diff --git a/CHANGELOG.md b/CHANGELOG.md deleted file mode 100644 index 468ef52c129be07f036f97561e793005ad9ec90b..0000000000000000000000000000000000000000 --- a/CHANGELOG.md +++ /dev/null @@ -1,27 +0,0 @@ -# Changelog for OneFlow v0.8.0 - -## v0.8.0-dev - -### 1. Enhancements - -#### Eager - -- placeholder - -#### System - -- placeholder - -#### Build - -### 2. Bug fixes - -#### Placeholder - -### 3. Deprecations - -#### Single client - -## v0.7.0 - -The CHANGELOG for v0.7.0 releases can be found [in the v0.7.0 tag](https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.7.0). diff --git a/CMakeLists.txt b/CMakeLists.txt index ca778ee8ff31fb7fa54086bca20ffa701ed109fd..c956e7ccc1b9f38d1b8f6a386523c352b423d2e3 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -6,9 +6,11 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON CACHE BOOL "") option(THIRD_PARTY "Build third party" ON) option(ONEFLOW "Build oneflow" ON) + if(NOT THIRD_PARTY AND NOT ONEFLOW) message(FATAL_ERROR "at least one of flags THIRD_PARTY and ONEFLOW should be ON") endif() + option(USE_CLANG_FORMAT "" OFF) option(USE_CLANG_TIDY "" OFF) option(BUILD_PYTHON "" ON) @@ -16,6 +18,7 @@ option(BUILD_CPP_API "Option to build OneFlow C++ API (beta)" OFF) option(BUILD_RDMA "" OFF) option(BUILD_CUDA "" ON) option(BUILD_ROCM "" OFF) +option(BUILD_ROCM_GRAPHS "" OFF) option(BUILD_TESTING "" OFF) option(BUILD_GIT_VERSION "" ON) option(BUILD_PROFILER "" OFF) @@ -29,6 +32,7 @@ option(OF_SOFTMAX_USE_FAST_MATH "" ON) option(OF_LAYER_NORM_USE_FAST_MATH "" ON) option(TREAT_WARNINGS_AS_ERRORS "" ON) option(MAYBE_NEED_ERROR_MSG_CHECK "" OFF) + # Reference: # https://medium.com/@alasher/colored-c-compiler-output-with-ninja-clang-gcc-10bfe7f2b949 option(OF_FORCE_COLORED_DIAGNOSTICS "Always produce ANSI-colored diagnostics (GNU/Clang only)." ON) @@ -37,18 +41,14 @@ if (BUILD_CUDA AND BUILD_ROCM) message(FATAL_ERROR "Compile with cuda and rocm enabled simultaneously are not supported right now.") endif() -if (BUILD_ROCM) - # Search for rocm in common locations - list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}/hip ${ROCM_PATH} ${ROCM_PATH}/llvm) -endif() - +set(ONEFLOW_CURRENT_VERSION 0.8.1.dev CACHE STRING "") -set(ONEFLOW_CURRENT_VERSION 0.7.0.dev CACHE STRING "") if(BUILD_FOR_CI) set(ONEFLOW_CURRENT_VERSION ci) endif() set(LLVM_PROVIDER "in-tree" CACHE STRING "in-tree, install") + if(NOT WITH_MLIR) set(LLVM_PROVIDER "install" CACHE STRING "in-tree will build LLVM's ALL, not what we want when not building MLIR" FORCE) @@ -70,6 +70,14 @@ set(CUDNN_STATIC OFF CACHE BOOL "") project(oneflow C CXX) +if (BUILD_ROCM) + # Search for rocm in common locations + #FIND_PACKAGE(HIP REQUIRED) + list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH} ${ROCM_PATH}/hip ${ROCM_PATH}/llvm) + set(CMAKE_MODULE_PATH "${HIP_PATH}/cmake" ${CMAKE_MODULE_PATH}) + set(CMAKE_MODULE_PATH "${HIP_PATH}/lib/cmake/hip" ${CMAKE_MODULE_PATH}) +endif() + if(NOT CMAKE_BUILD_TYPE) message(STATUS "No build type selected, default to Release") set(CMAKE_BUILD_TYPE "Release" CACHE STRING "Build type (default Release)" FORCE) @@ -81,13 +89,16 @@ if(NOT CMAKE_BUILD_TYPE MATCHES "^(Debug|Release|RelWithDebInfo|MinSizeRel)$") "Expected CMAKE_BUILD_TYPE is Debug, Release, RelWithDebInfo or MinSizeRel, got ${CMAKE_BUILD_TYPE}" ) endif() + message(STATUS "CMAKE_BUILD_TYPE: ${CMAKE_BUILD_TYPE}") set(COMPILER_VERSION_ERROR_MSG "At least gcc 7, clang 5 or Apple clang 12 is supported.") + if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU") if("${CMAKE_CXX_COMPILER_VERSION}" VERSION_LESS 7) message(FATAL_ERROR ${COMPILER_VERSION_ERROR_MSG}) endif() + if(CPU_THREADING_RUNTIME STREQUAL "OMP") set(OMP_FLAGS "-fopenmp") endif() @@ -102,6 +113,7 @@ elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang") ) endif() endif() + if("${CMAKE_CXX_COMPILER_VERSION}" VERSION_LESS 5) message(FATAL_ERROR ${COMPILER_VERSION_ERROR_MSG}) endif() @@ -117,6 +129,7 @@ set(oneflow_cmake_dir ${PROJECT_SOURCE_DIR}/cmake) get_filename_component(real_src_dir "${CMAKE_SOURCE_DIR}" REALPATH) get_filename_component(real_bin_dir "${CMAKE_BINARY_DIR}" REALPATH) + if("${real_src_dir}" STREQUAL "${real_bin_dir}") message(FATAL_ERROR "In-source build not allowed") endif() @@ -132,32 +145,40 @@ if(NOT DEFINED USE_CXX11_ABI) set(USE_CXX11_ABI ${CXX11_ABI_AVAILABLE}) elseif(USE_CXX11_ABI) check_cxx11_abi(CXX11_ABI_AVAILABLE) + if(NOT CXX11_ABI_AVAILABLE) message(FATAL_ERROR "cxx11 abi is not available for current compiler") endif() endif() + message(STATUS "USE_CXX11_ABI: ${USE_CXX11_ABI}") if(WITH_MLIR) add_definitions(-DWITH_MLIR) + if(WITH_MLIR_CUDA_CODEGEN) add_definitions(-DWITH_MLIR_CUDA_CODEGEN) endif() endif() + if(WITH_COCOAPI) add_definitions(-DWITH_COCOAPI) endif() + if(USE_CXX11_ABI) add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) else() add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) endif() + if(BUILD_PROFILER) add_definitions(-DOF_ENABLE_PROFILER) endif() + if(OF_SOFTMAX_USE_FAST_MATH) add_definitions(-DOF_SOFTMAX_USE_FAST_MATH) endif() + if(OF_LAYER_NORM_USE_FAST_MATH) add_definitions(-DOF_LAYER_NORM_USE_FAST_MATH) endif() @@ -178,14 +199,17 @@ if(OF_FORCE_COLORED_DIAGNOSTICS) $<$:$<$:-fcolor-diagnostics>> $<$:$<$:-fcolor-diagnostics>>) endif() + if(RPC_BACKEND MATCHES "GRPC") add_definitions(-DRPC_BACKEND_GRPC) message(STATUS "RPC backend enabled: gRPC") set(SUPPORTED_RPC_BACKEND_FOUND 1) endif() + if(WITH_ONEDNN) add_definitions(-DWITH_ONEDNN) endif() + add_definitions(-DRPC_BACKEND_LOCAL) message(STATUS "RPC backend enabled: local") enable_testing() @@ -236,19 +260,37 @@ if(WIN32) endif() endforeach() - #set(CMAKE_EXE_LINKER_FLAGS_DEBUG "${CMAKE_EXE_LINKER_FLAGS} /DEBUG:FASTLINK") + # set(CMAKE_EXE_LINKER_FLAGS_DEBUG "${CMAKE_EXE_LINKER_FLAGS} /DEBUG:FASTLINK") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /D_ITERATOR_DEBUG_LEVEL=0") else() set(EXTRA_CXX_FLAGS "-std=c++14 -Wall -Wno-sign-compare -Wno-unused-function -fPIC") + if(APPLE) set(EXTRA_CXX_FLAGS "${EXTRA_CXX_FLAGS} -Wno-deprecated-declarations") endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${EXTRA_CXX_FLAGS}") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} ${EXTRA_CXX_FLAGS}") set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${EXTRA_CXX_FLAGS}") set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} ${EXTRA_CXX_FLAGS}") endif() +if((ENABLE_ASAN OR ENABLE_UBSAN) AND ENABLE_TSAN) + message(FATAL_ERROR "Only ASAN and UBSAN can be enabled at the same time.") +endif() +if(ENABLE_ASAN) + add_compile_options(-fsanitize=address -fno-omit-frame-pointer) + add_link_options(-fsanitize=address -fno-omit-frame-pointer) +endif() +if(ENABLE_UBSAN) + add_compile_options(-fsanitize=undefined) + add_link_options(-fsanitize=undefined) +endif() +if(ENABLE_TSAN) + add_compile_options(-fsanitize=thread) + add_link_options(-fsanitize=thread) +endif() + if(BUILD_PYTHON) set(ONEFLOW_INCLUDE_DIR "${ONEFLOW_PYTHON_DIR}/oneflow/include") endif(BUILD_PYTHON) @@ -274,38 +316,55 @@ set(KINETO_URL use_mirror(VARIABLE KINETO_URL URL ${KINETO_URL}) set(KINETO_MD5 f9b550591b3899fb267270c19484933f) +set(CUTLASS_URL + https://github.com/Oneflow-Inc/cutlass/archive/34b3d940ad5e0e20776d0372b4e70092e67c268c.zip) +use_mirror(VARIABLE CUTLASS_URL URL ${CUTLASS_URL}) +set(CUTLASS_MD5 b75e600310e576811d89d37578d66fdb) + include(cuda) -add_subdirectory(external) -include(third_party) if(BUILD_CUDA) # NOTE: if you want to use source PTX with a version different from produced PTX/binary, you should add flags if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) list(APPEND CMAKE_CUDA_ARCHITECTURES 60-real) + # Tesla P40/P4, Quadro Pxxx/Pxxxx, GeForce GTX 10xx, TITAN X/Xp list(APPEND CMAKE_CUDA_ARCHITECTURES 61-real) + # V100, TITAN V list(APPEND CMAKE_CUDA_ARCHITECTURES 70-real) + if(CUDA_VERSION VERSION_GREATER_EQUAL "10.0") # T4, Quadro RTX xxxx, Txxxx, Geforce RTX 20xx, TITAN RTX list(APPEND CMAKE_CUDA_ARCHITECTURES 75-real) endif() + if(CUDA_VERSION VERSION_GREATER_EQUAL "11.0") # A100 list(APPEND CMAKE_CUDA_ARCHITECTURES 80-real) endif() + if(CUDA_VERSION VERSION_GREATER_EQUAL "11.1") # GeForce RTX 30xx list(APPEND CMAKE_CUDA_ARCHITECTURES 86-real) endif() - if(CUDA_VERSION VERSION_GREATER_EQUAL "11.0") - list(APPEND CMAKE_CUDA_ARCHITECTURES 80-virtual) - elseif(CUDA_VERSION VERSION_GREATER_EQUAL "10.0") - list(APPEND CMAKE_CUDA_ARCHITECTURES 75-virtual) - else() - list(APPEND CMAKE_CUDA_ARCHITECTURES 70-virtual) + + if(CUDA_VERSION VERSION_GREATER_EQUAL "11.8") + # GeForce RTX 40xx + list(APPEND CMAKE_CUDA_ARCHITECTURES 89-real) + # NVIDIA H100 + list(APPEND CMAKE_CUDA_ARCHITECTURES 90-real) endif() endif() + + foreach(CUDA_ARCH ${CMAKE_CUDA_ARCHITECTURES}) + if(CUDA_ARCH MATCHES "^([0-9]+)\\-real$") + list(APPEND CUDA_REAL_ARCHS_LIST ${CMAKE_MATCH_1}) + elseif(CUDA_ARCH MATCHES "^([0-9]+)$") + list(APPEND CUDA_REAL_ARCHS_LIST ${CMAKE_MATCH_1}) + endif() + endforeach() + enable_language(CUDA) include_directories(${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES}) message(STATUS "CMAKE_CUDA_ARCHITECTURES: ${CMAKE_CUDA_ARCHITECTURES}") @@ -316,16 +375,22 @@ if(BUILD_CUDA) set(CUDA_NVCC_THREADS_NUMBER "4" CACHE STRING "") list(APPEND CUDA_NVCC_FLAGS -t ${CUDA_NVCC_THREADS_NUMBER}) endif() + list(APPEND CUDA_NVCC_FLAGS "-Xcompiler=-fno-strict-aliasing") message(STATUS "CUDA_NVCC_FLAGS: " ${CUDA_NVCC_FLAGS}) list(JOIN CUDA_NVCC_FLAGS " " CMAKE_CUDA_FLAGS) endif() endif() +add_subdirectory(external) +include(third_party) + message(STATUS "CMAKE_CXX_COMPILER_VERSION: " ${CMAKE_CXX_COMPILER_VERSION}) add_custom_target(oneflow_deps ALL DEPENDS prepare_oneflow_third_party) + # skip oneflow cmake to avoid errors caused by the absences of python-dev, proto src if(ONEFLOW) include(oneflow) endif() + add_subdirectory(ci) diff --git a/README.md b/README.md index 15f73aed805a6fffadf7a17b0ccf9f50a410787e..dbcfc724b133e710cfea5305d46085f732481fc0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ # OneFlow OneFlow is a deep learning framework designed to be **user-friendly, scalable and efficient**. With OneFlow, it is easy to: + - program a model with **PyTorch-like API** - scale a model to n-dimensional-parallel/distributed execution with the **Global View API** - accelerate/deploy a model with the **Static Graph Compiler**. @@ -12,8 +13,8 @@ OneFlow is a deep learning framework designed to be **user-friendly, scalable an ## Latest News -- Version 0.8.0 is out! - - [Full changelog](https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.8.0) +- Version 0.9.0 is out! + - [Full changelog](https://github.com/Oneflow-Inc/oneflow/releases/tag/v0.9.0) ## Publication @@ -60,14 +61,14 @@ OneFlow is a deep learning framework designed to be **user-friendly, scalable an - To install nightly release of OneFlow with CUDA support: ```bash - python3 -m pip install --pre oneflow -f https://staging.oneflow.info/branch/master/cu102 + python3 -m pip install --pre oneflow -f https://staging.oneflow.info/branch/master/cu117 ``` - To install other available builds for different variants: - Stable ```bash - python3 -m pip install --find-links https://release.oneflow.info oneflow==0.8.0+[PLATFORM] + python3 -m pip install --find-links https://release.oneflow.info oneflow==0.9.0+cu117 ``` - Nightly ``` @@ -76,7 +77,7 @@ OneFlow is a deep learning framework designed to be **user-friendly, scalable an - All available `[PLATFORM]`: | Platform |CUDA Driver Version| Supported GPUs | |---|---|---| - | cu112 | >= 450.80.02 | GTX 10xx, RTX 20xx, A100, RTX 30xx | + | cu117 | >= 450.80.02 | GTX 10xx, RTX 20xx, A100, RTX 30xx | | cu102 | >= 440.33 | GTX 10xx, RTX 20xx | | cpu | N/A | N/A | @@ -89,8 +90,7 @@ OneFlow is a deep learning framework designed to be **user-friendly, scalable an ### Use docker image ``` -docker pull oneflowinc/oneflow:nightly-cuda10.2 -docker pull oneflowinc/oneflow:nightly-cuda11.2 +docker pull oneflowinc/oneflow:nightly-cuda11.7 ``` ### Build from Source diff --git a/audit_dtk-22.04.2.py b/audit_dtk-22.04.2.py deleted file mode 100644 index 6646a3b4578001be9f2d3d8731f6d8216816d6f8..0000000000000000000000000000000000000000 --- a/audit_dtk-22.04.2.py +++ /dev/null @@ -1,21 +0,0 @@ -# Monkey patch to not ship libjvm.so in pypi wheels -import sys - -from auditwheel.main import main -from auditwheel.policy import _POLICIES as POLICIES - -# libjvm is loaded dynamically; do not include it -for p in POLICIES: - p['lib_whitelist'].append('librccl.so.1') - p['lib_whitelist'].append('libhipblas.so.0') - p['lib_whitelist'].append('libhiprand.so.1') - p['lib_whitelist'].append('librocrand.so.1') - p['lib_whitelist'].append('libMIOpen.so.1') - p['lib_whitelist'].append('libgalaxyhip.so.4') - p['lib_whitelist'].append('librocm_smi64.so.2') - p['lib_whitelist'].append('librocsolver.so.0 ') - p['lib_whitelist'].append('librocblas.so.0') - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/audit_dtk-22.10.py b/audit_dtk-22.10.py deleted file mode 100644 index db4500751f55320aa4659349ca358c9a746365c1..0000000000000000000000000000000000000000 --- a/audit_dtk-22.10.py +++ /dev/null @@ -1,21 +0,0 @@ -# Monkey patch to not ship libjvm.so in pypi wheels -import sys - -from auditwheel.main import main -from auditwheel.policy import _POLICIES as POLICIES - -# libjvm is loaded dynamically; do not include it -for p in POLICIES: - p['lib_whitelist'].append('librccl.so.1') - p['lib_whitelist'].append('libhipblas.so.0') - p['lib_whitelist'].append('libhiprand.so.1') - p['lib_whitelist'].append('librocrand.so.1') - p['lib_whitelist'].append('libMIOpen.so.1') - p['lib_whitelist'].append('libgalaxyhip.so.5') - p['lib_whitelist'].append('librocm_smi64.so.2') - p['lib_whitelist'].append('librocsolver.so.0 ') - p['lib_whitelist'].append('librocblas.so.0') - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/ci/check/run_clang_format.py b/ci/check/run_clang_format.py index 2b62e017a4171851fb5f3e8a30158c09c7d1a114..adc34054c725e7cf67f7bb94ff6919c9e1f7f23d 100644 --- a/ci/check/run_clang_format.py +++ b/ci/check/run_clang_format.py @@ -130,6 +130,9 @@ if __name__ == "__main__": files = filter( lambda p: p.suffix in exts, pathlib.Path(args.source_dir).rglob("*"), ) + files = filter( + lambda p: not "oneflow/user/kernels/fmha_flash_attention/" in str(p), files, + ) loop = asyncio.get_event_loop() files = [str(f) for f in files] clang_fmt_args = "-dry-run --Werror" diff --git a/ci/check/run_clang_tidy.py b/ci/check/run_clang_tidy.py index 6807aaf9504ecf7ce848078c2c6c61d32b960243..4ed9555e45dc5443d1770af9a38a8afe60b01b7e 100644 --- a/ci/check/run_clang_tidy.py +++ b/ci/check/run_clang_tidy.py @@ -59,9 +59,9 @@ async def run_command(cmd=None, dry=False, name=None): def download(build_dir, dry=False) -> Optional[List[str]]: urls = [ - "https://github.com/Oneflow-Inc/llvm-project/releases/download/llvmorg-13.0.0-maybe/clang-tidy-13.AppImage" + "https://github.com/Oneflow-Inc/llvm-project/releases/download/maybe-14.0.4/clang-tidy-14.AppImage" if os.getenv("CI") - else "https://oneflow-static.oss-cn-beijing.aliyuncs.com/bin/clang-tidy/linux-x86_64/clang-tidy-13.AppImage", + else "https://oneflow-static.oss-cn-beijing.aliyuncs.com/bin/clang-tidy/linux-x86_64/clang-tidy-14.AppImage", "https://raw.githubusercontent.com/oneflow-inc/llvm-project/maybe/clang-tools-extra/clang-tidy/tool/clang-tidy-diff.py", ] dst_dir = f"{build_dir}/cache/bin" diff --git a/ci/clang/build-llvm.sh b/ci/clang/build-llvm.sh index 1200d26f079d7e1a3a9f59d5701685463f6b7bd1..205347a0dfeffa4b369ecd02698623b27cfabf87 100644 --- a/ci/clang/build-llvm.sh +++ b/ci/clang/build-llvm.sh @@ -1,10 +1,11 @@ set -ex -export PATH=/usr/lib/llvm-12/bin:/usr/lib/llvm-13/bin:/usr/lib64/ccache:/root/.local/bin:$PATH +export PATH=/usr/lib/llvm-15/bin:/usr/lib64/ccache:/root/.local/bin:$PATH # clean python dir cd ${ONEFLOW_CI_SRC_DIR} ${ONEFLOW_CI_PYTHON_EXE} -m pip install -i https://mirrors.aliyun.com/pypi/simple --user -r ci/fixed-dev-requirements.txt cd python +git config --global --add safe.directory ${ONEFLOW_CI_SRC_DIR} git clean -nXd -e \!dist -e \!dist/** git clean -fXd -e \!dist -e \!dist/** diff --git a/ci/manylinux/build-gcc7.sh b/ci/manylinux/build-gcc7.sh index 42244968a0e8baf3d25af21ce4ea34b1eb04e221..9d11db908db803aa5141f069773fcada33ab3b77 100644 --- a/ci/manylinux/build-gcc7.sh +++ b/ci/manylinux/build-gcc7.sh @@ -6,9 +6,11 @@ ld --version # clean python dir cd ${ONEFLOW_CI_SRC_DIR} ${ONEFLOW_CI_PYTHON_EXE} -m pip install -i https://mirrors.aliyun.com/pypi/simple --user -r ci/fixed-dev-requirements.txt +${ONEFLOW_CI_PYTHON_EXE} -m pip install -i https://mirrors.aliyun.com/pypi/simple --user auditwheel cd python function clean_artifacts { + git config --global --add safe.directory ${ONEFLOW_CI_SRC_DIR} git clean -nXd -e \!dist -e \!dist/** git clean -fXd -e \!dist -e \!dist/** } diff --git a/ci/manylinux/build.sh b/ci/manylinux/build.sh index e291d5a17b11c953f58ae0f39e2eeb54ae7101ee..24a089862948738d03c4c8c677315eeed24769f2 100644 --- a/ci/manylinux/build.sh +++ b/ci/manylinux/build.sh @@ -5,9 +5,11 @@ ld --version # clean python dir cd ${ONEFLOW_CI_SRC_DIR} ${ONEFLOW_CI_PYTHON_EXE} -m pip install -i https://mirrors.aliyun.com/pypi/simple --user -r ci/fixed-dev-requirements.txt +${ONEFLOW_CI_PYTHON_EXE} -m pip install -i https://mirrors.aliyun.com/pypi/simple --user auditwheel cd python function clean_artifacts { + git config --global --add safe.directory ${ONEFLOW_CI_SRC_DIR} git clean -nXd -e \!dist -e \!dist/** git clean -fXd -e \!dist -e \!dist/** } diff --git a/ci/test/1node_model_serve_test.sh b/ci/test/1node_model_serve_test.sh deleted file mode 100644 index beeec3c18b32a9775c2d89ce1d7672b31085d7a1..0000000000000000000000000000000000000000 --- a/ci/test/1node_model_serve_test.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash -set -xe - -src_dir=${ONEFLOW_SRC_DIR:-"$PWD"} -test_tmp_dir=${ONEFLOW_TEST_TMP_DIR:-"./test_tmp_dir"} - -rm -rf $test_tmp_dir -mkdir -p $test_tmp_dir -cp -r $src_dir/python/oneflow/compatible/single_client/test $test_tmp_dir -cd $test_tmp_dir - -export ONEFLOW_TEST_DEVICE_NUM=1 -python3 -m unittest discover test/serving --failfast --verbose diff --git a/ci/test/dry_run_test.sh b/ci/test/dry_run_test.sh deleted file mode 100644 index 332cf2901eb7c81e8e341f8e51f0cc5d2e7799e6..0000000000000000000000000000000000000000 --- a/ci/test/dry_run_test.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/bin/bash -set -xe - -export PYTHONUNBUFFERED=1 - -src_dir=${ONEFLOW_SRC_DIR:-"$PWD"} -test_tmp_dir=${ONEFLOW_TEST_TMP_DIR:-"./test_tmp_dir"} - - -rm -rf $test_tmp_dir -mkdir -p $test_tmp_dir -cp -r $src_dir/python/oneflow/compatible/single_client/benchmarks $test_tmp_dir -cd $test_tmp_dir/benchmarks - -export ONEFLOW_DRY_RUN=1 -# turn on ONEFLOW_DEBUG_MODE will cause protobuf err -# export ONEFLOW_DEBUG_MODE=1 - -node_num=2 -generated_node_list=$(seq -f "mockhost%02g" -s, $node_num) - -# heaptrack -# valgrind --tool=massif --threshold=0.0001 -# /usr/bin/time -v -time python3 bert_benchmark/run_pretraining.py \ - --learning_rate=1e-4 \ - --weight_decay_rate=0.01 \ - --batch_size_per_device=24 \ - --iter_num=5 \ - --loss_print_every_n_iter=1 \ - --data_dir="/dataset/bert/bert_seq_len_128_repeat1024" \ - --data_part_num=1 \ - --seq_length=128 \ - --max_predictions_per_seq=20 \ - --num_hidden_layers=12 \ - --num_attention_heads=12 \ - --max_position_embeddings=512 \ - --type_vocab_size=2 \ - --vocab_size=30522 \ - --attention_probs_dropout_prob=0.1 \ - --hidden_dropout_prob=0.1 \ - --hidden_size_per_head=64 \ - --node_list=${generated_node_list} \ - --node_num=${node_num} \ - --gpu_num_per_node=8 diff --git a/ci/test/test_mock_function.sh b/ci/test/test_mock_function.sh new file mode 100644 index 0000000000000000000000000000000000000000..21b26b17784d876091c5e65e423c0b8263026543 --- /dev/null +++ b/ci/test/test_mock_function.sh @@ -0,0 +1,17 @@ +#!/bin/bash +set -e +MOCK_UNITTEST=$PWD/python/oneflow/test/misc/test_mock_scope.py + +python3 $MOCK_UNITTEST --failfast --verbose +# testing import * +python3 -c " +import oneflow +import oneflow.nn +import oneflow.mock_torch as mock; mock.enable(); +from torch.sbp import *; assert(sbp == oneflow.sbp.sbp); +from torch import *; assert(randn == oneflow.randn); +from torch.nn import *; assert(Graph == oneflow.nn.Graph); +mock.disable(); +from torch import *; assert(randn != oneflow.randn); +from torch.nn import *; assert(Graph != oneflow.nn.Graph); +" diff --git a/ci/test/test_mock_script.sh b/ci/test/test_mock_script.sh new file mode 100644 index 0000000000000000000000000000000000000000..109a3a83eedbb56986f6555c24c2fd5b814eab59 --- /dev/null +++ b/ci/test/test_mock_script.sh @@ -0,0 +1,37 @@ +#!/bin/bash +set -e +MOCK_TORCH=$PWD/python/oneflow/test/misc/test_mock_simple.py + +same_or_exit() { + if [[ "$(python3 $MOCK_TORCH)" != *"$1"* ]]; then + exit 1 + fi +} +eval $(python3 -m oneflow.mock_torch) # test call to python module, default argument is enable +same_or_exit "True" + +# testing import +python3 -c 'import torch; torch.randn(2,3)' +python3 -c 'import torch.nn; torch.nn.Graph' +python3 -c 'import torch.version; torch.version.__version__' +python3 -c 'from torch import *; randn(2,3)' +python3 -c 'from torch.nn import *; Graph' +python3 -c 'from torch.sbp import *; sbp' +python3 -c 'from torch import nn; nn.Graph' +python3 -c 'from torch.version import __version__' +python3 -c 'import torch; torch.no_exist' 2>&1 >/dev/null | grep -q 'NotImplementedError' + +eval $(python3 -m oneflow.mock_torch disable) +same_or_exit "False" +eval $(python3 -m oneflow.mock_torch enable) +same_or_exit "True" +eval $(python3 -m oneflow.mock_torch disable) # recover +same_or_exit "False" +eval $(oneflow-mock-torch) # test scripts +same_or_exit "True" +eval $(oneflow-mock-torch disable) +same_or_exit "False" +eval $(oneflow-mock-torch enable) +same_or_exit "True" +eval $(oneflow-mock-torch disable) +same_or_exit "False" diff --git a/cmake/caches/ci/cpu-asan-ubsan.cmake b/cmake/caches/ci/cpu-asan-ubsan.cmake new file mode 100644 index 0000000000000000000000000000000000000000..16449614c5c596b0bec74da6de61e3a0d85f2c81 --- /dev/null +++ b/cmake/caches/ci/cpu-asan-ubsan.cmake @@ -0,0 +1,19 @@ +set(BUILD_CUDA NO CACHE BOOL "") +set(BUILD_GIT_VERSION YES CACHE BOOL "") +set(BUILD_TESTING YES CACHE BOOL "") +set(WITH_ONEDNN YES CACHE BOOL "") +set(TREAT_WARNINGS_AS_ERRORS YES CACHE BOOL "") +set(THIRD_PARTY_MIRROR aliyun CACHE STRING "") +set(PIP_INDEX_MIRROR "https://pypi.tuna.tsinghua.edu.cn/simple" CACHE STRING "") +set(CMAKE_BUILD_TYPE RelWithDebInfo CACHE STRING "") +set(CMAKE_GENERATOR Ninja CACHE STRING "") +set(BUILD_CPP_API ON CACHE BOOL "") +set(WITH_MLIR ON CACHE BOOL "") +set(BUILD_FOR_CI ON CACHE BOOL "") +set(BUILD_SHARED_LIBS ON CACHE BOOL "") +set(CMAKE_C_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CXX_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CPU_THREADING_RUNTIME "SEQ" CACHE STRING "") +set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF CACHE BOOL "") +set(ENABLE_ASAN ON CACHE BOOL "") +set(ENABLE_UBSAN OFF CACHE BOOL "") diff --git a/cmake/caches/ci/cpu-tsan.cmake b/cmake/caches/ci/cpu-tsan.cmake new file mode 100644 index 0000000000000000000000000000000000000000..198dced5472108f2df73035895261abb0ff1418f --- /dev/null +++ b/cmake/caches/ci/cpu-tsan.cmake @@ -0,0 +1,18 @@ +set(BUILD_CUDA NO CACHE BOOL "") +set(BUILD_GIT_VERSION YES CACHE BOOL "") +set(BUILD_TESTING YES CACHE BOOL "") +set(WITH_ONEDNN YES CACHE BOOL "") +set(TREAT_WARNINGS_AS_ERRORS YES CACHE BOOL "") +set(THIRD_PARTY_MIRROR aliyun CACHE STRING "") +set(PIP_INDEX_MIRROR "https://pypi.tuna.tsinghua.edu.cn/simple" CACHE STRING "") +set(CMAKE_BUILD_TYPE RelWithDebInfo CACHE STRING "") +set(CMAKE_GENERATOR Ninja CACHE STRING "") +set(BUILD_CPP_API ON CACHE BOOL "") +set(WITH_MLIR ON CACHE BOOL "") +set(BUILD_FOR_CI ON CACHE BOOL "") +set(BUILD_SHARED_LIBS ON CACHE BOOL "") +set(CMAKE_C_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CXX_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CPU_THREADING_RUNTIME "SEQ" CACHE STRING "") +set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF CACHE BOOL "") +set(ENABLE_TSAN ON CACHE BOOL "") diff --git a/cmake/caches/ci/llvm/cuda-75-clang.cmake b/cmake/caches/ci/llvm/cuda-75-clang.cmake index c66a6930efafba67b75f44e8443f171993314050..8be73adb75e9cc9c639fa8fb6ef21aae7a358b2c 100644 --- a/cmake/caches/ci/llvm/cuda-75-clang.cmake +++ b/cmake/caches/ci/llvm/cuda-75-clang.cmake @@ -4,6 +4,7 @@ set(CMAKE_CUDA_COMPILER "clang++" CACHE STRING "") set(CMAKE_EXE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") set(CMAKE_MODULE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") set(CMAKE_SHARED_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(WITH_MLIR YES CACHE BOOL "") set(BUILD_SHARED_LIBS YES CACHE BOOL "") set(BUILD_CUDA YES CACHE BOOL "") set(CMAKE_CUDA_ARCHITECTURES "75;52-real" CACHE STRING "") diff --git a/cmake/caches/cn/fast/mlir-cuda-80.cmake b/cmake/caches/cn/fast/mlir-cuda-80.cmake new file mode 100644 index 0000000000000000000000000000000000000000..8031bb7ca74acada5e2bb1154971ab65108bc4b3 --- /dev/null +++ b/cmake/caches/cn/fast/mlir-cuda-80.cmake @@ -0,0 +1,28 @@ +set(BUILD_SHARED_LIBS YES CACHE BOOL "") +# uncomment only if you know what you are doing +# set(CMAKE_LINK_DEPENDS_NO_SHARED YES CACHE BOOL "") +set(BUILD_CUDA YES CACHE BOOL "") +set(BUILD_GIT_VERSION NO CACHE BOOL "") +set(TREAT_WARNINGS_AS_ERRORS YES CACHE BOOL "") +set(BUILD_HWLOC NO CACHE BOOL "") +set(BUILD_TESTING OFF CACHE BOOL "") +set(WITH_MLIR YES CACHE BOOL "") +set(WITH_MLIR_CUDA_CODEGEN YES CACHE BOOL "") +set(THIRD_PARTY_MIRROR aliyun CACHE STRING "") +set(PIP_INDEX_MIRROR "https://pypi.tuna.tsinghua.edu.cn/simple" CACHE STRING "") +set(CMAKE_BUILD_TYPE RelWithDebInfo CACHE STRING "") +set(CMAKE_GENERATOR Ninja CACHE STRING "") +set(CMAKE_CUDA_ARCHITECTURES "80" CACHE STRING "") +set(CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda CACHE STRING "") +set(CUDNN_ROOT_DIR /usr/local/cudnn CACHE STRING "") +set(CMAKE_C_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CXX_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CUDA_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF CACHE BOOL "") +set(CMAKE_EXE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CMAKE_MODULE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CMAKE_SHARED_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CPU_THREADING_RUNTIME SEQ CACHE STRING + "when using lld with TBB enabled, there will be linkage error") +set(BUILD_HWLOC OFF CACHE BOOL "") +set(WITH_ONEDNN OFF CACHE BOOL "") diff --git a/cmake/caches/cn/fast/mlir-cuda-86.cmake b/cmake/caches/cn/fast/mlir-cuda-86.cmake new file mode 100644 index 0000000000000000000000000000000000000000..1be4514d4da2bb6e411737eda093082a21717571 --- /dev/null +++ b/cmake/caches/cn/fast/mlir-cuda-86.cmake @@ -0,0 +1,28 @@ +set(BUILD_SHARED_LIBS YES CACHE BOOL "") +# uncomment only if you know what you are doing +# set(CMAKE_LINK_DEPENDS_NO_SHARED YES CACHE BOOL "") +set(BUILD_CUDA YES CACHE BOOL "") +set(BUILD_GIT_VERSION NO CACHE BOOL "") +set(TREAT_WARNINGS_AS_ERRORS YES CACHE BOOL "") +set(BUILD_HWLOC NO CACHE BOOL "") +set(BUILD_TESTING OFF CACHE BOOL "") +set(WITH_MLIR YES CACHE BOOL "") +set(WITH_MLIR_CUDA_CODEGEN YES CACHE BOOL "") +set(THIRD_PARTY_MIRROR aliyun CACHE STRING "") +set(PIP_INDEX_MIRROR "https://pypi.tuna.tsinghua.edu.cn/simple" CACHE STRING "") +set(CMAKE_BUILD_TYPE RelWithDebInfo CACHE STRING "") +set(CMAKE_GENERATOR Ninja CACHE STRING "") +set(CMAKE_CUDA_ARCHITECTURES "86" CACHE STRING "") +set(CUDA_TOOLKIT_ROOT_DIR /usr/local/cuda CACHE STRING "") +set(CUDNN_ROOT_DIR /usr/local/cudnn CACHE STRING "") +set(CMAKE_C_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CXX_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_CUDA_COMPILER_LAUNCHER ccache CACHE STRING "") +set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF CACHE BOOL "") +set(CMAKE_EXE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CMAKE_MODULE_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CMAKE_SHARED_LINKER_FLAGS_INIT "-fuse-ld=lld" CACHE STRING "") +set(CPU_THREADING_RUNTIME SEQ CACHE STRING + "when using lld with TBB enabled, there will be linkage error") +set(BUILD_HWLOC OFF CACHE BOOL "") +set(WITH_ONEDNN OFF CACHE BOOL "") diff --git a/cmake/cuda.cmake b/cmake/cuda.cmake index a07e6953a500870266fceb479b7a98394fb4aa5f..cd0e424b9477c47a08b663e1a9042b21328aea83 100644 --- a/cmake/cuda.cmake +++ b/cmake/cuda.cmake @@ -1,11 +1,4 @@ -option(CUDA_STATIC "" ON) - if(BUILD_CUDA) - if((NOT CUDA_STATIC) OR BUILD_SHARED_LIBS) - set(OF_CUDA_LINK_DYNAMIC_LIBRARY ON) - else() - set(OF_CUDA_LINK_DYNAMIC_LIBRARY OFF) - endif() if(DEFINED CUDA_TOOLKIT_ROOT_DIR) message(WARNING "CUDA_TOOLKIT_ROOT_DIR is deprecated, use CUDAToolkit_ROOT instead") set(CUDAToolkit_ROOT ${CUDA_TOOLKIT_ROOT_DIR}) @@ -32,9 +25,22 @@ if(BUILD_CUDA) message(FATAL_ERROR "CUDA_VERSION empty") endif() message(STATUS "CUDA_VERSION: ${CUDA_VERSION}") + if(CUDA_VERSION VERSION_GREATER_EQUAL "11.6") + set(CUDA_STATIC OFF CACHE BOOL "") + else() + set(CUDA_STATIC ON CACHE BOOL "") + endif() + + if((NOT CUDA_STATIC) OR BUILD_SHARED_LIBS) + set(OF_CUDA_LINK_DYNAMIC_LIBRARY ON) + else() + set(OF_CUDA_LINK_DYNAMIC_LIBRARY OFF) + endif() + if(OF_CUDA_LINK_DYNAMIC_LIBRARY) list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cublas) list(APPEND VENDOR_CUDA_LIBRARIES CUDA::curand) + list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cufft) if(CUDA_VERSION VERSION_GREATER_EQUAL "10.1") list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cublasLt) endif() @@ -46,6 +52,7 @@ if(BUILD_CUDA) else() list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cublas_static) list(APPEND VENDOR_CUDA_LIBRARIES CUDA::curand_static) + list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cufft_static) if(CUDA_VERSION VERSION_GREATER_EQUAL "10.1") list(APPEND VENDOR_CUDA_LIBRARIES CUDA::cublasLt_static) endif() diff --git a/cmake/oneflow.cmake b/cmake/oneflow.cmake index fff0d9d98f3d3079e16a96d8c7f6dd7ea6f6d737..0940bb1737f4b3df157232f4af2aa60089118763 100644 --- a/cmake/oneflow.cmake +++ b/cmake/oneflow.cmake @@ -45,7 +45,7 @@ file( "${PROJECT_SOURCE_DIR}/oneflow/user/*.*" "${PROJECT_SOURCE_DIR}/oneflow/api/*.*" "${PROJECT_SOURCE_DIR}/oneflow/maybe/*.*" - "${PROJECT_SOURCE_DIR}/oneflow/extension/python/*.*") + "${PROJECT_SOURCE_DIR}/oneflow/extension/*.*") foreach(oneflow_single_file ${oneflow_all_src}) # Verify whether this file is for other platforms @@ -80,6 +80,21 @@ foreach(oneflow_single_file ${oneflow_all_src}) if(BUILD_CUDA) list(APPEND of_all_obj_cc ${oneflow_single_file}) endif() + if(BUILD_ROCM) + if("${oneflow_single_file}" MATCHES "^${PROJECT_SOURCE_DIR}/oneflow/(core|user)/.*\\.cu$") + get_filename_component(oneflow_single_file_hip_cpp_dir ${oneflow_single_file} DIRECTORY) + get_filename_component(oneflow_single_file_hip_cpp ${oneflow_single_file} NAME_WE) + add_custom_command( + OUTPUT "${oneflow_single_file_hip_cpp_dir}/${oneflow_single_file_hip_cpp}_hip.cpp" + COMMAND ${CMAKE_COMMAND} -E copy "${oneflow_single_file}" "${oneflow_single_file_hip_cpp_dir}/${oneflow_single_file_hip_cpp}_hip.cpp" + DEPENDS "${oneflow_single_file}" + ) + list(APPEND of_all_obj_cc ${oneflow_single_file_hip_cpp_dir}/${oneflow_single_file_hip_cpp}_hip.cpp) + endif() + if("${oneflow_single_file}" MATCHES "^${PROJECT_SOURCE_DIR}/oneflow/(core|user)/.*\\.cuh$") + list(APPEND of_all_obj_cc ${oneflow_single_file}) + endif() + endif() set(group_this ON) endif() @@ -96,8 +111,7 @@ foreach(oneflow_single_file ${oneflow_all_src}) set(group_this ON) endif() - if("${oneflow_single_file}" MATCHES - "^${PROJECT_SOURCE_DIR}/oneflow/extension/python/.*\\.(h|cpp)$") + if("${oneflow_single_file}" MATCHES "^${PROJECT_SOURCE_DIR}/oneflow/extension/.*\\.(c|h|cpp)$") list(APPEND of_pyext_obj_cc ${oneflow_single_file}) set(group_this ON) endif() @@ -105,7 +119,7 @@ foreach(oneflow_single_file ${oneflow_all_src}) if("${oneflow_single_file}" MATCHES "^${PROJECT_SOURCE_DIR}/oneflow/(core|user|maybe)/.*\\.cpp$") if("${oneflow_single_file}" MATCHES - "^${PROJECT_SOURCE_DIR}/oneflow/(core|user|maybe)/.*_test\\.cpp$") + "^${PROJECT_SOURCE_DIR}/oneflow/(core|user|maybe|thread)/.*_test\\.cpp$") # test file list(APPEND of_all_test_cc ${oneflow_single_file}) elseif(APPLE AND "${oneflow_single_file}" MATCHES @@ -136,6 +150,7 @@ add_custom_target( of_format COMMAND ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/ci/check/run_license_format.py -i ${CMAKE_CURRENT_SOURCE_DIR}/oneflow --fix + --exclude="oneflow/user/kernels/fmha_flash_attention" COMMAND ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/ci/check/run_license_format.py -i ${ONEFLOW_PYTHON_DIR} --fix --exclude="oneflow/include" --exclude="oneflow/core" COMMAND ${Python_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/ci/check/run_clang_format.py --source_dir @@ -254,20 +269,22 @@ if("${LLVM_MONO_REPO_URL}" STREQUAL "https://github.com/llvm/llvm-project/archive/6a9bbd9f20dcd700e28738788bb63a160c6c088c.zip" OR "${LLVM_MONO_REPO_URL}" STREQUAL "https://github.com/llvm/llvm-project/archive/32805e60c9de1f82887cd2af30d247dcabd2e1d3.zip" + OR "${LLVM_MONO_REPO_URL}" STREQUAL + "https://github.com/llvm/llvm-project/archive/6d6268dcbf0f48e43f6f9fe46b3a28c29ba63c7d.zip" OR "${LLVM_MONO_REPO_MD5}" STREQUAL "f2f17229cf21049663b8ef4f2b6b8062" OR "${LLVM_MONO_REPO_MD5}" STREQUAL "6b7c6506d5922de9632c8ff012b2f945" OR "${LLVM_MONO_REPO_MD5}" STREQUAL "e0ea669a9f0872d35bffda5ec6c5ac6f" OR "${LLVM_MONO_REPO_MD5}" STREQUAL "241a333828bba1efa35aff4c4fc2ce87" OR "${LLVM_MONO_REPO_MD5}" STREQUAL "075fbfdf06cb3f02373ea44971af7b03" - OR "${LLVM_MONO_REPO_MD5}" STREQUAL "e412dc61159b5e929b0c94e44b11feb2") + OR "${LLVM_MONO_REPO_MD5}" STREQUAL "e412dc61159b5e929b0c94e44b11feb2" + OR "${LLVM_MONO_REPO_MD5}" STREQUAL "334997b4879aba15d9323a732356cf2a") unset(LLVM_MONO_REPO_URL CACHE) unset(LLVM_MONO_REPO_MD5 CACHE) endif() -set(LLVM_MONO_REPO_URL - "https://github.com/llvm/llvm-project/archive/6d6268dcbf0f48e43f6f9fe46b3a28c29ba63c7d.zip" +set(LLVM_MONO_REPO_URL "https://github.com/llvm/llvm-project/archive/refs/tags/llvmorg-15.0.6.zip" CACHE STRING "") use_mirror(VARIABLE LLVM_MONO_REPO_URL URL ${LLVM_MONO_REPO_URL}) -set(LLVM_MONO_REPO_MD5 "334997b4879aba15d9323a732356cf2a" CACHE STRING "") +set(LLVM_MONO_REPO_MD5 "1ccc00accc87a1a5d42a275d6e31cd8c" CACHE STRING "") set(ONEFLOW_BUILD_ROOT_DIR "${PROJECT_BINARY_DIR}") add_subdirectory(${PROJECT_SOURCE_DIR}/oneflow/ir) if(WITH_MLIR) @@ -306,9 +323,9 @@ elseif(UNIX) ${oneflow_third_party_libs} ${EXTERNAL_TARGETS} -Wl,--no-whole-archive + -Wl,--as-needed -ldl - -lrt - -Wl,--version-script ${PROJECT_SOURCE_DIR}/version_script.lds) + -lrt) if(BUILD_CUDA) target_link_libraries(oneflow CUDA::cudart_static) endif() @@ -317,6 +334,43 @@ elseif(WIN32) set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /WHOLEARCHIVE:oneflow") endif() +if (BUILD_ROCM) + # AMD compiler fails to compile these three files with '-O1/2/3'. + # The value of `COMPILE_OPTIONS` target property is added after CMAKE__FLAGS_, + # so '-O0' will override '-O1/2/3'. + set_source_files_properties(${PROJECT_SOURCE_DIR}/oneflow/user/kernels/median_with_indices_kernel_hip.cpp + ${PROJECT_SOURCE_DIR}/oneflow/user/kernels/radix_sort_top_k_kernel_hip.cpp + ${PROJECT_SOURCE_DIR}/oneflow/user/kernels/arg_sort_kernel_hip.cpp + #${PROJECT_SOURCE_DIR}/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math1_hip.cpp + PROPERTIES COMPILE_OPTIONS "-O0") +endif() + +if(BUILD_CUDA) + string(JOIN "," CUDA_REAL_ARCHS ${CUDA_REAL_ARCHS_LIST}) + set_source_files_properties(${PROJECT_SOURCE_DIR}/oneflow/core/hardware/cuda_device_descriptor.cpp + PROPERTIES COMPILE_FLAGS "-DCUDA_REAL_ARCHS=\"${CUDA_REAL_ARCHS}\"") +endif() + +if(BUILD_CUDA AND WITH_CUTLASS) + if(CUDA_VERSION VERSION_GREATER_EQUAL "10.1") + add_definitions(-DCUTLASS_ENABLE_TENSOR_CORE_MMA=1) + endif() + + set_property( + SOURCE ${PROJECT_SOURCE_DIR}/oneflow/user/kernels/fused_multi_head_attention_inference_kernel.cu + APPEND PROPERTY INCLUDE_DIRECTORIES + ${CUTLASS_INSTALL_DIR}/examples/41_fused_multi_head_attention) + set_property(SOURCE ${PROJECT_SOURCE_DIR}/oneflow/user/kernels/fused_glu_kernel.cu APPEND + PROPERTY INCLUDE_DIRECTORIES ${CUTLASS_INSTALL_DIR}/examples/45_dual_gemm) + if("${CMAKE_CUDA_COMPILER_ID}" STREQUAL "NVIDIA") + set_property( + SOURCE + ${PROJECT_SOURCE_DIR}/oneflow/user/kernels/fused_multi_head_attention_inference_kernel.cu + APPEND + PROPERTY COMPILE_OPTIONS "--use_fast_math") + endif() +endif() + # oneflow api common if(BUILD_PYTHON OR BUILD_CPP_API) file(GLOB_RECURSE of_api_common_files ${PROJECT_SOURCE_DIR}/oneflow/api/common/*.h @@ -343,6 +397,8 @@ if(BUILD_PYTHON) add_dependencies(of_pyext_obj oneflow) pybind11_add_module(oneflow_internal ${PYBIND11_SRCS} ${of_pybind_obj_cc} ${PYBIND_REGISTRY_CC}) + set_property(TARGET oneflow_internal APPEND PROPERTY BUILD_RPATH "\$ORIGIN/../nvidia/cublas/lib") + set_property(TARGET oneflow_internal APPEND PROPERTY BUILD_RPATH "\$ORIGIN/../nvidia/cudnn/lib") set_compile_options_to_oneflow_target(oneflow_internal) set_property(TARGET oneflow_internal PROPERTY CXX_VISIBILITY_PRESET "default") add_dependencies(oneflow_internal of_functional_obj of_functional_tensor_obj of_op_schema) @@ -419,6 +475,9 @@ if(BUILD_TESTING) oneflow_add_test(oneflow_testexe SRCS ${of_all_test_cc} TEST_NAME oneflow_test) target_link_libraries(oneflow_testexe ${of_libs} ${oneflow_third_party_libs} glog::glog ${oneflow_test_libs}) + if(WITH_MLIR) + target_link_libraries(oneflow_testexe MLIROneFlowExtension) + endif() endif() if(BUILD_CPP_API) @@ -524,6 +583,10 @@ if(BUILD_CPP_API) if(BUILD_CUDA) checkdirandappendslash(DIR ${NCCL_LIBRARY_DIR} OUTPUT NCCL_LIBRARY_DIR_APPENDED) list(APPEND LIBONEFLOW_THIRD_PARTY_DIRS ${NCCL_LIBRARY_DIR_APPENDED}) + if(WITH_CUTLASS) + checkdirandappendslash(DIR ${CUTLASS_LIBRARY_DIR} OUTPUT CUTLASS_LIBRARY_DIR_APPENDED) + list(APPEND LIBONEFLOW_THIRD_PARTY_DIRS ${CUTLASS_LIBRARY_DIR_APPENDED}) + endif() endif() install( @@ -555,6 +618,7 @@ if(BUILD_CPP_API) llvm-PerfectShuffle llvm-tblgen mlir-tblgen + mlir-pdll obj2yaml oneflow_tblgen yaml-bench diff --git a/cmake/op_schema.cmake b/cmake/op_schema.cmake index 5017fab574e25355bf0cc209c574e1547b36f7ac..3f0d9595d0a396c7ddedb0e1e6569cf03cadf400 100644 --- a/cmake/op_schema.cmake +++ b/cmake/op_schema.cmake @@ -38,7 +38,13 @@ set(ONEFLOW_OP_GROUPS "TRIGONOMETRIC" "UNARY" "UPSAMPLE" - "ONE_EMBEDDING") + "ONE_EMBEDDING" + "LINEAR_ALGEBRA" + "SYSTEM") +if(WITH_MLIR) + list(APPEND ONEFLOW_OP_GROUPS "MLIR_JIT") +endif(WITH_MLIR) + foreach(OP_GROUP_NAME IN LISTS ONEFLOW_OP_GROUPS) list(APPEND ONEFLOW_SCHEMA_TABLEGEN_FLAGS "-DGET_ONEFLOW_${OP_GROUP_NAME}_OP_DEFINITIONS") endforeach() diff --git a/cmake/third_party.cmake b/cmake/third_party.cmake index 97789f68e91a7341d0fe9647044b5b39ee17a6f9..9990ed0bb76ce1fd46a77b4b8a1007a9654f6026 100644 --- a/cmake/third_party.cmake +++ b/cmake/third_party.cmake @@ -137,7 +137,8 @@ endif() list(APPEND ONEFLOW_THIRD_PARTY_INCLUDE_DIRS ${RE2_INCLUDE_DIR}) if(BUILD_CUDA) - if(CUDA_VERSION VERSION_GREATER_EQUAL "11.0") + # Always use third_party/cub for Clang CUDA in case of compatibility issues + if("${CMAKE_CUDA_COMPILER_ID}" STREQUAL "NVIDIA" AND CUDA_VERSION VERSION_GREATER_EQUAL "11.0") if(CMAKE_CXX_STANDARD LESS 14) add_definitions(-DTHRUST_IGNORE_DEPRECATED_CPP_DIALECT) add_definitions(-DCUB_IGNORE_DEPRECATED_CPP11) @@ -150,6 +151,7 @@ if(BUILD_CUDA) list(APPEND oneflow_third_party_dependencies cub_copy_headers_to_destination) endif() include(nccl) + include(cutlass) list(APPEND oneflow_third_party_libs ${NCCL_LIBRARIES}) list(APPEND oneflow_third_party_libs ${CUDNN_LIBRARIES}) @@ -159,12 +161,19 @@ if(BUILD_CUDA) list(APPEND ONEFLOW_THIRD_PARTY_INCLUDE_DIRS ${CUDNN_INCLUDE_DIRS} ${CUB_INCLUDE_DIR} ${NCCL_INCLUDE_DIR}) -endif() + if(WITH_CUTLASS) + list(APPEND oneflow_third_party_dependencies cutlass) + list(APPEND oneflow_third_party_dependencies cutlass_copy_examples_to_destination) + list(APPEND oneflow_third_party_libs ${CUTLASS_LIBRARIES}) + list(APPEND ONEFLOW_THIRD_PARTY_INCLUDE_DIRS ${CUTLASS_INCLUDE_DIR}) + endif() +endif() if (BUILD_ROCM) # Find rocm packages find_package(hip) + find_package(hipfft) find_package(hipblas) find_package(hipcub) find_package(hiprand) @@ -173,11 +182,31 @@ if (BUILD_ROCM) find_package(rccl) add_definitions(-DWITH_ROCM) add_definitions(-D__HIP_PLATFORM_HCC__) - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_HCC__ --gpu-max-threads-per-block=1024") - set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__ --gpu-max-threads-per-block=1024") + add_definitions(-D__HIPCC__) + if (BUILD_ROCM_GRAPHS) + add_definitions(-DWITH_ROCM_GRAPHS) + endif() + + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__ -D__HIPCC__") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_HCC__ -D__HIPCC__") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --gpu-max-threads-per-block=1024") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_HIP") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-macro-redefined") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-inconsistent-missing-override") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-exceptions") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-shift-count-negative") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-shift-count-overflow") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-command-line-argument") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-duplicate-decl-specifier") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-implicit-int-float-conversion") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-pass-failed") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-gpu-rdc") + set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -mcmodel=large") set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} -mcmodel=large") + list(APPEND oneflow_third_party_libs hip::device) + list(APPEND oneflow_third_party_libs hip::hipfft) list(APPEND oneflow_third_party_libs roc::hipblas) list(APPEND oneflow_third_party_libs hip::hipcub) list(APPEND oneflow_third_party_libs roc::rocrand) @@ -185,17 +214,18 @@ if (BUILD_ROCM) list(APPEND oneflow_third_party_libs MIOpen) link_directories(${ROCM_PATH}/rccl/lib) list(APPEND oneflow_third_party_libs rccl) - list(APPEND ONEFLOW_THIRD_PARTY_INCLUDE_DIRS ${HIP_INCLUDE_DIRS} + list(APPEND ONEFLOW_THIRD_PARTY_INCLUDE_DIRS ${HIP_INCLUDE_DIRS} + ${HIPFFT_INCLUDE_DIRS} ${HIPBLAS_INCLUDE_DIRS} ${HIPCUB_INCLUDE_DIRS} "${ROCM_PATH}/hiprand/include" "${ROCM_PATH}/rocrand/include" + "${ROCM_PATH}/roctracer/include" ${MIOPEN_INCLUDE_DIRS} ${RCCL_INCLUDE_DIRS}) message(STATUS "ONEFLOW_THIRD_PARTY_INCLUDE_DIRS: ${ONEFLOW_THIRD_PARTY_INCLUDE_DIRS}") endif() - if(BUILD_RDMA) if(UNIX) include(CheckIncludeFiles) diff --git a/cmake/third_party/cutlass.cmake b/cmake/third_party/cutlass.cmake new file mode 100644 index 0000000000000000000000000000000000000000..50db49ea4fae69a174545298900b73ad56671c96 --- /dev/null +++ b/cmake/third_party/cutlass.cmake @@ -0,0 +1,104 @@ +include(ExternalProject) + +if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + set(WITH_CUTLASS_INIT OFF) +else() + set(WITH_CUTLASS_INIT ON) +endif() + +set(WITH_CUTLASS ${WITH_CUTLASS_INIT} CACHE BOOL "") + +if(WITH_CUTLASS) + + add_definitions(-DWITH_CUTLASS) + + find_package(Threads) + + set(CUTLASS_PROJECT cutlass) + + set(CUTLASS_INSTALL_DIR ${THIRD_PARTY_DIR}/cutlass) + + set(CUTLASS_INCLUDE_DIR ${CUTLASS_INSTALL_DIR}/include CACHE PATH "" FORCE) + set(CUTLASS_LIBRARY_DIR ${CUTLASS_INSTALL_DIR}/lib CACHE PATH "" FORCE) + set(CUTLASS_LIBRARIES ${CUTLASS_LIBRARY_DIR}/libcutlass.so) + set(CUTLASS_SOUREC_DIR ${CMAKE_CURRENT_BINARY_DIR}/cutlass/src/cutlass/) + + foreach(arch ${CUDA_REAL_ARCHS_LIST}) + if(arch GREATER_EQUAL 70) + list(APPEND CUTLASS_REAL_ARCHS ${arch}) + endif() + endforeach() + + if(THIRD_PARTY) + ExternalProject_Add( + ${CUTLASS_PROJECT} + PREFIX cutlass + URL ${CUTLASS_URL} + URL_MD5 ${CUTLASS_MD5} + UPDATE_COMMAND "" + BUILD_BYPRODUCTS ${CUTLASS_LIBRARIES} + CMAKE_ARGS -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE} + -DCMAKE_CXX_FLAGS:STRING=${CMAKE_CXX_FLAGS} + -DCMAKE_CXX_FLAGS_DEBUG:STRING=${CMAKE_CXX_FLAGS_DEBUG} + -DCMAKE_CXX_FLAGS_RELEASE:STRING=${CMAKE_CXX_FLAGS_RELEASE} + CMAKE_CACHE_ARGS + -DCMAKE_CUDA_COMPILER:STRING=${CUDAToolkit_NVCC_EXECUTABLE} + -DCMAKE_C_COMPILER_LAUNCHER:STRING=${CMAKE_C_COMPILER_LAUNCHER} + -DCMAKE_CXX_COMPILER_LAUNCHER:STRING=${CMAKE_CXX_COMPILER_LAUNCHER} + -DCMAKE_INSTALL_PREFIX:PATH=${CUTLASS_INSTALL_DIR} + -DCMAKE_INSTALL_LIBDIR:PATH=${CUTLASS_LIBRARY_DIR} + -DCMAKE_INSTALL_MESSAGE:STRING=${CMAKE_INSTALL_MESSAGE} + -DCMAKE_BUILD_TYPE:STRING=${CMAKE_BUILD_TYPE} + -DCUTLASS_LIBRARY_OPERATIONS:STRING=conv2d + -DCUTLASS_LIBRARY_KERNELS:STRING=simt_hfprop_*,tensorop_f16_*fprop,tensorop_h*fprop + -DCUTLASS_ENABLE_EXAMPLES:BOOL=OFF + -DCUTLASS_ENABLE_PROFILER:BOOL=OFF + -DCUTLASS_ENABLE_LIBRARY:BOOL=ON + -DCUTLASS_NVCC_ARCHS:STRING=${CUTLASS_REAL_ARCHS} + -DCUTLASS_ENABLE_TESTS:BOOL=OFF + -DCUTLASS_UNITY_BUILD_ENABLED:BOOL=ON + -DCUTLASS_LIBRARY_DEBUG_POSTFIX:STRING= + -DCUTLASS_NVCC_EMBED_PTX:BOOL=OFF) + + add_custom_target(cutlass_copy_examples_to_destination DEPENDS cutlass) + set(CUTLASS_SOURCE_EXAMPLES_DIR ${CUTLASS_SOUREC_DIR}/examples) + + set(CUTLASS_INSTALL_EXAMPLES_FILES + "41_fused_multi_head_attention/iterators/make_residual_last.h" + "41_fused_multi_head_attention/iterators/epilogue_predicated_tile_iterator.h" + "41_fused_multi_head_attention/iterators/predicated_tile_iterator_residual_last.h" + "41_fused_multi_head_attention/iterators/predicated_tile_access_iterator_residual_last.h" + "41_fused_multi_head_attention/mma_from_smem.h" + "41_fused_multi_head_attention/epilogue_rescale_output.h" + "41_fused_multi_head_attention/attention_scaling_coefs_updater.h" + "41_fused_multi_head_attention/gemm_kernel_utils.h" + "41_fused_multi_head_attention/fmha_grouped_problem_visitor.h" + "41_fused_multi_head_attention/fmha_grouped.h" + "41_fused_multi_head_attention/default_fmha_grouped.h" + "41_fused_multi_head_attention/epilogue_pipelined.h" + "41_fused_multi_head_attention/epilogue_thread_apply_logsumexp.h" + "41_fused_multi_head_attention/kernel_forward.h" + "41_fused_multi_head_attention/gemm/custom_mma_multistage.h" + "41_fused_multi_head_attention/gemm/custom_mma_base.h" + "41_fused_multi_head_attention/gemm/custom_mma.h" + "41_fused_multi_head_attention/gemm/custom_mma_pipelined.h" + "41_fused_multi_head_attention/find_default_mma.h" + "41_fused_multi_head_attention/debug_utils.h" + "45_dual_gemm/test_run.h" + "45_dual_gemm/kernel/dual_gemm.h" + "45_dual_gemm/device/dual_gemm.h" + "45_dual_gemm/dual_gemm_run.h" + "45_dual_gemm/thread/left_silu_and_mul.h" + "45_dual_gemm/threadblock/dual_mma_multistage.h" + "45_dual_gemm/threadblock/dual_epilogue.h" + "45_dual_gemm/threadblock/dual_mma_base.h") + + foreach(filename ${CUTLASS_INSTALL_EXAMPLES_FILES}) + add_custom_command( + TARGET cutlass_copy_examples_to_destination + COMMAND ${CMAKE_COMMAND} -E copy_if_different ${CUTLASS_SOURCE_EXAMPLES_DIR}/${filename} + ${CUTLASS_INSTALL_DIR}/examples/${filename}) + endforeach() + + endif(THIRD_PARTY) +endif(WITH_CUTLASS) diff --git a/cmake/third_party/eigen.cmake b/cmake/third_party/eigen.cmake index 8a509c4b20d7d1db18e2df75c9bc0509b18fab76..fb86602bd7711d82a7447a812a975168371aeccd 100644 --- a/cmake/third_party/eigen.cmake +++ b/cmake/third_party/eigen.cmake @@ -7,12 +7,9 @@ set(EIGEN_URL https://github.com/Oneflow-Inc/eigen-git-mirror/archive/refs/tags/ set(EIGEN_MD5 a23cb70e12d1bf9b09cb28af51bc26ae) use_mirror(VARIABLE EIGEN_URL URL ${EIGEN_URL}) -add_definitions(-DEIGEN_NO_AUTOMATIC_RESIZING) if(BUILD_CUDA) add_definitions(-DEIGEN_USE_GPU) endif() -add_definitions(-DEIGEN_NO_MALLOC) -#add_definitions(-DEIGEN_NO_AUTOMATIC_RESIZING -DEIGEN_NO_MALLOC -DEIGEN_USE_GPU) if(THIRD_PARTY) diff --git a/cmake/third_party/nccl.cmake b/cmake/third_party/nccl.cmake index 29e89f8b4c01dcaf725f4d486ea170c7f0d15bbd..c9b014030f767e71a82b26e96290a4c1fb8ab681 100644 --- a/cmake/third_party/nccl.cmake +++ b/cmake/third_party/nccl.cmake @@ -34,11 +34,36 @@ else() set(NCCL_INCLUDE_DIR ${NCCL_INSTALL_DIR}/include) set(NCCL_LIBRARY_DIR ${NCCL_INSTALL_DIR}/lib) - set(NCCL_URL https://github.com/NVIDIA/nccl/archive/refs/tags/v2.12.10-1.tar.gz) + # Versions 2.13 and above may cause deadlocks + if(CUDA_VERSION VERSION_GREATER_EQUAL "11.8") + set(NCCL_URL https://github.com/NVIDIA/nccl/archive/refs/tags/v2.15.1-1.tar.gz) + set(NCCL_MD5 37b787ff8934cd9374b4612f663c17fa) + else() + set(NCCL_URL https://github.com/NVIDIA/nccl/archive/refs/tags/v2.12.10-1.tar.gz) + set(NCCL_MD5 bdb91f80b78c99831f09ca8bb28a1032) + endif() + use_mirror(VARIABLE NCCL_URL URL ${NCCL_URL}) list(APPEND NCCL_LIBRARIES ${NCCL_LIBRARY_DIR}/${NCCL_LIBRARY_NAME}) + set(NCCL_ARCHS_LIST ${CUDA_REAL_ARCHS_LIST}) + + # remove redundant archs, https://github.com/NVIDIA/nccl/blob/cb111f764a6d46370f24f75101d6b219bb2dda54/makefiles/common.mk#L28 + if("70" IN_LIST NCCL_ARCHS_LIST AND "75" IN_LIST NCCL_ARCHS_LIST) + list(REMOVE_ITEM NCCL_ARCHS_LIST "75") + endif() + if("80" IN_LIST NCCL_ARCHS_LIST AND "86" IN_LIST NCCL_ARCHS_LIST) + list(REMOVE_ITEM NCCL_ARCHS_LIST "86") + endif() + if("80" IN_LIST NCCL_ARCHS_LIST AND "89" IN_LIST NCCL_ARCHS_LIST) + list(REMOVE_ITEM NCCL_ARCHS_LIST "89") + endif() + + foreach(arch ${NCCL_ARCHS_LIST}) + string(APPEND NCCL_GENCODE "-gencode=arch=compute_${arch},code=sm_${arch} ") + endforeach() + if(THIRD_PARTY) include(ProcessorCount) @@ -47,11 +72,12 @@ else() nccl PREFIX nccl URL ${NCCL_URL} - URL_MD5 bdb91f80b78c99831f09ca8bb28a1032 + URL_MD5 ${NCCL_MD5} UPDATE_COMMAND "" CONFIGURE_COMMAND "" BUILD_IN_SOURCE 1 BUILD_COMMAND make -j${PROC_NUM} src.build CUDA_HOME=${CUDATOOLKIT_BIN_ROOT} + NVCC_GENCODE=${NCCL_GENCODE} INSTALL_COMMAND make src.install PREFIX=${NCCL_INSTALL_DIR} BUILD_BYPRODUCTS ${NCCL_LIBRARIES}) diff --git a/dev-requirements.txt b/dev-requirements.txt index b6e64ecf514d166d6624500c189da9420d840dc7..16f3fb0cc3ce38308dccb9fe21b2f91d3ff41f86 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -14,3 +14,4 @@ dataclasses; python_version<"3.7" cmakelang==0.6.13 pytest-xdist rich +portalocker diff --git a/docs/source/auto_parallel.rst b/docs/source/auto_parallel.rst new file mode 100644 index 0000000000000000000000000000000000000000..ffa2971240304013d73d5a78dd52d7bdb41e835b --- /dev/null +++ b/docs/source/auto_parallel.rst @@ -0,0 +1,70 @@ +Auto Parallelism +==================================================== + +As the scale of deep-learning models grows larger and larger, distributed training, +or parallelism, is needed. Data parallelism and model parallelism has been designed +to speed up the training and solve memory issues. + +In oneflow, SBP signature enables users to configure parallelism policy easily. +However, users still need to specify the SBP property for each operator, or most of them. +Users might spend a couple of days digging into the detail of parallelism and get a +low throughput just because of a slight mistake in the configuration of SBP signature. + +.. note:: + + It only works on :doc:`graph` mode. + + +Our strength +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To get rid of all those configurations for SBP signatures, we developed auto parallelism. +Still, configurations of placement are necessary and we have not supported auto placement +yet. If you read this paragraph before you rush into any SBP stuff, then congratulation, +you do not need to learn SBPs. You can start writing your code as you did under CPU mode. +Our auto parallelism would generate a fast strategy customized for your specific models, +the size of parameters, and the number of available GPUs. + + +How to use auto parallelism? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You just need to simply enable the configuration settings in the model +of :doc:`graph` . + +Example:: + + import oneflow as flow + class SubclassGraph(flow.nn.Graph): + def __init__(self): + super().__init__() # MUST be called + # auto parallelism configuration + self.config.enable_auto_parallel(True) + # other configurations about auto parallelism + # ...... + + def build(self): + pass + +.. warning:: + + If you enable auto parallelism, OneFlow will take care of the SBP configurations + of operators except for explicit ``to_global`` functions. + + +Configuration API for auto parallelism +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. currentmodule:: oneflow.nn.graph.graph_config.GraphConfig + +.. autosummary:: + :toctree: generated + :nosignatures: + + enable_auto_parallel + enable_auto_parallel_ignore_user_sbp_config + set_auto_parallel_computation_cost_ratio + set_auto_parallel_wait_time + enable_auto_parallel_trunk_algo + enable_auto_parallel_sbp_collector + diff --git a/docs/source/autograd.rst b/docs/source/autograd.rst index c59d5834010ad0e4c5c6b05400fa9d988b740325..6d5ab4bd7f0b1ae3998ab4f2a1657854711103ba 100644 --- a/docs/source/autograd.rst +++ b/docs/source/autograd.rst @@ -1,12 +1,87 @@ oneflow.autograd -================================================ -Functions and classes for autograd. ---------------------------------------------------- +==================================================== + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/autograd.html + +``oneflow.autograd`` provides classes and functions implementing automatic differentiation of arbitrary scalar +valued functions. It requires minimal changes to the existing code - you only need to declare ``Tensor`` s +for which gradients should be computed with the ``requires_grad=True`` keyword. As of now, we only support +autograd for floating point ``Tensor`` types ( half, float, double and bfloat16). + + .. currentmodule:: oneflow.autograd -.. autoclass:: oneflow.autograd.Function - :members: apply, - :special-members: __call__, -.. automodule:: oneflow.autograd - :members: grad, - backward, +.. autosummary:: + :toctree: generated + :nosignatures: + + backward + grad + +Locally disabling gradient computation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. autosummary:: + :toctree: generated + :nosignatures: + + no_grad + enable_grad + set_grad_enabled + inference_mode + +.. TODO(wyg): uncomment this after aligning accumulate grad +.. Default gradient layouts +.. ^^^^^^^^^^^^^^^^^^^^^^^^ + +.. A ``param.grad`` is accumulated by replacing ``.grad`` with a +.. new tensor ``.grad + new grad`` during :func:`oneflow.autograd.backward()` or +.. :func:`oneflow.Tensor.backward()`. + +In-place operations on Tensors +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Supporting in-place operations in autograd is a hard matter, and we discourage +their use in most cases. Autograd's aggressive buffer freeing and reuse makes +it very efficient and there are very few occasions when in-place operations +actually lower memory usage by any significant amount. Unless you're operating +under heavy memory pressure, you might never need to use them. + +Tensor autograd functions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. autosummary:: + :nosignatures: + + oneflow.Tensor.grad + oneflow.Tensor.requires_grad + oneflow.Tensor.is_leaf + oneflow.Tensor.backward + oneflow.Tensor.detach + oneflow.Tensor.register_hook + oneflow.Tensor.retain_grad + +Function +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autoclass:: Function +.. currentmodule:: oneflow.autograd +.. autosummary:: + :toctree: generated + :nosignatures: + + Function.forward + Function.backward + Function.apply + +Context method mixins +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +When creating a new :class:`Function`, the following methods are available to `ctx`. + +.. currentmodule:: oneflow._oneflow_internal.autograd.Function +.. autosummary:: + :toctree: generated + :nosignatures: + + FunctionCtx.mark_non_differentiable + FunctionCtx.save_for_backward + FunctionCtx.saved_tensors diff --git a/docs/source/comm.rst b/docs/source/comm.rst deleted file mode 100644 index cdfc227c929a32272f933f6e1570be600a197fff..0000000000000000000000000000000000000000 --- a/docs/source/comm.rst +++ /dev/null @@ -1,17 +0,0 @@ -oneflow.comm -=================================== -oneflow communication function ----------------------------------- -.. currentmodule:: oneflow.comm -.. automodule:: oneflow.comm - :members: all_reduce, - all_gather, - broadcast, - scatter, - all_to_all, - reduce, - gather, - reduce_scatter, - send, - recv, - barrier, diff --git a/docs/source/conf.py b/docs/source/conf.py index 3bb697192340def57796c07a0137b870b3e033f3..e702d5e823f69782a12de454f2eaa91fbcfa7178 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -45,9 +45,14 @@ extensions = [ "sphinx.ext.autodoc", "sphinx.ext.napoleon", "recommonmark", + "sphinx.ext.autosummary", "sphinx_copybutton", ] +# build the templated autosummary files +autosummary_generate = True +numpydoc_show_class_members = False + # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] @@ -107,7 +112,6 @@ html_static_path = ["_static"] # # html_sidebars = {} - # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. diff --git a/docs/source/cuda.rst b/docs/source/cuda.rst index 97d62a8105bbefbdec6bfca4354045fab2b758cb..fbdf91e3d6446809ccfe6a8876be51b28f222254 100644 --- a/docs/source/cuda.rst +++ b/docs/source/cuda.rst @@ -1,22 +1,57 @@ oneflow.cuda =================================== -ONEFLOW.CUDA ----------------------------------- + +.. The documentation is referenced from: https://pytorch.org/docs/1.10/cuda.html. + .. currentmodule:: oneflow.cuda -.. automodule:: oneflow.cuda - :members: is_available, - device_count, - current_device, - set_device, - synchronize, - manual_seed_all, - manual_seed, - empty_cache, - HalfTensor, - FloatTensor, - DoubleTensor, - BoolTensor, - ByteTensor, - CharTensor, - IntTensor, - LongTensor, \ No newline at end of file + +.. autosummary:: + :toctree: generated + :nosignatures: + + is_available + device_count + current_device + set_device + synchronize + get_device_properties + get_device_capability + get_device_name + +.. note:: + The :attr:`current_device` returns local rank as device index. It is different from the 'torch.current_device()' in PyTorch. + + +Random Number Generator +------------------------- +.. autosummary:: + :toctree: generated + :nosignatures: + + manual_seed_all + manual_seed + + +GPU tensor +----------------------------- +.. autosummary:: + :toctree: generated + :nosignatures: + + HalfTensor + FloatTensor + DoubleTensor + BoolTensor + ByteTensor + CharTensor + IntTensor + LongTensor + +Memory management +----------------------------- +.. autosummary:: + :toctree: generated + :nosignatures: + + empty_cache + \ No newline at end of file diff --git a/docs/source/distributed.rst b/docs/source/distributed.rst index 64a841af9664d5d4dad2dc2eb958bf71b03dbf75..159d6e2a34c528cc3a0f41fb2343ceb06bf67184 100644 --- a/docs/source/distributed.rst +++ b/docs/source/distributed.rst @@ -1,6 +1,223 @@ oneflow.distributed ========================================================= +.. note :: + Please refer to `OneFlow Distributed Overview `__ + for a brief introduction to all features related to distributed training. + +OneFlow provides two ways to accomplish `Distributed Training`: + +- The first way is that users are recommended to use OneFlow's global Tensor for distributed training. Global Tensor regards the computing cluster as a supercomputing device, allowing users to write distributed training code just like in a single-machine environment. + +- OneFlow also provides a DDP（DistributedDataParallel） module aligned with PyTorch. DDP has been well-known and widely used in data parallelism by the majority of PyTorch users. Also see `PyTorch DDP introduction `_. + + + +Basic +------------------------------- +When you start distributed training in OneFlow, the following functions can be used. + +.. currentmodule:: oneflow.env + +.. autosummary:: + :toctree: generated + :nosignatures: + + get_world_size + get_rank + get_local_rank + get_node_size + init_rdma + rdma_is_initialized + + +`Global Tensor` +-------------------------------------------------------------- + +Construct `Global Tensor` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +A `Global Tensor` can be created with a ``placement`` and a ``sbp``. The ``placement`` describes the physical devices of the global tensor will be allocated, and the ``sbp`` describes its distribution among these devices. + +:: + + >>>import oneflow as flow + >>> # Place a global tensor on cuda device of rank(process) 0 and 1 + >>> placement = flow.placement(type="cuda", ranks=[0, 1]) + >>> # Each rank's local data is a part data as a result of spliting global data on dim 0 + >>> sbp = flow.sbp.split(dim=0) + >>> # Create a global tensor by randn + >>> x = flow.randn(4, 5, placement=placement, sbp=sbp) + >>> x.shape + oneflow.Size([4, 5]) + + +Convert `Local Tensor` to `Global Tensor` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +With ``Tensor.to_global`` interface, `Local Tensor` can create a `Global Tensor` and use that `Local Tensor` as its local component at the current node. + +Two `local tensors` with the shape of ``(2,5)`` are created separately on two devices. While after the ``to_global`` method, the `global tensor` with a shape of ``(4,5)`` is obtained. + +Code running on Node 0 + +:: + + import oneflow as flow + + x = flow.randn(2,5) + placement = flow.placement("cuda", [0,1]) + sbp = flow.sbp.split(0) + x_global = x.to_global(placement=placement, sbp=sbp) + x_global.shape + +Code running on Node 1 + +:: + + import oneflow as flow + + x = flow.randn(2,5) + placement = flow.placement("cuda", [0,1]) + sbp = flow.sbp.split(0) + x_global = x.to_global(placement=placement, sbp=sbp) + x_global.shape + +Redistribute `Global Tensor` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Redistributing a `Global Tensor` means moving its data to another device group (or placement), or changing its data distribution (or SBP) across the group, or both at the same time. The redistributed tensor is still a `Global Tensor`. + +:: + + >>> import oneflow as flow + >>> x = flow.tensor([1.0, 2.0], placement=flow.placement("cuda", ranks=[0, 1]), sbp=flow.sbp.split(0)) + >>> y = x.to_global(placement=flow.placement("cuda", ranks=[2, 3]), sbp=flow.sbp.broadcast) + +According to the operator's semantics, OneFlow defines a sequence of valid input and output SBP combinations for each built-in operator. So OneFlow could automatically redistribute the `Global Tensor` to satisfy the operator's SBP requirements for its input Tensor. For example, the following code: + +:: + + >>> import oneflow as flow + >>> x = flow.randn(4, 4, + placement=flow.placement("cuda", ranks=[0, 1]), + sbp=flow.sbp.split(0)) + >>> y = flow.randn(4, 4, + placement=flow.placement("cuda", ranks=[0, 1]), + sbp=flow.sbp.split(1)) + >>> z = x + y + +When ``x + y`` is executed, since x is split along dimension ``0`` and y is split along dimension ``1``, their local components at each node can not be added directly, then OneFlow will automatically redistribute one of x and y to make them have the same SBP, and complete the add operation successfully. + +.. note :: + - Global Tensor can not be used in combination with DDP currently. + - Global Tensor requires all devices to execute at the same pace, otherwise, it may cause multi-process deadlock. + +Get Local Tensor from Global Tensor +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +With ``Tensor.to_local`` interface, the `Global Tensor` can return its local component at the current node. + +:: + + y = x.to_local() + y.is_local + True + y + tensor([[ 2.9186e-01, -3.9442e-01, 4.7072e-04, -3.2216e-01, 1.7788e-01], + [-4.5284e-01, 1.2361e-01, -3.5962e-01, 2.6651e-01, 1.2951e+00]], + device='cuda:0', dtype=oneflow.float32) + + +DistributedDataParallel +-------------------------------------------------------------- + +For more information about DistributedDataParallel, see ``nn.parallel.DistributedDataParallel`` + +The following script shows the process of using ``oneflow.nn.parallel.DistributedDataParallel`` for training data parallel: + +.. code-block:: + + import oneflow as flow + from oneflow.nn.parallel import DistributedDataParallel as ddp + + train_x = [ + flow.tensor([[1, 2], [2, 3]], dtype=flow.float32), + flow.tensor([[4, 6], [3, 1]], dtype=flow.float32), + ] + train_y = [ + flow.tensor([[8], [13]], dtype=flow.float32), + flow.tensor([[26], [9]], dtype=flow.float32), + ] + + + class Model(flow.nn.Module): + def __init__(self): + super().__init__() + self.lr = 0.01 + self.iter_count = 500 + self.w = flow.nn.Parameter(flow.tensor([[0], [0]], dtype=flow.float32)) + + def forward(self, x): + x = flow.matmul(x, self.w) + return x + + + m = Model().to("cuda") + m = ddp(m) + loss = flow.nn.MSELoss(reduction="sum") + optimizer = flow.optim.SGD(m.parameters(), m.lr) + + for i in range(0, m.iter_count): + rank = flow.env.get_rank() + x = train_x[rank].to("cuda") + y = train_y[rank].to("cuda") + + y_pred = m(x) + l = loss(y_pred, y) + if (i + 1) % 50 == 0: + print(f"{i+1}/{m.iter_count} loss:{l}") + + optimizer.zero_grad() + l.backward() + optimizer.step() + + print(f"\nw:{m.w}") + +There are only two differences between the data parallelism training code and the stand-alone single-card script: + +- Use `DistributedDataParallel` to wrap the module object (`m = ddp(m)`) +- Use `get_rank` to get the current device number and distribute the data to the device. + +Then use `launcher` to run the script, leave everything else to OneFlow, which makes distributed training as simple as stand-alone single-card training: + +:: + + python3 -m oneflow.distributed.launch --nproc_per_node 2 ./ddp_train.py + + +Communication collectives +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. currentmodule:: oneflow.comm + +.. autosummary:: + :toctree: generated + :nosignatures: + + all_reduce + all_gather + all_gather_into_tensor + all_to_all + broadcast + barrier + gather + reduce + reduce_scatter + reduce_scatter_tensor + recv + scatter + send + +Launching distributed training +-------------------------------------------------------------- + .. currentmodule:: oneflow.distributed run commands below to see more about usage. diff --git a/docs/source/distributions.rst b/docs/source/distributions.rst new file mode 100644 index 0000000000000000000000000000000000000000..e482697b30f6011e5fc719c202194022a0dd5fc5 --- /dev/null +++ b/docs/source/distributions.rst @@ -0,0 +1,17 @@ +oneflow.distributions +================================================== + +.. contents:: oneflow.distributions + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + +.. currentmodule:: oneflow.distributions +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Distribution + Categorical diff --git a/docs/source/env.rst b/docs/source/env.rst deleted file mode 100644 index 3738f0a67c5f7701519709be8e987003025b2adb..0000000000000000000000000000000000000000 --- a/docs/source/env.rst +++ /dev/null @@ -1,12 +0,0 @@ -oneflow.env -=================================== -Environment ----------------------------------- -.. currentmodule:: oneflow - -.. autofunction:: oneflow.env.get_world_size -.. autofunction:: oneflow.env.get_rank -.. autofunction:: oneflow.env.get_local_rank -.. autofunction:: oneflow.env.get_node_size -.. autofunction:: oneflow.env.init_rdma -.. autofunction:: oneflow.env.rdma_is_initialized diff --git a/docs/source/environment_variables.rst b/docs/source/environment_variables.rst new file mode 100644 index 0000000000000000000000000000000000000000..4f296a84ee0535be4a491092d5f2f03df02fdefe --- /dev/null +++ b/docs/source/environment_variables.rst @@ -0,0 +1,473 @@ +Environment Variables +================================================ + +OneFlow has an extensive set of environment variables to tune for specific usage. + +`ONEFLOW_COMM_NET_IB_HCA `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +When there are multiple IB NIC(which can be checked by ``ibstatus`` on the server, the system uses the first IB NIC for comm_net communication by default. + +When this environment variable is set, the system will check all IB NIC and find the NIC with the corresponding name. `#5626 `_ + +Values accepted +^^^^^^^^^^^^^^^ +The default value is empty, such as ``mlx5_0:1``、 ``mlx5_1:1``. When the port is 0, the default value is 1, representing the first port. + +`ONEFLOW_COMM_NET_IB_GID_INDEX `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +For the query of `ibv_query_gid `_, and 0 represents success. It often used with ``ONEFLOW_COMM_NET_IB_HCA``. GID means the Global ID, QP under RoCE network must be built by this value, instead of just using the LID as in the IB network. `#5626 `_ + +Values accepted +^^^^^^^^^^^^^^^ +The default value is 0, representing the port index value + +`ONEFLOW_COMM_NET_IB_QUEUE_DEPTH `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Queue length of jobs in IB network. + +This value effectively controls the size of the module without instead of using IB's default size, such as ``ONEFLOW_COMM_NET_IB_MEM_BLOCK_SIZE``. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``1024``, receiving ``int64_t``. The system would compare with ``max_qp_wr`` (Maximum number of outstanding WR on any work queue), and take the smaller one. + +`ONEFLOW_COMM_NET_IB_MEM_BLOCK_SIZE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The size of the module read when communicating. + +The value can calculate the amount of module, and transmit it after encapsulation. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``8388608`` (8M) + +`ONEFLOW_STREAM_CUDA_EVENT_FLAG_BLOCKING_SYNC `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Represents stream, and marks Blocking synchronization in cuda. `Detailed information `_, `#5612 `_, `#5837 `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_LIBIBVERBS_PATH `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +To load the DynamicLibrary by dlopen at runtime, to find symbols of ibverbs functions by dlopen without linking during compile for better compatibility. `#4852 `_. + +If it failed, it will output ``libibverbs not available, ibv_fork_init skipped``, if it worked, the ``import oneflow`` will output such as ``loaded library: /usr/lib/x86_64-linux-gnu/libibverbs.so.1`` + +Values accepted +^^^^^^^^^^^^^^^ +The default value is empty, but will load ``libibverbs.so.1``, ``libibverbs.so``. + +`ONEFLOW_DEBUG_MODE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Enable ``debug`` mode, ``ONEFLOW_DEBUG`` can do. + +If ``debug`` mode is on, it will output more INFO level logs, different ``prototxt`` and ``dot`` to files. The automatically inserted boxing information will be printed to the log file under eager global mode. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is empty, but will receive any string. + +`ONEFLOW_DRY_RUN `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Only for test running, it can generate log files like ``dot``. + +Exit once the test is succeed, do not try real training. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is empty, but will receive any string. + +`ONEFLOW_DEBUG_KERNEL_SYNC_CHECK_NUMERICS `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Only used when debugging because the performance would be affected, it could detect which op in the network appears nan or inf. + +It will create ``CpuCheckNumericsKernelObserver`` under ``cpu`` , and ``CudaCheckNumericsKernelObserver`` under ``cuda`` `#6052 `_ . + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_DEBUG_KERNEL_SYNC_CHECK `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Only used when debugging because the performance would be affected. + +It will create ``SyncCheckKernelObserver`` and will be synced after each kernel. + +It could be used to debug cuda errors. `#6052 `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_PROFILER_KERNEL_PROFILE_CUDA_MEMORY_BANDWIDTH `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Used when generate profiler files by nsys. + +Profiler is only valid for lazy temporarily. + +It can estimate the memory bandwidth reached by kernel by counting the execution time of the GPU kernel and the size of the input and output memory, and help find potential kernels that can be optimized. `Details `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``. When using, the compiled package needs to enable ``BUILD_PROFILER``. + +`ONEFLOW_PROFILER_KERNEL_PROFILE_KERNEL_FORWARD_RANGE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The same as above. collect `op name `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``. When using, the compiled package needs to enable ``BUILD_PROFILER``. + +`ONEFLOW_KERNEL_DISABLE_BLOB_ACCESS_CHECKER `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Only use blob_access_checker after enabling, because blob_access_checker is for correctness assurance, and closing it in some cases can increase the kernel overhead. `#5728 `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_KERNEL_ENABLE_CUDA_GRAPH `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Takes effect under ``WITH_CUDA_GRAPHS`` and the default value is ``false``. It uses more memory, so when there's just enough memory, it won't run. + +Turning on CUDA_GRAPH will use up more memory CUDA Graphs support. `#5868 `_ + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_ACTOR_ENABLE_LIGHT_ACTOR `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +LightActor is a new type of Actor that only handles NormalForward and similar tasks where all regst_num is 1 or tasks with only one kernel. `#5868 `_. ``export ONEFLOW_KERNEL_ENABLE_CUDA_GRAPH=1`` (Would use more memories), ``export ONEFLOW_THREAD_ENABLE_LOCAL_MESSAGE_QUEUE=1``, ``export ONEFLOW_KERNEL_DISABLE_BLOB_ACCESS_CHECKER=1``, ``export ONEFLOW_ACTOR_ENABLE_LIGHT_ACTOR=1``, ``export ONEFLOW_STREAM_REUSE_CUDA_EVENT=1`` can be used together. + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_THREAD_ENABLE_LOCAL_MESSAGE_QUEUE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +`#5720 `_. It is used to enable local message queue, ``oneflow.config.thread_enable_local_message_queue(True)`` is no longer used. + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``false``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_PERSISTENT_IN_STREAM_BUFFER_SIZE_BYTES `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Represents the size of each read from disk. `#5162 `_ + +Values accepted +^^^^^^^^^^^^^^^ +The default value is empty. If an invalid string or negative number is entered, the default value would be ``32 * 1024``; 32KB. + +`ONEFLOW_DECODER_ENABLE_NVJPEG_HARDWARE_ACCELERATION `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +``NVJPEG_VER_MAJOR`` need to be bigger than ``11``. It can accelerate nvjpeg hardware, warm up jpeg decoder and hw_jpeg decoder, `#5851 `_. + +Hardware JPEG decoder and NVIDIA nvJPEG library on NVIDIA A100 GPUs + +Values accepted +^^^^^^^^^^^^^^^ +Define and set to ``true``, and would be ``true`` only when the value is ``1``, ``true``, ``yes``, ``on`` and ``y``. + +`ONEFLOW_SERVING_DEBUG `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +For printing information of OneFlow Serving Debug + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_DISABLE_VIEW `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +To disable view mechanism, which means op related to view would stop running. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to disable Middle Node. When it is false, all inter-SBP communication is supported + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_ONE_EMBEDDING_DISABLE_NUMA_AWARE_ALLOCATION `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to disable NUMA_AWARE memory allocation when the OneEmbedding module allocates video memory. + +NUMA_AWARE memory allocation means that when allocating pinned host memory, the cpu close to the gpu will be considered (for example, if it is gpu 0 1, memory will be allocated on cpu0) + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_EP_CUDA_ENABLE_TF32_EXECUTION `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to allow CUDA to use TF32 numeric types for computation + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``true`` + +`ONEFLOW_FUNCTOR_DISABLE_FUSED_MLP `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to disable the fused_mlp operator implemented by cublasLt in FusedMLPFunctor, if disabled, it will degenerate into a multiple matrix multiplication operation. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_ONE_EMBEDDING_EMBEDDING_SHUFFLE_INDEPENTENT_STREAM `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to put the EmbeddingShuffle of the OneEmbedding module on a separate stream for overlapping execution. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_ONE_EMBEDDING_GRADIENT_SHUFFLE_USE_FP16 `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to allow the EmbeddingGradientShuffle operator of the OneEmbedding module to use the FP16 data type in the AMP case. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``true`` + +`ONEFLOW_ONE_EMBEDDING_NOT_FUSE_CAST_TO_UPDATE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to disable the fusion of cast type conversion and parameter update of OneEmbedding parameters into one operator in the case of AMP + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_DEBUG_KERNEL_SYNC_CHECK_NUMERICS_DUMP `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +When the value appears NaN or Inf, save the data Dump. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_MLIR_ENABLE_IR_PRINTING `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control whether to print ir when running each pass when debugging + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_MLIR_STDOUT `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control whether MLIR outputs log information in the console + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_MLIR_DUMP_IR `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control whether to dump ir files + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_MLIR_ENABLE_ROUND_TRIP `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control whether Oneflow Job goes into MLIR + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_KERNEL_REDUCE_SUM_USE_MATMUL `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +whether to use matrix multiplication for reduce_sum + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Whether to quantify the shuffle application communication in the case of OneEmbedding multi-card + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``false`` + +`ONEFLOW_TENSOR_BUFFER_ALIGNED_SIZE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Align size when allocating TensorBuffer memory + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``1024`` + +`ONEFLOW_TENSOR_BUFFER_POOL_THREAD_LOCAL_CACHE_SIZE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control the size of ``thread_local_cache`` in TensorBufferPool + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``64`` + +`ONEFLOW_GRPC_MAX_MESSAGE_BYTE_SIZE `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Set the maximum size of the gRPC transport message + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``-1`` + +`ONEFLOW_ONE_EMBEDDING_PERSISTENT_TABLE_CAPACITY_HINT `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control the initial capacity of the PersistentTable of OneEmbedding to avoid frequent expansion + +Values accepted +^^^^^^^^^^^^^^^ +OneEmbedding will calculate according to the actual situation, and users can also choose to configure a larger capacity. + +`ONEFLOW_ONE_EMBEDDING_PERSISTENT_TABLE_NUM_WORKERS `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +The number of threads used for reading and writing the PersistentTable of OneEmbedding + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``4`` + +`ONEFLOW_EP_CUDA_CONST_BUFFER_ELEMENT_COUNT `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Specify the size of the all zero and all one buffers on the CUDA device. + +This buffer can be used with matrix multiplication to implement operations such as reduce_sum + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``1024x1024`` + +`OMP_NUM_THREADS `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Set the number of threads used by OMP + +Values accepted +^^^^^^^^^^^^^^^ +The default value will be generated by specific `computational logic `_. + +`SBP_INFER_RULE_TAG `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Specify SBP derivation rules + +Values accepted +^^^^^^^^^^^^^^^ +When the default vaule is ``1`` , select the SBP that satisfies the producer or the SBP with the smallest cost as much as possible. + +When the default value is ``2``, select the SBP that matches the most. + +When the default value is ``3``, select the SBP with the smallest cost. + +`ONEFLOW_TENSOR_BUFFER_GROWTH_FACTOR `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control the growth factor of TensorBuffer + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``1.0`` + +`ONEFLOW_TENSOR_BUFFER_SHRINK_FACTOR `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Controls the shrink factor of TensorBuffer + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``0.7`` + +`ONEFLOW_TENSOR_BUFFER_POOL_SIZE_FACTOR `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Controls the size factor of TensorBuffer + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``2.0`` + +`AUTO_PARALLEL_TRANSFER_COST `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Control the size of the automatic parallel transfer cost + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``1.65e8`` + + +`ONEFLOW_DEBUG_PASS `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Pass names and print job before and after a specific pass, such as ``export ONEFLOW_DEBUG_PASS="FuseAddToOutputPass``. + +Or ALL, print job before and after a specific pass, such as ``export ONEFLOW_DEBUG_PASS="ALL"``. + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``empty`` + +`ONEFLOW_PROFILER_HOST_THREAD_NAME_PREFIX `_ +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +Add a prefix to the name of the named host thread in the profiling context to facilitate sorting in the visualization tool (nsight) + +Values accepted +^^^^^^^^^^^^^^^ +The default value is ``empty`` diff --git a/docs/source/functional.rst b/docs/source/functional.rst deleted file mode 100644 index 1f8ffc40071364e352e67e93b9202761d837f0fb..0000000000000000000000000000000000000000 --- a/docs/source/functional.rst +++ /dev/null @@ -1,52 +0,0 @@ -oneflow.nn.functional -=========================================== -Functional operations for neural networks -------------------------------------------- -.. currentmodule:: oneflow.nn.functional -.. autofunction:: conv1d -.. autofunction:: conv2d -.. autofunction:: conv3d -.. autofunction:: conv_transpose1d -.. autofunction:: conv_transpose2d -.. autofunction:: conv_transpose3d -.. autofunction:: adaptive_avg_pool1d -.. autofunction:: adaptive_avg_pool2d -.. autofunction:: adaptive_avg_pool3d -.. autofunction:: relu -.. autofunction:: hardsigmoid -.. autofunction:: hardshrink -.. autofunction:: hardswish -.. autofunction:: hardtanh -.. autofunction:: normalize -.. autofunction:: layer_norm -.. autofunction:: leaky_relu -.. autofunction:: elu -.. autofunction:: celu -.. autofunction:: selu -.. autofunction:: sigmoid -.. autofunction:: pad -.. autofunction:: prelu -.. autofunction:: logsigmoid -.. autofunction:: log_softmax -.. autofunction:: gelu -.. autofunction:: glu -.. autofunction:: softsign -.. autofunction:: softmax -.. autofunction:: softplus -.. autofunction:: tanh -.. autofunction:: threshold -.. autofunction:: softshrink -.. autofunction:: silu -.. autofunction:: mish -.. autofunction:: one_hot -.. autofunction:: triplet_margin_loss -.. autofunction:: dropout -.. autofunction:: affine_grid -.. autofunction:: grid_sample -.. autofunction:: interpolate -.. autofunction:: ctc_greedy_decoder -.. autofunction:: sparse_softmax_cross_entropy -.. autofunction:: embedding -.. autofunction:: linear -.. autofunction:: cosine_similarity -.. autofunction:: cross_entropy diff --git a/docs/source/graph.rst b/docs/source/graph.rst index 59198b2dbdb45ae8b45267aa003e10f753b5887a..a0df0ad46a76d8743e604880d595f4b9a23620e4 100644 --- a/docs/source/graph.rst +++ b/docs/source/graph.rst @@ -1,39 +1,168 @@ oneflow.nn.Graph ============================================================ Base class for running neural networks in Static Graph Mode. + +Currently, there are two main ways to run models in deep learning frameworks, namely dynamic graphs and static graphs , which are also conventionally referred to as :ref:`dynamic graph` and :ref:`static graph` in OneFlow. + +Both approaches have their advantages and disadvantages, and OneFlow provides support for both approaches, with Eager mode being the default. + +Generally speaking, dynamic graphs are easier to use and static graphs have more performance advantages. :class:`oneflow.nn.Graph` module is provided by OneFlow to allow users to build static graphs and train models with Eager-like programming conventions. + +.. contents:: oneflow.nn.Graph + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + +.. _dynamic graph: + +Eager Mode to Static Graph Mode +------------------------------------------------------------ + +OneFlow runs in Eager mode by default. + +OneFlow's nn.Graph is programmed in a style very similar to Eager Mode, so it is possible to make small changes and get large performance gains. + +The following script shows the process of building a neural network in eager mode using the interface under ``oneflow.nn`` : + + +.. code-block:: + + import oneflow as flow + import oneflow.nn as nn + + class ModuleMyLinear(nn.Module): + def __init__(self, in_features, out_features): + super().__init__() + self.weight = nn.Parameter(flow.randn(in_features, out_features)) + self.bias = nn.Parameter(flow.randn(out_features)) + + def forward(self, input): + return flow.matmul(input, self.weight) + self.bias + + linear_model = ModuleMyLinear(4, 3) + + +Eager ``nn.Module`` can be reused by ``nn.Graph``. The above script for eager mode can be changed to static Graph mode by adding just a few lines of code, which consists of the following steps: + +- Define your customized graph as a subclass of ``nn.Graph`` +- At the beginning of __init__. Call super().__init__() to let OneFlow do the necessary initialization of the Graph +- Reuse the ``nn.Module`` object in Eager mode in __init__ (self.model = model) +- Describe the computation in the ``build`` method +- Instantiate your graph then call it. + +.. code-block:: + + class GraphMyLinear(nn.Graph): + def __init__(self): + super().__init__() + self.model = linear_model + + def build(self, input): + return self.model(input) + + graph_mylinear = GraphMyLinear() + input = flow.randn(1, 4) + out = graph_mylinear(input) + print(out) + + tensor([[-0.3298, -3.7907, 0.1661]], dtype=oneflow.float32) + +.. _static graph: + +Static Graph Mode ------------------------------------------------------------ -.. currentmodule:: oneflow.nn -.. autoclass:: oneflow.nn.Graph - :members: __init__, - build, - __call__, - add_optimizer, - set_grad_scaler, - state_dict, - load_state_dict, - name, - debug, - __repr__, - :member-order: bysource - - - -.. autoclass:: oneflow.nn.graph.graph_config.GraphConfig - :members: enable_amp, - enable_zero, - allow_fuse_model_update_ops, - allow_fuse_add_to_output, - allow_fuse_cast_scale, - set_gradient_accumulation_steps, - enable_cudnn_conv_heuristic_search_algo, - enable_straighten_algorithm, - :member-order: bysource - - - -.. autoclass:: oneflow.nn.graph.block_config.BlockConfig - :members: stage_id, - set_stage, - activation_checkpointing, - :member-order: bysource + + +Constructing a Graph +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Base class for training or evaluating a neural network in static graph mode. + +.. currentmodule:: oneflow.nn.Graph + +.. autosummary:: + :toctree: generated + :nosignatures: + + __init__ + build + add_optimizer + set_grad_scaler + +Executing a Graph +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Call a nn.Graph instance to run a customized graph. + +.. currentmodule:: oneflow.nn.Graph + +.. autosummary:: + :toctree: generated + :nosignatures: + + __call__ + + + +Config options on a Graph +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Optimization options of a nn.Graph. + +.. currentmodule:: oneflow.nn.graph.graph_config.GraphConfig + +.. autosummary:: + :toctree: generated + :nosignatures: + + enable_amp + enable_zero + allow_fuse_model_update_ops + allow_fuse_add_to_output + allow_fuse_cast_scale + set_gradient_accumulation_steps + enable_cudnn_conv_heuristic_search_algo + enable_straighten_algorithm + enable_compress_memory + + +Config options on a GraphModule +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +GraphModule is the graph representation of a nn.Module in a nn.Graph. + +When an nn.Module is added into an nn.Graph, it is wrapped into a ProxyModule. The ProxyModule has a GraphModule inside it. +You can get and set the GraphModule to enable graph optimization on the nn.Module. + +.. currentmodule:: oneflow.nn.graph.graph_block.GraphModule + +.. autosummary:: + :toctree: generated + :nosignatures: + + set_stage + activation_checkpointing + +Save & Load a Model +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. currentmodule:: oneflow.nn.Graph + +.. autosummary:: + :toctree: generated + :nosignatures: + + state_dict + load_state_dict + + +Debug a Graph +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: generated + :nosignatures: + + __repr__ + debug + name + + diff --git a/docs/source/hub.rst b/docs/source/hub.rst new file mode 100644 index 0000000000000000000000000000000000000000..5b64ff577f5cd85f8c94540c602b3389e97f576b --- /dev/null +++ b/docs/source/hub.rst @@ -0,0 +1,157 @@ +oneflow.hub +=================================== + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/hub.html + +Oneflow Hub is a pre-trained model repository designed to facilitate research reproducibility. + +Publishing models +----------------- + +Oneflow Hub supports publishing pre-trained models(model definitions and pre-trained weights) +to a github repository by adding a simple ``hubconf.py`` file; + +``hubconf.py`` can have multiple entrypoints. Each entrypoint is defined as a python function +(example: a pre-trained model you want to publish). + +:: + + def entrypoint_name(*args, **kwargs): + # args & kwargs are optional, for models which take positional/keyword arguments. + ... + +How to implement an entrypoint? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Here is a code snippet specifies an entrypoint for ``resnet18`` model if we expand +the implementation in ``Oneflow-Inc/vision/hubconf.py``. +In most case importing the right function in ``hubconf.py`` is sufficient. Here we +just want to use the expanded version as an example to show how it works. +You can see the full script in +`Oneflow-Inc/vision repo `_ + +:: + + dependencies = ['oneflow'] + from flowvision.models.resnet import resnet18 as _resnet18 + + # resnet18 is the name of entrypoint + def resnet18(pretrained=False, **kwargs): + """ # This docstring shows up in hub.help() + Resnet18 model + pretrained (bool): kwargs, load pretrained weights into the model + """ + # Call the model, load pretrained weights + model = _resnet18(pretrained=pretrained, **kwargs) + return model + + +- ``dependencies`` variable is a **list** of package names required to **load** the model. Note this might + be slightly different from dependencies required for training a model. +- ``args`` and ``kwargs`` are passed along to the real callable function. +- Docstring of the function works as a help message. It explains what does the model do and what + are the allowed positional/keyword arguments. It's highly recommended to add a few examples here. +- Entrypoint function can either return a model(nn.module), or auxiliary tools to make the user workflow smoother, e.g. tokenizers. +- Callables prefixed with underscore are considered as helper functions which won't show up in :func:`oneflow.hub.list()`. +- Pretrained weights can either be stored locally in the github repo, or loadable by + :func:`oneflow.hub.load_state_dict_from_url()`. If less than 2GB, it's recommended to attach it to a `project release `_ + and use the url from the release. + In the example above ``flowvision.models.resnet.resnet18`` handles ``pretrained``, alternatively you can put the following logic in the entrypoint definition. + +:: + + if pretrained: + # For checkpoint saved in local github repo, e.g. =weights/save.pth + dirname = os.path.dirname(__file__) + checkpoint = os.path.join(dirname, ) + state_dict = oneflow.load(checkpoint) + model.load_state_dict(state_dict) + + # For checkpoint saved elsewhere + checkpoint = 'https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip' + model.load_state_dict(oneflow.hub.load_state_dict_from_url(checkpoint, progress=False)) + + +Important Notice +^^^^^^^^^^^^^^^^ + +- The published models should be at least in a branch/tag. It can't be a random commit. + + +Loading models from Hub +----------------------- + +OneFlow Hub provides convenient APIs to explore all available models in hub +through :func:`oneflow.hub.list()`, show docstring and examples through +:func:`oneflow.hub.help()` and load the pre-trained models using +:func:`oneflow.hub.load()`. + + +.. automodule:: oneflow.hub + +.. autofunction:: list + +.. autofunction:: help + +.. autofunction:: load + +.. autofunction:: download_url_to_file + +.. autofunction:: load_state_dict_from_url + +Running a loaded model: +^^^^^^^^^^^^^^^^^^^^^^^ + +Note that ``*args`` and ``**kwargs`` in :func:`oneflow.hub.load()` are used to +**instantiate** a model. After you have loaded a model, how can you find out +what you can do with the model? +A suggested workflow is + +- ``dir(model)`` to see all available methods of the model. +- ``help(model.foo)`` to check what arguments ``model.foo`` takes to run + +To help users explore without referring to documentation back and forth, we strongly +recommend repo owners make function help messages clear and succinct. It's also helpful +to include a minimal working example. + +Where are my downloaded models saved? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The locations are used in the order of + +- Calling ``hub.set_dir()`` +- ``$ONEFLOW_HOME/hub``, if environment variable ``ONEFLOW_HOME`` is set. +- ``$XDG_CACHE_HOME/oneflow/hub``, if environment variable ``XDG_CACHE_HOME`` is set. +- ``~/.cache/oneflow/hub`` + +.. autofunction:: get_dir + +.. autofunction:: set_dir + +Caching logic +^^^^^^^^^^^^^ + +By default, we don't clean up files after loading it. Hub uses the cache by default if it already exists in the +directory returned by :func:`~oneflow.hub.get_dir()`. + +Users can force a reload by calling ``hub.load(..., force_reload=True)``. This will delete +the existing github folder and downloaded weights, reinitialize a fresh download. This is useful +when updates are published to the same branch, users can keep up with the latest release. + + +Known limitations: +^^^^^^^^^^^^^^^^^^ +Oneflow hub works by importing the package as if it was installed. There are some side effects +introduced by importing in Python. For example, you can see new items in Python caches +``sys.modules`` and ``sys.path_importer_cache`` which is normal Python behavior. +This also means that you may have import errors when importing different models +from different repos, if the repos have the same sub-package names (typically, a +``model`` subpackage). A workaround for these kinds of import errors is to +remove the offending sub-package from the ``sys.modules`` dict; more details can +be found in `this github issue +`_. + +A known limitation that is worth mentioning here: users **CANNOT** load two different branches of +the same repo in the **same python process**. It's just like installing two packages with the +same name in Python, which is not good. Cache might join the party and give you surprises if you +actually try that. Of course it's totally fine to load them in separate processes. diff --git a/docs/source/image.rst b/docs/source/image.rst index 6cb5ec8cad966608f19d4ef1140d471d8166eaf6..9917330cdb21ab9114dc1d4b61a8f35d707caf1c 100644 --- a/docs/source/image.rst +++ b/docs/source/image.rst @@ -3,9 +3,14 @@ oneflow.nn.image Image operations for neural networks -------------------------------------- .. currentmodule:: oneflow.nn.image -.. automodule:: oneflow.nn.image - :members: Resize, - batch_align, - decode, - flip, - normalize +.. autosummary:: + :toctree: generated + :nosignatures: + + Resize + batch_align + decode + flip + normalize + + diff --git a/docs/source/index.rst b/docs/source/index.rst index 37ae35d0594763d09217dae6c468894ded33d393..6546e3063b155edf134a25a261c8d10a68157150 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,5 +1,12 @@ OneFlow API Reference =================================== + + +Distributed performance (high efficiency) is the core technical difficulty of deep learning frameworks. + +OneFlow upholds the core concept and architecture of static compilation and streaming parallelism around performance improvement and heterogeneous distributed scaling, solving the challenge of memory wall at cluster level with world-leading technology. + + .. toctree:: :maxdepth: 1 @@ -12,23 +19,27 @@ OneFlow API Reference :caption: OneFlow Python API oneflow + nn + nn.functional tensor tensor_attributes - nn - functional + type_info autograd cuda distributed + distributions + hub linalg nn.init optim - module graph + auto_parallel image - utils - env - comm + utils.data + utils.global_view + utils.tensor one_embedding + environment_variables diff --git a/docs/source/linalg.rst b/docs/source/linalg.rst index 641ea31adcc5565e10cf0d578ce44ba3ef819738..cc5cec2cb985715289b6bf7c26bc077ef12044d4 100644 --- a/docs/source/linalg.rst +++ b/docs/source/linalg.rst @@ -1,8 +1,22 @@ oneflow.linalg =================================== -OneFlow linear algebra operations. ----------------------------------- + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/linalg.html + +Common linear algebra operations. + +Matrix Properties +----------------- + .. currentmodule:: oneflow.linalg -.. autofunction:: oneflow.linalg.matrix_norm -.. autofunction:: oneflow.linalg.norm -.. autofunction:: oneflow.linalg.vector_norm +.. autosummary:: + :toctree: generated + :nosignatures: + + norm + vector_norm + matrix_norm + diagonal + inv + cross diff --git a/docs/source/module.rst b/docs/source/module.rst deleted file mode 100644 index 3605cee1f7f0f5e6c5ad68eb87d6bf0d07e138f5..0000000000000000000000000000000000000000 --- a/docs/source/module.rst +++ /dev/null @@ -1,8 +0,0 @@ -oneflow.nn.Module -================================================ -Module class for building neural networks ---------------------------------------------------- -.. currentmodule:: oneflow.nn -.. autoclass:: oneflow.nn.Module - :members: - diff --git a/docs/source/nn.functional.rst b/docs/source/nn.functional.rst new file mode 100644 index 0000000000000000000000000000000000000000..d22580aa07acb3ac3b115eb076db6ef4e568adb8 --- /dev/null +++ b/docs/source/nn.functional.rst @@ -0,0 +1,178 @@ +oneflow.nn.functional +=========================================== + +.. The documentation is referenced from: https://pytorch.org/docs/1.10/nn.functional.html. + +.. contents:: oneflow.nn.functional + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + +.. currentmodule:: oneflow.nn.functional + +Convolution functions +------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + conv1d + conv2d + conv3d + conv_transpose1d + conv_transpose2d + conv_transpose3d + fold + unfold + +BatchNorm functions +-------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + batch_norm + +Pooling functions +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + avg_pool1d + avg_pool2d + avg_pool3d + max_pool1d + max_pool2d + max_pool3d + max_unpool1d + max_unpool2d + max_unpool3d + adaptive_avg_pool1d + adaptive_avg_pool2d + adaptive_avg_pool3d + adaptive_max_pool1d + adaptive_max_pool2d + adaptive_max_pool3d + +Non-linear activation functions +------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + threshold + relu + hardtanh + hardswish + relu6 + elu + selu + celu + leaky_relu + prelu + glu + gelu + quick_gelu + logsigmoid + hardshrink + softsign + softplus + softmax + softshrink + log_softmax + gumbel_softmax + tanh + sigmoid + hardsigmoid + silu + mish + layer_norm + normalize + +Linear functions +---------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + linear + +Dropout functions +----------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + dropout + dropout1d + dropout2d + dropout3d + +Sparse functions +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + embedding + one_hot + +Distance functions +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + cosine_similarity + pairwise_distance + + +Loss functions +-------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + sparse_softmax_cross_entropy + cross_entropy + l1_loss + mse_loss + smooth_l1_loss + triplet_margin_loss + binary_cross_entropy + binary_cross_entropy_with_logits + +Vision functions +---------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + deform_conv2d + pad + interpolate + upsample + grid_sample + affine_grid + +Greedy decoder +---------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + ctc_greedy_decoder + diff --git a/docs/source/nn.init.rst b/docs/source/nn.init.rst index 68dbf908829e702d004a57bd5c348f1df8324c6c..6005440f1495a6007ebcfd3397cb206d5a986e26 100644 --- a/docs/source/nn.init.rst +++ b/docs/source/nn.init.rst @@ -1,11 +1,19 @@ oneflow.nn.init -=================================== -Operators for initialization ----------------------------------- -.. currentmodule:: oneflow.nn.init +=============== + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/nn.init.html -.. autofunction:: oneflow.nn.init.xavier_uniform_ -.. autofunction:: oneflow.nn.init.xavier_normal_ -.. autofunction:: oneflow.nn.init.kaiming_uniform_ -.. autofunction:: oneflow.nn.init.kaiming_normal_ -.. autofunction:: oneflow.nn.init.orthogonal_ +.. currentmodule:: oneflow.nn.init +.. autofunction:: calculate_gain +.. autofunction:: uniform_ +.. autofunction:: normal_ +.. autofunction:: constant_ +.. autofunction:: ones_ +.. autofunction:: zeros_ +.. autofunction:: xavier_uniform_ +.. autofunction:: xavier_normal_ +.. autofunction:: kaiming_uniform_ +.. autofunction:: kaiming_normal_ +.. autofunction:: trunc_normal_ +.. autofunction:: orthogonal_ diff --git a/docs/source/nn.rst b/docs/source/nn.rst index 18be2d6abfa3b3a4096d91253fb8925583af63e0..6daf5d80403aae421167ad6d3b72fcd921452a24 100644 --- a/docs/source/nn.rst +++ b/docs/source/nn.rst @@ -1,112 +1,391 @@ oneflow.nn =================================== -Operators for neural networks + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/nn.html + +These are the basic building blocks for graphs: + +.. contents:: oneflow.nn + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + +.. currentmodule:: oneflow.nn +.. autosummary:: + :toctree: generated + :nosignatures: + :template: + + Parameter + + +Containers ---------------------------------- .. currentmodule:: oneflow.nn -.. automodule:: oneflow.nn - :members: AdaptiveAvgPool1d, - AdaptiveAvgPool2d, - AdaptiveAvgPool3d, - AvgPool1d, - AvgPool2d, - AvgPool3d, - BCELoss, - BCEWithLogitsLoss, - BatchNorm1d, - BatchNorm2d, - BatchNorm3d, - COCOReader, - CTCLoss, - CoinFlip, - ConstantPad1d, - ConstantPad2d, - ConstantPad3d, - Conv1d, - Conv2d, - Conv3d, - ConvTranspose1d, - ConvTranspose2d, - ConvTranspose3d, - CosineSimilarity, - CombinedMarginLoss, - CropMirrorNormalize, - CrossEntropyLoss, - Dropout, - ELU, - CELU, - Embedding, - Flatten, - GELU, - GLU, - GroupNorm, - Hardsigmoid, - Hardshrink, - Hardswish, - Hardtanh, - Identity, - InstanceNorm1d, - InstanceNorm2d, - InstanceNorm3d, - KLDivLoss, - L1Loss, - LayerNorm, - LeakyReLU, - Linear, - LogSigmoid, - LogSoftmax, - MSELoss, - MarginRankingLoss, - TripletMarginLoss, - MaxPool1d, - MaxPool2d, - MaxPool3d, - ModuleDict, - ModuleList, - Mish, - NLLLoss, - OFRecordImageDecoder, - OFRecordImageDecoderRandomCrop, - OFRecordRawDecoder, - OFRecordReader, - OFRecordBytesDecoder, - PReLU, - Parameter, - ParameterDict, - ParameterList, - PixelShuffle, - ReLU, - ReLU6, - ReflectionPad2d, - ReplicationPad2d, - Sequential, - SELU, - SiLU, - Sigmoid, - SmoothL1Loss, - Softmax, - Softplus, - Softshrink, - Softsign, - Tanh, - Threshold, - Upsample, - UpsamplingBilinear2d, - UpsamplingNearest2d, - ZeroPad2d, - MinMaxObserver, - MovingAverageMinMaxObserver, - FakeQuantization, - Quantization, - FusedBatchNorm1d, - FusedBatchNorm2d, - FusedBatchNorm3d, - FusedMLP, - -.. autofunction:: oneflow.nn.modules.pixelshuffle.PixelShufflev2 - -.. autofunction:: oneflow.nn.parallel.DistributedDataParallel + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Module + Sequential + ModuleList + ModuleDict + ParameterList + ParameterDict + +nn.Module +---------------------------------- +.. currentmodule:: oneflow.nn.Module + +.. autosummary:: + :toctree: generated + :nosignatures: + + add_module + apply + buffers + children + cpu + cuda + double + train + eval + extra_repr + float + forward + load_state_dict + modules + named_buffers + named_children + named_modules + named_parameters + parameters + register_buffer + register_forward_hook + register_forward_pre_hook + register_parameter + requires_grad_ + state_dict + to + zero_grad + + + +Containers + +Convolution Layers +---------------------------------- +.. currentmodule:: oneflow +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Conv1d + nn.Conv2d + nn.Conv3d + nn.ConvTranspose1d + nn.ConvTranspose2d + nn.ConvTranspose3d + nn.Unfold + nn.Fold + +Pooling Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.MaxPool1d + nn.MaxPool2d + nn.MaxPool3d + nn.MaxUnpool1d + nn.MaxUnpool2d + nn.MaxUnpool3d + nn.AdaptiveAvgPool1d + nn.AdaptiveAvgPool2d + nn.AdaptiveAvgPool3d + nn.AdaptiveMaxPool1d + nn.AdaptiveMaxPool2d + nn.AdaptiveMaxPool3d + nn.AvgPool1d + nn.AvgPool2d + nn.AvgPool3d + +Padding Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.ConstantPad1d + nn.ConstantPad2d + nn.ConstantPad3d + nn.ReflectionPad1d + nn.ReflectionPad2d + nn.ReplicationPad1d + nn.ReplicationPad2d + nn.ZeroPad2d + +Non-linear Activations (weighted sum, nonlinearity) +---------------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.ELU + nn.Hardshrink + nn.Hardsigmoid + nn.Hardswish + nn.Hardtanh + nn.LeakyReLU + nn.LogSigmoid + nn.PReLU + nn.ReLU + nn.ReLU6 + nn.SELU + nn.CELU + nn.GELU + nn.QuickGELU + nn.SiLU + nn.Sigmoid + nn.Mish + nn.Softplus + nn.Softshrink + nn.Softsign + nn.Tanh + nn.Threshold + nn.GLU + +Non-linear Activations (other) +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Softmax + nn.LogSoftmax + +Normalization Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.BatchNorm1d + nn.BatchNorm2d + nn.BatchNorm3d + nn.SyncBatchNorm + nn.FusedBatchNorm1d + nn.FusedBatchNorm2d + nn.FusedBatchNorm3d + nn.GroupNorm + nn.InstanceNorm1d + nn.InstanceNorm2d + nn.InstanceNorm3d + nn.LayerNorm + nn.RMSLayerNorm + nn.RMSNorm + +Recurrent Layers +---------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.RNN + nn.LSTM + nn.GRU + nn.RNNCell + nn.LSTMCell + nn.GRUCell + +Linear Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Identity + nn.Linear + +Dropout Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Dropout + nn.Dropout1d + nn.Dropout2d + nn.Dropout3d + +Sparse Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Embedding + +Distance Functions +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.CosineSimilarity + nn.PairwiseDistance + +Loss Functions +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.BCELoss + nn.BCEWithLogitsLoss + nn.CTCLoss + nn.CombinedMarginLoss + nn.CrossEntropyLoss + nn.KLDivLoss + nn.L1Loss + nn.MSELoss + nn.MarginRankingLoss + nn.NLLLoss + nn.SmoothL1Loss + nn.TripletMarginLoss + +Vision Layers +---------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.PixelShuffle + nn.Upsample + nn.UpsamplingBilinear2d + nn.UpsamplingNearest2d + + +DataParallel Layers (multi-GPU, distributed) +-------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.parallel.DistributedDataParallel + + +Data loading and preprocessing Layers +---------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + nn.COCOReader + nn.CoinFlip + nn.CropMirrorNormalize + nn.OFRecordBytesDecoder + nn.OFRecordImageDecoder + nn.OFRecordImageDecoderRandomCrop + nn.OFRecordRawDecoder + nn.OFRecordReader + +Quantization Aware Training +-------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + nn.MinMaxObserver + nn.MovingAverageMinMaxObserver + nn.FakeQuantization + nn.QatConv1d + nn.QatConv2d + nn.QatConv3d + +Utilities +--------- + +From the ``oneflow.nn.utils`` module .. currentmodule:: oneflow.nn.utils -.. autofunction:: oneflow.nn.utils.clip_grad_norm_ -.. autofunction:: oneflow.nn.utils.weight_norm -.. autofunction:: oneflow.nn.utils.remove_weight_norm +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + clip_grad_norm_ + clip_grad_value_ + weight_norm + remove_weight_norm + +Utility functions in other modules + +.. currentmodule:: oneflow +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.utils.rnn.PackedSequence + nn.utils.rnn.pack_padded_sequence + nn.utils.rnn.pad_packed_sequence + nn.utils.rnn.pad_sequence + nn.utils.rnn.pack_sequence + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + nn.Flatten + +Quantized Functions +-------------------- + +Quantization refers to techniques for performing computations and +storing tensors at lower bitwidths than floating point precision. + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: + + nn.FakeQuantization + nn.MinMaxObserver + nn.MovingAverageMinMaxObserver + nn.Quantization diff --git a/docs/source/one_embedding.rst b/docs/source/one_embedding.rst index 9ac93c1f6acc1f968a50b631d3f70d10168313ed..408d7cad91e49b3715d22bd0e548fcb7f8e94551 100644 --- a/docs/source/one_embedding.rst +++ b/docs/source/one_embedding.rst @@ -1,26 +1,205 @@ oneflow.one_embedding =================================== -OneFlow one_embedding operations. + +Embedding is an important component of recommender system, and it has also spread to many fields outside recommender systems. Each framework provides basic operators for Embedding, for example, ``flow.nn.Embedding`` in OneFlow: + +:: + + import numpy as np + import oneflow as flow + indices = flow.tensor([[1, 2, 4, 5], [4, 3, 2, 9]], dtype=flow.int) + embedding = flow.nn.Embedding(10, 3) + y = embedding(indices) + + +OneEmbedding is the large-scale Embedding solution that OneFlow provides to solve the problem of large-scale deep recommender systems. OneEmbedding has the following advantages compared to ordinary opeartors: + + - With Flexible hierarchical storage, OneEmbedding can place the Embedding table on GPU memory, CPU memory or SSD, and allow high-speed devices to be used as caches for low-speed devices to achieve both speed and capacity. + + - OneEmbedding supports dynamic expansion. + +.. note :: + Please refer to `Large-Scale Embedding Solution: OneEmbedding `__ + for a brief introduction to all features related to OneEmbedding. + +Configure Embedding Table ---------------------------------- -.. currentmodule:: oneflow.one_embedding -.. autoclass:: MultiTableEmbedding - :members: forward, - save_snapshot, - load_snapshot, -.. autofunction:: oneflow.one_embedding.MultiTableEmbedding.forward -.. autoclass:: MultiTableMultiColumnEmbedding - :members: forward, - save_snapshot, - load_snapshot, -.. autofunction:: oneflow.one_embedding.MultiTableMultiColumnEmbedding.forward -.. autofunction:: oneflow.one_embedding.make_device_mem_store_options -.. autofunction:: oneflow.one_embedding.make_cached_ssd_store_options -.. autofunction:: oneflow.one_embedding.make_cached_host_mem_store_options -.. autofunction:: oneflow.one_embedding.make_uniform_initializer -.. autofunction:: oneflow.one_embedding.make_normal_initializer + +OneEmbedding supports simultaneous creation of multiple Embedding table. The following codes configured three Embedding tables. + +.. code-block:: + + import oneflow as flow + import oneflow.nn as nn + import numpy as np + + tables = [ + flow.one_embedding.make_table_options( + flow.one_embedding.make_uniform_initializer(low=-0.1, high=0.1) + ), + flow.one_embedding.make_table_options( + flow.one_embedding.make_uniform_initializer(low=-0.05, high=0.05) + ), + flow.one_embedding.make_table_options( + flow.one_embedding.make_uniform_initializer(low=-0.15, high=0.15) + ), + ] + +When configuring the Embedding table, you need to specify the initialization method. The above Embedding tables are initialized in the ``uniform`` method. The result of configuring the Embedding table is stored in the ``tables`` variable + .. autofunction:: oneflow.one_embedding.make_table_options .. autofunction:: oneflow.one_embedding.make_table + +initialization method +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. currentmodule:: oneflow.one_embedding + +.. autosummary:: + :toctree: generated + :nosignatures: + + make_uniform_initializer + make_normal_initializer + + +Configure the Storage Attribute of the Embedding Table +-------------------------------------------------------------------- +Then run the following codes to configure the storage attribute of the Embedding table: + +.. code-block:: + + store_options = flow.one_embedding.make_cached_ssd_store_options( + cache_budget_mb=8142, + persistent_path="/your_path_to_ssd", + capacity=40000000, + size_factor=1, + physical_block_size=4096 + ) + +Storage Method +^^^^^^^^^^^^^^^^^^^^ + +.. currentmodule:: oneflow.one_embedding + +.. autosummary:: + :toctree: generated + :nosignatures: + + make_device_mem_store_options + make_cached_ssd_store_options + make_cached_host_mem_store_options + +.. note :: + + Please refer to `Large-Scale Embedding Solution: OneEmbedding `__ + for a brief introduction to learn about How to Choose the Proper Storage Configuration + + +Instantiate Embedding +-------------------------------------------------------------------- +After the above configuration is completed, you can use MultiTableEmbedding to get the instantiated Embedding layer. + +.. code-block:: + + embedding_size = 128 + embedding = flow.one_embedding.MultiTableEmbedding( + name="my_embedding", + embedding_dim=embedding_size, + dtype=flow.float, + key_type=flow.int64, + tables=tables, + store_options=store_options, + ) + + embedding.to("cuda") + +.. note :: + + Please refer to `Large-Scale Embedding Solution: OneEmbedding `__ + for a brief introduction to learn about Feature ID and Multi-Table Query. + + +MultiTableEmbedding +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: oneflow.one_embedding.MultiTableEmbedding + +.. currentmodule:: oneflow.one_embedding.MultiTableEmbedding + +.. autosummary:: + :toctree: generated + :nosignatures: + + forward + save_snapshot + load_snapshot + +MultiTableMultiColumnEmbedding +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: oneflow.one_embedding.MultiTableMultiColumnEmbedding + +.. currentmodule:: oneflow.one_embedding.MultiTableMultiColumnEmbedding + +.. autosummary:: + :toctree: generated + :nosignatures: + + forward + save_snapshot + load_snapshot + +Construct Graph for Training +-------------------------------------------------------------------- +OneEmbedding is only supported in Graph mode. + +.. code-block:: + + num_tables = 3 + mlp = flow.nn.FusedMLP( + in_features=embedding_size * num_tables, + hidden_features=[512, 256, 128], + out_features=1, + skip_final_activation=True, + ) + mlp.to("cuda") + + class TrainGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + self.embedding_lookup = embedding + self.mlp = mlp + self.add_optimizer( + flow.optim.SGD(self.embedding_lookup.parameters(), lr=0.1, momentum=0.0) + ) + self.add_optimizer( + flow.optim.SGD(self.mlp.parameters(), lr=0.1, momentum=0.0) + ) + def build(self, ids): + embedding = self.embedding_lookup(ids) + loss = self.mlp(flow.reshape(embedding, (-1, num_tables * embedding_size))) + loss = loss.sum() + loss.backward() + return loss + +.. note :: + + Please refer to `Distributed Training: OneEmbedding `__ + for a brief introduction to learn about Graph For Training + + +Persistent Read & Write +----------------------------------------------- +.. currentmodule:: oneflow.one_embedding + +.. autosummary:: + :toctree: generated + :nosignatures: + + make_persistent_table_reader + make_persistent_table_writer + .. automodule:: oneflow.one_embedding :members: Ftrl -.. autofunction:: oneflow.one_embedding.make_persistent_table_reader -.. autofunction:: oneflow.one_embedding.make_persistent_table_writer + diff --git a/docs/source/oneflow.rst b/docs/source/oneflow.rst index 77b3aff6ee51dfd4bf2a6bb769f4c1ae36fdc3d7..40f9378db4f9a9cb5549ebe352a45200eaac3269 100644 --- a/docs/source/oneflow.rst +++ b/docs/source/oneflow.rst @@ -1,206 +1,417 @@ oneflow =================================== -oneflow ----------------------------------- + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/torch.html + +The oneflow package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities. + +It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0 + .. currentmodule:: oneflow -.. automodule:: oneflow - :members: adaptive_avg_pool1d, - adaptive_avg_pool2d, - adaptive_avg_pool3d, - abs, - acos, - acosh, - add, - addcmul, - addmm, - all, - amin, - amax, - any, - arccos, - arcsin, - arcsinh, - arccosh, - arctan, - arctanh, - argmax, - argmin, - arange, - argsort, - argwhere, - asin, - asinh, - atan, - atan2, - atanh, - bernoulli, - broadcast_like, - batch_gather, - bmm, - cat, - concat, - cast, - ceil, - chunk, - clamp, - clip, - cos, - cosh, - diag, - select, - diagonal, - movedim, - tensor_split, - hsplit, - vsplit, - as_strided, - div, - dot, - eq, - einsum, - equal, - expand, - eye, - exp, - expm1, - erf, - erfc, - erfinv, - flatten, - flip, - floor, - floor_, - fmod, - full, - gather, - gather_nd, - gelu, - gt, - in_top_k, - index_select, - linspace, - logical_and, - logical_or, - logical_not, - logical_xor, - load, - log, - log2, - log1p, - lt, - le, - masked_fill, - masked_select, - matmul, - mm, - mv, - narrow, - max, - mean, - median, - mish, - min, - meshgrid, - mul, - neg, - negative, - new_ones, - nonzero, - normal, - numel, - ne, - empty, - ones, - ones_like, - pow, - prod, - rand, - randn, - repeat, - repeat_interleave, - reshape, - randint, - randperm, - reciprocal, - roc_auc_score, - roll, - round, - rsqrt, - save, - scatter, - scatter_add, - scatter_nd, - tensor_scatter_nd_update, - sin, - sin_, - sinh, - sign, - selu, - silu, - slice, - slice_update, - softsign, - sort, - softplus, - sigmoid, - softmax, - squeeze, - split, - stack, - std, - sub, - sum, - sqrt, - square, - swapaxes, - swapdims, - tan, - tanh, - tensor, - tensordot, - tile, - transpose, - t, - tril, - unsqueeze, - unbind, - permute, - var, - where, - zeros, - zeros_like, - is_nonzero, - is_tensor, - no_grad, - set_grad_enabled, - enable_grad, - inference_mode, - is_grad_enabled, - is_floating_point, - set_printoptions, - decode_onerec, - from_numpy, - as_tensor, - cumsum, - topk, - nms, - cumprod, - HalfTensor, - FloatTensor, - DoubleTensor, - BoolTensor, - ByteTensor, - CharTensor, - IntTensor, - LongTensor, - seed, - manual_seed, - initial_seed, - get_rng_state, - set_rng_state, - isnan, - isinf, - searchsorted - -.. autofunction:: oneflow.relu -.. autofunction:: oneflow.set_num_threads + + +Tensor +------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + BoolTensor + ByteTensor + CharTensor + DoubleTensor + FloatTensor + HalfTensor + IntTensor + LongTensor + + +.. autosummary:: + :toctree: generated + :nosignatures: + + is_tensor + is_floating_point + is_nonzero + numel + set_printoptions + get_default_dtype + set_default_dtype + set_default_tensor_type + +.. _tensor-creation-ops: + +Creation Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. note:: + Random sampling creation ops are listed under :ref:`random-sampling` and + include: + :func:`oneflow.rand` + :func:`oneflow.randn` + :func:`oneflow.randint` + :func:`oneflow.randperm` + +.. autosummary:: + :toctree: generated + :nosignatures: + + tensor + as_tensor + as_strided + from_numpy + zeros + zeros_like + ones + ones_like + randn_like + randint_like + masked_fill + new_ones + arange + linspace + eye + empty + empty_like + full + full_like + tensor_scatter_nd_update + logspace + +.. _indexing-slicing-joining: + +Indexing, Slicing, Joining, Mutating Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + argwhere + atleast_1d + atleast_2d + atleast_3d + cat + column_stack + concat + chunk + dstack + expand + gather + gather_nd + batch_gather + hsplit + hstack + vsplit + vstack + index_select + index_add + masked_select + movedim + narrow + nonzero + permute + repeat + reshape + row_stack + select + scatter + scatter_add + scatter_nd + slice + slice_update + split + squeeze + stack + swapaxes + swapdims + t + tile + transpose + unbind + unsqueeze + where + tensor_split + +.. _random-sampling: + +Random sampling +------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + seed + manual_seed + initial_seed + get_rng_state + set_rng_state + bernoulli + normal + rand + randint + randn + randperm + multinomial + +In-place random sampling +~~~~~~~~~~~~~~~~~~~~~~~~ + +There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation: +- :func:`oneflow.Tensor.normal_` - in-place version of :func:`oneflow.normal` +- :func:`oneflow.Tensor.uniform_` - numbers sampled from the continuous uniform distribution + + + +Serialization +------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + save + load + +Parallelism +------------------------------------------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + set_num_threads + + +Locally disabling gradient computation +------------------------------------------- +The context managers :func:`oneflow.no_grad`, :func:`oneflow.enable_grad`, and +:func:`oneflow.set_grad_enabled` are helpful for locally disabling and enabling +gradient computation. These context managers are thread local, so they won't +work if you send work to another thread using the ``threading`` module, etc. + +Examples:: + + >>> import oneflow + >>> x = oneflow.zeros(1, requires_grad=True) + >>> with oneflow.no_grad(): + ... y = x * 2 + >>> y.requires_grad + False + + >>> with oneflow.set_grad_enabled(False): + ... y = x * 2 + >>> y.requires_grad + False + + >>> with oneflow.set_grad_enabled(True): + ... y = x * 2 + >>> y.requires_grad + True + +.. autosummary:: + :toctree: generated + :nosignatures: + + no_grad + set_grad_enabled + enable_grad + is_grad_enabled + inference_mode + +Math operations +------------------------------------------- + +Pointwise Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + abs + acos + acosh + arccos + arccosh + add + addcdiv + addcmul + asin + asinh + arcsin + arcsinh + atan + atanh + arctan + arctanh + atan2 + ceil + clamp + clamp_min + clamp_max + clip + cos + cosh + div + erf + erfc + erfinv + exp + expm1 + floor + floor_ + fmod + gelu + quick_gelu + log + log1p + log2 + log10 + logical_and + logical_not + logical_or + logical_xor + mish + mul + neg + negative + pow + reciprocal + round + rsqrt + selu + softmax + softplus + softsign + silu + sigmoid + sign + sin + sinh + sin_ + sqrt + square + sub + tan + tanh + trunc + floor_divide + +Reduction Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + argmax + argmin + amax + amin + any + max + min + mean + median + prod + nansum + std + sum + logsumexp + var + norm + all + + +Comparison Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + argsort + eq + equal + gt + isinf + isnan + le + lt + ne + sort + topk + ge + greater + greater_equal + maximum + minimum + not_equal + isclose + allclose + +Spectral Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + hann_window + +Other Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + adaptive_avg_pool1d + adaptive_avg_pool2d + adaptive_avg_pool3d + broadcast_like + cast + cumprod + cumsum + decode_onerec + diag + diagonal + einsum + flatten + flip + in_top_k + meshgrid + nms + roc_auc_score + roll + searchsorted + tensordot + tril + repeat_interleave + triu + cross + bincount + broadcast_shapes + broadcast_tensors + broadcast_to + unique + +BLAS and LAPACK Operations +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autosummary:: + :toctree: generated + :nosignatures: + + addmm + bmm + baddbmm + dot + matmul + mm + mv + diff --git a/docs/source/optim.rst b/docs/source/optim.rst index 3ebb75161b60c605db0a3bd5dc1860d29b7f28b2..6743036f61ba72250e831cd9c4d633ad0f6e1742 100644 --- a/docs/source/optim.rst +++ b/docs/source/optim.rst @@ -1,24 +1,324 @@ oneflow.optim =================================== -Optimizers ----------------------------------- + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/optim.html + +oneflow.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can be also easily integrated in the future. + +How to use an optimizer +----------------------- + +To use :mod:`oneflow.optim` you have to construct an optimizer object, that will hold +the current state and will update the parameters based on the computed gradients. + +Constructing it +^^^^^^^^^^^^^^^ + +To construct an :class:`Optimizer` you have to give it an iterable containing the +parameters (all should be :class:`~oneflow.autograd.Variable` s) to optimize. Then, +you can specify optimizer-specific options such as the learning rate, weight decay, etc. + +.. note:: + If you need to move a model to GPU via ``.cuda()``, please do so before + constructing optimizers for it. Parameters of a model after ``.cuda()`` + will be different objects with those before the call. + + In general, you should make sure that optimized parameters live in + consistent locations when optimizers are constructed and used. + +Example:: + + import oneflow + import oneflow.nn as nn + import oneflow.optim as optim + + model = nn.Linear(16, 3) + optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) + +Per-parameter options +^^^^^^^^^^^^^^^^^^^^^ + +:class:`Optimizer` also support specifying per-parameter options. To do this, instead +of passing an iterable of :class:`~oneflow.autograd.Variable`, pass in an iterable of +:class:`dict`. Each of them will define a separate parameter group, and should contain +a ``params`` key, containing a list of parameters belonging to it. Other keys +should match the keyword arguments accepted by the optimizers, and will be used +as optimization options for this group. + +.. note:: + + You can still pass options as keyword arguments. They will be used as + defaults, in the groups that didn't override them. This is useful when you + only want to vary a single option, while keeping all others consistent + between parameter groups. + + +For example, this is very useful when one wants to specify per-layer learning rates:: + + import oneflow.nn as nn + import oneflow.optim as optim + + + class Model(nn.Module): + def __init__(self): + super(Model, self).__init__() + self.base = nn.Linear(64, 32) + self.classifier = nn.Linear(32, 10) + + def forward(self, x): + out = self.base(x) + out = self.classifier(out) + return out + + + model = Model() + optim.SGD( + [ + {"params": model.base.parameters()}, + {"params": model.classifier.parameters(), "lr": 1e-3}, + ], + lr=1e-2, + momentum=0.9, + ) + + +This means that ``model.base``'s parameters will use the default learning rate of ``1e-2``, +``model.classifier``'s parameters will use a learning rate of ``1e-3``, and a momentum of +``0.9`` will be used for all parameters. + +Taking an optimization step +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +All optimizers implement a :func:`~Optimizer.step` method, that updates the +parameters. It can be used in two ways: + +``optimizer.step()`` +~~~~~~~~~~~~~~~~~~~~ + +This is a simplified version supported by most optimizers. The function can be +called once the gradients are computed using e.g. +:func:`~oneflow.autograd.Variable.backward`. + +Example:: + + import oneflow + import oneflow.nn as nn + import oneflow.nn.functional as F + import oneflow.optim as optim + from oneflow.utils.data import Dataset, DataLoader + + + class CustomDataset(Dataset): + def __init__(self, num): + self.inputs = oneflow.randn(num, 1) + self.targets = oneflow.sin(self.inputs) + + def __len__(self): + return self.inputs.shape[0] + + def __getitem__(self, index): + return self.inputs[index], self.targets[index] + + + class Model(nn.Module): + def __init__(self, input_size): + super(Model, self).__init__() + self.linear1 = nn.Linear(input_size, 64) + self.linear2 = nn.Linear(64, input_size) + + def forward(self, x): + out = self.linear1(x) + return self.linear2(F.relu(out)) + + + dataset = CustomDataset(10000) + dataloader = DataLoader(dataset, batch_size=10) + model = Model(1) + loss_fn = nn.MSELoss() + optimizer = optim.SGD(model.parameters(), lr=1e-3) + + for epoch in range(100): + for input, target in dataloader: + optimizer.zero_grad() + output = model(input) + loss = loss_fn(output, target) + loss.backward() + optimizer.step() + +.. _optimizer-algorithms: + .. currentmodule:: oneflow.optim -.. automodule:: oneflow.optim - :members: Adam, - Adagrad, - AdamW, - Optimizer, - RMSprop, - SGD, - LAMB, - lr_scheduler - -.. automodule:: oneflow.optim.lr_scheduler - :members: CosineDecayLR, - CosineAnnealingLR, - LambdaLR, - StepLR, - MultiStepLR, - ExponentialLR, - ReduceLROnPlateau, - PolynomialLR + + +Base class +---------- + +.. autoclass:: Optimizer + +.. autosummary:: + :toctree: generated + :nosignatures: + + Optimizer.add_param_group + Optimizer.load_state_dict + Optimizer.state_dict + Optimizer.step + Optimizer.zero_grad + +Algorithms +---------- + +.. autosummary:: + :toctree: generated + :nosignatures: + + Adagrad + Adam + AdamW + LAMB + RMSprop + SGD + +Adjust Learning Rate +-------------------- + +:mod:`oneflow.optim.lr_scheduler` provides several methods to adjust the learning +rate based on the number of epochs. :class:`oneflow.optim.lr_scheduler.ReduceLROnPlateau` +allows dynamic learning rate reducing based on some validation measurements. + +Learning rate scheduling should be applied after optimizer's update; e.g., you +should write your code this way: + +Example:: + + import oneflow + import oneflow.nn as nn + import oneflow.nn.functional as F + import oneflow.optim as optim + from oneflow.utils.data import Dataset, DataLoader + + + class CustomDataset(Dataset): + def __init__(self, num): + self.inputs = oneflow.randn(num, 1) + self.targets = oneflow.sin(self.inputs) + + def __len__(self): + return self.inputs.shape[0] + + def __getitem__(self, index): + return self.inputs[index], self.targets[index] + + + class Model(nn.Module): + def __init__(self, input_size): + super(Model, self).__init__() + self.linear1 = nn.Linear(input_size, 64) + self.linear2 = nn.Linear(64, input_size) + + def forward(self, x): + out = self.linear1(x) + return self.linear2(F.relu(out)) + + + dataset = CustomDataset(10000) + dataloader = DataLoader(dataset, batch_size=10) + model = Model(1) + loss_fn = nn.MSELoss() + optimizer = optim.SGD(model.parameters(), lr=1e-3) + scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9) + + for epoch in range(20): + for input, target in dataloader: + optimizer.zero_grad() + output = model(input) + loss = loss_fn(output, target) + loss.backward() + optimizer.step() + scheduler.step() + +Most learning rate schedulers can be chained (also referred to as +chaining schedulers). + +Example:: + + import oneflow + import oneflow.nn as nn + import oneflow.nn.functional as F + import oneflow.optim as optim + from oneflow.utils.data import Dataset, DataLoader + + + class CustomDataset(Dataset): + def __init__(self, num): + self.inputs = oneflow.randn(num, 1) + self.targets = oneflow.sin(self.inputs) + + def __len__(self): + return self.inputs.shape[0] + + def __getitem__(self, index): + return self.inputs[index], self.targets[index] + + + class Model(nn.Module): + def __init__(self, input_size): + super(Model, self).__init__() + self.linear1 = nn.Linear(input_size, 64) + self.linear2 = nn.Linear(64, input_size) + + def forward(self, x): + out = self.linear1(x) + return self.linear2(F.relu(out)) + + + dataset = CustomDataset(10000) + dataloader = DataLoader(dataset, batch_size=10) + model = Model(1) + loss_fn = nn.MSELoss() + optimizer = optim.SGD(model.parameters(), lr=1e-3) + scheduler1 = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9) + scheduler2 = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 10], gamma=0.1) + + for epoch in range(20): + for input, target in dataloader: + optimizer.zero_grad() + output = model(input) + loss = loss_fn(output, target) + loss.backward() + optimizer.step() + scheduler1.step() + scheduler2.step() + +In many places in the documentation, we will use the following template to refer to schedulers +algorithms. + + >>> scheduler = ... + >>> for epoch in range(100): + >>> train(...) + >>> validate(...) + >>> scheduler.step() + +.. warning:: + If you use the learning rate scheduler (calling ``scheduler.step()``) before the optimizer's update + (calling ``optimizer.step()``), this will skip the first value of the learning rate schedule. Please + check if you are calling ``scheduler.step()`` at the wrong time. + +.. autosummary:: + :toctree: generated + :nosignatures: + + lr_scheduler.CosineAnnealingLR + lr_scheduler.CosineDecayLR + lr_scheduler.ExponentialLR + lr_scheduler.LambdaLR + lr_scheduler.MultiStepLR + lr_scheduler.PolynomialLR + lr_scheduler.ReduceLROnPlateau + lr_scheduler.StepLR + lr_scheduler.ConstantLR + lr_scheduler.LinearLR + lr_scheduler.ChainedScheduler + lr_scheduler.SequentialLR + lr_scheduler.CosineAnnealingWarmRestarts diff --git a/docs/source/tensor.rst b/docs/source/tensor.rst index 0c1f4248f5b60f066559a7a9263969543a42d08e..93c547e23e04452bec7b27f305f8f2eb3236d82f 100644 --- a/docs/source/tensor.rst +++ b/docs/source/tensor.rst @@ -1,200 +1,390 @@ oneflow.Tensor =================================== -OneFlow Tensor Class ----------------------------------- + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/tensors.html + +A :class:`oneflow.Tensor` is a multi-dimensional matrix containing elements of +a single data type. + .. currentmodule:: oneflow -.. autoclass:: oneflow.Tensor - :members: abs, - acos, - acosh, - add, - add_, - addcmul, - addcmul_, - addmm, - amin, - amax, - arccos, - arccosh, - arcsin, - arcsinh, - arctan, - arctanh, - argmax, - argmin, - argsort, - argwhere, - asin, - asinh, - atan, - atan2, - atanh, - backward, - bmm, - byte, - cast, - ceil, - chunk, - clamp, - clamp_, - clip, - clip_, - clone, - copy_, - cos, - cosh, - cpu, - cuda, - data, - dot, - detach, - device, - placement, - sbp, - diag, - diagonal, - dim, - div, - div_, - double, - dtype, - element_size, - eq, - erf, - erfc, - erfinv, - erfinv_, - exp, - expand, - expand_as, - expm1, - fill_, - flatten, - flip, - float, - floor, - floor_, - fmod, - gather, - ge, - gelu, - get_device, - grad, - grad_fn, - gt, - half, - in_top_k, - index_select, - int, - is_global, - is_contiguous, - is_cuda, - is_floating_point, - is_lazy, - is_leaf, - item, - le, - log, - log1p, - logical_and, - logical_or, - logical_not, - logical_xor, - long, - lt, - masked_fill, - masked_select, - matmul, - mm, - mv, - max, - mean, - min, - mish, - mul, - mul_, - narrow, - ndim, - ndimension, - ne, - negative, - nelement, - new_empty, - new_ones, - new_zeros, - nonzero, - norm, - normal_, - numel, - numpy, - permute, - pow, - prod, - reciprocal, - register_hook, - relu, - repeat, - repeat_interleave, - requires_grad, - requires_grad_, - reshape, - retain_grad, - roll, - round, - rsqrt, - selu, - shape, - sigmoid, - sign, - silu, - sin, - sin_, - sinh, - size, - softmax, - softplus, - softsign, - sort, - split, - sqrt, - square, - squeeze, - std, - storage_offset, - stride, - sum, - swapaxes, - swapdims, - sub, - sub_, - tan, - tanh, - tile, - to, - local_to_global, - global_to_global, - to_global, - to_local, - to_consistent, - tolist, - topk, - transpose, - tril, - triu, - type_as, - type, - t, - T, - unbind, - unfold, - uniform_, - unsqueeze, - var, - view, - view_as, - where, - zero_, - nms, - pin_memory, - is_pinned, +Data types +---------- + +OneFlow defines 8 Tensor types with CPU and GPU variants which are as follows: + +======================================= =============================================== =============================== ================================== +Data type dtype CPU tensor GPU tensor +======================================= =============================================== =============================== ================================== +Boolean ``oneflow.bool`` :class:`oneflow.BoolTensor` :class:`oneflow.cuda.BoolTensor` +8-bit integer (unsigned) ``oneflow.uint8`` :class:`oneflow.ByteTensor` :class:`oneflow.cuda.ByteTensor` +8-bit integer (signed) ``oneflow.int8`` :class:`oneflow.CharTensor` :class:`oneflow.cuda.CharTensor` +64-bit floating point ``oneflow.float64`` or ``oneflow.double`` :class:`oneflow.DoubleTensor` :class:`oneflow.cuda.DoubleTensor` +32-bit floating point ``oneflow.float32`` or ``oneflow.float`` :class:`oneflow.FloatTensor` :class:`oneflow.cuda.FloatTensor` +16-bit floating point ``oneflow.float16`` or ``oneflow.half`` :class:`oneflow.HalfTensor` :class:`oneflow.cuda.HalfTensor` +32-bit integer (signed) ``oneflow.int32`` or ``oneflow.int`` :class:`oneflow.IntTensor` :class:`oneflow.cuda.IntTensor` +64-bit integer (signed) ``oneflow.int64`` or ``oneflow.long`` :class:`oneflow.LongTensor` :class:`oneflow.cuda.LongTensor` +======================================= =============================================== =============================== ================================== + +Initializing and basic operations +--------------------------------- + +A tensor can be constructed from a Python :class:`list` or sequence using the +:func:`oneflow.tensor` constructor: + +:: + + >>> import oneflow + >>> import numpy as np + >>> oneflow.tensor([[1., -1.], [1., -1.]]) + tensor([[ 1., -1.], + [ 1., -1.]], dtype=oneflow.float32) + >>> oneflow.tensor(np.array([[1, 2, 3], [4, 5, 6]])) + tensor([[ 1, 2, 3], + [ 4, 5, 6]], dtype=oneflow.int64) + +.. warning:: + + :func:`oneflow.tensor` always copies :attr:`data`. If you have a Tensor + :attr:`data` and just want to change its ``requires_grad`` flag, use + :meth:`~oneflow.Tensor.requires_grad_` or + :meth:`~oneflow.Tensor.detach` to avoid a copy. + If you have a numpy array and want to avoid a copy, use + :func:`oneflow.as_tensor`. + +.. A tensor of specific data type can be constructed by passing a :class:`oneflow.dtype` and/or a :class:`oneflow.device` to a constructor or tensor creation op: + +:: + + >>> import oneflow + >>> oneflow.zeros([2, 4], dtype=oneflow.int32) + tensor([[ 0, 0, 0, 0], + [ 0, 0, 0, 0]], dtype=oneflow.int32) + >>> cuda0 = oneflow.device('cuda:0') + >>> oneflow.ones([2, 4], dtype=oneflow.float64, device=cuda0) + tensor([[ 1., 1., 1., 1.], + [ 1., 1., 1., 1.]], device='cuda:0', dtype=oneflow.float64) + +For more information about building tensors, see :ref:`tensor-creation-ops` + +The contents of a tensor can be accessed and modified using Python's indexing +and slicing notation: + +:: + + >>> import oneflow + >>> x = oneflow.tensor([[1, 2, 3], [4, 5, 6]]) + >>> print(x[1][2]) + tensor(6, dtype=oneflow.int64) + >>> x[0][1] = 8 + >>> print(x) + tensor([[1, 8, 3], + [4, 5, 6]], dtype=oneflow.int64) + +Use :meth:`oneflow.Tensor.item` to get a Python number from a tensor containing a +single value: + +:: + + >>> import oneflow + >>> x = oneflow.tensor([[1]]) + >>> x + tensor([[1]], dtype=oneflow.int64) + >>> x.item() + 1 + >>> x = oneflow.tensor(2.5) + >>> x + tensor(2.5000, dtype=oneflow.float32) + >>> x.item() + 2.5 + +For more information about indexing, see :ref:`indexing-slicing-joining` + +A tensor can be created with :attr:`requires_grad=True` so that +:mod:`oneflow.autograd` records operations on them for automatic differentiation. + +:: + + >>> import oneflow + >>> x = oneflow.tensor([[1., -1.], [1., 1.]], requires_grad=True) + >>> out = x.pow(2).sum() + >>> out.backward() + >>> x.grad + tensor([[ 2., -2.], + [ 2., 2.]], dtype=oneflow.float32) + +.. note:: + For more information on the :class:`oneflow.dtype`, :class:`oneflow.device`, and + :class:`oneflow.layout` attributes of a :class:`oneflow.Tensor`, see + :ref:`tensor-attributes-doc`. + +.. note:: + Methods which mutate a tensor are marked with an underscore suffix. + For example, :func:`oneflow.FloatTensor.add_` computes the absolute value + in-place and returns the modified tensor, while :func:`oneflow.FloatTensor.add` + computes the result in a new tensor. + +.. note:: + To change an existing tensor's :class:`oneflow.device` and/or :class:`oneflow.dtype`, consider using + :meth:`~oneflow.Tensor.to` method of Tensor object. + +.. warning:: + Current implementation of :class:`oneflow.Tensor` introduces memory overhead, + thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors. + If this is your case, consider using one large structure. + +Tensor class reference +---------------------- + +.. class:: Tensor() + + There are a few main ways to create a tensor, depending on your use case. + + - To create a tensor with pre-existing data, use :func:`oneflow.tensor`. + - To create a tensor with specific size, use ``oneflow.*`` tensor creation + ops (see :ref:`tensor-creation-ops`). + - To create a tensor with the same size (and similar types) as another tensor, + use ``oneflow.*_like`` tensor creation ops + (see :ref:`tensor-creation-ops`). + +.. currentmodule:: oneflow +.. autosummary:: + :toctree: generated + :nosignatures: + + Tensor.new_empty + Tensor.new_ones + Tensor.new_zeros + Tensor.new_full + Tensor.new_tensor + Tensor.is_cuda + Tensor.is_global + Tensor.device + Tensor.grad + Tensor.ndim + Tensor.abs + Tensor.acos + Tensor.acosh + Tensor.add + Tensor.add_ + Tensor.addcdiv + Tensor.addcdiv_ + Tensor.addcmul + Tensor.addcmul_ + Tensor.addmm + Tensor.all + Tensor.amin + Tensor.amax + Tensor.any + Tensor.arccos + Tensor.arccosh + Tensor.arcsin + Tensor.arcsinh + Tensor.arctan + Tensor.arctanh + Tensor.argmax + Tensor.argmin + Tensor.argsort + Tensor.argwhere + Tensor.asin + Tensor.asinh + Tensor.atan + Tensor.atan2 + Tensor.atanh + Tensor.backward + Tensor.bmm + Tensor.bool + Tensor.byte + Tensor.cast + Tensor.ceil + Tensor.chunk + Tensor.clamp + Tensor.clamp_ + Tensor.clip + Tensor.clip_ + Tensor.clone + Tensor.contiguous + Tensor.copy_ + Tensor.cos + Tensor.cosh + Tensor.cpu + Tensor.cuda + Tensor.cumprod + Tensor.cumsum + Tensor.data + Tensor.dot + Tensor.detach + Tensor.placement + Tensor.sbp + Tensor.diag + Tensor.diagonal + Tensor.dim + Tensor.div + Tensor.div_ + Tensor.double + Tensor.dtype + Tensor.element_size + Tensor.eq + Tensor.equal + Tensor.erf + Tensor.erfc + Tensor.erfinv + Tensor.erfinv_ + Tensor.exp + Tensor.expand + Tensor.expand_as + Tensor.expm1 + Tensor.fill_ + Tensor.flatten + Tensor.flip + Tensor.float + Tensor.floor + Tensor.floor_ + Tensor.floor_divide + Tensor.fmod + Tensor.gather + Tensor.ge + Tensor.get_device + Tensor.grad_fn + Tensor.gt + Tensor.half + Tensor.in_top_k + Tensor.index_select + Tensor.index_add + Tensor.index_add_ + Tensor.int + Tensor.is_contiguous + Tensor.is_floating_point + Tensor.is_lazy + Tensor.is_leaf + Tensor.isinf + Tensor.isnan + Tensor.item + Tensor.le + Tensor.log + Tensor.log1p + Tensor.log2 + Tensor.log10 + Tensor.logical_and + Tensor.logical_or + Tensor.logical_not + Tensor.logical_xor + Tensor.long + Tensor.lt + Tensor.masked_fill + Tensor.masked_fill_ + Tensor.masked_select + Tensor.matmul + Tensor.mm + Tensor.mv + Tensor.max + Tensor.maximum + Tensor.median + Tensor.mean + Tensor.min + Tensor.minimum + Tensor.mish + Tensor.mul + Tensor.mul_ + Tensor.nansum + Tensor.narrow + Tensor.ndimension + Tensor.ne + Tensor.neg + Tensor.negative + Tensor.nelement + Tensor.nonzero + Tensor.norm + Tensor.normal_ + Tensor.numel + Tensor.numpy + Tensor.permute + Tensor.pow + Tensor.prod + Tensor.reciprocal + Tensor.register_hook + Tensor.relu + Tensor.repeat + Tensor.repeat_interleave + Tensor.requires_grad + Tensor.requires_grad_ + Tensor.reshape + Tensor.reshape_as + Tensor.retain_grad + Tensor.roll + Tensor.round + Tensor.rsqrt + Tensor.selu + Tensor.shape + Tensor.sigmoid + Tensor.sign + Tensor.silu + Tensor.sin + Tensor.sin_ + Tensor.sinh + Tensor.size + Tensor.softmax + Tensor.softplus + Tensor.softsign + Tensor.sort + Tensor.split + Tensor.sqrt + Tensor.square + Tensor.squeeze + Tensor.squeeze_ + Tensor.std + Tensor.storage_offset + Tensor.stride + Tensor.logsumexp + Tensor.sum + Tensor.swapaxes + Tensor.swapdims + Tensor.sub + Tensor.sub_ + Tensor.tan + Tensor.tanh + Tensor.tile + Tensor.to + Tensor.local_to_global + Tensor.global_to_global + Tensor.to_global + Tensor.to_local + Tensor.to_consistent + Tensor.tolist + Tensor.topk + Tensor.transpose + Tensor.tril + Tensor.triu + Tensor.trunc + Tensor.type_as + Tensor.type + Tensor.t + Tensor.T + Tensor.unbind + Tensor.unfold + Tensor.uniform_ + Tensor.unsqueeze + Tensor.unsqueeze_ + Tensor.as_strided + Tensor.as_strided_ + Tensor.var + Tensor.view + Tensor.view_as + Tensor.where + Tensor.zero_ + Tensor.nms + Tensor.pin_memory + Tensor.is_pinned + Tensor.cross + Tensor.scatter + Tensor.scatter_ + Tensor.scatter_add + Tensor.scatter_add_ + Tensor.bernoulli + Tensor.bernoulli_ + Tensor.bincount + Tensor.isclose + Tensor.allclose + Tensor.broadcast_to + Tensor.unique diff --git a/docs/source/tensor_attributes.rst b/docs/source/tensor_attributes.rst index 1890adf8a693a371d66e234427d4012cc7cfad77..e1f29973303e82a9a9a5708e25af7b06b0243ced 100644 --- a/docs/source/tensor_attributes.rst +++ b/docs/source/tensor_attributes.rst @@ -1,17 +1,203 @@ .. currentmodule:: oneflow +.. _tensor-attributes-doc: + Tensor Attributes ============================================================= + +.. The documentation is referenced from: https://pytorch.org/docs/1.10/tensor_attributes.html. + + Each local ``oneflow.Tensor`` has a :class:`oneflow.dtype`, :class:`oneflow.device`, and global ``oneflow.Tensor`` has a :class:`oneflow.dtype`, :class:`oneflow.placement`, :class:`oneflow.sbp`. +.. contents:: oneflow + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + + +.. _dtype-doc: + +oneflow.dtype +----------------------- + +.. class:: dtype + +A :class:`oneflow.dtype` is an object that represents the data type of a +:class:`oneflow.Tensor`. Oneflow has eight different data types: + +======================================= =============================================== =============================== ================================== +Data type dtype CPU tensor GPU tensor +======================================= =============================================== =============================== ================================== +Boolean ``oneflow.bool`` :class:`oneflow.BoolTensor` :class:`oneflow.cuda.BoolTensor` +8-bit integer (unsigned) ``oneflow.uint8`` :class:`oneflow.ByteTensor` :class:`oneflow.cuda.ByteTensor` +8-bit integer (signed) ``oneflow.int8`` :class:`oneflow.CharTensor` :class:`oneflow.cuda.CharTensor` +64-bit floating point ``oneflow.float64`` or ``oneflow.double`` :class:`oneflow.DoubleTensor` :class:`oneflow.cuda.DoubleTensor` +32-bit floating point ``oneflow.float32`` or ``oneflow.float`` :class:`oneflow.FloatTensor` :class:`oneflow.cuda.FloatTensor` +16-bit floating point ``oneflow.float16`` or ``oneflow.half`` :class:`oneflow.HalfTensor` :class:`oneflow.cuda.HalfTensor` +32-bit integer (signed) ``oneflow.int32`` or ``oneflow.int`` :class:`oneflow.IntTensor` :class:`oneflow.cuda.IntTensor` +64-bit integer (signed) ``oneflow.int64`` or ``oneflow.long`` :class:`oneflow.LongTensor` :class:`oneflow.cuda.LongTensor` +======================================= =============================================== =============================== ================================== + + +To find out if a :class:`oneflow.dtype` is a floating point data type, the property :attr:`is_floating_point` +can be used, which returns ``True`` if the data type is a floating point data type. + +.. _type-promotion-doc: + +When the dtypes of inputs to an arithmetic operation (`add`, `sub`, `div`, `mul`) differ, we promote +by finding the minimum dtype that satisfies the following rules: + +* If the type of a scalar operand is of a higher category than tensor operands + (where complex > floating > integral > boolean), we promote to a type with sufficient size to hold + all scalar operands of that category. +* If a zero-dimension tensor operand has a higher category than dimensioned operands, + we promote to a type with sufficient size and category to hold all zero-dim tensor operands of + that category. +* If there are no higher-category zero-dim operands, we promote to a type with sufficient size + and category to hold all dimensioned operands. + +A floating point scalar operand has dtype `oneflow.get_default_dtype()` and an integral +non-boolean scalar operand has dtype `oneflow.int64`. Unlike numpy, we do not inspect +values when determining the minimum `dtypes` of an operand. Quantized and complex types +are not yet supported. + +Promotion Examples:: + + >>> float_tensor = oneflow.ones(1, dtype=oneflow.float) + >>> double_tensor = oneflow.ones(1, dtype=oneflow.double) + >>> int_tensor = oneflow.ones(1, dtype=oneflow.int) + >>> long_tensor = oneflow.ones(1, dtype=oneflow.long) + >>> uint_tensor = oneflow.ones(1, dtype=oneflow.uint8) + >>> double_tensor = oneflow.ones(1, dtype=oneflow.double) + >>> bool_tensor = oneflow.ones(1, dtype=oneflow.bool) + # zero-dim tensors + >>> long_zerodim = oneflow.tensor(1, dtype=oneflow.long) + >>> int_zerodim = oneflow.tensor(1, dtype=oneflow.int) + + >>> a,b=oneflow.tensor(5),oneflow.tensor(5) + >>> oneflow.add(a, b).dtype + oneflow.int64 + # 5 is an int64, but does not have higher category than int_tensor so is not considered. + >>> (int_tensor + 5).dtype + oneflow.int32 + >>> (int_tensor + long_zerodim).dtype + oneflow.int64 + >>> (long_tensor + int_tensor).dtype + oneflow.int64 + >>> (bool_tensor + long_tensor).dtype + oneflow.int64 + >>> (bool_tensor + uint_tensor).dtype + oneflow.uint8 + >>> (float_tensor + double_tensor).dtype + oneflow.float64 + >>> (bool_tensor + int_tensor).dtype + oneflow.int32 + # Since long is a different kind than float, result dtype only needs to be large enough + # to hold the float. + >>> oneflow.add(long_tensor, float_tensor).dtype + oneflow.float32 + +When the output tensor of an arithmetic operation is specified, we allow casting to its `dtype` except that: + * An integral output tensor cannot accept a floating point tensor. + * A boolean output tensor cannot accept a non-boolean tensor. + * A non-complex output tensor cannot accept a complex tensor + +Casting Examples:: + + # allowed: + >>> float_tensor *= float_tensor + >>> float_tensor *= int_tensor + >>> float_tensor *= uint_tensor + >>> float_tensor *= bool_tensor + >>> int_tensor *= uint_tensor + + # disallowed (RuntimeError: result type can't be cast to the desired output type): + >>> float_tensor *= double_tensor + >>> int_tensor *= float_tensor + >>> int_tensor *= long_tensor + >>> uint_tensor *= int_tensor + >>> bool_tensor *= int_tensor + >>> bool_tensor *= uint_tensor + +.. _device-doc: + oneflow.device --------------------------------------------------------------- -.. autoclass:: oneflow.device +------------------------ + +.. class:: device + +A :class:`oneflow.device` is an object representing the device on which a :class:`oneflow.Tensor` is +or will be allocated. + +The :class:`oneflow.device` contains a device type (``'cpu'`` or ``'cuda'``) and optional device +ordinal for the device type. If the device ordinal is not present, this object will always represent +the current device for the device type, even after :func:`oneflow.cuda.set_device()` is called; e.g., +a :class:`oneflow.Tensor` constructed with device ``'cuda'`` is equivalent to ``'cuda:X'`` where X is +the result of :func:`oneflow.cuda.current_device()`. + +A :class:`oneflow.Tensor`'s device can be accessed via the :attr:`Tensor.device` property. + +A :class:`oneflow.device` can be constructed via a string or via a string and device ordinal + +Via a string: +:: + + >>> oneflow.device('cuda:0') + device(type='cuda', index=0) + + >>> oneflow.device('cpu') + device(type='cpu', index=0) + + >>> oneflow.device('cuda') # current cuda device + device(type='cuda', index=0) + +Via a string and device ordinal: + +:: + + >>> oneflow.device('cuda', 0) + device(type='cuda', index=0) + + >>> oneflow.device('cpu', 0) + device(type='cpu', index=0) + +.. note:: + The :class:`oneflow.device` argument in functions can generally be substituted with a string. + This allows for fast prototyping of code. + + >>> # Example of a function that takes in a oneflow.device + >>> cuda1 = oneflow.device('cuda:1') + >>> oneflow.randn((2,3), device=cuda1) + + >>> # You can substitute the oneflow.device with a string + >>> oneflow.randn((2,3), device='cuda:1') + +.. note:: + For legacy reasons, a device can be constructed via a single device ordinal, which is treated + as a cuda device. This matches :meth:`Tensor.get_device`, which returns an ordinal for cuda + tensors and is not supported for cpu tensors. + + >>> oneflow.device(1) + device(type='cuda', index=1) + +.. note:: + Methods which take a device will generally accept a (properly formatted) string + or (legacy) integer device ordinal, i.e. the following are all equivalent: + + >>> oneflow.randn((2,3), device=oneflow.device('cuda:1')) + >>> oneflow.randn((2,3), device='cuda:1') + >>> oneflow.randn((2,3), device=1) # legacy oneflow.placement -------------------------------------------------------------- .. autoclass:: oneflow.placement +oneflow.placement.all +-------------------------------------------------------------- +.. autofunction:: oneflow.placement.all + oneflow.env.all_device_placement -------------------------------------------------------------- .. autofunction:: oneflow.env.all_device_placement diff --git a/docs/source/type_info.rst b/docs/source/type_info.rst new file mode 100644 index 0000000000000000000000000000000000000000..edd7a11f88045a3504505a28ee27362f324d64f9 --- /dev/null +++ b/docs/source/type_info.rst @@ -0,0 +1,80 @@ +.. currentmodule:: oneflow + +.. _type-info-doc: + +Type Info +========= + +.. The documentation is referenced from: https://pytorch.org/docs/1.10/type_info.html. + +The numerical properties of a :class:`oneflow.dtype` can be accessed through either the :class:`oneflow.finfo` or the :class:`oneflow.iinfo`. + + +.. contents:: oneflow + :depth: 2 + :local: + :class: this-will-duplicate-information-and-it-is-still-useful-here + :backlinks: top + +oneflow.finfo +------------- + +.. class:: oneflow.finfo + +A :class:`oneflow.finfo` is an object that represents the numerical properties of a floating point :class:`oneflow.dtype`, (i.e. ``oneflow.float32``, ``oneflow.float64`` and ``oneflow.float16``). This is similar to `numpy.finfo `_. + +A :class:`oneflow.finfo` provides the following attributes: + +================== ======= ========================================================================== +Name Type Description +================== ======= ========================================================================== +bits int The number of bits occupied by the type. +eps float The smallest representable number such that ``1.0 + eps != 1.0``. +min float The largest representable number. +max float The smallest representable number (typically ``-max``). +tiny float The smallest positive normal number. See notes. +resolution float The approximate decimal resolution of this type, i.e., ``10**-precision``. +================== ======= ========================================================================== + +For example: + +.. code-block:: + + >>> import oneflow as flow + >>> flow.finfo() + finfo(resolution=1e-06, min=-3.40282e+38, max=3.40282e+38, eps=1.19209e-07, tiny=1.17549e-38, dtype=oneflow.float32, bits=32) + >>> flow.finfo(flow.float) + finfo(resolution=1e-06, min=-3.40282e+38, max=3.40282e+38, eps=1.19209e-07, tiny=1.17549e-38, dtype=oneflow.float32, bits=32) + >>> flow.finfo(flow.float16).bits + 16 + >>> flow.finfo(flow.float16).max + 65504.0 + +oneflow.iinfo +------------- + +.. class:: oneflow.iinfo + +A :class:`oneflow.iinfo` is an object that represents the numerical properties of a integer :class:`oneflow.dtype` (i.e. ``oneflow.uint8``, ``oneflow.int8``, ``oneflow.int16``, ``oneflow.int32``, and ``oneflow.int64``). This is similar to `numpy.iinfo `_. + +A :class:`oneflow.iinfo` provides the following attributes: + +================== ======= ========================================================================== +Name Type Description +================== ======= ========================================================================== +bits int The number of bits occupied by the type. +min float The largest representable number. +max float The smallest representable number. +================== ======= ========================================================================== + +For example: + +.. code-block :: + + >>> import oneflow as flow + >>> flow.iinfo(flow.int8) + iinfo(min=-128, max=127, dtype=oneflow.int8, bits=8) + >>> flow.iinfo(flow.int).max + 2147483647 + >>> flow.iinfo(flow.int).bits + 32 diff --git a/docs/source/utils.data.rst b/docs/source/utils.data.rst new file mode 100644 index 0000000000000000000000000000000000000000..09f7f8afaead88477edde3049cc3218ea2b25aaf --- /dev/null +++ b/docs/source/utils.data.rst @@ -0,0 +1,426 @@ +oneflow.utils.data +=================================== + +.. The documentation is referenced from: + https://pytorch.org/docs/1.10/data.html + +.. automodule:: oneflow.utils.data + +At the heart of Oneflow data loading utility is the :class:`oneflow.utils.data.DataLoader` +class. It represents a Python iterable over a dataset, with support for + +* `map-style and iterable-style datasets `_, + +* `customizing data loading order `_, + +* `automatic batching `_, + +* `single- and multi-process data loading `_, + +* `automatic memory pinning `_. + +These options are configured by the constructor arguments of a +:class:`~oneflow.utils.data.DataLoader`, which has signature:: + + DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, + batch_sampler=None, num_workers=0, collate_fn=None, + pin_memory=False, drop_last=False, timeout=0, + worker_init_fn=None, *, prefetch_factor=2, + persistent_workers=False) + +The sections below describe in details the effects and usages of these options. + +Dataset Types +------------- + +The most important argument of :class:`~oneflow.utils.data.DataLoader` +constructor is :attr:`dataset`, which indicates a dataset object to load data +from. Oneflow supports two different types of datasets: + +* `map-style datasets `_, + +* `iterable-style datasets `_. + +Map-style datasets +^^^^^^^^^^^^^^^^^^ + +A map-style dataset is one that implements the :meth:`__getitem__` and +:meth:`__len__` protocols, and represents a map from (possibly non-integral) +indices/keys to data samples. + +For example, such a dataset, when accessed with ``dataset[idx]``, could read +the ``idx``-th image and its corresponding label from a folder on the disk. + +See :class:`~oneflow.utils.data.Dataset` for more details. + +Iterable-style datasets +^^^^^^^^^^^^^^^^^^^^^^^ + +An iterable-style dataset is an instance of a subclass of :class:`~oneflow.utils.data.IterableDataset` +that implements the :meth:`__iter__` protocol, and represents an iterable over +data samples. This type of datasets is particularly suitable for cases where +random reads are expensive or even improbable, and where the batch size depends +on the fetched data. + +For example, such a dataset, when called ``iter(dataset)``, could return a +stream of data reading from a database, a remote server, or even logs generated +in real time. + +See :class:`~oneflow.utils.data.IterableDataset` for more details. + +.. note:: When using an :class:`~oneflow.utils.data.IterableDataset` with + `multi-process data loading `_. The same + dataset object is replicated on each worker process, and thus the + replicas must be configured differently to avoid duplicated data. See + :class:`~oneflow.utils.data.IterableDataset` documentations for how to + achieve this. + +Data Loading Order and :class:`~oneflow.utils.data.Sampler` +----------------------------------------------------------- + +For `iterable-style datasets `_, data loading order +is entirely controlled by the user-defined iterable. This allows easier +implementations of chunk-reading and dynamic batch size (e.g., by yielding a +batched sample at each time). + +The rest of this section concerns the case with +`map-style datasets `_. :class:`oneflow.utils.data.Sampler` +classes are used to specify the sequence of indices/keys used in data loading. +They represent iterable objects over the indices to datasets. E.g., in the +common case with stochastic gradient decent (SGD), a +:class:`~oneflow.utils.data.Sampler` could randomly permute a list of indices +and yield each one at a time, or yield a small number of them for mini-batch +SGD. + +A sequential or shuffled sampler will be automatically constructed based on the :attr:`shuffle` argument to a :class:`~oneflow.utils.data.DataLoader`. +Alternatively, users may use the :attr:`sampler` argument to specify a +custom :class:`~oneflow.utils.data.Sampler` object that at each time yields +the next index/key to fetch. + +A custom :class:`~oneflow.utils.data.Sampler` that yields a list of batch +indices at a time can be passed as the :attr:`batch_sampler` argument. +Automatic batching can also be enabled via :attr:`batch_size` and +:attr:`drop_last` arguments. See +`the next section `_ for more details +on this. + +.. note:: + Neither :attr:`sampler` nor :attr:`batch_sampler` is compatible with + iterable-style datasets, since such datasets have no notion of a key or an + index. + +Loading Batched and Non-Batched Data +------------------------------------ + +:class:`~oneflow.utils.data.DataLoader` supports automatically collating +individual fetched data samples into batches via arguments +:attr:`batch_size`, :attr:`drop_last`, :attr:`batch_sampler`, and +:attr:`collate_fn` (which has a default function). + + +Automatic batching (default) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This is the most common case, and corresponds to fetching a minibatch of +data and collating them into batched samples, i.e., containing Tensors with +one dimension being the batch dimension (usually the first). + +When :attr:`batch_size` (default ``1``) is not ``None``, the data loader yields +batched samples instead of individual samples. :attr:`batch_size` and +:attr:`drop_last` arguments are used to specify how the data loader obtains +batches of dataset keys. For map-style datasets, users can alternatively +specify :attr:`batch_sampler`, which yields a list of keys at a time. + +.. note:: + The :attr:`batch_size` and :attr:`drop_last` arguments essentially are used + to construct a :attr:`batch_sampler` from :attr:`sampler`. For map-style + datasets, the :attr:`sampler` is either provided by user or constructed + based on the :attr:`shuffle` argument. For iterable-style datasets, the + :attr:`sampler` is a dummy infinite one. See + `this section `_ on more details on + samplers. + +.. note:: + When fetching from + `iterable-style datasets `_ with + `multi-processing `_, the :attr:`drop_last` + argument drops the last non-full batch of each worker's dataset replica. + +After fetching a list of samples using the indices from sampler, the function +passed as the :attr:`collate_fn` argument is used to collate lists of samples +into batches. + +In this case, loading from a map-style dataset is roughly equivalent with:: + + for indices in batch_sampler: + yield collate_fn([dataset[i] for i in indices]) + +and loading from an iterable-style dataset is roughly equivalent with:: + + dataset_iter = iter(dataset) + for indices in batch_sampler: + yield collate_fn([next(dataset_iter) for _ in indices]) + +A custom :attr:`collate_fn` can be used to customize collation, e.g., padding +sequential data to max length of a batch. See +`this section `_ on more about :attr:`collate_fn`. + +Disable automatic batching +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In certain cases, users may want to handle batching manually in dataset code, +or simply load individual samples. For example, it could be cheaper to directly +load batched data (e.g., bulk reads from a database or reading continuous +chunks of memory), or the batch size is data dependent, or the program is +designed to work on individual samples. Under these scenarios, it's likely +better to not use automatic batching (where :attr:`collate_fn` is used to +collate the samples), but let the data loader directly return each member of +the :attr:`dataset` object. + +When both :attr:`batch_size` and :attr:`batch_sampler` are ``None`` (default +value for :attr:`batch_sampler` is already ``None``), automatic batching is +disabled. Each sample obtained from the :attr:`dataset` is processed with the +function passed as the :attr:`collate_fn` argument. + +**When automatic batching is disabled**, the default :attr:`collate_fn` simply +converts NumPy arrays into Oneflow Tensors, and keeps everything else untouched. + +In this case, loading from a map-style dataset is roughly equivalent with:: + + for index in sampler: + yield collate_fn(dataset[index]) + +and loading from an iterable-style dataset is roughly equivalent with:: + + for data in iter(dataset): + yield collate_fn(data) + +See `this section `_ on more about :attr:`collate_fn`. + +.. _dataloader-collate_fn: + +Working with :attr:`collate_fn` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The use of :attr:`collate_fn` is slightly different when automatic batching is +enabled or disabled. + +**When automatic batching is disabled**, :attr:`collate_fn` is called with +each individual data sample, and the output is yielded from the data loader +iterator. In this case, the default :attr:`collate_fn` simply converts NumPy +arrays in Oneflow tensors. + +**When automatic batching is enabled**, :attr:`collate_fn` is called with a list +of data samples at each time. It is expected to collate the input samples into +a batch for yielding from the data loader iterator. The rest of this section +describes the behavior of the default :attr:`collate_fn` +(:func:`~oneflow.utils.data.default_collate`). + +For instance, if each data sample consists of a 3-channel image and an integral +class label, i.e., each element of the dataset returns a tuple +``(image, class_index)``, the default :attr:`collate_fn` collates a list of +such tuples into a single tuple of a batched image tensor and a batched class +label Tensor. In particular, the default :attr:`collate_fn` has the following +properties: + +* It always prepends a new dimension as the batch dimension. + +* It automatically converts NumPy arrays and Python numerical values into + Oneflow Tensors. + +* It preserves the data structure, e.g., if each sample is a dictionary, it + outputs a dictionary with the same set of keys but batched Tensors as values + (or lists if the values can not be converted into Tensors). Same + for ``list`` s, ``tuple`` s, ``namedtuple`` s, etc. + +Users may use customized :attr:`collate_fn` to achieve custom batching, e.g., +collating along a dimension other than the first, padding sequences of +various lengths, or adding support for custom data types. + +If you run into a situation where the outputs of :class:`~oneflow.utils.data.DataLoader` +have dimensions or type that is different from your expectation, you may +want to check your :attr:`collate_fn`. + +Single- and Multi-process Data Loading +-------------------------------------- + +A :class:`~oneflow.utils.data.DataLoader` uses single-process data loading by +default. + +Within a Python process, the +`Global Interpreter Lock (GIL) `_ +prevents true fully parallelizing Python code across threads. To avoid blocking +computation code with data loading, Oneflow provides an easy switch to perform +multi-process data loading by simply setting the argument :attr:`num_workers` +to a positive integer. + +Single-process data loading (default) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In this mode, data fetching is done in the same process a +:class:`~oneflow.utils.data.DataLoader` is initialized. Therefore, data loading +may block computing. However, this mode may be preferred when resource(s) used +for sharing data among processes (e.g., shared memory, file descriptors) is +limited, or when the entire dataset is small and can be loaded entirely in +memory. Additionally, single-process loading often shows more readable error +traces and thus is useful for debugging. + + +Multi-process data loading +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Setting the argument :attr:`num_workers` as a positive integer will +turn on multi-process data loading with the specified number of loader worker +processes. + +.. warning:: + After several iterations, the loader worker processes will consume + the same amount of CPU memory as the parent process for all Python + objects in the parent process which are accessed from the worker + processes. This can be problematic if the Dataset contains a lot of + data (e.g., you are loading a very large list of filenames at Dataset + construction time) and/or you are using a lot of workers (overall + memory usage is ``number of workers * size of parent process``). The + simplest workaround is to replace Python objects with non-refcounted + representations such as Pandas, Numpy or PyArrow objects. + +In this mode, each time an iterator of a :class:`~oneflow.utils.data.DataLoader` +is created (e.g., when you call ``enumerate(dataloader)``), :attr:`num_workers` +worker processes are created. At this point, the :attr:`dataset`, +:attr:`collate_fn`, and :attr:`worker_init_fn` are passed to each +worker, where they are used to initialize, and fetch data. This means that +dataset access together with its internal IO, transforms +(including :attr:`collate_fn`) runs in the worker process. + +For map-style datasets, the main process generates the indices using +:attr:`sampler` and sends them to the workers. So any shuffle randomization is +done in the main process which guides loading by assigning indices to load. + +For iterable-style datasets, since each worker process gets a replica of the +:attr:`dataset` object, naive multi-process loading will often result in +duplicated data. Using :attr:`worker_init_fn`, users may configure each replica independently. (See +:class:`~oneflow.utils.data.IterableDataset` documentations for how to achieve +this. ) For similar reasons, in multi-process loading, the :attr:`drop_last` +argument drops the last non-full batch of each worker's iterable-style dataset +replica. + +Workers are shut down once the end of the iteration is reached, or when the +iterator becomes garbage collected. + +.. warning:: + It is generally not recommended to return CUDA tensors in multi-process + loading because of many subtleties in using CUDA and sharing CUDA tensors in + multiprocessing. Instead, we recommend + using `automatic memory pinning `_ (i.e., setting + :attr:`pin_memory=True`), which enables fast data transfer to CUDA-enabled + GPUs. + +Platform-specific behaviors +""""""""""""""""""""""""""" + +Since workers rely on Python :py:mod:`multiprocessing`, worker launch behavior is +different on Windows compared to Unix. + +* On Unix, :func:`fork()` is the default :py:mod:`multiprocessing` start method. + Using :func:`fork`, child workers typically can access the :attr:`dataset` and + Python argument functions directly through the cloned address space. + +* On Windows or MacOS, :func:`spawn()` is the default :py:mod:`multiprocessing` start method. + Using :func:`spawn()`, another interpreter is launched which runs your main script, + followed by the internal worker function that receives the :attr:`dataset`, + :attr:`collate_fn` and other arguments through :py:mod:`pickle` serialization. + +This separate serialization means that you should take two steps to ensure you +are compatible with Windows while using multi-process data loading: + +- Wrap most of you main script's code within ``if __name__ == '__main__':`` block, + to make sure it doesn't run again (most likely generating error) when each worker + process is launched. You can place your dataset and :class:`~oneflow.utils.data.DataLoader` + instance creation logic here, as it doesn't need to be re-executed in workers. + +- Make sure that any custom :attr:`collate_fn`, :attr:`worker_init_fn` + or :attr:`dataset` code is declared as top level definitions, outside of the + ``__main__`` check. This ensures that they are available in worker processes. + (this is needed since functions are pickled as references only, not ``bytecode``.) + +.. _data-loading-randomness: + +Randomness in multi-process data loading +"""""""""""""""""""""""""""""""""""""""""" + +By default, each worker will have its Oneflow seed set to ``base_seed + worker_id``, +where ``base_seed`` is a long generated by main process using its RNG (thereby, +consuming a RNG state mandatorily) or a specified :attr:`generator`. However, seeds for other +libraries may be duplicated upon initializing workers, causing each worker to return +identical random numbers. + +In :attr:`worker_init_fn`, you may access the Oneflow seed set for each worker +with :func:`oneflow.initial_seed()`, and use it to seed other libraries before data +loading. + +Memory Pinning +-------------- + +Host to GPU copies are much faster when they originate from pinned (page-locked) +memory. See `cuda-memory-pinning` for more details on when and how to use +pinned memory generally. + +For data loading, passing :attr:`pin_memory=True` to a +:class:`~oneflow.utils.data.DataLoader` will automatically put the fetched data +Tensors in pinned memory, and thus enables faster data transfer to CUDA-enabled +GPUs. + +The default memory pinning logic only recognizes Tensors and maps and iterables +containing Tensors. By default, if the pinning logic sees a batch that is a +custom type (which will occur if you have a :attr:`collate_fn` that returns a +custom batch type), or if each element of your batch is a custom type, the +pinning logic will not recognize them, and it will return that batch (or those +elements) without pinning the memory. To enable memory pinning for custom +batch or data type(s), define a :meth:`pin_memory` method on your custom +type(s). + +See the example below. + +Example:: + + class SimpleCustomBatch: + def __init__(self, data): + transposed_data = list(zip(*data)) + self.inp = oneflow.stack(transposed_data[0], 0) + self.tgt = oneflow.stack(transposed_data[1], 0) + + # custom memory pinning method on custom type + def pin_memory(self): + self.inp = self.inp.pin_memory() + self.tgt = self.tgt.pin_memory() + return self + + def collate_wrapper(batch): + return SimpleCustomBatch(batch) + + inps = oneflow.arange(10 * 5, dtype=oneflow.float32).view(10, 5) + tgts = oneflow.arange(10 * 5, dtype=oneflow.float32).view(10, 5) + dataset = TensorDataset(inps, tgts) + + loader = DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper, + pin_memory=True) + + for batch_ndx, sample in enumerate(loader): + print(sample.inp.is_pinned()) + print(sample.tgt.is_pinned()) + + +.. autoclass:: DataLoader +.. autoclass:: Dataset +.. autoclass:: IterableDataset +.. autoclass:: TensorDataset +.. autoclass:: ConcatDataset +.. autoclass:: Subset +.. autofunction:: oneflow.utils.data.random_split +.. autoclass:: oneflow.utils.data.Sampler +.. autoclass:: oneflow.utils.data.SequentialSampler +.. autoclass:: oneflow.utils.data.RandomSampler +.. autoclass:: oneflow.utils.data.SubsetRandomSampler +.. autoclass:: oneflow.utils.data.BatchSampler +.. autoclass:: oneflow.utils.data.distributed.DistributedSampler + diff --git a/docs/source/utils.global_view.rst b/docs/source/utils.global_view.rst new file mode 100644 index 0000000000000000000000000000000000000000..69ebf0efa4f1e48802a1e522d092239ec10f8859 --- /dev/null +++ b/docs/source/utils.global_view.rst @@ -0,0 +1,15 @@ +oneflow.utils.global_view +====================================== +Some global view Ops +-------------------------------------- +.. currentmodule:: oneflow.utils.global_view + +.. autosummary:: + :toctree: generated + :nosignatures: + + to_global + to_local + global_mode + current_global_mode + diff --git a/docs/source/utils.rst b/docs/source/utils.rst deleted file mode 100644 index 449893020e514f097169a86ce8e4469d10800b7e..0000000000000000000000000000000000000000 --- a/docs/source/utils.rst +++ /dev/null @@ -1,27 +0,0 @@ -oneflow.utils -=================================== -Utils ----------------------------------- -.. currentmodule:: oneflow.utils -.. automodule:: oneflow.utils.data - :members: DataLoader, - Dataset, - IterableDataset, - TensorDataset, - ConcatDataset, - Subset, - random_split, - Sampler, - SequentialSampler, - RandomSampler, - SubsetRandomSampler, - BatchSampler - - -.. currentmodule:: oneflow.utils -.. automodule:: oneflow.utils.data.distributed - :members: DistributedSampler - - -.. autofunction:: oneflow.utils.from_torch -.. autofunction:: oneflow.utils.to_torch diff --git a/docs/source/utils.tensor.rst b/docs/source/utils.tensor.rst new file mode 100644 index 0000000000000000000000000000000000000000..74890f297be79ef772e910b52c6cea386a3c7063 --- /dev/null +++ b/docs/source/utils.tensor.rst @@ -0,0 +1,13 @@ +oneflow.utils.tensor +========================================================== +Some torch-related Ops are suitable for tensor conversion. +---------------------------------------------------------- +.. currentmodule:: oneflow.utils.tensor + +.. autosummary:: + :toctree: generated + :nosignatures: + + from_torch + to_torch + diff --git a/external/CMakeLists.txt b/external/CMakeLists.txt index bc8aae2b85dcf3f741c71384d4a510104d35eb53..0985dfbea85cab1fa03313430e641e6e65a2939b 100644 --- a/external/CMakeLists.txt +++ b/external/CMakeLists.txt @@ -15,4 +15,5 @@ add_subdirectory(kineto) list(APPEND EXTERNAL_TARGETS kineto) mark_targets_as_system(${EXTERNAL_TARGETS}) + set_property(GLOBAL PROPERTY EXTERNAL_TARGETS ${EXTERNAL_TARGETS}) diff --git a/external/kineto/CMakeLists.txt b/external/kineto/CMakeLists.txt index 0bae1a0621e796985ca00ed01a7d107335580f37..0ffccf63079f912adc1a5220fcb15c0d55ff532c 100644 --- a/external/kineto/CMakeLists.txt +++ b/external/kineto/CMakeLists.txt @@ -34,7 +34,9 @@ list( $ENV{CUPTI_ROOT}/lib /usr/lib ${CUDA_SOURCE_DIR}/targets/x86_64-linux/lib64 - ${CUDA_SOURCE_DIR}/extras/CUPTI/lib64) + ${CUDA_SOURCE_DIR}/targets/x86_64-linux/lib + ${CUDA_SOURCE_DIR}/extras/CUPTI/lib64 + ${CUDA_SOURCE_DIR}/extras/CUPTI/lib) find_library( CUDA_cupti_LIBRARY diff --git a/oneflow/api/common/ofblob.h b/oneflow/api/common/ofblob.h deleted file mode 100644 index 055c28f3139196a48ff0756f89d36a1076abaca9..0000000000000000000000000000000000000000 --- a/oneflow/api/common/ofblob.h +++ /dev/null @@ -1,57 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifndef ONEFLOW_API_COMMON_OFBLOB_H_ -#define ONEFLOW_API_COMMON_OFBLOB_H_ - -#include "oneflow/core/common/just.h" -#include "oneflow/core/register/ofblob.h" - -namespace oneflow { - -template -struct BlobBufferCopyUtil { - static Maybe From(uint64_t of_blob_ptr, const T* buf_ptr, size_t size) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyFrom(buf_ptr, size); - return Maybe::Ok(); - } - - static Maybe To(uint64_t of_blob_ptr, T* buf_ptr, size_t size) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(buf_ptr, size); - return Maybe::Ok(); - } -}; - -template<> -struct BlobBufferCopyUtil { - static Maybe From(uint64_t of_blob_ptr, const void* buf_ptr, size_t size) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyFrom(buf_ptr, size); - return Maybe::Ok(); - } - - static Maybe To(uint64_t of_blob_ptr, void* buf_ptr, size_t size) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(buf_ptr, size); - return Maybe::Ok(); - } -}; - -} // namespace oneflow - -#endif // !ONEFLOW_API_COMMON_OFBLOB_H_ diff --git a/oneflow/api/common/sbp.h b/oneflow/api/common/sbp.h index 423c92a1633450b85a7124d288703a740466895c..9745d641df3d0b701b82ada514cb20acf9bceda0 100644 --- a/oneflow/api/common/sbp.h +++ b/oneflow/api/common/sbp.h @@ -26,7 +26,9 @@ namespace oneflow { namespace api { -inline Maybe SbpToString(Symbol sbp_sym) { +// NOTE: The api inferface will print the whole name of sbp. + +inline Maybe ApiSbpToString(Symbol sbp_sym) { std::string sbp_str = "oneflow.sbp."; if (sbp_sym->has_broadcast_parallel()) { sbp_str += "broadcast"; @@ -40,11 +42,11 @@ inline Maybe SbpToString(Symbol sbp_sym) { return sbp_str; } -inline Maybe NdSbpToString(Symbol nd_sbp) { +inline Maybe ApiNdSbpToString(Symbol nd_sbp) { std::string str = "("; for (int i = 0; i < nd_sbp->sbp_parallel_size(); ++i) { if (i > 0) { str += ", "; } - str += *JUST(SbpToString(SymbolOf(nd_sbp->sbp_parallel(i)))); + str += *JUST(ApiSbpToString(SymbolOf(nd_sbp->sbp_parallel(i)))); } if (nd_sbp->sbp_parallel_size() == 1) { str += ","; } str += ")"; diff --git a/oneflow/api/common/variable_tensor_mgr.h b/oneflow/api/common/variable_tensor_mgr.h index 082edbd26bbdfe7dfc34f7fd0dd31a21aec9c3bb..dcf1895ff1750dc50cd5d80c5d04bbb48e390800 100644 --- a/oneflow/api/common/variable_tensor_mgr.h +++ b/oneflow/api/common/variable_tensor_mgr.h @@ -28,9 +28,10 @@ inline Maybe FillVariableTensorMgr( auto mgr = Singleton::Get(); return mgr->Fill(variable_op_names, variable_tensors); } -inline void ClearVariableTensorMgr() { + +inline void ResetVariableTensorMgr() { auto mgr = Singleton::Get(); - mgr->Clear(); + mgr->Reset(); } inline std::tuple, std::vector>> diff --git a/oneflow/api/cpp/embedding/embedding.cpp b/oneflow/api/cpp/embedding/embedding.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7ca5330f5e8d7faaebcbdb9f43b8d825b12fdaf --- /dev/null +++ b/oneflow/api/cpp/embedding/embedding.cpp @@ -0,0 +1,46 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/api/cpp/embedding/embedding.h" +#include "oneflow/core/embedding/embedding_manager.h" + +namespace oneflow_api { +namespace embedding { + +std::string CreateKeyValueStore(const std::string& key_value_store_options, int64_t local_rank_id, + int64_t rank_id, int64_t world_size) { + oneflow::embedding::KeyValueStoreOptions options(key_value_store_options); +#if defined(WITH_CUDA) || defined(WITH_ROCM) + oneflow::Singleton::Get()->CreateKeyValueStore( + options, local_rank_id, rank_id, world_size); + return options.Name(); +#else + UNIMPLEMENTED() << "OneEmbedding Only Support with CUDA"; +#endif + return ""; +} + +void LoadSnapshot(const std::string& snapshot_name, const std::string& embedding_name, + int64_t local_rank_id, int64_t rank_id) { +#if defined(WITH_CUDA) || defined(WITH_ROCM) + oneflow::Singleton::Get()->LoadSnapshot( + embedding_name, local_rank_id, rank_id, snapshot_name); +#else + UNIMPLEMENTED() << "OneEmbedding Only Support with CUDA"; +#endif +} + +} // namespace embedding +} // namespace oneflow_api diff --git a/oneflow/api/cpp/embedding/embedding.h b/oneflow/api/cpp/embedding/embedding.h new file mode 100644 index 0000000000000000000000000000000000000000..87b5617fde88da16c40cac9d2844eebd2b4e7f07 --- /dev/null +++ b/oneflow/api/cpp/embedding/embedding.h @@ -0,0 +1,35 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_CPP_ONE_EMBEDDING_ONE_EMBEDDING_H_ +#define ONEFLOW_API_CPP_ONE_EMBEDDING_ONE_EMBEDDING_H_ + +#include + +namespace oneflow_api { +namespace embedding { + +// CreateKeyValueStore returns embedding name in the options. +std::string CreateKeyValueStore(const std::string& key_value_store_options, int64_t local_rank_id, + int64_t rank_id, + int64_t world_size); // key_value_store_options is + // a serialized json string. +void LoadSnapshot(const std::string& snapshot_name, const std::string& embedding_name, + int64_t local_rank_id, int64_t rank_id); + +} // namespace embedding +} // namespace oneflow_api + +#endif // ONEFLOW_API_CPP_ONE_EMBEDDING_ONE_EMBEDDING_H_ diff --git a/oneflow/api/cpp/env.cpp b/oneflow/api/cpp/env.cpp index f55550aa9ad132267940c1a1b16919634ce77e95..b84430df4ba9e936205ad72e93c238640afe47ef 100644 --- a/oneflow/api/cpp/env.cpp +++ b/oneflow/api/cpp/env.cpp @@ -18,7 +18,7 @@ limitations under the License. #include "oneflow/api/cpp/env.h" #include "oneflow/api/cpp/env_impl.h" #include "oneflow/core/framework/shut_down_util.h" -#include "oneflow/core/thread/thread_consistent_id.h" +#include "oneflow/core/thread/thread_global_id.h" namespace oneflow_api { void initialize() { @@ -29,7 +29,7 @@ void initialize() { void release() { if (of::Singleton::Get() != nullptr) { of::Singleton::Delete(); } of::SetShuttingDown(); - of::ResetThisThreadUniqueConsistentId().GetOrThrow(); + of::ResetThisThreadUniqueGlobalId().GetOrThrow(); } } // namespace oneflow_api diff --git a/oneflow/api/cpp/env_impl.cpp b/oneflow/api/cpp/env_impl.cpp index 2a50ba9871a9f41a0ed9bf628e68bd202ced692f..8e5f8882e64736dd25fb0c59d5739b03093e74f1 100644 --- a/oneflow/api/cpp/env_impl.cpp +++ b/oneflow/api/cpp/env_impl.cpp @@ -107,6 +107,9 @@ void CompleteEnvProto(of::EnvProto& env_proto) { if (HasEnvVar("GLOG_logbuflevel")) { cpp_logging_conf->set_logbuflevel(GetEnvVar("GLOG_logbuflevel", -1)); } + if (HasEnvVar("GLOG_minloglevel")) { + cpp_logging_conf->set_minloglevel(GetEnvVar("GLOG_minloglevel", -1)); + } } } // namespace @@ -119,15 +122,15 @@ OneFlowEnv::OneFlowEnv() { of::ConfigProto config_proto; config_proto.mutable_resource()->set_cpu_device_num(1); // useless, will be set in TryInit const int64_t session_id = of::NewSessionId(); - CHECK_JUST(of::RegsiterSession(session_id)); config_proto.set_session_id(session_id); - + CHECK(of::RegsterSessionId(session_id)); session_ctx_ = std::make_shared(env_ctx_); CHECK_JUST(session_ctx_->TryInit(config_proto)); } OneFlowEnv::~OneFlowEnv() { session_ctx_.reset(); + CHECK(of::ClearSessionId(CHECK_JUST(of::GetDefaultSessionId()))); env_ctx_.reset(); } diff --git a/oneflow/api/cpp/framework/graph.cpp b/oneflow/api/cpp/framework/graph.cpp index b4010fd3ca5df05a569aaa1b57d03ada51d5e07c..fa3c2bf657e73fcbb7db24baacc0258969992ac0 100644 --- a/oneflow/api/cpp/framework/graph.cpp +++ b/oneflow/api/cpp/framework/graph.cpp @@ -13,8 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ - -#include "oneflow/api/common/ofblob.h" +#include "nlohmann/json.hpp" #include "oneflow/api/common/variable_tensor_mgr.h" #include "oneflow/api/cpp/env_impl.h" #include "oneflow/api/cpp/framework/device.h" @@ -23,6 +22,7 @@ limitations under the License. #include "oneflow/api/cpp/framework/ivalue.h" #include "oneflow/api/cpp/framework/shape.h" #include "oneflow/api/cpp/framework/tensor.h" +#include "oneflow/api/cpp/embedding/embedding.h" #include "oneflow/api/common/job_build_and_infer_ctx.h" #include "oneflow/api/python/job_build/job_build_and_infer.h" #include "oneflow/core/common/data_type.pb.h" @@ -32,6 +32,8 @@ limitations under the License. #include "oneflow/core/common/shape.h" #include "oneflow/core/common/symbol.h" #include "oneflow/core/common/util.h" +#include "oneflow/core/embedding/posix_file.h" +#include "oneflow/core/eager/eager_blob_object.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/multi_client_session_context.h" @@ -52,6 +54,8 @@ limitations under the License. #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/job/scope.h" #include "oneflow/core/job/session.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/memory/memory_case_util.h" #include "oneflow/core/operator/interface_blob_conf.pb.h" #include "oneflow/core/operator/op_conf.pb.h" #include "oneflow/core/register/logical_blob_id.pb.h" @@ -108,6 +112,30 @@ Shape OfShapeToOfApiShape(const of::Shape& of_shape) { return Shape(dims); } +#ifdef __linux__ + +void LoadOneEmbedding(const std::string& model_path, const Device& device) { + const std::string one_embedding_info_name("one_embedding_options.json"); + const std::string one_embedding_info_save_path( + oneflow::JoinPath(model_path, one_embedding_info_name)); + if (oneflow::embedding::PosixFile::FileExists(one_embedding_info_save_path)) { + std::ifstream one_embedding_info_file(one_embedding_info_save_path); + auto one_embedding_json = nlohmann::json::parse(one_embedding_info_file); + for (auto& it : one_embedding_json["embedding"]) { + const std::string snapshot_path = it["snapshot"]; + auto kv_options_json = it["kv_options"]; + std::string embedding_name = embedding::CreateKeyValueStore(kv_options_json.dump(), + /*local_rank_id=*/0, + /*rank_id=*/0, + /*world_size=*/1); + embedding::LoadSnapshot(snapshot_path, embedding_name, /*local_rank_id=*/0, + /*rank_id=*/0); + } + } +} + +#endif // __linux__ + } // namespace class Graph::GraphImpl final { @@ -202,6 +230,9 @@ IValue Graph::Forward(const IValue& inputs) { void Graph::set_batch_size(int batch_size) { graph_->set_batch_size(batch_size); } Graph Graph::Load(const std::string& model_path, const Device& device) { +#ifdef __linux__ + LoadOneEmbedding(model_path, device); +#endif // __linux__ Graph graph(model_path, device); return graph; } @@ -306,7 +337,7 @@ of::Maybe Graph::GraphImpl::AddOp(of::OperatorConf op_conf) { 0, batch_size_); } auto* ctx = JUST(of::GetCurInferCtx()); - JUST(ctx->AddAndInferConsistentOp(op_conf)); + JUST(ctx->AddAndInferGlobalOp(op_conf)); return of::Maybe::Ok(); } @@ -374,11 +405,12 @@ of::Maybe Graph::GraphImpl::LoadCheckpoint() { ss << variable_file.rdbuf(); return ss.str(); }(); - const auto& callback = [&](uint64_t of_blob_ptr) { - CHECK_JUST(of::BlobBufferCopyUtil::From( - of_blob_ptr, buffer.data(), - variable_tensor->shape()->elem_cnt() - * of::GetSizeOfDataType(variable_tensor->dtype()->data_type()))); + const auto& callback = [&](of::ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + of::AutoMemcpy(stream, eager_blob_object->mut_dptr(), buffer.data(), + variable_tensor->shape()->elem_cnt() + * of::GetSizeOfDataType(variable_tensor->dtype()->data_type()), + eager_blob_object->mem_case(), of::memory::MakeHostMemCase()); }; JUST(of::one::SyncAccessTensorWithTimeOut(variable_tensor, callback, "mut")); } diff --git a/oneflow/api/cpp/framework/tensor.cpp b/oneflow/api/cpp/framework/tensor.cpp index 95437a7219e343a62d541ba77fc6442d0c76119b..670e467cdb819643fbf913ddf557e85ee5981897 100644 --- a/oneflow/api/cpp/framework/tensor.cpp +++ b/oneflow/api/cpp/framework/tensor.cpp @@ -21,9 +21,8 @@ limitations under the License. #include "oneflow/core/functional/functional.h" #include "oneflow/core/framework/dtype.h" #include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/framework/instructions_builder.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/api/common/ofblob.h" #include "oneflow/core/framework/dtype.h" #include "oneflow/core/vm/virtual_machine.h" @@ -68,14 +67,14 @@ Device Tensor::device() const { DType Tensor::dtype() const { return static_cast(tensor_->dtype()->data_type()); } void Tensor::zeros_() { - std::shared_ptr local_tensor = - tensor_->AsMirroredTensor().GetPtrOrThrow(); + std::shared_ptr local_tensor = tensor_->AsLocalTensor().GetPtrOrThrow(); of::PhysicalRun([&](of::InstructionsBuilder* builder) -> of::Maybe { JUST(builder->AccessBlobByCallback( local_tensor, - [](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AsyncAutoMemset(0); + [](of::ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + of::AutoMemset(stream, eager_blob_object->mut_dptr(), 0, + eager_blob_object->ByteSizeOfBlobBody(), eager_blob_object->mem_case()); }, "mut")); return of::Maybe::Ok(); @@ -85,14 +84,16 @@ void Tensor::zeros_() { Tensor Tensor::from_buffer(const void* buffer, const Shape& shape, const Device& device, const DType& dtype) { Tensor tensor(shape, device, dtype); - std::shared_ptr local_tensor = - tensor.tensor_->AsMirroredTensor().GetPtrOrThrow(); + std::shared_ptr local_tensor = + tensor.tensor_->AsLocalTensor().GetPtrOrThrow(); of::PhysicalRun([&](of::InstructionsBuilder* builder) -> of::Maybe { return builder->AccessBlobByCallback( local_tensor, - [buffer, shape, dtype](uint64_t ofblob_ptr) { - CHECK_JUST(of::BlobBufferCopyUtil::From(ofblob_ptr, buffer, - shape.Count(0) * GetDTypeSize(dtype))); + [buffer, shape, dtype](of::ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + of::AutoMemcpy(stream, eager_blob_object->mut_dptr(), buffer, + shape.Count(0) * GetDTypeSize(dtype), eager_blob_object->mem_case(), + of::memory::MakeHostMemCase()); }, "mut"); }).GetOrThrow(); @@ -101,14 +102,16 @@ Tensor Tensor::from_buffer(const void* buffer, const Shape& shape, const Device& template void Tensor::copy_to(T* buffer) const { - std::shared_ptr local_tensor = - tensor_->AsMirroredTensor().GetPtrOrThrow(); + std::shared_ptr local_tensor = tensor_->AsLocalTensor().GetPtrOrThrow(); const auto shape = this->shape(); - const auto& Callback = [buffer, shape](uint64_t ofblob_ptr) { - CHECK_JUST(of::BlobBufferCopyUtil::To(ofblob_ptr, buffer, shape.Count(0))); + const auto& Callback = [buffer, shape]( + of::ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + of::AutoMemcpy(stream, buffer, eager_blob_object->mut_dptr(), shape.Count(0) * sizeof(T), + of::memory::MakeHostMemCase(), eager_blob_object->mem_case()); }; - auto btb = std::make_shared(1); + auto btb = std::make_shared(); CHECK_JUST(of::PhysicalRun([&](of::InstructionsBuilder* builder) -> of::Maybe { return builder->SyncAccessBlobByCallback(local_tensor, btb, Callback, "const"); })); diff --git a/oneflow/api/cpp/tests/api_test.cpp b/oneflow/api/cpp/tests/api_test.cpp index 1fbc790bcc2423edcf5bc2f88d4f76acf1a84aa3..a25dfa0c14f12e67d6545367814fa6361917e842 100644 --- a/oneflow/api/cpp/tests/api_test.cpp +++ b/oneflow/api/cpp/tests/api_test.cpp @@ -30,13 +30,8 @@ limitations under the License. namespace oneflow_api { -namespace { - -std::mt19937 rng(std::random_device{}()); - -} - Shape RandomShape() { + thread_local static std::mt19937 rng(std::random_device{}()); std::uniform_int_distribution<> dist_ndim(1, 4), dist_dims(16, 64); std::vector dims(dist_ndim(rng), 0); for (auto& x : dims) { x = dist_dims(rng); } @@ -45,6 +40,7 @@ Shape RandomShape() { template std::vector RandomData(size_t size) { + thread_local static std::mt19937 rng(std::random_device{}()); std::uniform_int_distribution<> dist(-100, 100); std::vector data(size); for (auto& x : data) { x = static_cast(dist(rng)); } diff --git a/oneflow/api/cpp/tests/graph_test.cpp b/oneflow/api/cpp/tests/graph_test.cpp index 64cb6da3b09712779f5d450a2b13e89f8b036486..389f206ad869b407d968a59b286a62fddd3baad0 100644 --- a/oneflow/api/cpp/tests/graph_test.cpp +++ b/oneflow/api/cpp/tests/graph_test.cpp @@ -64,27 +64,7 @@ TEST(Api, graph_cpu_test) { Forward(graph, device, 1); } -#ifdef WITH_CUDA -TEST(Api, graph_gpu_test) { - EnvScope scope; - Device device("cuda", 0); - Graph graph = LoadGraph(device); - Forward(graph, device); -} - -TEST(Api, graph_multi_gpu_test) { - EnvScope scope; - Device device("cuda", 0); - Graph graph = LoadGraph(device); - Forward(graph, device); - - Device device1("cuda", 1); - Graph graph1 = LoadGraph(device1); - Forward(graph1, device1); -} -#endif - -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) TEST(Api, graph_gpu_test) { EnvScope scope; Device device("cuda", 0); @@ -112,7 +92,7 @@ TEST(Api, graph_cpu_batching_test) { Forward(graph, device, 10); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) TEST(Api, graph_gpu_batching_test) { EnvScope scope; Device device("cuda", 0); diff --git a/oneflow/api/cpp/tests/graph_test_model/affine_with_parameter/model.mlir b/oneflow/api/cpp/tests/graph_test_model/affine_with_parameter/model.mlir index 15a53af1f484b6a9e00c3bfc7299979037bd059a..23f26d6a796c43bb1e341ad3d503b5f95c65cf6e 100644 --- a/oneflow/api/cpp/tests/graph_test_model/affine_with_parameter/model.mlir +++ b/oneflow/api/cpp/tests/graph_test_model/affine_with_parameter/model.mlir @@ -1,8 +1,8 @@ module { oneflow.job @MyGraph_0(%arg0: tensor<1x3xf32>) -> tensor<1x4xf32> { %output = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_MyGraph_0-input_0", output_lbns = ["_MyGraph_0-input_0/out"], scope_symbol_id = 4611686018427469823 : i64, shape = [1 : si64, 3 : si64]} : (tensor<1x3xf32>) -> tensor<1x3xf32> - %output_0 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], nd_sbp = ["B"], op_name = "model.a", output_lbns = ["model.a/out"], scope_symbol_id = 4611686018427482111 : i64, shape = [3 : si64, 4 : si64]} : () -> tensor<3x4xf32> - %output_1 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], nd_sbp = ["B"], op_name = "model.b", output_lbns = ["model.b/out"], scope_symbol_id = 4611686018427494399 : i64, shape = [4 : si64]} : () -> tensor<4xf32> + %output_0 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], parallel = #sbp.parallel<[] -> [#sbp.B]>, op_name = "model.a", output_lbns = ["model.a/out"], scope_symbol_id = 4611686018427482111 : i64, shape = [3 : si64, 4 : si64]} : () -> tensor<3x4xf32> + %output_1 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], parallel = #sbp.parallel<[] -> [#sbp.B]>, op_name = "model.b", output_lbns = ["model.b/out"], scope_symbol_id = 4611686018427494399 : i64, shape = [4 : si64]} : () -> tensor<4xf32> %0 = "oneflow.matmul"(%output, %output_0) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "model-matmul_0", output_lbns = ["model-matmul_0/out_0"], scope_symbol_id = 4611686018427486207 : i64, transpose_a = false, transpose_b = false} : (tensor<1x3xf32>, tensor<3x4xf32>) -> tensor<1x4xf32> %1 = "oneflow.broadcast_add"(%0, %output_1) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "model-broadcast_add_1", output_lbns = ["model-broadcast_add_1/z_0"], scope_symbol_id = 4611686018427486207 : i64} : (tensor<1x4xf32>, tensor<4xf32>) -> tensor<1x4xf32> %output_2 = "oneflow.output"(%1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_MyGraph_0-output_0", output_lbns = ["_MyGraph_0-output_0/out"], scope_symbol_id = 4611686018427469823 : i64, shape = [1 : si64, 4 : si64]} : (tensor<1x4xf32>) -> tensor<1x4xf32> diff --git a/oneflow/api/cpp/tests/one_embedding_test.cpp b/oneflow/api/cpp/tests/one_embedding_test.cpp new file mode 100644 index 0000000000000000000000000000000000000000..48b93f838e6a55cbf0b8aea3c9d2ebc29138867a --- /dev/null +++ b/oneflow/api/cpp/tests/one_embedding_test.cpp @@ -0,0 +1,48 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/api/cpp/tests/api_test.h" + +namespace oneflow_api { + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +TEST(Api, embedding_test) { + EnvScope scope; + Device device("cuda"); + Graph graph = Graph::Load("/path/to/embedding", device); + int64_t batch_size = 10000; + int64_t num_features = 39; + + std::vector data(batch_size * num_features); + std::fill(data.begin(), data.end(), 1); + std::vector inputs; + inputs.emplace_back( + Tensor::from_buffer(data.data(), Shape({batch_size, num_features}), device, DType::kInt64)); + + const auto& value = graph.Forward(inputs); + + ASSERT_TRUE(value.IsTensor()); + Tensor output = value.ToTensor(); + Shape shape = output.shape(); + ASSERT_EQ(shape.At(0), batch_size); + ASSERT_EQ(shape.At(1), 1); + + std::vector buf(batch_size); + output.copy_to(buf.data()); +} +#endif + +} // namespace oneflow_api diff --git a/oneflow/api/cpp/tests/tensor_test.cpp b/oneflow/api/cpp/tests/tensor_test.cpp index a16bbb7c3b51f5b7329636e2db3f88603e6ba2d3..33107233bdf16b24fcc2c5693f1c4bdd20c4a100 100644 --- a/oneflow/api/cpp/tests/tensor_test.cpp +++ b/oneflow/api/cpp/tests/tensor_test.cpp @@ -25,16 +25,7 @@ TEST(Api, device) { auto device = Device("cpu"); ASSERT_EQ(device.type(), "cpu"); -#ifdef WITH_CUDA - device = Device("cuda:0"); - ASSERT_EQ(device.type(), "cuda"); - ASSERT_EQ(device.device_id(), 0); - - device = Device("cuda", 1); - ASSERT_EQ(device.type(), "cuda"); - ASSERT_EQ(device.device_id(), 1); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) device = Device("cuda:0"); ASSERT_EQ(device.type(), "cuda"); ASSERT_EQ(device.device_id(), 0); diff --git a/oneflow/api/python/autograd/autograd.cpp b/oneflow/api/python/autograd/autograd.cpp index f9042c3d671ac3f6e5031cdcfbcd81d93339312c..108a26ddce536ea950526d9afbde91f1649d4462 100644 --- a/oneflow/api/python/autograd/autograd.cpp +++ b/oneflow/api/python/autograd/autograd.cpp @@ -16,9 +16,13 @@ limitations under the License. #include #include +#include #include #include "oneflow/api/python/of_api_registry.h" +#include "oneflow/api/python/job_build/job_build_and_infer.h" +#include "oneflow/core/common/throw.h" #include "oneflow/core/framework/dtype.h" +#include "oneflow/core/framework/scope_util.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/autograd/autograd_engine.h" @@ -26,6 +30,7 @@ limitations under the License. #include "oneflow/core/functional/functional.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/container_util.h" +#include "oneflow/core/framework/saved_tensor_hooks.h" namespace oneflow { namespace autograd { @@ -50,7 +55,8 @@ Maybe CheckAndInitOutGrads(const one::TensorTuple& outputs, << " gradients"; for (int i = 0; i < outputs.size(); ++i) { CHECK_OR_RETURN(outputs.at(i)->requires_grad()) - << "All output tensors `.requires_grad` should be true"; + << "\nRuntimeError: element " << i + << " of tensors does not require grad and does not have a grad_fn"; if (!outputs.at(i)->grad_fn_node()) { CHECK_OR_RETURN(outputs.at(i)->is_leaf()) << "output[" << i << "] doesn't have grad_fn and it is not leaf tensor!\n" @@ -66,7 +72,6 @@ Maybe CheckAndInitOutGrads(const one::TensorTuple& outputs, CHECK_OR_RETURN(*(outputs.at(i)->shape()) == *(out_grads.at(i)->shape())) << "out_grad's shape must be same as output's (" << outputs.at(i)->shape()->ToString() << " vs " << out_grads.at(i)->shape()->ToString() << ")"; - // if (outputs.at(i)->dtype() != out_grads.at(i)->dtype()) { if (JUST(oneflow::VectorAt(outputs, i))->dtype() != JUST(oneflow::VectorAt(out_grads, i))->dtype()) { JUST(oneflow::VectorAt(*gradients, i)) = @@ -76,6 +81,7 @@ Maybe CheckAndInitOutGrads(const one::TensorTuple& outputs, } } } + if (LazyMode::is_enabled()) { JUST(MarkOutputGradients(outputs, *gradients)); } return gradients; } @@ -83,6 +89,7 @@ Maybe CheckAndInitOutGrads(const one::TensorTuple& outputs, Maybe Backward(const one::TensorTuple& outputs, const one::TensorTuple& out_grads, bool retain_graph, bool create_graph) { + BackwardPassScopeGuard backward_guard; if (create_graph) { retain_graph = true; } std::shared_ptr gradients = JUST(CheckAndInitOutGrads(outputs, out_grads)); JUST(one::GetThreadLocalAutogradEngine()->RunBackwardAndSaveGrads4LeafTensorIf( @@ -93,6 +100,7 @@ Maybe Backward(const one::TensorTuple& outputs, const one::Ten Maybe Grad(const one::TensorTuple& outputs, const one::TensorTuple& inputs, const one::TensorTuple& out_grads, bool retain_graph, bool create_graph) { + BackwardPassScopeGuard backward_guard; if (create_graph) { retain_graph = true; } if (inputs.empty()) { return Backward(outputs, out_grads, retain_graph, create_graph); } CHECK_OR_RETURN(std::all_of( @@ -104,9 +112,80 @@ Maybe Grad(const one::TensorTuple& outputs, const one::TensorT outputs, inputs, *gradients, retain_graph, create_graph); } +namespace py = pybind11; + +class PySavedTensorHook final : public one::SavedTensorHook { + public: + PySavedTensorHook(const py::function& pack_hook, const py::function& unpack_hook) + : pack_hook_(pack_hook), unpack_hook_(unpack_hook) {} + + void pack(const std::shared_ptr& tensor) { + py::gil_scoped_acquire acquire; + py::object packed = pack_hook_(tensor); + data_ = packed.release().ptr(); + } + std::shared_ptr unpack() { + py::gil_scoped_acquire acquire; + py::object obj = py::cast(data_); + py::object x = unpack_hook_(obj); + std::shared_ptr tensor; + try { + tensor = py::cast>(x); + } catch (const py::cast_error& e) { + THROW(RuntimeError) << "unpack_hook should return a Tensor, but got `" + << py::str(x.get_type()).cast() << "` instead"; + } + return tensor; + } + + private: + PyObject* data_ = nullptr; + py::function pack_hook_; + py::function unpack_hook_; +}; + +class PySavedTensorHookCreator final : public one::SavedTensorHookCreator { + public: + std::unique_ptr new_saved_tensor_hook() const override { + if (hooks_.empty()) { return nullptr; } + return std::make_unique(hooks_.back().first, hooks_.back().second); + } + void append_new_hooks(const py::function& pack_hook, const py::function& unpack_hook) { + hooks_.emplace_back(pack_hook, unpack_hook); + } + void pop_hooks() { + CHECK_OR_THROW(!hooks_.empty()) << "pop_hooks should not be called when there are no hooks"; + hooks_.pop_back(); + } + + private: + small_vector, 1> hooks_; +}; + ONEFLOW_API_PYBIND11_MODULE("autograd", m) { m.def("backward", &Backward); m.def("grad", &Grad); + m.def_submodule("graph") + .def("register_saved_tensors_hook_manager", + []() { + Singleton::SetAllocated(new PySavedTensorHookCreator()); + }) + .def("append_new_hooks", + [](const py::function& pack_hook, const py::function& unpack_hook) { + PySavedTensorHookCreator* creator = dynamic_cast( + Singleton::Get()); + CHECK_NOTNULL_OR_THROW(creator) + << "`register_saved_tensors_hook_manager` should be called " + "before calling `append_new_hooks`"; + creator->append_new_hooks(pack_hook, unpack_hook); + }) + .def("pop_hooks", []() { + PySavedTensorHookCreator* creator = + dynamic_cast(Singleton::Get()); + CHECK_NOTNULL_OR_THROW(creator) << "`register_saved_tensors_hook_manager` should be called " + "before calling `pop_hooks`"; + creator->pop_hooks(); + }); } } // namespace autograd diff --git a/oneflow/api/python/autograd/autograd_function.cpp b/oneflow/api/python/autograd/autograd_function.cpp index 5c279a71d4bcaee2f63ce64b59cbfbeeda635e96..bf16e3699ca533c8c130163c4f4d6667bd747b63 100644 --- a/oneflow/api/python/autograd/autograd_function.cpp +++ b/oneflow/api/python/autograd/autograd_function.cpp @@ -38,17 +38,22 @@ Maybe UnpackTensorTuple(const py::object& input) { tp.emplace_back(input.cast>()); } else if (py::isinstance(input)) { auto tuple = input.cast(); + tp.resize(tuple.size()); for (int i = 0; i < tuple.size(); ++i) { PyObject* obj = tuple[i].ptr(); - if (!one::PyTensor_Check(obj)) { + if (obj == Py_None) { + // do nothing + } else if (one::PyTensor_Check(obj)) { + tp[i] = one::PyTensor_Unpack(obj); + } else { return Error::RuntimeError() - << "expected Tensor as element " << i << ", but got " + << "expected Tensor or None as element " << i << ", but got " << one::functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(obj))); } - tp.emplace_back(one::PyTensor_Unpack(obj)); } } else { - return Error::RuntimeError() << "Only support tensor or list of tensors"; + return Error::RuntimeError() + << "autograd.Function's output only support tensor or list of tensors"; } return tp; } @@ -90,22 +95,6 @@ ONEFLOW_API_PYBIND11_MODULE("autograd", m) { *input_tensor_tuple)); return PackTensorTuple(*res); }); - - py::class_>( - m, "FunctionAutoGradCaptureState") - .def(py::init([]() { return std::make_shared(); })) - .def("save_for_backward", - [](FunctionAutoGradCaptureState& ctx, const py::args& input) { - const auto& tensors = UnpackTensorTuple(input).GetOrThrow(); - for (const auto& tensor : tensors) { ctx.SaveTensorForBackward(tensor); } - }) - .def_property_readonly( - "saved_tensors", - [](const FunctionAutoGradCaptureState& ctx) { return py::cast(ctx.SavedTensors()); }) - .def("mark_non_differentiable", [](FunctionAutoGradCaptureState& ctx, const py::args& input) { - const auto& tensors = UnpackTensorTuple(input).GetOrThrow(); - for (const auto& tensor : tensors) { ctx.MarkNonDifferentiable(tensor); } - }); } } // namespace one diff --git a/oneflow/api/python/autograd/autograd_function_state.cpp b/oneflow/api/python/autograd/autograd_function_state.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4635031f0f94d632a9128691f5480ac1a497ab2b --- /dev/null +++ b/oneflow/api/python/autograd/autograd_function_state.cpp @@ -0,0 +1,214 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/api/python/autograd/autograd_function_state.h" + +#include +#include "oneflow/api/python/exception/exception.h" +#include "oneflow/api/python/functional/common.h" +#include "oneflow/api/python/of_api_registry.h" + +namespace py = pybind11; +namespace oneflow { +namespace one { +namespace { +inline FunctionAutoGradCaptureState* CheckAndGetStateData(PyAutogradFunctionState* state) { + if (!state->data.lock()) { + PyErr_Format(PyExc_RuntimeError, "Data is deallocated. Please don't hold context outside " + "autograd.Function.forward or autograd.Function.backward"); + return nullptr; + } + return state->data.lock().get(); +} +} // namespace + +#if PY_VERSION_HEX < 0x03070000 +#define PYGETSET_NAME(name) const_cast(name) +#else +#define PYGETSET_NAME(name) (name) +#endif + +#define PY_XINCREF(p) (({ Py_XINCREF(p); }), (p)) + +static PyObject* PyAutogradFunctionState_new(PyTypeObject* type, PyObject* args, PyObject* kwds) { + PyAutogradFunctionState* self = (PyAutogradFunctionState*)type->tp_alloc(type, 0); + if (self != NULL) { + self->dynamic_attr_dict = PyDict_New(); + if (self->dynamic_attr_dict == NULL) { + Py_DECREF(self); + return NULL; + } + } + return (PyObject*)self; +} + +static void PyAutogradFunctionState_dealloc(PyAutogradFunctionState* self) { + Py_XDECREF(self->dynamic_attr_dict); + Py_TYPE(self)->tp_free((PyObject*)self); +} + +// PyMethodDef start +static PyObject* PyAutogradFunctionState_save_for_backward(PyObject* self, PyObject* args) { + HANDLE_ERRORS + auto* _self = (PyAutogradFunctionState*)self; + if (!functional::PyTensorSequenceCheck(args)) { + return PyErr_Format(PyExc_TypeError, "save_for_backward() only support Tensor or Tensors"); + } + const std::vector>& tensor_list = + functional::PyUnpackTensorSequence(args); + for (const auto& tensor : tensor_list) { + CheckAndGetStateData(_self)->SaveTensorForBackward(tensor); + } + Py_RETURN_NONE; + END_HANDLE_ERRORS +} + +static PyObject* PyAutogradFunctionState_mark_non_differentiable(PyObject* self, PyObject* args) { + HANDLE_ERRORS + auto* _self = (PyAutogradFunctionState*)self; + if (!functional::PyTensorSequenceCheck(args)) { + return PyErr_Format(PyExc_TypeError, "save_for_backward() only support Tensor or Tensors"); + } + const std::vector>& tensor_list = + functional::PyUnpackTensorSequence(args); + for (const auto& tensor : tensor_list) { + CheckAndGetStateData(_self)->MarkNonDifferentiable(tensor); + } + Py_RETURN_NONE; + END_HANDLE_ERRORS +} + +static PyObject* PyAutogradFunctionState_is_data_valid(PyObject* self) { + auto* _self = (PyAutogradFunctionState*)self; + return functional::CastToPyObject(_self->data.lock() != nullptr); +} + +static PyMethodDef PyAutogradFunctionState_methods[] = { + {"save_for_backward", (PyCFunction)PyAutogradFunctionState_save_for_backward, METH_VARARGS, + NULL}, + {"mark_non_differentiable", (PyCFunction)PyAutogradFunctionState_mark_non_differentiable, + METH_VARARGS, NULL}, + {"_is_data_valid", (PyCFunction)PyAutogradFunctionState_is_data_valid, METH_NOARGS, NULL}, + {NULL} /* Sentinel */ +}; +// PyMethodDef end + +// PyAutogradFunctionState_getset start +static PyObject* PyAutogradFunctionState_saved_tensors(PyObject* self, void*) { + auto* _self = (PyAutogradFunctionState*)self; + return functional::CastToPyObject>( + CheckAndGetStateData(_self)->SavedTensors()); +} + +static PyObject* PyAutogradFunctionState_get_dict(PyObject* self, PyObject* args) { + HANDLE_ERRORS + auto* _self = (PyAutogradFunctionState*)self; + return _self->dynamic_attr_dict; + Py_RETURN_NONE; + END_HANDLE_ERRORS +} + +static PyGetSetDef PyAutogradFunctionState_properties[] = { + {PYGETSET_NAME("saved_tensors"), (getter)PyAutogradFunctionState_saved_tensors, NULL, NULL, + NULL}, + {PYGETSET_NAME("__dict__"), (getter)PyAutogradFunctionState_get_dict, NULL, NULL, NULL}, + {NULL} /* Sentinel */ +}; +// PyAutogradFunctionState_getset end + +PyObject* PyAutogradFunctionState_getattro(PyObject* self, PyObject* attr) { + PyObject* res = NULL; + res = PyDict_GetItem(((PyAutogradFunctionState*)self)->dynamic_attr_dict, attr); + if (!res) { + // Not found attr in dynamic_attr_dict, try to find it in tp_dict + res = PyObject_GenericGetAttr(self, attr); + if (!res) { + return PyErr_Format(PyExc_AttributeError, "attribute %s not found", PyUnicode_AsUTF8(attr)); + } + } + return res; +} + +int PyAutogradFunctionState_setattro(PyObject* self, PyObject* attr, PyObject* value) { + auto* _self = (PyAutogradFunctionState*)self; + return PyDict_SetItem(_self->dynamic_attr_dict, attr, value); +} + +PyTypeObject PyAutogradFunctionState_Type = { + PyVarObject_HEAD_INIT(NULL, 0) "oneflow.autograd.Function.FunctionCtx", /* tp_name */ + sizeof(PyAutogradFunctionState), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor)PyAutogradFunctionState_dealloc, /* tp_dealloc */ + 0, /* tp_vectorcall_offset */ + NULL, /* tp_getattr */ + NULL, /* tp_setattr */ + NULL, /* tp_reserved */ + NULL, /* tp_repr */ + NULL, /* tp_as_number */ + NULL, /* tp_as_sequence */ + NULL, /* tp_as_mapping */ + NULL, /* tp_hash */ + NULL, /* tp_call */ + NULL, /* tp_str */ + PyAutogradFunctionState_getattro, /* tp_getattro */ + PyAutogradFunctionState_setattro, /* tp_setattro */ + NULL, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */ + NULL, /* tp_doc */ + NULL, /* tp_traverse */ + NULL, /* tp_clear */ + NULL, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ + NULL, /* tp_iter */ + NULL, /* tp_iternext */ + PyAutogradFunctionState_methods, /* tp_methods */ + NULL, /* tp_members */ + PyAutogradFunctionState_properties, /* tp_getset */ + 0, /* tp_base */ + NULL, /* tp_dict */ + NULL, /* tp_descr_get */ + NULL, /* tp_descr_set */ + offsetof(PyAutogradFunctionState, dynamic_attr_dict), /* tp_dictoffset */ + NULL, /* tp_init */ + NULL, /* tp_alloc */ + PyAutogradFunctionState_new, /* tp_new */ + NULL, /* tp_free */ +}; + +PyObject* PyAutogradFunctionState_NewFromPtr( + const std::shared_ptr& data) { + if (!data) { Py_RETURN_NONE; } + if (data->pyobject()) { return PY_XINCREF((PyObject*)data->pyobject()); } + auto* self = (PyAutogradFunctionState*)(PyObject_CallObject( + (PyObject*)&PyAutogradFunctionState_Type, NULL)); + if (self) { + PY_XINCREF(self); + self->data = data; + CheckAndGetStateData(self)->set_pyobject_ptr( + std::unique_ptr(self, [](void* ptr) { Py_DECREF((PyObject*)ptr); })); + } + return (PyObject*)self; +} + +ONEFLOW_API_PYBIND11_MODULE("autograd.Function", m) { + if (PyType_Ready(&PyAutogradFunctionState_Type) < 0) { return; } + Py_INCREF(&PyAutogradFunctionState_Type); + if (PyModule_AddObject(m.ptr(), "FunctionCtx", (PyObject*)&PyAutogradFunctionState_Type) < 0) { + return; + } +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/api/python/autograd/autograd_function_state.h b/oneflow/api/python/autograd/autograd_function_state.h new file mode 100644 index 0000000000000000000000000000000000000000..3873ad6f65dac8c1e12a8bfbeac005f1147a1721 --- /dev/null +++ b/oneflow/api/python/autograd/autograd_function_state.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_PYTHON_AUTOGRAD_AUTOGRAD_FUNCTION_STATE_H_ +#define ONEFLOW_API_PYTHON_AUTOGRAD_AUTOGRAD_FUNCTION_STATE_H_ + +#include +#include + +#include "oneflow/core/framework/op_expr_grad_function.h" + +namespace oneflow { +namespace one { + +typedef struct { + PyObject_HEAD; + PyObject* dynamic_attr_dict; + std::weak_ptr data; +} PyAutogradFunctionState; + +extern PyTypeObject PyAutogradFunctionState_Type; + +inline bool PyAutogradFunctionState_Check(PyObject* state) { + return PyObject_TypeCheck(state, &PyAutogradFunctionState_Type); +} + +PyObject* PyAutogradFunctionState_NewFromPtr( + const std::shared_ptr& data); + +} // namespace one +} // namespace oneflow + +#endif // ONEFLOW_API_PYTHON_AUTOGRAD_AUTOGRAD_FUNCTION_STATE_H_ diff --git a/oneflow/api/python/caster/autograd_function_state.h b/oneflow/api/python/caster/autograd_function_state.h new file mode 100644 index 0000000000000000000000000000000000000000..42f7e16bbf248f3668890bbb80000cbbf8d3ef88 --- /dev/null +++ b/oneflow/api/python/caster/autograd_function_state.h @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_PYTHON_CASTER_AUTOGRAD_FUNCTION_STATE_H_ +#define ONEFLOW_API_PYTHON_CASTER_AUTOGRAD_FUNCTION_STATE_H_ + +#include + +#include "oneflow/api/python/caster/common.h" +#include "oneflow/api/python/autograd/autograd_function_state.h" + +namespace py = pybind11; + +namespace pybind11 { +namespace detail { + +template +struct autograd_function_state_type_caster { + public: + bool load(handle src, bool convert) { + using namespace oneflow::one; + value_ = nullptr; + if (!src) { return false; } + if (src.is_none()) { return true; } + if (!PyAutogradFunctionState_Check(src.ptr())) { return false; } + value_ = ((PyAutogradFunctionState*)src.ptr())->data; + return true; + } + + template + static handle cast(U&& src, return_value_policy policy, handle parent) { + using namespace oneflow::one; + return reinterpret_steal( + PyAutogradFunctionState_NewFromPtr( + std::const_pointer_cast(src))) + .release(); + } + + operator std::shared_ptr*() { return &value_; } + operator std::shared_ptr&() { return value_; } + operator std::shared_ptr&&() && { return std::move(value_); } + + static constexpr auto name = _("autograd_function_state"); + + protected: + std::shared_ptr value_; +}; + +template<> +struct type_caster> + : public autograd_function_state_type_caster {}; +template<> +struct type_caster> + : public autograd_function_state_type_caster { +}; + +} // namespace detail +} // namespace pybind11 + +#endif // ONEFLOW_API_PYTHON_CASTER_AUTOGRAD_FUNCTION_STATE_H_ diff --git a/oneflow/api/python/caster/common.h b/oneflow/api/python/caster/common.h index d9296b74331e957e6b3b6ddcc71984b20e0d576d..ca2b2abab273177107e42fb81aa2a3fd589637fe 100644 --- a/oneflow/api/python/caster/common.h +++ b/oneflow/api/python/caster/common.h @@ -13,8 +13,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include +#ifndef ONEFLOW_API_PYTHON_CASTER_COMMON_H_ +#define ONEFLOW_API_PYTHON_CASTER_COMMON_H_ +#include #include namespace pybind11 { @@ -49,3 +51,5 @@ using IsSupportedByPybind11WhenInsideSharedPtr = } // namespace detail } // namespace pybind11 + +#endif // ONEFLOW_API_PYTHON_CASTER_COMMON_H_ diff --git a/oneflow/api/python/caster/maybe.h b/oneflow/api/python/caster/maybe.h index 1cc674e82f21391efdb12efa407eb50a44d741fe..1f7c846129f9f4d7864a316deff2b1696c54a59c 100644 --- a/oneflow/api/python/caster/maybe.h +++ b/oneflow/api/python/caster/maybe.h @@ -13,6 +13,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_API_PYTHON_CASTER_MAYBE_H_ +#define ONEFLOW_API_PYTHON_CASTER_MAYBE_H_ #include #include "oneflow/api/python/caster/common.h" @@ -84,7 +86,7 @@ template<> struct maybe_caster> { template static handle cast(T&& src, return_value_policy policy, handle parent) { - if (!src.IsOk()) { oneflow::ThrowError(src.error()); } + if (!src.IsOk()) { oneflow::ThrowError(src.stacked_error()); } return none().inc_ref(); } @@ -104,3 +106,5 @@ struct type_caster> : public maybe_caster> {}; } // namespace detail } // namespace pybind11 + +#endif // ONEFLOW_API_PYTHON_CASTER_MAYBE_H_ diff --git a/oneflow/api/python/caster/optional.h b/oneflow/api/python/caster/optional.h index 0db0d70a65a720833fdb678e8ea4a67988cb8e39..9cd3218c87ee4630d05462469143e8afd1b91b5d 100644 --- a/oneflow/api/python/caster/optional.h +++ b/oneflow/api/python/caster/optional.h @@ -13,6 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_API_PYTHON_CASTER_OPTIONAL_H_ +#define ONEFLOW_API_PYTHON_CASTER_OPTIONAL_H_ + #include #include "oneflow/api/python/caster/common.h" @@ -109,3 +112,5 @@ struct type_caster> : public oneflow_optional_caster> {} } // namespace detail } // namespace pybind11 + +#endif // ONEFLOW_API_PYTHON_CASTER_OPTIONAL_H_ diff --git a/oneflow/api/python/caster/size.h b/oneflow/api/python/caster/size.h new file mode 100644 index 0000000000000000000000000000000000000000..0d4b314851a25c902f97dbcde6ca79fb01b202b2 --- /dev/null +++ b/oneflow/api/python/caster/size.h @@ -0,0 +1,106 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_PYTHON_CASTER_SIZE_H_ +#define ONEFLOW_API_PYTHON_CASTER_SIZE_H_ +#include +#include +#include + +#include "oneflow/api/python/framework/size.h" +#include "oneflow/core/common/shape.h" + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +class shape : public object { + public: + PYBIND11_OBJECT_CVT(shape, object, oneflow::TensorSize_Check, raw_shape) + explicit shape(size_t size = 0) : object(oneflow::TensorSize_New((ssize_t)size), stolen_t{}) { + if (!m_ptr) pybind11_fail("Could not allocate tensor size object!"); + } + size_t size() const { return (size_t)PyTuple_Size(m_ptr); } + bool empty() const { return size() == 0; } + detail::tuple_accessor operator[](size_t index) const { return {*this, index}; } + detail::item_accessor operator[](handle h) const { return object::operator[](h); } + detail::tuple_iterator begin() const { return {*this, 0}; } + detail::tuple_iterator end() const { return {*this, PyTuple_GET_SIZE(m_ptr)}; } + + private: + static PyObject* raw_shape(PyObject* op) { + if (oneflow::TensorSize_Check(op)) return handle(op).inc_ref().ptr(); + return PyObject_CallFunctionObjArgs((PyObject*)&oneflow::TensorSize_Type, op, NULL); + } +}; + +PYBIND11_NAMESPACE_BEGIN(detail) + +template +struct shape_type_caster { + public: + bool load(handle src, bool convert) { + value_ = nullptr; + if (src && src.is_none()) { return true; } + if (!oneflow::TensorSize_Check(src.ptr())) { return false; } + value_ = std::make_shared(oneflow::TensorSize_AsShape(src.ptr())); + return true; + } + + template + static handle cast(U&& src, return_value_policy /*policy*/, handle /*parent*/) { + return cast_impl(std::forward(src)); + } + + template + static handle cast(U* src, return_value_policy policy, handle parent) { + if (!src) { return none().release(); } + return cast(*src, policy, parent); + } + + operator T*() { return value_.get(); } + operator T&() { return *value_; } + operator T&&() && { return std::move(*value_); } + + operator std::shared_ptr*() { return &value_; } + operator std::shared_ptr&() { return value_; } + operator std::shared_ptr&&() && { return std::move(value_); } + + static constexpr auto name = _("shape"); + template + using cast_op_type = pybind11::detail::cast_op_type>; + + private: + static handle cast_impl(const oneflow::Shape& src) { + return reinterpret_steal(oneflow::TensorSize_NewFromShape(src)).release(); + } + static handle cast_impl(const std::shared_ptr& src) { + return reinterpret_steal(oneflow::TensorSize_NewFromShape(*src)).release(); + } + + protected: + std::shared_ptr value_; +}; + +template<> +struct type_caster : public shape_type_caster {}; +template<> +struct type_caster> : public shape_type_caster {}; +template<> +struct type_caster> + : public shape_type_caster {}; + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) + +#endif // ONEFLOW_API_PYTHON_CASTER_SIZE_H_ diff --git a/oneflow/api/python/caster/tensor.h b/oneflow/api/python/caster/tensor.h index 06bb85477ee8e5a3d31d44cdfd4b24e7bbc8b754..cecfe3c9a81a640a6c8b2aea84a9a573e727cfe1 100644 --- a/oneflow/api/python/caster/tensor.h +++ b/oneflow/api/python/caster/tensor.h @@ -13,6 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_API_PYTHON_CASTER_TENSOR_H_ +#define ONEFLOW_API_PYTHON_CASTER_TENSOR_H_ + #include #include "oneflow/api/python/caster/common.h" @@ -100,3 +103,5 @@ struct type_caster> } // namespace detail } // namespace pybind11 + +#endif // ONEFLOW_API_PYTHON_CASTER_TENSOR_H_ diff --git a/oneflow/api/python/dlpack/converter.cpp b/oneflow/api/python/dlpack/converter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5e2a352a1fe6abb0848a4fb2bfc6884943c4953f --- /dev/null +++ b/oneflow/api/python/dlpack/converter.cpp @@ -0,0 +1,237 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/api/python/dlpack/dlpack.h" +#include "oneflow/api/python/exception/exception.h" +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/tensor_util.h" + +namespace oneflow { + +Maybe> ToOneFlowDevice(const DLDevice& ctx) { + switch (ctx.device_type) { + case DLDeviceType::kDLCPU: return JUST(Device::New("cpu")); +#if defined(WITH_CUDA) || defined(WITH_ROCM) + case DLDeviceType::kDLCUDA: return JUST(Device::New("cuda", ctx.device_id)); +#endif + default: UNIMPLEMENTED_THEN_RETURN() << "Unsupported device type: " << ctx.device_type; + } +} + +Maybe ToOneFlowDataType(const DLDataType& dtype) { + DataType ofdtype = DataType::kInvalidDataType; + CHECK_EQ_OR_RETURN(dtype.lanes, 1) << "OneFlow does not support lanes != 1"; + switch (dtype.code) { + case DLDataTypeCode::kDLUInt: + switch (dtype.bits) { + case 8: ofdtype = DataType::kUInt8; break; + default: + UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: " << dtype.code << dtype.bits; + } + break; + case DLDataTypeCode::kDLInt: + switch (dtype.bits) { + case 8: ofdtype = DataType::kInt8; break; + case 16: ofdtype = DataType::kInt16; break; + case 32: ofdtype = DataType::kInt32; break; + case 64: ofdtype = DataType::kInt64; break; + default: + UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: " << dtype.code << dtype.bits; + } + break; + case DLDataTypeCode::kDLFloat: + switch (dtype.bits) { + case 16: ofdtype = DataType::kFloat16; break; + case 32: ofdtype = DataType::kFloat; break; + case 64: ofdtype = DataType::kDouble; break; + default: + UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: " << dtype.code << dtype.bits; + } + break; + case DLDataTypeCode::kDLBfloat: + switch (dtype.bits) { + case 16: ofdtype = DataType::kBFloat16; break; + default: UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: bfloat" << dtype.bits; + } + break; + case DLDataTypeCode::kDLComplex: + UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: complex" << dtype.bits; + break; + default: UNIMPLEMENTED_THEN_RETURN() << "Unsupported code " << dtype.code; + } + CHECK_NE_OR_RETURN(ofdtype, DataType::kInvalidDataType); + return ofdtype; +} + +Maybe fromDLPack(const DLManagedTensor* src) { + using namespace one; + const auto& dl_tensor = src->dl_tensor; + + Symbol device = JUST(ToOneFlowDevice(dl_tensor.device)); + DataType dtype = JUST(ToOneFlowDataType(dl_tensor.dtype)); + + // Build TensorMeta + const Shape shape(dl_tensor.shape, dl_tensor.shape + dl_tensor.ndim); + Symbol tensor_meta; + if (dl_tensor.strides) { + const auto stride = Stride(dl_tensor.strides, dl_tensor.strides + dl_tensor.ndim); + tensor_meta = SymbolOf(LocalTensorMeta(shape, stride, dtype, device)); + } else { + tensor_meta = SymbolOf(LocalTensorMeta(shape, dtype, device)); + } + + // Build TensorBuffer + const auto& Free = [src](char* dptr) { + if (src->deleter) { + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast) + src->deleter(const_cast(src)); + } + }; + + size_t array_size_in_bytes = shape.elem_cnt() * GetSizeOfDataType(dtype); + auto tensor_data = std::make_shared(); + tensor_data->set_blob_dptr( + std::unique_ptr>(static_cast(dl_tensor.data), Free), + array_size_in_bytes); + + // Build TensorStorage: decrease ndarray reference count before releasing + auto tensor_storage = std::make_shared(tensor_data); + + // Build Tensor + auto tensor_impl = std::make_shared(tensor_storage, + /*requires_grad=*/false, + /*ls_leaf=*/true); + + // Init blob + JUST(tensor_impl->InitEagerBlobObject(tensor_meta, NewLocalDepObject())); + const auto& stream = JUST(GetDefaultStreamByDevice(device)); + const auto& eager_blob_object = JUST(tensor_impl->eager_blob_object()); + JUST(eager_blob_object->init_producer_stream(stream)); + eager_blob_object->set_last_used_stream(stream); + return std::static_pointer_cast(std::make_shared(tensor_impl)); +} + +Maybe ToDLDevice(Symbol ofdevice) { + DLDevice ctx; + ctx.device_id = ofdevice->device_id(); + switch (ofdevice->enum_type()) { + case DeviceType::kCPU: ctx.device_type = DLDeviceType::kDLCPU; break; +#if defined(WITH_CUDA) || defined(WITH_ROCM) + case DeviceType::kCUDA: ctx.device_type = DLDeviceType::kDLCUDA; break; +#endif + default: UNIMPLEMENTED_THEN_RETURN() << "Unsupported device type: " << ofdevice->type(); + } + return ctx; +} + +Maybe ToDLDataType(DataType ofdtype) { + DLDataType dtype; + dtype.lanes = 1; + dtype.bits = GetSizeOfDataType(ofdtype) * 8; + switch (ofdtype) { + case DataType::kUInt8: dtype.code = DLDataTypeCode::kDLUInt; break; + case DataType::kInt8: dtype.code = DLDataTypeCode::kDLInt; break; + case DataType::kInt16: dtype.code = DLDataTypeCode::kDLInt; break; + case DataType::kInt32: dtype.code = DLDataTypeCode::kDLInt; break; + case DataType::kInt64: dtype.code = DLDataTypeCode::kDLInt; break; + case DataType::kFloat16: dtype.code = DLDataTypeCode::kDLFloat; break; + case DataType::kFloat: dtype.code = DLDataTypeCode::kDLFloat; break; + case DataType::kDouble: dtype.code = DLDataTypeCode::kDLFloat; break; + case DataType::kBFloat16: dtype.code = DLDataTypeCode::kDLBfloat; break; + default: UNIMPLEMENTED_THEN_RETURN() << "Unsupported data type: " << DataType_Name(ofdtype); + } + return dtype; +} + +// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init) +struct ATenDLMTensor { + std::shared_ptr handle; + DLManagedTensor tensor; +}; + +void deleter(DLManagedTensor* arg) { delete static_cast(arg->manager_ctx); } + +Maybe toDLPack(const std::shared_ptr& src) { + auto shape = *src->shape(); + auto strides = *JUST(src->stride()); + // create a new tensor with possibly normalized strides + // Reference: + // https://github.com/pytorch/pytorch/issues/83069 + // https://github.com/pytorch/pytorch/issues/82610 + for (int i = 0; i < src->ndim(); i++) { + if (shape[i] <= 1) { strides[i] = 1; } + } + + ATenDLMTensor* atDLMTensor(new ATenDLMTensor); + atDLMTensor->handle = src; + atDLMTensor->tensor.manager_ctx = atDLMTensor; + atDLMTensor->tensor.deleter = &deleter; + JUST(one::SyncAccessTensorWithTimeOut( + src, + [&](ep::Stream*, const std::shared_ptr& tensor) { + atDLMTensor->tensor.dl_tensor.data = tensor->mut_raw_dptr(); + }, + "const")); + auto dldevice = JUST(ToDLDevice(JUST(src->device()))); + auto dldtype = JUST(ToDLDataType(src->dtype()->data_type())); + atDLMTensor->tensor.dl_tensor.device = *dldevice; + atDLMTensor->tensor.dl_tensor.ndim = src->ndim(); + atDLMTensor->tensor.dl_tensor.dtype = *dldtype; + atDLMTensor->tensor.dl_tensor.shape = + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast) + const_cast(src->shape()->data()); + atDLMTensor->tensor.dl_tensor.strides = + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast) + const_cast(JUST(src->stride())->data()); + atDLMTensor->tensor.dl_tensor.byte_offset = 0; + return &(atDLMTensor->tensor); +} + +// This function is mostly copied from PyTorch +void DLPack_Capsule_Destructor(PyObject* data) { + if (likely(!PyCapsule_IsValid(data, "dltensor"))) { + // early out, see DLPack spec: if a consuming library sets the capsule + // name to something else, they own it and we don't need to do anything + return; + } + HANDLE_ERRORS + // Causes overheads for validity checks again, but this case is rare + // since consuming libraries should rename the capsule according to spec. + // Note that this cannot set a python error (we checked validity above), + // so we don't need to handle python error state here. + DLManagedTensor* dlMTensor = (DLManagedTensor*)PyCapsule_GetPointer(data, "dltensor"); + // the dlMTensor has not been consumed, call deleter ourselves. + // DLPack spec mentions that deleter may be NULL, but deleter from + // `flow.to_dlpack` is never NULL, so no need for an additional check here. + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast) + dlMTensor->deleter(const_cast(dlMTensor)); + END_HANDLE_ERRORS_RET() +} + +namespace py = pybind11; + +ONEFLOW_API_PYBIND11_MODULE("", m) { + m.def("to_dlpack", [](const std::shared_ptr& tensor) -> Maybe { + DLManagedTensor* dlMTensor = JUST(toDLPack(tensor)); + return py::capsule(dlMTensor, "dltensor", DLPack_Capsule_Destructor); + }); + // from_dlpack is exported in tensor_api.yaml +} + +} // namespace oneflow diff --git a/oneflow/api/python/dlpack/converter.h b/oneflow/api/python/dlpack/converter.h new file mode 100644 index 0000000000000000000000000000000000000000..6f35471ed2c047ae8222ed67f31eaa168115df54 --- /dev/null +++ b/oneflow/api/python/dlpack/converter.h @@ -0,0 +1,28 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/api/python/dlpack/dlpack.h" +#include "oneflow/core/common/maybe.h" + +namespace oneflow { + +namespace one { +class Tensor; +} + +Maybe fromDLPack(const DLManagedTensor* src); +Maybe toDLPack(const std::shared_ptr& src); + +} // namespace oneflow diff --git a/oneflow/api/python/dlpack/dlpack.h b/oneflow/api/python/dlpack/dlpack.h new file mode 100644 index 0000000000000000000000000000000000000000..f63ecde8e6a7241810ce97ef804e7c636ac60b80 --- /dev/null +++ b/oneflow/api/python/dlpack/dlpack.h @@ -0,0 +1,244 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +/*! + * Copyright (c) 2017 by Contributors + * \file dlpack.h + * \brief The common header of DLPack. + */ +#ifndef DLPACK_DLPACK_H_ +#define DLPACK_DLPACK_H_ + +/** + * \brief Compatibility with C++ + */ +#ifdef __cplusplus +#define DLPACK_EXTERN_C extern "C" +#else +#define DLPACK_EXTERN_C +#endif + +/*! \brief The current version of dlpack */ +#define DLPACK_VERSION 70 + +/*! \brief The current ABI version of dlpack */ +#define DLPACK_ABI_VERSION 1 + +/*! \brief DLPACK_DLL prefix for windows */ +#ifdef _WIN32 +#ifdef DLPACK_EXPORTS +#define DLPACK_DLL __declspec(dllexport) +#else +#define DLPACK_DLL __declspec(dllimport) +#endif +#else +#define DLPACK_DLL +#endif + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif +/*! + * \brief The device type in DLDevice. + */ +#ifdef __cplusplus +typedef enum : int32_t { +#else +typedef enum { +#endif + /*! \brief CPU device */ + kDLCPU = 1, + /*! \brief CUDA GPU device */ + kDLCUDA = 2, + /*! + * \brief Pinned CUDA CPU memory by cudaMallocHost + */ + kDLCUDAHost = 3, + /*! \brief OpenCL devices. */ + kDLOpenCL = 4, + /*! \brief Vulkan buffer for next generation graphics. */ + kDLVulkan = 7, + /*! \brief Metal for Apple GPU. */ + kDLMetal = 8, + /*! \brief Verilog simulator buffer */ + kDLVPI = 9, + /*! \brief ROCm GPUs for AMD GPUs */ + kDLROCM = 10, + /*! + * \brief Pinned ROCm CPU memory allocated by hipMallocHost + */ + kDLROCMHost = 11, + /*! + * \brief Reserved extension device type, + * used for quickly test extension device + * The semantics can differ depending on the implementation. + */ + kDLExtDev = 12, + /*! + * \brief CUDA managed/unified memory allocated by cudaMallocManaged + */ + kDLCUDAManaged = 13, + /*! + * \brief Unified shared memory allocated on a oneAPI non-partititioned + * device. Call to oneAPI runtime is required to determine the device + * type, the USM allocation type and the sycl context it is bound to. + * + */ + kDLOneAPI = 14, + /*! \brief GPU support for next generation WebGPU standard. */ + kDLWebGPU = 15, + /*! \brief Qualcomm Hexagon DSP */ + kDLHexagon = 16, +} DLDeviceType; + +/*! + * \brief A Device for Tensor and operator. + */ +typedef struct { + /*! \brief The device type used in the device. */ + DLDeviceType device_type; + /*! + * \brief The device index. + * For vanilla CPU memory, pinned memory, or managed memory, this is set to 0. + */ + int32_t device_id; +} DLDevice; + +/*! + * \brief The type code options DLDataType. + */ +typedef enum { + /*! \brief signed integer */ + kDLInt = 0U, + /*! \brief unsigned integer */ + kDLUInt = 1U, + /*! \brief IEEE floating point */ + kDLFloat = 2U, + /*! + * \brief Opaque handle type, reserved for testing purposes. + * Frameworks need to agree on the handle data type for the exchange to be well-defined. + */ + kDLOpaqueHandle = 3U, + /*! \brief bfloat16 */ + kDLBfloat = 4U, + /*! + * \brief complex number + * (C/C++/Python layout: compact struct per complex number) + */ + kDLComplex = 5U, +} DLDataTypeCode; + +/*! + * \brief The data type the tensor can hold. The data type is assumed to follow the + * native endian-ness. An explicit error message should be raised when attempting to + * export an array with non-native endianness + * + * Examples + * - float: type_code = 2, bits = 32, lanes=1 + * - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4 + * - int8: type_code = 0, bits = 8, lanes=1 + * - std::complex: type_code = 5, bits = 64, lanes = 1 + */ +typedef struct { + /*! + * \brief Type code of base types. + * We keep it uint8_t instead of DLDataTypeCode for minimal memory + * footprint, but the value should be one of DLDataTypeCode enum values. + * */ + uint8_t code; + /*! + * \brief Number of bits, common choices are 8, 16, 32. + */ + uint8_t bits; + /*! \brief Number of lanes in the type, used for vector types. */ + uint16_t lanes; +} DLDataType; + +/*! + * \brief Plain C Tensor object, does not manage memory. + */ +typedef struct { + /*! + * \brief The data pointer points to the allocated data. This will be CUDA + * device pointer or cl_mem handle in OpenCL. It may be opaque on some device + * types. This pointer is always aligned to 256 bytes as in CUDA. The + * `byte_offset` field should be used to point to the beginning of the data. + * + * Note that as of Nov 2021, multiply libraries (CuPy, PyTorch, TensorFlow, + * TVM, perhaps others) do not adhere to this 256 byte aligment requirement + * on CPU/CUDA/ROCm, and always use `byte_offset=0`. This must be fixed + * (after which this note will be updated); at the moment it is recommended + * to not rely on the data pointer being correctly aligned. + * + * For given DLTensor, the size of memory required to store the contents of + * data is calculated as follows: + * + * \code{.c} + * static inline size_t GetDataSize(const DLTensor* t) { + * size_t size = 1; + * for (tvm_index_t i = 0; i < t->ndim; ++i) { + * size *= t->shape[i]; + * } + * size *= (t->dtype.bits * t->dtype.lanes + 7) / 8; + * return size; + * } + * \endcode + */ + void* data; + /*! \brief The device of the tensor */ + DLDevice device; + /*! \brief Number of dimensions */ + int32_t ndim; + /*! \brief The data type of the pointer*/ + DLDataType dtype; + /*! \brief The shape of the tensor */ + int64_t* shape; + /*! + * \brief strides of the tensor (in number of elements, not bytes) + * can be NULL, indicating tensor is compact and row-majored. + */ + int64_t* strides; + /*! \brief The offset in bytes to the beginning pointer to data */ + uint64_t byte_offset; +} DLTensor; + +/*! + * \brief C Tensor object, manage memory of DLTensor. This data structure is + * intended to facilitate the borrowing of DLTensor by another framework. It is + * not meant to transfer the tensor. When the borrowing framework doesn't need + * the tensor, it should call the deleter to notify the host that the resource + * is no longer needed. + */ +typedef struct DLManagedTensor { + /*! \brief DLTensor which is being memory managed */ + DLTensor dl_tensor; + /*! \brief the context of the original host framework of DLManagedTensor in + * which DLManagedTensor is used in the framework. It can also be NULL. + */ + void* manager_ctx; + /*! \brief Destructor signature void (*)(void*) - this should be called + * to destruct manager_ctx which holds the DLManagedTensor. It can be NULL + * if there is no way for the caller to provide a reasonable destructor. + * The destructors deletes the argument self as well. + */ + void (*deleter)(struct DLManagedTensor* self); +} DLManagedTensor; +#ifdef __cplusplus +} // DLPACK_EXTERN_C +#endif +#endif // DLPACK_DLPACK_H_ diff --git a/oneflow/api/python/eager/eager.cpp b/oneflow/api/python/eager/eager.cpp index 1e9f190c87fc04834ddd5044eb0eb261c6e63dd5..f36867b72248eb5aac18023b9fe0e75594a37e5c 100644 --- a/oneflow/api/python/eager/eager.cpp +++ b/oneflow/api/python/eager/eager.cpp @@ -22,7 +22,9 @@ ONEFLOW_API_PYBIND11_MODULE("eager", m) { using namespace oneflow; namespace py = pybind11; m.def( - "Sync", []() { return vm::ClusterSync(); }, py::call_guard()); + "Sync", []() { return vm::CurrentRankSync(); }, py::call_guard()); + m.def( + "ClusterSync", []() { return vm::ClusterSync(); }, py::call_guard()); py::class_>( diff --git a/oneflow/api/python/env/env.cpp b/oneflow/api/python/env/env.cpp index 78ffc690bb1f7c3dbbf68ba3709c08bf9b9d7ff8..4a1df361fee024fd6392cab14c137cee2477e905 100644 --- a/oneflow/api/python/env/env.cpp +++ b/oneflow/api/python/env/env.cpp @@ -18,30 +18,85 @@ limitations under the License. #include "oneflow/api/python/of_api_registry.h" #include "oneflow/core/job/env_global_objects_scope.h" #include "oneflow/core/common/singleton.h" +#include "oneflow/core/job/graph_scope_vars.h" #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/framework/shut_down_util.h" #include "oneflow/core/device/cuda_util.h" +#ifdef WITH_CUDA +#include +#endif // WITH_CUDA +#ifdef WITH_ROCM +#include +#endif // WITH_ROCM + namespace py = pybind11; namespace oneflow { +#ifdef WITH_CUDA + +void RegisterCudaDeviceProperties(py::module& m) { + py::class_(m, "_CudaDeviceProperties", py::module_local()) + .def(py::init<>()) + .def_readonly("name", &cudaDeviceProp::name) + .def_readonly("major", &cudaDeviceProp::major) + .def_readonly("minor", &cudaDeviceProp::minor) + .def_readonly("is_multi_gpu_board", &cudaDeviceProp::isMultiGpuBoard) + .def_readonly("is_integrated", &cudaDeviceProp::integrated) + .def_readonly("multi_processor_count", &cudaDeviceProp::multiProcessorCount) + .def_readonly("total_memory", &cudaDeviceProp::totalGlobalMem) + .def("__repr__", [](const cudaDeviceProp& prop) { + std::ostringstream stream; + stream << "_CudaDeviceProperties(name='" << prop.name << "', major=" << prop.major + << ", minor=" << prop.minor + << ", total_memory=" << prop.totalGlobalMem / (1024 * 1024) + << "MB, multi_processor_count=" << prop.multiProcessorCount << ")"; + return stream.str(); + }); +} + +#endif // WITH_CUDA + +#ifdef WITH_ROCM + +void RegisterCudaDeviceProperties(py::module& m) { + py::class_(m, "_CudaDeviceProperties", py::module_local()) + .def(py::init<>()) + .def_readonly("name", &hipDeviceProp_t::name) + .def_readonly("major", &hipDeviceProp_t::major) + .def_readonly("minor", &hipDeviceProp_t::minor) + .def_readonly("is_multi_gpu_board", &hipDeviceProp_t::isMultiGpuBoard) + .def_readonly("is_integrated", &hipDeviceProp_t::integrated) + .def_readonly("multi_processor_count", &hipDeviceProp_t::multiProcessorCount) + .def_readonly("total_memory", &hipDeviceProp_t::totalGlobalMem) + .def("__repr__", [](const hipDeviceProp_t& prop) { + std::ostringstream stream; + stream << "_CudaDeviceProperties(name='" << prop.name << "', major=" << prop.major + << ", minor=" << prop.minor + << ", total_memory=" << prop.totalGlobalMem / (1024 * 1024) + << "MB, multi_processor_count=" << prop.multiProcessorCount << ")"; + return stream.str(); + }); +} + +#endif // WITH_ROCM + Maybe SwitchToShuttingDownPhase(EnvGlobalObjectsScope* env, bool is_normal_exit) { + JUST(env->init_is_normal_exit(is_normal_exit)); + SetShuttingDown(true); if (is_normal_exit) { JUST(vm::ClusterSync()); auto* vm = JUST(SingletonMaybe()); JUST(vm->CloseVMThreads()); } - JUST(env->init_is_normal_exit(is_normal_exit)); - SetShuttingDown(true); return Maybe::Ok(); } ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("CurrentResource", &CurrentResource); m.def("EnvResource", &EnvResource); - m.def("EnableEagerEnvironment", &EnableEagerEnvironment); py::class_>( m, "EnvContext") @@ -57,19 +112,30 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("GetLocalRank", &GetLocalRank); m.def("InitRDMA", &InitRDMA); m.def("RDMAIsInitialized", &RDMAIsInitialized); + m.def("DestoryRDMA", &DestoryRDMA); m.def("CudaGetDeviceCount", &CudaGetDeviceCount); m.def("EmptyCache", &EmptyCache); #ifdef WITH_CUDA + RegisterCudaDeviceProperties(m); m.def("GetCudaDeviceIndex", &GetCudaDeviceIndex); m.def("SetCudaDeviceIndex", &SetCudaDeviceIndex); m.def("CudaSynchronize", &CudaSynchronize); m.def("GetCUDAMemoryUsed", &GetCUDAMemoryUsed); + m.def( + "_get_device_properties", + [](int device) -> cudaDeviceProp* { return GetDeviceProperties(device); }, + py::return_value_policy::reference); #endif // WITH_CUDA #ifdef WITH_ROCM + RegisterCudaDeviceProperties(m); m.def("GetCudaDeviceIndex", &GetCudaDeviceIndex); m.def("SetCudaDeviceIndex", &SetCudaDeviceIndex); m.def("CudaSynchronize", &CudaSynchronize); m.def("GetCUDAMemoryUsed", &GetCUDAMemoryUsed); + m.def( + "_get_device_properties", + [](int device) -> hipDeviceProp_t* { return GetDeviceProperties(device); }, + py::return_value_policy::reference); #endif // WITH_ROCM m.def("SetFLAGS_alsologtostderr", &SetFLAGS_alsologtostderr); m.def("GetFLAGS_alsologtostderr", &GetFLAGS_alsologtostderr); @@ -81,6 +147,10 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("GetGraphDebugMaxPyStackDepth", &GetGraphDebugMaxPyStackDepth); m.def("SetGraphDebugMode", &SetGraphDebugMode); m.def("GetGraphDebugMode", &GetGraphDebugMode); + m.def("SetGraphDebugOnlyUserPyStack", &SetGraphDebugOnlyUserPyStack); + m.def("GetGraphDebugOnlyUserPyStack", &GetGraphDebugOnlyUserPyStack); + m.def("InitPythonPathsToBeKeptAndFilteredForDebugging", + &InitPythonPathsToBeKeptAndFilteredForDebugging); } } // namespace oneflow diff --git a/oneflow/api/python/env/env.h b/oneflow/api/python/env/env.h index f52f82914afae4e0468720e0c92d37d7751c9bcb..0fdd500f7d12cb02772180cc103e10db4c3d47f2 100644 --- a/oneflow/api/python/env/env.h +++ b/oneflow/api/python/env/env.h @@ -20,7 +20,6 @@ limitations under the License. #include #include "oneflow/core/common/protobuf.h" #include "oneflow/core/common/singleton.h" -#include "oneflow/core/job/cluster.h" #include "oneflow/core/job/cluster_instruction.h" #include "oneflow/core/job/env_global_objects_scope.h" #include "oneflow/core/job/global_for.h" @@ -44,12 +43,6 @@ inline Maybe EnvResource() { return PbMessage2TxtString(Singleton::Get()->resource()); } -inline Maybe EnableEagerEnvironment(bool enable_eager_execution) { - CHECK_NOTNULL_OR_RETURN((Singleton::Get())); - *Singleton::Get() = enable_eager_execution; - return Maybe::Ok(); -} - inline Maybe CurrentMachineId() { return GlobalProcessCtx::Rank(); } inline Maybe GetRank() { return GlobalProcessCtx::Rank(); } diff --git a/oneflow/api/python/framework/autocast.cpp b/oneflow/api/python/framework/autocast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fc97b249ef11d6c7a46f0c900e9320f416316cc7 --- /dev/null +++ b/oneflow/api/python/framework/autocast.cpp @@ -0,0 +1,90 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/api/python/of_api_registry.h" + +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/autocast.h" + +namespace py = pybind11; + +namespace oneflow { + +class AutoCastMode { + public: + OF_DISALLOW_COPY_AND_MOVE(AutoCastMode); + + AutoCastMode(const std::string& device_type, Symbol dtype, bool enabled, + bool cache_enabled) + : prev_enabled_(autocast::is_enabled()), + prev_cache_enabled_(autocast::is_autocast_cache_enabled()), + prev_device_type_(autocast::get_autocast_device_type()), + prev_dtype_(autocast::get_autocast_dtype()), + prev_gpu_dtype_(autocast::get_autocast_gpu_dtype()), + prev_cpu_dtype_(autocast::get_autocast_cpu_dtype()) { + // update autocast state + autocast::set_enabled(enabled); + autocast::set_autocast_cache_enabled(cache_enabled); + if (device_type == "cpu") { + autocast::set_autocast_device_type(kCPU); + autocast::set_autocast_dtype(dtype); + autocast::set_autocast_cpu_dtype(dtype); + } else if (device_type == "cuda") { + autocast::set_autocast_device_type(kCUDA); + autocast::set_autocast_dtype(dtype); + autocast::set_autocast_gpu_dtype(dtype); + } else { + THROW(RuntimeError) << "User specified autocast device_type must be 'cuda' or 'cpu'"; + } + } + + ~AutoCastMode() { + autocast::set_enabled(prev_enabled_); + autocast::set_autocast_cache_enabled(prev_cache_enabled_); + autocast::set_autocast_device_type(prev_device_type_); + autocast::set_autocast_dtype(prev_dtype_); + autocast::set_autocast_gpu_dtype(prev_gpu_dtype_); + autocast::set_autocast_cpu_dtype(prev_cpu_dtype_); + } + + private: + bool prev_enabled_; + bool prev_cache_enabled_; + DeviceType prev_device_type_; + Symbol prev_dtype_; + Symbol prev_gpu_dtype_; + Symbol prev_cpu_dtype_; +}; + +ONEFLOW_API_PYBIND11_MODULE("", m) { + py::class_>(m, "AutoCastMode") + .def(py::init([](const std::string& device_type, Symbol dtype, bool enabled, + bool cache_enabled) { + return std::make_shared(device_type, dtype, enabled, cache_enabled); + })); + + m.def("is_autocast_enabled", autocast::is_enabled); + m.def("set_autocast_enabled", autocast::set_enabled); + m.def("get_autocast_gpu_dtype", autocast::get_autocast_gpu_dtype); + m.def("get_autocast_cpu_dtype", autocast::get_autocast_cpu_dtype); + m.def("set_autocast_gpu_dtype", autocast::set_autocast_gpu_dtype); + m.def("set_autocast_cpu_dtype", autocast::set_autocast_cpu_dtype); + m.def("is_autocast_cache_enabled", autocast::is_autocast_cache_enabled); + m.def("set_autocast_cache_enabled", autocast::set_autocast_cache_enabled); + m.def("clear_autocast_cache", autocast::clear_cache); +} + +} // namespace oneflow diff --git a/oneflow/api/python/framework/device.cpp b/oneflow/api/python/framework/device.cpp index 06d5a733bfbc08b16d7594c39bf291461fa98f89..4a286c2a53c1c87e973312c7e9d8e42b4599973b 100644 --- a/oneflow/api/python/framework/device.cpp +++ b/oneflow/api/python/framework/device.cpp @@ -20,6 +20,7 @@ limitations under the License. #include "oneflow/core/framework/device.h" #include "oneflow/core/common/str_util.h" #include "oneflow/core/control/global_process_ctx.h" +#include "oneflow/core/ep/include/device.h" namespace py = pybind11; @@ -39,6 +40,10 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { .def("__repr__", [](const Symbol& d) { return d->ToRepr(); }) .def(py::self == py::self) .def(py::hash(py::self)); + + m.def( + "max_alignment_size", []() { return ep::kMaxAlignmentRequirement; }, + py::return_value_policy::copy); } } // namespace oneflow diff --git a/oneflow/api/python/framework/dtype.cpp b/oneflow/api/python/framework/dtype.cpp index d65888329043639aafd5892e9bc1f749fdd5c395..8b54c472b35eea0e5e228f68db95af1728d44672 100644 --- a/oneflow/api/python/framework/dtype.cpp +++ b/oneflow/api/python/framework/dtype.cpp @@ -16,7 +16,10 @@ limitations under the License. #include #include #include "oneflow/api/python/of_api_registry.h" +#include "oneflow/api/python/framework/tensortype.h" +#include "oneflow/api/python/functional/common.h" #include "oneflow/core/framework/dtype.h" + namespace py = pybind11; namespace oneflow { @@ -66,6 +69,19 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { m.attr("complex32") = &CHECK_JUST(DType::Get(DataType::kComplex32)); m.attr("complex64") = &CHECK_JUST(DType::Get(DataType::kComplex64)); m.attr("complex128") = &CHECK_JUST(DType::Get(DataType::kComplex128)); + + py::options options; + options.disable_function_signatures(); + m.def("get_default_dtype", []() { return GetDefaultDType(); }); + m.def("set_default_dtype", + [](const Symbol& dtype) { SetDefaultDType(dtype).GetOrThrow(); }); + m.def("set_default_tensor_type", [](const py::object& tensor_type) { + if (one::PyTensorType_Check(tensor_type.ptr())) { + CHECK_JUST(SetDefaultDType(one::PyTensorType_UnpackDType(tensor_type.ptr()))); + } else { + throw py::type_error("invalid type object"); + } + }); } } // namespace oneflow diff --git a/oneflow/api/python/framework/foreign_callback.cpp b/oneflow/api/python/framework/foreign_callback.cpp deleted file mode 100644 index fcb6ff2e3c1efef3356ba3568a92b1aed2dc7296..0000000000000000000000000000000000000000 --- a/oneflow/api/python/framework/foreign_callback.cpp +++ /dev/null @@ -1,55 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include -#include "oneflow/api/python/of_api_registry.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/foreign_callback.h" - -namespace py = pybind11; - -namespace oneflow { - -class PyForeignCallback : public ForeignCallback { - public: - // Inherit the constructors - using ForeignCallback::ForeignCallback; - - // Trampoline (need one for each virtual function) - void OfBlobCall(int64_t unique_id, int64_t ofblob_ptr) const override { - PYBIND11_OVERRIDE(void, /* Return type */ - ForeignCallback, /* Parent class */ - OfBlobCall, /* Name of function in C++ (must match Python name) */ - unique_id, ofblob_ptr /* Argument(s) */ - ); - } - - void RemoveForeignCallback(int64_t unique_id) const override { - PYBIND11_OVERRIDE(void, ForeignCallback, RemoveForeignCallback, unique_id); - } -}; - -} // namespace oneflow - -ONEFLOW_API_PYBIND11_MODULE("", m) { - using namespace oneflow; - - py::class_>( - m, "ForeignCallback") - .def(py::init<>()) - .def("OfBlobCall", &ForeignCallback::OfBlobCall) - .def("RemoveForeignCallback", &ForeignCallback::RemoveForeignCallback); -} diff --git a/oneflow/api/python/framework/foreign_watcher.cpp b/oneflow/api/python/framework/foreign_watcher.cpp deleted file mode 100644 index 9ce0f11a069ac6ad3dcb3414c41649a489e40e06..0000000000000000000000000000000000000000 --- a/oneflow/api/python/framework/foreign_watcher.cpp +++ /dev/null @@ -1,43 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include -#include "oneflow/api/python/of_api_registry.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/foreign_watcher.h" - -namespace py = pybind11; - -namespace oneflow { - -class PyForeignWatcher : public ForeignWatcher { - public: - using ForeignWatcher::ForeignWatcher; - - void Call(const std::string& handler_uuid, int64_t ofblob_ptr) const override { - PYBIND11_OVERRIDE(void, ForeignWatcher, Call, handler_uuid, ofblob_ptr); - } -}; - -} // namespace oneflow - -ONEFLOW_API_PYBIND11_MODULE("", m) { - using namespace oneflow; - - py::class_>(m, "ForeignWatcher") - .def(py::init<>()) - .def("Call", &ForeignWatcher::Call); -} diff --git a/oneflow/api/python/framework/framework.cpp b/oneflow/api/python/framework/framework.cpp index a18b205b3df2f272b65c3365c16248f7cc37e21f..314b207fac8bc9b22a242f9b0ac306b3ec077220 100644 --- a/oneflow/api/python/framework/framework.cpp +++ b/oneflow/api/python/framework/framework.cpp @@ -25,30 +25,11 @@ namespace py = pybind11; namespace oneflow { ONEFLOW_API_PYBIND11_MODULE("", m) { - m.def("RegisterGlobalForeignCallback", &RegisterGlobalForeignCallback); - m.def("DestroyGlobalForeignCallback", &DestroyGlobalForeignCallback); - m.def("RegisterGlobalWatcher", &RegisterGlobalWatcher); - m.def("LaunchJob", &LaunchJob, py::call_guard()); - - m.def("GetSerializedInterUserJobInfo", - []() -> Maybe { return py::bytes(*JUST(GetSerializedInterUserJobInfo())); }); - m.def("GetSerializedJobSet", - []() -> Maybe { return py::bytes(*JUST(GetSerializedJobSet())); }); - m.def("GetSerializedStructureGraph", &GetSerializedStructureGraph /* a prototxt saved to file*/); m.def("GetSerializedCurrentJob", []() -> Maybe { return py::bytes(*JUST(GetSerializedCurrentJob())); }); - m.def("GetFunctionConfigDef", &GetFunctionConfigDef); m.def("GetScopeConfigDef", &GetScopeConfigDef); - m.def("GetMachine2DeviceIdListOFRecordFromParallelConf", - &GetSerializedMachineId2DeviceIdListOFRecord); - - m.def("LoadSavedModel", - [](const std::string& saved_model_meta_file, bool is_prototxt_file) -> Maybe { - return py::bytes(*JUST(LoadSavedModel(saved_model_meta_file, is_prototxt_file))); - }); - m.def("EagerExecutionEnabled", EagerExecutionEnabled); m.def("LoadLibrary", &LoadLibrary); } diff --git a/oneflow/api/python/framework/framework.h b/oneflow/api/python/framework/framework.h index 4c237d2c35d018b6f0371b7f555e7732e566e1ee..e44db35a2e81dcd98689f4912e16e22bee9bf767 100644 --- a/oneflow/api/python/framework/framework.h +++ b/oneflow/api/python/framework/framework.h @@ -25,82 +25,14 @@ limitations under the License. #include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" #include "oneflow/core/job/job_desc.h" #include "oneflow/core/job/inter_user_job_info.pb.h" -#include "oneflow/core/job/foreign_callback.h" -#include "oneflow/core/job/foreign_watcher.h" #include "oneflow/core/job/job_instance.h" #include "oneflow/core/job/oneflow.h" #include "oneflow/core/job/placement.pb.h" #include "oneflow/core/framework/config_def.h" #include "oneflow/core/framework/load_library.h" -#include "oneflow/core/serving/saved_model.pb.h" namespace oneflow { -inline Maybe RegisterGlobalForeignCallback(const std::shared_ptr& callback) { - CHECK_ISNULL_OR_RETURN(Singleton>::Get()) - << "foreign callback registered"; - // Singleton::SetAllocated is preferred since Singleton::New will output logs but - // glog is not constructed yet. - Singleton>::SetAllocated( - new std::shared_ptr(callback)); - return Maybe::Ok(); -} - -inline Maybe DestroyGlobalForeignCallback() { - if (Singleton>::Get()) { - Singleton>::Delete(); - } - return Maybe::Ok(); -} - -inline Maybe RegisterGlobalWatcher(const std::shared_ptr& watcher) { - CHECK_ISNULL_OR_RETURN(Singleton>::Get()) - << "foreign watcher registered"; - // Singleton::SetAllocated is preferred since Singleton::New will output logs but - // glog is not constructed yet. - Singleton>::SetAllocated( - new std::shared_ptr(watcher)); - return Maybe::Ok(); -} - -inline Maybe LaunchJob(const std::shared_ptr& cb) { - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - CHECK_NOTNULL_OR_RETURN(Singleton::Get()); - const auto& job_name = cb->job_name(); - auto* buffer_mgr = Singleton>>::Get(); - int64_t job_id = Singleton::Get()->at(job_name); - if (IsPullJob(job_name, *Singleton::Get())) { - buffer_mgr->Get(GetForeignOutputBufferName(job_name))->Push(cb); - } - if (IsPushJob(job_name, *Singleton::Get())) { - buffer_mgr->Get(GetForeignInputBufferName(job_name))->Push(cb); - } - buffer_mgr->Get(GetCallbackNotifierBufferName(job_name))->Push(cb); - Singleton>::Get()->Get(kBufferNameGlobalWaitJobId)->Push(job_id); - return Maybe::Ok(); -} - -inline Maybe GetSerializedStructureGraph() { - const auto* job_ctx_mgr = Singleton::Get(); - CHECK_NOTNULL_OR_RETURN(job_ctx_mgr); - return job_ctx_mgr->structure_graph(); -} - -inline Maybe GetSerializedInterUserJobInfo() { - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - CHECK_NOTNULL_OR_RETURN(Singleton::Get()); - CHECK_NOTNULL_OR_RETURN(Singleton::Get()); - return Singleton::Get()->SerializeAsString(); -} - -inline Maybe GetJobSet() { - auto* job_ctx_mgr = JUST(GlobalJobBuildAndInferCtxMgr()); - CHECK_NOTNULL_OR_RETURN(job_ctx_mgr); - return job_ctx_mgr->job_set(); -} - -inline Maybe GetSerializedJobSet() { return JUST(GetJobSet()).SerializeAsString(); } - inline Maybe GetSerializedCurrentJob() { auto* job_ctx_mgr = Singleton::Get(); CHECK_NOTNULL_OR_RETURN(job_ctx_mgr); @@ -130,17 +62,6 @@ inline Maybe GetSerializedMachineId2DeviceIdListOFRecord( return PbMessage2TxtString(*JUST(ParseMachineAndDeviceIdList(parallel_conf))); } -inline Maybe LoadSavedModel(const std::string& saved_model_meta_file, - bool is_prototxt_file) { - SavedModel saved_model_proto; - if (is_prototxt_file) { - CHECK_OR_RETURN(TryParseProtoFromTextFile(saved_model_meta_file, &saved_model_proto)); - } else { - CHECK_OR_RETURN(TryParseProtoFromPbFile(saved_model_meta_file, &saved_model_proto)); - } - return saved_model_proto.SerializeAsString(); -} - inline Maybe LoadLibraryNow(const std::string& lib_path) { return LoadLibrary(lib_path); } } // namespace oneflow diff --git a/oneflow/api/python/framework/global_mode.cpp b/oneflow/api/python/framework/global_mode.cpp new file mode 100644 index 0000000000000000000000000000000000000000..31e13ec4910d64e4de7b62d7f9a9f8485af0b185 --- /dev/null +++ b/oneflow/api/python/framework/global_mode.cpp @@ -0,0 +1,83 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/job/global_mode.h" + +namespace py = pybind11; + +namespace oneflow { + +ONEFLOW_API_PYBIND11_MODULE("global_view", m) { + py::class_>(m, "global_mode") + .def(py::init([](const bool enabled) { + if (enabled) { + THROW(RuntimeError) << "To enable global mode, placement and sbp must be provided."; + } + return std::make_shared(enabled); + })) + .def(py::init([](const bool enabled, const Symbol& placement, + const std::vector>& sbp) { + if (!enabled) { + THROW(RuntimeError) + << "To disable global mode, placement and sbp must not be provided."; + } + return std::make_shared(enabled, CHECK_JUST(GetNdSbp(sbp)), + placement); + }), + py::arg("enabled").none(false), py::arg("placement").none(false), + py::arg("sbp").none(false)) + .def(py::init([](const bool enabled, const Symbol& placement, + const Symbol& sbp) { + return std::make_shared(enabled, CHECK_JUST(SbpToNdSbp(sbp)), + placement); + }), + py::arg("enabled").none(false), py::arg("placement").none(false), + py::arg("sbp").none(false)) + .def("__enter__", [](const GlobalMode::Guard& guard_obj) {}) + .def("__exit__", [](const GlobalMode::Guard& guard_obj, const py::object& type, + const py::object& value, const py::object& traceback) {}); + + py::class_>(m, "current_global_mode") + .def(py::init([]() { return std::make_shared(); })) + .def_property_readonly("is_enabled", [](const GlobalMode& gm) { return gm.is_enabled(); }) + .def_property_readonly("sbp", + [](const GlobalMode& gm) { + if (!gm.is_enabled()) { + THROW(RuntimeError) + << "Current global mode is disabled, there is no sbp."; + } + const auto& nd_sbp = gm.nd_sbp(); + auto tuple = py::tuple(nd_sbp->sbp_parallel_size()); + for (int i = 0; i < nd_sbp->sbp_parallel_size(); ++i) { + tuple[i] = SymbolOf(nd_sbp->sbp_parallel(i)); + } + return tuple; + }) + .def_property_readonly("placement", [](const GlobalMode& gm) { + if (!gm.is_enabled()) { + THROW(RuntimeError) << "Current global mode is disabled, there is no placement."; + } + return gm.parallel_desc(); + }); +} + +} // namespace oneflow diff --git a/oneflow/api/python/framework/instructions_builder.cpp b/oneflow/api/python/framework/instructions_builder.cpp index dd3447980781384c91c5acc725322dadb08c7709..a220b0153c3283f5b9fe5fa31de1e2feb8fa7be0 100644 --- a/oneflow/api/python/framework/instructions_builder.cpp +++ b/oneflow/api/python/framework/instructions_builder.cpp @@ -44,29 +44,29 @@ ONEFLOW_API_PYBIND11_MODULE("deprecated", m) { [](const std::shared_ptr& builder, int64_t session_id, const std::string& job_conf_str, const std::string& device_tag, const std::vector& machine_device_ids, - const std::shared_ptr& hierarchy, bool is_mirrored) -> Maybe { + const std::shared_ptr& hierarchy, bool is_local) -> Maybe { JobConfigProto job_conf; CHECK_OR_RETURN(TxtString2PbMessage(job_conf_str, &job_conf)) << Error::RuntimeError() << "job conf parse failed"; return builder->BuildInitialScope(session_id, job_conf, device_tag, machine_device_ids, - hierarchy, is_mirrored); + hierarchy, is_local); }, py::arg("session_id").none(false), py::arg("job_conf_str").none(false), py::arg("device_tag").none(false), py::arg("machine_device_ids").none(false), - py::arg("hierarchy").none(true), py::arg("is_mirrored").none(false)) + py::arg("hierarchy").none(true), py::arg("is_local").none(false)) .def( "BuildInitialScopeWithPlacement", [](const std::shared_ptr& builder, int64_t session_id, const std::string& job_conf_str, Symbol placement, - bool is_mirrored) -> Maybe { + bool is_local) -> Maybe { JobConfigProto job_conf; CHECK_OR_RETURN(TxtString2PbMessage(job_conf_str, &job_conf)) << Error::RuntimeError() << "job conf parse failed"; return builder->BuildInitialScopeWithPlacement(session_id, job_conf, placement, - is_mirrored); + is_local); }, py::arg("session_id").none(false), py::arg("job_conf_str").none(false), - py::arg("placement").none(false), py::arg("is_mirrored").none(false)) + py::arg("placement").none(false), py::arg("is_local").none(false)) .def("BuildScopeWithNewParallelDesc", &InstructionsBuilder::BuildScopeWithNewParallelDesc, py::arg("scope").none(false), py::arg("device_tag").none(false), py::arg("machine_device_ids").none(false), py::arg("hierarchy").none(true)) @@ -79,7 +79,7 @@ ONEFLOW_API_PYBIND11_MODULE("deprecated", m) { << Error::RuntimeError() << "parallel conf parse failed"; return builder->BuildScopeWithNewParallelConf(scope, parallel_conf); }) - .def("BuildScopeWithNewIsMirrored", &InstructionsBuilder::BuildScopeWithNewIsMirrored) + .def("BuildScopeWithNewIsLocal", &InstructionsBuilder::BuildScopeWithNewIsLocal) .def("BuildScopeWithNewScopeName", &InstructionsBuilder::BuildScopeWithNewScopeName) .def("BuildScopeByProtoStrSetter", &InstructionsBuilder::BuildScopeByProtoStrSetter); diff --git a/oneflow/api/python/framework/job_instance.cpp b/oneflow/api/python/framework/job_instance.cpp deleted file mode 100644 index 367c39326e6627b2de84b9204769e9375b15ade5..0000000000000000000000000000000000000000 --- a/oneflow/api/python/framework/job_instance.cpp +++ /dev/null @@ -1,72 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include -#include -#include "oneflow/api/python/of_api_registry.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/job_instance.h" - -namespace py = pybind11; - -namespace oneflow { - -class PyJobInstance : public JobInstance { - public: - // Inherit the constructors - using JobInstance::JobInstance; - - // Trampoline (need one for each virtual function) - std::string job_name() const override { - PYBIND11_OVERRIDE(std::string, /* Return type */ - JobInstance, /* Parent class */ - job_name, /* Name of function in C++ (must match Python name) */ - ); - } - - std::string sole_input_op_name_in_user_job() const override { - PYBIND11_OVERRIDE(std::string, JobInstance, sole_input_op_name_in_user_job, ); - } - - std::string sole_output_op_name_in_user_job() const override { - PYBIND11_OVERRIDE(std::string, JobInstance, sole_output_op_name_in_user_job, ); - } - - void PushBlob(uint64_t ofblob_ptr) const override { - PYBIND11_OVERRIDE(void, JobInstance, PushBlob, ofblob_ptr); - } - - void PullBlob(uint64_t ofblob_ptr) const override { - PYBIND11_OVERRIDE(void, JobInstance, PullBlob, ofblob_ptr); - } - - void Finish() const override { PYBIND11_OVERRIDE(void, JobInstance, Finish, ); } -}; - -} // namespace oneflow - -ONEFLOW_API_PYBIND11_MODULE("", m) { - using namespace oneflow; - - py::class_>(m, "JobInstance") - .def(py::init<>()) - .def("job_name", &JobInstance::job_name) - .def("sole_input_op_name_in_user_job", &JobInstance::sole_input_op_name_in_user_job) - .def("sole_output_op_name_in_user_job", &JobInstance::sole_output_op_name_in_user_job) - .def("PushBlob", &JobInstance::PushBlob) - .def("PullBlob", &JobInstance::PullBlob) - .def("Finish", &JobInstance::Finish); -} diff --git a/oneflow/api/python/framework/nn_graph.cpp b/oneflow/api/python/framework/nn_graph.cpp index aa78605dab09ffb5d3334bd441db6d31810b554b..9e2fe2982bc153c44809153160f893f5807c200a 100644 --- a/oneflow/api/python/framework/nn_graph.cpp +++ b/oneflow/api/python/framework/nn_graph.cpp @@ -86,19 +86,30 @@ ONEFLOW_API_PYBIND11_MODULE("nn.graph.", m) { m.def("RunLazyNNGraph", &RunLazyNNGraph); m.def("SoftSyncNNGraphBuffers", &SoftSyncNNGraphBuffers); m.def("AddTensorAsGraphLoss", &AddTensorAsGraphLoss); + m.def("MarkVariableGradients", [](const std::vector>& variables, + const std::vector>& gradients) { + one::TensorTuple variable_tuple(variables.size()); + one::TensorTuple gradient_tuple(gradients.size()); + for (int i = 0; i < variables.size(); ++i) { variable_tuple[i] = variables[i]; } + for (int i = 0; i < gradients.size(); ++i) { gradient_tuple[i] = gradients[i]; } + return MarkVariableGradients(variable_tuple, gradient_tuple); + }); m.def("ConvertJobToTosaIR", [](const std::string& serialized_job) -> Maybe { Job job; - CHECK_OR_RETURN(TxtString2PbMessage(serialized_job, &job)) - << "serialized job conversion failed."; + CHECK_OR_RETURN(job.ParseFromString(serialized_job)) << "serialized job conversion failed."; return ConvertJobToTosaIR(&job); }); - m.def("SaveJobToIR", - [](const std::string& serialized_job, const std::string& path) -> Maybe { - Job job; - CHECK_OR_RETURN(TxtString2PbMessage(serialized_job, &job)) - << "serialized job conversion failed."; - return SaveJobToIR(&job, path); - }); + m.def( + "SaveJobToIR", [](const std::string& serialized_job, const std::string& path) -> Maybe { + Job job; + CHECK_OR_RETURN(job.ParseFromString(serialized_job)) << "serialized job conversion failed."; + return SaveJobToIR(&job, path); + }); + m.def("ConvertJobToIR", [](const std::string& serialized_job) -> Maybe { + Job job; + CHECK_OR_RETURN(job.ParseFromString(serialized_job)) << "serialized job conversion failed."; + return ConvertJobToIR(&job); + }); m.def("LoadSerializedJobFromIR", [](const std::string& path) -> Maybe { Job job; JUST(LoadJobFromIR(&job, path)); diff --git a/oneflow/api/python/framework/one_embedding.cpp b/oneflow/api/python/framework/one_embedding.cpp index 1f6cc72120f0334ce5da8dba398116326491c1c9..1d02c059531ff6f644b1e7a5b09d29c1922a32f3 100644 --- a/oneflow/api/python/framework/one_embedding.cpp +++ b/oneflow/api/python/framework/one_embedding.cpp @@ -222,6 +222,7 @@ class PersistentTableReaderImpl : public PersistentTableReader { options.value_size = storage_dim * sizeof(Value); options.target_chunk_size_mb = target_chunk_size_mb; options.physical_block_size = physical_block_size; + options.read_only = true; tables_[i] = NewPersistentTable(options); iterators_[i] = std::unique_ptr(tables_[i]->ReadSnapshot(snapshot_name)); diff --git a/oneflow/api/python/framework/random_generator.cpp b/oneflow/api/python/framework/random_generator.cpp index 6c622d893983b21d1412484507d2de22c7c36761..b7b5f8937d57f9c1ffdaa3f8eadeede59f330dc0 100644 --- a/oneflow/api/python/framework/random_generator.cpp +++ b/oneflow/api/python/framework/random_generator.cpp @@ -37,10 +37,10 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { })) .def("manual_seed", [](const std::shared_ptr& generator, - const py::object& seed) -> Maybe { - int64_t seed_val = JUST(one::functional::PyUnpackLong(seed.ptr())); + const py::object& seed) -> std::shared_ptr { + int64_t seed_val = (one::functional::PyUnpackLong(seed.ptr())).GetOrThrow(); generator->set_current_seed(seed_val); - return Maybe::Ok(); + return generator; }) .def("initial_seed", &one::Generator::current_seed) .def("seed", &one::Generator::seed) diff --git a/oneflow/api/python/framework/scope_util.cpp b/oneflow/api/python/framework/scope_util.cpp index d0a5de5b3241774f67e0a79b8f091c0beb72001c..06799b7f63882d067c08fed9d4a9143917028f8d 100644 --- a/oneflow/api/python/framework/scope_util.cpp +++ b/oneflow/api/python/framework/scope_util.cpp @@ -25,10 +25,10 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("GetCurrentScope", &GetCurrentScope); m.def("MakeInitialScope", [](const std::string& job_conf_str, Symbol placement, - bool is_mirrored) -> Maybe { + bool is_local) -> Maybe { JobConfigProto job_conf; CHECK_OR_RETURN(TxtString2PbMessage(job_conf_str, &job_conf)) << "job conf parse failed"; - return MakeInitialScope(job_conf, placement, is_mirrored); + return MakeInitialScope(job_conf, placement, is_local); }); m.def("InitGlobalScopeStack", &InitThreadLocalScopeStack); diff --git a/oneflow/api/python/framework/session_util.cpp b/oneflow/api/python/framework/session_util.cpp index 0c5134779d30d2c1c5c41467aadc3c02347cb814..54aab482c48cc271ef32c488cd8a0565bb10c953 100644 --- a/oneflow/api/python/framework/session_util.cpp +++ b/oneflow/api/python/framework/session_util.cpp @@ -13,7 +13,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include #include "oneflow/api/python/of_api_registry.h" #include "oneflow/core/framework/session_util.h" @@ -22,20 +21,9 @@ namespace py = pybind11; namespace oneflow { ONEFLOW_API_PYBIND11_MODULE("", m) { - py::class_>(m, "Session") - .def_property_readonly("id", &Session::id) - .def("push_mirrored_strategy_enabled", &Session::PushMirroredStrategyEnabled) - .def("pop_mirrored_strategy_enabled", &Session::PopMirroredStrategyEnabled) - .def("is_mirrored_strategy_enabled", &Session::IsMirroredStrategyEnabled) - .def("is_consistent_strategy_enabled", &Session::IsConsistentStrategyEnabled) - .def("is_mirrored_strategy_enabled_stack_size", - [](const Session* sess) { return sess->is_mirrored_strategy_enabled_stack()->size(); }); - - m.def("GetDefaultSessionId", &GetDefaultSessionId); - m.def("RegsiterSession", &RegsiterSession); - - m.def("GetDefaultSession", &GetDefaultSession); - m.def("ClearSessionById", &ClearSessionById); + m.def("GetDefaultSessionId", []() -> int64_t { return GetDefaultSessionId().GetOrThrow(); }); + m.def("RegsterSessionId", &RegsterSessionId); + m.def("ClearSessionId", &ClearSessionId); } } // namespace oneflow diff --git a/oneflow/api/python/framework/size.h b/oneflow/api/python/framework/size.h index 727318b627784579eddf316aa362ab22b58a3627..2829853828fcb3fddf857c912997d882e28e1c0b 100644 --- a/oneflow/api/python/framework/size.h +++ b/oneflow/api/python/framework/size.h @@ -37,85 +37,4 @@ Shape TensorSize_AsShape(PyObject* self); } // namespace oneflow -PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) - -class shape : public object { - public: - PYBIND11_OBJECT_CVT(shape, object, oneflow::TensorSize_Check, raw_shape) - explicit shape(size_t size = 0) : object(oneflow::TensorSize_New((ssize_t)size), stolen_t{}) { - if (!m_ptr) pybind11_fail("Could not allocate tensor size object!"); - } - size_t size() const { return (size_t)PyTuple_Size(m_ptr); } - bool empty() const { return size() == 0; } - detail::tuple_accessor operator[](size_t index) const { return {*this, index}; } - detail::item_accessor operator[](handle h) const { return object::operator[](h); } - detail::tuple_iterator begin() const { return {*this, 0}; } - detail::tuple_iterator end() const { return {*this, PyTuple_GET_SIZE(m_ptr)}; } - - private: - static PyObject* raw_shape(PyObject* op) { - if (oneflow::TensorSize_Check(op)) return handle(op).inc_ref().ptr(); - return PyObject_CallFunctionObjArgs((PyObject*)&oneflow::TensorSize_Type, op, NULL); - } -}; - -PYBIND11_NAMESPACE_BEGIN(detail) - -template -struct shape_type_caster { - public: - bool load(handle src, bool convert) { - value_ = nullptr; - if (src && src.is_none()) { return true; } - if (!oneflow::TensorSize_Check(src.ptr())) { return false; } - value_ = std::make_shared(oneflow::TensorSize_AsShape(src.ptr())); - return true; - } - - template - static handle cast(U&& src, return_value_policy /*policy*/, handle /*parent*/) { - return cast_impl(std::forward(src)); - } - - template - static handle cast(U* src, return_value_policy policy, handle parent) { - if (!src) { return none().release(); } - return cast(*src, policy, parent); - } - - operator T*() { return value_.get(); } - operator T&() { return *value_; } - operator T&&() && { return std::move(*value_); } - - operator std::shared_ptr*() { return &value_; } - operator std::shared_ptr&() { return value_; } - operator std::shared_ptr&&() && { return std::move(value_); } - - static constexpr auto name = _("shape"); - template - using cast_op_type = pybind11::detail::cast_op_type>; - - private: - static handle cast_impl(const oneflow::Shape& src) { - return reinterpret_steal(oneflow::TensorSize_NewFromShape(src)).release(); - } - static handle cast_impl(const std::shared_ptr& src) { - return reinterpret_steal(oneflow::TensorSize_NewFromShape(*src)).release(); - } - - protected: - std::shared_ptr value_; -}; - -template<> -struct type_caster : public shape_type_caster {}; -template<> -struct type_caster> : public shape_type_caster {}; -template<> -struct type_caster> - : public shape_type_caster {}; - -PYBIND11_NAMESPACE_END(detail) -PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) - #endif // ONEFLOW_API_PYTHON_FRAMEWORK_SIZE_H_ diff --git a/oneflow/api/python/framework/stream.cpp b/oneflow/api/python/framework/stream.cpp new file mode 100644 index 0000000000000000000000000000000000000000..45145e25e601e669648dfb66d273e5f2e2b113f9 --- /dev/null +++ b/oneflow/api/python/framework/stream.cpp @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/api/python/framework/thread.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/framework/stream_set.h" +#include "oneflow/core/framework/stream_guard.h" + +namespace py = pybind11; + +ONEFLOW_API_PYBIND11_MODULE("", m) { + using namespace oneflow; + py::class_>(m, "StreamSet") + .def(py::init([](const AsyncThread& async_thread) { + return StreamSet::New(async_thread.thread_uid()).GetPtrOrThrow(); + })); + + py::class_>(m, "StreamGuard") + .def(py::init([](const std::shared_ptr& stream_set) { + auto stream_converter = std::make_shared(stream_set); + return std::make_shared(stream_converter); + })); +} diff --git a/oneflow/api/python/framework/tensor.cpp b/oneflow/api/python/framework/tensor.cpp index 0ddd612b698afc262182456b794884475dd7681a..ca920f7f62663b92b21f7b9d996603a305613f8a 100644 --- a/oneflow/api/python/framework/tensor.cpp +++ b/oneflow/api/python/framework/tensor.cpp @@ -25,7 +25,6 @@ limitations under the License. #include "oneflow/api/python/functional/functional_api.yaml.pybind.h" #include "oneflow/api/python/functional/tensor_api.yaml.pybind.h" #include "oneflow/api/python/of_api_registry.h" -#include "oneflow/api/python/ofblob/ofblob.e.h" #include "oneflow/api/python/utils/tensor_utils.h" #include "oneflow/core/autograd/autograd_engine.h" #include "oneflow/core/framework/tensor.h" @@ -36,6 +35,7 @@ limitations under the License. #include "oneflow/core/framework/placement_utils.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/tensor_index.h" +#include "oneflow/core/kernel/kernel_util.h" namespace py = pybind11; @@ -55,29 +55,84 @@ namespace one { PyTypeObject* PyTensorObject_Type = NULL; PyTypeObject* PyParameterObject_Type = NULL; +namespace { + +template +struct AllocType {}; +#define DEFINE_ALLOC_TYPE(type) \ + template<> \ + struct AllocType { \ + static PyTypeObject** value; \ + }; \ + PyTypeObject** AllocType::value = &Py##type##Object_Type + +DEFINE_ALLOC_TYPE(Tensor); +DEFINE_ALLOC_TYPE(Parameter); +#undef DEFINE_ALLOC_TYPE + +template +PyObject* PyTensor_wrap(const std::shared_ptr& data, PyTensorObject* bind_pyobj) { + if (!data) { Py_RETURN_NONE; } + PyObject* py_tensor = (PyObject*)data->pyobject(); + if (bind_pyobj == nullptr && py_tensor) { + // Has been wrapped by python before + if (data->owns_pyobj()) { + // PyTensor are not alive in python side, so we flip back the ownership to PyTensor + data->set_owns_pyobj(false); + ((PyTensorObject*)py_tensor)->data = data; + // NOTE: Needn't incref here, because the reference count of py_tensor is already increased + return py_tensor; + } else { + // PyTensor is alive, so we directly incref it and return it + Py_XINCREF(py_tensor); + return py_tensor; + } + } else { + // Has not been wrapped by python before, so we create a new PyTensor and give it the ownership + if (bind_pyobj == nullptr) { + bind_pyobj = (PyTensorObject*)PyTensorObject_Type->tp_alloc(*AllocType::value, 0); + } + bind_pyobj->data = data; + if (py_tensor) { + // If it has bind pyobj, reset the shared_ptr in origin PyTensorObject + ((PyTensorObject*)py_tensor)->data.reset(); + } + bind_pyobj->data->set_pyobject_ptr(std::unique_ptr( + bind_pyobj, [](void* ptr) { Py_DECREF((PyObject*)ptr); })); + bind_pyobj->data->set_owns_pyobj(false); + return (PyObject*)bind_pyobj; + } +} + +bool PyTensor_tryResurrect(PyObject* py_tensor) { + auto* self = (PyTensorObject*)py_tensor; + if (self->data) { + // PyTensor holds the ownership, now we flip it back to C++ and resurrect python object + // temporarily + auto tensor = self->data; + self->data.reset(); + tensor->set_owns_pyobj(true); + Py_XINCREF(py_tensor); + return true; + } + // Otherwise, PyTensor was already not alive in python side + return false; +} + +} // namespace + static int PyTensorObject_init(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS auto* temp = functional::_legacy_tensor_ctor(NULL, args, kwargs); if (PyErr_Occurred()) { throw py::error_already_set(); } - auto* _self = (PyTensorObject*)self; - _self->data = PyTensor_Unpack(temp); - _self->data->set_pyobject(self); - - // reset temp data to prevent clearing the pyobject - // when the temp is deallocated - ((PyTensorObject*)temp)->data.reset(); - Py_XDECREF(temp); + PyTensor_wrap(PyTensor_Unpack(temp), (PyTensorObject*)self); return 0; END_HANDLE_ERRORS_RET(-1) } static void PyTensorObject_dealloc(PyObject* self) { - auto* _self = (PyTensorObject*)self; - // clear pyobject - if (_self->data) { - _self->data->set_pyobject(NULL); - _self->data.reset(); - } + if (PyTensor_tryResurrect(self)) { return; } + // clear __dict__ PyObject** dict_ptr = _PyObject_GetDictPtr(self); if (dict_ptr) { Py_CLEAR(*dict_ptr); } @@ -96,9 +151,9 @@ static int PyParameterObject_init(PyObject* self, PyObject* args, PyObject* kwar return -1; } if (self) { - auto* _self = (PyTensorObject*)self; - _self->data = ASSERT_PTR(Parameter::MakeTensor(PyTensor_Unpack(data), requires_grad)); - _self->data->set_pyobject(self); + PyTensor_wrap( + ASSERT_PTR(Parameter::MakeTensor(PyTensor_Unpack(data), requires_grad)), + (PyTensorObject*)self); } return 0; END_HANDLE_ERRORS_RET(-1) @@ -186,6 +241,16 @@ static PyObject* PyTensorObject_is_pinned(PyObject* self, PyObject* unused) { END_HANDLE_ERRORS } +static PyObject* PyTensorObject_is_floating_point(PyObject* self, PyObject* unused) { + HANDLE_ERRORS + if (PyTensor_Unpack(self)->dtype()->is_floating_point()) { + Py_RETURN_TRUE; + } else { + Py_RETURN_FALSE; + } + END_HANDLE_ERRORS +} + static PyObject* PyTensorObject_requires_grad_(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS int requires_grad = 1; @@ -203,11 +268,7 @@ static PyObject* PyTensorObject_requires_grad_(PyObject* self, PyObject* args, P static PyObject* PyTensorObject_retain_grad(PyObject* self, PyObject* unused) { HANDLE_ERRORS const auto& t = PyTensor_Unpack(self); - if (!t->requires_grad()) { - return PyErr_Format(PyExc_RuntimeError, - "can't retain_grad on Tensor that has requires_grad=False"); - } - ASSERT(t->set_retain_grad(true)); + CHECK_JUST(t->set_retain_grad(true)); Py_RETURN_NONE; END_HANDLE_ERRORS } @@ -226,7 +287,48 @@ static PyObject* PyTensorObject_clone(PyObject* self, PyObject* unused) { static PyObject* PyTensorObject_zero_(PyObject* self, PyObject* unused) { HANDLE_ERRORS - ASSERT(EagerMirroredTensorZeros(PyTensor_Unpack(self))); + ASSERT(EagerLocalTensorZeros(PyTensor_Unpack(self))); + Py_XINCREF(self); + return self; + END_HANDLE_ERRORS +} + +std::vector> RawSbpBToP(Symbol nd_sbp) { + std::vector> new_nd_sbp; + for (const auto& old_sbp : nd_sbp->sbp_parallel()) { + SbpParallel new_sbp = old_sbp; + if (new_sbp.has_broadcast_parallel()) { new_sbp.mutable_partial_sum_parallel(); } + new_nd_sbp.push_back(SymbolOf(new_sbp)); + } + return new_nd_sbp; +} + +static constexpr auto* SbpBToP = DECORATE(&RawSbpBToP, ThreadLocalCached); + +static PyObject* PyTensorObject_zero_grad(PyObject* self, PyObject* args, PyObject* kwargs) { + HANDLE_ERRORS + int set_to_none = 0; + static const char* keywords[2] = {"set_to_none", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|p:_zero_grad_", const_cast(keywords), + &set_to_none)) { + return NULL; + } + const auto& t = PyTensor_Unpack(self); + const auto acc_grad = ASSERT_PTR(t->acc_grad()); + if (acc_grad) { + if (set_to_none) { + ASSERT(t->set_acc_grad(NULL)); + } else { + ASSERT(EagerLocalTensorZeros(acc_grad)); + if (acc_grad->is_global() && acc_grad->is_eager()) { + const auto local_tensor = ASSERT_PTR(functional::GlobalToLocal(acc_grad, false)); + const auto p = ASSERT_PTR(functional::LocalToGlobal( + local_tensor, ASSERT(acc_grad->parallel_desc()), SbpBToP(ASSERT(acc_grad->nd_sbp())), + *acc_grad->shape(), acc_grad->dtype(), false, false)); + ASSERT(acc_grad->set_data(p)); + } + } + } Py_XINCREF(self); return self; END_HANDLE_ERRORS @@ -269,17 +371,35 @@ static PyObject* PyTensorObject_to_numpy(PyObject* self, PyObject* unused) { DataType data_type = t->dtype()->data_type(); switch (data_type) { #define SWITCH_EAGER_TENSOR_TO_NUMPY(cpp_type, of_type) \ - case of_type: return ASSERT(EagerMirroredTensorToNumpy(self)); + case of_type: return ASSERT(EagerLocalTensorToNumpy(self)); OF_PP_FOR_EACH_TUPLE(SWITCH_EAGER_TENSOR_TO_NUMPY, POD_DATA_TYPE_SEQ) - case DataType::kFloat16: return ASSERT(EagerMirroredTensorToNumpy(self)); + case DataType::kFloat16: return ASSERT(EagerLocalTensorToNumpy(self)); default: { - return PyErr_Format(PyExc_RuntimeError, "Invalid datatype"); + return PyErr_Format(PyExc_RuntimeError, + ("Invalid datatype " + DataType_Name(data_type)).data()); } } #undef SWITCH_EAGER_TENSOR_TO_NUMPY END_HANDLE_ERRORS } +static PyObject* PyTensorObject_item(PyObject* self, PyObject* unused) { + HANDLE_ERRORS + const auto& t = PyTensor_Unpack(self); + DataType data_type = t->dtype()->data_type(); + switch (data_type) { +#define CASE_SCALAR_TENSOR_TO_SCALAR(cpp_type, of_type) \ + case of_type: return ASSERT(EagerLocalTensorItem(t)); + OF_PP_FOR_EACH_TUPLE(CASE_SCALAR_TENSOR_TO_SCALAR, POD_AND_HALF_DATA_TYPE_SEQ); + default: { + return PyErr_Format(PyExc_RuntimeError, + ("Invalid datatype " + DataType_Name(data_type)).data()); + } + } +#undef CASE_SCALAR_TENSOR_TO_SCALAR + END_HANDLE_ERRORS +} + static PyObject* PyTensorObject_type(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS const auto& tensor = PyTensor_Unpack(self); @@ -299,6 +419,10 @@ static PyObject* PyTensorObject_type(PyObject* self, PyObject* args, PyObject* k PyTensorType_FromDTypeAndDeviceType(tensor->dtype(), ASSERT(tensor->device())->enum_type()); return PyUnicode_FromString(((PyTensorType*)tensor_type)->name); } + if (PyTensorMetaClass_CheckExact(tensor_type)) { + Optional device = "cpu"; + return PyTensor_New(ASSERT_PTR(functional::To(tensor, device, DType::Float(), /*copy=*/false))); + } if (PyUnicode_Check(tensor_type)) { tensor_type = PyTensorType_FromString(PyUnicode_AsUTF8(tensor_type)); } @@ -319,41 +443,38 @@ static PyObject* PyTensorObject_type(PyObject* self, PyObject* args, PyObject* k END_HANDLE_ERRORS } -#define DEFINE_TENSOR_METHOD(T, type_proto) \ - static PyObject* PyTensorObject__copy_to_numpy_##T(PyObject* self, PyObject* array) { \ - HANDLE_ERRORS \ - ASSERT(CopyBetweenMirroredTensorAndNumpy(PyTensor_Unpack(self), array, \ - BlobNumpyCopyUtil::To, "const", \ - /*block_host_until_done=*/true)); \ - Py_RETURN_NONE; \ - END_HANDLE_ERRORS \ - } \ - static PyObject* PyTensorObject__copy_from_numpy_##T(PyObject* self, PyObject* array) { \ - HANDLE_ERRORS \ - auto* copied = PyArray_NewCopy((PyArrayObject*)array, NPY_CORDER); \ - ASSERT(CopyBetweenMirroredTensorAndNumpy(PyTensor_Unpack(self), copied, \ - BlobNumpyCopyUtil::From, "mut", \ - /*block_host_until_done=*/false)); \ - Py_DECREF(copied); \ - Py_RETURN_NONE; \ - END_HANDLE_ERRORS \ - } -OF_PP_FOR_EACH_TUPLE(DEFINE_TENSOR_METHOD, POD_DATA_TYPE_SEQ) -#undef DEFINE_TENSOR_METHOD +namespace { +void CopyFromNumpyArray(ep::Stream* stream, + const std::shared_ptr& eager_blob_object, + const NumPyArrayPtr& array_ptr) { + SyncAutoMemcpy(stream, eager_blob_object->mut_dptr(), array_ptr.data(), + eager_blob_object->ByteSizeOfBlobBody(), eager_blob_object->mem_case(), + memory::MakeHostMemCase()); +} -static PyObject* PyTensorObject__get_copy_mirrored_tensor_to_numpy_func_name(PyObject* self, - PyObject* unused) { +void CopyToNumpyArray(ep::Stream* stream, + const std::shared_ptr& eager_blob_object, + const NumPyArrayPtr& array_ptr) { + SyncAutoMemcpy(stream, array_ptr.data(), eager_blob_object->dptr(), + eager_blob_object->ByteSizeOfBlobBody(), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); +} +} // namespace + // +static PyObject* PyTensorObject__copy_to_numpy(PyObject* self, PyObject* array) { HANDLE_ERRORS - return functional::CastToPyObject( - GetCopyMirroredTensorToNumpyFuncName(PyTensor_Unpack(self)->dtype()->data_type())); + ASSERT(CopyBetweenLocalTensorAndNumpy(PyTensor_Unpack(self), array, CopyToNumpyArray, "const", + /*block_host_until_done=*/true)); + Py_RETURN_NONE; END_HANDLE_ERRORS } - -static PyObject* PyTensorObject__get_copy_mirrored_tensor_from_numpy_func_name(PyObject* self, - PyObject* unused) { +static PyObject* PyTensorObject__copy_from_numpy(PyObject* self, PyObject* array) { HANDLE_ERRORS - return functional::CastToPyObject( - GetCopyMirroredTensorFromNumpyFuncName(PyTensor_Unpack(self)->dtype()->data_type())); + auto* copied = PyArray_NewCopy((PyArrayObject*)array, NPY_CORDER); + ASSERT(CopyBetweenLocalTensorAndNumpy(PyTensor_Unpack(self), copied, CopyFromNumpyArray, "mut", + /*block_host_until_done=*/false)); + Py_DECREF(copied); + Py_RETURN_NONE; END_HANDLE_ERRORS } @@ -388,28 +509,24 @@ static PyMethodDef PyTensorObject_methods[] = { {"contiguous_", PyTensorObject_contiguous_, METH_NOARGS, NULL}, {"pin_memory", PyTensorObject_pin_memory, METH_NOARGS, NULL}, {"is_pinned", PyTensorObject_is_pinned, METH_NOARGS, NULL}, + {"is_floating_point", PyTensorObject_is_floating_point, METH_NOARGS, NULL}, {"requires_grad_", (PyCFunction)PyTensorObject_requires_grad_, METH_VARARGS | METH_KEYWORDS, NULL}, {"retain_grad", PyTensorObject_retain_grad, METH_NOARGS, NULL}, {"detach", PyTensorObject_detach, METH_NOARGS, NULL}, {"clone", PyTensorObject_clone, METH_NOARGS, NULL}, {"zero_", PyTensorObject_zero_, METH_NOARGS, NULL}, + {"_zero_grad_", (PyCFunction)PyTensorObject_zero_grad, METH_VARARGS | METH_KEYWORDS, NULL}, {"register_hook", PyTensorObject_register_hook, METH_O, NULL}, {"_register_post_grad_accumulation_hook", PyTensorObject__register_post_grad_accumulation_hook, METH_O, NULL}, {"global_id", PyTensorObject_global_id, METH_NOARGS, NULL}, {"check_meta_consistency", PyTensorObject_check_meta_consistency, METH_NOARGS, NULL}, {"to_numpy", PyTensorObject_to_numpy, METH_NOARGS, NULL}, + {"item", PyTensorObject_item, METH_NOARGS, NULL}, {"type", (PyCFunction)PyTensorObject_type, METH_VARARGS | METH_KEYWORDS, NULL}, -#define DEFINE_TENSOR_METHOD(T, type_proto) \ - {"_copy_to_numpy_" #T, PyTensorObject__copy_to_numpy_##T, METH_O, NULL}, \ - {"_copy_from_numpy_" #T, PyTensorObject__copy_from_numpy_##T, METH_O, NULL}, - OF_PP_FOR_EACH_TUPLE(DEFINE_TENSOR_METHOD, POD_DATA_TYPE_SEQ) -#undef DEFINE_TENSOR_METHOD - {"_get_copy_mirrored_tensor_to_numpy_func_name", - PyTensorObject__get_copy_mirrored_tensor_to_numpy_func_name, METH_NOARGS, NULL}, - {"_get_copy_mirrored_tensor_from_numpy_func_name", - PyTensorObject__get_copy_mirrored_tensor_from_numpy_func_name, METH_NOARGS, NULL}, + {"_copy_to_numpy", PyTensorObject__copy_to_numpy, METH_O, NULL}, + {"_copy_from_numpy", PyTensorObject__copy_from_numpy, METH_O, NULL}, {"_register_storage_delete_hook", PyTensorObject__register_storage_delete_hook, METH_O, NULL}, {NULL}}; @@ -451,16 +568,6 @@ static int PyTensorObject_set_grad(PyObject* self, PyObject* grad, void* unused) END_HANDLE_ERRORS_RET(-1) } -static PyObject* PyTensorObject__is_grad_acc_inplace(PyObject* self, void* unused) { - return functional::CastToPyObject(PyTensor_Unpack(self)->autograd_meta()->is_grad_acc_inplace()); -} - -static int PyTensorObject_set__is_grad_acc_inplace(PyObject* self, PyObject* is_inplace, - void* unused) { - PyTensor_Unpack(self)->mut_autograd_meta()->set_is_grad_acc_inplace(is_inplace); - return 0; -} - static PyObject* PyTensorObject_data(PyObject* self, void* unused) { HANDLE_ERRORS return PyTensor_New(ASSERT_PTR(PyTensor_Unpack(self)->data())); @@ -509,7 +616,7 @@ static PyObject* PyTensorObject_is_eager(PyObject* self, void* unused) { } static PyObject* PyTensorObject_is_global(PyObject* self, void* unused) { - return functional::CastToPyObject(PyTensor_Unpack(self)->is_consistent()); + return functional::CastToPyObject(PyTensor_Unpack(self)->is_global()); } static PyObject* PyTensorObject_is_local(PyObject* self, void* unused) { @@ -548,8 +655,6 @@ static PyGetSetDef PyTensorObject_properties[] = { {PYGETSET_NAME("is_cuda"), (getter)PyTensorObject_is_cuda, NULL, NULL, NULL}, {PYGETSET_NAME("grad"), (getter)PyTensorObject_grad, (setter)PyTensorObject_set_grad, NULL, NULL}, - {PYGETSET_NAME("_is_grad_acc_inplace"), (getter)PyTensorObject__is_grad_acc_inplace, - (setter)PyTensorObject_set__is_grad_acc_inplace, NULL, NULL}, {PYGETSET_NAME("data"), (getter)PyTensorObject_data, (setter)PyTensorObject_set_data, NULL, NULL}, {PYGETSET_NAME("grad_fn"), (getter)PyTensorObject_grad_fn, NULL, NULL, NULL}, @@ -657,35 +762,17 @@ static PyTypeObject* MakeParameterType() { } PyObject* PyTensor_New(const std::shared_ptr& data) { - if (!data) { Py_RETURN_NONE; } - if (data->pyobject()) { return PY_XINCREF((PyObject*)(data->pyobject())); } - auto* self = (PyTensorObject*)PyTensorObject_Type->tp_alloc(PyTensorObject_Type, 0); - if (self) { - self->data = data; - self->data->set_pyobject(self); - } - return (PyObject*)self; + return PyTensor_wrap(data, /*bind_pyobj=*/nullptr); } PyObject* PyParameter_New(const std::shared_ptr& data) { - if (!data) { Py_RETURN_NONE; } - if (data->pyobject()) { return PY_XINCREF((PyObject*)(data->pyobject())); } - auto* self = (PyTensorObject*)PyTensorObject_Type->tp_alloc(PyParameterObject_Type, 0); - if (self) { - self->data = data; - self->data->set_pyobject(self); - } - return (PyObject*)self; + return PyTensor_wrap(data, /*bind_pyobj=*/nullptr); } PyObject* PyParameter_New(const std::shared_ptr& data, bool requires_grad) { if (!data) { Py_RETURN_NONE; } - auto* self = (PyTensorObject*)PyTensorObject_Type->tp_alloc(PyParameterObject_Type, 0); - if (self) { - self->data = ASSERT_PTR(Parameter::MakeTensor(data, requires_grad)); - self->data->set_pyobject(self); - } - return (PyObject*)self; + return PyTensor_wrap(ASSERT_PTR(Parameter::MakeTensor(data, requires_grad)), + /*bind_pyobj=*/nullptr); } } // namespace one diff --git a/oneflow/api/python/framework/tensor.h b/oneflow/api/python/framework/tensor.h index e919b8273b6539f74eaf7c1fc84ef167c63ba165..72e6b33b3a37d4e73ed9da8b949155e13657b1d1 100644 --- a/oneflow/api/python/framework/tensor.h +++ b/oneflow/api/python/framework/tensor.h @@ -31,6 +31,10 @@ typedef struct { extern PyTypeObject* PyTensorObject_Type; extern PyTypeObject* PyParameterObject_Type; +inline bool PyTensorMetaClass_CheckExact(PyObject* obj) { + return obj == (PyObject*)PyTensorObject_Type; +} + inline bool PyTensor_Check(PyObject* op) { return PyObject_TypeCheck(op, PyTensorObject_Type); } inline bool PyTensor_CheckExact(PyObject* op) { diff --git a/oneflow/api/python/framework/tensor_functions.cpp b/oneflow/api/python/framework/tensor_functions.cpp index 858336ed05d9e76be7377e77fdb4890ab10c41c0..519db057a91275139d0cc4d80984eb4d30e6c6c7 100644 --- a/oneflow/api/python/framework/tensor_functions.cpp +++ b/oneflow/api/python/framework/tensor_functions.cpp @@ -23,6 +23,10 @@ limitations under the License. #include "oneflow/core/functional/functional.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/common/wrap_dim_utils.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/api/python/functional/tensor_api.yaml.h" +#include "oneflow/extension/python/numpy.h" +#include "oneflow/api/python/utils/tensor_utils.h" namespace oneflow { namespace one { @@ -31,14 +35,35 @@ namespace one { #define ASSERT_PTR(x) (x).GetPtrOrThrow() using functional::PyObjectPtr; - -static PyObject* concat_self(PyObject* self, PyObject* args) { +namespace { +PyObject* concat_self(PyObject* self, PyObject* args) { PyObjectPtr self_tuple(PyTuple_Pack(1, self)); PyObject* tuple = PySequence_Concat(self_tuple.get(), args); CHECK_OR_THROW(tuple != NULL); return tuple; } +PyObject* ndarray_judgment_and_compatibility(PyObject* self, PyObject* other) { + if (PyArray_Check(other)) { + const auto& tensor = PyTensor_Unpack(self); + CHECK_OR_THROW(!tensor->is_cuda()) + << Error::RuntimeError() << "Can't convert cuda device type tensor to numpy"; + if (tensor->is_global()) { + Symbol placement = ASSERT(tensor->parallel_desc()); + auto ndsbp = ASSERT(tensor->nd_sbp()); + std::vector> sbp(ndsbp->sbp_parallel_size(), + ASSERT(MakeBroadcastSbpParallel())); + other = functional::CastToPyObject(MakeGlobalTensorFromData(other, tensor->dtype(), placement, + sbp, /*requires_grad=*/false)); + } else { + other = functional::CastToPyObject(functional::LocalTensorSharedNumpyData(other)); + } + } + return other; +} + +} // namespace + #define NB_UNARY_FUNC(func_name, bind_func) \ static PyObject* func_name(PyObject* self) { \ HANDLE_ERRORS \ @@ -52,12 +77,13 @@ static PyObject* concat_self(PyObject* self, PyObject* args) { #define NB_BINARY_FUNC(func_name, bind_func) \ static PyObject* func_name(PyObject* a, PyObject* b) { \ HANDLE_ERRORS \ + b = ndarray_judgment_and_compatibility(a, b); \ PyObjectPtr tuple(PyTuple_Pack(2, a, b)); \ auto* result = bind_func(NULL, tuple.get(), NULL); \ if (PyErr_Occurred()) { throw py::error_already_set(); } \ return result; \ END_HANDLE_ERRORS \ - } + } // namespace one NB_UNARY_FUNC(PyTensorObject_nb_absolute, functional::abs); NB_UNARY_FUNC(PyTensorObject_nb_negative, functional::negative); @@ -76,8 +102,9 @@ NB_BINARY_FUNC(PyTensorObject_nb_floor_div, functional::floor_divide); NB_BINARY_FUNC(PyTensorObject_nb_true_div, functional::div); NB_BINARY_FUNC(PyTensorObject_nb_matrix_multiply, functional::matmul); -static PyObject* PyTensorObject_nb_pow(PyObject* a, PyObject* b, PyObject* unsed) { +static PyObject* PyTensorObject_nb_pow(PyObject* a, PyObject* b, PyObject* unused) { HANDLE_ERRORS + b = ndarray_judgment_and_compatibility(a, b); PyObjectPtr tuple(PyTuple_Pack(2, a, b)); PyObject* result = functional::pow(NULL, tuple.get(), NULL); if (PyErr_Occurred()) { throw py::error_already_set(); } @@ -99,6 +126,7 @@ static PyObject* PyTensorObject_nb_invert(PyObject* self) { #define NB_INPLACE_BINARY_FUNC(func_name, bind_func) \ static PyObject* func_name(PyObject* a, PyObject* b) { \ HANDLE_ERRORS \ + b = ndarray_judgment_and_compatibility(a, b); \ PyObjectPtr tuple(PyTuple_Pack(2, a, b)); \ PyObjectPtr dict(PyDict_New()); \ CHECK_OR_THROW(PyDict_SetItemString(dict.get(), "inplace", Py_True) > -1); \ @@ -115,7 +143,7 @@ NB_INPLACE_BINARY_FUNC(PyTensorObject_nb_inplace_sub, functional::sub); NB_BINARY_FUNC(PyTensorObject_nb_inplace_mul, functional::mul_); NB_BINARY_FUNC(PyTensorObject_nb_inplace_true_div, functional::div_); -PyObject* PyTensorObject_nb_inplace_pow(PyObject* a, PyObject* b, PyObject* unsed) { +PyObject* PyTensorObject_nb_inplace_pow(PyObject* a, PyObject* b, PyObject* unused) { HANDLE_ERRORS PyObjectPtr tuple(PyTuple_Pack(2, a, b)); PyObjectPtr dict(PyDict_New()); @@ -193,6 +221,7 @@ UNARY_METHOD(PyTensorObject_selu, functional::Selu); UNARY_METHOD(PyTensorObject_softsign, functional::SoftSign); UNARY_METHOD(PyTensorObject_log1p, functional::Log1p); UNARY_METHOD(PyTensorObject_log2, functional::Log2); +UNARY_METHOD(PyTensorObject_log10, functional::Log10); UNARY_METHOD(PyTensorObject_reciprocal, functional::Reciprocal); UNARY_METHOD(PyTensorObject_ceil, functional::Ceil); UNARY_METHOD(PyTensorObject_erf, functional::Erf); @@ -246,6 +275,7 @@ DIRECT_PASS_FUNC(PyTensorObject_fmod, functional::fmod) DIRECT_PASS_FUNC(PyTensorObject_logical_and, functional::logical_and) DIRECT_PASS_FUNC(PyTensorObject_logical_or, functional::logical_or) DIRECT_PASS_FUNC(PyTensorObject_logical_xor, functional::logical_xor) +DIRECT_PASS_FUNC(PyTensorObject_equal, functional::equal) DIRECT_PASS_FUNC(PyTensorObject_ne, functional::not_equal) DIRECT_PASS_FUNC(PyTensorObject_lt, functional::less) DIRECT_PASS_FUNC(PyTensorObject_le, functional::less_equal) @@ -256,17 +286,26 @@ DIRECT_PASS_FUNC(PyTensorObject_amin, functional::amin) DIRECT_PASS_FUNC(PyTensorObject_amax, functional::amax) DIRECT_PASS_FUNC(PyTensorObject_addcmul, functional::addcmul) DIRECT_PASS_FUNC(PyTensorObject_addcmul_, functional::addcmul_) +DIRECT_PASS_FUNC(PyTensorObject_addcdiv, functional::addcdiv) +DIRECT_PASS_FUNC(PyTensorObject_addcdiv_, functional::addcdiv_) +DIRECT_PASS_FUNC(PyTensorObject_flip, functional::flip) DIRECT_PASS_FUNC(PyTensorObject_clip, functional::clip) DIRECT_PASS_FUNC(PyTensorObject_clip_, functional::clip_) DIRECT_PASS_FUNC(PyTensorObject_clamp, functional::clamp) +DIRECT_PASS_FUNC(PyTensorObject_clamp_min, functional::clamp_min) +DIRECT_PASS_FUNC(PyTensorObject_clamp_max, functional::clamp_max) DIRECT_PASS_FUNC(PyTensorObject_clamp_, functional::clamp_) +DIRECT_PASS_FUNC(PyTensorObject_clamp_min_, functional::clamp_min_) +DIRECT_PASS_FUNC(PyTensorObject_clamp_max_, functional::clamp_max_) DIRECT_PASS_FUNC(PyTensorObject_flatten, functional::flatten) DIRECT_PASS_FUNC(PyTensorObject_in_top_k, functional::in_top_k) DIRECT_PASS_FUNC(PyTensorObject_index_select, functional::index_select) +DIRECT_PASS_FUNC(PyTensorObject_logsumexp, functional::logsumexp) DIRECT_PASS_FUNC(PyTensorObject_maximum, functional::maximum) DIRECT_PASS_FUNC(PyTensorObject_minimum, functional::minimum) DIRECT_PASS_FUNC(PyTensorObject_tril, functional::tril) DIRECT_PASS_FUNC(PyTensorObject_triu, functional::triu) +DIRECT_PASS_FUNC(PyTensorObject_triu_, functional::triu_) DIRECT_PASS_FUNC(PyTensorObject_softmax, functional::softmax) DIRECT_PASS_FUNC(PyTensorObject_log_softmax, functional::log_softmax) DIRECT_PASS_FUNC(PyTensorObject_roll, functional::roll) @@ -281,9 +320,19 @@ DIRECT_PASS_FUNC(PyTensorObject_min, functional::min) DIRECT_PASS_FUNC(PyTensorObject_median, functional::median) DIRECT_PASS_FUNC(PyTensorObject_pow, functional::pow) DIRECT_PASS_FUNC(PyTensorObject_chunk, functional::chunk) +DIRECT_PASS_FUNC(PyTensorObject_split, functional::split) DIRECT_PASS_FUNC(PyTensorObject_narrow, functional::narrow) DIRECT_PASS_FUNC(PyTensorObject_masked_fill, functional::masked_fill) +DIRECT_PASS_FUNC(PyTensorObject_masked_fill_, functional::masked_fill_) DIRECT_PASS_FUNC(PyTensorObject_dot, functional::dot) +DIRECT_PASS_FUNC(PyTensorObject_nansum, functional::reduce_nansum) +DIRECT_PASS_FUNC(PyTensorObject_bernoulli, functional::bernoulli) +DIRECT_PASS_FUNC(PyTensorObject_bernoulli_, functional::bernoulli_) +DIRECT_PASS_FUNC(PyTensorObject_bincount, functional::bincount) +DIRECT_PASS_FUNC(PyTensorObject_isclose, functional::isclose) +DIRECT_PASS_FUNC(PyTensorObject_broadcast_to, functional::broadcast_to) +DIRECT_PASS_FUNC(PyTensorObject_unique, functional::unique) +DIRECT_PASS_FUNC(PyTensorObject_topk, functional::topk) // functions that parsing at Python C api layer static PyObject* PyTensorObject_byte(PyObject* self, PyObject* unused) { @@ -569,11 +618,13 @@ REDUCE_FUNC(PyTensorObject_mean, functional::reduce_mean, functional::ReduceMean END_HANDLE_ERRORS \ } +DATATYPE_FUNC(PyTensorObject_bool, DType::Bool()); DATATYPE_FUNC(PyTensorObject_int, DType::Int32()); DATATYPE_FUNC(PyTensorObject_long, DType::Int64()); DATATYPE_FUNC(PyTensorObject_half, DType::Float16()); DATATYPE_FUNC(PyTensorObject_float, DType::Float()); DATATYPE_FUNC(PyTensorObject_double, DType::Double()); +DATATYPE_FUNC(PyTensorObject_bfloat16, DType::BFloat16()); static PyObject* PyTensorObject_view(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS @@ -639,16 +690,20 @@ static PyObject* PyTensorObject_local_to_global(PyObject* self, PyObject* args, CHECK_OR_THROW(tensor->is_local()) << Error::RuntimeError() << "input must be a local tensor"; PyObject* placement_obj = Py_None; PyObject* sbp_obj = Py_None; - bool check_meta = true; - static const char* keywords[4] = {"placement", "sbp", "check_meta", NULL}; - if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|OO$O!:local_to_global", + PyObject* check_meta_obj = Py_True; + PyObject* copy_obj = Py_False; + static const char* keywords[5] = {"placement", "sbp", "check_meta", "copy", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|OO$O!O!:local_to_global", const_cast(keywords), &placement_obj, &sbp_obj, - &PyBool_Type, &check_meta)) { + &PyBool_Type, &check_meta_obj, &PyBool_Type, ©_obj)) { return NULL; - }; + } + const bool check_meta = (check_meta_obj == Py_True); + const bool copy = (copy_obj == Py_True); - CHECK_OR_THROW(placement_obj != Py_None && sbp_obj != Py_None) << Error::InvalidValueError( - "Converting a local tensor to global tensor must have placement and sbp parameters."); + CHECK_OR_THROW(placement_obj != Py_None && sbp_obj != Py_None) + << Error::InvalidValueError() + << "Converting a local tensor to global tensor must have placement and sbp parameters."; CHECK_OR_THROW(functional::PyParallelDescCheck(placement_obj)) << Error::TypeError() << "Invalid parameter placement with type " << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(placement_obj))); @@ -662,29 +717,31 @@ static PyObject* PyTensorObject_local_to_global(PyObject* self, PyObject* args, << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(sbp_obj))); sbp = functional::PyUnpackSbpParallelSequence(sbp_obj); } - return PyTensor_New(ASSERT_PTR(functional::ToConsistent( - tensor, functional::PyUnpackParallelDesc(placement_obj), sbp, {}, check_meta))); + return PyTensor_New(ASSERT_PTR(functional::ToGlobal( + tensor, functional::PyUnpackParallelDesc(placement_obj), sbp, {}, check_meta, copy))); END_HANDLE_ERRORS } static PyObject* PyTensorObject_global_to_global(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS auto tensor = PyTensor_Unpack(self); - CHECK_OR_THROW(tensor->is_consistent()) - << Error::RuntimeError() << "input must be a global tensor"; + CHECK_OR_THROW(tensor->is_global()) << Error::RuntimeError() << "input must be a global tensor"; PyObject* placement_obj = Py_None; PyObject* sbp_obj = Py_None; PyObject* grad_sbp_obj = Py_None; Symbol placement; std::vector> sbp; std::vector> grad_sbp; - bool check_meta = false; - static const char* keywords[5] = {"placement", "sbp", "grad_sbp", "check_meta", NULL}; - if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|OO$OO!:global_to_global", + PyObject* check_meta_obj = Py_False; + PyObject* copy_obj = Py_False; + static const char* keywords[6] = {"placement", "sbp", "grad_sbp", "check_meta", "copy", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|OO$OO!O!:global_to_global", const_cast(keywords), &placement_obj, &sbp_obj, - &grad_sbp_obj, &PyBool_Type, &check_meta)) { + &grad_sbp_obj, &PyBool_Type, &check_meta_obj, ©_obj)) { return NULL; - }; + } + const bool check_meta = (check_meta_obj == Py_True); + const bool copy = (copy_obj == Py_True); // sbp CHECK_OR_THROW(sbp_obj == Py_None || functional::PySbpParallelCheck(sbp_obj) @@ -721,7 +778,7 @@ static PyObject* PyTensorObject_global_to_global(PyObject* self, PyObject* args, grad_sbp = functional::PyUnpackSbpParallelSequence(grad_sbp_obj); } return PyTensor_New( - ASSERT_PTR(functional::ToConsistent(tensor, placement, sbp, grad_sbp, check_meta))); + ASSERT_PTR(functional::ToGlobal(tensor, placement, sbp, grad_sbp, check_meta, copy))); END_HANDLE_ERRORS } @@ -729,7 +786,7 @@ static PyObject* PyTensorObject_to_global(PyObject* self, PyObject* args, PyObje HANDLE_ERRORS const auto& tensor = PyTensor_Unpack(self); PyObject* result = NULL; - if (tensor->is_consistent()) + if (tensor->is_global()) result = PyTensorObject_global_to_global(self, args, kwargs); else { result = PyTensorObject_local_to_global(self, args, kwargs); @@ -740,57 +797,116 @@ static PyObject* PyTensorObject_to_global(PyObject* self, PyObject* args, PyObje END_HANDLE_ERRORS } -static PyObject* PyTensorObject_to_local(PyObject* self, PyObject* unused) { +static PyObject* PyTensorObject_to_local(PyObject* self, PyObject* unused, PyObject* kwargs) { HANDLE_ERRORS auto tensor = PyTensor_Unpack(self); - CHECK_OR_THROW(tensor->is_consistent()) + CHECK_OR_THROW(tensor->is_global()) << Error::RuntimeError() << "Expected global tensor for to_local but got local tensor!"; - return PyTensor_New(ASSERT_PTR(functional::ConsistentToLocal(tensor))); + bool copy = false; + static const char* keywords[2] = {"copy", NULL}; + if (!PyArg_ParseTupleAndKeywords(unused, kwargs, "|$O!:to_local", const_cast(keywords), + &PyBool_Type, ©)) { + return NULL; + }; + return PyTensor_New(ASSERT_PTR(functional::GlobalToLocal(tensor, /*copy=*/copy))); END_HANDLE_ERRORS } -int PyTensorObject_setitem(PyObject* self, PyObject* item, PyObject* value) { +static PyObject* PyTensorObject_type_as(PyObject* self, PyObject* args, PyObject* kwargs) { HANDLE_ERRORS - auto tensor = PyTensor_Unpack(self); + auto self_tensor = PyTensor_Unpack(self); + PyObject* other = NULL; + static const char* keywords[2] = {"other", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|:type_as", const_cast(keywords), + &other)) { + return NULL; + } + + // target is local + auto other_tensor = PyTensor_Unpack(other); + if (other_tensor->is_local()) { + Optional> device = ASSERT(other_tensor->device()); + if (self_tensor->is_global()) { + self_tensor = ASSERT_PTR(functional::GlobalToLocal(self_tensor, /*copy=*/false)); + } + return PyTensor_New( + ASSERT_PTR(functional::To(self_tensor, device, other_tensor->dtype(), /*copy=*/false))); + } + + // target is global std::shared_ptr value_tensor; + value_tensor = ASSERT_PTR(functional::To(self_tensor, other_tensor->dtype(), /*copy=*/false)); + Symbol placement = ASSERT(other_tensor->parallel_desc()); + std::vector> sbp; + auto ndsbp = ASSERT(other_tensor->nd_sbp()); + for (int32_t i = 0; i < ndsbp->sbp_parallel_size(); i++) { + sbp.emplace_back(ndsbp->sbp_parallel(i)); + } + return PyTensor_New( + ASSERT_PTR(functional::ToGlobal(value_tensor, placement, sbp, {}, true, /*copy=*/false))); + END_HANDLE_ERRORS +} + +int PyTensorObject_setitem(PyObject* self, PyObject* item, PyObject* value) { + HANDLE_ERRORS CHECK_OR_THROW(functional::PyTensorIndexCheck(item)) << Error::TypeError() << "tensor_setitem(): argument 'index' must be index, not " << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(item))); CHECK_OR_THROW(functional::PyScalarCheck(value) || PyTensor_Check(value)) << Error::TypeError() << "tensor_setitem(): argument 'value' must be tensor or scalar, not " << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(value))); + const auto& index_item = functional::PyUnpackTensorIndex(item); - if (tensor->is_consistent()) { - Symbol placement = ASSERT(tensor->parallel_desc()); - auto ndsbp = ASSERT(tensor->nd_sbp()); - std::vector> sbp(ndsbp->sbp_parallel_size(), - ASSERT(MakeBroadcastSbpParallel())); - if (functional::PyScalarCheck(value)) { - Scalar value_scalar = functional::PyUnpackScalar(value); - value_tensor = ASSERT_PTR( - functional::ConsistentConstant({1}, value_scalar, tensor->dtype(), placement, sbp)); - } else { - value_tensor = PyTensor_Unpack(value); - CHECK_OR_THROW(value_tensor->is_consistent()) - << Error::RuntimeError() - << "tensor_setitem(): value must be a global tensor when self is global"; - value_tensor = ASSERT_PTR(functional::ToConsistent(value_tensor, placement, sbp, {}, true)); + auto tensor = PyTensor_Unpack(self); + // NOTE: use masked_fill_(local,global) to avoid D2H in TensorSetItem if index is bool tensor + if (functional::PyScalarCheck(value) && index_item.size() == 1 && index_item[0].IsTensor()) { + const auto& index_tensor = index_item[0].tensor(); + if (index_tensor->shape() == tensor->shape() + && (index_tensor->dtype() == DType::Bool() || index_tensor->dtype() == DType::UInt8())) { + ASSERT_PTR( + functional::MaskedFillInplace(tensor, index_tensor, functional::PyUnpackScalar(value))); + return 0; } - } else { - if (functional::PyScalarCheck(value)) { - Scalar value_scalar = functional::PyUnpackScalar(value); - value_tensor = ASSERT_PTR( - functional::Constant({1}, value_scalar, tensor->dtype(), ASSERT(tensor->device()))); + } + + std::shared_ptr value_tensor; + { + if (tensor->is_global()) { + Symbol placement = ASSERT(tensor->parallel_desc()); + auto ndsbp = ASSERT(tensor->nd_sbp()); + std::vector> sbp(ndsbp->sbp_parallel_size(), + ASSERT(MakeBroadcastSbpParallel())); + if (functional::PyScalarCheck(value)) { + Scalar value_scalar = functional::PyUnpackScalar(value); + value_tensor = ASSERT_PTR( + functional::GlobalConstant(Shape({}), value_scalar, tensor->dtype(), placement, sbp)); + } else { + value_tensor = PyTensor_Unpack(value); + CHECK_OR_THROW(value_tensor->is_global()) + << Error::RuntimeError() + << "tensor_setitem(): value must be a global tensor when self is global"; + value_tensor = ASSERT_PTR( + functional::ToGlobal(value_tensor, placement, sbp, {}, true, /*copy=*/false)); + } } else { - value_tensor = PyTensor_Unpack(value); - CHECK_OR_THROW(value_tensor->is_local()) - << Error::RuntimeError() - << "tensor_setitem(): value must be a local tensor when self is local"; - Optional> device = ASSERT(tensor->device()); - value_tensor = ASSERT_PTR(functional::To(value_tensor, device, value_tensor->dtype(), false)); + if (functional::PyScalarCheck(value)) { + // NOTE: initialize value_tensor in eager mode + LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled=*/false); + Scalar value_scalar = functional::PyUnpackScalar(value); + value_tensor = ASSERT_PTR(functional::Constant(Shape({}), value_scalar, tensor->dtype(), + ASSERT(tensor->device()))); + } else { + value_tensor = PyTensor_Unpack(value); + CHECK_OR_THROW(value_tensor->is_local()) + << Error::RuntimeError() + << "tensor_setitem(): value must be a local tensor when self is local"; + Optional> device = ASSERT(tensor->device()); + value_tensor = + ASSERT_PTR(functional::To(value_tensor, device, value_tensor->dtype(), false)); + } } } - ASSERT(functional::TensorSetItem(tensor, functional::PyUnpackTensorIndex(item), value_tensor)); + ASSERT(functional::TensorSetItem(tensor, index_item, value_tensor)); return 0; END_HANDLE_ERRORS_RET(-1) } @@ -812,18 +928,23 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"diagonal", (PyCFunction)PyTensorObject_diagonal, METH_VARARGS | METH_KEYWORDS, NULL}, {"addcmul", (PyCFunction)PyTensorObject_addcmul, METH_VARARGS | METH_KEYWORDS, NULL}, {"addcmul_", (PyCFunction)PyTensorObject_addcmul_, METH_VARARGS | METH_KEYWORDS, NULL}, + {"addcdiv", (PyCFunction)PyTensorObject_addcdiv, METH_VARARGS | METH_KEYWORDS, NULL}, + {"addcdiv_", (PyCFunction)PyTensorObject_addcdiv_, METH_VARARGS | METH_KEYWORDS, NULL}, {"matmul", (PyCFunction)PyTensorObject_matmul, METH_VARARGS | METH_KEYWORDS, NULL}, + {"bool", PyTensorObject_bool, METH_NOARGS, NULL}, {"int", PyTensorObject_int, METH_NOARGS, NULL}, {"long", PyTensorObject_long, METH_NOARGS, NULL}, {"half", PyTensorObject_half, METH_NOARGS, NULL}, {"float", PyTensorObject_float, METH_NOARGS, NULL}, {"double", PyTensorObject_double, METH_NOARGS, NULL}, + {"bfloat16", PyTensorObject_bfloat16, METH_NOARGS, NULL}, {"local_to_global", (PyCFunction)PyTensorObject_local_to_global, METH_VARARGS | METH_KEYWORDS, NULL}, {"global_to_global", (PyCFunction)PyTensorObject_global_to_global, METH_VARARGS | METH_KEYWORDS, NULL}, - {"to_local", PyTensorObject_to_local, METH_NOARGS, NULL}, + {"to_local", (PyCFunction)PyTensorObject_to_local, METH_VARARGS | METH_KEYWORDS, NULL}, {"to_global", (PyCFunction)PyTensorObject_to_global, METH_VARARGS | METH_KEYWORDS, NULL}, + {"type_as", (PyCFunction)PyTensorObject_type_as, METH_VARARGS | METH_KEYWORDS, NULL}, {"cpu", PyTensorObject_cpu, METH_NOARGS, NULL}, {"cuda", (PyCFunction)PyTensorObject_cuda, METH_VARARGS | METH_KEYWORDS, NULL}, {"var", (PyCFunction)PyTensorObject_var, METH_VARARGS | METH_KEYWORDS, NULL}, @@ -839,6 +960,7 @@ PyMethodDef PyTensorObject_extra_methods[] = { // macro DIRECT_PASS_FUNC {"floor_divide", (PyCFunction)PyTensorObject_floor_divide, METH_VARARGS | METH_KEYWORDS, NULL}, {"atan2", (PyCFunction)PyTensorObject_atan2, METH_VARARGS | METH_KEYWORDS, NULL}, + {"equal", (PyCFunction)PyTensorObject_equal, METH_VARARGS | METH_KEYWORDS, NULL}, {"gt", (PyCFunction)PyTensorObject_gt, METH_VARARGS | METH_KEYWORDS, NULL}, {"ge", (PyCFunction)PyTensorObject_ge, METH_VARARGS | METH_KEYWORDS, NULL}, {"div", (PyCFunction)PyTensorObject_div, METH_VARARGS | METH_KEYWORDS, NULL}, @@ -853,10 +975,15 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"ne", (PyCFunction)PyTensorObject_ne, METH_VARARGS | METH_KEYWORDS, NULL}, {"lt", (PyCFunction)PyTensorObject_lt, METH_VARARGS | METH_KEYWORDS, NULL}, {"le", (PyCFunction)PyTensorObject_le, METH_VARARGS | METH_KEYWORDS, NULL}, + {"flip", (PyCFunction)PyTensorObject_flip, METH_VARARGS | METH_KEYWORDS, NULL}, {"clip", (PyCFunction)PyTensorObject_clip, METH_VARARGS | METH_KEYWORDS, NULL}, {"clip_", (PyCFunction)PyTensorObject_clip_, METH_VARARGS | METH_KEYWORDS, NULL}, {"clamp", (PyCFunction)PyTensorObject_clamp, METH_VARARGS | METH_KEYWORDS, NULL}, + {"clamp_min", (PyCFunction)PyTensorObject_clamp_min, METH_VARARGS | METH_KEYWORDS, NULL}, + {"clamp_max", (PyCFunction)PyTensorObject_clamp_max, METH_VARARGS | METH_KEYWORDS, NULL}, {"clamp_", (PyCFunction)PyTensorObject_clamp_, METH_VARARGS | METH_KEYWORDS, NULL}, + {"clamp_min_", (PyCFunction)PyTensorObject_clamp_min_, METH_VARARGS | METH_KEYWORDS, NULL}, + {"clamp_max_", (PyCFunction)PyTensorObject_clamp_max_, METH_VARARGS | METH_KEYWORDS, NULL}, {"flatten", (PyCFunction)PyTensorObject_flatten, METH_VARARGS | METH_KEYWORDS, NULL}, {"in_top_k", (PyCFunction)PyTensorObject_in_top_k, METH_VARARGS | METH_KEYWORDS, NULL}, {"index_select", (PyCFunction)PyTensorObject_index_select, METH_VARARGS | METH_KEYWORDS, NULL}, @@ -864,6 +991,7 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"minimum", (PyCFunction)PyTensorObject_minimum, METH_VARARGS | METH_KEYWORDS, NULL}, {"tril", (PyCFunction)PyTensorObject_tril, METH_VARARGS | METH_KEYWORDS, NULL}, {"triu", (PyCFunction)PyTensorObject_triu, METH_VARARGS | METH_KEYWORDS, NULL}, + {"triu_", (PyCFunction)PyTensorObject_triu_, METH_VARARGS | METH_KEYWORDS, NULL}, {"softmax", (PyCFunction)PyTensorObject_softmax, METH_VARARGS | METH_KEYWORDS, NULL}, {"log_softmax", (PyCFunction)PyTensorObject_log_softmax, METH_VARARGS | METH_KEYWORDS, NULL}, {"roll", (PyCFunction)PyTensorObject_roll, METH_VARARGS | METH_KEYWORDS, NULL}, @@ -879,9 +1007,19 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"median", (PyCFunction)PyTensorObject_median, METH_VARARGS | METH_KEYWORDS, NULL}, {"pow", (PyCFunction)PyTensorObject_pow, METH_VARARGS | METH_KEYWORDS, NULL}, {"chunk", (PyCFunction)PyTensorObject_chunk, METH_VARARGS | METH_KEYWORDS, NULL}, + {"split", (PyCFunction)PyTensorObject_split, METH_VARARGS | METH_KEYWORDS, NULL}, {"narrow", (PyCFunction)PyTensorObject_narrow, METH_VARARGS | METH_KEYWORDS, NULL}, {"masked_fill", (PyCFunction)PyTensorObject_masked_fill, METH_VARARGS | METH_KEYWORDS, NULL}, + {"masked_fill_", (PyCFunction)PyTensorObject_masked_fill_, METH_VARARGS | METH_KEYWORDS, NULL}, {"dot", (PyCFunction)PyTensorObject_dot, METH_VARARGS | METH_KEYWORDS, NULL}, + {"nansum", (PyCFunction)PyTensorObject_nansum, METH_VARARGS | METH_KEYWORDS, NULL}, + {"bernoulli", (PyCFunction)PyTensorObject_bernoulli, METH_VARARGS | METH_KEYWORDS, NULL}, + {"bernoulli_", (PyCFunction)PyTensorObject_bernoulli_, METH_VARARGS | METH_KEYWORDS, NULL}, + {"bincount", (PyCFunction)PyTensorObject_bincount, METH_VARARGS | METH_KEYWORDS, NULL}, + {"isclose", (PyCFunction)PyTensorObject_isclose, METH_VARARGS | METH_KEYWORDS, NULL}, + {"broadcast_to", (PyCFunction)PyTensorObject_broadcast_to, METH_VARARGS | METH_KEYWORDS, NULL}, + {"unique", (PyCFunction)PyTensorObject_unique, METH_VARARGS | METH_KEYWORDS, NULL}, + {"topk", (PyCFunction)PyTensorObject_topk, METH_VARARGS | METH_KEYWORDS, NULL}, // macro UNARY_METHOD {"abs", PyTensorObject_abs, METH_NOARGS, NULL}, @@ -908,6 +1046,7 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"softsign", PyTensorObject_softsign, METH_NOARGS, NULL}, {"log1p", PyTensorObject_log1p, METH_NOARGS, NULL}, {"log2", PyTensorObject_log2, METH_NOARGS, NULL}, + {"log10", PyTensorObject_log10, METH_NOARGS, NULL}, {"reciprocal", PyTensorObject_reciprocal, METH_NOARGS, NULL}, {"asin", PyTensorObject_asin, METH_NOARGS, NULL}, {"arcsin", PyTensorObject_asin, METH_NOARGS, NULL}, @@ -942,6 +1081,7 @@ PyMethodDef PyTensorObject_extra_methods[] = { {"view_as", (PyCFunction)PyTensorObject_view_as, METH_VARARGS | METH_KEYWORDS, NULL}, {"permute", (PyCFunction)PyTensorObject_permute, METH_VARARGS | METH_KEYWORDS, NULL}, {"transpose", (PyCFunction)PyTensorObject_transpose, METH_VARARGS | METH_KEYWORDS, NULL}, + {"logsumexp", (PyCFunction)PyTensorObject_logsumexp, METH_VARARGS | METH_KEYWORDS, NULL}, {NULL}, }; @@ -954,7 +1094,7 @@ PyObject* PyTensorObject_richcompare(PyObject* self, PyObject* other, int op) { case Py_LE: return functional::less_equal(NULL, tuple.get(), NULL); case Py_EQ: { if (self == Py_None || other == Py_None) return Py_False; - return functional::equal(NULL, tuple.get(), NULL); + return functional::broadcast_equal(NULL, tuple.get(), NULL); } case Py_NE: return functional::not_equal(NULL, tuple.get(), NULL); case Py_GT: return functional::greater(NULL, tuple.get(), NULL); diff --git a/oneflow/api/python/framework/thread.cpp b/oneflow/api/python/framework/thread.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7bd88c11b1d153fc3c77bf516a76f4a506a1300d --- /dev/null +++ b/oneflow/api/python/framework/thread.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/api/python/framework/thread.h" +#include "oneflow/core/common/env_var/vm.h" + +namespace py = pybind11; + +namespace oneflow { + +namespace { + +class UsingThreadUidSet final { + public: + UsingThreadUidSet() + : using_thread_uids_({Stream::kDefaultStreamThreadUid}), + thread_limits_(using_thread_uids_.size() + + ThreadLocalEnvInteger()) {} + ~UsingThreadUidSet() = default; + + Maybe Get() { + std::unique_lock lock(mutex_); + CHECK_LT_OR_RETURN(using_thread_uids_.size(), thread_limits_) + << "can not create more worker threads. please check your code or increase environment " + "variable ONEFLOW_VM_WORKER_THREAD_LIMIT(default value:" + << ThreadLocalEnvInteger() << ")"; + for (int i = 0; i < using_thread_uids_.size() + 1; ++i) { + if (using_thread_uids_.count(i) == 0) { + using_thread_uids_.insert(i); + return i; + } + } + UNIMPLEMENTED_THEN_RETURN(); + } + + Maybe Put(int64_t thread_uid) { + std::unique_lock lock(mutex_); + CHECK_NE_OR_RETURN(thread_uid, Stream::kDefaultStreamThreadUid) + << "default thread_uid should not be erased. value: " << thread_uid; + CHECK_OR_RETURN(using_thread_uids_.erase(thread_uid) > 0) + << "no thread_uid found. (current: " << thread_uid << ")."; + return Maybe::Ok(); + } + + private: + std::set using_thread_uids_; + size_t thread_limits_; + std::mutex mutex_; +}; + +UsingThreadUidSet* MutUsingThreadUidSet() { + static UsingThreadUidSet thread_uid_set; + return &thread_uid_set; +} + +} // namespace + +/*static*/ Maybe AsyncThread::New() { + return std::shared_ptr(new AsyncThread(JUST(MutUsingThreadUidSet()->Get()))); +} + +AsyncThread::~AsyncThread() { MutUsingThreadUidSet()->Put(thread_uid_).GetOrThrow(); } + +} // namespace oneflow + +ONEFLOW_API_PYBIND11_MODULE("", m) { + using namespace oneflow; + py::class_>(m, "AsyncThread").def(py::init([]() { + return AsyncThread::New().GetPtrOrThrow(); + })); +} diff --git a/oneflow/api/python/framework/thread.h b/oneflow/api/python/framework/thread.h new file mode 100644 index 0000000000000000000000000000000000000000..43e222ebeb9a4d96a9bd1c39789eaa1b64056a3d --- /dev/null +++ b/oneflow/api/python/framework/thread.h @@ -0,0 +1,41 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_PYTHON_FRAMEWORK_THREAD_H_ +#define ONEFLOW_API_PYTHON_FRAMEWORK_THREAD_H_ + +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { + +class AsyncThread final { + public: + OF_DISALLOW_COPY_AND_MOVE(AsyncThread); + ~AsyncThread(); + + static Maybe New(); + + int64_t thread_uid() const { return thread_uid_; } + + private: + AsyncThread(int64_t thread_uid) : thread_uid_(thread_uid) {} + + int64_t thread_uid_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_API_PYTHON_FRAMEWORK_THREAD_H_ diff --git a/oneflow/api/python/framework/typeinfo.cpp b/oneflow/api/python/framework/typeinfo.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a7fe1bdc219ca274e557de855fd7a399657742d5 --- /dev/null +++ b/oneflow/api/python/framework/typeinfo.cpp @@ -0,0 +1,298 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/api/python/exception/exception.h" +#include "oneflow/api/python/functional/common.h" +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/api/python/framework/typeinfo.h" + +namespace oneflow { +namespace one { + +#define ASSERT(x) (x).GetOrThrow() +#if PY_VERSION_HEX < 0x03070000 +#define PYGETSET_NAME(name) const_cast(name) +#else +#define PYGETSET_NAME(name) (name) +#endif + +using functional::PyObjectPtr; + +#define INT_TYPE INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ +// TODO(WangYi): support bf16 +#define FLOAT_TYPE FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ + +PyObject* PyGetMaxVal(DataType datatype) { +#define GET_INT_MAX_VAL(cpp_type, of_datatype) \ + case of_datatype: \ + return PyLong_FromLong(GetMaxVal>()); +#define GET_FLOAT_MAX_VAL(cpp_type, of_datatype) \ + case of_datatype: return PyFloat_FromDouble(GetMaxVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(GET_INT_MAX_VAL, INT_TYPE); + OF_PP_FOR_EACH_TUPLE(GET_FLOAT_MAX_VAL, FLOAT_TYPE); + default: return NULL; + +#undef GET_INT_MAX_VAL +#undef GET_FLOAT_MAX_VAL + } +} + +PyObject* PyGetMinVal(DataType datatype) { +#define GET_INT_MIN_VAL(cpp_type, of_datatype) \ + case of_datatype: \ + return PyLong_FromLong(GetMinVal>()); +#define GET_FLOAT_MIN_VAL(cpp_type, of_datatype) \ + case of_datatype: return PyFloat_FromDouble(GetMinVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(GET_INT_MIN_VAL, INT_TYPE); + OF_PP_FOR_EACH_TUPLE(GET_FLOAT_MIN_VAL, FLOAT_TYPE); + default: return NULL; + +#undef GET_INT_MIN_VAL +#undef GET_FLOAT_MIN_VAL + } +} + +#define GET_FLOAT_RESOLUTION(cpp_type, of_datatype) \ + case of_datatype: \ + return PyFloat_FromDouble( \ + std::pow(10, -std::numeric_limits>::digits10)); + +#define GET_FLOAT_EPS(cpp_type, of_datatype) \ + case of_datatype: \ + return PyFloat_FromDouble(std::numeric_limits>::epsilon()); + +#define GET_FLOAT_TINY(cpp_type, of_datatype) \ + case of_datatype: \ + return PyFloat_FromDouble(std::numeric_limits>::min()); + +PyTypeObject PyIInfoType = { + PyVarObject_HEAD_INIT(NULL, 0) "oneflow.iinfo", // tp_name + sizeof(PyDTypeInfo), // tp_basicsize +}; + +PyTypeObject PyFInfoType = { + PyVarObject_HEAD_INIT(NULL, 0) "oneflow.finfo", // tp_name + sizeof(PyDTypeInfo), // tp_basicsize +}; + +static PyObject* PyIInfo_new(PyTypeObject* self, PyObject* args, PyObject* kwargs) { + HANDLE_ERRORS + PyObject* dtype_obj = NULL; + static const char* keywords[2] = {"type", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O:iinfo", const_cast(keywords), + &dtype_obj)) { + return NULL; + } + CHECK_OR_THROW(functional::PyDTypeCheck(dtype_obj)) + << Error::TypeError() << "iinfo(): argument 'type' must be oneflow.dtype, but found " + << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(dtype_obj))); + + auto* self = (PyDTypeInfo*)PyIInfoType.tp_alloc(&PyIInfoType, 0); + if (!self) { throw py::error_already_set(); } + self->dtype = functional::PyUnpackDType(dtype_obj); + CHECK_OR_THROW(!self->dtype->is_floating_point() && !self->dtype->is_complex()) + << Error::TypeError() + << "oneflow.iinfo() requires an integer input type. Use oneflow.finfo to handle '" + << self->dtype->name() << "' "; + return (PyObject*)self; + END_HANDLE_ERRORS +} + +static PyObject* PyFInfo_new(PyTypeObject* self, PyObject* args, PyObject* kwargs) { + HANDLE_ERRORS + PyObject* dtype_obj = functional::CastToPyObject(DType::Float()); + static const char* keywords[2] = {"type", NULL}; + if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|O:finfo", const_cast(keywords), + &dtype_obj)) { + return NULL; + } + CHECK_OR_THROW(functional::PyDTypeCheck(dtype_obj)) + << Error::TypeError() << "finfo(): argument 'type' must be oneflow.dtype, but found " + << functional::PyStringAsString(PyObject_Str((PyObject*)Py_TYPE(dtype_obj))); + + auto* self = (PyDTypeInfo*)PyFInfoType.tp_alloc(&PyFInfoType, 0); + if (!self) { throw py::error_already_set(); } + self->dtype = functional::PyUnpackDType(dtype_obj); + CHECK_OR_THROW(self->dtype->is_floating_point() && !self->dtype->is_complex()) + << Error::TypeError() + << "oneflow.finfo() requires a float input type. Use oneflow.iinfo to handle '" + << self->dtype->name() << "' "; + // TODO (wangyi): support bfloat16 + CHECK_OR_THROW(self->dtype->data_type() != kBFloat16) + << Error::TypeError() << "bfloat16 is not supported yet by oneflow.finfo"; + return (PyObject*)self; + END_HANDLE_ERRORS +} + +static PyObject* PyDInfo_bits(PyObject* self, void*) { + HANDLE_ERRORS + size_t bits = ASSERT(((PyDTypeInfo*)self)->dtype->bytes()) * 8; + return PyLong_FromSize_t(bits); + END_HANDLE_ERRORS +} + +static PyObject* PyDInfo_min(PyObject* self, void*) { + HANDLE_ERRORS + DataType datatype = PyDTypeInfo_UnpackDataType(self); + PyObject* result = PyGetMinVal(datatype); + if (!result) { + THROW(RuntimeError) << PyDTypeInfo_UnpackDType(self)->name() << " not supported by " + << self->ob_type->tp_name; + } + return result; + END_HANDLE_ERRORS +} + +static PyObject* PyDInfo_max(PyObject* self, void*) { + HANDLE_ERRORS + DataType datatype = PyDTypeInfo_UnpackDataType(self); + PyObject* result = PyGetMaxVal(datatype); + if (!result) { + THROW(RuntimeError) << PyDTypeInfo_UnpackDType(self)->name() << " not supported by " + << self->ob_type->tp_name; + } + return result; + END_HANDLE_ERRORS +} + +static PyObject* PyFInfo_resolution(PyObject* self, void*) { + HANDLE_ERRORS + DataType datatype = PyDTypeInfo_UnpackDataType(self); + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(GET_FLOAT_RESOLUTION, FLOAT_TYPE); + default: + THROW(RuntimeError) << PyDTypeInfo_UnpackDType(self)->name() + << " not supported by oneflow.finfo"; + return NULL; + } + END_HANDLE_ERRORS +} + +static PyObject* PyFInfo_eps(PyObject* self, void*) { + HANDLE_ERRORS + DataType datatype = PyDTypeInfo_UnpackDataType(self); + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(GET_FLOAT_EPS, FLOAT_TYPE); + default: + THROW(RuntimeError) << PyDTypeInfo_UnpackDType(self)->name() + << " not supported by oneflow.finfo"; + return NULL; + } + END_HANDLE_ERRORS +} + +static PyObject* PyFInfo_tiny(PyObject* self, void*) { + HANDLE_ERRORS + DataType datatype = PyDTypeInfo_UnpackDataType(self); + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(GET_FLOAT_TINY, FLOAT_TYPE); + default: + THROW(RuntimeError) << PyDTypeInfo_UnpackDType(self)->name() + << " not supported by oneflow.finfo"; + return NULL; + } + END_HANDLE_ERRORS +} + +static PyObject* PyDInfo_dtype(PyObject* self, void*) { + HANDLE_ERRORS + std::string name = ((PyDTypeInfo*)self)->dtype->name(); + name = name.erase(0, name.find('.') + 1); + return PyUnicode_FromString(name.data()); + END_HANDLE_ERRORS +} + +static PyObject* PyIInfo_str(PyObject* self) { + HANDLE_ERRORS + std::ostringstream oss; + oss << "iinfo(min=" << PyLong_AS_LONG(PyDInfo_min((PyObject*)self, NULL)) << ", "; + oss << "max=" << PyLong_AS_LONG(PyDInfo_max((PyObject*)self, NULL)) << ", "; + oss << "dtype=" << PyDTypeInfo_UnpackDType(self)->name() << ", "; + oss << "bits=" << PyLong_AS_LONG(PyDInfo_bits((PyObject*)self, NULL)) << ")"; + return PyUnicode_FromString(oss.str().data()); + END_HANDLE_ERRORS +} + +static PyObject* PyFInfo_str(PyObject* self) { + HANDLE_ERRORS + std::ostringstream oss; + oss << "finfo(resolution=" << PyFloat_AS_DOUBLE(PyFInfo_resolution((PyObject*)self, NULL)) + << ", "; + oss << "min=" << PyFloat_AS_DOUBLE(PyDInfo_min((PyObject*)self, NULL)) << ", "; + oss << "max=" << PyFloat_AS_DOUBLE(PyDInfo_max((PyObject*)self, NULL)) << ", "; + oss << "eps=" << PyFloat_AS_DOUBLE(PyFInfo_eps((PyObject*)self, NULL)) << ", "; + oss << "tiny=" << PyFloat_AS_DOUBLE(PyFInfo_tiny((PyObject*)self, NULL)) << ", "; + oss << "dtype=" << PyDTypeInfo_UnpackDType(self)->name() << ", "; + oss << "bits=" << PyLong_AS_LONG(PyDInfo_bits((PyObject*)self, NULL)) << ")"; + return PyUnicode_FromString(oss.str().data()); + END_HANDLE_ERRORS +} + +static struct PyGetSetDef PyIInfo_properties[] = { + {PYGETSET_NAME("bits"), (getter)PyDInfo_bits, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("max"), (getter)PyDInfo_max, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("min"), (getter)PyDInfo_min, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("dtype"), (getter)PyDInfo_dtype, nullptr, nullptr, nullptr}, + {nullptr}, +}; + +static struct PyGetSetDef PyFInfo_properties[] = { + {PYGETSET_NAME("bits"), (getter)PyDInfo_bits, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("max"), (getter)PyDInfo_max, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("min"), (getter)PyDInfo_min, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("resolution"), (getter)PyFInfo_resolution, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("eps"), (getter)PyFInfo_eps, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("tiny"), (getter)PyFInfo_tiny, nullptr, nullptr, nullptr}, + {PYGETSET_NAME("dtype"), (getter)PyDInfo_dtype, nullptr, nullptr, nullptr}, + {nullptr}, +}; + +static void init_info_type() { + PyIInfoType.tp_flags = Py_TPFLAGS_DEFAULT; + PyIInfoType.tp_str = (reprfunc)PyIInfo_str; + PyIInfoType.tp_repr = (reprfunc)PyIInfo_str; + PyIInfoType.tp_new = (newfunc)PyIInfo_new; + PyIInfoType.tp_getset = PyIInfo_properties; + if (PyType_Ready(&PyIInfoType) < 0) { return; } + + PyFInfoType.tp_flags = Py_TPFLAGS_DEFAULT; + PyFInfoType.tp_str = (reprfunc)PyFInfo_str; + PyFInfoType.tp_repr = (reprfunc)PyFInfo_str; + PyFInfoType.tp_new = (newfunc)PyFInfo_new; + PyFInfoType.tp_getset = PyFInfo_properties; + if (PyType_Ready(&PyFInfoType) < 0) { return; } +} + +ONEFLOW_API_PYBIND11_MODULE("_C", m) { + init_info_type(); + if (PyModule_AddObject(m.ptr(), "iinfo", (PyObject*)&PyIInfoType) < 0) return; + if (PyModule_AddObject(m.ptr(), "finfo", (PyObject*)&PyFInfoType) < 0) return; +} + +} // namespace one +} // namespace oneflow +#undef ASSERT +#undef GET_FLOAT_RESOLUTION +#undef GET_FLOAT_EPS +#undef GET_FLOAT_TINY +#undef INT_TYPE +#undef FLOAT_TYPE +#undef PYGETSET_NAME \ No newline at end of file diff --git a/oneflow/api/python/framework/typeinfo.h b/oneflow/api/python/framework/typeinfo.h new file mode 100644 index 0000000000000000000000000000000000000000..85645e1bcfaf3eebabc3335aa9adb354f3cc2c39 --- /dev/null +++ b/oneflow/api/python/framework/typeinfo.h @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_API_PYTHON_FRAMEWORK_TYPEINFO_H_ +#define ONEFLOW_API_PYTHON_FRAMEWORK_TYPEINFO_H_ + +#include +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/dtype.h" + +namespace oneflow { +namespace one { + +typedef struct { + PyObject_HEAD; + Symbol dtype; +} PyDTypeInfo; + +extern PyTypeObject PyIInfoType; +extern PyTypeObject PyFInfoType; + +inline bool PyIInfo_Check(PyObject* obj) { return PyObject_TypeCheck(obj, &PyIInfoType); } +inline bool PyFInfo_Check(PyObject* obj) { return PyObject_TypeCheck(obj, &PyFInfoType); } +inline bool PyDTypeInfo_Check(PyObject* obj) { return PyIInfo_Check(obj) || PyFInfo_Check(obj); } + +inline Symbol PyDTypeInfo_UnpackDType(PyObject* obj) { + assert(PyDTypeInfo_Check(obj)); + return ((PyDTypeInfo*)obj)->dtype; +} + +inline DataType PyDTypeInfo_UnpackDataType(PyObject* obj) { + assert(PyDTypeInfo_Check(obj)); + return ((PyDTypeInfo*)obj)->dtype->data_type(); +} + +} // namespace one +} // namespace oneflow +#endif // ONEFLOW_API_PYTHON_FRAMEWORK_TYPEINFO_H_ diff --git a/oneflow/api/python/framework/variable_tensor_mgr.cpp b/oneflow/api/python/framework/variable_tensor_mgr.cpp index 1d9b0f64b47ca66a457fcb6d22e8c05d2a892f78..56d664961d17b37e56b648c241910a22c1b9be0c 100644 --- a/oneflow/api/python/framework/variable_tensor_mgr.cpp +++ b/oneflow/api/python/framework/variable_tensor_mgr.cpp @@ -26,7 +26,7 @@ namespace oneflow { ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("FillVariableTensorMgr", &FillVariableTensorMgr); m.def("DumpVariableTensorMgr", &DumpVariableTensorMgr); - m.def("ClearVariableTensorMgr", &ClearVariableTensorMgr); + m.def("ResetVariableTensorMgr", &ResetVariableTensorMgr); } } // namespace oneflow diff --git a/oneflow/api/python/functional/common.cpp b/oneflow/api/python/functional/common.cpp index 96c5b165f7ecdbd234a717e934ba72bd05f2be44..47446bae9e4e01e38082070a8db4de344c1684e4 100644 --- a/oneflow/api/python/functional/common.cpp +++ b/oneflow/api/python/functional/common.cpp @@ -13,7 +13,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ - #include "oneflow/api/python/functional/common.h" #include #include @@ -28,12 +27,62 @@ limitations under the License. #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/framework/random_generator.h" +#include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/functional/tensor_index.h" - +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/vm/virtual_machine.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/framework/tensor_util.h" namespace oneflow { namespace one { namespace functional { +namespace detail { + +namespace { + +template +Maybe GetItemInPyScalarTensor(PyObject* obj) { + return GetItemInScalarTensor(PyTensor_Unpack(obj)); +} + +} // namespace + +template::value, int>::type = 0> +bool isinstance_fast(PyObject* obj) { + static auto type = py::detail::get_type_handle(typeid(T), false); + if (!type) { return false; } + const auto result = PyObject_IsInstance(obj, type.ptr()); + if (result == -1) { throw py::error_already_set(); } + return result != 0; +} + +template::value + && !py::detail::is_shared_ptr::value, + int>::type = 0> +const T& cast_fast(PyObject* obj) { + auto vh = reinterpret_cast(obj)->get_value_and_holder(); + auto*& vptr = vh.value_ptr(); + if (!vptr) { + throw py::cast_error("Unable to cast from object to T& since lazy allocation is not allowed " + "for fast cast, please use pybind11::cast instead"); + } + return *reinterpret_cast(&vptr); +} + +template::value + && py::detail::is_shared_ptr::value, + int>::type = 0> +const T& cast_fast(PyObject* obj) { + auto vh = reinterpret_cast(obj)->get_value_and_holder(); + if (!vh.holder_constructed()) { + throw py::cast_error("Unable to cast from non-held to held instance (T& to Holder)"); + } + return vh.template holder(); +} + +} // namespace detail + bool PySequenceCheck(PyObject* obj, const std::function& item_check) { bool is_tuple = PyTuple_Check(obj); if (!is_tuple && !PyList_Check(obj)) { return false; } @@ -44,12 +93,15 @@ bool PySequenceCheck(PyObject* obj, const std::function& item_c } bool PyLongSequenceCheck(PyObject* obj) { - return PySequenceCheck(obj, [](PyObject* item) { return PyLong_Check(item); }); + return PySequenceCheck( + obj, [](PyObject* item) { return PyLong_Check(item) || PyIntegerScalarTensorCheck(item); }); } -bool PyFloatSquenceCheck(PyObject* obj) { - return PySequenceCheck(obj, - [](PyObject* item) { return PyFloat_Check(item) || PyLong_Check(item); }); +bool PyFloatSequenceCheck(PyObject* obj) { + return PySequenceCheck(obj, [](PyObject* item) { + return PyFloat_Check(item) || PyLong_Check(item) || PyFloatScalarTensorCheck(item) + || PyIntegerScalarTensorCheck(item); + }); } bool PyStringCheck(PyObject* obj) { return PyBytes_Check(obj) || PyUnicode_Check(obj); } @@ -82,14 +134,10 @@ std::vector> PyUnpackTensorSequence(PyObject* obj) { } // TensorTuple -bool PyTensorTupleCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance(handle); -} +bool PyTensorTupleCheck(PyObject* obj) { return detail::isinstance_fast(obj); } std::shared_ptr PyUnpackTensorTuple(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::cast>(handle); + return detail::cast_fast>(obj); } // Scalar @@ -107,16 +155,60 @@ Scalar PyUnpackScalar(PyObject* obj) { return 0; } -// DType -bool PyDTypeCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance>(handle); +// Scalar Tensor +bool PyScalarTensorCheck(PyObject* obj) { + if (!LazyMode::is_enabled() && PyTensor_Check(obj)) { + const auto& tensor = PyTensor_Unpack(obj); + return tensor->shape()->size() == 0 && IsPODDataType(tensor->dtype()->data_type()); + } + return false; } -Symbol PyUnpackDType(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return *py::cast*>(handle); + +Scalar PyUnpackScalarTensor(PyObject* obj) { + if (PyBoolScalarTensorCheck(obj)) { + return PyUnpackBoolScalarTensor(obj); + } else if (PyIntegerScalarTensorCheck(obj)) { + return PyUnpackIntegerScalarTensor_AsLongLong(obj); + } else if (PyFloatScalarTensorCheck(obj)) { + return PyUnpackFloatScalarTensor_AsDouble(obj); + } + THROW(RuntimeError) << "The object is not scalar tensor, but is " << Py_TYPE(obj)->tp_name + << "with data type: " + << DataType_Name(PyTensor_Unpack(obj)->dtype()->data_type()); + return 0; } +#define SWITCH_SCALAR_TENSOR_TO_SCALAR(cpp_type, of_type) \ + case of_type: \ + return detail::GetItemInPyScalarTensor(obj).GetOrThrow(); + +#define SCALAR_TENSOR_UNPACK_FUNC_IMPL(func_name, return_type, type_seq) \ + return_type func_name(PyObject* obj) { \ + const auto& tensor = PyTensor_Unpack(obj); \ + DataType data_type = tensor->dtype()->data_type(); \ + switch (data_type) { \ + OF_PP_FOR_EACH_TUPLE(SWITCH_SCALAR_TENSOR_TO_SCALAR, type_seq) \ + default: { \ + throw py::cast_error("Cannot get ##cpp##type from scalar tensor with data type: " \ + + DataType_Name(data_type)); \ + } \ + } \ + } + +SCALAR_TENSOR_UNPACK_FUNC_IMPL(PyUnpackBoolScalarTensor, bool, + BOOL_DATA_TYPE_SEQ CHAR_DATA_TYPE_SEQ); +SCALAR_TENSOR_UNPACK_FUNC_IMPL(PyUnpackIntegerScalarTensor_AsLongLong, long long, + INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ + CHAR_DATA_TYPE_SEQ); +SCALAR_TENSOR_UNPACK_FUNC_IMPL(PyUnpackFloatScalarTensor_AsDouble, double, + FLOATING_DATA_TYPE_SEQ INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ); +#undef SWITCH_SCALAR_TENSOR_TO_SCALAR +#undef SCALAR_TENSOR_UNPACK_FUNC_IMPL + +// DType +bool PyDTypeCheck(PyObject* obj) { return detail::isinstance_fast>(obj); } +Symbol PyUnpackDType(PyObject* obj) { return *detail::cast_fast*>(obj); } + // DType list bool PyDTypeSequenceCheck(PyObject* obj) { return PySequenceCheck(obj, [](PyObject* item) { return PyDTypeCheck(item); }); @@ -125,55 +217,54 @@ std::vector> PyUnpackDTypeSequence(PyObject* obj) { return PyUnpackSequence>(obj, [](PyObject* item) { return PyUnpackDType(item); }); } +// Shape +bool PyShapeCheck(PyObject* obj) { return PyLongSequenceCheck(obj); } + +Shape PyUnpackShape(PyObject* obj) { + bool is_tuple = PyTuple_Check(obj); + CHECK_OR_THROW(is_tuple || PyList_Check(obj)) + << "The object is not list or tuple, but is " << Py_TYPE(obj)->tp_name; + size_t size = is_tuple ? PyTuple_GET_SIZE(obj) : PyList_GET_SIZE(obj); + DimVector values(size); + for (int i = 0; i < size; ++i) { + PyObject* item = is_tuple ? PyTuple_GET_ITEM(obj, i) : PyList_GET_ITEM(obj, i); + values[i] = PyLong_AsLongLong(item); + } + return Shape(values); +} + // Shape list bool PyShapeSequenceCheck(PyObject* obj) { return PySequenceCheck(obj, [](PyObject* item) { return PyLongSequenceCheck(item); }); } std::vector PyUnpackShapeSequence(PyObject* obj) { - return PyUnpackSequence(obj, [](PyObject* item) -> Shape { - const auto& shape = PyUnpackLongSequence(item); - return Shape(DimVector(shape.begin(), shape.end())); - }); + return PyUnpackSequence(obj, [](PyObject* item) -> Shape { return PyUnpackShape(item); }); } // Generator -bool PyGeneratorCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance(handle); -} +bool PyGeneratorCheck(PyObject* obj) { return detail::isinstance_fast(obj); } std::shared_ptr PyUnpackGenerator(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::cast>(handle); + return detail::cast_fast>(obj); } // Device -bool PyDeviceCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance>(handle); -} +bool PyDeviceCheck(PyObject* obj) { return detail::isinstance_fast>(obj); } Symbol PyUnpackDevice(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return *py::cast>>(handle); + return *detail::cast_fast>>(obj); } // Placement bool PyParallelDescCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance>(handle); + return detail::isinstance_fast>(obj); } Symbol PyUnpackParallelDesc(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return *py::cast>>(handle); + return *detail::cast_fast>>(obj); } // SBP -bool PySbpParallelCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance>(handle); -} +bool PySbpParallelCheck(PyObject* obj) { return detail::isinstance_fast>(obj); } Symbol PyUnpackSbpParallel(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return *py::cast>>(handle); + return *detail::cast_fast>>(obj); } // SBP list @@ -280,14 +371,10 @@ TensorIndex PyUnpackTensorIndex(PyObject* obj) { } // OpExpr -bool PyOpExprCheck(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::isinstance(handle); -} +bool PyOpExprCheck(PyObject* obj) { return detail::isinstance_fast(obj); } std::shared_ptr PyUnpackOpExpr(PyObject* obj) { - auto handle = py::reinterpret_borrow(obj); - return py::cast>(handle); + return detail::cast_fast>(obj); } // int64_t diff --git a/oneflow/api/python/functional/common.h b/oneflow/api/python/functional/common.h index 749040bac771d7161c2b5ef57297005873c83de3..fd110dfc0fe0bf158da4161c3011104cc872c4a4 100644 --- a/oneflow/api/python/functional/common.h +++ b/oneflow/api/python/functional/common.h @@ -21,6 +21,8 @@ limitations under the License. #include #include "oneflow/api/python/framework/tensor.h" +#include "oneflow/api/python/caster/maybe.h" +#include "oneflow/api/python/caster/optional.h" #include "oneflow/core/common/throw.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/preprocessor.h" @@ -52,11 +54,11 @@ struct PyObjectPtrDeleter { using PyObjectPtr = std::unique_ptr; -#define INTEGER_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(int32_t) \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t) \ - OF_PP_MAKE_TUPLE_SEQ(int64_t) \ - OF_PP_MAKE_TUPLE_SEQ(uint64_t) \ +#define INTEGER_AND_BOOL_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(int32_t) \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t) \ + OF_PP_MAKE_TUPLE_SEQ(uint64_t) \ OF_PP_MAKE_TUPLE_SEQ(bool) #define FLOATING_TYPE_SEQ \ @@ -80,20 +82,47 @@ inline std::vector PyUnpackSequence(PyObject* obj, UnpackItemFunc unpack_item return values; } +// Scalar Tensor +bool PyScalarTensorCheck(PyObject* obj); +Scalar PyUnpackScalarTensor(PyObject* obj); + +#define DefinePyTypeScalarTensorCheck(type, type_check_func) \ + inline bool Py##type##ScalarTensorCheck(PyObject* obj) { \ + return PyScalarTensorCheck(obj) \ + && type_check_func(PyTensor_Unpack(obj)->dtype()->data_type()); \ + } + +DefinePyTypeScalarTensorCheck(Bool, IsBoolDataType); // PyBoolScalarTensorCheck +DefinePyTypeScalarTensorCheck(Integer, IsIntegralDataType); // PyIntegerScalarTensorCheck +DefinePyTypeScalarTensorCheck(Float, IsFloatingDataType); // PyFloatScalarTensorCheck +#undef DefinePyTypeScalarTensorCheck + +bool PyUnpackBoolScalarTensor(PyObject* obj); +long long PyUnpackIntegerScalarTensor_AsLongLong(PyObject* obj); +double PyUnpackFloatScalarTensor_AsDouble(PyObject* obj); + // Integer/Float list bool PyLongSequenceCheck(PyObject* obj); -bool PyFloatSquenceCheck(PyObject* obj); +bool PyFloatSequenceCheck(PyObject* obj); template inline std::vector PyUnpackLongSequence(PyObject* obj) { - return PyUnpackSequence( - obj, [](PyObject* item) -> T { return static_cast(PyLong_AsLongLong(item)); }); + return PyUnpackSequence(obj, [](PyObject* item) -> T { + if (PyIntegerScalarTensorCheck(item)) { + return static_cast(PyUnpackIntegerScalarTensor_AsLongLong(item)); + } + return static_cast(PyLong_AsLongLong(item)); + }); } template inline std::vector PyUnpackFloatSequence(PyObject* obj) { - return PyUnpackSequence( - obj, [](PyObject* item) -> T { return static_cast(PyFloat_AsDouble(item)); }); + return PyUnpackSequence(obj, [](PyObject* item) -> T { + if (PyFloatScalarTensorCheck(item)) { + return static_cast(PyUnpackFloatScalarTensor_AsDouble(item)); + } + return static_cast(PyFloat_AsDouble(item)); + }); } // String @@ -124,6 +153,10 @@ Symbol PyUnpackDType(PyObject* obj); bool PyDTypeSequenceCheck(PyObject* obj); std::vector> PyUnpackDTypeSequence(PyObject* obj); +// Shape +bool PyShapeCheck(PyObject* obj); +Shape PyUnpackShape(PyObject* obj); + // Shape list bool PyShapeSequenceCheck(PyObject* obj); std::vector PyUnpackShapeSequence(PyObject* obj); diff --git a/oneflow/api/python/functional/dispatch_stateful_ops.cpp b/oneflow/api/python/functional/dispatch_stateful_ops.cpp index f123ba39f4396392fbef597d0bff4aba140ec303..b1f40de9b9e75482491b6d9079d022d3d93be026 100644 --- a/oneflow/api/python/functional/dispatch_stateful_ops.cpp +++ b/oneflow/api/python/functional/dispatch_stateful_ops.cpp @@ -16,7 +16,9 @@ limitations under the License. #include "oneflow/core/common/scalar.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/framework/op_interpreter/lazy_op_interpreter.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" @@ -33,19 +35,26 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor( "DispatchFeedInput", [](const std::shared_ptr& op, const std::shared_ptr& input) -> Maybe { - return OpInterpUtil::Dispatch(*op, {input}); + const auto& origin_input = JUST(OpInterpUtil::Dispatch(*op, {input})); + // Unpack input when do grad acc + return GradAccTryInsertUnpackAfterInput(origin_input); }); m.add_functor( "DispatchFetchOutput", [](const std::shared_ptr& op, const std::shared_ptr& input) -> Maybe { - return OpInterpUtil::Dispatch(*op, {input}); + // Pack output when do grad acc + const auto& pack_input = JUST(GradAccTryInsertPackBeforeOutput(input)); + return OpInterpUtil::Dispatch(*op, {pack_input}); }); m.add_functor("DispatchFeedVariable", [](const std::shared_ptr& op, const std::shared_ptr& input, const Scalar& l2) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("l2", l2.As())); - return OpInterpUtil::Dispatch(*op, {input}, attrs); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("l2"); + attrs.SetAllAttrs(l2.As()); + const auto& origin_var = + JUST(OpInterpUtil::Dispatch(*op, {input}, attrs)); + // Repeat variable when do grad acc + return GradAccTryInsertRepeatAfterVar(origin_var); }); m.add_functor( "DispatchOfrecordReader", @@ -53,16 +62,12 @@ ONEFLOW_FUNCTION_LIBRARY(m) { const std::string& part_name_prefix, int32_t part_name_suffix_length, int32_t batch_size, int32_t shuffle_buffer_size, bool random_shuffle, bool shuffle_after_epoch, int64_t seed, const Optional>& device) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_dir", data_dir)); - JUST(attrs.SetAttr("data_part_num", data_part_num)); - JUST(attrs.SetAttr("part_name_prefix", part_name_prefix)); - JUST(attrs.SetAttr("part_name_suffix_length", part_name_suffix_length)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("shuffle_buffer_size", shuffle_buffer_size)); - JUST(attrs.SetAttr("random_shuffle", random_shuffle)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("seed", seed)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "data_dir", "data_part_num", "part_name_prefix", "part_name_suffix_length", + "batch_size", "shuffle_buffer_size", "random_shuffle", "shuffle_after_epoch", "seed"); + attrs.SetAllAttrs(data_dir, data_part_num, part_name_prefix, part_name_suffix_length, + batch_size, shuffle_buffer_size, random_shuffle, shuffle_after_epoch, + seed); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, JUST(device))); }); m.add_functor( @@ -72,17 +77,13 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int32_t shuffle_buffer_size, bool random_shuffle, bool shuffle_after_epoch, int64_t seed, const Symbol& placement, const std::vector>& sbp_tuple) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_dir", data_dir)); - JUST(attrs.SetAttr("data_part_num", data_part_num)); - JUST(attrs.SetAttr("part_name_prefix", part_name_prefix)); - JUST(attrs.SetAttr("part_name_suffix_length", part_name_suffix_length)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("shuffle_buffer_size", shuffle_buffer_size)); - JUST(attrs.SetAttr("random_shuffle", random_shuffle)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("seed", seed)); - JUST(attrs.SetAttr("nd_sbp", *JUST(GetNdSbpStrList(sbp_tuple)))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "data_dir", "data_part_num", "part_name_prefix", "part_name_suffix_length", + "batch_size", "shuffle_buffer_size", "random_shuffle", "shuffle_after_epoch", "seed", + "nd_sbp"); + attrs.SetAllAttrs(data_dir, data_part_num, part_name_prefix, part_name_suffix_length, + batch_size, shuffle_buffer_size, random_shuffle, shuffle_after_epoch, + seed, *JUST(GetNdSbpStrList(sbp_tuple))); auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -91,35 +92,29 @@ ONEFLOW_FUNCTION_LIBRARY(m) { [](const std::shared_ptr& op, const std::shared_ptr& input, const std::string& name, const Shape& shape, const Symbol& data_type, bool dim1_varying_length, bool truncate) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("name", name)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("data_type", data_type->data_type())); - JUST(attrs.SetAttr("dim1_varying_length", dim1_varying_length)); - JUST(attrs.SetAttr("truncate", truncate)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("name", "shape", "data_type", + "dim1_varying_length", "truncate"); + attrs.SetAllAttrs(name, shape, data_type->data_type(), dim1_varying_length, + truncate); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor( "DispatchCoinFlip", [](const std::shared_ptr& op, int64_t batch_size, Scalar probability, int64_t seed, bool has_seed, const Optional>& device) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("probability", probability.As())); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("seed", seed)); - JUST(attrs.SetAttr("has_seed", has_seed)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("probability", "batch_size", "seed", "has_seed"); + attrs.SetAllAttrs(probability.As(), batch_size, seed, has_seed); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, JUST(device))); }); m.add_functor("DispatchCoinFlip", [](const std::shared_ptr& op, int64_t batch_size, Scalar probability, int64_t seed, bool has_seed, const Symbol& placement, const std::vector>& sbp_tuple) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("probability", probability.As())); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("seed", seed)); - JUST(attrs.SetAttr("has_seed", has_seed)); - JUST(attrs.SetAttr("nd_sbp", *JUST(GetNdSbpStrList(sbp_tuple)))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("probability", "batch_size", "seed", + "has_seed", "nd_sbp"); + attrs.SetAllAttrs(probability.As(), batch_size, seed, has_seed, + *JUST(GetNdSbpStrList(sbp_tuple))); auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch( *op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -128,8 +123,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { "DispatchDistributedPariticalFCSample", [](const std::shared_ptr& op, const std::shared_ptr& weight, const std::shared_ptr& label, const int64_t& num_sample) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_sample", num_sample)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_sample"); + attrs.SetAllAttrs(num_sample); return OpInterpUtil::Dispatch(*op, {weight, label}, attrs); }); m.add_functor( @@ -138,16 +133,11 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int64_t crop_w, float crop_pos_x, float crop_pos_y, const std::vector& mean, const std::vector& std, const Symbol& output_dtype, const std::string& output_layout, const std::string& color_space) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("color_space", color_space)); - JUST(attrs.SetAttr("output_layout", output_layout)); - JUST(attrs.SetAttr("mean", mean)); - JUST(attrs.SetAttr("std", std)); - JUST(attrs.SetAttr("crop_h", crop_h)); - JUST(attrs.SetAttr("crop_w", crop_w)); - JUST(attrs.SetAttr("crop_pos_x", crop_pos_x)); - JUST(attrs.SetAttr("crop_pos_y", crop_pos_y)); - JUST(attrs.SetAttr("output_dtype", output_dtype->data_type())); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("color_space", "output_layout", "mean", "std", "crop_h", + "crop_w", "crop_pos_x", "crop_pos_y", "output_dtype"); + attrs.SetAllAttrs(color_space, output_layout, mean, std, crop_h, crop_w, crop_pos_x, + crop_pos_y, output_dtype->data_type()); return OpInterpUtil::Dispatch(*op, input, attrs); }); m.add_functor( @@ -156,16 +146,11 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int64_t crop_w, float crop_pos_x, float crop_pos_y, const std::vector& mean, const std::vector& std, const Symbol& output_dtype, const std::string& output_layout, const std::string& color_space) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("color_space", color_space)); - JUST(attrs.SetAttr("output_layout", output_layout)); - JUST(attrs.SetAttr("mean", mean)); - JUST(attrs.SetAttr("std", std)); - JUST(attrs.SetAttr("crop_h", crop_h)); - JUST(attrs.SetAttr("crop_w", crop_w)); - JUST(attrs.SetAttr("crop_pos_x", crop_pos_x)); - JUST(attrs.SetAttr("crop_pos_y", crop_pos_y)); - JUST(attrs.SetAttr("output_dtype", output_dtype->data_type())); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("color_space", "output_layout", "mean", "std", "crop_h", + "crop_w", "crop_pos_x", "crop_pos_y", "output_dtype"); + attrs.SetAllAttrs(color_space, output_layout, mean, std, crop_h, crop_w, crop_pos_x, + crop_pos_y, output_dtype->data_type()); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor( @@ -174,22 +159,18 @@ ONEFLOW_FUNCTION_LIBRARY(m) { const std::string& name, const std::string& color_space, const std::vector& random_area, const std::vector& random_aspect_ratio, int32_t num_attempts, int64_t seed, bool has_seed) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("name", name)); - JUST(attrs.SetAttr("color_space", color_space)); - JUST(attrs.SetAttr("num_attempts", num_attempts)); - JUST(attrs.SetAttr("seed", seed)); - JUST(attrs.SetAttr("has_seed", has_seed)); - JUST(attrs.SetAttr("random_area", random_area)); - JUST(attrs.SetAttr("random_aspect_ratio", random_aspect_ratio)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("name", "color_space", "num_attempts", "seed", + "has_seed", "random_area", "random_aspect_ratio"); + attrs.SetAllAttrs(name, color_space, num_attempts, seed, has_seed, random_area, + random_aspect_ratio); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchOfrecordImageDecoder", [](const std::shared_ptr& op, const std::shared_ptr& input, const std::string& name, const std::string& color_space) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("name", name)); - JUST(attrs.SetAttr("color_space", color_space)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("name", "color_space"); + attrs.SetAllAttrs(name, color_space); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchImageDecoderRandomCropResize", @@ -198,18 +179,13 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int64_t max_num_pixels, float random_area_min, float random_area_max, float random_aspect_ratio_min, float random_aspect_ratio_max, int64_t warmup_size, int64_t num_attempts) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("target_width", target_width)); - JUST(attrs.SetAttr("target_height", target_height)); - JUST(attrs.SetAttr("seed", seed)); - JUST(attrs.SetAttr("num_workers", num_workers)); - JUST(attrs.SetAttr("max_num_pixels", max_num_pixels)); - JUST(attrs.SetAttr("random_area_min", random_area_min)); - JUST(attrs.SetAttr("random_area_max", random_area_max)); - JUST(attrs.SetAttr("random_aspect_ratio_min", random_aspect_ratio_min)); - JUST(attrs.SetAttr("random_aspect_ratio_max", random_aspect_ratio_max)); - JUST(attrs.SetAttr("warmup_size", warmup_size)); - JUST(attrs.SetAttr("num_attempts", num_attempts)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "target_width", "target_height", "seed", "num_workers", "max_num_pixels", + "random_area_min", "random_area_max", "random_aspect_ratio_min", + "random_aspect_ratio_max", "warmup_size", "num_attempts"); + attrs.SetAllAttrs(target_width, target_height, seed, num_workers, max_num_pixels, + random_area_min, random_area_max, random_aspect_ratio_min, + random_aspect_ratio_max, warmup_size, num_attempts); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor( @@ -217,26 +193,22 @@ ONEFLOW_FUNCTION_LIBRARY(m) { [](const std::shared_ptr& op, const std::shared_ptr& input, const std::vector& out_shapes, const std::vector>& out_dtypes, bool dynamic_out) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("out_shapes", out_shapes)); - JUST(attrs.SetAttr("dynamic_out", dynamic_out)); auto out_data_types = std::vector(); for (auto it = out_dtypes.begin(); it != out_dtypes.end(); it++) { out_data_types.emplace_back((*it)->data_type()); } - JUST(attrs.SetAttr("out_dtypes", out_data_types)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("out_shapes", "dynamic_out", "out_dtypes"); + attrs.SetAllAttrs(out_shapes, dynamic_out, out_data_types); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchImageResizeKeepAspectRatio", [](const std::shared_ptr& op, const std::shared_ptr& input, int32_t target_size, int32_t min_size, int32_t max_size, bool resize_longer, const std::string& interpolation_type) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("target_size", target_size)); - JUST(attrs.SetAttr("min_size", min_size)); - JUST(attrs.SetAttr("max_size", max_size)); - JUST(attrs.SetAttr("resize_longer", resize_longer)); - JUST(attrs.SetAttr("interpolation_type", interpolation_type)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "target_size", "min_size", "max_size", "resize_longer", "interpolation_type"); + attrs.SetAllAttrs(target_size, min_size, max_size, resize_longer, + interpolation_type); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchImageResizeToFixed", @@ -244,89 +216,76 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int64_t target_width, int64_t target_height, int64_t channels, const Symbol& data_type, const std::string& interpolation_type) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("target_width", target_width)); - JUST(attrs.SetAttr("target_height", target_height)); - JUST(attrs.SetAttr("channels", channels)); - JUST(attrs.SetAttr("data_type", data_type->data_type())); - JUST(attrs.SetAttr("interpolation_type", interpolation_type)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("target_width", "target_height", "channels", + "data_type", "interpolation_type"); + attrs.SetAllAttrs(target_width, target_height, channels, data_type->data_type(), + interpolation_type); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor( "DispatchImageDecode", [](const std::shared_ptr& op, const std::shared_ptr& input, const std::string& color_space, const Symbol& data_type) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("color_space", color_space)); - JUST(attrs.SetAttr("data_type", data_type->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("color_space", "data_type"); + attrs.SetAllAttrs(color_space, data_type->data_type()); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchImageNormalize", [](const std::shared_ptr& op, const std::shared_ptr& input, const std::vector& mean, const std::vector& std) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("std", std)); - JUST(attrs.SetAttr("mean", mean)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("std", "mean"); + attrs.SetAllAttrs(std, mean); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); - m.add_functor( - "DispatchCOCOReader", - [](const std::shared_ptr& op, const std::string& image_dir, - const std::string& annotation_file, int64_t batch_size, bool shuffle_after_epoch, - int64_t random_seed, bool group_by_ratio, bool remove_images_without_annotations, - bool stride_partition, int64_t session_id, - const Optional>& device) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("session_id", session_id)); - JUST(attrs.SetAttr("annotation_file", annotation_file)); - JUST(attrs.SetAttr("image_dir", image_dir)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("random_seed", random_seed)); - JUST(attrs.SetAttr("group_by_ratio", group_by_ratio)); - JUST(attrs.SetAttr("remove_images_without_annotations", remove_images_without_annotations)); - JUST(attrs.SetAttr("stride_partition", stride_partition)); - return OpInterpUtil::Dispatch(*op, {}, - OpExprInterpContext(attrs, JUST(device))); - }); - m.add_functor( - "DispatchCOCOReader", - [](const std::shared_ptr& op, const std::string& image_dir, - const std::string& annotation_file, int64_t batch_size, bool shuffle_after_epoch, - int64_t random_seed, bool group_by_ratio, bool remove_images_without_annotations, - bool stride_partition, int64_t session_id, const Symbol& placement, - const std::vector>& sbp_tuple) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("session_id", session_id)); - JUST(attrs.SetAttr("annotation_file", annotation_file)); - JUST(attrs.SetAttr("image_dir", image_dir)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("random_seed", random_seed)); - JUST(attrs.SetAttr("group_by_ratio", group_by_ratio)); - JUST(attrs.SetAttr("remove_images_without_annotations", remove_images_without_annotations)); - JUST(attrs.SetAttr("stride_partition", stride_partition)); - JUST(attrs.SetAttr("nd_sbp", *JUST(GetNdSbpStrList(sbp_tuple)))); - auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); - return OpInterpUtil::Dispatch(*op, {}, - OpExprInterpContext(attrs, placement, nd_sbp)); - }); + m.add_functor("DispatchCOCOReader", + [](const std::shared_ptr& op, const std::string& image_dir, + const std::string& annotation_file, int64_t batch_size, bool shuffle_after_epoch, + int64_t random_seed, bool group_by_ratio, bool remove_images_without_annotations, + bool stride_partition, int64_t session_id, + const Optional>& device) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "session_id", "annotation_file", "image_dir", "batch_size", + "shuffle_after_epoch", "random_seed", "group_by_ratio", + "remove_images_without_annotations", "stride_partition"); + attrs.SetAllAttrs(session_id, annotation_file, image_dir, batch_size, + shuffle_after_epoch, random_seed, group_by_ratio, + remove_images_without_annotations, stride_partition); + return OpInterpUtil::Dispatch( + *op, {}, OpExprInterpContext(attrs, JUST(device))); + }); + m.add_functor("DispatchCOCOReader", + [](const std::shared_ptr& op, const std::string& image_dir, + const std::string& annotation_file, int64_t batch_size, bool shuffle_after_epoch, + int64_t random_seed, bool group_by_ratio, bool remove_images_without_annotations, + bool stride_partition, int64_t session_id, const Symbol& placement, + const std::vector>& sbp_tuple) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "session_id", "annotation_file", "image_dir", "batch_size", + "shuffle_after_epoch", "random_seed", "group_by_ratio", + "remove_images_without_annotations", "stride_partition", "nd_sbp"); + attrs.SetAllAttrs(session_id, annotation_file, image_dir, batch_size, + shuffle_after_epoch, random_seed, group_by_ratio, + remove_images_without_annotations, stride_partition, + *JUST(GetNdSbpStrList(sbp_tuple))); + auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); + return OpInterpUtil::Dispatch( + *op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); + }); m.add_functor( "DispatchImageBatchAlign", [](const std::shared_ptr& op, const std::shared_ptr& input, int32_t alignment, const Shape& shape, const Symbol& data_type, bool dynamic_out) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("data_type", data_type->data_type())); - JUST(attrs.SetAttr("alignment", alignment)); - JUST(attrs.SetAttr("dynamic_out", dynamic_out)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "data_type", "alignment", "dynamic_out"); + attrs.SetAllAttrs(shape, data_type->data_type(), alignment, dynamic_out); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor("DispatchOfrecordBytesDecoder", [](const std::shared_ptr& op, const std::shared_ptr& input, const std::string& name) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("name", name)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("name"); + attrs.SetAllAttrs(name); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); m.add_functor( @@ -335,15 +294,11 @@ ONEFLOW_FUNCTION_LIBRARY(m) { const int64_t batch_size, const bool random_shuffle, const std::string& shuffle_mode, const int32_t shuffle_buffer_size, const bool shuffle_after_epoch, int64_t random_seed, const bool verify_example, const Optional>& device) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("files", files)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("random_shuffle", random_shuffle)); - JUST(attrs.SetAttr("shuffle_mode", shuffle_mode)); - JUST(attrs.SetAttr("shuffle_buffer_size", shuffle_buffer_size)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("seed", random_seed)); - JUST(attrs.SetAttr("verify_example", verify_example)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "files", "batch_size", "random_shuffle", "shuffle_mode", "shuffle_buffer_size", + "shuffle_after_epoch", "seed", "verify_example"); + attrs.SetAllAttrs(files, batch_size, random_shuffle, shuffle_mode, shuffle_buffer_size, + shuffle_after_epoch, random_seed, verify_example); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, JUST(device))); }); m.add_functor( @@ -353,16 +308,12 @@ ONEFLOW_FUNCTION_LIBRARY(m) { const int32_t shuffle_buffer_size, const bool shuffle_after_epoch, int64_t random_seed, const bool verify_example, const Symbol& placement, const std::vector>& sbp_tuple) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("files", files)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("random_shuffle", random_shuffle)); - JUST(attrs.SetAttr("shuffle_mode", shuffle_mode)); - JUST(attrs.SetAttr("shuffle_buffer_size", shuffle_buffer_size)); - JUST(attrs.SetAttr("shuffle_after_epoch", shuffle_after_epoch)); - JUST(attrs.SetAttr("seed", random_seed)); - JUST(attrs.SetAttr("verify_example", verify_example)); - JUST(attrs.SetAttr("nd_sbp", *JUST(GetNdSbpStrList(sbp_tuple)))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "files", "batch_size", "random_shuffle", "shuffle_mode", "shuffle_buffer_size", + "shuffle_after_epoch", "seed", "verify_example", "nd_sbp"); + attrs.SetAllAttrs(files, batch_size, random_shuffle, shuffle_mode, shuffle_buffer_size, + shuffle_after_epoch, random_seed, verify_example, + *JUST(GetNdSbpStrList(sbp_tuple))); auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -373,17 +324,11 @@ ONEFLOW_FUNCTION_LIBRARY(m) { int64_t label_length, int64_t num_samples, int64_t batch_size, const Symbol& dtype, const std::vector& split_sizes, int64_t split_index, bool shuffle, int64_t random_seed, const Optional>& device) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_file_prefix", data_file_prefix)); - JUST(attrs.SetAttr("seq_length", seq_length)); - JUST(attrs.SetAttr("label_length", label_length)); - JUST(attrs.SetAttr("num_samples", num_samples)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("split_sizes", split_sizes)); - JUST(attrs.SetAttr("split_index", split_index)); - JUST(attrs.SetAttr("shuffle", shuffle)); - JUST(attrs.SetAttr("random_seed", random_seed)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "data_file_prefix", "seq_length", "label_length", "num_samples", "batch_size", "dtype", + "split_sizes", "split_index", "shuffle", "random_seed"); + attrs.SetAllAttrs(data_file_prefix, seq_length, label_length, num_samples, batch_size, + dtype->data_type(), split_sizes, split_index, shuffle, random_seed); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, JUST(device))); }); m.add_functor( @@ -393,17 +338,11 @@ ONEFLOW_FUNCTION_LIBRARY(m) { const std::vector& split_sizes, int64_t split_index, bool shuffle, int64_t random_seed, const Symbol& placement, const std::vector>& sbp_tuple) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_file_prefix", data_file_prefix)); - JUST(attrs.SetAttr("seq_length", seq_length)); - JUST(attrs.SetAttr("label_length", label_length)); - JUST(attrs.SetAttr("num_samples", num_samples)); - JUST(attrs.SetAttr("batch_size", batch_size)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("split_sizes", split_sizes)); - JUST(attrs.SetAttr("split_index", split_index)); - JUST(attrs.SetAttr("shuffle", shuffle)); - JUST(attrs.SetAttr("random_seed", random_seed)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "data_file_prefix", "seq_length", "label_length", "num_samples", "batch_size", "dtype", + "split_sizes", "split_index", "shuffle", "random_seed"); + attrs.SetAllAttrs(data_file_prefix, seq_length, label_length, num_samples, batch_size, + dtype->data_type(), split_sizes, split_index, shuffle, random_seed); auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -412,66 +351,50 @@ ONEFLOW_FUNCTION_LIBRARY(m) { [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, double scale, float l1, float l2, bool centered, float epsilon, float decay_rate, float weight_decay) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("centered", centered)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("decay_rate", decay_rate)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); - return Maybe::Ok(); - }); - m.add_functor("DispatchAdamUpdate", - [](const std::shared_ptr& op, const TensorTuple& inputs, - float learning_rate, float bias_correction1, float bias_correction2, - double scale, float l1, float l2, float beta1, float beta2, float epsilon, - float weight_decay, bool amsgrad, bool do_bias_correction) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("bias_correction1_val", bias_correction1)); - JUST(attrs.SetAttr("bias_correction2_val", bias_correction2)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("beta1", beta1)); - JUST(attrs.SetAttr("beta2", beta2)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("amsgrad", amsgrad)); - JUST(attrs.SetAttr("do_bias_correction", do_bias_correction)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", + "l2", "centered", "epsilon", + "decay_rate", "weight_decay"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, centered, epsilon, decay_rate, + weight_decay); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); + m.add_functor( + "DispatchAdamUpdate", + [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, + float bias_correction1, float bias_correction2, double scale, float l1, float l2, + float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "learning_rate_val", "bias_correction1_val", "bias_correction2_val", "scale", "l1", + "l2", "beta1", "beta2", "epsilon", "weight_decay", "amsgrad", "do_bias_correction"); + attrs.SetAllAttrs(learning_rate, bias_correction1, bias_correction2, scale, l1, l2, beta1, + beta2, epsilon, weight_decay, amsgrad, do_bias_correction); + JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); + return Maybe::Ok(); + }); m.add_functor("DispatchAdagradUpdate", [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, double scale, float l1, float l2, float lr_decay, float weight_decay, float epsilon, int32_t train_step) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("lr_decay", lr_decay)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("train_step_val", train_step)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", + "l2", "lr_decay", "weight_decay", + "epsilon", "train_step_val"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, lr_decay, weight_decay, epsilon, + train_step); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); m.add_functor( "DispatchMomentumUpdate", [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, - double scale, float l1, float l2, float beta, float weight_decay) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("beta", beta)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); + double scale, float l1, float l2, float beta, float dampening, bool nesterov, + bool maximize, float weight_decay) -> Maybe { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", "l2", "beta", + "dampening", "nesterov", "maximize", "weight_decay"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, beta, dampening, nesterov, maximize, + weight_decay); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); @@ -479,12 +402,9 @@ ONEFLOW_FUNCTION_LIBRARY(m) { "DispatchSgdUpdate", [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, double scale, float l1, float l2, float weight_decay) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", "l2", + "weight_decay"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, weight_decay); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); @@ -493,18 +413,12 @@ ONEFLOW_FUNCTION_LIBRARY(m) { float learning_rate, float bias_correction1, float bias_correction2, double scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool do_bias_correction) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("bias_correction1_val", bias_correction1)); - JUST(attrs.SetAttr("bias_correction2_val", bias_correction2)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("beta1", beta1)); - JUST(attrs.SetAttr("beta2", beta2)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("do_bias_correction", do_bias_correction)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "learning_rate_val", "bias_correction1_val", "bias_correction2_val", "scale", + "l1", "l2", "beta1", "beta2", "epsilon", "weight_decay", + "do_bias_correction"); + attrs.SetAllAttrs(learning_rate, bias_correction1, bias_correction2, scale, l1, + l2, beta1, beta2, epsilon, weight_decay, do_bias_correction); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); @@ -512,27 +426,61 @@ ONEFLOW_FUNCTION_LIBRARY(m) { [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, double scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("learning_rate_val", learning_rate)); - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("l1", l1)); - JUST(attrs.SetAttr("l2", l2)); - JUST(attrs.SetAttr("lr_power", lr_power)); - JUST(attrs.SetAttr("lambda1", lambda1)); - JUST(attrs.SetAttr("lambda2", lambda2)); - JUST(attrs.SetAttr("beta", beta)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", + "l2", "lr_power", "lambda1", + "lambda2", "beta", "weight_decay"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, lr_power, lambda1, lambda2, beta, + weight_decay); JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); return Maybe::Ok(); }); - m.add_functor("DispatchEagerNcclAllReduce", + m.add_functor( + "DispatchAdadeltaUpdate", + [](const std::shared_ptr& op, const TensorTuple& inputs, float learning_rate, + double scale, float l1, float l2, float rho, float epsilon, bool maximize, + float weight_decay) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "l1", "l2", + "rho", "epsilon", "maximize", "weight_decay"); + attrs.SetAllAttrs(learning_rate, scale, l1, l2, rho, epsilon, maximize, weight_decay); + JUST(OpInterpUtil::Dispatch(*op, inputs, attrs)); + return Maybe::Ok(); + }); + m.add_functor("DispatchEagerCclAllReduce", [](const std::shared_ptr& op, const std::shared_ptr& input, - const std::string& parallel_conf, bool async_launch) -> Maybe { - MutableAttrMap attrs; - JUST(attrs.SetAttr("parallel_conf", parallel_conf)); - JUST(attrs.SetAttr("async_launch", async_launch)); + const std::string& parallel_conf) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("parallel_conf"); + attrs.SetAllAttrs(parallel_conf); return OpInterpUtil::Dispatch(*op, {input}, attrs); }); + m.add_functor( + "DispatchRawReader", + [](const std::shared_ptr& op, const std::vector& files, + const Shape& shape, const Symbol& data_type, const int64_t batch_size, + const bool random_shuffle, const int64_t shuffle_block_size, int64_t random_seed, + const Optional>& device) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("files", "shape", "data_type", "batch_size", + "random_shuffle", "shuffle_block_size", "seed", + "nd_sbp"); + attrs.SetAllAttrs(files, shape, data_type->data_type(), batch_size, random_shuffle, + shuffle_block_size, random_seed, std::vector()); + return OpInterpUtil::Dispatch(*op, {}, OpExprInterpContext(attrs, JUST(device))); + }); + m.add_functor("DispatchRawReader", + [](const std::shared_ptr& op, const std::vector& files, + const Shape& shape, const Symbol& data_type, const int64_t batch_size, + const bool random_shuffle, const int64_t shuffle_block_size, int64_t random_seed, + const Symbol& placement, + const std::vector>& sbp_tuple) -> Maybe { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "files", "shape", "data_type", "batch_size", "random_shuffle", + "shuffle_block_size", "seed", "nd_sbp"); + attrs.SetAllAttrs(files, shape, data_type->data_type(), batch_size, + random_shuffle, shuffle_block_size, random_seed, + *JUST(GetNdSbpStrList(sbp_tuple))); + auto nd_sbp = JUST(GetNdSbp(sbp_tuple)); + return OpInterpUtil::Dispatch( + *op, {}, OpExprInterpContext(attrs, placement, nd_sbp)); + }); } } // namespace impl diff --git a/oneflow/api/python/functional/dispatch_stateful_ops.yaml b/oneflow/api/python/functional/dispatch_stateful_ops.yaml index bcd2848b6cd5fc927eb9566263b1cf9da6e8fc5c..62cdc7d7e8d949a80e7ac65797f9149a791b6fcd 100644 --- a/oneflow/api/python/functional/dispatch_stateful_ops.yaml +++ b/oneflow/api/python/functional/dispatch_stateful_ops.yaml @@ -137,7 +137,7 @@ bind_python: True - name: "dispatch_momentum_update" - signature: "Void (OpExpr op, TensorTuple inputs, Float learning_rate=0, Double scale=1.0, Float l1=0, Float l2=0, Float beta=0.9, Float weight_decay=0) => DispatchMomentumUpdate" + signature: "Void (OpExpr op, TensorTuple inputs, Float learning_rate=0, Double scale=1.0, Float l1=0, Float l2=0, Float beta=0.9, Float dampening=0.0, Bool nesterov=False, Bool maximize=False, Float weight_decay=0) => DispatchMomentumUpdate" bind_python: True - name: "dispatch_sgd_update" @@ -152,6 +152,18 @@ signature: "Void (OpExpr op, TensorTuple inputs, Float learning_rate=0, Double scale=1.0, Float l1=0, Float l2=0, Float lr_power, Float lambda1, Float lambda2, Float beta, Float weight_decay=0) => DispatchFtrlUpdate" bind_python: True -- name: "dispatch_eager_nccl_all_reduce" - signature: "Tensor (OpExpr op, Tensor input, String parallel_conf, Bool async_launch=False) => DispatchEagerNcclAllReduce" +- name: "dispatch_adadelta_update" + signature: "Void (OpExpr op, TensorTuple inputs, Float learning_rate=0, Double scale=1.0, Float l1=0, Float l2=0, Float rho, Float epsilon, Bool maximize, Float weight_decay=0) => DispatchAdadeltaUpdate" + bind_python: True + + +- name: "dispatch_eager_ccl_all_reduce" + signature: "Tensor (OpExpr op, Tensor input, String parallel_conf) => DispatchEagerCclAllReduce" + bind_python: True + +- name: "dispatch_raw_reader" + signature: [ + "Tensor (OpExpr op, StringList files, Shape shape, DataType data_type, Int64 batch_size, Bool random_shuffle, Int64 shuffle_block_size, Int64 random_seed=-1, Device device=None) => DispatchRawReader", + "Tensor (OpExpr op, StringList files, Shape shape, DataType data_type, Int64 batch_size, Bool random_shuffle, Int64 shuffle_block_size, Int64 random_seed=-1, Placement placement, SbpList sbp) => DispatchRawReader", + ] bind_python: True diff --git a/oneflow/api/python/functional/indexing.cpp b/oneflow/api/python/functional/indexing.cpp index 1f22330a0fadead5c214d0bfae326733bc4ee6b4..c45c899ac30b6ace34f4710f345ba2d1bf64ff17 100644 --- a/oneflow/api/python/functional/indexing.cpp +++ b/oneflow/api/python/functional/indexing.cpp @@ -20,7 +20,6 @@ limitations under the License. #include "oneflow/api/python/functional/common.h" #include "oneflow/extension/python/numpy.h" #include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/functional/functional.h" @@ -68,7 +67,7 @@ DataType InferScalarType(PyObject* object) { return numpy::NumpyTypeToOFDataType(PyArray_DescrFromScalar(object)->type_num).GetOrThrow(); } else if (PySequence_Check(object)) { int64_t length = PySequence_Length(object); - CHECK_GT_OR_THROW(length, 0) << "Index should not be empty."; + if (length == 0) { return DataType::kInt64; } DataType scalar_type = DataType::kInvalidDataType; for (int64_t i = 0; i < length; ++i) { PyObjectPtr item(PySequence_GetItem(object, i)); @@ -126,16 +125,18 @@ void RecursiveParseAndAssign(PyObject* object, char* data, const int& ndims, con } } -void ParseArrayToBlob(PyObject* object, Blob* blob) { - const DataType dtype = blob->data_type(); - const int ndims = blob->shape().NumAxes(); +void ParseArrayToTensor(PyObject* object, + const std::shared_ptr& eager_blob_object) { + const DataType dtype = eager_blob_object->data_type(); + const int ndims = eager_blob_object->shape().NumAxes(); DimVector strides(ndims); int64_t size = 1; for (int i = ndims - 1; i >= 0; --i) { strides[i] = size; - size *= blob->shape().At(i); + size *= eager_blob_object->shape().At(i); } - RecursiveParseAndAssign(object, blob->mut_dptr(), ndims, 0, blob->shape(), strides, dtype); + RecursiveParseAndAssign(object, eager_blob_object->mut_dptr(), ndims, 0, + eager_blob_object->shape(), strides, dtype); } Shape InferArraySizes(PyObject* object) { @@ -144,7 +145,6 @@ Shape InferArraySizes(PyObject* object) { PyObjectPtr handle; while (PySequence_Check(seq)) { int64_t length = PySequence_Length(seq); - CHECK_GT_OR_THROW(length, 0) << "Index should not be empty."; sizes.emplace_back(length); CHECK_LE_OR_THROW(sizes.size(), /*MAX_DIMS=*/128) << "Too many dimensions " << Py_TYPE(seq)->tp_name; @@ -156,6 +156,8 @@ Shape InferArraySizes(PyObject* object) { } Maybe ConvertToIndexingTensor(PyObject* object) { + // NOTE: convert data to indexing will ensure in eager mode + LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); const DataType dtype = InferScalarType(object); const auto& device = JUST(Device::New("cpu")); @@ -178,11 +180,11 @@ Maybe ConvertToIndexingTensor(PyObject* object) { JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->AccessBlobByCallback( - JUST(tensor->AsMirroredTensor()), - [handle](uint64_t ofblob_ptr) { - auto* of_blob = reinterpret_cast(ofblob_ptr); + JUST(tensor->AsLocalTensor()), + [handle](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { CHECK_JUST(Singleton::Get()->WithScopedAcquire([&]() -> Maybe { - ParseArrayToBlob(handle.get(), of_blob->mut_blob()); + ParseArrayToTensor(handle.get(), eager_blob_object); return Maybe::Ok(); })); }, @@ -211,7 +213,7 @@ IndexItem UnpackIndexItem(PyObject* object) { } else if (PySequence_Check(object)) { return IndexItem(ConvertToIndexingTensor(object).GetPtrOrThrow()); } - THROW(TypeError) << "Invalid index " << Py_TYPE(object)->tp_name; + THROW(IndexError) << "Invalid index " << Py_TYPE(object)->tp_name; return IndexItem(); } diff --git a/oneflow/api/python/functional/python_arg.cpp b/oneflow/api/python/functional/python_arg.cpp index 03824d5e4921b6435adf267b188bd0ff89236a2d..1a097b81cd4d00889c0286f74f43f1eae0591430 100644 --- a/oneflow/api/python/functional/python_arg.cpp +++ b/oneflow/api/python/functional/python_arg.cpp @@ -37,6 +37,9 @@ namespace functional { #define INSTANCE_OBJECT_AS_INTEGER(T) \ template<> \ T PythonArg::ObjectAs() const { \ + if (PyIntegerScalarTensorCheck(object_)) { \ + return static_cast(PyUnpackIntegerScalarTensor_AsLongLong(object_)); \ + } \ return static_cast(PyLong_AsLongLong(object_)); \ } \ template<> \ @@ -51,12 +54,15 @@ namespace functional { return std::make_shared>(ObjectAs>()); \ } -OF_PP_FOR_EACH_TUPLE(INSTANCE_OBJECT_AS_INTEGER, INTEGER_TYPE_SEQ) +OF_PP_FOR_EACH_TUPLE(INSTANCE_OBJECT_AS_INTEGER, INTEGER_AND_BOOL_TYPE_SEQ) #undef INSTANCE_OBJECT_AS_INTEGER #define INSTANCE_OBJECT_AS_FLOAT(T) \ template<> \ T PythonArg::ObjectAs() const { \ + if (PyFloatScalarTensorCheck(object_)) { \ + return static_cast(PyUnpackFloatScalarTensor_AsDouble(object_)); \ + } \ return static_cast(PyFloat_AsDouble(object_)); \ } \ template<> \ @@ -88,6 +94,7 @@ INSTANCE_OBJECT_AS_SHARED_PTR(std::string) template<> Scalar PythonArg::ObjectAs() const { + if (PyScalarTensorCheck(object_)) { return PyUnpackScalarTensor(object_); } return PyUnpackScalar(object_); } INSTANCE_OBJECT_AS_SHARED_PTR(Scalar) @@ -120,8 +127,7 @@ INSTANCE_OBJECT_AS_SHARED_PTR(std::vector>) template<> Shape PythonArg::ObjectAs() const { - const auto& shape = PyUnpackLongSequence(object_); - return Shape(DimVector(shape.begin(), shape.end())); + return PyUnpackShape(object_); } INSTANCE_OBJECT_AS_SHARED_PTR(Shape) @@ -197,7 +203,9 @@ bool PythonArg::TypeCheck(ValueType type) const { case kUINT32: case kINT64: case kUINT64: - case kBOOL: return PyLong_Check(object_) || numpy::PyArrayCheckLongScalar(object_); + case kBOOL: + return PyLong_Check(object_) || numpy::PyArrayCheckLongScalar(object_) + || PyIntegerScalarTensorCheck(object_) || PyBoolScalarTensorCheck(object_); case kINT32_LIST: case kUINT32_LIST: case kINT64_LIST: @@ -206,16 +214,17 @@ bool PythonArg::TypeCheck(ValueType type) const { case kFLOAT: case kDOUBLE: return PyFloat_Check(object_) || PyLong_Check(object_) - || numpy::PyArrayCheckFloatScalar(object_) || numpy::PyArrayCheckLongScalar(object_); + || numpy::PyArrayCheckFloatScalar(object_) || numpy::PyArrayCheckLongScalar(object_) + || PyFloatScalarTensorCheck(object_) || PyIntegerScalarTensorCheck(object_); case kFLOAT_LIST: case kDOUBLE_LIST: - return PyFloatSquenceCheck(object_) + return PyFloatSequenceCheck(object_) || (size_ > 0 && (PyFloat_Check(object_) || PyLong_Check(object_))); case kSTRING: return PyStringCheck(object_); case kSTRING_LIST: return PyStringSequenceCheck(object_); case kSCALAR: return PyScalarCheck(object_) || numpy::PyArrayCheckLongScalar(object_) - || numpy::PyArrayCheckFloatScalar(object_); + || numpy::PyArrayCheckFloatScalar(object_) || PyScalarTensorCheck(object_); case kTENSOR: case kTENSOR_REF: return PyTensor_Check(object_); case kTENSOR_TUPLE: return PyTensorTupleCheck(object_) || PyTensorSequenceCheck(object_); @@ -224,7 +233,7 @@ bool PythonArg::TypeCheck(ValueType type) const { case kGENERATOR: case kGENERATOR_REF: return PyGeneratorCheck(object_); case kTENSOR_INDEX: return PyTensorIndexCheck(object_); - case kDEVICE: return PyDeviceCheck(object_) || PyStringCheck(object_); + case kDEVICE: return PyStringCheck(object_) || PyDeviceCheck(object_); case kPARALLEL_DESC: return PyParallelDescCheck(object_); case kSBP_PARALLEL: return PySbpParallelCheck(object_); case kSBP_PARALLEL_LIST: @@ -240,8 +249,6 @@ bool PythonArg::TypeCheck(ValueType type) const { return false; } -bool PythonArgCheck(const PythonArg& arg, ValueType type) { return arg.TypeCheck(type); } - } // namespace functional } // namespace one } // namespace oneflow diff --git a/oneflow/api/python/functional/python_arg.h b/oneflow/api/python/functional/python_arg.h index 1c2dd490ccb3f5e679d5b2e052ea8e9550a8ecbc..085e4ee6a484a45b5331215156b724cc2c2c447d 100644 --- a/oneflow/api/python/functional/python_arg.h +++ b/oneflow/api/python/functional/python_arg.h @@ -61,22 +61,13 @@ struct optional_traits> { class PythonArg { public: PythonArg() = default; - PythonArg(const py::object& object, int size = 0) : PythonArg(object.ptr(), size) {} + PythonArg(PyObject* object, int size = 0) : object_(object), default_val_(), size_(size), tag_(HAS_OBJECT) {} - PythonArg(const std::shared_ptr& value, int size = 0) + PythonArg(const detail::DefaultVal* value, int size = 0) : object_(nullptr), default_val_(value), size_(size), tag_(HAS_DEFAULT) {} - template::value, int>::type = 0> - PythonArg(const T& value, int size = 0) - : object_(nullptr), - default_val_(std::make_shared>(value)), - size_(size), - tag_(HAS_DEFAULT) {} - - virtual ~PythonArg() = default; - template::value, int>::type = 0> T As() const { if (tag_ == HAS_DEFAULT) { @@ -109,13 +100,11 @@ class PythonArg { T ObjectAs() const; PyObject* object_; - std::shared_ptr default_val_; + const detail::DefaultVal* default_val_; size_t size_; enum { HAS_OBJECT, HAS_DEFAULT, HAS_NONE } tag_; }; -bool PythonArgCheck(const PythonArg& arg, ValueType type); - } // namespace functional } // namespace one } // namespace oneflow diff --git a/oneflow/api/python/functional/python_arg_parser.cpp b/oneflow/api/python/functional/python_arg_parser.cpp index ed6fa5003bff0830e98c70fb4a87e641ebcff182..25c678f88d514c77b5d92e4592b75df9c7282490 100644 --- a/oneflow/api/python/functional/python_arg_parser.cpp +++ b/oneflow/api/python/functional/python_arg_parser.cpp @@ -30,7 +30,7 @@ void FunctionSchema::ReportKwargsError(PyObject* kwargs, size_t nargs) const { int64_t index = -1; const std::string string_key = PyStringAsString(key); for (int i = 0; i < def_->argument_def.size(); ++i) { - const auto& arg = def_->argument_def.at(i); + const auto& arg = def_->argument_def[i]; if (arg.name == string_key) { index = i; break; @@ -56,7 +56,7 @@ bool FunctionSchema::Parse(PyObject* args, PyObject* kwargs, PythonArg* parsed_a size_t remaining_kwargs = kwargs ? PyDict_Size(kwargs) : 0; if (max_pos_nargs_ == 1) { - const auto& type = def_->argument_def.at(0).type; + const auto& type = def_->argument_def[0].type; treat_args_as_list = IsIntegralListType(type) || type == kSHAPE || type == kTENSOR_TUPLE; } if (nargs > max_pos_nargs_ && !treat_args_as_list) { @@ -68,7 +68,7 @@ bool FunctionSchema::Parse(PyObject* args, PyObject* kwargs, PythonArg* parsed_a } int arg_pos = 0; for (int i = 0; i < def_->argument_def.size(); ++i) { - const auto& param = def_->argument_def.at(i); + const auto& param = def_->argument_def[i]; PyObject* obj = NULL; if (args && arg_pos < nargs) { if (param.keyword_only) { @@ -77,10 +77,10 @@ bool FunctionSchema::Parse(PyObject* args, PyObject* kwargs, PythonArg* parsed_a } return false; } - obj = PyTuple_GetItem(args, arg_pos); + obj = PyTuple_GET_ITEM(args, arg_pos); } else if (kwargs) { obj = PyDict_GetItemString(kwargs, param.name.c_str()); - if (obj) { remaining_kwargs--; } + if (obj) { --remaining_kwargs; } } if (obj) { @@ -89,10 +89,10 @@ bool FunctionSchema::Parse(PyObject* args, PyObject* kwargs, PythonArg* parsed_a obj = args; arg_pos = nargs; } else { - arg_pos++; + ++arg_pos; } PythonArg arg(obj, param.size); - if ((obj == Py_None && param.optional) || PythonArgCheck(arg, param.type)) { + if ((obj == Py_None && param.optional) || arg.TypeCheck(param.type)) { parsed_args[i] = arg; } else { if (raise_exception) { @@ -109,7 +109,7 @@ bool FunctionSchema::Parse(PyObject* args, PyObject* kwargs, PythonArg* parsed_a } return false; } - parsed_args[i] = param.default_value; + parsed_args[i] = param.default_value.get(); } } if (remaining_kwargs > 0) { diff --git a/oneflow/api/python/functional/python_frame.h b/oneflow/api/python/functional/python_frame.h index c6db38dac15e2297958591afe5704e2e9aac9673..e94d5fc74409b6caa8aa854d13d2fc195c2c1885 100644 --- a/oneflow/api/python/functional/python_frame.h +++ b/oneflow/api/python/functional/python_frame.h @@ -17,33 +17,75 @@ limitations under the License. #define ONEFLOW_API_PYTHON_FUNCTIONAL_PYTHON_FRAME_H_ #include +#include +#include +#include #include "oneflow/api/python/functional/common.h" #include "oneflow/core/framework/op_interpreter/dispatch_frame.h" #include "oneflow/core/job/graph_scope_vars.h" +#include "oneflow/core/profiler/profiler.h" namespace oneflow { namespace one { namespace functional { namespace { -std::string get_cur_frame_stack_str(int32_t max_stack_depth) { - std::string cur_f_str; - PyFrameObject* cur_frame = PyEval_GetFrame(); - for (int32_t i = 0; i < max_stack_depth; i++) { - if (cur_frame == NULL) break; - const int32_t stack_index = (-1) * i - 1; - cur_f_str = "Python Stack[" + std::to_string(stack_index) - + "]: " + PyObjectToReprStr((PyObject*)cur_frame) + "; " + cur_f_str; - cur_frame = cur_frame->f_back; + +// get a formatted stack frame representation +// example: Python Stack[-10]: '__call__' at '.../graph/graph.py': line 219 +std::string get_python_frame_str_repr(int32_t stack_index, PyFrameObject* frame) { + if (frame == NULL) return ""; + PyCodeObject* code = frame->f_code; + std::string repr = "Python Stack[" + std::to_string(stack_index) + "]: "; + std::string file_name = PyObjectToReprStr(code->co_filename); + std::string code_name = PyObjectToReprStr(code->co_name); + int line_number = PyFrame_GetLineNumber(frame); + + return repr + code_name + " at " + file_name + ": line " + std::to_string(line_number) + "; "; +} + +bool check_if_python_file_should_be_filtered(const std::string& path) { + const auto& paths_to_be_kept = GetPythonPathsToBeKeptForDebugging(); + for (int i = 0; i < paths_to_be_kept.size(); ++i) { + const std::string& path_to_be_kept = paths_to_be_kept[i]; + if (path.size() > path_to_be_kept.size()) { + if (path.substr(0, path_to_be_kept.size()) == path_to_be_kept) { return false; } + } } - return cur_f_str; + + const auto& paths_to_be_filtered = GetPythonPathsToBeFilteredForDebugging(); + for (int i = 0; i < paths_to_be_filtered.size(); ++i) { + const std::string& path_to_be_filtered = paths_to_be_filtered[i]; + if (path.size() > path_to_be_filtered.size()) { + if (path.substr(0, path_to_be_filtered.size()) == path_to_be_filtered) { return true; } + } + } + + return false; +} + +bool check_if_frame_should_be_filtered(PyFrameObject* frame) { + std::string frame_file_name = PyObjectToReprStr(frame->f_code->co_filename); + frame_file_name = frame_file_name.substr(1, frame_file_name.size() - 2); // get rid of ' ' + return check_if_python_file_should_be_filtered(frame_file_name); +} + +bool check_if_should_skip_this_frame(PyFrameObject* frame) { + const bool only_user_py_stack = GetGraphDebugOnlyUserPyStack(); + if (only_user_py_stack) { return check_if_frame_should_be_filtered(frame); } + return false; } int32_t get_cur_stack_depth() { int32_t current_stack_depth = 0; PyFrameObject* f = PyEval_GetFrame(); while (f) { + if (check_if_should_skip_this_frame(f)) { + f = f->f_back; + continue; + } + current_stack_depth++; f = f->f_back; } @@ -51,20 +93,40 @@ int32_t get_cur_stack_depth() { } std::string get_cur_frame_stack_str() { - const bool debug_mode = GetGraphDebugMode(); const int32_t max_stack_depth = GetGraphDebugMaxPyStackDepth(); - if (debug_mode) { // show more info for the stack trace in debug mode - int32_t current_stack_depth = get_cur_stack_depth(); - std::string cur_f_str = get_cur_frame_stack_str(max_stack_depth); - if (current_stack_depth > max_stack_depth) { // show how many stack depth remaining to be shown - int32_t remaining_stack_depth = current_stack_depth - max_stack_depth; - cur_f_str += " ... " + std::to_string(remaining_stack_depth) + " more; "; + std::string cur_f_str; + PyFrameObject* cur_frame = PyEval_GetFrame(); + + int i = 0; + while (i < max_stack_depth) { + if (cur_frame == NULL) break; + + const int32_t stack_index = (-1) * i - 1; + + if (check_if_should_skip_this_frame(cur_frame)) { + cur_frame = cur_frame->f_back; + continue; + } + + i++; + cur_f_str = get_python_frame_str_repr(stack_index, cur_frame) + cur_f_str; + cur_frame = cur_frame->f_back; + } + + const bool debug_mode = + GetGraphDebugMode(); // show how may stack frames remain to be shown in debug mode + if (debug_mode) { + const int32_t current_stack_depth = get_cur_stack_depth(); + if (current_stack_depth > max_stack_depth) { + cur_f_str += "... " + std::to_string(current_stack_depth - max_stack_depth) + " more"; } - return cur_f_str; + } else { + if (cur_frame != NULL) { cur_f_str += " ... more"; } } - return get_cur_frame_stack_str(max_stack_depth); + return cur_f_str; } + } // namespace class PythonFrameGuard { diff --git a/oneflow/api/python/functional/python_return_types.h b/oneflow/api/python/functional/python_return_types.h new file mode 100644 index 0000000000000000000000000000000000000000..c65efcb9084377c7217cf30b9839699949bcc559 --- /dev/null +++ b/oneflow/api/python/functional/python_return_types.h @@ -0,0 +1,77 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// This code is referenced from: +// https://github.com/pytorch/pytorch/blob/master/torch/csrc/utils/structseq.cpp + +#ifndef ONEFLOW_API_PYTHON_FUNCTIONAL_PYTHON_RETURN_TYPES_H_ +#define ONEFLOW_API_PYTHON_FUNCTIONAL_PYTHON_RETURN_TYPES_H_ + +#include +#include +#include +#include + +#include "oneflow/api/python/exception/exception.h" +#include "oneflow/api/python/functional/common.h" + +namespace oneflow { +namespace one { +namespace functional { + +PyObject* returned_structseq_repr(PyStructSequence* obj) { + HANDLE_ERRORS + PyTypeObject* tp = Py_TYPE(obj); + PyObjectPtr tuple((PyObject*)obj); + if (tuple == nullptr) { return nullptr; } + + std::stringstream ss; + ss << tp->tp_name << "(\n"; + Py_ssize_t num_elements = Py_SIZE(obj); + + for (Py_ssize_t i = 0; i < num_elements; i++) { + const char* cname = tp->tp_members[i].name; + if (cname == nullptr) { + PyErr_Format(PyExc_SystemError, + "In structseq_repr(), member %zd name is nullptr" + " for type %.500s", + i, tp->tp_name); + return nullptr; + } + + PyObject* val = PyTuple_GetItem(tuple.get(), i); + if (val == nullptr) { return nullptr; } + + auto repr = PyObjectPtr(PyObject_Repr(val)); + if (repr == nullptr) { return nullptr; } + + const char* crepr = PyUnicode_AsUTF8(repr.get()); + if (crepr == nullptr) { return nullptr; } + + ss << cname << '=' << crepr; + if (i < num_elements - 1) { ss << ",\n"; } + } + ss << ")"; + + return PyUnicode_FromString(ss.str().c_str()); + END_HANDLE_ERRORS +} + +} // namespace functional +} // namespace one +} // namespace oneflow + +#endif // ONEFLOW_API_PYTHON_FUNCTIONAL_PYTHON_RETURN_TYPES_H_ diff --git a/oneflow/api/python/functional/tensor_api.cpp b/oneflow/api/python/functional/tensor_api.cpp index 156bf861388ac876725e65438a4fd943f7e43575..71809c1dd12e36a6977241391ed3711d4f7a80d8 100644 --- a/oneflow/api/python/functional/tensor_api.cpp +++ b/oneflow/api/python/functional/tensor_api.cpp @@ -17,6 +17,7 @@ limitations under the License. #include #include "oneflow/api/python/utils/tensor_utils.h" +#include "oneflow/api/python/dlpack/converter.h" #include "oneflow/api/python/framework/size.h" #include "oneflow/api/python/functional/common.h" #include "oneflow/api/python/functional/tensor_api.yaml.h" @@ -50,6 +51,11 @@ class TensorWithDataFunctor { // even if in nn.Graph build (module forward function), if you create a flow.Tensor, // its a eager tensor by Run functional::Empty() in LazyMode::Grad(false) LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); + if (GlobalMode::is_enabled()) { + return JUST( + functional::GlobalTensorWithData(data, dtype, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), requires_grad)); + } if (PyTensor_Check(data)) { // Throw warnings like pytorch. @@ -70,7 +76,7 @@ class TensorWithDataFunctor { } }; -class ConsistentTensorWithDataFunctor { +class GlobalTensorWithDataFunctor { public: Maybe operator()(PyObject* data, const Optional>& dtype, const Symbol& placement, @@ -93,8 +99,8 @@ class ConsistentTensorWithDataFunctor { const auto& other = PyTensor_Unpack(data); return MakeTensorFromOtherTensor(other, dtype, placement, sbp_tuple, requires_grad); } - // Make consistent tensor from python sequence or numpy array. - return MakeConsistentTensorFromData(data, dtype, placement, sbp_tuple, requires_grad); + // Make global tensor from python sequence or numpy array. + return MakeGlobalTensorFromData(data, dtype, placement, sbp_tuple, requires_grad); } }; @@ -106,13 +112,13 @@ class TensorEmptyCtorFunctor { } }; -class ConsistentTensorEmptyCtorFunctor { +class GlobalTensorEmptyCtorFunctor { public: Maybe operator()(const Symbol& placement, const std::vector>& sbp_tuple) const { Shape shape(DimVector{0}); JUST(CheckDeviceIdsIsValid(placement)); - return ConsistentTensorWithShapeCtor(shape, placement, sbp_tuple); + return GlobalTensorWithShapeCtor(shape, placement, sbp_tuple); } }; @@ -122,7 +128,7 @@ class TensorWithOtherCtorFunctor { // NOTE(chengcheng): flow.Tensor or flow.tensor ONLY created by EagerTensor now. LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); bool is_pinned = false; - if (other->is_local()) { is_pinned = JUST(CHECK_JUST(other->AsMirroredTensor())->is_pinned()); } + if (other->is_local()) { is_pinned = JUST(CHECK_JUST(other->AsLocalTensor())->is_pinned()); } return MakeTensorFromOtherTensor(other, is_pinned); } }; @@ -141,11 +147,11 @@ class TensorWithDataCtorFunctor { // NOTE(chengcheng): flow.Tensor or flow.tensor ONLY created by EagerTensor now. LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); - const auto& dtype = DType::Float(); + const auto& dtype = GetDefaultDType(); if (PyTensor_Check(data)) { const auto& other = PyTensor_Unpack(data); const bool pin_memory = - other->is_local() ? JUST(JUST(other->AsMirroredTensor())->is_pinned()) : false; + other->is_local() ? JUST(JUST(other->AsLocalTensor())->is_pinned()) : false; return MakeTensorFromOtherTensor(other, dtype, device, /*requires_grad=*/false, /*pin_memory=*/pin_memory); } @@ -155,7 +161,7 @@ class TensorWithDataCtorFunctor { } }; -class ConsistentTensorWithDataCtorFunctor { +class GlobalTensorWithDataCtorFunctor { public: Maybe operator()(PyObject* data, const Symbol& placement, const std::vector>& sbp_tuple) const { @@ -164,23 +170,23 @@ class ConsistentTensorWithDataCtorFunctor { if (PyLong_Check(data)) { int64_t size = PyLong_AsLongLong(data); Shape shape(DimVector{size}); - return ConsistentTensorWithShapeCtor(shape, placement, sbp_tuple); + return GlobalTensorWithShapeCtor(shape, placement, sbp_tuple); } if (TensorSize_Check(data)) { - return ConsistentTensorWithShapeCtor(TensorSize_AsShape(data), placement, sbp_tuple); + return GlobalTensorWithShapeCtor(TensorSize_AsShape(data), placement, sbp_tuple); } // NOTE(chengcheng): flow.Tensor or flow.tensor ONLY created by EagerTensor now. LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); - const auto& dtype = DType::Float(); + const auto& dtype = GetDefaultDType(); if (PyTensor_Check(data)) { const auto& other = PyTensor_Unpack(data); return MakeTensorFromOtherTensor(other, dtype, placement, sbp_tuple, /*requires_grad=*/false); } - // Make consistent tensor from python sequence or numpy array. - return MakeConsistentTensorFromData(data, dtype, placement, sbp_tuple, /*requires_grad=*/false); + // Make global tensor from python sequence or numpy array. + return MakeGlobalTensorFromData(data, dtype, placement, sbp_tuple, /*requires_grad=*/false); } }; @@ -195,18 +201,18 @@ class TensorWithShapeCtorFunctor { } else { device_ = JUST(Device::New("cpu")); } - return functional::Empty(shape, DType::Float(), device_, /*pin_memory=*/false); + return functional::Empty(shape, GetDefaultDType(), device_, /*pin_memory=*/false); } }; -class ConsistentTensorWithShapeCtorFunctor { +class GlobalTensorWithShapeCtorFunctor { public: Maybe operator()(const Shape& shape, const Symbol& placement, const std::vector>& sbp_tuple) const { // NOTE(chengcheng): flow.Tensor or flow.tensor ONLY created by EagerTensor now. LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); JUST(CheckDeviceIdsIsValid(placement)); - return functional::ConsistentEmpty(shape, DType::Float(), placement, sbp_tuple); + return functional::GlobalEmpty(shape, GetDefaultDType(), placement, sbp_tuple); } }; @@ -220,7 +226,9 @@ class AssignLocalTensorFunctor { // JUST(CheckInplaceValid(ref)); // align check to torch CHECK_OR_RETURN(ref->is_local() && value->is_local()) << "Both ref and value must be local tensor."; - JUST(OpInterpUtil::Dispatch(*op_, {ref, value})); + std::shared_ptr src = value; + if (ref->dtype() != src->dtype()) { src = JUST(To(src, ref->dtype(), false)); } + JUST(OpInterpUtil::Dispatch(*op_, {ref, src})); return Maybe::Ok(); } @@ -228,6 +236,35 @@ class AssignLocalTensorFunctor { std::shared_ptr op_; }; +static std::vector get_shape_or_stride_from_numpy(size_t ndim, npy_intp* values) { + auto result = std::vector(ndim); + for (size_t i = 0; i < ndim; ++i) { result[i] = static_cast(values[i]); } + return result; +} + +class LocalTensorSharedDlPackDataFunctor { + public: + LocalTensorSharedDlPackDataFunctor() {} + Maybe operator()(PyObject* obj) const { + DLManagedTensor* dlMTensor = (DLManagedTensor*)PyCapsule_GetPointer(obj, "dltensor"); + CHECK_NOTNULL_OR_RETURN(dlMTensor) + << "from_dlpack received an invalid capsule. " + "Note that DLTensor capsules can be consumed only once, " + "so you might have already constructed a tensor from it once."; + + // `tensor` steals the ownership of the underlying storage. It also passes a + // destructor function that will be called when the underlying storage goes + // out of scope. When the destructor is called, the dlMTensor is destructed + // too. + auto tensor = fromDLPack(dlMTensor); + + // Make sure this capsule will never be used again. + PyCapsule_SetName(obj, "used_dltensor"); + + return tensor; + } +}; + class LocalTensorSharedNumpyDataFunctor { public: LocalTensorSharedNumpyDataFunctor() {} @@ -236,37 +273,45 @@ class LocalTensorSharedNumpyDataFunctor { return Error::TypeError() << "expected np.ndarray, but got " << Py_TYPE(obj)->tp_name; } auto* array = reinterpret_cast(obj); - // TODO(wyg): support non-contiguous array. - if (!PyArray_IS_C_CONTIGUOUS(array)) { - OF_LOG_ONCE(LOG(WARNING) << "OneFlow don't support non-contiguous array now, " - "and we will copy the array to a contiguous one."); - // PyArray_GETCONTIGUOUS will return a reference if array is already contiguous, - // otherwise return a (contiguous) copy of the array. - // Note: Increment the reference count for array occurs whether the array is continuous or not - array = PyArray_GETCONTIGUOUS(array); - } else { - Py_INCREF(obj); + const size_t ndim = PyArray_NDIM(array); + std::vector sizes = get_shape_or_stride_from_numpy(ndim, PyArray_DIMS(array)); + std::vector strides = get_shape_or_stride_from_numpy(ndim, PyArray_STRIDES(array)); + // NumPy strides use bytes. OneFlow strides use element counts. + // These checks are consistent with pytorch(v1.10.0): + // https://github.com/pytorch/pytorch/blob/v1.10.0/torch/csrc/utils/tensor_numpy.cpp#L171 + const auto element_size_in_bytes = PyArray_ITEMSIZE(array); + for (auto& stride : strides) { + if (stride % element_size_in_bytes != 0) { + return Error::InvalidValueError() + << "given numpy array strides not a multiple of the element byte size. " + << "Copy the numpy array to reallocate the memory."; + } + stride /= element_size_in_bytes; } + for (size_t i = 0; i < ndim; ++i) { + if (strides[i] < 0) { + return Error::InvalidValueError() + << "At least one stride in the given numpy array is negative, " + << "and tensors with negative strides are not currently supported. " + << "(You can probably work around this by making a copy of your array " + << " with array.copy().) "; + } + } + void* data_ptr = PyArray_DATA(array); + if (!PyArray_EquivByteorders(PyArray_DESCR(array)->byteorder, NPY_NATIVE)) { + return Error::InvalidValueError() + << "given numpy array has byte order different from the native byte order. " + << "Conversion between byte orders is currently not supported."; + } + Py_INCREF(obj); // Build TensorMeta - int32_t dim = PyArray_NDIM(array); - const npy_intp* dims_ptr = PyArray_SHAPE(array); - const auto shape = std::make_shared(DimVector(dims_ptr, dims_ptr + dim)); + const auto shape = Shape(DimVector(sizes.begin(), sizes.end())); + const auto stride = Stride(strides.begin(), strides.end()); DataType data_type = JUST(numpy::GetOFDataTypeFromNpArray(array)); Symbol device = JUST(Device::New("cpu")); - const npy_intp* stride_ptr = PyArray_STRIDES(array); - // stride - auto strides = std::make_shared(stride_ptr, stride_ptr + dim); - auto element_size_in_bytes = PyArray_ITEMSIZE(array); - // NumPy strides use bytes. OneFlow strides use element counts. - for (auto& stride_val : *strides) { - if (stride_val % element_size_in_bytes != 0) { - return Error::RuntimeError() << "given numpy array strides not a multiple of the element " - "byte size. Copy the numpy array to reallocate the memory."; - } - stride_val /= element_size_in_bytes; - } - auto tensor_meta = std::make_shared(shape, strides, data_type, device, 0); + + auto tensor_meta = SymbolOf(LocalTensorMeta(shape, stride, data_type, device)); // Build TensorBuffer const auto& Free = [array](char* dptr) { @@ -275,9 +320,9 @@ class LocalTensorSharedNumpyDataFunctor { return Maybe::Ok(); })); }; - void* data_ptr = PyArray_DATA(array); - auto array_size_in_bytes = PyArray_NBYTES(array); - auto tensor_data = std::make_shared(); + + const auto array_size_in_bytes = PyArray_NBYTES(array); + auto tensor_data = std::make_shared(); tensor_data->set_blob_dptr( std::unique_ptr>(static_cast(data_ptr), Free), array_size_in_bytes); @@ -286,17 +331,17 @@ class LocalTensorSharedNumpyDataFunctor { auto tensor_storage = std::make_shared(tensor_data); // Build Tensor - auto tensor_impl = std::make_shared(tensor_meta, tensor_storage, - /*requires_grad=*/false, - /*ls_leaf=*/true); + auto tensor_impl = std::make_shared(tensor_storage, + /*requires_grad=*/false, + /*ls_leaf=*/true); // Init blob - JUST(tensor_impl->InitEagerBlobObject(NewLocalDepObject())); + JUST(tensor_impl->InitEagerBlobObject(tensor_meta, NewLocalDepObject())); const auto& stream = JUST(GetDefaultStreamByDevice(device)); const auto& eager_blob_object = JUST(tensor_impl->eager_blob_object()); JUST(eager_blob_object->init_producer_stream(stream)); eager_blob_object->set_last_used_stream(stream); - std::shared_ptr out(new MirroredTensor(tensor_impl)); + std::shared_ptr out(new LocalTensor(tensor_impl)); return out; } }; @@ -305,16 +350,17 @@ class LocalTensorSharedNumpyDataFunctor { ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("TensorWithData"); - m.add_functor("ConsistentTensorWithData"); + m.add_functor("GlobalTensorWithData"); m.add_functor("TensorEmptyCtor"); - m.add_functor("ConsistentTensorEmptyCtor"); + m.add_functor("GlobalTensorEmptyCtor"); m.add_functor("TensorWithOtherCtor"); m.add_functor("TensorWithDataCtor"); - m.add_functor("ConsistentTensorWithDataCtor"); + m.add_functor("GlobalTensorWithDataCtor"); m.add_functor("TensorWithShapeCtor"); - m.add_functor("ConsistentTensorWithShapeCtor"); + m.add_functor("GlobalTensorWithShapeCtor"); m.add_functor("AssignLocalTensor"); m.add_functor("LocalTensorSharedNumpyData"); + m.add_functor("LocalTensorSharedDlPackData"); } } // namespace functional diff --git a/oneflow/api/python/functional/tensor_api.yaml b/oneflow/api/python/functional/tensor_api.yaml index 531f9f3280325c117302a203ef79240d07bbd199..6601997b66dc905f64d95ea1426d59f48a2ac480 100644 --- a/oneflow/api/python/functional/tensor_api.yaml +++ b/oneflow/api/python/functional/tensor_api.yaml @@ -17,7 +17,7 @@ "Tensor (PyObject* data, *, DataType dtype=None, Device device=None, Bool requires_grad=False, Bool pin_memory=False) => TensorWithData", "Tensor (PyObject* data, *, DataType dtype=None, Placement placement, - SbpList sbp, Bool requires_grad=False) => ConsistentTensorWithData", + SbpList sbp, Bool requires_grad=False) => GlobalTensorWithData", ] bind_python: True @@ -25,12 +25,12 @@ signature: [ "Tensor (*, Device device=None) => TensorEmptyCtor", - "Tensor (*, Placement placement, SbpList sbp) => ConsistentTensorEmptyCtor", + "Tensor (*, Placement placement, SbpList sbp) => GlobalTensorEmptyCtor", "Tensor (Tensor other) => TensorWithOtherCtor", "Tensor (PyObject* data, *, Device device=None) => TensorWithDataCtor", - "Tensor (PyObject* data, *, Placement placement, SbpList sbp) => ConsistentTensorWithDataCtor", + "Tensor (PyObject* data, *, Placement placement, SbpList sbp) => GlobalTensorWithDataCtor", "Tensor (Shape size, *, Device device=None) => TensorWithShapeCtor", - "Tensor (Shape size, *, Placement placement, SbpList sbp) => ConsistentTensorWithShapeCtor", + "Tensor (Shape size, *, Placement placement, SbpList sbp) => GlobalTensorWithShapeCtor", ] bind_python: True @@ -41,3 +41,7 @@ - name: "from_numpy" signature: "Tensor (PyObject* obj) => LocalTensorSharedNumpyData" bind_python: True + +- name: "from_dlpack" + signature: "Tensor (PyObject* obj) => LocalTensorSharedDlPackData" + bind_python: True diff --git a/oneflow/api/python/functional/value_types.h b/oneflow/api/python/functional/value_types.h index 9707875479c2e2998dfa3ed27793fe506079a21b..d9a9d35b38c090e8169f32d7112caac30d2733e8 100644 --- a/oneflow/api/python/functional/value_types.h +++ b/oneflow/api/python/functional/value_types.h @@ -94,6 +94,7 @@ enum ValueType : int { kATTR_REF, kDTYPE, kSHAPE, + kSHAPE_MAYBE, kGENERATOR, kGENERATOR_REF, kGENERATOR_MAYBE, @@ -152,6 +153,7 @@ VALUE_TYPE_OF_IMPL(Maybe, kTENSOR_TUPLE_MAYBE); VALUE_TYPE_OF_IMPL(Symbol, kDTYPE); VALUE_TYPE_OF_IMPL(std::vector>, kDTYPE_LIST); VALUE_TYPE_OF_IMPL(Shape, kSHAPE); +VALUE_TYPE_OF_IMPL(Maybe, kSHAPE_MAYBE); VALUE_TYPE_OF_IMPL(std::vector, kSHAPE_LIST); VALUE_TYPE_OF_IMPL(one::Generator, kGENERATOR); VALUE_TYPE_OF_IMPL(std::shared_ptr, kGENERATOR_REF); diff --git a/oneflow/api/python/ir.cpp b/oneflow/api/python/ir.cpp index 422242d37c48ec17471aef22a4507d6b43418b8e..1d8be3a9fd4582cf13b2d450f405ab1e24478dee 100644 --- a/oneflow/api/python/ir.cpp +++ b/oneflow/api/python/ir.cpp @@ -13,19 +13,109 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/common/singleton.h" +#include "oneflow/ir/oneflow-extension/include/PyAst/Ast.h" + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include #ifdef WITH_MLIR #include "oneflow/ir/include/OneFlow/Extension.h" #include "oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h" -#include +#include "oneflow/ir/oneflow-extension/include/OneFlow/OneFlowLRJITRegistry.h" #include "oneflow/api/python/of_api_registry.h" +#include +#include +#include namespace oneflow { - ONEFLOW_API_PYBIND11_MODULE("ir", m) { m.def("load_jit_shared_lib", [](const std::string& lib_path) { MutSharedLibPaths()->insert(lib_path); }); + + // TODO: this may be move to a common place for create global singleton. + m.def("create_global_lr_jit", []() { Singleton::New(); }); + + m.def("compile_and_register_lr_jit", [](const std::string& function_id, + std::shared_ptr& func, bool is_dump) { + Singleton::Get()->Register(function_id, *func.get(), is_dump); + }); + + // look up and execute the registered function for python api + m.def("get_lr", [](const std::string& function_id, float base_lr, float step) { + auto engine = Singleton::Get()->LookUp(function_id); + return engine(base_lr, step); + }); + + pybind11::class_>(m, "smt"); + + pybind11::class_>(m, "expr"); + + pybind11::class_>( + m, "FunctionDef"); + m.def("FunctionDef_", &pyast::FunctionDef::FunctionDef_); + + pybind11::class_>(m, "Return"); + m.def("Return_", &pyast::Return::Return_); + + pybind11::class_>(m, "Assign"); + m.def("Assign_", &pyast::Assign::Assign_); + + pybind11::class_>(m, "If"); + m.def("If_", &pyast::If::If_); + + pybind11::class_>(m, "Raise"); + m.def("Raise_", &pyast::Raise::Raise_); + + pybind11::class_>(m, "Assert"); + m.def("Assert_", &pyast::Assert::Assert_); + + pybind11::class_>(m, "Expr"); + m.def("Expr_", &pyast::Expr::Expr_); + + pybind11::class_>(m, "BoolOp"); + m.def("BoolOp_", &pyast::BoolOp::BoolOp_); + + pybind11::class_>(m, "BinOp"); + m.def("BinOp_", &pyast::BinOp::BinOp_); + + pybind11::class_>(m, "Lambda"); + m.def("Lambda_", &pyast::Lambda::Lambda_); + + pybind11::class_>(m, "Compare"); + m.def("Compare_", &pyast::Compare::Compare_); + + pybind11::class_>(m, "Call"); + m.def("Call_", &pyast::Call::Call_); + + pybind11::class_>(m, "Num"); + m.def("Num_", &pyast::Num::Num_); + + pybind11::class_>(m, "Constant"); + m.def("Constant_", &pyast::Constant::Constant_); + + pybind11::class_>(m, + "Attribute"); + m.def("Attribute_", &pyast::Attribute::Attribute_); + + pybind11::class_>(m, "Name"); + m.def("Name_", &pyast::Name::Name_); + + pybind11::class_>(m, "arguments"); + m.def("arguments_", &pyast::arguments::arguments_); + + pybind11::class_>(m, "arg"); + m.def("arg_", &pyast::arg::arg_); } } // namespace oneflow diff --git a/oneflow/api/python/job_build/job_build_and_infer.cpp b/oneflow/api/python/job_build/job_build_and_infer.cpp index 875e18863404a39ff0d91fda2ed2b35ea791557c..42c2633988642ce45e8a8b9d306e8444c6e71371 100644 --- a/oneflow/api/python/job_build/job_build_and_infer.cpp +++ b/oneflow/api/python/job_build/job_build_and_infer.cpp @@ -22,51 +22,50 @@ namespace py = pybind11; namespace oneflow { +Maybe MarkVariableGradients(const one::TensorTuple& variables, + const one::TensorTuple& gradients) { + CHECK_OR_RETURN(LazyMode::is_enabled()); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(variables.size(), gradients.size()); // NOLINT(maybe-need-error-msg) + HashMap variable_grad_lbns; + for (int i = 0; i < variables.size(); ++i) { + const std::string& variable_lbn = one::TensorNameScope::Global()->Lookup(variables[i]); + CHECK_OR_RETURN(!variable_lbn.empty()) + << "variable which index is " << i << " expected to have a tensor name"; + const std::string& gradient_lbn = one::TensorNameScope::Global()->Lookup(gradients[i]); + CHECK_OR_RETURN(!gradient_lbn.empty()) + << "gradient which index is " << i << " expected to have a tensor name"; + variable_grad_lbns.emplace(variable_lbn, gradient_lbn); + } + return JUST(GetCurInferCtx())->MarkVariableGradientBlobNames(variable_grad_lbns); +} + +Maybe MarkOutputGradients(const one::TensorTuple& outputs, + const one::TensorTuple& gradients) { + CHECK_OR_RETURN(LazyMode::is_enabled()); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), gradients.size()); // NOLINT(maybe-need-error-msg) + HashMap output_gradient_lbns; + for (int i = 0; i < outputs.size(); ++i) { + const std::string& output_lbn = one::TensorNameScope::Global()->Lookup(outputs[i]); + CHECK_OR_RETURN(!output_lbn.empty()) + << "output which index is " << i << " expected to have a tensor name"; + const std::string& gradient_lbn = one::TensorNameScope::Global()->Lookup(gradients[i]); + CHECK_OR_RETURN(!gradient_lbn.empty()) + << "gradient which index is " << i << " expected to have a tensor name"; + output_gradient_lbns.emplace(output_lbn, gradient_lbn); + } + return JUST(GetCurInferCtx())->MarkOutputGradientBlobNames(output_gradient_lbns); +} + ONEFLOW_API_PYBIND11_MODULE("", m) { m.def("JobBuildAndInferCtx_Open", &JobBuildAndInferCtx_Open); m.def("JobBuildAndInferCtx_GetCurrentJobName", &JobBuildAndInferCtx_GetCurrentJobName); m.def("JobBuildAndInferCtx_GetCurrentJobId", &JobBuildAndInferCtx_GetCurrentJobId); m.def("JobBuildAndInferCtx_Close", &JobBuildAndInferCtx_Close); - m.def("CurJobBuildAndInferCtx_CheckJob", &CurJobBuildAndInferCtx_CheckJob); m.def("CurJobBuildAndInferCtx_SetJobConf", &CurJobBuildAndInferCtx_SetJobConf); - m.def("CurJobBuildAndInferCtx_SetTrainConf", &CurJobBuildAndInferCtx_SetTrainConf); m.def("CurJobBuildAndInferCtx_Complete", &CurJobBuildAndInferCtx_Complete, py::call_guard()); - m.def("CurJobBuildAndInferCtx_Rebuild", &CurJobBuildAndInferCtx_Rebuild, - py::call_guard()); - m.def("CurJobBuildAndInferCtx_HasJobConf", &CurJobBuildAndInferCtx_HasJobConf); - m.def("CurJobBuildAndInferCtx_AddAndInferMirroredOp", - &CurJobBuildAndInferCtx_AddAndInferMirroredOp, py::call_guard()); - - m.def("CurJobBuildAndInferCtx_AddAndInferConsistentOp", - &CurJobBuildAndInferCtx_AddAndInferConsistentOp); - m.def("CurJobBuildAndInferCtx_AddLbiAndDiffWatcherUuidPair", - &CurJobBuildAndInferCtx_AddLbiAndDiffWatcherUuidPair); - - m.def("JobBuildAndInferCtx_GetSerializedIdListAsStaticShape", - &JobBuildAndInferCtx_GetSerializedIdListAsStaticShape); - m.def("JobBuildAndInferCtx_GetDataType", &JobBuildAndInferCtx_GetDataType); - m.def("JobBuildAndInferCtx_IsDynamic", &JobBuildAndInferCtx_IsDynamic); - - m.def("JobBuildAndInferCtx_IsDisableBoxing", &JobBuildAndInferCtx_IsDisableBoxing); - - m.def("JobBuildAndInferCtx_GetSplitAxisFromProducerView", - &JobBuildAndInferCtx_GetSplitAxisFromProducerView); - m.def("JobBuildAndInferCtx_GetSerializedParallelConfFromProducerView", - &JobBuildAndInferCtx_GetSerializedParallelConfFromProducerView); - - m.def("CurJobBuildAndInferCtx_AddLossLogicalBlobName", - &CurJobBuildAndInferCtx_AddLossLogicalBlobName); - - m.def("JobBuildAndInferCtx_IsMirroredBlob", &JobBuildAndInferCtx_IsMirroredBlob); - m.def("JobBuildAndInferCtx_MirroredBlobGetNumSubLbi", - &JobBuildAndInferCtx_MirroredBlobGetNumSubLbi); - m.def("JobBuildAndInferCtx_MirroredBlobGetSerializedSubLbi", - &JobBuildAndInferCtx_MirroredBlobGetSubLbi); - m.def("JobBuildAndInferCtx_CheckLbnValidAndExist", &JobBuildAndInferCtx_CheckLbnValidAndExist); - m.def("JobBuildAndInferCtx_GetOpBlobLbn", &JobBuildAndInferCtx_GetOpBlobLbn); } } // namespace oneflow diff --git a/oneflow/api/python/job_build/job_build_and_infer.h b/oneflow/api/python/job_build/job_build_and_infer.h index d5fc32814e3ccb252dc885d3aef99462eb886051..204801d6e6b1973d731c38e2fdcf78023a0d75e8 100644 --- a/oneflow/api/python/job_build/job_build_and_infer.h +++ b/oneflow/api/python/job_build/job_build_and_infer.h @@ -19,6 +19,7 @@ limitations under the License. #include "oneflow/core/job/global_for.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/framework/tensor_name_scope.h" #include "oneflow/core/job/job_build_and_infer_ctx.h" #include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" @@ -48,129 +49,13 @@ inline Maybe JobBuildAndInferCtx_Close() { return Maybe::Ok(); } -inline Maybe CurJobBuildAndInferCtx_CheckJob() { return JUST(GetCurInferCtx())->CheckJob(); } - inline Maybe CurJobBuildAndInferCtx_SetJobConf(const std::string& job_conf_str) { JobConfigProto job_conf; CHECK_OR_RETURN(TxtString2PbMessage(job_conf_str, &job_conf)) << "job conf parse failed"; return JUST(GetCurInferCtx())->SetJobConf(job_conf); } -inline Maybe CurJobBuildAndInferCtx_SetTrainConf(const std::string& train_conf_str) { - TrainConf train_conf; - CHECK_OR_RETURN(TxtString2PbMessage(train_conf_str, &train_conf)) << "train conf parse failed"; - return JUST(GetCurInferCtx())->SetTrainConf(train_conf); -} - inline Maybe CurJobBuildAndInferCtx_Complete() { return JUST(GetCurInferCtx())->Complete(); } -inline Maybe CurJobBuildAndInferCtx_Rebuild() { return JUST(GetCurInferCtx())->Rebuild(); } - -inline Maybe CurJobBuildAndInferCtx_HasJobConf() { - return JUST(GetCurInferCtx())->HasJobConf(); -} - -inline Maybe CurJobBuildAndInferCtx_AddAndInferMirroredOp( - const std::string& op_conf_str) { - OperatorConf op_conf; - CHECK_OR_RETURN(TxtString2PbMessage(op_conf_str, &op_conf)) << "operator conf parse failed"; - auto* ctx = JUST(GetCurInferCtx()); - const auto& op_attribute = JUST(ctx->AddAndInferMirroredOp(op_conf)); - return PbMessage2TxtString(*op_attribute); -} - -inline Maybe CurJobBuildAndInferCtx_AddAndInferConsistentOp( - const std::string& op_conf_str) { - OperatorConf op_conf; - CHECK_OR_RETURN(TxtString2PbMessage(op_conf_str, &op_conf)) << "operator conf parse failed"; - auto* ctx = JUST(GetCurInferCtx()); - const auto& op_attribute = JUST(ctx->AddAndInferConsistentOp(op_conf)); - return PbMessage2TxtString(*op_attribute); -} - -inline Maybe CurJobBuildAndInferCtx_AddLbiAndDiffWatcherUuidPair( - const std::string& lbi_uuid_pair_str) { - auto* ctx = JUST(GetCurInferCtx()); - LbiAndDiffWatcherUuidPair lbi_uuid_pair; - CHECK_OR_RETURN(TxtString2PbMessage(lbi_uuid_pair_str, &lbi_uuid_pair)) - << "LbiAndDiffWatcherUuidPair parse failed"; - return ctx->AddLbiAndDiffWatcherUuidPair(lbi_uuid_pair); -} - -inline Maybe JobBuildAndInferCtx_GetSerializedIdListAsStaticShape( - const std::string& job_name, const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - const auto& shape = JUST(ctx->GetStaticShape(lbn)); - Int64List id_list; - *id_list.mutable_value() = {shape->dim_vec().begin(), shape->dim_vec().end()}; - return PbMessage2TxtString(id_list); -} - -inline Maybe JobBuildAndInferCtx_GetDataType(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return JUST(ctx->GetDataType(lbn)); -} - -inline Maybe JobBuildAndInferCtx_IsDynamic(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return ctx->IsDynamic(lbn); -} - -inline Maybe JobBuildAndInferCtx_IsDisableBoxing(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return ctx->IsDisableBoxing(lbn); -} - -inline Maybe JobBuildAndInferCtx_GetSplitAxisFromProducerView( - const std::string& job_name, const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return PbMessage2TxtString(*JUST(ctx->GetSplitAxisFromProducerView(lbn))); -} - -inline Maybe JobBuildAndInferCtx_GetSerializedParallelConfFromProducerView( - const std::string& job_name, const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return PbMessage2TxtString(JUST(ctx->GetParallelDescFromProducerView(lbn))->parallel_conf()); -} - -inline Maybe CurJobBuildAndInferCtx_AddLossLogicalBlobName(const std::string& lbn) { - return JUST(GetCurInferCtx())->AddLossLogicalBlobName(lbn); -} - -inline Maybe JobBuildAndInferCtx_IsMirroredBlob(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return ctx->IsMirroredBlob(lbn); -} - -inline Maybe JobBuildAndInferCtx_MirroredBlobGetNumSubLbi(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return ctx->MirroredBlobGetNumSubLbi(lbn); -} - -inline Maybe JobBuildAndInferCtx_MirroredBlobGetSubLbi(const std::string& job_name, - const std::string& lbn, - int index) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return PbMessage2TxtString(*JUST(ctx->MirroredBlobGetSubLbi(lbn, index))); -} - -inline Maybe JobBuildAndInferCtx_CheckLbnValidAndExist(const std::string& job_name, - const std::string& lbn) { - auto* ctx = JUST(GetJobBuildAndInferCtx(job_name)); - JUST(ctx->CheckLbnValidAndExist(lbn)); - return Maybe::Ok(); -} - -inline Maybe JobBuildAndInferCtx_GetOpBlobLbn(const std::string& job_name, - const std::string& op_name, - const std::string bn_in_op) { - const auto* job_ctx = JUST(GetJobBuildAndInferCtx(job_name)); - return job_ctx->GetOpBlobLbn(op_name, bn_in_op); -} inline Maybe AddTensorAsGraphLoss(const std::shared_ptr& t) { CHECK_OR_RETURN(t->is_lazy()); @@ -180,6 +65,11 @@ inline Maybe AddTensorAsGraphLoss(const std::shared_ptr& t) { return JUST(GetCurInferCtx())->AddLossLogicalBlobName(loss_lbn); } +Maybe MarkVariableGradients(const one::TensorTuple& variables, + const one::TensorTuple& gradients); + +Maybe MarkOutputGradients(const one::TensorTuple& outputs, const one::TensorTuple& gradients); + } // namespace oneflow #endif // ONEFLOW_API_PYTHON_JOB_BUILD_JOB_BUILD_AND_INFER_H_ diff --git a/oneflow/api/python/of_api_registry.h b/oneflow/api/python/of_api_registry.h index 0c8064011f95bcd29e600823af256359f0e00014..e265790565f43ddcd4429890e697457da9b9eead 100644 --- a/oneflow/api/python/of_api_registry.h +++ b/oneflow/api/python/of_api_registry.h @@ -20,8 +20,10 @@ limitations under the License. #include #include #include "oneflow/api/python/caster/maybe.h" -#include "oneflow/api/python/caster/tensor.h" #include "oneflow/api/python/caster/optional.h" +#include "oneflow/api/python/caster/size.h" +#include "oneflow/api/python/caster/tensor.h" +#include "oneflow/api/python/caster/autograd_function_state.h" #include "oneflow/core/common/preprocessor.h" namespace oneflow { diff --git a/oneflow/api/python/ofblob/ofblob.cpp b/oneflow/api/python/ofblob/ofblob.cpp deleted file mode 100644 index 1f7ea0873845e0cdda57a89701b892185ecdc043..0000000000000000000000000000000000000000 --- a/oneflow/api/python/ofblob/ofblob.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include "oneflow/api/python/of_api_registry.h" -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/common/data_type_seq.h" -#include "oneflow/api/python/ofblob/ofblob.h" -#include "oneflow/api/python/ofblob/ofblob.e.h" - -namespace py = pybind11; - -ONEFLOW_API_PYBIND11_MODULE("", m) { - m.def("Ofblob_GetDataType", &Ofblob_GetDataType); - m.def("OfBlob_NumAxes", &OfBlob_NumAxes); - m.def("OfBlob_IsDynamic", &OfBlob_IsDynamic); - - m.def("OfBlob_CopyShapeTo", &OfBlob_CopyShapeTo); - m.def("OfBlob_CopyStaticShapeTo", &OfBlob_CopyStaticShapeTo); - m.def("OfBlob_CopyShapeFrom", &OfBlob_CopyShapeFrom); - - m.def("Dtype_GetOfBlobCopyToBufferFuncName", &Dtype_GetOfBlobCopyToBufferFuncName); - m.def("Dtype_GetOfBlobCopyFromBufferFuncName", &Dtype_GetOfBlobCopyFromBufferFuncName); - -#define EXPORT_COPY_DATA_API(T, type_proto) \ - m.def("OfBlob_CopyToBuffer_" OF_PP_STRINGIZE(T), \ - [](uint64_t of_blob_ptr, py::array_t array) { \ - oneflow::NumPyArrayPtr array_ptr(array.ptr()); \ - OfBlob_CopyToBuffer_##T(of_blob_ptr, array_ptr); \ - }); \ - m.def("OfBlob_CopyFromBuffer_" OF_PP_STRINGIZE(T), \ - [](uint64_t of_blob_ptr, py::array_t array) { \ - oneflow::NumPyArrayPtr array_ptr(array.ptr()); \ - OfBlob_CopyFromBuffer_##T(of_blob_ptr, array_ptr); \ - }); - OF_PP_FOR_EACH_TUPLE(EXPORT_COPY_DATA_API, POD_DATA_TYPE_SEQ); - -#undef EXPORT_COPY_DATA_API -} diff --git a/oneflow/api/python/ofblob/ofblob.e.h b/oneflow/api/python/ofblob/ofblob.e.h deleted file mode 100644 index a1715205cdfc2114e81c2490bfab623cdef11941..0000000000000000000000000000000000000000 --- a/oneflow/api/python/ofblob/ofblob.e.h +++ /dev/null @@ -1,82 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_E_H_ -#define ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_E_H_ - -#include "oneflow/core/common/foreign_lock_helper.h" -#include "oneflow/core/common/type_traits.h" -#include -#include -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/common/data_type_seq.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/api/common/ofblob.h" -#include "oneflow/extension/python/numpy.h" - -namespace py = pybind11; - -namespace oneflow { - -template -struct BlobNumpyCopyUtil { - static Maybe From(uint64_t of_blob_ptr, const NumPyArrayPtr& array) { - return BlobBufferCopyUtil::From(of_blob_ptr, (T*)array.data(), array.size()); - } - - static Maybe To(uint64_t of_blob_ptr, const NumPyArrayPtr& array) { - return BlobBufferCopyUtil::To(of_blob_ptr, (T*)array.data(), array.size()); - } -}; - -} // namespace oneflow - -#define DEFINE_COPIER(T, type_proto) \ - inline void OfBlob_CopyToBuffer_##T(uint64_t of_blob_ptr, const oneflow::NumPyArrayPtr& array) { \ - oneflow::BlobNumpyCopyUtil::To(of_blob_ptr, array).GetOrThrow(); \ - } \ - inline void OfBlob_CopyFromBuffer_##T(uint64_t of_blob_ptr, \ - const oneflow::NumPyArrayPtr& array) { \ - oneflow::BlobNumpyCopyUtil::From(of_blob_ptr, array).GetOrThrow(); \ - } - -OF_PP_FOR_EACH_TUPLE(DEFINE_COPIER, POD_DATA_TYPE_SEQ); - -#undef DEFINE_COPIER - -inline std::string Dtype_GetOfBlobCopyToBufferFuncName(int64_t dtype) { - using namespace oneflow; - static const HashMap data_type2func_name{ -#define DATA_TYPE_FUNC_NAME_PAIR(type_cpp, type_proto) \ - {type_proto, "OfBlob_CopyToBuffer_" #type_cpp}, - OF_PP_FOR_EACH_TUPLE(DATA_TYPE_FUNC_NAME_PAIR, POD_DATA_TYPE_SEQ) -#undef DATA_TYPE_FUNC_NAME_PAIR - }; - return data_type2func_name.at(dtype); -} - -inline std::string Dtype_GetOfBlobCopyFromBufferFuncName(int64_t dtype) { - using namespace oneflow; - static const HashMap data_type2func_name{ -#define DATA_TYPE_FUNC_NAME_PAIR(type_cpp, type_proto) \ - {type_proto, "OfBlob_CopyFromBuffer_" #type_cpp}, - OF_PP_FOR_EACH_TUPLE(DATA_TYPE_FUNC_NAME_PAIR, POD_DATA_TYPE_SEQ) -#undef DATA_TYPE_FUNC_NAME_PAIR - }; - return data_type2func_name.at(dtype); -} - -#endif // ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_E_H_ diff --git a/oneflow/api/python/ofblob/ofblob.h b/oneflow/api/python/ofblob/ofblob.h deleted file mode 100644 index 47c7095a56074c2d0aa0b5be5a37b1b93ad1fbdc..0000000000000000000000000000000000000000 --- a/oneflow/api/python/ofblob/ofblob.h +++ /dev/null @@ -1,71 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_H_ -#define ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_H_ - -#include "oneflow/core/common/type_traits.h" -#include -#include -#include "oneflow/core/register/ofblob.h" - -namespace py = pybind11; - -inline int Ofblob_GetDataType(uint64_t of_blob_ptr) { - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->data_type(); -} - -inline size_t OfBlob_NumAxes(uint64_t of_blob_ptr) { - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->NumAxes(); -} - -inline bool OfBlob_IsDynamic(uint64_t of_blob_ptr) { - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->is_dynamic(); -} - -inline void OfBlob_CopyShapeFrom(uint64_t of_blob_ptr, py::array_t array) { - py::buffer_info buf = array.request(); - int64_t* buf_ptr = (int64_t*)buf.ptr; - size_t size = buf.size; - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->CopyShapeFrom(buf_ptr, size); -} - -inline void OfBlob_CopyShapeTo(uint64_t of_blob_ptr, py::array_t array) { - py::buffer_info buf = array.request(); - int64_t* buf_ptr = (int64_t*)buf.ptr; - size_t size = buf.size; - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->CopyShapeTo(buf_ptr, size); -} - -inline void OfBlob_CopyStaticShapeTo(uint64_t of_blob_ptr, py::array_t array) { - py::buffer_info buf = array.request(); - int64_t* buf_ptr = (int64_t*)buf.ptr; - size_t size = buf.size; - using namespace oneflow; - auto* of_blob = reinterpret_cast(of_blob_ptr); - return of_blob->CopyStaticShapeTo(buf_ptr, size); -} - -#endif // ONEFLOW_API_PYTHON_OFBLOB_OFBLOB_H_ diff --git a/oneflow/api/python/rpc/ccl.cpp b/oneflow/api/python/rpc/ccl.cpp index 7c34b1372d1f5822028012c8da24663c051b760f..5115e9875eea3f45d8db39d52696fbbeb363f00a 100644 --- a/oneflow/api/python/rpc/ccl.cpp +++ b/oneflow/api/python/rpc/ccl.cpp @@ -35,12 +35,16 @@ Maybe CpuBroadcast(py::bytes* in, int64_t root) { CHECK_NOTNULL_OR_RETURN(in); PyBytes_AsStringAndSize(in->ptr(), &buffer, &length); } - JUST(ccl::Broadcast(&length, &length, sizeof(length), DataType::kChar, root, - parallel_desc, nullptr)); + const auto& meta_transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeMeta)); + JUST(ccl::CpuBroadcast(&length, &length, sizeof(length), root, parallel_desc, + meta_transport_token)); + const auto& data_transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); if (GlobalProcessCtx::Rank() == root) { - JUST(ccl::Broadcast(buffer, buffer, length, DataType::kChar, root, // NOLINT - parallel_desc, nullptr)); + JUST(ccl::CpuBroadcast(buffer, buffer, length, root, parallel_desc, // NOLINT + data_transport_token)); // NOLINT return *in; } else { // https://github.com/pybind/pybind11/issues/1236#issuecomment-527730864 @@ -51,8 +55,7 @@ Maybe CpuBroadcast(py::bytes* in, int64_t root) { bytesObject->ob_shash = -1; bytesObject->ob_sval[length] = '\0'; buffer = bytesObject->ob_sval; - JUST(ccl::Broadcast(nullptr, buffer, length, DataType::kChar, root, - parallel_desc, nullptr)); + JUST(ccl::CpuBroadcast(nullptr, buffer, length, root, parallel_desc, data_transport_token)); return py::reinterpret_steal(reinterpret_cast(bytesObject)); } } diff --git a/oneflow/api/python/rpc/consistent_rpc_token_scope.cpp b/oneflow/api/python/rpc/consistent_rpc_token_scope.cpp deleted file mode 100644 index f7795e07edf167598c656925bb59ede5958288a7..0000000000000000000000000000000000000000 --- a/oneflow/api/python/rpc/consistent_rpc_token_scope.cpp +++ /dev/null @@ -1,60 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include -#include -#include "oneflow/api/python/of_api_registry.h" -#include "oneflow/core/thread/thread_consistent_id.h" -#include "oneflow/core/framework/rank_group_rpc_util.h" -#include "oneflow/core/job/rank_group.h" -#include "oneflow/core/job/rank_group_scope.h" -#include "oneflow/core/common/symbol.h" - -namespace py = pybind11; - -namespace oneflow { - -namespace { - -Maybe InitConsistentTransportTokenScope(const std::string& thread_tag, - int64_t thread_consistent_id, - Symbol rank_group) { - JUST(InitThisThreadUniqueConsistentId(thread_consistent_id, thread_tag)); - static thread_local const auto& init_rank_group_scope = - JUST(RankGroupScope::MakeInitialRankGroupScope(rank_group)); - // no unused warning for `init_rank_group_scope`. - (void)(init_rank_group_scope); - return Maybe::Ok(); -} - -Maybe InitConsistentTransportTokenScope(const std::string& thread_tag, - int64_t thread_consistent_id) { - const auto& rank_group = JUST(RankGroup::DefaultRankGroup()); - JUST(InitConsistentTransportTokenScope(thread_tag, thread_consistent_id, rank_group)); - return Maybe::Ok(); -} - -Maybe ApiInitDefaultConsistentTransportTokenScope() { - return InitConsistentTransportTokenScope("main", kThreadConsistentIdMain); -} - -} // namespace - -ONEFLOW_API_PYBIND11_MODULE("", m) { - m.def("InitDefaultConsistentTransportTokenScope", &ApiInitDefaultConsistentTransportTokenScope); -} - -} // namespace oneflow diff --git a/oneflow/api/python/rpc/global_rpc_token_scope.cpp b/oneflow/api/python/rpc/global_rpc_token_scope.cpp new file mode 100644 index 0000000000000000000000000000000000000000..988e5122e75719b3944c30b8e912a7bfd259fc65 --- /dev/null +++ b/oneflow/api/python/rpc/global_rpc_token_scope.cpp @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/core/thread/thread_global_id.h" +#include "oneflow/core/framework/rank_group_rpc_util.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/job/rank_group_scope.h" +#include "oneflow/core/common/symbol.h" + +namespace py = pybind11; + +namespace oneflow { + +namespace { + +Maybe InitGlobalTransportTokenScope(const std::string& thread_tag, int64_t thread_global_id, + Symbol rank_group) { + JUST(InitThisThreadUniqueGlobalId(thread_global_id, thread_tag)); + static thread_local const auto& init_rank_group_scope = + JUST(RankGroupScope::MakeInitialRankGroupScope(rank_group)); + // no unused warning for `init_rank_group_scope`. + (void)(init_rank_group_scope); + return Maybe::Ok(); +} + +Maybe InitGlobalTransportTokenScope(const std::string& thread_tag, int64_t thread_global_id) { + const auto& rank_group = JUST(RankGroup::DefaultRankGroup()); + JUST(InitGlobalTransportTokenScope(thread_tag, thread_global_id, rank_group)); + return Maybe::Ok(); +} + +Maybe ApiInitDefaultGlobalTransportTokenScope() { + return InitGlobalTransportTokenScope("main", kThreadGlobalIdMain); +} + +} // namespace + +ONEFLOW_API_PYBIND11_MODULE("", m) { + m.def("InitDefaultGlobalTransportTokenScope", &ApiInitDefaultGlobalTransportTokenScope); +} + +} // namespace oneflow diff --git a/oneflow/api/python/session/session.cpp b/oneflow/api/python/session/session.cpp index 43bde7ae7cc008068f68baf8238b01253fa64b03..41760da1c437e7cac5e374c0919a6943c9ec8a1c 100644 --- a/oneflow/api/python/session/session.cpp +++ b/oneflow/api/python/session/session.cpp @@ -20,21 +20,12 @@ limitations under the License. #include "oneflow/core/job/session.h" #include "oneflow/core/job/env_global_objects_scope.h" #include "oneflow/core/framework/multi_client_session_context.h" -#include "oneflow/api/python/session/session.h" namespace py = pybind11; namespace oneflow { ONEFLOW_API_PYBIND11_MODULE("", m) { - m.def("IsSessionInited", &IsSessionInited); - m.def("InitLazyGlobalSession", &InitLazyGlobalSession); - m.def("InitEagerGlobalSession", &InitEagerGlobalSession); - m.def("DestroyLazyGlobalSession", &DestroyLazyGlobalSession); - - m.def("StartLazyGlobalSession", &StartLazyGlobalSession); - m.def("StopLazyGlobalSession", &StopLazyGlobalSession); - using namespace oneflow; py::class_>( m, "SessionContext") @@ -43,6 +34,8 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { [](MultiClientSessionContext& session, const std::string& config_proto_str) { return session.TryInit(config_proto_str).GetOrThrow(); }) + .def("try_close", + [](MultiClientSessionContext& session) { return session.TryClose().GetOrThrow(); }) .def("update_resource", [](MultiClientSessionContext& session, const std::string& reso_proto_str) { return session.UpdateResource(reso_proto_str).GetOrThrow(); diff --git a/oneflow/api/python/session/session.h b/oneflow/api/python/session/session.h deleted file mode 100644 index d73240d0e346eb444f89562b841fce74e7667f86..0000000000000000000000000000000000000000 --- a/oneflow/api/python/session/session.h +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_API_PYTHON_SESSION_SESSION_H_ -#define ONEFLOW_API_PYTHON_SESSION_SESSION_H_ - -#include -#include -#include "oneflow/core/common/protobuf.h" -#include "oneflow/core/control/ctrl_client.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/env_global_objects_scope.h" -#include "oneflow/core/job/session_global_objects_scope.h" -#include "oneflow/core/job/cluster_instruction.h" -#include "oneflow/core/job/oneflow.h" -#include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" -#include "oneflow/core/job/resource_desc.h" -#include "oneflow/core/framework/config_def.h" -#include "oneflow/core/framework/multi_client_session_context.h" -#include "oneflow/core/framework/nn_graph.h" -#include "oneflow/core/persistence/tee_persistent_log_stream.h" - -namespace oneflow { - -inline Maybe IsSessionInited() { - return Singleton::Get() != nullptr; -} - -inline void FixCpuDeviceNum(ConfigProto* config_proto) { - if (config_proto->resource().cpu_device_num() > 0) { return; } - config_proto->mutable_resource()->set_cpu_device_num(std::thread::hardware_concurrency()); -} - -inline Maybe InitEagerGlobalSession(const std::string& config_proto_str) { - CHECK_NOTNULL_OR_RETURN(Singleton::Get()) << "env not found"; - ConfigProto config_proto; - CHECK_OR_RETURN(TxtString2PbMessage(config_proto_str, &config_proto)) - << "failed to parse config_proto: " << config_proto_str; - FixCpuDeviceNum(&config_proto); - Singleton::Get()->PushKV("config_proto", config_proto); - - CHECK_ISNULL_OR_RETURN(Singleton::Get()); - Singleton::SetAllocated(new SessionGlobalObjectsScope()); - - JUST(Singleton::Get()->EagerInit(config_proto)); - VLOG(3) << "NewGlobal " << typeid(SessionGlobalObjectsScope).name(); - - return Maybe::Ok(); -} - -inline Maybe InitLazyGlobalSession(const std::string& config_proto_str) { - CHECK_NOTNULL_OR_RETURN(Singleton::Get()) << "env not found"; - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - - ClusterInstruction::MasterSendSessionStart(); - - ConfigProto config_proto; - CHECK_OR_RETURN(TxtString2PbMessage(config_proto_str, &config_proto)) - << "failed to parse config_proto: " << config_proto_str; - FixCpuDeviceNum(&config_proto); - Singleton::Get()->PushKV("config_proto", config_proto); - - CHECK_ISNULL_OR_RETURN(Singleton::Get()); - Singleton::SetAllocated(new SessionGlobalObjectsScope()); - JUST(Singleton::Get()->Init(config_proto)); - VLOG(3) << "NewGlobal " << typeid(SessionGlobalObjectsScope).name(); - return Maybe::Ok(); -} - -inline Maybe DestroyLazyGlobalSession() { - if (Singleton::Get() == nullptr) { return Maybe::Ok(); } - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - Singleton::Delete(); - return Maybe::Ok(); -} - -inline Maybe StartLazyGlobalSession() { - CHECK_NOTNULL_OR_RETURN(Singleton::Get()) << "session not found"; - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - const JobSet& job_set = Singleton::Get()->job_set(); - if (Singleton::Get()->enable_debug_mode()) { - TeePersistentLogStream::Create("job_set.prototxt")->Write(job_set); - } - if (job_set.job().empty()) { return Error::JobSetEmptyError() << "no function defined"; } - CHECK_ISNULL_OR_RETURN(Singleton::Get()); - Singleton::Get()->PushKV("session_job_set", job_set); - Singleton::New(job_set.inter_job_reuse_mem_strategy()); - Singleton::New(); - JUST(Singleton::Get()->Init(job_set)); - return Maybe::Ok(); -} - -inline Maybe StopLazyGlobalSession() { - if (Singleton::Get() == nullptr) { return Maybe::Ok(); } - CHECK_OR_RETURN(GlobalProcessCtx::IsThisProcessMaster()); - CHECK_NOTNULL_OR_RETURN(Singleton::Get()); - Singleton::Delete(); - Singleton::Delete(); - return Maybe::Ok(); -} - -} // namespace oneflow - -#endif // ONEFLOW_API_PYTHON_SESSION_SESSION_H_ diff --git a/oneflow/api/python/stack_getter.cpp b/oneflow/api/python/stack_getter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ad00ad756b78885bc56d35445abfa82e64640112 --- /dev/null +++ b/oneflow/api/python/stack_getter.cpp @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include + +#include "pybind11/pybind11.h" + +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/core/common/singleton.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" +#include "oneflow/extension/stack/python/stack_getter.h" + +namespace py = pybind11; + +namespace oneflow { + +ONEFLOW_API_PYBIND11_MODULE("", m) { + m.def("RegisterStackGetter", &RegisterPyStackGetter); + m.def("GetCurrentStack", []() { + auto* stack_getter = Singleton::Get(); + return stack_getter->GetFormattedStack(stack_getter->GetCurrentFrame()); + }); +} + +} // namespace oneflow diff --git a/oneflow/api/python/symbol/placement_symbol.cpp b/oneflow/api/python/symbol/placement_symbol.cpp index e775a9da79708c5dddf6a568c39387a789b9c34d..4b76d2284e8bed3826cc8d359a5f4f9981769712 100644 --- a/oneflow/api/python/symbol/placement_symbol.cpp +++ b/oneflow/api/python/symbol/placement_symbol.cpp @@ -257,8 +257,8 @@ ONEFLOW_API_PYBIND11_MODULE("", m) { .def("__str__", PlacementToString) .def("__repr__", PlacementToString) .def(py::self == py::self) - .def(py::hash(py::self)); - m.def("AllDevicePlacement", &PlacementSymbolExportUtil::AllDevicePlacement); + .def(py::hash(py::self)) + .def_static("all", &PlacementSymbolExportUtil::AllDevicePlacement); } } // namespace oneflow diff --git a/oneflow/api/python/symbol/sbp_symbol.cpp b/oneflow/api/python/symbol/sbp_symbol.cpp index 3185ff92f180fe26a3a2508c3dbdd0579f956117..4f7e1168414bc513ae0dfb23a33d9e227f4b978c 100644 --- a/oneflow/api/python/symbol/sbp_symbol.cpp +++ b/oneflow/api/python/symbol/sbp_symbol.cpp @@ -90,8 +90,8 @@ ONEFLOW_API_PYBIND11_MODULE("sbp", m) { m.attr("max_split_axis") = kMaxSplitAxis; py::class_, std::shared_ptr>>(m, "sbp", py::dynamic_attr()) - .def("__str__", &api::SbpToString) - .def("__repr__", &api::SbpToString) + .def("__str__", &api::ApiSbpToString) + .def("__repr__", &api::ApiSbpToString) .def(py::self == py::self) .def(py::hash(py::self)) .def("_ToAttrStr", diff --git a/oneflow/api/python/utils/tensor_utils.cpp b/oneflow/api/python/utils/tensor_utils.cpp index 6285dc9e63726ad21d03bf7d1454824386df9f4c..1b7517204d868c714e2979b1bec35a1d4cc22700 100644 --- a/oneflow/api/python/utils/tensor_utils.cpp +++ b/oneflow/api/python/utils/tensor_utils.cpp @@ -15,13 +15,14 @@ limitations under the License. */ #include "oneflow/api/python/utils/tensor_utils.h" -#include "oneflow/api/python/ofblob/ofblob.e.h" #include "oneflow/core/autograd/autograd_engine.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/common/switch_func.h" #include "oneflow/core/common/tensor_buffer.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/job/global_mode.h" +#include "oneflow/core/kernel/kernel_util.h" #include "oneflow/extension/python/numpy.h" #include "oneflow/core/common/decorator.h" #include "oneflow/core/framework/consistency_check.h" @@ -32,11 +33,11 @@ namespace py = pybind11; namespace oneflow { namespace one { -Maybe EagerMirroredTensorZeros(const std::shared_ptr& t) { +Maybe EagerLocalTensorZeros(const std::shared_ptr& t) { JUST(functional::CheckInplaceValid(t)); - std::shared_ptr local_tensor; + std::shared_ptr local_tensor; if (t->is_local()) { - local_tensor = JUST(t->AsMirroredTensor()); + local_tensor = JUST(t->AsLocalTensor()); } else { local_tensor = JUST(t->cur_rank_phy_tensor()); } @@ -44,9 +45,9 @@ Maybe EagerMirroredTensorZeros(const std::shared_ptr& t) { JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { JUST(builder->AccessBlobByCallback( local_tensor, - [](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AsyncAutoMemset(0); + [](ep::Stream* stream, const std::shared_ptr& eager_blob_object) { + AutoMemset(stream, eager_blob_object->mut_dptr(), 0, + eager_blob_object->ByteSizeOfBlobBody(), eager_blob_object->mem_case()); }, "mut")); return Maybe::Ok(); @@ -54,38 +55,25 @@ Maybe EagerMirroredTensorZeros(const std::shared_ptr& t) { return Maybe::Ok(); } -template -Maybe CopyMirroredTensorFromUntypedArray(const std::shared_ptr& tensor, - PyObject* array) { - return CopyBetweenMirroredTensorAndNumpy(tensor, array, BlobNumpyCopyUtil::From, "mut", - /*block_host_until_done=*/false); -} - -Maybe GetCopyMirroredTensorToNumpyFuncName(DataType dtype) { - using namespace oneflow; - static const HashMap> data_type2func_name{ -#define DATA_TYPE_FUNC_NAME_PAIR(type_cpp, type_proto) \ - {type_proto, std::make_shared("_copy_to_numpy_" #type_cpp)}, - OF_PP_FOR_EACH_TUPLE(DATA_TYPE_FUNC_NAME_PAIR, POD_DATA_TYPE_SEQ) -#undef DATA_TYPE_FUNC_NAME_PAIR - }; - return JUST(MapAt(data_type2func_name, static_cast(dtype))); +namespace { +void CopyFromNumpyArray(ep::Stream* stream, + const std::shared_ptr& eager_blob_object, + const NumPyArrayPtr& array_ptr) { + SyncAutoMemcpy(stream, eager_blob_object->mut_dptr(), array_ptr.data(), + eager_blob_object->ByteSizeOfBlobBody(), eager_blob_object->mem_case(), + memory::MakeHostMemCase()); } +} // namespace -Maybe GetCopyMirroredTensorFromNumpyFuncName(DataType dtype) { - using namespace oneflow; - static const HashMap> data_type2func_name{ -#define DATA_TYPE_FUNC_NAME_PAIR(type_cpp, type_proto) \ - {type_proto, std::make_shared("_copy_from_numpy_" #type_cpp)}, - OF_PP_FOR_EACH_TUPLE(DATA_TYPE_FUNC_NAME_PAIR, POD_DATA_TYPE_SEQ) -#undef DATA_TYPE_FUNC_NAME_PAIR - }; - return JUST(MapAt(data_type2func_name, static_cast(dtype))); +Maybe CopyLocalTensorFromUntypedArray(const std::shared_ptr& tensor, + PyObject* array) { + return CopyBetweenLocalTensorAndNumpy(tensor, array, CopyFromNumpyArray, "mut", + /*block_host_until_done=*/false); } Maybe, std::vector>>> MaybeGetTensorBufferShapesAndDTypes(const std::shared_ptr& t) { - const auto& tensor = JUST(t->AsMirroredTensor()); + const auto& tensor = JUST(t->AsLocalTensor()); if (tensor->dtype() != DType::TensorBuffer()) { return Error::RuntimeError() << "tensor buffer supported only"; } @@ -93,10 +81,11 @@ MaybeGetTensorBufferShapesAndDTypes(const std::shared_ptr& t) { std::vector shapes; std::vector> dtypes; - auto btb = std::make_shared(1); + auto btb = std::make_shared(); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->SyncAccessBlobByCallback( - tensor, btb, [](uint64_t) {}, "const"); + tensor, btb, [](ep::Stream* stream, const std::shared_ptr&) {}, + "const"); })); JUST(btb->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); @@ -136,41 +125,51 @@ Maybe TensorGetPyTupleOfSbp(const Tensor& tensor) { return tuple; } -#define MAKE_SWITCH_ENTRY(func_name, dtype) func_name -DEFINE_STATIC_SWITCH_FUNC(Maybe, CopyMirroredTensorFromUntypedArray, MAKE_SWITCH_ENTRY, - MAKE_DATA_TYPE_CTRV_SEQ(POD_AND_HALF_DATA_TYPE_SEQ)); - Maybe MakeLocalTensorFromData(PyObject* data, const Optional>& dtype, const Optional>& device, const bool requires_grad, const bool pin_memory) { - PyObject* array = NULL; + bool is_bfloat16_dtype = dtype ? JUST(dtype)->data_type() == DataType::kBFloat16 : false; + bool is_cuda_device = device ? JUST(device)->enum_type() == DeviceType::kCUDA : false; + if (is_bfloat16_dtype && is_cuda_device) { +#if (CUDA_VERSION < 11000) + return Error::RuntimeError() + << "Cannot create a bfloat16 tensor on gpu under cuda version: 11000"; +#endif // CUDA_VERSION >= 11000 +#ifdef WITH_ROCM + return Error::RuntimeError() + << "Cannot create a bfloat16 tensor on gpu under ROCm for now"; +#endif // WITH_ROCM + } PyArray_Descr* np_dtype = - dtype.has_value() + dtype.has_value() && !is_bfloat16_dtype ? PyArray_DescrFromType(JUST(numpy::OFDataTypeToNumpyType(JUST(dtype)->data_type()))) : nullptr; - // PyArray_FromAny steals a reference to np_dtype object, so no need to decref it. // NPY_ARRAY_DEFAULT is NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_BEHAVED, so the // array with NPY_ARRAY_DEFAULT flag is C-style contiguous. // NPY_ARRAY_FORCECAST is needed otherwise there will a segfault. - array = PyArray_FromAny(data, np_dtype, 0, 0, - NPY_ARRAY_DEFAULT | NPY_ARRAY_ENSURECOPY | NPY_ARRAY_FORCECAST, nullptr); - if (!array) { + // + // Even though PyArray_FromAny can cast the input array to the desired dtype + // if `dtype` argument is set, it fails to handle the following case: + // >> x = [flow.tensor([1, 2])] * 3 <-- x is a list of flow.Tensor + // >> y = flow.tensor(x, dtype=flow.float32) <-- returns nullptr + // However, the following case without `dtype` argument works well: + // >> x = [flow.tensor([1, 2])] * 3 + // >> y = flow.tensor(x) + // So we cast the input array to the desired dtype manually. + PyArrayObject* _array = reinterpret_cast( + PyArray_FromAny(data, nullptr, 0, 0, + NPY_ARRAY_DEFAULT | NPY_ARRAY_ENSURECOPY | NPY_ARRAY_FORCECAST, nullptr)); + if (!_array) { return Error::RuntimeError() << "Can not convert input data to a new numpy array."; } - // flow.tensor([1., 2.]).dtype should be flow.float32 rather than flow.float64 - if (!PyArray_Check(data)) { - int np_array_type = PyArray_TYPE(reinterpret_cast(array)); - // Cast to float if data is double sequence, rather than numpy array. - if (np_array_type == NPY_DOUBLE && np_dtype == nullptr) { - PyObject* fp32_array = PyArray_Cast(reinterpret_cast(array), NPY_FLOAT); - Py_DECREF(array); - array = fp32_array; - } - } + // PyArray_FromArray steals a reference to np_dtype object, so no need to decref it. + PyObject* array = PyArray_FromArray( + _array, np_dtype, NPY_ARRAY_DEFAULT | NPY_ARRAY_ENSURECOPY | NPY_ARRAY_FORCECAST); + Py_DECREF(_array); auto* np_arr = reinterpret_cast(array); const npy_intp* dims_ptr = PyArray_SHAPE(np_arr); const Shape shape(DimVector(dims_ptr, dims_ptr + PyArray_NDIM(np_arr))); - DataType data_type = JUST(numpy::GetOFDataTypeFromNpArray(np_arr)); + DataType np_data_type = JUST(numpy::GetOFDataTypeFromNpArray(np_arr)); Symbol device_; if (device) { @@ -179,10 +178,17 @@ Maybe MakeLocalTensorFromData(PyObject* data, const Optional tensor = JUST( - functional::Empty(shape, JUST(DType::Get(data_type)), device_, /*pin_memory=*/pin_memory)); - JUST(SwitchCopyMirroredTensorFromUntypedArray(SwitchCase(data_type), tensor, array)); + functional::Empty(shape, JUST(DType::Get(np_data_type)), device_, /*pin_memory=*/pin_memory)); + JUST(CopyLocalTensorFromUntypedArray(tensor, array)); Py_DECREF(array); + if (dtype && JUST(dtype)->data_type() != np_data_type) { + tensor = JUST(functional::To(tensor, JUST(dtype), false)); + } else if (!dtype && !PyArray_Check(data) && tensor->dtype()->is_floating_point() + && GetDefaultDType() != tensor->dtype()) { + // If it not assign dtype and created from PySequence, cast tensor to default floating dtype + tensor = JUST(functional::To(tensor, JUST(DType::Get(DataType::kFloat)), false)); + } JUST(tensor->set_requires_grad(requires_grad)); return tensor; } @@ -201,10 +207,10 @@ auto* CachedGetAllBroadcastNdSbp = DECORATE(&GetAllBroadcastNdSbp, ThreadLocal); } // namespace -Maybe MakeConsistentTensorFromData(PyObject* data, const Optional>& dtype, - Symbol placement, - const std::vector>& sbp_tuple, - const bool requires_grad) { +Maybe MakeGlobalTensorFromData(PyObject* data, const Optional>& dtype, + Symbol placement, + const std::vector>& sbp_tuple, + const bool requires_grad) { PyObject* array = NULL; if (PyArray_Check(data)) { // Only NPY_CORDER is supported, and returns a new C-style contiguous array. @@ -229,9 +235,13 @@ Maybe MakeConsistentTensorFromData(PyObject* data, const Optional device = JUST(Device::New(placement->device_tag())); - std::shared_ptr local_tensor = - JUST(functional::Empty(shape, JUST(DType::Get(data_type)), device, /*pin_memory=*/false)); - JUST(SwitchCopyMirroredTensorFromUntypedArray(SwitchCase(data_type), local_tensor, array)); + std::shared_ptr local_tensor; + { + GlobalMode::Guard guard(/* disable global mode */ false); + local_tensor = + JUST(functional::Empty(shape, JUST(DType::Get(data_type)), device, /*pin_memory=*/false)); + } + JUST(CopyLocalTensorFromUntypedArray(local_tensor, array)); Py_DECREF(array); // Cast to float if data is double sequence, rather than numpy array. @@ -246,14 +256,16 @@ Maybe MakeConsistentTensorFromData(PyObject* data, const Optional broadcast_nd_sbp = JUST(CachedGetAllBroadcastNdSbp(sbp_dims)); - std::shared_ptr broadcast_tensor = JUST(functional::LocalToConsistent( - local_tensor, placement, *JUST(GetSbpList(broadcast_nd_sbp)), shape, local_tensor->dtype())); + std::shared_ptr broadcast_tensor = JUST( + functional::LocalToGlobal(local_tensor, placement, *JUST(GetSbpList(broadcast_nd_sbp)), shape, + local_tensor->dtype(), /* sync_data */ true, /*copy=*/false)); std::vector> grad_sbp_tuple; - auto consistent_tensor = JUST(functional::ToConsistent(broadcast_tensor, placement, sbp_tuple, - grad_sbp_tuple, /* check_meta */ false)); - JUST(consistent_tensor->set_requires_grad(requires_grad)); - return consistent_tensor; + auto global_tensor = + JUST(functional::ToGlobal(broadcast_tensor, placement, sbp_tuple, grad_sbp_tuple, + /* check_meta */ false, /*copy=*/false)); + JUST(global_tensor->set_requires_grad(requires_grad)); + return global_tensor; } Maybe MakeTensorFromOtherTensor(const std::shared_ptr& other, @@ -265,9 +277,9 @@ Maybe MakeTensorFromOtherTensor(const std::shared_ptr& other, const Symbol& nd_sbp = JUST(other->nd_sbp()); const std::vector>& sbp_tuple = *JUST(GetSbpList(nd_sbp)); std::vector> grad_sbp_tuple; - // TODO:(zhaoluyang) consistent case support pin_memory - return functional::ToConsistent(other, JUST(other->parallel_desc()), sbp_tuple, grad_sbp_tuple, - /* check_meta */ false); + // TODO:(zhaoluyang) global case support pin_memory + return functional::ToGlobal(other, JUST(other->parallel_desc()), sbp_tuple, grad_sbp_tuple, + /* check_meta */ false, /*copy=*/false); } } @@ -283,7 +295,7 @@ Maybe MakeTensorFromOtherTensor(const std::shared_ptr& other, tensor = JUST(functional::Copy(other, device_->type(), device_->device_id(), pin_memory && !dtype.has_value())); } else { - tensor = JUST(functional::ConsistentToLocal(other)); + tensor = JUST(functional::GlobalToLocal(other, /*copy=*/false)); if (!device) { device_ = JUST(Device::New("cpu")); } tensor = JUST(functional::Copy(tensor, device_->type(), device_->device_id(), pin_memory && !dtype.has_value())); @@ -302,9 +314,9 @@ Maybe MakeTensorFromOtherTensor(const std::shared_ptr& other, const std::vector>& sbp_tuple, const bool requires_grad) { std::vector> grad_sbp_tuple; - bool check_meta = other->is_consistent() ? false : true; - std::shared_ptr tensor = - JUST(functional::ToConsistent(other, placement, sbp_tuple, grad_sbp_tuple, check_meta)); + bool check_meta = other->is_global() ? false : true; + std::shared_ptr tensor = JUST(functional::ToGlobal( + other, placement, sbp_tuple, grad_sbp_tuple, check_meta, /*copy=*/false)); if (dtype) { const Symbol& dtype_ = JUST(dtype); if (tensor->dtype() != dtype_) { diff --git a/oneflow/api/python/utils/tensor_utils.h b/oneflow/api/python/utils/tensor_utils.h index 7843890bfeafdd456c9bd413349f8ed2ae4d3dc5..d20e0ea755eb6d87110b106d6fff7749a9a110f2 100644 --- a/oneflow/api/python/utils/tensor_utils.h +++ b/oneflow/api/python/utils/tensor_utils.h @@ -29,10 +29,13 @@ limitations under the License. #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/common/stride.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/common/blocking_then_busy.h" #include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/common/foreign_lock_helper.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/api/python/functional/common.h" +#include "oneflow/core/framework/tensor_util.h" +#include "oneflow/core/profiler/profiler.h" namespace py = pybind11; @@ -55,13 +58,13 @@ struct format_descriptor { namespace oneflow { namespace one { -Maybe EagerMirroredTensorZeros(const std::shared_ptr& t); +Maybe EagerLocalTensorZeros(const std::shared_ptr& t); template -inline static Maybe EagerMirroredTensorToNumpy(PyObject* py_tensor) { +inline static Maybe EagerLocalTensorToNumpy(PyObject* py_tensor) { const auto& t = PyTensor_Unpack(py_tensor); - std::shared_ptr tensor = JUST(t->AsMirroredTensor()); + std::shared_ptr tensor = JUST(t->AsLocalTensor()); CHECK_OR_RETURN(JUST(tensor->device()) == JUST(Device::New("cpu"))); CHECK_OR_RETURN(tensor->is_eager()) << "eager tensors supported only."; // set base object attr @@ -74,12 +77,13 @@ inline static Maybe EagerMirroredTensorToNumpy(PyObject* py_tensor) { numpy::OFStrideToNumpyStride(*JUST(tensor->stride()), tensor->dtype()->data_type()); T* data_ptr = nullptr; - const auto& Callback = [&](uint64_t ofblob_ptr) { - data_ptr = reinterpret_cast(ofblob_ptr)->mut_blob()->mut_dptr(); + const auto& Callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + data_ptr = eager_blob_object->mut_dptr(); }; - auto btb = std::make_shared(1); + auto btb = std::make_shared(); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - return builder->SyncAccessBlobByCallback(tensor, btb, Callback, "mut"); + return builder->SyncAccessBlobByCallback(tensor, btb, Callback, "const"); })); JUST(btb->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); return py::array(py::buffer_info(data_ptr, sizeof(T), py::format_descriptor::format(), ndim, @@ -90,19 +94,43 @@ inline static Maybe EagerMirroredTensorToNumpy(PyObject* py_tensor) { } template -inline Maybe CopyBetweenMirroredTensorAndNumpy( +struct TensorTypeToPyType final { + typedef T type; +}; + +template<> +struct TensorTypeToPyType final { + typedef float type; +}; + +template<> +struct TensorTypeToPyType final { + typedef float type; +}; + +template +inline static Maybe EagerLocalTensorItem(const std::shared_ptr& tensor) { + // OF_PROFILER_RANGE_GUARD("EagerLocalTensorItem"); + T value = JUST(GetItemInScalarTensor(tensor)); + return functional::CastToPyObject(static_cast::type>(value)); +} + +inline Maybe CopyBetweenLocalTensorAndNumpy( const std::shared_ptr& t, PyObject* array, - Maybe (*Copy)(uint64_t, const NumPyArrayPtr&), const std::string& modifier, - bool block_host_until_done) { - auto tensor = JUST(t->AsMirroredTensor()); + void (*Copy)(ep::Stream*, const std::shared_ptr&, const NumPyArrayPtr&), + const std::string& modifier, bool block_host_until_done) { + auto tensor = JUST(t->AsLocalTensor()); + CHECK_OR_RETURN(tensor->is_contiguous()) << "contiguous tensors supported only."; CHECK_OR_RETURN(tensor->is_eager()) << "eager tensors supported only."; if (block_host_until_done) { NumPyArrayPtr array_ptr(array); - const auto& Callback = [array_ptr, Copy](uint64_t ofblob_ptr) { - CHECK_JUST(Copy(ofblob_ptr, array_ptr)); + const auto& Callback = [array_ptr, Copy]( + ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + Copy(stream, eager_blob_object, array_ptr); }; - auto btb = std::make_shared(1); + auto btb = std::make_shared(); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->SyncAccessBlobByCallback(tensor, btb, Callback, modifier); })); @@ -119,17 +147,16 @@ inline Maybe CopyBetweenMirroredTensorAndNumpy( JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->AccessBlobByCallback( tensor, - [array_ptr, Copy](uint64_t ofblob_ptr) { CHECK_JUST(Copy(ofblob_ptr, array_ptr)); }, + [array_ptr, Copy](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + Copy(stream, eager_blob_object, array_ptr); + }, modifier); })); } return Maybe::Ok(); } -Maybe GetCopyMirroredTensorToNumpyFuncName(DataType dtype); - -Maybe GetCopyMirroredTensorFromNumpyFuncName(DataType dtype); - Maybe, std::vector>>> MaybeGetTensorBufferShapesAndDTypes(const std::shared_ptr& t); @@ -144,10 +171,10 @@ Maybe MakeLocalTensorFromData(PyObject* data, const Optional>& device, const bool requires_grad, const bool pin_memory); -Maybe MakeConsistentTensorFromData(PyObject* data, const Optional>& dtype, - Symbol placement, - const std::vector>& sbp_tuple, - const bool requires_grad); +Maybe MakeGlobalTensorFromData(PyObject* data, const Optional>& dtype, + Symbol placement, + const std::vector>& sbp_tuple, + const bool requires_grad); Maybe MakeTensorFromOtherTensor(const std::shared_ptr& other, const bool pin_memory); diff --git a/oneflow/core/auto_parallel/algorithm_util.cpp b/oneflow/core/auto_parallel/algorithm_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..47540d0fc3fa30a40f94c1bb6cd7dff1e116d6ef --- /dev/null +++ b/oneflow/core/auto_parallel/algorithm_util.cpp @@ -0,0 +1,33 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/auto_parallel/algorithm_util.h" + +namespace oneflow { +namespace auto_parallel { + +// Inverse function of order +// The reason why we need the inverse_order, a.k.a id2order, instead of id2value is to eliminate +// equality. For example, we have v[0] < v[1] = v[2] < v[3] We do not know v[1] is before or after +// v[2] with comp(v[1], v[2]). But if we transfer it to order order[0] < order[1] < order[2] < +// order[3] We know the strict order. +void InverseOrder(const std::vector& order, std::vector& inverse_order) { + inverse_order.resize(order.size()); + for (int32_t i = 0; i < order.size(); i++) { inverse_order[order[i]] = i; } +} + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/algorithm_util.h b/oneflow/core/auto_parallel/algorithm_util.h new file mode 100644 index 0000000000000000000000000000000000000000..7fa77072adcac09f616c72860dae49106f3950bb --- /dev/null +++ b/oneflow/core/auto_parallel/algorithm_util.h @@ -0,0 +1,82 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_ALGORITHM_UTIL_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_ALGORITHM_UTIL_H_ + +#include +#include +#include +#include + +namespace oneflow { +namespace auto_parallel { + +// this function is to remove the i-th element from a vector in Constant time. +// the vector should not care about ordering. +// Be more careful about this function. Make sure that the traveling order of +// the vector goes from back to front. +template +void RemoveFrom(std::vector& v, int32_t i) { + v[i] = v.back(); + v.pop_back(); +} + +template +void CheckAndRemoveFrom(std::vector& v, T& t) { + for (int32_t i = v.size() - 1; i >= 0; i--) { + if (v[i] == t) { + RemoveFrom(v, i); + break; + } + } +} + +// Inverse function, which transfer a vector to an unordered_map. +template +void InverseFunction(const std::vector& v, std::unordered_map& inverse_map) { + inverse_map.clear(); + for (int32_t i = 0; i < v.size(); i++) { inverse_map[v[i]] = i; } +} + +// When you want to sort something but you can not move any elements, use order. +// Decide the order of sorting in a list v, we have +// v[order[i]] < v[order[j]] for all i +void DecideOrder(const T& v, std::vector& order, const Compare& comp) { + // Initialize order + order.resize(v.size()); + for (int32_t i = 0; i < v.size(); i++) { order[i] = i; } + // sort + std::sort(order.begin(), order.end(), [&](int32_t i, int32_t j) { return comp(v[i], v[j]); }); +} + +// Inverse function of order +// The reason why we need the inverse_order, a.k.a id2order, instead of id2value is to eliminate +// equality. For example, we have v[0] < v[1] = v[2] < v[3] We do not know v[1] is before or after +// v[2] with comp(v[1], v[2]). But if we transfer it to order order[0] < order[1] < order[2] < +// order[3] We know the strict order. +void InverseOrder(const std::vector& order, std::vector& inverse_order); + +} // namespace auto_parallel + +static const double kFloatDeviationMinus = 0.9999999; +static const double kFloatDeviationPlus = 1.0000001; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_ALGORITHM_UTIL_H_ diff --git a/oneflow/core/auto_parallel/binary_set.cpp b/oneflow/core/auto_parallel/binary_set.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b97cd7e6910af948de67792d97a7601711bcb936 --- /dev/null +++ b/oneflow/core/auto_parallel/binary_set.cpp @@ -0,0 +1,147 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/auto_parallel/binary_set.h" + +namespace oneflow { +namespace auto_parallel { + +namespace { +// A static function for initialization of log_2 mapping +std::unordered_map InitLog2() { + std::unordered_map log_2; + for (int32_t i = 0; i < 8 * sizeof(BinarySetEntryType); i++) { + log_2[static_cast(1 << i)] = i; + } + return log_2; +} + +// Initialization of log_2 mapping +// Take log2 of a integer value: 2^n -> n. +const std::unordered_map log_2 = InitLog2(); + +} // namespace + +// Constructor +BinarySet::BinarySet(int32_t size_of_set) : size_of_set_(size_of_set) { + int32_t k = (size_of_set - 1) / bit_entry_type_ + 1; + binary_set_values_.resize(k, 0); +} + +// Initialization if needed +void BinarySet::Initialize(int32_t size_of_set) { + size_of_set_ = size_of_set; + int32_t k = (size_of_set - 1) / bit_entry_type_ + 1; + binary_set_values_.resize(k, 0); +} + +// Clear all the elements in the set +void BinarySet::Clear() { binary_set_values_.assign(binary_set_values_.size(), 0); } + +// Check if i-th element in this subset +bool BinarySet::CheckExistence(int32_t i) const { + int32_t k = i / bit_entry_type_; + int32_t j = i % bit_entry_type_; + return bool((binary_set_values_[k] >> j) & 1); +} + +// Add i-th element into this subset +void BinarySet::AddEntry(int32_t i) { + int32_t k = i / bit_entry_type_; + int32_t j = i % bit_entry_type_; + binary_set_values_[k] |= (1 << j); +} +// Take i-th element out from this subset +void BinarySet::DeleteEntry(int32_t i) { + int32_t k = i / bit_entry_type_; + int32_t j = i % bit_entry_type_; + binary_set_values_[k] &= ~(1 << j); +} +// Get the union with another subset and store it into u +void BinarySet::UnionTo(const BinarySet& bs, BinarySet& u) { + for (int32_t k = 0; k < binary_set_values_.size(); k++) { + u.binary_set_values_[k] = binary_set_values_[k] | bs.binary_set_values_[k]; + } +} +// If this binary set intersects another one +bool BinarySet::IfIntersect(const BinarySet& bs) const { + int32_t min_bs_size = std::min(binary_set_values_.size(), bs.binary_set_values_.size()); + for (int32_t k = 0; k < min_bs_size; k++) { + if (binary_set_values_[k] & bs.binary_set_values_[k]) { return true; } + } + return false; +} +// Get the intersection with another subset and store it into i +void BinarySet::IntersectionTo(const BinarySet& bs, BinarySet& i) const { + int32_t min_bs_size = std::min(binary_set_values_.size(), bs.binary_set_values_.size()); + if (min_bs_size > i.binary_set_values_.size()) { i.binary_set_values_.resize(min_bs_size, 0); } + for (int32_t k = 0; k < binary_set_values_.size(); k++) { + i.binary_set_values_[k] = binary_set_values_[k] & bs.binary_set_values_[k]; + } +} +// Count number of elements in this subset +int32_t BinarySet::Total() const { + int32_t t = 0; + for (int32_t k = 0; k < binary_set_values_.size(); k++) { + BinarySetEntryType bsv = binary_set_values_[k]; + bsv = (bsv & 0x5555555555555555) + ((bsv >> 1) & 0x5555555555555555); + bsv = (bsv & 0x3333333333333333) + ((bsv >> 2) & 0x3333333333333333); + bsv = (bsv & 0x0F0F0F0F0F0F0F0F) + ((bsv >> 4) & 0x0F0F0F0F0F0F0F0F); + bsv = (bsv & 0x00FF00FF00FF00FF) + ((bsv >> 8) & 0x00FF00FF00FF00FF); + bsv = (bsv & 0x0000FFFF0000FFFF) + ((bsv >> 16) & 0x0000FFFF0000FFFF); + // bsv = (bsv & 0x00000000FFFFFFFF) + ((bsv >> 32) & 0x00000000FFFFFFFF); + t += int32_t(bsv); + } + return t; +} + +// Output all the elements in the subset +void BinarySet::Output(std::vector& out) const { + out.clear(); + for (int32_t i = 0; i < size_of_set_; i++) { + if (CheckExistence(i)) { out.emplace_back(i); } + } +} + +// Output all the elements in the subset +void BinarySet::QuickOutput(std::vector& out) const { + out.clear(); + for (int32_t i = 0; i < binary_set_values_.size(); i++) { + BinarySetEntryType x = binary_set_values_[i]; + BinarySetEntryType y = 0; + while (x) { + y = x; + x &= x - 1; + out.emplace_back(i * BinarySet::bit_entry_type_ + log_2.find(y - x)->second); + } + } +} + +// Add elements of input into this subset +void BinarySet::AddEntries(std::vector& in) { + for (int32_t i : in) { AddEntry(i); } +} + +// If two binary sets are equal to each other +bool BinarySet::operator==(const BinarySet& rhs) const { + if (size_of_set_ != rhs.size_of_set_) { return false; } + for (int32_t i = 0; i < binary_set_values_.size(); i++) { + if (binary_set_values_[i] != rhs.binary_set_values_[i]) { return false; } + } + return true; +} + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/binary_set.h b/oneflow/core/auto_parallel/binary_set.h new file mode 100644 index 0000000000000000000000000000000000000000..f93997bd5dcce9eccc51337f60f866e98eb7ffc9 --- /dev/null +++ b/oneflow/core/auto_parallel/binary_set.h @@ -0,0 +1,93 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_BINARY_SET_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_BINARY_SET_H_ + +#include +#include +#include +#include "oneflow/core/common/hash.h" + +namespace oneflow { +namespace auto_parallel { + +// log_2_ index only support 32-bit int. Don't know why. +// Don't have any other bugs for unsigned int. +using BinarySetEntryType = unsigned int; + +class BinarySet { + public: + BinarySet() {} + explicit BinarySet(int32_t size_of_set); + + // Initialization + void Initialize(int32_t size_of_set); + // Clear all the elements in the set + void Clear(); + // Check if i-th element in this subset + bool CheckExistence(int32_t i) const; + // Add i-th element into this subset + void AddEntry(int32_t i); + // Take i-th element out from this subset + void DeleteEntry(int32_t i); + // Get the union with another subset and store it into u + void UnionTo(const BinarySet& bs, BinarySet& u); + // If this binary set intersects another one + bool IfIntersect(const BinarySet& bs) const; + // Get the intersection with another subset and store it into i + void IntersectionTo(const BinarySet& bs, BinarySet& i) const; + // Count number of elements in this subset + int32_t Total() const; + // Output all the elements in the subset + void Output(std::vector& out) const; + // Output all the elements in the subset + void QuickOutput(std::vector& out) const; + // Add elements of input into this subset + void AddEntries(std::vector& in); + // If two binary sets are equal to each other + bool operator==(const BinarySet& rhs) const; + + inline int32_t GetSizeOfSet() const { return size_of_set_; }; + + private: + friend struct BinarySetHasher; + // binary_set_values_ contains a vector of 64-bit or 32-bit int. + // Each bit means whether an entry is in the set + std::vector binary_set_values_; + + int32_t size_of_set_ = -1; + + // total bits of the entry type in vector binary_set_values_. + static constexpr int32_t bit_entry_type_ = 8 * sizeof(BinarySetEntryType); +}; + +struct BinarySetHasher { + std::size_t operator()(const BinarySet& bs) const { + using std::hash; + using std::size_t; + + size_t h = 0; + for (int i = 0; i < bs.binary_set_values_.size(); i++) { + h = HashCombine(h, hash()(bs.binary_set_values_[i])); + } + return h; + }; +}; + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_BINARY_SET_H_ diff --git a/oneflow/core/auto_parallel/boxing_collector.cpp b/oneflow/core/auto_parallel/boxing_collector.cpp index 6308fd0855ba4b512bd261445d80d1816bc7bab2..02d1621d16d61c82f49057a3c492c8afc3f465a2 100644 --- a/oneflow/core/auto_parallel/boxing_collector.cpp +++ b/oneflow/core/auto_parallel/boxing_collector.cpp @@ -16,8 +16,10 @@ limitations under the License. #include #include +#include "oneflow/core/auto_parallel/algorithm_util.h" #include "oneflow/core/auto_parallel/boxing_collector.h" #include "oneflow/core/common/data_type.h" +#include "oneflow/core/common/device_type.pb.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/job/global_for.h" @@ -34,9 +36,9 @@ limitations under the License. namespace oneflow { namespace { -void DfsSetNdSbp(const std::vector<::oneflow::SbpParallel>& id2sbp_parallel, int32_t depth, - int32_t max_depth, NdSbp& nd_sbp, std::vector& nd_sbp_lists, - std::unordered_map<::oneflow::NdSbp, int32_t>& nd_sbp_universe) { +void DfsSetNdSbp(const std::vector& id2sbp_parallel, int32_t depth, int32_t max_depth, + NdSbp& nd_sbp, std::vector& nd_sbp_lists, + std::unordered_map& nd_sbp_universe) { if (depth == max_depth) { nd_sbp_universe[nd_sbp] = nd_sbp_lists.size(); nd_sbp_lists.push_back(nd_sbp); @@ -49,7 +51,7 @@ void DfsSetNdSbp(const std::vector<::oneflow::SbpParallel>& id2sbp_parallel, int } // Let a nd sbp be consistent with the given hierarchy number -Maybe SetNdSbpDim(NdSbp nd_sbp, int32_t hierarchy_num) { +Maybe SetNdSbpDim(const NdSbp& nd_sbp, int32_t hierarchy_num) { // Do not need to change if (nd_sbp.sbp_parallel_size() == hierarchy_num) { return nd_sbp; } // (S0, S0) -> S0 @@ -71,9 +73,63 @@ Maybe SetNdSbpDim(NdSbp nd_sbp, int32_t hierarchy_num) { return new_sbp; } +int32_t TotalNumSplit(const NdSbp& nd_sbp, const ParallelDesc& parallel_desc) { + int32_t total_num_split = 1; + for (int32_t i = 0; i < nd_sbp.sbp_parallel_size(); i++) { + if (nd_sbp.sbp_parallel(i).has_split_parallel()) { + total_num_split *= parallel_desc.hierarchy()->At(i); + } + } + return total_num_split; +} + +// Dealing with 1D sbp to 1D sbp +// Specifically, S -> P. +Maybe AskSbpCombinationFor1DSbp(const NdSbp& sbp_producer, const NdSbp& sbp_consumer, + const ParallelDesc& producer_parallel_desc, + const ParallelDesc& consumer_parallel_desc, + std::vector& middle_sbps, int32_t* diag_node_pos) { + if (sbp_consumer.sbp_parallel(0).has_partial_sum_parallel()) { + // Support [4]: P <--> [2, 2]: (P, P) + // Support {0, 1, 2, 3}: P <--> {2, 0, 6, 7}: (P, P) + if (producer_parallel_desc.parallel_num() == consumer_parallel_desc.parallel_num() + && sbp_producer.sbp_parallel(0).has_partial_sum_parallel()) { + return Maybe::Ok(); + } + + if (!sbp_producer.sbp_parallel(0).has_broadcast_parallel()) { + // S -> B -> P (Large cost!) + // TODO: Please implement S -> P directly. + // We do not support [3]: P <--> [2, 2]: (P, P) as well. + + int32_t hierarchy_size = 0; + if (producer_parallel_desc.hierarchy()->elem_cnt() + < consumer_parallel_desc.hierarchy()->elem_cnt()) { + // The diagonal node uses the parallel description from producer + // (S, S) -> (B, B) -> P/(P, P) or S -> B -> P/(P, P) + *diag_node_pos = 1; + hierarchy_size = producer_parallel_desc.hierarchy()->NumAxes(); + } else { + // The diagonal node uses the parallel description from consumer + // S/(S, S) -> B -> P or S/(S, S) -> (B, B) -> (P, P) + *diag_node_pos = 0; + hierarchy_size = consumer_parallel_desc.hierarchy()->NumAxes(); + } + + NdSbp broadcast_nd; + for (int32_t i = 0; i < hierarchy_size; i++) { + broadcast_nd.add_sbp_parallel(); + broadcast_nd.mutable_sbp_parallel(i)->mutable_broadcast_parallel(); + } + middle_sbps.emplace_back(broadcast_nd); + } + } + return Maybe::Ok(); +} + } // namespace -// A constructor with init, designed for uncustomized boxing collector +// A constructor with init, designed for pre-stored boxing collector BoxingCollector::BoxingCollector(int32_t max_axis) { CHECK_JUST(Init(max_axis)); } // Construct a boxing collector with given maximum number of axis @@ -92,6 +148,8 @@ Maybe BoxingCollector::Init(int32_t max_axis) { JUST(GenerateCombination4SamePlacement(3)); JUST(GenerateCombination4DiffHierarchy(this, this)); JUST(GenerateCombination4DiffPlacement(this, this)); + init_type_ = int32_t(enable_general_basic_communication + || Singleton::Get()->nccl_use_compute_stream()); return Maybe::Ok(); } @@ -106,6 +164,8 @@ Maybe BoxingCollector::Init(const BlobDesc& logical_blob_desc, // Get copy cost in lazy mode LazyMode::Guard enable_lazy_mode(true); JUST(GenerateCombination4SamePlacement(5, logical_blob_desc, parallel_desc)); + init_type_ = int32_t(enable_general_basic_communication + || Singleton::Get()->nccl_use_compute_stream()); return Maybe::Ok(); } @@ -173,6 +233,7 @@ void BoxingCollector::GenerateMap1d2nd() { // Generate the id Map from 1d sbp to nd sbp NdSbp nd_sbp; for (int32_t dim_sbp = 0; dim_sbp < hierarchy_num_; dim_sbp++) { nd_sbp.add_sbp_parallel(); } + id_1d_2_nd_.clear(); id_1d_2_nd_.resize(m, -1); for (int32_t id_1d = 0; id_1d < m; id_1d++) { for (int32_t dim_sbp = 0; dim_sbp < hierarchy_num_; dim_sbp++) { @@ -190,10 +251,13 @@ Maybe BoxingCollector::GenerateCombination4SamePlacement(int32_t max_middl // NOTE: The performance of this function are all the same with different hierarchy int32_t world_size = GlobalProcessCtx::WorldSize(); Shape hierarchy44({4 * world_size, 4 * world_size}); + int32_t virtual_range_size = hierarchy44.elem_cnt(); std::shared_ptr virtual_hierarchy = std::make_shared(hierarchy44); auto parallel_desc = JUST(ParallelDesc::New( "cpu", {"0:0-" + std::to_string(hierarchy44.elem_cnt() - 1)}, virtual_hierarchy)); - BlobDesc blob_desc({16, 16, 16, 16}, DataType::kInt8, /*is_dynamic=*/false); + BlobDesc blob_desc({virtual_range_size, virtual_range_size, virtual_range_size, + virtual_range_size, virtual_range_size, virtual_range_size}, + DataType::kInt8, /*is_dynamic=*/false); JUST(GenerateCombination4SamePlacement(max_middle_node_num, blob_desc, *parallel_desc)); return Maybe::Ok(); } @@ -204,7 +268,9 @@ Maybe BoxingCollector::GenerateCombination4SamePlacement(int32_t max_middl const ParallelDesc& parallel_desc) { // Store the origin transfer cost information int32_t n = nd_sbp_lists_.size(); + minimum_copy_cost_.clear(); minimum_copy_cost_.resize(n); + middle_nodes_.clear(); middle_nodes_.resize(n); for (int32_t i = 0; i < n; i++) { minimum_copy_cost_[i].resize(n); @@ -250,7 +316,7 @@ Maybe BoxingCollector::GenerateCombination4SamePlacement(int32_t max_middl minimum_copy_cost_[i][j] = curr_copy_cost; } } - // If the minimum copy cost remians infinity, adding one middle node does not make it. + // If the minimum copy cost remains infinity, adding one middle node does not make it. if (minimum_copy_cost_[i][j] > GetValidMaxCopyCost()) { continue; } // Find those middle nodes for (int32_t k = 0; k < n; k++) { @@ -291,6 +357,7 @@ Maybe BoxingCollector::GenerateCombination4DiffHierarchy( // Search the path that contains one of the diagonal sbp int32_t n = nd_sbp_lists_.size(); + diag_node_diff_hierarchy_.clear(); diag_node_diff_hierarchy_.resize(n); for (int32_t i = 0; i < n; i++) { diag_node_diff_hierarchy_[i].resize(n); @@ -309,7 +376,10 @@ Maybe BoxingCollector::GenerateCombination4DiffPlacement( BoxingCollector* boxing_collector_producer, BoxingCollector* boxing_collector_consumer) { // Virtual parallel and blob description int32_t world_size = GlobalProcessCtx::WorldSize(); - BlobDesc blob_desc({16, 16, 16, 16}, DataType::kInt8, /*is_dynamic=*/false); + int32_t virtual_range_size = 4 * world_size * (4 * world_size + 1); + BlobDesc blob_desc({virtual_range_size, virtual_range_size, virtual_range_size, + virtual_range_size, virtual_range_size, virtual_range_size}, + DataType::kInt8, /*is_dynamic=*/false); // Virtual placements before transfer Shape in_hierarchy44({4 * world_size + 1, 4 * world_size}); std::shared_ptr in_hierarchy = std::make_shared(in_hierarchy44); @@ -334,6 +404,7 @@ Maybe BoxingCollector::ComputeCostFor1DSbpDiffPlacement( // Number of 1d sbp int32_t m = id2sbp_parallel_.size(); // Compute the cost while transferring a 1D sbp between different placements + cost_4_diff_placement.clear(); cost_4_diff_placement.resize(m); for (int32_t id_1d_producer = 0; id_1d_producer < m; id_1d_producer++) { cost_4_diff_placement[id_1d_producer].resize(m, GetMaxVal()); @@ -364,6 +435,7 @@ Maybe BoxingCollector::GenerateCombination4DiffPlacement( // Search the path that contains two of the diagonal sbp int32_t n = nd_sbp_lists_.size(); + diag_node_diff_placement_.clear(); diag_node_diff_placement_.resize(n); for (int32_t i = 0; i < n; i++) { diag_node_diff_placement_[i].resize(n); @@ -496,64 +568,74 @@ Maybe BoxingCollector::AskSbpCombination(const NdSbp& sbp_producer, const if (ParseBooleanFromEnv("ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK", false)) { return Maybe::Ok(); } - // If compute_cost==false + 2D sbp + same placment + nccl logical + not (p->b), - // Use nccl logical send recv instead of middle node. - // Note that in op sbp inference, cost of middle nodes is still used for the moment. -#if defined(WITH_CUDA) || defined(WITH_ROCM) - if (compute_cost == false && producer_parallel_desc.hierarchy()->NumAxes() == 2 - && producer_parallel_desc == consumer_parallel_desc - && !(NdSbpHasPartialParallel(sbp_consumer)) && - // TODO(): When same dim 0 finished dealing with (*, P) -> (*, S) in nccl logical pass, open - // this condition. When dealing with (P, P) -> (B, S0), middle node will change it to (P, P) - // -> (P, S0) -> (B, S0), neither same dim 0 or send recv in nccl logical pass can deal with - // (P, P) -> (P, S0) at the moment. - // !(NdSbpHasPartialParallel(sbp_producer) && NdSbpHasBroadcastParallel(sbp_consumer)) && - Singleton::Get()->nccl_use_compute_stream()) { - VLOG(3) << "Middle node insertion is skipped when src sbp is " << NdSbpToString(sbp_producer) - << " dst sbp is " << NdSbpToString(sbp_consumer) - << ", because nccl logical send/recv can handle this."; + if (producer_parallel_desc == consumer_parallel_desc && sbp_producer == sbp_consumer) { return Maybe::Ok(); } -#endif // WITH_CUDA // Dealing with 1D sbp to 1D sbp - // Specifically, S -> P. if (Is1dSbp(sbp_producer) && Is1dSbp(sbp_consumer)) { - if (sbp_consumer.sbp_parallel(0).has_partial_sum_parallel()) { - // Support [4]: P <--> [2, 2]: (P, P) - // Support {0, 1, 2, 3}: P <--> {2, 0, 6, 7}: (P, P) - if (producer_parallel_desc.parallel_num() == consumer_parallel_desc.parallel_num() - && sbp_producer.sbp_parallel(0).has_partial_sum_parallel()) { - return Maybe::Ok(); - } + JUST(AskSbpCombinationFor1DSbp(sbp_producer, sbp_consumer, producer_parallel_desc, + consumer_parallel_desc, middle_sbps, diag_node_pos)); + // No middle nodes for the other 1d-sbp combinations + return Maybe::Ok(); + } - if (!sbp_producer.sbp_parallel(0).has_broadcast_parallel()) { - // S -> B -> P (Large cost!) - // TODO: Please implement S -> P directly. - // We do not support [3]: P <--> [2, 2]: (P, P) as well. - - int32_t hierarchy_size = 0; - if (producer_parallel_desc.hierarchy()->elem_cnt() - < consumer_parallel_desc.hierarchy()->elem_cnt()) { - // The diagonal node uses the parallel description from producer - // (S, S) -> (B, B) -> P/(P, P) or S -> B -> P/(P, P) - *diag_node_pos = 1; - hierarchy_size = producer_parallel_desc.hierarchy()->NumAxes(); - } else { - // The diagonal node uses the parallel description from consumer - // S/(S, S) -> B -> P or S/(S, S) -> (B, B) -> (P, P) - *diag_node_pos = 0; - hierarchy_size = consumer_parallel_desc.hierarchy()->NumAxes(); - } +#ifdef WITH_CUDA + // Use a general basic communication if no P in the consumer + if (((Singleton::Get()->nccl_use_compute_stream() + && producer_parallel_desc == consumer_parallel_desc) + || enable_general_basic_communication) + && (!NdSbpHasPartialParallel(sbp_consumer)) + && producer_parallel_desc.device_type() == DeviceType::kCUDA + && consumer_parallel_desc.device_type() == DeviceType::kCUDA) { + if (NdSbpHasPartialParallel(sbp_producer) && NdSbpHasBroadcastParallel(sbp_consumer)) { + // (?, P, ?)->(Si, Sj)->(?, B, ?), two-step transfer + // Directly applying general basic communication would have O(n^2) time complexity for P->B + // Using two-step transfer would reduce it to a linear cost + JUST(AskSbpCombination4GeneralBasicCommunication( + sbp_producer, sbp_consumer, logical_blob_desc, producer_parallel_desc, + consumer_parallel_desc, middle_sbps, diag_node_pos)); + } + // Otherwise, one-step transfer + return Maybe::Ok(); + } +#endif // WITH_CUDA - NdSbp broadcast_nd; - for (int32_t i = 0; i < hierarchy_size; i++) { - broadcast_nd.add_sbp_parallel(); - broadcast_nd.mutable_sbp_parallel(i)->mutable_broadcast_parallel(); - } - middle_sbps.emplace_back(broadcast_nd); - } - return Maybe::Ok(); +#ifdef WITH_ROCM + // Use a general basic communication if no P in the consumer + if (((Singleton::Get()->nccl_use_compute_stream() + && producer_parallel_desc == consumer_parallel_desc) + || enable_general_basic_communication) + && (!NdSbpHasPartialParallel(sbp_consumer)) + && producer_parallel_desc.device_type() == DeviceType::kCUDA + && consumer_parallel_desc.device_type() == DeviceType::kCUDA) { + if (NdSbpHasPartialParallel(sbp_producer) && NdSbpHasBroadcastParallel(sbp_consumer)) { + // (?, P, ?)->(Si, Sj)->(?, B, ?), two-step transfer + // Directly applying general basic communication would have O(n^2) time complexity for P->B + // Using two-step transfer would reduce it to a linear cost + JUST(AskSbpCombination4GeneralBasicCommunication( + sbp_producer, sbp_consumer, logical_blob_desc, producer_parallel_desc, + consumer_parallel_desc, middle_sbps, diag_node_pos)); + } + // Otherwise, one-step transfer + return Maybe::Ok(); + } +#endif // WITH_ROCM + + if (JUST(ComputeLazyCopyCostBetweenNdSbp(sbp_producer, sbp_consumer, logical_blob_desc, + producer_parallel_desc, consumer_parallel_desc, + /*requires_same_sbp=*/false)) + < GetValidMaxCopyCost()) { + return Maybe::Ok(); + } else { + int32_t require_init_type = + int32_t(enable_general_basic_communication + || Singleton::Get()->nccl_use_compute_stream()); + if (init_type_ != require_init_type) { + // We assemble the boxing table from S(0) to S(5). + // Those splitting in higher axes are considered in the customized boxing. + constexpr int32_t kRegularMaxSplitAxes = 6; + JUST(Init(kRegularMaxSplitAxes)); } } @@ -568,6 +650,7 @@ Maybe BoxingCollector::AskSbpCombination(const NdSbp& sbp_producer, const // Transfer for the same machines, devices and hierarchy. if (sbp_producer == sbp_consumer) { return Maybe::Ok(); } const auto& parallel_hierarchy = producer_parallel_desc.hierarchy(); + *diag_node_pos = 0; // Dealing with nD sbp, n>2 if (parallel_hierarchy->NumAxes() > 2) { @@ -675,7 +758,7 @@ Maybe BoxingCollector::AskSbpCombination4DiffPlacement( if (same_placement) { // Different hierarchies CHECK_OR_RETURN(diag_node_diff_hierarchy_.size() > 0) - << "Have not initialzie the combination table for different hierarchies yet! " + << "Have not initialized the combination table for different hierarchies yet! " "Please run JUST(GenerateCombination4DiffHierarchy(this, this)); " "before Asking sbp combination for different parallel description."; if (JUST(Ask1Combination4DiffPlacement( @@ -687,7 +770,7 @@ Maybe BoxingCollector::AskSbpCombination4DiffPlacement( } else { // Different placements CHECK_OR_RETURN(diag_node_diff_placement_.size() > 0) - << "Have not initialzie the combination table for different hierarchies yet! " + << "Have not initialized the combination table for different hierarchies yet! " "Please run JUST(GenerateCombination4DiffPlacement(this, this)); " "before Asking sbp combination for different parallel description."; if (JUST(Ask1Combination4DiffPlacement( @@ -787,9 +870,9 @@ Maybe BoxingCollector::Generate1Combination4DiffHierarchy( min_path_length = path_length; // Find a candidate with small cost - if (curr_cost < min_cost * 1.0000001) { + if (curr_cost < min_cost * kFloatDeviationPlus) { // Find a smaller cost, clear the previous path. - if (curr_cost < min_cost * 0.9999999) { + if (curr_cost < min_cost * kFloatDeviationMinus) { min_cost = curr_cost; diag_nodes.clear(); } @@ -1007,4 +1090,105 @@ Maybe BoxingCollector::FilterNdSbpList4LogicalShape(const BlobDesc& logica return Maybe::Ok(); } +// Ask for sbp combination for general basic communication +Maybe BoxingCollector::AskSbpCombination4GeneralBasicCommunication( + const NdSbp& sbp_producer, const NdSbp& sbp_consumer, const BlobDesc& logical_blob_desc, + const ParallelDesc& producer_parallel_desc, const ParallelDesc& consumer_parallel_desc, + std::vector& middle_sbps, int32_t* diag_node_pos) { + // (P, X) -> (B, X) || (X , P) -> (X, B), X is any SBP + // One step transfer, at most 50% reduction in the transfer cost, do not use middle nodes + if (producer_parallel_desc == consumer_parallel_desc + && producer_parallel_desc.hierarchy()->NumAxes() == 2 + && (sbp_producer.sbp_parallel(0) == sbp_consumer.sbp_parallel(0) + || sbp_producer.sbp_parallel(1) == sbp_consumer.sbp_parallel(1))) { + return Maybe::Ok(); + } + + // Not enough gain in transfer cost, do not use middle nodes + int32_t partial_ratio4producer = PartialRatio4Producer(sbp_producer, producer_parallel_desc); + int32_t broadcast_ratio4consumer = BroadcastRatio4Consumer(sbp_consumer, consumer_parallel_desc); + if (2 * (partial_ratio4producer + broadcast_ratio4consumer) + >= partial_ratio4producer * broadcast_ratio4consumer) { + return Maybe::Ok(); + } + + bool close2producer = true; + if (producer_parallel_desc.parallel_num() == consumer_parallel_desc.parallel_num()) { + // Get close to the one with more splits + close2producer = TotalNumSplit(sbp_producer, producer_parallel_desc) + > TotalNumSplit(sbp_consumer, consumer_parallel_desc); + } else { + // Get close to the one with more machines + close2producer = producer_parallel_desc.parallel_num() > consumer_parallel_desc.parallel_num(); + } + // Get the contiguous sbp + if (close2producer) { + JUST(AskCloseAllSplitSbp(sbp_producer, producer_parallel_desc, logical_blob_desc, middle_sbps)); + *diag_node_pos = 1; + } else { + JUST(AskCloseAllSplitSbp(sbp_consumer, consumer_parallel_desc, logical_blob_desc, middle_sbps)); + *diag_node_pos = 0; + } + return Maybe::Ok(); +} + +// Ask for a all-split sbp which is close to the original one +Maybe BoxingCollector::AskCloseAllSplitSbp(const NdSbp& nd_sbp, + const ParallelDesc& parallel_desc, + const BlobDesc& logical_blob_desc, + std::vector& middle_sbps) { + Shape remain_shape = logical_blob_desc.shape(); + Shape rest_split_shape = logical_blob_desc.shape(); + int32_t dim_shape = remain_shape.NumAxes(); + // Initialize the remains and splitting + // logical_blob_desc.shape() == remain_shape .* rest_split_shape; + for (int32_t i = 0; i < dim_shape; i++) { rest_split_shape.Set(i, 1); } + for (int32_t sbp_id = 0; sbp_id < nd_sbp.sbp_parallel_size(); sbp_id++) { + const auto& sbp = nd_sbp.sbp_parallel(sbp_id); + if (sbp.has_split_parallel()) { + int32_t axis = sbp.split_parallel().axis(); + int32_t split_num = parallel_desc.hierarchy()->At(sbp_id); + remain_shape.Set(axis, remain_shape.At(axis) / split_num); + rest_split_shape.Set(axis, rest_split_shape.At(axis) * split_num); + } + } + // Get the contiguous sbp + NdSbp new_sbp = nd_sbp; + for (int32_t sbp_id = 0; sbp_id < nd_sbp.sbp_parallel_size(); sbp_id++) { + const auto& sbp = nd_sbp.sbp_parallel(sbp_id); + int32_t split_num = parallel_desc.hierarchy()->At(sbp_id); + if (sbp.has_split_parallel()) { + int32_t axis = sbp.split_parallel().axis(); + // split shape is the total splitting number starting from sbp_id to the end + rest_split_shape.Set(axis, rest_split_shape.At(axis) / split_num); + } else { + // change P or B to S(axis) + int32_t axis = -1; + // 4096 is large enough, we might not have that much devices + int32_t min_split_num = 4096; + // We need to pick a suitable axis + for (int32_t i = 0; i < remain_shape.NumAxes(); i++) { + if (remain_shape.At(i) % split_num == 0) { + if (rest_split_shape.At(i) < min_split_num) { + // Pick the axis with smallest splitting number among the rest of the sbp + min_split_num = rest_split_shape.At(i); + axis = i; + } + } + } + // P, B -> S(axis) + if (axis >= 0) { + new_sbp.mutable_sbp_parallel(sbp_id)->mutable_split_parallel()->set_axis(axis); + remain_shape.Set(axis, remain_shape.At(axis) / split_num); + } else { + // Can not find a suitable contiguous sbp + return Maybe::Ok(); + } + } + } + // Add the new sbp into the middle node lists + middle_sbps.emplace_back(new_sbp); + return Maybe::Ok(); +} + } // namespace oneflow diff --git a/oneflow/core/auto_parallel/boxing_collector.h b/oneflow/core/auto_parallel/boxing_collector.h index 09ddfd48f13ed6a21d1dd639eff278ff849b548e..2b62126d1697152e8c370bf7b1957f9d593cd935 100644 --- a/oneflow/core/auto_parallel/boxing_collector.h +++ b/oneflow/core/auto_parallel/boxing_collector.h @@ -129,10 +129,19 @@ class BoxingCollector final { BoxingCollector* boxing_collector_producer, BoxingCollector* boxing_collector_consumer, const std::vector>& diag_nodes); + // Ask for sbp combination for general basic communication + Maybe AskSbpCombination4GeneralBasicCommunication( + const NdSbp& sbp_producer, const NdSbp& sbp_consumer, const BlobDesc& logical_blob_desc, + const ParallelDesc& producer_parallel_desc, const ParallelDesc& consumer_parallel_desc, + std::vector& middle_sbps, int32_t* diag_node_pos); + // Ask for a all-split sbp which is closed to the original one + Maybe AskCloseAllSplitSbp(const NdSbp& nd_sbp, const ParallelDesc& parallel_desc, + const BlobDesc& logical_blob_desc, + std::vector& middle_sbps); // Stores all the possible SbpParallel. - HashMap<::oneflow::SbpParallel, int32_t> sbp_parallel_universe_; + HashMap sbp_parallel_universe_; // Relationship between id and Sbp Parallel - std::vector<::oneflow::SbpParallel> id2sbp_parallel_; + std::vector id2sbp_parallel_; // minimum cost // minimum_copy_cost[producer][consumer] std::vector> minimum_copy_cost_; @@ -142,18 +151,23 @@ class BoxingCollector final { // nodes that needs to be inserted std::vector>>> middle_nodes_; // Stores all the possible NdSbp. - std::unordered_map<::oneflow::NdSbp, int32_t> nd_sbp_universe_; + std::unordered_map nd_sbp_universe_; // Relationship between id and Nd Sbp std::vector nd_sbp_lists_; - // The diagonal middle node for differe placements + // The diagonal middle node for different placements std::vector>>> diag_node_diff_placement_; - // The diagonal middle node for differe hierarchies in the same placement + // The diagonal middle node for different hierarchies in the same placement std::vector>>> diag_node_diff_hierarchy_; // Id Map from 1d sbp to 2d sbp // For example: B -> (B, B), S0 -> (S0, S0) std::vector id_1d_2_nd_; // The sbp size in the combination table int32_t hierarchy_num_; + // How the boxing collector is initialized + int32_t init_type_ = -1; + // Enable general basic communication or not + const bool enable_general_basic_communication = + ParseBooleanFromEnv("ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION", false); }; // class BoxingCollector } // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_collector.cpp b/oneflow/core/auto_parallel/sbp_collector.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fb2169263cfc53c5c6ceefdad6aefcb9f6a12bdb --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_collector.cpp @@ -0,0 +1,403 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/core/auto_parallel/sbp_collector.h" +#include "oneflow/core/auto_parallel/binary_set.h" +#include "oneflow/core/auto_parallel/sbp_util.h" +#include "oneflow/core/auto_parallel/sbp_constructor.h" + +namespace oneflow { + +namespace auto_parallel { + +namespace { +// Whether the given binary set intersects all the sbp sets of the consumers +bool IfIntersectAll( + const HashMap, BinarySet>& consumer_bn2sbp_set, + const BinarySet& bs) { + for (const auto& sbp_set_group : consumer_bn2sbp_set) { + if (!bs.IfIntersect(sbp_set_group.second)) { return false; } + } + + return true; +} + +// Find unique sbp sets +void FindUniqueSbpSets( + const HashMap, BinarySet>& consumer_bn2sbp_set, + const std::unordered_set& all_sbp_set, std::vector& accumulator, + BinarySet& unique_sbps) { + std::vector sbp_ids; + // count the number of sbp + for (const auto& sbp_set_group : consumer_bn2sbp_set) { + sbp_set_group.second.QuickOutput(sbp_ids); + for (int32_t sbp_id : sbp_ids) { accumulator[sbp_id]++; } + } + // find unique sbp and clear the accumulator + for (const auto& sbp_id : all_sbp_set) { + if (accumulator[sbp_id] == 1) { unique_sbps.AddEntry(sbp_id); } + accumulator[sbp_id] = 0; + } +} + +// Find unique sbp groups +void FindUniqueSbpGroups( + const HashMap, BinarySet>& consumer_bn2sbp_set, + const std::unordered_set& all_sbp_set, std::vector& accumulator, + BinarySet& bs_buffer, std::vector& unique_sbp_groups) { + // find the unique sbp sets + BinarySet unique_sbps(accumulator.size()); + FindUniqueSbpSets(consumer_bn2sbp_set, all_sbp_set, accumulator, unique_sbps); + + // A: {B, S0, S1, S2, S3}, C: {B, S0}, D: {B, S0} + // {S1, S2, S3} show up only once, a parallel candidate should not contain two of them + for (const auto& sbp_set_group : consumer_bn2sbp_set) { + unique_sbps.IntersectionTo(sbp_set_group.second, bs_buffer); + // Find those unique sbp groups with more than two sbp + // For example {B, S1, S2} is an impossible proxy candidate, + // since {S1, S2} is only contained by A but not contained by C and D. + // A could be either S1 or S2. The tensor do not need to be transferred to both S1 and S2. + if (bs_buffer.Total() >= 2) { unique_sbp_groups.push_back(bs_buffer); } + } + bs_buffer.Clear(); +} + +// If not contains two sbp from a same unique group +bool No2SbpFromSameUniqueGroup(const BinarySet& bs, + const std::vector& unique_sbp_groups) { + BinarySet intersection(bs.GetSizeOfSet()); + for (const auto& unique_sbp_group : unique_sbp_groups) { + bs.IntersectionTo(unique_sbp_group, intersection); + // For example {B, S1, S2} is an impossible proxy candidate, + // since {S1, S2} is only contained by A but not contained by C and D. + // A could be either S1 or S2. The tensor do not need to be transferred to both S1 and S2. + if (intersection.Total() >= 2) { return false; } + } + return true; +} +} // namespace + +// Default constructor for SbpCollector +// Don't allow any special case for broadcast! +SbpCollector::SbpCollector() { + // initialize Sbp Parallel Universe with broadcast. + // NdSbp sbp_broadcast; + // sbp_broadcast.mutable_broadcast_parallel(); + // nd_sbp_universe_[sbp_broadcast] = 0; + // id2nd_sbp_.push_back(sbp_broadcast); +} + +// Collect all the possible Sbp Parallel from a NdSbpSignature +void SbpCollector::CollectUniverse(const NdSbpSignature& nd_sbp_sig) { + for (auto& bn_sbp_pair : nd_sbp_sig.bn_in_op2nd_sbp()) { + if (nd_sbp_universe_.find(bn_sbp_pair.second) == nd_sbp_universe_.end()) { + int32_t curr_size = nd_sbp_universe_.size(); + nd_sbp_universe_[bn_sbp_pair.second] = curr_size; + id2nd_sbp_.push_back(bn_sbp_pair.second); + } + } +} +// Collect all the possible Sbp Parallel from a SbpNode +void SbpCollector::CollectUniverse(const SbpNode* sbp_node) { + for (auto& nd_sbp_sig : sbp_node->sbp_sig_list_) { CollectUniverse(nd_sbp_sig); } +} +// Collect all the possible Sbp Parallel from a SbpGraph +void SbpCollector::CollectUniverse(const SbpGraph& sbp_graph) { + for (auto* sbp_node : sbp_graph.node_list_) { CollectUniverse(sbp_node); } + accumulator_.resize(nd_sbp_universe_.size(), 0); + bs_buffer_.Initialize(nd_sbp_universe_.size()); +} + +// TODO: Auto Placement! +// It only collect the same sbp with the same parallel description +// In this moment their hierarchy is the same! + +// Initialize copy cost from producer to proxy of producer +void SbpCollector::InitializeCopyCostFromNode2Proxy(const SbpNode* sbp_proxy, + const LogicalBlobId& lbi) const { + // the only edge from producer to proxy of producer + SbpEdge* sbp_edge = sbp_proxy->edges_in_[0]; + SbpNode* sbp_node_producer = sbp_edge->start_node_; + sbp_edge->cost_.resize(sbp_node_producer->sbp_sig_list_.size()); + int32_t consumer_sbp_size = sbp_proxy->parallel_candidates_.size(); + // look through sbp signature in producer + for (int32_t sbp_id_producer = 0; sbp_id_producer < sbp_node_producer->sbp_sig_list_.size(); + sbp_id_producer++) { + sbp_edge->cost_[sbp_id_producer].resize(consumer_sbp_size, 0); + } + + // Assemble copy cost from producer to proxy of producer + OpNode* producer = sbp_node_producer->op_node_; + + // get parallel description. Number of devices. + const ParallelDesc& producer_parallel_desc = producer->parallel_desc(); + // Need to be careful, the logical blob description should be independent to current + // NdSbp. Use producer or op_node? + const BlobDesc& logical_blob_desc = producer->LogicalBlobDesc4Lbi(lbi); + const std::string& obn = *CHECK_JUST(producer->op().obn4lbi(lbi)); + + // A buffer to store the sbp parallel id + std::vector sbp_parallel_ids; + + // look through sbp signature in producer + for (int32_t sbp_id_producer = 0; sbp_id_producer < sbp_node_producer->sbp_sig_list_.size(); + sbp_id_producer++) { + // get sbp parallel for a logical blob in producer + const auto& producer_sbp_bn_in_op2sbp_parallel = + sbp_node_producer->sbp_sig_list_[sbp_id_producer].bn_in_op2nd_sbp(); + const NdSbp& sbp_producer = producer_sbp_bn_in_op2sbp_parallel.at(obn); + + // look through sbp parallel set in consumer + for (int32_t sbp_id_consumer = 0; sbp_id_consumer < consumer_sbp_size; sbp_id_consumer++) { + const BinarySet& sbp_parallel_set = sbp_proxy->parallel_candidates_[sbp_id_consumer]; + sbp_parallel_set.QuickOutput(sbp_parallel_ids); + + // look through all sbp parallels in a sbp parallel set + for (int32_t sbp_parallel_id : sbp_parallel_ids) { + // get sbp parallel for a logical blob in consumer + const NdSbp& sbp_consumer = id2nd_sbp_[sbp_parallel_id]; + + // compute copy cost for a specific logical blob + // Use the parallel description of producer as those for consumer for now. + sbp_edge->cost_[sbp_id_producer][sbp_id_consumer] += + CHECK_JUST(ComputeCopyCostWithMiddleNodes(sbp_producer, sbp_consumer, logical_blob_desc, + producer_parallel_desc, + producer_parallel_desc, /*is_same=*/false)); + } + } + } +} + +// Initialize copy cost from proxy of producer to consumers +void SbpCollector::InitializeCopyCostFromProxy2Consumer( + SbpNode* sbp_proxy, + const HashMap, BinarySet>& consumer_bn2sbp_set, + const HashMap& op_name2sbp_node) const { + // Connect sbp proxy and consumers + for (const auto& consumer_bn_group : consumer_bn2sbp_set) { + // consumer in cost model + SbpNode* sbp_node_consumer = op_name2sbp_node.find(consumer_bn_group.first.first)->second; + // input blob name of logical blob in consumer + const std::string& ibn = consumer_bn_group.first.second; + + // check is_mutable in consumer + OpNode* consumer = sbp_node_consumer->op_node_; + CHECK(!RequireSameSbp(consumer, ibn)) << "Create a proxy for an unsuitable consumer!\n"; + + // Connect sbp proxy and consumer + sbp_proxy->PointTo(sbp_node_consumer); + // the sbp edge connecting proxy and consumer + SbpEdge* sbp_edge = sbp_node_consumer->FindEdgeWithNode(sbp_proxy); + sbp_edge->cost_.resize(sbp_proxy->parallel_candidates_.size()); + int32_t consumer_sbp_size = sbp_node_consumer->sbp_sig_list_.size(); + + // look through sbp parallel set in proxy + for (int32_t sbp_id_producer = 0; sbp_id_producer < sbp_proxy->parallel_candidates_.size(); + sbp_id_producer++) { + // initialization for copy cost + sbp_edge->cost_[sbp_id_producer].resize(consumer_sbp_size, 0); + // get sbp parallel set for a logical blob in proxy + BinarySet& parallel_candidate = sbp_proxy->parallel_candidates_[sbp_id_producer]; + + // look through sbp signatures in consumers + for (int32_t sbp_id_consumer = 0; sbp_id_consumer < consumer_sbp_size; sbp_id_consumer++) { + // get sbp parallel for a logical blob in consumer + const auto& consumer_sbp_bn_in_op2sbp_parallel = + sbp_node_consumer->sbp_sig_list_[sbp_id_consumer].bn_in_op2nd_sbp(); + const NdSbp& sbp_consumer = consumer_sbp_bn_in_op2sbp_parallel.at(ibn); + + if ((!parallel_candidate.CheckExistence(nd_sbp_universe_.find(sbp_consumer)->second))) { + sbp_edge->cost_[sbp_id_producer][sbp_id_consumer] = GetMaxVal(); + } + } + } + } +} + +// Export list of possible combination of Sbp Parallels +void SbpCollector::ProxySbpCandidate(const OpGraph& op_graph, + const HashMap& op_name2sbp_node, + SbpGraph& sbp_graph) { + // If needed, we can output the mapping from operator name to its proxy. + // HashMap>& + // op_name2lbi2sbp_proxy; + + // mapping from a logical blob id to index + HashMap lbi2index; + // mapping from the index to producer, consumer and corresponding input blob name, possible sbp + // sets + std::vector index2producer; + std::vector> index2sbp_set; + // mapping from consumers and input blob names to an unordered_set of SBP Parallel. + std::vector, BinarySet>> index2consumer_bn2sbp_set; + + for (auto* consumer_sbp_node : sbp_graph.node_list_) { + auto* node = consumer_sbp_node->op_node_; + + OperatorConf::OpTypeCase op_type_case = node->op().op_conf().op_type_case(); + // If not support boxing, just skip it. + if (IsClassRegistered(op_type_case)) { return; } + for (const std::string& ibn : node->op().input_bns()) { + // Skip those blobs who enforce same SBP. + if (RequireSameSbp(node, ibn)) { + // Enforcing same SBP. Can not collect sbp from this blob. + continue; + } + + const LogicalBlobId& lbi = node->op().BnInOp2Lbi(ibn); + const OpNode& producer = node->ProducerOpNode4Lbi(lbi); + + // not building proxy for fixed operators + if (op_name2sbp_node.find(producer.op().op_name()) == op_name2sbp_node.end()) { return; } + // decide the index of a logical blob description + const auto& iterator_lbi = lbi2index.find(lbi); + int32_t index = 0; + if (iterator_lbi == lbi2index.end()) { + index = lbi2index.size(); + lbi2index[lbi] = index; + // map from lbi to the producer + index2producer.push_back(&producer); + // Initialize consumer_bns and the sbp sets + index2consumer_bn2sbp_set.resize(index + 1); + index2sbp_set.resize(index + 1); + } else { + index = iterator_lbi->second; + } + + // a set to store the id of all possible SBP Parallel for a downstream op + // should filter out repeated SBP Parallel by pre-storing them into an unordered_set + BinarySet& nd_sbp_ids = index2consumer_bn2sbp_set[index][{node->op().op_name(), ibn}]; + nd_sbp_ids.Initialize(nd_sbp_universe_.size()); + // The union sbp set of all the consumers + std::unordered_set& union_nd_sbp_ids = index2sbp_set[index]; + for (auto& sbp_sig : consumer_sbp_node->sbp_sig_list_) { + const auto& map = sbp_sig.bn_in_op2nd_sbp(); + const auto& iter = map.find(ibn); + CHECK(iter != map.end()) << "blob_name " << ibn << " not found in sbp signature"; + const NdSbp& consumer_sbp = iter->second; + // filter out repeated SBP + int32_t sbp_universe_id = nd_sbp_universe_.find(consumer_sbp)->second; + nd_sbp_ids.AddEntry(sbp_universe_id); + union_nd_sbp_ids.insert(sbp_universe_id); + } + } + }; + + // A set of binary set with broadcast only + // std::unordered_set parallel_candidates_initializer; + // BinarySet one_broadcast(nd_sbp_universe_.size()); + // one_broadcast.AddEntry(0); + // parallel_candidates_initializer.insert(std::move(one_broadcast)); + + // Decide if we should insert a proxy for each logical blob + for (auto& lbi_index : lbi2index) { + int32_t index = lbi_index.second; + // Only insert proxy for those blobs with multiple downstream consumers. + if (index2consumer_bn2sbp_set[index].size() < 2) { continue; } + // Maximum number of possible sbp in the proxy + int32_t max_num_sbp_proxy = + std::min(max_num_sbp_proxy_, index2consumer_bn2sbp_set[index].size()); + // producer in cost model + const std::string& producer_name = index2producer[index]->op().op_name(); + SbpNode* sbp_node_producer = op_name2sbp_node.find(producer_name)->second; + + const LogicalBlobId& lbi = lbi_index.first; + // store all the binary sets of SBP Parallel into an unordered_set. + // std::vector parallel_candidates; + + // generate sbp proxy + SbpNode* sbp_proxy = sbp_graph.GenerateNode(); + + // A: {B, S0, S1, S2, S3}, C: {B, S0}, D: {B, S0} + // {S1, S2, S3} show up only once, a parallel candidate should not contain two of them + std::vector unique_sbp_groups; + FindUniqueSbpGroups(index2consumer_bn2sbp_set[index], index2sbp_set[index], accumulator_, + bs_buffer_, unique_sbp_groups); + + // Depth first search to collect Sbp Parallel information for the whole sbp set + DfsSbpSet(0, max_num_sbp_proxy, index2sbp_set[index], index2sbp_set[index].begin(), + index2consumer_bn2sbp_set[index], unique_sbp_groups, sbp_proxy->parallel_candidates_); + + // Initialize computation cost + sbp_proxy->cost_.resize(sbp_proxy->parallel_candidates_.size(), 0); + + // Transfer a logical blob from producer to a sbp proxy of this blob + sbp_node_producer->PointTo(sbp_proxy); + + // Compute copy cost between producer and proxy + InitializeCopyCostFromNode2Proxy(sbp_proxy, lbi); + + // Build connection and compute copy cost between proxy and consumers + InitializeCopyCostFromProxy2Consumer(sbp_proxy, index2consumer_bn2sbp_set[index], + op_name2sbp_node); + + // Unloading + for (const auto& consumer_bn_group : index2consumer_bn2sbp_set[index]) { + // consumer in cost model + SbpNode* sbp_node_consumer = op_name2sbp_node.find(consumer_bn_group.first.first)->second; + // the sbp edge connecting producer and consumer + SbpEdge* edge_found = sbp_node_consumer->FindEdgeWithNode(sbp_node_producer); + // unload logical blob from sbp edges + edge_found->UnloadLbi(lbi); + // Do not clip this edge. Save it for wait time. + // clip this edge if it no longer carries any blob + // We don't clip edges before since we have transfer cost + // Now we clip edges, which makes the topology simpler + if (edge_found->EmptyLbi() && edge_found->wait_time_ <= 0.0 + && edge_found->wait_time_ > -0.5) { + sbp_graph.ClipEdge(edge_found); + } + } + } +} + +// Depth first search to collect Sbp Parallel information for different logical blob ids +void SbpCollector::DfsSbpSet( + int32_t depth, int32_t max_depth, const std::unordered_set& sbp_sets, + const std::unordered_set::iterator& start_it, + const HashMap, BinarySet>& consumer_bn2sbp_set, + const std::vector& unique_sbp_groups, std::vector& parallel_candidates) { + if (depth > 0) { + if (IfIntersectAll(consumer_bn2sbp_set, bs_buffer_) + && No2SbpFromSameUniqueGroup(bs_buffer_, unique_sbp_groups)) { + // store the binary set into an unordered_set + parallel_candidates.push_back(bs_buffer_); + } + } + if (depth >= max_depth) { return; } + + // go through the rest of the sbp parallel + std::unordered_set::iterator curr_it = start_it; + while (curr_it != sbp_sets.end()) { + // Take the value out + int32_t nd_sbp_num = *curr_it; + // Then move to the next pointer + ++curr_it; + if (accumulator_[nd_sbp_num] == 0) { + bs_buffer_.AddEntry(nd_sbp_num); + ++accumulator_[nd_sbp_num]; + DfsSbpSet(depth + 1, max_depth, sbp_sets, curr_it, consumer_bn2sbp_set, unique_sbp_groups, + parallel_candidates); + bs_buffer_.DeleteEntry(nd_sbp_num); + --accumulator_[nd_sbp_num]; + } + } +} + +} // namespace auto_parallel + +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_collector.h b/oneflow/core/auto_parallel/sbp_collector.h new file mode 100644 index 0000000000000000000000000000000000000000..ecb338a8261e8febfcc4e9edef1510e3a1744ff6 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_collector.h @@ -0,0 +1,90 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef SBP_COLLECTOR_ +#define SBP_COLLECTOR_ + +#include +#include +#include +#include +#include +#include "oneflow/core/auto_parallel/sbp_graph.h" +#include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/job/sbp_parallel.pb.h" +#include "oneflow/core/job/local_sig_infer_hint.h" +#include "oneflow/core/job/job_builder.h" +// #include "sbp_constructor.h" +#define DEBUG_COLLECTOR_ + +namespace oneflow { + +namespace auto_parallel { + +class SbpCollector { + public: + SbpCollector(); + + ~SbpCollector() {} + + // Collect all the possible Sbp Parallel from a SbpGraph + void CollectUniverse(const SbpGraph& sbp_graph); + + // Export list of possible combination of Sbp Parallels + void ProxySbpCandidate(const OpGraph& op_graph, + const HashMap& op_name2sbp_node, + SbpGraph& sbp_graph); + + private: + // Stores all the possible NdSbp. + std::unordered_map nd_sbp_universe_; + // Relationship between id and Sbp Parallel + std::vector id2nd_sbp_; + // Calculate number of downstream sbp + std::vector accumulator_; + // A binary set buffer to indicate sets of downstream sbp + BinarySet bs_buffer_; + + // Collect all the possible Sbp Parallel from a NdSbpSignature + void CollectUniverse(const NdSbpSignature& nd_sbp_sig); + // Collect all the possible Sbp Parallel from a SbpNode + void CollectUniverse(const SbpNode* sbp_node); + + // Initialize copy cost from producer to proxy of producer + void InitializeCopyCostFromNode2Proxy(const SbpNode* sbp_proxy, const LogicalBlobId& lbi) const; + + // Initialize copy cost from proxy of producer to consumers + void InitializeCopyCostFromProxy2Consumer( + SbpNode* sbp_proxy, + const HashMap, BinarySet>& consumer_bn2sbp_set, + const HashMap& op_name2sbp_node) const; + + // Maximum number of possible sbp in the proxy + const unsigned long max_num_sbp_proxy_ = 3; + + // Depth first search to collect Sbp Parallel information for the whole sbp set + void DfsSbpSet(int32_t depth, int32_t max_depth, const std::unordered_set& sbp_sets, + const std::unordered_set::iterator& sbp_set_it, + const HashMap, BinarySet>& consumer_bn2sbp_set, + const std::vector& unique_sbp_groups, + std::vector& parallel_candidates); +}; // class SbpCollector + +} // namespace auto_parallel + +} // namespace oneflow + +#endif // SBP_COLLECTOR_ diff --git a/oneflow/core/auto_parallel/sbp_constructor.cpp b/oneflow/core/auto_parallel/sbp_constructor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a3aa4f9d3ee7fc0c923c2b55dace6b22df43f92a --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_constructor.cpp @@ -0,0 +1,457 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/auto_parallel/sbp_constructor.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/sbp_util.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/job/sbp_parallel.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/job/job.pb.h" +#include "oneflow/core/auto_parallel/sbp_collector.h" + +namespace oneflow { + +namespace auto_parallel { + +Maybe SbpConstructor::Init(const OpGraph& op_graph, Job* job /*Maybe not use*/) { + JUST(InitSbpGraph(op_graph, *job)); + return Maybe::Ok(); +} + +Maybe SbpConstructor::InitSbpGraph(const OpGraph& op_graph, const Job& job) { + // TODO: process local node + JUST(GenerateNodeAndEdge(op_graph, job)); + JUST(FillSbpSignatureForOpNode(op_graph, job)); + JUST(InitComputationCost(op_graph)); + if (enable_trunk_algo_) { JUST(ApplyTrunkAlgo()); } + if (use_sbp_collector_) { + // Load logical blobs on all sbp edges. + LoadLbi2SbpEdge(op_graph); + // Use sbp collector to create sbp proxy for nodes with multiple downstream operators. + SbpCollector sbp_collector; + sbp_collector.CollectUniverse(sbp_graph_); + sbp_collector.ProxySbpCandidate(op_graph, op_name2sbp_node_, sbp_graph_); + } + + JUST(InitCopyCost(op_graph)); + // TODO: Set all the sbp signature id to be 0 for initialization. + // Could revert it back to + // sbp_graph_.RandomSbpSignature(use_sbp_collector_); + // after settling down the synchronization of sbp strategy. + sbp_graph_.SetDefaultSbpSig(); + double ori_cost = sbp_graph_.ComputeCost(); + LOG(INFO) << "Initial cost: " << ori_cost; + // If we do not prune those parallel cast ops, steal the initial strategy from user setting and + // semi-auto parallelism + if (!job.job_conf().enable_auto_parallel_ignore_user_sbp_config()) { + JUST(StealSbpSignatureFromOpNode(op_graph, job)); + ori_cost = sbp_graph_.ComputeCost(); + LOG(INFO) << "OpGraph cost: " << ori_cost; + } + return Maybe::Ok(); +} + +Maybe SbpConstructor::FindBestSbpSignature() { + double ori_cost = sbp_graph_.ComputeCost(); + LOG(INFO) << "Initial cost: " << ori_cost; + int elimination_num = sbp_graph_.NodeAndEdgeEliminations(); + LOG(INFO) << "Elimination number: " << elimination_num; + if (ori_cost > GetValidMaxCopyCost()) { + JUST(sbp_graph_.Find1Strategy4Greedy()); + ori_cost = sbp_graph_.ComputeCost(); + LOG(INFO) << "Greedy cost: " << ori_cost; + } + sbp_graph_.GreedyStrategy(4); + sbp_graph_.FinalizeSbp(); + + double final_cost = sbp_graph_.ComputeCost(); + LOG(INFO) << "Final cost: " << final_cost; + if (ori_cost + 1.0 < final_cost) { LOG(WARNING) << "ori_cost less than final_cost!!!"; } + // TODO: Restart searching with another original random strategy + CHECK_LT_OR_RETURN(final_cost, GetValidMaxCopyCost()) + << "Failed! Auto parallel can't find a strategy with reasonable cost!"; + return Maybe::Ok(); +} + +Maybe SbpConstructor::DumpNdSbpSignatureForJob(const OpGraph& op_graph, Job* job) { + for (auto& op_conf : *job->mutable_net()->mutable_op()) { + const OpNode* node = op_graph.OpNode4OpName(op_conf.name()); + SbpNode* sbp_node = op_name2sbp_node_[node->op().op_name()]; + const NdSbpSignature& nd_sbp_sig = sbp_node->FinalSbpSignature(); + // Update NdSbpSignature + (*job->mutable_job_parallel_view_conf() + ->mutable_op_name2nd_sbp_signature_conf())[node->op().op_name()] + .CopyFrom(nd_sbp_sig); + // If we have 1D SbpSignature Conf + if (node->parallel_desc().hierarchy()->NumAxes() == 1) { + // Update SbpSignature + SbpSignature sbp_signature; + NdSbpSignatureToSbpSignature(nd_sbp_sig, &sbp_signature); + (*job->mutable_job_parallel_view_conf() + ->mutable_op_name2sbp_signature_conf())[node->op().op_name()] + .CopyFrom(sbp_signature); + } + JUST(node->op().GetDumpNdSbpSignatureForOpConfFn()(nd_sbp_sig, &op_conf)); + } + return Maybe::Ok(); +} + +Maybe SbpConstructor::GenerateNodeAndEdge(const OpGraph& op_graph, const Job& job) { + JobParallelViewConf job_parallel_view_conf(job.job_parallel_view_conf()); + + // Collect op_node + std::vector op_node_list; + op_graph.ForEachNode([&](OpNode* op_node) { + // TODO: support local op + bool is_local_conf = false; + { + const auto& op_name2is_local = job_parallel_view_conf.op_name2is_local_parallel_view(); + const auto& iter = op_name2is_local.find(op_node->op().op_name()); + if (iter != op_name2is_local.end()) { is_local_conf = iter->second; } + } + CHECK(is_local_conf == false) << "Haven't deal with local operators."; + op_node_list.push_back(op_node); + }); + + // Decide the order to visit the op + std::vector order; + auto CompareOpName = [&](OpNode* a, OpNode* b) { + return a->op().op_name().compare(b->op().op_name()) > 0; + }; + auto_parallel::DecideOrder(op_node_list, order, CompareOpName); + std::vector output_order; + + // Create sbp nodes + for (int32_t i = 0; i < op_node_list.size(); i++) { + OpNode* op_node = op_node_list[order[i]]; + // Generate sbp node in cost model and link it with corresponding op node + SbpNode* sbp_node = sbp_graph_.GenerateNode(); + // Mapping from sbp_node to op_node + sbp_node->op_node_ = op_node; // TODO: SetOpNode() + op_name2sbp_node_[op_node->op().op_name()] = sbp_node; + } + // Create sbp edges + for (int32_t i = 0; i < op_node_list.size(); i++) { + OpNode* op_node = op_node_list[order[i]]; + // Get corresponding sbp node + SbpNode* sbp_node = op_name2sbp_node_[op_node->op().op_name()]; + std::vector output_node_list; + for (const auto* op_edge : op_node->out_edges()) { + output_node_list.push_back(op_edge->dst_node()); + } + auto_parallel::DecideOrder(output_node_list, output_order, CompareOpName); + for (int32_t j : output_order) { + const auto& end_node_name = output_node_list[j]->op().op_name(); + // Generate sbp edge in cost model + sbp_node->PointTo(op_name2sbp_node_[end_node_name]); + } + } + return Maybe::Ok(); +} + +Maybe SbpConstructor::FillSbpSignatureForOpNode(const OpGraph& op_graph, const Job& job) { + // TODO: use user sbp signature in JobParallelViewConf + // const JobParallelViewConf& job_parallel_view_conf(job.job_parallel_view_conf()); + JUST(op_graph.TopoForEachNodeWithErrorCaptured([&](OpNode* op_node) -> Maybe { + HashMap ibn2blob_desc; + auto FindShape4Blobs = [&](const PbRpf& bns) -> Maybe { + for (const std::string& ibn : bns) { + const LogicalBlobId& lbi = op_node->op().BnInOp2Lbi(ibn); + const BlobDesc* logical_blob_desc = &op_node->LogicalBlobDesc4Lbi(lbi); + ibn2blob_desc.emplace(ibn, logical_blob_desc); + } + return Maybe::Ok(); + }; + JUST(FindShape4Blobs(op_node->op().input_bns())); + JUST(FindShape4Blobs(op_node->op().output_bns())); + // Get logical blob description + auto LogicalBlobDesc4Ibn = [&](const std::string& ibn) -> Maybe { + const auto& it = ibn2blob_desc.find(ibn); + if (it == ibn2blob_desc.end()) { + return Error::InvalidValueError() + << "Cannot find corresponding blob description for input_blob_name : " + ibn + " in " + + op_node->op().op_name(); + } + return *(it->second); + }; + // Get all valid sbp_signatures + SbpNode* sbp_node = op_name2sbp_node_[op_node->op().op_name()]; + JUST(op_node->op().GetValidNdSbpSignatureList(LogicalBlobDesc4Ibn, op_node->parallel_desc(), + &sbp_node->sbp_sig_list_, /*check_output=*/true)); + sbp_node->InitializeSbp(); + return Maybe::Ok(); + })); + return Maybe::Ok(); +} + +Maybe SbpConstructor::StealSbpSignatureFromOpNode(const OpGraph& op_graph, const Job& job) { + // Steal some strategy from original op graph + for (auto* sbp_node : sbp_graph_.node_list_) { + // sbp_collectors do not have op_node + if (sbp_node->op_node_) { + for (int32_t sbp_id = 0; sbp_id < sbp_node->sbp_sig_list_.size(); sbp_id++) { + if (*JUST(sbp_node->op_node_->op().nd_sbp_signature()) == sbp_node->sbp_sig_list_[sbp_id]) { + sbp_node->final_sbp_sig_id_ = sbp_id; + break; + } + } + } + } + return Maybe::Ok(); +} + +Maybe SbpConstructor::InitComputationCost(const OpGraph& op_graph) { + // Compute computation cost for sbp nodes + JUST(op_graph.TopoForEachNodeWithErrorCaptured([&](OpNode* op_node) -> Maybe { + // get corresponding sbp node producer + SbpNode* sbp_node = op_name2sbp_node_[op_node->op().op_name()]; + // get parallel description. Number of devices. + const ParallelDesc& parallel_desc = op_node->parallel_desc(); + + CHECK_EQ_OR_RETURN(sbp_node->cost_.size(), sbp_node->sbp_sig_list_.size()); + auto LogicalBlobDesc4Bn = [&](const std::string& bn) -> const BlobDesc& { + const LogicalBlobId& lbi = op_node->op().BnInOp2Lbi(bn); + return op_node->LogicalBlobDesc4Lbi(lbi); + }; + for (int32_t sbp_id = 0; sbp_id < sbp_node->sbp_sig_list_.size(); sbp_id++) { + double comp_cost = JUST(op_node->op().GetComputeComplexity( + &sbp_node->sbp_sig_list_[sbp_id], LogicalBlobDesc4Bn, parallel_desc)); + if (comp_cost > GetValidMaxCopyCost()) { + sbp_node->cost_[sbp_id] = comp_cost; + } else { + sbp_node->cost_[sbp_id] = + cost_ratio_ * comp_cost + * JUST(op_node->op().GetInputOutputFastestTimeShape())->elem_cnt(); + } + } + return Maybe::Ok(); + })); + return Maybe::Ok(); +} + +Maybe SbpConstructor::InitCopyCost(const OpGraph& op_graph) { + // Compute copy cost for sbp edges + op_graph.ForEachNode([&](OpNode* op_node) { + // get corresponding sbp node consumer + SbpNode* sbp_node_consumer = op_name2sbp_node_[op_node->op().op_name()]; + // Initialize copy cost between two nodes + for (auto* sbp_edge : sbp_node_consumer->edges_in_) { + // producer sbp node + const auto* sbp_node_producer = sbp_edge->start_node_; + // skip it if proxy + if (!sbp_node_producer->op_node_) { continue; } + sbp_edge->cost_.resize(sbp_node_producer->sbp_sig_list_.size()); + int32_t consumer_sbp_size = sbp_node_consumer->sbp_sig_list_.size(); + // look through sbp signature in producer + for (int32_t i = 0; i < sbp_node_producer->sbp_sig_list_.size(); ++i) { + sbp_edge->cost_[i].resize(consumer_sbp_size, 0); + } + } + // Find all those cases with wait time + // Do not skip edges carrying no lbi + sbp_node_consumer->InitializeCopyCost(use_sbp_collector_); + }); + return Maybe::Ok(); +} + +Maybe SbpConstructor::ApplyTrunkAlgo() { + auto OpNode2MutableOpCtrlDeps = JUST(GetMutableOpCtrlDeps(*op_graph_)); + // Compute layer number for each node + int32_t max_min_layer = sbp_graph_.ComputeLayer(op_name2sbp_node_, *OpNode2MutableOpCtrlDeps); + // Accumulate cost on the trunk after initializing computation cost + sbp_graph_.FindTrunk(max_min_layer, op_name2sbp_node_); + return Maybe::Ok(); +} + +// Load logical blob ids onto sbp edges +void SbpConstructor::LoadLbi2SbpEdge(const OpGraph& op_graph) { + // Load logical blobs onto sbp edges + + for (auto* sbp_node_consumer : sbp_graph_.node_list_) { + auto* op_node = sbp_node_consumer->op_node_; + + // Loading logical blobs between two nodes + // look through input blobs + for (const std::string& ibn : op_node->op().input_bns()) { + // Each input blob has one source op node. + OpNode* producer = op_node->MutSrcNode4Ibn(ibn); + // producer sbp node + const auto* sbp_node_producer = op_name2sbp_node_[producer->op().op_name()]; + // TODO: recode this + auto* edge_found = sbp_node_consumer->FindEdgeWithNode(sbp_node_producer); + + CHECK(edge_found != NULL) << "SbpEdge not found while loading!" << std::endl; + + // Add copy cost for each blob + const LogicalBlobId& lbi = op_node->op().BnInOp2Lbi(ibn); + edge_found->LoadLbi(lbi); + } + }; +} + +Maybe SbpConstructor::CheckSbpAgreement(const Job& job) { + Job new_job; + new_job.CopyFrom(job); + OpGraph op_graph(new_job); + // Compare sbp in job + JUST(op_graph.TopoForEachNodeWithErrorCaptured([&](OpNode* op_node) -> Maybe { + const std::string& op_name = op_node->op().op_name(); + const NdSbpSignature& auto_parallel_sbp = + NdSbpSignature(job.job_parallel_view_conf().op_name2nd_sbp_signature_conf().at(op_name)); + const NdSbpSignature& new_sbp = op_node->nd_sbp_signature(); + CHECK_EQ_OR_RETURN(auto_parallel_sbp.bn_in_op2nd_sbp_size(), new_sbp.bn_in_op2nd_sbp_size()); + for (const auto& iter : auto_parallel_sbp.bn_in_op2nd_sbp()) { + const NdSbp& new_sbp_parallel = new_sbp.bn_in_op2nd_sbp().at(iter.first); + const NdSbp& auto_parallel_sbp = iter.second; + // According error message, we can find op_type in op_conf.proto with type_id and locate + // the error op type. + const std::string& error_mgs = + "Op: `" + op_name + "`(type_id: " + std::to_string(op_node->op().op_conf().op_type_case()) + + ") changed sbp from " + NdSbpToString(auto_parallel_sbp) + "(AutoParallel) to " + + NdSbpToString(new_sbp_parallel) + "(OpGraph) with blob_name: `" + iter.first + "`."; + CHECK_OR_RETURN(new_sbp_parallel == auto_parallel_sbp) << error_mgs; + } + return Maybe::Ok(); + })); + return Maybe::Ok(); +} + +Maybe>> SbpConstructor::GetMutableOpCtrlDeps( + const OpGraph& op_graph) { + auto IsMutableConsumedLbi = [](const Operator& op, const LogicalBlobId& lbi) -> bool { + for (const std::string& bn : op.input_bns()) { + if (op.BnInOp2Lbi(bn) == lbi && op.InputBlobModifier4Ibn(bn).is_mutable()) { return true; } + } + return false; + }; + const auto& IsReachable = op_graph.MakePredicatorIsOpNameDataOrCtrlReachable(); + HashMap> op_node2ctrl_in_op_names; + JUST(op_graph.MaybeForEachNode([&](OpNode* op_node) -> Maybe { + if (op_node->op().op_conf().has_variable_conf() == false) { return Maybe::Ok(); } + if (op_node->out_edges().size() <= 1) { return Maybe::Ok(); } + const Operator& variable_op = op_node->op(); + const LogicalBlobId& variable_lbi = variable_op.BnInOp2Lbi(variable_op.SoleObn()); + const OpNode* mutable_consumer = nullptr; + std::vector naive_consumers; + naive_consumers.reserve(op_node->out_edges().size()); + for (OpEdge* edge : op_node->out_edges()) { + const auto& op_conf = edge->dst_node()->op().op_conf(); + if (IsMutableConsumedLbi(edge->dst_node()->op(), variable_lbi)) { + CHECK_OR_RETURN(mutable_consumer == nullptr); + mutable_consumer = edge->dst_node(); + } else { + naive_consumers.emplace_back(&op_conf); + } + } + if (mutable_consumer == nullptr) { return Maybe::Ok(); } + for (const auto* fw_bw_op : naive_consumers) { + op_node2ctrl_in_op_names[mutable_consumer].insert(fw_bw_op->name()); + } + return Maybe::Ok(); + })); + // Filter ctrl edges if all ctrl_in_op_names are reachable + HashMap> filter_op_ctrl_deps; + for (const auto& pair : op_node2ctrl_in_op_names) { + const OpNode* op_node = pair.first; + for (const auto& fw_bw_op_name : pair.second) { + if (!IsReachable(fw_bw_op_name, op_node->op().op_name())) { + filter_op_ctrl_deps[op_node].insert(fw_bw_op_name); + } + } + } + return filter_op_ctrl_deps; +} + +// Print the graph with SBP in order +void SbpConstructor::PrintSBPGraphDebugInfo() { + // sbp constructor information + std::cout << "cost_ratio_:" << cost_ratio_ << std::endl; + std::cout << "wait_time_:" << sbp_graph_.wait_time_ << std::endl; + std::cout << "use_sbp_collector_" << use_sbp_collector_ << std::endl; + // test debug + std::cout << "Get Into Print Op Graph" << std::endl; + // Collect op_node + std::vector node_list; + for (const auto& op_name_sbp_node : op_name2sbp_node_) { + auto* op_node_ = op_name_sbp_node.second->op_node_; + if (op_node_) { node_list.push_back(op_node_); } + } + + // test debug + std::cout << "Deciding order" << std::endl; + // Decide the order to visit the op + std::vector order; + auto_parallel::DecideOrder(node_list, order, [&](OpNode* a, OpNode* b) { + return a->op().op_name().compare(b->op().op_name()) > 0; + }); + std::vector str_order; + + // test debug + std::cout << "Finish deciding order" << std::endl; + + for (int32_t i = 0; i < node_list.size(); i++) { + OpNode* op_node = node_list[order[i]]; + std::cout << op_node->op().op_name() << " (^_^):" << std::endl; + // get corresponding sbp node + const auto& it = op_name2sbp_node_.find(op_node->op().op_name()); + // Print debug information for sbp graph + CHECK(it != op_name2sbp_node_.end()); + const SbpNode* sbp_node = it->second; + std::cout << "Computation Cost: " << sbp_node->cost_[sbp_node->final_sbp_sig_id_]; + std::cout << ", Min Layer: " << sbp_node->min_layer_ << ", Max Layer: " << sbp_node->max_layer_ + << ", Tributary Layer: " << sbp_node->tributary_layer_ + << ", in trunk: " << sbp_node->on_trunk_ + << ", Remain Cost: " << sbp_node->acc_trunk_cost_ << std::endl; + // Sort before printing + const auto& op_input_bns = op_node->op().input_bns(); + auto CompareString = [](const std::string& a, const std::string& b) { + return a.compare(b) > 0; + }; + auto_parallel::DecideOrder(op_input_bns, str_order, CompareString); + const NdSbpSignature& sbp_signature = sbp_node->FinalSbpSignature(); + // Print out SBP information for input operator + for (int32_t j : str_order) { + const auto& ibn = op_input_bns[j]; + const auto& producer_node = op_node->SrcNode4Ibn(ibn); + std::cout << "Pre Op:" << producer_node.op().op_name() << ": " << ibn; + const auto& this_sbp_parallel = sbp_signature.bn_in_op2nd_sbp().at(ibn); + std::cout << ", " << NdSbpToString(this_sbp_parallel); + if (RequireSameSbp(op_node, ibn)) { std::cout << ", require same SBP"; } + std::cout << ", " + << op_node->LogicalBlobDesc4Lbi(op_node->op().BnInOp2Lbi(ibn)).shape().elem_cnt(); + std::cout << std::endl; + } + // Sort before printing + const auto& op_output_bns = op_node->op().output_bns(); + auto_parallel::DecideOrder(op_output_bns, str_order, CompareString); + // Print out SBP information for output blobs + for (int32_t j : str_order) { + const auto& obn = op_output_bns[j]; + std::cout << "Out Op:" << obn; + const auto& this_sbp_parallel = sbp_signature.bn_in_op2nd_sbp().at(obn); + std::cout << ", " << NdSbpToString(this_sbp_parallel); + std::cout << ", " + << op_node->LogicalBlobDesc4Lbi(op_node->op().BnInOp2Lbi(obn)).shape().elem_cnt(); + std::cout << std::endl; + } + std::cout << std::endl; + } +} + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_constructor.h b/oneflow/core/auto_parallel/sbp_constructor.h new file mode 100644 index 0000000000000000000000000000000000000000..a92b88a5d77f3e8be782969d4e0fdd9222e70991 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_constructor.h @@ -0,0 +1,83 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_SBP_CONSTRUCTOR_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_SBP_CONSTRUCTOR_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/auto_parallel/sbp_graph.h" +#include "oneflow/core/job/global_for.h" + +namespace oneflow { + +class OpGraph; +class Job; + +namespace auto_parallel { + +// A constructor which will assemble the sbp_graph with the information from oneflow. +// SbpGraph contains the algorithms for elimination and search which is mainly for the strategy +// itself. Constructor mainly deal with the assemblage of each node, edge and the cost computation, +// activation of functions. +class SbpConstructor final { + public: + OF_DISALLOW_COPY_AND_MOVE(SbpConstructor); + SbpConstructor() = delete; + SbpConstructor(const OpGraph& op_graph, Job* job) + : cost_ratio_(job->job_conf().auto_parallel_computation_cost_ratio()), + enable_trunk_algo_(job->job_conf().enable_auto_parallel_trunk_algo()), + use_sbp_collector_(!Singleton::Get() + ->resource() + .disable_group_boxing_by_dst_parallel() + && job->job_conf().enable_auto_parallel_sbp_collector()), + op_graph_(&op_graph) { + sbp_graph_.SetWaitTime(job->job_conf().auto_parallel_wait_time()); + CHECK_JUST(Init(op_graph, job)); + } + ~SbpConstructor() = default; + + Maybe Init(const OpGraph& op_graph, Job* job); + Maybe FindBestSbpSignature(); + Maybe DumpNdSbpSignatureForJob(const OpGraph& op_graph, Job* job); + // Re-build OpGraph and check all sbp is same between op_graph and job + Maybe CheckSbpAgreement(const Job& job); + // Print the graph with SBP in order + void PrintSBPGraphDebugInfo(); + + private: + Maybe InitSbpGraph(const OpGraph& op_graph, const Job& job); + Maybe GenerateNodeAndEdge(const OpGraph& op_graph, const Job& job); + Maybe FillSbpSignatureForOpNode(const OpGraph& op_graph, const Job& job); + Maybe StealSbpSignatureFromOpNode(const OpGraph& op_graph, const Job& job); + Maybe InitComputationCost(const OpGraph& op_graph); + Maybe InitCopyCost(const OpGraph& op_graph); + Maybe ApplyTrunkAlgo(); + Maybe>> GetMutableOpCtrlDeps(const OpGraph& op_graph); + // Load logical blob ids onto sbp edges + void LoadLbi2SbpEdge(const OpGraph& op_graph); + + double cost_ratio_; + bool enable_trunk_algo_; + bool use_sbp_collector_; + SbpGraph sbp_graph_; + const OpGraph* op_graph_; + HashMap op_name2sbp_node_; +}; + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_SBP_CONSTRUCTOR_H_ diff --git a/oneflow/core/auto_parallel/sbp_edge.cpp b/oneflow/core/auto_parallel/sbp_edge.cpp new file mode 100644 index 0000000000000000000000000000000000000000..649cf3b15153bdd9dc39594bae4777e8f94892bc --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_edge.cpp @@ -0,0 +1,332 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include +#include +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/auto_parallel/sbp_edge.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/sbp_graph.h" +#include "oneflow/core/auto_parallel/sbp_util.h" +#include "oneflow/core/graph/op_graph.h" + +namespace oneflow { +namespace auto_parallel { + +// function in cpp. Should be put in one file due to use of template +// Otherwise we will need to declare specific template at the end of cpp file. + +SbpEdge::SbpEdge(SbpNode* start_node, SbpNode* mid_node, SbpNode* end_node, SbpEdge* first_edge, + SbpEdge* second_edge) + : start_node_(start_node), mid_node_(mid_node), end_node_(end_node) { + edge_list_.emplace_back(first_edge); + edge_list_.emplace_back(second_edge); +}; + +// Deconstructor +SbpEdge::~SbpEdge() { + if (mid_node_ != nullptr) { delete mid_node_; } + for (auto& this_edge : edge_list_) { delete this_edge; } +} + +void SbpEdge::SummarizeCost() { + if (mid_node_) { + cost_.resize(start_node_->cost_.size()); + mid_node_sbp_sig_.resize(start_node_->cost_.size()); + int32_t end_node_sbp_size = end_node_->cost_.size(); + int32_t mid_node_sbp_size = mid_node_->cost_.size(); + for (int32_t sbp_start = 0; sbp_start < cost_.size(); sbp_start++) { + cost_[sbp_start].resize(end_node_sbp_size); + mid_node_sbp_sig_[sbp_start].resize(end_node_sbp_size); + for (int32_t sbp_end = 0; sbp_end < end_node_sbp_size; sbp_end++) { + for (int32_t sbp_mid = 0; sbp_mid < mid_node_sbp_size; sbp_mid++) { + // Add middle node cost + double temp_cost = mid_node_->cost_[sbp_mid]; + // Add first edge cost + if (edge_list_[0]->start_node_ == start_node_) { + temp_cost += edge_list_[0]->cost_[sbp_start][sbp_mid]; + } else { + temp_cost += edge_list_[0]->cost_[sbp_mid][sbp_start]; + } + // Add second edge cost + if (edge_list_[1]->end_node_ == end_node_) { + temp_cost += edge_list_[1]->cost_[sbp_mid][sbp_end]; + } else { + temp_cost += edge_list_[1]->cost_[sbp_end][sbp_mid]; + } + + // Compare and look for the minimum cost + if (sbp_mid == 0) { + cost_[sbp_start][sbp_end] = temp_cost; + mid_node_sbp_sig_[sbp_start][sbp_end] = sbp_mid; + } else if (temp_cost < cost_[sbp_start][sbp_end]) { + cost_[sbp_start][sbp_end] = temp_cost; + mid_node_sbp_sig_[sbp_start][sbp_end] = sbp_mid; + } + } + } + } + } else { + cost_.resize(start_node_->cost_.size()); + int32_t end_node_sbp_size = end_node_->cost_.size(); + for (int32_t sbp_start = 0; sbp_start < cost_.size(); sbp_start++) { + cost_[sbp_start].resize(end_node_sbp_size); + for (int32_t sbp_end = 0; sbp_end < end_node_sbp_size; sbp_end++) { + cost_[sbp_start][sbp_end] = 0; + for (int32_t edge_num = 0; edge_num < edge_list_.size(); edge_num++) { + if (edge_list_[edge_num]->start_node_ == start_node_) { + cost_[sbp_start][sbp_end] += edge_list_[edge_num]->cost_[sbp_start][sbp_end]; + } else { + cost_[sbp_start][sbp_end] += edge_list_[edge_num]->cost_[sbp_end][sbp_start]; + } + } + } + } + } +} + +void SbpEdge::DuplicateCost( + bool merged_node_is_start_node, bool duplicating_first_node, + const std::vector>& merged_sig_id2children_sig_id) { + const int32_t num_sig = merged_sig_id2children_sig_id.size(); + std::vector> temp_cost; + std::vector> temp_mid_node_sbp_sig; + if (merged_node_is_start_node) { + temp_cost.resize(num_sig); + if (mid_node_) { temp_mid_node_sbp_sig.resize(num_sig); } + for (int32_t i = 0; i < num_sig; i++) { + const int32_t sig_idx = duplicating_first_node ? merged_sig_id2children_sig_id[i].first + : merged_sig_id2children_sig_id[i].second; + temp_cost[i] = cost_[sig_idx]; + if (mid_node_) { temp_mid_node_sbp_sig[i] = mid_node_sbp_sig_[sig_idx]; } + } + } else { + const int32_t num_start_sig = cost_.size(); + temp_cost.resize(num_start_sig); + if (mid_node_) { temp_mid_node_sbp_sig.resize(num_start_sig); } + for (int32_t i = 0; i < num_start_sig; i++) { + temp_cost[i].resize(num_sig); + if (mid_node_) { temp_mid_node_sbp_sig[i].resize(num_sig); } + for (int32_t j = 0; j < num_sig; j++) { + const int32_t sig_idx = duplicating_first_node ? merged_sig_id2children_sig_id[j].first + : merged_sig_id2children_sig_id[j].second; + temp_cost[i][j] = cost_[i][sig_idx]; + if (mid_node_) { temp_mid_node_sbp_sig[i][j] = mid_node_sbp_sig_[i][sig_idx]; } + } + } + } + + cost_ = temp_cost; + if (mid_node_) { mid_node_sbp_sig_ = temp_mid_node_sbp_sig; } +} + +void SbpEdge::FinalizeSbp() { + // Finalize Sbp for mid_node_ + if (mid_node_) { + mid_node_->final_sbp_sig_id_ = + mid_node_sbp_sig_[start_node_->final_sbp_sig_id_][end_node_->final_sbp_sig_id_]; + mid_node_->FinalizeSbp(); + } + for (const auto& this_edge : edge_list_) { this_edge->FinalizeSbp(); } +} + +double SbpEdge::GreedyStrategy() { + // Sbp combination of the minimum cost + int32_t min_sbp_start = start_node_->final_sbp_sig_id_, + min_sbp_end = end_node_->final_sbp_sig_id_; + // An unordered_map to evaluate cost between two edge nodes and other nodes. + std::unordered_map node_list_id2nbh_id = {{start_node_->node_list_id_, 0}, + {end_node_->node_list_id_, 1}}; + // pre-compute and store the current cost between end_node_ and outside. + std::vector end_node_out_cost(end_node_->cost_.size()); + for (int32_t sbp_end = 0; sbp_end < cost_[0].size(); sbp_end++) { + end_node_->final_sbp_sig_id_ = sbp_end; + end_node_out_cost[sbp_end] = end_node_->EvalOutNbhCost(node_list_id2nbh_id); + } + // pre-compute and store the current cost between start_node_ and outside. + std::vector start_node_out_cost(start_node_->cost_.size()); + for (int32_t sbp_start = 0; sbp_start < cost_.size(); sbp_start++) { + start_node_->final_sbp_sig_id_ = sbp_start; + start_node_out_cost[sbp_start] = start_node_->EvalOutNbhCost(node_list_id2nbh_id); + } + // Current Cost, Minimum Cost, Cost with original sbp + double curr_cost = 0.0; + double min_cost = start_node_out_cost[min_sbp_start] + end_node_out_cost[min_sbp_end] + + cost_[min_sbp_start][min_sbp_end]; + double original_cost = min_cost; + + for (int32_t sbp_start = 0; sbp_start < cost_.size(); sbp_start++) { + for (int32_t sbp_end = 0; sbp_end < cost_[0].size(); sbp_end++) { + // compute Current Cost for Neighborhood of edge + end_node_->final_sbp_sig_id_ = sbp_end; + curr_cost = + start_node_out_cost[sbp_start] + end_node_out_cost[sbp_end] + cost_[sbp_start][sbp_end]; + // Find the minimum current cost + if (curr_cost < min_cost) { + min_cost = curr_cost; + min_sbp_start = sbp_start; + min_sbp_end = sbp_end; + } + } + } + start_node_->final_sbp_sig_id_ = min_sbp_start; + end_node_->final_sbp_sig_id_ = min_sbp_end; + return min_cost - original_cost; +} + +// Get the minimum element in Cost + +double SbpEdge::GetMinCost() { + // used the stored value if pre-computed. + if (min_cost_ >= 0) { return min_cost_; } + // Check the size of Cost + CHECK(cost_.size() > 0) << "Cost not initialized!" << std::endl; + // Compute the min_cost + min_cost_ = *std::min_element(cost_[0].begin(), cost_[0].end()); + for (int32_t i = 1; i < cost_.size(); i++) { + double min_cost_row = *std::min_element(cost_[i].begin(), cost_[i].end()); + if (min_cost_row < min_cost_) { min_cost_ = min_cost_row; } + } + return min_cost_; +} + +// Get the maximum element in Cost + +double SbpEdge::GetMaxCost() const { + // used the stored value if pre-computed. + // if (max_cost >= 0) return max_cost; + // Check the size of Cost + CHECK(cost_.size() > 0) << "Cost not initialized!" << std::endl; + // Compute the max_cost + double max_cost = -1.0; + for (int32_t i = 0; i < cost_.size(); i++) { + for (int32_t j = 0; j < cost_[i].size(); j++) { + if (cost_[i][j] < GetValidMaxCopyCost() && cost_[i][j] > max_cost) { max_cost = cost_[i][j]; } + } + } + return max_cost; +} + +// Assemble copy cost + +void SbpEdge::InitializeCopyCost(const std::string& ibn, bool use_sbp_collector) { + // In this part, we assemble the cost from nodes to nodes. + if (start_node_->op_node_ && end_node_->op_node_) { + OpNode* consumer = end_node_->op_node_; + + // Add copy cost for each blob + const LogicalBlobId& lbi = consumer->op().BnInOp2Lbi(ibn); + + // Check whether lbi is transferred by this edge + if (use_sbp_collector && !SearchLbi(lbi)) { return; } + + OpNode* producer = start_node_->op_node_; + const std::string& producer_lbn = *CHECK_JUST(producer->op().obn4lbi(lbi)); + const ParallelDesc& producer_parallel_desc = + *CHECK_JUST(producer->op().GetParallelDesc4BnInOp(producer_lbn)); + const ParallelDesc& consumer_parallel_desc = + *CHECK_JUST(consumer->op().GetParallelDesc4BnInOp(ibn)); + + // Need to be careful, the logical blob description should be independent to current + // SbpParallel. Use producer or op_node? + const BlobDesc& logical_blob_desc = producer->LogicalBlobDesc4Lbi(lbi); + const std::string& obn = *CHECK_JUST(producer->op().obn4lbi(lbi)); + // If we are deciding whether we need the wait time, then make require_same_sbp true. + // B->S cause cudaEventSynchronize in current implementation. + bool require_same_sbp = RequireSameSbp(consumer, ibn); + int32_t consumer_sbp_size = end_node_->sbp_sig_list_.size(); + LazyMode::Guard enable_lazy_mode(true); + + // look through sbp signature in producer + for (int32_t sbp_id_producer = 0; sbp_id_producer < start_node_->sbp_sig_list_.size(); + sbp_id_producer++) { + // get sbp parallel for a logical blob in producer + const auto& producer_sbp_bn_in_op2sbp_parallel = + start_node_->sbp_sig_list_[sbp_id_producer].bn_in_op2nd_sbp(); + const NdSbp& sbp_producer = producer_sbp_bn_in_op2sbp_parallel.at(obn); + + // look through sbp signature in consumer + for (int32_t sbp_id_consumer = 0; sbp_id_consumer < consumer_sbp_size; sbp_id_consumer++) { + // get sbp parallel for a logical blob in consumer + const auto& consumer_sbp_bn_in_op2sbp_parallel = + end_node_->sbp_sig_list_[sbp_id_consumer].bn_in_op2nd_sbp(); + const NdSbp& sbp_consumer = consumer_sbp_bn_in_op2sbp_parallel.at(ibn); + + // compute copy cost for a specific logical blob + double curr_edge_cost = CHECK_JUST(ComputeCopyCostWithMiddleNodes( + sbp_producer, sbp_consumer, logical_blob_desc, producer_parallel_desc, + consumer_parallel_desc, require_same_sbp)); + if (curr_edge_cost < GetValidMaxCopyCost()) { + cost_[sbp_id_producer][sbp_id_consumer] += + CHECK_JUST(producer->op().GetOpTimeShape())->elem_cnt() * curr_edge_cost; + } else { + cost_[sbp_id_producer][sbp_id_consumer] = curr_edge_cost; + } + } + } + } +} + +// Set the cut ratio + +double SbpEdge::GetCutRatio() const { + int32_t num = 0; + for (int32_t i = 0; i < cost_.size(); i++) { + for (int32_t j = 0; j < cost_[i].size(); j++) { + if (cost_[i][j] < GetValidMaxCopyCost()) { num++; } + } + } + return double(num) / double(cost_.size() * cost_[0].size()); +} + +// find the cut ratio +// (#c>GetValidMaxCopyCost() in Cost)/(#c in Cost) + +double SbpEdge::FindCutRatio(int32_t threshold) const { + double cut_ratio = GetCutRatio(); + // lift the cut ratio to 1 to filter out some improper couples to avoid unlimited merging + double n = cost_.size(); + double m = cost_[0].size(); + double num = cut_ratio * n * m; + cut_ratio += 0.16 * (n + m) / double(threshold); + if (num <= n * 2 || num <= m * 2 || (num <= threshold && cut_ratio < 0.51)) { + return cut_ratio; + } else { + return 1.0; + } +} + +// load a logical blob +void SbpEdge::LoadLbi(const LogicalBlobId& lbi) { carry_lbis_.insert(lbi); } + +// check the existence of a logical blob +bool SbpEdge::SearchLbi(const LogicalBlobId& lbi) const { + return carry_lbis_.find(lbi) != carry_lbis_.end(); +} + +// unload a logical blob +void SbpEdge::UnloadLbi(const LogicalBlobId& lbi) { + if (carry_lbis_.erase(lbi) == 0) { std::cout << "Unload an empty lbi!" << std::endl; } +} + +// Not carrying any blob +bool SbpEdge::EmptyLbi() const { return carry_lbis_.empty(); } + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_edge.h b/oneflow/core/auto_parallel/sbp_edge.h new file mode 100644 index 0000000000000000000000000000000000000000..83ada89eb53ccc572c0b22378be767c457f00971 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_edge.h @@ -0,0 +1,140 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_SBP_EDGE_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_SBP_EDGE_H_ + +#include +#include +#include +#include "oneflow/core/common/util.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/sbp_util.h" +#include "oneflow/core/graph/op_graph.h" + +namespace oneflow { +namespace auto_parallel { + +// An edge structure to deal with the SBP strategy. +// Please see SbpGraph for the whole algorithm and introduction. +class SbpEdge final { + /* There are 3 types of edges: + * 1. start_node_ -> end_node_ + * Nothing special + * 2. Multiple start_node_ -> end_node_ + * edge_list_ will store all the edges which goes from start_node_ to end_node_ + * 3. start_node_ -> mid_node_ -> end_node_ + * It will pass by a middle node. + */ + public: + // Constructor for type 1 & 2 + SbpEdge(SbpNode* start_node, SbpNode* end_node) : start_node_(start_node), end_node_(end_node) { + mid_node_ = nullptr; + } + // Constructor for type 3 + SbpEdge(SbpNode* start_node, SbpNode* mid_node, SbpNode* end_node, SbpEdge* first_edge, + SbpEdge* second_edge); + + // Deconstructor + ~SbpEdge(); + + OF_DISALLOW_COPY_AND_MOVE(SbpEdge); + bool operator==(const SbpEdge& other) { return this == &other; } + + // Update copy cost for type 2 and 3 + void SummarizeCost(); + // Duplicate Cost. Designed for merging two nodes. + void DuplicateCost(bool merged_node_is_start_node, bool duplicating_first_node, + const std::vector>& merged_sig_id2children_sig_id); + // Determine Final SbpSignature for attachment of this edge + void FinalizeSbp(); + // Use Greedy Strategy to pick the sbp signature with minimum cost for this + // edge. You should have an initial strategy before running this. And the + // graph should be fully eliminated. + double GreedyStrategy(); + + // load a logical blob + void LoadLbi(const LogicalBlobId& lbi); + + // check the existence of a logical blob + bool SearchLbi(const LogicalBlobId& lbi) const; + + // unload a logical blob + void UnloadLbi(const LogicalBlobId& lbi); + + // Not carrying any blob + bool EmptyLbi() const; + + // Get the minimum element in Cost + double GetMinCost(); + // Get the maximum element in Cost + double GetMaxCost() const; + + // Assemble copy cost + void InitializeCopyCost(const std::string& ibn, bool use_sbp_collector); + + // find the cut ratio + // (#c>GetValidMaxCopyCost() in Cost)/(#c in Cost) + // But we would lift the cut ratio to 1 to filter out some improper couples + double FindCutRatio(int32_t threshold) const; + // Get the cut ratio + double GetCutRatio() const; + + private: + friend class SbpNode; + friend class SbpGraph; + friend class SbpCollector; + friend class SbpConstructor; + + // The edge point from start_node_ to end_node_ + // It will have a middle node if and only if type 3 + SbpNode *start_node_, *mid_node_, *end_node_; + // Cost[sbp_i][sbp_j] is the total cost from start_node_ with sbp_i to end_node_ + // with sbp_j + std::vector> cost_; + // SbpSignature for mid_node_ with corresponding Cost if type 3, empty otherwise + std::vector> mid_node_sbp_sig_; + // Contained edge list: + // empty if type 1, + // Parallel edges if type 2, + // succeed edges if type 3 + // the edge list might have reverse direction: + // example 1: type 3 edge_list_ contain two edges: + // mid_node_ -> start_node_, mid_node_ -> end_node_; + // example 2: type 2 edge_list_ contain three edges: + // start_node_ -> end_node_, end_node_ -> start_node_, start_node_ -> end_node_; + std::vector edge_list_; + // Time waiting for other gpus. pthread_cond_wait + double wait_time_ = -1.0; + + // a set of ids of logical blobs carried/transferred on this sbp edge + std::unordered_set carry_lbis_; + + // Minimum and maximum cost would not be changed by eliminations, which will generate new edges. + // Also would not be changed by node merging, which will only perform cost copy for the expanding + // dimensions. + // Minimum cost in the 2D array Cost. + // Would be initialized after GetMinCost(); + // Only used in the final graph. + double min_cost_ = -1.0; +}; + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_SBP_EDGE_H_ diff --git a/oneflow/core/auto_parallel/sbp_graph.cpp b/oneflow/core/auto_parallel/sbp_graph.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7410ff51a8522042013d97234310bb97fe38024f --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_graph.cpp @@ -0,0 +1,809 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include +#include "oneflow/core/auto_parallel/binary_set.h" +#include "oneflow/core/auto_parallel/sbp_graph.h" +#include "oneflow/core/auto_parallel/sbp_edge.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/algorithm_util.h" + +namespace oneflow { +namespace auto_parallel { + +// function in cpp. Should be put in one file due to use of template +// Otherwise we will need to declare specific template at the end of cpp file. + +namespace { +static const int32_t kMinNodeInGraphForMerging = 4; +} // anonymous namespace + +// Generate a node + +SbpNode* SbpGraph::GenerateNode() { + SbpNode* this_node = new SbpNode(); + node_list_.emplace_back(this_node); + this_node->node_list_id_ = node_list_.size() - 1; + return this_node; +} + +void SbpGraph::RemoveFromNodeList(SbpNode* this_node) { + if (this_node->node_list_id_ < 0) { return; } + node_list_.back()->node_list_id_ = this_node->node_list_id_; + RemoveFrom(node_list_, this_node->node_list_id_); + this_node->node_list_id_ = -1; +} + +SbpGraph::~SbpGraph() { + for (auto this_node : node_list_) { delete this_node; } + node_list_.clear(); +} + +void SbpGraph::RandomSbpSignature(bool use_sbp_collector) const { + for (const auto& this_node : node_list_) { + if (this_node->sbp_sig_list_.size() > 0) { + this_node->final_sbp_sig_id_ = rand() % this_node->sbp_sig_list_.size(); + } else { + // It must be a proxy when this_node->sbp_sig_list_.size() == 0 + this_node->final_sbp_sig_id_ = rand() % this_node->parallel_candidates_.size(); + } + } +}; + +void SbpGraph::SetDefaultSbpSig() const { + for (const auto& this_node : node_list_) { this_node->final_sbp_sig_id_ = 0; } +}; + +double SbpGraph::ComputeCost() const { + // Over All Cost under current strategy + double graph_cost_ = 0; + for (const auto& this_node : node_list_) { + int32_t this_id = this_node->final_sbp_sig_id_; + + graph_cost_ += this_node->cost_[this_id]; + for (const auto& edge_out : this_node->edges_out_) { + graph_cost_ += edge_out->cost_[this_id][edge_out->end_node_->final_sbp_sig_id_]; + } + } + return graph_cost_; +} + +int32_t SbpGraph::NodeElimination(SbpNode* this_node) { + if (this_node->edges_in_.size() + this_node->edges_out_.size() == 2) { + std::vector two_nodes; + for (const auto& one_edge : this_node->edges_in_) two_nodes.emplace_back(one_edge->start_node_); + for (const auto& one_edge : this_node->edges_out_) two_nodes.emplace_back(one_edge->end_node_); + + // If a node is pointing to itself, could happen when shrink from a circle + if (two_nodes[0] == two_nodes[1]) { + int32_t elimination_number = 0; + if (this_node->edges_out_.empty()) { + elimination_number += EdgeElimination(two_nodes[0]); + } else { + elimination_number += EdgeElimination(this_node); + } + + elimination_number += ChildElimination(this_node); + return elimination_number; + } + + std::vector two_edges(this_node->edges_in_); + two_edges.insert(two_edges.end(), this_node->edges_out_.begin(), this_node->edges_out_.end()); + + int32_t edges_in_size = this_node->edges_in_.size(); + + SbpEdge* e = new SbpEdge(two_nodes[0], this_node, two_nodes[1], two_edges[0], two_edges[1]); + e->SummarizeCost(); + // check and remove the edge_in with new edge in graph + for (int32_t i = 0; i < edges_in_size; i++) { + CheckAndRemoveFrom(two_nodes[i]->edges_out_, two_edges[i]); + } + // check and remove the edge_out with new edge in graph + for (int32_t i = edges_in_size; i < 2; i++) { + CheckAndRemoveFrom(two_nodes[i]->edges_in_, two_edges[i]); + } + // Let e take control of edge_list_ completely by disconnecting MidNode + e->mid_node_->edges_out_.clear(); + e->mid_node_->edges_in_.clear(); + + // Insert new compound edge into graph + two_nodes[0]->edges_out_.emplace_back(e); + two_nodes[1]->edges_in_.emplace_back(e); + + // eliminate the node from graph by swapping with the last element and + // popping + RemoveFromNodeList(this_node); + + // successfully eliminate this node + return 1; + } + // can not eliminate this node + return 0; +} + +int32_t SbpGraph::NodeAndEdgeEliminations() { + // Total elimination number + int32_t total_elimination_num = 0; + int32_t elimination_num = 1; + // repeat these kinds of elimination until stuck + while (elimination_num > 0) { + elimination_num = 0; + for (int32_t i = node_list_.size() - 1; i >= 0; i--) { + elimination_num += NodeElimination(node_list_[i]); + } + + for (int32_t i = node_list_.size() - 1; i >= 0; i--) { + elimination_num += EdgeElimination(node_list_[i]); + } + + for (int32_t i = node_list_.size() - 1; i >= 0; i--) { + elimination_num += ChildElimination(node_list_[i]); + } + + if (elimination_num == 0 && node_list_.size() > 2) { + elimination_num += PickAndMerge(); + for (int32_t i = node_list_.size() - 1; i >= 0; i--) { + elimination_num += EdgeElimination(node_list_[i]); + } + } + + total_elimination_num += elimination_num; + } + + return total_elimination_num; +} + +int32_t SbpGraph::EdgeElimination(SbpNode* this_node) const { + // Remove all edges with (start_node -> end_node) from edges_in_ of end_node + auto RemoveFromEdgesIn = [](SbpNode* start_node, SbpNode* end_node) -> void { + for (int32_t i = end_node->edges_in_.size() - 1; i >= 0; i--) { + if (start_node == end_node->edges_in_[i]->start_node_) { + RemoveFrom(end_node->edges_in_, i); + } + } + }; + auto LookForParallelEdge = [](SbpEdge*& e, SbpNode* start_node, SbpNode* end_node, + bool if_reverse, int32_t stop_sign) -> int32_t { + // elimination edges with specific start node and end node in + // start_node->edges_out_ from index stop sign to the end. + // start_node->edges_out_[stop_sign] not included and need special treatment + // after this process. + int32_t elimination_num = 0; + for (int32_t j = start_node->edges_out_.size() - 1; j > stop_sign; j--) { + if (end_node == start_node->edges_out_[j]->end_node_) { + if (!e) { + if (if_reverse) { + e = new SbpEdge(end_node, start_node); + } else { + e = new SbpEdge(start_node, end_node); + } + } + // edge elimination + e->edge_list_.emplace_back(start_node->edges_out_[j]); + elimination_num++; + RemoveFrom(start_node->edges_out_, j); + } + } + return elimination_num; + }; + + int32_t elimination_num = 0; + + for (int32_t i = 0; i < this_node->edges_out_.size(); i++) { + SbpEdge* e = nullptr; + // Find and delete Parallel Edges from edges_out_ + elimination_num += LookForParallelEdge(e, this_node, this_node->edges_out_[i]->end_node_, + /*if_reverse=*/false, i); + elimination_num += LookForParallelEdge(e, this_node->edges_out_[i]->end_node_, this_node, + /*if_reverse=*/true, /*stop_sign=*/-1); + if (e) { + // Delete Parallel Edges from edges_in_ + RemoveFromEdgesIn(this_node, e->end_node_); + RemoveFromEdgesIn(e->end_node_, this_node); + // Add the compound edge + e->edge_list_.emplace_back(this_node->edges_out_[i]); + this_node->edges_out_[i] = e; + e->SummarizeCost(); + e->end_node_->edges_in_.emplace_back(e); + } + } + return elimination_num; +} + +int32_t SbpGraph::ChildElimination(SbpNode* this_node) { + if (this_node->EliminateItselfAsChild()) { + // eliminate this node from global node list + RemoveFromNodeList(this_node); + // successfully eliminate this node + return 1; + } else { + // can not eliminate this node + return 0; + } +} + +// Merge two nodes + +int32_t SbpGraph::NodeMerging(SbpNode* first, SbpNode* second) { + SbpNode* new_node = new SbpNode(first, second); + + // Adjust node_list_ + RemoveFromNodeList(first); + RemoveFromNodeList(second); + + new_node->node_list_id_ = node_list_.size(); + node_list_.emplace_back(new_node); + + return 1; +} + +void SbpGraph::FinalizeSbp() const { + for (const auto& this_node : node_list_) { this_node->FinalizeSbp(); } +} + +double SbpGraph::GreedyStrategy(bool for_node) const { + // Overall, this function should be replaced by GreedyStrategy(nbh_num); + // Total Cost Reduce & Cost Reduce for one loop + double total_cost_reduction = 0, cost_reduction = 0; + for (int32_t step = node_list_.size(); step >= 0; step--) { + cost_reduction = 0; + for (SbpNode* this_node : node_list_) { + // Use GreedyStrategy on Nodes if there is one node left for this + // connected component. Otherwise, Use GreedyStrategy on Edges. + if (for_node || this_node->edges_in_.size() + this_node->edges_out_.size() == 0) { + cost_reduction += this_node->GreedyStrategy(); + } else { + // GreedyStrategy on Edges. + for (SbpEdge* this_edge : this_node->edges_out_) { + double second_rdc = this_edge->GreedyStrategy(); + cost_reduction += second_rdc; + } + } + } + if (cost_reduction == 0) { break; } + total_cost_reduction += cost_reduction; + } + return total_cost_reduction; +} + +double SbpGraph::GreedyStrategy(int32_t nbh_num) const { + // nbh_num is the maximum number of neighborhood to adjust sbp strategy in each step + // Total Cost Reduce & Cost Reduce for one loop + double total_cost_reduction = 0, cost_reduction = 0; + // A global buffer to store part of the one ring neighborhood. + std::vector nbh_id2node_list_id; + // Not accept a number lower than 1 + if (nbh_num < 1) { nbh_num = 1; } + nbh_id2node_list_id.resize(nbh_num); + std::vector original_sbp_sig_id(nbh_num); + // store all the node_list_id whose corresponding nodes will be visited + // We can use unordered_map to do this but vector is faster + std::vector pre_visit_node_list(node_list_.size() + 1); + for (int32_t nbh_id = 0; nbh_id < node_list_.size(); nbh_id++) { + pre_visit_node_list[nbh_id] = nbh_id; + } + int32_t head = 0, tail = node_list_.size(); + // whether a node_list_id is in pre_visit_node_list + std::vector pre_visit_tags(node_list_.size(), true); + int32_t step = 0; + // 1 ring neighborhood buffer + std::vector nbh_1ring(nbh_num); + // 2 ring neighborhood buffer + std::vector nbh_2ring; + std::vector node_tags(node_list_.size(), false); + std::vector nbh_1ring_buffer; + + while (head != tail && step < node_list_.size()) { + auto* this_node = node_list_[pre_visit_node_list[head]]; + if (nbh_num <= 1) { + // Greedy strategy on nodes, here we use nbh_1ring to store the nbh_id2node_list_id + // information for reutilization + nbh_1ring[0] = this_node->node_list_id_; + // store the original sbp signature of the 1-ring neighborhood for comparison + original_sbp_sig_id[0] = this_node->final_sbp_sig_id_; + cost_reduction = NbhGreedyStrategy(nbh_1ring); + } else { + // Use GreedyStrategy on the one ring neighborhood of this node. + this_node->OneRingNeighborhood(nbh_1ring); + // store the original sbp signature of the 1-ring neighborhood for comparison + original_sbp_sig_id.resize(nbh_1ring.size()); + for (int32_t nbh_id = 0; nbh_id < nbh_1ring.size(); nbh_id++) { + original_sbp_sig_id[nbh_id] = node_list_[nbh_1ring[nbh_id]]->final_sbp_sig_id_; + } + if (nbh_1ring.size() <= nbh_num) { + cost_reduction = NbhGreedyStrategy(nbh_1ring); + } else { + // Use GreedyStrategy on part of the one ring neighborhood. + // Loop through the neighborhood. Each loop should contain the centroid. + + // Initialize part of the one ring neighborhood + int32_t nbh_1ring_id = nbh_1ring.size() - nbh_num; + for (int32_t nbh_id = 1; nbh_id < nbh_num; ++nbh_id) { + nbh_id2node_list_id[nbh_id] = nbh_1ring[++nbh_1ring_id]; + } + // loop through the one ring neighborhood + cost_reduction = 0; + int32_t nbh_id = 0; + for (nbh_1ring_id = 0; nbh_1ring_id < nbh_1ring.size(); ++nbh_1ring_id) { + nbh_id2node_list_id[nbh_id] = nbh_1ring[nbh_1ring_id]; + cost_reduction += NbhGreedyStrategy(nbh_id2node_list_id); + // nbh_id for the next step + if (++nbh_id >= nbh_num) { nbh_id = 1; } + } + } + } + // change of strategies + if (cost_reduction != 0) { + // Add neighborhood into pre-visited node list for each node with changing strategy + for (int32_t nbh_id = 0; nbh_id < nbh_1ring.size(); nbh_id++) { + // If changes occur + if (original_sbp_sig_id[nbh_id] != node_list_[nbh_1ring[nbh_id]]->final_sbp_sig_id_) { + // schedule to visit the neighborhood of that changing node + node_list_[nbh_1ring[nbh_id]]->NRingNeighborhood(2, nbh_2ring, nbh_1ring_buffer, + node_list_, node_tags); + for (int32_t nbh_node_list_id : nbh_2ring) { + // Put them into the pre-visited node list + if (!pre_visit_tags[nbh_node_list_id]) { + pre_visit_node_list[tail] = nbh_node_list_id; + pre_visit_tags[nbh_node_list_id] = true; + tail++; + if (tail == pre_visit_node_list.size()) { tail = 0; } + } + } + } + } + } + // Finish visiting + pre_visit_tags[pre_visit_node_list[head]] = false; + head++; + if (head == pre_visit_node_list.size()) { + head = 0; + step++; + } + + total_cost_reduction += cost_reduction; + } + return total_cost_reduction; +} + +void SbpGraph::DfsAddNbhCost(std::vector& nbh_id2node_list_id, + std::unordered_map& node_list_id2nbh_id, + std::vector& order2nbh_id, std::vector& nbh_id2order, + std::vector& order2acc_min_in_nbh_cost, + std::vector>& out_nbh_costs, + std::vector>& nbh_id2order2sbp_id, + std::vector& min_sbp_sig_id, double& min_cost, int32_t order, + double curr_cost) const { + // We have finished visiting the neighborhood + if (order >= nbh_id2node_list_id.size()) { + // relative difference > 1e-12 + if (curr_cost < min_cost * kFloatDeviationMinus) { + min_cost = curr_cost; + for (int32_t nbh_id = 0; nbh_id < nbh_id2node_list_id.size(); nbh_id++) { + min_sbp_sig_id[nbh_id] = node_list_[nbh_id2node_list_id[nbh_id]]->final_sbp_sig_id_; + } + } + return; + } + // Pruning, remove all those branch with large cost + if (curr_cost + order2acc_min_in_nbh_cost[order] >= min_cost) { return; } + // Deep first search in the next order + int32_t nbh_id = order2nbh_id[order]; + SbpNode* sbp_node = node_list_[nbh_id2node_list_id[nbh_id]]; + for (int32_t sbp_id : nbh_id2order2sbp_id[nbh_id]) { + sbp_node->final_sbp_sig_id_ = sbp_id; + DfsAddNbhCost(nbh_id2node_list_id, node_list_id2nbh_id, order2nbh_id, nbh_id2order, + order2acc_min_in_nbh_cost, out_nbh_costs, nbh_id2order2sbp_id, min_sbp_sig_id, + min_cost, order + 1, + curr_cost + out_nbh_costs[nbh_id][sbp_id] + + sbp_node->EvalInNbhCost(node_list_id2nbh_id, nbh_id2order)); + } +} + +bool SbpGraph::DfsFindReasonableCost(std::vector& nbh_id2node_list_id, + std::unordered_map& node_list_id2nbh_id, + std::vector& nbh_id2order, int32_t nbh_id) const { + // We found such a strategy + if (nbh_id == nbh_id2order.size()) { return true; } + SbpNode* sbp_node = node_list_[nbh_id2node_list_id[nbh_id]]; + // Start from B. + for (int32_t sbp_id = sbp_node->cost_.size() - 1; sbp_id >= 0; sbp_id--) { + sbp_node->final_sbp_sig_id_ = sbp_id; + // If the cost for this node is reasonable, then go to the next one + if (sbp_node->cost_[sbp_id] + sbp_node->EvalInNbhCost(node_list_id2nbh_id, nbh_id2order) + < GetValidMaxCopyCost()) { + if (DfsFindReasonableCost(nbh_id2node_list_id, node_list_id2nbh_id, nbh_id2order, + nbh_id + 1)) { + // If we found one strategy, then exist the Dfs. + return true; + } + } + } + // Can not find a reasonable strategy with the setting for previous nodes. + // Go back and change the previous node. + return false; +} + +// Find one strategy with finite cost for adjustment + +Maybe SbpGraph::Find1Strategy4Greedy() const { + std::vector nbh_id2node_list_id; + std::vector not_visited(node_list_.size(), true); + std::vector nbh_1ring; + int32_t head = 0; + int32_t tail = 0; + std::vector node_cut_ratios(node_list_.size()); + // Initialize cut ratio for all the nodes + for (int32_t node_list_id = 0; node_list_id < node_list_.size(); node_list_id++) { + node_cut_ratios[node_list_id] = node_list_[node_list_id]->GetCutRatio(); + } + // If have not visited all the nodes + while (tail < node_list_.size()) { + // Find the node with the minimum cut ratio + int32_t node_with_min_cut_ratio = -1; + double min_cut_ratio = 2.0; + for (int32_t node_list_id = 0; node_list_id < node_list_.size(); node_list_id++) { + if (not_visited[node_list_id]) { + double curr_cut_ratio = node_cut_ratios[node_list_id]; + if (curr_cut_ratio < min_cut_ratio) { + min_cut_ratio = curr_cut_ratio; + node_with_min_cut_ratio = node_list_id; + } + } + } + // put this node into the open set + nbh_id2node_list_id.push_back(node_with_min_cut_ratio); + not_visited[node_with_min_cut_ratio] = false; + tail++; + // BFS + while (head < tail) { + // look for the neighborhood of the head + int32_t node_list_id = nbh_id2node_list_id[head]; + node_list_[node_list_id]->OneRingNeighborhood(nbh_1ring); + // sort + std::sort(nbh_1ring.begin(), nbh_1ring.end(), + [&](int32_t i, int32_t j) { return node_cut_ratios[i] < node_cut_ratios[j]; }); + for (int32_t curr_id : nbh_1ring) { + if (not_visited[curr_id]) { + nbh_id2node_list_id.push_back(curr_id); + tail++; + not_visited[curr_id] = false; + } + } + head++; + } + } + // mapping from the node_list_id to the id in the nbh_id2node_list_id + std::unordered_map node_list_id2nbh_id; + InverseFunction(nbh_id2node_list_id, node_list_id2nbh_id); + // Initial an ordinary order + std::vector nbh_id2order(nbh_id2node_list_id.size()); + for (int32_t nbh_id = 0; nbh_id < nbh_id2node_list_id.size(); nbh_id++) { + nbh_id2order[nbh_id] = nbh_id; + } + // Combining deep first search and pruning based on cut ratio + CHECK(DfsFindReasonableCost(nbh_id2node_list_id, node_list_id2nbh_id, nbh_id2order, /*nbh_id=*/0)) + << "Can't find a reasonable strategy!"; + return Maybe::Ok(); +} + +// Use brute force to search for a strategy with minimum cost for a neighborhood + +double SbpGraph::NbhGreedyStrategy(std::vector& nbh_id2node_list_id) const { + // number of nodes in the neighborhood + int32_t num_nbh = nbh_id2node_list_id.size(); + // mapping from the node_list_id to the id in the nbh_id2node_list_id + std::unordered_map node_list_id2nbh_id; + InverseFunction(nbh_id2node_list_id, node_list_id2nbh_id); + // a sbp signature id set minimizing the overall cost, store the original one as default + std::vector min_sbp_sig_id(num_nbh); + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + min_sbp_sig_id[nbh_id] = node_list_[nbh_id2node_list_id[nbh_id]]->final_sbp_sig_id_; + } + + // pre-compute and store the cost between neighborhood and outside nodes under different sbp for + // each node within the neighborhood + std::vector> out_nbh_costs(num_nbh); + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + SbpNode* sbp_node = node_list_[nbh_id2node_list_id[nbh_id]]; + out_nbh_costs[nbh_id].resize(sbp_node->cost_.size()); + for (int32_t sbp_id = sbp_node->cost_.size() - 1; sbp_id >= 0; sbp_id--) { + sbp_node->final_sbp_sig_id_ = sbp_id; + out_nbh_costs[nbh_id][sbp_id] = sbp_node->EvalOutNbhCost(node_list_id2nbh_id); + } + } + // pre-compute and store the order of the out_nbh_costs + std::vector> nbh_id2order2sbp_id(num_nbh); + auto CompareDoubleLess = [](double a, double b) { return a < b; }; + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + DecideOrder(out_nbh_costs[nbh_id], nbh_id2order2sbp_id[nbh_id], CompareDoubleLess); + } + + // Decide the order to go through the neighborhood. + // Should visit those nodes with a larger difference in the out cost first. + std::vector out_nbh_cost_diff(num_nbh); + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + out_nbh_cost_diff[nbh_id] = + *std::max_element(out_nbh_costs[nbh_id].begin(), out_nbh_costs[nbh_id].end()) + - *std::min_element(out_nbh_costs[nbh_id].begin(), out_nbh_costs[nbh_id].end()); + } + std::vector order2nbh_id; + DecideOrder(out_nbh_cost_diff, order2nbh_id, [](double a, double b) { return a > b; }); + // Find the inverse map of order + std::vector nbh_id2order; + InverseOrder(order2nbh_id, nbh_id2order); + + // Current Cost, Minimum Cost, Cost with original sbp + double original_cost = 0; + // Recover original sbp + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + node_list_[nbh_id2node_list_id[nbh_id]]->final_sbp_sig_id_ = min_sbp_sig_id[nbh_id]; + } + // Compute cost with original sbp + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + SbpNode* sbp_node = node_list_[nbh_id2node_list_id[nbh_id]]; + original_cost += out_nbh_costs[nbh_id][min_sbp_sig_id[nbh_id]]; + original_cost += sbp_node->EvalInNbhCost(node_list_id2nbh_id, nbh_id2order); + } + double min_cost = original_cost; + // Accumulate minimum cost from the current node to the end of the neighborhood node list. + // The accumulated cost include the current node. + std::vector order2acc_min_in_nbh_cost(num_nbh); + order2acc_min_in_nbh_cost[num_nbh - 1] = + *std::min_element(out_nbh_costs[order2nbh_id[num_nbh - 1]].begin(), + out_nbh_costs[order2nbh_id[num_nbh - 1]].end()); + for (int32_t order = num_nbh - 2; order >= 0; order--) { + int32_t nbh_id = order2nbh_id[order]; + order2acc_min_in_nbh_cost[order] = + order2acc_min_in_nbh_cost[order + 1] + + *std::min_element(out_nbh_costs[nbh_id].begin(), out_nbh_costs[nbh_id].end()) + + node_list_[nbh_id2node_list_id[nbh_id]]->EvalMinInNbhCost(node_list_id2nbh_id, + nbh_id2order); + } + // Use brute force (DFS) to adjust for the best strategy in the neighborhood. + DfsAddNbhCost(nbh_id2node_list_id, node_list_id2nbh_id, order2nbh_id, nbh_id2order, + order2acc_min_in_nbh_cost, out_nbh_costs, nbh_id2order2sbp_id, min_sbp_sig_id, + min_cost, /*order=*/0, /*curr_cost=*/0); + // Use the sbp strategy with minimum cost + for (int32_t nbh_id = 0; nbh_id < num_nbh; nbh_id++) { + node_list_[nbh_id2node_list_id[nbh_id]]->final_sbp_sig_id_ = min_sbp_sig_id[nbh_id]; + } + + if (min_cost < original_cost) { + // Directly return (min_cost - original_cost) might have floating point error up to 3e-16 + // For example, original_cost: 2.22507e+06, min_cost: 2.22507e+06, + // diff: -4.65661e-10, relative diff:2.09279e-16 + // Therefore, we use a threshold to filter out such fake true detection to + // avoid unlimited search. + if (original_cost * kFloatDeviationMinus > min_cost) { return min_cost - original_cost; } + } + return 0.0; +} + +// Select and Merge two nodes + +int32_t SbpGraph::PickAndMerge() { + if (node_list_.size() < kMinNodeInGraphForMerging) { return 0; } + // Pick the one with the smallest cut ratio + double min_cut_ratio = 1.0; + double curr_cut_ratio = 0.0; + SbpEdge* merging_edge = nullptr; + for (int32_t i = 0; i < node_list_.size(); i++) { + for (SbpEdge* edge_in : node_list_[i]->edges_in_) { + curr_cut_ratio = edge_in->FindCutRatio(threshold_); + if (curr_cut_ratio < min_cut_ratio) { + min_cut_ratio = curr_cut_ratio; + merging_edge = edge_in; + } + } + } + + if (merging_edge != nullptr) { + // Merge two nodes on the edge with the minimum cut ratio + return NodeMerging(merging_edge->start_node_, merging_edge->end_node_); + } else { + // Pick the couple with the largest similar neighborhood + std::vector node_binary_sets(node_list_.size()); + for (int32_t i = 0; i < node_list_.size(); i++) { + // Transfer edge to binary set + node_binary_sets[i].Initialize(node_list_.size()); + node_binary_sets[i].AddEntry(i); + for (const SbpEdge* edge_in : node_list_[i]->edges_in_) { + node_binary_sets[i].AddEntry(edge_in->start_node_->node_list_id_); + } + for (const SbpEdge* edge_out : node_list_[i]->edges_out_) { + node_binary_sets[i].AddEntry(edge_out->start_node_->node_list_id_); + } + } + // Find two nodes with largest common subset + // buffer of binary set + BinarySet buffer_binary_set(node_list_.size()); + // Number of common edges + int32_t max_comm_edge_num = 0, curr_comm_edge_num = 0; + int32_t min_node_pair[2]; + // Number of Sbp Signature in merged node + int32_t min_sbp_num = 0, curr_sbp_num = 0; + for (int32_t i = 0; i < node_list_.size(); i++) { + for (int32_t j = i + 1; j < node_list_.size(); j++) { + curr_sbp_num = node_list_[i]->cost_.size() * node_list_[j]->cost_.size(); + if (curr_sbp_num <= threshold_) { + node_binary_sets[i].IntersectionTo(node_binary_sets[j], buffer_binary_set); + curr_comm_edge_num = buffer_binary_set.Total(); + if (curr_comm_edge_num > max_comm_edge_num + || (curr_comm_edge_num == max_comm_edge_num && curr_sbp_num < min_sbp_num)) { + min_node_pair[0] = i; + min_node_pair[1] = j; + max_comm_edge_num = curr_comm_edge_num; + min_sbp_num = curr_sbp_num; + } + } + } + } + if (max_comm_edge_num > 0) { + return NodeMerging(node_list_[min_node_pair[0]], node_list_[min_node_pair[1]]); + } else { + return 0; + } + } +} + +// Clip an edge, remove it from graph + +void SbpGraph::ClipEdge(SbpEdge* this_edge) const { + CheckAndRemoveFrom(this_edge->end_node_->edges_in_, this_edge); + CheckAndRemoveFrom(this_edge->start_node_->edges_out_, this_edge); + delete this_edge; +} + +// Compute the minimum and maximum layer of each node in the graph + +int32_t SbpGraph::ComputeLayer( + HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps) const { + // Compute minimum layer + for (SbpNode* this_node : node_list_) { + this_node->GetMinLayer(op_name2sbp_node, op_node2mutable_op_ctrl_deps); + } + // Find the largest minimum layer + int32_t max_min_layer = -1; + for (SbpNode* this_node : node_list_) { + if (max_min_layer < this_node->min_layer_) { max_min_layer = this_node->min_layer_; } + } + // Compute maximum layer + for (SbpNode* this_node : node_list_) { + this_node->SpreadMaxLayer(op_name2sbp_node, op_node2mutable_op_ctrl_deps); + } + for (SbpNode* this_node : node_list_) { this_node->LiftMaxLayer(max_min_layer); } + return max_min_layer; +} + +// Find the trunk of the sbp graph, then reduce the wait time for tributaries + +void SbpGraph::FindTrunk(int32_t max_min_layer, + HashMap& op_name2sbp_node) const { + // Summarize cost for each layer, on the trunk or tributaries + std::vector trunk_cost(max_min_layer + 1, 0); + for (SbpNode* this_node : node_list_) { + trunk_cost[this_node->min_layer_] += this_node->GetMinCost(); + } + // Decide trunks + double acc_cost = 0; + // All the nodes with MinLayer>=trunk_end_id would be considered as trunks + int32_t trunk_end_id = max_min_layer; + for (int32_t layer_id = max_min_layer; layer_id >= 0; layer_id--) { + acc_cost += trunk_cost[layer_id]; + if (acc_cost > 0.5 * wait_time_) { + trunk_end_id = layer_id; + break; + } + } + // Find out all the nodes on the trunk. + for (SbpNode* this_node : node_list_) { + if (this_node->min_layer_ >= trunk_end_id) { this_node->SpreadTrunk(op_name2sbp_node); } + } + + // Compute maximum layer for tributaries + // Clear counter and initialize tributary layer for each sbp node + for (SbpNode* this_node : node_list_) { + this_node->counter_ = 0; + this_node->DropTributaryLayer(max_min_layer); + } + // Count the number of consumers and downstream nodes + for (SbpNode* this_node : node_list_) { this_node->RaiseConsumerNum(op_name2sbp_node); } + // Compute maximum layer for tributaries + for (SbpNode* this_node : node_list_) { this_node->SpreadTributaryLayer(op_name2sbp_node); } + + // Summarize cost for each layer on the trunk, store it to avoid subtraction of large values. + trunk_cost.assign(max_min_layer + 1, 0); + // tributary cost start from each min layer + std::vector tributary_cost(max_min_layer + 1, 0); + // tributary cost would be outdated after Max Layer (before Max Layer + 1) + std::vector outdated_tributary_cost(max_min_layer + 1, 0); + // number of operators in the trunk + std::vector> trunk_ops(max_min_layer + 1); + + for (SbpNode* this_node : node_list_) { + if (this_node->on_trunk_) { + trunk_cost[this_node->min_layer_] += this_node->GetMinCost(); + trunk_ops[this_node->min_layer_].emplace_back(this_node); + } else { + double curr_min_cost = this_node->GetMinCost(); + tributary_cost[this_node->min_layer_] += curr_min_cost; + outdated_tributary_cost[this_node->tributary_layer_] += curr_min_cost; + } + } + // Accumulate the cost from the consumer to the end, not including itself + std::vector acc_trunk_cost(max_min_layer + 1, 0); + for (int32_t layer_id = max_min_layer; layer_id > 0; layer_id--) { + acc_trunk_cost[layer_id - 1] = acc_trunk_cost[layer_id] + trunk_cost[layer_id]; + } + + // Clear counter for each sbp node + for (SbpNode* this_node : node_list_) { this_node->counter_ = 0; } + // Count the number of consumers and downstream nodes + for (SbpNode* this_node : node_list_) { this_node->RaiseConsumerNum(op_name2sbp_node); } + // Reduce the wait time for tributaries + for (SbpNode* this_node : node_list_) { + this_node->SpreadAvailWaitTime(trunk_cost, acc_trunk_cost, op_name2sbp_node, wait_time_); + } + + // Reduce the wait time for trunk from the end to the begin + double acc_tributary_cost = outdated_tributary_cost[max_min_layer]; + double used_tributary_cost = 0.0; + double curr_wait_time = 0.0; + for (int32_t layer_id = max_min_layer - 1; layer_id >= 0; layer_id--) { + // Can not move it backward since we need to do this at the 0th layer. + // At some moment, the cost haven't been used would disappear. + if (tributary_cost[layer_id + 1] > used_tributary_cost) { + acc_tributary_cost -= tributary_cost[layer_id + 1] - used_tributary_cost; + used_tributary_cost = 0.0; + if (acc_tributary_cost < 0.0) { + // should not happen besides floating point error + std::cout << "Caution! Current accumulated tributary cost is: " << acc_tributary_cost + << std::endl; + acc_tributary_cost = 0.0; + } + } else { + used_tributary_cost -= tributary_cost[layer_id + 1]; + } + // accumulate tributary cost at this layer + acc_tributary_cost += outdated_tributary_cost[layer_id]; + // If we have more cost in tributaries, we reduce the wait time + // This code maintains ( acc_tributary_cost + used_tributary_cost ) + if (acc_tributary_cost > 0.0) { + if (acc_tributary_cost > wait_time_) { + curr_wait_time = 0.0; + acc_tributary_cost -= wait_time_; + used_tributary_cost += wait_time_; + } else { + curr_wait_time = wait_time_ - acc_tributary_cost; + used_tributary_cost += acc_tributary_cost; + acc_tributary_cost = 0.0; + } + // Reduce the wait time in the trunk + for (SbpNode* this_node : trunk_ops[layer_id]) { + this_node->SetTrunkWaitTime(curr_wait_time); + } + } + } +} + +// Set wait time +void SbpGraph::SetWaitTime(double wait_time) { wait_time_ = wait_time; } + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_graph.h b/oneflow/core/auto_parallel/sbp_graph.h new file mode 100644 index 0000000000000000000000000000000000000000..81c87a18f338c941f584873f83470c9999cadd10 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_graph.h @@ -0,0 +1,139 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_SBP_GRAPH_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_SBP_GRAPH_H_ + +#include +#include +#include "oneflow/core/auto_parallel/binary_set.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/sbp_edge.h" +#include "oneflow/core/auto_parallel/algorithm_util.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace auto_parallel { + +// A graph structure to deal with the SBP strategy. +// It contains a lot of eliminations to shrink the topography structure of the original graph. +// Furthermore, it contains some adjustment tricks for search a good strategy in the shrunk graph. +class SbpGraph final { + public: + // Constructor + SbpGraph() = default; + + // Deconstructor + ~SbpGraph(); + + OF_DISALLOW_COPY_AND_MOVE(SbpGraph); + bool operator==(const SbpGraph& other) { return this == &other; } + + // Randomly assign a SbpSignature strategy + void RandomSbpSignature(bool use_sbp_collector) const; + // assign 0 to a SbpSignature strategy to avoid randomness + void SetDefaultSbpSig() const; + + // Compute Cost for current strategy + double ComputeCost() const; + + // Generate a node + SbpNode* GenerateNode(); + + // Merge all parallel edges & Check and eliminate all nodes with only one + // degree-in and one degree-out + int32_t NodeAndEdgeEliminations(); + + // Finalize Sbp Cost for the whole graph + void FinalizeSbp() const; + + // Use Greedy Strategy to decide Sbp for Nodes in node_list_. Should be used + // after we have a initial strategy. + // Set for_node to be true will only use GreedyStrategy on Nodes. + double GreedyStrategy(bool for_node) const; + // Use greedy strategy on the one ring neighborhood with the maximum number of points nbh_num. + double GreedyStrategy(int32_t nbh_num = 4) const; + + // Find one strategy with finite cost for adjustment + Maybe Find1Strategy4Greedy() const; + // Use brute force to search for a strategy with minimum cost for a neighborhood + double NbhGreedyStrategy(std::vector& nbh_id2node_list_id) const; + + // Set threshold_ for SbpNode Merging + void SetThreshold(int32_t threshold) { threshold_ = threshold; } + + // Clip an edge, remove it from graph + // Clipping an edge will also delete the nodes and edges contained in this edge. Though not + // suffering from any compiling and runtime bugs, clipping an edge on a shrunk graph is not + // recommended. We should carefully think about it before any clipping. + void ClipEdge(SbpEdge* this_edge) const; + + // Compute the minimum and maximum layer of each node in the graph + int32_t ComputeLayer( + HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps) const; + + // Find the trunk of the sbp graph, then reduce the wait time for tributaries + void FindTrunk(int32_t max_min_layer, HashMap& op_name2sbp_node) const; + + // Set wait time + void SetWaitTime(double wait_time); + + private: + friend class SbpCollector; + friend class SbpConstructor; + + // All the nodes + std::vector node_list_; + + // Limitation: Merged node should not have a number of Sbp Signature greater + // than threshold. + int32_t threshold_ = 100; + // Overlayable wait time for copy cost, which occurs before communication between devices. + double wait_time_ = 16500.0; + + // Remove a node from the node list + void RemoveFromNodeList(SbpNode* this_node); + + // Check and eliminate one node with only one degree-in and one degree-out + int32_t NodeElimination(SbpNode* this_node); + // Merge all parallel edges with given start_node_ and end_node_ + int32_t EdgeElimination(SbpNode* this_node) const; + // Check and eliminate one child node + int32_t ChildElimination(SbpNode* this_node); + + // Merge two nodes + int32_t NodeMerging(SbpNode* first, SbpNode* second); + // Select two nodes and merge them + int32_t PickAndMerge(); + + void DfsAddNbhCost(std::vector& nbh_id2node_list_id, + std::unordered_map& node_list_id2nbh_id, + std::vector& order2nbh_id, std::vector& nbh_id2order, + std::vector& order2acc_min_in_nbh_cost, + std::vector>& out_nbh_costs, + std::vector>& nbh_id2order2sbp_id, + std::vector& min_sbp_sig_id, double& min_cost, int32_t order, + double curr_cost) const; + + bool DfsFindReasonableCost(std::vector& nbh_id2node_list_id, + std::unordered_map& node_list_id2nbh_id, + std::vector& nbh_id2order, int32_t nbh_id) const; +}; + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_SBP_GRAPH_H_ diff --git a/oneflow/core/auto_parallel/sbp_node.cpp b/oneflow/core/auto_parallel/sbp_node.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8da4be4dc4188c0d220a24c330a895300a69c252 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_node.cpp @@ -0,0 +1,708 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include +#include +#include +#include "oneflow/core/auto_parallel/binary_set.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/auto_parallel/algorithm_util.h" +#include "oneflow/core/job/sbp_parallel.pb.h" +#include "oneflow/core/auto_parallel/sbp_node.h" +#include "oneflow/core/auto_parallel/sbp_edge.h" +#include "oneflow/core/auto_parallel/sbp_graph.h" + +namespace oneflow { +namespace auto_parallel { + +// function in cpp. Should be put in one file due to use of template +// Otherwise we will need to declare specific template at the end of cpp file. + +SbpNode::SbpNode(SbpNode* first, SbpNode* second) { + half_node_.resize(2); + half_node_[0] = first; + half_node_[1] = second; + + // Get the edge between first and second + // NOTE: It must zero or one edge between them + SbpEdge* common_edge = nullptr; + for (int32_t k = 0; k < first->edges_in_.size(); k++) { + if (first->edges_in_[k]->start_node_ == second) { + // CHECK_ISNULL(edge); + common_edge = first->edges_in_[k]; + } + } + for (int32_t k = 0; k < first->edges_out_.size(); k++) { + if (first->edges_out_[k]->end_node_ == second) { common_edge = first->edges_out_[k]; } + } + + // Find all available merged-SbpSignature(edge's cost less than threshold). + if (common_edge) { + double min_cost = GetMaxVal(); + for (const auto& row : common_edge->cost_) { + for (const double& c : row) min_cost = std::min(min_cost, c); + } + // If there is no one case can choose, we will blow up + for (int32_t i = 0; i < first->cost_.size(); i++) { + for (int32_t j = 0; j < second->cost_.size(); j++) { + const double edge_cost = + common_edge->start_node_ == first ? common_edge->cost_[i][j] : common_edge->cost_[j][i]; + if (edge_cost < GetValidMaxCopyCost()) { + merged_sig_id2children_sig_id_.emplace_back(std::make_pair(i, j)); + cost_.emplace_back(edge_cost + first->cost_[i] + second->cost_[j]); + } + } + } + CHECK(merged_sig_id2children_sig_id_.size() > 0) + << "0 size for merge child edge, min cost: " << min_cost; + } else { + for (int32_t i = 0; i < first->cost_.size(); i++) { + for (int32_t j = 0; j < second->cost_.size(); j++) { + merged_sig_id2children_sig_id_.emplace_back(std::make_pair(i, j)); + cost_.emplace_back(first->cost_[i] + second->cost_[j]); + } + } + } + + // Initialize default sbp choice + // If the original sbp pair does not go through, then use 0 as default. + final_sbp_sig_id_ = 0; + // Track the original strategy + for (int32_t sig_id = 0; sig_id < merged_sig_id2children_sig_id_.size(); sig_id++) { + if (merged_sig_id2children_sig_id_[sig_id].first == first->final_sbp_sig_id_ + && merged_sig_id2children_sig_id_[sig_id].second == second->final_sbp_sig_id_) { + final_sbp_sig_id_ = sig_id; + } + } + + // Merge edges_in_ + edges_in_.reserve(first->edges_in_.size() + second->edges_in_.size()); + edges_in_.insert(edges_in_.end(), first->edges_in_.begin(), first->edges_in_.end()); + edges_in_.insert(edges_in_.end(), second->edges_in_.begin(), second->edges_in_.end()); + // Merge edges_out_ + edges_out_.reserve(first->edges_out_.size() + second->edges_out_.size()); + edges_out_.insert(edges_out_.end(), first->edges_out_.begin(), first->edges_out_.end()); + edges_out_.insert(edges_out_.end(), second->edges_out_.begin(), second->edges_out_.end()); + // Merge SbpEdge Cost + for (SbpEdge*& this_edge : first->edges_in_) { + this_edge->DuplicateCost(false, true, merged_sig_id2children_sig_id_); + this_edge->end_node_ = this; + } + for (SbpEdge*& this_edge : first->edges_out_) { + this_edge->DuplicateCost(true, true, merged_sig_id2children_sig_id_); + this_edge->start_node_ = this; + } + for (SbpEdge*& this_edge : second->edges_in_) { + this_edge->DuplicateCost(false, false, merged_sig_id2children_sig_id_); + this_edge->end_node_ = this; + } + for (SbpEdge*& this_edge : second->edges_out_) { + this_edge->DuplicateCost(true, false, merged_sig_id2children_sig_id_); + this_edge->start_node_ = this; + } + // Remove edges from original nodes + first->edges_in_.clear(); + first->edges_out_.clear(); + second->edges_in_.clear(); + second->edges_out_.clear(); + + // Move edges between two nodes to each half node + for (int32_t k = edges_out_.size() - 1; k >= 0; k--) { + if (edges_out_[k]->end_node_ == this) { + // Remove this edge from edges_out_ and edges_in_ and put it inside the node + CheckAndRemoveFrom(edges_in_, edges_out_[k]); + first->edges_out_.emplace_back(edges_out_[k]); + second->edges_in_.emplace_back(edges_out_[k]); + RemoveFrom(edges_out_, k); + } + } +} + +SbpNode::~SbpNode() { + for (auto& edge_out : edges_out_) { delete edge_out; } + for (auto& child_node : children_) { + if (child_node->edges_in_.size()) { delete child_node->edges_in_[0]; } + delete child_node; + } + for (auto& half_node : half_node_) { delete half_node; } +} + +void SbpNode::InitializeSbp() { + global_sbp_sig_size_ = sbp_sig_list_.size(); + cost_.resize(sbp_sig_list_.size()); +}; + +// Let one node point to another + +void SbpNode::StartPointToEnd(SbpNode* start_node, SbpNode* end_node) { + // generate the edge between them + SbpEdge* e = new SbpEdge(start_node, end_node); + start_node->edges_out_.emplace_back(e); + end_node->edges_in_.emplace_back(e); +}; + +void SbpNode::PointFrom(SbpNode* start_node) { StartPointToEnd(start_node, this); }; + +void SbpNode::PointTo(SbpNode* end_node) { StartPointToEnd(this, end_node); }; + +void SbpNode::SummarizeCost() { + if (children_.size() == child_node_sbp_sig_.size()) { return; } + int32_t previous_children_size = child_node_sbp_sig_.size(); + child_node_sbp_sig_.resize(children_.size()); + // Only deal with new children_ + for (int32_t child = previous_children_size; child < children_.size(); child++) { + child_node_sbp_sig_[child].resize(cost_.size()); + + for (int32_t sbp_this = 0; sbp_this < cost_.size(); sbp_this++) { + double min_cost = 0, curr_cost = 0; + for (int32_t sbp_child = 0; sbp_child < children_[child]->cost_.size(); sbp_child++) { + if (children_[child]->edges_in_.size()) { + // edge in graph: father -> child + curr_cost = children_[child]->edges_in_[0]->cost_[sbp_this][sbp_child] + + children_[child]->cost_[sbp_child]; + + } else { + // edge in graph: child -> father + curr_cost = children_[child]->edges_out_[0]->cost_[sbp_child][sbp_this] + + children_[child]->cost_[sbp_child]; + } + // update min_cost with fixed SbpSignature for this node and child node + if (sbp_child == 0 || curr_cost < min_cost) { + min_cost = curr_cost; + child_node_sbp_sig_[child][sbp_this] = sbp_child; + } + } + // Add the cost for child node to this node + cost_[sbp_this] += min_cost; + } + } +} + +bool SbpNode::EliminateItselfAsChild() { + if (edges_in_.size() + edges_out_.size() == 1) { + if (edges_in_.size()) { + // edge in graph: father -> this_node + SbpNode* father = edges_in_[0]->start_node_; + father->children_.emplace_back(this); + CheckAndRemoveFrom(father->edges_out_, edges_in_[0]); + father->SummarizeCost(); + } else { + // edge in graph: this_node -> father + SbpNode* father = edges_out_[0]->end_node_; + father->children_.emplace_back(this); + CheckAndRemoveFrom(father->edges_in_, edges_out_[0]); + father->SummarizeCost(); + } + // successfully eliminate this node + return true; + } + // can not eliminate this node + return false; +} + +void SbpNode::FinalizeSbp() { + if (!half_node_.empty()) { + // Finalize Sbp of merged nodes + half_node_[0]->final_sbp_sig_id_ = merged_sig_id2children_sig_id_[final_sbp_sig_id_].first; + half_node_[1]->final_sbp_sig_id_ = merged_sig_id2children_sig_id_[final_sbp_sig_id_].second; + } + + // Finalize Sbp of children_ + for (int32_t i = 0; i < children_.size(); i++) { + children_[i]->final_sbp_sig_id_ = child_node_sbp_sig_[i][this->final_sbp_sig_id_]; + } + + // Finalize Sbp of half_node_ Attachment + if (!half_node_.empty()) { + half_node_[0]->FinalizeSbp(); + half_node_[1]->FinalizeSbp(); + } + + // Finalize Sbp of edges in edges_out_ + for (const auto& edge_out : edges_out_) { edge_out->FinalizeSbp(); } + + // Finalize Sbp again in case of the node on the other side is not finalized + // yet. This may happen when Two side of an edge merged into two larger nodes + // and this edge is just a sub edge. + for (const auto& edge_in : edges_in_) { edge_in->FinalizeSbp(); } + + // Finalize Sbp of children_ Attachment + for (int32_t i = 0; i < children_.size(); i++) { + children_[i]->FinalizeSbp(); + for (const auto& edge_in : children_[i]->edges_in_) { edge_in->FinalizeSbp(); } + } +} + +double SbpNode::GreedyStrategy() { + // Current Cost, Minimum Cost, Cost with original sbp + double curr_cost = 0; + double original_cost = EvalNbhCost(); + double min_cost = original_cost; + int32_t min_sbp = final_sbp_sig_id_; + for (int32_t sbp = 0; sbp < cost_.size(); sbp++) { + final_sbp_sig_id_ = sbp; + curr_cost = EvalNbhCost(); + if (curr_cost < min_cost) { + min_cost = curr_cost; + min_sbp = sbp; + } + } + final_sbp_sig_id_ = min_sbp; + return min_cost - original_cost; +} + +double SbpNode::EvalNbhCost() const { + // Current Cost, Minimum Cost, Cost with original sbp + double curr_cost = cost_[final_sbp_sig_id_]; + for (SbpEdge* this_edge : edges_in_) { + curr_cost += this_edge->cost_[this_edge->start_node_->final_sbp_sig_id_][final_sbp_sig_id_]; + } + for (SbpEdge* this_edge : edges_out_) { + curr_cost += this_edge->cost_[final_sbp_sig_id_][this_edge->end_node_->final_sbp_sig_id_]; + } + return curr_cost; +} + +double SbpNode::EvalOutNbhCost( + const std::unordered_map& node_list_id2nbh_id) const { + // check if this node is in the node list + CHECK(node_list_id_ >= 0) << "Compute out cost for a node out of the node list" << std::endl; + // Cost with original sbp + double curr_cost = cost_[final_sbp_sig_id_]; + for (SbpEdge* this_edge : edges_in_) { + // if the start node is not in the neighborhood + if (node_list_id2nbh_id.find(this_edge->start_node_->node_list_id_) + == node_list_id2nbh_id.end()) { + curr_cost += this_edge->cost_[this_edge->start_node_->final_sbp_sig_id_][final_sbp_sig_id_]; + } + } + for (SbpEdge* this_edge : edges_out_) { + // if the end node is not in the neighborhood + if (node_list_id2nbh_id.find(this_edge->end_node_->node_list_id_) + == node_list_id2nbh_id.end()) { + curr_cost += this_edge->cost_[final_sbp_sig_id_][this_edge->end_node_->final_sbp_sig_id_]; + } + } + return curr_cost; +} + +// Compute the cost between this node and adjacent nodes with a lower order + +double SbpNode::EvalInNbhCost(const std::unordered_map& node_list_id2nbh_id, + const std::vector& nbh_id2order) const { + // check if this node is in the node list + CHECK(node_list_id_ >= 0) << "Compute in cost for a node out of the node list"; + // check if the node is in the neighborhood + const auto& this_it = node_list_id2nbh_id.find(node_list_id_); + CHECK(this_it != node_list_id2nbh_id.end()) + << "Compute in cost for a node out of the neighborhood"; + // Compute the minimum cost between this node and adjacent nodes with a lower order + int32_t order = nbh_id2order[this_it->second]; + double curr_cost = 0; + for (SbpEdge* this_edge : edges_in_) { + const auto& it = node_list_id2nbh_id.find(this_edge->start_node_->node_list_id_); + // if the start node is in the neighborhood + if (it != node_list_id2nbh_id.end() && nbh_id2order[it->second] < order) { + curr_cost += this_edge->cost_[this_edge->start_node_->final_sbp_sig_id_][final_sbp_sig_id_]; + // End this function and return infinity. + if (curr_cost > GetValidMaxCopyCost()) { return GetMaxVal(); } + } + } + for (SbpEdge* this_edge : edges_out_) { + const auto& it = node_list_id2nbh_id.find(this_edge->end_node_->node_list_id_); + // if the end node is in the neighborhood + if (it != node_list_id2nbh_id.end() && nbh_id2order[it->second] < order) { + curr_cost += this_edge->cost_[final_sbp_sig_id_][this_edge->end_node_->final_sbp_sig_id_]; + if (curr_cost > GetValidMaxCopyCost()) { return GetMaxVal(); } + } + } + return curr_cost; +} + +double SbpNode::EvalMinInNbhCost(const std::unordered_map& node_list_id2nbh_id, + const std::vector& nbh_id2order) const { + // check if this node is in the node list + CHECK(node_list_id_ >= 0) << "Compute out cost for a node out of the node list" << std::endl; + // check if the node is in the neighborhood + const auto& this_it = node_list_id2nbh_id.find(node_list_id_); + CHECK(this_it != node_list_id2nbh_id.end()) + << "Compute out cost for a node out of the neighborhood" << std::endl; + // Compute the minimum cost between this node and adjacent nodes with a higher order + int32_t order = nbh_id2order[this_it->second]; + double curr_cost = 0; + for (SbpEdge* this_edge : edges_in_) { + const auto& it = node_list_id2nbh_id.find(this_edge->start_node_->node_list_id_); + // if the start node is in the neighborhood + if (it != node_list_id2nbh_id.end() && nbh_id2order[it->second] > order) { + curr_cost += this_edge->GetMinCost(); + } + } + for (SbpEdge* this_edge : edges_out_) { + const auto& it = node_list_id2nbh_id.find(this_edge->end_node_->node_list_id_); + // if the end node is in the neighborhood + if (it != node_list_id2nbh_id.end() && nbh_id2order[it->second] > order) { + curr_cost += this_edge->GetMinCost(); + } + } + return curr_cost; +} + +void SbpNode::OneRingNeighborhood(std::vector& nbh_1ring) const { + nbh_1ring.resize(edges_in_.size() + edges_out_.size() + 1); + int32_t nbh_id = 0; + nbh_1ring[nbh_id] = node_list_id_; + for (SbpEdge* this_edge : edges_in_) { + nbh_id++; + nbh_1ring[nbh_id] = this_edge->start_node_->node_list_id_; + } + for (SbpEdge* this_edge : edges_out_) { + nbh_id++; + nbh_1ring[nbh_id] = this_edge->end_node_->node_list_id_; + } +} + +// Get the n ring neighborhood of this node +// Pre-allocate buffer, which will be faster. + +void SbpNode::NRingNeighborhood(int32_t n, std::vector& nbh_n_ring, + std::vector& nbh_1ring, + const std::vector& node_list, + std::vector& node_tags) const { + // Initialize 0 ring + if (n <= 0) { n = 0; } + nbh_n_ring.resize(1); + nbh_n_ring[0] = node_list_id_; + node_tags[node_list_id_] = true; + int32_t l = 0; + // do ring expansion for n times + for (int32_t i = 0; i < n; i++) { + for (int32_t r = nbh_n_ring.size(); l < r; l++) { + node_list[nbh_n_ring[l]]->OneRingNeighborhood(nbh_1ring); + for (auto nbh_id : nbh_1ring) { + if (!node_tags[nbh_id]) { + nbh_n_ring.push_back(nbh_id); + node_tags[nbh_id] = true; + } + } + } + } + // Recover false for buffer + for (auto nbh_id : nbh_n_ring) { node_tags[nbh_id] = false; } +} + +// Get or compute the minimum layer of this node + +int32_t SbpNode::GetMinLayer( + const HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps) { + if (min_layer_ >= 0) { return min_layer_; } + if (!op_node_) { return min_layer_; } + for (SbpEdge* this_edge : edges_in_) { + int32_t producer_min_layer = + this_edge->start_node_->GetMinLayer(op_name2sbp_node, op_node2mutable_op_ctrl_deps); + if (producer_min_layer > min_layer_) { min_layer_ = producer_min_layer; } + } + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { + int32_t producer_min_layer = + it->second->GetMinLayer(op_name2sbp_node, op_node2mutable_op_ctrl_deps); + if (producer_min_layer > min_layer_) { min_layer_ = producer_min_layer; } + } + } + if (op_node2mutable_op_ctrl_deps.find(op_node_) != op_node2mutable_op_ctrl_deps.end()) { + for (const auto& ctrl_in_op_name : op_node2mutable_op_ctrl_deps.at(op_node_)) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { + int32_t producer_min_layer = + it->second->GetMinLayer(op_name2sbp_node, op_node2mutable_op_ctrl_deps); + if (producer_min_layer > min_layer_) { min_layer_ = producer_min_layer; } + } + } + } + return ++min_layer_; +} + +// Spread the minimum layer to compute the maximum layer of producers + +void SbpNode::SpreadMaxLayer( + const HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps) { + if (min_layer_ <= 0) { return; } + int32_t producer_max_lay = min_layer_ - 1; + for (SbpEdge* this_edge : edges_in_) { this_edge->start_node_->DropMaxLayer(producer_max_lay); } + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { it->second->DropMaxLayer(producer_max_lay); } + } + if (op_node2mutable_op_ctrl_deps.find(op_node_) != op_node2mutable_op_ctrl_deps.end()) { + for (const auto& ctrl_in_op_name : op_node2mutable_op_ctrl_deps.at(op_node_)) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { it->second->DropMaxLayer(producer_max_lay); } + } + } +} + +// Drop down the maximum layer with the minimum layer from consumer + +void SbpNode::DropMaxLayer(int32_t upper_bound) { + if (upper_bound < max_layer_ || max_layer_ < 0) { max_layer_ = upper_bound; } +} +// Set max_layer_ = min_layer_ if this node does not have any consumer +// This is the end of the whole graph +// We could also set it to be the maximum of the min_layer_ in the graph. (It should be the same.) + +void SbpNode::LiftMaxLayer() { + if (max_layer_ < min_layer_) { max_layer_ = min_layer_; } +} +// Set max_layer_ = upper_bound if this node does not have any consumer + +void SbpNode::LiftMaxLayer(int32_t upper_bound) { + if (max_layer_ < min_layer_) { max_layer_ = upper_bound; } +} + +// Get the minimum element in Cost + +double SbpNode::GetMinCost() const { + // Check the size of Cost + CHECK(cost_.size() > 0) << "Cost not initialized!" << std::endl; + // Compute the min_comp_cost + return *std::min_element(cost_.begin(), cost_.end()); +} + +// Set the cut ratio + +double SbpNode::GetCutRatio() const { + double curr_cut_ratio = 1.0; + for (auto* this_edge : edges_in_) { curr_cut_ratio *= this_edge->GetCutRatio(); } + for (auto* this_edge : edges_out_) { curr_cut_ratio *= this_edge->GetCutRatio(); } + return curr_cut_ratio; +} + +// Judge if this node is on the trunk +// If so, judge it for its producer/upstream nodes + +void SbpNode::SpreadTrunk(const HashMap& op_name2sbp_node) { + // Skip it if this node is already judged. + if (on_trunk_) { return; } + // Skip sbp proxy. This is before we have proxy. + if (min_layer_ < 0) { return; } + on_trunk_ = true; + // If I am in the trunk, then all the children with (min_layer_ >= my layer id - 1) would be + // considered as in the trunk + for (SbpEdge* this_edge : edges_in_) { + if (this_edge->start_node_->min_layer_ >= min_layer_ - 1) { + this_edge->start_node_->SpreadTrunk(op_name2sbp_node); + } + } + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end() && it->second->min_layer_ >= min_layer_ - 1) { + it->second->SpreadTrunk(op_name2sbp_node); + } + } +} + +// Count consumers and any downstream nodes defined by control edges + +void SbpNode::RaiseConsumerNum(const HashMap& op_name2sbp_node) { + // Should clear it before running. + // skip the proxy nodes and the sources + if (min_layer_ <= 0) { return; } + for (SbpEdge* this_edge : edges_in_) { this_edge->start_node_->counter_++; } + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { it->second->counter_++; } + } +} + +// Compute the minimal available wait time for producers or upstream nodes + +void SbpNode::SpreadAvailWaitTime(const std::vector& trunk_cost, + const std::vector& acc_trunk_cost, + const HashMap& op_name2sbp_node, + double wait_time) { + // skip the proxy nodes and the sources + if (min_layer_ <= 0) { return; } + // Have not finished spreading for consumers or downstream nodes or already visited. + if (counter_) { return; } + if (on_trunk_) { + // Nodes on the trunk does not have any accumulate cost + acc_trunk_cost_ = 0; + } else { + if (acc_trunk_cost_ < 0) { + // Do not have any consumer or downstream node + acc_trunk_cost_ = acc_trunk_cost[min_layer_ - 1]; + } else { + // Add the trunk cost at this layer + acc_trunk_cost_ += trunk_cost[min_layer_]; + } + } + + // Reduce the wait time for edges_in_, put the rest of the trunk cost in the producers + for (SbpEdge* this_edge : edges_in_) { + CHECK(this_edge->wait_time_ < 0) + << "Double assign values into wait_time_ of this edge!" << std::endl; + SbpNode* producer = this_edge->start_node_; + // Accumulate the cost from the start node to this node + double curr_trunk_cost = + acc_trunk_cost_ + acc_trunk_cost[producer->min_layer_] - acc_trunk_cost[min_layer_ - 1]; + if (curr_trunk_cost >= wait_time) { + // Remain cost in the trunk is able to cover all the wait time + this_edge->wait_time_ = 0.0; + curr_trunk_cost -= wait_time; + } else { + // Remain cost in the trunk can only cover partial wait time + this_edge->wait_time_ = wait_time - curr_trunk_cost; + curr_trunk_cost = 0.0; + } + // Reducing non-matching edges + // For example: + // (1) P->S0->S0->S0->B + // (2) p->B->B->B->B + // We would use (2) when the tensor is relatively tiny. + // Do not inherit trunk cost for nodes on the trunk + if (!producer->on_trunk_) { + // Inherit the minimal of the trunk cost from consumers + producer->DropAvailWaitTime(curr_trunk_cost); + } + producer->counter_--; + producer->SpreadAvailWaitTime(trunk_cost, acc_trunk_cost, op_name2sbp_node, wait_time); + } + // Put the rest the trunk cost in the upstream nodes. + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { + SbpNode* producer = it->second; + // Do not inherit trunk cost for nodes on the trunk + if (!producer->on_trunk_) { + // Accumulate the cost from the start node to this node + double curr_trunk_cost = + acc_trunk_cost_ + acc_trunk_cost[producer->min_layer_] - acc_trunk_cost[min_layer_ - 1]; + // Inherit the minimal of the trunk cost from consumers + producer->DropAvailWaitTime(curr_trunk_cost); + } + producer->counter_--; + producer->SpreadAvailWaitTime(trunk_cost, acc_trunk_cost, op_name2sbp_node, wait_time); + } + } + // Set counter_ to be -1, do not visit it again. + counter_--; +} + +// Drop down the available wait time with the minimum cost from downstream + +void SbpNode::DropAvailWaitTime(double curr_trunk_cost) { + if (acc_trunk_cost_ < 0.0 || acc_trunk_cost_ > curr_trunk_cost) { + acc_trunk_cost_ = curr_trunk_cost; + } +} + +// Assemble copy cost for all the incoming edges + +void SbpNode::InitializeCopyCost(bool use_sbp_collector) { + for (SbpEdge* this_edge : edges_in_) { + const auto* sbp_node_producer = this_edge->start_node_; + OpNode* producer = sbp_node_producer->op_node_; + + // skip it if proxy + if (use_sbp_collector && !producer) { continue; } + // look through input blobs + for (const std::string& ibn : op_node_->op().input_bns()) { + if (producer->op().op_name() == op_node_->SrcNode4Ibn(ibn).op().op_name()) { + this_edge->InitializeCopyCost(ibn, use_sbp_collector); + } + } + // Add Wait time + for (auto& cost_row : this_edge->cost_) { + for (auto& cost_value : cost_row) { + // If transferring between devices, we need to add wait time. + if (cost_value > 0.0) { cost_value += this_edge->wait_time_; } + } + } + } +} + +// Reduce and set the wait time for op in the trunk + +void SbpNode::SetTrunkWaitTime(double trunk_wait_time) { + // only reduce the wait time for operators in the trunk + if (on_trunk_) { + // Reduce the wait time for edges_out_ + for (SbpEdge* edge_out : edges_out_) { + if (edge_out->wait_time_ < 0.0 || edge_out->wait_time_ > trunk_wait_time) { + edge_out->wait_time_ = trunk_wait_time; + } + } + // Might reduce it for edges_in_ + } +} + +// Drop down the maximum layer with the minimum layer from consumer + +void SbpNode::DropTributaryLayer(int32_t upper_bound) { + if (upper_bound < tributary_layer_ || tributary_layer_ < 0) { tributary_layer_ = upper_bound; } +} + +// Compute maximum layer for tributaries + +void SbpNode::SpreadTributaryLayer(const HashMap& op_name2sbp_node) { + if (counter_ || min_layer_ <= 0) { return; } + int32_t producer_max_lay = 0; + if (on_trunk_) { + producer_max_lay = min_layer_ - 1; + } else { + // On a tributary, the operator could be run later. + producer_max_lay = tributary_layer_; + // producer_max_lay = tributary_layer_ - 1; + } + for (SbpEdge* this_edge : edges_in_) { + this_edge->start_node_->DropTributaryLayer(producer_max_lay); + if (--this_edge->start_node_->counter_ == 0) { + this_edge->start_node_->SpreadTributaryLayer(op_name2sbp_node); + } + } + for (const auto& ctrl_in_op_name : op_node_->op().op_conf().ctrl_in_op_name()) { + const auto& it = op_name2sbp_node.find(ctrl_in_op_name); + if (it != op_name2sbp_node.end()) { + it->second->DropTributaryLayer(producer_max_lay); + if (--it->second->counter_ == 0) { it->second->SpreadTributaryLayer(op_name2sbp_node); } + } + } + counter_--; +} + +SbpEdge* SbpNode::FindEdgeWithNode(const SbpNode* other_node) const { + for (auto* sbp_edge : edges_in_) { + if (sbp_edge->start_node_ == other_node) { return sbp_edge; } + } + for (auto* sbp_edge : edges_out_) { + if (sbp_edge->end_node_ == other_node) { return sbp_edge; } + } + return nullptr; +}; + +// Decide to use this SbpSignature +const NdSbpSignature& SbpNode::FinalSbpSignature() const { + CHECK(!sbp_sig_list_.empty()) << "Asking for sbp signature for an empty node"; + return sbp_sig_list_[final_sbp_sig_id_]; +}; + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_node.h b/oneflow/core/auto_parallel/sbp_node.h new file mode 100644 index 0000000000000000000000000000000000000000..1f8c6cef815e2b869327bf53fe4fd84bf416160b --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_node.h @@ -0,0 +1,197 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_SBP_NODE_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_SBP_NODE_H_ + +#include +#include +#include +#include +#include "oneflow/core/auto_parallel/binary_set.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/auto_parallel/algorithm_util.h" +#include "oneflow/core/job/sbp_parallel.pb.h" + +namespace oneflow { +namespace auto_parallel { + +class SbpEdge; + +// A node structure to deal with the SBP strategy. +// Please see SbpGraph for the whole algorithm and introduction. +class SbpNode final { + public: + // default constructor + SbpNode() : final_sbp_sig_id_(0) {} + + // This constructor is to merge two node into one + SbpNode(SbpNode* first, SbpNode* second); + + ~SbpNode(); + + OF_DISALLOW_COPY_AND_MOVE(SbpNode); + bool operator==(const SbpNode& other) { return this == &other; } + + // another node point to this node + void PointFrom(SbpNode* start_node); + // this node point to another node + void PointTo(SbpNode* end_node); + + SbpEdge* FindEdgeWithNode(const SbpNode* other_node) const; + + // Check and eliminate one child node. + // Only used by SbpGraph since it need to remove it from the NodeList after this. + bool EliminateItselfAsChild(); + + // Initialize SbpSignature from Signature Objects + void InitializeSbp(); + // Decide to use this SbpSignature + const NdSbpSignature& FinalSbpSignature() const; + + // Recompute Computation Cost after adding child nodes in it + void SummarizeCost(); + // Determine Final SbpSignature for attachment of this node + void FinalizeSbp(); + // Use Greedy Strategy to pick the sbp signature with minimum cost for this + // node You should have an initial strategy before running this + double GreedyStrategy(); + // Evaluate summery of cost between neighborhood and outside nodes + double EvalOutNbhCost(const std::unordered_map& node_list_id2nbh_id) const; + // Evaluate summery of cost within neighborhood + // We only accumulate the edge cost with a lower order. + double EvalInNbhCost(const std::unordered_map& node_list_id2nbh_id, + const std::vector& nbh_id2order) const; + // Evaluate summery of cost within neighborhood + // We only accumulate the minimum edge cost with a higher order. + double EvalMinInNbhCost(const std::unordered_map& node_list_id2nbh_id, + const std::vector& nbh_id2order) const; + // Get the one ring neighborhood of this node, which is itself and all the adjacent nodes. + void OneRingNeighborhood(std::vector& nbh_1ring) const; + // Get the n ring neighborhood of this node + // Pre-allocate buffer, which will be faster. + void NRingNeighborhood(int32_t n, std::vector& nbh_n_ring, + std::vector& nbh_1ring, const std::vector& node_list, + std::vector& node_tags) const; + + // Get or compute the minimum layer of this node + int32_t GetMinLayer( + const HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps); + // Spread the minimum layer to compute the maximum layer of producers + void SpreadMaxLayer( + const HashMap& op_name2sbp_node, + const HashMap>& op_node2mutable_op_ctrl_deps); + // Set max_layer_ = min_layer_ if this node does not have any consumer + void LiftMaxLayer(); + // Set max_layer_ = upper_bound if this node does not have any consumer + void LiftMaxLayer(int32_t upper_bound); + // Compute maximum layer for tributaries + void SpreadTributaryLayer(const HashMap& op_name2sbp_node); + // Drop down the tributary layer + void DropTributaryLayer(int32_t upper_bound); + + // Get the minimum element in Cost + double GetMinCost() const; + // get the cut ratio + double GetCutRatio() const; + + // Judge if this node is on the trunk + // If so, judge it for its producer/upstream nodes + void SpreadTrunk(const HashMap& op_name2sbp_node); + // Count consumers and any downstream nodes defined by control edges + // for producers or upstream nodes + void RaiseConsumerNum(const HashMap& op_name2sbp_node); + // Compute the minimal available wait time for producers or upstream nodes + void SpreadAvailWaitTime(const std::vector& trunk_cost, + const std::vector& acc_trunk_cost, + const HashMap& op_name2sbp_node, + double wait_time); + // Reduce and set the wait time for op in the trunk + void SetTrunkWaitTime(double trunk_wait_time); + + // Assemble copy cost for all the incoming edges + void InitializeCopyCost(bool use_sbp_collector); + + private: + friend class SbpEdge; + friend class SbpGraph; + friend class SbpCollector; + friend class SbpConstructor; + + // compound edge in + std::vector edges_in_; + // compound edge out + std::vector edges_out_; + + // Location in node_list of SbpGraph + int32_t node_list_id_ = -1; + // Global SbpSignature List Size + int32_t global_sbp_sig_size_ = -1; + // Decide to use SbpSignature with this id + int32_t final_sbp_sig_id_; + // Available SbpSignature object for this node + std::vector sbp_sig_list_; + // Cost[sbp] is Computation Cost when using sbp_sig_list_[sbp] + std::vector cost_; + + // Child node list + std::vector children_; + // SbpSignature for each child node when using specific SbpSignature for this + // node Its dimension is Number of Child Nodes * Number of Available + // SbpSignatures for this node + std::vector> child_node_sbp_sig_; + + // Merge two nodes into this compound node + std::vector half_node_; + // We should delete those merged-signatures which has very large cost for speed up + // New sbp_sig_list_ index map to each half_node_'s sig_index + std::vector> merged_sig_id2children_sig_id_; + + std::vector parallel_candidates_; + + OpNode* op_node_ = nullptr; + + // We divide the sbp graph into multiple layers. + // min_layer_ is the minimum layer number to run this op as soon as possible. + // max_layer_ is the maximum layer number without slowing down the whole process of the graph. + // producer.max_layer_ < this_node.min_layer_ <= this_node.max_layer_ < consumer.min_layer_ + int32_t min_layer_ = -1, max_layer_ = -1; + // Maximum layer in tributaries + int32_t tributary_layer_ = -1; + // Whether we are on the trunk + bool on_trunk_ = false; + // A counter_ buffer for topological traversal or something else + int32_t counter_ = 0; + // Accumulate trunk cost from consumer to the end + double acc_trunk_cost_ = -1.0; + + // Let one node point to another + void StartPointToEnd(SbpNode* start_node, SbpNode* end_node); + + // Evaluate summery of cost in 1-ring neighborhood. + double EvalNbhCost() const; + // Drop down the maximum layer with the minimum layer from consumer + void DropMaxLayer(int32_t upper_bound); + // Drop down the available wait time with the minimum cost from downstream + void DropAvailWaitTime(double curr_trunk_cost); +}; // class SbpNode + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_SBP_NODE_H_ diff --git a/oneflow/core/auto_parallel/sbp_util.cpp b/oneflow/core/auto_parallel/sbp_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4e69816cdf1ea477b21392e05beb1c2fb2d71152 --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_util.cpp @@ -0,0 +1,40 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/core/auto_parallel/sbp_util.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/job/sbp_parallel.h" +#include "oneflow/core/graph/boxing/hierarchical_sub_task_graph_builder_impl.h" + +namespace oneflow { +namespace auto_parallel { + +// Judge whether we need the same SBP for both producer and consumer +bool RequireSameSbp(const OpNode* consumer, const std::string& ibn) { + // is mutable + const auto& input_blob_modifier_ = consumer->op().InputBlobModifier4Ibn(ibn); + if (input_blob_modifier_.has_is_mutable() && input_blob_modifier_.is_mutable()) { return true; } + // kOFRecord or kTensorBuffer don't accept boxing + const LogicalBlobId& lbi = consumer->op().BnInOp2Lbi(ibn); + const OpNode& producer = consumer->ProducerOpNode4Lbi(lbi); + const BlobDesc& logical_blob_desc = producer.LogicalBlobDesc4Lbi(lbi); + return (logical_blob_desc.data_type() == DataType::kOFRecord + || logical_blob_desc.data_type() == DataType::kTensorBuffer); +} + +} // namespace auto_parallel +} // namespace oneflow diff --git a/oneflow/core/auto_parallel/sbp_util.h b/oneflow/core/auto_parallel/sbp_util.h new file mode 100644 index 0000000000000000000000000000000000000000..ee64abb11036fa6daffea0a682f63bf8417df73b --- /dev/null +++ b/oneflow/core/auto_parallel/sbp_util.h @@ -0,0 +1,30 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_AUTO_PARALLEL_SBP_UTIL_H_ +#define ONEFLOW_CORE_AUTO_PARALLEL_SBP_UTIL_H_ + +#include "oneflow/core/graph/op_graph.h" + +namespace oneflow { +namespace auto_parallel { + +// Judge whether we need the same SBP for both producer and consumer +bool RequireSameSbp(const OpNode* consumer, const std::string& ibn); + +} // namespace auto_parallel +} // namespace oneflow + +#endif // ONEFLOW_CORE_AUTO_PARALLEL_SBP_UTIL_H_ diff --git a/oneflow/core/autograd/autograd_engine.cpp b/oneflow/core/autograd/autograd_engine.cpp index bf29b1c117f9be6347c9011404a9de18ff8fd644..01d708619955b82ad1c798f67e20bdba528f5067 100644 --- a/oneflow/core/autograd/autograd_engine.cpp +++ b/oneflow/core/autograd/autograd_engine.cpp @@ -17,17 +17,25 @@ limitations under the License. #include #include #include +#include "fmt/core.h" +#include "fmt/format.h" #include "oneflow/core/autograd/autograd_engine.h" #include "oneflow/core/autograd/autograd_meta.h" +#include "oneflow/core/autograd/autograd_mode.h" +#include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_arg.h" +#include "oneflow/core/framework/tensor_methods.h" +#include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/autograd/autograd_mode.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/framework/global_param_grad_sync_mode.h" -#include "oneflow/core/common/container_util.h" +#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/profiler/profiler.h" +#include "oneflow/core/common/env_var/debug_mode.h" +#include "oneflow/core/persistence/tee_persistent_log_stream.h" namespace oneflow { namespace one { @@ -75,20 +83,16 @@ bool IsReadyToRun(const std::vector>& out_meta_dat Maybe CopyOrAccGrad(AutogradMeta* autograd_meta, bool autograd_mode) { autograd::AutoGradMode mode(autograd_mode); - auto current_grad = JUST(autograd_meta->current_grad()->GetAccTensor({})); + auto current_grad = JUST(autograd_meta->current_grad_value()); if (!current_grad) { return Maybe::Ok(); } if (autograd_meta->acc_grad()) { - // Should not inplace accumulate grad. For example, - // >>> z = x + y - // >>> p = x / z - // >>> p.sum().backward() - // - // As we know that dx = dz + dp / z and dy = dz, so it will lead to wrong value - // for dy if dx is shared with dz. - const auto& output = JUST(functional::Add(autograd_meta->acc_grad(), current_grad, /*alpha=*/1, - /*inplace=*/autograd_meta->is_grad_acc_inplace())); - JUST(autograd_meta->set_acc_grad(output)); + JUST(functional::Add(autograd_meta->acc_grad(), current_grad, /*alpha=*/1.0, + /*inplace=*/true)); } else { + // NOTE: acc_grad can not share data with current_grad, because accumulate acc_grad + // with inplace operation and it maybe change current_grad to get wrong result. + // See more details in https://github.com/Oneflow-Inc/oneflow/issues/8248 + if (!LazyMode::is_enabled()) { current_grad = JUST(functional::Identity(current_grad)); } JUST(autograd_meta->set_acc_grad(current_grad)); } for (const auto& hook : autograd_meta->post_grad_accumulation_hooks()) { @@ -99,47 +103,50 @@ Maybe CopyOrAccGrad(AutogradMeta* autograd_meta, bool autograd_mode) { return Maybe::Ok(); } -Maybe RawTorchConsistentTensor(const std::shared_ptr& tensor) { +Maybe RawTouchGlobalTensor(const std::shared_ptr& tensor) { // Do nothing. return Maybe::Ok(); } -static constexpr auto* TorchConsistentTensor = - DECORATE(&RawTorchConsistentTensor, CheckConsistentTensorMeta); +static constexpr auto* TouchGlobalTensor = DECORATE(&RawTouchGlobalTensor, CheckGlobalTensorMeta); -Maybe CheckConsistentTensorsMeta(const TensorTuple& tensor_tuple) { +Maybe CheckGlobalTensorsMeta(const TensorTuple& tensor_tuple) { for (const auto& tensor : tensor_tuple) { - if (tensor->is_consistent()) { JUST(TorchConsistentTensor(tensor)); } + if (tensor->is_global() && tensor->is_eager()) { JUST(TouchGlobalTensor(tensor)); } } return Maybe::Ok(); } +std::string GetDebugGraphFileName(const std::string& mode, const std::string& suffix) { + return fmt::format("autograd_{}_rank{}_suffix_graph.dot", mode, GlobalProcessCtx::Rank(), suffix); +} + } // namespace Maybe AutogradEngine::RunBackwardAndSaveGrads4LeafTensorIf(const TensorTuple& outputs, const TensorTuple& out_grads, bool retain_graph, bool create_graph) { - JUST(CheckConsistentTensorsMeta(outputs)); - JUST(CheckConsistentTensorsMeta(out_grads)); - DisableCheckConsistentTensorMetaScope disable_meta_check; + JUST(CheckGlobalTensorsMeta(outputs)); + JUST(CheckGlobalTensorsMeta(out_grads)); + DisableCheckGlobalTensorMetaScope disable_meta_check; return RunBackwardAndSaveGrads4LeafTensor(outputs, out_grads, retain_graph, create_graph); } Maybe AutogradEngine::RunBackwardAndReturnInputsTensorGradIf( const TensorTuple& outputs, const TensorTuple& inputs, const TensorTuple& out_grads, bool retain_graph, bool create_graph) { - JUST(CheckConsistentTensorsMeta(outputs)); - JUST(CheckConsistentTensorsMeta(inputs)); - JUST(CheckConsistentTensorsMeta(out_grads)); - DisableCheckConsistentTensorMetaScope disable_meta_check; + JUST(CheckGlobalTensorsMeta(outputs)); + JUST(CheckGlobalTensorsMeta(inputs)); + JUST(CheckGlobalTensorsMeta(out_grads)); + DisableCheckGlobalTensorMetaScope disable_meta_check; return RunBackwardAndReturnInputsTensorGrad(outputs, inputs, out_grads, retain_graph, create_graph); } -Maybe FunctionNode::AccGrad4RetainGradTensor() { +Maybe FunctionNode::AccGrad4RetainGradTensor(bool create_graph) { for (const std::shared_ptr& out : output_meta_data_) { - if (out->retain_grad()) { JUST(CopyOrAccGrad(out.get(), /*autograd_mode=*/false)); } + if (out->retain_grad()) { JUST(CopyOrAccGrad(out.get(), create_graph)); } } return Maybe::Ok(); } @@ -149,17 +156,18 @@ Maybe FunctionNode::AccGrad4LeafTensor(bool create_graph) { auto& out = output_meta_data_[i]; if (out->is_leaf() && out->requires_grad()) { - JUST(CopyOrAccGrad(out.get(), /*autograd_mode=*/false)); + JUST(CopyOrAccGrad(out.get(), /*autograd_mode=*/create_graph)); // control acc_grad to do boxing conditionally const auto& acc_grad = out->acc_grad(); - if (GlobalGradSyncMode::is_enabled() && acc_grad->is_consistent()) { + if (!LazyMode::is_enabled() && GlobalGradSyncMode::is_enabled() && acc_grad->is_global() + && acc_grad->is_eager()) { auto& tensor_info = output_tensor_infos_[i]; const auto& placement = JUST(tensor_info.placement()); const auto& nd_sbp = JUST(tensor_info.sbp()); JUST(out->set_acc_grad( - JUST(functional::ToConsistent(acc_grad, placement, *JUST(GetSbpList(nd_sbp)), - GetNoneSbpList(), /* check_meta */ false)))); + JUST(functional::ToGlobal(acc_grad, placement, *JUST(GetSbpList(nd_sbp)), + GetNoneSbpList(), /* check_meta */ false, /*copy=*/false)))); } } } @@ -182,22 +190,30 @@ Maybe FunctionNode::Apply(bool create_graph) { TensorTuple output_grads(output_meta_data_.size()); for (int i = 0; i < output_meta_data_.size(); ++i) { if (output_meta_data_.at(i)->current_grad()->Empty()) { - output_grads.at(i) = JUST(output_tensor_infos_.at(i).zeros()); + // Only initialize out_grads for those requires_grad outputs + if (output_meta_data_[i]->requires_grad()) { + output_grads[i] = JUST(output_tensor_infos_[i].zeros()); + } } else { - const auto& hooks = JUST(oneflow::VectorAt(output_meta_data_, i))->hooks(); JUST(oneflow::VectorAt(output_grads, i)) = - JUST(JUST(oneflow::VectorAt(output_meta_data_, i))->current_grad()->GetAccTensor(hooks)); + JUST(JUST(oneflow::VectorAt(output_meta_data_, i))->current_grad_value()); } } JUST(backward_fn_->body(output_grads, &input_grads, create_graph)); for (int i = 0; i < input_meta_data_.size(); ++i) { if (JUST(VectorAt(input_grads, i))) { - CHECK_NOTNULL_OR_RETURN(input_meta_data_.at(i)) + CHECK_NOTNULL_OR_RETURN(input_meta_data_[i]) << name_ << " calculate grad for tensor which requires_grad is False. Please submit an issue in " "`https://github.com/Oneflow-Inc/oneflow/issues` and we will fix it as soon as " "possible"; - JUST(input_meta_data_.at(i)->current_grad()->PushPartialTensor(input_grads.at(i))); + JUST(input_meta_data_[i]->current_grad()->PushPartialTensor(JUST(VectorAt(input_grads, i)))); + } else { + CHECK_OR_RETURN(!input_meta_data_[i]) + << name() << "'s input[" << i + << "] need calculate grad but got nullptr. Please submit an issue in " + "`https://github.com/Oneflow-Inc/oneflow/issues` and we will fix it as soon as " + "possible;"; } } return true; @@ -247,15 +263,64 @@ GraphTask::GraphTask(const TensorTuple& outputs, bool retain_graph, bool create_ for (const auto& out_tensor : outputs) { FunctionNode* node = out_tensor->mut_grad_fn_node().get(); roots_.emplace_back(node); - dependencies_.insert(std::make_pair(node, 0)); } } +Maybe GraphTask::WriteGraphToDotFile(const std::string& file_name) const { + auto ExecInfoToDotString = [](const ExecInfo& exec_info) -> std::string { + std::stringstream ss; + ss << "ExecInfo{\\l"; + ss << "\tdependencies: " << exec_info.dependencies << "\\l"; + ss << "\tneed_execute: " << exec_info.need_execute << "\\l"; + if (exec_info.capture_indices) { + ss << "\tcapture_indices: ["; + for (const auto& out_idx_and_capture_idx : *exec_info.capture_indices) { + ss << out_idx_and_capture_idx.second << ", "; + } + ss << "]\\l"; + } + ss << "}\\l"; + return ss.str(); + }; + + auto log_stream = TeePersistentLogStream::Create(file_name); + std::vector lines; + lines.emplace_back("digraph AutogradTaskGraph {"); + lines.emplace_back("\tmargin=\"1.5\";"); + lines.emplace_back("\tnode [shape=box];"); + for (auto iter = grad_fn2exec_info_.begin(); iter != grad_fn2exec_info_.end(); ++iter) { + const FunctionNode* node = iter->first; + const ExecInfo& exec_info = iter->second; + // write label attribute + std::string node_color = "black"; + if (exec_info.dependencies == 0 && exec_info.need_execute) { // start node + node_color = "red"; + } else if (exec_info.need_execute && exec_info.capture_indices) { // end node + node_color = "green"; + } + lines.emplace_back(fmt::format( + "\t\"{}\" [label=\"{}\\l{}\\l{}\", color={}];", static_cast(node), + node->name(), static_cast(node), ExecInfoToDotString(exec_info), node_color)); + // write edge + for (const auto& next_fn : node->next_functions()) { + lines.emplace_back(fmt::format("\t\"{}\" -> \"{}\";", static_cast(node), + static_cast(next_fn.get()))); + } + } + lines.emplace_back("}"); + log_stream << fmt::format("{}", fmt::join(lines, "\n")); + log_stream->Flush(); + return Maybe::Ok(); +} + // Computes the number of dependencies for each FunctionNode Maybe GraphTask::ComputeDependencies() { HashSet seen; std::stack stack; - for (FunctionNode* node : roots_) { stack.push(node); } + for (FunctionNode* node : roots_) { + stack.push(node); + grad_fn2exec_info_[node].need_execute = true; + } while (!stack.empty()) { FunctionNode* node = stack.top(); @@ -263,7 +328,9 @@ Maybe GraphTask::ComputeDependencies() { if (/*bool has_seen=*/!seen.insert(node).second) { continue; } for (const auto& next_grad_fn : node->next_functions()) { FunctionNode* next_node = next_grad_fn.get(); - dependencies_[next_node] += 1; + ExecInfo& exec_info = grad_fn2exec_info_[next_node]; + exec_info.dependencies += 1; + exec_info.need_execute = true; if (seen.find(next_node) == seen.end()) { stack.push(next_node); } } } @@ -288,9 +355,17 @@ Maybe GraphTask::ComputeDependenciesAndPruneNode(const TensorTuple& inputs } }; - for (const auto& input : inputs) { - CHECK_NOTNULL_OR_RETURN(input->mut_grad_fn_node().get()); - need_execute_.insert(input->mut_grad_fn_node().get()); + // initialize all variable to capture grad for input tensors + captured_grads_ = std::make_shared(inputs.size()); + for (int idx = 0; idx < inputs.size(); idx++) { + const auto& input = inputs[idx]; + CHECK_NOTNULL_OR_RETURN(input->mut_grad_fn_node().get()); // NOLINT(maybe-need-error-msg) + ExecInfo& exec_info = grad_fn2exec_info_[input->mut_grad_fn_node().get()]; + exec_info.need_execute = true; + if (!exec_info.capture_indices) { + exec_info.capture_indices = std::make_unique>>(); + } + exec_info.capture_indices->emplace_back(std::make_pair(input->get_grad_fn_output_index(), idx)); } HashSet seen; @@ -305,18 +380,17 @@ Maybe GraphTask::ComputeDependenciesAndPruneNode(const TensorTuple& inputs continue; } if (FunctionNode* node = frame.GetNextFunction()) { - dependencies_[node] += 1; + grad_fn2exec_info_[node].dependencies += 1; if (seen.find(node) == seen.end()) { stack.push(NodeFrame(node)); continue; // recurse } } else { - bool need_execute = + grad_fn2exec_info_[frame.node_].need_execute |= std::any_of(frame.node_->next_functions().begin(), frame.node_->next_functions().end(), [&](const std::shared_ptr& fn) { - return need_execute_.find(fn.get()) != need_execute_.end(); + return grad_fn2exec_info_[fn.get()].need_execute; }); - if (need_execute) { need_execute_.insert(frame.node_); } seen.insert(frame.node_); stack.pop(); } @@ -327,26 +401,38 @@ Maybe GraphTask::ComputeDependenciesAndPruneNode(const TensorTuple& inputs Maybe GraphTask::Apply(bool save_grad_for_leaf) { std::queue queue; for (FunctionNode* node : roots_) { - if (dependencies_[node] == 0) { queue.push(node); } + if (grad_fn2exec_info_[node].dependencies == 0) { queue.push(node); } } while (!queue.empty()) { FunctionNode* node = queue.front(); queue.pop(); - if (!need_execute_.empty() && need_execute_.find(node) == need_execute_.end()) { + auto& exec_info = grad_fn2exec_info_[node]; + + if (!exec_info.need_execute) { node->ReleaseOutTensorArgs(); continue; } + BackwardPassScopeGuard backward_guard(node->scope()); if (/*bool not_ready_to_apply=*/!(JUST(node->Apply(create_graph_)))) { continue; } + if (exec_info.capture_indices) { + CHECK_NOTNULL_OR_RETURN(captured_grads_.get()) << "captured grads in GraphTask is nullptr"; + for (const auto& out_idx_and_capture_idx : *exec_info.capture_indices) { + JUST(VectorAt(*captured_grads_, out_idx_and_capture_idx.second)) = + JUST(JUST(VectorAt(node->output_meta_data_, out_idx_and_capture_idx.first)) + ->current_grad_value()); + } + } if (save_grad_for_leaf) { JUST(node->AccGrad4LeafTensor(create_graph_)); } - JUST(node->AccGrad4RetainGradTensor()); + JUST(node->AccGrad4RetainGradTensor(create_graph_)); node->ReleaseOutTensorArgs(); if (!retain_graph_) { node->ReleaseData(); } for (const auto& next_grad_fn : node->next_functions()) { FunctionNode* next_node = next_grad_fn.get(); - dependencies_[next_node] -= 1; - if (dependencies_[next_node] == 0) { queue.push(next_node); } + int32_t& dependencies = grad_fn2exec_info_[next_node].dependencies; + dependencies -= 1; + if (dependencies == 0) { queue.push(next_node); } } } return Maybe::Ok(); @@ -361,6 +447,10 @@ Maybe GraphAutogradEngine::RunBackwardAndSaveGrads4LeafTensor(const Tensor } GraphTask graph_task(outputs, retain_graph, create_graph); JUST(graph_task.ComputeDependencies()); + if (IsInDebugMode()) { + JUST( + graph_task.WriteGraphToDotFile(GetDebugGraphFileName("backward", std::to_string(clock())))); + } JUST(graph_task.Apply(/*save_grad_for_leaf=*/true)); return Maybe::Ok(); } @@ -368,34 +458,23 @@ Maybe GraphAutogradEngine::RunBackwardAndSaveGrads4LeafTensor(const Tensor Maybe GraphAutogradEngine::RunBackwardAndReturnInputsTensorGrad( const TensorTuple& outputs, const TensorTuple& inputs, const TensorTuple& out_grads, bool retain_graph, bool create_graph) { - std::shared_ptr input_current_grad = std::make_shared(inputs.size()); - GraphTask graph_task(outputs, retain_graph, create_graph); - std::vector ori_retain_grad(inputs.size()); - for (int i = 0; i < inputs.size(); ++i) { - ori_retain_grad.at(i) = inputs.at(i)->retain_grad(); - JUST(inputs.at(i)->set_retain_grad(true)); - } for (int i = 0; i < outputs.size(); ++i) { JUST(JUST(outputs.at(i)->current_grad())->PushPartialTensor(out_grads.at(i))); } + GraphTask graph_task(outputs, retain_graph, create_graph); JUST(graph_task.ComputeDependenciesAndPruneNode(inputs)); - JUST(graph_task.Apply(/*save_grad_for_leaf=*/false)); - - // Gets input grads and resume retain_grad - for (int i = 0; i < inputs.size(); ++i) { - input_current_grad->at(i) = JUST(inputs.at(i)->acc_grad()); - if (!ori_retain_grad.at(i)) { - JUST(inputs.at(i)->set_acc_grad(nullptr)); - JUST(inputs.at(i)->set_retain_grad(false)); - } + if (IsInDebugMode()) { + JUST(graph_task.WriteGraphToDotFile(GetDebugGraphFileName("grad", std::to_string(clock())))); } - return input_current_grad; + JUST(graph_task.Apply(/*save_grad_for_leaf=*/false)); + return graph_task.GetCapturedGrads(); } Maybe GraphAutogradEngine::AddNode( const std::string& name, const std::shared_ptr& backward_fn, const TensorTuple& inputs, TensorTuple* outputs) { + OF_PROFILER_RANGE_PUSH("AddAccumulateFunctionNode"); // Firstly push function_node of tensor in stack which is leaf and requires_grad for (const std::shared_ptr& in_tensor : inputs) { if (in_tensor->is_leaf() && in_tensor->requires_grad()) { @@ -403,11 +482,17 @@ Maybe GraphAutogradEngine::AddNode( } } + OF_PROFILER_RANGE_POP(); + OF_PROFILER_RANGE_PUSH("set_grad_fn_node"); std::shared_ptr func_node = GraphFunctionNode::New(name, backward_fn, inputs, *outputs); - for (const std::shared_ptr& out_tensor : *outputs) { + for (int i = 0; i < outputs->size(); ++i) { + const std::shared_ptr& out_tensor = JUST(VectorAt(*outputs, i)); out_tensor->set_grad_fn_node(func_node); + out_tensor->set_grad_fn_output_index(i); } + if (LazyMode::is_enabled()) { func_node->set_scope(JUST(GetCurrentScope())); } + OF_PROFILER_RANGE_POP(); return func_node; } @@ -423,6 +508,10 @@ Maybe AddAccumulateFunctionNode(const std::shared_ptr& tensor) { backward_fn->status = []() { return false; }; tensor->set_grad_fn_node(GraphFunctionNode::New( "accumulate_grad", backward_fn, /*inputs=*/TensorTuple{}, /*outputs*/ TensorTuple{tensor})); + tensor->set_grad_fn_output_index(0); + if (LazyMode::is_enabled()) { + tensor->mut_grad_fn_node()->set_scope(JUST(GetTensorScope(tensor))); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/autograd_engine.h b/oneflow/core/autograd/autograd_engine.h index 2a0aafdf5b85f6f8c8c702007d6518d5bb288ba4..3b4d6f8d15d067c19f52047aaadba56adcd22fde 100644 --- a/oneflow/core/autograd/autograd_engine.h +++ b/oneflow/core/autograd/autograd_engine.h @@ -17,12 +17,15 @@ limitations under the License. #ifndef ONEFLOW_CORE_AUTOGRAD_AUTOGRAD_ENGINE_H_ #define ONEFLOW_CORE_AUTOGRAD_AUTOGRAD_ENGINE_H_ +#include #include -#include #include -#include -#include "oneflow/core/common/util.h" +#include + #include "oneflow/core/autograd/autograd_meta.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/framework/scope_util.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { @@ -45,7 +48,7 @@ class FunctionNode { Maybe Apply(bool create_graph); Maybe AccGrad4LeafTensor(bool create_graph); - Maybe AccGrad4RetainGradTensor(); + Maybe AccGrad4RetainGradTensor(bool create_graph); void ReleaseOutTensorArgs(); // Releases the eventual c++ std::function for backward if retain_graph=False to avoid calling // `Apply` in second time @@ -56,10 +59,14 @@ class FunctionNode { } const std::string& name() const { return name_; } + const std::shared_ptr& scope() const { return scope_; } + void set_scope(const std::shared_ptr& scope) { scope_ = scope; } + protected: + friend class GraphTask; explicit FunctionNode(const std::string& name, const std::shared_ptr& backward_fn) - : name_(name), backward_fn_(backward_fn) {} + : name_(name), backward_fn_(backward_fn), scope_(nullptr) {} const std::string name_; std::vector> next_functions_; @@ -70,6 +77,9 @@ class FunctionNode { // Actual backward function builds in `AutogradInterpreter` to calculate one backward op std::shared_ptr backward_fn_; + + // The execution scope + std::shared_ptr scope_; }; class AutogradEngine { @@ -130,13 +140,26 @@ class GraphTask final { Maybe ComputeDependencies(); Maybe ComputeDependenciesAndPruneNode(const TensorTuple& inputs); Maybe Apply(bool save_grad_for_leaf); + std::shared_ptr GetCapturedGrads() const { return captured_grads_; } + Maybe WriteGraphToDotFile(const std::string& file_name) const; private: + class ExecInfo { + public: + ExecInfo() = default; + + int32_t dependencies = 0; + bool need_execute = false; + // Used in autograd.grad interface, to record which grad of tensor will be captured. + // The pair means: + std::unique_ptr>> capture_indices; + }; + bool retain_graph_; bool create_graph_; std::vector roots_; - HashMap dependencies_; - HashSet need_execute_; + HashMap grad_fn2exec_info_; + std::shared_ptr captured_grads_; }; class GraphAutogradEngine final : public AutogradEngine { diff --git a/oneflow/core/autograd/autograd_meta.cpp b/oneflow/core/autograd/autograd_meta.cpp index cad95cedad060213834d58839820891f7efe62f1..2788ea81330c9b276b0304e923bbc340059aa5cb 100644 --- a/oneflow/core/autograd/autograd_meta.cpp +++ b/oneflow/core/autograd/autograd_meta.cpp @@ -25,9 +25,12 @@ namespace oneflow { namespace one { TensorInfo::TensorInfo(const Tensor& tensor) : shape_(tensor.shape()), dtype_(tensor.dtype()) { - if (TRY(tensor.device()).IsOk()) { device_ = CHECK_JUST(tensor.device()); } - if (TRY(tensor.parallel_desc()).IsOk()) { parallel_desc_ = CHECK_JUST(tensor.parallel_desc()); } - if (TRY(tensor.nd_sbp()).IsOk()) { nd_sbp_ = CHECK_JUST(tensor.nd_sbp()); } + if (tensor.is_global()) { + parallel_desc_ = CHECK_JUST(tensor.parallel_desc()); + nd_sbp_ = CHECK_JUST(tensor.nd_sbp()); + } else { + device_ = CHECK_JUST(tensor.device()); + } } Maybe>&> GetSbpTuple(Symbol nd_sbp) { @@ -52,7 +55,7 @@ Maybe TensorInfo::zeros() const { const auto& parallel_desc = JUST(parallel_desc_); const auto& nd_sbp = JUST(nd_sbp_); const auto& sbp_tuple = JUST(GetSbpTuple(nd_sbp)); - return functional::ConsistentConstant(*shape_.get(), 0, dtype_, parallel_desc, sbp_tuple); + return functional::GlobalConstant(*shape_.get(), 0, dtype_, parallel_desc, sbp_tuple); } } @@ -60,18 +63,26 @@ AutogradMeta::AutogradMeta(bool requires_grad, bool is_leaf) : is_leaf_(is_leaf), requires_grad_(requires_grad), retain_grad_(false), - is_grad_acc_inplace_(false), current_grad_(new TensorArg) {} Maybe AutogradMeta::set_acc_grad(const std::shared_ptr& grad) { if (const auto& static_zeros_tensor = std::dynamic_pointer_cast(grad)) { - acc_grad_ = JUST(static_zeros_tensor->AsMirroredTensor()); + acc_grad_ = JUST(static_zeros_tensor->AsLocalTensor()); } else { acc_grad_ = grad; } return Maybe::Ok(); } +Maybe AutogradMeta::current_grad_value() const { + std::shared_ptr res = JUST(current_grad_->GetAccTensor()); + for (const auto& hook : hooks_) { + const auto& new_tensor = hook(res); + if (new_tensor) { res = new_tensor; } + } + return res; +} + } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/autograd_meta.h b/oneflow/core/autograd/autograd_meta.h index 73432a62968091a550b22fb95201bab0082e27da..b48c242e4bae07690646f15c9dc069eb112dd639 100644 --- a/oneflow/core/autograd/autograd_meta.h +++ b/oneflow/core/autograd/autograd_meta.h @@ -36,7 +36,7 @@ namespace one { class Tensor; class TensorArg; -class MirroredTensor; +class LocalTensor; class AutogradMeta final { public: @@ -46,7 +46,8 @@ class AutogradMeta final { // Getters const std::shared_ptr& acc_grad() const { return acc_grad_; } const std::shared_ptr& current_grad() const { return current_grad_; } - bool is_grad_acc_inplace() const { return is_grad_acc_inplace_; } + // get current grad processed by hooks + Maybe current_grad_value() const; bool requires_grad() const { return requires_grad_; } bool is_leaf() const { return is_leaf_; } bool retain_grad() const { return retain_grad_; } @@ -59,7 +60,6 @@ class AutogradMeta final { // Setters Maybe set_acc_grad(const std::shared_ptr& grad); std::shared_ptr mut_acc_grad() { return acc_grad_; } - void set_is_grad_acc_inplace(bool is_inplace) { is_grad_acc_inplace_ = is_inplace; } void set_requires_grad(bool requires_grad) { requires_grad_ = requires_grad; } void set_retain_grad(bool retain_grad) { retain_grad_ = retain_grad; } void set_is_leaf(bool is_leaf) { is_leaf_ = is_leaf; } @@ -77,10 +77,6 @@ class AutogradMeta final { // Only meaningful on non_leaf Tensors (must be false otherwise) bool retain_grad_; - // Control whether grad accumulation is inplace. Don't change it - // unless you know what you are doing - bool is_grad_acc_inplace_; - std::shared_ptr acc_grad_; std::shared_ptr current_grad_; std::vector hooks_; @@ -104,8 +100,8 @@ class TensorInfo final { std::shared_ptr shape_; Symbol dtype_; Optional> device_; // for local tensor - Optional> parallel_desc_; // for consistent tensor - Optional> nd_sbp_; // for consistent tensor + Optional> parallel_desc_; // for global tensor + Optional> nd_sbp_; // for global tensor }; } // namespace one diff --git a/oneflow/core/autograd/gradient_funcs/activation.cpp b/oneflow/core/autograd/gradient_funcs/activation.cpp index 8d30776e5c48f4b6dc01760058c4a9d79145410e..e31781712a9876e293cb217b926289118f0aae45 100644 --- a/oneflow/core/autograd/gradient_funcs/activation.cpp +++ b/oneflow/core/autograd/gradient_funcs/activation.cpp @@ -108,6 +108,50 @@ class GeLU : public BaseActivation { } }; +class FastGeLU : public BaseActivation { + public: + Maybe Apply(const BaseActivationCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + if (ctx->requires_grad) { + const auto& x = ctx->SavedTensors().at(0); + in_grads->at(0) = JUST(functional::FastGeluGrad(out_grads.at(0), x)); + } + return Maybe::Ok(); + } +}; + +struct QuickGeluCaptureState : public AutoGradCaptureState { + bool requires_grad = false; +}; + +class QuickGeLU : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(QuickGeluCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + ctx->requires_grad = inputs.at(0)->requires_grad(); + if (!ctx->requires_grad) { return Maybe::Ok(); } + ctx->SaveTensorForBackward(inputs.at(0)); + return Maybe::Ok(); + } + + Maybe Apply(const QuickGeluCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + in_grads->resize(1); + if (ctx->requires_grad) { + const auto& x = ctx->SavedTensors().at(0); + in_grads->at(0) = JUST(functional::QuickGeluGrad(out_grads.at(0), x)); + } + return Maybe::Ok(); + } +}; + class HardSigmoid : public BaseActivation { public: Maybe Apply(const BaseActivationCaptureState* ctx, const TensorTuple& out_grads, @@ -558,6 +602,8 @@ REGISTER_OP_EXPR_GRAD_FUNCTION("prelu", PReLU); REGISTER_OP_EXPR_GRAD_FUNCTION("threshold", Threshold); REGISTER_OP_EXPR_GRAD_FUNCTION("softplus", Softplus); REGISTER_OP_EXPR_GRAD_FUNCTION("softshrink", SoftShrink); +REGISTER_OP_EXPR_GRAD_FUNCTION("fast_gelu", FastGeLU); +REGISTER_OP_EXPR_GRAD_FUNCTION("quick_gelu", QuickGeLU); } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/adaptive_pool.cpp b/oneflow/core/autograd/gradient_funcs/adaptive_avg_pool.cpp similarity index 100% rename from oneflow/core/autograd/gradient_funcs/adaptive_pool.cpp rename to oneflow/core/autograd/gradient_funcs/adaptive_avg_pool.cpp diff --git a/oneflow/core/autograd/gradient_funcs/adaptive_max_pool.cpp b/oneflow/core/autograd/gradient_funcs/adaptive_max_pool.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e20a36da9a77b6278ad25b6249063b7462103cf6 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/adaptive_max_pool.cpp @@ -0,0 +1,93 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct AdaptiveMaxPoolCaptureState : public AutoGradCaptureState { + bool requires_grad = false; +}; + +class AdaptiveMaxPoolNdGrad : public OpExprGradFunction { + public: + using OpExprGradFunction::Init; + + Maybe Init(const OpExpr& op, const int& ndims); + Maybe Capture(AdaptiveMaxPoolCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const AdaptiveMaxPoolCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + int32_t ndims_ = 0; +}; + +Maybe AdaptiveMaxPoolNdGrad::Init(const OpExpr& op, const int& ndims) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + ndims_ = ndims; + return Maybe::Ok(); +} + +Maybe AdaptiveMaxPoolNdGrad::Capture(AdaptiveMaxPoolCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + ctx->requires_grad = inputs.at(0)->requires_grad(); + if (!ctx->requires_grad) { return Maybe::Ok(); } + + ctx->SaveTensorForBackward(inputs.at(0)); + ctx->SaveTensorForBackward(outputs.at(1)); + return Maybe::Ok(); +} + +Maybe AdaptiveMaxPoolNdGrad::Apply(const AdaptiveMaxPoolCaptureState* ctx, + const TensorTuple& out_grads, + TensorTuple* in_grads) const { + if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 2); // NOLINT(maybe-need-error-msg) + const std::shared_ptr& x = ctx->SavedTensors().at(0); + const std::shared_ptr& index = ctx->SavedTensors().at(1); + in_grads->resize(1); + in_grads->at(0) = JUST(functional::AdaptiveMaxPoolNdGrad(x, out_grads.at(0), index, ndims_)); + return Maybe::Ok(); +} + +class AdaptiveMaxPool1dGrad final : public AdaptiveMaxPoolNdGrad { + public: + Maybe Init(const OpExpr& op) override { return AdaptiveMaxPoolNdGrad::Init(op, 1); } +}; + +class AdaptiveMaxPool2dGrad final : public AdaptiveMaxPoolNdGrad { + public: + Maybe Init(const OpExpr& op) override { return AdaptiveMaxPoolNdGrad::Init(op, 2); } +}; + +class AdaptiveMaxPool3dGrad final : public AdaptiveMaxPoolNdGrad { + public: + Maybe Init(const OpExpr& op) override { return AdaptiveMaxPoolNdGrad::Init(op, 3); } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool1d", AdaptiveMaxPool1dGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool2d", AdaptiveMaxPool2dGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool3d", AdaptiveMaxPool3dGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/amp_white_identity.cpp b/oneflow/core/autograd/gradient_funcs/amp_white_identity.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b18f887849e579fc8c04f5ae315dfd1b65d8d206 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/amp_white_identity.cpp @@ -0,0 +1,62 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +enum class AmpIdentityType { + kWhite = 0, + kBlack, +}; + +struct AmpIdentityCaptureState : public AutoGradCaptureState {}; + +template +class AmpIdentityGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); + } + + Maybe Capture(AmpIdentityCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + return Maybe::Ok(); + } + + Maybe Apply(const AmpIdentityCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(1); + if (type == AmpIdentityType::kWhite) { + (*in_grads)[0] = JUST(functional::AmpWhiteIdentity(out_grads[0])); + } else if (type == AmpIdentityType::kBlack) { + (*in_grads)[0] = JUST(functional::AmpBlackIdentity(out_grads[0])); + } else { + (*in_grads)[0] = out_grads[0]; + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("amp_white_identity", AmpIdentityGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("amp_black_identity", AmpIdentityGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/as_strided.cpp b/oneflow/core/autograd/gradient_funcs/as_strided.cpp index ca588e2986c7e434f8d7a012bc488b0f9e45f447..9d38cd8e1ae1fd8d4066359437ae22ce7f635d8d 100644 --- a/oneflow/core/autograd/gradient_funcs/as_strided.cpp +++ b/oneflow/core/autograd/gradient_funcs/as_strided.cpp @@ -22,9 +22,9 @@ namespace oneflow { namespace one { struct AsStridedCaptureState : public AutoGradCaptureState { - std::vector size; - std::vector stride; - int32_t storage_offset = 0; + std::vector size; + std::vector stride; + int64_t storage_offset = 0; bool requires_grad = false; }; @@ -55,9 +55,9 @@ Maybe AsStrided::Capture(AsStridedCaptureState* ctx, const TensorTuple& in ctx->SaveTensorForBackward(inputs.at(0)); ComposedAttrMap composed_attrs(attrs, base_attrs_); - ctx->size = JUST(composed_attrs.GetAttr>("size")); - ctx->stride = JUST(composed_attrs.GetAttr>("stride")); - ctx->storage_offset = JUST(composed_attrs.GetAttr("storage_offset")); + ctx->size = JUST(composed_attrs.GetAttr>("size")); + ctx->stride = JUST(composed_attrs.GetAttr>("stride")); + ctx->storage_offset = JUST(composed_attrs.GetAttr("storage_offset")); return Maybe::Ok(); } @@ -67,9 +67,9 @@ Maybe AsStrided::Apply(const AsStridedCaptureState* ctx, const TensorTuple CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) const auto& input = ctx->SavedTensors().at(0); - std::vector size = ctx->size; - std::vector stride = ctx->stride; - int32_t storage_offset = ctx->storage_offset; + std::vector size = ctx->size; + std::vector stride = ctx->stride; + int64_t storage_offset = ctx->storage_offset; in_grads->at(0) = JUST(functional::AsStridedGrad(out_grads.at(0), input, size, stride, storage_offset)); diff --git a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy.cpp b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy.cpp index c05a72bed2c02975ae598aa29aa1215e2c4218e4..9cc63158521230f4771a689c9b11bd0dd5664ee3 100644 --- a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy.cpp +++ b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy.cpp @@ -20,7 +20,9 @@ namespace oneflow { namespace one { struct BinaryCrossEntropyCaptureState : public AutoGradCaptureState { - bool requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + bool has_weight = false; }; class BinaryCrossEntropy : public OpExprGradFunction { @@ -30,46 +32,42 @@ class BinaryCrossEntropy : public OpExprGradFunction Apply(const BinaryCrossEntropyCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override; - - private: - AttrMap base_attrs_; }; -Maybe BinaryCrossEntropy::Init(const OpExpr& op) { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) - base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); - return Maybe::Ok(); -} +Maybe BinaryCrossEntropy::Init(const OpExpr& op) { return Maybe::Ok(); } + Maybe BinaryCrossEntropy::Capture(BinaryCrossEntropyCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ctx->requires_grad = inputs.at(0)->requires_grad(); - if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_OR_RETURN(inputs.size() >= 2 && inputs.size() <= 3); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = inputs[0]->requires_grad(); + ctx->target_requires_grad = inputs[1]->requires_grad(); + ctx->has_weight = inputs.size() == 3; - ComposedAttrMap composed_attrs(attrs, base_attrs_); - ctx->SaveTensorForBackward(inputs.at(0)); // input - ctx->SaveTensorForBackward(inputs.at(1)); // target - if (inputs.size() == 3) { - ctx->SaveTensorForBackward(inputs.at(2)); // weight + ctx->SaveTensorForBackward(inputs[0]); // input + ctx->SaveTensorForBackward(inputs[1]); // target + if (ctx->has_weight) { + ctx->SaveTensorForBackward(inputs[2]); // weight } return Maybe::Ok(); } Maybe BinaryCrossEntropy::Apply(const BinaryCrossEntropyCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - if (!ctx->requires_grad) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - const auto& dy = out_grads.at(0); - const auto& input = ctx->SavedTensors().at(0); - const auto& target = ctx->SavedTensors().at(1); - in_grads->resize(ctx->SavedTensors().size()); + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), + 2 + ctx->has_weight); // NOLINT(maybe-need-error-msg) + in_grads->resize(2 + ctx->has_weight); + + const auto& dy = out_grads[0]; + const auto& input = ctx->SavedTensors()[0]; + const auto& target = ctx->SavedTensors()[1]; + const auto& weight = ctx->has_weight ? Optional(ctx->SavedTensors()[2]) : NullOpt; - if (ctx->SavedTensors().size() == 3) { - const auto& weight = ctx->SavedTensors().at(2); - in_grads->at(0) = JUST(functional::BinaryCrossEntropyLossGrad(dy, input, target, weight)); - } else { - in_grads->at(0) = JUST(functional::BinaryCrossEntropyLossGrad(dy, input, target, NullOpt)); + if (ctx->input_requires_grad) { + (*in_grads)[0] = JUST(functional::BinaryCrossEntropyLossGrad(dy, input, target, weight)); + } + if (ctx->target_requires_grad) { + (*in_grads)[1] = JUST(functional::BinaryCrossEntropyLossTargetGrad(dy, input, target, weight)); } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits.cpp b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits.cpp index f48adb41da7fcb7591eb981947bda3146eb8e3ba..15c370e72cd6b7b73f8d513fa1871fb7ecca3896 100644 --- a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits.cpp +++ b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits.cpp @@ -20,7 +20,9 @@ namespace oneflow { namespace one { struct BinaryCrossEntropyWithLogitsCaptureState : public AutoGradCaptureState { - bool requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + bool has_weight = false; bool has_pos_weight = false; }; @@ -47,53 +49,51 @@ Maybe BinaryCrossEntropyWithLogits::Capture(BinaryCrossEntropyWithLogitsCa const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ctx->requires_grad = inputs.at(0)->requires_grad(); - if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_OR_RETURN(inputs.size() >= 2 && inputs.size() <= 4); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = inputs[0]->requires_grad(); + ctx->target_requires_grad = inputs[1]->requires_grad(); ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->has_pos_weight = JUST(composed_attrs.GetAttr("has_pos_weight")); - ctx->SaveTensorForBackward(inputs.at(0)); // input - ctx->SaveTensorForBackward(inputs.at(1)); // target + ctx->has_weight = inputs.size() == 4 || (inputs.size() == 3 && !ctx->has_pos_weight); + ctx->SaveTensorForBackward(inputs[0]); // input + ctx->SaveTensorForBackward(inputs[1]); // target + if (inputs.size() == 3) { - ctx->SaveTensorForBackward(inputs.at(2)); // weight or pos_weight + ctx->SaveTensorForBackward(inputs[2]); // weight or pos_weight } if (inputs.size() == 4) { - ctx->SaveTensorForBackward(inputs.at(2)); // weight - ctx->SaveTensorForBackward(inputs.at(3)); // pos_weight + ctx->SaveTensorForBackward(inputs[2]); // weight + ctx->SaveTensorForBackward(inputs[3]); // pos_weight } return Maybe::Ok(); } Maybe BinaryCrossEntropyWithLogits::Apply(const BinaryCrossEntropyWithLogitsCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - if (!ctx->requires_grad) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - const auto& dy = out_grads.at(0); - const auto& input = ctx->SavedTensors().at(0); - const auto& target = ctx->SavedTensors().at(1); + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), + 2 + ctx->has_weight + ctx->has_pos_weight); // NOLINT(maybe-need-error-msg) + const auto& dy = out_grads[0]; + const auto& input = ctx->SavedTensors()[0]; + const auto& target = ctx->SavedTensors()[1]; in_grads->resize(ctx->SavedTensors().size()); - if (ctx->SavedTensors().size() == 3) { - if (ctx->has_pos_weight) { - const auto& pos_weight = ctx->SavedTensors().at(2); - in_grads->at(0) = JUST( - functional::BinaryCrossEntropyWithLogitsLossGrad(dy, input, target, NullOpt, pos_weight)); - } else { - const auto& weight = ctx->SavedTensors().at(2); - in_grads->at(0) = JUST( - functional::BinaryCrossEntropyWithLogitsLossGrad(dy, input, target, weight, NullOpt)); - } - } else if (ctx->SavedTensors().size() == 4) { - const auto& weight = ctx->SavedTensors().at(2); - const auto& pos_weight = ctx->SavedTensors().at(3); - in_grads->at(0) = JUST( + size_t pos_weight_index = ctx->has_weight ? 3 : 2; + auto weight = ctx->has_weight ? Optional(ctx->SavedTensors()[2]) : NullOpt; + auto pos_weight = + ctx->has_pos_weight ? Optional(ctx->SavedTensors()[pos_weight_index]) : NullOpt; + + if (ctx->input_requires_grad) { + (*in_grads)[0] = JUST( functional::BinaryCrossEntropyWithLogitsLossGrad(dy, input, target, weight, pos_weight)); - } else { - in_grads->at(0) = - JUST(functional::BinaryCrossEntropyWithLogitsLossGrad(dy, input, target, NullOpt, NullOpt)); } + if (ctx->target_requires_grad) { + (*in_grads)[1] = JUST(functional::BinaryCrossEntropyWithLogitsLossTargetGrad( + dy, input, target, weight, pos_weight)); + } + return Maybe::Ok(); } REGISTER_OP_EXPR_GRAD_FUNCTION("binary_cross_entropy_with_logits", BinaryCrossEntropyWithLogits); diff --git a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp index 879c743beb7f17dc09be517c3b7103b750ef50fc..66a2c097300bf59d223a1f4ce79daf64ee9df647 100644 --- a/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp +++ b/oneflow/core/autograd/gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp @@ -21,8 +21,8 @@ namespace oneflow { namespace one { struct BinaryCrossEntropyWithLogitsReduceMeanCaptureState : public AutoGradCaptureState { - bool requires_grad = false; - bool has_pos_weight = false; + bool input_requires_grad = false; + bool target_requires_grad = false; }; class BinaryCrossEntropyWithLogitsReduceMean @@ -34,25 +34,19 @@ class BinaryCrossEntropyWithLogitsReduceMean const AttrMap& attrs) const override; Maybe Apply(const BinaryCrossEntropyWithLogitsReduceMeanCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override; - - private: - AttrMap base_attrs_; }; Maybe BinaryCrossEntropyWithLogitsReduceMean::Init(const OpExpr& op) { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr) << "fw_op_expr should not be null. "; - base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); return Maybe::Ok(); } Maybe BinaryCrossEntropyWithLogitsReduceMean::Capture( BinaryCrossEntropyWithLogitsReduceMeanCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ctx->requires_grad = JUST(VectorAt(inputs, 0))->requires_grad(); - if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = JUST(VectorAt(inputs, 0))->requires_grad(); + ctx->target_requires_grad = JUST(VectorAt(inputs, 1))->requires_grad(); - ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); // input ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // target return Maybe::Ok(); @@ -61,14 +55,20 @@ Maybe BinaryCrossEntropyWithLogitsReduceMean::Capture( Maybe BinaryCrossEntropyWithLogitsReduceMean::Apply( const BinaryCrossEntropyWithLogitsReduceMeanCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - if (!ctx->requires_grad) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "out_grads size should be equal to 1. "; + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) const auto& dy = JUST(VectorAt(out_grads, 0)); const auto& input = JUST(VectorAt(ctx->SavedTensors(), 0)); const auto& target = JUST(VectorAt(ctx->SavedTensors(), 1)); - in_grads->resize(ctx->SavedTensors().size()); - JUST(VectorAt(*in_grads, 0)) = - JUST(functional::BinaryCrossEntropyWithLogitsReduceMeanLossGrad(dy, input, target)); + in_grads->resize(2); + + if (ctx->input_requires_grad) { + (*in_grads)[0] = + JUST(functional::BinaryCrossEntropyWithLogitsReduceMeanLossGrad(dy, input, target)); + } + if (ctx->target_requires_grad) { + (*in_grads)[1] = + JUST(functional::BinaryCrossEntropyWithLogitsReduceMeanLossTargetGrad(dy, input, target)); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/broadcast_binary_ops.cpp b/oneflow/core/autograd/gradient_funcs/broadcast_binary_ops.cpp index b01d63a1e8f53bf559f888b96942da2e841e9161..0b3c367b5a5e26cda5b9f261a4f66bd41f6081d8 100644 --- a/oneflow/core/autograd/gradient_funcs/broadcast_binary_ops.cpp +++ b/oneflow/core/autograd/gradient_funcs/broadcast_binary_ops.cpp @@ -232,13 +232,12 @@ class BroadcastPow : public BroadcastBinaryGrad { TensorTuple* in_grads) const override { const auto& x = ctx->SavedTensors().at(ctx->x_index); const auto& y = ctx->SavedTensors().at(ctx->y_index); - const auto& z = ctx->SavedTensors().at(ctx->z_index); in_grads->resize(2); if (ctx->x_requires_grad) { - in_grads->at(0) = JUST(functional::BroadcastPowXGrad(out_grads.at(0), x, y, z)); + (*in_grads)[0] = JUST(functional::BroadcastPowXGrad(x, y, out_grads[0])); } if (ctx->y_requires_grad) { - in_grads->at(1) = JUST(functional::BroadcastPowYGrad(out_grads.at(0), x, y, z)); + (*in_grads)[1] = JUST(functional::BroadcastPowYGrad(x, y, out_grads[0])); } return Maybe::Ok(); } @@ -246,9 +245,8 @@ class BroadcastPow : public BroadcastBinaryGrad { protected: Maybe SaveTensorForBackward(BroadcastBinaryCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs) const override { - ctx->x_index = ctx->SaveTensorForBackward(inputs.at(0)); - ctx->y_index = ctx->SaveTensorForBackward(inputs.at(1)); - ctx->z_index = ctx->SaveTensorForBackward(outputs.at(0)); + ctx->x_index = ctx->SaveTensorForBackward(inputs[0]); + ctx->y_index = ctx->SaveTensorForBackward(inputs[1]); return Maybe::Ok(); } }; @@ -348,5 +346,80 @@ class BroadcastMaximum : public BroadcastMinMax { REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_minimum", BroadcastMinimum); REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_maximum", BroadcastMaximum); +class BroadcastFMod : public BroadcastBinaryGrad { + public: + Maybe Apply(const BroadcastBinaryCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + const auto& out_shape = *(JUST(VectorAt(out_grads, 0))->shape()); + in_grads->resize(2); + if (ctx->x_requires_grad || ctx->y_requires_grad) { + const auto& x = JUST(VectorAt(ctx->SavedTensors(), ctx->x_index)); + const auto& y = JUST(VectorAt(ctx->SavedTensors(), ctx->y_index)); + auto broad_x_ = x; + auto broad_y_ = y; + if (ctx->broadcast_x) { + const auto& x_shape = *(x->shape()); + const Shape& left_extended_x_shape = + CreateLeftExtendedShape(ShapeView(x_shape), out_shape.NumAxes()); + if (left_extended_x_shape == out_shape) { + broad_x_ = JUST(functional::ReshapeLike(x, JUST(VectorAt(out_grads, 0)))); + } else { + const AxisVector& broadcast_axis_vec = left_extended_x_shape.Axes4BroadcastTo(out_shape); + const std::vector x_axis = + std::vector{broadcast_axis_vec.begin(), broadcast_axis_vec.end()}; + broad_x_ = JUST(functional::BroadcastLike(x, JUST(VectorAt(out_grads, 0)), x_axis)); + } + } + if (ctx->broadcast_y) { + const auto& y_shape = *(y->shape()); + const Shape& left_extended_y_shape = + CreateLeftExtendedShape(ShapeView(y_shape), out_shape.NumAxes()); + if (left_extended_y_shape == out_shape) { + broad_y_ = JUST(functional::ReshapeLike(y, JUST(VectorAt(out_grads, 0)))); + } else { + const AxisVector& broadcast_axis_vec = left_extended_y_shape.Axes4BroadcastTo(out_shape); + const std::vector y_axis = + std::vector{broadcast_axis_vec.begin(), broadcast_axis_vec.end()}; + broad_y_ = JUST(functional::BroadcastLike(y, JUST(VectorAt(out_grads, 0)), y_axis)); + } + } + if (ctx->x_requires_grad) { + if (ctx->broadcast_x) { + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::BroadcastReduceSumLike(JUST(VectorAt(out_grads, 0)), x)); + } else { + JUST(VectorAt(*in_grads, 0)) = JUST(VectorAt(out_grads, 0)); + } + } + if (ctx->y_requires_grad) { + auto result = JUST(functional::TruncDiv(broad_x_, broad_y_)); + result = JUST(functional::Mul(JUST(VectorAt(out_grads, 0)), result)); + JUST(functional::ScalarMul(result, Scalar(-1.f), true)); + if (ctx->broadcast_y) { + in_grads->at(1) = JUST(functional::BroadcastReduceSumLike(result, y)); + } else { + in_grads->at(1) = result; + } + } + } + return Maybe::Ok(); + } + + protected: + Maybe SaveTensorForBackward(BroadcastBinaryCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs) const override { + if (ctx->x_requires_grad && ctx->broadcast_x) { + ctx->x_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); + } + if (ctx->y_requires_grad) { + ctx->x_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); + ctx->y_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_fmod", BroadcastFMod); + } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/broadcast_floor_mod.cpp b/oneflow/core/autograd/gradient_funcs/broadcast_floor_mod.cpp deleted file mode 100644 index 36e98d078cd0838753a2b749fe6b89e8c1a9fe75..0000000000000000000000000000000000000000 --- a/oneflow/core/autograd/gradient_funcs/broadcast_floor_mod.cpp +++ /dev/null @@ -1,49 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/framework/op_expr_grad_function.h" - -namespace oneflow { -namespace one { - -struct BroadcastFModCaptureState : public AutoGradCaptureState { - bool requires_grad; -}; - -class BroadcastFMod : public OpExprGradFunction { - public: - Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } - - Maybe Capture(BroadcastFModCaptureState* ctx, const TensorTuple& inputs, - const TensorTuple& outputs, const AttrMap& attrs) const override { - CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) - ctx->requires_grad = inputs.at(0)->requires_grad(); - return Maybe::Ok(); - } - - Maybe Apply(const BroadcastFModCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const override { - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - in_grads->resize(2); - if (ctx->requires_grad) { in_grads->at(0) = out_grads.at(0); } - return Maybe::Ok(); - } -}; - -REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_fmod", BroadcastFMod); - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/consistent_cast.cpp b/oneflow/core/autograd/gradient_funcs/consistent_cast.cpp deleted file mode 100644 index e692aa2f755b252057b067d6a4972df3aacf52af..0000000000000000000000000000000000000000 --- a/oneflow/core/autograd/gradient_funcs/consistent_cast.cpp +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/op_expr_grad_function.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" -#include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/functional/functional.h" - -namespace oneflow { -namespace one { - -struct CastConsistentCaptureState : public AutoGradCaptureState { - Symbol parallel_desc; - Symbol nd_sbp; - std::shared_ptr shape; - Symbol dtype; -}; - -class CastToConsistent : public OpExprGradFunction { - public: - Maybe Init(const OpExpr& op) override { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) - const std::string& op_name = fw_op_expr->op_name(); - grad_op_ = JUST(one::CastFromConsistentOpExpr::New(GradientOpName(op_name))); - return Maybe::Ok(); - } - - Maybe Capture(CastConsistentCaptureState* ctx, const TensorTuple& inputs, - const TensorTuple& outputs, - const OpExprInterpContext& interp_ctx) const override { - ctx->parallel_desc = JUST(interp_ctx.parallel_desc); - ctx->nd_sbp = JUST(GetDualNdSbp(JUST(interp_ctx.nd_sbp))); - return Maybe::Ok(); - } - - Maybe Apply(const CastConsistentCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const override { - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - std::shared_ptr out_grad = out_grads.at(0); - CHECK_OR_RETURN(out_grad->is_consistent()) - << Error::RuntimeError() - << "Expected global tensor for cast_to_consistent but got local tensor"; - { - Symbol nd_sbp_constraint = ctx->nd_sbp; - Symbol parallel_desc_constraint = ctx->parallel_desc; - out_grad = JUST(functional::ToConsistent(out_grad, parallel_desc_constraint, - *JUST(GetSbpList(nd_sbp_constraint)), - GetNoneSbpList(), /* check_meta */ false)); - } - in_grads->at(0) = JUST(OpInterpUtil::Dispatch(*grad_op_, {out_grad})); - return Maybe::Ok(); - } - - private: - std::shared_ptr grad_op_; -}; - -REGISTER_OP_EXPR_GRAD_FUNCTION("cast_to_consistent", CastToConsistent); - -class CastFromConsistent : public OpExprGradFunction { - public: - Maybe Init(const OpExpr& op) override { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) - const std::string& op_name = fw_op_expr->op_name(); - grad_op_ = JUST(one::CastToConsistentOpExpr::New(GradientOpName(op_name))); - return Maybe::Ok(); - } - - Maybe Capture(CastConsistentCaptureState* ctx, const TensorTuple& inputs, - const TensorTuple& outputs, const AttrMap& attrs) const override { - const auto& input = inputs.at(0); - CHECK_OR_RETURN(input->is_consistent()) - << Error::RuntimeError() - << "Expected global tensor for cast_from_consistent but got local tensor"; - ctx->parallel_desc = JUST(input->parallel_desc()); - ctx->nd_sbp = JUST(input->nd_sbp()); - ctx->shape = input->shape(); - ctx->dtype = input->dtype(); - return Maybe::Ok(); - } - - Maybe Apply(const CastConsistentCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const override { - const auto& dual_nd_sbp = JUST(GetDualNdSbp(ctx->nd_sbp)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", *ctx->shape)); - JUST(attrs.SetAttr("dtype", ctx->dtype->data_type())); - in_grads->at(0) = JUST(OpInterpUtil::Dispatch( - *grad_op_, {out_grads.at(0)}, OpExprInterpContext(attrs, ctx->parallel_desc, dual_nd_sbp))); - return Maybe::Ok(); - } - - private: - std::shared_ptr grad_op_; -}; - -REGISTER_OP_EXPR_GRAD_FUNCTION("cast_from_consistent", CastFromConsistent); - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/consistent_to_consistent.cpp b/oneflow/core/autograd/gradient_funcs/consistent_to_consistent.cpp deleted file mode 100644 index a77f1ff3422064040f3e2114a9a71adbdb85cd57..0000000000000000000000000000000000000000 --- a/oneflow/core/autograd/gradient_funcs/consistent_to_consistent.cpp +++ /dev/null @@ -1,74 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/id_util.h" -#include "oneflow/core/framework/op_expr_grad_function.h" -#include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/functional/functional.h" -#include "oneflow/core/common/optional.h" - -namespace oneflow { -namespace one { - -struct ConsistentToConsistentState : public AutoGradCaptureState { - Symbol parallel_desc; - Symbol nd_sbp; -}; - -class ConsistentToConsistentGradFunction : public OpExprGradFunction { - public: - Maybe Init(const OpExpr& op) override { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) - grad_nd_sbp_ = fw_op_expr->grad_nd_sbp(); - return Maybe::Ok(); - } - - Maybe Capture(ConsistentToConsistentState* ctx, const TensorTuple& inputs, - const TensorTuple& outputs, - const OpExprInterpContext& interp_ctx) const override { - CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) - ctx->parallel_desc = JUST(inputs.at(0)->parallel_desc()); - ctx->nd_sbp = JUST(inputs.at(0)->nd_sbp()); - return Maybe::Ok(); - } - - Maybe Apply(const ConsistentToConsistentState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const override { - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - const auto& out_grad = out_grads.at(0); - CHECK_OR_RETURN(out_grad->is_consistent()) - << Error::RuntimeError() - << "Expected global tensor for consistent_to_consistent but got local tensor"; - in_grads->resize(1); - const auto& grad_nd_sbp = grad_nd_sbp_.value_or(JUST(out_grad->nd_sbp())); - const auto& grad_sbp_list = JUST(GetSbpList(grad_nd_sbp)); - const auto& grad_grad_sbp_list = JUST(GetSbpList(ctx->nd_sbp)); - (*in_grads)[0] = JUST(one::functional::ToConsistent( - out_grad, ctx->parallel_desc, *grad_sbp_list, *grad_grad_sbp_list, /* check_meta */ false)); - return Maybe::Ok(); - } - - private: - Optional> grad_nd_sbp_; -}; - -REGISTER_OP_EXPR_GRAD_FUNCTION("consistent_to_consistent", ConsistentToConsistentGradFunction); - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/conv.cpp b/oneflow/core/autograd/gradient_funcs/conv.cpp index 7ef166f45710664d38764801b83d54f7c95d3428..c5f7517725a8e2335b5e989edd9318b42b517720 100644 --- a/oneflow/core/autograd/gradient_funcs/conv.cpp +++ b/oneflow/core/autograd/gradient_funcs/conv.cpp @@ -26,6 +26,8 @@ namespace one { struct ConvolutionNdCaptureState : public AutoGradCaptureState { bool input_requires_grad = false; bool weight_requires_grad = false; + bool has_bias = false; + bool bias_requires_grad = false; size_t input_index; size_t weight_index; @@ -58,10 +60,17 @@ Maybe ConvolutionNd::Init(const OpExpr& op) { Maybe ConvolutionNd::Capture(ConvolutionNdCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(inputs.size() == 2 || inputs.size() == 3); // NOLINT(maybe-need-error-msg) ctx->input_requires_grad = inputs.at(0)->requires_grad(); ctx->weight_requires_grad = inputs.at(1)->requires_grad(); - if (!ctx->input_requires_grad && !ctx->weight_requires_grad) { return Maybe::Ok(); } + if (inputs.size() == 3) { + ctx->has_bias = true; + ctx->bias_requires_grad = inputs.at(2)->requires_grad(); + } + + if (!ctx->input_requires_grad && !ctx->weight_requires_grad && !ctx->bias_requires_grad) { + return Maybe::Ok(); + } if (ctx->input_requires_grad) { ctx->weight_index = ctx->SaveTensorForBackward(inputs.at(1)); // weight } @@ -79,7 +88,11 @@ Maybe ConvolutionNd::Capture(ConvolutionNdCaptureState* ctx, const TensorT Maybe ConvolutionNd::Apply(const ConvolutionNdCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - in_grads->resize(2); + if (ctx->has_bias) { + in_grads->resize(3); + } else { + in_grads->resize(2); + } size_t num_spatial_dims = ctx->kernel_size.size(); if (ctx->input_requires_grad) { const auto& weight = ctx->SavedTensors().at(ctx->weight_index); @@ -94,6 +107,18 @@ Maybe ConvolutionNd::Apply(const ConvolutionNdCaptureState* ctx, const Ten out_grads.at(0), input, num_spatial_dims, ctx->kernel_size, ctx->strides, ctx->padding_before, ctx->dilation_rate, ctx->groups, ctx->data_format)); } + if (ctx->bias_requires_grad) { + std::vector dim; + for (int i = 0; i < out_grads.at(0)->shape()->NumAxes(); ++i) { + if ((ctx->data_format == "channels_first" && i == 1) + || (ctx->data_format == "channels_last" + && i == out_grads.at(0)->shape()->NumAxes() - 1)) { + continue; + } + dim.push_back(i); + } + in_grads->at(2) = JUST(functional::ReduceSum(out_grads.at(0), dim, false)); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/copy.cpp b/oneflow/core/autograd/gradient_funcs/copy.cpp index 9a3f1afc08653b0fc101a73c6b125d890b1e119b..624a1a4c2c821856e659e5af92d95490e356a1e9 100644 --- a/oneflow/core/autograd/gradient_funcs/copy.cpp +++ b/oneflow/core/autograd/gradient_funcs/copy.cpp @@ -38,8 +38,14 @@ class Copy : public OpExprGradFunction { Maybe Capture(CopyCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const override { - ctx->device_type = JUST(inputs.at(0)->device())->type(); - ctx->device_id = JUST(inputs.at(0)->device())->device_id(); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + if (inputs[0]->is_global()) { + ctx->device_type = JUST(inputs[0]->parallel_desc())->device_tag(); + ctx->device_id = 0; // global tensor only has one local device + } else { + ctx->device_type = JUST(inputs[0]->device())->type(); + ctx->device_id = JUST(inputs[0]->device())->device_id(); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/ctc_loss.cpp b/oneflow/core/autograd/gradient_funcs/ctc_loss.cpp index 8c81dda4ad454f91323629f4073677834e9ed835..86b2611d6340f0ab1ed9427e98f6948d1ce76004 100644 --- a/oneflow/core/autograd/gradient_funcs/ctc_loss.cpp +++ b/oneflow/core/autograd/gradient_funcs/ctc_loss.cpp @@ -57,7 +57,7 @@ Maybe CTCLoss::Capture(CTCLossCaptureState* ctx, const TensorTuple& inputs ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->max_target_length = JUST(composed_attrs.GetAttr("max_target_length")); - ctx->blank = JUST(composed_attrs.GetAttr("blank")); + ctx->blank = JUST(composed_attrs.GetAttr("blank")); ctx->zero_infinity = JUST(composed_attrs.GetAttr("zero_infinity")); CHECK_EQ_OR_RETURN(inputs.size(), 4); // NOLINT(maybe-need-error-msg) diff --git a/oneflow/core/autograd/gradient_funcs/cublas_fused_mlp.cpp b/oneflow/core/autograd/gradient_funcs/cublas_fused_mlp.cpp index 4376891f696ee52233c6c4154f493256ed2133df..16249eafd6989bb8d7f083895beb8a54242b9f3e 100644 --- a/oneflow/core/autograd/gradient_funcs/cublas_fused_mlp.cpp +++ b/oneflow/core/autograd/gradient_funcs/cublas_fused_mlp.cpp @@ -81,7 +81,7 @@ Maybe CublasFusedMLP::Capture(CublasFusedMLPCaptureState* ctx, const Tenso ctx->SaveTensorForBackward( JUST(VectorAt(outputs, i + 1))); // cublas aux. need minus 1. idx_sum:2+2w } - for (int32_t i = 0; i < weight_num - 1; i++) { + for (int32_t i = 0; i < weight_num; i++) { ctx->SaveTensorForBackward(JUST(VectorAt(outputs, i + 1 + weight_num))); // hidden. } @@ -103,14 +103,7 @@ Maybe CublasFusedMLP::Apply(const CublasFusedMLPCaptureState* ctx, JUST(VectorAt(ctx->SavedTensors(), 1 + weight_num)))); } - // step2: use reduce_sum to get last layer's bias grad. - std::vector reduce_axes_vec{0}; - if (JUST(VectorAt(ctx->biases_requires_grad, weight_num - 1))) { - JUST(VectorAt(*in_grads, 2 * weight_num)) = - JUST(functional::ReduceSum(last_bias_dy, reduce_axes_vec, false)); - } - - TensorTuple hiddens(weight_num - 1); + TensorTuple hiddens(weight_num); TensorTuple weights(weight_num); TensorTuple cublas_auxs(weight_num); TensorTuple dgrad(weight_num); @@ -125,56 +118,90 @@ Maybe CublasFusedMLP::Apply(const CublasFusedMLPCaptureState* ctx, cublas_auxs[i] = JUST(VectorAt(ctx->SavedTensors(), i + 2 + weight_num)); } - for (int32_t i = 0; i < weight_num - 1; ++i) { + for (int32_t i = 0; i < weight_num; ++i) { hiddens[i] = JUST(VectorAt(ctx->SavedTensors(), i + 2 + 2 * weight_num)); } std::shared_ptr cublas_dy = last_bias_dy; - for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { - // If it is final layer, we use out_grads[0] as dy. - if (hidden_layer_idx != weight_num - 1) { - cublas_dy = JUST(VectorAt(dgrad, hidden_layer_idx + 1)); + + // Use Fully Fused MLP Backward. + if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSED_MLP_ASYNC_GRAD", false)) { + const std::vector alpha_list(weight_num - 1, 1.0); + const auto& fused_mlp_grad = + JUST(functional::FusedMLPGrad(cublas_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), weights, + cublas_auxs, hiddens, alpha_list)); + if (ctx->x_requires_grad) { + // dx: + JUST(VectorAt(*in_grads, 0)) = fused_mlp_grad->at(0); } - /* - Here we use cublas to compute bias + relu + matmul grad. - Then use Matmul to compute weight grad. - */ - const auto& matmul_relu_bias_bgrad = JUST(functional::CublasBiasAddReluMatmulGrad( - cublas_dy, JUST(VectorAt(weights, hidden_layer_idx)), - JUST(VectorAt(cublas_auxs, hidden_layer_idx - 1)), /*alpha=*/1.0)); - - // dgrad - dgrad.at(hidden_layer_idx) = matmul_relu_bias_bgrad->at(0); // NOLINT - - if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx - 1)))) { - // dbias - JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx)) = - matmul_relu_bias_bgrad->at(1); // NOLINT + + for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > -1; hidden_layer_idx--) { + if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx)))) { + // dbias + JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx + 1)) = + fused_mlp_grad->at(1 + hidden_layer_idx); // NOLINT + } + + // dw + if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { + JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = + fused_mlp_grad->at(1 + weight_num + hidden_layer_idx); + } } - // dw - if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { - JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = JUST(functional::MatMul( - cublas_dy, JUST(VectorAt(hiddens, hidden_layer_idx - 1)), true, false, 1.0)); + } else { + // step2: use reduce_sum to get last layer's bias grad. + std::vector reduce_axes_vec{0}; + if (JUST(VectorAt(ctx->biases_requires_grad, weight_num - 1))) { + JUST(VectorAt(*in_grads, 2 * weight_num)) = + JUST(functional::ReduceSum(last_bias_dy, reduce_axes_vec, false)); } - } - // For the first layer, we need to use 2 matmul to get grads. - std::shared_ptr last_dy; - if (weight_num != 1) { - last_dy = JUST(VectorAt(dgrad, 1)); - } else { - last_dy = last_bias_dy; - } + for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { + // If it is final layer, we use out_grads[0] as dy. + if (hidden_layer_idx != weight_num - 1) { + cublas_dy = JUST(VectorAt(dgrad, hidden_layer_idx + 1)); + } + /* + Here we use cublas to compute bias + relu + matmul grad. + Then use Matmul to compute weight grad. + */ + const auto& matmul_relu_bias_bgrad = JUST(functional::CublasBiasAddReluMatmulGrad( + cublas_dy, JUST(VectorAt(weights, hidden_layer_idx)), + JUST(VectorAt(cublas_auxs, hidden_layer_idx - 1)), /*alpha=*/1.0)); + + // dgrad + dgrad.at(hidden_layer_idx) = matmul_relu_bias_bgrad->at(0); // NOLINT + + if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx - 1)))) { + // dbias + JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx)) = + matmul_relu_bias_bgrad->at(1); // NOLINT + } + // dw + if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { + JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = JUST(functional::MatMul( + cublas_dy, JUST(VectorAt(hiddens, hidden_layer_idx - 1)), true, false, 1.0)); + } + } - if (ctx->x_requires_grad) { - // dx: - JUST(VectorAt(*in_grads, 0)) = - JUST(functional::MatMul(last_dy, JUST(VectorAt(weights, 0)), false, false, 1.0)); - } - if (JUST(VectorAt(ctx->weights_requires_grad, 0))) { - // dw: - JUST(VectorAt(*in_grads, 1)) = - JUST(functional::MatMul(last_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), true, false, 1.0)); + // For the first layer, we need to use 2 matmul to get grads. + std::shared_ptr last_dy; + if (weight_num != 1) { + last_dy = JUST(VectorAt(dgrad, 1)); + } else { + last_dy = last_bias_dy; + } + + if (ctx->x_requires_grad) { + // dx: + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::MatMul(last_dy, JUST(VectorAt(weights, 0)), false, false, 1.0)); + } + if (JUST(VectorAt(ctx->weights_requires_grad, 0))) { + // dw: + JUST(VectorAt(*in_grads, 1)) = JUST( + functional::MatMul(last_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), true, false, 1.0)); + } } return Maybe::Ok(); diff --git a/oneflow/core/autograd/gradient_funcs/deform_conv.cpp b/oneflow/core/autograd/gradient_funcs/deform_conv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1e5477ecb5c94786bbc239acdfd88f3065f6522e --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/deform_conv.cpp @@ -0,0 +1,132 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct DeformConvNdCaptureState : public AutoGradCaptureState { + bool input_requires_grad = false; + bool offset_requires_grad = false; + bool weight_requires_grad = false; + bool mask_requires_grad = false; + bool bias_requires_grad = false; + int32_t stride_h = 0; + int32_t stride_w = 0; + int32_t pad_h = 0; + int32_t pad_w = 0; + int32_t dilation_h = 0; + int32_t dilation_w = 0; + int32_t groups = 0; + int32_t offset_groups = 0; + bool use_mask = false; +}; + +class DeformConvNd : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(DeformConvNdCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const DeformConvNdCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe DeformConvNd::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe DeformConvNd::Capture(DeformConvNdCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ctx->input_requires_grad = inputs.at(0)->requires_grad(); + ctx->weight_requires_grad = inputs.at(1)->requires_grad(); + ctx->offset_requires_grad = inputs.at(2)->requires_grad(); + ctx->mask_requires_grad = inputs.at(3)->requires_grad(); + + ctx->SaveTensorForBackward(inputs.at(0)); // input + ctx->SaveTensorForBackward(inputs.at(1)); // weight + ctx->SaveTensorForBackward(inputs.at(2)); // offset + ctx->SaveTensorForBackward(inputs.at(3)); // mask + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + + ctx->use_mask = JUST(composed_attrs.GetAttr("use_mask")); + ctx->stride_h = JUST(composed_attrs.GetAttr("stride_h")); + ctx->stride_w = JUST(composed_attrs.GetAttr("stride_w")); + ctx->pad_h = JUST(composed_attrs.GetAttr("pad_h")); + ctx->pad_w = JUST(composed_attrs.GetAttr("pad_w")); + ctx->dilation_h = JUST(composed_attrs.GetAttr("dilation_h")); + ctx->dilation_w = JUST(composed_attrs.GetAttr("dilation_w")); + ctx->groups = JUST(composed_attrs.GetAttr("groups")); + ctx->offset_groups = JUST(composed_attrs.GetAttr("offset_groups")); + + return Maybe::Ok(); +} + +Maybe DeformConvNd::Apply(const DeformConvNdCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + in_grads->resize(5); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + const auto& input = ctx->SavedTensors().at(0); + const auto& weight = ctx->SavedTensors().at(1); + const auto& offset = ctx->SavedTensors().at(2); + const auto& mask = ctx->SavedTensors().at(3); + const auto& output_grad = out_grads.at(0); + if (ctx->input_requires_grad || ctx->offset_requires_grad || ctx->mask_requires_grad) { + std::shared_ptr grads_tuple; + if (ctx->use_mask) { + grads_tuple = JUST(functional::DeformConv2dInputGrad( + output_grad, input, weight, offset, mask, ctx->stride_h, ctx->stride_w, ctx->pad_h, + ctx->pad_w, ctx->dilation_h, ctx->dilation_w, ctx->groups, ctx->offset_groups, + ctx->use_mask)); + } else { + grads_tuple = JUST(functional::DeformConv2dInputGrad( + output_grad, input, weight, offset, NullOpt, ctx->stride_h, ctx->stride_w, ctx->pad_h, + ctx->pad_w, ctx->dilation_h, ctx->dilation_w, ctx->groups, ctx->offset_groups, + ctx->use_mask)); + } + if (ctx->input_requires_grad) { + in_grads->at(0) = grads_tuple->at(0); // input_grad + } + if (ctx->offset_requires_grad) { + in_grads->at(2) = grads_tuple->at(1); // offset_grad + } + if (ctx->use_mask && ctx->mask_requires_grad) { + in_grads->at(3) = grads_tuple->at(2); // mask_grad + } + } + + if (ctx->weight_requires_grad) { // weight_grad + in_grads->at(1) = JUST(functional::DeformConv2dParamGrad( + output_grad, input, weight, offset, mask, ctx->stride_h, ctx->stride_w, ctx->pad_h, + ctx->pad_w, ctx->dilation_h, ctx->dilation_w, ctx->groups, ctx->offset_groups, + ctx->use_mask)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("deform_conv2d", DeformConvNd); + +} // namespace one +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/autograd/gradient_funcs/dim_scatter.cpp b/oneflow/core/autograd/gradient_funcs/dim_scatter.cpp index 0edd67ce6a35febbee93a706011b6b440af7b905..e02d1b092c022445badfcca91e784b9037b768d9 100644 --- a/oneflow/core/autograd/gradient_funcs/dim_scatter.cpp +++ b/oneflow/core/autograd/gradient_funcs/dim_scatter.cpp @@ -26,10 +26,9 @@ struct DimScatterCaptureState : public AutoGradCaptureState { bool input_requires_grad; bool src_requires_grad; }; +enum class ScatterType { kUpdate, kAdd, kMultiply }; -enum SCATTER_TYPE { SCATTER_UPDATE, SCATTER_ADD }; - -template +template class DimScatter : public OpExprGradFunction { public: Maybe Init(const OpExpr& op) override; @@ -37,14 +36,12 @@ class DimScatter : public OpExprGradFunction { const TensorTuple& outputs, const AttrMap& attrs) const override; Maybe Apply(const DimScatterCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override; - Maybe ApplyCommon(const DimScatterCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const; private: AttrMap base_attrs_; }; -template +template Maybe DimScatter::Init(const OpExpr& op) { const UserOpExpr* fw_op_expr = dynamic_cast(&op); CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) @@ -52,7 +49,7 @@ Maybe DimScatter::Init(const OpExpr& op) { return Maybe::Ok(); } -template +template Maybe DimScatter::Capture(DimScatterCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) @@ -63,55 +60,46 @@ Maybe DimScatter::Capture(DimScatterCaptureState* ctx, const TensorTupl if ((!ctx->input_requires_grad) && (!ctx->src_requires_grad)) { return Maybe::Ok(); } ctx->SaveTensorForBackward(inputs.at(1)); // index saved + if (T == ScatterType::kMultiply) { + ctx->SaveTensorForBackward(inputs.at(2)); // src saved + } ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->dim = JUST(composed_attrs.GetAttr("dim")); return Maybe::Ok(); } -template -Maybe DimScatter::ApplyCommon(const DimScatterCaptureState* ctx, - const TensorTuple& out_grads, TensorTuple* in_grads) const { - const std::shared_ptr& index = ctx->SavedTensors().at(0); +template +Maybe DimScatter::Apply(const DimScatterCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + if ((!ctx->input_requires_grad) && (!ctx->src_requires_grad)) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) in_grads->resize(3); + const std::shared_ptr& index = ctx->SavedTensors().at(0); + if (ctx->src_requires_grad) { in_grads->at(2) = JUST(functional::DimGather(out_grads.at(0), ctx->dim, index, false)); } - return Maybe::Ok(); -} - -template<> -Maybe DimScatter::Apply(const DimScatterCaptureState* ctx, - const TensorTuple& out_grads, - TensorTuple* in_grads) const { - if ((!ctx->input_requires_grad) && (!ctx->src_requires_grad)) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - JUST(ApplyCommon(ctx, out_grads, in_grads)); if (ctx->input_requires_grad) { - const std::shared_ptr& index = ctx->SavedTensors().at(0); - in_grads->at(0) = - JUST(functional::DimScatterUpdateScalar(out_grads.at(0), ctx->dim, index, 0.0f)); + if (T == ScatterType::kAdd) { in_grads->at(0) = out_grads.at(0); } + + if (T == ScatterType::kUpdate) { + in_grads->at(0) = JUST(functional::DimScatterUpdateScalar(out_grads.at(0), ctx->dim, index, + 0.0f, /*inplace*/ false)); + } + + if (T == ScatterType::kMultiply) { + const std::shared_ptr& src = ctx->SavedTensors().at(1); + in_grads->at(0) = + JUST(functional::DimScatterMul(out_grads.at(0), ctx->dim, index, src, /*inplace*/ false)); + } } return Maybe::Ok(); } -template<> -Maybe DimScatter::Apply(const DimScatterCaptureState* ctx, - const TensorTuple& out_grads, - TensorTuple* in_grads) const { - if ((!ctx->input_requires_grad) && (!ctx->src_requires_grad)) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - - JUST(ApplyCommon(ctx, out_grads, in_grads)); - - if (ctx->input_requires_grad) { in_grads->at(0) = out_grads.at(0); } - - return Maybe::Ok(); -} - class DimScatterUpdateScalar : public OpExprGradFunction { public: Maybe Init(const OpExpr& op) override; @@ -156,18 +144,15 @@ Maybe DimScatterUpdateScalar::Apply(const DimScatterCaptureState* ctx, const std::shared_ptr& index = ctx->SavedTensors().at(0); in_grads->resize(2); - - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", ctx->dim)); - JUST(attrs.SetAttr("src_scalar", 0.0f)); - in_grads->at(0) = - JUST(functional::DimScatterUpdateScalar(out_grads.at(0), ctx->dim, index, 0.0f)); + in_grads->at(0) = JUST(functional::DimScatterUpdateScalar(out_grads.at(0), ctx->dim, index, 0.0f, + /*inplace*/ false)); return Maybe::Ok(); } -REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_update", DimScatter); -REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_add", DimScatter); +REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_update", DimScatter); +REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_add", DimScatter); +REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_mul", DimScatter); REGISTER_OP_EXPR_GRAD_FUNCTION("dim_scatter_update_scalar", DimScatterUpdateScalar); } // namespace one diff --git a/oneflow/core/autograd/gradient_funcs/eager_ccl_broadcast.cpp b/oneflow/core/autograd/gradient_funcs/eager_ccl_broadcast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d0ffcf9aaf1f9479038900639175dced40d8a258 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/eager_ccl_broadcast.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/id_util.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" + +namespace oneflow { + +namespace one { + +namespace { + +Maybe EagerCclReduce(Symbol parallel_desc, int64_t root) { + return one::OpBuilder("eager_ccl_reduce", *JUST(UniqueStr("eager_ccl_reduce"))) + .Input("in") + .Output("out") + .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Attr("root", root) + .Build(); +} + +Maybe FindOrCreatEagerCclReduceOpExpr(Symbol parallel_desc, + int64_t root) { + thread_local HashMap, int64_t>, std::shared_ptr> + parallel_desc_and_root_device2eager_nccl_reduce; + const auto& key = std::make_pair(parallel_desc, root); + auto iter = parallel_desc_and_root_device2eager_nccl_reduce.find(key); + if (iter == parallel_desc_and_root_device2eager_nccl_reduce.end()) { + std::shared_ptr op_expr = JUST(EagerCclReduce(parallel_desc, root)); + iter = parallel_desc_and_root_device2eager_nccl_reduce.emplace(key, op_expr).first; + } + return iter->second; +} + +} // namespace + +struct EagerCclBroadcastCaptureState : public AutoGradCaptureState { // NOLINT + Symbol parallel_desc; + int64_t root; +}; + +class EagerCclBroadcast : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); + return Maybe::Ok(); + } + + Maybe Capture(EagerCclBroadcastCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, + const OpExprInterpContext& interp_ctx) const override { + ctx->root = JUST(interp_ctx.attrs.GetAttr("root")); + ctx->parallel_desc = JUST(interp_ctx.parallel_desc); + return Maybe::Ok(); + } + + Maybe Apply(const EagerCclBroadcastCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + const auto& grad_op = JUST(FindOrCreatEagerCclReduceOpExpr(ctx->parallel_desc, ctx->root)); + in_grads->resize(1); + in_grads->at(0) = JUST(OpInterpUtil::Dispatch(*grad_op, {out_grads.at(0)})); + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("eager_ccl_broadcast", EagerCclBroadcast); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/eager_nccl_broadcast.cpp b/oneflow/core/autograd/gradient_funcs/eager_nccl_broadcast.cpp deleted file mode 100644 index 496629adb4dec06d152109c7f32485919e34c0cf..0000000000000000000000000000000000000000 --- a/oneflow/core/autograd/gradient_funcs/eager_nccl_broadcast.cpp +++ /dev/null @@ -1,85 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/id_util.h" -#include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_expr_grad_function.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" - -namespace oneflow { - -namespace one { - -namespace { - -Maybe EagerNcclReduce(Symbol parallel_desc, int64_t root) { - return one::OpBuilder("eager_nccl_reduce", *JUST(UniqueStr("eager_nccl_reduce"))) - .Input("in") - .Output("out") - .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) - .Attr("root", root) - .Build(); -} - -Maybe FindOrCreatEagerNcclReduceOpExpr(Symbol parallel_desc, - int64_t root) { - thread_local HashMap, int64_t>, std::shared_ptr> - parallel_desc_and_root_device2eager_nccl_reduce; - const auto& key = std::make_pair(parallel_desc, root); - auto iter = parallel_desc_and_root_device2eager_nccl_reduce.find(key); - if (iter == parallel_desc_and_root_device2eager_nccl_reduce.end()) { - std::shared_ptr op_expr = JUST(EagerNcclReduce(parallel_desc, root)); - iter = parallel_desc_and_root_device2eager_nccl_reduce.emplace(key, op_expr).first; - } - return iter->second; -} - -} // namespace - -struct EagerNcclBroadcastCaptureState : public AutoGradCaptureState { - Symbol parallel_desc; - int64_t root; -}; - -class EagerNcclBroadcast : public OpExprGradFunction { - public: - Maybe Init(const OpExpr& op) override { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); - return Maybe::Ok(); - } - - Maybe Capture(EagerNcclBroadcastCaptureState* ctx, const TensorTuple& inputs, - const TensorTuple& outputs, - const OpExprInterpContext& interp_ctx) const override { - ctx->root = JUST(interp_ctx.attrs.GetAttr("root")); - ctx->parallel_desc = JUST(interp_ctx.parallel_desc); - return Maybe::Ok(); - } - - Maybe Apply(const EagerNcclBroadcastCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const override { - const auto& grad_op = JUST(FindOrCreatEagerNcclReduceOpExpr(ctx->parallel_desc, ctx->root)); - in_grads->resize(1); - in_grads->at(0) = JUST(OpInterpUtil::Dispatch(*grad_op, {out_grads.at(0)})); - return Maybe::Ok(); - } -}; - -REGISTER_OP_EXPR_GRAD_FUNCTION("eager_nccl_broadcast", EagerNcclBroadcast); - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/expand.cpp b/oneflow/core/autograd/gradient_funcs/expand.cpp index de8c2297d9dde756b8d7d14995ab4bebc98b93e3..80826e15e5717e93209d8563f947f3fd816aef64 100644 --- a/oneflow/core/autograd/gradient_funcs/expand.cpp +++ b/oneflow/core/autograd/gradient_funcs/expand.cpp @@ -21,9 +21,10 @@ namespace oneflow { namespace one { struct ExpandCaptureState : public AutoGradCaptureState { - std::vector logical_out_shape; - std::vector logical_expand_shape; bool requires_grad; + int32_t lpad; + bool keep_dims; + std::vector reduce_dims; }; class Expand : public OpExprGradFunction { @@ -33,39 +34,51 @@ class Expand : public OpExprGradFunction { const TensorTuple& outputs, const AttrMap& attrs) const override; Maybe Apply(const ExpandCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override; - - private: - AttrMap base_attrs_; }; Maybe Expand::Init(const OpExpr& op) { const UserOpExpr* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); - base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) return Maybe::Ok(); } Maybe Expand::Capture(ExpandCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ctx->requires_grad = inputs.at(0)->requires_grad(); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = inputs[0]->requires_grad(); if (!ctx->requires_grad) { return Maybe::Ok(); } - ComposedAttrMap composed_attrs(attrs, base_attrs_); - ctx->logical_out_shape = JUST(composed_attrs.GetAttr>("logical_in_shape")); - ctx->logical_expand_shape = - JUST(composed_attrs.GetAttr>("logical_expand_shape")); + const Shape& in_shape = *inputs[0]->shape(); + const Shape& expand_shape = *outputs[0]->shape(); + ctx->lpad = expand_shape.size() - in_shape.size(); + ctx->keep_dims = (in_shape.size() > 0); + ctx->reduce_dims.reserve(expand_shape.size()); + if (ctx->keep_dims) { + for (size_t i = 0; i < expand_shape.size(); ++i) { + const auto& t_dim = expand_shape[i]; + const auto& dim = i < ctx->lpad ? 1 : in_shape[i - ctx->lpad]; + if (dim != t_dim) { ctx->reduce_dims.push_back(i); } + } + } else { + for (int32_t axis = 0; axis < expand_shape.size(); ++axis) { ctx->reduce_dims.push_back(axis); } + } return Maybe::Ok(); } Maybe Expand::Apply(const ExpandCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { if (!ctx->requires_grad) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); - MutableAttrMap attrs; - JUST(attrs.SetAttr>("logical_out_shape", ctx->logical_out_shape)); - JUST(attrs.SetAttr>("logical_expand_shape", ctx->logical_expand_shape)); - in_grads->at(0) = JUST( - functional::ExpandGrad(out_grads.at(0), ctx->logical_out_shape, ctx->logical_expand_shape)); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + in_grads->at(0) = out_grads[0]; + if (ctx->reduce_dims.size() > 0) { + in_grads->at(0) = + JUST(functional::ReduceSum(in_grads->at(0), ctx->reduce_dims, ctx->keep_dims)); + } + if (ctx->lpad > 0 && ctx->keep_dims) { + in_grads->at(0) = JUST(functional::Flatten(in_grads->at(0), 0, ctx->lpad)); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/fold.cpp b/oneflow/core/autograd/gradient_funcs/fold.cpp index 0ec515549697f6aea10969bf286a6f1182ed4f19..e175961f562d2edc13ceafb8e465b8e79222584d 100644 --- a/oneflow/core/autograd/gradient_funcs/fold.cpp +++ b/oneflow/core/autograd/gradient_funcs/fold.cpp @@ -66,8 +66,8 @@ Maybe Fold::Apply(const FoldInterpState* ctx, const TensorTuple& out_grads if (!ctx->requires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); in_grads->resize(1); - in_grads->at(0) = JUST(functional::Unfold(out_grads.at(0), ctx->data_format, ctx->kernel_size, - ctx->dilation_rate, ctx->padding, ctx->strides)); + in_grads->at(0) = JUST(functional::Unfold(out_grads.at(0), ctx->kernel_size, ctx->dilation_rate, + ctx->padding, ctx->strides, ctx->data_format)); return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/fused_bias_add_scale_mask_softmax_dropout.cpp b/oneflow/core/autograd/gradient_funcs/fused_bias_add_scale_mask_softmax_dropout.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7716721dcae56c6800c6b30e85c5b6b0b7adf92 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_bias_add_scale_mask_softmax_dropout.cpp @@ -0,0 +1,113 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" + +namespace oneflow { +namespace one { + +struct FusedBiasAddScaleMaskSoftmaxDropoutCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool bias_requires_grad = false; + bool bias_broadcast = false; + int softmax_y_index = -1; + int bias_index = -1; + int mask_index = -1; + int dropout_mask_index = -1; + float scale = 1.0; + float dropout_scale = 1.0; +}; + +class FusedBiasAddScaleMaskSoftmaxDropoutGradFunction + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(FusedBiasAddScaleMaskSoftmaxDropoutCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(outputs.size(), 2); // (y, softmax_y) + CHECK_EQ_OR_RETURN(inputs.size(), 4); // (x, bias, mask, dropout_mask) + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->bias_requires_grad = inputs.at(1)->requires_grad(); + + if (!ctx->x_requires_grad && !ctx->bias_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->scale = JUST(composed_attrs.GetAttr("scale_value")); + ctx->dropout_scale = JUST(composed_attrs.GetAttr("dropout_scale_value")); + + if (ctx->x_requires_grad) { + ctx->mask_index = ctx->SaveTensorForBackward(inputs.at(2)); // mask + ctx->dropout_mask_index = ctx->SaveTensorForBackward(inputs.at(3)); // dropout_mask + ctx->softmax_y_index = ctx->SaveTensorForBackward(outputs.at(1)); // softmax_y + } + + if (ctx->bias_requires_grad) { + ctx->bias_broadcast = (inputs.at(0)->shape() != inputs.at(1)->shape()); + if (ctx->bias_broadcast) { + ctx->bias_index = ctx->SaveTensorForBackward(inputs.at(1)); // bias + } + } + + return Maybe::Ok(); + } + + Maybe Apply(const FusedBiasAddScaleMaskSoftmaxDropoutCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override { + if (!ctx->x_requires_grad && !ctx->bias_requires_grad) { return Maybe::Ok(); } + + CHECK_EQ_OR_RETURN(out_grads.size(), 2); // (dy, d_softmax_y) + in_grads->resize(4); // (x, bias, mask, dropout_mask) + + const auto& saved_tensors = ctx->SavedTensors(); + const auto& dy = out_grads.at(0); + CHECK_GE_OR_RETURN(saved_tensors.size(), 3); // (mask, dropout_mask, softmax_y, [bias]) + + if (ctx->x_requires_grad || ctx->bias_requires_grad) { + const auto& mask = saved_tensors.at(ctx->mask_index); + const auto& dropout_mask = saved_tensors.at(ctx->dropout_mask_index); + const auto& softmax_y = saved_tensors.at(ctx->softmax_y_index); + in_grads->at(0) = JUST(functional::FusedScaleMaskSoftmaxDropoutGrad( + softmax_y, dy, mask, dropout_mask, ctx->scale, ctx->dropout_scale)); + } + + if (ctx->bias_requires_grad) { + if (ctx->bias_broadcast) { + const auto& bias = saved_tensors.at(ctx->bias_index); + in_grads->at(1) = JUST(functional::BroadcastReduceSumLike(in_grads->at(0), bias)); + } else { + in_grads->at(1) = in_grads->at(0); + } + } + + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_bias_add_scale_mask_softmax_dropout", + FusedBiasAddScaleMaskSoftmaxDropoutGradFunction); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_center.cpp b/oneflow/core/autograd/gradient_funcs/fused_center.cpp new file mode 100644 index 0000000000000000000000000000000000000000..02c65930182765bd183f38abc86039c6f3e92e62 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_center.cpp @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +const int32_t INPUT_LEN = 8; +struct FusedCenterCaptureState : public AutoGradCaptureState { + std::vector requires_grad; +}; + +class FusedCenterGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedCenterCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), INPUT_LEN); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + for (int i = 0; i < INPUT_LEN; i++) { + ctx->requires_grad.push_back(inputs.at(i)->requires_grad()); + } + for (int i = 0; i < INPUT_LEN; i++) { ctx->SaveTensorForBackward(inputs.at(i)); } + + return Maybe::Ok(); + } + + Maybe Apply(const FusedCenterCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& rho2_diff = out_grads.at(0); + + const auto& b1_x1 = ctx->SavedTensors().at(0); + const auto& b1_x2 = ctx->SavedTensors().at(1); + const auto& b2_x1 = ctx->SavedTensors().at(2); + const auto& b2_x2 = ctx->SavedTensors().at(3); + const auto& b1_y1 = ctx->SavedTensors().at(4); + const auto& b1_y2 = ctx->SavedTensors().at(5); + const auto& b2_y1 = ctx->SavedTensors().at(6); + const auto& b2_y2 = ctx->SavedTensors().at(7); + + in_grads->resize(INPUT_LEN); + auto result = JUST(functional::FusedCenterGrad(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, + b2_y2, rho2_diff)); + + CHECK_EQ_OR_RETURN(result->size(), INPUT_LEN); + for (int i = 0; i < INPUT_LEN; i++) { + if (ctx->requires_grad[i]) { in_grads->at(i) = result->at(i); } + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_center_dist", FusedCenterGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_fast_gelu_mul.cpp b/oneflow/core/autograd/gradient_funcs/fused_fast_gelu_mul.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cb8ecdad59e7f867ea76f5515e1245be39f10b98 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_fast_gelu_mul.cpp @@ -0,0 +1,66 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct FusedFastGeluMulGradCaptureState : public AutoGradCaptureState { + bool requires_grad = true; +}; + +class FusedFastGeluMulGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedFastGeluMulGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // (in, multiplier) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // (out,) + ctx->requires_grad = inputs.at(0)->requires_grad() || inputs.at(1)->requires_grad(); + if (ctx->requires_grad) { + ctx->SaveTensorForBackward(inputs.at(0)); // in + ctx->SaveTensorForBackward(inputs.at(1)); // multiplier + } + return Maybe::Ok(); + } + + Maybe Apply(const FusedFastGeluMulGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + if (!ctx->requires_grad) { return Maybe::Ok(); } + + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& out_diff = out_grads.at(0); + + const auto& saved_tensors = ctx->SavedTensors(); + CHECK_EQ_OR_RETURN(saved_tensors.size(), 2); + const auto& in = saved_tensors.at(0); + const auto& multiplier = saved_tensors.at(1); + + in_grads->resize(2); // (in_diff, multiplier_diff) + auto result = JUST(functional::FusedFastGeluMulGrad(out_diff, in, multiplier)); + CHECK_EQ_OR_RETURN(result->size(), 2); + in_grads->at(0) = result->at(0); + in_grads->at(1) = result->at(1); + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_fast_gelu_mul", FusedFastGeluMulGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_boundding_boxes_coord.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_boundding_boxes_coord.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7555efdb146a3858ec71d5f19f16e549f98aa718 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_boundding_boxes_coord.cpp @@ -0,0 +1,71 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +const int32_t INPUT_LEN = 8; +struct FusedGetBounddingBoxesCoordGradCaptureState : public AutoGradCaptureState { + std::vector requires_grad; +}; + +class FusedGetBounddingBoxesCoordGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedGetBounddingBoxesCoordGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), INPUT_LEN); + CHECK_EQ_OR_RETURN(outputs.size(), INPUT_LEN); + for (int i = 0; i < INPUT_LEN; i++) { + ctx->requires_grad.push_back(inputs.at(i)->requires_grad()); + } + + return Maybe::Ok(); + } + + Maybe Apply(const FusedGetBounddingBoxesCoordGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), INPUT_LEN); + const auto& b1_x1_diff = out_grads.at(0); + const auto& b1_x2_diff = out_grads.at(1); + const auto& b1_y1_diff = out_grads.at(2); + const auto& b1_y2_diff = out_grads.at(3); + const auto& b2_x1_diff = out_grads.at(4); + const auto& b2_x2_diff = out_grads.at(5); + const auto& b2_y1_diff = out_grads.at(6); + const auto& b2_y2_diff = out_grads.at(7); + + in_grads->resize(8); + auto result = JUST(functional::FusedGetBounddingBoxesCoordGrad( + b1_x1_diff, b1_x2_diff, b1_y1_diff, b1_y2_diff, b2_x1_diff, b2_x2_diff, b2_y1_diff, + b2_y2_diff)); + CHECK_EQ_OR_RETURN(result->size(), INPUT_LEN); + for (int i = 0; i < result->size(); i++) { + if (ctx->requires_grad[i]) { in_grads->at(i) = result->at(i); } + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_boundding_boxes_coord", FusedGetBounddingBoxesCoordGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_ciou_diagonal_angle.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_ciou_diagonal_angle.cpp new file mode 100644 index 0000000000000000000000000000000000000000..61f45e8b55536b44b004d2b5886dbad0eb5e881e --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_ciou_diagonal_angle.cpp @@ -0,0 +1,73 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +const int32_t INPUT_LEN = 4; +struct FusedCiouAngleCaptureState : public AutoGradCaptureState { + std::vector requires_grad; + float eps = 1e-8; +}; + +class FusedGetCiouDiagonalAngleGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedCiouAngleCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), INPUT_LEN); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + + for (int i = 0; i < INPUT_LEN; i++) { + ctx->requires_grad.push_back(inputs.at(i)->requires_grad()); + } + for (int i = 0; i < INPUT_LEN; i++) { ctx->SaveTensorForBackward(inputs.at(i)); } + + ComposedAttrMap composed_attrs(attrs); + ctx->eps = JUST(composed_attrs.GetAttr("eps")); + + return Maybe::Ok(); + } + + Maybe Apply(const FusedCiouAngleCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& v_diff = out_grads.at(0); + + const auto& w1 = ctx->SavedTensors().at(0); + const auto& h1 = ctx->SavedTensors().at(1); + const auto& w2 = ctx->SavedTensors().at(2); + const auto& h2 = ctx->SavedTensors().at(3); + + auto result = JUST(functional::FusedGetCiouDiagonalAngleGrad(w1, h1, w2, h2, v_diff, ctx->eps)); + CHECK_EQ_OR_RETURN(result->size(), INPUT_LEN); + + in_grads->resize(INPUT_LEN); + for (int i = 0; i < INPUT_LEN; i++) { + if (ctx->requires_grad[i]) { in_grads->at(i) = result->at(i); } + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_ciou_diagonal_angle", FusedGetCiouDiagonalAngleGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_ciou_result.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_ciou_result.cpp new file mode 100644 index 0000000000000000000000000000000000000000..014425e6cb0e7c1cf1bdd0b09fd7126606539a05 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_ciou_result.cpp @@ -0,0 +1,82 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct FusedGetCiouResultGradCaptureState : public AutoGradCaptureState { + bool v_requires_grad = false; + bool iou_requires_grad = false; + bool rho2_requires_grad = false; + bool c2_requires_grad = false; +}; + +class FusedGetCiouResultGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedGetCiouResultGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 4); + CHECK_EQ_OR_RETURN(outputs.size(), 2); + ctx->v_requires_grad = inputs.at(0)->requires_grad(); + ctx->iou_requires_grad = inputs.at(1)->requires_grad(); + ctx->rho2_requires_grad = inputs.at(2)->requires_grad(); + ctx->c2_requires_grad = inputs.at(3)->requires_grad(); + if (ctx->v_requires_grad && ctx->iou_requires_grad && ctx->rho2_requires_grad + && ctx->c2_requires_grad) { + ctx->SaveTensorForBackward(outputs.at(1)); // alpha + ctx->SaveTensorForBackward(inputs.at(2)); // rho2 + ctx->SaveTensorForBackward(inputs.at(3)); // c2 + } + return Maybe::Ok(); + } + + Maybe Apply(const FusedGetCiouResultGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 2); + const auto& dy = out_grads.at(0); + + const auto& saved_tensors = ctx->SavedTensors(); + CHECK_EQ_OR_RETURN(saved_tensors.size(), 3); + const auto& alpha = saved_tensors.at(0); + const auto& rho2 = saved_tensors.at(1); + const auto& c2 = saved_tensors.at(2); + + in_grads->resize(4); + auto result = JUST(functional::FusedGetCiouResultGrad(dy, alpha, rho2, c2)); + CHECK_EQ_OR_RETURN(result->size(), 4); + if (ctx->v_requires_grad && ctx->iou_requires_grad && ctx->rho2_requires_grad + && ctx->c2_requires_grad) { + in_grads->at(0) = result->at(0); + in_grads->at(1) = result->at(1); + in_grads->at(2) = result->at(2); + in_grads->at(3) = result->at(3); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_ciou_result", FusedGetCiouResultGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_convex_diagonal_squared.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_convex_diagonal_squared.cpp new file mode 100644 index 0000000000000000000000000000000000000000..899e54999ee54e345c5d623a3827f1fb48caaa80 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_convex_diagonal_squared.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +const int32_t INPUT_LEN = 8; +struct FusedGetConvexDiagonalSquaredCaptureState : public AutoGradCaptureState { + std::vector requires_grad; + float eps = 1e-8; +}; + +class FusedGetConvexDiagonalSquaredGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(FusedGetConvexDiagonalSquaredCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), INPUT_LEN); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + for (int i = 0; i < INPUT_LEN; i++) { + ctx->requires_grad.push_back(inputs.at(i)->requires_grad()); + } + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->eps = JUST(composed_attrs.GetAttr("eps")); + for (int i = 0; i < INPUT_LEN; i++) { ctx->SaveTensorForBackward(inputs.at(i)); } + + return Maybe::Ok(); + } + + Maybe Apply(const FusedGetConvexDiagonalSquaredCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& c2_diff = out_grads.at(0); + + const auto& b1_x1 = ctx->SavedTensors().at(0); + const auto& b1_x2 = ctx->SavedTensors().at(1); + const auto& b2_x1 = ctx->SavedTensors().at(2); + const auto& b2_x2 = ctx->SavedTensors().at(3); + const auto& b1_y1 = ctx->SavedTensors().at(4); + const auto& b1_y2 = ctx->SavedTensors().at(5); + const auto& b2_y1 = ctx->SavedTensors().at(6); + const auto& b2_y2 = ctx->SavedTensors().at(7); + + in_grads->resize(INPUT_LEN); + auto result = JUST(functional::FusedGetConvexDiagonalSquaredGrad( + c2_diff, b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, ctx->eps)); + + CHECK_EQ_OR_RETURN(result->size(), INPUT_LEN); + for (int i = 0; i < INPUT_LEN; i++) { + if (ctx->requires_grad[i]) { in_grads->at(i) = result->at(i); } + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_convex_diagonal_squared", + FusedGetConvexDiagonalSquaredGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_intersection_area.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_intersection_area.cpp new file mode 100644 index 0000000000000000000000000000000000000000..659eb55f40ef2a576ce82bd3710ea987447177dd --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_intersection_area.cpp @@ -0,0 +1,73 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +const int32_t INPUT_LEN = 8; +struct FusedGetIntersectionAreaCaptureState : public AutoGradCaptureState { + std::vector requires_grad; +}; + +class FusedGetIntersectionAreaGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedGetIntersectionAreaCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), INPUT_LEN); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + for (int i = 0; i < INPUT_LEN; i++) { + ctx->requires_grad.push_back(inputs.at(i)->requires_grad()); + } + for (int i = 0; i < INPUT_LEN; i++) { ctx->SaveTensorForBackward(inputs.at(i)); } + + return Maybe::Ok(); + } + + Maybe Apply(const FusedGetIntersectionAreaCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& rho2_diff = out_grads.at(0); + + const auto& b1_x1 = ctx->SavedTensors().at(0); + const auto& b1_x2 = ctx->SavedTensors().at(1); + const auto& b2_x1 = ctx->SavedTensors().at(2); + const auto& b2_x2 = ctx->SavedTensors().at(3); + const auto& b1_y1 = ctx->SavedTensors().at(4); + const auto& b1_y2 = ctx->SavedTensors().at(5); + const auto& b2_y1 = ctx->SavedTensors().at(6); + const auto& b2_y2 = ctx->SavedTensors().at(7); + + in_grads->resize(INPUT_LEN); + auto result = JUST(functional::FusedGetIntersectionAreaGrad(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, + b1_y2, b2_y1, b2_y2, rho2_diff)); + + CHECK_EQ_OR_RETURN(result->size(), INPUT_LEN); + for (int i = 0; i < INPUT_LEN; i++) { + if (ctx->requires_grad[i]) { in_grads->at(i) = result->at(i); } + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_intersection_area", FusedGetIntersectionAreaGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_get_iou.cpp b/oneflow/core/autograd/gradient_funcs/fused_get_iou.cpp new file mode 100644 index 0000000000000000000000000000000000000000..87664fae8b51bea97e753b737577142337eb6965 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_get_iou.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/placed_nd_sbp.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct FusedGetIouGradCaptureState : public AutoGradCaptureState { + bool requires_grad = true; + float eps = 1e-8; +}; + +class FusedGetIouGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(FusedGetIouGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 5); + CHECK_EQ_OR_RETURN(outputs.size(), 1); + ctx->requires_grad = inputs.at(0)->requires_grad() && inputs.at(1)->requires_grad() + && inputs.at(4)->requires_grad(); + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->eps = JUST(composed_attrs.GetAttr("eps")); + if (ctx->requires_grad) { + ctx->SaveTensorForBackward(inputs.at(0)); // w1 + ctx->SaveTensorForBackward(inputs.at(1)); // h1 + ctx->SaveTensorForBackward(inputs.at(2)); // w2 + ctx->SaveTensorForBackward(inputs.at(3)); // h2 + ctx->SaveTensorForBackward(inputs.at(4)); // inter + } + return Maybe::Ok(); + } + + Maybe Apply(const FusedGetIouGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); + const auto& diou = out_grads.at(0); + + const auto& saved_tensors = ctx->SavedTensors(); + CHECK_EQ_OR_RETURN(saved_tensors.size(), 5); + const auto& w1 = saved_tensors.at(0); + const auto& h1 = saved_tensors.at(1); + const auto& w2 = saved_tensors.at(2); + const auto& h2 = saved_tensors.at(3); + const auto& inter = saved_tensors.at(4); + + in_grads->resize(5); + auto result = JUST(functional::FusedGetIouGrad(diou, w1, h1, w2, h2, inter, ctx->eps)); + CHECK_EQ_OR_RETURN(result->size(), 3); + if (ctx->requires_grad) { + in_grads->at(0) = result->at(0); + in_grads->at(1) = result->at(1); + in_grads->at(4) = result->at(2); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_get_iou", FusedGetIouGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_matmul_bias.cpp b/oneflow/core/autograd/gradient_funcs/fused_matmul_bias.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5d4b0991f721d585e0e1a3758bb6debfa181e466 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_matmul_bias.cpp @@ -0,0 +1,92 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { + +namespace one { + +struct FusedMatmulBiasCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool weight_requires_grad = false; + bool bias_requires_grad = false; +}; + +class FusedMatmulBias : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(FusedMatmulBiasCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const FusedMatmulBiasCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + protected: + AttrMap base_attrs_; +}; + +Maybe FusedMatmulBias::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe FusedMatmulBias::Capture(FusedMatmulBiasCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + CHECK_GE_OR_RETURN(inputs.size(), 3) + << "x, weight, and bias, [add_to_output] should all be included"; + ctx->x_requires_grad = JUST(VectorAt(inputs, 0))->requires_grad(); + ctx->weight_requires_grad = JUST(VectorAt(inputs, 1))->requires_grad(); + ctx->bias_requires_grad = JUST(VectorAt(inputs, 2))->requires_grad(); + + ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); + ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); + + return Maybe::Ok(); +} + +Maybe FusedMatmulBias::Apply(const FusedMatmulBiasCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "FusedMatmulBias more than one output"; + const auto& x = ctx->SavedTensors().at(0); + const auto& weight = ctx->SavedTensors().at(1); + + if (ctx->x_requires_grad) { + in_grads->at(0) = + JUST(functional::MatMul(JUST(VectorAt(out_grads, 0)), weight, false, false, 1.0)); + } + if (ctx->weight_requires_grad) { + in_grads->at(1) = JUST(functional::BroadcastMatmulGradB(JUST(VectorAt(out_grads, 0)), x, 1.0)); + } + if (ctx->bias_requires_grad) { + const int64_t num_axes = out_grads.at(0)->shape()->NumAxes(); + std::vector reduce_axes_vec; + reduce_axes_vec.reserve(num_axes - 1); + for (int i = 0; i < num_axes - 1; i++) { reduce_axes_vec.push_back(i); } + in_grads->at(2) = + JUST(functional::ReduceSum(JUST(VectorAt(out_grads, 0)), reduce_axes_vec, false)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_matmul_bias", FusedMatmulBias); + +} // namespace one + +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/fused_matmul_bias_add_relu_dropout.cpp b/oneflow/core/autograd/gradient_funcs/fused_matmul_bias_add_relu_dropout.cpp index 32e38b0da187da3e70a726d74d249a8dd5d69d10..f7eb910ceed557225b8af2613419fc387de5ea90 100644 --- a/oneflow/core/autograd/gradient_funcs/fused_matmul_bias_add_relu_dropout.cpp +++ b/oneflow/core/autograd/gradient_funcs/fused_matmul_bias_add_relu_dropout.cpp @@ -84,7 +84,7 @@ Maybe FusedMatmulBiasAddReluDropout::Capture(FusedMatmulBiasAddReluDropout ctx->SaveTensorForBackward( JUST(VectorAt(outputs, i + 1))); // cublas aux. need minus 1. idx_sum:2+2w } - for (int32_t i = 0; i < weight_num - 1; i++) { + for (int32_t i = 0; i < weight_num; i++) { ctx->SaveTensorForBackward(JUST(VectorAt(outputs, i + 1 + weight_num))); // hidden. } @@ -101,7 +101,7 @@ Maybe FusedMatmulBiasAddReluDropout::Apply( int32_t weight_num = ctx->weight_num; in_grads->resize(1 + 2 * weight_num); - TensorTuple hiddens(weight_num - 1); + TensorTuple hiddens(weight_num); TensorTuple weights(weight_num); TensorTuple cublas_auxs(weight_num); TensorTuple dgrad(weight_num); @@ -117,9 +117,10 @@ Maybe FusedMatmulBiasAddReluDropout::Apply( cublas_auxs[i] = JUST(VectorAt(ctx->SavedTensors(), i + 2 + weight_num)); } - for (int32_t i = 0; i < weight_num - 1; ++i) { + for (int32_t i = 0; i < weight_num; ++i) { hiddens[i] = JUST(VectorAt(ctx->SavedTensors(), i + 2 + 2 * weight_num)); } + float rate = ctx->dropout_rate_list.at(weight_num - 1); float scale = 0.0f; if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } @@ -136,62 +137,93 @@ Maybe FusedMatmulBiasAddReluDropout::Apply( cublas_auxs[weight_num - 1], scale)); } - // step2: use reduce_sum to get last layer's bias grad. - std::vector reduce_axes_vec{0}; - if (JUST(VectorAt(ctx->biases_requires_grad, weight_num - 1))) { - JUST(VectorAt(*in_grads, 2 * weight_num)) = - JUST(functional::ReduceSum(last_bias_dy, reduce_axes_vec, false)); - } + if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSED_MLP_ASYNC_GRAD", false)) { + std::vector alpha_list(weight_num - 1, 1.0); + for (int i = 0; i < weight_num - 1; i++) { + rate = ctx->dropout_rate_list.at(i); + scale = 1.0; + if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } + alpha_list.at(i) = scale; + } + const auto& fused_mlp_grad = + JUST(functional::FusedMLPGrad(last_bias_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), weights, + cublas_auxs, hiddens, alpha_list)); + if (ctx->x_requires_grad) { + // dx: + JUST(VectorAt(*in_grads, 0)) = fused_mlp_grad->at(0); + } - std::shared_ptr cublas_dy = last_bias_dy; - for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { - // If it is final layer, we use out_grads[0] as dy. - if (hidden_layer_idx != weight_num - 1) { - cublas_dy = JUST(VectorAt(dgrad, hidden_layer_idx + 1)); + for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > -1; hidden_layer_idx--) { + if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx)))) { + // dbias + JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx + 1)) = + fused_mlp_grad->at(1 + hidden_layer_idx); // NOLINT + } + + // dw + if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { + JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = + fused_mlp_grad->at(1 + weight_num + hidden_layer_idx); + } } - rate = ctx->dropout_rate_list.at(hidden_layer_idx - 1); - scale = 1.0; - if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } - /* - Here we use cublas to compute bias + relu + matmul grad. - Then use Matmul to compute weight grad. - */ - const auto& matmul_relu_bias_bgrad = JUST(functional::CublasBiasAddReluMatmulGrad( - cublas_dy, JUST(VectorAt(weights, hidden_layer_idx)), - JUST(VectorAt(cublas_auxs, hidden_layer_idx - 1)), /*alpha=*/scale)); - - // dgrad - dgrad.at(hidden_layer_idx) = matmul_relu_bias_bgrad->at(0); // NOLINT - - if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx - 1)))) { - // dbias - JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx)) = - matmul_relu_bias_bgrad->at(1); // NOLINT + } else { + // step2: use reduce_sum to get last layer's bias grad. + std::vector reduce_axes_vec{0}; + if (JUST(VectorAt(ctx->biases_requires_grad, weight_num - 1))) { + JUST(VectorAt(*in_grads, 2 * weight_num)) = + JUST(functional::ReduceSum(last_bias_dy, reduce_axes_vec, false)); } - // dw - if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { - JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = JUST(functional::MatMul( - cublas_dy, JUST(VectorAt(hiddens, hidden_layer_idx - 1)), true, false, 1.0)); + + std::shared_ptr cublas_dy = last_bias_dy; + for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { + // If it is final layer, we use out_grads[0] as dy. + if (hidden_layer_idx != weight_num - 1) { + cublas_dy = JUST(VectorAt(dgrad, hidden_layer_idx + 1)); + } + rate = ctx->dropout_rate_list.at(hidden_layer_idx - 1); + scale = 1.0; + if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } + /* + Here we use cublas to compute bias + relu + matmul grad. + Then use Matmul to compute weight grad. + */ + const auto& matmul_relu_bias_bgrad = JUST(functional::CublasBiasAddReluMatmulGrad( + cublas_dy, JUST(VectorAt(weights, hidden_layer_idx)), + JUST(VectorAt(cublas_auxs, hidden_layer_idx - 1)), /*alpha=*/scale)); + + // dgrad + dgrad.at(hidden_layer_idx) = matmul_relu_bias_bgrad->at(0); // NOLINT + + if (JUST(VectorAt(ctx->biases_requires_grad, (hidden_layer_idx - 1)))) { + // dbias + JUST(VectorAt(*in_grads, weight_num + hidden_layer_idx)) = + matmul_relu_bias_bgrad->at(1); // NOLINT + } + // dw + if (JUST(VectorAt(ctx->weights_requires_grad, hidden_layer_idx))) { + JUST(VectorAt(*in_grads, (1 + hidden_layer_idx))) = JUST(functional::MatMul( + cublas_dy, JUST(VectorAt(hiddens, hidden_layer_idx - 1)), true, false, 1.0)); + } } - } - // For the first layer, we need to use 2 matmul to get grads. - std::shared_ptr last_dy; - if (weight_num != 1) { - last_dy = JUST(VectorAt(dgrad, 1)); - } else { - last_dy = last_bias_dy; - } + // For the first layer, we need to use 2 matmul to get grads. + std::shared_ptr last_dy; + if (weight_num != 1) { + last_dy = JUST(VectorAt(dgrad, 1)); + } else { + last_dy = last_bias_dy; + } - if (ctx->x_requires_grad) { - // dx: - JUST(VectorAt(*in_grads, 0)) = - JUST(functional::MatMul(last_dy, JUST(VectorAt(weights, 0)), false, false, 1.0)); - } - if (JUST(VectorAt(ctx->weights_requires_grad, 0))) { - // dw: - JUST(VectorAt(*in_grads, 1)) = - JUST(functional::MatMul(last_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), true, false, 1.0)); + if (ctx->x_requires_grad) { + // dx: + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::MatMul(last_dy, JUST(VectorAt(weights, 0)), false, false, 1.0)); + } + if (JUST(VectorAt(ctx->weights_requires_grad, 0))) { + // dw: + JUST(VectorAt(*in_grads, 1)) = JUST( + functional::MatMul(last_dy, JUST(VectorAt(ctx->SavedTensors(), 0)), true, false, 1.0)); + } } return Maybe::Ok(); diff --git a/oneflow/core/autograd/gradient_funcs/fused_weighted_sum.cpp b/oneflow/core/autograd/gradient_funcs/fused_weighted_sum.cpp new file mode 100644 index 0000000000000000000000000000000000000000..33dd55ff3dc9064d0e4786522a1c218ca6d08667 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/fused_weighted_sum.cpp @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" + +namespace oneflow { +namespace one { + +struct FusedWeightedSumCaptureState : public AutoGradCaptureState { + std::vector requires_grad; + std::vector weights; + float alpha{}; +}; + +class FusedWeightedSum : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(FusedWeightedSumCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + ctx->requires_grad.resize(inputs.size()); + ctx->weights = JUST(attrs.GetAttr>("weights")); + ctx->alpha = JUST(attrs.GetAttr("alpha")); + CHECK_EQ_OR_RETURN(ctx->weights.size(), inputs.size()); + for (int i = 0; i < inputs.size(); ++i) { ctx->requires_grad[i] = inputs[i]->requires_grad(); } + return Maybe::Ok(); + } + + Maybe Apply(const FusedWeightedSumCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(ctx->requires_grad.size()); + for (int i = 0; i < ctx->requires_grad.size(); ++i) { + if (ctx->requires_grad[i]) { + (*in_grads)[i] = + JUST(functional::ScalarMul(out_grads[0], ctx->weights[i] * ctx->alpha, false)); + } + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("fused_weighted_sum", FusedWeightedSum); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/global_cast.cpp b/oneflow/core/autograd/gradient_funcs/global_cast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..781456fd5202a80eeb65db99feb7ab006907c511 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/global_cast.cpp @@ -0,0 +1,117 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/mutable_attr_map.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" +#include "oneflow/core/framework/tensor_rpc_util.h" +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct CastGlobalCaptureState : public AutoGradCaptureState { + Symbol parallel_desc; + Symbol nd_sbp; + std::shared_ptr shape; + Symbol dtype; +}; + +class LocalToGlobal : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + const std::string& op_name = fw_op_expr->op_name(); + grad_op_ = JUST(one::GlobalToLocalOpExpr::New(GradientOpName(op_name))); + return Maybe::Ok(); + } + + Maybe Capture(CastGlobalCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, + const OpExprInterpContext& interp_ctx) const override { + ctx->parallel_desc = JUST(interp_ctx.parallel_desc); + ctx->nd_sbp = JUST(GetDualNdSbp(JUST(interp_ctx.nd_sbp))); + return Maybe::Ok(); + } + + Maybe Apply(const CastGlobalCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + std::shared_ptr out_grad = out_grads.at(0); + CHECK_OR_RETURN(out_grad->is_global()) + << Error::RuntimeError() + << "Expected global tensor for local_to_global but got local tensor"; + { + Symbol nd_sbp_constraint = ctx->nd_sbp; + Symbol parallel_desc_constraint = ctx->parallel_desc; + out_grad = JUST(functional::ToGlobal(out_grad, parallel_desc_constraint, + *JUST(GetSbpList(nd_sbp_constraint)), GetNoneSbpList(), + /* check_meta */ false, /*copy=*/false)); + } + in_grads->at(0) = JUST(OpInterpUtil::Dispatch(*grad_op_, {out_grad})); + return Maybe::Ok(); + } + + private: + std::shared_ptr grad_op_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("local_to_global", LocalToGlobal); + +class GlobalToLocal : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + const std::string& op_name = fw_op_expr->op_name(); + grad_op_ = JUST(one::LocalToGlobalOpExpr::New(GradientOpName(op_name))); + return Maybe::Ok(); + } + + Maybe Capture(CastGlobalCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + const auto& input = inputs.at(0); + CHECK_OR_RETURN(input->is_global()) + << Error::RuntimeError() + << "Expected global tensor for global_to_local but got local tensor"; + ctx->parallel_desc = JUST(input->parallel_desc()); + ctx->nd_sbp = JUST(input->nd_sbp()); + ctx->shape = input->shape(); + ctx->dtype = input->dtype(); + return Maybe::Ok(); + } + + Maybe Apply(const CastGlobalCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + const auto& dual_nd_sbp = JUST(GetDualNdSbp(ctx->nd_sbp)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "sync_data"); + attrs.SetAllAttrs(*ctx->shape, ctx->dtype->data_type(), true); + in_grads->at(0) = JUST(OpInterpUtil::Dispatch( + *grad_op_, {out_grads.at(0)}, OpExprInterpContext(attrs, ctx->parallel_desc, dual_nd_sbp))); + return Maybe::Ok(); + } + + private: + std::shared_ptr grad_op_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("global_to_local", GlobalToLocal); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/global_to_global.cpp b/oneflow/core/autograd/gradient_funcs/global_to_global.cpp new file mode 100644 index 0000000000000000000000000000000000000000..10a8e409699e4f6ba166554a2e2028da4b991e35 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/global_to_global.cpp @@ -0,0 +1,81 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/id_util.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/optional.h" + +namespace oneflow { +namespace one { + +struct GlobalToGlobalState : public AutoGradCaptureState { + Symbol parallel_desc; + Symbol nd_sbp; +}; + +class GlobalToGlobalGradFunction : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + grad_nd_sbp_ = fw_op_expr->grad_nd_sbp(); + return Maybe::Ok(); + } + + Maybe Capture(GlobalToGlobalState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, + const OpExprInterpContext& interp_ctx) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->parallel_desc = JUST(inputs.at(0)->parallel_desc()); + ctx->nd_sbp = JUST(inputs.at(0)->nd_sbp()); + return Maybe::Ok(); + } + + Maybe Apply(const GlobalToGlobalState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + const auto& out_grad = out_grads.at(0); + CHECK_OR_RETURN(out_grad->is_global()) + << Error::RuntimeError() + << "Expected global tensor for global_to_global but got local tensor"; + in_grads->resize(1); + const auto& grad_nd_sbp = grad_nd_sbp_.value_or(JUST(out_grad->nd_sbp())); + const auto& grad_sbp_list = JUST(GetSbpList(grad_nd_sbp)); + + if (LazyMode::is_enabled()) { + (*in_grads)[0] = JUST(one::functional::ToGlobal(out_grad, ctx->parallel_desc, *grad_sbp_list, + {}, /* check_meta */ false, /*copy=*/false)); + } else { + const auto& grad_grad_sbp_list = JUST(GetSbpList(ctx->nd_sbp)); + (*in_grads)[0] = JUST(one::functional::ToGlobal(out_grad, ctx->parallel_desc, *grad_sbp_list, + *grad_grad_sbp_list, /* check_meta */ false, + /*copy=*/false)); + } + return Maybe::Ok(); + } + + private: + Optional> grad_nd_sbp_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("global_to_global", GlobalToGlobalGradFunction); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/gradient_accumulation.cpp b/oneflow/core/autograd/gradient_funcs/gradient_accumulation.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fd0c198007ae0a2176af13fe951f69b6d8fbeee7 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/gradient_accumulation.cpp @@ -0,0 +1,184 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct GradAccRepeatCaptureState : public AutoGradCaptureState { + int32_t repeat_num = 1; +}; + +class GradAccRepeat : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(GradAccRepeatCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const GradAccRepeatCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe GradAccRepeat::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe GradAccRepeat::Capture(GradAccRepeatCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->repeat_num = JUST(composed_attrs.GetAttr("repeat_num")); + return Maybe::Ok(); +} + +Maybe GradAccRepeat::Apply(const GradAccRepeatCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + (*in_grads)[0] = JUST(functional::GradAccCollect(out_grads[0], ctx->repeat_num)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("repeat", GradAccRepeat); + +struct GradAccCollectCaptureState : public AutoGradCaptureState { + int32_t max_acc_num = 1; +}; + +class GradAccCollect : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(GradAccCollectCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const GradAccCollectCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe GradAccCollect::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe GradAccCollect::Capture(GradAccCollectCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->max_acc_num = JUST(composed_attrs.GetAttr("max_acc_num")); + return Maybe::Ok(); +} + +Maybe GradAccCollect::Apply(const GradAccCollectCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + (*in_grads)[0] = JUST(functional::GradAccRepeat(out_grads[0], ctx->max_acc_num)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("acc", GradAccCollect); + +struct GradAccPackCaptureState : public AutoGradCaptureState { + int32_t pack_num = 1; +}; + +class GradAccPack : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(GradAccPackCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const GradAccPackCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe GradAccPack::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe GradAccPack::Capture(GradAccPackCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->pack_num = JUST(composed_attrs.GetAttr("pack_num")); + return Maybe::Ok(); +} + +Maybe GradAccPack::Apply(const GradAccPackCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + (*in_grads)[0] = JUST(functional::GradAccUnpack(out_grads[0], ctx->pack_num)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("pack", GradAccPack); + +struct GradAccUnpackCaptureState : public AutoGradCaptureState { + int32_t unpack_num = 1; +}; + +class GradAccUnpack : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(GradAccUnpackCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const GradAccUnpackCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe GradAccUnpack::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe GradAccUnpack::Capture(GradAccUnpackCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->unpack_num = JUST(composed_attrs.GetAttr("unpack_num")); + return Maybe::Ok(); +} + +Maybe GradAccUnpack::Apply(const GradAccUnpackCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + (*in_grads)[0] = JUST(functional::GradAccPack(out_grads[0], ctx->unpack_num)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("unpack", GradAccUnpack); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/graph_feed_and_fetch.cpp b/oneflow/core/autograd/gradient_funcs/graph_feed_and_fetch.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c346af6e2d980b61b9672a421ec95a88a8ab0d45 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/graph_feed_and_fetch.cpp @@ -0,0 +1,49 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/job/lazy_mode.h" + +namespace oneflow { +namespace one { + +struct GraphFeedAndFetchCaptureState : public AutoGradCaptureState { + bool requires_grad = false; +}; + +class GraphFeedAndFetch : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(GraphFeedAndFetchCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = inputs.at(0)->requires_grad(); + return Maybe::Ok(); + } + + Maybe Apply(const GraphFeedAndFetchCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + if (ctx->requires_grad) { in_grads->at(0) = out_grads.at(0); } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("graph_feed_and_fetch", GraphFeedAndFetch); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/group_norm.cpp b/oneflow/core/autograd/gradient_funcs/group_norm.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ad77c8ee074bb2814753d2bc3bcdc69026772632 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/group_norm.cpp @@ -0,0 +1,122 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct GroupNormCaptureState : public AutoGradCaptureState { + double epsilon = 1e-5; + bool x_requires_grad = true; + bool affine = true; + int32_t num_groups = 1; + size_t x_index = 0; + size_t mean_index = 1; + size_t inv_variance_index = 2; + size_t gamma_index = 3; + std::string data_format; + std::string activation; +}; + +class GroupNorm : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + + Maybe Capture(GroupNormCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + + Maybe Apply(const GroupNormCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; + std::string op_name_; +}; + +Maybe GroupNorm::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + op_name_ = fw_op_expr->op_name(); + return Maybe::Ok(); +} + +Maybe GroupNorm::Capture(GroupNormCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->affine = JUST(composed_attrs.GetAttr("affine")); + ctx->epsilon = JUST(composed_attrs.GetAttr("epsilon")); + ctx->num_groups = JUST(composed_attrs.GetAttr("num_groups")); + ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); + ctx->activation = JUST(composed_attrs.GetAttr("activation")); + if (ctx->affine) { + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + } else { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + } + CHECK_EQ_OR_RETURN(outputs.size(), 3); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + if (ctx->x_requires_grad || ctx->affine) { + ctx->x_index = ctx->SaveTensorForBackward(inputs.at(0)); + ctx->mean_index = ctx->SaveTensorForBackward(outputs.at(1)); + ctx->inv_variance_index = ctx->SaveTensorForBackward(outputs.at(2)); + if (ctx->affine) { + ctx->gamma_index = ctx->SaveTensorForBackward(inputs.at(1)); // save gamma. + } + } + return Maybe::Ok(); +} + +Maybe GroupNorm::Apply(const GroupNormCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(ctx->data_format, "channels_first"); + CHECK_EQ_OR_RETURN(ctx->activation, "none"); + const auto& saved_tensors = ctx->SavedTensors(); + if (ctx->affine) { + in_grads->resize(3); + } else { + in_grads->resize(1); + } + const auto& dy = out_grads.at(0); + const auto& x = saved_tensors.at(ctx->x_index); + const auto& mean = saved_tensors.at(ctx->mean_index); + const auto& inv_variance = saved_tensors.at(ctx->inv_variance_index); + + if (ctx->affine) { + const auto& results = JUST(functional::GroupNormParamGrad(dy, x, mean, inv_variance)); + in_grads->at(1) = results->at(0); // For gamma. + in_grads->at(2) = results->at(1); // For beta. + } + if (ctx->x_requires_grad) { + if (ctx->affine) { + std::shared_ptr gamma = saved_tensors.at(ctx->gamma_index); + in_grads->at(0) = JUST(functional::GroupNormGrad(dy, x, mean, inv_variance, gamma, + ctx->num_groups, ctx->epsilon)); + } else { + in_grads->at(0) = JUST(functional::GroupNormGrad(dy, x, mean, inv_variance, NullOpt, + ctx->num_groups, ctx->epsilon)); + } + } + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("group_norm", GroupNorm); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/identity.cpp b/oneflow/core/autograd/gradient_funcs/identity.cpp index 83ad45b13d985478434c99ed1bfeba0e2fd0ab33..c25e7df9aa22da64dfa7e7332bd1b6036479ee85 100644 --- a/oneflow/core/autograd/gradient_funcs/identity.cpp +++ b/oneflow/core/autograd/gradient_funcs/identity.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { namespace one { @@ -37,7 +38,15 @@ class Identity : public OpExprGradFunction { TensorTuple* in_grads) const override { CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) in_grads->resize(1); - if (ctx->requires_grad) { in_grads->at(0) = out_grads.at(0); } + if (ctx->requires_grad) { + if (LazyMode::is_enabled()) { + // requires an intermediate node to avoid redundant memory copy or commnet + // communication in lazy mode + in_grads->at(0) = JUST(functional::Identity(out_grads.at(0))); + } else { + in_grads->at(0) = out_grads.at(0); + } + } return Maybe::Ok(); } }; diff --git a/oneflow/core/autograd/gradient_funcs/inv.cpp b/oneflow/core/autograd/gradient_funcs/inv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4aefd45c8f4d718d3815f47f77b1fae90c8261a8 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/inv.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct InvCaptureState : public AutoGradCaptureState { + bool requires_grad = false; +}; + +class Inv : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(InvCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override { + ctx->requires_grad = JUST(VectorAt(inputs, 0))->requires_grad(); + if (ctx->requires_grad) { ctx->SaveTensorForBackward(JUST(VectorAt(outputs, 0))); } + return Maybe::Ok(); + } + Maybe Apply(const InvCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + if (ctx->requires_grad) { + const auto& output = JUST(VectorAt(ctx->SavedTensors(), 0)); + const auto& dy = JUST(VectorAt(out_grads, 0)); + JUST(VectorAt(*in_grads, 0)) = JUST(functional::Negative(JUST(functional::MatMul( + output, JUST(functional::MatMul(dy, output, false, true, 1.0)), true, false, 1.0)))); + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("inv", Inv); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/kl_div.cpp b/oneflow/core/autograd/gradient_funcs/kl_div.cpp index 60d8f7d3f5750c53688a1513a3d37968ad2d8018..ac17b22653173d919d034523f4838c3076db6f1e 100644 --- a/oneflow/core/autograd/gradient_funcs/kl_div.cpp +++ b/oneflow/core/autograd/gradient_funcs/kl_div.cpp @@ -20,7 +20,8 @@ namespace oneflow { namespace one { struct KLDivLossCaptureState : public AutoGradCaptureState { - bool requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; bool log_target = false; }; @@ -44,25 +45,31 @@ Maybe KLDivLoss::Init(const OpExpr& op) { } Maybe KLDivLoss::Capture(KLDivLossCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ctx->requires_grad = inputs.at(0)->requires_grad(); - if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = inputs[0]->requires_grad(); + ctx->target_requires_grad = inputs[1]->requires_grad(); ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->log_target = JUST(composed_attrs.GetAttr("log_target")); - ctx->SaveTensorForBackward(inputs.at(0)); // input - ctx->SaveTensorForBackward(inputs.at(1)); // target + ctx->SaveTensorForBackward(inputs[0]); // input + ctx->SaveTensorForBackward(inputs[1]); // target return Maybe::Ok(); } Maybe KLDivLoss::Apply(const KLDivLossCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), 2); // NOLINT(maybe-need-error-msg) + const auto& dy = out_grads[0]; + const auto& input = ctx->SavedTensors()[0]; + const auto& target = ctx->SavedTensors()[1]; + in_grads->resize(2); - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - const auto& dy = out_grads.at(0); - const auto& input = ctx->SavedTensors().at(0); - const auto& target = ctx->SavedTensors().at(1); - in_grads->resize(ctx->SavedTensors().size()); - in_grads->at(0) = JUST(functional::KLDivLossGrad(dy, input, target, ctx->log_target)); + if (ctx->input_requires_grad) { + (*in_grads)[0] = JUST(functional::KLDivLossGrad(dy, input, target, ctx->log_target)); + } + if (ctx->target_requires_grad) { + (*in_grads)[1] = JUST(functional::KLDivLossTargetGrad(dy, input, target, ctx->log_target)); + } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/layer_norm.cpp b/oneflow/core/autograd/gradient_funcs/layer_norm.cpp index 996750022e1f455c314a0de08cc2f96692a4e8d6..4a0835247ee3390ad102c333e87108d2bcc50028 100644 --- a/oneflow/core/autograd/gradient_funcs/layer_norm.cpp +++ b/oneflow/core/autograd/gradient_funcs/layer_norm.cpp @@ -108,10 +108,10 @@ Maybe LayerNorm::Apply(const LayerNormCaptureState* ctx, const TensorTuple std::shared_ptr inv_variance = saved_tensors.at(ctx->inv_variance_index); if (ctx->has_affine) { - // Use LayerNormParamGrad(Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Int64 - // begin_params_axis, Double epsilon). - const auto& results = JUST( - functional::LayerNormParamGrad(dy, x, mean, inv_variance, begin_params_axis, ctx->epsilon)); + // Use LayerNormParamGrad(Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, + // Int64 begin_params_axis) + const auto& results = + JUST(functional::LayerNormParamGrad(dy, x, mean, inv_variance, begin_params_axis)); in_grads->at(1) = results->at(0); // For gamma. in_grads->at(2) = results->at(1); // For beta. } diff --git a/oneflow/core/autograd/gradient_funcs/linalg_cross.cpp b/oneflow/core/autograd/gradient_funcs/linalg_cross.cpp new file mode 100644 index 0000000000000000000000000000000000000000..aa4c58a27e4148af0cdec2c014940cb49ad6cecc --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/linalg_cross.cpp @@ -0,0 +1,80 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/just.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional_api.yaml.h" + +namespace oneflow { +namespace one { + +struct LinalgCrossCaptureState : public AutoGradCaptureState { + int64_t dim = -1; + bool input_requires_grad = false; + bool other_requires_grad = false; +}; + +class LinalgCross : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(LinalgCrossCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const LinalgCrossCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe LinalgCross::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe LinalgCross::Capture(LinalgCrossCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ctx->input_requires_grad = inputs.at(0)->requires_grad(); + ctx->other_requires_grad = inputs.at(1)->requires_grad(); + + if (ctx->input_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); } + if (ctx->other_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->dim = JUST(composed_attrs.GetAttr("dim")); + return Maybe::Ok(); +} + +Maybe LinalgCross::Apply(const LinalgCrossCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + in_grads->resize(ctx->SavedTensors().size()); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + + if (ctx->input_requires_grad) { + in_grads->at(0) = + JUST(functional::LinalgCross(ctx->SavedTensors().at(0), out_grads.at(0), ctx->dim)); + } + if (ctx->other_requires_grad) { + in_grads->at(1) = JUST(functional::LinalgCross( + out_grads.at(0), ctx->SavedTensors().at(ctx->input_requires_grad ? 1 : 0), ctx->dim)); + } + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("linalg_cross", LinalgCross); + +} // namespace one +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/autograd/gradient_funcs/log_softmax.cpp b/oneflow/core/autograd/gradient_funcs/log_softmax.cpp index acf199514d6117af58e2fa2a5946e008b7c39ebb..0ba25a4a3c53e1d5242e77554cea3ce81903ad11 100644 --- a/oneflow/core/autograd/gradient_funcs/log_softmax.cpp +++ b/oneflow/core/autograd/gradient_funcs/log_softmax.cpp @@ -38,39 +38,23 @@ class LogSoftmax : public OpExprGradFunction { std::shared_ptr grad_op_; }; -Maybe LogSoftmax::Init(const OpExpr& op) { - const auto* fw_op_expr = dynamic_cast(&op); - CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) - const std::string& op_name = fw_op_expr->op_name(); - base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); - grad_op_ = JUST(one::OpBuilder("log_softmax_grad", GradientOpName(op_name)) - .Input("prob") - .Input("dy") - .Output("dx") - .Build()); - return Maybe::Ok(); -} +Maybe LogSoftmax::Init(const OpExpr& op) { return Maybe::Ok(); } Maybe LogSoftmax::Capture(LogSoftmaxCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const { - ComposedAttrMap composed_attrs(attrs, base_attrs_); CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) ctx->requires_grad = inputs.at(0)->requires_grad(); - - if (!ctx->requires_grad) return Maybe::Ok(); - ctx->SaveTensorForBackward(outputs.at(0)); return Maybe::Ok(); } Maybe LogSoftmax::Apply(const LogSoftmaxCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const { - if (!ctx->requires_grad) return Maybe::Ok(); CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) const auto& dy = out_grads.at(0); - const auto& prob = ctx->SavedTensors().at(0); + const auto& y = ctx->SavedTensors().at(0); in_grads->resize(1); - in_grads->at(0) = JUST(OpInterpUtil::Dispatch(*grad_op_, {prob, dy})); + in_grads->at(0) = JUST(functional::LogSoftmaxGrad(dy, y)); return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/math_unary_op.cpp b/oneflow/core/autograd/gradient_funcs/math_unary_op.cpp index aaa11fb7e28d140322f6ab04682676f5a4a82dbe..dabb1ae3c2a761f4da53ee6c7e510b8450a4cd83 100644 --- a/oneflow/core/autograd/gradient_funcs/math_unary_op.cpp +++ b/oneflow/core/autograd/gradient_funcs/math_unary_op.cpp @@ -30,7 +30,7 @@ typedef Maybe (*UnaryBwFunc)(const std::shared_ptr&, const std::shared_ptr&); template -class UnaryMathOp : public OpExprGradFunction { +class UnaryMathBwdWithDyXOp : public OpExprGradFunction { Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } Maybe Capture(UnaryMathCaptureState* ctx, const TensorTuple& inputs, @@ -52,20 +52,96 @@ class UnaryMathOp : public OpExprGradFunction { std::shared_ptr grad_op_; }; -#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS(op_type_name, op_cls) \ - class op_cls##Cls final : public UnaryMathOp {}; \ +template +class UnaryMathBwdWithDyYOp : public OpExprGradFunction { + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(UnaryMathCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->SaveTensorForBackward(outputs.at(0)); + return Maybe::Ok(); + } + + Maybe Apply(const UnaryMathCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + if (!ctx->x_requires_grad) { return Maybe::Ok(); } + const auto& y = ctx->SavedTensors().at(0); + in_grads->at(0) = JUST(BwFunc(y, out_grads.at(0))); + return Maybe::Ok(); + } + + protected: + std::shared_ptr grad_op_; +}; + +class UnaryMathBwdWithFillZeroOp : public OpExprGradFunction { + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(UnaryMathCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + return Maybe::Ok(); + } + + Maybe Apply(const UnaryMathCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + if (!ctx->x_requires_grad) { return Maybe::Ok(); } + in_grads->at(0) = JUST(functional::ZerosLike(out_grads[0])); + return Maybe::Ok(); + } + + protected: + std::shared_ptr grad_op_; +}; + +#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_X_CLASS(op_type_name, op_cls) \ + class op_cls##Cls final : public UnaryMathBwdWithDyXOp {}; \ REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, op_cls##Cls); -OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS, MATH_UNARY_ELEMENTWISE_FUNC_SEQ); -OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS, +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_X_CLASS, + MATH_UNARY_ELEMENTWISE_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ); +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_X_CLASS, OF_PP_MAKE_TUPLE_SEQ("tanh", Tanh)); -// higher order derivative -OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS, - OF_PP_MAKE_TUPLE_SEQ("sin_grad", SinGrad)); -OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS, - OF_PP_MAKE_TUPLE_SEQ("cos_grad", CosGrad)); +#undef INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_X_CLASS + +#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_Y_CLASS(op_type_name, op_cls) \ + class op_cls##Cls final : public UnaryMathBwdWithDyYOp {}; \ + REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, op_cls##Cls); + +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_Y_CLASS, + MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_DY_Y_SEQ); +#undef INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_DY_Y_CLASS + +#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_FILL_CLASS(op_type_name, op_cls) \ + class op_cls##Cls final : public UnaryMathBwdWithDyYOp {}; \ + REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, UnaryMathBwdWithFillZeroOp); + +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_FILL_CLASS, + MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_FILL_SEQ); +#undef INSTANTIAT_AND_REGISTER_UNARY_MATHOP_WITH_FILL_CLASS + +class NegativeOp : public OpExprGradFunction { + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(UnaryMathCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + return Maybe::Ok(); + } + + Maybe Apply(const UnaryMathCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + if (!ctx->x_requires_grad) { return Maybe::Ok(); } + in_grads->at(0) = JUST(functional::Negative(out_grads[0])); + return Maybe::Ok(); + } + + protected: + std::shared_ptr grad_op_; +}; +REGISTER_OP_EXPR_GRAD_FUNCTION("negative", NegativeOp); -#undef INSTANTIAT_AND_REGISTER_UNARY_MATHOP_CLASS } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/matmul.cpp b/oneflow/core/autograd/gradient_funcs/matmul.cpp index 5269f77acd2cced6e62689d7f0468a9b917f9e0e..96cb0e5a4ff9c18bfe4fb97771f59aabf451092d 100644 --- a/oneflow/core/autograd/gradient_funcs/matmul.cpp +++ b/oneflow/core/autograd/gradient_funcs/matmul.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" namespace oneflow { namespace one { @@ -102,40 +103,194 @@ Maybe Matmul::Apply(const MatmulCaptureState* ctx, const TensorTuple& out_ return Maybe::Ok(); } -class BroadcastMatmul : public Matmul { +struct BroadcastMatmulCaptureState : public AutoGradCaptureState { + bool transpose_a = false; + bool transpose_b = false; + double alpha = 1.0; + bool requires_grad_a = true; + bool requires_grad_b = true; + size_t a_index = 0; + size_t b_index = 1; + bool broadcast_a = false; + bool broadcast_b = false; + int64_t b_num_axes = 0; +}; + +class BroadcastMatmul : public OpExprGradFunction { public: - Maybe Apply(const MatmulCaptureState* ctx, const TensorTuple& out_grads, + Maybe Init(const OpExpr& op) override; + Maybe Capture(BroadcastMatmulCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const BroadcastMatmulCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override; + + protected: + AttrMap base_attrs_; }; -Maybe BroadcastMatmul::Apply(const MatmulCaptureState* ctx, const TensorTuple& out_grads, - TensorTuple* in_grads) const { +Maybe BroadcastMatmul::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr) << "fw_op_expr should not be null. "; + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + + return Maybe::Ok(); +} + +Maybe BroadcastMatmul::Capture(BroadcastMatmulCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ctx->requires_grad_a = JUST(VectorAt(inputs, 0))->requires_grad(); + ctx->requires_grad_b = JUST(VectorAt(inputs, 1))->requires_grad(); if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + const auto a_shape = JUST(VectorAt(inputs, 0))->shape(); + const auto b_shape = JUST(VectorAt(inputs, 1))->shape(); + + const int64_t a_num_axes = a_shape->NumAxes(); + const int64_t b_num_axes = b_shape->NumAxes(); + + const size_t num_max_batch_dims = std::max(a_num_axes, b_num_axes) - 2; + auto MakeGetBatchDim = [num_max_batch_dims](size_t num_dims, const Shape& shape_dim) { + const int64_t num_batch_dims = num_dims - 2; + const int64_t num_padding_dims = num_max_batch_dims - num_batch_dims; + return [num_padding_dims, shape_dim](size_t index) { + return index < num_padding_dims ? 1 : shape_dim.At(index - num_padding_dims); + }; + }; + auto GetABatchDim = MakeGetBatchDim(a_num_axes, *a_shape); + auto GetBBatchDim = MakeGetBatchDim(b_num_axes, *b_shape); + bool broadcast_a = false; + bool broadcast_b = false; + + for (int32_t i = 0; i < num_max_batch_dims; i++) { + if (GetABatchDim(i) < GetBBatchDim(i) || a_num_axes < b_num_axes) { + broadcast_a = true; + break; + } + } + + for (int32_t i = 0; i < num_max_batch_dims; i++) { + if (GetBBatchDim(i) < GetABatchDim(i) || b_num_axes < a_num_axes) { + broadcast_b = true; + break; + } + } + + if (b_num_axes == 2 && !ctx->transpose_a) { + // In this case, we can directly use `broadcast_matmul_grad_b` OP to generate Grad instead of + // broadcast_matmul+reduce_sum_like. + broadcast_b = false; + } + + ctx->broadcast_a = broadcast_a; + ctx->broadcast_b = broadcast_b; + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->transpose_a = JUST(composed_attrs.GetAttr("transpose_a")); + ctx->transpose_b = JUST(composed_attrs.GetAttr("transpose_b")); + ctx->alpha = JUST(composed_attrs.GetAttr("alpha")); + + if (ctx->requires_grad_a) { + ctx->b_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // input b + if (broadcast_a) { + ctx->a_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); // input a + } + } + + if (ctx->requires_grad_b) { + ctx->b_num_axes = JUST(VectorAt(inputs, 1))->shape()->NumAxes(); + ctx->a_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); // input a + if (broadcast_b) { + ctx->b_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // input b + } + } + return Maybe::Ok(); +} + +Maybe BroadcastMatmul::Apply(const BroadcastMatmulCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "Out grad size should be equal to 1. "; in_grads->resize(2); + const auto out_shape = JUST(VectorAt(out_grads, 0))->shape(); + const int64_t out_num_axes = out_shape->NumAxes(); + const size_t num_max_batch_dims = out_num_axes - 2; + auto MakeGetBatchDim = [num_max_batch_dims](size_t num_dims, const Shape& shape_dim) { + const int64_t num_batch_dims = num_dims - 2; + const int64_t num_padding_dims = num_max_batch_dims - num_batch_dims; + return [num_padding_dims, shape_dim](size_t index) { + return index < num_padding_dims ? 1 : shape_dim.At(index - num_padding_dims); + }; + }; + auto GetOutBatchDim = MakeGetBatchDim(out_num_axes, *out_shape); if (ctx->requires_grad_a) { + std::shared_ptr broadcast_grad_a; const auto& input_b = ctx->SavedTensors().at(ctx->b_index); if (ctx->transpose_a) { - in_grads->at(0) = - JUST(functional::MatMul(input_b, out_grads.at(0), ctx->transpose_b, true, ctx->alpha)); + broadcast_grad_a = JUST(functional::MatMul(input_b, JUST(VectorAt(out_grads, 0)), + ctx->transpose_b, true, ctx->alpha)); } else { - in_grads->at(0) = JUST( - functional::MatMul(out_grads.at(0), input_b, false, !(ctx->transpose_b), ctx->alpha)); + broadcast_grad_a = JUST(functional::MatMul(JUST(VectorAt(out_grads, 0)), input_b, false, + !(ctx->transpose_b), ctx->alpha)); + } + if (ctx->broadcast_a) { + const auto& input_a = JUST(VectorAt(ctx->SavedTensors(), ctx->a_index)); + const auto a_shape = input_a->shape(); + const int64_t a_num_axes = a_shape->NumAxes(); + + std::vector a_reduce_vec; + auto GetABatchDim = MakeGetBatchDim(a_num_axes, *a_shape); + const int64_t a_out_num_dim_differ = out_num_axes - a_num_axes; + for (int32_t i = 0; i < out_num_axes - 2; i++) { + if (GetOutBatchDim(i) > GetABatchDim(i) + || (GetOutBatchDim(i) == 1 && i < a_out_num_dim_differ)) { + a_reduce_vec.push_back(i); + } + } + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::ReduceSumLike(broadcast_grad_a, input_a, a_reduce_vec)); + } else { + JUST(VectorAt(*in_grads, 0)) = broadcast_grad_a; } } if (ctx->requires_grad_b) { const auto& input_a = ctx->SavedTensors().at(ctx->a_index); - if (ctx->transpose_b) { - in_grads->at(1) = - JUST(functional::BroadcastMatmulGradB(out_grads.at(0), input_a, ctx->alpha)); + if (ctx->b_num_axes == 2 && !ctx->transpose_a) { + if (ctx->transpose_b) { + JUST(VectorAt(*in_grads, 1)) = JUST( + functional::BroadcastMatmulGradB(JUST(VectorAt(out_grads, 0)), input_a, ctx->alpha)); + } else { + JUST(VectorAt(*in_grads, 1)) = JUST( + functional::BroadcastMatmulGradB(input_a, JUST(VectorAt(out_grads, 0)), ctx->alpha)); + } } else { - in_grads->at(1) = - JUST(functional::BroadcastMatmulGradB(input_a, out_grads.at(0), ctx->alpha)); + std::shared_ptr broadcast_grad_b; + if (ctx->transpose_b) { + broadcast_grad_b = JUST(functional::MatMul(JUST(VectorAt(out_grads, 0)), input_a, true, + ctx->transpose_a, ctx->alpha)); + } else { + broadcast_grad_b = JUST(functional::MatMul(input_a, JUST(VectorAt(out_grads, 0)), + !ctx->transpose_a, false, ctx->alpha)); + } + if (ctx->broadcast_b) { + const auto& input_b = JUST(VectorAt(ctx->SavedTensors(), ctx->b_index)); + const auto b_shape = input_b->shape(); + std::vector b_reduce_vec; + auto GetBBatchDim = MakeGetBatchDim(ctx->b_num_axes, *b_shape); + const int64_t b_out_num_dim_differ = out_num_axes - ctx->b_num_axes; + for (int32_t i = 0; i < out_num_axes - 2; i++) { + if (GetOutBatchDim(i) > GetBBatchDim(i) + || (GetOutBatchDim(i) == 1 && i < b_out_num_dim_differ)) { + b_reduce_vec.push_back(i); + } + } + JUST(VectorAt(*in_grads, 1)) = + JUST(functional::ReduceSumLike(broadcast_grad_b, input_b, b_reduce_vec)); + } else { + JUST(VectorAt(*in_grads, 1)) = broadcast_grad_b; + } } } - return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/matrix_vector_product.cpp b/oneflow/core/autograd/gradient_funcs/matrix_vector_product.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7070e58b00742ca8f7048e82c80d56ee432da155 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/matrix_vector_product.cpp @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct MatrixVectorProductCaptureState : public AutoGradCaptureState { + bool requires_grad_a = false; + bool requires_grad_b = false; + size_t a_index = 0; + size_t b_index = 1; +}; + +class MatrixVectorProduct : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(MatrixVectorProductCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const MatrixVectorProductCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + protected: + AttrMap base_attrs_; +}; + +Maybe MatrixVectorProduct::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr) << "fw_op_expr should not be null. "; + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + + return Maybe::Ok(); +} + +Maybe MatrixVectorProduct::Capture(MatrixVectorProductCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + ctx->requires_grad_a = JUST(VectorAt(inputs, 0))->requires_grad(); + ctx->requires_grad_b = JUST(VectorAt(inputs, 1))->requires_grad(); + if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + if (ctx->requires_grad_a) { + ctx->b_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // input b + } + if (ctx->requires_grad_b) { + ctx->a_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); // input a + } + return Maybe::Ok(); +} + +Maybe MatrixVectorProduct::Apply(const MatrixVectorProductCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "Out grad size should be equal to 1. "; + + in_grads->resize(2); + if (ctx->requires_grad_a) { + const auto& input_b = JUST(VectorAt(ctx->SavedTensors(), ctx->b_index)); + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::MatrixVectorProductGradA(JUST(VectorAt(out_grads, 0)), input_b)); + } + + if (ctx->requires_grad_b) { + const auto& input_a = JUST(VectorAt(ctx->SavedTensors(), ctx->a_index)); + JUST(VectorAt(*in_grads, 1)) = + JUST(functional::MatrixVectorProductGradB(JUST(VectorAt(out_grads, 0)), input_a)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("matrix_vector_product", MatrixVectorProduct); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/max_unpool.cpp b/oneflow/core/autograd/gradient_funcs/max_unpool.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d4ed9f1ecd91c3f7a86d5932a0cb7c26d5629336 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/max_unpool.cpp @@ -0,0 +1,89 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" + +namespace oneflow { +namespace one { + +namespace { + +struct MaxUnpoolCaptureState : public AutoGradCaptureState { + bool requires_grad = false; + size_t input_index = 0; + size_t indices_index = 0; +}; + +using FuncType = decltype(functional::MaxUnpool1dGrad); + +template +class MaxUnpoolNdGrad : public OpExprGradFunction { + public: + virtual ~MaxUnpoolNdGrad() = default; + + using OpExprGradFunction::Init; + + Maybe Init(const OpExpr& op) override; + Maybe Capture(MaxUnpoolCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const MaxUnpoolCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +template +Maybe MaxUnpoolNdGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +template +Maybe MaxUnpoolNdGrad::Capture(MaxUnpoolCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ctx->requires_grad = inputs.at(0)->requires_grad(); + if (!ctx->requires_grad) { return Maybe::Ok(); } + ctx->input_index = ctx->SaveTensorForBackward(inputs.at(0)); + ctx->indices_index = ctx->SaveTensorForBackward(inputs.at(1)); + return Maybe::Ok(); +} + +template +Maybe MaxUnpoolNdGrad::Apply(const MaxUnpoolCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + if (!ctx->requires_grad) { return Maybe::Ok(); } + CHECK_LE_OR_RETURN(out_grads.size(), 2); // NOLINT(maybe-need-error-msg) + + const auto& input = ctx->SavedTensors().at(ctx->input_index); + const auto& indices = ctx->SavedTensors().at(ctx->indices_index); + + in_grads->resize(2); + (*in_grads)[0] = JUST(F(input, indices, out_grads[0])); + + return Maybe::Ok(); +} + +} // namespace + +REGISTER_OP_EXPR_GRAD_FUNCTION("max_unpool_1d", MaxUnpoolNdGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("max_unpool_2d", MaxUnpoolNdGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("max_unpool_3d", MaxUnpoolNdGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/median.cpp b/oneflow/core/autograd/gradient_funcs/median.cpp index 49c909006deee76c10a4bab9c192c22e7bebd248..af70f053508c10a35dc8f9c62d59129230722085 100644 --- a/oneflow/core/autograd/gradient_funcs/median.cpp +++ b/oneflow/core/autograd/gradient_funcs/median.cpp @@ -92,10 +92,10 @@ class MedianWithIndices : public OpExprGradFunctionSavedTensors(), 0)); const auto& indices = JUST(functional::Unsqueeze(JUST(VectorAt(ctx->SavedTensors(), 1)), -1)); const auto& dout = JUST(functional::Unsqueeze(JUST(VectorAt(out_grads, 0)), -1)); - JUST(VectorAt(*in_grads, 0)) = JUST( - functional::DimScatter(JUST(functional::Constant(*(input->shape()), Scalar(0), - *dout->dtype(), JUST(dout->device()))), - -1, indices, dout)); + JUST(VectorAt(*in_grads, 0)) = JUST(functional::DimScatterUpdate( + JUST(functional::Constant(*(input->shape()), Scalar(0), *dout->dtype(), + JUST(dout->device()))), + -1, indices, dout, /*inplace*/ false)); } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/narrow.cpp b/oneflow/core/autograd/gradient_funcs/narrow.cpp index dfc818db3895ce8b6b09808fda89bc5e679315e5..c3f0ebea80930d9844df3561eb72ff821767b036 100644 --- a/oneflow/core/autograd/gradient_funcs/narrow.cpp +++ b/oneflow/core/autograd/gradient_funcs/narrow.cpp @@ -71,8 +71,8 @@ class Narrow : public OpExprGradFunction { functional::Empty(ctx->shape, dy->dtype(), JUST(dy->device()), /*pin_memory=*/false)); } else { like = JUST( - functional::ConsistentEmpty(ctx->shape, dy->dtype(), JUST(dy->parallel_desc()), - *JUST(private_details::RawGetSbpList(JUST(dy->nd_sbp()))))); + functional::GlobalEmpty(ctx->shape, dy->dtype(), JUST(dy->parallel_desc()), + *JUST(private_details::RawGetSbpList(JUST(dy->nd_sbp()))))); } in_grads->resize(1); in_grads->at(0) = JUST(functional::NarrowGrad(dy, like, ctx->dim, ctx->start, ctx->length)); diff --git a/oneflow/core/autograd/gradient_funcs/one_embedding_fused_lookup.cpp b/oneflow/core/autograd/gradient_funcs/one_embedding_fused_lookup.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f100d98d5e0106aff12c9a903d8145900b3248c8 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/one_embedding_fused_lookup.cpp @@ -0,0 +1,68 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct OneEmbeddingFusedLookupCaptureState : public AutoGradCaptureState { + bool requires_grad{}; + std::string embedding_name{}; + int64_t line_size{}; + int64_t embedding_size{}; + int shadow_index{}; + int ids_index{}; + int input_num{}; +}; + +class OneEmbeddingFusedLookup : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(OneEmbeddingFusedLookupCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_GE_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = inputs.at(0)->requires_grad(); // shadow + ctx->shadow_index = ctx->SaveTensorForBackward(inputs.at(0)); // shadow + ctx->ids_index = ctx->SaveTensorForBackward(inputs.at(1)); // id + ctx->embedding_name = JUST(attrs.GetAttr("embedding_name")); + ctx->line_size = JUST(attrs.GetAttr("line_size")); + ctx->embedding_size = JUST(attrs.GetAttr("embedding_size")); + ctx->input_num = inputs.size(); + return Maybe::Ok(); + } + + Maybe Apply(const OneEmbeddingFusedLookupCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(ctx->input_num); + const auto& saved_tensors = ctx->SavedTensors(); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + if (ctx->requires_grad) { + JUST(functional::OneEmbeddingFusedLookupGrad( + saved_tensors.at(ctx->ids_index), JUST(VectorAt(out_grads, 0)), ctx->embedding_name, + ctx->line_size, ctx->embedding_size)); + (*in_grads)[0] = JUST(functional::ZerosLike(saved_tensors.at(ctx->shadow_index))); + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("one_embedding_fused_lookup", OneEmbeddingFusedLookup); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/padding.cpp b/oneflow/core/autograd/gradient_funcs/padding.cpp index 8f3ac807bc84214354a062ab627952e447f24631..7866f294aa71e26c71da5e4c17a697b619f813e1 100644 --- a/oneflow/core/autograd/gradient_funcs/padding.cpp +++ b/oneflow/core/autograd/gradient_funcs/padding.cpp @@ -20,12 +20,12 @@ limitations under the License. namespace oneflow { namespace one { -struct Pad2dCaptureState : public AutoGradCaptureState { - bool requires_grad; - std::vector paddings; +struct PadNdCaptureState : public AutoGradCaptureState { + bool requires_grad = false; + std::vector paddings{}; }; -class Pad2d : public OpExprGradFunction { +class PadNd : public OpExprGradFunction { public: Maybe Init(const OpExpr& op) override { const UserOpExpr* fw_op_expr = dynamic_cast(&op); @@ -34,7 +34,7 @@ class Pad2d : public OpExprGradFunction { return Maybe::Ok(); } - Maybe Capture(Pad2dCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, + Maybe Capture(PadNdCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const override { CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) @@ -50,9 +50,9 @@ class Pad2d : public OpExprGradFunction { AttrMap base_attrs_; }; -class ReflectionPad2d : public Pad2d { +class ReflectionPadNd : public PadNd { public: - Maybe Apply(const Pad2dCaptureState* ctx, const TensorTuple& out_grads, + Maybe Apply(const PadNdCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) in_grads->resize(1); @@ -64,9 +64,9 @@ class ReflectionPad2d : public Pad2d { } }; -class ReplicationPad2d : public Pad2d { +class ReplicationPadNd : public PadNd { public: - Maybe Apply(const Pad2dCaptureState* ctx, const TensorTuple& out_grads, + Maybe Apply(const PadNdCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) in_grads->resize(1); @@ -121,8 +121,10 @@ class ConstantPadNd : public OpExprGradFunction { }; REGISTER_OP_EXPR_GRAD_FUNCTION("pad", ConstantPadNd); -REGISTER_OP_EXPR_GRAD_FUNCTION("reflection_pad2d", ReflectionPad2d); -REGISTER_OP_EXPR_GRAD_FUNCTION("replication_pad2d", ReplicationPad2d); +REGISTER_OP_EXPR_GRAD_FUNCTION("reflection_pad1d", ReflectionPadNd); +REGISTER_OP_EXPR_GRAD_FUNCTION("reflection_pad2d", ReflectionPadNd); +REGISTER_OP_EXPR_GRAD_FUNCTION("replication_pad1d", ReplicationPadNd); +REGISTER_OP_EXPR_GRAD_FUNCTION("replication_pad2d", ReplicationPadNd); } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/reduce_ops.cpp b/oneflow/core/autograd/gradient_funcs/reduce_ops.cpp index 1ce85132a841178c5382d90b1a43ec013aa8146b..c1470215b7479d5da2c3b9020757717bcd3a0e96 100644 --- a/oneflow/core/autograd/gradient_funcs/reduce_ops.cpp +++ b/oneflow/core/autograd/gradient_funcs/reduce_ops.cpp @@ -64,6 +64,7 @@ Maybe ReduceSum::Apply(const ReduceSumCaptureState* ctx, const TensorTuple } REGISTER_OP_EXPR_GRAD_FUNCTION("reduce_sum", ReduceSum); +REGISTER_OP_EXPR_GRAD_FUNCTION("reduce_nansum", ReduceSum); struct ReduceProdOpInterpState : public AutoGradCaptureState { std::vector axis; diff --git a/oneflow/core/autograd/gradient_funcs/reduce_sum_like.cpp b/oneflow/core/autograd/gradient_funcs/reduce_sum_like.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1974ce54850940d3a4fbd6cbc5af8b3cf9330da4 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/reduce_sum_like.cpp @@ -0,0 +1,76 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" + +namespace oneflow { +namespace one { + +struct ReduceSumLikeCaptureState : public AutoGradCaptureState { + bool requires_grad = false; + std::vector axis; +}; + +class ReduceSumLike : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(ReduceSumLikeCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const ReduceSumLikeCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe ReduceSumLike::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe ReduceSumLike::Capture(ReduceSumLikeCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = inputs.at(0)->requires_grad(); + CHECK_OR_RETURN(!inputs.at(1)->requires_grad()) + << Error::RuntimeError() << "like tensor does not require grad"; + if (!ctx->requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->axis = JUST(composed_attrs.GetAttr>("axis")); + ctx->SaveTensorForBackward(inputs.at(0)); + return Maybe::Ok(); +} + +Maybe ReduceSumLike::Apply(const ReduceSumLikeCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + const auto& x = ctx->SavedTensors().at(0); + in_grads->resize(2); + in_grads->at(0) = JUST(functional::BroadcastLike(out_grads.at(0), x, ctx->axis)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("reduce_sum_like", ReduceSumLike); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/reshape.cpp b/oneflow/core/autograd/gradient_funcs/reshape.cpp index ac1ce5494699f2376a822ac0463a15e74fb343b6..6e8100a07a849409174cceff0d621dfe1703b550 100644 --- a/oneflow/core/autograd/gradient_funcs/reshape.cpp +++ b/oneflow/core/autograd/gradient_funcs/reshape.cpp @@ -28,7 +28,7 @@ struct ReshapeCaptureState : public AutoGradCaptureState { DimVector input_shape_vec; }; -class ReshapeOpExprGrad : public OpExprGradFunction { +class ReshapeGrad : public OpExprGradFunction { public: Maybe Init(const OpExpr& op) override { const auto* fw_op_expr = dynamic_cast(&op); @@ -51,7 +51,34 @@ class ReshapeOpExprGrad : public OpExprGradFunction { } }; -REGISTER_OP_EXPR_GRAD_FUNCTION("reshape", ReshapeOpExprGrad); +class ReshapeLikeGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); + } + + Maybe Capture(ReshapeCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!inputs.at(1)->requires_grad()) + << "ReshapeLikeOp's input[1] need not requires_grad."; + ctx->input_shape_vec = inputs.at(0)->shape()->dim_vec(); + return Maybe::Ok(); + } + + Maybe Apply(const ReshapeCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + Shape shape(ctx->input_shape_vec); + in_grads->at(0) = JUST(functional::Reshape(out_grads.at(0), shape)); + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("reshape", ReshapeGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("reshape_like", ReshapeLikeGrad); } // namespace one } // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/rms_norm.cpp b/oneflow/core/autograd/gradient_funcs/rms_norm.cpp new file mode 100644 index 0000000000000000000000000000000000000000..32e778b6304cf92339985d7efdf9071b9abe64ab --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/rms_norm.cpp @@ -0,0 +1,99 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct RMSNormCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool weight_requires_grad = false; + int x_index = -1; + int inv_rms_index = -1; + int weight_index = -1; +}; + +class RMSNormGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(RMSNormCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const RMSNormCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; +}; + +Maybe RMSNormGrad::Capture(RMSNormCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // (x, [weight]) + CHECK_GE_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_LE_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + // (y, inv_rms) + CHECK_EQ_OR_RETURN(outputs.size(), 2); // NOLINT(maybe-need-error-msg) + + // save x + ctx->x_requires_grad = inputs[0]->requires_grad(); + ctx->x_index = ctx->SaveTensorForBackward(inputs[0]); + + // save weight + ctx->weight_requires_grad = false; + if (inputs.size() > 1) { + ctx->weight_requires_grad = inputs[1]->requires_grad(); + ctx->weight_index = ctx->SaveTensorForBackward(inputs[1]); + } + + // save inv_rms + if (ctx->x_requires_grad || ctx->weight_requires_grad) { + ctx->inv_rms_index = ctx->SaveTensorForBackward(outputs[1]); + } + return Maybe::Ok(); +} + +Maybe RMSNormGrad::Apply(const RMSNormCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + // (x, inv_rms) or (x, weight, inv_rms) + const auto& saved_tensors = ctx->SavedTensors(); + CHECK_GE_OR_RETURN(saved_tensors.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_LE_OR_RETURN(saved_tensors.size(), 3); // NOLINT(maybe-need-error-msg) + + // (dy, inv_rms_diff) + CHECK_EQ_OR_RETURN(out_grads.size(), 2); // NOLINT(maybe-need-error-msg) + const auto& dy = out_grads[0]; + const auto& x = saved_tensors.at(ctx->x_index); + const auto& inv_rms = saved_tensors.at(ctx->inv_rms_index); + + // (x_grad, weight_grad) + in_grads->resize(2); + if (ctx->x_requires_grad) { + if (saved_tensors.size() == 3) { + const auto& weight = saved_tensors.at(ctx->weight_index); + in_grads->at(0) = JUST(functional::RMSNormGrad(dy, x, inv_rms, weight, /*param_grad*/ false)); + } else { + in_grads->at(0) = + JUST(functional::RMSNormGrad(dy, x, inv_rms, /*weight*/ NullOpt, /*param_grad*/ false)); + } + } + if (ctx->weight_requires_grad) { + in_grads->at(1) = + JUST(functional::RMSNormGrad(dy, x, inv_rms, /*weight*/ NullOpt, /*param_grad*/ true)); + } + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("rms_norm", RMSNormGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/scalar_floordiv.cpp b/oneflow/core/autograd/gradient_funcs/scalar_floordiv.cpp index 61de20569485973b21912478827fdd6aa8a5d995..a037478e00e03b83ef3ec3684102063ff10c87f7 100644 --- a/oneflow/core/autograd/gradient_funcs/scalar_floordiv.cpp +++ b/oneflow/core/autograd/gradient_funcs/scalar_floordiv.cpp @@ -13,15 +13,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/framework/attr_map.h" + #include "oneflow/core/framework/op_expr_grad_function.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" namespace oneflow { namespace one { -// FloorDiv derivatives function isn't exists. (author: zhengzekang) -struct ScalarFloorDivCaptureState : public AutoGradCaptureState {}; +struct ScalarFloorDivCaptureState : public AutoGradCaptureState { + bool requires_grad = true; +}; class ScalarFloorDiv : public OpExprGradFunction { public: @@ -29,17 +31,20 @@ class ScalarFloorDiv : public OpExprGradFunction { Maybe Capture(ScalarFloorDivCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = JUST(VectorAt(inputs, 0))->requires_grad(); return Maybe::Ok(); } Maybe Apply(const ScalarFloorDivCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { - UNIMPLEMENTED_THEN_RETURN() << "RuntimeError: derivative for floor_divide is not implemented"; + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + if (ctx->requires_grad) { + JUST(VectorAt(*in_grads, 0)) = JUST(functional::ZerosLike(JUST(VectorAt(out_grads, 0)))); + } return Maybe::Ok(); } - - private: - AttrMap base_attrs_; }; REGISTER_OP_EXPR_GRAD_FUNCTION("scalar_floordiv", ScalarFloorDiv); diff --git a/oneflow/core/autograd/gradient_funcs/scalar_pow.cpp b/oneflow/core/autograd/gradient_funcs/scalar_pow.cpp index 6bdba849ec6bf56424d2675691b731ac94eb78de..fc265a9e1306f3361cc4f822b806b940b43079f9 100644 --- a/oneflow/core/autograd/gradient_funcs/scalar_pow.cpp +++ b/oneflow/core/autograd/gradient_funcs/scalar_pow.cpp @@ -55,7 +55,6 @@ class ScalarPow : public OpExprGradFunction { Maybe Apply(const ScalarPowCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { const auto& x = ctx->SavedTensors().at(0); - MutableAttrMap attrs; in_grads->resize(1); if (ctx->requires_grad) { in_grads->at(0) = JUST(functional::ScalarPowGrad(x, out_grads.at(0), ctx->operand)); @@ -64,7 +63,6 @@ class ScalarPow : public OpExprGradFunction { } private: - std::shared_ptr grad_op_; AttrMap base_attrs_; }; @@ -100,7 +98,6 @@ class ScalarReversePow : public OpExprGradFunction { Maybe Apply(const ScalarPowCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { const auto& x = ctx->SavedTensors()[0]; - MutableAttrMap attrs; in_grads->resize(1); if (ctx->requires_grad) { (*in_grads)[0] = JUST(functional::ScalarReversePowGrad(x, out_grads[0], ctx->operand)); @@ -109,7 +106,6 @@ class ScalarReversePow : public OpExprGradFunction { } private: - std::shared_ptr grad_op_; AttrMap base_attrs_; }; diff --git a/oneflow/core/autograd/gradient_funcs/scalar_truncdiv.cpp b/oneflow/core/autograd/gradient_funcs/scalar_truncdiv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5617f313628de675797d4589d02017bb1bc4c881 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/scalar_truncdiv.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct ScalarTruncDivCaptureState : public AutoGradCaptureState { + bool requires_grad = true; +}; + +class ScalarTruncDiv : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(ScalarTruncDivCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->requires_grad = inputs.at(0)->requires_grad(); + return Maybe::Ok(); + } + + Maybe Apply(const ScalarTruncDivCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + if (ctx->requires_grad) { + JUST(VectorAt(*in_grads, 0)) = JUST(functional::ZerosLike(JUST(VectorAt(out_grads, 0)))); + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("scalar_truncdiv", ScalarTruncDiv); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/slice.cpp b/oneflow/core/autograd/gradient_funcs/slice.cpp index 59aad3fb1d61fc1bfa7a0b41486268af5e9fa301..628a7e73e303d5c7fa4df7a1b150a89b34c2293c 100644 --- a/oneflow/core/autograd/gradient_funcs/slice.cpp +++ b/oneflow/core/autograd/gradient_funcs/slice.cpp @@ -98,7 +98,7 @@ class SliceUpdate : public OpExprGradFunction { if (ctx->requires_grad_ref) { ctx->value_shape = *(inputs[1]->shape()); - if (inputs[1]->is_consistent()) { ctx->value_sbp = JUST(inputs[1]->nd_sbp()); } + if (inputs[1]->is_global()) { ctx->value_sbp = JUST(inputs[1]->nd_sbp()); } } return Maybe::Ok(); } @@ -114,8 +114,7 @@ class SliceUpdate : public OpExprGradFunction { JUST(out_grads[0]->device()))); } else { const auto& parallel_desc = JUST(out_grads[0]->parallel_desc()); - zeros = - JUST(functional::ConsistentConstant(ctx->value_shape, 0, out_grads[0]->dtype(), + zeros = JUST(functional::GlobalConstant(ctx->value_shape, 0, out_grads[0]->dtype(), parallel_desc, *JUST(GetSbpList(ctx->value_sbp)))); } (*in_grads)[0] = JUST(functional::SliceUpdate(out_grads[0], zeros, ctx->start, ctx->stop, diff --git a/oneflow/core/autograd/gradient_funcs/smooth_l1_loss.cpp b/oneflow/core/autograd/gradient_funcs/smooth_l1_loss.cpp index 1d3f069c293dd8a4419ce8aebaeb779abad9c05d..69ff0faf3e68bd7e264979547b056830ffa00efc 100644 --- a/oneflow/core/autograd/gradient_funcs/smooth_l1_loss.cpp +++ b/oneflow/core/autograd/gradient_funcs/smooth_l1_loss.cpp @@ -22,7 +22,8 @@ namespace oneflow { namespace one { struct SmoothL1LossCaptureState : public AutoGradCaptureState { - bool requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; float beta = 0.0; }; @@ -37,13 +38,13 @@ class SmoothL1Loss : public OpExprGradFunction { Maybe Capture(SmoothL1LossCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const AttrMap& attrs) const override { - ctx->requires_grad = inputs.at(0)->requires_grad(); // prediction - if (!ctx->requires_grad) { return Maybe::Ok(); } - CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) - ctx->SaveTensorForBackward(inputs.at(0)); // prediction - ctx->SaveTensorForBackward(inputs.at(1)); // label + ctx->input_requires_grad = inputs.at(0)->requires_grad(); // input + ctx->target_requires_grad = inputs.at(1)->requires_grad(); // target + + ctx->SaveTensorForBackward(inputs.at(0)); // input + ctx->SaveTensorForBackward(inputs.at(1)); // target ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->beta = JUST(composed_attrs.GetAttr("beta")); @@ -52,15 +53,15 @@ class SmoothL1Loss : public OpExprGradFunction { Maybe Apply(const SmoothL1LossCaptureState* ctx, const TensorTuple& out_grads, TensorTuple* in_grads) const override { - if (!ctx->requires_grad) { return Maybe::Ok(); } - - CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), 2); // NOLINT(maybe-need-error-msg) in_grads->resize(2); + const auto& input = ctx->SavedTensors().at(0); + const auto& target = ctx->SavedTensors().at(1); + const auto& grad = JUST(functional::SmoothL1LossGrad(out_grads[0], input, target, ctx->beta)); - const auto& prediction = ctx->SavedTensors().at(0); - const auto& label = ctx->SavedTensors().at(1); - in_grads->at(0) = - JUST(functional::SmoothL1LossGrad(out_grads.at(0), prediction, label, ctx->beta)); + if (ctx->input_requires_grad) { (*in_grads)[0] = grad; } + if (ctx->target_requires_grad) { (*in_grads)[1] = JUST(functional::Negative(grad)); } return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy.cpp b/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy.cpp index df3cb602cd1357b914cfa2696c93f5631c857a88..3aeaca8f82b8c4283ac0c4019bebba73c7f8e5aa 100644 --- a/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy.cpp +++ b/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy.cpp @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/attr_map.h" #include "oneflow/core/framework/op_expr_grad_function.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" @@ -50,10 +51,10 @@ Maybe SparseSoftmaxCrossEntropy::Capture(SparseSoftmaxCrossEntropyCaptureS const AttrMap& attrs) const { ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->depth = JUST(composed_attrs.GetAttr("depth")); - CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) - CHECK_EQ_OR_RETURN(outputs.size(), 2); // NOLINT(maybe-need-error-msg) - ctx->SaveTensorForBackward(outputs.at(0)); // prob - ctx->SaveTensorForBackward(inputs.at(1)); // label + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 2); // NOLINT(maybe-need-error-msg) + ctx->SaveTensorForBackward(JUST(VectorAt(outputs, 0))); // prob + ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // label return Maybe::Ok(); } @@ -61,15 +62,14 @@ Maybe SparseSoftmaxCrossEntropy::Apply(const SparseSoftmaxCrossEntropyCapt const TensorTuple& out_grads, TensorTuple* in_grads) const { CHECK_EQ_OR_RETURN(out_grads.size(), 2); // NOLINT(maybe-need-error-msg) - const auto& dy = out_grads.at(1); - const auto& prob = ctx->SavedTensors().at(0); - const auto& label = ctx->SavedTensors().at(1); - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", ctx->depth)); + const auto& dy = JUST(VectorAt(out_grads, 1)); + const auto& prob = JUST(VectorAt(ctx->SavedTensors(), 0)); + const auto& label = JUST(VectorAt(ctx->SavedTensors(), 1)); // SparseSoftmaxCrossEntropy has 2 inputs (prediction and label), and the second input does not // require gradient. in_grads->resize(2); - in_grads->at(0) = JUST(functional::SparseSoftmaxCrossEntropyGrad(dy, prob, label, ctx->depth)); + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::SparseSoftmaxCrossEntropyGrad(dy, prob, label, ctx->depth)); return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy_ms.cpp b/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy_ms.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2a5159a86363ab77f5fcfab68969b98b3dffd4bf --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/sparse_softmax_cross_entropy_ms.cpp @@ -0,0 +1,80 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct SparseSoftmaxCrossEntropyMsCaptureState : public AutoGradCaptureState { + int64_t depth = 0; +}; + +class SparseSoftmaxCrossEntropyMs + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(SparseSoftmaxCrossEntropyMsCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const SparseSoftmaxCrossEntropyMsCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe SparseSoftmaxCrossEntropyMs::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe SparseSoftmaxCrossEntropyMs::Capture(SparseSoftmaxCrossEntropyMsCaptureState* ctx, + const TensorTuple& inputs, + const TensorTuple& outputs, + const AttrMap& attrs) const { + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->depth = JUST(composed_attrs.GetAttr("depth")); + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 2); // NOLINT(maybe-need-error-msg) + ctx->SaveTensorForBackward(JUST(VectorAt(outputs, 0))); // prob + ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // label + return Maybe::Ok(); +} + +Maybe SparseSoftmaxCrossEntropyMs::Apply(const SparseSoftmaxCrossEntropyMsCaptureState* ctx, + const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 2); // NOLINT(maybe-need-error-msg) + const auto& dy = JUST(VectorAt(out_grads, 1)); + const auto& prob = JUST(VectorAt(ctx->SavedTensors(), 0)); + const auto& label = JUST(VectorAt(ctx->SavedTensors(), 1)); + // SparseSoftmaxCrossEntropy has 2 inputs (prediction and label), and the second input does not + // require gradient. + in_grads->resize(2); + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::SparseSoftmaxCrossEntropyMsGrad(dy, prob, label, ctx->depth)); + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("sparse_softmax_cross_entropy_ms", SparseSoftmaxCrossEntropyMs); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/trunc.cpp b/oneflow/core/autograd/gradient_funcs/trunc.cpp new file mode 100644 index 0000000000000000000000000000000000000000..52a78b50f513ef3b7daf9edead18fa206e4e905c --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/trunc.cpp @@ -0,0 +1,60 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" + +namespace oneflow { +namespace one { + +struct TruncCaptureState : public AutoGradCaptureState { + bool requires_grad = false; +}; + +class Trunc : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(TruncCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override; + Maybe Apply(const TruncCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; +}; + +Maybe Trunc::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); +} + +Maybe Trunc::Capture(TruncCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + ctx->requires_grad = inputs.at(0)->requires_grad(); + if (!ctx->requires_grad) { return Maybe::Ok(); } + return Maybe::Ok(); +} + +Maybe Trunc::Apply(const TruncCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + if (ctx->requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("trunc", Trunc); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/gradient_funcs/unfold.cpp b/oneflow/core/autograd/gradient_funcs/unfold.cpp index 80ece9e16d796139db98d78d94c3dc0a7c5e294f..6e6098edc07f6b857f2a2bbee22bade7a330aae2 100644 --- a/oneflow/core/autograd/gradient_funcs/unfold.cpp +++ b/oneflow/core/autograd/gradient_funcs/unfold.cpp @@ -73,8 +73,8 @@ Maybe Unfold::Apply(const UnfoldInterpState* ctx, const TensorTuple& out_g CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) in_grads->resize(1); in_grads->at(0) = - JUST(functional::Fold(out_grads.at(0), ctx->data_format, ctx->output_size, ctx->kernel_size, - ctx->dilation_rate, ctx->padding, ctx->strides)); + JUST(functional::Fold(out_grads.at(0), ctx->output_size, ctx->kernel_size, ctx->dilation_rate, + ctx->padding, ctx->strides, ctx->data_format)); return Maybe::Ok(); } diff --git a/oneflow/core/autograd/gradient_funcs/upsample.cpp b/oneflow/core/autograd/gradient_funcs/upsample.cpp index bd1a71d3975e102e33ca3a8248033517454e50b0..4a4b7de3d0c3d13d221b575e3b9b36470c735d34 100644 --- a/oneflow/core/autograd/gradient_funcs/upsample.cpp +++ b/oneflow/core/autograd/gradient_funcs/upsample.cpp @@ -100,7 +100,7 @@ class UpsampleNearest2D : public OpExprGradFunctionheight_scale = JUST(composed_attrs.GetAttr("height_scale")); ctx->width_scale = JUST(composed_attrs.GetAttr("width_scale")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -112,7 +112,6 @@ class UpsampleNearest2D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleNearest2DGrad( @@ -151,7 +150,7 @@ class UpsampleBilinear2D : public OpExprGradFunctionheight_scale = JUST(composed_attrs.GetAttr("height_scale")); ctx->width_scale = JUST(composed_attrs.GetAttr("width_scale")); ctx->align_corners = JUST(composed_attrs.GetAttr("align_corners")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -163,7 +162,6 @@ class UpsampleBilinear2D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleBilinear2DGrad( @@ -200,7 +198,7 @@ class UpsampleLinear1D : public OpExprGradFunction ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->scale_factor = JUST(composed_attrs.GetAttr("scale_factor")); ctx->align_corners = JUST(composed_attrs.GetAttr("align_corners")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -212,7 +210,6 @@ class UpsampleLinear1D : public OpExprGradFunction TensorTuple* in_grads) const override { if (!ctx->requires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleLinear1DGrad( @@ -247,7 +244,7 @@ class UpsampleNearest1D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } ComposedAttrMap composed_attrs(attrs, base_attrs_); ctx->scale_factor = JUST(composed_attrs.GetAttr("scale_factor")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -259,7 +256,6 @@ class UpsampleNearest1D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST( @@ -298,7 +294,7 @@ class UpsampleBicubic2D : public OpExprGradFunctionheight_scale = JUST(composed_attrs.GetAttr("height_scale")); ctx->width_scale = JUST(composed_attrs.GetAttr("width_scale")); ctx->align_corners = JUST(composed_attrs.GetAttr("align_corners")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -310,7 +306,6 @@ class UpsampleBicubic2D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleBicubic2DGrad( @@ -348,7 +343,7 @@ class UpsampleNearest3D : public OpExprGradFunctiondepth_scale = JUST(composed_attrs.GetAttr("depth_scale")); ctx->height_scale = JUST(composed_attrs.GetAttr("height_scale")); ctx->width_scale = JUST(composed_attrs.GetAttr("width_scale")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -360,7 +355,6 @@ class UpsampleNearest3D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleNearest3DGrad( @@ -401,7 +395,7 @@ class UpsampleTrilinear3D : public OpExprGradFunctionheight_scale = JUST(composed_attrs.GetAttr("height_scale")); ctx->width_scale = JUST(composed_attrs.GetAttr("width_scale")); ctx->align_corners = JUST(composed_attrs.GetAttr("align_corners")); - if (base_attrs_.find("output_size") != base_attrs_.end()) { + if (composed_attrs.Has("output_size")) { ctx->output_size = JUST(composed_attrs.GetAttr>("output_size")); } ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); @@ -413,7 +407,6 @@ class UpsampleTrilinear3D : public OpExprGradFunctionrequires_grad) { return Maybe::Ok(); } CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) - MutableAttrMap attrs; const std::shared_ptr& x = ctx->SavedTensors().at(0); in_grads->resize(1); JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::UpsampleTrilinear3DGrad( @@ -430,4 +423,4 @@ class UpsampleTrilinear3D : public OpExprGradFunction Variance::Apply(const VarianceState* ctx, const TensorTuple& out_gra TensorTuple* in_grads) const { // TODO(): replace it using kernel const std::shared_ptr& x = ctx->SavedTensors().at(0); + DataType data_type = x->dtype()->data_type(); + CHECK_NE_OR_RETURN(data_type, DataType::kBFloat16) + << Error::RuntimeError() << "Variance op not support backward for bfloat16 yet!"; size_t correction = ctx->unbiased ? 1 : 0; size_t elem_cnt = 1; CHECK_OR_RETURN(ctx->axis.size() > 0) diff --git a/oneflow/core/autograd/gradient_funcs/vector_matrix_product.cpp b/oneflow/core/autograd/gradient_funcs/vector_matrix_product.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e59b9b7fae009374c46685073ad38741f45c23b5 --- /dev/null +++ b/oneflow/core/autograd/gradient_funcs/vector_matrix_product.cpp @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct VectorMatrixProductCaptureState : public AutoGradCaptureState { + bool requires_grad_a = false; + bool requires_grad_b = false; + size_t a_index = 0; + size_t b_index = 1; +}; + +class VectorMatrixProduct : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(VectorMatrixProductCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const VectorMatrixProductCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + protected: + AttrMap base_attrs_; +}; + +Maybe VectorMatrixProduct::Init(const OpExpr& op) { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr) << "fw_op_expr should not be null. "; + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + + return Maybe::Ok(); +} + +Maybe VectorMatrixProduct::Capture(VectorMatrixProductCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + ctx->requires_grad_a = JUST(VectorAt(inputs, 0))->requires_grad(); + ctx->requires_grad_b = JUST(VectorAt(inputs, 1))->requires_grad(); + if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + if (ctx->requires_grad_a) { + ctx->b_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 1))); // input b + } + if (ctx->requires_grad_b) { + ctx->a_index = ctx->SaveTensorForBackward(JUST(VectorAt(inputs, 0))); // input a + } + return Maybe::Ok(); +} + +Maybe VectorMatrixProduct::Apply(const VectorMatrixProductCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + if (!ctx->requires_grad_a && !ctx->requires_grad_b) { return Maybe::Ok(); } + CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "Out grad size should be equal to 1. "; + + in_grads->resize(2); + if (ctx->requires_grad_a) { + const auto& input_b = JUST(VectorAt(ctx->SavedTensors(), ctx->b_index)); + JUST(VectorAt(*in_grads, 0)) = + JUST(functional::VectorMatrixProductGradA(JUST(VectorAt(out_grads, 0)), input_b)); + } + + if (ctx->requires_grad_b) { + const auto& input_a = JUST(oneflow::VectorAt(ctx->SavedTensors(), ctx->a_index)); + JUST(VectorAt(*in_grads, 1)) = + JUST(functional::VectorMatrixProductGradB(JUST(VectorAt(out_grads, 0)), input_a)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("vector_matrix_product", VectorMatrixProduct); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/activation.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/activation.cpp new file mode 100644 index 0000000000000000000000000000000000000000..14d20b3014271c0091c4a7c141661f9ae0a27881 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/activation.cpp @@ -0,0 +1,556 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/scalar.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct BaseActivationGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; +}; + +typedef Maybe (*NoParamActivationBwFunc)(const std::shared_ptr&, + const std::shared_ptr&); + +template +class NoParamActivationGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(BaseActivationGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, x + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(1)->requires_grad(); + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); + + if (!ctx->x_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ctx->SaveTensorForBackward(inputs.at(1)); + if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + + return Maybe::Ok(); + } + + Maybe Apply(const BaseActivationGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& x = ctx->SavedTensors().at(0); + + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(1); + in_grads->at(1) = JUST(functional::Mul(out_grads.at(0), JUST(BwBwFunc(x, grad)))); + } + if (ctx->grad_requires_grad) { in_grads->at(0) = JUST(BwFunc(out_grads.at(0), x)); } + return Maybe::Ok(); + } +}; + +#define INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS(op_type_name, op_cls) \ + class op_cls##GradGradCls final \ + : public NoParamActivationGradGrad { \ + }; \ + REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, op_cls##GradGradCls); + +// first order backward param: (dy, x) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("mish_grad", Mish) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("gelu_grad", Gelu) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("silu_grad", Silu) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("selu_grad", Selu) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("softsign_grad", SoftSign) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("hardsigmoid_grad", HardSigmoid) +INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS("hardswish_grad", HardSwish) + +#undef INSTANTIAT_AND_REGISTER_NOPARAM_ACTIVATION_CLASS + +struct HardShrinkGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool grad_requires_grad = false; + double lambd = 0.5; +}; + +class HardShrinkGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + Maybe Capture(HardShrinkGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // y, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->y_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->y_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->lambd = JUST(composed_attrs.GetAttr("lambd")); + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const HardShrinkGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + + if (ctx->y_requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& y = ctx->SavedTensors().at(0); + in_grads->at(1) = JUST(functional::HardShrinkGrad(y, out_grads.at(0), ctx->lambd)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +struct SoftShrinkGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool grad_requires_grad = false; + double alpha = 0.5; +}; + +class SoftShrinkGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + Maybe Capture(SoftShrinkGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // y, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->y_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->y_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->alpha = JUST(composed_attrs.GetAttr("alpha")); + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const SoftShrinkGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + + if (ctx->y_requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& y = ctx->SavedTensors().at(0); + in_grads->at(1) = JUST(functional::SoftShrinkGrad(y, out_grads.at(0), ctx->alpha)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +struct ReluGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool grad_requires_grad = false; +}; + +class ReluGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(ReluGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, y + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->y_requires_grad = inputs.at(1)->requires_grad(); + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); + + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); } + return Maybe::Ok(); + } + Maybe Apply(const ReluGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + if (ctx->y_requires_grad) { in_grads->at(1) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& y = ctx->SavedTensors().at(0); + in_grads->at(0) = JUST(functional::ReluGrad(out_grads.at(0), y)); + } + return Maybe::Ok(); + } +}; + +struct LeakyReluGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + float alpha = 0.01; +}; + +class LeakyReluGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(LeakyReluGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->x_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->alpha = JUST(composed_attrs.GetAttr("alpha")); + + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const LeakyReluGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + if (ctx->x_requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& x = ctx->SavedTensors().at(0); + in_grads->at(1) = JUST(functional::LeakyReluGrad(x, out_grads.at(0), ctx->alpha)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +struct SoftplusGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + double beta = 1.0; + double threshold = 20.0; +}; + +class SoftplusGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(SoftplusGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->x_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->beta = JUST(composed_attrs.GetAttr("beta")); + ctx->threshold = JUST(composed_attrs.GetAttr("threshold")); + + ctx->SaveTensorForBackward(inputs.at(0)); + if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); } + return Maybe::Ok(); + } + + Maybe Apply(const SoftplusGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& x = ctx->SavedTensors().at(0); + + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(1); + in_grads->at(0) = JUST(functional::Mul( + out_grads.at(0), JUST(functional::SoftplusGradGrad(x, grad, ctx->beta, ctx->threshold)))); + } + if (ctx->grad_requires_grad) { + in_grads->at(1) = + JUST(functional::SoftplusGrad(x, out_grads.at(0), ctx->beta, ctx->threshold)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +struct HardTanhGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool grad_requires_grad = false; + double min_val = -1.0; + double max_val = 1.0; +}; + +class HardTanhGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + Maybe Capture(HardTanhGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // y, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->y_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->y_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->min_val = JUST(composed_attrs.GetAttr("min_val")); + ctx->max_val = JUST(composed_attrs.GetAttr("max_val")); + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const HardTanhGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + + if (ctx->y_requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& y = ctx->SavedTensors().at(0); + in_grads->at(1) = + JUST(functional::HardTanhGrad(y, out_grads.at(0), ctx->min_val, ctx->max_val)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +struct EluGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + double alpha = 1.0; +}; + +class EluGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(EluGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->alpha = JUST(composed_attrs.GetAttr("alpha")); + + if (!ctx->x_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + ctx->SaveTensorForBackward(inputs.at(0)); + if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); } + return Maybe::Ok(); + } + + Maybe Apply(const EluGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& x = ctx->SavedTensors().at(0); + + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(1); + in_grads->at(0) = JUST( + functional::Mul(out_grads.at(0), JUST(functional::EluGradGrad(x, grad, ctx->alpha)))); + } + if (ctx->grad_requires_grad) { + in_grads->at(1) = JUST(functional::EluGrad(x, out_grads.at(0), ctx->alpha)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +class CeluGradGrad : public EluGradGrad { + public: + Maybe Apply(const EluGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& x = ctx->SavedTensors().at(0); + + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(1); + in_grads->at(0) = JUST( + functional::CeluGradGrad(x, JUST(functional::Mul(out_grads.at(0), (grad))), ctx->alpha)); + } + if (ctx->grad_requires_grad) { + in_grads->at(1) = JUST(functional::CeluGrad(x, out_grads.at(0), ctx->alpha)); + } + return Maybe::Ok(); + } +}; + +struct PReluGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool alpha_requires_grad = false; + size_t grad_index = 0; + size_t input_index = 1; + size_t alpha_index = 2; +}; + +class PReluGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(PReluGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, x, alpha + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); // grad + ctx->input_requires_grad = inputs.at(1)->requires_grad(); // input + ctx->alpha_requires_grad = inputs.at(2)->requires_grad(); // alpha + + ctx->input_index = ctx->SaveTensorForBackward(inputs.at(1)); + ctx->alpha_index = ctx->SaveTensorForBackward(inputs.at(2)); + ctx->grad_index = ctx->SaveTensorForBackward(inputs.at(0)); + + return Maybe::Ok(); + } + + Maybe Apply(const PReluGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(3); + + const auto& input = ctx->SavedTensors().at(ctx->input_index); + const auto& alpha = ctx->SavedTensors().at(ctx->alpha_index); + const auto& grad = ctx->SavedTensors().at(ctx->grad_index); + const auto& grad_for_input = out_grads.at(0); + const auto& grad_for_alpha = out_grads.at(1); + const auto& condition = JUST(functional::ScalarLogicalLess(input, Scalar(0.0))); + const auto& zero_grad = JUST(functional::ZerosLike(alpha)); // alpha can broadcast to input + + if (ctx->grad_requires_grad) { + auto input_mul_grad = JUST(functional::Mul(alpha, grad_for_input)); + auto alpha_mul_grad = JUST(functional::Mul(input, grad_for_alpha)); + auto result = JUST(functional::Add(input_mul_grad, alpha_mul_grad, /*alpha=*/Scalar(1.0), + /*inplace*/ false)); + in_grads->at(0) = JUST(functional::Where(condition, result, grad_for_input)); + } + if (ctx->input_requires_grad) { + auto result = JUST(functional::Mul(grad, grad_for_alpha)); + in_grads->at(1) = JUST(functional::Where(condition, result, zero_grad)); + } + if (ctx->alpha_requires_grad) { + auto result = JUST(functional::Mul(grad, grad_for_input)); + in_grads->at(2) = JUST(functional::Where(condition, result, zero_grad)); + } + return Maybe::Ok(); + } + + private: + std::shared_ptr grad_op_; +}; + +struct ThresholdGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + double threshold = 0.0; +}; + +class ThresholdGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(ThresholdGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!ctx->x_requires_grad && !ctx->grad_requires_grad) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->threshold = JUST(composed_attrs.GetAttr("threshold_val")); + + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const ThresholdGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + if (ctx->x_requires_grad) { in_grads->at(0) = JUST(functional::ZerosLike(out_grads.at(0))); } + if (ctx->grad_requires_grad) { + const auto& x = ctx->SavedTensors().at(0); + in_grads->at(1) = JUST(functional::ThresholdGrad(x, out_grads.at(0), ctx->threshold)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("relu_grad", ReluGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("elu_grad", EluGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("celu_grad", CeluGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("prelu_grad", PReluGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("hardshrink_grad", HardShrinkGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("softshrink_grad", SoftShrinkGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("leaky_relu_grad", LeakyReluGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("hardtanh_grad", HardTanhGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("threshold_grad", ThresholdGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("softplus_grad", SoftplusGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/avg_pool.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/avg_pool.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d398d28c5d20133c7316a87d4e07c2bdf076f079 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/avg_pool.cpp @@ -0,0 +1,158 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct AdaptiveAvgPoolNDGradGradCaptureState : public AutoGradCaptureState { + bool input_requires_grad = false; + bool grad_requires_grad = false; + std::vector pool_output_size; +}; + +template +class AdaptiveAvgPoolNdNdGradGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(AdaptiveAvgPoolNDGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, x + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + if (ctx->grad_requires_grad) { + const auto& grad_shape = *inputs[0]->shape(); + if (ndims == 1) { + ctx->pool_output_size = {grad_shape[grad_shape.size() - 1]}; + } else if (ndims == 2) { + ctx->pool_output_size = {grad_shape[grad_shape.size() - 2], + grad_shape[grad_shape.size() - 1]}; + } else if (ndims == 3) { + ctx->pool_output_size = {grad_shape[grad_shape.size() - 3], + grad_shape[grad_shape.size() - 2], + grad_shape[grad_shape.size() - 1]}; + } else { + UNIMPLEMENTED_THEN_RETURN(); + } + } + return Maybe::Ok(); + } + + Maybe Apply(const AdaptiveAvgPoolNDGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(2); + + if (ctx->grad_requires_grad) { + if (ndims == 1) { + (*in_grads)[0] = JUST(functional::AdaptiveAvgPool1D(out_grads[0], ctx->pool_output_size)); + } else if (ndims == 2) { + (*in_grads)[0] = JUST(functional::AdaptiveAvgPool2D(out_grads[0], ctx->pool_output_size)); + } else if (ndims == 3) { + (*in_grads)[0] = JUST(functional::AdaptiveAvgPool3D(out_grads[0], ctx->pool_output_size)); + } else { + UNIMPLEMENTED_THEN_RETURN(); + } + } + if (ctx->input_requires_grad) { (*in_grads)[1] = JUST(functional::ZerosLike(out_grads[0])); } + return Maybe::Ok(); + } +}; + +struct AvgPoolGradGradCaptureState : public AutoGradCaptureState { + bool input_requires_grad = false; + bool grad_requires_grad = false; + + std::string data_format; + std::vector padding; + std::vector kernel_size; + std::vector stride; + bool ceil_mode = false; + bool count_include_pad = false; + int32_t divisor_override = 0; +}; + +class AvgPoolNdGradGrad : public OpExprGradFunction { + public: + virtual ~AvgPoolNdGradGrad() = default; + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + Maybe Capture(AvgPoolGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, x + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); + ctx->padding = JUST(composed_attrs.GetAttr>("padding")); + ctx->kernel_size = JUST(composed_attrs.GetAttr>("kernel_size")); + ctx->stride = JUST(composed_attrs.GetAttr>("stride")); + ctx->ceil_mode = JUST(composed_attrs.GetAttr("ceil_mode")); + ctx->count_include_pad = JUST(composed_attrs.GetAttr("count_include_pad")); + ctx->divisor_override = JUST(composed_attrs.GetAttr("divisor_override")); + + return Maybe::Ok(); + } + Maybe Apply(const AvgPoolGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(2); + + if (ctx->grad_requires_grad) { + int32_t ndims = ctx->kernel_size.size(); + const auto pool_op = + (ndims == 1 ? functional::AvgPool1D + : (ndims == 2 ? functional::AvgPool2D + : (ndims == 3 ? functional::AvgPool3D : nullptr))); + CHECK_NOTNULL_OR_RETURN(pool_op); // NOLINT(maybe-need-error-msg) + (*in_grads)[0] = + JUST(pool_op(out_grads[0], ctx->kernel_size, ctx->stride, ctx->padding, ctx->ceil_mode, + ctx->count_include_pad, ctx->divisor_override, ctx->data_format)); + } + if (ctx->input_requires_grad) { (*in_grads)[1] = JUST(functional::ZerosLike(out_grads[0])); } + + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("avg_pool_1d_grad", AvgPoolNdGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("avg_pool_2d_grad", AvgPoolNdGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("avg_pool_3d_grad", AvgPoolNdGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_avg_pool1d_grad", AdaptiveAvgPoolNdNdGradGrad<1>); +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_avg_pool2d_grad", AdaptiveAvgPoolNdNdGradGrad<2>); +REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_avg_pool3d_grad", AdaptiveAvgPoolNdNdGradGrad<3>); +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_loss.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_loss.cpp new file mode 100644 index 0000000000000000000000000000000000000000..bdc87c2eb2c54ceed0fc4043ff55f3bbf81aed60 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_loss.cpp @@ -0,0 +1,114 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct BinaryCrossEntropyGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + bool has_weight = false; +}; + +class BinaryCrossEntropyGradGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(BinaryCrossEntropyGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const BinaryCrossEntropyGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; +}; + +Maybe BinaryCrossEntropyGradGrad::Init(const OpExpr& op) { return Maybe::Ok(); } + +Maybe BinaryCrossEntropyGradGrad::Capture(BinaryCrossEntropyGradGradCaptureState* ctx, + const TensorTuple& inputs, + const TensorTuple& outputs, + const AttrMap& attrs) const { + // dy, input, target[, weight] + CHECK_OR_RETURN(inputs.size() >= 3 && inputs.size() <= 4); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->target_requires_grad = inputs[2]->requires_grad(); + ctx->has_weight = inputs.size() == 4; + + ctx->SaveTensorForBackward(inputs[0]); // grad + ctx->SaveTensorForBackward(inputs[1]); // input + ctx->SaveTensorForBackward(inputs[2]); // target + if (ctx->has_weight) { + ctx->SaveTensorForBackward(inputs[3]); // weight + } + return Maybe::Ok(); +} +Maybe BinaryCrossEntropyGradGrad::Apply(const BinaryCrossEntropyGradGradCaptureState* ctx, + const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), + 3 + ctx->has_weight); // NOLINT(maybe-need-error-msg) + in_grads->resize(3 + ctx->has_weight); + const auto& grad = ctx->SavedTensors()[0]; + const auto& input = ctx->SavedTensors()[1]; + const auto& target = ctx->SavedTensors()[2]; + + // dx = grad * [-target/input + (1-target)/(1-input)] + // grad_for_grad = out_grad * [-target/input + (1-target)/(1-input)] + // grad_for_input = out_grad * grad * [target/(input*input) + (1-target)/((1-input)*(1-input))] + // = out_grad * grad * [(input*input-2*input*target+target)/(input*(1-input))^2] + // grad_for_target = out_grad * grad * [1/(input*(1-input))] + if (ctx->grad_requires_grad) { + const auto& weight = ctx->has_weight ? Optional(ctx->SavedTensors()[3]) : NullOpt; + (*in_grads)[0] = + JUST(functional::BinaryCrossEntropyLossGrad(out_grads[0], input, target, weight)); + } + if (ctx->input_requires_grad) { + auto one_sub_input = JUST(functional::ScalarSub(1, input, /*alpha=*/1)); + auto input_mul_target = JUST(functional::Mul(input, target)); + auto numerator = + JUST(functional::sequence_function(functional::Square) + .then(std::bind(functional::Sub, std::placeholders::_1, input_mul_target, + /*alpha=*/2, /*inplace=*/false)) + .then([&target](const std::shared_ptr& in) { + return functional::Add(in, target, /*alpha=*/1, /*inplace=*/false); + }) + .call(input)); + auto res = JUST(functional::sequence_function(functional::Mul) + .then(functional::Square) + .then(std::bind(functional::Div, numerator, std::placeholders::_1)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::Mul, std::placeholders::_1, grad)) + .call(input, one_sub_input)); + (*in_grads)[1] = ctx->has_weight ? JUST(functional::Mul(ctx->SavedTensors()[3], res)) : res; + } + if (ctx->target_requires_grad) { + auto input_sub_one = JUST(functional::ScalarAdd(-1, input, /*alpha=*/1)); + auto res = JUST(functional::sequence_function(functional::Mul) + .then(std::bind(functional::LogGrad, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::Mul, std::placeholders::_1, grad)) + .call(input, input_sub_one)); + (*in_grads)[2] = ctx->has_weight ? JUST(functional::Mul(ctx->SavedTensors()[3], res)) : res; + } + + return Maybe::Ok(); +} +REGISTER_OP_EXPR_GRAD_FUNCTION("binary_cross_entropy_grad", BinaryCrossEntropyGradGrad); +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fea7ed5234e2d60293193726600b4900710e5974 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits.cpp @@ -0,0 +1,139 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct BinaryCrossEntropyWithLogitsGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + bool has_weight = false; + bool has_pos_weight = false; +}; + +class BinaryCrossEntropyWithLogitsGradGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(BinaryCrossEntropyWithLogitsGradGradCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override; + Maybe Apply(const BinaryCrossEntropyWithLogitsGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe BinaryCrossEntropyWithLogitsGradGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} +Maybe BinaryCrossEntropyWithLogitsGradGrad::Capture( + BinaryCrossEntropyWithLogitsGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // dy, input, target[, weight][, pos_weight] + CHECK_OR_RETURN(inputs.size() >= 3 && inputs.size() <= 5); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->target_requires_grad = inputs[2]->requires_grad(); + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->has_pos_weight = JUST(composed_attrs.GetAttr("has_pos_weight")); + ctx->has_weight = inputs.size() == 5 || (inputs.size() == 4 && !ctx->has_pos_weight); + ctx->SaveTensorForBackward(inputs[0]); // grad + ctx->SaveTensorForBackward(inputs[1]); // input + ctx->SaveTensorForBackward(inputs[2]); // target + + if (inputs.size() == 4) { + ctx->SaveTensorForBackward(inputs[3]); // weight or pos_weight + } + if (inputs.size() == 5) { + ctx->SaveTensorForBackward(inputs[3]); // weight + ctx->SaveTensorForBackward(inputs[4]); // pos_weight + } + return Maybe::Ok(); +} +Maybe BinaryCrossEntropyWithLogitsGradGrad::Apply( + const BinaryCrossEntropyWithLogitsGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(ctx->SavedTensors().size(), + 3 + ctx->has_weight + ctx->has_pos_weight); // NOLINT(maybe-need-error-msg) + in_grads->resize(3 + ctx->has_weight + ctx->has_pos_weight); + const auto& grad = ctx->SavedTensors()[0]; + const auto& input = ctx->SavedTensors()[1]; + const auto& target = ctx->SavedTensors()[2]; + const size_t pos_weight_index = ctx->has_weight ? 4 : 3; + const auto& weight = ctx->has_weight ? Optional(ctx->SavedTensors()[3]) : NullOpt; + const auto& pos_weight = + ctx->has_pos_weight ? Optional(ctx->SavedTensors()[pos_weight_index]) : NullOpt; + + // dx = grad * weight * (-target*(1-input.sigmoid())*pos_weight + input.sigmoid()*(1-target)) + // grad_for_input = out_grad * grad * weight * sig * (1-sig) * [pos_weight * target + 1 - target] + // grad_for_target = -out_grad * grad * weight * [pos_weight + sig - pos_weight * sig] + if (ctx->grad_requires_grad) { + (*in_grads)[0] = JUST(functional::BinaryCrossEntropyWithLogitsLossGrad( + out_grads[0], input, target, weight, pos_weight)); + } + if (ctx->input_requires_grad) { + auto res = JUST(functional::sequence_function(functional::Sigmoid) + .then(std::bind(functional::SigmoidGrad, std::placeholders::_1, grad)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .call(input)); + if (ctx->has_pos_weight) { + res = JUST(functional::sequence_function(functional::Mul) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/Scalar(1)); + }) + .then(std::bind(functional::Sub, std::placeholders::_1, target, /*alpha=*/1, + /*inplace=*/false)) + .then(std::bind(functional::Mul, std::placeholders::_1, res)) + .call(JUST(pos_weight), target)); + } + if (ctx->has_weight) { res = JUST(functional::Mul(res, JUST(weight))); } + (*in_grads)[1] = res; + } + if (ctx->target_requires_grad) { + auto res = JUST(functional::sequence_function(functional::Mul) + .then(functional::Negative) + .call(out_grads[0], grad)); + if (ctx->has_pos_weight) { + auto sig = JUST(functional::Sigmoid(input)); + auto one_sub_sig = JUST(functional::ScalarSub(1, sig, /*alpha=*/1)); + res = JUST(functional::sequence_function(functional::Mul) + .then([&sig](const std::shared_ptr& input) { + return functional::Add(input, sig, /*alpha=*/Scalar(1), /*inplace=*/false); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, res)) + .call(one_sub_sig, JUST(pos_weight))); + } + if (ctx->has_weight) { res = JUST(functional::Mul(res, JUST(weight))); } + (*in_grads)[2] = res; + } + + return Maybe::Ok(); +} +REGISTER_OP_EXPR_GRAD_FUNCTION("binary_cross_entropy_with_logits_grad", + BinaryCrossEntropyWithLogitsGradGrad); +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d4d00cf999c58758064852a62b51e6aca91cebd2 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/binary_cross_entropy_with_logits_reduce_mean.cpp @@ -0,0 +1,116 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct BinaryCrossEntropyWithLogitsReduceMeanGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + + size_t grad_index = 0; + size_t input_index = 0; + size_t target_index = 0; +}; + +class BinaryCrossEntropyWithLogitsReduceMeanGradGrad + : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(BinaryCrossEntropyWithLogitsReduceMeanGradGradCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override; + Maybe Apply(const BinaryCrossEntropyWithLogitsReduceMeanGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const override; +}; + +Maybe BinaryCrossEntropyWithLogitsReduceMeanGradGrad::Init(const OpExpr& op) { + return Maybe::Ok(); +} + +Maybe BinaryCrossEntropyWithLogitsReduceMeanGradGrad::Capture( + BinaryCrossEntropyWithLogitsReduceMeanGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // dy, input, target + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->target_requires_grad = inputs[2]->requires_grad(); + + if (ctx->input_requires_grad || ctx->target_requires_grad) { + ctx->grad_index = ctx->SaveTensorForBackward(inputs[0]); // grad + } + if (ctx->input_requires_grad || ctx->grad_requires_grad) { + ctx->input_index = ctx->SaveTensorForBackward(inputs[1]); // input + } + if (ctx->grad_requires_grad) { + ctx->target_index = ctx->SaveTensorForBackward(inputs[2]); // target + } + return Maybe::Ok(); +} + +Maybe BinaryCrossEntropyWithLogitsReduceMeanGradGrad::Apply( + const BinaryCrossEntropyWithLogitsReduceMeanGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(3); + + // dx = grad * weight * (input.sigmoid() - target) + // grad_for_input = out_grad * grad * weight * sig * (1-sig) + // grad_for_target = -out_grad * grad * weight + if (ctx->grad_requires_grad) { + const auto& input = JUST(VectorAt(ctx->SavedTensors(), ctx->input_index)); + const auto& target = JUST(VectorAt(ctx->SavedTensors(), ctx->target_index)); + (*in_grads)[0] = JUST( + functional::sequence_function(functional::Sigmoid) + .then(std::bind(functional::Sub, std::placeholders::_1, target, /*alpha=*/1, + /*inplace=*/false)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::ReduceMean, std::placeholders::_1, std::vector{}, + /*keepdim=*/false)) + .call(input)); + } + if (ctx->input_requires_grad) { + const auto& grad = JUST(VectorAt(ctx->SavedTensors(), ctx->grad_index)); + const auto& input = JUST(VectorAt(ctx->SavedTensors(), ctx->input_index)); + const auto& mean_grad = JUST(functional::ScalarMul(1.0 / out_grads[0]->nelement(), grad)); + (*in_grads)[1] = + JUST(functional::sequence_function(functional::Sigmoid) + .then(std::bind(functional::SigmoidGrad, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::Mul, std::placeholders::_1, mean_grad)) + .call(input)); + } + if (ctx->target_requires_grad) { + const auto& grad = JUST(VectorAt(ctx->SavedTensors(), ctx->grad_index)); + const auto& mean_grad = JUST(functional::ScalarMul(1.0 / out_grads[0]->nelement(), grad)); + (*in_grads)[2] = JUST(functional::sequence_function(functional::Mul) + .then(functional::Negative) + .call(out_grads[0], mean_grad)); + } + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("binary_cross_entropy_with_logits_reduce_mean_grad", + BinaryCrossEntropyWithLogitsReduceMeanGradGrad); + +} // namespace one + +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/conv.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/conv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ad5c328bf790e1646d712159a986c6e88b3d5073 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/conv.cpp @@ -0,0 +1,228 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct ConvDataGradGradCaptureState : public AutoGradCaptureState { + bool w_requires_grad = false; + bool grad_requires_grad = false; + + size_t w_index = 0; + size_t grad_index = 0; + + std::string data_format; + std::vector padding_before; + std::vector kernel_size; + std::vector strides; + std::vector dilation_rate; + int32_t groups = 0; +}; + +class ConvDataGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(ConvDataGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const ConvDataGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe ConvDataGradGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe ConvDataGradGrad::Capture(ConvDataGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // input: dy, w, x_like, [add to output] + // output: dx + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->w_requires_grad = inputs.at(1)->requires_grad(); + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); + + if (ctx->grad_requires_grad) { ctx->w_index = ctx->SaveTensorForBackward(inputs.at(1)); } + if (ctx->w_requires_grad) { ctx->grad_index = ctx->SaveTensorForBackward(inputs.at(0)); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); + ctx->padding_before = JUST(composed_attrs.GetAttr>("padding_before")); + ctx->kernel_size = JUST(composed_attrs.GetAttr>("kernel_size")); + ctx->strides = JUST(composed_attrs.GetAttr>("strides")); + ctx->dilation_rate = JUST(composed_attrs.GetAttr>("dilation_rate")); + ctx->groups = JUST(composed_attrs.GetAttr("groups")); + return Maybe::Ok(); +} + +Maybe ConvDataGradGrad::Apply(const ConvDataGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + in_grads->resize(3); + size_t num_spatial_dims = ctx->kernel_size.size(); + + // first order forward: ConvND + // x * w = y ( * => convolution) + // first order backward: + // x_grad = y_grad * w.rot180 (y.shape * w.shape -> x.shape) call ConvDataGrad + // w_grad = x * y_grad (x.shape * y.shape -> w.shape) call ConvFilterGrad + + // second order forward (first order backward): ConvDataGrad + // y_grad * w.rot180 = x_grad + // second order forward: + // w_grad_grad = out_grads_x * y_grad (x.shape * y.shape -> w.shape) call ConvFilterGrad + // grad_for_y_grad = out_grads_x * w (x.shape * w.shape -> y.shape) call ConvND + + // w_grad_grad + if (ctx->w_requires_grad) { + const auto& grad = ctx->SavedTensors().at(ctx->grad_index); + in_grads->at(1) = JUST(functional::ConvFilterGrad( + grad, out_grads.at(0), num_spatial_dims, ctx->kernel_size, ctx->strides, + ctx->padding_before, ctx->dilation_rate, ctx->groups, ctx->data_format)); + } + + // grad_for_y_grad + if (ctx->grad_requires_grad) { + const auto& w = ctx->SavedTensors().at(ctx->w_index); + const int32_t ndims = ctx->kernel_size.size(); + const auto conv_op = (ndims == 1 ? functional::Conv1d + : (ndims == 2 ? functional::Conv2d + : (ndims == 3 ? functional::Conv3d : nullptr))); + CHECK_NOTNULL_OR_RETURN(conv_op); // NOLINT(maybe-need-error-msg) + in_grads->at(0) = + JUST(conv_op(out_grads.at(0), w, Optional(), ctx->strides, ctx->padding_before, + ctx->dilation_rate, ctx->groups, ctx->data_format)); + } + + return Maybe::Ok(); +} + +struct ConvFilterGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + + size_t x_index = 0; + size_t grad_index = 0; + + std::string data_format; + std::vector padding_before; + std::vector kernel_size; + std::vector strides; + std::vector dilation_rate; + int32_t groups = 0; +}; + +class ConvFilterGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(ConvFilterGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const ConvFilterGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe ConvFilterGradGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe ConvFilterGradGrad::Capture(ConvFilterGradGradCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + // input: dy, x + // output: dw + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(1)->requires_grad(); + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); + + ctx->x_index = ctx->SaveTensorForBackward(inputs.at(1)); + if (ctx->x_requires_grad) { ctx->grad_index = ctx->SaveTensorForBackward(inputs.at(0)); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->data_format = JUST(composed_attrs.GetAttr("data_format")); + ctx->padding_before = JUST(composed_attrs.GetAttr>("padding_before")); + ctx->kernel_size = JUST(composed_attrs.GetAttr>("kernel_size")); + ctx->strides = JUST(composed_attrs.GetAttr>("strides")); + ctx->dilation_rate = JUST(composed_attrs.GetAttr>("dilation_rate")); + ctx->groups = JUST(composed_attrs.GetAttr("groups")); + return Maybe::Ok(); +} + +Maybe ConvFilterGradGrad::Apply(const ConvFilterGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + in_grads->resize(2); + size_t num_spatial_dims = ctx->kernel_size.size(); + + // first order forward: ConvND + // x * w = y ( * => convolution) + // first order backward: + // x_grad = y_grad * w.rot180 (y.shape * w.shape -> x.shape) call ConvDataGrad + // w_grad = x * y_grad (x.shape * y.shape -> w.shape) call ConvFilterGrad + + // second order forward (first order backward): ConvFilterGrad + // x * y_grad = w_grad + // second order backward: + // x_grad_grad = out_grads_w * y_grad.rot180 (y.shape * w.shape -> x.shape) call ConvDataGrad + // grad_for_y_grad = x * out_grads_w (x.shape * w.shape -> y.shape) call ConvND + + // x_grad_grad + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(ctx->grad_index); + const auto& x = ctx->SavedTensors().at(ctx->x_index); + in_grads->at(1) = JUST(functional::ConvDataGrad( + grad, out_grads.at(0), JUST(x->detach()), num_spatial_dims, ctx->kernel_size, ctx->strides, + ctx->padding_before, ctx->dilation_rate, ctx->groups, ctx->data_format)); + } + + // grad_for_y_grad + if (ctx->grad_requires_grad) { + const auto& x = ctx->SavedTensors().at(ctx->x_index); + const int32_t ndims = ctx->kernel_size.size(); + const auto conv_op = (ndims == 1 ? functional::Conv1d + : (ndims == 2 ? functional::Conv2d + : (ndims == 3 ? functional::Conv3d : nullptr))); + CHECK_NOTNULL_OR_RETURN(conv_op); // NOLINT(maybe-need-error-msg) + in_grads->at(0) = + JUST(conv_op(x, out_grads.at(0), Optional(), ctx->strides, ctx->padding_before, + ctx->dilation_rate, ctx->groups, ctx->data_format)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("conv_data_grad", ConvDataGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("conv_filter_grad", ConvFilterGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/div.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/div.cpp new file mode 100644 index 0000000000000000000000000000000000000000..37d42b9da351eaa4d53ec1ffb8975e32ac8dc091 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/div.cpp @@ -0,0 +1,102 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct DivGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool z_requires_grad = false; + bool grad_requires_grad = false; + + size_t y_index = 0; + size_t z_index = 1; + size_t grad_index = 2; +}; + +class DivGradGrad : public OpExprGradFunction { + // div_grad = -x/(y*y)*dz = -z/y*dz + // div_grad_y = out_grad * z*dz/(y*y) + // div_grad_z = out_grad * -dz/y + // div_grad_dz = out_grad * -z/y + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(DivGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dz, z, y + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs.at(0)->requires_grad(); + ctx->z_requires_grad = inputs.at(1)->requires_grad(); + ctx->y_requires_grad = inputs.at(2)->requires_grad(); + + ctx->y_index = ctx->SaveTensorForBackward(inputs.at(2)); + if (ctx->y_requires_grad || ctx->grad_requires_grad) { + ctx->z_index = ctx->SaveTensorForBackward(inputs.at(1)); + } + if (ctx->y_requires_grad || ctx->z_requires_grad) { + ctx->grad_index = ctx->SaveTensorForBackward(inputs.at(0)); + } + + return Maybe::Ok(); + } + + Maybe Apply(const DivGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(3); + const auto& y = ctx->SavedTensors().at(ctx->y_index); + + if (ctx->grad_requires_grad) { + const auto& z = ctx->SavedTensors().at(ctx->z_index); + in_grads->at(0) = JUST(functional::sequence_function(functional::Mul) + .then(functional::Negative) + .then(std::bind(functional::Div, std::placeholders::_1, y)) + .call(out_grads.at(0), z)); + } + if (ctx->z_requires_grad) { + const auto& grad = ctx->SavedTensors().at(ctx->grad_index); + in_grads->at(1) = JUST(functional::sequence_function(functional::Mul) + .then(functional::Negative) + .then(std::bind(functional::Div, std::placeholders::_1, y)) + .call(out_grads.at(0), grad)); + } + if (ctx->y_requires_grad) { + const auto& z = ctx->SavedTensors().at(ctx->z_index); + const auto& grad = ctx->SavedTensors().at(ctx->grad_index); + in_grads->at(2) = JUST( + functional::sequence_function(functional::Mul) + .then(std::bind(functional::BroadcastReduceSumLike, std::placeholders::_1, y)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads.at(0))) + .then(std::bind(functional::Div, std::placeholders::_1, JUST(functional::Square(y)))) + .call(z, grad)); + } + + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_div_grad", DivGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/kl_div_loss.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/kl_div_loss.cpp new file mode 100644 index 0000000000000000000000000000000000000000..42da43c42084d784efa98c911b5b6b376b921272 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/kl_div_loss.cpp @@ -0,0 +1,89 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct KLDivLossGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + bool log_target = false; + + size_t input_index = 0; + size_t target_index = 0; +}; + +class KLDivLossGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(KLDivLossGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const KLDivLossGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe KLDivLossGradGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} +Maybe KLDivLossGradGrad::Capture(KLDivLossGradGradCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + // grad, input, target + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->target_requires_grad = inputs[2]->requires_grad(); + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->log_target = JUST(composed_attrs.GetAttr("log_target")); + + if (ctx->grad_requires_grad) { + ctx->input_index = ctx->SaveTensorForBackward(inputs[1]); // input + ctx->target_index = ctx->SaveTensorForBackward(inputs[2]); // target + } + + return Maybe::Ok(); +} +Maybe KLDivLossGradGrad::Apply(const KLDivLossGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(3); + + if (ctx->grad_requires_grad) { + const auto& input = JUST(VectorAt(ctx->SavedTensors(), ctx->input_index)); + const auto& target = JUST(VectorAt(ctx->SavedTensors(), ctx->target_index)); + (*in_grads)[0] = JUST(functional::KLDivLossGrad(out_grads[0], input, target, ctx->log_target)); + } + if (ctx->input_requires_grad) { (*in_grads)[1] = JUST(functional::ZerosLike(out_grads[0])); } + if (ctx->target_requires_grad) { (*in_grads)[2] = JUST(functional::ZerosLike(out_grads[0])); } + + return Maybe::Ok(); +} +REGISTER_OP_EXPR_GRAD_FUNCTION("kl_div_loss_grad", KLDivLossGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/log_softmax.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/log_softmax.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ef30d203d162cca2fe053d2dbab58402f1c6dce8 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/log_softmax.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct LogSoftmaxGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool dy_requires_grad = false; +}; + +class LogSoftmaxGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(LogSoftmaxGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const LogSoftmaxGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; +}; + +Maybe LogSoftmaxGradGrad::Init(const OpExpr& op) { return Maybe::Ok(); } + +Maybe LogSoftmaxGradGrad::Capture(LogSoftmaxGradGradCaptureState* ctx, + const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const { + // y, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->y_requires_grad = inputs[0]->requires_grad(); + ctx->dy_requires_grad = inputs[1]->requires_grad(); + + ctx->SaveTensorForBackward(inputs[0]); + if (ctx->y_requires_grad) ctx->SaveTensorForBackward(inputs[1]); + return Maybe::Ok(); +} + +Maybe LogSoftmaxGradGrad::Apply(const LogSoftmaxGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + in_grads->resize(2); + const auto& y = ctx->SavedTensors()[0]; + const std::vector reduce_axis{static_cast(y->ndim() - 1)}; + + if (ctx->y_requires_grad) { + const auto& dy = ctx->SavedTensors()[1]; + in_grads->at(0) = + JUST(functional::sequence_function(functional::ReduceSum) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::Mul, std::placeholders::_1, JUST(functional::Exp(y)))) + .then(functional::Negative) + .call(dy, reduce_axis, true)); + } + if (ctx->dy_requires_grad) { + in_grads->at(1) = + JUST(functional::sequence_function(functional::Exp) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .then(std::bind(functional::ReduceSum, std::placeholders::_1, reduce_axis, + /*keepdim=*/true)) + .then(std::bind(functional::Sub, out_grads[0], std::placeholders::_1, /*alpha=*/1, + /*inplace=*/false)) + .call(y)); + } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("log_softmax_grad", LogSoftmaxGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/math_unary_op.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/math_unary_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6134de2114960843cf4d1b4593ba0442a24d181f --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/math_unary_op.cpp @@ -0,0 +1,133 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct UnaryMathGradGradState : public AutoGradCaptureState { + bool input_requires_grad = false; + bool grad_requires_grad = false; +}; + +typedef Maybe (*UnaryBwFunc)(const std::shared_ptr&, + const std::shared_ptr&); + +template +class UnaryMathGradGrad : public OpExprGradFunction { + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(UnaryMathGradGradState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = inputs[0]->requires_grad(); + ctx->grad_requires_grad = inputs[1]->requires_grad(); + ctx->SaveTensorForBackward(inputs[0]); + if (ctx->input_requires_grad) { ctx->SaveTensorForBackward(inputs[1]); } + return Maybe::Ok(); + } + Maybe Apply(const UnaryMathGradGradState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& input = ctx->SavedTensors()[0]; + if (ctx->input_requires_grad) { + const auto& grad = ctx->SavedTensors()[1]; + (*in_grads)[0] = JUST(functional::Mul(out_grads[0], JUST(BwBwFunc(input, grad)))); + } + if (ctx->grad_requires_grad) { (*in_grads)[1] = JUST(BwFunc(input, out_grads[0])); } + return Maybe::Ok(); + } +}; + +template +class UnaryMathGradGradWithZeroDDX : public OpExprGradFunction { + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(UnaryMathGradGradState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->input_requires_grad = inputs[0]->requires_grad(); + ctx->grad_requires_grad = inputs[1]->requires_grad(); + ctx->SaveTensorForBackward(inputs[0]); + return Maybe::Ok(); + } + Maybe Apply(const UnaryMathGradGradState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + const auto& input = ctx->SavedTensors()[0]; + if (ctx->input_requires_grad) { (*in_grads)[0] = JUST(functional::ZerosLike(input)); } + if (ctx->grad_requires_grad) { (*in_grads)[1] = JUST(BwFunc(input, out_grads[0])); } + return Maybe::Ok(); + } +}; + +// TODO: Lgamma, first order backward unimplemented +#define MATH_UNARY_ELEMENTWISE_GRAD_GRAD_DY_X_FUNC_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("sin_grad", Sin) \ + OF_PP_MAKE_TUPLE_SEQ("cos_grad", Cos) \ + OF_PP_MAKE_TUPLE_SEQ("tan_grad", Tan) \ + OF_PP_MAKE_TUPLE_SEQ("sinh_grad", Sinh) \ + OF_PP_MAKE_TUPLE_SEQ("cosh_grad", Cosh) \ + OF_PP_MAKE_TUPLE_SEQ("tanh_grad", Tanh) \ + OF_PP_MAKE_TUPLE_SEQ("asin_grad", Asin) \ + OF_PP_MAKE_TUPLE_SEQ("acos_grad", Acos) \ + OF_PP_MAKE_TUPLE_SEQ("atan_grad", Atan) \ + OF_PP_MAKE_TUPLE_SEQ("asinh_grad", Asinh) \ + OF_PP_MAKE_TUPLE_SEQ("acosh_grad", Acosh) \ + OF_PP_MAKE_TUPLE_SEQ("atanh_grad", Atanh) \ + OF_PP_MAKE_TUPLE_SEQ("erf_grad", Erf) \ + OF_PP_MAKE_TUPLE_SEQ("erfc_grad", Erfc) \ + OF_PP_MAKE_TUPLE_SEQ("exp_grad", Exp) \ + OF_PP_MAKE_TUPLE_SEQ("expm1_grad", Expm1) \ + OF_PP_MAKE_TUPLE_SEQ("log_grad", Log) \ + OF_PP_MAKE_TUPLE_SEQ("log_sigmoid_grad", LogSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ("log2_grad", Log2) \ + OF_PP_MAKE_TUPLE_SEQ("log1p_grad", Log1p) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_grad", Reciprocal) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan_grad", ReciprocalNoNan) \ + OF_PP_MAKE_TUPLE_SEQ("rsqrt_grad", Rsqrt) \ + OF_PP_MAKE_TUPLE_SEQ("sqrt_grad", Sqrt) \ + OF_PP_MAKE_TUPLE_SEQ("square_grad", Square) + +#define MATH_UNARY_ELEMENTWISE_GRAD_GRAD_DY_Y_FUNC_SEQ OF_PP_MAKE_TUPLE_SEQ("sigmoid_grad", Sigmoid) + +#define MATH_UNARY_ELEMENTWISE_GRAD_GRAD_ZERO_DDX_FUNC_SEQ OF_PP_MAKE_TUPLE_SEQ("abs_grad", Abs) + +#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_GRAD_GRAD_CLASS(op_type_name, op_cls) \ + class op_cls##GradGradCls final \ + : public UnaryMathGradGrad {}; \ + REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, op_cls##GradGradCls); + +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_GRAD_GRAD_CLASS, + MATH_UNARY_ELEMENTWISE_GRAD_GRAD_DY_X_FUNC_SEQ); + +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_GRAD_GRAD_CLASS, + MATH_UNARY_ELEMENTWISE_GRAD_GRAD_DY_Y_FUNC_SEQ); + +#define INSTANTIAT_AND_REGISTER_UNARY_MATHOP_GRAD_GRAD_ZERO_DDX_CLASS(op_type_name, op_cls) \ + class op_cls##GradGradCls final \ + : public UnaryMathGradGradWithZeroDDX {}; \ + REGISTER_OP_EXPR_GRAD_FUNCTION(op_type_name, op_cls##GradGradCls); +OF_PP_FOR_EACH_TUPLE(INSTANTIAT_AND_REGISTER_UNARY_MATHOP_GRAD_GRAD_ZERO_DDX_CLASS, + MATH_UNARY_ELEMENTWISE_GRAD_GRAD_ZERO_DDX_FUNC_SEQ); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/matmul.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/matmul.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e83f3c2bd823e8e30682f40e630aba50e62b1dbb --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/matmul.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct BroadcastMatmulGradBGradCaptureState : public AutoGradCaptureState { + bool a_requires_grad = false; + bool b_requires_grad = false; + size_t a_index = 0; + size_t b_index = 1; + double alpha = 1.0; +}; + +class BroadcastMatmulGradBGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const UserOpExpr* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + }; + Maybe Capture(BroadcastMatmulGradBGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->a_requires_grad = inputs.at(0)->requires_grad(); + ctx->b_requires_grad = inputs.at(1)->requires_grad(); + if (ctx->a_requires_grad) { ctx->b_index = ctx->SaveTensorForBackward(inputs.at(1)); } + if (ctx->b_requires_grad) { ctx->a_index = ctx->SaveTensorForBackward(inputs.at(0)); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->alpha = JUST(composed_attrs.GetAttr("alpha")); + + return Maybe::Ok(); + } + Maybe Apply(const BroadcastMatmulGradBGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(2); + + // for matmul: input_a[dims..., m, k] * input_b[k, n] -> [dims..., m, n] + // if forward: BroadcastMatmulGradB(input_a, JUST(VectorAt(out_grads, 0)), ctx->alpha)) + // then: a.shape = [dims..., m, k], b.shape = [dims..., m, n], grad.shape = [k, n] + // if forward: BroadcastMatmulGradB(JUST(VectorAt(out_grads, 0)), input_a, ctx->alpha)) + // then: a.shape = [dims..., m, n], b.shape = [dims..., m, k], grad.shape = [n, k] + if (ctx->a_requires_grad) { + const auto& b = ctx->SavedTensors()[ctx->b_index]; + in_grads->at(0) = JUST(functional::MatMul(b, out_grads.at(0), false, true, ctx->alpha)); + } + if (ctx->b_requires_grad) { + const auto& a = ctx->SavedTensors()[ctx->a_index]; + in_grads->at(1) = JUST(functional::MatMul(a, out_grads.at(0), false, false, ctx->alpha)); + } + + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("broadcast_matmul_grad_b", BroadcastMatmulGradBGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/max_pool.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/max_pool.cpp new file mode 100644 index 0000000000000000000000000000000000000000..884dceb4d3594416f2e8fae92828c17f443fed30 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/max_pool.cpp @@ -0,0 +1,68 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { +namespace one { + +struct MaxPoolGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; +}; + +template +class MaxPoolNdGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + + Maybe Capture(MaxPoolGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // dy, x, indice + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + if (ctx->grad_requires_grad) { ctx->SaveTensorForBackward(inputs[2]); } + + return Maybe::Ok(); + } + + Maybe Apply(const MaxPoolGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(3); + + if (ctx->grad_requires_grad) { + const auto& indices = JUST(VectorAt(ctx->SavedTensors(), 0)); + (*in_grads)[0] = JUST(functional::MaxPoolNdGradGrad(out_grads[0], indices, ndims)); + } + if (ctx->input_requires_grad) { (*in_grads)[1] = JUST(functional::ZerosLike(out_grads[0])); } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("max_pool_1d_grad", MaxPoolNdGradGrad<1>); +REGISTER_OP_EXPR_GRAD_FUNCTION("max_pool_2d_grad", MaxPoolNdGradGrad<2>); +REGISTER_OP_EXPR_GRAD_FUNCTION("max_pool_3d_grad", MaxPoolNdGradGrad<3>); +// REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool1d_grad", MaxPoolNdGradGrad<1>); +// REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool2d_grad", MaxPoolNdGradGrad<2>); +// REGISTER_OP_EXPR_GRAD_FUNCTION("adaptive_max_pool3d_grad", MaxPoolNdGradGrad<3>); +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/nll_loss.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/nll_loss.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6248cd6c9122a4f0542a61b5c39b2ce648628d9b --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/nll_loss.cpp @@ -0,0 +1,93 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { + +namespace one { + +struct NLLCaptureState : public AutoGradCaptureState { + bool input_requires_grad = false; + bool grad_requires_grad = false; + bool has_weight = false; + int64_t ignore_index = -100; +}; + +class NLLLossGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(NLLCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, + const AttrMap& attrs) const override; + Maybe Apply(const NLLCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; + + private: + AttrMap base_attrs_; +}; + +Maybe NLLLossGradGrad::Init(const OpExpr& op) { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); +} + +Maybe NLLLossGradGrad::Capture(NLLCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // dy, input, target[, weight] + CHECK_OR_RETURN(inputs.size() >= 3 && inputs.size() <= 4); // NOLINT(maybe-need-error-msg) + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->has_weight = inputs.size() == 4; + + if (ctx->grad_requires_grad) { + ctx->SaveTensorForBackward(inputs[2]); + if (ctx->has_weight) { ctx->SaveTensorForBackward(inputs[3]); } // weight + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->ignore_index = JUST(composed_attrs.GetAttr("ignore_index")); + } + + return Maybe::Ok(); +} + +Maybe NLLLossGradGrad::Apply(const NLLCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(3 + ctx->has_weight); + + if (ctx->grad_requires_grad) { + const auto& target = JUST(VectorAt(ctx->SavedTensors(), 0)); + if (ctx->has_weight) { + auto weight = JUST(VectorAt(ctx->SavedTensors(), 1)); + (*in_grads)[0] = + JUST(functional::NLLLoss(out_grads[0], target, weight, ctx->ignore_index, "none")); + } else { + (*in_grads)[0] = + JUST(functional::NLLLoss(out_grads[0], target, NullOpt, ctx->ignore_index, "none")); + } + } + if (ctx->input_requires_grad) { (*in_grads)[1] = JUST(functional::ZerosLike(out_grads[0])); } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("nll_grad", NLLLossGradGrad); + +} // namespace one + +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/pow.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/pow.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a8be9fe8fa75b6cfed5333eff83627c3f87f6ad0 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/pow.cpp @@ -0,0 +1,179 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { +struct PowXGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool y_requires_grad = false; + bool dz_requires_grad = false; + + size_t x_index = 0; + size_t y_index = 1; + size_t dz_index = 2; +}; + +class PowXGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(PowXGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, y, dz + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->y_requires_grad = inputs.at(1)->requires_grad(); + ctx->dz_requires_grad = inputs.at(2)->requires_grad(); + + ctx->x_index = ctx->SaveTensorForBackward(inputs.at(0)); + ctx->y_index = ctx->SaveTensorForBackward(inputs.at(1)); + if (ctx->x_requires_grad || ctx->y_requires_grad) { + ctx->dz_index = ctx->SaveTensorForBackward(inputs.at(2)); + } + return Maybe::Ok(); + } + + Maybe Apply(const PowXGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(3); + const auto& x = ctx->SavedTensors().at(ctx->x_index); + const auto& y = ctx->SavedTensors().at(ctx->y_index); + + // dx = y * x^(y-1) * dz + // grad_for_x = out_grads * dz * y * [x^(y-1)]' + // grad_for_y = out_grads * dz * [x^(y-1) * (1 + y * ln(x))] + // grad_for_dz = out_grads * y * x^(y-1) + + if (ctx->x_requires_grad || ctx->y_requires_grad) { + const auto& dz = ctx->SavedTensors().at(ctx->dz_index); + const auto& y_sub_one = JUST(functional::ScalarSub(y, 1, /*alpha=*/1, /*inplace=*/false)); + if (ctx->x_requires_grad) { + in_grads->at(0) = JUST(functional::sequence_function(functional::PowXGrad) + .then(std::bind(functional::Mul, std::placeholders::_1, y)) + .then(std::bind(functional::Mul, std::placeholders::_1, dz)) + .call(x, y_sub_one, out_grads.at(0))); + } + if (ctx->y_requires_grad) { + in_grads->at(1) = + JUST(functional::sequence_function(functional::Log) + .then(std::bind(functional::Mul, std::placeholders::_1, y)) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/1); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, + JUST(functional::Pow(x, y_sub_one)))) + .then(std::bind(functional::Mul, std::placeholders::_1, dz)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads.at(0))) + .call(x)); + } + } + if (ctx->dz_requires_grad) { + in_grads->at(2) = JUST(functional::PowXGrad(x, y, out_grads.at(0))); + } + return Maybe::Ok(); + } +}; + +struct PowYGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool y_requires_grad = false; + bool dz_requires_grad = false; + + size_t x_index = 0; + size_t y_index = 1; + size_t dz_index = 2; + size_t dy_index = 3; +}; + +class PowYGradGrad : public OpExprGradFunction { + public: + // dy = x^y*ln(x)*dz = z*ln(x)*dz + Maybe Init(const OpExpr& op) override { return Maybe::Ok(); } + Maybe Capture(PowYGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // x, y, dz + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->y_requires_grad = inputs.at(1)->requires_grad(); + ctx->dz_requires_grad = inputs.at(2)->requires_grad(); + + ctx->x_index = ctx->SaveTensorForBackward(inputs.at(0)); + if (ctx->x_requires_grad || ctx->y_requires_grad) { + ctx->y_index = ctx->SaveTensorForBackward(inputs.at(1)); + } + if (ctx->x_requires_grad) { ctx->dz_index = ctx->SaveTensorForBackward(inputs.at(2)); } + if (ctx->y_requires_grad) { ctx->dy_index = ctx->SaveTensorForBackward(outputs.at(0)); } + + return Maybe::Ok(); + } + + Maybe Apply(const PowYGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(3); + const auto& x = ctx->SavedTensors().at(ctx->x_index); + + // dy = x^y * ln(x) * dz = z * ln(x) * dz + // grad_for_x = out_grads * dz * [x^(y-1) * (1 + y * ln(x))] + // grad_for_y = out_grads * dy' = out_grads * dy * ln(x) + // grad_for_dz = out_grads * x^y * ln(x) + + if (ctx->x_requires_grad) { + const auto& y = ctx->SavedTensors().at(ctx->y_index); + const auto& dz = ctx->SavedTensors().at(ctx->dz_index); + const auto& y_sub_one = JUST(functional::ScalarSub(y, 1, /*alpha=*/1, /*inplace=*/false)); + in_grads->at(0) = + JUST(functional::sequence_function(functional::Log) + .then(std::bind(functional::Mul, std::placeholders::_1, y)) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/1); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, + JUST(functional::Pow(x, y_sub_one)))) + .then(std::bind(functional::Mul, std::placeholders::_1, dz)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads.at(0))) + .call(x)); + } + + if (ctx->y_requires_grad) { + const auto& dy = ctx->SavedTensors().at(ctx->dy_index); + in_grads->at(1) = + JUST(functional::sequence_function(functional::Log) + .then(std::bind(functional::Mul, std::placeholders::_1, dy)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads.at(0))) + .call(x)); + } + + if (ctx->dz_requires_grad) { + const auto& y = ctx->SavedTensors().at(ctx->y_index); + in_grads->at(2) = JUST(functional::PowYGrad(x, y, out_grads.at(0))); + } + return Maybe::Ok(); + } +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("pow_x_grad", PowXGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("pow_y_grad", PowYGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/scalar_pow.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/scalar_pow.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ced6914a8dc6e42ab813a5ff31efbc2155222d77 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/scalar_pow.cpp @@ -0,0 +1,153 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct ScalarPowGradGradCaptureState : public AutoGradCaptureState { + bool x_requires_grad = false; + bool grad_requires_grad = false; + Scalar operand; +}; + +class ScalarPowGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); + } + + Maybe Capture(ScalarPowGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!(ctx->x_requires_grad || ctx->grad_requires_grad)) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + bool has_float_operand = JUST(composed_attrs.GetAttr("has_float_operand")); + if (has_float_operand) { + ctx->operand = Scalar(JUST(composed_attrs.GetAttr("float_operand"))); + } else { + ctx->operand = Scalar(JUST(composed_attrs.GetAttr("int_operand"))); + } + ctx->SaveTensorForBackward(inputs.at(0)); + if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(inputs.at(1)); } + return Maybe::Ok(); + } + + Maybe Apply(const ScalarPowGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + const auto& x = ctx->SavedTensors().at(0); + in_grads->resize(2); + + // z = x^a, dx = a * x^(a-1) * dz + // grad_for_x = out_grad * a * dz * [x^(a-1)]' + // grad_for_dz = out_grad * [x^a]' + + if (ctx->x_requires_grad) { + const auto& grad = ctx->SavedTensors().at(1); + const auto operand_sub_one = ctx->operand - Scalar(1); + in_grads->at(0) = JUST( + functional::sequence_function(functional::Mul) + .then(std::bind(functional::ScalarPowGrad, x, std::placeholders::_1, operand_sub_one)) + .then([&ctx](const std::shared_ptr& input) { + return functional::ScalarMul(ctx->operand, input); + }) + .call(grad, out_grads.at(0))); + } + if (ctx->grad_requires_grad) { + in_grads->at(1) = JUST(functional::ScalarPowGrad(x, out_grads.at(0), ctx->operand)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +class ScalarReversePowGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); + } + + Maybe Capture(ScalarPowGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + + ctx->x_requires_grad = inputs.at(0)->requires_grad(); + ctx->grad_requires_grad = inputs.at(1)->requires_grad(); + if (!(ctx->x_requires_grad || ctx->grad_requires_grad)) { return Maybe::Ok(); } + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + bool has_float_operand = JUST(composed_attrs.GetAttr("has_float_operand")); + if (has_float_operand) { + ctx->operand = Scalar(JUST(composed_attrs.GetAttr("float_operand"))); + } else { + ctx->operand = Scalar(JUST(composed_attrs.GetAttr("int_operand"))); + } + ctx->SaveTensorForBackward(inputs.at(0)); + if (ctx->x_requires_grad) { ctx->SaveTensorForBackward(outputs.at(0)); } + return Maybe::Ok(); + } + + Maybe Apply(const ScalarPowGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + const auto& x = ctx->SavedTensors().at(0); + in_grads->resize(2); + + // z = a^x, dx = a^x * ln(a) * dz + // grad_for_x = out_grad * dz * a^x * ln(a) * ln(a) + // grad_for_dz = out_grad * [a^x]' + + if (ctx->x_requires_grad) { + const auto& dx = ctx->SavedTensors().at(1); + const auto log_operand = std::log(ctx->operand.As()); + in_grads->at(0) = JUST(functional::sequence_function(functional::Mul) + .then([&log_operand](const std::shared_ptr& input) { + return functional::ScalarMul(log_operand, input); + }) + .call(dx, out_grads.at(0))); + } + if (ctx->grad_requires_grad) { + in_grads->at(1) = JUST(functional::ScalarReversePowGrad(x, out_grads.at(0), ctx->operand)); + } + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("scalar_pow_grad", ScalarPowGradGrad); +REGISTER_OP_EXPR_GRAD_FUNCTION("scalar_reverse_pow_grad", ScalarReversePowGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/slice.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/slice.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8120d3768ce16994754c8d06de04cbb91dd9061e --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/slice.cpp @@ -0,0 +1,67 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct SliceGradGradCaptureState : public AutoGradCaptureState { + std::vector start; + std::vector stop; + std::vector step; +}; + +class SliceGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(SliceGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->start = JUST(composed_attrs.GetAttr>("start")); + ctx->stop = JUST(composed_attrs.GetAttr>("stop")); + ctx->step = JUST(composed_attrs.GetAttr>("step")); + return Maybe::Ok(); + } + + Maybe Apply(const SliceGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + in_grads->resize(1); + in_grads->at(0) = JUST(functional::Slice(out_grads.at(0), ctx->start, ctx->stop, ctx->step, + /*enable_view_slice=*/false)); + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("slice_grad", SliceGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/smooth_l1_loss.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/smooth_l1_loss.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d5712b7159a87dcc5f66eb5aa40748e3ead46889 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/smooth_l1_loss.cpp @@ -0,0 +1,103 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct SmoothL1LossGradGradCaptureState : public AutoGradCaptureState { + bool grad_requires_grad = false; + bool input_requires_grad = false; + bool target_requires_grad = false; + size_t grad_index = 0; + size_t input_index = 0; + size_t target_index = 0; + float beta = 0.0; +}; + +class SmoothL1LossGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override { + const auto* fw_op_expr = dynamic_cast(&op); + CHECK_NOTNULL_OR_RETURN(fw_op_expr); // NOLINT(maybe-need-error-msg) + base_attrs_ = MakeAttrMapFromUserOpConf(fw_op_expr->proto()); + return Maybe::Ok(); + } + + Maybe Capture(SmoothL1LossGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override { + // grad, input, target + CHECK_EQ_OR_RETURN(inputs.size(), 3); // NOLINT(maybe-need-error-msg) + + ctx->grad_requires_grad = inputs[0]->requires_grad(); + ctx->input_requires_grad = inputs[1]->requires_grad(); + ctx->target_requires_grad = inputs[2]->requires_grad(); + + if (ctx->input_requires_grad || ctx->target_requires_grad) { + ctx->grad_index = ctx->SaveTensorForBackward(inputs[0]); + } + ctx->input_index = ctx->SaveTensorForBackward(inputs[1]); + ctx->target_index = ctx->SaveTensorForBackward(inputs[2]); + + ComposedAttrMap composed_attrs(attrs, base_attrs_); + ctx->beta = JUST(composed_attrs.GetAttr("beta")); + return Maybe::Ok(); + } + + Maybe Apply(const SmoothL1LossGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override { + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(3); + const auto& input = JUST(VectorAt(ctx->SavedTensors(), ctx->input_index)); + const auto& target = JUST(VectorAt(ctx->SavedTensors(), ctx->target_index)); + + if (ctx->grad_requires_grad) { + (*in_grads)[0] = JUST(functional::SmoothL1LossGrad(out_grads[0], input, target, ctx->beta)); + } + if (ctx->input_requires_grad || ctx->target_requires_grad) { + const auto& grad = JUST(VectorAt(ctx->SavedTensors(), ctx->grad_index)); + auto condition = JUST(functional::sequence_function(functional::Sub) + .then(functional::Abs) + .then([&ctx](const std::shared_ptr& input) { + return functional::ScalarLogicalLess(input, ctx->beta); + }) + .call(input, target, /*alpha=*/1, /*inplace=*/false)); + auto out = JUST(functional::sequence_function(functional::Mul) + .then(std::bind(functional::Mul, std::placeholders::_1, condition)) + .then([&ctx](const std::shared_ptr& input) { + double inv_beta = ctx->beta == 0.0 ? 0.0 : 1.0 / ctx->beta; + return functional::ScalarMul(inv_beta, input); + }) + .call(out_grads[0], grad)); + if (ctx->input_requires_grad) { (*in_grads)[1] = out; } + if (ctx->target_requires_grad) { (*in_grads)[2] = JUST(functional::Negative(out)); } + } + + return Maybe::Ok(); + } + + private: + AttrMap base_attrs_; +}; + +REGISTER_OP_EXPR_GRAD_FUNCTION("smooth_l1_loss_grad", SmoothL1LossGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/autograd/higher_order_gradient_funcs/softmax.cpp b/oneflow/core/autograd/higher_order_gradient_funcs/softmax.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cb29a44857f69f19ea5c2ebd0297b8d4a9dccd41 --- /dev/null +++ b/oneflow/core/autograd/higher_order_gradient_funcs/softmax.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/sequence_function.h" + +namespace oneflow { +namespace one { + +struct SoftmaxGradGradCaptureState : public AutoGradCaptureState { + bool y_requires_grad = false; + bool dy_requires_grad = false; +}; + +class SoftmaxGradGrad : public OpExprGradFunction { + public: + Maybe Init(const OpExpr& op) override; + Maybe Capture(SoftmaxGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const override; + Maybe Apply(const SoftmaxGradGradCaptureState* ctx, const TensorTuple& out_grads, + TensorTuple* in_grads) const override; +}; + +Maybe SoftmaxGradGrad::Init(const OpExpr& op) { return Maybe::Ok(); } + +Maybe SoftmaxGradGrad::Capture(SoftmaxGradGradCaptureState* ctx, const TensorTuple& inputs, + const TensorTuple& outputs, const AttrMap& attrs) const { + // y, dy + CHECK_EQ_OR_RETURN(inputs.size(), 2); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs.size(), 1); // NOLINT(maybe-need-error-msg) + ctx->y_requires_grad = inputs[0]->requires_grad(); + ctx->dy_requires_grad = inputs[1]->requires_grad(); + + ctx->SaveTensorForBackward(inputs[0]); + if (ctx->y_requires_grad) ctx->SaveTensorForBackward(inputs[1]); + return Maybe::Ok(); +} + +Maybe SoftmaxGradGrad::Apply(const SoftmaxGradGradCaptureState* ctx, + const TensorTuple& out_grads, TensorTuple* in_grads) const { + in_grads->resize(2); + const auto& y = ctx->SavedTensors()[0]; + + if (ctx->y_requires_grad) { + const auto& dy = ctx->SavedTensors()[1]; + const std::vector reduce_axis{static_cast(y->ndim() - 1)}; + const auto& a = JUST(functional::sequence_function(functional::Mul) + .then(std::bind(functional::ReduceSum, std::placeholders::_1, + reduce_axis, /*keepdim=*/true)) + .then(std::bind(functional::Mul, std::placeholders::_1, dy)) + .call(y, out_grads[0])); + const auto& b = JUST(functional::sequence_function(functional::Mul) + .then(std::bind(functional::ReduceSum, std::placeholders::_1, + reduce_axis, /*keepdim=*/true)) + .then(std::bind(functional::Mul, std::placeholders::_1, out_grads[0])) + .call(y, dy)); + in_grads->at(0) = JUST(functional::sequence_function(functional::Mul) + .then(std::bind(functional::Sub, std::placeholders::_1, a, + /*alpha=*/1, /*inplace=*/false)) + .then(std::bind(functional::Sub, std::placeholders::_1, b, + /*alpha=*/1, /*inplace=*/false)) + .call(out_grads[0], dy)); + } + if (ctx->dy_requires_grad) { in_grads->at(1) = JUST(functional::SoftmaxGrad(out_grads[0], y)); } + + return Maybe::Ok(); +} + +REGISTER_OP_EXPR_GRAD_FUNCTION("softmax_grad", SoftmaxGradGrad); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/boxing/asymmetric_broadcast.cpp b/oneflow/core/boxing/asymmetric_broadcast.cpp index 9d6ed18891582f8161b710c258247d6e1f5e5ce4..d27a2a15dbe7a5e6a2dd64182a7e8e17a4556379 100644 --- a/oneflow/core/boxing/asymmetric_broadcast.cpp +++ b/oneflow/core/boxing/asymmetric_broadcast.cpp @@ -39,6 +39,8 @@ Maybe RawCheckAsymmetricBroadcast(Symbol in, Symbolnd_sbp())); CHECK_OR_RETURN(out->placement()->Bigger(*in->placement()) || in->placement()->Bigger(*out->placement())); + CHECK_OR_RETURN(in->placement()->device_type() == DeviceType::kCPU + || in->placement()->device_type() == DeviceType::kCUDA); // NOLINTEND(maybe-need-error-msg) return Maybe::Ok(); } @@ -76,16 +78,19 @@ Maybe CalBroadcastRoot(Symbol src_parallel_desc, static constexpr auto* CachedGetBroadcastRoot = DECORATE(&CalBroadcastRoot, ThreadLocalCached); -Maybe EagerNcclBroadcast(Symbol parallel_desc, int64_t root) { - return one::OpBuilder("eager_nccl_broadcast", *JUST(UniqueStr("eager_nccl_broadcast"))) +Maybe EagerCclBroadcast(Symbol parallel_desc, int64_t root, + const Shape& shape) { + return one::OpBuilder("eager_ccl_broadcast", *JUST(UniqueStr("eager_ccl_broadcast"))) .Input("in") .Output("out") .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Attr>("shape_list", {shape}) .Attr("root", root) .Build(); } -static constexpr auto* CachedEagerNcclBroadcast = DECORATE(&EagerNcclBroadcast, ThreadLocalCached); +static constexpr auto* CachedEagerCclBroadcast = + DECORATE(&EagerCclBroadcast, ThreadLocalCachedCopiable); } // namespace Maybe AsymmetricBroadcast(const std::shared_ptr& tensor, @@ -105,26 +110,19 @@ Maybe AsymmetricBroadcast(const std::shared_ptr& tenso if (out->placement()->Bigger(*in->placement())) { const auto& out_parallel_id = JUST(GetParallelId4CurrentProcessCtx(out_placement)); if (out_parallel_id->has_value()) { - const auto& in_parallel_id = JUST(GetParallelId4CurrentProcessCtx(in_placement)); - if (!in_parallel_id->has_value()) { - const std::string& device_type = in_placement->device_tag(); - local_tensor = - JUST(one::functional::Empty(*tensor->shape(), tensor->dtype(), - JUST(Device::New(device_type)), /*pin_memory=*/false)); - } const auto& broadcast_group = JUST(GetBroadcastGroup(in_placement, out_placement)); Symbol broadcast_placement_cur_rank = JUST(MapAt(*broadcast_group, GlobalProcessCtx::Rank())); int64_t root = JUST(CachedGetBroadcastRoot(in_placement, broadcast_placement_cur_rank)); std::shared_ptr op_expr = - JUST(CachedEagerNcclBroadcast(broadcast_placement_cur_rank, root)); + JUST(CachedEagerCclBroadcast(broadcast_placement_cur_rank, root, *tensor->shape())); local_tensor = JUST(one::OpInterpUtil::Dispatch(*op_expr, {local_tensor})); } } - return one::functional::LocalToConsistent(local_tensor, out_placement, - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype()); + return one::functional::LocalToGlobal(local_tensor, out_placement, + *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/false); } COMMAND(RegisterBoxingFunction("asymmetric-broadcast", CheckAsymmetricBroadcast, diff --git a/oneflow/core/boxing/boxing_interpreter_status.h b/oneflow/core/boxing/boxing_interpreter_status.h index c368d6fb75340d5e51d696fa2973e520f5b50a72..3e3e5459d103c7eb0725945634d560244a7821e7 100644 --- a/oneflow/core/boxing/boxing_interpreter_status.h +++ b/oneflow/core/boxing/boxing_interpreter_status.h @@ -85,17 +85,15 @@ namespace std { template<> struct hash { size_t operator()(const oneflow::BoxingInterpreterStatus& status) const { + using namespace oneflow; size_t ret = 0; - for (const auto& boxing_name : *status.sorted_boxing_names()) { - ret ^= std::hash()(boxing_name); - } - const auto& placed_nd_sbp_hash = std::hash(); - ret ^= placed_nd_sbp_hash(*status.src_placed_nd_sbp()); + for (const auto& boxing_name : *status.sorted_boxing_names()) { AddHash(&ret, boxing_name); } + AddHash(&ret, *status.src_placed_nd_sbp()); for (const auto& mid_placed_nd_sbp : *status.mid_placed_nd_sbp()) { - ret ^= placed_nd_sbp_hash(*mid_placed_nd_sbp); + AddHash(&ret, *mid_placed_nd_sbp); } - ret ^= placed_nd_sbp_hash(*status.dst_placed_nd_sbp()); - return hash()(ret); + AddHash(&ret, *status.dst_placed_nd_sbp()); + return ret; } }; diff --git a/oneflow/core/boxing/ccl_boxing_function.cpp b/oneflow/core/boxing/ccl_boxing_function.cpp index 456cdf8836ec21ef5e0935d67ecc7a6546c108cc..d3fa9fd2e597a2a816eabebc1935fb6a825f226a 100644 --- a/oneflow/core/boxing/ccl_boxing_function.cpp +++ b/oneflow/core/boxing/ccl_boxing_function.cpp @@ -13,16 +13,55 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/framework/id_util.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/boxing/eager_boxing_interpreter.h" #include "oneflow/core/common/decorator.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/framework/user_op_registry_manager.h" namespace oneflow { namespace { +class EagerBoxingKernelRegContext final : public user_op::KernelRegContext { + public: + explicit EagerBoxingKernelRegContext(DeviceType device_type) : device_type_(device_type) {} + ~EagerBoxingKernelRegContext() = default; + + DeviceType device_type() const override { return device_type_; } + const ParallelContext& parallel_ctx() const override { PRINT_BUG_PROMPT_AND_ABORT(); } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + const std::vector>& inputs() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + const std::vector>& outputs() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + + const user_op::UserOpConfWrapper& user_op_conf() const override { PRINT_BUG_PROMPT_AND_ABORT(); } + + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + + private: + DeviceType device_type_; +}; + +Maybe RawCheckCclKernelRegistered(const std::string& op_type_name, DeviceType device_type) { + EagerBoxingKernelRegContext reg_ctx(device_type); + return user_op::UserOpRegistryMgr::Get().IsOpKernelRegistered(op_type_name, reg_ctx); +} + +static constexpr auto* CheckCclKernelRegistered = + DECORATE(&RawCheckCclKernelRegistered, ThreadLocalCachedCopiable); + Maybe RawCheckCclP2B(Symbol in, Symbol out, const Shape& logical_shape) { // NOLINTBEGIN(maybe-need-error-msg) @@ -33,8 +72,9 @@ Maybe RawCheckCclP2B(Symbol in, Symbol out, CHECK_OR_RETURN(NdSbpIsAllBroadcast(*out->nd_sbp())); CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_OR_RETURN(in->placement()->device_type() == DeviceType::kCPU - || in->placement()->device_type() == DeviceType::kCUDA); + CHECK_OR_RETURN( // NOLINT + JUST(CheckCclKernelRegistered("eager_ccl_all_reduce", // NOLINT + in->placement()->device_type()))); // NOLINT // NOLINTEND(maybe-need-error-msg) return Maybe::Ok(); } @@ -53,8 +93,9 @@ Maybe RawCheckCclP2S(Symbol in, Symbol out, CHECK_OR_RETURN(logical_shape.At(0) % in->placement()->parallel_num() == 0); CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_OR_RETURN(in->placement()->device_type() == DeviceType::kCPU - || in->placement()->device_type() == DeviceType::kCUDA); + CHECK_OR_RETURN( // NOLINT + JUST(CheckCclKernelRegistered("eager_ccl_reduce_scatter", // NOLINT + in->placement()->device_type()))); // NOLINT // NOLINTEND(maybe-need-error-msg) return Maybe::Ok(); } @@ -74,8 +115,9 @@ Maybe RawCheckCclS2B(Symbol in, Symbol out, CHECK_OR_RETURN(logical_shape.At(0) % in->placement()->parallel_num() == 0); CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_OR_RETURN(in->placement()->device_type() == DeviceType::kCPU - || in->placement()->device_type() == DeviceType::kCUDA); + CHECK_OR_RETURN( // NOLINT + JUST(CheckCclKernelRegistered("eager_ccl_all_gather", // NOLINT + in->placement()->device_type()))); // NOLINT // NOLINTEND(maybe-need-error-msg) return Maybe::Ok(); } @@ -122,7 +164,7 @@ Maybe CclP2B(const std::shared_ptr& tensor, Symbolplacement())) << ")"; - return JUST(one::functional::ConsistentAllReduce(tensor)); + return JUST(one::functional::GlobalAllReduce(tensor)); } Maybe CclP2S(const std::shared_ptr& tensor, Symbol in, @@ -137,7 +179,7 @@ Maybe CclP2S(const std::shared_ptr& tensor, Symbolplacement())) << ")"; - return JUST(one::functional::ConsistentReduceScatter(tensor, "sum")); + return JUST(one::functional::GlobalReduceScatter(tensor, "sum")); } Maybe CclS2B(const std::shared_ptr& tensor, Symbol in, @@ -151,7 +193,7 @@ Maybe CclS2B(const std::shared_ptr& tensor, Symbolplacement())) << ")"; - return JUST(one::functional::ConsistentAllGather(tensor)); + return JUST(one::functional::GlobalAllGather(tensor)); } Maybe CclS2S(const std::shared_ptr& tensor, Symbol in, @@ -165,7 +207,7 @@ Maybe CclS2S(const std::shared_ptr& tensor, Symbolplacement())) << ")"; - return JUST(one::functional::ConsistentS2S(tensor, *JUST(GetSbpList(out->nd_sbp())))); + return JUST(one::functional::GlobalS2S(tensor, *JUST(GetSbpList(out->nd_sbp())))); } COMMAND(RegisterBoxingFunction("ccl-p-to-b", CheckCclP2B, &CclP2B)); diff --git a/oneflow/core/boxing/cuda_copy_boxing_interpreter.cpp b/oneflow/core/boxing/cuda_copy_boxing_interpreter.cpp index f8e2dd5cf1f6ae353283024d9285ac51e15eb5ce..ef7b6c598c559e4baefb09495ff8152f0c363dac 100644 --- a/oneflow/core/boxing/cuda_copy_boxing_interpreter.cpp +++ b/oneflow/core/boxing/cuda_copy_boxing_interpreter.cpp @@ -63,17 +63,11 @@ Maybe CopyBoxingFunction(const std::shared_ptr& tensor << Error::RuntimeError() << "The placement of input tensor (" << *JUST(PlacementToString(tensor_placement)) << ") must match the input placement (" << *JUST(PlacementToString(in->placement())) << ")"; - std::shared_ptr local_tensor = JUST(tensor->cur_rank_phy_tensor()); - const auto& out_parallel_id = JUST(GetParallelId4CurrentProcessCtx(out->placement())); - if (!out_parallel_id->has_value()) { - const std::string& device_type = tensor_placement->device_tag(); - local_tensor = JUST(one::functional::Empty( - *JUST(GetPhysicalShape(*tensor->shape(), *tensor_nd_sbp, *tensor_placement, 0)), - tensor->dtype(), JUST(Device::New(device_type)), /*pin_memory=*/false)); - } + const std::shared_ptr& local_tensor = JUST(tensor->cur_rank_phy_tensor()); const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } COMMAND(RegisterBoxingFunction("copy-h2d", &CheckCopyH2D, &CopyBoxingFunction)); diff --git a/oneflow/core/boxing/eager_boxing_interpreter.cpp b/oneflow/core/boxing/eager_boxing_interpreter.cpp index 6ce3e023ac2915f71b5c408266ae03ca19b50e98..99d2f6e3548878d22d205230487c9c8ee287c590 100644 --- a/oneflow/core/boxing/eager_boxing_interpreter.cpp +++ b/oneflow/core/boxing/eager_boxing_interpreter.cpp @@ -38,7 +38,7 @@ Maybe EagerBoxingInterpreter::Interpret(const std::shared_ptr in_parallel_desc, Symbol out_parallel_desc) const { JUST(CheckEagerBoxingDataType(input->dtype()->data_type())); - DisableCheckConsistentTensorMetaScope disable_meta_check; + DisableCheckGlobalTensorMetaScope disable_meta_check; const auto& tensor = JUST(InterpretImpl(input, in_nd_sbp, out_nd_sbp, in_parallel_desc, out_parallel_desc)); const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); diff --git a/oneflow/core/boxing/eager_boxing_interpreter_mgr.cpp b/oneflow/core/boxing/eager_boxing_interpreter_mgr.cpp index 56b3a9286c5e4a1b05812c0045fdc3b949d99a75..dbb05b324e16c3b0b5bbf66f0cc3d962184396bc 100644 --- a/oneflow/core/boxing/eager_boxing_interpreter_mgr.cpp +++ b/oneflow/core/boxing/eager_boxing_interpreter_mgr.cpp @@ -38,6 +38,13 @@ Maybe OptionalCudaCopy(const std::shared_ptr& core_b core_boxing_expr, JUST(OptionalBoxing("copy-d2h")))))); } +Maybe OptionalCpuCopy(const std::shared_ptr& core_boxing_expr) { + return JUST(BoxingExpr(JUST(ReplaceInDeviceType(DeviceType::kCPU)), + JUST(OptionalBoxing("copy-d2h")), + JUST(BoxingExpr(JUST(ReplaceOutDeviceType(DeviceType::kCPU)), + core_boxing_expr, JUST(OptionalBoxing("copy-h2d")))))); +} + Maybe SymmetricOneDimSxToBBoxingExpr() { return JUST(BoxingExpr(JUST(InPlacementAndSplit(0)), JUST(OptionalBoxing("ccl-s-to-s")), JUST(BoxingExpr("ccl-s-to-b")))); @@ -152,7 +159,7 @@ Maybe RawMainBoxingExpr() { | JUST(SymmetricNDimToOneDimBoxingExpr()) | JUST(GenericBoxingExpr()); // clang-format on - return core | JUST(OptionalCudaCopy(core)); + return core | JUST(OptionalCudaCopy(core)) | JUST(OptionalCpuCopy(core)); } } // namespace diff --git a/oneflow/core/boxing/flatten_hierarchy.cpp b/oneflow/core/boxing/flatten_hierarchy.cpp index fd4030e1507be84ea320a855e6a4f12f58c16ecb..77e38bfc5a2c2032ec8ae74d1c8f3e74d2a8237d 100644 --- a/oneflow/core/boxing/flatten_hierarchy.cpp +++ b/oneflow/core/boxing/flatten_hierarchy.cpp @@ -69,8 +69,9 @@ Maybe FlattenHierarchy(const std::shared_ptr& tensor, << *JUST(PlacementToString(in->placement())) << ")"; const auto& local_tensor = JUST(tensor->cur_rank_phy_tensor()); const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("flatten-hierarchy", CheckFlattenHierarchy, &FlattenHierarchy)); diff --git a/oneflow/core/boxing/generic_symmetric_nd_sbp_boxing.cpp b/oneflow/core/boxing/generic_symmetric_nd_sbp_boxing.cpp index 9af98b8591606fc787cd772cc52190ed9082fffd..5e245e5bbffd8d19501fbee888088f636e61258e 100644 --- a/oneflow/core/boxing/generic_symmetric_nd_sbp_boxing.cpp +++ b/oneflow/core/boxing/generic_symmetric_nd_sbp_boxing.cpp @@ -163,9 +163,9 @@ Maybe GenericSymmetricNdSbpBoxing(const std::shared_ptrshape()->ToString() << ") does not match global tensor (" << logical_shape->ToString() << ")!"; - std::shared_ptr sub_global_tensor = JUST(one::functional::LocalToConsistent( + std::shared_ptr sub_global_tensor = JUST(one::functional::LocalToGlobal( local_tensor, sub_parallel_desc, *JUST(GetSbpList(one_dim_nd_sbp)), sub_logical_shape, - local_tensor->dtype())); + local_tensor->dtype(), /* sync_data */ false, /*copy=*/false)); sub_global_tensor = JUST(Apply1DBoxing(sub_global_tensor, one_dim_nd_sbp, JUST(SbpToNdSbp(broadcast_sbp)), @@ -175,9 +175,9 @@ Maybe GenericSymmetricNdSbpBoxing(const std::shared_ptrdtype())); + output = JUST(one::functional::LocalToGlobal( + local_tensor, in_parallel_desc, *JUST(GetSbpList(new_nd_sbp)), *logical_shape, + local_tensor->dtype(), /* sync_data */ false, /*copy=*/false)); } CHECK_OR_RETURN(IsAllBroadcastNdSbpAfterDim(JUST(output->nd_sbp()), first_diff_sbp_dim)) @@ -202,9 +202,9 @@ Maybe GenericSymmetricNdSbpBoxing(const std::shared_ptr local_tensor = JUST(output->cur_rank_phy_tensor()); - std::shared_ptr sub_global_tensor = JUST(one::functional::LocalToConsistent( + std::shared_ptr sub_global_tensor = JUST(one::functional::LocalToGlobal( local_tensor, sub_parallel_desc, *JUST(GetSbpList(JUST(SbpToNdSbp(broadcast_sbp)))), - *sub_logical_shape, local_tensor->dtype())); + *sub_logical_shape, local_tensor->dtype(), /* sync_data */ false, /*copy=*/false)); const auto& one_dim_nd_sbp = JUST(SbpToNdSbp(sbp_parallel)); sub_global_tensor = JUST(Apply1DBoxing(sub_global_tensor, JUST(SbpToNdSbp(broadcast_sbp)), @@ -223,18 +223,18 @@ Maybe GenericSymmetricNdSbpBoxing(const std::shared_ptrdtype())); + output = JUST(one::functional::LocalToGlobal( + local_tensor, in_parallel_desc, *JUST(GetSbpList(new_nd_sbp)), *logical_shape, + local_tensor->dtype(), /* sync_data */ false, /*copy=*/false)); // physical_shape of this axis is logical shape of next axis sub_logical_shape = physical_shape; } } else { - one::ConsistentTensorMeta tensor_meta(input->shape(), input->dtype()->data_type(), out_nd_sbp, - out_parallel_desc); - const auto& tensor_impl = JUST( - one::EagerConsistentTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); - output = std::make_shared(tensor_impl); + one::GlobalTensorMeta tensor_meta(*input->shape(), input->dtype()->data_type(), out_nd_sbp, + out_parallel_desc); + const auto& tensor_impl = + JUST(one::EagerGlobalTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); + output = std::make_shared(tensor_impl); } return output; diff --git a/oneflow/core/boxing/identity_boxing_interpreter.cpp b/oneflow/core/boxing/identity_boxing_interpreter.cpp index d2b5a8b6dfc5a02110932dc586b57fe2fdbed434..c9a535dfaae80729139245369cac2dd9eb09b42e 100644 --- a/oneflow/core/boxing/identity_boxing_interpreter.cpp +++ b/oneflow/core/boxing/identity_boxing_interpreter.cpp @@ -49,8 +49,9 @@ Maybe GetIdentity(const std::shared_ptr& tensor, Symbo // reset sbp if parallel_num == 1 and reset transport_token const auto& local_tensor = JUST(tensor->cur_rank_phy_tensor()); const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("identity", DECORATE(&RawCheckIdentity, ThreadLocalCachedCopiable), diff --git a/oneflow/core/boxing/naive_1_to_p_boxing.cpp b/oneflow/core/boxing/naive_1_to_p_boxing.cpp index e20db61e8b0b72ff6e8b905f5938317935c0afee..882b6d977ce8b8f276271114718ad0a486a6bd41 100644 --- a/oneflow/core/boxing/naive_1_to_p_boxing.cpp +++ b/oneflow/core/boxing/naive_1_to_p_boxing.cpp @@ -67,9 +67,9 @@ Maybe Naive1ToP(const std::shared_ptr& tensor, Symbol< local_tensor = JUST(one::functional::Constant(*tensor->shape(), 0, tensor->dtype(), JUST(Device::New(device_type)))); } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("naive-1-to-p", CheckNaive1ToP, &Naive1ToP)); diff --git a/oneflow/core/boxing/naive_b_to_1_boxing.cpp b/oneflow/core/boxing/naive_b_to_1_boxing.cpp index 52806f8c7df7d6866a9a34415a217e9d0107d6f7..f9880878008b911c1da5b009d924548bf983f743 100644 --- a/oneflow/core/boxing/naive_b_to_1_boxing.cpp +++ b/oneflow/core/boxing/naive_b_to_1_boxing.cpp @@ -52,9 +52,9 @@ Maybe NaiveBTo1(const std::shared_ptr& tensor, Symbol< << *JUST(PlacementToString(in->placement())) << ")"; std::shared_ptr local_tensor = JUST(tensor->cur_rank_phy_tensor()); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("naive-b-to-1", CheckNaiveBTo1, &NaiveBTo1)); diff --git a/oneflow/core/boxing/naive_b_to_s_boxing.cpp b/oneflow/core/boxing/naive_b_to_s_boxing.cpp index 6df0f8345df19c4265493918221033de3b7f5767..75136e199afbf328df843758dd89ae57da6ff977 100644 --- a/oneflow/core/boxing/naive_b_to_s_boxing.cpp +++ b/oneflow/core/boxing/naive_b_to_s_boxing.cpp @@ -74,8 +74,9 @@ Maybe NaiveBToS(const std::shared_ptr& tensor, Symbol< } } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } static constexpr auto* NaiveBToSWithAutoConvert = diff --git a/oneflow/core/boxing/naive_p_to_b_boxing.cpp b/oneflow/core/boxing/naive_p_to_b_boxing.cpp index a348d73ed3e2143771198c83388840be8a9149b7..ecc493c9e792380110c8971bd31a98a4cf14f157 100644 --- a/oneflow/core/boxing/naive_p_to_b_boxing.cpp +++ b/oneflow/core/boxing/naive_p_to_b_boxing.cpp @@ -74,8 +74,9 @@ Maybe NaivePToB(const std::shared_ptr& tensor, Symbol< } const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } static constexpr auto* NaivePToBWithAutoConvert = diff --git a/oneflow/core/boxing/naive_p_to_s_boxing.cpp b/oneflow/core/boxing/naive_p_to_s_boxing.cpp index 9057a0b6fd56b653c5ab5d2930e225ad0b4eac67..db8f2fab940791bdc7cc7aef4c90cbf05b1be274 100644 --- a/oneflow/core/boxing/naive_p_to_s_boxing.cpp +++ b/oneflow/core/boxing/naive_p_to_s_boxing.cpp @@ -73,8 +73,9 @@ Maybe NaivePToS(const std::shared_ptr& tensor, Symbol< } } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ true, /*copy=*/false)); } static constexpr auto* NaivePToSWithAutoConvert = diff --git a/oneflow/core/boxing/naive_s_to_b_boxing.cpp b/oneflow/core/boxing/naive_s_to_b_boxing.cpp index 04c6f3d67e65e2bcfdefc6745778fe31945e0d52..50aff12e2a8cf3a3a15b03f8ba54ea6dfe92b3b2 100644 --- a/oneflow/core/boxing/naive_s_to_b_boxing.cpp +++ b/oneflow/core/boxing/naive_s_to_b_boxing.cpp @@ -73,8 +73,9 @@ Maybe NaiveSToB(const std::shared_ptr& tensor, Symbol< } const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } static constexpr auto* NaiveSToBWithAutoConvert = diff --git a/oneflow/core/boxing/naive_s_to_p_boxing.cpp b/oneflow/core/boxing/naive_s_to_p_boxing.cpp index 26858ea5b5556ec63302fa8c61adff6247e06e43..e85010b094e79ce7956f8f374d892bf805213862 100644 --- a/oneflow/core/boxing/naive_s_to_p_boxing.cpp +++ b/oneflow/core/boxing/naive_s_to_p_boxing.cpp @@ -73,8 +73,9 @@ Maybe NaiveSToP(const std::shared_ptr& tensor, Symbol< } const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } static constexpr auto* NaiveSToPWithAutoConvert = diff --git a/oneflow/core/boxing/naive_s_to_s_boxing.cpp b/oneflow/core/boxing/naive_s_to_s_boxing.cpp index caf77a0fcda235ee7f8008ea7f51954b51c42348..d813cfdcd2d254bf5a69c06d4ee561b2b35ddd0d 100644 --- a/oneflow/core/boxing/naive_s_to_s_boxing.cpp +++ b/oneflow/core/boxing/naive_s_to_s_boxing.cpp @@ -71,8 +71,9 @@ Maybe NaiveSToS(const std::shared_ptr& tensor, Symbol< } } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *out_sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *out_sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/false)); } static constexpr auto* NaiveSToSWithAutoConvert = diff --git a/oneflow/core/boxing/nccl_boxing_function.cpp b/oneflow/core/boxing/nccl_boxing_function.cpp deleted file mode 100644 index 2a35140cbce6295c48aa3b92f6f468758d25c034..0000000000000000000000000000000000000000 --- a/oneflow/core/boxing/nccl_boxing_function.cpp +++ /dev/null @@ -1,154 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/job/nd_sbp_util.h" -#include "oneflow/core/boxing/eager_boxing_interpreter.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/functional/functional.h" - -namespace oneflow { - -namespace { - -bool IsSplitSbp(Symbol sbp_parallel) { return sbp_parallel->has_split_parallel(); } - -Maybe RawCheckNcclP2B(Symbol in, Symbol out, - const Shape& logical_shape) { - // NOLINTBEGIN(maybe-need-error-msg) - CHECK_EQ_OR_RETURN(in->nd_sbp()->sbp_parallel_size(), 1); - CHECK_EQ_OR_RETURN(out->nd_sbp()->sbp_parallel_size(), 1); - CHECK_OR_RETURN(NdSbpIsAllPartialSum(*in->nd_sbp())); - CHECK_OR_RETURN(NdSbpIsAllBroadcast(*out->nd_sbp())); - - CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_EQ_OR_RETURN(in->placement()->device_type(), DeviceType::kCUDA); - // NOLINTEND(maybe-need-error-msg) - return Maybe::Ok(); -} - -static constexpr auto* CheckNcclP2B = DECORATE(&RawCheckNcclP2B, ThreadLocalCachedCopiable); - -Maybe RawCheckNcclP2S(Symbol in, Symbol out, - const Shape& logical_shape) { - // NOLINTBEGIN(maybe-need-error-msg) - CHECK_EQ_OR_RETURN(in->nd_sbp()->sbp_parallel_size(), 1); - CHECK_EQ_OR_RETURN(out->nd_sbp()->sbp_parallel_size(), 1); - CHECK_OR_RETURN(NdSbpIsAllPartialSum(*in->nd_sbp())); - CHECK_OR_RETURN(NdSbpIsAllSplit(*out->nd_sbp(), 0)); - - CHECK_GT_OR_RETURN(logical_shape.NumAxes(), 0); - CHECK_OR_RETURN(logical_shape.At(0) % in->placement()->parallel_num() == 0); - - CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_EQ_OR_RETURN(in->placement()->device_type(), DeviceType::kCUDA); - // NOLINTEND(maybe-need-error-msg) - return Maybe::Ok(); -} - -static constexpr auto* CheckNcclP2S = DECORATE(&RawCheckNcclP2S, ThreadLocalCachedCopiable); - -Maybe RawCheckNcclS2B(Symbol in, Symbol out, - const Shape& logical_shape) { - // NOLINTBEGIN(maybe-need-error-msg) - CHECK_EQ_OR_RETURN(in->nd_sbp()->sbp_parallel_size(), 1); - CHECK_EQ_OR_RETURN(out->nd_sbp()->sbp_parallel_size(), 1); - CHECK_OR_RETURN(NdSbpIsAllSplit(*in->nd_sbp(), 0)); - CHECK_OR_RETURN(NdSbpIsAllBroadcast(*out->nd_sbp())); - - CHECK_GT_OR_RETURN(logical_shape.NumAxes(), 0); - CHECK_OR_RETURN(logical_shape.At(0) % in->placement()->parallel_num() == 0); - - CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_EQ_OR_RETURN(in->placement()->device_type(), DeviceType::kCUDA); - // NOLINTEND(maybe-need-error-msg) - return Maybe::Ok(); -} - -static constexpr auto* CheckNcclS2B = DECORATE(&RawCheckNcclS2B, ThreadLocalCachedCopiable); - -Maybe RawCheckNcclS2S(Symbol in, Symbol out, - const Shape& logical_shape) { - // NOLINTBEGIN(maybe-need-error-msg) - CHECK_EQ_OR_RETURN(in->nd_sbp()->sbp_parallel_size(), 1); - CHECK_EQ_OR_RETURN(out->nd_sbp()->sbp_parallel_size(), 1); - - CHECK_OR_RETURN(IsSplitSbp(in->nd_sbp()->sbp_parallel(0))); - CHECK_OR_RETURN(IsSplitSbp(out->nd_sbp()->sbp_parallel(0))); - CHECK_NE_OR_RETURN(in->nd_sbp()->sbp_parallel(0).split_parallel().axis(), - out->nd_sbp()->sbp_parallel(0).split_parallel().axis()); - - int64_t in_split_axis = in->nd_sbp()->sbp_parallel(0).split_parallel().axis(); - int64_t out_split_axis = out->nd_sbp()->sbp_parallel(0).split_parallel().axis(); - CHECK_GT_OR_RETURN(logical_shape.NumAxes(), in_split_axis); - CHECK_GT_OR_RETURN(logical_shape.NumAxes(), out_split_axis); - CHECK_OR_RETURN(logical_shape.At(in_split_axis) % in->placement()->parallel_num() == 0); - CHECK_OR_RETURN(logical_shape.At(out_split_axis) % in->placement()->parallel_num() == 0); - - CHECK_OR_RETURN(in->placement() == out->placement()); - CHECK_EQ_OR_RETURN(in->placement()->device_type(), DeviceType::kCUDA); - // NOLINTEND(maybe-need-error-msg) - return Maybe::Ok(); -} - -static constexpr auto* CheckNcclS2S = DECORATE(&RawCheckNcclS2S, ThreadLocalCachedCopiable); - -} // namespace - -Maybe NcclP2B(const std::shared_ptr& tensor, Symbol in, - Symbol out) { - const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); - CHECK_OR_RETURN(tensor_nd_sbp == in->nd_sbp()); // NOLINT(maybe-need-error-msg) - const auto& tensor_placement = JUST(tensor->parallel_desc()); - CHECK_OR_RETURN(tensor_placement == in->placement()); // NOLINT(maybe-need-error-msg) - - return JUST(one::functional::ConsistentAllReduce(tensor)); -} - -Maybe NcclP2S(const std::shared_ptr& tensor, Symbol in, - Symbol out) { - const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); - CHECK_OR_RETURN(tensor_nd_sbp == in->nd_sbp()); // NOLINT(maybe-need-error-msg) - const auto& tensor_placement = JUST(tensor->parallel_desc()); - CHECK_OR_RETURN(tensor_placement == in->placement()); // NOLINT(maybe-need-error-msg) - - return JUST(one::functional::ConsistentReduceScatter(tensor, "sum")); -} - -Maybe NcclS2B(const std::shared_ptr& tensor, Symbol in, - Symbol out) { - const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); - CHECK_OR_RETURN(tensor_nd_sbp == in->nd_sbp()); // NOLINT(maybe-need-error-msg) - const auto& tensor_placement = JUST(tensor->parallel_desc()); - CHECK_OR_RETURN(tensor_placement == in->placement()); // NOLINT(maybe-need-error-msg) - - return JUST(one::functional::ConsistentAllGather(tensor)); -} - -Maybe NcclS2S(const std::shared_ptr& tensor, Symbol in, - Symbol out) { - const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); - CHECK_OR_RETURN(tensor_nd_sbp == in->nd_sbp()); // NOLINT(maybe-need-error-msg) - const auto& tensor_placement = JUST(tensor->parallel_desc()); - CHECK_OR_RETURN(tensor_placement == in->placement()); // NOLINT(maybe-need-error-msg) - return JUST(one::functional::ConsistentS2S(tensor, *JUST(GetSbpList(out->nd_sbp())))); -} - -COMMAND(RegisterBoxingFunction("nccl-p-to-b", CheckNcclP2B, &NcclP2B)); -COMMAND(RegisterBoxingFunction("nccl-p-to-s", CheckNcclP2S, &NcclP2S)); -COMMAND(RegisterBoxingFunction("nccl-s-to-b", CheckNcclS2B, &NcclS2B)); -COMMAND(RegisterBoxingFunction("nccl-s-to-s", CheckNcclS2S, &NcclS2S)); - -} // namespace oneflow diff --git a/oneflow/core/boxing/nd_sbp_dim_reduce_boxing.cpp b/oneflow/core/boxing/nd_sbp_dim_reduce_boxing.cpp index 1aa51a7dde6a97149dc222de4a3af3376a7a629c..4c507a1edbd539162e39945dfe13bae34cf60895 100644 --- a/oneflow/core/boxing/nd_sbp_dim_reduce_boxing.cpp +++ b/oneflow/core/boxing/nd_sbp_dim_reduce_boxing.cpp @@ -27,7 +27,7 @@ namespace oneflow { namespace { Maybe, Symbol>> RawInOutPlacedNdSbpDimReduce( - Symbol in, Symbol out) { + Symbol in, Symbol out, const Shape& logical_shape) { // reduce hierarchy ParallelDesc reduced_in_placement = *in->placement(); ParallelDesc reduced_out_placement = *out->placement(); @@ -35,14 +35,14 @@ Maybe, Symbol>> RawInOutPlacedNdSbpD NdSbp reduced_out_nd_sbp; InOutParallelDimReduce(*in->placement(), *out->placement(), *in->nd_sbp(), *out->nd_sbp(), &reduced_in_placement, &reduced_out_placement, &reduced_in_nd_sbp, - &reduced_out_nd_sbp); + &reduced_out_nd_sbp, logical_shape); return std::make_tuple( JUST(PlacedNdSbp::New(SymbolOf(reduced_in_nd_sbp), SymbolOf(reduced_in_placement))), JUST(PlacedNdSbp::New(SymbolOf(reduced_out_nd_sbp), SymbolOf(reduced_out_placement)))); } constexpr auto* InOutPlacedNdSbpDimReduce = - DECORATE(&RawInOutPlacedNdSbpDimReduce, ThreadLocalCached); + DECORATE(&RawInOutPlacedNdSbpDimReduce, ThreadLocalCachedCopiable); // NOLINTBEGIN(maybe-need-error-msg) Maybe RawCheckParallelDimReduce(Symbol in, Symbol out, @@ -51,7 +51,7 @@ Maybe RawCheckParallelDimReduce(Symbol in, Symbolplacement()->device_tag(), out->placement()->device_tag()); Symbol reduced_in; Symbol reduced_out; - std::tie(reduced_in, reduced_out) = *JUST(InOutPlacedNdSbpDimReduce(in, out)); + std::tie(reduced_in, reduced_out) = *JUST(InOutPlacedNdSbpDimReduce(in, out, logical_shape)); for (int64_t in_parallel_id = 0; in_parallel_id < in->placement()->parallel_num(); ++in_parallel_id) { @@ -102,13 +102,13 @@ Maybe ParallelDimReduce(const std::shared_ptr& tensor, Symbol reduced_in; Symbol reduced_out; - std::tie(reduced_in, reduced_out) = *JUST(InOutPlacedNdSbpDimReduce(in, out)); + std::tie(reduced_in, reduced_out) = *JUST(InOutPlacedNdSbpDimReduce(in, out, *tensor->shape())); const std::shared_ptr& local_tensor = JUST(tensor->cur_rank_phy_tensor()); - std::shared_ptr reduced_in_tensor = JUST(one::functional::LocalToConsistent( + std::shared_ptr reduced_in_tensor = JUST(one::functional::LocalToGlobal( local_tensor, reduced_in->placement(), *JUST(GetSbpList(reduced_in->nd_sbp())), - *tensor->shape(), tensor->dtype())); + *tensor->shape(), tensor->dtype(), /* sync_data */ false, /*copy=*/false)); const auto& boxing_interpreter = JUST(Singleton::Get()->GetEagerBoxingInterpreter( @@ -124,9 +124,9 @@ Maybe ParallelDimReduce(const std::shared_ptr& tensor, const std::shared_ptr& reduced_out_local_tensor = JUST(reduced_out_tensor->cur_rank_phy_tensor()); - return JUST(one::functional::LocalToConsistent(reduced_out_local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + reduced_out_local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), + *tensor->shape(), tensor->dtype(), /* sync_data */ false, /*copy=*/false)); } COMMAND(RegisterBoxingFunction("nd-sbp-dim-reduce", CheckParallelDimReduce, &ParallelDimReduce)); diff --git a/oneflow/core/boxing/one_to_one_boxing.cpp b/oneflow/core/boxing/one_to_one_boxing.cpp index 0b8946de643ce44fd0285166a8a4ab93af83de2d..1aaafeb26c15b1574910f35c0ecacfef0512264a 100644 --- a/oneflow/core/boxing/one_to_one_boxing.cpp +++ b/oneflow/core/boxing/one_to_one_boxing.cpp @@ -19,6 +19,7 @@ limitations under the License. #include "oneflow/core/boxing/eager_boxing_interpreter.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/common/decorator.h" +#include "oneflow/user/kernels/communicate_util.h" namespace oneflow { @@ -31,6 +32,7 @@ Maybe RawCheckNaiveOneToOne(Symbol in, Symbol ou CHECK_EQ_OR_RETURN(out->placement()->parallel_num(), 1); CHECK_EQ_OR_RETURN(in->placement()->device_tag(), out->placement()->device_tag()); CHECK_OR_RETURN(in->placement() != out->placement()); + CHECK_OR_RETURN(IsSendAndRecvRegistered(in->placement()->device_type())); // NOLINT return Maybe::Ok(); } // NOLINTEND(maybe-need-error-msg) @@ -56,7 +58,9 @@ Maybe NaiveOneToOne(const std::shared_ptr& tensor, Sym int64_t src = JUST(tensor_placement->MachineId4ParallelId(0)); int64_t dst = JUST(out->placement()->MachineId4ParallelId(0)); + bool copy = true; if (src != dst) { + copy = false; if (GlobalProcessCtx::Rank() == src) { JUST(one::functional::Send(local_tensor, dst, /* send_meta */ false)); } @@ -65,9 +69,9 @@ Maybe NaiveOneToOne(const std::shared_ptr& tensor, Sym JUST(local_tensor->device()), NullOpt)); } } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/copy)); } COMMAND(RegisterBoxingFunction("naive-1-to-1", CheckNaiveOneToOne, &NaiveOneToOne)); diff --git a/oneflow/core/boxing/slice_boxing_util.cpp b/oneflow/core/boxing/slice_boxing_util.cpp index bea946177b85096932f9b8ed7a239328ebd23a2e..81ec16ebb3a2d35b0cb53690b53edf19efa06fec 100644 --- a/oneflow/core/boxing/slice_boxing_util.cpp +++ b/oneflow/core/boxing/slice_boxing_util.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" #include "oneflow/core/boxing/eager_boxing_logger.h" #include "oneflow/core/boxing/eager_boxing_interpreter.h" +#include "oneflow/user/kernels/communicate_util.h" namespace oneflow { @@ -26,10 +27,7 @@ namespace private_details { Maybe PreprocessInputTensor4SliceBoxing(const std::shared_ptr& tensor, const std::string& log_prefix) { const auto& tensor_placement = JUST(tensor->parallel_desc()); - if (tensor_placement->device_type() == DeviceType::kCPU - || tensor_placement->device_type() == DeviceType::kCUDA) { - return tensor; - } + if (IsSendAndRecvRegistered(tensor_placement->device_type())) { return tensor; } const auto& tensor_nd_sbp = JUST(tensor->nd_sbp()); Symbol new_placement = JUST(ReplaceDeviceType(tensor_placement, DeviceType::kCPU)); diff --git a/oneflow/core/boxing/symmetric_acyclic_nd_sbp_boxing.cpp b/oneflow/core/boxing/symmetric_acyclic_nd_sbp_boxing.cpp index 0f5714b9585ba79b9e0384b7eb73159be045fe1d..5e5d131819e797ef9fc775b449ff10031f333022 100644 --- a/oneflow/core/boxing/symmetric_acyclic_nd_sbp_boxing.cpp +++ b/oneflow/core/boxing/symmetric_acyclic_nd_sbp_boxing.cpp @@ -29,30 +29,17 @@ namespace oneflow { namespace { -Maybe MakeToConsistentOpExpr() { - std::shared_ptr op_expr = - JUST(one::CastToConsistentOpExpr::New(*JUST(UniqueStr("cast_to_consistent")))); - return op_expr; -} - -static constexpr auto* GetLocalToConsistentOpExpr = - DECORATE(&MakeToConsistentOpExpr, ThreadLocalCachedCopiable); - -Maybe ReinterpterConsistentTensor(const std::shared_ptr& tensor, - const Shape& shape, - Symbol parallel_desc, - Symbol nd_sbp) { - const auto& op = JUST(GetLocalToConsistentOpExpr()); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", tensor->dtype()->data_type())); +Maybe ReinterpterGlobalTensor(const std::shared_ptr& tensor, + const Shape& shape, Symbol parallel_desc, + Symbol nd_sbp) { const auto& parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); std::shared_ptr pyhsical_shape = JUST(GetPhysicalShape(shape, *nd_sbp, *parallel_desc, JUST(*parallel_id))); std::shared_ptr x = JUST(tensor->cur_rank_phy_tensor()); if (*x->shape() != *pyhsical_shape) { x = JUST(one::functional::Reshape(x, *pyhsical_shape)); } - return JUST(one::OpInterpUtil::Dispatch( - *op, {x}, one::OpExprInterpContext(attrs, parallel_desc, nd_sbp))); + return JUST(one::functional::LocalToGlobal(x, parallel_desc, *JUST(GetSbpList(nd_sbp)), shape, + tensor->dtype(), /* sync_data */ false, + /*copy=*/false)); } Maybe Apply1DBoxing(const std::shared_ptr& input, Symbol in_nd_sbp, @@ -81,7 +68,7 @@ Maybe RawCheckSymmetricAcyclicNdSbpBoxing(Symbol in, Symbol

SymmetricAcyclicNdSbpBoxing(const std::shared_ptr output; const auto& out_parallel_id = JUST(GetParallelId4CurrentProcessCtx(out_parallel_desc)); if (out_parallel_id->has_value()) { - const auto& tensor_meta = JUST(input->consistent_tensor_meta()); + const auto& tensor_meta = JUST(input->global_tensor_meta()); const auto& naive_transformations = JUST(DecomposeIntoNaiveTransformations(tensor_meta, out_nd_sbp)); std::shared_ptr tensor = input; for (const auto& naive_transformation : *naive_transformations) { - const auto& sub_tensor_meta = naive_transformation.consistent_tensor_meta; - tensor = JUST(ReinterpterConsistentTensor(tensor, sub_tensor_meta->shape(), - sub_tensor_meta->parallel_desc(), - sub_tensor_meta->nd_sbp())); + const auto& sub_tensor_meta = naive_transformation.global_tensor_meta; + tensor = JUST(ReinterpterGlobalTensor(tensor, sub_tensor_meta->shape(), + sub_tensor_meta->parallel_desc(), + sub_tensor_meta->nd_sbp())); tensor = JUST(Apply1DBoxing(tensor, sub_tensor_meta->nd_sbp(), naive_transformation.dst_nd_sbp, sub_tensor_meta->parallel_desc(), sub_tensor_meta->parallel_desc())); } - output = - JUST(ReinterpterConsistentTensor(tensor, *input->shape(), out_parallel_desc, out_nd_sbp)); + output = JUST(ReinterpterGlobalTensor(tensor, *input->shape(), out_parallel_desc, out_nd_sbp)); } else { - one::ConsistentTensorMeta tensor_meta(input->shape(), input->dtype()->data_type(), out_nd_sbp, - out_parallel_desc); - const auto& tensor_impl = JUST( - one::EagerConsistentTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); - output = std::make_shared(tensor_impl); + one::GlobalTensorMeta tensor_meta(*input->shape(), input->dtype()->data_type(), out_nd_sbp, + out_parallel_desc); + const auto& tensor_impl = + JUST(one::EagerGlobalTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); + output = std::make_shared(tensor_impl); } return output; } diff --git a/oneflow/core/boxing/symmetric_b_to_p_boxing.cpp b/oneflow/core/boxing/symmetric_b_to_p_boxing.cpp index a93beb8232834a6ca9a326f912ccef7dff1efa2e..fddc31333a6940fa29f903ab5220e25933e36b62 100644 --- a/oneflow/core/boxing/symmetric_b_to_p_boxing.cpp +++ b/oneflow/core/boxing/symmetric_b_to_p_boxing.cpp @@ -63,9 +63,9 @@ Maybe SymmetricBToP(const std::shared_ptr& tensor, Sym } else { local_tensor = JUST(one::functional::ZerosLike(local_tensor)); } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("symmetric-b-to-p", CheckSymmetricBToP, &SymmetricBToP)); diff --git a/oneflow/core/boxing/symmetric_b_to_s_boxing.cpp b/oneflow/core/boxing/symmetric_b_to_s_boxing.cpp index ac477b4b5aba8b332079f1263e0acdf100b51525..c5e25be642d4e827cecf49a2c0f89dfe223c33af 100644 --- a/oneflow/core/boxing/symmetric_b_to_s_boxing.cpp +++ b/oneflow/core/boxing/symmetric_b_to_s_boxing.cpp @@ -45,6 +45,8 @@ Maybe RawCheckSymmetricB2S(Symbol in, Symbol out CHECK_OR_RETURN(IsSplitSbp(SymbolOf(out->nd_sbp()->sbp_parallel(0)))); CHECK_OR_RETURN(in->placement() == out->placement()); + CHECK_OR_RETURN(in->placement()->device_type() == DeviceType::kCPU + || in->placement()->device_type() == DeviceType::kCUDA); return Maybe::Ok(); } // NOLINTEND(maybe-need-error-msg) @@ -92,9 +94,9 @@ Maybe SymmetricB2S(const std::shared_ptr& tensor, Symb /*enable_view_slice=*/false)); } - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), - *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), - tensor->dtype())); + return JUST(one::functional::LocalToGlobal( + local_tensor, out->placement(), *JUST(GetSbpList(out->nd_sbp())), *tensor->shape(), + tensor->dtype(), /* sync_data */ false, /*copy=*/false)); } COMMAND(RegisterBoxingFunction("symmetric-b-to-s", CheckSymmetricB2S, &SymmetricB2S)); diff --git a/oneflow/core/boxing/unflatten_hierarchy.cpp b/oneflow/core/boxing/unflatten_hierarchy.cpp index 94b690d9415adf80a65a9709c3de50e0be31f55b..4d42324bb8554baeb60c99a7ff608cf37a54a8a0 100644 --- a/oneflow/core/boxing/unflatten_hierarchy.cpp +++ b/oneflow/core/boxing/unflatten_hierarchy.cpp @@ -70,8 +70,9 @@ Maybe UnflattenHierarchy(const std::shared_ptr& tensor << *JUST(PlacementToString(in->placement())) << ")"; const auto& local_tensor = JUST(tensor->cur_rank_phy_tensor()); const auto& sbp_list = JUST(GetSbpList(out->nd_sbp())); - return JUST(one::functional::LocalToConsistent(local_tensor, out->placement(), *sbp_list, - *tensor->shape(), tensor->dtype())); + return JUST(one::functional::LocalToGlobal(local_tensor, out->placement(), *sbp_list, + *tensor->shape(), tensor->dtype(), + /* sync_data */ false, /*copy=*/true)); } COMMAND(RegisterBoxingFunction("unflatten-hierarchy", CheckUnflattenHierarchy, diff --git a/oneflow/core/ccl/ccl.cpp b/oneflow/core/ccl/ccl.cpp index 62330ef13147c47a7494c223dd7157993a0633df..018e10cf94d58f4d44d34873e9e5d172f8b79952 100644 --- a/oneflow/core/ccl/ccl.cpp +++ b/oneflow/core/ccl/ccl.cpp @@ -24,11 +24,7 @@ limitations under the License. #include "oneflow/core/rpc/include/global_process_ctx.h" #include "oneflow/core/thread/thread_manager.h" #include "oneflow/core/job/eager_nccl_comm_manager.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif #include "oneflow/core/common/constant.h" namespace oneflow { @@ -51,275 +47,8 @@ Maybe InitBroadcastRankHeap(std::vector* ranks, const ParallelDes return Maybe::Ok(); } -int64_t RingDecrease(int64_t n, int64_t size) { return (n - 1 + size) % size; } - -int64_t RingIncrease(int64_t n, int64_t size) { return (n + 1 + size) % size; } - -template -void VecAdd(size_t size, T* out, const T* in0, const T* in1) { - size_t thread_num = Singleton::Get()->thread_num(); - BalancedSplitter bs(size, thread_num); - MultiThreadLoop(thread_num, [&](size_t thread_idx) { - size_t end = bs.At(thread_idx).end(); - for (size_t i = bs.At(thread_idx).begin(); i < end; ++i) { out[i] = in0[i] + in1[i]; } - }); -} - } // namespace -template -struct DtypeAllReduce; - -template -struct DtypeAllReduce { - static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, - Symbol parallel_desc) { - int64_t parallel_num = parallel_desc->parallel_num(); - if (parallel_num == 1) { - if (void_in != void_out) { std::memcpy(void_out, void_in, elem_cnt * sizeof(T)); } - return Maybe::Ok(); - } - const T* in = reinterpret_cast(void_in); - T* out = reinterpret_cast(void_out); - BalancedSplitter bs(elem_cnt, parallel_num); - auto recv_buffer = std::make_unique(bs.At(0).size()); - Optional parallel_id; - JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); - const auto& rank_group = JUST(RankGroup::New(parallel_desc)); - TransportToken transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); - for (int64_t i = 0, part_id = JUST(parallel_id); i < parallel_num - 1; - ++i, part_id = RingDecrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - const T* send_ptr = nullptr; - if (i == 0) { - send_ptr = &in[bs.At(send_part_id).begin()]; - } else { - send_ptr = &out[bs.At(send_part_id).begin()]; - } - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = RingDecrease(part_id, parallel_num); - T* recv_ptr = recv_buffer.get(); - size_t recv_size = bs.At(recv_part_id).size(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }); - if (send_size > 0) { - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - } - if (recv_size > 0) { - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - } - JUST(ctx.WaitDone()); - const T* cur_in = &in[bs.At(recv_part_id).begin()]; - T* cur_out = &out[bs.At(recv_part_id).begin()]; - if (recv_size > 0) { VecAdd(recv_size, cur_out, cur_in, recv_ptr); } - } - for (int64_t i = 0, part_id = RingIncrease(JUST(parallel_id), parallel_num); - i < parallel_num - 1; ++i, part_id = RingDecrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - const T* send_ptr = &out[bs.At(send_part_id).begin()]; - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = RingDecrease(part_id, parallel_num); - T* recv_ptr = &out[bs.At(recv_part_id).begin()]; - size_t recv_size = bs.At(recv_part_id).size(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }); - if (send_size > 0) { - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - } - if (recv_size > 0) { - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - } - JUST(ctx.WaitDone()); - } - return Maybe::Ok(); - } -}; - -#define MAKE_ALL_REDUCE_ENTRY(func_name, T, reduce_type) func_name::Call - -DEFINE_STATIC_SWITCH_FUNC(Maybe, DtypeAllReduce, MAKE_ALL_REDUCE_ENTRY, - MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), CCL_REDUCE_TYPE_CTRV_SEQ); - -#undef MAKE_ALL_REDUCE_ENTRY - -template<> -Maybe AllReduce(const void* in, void* out, size_t elem_cnt, DataType dtype, - ReduceType reduce_type, Symbol parallel_desc, - ep::Stream* stream) { - return SwitchDtypeAllReduce(SwitchCase(dtype, reduce_type), in, out, elem_cnt, parallel_desc); -} - -template -struct DtypeReduceScatter; - -template -struct DtypeReduceScatter { - static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, - Symbol parallel_desc) { - int64_t parallel_num = parallel_desc->parallel_num(); - if (parallel_num == 1) { - if (void_in != void_out) { std::memcpy(void_out, void_in, elem_cnt * sizeof(T)); } - return Maybe::Ok(); - } - - const T* in = reinterpret_cast(void_in); - T* out = reinterpret_cast(void_out); - - BalancedSplitter bs(elem_cnt * parallel_num, parallel_num); - const auto& opt_parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); - CHECK_OR_RETURN(opt_parallel_id->has_value()); - int64_t parallel_id = JUST(*opt_parallel_id); - - auto recv_buffer = std::make_unique(bs.At(0).size()); - const auto& rank_group = JUST(RankGroup::New(parallel_desc)); - - TransportToken transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); - for (int64_t i = 0, part_id = RingDecrease(parallel_id, parallel_num); i < parallel_num - 1; - ++i, part_id = RingDecrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - const T* send_ptr = nullptr; - if (i == 0) { - send_ptr = &in[bs.At(send_part_id).begin()]; - } else { - send_ptr = out; - } - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = RingDecrease(part_id, parallel_num); - T* recv_ptr = recv_buffer.get(); - size_t recv_size = bs.At(recv_part_id).size(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }); - if (send_size > 0) { - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - } - if (recv_size > 0) { - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - } - JUST(ctx.WaitDone()); - const T* cur_in = &in[bs.At(recv_part_id).begin()]; - if (recv_size > 0) { VecAdd(recv_size, out, cur_in, recv_ptr); } - } - return Maybe::Ok(); - } -}; - -#define MAKE_REDUCE_SCATTER_ENTRY(func_name, T, reduce_type) func_name::Call - -DEFINE_STATIC_SWITCH_FUNC(Maybe, DtypeReduceScatter, MAKE_REDUCE_SCATTER_ENTRY, - MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), CCL_REDUCE_TYPE_CTRV_SEQ); - -#undef MAKE_REDUCE_SCATTER_ENTRY - -template<> -Maybe ReduceScatter(const void* in, void* out, size_t elem_cnt, - DataType dtype, ReduceType reduce_type, - Symbol parallel_desc, - ep::Stream* stream) { - return SwitchDtypeReduceScatter(SwitchCase(dtype, reduce_type), in, out, elem_cnt, parallel_desc); -} - -template<> -Maybe AllGather(const void* in, void* out, size_t elem_cnt, DataType dtype, - Symbol parallel_desc, ep::Stream* stream) { - int64_t parallel_num = parallel_desc->parallel_num(); - if (parallel_num == 1) { - if (in != out) { std::memcpy(out, in, elem_cnt * GetSizeOfDataType(dtype)); } - return Maybe::Ok(); - } - char* char_out = reinterpret_cast(out); - size_t chunk_size = elem_cnt * GetSizeOfDataType(dtype); - BalancedSplitter bs(chunk_size * parallel_num, parallel_num); - const auto& opt_parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); - CHECK_OR_RETURN(opt_parallel_id->has_value()); - const auto& rank_group = JUST(RankGroup::New(parallel_desc)); - TransportToken transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); - int64_t parallel_id = JUST(*opt_parallel_id); - // In-place operation will happen if in == out + parallel_id * chunk_size - if (in != &char_out[parallel_id * chunk_size]) { - memcpy(&char_out[parallel_id * chunk_size], in, chunk_size); - } - for (int64_t i = 0, part_id = parallel_id; i < parallel_num - 1; - ++i, part_id = RingDecrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - const void* send_ptr = &char_out[bs.At(send_part_id).begin()]; - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = RingDecrease(part_id, parallel_num); - void* recv_ptr = &char_out[bs.At(recv_part_id).begin()]; - size_t recv_size = bs.At(recv_part_id).size(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size; - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size; - *Cb = [] {}; - return Maybe::Ok(); - }); - if (send_size > 0) { - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - } - if (recv_size > 0) { - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - } - JUST(ctx.WaitDone()); - } - return Maybe::Ok(); -} - -template<> -Maybe Broadcast(const void* in, void* out, size_t elem_cnt, DataType dtype, - int64_t root, Symbol parallel_desc, - ep::Stream* stream) { - CHECK_EQ_OR_RETURN(parallel_desc->device_type(), DeviceType::kCPU); - CHECK_OR_RETURN(IsPODDataType(dtype)); - size_t buffer_size = elem_cnt * GetSizeOfDataType(dtype); - const auto& transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); - return CpuBroadcast(in, out, buffer_size, root, parallel_desc, transport_token); -} - Maybe CpuBroadcast(const void* in, void* out, size_t buffer_size, int64_t root, Symbol parallel_desc, const TransportToken& transport_token) { @@ -351,171 +80,7 @@ Maybe CpuBroadcast(const void* in, void* out, size_t buffer_size, int64_t return Maybe::Ok(); } -template -struct DtypeReduce; - -template -struct DtypeReduce { - static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, int64_t root, - Symbol parallel_desc) { - const T* in = reinterpret_cast(void_in); - T* out = reinterpret_cast(void_out); - - int64_t parallel_num = parallel_desc->parallel_num(); - BalancedSplitter bs(elem_cnt, parallel_num); - - size_t size = root == GlobalProcessCtx::Rank() && void_in != void_out ? 0 : bs.At(0).size(); - T* tmp_out = nullptr; - // void_out is only used on rank root and ignored for other ranks. - auto tmp_out_buffer = std::make_unique(size); - int64_t parallel_id_of_root = - JUST(parallel_desc->ParallelId4MachineDeviceId(root, GlobalProcessCtx::LocalRank(root))); - if (root == GlobalProcessCtx::Rank() && void_in != void_out) { - tmp_out = &reinterpret_cast(void_out)[bs.At(parallel_id_of_root).begin()]; - } else { - tmp_out = tmp_out_buffer.get(); - } - - auto recv_buffer = std::make_unique(bs.At(0).size()); - Optional parallel_id; - JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); - const auto& rank_group = JUST(RankGroup::New(parallel_desc)); - TransportToken transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); - for (int64_t i = 0, part_id = RingDecrease(JUST(parallel_id), parallel_num); - i < parallel_num - 1; ++i, part_id = RingDecrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - const T* send_ptr = nullptr; - if (i == 0) { - send_ptr = &in[bs.At(send_part_id).begin()]; - } else { - send_ptr = tmp_out; - } - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = RingDecrease(part_id, parallel_num); - T* recv_ptr = recv_buffer.get(); - size_t recv_size = bs.At(recv_part_id).size(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }); - if (send_size > 0) { - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - } - if (recv_size > 0) { - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - } - JUST(ctx.WaitDone()); - const T* cur_in = &in[bs.At(recv_part_id).begin()]; - if (recv_size > 0) { VecAdd(recv_size, tmp_out, cur_in, recv_ptr); } - } - - if (root == GlobalProcessCtx::Rank() && void_in == void_out) { - memcpy(&out[bs.At(parallel_id_of_root).begin()], tmp_out, - bs.At(parallel_id_of_root).size() * sizeof(T)); - } - - for (int64_t i = 0, part_id = RingIncrease(parallel_id_of_root, parallel_num); - i < parallel_num - 1; ++i, part_id = RingIncrease(part_id, parallel_num)) { - int64_t send_part_id = part_id; - int64_t src_rank = JUST(parallel_desc->MachineId4ParallelId(send_part_id)); - const T* send_ptr = tmp_out; - size_t send_size = bs.At(send_part_id).size(); - int64_t recv_part_id = part_id; - T* recv_ptr = &out[bs.At(recv_part_id).begin()]; - size_t recv_size = bs.At(recv_part_id).size(); - - if (send_size > 0 && src_rank == GlobalProcessCtx::Rank()) { - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = const_cast(send_ptr); - *size = send_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - UNIMPLEMENTED_THEN_RETURN(); - }); - JUST(TransportUtil::SendDataToRank(root, transport_token, &ctx)); - JUST(ctx.WaitDone()); - } - if (recv_size > 0 && root == GlobalProcessCtx::Rank()) { - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - UNIMPLEMENTED_THEN_RETURN(); - }, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_ptr; - *size = recv_size * sizeof(T); - *Cb = [] {}; - return Maybe::Ok(); - }); - JUST(TransportUtil::ReceiveDataFromRank(src_rank, transport_token, &ctx)); - JUST(ctx.WaitDone()); - } - } - return Maybe::Ok(); - } -}; - -#define MAKE_REDUCE_ENTRY(func_name, T, reduce_type) func_name::Call - -DEFINE_STATIC_SWITCH_FUNC(Maybe, DtypeReduce, MAKE_REDUCE_ENTRY, - MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), CCL_REDUCE_TYPE_CTRV_SEQ); - -#undef MAKE_REDUCE_ENTRY - -template<> -Maybe Reduce(const void* in, void* out, size_t elem_cnt, DataType dtype, - ReduceType reduce_type, int64_t root, - Symbol parallel_desc, ep::Stream* stream) { - return SwitchDtypeReduce(SwitchCase(dtype, reduce_type), in, out, elem_cnt, root, parallel_desc); -} - -#ifdef WITH_CUDA -std::pair RawGetNcclCommAndPeerNcclRank(int64_t peer_process_id) { - std::set> device_set; - const int64_t& rank = GlobalProcessCtx::Rank(); - const int64_t peer_nccl_rank = (peer_process_id > rank) ? 1 : 0; - device_set.emplace(rank, GlobalProcessCtx::LocalRank()); - device_set.emplace(peer_process_id, GlobalProcessCtx::LocalRank(peer_process_id)); - return {CHECK_NOTNULL(Singleton::Get())->GetCommForDevice(device_set), - peer_nccl_rank}; -} -auto* GetNcclCommAndPeerNcclRank = DECORATE(&RawGetNcclCommAndPeerNcclRank, ThreadLocal); -#endif - -#ifdef WITH_ROCM -std::pair RawGetNcclCommAndPeerNcclRank(int64_t peer_process_id) { - std::set> device_set; - const int64_t& rank = GlobalProcessCtx::Rank(); - const int64_t peer_nccl_rank = (peer_process_id > rank) ? 1 : 0; - device_set.emplace(rank, GlobalProcessCtx::LocalRank()); - device_set.emplace(peer_process_id, GlobalProcessCtx::LocalRank(peer_process_id)); - return {CHECK_NOTNULL(Singleton::Get())->GetCommForDevice(device_set), - peer_nccl_rank}; -} -auto* GetNcclCommAndPeerNcclRank = DECORATE(&RawGetNcclCommAndPeerNcclRank, ThreadLocal); -#endif - - -template<> -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, - ep::Stream* stream) { - CHECK_OR_RETURN(IsPODDataType(dtype)); - size_t buffer_size = elem_cnt * GetSizeOfDataType(dtype); +Maybe CpuSend(const void* in, size_t buffer_size, int64_t dst) { TransportToken transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); NaiveAsyncTransportCtx transport_ctx( transport_token, @@ -533,41 +98,7 @@ Maybe Send(const void* in, size_t elem_cnt, DataType dty return Maybe::Ok(); } -#ifdef WITH_CUDA -template<> -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, - ep::Stream* stream) { -#if NCCL_VERSION_CODE >= 2700 - CHECK_OR_RETURN(IsPODDataType(dtype)); - const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(dst); - OF_NCCL_CHECK_OR_RETURN(ncclSend(in, elem_cnt, GetNcclDataType(dtype), comm_and_peer_rank.second, - comm_and_peer_rank.first, - stream->As()->cuda_stream())); - return Maybe::Ok(); -#else - UNIMPLEMENTED_THEN_RETURN() << "GPU send is only supported when nccl version >= 2.7" -#endif -} -#endif - -#ifdef WITH_ROCM -template<> -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, - ep::Stream* stream) { - CHECK_OR_RETURN(IsPODDataType(dtype)); - const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(dst); - OF_NCCL_CHECK_OR_RETURN(ncclSend(in, elem_cnt, GetNcclDataType(dtype), comm_and_peer_rank.second, - comm_and_peer_rank.first, - stream->As()->cuda_stream())); - return Maybe::Ok(); -} -#endif - -template<> -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, - ep::Stream* stream) { - CHECK_OR_RETURN(IsPODDataType(dtype)); - size_t buffer_size = elem_cnt * GetSizeOfDataType(dtype); +Maybe CpuRecv(void* out, size_t buffer_size, int64_t src) { TransportToken transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); NaiveAsyncTransportCtx transport_ctx( transport_token, @@ -585,35 +116,5 @@ Maybe Recv(void* out, size_t elem_cnt, DataType dtype, i return Maybe::Ok(); } -#ifdef WITH_CUDA -template<> -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, - ep::Stream* stream) { -#if NCCL_VERSION_CODE >= 2700 - CHECK_OR_RETURN(IsPODDataType(dtype)); - const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(src); - OF_NCCL_CHECK_OR_RETURN(ncclRecv(out, elem_cnt, GetNcclDataType(dtype), comm_and_peer_rank.second, - comm_and_peer_rank.first, - stream->As()->cuda_stream())); - return Maybe::Ok(); -#else - UNIMPLEMENTED_THEN_RETURN() << "GPU recv is only supported when nccl version >= 2.7" -#endif -} -#endif - -#ifdef WITH_ROCM -template<> -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, - ep::Stream* stream) { - CHECK_OR_RETURN(IsPODDataType(dtype)); - const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(src); - OF_NCCL_CHECK_OR_RETURN(ncclRecv(out, elem_cnt, GetNcclDataType(dtype), comm_and_peer_rank.second, - comm_and_peer_rank.first, - stream->As()->cuda_stream())); - return Maybe::Ok(); -} -#endif - } // namespace ccl } // namespace oneflow diff --git a/oneflow/core/ccl/ccl.h b/oneflow/core/ccl/ccl.h index a610d6b5a705dc4e179b3d59638dc50a0190a578..c3a0ceaa352a570116458fcd046caac5c4866ff0 100644 --- a/oneflow/core/ccl/ccl.h +++ b/oneflow/core/ccl/ccl.h @@ -24,55 +24,15 @@ limitations under the License. namespace oneflow { -class DeviceCtx; class ParallelDesc; class TransportToken; // collective communication library namespace ccl { -#define CCL_REDUCE_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(kSum) +Maybe CpuSend(const void* in, size_t buffer_size, int64_t dst); -enum ReduceType { - kInvalidReduceFunctorType = 0, -#define DEFINE_REDUCE_TYPE_ENUM_VALUE(enum_value) enum_value, - OF_PP_FOR_EACH_TUPLE(DEFINE_REDUCE_TYPE_ENUM_VALUE, CCL_REDUCE_TYPE_SEQ) -#undef DEFINE_REDUCE_TYPE_ENUM_VALUE - kReduceTypeSize -}; - -#define CCL_REDUCE_TYPE_CTRV_SEQ \ - MAKE_TYPED_CTRV_SEQ(ReduceType, \ - OF_PP_FOR_EACH_TUPLE(OF_PP_I_MAKE_REPLICATE_TUPLE_SEQ, CCL_REDUCE_TYPE_SEQ)) - -template -Maybe AllReduce(const void* in, void* out, size_t elem_cnt, DataType dtype, - ReduceType reduce_type, Symbol parallel_desc, - ep::Stream* stream); - -template -Maybe ReduceScatter(const void* in, void* out, size_t elem_cnt, DataType dtype, - ReduceType reduce_type, Symbol parallel_desc, - ep::Stream* stream); - -template -Maybe AllGather(const void* in, void* out, size_t elem_cnt, DataType dtype, - Symbol parallel_desc, ep::Stream* stream); - -template -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, ep::Stream* stream); - -template -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, ep::Stream* stream); - -template -Maybe Broadcast(const void* in, void* out, size_t elem_cnt, DataType dtype, int64_t root, - Symbol parallel_desc, ep::Stream* stream); - -template -Maybe Reduce(const void* in, void* out, size_t elem_cnt, DataType dtype, - ReduceType reduce_type, int64_t root, Symbol parallel_desc, - ep::Stream* stream); +Maybe CpuRecv(void* out, size_t buffer_size, int64_t src); Maybe CpuBroadcast(const void* in, void* out, size_t buffer_size, int64_t root, Symbol parallel_desc, const TransportToken& transport_token); diff --git a/oneflow/core/comm_network/ibverbs/ibverbs_comm_network.cpp b/oneflow/core/comm_network/ibverbs/ibverbs_comm_network.cpp index 10da873134ca07a50ad3188db2186a8878aee8ed..78f3d4945acc4e92169c0e4cf1e5a443c96a625e 100644 --- a/oneflow/core/comm_network/ibverbs/ibverbs_comm_network.cpp +++ b/oneflow/core/comm_network/ibverbs/ibverbs_comm_network.cpp @@ -71,9 +71,11 @@ IBVerbsCommNet::~IBVerbsCommNet() { for (IBVerbsQP* qp : qp_vec_) { if (qp) { delete qp; } } - CHECK_EQ(ibv::wrapper.ibv_destroy_cq(cq_), 0); - CHECK_EQ(ibv::wrapper.ibv_dealloc_pd(pd_), 0); - CHECK_EQ(ibv::wrapper.ibv_close_device(context_), 0); + PCHECK(ibv::wrapper.ibv_destroy_cq(cq_) == 0); + PCHECK(ibv::wrapper.ibv_dealloc_pd(pd_) == 0); + CHECK_EQ(ibv::wrapper.ibv_close_device(context_), 0) + << "Error, failed to close the IB device " + << ibv::wrapper.ibv_get_device_name(context_->device); } void IBVerbsCommNet::SendActorMsg(int64_t dst_machine_id, const ActorMsg& msg) { @@ -127,20 +129,21 @@ IBVerbsCommNet::IBVerbsCommNet() : CommNetIf(), poll_exit_flag_(ATOMIC_FLAG_INIT CHECK(device != nullptr) << "No IB device match " << user_device; } context_ = ibv::wrapper.ibv_open_device(device); - CHECK(context_); + CHECK(context_ != NULL) << "Error, failed to open the IB device " + << ibv::wrapper.ibv_get_device_name(device); ibv::wrapper.ibv_free_device_list(device_list); pd_ = ibv::wrapper.ibv_alloc_pd(context_); - CHECK(pd_); + CHECK(pd_) << "Error, ibv_alloc_pd() allocates a Protection Domain (PD) failed"; ibv_device_attr device_attr{}; - CHECK_EQ(ibv::wrapper.ibv_query_device(context_, &device_attr), 0); + PCHECK(ibv::wrapper.ibv_query_device(context_, &device_attr) == 0); cq_ = ibv::wrapper.ibv_create_cq(context_, device_attr.max_cqe, nullptr, nullptr, 0); - CHECK(cq_); + PCHECK(cq_); ibv_port_attr port_attr{}; const uint8_t port = user_port == 0 ? 1 : user_port; - CHECK_EQ(ibv::wrapper.ibv_query_port_wrap(context_, port, &port_attr), 0); + PCHECK(ibv::wrapper.ibv_query_port_wrap(context_, port, &port_attr) == 0); ibv_gid gid{}; const int64_t gid_index = ParseIntegerFromEnv("ONEFLOW_COMM_NET_IB_GID_INDEX", 0); - CHECK_EQ(ibv::wrapper.ibv_query_gid(context_, port, gid_index, &gid), 0); + PCHECK(ibv::wrapper.ibv_query_gid(context_, port, gid_index, &gid) == 0); VLOG(1) << "Using IB device " << device->name << " port " << static_cast(port) << " gid index " << gid_index; int64_t this_machine_id = GlobalProcessCtx::Rank(); diff --git a/oneflow/core/comm_network/ibverbs/ibverbs_memory_desc.cpp b/oneflow/core/comm_network/ibverbs/ibverbs_memory_desc.cpp index c06d9805bb818aa29865f306aa62e61f010ab5d8..cc75d19331bdc875c02c7987aed22ed24716fc01 100644 --- a/oneflow/core/comm_network/ibverbs/ibverbs_memory_desc.cpp +++ b/oneflow/core/comm_network/ibverbs/ibverbs_memory_desc.cpp @@ -26,10 +26,10 @@ IBVerbsMemDesc::IBVerbsMemDesc(ibv_pd* pd, void* mem_ptr, size_t byte_size) mr_ = ibv::wrapper.ibv_reg_mr_wrap( pd, mem_ptr, byte_size, IBV_ACCESS_LOCAL_WRITE | IBV_ACCESS_REMOTE_WRITE | IBV_ACCESS_REMOTE_READ); - CHECK(mr_); + PCHECK(mr_); } -IBVerbsMemDesc::~IBVerbsMemDesc() { CHECK_EQ(ibv::wrapper.ibv_dereg_mr(mr_), 0); } +IBVerbsMemDesc::~IBVerbsMemDesc() { PCHECK(ibv::wrapper.ibv_dereg_mr(mr_) == 0); } } // namespace oneflow diff --git a/oneflow/core/comm_network/ibverbs/ibverbs_qp.cpp b/oneflow/core/comm_network/ibverbs/ibverbs_qp.cpp index af3769560667e4a08f5027c6465f2344fce9c296..b700219ba1f6c3f6bc7f95d608656af60f2cc626 100644 --- a/oneflow/core/comm_network/ibverbs/ibverbs_qp.cpp +++ b/oneflow/core/comm_network/ibverbs/ibverbs_qp.cpp @@ -40,7 +40,7 @@ IBVerbsQP::IBVerbsQP(ibv_context* ctx, ibv_pd* pd, const struct ibv_port_attr& p port_num_ = port_num; // qp_ ibv_device_attr device_attr{}; - CHECK_EQ(ibv::wrapper.ibv_query_device(ctx, &device_attr), 0); + PCHECK(ibv::wrapper.ibv_query_device(ctx, &device_attr) == 0); const int64_t user_queue_depth = ParseIntegerFromEnv("ONEFLOW_COMM_NET_IB_QUEUE_DEPTH", kDefaultQueueDepth); const uint32_t queue_depth = std::min(device_attr.max_qp_wr, user_queue_depth); @@ -57,7 +57,7 @@ IBVerbsQP::IBVerbsQP(ibv_context* ctx, ibv_pd* pd, const struct ibv_port_attr& p qp_init_attr.qp_type = IBV_QPT_RC; qp_init_attr.sq_sig_all = 1; qp_ = ibv::wrapper.ibv_create_qp(pd, &qp_init_attr); - CHECK(qp_); + PCHECK(qp_); // recv_msg_buf_ recv_msg_buf_.assign(queue_depth, nullptr); FOR_RANGE(size_t, i, 0, recv_msg_buf_.size()) { recv_msg_buf_.at(i) = new ActorMsgMR(pd_); } @@ -71,7 +71,7 @@ IBVerbsQP::IBVerbsQP(ibv_context* ctx, ibv_pd* pd, const struct ibv_port_attr& p } IBVerbsQP::~IBVerbsQP() { - CHECK_EQ(ibv::wrapper.ibv_destroy_qp(qp_), 0); + PCHECK(ibv::wrapper.ibv_destroy_qp(qp_) == 0); while (send_msg_buf_.empty() == false) { delete send_msg_buf_.front(); send_msg_buf_.pop(); @@ -90,9 +90,9 @@ void IBVerbsQP::Connect(const IBVerbsConnectionInfo& peer_info) { qp_attr.port_num = port_num_; qp_attr.qp_access_flags = IBV_ACCESS_LOCAL_WRITE | IBV_ACCESS_REMOTE_WRITE | IBV_ACCESS_REMOTE_READ; - CHECK_EQ(ibv::wrapper.ibv_modify_qp( - qp_, &qp_attr, IBV_QP_STATE | IBV_QP_PKEY_INDEX | IBV_QP_PORT | IBV_QP_ACCESS_FLAGS), - 0); + PCHECK(ibv::wrapper.ibv_modify_qp( + qp_, &qp_attr, IBV_QP_STATE | IBV_QP_PKEY_INDEX | IBV_QP_PORT | IBV_QP_ACCESS_FLAGS) + == 0); // IBV_QPS_RTR memset(&qp_attr, 0, sizeof(ibv_qp_attr)); @@ -120,11 +120,11 @@ void IBVerbsQP::Connect(const IBVerbsConnectionInfo& peer_info) { qp_attr.rq_psn = 0; qp_attr.max_dest_rd_atomic = 1; qp_attr.min_rnr_timer = 12; - CHECK_EQ(ibv::wrapper.ibv_modify_qp(qp_, &qp_attr, - IBV_QP_STATE | IBV_QP_AV | IBV_QP_PATH_MTU | IBV_QP_DEST_QPN - | IBV_QP_RQ_PSN | IBV_QP_MAX_DEST_RD_ATOMIC - | IBV_QP_MIN_RNR_TIMER), - 0); + PCHECK(ibv::wrapper.ibv_modify_qp(qp_, &qp_attr, + IBV_QP_STATE | IBV_QP_AV | IBV_QP_PATH_MTU | IBV_QP_DEST_QPN + | IBV_QP_RQ_PSN | IBV_QP_MAX_DEST_RD_ATOMIC + | IBV_QP_MIN_RNR_TIMER) + == 0); // IBV_QPS_RTS memset(&qp_attr, 0, sizeof(ibv_qp_attr)); @@ -134,11 +134,10 @@ void IBVerbsQP::Connect(const IBVerbsConnectionInfo& peer_info) { qp_attr.retry_cnt = 7; qp_attr.rnr_retry = 7; qp_attr.timeout = 14; - CHECK_EQ(ibv::wrapper.ibv_modify_qp(qp_, &qp_attr, - IBV_QP_STATE | IBV_QP_SQ_PSN | IBV_QP_MAX_QP_RD_ATOMIC - | IBV_QP_RETRY_CNT | IBV_QP_RNR_RETRY | IBV_QP_TIMEOUT), - - 0); + PCHECK(ibv::wrapper.ibv_modify_qp(qp_, &qp_attr, + IBV_QP_STATE | IBV_QP_SQ_PSN | IBV_QP_MAX_QP_RD_ATOMIC + | IBV_QP_RETRY_CNT | IBV_QP_RNR_RETRY | IBV_QP_TIMEOUT) + == 0); } void IBVerbsQP::PostAllRecvRequest() { @@ -197,7 +196,7 @@ void IBVerbsQP::EnqueuePostSendReadWR(ibv_send_wr wr, ibv_sge sge) { if (num_outstanding_send_wr_ < max_outstanding_send_wr_) { num_outstanding_send_wr_++; ibv_send_wr* bad_wr = nullptr; - CHECK_EQ(ibv_post_send(qp_, &wr, &bad_wr), 0); + PCHECK(ibv_post_send(qp_, &wr, &bad_wr) == 0); } else { std::pair ibv_send_wr_sge = std::make_pair(wr, sge); pending_send_wr_queue_.push(ibv_send_wr_sge); @@ -239,7 +238,7 @@ void IBVerbsQP::PostPendingSendWR() { wr.sg_list = &ibv_send_wr_sge.second; pending_send_wr_queue_.pop(); ibv_send_wr* bad_wr = nullptr; - CHECK_EQ(ibv_post_send(qp_, &wr, &bad_wr), 0); + PCHECK(ibv_post_send(qp_, &wr, &bad_wr) == 0); } else { if (num_outstanding_send_wr_ > 0) { num_outstanding_send_wr_--; } } @@ -258,7 +257,7 @@ void IBVerbsQP::PostRecvRequest(ActorMsgMR* msg_mr) { wr.sg_list = &sge; wr.num_sge = 1; ibv_recv_wr* bad_wr = nullptr; - CHECK_EQ(ibv_post_recv(qp_, &wr, &bad_wr), 0); + PCHECK(ibv_post_recv(qp_, &wr, &bad_wr) == 0); } ActorMsgMR* IBVerbsQP::GetOneSendMsgMRFromBuf() { diff --git a/oneflow/core/common/bfloat16.h b/oneflow/core/common/bfloat16.h new file mode 100644 index 0000000000000000000000000000000000000000..a013fcdebc02ef347f6b5045d3964b604827e88c --- /dev/null +++ b/oneflow/core/common/bfloat16.h @@ -0,0 +1,314 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_BFLOAT16_H_ +#define ONEFLOW_CORE_COMMON_BFLOAT16_H_ + +#include +#include +#include +#include + +namespace oneflow { + +#if defined(__CUDACC__) +#define OF_DEVICE_FUNCTION __device__ __host__ __forceinline__ +#else +#define OF_DEVICE_FUNCTION inline +#endif + +struct alignas(2) bfloat16 { + uint16_t x; + + bfloat16() = default; + bfloat16(const bfloat16& o) = default; + bfloat16& operator=(const bfloat16& o) = default; + bfloat16(bfloat16&& o) = default; + bfloat16& operator=(bfloat16&& o) = default; + ~bfloat16() = default; + + struct from_bits_t {}; + static constexpr inline from_bits_t from_bits() { return from_bits_t(); } + + constexpr inline bfloat16(unsigned short bits, from_bits_t) : x(bits){}; + + // reference: pytorch/c10/util/BFloat16.h + // https://github.com/pytorch/pytorch/blob/release/1.12/c10/util/BFloat16.h + bfloat16(float value) { + if (std::isnan(value)) { + x = 0x7FC0; + } else { + union { + uint32_t U32; + float F32; + }; + + F32 = value; + uint32_t rounding_bias = ((U32 >> 16) & 1) + 0x7FFFU; + x = static_cast((U32 + rounding_bias) >> 16); + } + } + + inline operator float() const { + float res = 0; + uint32_t tmp = x; + tmp <<= 16; + std::memcpy(&res, &tmp, sizeof(tmp)); + return res; + } + + inline bool operator==(const bfloat16& other) const { return x == other.x; } + + inline explicit operator bool() const { return (x & 0x7fff) != 0; } + + inline explicit operator int8_t() const { return static_cast(static_cast(*this)); } + + inline explicit operator uint8_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator int16_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator uint16_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator int32_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator uint32_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator int64_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator uint64_t() const { + return static_cast(static_cast(*this)); + } + + inline explicit operator double() const { return static_cast(static_cast(*this)); } +}; + +// Arithmetic + +inline bfloat16 operator+(const bfloat16& a, const bfloat16& b) { + return static_cast(a) + static_cast(b); +} + +inline bfloat16 operator-(const bfloat16& a, const bfloat16& b) { + return static_cast(a) - static_cast(b); +} + +inline bfloat16 operator*(const bfloat16& a, const bfloat16& b) { + return static_cast(a) * static_cast(b); +} + +inline bfloat16 operator/(const bfloat16& a, const bfloat16& b) { + return static_cast(a) / static_cast(b); +} + +inline bfloat16 operator-(const bfloat16& a) { + bfloat16 output; + output.x = a.x ^ 0x8000U; + return output; +} + +inline bfloat16& operator+=(bfloat16& a, const bfloat16& b) { + a = a + b; + return a; +} + +inline bfloat16& operator-=(bfloat16& a, const bfloat16& b) { + a = a - b; + return a; +} + +inline bfloat16& operator*=(bfloat16& a, const bfloat16& b) { + a = a * b; + return a; +} + +inline bfloat16& operator/=(bfloat16& a, const bfloat16& b) { + a = a / b; + return a; +} + +inline bfloat16& operator|(bfloat16& a, const bfloat16& b) { + a.x = a.x | b.x; + return a; +} + +inline bfloat16& operator^(bfloat16& a, const bfloat16& b) { + a.x = a.x ^ b.x; + return a; +} + +inline bfloat16& operator&(bfloat16& a, const bfloat16& b) { + a.x = a.x & b.x; + return a; +} + +// Arithmetic with floats + +inline float operator+(bfloat16 a, float b) { return static_cast(a) + b; } +inline float operator-(bfloat16 a, float b) { return static_cast(a) - b; } +inline float operator*(bfloat16 a, float b) { return static_cast(a) * b; } +inline float operator/(bfloat16 a, float b) { return static_cast(a) / b; } + +inline float operator+(float a, bfloat16 b) { return a + static_cast(b); } +inline float operator-(float a, bfloat16 b) { return a - static_cast(b); } +inline float operator*(float a, bfloat16 b) { return a * static_cast(b); } +inline float operator/(float a, bfloat16 b) { return a / static_cast(b); } + +inline float& operator+=(float& a, const bfloat16& b) { return a += static_cast(b); } +inline float& operator-=(float& a, const bfloat16& b) { return a -= static_cast(b); } +inline float& operator*=(float& a, const bfloat16& b) { return a *= static_cast(b); } +inline float& operator/=(float& a, const bfloat16& b) { return a /= static_cast(b); } + +// Arithmetic with doubles + +inline double operator+(bfloat16 a, double b) { return static_cast(a) + b; } +inline double operator-(bfloat16 a, double b) { return static_cast(a) - b; } +inline double operator*(bfloat16 a, double b) { return static_cast(a) * b; } +inline double operator/(bfloat16 a, double b) { return static_cast(a) / b; } + +inline double operator+(double a, bfloat16 b) { return a + static_cast(b); } +inline double operator-(double a, bfloat16 b) { return a - static_cast(b); } +inline double operator*(double a, bfloat16 b) { return a * static_cast(b); } +inline double operator/(double a, bfloat16 b) { return a / static_cast(b); } + +// Arithmetic with int32_t + +inline bfloat16 operator+(bfloat16 a, int32_t b) { return a + static_cast(b); } +inline bfloat16 operator-(bfloat16 a, int32_t b) { return a - static_cast(b); } +inline bfloat16 operator*(bfloat16 a, int32_t b) { return a * static_cast(b); } +inline bfloat16 operator/(bfloat16 a, int32_t b) { return a / static_cast(b); } + +inline bfloat16 operator+(int32_t a, bfloat16 b) { return static_cast(a) + b; } +inline bfloat16 operator-(int32_t a, bfloat16 b) { return static_cast(a) - b; } +inline bfloat16 operator*(int32_t a, bfloat16 b) { return static_cast(a) * b; } +inline bfloat16 operator/(int32_t a, bfloat16 b) { return static_cast(a) / b; } + +// Arithmetic with int64_t + +inline bfloat16 operator+(bfloat16 a, int64_t b) { return a + static_cast(b); } +inline bfloat16 operator-(bfloat16 a, int64_t b) { return a - static_cast(b); } +inline bfloat16 operator*(bfloat16 a, int64_t b) { return a * static_cast(b); } +inline bfloat16 operator/(bfloat16 a, int64_t b) { return a / static_cast(b); } + +inline bfloat16 operator+(int64_t a, bfloat16 b) { return static_cast(a) + b; } +inline bfloat16 operator-(int64_t a, bfloat16 b) { return static_cast(a) - b; } +inline bfloat16 operator*(int64_t a, bfloat16 b) { return static_cast(a) * b; } +inline bfloat16 operator/(int64_t a, bfloat16 b) { return static_cast(a) / b; } + +// Comparison operators + +inline bool operator>(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) > static_cast(rhs); +} + +inline bool operator>=(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) >= static_cast(rhs); +} + +inline bool operator<(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) < static_cast(rhs); +} + +inline bool operator<=(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) <= static_cast(rhs); +} + +inline bool operator==(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) == static_cast(rhs); +} + +inline bool operator!=(bfloat16& lhs, bfloat16& rhs) { + return static_cast(lhs) != static_cast(rhs); +} + +} // namespace oneflow + +namespace std { + +inline bool isnan(const oneflow::bfloat16& value) { return (value.x & 0x7FFFU) > 0x07F80U; } + +inline bool isinf(const oneflow::bfloat16& value) { return value.x == 0x07F80U; } + +inline bool isfinite(const oneflow::bfloat16& value) { return !isinf(value) && !isnan(value); } + +template<> +class numeric_limits { + public: + static constexpr bool is_signed = true; + static constexpr bool is_specialized = true; + static constexpr bool is_integer = false; + static constexpr bool is_exact = false; + static constexpr bool has_infinity = true; + static constexpr bool has_quiet_NaN = true; + static constexpr bool has_signaling_NaN = true; + static constexpr auto has_denorm = numeric_limits::has_denorm; + static constexpr auto has_denorm_loss = numeric_limits::has_denorm_loss; + static constexpr auto round_style = numeric_limits::round_style; + static constexpr bool is_iec559 = false; + static constexpr bool is_bounded = true; + static constexpr bool is_modulo = false; + static constexpr int digits = 8; + static constexpr int digits10 = 2; + static constexpr int max_digits10 = 4; + static constexpr int radix = 2; + static constexpr int min_exponent = -125; + static constexpr int min_exponent10 = -37; + static constexpr int max_exponent = 128; + static constexpr int max_exponent10 = 38; + static constexpr auto traps = numeric_limits::traps; + static constexpr auto tinyness_before = numeric_limits::tinyness_before; + static constexpr oneflow::bfloat16 min() { + return oneflow::bfloat16(0x0080U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 lowest() { + return oneflow::bfloat16(0xFF7FU, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 max() { + return oneflow::bfloat16(0x7F7FU, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 epsilon() { + return oneflow::bfloat16(0x3C00U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 round_error() { + return oneflow::bfloat16(0x3F00U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 infinity() { + return oneflow::bfloat16(0x7F80U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 quiet_NaN() { + return oneflow::bfloat16(0x7FC0U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 signaling_NaN() { + return oneflow::bfloat16(0x7F80U, oneflow::bfloat16::from_bits()); + } + static constexpr oneflow::bfloat16 denorm_min() { + return oneflow::bfloat16(0x0001U, oneflow::bfloat16::from_bits()); + } +}; + +} // namespace std + +#endif // ONEFLOW_CORE_COMMON_BFLOAT16_H_ diff --git a/oneflow/core/common/bfloat16_math.h b/oneflow/core/common/bfloat16_math.h new file mode 100644 index 0000000000000000000000000000000000000000..8851d0944cc039436dfbbbe64cab99a58eacd314 --- /dev/null +++ b/oneflow/core/common/bfloat16_math.h @@ -0,0 +1,66 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_BFLOAT16_MATH_H_ +#define ONEFLOW_CORE_COMMON_BFLOAT16_MATH_H_ + +#include "oneflow/core/common/bfloat16.h" + +namespace std { + +// reference: pytorch/c10/util/BFloat16-math.h +// https://github.com/pytorch/pytorch/blob/release/1.12/c10/util/BFloat16-math.h +inline oneflow::bfloat16 acos(oneflow::bfloat16 a) { return std::acos(static_cast(a)); } +inline oneflow::bfloat16 asin(oneflow::bfloat16 a) { return std::asin(static_cast(a)); } +inline oneflow::bfloat16 atan(oneflow::bfloat16 a) { return std::atan(static_cast(a)); } +inline oneflow::bfloat16 erf(oneflow::bfloat16 a) { return std::erf(static_cast(a)); } +inline oneflow::bfloat16 erfc(oneflow::bfloat16 a) { return std::erfc(static_cast(a)); } +inline oneflow::bfloat16 exp(oneflow::bfloat16 a) { return std::exp(static_cast(a)); } +inline oneflow::bfloat16 expm1(oneflow::bfloat16 a) { return std::expm1(static_cast(a)); } +inline oneflow::bfloat16 log(oneflow::bfloat16 a) { return std::log(static_cast(a)); } +inline oneflow::bfloat16 log10(oneflow::bfloat16 a) { return std::log10(static_cast(a)); } +inline oneflow::bfloat16 log1p(oneflow::bfloat16 a) { return std::log1p(static_cast(a)); } +inline oneflow::bfloat16 log2(oneflow::bfloat16 a) { return std::log2(static_cast(a)); } +inline oneflow::bfloat16 ceil(oneflow::bfloat16 a) { return std::ceil(static_cast(a)); } +inline oneflow::bfloat16 cos(oneflow::bfloat16 a) { return std::cos(static_cast(a)); } +inline oneflow::bfloat16 floor(oneflow::bfloat16 a) { return std::floor(static_cast(a)); } +inline oneflow::bfloat16 nearbyint(oneflow::bfloat16 a) { + return std::nearbyint(static_cast(a)); +} +inline oneflow::bfloat16 sin(oneflow::bfloat16 a) { return std::sin(static_cast(a)); } +inline oneflow::bfloat16 tan(oneflow::bfloat16 a) { return std::tan(static_cast(a)); } +inline oneflow::bfloat16 sinh(oneflow::bfloat16 a) { return std::sinh(static_cast(a)); } +inline oneflow::bfloat16 cosh(oneflow::bfloat16 a) { return std::cosh(static_cast(a)); } +inline oneflow::bfloat16 tanh(oneflow::bfloat16 a) { return std::tanh(static_cast(a)); } +inline oneflow::bfloat16 trunc(oneflow::bfloat16 a) { return std::trunc(static_cast(a)); } +inline oneflow::bfloat16 lgamma(oneflow::bfloat16 a) { return std::lgamma(static_cast(a)); } +inline oneflow::bfloat16 sqrt(oneflow::bfloat16 a) { return std::sqrt(static_cast(a)); } +inline oneflow::bfloat16 rsqrt(oneflow::bfloat16 a) { + return 1.0 / std::sqrt(static_cast(a)); +} +inline oneflow::bfloat16 abs(oneflow::bfloat16 a) { return std::abs(static_cast(a)); } +inline oneflow::bfloat16 pow(oneflow::bfloat16 a, double b) { + return std::pow(static_cast(a), b); +} +inline oneflow::bfloat16 pow(oneflow::bfloat16 a, oneflow::bfloat16 b) { + return std::pow(static_cast(a), static_cast(b)); +} +inline oneflow::bfloat16 fmod(oneflow::bfloat16 a, oneflow::bfloat16 b) { + return std::fmod(static_cast(a), static_cast(b)); +} + +} // namespace std + +#endif // ONEFLOW_CORE_COMMON_BFLOAT16_MATH_H_ diff --git a/oneflow/core/common/bfloat16_test.cpp b/oneflow/core/common/bfloat16_test.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8ae63acfbbbee37be845c7188934d08cee49b834 --- /dev/null +++ b/oneflow/core/common/bfloat16_test.cpp @@ -0,0 +1,129 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "gtest/gtest.h" +#include "oneflow/core/common/bfloat16.h" +#include "oneflow/core/common/bfloat16_math.h" + +namespace oneflow { +namespace test { + +float float_from_bytes(uint32_t sign, uint32_t exponent, uint32_t fraction) { + // reference: pytorch/c10/test/util/bfloat16_test.cpp + // https://github.com/pytorch/pytorch/blob/release/1.12/c10/test/util/bfloat16_test.cpp + uint32_t bytes = 0; + bytes |= sign; + bytes <<= 8; + bytes |= exponent; + bytes <<= 23; + bytes |= fraction; + float res = NAN; + std::memcpy(&res, &bytes, sizeof(res)); + return res; +} + +TEST(BFLOAT16MATH, Add) { + // 6.25 + float input = float_from_bytes(0, 0, 0x40C80000U); + // 7.25 + float expected = float_from_bytes(0, 0, 0x40E80000U); + + bfloat16 b(input); + b = b + 1; + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Sub) { + // 7.25 + float input = float_from_bytes(0, 0, 0x40E80000U); + // 6.25 + float expected = float_from_bytes(0, 0, 0x40C80000U); + + bfloat16 b(input); + b = b - 1; + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Mul) { + // 3.125 + float input = float_from_bytes(0, 0, 0x40480000U); + // 6.25 + float expected = float_from_bytes(0, 0, 0x40C80000U); + + bfloat16 b(input); + b = b * 2; + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Div) { + // 6.25 + float input = float_from_bytes(0, 0, 0x40C80000U); + // 3.125 + float expected = float_from_bytes(0, 0, 0x40480000U); + + bfloat16 b(input); + b = b / 2; + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Log2) { + // 16 + float input = float_from_bytes(0, 0, 0x41800000U); + // 4 + float expected = float_from_bytes(0, 0, 0x40800000U); + + bfloat16 b(input); + b = std::log2(b); + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Log10) { + // 100 + float input = float_from_bytes(0, 0, 0x42C80000U); + // 2 + float expected = float_from_bytes(0, 0, 0x40000000U); + + bfloat16 b(input); + b = std::log10(b); + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +TEST(BFLOAT16MATH, Sqrt) { + // 25 + float input = float_from_bytes(0, 0, 0x41C80000U); + // 5 + float expected = float_from_bytes(0, 0, 0x40A00000U); + + bfloat16 b(input); + b = std::sqrt(b); + + float res = static_cast(b); + EXPECT_EQ(res, expected); +} + +} // namespace test +} // namespace oneflow diff --git a/oneflow/core/common/blas.h b/oneflow/core/common/blas.h index 68958d4136db19360e7969475387ad7438277113..ae1474ddff5a4ad9a980aea23f37f481693ceaa2 100644 --- a/oneflow/core/common/blas.h +++ b/oneflow/core/common/blas.h @@ -18,27 +18,23 @@ limitations under the License. #include #include -#ifdef WITH_CUDA -#include -#endif // WITH_CUDA -#ifdef WITH_ROCM -#include -#endif // WITH_ROCM #include "oneflow/core/common/cblas.h" #include "oneflow/core/common/preprocessor.h" namespace oneflow { -#define BLAS_NAME_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(dot) \ - OF_PP_MAKE_TUPLE_SEQ(swap) \ - OF_PP_MAKE_TUPLE_SEQ(copy) \ - OF_PP_MAKE_TUPLE_SEQ(axpy) \ - OF_PP_MAKE_TUPLE_SEQ(scal) \ - OF_PP_MAKE_TUPLE_SEQ(gemv) \ - OF_PP_MAKE_TUPLE_SEQ(gemm) \ - OF_PP_MAKE_TUPLE_SEQ(gemmBatched) \ - OF_PP_MAKE_TUPLE_SEQ(gemmStridedBatched) +#define BLAS_NAME_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(dot) \ + OF_PP_MAKE_TUPLE_SEQ(swap) \ + OF_PP_MAKE_TUPLE_SEQ(copy) \ + OF_PP_MAKE_TUPLE_SEQ(axpy) \ + OF_PP_MAKE_TUPLE_SEQ(scal) \ + OF_PP_MAKE_TUPLE_SEQ(gemv) \ + OF_PP_MAKE_TUPLE_SEQ(gemm) \ + OF_PP_MAKE_TUPLE_SEQ(gemmBatched) \ + OF_PP_MAKE_TUPLE_SEQ(gemmStridedBatched) \ + OF_PP_MAKE_TUPLE_SEQ(getrfBatched) \ + OF_PP_MAKE_TUPLE_SEQ(getriBatched) #define CBLAS_TEMPLATE(name) \ template \ diff --git a/oneflow/core/common/blocking_then_busy.h b/oneflow/core/common/blocking_then_busy.h index 32316311c3be1ba691d852ce2a7d742ff7054472..e76da6a123bcf7b44436269982ca7e47afb05349 100644 --- a/oneflow/core/common/blocking_then_busy.h +++ b/oneflow/core/common/blocking_then_busy.h @@ -17,7 +17,7 @@ limitations under the License. #define ONEFLOW_CORE_COMMON_BLOCKING_THEN_BUSY_H_ #include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/blocking_counter.h" +#include "oneflow/core/common/notifier.h" #include "oneflow/core/common/spin_counter.h" namespace oneflow { @@ -26,20 +26,22 @@ class BlockingThenBusy final { public: BlockingThenBusy(const BlockingThenBusy&) = delete; BlockingThenBusy(BlockingThenBusy&&) = delete; - BlockingThenBusy() = delete; - explicit BlockingThenBusy(int cnt) : blocking_counter_(cnt), spin_counter_(cnt) {} + constexpr static int kCnt = 1; + BlockingThenBusy() : notifier_(), spin_counter_(kCnt) {} - BlockingCounter* mut_blocking_counter() { return &blocking_counter_; } + Notifier* mut_notifier() { return ¬ifier_; } SpinCounter* mut_spin_counter() { return &spin_counter_; } + void Reset() { mut_spin_counter()->Reset(kCnt); } + Maybe WaitUntilCntEqualZero(const std::function()>& StopAfterTimeout) { - JUST(blocking_counter_.WaitUntilCntEqualZero(StopAfterTimeout)); + JUST(notifier_.TimedWaitAndClearNotifiedCnt(StopAfterTimeout)); JUST(spin_counter_.WaitUntilCntEqualZero()); return Maybe::Ok(); } private: - BlockingCounter blocking_counter_; + Notifier notifier_; SpinCounter spin_counter_; }; diff --git a/oneflow/core/common/buffer_manager.h b/oneflow/core/common/buffer_manager.h index a292c3a8cd5875f0b45c53674651633688f85726..5a1597f089313b96bfd2a1c4e1ff9c5999ac876b 100644 --- a/oneflow/core/common/buffer_manager.h +++ b/oneflow/core/common/buffer_manager.h @@ -70,23 +70,13 @@ inline std::string GetOutputCriticalSectionCallbackBufferName(const std::string& return prefix + job_name; } -inline std::string GetForeignInputBufferName(const std::string& job_name) { - static const std::string prefix = "ForeignInput-"; - return prefix + job_name; -} - -inline std::string GetForeignOutputBufferName(const std::string& job_name) { - static const std::string prefix = "ForeignOutput-"; - return prefix + job_name; -} - inline std::string GetInputBufferName(const std::string& job_name, const std::string& op_name) { - static const std::string prefix = "ForeignInput-"; + static const std::string prefix = "Input-"; return prefix + job_name + "-" + op_name; } inline std::string GetOutputBufferName(const std::string& job_name, const std::string& op_name) { - static const std::string prefix = "ForeignOutput-"; + static const std::string prefix = "Output-"; return prefix + job_name + "-" + op_name; } diff --git a/oneflow/core/common/constant.h b/oneflow/core/common/constant.h index 3f8b331bdb4442ab6502964c134e93a7286eda86..7760e1611284dea2fcda9e2a5417ea129c38cb7b 100644 --- a/oneflow/core/common/constant.h +++ b/oneflow/core/common/constant.h @@ -24,6 +24,7 @@ static const int64_t kInvalidSessionId = -1; static const std::string kNoPassTag = ""; static const std::string kMainOp = "main_op"; static const int64_t kMaxSplitAxis = 6; +constexpr size_t kMaxNumDims = 8; static const std::string kAsymmetricCodeErrorMsg = "Maybe executing different code in different ranks, please check if the code is branched and " "operates on the global tensor."; diff --git a/oneflow/core/common/container_util.h b/oneflow/core/common/container_util.h index 2f8013b0e4fdd9cc565d6293e074d8fe3dae989f..9a837094726598d0216b9282375fd6cce8fac367 100644 --- a/oneflow/core/common/container_util.h +++ b/oneflow/core/common/container_util.h @@ -82,18 +82,6 @@ std::string Join(const T& con, const std::string& delimiter) { return os.str(); } -template -using SmallSet = std::vector; - -template -std::pair::iterator, bool> SmallSetInsert(SmallSet* vec, const T& elem) { - for (auto iter = vec->begin(); iter != vec->end(); ++iter) { - if (*iter == elem) { return std::make_pair(iter, false); } - } - vec->push_back(elem); - return std::make_pair(--vec->end(), true); -} - } // namespace oneflow #endif // ONEFLOW_CORE_COMMON_CONTAINER_UTIL_H_ diff --git a/oneflow/core/common/cost_util.h b/oneflow/core/common/cost_util.h new file mode 100644 index 0000000000000000000000000000000000000000..173a1efa0e714b40e177b3a5f0c303aa617f4e0a --- /dev/null +++ b/oneflow/core/common/cost_util.h @@ -0,0 +1,109 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_TIME_UTIL_H_ +#define ONEFLOW_CORE_COMMON_TIME_UTIL_H_ + +#include +#include +#include + +#include "nlohmann/json.hpp" + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/mem_util.h" +#include "oneflow/core/job/utils/progress_bar.h" + +namespace oneflow { + +template +struct Duration { + static const std::string& Repr() { + static const std::string repr = ""; + return repr; + } +}; + +#define DEFINE_DURATION_TRAIT(time_type) \ + template<> \ + struct Duration { \ + static const std::string& Repr() { \ + static const std::string repr = #time_type; \ + return repr; \ + } \ + }; + +DEFINE_DURATION_TRAIT(nanoseconds) +DEFINE_DURATION_TRAIT(microseconds) +DEFINE_DURATION_TRAIT(milliseconds) +DEFINE_DURATION_TRAIT(seconds) +DEFINE_DURATION_TRAIT(minutes) +DEFINE_DURATION_TRAIT(hours) +#undef DEFINE_DURATION_TRAIT + +template +class CostCounter final { + public: + OF_DISALLOW_COPY_AND_MOVE(CostCounter); + explicit CostCounter(bool with_log = true, bool with_mem = false) + : with_log_(with_log), with_mem_(with_mem) {} + ~CostCounter() = default; + + void Count(const std::string& log_prefix = "", int v_log_level = 0, bool log_progress = false); + + private: + using Clock = std::conditional_t; + + Clock::time_point start_{Clock::now()}; + bool with_log_{false}; + bool with_mem_{false}; +}; + +template +void CostCounter::Count(const std::string& log_prefix, int v_log_level, + bool log_progress) { + if (log_progress) { CHECK_JUST(LogProgress(log_prefix)); } + + const auto end = Clock::now(); + if (FLAGS_minloglevel <= 0 && VLOG_IS_ON(v_log_level) && with_log_ && v_log_level >= 0) { + // only do time/mem count and log when glog level is INFO and VLOG level is matched. + auto dur = std::chrono::duration_cast(end - start_).count(); + + nlohmann::json json_log; + json_log["loc"] = log_prefix; + json_log["time_cost"] = std::to_string(dur) + " " + Duration::Repr(); + + if (with_mem_) { +#ifdef __linux__ + double vm = 0, rss = 0; + ProcessMemUsage(&vm, &rss); + json_log["mem_rss"] = std::to_string(rss) + " MB"; +#endif // __linux__ + } + + if (v_log_level == 0) { + LOG(INFO) << "[count log]" << json_log.dump(); + } else { + VLOG(v_log_level) << "[count log]" << json_log.dump(); + } + } + start_ = end; + return; +} + +} // namespace oneflow + +#endif // ONEFLOW_CORE_COMMON_TIME_UTIL_H_ diff --git a/oneflow/core/common/data_type.cpp b/oneflow/core/common/data_type.cpp index 467e5bf5fe2e068b2ec91c2beabd0b402d458352..ba42d8a616ffea83ec1ec354c9124e46bd49016f 100644 --- a/oneflow/core/common/data_type.cpp +++ b/oneflow/core/common/data_type.cpp @@ -46,6 +46,15 @@ bool IsFloatingDataType(DataType data_type) { } #undef FLOATING_CASE } +bool IsHalfDataType(DataType data_type) { + switch (data_type) { +#define HALF_CASE(type_cpp, type_proto) \ + case type_proto: return true; + OF_PP_FOR_EACH_TUPLE(HALF_CASE, FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ) + default: return false; + } +#undef HALF_CASE +} bool IsPODDataType(DataType data_type) { switch (data_type) { #define POD_CASE(type_cpp, type_proto) \ @@ -77,7 +86,8 @@ bool IsSupportRequireGradDataType(DataType data_type) { switch (data_type) { #define REQUIRE_GRAD_CASE(type_cpp, type_proto) \ case type_proto: return true; - OF_PP_FOR_EACH_TUPLE(REQUIRE_GRAD_CASE, FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) + OF_PP_FOR_EACH_TUPLE(REQUIRE_GRAD_CASE, + FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ) default: return false; } #undef REQUIRE_GRAD_CASE @@ -130,6 +140,58 @@ size_t GetSizeOfDataType(DataType data_type) { } } +int64_t GetIntMaxVal(DataType datatype) { +#define SWITCH_INT_TYPE(cpp_type, of_datatype) \ + case of_datatype: return static_cast(GetMaxVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(SWITCH_INT_TYPE, INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) + default: + LOG(FATAL) << "invalid data_type: " << DataType_Name(datatype) + << " for GetIntMaxVal(DataType)"; + } +#undef SWITCH_INT_TYPE +} + +int64_t GetIntMinVal(DataType datatype) { +#define SWITCH_INT_TYPE(cpp_type, of_datatype) \ + case of_datatype: return static_cast(GetMinVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(SWITCH_INT_TYPE, INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) + default: + LOG(FATAL) << "invalid data_type: " << DataType_Name(datatype) + << " for GetIntMinVal(DataType)"; + } +#undef SWITCH_INT_TYPE +} + +double GetFloatMaxVal(DataType datatype) { +#define SWITCH_FLOAT_TYPE(cpp_type, of_datatype) \ + case of_datatype: return static_cast(GetMaxVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(SWITCH_FLOAT_TYPE, FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) + default: + LOG(FATAL) << "invalid data_type: " << DataType_Name(datatype) + << " for GetFloatMaxVal(DataType)"; + } +#undef SWITCH_FLOAT_TYPE +} + +double GetFloatMinVal(DataType datatype) { +#define SWITCH_FLOAT_TYPE(cpp_type, of_datatype) \ + case of_datatype: return static_cast(GetMinVal>()); + + switch (datatype) { + OF_PP_FOR_EACH_TUPLE(SWITCH_FLOAT_TYPE, FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) + default: + LOG(FATAL) << "invalid data_type: " << DataType_Name(datatype) + << " for GetFloatMinVal(DataType)"; + } +#undef SWITCH_INT_TYPE +} + namespace { void CheckDataType() { diff --git a/oneflow/core/common/data_type.h b/oneflow/core/common/data_type.h index 41302430017fa9402791d3829706a633c19351cc..204c55ff67bb8974156311208c77f5a846579d88 100644 --- a/oneflow/core/common/data_type.h +++ b/oneflow/core/common/data_type.h @@ -20,11 +20,17 @@ limitations under the License. #include #if defined(WITH_CUDA) #include +#include +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 #endif #if defined(WITH_ROCM) +#include #include #endif - +#include "oneflow/core/common/bfloat16.h" +#include "oneflow/core/common/bfloat16_math.h" #include "oneflow/core/common/data_type.pb.h" #include "oneflow/core/common/data_type_seq.h" #include "oneflow/core/record/record.pb.h" @@ -34,8 +40,18 @@ limitations under the License. namespace oneflow { +template<> +struct IsScalarType final { + static const bool value = true; +}; + typedef half_float::half float16; +template<> +struct IsScalarType final { + static const bool value = true; +}; + template struct IsFloat16; @@ -103,19 +119,23 @@ struct GetDataType : std::integral_constant {}; template<> \ struct GetDataType : std::integral_constant {}; \ inline type_cpp GetTypeByDataType(std::integral_constant) { return {}; } -OF_PP_FOR_EACH_TUPLE(SPECIALIZE_GET_DATA_TYPE, ALL_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ); +OF_PP_FOR_EACH_TUPLE(SPECIALIZE_GET_DATA_TYPE, + ALL_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ); #undef SPECIALIZE_GET_DATA_TYPE template struct GetDataType::value>::type> : std::integral_constant {}; +#if CUDA_VERSION >= 11000 +template<> +struct GetDataType : std::integral_constant {}; +#endif + template using DataTypeToType = decltype(GetTypeByDataType(std::integral_constant{})); -#if defined(__CUDACC__) -#define OF_DEVICE_FUNC __device__ __host__ __forceinline__ -#elif defined(__HIPCC__) +#if defined(__CUDACC__) || defined(__HIPCC__) #define OF_DEVICE_FUNC __device__ __host__ __forceinline__ #else #define OF_DEVICE_FUNC inline @@ -240,7 +260,14 @@ struct DevDType { static_assert(sizeof(float16) == sizeof(half), "sizeof(float16) != sizeof(half)"); typedef half type; }; -#endif +#if CUDA_VERSION >= 11000 +template<> +struct DevDType { + static_assert(sizeof(bfloat16) == sizeof(nv_bfloat16), "sizeof(bfloat16) != sizeof(nv_bfloat16)"); + typedef nv_bfloat16 type; +}; +#endif // CUDA_VERSION >= 11000 +#endif // defined(WITH_CUDA) #if defined(WITH_ROCM) template<> @@ -248,13 +275,21 @@ struct DevDType { static_assert(sizeof(float16) == sizeof(half), "sizeof(float16) != sizeof(half)"); typedef half type; }; -#endif +// #if CUDA_VERSION >= 11000 +// template<> +// struct DevDType { +// static_assert(sizeof(bfloat16) == sizeof(nv_bfloat16), "sizeof(bfloat16) != sizeof(nv_bfloat16)"); +// typedef nv_bfloat16 type; +// }; +// #endif // CUDA_VERSION >= 11000 +#endif // defined(WITH_ROCM) // Func bool IsBoolDataType(DataType data_type); bool IsIntegralDataType(DataType data_type); bool IsFloatingDataType(DataType data_type); +bool IsHalfDataType(DataType data_type); bool IsSupportRequireGradDataType(DataType data_type); bool IsPODDataType(DataType data_type); bool IsPODAndHalfDataType(DataType data_type); @@ -269,11 +304,16 @@ inline bool operator==(const OptInt64& lhs, const OptInt64& rhs) { template void CheckDataType(DataType data_type) { - LOG_IF(FATAL, (std::is_same::value == false && std::is_same::value == false && std::is_same::value == false + LOG_IF(FATAL, (std::is_same::value == false && std::is_same::value == false && data_type != DataType::kChar && data_type != GetDataType::value)) << data_type << " " << GetDataType::value; } +int64_t GetIntMaxVal(DataType datatype); +int64_t GetIntMinVal(DataType datatype); +double GetFloatMaxVal(DataType datatype); +double GetFloatMinVal(DataType datatype); + } // namespace oneflow #endif // ONEFLOW_CORE_COMMON_DATA_TYPE_H_ diff --git a/oneflow/core/common/data_type_converter.h b/oneflow/core/common/data_type_converter.h index 1b84db5f4363c2212fde339caabfc7e6be9f96f6..6a40750684ef0eff69061d9f0173edd543bfdea5 100644 --- a/oneflow/core/common/data_type_converter.h +++ b/oneflow/core/common/data_type_converter.h @@ -352,11 +352,10 @@ struct ConvertIntInt { OF_DEVICE_FUNC static const Out ConvertSatNorm(In value) { #if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) return cuda_round_helper(__saturatef(value * (1.0f / GetMaxVal())) * GetMaxVal()); - } #else return value < 0 ? 0 : ConvertNorm(value); - } #endif + } }; // Converts between integral types diff --git a/oneflow/core/common/data_type_seq.h b/oneflow/core/common/data_type_seq.h index d3b9d2ab1321af0033e77741ac9a81b6fa3cb487..99f441eab90fcb8a30b23bc7ee7752b0836da06c 100644 --- a/oneflow/core/common/data_type_seq.h +++ b/oneflow/core/common/data_type_seq.h @@ -43,7 +43,7 @@ limitations under the License. #define POD_DATA_TYPE_SEQ \ ARITHMETIC_DATA_TYPE_SEQ CHAR_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ -#define POD_AND_HALF_DATA_TYPE_SEQ POD_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ +#define POD_AND_HALF_DATA_TYPE_SEQ POD_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ #define PB_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(OFRecord, DataType::kOFRecord) #define ALL_DATA_TYPE_SEQ POD_DATA_TYPE_SEQ PB_DATA_TYPE_SEQ @@ -53,12 +53,20 @@ limitations under the License. #define FLOAT16_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float16, DataType::kFloat16) +#define BFLOAT16_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(bfloat16, DataType::kBFloat16) + #if defined(WITH_CUDA) #define HALF_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) -#endif +#if CUDA_VERSION >= 11000 +#define NV_BFLOAT16_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) +#endif // CUDA_VERSION >= 11000 +#endif // defined(WITH_CUDA) #if defined(WITH_ROCM) #define HALF_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) +// #if CUDA_VERSION >= 11000 +// #define NV_BFLOAT16_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) +// #endif // CUDA_VERSION >= 11000 #endif #define IMAGE_DATA_TYPE_SEQ \ diff --git a/oneflow/core/common/device_type.proto b/oneflow/core/common/device_type.proto index 731d16b2f8b6443284883042a7bb7e2baa2690d5..2b94416c8cbe964d71c3970940f73657a86b4310 100644 --- a/oneflow/core/common/device_type.proto +++ b/oneflow/core/common/device_type.proto @@ -6,5 +6,4 @@ enum DeviceType { kCPU = 1; kCUDA = 2; kMockDevice = 3; // pseudo device for test. - kROCm = 4; } diff --git a/oneflow/core/common/env_var/bootstrap.h b/oneflow/core/common/env_var/bootstrap.h new file mode 100644 index 0000000000000000000000000000000000000000..cf82a02efdc46d6b55670cd97678fe9cc5859fc2 --- /dev/null +++ b/oneflow/core/common/env_var/bootstrap.h @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_ENV_VAR_BOOTSTRAP_H_ +#define ONEFLOW_CORE_COMMON_ENV_VAR_BOOTSTRAP_H_ + +#include "oneflow/core/common/env_var/env_var.h" + +namespace oneflow { + +DEFINE_ENV_INTEGER(ONEFLOW_RPC_BOOTSTRAP_SERVER_SLEEP_SECONDS, 20); +DEFINE_ENV_INTEGER(ONEFLOW_RPC_BOOTSTRAP_SERVER_MAX_RETRY_TIMES, 3); +DEFINE_ENV_INTEGER(ONEFLOW_RPC_CLIENT_SLEEP_SECONDS, 5); +DEFINE_ENV_INTEGER(ONEFLOW_RPC_CLIENT_MAX_RETRY_TIMES, 6); + +} // namespace oneflow +#endif // ONEFLOW_CORE_COMMON_ENV_VAR_BOOTSTRAP_H_ diff --git a/oneflow/core/common/env_var/debug_mode.h b/oneflow/core/common/env_var/debug_mode.h index 9f2790e49c8d640326bff54afe8448d33754d585..e2e5b98c9511217294a08fbbe032a67c98dcee05 100644 --- a/oneflow/core/common/env_var/debug_mode.h +++ b/oneflow/core/common/env_var/debug_mode.h @@ -25,6 +25,13 @@ DEFINE_ENV_BOOL(ONEFLOW_DEBUG, false); inline bool IsInDebugMode() { return EnvBool() || EnvBool(); } +DEFINE_ENV_BOOL(ENABLE_LOGICAL_CHAIN, false); +inline bool EnableLogicalChain() { return EnvBool(); } + +inline bool IsPythonStackGetterEnabled() { + return ParseBooleanFromEnv("ONEFLOW_PYTHON_STACK_GETTER", IsInDebugMode()); +} + } // namespace oneflow #endif // ONEFLOW_CORE_COMMON_ENV_VAR_DEBUG_MODE_H_ diff --git a/oneflow/core/common/env_var/eager.h b/oneflow/core/common/env_var/eager.h new file mode 100644 index 0000000000000000000000000000000000000000..0bdcb680d986fe55a99cdd58d6e65008a4037f85 --- /dev/null +++ b/oneflow/core/common/env_var/eager.h @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_ENV_VAR_EAGER_H_ +#define ONEFLOW_CORE_COMMON_ENV_VAR_EAGER_H_ + +#include "oneflow/core/common/env_var/env_var.h" + +namespace oneflow { + +// NOTE: use env variable 'ONEFLOW_EAGER_ENABLE_LOCAL_INFER_CACHE' indicate whether the +// use infer cache in naive local op interpret. +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_EAGER_ENABLE_LOCAL_INFER_CACHE, true); + +// NOTE: use env variable 'ONEFLOW_EAGER_TENSOR_INFER_CACHE_SIZE' indicate the size of +// infer cache in op interpret. +DEFINE_THREAD_LOCAL_ENV_INTEGER(ONEFLOW_EAGER_TENSOR_INFER_CACHE_SIZE, 128 * 1024); + +} // namespace oneflow +#endif // ONEFLOW_CORE_COMMON_ENV_VAR_EAGER_H_ diff --git a/oneflow/core/common/env_var/stream.h b/oneflow/core/common/env_var/stream.h new file mode 100644 index 0000000000000000000000000000000000000000..56a73dff3db9bcd8cb8e8cf9610b3a926aea27de --- /dev/null +++ b/oneflow/core/common/env_var/stream.h @@ -0,0 +1,27 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_ENV_VAR_STREAM_H_ +#define ONEFLOW_CORE_COMMON_ENV_VAR_STREAM_H_ + +#include "oneflow/core/common/env_var/env_var.h" + +namespace oneflow { + +DEFINE_THREAD_LOCAL_ENV_INTEGER(ONEFLOW_DEVICE_STREAM_MAX_SIZE, 16); +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_STREAM_ENABLE_H2D_STREAM, false); + +} // namespace oneflow +#endif // ONEFLOW_CORE_COMMON_ENV_VAR_STREAM_H_ diff --git a/oneflow/core/common/env_var/vm.h b/oneflow/core/common/env_var/vm.h index 662f4093b1e2f2cdff7b1ff12da079e3098cbc42..c62adf6a13a6c952f522a8c96d06cdb919713e26 100644 --- a/oneflow/core/common/env_var/vm.h +++ b/oneflow/core/common/env_var/vm.h @@ -20,7 +20,12 @@ limitations under the License. namespace oneflow { -DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_VM_WORKLOAD_ON_SCHEDULER_THREAD, false); +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_VM_COMPUTE_ON_WORKER_THREAD, true); +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_VM_ENABLE_STREAM_WAIT, true); +DEFINE_THREAD_LOCAL_ENV_INTEGER(ONEFLOW_VM_PENDING_HANDLE_WINDOW_SIZE, 10) +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_VM_ENABLE_SCHEDULE_YIELD, true) +DEFINE_THREAD_LOCAL_ENV_INTEGER(ONEFLOW_VM_WORKER_THREAD_LIMIT, 16); +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_VM_MULTI_THREAD, true); -} +} // namespace oneflow #endif // ONEFLOW_CORE_COMMON_ENV_VAR_VM_H_ diff --git a/oneflow/core/common/error.cpp b/oneflow/core/common/error.cpp index 26dad6e7163a9c6c70baaaec6ba90ab1495541be..8af935f96c618e93480775d8a970934e6d8af198 100644 --- a/oneflow/core/common/error.cpp +++ b/oneflow/core/common/error.cpp @@ -14,15 +14,22 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +#include "fmt/core.h" +#include "fmt/color.h" +#include "fmt/ostream.h" #include "oneflow/core/common/error.h" #include "oneflow/core/common/exception.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/error_util.h" #include "oneflow/core/common/env_var/debug_mode.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" +#include "oneflow/core/thread/thread_manager.h" namespace oneflow { +StackedError::StackedError() : stack_frame_(), error_proto_(new ErrorProto()) {} + namespace { void LogError(const Error& error) { @@ -30,234 +37,220 @@ void LogError(const Error& error) { LOG(ERROR) << error->msg(); } -std::shared_ptr* MutThreadLocalError() { - thread_local std::shared_ptr error; +std::shared_ptr* MutThreadLocalError() { + thread_local std::shared_ptr error; return &error; } } // namespace -Error&& Error::AddStackFrame(const std::string& file, const int64_t& line, - const std::string& function) { - auto* stack_frame = error_proto_->add_stack_frame(); - stack_frame->set_file(file); - stack_frame->set_line(line); - stack_frame->set_function(function); +Error&& Error::AddStackFrame(Symbol error_stack_frame) { + stacked_error_->add_stack_frame(error_stack_frame); return std::move(*this); } void Error::Merge(const Error& other) { - std::string error_summary{error_proto_->error_summary()}; - std::string msg{error_proto_->msg()}; - error_proto_->MergeFrom(*other.error_proto_); - // MergeFrom will overwrite singular field, so restore it. - if (!error_summary.empty()) { - error_proto_->set_error_summary(error_summary + " " + error_proto_->error_summary()); - } - if (!msg.empty()) { error_proto_->set_msg(msg + " " + error_proto_->msg()); } + auto* error_proto = stacked_error_->mut_error_proto(); + error_proto->MergeFrom(*other.stacked_error_->error_proto()); } -Error::operator std::string() const { return error_proto_->DebugString(); } +Error::operator std::string() const { return stacked_error_->DebugString(); } -Error Error::Ok() { return std::make_shared(); } +Error Error::Ok() { return std::make_shared(); } Error Error::ProtoParseFailedError() { - auto error = std::make_shared(); - error->mutable_proto_parse_failed_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_proto_parse_failed_error(); return error; } Error Error::JobSetEmptyError() { - auto error = std::make_shared(); - error->mutable_job_set_empty_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_set_empty_error(); return error; } Error Error::DeviceTagNotFoundError() { - auto error = std::make_shared(); - error->mutable_device_tag_not_found_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_device_tag_not_found_error(); return error; } -Error Error::InvalidValueError(const std::string& error_summary) { - auto error = std::make_shared(); - error->set_error_summary(error_summary); - error->mutable_invalid_value_error(); +Error Error::InvalidValueError() { + auto error = std::make_shared(); + error->mut_error_proto()->mutable_invalid_value_error(); return error; } Error Error::IndexError() { - auto error = std::make_shared(); - error->mutable_index_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_index_error(); return error; } Error Error::TypeError() { - auto error = std::make_shared(); - error->mutable_type_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_type_error(); return error; } Error Error::TimeoutError() { - auto error = std::make_shared(); - error->mutable_timeout_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_timeout_error(); return error; } Error Error::JobNameExistError() { - auto error = std::make_shared(); - error->mutable_job_name_exist_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_name_exist_error(); return error; } Error Error::JobNameEmptyError() { - auto error = std::make_shared(); - error->mutable_job_name_empty_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_name_empty_error(); return error; } Error Error::JobNameNotEqualError() { - auto error = std::make_shared(); - error->mutable_job_name_not_equal_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_name_not_equal_error(); return error; } Error Error::NoJobBuildAndInferCtxError() { - auto error = std::make_shared(); - error->mutable_no_job_build_and_infer_ctx_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_no_job_build_and_infer_ctx_error(); return error; } Error Error::JobConfFrozenError() { - auto error = std::make_shared(); - error->mutable_job_conf_frozen_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_conf_frozen_error(); return error; } Error Error::JobConfNotSetError() { - auto error = std::make_shared(); - error->mutable_job_conf_not_set_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_conf_not_set_error(); return error; } Error Error::JobConfRepeatedSetError() { - auto error = std::make_shared(); - error->mutable_job_conf_repeated_set_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_conf_repeated_set_error(); return error; } Error Error::JobTypeNotSetError() { - auto error = std::make_shared(); - error->mutable_job_type_not_set_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_job_type_not_set_error(); return error; } Error Error::LogicalBlobNameNotExistError() { - auto error = std::make_shared(); - error->mutable_logical_blob_name_not_exist_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_logical_blob_name_not_exist_error(); return error; } Error Error::LogicalBlobNameExistError() { - auto error = std::make_shared(); - error->mutable_logical_blob_name_exist_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_logical_blob_name_exist_error(); return error; } Error Error::LogicalBlobNameInvalidError() { - auto error = std::make_shared(); - error->mutable_logical_blob_name_invalid_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_logical_blob_name_invalid_error(); return error; } Error Error::OpNameExistError() { - auto error = std::make_shared(); - error->mutable_op_name_exist_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_op_name_exist_error(); return error; } Error Error::OpConfDeviceTagNoSetError() { - auto error = std::make_shared(); - error->mutable_op_conf_device_tag_no_set_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_op_conf_device_tag_no_set_error(); return error; } Error Error::PlacementError() { - auto error = std::make_shared(); - error->mutable_placement_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_placement_error(); return error; } Error Error::BlobSplitAxisInferError() { - auto error = std::make_shared(); - error->mutable_blob_split_axis_infer_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_blob_split_axis_infer_error(); return error; } Error Error::UnknownJobBuildAndInferError() { - auto error = std::make_shared(); - error->mutable_unknown_job_build_and_infer_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_unknown_job_build_and_infer_error(); return error; } Error Error::CheckFailedError() { - auto error = std::make_shared(); - error->mutable_check_failed_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_check_failed_error(); return error; } Error Error::ValueNotFoundError() { - auto error = std::make_shared(); - error->mutable_value_not_found_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_value_not_found_error(); return error; } Error Error::TodoError() { - auto error = std::make_shared(); - error->mutable_todo_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_todo_error(); return error; } Error Error::UnimplementedError() { - auto error = std::make_shared(); - error->mutable_unimplemented_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_unimplemented_error(); return error; } Error Error::RuntimeError() { - auto error = std::make_shared(); - error->mutable_runtime_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_runtime_error(); return error; } Error Error::OutOfMemoryError() { - auto error = std::make_shared(); - error->mutable_out_of_memory_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_out_of_memory_error(); return error; } Error Error::BoxingNotSupportedError() { - auto error = std::make_shared(); - error->mutable_boxing_not_supported_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_boxing_not_supported_error(); return error; } -Error Error::OpKernelNotFoundError(const std::string& error_summary, - const std::vector& error_msgs) { - auto error = std::make_shared(); - error->set_error_summary(error_summary); - auto* op_kernel_not_found_error = error->mutable_op_kernel_not_found_error(); +Error Error::OpKernelNotFoundError(const std::vector& error_msgs) { + auto error = std::make_shared(); + auto* op_kernel_not_found_error = error->mut_error_proto()->mutable_op_kernel_not_found_error(); for (const auto& msg : error_msgs) { op_kernel_not_found_error->add_op_kernels_not_found_debug_str(msg); } return error; } -Error Error::MultipleOpKernelsMatchedError(const std::string& error_summary, - const std::vector& error_msgs) { - auto error = std::make_shared(); - error->set_error_summary(error_summary); - auto* multiple_op_kernels_matched_error = error->mutable_multiple_op_kernels_matched_error(); +Error Error::MultipleOpKernelsMatchedError(const std::vector& error_msgs) { + auto error = std::make_shared(); + auto* multiple_op_kernels_matched_error = + error->mut_error_proto()->mutable_multiple_op_kernels_matched_error(); for (const auto& msg : error_msgs) { multiple_op_kernels_matched_error->add_matched_op_kernels_debug_str(msg); } @@ -266,8 +259,9 @@ Error Error::MultipleOpKernelsMatchedError(const std::string& error_summary, Error Error::MemoryZoneOutOfMemoryError(int64_t machine_id, int64_t mem_zone_id, uint64_t calc, uint64_t available, const std::string& device_tag) { - auto error = std::make_shared(); - auto* memory_zone_out_of_memory_error = error->mutable_memory_zone_out_of_memory_error(); + auto error = std::make_shared(); + auto* memory_zone_out_of_memory_error = + error->mut_error_proto()->mutable_memory_zone_out_of_memory_error(); memory_zone_out_of_memory_error->add_machine_id(std::to_string(machine_id)); memory_zone_out_of_memory_error->add_mem_zone_id(std::to_string(mem_zone_id)); memory_zone_out_of_memory_error->add_device_tag(device_tag); @@ -276,79 +270,100 @@ Error Error::MemoryZoneOutOfMemoryError(int64_t machine_id, int64_t mem_zone_id, return error; } -Error Error::LossBlobNotFoundError(const std::string& error_summary) { - auto error = std::make_shared(); - error->mutable_loss_blob_not_found_error(); - error->set_error_summary(error_summary); +Error Error::LossBlobNotFoundError() { + auto error = std::make_shared(); + error->mut_error_proto()->mutable_loss_blob_not_found_error(); return error; } Error Error::RwMutexedObjectNotFoundError() { - auto error = std::make_shared(); - error->mutable_rw_mutexed_object_not_found_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_rw_mutexed_object_not_found_error(); return error; } Error Error::GradientFunctionNotFoundError() { - auto error = std::make_shared(); - error->mutable_gradient_function_not_found_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_gradient_function_not_found_error(); return error; } Error Error::SymbolIdUninitializedError() { - auto error = std::make_shared(); - error->mutable_symbol_id_uninitialized_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_symbol_id_uninitialized_error(); return error; } Error Error::CompileOptionWrongError() { - auto error = std::make_shared(); - error->mutable_compile_option_wrong_error(); + auto error = std::make_shared(); + error->mut_error_proto()->mutable_compile_option_wrong_error(); return error; } Error Error::InputDeviceNotMatchError() { - auto error = std::make_shared(); - auto* input_device_not_match_error = error->mutable_input_device_not_match_error(); + auto error = std::make_shared(); + auto* input_device_not_match_error = + error->mut_error_proto()->mutable_input_device_not_match_error(); input_device_not_match_error->add_info( std::string("Input tensors are at different devices, please try to use tensor.to or " "module.to to correct it.")); return error; } -std::string GetStackedErrorString(const std::shared_ptr& error) { +std::string GetStackedErrorString(const std::shared_ptr& error) { const auto& maybe_error = TRY(FormatErrorStr(error)); - const auto& error_str = maybe_error.GetDataAndErrorProto(error->DebugString()); - CHECK_NE(error->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET); + const auto& error_str = maybe_error.GetDataAndStackedError(error->DebugString()); + CHECK_NE(error->error_proto()->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET); return error_str.first; } -std::string GetErrorString(const std::shared_ptr& error) { +std::string GetErrorString(const std::shared_ptr& error) { + std::string error_str; if (IsInDebugMode()) { - return GetStackedErrorString(error); + error_str = GetStackedErrorString(error); } else { - if (error->msg().empty() && error->stack_frame().size() > 0) { - return error->stack_frame(0).error_msg(); - } else { - return error->msg(); + error_str = error->error_proto()->msg(); + } + if (error_str.empty()) { error_str = ""; } + return error_str; +} + +void ThrowError(const std::shared_ptr& error) { + std::string error_str; + fmt::format_to(std::back_inserter(error_str), "{}: {}\n", + fmt::styled("Error", fmt::emphasis::bold | fmt::fg(fmt::color::red)), + GetErrorString(error)); + // Append foreign stack trace (e.g. Python stack trace) when it is available. + if (ForeignFrameThreadLocalGuard::Current().has_value()) { + auto frame = *CHECK_JUST(ForeignFrameThreadLocalGuard::Current()); + if (!IsMainThread()) { + if (auto* stack_getter = Singleton::Get()) { + fmt::format_to(std::back_inserter(error_str), + fmt::emphasis::bold | fmt::fg(fmt::color::dark_orange), + "Related Python stack trace:\n"); + fmt::format_to(std::back_inserter(error_str), "{}", stack_getter->GetFormattedStack(frame)); + } else { + fmt::format_to( + std::back_inserter(error_str), + "You can set {} or {} to 1 to get the Python stack of the error.", + fmt::styled("ONEFLOW_DEBUG", fmt::emphasis::bold | fmt::fg(fmt::color::dark_orange)), + fmt::styled("ONEFLOW_PYTHON_STACK_GETTER", + fmt::emphasis::bold | fmt::fg(fmt::color::dark_orange))); + } } } -} - -void ThrowError(const std::shared_ptr& error) { *MutThreadLocalError() = error; - if (error->has_runtime_error()) { throw RuntimeException(GetErrorString(error)); } - if (error->has_type_error()) { throw TypeException(GetErrorString(error)); } - if (error->has_index_error()) { throw IndexException(GetErrorString(error)); } - if (error->has_unimplemented_error()) { throw NotImplementedException(GetErrorString(error)); } + if ((*error)->has_runtime_error()) { throw RuntimeException(error_str); } + if ((*error)->has_type_error()) { throw TypeException(error_str); } + if ((*error)->has_index_error()) { throw IndexException(error_str); } + if ((*error)->has_unimplemented_error()) { throw NotImplementedException(error_str); } throw Exception(GetStackedErrorString(error)); } -const std::shared_ptr& ThreadLocalError() { return *MutThreadLocalError(); } - -const char* kOfBugIssueUploadPrompt = - "This is a oneflow bug, please submit issues in " - "'https://github.com/Oneflow-Inc/oneflow/issues' include the log information of the error, the " - "minimum reproduction code, and the system information."; +const std::shared_ptr& ThreadLocalError() { return *MutThreadLocalError(); } +const char* kOfBugIssueUploadPrompt = "This is a oneflow bug, please submit an issue at " + "'https://github.com/Oneflow-Inc/oneflow/issues' including " + "the log information of the error, the " + "minimum reproduction code, and the system information."; } // namespace oneflow diff --git a/oneflow/core/common/error.h b/oneflow/core/common/error.h index 6aa96c729d7c33c0b5a570db82f228f700489085..9da18596d39c30220d388218be43094d70db94b1 100644 --- a/oneflow/core/common/error.h +++ b/oneflow/core/common/error.h @@ -18,32 +18,114 @@ limitations under the License. #include #include +#include +#include #include "oneflow/core/common/error.pb.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/common/hash.h" namespace oneflow { +class ErrorStackFrame final { + public: + ErrorStackFrame(const ErrorStackFrame&) = default; + ErrorStackFrame(const std::string& file, int64_t line, const std::string& function) + : file_(file), line_(line), function_(function), code_text_() {} + ErrorStackFrame(const std::string& file, int64_t line, const std::string& function, + const std::string& code_text) + : file_(file), line_(line), function_(function), code_text_(code_text) {} + + bool operator==(const ErrorStackFrame& other) const { + return this->file_ == other.file_ && this->line_ == other.line_ + && this->function_ == other.function_ && this->code_text_ == other.code_text_; + } + + const std::string& file() const { return file_; } + int64_t line() const { return line_; } + const std::string& function() const { return function_; } + const std::string& code_text() const { return code_text_; } + + std::string DebugString() const { + return file_ + ":" + std::to_string(line_) + " " + function_ + "\n\t" + code_text_ + "\n"; + } + + private: + std::string file_; + int64_t line_; + std::string function_; + std::string code_text_; +}; + +} // namespace oneflow + +namespace std { + +template<> +struct hash<::oneflow::ErrorStackFrame> final { + size_t operator()(const ::oneflow::ErrorStackFrame& frame) const { + using namespace oneflow; + return Hash(frame.file(), frame.line(), frame.function(), frame.code_text()); + } +}; + +} // namespace std + +namespace oneflow { + +class StackedError final { + public: + StackedError(); + StackedError(const StackedError&) = default; + + constexpr static int kStackReservedSize = 16; + using FrameVector = small_vector, kStackReservedSize>; + + const ErrorProto* operator->() const { return error_proto().get(); } + ErrorProto* operator->() { return mut_error_proto(); } + + // Getters + const FrameVector& stack_frame() const { return stack_frame_; } + const std::shared_ptr& error_proto() const { return error_proto_; } + std::string DebugString() const { + std::string str; + for (const auto& frame : stack_frame()) { str += frame->DebugString() + "\n"; } + str += error_proto()->DebugString(); + return str; + } + + // Setters + void add_stack_frame(Symbol error_frame) { stack_frame_.push_back(error_frame); } + ErrorProto* mut_error_proto() { return const_cast(error_proto_.get()); } + + private: + FrameVector stack_frame_; + std::shared_ptr error_proto_; +}; + +std::string GetErrorString(const std::shared_ptr& error); + class Error final { public: - Error(const std::shared_ptr& error_proto) : error_proto_(error_proto) {} + Error(const std::shared_ptr& stacked_error) + : stacked_error_(stacked_error), msg_collecting_mode_(kMergeMessage) {} Error(const Error&) = default; ~Error() = default; - std::shared_ptr error_proto() const { return error_proto_; } - const ErrorProto* operator->() const { return error_proto_.get(); } - ErrorProto* operator->() { return error_proto_.get(); } + std::shared_ptr stacked_error() const { return stacked_error_; } + const ErrorProto* operator->() const { return stacked_error_->error_proto().get(); } + ErrorProto* operator->() { return stacked_error_->mut_error_proto(); } operator std::string() const; - void Assign(const Error& other) { error_proto_ = other.error_proto_; } + void Assign(const Error& other) { stacked_error_ = other.stacked_error_; } void Merge(const Error& other); - // r-value reference is used to supporting expressions like `Error().AddStackFrame("foo.cpp", - // ,"line", "Bar") << "invalid value"` because operator<<() need r-value reference - Error&& AddStackFrame(const std::string& file, const int64_t& line, const std::string& function); + Error&& AddStackFrame(Symbol error_stack_frame); static Error Ok(); static Error ProtoParseFailedError(); static Error JobSetEmptyError(); static Error DeviceTagNotFoundError(); - static Error InvalidValueError(const std::string& error_summary); + static Error InvalidValueError(); static Error IndexError(); static Error TypeError(); static Error TimeoutError(); @@ -72,11 +154,9 @@ class Error final { static Error BoxingNotSupportedError(); static Error MemoryZoneOutOfMemoryError(int64_t machine_id, int64_t mem_zone_id, uint64_t calc, uint64_t available, const std::string& device_type); - static Error OpKernelNotFoundError(const std::string& error_summary, - const std::vector& error_msgs); - static Error MultipleOpKernelsMatchedError(const std::string& error_summary, - const std::vector& error_msgs); - static Error LossBlobNotFoundError(const std::string& error_summary); + static Error OpKernelNotFoundError(const std::vector& error_msgs); + static Error MultipleOpKernelsMatchedError(const std::vector& error_msgs); + static Error LossBlobNotFoundError(); static Error RwMutexedObjectNotFoundError(); @@ -90,22 +170,39 @@ class Error final { static Error InputDeviceNotMatchError(); + enum MsgCollectingMode { + kInvalidMsgCollectingMode = 0, + kMergeMessage, + kOverrideThenMergeMessage, + }; + + MsgCollectingMode msg_collecting_mode() const { return msg_collecting_mode_; } + void set_msg_collecting_mode(MsgCollectingMode val) { msg_collecting_mode_ = val; } + private: - std::shared_ptr error_proto_; + std::shared_ptr stacked_error_; + MsgCollectingMode msg_collecting_mode_; }; -void ThrowError(const std::shared_ptr& error); -const std::shared_ptr& ThreadLocalError(); +void ThrowError(const std::shared_ptr& error); +const std::shared_ptr& ThreadLocalError(); + +inline Error& operator<<(Error& error, Error::MsgCollectingMode mode) { + error.set_msg_collecting_mode(mode); + return error; +} template Error& operator<<(Error& error, const T& x) { std::ostringstream ss; ss << x; - if (error->stack_frame().empty()) { + if (error.msg_collecting_mode() == Error::kMergeMessage) { error->set_msg(error->msg() + ss.str()); + } else if (error.msg_collecting_mode() == Error::kOverrideThenMergeMessage) { + error->set_msg(ss.str()); + error.set_msg_collecting_mode(Error::kMergeMessage); } else { - auto* stack_frame_top = error->mutable_stack_frame(error->stack_frame_size() - 1); - stack_frame_top->set_error_msg(stack_frame_top->error_msg() + ss.str()); + LOG(FATAL) << "UNIMPLEMENTED"; } return error; } diff --git a/oneflow/core/common/error.proto b/oneflow/core/common/error.proto index 766a6f7f5401004a9d82497aca2ed5cc9a8e7efd..ee241e8573b052572ba556ba057dfeffd9c14ecd 100644 --- a/oneflow/core/common/error.proto +++ b/oneflow/core/common/error.proto @@ -119,13 +119,6 @@ message InputDeviceNotMatchError { repeated string info = 1; } -message ErrorStackFrame { - required string file = 1; - required int64 line = 2; - required string function = 3; - required string error_msg = 4; -} - message SymbolIdUninitializedError {} message InvalidValueError {} @@ -138,9 +131,8 @@ message TimeoutError {} message ValueNotFoundError {} message ErrorProto { - optional string error_summary = 1 [default = ""]; - optional string msg = 2 [default = ""]; - repeated ErrorStackFrame stack_frame = 3; + optional string msg = 1 [default = ""]; + optional string frame_msg = 2 [default = ""]; oneof error_type { ConfigAssertFailedError config_assert_failed_error = 12; ConfigResourceUnavailableError config_resource_unavailable_error = 13; diff --git a/oneflow/core/common/error_util.cpp b/oneflow/core/common/error_util.cpp index 7f35e5523c9298b6fc894849a67a513a9b7b0d2f..adc636fc1f003a8ba9f7369fedca5fd51354644b 100644 --- a/oneflow/core/common/error_util.cpp +++ b/oneflow/core/common/error_util.cpp @@ -108,25 +108,18 @@ Maybe FormatMsgOfStackFrame(std::string error_msg, bool is_last_sta return ss.str(); } -// the error_summary and msg in error proto -std::string FormatErrorSummaryAndMsgOfErrorProto(const std::shared_ptr& error) { - std::stringstream ss; - if (error->has_error_summary()) { ss << error->error_summary(); } - if (error->has_msg()) { ss << (ss.str().size() != 0 ? "\n" + error->msg() : error->msg()); } - return ss.str(); -} - // the msg in error type instance. -Maybe FormatMsgOfErrorType(const std::shared_ptr& error) { - CHECK_NE_OR_RETURN(error->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET) +Maybe FormatMsgOfErrorType(const std::shared_ptr& error) { + const auto& error_proto = error->error_proto(); + CHECK_NE_OR_RETURN(error_proto->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET) << Error::RuntimeError() << "Parse error failed, unknown error type"; std::stringstream ss; - const google::protobuf::Descriptor* error_des = error->GetDescriptor(); + const google::protobuf::Descriptor* error_des = error_proto->GetDescriptor(); const google::protobuf::OneofDescriptor* oneof_field_des = error_des->FindOneofByName("error_type"); - const google::protobuf::Reflection* error_ref = error->GetReflection(); + const google::protobuf::Reflection* error_ref = error_proto->GetReflection(); const google::protobuf::FieldDescriptor* field_des = - error_ref->GetOneofFieldDescriptor(*error, oneof_field_des); + error_ref->GetOneofFieldDescriptor(*error_proto, oneof_field_des); CHECK_OR_RETURN(field_des != nullptr); ss << "Error Type: " << field_des->full_name(); return ss.str(); @@ -134,20 +127,17 @@ Maybe FormatMsgOfErrorType(const std::shared_ptr& error } // namespace -Maybe FormatErrorStr(const std::shared_ptr& error) { +Maybe FormatErrorStr(const std::shared_ptr& error) { std::stringstream ss; + ss << error->error_proto()->msg(); + ss << error->error_proto()->frame_msg(); // Get msg from stack frame of error proto - for (auto stack_frame = error->mutable_stack_frame()->rbegin(); - stack_frame < error->mutable_stack_frame()->rend(); stack_frame++) { + for (auto iter = error->stack_frame().rbegin(); iter < error->stack_frame().rend(); iter++) { + auto stack_frame = *iter; ss << FormatFileOfStackFrame(stack_frame->file()) << FormatLineOfStackFrame(stack_frame->line()) << FormatFunctionOfStackFrame(stack_frame->function()) - << *JUST(FormatMsgOfStackFrame(stack_frame->error_msg(), - stack_frame == error->mutable_stack_frame()->rend() - 1)); - } - // Get msg from error summary and msg of error proto - std::string error_summary_and_msg_of_error_proto = FormatErrorSummaryAndMsgOfErrorProto(error); - if (error_summary_and_msg_of_error_proto.size() != 0) { - ss << "\n" << error_summary_and_msg_of_error_proto; + << *JUST(FormatMsgOfStackFrame(stack_frame->code_text(), + iter == error->stack_frame().rend() - 1)); } // Get msg from error type of error proto std::string msg_of_error_type = *JUST(FormatMsgOfErrorType(error)); diff --git a/oneflow/core/common/error_util.h b/oneflow/core/common/error_util.h index c4e4d6789a237b8d0b5823a358b09e35caab194c..1dd2a1e8102efe0788c97f71df7e844acbc07f5b 100644 --- a/oneflow/core/common/error_util.h +++ b/oneflow/core/common/error_util.h @@ -22,7 +22,7 @@ limitations under the License. namespace oneflow { -Maybe FormatErrorStr(const std::shared_ptr& error); +Maybe FormatErrorStr(const std::shared_ptr& error); } // namespace oneflow diff --git a/oneflow/core/common/hash.h b/oneflow/core/common/hash.h new file mode 100644 index 0000000000000000000000000000000000000000..27d9b8316e7074918b52af141d49e9a0f5f9cac2 --- /dev/null +++ b/oneflow/core/common/hash.h @@ -0,0 +1,64 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_HASH_H_ +#define ONEFLOW_CORE_COMMON_HASH_H_ +#include + +namespace oneflow { + +inline size_t HashCombine(size_t lhs, size_t rhs) { + return lhs ^ (rhs + 0x9e3779b9 + (lhs << 6U) + (lhs >> 2U)); +} + +inline void HashCombine(size_t* seed, size_t hash) { *seed = HashCombine(*seed, hash); } + +template +inline void AddHash(size_t* seed, const T&... v) { + __attribute__((__unused__)) int dummy[] = {(HashCombine(seed, std::hash()(v)), 0)...}; +} + +template +inline size_t Hash(const T& v1, const Ts&... vn) { + size_t seed = std::hash()(v1); + + AddHash(&seed, vn...); + + return seed; +} + +} // namespace oneflow + +namespace std { + +template +struct hash> { + std::size_t operator()(const std::pair& p) const { + return oneflow::Hash(p.first, p.second); + } +}; + +template +struct hash> { + std::size_t operator()(const std::vector& vec) const { + std::size_t hash_value = vec.size(); + for (const auto& elem : vec) { oneflow::AddHash(&hash_value, elem); } + return hash_value; + } +}; + +} // namespace std + +#endif // ONEFLOW_CORE_COMMON_HASH_H_ diff --git a/oneflow/core/common/just.h b/oneflow/core/common/just.h index bc00c2eddea1c4b0a8f6ace86d1d301bd54259f2..d419fe9b26306f4304402325eae98d6b63ef85de 100644 --- a/oneflow/core/common/just.h +++ b/oneflow/core/common/just.h @@ -17,9 +17,12 @@ limitations under the License. #ifndef ONEFLOW_CORE_COMMON_JUST_H_ #define ONEFLOW_CORE_COMMON_JUST_H_ +#include #include #include #include "oneflow/core/common/error.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/core/common/symbol.h" #include "oneflow/core/common/preprocessor.h" namespace oneflow { @@ -30,29 +33,43 @@ class Maybe; template class Optional; -Maybe FormatErrorStr(const std::shared_ptr&); +Maybe FormatErrorStr(const std::shared_ptr&); namespace { -std::string GetFormatedSerializedError(const std::shared_ptr&); +std::string GetFormatedSerializedError(const std::shared_ptr&); } namespace private_details { -inline std::shared_ptr&& JustErrorAddStackFrame(std::shared_ptr&& err, - const std::string& file, int64_t line, - const std::string& func, - const std::string& message) { - auto* stack_frame = err->add_stack_frame(); - stack_frame->set_file(file); - stack_frame->set_line(line); - stack_frame->set_function(func); - stack_frame->set_error_msg(message); - +inline std::shared_ptr&& JustErrorAddStackFrame( + std::shared_ptr&& err, Symbol error_stack_frame) { + err->add_stack_frame(error_stack_frame); return std::move(err); } +template +Error&& AddFrameMessage(Error&& error, const T& x) { + std::ostringstream ss; + ss << x; + error->set_frame_msg(error->frame_msg() + ss.str()); + return std::move(error); +} + +template<> +inline Error&& AddFrameMessage(Error&& error, const std::stringstream& x) { + AddFrameMessage(std::move(error), x.str()); + return std::move(error); +} + +template<> +inline Error&& AddFrameMessage(Error&& error, const std::ostream& x) { + AddFrameMessage(std::move(error), x.rdbuf()); + return std::move(error); +} + template -Error&& JustErrorAddMessage(Error&& err, T&&... msg) { - __attribute__((unused)) int dummy[] = {((void)(std::move(err) << std::forward(msg)), 0)...}; +Error&& JustErrorAddFrameMessage(Error&& err, T&&... msg) { + __attribute__((unused)) int dummy[] = { + ((void)(AddFrameMessage(std::move(err), std::forward(msg))), 0)...}; return std::move(err); } @@ -67,13 +84,13 @@ bool JustIsOk(const Optional& val) { } template -std::shared_ptr JustGetError(const Maybe& val) { - return val.error(); +std::shared_ptr JustGetError(const Maybe& val) { + return val.stacked_error(); } template -std::shared_ptr JustGetError(const Optional&) { - return Error::ValueNotFoundError().error_proto(); +std::shared_ptr JustGetError(const Optional&) { + return Error::ValueNotFoundError().stacked_error(); } template @@ -91,55 +108,68 @@ typename std::remove_const::type>::type&& Remo #if defined(__GNUC__) || defined(__CUDACC__) || defined(__clang__) -#define JUST(...) \ - ::oneflow::private_details::RemoveRValConst(({ \ - auto&& _just_value_to_check_ = __JustStackCheckWrapper__(__VA_ARGS__); \ - if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ - return ::oneflow::private_details::JustErrorAddStackFrame( \ - ::oneflow::private_details::JustGetError(_just_value_to_check_), __FILE__, __LINE__, \ - __FUNCTION__, OF_PP_STRINGIZE(__VA_ARGS__)); \ - } \ - std::forward(_just_value_to_check_); \ +#define JUST(...) \ + ::oneflow::private_details::RemoveRValConst(({ \ + auto&& _just_value_to_check_ = __JustStackCheckWrapper__(__VA_ARGS__); \ + if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ + return ::oneflow::private_details::JustErrorAddStackFrame( \ + ::oneflow::private_details::JustGetError(_just_value_to_check_), \ + [](const char* function) { \ + thread_local static auto frame = ::oneflow::SymbolOf( \ + ::oneflow::ErrorStackFrame(__FILE__, __LINE__, function, #__VA_ARGS__)); \ + return frame; \ + }(__FUNCTION__)); \ + } \ + std::forward(_just_value_to_check_); \ })).Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() #define CHECK_JUST(...) \ ([&](const char* _just_closure_func_name_) { \ auto&& _just_value_to_check_ = __JustStackCheckWrapper__(__VA_ARGS__); \ if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ - LOG(FATAL) << ::oneflow::GetFormatedSerializedError( \ + thread_local static auto frame = ::oneflow::SymbolOf( \ + ::oneflow::ErrorStackFrame(__FILE__, __LINE__, _just_closure_func_name_, #__VA_ARGS__)); \ + THROW(RuntimeError) << ::oneflow::GetErrorString( \ ::oneflow::private_details::JustErrorAddStackFrame( \ - ::oneflow::private_details::JustGetError(_just_value_to_check_), __FILE__, __LINE__, \ - _just_closure_func_name_, OF_PP_STRINGIZE(__VA_ARGS__))); \ + ::oneflow::private_details::JustGetError(_just_value_to_check_), frame)); \ } \ return std::forward(_just_value_to_check_); \ })(__FUNCTION__) \ .Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() -#define JUST_MSG(value, ...) \ - ::oneflow::private_details::RemoveRValConst(({ \ - auto&& _just_value_to_check_ = (value); \ - if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ - return ::oneflow::private_details::JustErrorAddMessage( \ - ::oneflow::Error(::oneflow::private_details::JustGetError(_just_value_to_check_)) \ - .AddStackFrame(__FILE__, __LINE__, __FUNCTION__), \ - OF_PP_STRINGIZE(value), ": ", __VA_ARGS__); \ - } \ - std::forward(_just_value_to_check_); \ +#define JUST_MSG(value, ...) \ + ::oneflow::private_details::RemoveRValConst(({ \ + auto&& _just_value_to_check_ = (value); \ + if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ + return ::oneflow::private_details::JustErrorAddFrameMessage( \ + ::oneflow::Error(::oneflow::private_details::JustGetError(_just_value_to_check_)) \ + .AddStackFrame([](const char* function) { \ + thread_local static auto frame = ::oneflow::SymbolOf( \ + ::oneflow::ErrorStackFrame(__FILE__, __LINE__, function, #value)); \ + return frame; \ + }(__FUNCTION__)), \ + "\nError message from " __FILE__, ":", __LINE__, "\n\t", #value, ": ", __VA_ARGS__, \ + "\n"); \ + } \ + std::forward(_just_value_to_check_); \ })).Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() -#define CHECK_JUST_MSG(value, ...) \ - ([&](const char* _just_closure_func_name_) { \ - auto&& _just_value_to_check_ = (value); \ - if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ - LOG(FATAL) << ::oneflow::GetFormatedSerializedError( \ - ::oneflow::private_details::JustErrorAddMessage( \ - ::oneflow::Error(::oneflow::private_details::JustGetError(_just_value_to_check_)) \ - .AddStackFrame(__FILE__, __LINE__, _just_closure_func_name_), \ - OF_PP_STRINGIZE(value), ": ", __VA_ARGS__) \ - .error_proto()); \ - } \ - return std::forward(_just_value_to_check_); \ - })(__FUNCTION__) \ +#define CHECK_JUST_MSG(value, ...) \ + ([&](const char* _just_closure_func_name_) { \ + auto&& _just_value_to_check_ = (value); \ + if (!::oneflow::private_details::JustIsOk(_just_value_to_check_)) { \ + thread_local static auto frame = ::oneflow::SymbolOf( \ + ::oneflow::ErrorStackFrame(__FILE__, __LINE__, _just_closure_func_name_, #value)); \ + THROW(RuntimeError) << ::oneflow::GetErrorString( \ + ::oneflow::private_details::JustErrorAddFrameMessage( \ + ::oneflow::Error(::oneflow::private_details::JustGetError(_just_value_to_check_)) \ + .AddStackFrame(frame), \ + "\nError message from " __FILE__, ":", __LINE__, "\n\t", #value, ": ", __VA_ARGS__, \ + "\n") \ + .stacked_error()); \ + } \ + return std::forward(_just_value_to_check_); \ + })(__FUNCTION__) \ .Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() #define JUST_OPT(...) \ diff --git a/oneflow/core/common/math_util.h b/oneflow/core/common/math_util.h index 04374112db5a5d442b4d1b1157a5d5b2f8e01e9a..b0361f90c734f69cb8cff89eef48183e3afed0bc 100644 --- a/oneflow/core/common/math_util.h +++ b/oneflow/core/common/math_util.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef ONEFLOW_CORE_COMMON_MATH_UTIL_H_ #define ONEFLOW_CORE_COMMON_MATH_UTIL_H_ #include +#include "data_type.h" +#include "oneflow/core/common/util.h" namespace oneflow { @@ -23,6 +25,24 @@ int64_t Gcd(int64_t m, int64_t n); int64_t Lcm(int64_t m, int64_t n); +template +OF_DEVICE_FUNC T DeviceMin(T a, T b) { +#if defined(__CUDA_ARCH__) + return a < b ? a : b; +#else + return std::min(a, b); +#endif +} + +template +OF_DEVICE_FUNC T DeviceMax(T a, T b) { +#if defined(__CUDA_ARCH__) + return a > b ? a : b; +#else + return std::max(a, b); +#endif +} + } // namespace oneflow #endif // ONEFLOW_CORE_COMMON_MATH_UTIL_H_ diff --git a/oneflow/core/common/maybe.h b/oneflow/core/common/maybe.h index d167b167e73026abf20944bcde450c4d6478c410..519632bc9194b793151980ce7455d71ed0fb77b1 100644 --- a/oneflow/core/common/maybe.h +++ b/oneflow/core/common/maybe.h @@ -44,10 +44,10 @@ class Maybe::value || IsScala public: Maybe(const T& data) : data_or_error_(std::make_shared(data)) {} Maybe(T&& data) : data_or_error_(std::make_shared(std::move(data))) {} - Maybe(const Error& error) : data_or_error_(error.error_proto()) {} + Maybe(const Error& error) : data_or_error_(error.stacked_error()) {} Maybe(const std::shared_ptr& data) : data_or_error_(data) {} Maybe(std::shared_ptr&& data) : data_or_error_(std::move(data)) {} - Maybe(const std::shared_ptr& error) : data_or_error_(error) {} + Maybe(const std::shared_ptr& error) : data_or_error_(error) {} Maybe(const Maybe&) = default; Maybe(Maybe&& other) : data_or_error_(std::move(other.data_or_error_)) {} ~Maybe() = default; @@ -56,65 +56,69 @@ class Maybe::value || IsScala std::shared_ptr Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() const { return data_or_error_.template Get(); } - std::shared_ptr error() const { return data_or_error_.template Get(); } + std::shared_ptr stacked_error() const { + return data_or_error_.template Get(); + } + std::shared_ptr error() const { return stacked_error()->error_proto(); } std::string GetSerializedError() const { CHECK(!IsOk()); - return GetFormatedSerializedError(this->error()); + return GetFormatedSerializedError(this->stacked_error()); } template - Type GetDataAndSerializedErrorProto(std::string* error_str, const Type& default_for_error) const { + Type GetDataAndSerializedStackedError(std::string* error_str, + const Type& default_for_error) const { static_assert(std::is_same::value, "error type for argument 1"); if (IsOk()) { - *error_str = ErrorProto().DebugString(); + *error_str = StackedError().DebugString(); return *Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } else { - *error_str = this->error()->DebugString(); + *error_str = this->stacked_error()->DebugString(); return default_for_error; } } template - std::pair> GetDataAndErrorProto( + std::pair> GetDataAndStackedError( const Type& default_for_error) const { if (IsOk()) { return std::make_pair(*Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(), - std::shared_ptr()); + std::shared_ptr()); } else { - return std::make_pair(default_for_error, error()); + return std::make_pair(default_for_error, stacked_error()); } } - std::pair, std::shared_ptr> GetDataPtrAndErrorProto() const { + std::pair, std::shared_ptr> GetDataPtrAndStackedError() const { if (IsOk()) { return std::make_pair(Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(), - std::shared_ptr()); + std::shared_ptr()); } else { - return std::make_pair(std::shared_ptr(), error()); + return std::make_pair(std::shared_ptr(), stacked_error()); } } template Type GetOrThrow() const { - if (!IsOk()) { ThrowError(error()); } + if (!IsOk()) { ThrowError(stacked_error()); } return *Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } std::shared_ptr GetPtrOrThrow() const { - if (!IsOk()) { ThrowError(error()); } + if (!IsOk()) { ThrowError(stacked_error()); } return Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } private: - EitherPtr data_or_error_; + EitherPtr data_or_error_; }; template class Maybe::value>::type> final { public: - Maybe(const Error& error) : error_or_scalar_(error.error_proto()) { CheckError(); } - Maybe(const std::shared_ptr& error) : error_or_scalar_(error) { CheckError(); } + Maybe(const Error& error) : error_or_scalar_(error.stacked_error()) { CheckError(); } + Maybe(const std::shared_ptr& error) : error_or_scalar_(error) { CheckError(); } Maybe(const Maybe&) = default; Maybe(Maybe&&) = default; ~Maybe() = default; @@ -123,31 +127,32 @@ class Maybe::value>::type> fina bool IsOk() const { return error_or_scalar_.IsScalar(); } void Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() const {} - std::shared_ptr error() const { return error_or_scalar_.shared_ptr(); } + std::shared_ptr stacked_error() const { return error_or_scalar_.shared_ptr(); } + std::shared_ptr error() const { return stacked_error()->error_proto(); } std::string GetSerializedError() const { CHECK(!IsOk()); - return GetFormatedSerializedError(this->error()); + return GetFormatedSerializedError(this->stacked_error()); } - void GetDataAndSerializedErrorProto(std::string* error_str) const { + void GetDataAndSerializedStackedError(std::string* error_str) const { if (IsOk()) { - *error_str = ErrorProto().DebugString(); + *error_str = StackedError().DebugString(); } else { - *error_str = this->error()->DebugString(); + *error_str = this->stacked_error()->DebugString(); } } - std::shared_ptr GetDataAndErrorProto() const { + std::shared_ptr GetDataAndStackedError() const { if (IsOk()) { - return std::shared_ptr(); + return std::shared_ptr(); } else { - return error(); + return stacked_error(); } } void GetOrThrow() const { - if (!IsOk()) { ThrowError(error()); } + if (!IsOk()) { ThrowError(stacked_error()); } return Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } @@ -157,12 +162,12 @@ class Maybe::value>::type> fina CHECK_NE(this->error()->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET); } - SharedOrScalar error_or_scalar_; + SharedOrScalar error_or_scalar_; }; -inline const std::shared_ptr& UninitializedValueError() { +inline const std::shared_ptr& UninitializedValueError() { static thread_local const auto& error = - Error::InvalidValueError("uninitialized value").error_proto(); + (Error::InvalidValueError() << "uninitialized value").stacked_error(); return error; } @@ -170,8 +175,8 @@ template class Maybe::value>::type> final { public: Maybe(T data) : error_or_scalar_(data) {} - Maybe(const Error& error) : error_or_scalar_(error.error_proto()) { CheckError(); } - Maybe(const std::shared_ptr& error) : error_or_scalar_(error) { CheckError(); } + Maybe(const Error& error) : error_or_scalar_(error.stacked_error()) { CheckError(); } + Maybe(const std::shared_ptr& error) : error_or_scalar_(error) { CheckError(); } Maybe() : error_or_scalar_(UninitializedValueError()) {} Maybe(const Maybe&) = default; Maybe(Maybe&&) = default; @@ -183,34 +188,36 @@ class Maybe::value>::type> final { T Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() const { return error_or_scalar_.scalar_value(); } - std::shared_ptr error() const { return error_or_scalar_.shared_ptr(); } + std::shared_ptr stacked_error() const { return error_or_scalar_.shared_ptr(); } + std::shared_ptr error() const { return stacked_error()->error_proto(); } std::string GetSerializedError() const { CHECK(!IsOk()); - return GetFormatedSerializedError(this->error()); + return GetFormatedSerializedError(this->stacked_error()); } - T GetDataAndSerializedErrorProto(std::string* error_str, const T& default_for_error) const { + T GetDataAndSerializedStackedError(std::string* error_str, const T& default_for_error) const { if (IsOk()) { - *error_str = ErrorProto().DebugString(); + *error_str = StackedError().DebugString(); return Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } else { - *error_str = this->error()->DebugString(); + *error_str = this->stacked_error()->DebugString(); return default_for_error; } } - std::pair> GetDataAndErrorProto(const T& default_for_error) const { + std::pair> GetDataAndStackedError( + const T& default_for_error) const { if (IsOk()) { return std::make_pair(Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(), - std::shared_ptr()); + std::shared_ptr()); } else { - return std::make_pair(default_for_error, error()); + return std::make_pair(default_for_error, stacked_error()); } } T GetOrThrow() const { - if (!IsOk()) { ThrowError(error()); } + if (!IsOk()) { ThrowError(stacked_error()); } return Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } @@ -219,7 +226,7 @@ class Maybe::value>::type> final { CHECK_NE(this->error()->error_type_case(), ErrorProto::ERROR_TYPE_NOT_SET); } - SharedOrScalar error_or_scalar_; + SharedOrScalar error_or_scalar_; }; template @@ -232,7 +239,7 @@ class Maybe::value || IsScala public: Maybe(T data) : maybe_ptr_(&data) {} Maybe(const Error& error) : maybe_ptr_(error) {} - Maybe(const std::shared_ptr& error) : maybe_ptr_(error) {} + Maybe(const std::shared_ptr& error) : maybe_ptr_(error) {} Maybe(const Maybe&) = default; Maybe(Maybe&&) = default; ~Maybe() = default; @@ -241,19 +248,20 @@ class Maybe::value || IsScala T Data_YouAreNotAllowedToCallThisFuncOutsideThisFile() const { return *maybe_ptr_.Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } - std::shared_ptr error() const { return maybe_ptr_.error(); } + std::shared_ptr stacked_error() const { return maybe_ptr_.stacked_error(); } + std::shared_ptr error() const { return stacked_error()->error_proto(); } std::string GetSerializedError() const { CHECK(!IsOk()); return maybe_ptr_.GetSerializedError(); } - T GetDataAndSerializedErrorProto(std::string* error_str) const { - return *maybe_ptr_.GetDataAndSerializedErrorProto(error_str, static_cast(nullptr)); + T GetDataAndSerializedStackedError(std::string* error_str) const { + return *maybe_ptr_.GetDataAndSerializedStackedError(error_str, static_cast(nullptr)); } T GetOrThrow() const { - if (!IsOk()) { ThrowError(error()); } + if (!IsOk()) { ThrowError(stacked_error()); } return Data_YouAreNotAllowedToCallThisFuncOutsideThisFile(); } @@ -262,10 +270,10 @@ class Maybe::value || IsScala }; namespace { -std::string GetFormatedSerializedError(const std::shared_ptr& error_proto) { +std::string GetFormatedSerializedError(const std::shared_ptr& stacked_error) { // return error msg got from formatted function or debugstring. - const auto& maybe_error = TRY(FormatErrorStr(error_proto)); - const auto& error_str = maybe_error.GetDataAndErrorProto(error_proto->DebugString()); + const auto& maybe_error = TRY(FormatErrorStr(stacked_error)); + const auto& error_str = maybe_error.GetDataAndStackedError(stacked_error->DebugString()); return error_str.first; } } // namespace @@ -276,18 +284,32 @@ std::string GetFormatedSerializedError(const std::shared_ptr& error_ GOOGLE_PREDICT_BRANCH_NOT_TAKEN(!maybe.IsOk());) \ LOG(FATAL) << OF_PP_STRINGIZE(__VA_ARGS__) << " is not OK:\n" << maybe.GetSerializedError() -#define OF_RETURN_IF_ERROR(...) \ - for (auto&& maybe_##__LINE__ = __JustStackCheckWrapper__(__VA_ARGS__); \ - !maybe_##__LINE__.IsOk();) \ - return Error(maybe_##__LINE__.error()).AddStackFrame(__FILE__, __LINE__, __FUNCTION__) - -#define OF_TODO() return Error::TodoError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) -#define OF_UNIMPLEMENTED() \ - return Error::UnimplementedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) - -#define OF_RUNTIME_ERROR() \ - return Error::RuntimeError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) << "RuntimeError " \ - ": " +#define OF_RETURN_IF_ERROR(...) \ + for (auto&& maybe_##__LINE__ = __JustStackCheckWrapper__(__VA_ARGS__); \ + !maybe_##__LINE__.IsOk();) \ + return Error(maybe_##__LINE__.stacked_error()).AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) + +#define OF_TODO() \ + return Error::TodoError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) +#define OF_UNIMPLEMENTED() \ + return Error::UnimplementedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) + +#define OF_RUNTIME_ERROR() \ + return Error::RuntimeError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ + << "RuntimeError " \ + ": " #define RETURN_ERROR_WITH_BUG_PROMPT() OF_RUNTIME_ERROR() << kOfBugIssueUploadPrompt #define OF_LOG_ONCE(x) \ @@ -299,32 +321,51 @@ std::string GetFormatedSerializedError(const std::shared_ptr& error_ } \ } -#define OF_COMPLIE_OPTION_ERROR() \ - return Error::CompileOptionWrongError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) \ +#define OF_COMPLIE_OPTION_ERROR() \ + return Error::CompileOptionWrongError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ << "Compile option wrong: " -#define CHECK_OR_RETURN(expr) \ - if (!(expr)) \ - return Error::CheckFailedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) \ - << "Check failed: " << OF_PP_STRINGIZE(expr) << " " - -#define CHECK_EQ_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) == (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " - -#define CHECK_GE_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) >= (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " - -#define CHECK_GT_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) > (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " - -#define CHECK_LE_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) <= (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " - -#define CHECK_LT_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) < (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " - -#define CHECK_NE_OR_RETURN(lhs, rhs) \ - CHECK_OR_RETURN((lhs) != (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " +#define CHECK_OR_RETURN(expr) \ + if (!(expr)) \ + return Error::CheckFailedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ + << "Check failed: " << OF_PP_STRINGIZE(expr) << " " << Error::kOverrideThenMergeMessage + +#define CHECK_OR_RETURN_ERROR(expr) \ + if (!(expr)) \ + return Error::CheckFailedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) + +#define CHECK_EQ_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) == (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage + +#define CHECK_GE_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) >= (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage + +#define CHECK_GT_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) > (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage + +#define CHECK_LE_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) <= (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage + +#define CHECK_LT_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) < (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage + +#define CHECK_NE_OR_RETURN(lhs, rhs) \ + CHECK_OR_RETURN((lhs) != (rhs)) << "(" << (lhs) << " vs " << (rhs) << ") " \ + << Error::kOverrideThenMergeMessage #define CHECK_STREQ_OR_RETURN(lhs, rhs) CHECK_EQ_OR_RETURN(std::string(lhs), std::string(rhs)) diff --git a/oneflow/core/common/maybe_test.cpp b/oneflow/core/common/maybe_test.cpp index b58f8fcd5e9983d15ecb75f4a9d1404160923793..daeb77f2b1df5299fea0f23e428abf650a5981db 100644 --- a/oneflow/core/common/maybe_test.cpp +++ b/oneflow/core/common/maybe_test.cpp @@ -17,6 +17,7 @@ limitations under the License. #include "gtest/gtest.h" #include #include +#include "oneflow/core/common/exception.h" #include "oneflow/core/common/util.h" namespace oneflow { @@ -24,7 +25,7 @@ namespace test { TEST(Maybe, JUST_MSG) { auto f = [](int x) -> Maybe { - if (x > 10) { return Error::InvalidValueError("") << "input value " << x; } + if (x > 10) { return Error::InvalidValueError() << "input value " << x; } return 233; }; @@ -44,18 +45,22 @@ TEST(Maybe, JUST_MSG) { auto data = CHECK_JUST(i(1)); ASSERT_EQ(data, 233); - auto err = i(10.123).error(); - ASSERT_EQ(err->msg(), "input value 53"); - ASSERT_EQ(err->stack_frame(0).error_msg(), "f(y): input value g(10)"); - ASSERT_EQ(err->stack_frame(1).error_msg(), "h(y): input value int(10.123)"); - - // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto) - ASSERT_EXIT(CHECK_JUST(i(10.234)), testing::KilledBySignal(SIGABRT), R"(input value 53)"); + auto err = i(10.123).stacked_error(); + ASSERT_EQ(err->error_proto()->msg(), R"(input value 53)"); + ASSERT_GE(err->stack_frame().size(), 2); + ASSERT_EQ(err->stack_frame().at(0)->code_text(), "f(y)"); + ASSERT_EQ(err->stack_frame().at(1)->code_text(), "h(y)"); + + try { + CHECK_JUST(i(10.234)); + } catch (const RuntimeException& e) { + EXPECT_TRUE(std::string(e.what()).find(R"(input value 53)") != std::string::npos); + } } TEST(Maybe, CHECK_OK) { auto f = [](int x) -> Maybe { - if (x > 10) { return Error::InvalidValueError("") << "input value " << x; } + if (x > 10) { return Error::InvalidValueError() << "input value " << x; } return 233; }; diff --git a/oneflow/core/common/mem_util.cpp b/oneflow/core/common/mem_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0ce0778cbecbb9a04d4b0d9493b6d518de2715f5 --- /dev/null +++ b/oneflow/core/common/mem_util.cpp @@ -0,0 +1,62 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/mem_util.h" + +#include +#include + +namespace oneflow { + +namespace { +struct ProcStat { + std::string pid, comm, state, ppid, pgrp, session, tty_nr; + std::string tpgid, flags, minflt, cminflt, majflt, cmajflt; + std::string utime, stime, cutime, cstime, priority, nice; + std::string num_threads, itrealvalue, starttime; + unsigned long vsize = 0; + long rss = 0; +}; + +} // namespace + +// Reference: https://stackoverflow.com/questions/669438/how-to-get-memory-usage-at-runtime-using-c +void ProcessMemUsage(double* vm_usage, double* resident_set) { + *vm_usage = 0.0; + *resident_set = 0.0; + +#ifdef __linux__ + // 'file' stat seems to give the most reliable results + std::ifstream stat_stream("/proc/self/stat", std::ios_base::in); + ProcStat proc_stat; + stat_stream >> proc_stat.pid >> proc_stat.comm >> proc_stat.state >> proc_stat.ppid + >> proc_stat.pgrp >> proc_stat.session >> proc_stat.tty_nr >> proc_stat.tpgid + >> proc_stat.flags >> proc_stat.minflt >> proc_stat.cminflt >> proc_stat.majflt + >> proc_stat.cmajflt >> proc_stat.utime >> proc_stat.stime >> proc_stat.cutime + >> proc_stat.cstime >> proc_stat.priority >> proc_stat.nice >> proc_stat.num_threads + >> proc_stat.itrealvalue >> proc_stat.starttime >> proc_stat.vsize + >> proc_stat.rss; // don't care about the rest + + stat_stream.close(); + + long page_size_kb = sysconf(_SC_PAGE_SIZE); // in case x86-64 is configured to use 2MB pages + // return with MB + *vm_usage = proc_stat.vsize >> 20; + // return with MB + *resident_set = (proc_stat.rss * page_size_kb) >> 20; +#endif // __linux__ +} + +} // namespace oneflow diff --git a/oneflow/core/common/mem_util.h b/oneflow/core/common/mem_util.h new file mode 100644 index 0000000000000000000000000000000000000000..6c224ab5c4dbf7d465c5f586214e5381e5bdd6b1 --- /dev/null +++ b/oneflow/core/common/mem_util.h @@ -0,0 +1,35 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_MEM_UTIL_H_ +#define ONEFLOW_CORE_COMMON_MEM_UTIL_H_ + +#include +#include +#include + +#include "oneflow/core/common/util.h" + +namespace oneflow { +void ProcessMemUsage(double* vm_usage, double* resident_set); +} // namespace oneflow + +#define LOG_MEM(...) \ + double vm_ = 0, rss_ = 0; \ + ProcessMemUsage(&vm_, &rss_); \ + VLOG(1) << "File " __FILE__ << ", Line " << __LINE__ << ", Func " << __FUNCTION__ \ + << ", Mem size RSS " << rss_ << "MB." + +#endif // ONEFLOW_CORE_COMMON_MEM_UTIL_H_ diff --git a/oneflow/core/common/nd_index_offset_helper.h b/oneflow/core/common/nd_index_offset_helper.h index 89bd0a90763dda0b77fe764aebdbb3e1d42b6993..19fabc445b2dfb9838b960902ba3d6919eec84df 100644 --- a/oneflow/core/common/nd_index_offset_helper.h +++ b/oneflow/core/common/nd_index_offset_helper.h @@ -24,7 +24,8 @@ namespace oneflow { template class NdIndexOffsetHelper { public: - NdIndexOffsetHelper() {} + OF_DEVICE_FUNC NdIndexOffsetHelper() = default; + template OF_DEVICE_FUNC explicit NdIndexOffsetHelper(T d0, Ts... dims) { constexpr int n = 1 + sizeof...(dims); @@ -53,15 +54,14 @@ class NdIndexOffsetHelper { InitStrides(dims_arr, n); } - ~NdIndexOffsetHelper() = default; + virtual ~NdIndexOffsetHelper() = default; OF_DEVICE_FUNC T NdIndexToOffset(const T* index) const { T offset = 0; #ifdef __CUDA_ARCH__ #pragma unroll #endif - for (int i = 0; i < N - 1; ++i) { offset += index[i] * stride_[i]; } - offset += index[N - 1]; + for (int i = 0; i < N; ++i) { offset += index[i] * stride_[i]; } return offset; } @@ -146,7 +146,7 @@ class NdIndexOffsetHelper { OF_DEVICE_FUNC constexpr int Size() const { return N; } - private: + protected: OF_DEVICE_FUNC void InitStrides(const T* dims, const int n) { for (int i = n - 1; i < N; ++i) { stride_[i] = 1; } for (int i = n - 2; i >= 0; --i) { stride_[i] = dims[i + 1] * stride_[i + 1]; } @@ -155,6 +155,36 @@ class NdIndexOffsetHelper { T stride_[N]; }; +template +class NdIndexStrideOffsetHelper : public NdIndexOffsetHelper { + public: + OF_DEVICE_FUNC NdIndexStrideOffsetHelper() = default; + OF_DEVICE_FUNC explicit NdIndexStrideOffsetHelper(const T* strides) { + for (int i = 0; i < N; ++i) { stride_[i] = strides[i]; } + } + + template + OF_DEVICE_FUNC explicit NdIndexStrideOffsetHelper(const U* strides) { + for (int i = 0; i < N; ++i) { stride_[i] = static_cast(strides[i]); } + } + + OF_DEVICE_FUNC explicit NdIndexStrideOffsetHelper(const T* strides, int n) { + for (int i = 0; i < N; ++i) { + if (i < n) { stride_[i] = strides[i]; } + } + } + + template + OF_DEVICE_FUNC explicit NdIndexStrideOffsetHelper(const U* strides, int n) { + for (int i = 0; i < N; ++i) { + if (i < n) { stride_[i] = static_cast(strides[i]); } + } + } + + private: + using NdIndexOffsetHelper::stride_; +}; + } // namespace oneflow #endif // ONEFLOW_CORE_COMMON_ND_INDEX_OFFSET_HELPER_H_ diff --git a/oneflow/core/common/nd_index_offset_helper_test.cpp b/oneflow/core/common/nd_index_offset_helper_test.cpp index f229628ae210590635d7646c91686bc5c4cf43e4..0505fbb02f6d2832086d4288c3f0d1687af6fcd9 100644 --- a/oneflow/core/common/nd_index_offset_helper_test.cpp +++ b/oneflow/core/common/nd_index_offset_helper_test.cpp @@ -19,6 +19,7 @@ limitations under the License. #include #include "gtest/gtest.h" #define private public +#define protected public #include "oneflow/core/common/nd_index_offset_helper.h" namespace oneflow { @@ -142,6 +143,35 @@ TEST(NdIndexOffsetHelper, constructor) { test_constructor(); } +template +void test_stride_constructor() { + const T d1 = 5; + const T d2 = 6; + + const U u1 = 5; + const U u2 = 6; + + std::vector strides({d1 * d2, d2, 1}); + std::vector strides_u({u1 * u2, u2, 1}); + + const NdIndexStrideOffsetHelper helper1(strides.data()); + const NdIndexStrideOffsetHelper helper2(strides.data(), strides.size()); + const NdIndexStrideOffsetHelper helper3(strides_u.data()); + const NdIndexStrideOffsetHelper helper4(strides_u.data(), strides_u.size()); + + for (int i = 0; i < 3; i++) { + ASSERT_EQ(helper1.stride_[i], strides[i]); + ASSERT_EQ(helper2.stride_[i], strides[i]); + ASSERT_EQ(helper3.stride_[i], strides_u[i]); + ASSERT_EQ(helper4.stride_[i], strides_u[i]); + } +} + +TEST(NdIndexStrideOffsetHelper, constructor) { + test_stride_constructor(); + test_stride_constructor(); +} + } // namespace test } // namespace oneflow diff --git a/oneflow/core/common/notifier.cpp b/oneflow/core/common/notifier.cpp index dcd38e8d7d1db93611c4887796fa48f0a9637a6a..eaccd01389b7ac69881cf735d3695157f4f9486f 100644 --- a/oneflow/core/common/notifier.cpp +++ b/oneflow/core/common/notifier.cpp @@ -14,6 +14,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/common/notifier.h" +#include "oneflow/core/common/foreign_lock_helper.h" +#include "oneflow/core/common/env_var/env_var.h" namespace oneflow { @@ -37,6 +39,30 @@ NotifierStatus Notifier::WaitAndClearNotifiedCnt() { return kNotifierStatusSuccess; } +Maybe Notifier::TimedWaitAndClearNotifiedCnt(size_t timeout_seconds) { + return Singleton::Get()->WithScopedRelease([&, this]() -> Maybe { + std::chrono::duration seconds(timeout_seconds); + std::unique_lock lock(mutex_); + CHECK_OR_RETURN(cond_.wait_for(lock, seconds, [this]() { + return notified_cnt_ > 0 || is_closed_; + })) << Error::TimeoutError(); + CHECK_GT_OR_RETURN(notified_cnt_, 0) << "notifier closed."; + notified_cnt_ = 0; + return Maybe::Ok(); + }); +} + +Maybe Notifier::TimedWaitAndClearNotifiedCnt( + const std::function()>& StopWaitingAfterTimeout) { + while (true) { + auto status = TRY(TimedWaitAndClearNotifiedCnt(EnvInteger())); + if (status.IsOk()) { return status; } + if (!status.error()->has_timeout_error()) { return status; } + if (JUST(StopWaitingAfterTimeout())) { return status; } + } + UNIMPLEMENTED_THEN_RETURN(); +} + void Notifier::Close() { std::unique_lock lock(mutex_); is_closed_ = true; diff --git a/oneflow/core/common/notifier.h b/oneflow/core/common/notifier.h index dd1e9a59a000c6dc128c335b832b4252c47b354d..aaf25bc06cbf0d7838712cd79e68825f7159d5b6 100644 --- a/oneflow/core/common/notifier.h +++ b/oneflow/core/common/notifier.h @@ -32,6 +32,10 @@ class Notifier final { NotifierStatus WaitAndClearNotifiedCnt(); void Close(); + Maybe TimedWaitAndClearNotifiedCnt(size_t timeout_seconds); + Maybe TimedWaitAndClearNotifiedCnt( + const std::function()>& StopWaitingAfterTimeout); + private: size_t notified_cnt_; std::mutex mutex_; diff --git a/oneflow/core/common/op_args_reserved_size.h b/oneflow/core/common/op_args_reserved_size.h new file mode 100644 index 0000000000000000000000000000000000000000..83c97e03b822523722addfaf5af5ead7071cc689 --- /dev/null +++ b/oneflow/core/common/op_args_reserved_size.h @@ -0,0 +1,25 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_OP_ARGS_RESERVED_SIZE_H_ +#define ONEFLOW_CORE_COMMON_OP_ARGS_RESERVED_SIZE_H_ + +namespace oneflow { + +constexpr static int kOpArgsReservedSize = 4; + +} + +#endif // ONEFLOW_CORE_COMMON_OP_ARGS_RESERVED_SIZE_H_ diff --git a/oneflow/core/common/op_args_vector.h b/oneflow/core/common/op_args_vector.h new file mode 100644 index 0000000000000000000000000000000000000000..8aacdf19fdcebcb989b4890bd552ab81e3c2cfba --- /dev/null +++ b/oneflow/core/common/op_args_vector.h @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_OP_ARGS_VECTOR_H_ +#define ONEFLOW_CORE_COMMON_OP_ARGS_VECTOR_H_ + +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/common/op_args_reserved_size.h" + +namespace oneflow { + +template +using OpArgsVector = small_vector; + +} + +#endif // ONEFLOW_CORE_COMMON_OP_ARGS_VECTOR_H_ diff --git a/oneflow/core/common/protobuf.h b/oneflow/core/common/protobuf.h index f8fac83cf853b16004ba40d901785c4c84538d58..ef35d371c17731083f5e2810db72b666db61a1c1 100644 --- a/oneflow/core/common/protobuf.h +++ b/oneflow/core/common/protobuf.h @@ -227,31 +227,31 @@ struct hash { template<> struct hash { size_t operator()(const oneflow::LogicalBlobId& lbi) const { - const auto& str_hash = std::hash(); - return str_hash(lbi.op_name()) ^ str_hash(lbi.blob_name()); + using namespace oneflow; + return Hash(lbi.op_name(), lbi.blob_name()); } }; template<> struct hash { size_t operator()(const oneflow::OpBlobArg& oba) const { - const auto& str_hash = std::hash(); - return str_hash(oba.op_name()) ^ str_hash(oba.bn_in_op()); + using namespace oneflow; + return Hash(oba.op_name(), oba.bn_in_op()); } }; template<> struct hash { size_t operator()(const oneflow::SbpParallel& sbp_parallel) const { - const auto& str_hash = std::hash(); + using namespace oneflow; size_t ret = 0; if (sbp_parallel.has_broadcast_parallel()) { - ret ^= str_hash("B"); + AddHash(&ret, std::string("B")); } else if (sbp_parallel.has_partial_sum_parallel()) { - ret ^= str_hash("P"); + AddHash(&ret, std::string("P")); } else if (sbp_parallel.has_split_parallel()) { - ret ^= str_hash("S"); - ret ^= std::hash()(sbp_parallel.split_parallel().axis()); + AddHash(&ret, std::string("S")); + AddHash(&ret, sbp_parallel.split_parallel().axis()); } else { UNIMPLEMENTED(); } diff --git a/oneflow/core/common/registry_error.cpp b/oneflow/core/common/registry_error.cpp index e5a033001dba0e96b68532dfd9a16f33d87c0dba..99965740d2d7ba309d765f0e4b0b23b7b575909a 100644 --- a/oneflow/core/common/registry_error.cpp +++ b/oneflow/core/common/registry_error.cpp @@ -19,15 +19,15 @@ limitations under the License. namespace oneflow { namespace { -std::shared_ptr* MutRegistryError() { - static std::shared_ptr registry_error; +std::shared_ptr* MutRegistryError() { + static std::shared_ptr registry_error; return ®istry_error; } } // namespace Maybe CheckAndClearRegistryFlag() { if (!*MutRegistryError()) { return Maybe::Ok(); } - std::shared_ptr registry_error_old = *MutRegistryError(); + std::shared_ptr registry_error_old = *MutRegistryError(); *MutRegistryError() = nullptr; return registry_error_old; } @@ -35,7 +35,7 @@ Maybe CheckAndClearRegistryFlag() { void CatchRegistryError(const std::function()>& handler) { const auto& maybe_error = TRY(handler()); if (!maybe_error.IsOk()) { - if (!*MutRegistryError()) { *MutRegistryError() = maybe_error.error(); } + if (!*MutRegistryError()) { *MutRegistryError() = maybe_error.stacked_error(); } } } diff --git a/oneflow/core/common/scalar.cpp b/oneflow/core/common/scalar.cpp index d560f08b43a961900742e70deca86143bbd77098..1c497887e3b1e728913c2302c1cbdf9a1a0e795d 100644 --- a/oneflow/core/common/scalar.cpp +++ b/oneflow/core/common/scalar.cpp @@ -29,7 +29,7 @@ namespace oneflow { } \ return *this; \ } \ - Scalar Scalar::operator op(const Scalar& other) { \ + Scalar Scalar::operator op(const Scalar& other) const { \ if (IsFloatingPoint() || other.IsFloatingPoint()) { \ double val = As() op other.As(); \ return Scalar(val); \ diff --git a/oneflow/core/common/scalar.h b/oneflow/core/common/scalar.h index 275c70bb5708a0d165d9af7e94738b0372176bad..b0bfca23493131339222ba99f033f8877f2e9c80 100644 --- a/oneflow/core/common/scalar.h +++ b/oneflow/core/common/scalar.h @@ -29,28 +29,28 @@ class Scalar { Scalar() : Scalar(int32_t(0)) {} template::value, int>::type = 0> - Scalar(const T& value) : value_{.b = value}, active_tag_(HAS_B) {} + OF_DEVICE_FUNC Scalar(const T& value) : value_{.b = value}, active_tag_(HAS_B) {} template::value && std::is_signed::value, int>::type = 0> - Scalar(const T& value) : value_{.s = value}, active_tag_(HAS_S) {} + OF_DEVICE_FUNC Scalar(const T& value) : value_{.s = value}, active_tag_(HAS_S) {} template::value && std::is_unsigned::value && !std::is_same::value, int>::type = 0> - Scalar(const T& value) : value_{.u = value}, active_tag_(HAS_U) {} + OF_DEVICE_FUNC Scalar(const T& value) : value_{.u = value}, active_tag_(HAS_U) {} template::value, int>::type = 0> - Scalar(const T& value) : value_{.d = value}, active_tag_(HAS_D) {} + OF_DEVICE_FUNC Scalar(const T& value) : value_{.d = value}, active_tag_(HAS_D) {} template::value, int>::type = 0> - Scalar& operator=(const T& value) { + OF_DEVICE_FUNC Scalar& operator=(const T& value) { *this = Scalar(value); return *this; } - Scalar& operator=(const Scalar& other) { + OF_DEVICE_FUNC Scalar& operator=(const Scalar& other) { value_ = other.value_; active_tag_ = other.active_tag_; return *this; @@ -78,10 +78,10 @@ class Scalar { bool IsSigned() const { return active_tag_ == HAS_S || active_tag_ == HAS_D; } bool IsUnsigned() const { return active_tag_ == HAS_U; } - Scalar operator+(const Scalar& other); - Scalar operator-(const Scalar& other); - Scalar operator*(const Scalar& other); - Scalar operator/(const Scalar& other); + Scalar operator+(const Scalar& other) const; + Scalar operator-(const Scalar& other) const; + Scalar operator*(const Scalar& other) const; + Scalar operator/(const Scalar& other) const; Scalar& operator+=(const Scalar& other); Scalar& operator-=(const Scalar& other); diff --git a/oneflow/core/common/shape.cpp b/oneflow/core/common/shape.cpp index 94631c6d5e48e228c76b92c3d92e27a90c8ec10e..c39ad652a2ef525265b2fc2e8dcff570bd895c93 100644 --- a/oneflow/core/common/shape.cpp +++ b/oneflow/core/common/shape.cpp @@ -220,4 +220,14 @@ Maybe Shape::Slice(int64_t start_dim, int64_t end_dim) const { return shape; } +bool Shape::operator==(const Shape& rhs) const { + if (is_initialized_ != rhs.is_initialized_) { return false; } + if (is_initialized_ == false) { return true; } + if (this->NumAxes() != rhs.NumAxes()) { return false; } + FOR_RANGE(int, i, 0, this->NumAxes()) { + if (this->At(i) != rhs.At(i)) { return false; } + } + return true; +} + } // namespace oneflow diff --git a/oneflow/core/common/shape.h b/oneflow/core/common/shape.h index 6805dc21caf4e754729ef60e72f92c420feb9bd7..4e5f5d48fc1377945e71f5370e8946f5e0cc21fc 100644 --- a/oneflow/core/common/shape.h +++ b/oneflow/core/common/shape.h @@ -147,6 +147,8 @@ class Shape final : public DimVector, public MutShapeMixIn { Maybe Slice(int64_t start_dim, int64_t end_dim) const; + bool operator==(const Shape& rhs) const; + private: // Set default value here because some constructors are inherited from DimVector // TODO(daquexian): remove this field and make it initializied by construction @@ -170,6 +172,7 @@ namespace std { template<> struct hash { size_t operator()(const oneflow::Shape& shape) const { + if (!shape.is_initialized()) { return 0; } size_t ret = shape.NumAxes(); FOR_RANGE(int, i, 0, shape.NumAxes()) { oneflow::AddHash(&ret, shape.At(i)); } return ret; diff --git a/oneflow/core/common/shape_view.cpp b/oneflow/core/common/shape_view.cpp index f3aa873558210bcd16b2085214e0ccf5d80e8045..61e58d5e467793c2216f2f1ff287e15480d91d45 100644 --- a/oneflow/core/common/shape_view.cpp +++ b/oneflow/core/common/shape_view.cpp @@ -36,6 +36,7 @@ std::ostream& operator<<(std::ostream& out, ShapeView shape) { } void MutShapeView::set_shape(ShapeView shape) { + if (shape.ptr() == mut_ptr() && shape.NumAxes() == NumAxes()) { return; } CHECK_EQ(NumAxes(), shape.NumAxes()); std::copy(shape.ptr(), shape.ptr() + shape.NumAxes(), mut_ptr()); } diff --git a/oneflow/core/common/small_vector.h b/oneflow/core/common/small_vector.h index 6aee5359f2b45f6268ec21a5c5e6d18f35952b55..5e0213e783b58d605341592d2b3edc054a6e2c42 100644 --- a/oneflow/core/common/small_vector.h +++ b/oneflow/core/common/small_vector.h @@ -25,6 +25,7 @@ class small_vector : public llvm::SmallVector { using Base = llvm::SmallVector; public: + constexpr static size_t kInitialSize = N; // https://stackoverflow.com/questions/27954940/a-using-statement-compiles-with-g-fails-compilation-with-clang using Base::Base; @@ -36,6 +37,10 @@ class small_vector : public llvm::SmallVector { CHECK_LT(idx, Base::size()); return (*this)[idx]; } + typename Base::reference operator[](typename Base::size_type idx) { return this->data()[idx]; } + typename Base::const_reference operator[](typename Base::size_type idx) const { + return this->data()[idx]; + } typename Base::const_iterator cbegin() const { return (typename Base::const_iterator)this->BeginX; } diff --git a/oneflow/core/common/spin_counter.cpp b/oneflow/core/common/spin_counter.cpp index 325f9c95117a7acaebcefa1be5c996798aeac26e..34064f162b2fcc191d9a5efc976c215d97797575 100644 --- a/oneflow/core/common/spin_counter.cpp +++ b/oneflow/core/common/spin_counter.cpp @@ -22,7 +22,7 @@ namespace oneflow { Maybe SpinCounter::WaitUntilCntEqualZero() const { return Singleton::Get()->WithScopedRelease([&]() -> Maybe { - while (cnt_val_ > 0) {}; + while (cnt_val_ > 0) {} return Maybe::Ok(); }); } diff --git a/oneflow/core/common/spin_counter.h b/oneflow/core/common/spin_counter.h index 0e4406a4ba9433671e7d8a4df33c694e9c528741..c1eeebd3b76adbe2e9e9175bad77ea6c7eb0c442 100644 --- a/oneflow/core/common/spin_counter.h +++ b/oneflow/core/common/spin_counter.h @@ -31,6 +31,7 @@ class SpinCounter final { explicit SpinCounter(int64_t cnt_val) : cnt_val_(cnt_val) {} int64_t Decrease() { return --cnt_val_; } + void Reset(int64_t cnt_val) { cnt_val_ = cnt_val; } Maybe WaitUntilCntEqualZero() const; private: diff --git a/oneflow/core/common/steady_vector.h b/oneflow/core/common/steady_vector.h index f2a7e06877a6b1b4649de91e0f504051fff42be8..e92d47b7bb381d32fe046ccd4de97ec6e618093d 100644 --- a/oneflow/core/common/steady_vector.h +++ b/oneflow/core/common/steady_vector.h @@ -34,7 +34,7 @@ class SteadyVector { using size_type = size_t; // thread safe. - size_t size() const { return size_; } + size_t size() const { return size_.load(std::memory_order_acquire); } // thread safe. const T& at(size_t index) const { @@ -51,12 +51,10 @@ class SteadyVector { return granularity2data_[gran].get()[index - start]; } - void push_back(const T& elem) { *MutableOrAdd(size_) = elem; } - - // `index` shoule be <= size() - T* MutableOrAdd(size_t index) { + // `index` should be <= size() + void SetOrAdd(size_t index, T value) { std::unique_lock lock(mutex_); - size_t size = size_; + size_t size = size_.load(std::memory_order_relaxed); CHECK_LE(index, size) << "index out of range"; if (index == size) { int granularity = GetGranularity(size); @@ -64,11 +62,15 @@ class SteadyVector { CHECK_LT(granularity, N); granularity2data_[granularity].reset(new T[1 << granularity]); } - ++size_; + *Mutable(index) = std::move(value); + size_.fetch_add(1, std::memory_order_release); + } else { + *Mutable(index) = std::move(value); } - return Mutable(index); } + void push_back(const T& elem) { SetOrAdd(size_, elem); } + private: T* Mutable(size_t index) { int gran = 0; diff --git a/oneflow/core/common/stream_role.h b/oneflow/core/common/stream_role.h deleted file mode 100644 index 424f21c70db180eefbf2bfa34ebc496dd1c3391d..0000000000000000000000000000000000000000 --- a/oneflow/core/common/stream_role.h +++ /dev/null @@ -1,77 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_COMMON_STREAM_ROLE_H_ -#define ONEFLOW_CORE_COMMON_STREAM_ROLE_H_ - -#include -#include -#include "oneflow/core/common/preprocessor.h" -#include "glog/logging.h" - -namespace oneflow { - -enum class StreamRole { - kInvalid = 0, - kCompute, - kHost2Device, - kDevice2Host, - kSyncedLaunchedCommNet, - kAsyncedLaunchedCommNet, - kBarrier, - kCriticalSection, - kLazyJobLauncher, - kPinnedCompute -}; - -template -struct StreamRoleVisitor { - template - static auto Visit(StreamRole stream_role, Args&&... args) { - switch (stream_role) { - case StreamRole::kInvalid: LOG(FATAL) << "invalid stream role"; - case StreamRole::kCompute: return DerivedT::VisitCompute(std::forward(args)...); - case StreamRole::kHost2Device: return DerivedT::VisitHost2Device(std::forward(args)...); - case StreamRole::kDevice2Host: return DerivedT::VisitDevice2Host(std::forward(args)...); - case StreamRole::kSyncedLaunchedCommNet: - return DerivedT::VisitSyncedLaunchedCommNet(std::forward(args)...); - case StreamRole::kAsyncedLaunchedCommNet: - return DerivedT::VisitAsyncedLaunchedCommNet(std::forward(args)...); - case StreamRole::kBarrier: return DerivedT::VisitBarrier(std::forward(args)...); - case StreamRole::kCriticalSection: - return DerivedT::VisitCriticalSection(std::forward(args)...); - case StreamRole::kLazyJobLauncher: - return DerivedT::VisitLazyJobLauncher(std::forward(args)...); - case StreamRole::kPinnedCompute: - return DerivedT::VisitPinnedCompute(std::forward(args)...); - } - LOG(FATAL) << "invalid stream role"; - } -}; - -} // namespace oneflow - -namespace std { - -template<> -struct hash final { - size_t operator()(const oneflow::StreamRole& stream_role) const { - return static_cast(stream_role); - } -}; - -} // namespace std - -#endif // ONEFLOW_CORE_COMMON_STREAM_ROLE_H_ diff --git a/oneflow/core/common/stream_type.h b/oneflow/core/common/stream_type.h new file mode 100644 index 0000000000000000000000000000000000000000..b371159f7520b376580d400979804cecb99175e7 --- /dev/null +++ b/oneflow/core/common/stream_type.h @@ -0,0 +1,73 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_STREAM_TYPE_H_ +#define ONEFLOW_CORE_COMMON_STREAM_TYPE_H_ + +#include +#include +#include "oneflow/core/common/preprocessor.h" +#include "glog/logging.h" + +namespace oneflow { + +enum class StreamType { + kInvalid = 0, + kCompute, + kHost2Device, + kDevice2Host, + kCcl, + kBarrier, + kCriticalSection, + kLazyJobLauncher, + kPinnedCompute +}; + +template +struct StreamTypeVisitor { + template + static auto Visit(StreamType stream_type, Args&&... args) { + switch (stream_type) { + case StreamType::kInvalid: LOG(FATAL) << "invalid stream type"; + case StreamType::kCompute: return DerivedT::VisitCompute(std::forward(args)...); + case StreamType::kHost2Device: return DerivedT::VisitHost2Device(std::forward(args)...); + case StreamType::kDevice2Host: return DerivedT::VisitDevice2Host(std::forward(args)...); + case StreamType::kCcl: return DerivedT::VisitCcl(std::forward(args)...); + case StreamType::kBarrier: return DerivedT::VisitBarrier(std::forward(args)...); + case StreamType::kCriticalSection: + return DerivedT::VisitCriticalSection(std::forward(args)...); + case StreamType::kLazyJobLauncher: + return DerivedT::VisitLazyJobLauncher(std::forward(args)...); + case StreamType::kPinnedCompute: + return DerivedT::VisitPinnedCompute(std::forward(args)...); + } + LOG(FATAL) << "invalid stream type"; + } +}; + +} // namespace oneflow + +namespace std { + +template<> +struct hash final { + size_t operator()(const oneflow::StreamType& stream_type) const { + return static_cast(stream_type); + } +}; + +} // namespace std + +#endif // ONEFLOW_CORE_COMMON_STREAM_TYPE_H_ diff --git a/oneflow/core/common/stride.cpp b/oneflow/core/common/stride.cpp index 38552a832f907542daf70c235972cbf98d4ab41a..ab130076065e65f9862319f19577993adbc3fad3 100644 --- a/oneflow/core/common/stride.cpp +++ b/oneflow/core/common/stride.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/common/stride.h" +#include "oneflow/core/common/constant.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/common/cplusplus_17.h" @@ -29,7 +30,7 @@ Stride::Stride(const Shape& shape) { std::multiplies<>{}); } else if (ndim > 0 && shape.elem_cnt() == 0) { // 0-size shape - std::vector tmp_shape(ndim); + small_vector tmp_shape(ndim); for (int64_t i = 0; i < ndim; ++i) { tmp_shape[i] = shape.At(i) > 0 ? shape.At(i) : 1; } std::exclusive_scan(tmp_shape.rbegin(), tmp_shape.rend(), rbegin(), (int64_t)1, std::multiplies<>{}); diff --git a/oneflow/core/common/symbol.h b/oneflow/core/common/symbol.h index 2f5d4e927273f4f1440f3bcb95fbf7491cdd7443..a5ef7699e2b5b646d9ae480a7896d56713815e5e 100644 --- a/oneflow/core/common/symbol.h +++ b/oneflow/core/common/symbol.h @@ -22,7 +22,6 @@ limitations under the License. #include #include #include "oneflow/core/common/type_traits.h" -#include "oneflow/core/common/maybe.h" #include "oneflow/core/common/hash_eq_trait_ptr.h" namespace oneflow { @@ -128,12 +127,6 @@ struct SymbolUtil final { static const std::shared_ptr& GetOrCreatePtr(const T& obj) { return LocalThreadGetOr(obj); } - static Maybe> GetSymbolByExistedRawPtr(const T* ptr) { - CHECK_GT_OR_RETURN(ThreadLocalSymbolPtrSet()->count(ptr), 0) << "ptr: " << ptr; - Symbol symbol; - symbol.ptr_ = ptr; - return symbol; - } }; template diff --git a/oneflow/core/framework/tensor_desc.cpp b/oneflow/core/common/tensor_desc.cpp similarity index 85% rename from oneflow/core/framework/tensor_desc.cpp rename to oneflow/core/common/tensor_desc.cpp index b13dd5dac390154ef35c82ddacbcd6312633bb42..661cf878ba70c69016af42c8ad15bb84ada8062a 100644 --- a/oneflow/core/framework/tensor_desc.cpp +++ b/oneflow/core/common/tensor_desc.cpp @@ -13,17 +13,18 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" +#include "oneflow/core/register/blob_desc.pb.h" namespace oneflow { namespace user_op { TensorDesc& TensorDesc::operator=(const TensorDesc& rhs) { - *this->mut_shape() = rhs.shape(); - *this->mut_stride() = rhs.stride(); - *this->mut_data_type() = rhs.data_type(); - *this->mut_is_dynamic() = rhs.is_dynamic(); + this->set_shape(rhs.shape()); + this->set_stride(rhs.stride()); + this->set_data_type(rhs.data_type()); + this->set_is_dynamic(rhs.is_dynamic()); return *this; } diff --git a/oneflow/core/common/tensor_desc.h b/oneflow/core/common/tensor_desc.h new file mode 100644 index 0000000000000000000000000000000000000000..2125835698f3325dde9995f46be17e9f44b955bf --- /dev/null +++ b/oneflow/core/common/tensor_desc.h @@ -0,0 +1,80 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_COMMON_TENSOR_DESC_H_ +#define ONEFLOW_CORE_COMMON_TENSOR_DESC_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/shape.h" +#include "oneflow/core/common/stride.h" +#include "oneflow/core/common/data_type.pb.h" + +namespace oneflow { + +class BlobDescProto; + +namespace user_op { + +class TensorDesc { + public: + virtual ~TensorDesc() = default; + TensorDesc& operator=(const TensorDesc& rhs); + bool operator==(const TensorDesc&) const; + + virtual const Shape& shape() const = 0; + virtual void set_shape(const Shape& shape) = 0; + virtual const Stride& stride() const = 0; + virtual void set_stride(const Stride& stride) = 0; + virtual DataType data_type() const = 0; + virtual void set_data_type(DataType data_type) = 0; + + virtual bool is_dynamic() const = 0; + virtual void set_is_dynamic(bool is_dynamic) = 0; + + protected: + TensorDesc() = default; +}; + +class NaiveTensorDesc final : public TensorDesc { + public: + NaiveTensorDesc() = default; + ~NaiveTensorDesc() override = default; + NaiveTensorDesc(const NaiveTensorDesc&); + NaiveTensorDesc(const BlobDescProto&); + + NaiveTensorDesc& operator=(const BlobDescProto&); + + const Shape& shape() const override { return shape_; } + void set_shape(const Shape& shape) override { shape_ = shape; } + const Stride& stride() const override { return stride_; } + void set_stride(const Stride& stride) override { stride_ = stride; } + DataType data_type() const override { return data_type_; } + void set_data_type(DataType data_type) override { data_type_ = data_type; } + + bool is_dynamic() const override { return is_dynamic_; } + void set_is_dynamic(bool is_dynamic) override { is_dynamic_ = is_dynamic; } + + private: + Shape shape_; + Stride stride_; + DataType data_type_; + bool is_dynamic_; +}; + +} // namespace user_op + +} // namespace oneflow + +#endif // ONEFLOW_CORE_COMMON_TENSOR_DESC_H_ diff --git a/oneflow/core/common/tensor_meta.cpp b/oneflow/core/common/tensor_meta.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1828ea60a269b6c78910fd5c392e645641a517f1 --- /dev/null +++ b/oneflow/core/common/tensor_meta.cpp @@ -0,0 +1,168 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/tensor_meta.h" +#include "oneflow/core/common/stride.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/common/shape_view.h" + +namespace oneflow { +namespace one { + +MutTensorMeta::MutTensorMeta() + : TensorMeta(kInvalidDataType), + shape_(std::make_shared()), + stride_(std::make_shared()) {} + +MutTensorMeta::MutTensorMeta(const std::shared_ptr& shape, DataType dtype) + : TensorMeta(dtype), + shape_(std::make_shared(*shape)), + stride_(std::make_shared(*shape)) {} + +MutTensorMeta::MutTensorMeta(const std::shared_ptr& shape, + const std::shared_ptr& stride, DataType dtype) + : TensorMeta(dtype), + shape_(std::make_shared(*shape)), + stride_(std::make_shared(*stride)) {} + +MutTensorMeta::MutTensorMeta(const Shape& shape, DataType dtype) + : TensorMeta(dtype), + shape_(std::make_shared(shape)), + stride_(std::make_shared(shape)) {} + +MutTensorMeta::MutTensorMeta(const Shape& shape, const Stride& stride, DataType dtype) + : TensorMeta(dtype), + shape_(std::make_shared(shape)), + stride_(std::make_shared(stride)) {} + +bool MutTensorMeta::operator==(const MutTensorMeta& other) const { + // It's correct to ignore is_dynamic_ field. + return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() + && this->stride() == other.stride(); +} + +size_t MutTensorMeta::CalcHashValue() const { + // It's correct to ignore is_dynamic_ field. + return Hash(*shape_ptr(), dtype(), stride()); +} + +ConstTensorMeta::ConstTensorMeta() + : TensorMeta(kInvalidDataType), shape_(SymbolOf(Shape())), stride_(SymbolOf(Stride())) {} + +ConstTensorMeta::ConstTensorMeta(Symbol shape, DataType dtype) + : TensorMeta(dtype), shape_(shape), stride_(SymbolOf(Stride(*shape))) {} + +ConstTensorMeta::ConstTensorMeta(Symbol shape, Symbol stride, DataType dtype) + : TensorMeta(dtype), shape_(shape), stride_(stride) {} + +bool ConstTensorMeta::operator==(const ConstTensorMeta& other) const { + // It's correct to ignore is_dynamic_ field. + return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() + && this->stride() == other.stride(); +} + +size_t ConstTensorMeta::CalcHashValue() const { + // It's correct to ignore is_dynamic_ field. + return Hash(*shape_ptr(), dtype(), stride()); +} + +LocalTensorMeta::LocalTensorMeta() + : ConstTensorMeta(SymbolOf(Shape()), SymbolOf(Stride()), DataType::kInvalidDataType), + device_(Symbol()) {} + +LocalTensorMeta::LocalTensorMeta(Symbol shape, DataType dtype, Symbol device) + : ConstTensorMeta(shape, SymbolOf(Stride(*shape)), dtype), device_(device) {} + +LocalTensorMeta::LocalTensorMeta(Symbol shape, Symbol stride, DataType dtype, + Symbol device) + : ConstTensorMeta(shape, stride, dtype), device_(device) {} + +bool LocalTensorMeta::operator==(const LocalTensorMeta& other) const { + // It's correct to ignore is_dynamic_ field. + return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() + && this->device() == other.device() && this->stride() == other.stride(); +} + +size_t LocalTensorMeta::CalcHashValue() const { + // It's correct to ignore is_dynamic_ field. + return Hash(*shape_ptr(), dtype(), device(), stride()); +} + +MutLocalTensorMeta::MutLocalTensorMeta() + : MutTensorMeta(std::make_shared(), std::make_shared(), + kInvalidDataType), + device_(Symbol()) {} + +MutLocalTensorMeta::MutLocalTensorMeta(const std::shared_ptr& shape, DataType dtype, + Symbol device) + : MutTensorMeta(shape, std::make_shared(*shape), dtype), device_(device) {} + +MutLocalTensorMeta::MutLocalTensorMeta(const std::shared_ptr& shape, + const std::shared_ptr& stride, DataType dtype, + Symbol device) + : MutTensorMeta(shape, stride, dtype), device_(device) {} + +MutLocalTensorMeta::MutLocalTensorMeta(const Shape& shape, DataType dtype, Symbol device) + : MutTensorMeta(shape, Stride(shape), dtype), device_(device) {} + +MutLocalTensorMeta::MutLocalTensorMeta(const Shape& shape, const Stride& stride, DataType dtype, + Symbol device) + : MutTensorMeta(shape, stride, dtype), device_(device) {} + +bool MutLocalTensorMeta::operator==(const MutLocalTensorMeta& other) const { + // It's correct to ignore is_dynamic_ field. + return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() + && *this->device() == *other.device() && this->stride() == other.stride(); +} + +size_t MutLocalTensorMeta::CalcHashValue() const { + // It's correct to ignore is_dynamic_ field. + return Hash(*shape_ptr(), dtype(), *device(), stride()); +} + +bool GlobalTensorMeta::operator==(const GlobalTensorMeta& other) const { + // It's correct to ignore is_dynamic_ field. + return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() + && this->nd_sbp() == other.nd_sbp() && this->parallel_desc() == other.parallel_desc(); +} + +size_t GlobalTensorMeta::CalcHashValue() const { + return Hash(*shape_ptr(), dtype(), nd_sbp(), parallel_desc()); +} + +bool IsContiguous(const Shape& shape, const Stride& stride) { + if (!shape.is_initialized()) { return true; } + return IsContiguous(ShapeView(shape), stride); +} + +bool IsContiguous(const ShapeView& shape_view, const Stride& stride) { + if (shape_view.NumAxes() < 1 || shape_view.elem_cnt() <= 1) { return true; } + int64_t dim = shape_view.NumAxes(); + int64_t expected_stride = 1; + bool contig_if_nonempty = true; + for (int64_t i = dim - 1; i >= 0; --i) { + // Contiguous by default when any dim is equal to zero + // https://stackoverflow.com/questions/31681324/identify-contiguous-segments-of-a-non-contiguous-numpy-array + if (shape_view.At(i) == 0) { return true; } + if (contig_if_nonempty && shape_view.At(i) != 1) { + if (stride.at(i) != expected_stride) { contig_if_nonempty = false; } + expected_stride *= shape_view.At(i); + } + } + return contig_if_nonempty; +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/common/tensor_meta.h b/oneflow/core/common/tensor_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..07a2b501d68d84f22d3dca5535e228b8b5450510 --- /dev/null +++ b/oneflow/core/common/tensor_meta.h @@ -0,0 +1,239 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_COMMON_TENSOR_META_H_ +#define ONEFLOW_COMMON_TENSOR_META_H_ + +#include +#include "oneflow/core/common/tensor_desc.h" +#include "oneflow/core/common/symbol.h" + +namespace oneflow { + +class NdSbp; +class Shape; +class Stride; +class Device; +class ParallelDesc; + +namespace one { + +bool IsContiguous(const Shape& shape, const Stride& stride); +bool IsContiguous(const ShapeView& shape_view, const Stride& stride); + +class TensorMeta : public user_op::TensorDesc { + public: + TensorMeta(DataType dtype) : data_type_(dtype), is_dynamic_(false) {} + TensorMeta(const TensorMeta& other) = default; + TensorMeta(TensorMeta&&) = default; + virtual ~TensorMeta() = default; + + virtual const std::shared_ptr& shape_ptr() const = 0; + virtual const std::shared_ptr& stride_ptr() const = 0; + virtual bool is_contiguous() const = 0; + + DataType dtype() const { return data_type_; } + DataType data_type() const override { return data_type_; } + bool is_dynamic() const override { return is_dynamic_; } + + virtual void set_shape(const Shape& shape) override { PRINT_BUG_PROMPT_AND_ABORT(); } + virtual void set_stride(const Stride& stride) override { PRINT_BUG_PROMPT_AND_ABORT(); } + virtual void set_data_type(DataType data_type) override { PRINT_BUG_PROMPT_AND_ABORT(); } + virtual void set_is_dynamic(bool is_dynamic) override { PRINT_BUG_PROMPT_AND_ABORT(); } + + protected: + DataType data_type_; + bool is_dynamic_; +}; + +class MutTensorMeta : public TensorMeta { + public: + // uninitialized MutTensorMeta. + MutTensorMeta(); + MutTensorMeta(const MutTensorMeta& other) + : TensorMeta(other), + shape_(std::make_shared(*other.shape_)), + stride_(std::make_shared(*other.stride_)) {} + MutTensorMeta(const std::shared_ptr& shape, DataType dtype); + MutTensorMeta(const std::shared_ptr& shape, + const std::shared_ptr& stride, DataType dtype); + MutTensorMeta(const Shape& shape, DataType dtype); + MutTensorMeta(const Shape& shape, const Stride& stride, DataType dtype); + virtual ~MutTensorMeta() = default; + + const std::shared_ptr& shape_ptr() const override { return shape_; } + const std::shared_ptr& stride_ptr() const override { return stride_; } + const Shape& shape() const override { return *shape_; } + const Stride& stride() const override { return *stride_; } + bool is_contiguous() const override { return IsContiguous(*shape_, *stride_); } + + void set_shape(const Shape& shape) override { *const_cast(shape_.get()) = shape; } + void set_stride(const Stride& stride) override { *const_cast(stride_.get()) = stride; } + void set_data_type(DataType data_type) override { data_type_ = data_type; } + void set_is_dynamic(bool is_dynamic) override { is_dynamic_ = is_dynamic; } + + bool operator==(const MutTensorMeta& other) const; + size_t CalcHashValue() const; + + MutTensorMeta& operator=(const MutTensorMeta& other) { + this->data_type_ = other.data_type_; + this->is_dynamic_ = other.is_dynamic_; + this->shape_ = std::make_shared(*other.shape_); + this->stride_ = std::make_shared(*other.stride_); + return *this; + } + + protected: + std::shared_ptr shape_; + std::shared_ptr stride_; +}; + +class ConstTensorMeta : public TensorMeta { + public: + // uninitialized ConstTensorMeta. + ConstTensorMeta(); + ConstTensorMeta(const ConstTensorMeta&) = default; + ConstTensorMeta(Symbol shape, DataType dtype); + ConstTensorMeta(Symbol shape, Symbol stride, DataType dtype); + ConstTensorMeta(const Shape& shape, DataType dtype) : ConstTensorMeta(SymbolOf(shape), dtype) {} + ConstTensorMeta(const Shape& shape, const Stride& stride, DataType dtype) + : ConstTensorMeta(SymbolOf(shape), SymbolOf(stride), dtype) {} + virtual ~ConstTensorMeta() = default; + + const std::shared_ptr& shape_ptr() const override { + return shape_.shared_from_symbol(); + } + const std::shared_ptr& stride_ptr() const override { + return stride_.shared_from_symbol(); + } + const Shape& shape() const override { return *shape_; } + const Stride& stride() const override { return *stride_; } + bool is_contiguous() const override { return IsContiguous(*shape_, *stride_); } + + bool operator==(const ConstTensorMeta& other) const; + size_t CalcHashValue() const; + + ConstTensorMeta& operator=(const ConstTensorMeta& other) { + this->data_type_ = other.data_type_; + this->is_dynamic_ = other.is_dynamic_; + this->shape_ = other.shape_; + this->stride_ = other.stride_; + return *this; + } + + protected: + Symbol shape_; + Symbol stride_; +}; + +class LocalTensorMeta : public ConstTensorMeta { + public: + // uninitialized LocalTensorMeta. + LocalTensorMeta(); + LocalTensorMeta(const LocalTensorMeta&) = default; + LocalTensorMeta(Symbol shape, DataType dtype, Symbol device); + LocalTensorMeta(Symbol shape, Symbol stride, DataType dtype, + Symbol device); + LocalTensorMeta(const Shape& shape, DataType dtype, Symbol device) + : LocalTensorMeta(SymbolOf(shape), dtype, device) {} + LocalTensorMeta(const Shape& shape, const Stride& stride, DataType dtype, Symbol device) + : LocalTensorMeta(SymbolOf(shape), SymbolOf(stride), dtype, device) {} + virtual ~LocalTensorMeta() = default; + + const Symbol& device() const { return device_; } + + bool operator==(const LocalTensorMeta& other) const; + size_t CalcHashValue() const; + + LocalTensorMeta& operator=(const LocalTensorMeta& other) = default; + + private: + Symbol device_; +}; + +class MutLocalTensorMeta : public MutTensorMeta { + public: + // uninitialized MutLocalTensorMeta. + MutLocalTensorMeta(); + MutLocalTensorMeta(const MutLocalTensorMeta&) = default; + MutLocalTensorMeta(const std::shared_ptr& shape, DataType dtype, + Symbol device); + MutLocalTensorMeta(const std::shared_ptr& shape, + const std::shared_ptr& stride, DataType dtype, + Symbol device); + MutLocalTensorMeta(const Shape& shape, DataType dtype, Symbol device); + MutLocalTensorMeta(const Shape& shape, const Stride& stride, DataType dtype, + Symbol device); + virtual ~MutLocalTensorMeta() = default; + + const Symbol& device() const { return device_; } + + Symbol* mut_device() { return &device_; } + + bool operator==(const MutLocalTensorMeta& other) const; + size_t CalcHashValue() const; + + MutLocalTensorMeta& operator=(const MutLocalTensorMeta& other) = default; + + private: + Symbol device_; +}; + +class GlobalTensorMeta : public ConstTensorMeta { + public: + GlobalTensorMeta(Symbol shape, DataType dtype, Symbol nd_sbp, + Symbol parallel_desc) + : ConstTensorMeta(shape, dtype), nd_sbp_(nd_sbp), parallel_desc_(parallel_desc) {} + GlobalTensorMeta(const Shape& shape, DataType dtype, Symbol nd_sbp, + Symbol parallel_desc) + : GlobalTensorMeta(SymbolOf(shape), dtype, nd_sbp, parallel_desc) {} + GlobalTensorMeta(const GlobalTensorMeta&) = default; + GlobalTensorMeta(GlobalTensorMeta&&) = default; + virtual ~GlobalTensorMeta() = default; + + bool operator==(const GlobalTensorMeta& other) const; + + Symbol nd_sbp() const { return nd_sbp_; } + Symbol parallel_desc() const { return parallel_desc_; } + + size_t CalcHashValue() const; + + private: + Symbol nd_sbp_; + Symbol parallel_desc_; +}; + +} // namespace one +} // namespace oneflow + +namespace std { + +template<> +struct hash final { + size_t operator()(const oneflow::one::LocalTensorMeta& local_tensor_meta) const { + return local_tensor_meta.CalcHashValue(); + } +}; + +template<> +struct hash final { + size_t operator()(const oneflow::one::GlobalTensorMeta& global_tensor_meta) const { + return global_tensor_meta.CalcHashValue(); + } +}; + +} // namespace std + +#endif // ONEFLOW_COMMON_TENSOR_META_H_ diff --git a/oneflow/core/common/thread_local_guard.h b/oneflow/core/common/thread_local_guard.h index cc99be9793732b1368f779fb517666368e900189..39eb04f6e091cffbbb675c0d4b7a096faed9d9d9 100644 --- a/oneflow/core/common/thread_local_guard.h +++ b/oneflow/core/common/thread_local_guard.h @@ -25,6 +25,10 @@ namespace oneflow { template class ThreadLocalGuard { public: + ThreadLocalGuard() { + old_value_ = *MutThreadLocalValue(); + *MutThreadLocalValue() = Optional(); + } explicit ThreadLocalGuard(const T& value) { old_value_ = *MutThreadLocalValue(); *MutThreadLocalValue() = Optional(value); diff --git a/oneflow/core/common/throw.h b/oneflow/core/common/throw.h index da7d433c3e2f6a288dc44450b9783349582bc5b0..ac1df7a737cef4a87150aa061034a85594bd862d 100644 --- a/oneflow/core/common/throw.h +++ b/oneflow/core/common/throw.h @@ -23,21 +23,29 @@ namespace oneflow { namespace details { struct Throw final { - void operator=(Error&& error) { ThrowError(error.error_proto()); } + void operator=(Error&& error) { ThrowError(error.stacked_error()); } }; } // namespace details } // namespace oneflow -#define THROW(err_type) \ - oneflow::details::Throw() = \ - oneflow::Error::err_type().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) - -#define CHECK_OR_THROW(expr) \ - if (!(expr)) \ - oneflow::details::Throw() = \ - oneflow::Error::CheckFailedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) \ +#define THROW(err_type) \ + ::oneflow::details::Throw() = \ + ::oneflow::Error::err_type().AddStackFrame([](const char* function) { \ + thread_local static auto frame = \ + ::oneflow::SymbolOf(::oneflow::ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) + +#define CHECK_OR_THROW(expr) \ + if (!(expr)) \ + ::oneflow::details::Throw() = \ + ::oneflow::Error::CheckFailedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = \ + ::oneflow::SymbolOf(::oneflow::ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ << "Check failed: " << OF_PP_STRINGIZE(expr) << ": " #define CHECK_EQ_OR_THROW(lhs, rhs) \ @@ -66,12 +74,20 @@ struct Throw final { #define CHECK_ISNULL_OR_THROW(ptr) CHECK_OR_THROW(ptr == nullptr) -#define TODO_THEN_THROW() \ - oneflow::details::Throw() = \ - oneflow::Error::TodoError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) - -#define UNIMPLEMENTED_THEN_THROW() \ - oneflow::details::Throw() = \ - oneflow::Error::UnimplementedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) +#define TODO_THEN_THROW() \ + ::oneflow::details::Throw() = \ + ::oneflow::Error::TodoError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = \ + ::oneflow::SymbolOf(::oneflow::ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) + +#define UNIMPLEMENTED_THEN_THROW() \ + ::oneflow::details::Throw() = \ + ::oneflow::Error::UnimplementedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = \ + ::oneflow::SymbolOf(::oneflow::ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) #endif // ONEFLOW_CORE_COMMON_THROW_H_ diff --git a/oneflow/core/common/util.h b/oneflow/core/common/util.h index e452a07a385c5d3b9e582b1f8ac15ce7f392288f..a36517b134776440a47eea972ab0d220d20e8f46 100644 --- a/oneflow/core/common/util.h +++ b/oneflow/core/common/util.h @@ -38,56 +38,13 @@ limitations under the License. #include "oneflow/core/common/hash_container.h" #include "oneflow/core/common/meta_util.hpp" #include "oneflow/core/common/singleton.h" +#include "oneflow/core/common/hash.h" #include "oneflow/core/common/cpp_attribute.h" #define CHECK_ISNULL(e) CHECK((e) == nullptr) namespace oneflow { -inline size_t HashCombine(size_t lhs, size_t rhs) { - return lhs ^ (rhs + 0x9e3779b9 + (lhs << 6U) + (lhs >> 2U)); -} - -inline void HashCombine(size_t* seed, size_t hash) { *seed = HashCombine(*seed, hash); } - -template -inline void AddHash(size_t* seed, const T&... v) { - __attribute__((__unused__)) int dummy[] = {(HashCombine(seed, std::hash()(v)), 0)...}; -} - -template -inline size_t Hash(const T& v1, const Ts&... vn) { - size_t seed = std::hash()(v1); - - AddHash(&seed, vn...); - - return seed; -} - -} // namespace oneflow - -namespace std { - -template -struct hash> { - std::size_t operator()(const std::pair& p) const { - return oneflow::Hash(p.first, p.second); - } -}; - -template -struct hash> { - std::size_t operator()(const std::vector& vec) const { - std::size_t hash_value = vec.size(); - for (const auto& elem : vec) { oneflow::AddHash(&hash_value, elem); } - return hash_value; - } -}; - -} // namespace std - -namespace oneflow { - #define OF_DISALLOW_COPY(ClassName) \ ClassName(const ClassName&) = delete; \ ClassName& operator=(const ClassName&) = delete diff --git a/oneflow/core/common/wrap_dim_utils.h b/oneflow/core/common/wrap_dim_utils.h index 929b203cf45c7fee7cdfd9bc82ba21cbe2238191..1bcac04dd632ccbacb3dfe873ddb472efefe2fb5 100644 --- a/oneflow/core/common/wrap_dim_utils.h +++ b/oneflow/core/common/wrap_dim_utils.h @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include #include "oneflow/core/common/maybe.h" namespace oneflow { @@ -37,4 +38,23 @@ static inline Maybe maybe_wrap_dim(int64_t dim, int64_t dim_post_expr, if (dim < 0) dim += dim_post_expr; return dim; } + +// align with pytorch: `aten/src/ATen/WrapDimUtilsMulti.h` +constexpr size_t dim_bitset_size = 64; + +static inline Maybe> dim_list_to_bitset( + const std::vector& dims, int64_t ndims) { + CHECK_LE_OR_RETURN(ndims, (int64_t)dim_bitset_size) + << Error::RuntimeError() << "Only tensors with up to " << dim_bitset_size + << " dims are supported"; + std::bitset seen; + for (int32_t i = 0; i < dims.size(); i++) { + size_t dim = JUST(maybe_wrap_dim(dims[i], ndims)); + CHECK_OR_RETURN_ERROR(!seen[dim]) << Error::RuntimeError() << "The dim " << dim + << " appears multiple times in the list of dims"; + seen[dim] = true; + } + return seen; +} + } // namespace oneflow diff --git a/oneflow/core/control/rank_info_bootstrap_server.cpp b/oneflow/core/control/rank_info_bootstrap_server.cpp index c467dbe198a907fc1fb82554e6c9ad565f6248f2..c874f154b9ce9dc2c0d9da39674fb305e5a2a7bf 100644 --- a/oneflow/core/control/rank_info_bootstrap_server.cpp +++ b/oneflow/core/control/rank_info_bootstrap_server.cpp @@ -13,8 +13,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/control/rank_info_bootstrap_server.h" +#include +#include +#include #include "grpc/grpc_posix.h" +#include "oneflow/core/common/env_var/bootstrap.h" +#include "oneflow/core/control/rank_info_bootstrap_server.h" namespace oneflow { @@ -29,12 +33,25 @@ std::string GetHostFromUri(const std::string& uri) { return uri.substr(first_delimiter_pos + 1, second_delimiter_pos - first_delimiter_pos - 1); } +int64_t rpc_bootstrap_server_sleep_seconds() { + static const int64_t rpc_bootstrap_server_sleep_seconds = + EnvInteger(); + return rpc_bootstrap_server_sleep_seconds; +} + +int64_t rpc_bootstrap_server_max_retry_times() { + static const int64_t rpc_bootstrap_server_max_retry_times = + EnvInteger(); + return rpc_bootstrap_server_max_retry_times; +} + } // namespace RankInfoBootstrapServer::RankInfoBootstrapServer(const BootstrapConf& bootstrap_conf) : BootstrapServer(), port_(0), world_size_(bootstrap_conf.world_size()) { Init(); - int p = (bootstrap_conf.rank() == 0 ? bootstrap_conf.master_addr().port() : 0); + const int64_t rank = bootstrap_conf.rank(); + int p = (rank == 0 ? bootstrap_conf.master_addr().port() : 0); grpc::ServerBuilder server_builder; server_builder.SetMaxMessageSize(INT_MAX); server_builder.AddListeningPort("0.0.0.0:" + std::to_string(p), grpc::InsecureServerCredentials(), @@ -43,10 +60,59 @@ RankInfoBootstrapServer::RankInfoBootstrapServer(const BootstrapConf& bootstrap_ server_builder.RegisterService(grpc_service_.get()); cq_ = server_builder.AddCompletionQueue(); grpc_server_ = server_builder.BuildAndStart(); - if (bootstrap_conf.rank() == 0) { CHECK_EQ(p, port()) << "Port " << p << " is unavailable"; } + if (rank == 0) { CHECK_EQ(p, port()) << "Port " << p << " is unavailable"; } LOG(INFO) << "RankInfoBootstrapServer listening on " << "0.0.0.0:" + std::to_string(port()); loop_thread_ = std::thread(&RankInfoBootstrapServer::HandleRpcs, this); + if (rank == 0) { + rank2host_ = std::make_shared>(world_size_, ""); + // NOTE: use check_thread_ to check RankInfoBootstrapServer status on rank 0 + // if size of ready ranks == total ranks(world_size), means status is ok. + // otherwise, it indicates that other ranks' server have not been created successfully! + check_thread_ = std::thread(&RankInfoBootstrapServer::CheckServerStatus, this); + } +} + +void RankInfoBootstrapServer::CheckServerStatus() { + bool status_ok = false; + int64_t skip_warning_times = 1; + int64_t retry_idx = 0; + // lambda function to get valid rank num of rank2host_ + auto GetValidRank2HostSize = [](const std::shared_ptr>& rank2host) { + int64_t valid_size = 0; + for (int64_t i = 0; i < rank2host->size(); ++i) { + if (rank2host->at(i) == "") { continue; } + valid_size += 1; + } + return valid_size; + }; + + for (; retry_idx < rpc_bootstrap_server_max_retry_times(); ++retry_idx) { + std::this_thread::sleep_for(std::chrono::seconds(rpc_bootstrap_server_sleep_seconds())); + int64_t valid_size = 0; + { + std::lock_guard lock(lock_); + valid_size = GetValidRank2HostSize(rank2host_); + } + CHECK(valid_size <= world_size_); + if (valid_size == world_size_) { + status_ok = true; + break; + } else { + if (retry_idx >= skip_warning_times) { + LOG(WARNING) << "BootstrapServer not ready, rpc server on some rank have not been created " + "successfully. Failed at " + << retry_idx + 1 << " times, total ranks(world_size): " << world_size_ + << ", ready ranks: " << valid_size; + } + } + } + + if (!status_ok) { + LOG(FATAL) << "CheckServerStatus() failed, rpc server on some rank are not ready, please check " + "whether the processes on all ranks are " + "created successfully."; + } } Maybe&> RankInfoBootstrapServer::rank2host() const { @@ -59,6 +125,7 @@ void RankInfoBootstrapServer::OnLoadServer(CtrlCall* ca CHECK_GE(rank, 0); CHECK_LT(rank, world_size_); if (!rank2host_) { rank2host_ = std::make_shared>(world_size_); } + std::lock_guard lock(lock_); rank2host_->at(rank) = GetHostFromUri(call->server_ctx().peer()); call->SendResponse(); EnqueueRequest(); diff --git a/oneflow/core/control/rank_info_bootstrap_server.h b/oneflow/core/control/rank_info_bootstrap_server.h index 8e10d49040942cdb7611aa98b3b53662d4fc373b..7e85b4961dddc49aadc336917e536348e1fb6e87 100644 --- a/oneflow/core/control/rank_info_bootstrap_server.h +++ b/oneflow/core/control/rank_info_bootstrap_server.h @@ -26,7 +26,9 @@ namespace oneflow { class RankInfoBootstrapServer final : public BootstrapServer { public: OF_DISALLOW_COPY_AND_MOVE(RankInfoBootstrapServer); - ~RankInfoBootstrapServer() override = default; + ~RankInfoBootstrapServer() override { + if (check_thread_.joinable()) { check_thread_.join(); } + } RankInfoBootstrapServer(const BootstrapConf& bootstrap_conf); @@ -35,9 +37,12 @@ class RankInfoBootstrapServer final : public BootstrapServer { private: void OnLoadServer(CtrlCall* call) override; + void CheckServerStatus(); int port_; const int64_t world_size_; + std::mutex lock_; + std::thread check_thread_; // use std::shared_ptr as std::optional std::shared_ptr> rank2host_; }; diff --git a/oneflow/core/control/rpc_client.cpp b/oneflow/core/control/rpc_client.cpp index 3984aab4e3ef5dd189747d4031f1bd8fab93bc5b..98043e7d2970f8adc0db2e90a7e026d453c9d274 100644 --- a/oneflow/core/control/rpc_client.cpp +++ b/oneflow/core/control/rpc_client.cpp @@ -16,13 +16,22 @@ limitations under the License. #include "oneflow/core/control/rpc_client.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/job/env_desc.h" +#include "oneflow/core/common/env_var/bootstrap.h" namespace oneflow { namespace { -const int32_t max_retry_num = 60; -const int64_t sleep_seconds = 10; +int64_t rpc_client_max_retry_times() { + static const int64_t rpc_client_max_retry_times = + EnvInteger(); + return rpc_client_max_retry_times; +} + +int64_t rpc_client_sleep_seconds() { + static const int64_t rpc_client_sleep_seconds = EnvInteger(); + return rpc_client_sleep_seconds; +} #define GRPC_CHECK(x) CHECK_EQ(x.error_code(), grpc::StatusCode::OK) @@ -179,23 +188,28 @@ void RpcClient::LoadServer(const std::string& server_addr, CtrlService::Stub* st void RpcClient::LoadServer(const LoadServerRequest& request, CtrlService::Stub* stub) { int32_t retry_idx = 0; - for (; retry_idx < max_retry_num; ++retry_idx) { + int32_t skip_warning_times = 3; + for (; retry_idx < rpc_client_max_retry_times(); ++retry_idx) { grpc::ClientContext client_ctx; LoadServerResponse response; grpc::Status st = stub->CallMethod(&client_ctx, request, &response); if (st.error_code() == grpc::StatusCode::OK) { - VLOG(3) << "LoadServer " << request.addr() << " Successful at " << retry_idx << " times"; + VLOG(3) << "LoadServer " << request.addr() << " Successful at " << retry_idx + 1 << " times"; break; } else if (st.error_code() == grpc::StatusCode::UNAVAILABLE) { - LOG(WARNING) << "LoadServer " << request.addr() << " Failed at " << retry_idx << " times" - << " error_code " << st.error_code() << " error_message " << st.error_message(); - std::this_thread::sleep_for(std::chrono::seconds(sleep_seconds)); + if (retry_idx >= skip_warning_times) { + LOG(WARNING) << "LoadServer " << request.addr() << " Failed at " << retry_idx + 1 + << " times" + << " error_code: " << st.error_code() + << " error_message: " << st.error_message(); + } + std::this_thread::sleep_for(std::chrono::seconds(rpc_client_sleep_seconds())); continue; } else { LOG(FATAL) << st.error_message(); } } - CHECK_LT(retry_idx, max_retry_num); + CHECK_LT(retry_idx, rpc_client_max_retry_times()); } CtrlService::Stub* RpcClient::GetThisStub() { return stubs_[GlobalProcessCtx::Rank()].get(); } diff --git a/oneflow/core/cuda/atomic.cuh b/oneflow/core/cuda/atomic.cuh index a227d441cebeb34fd678a2d58b68b5cb4addb7f8..0f9bd6fd3070552e0ee55c0b09eee16c046900c2 100644 --- a/oneflow/core/cuda/atomic.cuh +++ b/oneflow/core/cuda/atomic.cuh @@ -16,7 +16,14 @@ limitations under the License. #ifndef ONEFLOW_CORE_CUDA_ATOMIC_H_ #define ONEFLOW_CORE_CUDA_ATOMIC_H_ -#if defined(__CUDACC__) +#if defined(__CUDACC__) || defined(__HIPCC__) + +#ifdef WITH_ROCM + +#include +#include + +#else #include #include @@ -25,6 +32,9 @@ limitations under the License. #if CUDA_VERSION >= 11000 #include #endif // CUDA_VERSION >= 11000 + +#endif + namespace oneflow { namespace cuda { @@ -34,58 +44,90 @@ namespace atomic { namespace internal { template -__device__ __forceinline__ T CastCASImpl(T* address, T compare, T val) { - static_assert(sizeof(T) == sizeof(U), ""); - U ret = atomicCAS(reinterpret_cast(address), *(reinterpret_cast(&compare)), - *(reinterpret_cast(&val))); - return *(reinterpret_cast(&ret)); -} +struct CastCASImpl { + __device__ __forceinline__ T operator()(T* address, T compare, T val, bool* success) const { + static_assert(sizeof(T) == sizeof(U), ""); + U assumed = *(reinterpret_cast(&compare)); + U ret = atomicCAS(reinterpret_cast(address), assumed, *(reinterpret_cast(&val))); + *success = (ret == assumed); + return *(reinterpret_cast(&ret)); + } +}; + +#if __CUDA_ARCH__ < 700 || (defined(__clang__) && defined(__CUDA__)) + +template +struct CastCASImpl { + __device__ __forceinline__ T operator()(T* address, T compare, T val, bool* success) const { + static_assert(sizeof(T) == sizeof(unsigned short int), ""); + size_t offset = reinterpret_cast(address) & 0x2; + unsigned int* address_as_ui = + reinterpret_cast(reinterpret_cast(address) - offset); + unsigned int old = *address_as_ui; + unsigned int assumed = *(reinterpret_cast(&compare)); + unsigned int newval = *(reinterpret_cast(&val)); + + assumed = offset ? (old & 0xffff) | (assumed << 16) : (old & 0xffff0000) | assumed; + newval = offset ? (old & 0xffff) | (newval << 16) : (old & 0xffff0000) | newval; + + unsigned int ret = atomicCAS(address_as_ui, assumed, newval); + *success = (ret == assumed); + ret = offset ? (ret >> 16) : (ret & 0xffff); + return *(reinterpret_cast(&ret)); + } +}; + +#endif // __CUDA_ARCH__ template __device__ __forceinline__ typename std::enable_if::type -CASImpl(T* address, T compare, T val) { - return CastCASImpl(address, compare, val); +CASImpl(T* address, T compare, T val, bool* success) { + return CastCASImpl()(address, compare, val, success); } template __device__ __forceinline__ typename std::enable_if::type - CASImpl(T* address, T compare, T val) { - return CastCASImpl(address, compare, val); + CASImpl(T* address, T compare, T val, bool* success) { + return CastCASImpl()(address, compare, val, success); } template __device__ __forceinline__ typename std::enable_if::type -CASImpl(T* address, T compare, T val) { -#if __CUDA_ARCH__ >= 700 - return CastCASImpl(address, compare, val); -#else - __trap(); - return 0; -#endif // __CUDA_ARCH__ >= 700 +CASImpl(T* address, T compare, T val, bool* success) { + return CastCASImpl()(address, compare, val, success); } -__device__ __forceinline__ int CASImpl(int* address, int compare, int val) { - return atomicCAS(address, compare, val); +__device__ __forceinline__ int CASImpl(int* address, int compare, int val, bool* success) { + int ret = atomicCAS(address, compare, val); + *success = (ret == compare); + return ret; } __device__ __forceinline__ unsigned int CASImpl(unsigned int* address, unsigned int compare, - unsigned int val) { - return atomicCAS(address, compare, val); + unsigned int val, bool* success) { + unsigned int ret = atomicCAS(address, compare, val); + *success = (ret == compare); + return ret; } __device__ __forceinline__ unsigned long long int CASImpl(unsigned long long int* address, unsigned long long int compare, - unsigned long long int val) { - return atomicCAS(address, compare, val); + unsigned long long int val, + bool* success) { + unsigned long long int ret = atomicCAS(address, compare, val); + *success = (ret == compare); + return ret; } #if __CUDA_ARCH__ >= 700 __device__ __forceinline__ unsigned short int CASImpl(unsigned short int* address, unsigned short int compare, - unsigned short int val) { - return atomicCAS(address, compare, val); + unsigned short int val, bool* success) { + unsigned short int ret = atomicCAS(address, compare, val); + *success = (ret == compare); + return ret; } #endif // __CUDA_ARCH__ >= 700 @@ -99,10 +141,11 @@ template class BinaryOp> __device__ __forceinline__ T AtomicCASBinaryImpl(T* address, T val) { T old = *address; T assumed; + bool success = false; do { assumed = old; - old = CASImpl(address, assumed, BinaryOp()(old, val)); - } while (old != assumed); + old = CASImpl(address, assumed, BinaryOp()(old, val), &success); + } while (!success); return old; } @@ -156,17 +199,41 @@ __device__ __forceinline__ nv_bfloat16 AddImpl(nv_bfloat16* address, nv_bfloat16 return atomicAdd(address, val); } +__device__ __forceinline__ nv_bfloat162 AddImpl(nv_bfloat162* address, nv_bfloat162 val) { + return atomicAdd(address, val); +} + #endif // __CUDA_ARCH__ >= 800 -#if __CUDA_ARCH__ < 530 +#if (__CUDA_ARCH__ < 530) && !defined(WITH_ROCM) __device__ __forceinline__ half2 AddImpl(half2* address, half2 val) { - __trap(); + TRAP(); return val; } #endif // __CUDA_ARCH__ < 530 +#ifdef WITH_ROCM + +__device__ __forceinline__ double AddImpl(double* address, double val) { + return atomicAdd(address, val); +} + +__device__ __forceinline__ half AddImpl(half* address, half val) { + float address_value = __half2float(*address); + return __float2half(atomicAdd(&address_value, __half2float(val))); } + +__device__ __forceinline__ half2 AddImpl(half2* address, half2 val) { + half2 res; + float2 address_value = __half22float2(*address); + res.data.x = __float2half(atomicAdd(&address_value.x, __half2float(val.data.x))); + res.data.y = __float2half(atomicAdd(&address_value.y, __half2float(val.data.y))); + return res; +} + +#endif + } // namespace internal template @@ -181,7 +248,8 @@ __device__ __forceinline__ typename std::enable_if::value, T> template __device__ __forceinline__ T CAS(T* address, U compare, V val) { - return internal::CASImpl(address, Cast(compare), Cast(val)); + bool success = false; + return internal::CASImpl(address, Cast(compare), Cast(val), &success); } template @@ -189,6 +257,56 @@ __device__ __forceinline__ T Add(T* address, U val) { return internal::AddImpl(address, Cast(val)); } +__device__ __forceinline__ float Mul(int32_t* address, const int32_t val) { + int32_t old = *address, assumed; + do { + assumed = old; + old = atomicCAS(address, assumed, val * assumed); + } while (assumed != old); + return old; +} + +__device__ __forceinline__ float Mul(uint32_t* address, const uint32_t val) { + uint32_t old = *address, assumed; + do { + assumed = old; + old = atomicCAS(address, assumed, val * assumed); + } while (assumed != old); + return old; +} + +__device__ __forceinline__ float Mul(uint64_t* address, const uint64_t val) { + static_assert(sizeof(uint64_t) == sizeof(unsigned long long int), ""); + unsigned long long int old = *reinterpret_cast(address), assumed; + do { + assumed = old; + old = atomicCAS(reinterpret_cast(address), assumed, + static_cast(val) * assumed); + } while (assumed != old); + return old; +} + +__device__ __forceinline__ float Mul(float* address, const float val) { + int32_t* address_as_int = reinterpret_cast(address); + int32_t old = *address_as_int, assumed; + do { + assumed = old; + old = atomicCAS(address_as_int, assumed, __float_as_int(val * __int_as_float(assumed))); + } while (assumed != old); + return __int_as_float(old); +} + +__device__ __forceinline__ float Mul(double* address, const double val) { + unsigned long long int* address_as_ull = reinterpret_cast(address); + unsigned long long int old = *address_as_ull, assumed; + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val * __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); +} + __device__ __forceinline__ float Max(float* address, const float val) { int* address_as_i = (int*)address; int old = *address_as_i; diff --git a/oneflow/core/cuda/elementwise.cuh b/oneflow/core/cuda/elementwise.cuh index ba50f369c5d8ce603510c5e281c019883f032e83..edce202c5a50238f07707342c85fe8347ca222fc 100644 --- a/oneflow/core/cuda/elementwise.cuh +++ b/oneflow/core/cuda/elementwise.cuh @@ -16,7 +16,13 @@ limitations under the License. #ifndef ONEFLOW_CORE_CUDA_ELEMENTWISE_H_ #define ONEFLOW_CORE_CUDA_ELEMENTWISE_H_ +#ifdef WITH_ROCM +#include +#else #include +#endif + +#include "oneflow/core/ep/include/gpu_macro.h" #include #include #include @@ -30,25 +36,25 @@ namespace elementwise { constexpr int kBlockSize = 256; constexpr int kNumWaves = 32; -inline cudaError_t GetNumBlocks(int64_t n, int* num_blocks) { +inline GPU(Error_t) GetNumBlocks(int64_t n, int* num_blocks) { int dev; { - cudaError_t err = cudaGetDevice(&dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } } int sm_count; { - cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } } int tpm; { - cudaError_t err = cudaDeviceGetAttribute(&tpm, cudaDevAttrMaxThreadsPerMultiProcessor, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&tpm, GPUMaxThreadsPerMultiProcessor, dev); + if (err != GPU(Success)) { return err; } } *num_blocks = std::max(1, std::min((n + kBlockSize - 1) / kBlockSize, sm_count * tpm / kBlockSize * kNumWaves)); - return cudaSuccess; + return GPU(Success); } template @@ -113,24 +119,24 @@ class HasApply2 { template __device__ typename std::enable_if::value == true && pack_size % 2 == 0, Packed>::type -ApplyPack(const FunctorT& functor, const IN... in[pack_size]) { +ApplyPack(const FunctorT& functor, const Packed... in) { Packed ret; #pragma unroll - for (int j = 0; j < pack_size; j += 2) { functor.Apply2(ret.elem + j, (in + j)...); } + for (int j = 0; j < pack_size; j += 2) { functor.Apply2(ret.elem + j, (in.elem + j)...); } return ret; } template __device__ typename std::enable_if::value == false || pack_size % 2 != 0, Packed>::type -ApplyPack(const FunctorT& functor, const IN... in[pack_size]) { +ApplyPack(const FunctorT& functor, const Packed... in) { Packed ret; #pragma unroll - for (int j = 0; j < pack_size; ++j) { ret.elem[j] = functor((in[j])...); } + for (int j = 0; j < pack_size; ++j) { ret.elem[j] = functor((in.elem[j])...); } return ret; } -template +template __global__ void __launch_bounds__(kBlockSize) ApplyGeneric(FactoryT factory, int64_t n_pack, Packed* pack_r, const Packed*... pack_in, int64_t n_tail, R* tail_r, @@ -138,9 +144,9 @@ __global__ void __launch_bounds__(kBlockSize) auto functor = factory(); const int global_tid = blockIdx.x * kBlockSize + threadIdx.x; for (int64_t i = global_tid; i < n_pack; i += blockDim.x * gridDim.x) { - pack_r[i] = ApplyPack(functor, (pack_in[i].elem)...); + pack_r[i] = ApplyPack(functor, (pack_in[i])...); } - if (tail && global_tid < n_tail) { tail_r[global_tid] = functor((tail_in[global_tid])...); } + if (global_tid < n_tail) { tail_r[global_tid] = functor((tail_in[global_tid])...); } } template @@ -153,41 +159,39 @@ struct SimpleFactory { }; template -bool IsAligendForPack() { +bool IsAlignedForPack() { return true; } template -bool IsAligendForPack(const T* ptr, const Args*... others) { +bool IsAlignedForPack(const T* ptr, const Args*... others) { return reinterpret_cast(ptr) % sizeof(Pack) == 0 - && IsAligendForPack(others...); + && IsAlignedForPack(others...); } template -cudaError_t LaunchKernel(FactoryT factory, int64_t n, R* r, const IN*... in, cudaStream_t stream) { +GPU(Error_t) LaunchKernel(FactoryT factory, int64_t n, R* r, const IN*... in, GPU(Stream_t) stream) { const int64_t n_pack = n / pack_size; const int64_t tail_offset = n_pack * pack_size; const int64_t n_tail = n - tail_offset; int num_blocks; { - cudaError_t err = GetNumBlocks(n_pack, &num_blocks); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(n_pack, &num_blocks); + if (err != GPU(Success)) { return err; } } - auto func = n_tail > 0 ? ApplyGeneric - : ApplyGeneric; - func<<>>( + ApplyGeneric<<>>( factory, n_pack, reinterpret_cast*>(r), (reinterpret_cast*>(in))..., n_tail, r + tail_offset, (in + tail_offset)...); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template struct GenericLauncher { - static cudaError_t Launch(FactoryT factory, int64_t n, R* r, const IN*... in, - cudaStream_t stream) { + static GPU(Error_t) Launch(FactoryT factory, int64_t n, R* r, const IN*... in, + GPU(Stream_t) stream) { constexpr int max_pack_size = PackSize(); - if (IsAligendForPack(r, in...)) { + if (IsAlignedForPack(r, in...)) { return LaunchKernel(factory, n, r, in..., stream); } else { return LaunchKernel<1, FactoryT, R, IN...>(factory, n, r, in..., stream); @@ -196,37 +200,37 @@ struct GenericLauncher { }; template -inline cudaError_t UnaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, - cudaStream_t stream) { +inline GPU(Error_t) UnaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, + GPU(Stream_t) stream) { return GenericLauncher::Launch(factory, n, r, a, stream); } template -inline cudaError_t Unary(FunctorT functor, int64_t n, R* r, const A* a, cudaStream_t stream) { +inline GPU(Error_t) Unary(FunctorT functor, int64_t n, R* r, const A* a, GPU(Stream_t) stream) { return UnaryWithFactory(SimpleFactory(functor), n, r, a, stream); } template -inline cudaError_t BinaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, - cudaStream_t stream) { +inline GPU(Error_t) BinaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, + GPU(Stream_t) stream) { return GenericLauncher::Launch(factory, n, r, a, b, stream); } template -inline cudaError_t Binary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, - cudaStream_t stream) { +inline GPU(Error_t) Binary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, + GPU(Stream_t) stream) { return BinaryWithFactory(SimpleFactory(functor), n, r, a, b, stream); } template -inline cudaError_t TernaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, - const C* c, cudaStream_t stream) { +inline GPU(Error_t) TernaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, + const C* c, GPU(Stream_t) stream) { return GenericLauncher::Launch(factory, n, r, a, b, c, stream); } template -inline cudaError_t Ternary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, const C* c, - cudaStream_t stream) { +inline GPU(Error_t) Ternary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, const C* c, + GPU(Stream_t) stream) { return TernaryWithFactory(SimpleFactory(functor), n, r, a, b, c, stream); } diff --git a/oneflow/core/cuda/layer_norm.cuh b/oneflow/core/cuda/layer_norm.cuh index f4a6f523aa72e03ea163cd1f31277d6b58f4a98e..9c54ff8c738da73c09655d1bc4fad55b91bdad2b 100644 --- a/oneflow/core/cuda/layer_norm.cuh +++ b/oneflow/core/cuda/layer_norm.cuh @@ -17,8 +17,14 @@ limitations under the License. #ifndef ONEFLOW_CORE_CUDA_LAYER_NORM_H_ #define ONEFLOW_CORE_CUDA_LAYER_NORM_H_ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include #include +#endif + #include namespace oneflow { @@ -27,7 +33,11 @@ namespace cuda { namespace layer_norm { +#ifdef WITH_ROCM +constexpr int kWarpSize = 64; +#else constexpr int kWarpSize = 32; +#endif template struct SumOp { @@ -42,14 +52,22 @@ struct MaxOp { template class ReductionOp, typename T, int thread_group_width = kWarpSize> __inline__ __device__ T WarpAllReduce(T val) { for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { +#ifdef WITH_ROCM + val = ReductionOp()(val, __shfl_xor(val, mask, thread_group_width)); +#else val = ReductionOp()(val, __shfl_xor_sync(0xffffffff, val, mask, thread_group_width)); +#endif } return val; } template class ReductionOp, typename T, int block_size> __inline__ __device__ T BlockAllReduce(T val) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; __shared__ T result_broadcast; T result = BlockReduce(temp_storage).Reduce(val, ReductionOp()); @@ -93,26 +111,26 @@ __inline__ __device__ double Rsqrt(double x) { } template -inline cudaError_t GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, +inline GPU(Error_t) GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, int64_t max_blocks, int64_t waves, int* num_blocks) { int dev; { - cudaError_t err = cudaGetDevice(&dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } } int sm_count; { - cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } } int max_active_blocks; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_active_blocks, func, + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)(&max_active_blocks, func, block_size, dynamic_smem_size); } *num_blocks = std::max(1, std::min(max_blocks, sm_count * max_active_blocks * waves)); - return cudaSuccess; + return GPU(Success); } template @@ -132,6 +150,34 @@ struct DefaultComputeType { }; #endif // CUDA_VERSION >= 11000 +template +class HasCanPackAs { + typedef char one; + struct two { + char x[2]; + }; + + template + static one test(decltype(&C::CanPackAs)); + template + static two test(...); + + public: + enum { value = sizeof(test(0)) == sizeof(char) }; +}; + +template +typename std::enable_if::value == true, bool>::type CanPackAs(T t, + size_t pack_size) { + return t.CanPackAs(pack_size); +} + +template +typename std::enable_if::value == false, bool>::type CanPackAs(T t, + size_t pack_size) { + return true; +} + template struct GetPackType { using type = typename std::aligned_storage::type; @@ -152,6 +198,7 @@ union Pack { template struct DirectLoad { + using LoadType = DST; DirectLoad(const SRC* src, int64_t row_size) : src(src), row_size(row_size) {} template __device__ void load(DST* dst, int64_t row, int64_t col) const { @@ -210,9 +257,15 @@ __inline__ __device__ void WelfordWarpReduce(T thread_mean, T thread_m2, T threa *m2 = thread_m2; *count = thread_count; for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { +#ifdef WITH_ROCM + T b_mean = __shfl_down(*mean, mask, thread_group_width); + T b_m2 = __shfl_down(*m2, mask, thread_group_width); + T b_count = __shfl_down(*count, mask, thread_group_width); +#else T b_mean = __shfl_down_sync(0xffffffff, *mean, mask, thread_group_width); T b_m2 = __shfl_down_sync(0xffffffff, *m2, mask, thread_group_width); T b_count = __shfl_down_sync(0xffffffff, *count, mask, thread_group_width); +#endif WelfordCombine(b_mean, b_m2, b_count, mean, m2, count); } } @@ -221,9 +274,16 @@ template __inline__ __device__ void WelfordWarpAllReduce(T thread_mean, T thread_m2, T thread_count, T* mean, T* m2, T* count) { WelfordWarpReduce(thread_mean, thread_m2, thread_count, mean, m2, count); +#ifdef WITH_ROCM + *mean = __shfl(*mean, 0, thread_group_width); + *m2 = __shfl(*m2, 0, thread_group_width); + *count = __shfl(*count, 0, thread_group_width); +#else *mean = __shfl_sync(0xffffffff, *mean, 0, thread_group_width); *m2 = __shfl_sync(0xffffffff, *m2, 0, thread_group_width); *count = __shfl_sync(0xffffffff, *count, 0, thread_group_width); +#endif + } template @@ -258,7 +318,11 @@ __inline__ __device__ void WelfordBlockAllReduce(T thread_mean, T thread_m2, T t warp_m2 = static_cast(0); warp_count = static_cast(0); } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif T block_mean = 0; T block_m2 = 0; T block_count = 0; @@ -275,17 +339,21 @@ __inline__ __device__ void WelfordBlockAllReduce(T thread_mean, T thread_m2, T t *result_count = count_result_broadcast; } -template +template __global__ void LayerNormWarpImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - static_assert(cols_per_thread % pack_size == 0, ""); + using LoadType = typename LOAD::LoadType; + static_assert(max_cols_per_thread % pack_size == 0, ""); + static_assert(min_cols_per_thread % pack_size == 0, ""); static_assert(thread_group_width <= kWarpSize, ""); static_assert(kWarpSize % thread_group_width == 0, ""); - constexpr int num_packs = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - ComputeType buf[rows_per_access][cols_per_thread]; + constexpr int max_num_packs = max_cols_per_thread / pack_size; + constexpr int min_num_packs = min_cols_per_thread / pack_size; + assert(cols <= max_cols_per_thread * thread_group_width); + ComputeType buf[rows_per_access][max_cols_per_thread]; const int64_t global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; const int64_t num_global_thread_group = gridDim.x * blockDim.y; const int64_t lane_id = threadIdx.x; @@ -301,13 +369,27 @@ __global__ void LayerNormWarpImpl(LOAD load, STORE store, const int64_t rows, co thread_count[row_id] = 0; ComputeType* row_buf = buf[row_id]; #pragma unroll - for (int pack_id = 0; pack_id < num_packs; ++pack_id) { + for (int pack_id = 0; pack_id < min_num_packs; ++pack_id) { + const int col = (pack_id * thread_group_width + lane_id) * pack_size; + const int pack_offset = pack_id * pack_size; + LoadType pack[pack_size]; + load.template load(pack, row + row_id, col); +#pragma unroll + for (int i = 0; i < pack_size; ++i) { + row_buf[pack_offset + i] = static_cast(pack[i]); + WelfordCombine(row_buf[pack_offset + i], thread_mean + row_id, thread_m2 + row_id, + thread_count + row_id); + } + } + for (int pack_id = min_num_packs; pack_id < max_num_packs; ++pack_id) { const int col = (pack_id * thread_group_width + lane_id) * pack_size; const int pack_offset = pack_id * pack_size; if (!padding || col < cols) { - load.template load(row_buf + pack_offset, row + row_id, col); + LoadType pack[pack_size]; + load.template load(pack, row + row_id, col); #pragma unroll for (int i = 0; i < pack_size; ++i) { + row_buf[pack_offset + i] = static_cast(pack[i]); WelfordCombine(row_buf[pack_offset + i], thread_mean + row_id, thread_m2 + row_id, thread_count + row_id); } @@ -336,11 +418,16 @@ __global__ void LayerNormWarpImpl(LOAD load, STORE store, const int64_t rows, co inv_variance[global_row_id] = row_inv_var; } #pragma unroll - for (int i = 0; i < cols_per_thread; ++i) { + for (int i = 0; i < max_cols_per_thread; ++i) { row_buf[i] = (row_buf[i] - row_mean) * row_inv_var; } #pragma unroll - for (int i = 0; i < num_packs; ++i) { + for (int i = 0; i < min_num_packs; ++i) { + const int col = (i * thread_group_width + lane_id) * pack_size; + store.template store(row_buf + i * pack_size, global_row_id, col); + } +#pragma unroll + for (int i = min_num_packs; i < max_num_packs; ++i) { const int col = (i * thread_group_width + lane_id) * pack_size; if (!padding || col < cols) { store.template store(row_buf + i * pack_size, global_row_id, col); @@ -350,9 +437,10 @@ __global__ void LayerNormWarpImpl(LOAD load, STORE store, const int64_t rows, co } } -template -inline cudaError_t LaunchLayerNormWarpImpl(cudaStream_t stream, LOAD load, STORE store, +template +inline GPU(Error_t) LaunchLayerNormWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { @@ -365,171 +453,129 @@ inline cudaError_t LaunchLayerNormWarpImpl(cudaStream_t stream, LOAD load, STORE (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; int grid_dim_x; { - cudaError_t err = - GetNumBlocks(LayerNormWarpImpl, - block_size, 0, num_blocks, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks( + LayerNormWarpImpl, + block_size, 0, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } - LayerNormWarpImpl + LayerNormWarpImpl <<>>(load, store, rows, cols, epsilon, mean, inv_variance); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } -template -inline cudaError_t DispatchLayerNormWarpImplPadding(cudaStream_t stream, LOAD load, STORE store, +template +inline GPU(Error_t) DispatchLayerNormWarpImplPadding(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchLayerNormWarpImpl( + if (cols == max_cols_per_thread * thread_group_width) { + // when not padding, min_cols_per_thread must equals to max_cols_per_thread, pass + // max_cols_per_thread as min_cols_per_thread and max_cols_per_thread param. + return LaunchLayerNormWarpImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance); } else { - return LaunchLayerNormWarpImpl( + return LaunchLayerNormWarpImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance); } } template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, +typename std::enable_if::type DispatchLayerNormWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return cudaErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ + if (cols <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (cols <= (thread_group_width)*pack_size) { \ + if (rows % 2 == 0) { \ + return DispatchLayerNormWarpImplPadding( \ + stream, load, store, rows, cols, epsilon, mean, inv_variance); \ + } else { \ + return DispatchLayerNormWarpImplPadding( \ + stream, load, store, rows, cols, epsilon, mean, inv_variance); \ + } \ } DEFINE_ONE_ELIF(4) DEFINE_ONE_ELIF(8) DEFINE_ONE_ELIF(16) DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if (cols <= (max_col)*kWarpSize) { \ + return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, \ + epsilon, mean, inv_variance); \ } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) + DEFINE_ONE_ELIF(2, 1) + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, +typename std::enable_if::type DispatchLayerNormWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return cudaErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return cudaErrorInvalidValue; + if (cols <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (cols <= (thread_group_width)*pack_size) { \ + if (rows % 2 == 0) { \ + return DispatchLayerNormWarpImplPadding( \ + stream, load, store, rows, cols, epsilon, mean, inv_variance); \ + } else { \ + return DispatchLayerNormWarpImplPadding( \ + stream, load, store, rows, cols, epsilon, mean, inv_variance); \ + } \ } -} -template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return cudaErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) DEFINE_ONE_ELIF(4) DEFINE_ONE_ELIF(8) DEFINE_ONE_ELIF(16) DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if ((cols <= (max_col)*kWarpSize) && (cols > (min_col)*kWarpSize)) { \ + return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, \ + epsilon, mean, inv_variance); \ } - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template struct DispatchLayerNormWarpImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols % 4 == 0) { - return DispatchLayerNormWarpImplCols( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else if (cols % 2 == 0) { + if (cols % 2 == 0 && CanPackAs(load, 2) && CanPackAs(store, 2)) { return DispatchLayerNormWarpImplCols( stream, load, store, rows, cols, epsilon, mean, inv_variance); } else { @@ -540,7 +586,7 @@ struct DispatchLayerNormWarpImplPackSize { }; template -inline cudaError_t DispatchLayerNormWarpImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) DispatchLayerNormWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { @@ -552,8 +598,9 @@ template(shared_buf); + auto* buf = reinterpret_cast(shared_buf); const int tid = threadIdx.x; assert(cols % pack_size == 0); const int num_packs = static_cast(cols) / pack_size; @@ -562,12 +609,12 @@ __global__ void LayerNormBlockSMemImpl(LOAD load, STORE store, const int64_t row ComputeType thread_m2 = 0; ComputeType thread_count = 0; for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; + LoadType pack[pack_size]; load.template load(pack, row, pack_id * pack_size); #pragma unroll for (int i = 0; i < pack_size; ++i) { buf[i * num_packs + pack_id] = pack[i]; - WelfordCombine(pack[i], &thread_mean, &thread_m2, &thread_count); + WelfordCombine(static_cast(pack[i]), &thread_mean, &thread_m2, &thread_count); } } ComputeType row_mean = 0; @@ -585,7 +632,7 @@ __global__ void LayerNormBlockSMemImpl(LOAD load, STORE store, const int64_t row ComputeType pack[pack_size]; #pragma unroll for (int i = 0; i < pack_size; ++i) { - pack[i] = (buf[i * num_packs + pack_id] - row_mean) * row_inv_var; + pack[i] = (static_cast(buf[i * num_packs + pack_id]) - row_mean) * row_inv_var; } store.template store(pack, row, pack_id * pack_size); } @@ -593,88 +640,152 @@ __global__ void LayerNormBlockSMemImpl(LOAD load, STORE store, const int64_t row } template -inline cudaError_t LaunchLayerNormBlockSMemImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) LaunchLayerNormBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, int smem, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = + GPU(Error_t) err = GetNumBlocks(LayerNormBlockSMemImpl, block_size, smem, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } LayerNormBlockSMemImpl <<>>(load, store, rows, cols, epsilon, mean, inv_variance); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) MaximizeDynamicSharedMemorySize(Func func, const int max_smem_size) { + GPU(FuncAttributes) attr{}; +#ifdef WITH_ROCM + GPU(Error_t) err = GPU(FuncGetAttributes)(&attr, (const void*)func); +#else + GPU(Error_t) err = GPU(FuncGetAttributes)(&attr, func); +#endif + if (err != GPU(Success)) { return err; } + constexpr int reserved_smem = 1024; // 1K +#ifdef WITH_ROCM + return GPU(FuncSetAttribute)((const void*)func, GPU(FuncAttributeMaxDynamicSharedMemorySize), + max_smem_size - attr.sharedSizeBytes - reserved_smem); +#else + return GPU(FuncSetAttribute)(func, GPU(FuncAttributeMaxDynamicSharedMemorySize), + max_smem_size - attr.sharedSizeBytes - reserved_smem); +#endif + } template -inline cudaError_t TryDispatchLayerNormBlockSMemImplBlockSize( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, +inline GPU(Error_t) TryDispatchLayerNormBlockSMemImplBlockSize( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance, bool* success) { constexpr int block_size_conf_1 = 128; constexpr int block_size_conf_2 = 256; constexpr int block_size_conf_3 = 512; constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType); - int max_active_blocks_conf_1; + int dev = 0; + { + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } + } + + int sm_count = 0; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } + } + + static const bool max_smem_configed = [=]() { + int max_smem_size = 0; + GPU(Error_t) err = + GPU(DeviceGetAttribute)(&max_smem_size, GPUMaxSharedMemoryPerBlockOptin, dev); + if (err != GPU(Success)) { return false; } + + err = MaximizeDynamicSharedMemorySize( + LayerNormBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + + return true; + }(); + + const size_t smem = cols * sizeof(typename LOAD::LoadType); + + int max_active_blocks_conf_1; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_1, LayerNormBlockSMemImpl, block_size_conf_1, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_1 <= 0) { *success = false; - return cudaSuccess; + return GPU(Success); } + int max_active_blocks_conf_4; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_4, LayerNormBlockSMemImpl, block_size_conf_4, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_4 == max_active_blocks_conf_1 + || (max_active_blocks_conf_4 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormBlockSMemImpl( stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); } + int max_active_blocks_conf_3; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_3, LayerNormBlockSMemImpl, block_size_conf_3, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_3 == max_active_blocks_conf_1 + || (max_active_blocks_conf_3 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormBlockSMemImpl( stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); } + int max_active_blocks_conf_2; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_2, LayerNormBlockSMemImpl, block_size_conf_2, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_2 == max_active_blocks_conf_1 + || (max_active_blocks_conf_2 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormBlockSMemImpl( stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); } + *success = true; return LaunchLayerNormBlockSMemImpl( stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); @@ -682,13 +793,13 @@ inline cudaError_t TryDispatchLayerNormBlockSMemImplBlockSize( template struct TryDispatchLayerNormBlockSMemImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance, bool* success) { - if (cols % 4 == 0) { + if (cols % 4 == 0 && CanPackAs(load, 4) && CanPackAs(store, 4)) { return TryDispatchLayerNormBlockSMemImplBlockSize( stream, load, store, rows, cols, epsilon, mean, inv_variance, success); - } else if (cols % 2 == 0) { + } else if (cols % 2 == 0 && CanPackAs(load, 2) && CanPackAs(store, 2)) { return TryDispatchLayerNormBlockSMemImplBlockSize( stream, load, store, rows, cols, epsilon, mean, inv_variance, success); } else { @@ -699,7 +810,7 @@ struct TryDispatchLayerNormBlockSMemImplPackSize { }; template -inline cudaError_t TryDispatchLayerNormBlockSMemImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) TryDispatchLayerNormBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance, bool* success) { @@ -708,9 +819,10 @@ inline cudaError_t TryDispatchLayerNormBlockSMemImpl(cudaStream_t stream, LOAD l } template -__global__ void LayerNormBlockUncachedImpl(LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, - ComputeType* mean, ComputeType* inv_variance) { +__global__ void __launch_bounds__(1024) + LayerNormBlockUncachedImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols, + const double epsilon, ComputeType* mean, ComputeType* inv_variance) { + using LoadType = typename LOAD::LoadType; const int tid = threadIdx.x; assert(cols % pack_size == 0); const int num_packs = static_cast(cols) / pack_size; @@ -719,11 +831,11 @@ __global__ void LayerNormBlockUncachedImpl(LOAD load, STORE store, const int64_t ComputeType thread_m2 = 0; ComputeType thread_count = 0; for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; + LoadType pack[pack_size]; load.template load(pack, row, pack_id * pack_size); #pragma unroll for (int i = 0; i < pack_size; ++i) { - WelfordCombine(pack[i], &thread_mean, &thread_m2, &thread_count); + WelfordCombine(static_cast(pack[i]), &thread_mean, &thread_m2, &thread_count); } } ComputeType row_mean = 0; @@ -738,18 +850,21 @@ __global__ void LayerNormBlockUncachedImpl(LOAD load, STORE store, const int64_t inv_variance[row] = row_inv_var; } for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; + LoadType pack[pack_size]; + ComputeType dst_pack[pack_size]; const int pack_offset = pack_id * pack_size; load.template load(pack, row, pack_offset); #pragma unroll - for (int i = 0; i < pack_size; ++i) { pack[i] = (pack[i] - row_mean) * row_inv_var; } - store.template store(pack, row, pack_offset); + for (int i = 0; i < pack_size; ++i) { + dst_pack[i] = (static_cast(pack[i]) - row_mean) * row_inv_var; + } + store.template store(dst_pack, row, pack_offset); } } } template -inline cudaError_t LaunchLayerNormBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) LaunchLayerNormBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { @@ -757,25 +872,25 @@ inline cudaError_t LaunchLayerNormBlockUncachedImpl(cudaStream_t stream, LOAD lo constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = + GPU(Error_t) err = GetNumBlocks(LayerNormBlockUncachedImpl, block_size, 0, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } LayerNormBlockUncachedImpl <<>>(load, store, rows, cols, epsilon, mean, inv_variance); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template struct DispatchLayerNormBlockUncachedImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols % 4 == 0) { + if (cols % 4 == 0 && CanPackAs(load, 4) && CanPackAs(store, 4)) { return LaunchLayerNormBlockUncachedImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else if (cols % 2 == 0) { + } else if (cols % 2 == 0 && CanPackAs(load, 2) && CanPackAs(store, 2)) { return LaunchLayerNormBlockUncachedImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance); } else { @@ -786,7 +901,7 @@ struct DispatchLayerNormBlockUncachedImplPackSize { }; template -inline cudaError_t DispatchLayerNormBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) DispatchLayerNormBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { @@ -795,8 +910,8 @@ inline cudaError_t DispatchLayerNormBlockUncachedImpl(cudaStream_t stream, LOAD } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLayerNorm(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLayerNorm(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { if (cols <= 1024) { @@ -805,22 +920,22 @@ DispatchLayerNorm(cudaStream_t stream, LOAD load, STORE store, const int64_t row } else { bool dispatch_smem_impl_success; { - cudaError_t err = TryDispatchLayerNormBlockSMemImpl( + GPU(Error_t) err = TryDispatchLayerNormBlockSMemImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchLayerNormBlockUncachedImpl( stream, load, store, rows, cols, epsilon, mean, inv_variance); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLayerNorm(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLayerNorm(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, const double epsilon, ComputeType* mean, ComputeType* inv_variance) { return DispatchLayerNormBlockUncachedImpl( @@ -836,18 +951,22 @@ dx = cols * dy - sum_stats1 - normalized * sum_stats2 dx *= inv_var / cols */ template + int pack_size, int max_cols_per_thread, int min_cols_per_thread, int thread_group_width, + int rows_per_access> __global__ void LayerNormGradWarpImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - static_assert(cols_per_thread % pack_size == 0, ""); - constexpr int pack_per_thread = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); + using LoadTypeX = typename LOAD_X::LoadType; + using LoadTypeDy = typename LOAD_SCALED_DY::LoadType; + static_assert(max_cols_per_thread % pack_size == 0, ""); + static_assert(min_cols_per_thread % pack_size == 0, ""); + constexpr int max_num_packs = max_cols_per_thread / pack_size; + constexpr int min_num_packs = min_cols_per_thread / pack_size; + assert(cols <= max_cols_per_thread * thread_group_width); static_assert(thread_group_width <= kWarpSize, ""); static_assert(kWarpSize % thread_group_width == 0, ""); - ComputeType normalized_buf[rows_per_access][cols_per_thread]; - ComputeType dy_buf[rows_per_access][cols_per_thread]; + ComputeType normalized_buf[rows_per_access][max_cols_per_thread]; + ComputeType dy_buf[rows_per_access][max_cols_per_thread]; const ComputeType one_over_cols = static_cast(1.0) / static_cast(cols); const int64_t global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; const int64_t num_global_thread_group = gridDim.x * blockDim.y; @@ -867,18 +986,40 @@ __global__ void LayerNormGradWarpImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_ ComputeType* row_normalized_buf = normalized_buf[row_id]; ComputeType* row_dy_buf = dy_buf[row_id]; #pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { + for (int pack_id = 0; pack_id < min_num_packs; ++pack_id) { const int col = (pack_id * thread_group_width + lane_id) * pack_size; const int pack_offset = pack_id * pack_size; - if (!padding || col < cols) { - load_x.template load(row_normalized_buf + pack_offset, global_row_id, col); - load_scaled_dy.template load(row_dy_buf + pack_offset, global_row_id, col); + LoadTypeX pack_x[pack_size]; + LoadTypeDy pack_dy[pack_size]; + load_x.template load(pack_x, global_row_id, col); + load_scaled_dy.template load(pack_dy, global_row_id, col); +#pragma unroll + for (int i = 0; i < pack_size; ++i) { + const int col_id = pack_offset + i; + // row_normalized_buf store x + row_normalized_buf[col_id] = + (static_cast(pack_x[i]) - mean_val) * inv_variance_buf[row_id]; + row_dy_buf[col_id] = static_cast(pack_dy[i]); + sum_stats1[row_id] += row_dy_buf[col_id]; + sum_stats2[row_id] += row_dy_buf[col_id] * row_normalized_buf[col_id]; + } + } +#pragma unroll + for (int pack_id = min_num_packs; pack_id < max_num_packs; ++pack_id) { + const int col = (pack_id * thread_group_width + lane_id) * pack_size; + const int pack_offset = pack_id * pack_size; + if (col < cols) { + LoadTypeX pack_x[pack_size]; + LoadTypeDy pack_dy[pack_size]; + load_x.template load(pack_x, global_row_id, col); + load_scaled_dy.template load(pack_dy, global_row_id, col); #pragma unroll for (int i = 0; i < pack_size; ++i) { const int col_id = pack_offset + i; // row_normalized_buf store x row_normalized_buf[col_id] = - (row_normalized_buf[col_id] - mean_val) * inv_variance_buf[row_id]; + (static_cast(pack_x[i]) - mean_val) * inv_variance_buf[row_id]; + row_dy_buf[col_id] = static_cast(pack_dy[i]); sum_stats1[row_id] += row_dy_buf[col_id]; sum_stats2[row_id] += row_dy_buf[col_id] * row_normalized_buf[col_id]; } @@ -901,16 +1042,29 @@ __global__ void LayerNormGradWarpImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_ ComputeType* row_dy_buf = dy_buf[row_id]; const ComputeType inv_variance_over_cols = inv_variance_buf[row_id] * one_over_cols; #pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { + for (int pack_id = 0; pack_id < min_num_packs; ++pack_id) { const int col = (pack_id * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { + const int pack_offset = pack_id * pack_size; + for (int i = 0; i < pack_size; ++i) { + const int col_id = pack_offset + i; + row_dy_buf[col_id] = (cols * row_dy_buf[col_id] - warp_sum_stats1[row_id] + - row_normalized_buf[col_id] * warp_sum_stats2[row_id]) + * inv_variance_over_cols; + } + store.template store(row_dy_buf + pack_offset, global_row_id, col); + } +#pragma unroll + for (int pack_id = min_num_packs; pack_id < max_num_packs; ++pack_id) { + const int col = (pack_id * thread_group_width + lane_id) * pack_size; + if (col < cols) { + const int pack_offset = pack_id * pack_size; for (int i = 0; i < pack_size; ++i) { - const int col_id = pack_id * pack_size + i; + const int col_id = pack_offset + i; row_dy_buf[col_id] = (cols * row_dy_buf[col_id] - warp_sum_stats1[row_id] - row_normalized_buf[col_id] * warp_sum_stats2[row_id]) * inv_variance_over_cols; } - store.template store(row_dy_buf + pack_id * pack_size, global_row_id, col); + store.template store(row_dy_buf + pack_offset, global_row_id, col); } } } @@ -918,9 +1072,9 @@ __global__ void LayerNormGradWarpImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_ } template -inline cudaError_t LaunchLayerNormGradWarpImpl(cudaStream_t stream, LOAD_X load_x, + int pack_size, int max_cols_per_thread, int min_cols_per_thread, int thread_group_width, + int rows_per_access> +inline GPU(Error_t) LaunchLayerNormGradWarpImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, @@ -934,143 +1088,100 @@ inline cudaError_t LaunchLayerNormGradWarpImpl(cudaStream_t stream, LOAD_X load_ (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; int grid_dim_x; { - cudaError_t err = GetNumBlocks( - LayerNormGradWarpImpl, - block_size, 0, num_blocks, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = + GetNumBlocks(LayerNormGradWarpImpl, + block_size, 0, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } - LayerNormGradWarpImpl + LayerNormGradWarpImpl <<>>(load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t DispatchLayerNormGradWarpImplPadding(cudaStream_t stream, LOAD_X load_x, + int pack_size, int max_cols_per_thread, int min_cols_per_thread, int thread_group_width, + int rows_per_access> +inline GPU(Error_t) DispatchLayerNormGradWarpImplPadding(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - if (cols == cols_per_thread * thread_group_width) { + if (cols == max_cols_per_thread * thread_group_width) { + // when not padding, min_cols_per_thread must equals to max_cols_per_thread, pass + // max_cols_per_thread as min_cols_per_thread and max_cols_per_thread param. return LaunchLayerNormGradWarpImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); + max_cols_per_thread, max_cols_per_thread, thread_group_width, + rows_per_access>(stream, load_x, load_scaled_dy, store, mean, + inv_variance, rows, cols); } else { return LaunchLayerNormGradWarpImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } -} - -template -typename std::enable_if::type DispatchLayerNormGradWarpImplCols( - cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } else { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return cudaErrorInvalidValue; + max_cols_per_thread, min_cols_per_thread, thread_group_width, + rows_per_access>(stream, load_x, load_scaled_dy, store, mean, + inv_variance, rows, cols); } } template -typename std::enable_if::type DispatchLayerNormGradWarpImplCols( - cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, +typename std::enable_if::type DispatchLayerNormGradWarpImplCols( + GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } else { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } \ + if (cols <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (cols <= (thread_group_width)*pack_size) { \ + if (rows % 2 == 0) { \ + return DispatchLayerNormGradWarpImplPadding( \ + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ + } else { \ + return DispatchLayerNormGradWarpImplPadding( \ + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ + } \ } DEFINE_ONE_ELIF(4) DEFINE_ONE_ELIF(8) DEFINE_ONE_ELIF(16) DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if (cols <= (max_col)*kWarpSize) { \ return DispatchLayerNormGradWarpImplPadding( \ + pack_size, max_col, min_col, kWarpSize, 1>( \ stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) + DEFINE_ONE_ELIF(2, 1) + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template struct DispatchLayerNormGradWarpImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - if (cols % 2 == 0) { - return DispatchLayerNormGradWarpImplCols( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } else { - return DispatchLayerNormGradWarpImplCols( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } + return DispatchLayerNormGradWarpImplCols( + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); } }; template -inline cudaError_t DispatchLayerNormGradWarpImpl(cudaStream_t stream, LOAD_X load_x, +inline GPU(Error_t) DispatchLayerNormGradWarpImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, @@ -1085,9 +1196,11 @@ __global__ void LayerNormGradBlockSMemImpl(LOAD_X load_x, LOAD_SCALED_DY load_sc STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { + using LoadTypeX = typename LOAD_X::LoadType; + using LoadTypeDy = typename LOAD_SCALED_DY::LoadType; extern __shared__ __align__(sizeof(double)) unsigned char grad_shared_buf[]; - auto* normalized_buf = reinterpret_cast(grad_shared_buf); - auto* dy_buf = normalized_buf + cols; + auto* normalized_buf = reinterpret_cast(grad_shared_buf); + auto* dy_buf = reinterpret_cast(normalized_buf + cols); const int tid = threadIdx.x; assert(cols % pack_size == 0); const int num_packs = static_cast(cols) / pack_size; @@ -1099,18 +1212,19 @@ __global__ void LayerNormGradBlockSMemImpl(LOAD_X load_x, LOAD_SCALED_DY load_sc const ComputeType inv_variance_val = inv_variance[row]; const ComputeType inv_variance_over_cols = inv_variance_val * one_over_cols; for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; + LoadTypeX x_pack[pack_size]; + LoadTypeDy dy_pack[pack_size]; load_x.template load(x_pack, row, pack_id * pack_size); load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); #pragma unroll for (int i = 0; i < pack_size; ++i) { const int buf_offset = i * num_packs + pack_id; - ComputeType normalized = (x_pack[i] - mean_val) * inv_variance_val; - normalized_buf[buf_offset] = normalized; + ComputeType normalized = + (static_cast(x_pack[i]) - mean_val) * inv_variance_val; + normalized_buf[buf_offset] = static_cast(normalized); dy_buf[buf_offset] = dy_pack[i]; - sum_stats1 += dy_pack[i]; - sum_stats2 += dy_pack[i] * normalized; + sum_stats1 += static_cast(dy_pack[i]); + sum_stats2 += static_cast(dy_pack[i]) * normalized; } } const ComputeType row_sum_stats1 = BlockAllReduce(sum_stats1); @@ -1120,8 +1234,8 @@ __global__ void LayerNormGradBlockSMemImpl(LOAD_X load_x, LOAD_SCALED_DY load_sc #pragma unroll for (int i = 0; i < pack_size; ++i) { const int buf_offset = i * num_packs + pack_id; - pack[i] = (cols * dy_buf[buf_offset] - row_sum_stats1 - - normalized_buf[buf_offset] * row_sum_stats2) + pack[i] = (cols * static_cast(dy_buf[buf_offset]) - row_sum_stats1 + - static_cast(normalized_buf[buf_offset]) * row_sum_stats2) * inv_variance_over_cols; } store.template store(pack, row, pack_id * pack_size); @@ -1131,7 +1245,7 @@ __global__ void LayerNormGradBlockSMemImpl(LOAD_X load_x, LOAD_SCALED_DY load_sc template -inline cudaError_t LaunchLayerNormGradBlockSMemImpl(cudaStream_t stream, LOAD_X load_x, +inline GPU(Error_t) LaunchLayerNormGradBlockSMemImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, int smem, @@ -1139,86 +1253,139 @@ inline cudaError_t LaunchLayerNormGradBlockSMemImpl(cudaStream_t stream, LOAD_X constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = GetNumBlocks(LayerNormGradBlockSMemImpl, block_size, smem, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } LayerNormGradBlockSMemImpl <<>>(load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t TryDispatchLayerNormGradBlockSMemImplBlockSize( - cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, +inline GPU(Error_t) TryDispatchLayerNormGradBlockSMemImplBlockSize( + GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols, bool* success) { constexpr int block_size_conf_1 = 128; constexpr int block_size_conf_2 = 256; constexpr int block_size_conf_3 = 512; constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType) * 2; + + int dev = 0; + { + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } + } + + int sm_count = 0; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } + } + + static const bool max_smem_configed = [=]() { + int max_smem_size = 0; + GPU(Error_t) err = + GPU(DeviceGetAttribute)(&max_smem_size, GPUMaxSharedMemoryPerBlockOptin, dev); + if (err != GPU(Success)) { return false; } + + err = MaximizeDynamicSharedMemorySize( + LayerNormGradBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormGradBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormGradBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + err = MaximizeDynamicSharedMemorySize( + LayerNormGradBlockSMemImpl, + max_smem_size); + if (err != GPU(Success)) { return false; } + + return true; + }(); + + using LoadTypeX = typename LOAD_X::LoadType; + using LoadTypeDy = typename LOAD_SCALED_DY::LoadType; + const size_t smem = cols * (sizeof(LoadTypeX) + sizeof(LoadTypeDy)); + int max_active_blocks_conf_1; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_1, LayerNormGradBlockSMemImpl, block_size_conf_1, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_1 <= 0) { *success = false; - return cudaSuccess; + return GPU(Success); } + int max_active_blocks_conf_4; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_4, LayerNormGradBlockSMemImpl, block_size_conf_4, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_4 == max_active_blocks_conf_1 + || (max_active_blocks_conf_4 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormGradBlockSMemImpl( stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); } + int max_active_blocks_conf_3; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_3, LayerNormGradBlockSMemImpl, block_size_conf_3, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_3 == max_active_blocks_conf_1 + || (max_active_blocks_conf_3 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormGradBlockSMemImpl( stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); } + int max_active_blocks_conf_2; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_2, LayerNormGradBlockSMemImpl, block_size_conf_2, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { + if (max_active_blocks_conf_2 == max_active_blocks_conf_1 + || (max_active_blocks_conf_2 > 0 && rows <= sm_count)) { *success = true; return LaunchLayerNormGradBlockSMemImpl( stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); } + *success = true; return LaunchLayerNormGradBlockSMemImpl(stream, load_x, load_scaled_dy, store, @@ -1227,10 +1394,11 @@ inline cudaError_t TryDispatchLayerNormGradBlockSMemImplBlockSize( template struct TryDispatchLayerNormGradBlockSMemImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols, bool* success) { - if (cols % 2 == 0) { + if (cols % 2 == 0 && CanPackAs(load_x, 2) + && CanPackAs(load_scaled_dy, 2) && CanPackAs(store, 2)) { return TryDispatchLayerNormGradBlockSMemImplBlockSize( stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, success); @@ -1243,7 +1411,7 @@ struct TryDispatchLayerNormGradBlockSMemImplPackSize { }; template -inline cudaError_t TryDispatchLayerNormGradBlockSMemImpl(cudaStream_t stream, LOAD_X load_x, +inline GPU(Error_t) TryDispatchLayerNormGradBlockSMemImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, @@ -1260,6 +1428,8 @@ __global__ void LayerNormGradBlockUncachedImpl(LOAD_X load_x, LOAD_SCALED_DY loa STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { + using LoadTypeX = typename LOAD_X::LoadType; + using LoadTypeDy = typename LOAD_SCALED_DY::LoadType; const int tid = threadIdx.x; assert(cols % pack_size == 0); const int num_packs = static_cast(cols) / pack_size; @@ -1271,75 +1441,134 @@ __global__ void LayerNormGradBlockUncachedImpl(LOAD_X load_x, LOAD_SCALED_DY loa ComputeType sum_stats1 = 0; ComputeType sum_stats2 = 0; for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); - + const int pack_offset = pack_id * pack_size; + LoadTypeX x_pack[pack_size]; + LoadTypeDy dy_pack[pack_size]; + load_x.template load(x_pack, row, pack_offset); + load_scaled_dy.template load(dy_pack, row, pack_offset); #pragma unroll for (int i = 0; i < pack_size; ++i) { - sum_stats1 += dy_pack[i]; - sum_stats2 += dy_pack[i] * (x_pack[i] - mean_val) * inv_variance_val; + sum_stats1 += static_cast(dy_pack[i]); + sum_stats2 += static_cast(dy_pack[i]) + * (static_cast(x_pack[i]) - mean_val) * inv_variance_val; } } const ComputeType row_sum_stats1 = BlockAllReduce(sum_stats1); const ComputeType row_sum_stats2 = BlockAllReduce(sum_stats2); for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); + const int pack_offset = pack_id * pack_size; + LoadTypeX x_pack[pack_size]; + LoadTypeDy dy_pack[pack_size]; + ComputeType dx_pack[pack_size]; + load_x.template load(x_pack, row, pack_offset); + load_scaled_dy.template load(dy_pack, row, pack_offset); #pragma unroll for (int i = 0; i < pack_size; ++i) { - dy_pack[i] = (cols * dy_pack[i] - row_sum_stats1 - - (x_pack[i] - mean_val) * inv_variance_val * row_sum_stats2) - * inv_variance_over_cols; + dx_pack[i] = + (cols * static_cast(dy_pack[i]) - row_sum_stats1 + - (static_cast(x_pack[i]) - mean_val) * inv_variance_val * row_sum_stats2) + * inv_variance_over_cols; } - store.template store(dy_pack, row, pack_id * pack_size); + store.template store(dx_pack, row, pack_offset); } } } template -inline cudaError_t LaunchLayerNormGradBlockUncachedImpl(cudaStream_t stream, LOAD_X load_x, + int pack_size, int block_size> +inline GPU(Error_t) LaunchLayerNormGradBlockUncachedImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - constexpr int block_size = 1024; constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = + GPU(Error_t) err = GetNumBlocks(LayerNormGradBlockUncachedImpl, block_size, 0, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } LayerNormGradBlockUncachedImpl <<>>(load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); +} + +template +inline GPU(Error_t) TryDispatchLaunchLayerNormGradBlockUncachedImplBlockSize( + GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, + const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, + const int64_t cols) { + int max_active_blocks = 0; + constexpr int block_size_conf_1 = 1024; + { + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( + &max_active_blocks, + LayerNormGradBlockUncachedImpl, + block_size_conf_1, 0); + if (max_active_blocks > 0) { + return LaunchLayerNormGradBlockUncachedImpl( + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); + } + } + constexpr int block_size_conf_2 = 512; + { + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( + &max_active_blocks, + LayerNormGradBlockUncachedImpl, + block_size_conf_2, 0); + if (max_active_blocks > 0) { + return LaunchLayerNormGradBlockUncachedImpl( + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); + } + } + constexpr int block_size_conf_3 = 256; + { + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( + &max_active_blocks, + LayerNormGradBlockUncachedImpl, + block_size_conf_2, 0); + if (max_active_blocks > 0) { + return LaunchLayerNormGradBlockUncachedImpl( + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); + } + } + constexpr int block_size_conf_4 = 128; + return LaunchLayerNormGradBlockUncachedImpl( + stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); } template struct DispatchLayerNormGradBlockUncachedImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { - if (cols % 2 == 0 && cols > kWarpSize) { - return LaunchLayerNormGradBlockUncachedImpl( + if (cols % 2 == 0 && CanPackAs(load_x, 2) + && CanPackAs(load_scaled_dy, 2) && CanPackAs(store, 2) + && cols > kWarpSize) { + return TryDispatchLaunchLayerNormGradBlockUncachedImplBlockSize( stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); } else { - return LaunchLayerNormGradBlockUncachedImpl( + return TryDispatchLaunchLayerNormGradBlockUncachedImplBlockSize( stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); } } }; template -inline cudaError_t DispatchLayerNormGradBlockUncachedImpl(cudaStream_t stream, LOAD_X load_x, +inline GPU(Error_t) DispatchLayerNormGradBlockUncachedImpl(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, @@ -1350,8 +1579,8 @@ inline cudaError_t DispatchLayerNormGradBlockUncachedImpl(cudaStream_t stream, L } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLayerNormGrad(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLayerNormGrad(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { if (cols <= 1024) { @@ -1360,23 +1589,23 @@ DispatchLayerNormGrad(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_sc } else { bool dispatch_smem_impl_success; { - cudaError_t err = + GPU(Error_t) err = TryDispatchLayerNormGradBlockSMemImpl( stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchLayerNormGradBlockUncachedImpl( stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLayerNormGrad(cudaStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLayerNormGrad(GPU(Stream_t) stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, const int64_t cols) { return DispatchLayerNormGradBlockUncachedImpl( diff --git a/oneflow/core/cuda/rms_norm.cuh b/oneflow/core/cuda/rms_norm.cuh new file mode 100644 index 0000000000000000000000000000000000000000..0e93d8407fd518c1b8b4ff040e5af68923a033c3 --- /dev/null +++ b/oneflow/core/cuda/rms_norm.cuh @@ -0,0 +1,1031 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_CUDA_RMS_NORM_H_ +#define ONEFLOW_CORE_CUDA_RMS_NORM_H_ + +#include "oneflow/core/cuda/layer_norm.cuh" + +namespace oneflow { +namespace cuda { +namespace rms_norm { + +#ifdef WITH_ROCM +constexpr int kWarpSize = 64; +#else +constexpr int kWarpSize = 32; +#endif + +template +__inline__ __device__ T WarpReduceSum(T val) { +#ifdef WITH_ROCM + for (int mask = 32; mask > 0; mask /= 2) { val += __shfl_down(val, mask); } +#else + for (int mask = 16; mask > 0; mask /= 2) { val += __shfl_down_sync(0xffffffff, val, mask); } +#endif + return val; +} + +template +__global__ void RmsNormWarpImpl(LOAD load, STORE store, const int nrow, const int ncol, + const double eps, ComputeType* inv_rms) { + static_assert(max_cols_per_thread % pack_size == 0, ""); + static_assert(min_cols_per_thread % pack_size == 0, ""); + static_assert(thread_group_width <= kWarpSize, ""); + static_assert(kWarpSize % thread_group_width == 0, ""); + constexpr int max_packs = max_cols_per_thread / pack_size; + constexpr int min_packs = min_cols_per_thread / pack_size; + assert(ncol <= max_cols_per_thread * thread_group_width); + + ComputeType buf[rows_per_access][max_cols_per_thread]; + const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; + const int num_global_thread_groups = gridDim.x * blockDim.y; + for (int row_i = global_thread_group_id; row_i < nrow; row_i += num_global_thread_groups) { + ComputeType thread_square_sum[rows_per_access]; +#pragma unroll + for (int row_j = 0; row_j < rows_per_access; ++row_j) { + thread_square_sum[row_j] = 0; + ComputeType* row_buf = buf[row_j]; + const int row = row_i * rows_per_access + row_j; +#pragma unroll + for (int pack_i = 0; pack_i < min_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + load.template load(row_buf + pack_offset, row, col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + thread_square_sum[row_j] += row_buf[pack_offset + pack_j] * row_buf[pack_offset + pack_j]; + } + } +#pragma unroll + for (int pack_i = min_packs; pack_i < max_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + if (!padding || col < ncol) { + load.template load(row_buf + pack_offset, row, col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + thread_square_sum[row_j] += + row_buf[pack_offset + pack_j] * row_buf[pack_offset + pack_j]; + } + } else { +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + row_buf[pack_i * pack_size + pack_j] = 0; + } + } + } + } + ComputeType warp_square_sum[rows_per_access]; +#pragma unroll + for (int row_j = 0; row_j < rows_per_access; ++row_j) { + const int row = row_i * rows_per_access + row_j; + ComputeType* row_buf = buf[row_j]; + warp_square_sum[row_j] = + layer_norm::WarpAllReduce( + thread_square_sum[row_j]); + ComputeType row_square_mean = + layer_norm::Div(warp_square_sum[row_j], static_cast(ncol)); + ComputeType row_inv_rms = layer_norm::Rsqrt(row_square_mean + static_cast(eps)); + if (threadIdx.x == 0) { inv_rms[row] = row_inv_rms; } +#pragma unroll + for (int col = 0; col < max_cols_per_thread; ++col) { row_buf[col] *= row_inv_rms; } +#pragma unroll + for (int pack_i = 0; pack_i < min_packs; ++pack_i) { + const int col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + store.template store(row_buf + pack_i * pack_size, row, col); + } +#pragma unroll + for (int pack_i = min_packs; pack_i < max_packs; ++pack_i) { + const int col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + if (!padding || col < ncol) { + store.template store(row_buf + pack_i * pack_size, row, col); + } + } + } + } +} + +template +GPU(Error_t) LaunchRmsNormWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t nrow, + const int64_t ncol, const double eps, ComputeType* inv_rms) { + constexpr int block_size = 128; + constexpr int waves = 32; + static_assert(block_size % thread_group_width == 0, ""); + constexpr int thread_groups_per_block = block_size / thread_group_width; + const int64_t num_blocks = + (nrow / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormWarpImpl, + block_size, 0, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + dim3 block_dim(thread_group_width, thread_groups_per_block); + RmsNormWarpImpl + <<>>(load, store, static_cast(nrow), + static_cast(ncol), eps, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) DispatchLaunchRmsNormWarpImplPadding(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + if (ncol == max_cols_per_thread * thread_group_width) { + // when not padding, min_cols_per_thread must equals to max_cols_per_thread, pass + // max_cols_per_thread as min_cols_per_thread and max_cols_per_thread param. + return LaunchRmsNormWarpImpl( + stream, load, store, nrow, ncol, eps, inv_rms); + } else { + return LaunchRmsNormWarpImpl( + stream, load, store, nrow, ncol, eps, inv_rms); + } +} + +template +typename std::enable_if::type DispatchLaunchRmsNormWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + if (ncol <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (ncol <= (thread_group_width)*pack_size) { \ + if (nrow % 2 == 0) { \ + return DispatchLaunchRmsNormWarpImplPadding( \ + stream, load, store, nrow, ncol, eps, inv_rms); \ + } else { \ + return DispatchLaunchRmsNormWarpImplPadding( \ + stream, load, store, nrow, ncol, eps, inv_rms); \ + } \ + } + DEFINE_ONE_ELIF(4) + DEFINE_ONE_ELIF(8) + DEFINE_ONE_ELIF(16) + DEFINE_ONE_ELIF(32) +#undef DEFINE_ONE_ELIF +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if (ncol <= (max_col)*kWarpSize) { \ + return DispatchLaunchRmsNormWarpImplPadding(stream, load, store, nrow, \ + ncol, eps, inv_rms); \ + } + DEFINE_ONE_ELIF(2, 1) + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) +#undef DEFINE_ONE_ELIF + else { + return GPU(ErrorInvalidValue); + } +} + +template +typename std::enable_if::type DispatchLaunchRmsNormWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + if (ncol <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (ncol <= (thread_group_width)*pack_size) { \ + if (nrow % 2 == 0) { \ + return DispatchLaunchRmsNormWarpImplPadding( \ + stream, load, store, nrow, ncol, eps, inv_rms); \ + } else { \ + return DispatchLaunchRmsNormWarpImplPadding( \ + stream, load, store, nrow, ncol, eps, inv_rms); \ + } \ + } + DEFINE_ONE_ELIF(4) + DEFINE_ONE_ELIF(8) + DEFINE_ONE_ELIF(16) + DEFINE_ONE_ELIF(32) +#undef DEFINE_ONE_ELIF +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if ((ncol <= (max_col)*kWarpSize) && (ncol > (min_col)*kWarpSize)) { \ + return DispatchLaunchRmsNormWarpImplPadding(stream, load, store, nrow, \ + ncol, eps, inv_rms); \ + } + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) +#undef DEFINE_ONE_ELIF + else { + return GPU(ErrorInvalidValue); + } +} + +template +GPU(Error_t) DispatchLaunchRmsNormWarpImplPackSize(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + if (ncol % 2 == 0 && layer_norm::CanPackAs(load, 2) + && layer_norm::CanPackAs(store, 2)) { + return DispatchLaunchRmsNormWarpImplCols(stream, load, store, nrow, + ncol, eps, inv_rms); + } else { + return DispatchLaunchRmsNormWarpImplCols(stream, load, store, nrow, + ncol, eps, inv_rms); + } +} + +template +GPU(Error_t) DispatchLaunchRmsNormWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, const double eps, + ComputeType* inv_rms) { + return DispatchLaunchRmsNormWarpImplPackSize(stream, load, store, nrow, ncol, eps, inv_rms); +} + +template +__global__ void RmsNormBlockSMemImpl(LOAD load, STORE store, const int nrow, const int ncol, + const double eps, ComputeType* inv_rms) { + extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[]; + auto* buf = reinterpret_cast(shared_buf); + assert(ncol % pack_size == 0); + const int num_packs = ncol / pack_size; + for (int row = blockIdx.x; row < nrow; row += gridDim.x) { + ComputeType thread_square_sum = 0; + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += block_size) { + ComputeType pack[pack_size]; + const int col = pack_i * pack_size; + load.template load(pack, row, col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + buf[pack_i * pack_size + pack_j] = pack[pack_j]; + thread_square_sum += pack[pack_j] * pack[pack_j]; + } + } + ComputeType row_square_sum = + layer_norm::BlockAllReduce(thread_square_sum); + ComputeType row_square_mean = layer_norm::Div(row_square_sum, static_cast(ncol)); + ComputeType row_inv_rms = layer_norm::Rsqrt(row_square_mean + static_cast(eps)); + if (threadIdx.x == 0) { inv_rms[row] = row_inv_rms; } + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += block_size) { + ComputeType pack[pack_size]; +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + pack[pack_j] = buf[pack_i * pack_size + pack_j] * row_inv_rms; + } + const int col = pack_i * pack_size; + store.template store(pack, row, col); + } + } +} + +template +GPU(Error_t) LaunchRmsNormBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, + size_t smem_size, const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + constexpr int waves = 32; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormBlockSMemImpl, block_size, + smem_size, nrow, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + RmsNormBlockSMemImpl + <<>>(load, store, nrow, ncol, eps, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) TryDispatchLaunchRmsNormBlockSMemImplBlockSize(GPU(Stream_t) stream, LOAD load, + STORE store, const int64_t nrow, + const int64_t ncol, const double eps, + ComputeType* inv_rms, bool* success) { + constexpr int block_size_conf_1 = 128; + constexpr int block_size_conf_2 = 256; + constexpr int block_size_conf_3 = 512; + constexpr int block_size_conf_4 = 1024; + const size_t smem_size = ncol * sizeof(ComputeType); + int max_active_blocks = 0; + int num_blocks = 0; + +#define SELECT_BLOCK_SIZE_CONF(block_size_conf) \ + { \ + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( \ + &num_blocks, RmsNormBlockSMemImpl, \ + block_size_conf, smem_size); \ + if (err != GPU(Success)) { return err; } \ + if (max_active_blocks == 0) { \ + if (num_blocks <= max_active_blocks) { \ + *success = false; \ + return GPU(Success); \ + } \ + max_active_blocks = num_blocks; \ + } else { \ + if (num_blocks == max_active_blocks) { \ + *success = true; \ + return LaunchRmsNormBlockSMemImpl( \ + stream, load, store, smem_size, nrow, ncol, eps, inv_rms); \ + } \ + } \ + } + + SELECT_BLOCK_SIZE_CONF(block_size_conf_1) + SELECT_BLOCK_SIZE_CONF(block_size_conf_4) + SELECT_BLOCK_SIZE_CONF(block_size_conf_3) + SELECT_BLOCK_SIZE_CONF(block_size_conf_2) +#undef SELECT_BLOCK_SIZE_CONF + + *success = true; + return LaunchRmsNormBlockSMemImpl( + stream, load, store, smem_size, nrow, ncol, eps, inv_rms); +} + +template +GPU(Error_t) TryDispatchLaunchRmsNormBlockSMemImplPackSize(GPU(Stream_t) stream, LOAD load, + STORE store, const int64_t nrow, + const int64_t ncol, const double eps, + ComputeType* inv_rms, bool* success) { + if (ncol % 4 == 0 && layer_norm::CanPackAs(load, 4) + && layer_norm::CanPackAs(store, 4)) { + return TryDispatchLaunchRmsNormBlockSMemImplBlockSize( + stream, load, store, nrow, ncol, eps, inv_rms, success); + } else if (ncol % 2 == 0 && layer_norm::CanPackAs(load, 2) + && layer_norm::CanPackAs(store, 2)) { + return TryDispatchLaunchRmsNormBlockSMemImplBlockSize( + stream, load, store, nrow, ncol, eps, inv_rms, success); + } else { + return TryDispatchLaunchRmsNormBlockSMemImplBlockSize( + stream, load, store, nrow, ncol, eps, inv_rms, success); + } +} + +template +GPU(Error_t) TryDispatchLaunchRmsNormBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms, + bool* success) { + return TryDispatchLaunchRmsNormBlockSMemImplPackSize(stream, load, store, nrow, ncol, eps, + inv_rms, success); +} + +template +__global__ void RmsNormBlockUncachedImpl(LOAD load, STORE store, const int nrow, const int ncol, + const double eps, ComputeType* inv_rms) { + assert(ncol % pack_size == 0); + const int num_packs = ncol / pack_size; + for (int row = blockIdx.x; row < nrow; row += gridDim.x) { + ComputeType thread_square_sum = 0; + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += block_size) { + ComputeType pack[pack_size]; + const int col = pack_i * pack_size; + load.template load(pack, row, col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + thread_square_sum += pack[pack_j] * pack[pack_j]; + } + } + ComputeType row_square_sum = + layer_norm::BlockAllReduce(thread_square_sum); + ComputeType row_square_mean = layer_norm::Div(row_square_sum, static_cast(ncol)); + ComputeType row_inv_rms = layer_norm::Rsqrt(row_square_mean + static_cast(eps)); + if (threadIdx.x == 0) { inv_rms[row] = row_inv_rms; } + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += block_size) { + ComputeType pack[pack_size]; + const int col = pack_i * pack_size; + load.template load(pack, row, col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + pack[pack_j] = pack[pack_j] * row_inv_rms; + } + store.template store(pack, row, col); + } + } +} + +template +GPU(Error_t) LaunchRmsNormBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, const double eps, + ComputeType* inv_rms) { + constexpr int block_size = 1024; + constexpr int waves = 32; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormBlockUncachedImpl, block_size, 0, + nrow, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + RmsNormBlockUncachedImpl + <<>>(load, store, nrow, ncol, eps, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) DispatchLaunchRmsNormBlockUncachedImplPackSize(GPU(Stream_t) stream, LOAD load, + STORE store, const int64_t nrow, + const int64_t ncol, const double eps, + ComputeType* inv_rms) { + if (ncol % 4 == 0 && layer_norm::CanPackAs(load, 4) + && layer_norm::CanPackAs(store, 4)) { + return LaunchRmsNormBlockUncachedImpl(stream, load, store, nrow, + ncol, eps, inv_rms); + } else if (ncol % 2 == 0 && layer_norm::CanPackAs(load, 2) + && layer_norm::CanPackAs(store, 2)) { + return LaunchRmsNormBlockUncachedImpl(stream, load, store, nrow, + ncol, eps, inv_rms); + } else { + return LaunchRmsNormBlockUncachedImpl(stream, load, store, nrow, + ncol, eps, inv_rms); + } +} + +template +GPU(Error_t) DispatchLaunchRmsNormBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, + const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + return DispatchLaunchRmsNormBlockUncachedImplPackSize(stream, load, store, nrow, ncol, eps, + inv_rms); +} + +template +typename std::enable_if::value, GPU(Error_t)>::type LaunchRmsNorm( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + if (ncol <= 1024) { + return DispatchLaunchRmsNormWarpImpl(stream, load, store, nrow, ncol, eps, inv_rms); + } else { + bool dispatch_smem_impl_success = false; + { + GPU(Error_t) err = TryDispatchLaunchRmsNormBlockSMemImpl(stream, load, store, nrow, ncol, eps, + inv_rms, &dispatch_smem_impl_success); + if (err != GPU(Success)) { return err; } + } + if (!dispatch_smem_impl_success) { + return DispatchLaunchRmsNormBlockUncachedImpl(stream, load, store, nrow, ncol, eps, inv_rms); + } + return GPU(Success); + } +} + +template +typename std::enable_if::value, GPU(Error_t)>::type LaunchRmsNorm( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t nrow, const int64_t ncol, + const double eps, ComputeType* inv_rms) { + return DispatchLaunchRmsNormBlockUncachedImpl(stream, load, store, nrow, ncol, eps, inv_rms); +} + +template +__global__ void RmsNormGradWarpImpl(const int nrow, const int ncol, LOAD_X load_x, LOAD_DY load_dy, + STORE store, const ComputeType* inv_rms) { + static_assert(max_cols_per_thread % pack_size == 0, ""); + static_assert(min_cols_per_thread % pack_size == 0, ""); + static_assert(thread_group_width <= kWarpSize, ""); + static_assert(kWarpSize % thread_group_width == 0, ""); + assert(ncol <= max_cols_per_thread * thread_group_width); + + constexpr int max_packs = max_cols_per_thread / pack_size; + constexpr int min_packs = min_cols_per_thread / pack_size; + + ComputeType normalized_buf[rows_per_access][max_cols_per_thread]; + ComputeType dy_buf[rows_per_access][max_cols_per_thread]; + + const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; + const int num_global_thread_group = gridDim.x * blockDim.y; + for (int row_i = global_thread_group_id; row_i < nrow; row_i += num_global_thread_group) { + ComputeType sum_stats[rows_per_access]; + ComputeType inv_rms_buf[rows_per_access]; +#pragma unroll + for (int row_j = 0; row_j < rows_per_access; ++row_j) { + const int global_row = row_i * rows_per_access + row_j; + sum_stats[row_j] = 0; + inv_rms_buf[row_j] = inv_rms[global_row]; + ComputeType* row_normalized_buf = normalized_buf[row_j]; + ComputeType* row_dy_buf = dy_buf[row_j]; +#pragma unroll + for (int pack_i = 0; pack_i < min_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int global_col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + load_x.template load(row_normalized_buf + pack_offset, global_row, global_col); + load_dy.template load(row_dy_buf + pack_offset, global_row, global_col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + row_normalized_buf[col] = row_normalized_buf[col] * inv_rms_buf[row_j]; + sum_stats[row_j] += row_dy_buf[col] * row_normalized_buf[col]; + } + } +#pragma unroll + for (int pack_i = min_packs; pack_i < max_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int global_col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + if (global_col < ncol) { + load_x.template load(row_normalized_buf + pack_offset, global_row, global_col); + load_dy.template load(row_dy_buf + pack_offset, global_row, global_col); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + row_normalized_buf[col] = row_normalized_buf[col] * inv_rms_buf[row_j]; + sum_stats[row_j] += row_dy_buf[col] * row_normalized_buf[col]; + } + } + } + } + ComputeType warp_sum_stats[rows_per_access]; +#pragma unroll + for (int row_j = 0; row_j < rows_per_access; ++row_j) { + warp_sum_stats[row_j] = + layer_norm::WarpAllReduce( + sum_stats[row_j]); + } +#pragma unroll + for (int row_j = 0; row_j < rows_per_access; ++row_j) { + const int global_row = row_i * rows_per_access + row_j; + ComputeType* row_normalized_buf = normalized_buf[row_j]; + ComputeType* row_dy_buf = dy_buf[row_j]; +#pragma unroll + for (int pack_i = 0; pack_i < min_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int global_col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + const ComputeType norm_val = + layer_norm::Div(row_normalized_buf[col], static_cast(ncol)); + row_dy_buf[col] = + (row_dy_buf[col] - norm_val * warp_sum_stats[row_j]) * inv_rms_buf[row_j]; + } + store.template store(row_dy_buf + pack_offset, global_row, global_col); + } +#pragma unroll + for (int pack_i = min_packs; pack_i < max_packs; ++pack_i) { + const int pack_offset = pack_i * pack_size; + const int global_col = (pack_i * thread_group_width + threadIdx.x) * pack_size; + if (global_col < ncol) { + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + const ComputeType norm_val = + layer_norm::Div(row_normalized_buf[col], static_cast(ncol)); + row_dy_buf[col] = + (row_dy_buf[col] - norm_val * warp_sum_stats[row_j]) * inv_rms_buf[row_j]; + } + store.template store(row_dy_buf + pack_offset, global_row, global_col); + } + } + } + } +} + +template +GPU(Error_t) LaunchRmsNormGradWarpImpl(GPU(Stream_t) stream, const int nrow, const int ncol, + LOAD_X load_x, LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + constexpr int block_size = 128; + constexpr int waves = 32; + static_assert(block_size % thread_group_width == 0, ""); + constexpr int thread_groups_per_block = block_size / thread_group_width; + const int64_t num_blocks = + (nrow / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormGradWarpImpl, + block_size, 0, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + dim3 block_dim(thread_group_width, thread_groups_per_block); + RmsNormGradWarpImpl + <<>>(nrow, ncol, load_x, load_dy, store, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +typename std::enable_if::type DispatchLaunchRmsNormGradWarpImplCols( + GPU(Stream_t) stream, const int64_t nrow, const int64_t ncol, LOAD_X load_x, LOAD_DY load_dy, + STORE store, const ComputeType* inv_rms) { + if (ncol <= 0) { return GPU(ErrorInvalidValue); } +#define DEFINE_ONE_ELIF(thread_group_width) \ + else if (ncol <= (thread_group_width)*pack_size) { \ + if (nrow % 2 == 0) { \ + return LaunchRmsNormGradWarpImpl(stream, nrow, ncol, load_x, \ + load_dy, store, inv_rms); \ + } else { \ + return LaunchRmsNormGradWarpImpl(stream, nrow, ncol, load_x, \ + load_dy, store, inv_rms); \ + } \ + } + DEFINE_ONE_ELIF(4) + DEFINE_ONE_ELIF(8) + DEFINE_ONE_ELIF(16) + DEFINE_ONE_ELIF(32) +#undef DEFINE_ONE_ELIF +#define DEFINE_ONE_ELIF(max_col, min_col) \ + else if (ncol <= (max_col)*kWarpSize) { \ + return LaunchRmsNormGradWarpImpl(stream, nrow, ncol, load_x, load_dy, \ + store, inv_rms); \ + } + DEFINE_ONE_ELIF(2, 1) + DEFINE_ONE_ELIF(4, 2) + DEFINE_ONE_ELIF(8, 4) + DEFINE_ONE_ELIF(12, 8) + DEFINE_ONE_ELIF(16, 12) + DEFINE_ONE_ELIF(20, 16) + DEFINE_ONE_ELIF(24, 20) + DEFINE_ONE_ELIF(28, 24) + DEFINE_ONE_ELIF(32, 28) +#undef DEFINE_ONE_ELIF + else { + return GPU(ErrorInvalidValue); + } +} + +template +GPU(Error_t) DispatchLaunchRmsNormGradWarpImplPackSize(GPU(Stream_t) stream, const int64_t nrow, + const int64_t ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + return DispatchLaunchRmsNormGradWarpImplCols( + stream, nrow, ncol, load_x, load_dy, store, inv_rms); +} + +template +__global__ void RmsNormGradBlockSMemImpl(const int nrow, const int ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, const ComputeType* inv_rms) { + extern __shared__ __align__(sizeof(double)) unsigned char dyn_smem[]; + // dynamic shared memory for caching x and dy + auto* normalized_buf = reinterpret_cast(dyn_smem); + auto* dy_buf = normalized_buf + ncol; + assert(ncol % pack_size == 0); + const int num_packs = ncol / pack_size; + for (int row = blockIdx.x; row < nrow; row += gridDim.x) { + ComputeType sum_stats = 0; + const ComputeType inv_rms_val = inv_rms[row]; + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += blockDim.x) { + ComputeType x_pack[pack_size]; + ComputeType dy_pack[pack_size]; + const int pack_offset = pack_i * pack_size; + load_x.template load(x_pack, row, pack_offset); + load_dy.template load(dy_pack, row, pack_offset); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + normalized_buf[col] = x_pack[pack_j] * inv_rms_val; + dy_buf[col] = dy_pack[pack_j]; + sum_stats += dy_buf[col] * normalized_buf[col]; + } + } + const ComputeType row_sum_stats = + layer_norm::BlockAllReduce(sum_stats); + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += blockDim.x) { + ComputeType pack[pack_size]; + const int pack_offset = pack_i * pack_size; +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const int col = pack_offset + pack_j; + const ComputeType norm_val = + layer_norm::Div(normalized_buf[col], static_cast(ncol)); + pack[pack_j] = (dy_buf[col] - norm_val * row_sum_stats) * inv_rms_val; + } + store.template store(pack, row, pack_offset); + } + } +} + +template +GPU(Error_t) LaunchRmsNormGradBlockSMemImpl(GPU(Stream_t) stream, const int64_t nrow, + const int64_t ncol, const size_t smem_size, + LOAD_X load_x, LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + constexpr int waves = 32; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormGradBlockSMemImpl, + block_size, smem_size, nrow, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + RmsNormGradBlockSMemImpl + <<>>( + static_cast(nrow), static_cast(ncol), load_x, load_dy, store, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) TryDispatchLaunchRmsNormGradBlockSMemImplBlockSize( + GPU(Stream_t) stream, const int64_t nrow, const int64_t ncol, LOAD_X load_x, LOAD_DY load_dy, + STORE store, const ComputeType* inv_rms, bool* success) { + constexpr int block_size_conf_1 = 128; + constexpr int block_size_conf_2 = 256; + constexpr int block_size_conf_3 = 512; + constexpr int block_size_conf_4 = 1024; + const size_t smem_size = ncol * sizeof(ComputeType) * 2; // ncol * 2 for caching x and dy both + int max_active_blocks = 0; + int num_blocks = 0; + +#define SELECT_BLOCK_SIZE_CONF(block_size_conf) \ + { \ + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( \ + &num_blocks, \ + RmsNormGradBlockSMemImpl, \ + block_size_conf, smem_size); \ + if (err != GPU(Success)) { return err; } \ + if (max_active_blocks == 0) { \ + if (num_blocks <= max_active_blocks) { \ + *success = false; \ + return GPU(Success); \ + } \ + max_active_blocks = num_blocks; \ + } else { \ + if (num_blocks == max_active_blocks) { \ + *success = true; \ + return LaunchRmsNormGradBlockSMemImpl(stream, nrow, ncol, smem_size, \ + load_x, load_dy, store, inv_rms); \ + } \ + } \ + } + + SELECT_BLOCK_SIZE_CONF(block_size_conf_1) + SELECT_BLOCK_SIZE_CONF(block_size_conf_4) + SELECT_BLOCK_SIZE_CONF(block_size_conf_3) + SELECT_BLOCK_SIZE_CONF(block_size_conf_2) +#undef SELECT_BLOCK_SIZE_CONF + + *success = true; + return LaunchRmsNormGradBlockSMemImpl(stream, nrow, ncol, smem_size, load_x, + load_dy, store, inv_rms); +} + +template +GPU(Error_t) TryDispatchLaunchRmsNormGradBlockSMemImplPackSize( + GPU(Stream_t) stream, const int64_t nrow, const int64_t ncol, LOAD_X load_x, LOAD_DY load_dy, + STORE store, const ComputeType* inv_rms, bool* success) { + if (ncol % 2 == 0 && layer_norm::CanPackAs(load_x, 2) + && layer_norm::CanPackAs(load_dy, 2) && layer_norm::CanPackAs(store, 2)) { + return TryDispatchLaunchRmsNormGradBlockSMemImplBlockSize(stream, nrow, ncol, load_x, + load_dy, store, inv_rms, success); + } else { + return TryDispatchLaunchRmsNormGradBlockSMemImplBlockSize(stream, nrow, ncol, load_x, + load_dy, store, inv_rms, success); + } +} + +template +__global__ void RmsNormGradBlockUncachedImpl(const int nrow, const int ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + assert(ncol % pack_size == 0); + const int num_packs = ncol / pack_size; + for (int row = blockIdx.x; row < nrow; row += gridDim.x) { + const ComputeType inv_rms_val = inv_rms[row]; + ComputeType sum_stats = 0; + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += blockDim.x) { + ComputeType x_pack[pack_size]; + ComputeType dy_pack[pack_size]; + const int pack_offset = pack_i * pack_size; + load_x.template load(x_pack, row, pack_offset); + load_dy.template load(dy_pack, row, pack_offset); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + sum_stats += dy_pack[pack_j] * x_pack[pack_j] * inv_rms_val; + } + } + const ComputeType row_sum_stats = + layer_norm::BlockAllReduce(sum_stats); + for (int pack_i = threadIdx.x; pack_i < num_packs; pack_i += blockDim.x) { + ComputeType x_pack[pack_size]; + ComputeType dy_pack[pack_size]; + const int pack_offset = pack_i * pack_size; + load_x.template load(x_pack, row, pack_offset); + load_dy.template load(dy_pack, row, pack_offset); +#pragma unroll + for (int pack_j = 0; pack_j < pack_size; ++pack_j) { + const ComputeType norm_val = + layer_norm::Div(x_pack[pack_j] * inv_rms_val, static_cast(ncol)); + dy_pack[pack_j] = (dy_pack[pack_j] - norm_val * row_sum_stats) * inv_rms_val; + } + store.template store(dy_pack, row, pack_offset); + } + } +} + +template +GPU(Error_t) LaunchRmsNormGradBlockUncachedImpl(GPU(Stream_t) stream, const int64_t nrow, + const int64_t ncol, LOAD_X load_x, LOAD_DY load_dy, + STORE store, const ComputeType* inv_rms) { + constexpr int waves = 32; + int grid_dim_x; + { + GPU(Error_t) err = layer_norm::GetNumBlocks( + RmsNormGradBlockUncachedImpl, + block_size, 0, nrow, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } + } + RmsNormGradBlockUncachedImpl + <<>>(nrow, ncol, load_x, load_dy, store, inv_rms); + return GPU(PeekAtLastError)(); +} + +template +GPU(Error_t) DispatchLaunchRmsNormGradBlockUncachedImplBlockSize(GPU(Stream_t) stream, + const int64_t nrow, + const int64_t ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + constexpr int block_size_conf_1 = 128; + constexpr int block_size_conf_2 = 256; + constexpr int block_size_conf_3 = 512; + constexpr int block_size_conf_4 = 1024; + int max_active_blocks = 0; + +#define SELECT_BLOCK_SIZE_CONF(block_size_conf) \ + { \ + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( \ + &max_active_blocks, \ + RmsNormGradBlockUncachedImpl, \ + block_size_conf, 0); \ + if (err != GPU(Success)) { return err; } \ + if (max_active_blocks > 0) { \ + return LaunchRmsNormGradBlockUncachedImpl(stream, nrow, ncol, load_x, \ + load_dy, store, inv_rms); \ + } \ + } + + SELECT_BLOCK_SIZE_CONF(block_size_conf_4) + SELECT_BLOCK_SIZE_CONF(block_size_conf_3) + SELECT_BLOCK_SIZE_CONF(block_size_conf_2) + SELECT_BLOCK_SIZE_CONF(block_size_conf_1) +#undef SELECT_BLOCK_SIZE_CONF + + return GPU(ErrorInvalidValue); +} + +template +GPU(Error_t) DispatchLaunchRmsNormGradBlockUncachedImplPackSize(GPU(Stream_t) stream, + const int64_t nrow, + const int64_t ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, + const ComputeType* inv_rms) { + if (ncol % 2 == 0 && layer_norm::CanPackAs(load_x, 2) + && layer_norm::CanPackAs(load_dy, 2) && layer_norm::CanPackAs(store, 2) + && ncol > kWarpSize) { + return DispatchLaunchRmsNormGradBlockUncachedImplBlockSize(stream, nrow, ncol, load_x, + load_dy, store, inv_rms); + } else { + return DispatchLaunchRmsNormGradBlockUncachedImplBlockSize(stream, nrow, ncol, load_x, + load_dy, store, inv_rms); + } +} + +template +typename std::enable_if::value, GPU(Error_t)>::type +LaunchRmsNormGrad(GPU(Stream_t) stream, const int64_t nrow, const int64_t ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, const ComputeType* inv_rms) { + if (ncol <= 1024) { + return DispatchLaunchRmsNormGradWarpImplPackSize(stream, nrow, ncol, load_x, load_dy, store, + inv_rms); + } else { + bool dispatch_smem_impl_success = false; + { + GPU(Error_t) err = TryDispatchLaunchRmsNormGradBlockSMemImplPackSize( + stream, nrow, ncol, load_x, load_dy, store, inv_rms, &dispatch_smem_impl_success); + if (err != GPU(Success)) { return err; } + } + if (!dispatch_smem_impl_success) { + return DispatchLaunchRmsNormGradBlockUncachedImplPackSize(stream, nrow, ncol, load_x, load_dy, + store, inv_rms); + } + return GPU(Success); + } +} + +template +typename std::enable_if::value, GPU(Error_t)>::type +LaunchRmsNormGrad(GPU(Stream_t) stream, const int64_t nrow, const int64_t ncol, LOAD_X load_x, + LOAD_DY load_dy, STORE store, const ComputeType* inv_rms) { + return DispatchLaunchRmsNormGradBlockUncachedImplPackSize(stream, nrow, ncol, load_x, load_dy, + store, inv_rms); +} + +template +__global__ void RmsNormParamGrad(int nrow, int ncol, const T* __restrict__ dy, + const T* __restrict__ x, const ComputeType* __restrict__ inv_rms, + T* __restrict__ b_weight_grad) { + __shared__ ComputeType dweight[kWarpSize][kWarpSize + 1]; + ComputeType dweight_sum[nproc_per_thread]; +#pragma unroll + for (int i = 0; i < nproc_per_thread; ++i) { dweight_sum[i] = 0; } + const int col = blockIdx.x * blockDim.x + threadIdx.x; + if (col < ncol) { + // a wave for one traverse (when nrow > warp_size * grad_dim_y) + for (int j = blockIdx.y * kWarpSize + threadIdx.y; j < nrow; j += kWarpSize * gridDim.y) { +#pragma unroll + for (int i = 0; i < nproc_per_thread; ++i) { + int row = j + i * blockDim.y; + if (row < nrow) { + int offset = row * ncol + col; + const ComputeType dy_val = static_cast(dy[offset]); + const ComputeType x_val = static_cast(x[offset]); + const ComputeType inv_rms_val = inv_rms[row]; + // collect dx from waves + dweight_sum[i] += dy_val * x_val * inv_rms_val; + } + } + } + } + // broadcast sum to the nproc_per_thread number rows + // each warp process the nproc_per_thread number rows of smem +#pragma unroll + for (int i = 0; i < nproc_per_thread; ++i) { + dweight[i * blockDim.y + threadIdx.y][threadIdx.x] = dweight_sum[i]; + } + __syncthreads(); + // transpose access for leveraging warp to reduce rows in a block +#pragma unroll + for (int i = 0; i < nproc_per_thread; ++i) { + // the first col of block threads is for storing the reduced sum of rows, + // and each first col thread is writing the nproc_per_thread number cols of output + const int row_in_block = threadIdx.y + i * blockDim.y; + const int col = blockIdx.x * blockDim.x + row_in_block; + if (col < ncol) { + // each warp process a col in which reduce sum all rows + ComputeType dweight_val = dweight[threadIdx.x][row_in_block]; + ComputeType global_dweight = WarpReduceSum(dweight_val); + if (threadIdx.x == 0) { + const int offset = blockIdx.y * ncol + col; + b_weight_grad[offset] = global_dweight; + } + } + } +} + +template +GPU(Error_t) GetGrid2Dim(const int64_t nrow, const int64_t ncol, int block_dim_x, int block_dim_y, + int* grid_dim_x, int* grid_dim_y) { + const int tile_size = block_dim_x; + if (nproc_per_thread * block_dim_y != tile_size) { return GPU(ErrorInvalidValue); } + *grid_dim_x = (ncol + tile_size - 1) / tile_size; + const int num_blocks_y = (nrow + tile_size - 1) / tile_size; + + using ComputeType = typename layer_norm::DefaultComputeType::type; + GPU(Error_t) err = layer_norm::GetNumBlocks(RmsNormParamGrad, + block_dim_x * block_dim_y, /*dynamic_smem_size*/ 0, + num_blocks_y, /*waves*/ 1, grid_dim_y); + if (err != GPU(Success)) { return err; } + return GPU(Success); +} + +} // namespace rms_norm +} // namespace cuda +} // namespace oneflow + +#endif // ONEFLOW_CORE_CUDA_RMS_NORM_H_ diff --git a/oneflow/core/cuda/softmax.cuh b/oneflow/core/cuda/softmax.cuh index 160daeb74052f51790044a8488550775767b7fff..6d10ffc81d366e640b0f9336c99603e5da3def51 100644 --- a/oneflow/core/cuda/softmax.cuh +++ b/oneflow/core/cuda/softmax.cuh @@ -17,10 +17,15 @@ limitations under the License. #ifndef ONEFLOW_CORE_CUDA_SOFTMAX_H_ #define ONEFLOW_CORE_CUDA_SOFTMAX_H_ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else +#include #include #include +#endif #include -#include #if CUDA_VERSION >= 11000 #include @@ -32,7 +37,11 @@ namespace cuda { namespace softmax { +#ifdef WITH_ROCM +constexpr int kWarpSize = 64; +#else constexpr int kWarpSize = 32; +#endif template struct SumOp { @@ -47,14 +56,22 @@ struct MaxOp { template class ReductionOp, typename T, int thread_group_width = kWarpSize> __inline__ __device__ T WarpAllReduce(T val) { for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { +#ifdef WITH_ROCM + val = ReductionOp()(val, __shfl_xor(val, mask)); +#else val = ReductionOp()(val, __shfl_xor_sync(0xffffffff, val, mask)); +#endif } return val; } template class ReductionOp, typename T, int block_size> __inline__ __device__ T BlockAllReduce(T val) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; __shared__ T result_broadcast; T result = BlockReduce(temp_storage).Reduce(val, ReductionOp()); @@ -68,12 +85,20 @@ __inline__ __device__ T Inf(); template<> __inline__ __device__ float Inf() { +#ifdef WITH_ROCM + return __int_as_float(0x7f800000U); +#else return CUDART_INF_F; +#endif } template<> __inline__ __device__ double Inf() { +#ifdef WITH_ROCM + return __longlong_as_double(0x7ff0000000000000ULL); +#else return CUDART_INF; +#endif } template @@ -126,26 +151,26 @@ __inline__ __device__ double Log(double x) { return log(x); } -inline cudaError_t GetNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, +inline GPU(Error_t) GetNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, int* num_blocks) { int dev; { - cudaError_t err = cudaGetDevice(&dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } } int sm_count; { - cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } } int tpm; { - cudaError_t err = cudaDeviceGetAttribute(&tpm, cudaDevAttrMaxThreadsPerMultiProcessor, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&tpm, GPUMaxThreadsPerMultiProcessor, dev); + if (err != GPU(Success)) { return err; } } *num_blocks = std::max(1, std::min(max_blocks, sm_count * tpm / block_size * waves)); - return cudaSuccess; + return GPU(Success); } template @@ -272,7 +297,7 @@ __global__ void SoftmaxWarpImpl(LOAD load, STORE store, const int64_t rows, cons row_buf[i] -= warp_max[row_id]; thread_sum[row_id] += Exp(row_buf[i]); } else { - __trap(); + TRAP(); } } } @@ -291,7 +316,7 @@ __global__ void SoftmaxWarpImpl(LOAD load, STORE store, const int64_t rows, cons } else if (algorithm == Algorithm::kLogSoftmax) { row_buf[i] -= Log(warp_sum[row_id]); } else { - __trap(); + TRAP(); } } #pragma unroll @@ -307,7 +332,7 @@ __global__ void SoftmaxWarpImpl(LOAD load, STORE store, const int64_t rows, cons template -inline cudaError_t LaunchSoftmaxWarpImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) LaunchSoftmaxWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { constexpr int block_size = 128; constexpr int waves = 32; @@ -318,18 +343,18 @@ inline cudaError_t LaunchSoftmaxWarpImpl(cudaStream_t stream, LOAD load, STORE s (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxWarpImpl <<>>(load, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t DispatchSoftmaxWarpImplPadding(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) DispatchSoftmaxWarpImplPadding(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { if (cols == cols_per_thread * thread_group_width) { return LaunchSoftmaxWarpImpl -typename std::enable_if::type DispatchSoftmaxWarpImplCols( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } +typename std::enable_if::type DispatchSoftmaxWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -403,14 +428,14 @@ typename std::enable_if::type DispatchSoftmaxWarpIm DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template -typename std::enable_if::type DispatchSoftmaxWarpImplCols( - cudaStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } +typename std::enable_if::type DispatchSoftmaxWarpImplCols( + GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -452,13 +477,13 @@ typename std::enable_if::type DispatchSoftmaxWarpIm DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template struct DispatchSoftmaxWarpImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { if (cols % 2 == 0) { return DispatchSoftmaxWarpImplCols(stream, load, @@ -471,7 +496,7 @@ struct DispatchSoftmaxWarpImplPackSize { }; template -inline cudaError_t DispatchSoftmaxWarpImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) DispatchSoftmaxWarpImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxWarpImplPackSize()(stream, load, store, rows, cols); @@ -520,7 +545,7 @@ __global__ void SoftmaxBlockSMemImpl(LOAD load, STORE store, const int64_t rows, } else if (algorithm == Algorithm::kLogSoftmax) { pack[i] = buf[i * num_packs + pack_id] - Log(row_sum); } else { - __trap(); + TRAP(); } } store.template store(pack, row, pack_id * pack_size); @@ -530,21 +555,21 @@ __global__ void SoftmaxBlockSMemImpl(LOAD load, STORE store, const int64_t rows, template -inline cudaError_t LaunchSoftmaxBlockSMemImpl(cudaStream_t stream, LOAD load, STORE store, int smem, +inline GPU(Error_t) LaunchSoftmaxBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, int smem, const int64_t rows, const int64_t cols) { constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxBlockSMemImpl <<>>(load, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream, LOAD load, +inline GPU(Error_t) TryDispatchSoftmaxBlockSMemImplBlockSize(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, bool* success) { constexpr int block_size_conf_1 = 128; @@ -554,23 +579,23 @@ inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream, const size_t smem = cols * sizeof(ComputeType); int max_active_blocks_conf_1; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_1, SoftmaxBlockSMemImpl, block_size_conf_1, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_1 <= 0) { *success = false; - return cudaSuccess; + return GPU(Success); } int max_active_blocks_conf_4; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_4, SoftmaxBlockSMemImpl, block_size_conf_4, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { *success = true; @@ -579,11 +604,11 @@ inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream, } int max_active_blocks_conf_3; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_3, SoftmaxBlockSMemImpl, block_size_conf_3, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { *success = true; @@ -592,11 +617,11 @@ inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream, } int max_active_blocks_conf_2; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_2, SoftmaxBlockSMemImpl, block_size_conf_2, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { *success = true; @@ -610,7 +635,7 @@ inline cudaError_t TryDispatchSoftmaxBlockSMemImplBlockSize(cudaStream_t stream, template struct TryDispatchSoftmaxBlockSMemImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, bool* success) { if (cols % 2 == 0) { return TryDispatchSoftmaxBlockSMemImplBlockSize( @@ -623,7 +648,7 @@ struct TryDispatchSoftmaxBlockSMemImplPackSize { }; template -inline cudaError_t TryDispatchSoftmaxBlockSMemImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) TryDispatchSoftmaxBlockSMemImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, bool* success) { return TryDispatchSoftmaxBlockSMemImplPackSize()( @@ -664,7 +689,7 @@ __global__ void SoftmaxBlockUncachedImpl(LOAD load, STORE store, const int64_t r } else if (algorithm == Algorithm::kLogSoftmax) { pack[i] = (pack[i] - row_max) - Log(row_sum); } else { - __trap(); + TRAP(); } } store.template store(pack, row, pack_id * pack_size); @@ -673,23 +698,23 @@ __global__ void SoftmaxBlockUncachedImpl(LOAD load, STORE store, const int64_t r } template -inline cudaError_t LaunchSoftmaxBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) LaunchSoftmaxBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { constexpr int block_size = 1024; constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxBlockUncachedImpl <<>>(load, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template struct DispatchSoftmaxBlockUncachedImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { if (cols % 2 == 0) { return LaunchSoftmaxBlockUncachedImpl( @@ -702,15 +727,15 @@ struct DispatchSoftmaxBlockUncachedImplPackSize { }; template -inline cudaError_t DispatchSoftmaxBlockUncachedImpl(cudaStream_t stream, LOAD load, STORE store, +inline GPU(Error_t) DispatchSoftmaxBlockUncachedImpl(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxBlockUncachedImplPackSize()( stream, load, store, rows, cols); } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchSoftmax(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { if (cols < 1024) { return DispatchSoftmaxWarpImpl( @@ -718,30 +743,30 @@ DispatchSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, } else { bool dispatch_smem_impl_success; { - cudaError_t err = + GPU(Error_t) err = TryDispatchSoftmaxBlockSMemImpl( stream, load, store, rows, cols, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchSoftmaxBlockUncachedImpl( stream, load, store, rows, cols); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchSoftmax(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxBlockUncachedImpl( stream, load, store, rows, cols); } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLogSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLogSoftmax(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { if (cols <= 1024) { return DispatchSoftmaxWarpImpl( @@ -749,22 +774,22 @@ DispatchLogSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t ro } else { bool dispatch_smem_impl_success; { - cudaError_t err = + GPU(Error_t) err = TryDispatchSoftmaxBlockSMemImpl( stream, load, store, rows, cols, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchSoftmaxBlockUncachedImpl( stream, load, store, rows, cols); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLogSoftmax(cudaStream_t stream, LOAD load, STORE store, const int64_t rows, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLogSoftmax(GPU(Stream_t) stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxBlockUncachedImpl( stream, load, store, rows, cols); @@ -807,7 +832,7 @@ __global__ void SoftmaxGradWarpImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, } else if (algorithm == Algorithm::kLogSoftmax) { thread_sum[row_id] += row_dy_buf[pack_offset + i]; } else { - __trap(); + TRAP(); } } } @@ -834,7 +859,7 @@ __global__ void SoftmaxGradWarpImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, } else if (algorithm == Algorithm::kLogSoftmax) { row_dy_buf[pack_offset + i] -= Exp(row_y_buf[pack_offset + i]) * warp_sum[row_id]; } else { - __trap(); + TRAP(); } } store.template store(row_dy_buf + pack_offset, row + row_id, col); @@ -847,7 +872,7 @@ __global__ void SoftmaxGradWarpImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, template -inline cudaError_t LaunchSoftmaxGradWarpImpl(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, +inline GPU(Error_t) LaunchSoftmaxGradWarpImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { constexpr int block_size = 128; constexpr int waves = 32; @@ -858,18 +883,18 @@ inline cudaError_t LaunchSoftmaxGradWarpImpl(cudaStream_t stream, LOAD_Y load_y, (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxGradWarpImpl <<>>(load_y, load_dy, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t DispatchSoftmaxGradWarpImplPadding(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) DispatchSoftmaxGradWarpImplPadding(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { if (cols == cols_per_thread * thread_group_width) { @@ -885,10 +910,10 @@ inline cudaError_t DispatchSoftmaxGradWarpImplPadding(cudaStream_t stream, LOAD_ template -typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( - cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, +typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( + GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -947,16 +972,16 @@ typename std::enable_if::type DispatchSoftmaxGradWa DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template -typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( - cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, +typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( + GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -999,14 +1024,14 @@ typename std::enable_if::type DispatchSoftmaxGradWa DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template struct DispatchSoftmaxGradWarpImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { if (cols % 2 == 0) { return DispatchSoftmaxGradWarpImplCols( @@ -1020,7 +1045,7 @@ struct DispatchSoftmaxGradWarpImplPackSize { template -inline cudaError_t DispatchSoftmaxGradWarpImpl(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, +inline GPU(Error_t) DispatchSoftmaxGradWarpImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxGradWarpImplPackSize()( @@ -1053,7 +1078,7 @@ __global__ void SoftmaxGradBlockSMemImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE s } else if (algorithm == Algorithm::kLogSoftmax) { thread_sum += dy_pack[i]; } else { - __trap(); + TRAP(); } } } @@ -1067,7 +1092,7 @@ __global__ void SoftmaxGradBlockSMemImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE s } else if (algorithm == Algorithm::kLogSoftmax) { pack[i] = dy_buf[i * num_packs + pack_id] - Exp(y_buf[i * num_packs + pack_id]) * row_sum; } else { - __trap(); + TRAP(); } } store.template store(pack, row, pack_id * pack_size); @@ -1077,23 +1102,23 @@ __global__ void SoftmaxGradBlockSMemImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE s template -inline cudaError_t LaunchSoftmaxGradBlockSMemImpl(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) LaunchSoftmaxGradBlockSMemImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, int smem, const int64_t rows, const int64_t cols) { constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxGradBlockSMemImpl <<>>(load_y, load_dy, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) TryDispatchSoftmaxGradBlockSMemImplBlockSize(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols, bool* success) { @@ -1104,25 +1129,25 @@ inline cudaError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(cudaStream_t str const size_t smem = cols * sizeof(ComputeType) * 2; int max_active_blocks_conf_1; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_1, SoftmaxGradBlockSMemImpl, block_size_conf_1, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_1 <= 0) { *success = false; - return cudaSuccess; + return GPU(Success); } int max_active_blocks_conf_4; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_4, SoftmaxGradBlockSMemImpl, block_size_conf_4, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { *success = true; @@ -1132,12 +1157,12 @@ inline cudaError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(cudaStream_t str } int max_active_blocks_conf_3; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_3, SoftmaxGradBlockSMemImpl, block_size_conf_3, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { *success = true; @@ -1147,12 +1172,12 @@ inline cudaError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(cudaStream_t str } int max_active_blocks_conf_2; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor( + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks_conf_2, SoftmaxGradBlockSMemImpl, block_size_conf_2, smem); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { *success = true; @@ -1169,7 +1194,7 @@ inline cudaError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(cudaStream_t str template struct TryDispatchSoftmaxGradBlockSMemImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols, bool* success) { if (cols % 2 == 0) { return TryDispatchSoftmaxGradBlockSMemImplBlockSize -inline cudaError_t TryDispatchSoftmaxGradBlockSMemImpl(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) TryDispatchSoftmaxGradBlockSMemImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols, bool* success) { @@ -1216,7 +1241,7 @@ __global__ void SoftmaxGradBlockUncachedImpl(LOAD_Y load_y, LOAD_DY load_dy, STO } else if (algorithm == Algorithm::kLogSoftmax) { thread_sum += dy_pack[i]; } else { - __trap(); + TRAP(); } } } @@ -1233,7 +1258,7 @@ __global__ void SoftmaxGradBlockUncachedImpl(LOAD_Y load_y, LOAD_DY load_dy, STO } else if (algorithm == Algorithm::kLogSoftmax) { dy_pack[i] -= Exp(y_pack[i]) * row_sum; } else { - __trap(); + TRAP(); } } store.template store(dy_pack, row, pack_id * pack_size); @@ -1243,26 +1268,26 @@ __global__ void SoftmaxGradBlockUncachedImpl(LOAD_Y load_y, LOAD_DY load_dy, STO template -inline cudaError_t LaunchSoftmaxGradBlockUncachedImpl(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) LaunchSoftmaxGradBlockUncachedImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { constexpr int block_size = 1024; constexpr int waves = 32; int grid_dim_x; { - cudaError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } } SoftmaxGradBlockUncachedImpl <<>>(load_y, load_dy, store, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template struct DispatchSoftmaxGradBlockUncachedImplPackSize { - cudaError_t operator()(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, + GPU(Error_t) operator()(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { if (cols % 2 == 0 && cols > kWarpSize) { return LaunchSoftmaxGradBlockUncachedImpl( @@ -1276,7 +1301,7 @@ struct DispatchSoftmaxGradBlockUncachedImplPackSize { template -inline cudaError_t DispatchSoftmaxGradBlockUncachedImpl(cudaStream_t stream, LOAD_Y load_y, +inline GPU(Error_t) DispatchSoftmaxGradBlockUncachedImpl(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxGradBlockUncachedImplPackSize -inline typename std::enable_if::value, cudaError_t>::type -DispatchSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchSoftmaxGrad(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { if (cols <= 1024) { return DispatchSoftmaxGradWarpImpl( @@ -1294,23 +1319,23 @@ DispatchSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE s } else { bool dispatch_smem_impl_success; { - cudaError_t err = TryDispatchSoftmaxGradBlockSMemImpl( stream, load_y, load_dy, store, rows, cols, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, store, rows, cols); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchSoftmaxGrad(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, store, @@ -1318,8 +1343,8 @@ DispatchSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE s } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLogSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLogSoftmaxGrad(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { if (cols <= 1024) { return DispatchSoftmaxGradWarpImpl( @@ -1327,23 +1352,23 @@ DispatchLogSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STOR } else { bool dispatch_smem_impl_success; { - cudaError_t err = TryDispatchSoftmaxGradBlockSMemImpl( stream, load_y, load_dy, store, rows, cols, &dispatch_smem_impl_success); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (!dispatch_smem_impl_success) { return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, store, rows, cols); } - return cudaSuccess; + return GPU(Success); } } template -inline typename std::enable_if::value, cudaError_t>::type -DispatchLogSoftmaxGrad(cudaStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, +inline typename std::enable_if::value, GPU(Error_t)>::type +DispatchLogSoftmaxGrad(GPU(Stream_t) stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, const int64_t cols) { return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, diff --git a/oneflow/core/cuda/unique.cuh b/oneflow/core/cuda/unique.cuh index 2a49a5885570b43a1fff6a37c6973a720ef9397d..e3b01422bded4e95f44d5ec970f43ae94ca2342e 100644 --- a/oneflow/core/cuda/unique.cuh +++ b/oneflow/core/cuda/unique.cuh @@ -16,8 +16,14 @@ limitations under the License. #ifndef ONEFLOW_CORE_CUDA_UNIQUE_H_ #define ONEFLOW_CORE_CUDA_UNIQUE_H_ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include #include +#endif + #include "oneflow/core/common/permutation_iterator.h" #include "oneflow/core/common/not_equal_to_previous_adjacent_iterator.h" @@ -49,82 +55,98 @@ __device__ __host__ __forceinline__ T* PtrOffset(void* ptr, size_t offset) { __device__ __host__ __forceinline__ size_t max(size_t a, size_t b) { return a > b ? a : b; } template -cudaError_t DoUnique(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, - void* workspace, size_t* workspace_size, cudaStream_t stream) { +GPU(Error_t) DoUnique(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, + void* workspace, size_t* workspace_size, GPU(Stream_t) stream) { size_t ws = *workspace_size; - cudaError_t err = cub::DeviceSelect::Unique( +#ifdef WITH_ROCM + GPU(Error_t) err = hipcub::DeviceSelect::Unique( + workspace, ws, sorted_in, unique, num_unique, n, stream); +#else + GPU(Error_t) err = cub::DeviceSelect::Unique( workspace, ws, sorted_in, unique, num_unique, n, stream); - if (err != cudaSuccess) { return err; } +#endif + if (err != GPU(Success)) { return err; } if (*workspace_size == 0) { *workspace_size = ws; } - return cudaSuccess; + return GPU(Success); } template -cudaError_t DoUniqueWithCounts(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, +GPU(Error_t) DoUniqueWithCounts(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, Index* counts, void* workspace, size_t* workspace_size, - cudaStream_t stream) { + GPU(Stream_t) stream) { size_t ws = *workspace_size; - cudaError_t err = cub::DeviceRunLengthEncode::Encode( +#ifdef WITH_ROCM + GPU(Error_t) err = hipcub::DeviceRunLengthEncode::Encode( + workspace, ws, sorted_in, unique, counts, num_unique, n, stream); +#else + GPU(Error_t) err = cub::DeviceRunLengthEncode::Encode( workspace, ws, sorted_in, unique, counts, num_unique, n, stream); - if (err != cudaSuccess) { return err; } +#endif + if (err != GPU(Success)) { return err; } if (*workspace_size == 0) { *workspace_size = ws; } - return cudaSuccess; + return GPU(Success); } template -cudaError_t DispatchOutputCounts(Flag flag, size_t n, const Key* sorted_in, Key* unique, +GPU(Error_t) DispatchOutputCounts(Flag flag, size_t n, const Key* sorted_in, Key* unique, Index* num_unique, Index* counts, void* workspace, - size_t* workspace_size, cudaStream_t stream) { + size_t* workspace_size, GPU(Stream_t) stream) { size_t ws = *workspace_size; if ((flag & kOutputCounts) != 0) { - cudaError_t err = DoUniqueWithCounts(n, sorted_in, unique, num_unique, counts, + GPU(Error_t) err = DoUniqueWithCounts(n, sorted_in, unique, num_unique, counts, workspace, &ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } else { - cudaError_t err = + GPU(Error_t) err = DoUnique(n, sorted_in, unique, num_unique, workspace, &ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (*workspace_size == 0) { *workspace_size = ws; } - return cudaSuccess; + return GPU(Success); } template -cudaError_t DoGenInverseIndices(size_t n, const Key* sorted_in, +GPU(Error_t) DoGenInverseIndices(size_t n, const Key* sorted_in, InverseIndicesIter inverse_indices_iter, void* workspace, - size_t* workspace_size, cudaStream_t stream) { + size_t* workspace_size, GPU(Stream_t) stream) { size_t ws = *workspace_size; NotEqualToPreviousAdjacentIterator unique_counting_iter(sorted_in, 0); - cudaError_t err = +#ifdef WITH_ROCM + GPU(Error_t) err = + hipcub::DeviceScan::InclusiveSum( + workspace, ws, unique_counting_iter, inverse_indices_iter, n, stream); +#else + GPU(Error_t) err = cub::DeviceScan::InclusiveSum( workspace, ws, unique_counting_iter, inverse_indices_iter, n, stream); - if (err != cudaSuccess) { return err; } +#endif + if (err != GPU(Success)) { return err; } if (*workspace_size == 0) { *workspace_size = ws; } - return cudaSuccess; + return GPU(Success); } template -cudaError_t DispatchOutputInverseIndices(Flag flag, size_t n, const Key* sorted_in, Key* unique, +GPU(Error_t) DispatchOutputInverseIndices(Flag flag, size_t n, const Key* sorted_in, Key* unique, Index* num_unique, InverseIndicesIter inverse_indices_iter, Index* counts, void* workspace, size_t* workspace_size, - cudaStream_t stream) { + GPU(Stream_t) stream) { size_t dispatch_with_counts_ws = *workspace_size; size_t do_gen_inverse_indices_ws = *workspace_size; { - cudaError_t err = + GPU(Error_t) err = DispatchOutputCounts(flag, n, sorted_in, unique, num_unique, counts, workspace, &dispatch_with_counts_ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if ((flag & kOutputInverseIndices) != 0) { - cudaError_t err = DoGenInverseIndices( + GPU(Error_t) err = DoGenInverseIndices( n, sorted_in, inverse_indices_iter, workspace, &do_gen_inverse_indices_ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (*workspace_size == 0) { *workspace_size = max(dispatch_with_counts_ws, do_gen_inverse_indices_ws); } - return cudaSuccess; + return GPU(Success); } template @@ -136,8 +158,8 @@ __global__ void IotaKernel(size_t n, T* out) { } template -cudaError_t DoSort(size_t n, const Key* in, Key* sorted, Index* sorted_indices, void* workspace, - size_t* workspace_size, cudaStream_t stream) { +GPU(Error_t) DoSort(size_t n, const Key* in, Key* sorted, Index* sorted_indices, void* workspace, + size_t* workspace_size, GPU(Stream_t) stream) { Index* indices; const size_t indices_size = GetCudaAlignedSize(n * sizeof(Index)); void* sort_workspace; @@ -147,7 +169,7 @@ cudaError_t DoSort(size_t n, const Key* in, Key* sorted, Index* sorted_indices, sort_workspace = nullptr; sort_ws = 0; } else { - if (*workspace_size <= indices_size) { return cudaErrorInvalidValue; } + if (*workspace_size <= indices_size) { return GPU(ErrorInvalidValue); } indices = PtrOffset(workspace, 0); sort_workspace = PtrOffset(workspace, indices_size); sort_ws = *workspace_size - indices_size; @@ -157,17 +179,22 @@ cudaError_t DoSort(size_t n, const Key* in, Key* sorted, Index* sorted_indices, const int num_blocks = static_cast((n + block_size - 1) / block_size); IotaKernel<<>>(n, indices); } - cudaError_t err = cub::DeviceRadixSort::SortPairs( +#ifdef WITH_ROCM + GPU(Error_t) err = hipcub::DeviceRadixSort::SortPairs( + sort_workspace, sort_ws, in, sorted, indices, sorted_indices, n, 0, sizeof(Key) * 8, stream); +#else + GPU(Error_t) err = cub::DeviceRadixSort::SortPairs( sort_workspace, sort_ws, in, sorted, indices, sorted_indices, n, 0, sizeof(Key) * 8, stream); - if (err != cudaSuccess) { return err; } +#endif + if (err != GPU(Success)) { return err; } if (*workspace_size == 0) { *workspace_size = indices_size + sort_ws; } - return cudaSuccess; + return GPU(Success); } template -cudaError_t DispatchInputSorted(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, +GPU(Error_t) DispatchInputSorted(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, Index* inverse_indices, Index* counts, void* workspace, - size_t* workspace_size, cudaStream_t stream) { + size_t* workspace_size, GPU(Stream_t) stream) { if ((flag & kInputSorted) != 0) { return DispatchOutputInverseIndices(flag, n, in, unique, num_unique, inverse_indices, counts, workspace, @@ -190,7 +217,7 @@ cudaError_t DispatchInputSorted(Flag flag, size_t n, const Key* in, Key* unique, do_inverse_indices_ws = 0; do_inverse_indices_workspace = nullptr; } else { - if (*workspace_size <= sort_buffer_size) { return cudaErrorInvalidValue; } + if (*workspace_size <= sort_buffer_size) { return GPU(ErrorInvalidValue); } sorted_in = PtrOffset(workspace, 0); sorted_indices = PtrOffset(workspace, sorted_in_size); do_sort_ws = *workspace_size - sort_buffer_size; @@ -199,38 +226,38 @@ cudaError_t DispatchInputSorted(Flag flag, size_t n, const Key* in, Key* unique, do_inverse_indices_workspace = do_sort_workspace; } { - cudaError_t err = DoSort(n, in, sorted_in, sorted_indices, do_sort_workspace, + GPU(Error_t) err = DoSort(n, in, sorted_in, sorted_indices, do_sort_workspace, &do_sort_ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } PermutationIterator inverse_indices_iter(inverse_indices, sorted_indices); { - cudaError_t err = DispatchOutputInverseIndices( + GPU(Error_t) err = DispatchOutputInverseIndices( flag, n, sorted_in, unique, num_unique, inverse_indices_iter, counts, do_inverse_indices_workspace, &do_inverse_indices_ws, stream); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } if (*workspace_size == 0) { *workspace_size = sort_buffer_size + max(do_sort_ws, do_inverse_indices_ws); } - return cudaSuccess; + return GPU(Success); } } } // namespace template -cudaError_t Launch(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, +GPU(Error_t) Launch(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, Index* inverse_indices, Index* counts, void* workspace, size_t workspace_size, - cudaStream_t stream) { - if (workspace_size == 0) { return cudaErrorInvalidValue; } + GPU(Stream_t) stream) { + if (workspace_size == 0) { return GPU(ErrorInvalidValue); } return DispatchInputSorted(flag, n, in, unique, num_unique, inverse_indices, counts, workspace, &workspace_size, stream); } template -cudaError_t GetWorkspaceSize(Flag flag, size_t n, size_t* workspace_size) { +GPU(Error_t) GetWorkspaceSize(Flag flag, size_t n, size_t* workspace_size) { *workspace_size = 0; return DispatchInputSorted(flag, n, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, workspace_size, 0); diff --git a/oneflow/core/device/cuda_util.cpp b/oneflow/core/device/cuda_util.cpp index 0f0b1f33cba68b08edcb9bb68d2927e4a3fb5b7e..414f87eb7be3a4ba067f370624932dbadce073ad 100644 --- a/oneflow/core/device/cuda_util.cpp +++ b/oneflow/core/device/cuda_util.cpp @@ -23,11 +23,7 @@ limitations under the License. #include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/platform/include/pthread_fork.h" #include "oneflow/core/device/device_context.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif #include "oneflow/core/vm/vm_util.h" #ifdef WITH_CUDA @@ -193,6 +189,10 @@ Maybe GetCUDAMemoryUsed() { int deviceCount = 0; cudaError_t error_id = cudaGetDeviceCount(&deviceCount); + if (error_id != cudaSuccess) { + return Error::RuntimeError() << "Error: GetCUDAMemoryUsed fails :" + << cudaGetErrorString(error_id); + } CHECK_OR_RETURN(deviceCount > 0) << "GPU device does not exist"; @@ -209,6 +209,26 @@ Maybe GetCUDAMemoryUsed() { return (total_memory - free_memory); } +static std::once_flag prop_init_flag; +static std::vector device_props; + +void InitDevicePropVectorSize() { + int device_count = GetCudaDeviceCount(); + device_props.resize(device_count); +} + +void InitDeviceProperties(int device_id) { + std::call_once(prop_init_flag, InitDevicePropVectorSize); + cudaDeviceProp prop{}; + OF_CUDA_CHECK(cudaGetDeviceProperties(&prop, device_id)); + device_props[device_id] = prop; +} + +cudaDeviceProp* GetDeviceProperties(int device_id) { + InitCudaContextOnce(device_id); + return &device_props[device_id]; +} + void InitCudaContextOnce(int device_id) { static int device_count = GetCudaDeviceCount(); static std::vector init_flags = std::vector(device_count); @@ -217,6 +237,7 @@ void InitCudaContextOnce(int device_id) { std::call_once(init_flags[device_id], [&]() { OF_CUDA_CHECK(cudaSetDevice(device_id)); OF_CUDA_CHECK(cudaDeviceSynchronize()); + InitDeviceProperties(device_id); }); } @@ -361,6 +382,10 @@ Maybe GetCUDAMemoryUsed() { int deviceCount = 0; hipError_t error_id = hipGetDeviceCount(&deviceCount); + if (error_id != hipSuccess) { + return Error::RuntimeError() << "Error: GetCUDAMemoryUsed fails :" + << hipGetErrorString(error_id); + } CHECK_OR_RETURN(deviceCount > 0) << "GPU device does not exist"; @@ -377,6 +402,26 @@ Maybe GetCUDAMemoryUsed() { return (total_memory - free_memory); } +static std::once_flag prop_init_flag; +static std::vector device_props; + +void InitDevicePropVectorSize() { + int device_count = GetCudaDeviceCount(); + device_props.resize(device_count); +} + +void InitDeviceProperties(int device_id) { + std::call_once(prop_init_flag, InitDevicePropVectorSize); + hipDeviceProp_t prop{}; + OF_CUDA_CHECK(hipGetDeviceProperties(&prop, device_id)); + device_props[device_id] = prop; +} + +hipDeviceProp_t* GetDeviceProperties(int device_id) { + InitCudaContextOnce(device_id); + return &device_props[device_id]; +} + void InitCudaContextOnce(int device_id) { static int device_count = GetCudaDeviceCount(); static std::vector init_flags = std::vector(device_count); @@ -385,11 +430,10 @@ void InitCudaContextOnce(int device_id) { std::call_once(init_flags[device_id], [&]() { OF_CUDA_CHECK(hipSetDevice(device_id)); OF_CUDA_CHECK(hipDeviceSynchronize()); + InitDeviceProperties(device_id); }); } - - #endif // WITH_ROCM } // namespace oneflow diff --git a/oneflow/core/device/cuda_util.cu b/oneflow/core/device/cuda_util.cu deleted file mode 100644 index 565a234d9a2f37eed5494dadec962f6f0b4a5539..0000000000000000000000000000000000000000 --- a/oneflow/core/device/cuda_util.cu +++ /dev/null @@ -1,34 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -int GetCudaSmVersion() { - int sm_version, device_ordinal; - OF_CUDA_CHECK(cudaGetDevice(&device_ordinal)); - OF_CUDA_CHECK(cub::SmVersion(sm_version, device_ordinal)); - return sm_version; -} - -int GetCudaPtxVersion() { - int ptx_version; - OF_CUDA_CHECK(cub::PtxVersion(ptx_version)); - return ptx_version; -} - -} // namespace oneflow diff --git a/oneflow/core/device/cuda_util.h b/oneflow/core/device/cuda_util.h index bde2ffeb22aad041af5f17929b0e13ec4c7ed2e1..20060d4bc4d0c1f23d35fe826b25c7408f9b8802 100644 --- a/oneflow/core/device/cuda_util.h +++ b/oneflow/core/device/cuda_util.h @@ -30,6 +30,9 @@ limitations under the License. #include #include #include +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 #include "oneflow/core/device/cuda_pseudo_half.h" #include "oneflow/core/ep/cuda/cuda_stream.h" @@ -82,7 +85,10 @@ const char* NvjpegGetErrorString(nvjpegStatus_t error); #define OF_NCCL_CHECK_OR_RETURN(condition) \ for (ncclResult_t _of_nccl_check_status = (condition); _of_nccl_check_status != ncclSuccess;) \ - return Error::CheckFailedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) \ + return Error::CheckFailedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ << "Check failed: " #condition " : " << ncclGetErrorString(_of_nccl_check_status) << " (" \ << _of_nccl_check_status << ") " @@ -152,16 +158,14 @@ class CublasMathModeGuard final { cublasMath_t new_mode_{}; }; -int GetCudaSmVersion(); - -int GetCudaPtxVersion(); - int GetCudaDeviceIndex(); int GetCudaDeviceCount(); Maybe GetCUDAMemoryUsed(); +cudaDeviceProp* GetDeviceProperties(int device_id); + void SetCudaDeviceIndex(int device_id); void CudaSynchronize(int device_id); @@ -184,7 +188,11 @@ cudaError_t CudaDriverGetPrimaryCtxActive(int dev, int* active); #include #include #include "oneflow/core/device/cuda_pseudo_half.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +// #if CUDA_VERSION >= 11000 +// #include +// #endif // CUDA_VERSION >= 11000 namespace oneflow { @@ -223,7 +231,10 @@ const char* CurandGetErrorString(hiprandStatus_t error); #define OF_NCCL_CHECK_OR_RETURN(condition) \ for (ncclResult_t _of_nccl_check_status = (condition); _of_nccl_check_status != ncclSuccess;) \ - return Error::CheckFailedError().AddStackFrame(__FILE__, __LINE__, __FUNCTION__) \ + return Error::CheckFailedError().AddStackFrame([](const char* function) { \ + thread_local static auto frame = SymbolOf(ErrorStackFrame(__FILE__, __LINE__, function)); \ + return frame; \ + }(__FUNCTION__)) \ << "Check failed: " #condition " : " << ncclGetErrorString(_of_nccl_check_status) << " (" \ << _of_nccl_check_status << ") " @@ -275,6 +286,8 @@ int GetCudaDeviceCount(); Maybe GetCUDAMemoryUsed(); +hipDeviceProp_t* GetDeviceProperties(int device_id); + void SetCudaDeviceIndex(int device_id); void CudaSynchronize(int device_id); diff --git a/oneflow/core/device/cudnn_conv_util.cpp b/oneflow/core/device/cudnn_conv_util.cpp index 157042bf0816fd5b47ea6d71fd7942fa7ef93b94..d3e329c0fe0b74f193f4dca840e4d46df976c03f 100644 --- a/oneflow/core/device/cudnn_conv_util.cpp +++ b/oneflow/core/device/cudnn_conv_util.cpp @@ -341,7 +341,10 @@ ManagedCudnnConvResource::ManagedCudnnConvResource(const CudnnConvArgs& args) } ManagedCudnnConvResource::~ManagedCudnnConvResource() { - if (handle_ != nullptr) { OF_CUDNN_CHECK(cudnnDestroy(handle_)); } + if (handle_ != nullptr) { + Singleton::Get()->Put(handle_); + handle_ = nullptr; + } if (x_dptr_ != nullptr) { OF_CUDA_CHECK(cudaFree(x_dptr_)); } if (w_dptr_ != nullptr) { OF_CUDA_CHECK(cudaFree(w_dptr_)); } if (y_dptr_ != nullptr) { OF_CUDA_CHECK(cudaFree(y_dptr_)); } @@ -349,7 +352,7 @@ ManagedCudnnConvResource::~ManagedCudnnConvResource() { } cudnnHandle_t ManagedCudnnConvResource::cudnn_handle() { - if (handle_ == nullptr) { OF_CUDNN_CHECK(cudnnCreate(&handle_)); } + if (handle_ == nullptr) { handle_ = Singleton::Get()->Get(); } return handle_; } @@ -392,7 +395,12 @@ bool operator==(const CudnnConvParams& a, const CudnnConvParams& b) { } DataType GetConvDescDataType(DataType data_type, bool pseudo_half) { - return (data_type == DataType::kFloat16 && pseudo_half) ? DataType::kFloat : data_type; + if (data_type == DataType::kFloat16 && pseudo_half) { + return DataType::kFloat; + } else if (data_type == DataType::kBFloat16) { + return DataType::kFloat; + } + return data_type; } cudnnStatus_t GetCudnnConvWorkspaceSize(const CudnnConvArgs& args, CudnnConvResource* res, @@ -669,25 +677,6 @@ perf_t GetBestAlgorithm(const CudnnConvArgs& args, CudnnConvResource* res, << ") requires memory " << perf_vec[0].memory; } -// #if HIPDNN_VERSION < 7500 -// // google [blacklist fft algorithms for strided dgrad] -// if (std::is_same::value) { -// int stride_dim = args.params.x_ndim - 2; -// bool blacklist = -// std::any_of(std::begin(args.params.stride), std::begin(args.params.stride) + stride_dim, -// [](int n) { return n != 1; }); -// if (blacklist -// && (static_cast(perf_vec[found_algo_idx].algo) -// == HIPDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING -// || static_cast(perf_vec[found_algo_idx].algo) -// == HIPDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)) { -// perf_t algo_perf; -// SetAlgo4Perf(args, res, &algo_perf, GetDefaultAlgo()); -// return algo_perf; -// } -// } -// #endif - return perf_vec.at(found_algo_idx); } @@ -864,39 +853,37 @@ CudnnConvArgs::CudnnConvArgs(const user_op::InferContext& ctx, DataType x_data_t wdesc(w_data_type, w_shape, data_format), cdesc(GetConvDescDataType(x_data_type, enable_pseudo_half), x_data_type, x_shape, ctx), heuristic(heuristic_search), - deterministic(use_deterministic_algo_only) { - std::memset(¶ms, 0, sizeof(CudnnConvParams)); - OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(xdesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.x_data_type, ¶ms.x_ndim, params.x_dims, - params.x_strides)); - OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(ydesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.y_data_type, ¶ms.y_ndim, params.y_dims, - params.y_strides)); - OF_CUDNN_CHECK(hipdnnGetFilterNdDescriptor(wdesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.w_data_type, ¶ms.w_format, ¶ms.w_ndim, - params.w_dims)); - hipdnnConvolutionMode_t mode; - int conv_dim_size = x_shape.NumAxes() - 2; - - for (int i=0; i<3; i++) { - params.padding[i] = cdesc.CD_padding[i]; - params.stride[i] = cdesc.CD_stride[i]; - params.dilation[i] = cdesc.CD_dilation[i]; - } + deterministic(use_deterministic_algo_only), + max_ws_size(max_workspace_size) { + // std::memset(¶ms, 0, sizeof(CudnnConvParams)); + // OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(xdesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.x_data_type, ¶ms.x_ndim, params.x_dims, + // params.x_strides)); + // OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(ydesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.y_data_type, ¶ms.y_ndim, params.y_dims, + // params.y_strides)); + // OF_CUDNN_CHECK(hipdnnGetFilterNdDescriptor(wdesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.w_data_type, ¶ms.w_format, ¶ms.w_ndim, + // params.w_dims)); + // hipdnnConvolutionMode_t mode; + // int conv_dim_size = x_shape.NumAxes() - 2; + + // for (int i=0; i<3; i++) { + // params.padding[i] = cdesc.CD_padding[i]; + // params.stride[i] = cdesc.CD_stride[i]; + // params.dilation[i] = cdesc.CD_dilation[i]; + // } - mode = cdesc.CD_mode; - params.data_type = cdesc.CD_data_type; + // mode = cdesc.CD_mode; + // params.data_type = cdesc.CD_data_type; - // OF_CUDNN_CHECK(cudnnGetConvolutionNdDescriptor(cdesc.Get(), CudnnConvParams::kConvMaxDims, - // &conv_dim_size, params.padding, params.stride, - // params.dilation, &mode, ¶ms.data_type)); - CHECK_EQ(params.x_data_type, params.w_data_type); - CHECK_EQ(params.x_ndim, params.w_ndim); - // CHECK_EQ(conv_dim_size + 2, params.x_ndim); + // CHECK_EQ(params.x_data_type, params.w_data_type); + // CHECK_EQ(params.x_ndim, params.w_ndim); + // // CHECK_EQ(conv_dim_size + 2, params.x_ndim); + + // params.groups = cdesc.CD_groups; + // params.max_ws_size = max_workspace_size; - params.groups = cdesc.CD_groups; - // OF_CUDNN_CHECK(cudnnGetConvolutionGroupCount(cdesc.Get(), ¶ms.groups)); - params.max_ws_size = max_workspace_size; } CudnnConvArgs::CudnnConvArgs(const user_op::KernelComputeContext& ctx, DataType x_data_type, @@ -910,51 +897,56 @@ CudnnConvArgs::CudnnConvArgs(const user_op::KernelComputeContext& ctx, DataType wdesc(w_data_type, w_shape, data_format), cdesc(GetConvDescDataType(x_data_type, enable_pseudo_half), x_data_type, x_shape, ctx), heuristic(heuristic_search), - deterministic(use_deterministic_algo_only) { - std::memset(¶ms, 0, sizeof(CudnnConvParams)); - OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(xdesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.x_data_type, ¶ms.x_ndim, params.x_dims, - params.x_strides)); - OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(ydesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.y_data_type, ¶ms.y_ndim, params.y_dims, - params.y_strides)); - OF_CUDNN_CHECK(hipdnnGetFilterNdDescriptor(wdesc.Get(), CudnnConvParams::kTensorMaxDims, - ¶ms.w_data_type, ¶ms.w_format, ¶ms.w_ndim, - params.w_dims)); - hipdnnConvolutionMode_t mode; - int conv_dim_size = x_shape.NumAxes() - 2; - - for (int i=0; i<3; i++) { - params.padding[i] = cdesc.CD_padding[i]; - params.stride[i] = cdesc.CD_stride[i]; - params.dilation[i] = cdesc.CD_dilation[i]; - } + deterministic(use_deterministic_algo_only), + max_ws_size(max_workspace_size) { + // std::memset(¶ms, 0, sizeof(CudnnConvParams)); + // OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(xdesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.x_data_type, ¶ms.x_ndim, params.x_dims, + // params.x_strides)); + // OF_CUDNN_CHECK(hipdnnGetTensorNdDescriptor(ydesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.y_data_type, ¶ms.y_ndim, params.y_dims, + // params.y_strides)); + // OF_CUDNN_CHECK(hipdnnGetFilterNdDescriptor(wdesc.Get(), CudnnConvParams::kTensorMaxDims, + // ¶ms.w_data_type, ¶ms.w_format, ¶ms.w_ndim, + // params.w_dims)); + // hipdnnConvolutionMode_t mode; + // int conv_dim_size = x_shape.NumAxes() - 2; + + // for (int i=0; i<3; i++) { + // params.padding[i] = cdesc.CD_padding[i]; + // params.stride[i] = cdesc.CD_stride[i]; + // params.dilation[i] = cdesc.CD_dilation[i]; + // } - mode = cdesc.CD_mode; - params.data_type = cdesc.CD_data_type; + // mode = cdesc.CD_mode; + // params.data_type = cdesc.CD_data_type; - // OF_CUDNN_CHECK(cudnnGetConvolutionNdDescriptor(cdesc.Get(), CudnnConvParams::kConvMaxDims, - // &conv_dim_size, params.padding, params.stride, - // params.dilation, &mode, ¶ms.data_type)); - CHECK_EQ(params.x_data_type, params.w_data_type); - CHECK_EQ(params.x_ndim, params.w_ndim); - // CHECK_EQ(conv_dim_size + 2, params.x_ndim); + // CHECK_EQ(params.x_data_type, params.w_data_type); + // CHECK_EQ(params.x_ndim, params.w_ndim); + // // CHECK_EQ(conv_dim_size + 2, params.x_ndim); + + // params.groups = cdesc.CD_groups; + // params.max_ws_size = max_workspace_size; - params.groups = cdesc.CD_groups; - // OF_CUDNN_CHECK(cudnnGetConvolutionGroupCount(cdesc.Get(), ¶ms.groups)); - params.max_ws_size = max_workspace_size; } ManagedCudnnConvResource::ManagedCudnnConvResource(const CudnnConvArgs& args) : handle_(nullptr), x_dptr_(nullptr), w_dptr_(nullptr), y_dptr_(nullptr), ws_dptr_(nullptr) { - x_byte_size_ = ByteSize4Tensor(args.params.x_dims, args.params.x_ndim, args.params.x_data_type); - w_byte_size_ = ByteSize4Tensor(args.params.w_dims, args.params.w_ndim, args.params.w_data_type); - y_byte_size_ = ByteSize4Tensor(args.params.y_dims, args.params.y_ndim, args.params.y_data_type); - ws_byte_size_ = args.params.max_ws_size; + // x_byte_size_ = ByteSize4Tensor(args.params.x_dims, args.params.x_ndim, args.params.x_data_type); + // w_byte_size_ = ByteSize4Tensor(args.params.w_dims, args.params.w_ndim, args.params.w_data_type); + // y_byte_size_ = ByteSize4Tensor(args.params.y_dims, args.params.y_ndim, args.params.y_data_type); + // ws_byte_size_ = args.params.max_ws_size; + x_byte_size_ = 0; + w_byte_size_ = 0; + y_byte_size_ = 0; + ws_byte_size_ = 0; } ManagedCudnnConvResource::~ManagedCudnnConvResource() { - if (handle_ != nullptr) { OF_CUDNN_CHECK(hipdnnDestroy(handle_)); } + if (handle_ != nullptr) { + Singleton::Get()->Put(handle_); + handle_ = nullptr; + } if (x_dptr_ != nullptr) { OF_CUDA_CHECK(hipFree(x_dptr_)); } if (w_dptr_ != nullptr) { OF_CUDA_CHECK(hipFree(w_dptr_)); } if (y_dptr_ != nullptr) { OF_CUDA_CHECK(hipFree(y_dptr_)); } @@ -962,7 +954,7 @@ ManagedCudnnConvResource::~ManagedCudnnConvResource() { } hipdnnHandle_t ManagedCudnnConvResource::cudnn_handle() { - if (handle_ == nullptr) { OF_CUDNN_CHECK(hipdnnCreate(&handle_)); } + if (handle_ == nullptr) { handle_ = Singleton::Get()->Get(); } return handle_; } @@ -1005,7 +997,12 @@ bool operator==(const CudnnConvParams& a, const CudnnConvParams& b) { } DataType GetConvDescDataType(DataType data_type, bool pseudo_half) { - return (data_type == DataType::kFloat16 && pseudo_half) ? DataType::kFloat : data_type; + if (data_type == DataType::kFloat16 && pseudo_half) { + return DataType::kFloat; + } else if (data_type == DataType::kBFloat16) { + return DataType::kFloat; + } + return data_type; } hipdnnStatus_t GetCudnnConvWorkspaceSize(const CudnnConvArgs& args, CudnnConvResource* res, @@ -1035,38 +1032,18 @@ struct CudnnConvAlgorithmSearch { static int GetAlgoMaxCount(CudnnConvResource* res) { int max_algo_cnt = 1; - // OF_CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithmMaxCount(res->cudnn_handle(), &max_algo_cnt)); return max_algo_cnt; } - // static void HeuristicSearch(const CudnnConvArgs& args, CudnnConvResource* res, - // std::vector* perf_vec) { - // int found_algo_cnt = 0; - // perf_vec->resize(GetAlgoMaxCount(res)); - // OF_CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm_v7( - // res->cudnn_handle(), args.xdesc.Get(), args.wdesc.Get(), args.cdesc.Get(), args.ydesc.Get(), - // perf_vec->size(), &found_algo_cnt, perf_vec->data())); - // // vector::resize does not affect the first found_algo_cnt elements. - // perf_vec->resize(found_algo_cnt); - // } - static void ExhaustiveSearch(CudnnConvArgs& args, CudnnConvResource* res, perf_t* perf) { int found_algo_cnt = 0; - size_t ws = 0; - hipdnnConvolutionFwdAlgo_t algo; - hipdnnGetConvolutionForwardWorkspaceSize(res->cudnn_handle(), args.xdesc.Get(), - args.wdesc.Get(), args.cdesc.Get(), - args.ydesc.Get(), algo, &ws); - res->ws_byte_size_ = ws; - res->set_ws(); - args.params.max_ws_size = ws; OF_CUDNN_CHECK(hipdnnFindConvolutionForwardAlgorithmEx( res->cudnn_handle(), args.xdesc.Get(), res->x_const_dptr(), args.wdesc.Get(), res->w_const_dptr(), args.cdesc.Get(), args.ydesc.Get(), res->y_mut_dptr(), 1, &found_algo_cnt, perf, res->ws_dptr(), - args.params.max_ws_size)); + args.max_ws_size)); } }; @@ -1076,40 +1053,18 @@ struct CudnnConvAlgorithmSearch { static int GetAlgoMaxCount(CudnnConvResource* res) { int max_algo_cnt = 1; - // OF_CUDNN_CHECK( - // cudnnGetConvolutionBackwardDataAlgorithmMaxCount(res->cudnn_handle(), &max_algo_cnt)); return max_algo_cnt; } - // static void HeuristicSearch(const CudnnConvArgs& args, CudnnConvResource* res, - // std::vector* perf_vec) { - // int found_algo_cnt = 0; - // perf_vec->resize(GetAlgoMaxCount(res)); - // OF_CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm_v7( - // res->cudnn_handle(), args.wdesc.Get(), args.ydesc.Get(), args.cdesc.Get(), args.xdesc.Get(), - // perf_vec->size(), &found_algo_cnt, perf_vec->data())); - // // vector::resize does not affect the first found_algo_cnt elements. - // perf_vec->resize(found_algo_cnt); - // } - static void ExhaustiveSearch(CudnnConvArgs& args, CudnnConvResource* res, perf_t* perf) { int found_algo_cnt = 0; - size_t ws = 0; - hipdnnConvolutionBwdDataAlgo_t algo; - hipdnnGetConvolutionBackwardDataWorkspaceSize(res->cudnn_handle(), args.wdesc.Get(), - args.ydesc.Get(), args.cdesc.Get(), - args.xdesc.Get(), algo, &ws); - - res->ws_byte_size_ = ws; - res->set_ws(); - args.params.max_ws_size = ws; OF_CUDNN_CHECK(hipdnnFindConvolutionBackwardDataAlgorithmEx( res->cudnn_handle(), args.wdesc.Get(), res->w_const_dptr(), args.ydesc.Get(), res->y_const_dptr(), args.cdesc.Get(), args.xdesc.Get(), res->x_mut_dptr(), 1, &found_algo_cnt, perf, res->ws_dptr(), - args.params.max_ws_size)); + args.max_ws_size)); } }; @@ -1119,40 +1074,18 @@ struct CudnnConvAlgorithmSearch { static int GetAlgoMaxCount(CudnnConvResource* res) { int max_algo_cnt = 1; - // OF_CUDNN_CHECK( - // cudnnGetConvolutionBackwardFilterAlgorithmMaxCount(res->cudnn_handle(), &max_algo_cnt)); return max_algo_cnt; } - // static void HeuristicSearch(const CudnnConvArgs& args, CudnnConvResource* res, - // std::vector* perf_vec) { - // int found_algo_cnt = 0; - // perf_vec->resize(GetAlgoMaxCount(res)); - // OF_CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm_v7( - // res->cudnn_handle(), args.xdesc.Get(), args.ydesc.Get(), args.cdesc.Get(), args.wdesc.Get(), - // perf_vec->size(), &found_algo_cnt, perf_vec->data())); - // // vector::resize does not affect the first found_algo_cnt elements. - // perf_vec->resize(found_algo_cnt); - // } - static void ExhaustiveSearch(CudnnConvArgs& args, CudnnConvResource* res, perf_t* perf) { int found_algo_cnt = 0; - size_t ws = 0; - hipdnnConvolutionBwdFilterAlgo_t algo; - hipdnnGetConvolutionBackwardFilterWorkspaceSize(res->cudnn_handle(), args.xdesc.Get(), - args.ydesc.Get(), args.cdesc.Get(), - args.wdesc.Get(), algo, &ws); - - res->ws_byte_size_ = ws; - res->set_ws(); - args.params.max_ws_size = ws; OF_CUDNN_CHECK(hipdnnFindConvolutionBackwardFilterAlgorithmEx( res->cudnn_handle(), args.xdesc.Get(), res->x_const_dptr(), args.ydesc.Get(), res->y_const_dptr(), args.cdesc.Get(), args.wdesc.Get(), res->w_mut_dptr(), 1, &found_algo_cnt, perf, res->ws_dptr(), - args.params.max_ws_size)); + args.max_ws_size)); } }; @@ -1200,4 +1133,6 @@ EXPLICIT_INSTANTIAT_CUDNN_CONV_ALGORITHM_INTERFACE(hipdnnConvolutionBwdFilterAlg } // namespace oneflow + #endif // WITH_ROCM + diff --git a/oneflow/core/device/cudnn_conv_util.h b/oneflow/core/device/cudnn_conv_util.h index c3c03a22a28227beeb3a68480791504edbbbf3b3..6f177bf4f0aedeeb84d766a097ef6f37d9ff4a94 100644 --- a/oneflow/core/device/cudnn_conv_util.h +++ b/oneflow/core/device/cudnn_conv_util.h @@ -308,6 +308,7 @@ struct CudnnConvArgs final { CudnnConvDesc cdesc; bool heuristic; bool deterministic; + size_t max_ws_size; OF_DISALLOW_COPY_AND_MOVE(CudnnConvArgs); CudnnConvArgs(const user_op::InferContext& ctx, DataType x_data_type, const ShapeView& x_shape, @@ -333,17 +334,14 @@ class CudnnConvResource { virtual const void* x_const_dptr() const = 0; virtual const void* y_const_dptr() const = 0; virtual void* ws_dptr() = 0; - virtual void set_ws() = 0; - size_t ws_byte_size_; }; class AllocatedCudnnConvResource final : public CudnnConvResource { public: AllocatedCudnnConvResource(hipdnnHandle_t handle, void* x_dptr, void* w_dptr, void* y_dptr, - void* ws_dptr, size_t ws_byte_size) - : handle_(handle), x_dptr_(x_dptr), w_dptr_(w_dptr), y_dptr_(y_dptr), ws_dptr_(ws_dptr), ws_byte_size_(ws_byte_size) {} - // ~AllocatedCudnnConvResource() = default; - ~AllocatedCudnnConvResource(){if (ws_dptr_ != nullptr) { OF_CUDA_CHECK(hipFree(ws_dptr_)); }} + void* ws_dptr) + : handle_(handle), x_dptr_(x_dptr), w_dptr_(w_dptr), y_dptr_(y_dptr), ws_dptr_(ws_dptr) {} + ~AllocatedCudnnConvResource() = default; hipdnnHandle_t cudnn_handle() override { return handle_; } const void* x_const_dptr() const override { return x_dptr_; } const void* w_const_dptr() const override { return w_dptr_; } @@ -351,13 +349,7 @@ class AllocatedCudnnConvResource final : public CudnnConvResource { void* x_mut_dptr() override { return x_dptr_; } void* w_mut_dptr() override { return w_dptr_; } void* y_mut_dptr() override { return y_dptr_; } - void* ws_dptr() override { - // return ws_dptr_; - if (ws_dptr_ == nullptr) { OF_CUDA_CHECK(hipMalloc(&ws_dptr_, ws_byte_size_)); } - return ws_dptr_; - } - void set_ws() { ws_byte_size_ = CudnnConvResource::ws_byte_size_; } - size_t ws_byte_size_; + void* ws_dptr() override { return ws_dptr_; } private: hipdnnHandle_t handle_; @@ -379,8 +371,6 @@ class ManagedCudnnConvResource final : public CudnnConvResource { const void* w_const_dptr() const override; const void* y_const_dptr() const override; void* ws_dptr() override; - void set_ws(){ ws_byte_size_ = CudnnConvResource::ws_byte_size_; } - size_t ws_byte_size_; private: hipdnnHandle_t handle_; @@ -391,7 +381,7 @@ class ManagedCudnnConvResource final : public CudnnConvResource { size_t x_byte_size_; size_t w_byte_size_; size_t y_byte_size_; - // size_t ws_byte_size_; + size_t ws_byte_size_; }; bool operator==(const CudnnConvParams& a, const CudnnConvParams& b); diff --git a/oneflow/core/device/cudnn_util.cpp b/oneflow/core/device/cudnn_util.cpp index 208089b38351c14f188ed0f7fc097f9f1dda3276..b7afc228f990390564798613b57ae2396fab1637 100644 --- a/oneflow/core/device/cudnn_util.cpp +++ b/oneflow/core/device/cudnn_util.cpp @@ -177,6 +177,45 @@ size_t GetCudnnDataTypeByteSize(cudnnDataType_t data_type) { return byte_size; } +CudnnHandlePool::~CudnnHandlePool() { + for (auto& pair : handle_list_map_) { + int64_t device_id = pair.first; + auto& handle_list = pair.second; + CudaCurrentDeviceGuard guard(device_id); + while (!handle_list.empty()) { + cudnnHandle_t handle = handle_list.back(); + handle_list.pop_back(); + OF_CUDNN_CHECK(cudnnDestroy(handle)); + } + } + handle_list_map_.clear(); +} + +cudnnHandle_t CudnnHandlePool::Get() { + int device_id; + OF_CUDA_CHECK(cudaGetDevice(&device_id)); + { + std::unique_lock lock(mutex_); + std::vector& handle_list = handle_list_map_[device_id]; + if (!handle_list.empty()) { + cudnnHandle_t handle = handle_list.back(); + handle_list.pop_back(); + return handle; + } + } + cudnnHandle_t handle; + OF_CUDNN_CHECK(cudnnCreate(&handle)); + return handle; +} + +void CudnnHandlePool::Put(cudnnHandle_t handle) { + int device_id; + OF_CUDA_CHECK(cudaGetDevice(&device_id)); + std::unique_lock lock(mutex_); + std::vector& handle_list = handle_list_map_[device_id]; + handle_list.push_back(handle); +} + #endif // WITH_CUDA #ifdef WITH_ROCM @@ -302,10 +341,6 @@ size_t GetCudnnDataTypeByteSize(hipdnnDataType_t data_type) { case HIPDNN_DATA_FLOAT: case HIPDNN_DATA_INT32: case HIPDNN_DATA_INT8x4: - // case CUDNN_DATA_UINT8x4: { - // byte_size = 4; - // break; - // } case HIPDNN_DATA_DOUBLE: { byte_size = 8; break; @@ -315,22 +350,9 @@ size_t GetCudnnDataTypeByteSize(hipdnnDataType_t data_type) { break; } case HIPDNN_DATA_INT8: { - // case CUDNN_DATA_UINT8: { byte_size = 1; break; } -// #if HIPDNN_VERSION > 7200 -// case CUDNN_DATA_INT8x32: { -// byte_size = 32; -// break; -// } -// #endif -// #if HIPDNN_VERSION >= 8100 -// case CUDNN_DATA_BFLOAT16: { -// byte_size = 2; -// break; -// } -// #endif default: { UNIMPLEMENTED(); } @@ -338,6 +360,45 @@ size_t GetCudnnDataTypeByteSize(hipdnnDataType_t data_type) { return byte_size; } +CudnnHandlePool::~CudnnHandlePool() { + for (auto& pair : handle_list_map_) { + int64_t device_id = pair.first; + auto& handle_list = pair.second; + CudaCurrentDeviceGuard guard(device_id); + while (!handle_list.empty()) { + hipdnnHandle_t handle = handle_list.back(); + handle_list.pop_back(); + OF_CUDNN_CHECK(hipdnnDestroy(handle)); + } + } + handle_list_map_.clear(); +} + +hipdnnHandle_t CudnnHandlePool::Get() { + int device_id; + OF_CUDA_CHECK(hipGetDevice(&device_id)); + { + std::unique_lock lock(mutex_); + std::vector& handle_list = handle_list_map_[device_id]; + if (!handle_list.empty()) { + hipdnnHandle_t handle = handle_list.back(); + handle_list.pop_back(); + return handle; + } + } + hipdnnHandle_t handle; + OF_CUDNN_CHECK(hipdnnCreate(&handle)); + return handle; +} + +void CudnnHandlePool::Put(hipdnnHandle_t handle) { + int device_id; + OF_CUDA_CHECK(hipGetDevice(&device_id)); + std::unique_lock lock(mutex_); + std::vector& handle_list = handle_list_map_[device_id]; + handle_list.push_back(handle); +} + #endif // WITH_ROCM template @@ -366,4 +427,34 @@ template const void* CudnnSPZeroPtr(); template const void* CudnnSPZeroPtr(); template const void* CudnnSPZeroPtr(); +const void* CudnnSPOnePtr(const DataType dtype) { + if (dtype == kDouble) { + return CudnnSPOnePtr(); + } else if (dtype == kFloat) { + return CudnnSPOnePtr(); + } else if (dtype == kFloat16) { + return CudnnSPOnePtr(); + } else if (dtype == kBFloat16) { + // NOTE(guoran): kBFloat16 use float OnePtr + return CudnnSPOnePtr(); + } else { + UNIMPLEMENTED(); + } +} + +const void* CudnnSPZeroPtr(const DataType dtype) { + if (dtype == kDouble) { + return CudnnSPZeroPtr(); + } else if (dtype == kFloat) { + return CudnnSPZeroPtr(); + } else if (dtype == kFloat16) { + return CudnnSPZeroPtr(); + } else if (dtype == kBFloat16) { + // NOTE(guoran): kBFloat16 use float ZeroPtr + return CudnnSPZeroPtr(); + } else { + UNIMPLEMENTED(); + } +} + } // namespace oneflow diff --git a/oneflow/core/device/cudnn_util.h b/oneflow/core/device/cudnn_util.h index 3193f982e063de5c6a81cf2a814f0e0c64cf4e4c..aba3f7f2df4e0628ae50fa97e41e3a473e3b886d 100644 --- a/oneflow/core/device/cudnn_util.h +++ b/oneflow/core/device/cudnn_util.h @@ -96,6 +96,22 @@ const void* CudnnSPOnePtr(); template const void* CudnnSPZeroPtr(); +const void* CudnnSPOnePtr(const DataType dtype); + +const void* CudnnSPZeroPtr(const DataType dtype); + +class CudnnHandlePool { + public: + CudnnHandlePool() = default; + ~CudnnHandlePool(); + cudnnHandle_t Get(); + void Put(cudnnHandle_t handle); + + private: + std::mutex mutex_; + HashMap> handle_list_map_; +}; + } // namespace oneflow #endif // WITH_CUDA @@ -177,6 +193,22 @@ const void* CudnnSPOnePtr(); template const void* CudnnSPZeroPtr(); +const void* CudnnSPOnePtr(const DataType dtype); + +const void* CudnnSPZeroPtr(const DataType dtype); + +class CudnnHandlePool { + public: + CudnnHandlePool() = default; + ~CudnnHandlePool(); + hipdnnHandle_t Get(); + void Put(hipdnnHandle_t handle); + + private: + std::mutex mutex_; + HashMap> handle_list_map_; +}; + } // namespace oneflow #endif // WITH_ROCM diff --git a/oneflow/core/eager/blob_instruction_type.cpp b/oneflow/core/eager/blob_instruction_type.cpp deleted file mode 100644 index 3cb6dd83befe29e571bebc6c2990ee343db29ff2..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/blob_instruction_type.cpp +++ /dev/null @@ -1,45 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/eager/blob_instruction_type.h" -#include "oneflow/core/vm/control_stream_type.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/register/register_manager.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/eager/eager_blob_object.h" - -namespace oneflow { -namespace vm { - -void AccessBlobByCallbackInstructionType::Compute(vm::Instruction* instruction) const { - const auto& phy_instr_operand = instruction->phy_instr_operand(); - CHECK(static_cast(phy_instr_operand)); - const auto* ptr = - dynamic_cast(phy_instr_operand.get()); - CHECK_NOTNULL(ptr); - DeviceCtx* device_ctx = instruction->stream().device_ctx().get(); - OfBlob ofblob(device_ctx->stream(), ptr->eager_blob_object()->blob()); - ptr->callback()(reinterpret_cast(&ofblob)); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/eager/blob_instruction_type.h b/oneflow/core/eager/blob_instruction_type.h deleted file mode 100644 index 99218a24fd1b975c228280484e4f4542b3224d4c..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/blob_instruction_type.h +++ /dev/null @@ -1,96 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_BLOB_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_BLOB_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/common/singleton_ptr.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/ep_event.h" -#include "oneflow/core/vm/ep_device_context.h" - -namespace oneflow { -namespace vm { - -class AccessBlobByCallbackInstructionType final : public vm::InstructionType { - public: - AccessBlobByCallbackInstructionType() = default; - ~AccessBlobByCallbackInstructionType() override = default; - - std::string DebugName(const vm::Instruction& instruction) const override { - return "AccessBlobByCallback"; - } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(vm::Instruction* instruction) const override; -}; - -class EpRecordEventInstructionType final : public vm::InstructionType { - public: - EpRecordEventInstructionType() = default; - ~EpRecordEventInstructionType() override = default; - - InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAsTailOnly; } - - void InitInstructionStatus(Instruction* instruction) const override { - auto* status_buffer = instruction->mut_status_buffer(); - auto* stream = instruction->mut_stream(); - instruction->stream_type().InitInstructionStatus(*stream, status_buffer); - auto* ep_device_ctx = static_cast(stream->device_ctx().get()); - auto* ep_event_provider = ep_device_ctx->ep_event_provider(); - const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); - auto* data_ptr = status_buffer->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->reset_ep_event(ep_event); - } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - std::string DebugName(const vm::Instruction&) const override { return "RecordEvent"; } - void Compute(vm::Instruction* instruction) const override {} -}; -} // namespace vm - -struct GetRecordEventInstructionType : public StreamRoleVisitor { - static Maybe VisitCompute(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitHost2Device(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitDevice2Host(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitSyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitAsyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitBarrier(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitCriticalSection(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitLazyJobLauncher(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitPinnedCompute(DeviceType device_type) { - return VisitCompute(device_type); - } -}; - -} // namespace oneflow -#endif // ONEFLOW_CORE_EAGER_BLOB_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/eager/call_context.h b/oneflow/core/eager/call_context.h index 0e7058c02928e7922fbbc6a53854b736a7643d39..0fd4ad7f7f39c9182efdcd56c4a934d655256a00 100644 --- a/oneflow/core/eager/call_context.h +++ b/oneflow/core/eager/call_context.h @@ -21,17 +21,14 @@ limitations under the License. #include "oneflow/core/framework/op_interpreter.h" #include "oneflow/core/common/shape_view.h" #include "oneflow/core/common/stride.h" +#include "oneflow/core/common/small_vector.h" namespace oneflow { namespace one { class StatefulLocalOpKernel; -class ConsistentTensorInferResult; - -using EagerBlobObjectList = std::vector>; -using EagerBlobObjectListPtr = - std::shared_ptr>>; +class GlobalTensorInferResult; } // namespace one @@ -60,10 +57,7 @@ class TmpTensor final : public user_op::Tensor { char* mut_tmp_buffer_ptr() { return tmp_buffer_ptr_; } - void init_tmp_buffer_ptr(char* ptr) { - CHECK_EQ(tmp_buffer_ptr_, nullptr); - tmp_buffer_ptr_ = ptr; - } + void set_tmp_buffer_ptr(char* ptr) { tmp_buffer_ptr_ = ptr; } private: std::shared_ptr mem_case_; @@ -73,35 +67,34 @@ class TmpTensor final : public user_op::Tensor { class CallContext { public: - CallContext( - ComposedAttrMap&& composed_attrs, const one::EagerBlobObjectListPtr& inputs, - const one::EagerBlobObjectListPtr& outputs, - const std::shared_ptr& consistent_tensor_infer_result, - const one::OpExprInterpContext& op_interp_ctx, const std::shared_ptr& mem_case) + CallContext(ComposedAttrMap&& composed_attrs, vm::EagerBlobObjectList&& inputs, + vm::EagerBlobObjectList&& outputs, + const std::shared_ptr& global_tensor_infer_result, + const one::OpExprInterpContext& op_interp_ctx, + const std::shared_ptr& mem_case) : composed_attrs_(std::move(composed_attrs)), - inputs_(inputs), - outputs_(outputs), - consistent_tensor_infer_result_(consistent_tensor_infer_result), + inputs_(std::move(inputs)), + outputs_(std::move(outputs)), + global_tensor_infer_result_(global_tensor_infer_result), op_interp_ctx_(op_interp_ctx), tmp_tensor_(mem_case) {} ~CallContext() = default; const ComposedAttrMap& composed_attrs() const { return composed_attrs_; } - const one::EagerBlobObjectListPtr& inputs() const { return inputs_; } - const one::EagerBlobObjectListPtr& outputs() const { return outputs_; } - const std::shared_ptr& consistent_tensor_infer_result() - const { - return consistent_tensor_infer_result_; + const vm::EagerBlobObjectList& inputs() const { return inputs_; } + const vm::EagerBlobObjectList& outputs() const { return outputs_; } + const std::shared_ptr& global_tensor_infer_result() const { + return global_tensor_infer_result_; } const one::OpExprInterpContext& op_interp_ctx() const { return op_interp_ctx_; } TmpTensor* mut_tmp_tensor() { return &tmp_tensor_; } private: const ComposedAttrMap composed_attrs_; - const one::EagerBlobObjectListPtr inputs_; - const one::EagerBlobObjectListPtr outputs_; - const std::shared_ptr consistent_tensor_infer_result_; + const vm::EagerBlobObjectList inputs_; + const vm::EagerBlobObjectList outputs_; + const std::shared_ptr global_tensor_infer_result_; const one::OpExprInterpContext op_interp_ctx_; TmpTensor tmp_tensor_; }; diff --git a/oneflow/core/eager/critical_section_instruction_type.h b/oneflow/core/eager/critical_section_instruction_type.h deleted file mode 100644 index c362e17ba6363d097ff3a6b1c92f4be692c5e66b..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/critical_section_instruction_type.h +++ /dev/null @@ -1,138 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_CRITICAL_SECTION_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_CRITICAL_SECTION_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/critical_section_status_querier.h" -#include "oneflow/core/eager/critical_section_phy_instr_operand.h" -#include "oneflow/core/job/critical_section_instance.h" -#include "oneflow/core/framework/nn_graph_if.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/common/buffer_manager.h" -#include "oneflow/core/common/singleton.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/vm/ref_cnt_instruction_status_querier.h" -#include "oneflow/core/profiler/profiler.h" - -namespace oneflow { - -namespace vm { - -class CriticalSectionBeginInstructionType final : public InstructionType { - public: - CriticalSectionBeginInstructionType(const CriticalSectionBeginInstructionType&) = delete; - CriticalSectionBeginInstructionType(CriticalSectionBeginInstructionType&&) = delete; - CriticalSectionBeginInstructionType& operator=(const CriticalSectionBeginInstructionType&) = - delete; - CriticalSectionBeginInstructionType& operator=(CriticalSectionBeginInstructionType&&) = delete; - CriticalSectionBeginInstructionType() = default; - ~CriticalSectionBeginInstructionType() = default; - - std::string DebugName(const vm::Instruction& instruction) const override { - return "CriticalSectionBegin"; - } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(vm::Instruction* instruction) const override { - OF_PROFILER_RANGE_GUARD("CriticalSectionBegin"); - { - auto ptr = instruction->phy_instr_operand(); - auto phy_instr_operand = std::dynamic_pointer_cast(ptr); - CHECK_NOTNULL(phy_instr_operand); - const auto& critical_section_instance = MakeCriticalSectionInstance(phy_instr_operand); - const auto& job_name = critical_section_instance->job_name(); - auto* buffer_mgr = Singleton>>::Get(); - for (int i = 0; i < phy_instr_operand->interfaces_op_names().size(); ++i) { - if (phy_instr_operand->interfaces_valid().at(i)) { - const std::string& interface_op_name = phy_instr_operand->interfaces_op_names().at(i); - const auto& buffer_name = - phy_instr_operand->GetInterfaceBufferName(job_name, interface_op_name); - buffer_mgr->Get(buffer_name)->Push(critical_section_instance); - } - } - const auto& callback_buffer_name = - phy_instr_operand->GetInterfaceCriticalSectionCallbackBufferName(job_name); - buffer_mgr->Get(callback_buffer_name)->Push(critical_section_instance); - const auto& wait_buffer_name = - phy_instr_operand->GetInterfaceCriticalSectionWaitBufferName(job_name); - buffer_mgr->Get(wait_buffer_name)->Push(critical_section_instance); - } - { - auto* status_buffer_data = instruction->mut_status_buffer()->mut_buffer(); - auto* status_querier = CriticalSectionStatusQuerier::MutCast(status_buffer_data); - status_querier->SetLaunched(std::make_shared()); - } - } - - private: - class NaiveCriticalSectionInstance final : public CriticalSectionInstance { - public: - NaiveCriticalSectionInstance( - const std::shared_ptr& phy_instr_operand, - const std::string& job_name) - : CriticalSectionInstance(), phy_instr_operand_(phy_instr_operand), job_name_(job_name) {} - - ~NaiveCriticalSectionInstance() override = default; - - const std::string& job_name() const override { return job_name_; } - - void AccessBlobByOpName(uint64_t ofblob_ptr, const std::string& op_name) const override { - phy_instr_operand_->AccessBlobByOpName(ofblob_ptr, op_name); - } - void Finish() const override { phy_instr_operand_->Finish(); } - - private: - std::shared_ptr phy_instr_operand_; - std::string job_name_; - }; - - std::shared_ptr MakeCriticalSectionInstance( - const std::shared_ptr& phy_instr_operand) const { - phy_instr_operand->FinishInvalidInterfaceEventRecords(); - const auto& job_name = phy_instr_operand->nn_graph()->job_name(); - return std::make_shared(phy_instr_operand, job_name); - } -}; - -class CriticalSectionEndInstructionType final : public InstructionType { - public: - CriticalSectionEndInstructionType(const CriticalSectionEndInstructionType&) = delete; - CriticalSectionEndInstructionType(CriticalSectionEndInstructionType&&) = delete; - CriticalSectionEndInstructionType& operator=(const CriticalSectionEndInstructionType&) = delete; - CriticalSectionEndInstructionType& operator=(CriticalSectionEndInstructionType&&) = delete; - CriticalSectionEndInstructionType() = default; - ~CriticalSectionEndInstructionType() = default; - - std::string DebugName(const vm::Instruction& instruction) const override { - return "CriticalSectionEnd"; - } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(vm::Instruction* instruction) const override { - const auto* ptr = instruction->phy_instr_operand().get(); - const auto* phy_instr_operand = dynamic_cast(ptr); - CHECK_NOTNULL(phy_instr_operand); - auto* status_buffer_data = instruction->mut_status_buffer()->mut_buffer(); - auto* status_querier = CriticalSectionStatusQuerier::MutCast(status_buffer_data); - status_querier->SetLaunched(phy_instr_operand->event_record()); - } -}; - -} // namespace vm -} // namespace oneflow -#endif // ONEFLOW_CORE_EAGER_CRITICAL_SECTION_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/eager/critical_section_phy_instr_operand.cpp b/oneflow/core/eager/critical_section_phy_instr_operand.cpp deleted file mode 100644 index 51bc0a4b82b68ca9f37ce78751c594e1742a87df..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/critical_section_phy_instr_operand.cpp +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/eager/critical_section_phy_instr_operand.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/stream.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/device/ep_based_event_record.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/vm/stream.h" - -namespace oneflow { -namespace vm { - -void CriticalSectionBeginPhyInstrOperand::ForEachMirroredObject( - const std::function& DoEach) const { - for (const auto& eager_blob_object : *eager_blob_objects_) { - DoEach(CHECK_JUST(eager_blob_object->compute_local_dep_object())); - } -} - -void CriticalSectionEndPhyInstrOperand::ForEachMirroredObject( - const std::function& DoEach) const { - DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); -} - -void CriticalSectionBeginPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - DoEach(vm_stream_->schedule_local_dep_object().get()); -} - -void CriticalSectionBeginPhyInstrOperand::FinishInvalidInterfaceEventRecords() { - for (const auto& op_name : interfaces_op_names()) { - size_t index = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); - if (!interfaces_valid().at(index)) { - const auto& iter = op_name2end_event_record_->find(op_name); - CHECK(iter != op_name2end_event_record_->end()); - iter->second->Init(std::make_shared()); - } - } -} - -void CriticalSectionBeginPhyInstrOperand::Finish() { - for (const auto& pair : *op_name2end_event_record_) { - pair.second->TryInit(std::make_shared()); - } -} - -void InputCriticalSectionBeginPhyInstrOperand::AccessBlobByOpName(uint64_t of_blob_ptr, - const std::string& op_name) { - int64_t i = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); - CHECK(interfaces_valid().at(i)); - OfBlob* of_blob = reinterpret_cast(of_blob_ptr); - const auto& eager_blob_object = eager_blob_objects_->at(i); - { - size_t header_size = of_blob->mut_blob()->blob_desc().ByteSizeOfBlobHeader(); - CHECK_EQ(header_size, eager_blob_object->shape().NumAxes() * sizeof(int64_t)); - std::memcpy(of_blob->mut_blob()->mut_header_ptr(), eager_blob_object->mut_header_ptr(), - header_size); - } - const auto& end_event_record = op_name2end_event_record_->at(op_name); - if (eager_blob_object->dptr() == nullptr) { - end_event_record->Init(std::make_shared()); - } else { - { - const size_t body_bytes = of_blob->blob().ByteSizeOfBlobBody(); - CHECK_EQ(eager_blob_object->ByteSizeOfBlobBody(), body_bytes); - AutoMemcpy(of_blob->stream(), of_blob->mut_blob()->mut_dptr(), eager_blob_object->dptr(), - body_bytes, of_blob->blob().mem_case(), eager_blob_object->mem_case()); - } - end_event_record->Init(EpBasedEventRecord::MakeEventRecord(of_blob->stream())); - } -} - -void OutputCriticalSectionBeginPhyInstrOperand::AccessBlobByOpName(uint64_t of_blob_ptr, - const std::string& op_name) { - int64_t i = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); - CHECK(interfaces_valid().at(i)); - OfBlob* of_blob = reinterpret_cast(of_blob_ptr); - auto& eager_blob_object = eager_blob_objects_->at(i); - of_blob->blob().shape_view().ToShape(eager_blob_object->mut_shape()); - const auto& end_event_record = op_name2end_event_record_->at(op_name); - if (eager_blob_object->dptr() == nullptr) { - end_event_record->Init(std::make_shared()); - } else { - { - const size_t body_bytes = of_blob->blob().ByteSizeOfBlobBody(); - CHECK_EQ(eager_blob_object->ByteSizeOfBlobBody(), body_bytes); - AutoMemcpy(of_blob->stream(), eager_blob_object->mut_dptr(), of_blob->blob().dptr(), - body_bytes, eager_blob_object->mem_case(), of_blob->blob().mem_case()); - } - end_event_record->Init(EpBasedEventRecord::MakeEventRecord(of_blob->stream())); - } -} - -void CriticalSectionEndPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - DoEach(vm_stream_->schedule_local_dep_object().get()); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/eager/critical_section_phy_instr_operand.h b/oneflow/core/eager/critical_section_phy_instr_operand.h deleted file mode 100644 index eac77d38c41289a8b15fdb6b568b636fd6802401..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/critical_section_phy_instr_operand.h +++ /dev/null @@ -1,295 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_CRITICAL_SECTION_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_EAGER_CRITICAL_SECTION_PHY_INSTR_OPERAND_H_ - -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/device/event_record.h" -#include "oneflow/core/framework/nn_graph_if.h" -#include "oneflow/core/common/buffer_manager.h" - -namespace oneflow { - -namespace one { - -using EagerBlobObjectListPtr = - std::shared_ptr>>; - -} - -namespace vm { - -class Stream; - -class CriticalSectionBeginPhyInstrOperand : public PhyInstrOperand { - public: - CriticalSectionBeginPhyInstrOperand(const CriticalSectionBeginPhyInstrOperand&) = delete; - CriticalSectionBeginPhyInstrOperand(CriticalSectionBeginPhyInstrOperand&&) = delete; - CriticalSectionBeginPhyInstrOperand& operator=(const CriticalSectionBeginPhyInstrOperand&) = - delete; - CriticalSectionBeginPhyInstrOperand& operator=(CriticalSectionBeginPhyInstrOperand&&) = delete; - virtual ~CriticalSectionBeginPhyInstrOperand() = default; - - explicit CriticalSectionBeginPhyInstrOperand( - const std::shared_ptr& nn_graph, - const one::EagerBlobObjectListPtr& eager_blob_objects, - const std::shared_ptr>>& - op_name2end_event_record, - vm::Stream* vm_stream) - : nn_graph_(nn_graph), - eager_blob_objects_(eager_blob_objects), - op_name2end_event_record_(op_name2end_event_record), - vm_stream_(vm_stream) {} - - const std::shared_ptr& nn_graph() const { return nn_graph_; } - const one::EagerBlobObjectListPtr& eager_blob_objects() const { return eager_blob_objects_; } - - void ForEachMirroredObject(const std::function&) const; - - void ForEachMutMirroredObject(const std::function&) const; - - virtual const std::vector& interfaces_op_names() const = 0; - virtual const std::vector& interfaces_valid() const = 0; - virtual std::string GetInterfaceBufferName(const std::string& job_name, - const std::string& op_name) const = 0; - virtual std::string GetInterfaceCriticalSectionCallbackBufferName( - const std::string& job_name) const = 0; - virtual std::string GetInterfaceCriticalSectionWaitBufferName( - const std::string& job_name) const = 0; - virtual void AccessBlobByOpName(uint64_t of_blob_ptr, const std::string& op_name) = 0; - - void FinishInvalidInterfaceEventRecords(); - void Finish(); - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - for (const auto& eager_blob_object : *eager_blob_objects_) { DoEach(eager_blob_object.get()); } - } - - protected: - std::shared_ptr nn_graph_; - one::EagerBlobObjectListPtr eager_blob_objects_; - std::shared_ptr>> - op_name2end_event_record_; - HashMap op_name2interface_index_; - vm::Stream* vm_stream_; -}; - -class InputCriticalSectionBeginPhyInstrOperand final : public CriticalSectionBeginPhyInstrOperand { - public: - InputCriticalSectionBeginPhyInstrOperand( - const std::shared_ptr& nn_graph, - const one::EagerBlobObjectListPtr& eager_blob_objects, - const std::shared_ptr>>& - op_name2end_event_record, - vm::Stream* vm_stream) - : CriticalSectionBeginPhyInstrOperand(nn_graph, eager_blob_objects, op_name2end_event_record, - vm_stream), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - CHECK_EQ(nn_graph->inputs_op_names().size(), eager_blob_objects->size()); - CHECK_EQ(nn_graph->inputs_op_names().size(), nn_graph->inputs_valid().size()); - for (int i = 0; i < nn_graph->inputs_op_names().size(); ++i) { - CHECK(op_name2interface_index_.emplace(nn_graph->inputs_op_names().at(i), i).second); - } - } - - ~InputCriticalSectionBeginPhyInstrOperand() override = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - // for inputs - void ForEachConstMirroredObject( - const std::function& DoEach) const { - ForEachMirroredObject(DoEach); - } - - // for outputs - const std::vector& interfaces_op_names() const override { - return nn_graph_->inputs_op_names(); - } - const std::vector& interfaces_valid() const override { return nn_graph_->inputs_valid(); } - std::string GetInterfaceBufferName(const std::string& job_name, - const std::string& op_name) const override { - return GetInputBufferName(job_name, op_name); - } - std::string GetInterfaceCriticalSectionCallbackBufferName( - const std::string& job_name) const override { - return GetInputCriticalSectionCallbackBufferName(job_name); - } - std::string GetInterfaceCriticalSectionWaitBufferName( - const std::string& job_name) const override { - return GetInputCriticalSectionWaitBufferName(job_name); - } - void AccessBlobByOpName(uint64_t of_blob_ptr, const std::string& op_name) override; - void ForEachMut2MirroredObject(const std::function&) const {} - - private: - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -class OutputCriticalSectionBeginPhyInstrOperand final : public CriticalSectionBeginPhyInstrOperand { - public: - OutputCriticalSectionBeginPhyInstrOperand( - const std::shared_ptr& nn_graph, - const one::EagerBlobObjectListPtr& eager_blob_objects, - const std::shared_ptr>>& - op_name2end_event_record, - vm::Stream* vm_stream) - : CriticalSectionBeginPhyInstrOperand(nn_graph, eager_blob_objects, op_name2end_event_record, - vm_stream), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - CHECK_EQ(nn_graph->outputs_op_names().size(), eager_blob_objects->size()); - CHECK_EQ(nn_graph->outputs_op_names().size(), nn_graph->outputs_valid().size()); - for (int i = 0; i < nn_graph->outputs_op_names().size(); ++i) { - CHECK(op_name2interface_index_.emplace(nn_graph->outputs_op_names().at(i), i).second); - } - } - - ~OutputCriticalSectionBeginPhyInstrOperand() override = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - // for inputs - void ForEachConstMirroredObject(const std::function&) const {} - - // for outputs - void ForEachMut2MirroredObject( - const std::function& DoEach) const { - ForEachMirroredObject(DoEach); - } - - const std::vector& interfaces_op_names() const override { - return nn_graph_->outputs_op_names(); - } - const std::vector& interfaces_valid() const override { return nn_graph_->outputs_valid(); } - std::string GetInterfaceBufferName(const std::string& job_name, - const std::string& op_name) const override { - return GetOutputBufferName(job_name, op_name); - } - std::string GetInterfaceCriticalSectionCallbackBufferName( - const std::string& job_name) const override { - return GetOutputCriticalSectionCallbackBufferName(job_name); - } - std::string GetInterfaceCriticalSectionWaitBufferName( - const std::string& job_name) const override { - return GetOutputCriticalSectionWaitBufferName(job_name); - } - void AccessBlobByOpName(uint64_t of_blob_ptr, const std::string& op_name) override; - - private: - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -class CriticalSectionEndPhyInstrOperand : public PhyInstrOperand { - public: - CriticalSectionEndPhyInstrOperand(const std::shared_ptr& eager_blob_object, - const std::shared_ptr& event_record, - vm::Stream* vm_stream) - : eager_blob_object_(eager_blob_object), event_record_(event_record), vm_stream_(vm_stream) {} - virtual ~CriticalSectionEndPhyInstrOperand() = default; - - const std::shared_ptr& event_record() const { return event_record_; } - - void ForEachMirroredObject(const std::function&) const; - - void ForEachMutMirroredObject(const std::function&) const; - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - DoEach(eager_blob_object_.get()); - } - - private: - std::shared_ptr eager_blob_object_; - std::shared_ptr event_record_; - vm::Stream* vm_stream_; -}; - -class InputCriticalSecondEndPhyInstrOperand final : public CriticalSectionEndPhyInstrOperand { - public: - InputCriticalSecondEndPhyInstrOperand(const std::shared_ptr& eager_blob_object, - const std::shared_ptr& event_record, - vm::Stream* vm_stream) - : CriticalSectionEndPhyInstrOperand(eager_blob_object, event_record, vm_stream), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - } - ~InputCriticalSecondEndPhyInstrOperand() override = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachConstMirroredObject( - const std::function& DoEach) const { - ForEachMirroredObject(DoEach); - } - - void ForEachMut2MirroredObject(const std::function&) const {} - - private: - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -class OutputCriticalSecondEndPhyInstrOperand final : public CriticalSectionEndPhyInstrOperand { - public: - OutputCriticalSecondEndPhyInstrOperand(const std::shared_ptr& eager_blob_object, - const std::shared_ptr& event_record, - vm::Stream* vm_stream) - : CriticalSectionEndPhyInstrOperand(eager_blob_object, event_record, vm_stream), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - } - ~OutputCriticalSecondEndPhyInstrOperand() override = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - // for inputs - void ForEachConstMirroredObject(const std::function&) const {} - - // for outputs - void ForEachMut2MirroredObject( - const std::function& DoEach) const { - ForEachMirroredObject(DoEach); - } - - private: - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_EAGER_CRITICAL_SECTION_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/eager/eager_blob_object.cpp b/oneflow/core/eager/eager_blob_object.cpp index 3f2a0e48da60c8e3b4b11ad9b47b10a9764a860c..c88bcb6ef1a5d969ec8d24e8cc537c3663b24489 100644 --- a/oneflow/core/eager/eager_blob_object.cpp +++ b/oneflow/core/eager/eager_blob_object.cpp @@ -18,53 +18,77 @@ limitations under the License. #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/shut_down_util.h" #include "oneflow/core/common/shape_vec.h" +#include "oneflow/core/common/tensor_meta.h" namespace oneflow { + namespace vm { -EagerBlobObject::EagerBlobObject(const std::shared_ptr& mem_case, - const std::shared_ptr& shape, - const std::shared_ptr& stride, DataType data_type, - const std::shared_ptr& tensor_storage, - const intrusive::shared_ptr& dep_object) +EagerBlobObject::EagerBlobObject( + const std::shared_ptr& mem_case, + const Symbol& static_local_tensor_meta, + const std::shared_ptr& dynamic_local_tensor_meta, + DataType data_type, const std::shared_ptr& tensor_storage, + const intrusive::shared_ptr& dep_object) : is_dynamic_(false), mem_case_(mem_case), data_type_(data_type), - shape_(shape), - stride_(stride), storage_offset_(0), tensor_storage_(tensor_storage), - mem_ptr_for_allocation_compuation_pipelining_(nullptr), - inited_mem_ptr_for_allocation_compuation_pipelining_(false), - is_non_pod_object_placement_newed_(false), - is_shape_synced_(true), compute_local_dep_object_(dep_object), - blob_desc_(shape, stride, data_type) { - CHECK(static_cast(shape)); - CHECK(static_cast(stride)); + static_local_tensor_meta_(static_local_tensor_meta), + dynamic_local_tensor_meta_(dynamic_local_tensor_meta) { CHECK(static_cast(tensor_storage)); } -Blob* EagerBlobObject::blob() { - if (!blob_) { - blob_.reset(new Blob(*mem_case_, &blob_desc_, mut_header_ptr(), mut_dptr())); +// user_op::TensorDesc overrides +const Shape& EagerBlobObject::shape() const { + if (dynamic_local_tensor_meta_) { + return dynamic_local_tensor_meta_->shape(); + } else { + return static_local_tensor_meta_->shape(); + } +} +const Stride& EagerBlobObject::stride() const { + if (dynamic_local_tensor_meta_) { + return dynamic_local_tensor_meta_->stride(); + } else { + return static_local_tensor_meta_->stride(); } - return blob_.get(); } -void EagerBlobObject::set_storage_offset(const int64_t offset) { storage_offset_ = offset; } +void EagerBlobObject::set_shape(const Shape& shape) { + CHECK(dynamic_local_tensor_meta_); + std::const_pointer_cast(dynamic_local_tensor_meta_)->set_shape(shape); +} +void EagerBlobObject::set_stride(const Stride& stride) { + CHECK(dynamic_local_tensor_meta_); + std::const_pointer_cast(dynamic_local_tensor_meta_)->set_stride(stride); +} -void EagerBlobObject::TryInitNonPODTypeEagerBlobObjectIfNeed() { - if (!IsPODDataType(data_type())) { - if (!is_non_pod_object_placement_newed_) { - InitNonPODTypeEagerBlobObjectIfNeed(tensor_storage_->non_pod_allocator(), this); - is_non_pod_object_placement_newed_ = true; - } +MutShapeView EagerBlobObject::mut_shape_view() { + CHECK(dynamic_local_tensor_meta_); + return *const_cast(dynamic_local_tensor_meta_->shape_ptr().get()); +} + +std::shared_ptr EagerBlobObject::shape_ptr() const { + if (dynamic_local_tensor_meta_) { + return dynamic_local_tensor_meta_->shape_ptr(); + } else { + return static_local_tensor_meta_->shape_ptr(); + } +} +std::shared_ptr EagerBlobObject::stride_ptr() const { + if (dynamic_local_tensor_meta_) { + return dynamic_local_tensor_meta_->stride_ptr(); + } else { + return static_local_tensor_meta_->stride_ptr(); } } -Maybe EagerBlobObject::TryAllocateBlobBodyMemory(DeviceCtx* device_ctx) { - vm::Allocator* allocator = device_ctx->mut_allocator(); +void EagerBlobObject::set_storage_offset(const int64_t offset) { storage_offset_ = offset; } + +Maybe EagerBlobObject::TryAllocateBlobBodyMemory(vm::Allocator* allocator) { size_t required_body_bytes = AlignedByteSizeOfBlobBody(); if (required_body_bytes == 0) { CHECK_ISNULL_OR_RETURN(tensor_storage_->blob_dptr()); @@ -81,10 +105,10 @@ Maybe EagerBlobObject::TryAllocateBlobBodyMemory(DeviceCtx* device_ctx) { }; tensor_storage_->set_blob_dptr(std::unique_ptr>(dptr, Free), required_body_bytes); - InitMemPtrForAllocationComputationPipelining(); + InitNonPODTypeEagerBlobObjectIfNeed(tensor_storage_->non_pod_allocator(), this); + return true; } - InitOrCheckMemPtrForAllocationComputationPipelining(); - return Maybe::Ok(); + return false; } } // namespace vm diff --git a/oneflow/core/eager/eager_blob_object.h b/oneflow/core/eager/eager_blob_object.h index ce39193881ba4fb485dbd6d0cbe9d8e1419c7a10..0b742c7cc87ae2a8ec17e86290bcb91b1381061f 100644 --- a/oneflow/core/eager/eager_blob_object.h +++ b/oneflow/core/eager/eager_blob_object.h @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/optional.h" +#include "oneflow/core/common/op_args_reserved_size.h" #include "oneflow/core/eager/local_dep_object.h" #include "oneflow/core/device/device_context.h" #include "oneflow/core/memory/memory_allocator.h" @@ -25,24 +26,34 @@ limitations under the License. #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/tensor_methods.h" #include "oneflow/core/framework/user_op_tensor.h" -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/register/blob.h" namespace oneflow { +namespace one { + +class LocalTensorMeta; +class MutLocalTensorMeta; + +} // namespace one + namespace vm { class TensorStorage { public: TensorStorage() - : non_pod_allocator_(std::make_unique()), + : blob_bytes_(0), + non_pod_allocator_(std::make_unique()), producer_stream_(NullOpt), last_used_stream_(NullOpt) {} - ~TensorStorage() { + virtual ~TensorStorage() { for (const auto& hook : storage_delete_hooks_) { hook(); } } + virtual bool is_allocated_in_vm() const = 0; + size_t blob_bytes() const { return blob_bytes_; } char* blob_dptr() { return blob_dptr_.get(); } @@ -84,58 +95,77 @@ class TensorStorage { std::vector> storage_delete_hooks_; }; +class InsideVmTensorStorage : public TensorStorage { + public: + InsideVmTensorStorage() = default; + ~InsideVmTensorStorage() = default; + + bool is_allocated_in_vm() const override { return true; } +}; + +class OutsideVmTensorStorage : public TensorStorage { + public: + OutsideVmTensorStorage() = default; + ~OutsideVmTensorStorage() = default; + + bool is_allocated_in_vm() const override { return false; } +}; + class EagerBlobObject final : public user_op::Tensor, public user_op::TensorDesc, public std::enable_shared_from_this { public: EagerBlobObject(const EagerBlobObject&) = delete; EagerBlobObject(EagerBlobObject&&) = delete; - EagerBlobObject(const std::shared_ptr& mem_case, const std::shared_ptr& shape, - const std::shared_ptr& stride, DataType data_type, - const std::shared_ptr& tensor_storage) - : EagerBlobObject(mem_case, shape, stride, data_type, tensor_storage, - intrusive::shared_ptr()) {} - EagerBlobObject(const std::shared_ptr& mem_case, const std::shared_ptr& shape, - const std::shared_ptr& stride, DataType data_type, - const std::shared_ptr& tensor_storage, + EagerBlobObject(const std::shared_ptr& mem_case, + const Symbol& static_local_tensor_meta, + const std::shared_ptr& dynamic_local_tensor_meta, + DataType data_type, const std::shared_ptr& tensor_storage) + : EagerBlobObject(mem_case, static_local_tensor_meta, dynamic_local_tensor_meta, data_type, + tensor_storage, intrusive::shared_ptr()) {} + EagerBlobObject(const std::shared_ptr& mem_case, + const Symbol& static_local_tensor_meta, + const std::shared_ptr& dynamic_local_tensor_meta, + DataType data_type, const std::shared_ptr& tensor_storage, const intrusive::shared_ptr& dep_object); ~EagerBlobObject() { tensor_storage_.reset(); } + const std::shared_ptr& mut_tensor_meta() { + return dynamic_local_tensor_meta_; + } + // Getters + const Symbol& tensor_meta() const { return static_local_tensor_meta_; } + // user_op::TensorDesc overrides - const Shape& shape() const override { return *shape_; } - Shape* mut_shape() override { return shape_.get(); } - const Stride& stride() const override { return *stride_; } - Stride* mut_stride() override { return stride_.get(); } + const Shape& shape() const override; + const Stride& stride() const override; DataType data_type() const override { return data_type_; } - DataType* mut_data_type() override { return &data_type_; } bool is_dynamic() const override { return is_dynamic_; } - bool* mut_is_dynamic() override { return &is_dynamic_; } + + void set_shape(const Shape& shape) override; + void set_stride(const Stride& stride) override; + void set_data_type(DataType data_type) override { data_type_ = data_type; } void set_is_dynamic(bool is_dynamic) override { is_dynamic_ = is_dynamic; } // user_op::Tensor overrides - ShapeView shape_view() const override { return *shape_; } - MutShapeView mut_shape_view() override { return *shape_; } + ShapeView shape_view() const override { return shape(); } + MutShapeView mut_shape_view() override; const MemoryCase& mem_case() const override { return *mem_case_; } const void* raw_dptr() const override { - CHECK(inited_mem_ptr_for_allocation_compuation_pipelining_) - << "mem_ptr_for_allocation_compuation_pipelining_ not initialized. Please check if there " - "are any EagerBlobObjects created outside vm"; - return mem_ptr_for_allocation_compuation_pipelining_ - + storage_offset_ * GetSizeOfDataType(data_type_); + char* ptr = tensor_storage_->blob_dptr(); + if (tensor_storage_->blob_bytes() > 0) { CHECK_NOTNULL(ptr); } + return ptr + storage_offset_ * GetSizeOfDataType(data_type_); } void* mut_raw_dptr() override { return const_cast(raw_dptr()); } void set_storage_offset(const int64_t offset); - [[deprecated("\"Blob\" will be removed in eager. Please avoid to use this method whenever " - "possible. Almost all methods of `Blob` are also in `EagerBlobObject`.")]] Blob* - blob(); - - Maybe TryAllocateBlobBodyMemory(DeviceCtx* device_ctx); + // Returns true if allocate successfully. + Maybe TryAllocateBlobBodyMemory(vm::Allocator* allocator); Maybe DeallocateBlobDataPtr() { tensor_storage_->Release(); - tensor_storage_.reset(new TensorStorage); + tensor_storage_.reset(new InsideVmTensorStorage()); return Maybe::Ok(); } void RegisterStorageDeleteHook(const std::function& hook) { @@ -149,10 +179,6 @@ class EagerBlobObject final : public user_op::Tensor, std::shared_ptr& tensor_storage() { return tensor_storage_; } - bool is_shape_synced() const { return is_shape_synced_; } - - void set_is_shape_synced(bool val) { is_shape_synced_ = val; } - const Optional>& producer_stream() const { return tensor_storage_->producer_stream(); } @@ -167,10 +193,10 @@ class EagerBlobObject final : public user_op::Tensor, tensor_storage_->set_last_used_stream(last_used_stream); } - std::shared_ptr shape_ptr() const { return shape_; } - std::shared_ptr stride_ptr() const { return stride_; } + std::shared_ptr shape_ptr() const; + std::shared_ptr stride_ptr() const; - size_t ByteSizeOfBlobBody() const { return shape_->elem_cnt() * GetSizeOfDataType(data_type_); } + size_t ByteSizeOfBlobBody() const { return shape().elem_cnt() * GetSizeOfDataType(data_type_); } size_t AlignedByteSizeOfBlobBody() const { return RoundUp(ByteSizeOfBlobBody(), kBlobBodyAlignSize); } @@ -179,52 +205,28 @@ class EagerBlobObject final : public user_op::Tensor, return RoundUp(ByteSizeOfBlobHeader(), kBlobHeaderAlignSize); } - const char* header_ptr() const { return reinterpret_cast(shape_->dim_vec().data()); } - char* mut_header_ptr() { return reinterpret_cast(shape_->dim_vec().data()); } - - void InitOrCheckMemPtrForAllocationComputationPipelining() { - auto* ptr = tensor_storage_->blob_dptr(); - if (inited_mem_ptr_for_allocation_compuation_pipelining_) { - CHECK_EQ(mem_ptr_for_allocation_compuation_pipelining_, ptr); - } else { - mem_ptr_for_allocation_compuation_pipelining_ = ptr; - inited_mem_ptr_for_allocation_compuation_pipelining_ = true; - } + const char* header_ptr() const { return reinterpret_cast(shape().dim_vec().data()); } + char* mut_header_ptr() { + return reinterpret_cast(const_cast(shape().dim_vec().data())); } - void TryInitNonPODTypeEagerBlobObjectIfNeed(); - private: - void InitMemPtrForAllocationComputationPipelining() { - auto* ptr = tensor_storage_->blob_dptr(); - CHECK(!inited_mem_ptr_for_allocation_compuation_pipelining_) - << "mem_ptr_for_allocation_compuation_pipelining_ has been initialized."; - mem_ptr_for_allocation_compuation_pipelining_ = ptr; - inited_mem_ptr_for_allocation_compuation_pipelining_ = true; - } - bool is_dynamic_; std::shared_ptr mem_case_; DataType data_type_; - std::shared_ptr shape_; - std::shared_ptr stride_; int64_t storage_offset_; std::shared_ptr tensor_storage_; - // For allocation-computation pipeline, the value of mem_ptr_for_allocation_compuation_pipelining_ - // are kept even after tensor_storage_.reset(). - char* mem_ptr_for_allocation_compuation_pipelining_; - bool inited_mem_ptr_for_allocation_compuation_pipelining_; - bool is_non_pod_object_placement_newed_; - std::atomic is_shape_synced_; - bool pin_memory_; intrusive::shared_ptr compute_local_dep_object_; - // NOTE: Will be removed soon. Avoid to use it whenever possible. - BlobDesc blob_desc_; - std::unique_ptr blob_; + Symbol static_local_tensor_meta_; + std::shared_ptr dynamic_local_tensor_meta_; }; +using EagerBlobObjectList = small_vector, kOpArgsReservedSize>; +using EagerBlobObjectListPtr = std::shared_ptr; + } // namespace vm + } // namespace oneflow #endif // ONEFLOW_CORE_EAGER_EAGER_BLOB_OBJECT_H_ diff --git a/oneflow/core/eager/lazy_job_instruction_type.h b/oneflow/core/eager/lazy_job_instruction_type.h deleted file mode 100644 index 66c5b261be383e5d57a2af9f44e1b40f753642f1..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/lazy_job_instruction_type.h +++ /dev/null @@ -1,130 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_LAZY_JOB_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_LAZY_JOB_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/lazy_job_device_context.h" -#include "oneflow/core/eager/lazy_job_phy_instr_operand.h" -#include "oneflow/core/framework/nn_graph_if.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/of_unused.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/job/job_instance.h" -#include "oneflow/core/common/buffer_manager.h" -#include "oneflow/core/common/singleton.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/vm/naive_instruction_status_querier.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/kernel/kernel_util.h" - -namespace oneflow { - -class LazyJobInstance final : public JobInstance { - public: - LazyJobInstance(const LazyJobInstance&) = delete; - LazyJobInstance(LazyJobInstance&&) = delete; - ~LazyJobInstance() override = default; - LazyJobInstance(const std::string& job_name, const std::function& finish_cb) - : job_name_(job_name), finish_cb_(finish_cb) {} - - std::string job_name() const override { return job_name_; } - void Finish() const override { finish_cb_(); } - - std::string sole_input_op_name_in_user_job() const override { - UNIMPLEMENTED(); - return std::string(); - } - std::string sole_output_op_name_in_user_job() const override { - UNIMPLEMENTED(); - return std::string(); - } - void PushBlob(uint64_t ofblob_ptr) const override { UNIMPLEMENTED(); } - void PullBlob(uint64_t ofblob_ptr) const override { UNIMPLEMENTED(); } - - private: - const std::string job_name_; - const std::function finish_cb_; -}; - -namespace vm { - -class LaunchLazyJobInstructionType final : public InstructionType { // NOLINT - public: - LaunchLazyJobInstructionType(const LaunchLazyJobInstructionType&) = delete; - LaunchLazyJobInstructionType(LaunchLazyJobInstructionType&&) = delete; - LaunchLazyJobInstructionType() = default; - ~LaunchLazyJobInstructionType() = default; - - std::string DebugName(const vm::Instruction&) const override { return "LaunchLazyJob"; } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(vm::Instruction* instruction) const override { - const auto& cur_nn_graph = GetCurNNGraph(instruction); - auto* device_ctx = GetLazyJobDeviceCtx(instruction); - - static thread_local int64_t run_id = 0; - { - OF_PROFILER_RANGE_GUARD("WaitUntilQueueEmptyIfFrontNNGraphNotEquals"); - device_ctx->WaitUntilQueueEmptyIfFrontNNGraphNotEquals(cur_nn_graph); - } - { - OF_PROFILER_RANGE_GUARD("Send all buffers to BufferMgr"); - const auto& job_instance = MakeJobInstance(instruction); - const auto& job_name = job_instance->job_name(); - auto* buffer_mgr = Singleton>>::Get(); - buffer_mgr->Get(GetCallbackNotifierBufferName(job_name))->Push(job_instance); - buffer_mgr->Get(GetSourceTickBufferName(job_name))->Push(job_instance); - } - OF_UNUSED(run_id); // disable compiler warning. - OF_PROFILER_RANGE_GUARD("EnqueueNNGraph"); - device_ctx->EnqueueNNGraph(cur_nn_graph); - } - - private: - LazyJobDeviceCtx* GetLazyJobDeviceCtx(Instruction* instruction) const { - auto* stream = instruction->mut_stream(); - auto* device_ctx = dynamic_cast(stream->device_ctx().get()); - CHECK_NOTNULL(device_ctx); - return device_ctx; - } - - std::shared_ptr GetCurNNGraph(Instruction* instruction) const { - const auto* ptr = instruction->phy_instr_operand().get(); - const auto* phy_instr_operand = dynamic_cast(ptr); - CHECK_NOTNULL(phy_instr_operand); - return phy_instr_operand->nn_graph(); - } - - std::shared_ptr MakeJobInstance(Instruction* instruction) const { - const auto* ptr = instruction->phy_instr_operand().get(); - const auto* phy_instr_operand = dynamic_cast(ptr); - CHECK_NOTNULL(phy_instr_operand); - const auto& nn_graph = phy_instr_operand->nn_graph(); - const auto& FinishCb = [this, instruction]() { - auto* device_ctx = GetLazyJobDeviceCtx(instruction); - device_ctx->DequeueNNGraph(); - auto* status_buffer = instruction->mut_status_buffer(); - NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer())->set_done(); - }; - return std::make_shared(nn_graph->job_name(), FinishCb); - } -}; - -} // namespace vm -} // namespace oneflow -#endif // ONEFLOW_CORE_EAGER_LAZY_JOB_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/eager/lazy_job_phy_instr_operand.cpp b/oneflow/core/eager/lazy_job_phy_instr_operand.cpp deleted file mode 100644 index 2418876bbbb2b74d0dd7ac2e37d4cea4c2f3d792..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/lazy_job_phy_instr_operand.cpp +++ /dev/null @@ -1,37 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/eager/lazy_job_phy_instr_operand.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/stream.h" -#include "oneflow/core/vm/virtual_machine.h" - -namespace oneflow { -namespace vm { - -void LaunchLazyJobPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - for (const auto& eager_blob_object : *param_blob_objects_) { - DoEach(CHECK_JUST(eager_blob_object->compute_local_dep_object())); - } - DoEach(CHECK_JUST(SingletonMaybe()) - ->FindOrCreateTransportLocalDepObject() - .Mutable()); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/eager/lazy_job_phy_instr_operand.h b/oneflow/core/eager/lazy_job_phy_instr_operand.h deleted file mode 100644 index 2a231fdd0d73465e549fc104897a998c26e77858..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/lazy_job_phy_instr_operand.h +++ /dev/null @@ -1,79 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_LAZY_JOB_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_EAGER_LAZY_JOB_PHY_INSTR_OPERAND_H_ - -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/eager/local_dep_object.h" -#include "oneflow/core/device/event_record.h" -#include "oneflow/core/eager/critical_section_phy_instr_operand.h" -#include "oneflow/core/framework/nn_graph_if.h" -#include "oneflow/core/common/notifier.h" - -namespace oneflow { - -namespace one { - -using EagerBlobObjectListPtr = - std::shared_ptr>>; - -} - -namespace vm { - -class LaunchLazyJobPhyInstrOperand final : public PhyInstrOperand { - public: - LaunchLazyJobPhyInstrOperand(const LaunchLazyJobPhyInstrOperand&) = delete; - LaunchLazyJobPhyInstrOperand(LaunchLazyJobPhyInstrOperand&&) = delete; - ~LaunchLazyJobPhyInstrOperand() override = default; - - LaunchLazyJobPhyInstrOperand(const std::shared_ptr& nn_graph, - const one::EagerBlobObjectListPtr& param_blob_objects) - : nn_graph_(nn_graph), - param_blob_objects_(param_blob_objects), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - stream_sequential_dependence_ = nullptr; - } - - const std::shared_ptr& nn_graph() const { return nn_graph_; } - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachConstMirroredObject(const std::function&) const {} - - void ForEachMutMirroredObject(const std::function&) const; - - void ForEachMut2MirroredObject(const std::function&) const {} - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - for (const auto& eager_blob_object : *param_blob_objects_) { DoEach(eager_blob_object.get()); } - } - - private: - std::shared_ptr nn_graph_; - one::EagerBlobObjectListPtr param_blob_objects_; - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_EAGER_LAZY_JOB_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/eager/local_dep_object.h b/oneflow/core/eager/local_dep_object.h index 0ec7ecac0221bff158dd23ae19f85ca8acfda764..edfe7d73c62c219c226d3be5cd94c50992fd8958 100644 --- a/oneflow/core/eager/local_dep_object.h +++ b/oneflow/core/eager/local_dep_object.h @@ -20,12 +20,16 @@ limitations under the License. #include "oneflow/core/vm/vm_object.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/common/op_args_reserved_size.h" #include "oneflow/core/framework/device.h" namespace oneflow { // LocalDepObject helps VirtualMachineEngine building instruction edges -using LocalDepObject = vm::MirroredObject; +using LocalDepObject = vm::Dependence; + +using DependenceVector = small_vector; intrusive::shared_ptr NewLocalDepObject(); diff --git a/oneflow/core/eager/op_call_instruction_type.cpp b/oneflow/core/eager/op_call_instruction_type.cpp deleted file mode 100644 index 9fafef279bf148d51c088339d2422e36c16863bd..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/op_call_instruction_type.cpp +++ /dev/null @@ -1,160 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/device_type.pb.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/common/protobuf.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else -#include "oneflow/core/ep/cuda/cuda_stream.h" -#endif -#include "oneflow/core/job/job_desc.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/allocator.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/eager/op_call_instruction_type.h" -#include "oneflow/core/eager/op_call_phy_instr_operand.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/job/foreign_callback.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/vm/symbol_storage.h" -#include "oneflow/core/operator/op_conf_symbol.h" -#include "oneflow/user/kernels/stateful_opkernel.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/profiler/profile_manager.h" -#include "oneflow/core/profiler/event_recorder.h" -#include "oneflow/core/common/cpp_attribute.h" - -namespace oneflow { -namespace vm { - -struct OpCallInstructionUtil final { - static inline Maybe Prepare(const vm::Instruction& instruction) { - auto* operand = GetCallPhyInstrOperand(instruction); - DeviceCtx* device_ctx = instruction.stream().device_ctx().get(); - JUST(AllocateOutputBlobsMemory(operand, device_ctx)); - if (unlikely(operand->need_temp_storage())) { - InferTempStorageSize(operand); - JUST(TryAllocateTempStorage(operand, device_ctx)); - // Since memory block is cached in allocator, it's safe to deallocate tmp buffer before - // kernel executed. - DeallocateTempStorage(operand, device_ctx); - } - return Maybe::Ok(); - } - - static inline void Compute(const vm::Instruction& instruction) { - auto* operand = GetCallPhyInstrOperand(instruction); - DeviceCtx* device_ctx = instruction.stream().device_ctx().get(); - if (!operand->is_all_outputs_pod()) { - for (const auto& blob_object : *operand->outputs()) { - blob_object->TryInitNonPODTypeEagerBlobObjectIfNeed(); - } - } - user_op::OpKernelState* state = nullptr; - user_op::OpKernelCache* cache = nullptr; - if (operand->user_opkernel()->has_state_or_cache()) { - TryInitOpKernelStateAndCache(operand, device_ctx, &state, &cache); - } - OpKernelCompute(operand, device_ctx, state, cache); - } - - static inline OpCallPhyInstrOperand* GetCallPhyInstrOperand(const vm::Instruction& instruction) { - auto* operand = CHECK_NOTNULL(instruction.phy_instr_operand().get()); - return CHECK_NOTNULL(dynamic_cast(operand)); - } - - private: - static inline void InferTempStorageSize(OpCallPhyInstrOperand* operand) { - auto* tmp_tensor = operand->mut_call_ctx()->mut_tmp_tensor(); - size_t temp_size = - operand->opkernel().InferTmpSize(&operand->call_ctx_, operand->user_opkernel()); - tmp_tensor->set_tmp_buffer_size(temp_size); - } - - static inline void TryInitOpKernelStateAndCache(OpCallPhyInstrOperand* operand, - DeviceCtx* device_ctx, - user_op::OpKernelState** state, - user_op::OpKernelCache** cache) { - OF_PROFILER_RANGE_GUARD("TryInitOpKernelStateAndCache"); - if (likely(operand->op_interp_ctx().state)) { - *state = operand->op_interp_ctx().state.get(); - // set state to nullptr so that state initialization in TryInitOpKernelStateAndCache will be - // skipped. - state = nullptr; - } - operand->mut_opkernel()->TryInitOpKernelStateAndCache(&operand->call_ctx_, device_ctx, - operand->user_opkernel(), state, cache); - } - - static inline Maybe AllocateOutputBlobsMemory(OpCallPhyInstrOperand* operand, - DeviceCtx* device_ctx) { - OF_PROFILER_RANGE_GUARD("AllocateOutputBlobsMemory"); - for (const auto& blob_object : *operand->outputs()) { - JUST(blob_object->TryAllocateBlobBodyMemory(device_ctx)); - } - return Maybe::Ok(); - } - - static inline Maybe TryAllocateTempStorage(OpCallPhyInstrOperand* operand, - DeviceCtx* device_ctx) { - OF_PROFILER_RANGE_GUARD("TryAllocateTempStorage"); - auto* tmp_tensor = operand->mut_call_ctx()->mut_tmp_tensor(); - size_t byte_size = tmp_tensor->tmp_buffer_size(); - if (byte_size > 0) { - char* mem_ptr = nullptr; - JUST(device_ctx->mut_allocator()->Allocate(&mem_ptr, byte_size)); - tmp_tensor->init_tmp_buffer_ptr(mem_ptr); - } - return Maybe::Ok(); - } - - static inline void OpKernelCompute(OpCallPhyInstrOperand* operand, DeviceCtx* device_ctx, - user_op::OpKernelState* state, user_op::OpKernelCache* cache) { - auto* call_ctx = &operand->call_ctx_; - auto* user_kernel = operand->user_opkernel(); - operand->mut_opkernel()->Compute(call_ctx, device_ctx, user_kernel, state, cache); - } - - static inline void DeallocateTempStorage(OpCallPhyInstrOperand* operand, DeviceCtx* device_ctx) { - OF_PROFILER_RANGE_GUARD("DeallocateTempStorage"); - auto* tmp_tensor = operand->mut_call_ctx()->mut_tmp_tensor(); - device_ctx->mut_allocator()->Deallocate(tmp_tensor->mut_tmp_buffer_ptr(), - tmp_tensor->tmp_buffer_size()); - } -}; - -Maybe OpCallInstructionType::Prepare(vm::Instruction* instruction) const { - return OpCallInstructionUtil::Prepare(*instruction); -} - -void OpCallInstructionType::Compute(vm::Instruction* instruction) const { - OpCallInstructionUtil::Compute(*instruction); -} - -std::string OpCallInstructionType::DebugName(const vm::Instruction& instruction) const { - auto* operand = CHECK_NOTNULL(instruction.phy_instr_operand().get()); - return CHECK_NOTNULL(dynamic_cast(operand))->opkernel().op_type_name() - + ":OpCall"; -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/eager/op_call_instruction_type.h b/oneflow/core/eager/op_call_instruction_type.h deleted file mode 100644 index eb5a4556e6c237a8fcc7913c9e65cfb8116b1995..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/op_call_instruction_type.h +++ /dev/null @@ -1,46 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_OP_CALL_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_OP_CALL_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/memory/memory_case.pb.h" - -namespace oneflow { -namespace vm { - -class OpCallInstructionType final : public vm::InstructionType { - public: - OpCallInstructionType() = default; - ~OpCallInstructionType() = default; - - Maybe Prepare(vm::Instruction* instruction) const override; - void Compute(vm::Instruction* instruction) const override; - - InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAtAnyPosition; } - - std::string DebugName(const vm::Instruction& instruction) const override; - - protected: - private: - Maybe MaybeCompute(vm::Instruction* instruction) const; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_EAGER_OP_CALL_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/eager/op_call_phy_instr_operand.cpp b/oneflow/core/eager/op_call_phy_instr_operand.cpp deleted file mode 100644 index 0e3f5f6bde77d6bf04cd1ecbca0a6a410b538105..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/op_call_phy_instr_operand.cpp +++ /dev/null @@ -1,107 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/eager/op_call_phy_instr_operand.h" -#include "oneflow/user/kernels/stateful_opkernel.h" -#include "oneflow/core/eager/dev_vm_dep_object_consume_mode.h" -#include "oneflow/core/framework/stream_is_comm_net_stream.h" -#include "oneflow/core/vm/stream.h" - -namespace oneflow { -namespace vm { - -OpCallPhyInstrOperand::OpCallPhyInstrOperand( - vm::Stream* vm_stream, const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& inputs, const one::EagerBlobObjectListPtr& outputs, - const std::shared_ptr& consistent_tensor_infer_result, - const one::OpExprInterpContext& op_interp_ctx, - const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode) - : vm_stream_(vm_stream), - call_ctx_(ComposedAttrMap(op_interp_ctx.attrs, opkernel->base_attrs()), inputs, outputs, - consistent_tensor_infer_result, op_interp_ctx, opkernel->mem_case()), - opkernel_(opkernel), - user_opkernel_(nullptr), - infer_tmp_size_fn_(nullptr), - need_temp_storage_(false), - dev_vm_dep_object_consume_mode_(dev_vm_dep_object_consume_mode), - input_dependences_(), - output_dependences_(), - is_all_outputs_pod_(false) { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - InitStreamSequentialDependence(); - for (const auto& blob_object : *outputs) { - is_all_outputs_pod_ = is_all_outputs_pod_ && IsPODDataType(blob_object->data_type()); - } -} - -Maybe OpCallPhyInstrOperand::Init() { - return mut_opkernel()->ChooseOpKernel(&call_ctx_, &user_opkernel_, &need_temp_storage_); -} - -void OpCallPhyInstrOperand::ForEachConstMirroredObject( - const std::function& DoEach) const { - const auto& input_list = inputs(); - for (int64_t index : opkernel().input_tuple_indexes4const_ibns()) { - const auto& input = input_list->at(index); - DoEach(CHECK_JUST(input->compute_local_dep_object())); - } -} - -void OpCallPhyInstrOperand::InitStreamSequentialDependence() { - auto* device_schedule_dep_object = vm_stream_->schedule_local_dep_object().get(); - if (IsCommNetStream::Visit(vm_stream_->stream_role())) { - // Sequantialize nccl instructions to avoid deadlock - stream_sequential_dependence_ = device_schedule_dep_object; - } else { - // Sequantialize instructions to avoid explosive memory allocation of source ops - if (dev_vm_dep_object_consume_mode() == one::DevVmDepObjectConsumeMode::MUTABLE) { - stream_sequential_dependence_ = device_schedule_dep_object; - } else if (opkernel().input_tuple_indexes4const_ibns().empty() - && opkernel().input_tuple_indexes4mut_ibns().empty()) { - stream_sequential_dependence_ = device_schedule_dep_object; - } - } -} - -void OpCallPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - const auto& opt_transport_dep_object = vm_stream_->transport_local_dep_object(); - if (opt_transport_dep_object.has_value()) { DoEach(CHECK_JUST(opt_transport_dep_object)->get()); } - - const auto& input_list = inputs(); - for (int64_t index : opkernel().input_tuple_indexes4mut_ibns()) { - const auto& input = input_list->at(index); - DoEach(CHECK_JUST(input->compute_local_dep_object())); - } - const auto& output_list = outputs(); - for (int64_t index : opkernel().output_tuple_indexes4mut_obns()) { - const auto& output = output_list->at(index); - DoEach(CHECK_JUST(output->compute_local_dep_object())); - } -} - -void OpCallPhyInstrOperand::ForEachMut2MirroredObject( - const std::function& DoEach) const { - const auto& output_list = outputs(); - for (int64_t index : opkernel().output_tuple_indexes4mut2_obns()) { - const auto& output = output_list->at(index); - DoEach(CHECK_JUST(output->compute_local_dep_object())); - } -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/eager/op_call_phy_instr_operand.h b/oneflow/core/eager/op_call_phy_instr_operand.h deleted file mode 100644 index 12c51fb064dabccb86017a7b837b2e210110c0d7..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/op_call_phy_instr_operand.h +++ /dev/null @@ -1,121 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_OP_CALL_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_EAGER_OP_CALL_PHY_INSTR_OPERAND_H_ - -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/eager/call_context.h" -#include "oneflow/core/eager/dev_vm_dep_object_consume_mode.h" -#include "oneflow/core/framework/user_op_kernel_registry.h" - -namespace oneflow { - -namespace user_op { - -class OpKernel; - -} // namespace user_op - -namespace vm { - -class Stream; - -struct OpCallInstructionUtil; - -class OpCallPhyInstrOperand final : public vm::PhyInstrOperand { - public: - OpCallPhyInstrOperand(const OpCallPhyInstrOperand&) = delete; - OpCallPhyInstrOperand(OpCallPhyInstrOperand&&) = delete; - ~OpCallPhyInstrOperand() override = default; - - template - static Maybe New(Args&&... args) { - auto* ptr = new OpCallPhyInstrOperand(std::forward(args)...); - JUST(ptr->Init()); - return std::shared_ptr(ptr); - } - - const one::StatefulOpKernel& opkernel() const { return *opkernel_; } - const one::EagerBlobObjectListPtr& inputs() const { return call_ctx_.inputs(); } - const one::EagerBlobObjectListPtr& outputs() const { return call_ctx_.outputs(); } - const AttrMap& attrs() const { return call_ctx_.op_interp_ctx().attrs; } - const one::OpExprInterpContext& op_interp_ctx() const { return call_ctx_.op_interp_ctx(); } - const one::DevVmDepObjectConsumeMode& dev_vm_dep_object_consume_mode() const { - return dev_vm_dep_object_consume_mode_; - } - - bool is_all_outputs_pod() const { return is_all_outputs_pod_; } - - one::StatefulOpKernel* mut_opkernel() { return opkernel_.get(); } - - template - Maybe ForEachOutputTensor(const DoEachT& DoEach) { - for (const auto& output : *outputs()) { JUST(DoEach(output.get())); } - return Maybe::Ok(); - } - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachConstMirroredObject(const std::function&) const; - - void ForEachMutMirroredObject(const std::function&) const; - - void ForEachMut2MirroredObject(const std::function&) const; - - bool need_temp_storage() const { return need_temp_storage_; } - const user_op::OpKernel* user_opkernel() const { return user_opkernel_; } - const user_op::InferTmpSizeFn& infer_tmp_size_fn() const { return *infer_tmp_size_fn_; } - - const std::shared_ptr& consistent_tensor_infer_result() - const { - return call_ctx_.consistent_tensor_infer_result(); - } - - eager::CallContext* mut_call_ctx() { return &call_ctx_; } - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - for (const auto& eager_blob_object : *call_ctx_.inputs()) { DoEach(eager_blob_object.get()); } - } - - private: - friend struct OpCallInstructionUtil; - OpCallPhyInstrOperand( - vm::Stream* vm_stream, const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& inputs, const one::EagerBlobObjectListPtr& outputs, - const std::shared_ptr& consistent_tensor_infer_result, - const one::OpExprInterpContext& op_interp_ctx, - const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode); - - Maybe Init(); - void InitStreamSequentialDependence(); - - vm::Stream* vm_stream_; - eager::CallContext call_ctx_; - std::shared_ptr opkernel_; - const user_op::OpKernel* user_opkernel_; - const user_op::InferTmpSizeFn* infer_tmp_size_fn_; - bool need_temp_storage_; - const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode_; - DependenceVector input_dependences_; - DependenceVector output_dependences_; - bool is_all_outputs_pod_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_EAGER_OP_CALL_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/eager/release_tensor_arg_phy_instr_operand.h b/oneflow/core/eager/release_tensor_arg_phy_instr_operand.h deleted file mode 100644 index 527509d07f09e35188c1976eb0278ae8631a3461..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/release_tensor_arg_phy_instr_operand.h +++ /dev/null @@ -1,71 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_RELEASE_TENSOR_ARG_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_EAGER_RELEASE_TENSOR_ARG_PHY_INSTR_OPERAND_H_ - -#include -#include -#include "oneflow/core/intrusive/intrusive.h" -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/eager/local_dep_object.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/stream.h" -#include "oneflow/core/vm/stream.h" - -namespace oneflow { - -namespace vm { - -class EagerBlobObject; - -class ReleaseTensorArgPhyInstrOperand : public PhyInstrOperand { - public: - ReleaseTensorArgPhyInstrOperand(const std::shared_ptr& eager_blob_object, - const Optional& stream) - : eager_blob_object_(eager_blob_object), output_dependences_() { - output_dependences_.push_back(CHECK_JUST(eager_blob_object->compute_local_dep_object())); - if (stream.has_value()) { - stream_sequential_dependence_ = CHECK_JUST(stream)->schedule_local_dep_object().get(); - } - } - ~ReleaseTensorArgPhyInstrOperand() override = default; - - const std::shared_ptr& eager_blob_object() const { - return eager_blob_object_; - } - - const DependenceVector& input_dependences() const override { - static thread_local DependenceVector empty{}; - return empty; - } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - DoEach(eager_blob_object_.get()); - } - - private: - std::shared_ptr eager_blob_object_; - DependenceVector output_dependences_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_EAGER_RELEASE_TENSOR_ARG_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/eager/release_tensor_instruction_type.h b/oneflow/core/eager/release_tensor_instruction_type.h deleted file mode 100644 index 38a56dfa33e7308c6f6444acb5434757c2c3d0f8..0000000000000000000000000000000000000000 --- a/oneflow/core/eager/release_tensor_instruction_type.h +++ /dev/null @@ -1,108 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_RELEASE_TENSOR_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_RELEASE_TENSOR_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/eager/release_tensor_arg_phy_instr_operand.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/common/singleton_ptr.h" - -namespace oneflow { - -namespace vm { - -class ReleaseTensorInstructionType : public vm::InstructionType { - public: - ReleaseTensorInstructionType() = default; - ~ReleaseTensorInstructionType() override = default; - - InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAtAnyPosition; } - - std::string DebugName(const vm::Instruction& instruction) const override { - return "ReleaseTensor"; - } - Maybe Prepare(vm::Instruction* instruction) const override { - const auto& eager_blob_object = GetEagerBlobObject(*instruction); - DataType data_type = eager_blob_object->data_type(); - if (IsPODDataType(data_type)) { Release(eager_blob_object); } - return Maybe::Ok(); - } - void Compute(vm::Instruction* instruction) const override { - const auto& eager_blob_object = GetEagerBlobObject(*instruction); - DataType data_type = eager_blob_object->data_type(); - if (!IsPODDataType(data_type)) { Release(eager_blob_object); } - } - void InitInstructionStatus(Instruction* instruction) const override { - auto* status_buffer = instruction->mut_status_buffer(); - auto* stream = instruction->mut_stream(); - instruction->stream_type().InitInstructionStatus(*stream, status_buffer); - auto* data_ptr = status_buffer->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->reset_ep_event(nullptr); - } - - private: - const std::shared_ptr& GetEagerBlobObject( - const vm::Instruction& instruction) const { - const auto& phy_instr_operand = instruction.phy_instr_operand(); - CHECK(static_cast(phy_instr_operand)); - const auto* ptr = - dynamic_cast(phy_instr_operand.get()); - CHECK_NOTNULL(ptr); - return ptr->eager_blob_object(); - } - void Release(const std::shared_ptr& eager_blob_object) const { - CHECK_JUST(eager_blob_object->DeallocateBlobDataPtr()); - } -}; - -} // namespace vm - -struct GetReleaseInstructionType : public StreamRoleVisitor { - static Maybe VisitCompute(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitHost2Device(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitDevice2Host(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitSyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitAsyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitBarrier(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitCriticalSection(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitLazyJobLauncher(DeviceType device_type) { - UNIMPLEMENTED_THEN_RETURN(); - } - static Maybe VisitPinnedCompute(DeviceType device_type) { - return VisitCompute(device_type); - } -}; - -} // namespace oneflow -#endif // ONEFLOW_CORE_EAGER_RELEASE_TENSOR_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/embedding/cache.h b/oneflow/core/embedding/cache.h index 6501bfb5541c8623344539f00778912f34469b1e..1474990aa27f10b545b4248685d76a511c4f0851 100644 --- a/oneflow/core/embedding/cache.h +++ b/oneflow/core/embedding/cache.h @@ -75,6 +75,9 @@ class Cache { } virtual void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, uint32_t* n_dumped, void* keys, void* values) = 0; + + virtual void ClearDirtyFlags() = 0; + virtual void Clear() = 0; }; diff --git a/oneflow/core/embedding/cache_test.cpp b/oneflow/core/embedding/cache_test.cpp index b047de4cf2559bc723154f2beb9054810eff780d..e62a594b5c06ed28c1efd3059f24fd0c11d11a45 100644 --- a/oneflow/core/embedding/cache_test.cpp +++ b/oneflow/core/embedding/cache_test.cpp @@ -462,7 +462,7 @@ TEST(Cache, FullCache) { // TestCache(cache.get(), line_size); // } -#endif +#endif // WITH_ROCM } // namespace diff --git a/oneflow/core/embedding/cached_key_value_store.cu b/oneflow/core/embedding/cached_key_value_store.cu index 47e7751c885caec86c0ec47d02559fa115d73871..24438fd752fa6afbe781613ae7842c0303a78d67 100644 --- a/oneflow/core/embedding/cached_key_value_store.cu +++ b/oneflow/core/embedding/cached_key_value_store.cu @@ -45,22 +45,26 @@ class CacheKeyValueStoreImpl : public KeyValueStore { OF_DISALLOW_COPY_AND_MOVE(CacheKeyValueStoreImpl); CacheKeyValueStoreImpl(std::unique_ptr&& store, std::unique_ptr&& cache) : store_(std::move(store)), cache_(std::move(cache)), synced_(true), max_query_length_(0) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); CHECK_EQ(store_->KeySize(), cache_->KeySize()); CHECK_EQ(store_->ValueSize(), cache_->ValueSize()); - OF_CUDA_CHECK(cudaMalloc(&num_buffer_, sizeof(uint32_t))); + OF_CUDA_CHECK(GPU(Malloc)(&num_buffer_, sizeof(uint32_t))); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_buffer_), sizeof(uint32_t))); +#else OF_CUDA_CHECK(cudaMallocHost(&host_num_buffer_, sizeof(uint32_t))); +#endif num_elems_per_value_ = store_->ValueSize() / sizeof(Elem); } ~CacheKeyValueStoreImpl() { CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(cudaFree(num_buffer_)); - OF_CUDA_CHECK(cudaFreeHost(host_num_buffer_)); + OF_CUDA_CHECK(GPU(Free)(num_buffer_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_buffer_)); if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFree(keys_buffer_)); - OF_CUDA_CHECK(cudaFree(values_buffer_)); - OF_CUDA_CHECK(cudaFree(indices_buffer0_)); - OF_CUDA_CHECK(cudaFree(indices_buffer1_)); + OF_CUDA_CHECK(GPU(Free)(keys_buffer_)); + OF_CUDA_CHECK(GPU(Free)(values_buffer_)); + OF_CUDA_CHECK(GPU(Free)(indices_buffer0_)); + OF_CUDA_CHECK(GPU(Free)(indices_buffer1_)); } cache_.reset(); store_.reset(); @@ -76,15 +80,15 @@ class CacheKeyValueStoreImpl : public KeyValueStore { if (query_length > cache_->MaxQueryLength()) { cache_->ReserveQueryLength(query_length); } if (query_length > store_->MaxQueryLength()) { store_->ReserveQueryLength(query_length); } if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFree(keys_buffer_)); - OF_CUDA_CHECK(cudaFree(values_buffer_)); - OF_CUDA_CHECK(cudaFree(indices_buffer0_)); - OF_CUDA_CHECK(cudaFree(indices_buffer1_)); + OF_CUDA_CHECK(GPU(Free)(keys_buffer_)); + OF_CUDA_CHECK(GPU(Free)(values_buffer_)); + OF_CUDA_CHECK(GPU(Free)(indices_buffer0_)); + OF_CUDA_CHECK(GPU(Free)(indices_buffer1_)); } - OF_CUDA_CHECK(cudaMalloc(&keys_buffer_, query_length * store_->KeySize())); - OF_CUDA_CHECK(cudaMalloc(&values_buffer_, query_length * store_->ValueSize())); - OF_CUDA_CHECK(cudaMalloc(&indices_buffer0_, query_length * sizeof(uint32_t))); - OF_CUDA_CHECK(cudaMalloc(&indices_buffer1_, query_length * sizeof(uint32_t))); + OF_CUDA_CHECK(GPU(Malloc)(&keys_buffer_, query_length * store_->KeySize())); + OF_CUDA_CHECK(GPU(Malloc)(&values_buffer_, query_length * store_->ValueSize())); + OF_CUDA_CHECK(GPU(Malloc)(&indices_buffer0_, query_length * sizeof(uint32_t))); + OF_CUDA_CHECK(GPU(Malloc)(&indices_buffer1_, query_length * sizeof(uint32_t))); max_query_length_ = query_length; } @@ -136,17 +140,17 @@ void CacheKeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_key } else { cache_->Get(stream, num_keys, keys, values, num_buffer_, keys_buffer_, indices_buffer0_); } - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_buffer_, num_buffer_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); const uint32_t num_cache_missing = *host_num_buffer_; if (num_cache_missing == 0) { - OF_CUDA_CHECK(cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), + OF_CUDA_CHECK(GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); return; } store_->Get(stream, num_cache_missing, keys_buffer_, values_buffer_, n_missing, indices_buffer1_); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, n_missing, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_buffer_, n_missing, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); const uint32_t num_store_missing = *host_num_buffer_; @@ -173,9 +177,12 @@ void CacheKeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_key std::lock_guard lock(mutex_); synced_ = false; auto cuda_stream = stream->As(); + if (cache_->Policy() != CacheOptions::Policy::kFull) { + OF_CUDA_CHECK(GPU(MemsetAsync)(num_buffer_, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); + } cache_->Put(stream, num_keys, keys, values, num_buffer_, keys_buffer_, values_buffer_); if (cache_->Policy() == CacheOptions::Policy::kFull) { return; } - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_buffer_, num_buffer_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); store_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_); @@ -187,6 +194,10 @@ void CacheKeyValueStoreImpl::FusedHalfUpdatePut(ep::Stream* stream, u const void* update, const float* lr, float scale) { std::lock_guard lock(mutex_); + if (cache_->Policy() != CacheOptions::Policy::kFull) { + OF_CUDA_CHECK(GPU(MemsetAsync)(num_buffer_, 0, sizeof(uint32_t), + stream->As()->cuda_stream())); + } if (cache_->Policy() != CacheOptions::Policy::kFull || cache_->ValueType() != DataType::kFloat) { UNIMPLEMENTED(); } @@ -221,17 +232,13 @@ void CacheKeyValueStoreImpl::LoadSnapshot( auto* cuda_stream = stream->As(); while (true) { iter->NextN(stream, max_query_length_, num_buffer_, keys_buffer_, values_buffer_); - OF_CUDA_CHECK(cudaDeviceSynchronize()); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(DeviceSynchronize)()); + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_buffer_, num_buffer_, sizeof(uint32_t), + GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(stream->Sync()); if (*host_num_buffer_ == 0) { return; } cache_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_, num_buffer_, nullptr, nullptr); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - cudaMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(stream->Sync()); - CHECK_EQ(*host_num_buffer_, 0); } } if (Hook) { @@ -267,13 +274,14 @@ void CacheKeyValueStoreImpl::SyncCacheToStore() { cache_->Dump(stream, start_key_index, std::min(start_key_index + max_query_length_, dump_capacity), num_buffer_, keys_buffer_, values_buffer_); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_buffer_, num_buffer_, sizeof(uint32_t), + GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(stream->Sync()); if (*host_num_buffer_ == 0) { continue; } store_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_); CHECK_JUST(stream->Sync()); } + cache_->ClearDirtyFlags(); device->DestroyStream(stream); synced_ = true; } diff --git a/oneflow/core/embedding/cached_key_value_store.hip.cpp b/oneflow/core/embedding/cached_key_value_store.hip.cpp deleted file mode 100644 index 88456a4af1c34f480f4b4d4820d244cc4f5827e2..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/cached_key_value_store.hip.cpp +++ /dev/null @@ -1,326 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/embedding/cached_key_value_store.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/ep/include/device_manager_registry.h" - -namespace oneflow { - -namespace embedding { - -namespace { -template -__global__ void PostStoreGetKernel(uint32_t num_cache_missing, uint32_t num_store_missing, - uint32_t num_elems_per_value, - const uint32_t* cache_missing_indices, - const uint32_t* store_missing_indices, const Elem* store_values, - Elem* values, uint32_t* missing_indices) { - const uint32_t num_cache_missing_elem = num_cache_missing * num_elems_per_value; - CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_cache_missing_elem) { - const uint32_t value_index = i / num_elems_per_value; - const uint32_t elem_index = i - value_index * num_elems_per_value; - values[cache_missing_indices[value_index] * num_elems_per_value + elem_index] = store_values[i]; - } - CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_store_missing) { - missing_indices[i] = cache_missing_indices[store_missing_indices[i]]; - } -} - -template -class CacheKeyValueStoreImpl : public KeyValueStore { - public: - OF_DISALLOW_COPY_AND_MOVE(CacheKeyValueStoreImpl); - CacheKeyValueStoreImpl(std::unique_ptr&& store, std::unique_ptr&& cache) - : store_(std::move(store)), cache_(std::move(cache)), synced_(true), max_query_length_(0) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - CHECK_EQ(store_->KeySize(), cache_->KeySize()); - CHECK_EQ(store_->ValueSize(), cache_->ValueSize()); - OF_CUDA_CHECK(hipMalloc(&num_buffer_, sizeof(uint32_t))); - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_buffer_), sizeof(uint32_t))); - num_elems_per_value_ = store_->ValueSize() / sizeof(Elem); - } - ~CacheKeyValueStoreImpl() { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipFree(num_buffer_)); - OF_CUDA_CHECK(hipHostFree(host_num_buffer_)); - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipFree(keys_buffer_)); - OF_CUDA_CHECK(hipFree(values_buffer_)); - OF_CUDA_CHECK(hipFree(indices_buffer0_)); - OF_CUDA_CHECK(hipFree(indices_buffer1_)); - } - cache_.reset(); - store_.reset(); - } - - uint32_t KeySize() const override { return store_->KeySize(); } - uint32_t ValueSize() const override { return store_->ValueSize(); } - uint32_t MaxQueryLength() const override { return max_query_length_; } - - void ReserveQueryLength(uint32_t query_length) override { - CudaCurrentDeviceGuard guard(device_index_); - if (query_length <= max_query_length_) { return; } - if (query_length > cache_->MaxQueryLength()) { cache_->ReserveQueryLength(query_length); } - if (query_length > store_->MaxQueryLength()) { store_->ReserveQueryLength(query_length); } - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipFree(keys_buffer_)); - OF_CUDA_CHECK(hipFree(values_buffer_)); - OF_CUDA_CHECK(hipFree(indices_buffer0_)); - OF_CUDA_CHECK(hipFree(indices_buffer1_)); - } - OF_CUDA_CHECK(hipMalloc(&keys_buffer_, query_length * store_->KeySize())); - OF_CUDA_CHECK(hipMalloc(&values_buffer_, query_length * store_->ValueSize())); - OF_CUDA_CHECK(hipMalloc(&indices_buffer0_, query_length * sizeof(uint32_t))); - OF_CUDA_CHECK(hipMalloc(&indices_buffer1_, query_length * sizeof(uint32_t))); - max_query_length_ = query_length; - } - - void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, - uint32_t* n_missing, uint32_t* missing_indices) override; - void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, - uint8_t* mask) override; - void Put(ep::Stream* stream, uint32_t num_keys, const void* keys, const void* values) override; - void FusedHalfUpdatePut(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, - const void* update, const float* lr, float scale) override; - bool IsFusionSupported() override { - return cache_->Policy() == CacheOptions::Policy::kFull - && cache_->ValueType() == DataType::kFloat; - } - bool SnapshotExists(const std::string& name) override; - void LoadSnapshot(const std::string& name) override; - void SaveSnapshot(const std::string& name) override; - void LoadSnapshot(const std::string& name, - const std::function& Hook) override; - - private: - void SyncCacheToStore(); - - std::unique_ptr store_; - std::unique_ptr cache_; - - uint32_t* num_buffer_{}; - uint32_t* host_num_buffer_{}; - Key* keys_buffer_{}; - Elem* values_buffer_{}; - uint32_t* indices_buffer0_{}; - uint32_t* indices_buffer1_{}; - int device_index_{}; - uint32_t max_query_length_; - uint32_t num_elems_per_value_{}; - std::recursive_mutex mutex_; - bool synced_; -}; - -template -void CacheKeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const void* keys, - void* values, uint32_t* n_missing, - uint32_t* missing_indices) { - std::lock_guard lock(mutex_); - auto cuda_stream = stream->As(); - if (cache_->Policy() == CacheOptions::Policy::kFull) { - cache_->Get(stream, num_keys, keys, values, n_missing, keys_buffer_, missing_indices); - return; - } else { - cache_->Get(stream, num_keys, keys, values, num_buffer_, keys_buffer_, indices_buffer0_); - } - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - const uint32_t num_cache_missing = *host_num_buffer_; - if (num_cache_missing == 0) { - OF_CUDA_CHECK(hipMemsetAsync(n_missing, 0, sizeof(uint32_t), - stream->As()->cuda_stream())); - return; - } - store_->Get(stream, num_cache_missing, keys_buffer_, values_buffer_, n_missing, indices_buffer1_); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, n_missing, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - const uint32_t num_store_missing = *host_num_buffer_; - RUN_CUDA_KERNEL((PostStoreGetKernel), stream, num_cache_missing * num_elems_per_value_, - num_cache_missing, num_store_missing, num_elems_per_value_, indices_buffer0_, - indices_buffer1_, values_buffer_, static_cast(values), missing_indices); -} - -template -void CacheKeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const void* keys, - void* values, uint8_t* mask) { - std::lock_guard lock(mutex_); - if (cache_->Policy() == CacheOptions::Policy::kFull) { - cache_->Get(stream, num_keys, keys, values, mask); - return; - } else { - UNIMPLEMENTED(); - } -} - -template -void CacheKeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_keys, const void* keys, - const void* values) { - std::lock_guard lock(mutex_); - synced_ = false; - auto cuda_stream = stream->As(); - cache_->Put(stream, num_keys, keys, values, num_buffer_, keys_buffer_, values_buffer_); - if (cache_->Policy() == CacheOptions::Policy::kFull) { return; } - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - store_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_); -} - -template -void CacheKeyValueStoreImpl::FusedHalfUpdatePut(ep::Stream* stream, uint32_t num_keys, - const void* keys, const void* values, - const void* update, const float* lr, - float scale) { - std::lock_guard lock(mutex_); - if (cache_->Policy() != CacheOptions::Policy::kFull || cache_->ValueType() != DataType::kFloat) { - UNIMPLEMENTED(); - } - synced_ = false; - cache_->FusedHalfUpdatePut(stream, num_keys, keys, values, update, lr, scale, num_buffer_, - keys_buffer_, values_buffer_); -} - -template -bool CacheKeyValueStoreImpl::SnapshotExists(const std::string& name) { - return store_->SnapshotExists(name); -} - -template -void CacheKeyValueStoreImpl::LoadSnapshot(const std::string& name) { - LoadSnapshot(name, nullptr); -} - -template -void CacheKeyValueStoreImpl::LoadSnapshot( - const std::string& name, const std::function& Hook) { - CudaCurrentDeviceGuard guard(device_index_); - std::lock_guard lock(mutex_); - CHECK_GT(max_query_length_, 0); - cache_->Clear(); - auto device = - Singleton::Get()->GetDevice(DeviceType::kCUDA, device_index_); - CHECK(device); - auto* stream = device->CreateStream(); - store_->LoadSnapshot(name, [&](KVIterator* iter) { - if (cache_->Policy() == CacheOptions::Policy::kFull) { - auto* cuda_stream = stream->As(); - while (true) { - iter->NextN(stream, max_query_length_, num_buffer_, keys_buffer_, values_buffer_); - OF_CUDA_CHECK(hipDeviceSynchronize()); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - hipMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(stream->Sync()); - if (*host_num_buffer_ == 0) { return; } - cache_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_, num_buffer_, nullptr, - nullptr); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - hipMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(stream->Sync()); - CHECK_EQ(*host_num_buffer_, 0); - } - } - if (Hook) { - iter->Reset(); - Hook(iter); - } - }); - device->DestroyStream(stream); - store_->LoadSnapshot(name); -} - -template -void CacheKeyValueStoreImpl::SaveSnapshot(const std::string& name) { - CudaCurrentDeviceGuard guard(device_index_); - std::lock_guard lock(mutex_); - SyncCacheToStore(); - store_->SaveSnapshot(name); -} - -template -void CacheKeyValueStoreImpl::SyncCacheToStore() { - if (synced_) { return; } - CudaCurrentDeviceGuard guard(device_index_); - auto device = - Singleton::Get()->GetDevice(DeviceType::kCUDA, device_index_); - CHECK(device); - auto* stream = device->CreateStream(); - auto* cuda_stream = stream->As(); - const uint64_t dump_capacity = cache_->DumpCapacity(); - CHECK_GT(max_query_length_, 0); - for (uint64_t start_key_index = 0; start_key_index < dump_capacity; - start_key_index += max_query_length_) { - cache_->Dump(stream, start_key_index, - std::min(start_key_index + max_query_length_, dump_capacity), num_buffer_, - keys_buffer_, values_buffer_); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_buffer_, num_buffer_, sizeof(uint32_t), - hipMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(stream->Sync()); - if (*host_num_buffer_ == 0) { continue; } - store_->Put(stream, *host_num_buffer_, keys_buffer_, values_buffer_); - CHECK_JUST(stream->Sync()); - } - device->DestroyStream(stream); - synced_ = true; -} - -template -std::unique_ptr DispatchElemType(std::unique_ptr&& store, - std::unique_ptr&& cache) { - const uint32_t value_size = store->ValueSize(); - if (value_size % sizeof(uint4) == 0) { - return std::unique_ptr( - new CacheKeyValueStoreImpl(std::move(store), std::move(cache))); - } else if (value_size % sizeof(uint64_t) == 0) { - return std::unique_ptr( - new CacheKeyValueStoreImpl(std::move(store), std::move(cache))); - } else if (value_size % sizeof(uint32_t) == 0) { - return std::unique_ptr( - new CacheKeyValueStoreImpl(std::move(store), std::move(cache))); - } else if (value_size % sizeof(uint16_t) == 0) { - return std::unique_ptr( - new CacheKeyValueStoreImpl(std::move(store), std::move(cache))); - } else { - return std::unique_ptr( - new CacheKeyValueStoreImpl(std::move(store), std::move(cache))); - } -} - -std::unique_ptr DispatchKeyType(std::unique_ptr&& store, - std::unique_ptr&& cache) { - const uint32_t key_size = store->KeySize(); - if (key_size == 4) { - return DispatchElemType(std::move(store), std::move(cache)); - } else if (key_size == 8) { - return DispatchElemType(std::move(store), std::move(cache)); - } else { - UNIMPLEMENTED(); - return nullptr; - } -} - -} // namespace - -std::unique_ptr NewCachedKeyValueStore(std::unique_ptr&& store, - std::unique_ptr&& cache) { - return DispatchKeyType(std::move(store), std::move(cache)); -} - -} // namespace embedding - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/embedding/embedding_manager.cpp b/oneflow/core/embedding/embedding_manager.cpp index 193a1c573d63e0c5bbc0bc729de7cba6de143306..7e0bed6b80bc15c917620647df7d390f2201468d 100644 --- a/oneflow/core/embedding/embedding_manager.cpp +++ b/oneflow/core/embedding/embedding_manager.cpp @@ -24,7 +24,470 @@ namespace embedding { #ifdef WITH_CUDA -constexpr size_t kDefaultMaxQueryLength = 65536; +constexpr size_t kDefaultMaxQueryLength = 131072; + +constexpr int64_t kRingBufferSize = 8; + +struct IdStatistics { + IdStatistics() : final_num_unique(0), iter(-1) {} + uint32_t final_num_unique; + std::vector num_unique_matrix; + int64_t iter; +}; + +#if CUDA_VERSION >= 11020 + +class DynamicTmpBufferAllocator final : public TmpBufferAllocator { + public: + OF_DISALLOW_COPY_AND_MOVE(DynamicTmpBufferAllocator); + DynamicTmpBufferAllocator(cudaStream_t stream, cudaMemPool_t pool) + : stream_(stream), mem_pool_(pool) {} + ~DynamicTmpBufferAllocator() override = default; + + void Allocate(void** ptr, size_t size) override { + OF_CUDA_CHECK(cudaMallocFromPoolAsync(ptr, GetCudaAlignedSize(size), mem_pool_, stream_)); + } + void Free(void* ptr) override { OF_CUDA_CHECK(cudaFreeAsync(ptr, stream_)); } + + private: + cudaStream_t stream_{}; + cudaMemPool_t mem_pool_{}; +}; + +class DynamicAllocationEmbeddingState final : public EmbeddingState { + public: + OF_DISALLOW_COPY_AND_MOVE(DynamicAllocationEmbeddingState); + DynamicAllocationEmbeddingState() + : lookup_values_(nullptr), + lookup_values_size_(0), + has_lookup_values_(false), + lookup_embeddings_(nullptr), + lookup_embeddings_size_(0), + has_lookup_embeddings_(false), + updated_values_(nullptr), + iter_(-1) { + OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + id_statistics_vec_.resize(kRingBufferSize); + cudaMemPoolProps poolProps = {}; + poolProps.allocType = cudaMemAllocationTypePinned; + poolProps.handleTypes = cudaMemHandleTypePosixFileDescriptor; + poolProps.location.type = cudaMemLocationTypeDevice; + poolProps.location.id = device_index_; + cudaMemPoolCreate(&mem_pool_, &poolProps); + uint64_t threshold = UINT64_MAX; + cudaMemPoolSetAttribute(mem_pool_, cudaMemPoolAttrReleaseThreshold, &threshold); + } + ~DynamicAllocationEmbeddingState() { + CudaCurrentDeviceGuard guard(device_index_); + if (has_lookup_values_) { OF_CUDA_CHECK(cudaFree(lookup_values_)); } + if (has_lookup_embeddings_) { OF_CUDA_CHECK(cudaFree(lookup_embeddings_)); } + OF_CUDA_CHECK(cudaMemPoolDestroy(mem_pool_)); + } + + std::unique_ptr NewTmpBufferAllocator( + user_op::KernelComputeContext* ctx) override { + return std::make_unique( + ctx->stream()->As()->cuda_stream(), mem_pool_); + } + + void OnEmbeddingLookupStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + iter_ = iter; + cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + uint32_t num_unique = this->GetIdNumUnique(iter); + size_t lookup_values_size = + GetCudaAlignedSize(num_unique * line_size * GetSizeOfDataType(unique_values->data_type())); + if (!has_lookup_values_ || lookup_values_size_ < lookup_values_size) { + if (has_lookup_values_) { OF_CUDA_CHECK(cudaFreeAsync(lookup_values_, cuda_stream)); } + OF_CUDA_CHECK( + cudaMallocFromPoolAsync(&lookup_values_, lookup_values_size, mem_pool_, cuda_stream)); + has_lookup_values_ = true; + lookup_values_size_ = lookup_values_size; + if (ctx->has_output("embeddings", 0)) { + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + const size_t lookup_embeddings_size = GetCudaAlignedSize( + num_unique * embedding_size * GetSizeOfDataType(embeddings->data_type())); + if (!has_lookup_embeddings_ || lookup_embeddings_size_ < lookup_values_size) { + if (has_lookup_embeddings_) { + OF_CUDA_CHECK(cudaFreeAsync(lookup_embeddings_, cuda_stream)); + } + OF_CUDA_CHECK(cudaMallocFromPoolAsync(&lookup_embeddings_, lookup_embeddings_size, + mem_pool_, cuda_stream)); + has_lookup_embeddings_ = true; + lookup_embeddings_size_ = lookup_embeddings_size; + } + } else { + lookup_embeddings_ = nullptr; + } + } + } + + void* LookupUniqueValues(int64_t iter) override { + CHECK_EQ(iter_, iter); + CHECK(has_lookup_values_); + return lookup_values_; + } + + void* LookupEmbeddings(int64_t iter) override { + CHECK_EQ(iter_, iter); + CHECK(has_lookup_embeddings_); + return lookup_embeddings_; + } + + void OnEmbeddingLookupEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + void OnEmbeddingGatherStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + const void* EmbeddingGatherIn(int64_t iter) override { + if (has_lookup_embeddings_) { + return lookup_embeddings_; + } else { + CHECK(has_lookup_values_); + return lookup_values_; + } + } + + void OnEmbeddingGatherEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + void OnEmbeddingShuffleStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + const void* EmbeddingShuffleCurRankEmbeddings(int64_t iter) override { + if (has_lookup_embeddings_) { + return lookup_embeddings_; + } else { + CHECK(has_lookup_values_); + return lookup_values_; + } + } + + void OnEmbeddingShuffleEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + void OnEmbeddingUpdateStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* updated_unique_embeddings = + ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); + const int64_t line_size = ctx->Attr("line_size"); + uint32_t num_unique = this->GetIdNumUnique(iter); + size_t update_values_size = GetCudaAlignedSize( + num_unique * line_size * GetSizeOfDataType(updated_unique_embeddings->data_type())); + OF_CUDA_CHECK(cudaMallocFromPoolAsync(&updated_values_, update_values_size, mem_pool_, + ctx->stream()->As()->cuda_stream())); + } + + const void* EmbeddingUpdateUniqueEmbeddings(int64_t iter) override { + CHECK_EQ(iter_, iter); + CHECK(has_lookup_values_); + return lookup_values_; + } + + void* EmbeddingUpdateUpdatedUniqueEmbeddings(int64_t iter) override { + CHECK_EQ(iter_, iter); + return updated_values_; + } + + void OnEmbeddingUpdateEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + void OnEmbeddingPutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + const void* EmbeddingPutUniqueEmbeddings(int64_t iter) override { + CHECK_EQ(iter_, iter); + return updated_values_; + } + + void OnEmbeddingPutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + OF_CUDA_CHECK( + cudaFreeAsync(updated_values_, ctx->stream()->As()->cuda_stream())); + } + + void OnEmbeddingFusedUpdatePutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + const void* EmbeddingFusedUpdatePutUniqueEmbeddings(int64_t iter) override { + CHECK_EQ(iter_, iter); + CHECK(has_lookup_values_); + return lookup_values_; + } + + void OnEmbeddingFusedUpdatePutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + // do nothing + } + + void SetIdFinalNumUnique(uint32_t final_num_unique, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).final_num_unique = final_num_unique; + id_statistics_vec_.at(index).iter = iter; + } + + void SetIdNumUniqueMatrix(const std::vector& num_unique_matrix, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).num_unique_matrix = num_unique_matrix; + id_statistics_vec_.at(index).iter = iter; + } + + uint32_t GetIdNumUnique(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.final_num_unique; + } + + const std::vector& GetIdNumUniqueMatrix(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.num_unique_matrix; + } + + private: + void* lookup_values_; + size_t lookup_values_size_; + bool has_lookup_values_; + void* lookup_embeddings_; + size_t lookup_embeddings_size_; + bool has_lookup_embeddings_; + void* updated_values_; + int64_t iter_; + std::vector id_statistics_vec_; + int device_index_{}; + cudaMemPool_t mem_pool_{}; + std::mutex mutex_; +}; + +#endif + +class StaticTmpBufferAllocator final : public TmpBufferAllocator { + public: + OF_DISALLOW_COPY_AND_MOVE(StaticTmpBufferAllocator); + StaticTmpBufferAllocator(void* ptr, size_t size) : ptr_(ptr), offset_(0), size_(size) {} + ~StaticTmpBufferAllocator() override = default; + + void Allocate(void** ptr, size_t size) override { + CHECK(ptr_ != nullptr); + CHECK_GE(offset_, 0); + size_t aligned_size = GetCudaAlignedSize(size); + CHECK_LE(offset_ + aligned_size, size_); + *ptr = reinterpret_cast(ptr_) + offset_; + offset_ += aligned_size; + } + + void Free(void* ptr) override { + // do nothing + } + + private: + void* ptr_; + int64_t offset_; + size_t size_; +}; + +class StaticAllocationEmbeddingState final : public EmbeddingState { + public: + OF_DISALLOW_COPY_AND_MOVE(StaticAllocationEmbeddingState); + StaticAllocationEmbeddingState() + : lookup_unique_values_(nullptr), + lookup_embeddings_(nullptr), + has_lookup_embeddings_(false), + embedding_shuffle_cur_rank_embeddings_(nullptr), + embedding_update_unique_embeddings_(nullptr), + embedding_update_updated_unique_embeddings_(nullptr), + embedding_put_unique_embeddings_(nullptr), + embedding_fused_update_put_unique_embeddings_(nullptr) { + id_statistics_vec_.resize(kRingBufferSize); + } + ~StaticAllocationEmbeddingState() override = default; + + std::unique_ptr NewTmpBufferAllocator( + user_op::KernelComputeContext* ctx) override { + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + return std::make_unique(tmp_buffer->mut_dptr(), + tmp_buffer->shape_view().elem_cnt()); + } + + void OnEmbeddingLookupStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); + lookup_unique_values_ = unique_values->mut_dptr(); + if (ctx->has_output("embeddings", 0)) { + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + has_lookup_embeddings_ = true; + lookup_embeddings_ = embeddings->mut_dptr(); + } + } + + void* LookupUniqueValues(int64_t iter) override { return lookup_unique_values_; } + + void* LookupEmbeddings(int64_t iter) override { + CHECK(has_lookup_embeddings_); + return lookup_embeddings_; + } + + void OnEmbeddingLookupEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + lookup_unique_values_ = nullptr; + lookup_embeddings_ = nullptr; + has_lookup_embeddings_ = false; + } + + void OnEmbeddingGatherStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + embedding_gather_in_ = in->dptr(); + } + + const void* EmbeddingGatherIn(int64_t iter) override { return embedding_gather_in_; } + + void OnEmbeddingGatherEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_gather_in_ = nullptr; + } + + void OnEmbeddingShuffleStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* cur_rank_embeddings = + ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0); + embedding_shuffle_cur_rank_embeddings_ = cur_rank_embeddings->dptr(); + } + + const void* EmbeddingShuffleCurRankEmbeddings(int64_t iter) override { + return embedding_shuffle_cur_rank_embeddings_; + } + + void OnEmbeddingShuffleEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_shuffle_cur_rank_embeddings_ = nullptr; + } + + void OnEmbeddingUpdateStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + user_op::Tensor* updated_unique_embeddings = + ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); + embedding_update_unique_embeddings_ = unique_embeddings->dptr(); + embedding_update_updated_unique_embeddings_ = updated_unique_embeddings->mut_dptr(); + } + + const void* EmbeddingUpdateUniqueEmbeddings(int64_t iter) override { + return embedding_update_unique_embeddings_; + } + + void* EmbeddingUpdateUpdatedUniqueEmbeddings(int64_t iter) override { + return embedding_update_updated_unique_embeddings_; + } + + void OnEmbeddingUpdateEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_update_unique_embeddings_ = nullptr; + embedding_update_updated_unique_embeddings_ = nullptr; + } + + void OnEmbeddingPutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + embedding_put_unique_embeddings_ = unique_embeddings->dptr(); + } + + const void* EmbeddingPutUniqueEmbeddings(int64_t iter) override { + return embedding_put_unique_embeddings_; + } + + void OnEmbeddingPutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_put_unique_embeddings_ = nullptr; + } + + void OnEmbeddingFusedUpdatePutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + embedding_fused_update_put_unique_embeddings_ = unique_embeddings->dptr(); + } + + const void* EmbeddingFusedUpdatePutUniqueEmbeddings(int64_t iter) override { + return embedding_fused_update_put_unique_embeddings_; + } + + void OnEmbeddingFusedUpdatePutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_fused_update_put_unique_embeddings_ = nullptr; + } + + void SetIdFinalNumUnique(uint32_t final_num_unique, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).final_num_unique = final_num_unique; + id_statistics_vec_.at(index).iter = iter; + } + + void SetIdNumUniqueMatrix(const std::vector& num_unique_matrix, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).num_unique_matrix = num_unique_matrix; + id_statistics_vec_.at(index).iter = iter; + } + + uint32_t GetIdNumUnique(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.final_num_unique; + } + + const std::vector& GetIdNumUniqueMatrix(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.num_unique_matrix; + } + + void* lookup_unique_values_; + void* lookup_embeddings_; + bool has_lookup_embeddings_; + const void* embedding_gather_in_; + const void* embedding_shuffle_cur_rank_embeddings_; + const void* embedding_update_unique_embeddings_; + void* embedding_update_updated_unique_embeddings_; + const void* embedding_put_unique_embeddings_; + const void* embedding_fused_update_put_unique_embeddings_; + std::vector id_statistics_vec_; + std::mutex mutex_; +}; + +EmbeddingState* EmbeddingManager::GetEmbeddingState(const std::string& embedding_name, + int64_t rank_id) { + std::pair map_key = std::make_pair(embedding_name, rank_id); + std::unique_lock lock(mutex_); + auto it = embedding_state_map_.find(map_key); + // for id shuffle test, not need to create table + if (it == embedding_state_map_.end()) { + LOG(INFO) << "create embedding state: " << embedding_name << "-" << rank_id; + if (UseDynamicMemoryAllocation()) { +#if CUDA_VERSION >= 11020 + it = + embedding_state_map_.emplace(map_key, std::make_unique()) + .first; +#else + UNIMPLEMENTED(); +#endif + } else { + it = embedding_state_map_.emplace(map_key, std::make_unique()) + .first; + } + } + return it->second.get(); +} KeyValueStore* EmbeddingManager::GetKeyValueStore(const std::string& embedding_name, int64_t rank_id) { @@ -66,6 +529,22 @@ void EmbeddingManager::CreateKeyValueStore(const KeyValueStoreOptions& key_value store->ReserveQueryLength(kDefaultMaxQueryLength); CHECK(key_value_store_map_.emplace(map_key, std::move(store)).second) << "Can't create an embedding with same name of an existing embedding, the name: " << name; + + if (UseDynamicMemoryAllocation()) { +#if CUDA_VERSION >= 11020 + CHECK(embedding_state_map_.emplace(map_key, std::make_unique()) + .second) + << "Can't create an embedding state with same name of an existing embedding, the name: " + << name; +#else + UNIMPLEMENTED(); +#endif + } else { + CHECK(embedding_state_map_.emplace(map_key, std::make_unique()) + .second) + << "Can't create an embedding state with same name of an existing embedding, the name: " + << name; + } } void EmbeddingManager::SaveSnapshot(const std::string& embedding_name, int64_t local_rank_id, @@ -101,6 +580,221 @@ void EmbeddingManager::LoadSnapshot(const std::string& embedding_name, int64_t l constexpr size_t kDefaultMaxQueryLength = 131072; +constexpr int64_t kRingBufferSize = 8; + +struct IdStatistics { + IdStatistics() : final_num_unique(0), iter(-1) {} + uint32_t final_num_unique; + std::vector num_unique_matrix; + int64_t iter; +}; + +class StaticTmpBufferAllocator final : public TmpBufferAllocator { + public: + OF_DISALLOW_COPY_AND_MOVE(StaticTmpBufferAllocator); + StaticTmpBufferAllocator(void* ptr, size_t size) : ptr_(ptr), offset_(0), size_(size) {} + ~StaticTmpBufferAllocator() override = default; + + void Allocate(void** ptr, size_t size) override { + CHECK(ptr_ != nullptr); + CHECK_GE(offset_, 0); + size_t aligned_size = GetCudaAlignedSize(size); + CHECK_LE(offset_ + aligned_size, size_); + *ptr = reinterpret_cast(ptr_) + offset_; + offset_ += aligned_size; + } + + void Free(void* ptr) override { + // do nothing + } + + private: + void* ptr_; + int64_t offset_; + size_t size_; +}; + +class StaticAllocationEmbeddingState final : public EmbeddingState { + public: + OF_DISALLOW_COPY_AND_MOVE(StaticAllocationEmbeddingState); + StaticAllocationEmbeddingState() + : lookup_unique_values_(nullptr), + lookup_embeddings_(nullptr), + has_lookup_embeddings_(false), + embedding_shuffle_cur_rank_embeddings_(nullptr), + embedding_update_unique_embeddings_(nullptr), + embedding_update_updated_unique_embeddings_(nullptr), + embedding_put_unique_embeddings_(nullptr), + embedding_fused_update_put_unique_embeddings_(nullptr) { + id_statistics_vec_.resize(kRingBufferSize); + } + ~StaticAllocationEmbeddingState() override = default; + + std::unique_ptr NewTmpBufferAllocator( + user_op::KernelComputeContext* ctx) override { + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + return std::make_unique(tmp_buffer->mut_dptr(), + tmp_buffer->shape_view().elem_cnt()); + } + + void OnEmbeddingLookupStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); + lookup_unique_values_ = unique_values->mut_dptr(); + if (ctx->has_output("embeddings", 0)) { + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + has_lookup_embeddings_ = true; + lookup_embeddings_ = embeddings->mut_dptr(); + } + } + + void* LookupUniqueValues(int64_t iter) override { return lookup_unique_values_; } + + void* LookupEmbeddings(int64_t iter) override { + CHECK(has_lookup_embeddings_); + return lookup_embeddings_; + } + + void OnEmbeddingLookupEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + lookup_unique_values_ = nullptr; + lookup_embeddings_ = nullptr; + has_lookup_embeddings_ = false; + } + + void OnEmbeddingGatherStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + embedding_gather_in_ = in->dptr(); + } + + const void* EmbeddingGatherIn(int64_t iter) override { return embedding_gather_in_; } + + void OnEmbeddingGatherEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_gather_in_ = nullptr; + } + + void OnEmbeddingShuffleStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* cur_rank_embeddings = + ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0); + embedding_shuffle_cur_rank_embeddings_ = cur_rank_embeddings->dptr(); + } + + const void* EmbeddingShuffleCurRankEmbeddings(int64_t iter) override { + return embedding_shuffle_cur_rank_embeddings_; + } + + void OnEmbeddingShuffleEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_shuffle_cur_rank_embeddings_ = nullptr; + } + + void OnEmbeddingUpdateStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + user_op::Tensor* updated_unique_embeddings = + ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); + embedding_update_unique_embeddings_ = unique_embeddings->dptr(); + embedding_update_updated_unique_embeddings_ = updated_unique_embeddings->mut_dptr(); + } + + const void* EmbeddingUpdateUniqueEmbeddings(int64_t iter) override { + return embedding_update_unique_embeddings_; + } + + void* EmbeddingUpdateUpdatedUniqueEmbeddings(int64_t iter) override { + return embedding_update_updated_unique_embeddings_; + } + + void OnEmbeddingUpdateEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_update_unique_embeddings_ = nullptr; + embedding_update_updated_unique_embeddings_ = nullptr; + } + + void OnEmbeddingPutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + embedding_put_unique_embeddings_ = unique_embeddings->dptr(); + } + + const void* EmbeddingPutUniqueEmbeddings(int64_t iter) override { + return embedding_put_unique_embeddings_; + } + + void OnEmbeddingPutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_put_unique_embeddings_ = nullptr; + } + + void OnEmbeddingFusedUpdatePutStart(user_op::KernelComputeContext* ctx, int64_t iter) override { + const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); + embedding_fused_update_put_unique_embeddings_ = unique_embeddings->dptr(); + } + + const void* EmbeddingFusedUpdatePutUniqueEmbeddings(int64_t iter) override { + return embedding_fused_update_put_unique_embeddings_; + } + + void OnEmbeddingFusedUpdatePutEnd(user_op::KernelComputeContext* ctx, int64_t iter) override { + embedding_fused_update_put_unique_embeddings_ = nullptr; + } + + void SetIdFinalNumUnique(uint32_t final_num_unique, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).final_num_unique = final_num_unique; + id_statistics_vec_.at(index).iter = iter; + } + + void SetIdNumUniqueMatrix(const std::vector& num_unique_matrix, int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + id_statistics_vec_.at(index).num_unique_matrix = num_unique_matrix; + id_statistics_vec_.at(index).iter = iter; + } + + uint32_t GetIdNumUnique(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.final_num_unique; + } + + const std::vector& GetIdNumUniqueMatrix(int64_t iter) override { + std::unique_lock lock(mutex_); + int64_t index = iter % kRingBufferSize; + const IdStatistics& statistics = id_statistics_vec_.at(index); + CHECK_EQ(statistics.iter, iter) + << "saved iter: " << statistics.iter << " current iter: " << iter; + return statistics.num_unique_matrix; + } + + void* lookup_unique_values_; + void* lookup_embeddings_; + bool has_lookup_embeddings_; + const void* embedding_gather_in_; + const void* embedding_shuffle_cur_rank_embeddings_; + const void* embedding_update_unique_embeddings_; + void* embedding_update_updated_unique_embeddings_; + const void* embedding_put_unique_embeddings_; + const void* embedding_fused_update_put_unique_embeddings_; + std::vector id_statistics_vec_; + std::mutex mutex_; +}; + +EmbeddingState* EmbeddingManager::GetEmbeddingState(const std::string& embedding_name, + int64_t rank_id) { + std::pair map_key = std::make_pair(embedding_name, rank_id); + std::unique_lock lock(mutex_); + auto it = embedding_state_map_.find(map_key); + // for id shuffle test, not need to create table + if (it == embedding_state_map_.end()) { + LOG(INFO) << "create embedding state: " << embedding_name << "-" << rank_id; + if (UseDynamicMemoryAllocation()) { + UNIMPLEMENTED(); + } else { + it = embedding_state_map_.emplace(map_key, std::make_unique()) + .first; + } + } + return it->second.get(); +} + KeyValueStore* EmbeddingManager::GetKeyValueStore(const std::string& embedding_name, int64_t rank_id) { std::pair map_key = std::make_pair(embedding_name, rank_id); @@ -141,6 +835,15 @@ void EmbeddingManager::CreateKeyValueStore(const KeyValueStoreOptions& key_value store->ReserveQueryLength(kDefaultMaxQueryLength); CHECK(key_value_store_map_.emplace(map_key, std::move(store)).second) << "Can't create an embedding with same name of an existing embedding, the name: " << name; + + if (UseDynamicMemoryAllocation()) { + UNIMPLEMENTED(); + } else { + CHECK(embedding_state_map_.emplace(map_key, std::make_unique()) + .second) + << "Can't create an embedding state with same name of an existing embedding, the name: " + << name; + } } void EmbeddingManager::SaveSnapshot(const std::string& embedding_name, int64_t local_rank_id, @@ -170,7 +873,7 @@ void EmbeddingManager::LoadSnapshot(const std::string& embedding_name, int64_t l } } -#endif // WITH_ROCM +#endif } // namespace embedding diff --git a/oneflow/core/embedding/embedding_manager.h b/oneflow/core/embedding/embedding_manager.h index 0eec7cf739a230837b15d137fc7e68d683f6bc16..e01820403a2f7dc9240704fbed8c1e2087d70ea0 100644 --- a/oneflow/core/embedding/embedding_manager.h +++ b/oneflow/core/embedding/embedding_manager.h @@ -20,36 +20,149 @@ limitations under the License. #include "oneflow/core/embedding/key_value_store.h" #include "oneflow/core/embedding/key_value_store_options.h" +#include "oneflow/core/framework/framework.h" namespace oneflow { namespace embedding { -#ifdef WITH_CUDA - -class EmbeddingManager final { +inline bool UseDynamicMemoryAllocation() { + static bool use_dynamic_memory_allocation = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION", false); +#if CUDA_VERSION >= 11020 + return use_dynamic_memory_allocation; +#else + if (use_dynamic_memory_allocation) { + LOG(WARNING) + << "Dynamic memory allocation only support when cuda_version greater equal than 11.2. "; + } + return false; +#endif +} + +inline bool UseEmbeddingShuffleP2PKernel(DataType embedding_dtype, DataType idx_dtype) { + static bool use_embedding_shuffle_p2p_env = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_EMBEDDING_SHUFFLE_USE_P2P", false); + static bool add_id_shuffle_copy_out_env = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ADD_ID_SHUFFLE_COPY_OUT", true); + static bool enable_quantized_comm = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); + if (use_embedding_shuffle_p2p_env) { + if (embedding_dtype != DataType::kFloat16 || idx_dtype != DataType::kUInt32) { + // p2p kernel only registered kFloat16 and kUint32. + return false; + } + if (!add_id_shuffle_copy_out_env) { + // when not enable id shuffle copy out, the ptrs change every iter. + return false; + } + if (enable_quantized_comm) { + // p2p kernel not support quantize comm. + return false; + } + if (UseDynamicMemoryAllocation()) { + // p2p kernel not support dynamic memory allocation. + return false; + } + } +#if CUDA_VERSION >= 11030 + return use_embedding_shuffle_p2p_env; +#else + if (use_embedding_shuffle_p2p_env) { + LOG(WARNING) + << "embedding shuffle p2p kernel only support when cuda_version greater equal than 11.3. "; + } + return false; +#endif +} + +inline bool UseEmbeddingGradientShuffleP2PKernel(DataType embedding_dtype, DataType idx_dtype) { + static bool use_embedding_gradient_shuffle_p2p_env = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_EMBEDDING_GRADIENT_SHUFFLE_USE_P2P", false); + static bool add_id_shuffle_copy_out_env = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ADD_ID_SHUFFLE_COPY_OUT", true); + static bool enable_quantized_comm = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); + if (use_embedding_gradient_shuffle_p2p_env) { + if (embedding_dtype != DataType::kFloat16 || idx_dtype != DataType::kUInt32) { + // p2p kernel only registered kFloat16 and kUint32. + return false; + } + if (!add_id_shuffle_copy_out_env) { + // when not enable id shuffle copy out, the ptrs change every iter. + return false; + } + if (enable_quantized_comm) { + // p2p kernel not support quantize comm. + return false; + } + if (UseDynamicMemoryAllocation()) { + // p2p kernel not support dynamic memory allocation. + return false; + } + } +#if CUDA_VERSION >= 11030 + return use_embedding_gradient_shuffle_p2p_env; +#else + if (use_embedding_gradient_shuffle_p2p_env) { + LOG(WARNING) << "embedding gradient shuffle p2p kernel only support when cuda_version greater " + "equal than 11.3. "; + } + return false; +#endif +} + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + +class TmpBufferAllocator { public: - EmbeddingManager() = default; - ~EmbeddingManager() = default; - - void SaveSnapshot(const std::string& embedding_name, int64_t local_rank_id, int64_t rank_id, - const std::string& snapshot_name); - void LoadSnapshot(const std::string& embedding_name, int64_t local_rank_id, int64_t rank_id, - const std::string& snapshot_name); + TmpBufferAllocator() = default; + virtual ~TmpBufferAllocator() = default; - KeyValueStore* GetKeyValueStore(const std::string& embedding_name, int64_t rank_id); - - void CreateKeyValueStore(const KeyValueStoreOptions& options, int64_t local_rank_id, - int64_t rank_id, int64_t world_size); - - private: - HashMap, std::unique_ptr> key_value_store_map_; - std::mutex mutex_; + virtual void Allocate(void** ptr, size_t size) = 0; + virtual void Free(void* ptr) = 0; }; -#endif // WITH_CUDA - -#ifdef WITH_ROCM +class EmbeddingState { + public: + EmbeddingState() = default; + virtual ~EmbeddingState() = default; + + virtual std::unique_ptr NewTmpBufferAllocator( + user_op::KernelComputeContext* ctx) = 0; + + virtual void OnEmbeddingLookupStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual void* LookupUniqueValues(int64_t iter) = 0; + virtual void* LookupEmbeddings(int64_t iter) = 0; + virtual void OnEmbeddingLookupEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void OnEmbeddingGatherStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual const void* EmbeddingGatherIn(int64_t iter) = 0; + virtual void OnEmbeddingGatherEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void OnEmbeddingShuffleStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual const void* EmbeddingShuffleCurRankEmbeddings(int64_t iter) = 0; + virtual void OnEmbeddingShuffleEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void OnEmbeddingUpdateStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual const void* EmbeddingUpdateUniqueEmbeddings(int64_t iter) = 0; + virtual void* EmbeddingUpdateUpdatedUniqueEmbeddings(int64_t iter) = 0; + virtual void OnEmbeddingUpdateEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void OnEmbeddingPutStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual const void* EmbeddingPutUniqueEmbeddings(int64_t iter) = 0; + virtual void OnEmbeddingPutEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void OnEmbeddingFusedUpdatePutStart(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + virtual const void* EmbeddingFusedUpdatePutUniqueEmbeddings(int64_t iter) = 0; + virtual void OnEmbeddingFusedUpdatePutEnd(user_op::KernelComputeContext* ctx, int64_t iter) = 0; + + virtual void SetIdFinalNumUnique(uint32_t final_num_unique, int64_t iter) = 0; + virtual void SetIdNumUniqueMatrix(const std::vector& num_unique_matrix, + int64_t iter) = 0; + virtual uint32_t GetIdNumUnique(int64_t iter) = 0; + virtual const std::vector& GetIdNumUniqueMatrix(int64_t iter) = 0; +}; class EmbeddingManager final { public: @@ -62,16 +175,17 @@ class EmbeddingManager final { const std::string& snapshot_name); KeyValueStore* GetKeyValueStore(const std::string& embedding_name, int64_t rank_id); - + EmbeddingState* GetEmbeddingState(const std::string& embedding_name, int64_t rank_id); void CreateKeyValueStore(const KeyValueStoreOptions& options, int64_t local_rank_id, int64_t rank_id, int64_t world_size); private: HashMap, std::unique_ptr> key_value_store_map_; + HashMap, std::unique_ptr> embedding_state_map_; std::mutex mutex_; }; -#endif // WITH_ROCM +#endif // WITH_CUDA } // namespace embedding } // namespace oneflow diff --git a/oneflow/core/embedding/full_cache.cu b/oneflow/core/embedding/full_cache.cu index 80bf342ec099f527a9c02639572610bcdae4b6aa..fbefa973574f9b6e4f6bd075278f92e6705d95f1 100644 --- a/oneflow/core/embedding/full_cache.cu +++ b/oneflow/core/embedding/full_cache.cu @@ -28,9 +28,9 @@ using Key128 = ulonglong2; namespace { -template -__device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, Index* table_size, - Key key, Index* out) { +template +__device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, bool* entry_dirty_flag, + Index* table_size, Key key, Index* out) { Key key_hi = (key | 0x1); Key key_lo = (key & 0x1); Index index_plus_one = 0; @@ -41,6 +41,10 @@ __device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, Inde index_plus_one = index + 1; *entry_index = ((index_plus_one << 1U) | key_lo); *out = index_plus_one; + if (dump_dirty_only) { + bool entry_flag_val = *entry_dirty_flag; + if (!entry_flag_val) { *entry_dirty_flag = true; } + } return true; } else if (old_entry_key == key_hi) { const Index entry_index_val = *entry_index; @@ -48,6 +52,10 @@ __device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, Inde // do nothing } else if ((entry_index_val & 0x1) == key_lo) { *out = (entry_index_val >> 1U); + if (dump_dirty_only) { + bool entry_flag_val = *entry_dirty_flag; + if (!entry_flag_val) { *entry_dirty_flag = true; } + } return true; } else { return false; @@ -59,15 +67,20 @@ __device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, Inde return false; } -template +template __device__ bool GetOrInsertOne(const size_t capacity, Key* table_keys, Index* table_indices, - Index* table_size, Key key, size_t hash, Index* out) { + bool* table_dirty_flags, Index* table_size, Key key, size_t hash, + Index* out) { const size_t start_idx = hash % capacity; for (size_t count = 0; count < capacity; ++count) { const size_t idx = (start_idx + count) % capacity; Key* entry_key = table_keys + idx; Index* entry_index = table_indices + idx; - if (TryGetOrInsert(entry_key, entry_index, table_size, key, out)) { return true; } + bool* entry_dirty_flag = dump_dirty_only ? table_dirty_flags + idx : nullptr; + if (TryGetOrInsert(entry_key, entry_index, entry_dirty_flag, + table_size, key, out)) { + return true; + } } return false; } @@ -94,15 +107,15 @@ __device__ bool GetOne(const size_t capacity, Key* table_keys, Index* table_indi return false; } -template +template __global__ void OrdinalEncodeKernel(uint64_t capacity, Key* table_keys, Index* table_indices, - Index* table_size, uint32_t num_keys, const Key* keys, - Index* context) { + bool* table_dirty_flags, Index* table_size, uint32_t num_keys, + const Key* keys, Index* context) { CUDA_1D_KERNEL_LOOP(i, num_keys) { Key key = keys[i]; uint64_t hash = FullCacheHash()(key); - bool success = GetOrInsertOne(capacity, table_keys, table_indices, table_size, key, - hash, context + i); + bool success = GetOrInsertOne( + capacity, table_keys, table_indices, table_dirty_flags, table_size, key, hash, context + i); assert(success); } } @@ -117,14 +130,20 @@ __global__ void OrdinalEncodeLookupKernel(uint64_t capacity, Key* table_keys, In } } -template +template __global__ void OrdinalEncodeDumpKernel(const Key* table_keys, const Index* table_indices, - uint64_t start_key_index, uint64_t end_key_index, - uint32_t* n_dumped, Key* keys, Index* context) { + const bool* table_dirty_flags, uint64_t start_key_index, + uint64_t end_key_index, uint32_t* n_dumped, Key* keys, + Index* context) { CUDA_1D_KERNEL_LOOP(i, (end_key_index - start_key_index)) { Key entry_key = table_keys[i + start_key_index]; Index entry_index = table_indices[i + start_key_index]; - if (entry_index != 0) { + bool dump_flag = (entry_index != 0); + if (dump_dirty_only) { + bool entry_dirty_flag = table_dirty_flags[i + start_key_index]; + dump_flag = (dump_flag && entry_dirty_flag); + } + if (dump_flag) { uint32_t index = cuda::atomic::Add(n_dumped, static_cast(1)); keys[index] = ((entry_key ^ 0x1) | (entry_index & 0x1)); context[index] = (entry_index >> 1U); @@ -177,7 +196,11 @@ __global__ void EncodeLookupKernel(uint32_t value_length, const Elem* cache_valu batch_start += global_n_warp * warp_size) { const uint32_t batch_n_key = min(n_keys - batch_start, warp_size); if (lane_id == 0) { batch_n_missing[warp_id] = 0; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif const uint32_t key_offset = batch_start + lane_id; if (key_offset < n_keys) { const Key key = keys[batch_start + lane_id]; @@ -191,14 +214,22 @@ __global__ void EncodeLookupKernel(uint32_t value_length, const Elem* cache_valu batch_missing_indices[warp_id][batch_missing_idx] = key_offset; } } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif const uint32_t batch_n_missing_t = batch_n_missing[warp_id]; if (lane_id == 0) { const uint32_t old_n_missing = cuda::atomic::Add(n_missing, static_cast(batch_n_missing_t)); batch_n_missing[warp_id] = old_n_missing; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif if (lane_id < batch_n_missing_t) { missing_keys[batch_n_missing[warp_id] + lane_id] = batch_missing_keys[warp_id][lane_id]; missing_indices[batch_n_missing[warp_id] + lane_id] = batch_missing_indices[warp_id][lane_id]; @@ -212,7 +243,11 @@ __global__ void EncodeLookupKernel(uint32_t value_length, const Elem* cache_valu cache_values[(row - 1) * value_length + col]; } } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } } @@ -252,7 +287,11 @@ __global__ void EncodeLookupMaskKernel(uint32_t value_length, const Elem* __rest batch_row_ids[warp_id][lane_id] = row; mask[key_offset] = row > 0; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif for (int i = 0; i < batch_n_key; ++i) { const Key key = batch_keys[warp_id][i]; const Index row = batch_row_ids[warp_id][i]; @@ -263,7 +302,11 @@ __global__ void EncodeLookupMaskKernel(uint32_t value_length, const Elem* __rest packed_cache_values[(row - 1) * packed_cols + col]; } } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } } @@ -314,7 +357,7 @@ __global__ typename std::enable_if::value, void>::typ FusedHalfUpdateKernel(uint32_t value_length, Elem* cache_values, uint32_t values_elem_cnt, const Index* context, const Elem* values, const half* update, const float* lr, float scale) { - __trap(); + TRAP(); } template @@ -333,33 +376,39 @@ template class OrdinalEncoder { public: OF_DISALLOW_COPY_AND_MOVE(OrdinalEncoder); - explicit OrdinalEncoder(uint64_t capacity, float load_factor) - : capacity_(capacity), table_capacity_(capacity / load_factor) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); - OF_CUDA_CHECK(cudaMalloc(&table_size_, sizeof(Index))); + explicit OrdinalEncoder(uint64_t capacity, float load_factor, bool if_dump_dirty) + : capacity_(capacity), + table_capacity_(capacity / load_factor), + if_dump_dirty_(if_dump_dirty) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); + OF_CUDA_CHECK(GPU(Malloc)(&table_size_, sizeof(Index))); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&table_size_host_), sizeof(Index))); +#else OF_CUDA_CHECK(cudaMallocHost(&table_size_host_, sizeof(Index))); - OF_CUDA_CHECK(cudaMalloc(&table_keys_, table_capacity_ * sizeof(Key))); - OF_CUDA_CHECK(cudaMalloc(&table_indices_, table_capacity_ * sizeof(Index))); +#endif + OF_CUDA_CHECK(GPU(Malloc)(&table_keys_, table_capacity_ * sizeof(Key))); + OF_CUDA_CHECK(GPU(Malloc)(&table_indices_, table_capacity_ * sizeof(Index))); + if (if_dump_dirty_) { + OF_CUDA_CHECK(GPU(Malloc)(&table_dirty_flags_, table_capacity_ * sizeof(bool))); + } Clear(); } ~OrdinalEncoder() { CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(cudaFree(table_size_)); - OF_CUDA_CHECK(cudaFreeHost(table_size_host_)); - OF_CUDA_CHECK(cudaFree(table_keys_)); - OF_CUDA_CHECK(cudaFree(table_indices_)); + OF_CUDA_CHECK(GPU(Free)(table_size_)); + OF_CUDA_CHECK(GPU(FreeHost)(table_size_host_)); + OF_CUDA_CHECK(GPU(Free)(table_keys_)); + OF_CUDA_CHECK(GPU(Free)(table_indices_)); + if (if_dump_dirty_) { OF_CUDA_CHECK(GPU(Free)(table_dirty_flags_)); } } - template + template void Encode(ep::Stream* stream, uint32_t num_keys, const Key* keys, Index* context) { if (insert) { - RUN_CUDA_KERNEL((OrdinalEncodeKernel), stream, num_keys, table_capacity_, - table_keys_, table_indices_, table_size_, num_keys, keys, context); - OF_CUDA_CHECK(cudaMemcpyAsync(table_size_host_, table_size_, sizeof(Index), cudaMemcpyDefault, - stream->As()->cuda_stream())); - CHECK_JUST(stream->Sync()); - CHECK_LT(*table_size_host_, capacity_) - << "The number of key is larger than cache size, please enlarge cache_memory_budget. "; + RUN_CUDA_KERNEL((OrdinalEncodeKernel), stream, num_keys, + table_capacity_, table_keys_, table_indices_, table_dirty_flags_, table_size_, + num_keys, keys, context); } else { RUN_CUDA_KERNEL((OrdinalEncodeLookupKernel), stream, num_keys, table_capacity_, table_keys_, table_indices_, num_keys, keys, context); @@ -368,17 +417,35 @@ class OrdinalEncoder { void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, uint32_t* n_dumped, Key* keys, Index* context) { - OF_CUDA_CHECK(cudaMemsetAsync(n_dumped, 0, sizeof(uint32_t), + OF_CUDA_CHECK(GPU(MemsetAsync)(n_dumped, 0, sizeof(uint32_t), + stream->As()->cuda_stream())); + RUN_CUDA_KERNEL((OrdinalEncodeDumpKernel), stream, + end_key_index - start_key_index, table_keys_, table_indices_, + table_dirty_flags_, start_key_index, end_key_index, n_dumped, keys, context); + } + + void DumpDirtyOnly(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, + uint32_t* n_dumped, Key* keys, Index* context) { + OF_CUDA_CHECK(GPU(MemsetAsync)(n_dumped, 0, sizeof(uint32_t), stream->As()->cuda_stream())); - RUN_CUDA_KERNEL((OrdinalEncodeDumpKernel), stream, end_key_index - start_key_index, - table_keys_, table_indices_, start_key_index, end_key_index, n_dumped, keys, - context); + RUN_CUDA_KERNEL((OrdinalEncodeDumpKernel), stream, + end_key_index - start_key_index, table_keys_, table_indices_, + table_dirty_flags_, start_key_index, end_key_index, n_dumped, keys, context); + } + + void ClearDirtyFlags() { + if (if_dump_dirty_) { + OF_CUDA_CHECK(GPU(Memset)(table_dirty_flags_, 0, table_capacity_ * sizeof(bool))); + } } void Clear() { - OF_CUDA_CHECK(cudaMemset(table_size_, 0, sizeof(Index))); - OF_CUDA_CHECK(cudaMemset(table_keys_, 0, table_capacity_ * sizeof(Key))); - OF_CUDA_CHECK(cudaMemset(table_indices_, 0, table_capacity_ * sizeof(Index))); + OF_CUDA_CHECK(GPU(Memset)(table_size_, 0, sizeof(Index))); + OF_CUDA_CHECK(GPU(Memset)(table_keys_, 0, table_capacity_ * sizeof(Key))); + OF_CUDA_CHECK(GPU(Memset)(table_indices_, 0, table_capacity_ * sizeof(Index))); + if (if_dump_dirty_) { + OF_CUDA_CHECK(GPU(Memset)(table_dirty_flags_, 0, table_capacity_ * sizeof(bool))); + } } uint64_t TableCapacity() const { return table_capacity_; } @@ -391,8 +458,10 @@ class OrdinalEncoder { int device_index_{}; Key* table_keys_; Index* table_indices_; + bool* table_dirty_flags_; uint64_t capacity_; uint64_t table_capacity_; + bool if_dump_dirty_; Index* table_size_{}; Index* table_size_host_{}; }; @@ -402,17 +471,22 @@ class CacheImpl : public Cache { public: OF_DISALLOW_COPY_AND_MOVE(CacheImpl); explicit CacheImpl(const CacheOptions& options) - : encoder_(options.capacity, options.load_factor), + : if_dump_dirty_(ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DUMP_DIRTY_ONLY", false)), + encoder_(options.capacity, options.load_factor, if_dump_dirty_), device_index_(-1), options_(options), max_query_length_(0) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); const uint64_t values_size = options.capacity * options.value_size; if (options.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(cudaMalloc(&values_, values_size)); + OF_CUDA_CHECK(GPU(Malloc)(&values_, values_size)); } else if (options.value_memory_kind == CacheOptions::MemoryKind::kHost) { if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_NUMA_AWARE_ALLOCATION", false)) { - OF_CUDA_CHECK(cudaMallocHost(&values_, values_size)); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&values_), values_size)); +#else + OF_CUDA_CHECK(cudaMallocHost(&values_, values_size)); +#endif } else { OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, reinterpret_cast(&values_), values_size)); @@ -425,13 +499,13 @@ class CacheImpl : public Cache { ~CacheImpl() { CudaCurrentDeviceGuard guard(device_index_); if (options_.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(cudaFree(values_)); + OF_CUDA_CHECK(GPU(Free)(values_)); } else if (options_.value_memory_kind == CacheOptions::MemoryKind::kHost) { - OF_CUDA_CHECK(cudaFreeHost(values_)); + OF_CUDA_CHECK(GPU(FreeHost)(values_)); } else { UNIMPLEMENTED(); } - if (max_query_length_ > 0) { OF_CUDA_CHECK(cudaFree(encoding_buffer_)); } + if (max_query_length_ > 0) { OF_CUDA_CHECK(GPU(Free)(encoding_buffer_)); } } uint64_t Capacity() const override { return options_.capacity; } @@ -447,8 +521,8 @@ class CacheImpl : public Cache { void ReserveQueryLength(uint32_t query_length) override { CudaCurrentDeviceGuard guard(device_index_); if (query_length <= max_query_length_) { return; } - if (max_query_length_ > 0) { OF_CUDA_CHECK(cudaFree(encoding_buffer_)); } - OF_CUDA_CHECK(cudaMalloc(&encoding_buffer_, query_length * sizeof(uint64_t))); + if (max_query_length_ > 0) { OF_CUDA_CHECK(GPU(Free)(encoding_buffer_)); } + OF_CUDA_CHECK(GPU(Malloc)(&encoding_buffer_, query_length * sizeof(uint64_t))); max_query_length_ = query_length; } @@ -465,15 +539,19 @@ class CacheImpl : public Cache { void Put(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, uint32_t* n_evicted, void* evicted_keys, void* evicted_values) override; + void FusedHalfUpdatePut(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, const void* update, const float* lr, float scale, uint32_t* n_evicted, void* evicted_keys, void* evicted_values) override; void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, uint32_t* n_dumped, void* keys, void* values) override; + void ClearDirtyFlags() override; + void Clear() override; private: + bool if_dump_dirty_; OrdinalEncoder encoder_; int device_index_; uint32_t num_elem_per_value_{}; @@ -488,10 +566,16 @@ void CacheImpl::Test(ep::Stream* stream, uint32_t n const void* keys, uint32_t* n_missing, void* missing_keys, uint32_t* missing_indices) { OF_CUDA_CHECK( - cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); + GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); if (n_keys == 0) { return; } CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); + if (if_dump_dirty_) { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } else { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; RUN_CUDA_KERNEL((LookupKernel), stream, values_elem_cnt, num_elem_per_value_, values_, values_elem_cnt, static_cast(keys), @@ -505,7 +589,7 @@ void CacheImpl::Get(ep::Stream* stream, uint32_t n_ uint32_t* n_missing, void* missing_keys, uint32_t* missing_indices) { OF_CUDA_CHECK( - cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); + GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); if (n_keys == 0) { return; } CHECK_LE(n_keys, max_query_length_); constexpr uint32_t block_size = 128; @@ -539,11 +623,15 @@ void CacheImpl::Put(ep::Stream* stream, uint32_t n_ const void* keys, const void* values, uint32_t* n_evicted, void* evicted_keys, void* evicted_values) { - OF_CUDA_CHECK( - cudaMemsetAsync(n_evicted, 0, sizeof(uint32_t), stream->As()->cuda_stream())); if (n_keys == 0) { return; } CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); + if (if_dump_dirty_) { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } else { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; RUN_CUDA_KERNEL((UpdateKernel), stream, values_elem_cnt / pack_size, num_elem_per_value_, values_, values_elem_cnt, encoding_buffer_, @@ -555,28 +643,43 @@ void CacheImpl::FusedHalfUpdatePut( ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, const void* update, const float* lr, float scale, uint32_t* n_evicted, void* evicted_keys, void* evicted_values) { if (!std::is_same::value) { UNIMPLEMENTED(); } - OF_CUDA_CHECK( - cudaMemsetAsync(n_evicted, 0, sizeof(uint32_t), stream->As()->cuda_stream())); if (n_keys == 0) { return; } CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); + if (if_dump_dirty_) { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } else { + encoder_.template Encode(stream, n_keys, static_cast(keys), + encoding_buffer_); + } const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; RUN_CUDA_KERNEL((FusedHalfUpdateKernel), stream, values_elem_cnt / pack_size, num_elem_per_value_, values_, values_elem_cnt, encoding_buffer_, static_cast(values), static_cast(update), lr, scale); } + template void CacheImpl::Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, uint32_t* n_dumped, void* keys, void* values) { - encoder_.Dump(stream, start_key_index, end_key_index, n_dumped, static_cast(keys), - encoding_buffer_); + if (if_dump_dirty_) { + encoder_.DumpDirtyOnly(stream, start_key_index, end_key_index, n_dumped, + static_cast(keys), encoding_buffer_); + } else { + encoder_.Dump(stream, start_key_index, end_key_index, n_dumped, static_cast(keys), + encoding_buffer_); + } RUN_CUDA_KERNEL((DumpValueKernel), stream, num_elem_per_value_ * (end_key_index - start_key_index), num_elem_per_value_, n_dumped, encoding_buffer_, values_, static_cast(values)); } +template +void CacheImpl::ClearDirtyFlags() { + encoder_.ClearDirtyFlags(); +} + template void CacheImpl::Clear() { encoder_.Clear(); diff --git a/oneflow/core/embedding/full_cache.h b/oneflow/core/embedding/full_cache.h index 465c8119a3b27a48ff08feaaf1e5f08ba8e556be..c7c03dd8aadbd9a33cc146625809e77f489d4fe4 100644 --- a/oneflow/core/embedding/full_cache.h +++ b/oneflow/core/embedding/full_cache.h @@ -23,16 +23,11 @@ namespace oneflow { namespace embedding { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) std::unique_ptr NewFullCache(const CacheOptions& options); #endif // WITH_CUDA -#ifdef WITH_ROCM - -std::unique_ptr NewFullCache(const CacheOptions& options); - -#endif // WITH_ROCM } // namespace embedding diff --git a/oneflow/core/embedding/full_cache.hip.cpp b/oneflow/core/embedding/full_cache.hip.cpp deleted file mode 100644 index 164ececd8133753abc139a5d66a55e8bbf33b0fc..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/full_cache.hip.cpp +++ /dev/null @@ -1,640 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/embedding/full_cache.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/embedding/hash_functions.hip.h" -#include "oneflow/core/hip/atomic.hip.h" - -namespace oneflow { - -namespace embedding { - -using Key32 = unsigned int; -using Key64 = unsigned long long int; -using Key128 = ulonglong2; - -namespace { - -template -__device__ bool TryGetOrInsert(Key* entry_key, volatile Index* entry_index, Index* table_size, - Key key, Index* out) { - Key key_hi = (key | 0x1); - Key key_lo = (key & 0x1); - Index index_plus_one = 0; - Key old_entry_key = cuda::atomic::CAS(entry_key, static_cast(0), key_hi); - while (index_plus_one == 0) { - if (old_entry_key == static_cast(0)) { - Index index = cuda::atomic::Add(table_size, static_cast(1)); - index_plus_one = index + 1; - *entry_index = ((index_plus_one << 1U) | key_lo); - *out = index_plus_one; - return true; - } else if (old_entry_key == key_hi) { - const Index entry_index_val = *entry_index; - if (entry_index_val == 0) { - // do nothing - } else if ((entry_index_val & 0x1) == key_lo) { - *out = (entry_index_val >> 1U); - return true; - } else { - return false; - } - } else { - return false; - } - } - return false; -} - -template -__device__ bool GetOrInsertOne(const size_t capacity, Key* table_keys, Index* table_indices, - Index* table_size, Key key, size_t hash, Index* out) { - const size_t start_idx = hash % capacity; - for (size_t count = 0; count < capacity; ++count) { - const size_t idx = (start_idx + count) % capacity; - Key* entry_key = table_keys + idx; - Index* entry_index = table_indices + idx; - if (TryGetOrInsert(entry_key, entry_index, table_size, key, out)) { return true; } - } - return false; -} - -template -__device__ bool GetOne(const size_t capacity, Key* table_keys, Index* table_indices, Key key, - size_t hash, Index* out) { - const size_t start_idx = hash % capacity; - for (size_t count = 0; count < capacity; ++count) { - const size_t idx = (start_idx + count) % capacity; - Key entry_key = table_keys[idx]; - Key entry_index = table_indices[idx]; - Key key_hi = (key | 0x1); - Key key_lo = (key & 0x1); - if (entry_key == 0) { break; } - if (entry_key == key_hi) { - if ((entry_index & 0x1) == key_lo) { - *out = (entry_index >> 1U); - return true; - } - } - } - *out = 0; - return false; -} - -template -__global__ void OrdinalEncodeKernel(uint64_t capacity, Key* table_keys, Index* table_indices, - Index* table_size, uint32_t num_keys, const Key* keys, - Index* context) { - CUDA_1D_KERNEL_LOOP(i, num_keys) { - Key key = keys[i]; - uint64_t hash = FullCacheHash()(key); - bool success = GetOrInsertOne(capacity, table_keys, table_indices, table_size, key, - hash, context + i); - assert(success); - } -} - -template -__global__ void OrdinalEncodeLookupKernel(uint64_t capacity, Key* table_keys, Index* table_indices, - uint32_t num_keys, const Key* keys, Index* context) { - CUDA_1D_KERNEL_LOOP(i, num_keys) { - Key key = keys[i]; - uint64_t hash = FullCacheHash()(key); - GetOne(capacity, table_keys, table_indices, key, hash, context + i); - } -} - -template -__global__ void OrdinalEncodeDumpKernel(const Key* table_keys, const Index* table_indices, - uint64_t start_key_index, uint64_t end_key_index, - uint32_t* n_dumped, Key* keys, Index* context) { - CUDA_1D_KERNEL_LOOP(i, (end_key_index - start_key_index)) { - Key entry_key = table_keys[i + start_key_index]; - Index entry_index = table_indices[i + start_key_index]; - if (entry_index != 0) { - uint32_t index = cuda::atomic::Add(n_dumped, static_cast(1)); - keys[index] = ((entry_key ^ 0x1) | (entry_index & 0x1)); - context[index] = (entry_index >> 1U); - } - } -} - -template -__global__ void LookupKernel(uint32_t value_length, const Elem* cache_values, - uint32_t values_elem_cnt, const Key* keys, const Index* context, - Elem* values, uint32_t* n_missing, Key* missing_keys, - uint32_t* missing_indices) { - CUDA_1D_KERNEL_LOOP(i, values_elem_cnt) { - const uint64_t key_id = i / value_length; - const uint64_t ctx = context[key_id]; - const uint64_t row_id = ctx - 1; - const uint64_t col_id = i - key_id * value_length; - if (ctx == 0) { - const Key missing_key = keys[key_id]; - if (col_id == 0) { - const uint32_t old_n_missing = cuda::atomic::Add(n_missing, static_cast(1)); - missing_keys[old_n_missing] = missing_key; - missing_indices[old_n_missing] = key_id; - } - continue; - } - if (return_value) { values[i] = cache_values[row_id * value_length + col_id]; } - } -} - -template -__global__ void EncodeLookupKernel(uint32_t value_length, const Elem* cache_values, - uint32_t values_elem_cnt, const Key* keys, const Index* context, - Elem* values, uint32_t* n_missing, Key* missing_keys, - uint32_t* missing_indices, const size_t capacity, - Key* table_keys, Index* table_indices) { - constexpr uint32_t warp_size = 32; - constexpr uint32_t n_warp_per_block = block_size / warp_size; - const uint32_t warp_id = threadIdx.x / warp_size; - const uint32_t lane_id = threadIdx.x % warp_size; - const uint32_t global_warp_id = blockIdx.x * n_warp_per_block + warp_id; - const uint32_t global_n_warp = gridDim.x * n_warp_per_block; - const uint32_t n_keys = values_elem_cnt / value_length; - __shared__ Key batch_keys[n_warp_per_block][warp_size]; - __shared__ Index batch_row_ids[n_warp_per_block][warp_size]; - __shared__ Key batch_missing_keys[n_warp_per_block][warp_size]; - __shared__ uint32_t batch_missing_indices[n_warp_per_block][warp_size]; - __shared__ uint32_t batch_n_missing[n_warp_per_block]; - for (uint32_t batch_start = global_warp_id * warp_size; batch_start < n_keys; - batch_start += global_n_warp * warp_size) { - const uint32_t batch_n_key = min(n_keys - batch_start, warp_size); - if (lane_id == 0) { batch_n_missing[warp_id] = 0; } - __syncthreads(); - const uint32_t key_offset = batch_start + lane_id; - if (key_offset < n_keys) { - const Key key = keys[batch_start + lane_id]; - const uint64_t hash = FullCacheHash()(key); - Index row; - GetOne(capacity, table_keys, table_indices, key, hash, &row); - batch_row_ids[warp_id][lane_id] = row; - if (row == 0) { - const uint32_t batch_missing_idx = atomicAdd(batch_n_missing + warp_id, 1); - batch_missing_keys[warp_id][batch_missing_idx] = key; - batch_missing_indices[warp_id][batch_missing_idx] = key_offset; - } - } - __syncthreads(); - const uint32_t batch_n_missing_t = batch_n_missing[warp_id]; - if (lane_id == 0) { - const uint32_t old_n_missing = - cuda::atomic::Add(n_missing, static_cast(batch_n_missing_t)); - batch_n_missing[warp_id] = old_n_missing; - } - __syncthreads(); - if (lane_id < batch_n_missing_t) { - missing_keys[batch_n_missing[warp_id] + lane_id] = batch_missing_keys[warp_id][lane_id]; - missing_indices[batch_n_missing[warp_id] + lane_id] = batch_missing_indices[warp_id][lane_id]; - } - for (int i = 0; i < batch_n_key; ++i) { - const Key key = batch_keys[warp_id][i]; - const Index row = batch_row_ids[warp_id][i]; - if (row == 0) { continue; } - for (int col = lane_id; col < value_length; col += warp_size) { - values[(batch_start + i) * value_length + col] = - cache_values[(row - 1) * value_length + col]; - } - } - __syncthreads(); - } -} - -template -struct alignas(sizeof(T) * pack_size) Pack { - T elem[pack_size]; -}; - -template -__global__ void EncodeLookupMaskKernel(uint32_t value_length, const Elem* __restrict__ cache_values, - uint32_t values_elem_cnt, const Key* __restrict__ keys, - const Index* __restrict__ context, Elem* __restrict__ values, - uint8_t* __restrict__ mask, const size_t capacity, - Key* __restrict__ table_keys, - Index* __restrict__ table_indices) { - const uint32_t packed_cols = value_length / pack_size; - auto* packed_values = reinterpret_cast*>(values); - const auto* packed_cache_values = reinterpret_cast*>(cache_values); - constexpr uint32_t warp_size = 32; - constexpr uint32_t n_warp_per_block = block_size / warp_size; - const uint32_t warp_id = threadIdx.x / warp_size; - const uint32_t lane_id = threadIdx.x % warp_size; - const uint32_t global_warp_id = blockIdx.x * n_warp_per_block + warp_id; - const uint32_t global_n_warp = gridDim.x * n_warp_per_block; - const uint32_t n_keys = values_elem_cnt / value_length; - __shared__ Key batch_keys[n_warp_per_block][warp_size]; - __shared__ Index batch_row_ids[n_warp_per_block][warp_size]; - for (uint32_t batch_start = global_warp_id * warp_size; batch_start < n_keys; - batch_start += global_n_warp * warp_size) { - const uint32_t batch_n_key = min(n_keys - batch_start, warp_size); - const uint32_t key_offset = batch_start + lane_id; - if (key_offset < n_keys) { - const Key key = keys[batch_start + lane_id]; - const uint64_t hash = FullCacheHash()(key); - Index row; - GetOne(capacity, table_keys, table_indices, key, hash, &row); - batch_row_ids[warp_id][lane_id] = row; - mask[key_offset] = row > 0; - } - __syncthreads(); - for (int i = 0; i < batch_n_key; ++i) { - const Key key = batch_keys[warp_id][i]; - const Index row = batch_row_ids[warp_id][i]; - if (row == 0) { continue; } -#pragma unroll 4 - for (int col = lane_id; col < packed_cols; col += warp_size) { - packed_values[(batch_start + i) * packed_cols + col] = - packed_cache_values[(row - 1) * packed_cols + col]; - } - } - __syncthreads(); - } -} - -template -__global__ void UpdateKernel(uint32_t value_length, Elem* cache_values, uint32_t values_elem_cnt, - const Index* context, const Elem* values) { - const int packed_values_elem_cnt = values_elem_cnt / pack_size; - const uint32_t packed_elem_cnt = value_length / pack_size; - auto* packed_cache_values = reinterpret_cast*>(cache_values); - auto* packed_values = reinterpret_cast*>(values); - CUDA_1D_KERNEL_LOOP(i, packed_values_elem_cnt) { - const uint64_t key_id = i / packed_elem_cnt; - const uint64_t ctx = context[key_id]; - if (ctx == 0) { continue; } - const uint64_t row_id = ctx - 1; - const uint64_t col_id = i - key_id * packed_elem_cnt; - packed_cache_values[row_id * packed_elem_cnt + col_id] = packed_values[i]; - } -} - -template -__global__ typename std::enable_if::value, void>::type -FusedHalfUpdateKernel(uint32_t value_length, Elem* __restrict__ cache_values, - uint32_t values_elem_cnt, const Index* __restrict__ context, - const Elem* __restrict__ values, const half* __restrict__ update, - const float* __restrict__ lr, float scale) { - const int packed_values_elem_cnt = values_elem_cnt / pack_size; - const uint32_t packed_elem_cnt = value_length / pack_size; - auto* packed_cache_values = reinterpret_cast*>(cache_values); - auto* packed_values = reinterpret_cast*>(values); - auto* packed_update = reinterpret_cast*>(update); - const float alpha = -*lr * scale; - CUDA_1D_KERNEL_LOOP(i, packed_values_elem_cnt) { - const uint64_t key_id = i / packed_elem_cnt; - const uint64_t ctx = context[key_id]; - if (ctx == 0) { continue; } - const uint64_t row_id = ctx - 1; - const uint64_t col_id = i - key_id * packed_elem_cnt; - Pack m = packed_values[i]; - Pack u = packed_update[i]; - for (size_t j = 0; j < pack_size; ++j) { m.elem[j] += static_cast(u.elem[j]) * alpha; } - packed_cache_values[row_id * packed_elem_cnt + col_id] = m; - } -} - -template -__global__ typename std::enable_if::value, void>::type -FusedHalfUpdateKernel(uint32_t value_length, Elem* cache_values, uint32_t values_elem_cnt, - const Index* context, const Elem* values, const half* update, const float* lr, - float scale) { - asm volatile("s_trap 0;"); -} - -template -__global__ void DumpValueKernel(uint32_t value_length, const uint32_t* n_dumped, - const Index* context, const Elem* cache_values, Elem* values) { - CUDA_1D_KERNEL_LOOP(i, *n_dumped * value_length) { - const uint64_t key_id = i / value_length; - const uint64_t ctx = context[key_id]; - const uint64_t row_id = ctx - 1; - const uint64_t col_id = i - key_id * value_length; - values[i] = cache_values[row_id * value_length + col_id]; - } -} - -template -class OrdinalEncoder { - public: - OF_DISALLOW_COPY_AND_MOVE(OrdinalEncoder); - explicit OrdinalEncoder(uint64_t capacity, float load_factor) - : capacity_(capacity), table_capacity_(capacity / load_factor) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - OF_CUDA_CHECK(hipMalloc(&table_size_, sizeof(Index))); - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&table_size_host_), sizeof(Index))); - OF_CUDA_CHECK(hipMalloc(&table_keys_, table_capacity_ * sizeof(Key))); - OF_CUDA_CHECK(hipMalloc(&table_indices_, table_capacity_ * sizeof(Index))); - Clear(); - } - ~OrdinalEncoder() { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipFree(table_size_)); - OF_CUDA_CHECK(hipHostFree(table_size_host_)); - OF_CUDA_CHECK(hipFree(table_keys_)); - OF_CUDA_CHECK(hipFree(table_indices_)); - } - - template - void Encode(ep::Stream* stream, uint32_t num_keys, const Key* keys, Index* context) { - if (insert) { - RUN_CUDA_KERNEL((OrdinalEncodeKernel), stream, num_keys, table_capacity_, - table_keys_, table_indices_, table_size_, num_keys, keys, context); - OF_CUDA_CHECK(hipMemcpyAsync(table_size_host_, table_size_, sizeof(Index), hipMemcpyDefault, - stream->As()->cuda_stream())); - CHECK_JUST(stream->Sync()); - CHECK_LT(*table_size_host_, capacity_) - << "The number of key is larger than cache size, please enlarge cache_memory_budget. "; - } else { - RUN_CUDA_KERNEL((OrdinalEncodeLookupKernel), stream, num_keys, table_capacity_, - table_keys_, table_indices_, num_keys, keys, context); - } - } - - void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, - uint32_t* n_dumped, Key* keys, Index* context) { - OF_CUDA_CHECK(hipMemsetAsync(n_dumped, 0, sizeof(uint32_t), - stream->As()->cuda_stream())); - RUN_CUDA_KERNEL((OrdinalEncodeDumpKernel), stream, end_key_index - start_key_index, - table_keys_, table_indices_, start_key_index, end_key_index, n_dumped, keys, - context); - } - - void Clear() { - OF_CUDA_CHECK(hipMemset(table_size_, 0, sizeof(Index))); - OF_CUDA_CHECK(hipMemset(table_keys_, 0, table_capacity_ * sizeof(Key))); - OF_CUDA_CHECK(hipMemset(table_indices_, 0, table_capacity_ * sizeof(Index))); - } - - uint64_t TableCapacity() const { return table_capacity_; } - - Key* table_keys() const { return table_keys_; } - - Index* table_indices() const { return table_indices_; } - - private: - int device_index_{}; - Key* table_keys_; - Index* table_indices_; - uint64_t capacity_; - uint64_t table_capacity_; - Index* table_size_{}; - Index* table_size_host_{}; -}; - -template -class CacheImpl : public Cache { - public: - OF_DISALLOW_COPY_AND_MOVE(CacheImpl); - explicit CacheImpl(const CacheOptions& options) - : encoder_(options.capacity, options.load_factor), - device_index_(-1), - options_(options), - max_query_length_(0) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - const uint64_t values_size = options.capacity * options.value_size; - if (options.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(hipMalloc(&values_, values_size)); - } else if (options.value_memory_kind == CacheOptions::MemoryKind::kHost) { - if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_NUMA_AWARE_ALLOCATION", false)) { - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&values_), values_size)); - } else { - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, reinterpret_cast(&values_), - values_size)); - } - } else { - UNIMPLEMENTED(); - } - num_elem_per_value_ = options_.value_size / sizeof(Elem); - } - ~CacheImpl() { - CudaCurrentDeviceGuard guard(device_index_); - if (options_.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(hipFree(values_)); - } else if (options_.value_memory_kind == CacheOptions::MemoryKind::kHost) { - OF_CUDA_CHECK(hipHostFree(values_)); - } else { - UNIMPLEMENTED(); - } - if (max_query_length_ > 0) { OF_CUDA_CHECK(hipFree(encoding_buffer_)); } - } - - uint64_t Capacity() const override { return options_.capacity; } - uint64_t DumpCapacity() const override { return encoder_.TableCapacity(); } - uint32_t KeySize() const override { return options_.key_size; } - - uint32_t ValueSize() const override { return options_.value_size; } - - DataType ValueType() const override { return options_.value_type; } - - uint32_t MaxQueryLength() const override { return max_query_length_; } - - void ReserveQueryLength(uint32_t query_length) override { - CudaCurrentDeviceGuard guard(device_index_); - if (query_length <= max_query_length_) { return; } - if (max_query_length_ > 0) { OF_CUDA_CHECK(hipFree(encoding_buffer_)); } - OF_CUDA_CHECK(hipMalloc(&encoding_buffer_, query_length * sizeof(uint64_t))); - max_query_length_ = query_length; - } - - CacheOptions::Policy Policy() const override { return CacheOptions::Policy::kFull; } - - void Test(ep::Stream* stream, uint32_t n_keys, const void* keys, uint32_t* n_missing, - void* missing_keys, uint32_t* missing_indices) override; - - void Get(ep::Stream* stream, uint32_t n_keys, const void* keys, void* values, uint32_t* n_missing, - void* missing_keys, uint32_t* missing_indices) override; - - void Get(ep::Stream* stream, uint32_t n_keys, const void* keys, void* values, - uint8_t* mask) override; - - void Put(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, - uint32_t* n_evicted, void* evicted_keys, void* evicted_values) override; - void FusedHalfUpdatePut(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, - const void* update, const float* lr, float scale, uint32_t* n_evicted, - void* evicted_keys, void* evicted_values) override; - void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, - uint32_t* n_dumped, void* keys, void* values) override; - - void Clear() override; - - private: - OrdinalEncoder encoder_; - int device_index_; - uint32_t num_elem_per_value_{}; - Elem* values_; - Index* encoding_buffer_{}; - CacheOptions options_; - uint32_t max_query_length_; -}; - -template -void CacheImpl::Test(ep::Stream* stream, uint32_t n_keys, - const void* keys, uint32_t* n_missing, - void* missing_keys, uint32_t* missing_indices) { - OF_CUDA_CHECK( - hipMemsetAsync(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); - if (n_keys == 0) { return; } - CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); - const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; - RUN_CUDA_KERNEL((LookupKernel), stream, values_elem_cnt, - num_elem_per_value_, values_, values_elem_cnt, static_cast(keys), - encoding_buffer_, nullptr, n_missing, static_cast(missing_keys), - missing_indices); -} - -template -void CacheImpl::Get(ep::Stream* stream, uint32_t n_keys, - const void* keys, void* values, - uint32_t* n_missing, void* missing_keys, - uint32_t* missing_indices) { - OF_CUDA_CHECK( - hipMemsetAsync(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); - if (n_keys == 0) { return; } - CHECK_LE(n_keys, max_query_length_); - constexpr uint32_t block_size = 128; - uint32_t grid_size = (n_keys + block_size - 1) / block_size; - const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; - EncodeLookupKernel - <<As()->cuda_stream()>>>( - num_elem_per_value_, values_, values_elem_cnt, static_cast(keys), - encoding_buffer_, static_cast(values), n_missing, static_cast(missing_keys), - missing_indices, encoder_.TableCapacity(), encoder_.table_keys(), - encoder_.table_indices()); -} - -template -void CacheImpl::Get(ep::Stream* stream, uint32_t n_keys, - const void* keys, void* values, uint8_t* mask) { - if (n_keys == 0) { return; } - CHECK_LE(n_keys, max_query_length_); - constexpr uint32_t block_size = 128; - uint32_t grid_size = (n_keys + block_size - 1) / block_size; - const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; - EncodeLookupMaskKernel - <<As()->cuda_stream()>>>( - num_elem_per_value_, values_, values_elem_cnt, static_cast(keys), - encoding_buffer_, static_cast(values), mask, encoder_.TableCapacity(), - encoder_.table_keys(), encoder_.table_indices()); -} - -template -void CacheImpl::Put(ep::Stream* stream, uint32_t n_keys, - const void* keys, const void* values, - uint32_t* n_evicted, void* evicted_keys, - void* evicted_values) { - OF_CUDA_CHECK( - hipMemsetAsync(n_evicted, 0, sizeof(uint32_t), stream->As()->cuda_stream())); - if (n_keys == 0) { return; } - CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); - const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; - RUN_CUDA_KERNEL((UpdateKernel), stream, values_elem_cnt / pack_size, - num_elem_per_value_, values_, values_elem_cnt, encoding_buffer_, - static_cast(values)); -} - -template -void CacheImpl::FusedHalfUpdatePut( - ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, const void* update, - const float* lr, float scale, uint32_t* n_evicted, void* evicted_keys, void* evicted_values) { - if (!std::is_same::value) { UNIMPLEMENTED(); } - OF_CUDA_CHECK( - hipMemsetAsync(n_evicted, 0, sizeof(uint32_t), stream->As()->cuda_stream())); - if (n_keys == 0) { return; } - CHECK_LE(n_keys, max_query_length_); - encoder_.template Encode(stream, n_keys, static_cast(keys), encoding_buffer_); - const uint32_t values_elem_cnt = n_keys * num_elem_per_value_; - RUN_CUDA_KERNEL((FusedHalfUpdateKernel), stream, - values_elem_cnt / pack_size, num_elem_per_value_, values_, values_elem_cnt, - encoding_buffer_, static_cast(values), - static_cast(update), lr, scale); -} -template -void CacheImpl::Dump(ep::Stream* stream, uint64_t start_key_index, - uint64_t end_key_index, uint32_t* n_dumped, - void* keys, void* values) { - encoder_.Dump(stream, start_key_index, end_key_index, n_dumped, static_cast(keys), - encoding_buffer_); - RUN_CUDA_KERNEL((DumpValueKernel), stream, - num_elem_per_value_ * (end_key_index - start_key_index), num_elem_per_value_, - n_dumped, encoding_buffer_, values_, static_cast(values)); -} - -template -void CacheImpl::Clear() { - encoder_.Clear(); -} - -template -std::unique_ptr DispatchValueType(const CacheOptions& options) { - if (options.value_type == DataType::kFloat) { - const size_t value_elem_cnt = options.value_size / sizeof(float); - const size_t half_warp = 16; - if (value_elem_cnt % 4 == 0 && value_elem_cnt / 4 > half_warp) { - return std::unique_ptr(new CacheImpl(options)); - } else if (value_elem_cnt % 2 == 0 && value_elem_cnt / 2 > half_warp) { - return std::unique_ptr(new CacheImpl(options)); - } else { - return std::unique_ptr(new CacheImpl(options)); - } - } else if (options.value_size % sizeof(ulonglong2) == 0) { - return std::unique_ptr(new CacheImpl(options)); - } else if (options.value_size % sizeof(uint64_t) == 0) { - return std::unique_ptr(new CacheImpl(options)); - } else if (options.value_size % sizeof(uint32_t) == 0) { - return std::unique_ptr(new CacheImpl(options)); - } else if (options.value_size % sizeof(uint16_t) == 0) { - return std::unique_ptr(new CacheImpl(options)); - } else { - return std::unique_ptr(new CacheImpl(options)); - } -} - -template -std::unique_ptr DispatchKeyType(const CacheOptions& options) { - if (options.key_size == sizeof(Key32)) { - return DispatchValueType(options); - } else if (options.key_size == sizeof(Key64)) { - return DispatchValueType(options); - } else { - UNIMPLEMENTED(); - return nullptr; - } -} - -std::unique_ptr DispatchIndexType(const CacheOptions& options) { - const int64_t table_capacity = static_cast(options.capacity) / options.load_factor; - if (table_capacity >= (1ULL << 31ULL)) { - return DispatchKeyType(options); - } else { - return DispatchKeyType(options); - } -} - -} // namespace - -std::unique_ptr NewFullCache(const CacheOptions& options) { - return DispatchIndexType(options); -} - -} // namespace embedding - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/embedding/hash_functions.hip.h b/oneflow/core/embedding/hash_functions.hip.h deleted file mode 100644 index 25c6eb0601e2cc1feb9e222c3bceec62045a5512..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/hash_functions.hip.h +++ /dev/null @@ -1,100 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EMBEDDING_HASH_FUNCTION_HIP_H_ -#define ONEFLOW_CORE_EMBEDDING_HASH_FUNCTION_HIP_H_ - -#include -#include "oneflow/core/common/data_type.h" - -namespace oneflow { - -namespace embedding { - -namespace { - -// From https://github.com/Cyan4973/xxHash/blob/dev/xxhash.h -static const uint64_t PRIME64_1 = - 0x9E3779B185EBCA87ULL; // 0b1001111000110111011110011011000110000101111010111100101010000111 -static const uint64_t PRIME64_2 = - 0xC2B2AE3D27D4EB4FULL; // 0b1100001010110010101011100011110100100111110101001110101101001111 -static const uint64_t PRIME64_3 = - 0x165667B19E3779F9ULL; // 0b0001011001010110011001111011000110011110001101110111100111111001 -static const uint64_t PRIME64_4 = - 0x85EBCA77C2B2AE63ULL; // 0b1000010111101011110010100111011111000010101100101010111001100011 -static const uint64_t PRIME64_5 = - 0x27D4EB2F165667C5ULL; // 0b0010011111010100111010110010111100010110010101100110011111000101 - -#define XXH_rotl64(x, r) (((x) << (r)) | ((x) >> (64 - (r)))) - -OF_DEVICE_FUNC uint64_t XXH64_round(uint64_t acc, uint64_t input) { - acc += input * PRIME64_2; - acc = XXH_rotl64(acc, 31); - acc *= PRIME64_1; - return acc; -} - -OF_DEVICE_FUNC uint64_t xxh64_uint64(uint64_t v, uint64_t seed) { - uint64_t acc = seed + PRIME64_5; - acc += sizeof(uint64_t); - acc = acc ^ XXH64_round(0, v); - acc = XXH_rotl64(acc, 27) * PRIME64_1; - acc = acc + PRIME64_4; - acc ^= (acc >> 33); - acc = acc * PRIME64_2; - acc = acc ^ (acc >> 29); - acc = acc * PRIME64_3; - acc = acc ^ (acc >> 32); - return acc; -} - -static const size_t kShardingHashSeed = 1; -static const size_t kLocalUniqueHashSeed = 2; -static const size_t kGlobalUniqueHashSeed = 3; -static const size_t kFullCacheHashSeed = 4; -static const size_t kLruCacheHashSeed = 5; - -} // namespace - -struct ShardingHash { - OF_DEVICE_FUNC size_t operator()(uint64_t v) { return xxh64_uint64(v, kShardingHashSeed); } - OF_DEVICE_FUNC size_t operator()(uint32_t v) { return xxh64_uint64(v, kShardingHashSeed); } - OF_DEVICE_FUNC size_t operator()(int32_t v) { - return xxh64_uint64(static_cast(v), kShardingHashSeed); - } - OF_DEVICE_FUNC size_t operator()(int64_t v) { - return xxh64_uint64(static_cast(v), kShardingHashSeed); - } -}; - -struct LocalUniqueHash { - OF_DEVICE_FUNC size_t operator()(uint64_t v) { return xxh64_uint64(v, kLocalUniqueHashSeed); } -}; - -struct GlobalUniqueHash { - OF_DEVICE_FUNC size_t operator()(uint64_t v) { return xxh64_uint64(v, kGlobalUniqueHashSeed); } -}; - -struct FullCacheHash { - OF_DEVICE_FUNC size_t operator()(uint64_t v) { return xxh64_uint64(v, kFullCacheHashSeed); } -}; - -struct LruCacheHash { - OF_DEVICE_FUNC size_t operator()(uint64_t v) { return xxh64_uint64(v, kLruCacheHashSeed); } -}; - -} // namespace embedding -} // namespace oneflow -#endif // ONEFLOW_CORE_EMBEDDING_HASH_FUNCTION_HIP_H_ \ No newline at end of file diff --git a/oneflow/core/embedding/key_value_store_test.cpp b/oneflow/core/embedding/key_value_store_test.cpp index 954f3eddf9dc7bd2be9ce011568bb5f2475b3373..c2df37c0f5dd411422ce2806f74308c658200bdd 100644 --- a/oneflow/core/embedding/key_value_store_test.cpp +++ b/oneflow/core/embedding/key_value_store_test.cpp @@ -263,6 +263,242 @@ TEST(MockKeyValueStore, Mock) { #endif // WITH_CUDA +#ifdef WITH_ROCM + +std::string CreateTempDirectory() { + const char* tmp_env = getenv("TMPDIR"); + const char* tmp_dir = tmp_env == nullptr ? "/tmp" : tmp_env; + std::string tpl = std::string(tmp_dir) + "/test_kv_XXXXXX"; + char* path = mkdtemp(const_cast(tpl.c_str())); + PCHECK(path != nullptr); + return std::string(path); +} + +bool HasCudaDevice() { + int device_count = 0; + if (hipGetDeviceCount(&device_count) != hipSuccess) { return false; } + if (device_count <= 0) { return false; } + return true; +} + +void TestKeyValueStore(KeyValueStore* store, size_t num_embeddings, size_t test_embeddings, + size_t embedding_vec_size) { + auto device = Singleton::Get()->GetDevice(DeviceType::kCUDA, 0); + ep::Stream* stream = device->CreateStream(); + + store->SaveSnapshot("init"); + + uint64_t* keys = nullptr; + float* values = nullptr; + float* values1 = nullptr; + uint64_t* keys_host = nullptr; + float* values_host = nullptr; + uint64_t* context = nullptr; + uint32_t* n_missing = nullptr; + uint32_t* host_n_missing = nullptr; + uint64_t* missing_keys = nullptr; + uint32_t* missing_indices = nullptr; + size_t keys_size = sizeof(uint64_t) * num_embeddings; + size_t values_size = sizeof(float) * embedding_vec_size * num_embeddings; + size_t context_size = sizeof(uint64_t) * num_embeddings; + const size_t batch_size = 128; + OF_CUDA_CHECK(hipMalloc(&keys, keys_size)); + OF_CUDA_CHECK(hipMalloc(&values, values_size)); + OF_CUDA_CHECK(hipMalloc(&values1, values_size)); + OF_CUDA_CHECK(hipMalloc(&context, context_size)); + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&keys_host), keys_size)); + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&values_host), values_size)); + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_n_missing), sizeof(uint32_t))); + OF_CUDA_CHECK(hipMalloc(&missing_keys, batch_size * sizeof(uint64_t))); + OF_CUDA_CHECK(hipMalloc(&missing_indices, batch_size * sizeof(uint32_t))); + OF_CUDA_CHECK(hipMalloc(&n_missing, sizeof(uint32_t))); + for (size_t i = 0; i < num_embeddings; ++i) { + uint64_t key = i + 1; + keys_host[i] = key; + for (size_t j = 0; j < embedding_vec_size; j++) { + values_host[i * embedding_vec_size + j] = key; + } + } + OF_CUDA_CHECK(hipMemcpy(keys, keys_host, keys_size, hipMemcpyDefault)); + OF_CUDA_CHECK(hipMemcpy(values, values_host, values_size, hipMemcpyDefault)); + + store->Put(stream, 0, keys, values); + OF_CUDA_CHECK(hipDeviceSynchronize()); + OF_CUDA_CHECK(hipGetLastError()); + + for (size_t offset = 0; offset < test_embeddings; offset += batch_size) { + const size_t num_keys = std::min(batch_size, test_embeddings - offset); + store->Get(stream, num_keys, keys + offset, values1 + offset * embedding_vec_size, n_missing, + missing_indices); + OF_CUDA_CHECK(hipMemcpy(host_n_missing, n_missing, sizeof(uint32_t), hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + ASSERT_EQ(*host_n_missing, num_keys); + store->Put(stream, num_keys, keys + offset, values + offset * embedding_vec_size); + } + + OF_CUDA_CHECK(hipDeviceSynchronize()); + + store->SaveSnapshot("final"); + + OF_CUDA_CHECK(hipMemset(values_host, 0, values_size)); + OF_CUDA_CHECK(hipMemset(values, 0, values_size)); + for (size_t offset = 0; offset < test_embeddings; offset += batch_size) { + const size_t num_keys = std::min(batch_size, test_embeddings - offset); + store->Get(stream, num_keys, keys + offset, values + offset * embedding_vec_size, n_missing, + missing_indices); + OF_CUDA_CHECK(hipMemcpy(host_n_missing, n_missing, sizeof(uint32_t), hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + ASSERT_EQ(*host_n_missing, 0); + } + OF_CUDA_CHECK(hipMemcpy(values_host, values, values_size, hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + for (size_t i = 0; i < test_embeddings; ++i) { + uint64_t key = keys_host[i]; + for (size_t j = 0; j < embedding_vec_size; j++) { + ASSERT_EQ(values_host[i * embedding_vec_size + j], key); + } + } + + store->LoadSnapshot("init"); + + for (size_t offset = 0; offset < test_embeddings; offset += batch_size) { + const size_t num_keys = std::min(batch_size, test_embeddings - offset); + store->Get(stream, num_keys, keys + offset, values1 + offset * embedding_vec_size, n_missing, + missing_indices); + OF_CUDA_CHECK(hipMemcpy(host_n_missing, n_missing, sizeof(uint32_t), hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + ASSERT_EQ(*host_n_missing, num_keys); + } + + store->LoadSnapshot("final"); + + OF_CUDA_CHECK(hipMemset(values_host, 0, values_size)); + OF_CUDA_CHECK(hipMemset(values, 0, values_size)); + for (size_t offset = 0; offset < test_embeddings; offset += batch_size) { + const size_t num_keys = std::min(batch_size, test_embeddings - offset); + store->Get(stream, num_keys, keys + offset, values + offset * embedding_vec_size, n_missing, + missing_indices); + OF_CUDA_CHECK(hipMemcpy(host_n_missing, n_missing, sizeof(uint32_t), hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + ASSERT_EQ(*host_n_missing, 0); + } + OF_CUDA_CHECK(hipMemcpy(values_host, values, values_size, hipMemcpyDefault)); + OF_CUDA_CHECK(hipDeviceSynchronize()); + for (size_t i = 0; i < test_embeddings; ++i) { + uint64_t key = keys_host[i]; + for (size_t j = 0; j < embedding_vec_size; j++) { + ASSERT_EQ(values_host[i * embedding_vec_size + j], key); + } + } + + OF_CUDA_CHECK(hipDeviceSynchronize()); + OF_CUDA_CHECK(hipGetLastError()); + OF_CUDA_CHECK(hipFree(keys)); + OF_CUDA_CHECK(hipFree(values)); + OF_CUDA_CHECK(hipFree(values1)); + OF_CUDA_CHECK(hipHostFree(keys_host)); + OF_CUDA_CHECK(hipHostFree(values_host)); + OF_CUDA_CHECK(hipHostFree(host_n_missing)); + OF_CUDA_CHECK(hipFree(n_missing)); + OF_CUDA_CHECK(hipFree(missing_keys)); + OF_CUDA_CHECK(hipFree(missing_indices)); + CHECK_JUST(stream->Sync()); + device->DestroyStream(stream); +} + +TEST(PersistentTableKeyValueStore, PersistentTableKeyValueStore) { + if (!HasCudaDevice()) { return; } + Singleton::New(); + PersistentTableKeyValueStoreOptions options{}; + uint32_t value_length = 128; + + std::string path = CreateTempDirectory(); + options.table_options.path = path; + options.table_options.value_size = value_length * sizeof(float); + options.table_options.key_size = GetSizeOfDataType(DataType::kUInt64); + options.table_options.physical_block_size = 512; + + std::unique_ptr store = NewPersistentTableKeyValueStore(options); + store->ReserveQueryLength(128); + TestKeyValueStore(store.get(), 1024, 1024, value_length); + store.reset(); + PosixFile::RecursiveDelete(path); + Singleton::Delete(); +} + +// TEST(CachedKeyValueStore, LRU) { +// if (!HasCudaDevice()) { return; } +// Singleton::New(); +// PersistentTableKeyValueStoreOptions store_options{}; +// std::string path = CreateTempDirectory(); +// store_options.table_options.path = path; +// uint32_t value_length = 128; +// store_options.table_options.value_size = value_length * sizeof(float); +// store_options.table_options.key_size = GetSizeOfDataType(DataType::kUInt64); +// store_options.table_options.physical_block_size = 512; +// std::unique_ptr store = NewPersistentTableKeyValueStore(store_options); +// CacheOptions cache_options{}; +// cache_options.policy = CacheOptions::Policy::kLRU; +// cache_options.value_memory_kind = CacheOptions::MemoryKind::kDevice; +// cache_options.value_size = 512; +// cache_options.capacity = 512; +// cache_options.key_size = 8; +// std::unique_ptr cache = NewCache(cache_options); +// std::unique_ptr cached_store = +// NewCachedKeyValueStore(std::move(store), std::move(cache)); +// cached_store->ReserveQueryLength(128); +// TestKeyValueStore(cached_store.get(), 1024, 1024, value_length); +// cached_store.reset(); +// PosixFile::RecursiveDelete(path); +// Singleton::Delete(); +// } + +TEST(CachedKeyValueStore, Full) { + if (!HasCudaDevice()) { return; } + Singleton::New(); + PersistentTableKeyValueStoreOptions store_options{}; + std::string path = CreateTempDirectory(); + store_options.table_options.path = path; + uint32_t value_length = 128; + store_options.table_options.value_size = value_length * sizeof(float); + store_options.table_options.key_size = GetSizeOfDataType(DataType::kUInt64); + store_options.table_options.physical_block_size = 512; + std::unique_ptr store = NewPersistentTableKeyValueStore(store_options); + CacheOptions cache_options{}; + cache_options.policy = CacheOptions::Policy::kFull; + cache_options.value_memory_kind = CacheOptions::MemoryKind::kHost; + cache_options.value_size = 512; + cache_options.capacity = 1024 * 2; + cache_options.key_size = 8; + std::unique_ptr cache = NewCache(cache_options); + std::unique_ptr cached_store = + NewCachedKeyValueStore(std::move(store), std::move(cache)); + cached_store->ReserveQueryLength(128); + TestKeyValueStore(cached_store.get(), 1024, 1024, value_length); + cached_store.reset(); + PosixFile::RecursiveDelete(path); + Singleton::Delete(); +} + +TEST(MockKeyValueStore, Mock) { + if (!HasCudaDevice()) { return; } + Singleton::New(); + MockKeyValueStoreOptions store_options{}; + std::string path = CreateTempDirectory(); + uint32_t value_length = 128; + store_options.value_size = value_length * sizeof(float); + store_options.key_size = GetSizeOfDataType(DataType::kUInt64); + std::unique_ptr store = NewMockKeyValueStore(store_options); + store->ReserveQueryLength(128); + TestKeyValueStore(store.get(), 1024, 1024, value_length); + store.reset(); + PosixFile::RecursiveDelete(path); + Singleton::Delete(); +} + +#endif // WITH_ROCM + + } // namespace } // namespace embedding diff --git a/oneflow/core/embedding/lru_cache.cu b/oneflow/core/embedding/lru_cache.cu index fda887f91ddeb2d91ebecc3194843b702bd53597..80759144892508c6427a0c92f498de6c3c4c9fbe 100644 --- a/oneflow/core/embedding/lru_cache.cu +++ b/oneflow/core/embedding/lru_cache.cu @@ -20,9 +20,14 @@ limitations under the License. #include "oneflow/core/device/cuda_util.h" #include "oneflow/core/embedding/hash_functions.cuh" #include +#ifdef WITH_ROCM +#include +#else #include +#endif -#if CUDA_VERSION >= 11000 && ((!defined(__CUDA_ARCH__)) || (__CUDA_ARCH__ >= 700)) +#if CUDA_VERSION >= 11000 && ((!defined(__CUDA_ARCH__)) || (__CUDA_ARCH__ >= 700)) \ + && !(defined(__clang__) && defined(__CUDA__)) #include #endif @@ -32,7 +37,11 @@ namespace embedding { namespace { +#ifdef WITH_ROCM +constexpr int kWarpSize = 64; +#else constexpr int kWarpSize = 32; +#endif constexpr int kNumWarpPerBlock = 4; constexpr int kBlockSize = kNumWarpPerBlock * kWarpSize; constexpr uint32_t kFullMask = 0xFFFFFFFFU; @@ -69,11 +78,19 @@ class WarpMutexAtomicImpl { ; } __threadfence(); - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } __device__ void Unlock(const ThreadContext& thread_ctx) { - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif __threadfence(); if (thread_ctx.lane_id == 0) { atomicExch(&flag_, 0); } } @@ -82,7 +99,8 @@ class WarpMutexAtomicImpl { int32_t flag_; }; -#if CUDA_VERSION >= 11000 && ((!defined(__CUDA_ARCH__)) || (__CUDA_ARCH__ >= 700)) +#if CUDA_VERSION >= 11000 && ((!defined(__CUDA_ARCH__)) || (__CUDA_ARCH__ >= 700)) \ + && !(defined(__clang__) && defined(__CUDA__)) class WarpMutexSemaphoreImpl { public: @@ -92,11 +110,19 @@ class WarpMutexSemaphoreImpl { __device__ void Lock(const ThreadContext& thread_ctx) { if (thread_ctx.lane_id == 0) { semaphore_.acquire(); } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } __device__ void Unlock(const ThreadContext& thread_ctx) { - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif if (thread_ctx.lane_id == 0) { semaphore_.release(); } } @@ -118,19 +144,20 @@ struct LruCacheContext { }; __global__ void InitCacheSetMutex(uint32_t n_set, void* mutex) { -#if CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 +#if CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 && !(defined(__clang__) && defined(__CUDA__)) using WarpMutex = WarpMutexSemaphoreImpl; #else using WarpMutex = WarpMutexAtomicImpl; -#endif // CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 +#endif // CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 && !(defined(__clang__) && + // defined(__CUDA__)) const uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < n_set) { new (reinterpret_cast(mutex) + idx) WarpMutex; } } template void ClearLruCacheContext(LruCacheContext* ctx) { - OF_CUDA_CHECK(cudaMemset(ctx->keys, 0, ctx->n_set * kWarpSize * sizeof(Key))); - OF_CUDA_CHECK(cudaMemset(ctx->ages, 0, ctx->n_set * kWarpSize * sizeof(uint8_t))); + OF_CUDA_CHECK(GPU(Memset)(ctx->keys, 0, ctx->n_set * kWarpSize * sizeof(Key))); + OF_CUDA_CHECK(GPU(Memset)(ctx->ages, 0, ctx->n_set * kWarpSize * sizeof(uint8_t))); InitCacheSetMutex<<<(ctx->n_set - 1 + 256) / 256, 256>>>(ctx->n_set, ctx->mutex); } @@ -141,11 +168,13 @@ void InitLruCacheContext(const CacheOptions& options, LruCacheContext const size_t lines_size_per_set = kWarpSize * line_size * sizeof(Elem); const size_t ages_size_per_set = kWarpSize * sizeof(uint8_t); int device = 0; - OF_CUDA_CHECK(cudaGetDevice(&device)); + OF_CUDA_CHECK(GPU(GetDevice)(&device)); int major = 0; +#ifdef WITH_CUDA OF_CUDA_CHECK(cudaDeviceGetAttribute(&major, cudaDevAttrComputeCapabilityMajor, device)); +#endif size_t mutex_size_per_set = 0; -#if CUDA_VERSION >= 11000 +#if CUDA_VERSION >= 11000 && !(defined(__clang__) && defined(__CUDA__)) if (major >= 7) { #if !defined(__CUDA_ARCH__) mutex_size_per_set = sizeof(WarpMutexSemaphoreImpl); @@ -157,19 +186,23 @@ void InitLruCacheContext(const CacheOptions& options, LruCacheContext } #else mutex_size_per_set = sizeof(WarpMutexAtomicImpl); -#endif // CUDA_VERSION >= 11000 +#endif // CUDA_VERSION >= 11000 && !(defined(__clang__) && defined(__CUDA__)) const size_t n_set = (options.capacity - 1 + kWarpSize) / kWarpSize; CHECK_GT(n_set, 0); ctx->n_set = n_set; ctx->line_size = line_size; const size_t keys_size = n_set * keys_size_per_set; - OF_CUDA_CHECK(cudaMalloc(&(ctx->keys), keys_size)); + OF_CUDA_CHECK(GPU(Malloc)(&(ctx->keys), keys_size)); const size_t lines_size = n_set * lines_size_per_set; if (options.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(cudaMalloc(&(ctx->lines), lines_size)); + OF_CUDA_CHECK(GPU(Malloc)(&(ctx->lines), lines_size)); } else if (options.value_memory_kind == CacheOptions::MemoryKind::kHost) { if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_NUMA_AWARE_ALLOCATION", false)) { +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&(ctx->lines)), lines_size)); +#else OF_CUDA_CHECK(cudaMallocHost(&(ctx->lines), lines_size)); +#endif } else { OF_CUDA_CHECK( NumaAwareCudaMallocHost(device, reinterpret_cast(&ctx->lines), lines_size)); @@ -179,45 +212,50 @@ void InitLruCacheContext(const CacheOptions& options, LruCacheContext } ctx->value_memory_kind = options.value_memory_kind; const size_t ages_size = n_set * ages_size_per_set; - OF_CUDA_CHECK(cudaMalloc(&(ctx->ages), ages_size)); + OF_CUDA_CHECK(GPU(Malloc)(&(ctx->ages), ages_size)); const size_t mutex_size = n_set * mutex_size_per_set; - OF_CUDA_CHECK(cudaMalloc(&(ctx->mutex), mutex_size)); + OF_CUDA_CHECK(GPU(Malloc)(&(ctx->mutex), mutex_size)); ClearLruCacheContext(ctx); } template void DestroyLruCacheContext(LruCacheContext* ctx) { - OF_CUDA_CHECK(cudaFree(ctx->keys)); + OF_CUDA_CHECK(GPU(Free)(ctx->keys)); if (ctx->value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(cudaFree(ctx->lines)); + OF_CUDA_CHECK(GPU(Free)(ctx->lines)); } else if (ctx->value_memory_kind == CacheOptions::MemoryKind::kHost) { - OF_CUDA_CHECK(cudaFreeHost(ctx->lines)); + OF_CUDA_CHECK(GPU(FreeHost)(ctx->lines)); } else { UNIMPLEMENTED(); } - OF_CUDA_CHECK(cudaFree(ctx->ages)); - OF_CUDA_CHECK(cudaFree(ctx->mutex)); + OF_CUDA_CHECK(GPU(Free)(ctx->ages)); + OF_CUDA_CHECK(GPU(Free)(ctx->mutex)); } template struct SetContext { -#if CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 +#if CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 && !(defined(__clang__) && defined(__CUDA__)) using WarpMutex = WarpMutexSemaphoreImpl; #else using WarpMutex = WarpMutexAtomicImpl; -#endif // CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 +#endif // CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 700 && !(defined(__clang__) && + // defined(__CUDA__)) __device__ SetContext(const LruCacheContext& ctx, uint32_t set_id) : keys(ctx.keys + set_id * kWarpSize), mutex(reinterpret_cast(ctx.mutex) + set_id), ages(ctx.ages + set_id * kWarpSize), - lines(ctx.lines + set_id * kWarpSize * ctx.line_size) {} + lines(ctx.lines + static_cast(set_id) * kWarpSize * ctx.line_size) {} __device__ int Lookup(const ThreadContext& thread_ctx, Key key) { const Key lane_key = keys[thread_ctx.lane_id]; const int lane_age = ages[thread_ctx.lane_id]; const bool lane_hit = (lane_key == key && lane_age != 0); +#ifdef WITH_ROCM + const unsigned hit_mask = __ballot(lane_hit); +#else const unsigned hit_mask = __ballot_sync(kFullMask, lane_hit); +#endif if (hit_mask != 0) { return __ffs(static_cast(hit_mask)) - 1; } else { @@ -238,19 +276,35 @@ struct SetContext { int insert_way = -1; const Key lane_key = keys[thread_ctx.lane_id]; int lane_age = ages[thread_ctx.lane_id]; +#ifdef WITH_ROCM + const unsigned hit_mask = __ballot(lane_key == key && lane_age != 0); +#else const unsigned hit_mask = __ballot_sync(kFullMask, lane_key == key && lane_age != 0); +#endif if (hit_mask != 0) { insert_way = __ffs(static_cast(hit_mask)) - 1; +#ifdef WITH_ROCM + const int insert_way_age = __shfl(lane_age, insert_way); +#else const int insert_way_age = __shfl_sync(kFullMask, lane_age, insert_way); +#endif if (lane_age > insert_way_age) { lane_age -= 1; } else if (thread_ctx.lane_id == insert_way) { lane_age = kWarpSize; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } if (insert_way == -1) { - const unsigned valid_mask = __ballot_sync(kFullMask, lane_age != 0); +#ifdef WITH_ROCM + const unsigned valid_mask = __ballot(lane_age != 0); +#else + const unsigned valid_mask = __ballot_sync(kFullMask, lane_age != 0); +#endif if (valid_mask != kFullMask) { insert_way = __popc(static_cast(valid_mask)); if (lane_age > 0) { @@ -259,7 +313,11 @@ struct SetContext { lane_age = kWarpSize; keys[insert_way] = key; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } } if (insert_way != -1) { ages[thread_ctx.lane_id] = lane_age; } @@ -270,15 +328,28 @@ struct SetContext { const ThreadContext& thread_ctx, Key key, int* way, Key* evicted_key) { const Key lane_key = keys[thread_ctx.lane_id]; int lane_age = ages[thread_ctx.lane_id]; +#ifdef WITH_ROCM + const int insert_way = __ffs(static_cast(__ballot(lane_age == 1))) - 1; +#else const int insert_way = __ffs(__ballot_sync(kFullMask, lane_age == 1)) - 1; +#endif + +#ifdef WITH_ROCM + *evicted_key = __shfl(lane_key, insert_way); +#else *evicted_key = __shfl_sync(kFullMask, lane_key, insert_way); +#endif if (thread_ctx.lane_id == insert_way) { keys[insert_way] = key; lane_age = kWarpSize; } else if (lane_age > 1) { lane_age -= 1; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif ages[thread_ctx.lane_id] = lane_age; *way = insert_way; } @@ -318,7 +389,11 @@ __global__ void GetKernel(LruCacheContext cache_ctx, uint32_t num_key block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif uint32_t n_warp_missing = 0; Key warp_missing_key = 0; uint32_t warp_missing_index = 0; @@ -333,7 +408,11 @@ __global__ void GetKernel(LruCacheContext cache_ctx, uint32_t num_key warp_missing_key = key; warp_missing_index = key_idx; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif n_warp_missing += 1; } else if (!test_only) { set_ctx.Read(cache_ctx, thread_ctx, way, values + key_idx * cache_ctx.line_size); @@ -342,15 +421,31 @@ __global__ void GetKernel(LruCacheContext cache_ctx, uint32_t num_key if (n_warp_missing > 0) { uint32_t base_missing_idx = 0; if (thread_ctx.lane_id == 0) { base_missing_idx = atomicAdd(n_missing_keys, n_warp_missing); } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif +#ifdef WITH_ROCM + base_missing_idx = __shfl(base_missing_idx, 0); +#else base_missing_idx = __shfl_sync(kFullMask, base_missing_idx, 0); +#endif if (thread_ctx.lane_id < n_warp_missing) { missing_keys[base_missing_idx + thread_ctx.lane_id] = warp_missing_key; missing_indices[base_missing_idx + thread_ctx.lane_id] = warp_missing_index; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } } @@ -371,7 +466,11 @@ __global__ void PutWithoutEvictingKernel(LruCacheContext cache_ctx, u block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif uint32_t n_warp_missing = 0; Key warp_missing_key = 0; uint32_t warp_missing_index = 0; @@ -390,7 +489,11 @@ __global__ void PutWithoutEvictingKernel(LruCacheContext cache_ctx, u warp_missing_key = key; warp_missing_index = key_idx; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif n_warp_missing += 1; } set_ctx.Unlock(thread_ctx); @@ -398,13 +501,25 @@ __global__ void PutWithoutEvictingKernel(LruCacheContext cache_ctx, u if (n_warp_missing > 0) { uint32_t base_missing_idx = 0; if (thread_ctx.lane_id == 0) { base_missing_idx = atomicAdd(n_missing, n_warp_missing); } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif +#ifdef WITH_ROCM + base_missing_idx = __shfl(base_missing_idx, 0); +#else base_missing_idx = __shfl_sync(kFullMask, base_missing_idx, 0); +#endif if (thread_ctx.lane_id < n_warp_missing) { missing_keys[base_missing_idx + thread_ctx.lane_id] = warp_missing_key; missing_indices[base_missing_idx + thread_ctx.lane_id] = warp_missing_index; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif } } } @@ -427,7 +542,11 @@ __global__ void EvictKernel(LruCacheContext cache_ctx, const Key* key block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif for (uint32_t i = 0; i < n_batch_keys; ++i) { const uint32_t key_idx = batch_offset + i; const Key key = block_keys[thread_ctx.warp_id_in_block][i]; @@ -438,7 +557,11 @@ __global__ void EvictKernel(LruCacheContext cache_ctx, const Key* key Key evicted_key = 0; set_ctx.Evict(cache_ctx, thread_ctx, key, &evicted_way, &evicted_key); if (thread_ctx.lane_id == 0) { evicted_keys[key_idx] = evicted_key; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif set_ctx.Read(cache_ctx, thread_ctx, evicted_way, evicted_values + cache_ctx.line_size * key_idx); set_ctx.Write(cache_ctx, thread_ctx, evicted_way, @@ -463,26 +586,52 @@ __global__ void DumpKernel(LruCacheContext cache_ctx, size_t start_ke lane_key = cache_ctx.keys[warp_start_key_index + thread_ctx.lane_id]; lane_age = cache_ctx.ages[warp_start_key_index + thread_ctx.lane_id]; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif warp_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = lane_key; warp_ages[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = lane_age; +#ifdef WITH_ROCM + const int key_count = __popc(static_cast(__ballot(lane_age != 0))); +#else const int key_count = __popc(__ballot_sync(kFullMask, lane_age != 0)); +#endif + if (key_count == 0) { continue; } uint32_t offset = 0; if (thread_ctx.lane_id == 0) { offset = atomicAdd(n_dumped, key_count); } +#ifdef WITH_ROCM + offset = __shfl(offset, 0); +#else offset = __shfl_sync(kFullMask, offset, 0); - __syncwarp(); +#endif + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif for (uint32_t i = 0; i < kWarpSize; ++i) { const Key key = warp_keys[thread_ctx.warp_id_in_block][i]; const Key age = warp_ages[thread_ctx.warp_id_in_block][i]; if (age == 0) { continue; } if (thread_ctx.lane_id == 0) { keys[offset] = key; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif for (uint32_t j = thread_ctx.lane_id; j < cache_ctx.line_size; j += kWarpSize) { values[offset * cache_ctx.line_size + j] = - cache_ctx.lines[(warp_start_key_index + i) * cache_ctx.line_size + j]; + cache_ctx + .lines[static_cast(warp_start_key_index + i) * cache_ctx.line_size + j]; } - __syncwarp(); + #ifdef WITH_ROCM +__syncthreads(); +#else +__syncwarp(); +#endif offset += 1; } } @@ -498,14 +647,14 @@ class LruCache : public Cache { query_indices_buffer_(nullptr), query_keys_buffer_(nullptr), value_type_(options.value_type) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); InitLruCacheContext(options, &ctx_); } ~LruCache() override { CudaCurrentDeviceGuard guard(device_index_); if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFree(query_indices_buffer_)); - OF_CUDA_CHECK(cudaFree(query_keys_buffer_)); + OF_CUDA_CHECK(GPU(Free)(query_indices_buffer_)); + OF_CUDA_CHECK(GPU(Free)(query_keys_buffer_)); } DestroyLruCacheContext(&ctx_); } @@ -520,11 +669,11 @@ class LruCache : public Cache { CudaCurrentDeviceGuard guard(device_index_); if (query_length < max_query_length_) { return; } if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFree(query_indices_buffer_)); - OF_CUDA_CHECK(cudaFree(query_keys_buffer_)); + OF_CUDA_CHECK(GPU(Free)(query_indices_buffer_)); + OF_CUDA_CHECK(GPU(Free)(query_keys_buffer_)); } - OF_CUDA_CHECK(cudaMalloc(&query_indices_buffer_, query_length * sizeof(uint32_t))); - OF_CUDA_CHECK(cudaMalloc(&query_keys_buffer_, query_length * sizeof(Key))); + OF_CUDA_CHECK(GPU(Malloc)(&query_indices_buffer_, query_length * sizeof(uint32_t))); + OF_CUDA_CHECK(GPU(Malloc)(&query_keys_buffer_, query_length * sizeof(Key))); max_query_length_ = query_length; } @@ -534,18 +683,19 @@ class LruCache : public Cache { void* missing_keys, uint32_t* missing_indices) override { CHECK_LE(n_keys, max_query_length_); auto cuda_stream = stream->As(); - OF_CUDA_CHECK(cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); if (n_keys == 0) { return; } cuda_stream->LaunchKernel(GetKernel, GetLaunchConfig(n_keys), ctx_, n_keys, static_cast(keys), nullptr, n_missing, static_cast(missing_keys), missing_indices); } + using Cache::Get; void Get(ep::Stream* stream, uint32_t n_keys, const void* keys, void* values, uint32_t* n_missing, void* missing_keys, uint32_t* missing_indices) override { CHECK_LE(n_keys, max_query_length_); auto cuda_stream = stream->As(); - OF_CUDA_CHECK(cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); if (n_keys == 0) { return; } cuda_stream->LaunchKernel(GetKernel, GetLaunchConfig(n_keys), ctx_, n_keys, static_cast(keys), static_cast(values), n_missing, @@ -556,7 +706,7 @@ class LruCache : public Cache { uint32_t* n_evicted, void* evicted_keys, void* evicted_values) override { CHECK_LE(n_keys, max_query_length_); auto cuda_stream = stream->As(); - OF_CUDA_CHECK(cudaMemsetAsync(n_evicted, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemsetAsync)(n_evicted, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); if (n_keys == 0) { return; } cuda_stream->LaunchKernel(PutWithoutEvictingKernel, GetLaunchConfig(n_keys), ctx_, n_keys, static_cast(keys), @@ -571,7 +721,7 @@ class LruCache : public Cache { void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, uint32_t* n_dumped, void* keys, void* values) override { auto cuda_stream = stream->As(); - OF_CUDA_CHECK(cudaMemsetAsync(n_dumped, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemsetAsync)(n_dumped, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); const uint64_t max_dump_keys = end_key_index - start_key_index; cuda_stream->LaunchKernel( DumpKernel, @@ -581,6 +731,11 @@ class LruCache : public Cache { static_cast(values)); } + void ClearDirtyFlags() override { + // do nothing. + return; + } + void Clear() override { ClearLruCacheContext(&ctx_); } private: diff --git a/oneflow/core/embedding/lru_cache.hip.cpp b/oneflow/core/embedding/lru_cache.hip.cpp deleted file mode 100644 index 8db00c564dffb019bc7ca47d9800914a9bd4bcc7..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/lru_cache.hip.cpp +++ /dev/null @@ -1,585 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -// Inspired by https://github.com/NVIDIA-Merlin/HugeCTR/blob/master/gpu_cache/src/nv_gpu_cache.cu - -#include "oneflow/core/embedding/lru_cache.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/embedding/hash_functions.hip.h" -#include -#include - -namespace oneflow { - -namespace embedding { - -namespace { - -constexpr int kWarpSize = 64; -constexpr int kNumWarpPerBlock = 2; -constexpr int kBlockSize = kNumWarpPerBlock * kWarpSize; -constexpr unsigned long long int kFullMask = 0xFFFFFFFFFFFFFFFFU; - -ep::CudaLaunchConfig GetLaunchConfig(uint32_t n_keys) { - return ep::CudaLaunchConfig((n_keys + kNumWarpPerBlock - 1) / kNumWarpPerBlock, - kWarpSize * kNumWarpPerBlock, 0); -} - -struct ThreadContext { - __device__ ThreadContext() { - const uint32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - global_warp_id = global_thread_id / kWarpSize; - warp_id_in_block = global_warp_id % kNumWarpPerBlock; // NOLINT - num_warps = gridDim.x * kNumWarpPerBlock; // NOLINT - lane_id = global_thread_id % kWarpSize; - } - - uint32_t global_warp_id; - uint32_t warp_id_in_block; - uint32_t num_warps; - uint32_t lane_id; -}; - -class WarpMutexAtomicImpl { - public: - OF_DISALLOW_COPY_AND_MOVE(WarpMutexAtomicImpl); - __device__ WarpMutexAtomicImpl() : flag_(0) {} - __device__ ~WarpMutexAtomicImpl() = default; - - __device__ void Lock(const ThreadContext& thread_ctx) { - if (thread_ctx.lane_id == 0) { - while (atomicCAS(&flag_, 0, 1) != 0) - ; - } - __threadfence(); - __syncthreads(); - } - - __device__ void Unlock(const ThreadContext& thread_ctx) { - __syncthreads(); - __threadfence(); - if (thread_ctx.lane_id == 0) { atomicExch(&flag_, 0); } - } - - private: - int32_t flag_; -}; - -template -struct LruCacheContext { - Key* keys; - Elem* lines; - uint8_t* ages; - void* mutex; - uint64_t n_set; - uint32_t line_size; - CacheOptions::MemoryKind value_memory_kind; -}; - -__global__ void InitCacheSetMutex(uint32_t n_set, void* mutex) { - - using WarpMutex = WarpMutexAtomicImpl; - - const uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x; - if (idx < n_set) { new (reinterpret_cast(mutex) + idx) WarpMutex; } -} - -template -void ClearLruCacheContext(LruCacheContext* ctx) { - OF_CUDA_CHECK(hipMemset(ctx->keys, 0, ctx->n_set * kWarpSize * sizeof(Key))); - OF_CUDA_CHECK(hipMemset(ctx->ages, 0, ctx->n_set * kWarpSize * sizeof(uint8_t))); - InitCacheSetMutex<<<(ctx->n_set - 1 + 256) / 256, 256>>>(ctx->n_set, ctx->mutex); -} - -template -void InitLruCacheContext(const CacheOptions& options, LruCacheContext* ctx) { - const size_t keys_size_per_set = kWarpSize * sizeof(Key); - const uint32_t line_size = options.value_size / sizeof(Elem); - const size_t lines_size_per_set = kWarpSize * line_size * sizeof(Elem); - const size_t ages_size_per_set = kWarpSize * sizeof(uint8_t); - int device = 0; - OF_CUDA_CHECK(hipGetDevice(&device)); - int major = 0; - OF_CUDA_CHECK(hipDeviceGetAttribute(&major, hipDeviceAttributeComputeCapabilityMajor, device)); - size_t mutex_size_per_set = 0; - - mutex_size_per_set = sizeof(WarpMutexAtomicImpl); - - const size_t n_set = (options.capacity - 1 + kWarpSize) / kWarpSize; - CHECK_GT(n_set, 0); - ctx->n_set = n_set; - ctx->line_size = line_size; - const size_t keys_size = n_set * keys_size_per_set; - OF_CUDA_CHECK(hipMalloc(&(ctx->keys), keys_size)); - const size_t lines_size = n_set * lines_size_per_set; - if (options.value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(hipMalloc(&(ctx->lines), lines_size)); - } else if (options.value_memory_kind == CacheOptions::MemoryKind::kHost) { - if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_NUMA_AWARE_ALLOCATION", false)) { - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&(ctx->lines)), lines_size)); - } else { - OF_CUDA_CHECK( - NumaAwareCudaMallocHost(device, reinterpret_cast(&ctx->lines), lines_size)); - } - } else { - UNIMPLEMENTED(); - } - ctx->value_memory_kind = options.value_memory_kind; - const size_t ages_size = n_set * ages_size_per_set; - OF_CUDA_CHECK(hipMalloc(&(ctx->ages), ages_size)); - const size_t mutex_size = n_set * mutex_size_per_set; - OF_CUDA_CHECK(hipMalloc(&(ctx->mutex), mutex_size)); - - ClearLruCacheContext(ctx); -} - -template -void DestroyLruCacheContext(LruCacheContext* ctx) { - OF_CUDA_CHECK(hipFree(ctx->keys)); - if (ctx->value_memory_kind == CacheOptions::MemoryKind::kDevice) { - OF_CUDA_CHECK(hipFree(ctx->lines)); - } else if (ctx->value_memory_kind == CacheOptions::MemoryKind::kHost) { - OF_CUDA_CHECK(hipHostFree(ctx->lines)); - } else { - UNIMPLEMENTED(); - } - OF_CUDA_CHECK(hipFree(ctx->ages)); - OF_CUDA_CHECK(hipFree(ctx->mutex)); -} - -template -struct SetContext { - - using WarpMutex = WarpMutexAtomicImpl; - - __device__ SetContext(const LruCacheContext& ctx, uint32_t set_id) - : keys(ctx.keys + set_id * kWarpSize), - mutex(reinterpret_cast(ctx.mutex) + set_id), - ages(ctx.ages + set_id * kWarpSize), - lines(ctx.lines + set_id * kWarpSize * ctx.line_size) {} - - __device__ int Lookup(const ThreadContext& thread_ctx, Key key) { - const Key lane_key = keys[thread_ctx.lane_id]; - const int lane_age = ages[thread_ctx.lane_id]; - const bool lane_hit = (lane_key == key && lane_age != 0); - const unsigned long long int hit_mask = __ballot(lane_hit); - if (hit_mask != 0) { - return __ffs(static_cast(hit_mask)) - 1; - } else { - return -1; - } - } - - __device__ void Read(const LruCacheContext& cache_ctx, const ThreadContext& thread_ctx, - int way, Elem* line) { - const Elem* from_line = lines + way * cache_ctx.line_size; - for (int i = thread_ctx.lane_id; i < cache_ctx.line_size; i += kWarpSize) { - line[i] = from_line[i]; - } - } - - __device__ int InsertWithoutEvicting(const LruCacheContext& cache_ctx, - const ThreadContext& thread_ctx, Key key) { - int insert_way = -1; - const Key lane_key = keys[thread_ctx.lane_id]; - int lane_age = ages[thread_ctx.lane_id]; - const unsigned long long int hit_mask = __ballot(lane_key == key && lane_age != 0); - if (hit_mask != 0) { - insert_way = __ffs(static_cast(hit_mask)) - 1; - const int insert_way_age = __shfl(lane_age, insert_way); - if (lane_age > insert_way_age) { - lane_age -= 1; - } else if (thread_ctx.lane_id == insert_way) { - lane_age = kWarpSize; - } - __syncthreads(); - } - if (insert_way == -1) { - const unsigned long long int valid_mask = __ballot(lane_age != 0); - if (valid_mask != kFullMask) { - insert_way = __popc(static_cast(valid_mask)); - if (lane_age > 0) { - lane_age -= 1; - } else if (thread_ctx.lane_id == insert_way) { - lane_age = kWarpSize; - keys[insert_way] = key; - } - __syncthreads(); - } - } - if (insert_way != -1) { ages[thread_ctx.lane_id] = lane_age; } - return insert_way; - } - - __device__ void Evict(const LruCacheContext& cache_ctx, - const ThreadContext& thread_ctx, Key key, int* way, Key* evicted_key) { - const Key lane_key = keys[thread_ctx.lane_id]; - int lane_age = ages[thread_ctx.lane_id]; - const int insert_way = __ffs(static_cast(__ballot(lane_age == 1))) - 1; - *evicted_key = __shfl(lane_key, insert_way); - if (thread_ctx.lane_id == insert_way) { - keys[insert_way] = key; - lane_age = kWarpSize; - } else if (lane_age > 1) { - lane_age -= 1; - } - __syncthreads(); - ages[thread_ctx.lane_id] = lane_age; - *way = insert_way; - } - - __device__ void Write(const LruCacheContext& cache_ctx, - const ThreadContext& thread_ctx, int way, const Elem* line) { - Elem* to_line = lines + way * cache_ctx.line_size; - for (int i = thread_ctx.lane_id; i < cache_ctx.line_size; i += kWarpSize) { - to_line[i] = line[i]; - } - } - - __device__ void Lock(const ThreadContext& thread_ctx) { mutex->Lock(thread_ctx); } - - __device__ void Unlock(const ThreadContext& thread_ctx) { mutex->Unlock(thread_ctx); } - - Key* keys; - Elem* lines; - uint8_t* ages; - WarpMutex* mutex; -}; - -template -__global__ void GetKernel(LruCacheContext cache_ctx, uint32_t num_keys, const Key* keys, - Elem* values, uint32_t* n_missing_keys, Key* missing_keys, - uint32_t* missing_indices) { - ThreadContext thread_ctx{}; - __shared__ Key block_keys[kNumWarpPerBlock][kWarpSize]; - __shared__ size_t block_set_ids[kNumWarpPerBlock][kWarpSize]; - for (uint32_t batch_offset = thread_ctx.global_warp_id * kWarpSize; batch_offset < num_keys; - batch_offset += thread_ctx.num_warps * kWarpSize) { - const uint32_t n_batch_keys = min(kWarpSize, num_keys - batch_offset); - if (thread_ctx.lane_id < n_batch_keys) { - const Key key = keys[batch_offset + thread_ctx.lane_id]; - const size_t hash = LruCacheHash()(key); - const uint32_t set_id = hash % cache_ctx.n_set; - block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; - block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; - } - __syncthreads(); - uint32_t n_warp_missing = 0; - Key warp_missing_key = 0; - uint32_t warp_missing_index = 0; - for (uint32_t i = 0; i < n_batch_keys; ++i) { - const uint32_t key_idx = batch_offset + i; - const Key key = block_keys[thread_ctx.warp_id_in_block][i]; - const size_t set_id = block_set_ids[thread_ctx.warp_id_in_block][i]; - SetContext set_ctx(cache_ctx, set_id); - const int way = set_ctx.Lookup(thread_ctx, key); - if (way < 0) { - if (thread_ctx.lane_id == n_warp_missing) { - warp_missing_key = key; - warp_missing_index = key_idx; - } - __syncthreads(); - n_warp_missing += 1; - } else if (!test_only) { - set_ctx.Read(cache_ctx, thread_ctx, way, values + key_idx * cache_ctx.line_size); - } - } - if (n_warp_missing > 0) { - uint32_t base_missing_idx = 0; - if (thread_ctx.lane_id == 0) { base_missing_idx = atomicAdd(n_missing_keys, n_warp_missing); } - __syncthreads(); - base_missing_idx = __shfl(base_missing_idx, 0); - if (thread_ctx.lane_id < n_warp_missing) { - missing_keys[base_missing_idx + thread_ctx.lane_id] = warp_missing_key; - missing_indices[base_missing_idx + thread_ctx.lane_id] = warp_missing_index; - } - __syncthreads(); - } - __syncthreads(); - } -} - -template -__global__ void PutWithoutEvictingKernel(LruCacheContext cache_ctx, uint32_t num_keys, - const Key* keys, const Elem* values, uint32_t* n_missing, - Key* missing_keys, uint32_t* missing_indices) { - ThreadContext thread_ctx{}; - __shared__ Key block_keys[kNumWarpPerBlock][kWarpSize]; - __shared__ size_t block_set_ids[kNumWarpPerBlock][kWarpSize]; - for (uint32_t batch_offset = thread_ctx.global_warp_id * kWarpSize; batch_offset < num_keys; - batch_offset += thread_ctx.num_warps * kWarpSize) { - const uint32_t n_batch_keys = min(kWarpSize, num_keys - batch_offset); - if (thread_ctx.lane_id < n_batch_keys) { - const Key key = keys[batch_offset + thread_ctx.lane_id]; - const size_t hash = LruCacheHash()(key); - const uint32_t set_id = hash % cache_ctx.n_set; - block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; - block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; - } - __syncthreads(); - uint32_t n_warp_missing = 0; - Key warp_missing_key = 0; - uint32_t warp_missing_index = 0; - for (uint32_t i = 0; i < n_batch_keys; ++i) { - const uint32_t key_idx = batch_offset + i; - const Key key = block_keys[thread_ctx.warp_id_in_block][i]; - const size_t set_id = block_set_ids[thread_ctx.warp_id_in_block][i]; - SetContext set_ctx(cache_ctx, set_id); - set_ctx.Lock(thread_ctx); - Key evicted_key = 0; - const int insert_way = set_ctx.InsertWithoutEvicting(cache_ctx, thread_ctx, key); - if (insert_way >= 0) { - set_ctx.Write(cache_ctx, thread_ctx, insert_way, values + cache_ctx.line_size * key_idx); - } else { - if (thread_ctx.lane_id == n_warp_missing) { - warp_missing_key = key; - warp_missing_index = key_idx; - } - __syncthreads(); - n_warp_missing += 1; - } - set_ctx.Unlock(thread_ctx); - } - if (n_warp_missing > 0) { - uint32_t base_missing_idx = 0; - if (thread_ctx.lane_id == 0) { base_missing_idx = atomicAdd(n_missing, n_warp_missing); } - __syncthreads(); - base_missing_idx = __shfl(base_missing_idx, 0); - if (thread_ctx.lane_id < n_warp_missing) { - missing_keys[base_missing_idx + thread_ctx.lane_id] = warp_missing_key; - missing_indices[base_missing_idx + thread_ctx.lane_id] = warp_missing_index; - } - __syncthreads(); - } - } -} - -template -__global__ void EvictKernel(LruCacheContext cache_ctx, const Key* keys, - const uint32_t* indices, const Elem* values, const uint32_t* n_evict, - Key* evicted_keys, Elem* evicted_values) { - ThreadContext thread_ctx{}; - uint32_t num_evict = *n_evict; - __shared__ Key block_keys[kNumWarpPerBlock][kWarpSize]; - __shared__ size_t block_set_ids[kNumWarpPerBlock][kWarpSize]; - for (uint32_t batch_offset = thread_ctx.global_warp_id * kWarpSize; batch_offset < num_evict; - batch_offset += thread_ctx.num_warps * kWarpSize) { - const uint32_t n_batch_keys = min(kWarpSize, num_evict - batch_offset); - if (thread_ctx.lane_id < n_batch_keys) { - const Key key = keys[batch_offset + thread_ctx.lane_id]; - const size_t hash = LruCacheHash()(key); - const uint32_t set_id = hash % cache_ctx.n_set; - block_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = key; - block_set_ids[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = set_id; - } - __syncthreads(); - for (uint32_t i = 0; i < n_batch_keys; ++i) { - const uint32_t key_idx = batch_offset + i; - const Key key = block_keys[thread_ctx.warp_id_in_block][i]; - const uint32_t set_id = block_set_ids[thread_ctx.warp_id_in_block][i]; - SetContext set_ctx(cache_ctx, set_id); - set_ctx.Lock(thread_ctx); - int evicted_way = -1; - Key evicted_key = 0; - set_ctx.Evict(cache_ctx, thread_ctx, key, &evicted_way, &evicted_key); - if (thread_ctx.lane_id == 0) { evicted_keys[key_idx] = evicted_key; } - __syncthreads(); - set_ctx.Read(cache_ctx, thread_ctx, evicted_way, - evicted_values + cache_ctx.line_size * key_idx); - set_ctx.Write(cache_ctx, thread_ctx, evicted_way, - values + cache_ctx.line_size * indices[key_idx]); - set_ctx.Unlock(thread_ctx); - } - } -} - -template -__global__ void DumpKernel(LruCacheContext cache_ctx, size_t start_key_index, - size_t end_key_index, uint32_t* n_dumped, Key* keys, Elem* values) { - ThreadContext thread_ctx{}; - __shared__ Key warp_keys[kNumWarpPerBlock][kWarpSize]; - __shared__ uint8_t warp_ages[kNumWarpPerBlock][kWarpSize]; - for (uint32_t warp_start_key_index = start_key_index + thread_ctx.global_warp_id * kWarpSize; - warp_start_key_index < end_key_index; - warp_start_key_index += thread_ctx.num_warps * kWarpSize) { - Key lane_key = 0; - uint8_t lane_age = 0; - if (warp_start_key_index + thread_ctx.lane_id < end_key_index) { - lane_key = cache_ctx.keys[warp_start_key_index + thread_ctx.lane_id]; - lane_age = cache_ctx.ages[warp_start_key_index + thread_ctx.lane_id]; - } - __syncthreads(); - warp_keys[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = lane_key; - warp_ages[thread_ctx.warp_id_in_block][thread_ctx.lane_id] = lane_age; - const int key_count = __popc(static_cast(__ballot(lane_age != 0))); - if (key_count == 0) { continue; } - uint32_t offset = 0; - if (thread_ctx.lane_id == 0) { offset = atomicAdd(n_dumped, key_count); } - offset = __shfl(offset, 0); - __syncthreads(); - for (uint32_t i = 0; i < kWarpSize; ++i) { - const Key key = warp_keys[thread_ctx.warp_id_in_block][i]; - const Key age = warp_ages[thread_ctx.warp_id_in_block][i]; - if (age == 0) { continue; } - if (thread_ctx.lane_id == 0) { keys[offset] = key; } - __syncthreads(); - for (uint32_t j = thread_ctx.lane_id; j < cache_ctx.line_size; j += kWarpSize) { - values[offset * cache_ctx.line_size + j] = - cache_ctx.lines[(warp_start_key_index + i) * cache_ctx.line_size + j]; - } - __syncthreads(); - offset += 1; - } - } -} - -template -class LruCache : public Cache { - public: - OF_DISALLOW_COPY_AND_MOVE(LruCache); - explicit LruCache(const CacheOptions& options) - : device_index_{}, - max_query_length_(0), - query_indices_buffer_(nullptr), - query_keys_buffer_(nullptr), - value_type_(options.value_type) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - InitLruCacheContext(options, &ctx_); - } - ~LruCache() override { - CudaCurrentDeviceGuard guard(device_index_); - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipFree(query_indices_buffer_)); - OF_CUDA_CHECK(hipFree(query_keys_buffer_)); - } - DestroyLruCacheContext(&ctx_); - } - - uint32_t KeySize() const override { return sizeof(Key); } - uint32_t ValueSize() const override { return sizeof(Elem) * ctx_.line_size; } - DataType ValueType() const override { return value_type_; } - uint64_t Capacity() const override { return ctx_.n_set * kWarpSize; } - uint32_t MaxQueryLength() const override { return max_query_length_; } - - void ReserveQueryLength(uint32_t query_length) override { - CudaCurrentDeviceGuard guard(device_index_); - if (query_length < max_query_length_) { return; } - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipFree(query_indices_buffer_)); - OF_CUDA_CHECK(hipFree(query_keys_buffer_)); - } - OF_CUDA_CHECK(hipMalloc(&query_indices_buffer_, query_length * sizeof(uint32_t))); - OF_CUDA_CHECK(hipMalloc(&query_keys_buffer_, query_length * sizeof(Key))); - max_query_length_ = query_length; - } - - CacheOptions::Policy Policy() const override { return CacheOptions::Policy::kLRU; } - - void Test(ep::Stream* stream, uint32_t n_keys, const void* keys, uint32_t* n_missing, - void* missing_keys, uint32_t* missing_indices) override { - CHECK_LE(n_keys, max_query_length_); - auto cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemsetAsync(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); - if (n_keys == 0) { return; } - cuda_stream->LaunchKernel(GetKernel, GetLaunchConfig(n_keys), ctx_, n_keys, - static_cast(keys), nullptr, n_missing, - static_cast(missing_keys), missing_indices); - } - - void Get(ep::Stream* stream, uint32_t n_keys, const void* keys, void* values, uint32_t* n_missing, - void* missing_keys, uint32_t* missing_indices) override { - CHECK_LE(n_keys, max_query_length_); - auto cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemsetAsync(n_missing, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); - if (n_keys == 0) { return; } - cuda_stream->LaunchKernel(GetKernel, GetLaunchConfig(n_keys), ctx_, n_keys, - static_cast(keys), static_cast(values), n_missing, - static_cast(missing_keys), missing_indices); - } - - void Put(ep::Stream* stream, uint32_t n_keys, const void* keys, const void* values, - uint32_t* n_evicted, void* evicted_keys, void* evicted_values) override { - CHECK_LE(n_keys, max_query_length_); - auto cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemsetAsync(n_evicted, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); - if (n_keys == 0) { return; } - cuda_stream->LaunchKernel(PutWithoutEvictingKernel, GetLaunchConfig(n_keys), ctx_, - n_keys, static_cast(keys), - static_cast(values), n_evicted, query_keys_buffer_, - query_indices_buffer_); - cuda_stream->LaunchKernel(EvictKernel, GetLaunchConfig(n_keys), ctx_, - query_keys_buffer_, query_indices_buffer_, - static_cast(values), n_evicted, - static_cast(evicted_keys), static_cast(evicted_values)); - } - - void Dump(ep::Stream* stream, uint64_t start_key_index, uint64_t end_key_index, - uint32_t* n_dumped, void* keys, void* values) override { - auto cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemsetAsync(n_dumped, 0, sizeof(uint32_t), cuda_stream->cuda_stream())); - const uint64_t max_dump_keys = end_key_index - start_key_index; - cuda_stream->LaunchKernel( - DumpKernel, - ep::CudaLaunchConfig((max_dump_keys + kNumWarpPerBlock - 1) / kNumWarpPerBlock, kBlockSize, - 0), - ctx_, start_key_index, end_key_index, n_dumped, static_cast(keys), - static_cast(values)); - } - - void Clear() override { ClearLruCacheContext(&ctx_); } - - private: - int device_index_; - uint32_t max_query_length_; - LruCacheContext ctx_; - uint32_t* query_indices_buffer_; - Key* query_keys_buffer_; - DataType value_type_; -}; - -template -std::unique_ptr DispatchValueType(const CacheOptions& options) { - if (options.value_size % sizeof(ulonglong2) == 0) { - return std::unique_ptr(new LruCache(options)); - } else if (options.value_size % sizeof(uint64_t) == 0) { - return std::unique_ptr(new LruCache(options)); - } else if (options.value_size % sizeof(uint32_t) == 0) { - return std::unique_ptr(new LruCache(options)); - } else if (options.value_size % sizeof(uint16_t) == 0) { - return std::unique_ptr(new LruCache(options)); - } else { - return std::unique_ptr(new LruCache(options)); - } -} - -std::unique_ptr DispatchKeyType(const CacheOptions& options) { - if (options.key_size == sizeof(uint32_t)) { - return DispatchValueType(options); - } else if (options.key_size == sizeof(uint64_t)) { - return DispatchValueType(options); - } else { - UNIMPLEMENTED(); - return nullptr; - } -} - -} // namespace - -std::unique_ptr NewLruCache(const CacheOptions& options) { return DispatchKeyType(options); } - -} // namespace embedding - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/embedding/mock_key_value_store.cu b/oneflow/core/embedding/mock_key_value_store.cu index f62c8779376a126632be7e13f758f383ec42ddaf..bfd08219c6ac762fca554386c4cb7dbba16f5bee 100644 --- a/oneflow/core/embedding/mock_key_value_store.cu +++ b/oneflow/core/embedding/mock_key_value_store.cu @@ -50,14 +50,14 @@ class IteratorImpl : public KVIterator { std::memcpy(reinterpret_cast(host_values_buffer_) + *host_num_buffer_ * value_size_, pos_->second.data(), value_size_); } - OF_CUDA_CHECK(cudaMemcpyAsync(n_result, host_num_buffer_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(n_result, host_num_buffer_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); const uint32_t num_keys = *host_num_buffer_; if (num_keys != 0) { - OF_CUDA_CHECK(cudaMemcpyAsync(keys, host_keys_buffer_, num_keys * key_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(values, host_values_buffer_, num_keys * value_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(keys, host_keys_buffer_, num_keys * key_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(values, host_values_buffer_, num_keys * value_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); } } @@ -80,7 +80,7 @@ class KeyValueStoreImpl : public KeyValueStore { OF_DISALLOW_COPY_AND_MOVE(KeyValueStoreImpl); explicit KeyValueStoreImpl(const MockKeyValueStoreOptions& options) : device_index_(-1), max_query_length_(0) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); key_size_ = options.key_size; value_size_ = options.value_size; OF_CUDA_CHECK(NumaAwareCudaMallocHost( @@ -97,11 +97,11 @@ class KeyValueStoreImpl : public KeyValueStore { ~KeyValueStoreImpl() { CudaCurrentDeviceGuard guard(device_index_); if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFreeHost(host_query_keys_)); - OF_CUDA_CHECK(cudaFreeHost(host_query_values_)); - OF_CUDA_CHECK(cudaFreeHost(host_missing_indices_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_values_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_missing_indices_)); } - OF_CUDA_CHECK(cudaFreeHost(host_n_missing_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_n_missing_)); } uint32_t KeySize() const override { return key_size_; } @@ -114,9 +114,9 @@ class KeyValueStoreImpl : public KeyValueStore { CudaCurrentDeviceGuard guard(device_index_); if (query_length <= max_query_length_) { return; } if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFreeHost(host_query_keys_)); - OF_CUDA_CHECK(cudaFreeHost(host_query_values_)); - OF_CUDA_CHECK(cudaFreeHost(host_missing_indices_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_values_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_missing_indices_)); } OF_CUDA_CHECK(NumaAwareCudaMallocHost( device_index_, reinterpret_cast(&host_query_keys_), key_size_ * query_length)); @@ -128,6 +128,7 @@ class KeyValueStoreImpl : public KeyValueStore { max_query_length_ = query_length; } + using KeyValueStore::Get; void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, uint32_t* n_missing, uint32_t* missing_indices) override; void Put(ep::Stream* stream, uint32_t num_keys, const void* keys, const void* values) override; @@ -158,11 +159,11 @@ void KeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const vo auto cuda_stream = stream->As(); CHECK_LE(num_keys, max_query_length_); if (num_keys == 0) { - OF_CUDA_CHECK(cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), + OF_CUDA_CHECK(GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); return; } - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_keys_, keys, key_size_ * num_keys, GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); *host_n_missing_ = 0; @@ -175,12 +176,12 @@ void KeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const vo *host_n_missing_ += 1; } } - OF_CUDA_CHECK(cudaMemcpyAsync(values, host_query_values_, num_keys * value_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(n_missing, host_n_missing_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(values, host_query_values_, num_keys * value_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(n_missing, host_n_missing_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(missing_indices, host_missing_indices_, - (*host_n_missing_) * sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(missing_indices, host_missing_indices_, + (*host_n_missing_) * sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); } @@ -191,10 +192,10 @@ void KeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_keys, const vo auto cuda_stream = stream->As(); CHECK_LE(num_keys, max_query_length_); if (num_keys == 0) { return; } - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_keys_, keys, key_size_ * num_keys, GPU(MemcpyDefault), cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_values_, values, value_size_ * num_keys, - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_values_, values, value_size_ * num_keys, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); for (uint32_t i = 0; i < num_keys; ++i) { store_[host_query_keys_[i]] = std::string( diff --git a/oneflow/core/embedding/mock_key_value_store.h b/oneflow/core/embedding/mock_key_value_store.h index 305cdd2a363b8eae56dd5cd844b695e3362618e7..edc285b2ad85562487766b6c4623a14ae607c3db 100644 --- a/oneflow/core/embedding/mock_key_value_store.h +++ b/oneflow/core/embedding/mock_key_value_store.h @@ -22,17 +22,7 @@ namespace oneflow { namespace embedding { -#ifdef WITH_CUDA - -struct MockKeyValueStoreOptions { - uint32_t key_size = 0; - uint32_t value_size = 0; -}; - -std::unique_ptr NewMockKeyValueStore(const MockKeyValueStoreOptions& options); - -#endif // WITH_CUDA -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) struct MockKeyValueStoreOptions { uint32_t key_size = 0; diff --git a/oneflow/core/embedding/mock_key_value_store.hip.cpp b/oneflow/core/embedding/mock_key_value_store.hip.cpp deleted file mode 100644 index de55f2ab51cab0332af51e5ae5e5e16e1715ba17..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/mock_key_value_store.hip.cpp +++ /dev/null @@ -1,249 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/embedding/mock_key_value_store.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace embedding { - -namespace { - -template -class IteratorImpl : public KVIterator { - public: - OF_DISALLOW_COPY_AND_MOVE(IteratorImpl); - IteratorImpl(HashMap* store, uint32_t key_size, uint32_t value_size, - uint32_t max_query_length, void* host_keys_buffer, void* host_values_buffer, - uint32_t* host_num_buffer) - : store_(store), - pos_(store->begin()), - key_size_(key_size), - value_size_(value_size), - max_query_length_(max_query_length), - host_keys_buffer_(host_keys_buffer), - host_values_buffer_(host_values_buffer), - host_num_buffer_(host_num_buffer) {} - ~IteratorImpl() override = default; - - void NextN(ep::Stream* stream, uint32_t n_request, uint32_t* n_result, void* keys, - void* values) override { - CHECK_LE(n_request, max_query_length_); - auto cuda_stream = stream->As(); - CHECK_JUST(cuda_stream->Sync()); - *host_num_buffer_ = 0; - while (*host_num_buffer_ < n_request && pos_ != store_->end()) { - reinterpret_cast(host_keys_buffer_)[*host_num_buffer_] = pos_->first; - std::memcpy(reinterpret_cast(host_values_buffer_) + *host_num_buffer_ * value_size_, - pos_->second.data(), value_size_); - } - OF_CUDA_CHECK(hipMemcpyAsync(n_result, host_num_buffer_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - const uint32_t num_keys = *host_num_buffer_; - if (num_keys != 0) { - OF_CUDA_CHECK(hipMemcpyAsync(keys, host_keys_buffer_, num_keys * key_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(values, host_values_buffer_, num_keys * value_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - } - } - - void Reset() override { pos_ = store_->begin(); } - - private: - HashMap* store_; - typename HashMap::iterator pos_; - uint32_t key_size_; - uint32_t value_size_; - uint32_t max_query_length_; - void* host_keys_buffer_; - void* host_values_buffer_; - uint32_t* host_num_buffer_; -}; - -template -class KeyValueStoreImpl : public KeyValueStore { - public: - OF_DISALLOW_COPY_AND_MOVE(KeyValueStoreImpl); - explicit KeyValueStoreImpl(const MockKeyValueStoreOptions& options) - : device_index_(-1), max_query_length_(0) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - key_size_ = options.key_size; - value_size_ = options.value_size; - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_keys_), key_size_ * max_query_length_)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_query_values_), - value_size_ * max_query_length_)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, reinterpret_cast(&host_n_missing_), - sizeof(uint32_t))); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_missing_indices_), - sizeof(uint32_t) * max_query_length_)); - } - ~KeyValueStoreImpl() { - CudaCurrentDeviceGuard guard(device_index_); - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipHostFree(host_query_keys_)); - OF_CUDA_CHECK(hipHostFree(host_query_values_)); - OF_CUDA_CHECK(hipHostFree(host_missing_indices_)); - } - OF_CUDA_CHECK(hipHostFree(host_n_missing_)); - } - - uint32_t KeySize() const override { return key_size_; } - - uint32_t ValueSize() const override { return value_size_; } - - uint32_t MaxQueryLength() const override { return max_query_length_; } - - void ReserveQueryLength(uint32_t query_length) override { - CudaCurrentDeviceGuard guard(device_index_); - if (query_length <= max_query_length_) { return; } - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipHostFree(host_query_keys_)); - OF_CUDA_CHECK(hipHostFree(host_query_values_)); - OF_CUDA_CHECK(hipHostFree(host_missing_indices_)); - } - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_keys_), key_size_ * query_length)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_values_), value_size_ * query_length)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_missing_indices_), - sizeof(uint32_t) * query_length)); - max_query_length_ = query_length; - } - - void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, - uint32_t* n_missing, uint32_t* missing_indices) override; - void Put(ep::Stream* stream, uint32_t num_keys, const void* keys, const void* values) override; - bool SnapshotExists(const std::string& name) override; - void LoadSnapshot(const std::string& name) override; - void LoadSnapshot(const std::string& name, - const std::function& Hook) override; - void SaveSnapshot(const std::string& name) override; - - private: - int device_index_; - uint32_t max_query_length_; - uint32_t key_size_; - uint32_t value_size_; - Key* host_query_keys_{}; - uint8_t* host_query_values_{}; - uint32_t* host_n_missing_{}; - uint32_t* host_missing_indices_{}; - HashMap store_; - HashMap> snapshots_; - std::mutex mutex_; -}; - -template -void KeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const void* keys, - void* values, uint32_t* n_missing, uint32_t* missing_indices) { - std::lock_guard lock(mutex_); - auto cuda_stream = stream->As(); - CHECK_LE(num_keys, max_query_length_); - if (num_keys == 0) { - OF_CUDA_CHECK(hipMemsetAsync(n_missing, 0, sizeof(uint32_t), - stream->As()->cuda_stream())); - return; - } - OF_CUDA_CHECK(hipMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, hipMemcpyDefault, - cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - *host_n_missing_ = 0; - for (uint32_t i = 0; i < num_keys; ++i) { - auto it = store_.find(host_query_keys_[i]); - if (it != store_.end()) { - std::memcpy(host_query_values_ + i * value_size_, it->second.data(), value_size_); - } else { - host_missing_indices_[*host_n_missing_] = i; - *host_n_missing_ += 1; - } - } - OF_CUDA_CHECK(hipMemcpyAsync(values, host_query_values_, num_keys * value_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(n_missing, host_n_missing_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(missing_indices, host_missing_indices_, - (*host_n_missing_) * sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); -} - -template -void KeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_keys, const void* keys, - const void* values) { - std::lock_guard lock(mutex_); - auto cuda_stream = stream->As(); - CHECK_LE(num_keys, max_query_length_); - if (num_keys == 0) { return; } - OF_CUDA_CHECK(hipMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, hipMemcpyDefault, - cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(host_query_values_, values, value_size_ * num_keys, - hipMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - for (uint32_t i = 0; i < num_keys; ++i) { - store_[host_query_keys_[i]] = std::string( - reinterpret_cast(host_query_values_) + i * value_size_, value_size_); - } -} - -template -bool KeyValueStoreImpl::SnapshotExists(const std::string& name) { - return snapshots_.find(name) != snapshots_.end(); -} - -template -void KeyValueStoreImpl::LoadSnapshot(const std::string& name) { - CudaCurrentDeviceGuard guard(device_index_); - LoadSnapshot(name, nullptr); -} - -template -void KeyValueStoreImpl::LoadSnapshot(const std::string& name, - const std::function& Hook) { - CudaCurrentDeviceGuard guard(device_index_); - store_ = snapshots_[name]; - if (Hook) { - IteratorImpl iterator(&store_, KeySize(), ValueSize(), max_query_length_, host_query_keys_, - host_query_values_, host_n_missing_); - Hook(&iterator); - } -} - -template -void KeyValueStoreImpl::SaveSnapshot(const std::string& name) { - CudaCurrentDeviceGuard guard(device_index_); - snapshots_[name] = store_; -} - -} // namespace - -std::unique_ptr NewMockKeyValueStore(const MockKeyValueStoreOptions& options) { - if (options.key_size == sizeof(uint64_t)) { - return std::unique_ptr(new KeyValueStoreImpl(options)); - } else if (options.key_size == sizeof(uint32_t)) { - return std::unique_ptr(new KeyValueStoreImpl(options)); - } else { - UNIMPLEMENTED(); - return nullptr; - } -} - -} // namespace embedding - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/embedding/persistent_table.cpp b/oneflow/core/embedding/persistent_table.cpp index bd11e604dba2ffea8ee43a29055c352d96554652..96a226d13c8d29e3947be6f569bce4f367657f46 100644 --- a/oneflow/core/embedding/persistent_table.cpp +++ b/oneflow/core/embedding/persistent_table.cpp @@ -395,6 +395,7 @@ class PersistentTableImpl : public PersistentTable { PosixFile writable_key_file_; uint64_t writable_key_file_chunk_id_; PosixFileLockGuard lock_; + bool read_only_; }; template @@ -405,14 +406,19 @@ PersistentTableImpl::PersistentTableImpl(const PersistentTableOptio physical_block_size_(options.physical_block_size), logical_block_size_(GetLogicalBlockSize(options.physical_block_size, value_size_)), blocks_buffer_(options.physical_block_size), - writable_key_file_chunk_id_(-1) { + writable_key_file_chunk_id_(-1), + read_only_(options.read_only) { const uint64_t capacity_hint = ParseIntegerFromEnv( "ONEFLOW_ONE_EMBEDDING_PERSISTENT_TABLE_CAPACITY_HINT", options.capacity_hint); if (capacity_hint > 0) { row_id_mapping_.reserve(capacity_hint); } PosixFile::RecursiveCreateDirectory(options.path, 0755); const std::string lock_filename = PosixFile::JoinPath(options.path, kLockFileName); const bool init = !PosixFile::FileExists(lock_filename); - lock_ = PosixFileLockGuard(PosixFile(lock_filename, O_CREAT | O_RDWR, 0644)); + if (read_only_) { + CHECK(!init) << "The table must be initialized in read only mode"; + } else { + lock_ = PosixFileLockGuard(PosixFile(lock_filename, O_CREAT | O_RDWR, 0644)); + } const uint64_t target_chunk_size = options.target_chunk_size_mb * 1024 * 1024; CHECK_GE(target_chunk_size, logical_block_size_); num_logical_blocks_per_chunk_ = target_chunk_size / logical_block_size_, @@ -442,7 +448,8 @@ PersistentTableImpl::PersistentTableImpl(const PersistentTableOptio for (auto& chunk : chunks) { if (value_files_.size() <= chunk.first) { value_files_.resize(chunk.first + 1); } CHECK_EQ(value_files_.at(chunk.first).fd(), -1); - PosixFile value_file(chunk.second, O_RDWR | O_DIRECT, 0644); + const int flags = read_only_ ? (O_RDONLY | O_DIRECT) : (O_RDWR | O_DIRECT); + PosixFile value_file(chunk.second, flags, 0644); value_files_.at(chunk.first) = std::move(value_file); } if (!value_files_.empty()) { @@ -523,6 +530,7 @@ void PersistentTableImpl::Get(uint32_t num_keys, const void* keys, template void PersistentTableImpl::PutBlocks(uint32_t num_keys, const void* keys, const void* blocks) { + CHECK(!read_only_); std::lock_guard lock(mutex_); const uint32_t num_blocks = RoundUp(num_keys, num_values_per_block_) / num_values_per_block_; const uint32_t num_padded_keys = num_blocks * num_values_per_block_; @@ -579,6 +587,7 @@ void PersistentTableImpl::PutBlocks(uint32_t num_keys, const void* template void PersistentTableImpl::Put(uint32_t num_keys, const void* keys, const void* values) { + CHECK(!read_only_); std::lock_guard lock(mutex_); const void* blocks_ptr = nullptr; if (value_size_ == logical_block_size_ @@ -656,6 +665,7 @@ void PersistentTableImpl::LoadSnapshotImpl(const std::string& name) template void PersistentTableImpl::SaveSnapshotImpl(const std::string& name) { + CHECK(!read_only_); std::lock_guard lock(mutex_); PosixFile::RecursiveCreateDirectory(SnapshotDirPath(name), 0755); std::ofstream list_ofs(SnapshotListFilePath(name)); @@ -704,13 +714,11 @@ template void PersistentTableImpl::LoadSnapshot( const std::string& name, const std::function& Hook) { std::lock_guard lock(mutex_); - int mmap_flags = MAP_SHARED; if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_PERSISTENT_TABLE_SNAPSHOT_LOAD_MAP_POPULATE", true)) { mmap_flags |= MAP_POPULATE; } - const std::string snapshot_base = SnapshotDirPath(name); const std::string snapshot_list = SnapshotListFilePath(name); row_id_mapping_.clear(); @@ -723,10 +731,8 @@ void PersistentTableImpl::LoadSnapshot( CHECK_EQ(index_file_size % sizeof(uint64_t), 0); if (index_file_size == 0) { return; } const size_t n_entries = index_file_size / sizeof(uint64_t); - // PosixMappedFile mapped_index(std::move(index_file), index_file_size, PROT_READ); PosixMappedFile mapped_index(std::move(index_file), index_file_size, PROT_READ, mmap_flags); PosixFile key_file(KeyFilePath(chunk_id), O_RDONLY, 0644); - // PosixMappedFile mapped_key(std::move(key_file), key_file.Size(), PROT_READ); PosixMappedFile mapped_key(std::move(key_file), key_file.Size(), PROT_READ, mmap_flags); const uint64_t* indices = static_cast(mapped_index.ptr()); const Key* keys = static_cast(mapped_key.ptr()); @@ -737,7 +743,6 @@ void PersistentTableImpl::LoadSnapshot( } if (Hook) { PosixFile value_file(ValueFilePath(chunk_id), O_RDONLY, 0644); - // PosixMappedFile mapped_value(std::move(value_file), value_file.Size(), PROT_READ); PosixMappedFile mapped_value(std::move(value_file), value_file.Size(), PROT_READ, mmap_flags); ChunkIteratorImpl chunk_iterator(value_size_, logical_block_size_, num_values_per_block_, num_values_per_chunk_, chunk_id, n_entries, keys, diff --git a/oneflow/core/embedding/persistent_table.h b/oneflow/core/embedding/persistent_table.h index 39520c6fa2b25dd25430892e5488a0b4436c7ab7..346fdda53c53d2bda15b3ec6ed0afa9560b7d017 100644 --- a/oneflow/core/embedding/persistent_table.h +++ b/oneflow/core/embedding/persistent_table.h @@ -29,6 +29,7 @@ struct PersistentTableOptions { uint64_t target_chunk_size_mb = 4 * 1024; uint16_t physical_block_size = 4096; uint64_t capacity_hint = 0; + bool read_only = false; }; class PersistentTable { diff --git a/oneflow/core/embedding/persistent_table_key_value_store.cu b/oneflow/core/embedding/persistent_table_key_value_store.cu index 1dd2379aff92ce7d0babd9a054f19c4368139a7f..1a5745db0c7823902ed0f268434415cfd193b849 100644 --- a/oneflow/core/embedding/persistent_table_key_value_store.cu +++ b/oneflow/core/embedding/persistent_table_key_value_store.cu @@ -49,14 +49,14 @@ class IteratorImpl : public KVIterator { auto cuda_stream = stream->As(); CHECK_JUST(cuda_stream->Sync()); base_iter_->Next(n_request, host_num_buffer_, host_keys_buffer_, host_values_buffer_); - OF_CUDA_CHECK(cudaMemcpyAsync(n_result, host_num_buffer_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(n_result, host_num_buffer_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); const uint32_t num_keys = *host_num_buffer_; if (num_keys != 0) { - OF_CUDA_CHECK(cudaMemcpyAsync(keys, host_keys_buffer_, num_keys * key_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(values, host_values_buffer_, num_keys * value_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(keys, host_keys_buffer_, num_keys * key_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(values, host_values_buffer_, num_keys * value_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); } } @@ -78,7 +78,7 @@ class KeyValueStoreImpl : public KeyValueStore { OF_DISALLOW_COPY_AND_MOVE(KeyValueStoreImpl); explicit KeyValueStoreImpl(const PersistentTableKeyValueStoreOptions& options) : device_index_(-1), max_query_length_(0) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); key_size_ = options.table_options.key_size; value_size_ = options.table_options.value_size; table_ = NewPersistentTable(options.table_options); @@ -96,11 +96,11 @@ class KeyValueStoreImpl : public KeyValueStore { ~KeyValueStoreImpl() { CudaCurrentDeviceGuard guard(device_index_); if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFreeHost(host_query_keys_)); - OF_CUDA_CHECK(cudaFreeHost(host_query_values_)); - OF_CUDA_CHECK(cudaFreeHost(host_missing_indices_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_values_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_missing_indices_)); } - OF_CUDA_CHECK(cudaFreeHost(host_n_missing_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_n_missing_)); } uint32_t KeySize() const override { return key_size_; } @@ -113,9 +113,9 @@ class KeyValueStoreImpl : public KeyValueStore { CudaCurrentDeviceGuard guard(device_index_); if (query_length <= max_query_length_) { return; } if (max_query_length_ != 0) { - OF_CUDA_CHECK(cudaFreeHost(host_query_keys_)); - OF_CUDA_CHECK(cudaFreeHost(host_query_values_)); - OF_CUDA_CHECK(cudaFreeHost(host_missing_indices_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_query_values_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_missing_indices_)); } OF_CUDA_CHECK(NumaAwareCudaMallocHost( device_index_, reinterpret_cast(&host_query_keys_), key_size_ * query_length)); @@ -127,6 +127,7 @@ class KeyValueStoreImpl : public KeyValueStore { max_query_length_ = query_length; } + using KeyValueStore::Get; void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, uint32_t* n_missing, uint32_t* missing_indices) override; void Put(ep::Stream* stream, uint32_t num_keys, const void* keys, const void* values) override; @@ -157,23 +158,23 @@ void KeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const vo auto cuda_stream = stream->As(); CHECK_LE(num_keys, max_query_length_); if (num_keys == 0) { - OF_CUDA_CHECK(cudaMemsetAsync(n_missing, 0, sizeof(uint32_t), + OF_CUDA_CHECK(GPU(MemsetAsync)(n_missing, 0, sizeof(uint32_t), stream->As()->cuda_stream())); return; } - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_keys_, keys, key_size_ * num_keys, GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); table_->Get(num_keys, host_query_keys_, host_query_values_, host_n_missing_, host_missing_indices_); - OF_CUDA_CHECK(cudaMemcpyAsync(values, host_query_values_, num_keys * value_size_, - cudaMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(n_missing, host_n_missing_, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(values, host_query_values_, num_keys * value_size_, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(n_missing, host_n_missing_, sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(missing_indices, host_missing_indices_, - (*host_n_missing_) * sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(missing_indices, host_missing_indices_, + (*host_n_missing_) * sizeof(uint32_t), GPU(MemcpyDefault), cuda_stream->cuda_stream())); } @@ -184,10 +185,10 @@ void KeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_keys, const vo auto cuda_stream = stream->As(); CHECK_LE(num_keys, max_query_length_); if (num_keys == 0) { return; } - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_keys_, keys, key_size_ * num_keys, GPU(MemcpyDefault), cuda_stream->cuda_stream())); - OF_CUDA_CHECK(cudaMemcpyAsync(host_query_values_, values, value_size_ * num_keys, - cudaMemcpyDefault, cuda_stream->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_query_values_, values, value_size_ * num_keys, + GPU(MemcpyDefault), cuda_stream->cuda_stream())); CHECK_JUST(cuda_stream->Sync()); table_->Put(num_keys, host_query_keys_, host_query_values_); } diff --git a/oneflow/core/embedding/persistent_table_key_value_store.h b/oneflow/core/embedding/persistent_table_key_value_store.h index 555c447e2a83a86b4ba323ce9c13aea6995bfa42..e51bca06dabcfa74fad8facd886bf2437e217a3c 100644 --- a/oneflow/core/embedding/persistent_table_key_value_store.h +++ b/oneflow/core/embedding/persistent_table_key_value_store.h @@ -23,7 +23,7 @@ namespace oneflow { namespace embedding { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) struct PersistentTableKeyValueStoreOptions { PersistentTableOptions table_options{}; @@ -33,16 +33,6 @@ std::unique_ptr NewPersistentTableKeyValueStore( const PersistentTableKeyValueStoreOptions& options); #endif // WITH_CUDA -#ifdef WITH_ROCM - -struct PersistentTableKeyValueStoreOptions { - PersistentTableOptions table_options{}; -}; - -std::unique_ptr NewPersistentTableKeyValueStore( - const PersistentTableKeyValueStoreOptions& options); - -#endif // WITH_ROCM } // namespace embedding diff --git a/oneflow/core/embedding/persistent_table_key_value_store.hip.cpp b/oneflow/core/embedding/persistent_table_key_value_store.hip.cpp deleted file mode 100644 index 46fca92d4d7c0f92983f762af1a7becd280c7b23..0000000000000000000000000000000000000000 --- a/oneflow/core/embedding/persistent_table_key_value_store.hip.cpp +++ /dev/null @@ -1,243 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/embedding/persistent_table_key_value_store.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/embedding/persistent_table.h" -#include -#include -#include -#include -#include - -namespace oneflow { - -namespace embedding { - -namespace { - -class IteratorImpl : public KVIterator { - public: - OF_DISALLOW_COPY_AND_MOVE(IteratorImpl); - IteratorImpl(PersistentTable::Iterator* base_iter, uint32_t key_size, uint32_t value_size, - uint32_t max_query_length, void* host_keys_buffer, void* host_values_buffer, - uint32_t* host_num_buffer) - : base_iter_(base_iter), - key_size_(key_size), - value_size_(value_size), - max_query_length_(max_query_length), - host_keys_buffer_(host_keys_buffer), - host_values_buffer_(host_values_buffer), - host_num_buffer_(host_num_buffer) {} - ~IteratorImpl() override = default; - - void NextN(ep::Stream* stream, uint32_t n_request, uint32_t* n_result, void* keys, - void* values) override { - CHECK_LE(n_request, max_query_length_); - auto cuda_stream = stream->As(); - CHECK_JUST(cuda_stream->Sync()); - base_iter_->Next(n_request, host_num_buffer_, host_keys_buffer_, host_values_buffer_); - OF_CUDA_CHECK(hipMemcpyAsync(n_result, host_num_buffer_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - const uint32_t num_keys = *host_num_buffer_; - if (num_keys != 0) { - OF_CUDA_CHECK(hipMemcpyAsync(keys, host_keys_buffer_, num_keys * key_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(values, host_values_buffer_, num_keys * value_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - } - } - - void Reset() override { base_iter_->Reset(); } - - private: - PersistentTable::Iterator* base_iter_; - uint32_t key_size_; - uint32_t value_size_; - uint32_t max_query_length_; - void* host_keys_buffer_; - void* host_values_buffer_; - uint32_t* host_num_buffer_; -}; - -template -class KeyValueStoreImpl : public KeyValueStore { - public: - OF_DISALLOW_COPY_AND_MOVE(KeyValueStoreImpl); - explicit KeyValueStoreImpl(const PersistentTableKeyValueStoreOptions& options) - : device_index_(-1), max_query_length_(0) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - key_size_ = options.table_options.key_size; - value_size_ = options.table_options.value_size; - table_ = NewPersistentTable(options.table_options); - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_keys_), key_size_ * max_query_length_)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_query_values_), - value_size_ * max_query_length_)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, reinterpret_cast(&host_n_missing_), - sizeof(uint32_t))); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_missing_indices_), - sizeof(uint32_t) * max_query_length_)); - } - ~KeyValueStoreImpl() { - CudaCurrentDeviceGuard guard(device_index_); - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipHostFree(host_query_keys_)); - OF_CUDA_CHECK(hipHostFree(host_query_values_)); - OF_CUDA_CHECK(hipHostFree(host_missing_indices_)); - } - OF_CUDA_CHECK(hipHostFree(host_n_missing_)); - } - - uint32_t KeySize() const override { return key_size_; } - - uint32_t ValueSize() const override { return value_size_; } - - uint32_t MaxQueryLength() const override { return max_query_length_; } - - void ReserveQueryLength(uint32_t query_length) override { - CudaCurrentDeviceGuard guard(device_index_); - if (query_length <= max_query_length_) { return; } - if (max_query_length_ != 0) { - OF_CUDA_CHECK(hipHostFree(host_query_keys_)); - OF_CUDA_CHECK(hipHostFree(host_query_values_)); - OF_CUDA_CHECK(hipHostFree(host_missing_indices_)); - } - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_keys_), key_size_ * query_length)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost( - device_index_, reinterpret_cast(&host_query_values_), value_size_ * query_length)); - OF_CUDA_CHECK(NumaAwareCudaMallocHost(device_index_, - reinterpret_cast(&host_missing_indices_), - sizeof(uint32_t) * query_length)); - max_query_length_ = query_length; - } - - void Get(ep::Stream* stream, uint32_t num_keys, const void* keys, void* values, - uint32_t* n_missing, uint32_t* missing_indices) override; - void Put(ep::Stream* stream, uint32_t num_keys, const void* keys, const void* values) override; - bool SnapshotExists(const std::string& name) override; - void LoadSnapshot(const std::string& name) override; - void LoadSnapshot(const std::string& name, - const std::function& Hook) override; - void SaveSnapshot(const std::string& name) override; - - private: - int device_index_; - uint32_t max_query_length_; - uint32_t key_size_; - uint32_t value_size_; - Key* host_query_keys_{}; - uint8_t* host_query_values_{}; - uint32_t* host_n_missing_{}; - uint32_t* host_missing_indices_{}; - - std::mutex mutex_; - std::unique_ptr table_; -}; - -template -void KeyValueStoreImpl::Get(ep::Stream* stream, uint32_t num_keys, const void* keys, - void* values, uint32_t* n_missing, uint32_t* missing_indices) { - std::lock_guard lock(mutex_); - auto cuda_stream = stream->As(); - CHECK_LE(num_keys, max_query_length_); - if (num_keys == 0) { - OF_CUDA_CHECK(hipMemsetAsync(n_missing, 0, sizeof(uint32_t), - stream->As()->cuda_stream())); - return; - } - OF_CUDA_CHECK(hipMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, hipMemcpyDefault, - cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - - table_->Get(num_keys, host_query_keys_, host_query_values_, host_n_missing_, - host_missing_indices_); - - OF_CUDA_CHECK(hipMemcpyAsync(values, host_query_values_, num_keys * value_size_, - hipMemcpyDefault, cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(n_missing, host_n_missing_, sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(missing_indices, host_missing_indices_, - (*host_n_missing_) * sizeof(uint32_t), hipMemcpyDefault, - cuda_stream->cuda_stream())); -} - -template -void KeyValueStoreImpl::Put(ep::Stream* stream, uint32_t num_keys, const void* keys, - const void* values) { - std::lock_guard lock(mutex_); - auto cuda_stream = stream->As(); - CHECK_LE(num_keys, max_query_length_); - if (num_keys == 0) { return; } - OF_CUDA_CHECK(hipMemcpyAsync(host_query_keys_, keys, key_size_ * num_keys, hipMemcpyDefault, - cuda_stream->cuda_stream())); - OF_CUDA_CHECK(hipMemcpyAsync(host_query_values_, values, value_size_ * num_keys, - hipMemcpyDefault, cuda_stream->cuda_stream())); - CHECK_JUST(cuda_stream->Sync()); - table_->Put(num_keys, host_query_keys_, host_query_values_); -} - -template -bool KeyValueStoreImpl::SnapshotExists(const std::string& name) { - return table_->SnapshotExists(name); -} - -template -void KeyValueStoreImpl::LoadSnapshot(const std::string& name) { - CudaCurrentDeviceGuard guard(device_index_); - LoadSnapshot(name, nullptr); -} - -template -void KeyValueStoreImpl::LoadSnapshot(const std::string& name, - const std::function& Hook) { - CudaCurrentDeviceGuard guard(device_index_); - if (Hook) { - table_->LoadSnapshot(name, [&](PersistentTable::Iterator* chunk_iterator) { - IteratorImpl iterator(chunk_iterator, KeySize(), ValueSize(), max_query_length_, - host_query_keys_, host_query_values_, host_n_missing_); - Hook(&iterator); - }); - } else { - table_->LoadSnapshot(name); - } -} - -template -void KeyValueStoreImpl::SaveSnapshot(const std::string& name) { - CudaCurrentDeviceGuard guard(device_index_); - table_->SaveSnapshot(name); -} - -} // namespace - -std::unique_ptr NewPersistentTableKeyValueStore( - const PersistentTableKeyValueStoreOptions& options) { - if (options.table_options.key_size == sizeof(uint64_t)) { - return std::unique_ptr(new KeyValueStoreImpl(options)); - } else if (options.table_options.key_size == sizeof(uint32_t)) { - return std::unique_ptr(new KeyValueStoreImpl(options)); - } else { - UNIMPLEMENTED(); - return nullptr; - } -} - -} // namespace embedding - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/embedding/posix_file.h b/oneflow/core/embedding/posix_file.h index 0b0430f0792d935f174f3eea14df546ea20ca22d..ea8592cb554dc5c2d19047e8ff18a5368d37d94e 100644 --- a/oneflow/core/embedding/posix_file.h +++ b/oneflow/core/embedding/posix_file.h @@ -141,15 +141,15 @@ class PosixFile final { class PosixMappedFile final { public: PosixMappedFile() : file_(), ptr_(nullptr) {} - // PosixMappedFile(PosixFile&& file, size_t size, int prot) : file_(std::move(file)), ptr_(nullptr) { - PosixMappedFile(PosixFile&& file, size_t size, int prot, int flags) : file_(std::move(file)), ptr_(nullptr) { + PosixMappedFile(PosixFile&& file, size_t size, int prot, int flags) + : file_(std::move(file)), ptr_(nullptr) { CHECK_NE(file_.fd(), -1); - // void* ptr = mmap(nullptr, size, prot, MAP_SHARED, file_.fd(), 0); void* ptr = mmap(nullptr, size, prot, flags, file_.fd(), 0); PCHECK(ptr != MAP_FAILED); ptr_ = ptr; } - PosixMappedFile(PosixFile&& file, size_t size, int prot) : PosixMappedFile(std::move(file), size, prot, MAP_SHARED) {} + PosixMappedFile(PosixFile&& file, size_t size, int prot) + : PosixMappedFile(std::move(file), size, prot, MAP_SHARED) {} PosixMappedFile(PosixMappedFile&& other) noexcept : PosixMappedFile() { *this = std::move(other); } diff --git a/oneflow/core/ep/common/primitive/batch_matmul.cpp b/oneflow/core/ep/common/primitive/batch_matmul.cpp index d5a059084c2ec97f183ce0a14faa6fd2d5fb4e1b..c25deb1bdb9b0b62cd49cba6ec52f05c8baa4c98 100644 --- a/oneflow/core/ep/common/primitive/batch_matmul.cpp +++ b/oneflow/core/ep/common/primitive/batch_matmul.cpp @@ -93,7 +93,6 @@ REGISTER_PRIMITIVE_FACTORY(DeviceType::kCPU, BatchMatmulFactory, REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BatchMatmulFactory, BatchMatmulFactoryImpl); #endif // WITH_CUDA - #ifdef WITH_ROCM REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BatchMatmulFactory, BatchMatmulFactoryImpl); diff --git a/oneflow/core/ep/common/primitive/binary_functor.h b/oneflow/core/ep/common/primitive/binary_functor.h index 67326ef35484219525a405ab5f92eb00a68b6755..0881770203fc0a19d280e5f30d704dfc2c12363e 100644 --- a/oneflow/core/ep/common/primitive/binary_functor.h +++ b/oneflow/core/ep/common/primitive/binary_functor.h @@ -19,6 +19,7 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/binary_op.h" #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/scalar.h" +#include namespace oneflow { @@ -124,6 +125,39 @@ struct BinaryFunctor { OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { return static_cast(src0 >= src1); } }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) + : atol(attr0.Value()), rtol(attr1.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + bool close = src0 == src1; + close |= (std::isnan(src0) and std::isnan(src1)); + if (atol == 0 and rtol == 0) return close; + Src allowed_error = static_cast(atol) + abs(static_cast(rtol) * src1); + Src actual_error = abs(src0 - src1); + close |= (std::isfinite(actual_error) and (actual_error <= allowed_error)); + return close; + } + float atol, rtol; +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) + : atol(attr0.Value()), rtol(attr1.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + bool close = src0 == src1; + if (atol == 0 and rtol == 0) return close; + Src allowed_error = static_cast(atol) + abs(static_cast(rtol) * src1); + Src actual_error = abs(src0 - src1); + close |= (std::isfinite(actual_error) and (actual_error <= allowed_error)); + return close; + } + float atol, rtol; +}; + template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} @@ -147,6 +181,81 @@ struct BinaryFunctor { } }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { return static_cast(src0 % src1); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { return src0 / src1; } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { return static_cast(src0 / src1); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + Src trunc_mod = src0 % src1; + return (trunc_mod != static_cast(0)) + && ((src1 < static_cast(0)) != (trunc_mod < static_cast(0))) + ? trunc_mod + src1 + : trunc_mod; + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC uint8_t operator()(uint8_t src0, uint8_t src1) const { return src0 % src1; } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC uint32_t operator()(uint32_t src0, uint32_t src1) const { return src0 % src1; } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC uint64_t operator()(uint64_t src0, uint64_t src1) const { return src0 % src1; } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : scalar_operand(attr0.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + return scalar_operand * (pow(src0, scalar_operand - static_cast(1))) * src1; + } + Src scalar_operand; +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : scalar_operand(attr0.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + return (pow(scalar_operand, src0)) * log(scalar_operand) * src1; + } + Src scalar_operand; +}; + template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} @@ -314,6 +423,226 @@ struct BinaryFunctor { const Src threshold; }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src zero = static_cast(0.0); + if (x == zero) { + return zero; + } else if (x < zero) { + return -dy; + } else { + return dy; + } + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * -rsqrt(static_cast(1.0) - x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * rsqrt(x * x - static_cast(1.0)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * rsqrt(static_cast(1.0) - x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * rsqrt(static_cast(1.0) + x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src one = static_cast(1.0); + return dy * (one / (one + x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src one = static_cast(1.0); + return dy * (one / (one - x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * (-sin(x)); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * sinh(x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * static_cast(2.0) * rsqrt(static_cast(M_PI)) * exp(-x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * -static_cast(2.0) * rsqrt(static_cast(M_PI)) * exp(-x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * exp(x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * exp(x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + // TODO(chengcheng): return: dy * digamma(x) + assert(false); + return 0.0; + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * (static_cast(1.0) / x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / (x * log(static_cast(2.0)))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / (x * log(static_cast(10.0)))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / (x + static_cast(1.0))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / (exp(x) + static_cast(1.0))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (-static_cast(1.0) / (x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + if (abs(x) <= static_cast(0.0)) { return static_cast(0.0); } + return dy * (-static_cast(1.0) / (x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(-1.0) / (static_cast(2.0) * sqrt(x * x * x))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src y) const { return dy * (y * (1.0 - y)); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * cos(x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * cosh(x); } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * static_cast(0.5) / sqrt(x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { return dy * static_cast(2.0) * x; } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src cos_val = cos(x); + return dy * (static_cast(1.0) / (cos_val * cos_val)); + } +}; + } // namespace broadcast_elementwise_binary } // namespace primitive } // namespace ep diff --git a/oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h b/oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h index f3b68ef33819cf78b0a7a3ea048f62afc743f9f4..e6369be59682672efb140ea2e6e452b10b216257 100644 --- a/oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h +++ b/oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h @@ -30,15 +30,6 @@ namespace broadcast_elementwise_binary { constexpr size_t kMaxNumDims = 8; -inline void CheckInplace(size_t num_dims, const int64_t* src0_dims, const void* src0, - const int64_t* src1_dims, const void* src1, const int64_t* dst_dims, - const void* dst) { - for (int64_t i = 0; i < num_dims; ++i) { - if (src0 == dst) { CHECK_EQ(src0_dims[i], dst_dims[i]); } - if (src1 == dst) { CHECK_EQ(src1_dims[i], dst_dims[i]); } - } -} - inline bool IsDimsEquals(size_t num_src0_dims, const int64_t* src0_dims, size_t num_src1_dims, const int64_t* src1_dims) { if (num_src0_dims != num_src1_dims) { return false; } @@ -48,22 +39,36 @@ inline bool IsDimsEquals(size_t num_src0_dims, const int64_t* src0_dims, size_t return true; } -#define BINARY_MATH_OP_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAdd) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSub) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMul) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kDiv) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMax) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMin) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kPow) - -#define BINARY_COMPARISION_OP_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kEqual) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kNotEqual) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLessThan) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLessEqual) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kGreaterThan) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kGreaterEqual) +#define BINARY_MATH_OP_SEQ_0 \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAdd) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSub) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMul) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kDiv) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMax) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kMin) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kPow) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kFmod) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kFloorDiv) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kTruncDiv) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kFloorMod) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kScalarExpPowerGrad) + +#define BINARY_MATH_OP_SEQ_1 \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kScalarBasePowerGrad) + +#define BINARY_MATH_OP_SEQ \ + BINARY_MATH_OP_SEQ_0 \ + BINARY_MATH_OP_SEQ_1 + +#define BINARY_COMPARISION_OP_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kEqual) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kNotEqual) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLessThan) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLessEqual) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kGreaterThan) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kGreaterEqual) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kIsCloseEqualNan) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kIsClose) #define BINARY_LOGICAL_OP_SEQ \ OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLogicalAnd) \ @@ -87,7 +92,39 @@ inline bool IsDimsEquals(size_t num_src0_dims, const int64_t* src0_dims, size_t OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSoftplusBackwardWithDyX) \ OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSoftshrinkBackwardWithDyY) \ OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kTanhBackwardWithDyX) \ - OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kThresholdBackwardWithDyX) + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kThresholdBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kFastGeluBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kQuickGeluBackwardWithDyX) + +#define BINARY_MATH_BACKWARD_OP_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAbsBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAcosBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAcoshBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAsinBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAsinhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAtanBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kAtanhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kCosBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kCoshBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kErfBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kErfcBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kExpBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kExpm1BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLgammaBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLogBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLog2BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLog10BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLog1pBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kLogSigmoidBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kReciprocalBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kReciprocalNoNanBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kRsqrtBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSinBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSigmoidBackwardWithDyY) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSinhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSqrtBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kSquareBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ(BinaryOp::kTanBackwardWithDyX) } // namespace broadcast_elementwise_binary } // namespace primitive diff --git a/oneflow/core/ep/common/primitive/broadcast_elementwise_unary.h b/oneflow/core/ep/common/primitive/broadcast_elementwise_unary.h new file mode 100644 index 0000000000000000000000000000000000000000..401c2b5715742b8bf42df1c4b44c3f98cd95735d --- /dev/null +++ b/oneflow/core/ep/common/primitive/broadcast_elementwise_unary.h @@ -0,0 +1,182 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_PRIMITIVE_COMMON_BROADCAST_ELEMENTWISE_UNARY +#define ONEFLOW_CORE_PRIMITIVE_COMMON_BROADCAST_ELEMENTWISE_UNARY + +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/ep/include/primitive/fast_integer_math.h" +#include "oneflow/core/ep/common/primitive/util.h" + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace broadcast_elementwise_unary { + +constexpr size_t kMaxNumDims = 8; + +template +class IndexToOffsetWithStrideCalculator { + public: + IndexToOffsetWithStrideCalculator() {} + + OF_DEVICE_FUNC explicit IndexToOffsetWithStrideCalculator(const T* strides) { + InitStrides(strides, N); + } + + template + OF_DEVICE_FUNC explicit IndexToOffsetWithStrideCalculator(const U* strides) { + T strides_arr[N]; + for (int i = 0; i < N; ++i) { strides_arr[i] = strides[i]; } + InitStrides(strides_arr, N); + } + + OF_DEVICE_FUNC explicit IndexToOffsetWithStrideCalculator(const T* strides, int n) { + InitStrides(strides, n); + } + + template + OF_DEVICE_FUNC explicit IndexToOffsetWithStrideCalculator(const U* strides, int n) { + T strides_arr[N]; + for (int i = 0; i < N; ++i) { + if (i < n) { strides_arr[i] = strides[i]; } + } + InitStrides(strides_arr, n); + } + + ~IndexToOffsetWithStrideCalculator() = default; + + OF_DEVICE_FUNC T NdIndexToOffset(const T* index) const { + T offset = 0; +#ifdef __CUDA_ARCH__ +#pragma unroll +#endif + for (int i = 0; i < N - 1; ++i) { offset += index[i] * stride_[i]; } + offset += index[N - 1]; + return offset; + } + + OF_DEVICE_FUNC T NdIndexToOffset(const T* index, int n) const { + assert(n <= N); + T offset = 0; +#ifdef __CUDA_ARCH__ +#pragma unroll +#endif + for (int i = 0; i < N; ++i) { + if (i < n) { offset += index[i] * stride_[i]; } + } + return offset; + } + + OF_DEVICE_FUNC constexpr int Size() const { return N; } + + private: + OF_DEVICE_FUNC void InitStrides(const T* strides, const int n) { + for (int i = n; i < N; ++i) { stride_[i] = 1; } + for (int i = n - 1; i >= 0; --i) { stride_[i] = strides[i]; } + } + + T stride_[N]; +}; + +template +class OffsetToIndexWithStrideCalculator { + public: + OffsetToIndexWithStrideCalculator() {} + + OF_DEVICE_FUNC explicit OffsetToIndexWithStrideCalculator(const T* dims) { + InitFastIntegerMath(dims, N); + } + + template + OF_DEVICE_FUNC explicit OffsetToIndexWithStrideCalculator(const U* dims) { + T dims_arr[N]; + for (int i = 0; i < N; ++i) { dims_arr[i] = dims[i]; } + InitFastIntegerMath(dims_arr, N); + } + + OF_DEVICE_FUNC explicit OffsetToIndexWithStrideCalculator(const T* dims, int n) { + InitFastIntegerMath(dims, n); + } + + template + OF_DEVICE_FUNC explicit OffsetToIndexWithStrideCalculator(const U* dims, int n) { + T dims_arr[N]; + for (int i = 0; i < N; ++i) { + if (i < n) { dims_arr[i] = dims[i]; } + } + InitFastIntegerMath(dims_arr, n); + } + + ~OffsetToIndexWithStrideCalculator() = default; + + OF_DEVICE_FUNC void OffsetToNdIndex(T offset, T* index) const { + T remaining = offset; +#ifdef __CUDA_ARCH__ +#pragma unroll +#endif + for (int i = 0; i < N - 1; ++i) { + const T idx = math_helper_[i].divides(remaining); + index[i] = idx; + remaining = remaining - math_helper_[i].mul(idx); + } + index[N - 1] = remaining; + } + + OF_DEVICE_FUNC void OffsetToNdIndex(T offset, T* index, int n) const { + assert(n <= N); + T remaining = offset; +#ifdef __CUDA_ARCH__ +#pragma unroll +#endif + for (int i = 0; i < N; ++i) { + if (i == n - 1) { break; } + if (i < n - 1) { + const T idx = math_helper_[i].divides(remaining); + index[i] = idx; + remaining = remaining - math_helper_[i].mul(idx); + } + } + index[n - 1] = remaining; + } + + OF_DEVICE_FUNC constexpr int Size() const { return N; } + + private: + OF_DEVICE_FUNC void InitFastIntegerMath(const T* dims, const int n) { + T stride_arr[N]; + for (int i = n - 1; i < N; ++i) { + stride_arr[i] = 1; + math_helper_[i] = FastIntegerMath(1); + } + for (int i = n - 2; i >= 0; --i) { + stride_arr[i] = dims[i + 1] * stride_arr[i + 1]; + math_helper_[i] = FastIntegerMath(stride_arr[i]); + } + } + FastIntegerMath math_helper_[N]; +}; + +#define UNARY_BROADCAST_OP_SEQ OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kIdentity) + +} // namespace broadcast_elementwise_unary +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif // ONEFLOW_CORE_PRIMITIVE_COMMON_BROADCAST_ELEMENTWISE_UNARY diff --git a/oneflow/core/ep/common/primitive/copy_nd.h b/oneflow/core/ep/common/primitive/copy_nd.h index d8680177e86e31d59d45c11d97848467625521ce..06038399c1e4252e047fdafe66bcf366a1efd26c 100644 --- a/oneflow/core/ep/common/primitive/copy_nd.h +++ b/oneflow/core/ep/common/primitive/copy_nd.h @@ -206,6 +206,7 @@ void SimplifyCopyNd(size_t num_dims, const int64_t* dst_dims, const int64_t* dst void SimplifyThenLaunch(Stream* stream, DataType data_type, size_t num_dims, void* dst, const int64_t* dst_dims, const int64_t* dst_pos, const void* src, const int64_t* src_dims, const int64_t* src_pos, const int64_t* extent) { + CHECK_GT(num_dims, 0) << "num_dims must greater than 0"; CHECK_LE(num_dims, kMaxNumDims); size_t simplified_num_dims = 0; int64_t simplified_dst_dims[kMaxNumDims]; diff --git a/oneflow/core/ep/common/primitive/elementwise_unary.h b/oneflow/core/ep/common/primitive/elementwise_unary.h index a1b84f1748191527a4b8587f60a540eb824b91ca..67bae80b710952311a2b7a3b006a519ec0385828 100644 --- a/oneflow/core/ep/common/primitive/elementwise_unary.h +++ b/oneflow/core/ep/common/primitive/elementwise_unary.h @@ -25,29 +25,72 @@ namespace primitive { #define UNARY_MATH_OP_SEQ OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kRelu) -#define UNARY_FLOATING_MATH_OP_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kElu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kCelu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kGelu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardSwish) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardSigmoid) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardShrink) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardTanh) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLeakyRelu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kMish) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSelu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSilu) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftShrink) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftSign) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftPlus) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kTanh) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kThreshold) +#define UNARY_FLOATING_MATH_OP_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kElu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kCelu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kGelu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardSwish) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardShrink) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kHardTanh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLeakyRelu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kMish) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSelu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSilu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftShrink) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftSign) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSoftPlus) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kTanh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kThreshold) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAbs) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAcos) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAcosh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAsin) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAsinh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAtan) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAtanh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kCeil) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kCos) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kCosh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kErf) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kErfc) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kExp) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kExpm1) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kFloor) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLgamma) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLog) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLog2) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLog10) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLog1p) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLogSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kNegative) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kReciprocal) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kReciprocalNoNan) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kRint) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kRound) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kRsqrt) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSign) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSin) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSinh) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSqrt) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSign) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kSquare) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kTan) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kTrunc) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kNotEqualZero) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kNanAssign) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kFastGelu) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kQuickGelu) + +#define UNARY_INT_MATH_OP_SEQ OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kAbs) #define UNARY_LOGICAL_OP_SEQ OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kLogicalNot) #define UNARY_UTILS_OP_SEQ \ OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kIsInf) \ - OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kIsNan) + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kIsNan) \ + OF_PP_MAKE_TUPLE_SEQ(UnaryOp::kIsFinite) } // namespace primitive } // namespace ep diff --git a/oneflow/core/ep/common/primitive/matmul.cpp b/oneflow/core/ep/common/primitive/matmul.cpp index 2baf7bb4b0962f0073ea8cc293c684c06b73ad8b..e3d0fc58ca236484c928eccfa49d724f6df04b51 100644 --- a/oneflow/core/ep/common/primitive/matmul.cpp +++ b/oneflow/core/ep/common/primitive/matmul.cpp @@ -60,12 +60,10 @@ REGISTER_PRIMITIVE_FACTORY(DeviceType::kCPU, MatmulFactory, MatmulFactoryImpl); #endif // WITH_CUDA - #ifdef WITH_ROCM REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MatmulFactory, MatmulFactoryImpl); #endif // WITH_ROCM - } // namespace } // namespace primitive diff --git a/oneflow/core/ep/common/primitive/unary_functor.h b/oneflow/core/ep/common/primitive/unary_functor.h index 2c43329be77fc5d7585100caebfbf815bdffc168..f0761adb9ef8081389f8aa28cd1849a023f05afc 100644 --- a/oneflow/core/ep/common/primitive/unary_functor.h +++ b/oneflow/core/ep/common/primitive/unary_functor.h @@ -28,9 +28,16 @@ namespace primitive { template struct UnaryFunctor; +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(src); } +}; + template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast( @@ -41,7 +48,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()), inv_alpha(1.0f / attr0.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { @@ -54,7 +61,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { if (src <= static_cast(-3)) { @@ -69,7 +76,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { if (src <= static_cast(-3)) { @@ -84,7 +91,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) : lambd(attr0.Value()) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : lambd(attr0.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return (src <= lambd && src >= -lambd) ? static_cast(0) : static_cast(src); @@ -95,7 +102,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : min_val(attr0.Value()), max_val(attr1.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { @@ -114,7 +121,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast((src > static_cast(0.0)) ? src : alpha * src); @@ -124,7 +131,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { Src soft_plus_val = log(static_cast(1) + exp(src)); @@ -137,7 +144,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { const Src zero_val = static_cast(0.0); @@ -151,7 +158,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(src / (static_cast(1) + exp(-src))); @@ -160,7 +167,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast((src > static_cast(0.0)) @@ -173,7 +180,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(src / (static_cast(1) + abs(src))); @@ -182,7 +189,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : beta(attr0.Value()), threshold(attr1.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { @@ -196,7 +203,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : alpha(attr0.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { if (src <= alpha && src >= -alpha) { @@ -212,7 +219,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : threshold(attr0.Value()), value(attr1.Value()) {} OF_DEVICE_FUNC Dst operator()(Src src) const { @@ -224,7 +231,7 @@ struct UnaryFunctor { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(!src); } }; @@ -243,6 +250,300 @@ struct UnaryFunctor { OF_DEVICE_FUNC bool operator()(Src src) const { return false; } }; +template +struct UnaryFunctor { + UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(Src src) const { return true; } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1); + OF_DEVICE_FUNC Dst operator()(Src src) const; +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(abs(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC uint8_t operator()(uint8_t src) const { return src; } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(exp(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(acos(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(acosh(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(asin(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(asinh(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(atan(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(atanh(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(ceil(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(cos(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(cosh(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(erf(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(erfc(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(expm1(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(floor(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(lgamma(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(log(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(log2(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(log10(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(log1p(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return static_cast(-log(static_cast(1.0) + exp(-src))); + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(-src); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return static_cast(static_cast(1.0) / src); + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + if (abs(src) <= static_cast(0.0)) { return static_cast(0.0); } + return static_cast(static_cast(1.0) / src); + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(rint(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(nearbyint(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(rsqrt(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return static_cast(static_cast(1.0) / (static_cast(1.0) + exp(-src))); + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + const Src zero = static_cast(0.0); + if (src > zero) { + return static_cast(1.0); + } else if (src < zero) { + return static_cast(-1.0); + } else { + return static_cast(0.0); + } + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(sin(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(sinh(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(sqrt(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(src * src); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(tan(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return static_cast(src != static_cast(0.0)); + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return std::isnan(src) ? static_cast(0.0) : src; + } +}; + } // namespace primitive } // namespace ep } // namespace oneflow diff --git a/oneflow/core/ep/common/primitive/util.h b/oneflow/core/ep/common/primitive/util.h index 20825ebdf749825b80f5cd8771b8a8e630b8752f..e4173ed21b86e58d6d0eaab92149b5d039936eae 100644 --- a/oneflow/core/ep/common/primitive/util.h +++ b/oneflow/core/ep/common/primitive/util.h @@ -37,6 +37,71 @@ bool IsPackSizeSupported(const size_t pack_size, size_t num_dims, const int64_t* && (reinterpret_cast(ptr) % (pack_size * sizeof(T)) == 0); } +inline void CheckInplace(size_t num_dims, const int64_t* src_dims_or_strides, const void* src, + const int64_t* dst_dims_or_strides, const void* dst) { + if (src == dst) { + for (int64_t i = 0; i < num_dims; ++i) { + CHECK_EQ(src_dims_or_strides[i], dst_dims_or_strides[i]); + } + } +} + +template +inline void SimplifyBroadcastDims(size_t num_src_dims, const int64_t* src_dims, + const int64_t* src_strides, size_t num_dst_dims, + const int64_t* dst_dims, const int64_t* dst_strides, + size_t* simplified_num_dims, int64_t* simplified_src_dims, + int64_t* simplified_src_strides, int64_t* simplified_dst_dims, + int64_t* simplified_dst_strides) { + *simplified_num_dims = 0; + std::pair sorted_dst_strides[max_num_dims]; + int64_t new_dst_dims[max_num_dims]; + int64_t new_src_dims[max_num_dims]; + int64_t new_dst_strides[max_num_dims]; + int64_t new_src_strides[max_num_dims]; + for (size_t i = 0; i < num_dst_dims; i++) { sorted_dst_strides[i] = {dst_strides[i], i}; } + std::sort(sorted_dst_strides, sorted_dst_strides + num_dst_dims, + [](auto pair1, auto pair2) { return pair1.first > pair2.first; }); + const int64_t num_src_padding_dims = num_dst_dims - num_src_dims; + // dimension completion + int64_t expanded_src_dims[max_num_dims]; + int64_t expanded_src_strides[max_num_dims]; + for (int64_t i = num_dst_dims - 1; i >= 0; i--) { + expanded_src_dims[i] = i < num_src_padding_dims ? 1 : src_dims[i - num_src_padding_dims]; + expanded_src_strides[i] = i < num_src_padding_dims ? 0 : src_strides[i - num_src_padding_dims]; + } + // dimension permutation + for (int64_t i = num_dst_dims - 1; i >= 0; i--) { + size_t idx = sorted_dst_strides[i].second; + new_dst_dims[i] = dst_dims[idx]; + new_dst_strides[i] = dst_strides[idx]; + new_src_dims[i] = expanded_src_dims[idx]; + new_src_strides[i] = expanded_src_strides[idx]; + } + // dimension merge + bool prev_broadcast_src = false; + for (int64_t i = 0; i < num_dst_dims; ++i) { + const bool broadcast_src = (new_src_dims[i] == 1); + if (new_dst_dims[i] == 1) { + continue; + } else if (*simplified_num_dims != 0 && prev_broadcast_src == broadcast_src + && (new_src_strides[i - 1] == new_src_strides[i] * new_src_dims[i]) + && (new_dst_strides[i - 1] == new_dst_strides[i] * new_dst_dims[i])) { + simplified_src_dims[*simplified_num_dims - 1] *= new_src_dims[i]; + simplified_dst_dims[*simplified_num_dims - 1] *= new_dst_dims[i]; + simplified_src_strides[*simplified_num_dims - 1] = new_src_strides[i]; + simplified_dst_strides[*simplified_num_dims - 1] = new_dst_strides[i]; + } else { + simplified_src_dims[*simplified_num_dims] = new_src_dims[i]; + simplified_dst_dims[*simplified_num_dims] = new_dst_dims[i]; + simplified_src_strides[*simplified_num_dims] = new_src_strides[i]; + simplified_dst_strides[*simplified_num_dims] = new_dst_strides[i]; + *simplified_num_dims += 1; + prev_broadcast_src = broadcast_src; + } + } +} + inline void SimplifyBroadcastDims(size_t num_a_dims, const int64_t* a_dims, size_t num_b_dims, const int64_t* b_dims, size_t num_c_dims, const int64_t* c_dims, size_t* simplified_num_dims, int64_t* simplified_broadcast_dims, diff --git a/oneflow/core/ep/cpu/cpu_device.cpp b/oneflow/core/ep/cpu/cpu_device.cpp index 14c0415b6ec0eed776975441acbff3bff789a96f..faf394b855d2e7f0bc665543c7be209d7c6d09e2 100644 --- a/oneflow/core/ep/cpu/cpu_device.cpp +++ b/oneflow/core/ep/cpu/cpu_device.cpp @@ -42,15 +42,13 @@ Maybe CpuDevice::Alloc(const AllocationOptions& options, void** ptr, size_ this->device_manager()->registry()->GetDevice(options.GetPinnedDeviceType(), // NOLINT options.GetPinnedDeviceIndex()); // NOLINT CHECK_OR_RETURN(device); - return device->AllocPinned(options, ptr, size); + JUST(device->AllocPinned(options, ptr, size)); } else { - *ptr = aligned_alloc(kMaxAlignmentRequirement, size); - if (*ptr == nullptr) { - return Error::RuntimeError() << "allocate failed"; - } else { - return Maybe::Ok(); - } + *ptr = aligned_alloc(kMaxAlignmentRequirement, RoundUp(size, kMaxAlignmentRequirement)); + if (*ptr == nullptr) { return Error::RuntimeError() << "allocate failed"; } } + memset(*ptr, 0, size); + return Maybe::Ok(); } void CpuDevice::Free(const AllocationOptions& options, void* ptr) { diff --git a/oneflow/core/ep/cpu/primitive/binary_functor.h b/oneflow/core/ep/cpu/primitive/binary_functor.h index d27dcbca34edc5c0e7a67ba14d24cbc88ccd0f45..4b25663a73a4c480c333d6564c1ca501dfc3f895 100644 --- a/oneflow/core/ep/cpu/primitive/binary_functor.h +++ b/oneflow/core/ep/cpu/primitive/binary_functor.h @@ -29,23 +29,224 @@ struct BinaryFunctor { }; template<> -struct BinaryFunctor { +struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - OF_DEVICE_FUNC bool operator()(bool src0, bool src1) const { - return static_cast(std::pow(static_cast(src0), static_cast(src1))); + OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { + return static_cast(std::pow(static_cast(src0), static_cast(src1))); } }; template<> -struct BinaryFunctor { +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return std::fmod(src0, src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { return std::fmod(src0, src1); } +}; + +template<> +struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { - return static_cast(std::pow(static_cast(src0), static_cast(src1))); + return static_cast(std::fmod(static_cast(src0), static_cast(src1))); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src0, bfloat16 src1) const { + return std::fmod(src0, src1); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return std::floor(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { + return std::floor(src0 / src1); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { + return static_cast(std::floor(static_cast(src0) / static_cast(src1))); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src0, bfloat16 src1) const { + return std::floor(src0 / src1); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return std::trunc(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { + return std::trunc(src0 / src1); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { + return static_cast(std::trunc(static_cast(src0) / static_cast(src1))); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src0, bfloat16 src1) const { + return std::trunc(src0 / src1); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { + float trunc_mod = std::fmod(src0, src1); + return (trunc_mod != static_cast(0)) + && ((src1 < static_cast(0)) != (trunc_mod < static_cast(0))) + ? trunc_mod + src1 + : trunc_mod; + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { + double trunc_mod = std::fmod(src0, src1); + return (trunc_mod != static_cast(0)) + && ((src1 < static_cast(0)) != (trunc_mod < static_cast(0))) + ? trunc_mod + src1 + : trunc_mod; + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src0, bfloat16 src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : scalar_operand(attr0.Value()) {} + + OF_DEVICE_FUNC float16 operator()(float16 src0, float16 src1) const { + return static_cast( + scalar_operand + * (std::pow(static_cast(src0), scalar_operand - static_cast(1))) + * static_cast(src1)); + } + float scalar_operand; +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(int src0, int src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); } }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(int8_t src0, int8_t src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(uint8_t src0, uint8_t src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(int src0, int src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : scalar_operand(attr0.Value()) {} + + OF_DEVICE_FUNC Dst operator()(float16 src0, float16 src1) const { + return static_cast(std::pow(scalar_operand, static_cast(src0)) + * std::log(scalar_operand) * static_cast(src1)); + } + float scalar_operand; +}; + template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} @@ -59,6 +260,39 @@ struct BinaryFunctor Src coef = std::sqrt(2.0 / std::acos(-1.0)); }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + // ref to: https://mlfromscratch.com/activation-functions-explained/#gelu + const Src one = static_cast(1); + const Src half = static_cast(0.5); + const Src pow3 = x * x * x; + const Src tanh_out = std::tanh(alpha * (x + beta * pow3)); + const Src dtanh = alpha * (half * x + beta * static_cast(1.5) * pow3); + return dy * (half + half * tanh_out + dtanh * (one - tanh_out * tanh_out)); + } + + private: + static constexpr Src alpha = static_cast(0.7978845608028654); + static constexpr Src beta = static_cast(0.044714998453855515); +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src one = static_cast(1.0); + const Src sigmoid = one / (one + exp(-x * alpha)); + return dy * (sigmoid + alpha * x * (sigmoid * (one - sigmoid))); + } + + private: + static constexpr Src alpha = static_cast(1.702); +}; + template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} @@ -69,6 +303,82 @@ struct BinaryFunctor } }; +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * -(static_cast(1.0) / sqrt(static_cast(1.0) - x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy / sqrt(x * x - static_cast(1.0)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / sqrt(static_cast(1.0) - x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * (static_cast(1.0) / sqrt(static_cast(1.0) + x * x)); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * static_cast(2.0) * (static_cast(1.0) / sqrt(static_cast(M_PI))) + * exp(-x * x); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + return dy * static_cast(-2.0) * (static_cast(1.0) / sqrt(static_cast(M_PI))) + * exp(-x * x); + } +}; + +#define SPECIALIZATION_CPU_BINARY_FUNCTOR(op, type) \ + template<> \ + struct BinaryFunctor { \ + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : int_functor(attr0, attr1) {} \ + \ + BinaryFunctor int_functor; \ + OF_DEVICE_FUNC type operator()(type src0, type src1) const { \ + return static_cast(int_functor(static_cast(src0), static_cast(src1))); \ + } \ + }; + +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kPow, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFmod, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFloorDiv, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kTruncDiv, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFloorMod, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kScalarBasePowerGrad, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kScalarExpPowerGrad, bool); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kPow, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFmod, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFloorDiv, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kTruncDiv, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kFloorMod, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kScalarBasePowerGrad, char); +SPECIALIZATION_CPU_BINARY_FUNCTOR(BinaryOp::kScalarExpPowerGrad, char); + } // namespace broadcast_elementwise_binary } // namespace primitive } // namespace ep diff --git a/oneflow/core/ep/cpu/primitive/broadcast_elementwise_binary.cpp b/oneflow/core/ep/cpu/primitive/broadcast_elementwise_binary.cpp index b663213758c926c38759611c50eb45d595bc4612..e2293f66fe7bda7fffdea150987513a2752bd2ac 100644 --- a/oneflow/core/ep/cpu/primitive/broadcast_elementwise_binary.cpp +++ b/oneflow/core/ep/cpu/primitive/broadcast_elementwise_binary.cpp @@ -45,6 +45,11 @@ float16 GetValue(Scalar value) { return static_cast(GetValue(value)); } +template<> +bfloat16 GetValue(Scalar value) { + return static_cast(GetValue(value)); +} + template struct BinaryLhsScalarFunctor { BinaryLhsScalarFunctor(Src scalar, Scalar attr0, Scalar attr1) @@ -247,8 +252,8 @@ void DispatchLaunch(Stream* stream, size_t num_src0_dims, const int64_t* src0_di SimplifyBroadcastDims(num_src0_dims, src0_dims, num_src1_dims, src1_dims, &simplified_num_dims, simplified_src0_dims, simplified_src1_dims, simplified_dst_dims); - CheckInplace(simplified_num_dims, simplified_src0_dims, src0, simplified_src1_dims, src1, - simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src0_dims, src0, simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src1_dims, src1, simplified_dst_dims, dst); if (IsDimsEquals(simplified_num_dims, simplified_src0_dims, simplified_num_dims, simplified_src1_dims)) { LaunchElementwise(cpu_stream, simplified_num_dims, simplified_src0_dims, @@ -260,16 +265,20 @@ void DispatchLaunch(Stream* stream, size_t num_src0_dims, const int64_t* src0_di } else if (simplified_num_dims == 1 && simplified_src1_dims[0] == 1) { LaunchBinaryRhsScalar(cpu_stream, *src1, simplified_src0_dims[0], src0, dst, attr0, attr1); - } else if (simplified_num_dims == 2 && simplified_src0_dims[0] == 1) { + } else if (simplified_num_dims == 2 && simplified_src0_dims[0] == 1 + && simplified_src0_dims[1] == simplified_src1_dims[1]) { LaunchRowWithMatrix(cpu_stream, simplified_src0_dims, src0, simplified_src1_dims, src1, dst, attr0, attr1); - } else if (simplified_num_dims == 2 && simplified_src1_dims[0] == 1) { + } else if (simplified_num_dims == 2 && simplified_src1_dims[0] == 1 + && simplified_src0_dims[1] == simplified_src1_dims[1]) { LaunchMatrixWithRow(cpu_stream, simplified_src0_dims, src0, simplified_src1_dims, src1, dst, attr0, attr1); - } else if (simplified_num_dims == 2 && simplified_src0_dims[1] == 1) { + } else if (simplified_num_dims == 2 && simplified_src0_dims[1] == 1 + && simplified_src0_dims[0] == simplified_src1_dims[0]) { LaunchColWithMatrix(cpu_stream, simplified_src0_dims, src0, simplified_src1_dims, src1, dst, attr0, attr1); - } else if (simplified_num_dims == 2 && simplified_src1_dims[1] == 1) { + } else if (simplified_num_dims == 2 && simplified_src1_dims[1] == 1 + && simplified_src0_dims[0] == simplified_src1_dims[0]) { LaunchMatrixWithCol(cpu_stream, simplified_src0_dims, src0, simplified_src1_dims, src1, dst, attr0, attr1); } else { @@ -405,8 +414,8 @@ class OneDnnBroadcastElementwiseBinaryImpl : public BroadcastElementwiseBinary { src1_dims, dst_dims); } - CheckInplace(num_dims, src_0_dims.data(), onednn_src0, src_1_dims.data(), onednn_src1, - dst_dims.data(), dst); + CheckInplace(num_dims, src_0_dims.data(), onednn_src0, dst_dims.data(), dst); + CheckInplace(num_dims, src_1_dims.data(), onednn_src1, dst_dims.data(), dst); auto src_0_md = dnnl::memory::desc( src_0_dims, src_onednn, @@ -564,7 +573,11 @@ class BroadcastElementwiseBinaryFactoryImpl : public BroadcastElementwiseBinaryF OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY, - BINARY_ACTIVATION_BACKWARD_OP_SEQ, CPU_PRIMITIVE_FLOATING_TYPE_SEQ)}; + BINARY_ACTIVATION_BACKWARD_OP_SEQ, CPU_PRIMITIVE_FLOATING_TYPE_SEQ) + + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, + BINARY_MATH_BACKWARD_OP_SEQ, CPU_PRIMITIVE_FLOATING_TYPE_SEQ)}; #undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_AND_LOGICAL_ENTRY #undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY diff --git a/oneflow/core/ep/cpu/primitive/broadcast_elementwise_unary.cpp b/oneflow/core/ep/cpu/primitive/broadcast_elementwise_unary.cpp new file mode 100644 index 0000000000000000000000000000000000000000..aff6cb0d26f35afd0eb9918a4130c989dde358d6 --- /dev/null +++ b/oneflow/core/ep/cpu/primitive/broadcast_elementwise_unary.cpp @@ -0,0 +1,233 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ep/common/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/ep/cpu/primitive/unary_functor.h" +#include "oneflow/core/ep/cpu/primitive/type_seq.h" +#include "oneflow/core/ep/cpu/cpu_stream.h" +#include "oneflow/core/ep/cpu/cpu_device.h" + +namespace oneflow { + +namespace ep { +namespace primitive { +namespace broadcast_elementwise_unary { + +namespace { + +bool IsContiguous(size_t num_dims, const int64_t* dims, const int64_t* strides) { + for (int i = num_dims - 1; i >= 0; i--) { + if ((i == num_dims - 1 && strides[i] != 1) + || (i != num_dims - 1 && strides[i] != dims[i + 1] * strides[i + 1])) { + return false; + } + } + return true; +} + +template +void LaunchScalarFill(CpuStream* stream, Dst* dst, const Src* src, size_t count, size_t stride, + Scalar attr0, Scalar attr1) { + auto functor = UnaryFunctor(attr0, attr1); + Dst scalar_value = functor(*src); + stream->ParallelFor(0, count, [dst, stride, scalar_value](int64_t begin, int64_t end) { + for (int64_t i = begin; i < end; i++) { dst[i * stride] = scalar_value; } + }); +} + +template +void LaunchTensorFill(CpuStream* stream, Dst* dst, const Src* src, size_t count, size_t dst_stride, + size_t src_stride, Scalar attr0, Scalar attr1) { + auto functor = UnaryFunctor(attr0, attr1); + stream->ParallelFor(0, count, + [functor, src, dst, src_stride, dst_stride](int64_t begin, int64_t end) { + for (int64_t i = begin; i < end; i++) { + dst[i * dst_stride] = functor(src[i * src_stride]); + } + }); +} + +template +void LaunchGeneral(CpuStream* stream, Dst* dst, const Src* src, size_t num_dims, + const int64_t* dst_dims, const int64_t* src_dims, const int64_t* dst_stride, + const int64_t* src_stride, Scalar attr0, Scalar attr1) { + bool contiguous_output = IsContiguous(num_dims, dst_dims, dst_stride); + const int64_t elem_cnt = GetElementCount(num_dims, dst_dims); + auto functor = UnaryFunctor(attr0, attr1); + stream->ParallelFor( + 0, elem_cnt, + [functor, src, dst, num_dims, src_dims, dst_dims, src_stride, dst_stride, contiguous_output]( + int64_t begin, int64_t end) { + auto src_index_to_offset_helper = + IndexToOffsetWithStrideCalculator(src_stride, num_dims); + auto dst_offset_to_index_helper = + OffsetToIndexWithStrideCalculator(dst_dims, num_dims); + auto dst_index_to_offset_helper = + IndexToOffsetWithStrideCalculator(dst_stride, num_dims); + int64_t src_index[kMaxNumDims]; + int64_t dst_index[kMaxNumDims]; + for (int64_t offset = begin; offset < end; offset++) { + dst_offset_to_index_helper.OffsetToNdIndex(offset, dst_index, num_dims); + for (int i = 0; i < kMaxNumDims; i++) { + if (i < num_dims) { + src_index[i] = (src_dims[i] != 1) ? dst_index[i] : 0; + } else { + src_index[i] = 0; + } + } + const int64_t src_offset = + src_index_to_offset_helper.NdIndexToOffset(src_index, num_dims); + if (!contiguous_output) { + const int64_t dst_offset = + dst_index_to_offset_helper.NdIndexToOffset(dst_index, num_dims); + dst[dst_offset] = functor(src[src_offset]); + } else { + dst[offset] = functor(src[src_offset]); + } + } + }); +} + +template +class BroadcastElementwiseUnaryImpl : public BroadcastElementwiseUnary { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnaryImpl); + BroadcastElementwiseUnaryImpl(Scalar attr0, Scalar attr1) : attr0(attr0), attr1(attr1) {} + ~BroadcastElementwiseUnaryImpl() override = default; + + void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, const void* src, + size_t num_dst_dims, const int64_t* dst_dims, void* dst) override { + CHECK_GT(num_src_dims, 0) << "num_src_dims must greater than 0"; + CHECK_GT(num_dst_dims, 0) << "num_dst_dims must greater than 0"; + int64_t src_strides[kMaxNumDims]; + int64_t dst_strides[kMaxNumDims]; + // init stride + for (int i = num_src_dims - 1; i < kMaxNumDims; ++i) { src_strides[i] = 1; } + for (int i = num_src_dims - 2; i >= 0; --i) { + src_strides[i] = src_dims[i + 1] * src_strides[i + 1]; + } + + for (int i = num_dst_dims - 1; i < kMaxNumDims; ++i) { dst_strides[i] = 1; } + for (int i = num_dst_dims - 2; i >= 0; --i) { + dst_strides[i] = dst_dims[i + 1] * dst_strides[i + 1]; + } + Launch(stream, num_src_dims, src_dims, src_strides, src, num_dst_dims, dst_dims, dst_strides, + dst); + } + + void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, + const int64_t* src_strides, const void* src_ptr, size_t num_dst_dims, + const int64_t* dst_dims, const int64_t* dst_strides, void* dst_ptr) override { + CHECK_GT(num_src_dims, 0) << "num_src_dims must greater than 0"; + CHECK_GT(num_dst_dims, 0) << "num_dst_dims must greater than 0"; + auto* cpu_stream = stream->As(); + Dst* dst = reinterpret_cast(dst_ptr); + const Src* src = reinterpret_cast(src_ptr); + size_t simplified_num_dims = 0; + int64_t simplified_src_dims[kMaxNumDims]; + int64_t simplified_dst_dims[kMaxNumDims]; + int64_t simplified_src_strides[kMaxNumDims]; + int64_t simplified_dst_strides[kMaxNumDims]; + SimplifyBroadcastDims(num_src_dims, src_dims, src_strides, num_dst_dims, dst_dims, + dst_strides, &simplified_num_dims, simplified_src_dims, + simplified_src_strides, simplified_dst_dims, + simplified_dst_strides); + CheckInplace(simplified_num_dims, simplified_src_dims, src, simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src_strides, src, simplified_dst_strides, dst); + if (simplified_num_dims == 1 && simplified_src_dims[0] == 1) { + const int64_t elem_cnt = simplified_dst_dims[0]; + const int64_t dst_stride = simplified_dst_strides[0]; + LaunchScalarFill(cpu_stream, dst, src, elem_cnt, dst_stride, attr0, + attr1); + } else if (simplified_num_dims == 1) { + const int64_t elem_cnt = simplified_src_dims[0]; + const int64_t src_stride = simplified_src_strides[0]; + const int64_t dst_stride = simplified_dst_strides[0]; + LaunchTensorFill(cpu_stream, dst, src, elem_cnt, dst_stride, src_stride, + attr0, attr1); + } else { + LaunchGeneral( + cpu_stream, dst, src, simplified_num_dims, simplified_dst_dims, simplified_src_dims, + simplified_dst_strides, simplified_src_strides, attr0, attr1); + } + } + + protected: + Scalar attr0, attr1; +}; + +template +std::unique_ptr NewBroadcastElementwiseUnary(Scalar attr0, + Scalar attr1) { + return std::unique_ptr( + new BroadcastElementwiseUnaryImpl(attr0, attr1)); +} + +class BroadcastElementwiseUnaryFactoryImpl : public BroadcastElementwiseUnaryFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnaryFactoryImpl); + BroadcastElementwiseUnaryFactoryImpl() = default; + ~BroadcastElementwiseUnaryFactoryImpl() override = default; + + std::unique_ptr New(UnaryOp op, DataType src_type, DataType dst_type, + size_t max_num_dims) override { + return New(op, src_type, dst_type, max_num_dims, Scalar(), Scalar()); + } + + std::unique_ptr New(UnaryOp op, DataType src_type, DataType dst_type, + size_t max_num_dims, Scalar attr0) override { + return New(op, src_type, dst_type, max_num_dims, attr0, Scalar()); + } + + std::unique_ptr New(UnaryOp unary_op, DataType src_type, + DataType dst_type, size_t max_num_dims, + Scalar attr0, Scalar attr1) override { + if (max_num_dims > kMaxNumDims) { return nullptr; } +#define MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY(unary_op, dtype_pair) \ + {std::make_tuple(unary_op, OF_PP_PAIR_SECOND(dtype_pair), OF_PP_PAIR_SECOND(dtype_pair)), \ + NewBroadcastElementwiseUnary}, + + static const std::map, + std::function(Scalar, Scalar)>> + new_broadcast_elementwise_unary_handle{ + // For All Type OP + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY, + UNARY_BROADCAST_OP_SEQ, CPU_PRIMITIVE_ALL_TYPE_SEQ)}; + +#undef MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY + + const auto iter = + new_broadcast_elementwise_unary_handle.find(std::make_tuple(unary_op, src_type, dst_type)); + if (iter != new_broadcast_elementwise_unary_handle.end()) { + return iter->second(attr0, attr1); + } else { + return nullptr; + } + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCPU, BroadcastElementwiseUnaryFactory, + BroadcastElementwiseUnaryFactoryImpl); + +} // namespace +} // namespace broadcast_elementwise_unary +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cpu/primitive/cast.cpp b/oneflow/core/ep/cpu/primitive/cast.cpp index 4f8cd2df4c2479ea6da09f2ea7ef393b57bd219f..ff3af6cddc03e34aee40239af8214deab795f0c1 100644 --- a/oneflow/core/ep/cpu/primitive/cast.cpp +++ b/oneflow/core/ep/cpu/primitive/cast.cpp @@ -23,10 +23,28 @@ namespace primitive { namespace { -template -void CastCpu(const From* from, To* to, size_t count) { - for (size_t i = 0; i < count; ++i) { to[i] = static_cast(from[i]); } -} +template +struct CpuCastFunctor { + static void Call(const From* from, To* to, size_t count) { + for (size_t i = 0; i < count; ++i) { to[i] = static_cast(from[i]); } + } +}; + +template +struct CpuCastFunctor::value)>::type> { + static void Call(const bfloat16* from, To* to, size_t count) { + for (size_t i = 0; i < count; ++i) { to[i] = static_cast(static_cast(from[i])); } + } +}; + +template +struct CpuCastFunctor::value)>::type> { + static void Call(const From* from, bfloat16* to, size_t count) { + for (size_t i = 0; i < count; ++i) { to[i] = bfloat16(static_cast(from[i])); } + } +}; template class CastImpl : public Cast { @@ -36,7 +54,8 @@ class CastImpl : public Cast { ~CastImpl() override = default; void Launch(Stream* stream, const void* from, void* to, size_t count) override { - CastCpu(reinterpret_cast(from), reinterpret_cast(to), count); + CpuCastFunctor::Call(reinterpret_cast(from), reinterpret_cast(to), + count); } }; @@ -56,7 +75,8 @@ std::unique_ptr NewCast() { CPU_PRIMITIVE_UINT64_TYPE_SEQ \ CPU_PRIMITIVE_FLOAT_TYPE_SEQ \ CPU_PRIMITIVE_DOUBLE_TYPE_SEQ \ - CPU_PRIMITIVE_FLOAT16_TYPE_SEQ + CPU_PRIMITIVE_FLOAT16_TYPE_SEQ \ + CPU_PRIMITIVE_BFLOAT16_TYPE_SEQ class CastFactoryImpl : public CastFactory { public: diff --git a/oneflow/core/ep/cpu/primitive/constant_pad.cpp b/oneflow/core/ep/cpu/primitive/constant_pad.cpp index 441d3bbd47e35c7f786d486747eeababe80a283c..2c776480a07a692d81d90504b8589b7922a2b6fe 100644 --- a/oneflow/core/ep/cpu/primitive/constant_pad.cpp +++ b/oneflow/core/ep/cpu/primitive/constant_pad.cpp @@ -56,6 +56,11 @@ float16 GetValue(Scalar value) { return static_cast(GetValue(value)); } +template<> +bfloat16 GetValue(Scalar value) { + return static_cast(GetValue(value)); +} + template void LaunchKernel(ConstantPadParams params, StorageType packed_pad_val) { ConstantPadKernel(params, packed_pad_val); @@ -163,6 +168,7 @@ template void SimplifyThenLaunch(size_t num_dims, const int64_t* src_dims, const void* src, const int64_t* padding_before, const int64_t* padding_after, T pad_val, void* dst) { + CHECK_GT(num_dims, 0) << "num_dims must greater than 0"; CHECK_LE(num_dims, kMaxNumDims); int64_t simplified_dst_dims[kMaxNumDims]; int64_t simplified_src_dims[kMaxNumDims]; diff --git a/oneflow/core/ep/cpu/primitive/elementwise_unary.cpp b/oneflow/core/ep/cpu/primitive/elementwise_unary.cpp index d6e69cbd17ae0f5c9e8c3fad5b533e5414e69b11..58da15cff96e1415e70dee992cedcde52f7c5b8e 100644 --- a/oneflow/core/ep/cpu/primitive/elementwise_unary.cpp +++ b/oneflow/core/ep/cpu/primitive/elementwise_unary.cpp @@ -88,9 +88,13 @@ class ElementwiseUnaryFactoryImpl : public ElementwiseUnaryFactory { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, UNARY_MATH_OP_SEQ, CPU_PRIMITIVE_NATIVE_TYPE_SEQ) // For Float Type OP + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, UNARY_FLOATING_MATH_OP_SEQ, + CPU_PRIMITIVE_FLOATING_TYPE_SEQ CPU_PRIMITIVE_BFLOAT16_TYPE_SEQ) + + // For Int Type OP OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, - UNARY_FLOATING_MATH_OP_SEQ, - CPU_PRIMITIVE_FLOATING_TYPE_SEQ) + UNARY_INT_MATH_OP_SEQ, CPU_PRIMITIVE_INT_TYPE_SEQ) // For Utils OP OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY, diff --git a/oneflow/core/ep/cpu/primitive/fill.cpp b/oneflow/core/ep/cpu/primitive/fill.cpp index 7e2d16c70920b61f45d0cd1351e163cfe474e299..b4dfc3def2a3993ef069826a16f0ec7e3fb349bd 100644 --- a/oneflow/core/ep/cpu/primitive/fill.cpp +++ b/oneflow/core/ep/cpu/primitive/fill.cpp @@ -34,6 +34,11 @@ float16 GetValue(Scalar value) { return static_cast(GetValue(value)); } +template<> +bfloat16 GetValue(Scalar value) { + return static_cast(GetValue(value)); +} + template class FillImpl : public Fill { public: diff --git a/oneflow/core/ep/cpu/primitive/tensor_fill.cpp b/oneflow/core/ep/cpu/primitive/tensor_fill.cpp new file mode 100644 index 0000000000000000000000000000000000000000..82fdd91af9e2cacb87d271c73ea5f117a3c3bf14 --- /dev/null +++ b/oneflow/core/ep/cpu/primitive/tensor_fill.cpp @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/include/primitive/tensor_fill.h" +#include "oneflow/core/ep/cpu/primitive/type_seq.h" + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace { + +template +class TensorFillImpl : public TensorFill { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFillImpl); + TensorFillImpl() = default; + ~TensorFillImpl() override = default; + + void Launch(Stream* stream, const void* src, void* dst, size_t count) override { + const T* value = reinterpret_cast(src); + std::fill_n(reinterpret_cast(dst), count, value[0]); + } +}; + +template +std::unique_ptr NewTensorFill() { + return std::unique_ptr(new TensorFillImpl()); +} + +class TensorFillFactoryImpl : public TensorFillFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFillFactoryImpl); + TensorFillFactoryImpl() = default; + ~TensorFillFactoryImpl() override = default; + + std::unique_ptr New(DataType data_type) override { +#define MAKE_NEW_FILL_ENTRY(type_cpp, type_proto) {type_proto, NewTensorFill}, + + static const std::map()>> new_fill_handle{ + OF_PP_FOR_EACH_TUPLE(MAKE_NEW_FILL_ENTRY, CPU_PRIMITIVE_ALL_TYPE_SEQ)}; +#undef MAKE_NEW_ADD_ENTRY + const auto it = new_fill_handle.find(data_type); + if (it != new_fill_handle.end()) { + return it->second(); + } else { + return nullptr; + } + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCPU, TensorFillFactory, TensorFillFactoryImpl); + +} // namespace + +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cpu/primitive/type_seq.h b/oneflow/core/ep/cpu/primitive/type_seq.h index 1433661ef549d7c69b1bed39831546f21a82e9d5..a3aefd2b41f2449ec693becccb9eb3511daba5d6 100644 --- a/oneflow/core/ep/cpu/primitive/type_seq.h +++ b/oneflow/core/ep/cpu/primitive/type_seq.h @@ -35,6 +35,7 @@ limitations under the License. #define CPU_PRIMITIVE_FLOAT_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) #define CPU_PRIMITIVE_DOUBLE_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) #define CPU_PRIMITIVE_FLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float16, DataType::kFloat16) +#define CPU_PRIMITIVE_BFLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(bfloat16, DataType::kBFloat16) #define CPU_PRIMITIVE_ONEDNN_BOOl_TYPE_SEQ \ OF_PP_MAKE_TUPLE_SEQ(dnnl::memory::data_type::u8, DataType::kBool) @@ -63,12 +64,19 @@ limitations under the License. #define CPU_PRIMITIVE_ALL_TYPE_SEQ \ CPU_PRIMITIVE_NATIVE_TYPE_SEQ \ - CPU_PRIMITIVE_FLOAT16_TYPE_SEQ + CPU_PRIMITIVE_FLOAT16_TYPE_SEQ \ + CPU_PRIMITIVE_BFLOAT16_TYPE_SEQ #define CPU_PRIMITIVE_FLOATING_TYPE_SEQ \ CPU_PRIMITIVE_FLOAT_TYPE_SEQ \ CPU_PRIMITIVE_DOUBLE_TYPE_SEQ +#define CPU_PRIMITIVE_INT_TYPE_SEQ \ + CPU_PRIMITIVE_INT8_TYPE_SEQ \ + CPU_PRIMITIVE_UINT8_TYPE_SEQ \ + CPU_PRIMITIVE_INT32_TYPE_SEQ \ + CPU_PRIMITIVE_INT64_TYPE_SEQ + #define UTIL_OPS_DATA_TYPE_SEQ \ CPU_PRIMITIVE_INT8_TYPE_SEQ \ CPU_PRIMITIVE_UINT8_TYPE_SEQ \ diff --git a/oneflow/core/ep/cpu/primitive/unary_functor.h b/oneflow/core/ep/cpu/primitive/unary_functor.h index 668cb790f11b374c8f1f800e90b42550364db9a7..e972b4e21cefa94e5a80eabfcf86f2e7e2d8d741 100644 --- a/oneflow/core/ep/cpu/primitive/unary_functor.h +++ b/oneflow/core/ep/cpu/primitive/unary_functor.h @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/ep/common/primitive/unary_functor.h" #include "oneflow/core/ep/cpu/primitive/type_seq.h" -#include namespace oneflow { namespace ep { @@ -23,7 +22,7 @@ namespace primitive { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(0.5) * src * (static_cast(1.0) + std::erf(inv_sqrt2 * src)); @@ -31,9 +30,42 @@ struct UnaryFunctor { Src inv_sqrt2 = std::sqrt(0.5); }; +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + // ref to: https://mlfromscratch.com/activation-functions-explained/#gelu + const Src half = static_cast(0.5); + const Src one = static_cast(1); + const Src tanh_in = alpha * (src + beta * src * src * src); + return half * src * (one + std::tanh(tanh_in)); + } + + private: + // constant ref to: + // https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/testdata/transform/fusion/fast_gelu.py + static constexpr Src alpha = static_cast(0.7978845608028654); + static constexpr Src beta = static_cast(0.044714998453855515); +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + const Src sigmoid = + static_cast(static_cast(1.0) / (static_cast(1.0) + exp(-src * alpha))); + return src * sigmoid; + } + + private: + static constexpr Src alpha = static_cast(1.702); +}; + template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return std::tanh(src); } }; @@ -66,6 +98,109 @@ struct UnaryFunctor { OF_DEVICE_FUNC bool operator()(double src) const { return std::isnan(src); } }; +template +struct UnaryFunctor { + UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(Src src) const { return std::isfinite(src); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(std::trunc(src)); } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + return static_cast(static_cast(1.0) / static_cast(std::sqrt(src))); + } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src) const { return std::abs(src); } +}; + +#define SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(op) \ + template<> \ + struct UnaryFunctor { \ + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + \ + UnaryFunctor float_functor; \ + OF_DEVICE_FUNC bfloat16 operator()(bfloat16 src) const { \ + return bfloat16(float_functor(static_cast(src))); \ + } \ + }; + +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kElu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCelu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kGelu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardSwish); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardSigmoid); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardShrink); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardTanh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLeakyRelu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kMish); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSelu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSilu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftShrink); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftSign); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftPlus); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kTanh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kThreshold); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAcos); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAcosh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAsin); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAsinh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAtan); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAtanh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCeil); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCos); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCosh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kErf); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kErfc); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kExp); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kExpm1); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kFloor); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLgamma); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog2); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog1p); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLogSigmoid); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRint); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRound); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRsqrt); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSigmoid); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSin); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSinh); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSqrt); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSquare); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kTan); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kReciprocalNoNan); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kNotEqualZero); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kFastGelu); +SPECIALIZATION_CPU_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kQuickGelu); + +template<> +struct UnaryFunctor { + UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(bfloat16 src) const { return std::isinf(src); } +}; + +template<> +struct UnaryFunctor { + UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(bfloat16 src) const { return std::isnan(src); } +}; + } // namespace primitive } // namespace ep } // namespace oneflow diff --git a/oneflow/core/ep/cuda/cuda_device.cpp b/oneflow/core/ep/cuda/cuda_device.cpp index d457cf4d4a42ae8b2044e1ff74c8941650b4c3bb..f061186706f4da29459d413b401cf306e4dda82b 100644 --- a/oneflow/core/ep/cuda/cuda_device.cpp +++ b/oneflow/core/ep/cuda/cuda_device.cpp @@ -55,6 +55,13 @@ CudaDevice::CudaDevice(int device_index, DeviceManager* device_manager) const_ones_buffer_bf16_(nullptr) { CudaCurrentDeviceGuard guard(device_index_); OF_CUDA_CHECK(cudaGetDeviceProperties(&properties_, device_index_)); + { + const char* env_name = "ONEFLOW_EP_CUDA_DEVICE_FLAGS"; + if (std::getenv(env_name) != nullptr) { + const unsigned int flags = ParseIntegerFromEnv(env_name, 0); + OF_CUDA_CHECK(cudaSetDeviceFlags(flags)); + } + } event_flags_ = cudaEventDisableTiming; if (ParseBooleanFromEnv("ONEFLOW_STREAM_CUDA_EVENT_FLAG_BLOCKING_SYNC", false)) { event_flags_ |= cudaEventBlockingSync; @@ -119,6 +126,10 @@ Maybe CudaDevice::Alloc(const AllocationOptions& options, void** ptr, size CHECK(!options.HasPinnedDevice()); cudaError_t err = cudaMalloc(ptr, size); if (err != cudaSuccess) { + if (err == cudaErrorMemoryAllocation) { + // NOTE:return out of memory error, so vm will try to shrink memory and rerun + return Error::OutOfMemoryError() << cudaGetErrorString(err); + } return Error::RuntimeError() << cudaGetErrorString(err); } else { return Maybe::Ok(); @@ -177,3 +188,176 @@ const void* CudaDevice::GetConstOnes(DataType data_type, size_t n) const { } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +#include +#include + +// #if CUDA_VERSION >= 11000 +// #include +// #endif + +namespace oneflow { + +namespace ep { + +namespace { + +constexpr size_t kDefaultConstBufElementCount = 1024 * 1024; + +template +void CreateConstBuffer(void** buf, T value, size_t n) { + OF_CUDA_CHECK(hipMalloc(buf, n * sizeof(T))); + std::vector host(n, value); + OF_CUDA_CHECK(hipMemcpy(*buf, host.data(), n * sizeof(T), hipMemcpyDefault)); +} + +} // namespace + +CudaDevice::CudaDevice(int device_index, DeviceManager* device_manager) + : device_index_(device_index), + event_flags_{}, + properties_{}, + device_manager_(device_manager), + const_buf_elem_cnt_(0), + const_zeros_buffer_(nullptr), + const_ones_buffer_fp32_(nullptr), + const_ones_buffer_fp16_(nullptr), + const_ones_buffer_bf16_(nullptr) { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(hipGetDeviceProperties(&properties_, device_index_)); + { + const char* env_name = "ONEFLOW_EP_CUDA_DEVICE_FLAGS"; + if (std::getenv(env_name) != nullptr) { + const unsigned int flags = ParseIntegerFromEnv(env_name, 0); + OF_CUDA_CHECK(hipSetDeviceFlags(flags)); + } + } + event_flags_ = hipEventDisableTiming; + if (ParseBooleanFromEnv("ONEFLOW_STREAM_CUDA_EVENT_FLAG_BLOCKING_SYNC", false)) { + event_flags_ |= hipEventBlockingSync; + } + const_buf_elem_cnt_ = ParseIntegerFromEnv("ONEFLOW_EP_CUDA_CONST_BUFFER_ELEMENT_COUNT", + kDefaultConstBufElementCount); + if (const_buf_elem_cnt_ > 0) { + CreateConstBuffer(&const_zeros_buffer_, static_cast(0), const_buf_elem_cnt_); + CreateConstBuffer(&const_ones_buffer_fp32_, static_cast(1.0), + const_buf_elem_cnt_); + CreateConstBuffer(&const_ones_buffer_fp16_, static_cast(1.0), const_buf_elem_cnt_); +// #if CUDA_VERSION >= 11000 +// CreateConstBuffer(&const_ones_buffer_bf16_, static_cast(1.0), +// const_buf_elem_cnt_); +// #endif + } +} + +CudaDevice::~CudaDevice() { + CudaCurrentDeviceGuard guard(device_index_); + for (auto* event : events_) { delete event; } + OF_CUDA_CHECK(hipFree(const_zeros_buffer_)); + OF_CUDA_CHECK(hipFree(const_ones_buffer_fp32_)); + OF_CUDA_CHECK(hipFree(const_ones_buffer_fp16_)); + OF_CUDA_CHECK(hipFree(const_ones_buffer_bf16_)); +} + +void CudaDevice::SetAsActiveDevice() { OF_CUDA_CHECK(hipSetDevice(device_index_)); } + +Stream* CudaDevice::CreateStream() { + CudaCurrentDeviceGuard guard(device_index_); + return new CudaStream(this); +} + +void CudaDevice::DestroyStream(Stream* stream) { + CudaCurrentDeviceGuard guard(device_index_); + delete stream; +} + +void CudaDevice::CreateEvents(Event** events, size_t count) { + size_t copied = 0; + { + std::lock_guard lock(events_mutex_); + copied = std::min(count, events_.size()); + size_t offset = events_.size() - copied; + std::copy(events_.begin() + offset, events_.end(), events); + events_.resize(offset); + } + if (copied != count) { + CudaCurrentDeviceGuard guard(device_index_); + for (size_t i = copied; i < count; ++i) { events[i] = new CudaEvent(event_flags_); } + } +} + +void CudaDevice::DestroyEvents(Event** events, size_t count) { + std::lock_guard lock(events_mutex_); + events_.insert(events_.end(), events, events + count); +} + +Maybe CudaDevice::Alloc(const AllocationOptions& options, void** ptr, size_t size) { + CudaCurrentDeviceGuard guard(device_index_); + CHECK(!options.HasPinnedDevice()); + hipError_t err = hipMalloc(ptr, size); + if (err != hipSuccess) { + if (err == hipErrorMemoryAllocation) { + // NOTE:return out of memory error, so vm will try to shrink memory and rerun + return Error::OutOfMemoryError() << hipGetErrorString(err); + } + return Error::RuntimeError() << hipGetErrorString(err); + } else { + return Maybe::Ok(); + } +} + +void CudaDevice::Free(const AllocationOptions& attr, void* ptr) { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(hipFree(ptr)); +} + +Maybe CudaDevice::AllocPinned(const AllocationOptions& options, void** ptr, size_t size) { + CudaCurrentDeviceGuard guard(device_index_); + hipError_t err = NumaAwareCudaMallocHost(device_index_, ptr, size); + if (err != hipSuccess) { + return Error::RuntimeError() << hipGetErrorString(err); + } else { + return Maybe::Ok(); + } +} + +void CudaDevice::FreePinned(const AllocationOptions& options, void* ptr) { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(hipHostFree(ptr)); +} + +const hipDeviceProp_t& CudaDevice::properties() const { return properties_; } + +const void* CudaDevice::GetConstZeros(DataType data_type, size_t n) const { + if (GetSizeOfDataType(data_type) * n + <= GetSizeOfDataType(DataType::kFloat) * const_buf_elem_cnt_) { + return const_zeros_buffer_; + } else { + return nullptr; + } +} + +const void* CudaDevice::GetConstOnes(DataType data_type, size_t n) const { + if (n <= const_buf_elem_cnt_) { + if (data_type == DataType::kFloat) { + return const_ones_buffer_fp32_; + } else if (data_type == DataType::kFloat16) { + return const_ones_buffer_fp16_; + } else if (data_type == DataType::kBFloat16) { + return const_ones_buffer_bf16_; + } else { + return nullptr; + } + } else { + return nullptr; + } +} + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + diff --git a/oneflow/core/ep/cuda/cuda_device.h b/oneflow/core/ep/cuda/cuda_device.h index 31aeb9648c057081d26f0f30a28193da2d1ec77a..4a4364c39ed0dcf7ade0289c61e0e4730a580420 100644 --- a/oneflow/core/ep/cuda/cuda_device.h +++ b/oneflow/core/ep/cuda/cuda_device.h @@ -75,4 +75,60 @@ class CudaDevice : public Device { #endif // WITH_CUDA +#ifdef WITH_ROCM + +#include + +namespace oneflow { + +namespace ep { + +class CudaDevice : public Device { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaDevice); + explicit CudaDevice(int device_index, DeviceManager* device_manager); + ~CudaDevice() override; + + void SetAsActiveDevice() override; + + DeviceType device_type() const override { return DeviceType::kCUDA; } + size_t device_index() const override { return device_index_; } + DeviceManager* device_manager() const override { return device_manager_; } + + Stream* CreateStream() override; + void DestroyStream(Stream* stream) override; + + void CreateEvents(Event** events, size_t count) override; + void DestroyEvents(Event** events, size_t count) override; + + Maybe Alloc(const AllocationOptions& options, void** ptr, size_t size) override; + void Free(const AllocationOptions& options, void* ptr) override; + Maybe AllocPinned(const AllocationOptions& options, void** ptr, size_t size) override; + void FreePinned(const AllocationOptions& options, void* ptr) override; + + const hipDeviceProp_t& properties() const; + + const void* GetConstZeros(DataType data_type, size_t n) const; + const void* GetConstOnes(DataType data_type, size_t n) const; + + private: + int device_index_; + std::mutex events_mutex_; + std::vector events_; + unsigned int event_flags_; + hipDeviceProp_t properties_; + DeviceManager* device_manager_; + int64_t const_buf_elem_cnt_; + void* const_zeros_buffer_; + void* const_ones_buffer_fp32_; + void* const_ones_buffer_fp16_; + void* const_ones_buffer_bf16_; +}; + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + #endif // ONEFLOW_CORE_EP_CUDA_CUDA_DEVICE_H_ diff --git a/oneflow/core/ep/cuda/cuda_device_manager.cpp b/oneflow/core/ep/cuda/cuda_device_manager.cpp index c045e6d9b7a28836c60f8734c57a5f19f9e7755b..18f1e34899f5e28b015222d37d7e0673c4f13a62 100644 --- a/oneflow/core/ep/cuda/cuda_device_manager.cpp +++ b/oneflow/core/ep/cuda/cuda_device_manager.cpp @@ -66,3 +66,55 @@ void CudaDeviceManager::SetActiveDeviceByIndex(size_t device_index) { } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace ep { + +CudaDeviceManager::CudaDeviceManager(DeviceManagerRegistry* registry) : registry_(registry) {} +CudaDeviceManager::~CudaDeviceManager() = default; + +DeviceManagerRegistry* CudaDeviceManager::registry() const { return registry_; } + +std::shared_ptr CudaDeviceManager::GetDevice(size_t device_index) { + std::lock_guard lock(devices_mutex_); + if (device_index < devices_.size() && devices_.at(device_index)) { + return devices_.at(device_index); + } + auto device = std::make_shared(device_index, this); + if (device_index >= devices_.size()) { devices_.resize(device_index + 1); } + devices_.at(device_index) = device; + return device; +} + +size_t CudaDeviceManager::GetDeviceCount(size_t primary_device_index) { + CudaCurrentDeviceGuard guard(primary_device_index); + return this->GetDeviceCount(); +} + +size_t CudaDeviceManager::GetDeviceCount() { + int count = 0; + hipError_t err = hipGetDeviceCount(&count); + if (err == hipErrorNoDevice || err == hipErrorInsufficientDriver) { return 0; } + OF_CUDA_CHECK(err); + return count; +} + +size_t CudaDeviceManager::GetActiveDeviceIndex() { + int device = 0; + OF_CUDA_CHECK(hipGetDevice(&device)); + return static_cast(device); +} + +void CudaDeviceManager::SetActiveDeviceByIndex(size_t device_index) { + OF_CUDA_CHECK(hipSetDevice(static_cast(device_index))); +} + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + diff --git a/oneflow/core/ep/cuda/cuda_device_manager.h b/oneflow/core/ep/cuda/cuda_device_manager.h index 1463d085d0edfbae1143c13df79a3ea2afaf52e9..7b3b495df423e81343837b9c538a8acb15853bc3 100644 --- a/oneflow/core/ep/cuda/cuda_device_manager.h +++ b/oneflow/core/ep/cuda/cuda_device_manager.h @@ -50,4 +50,36 @@ class CudaDeviceManager : public DeviceManager { #endif // WITH_CUDA +#ifdef WITH_ROCM + +namespace oneflow { +namespace ep { + +class CudaDevice; + +class CudaDeviceManager : public DeviceManager { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaDeviceManager); + CudaDeviceManager(DeviceManagerRegistry* registry); + ~CudaDeviceManager() override; + + DeviceManagerRegistry* registry() const override; + std::shared_ptr GetDevice(size_t device_index) override; + size_t GetDeviceCount(size_t primary_device_index) override; + size_t GetDeviceCount() override; + size_t GetActiveDeviceIndex() override; + void SetActiveDeviceByIndex(size_t device_index) override; + + private: + std::mutex devices_mutex_; + std::vector> devices_; + DeviceManagerRegistry* registry_; +}; + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + #endif // ONEFLOW_CORE_EP_CUDA_CUDA_DEVICE_MANAGER_H_ diff --git a/oneflow/core/ep/cuda/cuda_device_manager_factory.cpp b/oneflow/core/ep/cuda/cuda_device_manager_factory.cpp index 8404cb7fcc3b82be25b1cfb960ca2ba924d578ae..0aa9a1049e6529332765745c2f799145f41862fb 100644 --- a/oneflow/core/ep/cuda/cuda_device_manager_factory.cpp +++ b/oneflow/core/ep/cuda/cuda_device_manager_factory.cpp @@ -117,3 +117,102 @@ COMMAND(DeviceManagerRegistry::RegisterDeviceManagerFactory( } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +#include +#include +#include + +namespace oneflow { + +namespace ep { + +namespace { + +std::string GetCudaVersionString(int version) { + return std::to_string(version / 1000) + "." + std::to_string((version % 1000) / 10); +} + +bool GetCudnnVersion(size_t* major, size_t* minor, size_t* patch) { + miopenStatus_t status = miopenGetVersion(major, minor, patch); + if (status == miopenStatusSuccess) { + return true; + } else { + LOG(ERROR) << "Failed to get cuDNN version: " << miopenGetErrorString(status); + return false; + } +} + +bool GetCudnnVersionString(std::string* version) { + size_t version_major = 0; + size_t version_minor = 0; + size_t version_patch = 0; + if (!GetCudnnVersion(&version_major, &version_minor, &version_patch)) { return false; } + *version = std::to_string(version_major) + "." + std::to_string(version_minor) + "." + + std::to_string(version_patch); + return true; +} + +void CudaDumpVersionInfo() { + { + int cuda_runtime_version = 0; + hipError_t err = hipRuntimeGetVersion(&cuda_runtime_version); + if (err == hipSuccess) { + LOG(INFO) << "CUDA runtime version: " << GetCudaVersionString(cuda_runtime_version); + } else { + LOG(ERROR) << "Failed to get cuda runtime version: " << hipGetErrorString(err); + } + } + + { + std::string cudnn_version_string; + if (GetCudnnVersionString(&cudnn_version_string)) { + LOG(INFO) << "cuDNN version: " << cudnn_version_string; + } + } + + { + int nccl_version = 0; + ncclResult_t result = ncclGetVersion(&nccl_version); + if (result == ncclSuccess) { + int nccl_version_major = + (nccl_version >= 20900) ? (nccl_version / 10000) : (nccl_version / 1000); + int nccl_version_minor = + (nccl_version >= 20900) ? (nccl_version % 10000) / 100 : (nccl_version % 1000) / 100; + int nccl_version_patch = (nccl_version % 100); + LOG(INFO) << "NCCL version: " << nccl_version_major << "." << nccl_version_minor << "." + << nccl_version_patch; + } else { + LOG(ERROR) << "Failed to get NCCL version: " << ncclGetErrorString(result); + } + } +} + +class CudaDeviceManagerFactory : public DeviceManagerFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaDeviceManagerFactory); + CudaDeviceManagerFactory() = default; + ~CudaDeviceManagerFactory() override = default; + + std::unique_ptr NewDeviceManager(DeviceManagerRegistry* registry) override { + return std::make_unique(registry); + } + + DeviceType device_type() const override { return DeviceType::kCUDA; } + + std::string device_type_name() const override { return "cuda"; } + + void DumpVersionInfo() const override { CudaDumpVersionInfo(); } +}; + +COMMAND(DeviceManagerRegistry::RegisterDeviceManagerFactory( + std::make_unique())) + +} // namespace + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM diff --git a/oneflow/core/ep/cuda/cuda_event.cpp b/oneflow/core/ep/cuda/cuda_event.cpp index 0cabe3c36d9840528675253577d40b8fd65512e1..20efad76cccd9ccdbe4951dade5f377e3eca4bdc 100644 --- a/oneflow/core/ep/cuda/cuda_event.cpp +++ b/oneflow/core/ep/cuda/cuda_event.cpp @@ -54,3 +54,44 @@ cudaEvent_t CudaEvent::cuda_event() { return cuda_event_; } } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace ep { + +CudaEvent::CudaEvent(unsigned int flags) : cuda_event_{} { + OF_CUDA_CHECK(hipEventCreateWithFlags(&cuda_event_, flags)); +} + +CudaEvent::~CudaEvent() { OF_CUDA_CHECK(hipEventDestroy(cuda_event_)); } + +Maybe CudaEvent::QueryDone() { + hipError_t err = hipEventQuery(cuda_event_); + if (err == hipSuccess) { + return Maybe(true); + } else if (err == hipErrorNotReady) { + return Maybe(false); + } else { + return Error::RuntimeError() << hipGetErrorString(err); + } +} + +Maybe CudaEvent::Sync() { + hipError_t err = hipEventSynchronize(cuda_event_); + if (err == hipSuccess) { + return Maybe::Ok(); + } else { + return Error::RuntimeError() << hipGetErrorString(err); + } +} + +hipEvent_t CudaEvent::cuda_event() { return cuda_event_; } + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + diff --git a/oneflow/core/ep/cuda/cuda_event.h b/oneflow/core/ep/cuda/cuda_event.h index 1e20c5943b8fb1aa334e9b2b4901ec1eb7445f13..817b285659b5d7aab0b6bf797e441f74fd25d6ec 100644 --- a/oneflow/core/ep/cuda/cuda_event.h +++ b/oneflow/core/ep/cuda/cuda_event.h @@ -47,4 +47,33 @@ class CudaEvent : public Event { #endif // WITH_CUDA +#ifdef WITH_ROCM + +#include "oneflow/core/device/cuda_util.h" + +namespace oneflow { + +namespace ep { + +class CudaEvent : public Event { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaEvent); + explicit CudaEvent(unsigned int flags); + ~CudaEvent() override; + + Maybe QueryDone() override; + Maybe Sync() override; + + hipEvent_t cuda_event(); + + private: + hipEvent_t cuda_event_; +}; + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + #endif // ONEFLOW_CORE_EP_CUDA_CUDA_EVENT_H_ diff --git a/oneflow/core/ep/cuda/cuda_stream.cpp b/oneflow/core/ep/cuda/cuda_stream.cpp index 6236e1335f752801fb80e20969d6f66fd1ef99d1..11926d1905869ec8af151702d808000a7cf6487b 100644 --- a/oneflow/core/ep/cuda/cuda_stream.cpp +++ b/oneflow/core/ep/cuda/cuda_stream.cpp @@ -42,6 +42,61 @@ void SetAffinityByDevice(int dev_id) { node_device_desc->Topology()->SetMemoryAffinityByPCIBusID(cuda_device->PCIBusID()); } +void CheckVersionCompatibility(int compiletime_major, int compiletime_minor, int runtime_major, + int runtime_minor, const std::string& name) { + if (runtime_major != compiletime_major || runtime_minor < compiletime_minor) { + LOG(WARNING) << "Runtime version " << runtime_major << "." << runtime_minor << " of " << name + << " incompatible with compiletime version " << compiletime_major << "." + << compiletime_minor << "."; + } +} + +void CheckCudaRuntimeVersion() { +#if !defined(CUDART_VERSION) +#error +#endif // !defined(CUDART_VERSION) + const int compiletime_major = CUDART_VERSION / 1000; + const int compiletime_minor = CUDART_VERSION % 1000 / 10; + int runtime_version = 0; + OF_CUDA_CHECK(cudaRuntimeGetVersion(&runtime_version)); + const int runtime_major = runtime_version / 1000; + const int runtime_minor = runtime_version % 1000 / 10; + CheckVersionCompatibility(compiletime_major, compiletime_minor, runtime_major, runtime_minor, + "CUDA Runtime"); +} + +void CheckCublasVersion(cublasHandle_t handle) { +#if CUDA_VERSION >= 10020 +#if (!defined(CUBLAS_VER_MAJOR)) || (!defined(CUBLAS_VER_MINOR)) +#error +#endif // (!defined(CUBLAS_VER_MAJOR)) || (!defined(CUBLAS_VER_MINOR)) + int runtime_version = 0; + OF_CUBLAS_CHECK(cublasGetVersion(handle, &runtime_version)); + int runtime_major = 0; + int runtime_minor = 0; + if (runtime_version >= 100000) { + runtime_major = runtime_version / 10000; + runtime_minor = runtime_version % 10000 / 100; + } else { + runtime_major = runtime_version / 1000; + runtime_minor = runtime_version % 1000 / 100; + } + CheckVersionCompatibility(CUBLAS_VER_MAJOR, CUBLAS_VER_MINOR, runtime_major, runtime_minor, + "cuBLAS"); +#endif // CUDA_VERSION >= 10020 +} + +void CheckCudnnVersion() { +#if (!defined(CUDNN_MAJOR)) || (!defined(CUDNN_MINOR)) +#error +#endif // (!defined(CUDNN_MAJOR)) || (!defined(CUDNN_MINOR)) + int runtime_major = 0; + int runtime_minor = 0; + OF_CUDNN_CHECK(cudnnGetProperty(libraryPropertyType::MAJOR_VERSION, &runtime_major)); + OF_CUDNN_CHECK(cudnnGetProperty(libraryPropertyType::MINOR_VERSION, &runtime_minor)); + CheckVersionCompatibility(CUDNN_MAJOR, CUDNN_MINOR, runtime_major, runtime_minor, "cuDNN"); +} + } // namespace #ifdef WITH_CUDA_GRAPHS @@ -83,11 +138,26 @@ void CudaGraphExecutable::Reset() { CudaStream::CudaStream(CudaDevice* device) : device_index_(device->device_index()), device_(device) { CudaCurrentDeviceGuard guard(device_index_); + + const bool need_check_version = []() { + static std::atomic version_checked(false); + return version_checked.exchange(true) == false; + }(); + + if (need_check_version) { CheckCudaRuntimeVersion(); } + // cuda_stream - OF_CUDA_CHECK(cudaStreamCreate(&cuda_stream_)); + const char* stream_flags_env_name = "ONEFLOW_EP_CUDA_STREAM_FLAGS"; + if (std::getenv(stream_flags_env_name) != nullptr) { + const unsigned int stream_flags = ParseIntegerFromEnv(stream_flags_env_name, 0); + OF_CUDA_CHECK(cudaStreamCreateWithFlags(&cuda_stream_, stream_flags)); + } else { + OF_CUDA_CHECK(cudaStreamCreate(&cuda_stream_)); + } // cublas_handle OF_CUBLAS_CHECK(cublasCreate(&cublas_handle_)); OF_CUBLAS_CHECK(cublasSetStream(cublas_handle_, cuda_stream_)); + if (need_check_version) { CheckCublasVersion(cublas_handle_); } #if CUDA_VERSION >= 10010 // cublas_lt_handle OF_CUBLAS_CHECK(cublasLtCreate(&cublas_lt_handle_)); @@ -107,6 +177,7 @@ CudaStream::CudaStream(CudaDevice* device) // cudnn_handle OF_CUDNN_CHECK(cudnnCreate(&cudnn_handle_)); OF_CUDNN_CHECK(cudnnSetStream(cudnn_handle_, cuda_stream_)); + if (need_check_version) { CheckCudnnVersion(); } } CudaStream::~CudaStream() { @@ -147,6 +218,15 @@ void CudaStream::RecordEvent(Event* event) { OF_CUDA_CHECK(cudaEventRecord(cuda_event->cuda_event(), cuda_stream_)); } +Maybe CudaStream::GetAsyncError() { + cudaError_t err = cudaGetLastError(); + if (err == cudaSuccess) { + return Maybe::Ok(); + } else { + return Error::RuntimeError() << cudaGetErrorString(err) << " (" << err << ") "; + } +} + cudaStream_t CudaStream::cuda_stream() const { return cuda_stream_; } cublasHandle_t CudaStream::cublas_handle() const { return cublas_handle_; } @@ -196,3 +276,175 @@ void CudaStream::LaunchGraph(const CudaGraphExecutable* executable) { } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace ep { + +namespace { + +constexpr size_t kDefaultWorkspaceSize = 4 * 1024 * 1024; // 4M + +void SetAffinityByDevice(int dev_id) { + auto node_device_desc_mgr = Singleton::Get(); + if (node_device_desc_mgr == nullptr) { return; } + auto node_device_desc = node_device_desc_mgr->GetLocalNodeDeviceDescriptor(); + auto cuda_device = std::dynamic_pointer_cast( + node_device_desc->GetDevice(hardware::kCudaDeviceDescriptorClassName, dev_id)); + if (!cuda_device) { return; } + node_device_desc->Topology()->SetCPUAffinityByPCIBusID(cuda_device->PCIBusID()); + node_device_desc->Topology()->SetMemoryAffinityByPCIBusID(cuda_device->PCIBusID()); +} + +} // namespace + +#ifdef WITH_ROCM_GRAPHS + +CudaGraphExecutable::CudaGraphExecutable() : graph_exec_(nullptr), dev_(-1) {} + +CudaGraphExecutable::~CudaGraphExecutable() { Reset(); } + +void CudaGraphExecutable::Update(hipGraph_t graph) { + int dev = -1; + OF_CUDA_CHECK(hipGetDevice(&dev)); + if (dev != dev_) { Reset(); } + dev_ = dev; + if (graph_exec_ != nullptr) { + hipGraphExecUpdateResult update_result{}; + hipGraphNode_t error_node = nullptr; + OF_CUDA_CHECK(hipGraphExecUpdate(graph_exec_, graph, &error_node, &update_result)); + if (update_result == hipGraphExecUpdateSuccess) { return; } + } + Reset(); + OF_CUDA_CHECK(hipGraphInstantiate(&graph_exec_, graph, NULL, NULL, 0)); +} + +void CudaGraphExecutable::Launch(hipStream_t stream) const { + OF_CUDA_CHECK(hipGraphLaunch(graph_exec_, stream)); +} + +bool CudaGraphExecutable::IsInstantiated() const { return graph_exec_ != nullptr; } + +void CudaGraphExecutable::Reset() { + if (graph_exec_ != nullptr) { + CudaCurrentDeviceGuard guard(dev_); + OF_CUDA_CHECK(hipGraphExecDestroy(graph_exec_)); + } +} + +#endif // WITH_ROCM_GRAPHS + +CudaStream::CudaStream(CudaDevice* device) + : device_index_(device->device_index()), device_(device) { + CudaCurrentDeviceGuard guard(device_index_); + // cuda_stream + const char* stream_flags_env_name = "ONEFLOW_EP_CUDA_STREAM_FLAGS"; + if (std::getenv(stream_flags_env_name) != nullptr) { + const unsigned int stream_flags = ParseIntegerFromEnv(stream_flags_env_name, 0); + OF_CUDA_CHECK(hipStreamCreateWithFlags(&cuda_stream_, stream_flags)); + } else { + OF_CUDA_CHECK(hipStreamCreate(&cuda_stream_)); + } // cublas_handle + OF_CUBLAS_CHECK(hipblasCreate(&cublas_handle_)); + OF_CUBLAS_CHECK(hipblasSetStream(cublas_handle_, cuda_stream_)); + + workspace_size_ = kDefaultWorkspaceSize; + OF_CUDA_CHECK(hipMalloc(&workspace_, workspace_size_)); + + OF_CUDNN_CHECK(hipdnnCreate(&cudnn_handle_)); + + OF_CUDNN_CHECK(hipdnnSetStream(cudnn_handle_, cuda_stream_)); +} + +CudaStream::~CudaStream() { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(hipStreamSynchronize(cuda_stream_)); + OF_CUDNN_CHECK(hipdnnDestroy(cudnn_handle_)); + OF_CUBLAS_CHECK(hipblasDestroy(cublas_handle_)); + + OF_CUDA_CHECK(hipStreamDestroy(cuda_stream_)); + OF_CUDA_CHECK(hipFree(workspace_)); +} + +Maybe CudaStream::OnExecutionContextSetup() { + OF_CUDA_CHECK(hipSetDevice(device_index_)); + SetAffinityByDevice(device_index_); + return Maybe::Ok(); +} + +Maybe CudaStream::OnExecutionContextTeardown() { return Maybe::Ok(); } + +DeviceType CudaStream::device_type() const { return DeviceType::kCUDA; } + +CudaDevice* CudaStream::device() const { return device_; } + +Maybe CudaStream::Sync() { + hipError_t err = hipStreamSynchronize(cuda_stream_); + if (err == hipSuccess) { + return Maybe::Ok(); + } else { + return Error::RuntimeError() << hipGetErrorString(err) << " (" << err << ") "; + } +} + +void CudaStream::RecordEvent(Event* event) { + auto* cuda_event = static_cast(event); // NOLINT + OF_CUDA_CHECK(hipEventRecord(cuda_event->cuda_event(), cuda_stream_)); +} + +Maybe CudaStream::GetAsyncError() { + hipError_t err = hipGetLastError(); + if (err == hipSuccess) { + return Maybe::Ok(); + } else { + return Error::RuntimeError() << hipGetErrorString(err) << " (" << err << ") "; + } +} + +hipStream_t CudaStream::cuda_stream() const { return cuda_stream_; } + +hipblasHandle_t CudaStream::cublas_handle() const { return cublas_handle_; } + +void* CudaStream::cublas_workspace() const { return workspace_; } + +size_t CudaStream::cublas_workspace_size() const { return workspace_size_; } + +hipdnnHandle_t CudaStream::cudnn_handle() const { return cudnn_handle_; } + +const hipDeviceProp_t& CudaStream::device_properties() const { return device_->properties(); } + +int CudaStream::cuda_arch() const { + return device_->properties().major * 100 + device_->properties().minor * 10; +} + +#ifdef WITH_ROCM_GRAPHS + +void CudaStream::BeginGraphCapture() { + CHECK(!is_graph_capturing_); + is_graph_capturing_ = true; + OF_CUDA_CHECK(hipStreamBeginCapture(cuda_stream_, hipStreamCaptureModeThreadLocal)); +} + +void CudaStream::EndGraphCapture(CudaGraphExecutable* executable) { + hipGraph_t graph = nullptr; + OF_CUDA_CHECK(hipStreamEndCapture(cuda_stream_, &graph)); + executable->Update(graph); + OF_CUDA_CHECK(hipGraphDestroy(graph)); + is_graph_capturing_ = false; +} + +bool CudaStream::IsGraphCapturing() const { return is_graph_capturing_; } + +void CudaStream::LaunchGraph(const CudaGraphExecutable* executable) { + executable->Launch(cuda_stream_); +} + +#endif // WITH_ROCM_GRAPHS + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM diff --git a/oneflow/core/ep/cuda/cuda_stream.h b/oneflow/core/ep/cuda/cuda_stream.h index efea18ad5d1a6858f291310f677b34a7e43834f3..f4c8cfcfe2b251a551dcf9c4f0ed7857e32a85a1 100644 --- a/oneflow/core/ep/cuda/cuda_stream.h +++ b/oneflow/core/ep/cuda/cuda_stream.h @@ -79,6 +79,7 @@ class CudaStream : public Stream { CudaDevice* device() const override; Maybe Sync() override; void RecordEvent(Event* event) override; + Maybe GetAsyncError() override; Maybe OnExecutionContextSetup() override; Maybe OnExecutionContextTeardown() override; @@ -165,4 +166,139 @@ class CudaStream : public Stream { #endif // WITH_CUDA +#ifdef WITH_ROCM + +#include +#include "oneflow/core/hipdnn/hipdnn.h" + +// #if CUDA_VERSION >= 11000 +// #define WITH_ROCM_GRAPHS +// #endif // CUDA_VERSION >= 11000 + +#include "oneflow/core/device/cuda_util.h" + +namespace oneflow { + +namespace ep { + +class CudaDevice; + +#ifdef WITH_ROCM_GRAPHS + +class CudaGraphExecutable { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaGraphExecutable); + CudaGraphExecutable(); + ~CudaGraphExecutable(); + + void Update(hipGraph_t graph); + void Launch(hipStream_t stream) const; + bool IsInstantiated() const; + + private: + void Reset(); + + hipGraphExec_t graph_exec_; + int dev_; +}; + +#endif // WITH_ROCM_GRAPHS + +struct CudaLaunchConfig { + dim3 grid_dim; + dim3 block_dim; + size_t shared_mem_size; + CudaLaunchConfig() : grid_dim{}, block_dim{}, shared_mem_size(0) {} + + CudaLaunchConfig(unsigned int grid_size, unsigned int block_size, size_t shared_mem_size) + : grid_dim(grid_size), block_dim(block_size), shared_mem_size(shared_mem_size) {} +}; + +class CudaStream : public Stream { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaStream); + explicit CudaStream(CudaDevice* device); + ~CudaStream() override; + + static constexpr uint32_t kDefaultBlockSize = 256; + + DeviceType device_type() const override; + CudaDevice* device() const override; + Maybe Sync() override; + void RecordEvent(Event* event) override; + Maybe GetAsyncError() override; + + Maybe OnExecutionContextSetup() override; + Maybe OnExecutionContextTeardown() override; + + hipStream_t cuda_stream() const; + hipblasHandle_t cublas_handle() const; + + hipdnnHandle_t cudnn_handle() const; + void* cublas_workspace() const; + size_t cublas_workspace_size() const; + const hipDeviceProp_t& device_properties() const; + int cuda_arch() const; + + void InitLaunchConfigWithWaves(CudaLaunchConfig* config, size_t elem_cnt, size_t block_size, + size_t max_waves) const { + const uint32_t max_grid_size = max_waves * device_properties().multiProcessorCount + * (device_properties().maxThreadsPerMultiProcessor / block_size); + const uint32_t grid_size = + std::min(max_grid_size, (elem_cnt + block_size - 1) / block_size); + config->grid_dim = dim3(grid_size); + config->block_dim = dim3(block_size); + config->shared_mem_size = 0; + } + +#ifdef __HIPCC__ + template + void LaunchKernel(void (*kernel)(Params...), const CudaLaunchConfig& launch_config, + Args... args) { + kernel<<>>(args...); + } + + template + void LaunchKernel(void (*kernel)(Params...), size_t elem_cnt, size_t max_waves, Args... args) { + constexpr uint32_t block_size = kDefaultBlockSize; + CudaLaunchConfig config{}; + InitLaunchConfigWithWaves(&config, elem_cnt, block_size, max_waves); + LaunchKernel(kernel, config, args...); + } + + template + void LaunchKernelDefaultWaves(void (*kernel)(Params...), size_t elem_cnt, Args... args) { + const size_t default_waves = 32; + LaunchKernel(kernel, elem_cnt, default_waves, args...); + } +#endif // __HIPCC__ + +#ifdef WITH_ROCM_GRAPHS + void BeginGraphCapture(); + void EndGraphCapture(CudaGraphExecutable* executable); + bool IsGraphCapturing() const; + void LaunchGraph(const CudaGraphExecutable* executable); +#endif // WITH_ROCM_GRAPHS + + private: + hipStream_t cuda_stream_{}; + hipblasHandle_t cublas_handle_{}; + + hipdnnHandle_t cudnn_handle_{}; + int device_index_; + void* workspace_{}; + size_t workspace_size_{}; +#ifdef WITH_ROCM_GRAPHS + bool is_graph_capturing_{}; +#endif // WITH_ROCM_GRAPHS + CudaDevice* device_; +}; + +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + #endif // ONEFLOW_CORE_EP_CUDA_CUDA_STREAM_H_ diff --git a/oneflow/core/ep/cuda/primitive/add.cu b/oneflow/core/ep/cuda/primitive/add.cu index 4784fc227518b165d3abdf77adb821c2ae503c4f..a34009b4729e4fb2f3e5d1db7d3b5fcd964a052f 100644 --- a/oneflow/core/ep/cuda/primitive/add.cu +++ b/oneflow/core/ep/cuda/primitive/add.cu @@ -47,17 +47,17 @@ __global__ void AddGpu(const Args*... srcs, T* dst, size_t count) { } template -void LaunchAddGpu(cudaStream_t stream, const Args*... srcs, T* dst, size_t count) { +void LaunchAddGpu(GPU(Stream_t) stream, const Args*... srcs, T* dst, size_t count) { AddGpu <<>>(srcs..., dst, count); } template -void DispatchLaunch(cudaStream_t stream, const T* const* srcs, size_t arity, T* dst, size_t count) { +void DispatchLaunch(GPU(Stream_t) stream, const T* const* srcs, size_t arity, T* dst, size_t count) { if (arity == 0) { - OF_CUDA_CHECK(cudaMemsetAsync(dst, 0, count * sizeof(T), stream)); + OF_CUDA_CHECK(GPU(MemsetAsync)(dst, 0, count * sizeof(T), stream)); } else if (arity == 1) { - OF_CUDA_CHECK(cudaMemcpyAsync(dst, srcs[0], count * sizeof(T), cudaMemcpyDefault, stream)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(dst, srcs[0], count * sizeof(T), GPU(MemcpyDefault), stream)); } else if (arity == 2) { OF_CUDA_CHECK((cuda::elementwise::Binary, T, T, T>( AddFunctor(), count, dst, srcs[0], srcs[1], stream))); @@ -94,7 +94,7 @@ class AddImpl : public Add { using Add::Launch; void Launch(Stream* stream, const void* const* srcs, size_t arity, void* dst, size_t count) override { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); DispatchLaunch(cuda_stream, reinterpret_cast(srcs), arity, reinterpret_cast(dst), count); } diff --git a/oneflow/core/ep/cuda/primitive/binary_functor.cuh b/oneflow/core/ep/cuda/primitive/binary_functor.cuh index 459e6de2d13524f0a29bb199332e14f5230a7660..d7a07bda74d9ddf4388ed1c730084ad5f6fb3a45 100644 --- a/oneflow/core/ep/cuda/primitive/binary_functor.cuh +++ b/oneflow/core/ep/cuda/primitive/binary_functor.cuh @@ -29,27 +29,77 @@ struct BinaryFunctor { }; template<> -struct BinaryFunctor { +struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - OF_DEVICE_FUNC bool operator()(bool src0, bool src1) const { - return static_cast(pow(static_cast(src0), static_cast(src1))); + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return fmod(src0, src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { return fmod(src0, src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return floor(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { return floor(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { return truncf(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC double operator()(double src0, double src1) const { return trunc(src0 / src1); } +}; + +template<> +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC float operator()(float src0, float src1) const { + float trunc_mod = fmod(src0, src1); + return (trunc_mod != static_cast(0)) + && ((src1 < static_cast(0)) != (trunc_mod < static_cast(0))) + ? trunc_mod + src1 + : trunc_mod; } }; template<> -struct BinaryFunctor { +struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - OF_DEVICE_FUNC half operator()(half src0, half src1) const { - return static_cast(pow(static_cast(src0), static_cast(src1))); + OF_DEVICE_FUNC double operator()(double src0, double src1) const { + double trunc_mod = fmod(src0, src1); + return (trunc_mod != static_cast(0)) + && ((src1 < static_cast(0)) != (trunc_mod < static_cast(0))) + ? trunc_mod + src1 + : trunc_mod; } }; template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) { -#if defined(__CUDA_ARCH__) +#if defined(__CUDA_ARCH__) || defined( __HIP_DEVICE_COMPILE__) coef = sqrt(static_cast(2.0) / acos(static_cast(-1.0))); #else coef = std::sqrt(static_cast(2.0) / std::acos(static_cast(-1.0))); @@ -65,6 +115,39 @@ struct BinaryFunctor +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + // ref to: https://mlfromscratch.com/activation-functions-explained/#gelu + const Src one = static_cast(1); + const Src half = static_cast(0.5); + const Src pow3 = x * x * x; + const Src tanh_out = std::tanh(alpha * (x + beta * pow3)); + const Src dtanh = alpha * (half * x + beta * static_cast(1.5) * pow3); + return dy * (half + half * tanh_out + dtanh * (one - tanh_out * tanh_out)); + } + + private: + const Src alpha = static_cast(0.7978845608028654); + const Src beta = static_cast(0.044714998453855515); +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src one = static_cast(1.0); + const Src sigmoid = one / (one + exp(-x * alpha)); + return dy * (sigmoid + alpha * x * (sigmoid * (one - sigmoid))); + } + + private: + const Src alpha = static_cast(1.702); +}; + template struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} @@ -75,19 +158,114 @@ struct BinaryFunctor +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; -#if CUDA_VERSION >= 11000 + OF_DEVICE_FUNC Dst operator()(int src0, int src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; -template<> -struct BinaryFunctor { +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(int8_t src0, int8_t src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(uint8_t src0, uint8_t src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + BinaryFunctor float_functor; + + OF_DEVICE_FUNC Dst operator()(int src0, int src1) const { + return static_cast(float_functor(static_cast(src0), static_cast(src1))); + } +}; + +template +struct BinaryFunctor { OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { + const Src one = static_cast(1.0); + return dy * one / (one - static_cast(pow(x, 2))); + } +}; + +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) + : atol(attr0.Value()), rtol(attr1.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + bool close = src0 == src1; + close |= (isnan(src0) and isnan(src1)); + if (atol == 0 and rtol == 0) return close; + Src allowed_error = static_cast(atol) + abs(static_cast(rtol) * src1); + Src actual_error = abs(src0 - src1); + close |= (isfinite(actual_error) and (actual_error <= allowed_error)); + return close; + } + float atol, rtol; +}; - OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src0, nv_bfloat16 src1) const { - return static_cast(pow(static_cast(src0), static_cast(src1))); +template +struct BinaryFunctor { + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) + : atol(attr0.Value()), rtol(attr1.Value()) {} + + OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { + bool close = src0 == src1; + if (atol == 0 and rtol == 0) return close; + Src allowed_error = static_cast(atol) + abs(static_cast(rtol) * src1); + Src actual_error = abs(src0 - src1); + close |= (isfinite(actual_error) and (actual_error <= allowed_error)); + return close; } + float atol, rtol; }; +#define SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(op, type) \ + template \ + struct BinaryFunctor { \ + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + OF_DEVICE_FUNC Dst operator()(type src0, type src1) const { \ + return float_functor(static_cast(src0), static_cast(src1)); \ + } \ + BinaryFunctor float_functor; \ + }; +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, bool); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, int); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, char); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, int8_t); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, uint8_t); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsClose, int64_t); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, bool); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, int); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, char); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, int8_t); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, uint8_t); +SPECIALIZATION_INTEGRAL_CLOSENESS_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan, int64_t); + +/*********nv_bfloat16_kernel*******/ + +#if CUDA_VERSION >= 11000 + #define SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(op) \ template<> \ struct BinaryFunctor { \ @@ -99,6 +277,13 @@ struct BinaryFunctor \ + struct BinaryFunctor { \ + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + BinaryFunctor float_functor; \ + OF_DEVICE_FUNC Dst operator()(nv_bfloat16 src0, nv_bfloat16 src1) const { \ + return float_functor(__bfloat162float(src0), __bfloat162float(src1)); \ + } \ + }; +SPECIALIZATION_BFLOAT16_COMPARISON_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan) +SPECIALIZATION_BFLOAT16_COMPARISON_BINARY_FUNCTOR(BinaryOp::kIsClose) #endif // CUDA_VERSION >= 11000 @@ -129,6 +350,13 @@ SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kThresholdBackwardWithDy } \ }; +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kPow); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kFmod); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kFloorDiv); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kTruncDiv); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kFloorMod); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kScalarBasePowerGrad); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kScalarExpPowerGrad); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kEluBackwardWithDyX); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kCeluBackwardWithDyX); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kGeluBackwardWithDyX); @@ -142,6 +370,69 @@ SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSoftsignBackwardWithDyX); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSoftshrinkBackwardWithDyY); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kThresholdBackwardWithDyX); SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kTanhBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kFastGeluBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kQuickGeluBackwardWithDyX); + +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kAcosBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kAcoshBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kAsinBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kAsinhBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kCosBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kCoshBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kErfBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kErfcBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kExpBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kExpm1BackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kLog2BackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kLog10BackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kLogSigmoidBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kReciprocalNoNanBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kRsqrtBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSinBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSinhBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSqrtBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kTanBackwardWithDyX); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSigmoidBackwardWithDyY); +SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kAtanhBackwardWithDyX); + +#define SPECIALIZATION_HALF_COMPARISON_BINARY_FUNCTOR(op) \ + template \ + struct BinaryFunctor { \ + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + BinaryFunctor float_functor; \ + OF_DEVICE_FUNC Dst operator()(half src0, half src1) const { \ + return float_functor(__half2float(src0), __half2float(src1)); \ + } \ + }; + +SPECIALIZATION_HALF_COMPARISON_BINARY_FUNCTOR(BinaryOp::kIsCloseEqualNan) +SPECIALIZATION_HALF_COMPARISON_BINARY_FUNCTOR(BinaryOp::kIsClose) + +#define SPECIALIZATION_GPU_BINARY_FUNCTOR(op, type) \ + template<> \ + struct BinaryFunctor { \ + OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : int_functor(attr0, attr1) {} \ + \ + BinaryFunctor int_functor; \ + OF_DEVICE_FUNC type operator()(type src0, type src1) const { \ + return static_cast(int_functor(static_cast(src0), static_cast(src1))); \ + } \ + }; + +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kPow, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFmod, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFloorDiv, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kTruncDiv, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFloorMod, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kScalarBasePowerGrad, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kScalarExpPowerGrad, bool); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kPow, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFmod, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFloorDiv, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kTruncDiv, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kFloorMod, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kScalarBasePowerGrad, char); +SPECIALIZATION_GPU_BINARY_FUNCTOR(BinaryOp::kScalarExpPowerGrad, char); } // namespace broadcast_elementwise_binary } // namespace primitive diff --git a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cu b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cu index c6df3d5773d5a5ed0f7f9606b5949a973735f560..5500e863092b08271b0134a5bf7bc89352fceda5 100644 --- a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cu +++ b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cu @@ -85,7 +85,11 @@ class BroadcastElementwiseBinaryFactoryImpl : public BroadcastElementwiseBinaryF OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY, - BINARY_ACTIVATION_BACKWARD_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ)}; + BINARY_ACTIVATION_BACKWARD_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ) + + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY, + BINARY_MATH_BACKWARD_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ)}; #undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_AND_LOGICAL_ENTRY #undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY diff --git a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh index d3856b2c8d8035db0a8dc4e4c038c6251c22d19c..86edec160650a637298b18194c499d1be3a74e55 100644 --- a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh +++ b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/ep/include/primitive//broadcast_elementwise_binary.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" #include "oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h" #include "oneflow/core/ep/cuda/primitive/type_seq.h" #include "oneflow/core/ep/cuda/cuda_stream.h" @@ -299,8 +299,8 @@ void DispatchLaunch(Stream* stream, size_t num_src0_dims, const int64_t* src0_di SimplifyBroadcastDims(num_src0_dims, src0_dims, num_src1_dims, src1_dims, &simplified_num_dims, simplified_src0_dims, simplified_src1_dims, simplified_dst_dims); - CheckInplace(simplified_num_dims, simplified_src0_dims, src0, simplified_src1_dims, src1, - simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src0_dims, src0, simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src1_dims, src1, simplified_dst_dims, dst); if (IsDimsEquals(simplified_num_dims, simplified_src0_dims, simplified_num_dims, simplified_src1_dims)) { const int64_t elem_cnt = GetElementCount(simplified_num_dims, simplified_src0_dims); diff --git a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math.cu b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math.cu index 8c1989f5724e7e7ea826eb88fd1766eb43564e36..012ae9366b26c696a269836a9c136aa404d418ff 100644 --- a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math.cu +++ b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math.cu @@ -27,7 +27,7 @@ namespace broadcast_elementwise_binary { Scalar attr0, Scalar attr1); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, - BINARY_MATH_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ); + BINARY_MATH_OP_SEQ_0, CUDA_PRIMITIVE_ALL_TYPE_SEQ); } // namespace broadcast_elementwise_binary } // namespace primitive diff --git a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math1.cu b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math1.cu new file mode 100644 index 0000000000000000000000000000000000000000..bb6b8b64fce48322bc6e306ad27dad7d165f395c --- /dev/null +++ b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary_math1.cu @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh" + +namespace oneflow { + +namespace ep { +namespace primitive { +namespace broadcast_elementwise_binary { + +#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY(binary_op, data_type_pair) \ + template std::unique_ptr NewBroadcastElementwiseBinary< \ + binary_op, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(data_type_pair)>( \ + Scalar attr0, Scalar attr1); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, + BINARY_MATH_OP_SEQ_1, CUDA_PRIMITIVE_ALL_TYPE_SEQ); + +} // namespace broadcast_elementwise_binary +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cuda/primitive/broadcast_elementwise_unary.cu b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_unary.cu new file mode 100644 index 0000000000000000000000000000000000000000..c9ed3db140a3a3c7f3fec32a160b2d71b231928e --- /dev/null +++ b/oneflow/core/ep/cuda/primitive/broadcast_elementwise_unary.cu @@ -0,0 +1,420 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/ep/common/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/ep/cuda/primitive/unary_functor.cuh" +#include "oneflow/core/ep/cuda/primitive/type_seq.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/cuda/elementwise.cuh" + +namespace oneflow { + +namespace ep { +namespace primitive { +namespace broadcast_elementwise_unary { + +namespace { + +constexpr size_t kMaxPackSize = 4; + +template +size_t GetPackSize(size_t num_dims, const int64_t* src_dims, const void* src, + const int64_t* dst_dims, const void* dst) { + static_assert(max_pack_size > 0 && (max_pack_size & (max_pack_size - 1)) == 0, ""); + for (size_t pack_size = max_pack_size; pack_size > 2; pack_size /= 2) { + bool is_src_supported = IsPackSizeSupported(pack_size, num_dims, src_dims, src); + bool is_dst_supported = IsPackSizeSupported(pack_size, num_dims, dst_dims, dst); + if (is_src_supported && is_dst_supported) { return pack_size; } + } + return 1; +} + +template +struct BroadcastElementwiseUnaryParams { + IndexToOffsetWithStrideCalculator src_index_to_offset_helper; + OffsetToIndexWithStrideCalculator dst_offset_to_index_helper; + IndexToOffsetWithStrideCalculator dst_index_to_offset_helper; + size_t num_dims; + IndexType src_index_mask[max_dims]; + IndexType count{}; + const Src* src{}; + Dst* dst{}; + bool dst_is_contiguous; + Scalar attr0; + Scalar attr1; +}; + +template +struct UnaryScalarFunctor { + __host__ __device__ explicit UnaryScalarFunctor(Src scalar) : scalar(scalar) {} + __device__ Dst operator()() const { + return UnaryFunctor()(scalar); + } + const Src scalar; +}; + +template +struct UnaryScalarPtrFunctorFactory { + __host__ __device__ explicit UnaryScalarPtrFunctorFactory(const Src* scalar_ptr) + : scalar_ptr(scalar_ptr) {} + __device__ UnaryScalarFunctor operator()() const { + return UnaryScalarFunctor(*scalar_ptr); + } + const Src* scalar_ptr; +}; + +template +__global__ void BroadcastElementwiseUnaryGpu( + BroadcastElementwiseUnaryParams params) { + using LoadPack = cuda::elementwise::Packed; + using StorePack = cuda::elementwise::Packed; + const LoadPack* src = reinterpret_cast(params.src); + StorePack* dst = reinterpret_cast(params.dst); + + IndexType src_index[max_dims]; + IndexType dst_index[max_dims]; + size_t num_dims = params.num_dims; + auto functor = UnaryFunctor(params.attr0, params.attr1); + + CUDA_1D_KERNEL_LOOP_T(IndexType, offset, params.count) { + params.dst_offset_to_index_helper.OffsetToNdIndex(offset, dst_index, num_dims); +#pragma unroll + for (int i = 0; i < max_dims; ++i) { + if (i < num_dims) { src_index[i] = params.src_index_mask[i] * dst_index[i]; } + } + const IndexType src_offset = + params.src_index_to_offset_helper.NdIndexToOffset(src_index, num_dims); + LoadPack src_pack = src[src_offset]; + StorePack dst_pack; +#pragma unroll + for (int j = 0; j < pack_size; ++j) { dst_pack.elem[j] = functor(src_pack.elem[j]); } + IndexType dst_offset = offset; + if (!params.dst_is_contiguous) { + dst_offset = params.dst_index_to_offset_helper.NdIndexToOffset(dst_index, num_dims); + } + dst[dst_offset] = dst_pack; + } +} + +template +void LaunchKernel(CudaStream* stream, size_t num_dims, const int64_t* src_dims, + const int64_t* src_strides, const Src* src, const int64_t* dst_dims, + const int64_t* dst_strides, Dst* dst, bool continuous_output, Scalar attr0, + Scalar attr1, size_t count) { + BroadcastElementwiseUnaryParams params; + for (size_t i = 0; i < num_dims; ++i) { params.src_index_mask[i] = (src_dims[i] == 1) ? 0 : 1; } + params.src_index_to_offset_helper = + IndexToOffsetWithStrideCalculator(src_strides, num_dims); + params.dst_offset_to_index_helper = + OffsetToIndexWithStrideCalculator(dst_dims, num_dims); + params.dst_index_to_offset_helper = + IndexToOffsetWithStrideCalculator(dst_strides, num_dims); + params.num_dims = num_dims; + params.src = src; + params.dst = dst; + params.count = static_cast(count); + params.attr0 = attr0; + params.attr1 = attr1; + params.dst_is_contiguous = continuous_output; + + BroadcastElementwiseUnaryGpu + <<cuda_stream()>>>( + params); +} + +template +void DispatchIndexType(CudaStream* stream, size_t num_dims, const int64_t* src_dims, + const int64_t* src_strides, const Src* src, const int64_t* dst_dims, + const int64_t* dst_strides, Dst* dst, bool continuous_output, Scalar attr0, + Scalar attr1) { + size_t count = GetElementCount(num_dims, dst_dims); + if (count < GetMaxVal() / 2) { + LaunchKernel( + stream, num_dims, src_dims, src_strides, src, dst_dims, dst_strides, dst, continuous_output, + attr0, attr1, count); + } else { + LaunchKernel( + stream, num_dims, src_dims, src_strides, src, dst_dims, dst_strides, dst, continuous_output, + attr0, attr1, count); + } +} + +template +void DispatchPackSize(CudaStream* stream, size_t pack_size, size_t num_dims, + const int64_t* src_dims, const int64_t* src_strides, const Src* src, + const int64_t* dst_dims, const int64_t* dst_strides, Dst* dst, + bool continuous_output, Scalar attr0, Scalar attr1) { + void (*func)(CudaStream* /*stream*/, size_t /*num_dims*/, const int64_t* /*src_dims*/, + const int64_t* /*src_strides*/, const Src* /*src*/, const int64_t* /*dst_dims*/, + const int64_t* /*dst_strides*/, Dst* /*dst*/, bool /*continuous_output*/, + Scalar /*attr0*/, Scalar /*attr1*/) = nullptr; + if (pack_size == 1) { + func = DispatchIndexType; + } else if (pack_size == 4) { + func = DispatchIndexType; + } else { + UNIMPLEMENTED(); + } + func(stream, num_dims, src_dims, src_strides, src, dst_dims, dst_strides, dst, continuous_output, + attr0, attr1); +} + +template +void DispatchNumDims(CudaStream* stream, size_t pack_size, size_t num_dims, const int64_t* src_dims, + const int64_t* src_strides, const Src* src, const int64_t* dst_dims, + const int64_t* dst_strides, Dst* dst, bool continuous_output, Scalar attr0, + Scalar attr1) { + void (*func)(CudaStream* /*stream*/, size_t /*pack_size*/, size_t /*num_dims*/, + const int64_t* /*src_dims*/, const int64_t* /*src_strides*/, const Src* /*src*/, + const int64_t* /*dst_dims*/, const int64_t* /*dst_strides*/, Dst* /*dst*/, + bool /*continuous_output*/, Scalar /*attr0*/, Scalar /*attr1*/) = nullptr; + if (num_dims == 1) { + func = DispatchPackSize; + } else if (num_dims == 2) { + func = DispatchPackSize; + } else if (num_dims == 3) { + func = DispatchPackSize; + } else if (num_dims == 4) { + func = DispatchPackSize; + } else if (num_dims <= kMaxNumDims) { + func = DispatchPackSize; + } else { + UNIMPLEMENTED(); + } + func(stream, pack_size, num_dims, src_dims, src_strides, src, dst_dims, dst_strides, dst, + continuous_output, attr0, attr1); +} + +template +void LaunchWithSimplified(CudaStream* stream, size_t simplified_num_dims, + int64_t* simplified_src_dims, int64_t* simplified_src_strides, + const Src* src, int64_t* simplified_dst_dims, + int64_t* simplified_dst_strides, Dst* dst, Scalar attr0, Scalar attr1) { + CHECK_LE(simplified_num_dims, kMaxNumDims); + bool src_enable_pack = (simplified_src_strides[simplified_num_dims - 1] == 1); + bool dst_enable_pack = (simplified_dst_strides[simplified_num_dims - 1] == 1); + size_t pack_size = 1; + // TODO(zzk): this pack has bug, will be fixed in future + // if (src_enable_pack && dst_enable_pack) { + // pack_size = GetPackSize(simplified_num_dims, simplified_src_dims, + // src, + // simplified_dst_dims, dst); + // } + bool continuous_output = true; + for (int i = simplified_num_dims - 1; i >= 0; i--) { + if ((i == simplified_num_dims - 1 && simplified_dst_strides[i] != 1) + || (i != simplified_num_dims - 1 + && simplified_dst_strides[i] + != simplified_dst_strides[i + 1] * simplified_dst_dims[i + 1])) { + continuous_output = false; + break; + } + } + simplified_src_dims[simplified_num_dims - 1] /= pack_size; + simplified_dst_dims[simplified_num_dims - 1] /= pack_size; + DispatchNumDims(stream, pack_size, simplified_num_dims, simplified_src_dims, + simplified_src_strides, src, simplified_dst_dims, + simplified_dst_strides, dst, continuous_output, attr0, attr1); +} + +template +__global__ void LaunchFillKernel(UnaryFunctor functor, Dst* dst, + const Src* src, size_t pack_count, size_t count, size_t tail_count, + Dst* tail_dst) { + using StorePack = cuda::elementwise::Packed; + StorePack pack_value; + Dst value = functor(*src); +#pragma unroll + for (size_t i = 0; i < pack; ++i) { pack_value.elem[i] = value; } + StorePack* pack_dst = reinterpret_cast(dst); + CUDA_1D_KERNEL_LOOP_T(size_t, i, pack_count) { pack_dst[i] = pack_value; } + if (tail) { + CUDA_1D_KERNEL_LOOP_T(size_t, i, tail_count) { tail_dst[i] = value; } + } +} + +template +typename std::enable_if<(pack != 0), void>::type LaunchPackFill(CudaStream* stream, Dst* dst, + const Src* src, size_t count, + Scalar attr0, Scalar attr1) { + const size_t pack_count = count / pack; + const size_t tail_offset = pack_count * pack; + const size_t tail_count = count - tail_offset; + auto functor = UnaryFunctor(attr0, attr1); + if (tail_count > 0) { + LaunchFillKernel + <<cuda_stream()>>>( + functor, dst, src, pack_count, count, tail_count, dst + tail_offset); + } else { + LaunchFillKernel + <<cuda_stream()>>>( + functor, dst, src, pack_count, count, tail_count, dst + tail_offset); + } +} + +template +typename std::enable_if<(pack == 0), void>::type LaunchPackFill(CudaStream* stream, Dst* dst, + const Src* src, size_t count, + Scalar attr0, Scalar attr1) { + LOG(FATAL) << "wrong alignment"; +} + +template +void LaunchFill(CudaStream* stream, Dst* dst, const Src* src, size_t count, Scalar attr0, + Scalar attr1) { + auto uintptr = reinterpret_cast(dst); + if (uintptr % 16 == 0 && count * sizeof(Dst) >= 16) { + LaunchPackFill(stream, dst, src, count, attr0, attr1); + } else if (uintptr % 8 == 0 && count * sizeof(Dst) >= 8) { + LaunchPackFill(stream, dst, src, count, attr0, attr1); + } else if (uintptr % 4 == 0 && count * sizeof(Dst) >= 4) { + LaunchPackFill(stream, dst, src, count, attr0, attr1); + } else if (uintptr % 2 == 0 && count * sizeof(Dst) >= 2) { + LaunchPackFill(stream, dst, src, count, attr0, attr1); + } else { + LaunchPackFill(stream, dst, src, count, attr0, attr1); + } +} + +template +class BroadcastElementwiseUnaryImpl : public BroadcastElementwiseUnary { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnaryImpl); + BroadcastElementwiseUnaryImpl(Scalar attr0, Scalar attr1) : attr0(attr0), attr1(attr1) {} + ~BroadcastElementwiseUnaryImpl() override = default; + + void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, const void* src, + size_t num_dst_dims, const int64_t* dst_dims, void* dst) override { + CHECK_GT(num_src_dims, 0) << "num_src_dims must greater than 0"; + CHECK_GT(num_dst_dims, 0) << "num_dst_dims must greater than 0"; + int64_t src_strides[kMaxNumDims]; + int64_t dst_strides[kMaxNumDims]; + // init stride + for (int i = num_src_dims - 1; i < kMaxNumDims; ++i) { src_strides[i] = 1; } + for (int i = num_src_dims - 2; i >= 0; --i) { + src_strides[i] = src_dims[i + 1] * src_strides[i + 1]; + } + + for (int i = num_dst_dims - 1; i < kMaxNumDims; ++i) { dst_strides[i] = 1; } + for (int i = num_dst_dims - 2; i >= 0; --i) { + dst_strides[i] = dst_dims[i + 1] * dst_strides[i + 1]; + } + Launch(stream, num_src_dims, src_dims, src_strides, src, num_dst_dims, dst_dims, dst_strides, + dst); + } + + void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, + const int64_t* src_strides, const void* src_ptr, size_t num_dst_dims, + const int64_t* dst_dims, const int64_t* dst_strides, void* dst_ptr) override { + CHECK_GT(num_src_dims, 0) << "num_src_dims must greater than 0"; + CHECK_GT(num_dst_dims, 0) << "num_dst_dims must greater than 0"; + auto* cuda_stream = stream->As(); + Dst* dst = reinterpret_cast(dst_ptr); + const Src* src = reinterpret_cast(src_ptr); + size_t simplified_num_dims = 0; + int64_t simplified_src_dims[kMaxNumDims]; + int64_t simplified_dst_dims[kMaxNumDims]; + int64_t simplified_src_strides[kMaxNumDims]; + int64_t simplified_dst_strides[kMaxNumDims]; + SimplifyBroadcastDims(num_src_dims, src_dims, src_strides, num_dst_dims, dst_dims, + dst_strides, &simplified_num_dims, simplified_src_dims, + simplified_src_strides, simplified_dst_dims, + simplified_dst_strides); + CheckInplace(simplified_num_dims, simplified_src_dims, src, simplified_dst_dims, dst); + CheckInplace(simplified_num_dims, simplified_src_strides, src, simplified_dst_strides, dst); + if (simplified_num_dims == 1 && simplified_src_dims[0] == 1) { + const int64_t elem_cnt = simplified_dst_dims[0]; + LaunchFill(cuda_stream, dst, src, elem_cnt, attr0, attr1); + } else if (simplified_num_dims == 1 && simplified_src_strides[0] == 1 + && simplified_dst_strides[0] == 1) { + const int64_t elem_cnt = simplified_src_dims[0]; + auto functor = UnaryFunctor(attr0, attr1); + OF_CUDA_CHECK((cuda::elementwise::Unary( + functor, elem_cnt, dst, src, cuda_stream->cuda_stream()))); + } else { + LaunchWithSimplified( + cuda_stream, simplified_num_dims, simplified_src_dims, simplified_src_strides, src, + simplified_dst_dims, simplified_dst_strides, dst, attr0, attr1); + } + } + + protected: + Scalar attr0, attr1; +}; + +template +std::unique_ptr NewBroadcastElementwiseUnary(Scalar attr0, + Scalar attr1) { + return std::unique_ptr( + new BroadcastElementwiseUnaryImpl(attr0, attr1)); +} + +class BroadcastElementwiseUnaryFactoryImpl : public BroadcastElementwiseUnaryFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnaryFactoryImpl); + BroadcastElementwiseUnaryFactoryImpl() = default; + ~BroadcastElementwiseUnaryFactoryImpl() override = default; + + std::unique_ptr New(UnaryOp op, DataType src_type, DataType dst_type, + size_t max_num_dims) override { + return New(op, src_type, dst_type, max_num_dims, Scalar(), Scalar()); + } + + std::unique_ptr New(UnaryOp op, DataType src_type, DataType dst_type, + size_t max_num_dims, Scalar attr0) override { + return New(op, src_type, dst_type, max_num_dims, attr0, Scalar()); + } + + std::unique_ptr New(UnaryOp unary_op, DataType src_type, + DataType dst_type, size_t max_num_dims, + Scalar attr0, Scalar attr1) override { + if (max_num_dims > kMaxNumDims) { return nullptr; } +#define MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY(unary_op, dtype_pair) \ + {std::make_tuple(unary_op, OF_PP_PAIR_SECOND(dtype_pair), OF_PP_PAIR_SECOND(dtype_pair)), \ + NewBroadcastElementwiseUnary}, + + static const std::map, + std::function(Scalar, Scalar)>> + new_broadcast_elementwise_unary_handle{ + // For All Type OP + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY, + UNARY_BROADCAST_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ)}; + +#undef MAKE_NEW_SAME_DTYPE_BROADCAST_ELEMENTWISE_UNARY_ENTRY + + const auto iter = + new_broadcast_elementwise_unary_handle.find(std::make_tuple(unary_op, src_type, dst_type)); + if (iter != new_broadcast_elementwise_unary_handle.end()) { + return iter->second(attr0, attr1); + } else { + return nullptr; + } + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BroadcastElementwiseUnaryFactory, + BroadcastElementwiseUnaryFactoryImpl); + +} // namespace +} // namespace broadcast_elementwise_unary +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cuda/primitive/broadcast_matmul.cpp b/oneflow/core/ep/cuda/primitive/broadcast_matmul.cpp index 8df45385867d246a00b12c59b63168736cdf9e5a..4b37fa8e137058ad6c3e3f968eb73cf8a83c2ff4 100644 --- a/oneflow/core/ep/cuda/primitive/broadcast_matmul.cpp +++ b/oneflow/core/ep/cuda/primitive/broadcast_matmul.cpp @@ -57,6 +57,7 @@ cudaDataType_t GetCudaDataType(DataType data_type) { union CublasScalarParameter { double d; float s; + half h; }; CublasScalarParameter GetCublasScalarParameter(Scalar scalar, cudaDataType_t compute_type) { @@ -65,6 +66,8 @@ CublasScalarParameter GetCublasScalarParameter(Scalar scalar, cudaDataType_t com sp.d = scalar.Value(); } else if (compute_type == CUDA_R_32F) { sp.s = scalar.Value(); + } else if (compute_type == CUDA_R_16F) { + sp.h = static_cast(scalar.Value()); } else { UNIMPLEMENTED(); } @@ -75,7 +78,15 @@ cudaDataType_t GetComputeType(DataType data_type) { switch (data_type) { case kFloat: return CUDA_R_32F; case kDouble: return CUDA_R_64F; - case kFloat16: return CUDA_R_32F; + case kFloat16: { + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + if (allow_half_accumulation) { + return CUDA_R_16F; + } else { + return CUDA_R_32F; + } + } #if CUDA_VERSION >= 11000 case kBFloat16: return CUDA_R_32F; #endif // CUDA_VERSION >= 11000 @@ -207,3 +218,199 @@ REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BroadcastMatmulFactory, BroadcastM } // namespace oneflow #endif // WITH_CUDA + +#ifdef WITH_ROCM + +#include "oneflow/core/ep/include/primitive/primitive.h" +#include "oneflow/core/ep/include/primitive/broadcast_matmul.h" +#include "oneflow/core/ep/common/primitive/broadcast_matmul.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace broadcast_matmul { + +namespace internal { + +namespace { + +constexpr size_t kMaxNumDims = 8; + +Optional OptCudaDataType(DataType data_type) { + switch (data_type) { + case kFloat: return HIPBLAS_R_32F; + case kDouble: return HIPBLAS_R_64F; + case kFloat16: return HIPBLAS_R_16F; +// #if CUDA_VERSION >= 11000 +// case kBFloat16: return CUDA_R_16BF; +// #endif // CUDA_VERSION >= 11000 + default: return NullOpt; + } +} + +hipblasDatatype_t GetCudaDataType(DataType data_type) { + auto cuda_data_type = OptCudaDataType(data_type); + CHECK(cuda_data_type.has_value()); + return cuda_data_type.value_or(HIPBLAS_R_32F); +} + +union CublasScalarParameter { + double d; + float s; + half h; +}; + +CublasScalarParameter GetCublasScalarParameter(Scalar scalar, hipblasDatatype_t compute_type) { + CublasScalarParameter sp{}; + if (compute_type == HIPBLAS_R_64F) { + sp.d = scalar.Value(); + } else if (compute_type == HIPBLAS_R_32F) { + sp.s = scalar.Value(); + } else if (compute_type == HIPBLAS_R_16F) { + sp.h = static_cast(scalar.Value()); + } else { + UNIMPLEMENTED(); + } + return sp; +} + +hipblasDatatype_t GetComputeType(DataType data_type) { + switch (data_type) { + case kFloat: return HIPBLAS_R_32F; + case kDouble: return HIPBLAS_R_64F; + case kFloat16: { + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", true); + if (allow_half_accumulation) { + return HIPBLAS_R_16F; + } else { + return HIPBLAS_R_32F; + } + } +// #if CUDA_VERSION >= 11000 +// case kBFloat16: return HIPBLAS_R_32F; +// #endif // CUDA_VERSION >= 11000 + default: UNIMPLEMENTED(); return HIPBLAS_R_32F; + } +} + +void LaunchBroadcastMatmul(Stream* stream, DataType data_type, BlasTransposeType transpose_a, + BlasTransposeType transpose_b, int64_t num_batch_dims, + const int64_t* broadcast_batch_dims, const int64_t* a_batch_dims, + const int64_t* b_batch_dims, const int64_t* c_batch_dims, int64_t m, + int64_t n, int64_t k, Scalar alpha, const void* a, const void* b, + Scalar beta, void* c) { + auto* cuda_stream = stream->As(); + const auto cuda_data_type = GetCudaDataType(data_type); + const auto compute_type = GetComputeType(data_type); + const auto sp_alpha = GetCublasScalarParameter(alpha, compute_type); + const auto GetCublasOperation = [](BlasTransposeType transpose_type) { + if (transpose_type == BlasTransposeType::N) { + return HIPBLAS_OP_N; + } else if (transpose_type == BlasTransposeType::T) { + return HIPBLAS_OP_T; + } else { + UNIMPLEMENTED(); + return HIPBLAS_OP_N; + } + }; + const hipblasOperation_t cublas_trans_a = GetCublasOperation(transpose_b); + const hipblasOperation_t cublas_trans_b = GetCublasOperation(transpose_a); + const int cublas_m = n; + const int cublas_n = m; + const int cublas_k = k; + int cublas_lda = 0; + if (transpose_b == BlasTransposeType::N) { + cublas_lda = n; + } else if (transpose_b == BlasTransposeType::T) { + cublas_lda = k; + } else { + UNIMPLEMENTED(); + } + int cublas_ldb = 0; + if (transpose_a == BlasTransposeType::N) { + cublas_ldb = k; + } else if (transpose_a == BlasTransposeType::T) { + cublas_ldb = m; + } else { + UNIMPLEMENTED(); + } + const int cublas_ldc = n; + + hipblasGemmAlgo_t algo = HIPBLAS_GEMM_DEFAULT; + + if (num_batch_dims == 1 && c_batch_dims[0] != 1) { + const void* cublas_a = b; + const void* cublas_b = a; + void* cublas_c = c; + const int64_t a_batch_count = a_batch_dims[0]; + const int64_t b_batch_count = b_batch_dims[0]; + CHECK(a_batch_count == 1 || b_batch_count == 1 || a_batch_count == b_batch_count); + CHECK_GT(a_batch_count, 0); + CHECK_GT(b_batch_count, 0); + const int batch_count = std::max(a_batch_count, b_batch_count); + const long long int cublas_stride_a = b_batch_count == 1 ? 0 : cublas_m * cublas_k; + const long long int cublas_stride_b = a_batch_count == 1 ? 0 : cublas_k * cublas_n; + const long long int cublas_stride_c = cublas_m * cublas_n; + const auto sp_beta = GetCublasScalarParameter(beta, compute_type); + OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( + cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, cublas_k, + &sp_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_stride_a, cublas_b, cuda_data_type, + cublas_ldb, cublas_stride_b, &sp_beta, cublas_c, cuda_data_type, cublas_ldc, + cublas_stride_c, batch_count, compute_type, algo)); + } else { + auto func = [&](const void* batch_a, const void* batch_b, void* batch_c, Scalar batch_beta) { + const auto sp_beta = GetCublasScalarParameter(batch_beta, compute_type); + const void* cublas_a = batch_b; + const void* cublas_b = batch_a; + void* cublas_c = batch_c; + OF_CUBLAS_CHECK(hipblasGemmEx( + cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, + cublas_k, &sp_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_b, cuda_data_type, + cublas_ldb, &sp_beta, cublas_c, cuda_data_type, cublas_ldc, compute_type, algo)); + }; + ForEachMatmul(data_type, m, n, k, beta, num_batch_dims, broadcast_batch_dims, + a_batch_dims, b_batch_dims, c_batch_dims, a, b, c, func); + } +} + +class BroadcastMatmulFactoryImpl : public BroadcastMatmulFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastMatmulFactoryImpl); + BroadcastMatmulFactoryImpl() = default; + ~BroadcastMatmulFactoryImpl() override = default; + + std::unique_ptr New(DataType data_type, BlasTransposeType transpose_a, + BlasTransposeType transpose_b, + size_t max_num_dims) override { + auto cuda_data_type = OptCudaDataType(data_type); + if (max_num_dims <= kMaxNumDims && cuda_data_type.has_value()) { + return std::make_unique>(data_type, transpose_a, + transpose_b); + } else { + return nullptr; + } + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BroadcastMatmulFactory, BroadcastMatmulFactoryImpl); + +} // namespace + +} // namespace internal + +} // namespace broadcast_matmul + +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif // WITH_ROCM + diff --git a/oneflow/core/ep/cuda/primitive/constant_pad.cu b/oneflow/core/ep/cuda/primitive/constant_pad.cu index 2657dbb5e010e5066e788eea9f698e0a96df989b..2ff0ccdef3702de655633952c55d0ff36c807204 100644 --- a/oneflow/core/ep/cuda/primitive/constant_pad.cu +++ b/oneflow/core/ep/cuda/primitive/constant_pad.cu @@ -17,7 +17,11 @@ limitations under the License. #include "oneflow/core/ep/common/primitive/constant_pad.h" #include "oneflow/core/ep/cuda/primitive/type_seq.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include +#else #include +#endif namespace oneflow { @@ -186,6 +190,7 @@ template void SimplifyThenLaunch(Stream* stream, size_t num_dims, const int64_t* src_dims, const void* src, const int64_t* padding_before, const int64_t* padding_after, T pad_val, void* dst) { + CHECK_GT(num_dims, 0) << "num_dims must greater than 0"; CHECK_LE(num_dims, kMaxNumDims); int64_t simplified_dst_dims[kMaxNumDims]; int64_t simplified_src_dims[kMaxNumDims]; diff --git a/oneflow/core/ep/cuda/primitive/copy_nd.cu b/oneflow/core/ep/cuda/primitive/copy_nd.cu index 072821a375a7ff5a53163163998490fd1ffeff23..393c9f49e04f4ede6dc729a22236fd1e99a85d5d 100644 --- a/oneflow/core/ep/cuda/primitive/copy_nd.cu +++ b/oneflow/core/ep/cuda/primitive/copy_nd.cu @@ -17,8 +17,11 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/copy_nd.h" #include "oneflow/core/ep/common/primitive/copy_nd.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include +#else #include - +#endif namespace oneflow { namespace ep { @@ -49,7 +52,7 @@ __global__ void CopyNdKernel(CopyNdKernelParams params) { template void LaunchKernel(Stream* stream, CopyNdKernelParams params) { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); CopyNdKernel <<>>(params); } diff --git a/oneflow/core/ep/cuda/primitive/elementwise_unary.cu b/oneflow/core/ep/cuda/primitive/elementwise_unary.cu index 0de79724353403978d32e77069a05b41b1de79e7..ccf667d1e53fd1614dc4d0cad37ec597914eadfa 100644 --- a/oneflow/core/ep/cuda/primitive/elementwise_unary.cu +++ b/oneflow/core/ep/cuda/primitive/elementwise_unary.cu @@ -86,6 +86,10 @@ class ElementwiseUnaryFactoryImpl : public ElementwiseUnaryFactory { UNARY_FLOATING_MATH_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ) + // For Int Type OP + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, + UNARY_INT_MATH_OP_SEQ, CUDA_PRIMITIVE_INT_TYPE_SEQ) + // For Utils OP OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY, UNARY_UTILS_OP_SEQ, UTIL_OPS_DATA_TYPE_SEQ, diff --git a/oneflow/core/ep/cuda/primitive/fill.cu b/oneflow/core/ep/cuda/primitive/fill.cu index d34ec91649218a440422a238487bf95e838347bd..ae49aa90961014ff66db657375ad0c588ff4cf1c 100644 --- a/oneflow/core/ep/cuda/primitive/fill.cu +++ b/oneflow/core/ep/cuda/primitive/fill.cu @@ -71,20 +71,20 @@ nv_bfloat16 GetValue(Scalar value) { #endif // CUDA_VERSION >= 11000 template -typename std::enable_if<(pack != 0), void>::type LaunchPackFill(cudaStream_t stream, T* dst, +typename std::enable_if<(pack != 0), void>::type LaunchPackFill(GPU(Stream_t) stream, T* dst, T value, size_t count) { FillGpu <<>>(dst, value, count); } template -typename std::enable_if<(pack == 0), void>::type LaunchPackFill(cudaStream_t stream, T* dst, +typename std::enable_if<(pack == 0), void>::type LaunchPackFill(GPU(Stream_t) stream, T* dst, T value, size_t count) { LOG(FATAL) << "wrong alignment"; } template -void LaunchFill(cudaStream_t stream, T* dst, T value, size_t count) { +void LaunchFill(GPU(Stream_t) stream, T* dst, T value, size_t count) { auto uintptr = reinterpret_cast(dst); if (uintptr % 16 == 0) { LaunchPackFill(stream, dst, value, count); @@ -107,7 +107,7 @@ class FillImpl : public Fill { ~FillImpl() override = default; void Launch(Stream* stream, void* dst, Scalar value, size_t count) override { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); LaunchFill(cuda_stream, reinterpret_cast(dst), GetValue(value), count); } }; diff --git a/oneflow/core/ep/cuda/primitive/math_elementwise_unary_math_grad.cu b/oneflow/core/ep/cuda/primitive/math_elementwise_unary_math_grad.cu new file mode 100644 index 0000000000000000000000000000000000000000..ac6d76bb154b9b9aa38615811ad9fe4cc5373be7 --- /dev/null +++ b/oneflow/core/ep/cuda/primitive/math_elementwise_unary_math_grad.cu @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/cuda/primitive/broadcast_elementwise_binary.cuh" + +namespace oneflow { + +namespace ep { +namespace primitive { +namespace broadcast_elementwise_binary { + +#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY(binary_op, data_type_pair) \ + template std::unique_ptr NewBroadcastElementwiseBinary< \ + binary_op, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(data_type_pair)>( \ + Scalar attr0, Scalar attr1); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, + BINARY_MATH_BACKWARD_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ); + +} // namespace broadcast_elementwise_binary +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cuda/primitive/memcpy.cpp b/oneflow/core/ep/cuda/primitive/memcpy.cpp index f59bbf39c689cc0a7f586fa2fb570c30d790a21a..3342e64c17bc1a2d25c4437126b958887332285a 100644 --- a/oneflow/core/ep/cuda/primitive/memcpy.cpp +++ b/oneflow/core/ep/cuda/primitive/memcpy.cpp @@ -60,3 +60,51 @@ REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemcpyFactory, MemcpyFactoryImpl); } // namespace oneflow #endif + +#ifdef WITH_ROCM + +#include "oneflow/core/ep/include/primitive/memcpy.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace { + +class MemcpyImpl : public Memcpy { + public: + OF_DISALLOW_COPY_AND_MOVE(MemcpyImpl); + MemcpyImpl() = default; + ~MemcpyImpl() override = default; + + void Launch(Stream* stream, void* dst, const void* src, size_t count) override { + if (dst == src) { return; } + auto* cuda_stream = stream->As(); + OF_CUDA_CHECK(hipMemcpyAsync(dst, src, count, hipMemcpyDefault, cuda_stream->cuda_stream())); + } +}; + +class MemcpyFactoryImpl : public MemcpyFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(MemcpyFactoryImpl); + MemcpyFactoryImpl() = default; + ~MemcpyFactoryImpl() override = default; + + std::unique_ptr New(MemcpyKind kind) override { + return std::unique_ptr(new MemcpyImpl()); + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemcpyFactory, MemcpyFactoryImpl); + +} // namespace + +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif diff --git a/oneflow/core/ep/cuda/primitive/memset.cpp b/oneflow/core/ep/cuda/primitive/memset.cpp index fb42507b733591d4f5e54bdd0f073d20f5002621..814008ea920a1593f1c535e20fada6f04e4e8433 100644 --- a/oneflow/core/ep/cuda/primitive/memset.cpp +++ b/oneflow/core/ep/cuda/primitive/memset.cpp @@ -57,3 +57,49 @@ REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemsetFactory, MemsetFactoryImpl); } // namespace oneflow #endif + +#ifdef WITH_ROCM + +#include "oneflow/core/ep/include/primitive/memset.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace { + +class MemsetImpl : public Memset { + public: + OF_DISALLOW_COPY_AND_MOVE(MemsetImpl); + MemsetImpl() = default; + ~MemsetImpl() override = default; + + void Launch(Stream* stream, void* ptr, int value, size_t count) override { + auto* cuda_stream = stream->As(); + OF_CUDA_CHECK(hipMemsetAsync(ptr, value, count, cuda_stream->cuda_stream())); + } +}; + +class MemsetFactoryImpl : public MemsetFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(MemsetFactoryImpl); + MemsetFactoryImpl() = default; + ~MemsetFactoryImpl() override = default; + + std::unique_ptr New() override { return std::unique_ptr(new MemsetImpl()); } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemsetFactory, MemsetFactoryImpl); + +} // namespace + +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif + diff --git a/oneflow/core/ep/cuda/primitive/permute.cu b/oneflow/core/ep/cuda/primitive/permute.cu index cc365c1a367fc07a4b1baf82dfbe8f2ac9e66a98..6069f7e34803d1e24d9773497ae2ffdaa3f82e6a 100644 --- a/oneflow/core/ep/cuda/primitive/permute.cu +++ b/oneflow/core/ep/cuda/primitive/permute.cu @@ -16,7 +16,11 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/permute.h" #include "oneflow/core/ep/common/primitive/permute_impl.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include +#else #include +#endif namespace oneflow { @@ -192,7 +196,7 @@ __global__ void BatchTransposeMovement2Kernel(const void* src_ptr, void* dst_ptr } template -void LaunchBatchTransposeKernel(cudaStream_t& cuda_stream, +void LaunchBatchTransposeKernel(GPU(Stream_t)& cuda_stream, const PermuteKernelParams& params, const IndexType& num_batches, const IndexType& rows, const IndexType& cols) { @@ -264,7 +268,7 @@ void LaunchKernel(Stream* stream, const int64_t* src_dims, const void* src, cons void* dst, size_t count) { PermuteKernelParams params = MakePermuteParams(src_dims, src, permutation, dst, count); - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); if (num_dims == 2 || num_dims == 3) { IndexType num_batches; @@ -281,10 +285,12 @@ void LaunchKernel(Stream* stream, const int64_t* src_dims, const void* src, cons cuda_stream, params, num_batches, rows, cols); } } else { + if (params.count == 0) { return; } PermuteKernel <<>>(params); } } else { + if (params.count == 0) { return; } PermuteKernel <<>>(params); } diff --git a/oneflow/core/ep/cuda/primitive/softmax.cu b/oneflow/core/ep/cuda/primitive/softmax.cu index d9f553bbfd645052d9977d0444b5c59952a80257..bfeb97675d5bb3a6daef402876475840f4fdfe6d 100644 --- a/oneflow/core/ep/cuda/primitive/softmax.cu +++ b/oneflow/core/ep/cuda/primitive/softmax.cu @@ -32,7 +32,7 @@ enum class Algorithm { }; template -void SoftmaxGpu(cudaStream_t cuda_stream, size_t rows, size_t cols, const T* x, T* y) { +void SoftmaxGpu(GPU(Stream_t) cuda_stream, size_t rows, size_t cols, const T* x, T* y) { using ComputeType = typename cuda::softmax::DefaultComputeType::type; oneflow::cuda::softmax::DirectLoad load(x, cols); oneflow::cuda::softmax::DirectStore store(y, cols); @@ -55,7 +55,7 @@ class SoftmaxImpl : public SoftmaxBase { ~SoftmaxImpl() override = default; void Launch(Stream* stream, size_t rows, size_t cols, const void* x, void* y) override { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); SoftmaxGpu(cuda_stream, rows, cols, reinterpret_cast(x), reinterpret_cast(y)); } diff --git a/oneflow/core/ep/cuda/primitive/softmax_backward.cu b/oneflow/core/ep/cuda/primitive/softmax_backward.cu index ab71156c31faf2445aa691026d652c8b99ca2c17..151ad4b6c9a23c9e1ccb259c4ff6713ff16084c5 100644 --- a/oneflow/core/ep/cuda/primitive/softmax_backward.cu +++ b/oneflow/core/ep/cuda/primitive/softmax_backward.cu @@ -32,7 +32,7 @@ enum class Algorithm { }; template -void SoftmaxBackwardGpu(cudaStream_t cuda_stream, size_t rows, size_t cols, const T* y, const T* dy, +void SoftmaxBackwardGpu(GPU(Stream_t) cuda_stream, size_t rows, size_t cols, const T* y, const T* dy, T* dx) { using ComputeType = typename cuda::softmax::DefaultComputeType::type; cuda::softmax::DirectLoad load_y(y, cols); @@ -60,7 +60,7 @@ class SoftmaxBackwardImpl : public SoftmaxBackwardBase { void Launch(Stream* stream, size_t rows, size_t cols, const void* y, const void* dy, void* dx) override { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); SoftmaxBackwardGpu(cuda_stream, rows, cols, reinterpret_cast(y), reinterpret_cast(dy), reinterpret_cast(dx)); } diff --git a/oneflow/core/ep/cuda/primitive/tensor_fill.cu b/oneflow/core/ep/cuda/primitive/tensor_fill.cu new file mode 100644 index 0000000000000000000000000000000000000000..6553b93bd06f2d8ecde0b7e2d553c430dc5e2cca --- /dev/null +++ b/oneflow/core/ep/cuda/primitive/tensor_fill.cu @@ -0,0 +1,135 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/include/primitive/tensor_fill.h" +#include "oneflow/core/ep/cuda/primitive/type_seq.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { + +namespace ep { +namespace primitive { + +namespace { + +template +using Storage = typename std::aligned_storage::type; + +template +union Pack { + static constexpr size_t size = sizeof(T) * pack; + explicit __device__ __host__ Pack(const T value) { + static_assert(sizeof(Pack) == size, ""); + static_assert(alignof(Pack) == size, ""); +#pragma unroll + for (size_t i = 0; i < pack; ++i) { elem[i] = value; } + } + T elem[pack]; + Storage storage; +}; + +template +__global__ void TensorFillGpu(T* dst, const T* value, size_t count) { + const size_t pack_count = count / pack; + const T fill_value = value[0]; + Pack pack_value(fill_value); + auto* pack_dst = reinterpret_cast(dst); + CUDA_1D_KERNEL_LOOP_T(size_t, i, pack_count) { pack_dst[i] = pack_value.storage; } + T* tail_dst = dst + pack_count * pack; + const size_t tail_count = count - pack_count * pack; + CUDA_1D_KERNEL_LOOP_T(size_t, i, tail_count) { tail_dst[i] = fill_value; } +} + +template +typename std::enable_if<(pack != 0), void>::type LaunchPackTensorFill(GPU(Stream_t) stream, T* dst, + const T* value, + size_t count) { + TensorFillGpu + <<>>(dst, value, count); +} + +template +typename std::enable_if<(pack == 0), void>::type LaunchPackTensorFill(GPU(Stream_t) stream, T* dst, + const T* value, + size_t count) { + LOG(FATAL) << "wrong alignment"; +} + +template +void LaunchTensorFill(GPU(Stream_t) stream, T* dst, const T* value, size_t count) { + auto uintptr = reinterpret_cast(dst); + if (uintptr % 16 == 0) { + LaunchPackTensorFill(stream, dst, value, count); + } else if (uintptr % 8 == 0) { + LaunchPackTensorFill(stream, dst, value, count); + } else if (uintptr % 4 == 0) { + LaunchPackTensorFill(stream, dst, value, count); + } else if (uintptr % 2 == 0) { + LaunchPackTensorFill(stream, dst, value, count); + } else { + LaunchPackTensorFill(stream, dst, value, count); + } +} + +template +class TensorFillImpl : public TensorFill { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFillImpl); + TensorFillImpl() = default; + ~TensorFillImpl() override = default; + + void Launch(Stream* stream, const void* src, void* dst, size_t count) override { + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); + const T* value = reinterpret_cast(src); + LaunchTensorFill(cuda_stream, reinterpret_cast(dst), value, count); + } +}; + +template +std::unique_ptr NewTensorFill() { + return std::unique_ptr(new TensorFillImpl()); +} + +class TensorFillFactoryImpl : public TensorFillFactory { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFillFactoryImpl); + TensorFillFactoryImpl() = default; + ~TensorFillFactoryImpl() override = default; + + std::unique_ptr New(DataType data_type) override { +#define MAKE_NEW_TENSOR_FILL_ENTRY(type_cpp, type_proto) {type_proto, NewTensorFill}, + + static const std::map()>> new_fill_handle{ + OF_PP_FOR_EACH_TUPLE(MAKE_NEW_TENSOR_FILL_ENTRY, CUDA_PRIMITIVE_ALL_TYPE_SEQ)}; + +#undef MAKE_NEW_TENSOR_FILL_ENTRY + + const auto it = new_fill_handle.find(data_type); + if (it != new_fill_handle.end()) { + return it->second(); + } else { + return nullptr; + } + } +}; + +REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, TensorFillFactory, TensorFillFactoryImpl); + +} // namespace + +} // namespace primitive +} // namespace ep + +} // namespace oneflow diff --git a/oneflow/core/ep/cuda/primitive/type_seq.h b/oneflow/core/ep/cuda/primitive/type_seq.h index 86aab891ec845d24024c9ab3fda26049295b36ce..ac52828acdb4e17114a168ac0d1bf57b6cbed006 100644 --- a/oneflow/core/ep/cuda/primitive/type_seq.h +++ b/oneflow/core/ep/cuda/primitive/type_seq.h @@ -63,6 +63,12 @@ limitations under the License. CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ +#define CUDA_PRIMITIVE_INT_TYPE_SEQ \ + CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT32_TYPE_SEQ \ + CUDA_PRIMITIVE_INT64_TYPE_SEQ + #define UTIL_OPS_DATA_TYPE_SEQ \ CUDA_PRIMITIVE_INT8_TYPE_SEQ \ CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ @@ -75,4 +81,66 @@ limitations under the License. #endif // WITH_CUDA +#ifdef WITH_ROCM +#include +#include + +// #if CUDA_VERSION >= 11000 +// #include +// #endif // CUDA_VERSION >= 11000 + +#define CUDA_PRIMITIVE_BOOL_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(bool, DataType::kBool) +#define CUDA_PRIMITIVE_CHAR_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(char, DataType::kChar) +#define CUDA_PRIMITIVE_INT8_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) +#define CUDA_PRIMITIVE_UINT8_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) +#define CUDA_PRIMITIVE_INT32_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) +#define CUDA_PRIMITIVE_UINT32_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) +#define CUDA_PRIMITIVE_INT64_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) +#define CUDA_PRIMITIVE_UINT64_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) +#define CUDA_PRIMITIVE_FLOAT_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) +#define CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) +#define CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) + +// #if CUDA_VERSION >= 11000 +// #define CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) +// #else +#define CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ +// #endif // CUDA_VERSION >= 11000 + +#define CUDA_PRIMITIVE_ALL_TYPE_SEQ \ + CUDA_PRIMITIVE_BOOL_TYPE_SEQ \ + CUDA_PRIMITIVE_CHAR_TYPE_SEQ \ + CUDA_PRIMITIVE_INT8_TYPE_SEQ \ + CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT32_TYPE_SEQ \ + CUDA_PRIMITIVE_INT64_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ + CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ + CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ + +#define CUDA_PRIMITIVE_FLOATING_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ + CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ + CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ + +#define CUDA_PRIMITIVE_INT_TYPE_SEQ \ + CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT32_TYPE_SEQ \ + CUDA_PRIMITIVE_INT64_TYPE_SEQ + +#define UTIL_OPS_DATA_TYPE_SEQ \ + CUDA_PRIMITIVE_INT8_TYPE_SEQ \ + CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ + CUDA_PRIMITIVE_INT32_TYPE_SEQ \ + CUDA_PRIMITIVE_INT64_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ + CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ + CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ + CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ + +#endif // WITH_ROCM + #endif // ONEFLOW_CORE_EP_CUDA_PRIMITIVE_TYPE_SEQ_H_ diff --git a/oneflow/core/ep/cuda/primitive/unary_functor.cuh b/oneflow/core/ep/cuda/primitive/unary_functor.cuh index a511b8ed003e210d9cad974592f4e01a48b62820..cb1b956c7bccab2b5c86965d0cd3208c36112b78 100644 --- a/oneflow/core/ep/cuda/primitive/unary_functor.cuh +++ b/oneflow/core/ep/cuda/primitive/unary_functor.cuh @@ -17,14 +17,19 @@ limitations under the License. #include "oneflow/core/ep/cuda/primitive/type_seq.h" #include "oneflow/core/cuda/elementwise.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" - +#ifdef WITH_ROCM +#include +#include +#else +#include +#endif namespace oneflow { namespace ep { namespace primitive { template struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC Dst operator()(Src src) const { return static_cast(0.5) * src @@ -32,78 +37,236 @@ struct UnaryFunctor { } }; +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + // ref to: https://mlfromscratch.com/activation-functions-explained/#gelu + const Src half = static_cast(0.5); + const Src one = static_cast(1); + const Src tanh_in = alpha * (src + beta * src * src * src); + return half * src * (one + tanh(tanh_in)); + } + + private: + // constant ref to: + // https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/testdata/transform/fusion/fast_gelu.py + static constexpr Src alpha = static_cast(0.7978845608028654); + static constexpr Src beta = static_cast(0.044714998453855515); +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { + const Src sigmoid = + static_cast(static_cast(1.0) / (static_cast(1.0) + exp(-src * alpha))); + return src * sigmoid; + } + + private: + static constexpr Src alpha = static_cast(1.702); +}; + +namespace unary_functor_internal { + +namespace { + +OF_DEVICE_FUNC +float TanhApprox(float x) { +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + float r; + asm("tanh.approx.f32 %0,%1; \n\t" : "=f"(r) : "f"(x)); + return r; +#else + return tanhf(x); +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) +} + +} // namespace + +} // namespace unary_functor_internal + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} + + OF_DEVICE_FUNC half operator()(half src) const { +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + const float tanh_in = + __half2float(__float2half_rn(alpha) * (src + __float2half_rn(beta) * src * src * src)); + const float tanh_out = unary_functor_internal::TanhApprox(tanh_in); + return __float2half_rn(0.5F) * src * (__float2half_rn(1.0F) + __float2half_rn(tanh_out)); +#else + return static_cast(float_functor(static_cast(src))); +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + } + +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + __device__ void Apply2(half* dst, const half* src) const { + const half2 src2 = *(reinterpret_cast(src)); + const float2 tanh_in = __half22float2(__hmul2( + __float2half2_rn(alpha), + __hadd2(src2, __hmul2(__hmul2(__hmul2(__float2half2_rn(beta), src2), src2), src2)))); + float2 tanh_out; + tanh_out.x = unary_functor_internal::TanhApprox(tanh_in.x); + tanh_out.y = unary_functor_internal::TanhApprox(tanh_in.y); + const half2 dst2 = __hmul2(__hmul2(__float2half2_rn(0.5F), src2), + __hadd2(__float2half2_rn(1.0F), __float22half2_rn(tanh_out))); + *reinterpret_cast(dst) = dst2; + } +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + + private: + static constexpr float alpha = 0.7978845608028654F; + static constexpr float beta = 0.044714998453855515F; + UnaryFunctor float_functor; +}; + template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC float operator()(float src) const { return tanhf(src); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC double operator()(double src) const { return tanh(src); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC half operator()(half src) const { return __float2half(tanhf(__half2float(src))); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(half src) const { return isinf(__half2float(src)); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(float src) const { return isinf(src); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(double src) const { return isinf(src); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(half src) const { return isnan(__half2float(src)); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(float src) const { return isnan(src); } }; template<> struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} OF_DEVICE_FUNC bool operator()(double src) const { return isnan(src); } }; -#define SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(op) \ - template<> \ - struct UnaryFunctor { \ - UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ - \ - UnaryFunctor float_functor; \ - OF_DEVICE_FUNC half operator()(half src) const { \ - return __float2half(float_functor(__half2float(src))); \ - } \ +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(half src) const { return isfinite(__half2float(src)); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(float src) const { return isfinite(src); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC bool operator()(double src) const { return isfinite(src); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + __device__ half operator()(half src) const { return htrunc(src); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC float operator()(float src) const { return truncf(src); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + OF_DEVICE_FUNC double operator()(double src) const { return trunc(src); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + __device__ half operator()(half src) const { + return __hlt(src, static_cast(0)) ? __hneg(src) : src; + } +}; + +template +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + OF_DEVICE_FUNC Dst operator()(Src src) const { return isnan(src) ? static_cast(0.0) : src; } +}; + +#if CUDA_VERSION >= 11000 +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + + __device__ nv_bfloat16 operator()(nv_bfloat16 src) const { +#if CUDA_ARCH >= 800 + return __habs(src); +#else + return __float2bfloat16(abs(__bfloat162float(src))); +#endif // CUDA_ARCH >= 800 + } +}; +#endif // CUDA_VERSION >= 11000 + +#define SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(op) \ + template<> \ + struct UnaryFunctor { \ + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + \ + UnaryFunctor float_functor; \ + OF_DEVICE_FUNC half operator()(half src) const { \ + return __float2half(float_functor(__half2float(src))); \ + } \ }; SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kElu); @@ -114,20 +277,53 @@ SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSelu); SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSilu); SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSoftSign); SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSoftPlus); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAcos); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAcosh); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAsin); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAsinh); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAtan); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kAtanh); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kCeil); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kCos); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kCosh); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kErf); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kErfc); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kExp); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kExpm1); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kFloor); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLgamma); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLog); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLog2); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLog10); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLog1p); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kLogSigmoid); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kRint); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kRound); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kRsqrt); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSigmoid); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSin); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSinh); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSqrt); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSquare); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kTan); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kReciprocalNoNan); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kNotEqualZero); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kNanAssign); +SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kQuickGelu); /*********nv_bfloat16_kernel*******/ #if CUDA_VERSION >= 11000 -#define SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(op) \ - template<> \ - struct UnaryFunctor { \ - UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ - \ - UnaryFunctor float_functor; \ - OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src) const { \ - return __float2bfloat16(float_functor(__bfloat162float(src))); \ - } \ +#define SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(op) \ + template<> \ + struct UnaryFunctor { \ + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ + \ + UnaryFunctor float_functor; \ + OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src) const { \ + return __float2bfloat16(float_functor(__bfloat162float(src))); \ + } \ }; SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kElu); @@ -146,6 +342,40 @@ SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftSign); SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftPlus); SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kTanh); SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kThreshold); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAcos); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAcosh); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAsin); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAsinh); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAtan); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kAtanh); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCeil); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCos); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCosh); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kErf); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kErfc); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kExp); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kExpm1); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kFloor); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLgamma); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog2); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog10); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLog1p); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLogSigmoid); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRint); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRound); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kRsqrt); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSigmoid); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSin); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSinh); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSqrt); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSquare); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kTan); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kReciprocalNoNan); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kNotEqualZero); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kNanAssign); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kFastGelu); +SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kQuickGelu); template<> struct UnaryFunctor { @@ -160,8 +390,26 @@ struct UnaryFunctor { OF_DEVICE_FUNC bool operator()(nv_bfloat16 src) const { return isnan(__bfloat162float(src)); } }; +template<> +struct UnaryFunctor { + UnaryFunctor(Scalar attr0, Scalar attr1) {} -#endif + OF_DEVICE_FUNC bool operator()(nv_bfloat16 src) const { return isfinite(__bfloat162float(src)); } +}; + +template<> +struct UnaryFunctor { + OF_DEVICE_FUNC UnaryFunctor(Scalar attr0, Scalar attr1) {} + __device__ nv_bfloat16 operator()(nv_bfloat16 src) const { +#if CUDA_ARCH >= 800 + return htrunc(src); +#else + return __float2bfloat16(truncf(__bfloat162float(src))); +#endif // CUDA_ARCH >= 800 + } +}; + +#endif // CUDA_VERSION >= 11000 } // namespace primitive } // namespace ep diff --git a/oneflow/core/ep/include/device.h b/oneflow/core/ep/include/device.h index e7b62e18ad075ab0b83d73f22bdf87057fc0af3f..bd6e72cd2444e5692813c228dfe556cdf59736e1 100644 --- a/oneflow/core/ep/include/device.h +++ b/oneflow/core/ep/include/device.h @@ -21,6 +21,7 @@ limitations under the License. #include "oneflow/core/ep/include/event.h" #include "oneflow/core/ep/include/stream.h" #include "oneflow/core/ep/include/allocation_options.h" +#include "oneflow/core/ep/include/gpu_macro.h" namespace oneflow { diff --git a/oneflow/core/ep/include/event.h b/oneflow/core/ep/include/event.h index 8b3a67e492639621f37fe8ddfad4056c4da831ba..e3cebf981c8fe988044ea1d77bccf6b65c2c18fb 100644 --- a/oneflow/core/ep/include/event.h +++ b/oneflow/core/ep/include/event.h @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/common/maybe.h" +#include "oneflow/core/ep/include/gpu_macro.h" namespace oneflow { diff --git a/oneflow/core/ep/include/gpu_macro.h b/oneflow/core/ep/include/gpu_macro.h new file mode 100644 index 0000000000000000000000000000000000000000..7bb725e27be80b0096bb497652dffec5e882c4be --- /dev/null +++ b/oneflow/core/ep/include/gpu_macro.h @@ -0,0 +1,49 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_EP_GPU_MACRO_H_ +#define ONEFLOW_CORE_EP_GPU_MACRO_H_ + +#ifdef WITH_ROCM +#include + +#define GPU(str) hip##str +#define GPURAND(str) hiprand##str +#define GPUMultiProcessorCount hipDeviceAttributeMultiprocessorCount +#define GPUMaxThreadsPerMultiProcessor hipDeviceAttributeMaxThreadsPerMultiProcessor +#define GPUMaxSharedMemoryPerBlockOptin hipDeviceAttributeSharedMemPerBlockOptin + +__device__ __forceinline__ void TRAP() +{ + asm volatile("s_trap 0;"); +} + +#else +#include + +#define GPU(str) cuda##str +#define GPURAND(str) curand##str +#define GPUMultiProcessorCount cudaDevAttrMultiProcessorCount +#define GPUMaxThreadsPerMultiProcessor cudaDevAttrMaxThreadsPerMultiProcessor +#define GPUMaxSharedMemoryPerBlockOptin cudaDevAttrMaxSharedMemoryPerBlockOptin + +__device__ __forceinline__ void TRAP() +{ + __trap(); +} + +#endif + +#endif // ONEFLOW_CORE_EP_GPU_MACRO_H_ \ No newline at end of file diff --git a/oneflow/core/ep/include/primitive/binary_op.h b/oneflow/core/ep/include/primitive/binary_op.h index 0f7dd7c3db72ac0a0107deb47b658fa4c1442be5..e989b387aaeff8a5c6b5ca1326fd6c689c0a9a1b 100644 --- a/oneflow/core/ep/include/primitive/binary_op.h +++ b/oneflow/core/ep/include/primitive/binary_op.h @@ -32,6 +32,12 @@ enum class BinaryOp { kMax, kMin, kPow, + kFmod, + kFloorDiv, + kTruncDiv, + kFloorMod, + kScalarBasePowerGrad, + kScalarExpPowerGrad, // Comparision kEqual, kNotEqual, @@ -39,6 +45,8 @@ enum class BinaryOp { kLessEqual, kGreaterThan, kGreaterEqual, + kIsClose, + kIsCloseEqualNan, // Logical kLogicalAnd, kLogicalOr, @@ -62,7 +70,35 @@ enum class BinaryOp { kTanhBackwardWithDyX, kThresholdBackwardWithDyX, kSigmoidBackwardWithDyY, - + kAbsBackwardWithDyX, + kAcosBackwardWithDyX, + kAcoshBackwardWithDyX, + kAsinBackwardWithDyX, + kAsinhBackwardWithDyX, + kAtanBackwardWithDyX, + kAtanhBackwardWithDyX, + kCosBackwardWithDyX, + kCoshBackwardWithDyX, + kErfBackwardWithDyX, + kErfcBackwardWithDyX, + kExpBackwardWithDyX, + kExpm1BackwardWithDyX, + kLgammaBackwardWithDyX, + kLogBackwardWithDyX, + kLog2BackwardWithDyX, + kLog10BackwardWithDyX, + kLog1pBackwardWithDyX, + kLogSigmoidBackwardWithDyX, + kReciprocalBackwardWithDyX, + kReciprocalNoNanBackwardWithDyX, + kRsqrtBackwardWithDyX, + kSinBackwardWithDyX, + kSinhBackwardWithDyX, + kSqrtBackwardWithDyX, + kSquareBackwardWithDyX, + kTanBackwardWithDyX, + kFastGeluBackwardWithDyX, + kQuickGeluBackwardWithDyX, }; } diff --git a/oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h b/oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h new file mode 100644 index 0000000000000000000000000000000000000000..5e228ce4a145b8db7d25b153396e155e752d1c12 --- /dev/null +++ b/oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h @@ -0,0 +1,66 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_EP_PRIMITIVE_BROADCAST_ELEMENTWISE_UNARY_H_ +#define ONEFLOW_CORE_EP_PRIMITIVE_BROADCAST_ELEMENTWISE_UNARY_H_ + +#include "oneflow/core/ep/include/primitive/primitive.h" +#include "oneflow/core/ep/include/primitive/unary_op.h" +#include "oneflow/core/common/scalar.h" + +namespace oneflow { + +namespace ep { +namespace primitive { + +class BroadcastElementwiseUnary : public Primitive { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnary); + BroadcastElementwiseUnary() = default; + ~BroadcastElementwiseUnary() override = default; + + virtual void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, + const int64_t* src_strides, const void* src, size_t num_dst_dims, + const int64_t* dst_dims, const int64_t* dst_strides, void* dst) = 0; + + virtual void Launch(Stream* stream, size_t num_src_dims, const int64_t* src_dims, const void* src, + size_t num_dst_dims, const int64_t* dst_dims, void* dst) = 0; +}; + +class BroadcastElementwiseUnaryFactory : public Factory { + public: + OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseUnaryFactory); + BroadcastElementwiseUnaryFactory() = default; + ~BroadcastElementwiseUnaryFactory() override = default; + + virtual std::unique_ptr New(UnaryOp op, DataType src_type, + DataType dst_type, + size_t max_num_dims) = 0; + + virtual std::unique_ptr New(UnaryOp op, DataType src_type, + DataType dst_type, size_t max_num_dims, + Scalar attr0) = 0; + + virtual std::unique_ptr New(UnaryOp op, DataType src_type, + DataType dst_type, size_t max_num_dims, + Scalar attr0, Scalar attr1) = 0; +}; + +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif // ONEFLOW_CORE_EP_PRIMITIVE_BROADCAST_ELEMENTWISE_UNARY_H_ diff --git a/oneflow/core/ep/include/primitive/fast_integer_math.h b/oneflow/core/ep/include/primitive/fast_integer_math.h index 22ad5613a7186eadeaea4ed6bd4db8b178250716..488ef5f6faa05b6711da4b9432b5f64600676dc9 100644 --- a/oneflow/core/ep/include/primitive/fast_integer_math.h +++ b/oneflow/core/ep/include/primitive/fast_integer_math.h @@ -16,8 +16,10 @@ limitations under the License. #ifndef ONEFLOW_CORE_EP_PRIMITIVE_FAST_INTEGER_MATH_H_ #define ONEFLOW_CORE_EP_PRIMITIVE_FAST_INTEGER_MATH_H_ #include "oneflow/core/common/data_type.h" +#ifdef WITH_ROCM +#include "hip/device_functions.h" // /opt/rocm/hip/include/hip +#endif #include -#include "device_functions.h" // /opt/rocm/hip/include/hip namespace oneflow { diff --git a/oneflow/core/ep/include/primitive/tensor_fill.h b/oneflow/core/ep/include/primitive/tensor_fill.h new file mode 100644 index 0000000000000000000000000000000000000000..d9ac08661ff1d891065309aab69fb5242a3ad596 --- /dev/null +++ b/oneflow/core/ep/include/primitive/tensor_fill.h @@ -0,0 +1,49 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_EP_PRIMITIVE_TENSOR_FILL_H_ +#define ONEFLOW_CORE_EP_PRIMITIVE_TENSOR_FILL_H_ + +#include "oneflow/core/ep/include/primitive/primitive.h" + +namespace oneflow { + +namespace ep { +namespace primitive { + +class TensorFill : public Primitive { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFill); + TensorFill() = default; + ~TensorFill() override = default; + + virtual void Launch(Stream* stream, const void* src, void* dst, size_t count) = 0; +}; + +class TensorFillFactory : public Factory { + public: + OF_DISALLOW_COPY_AND_MOVE(TensorFillFactory); + TensorFillFactory() = default; + ~TensorFillFactory() override = default; + + virtual std::unique_ptr New(DataType data_type) = 0; +}; + +} // namespace primitive +} // namespace ep + +} // namespace oneflow + +#endif // ONEFLOW_CORE_EP_PRIMITIVE_TENSOR_FILL_H_ diff --git a/oneflow/core/ep/include/primitive/unary_op.h b/oneflow/core/ep/include/primitive/unary_op.h index 0c96d3b63b5865b91187c23956ffed78d3b4dbf8..8f014307fba34bcdf46077310d0be797aa8a9940 100644 --- a/oneflow/core/ep/include/primitive/unary_op.h +++ b/oneflow/core/ep/include/primitive/unary_op.h @@ -22,6 +22,7 @@ namespace ep { namespace primitive { enum class UnaryOp { + kIdentity, // activation op kElu, kCelu, @@ -40,13 +41,53 @@ enum class UnaryOp { kSoftPlus, kTanh, kThreshold, - + kFastGelu, + kQuickGelu, + // math op + kAbs, + kAcos, + kAcosh, + kAsin, + kAsinh, + kAtan, + kAtanh, + kCeil, + kCos, + kCosh, + kErf, + kErfc, + kExp, + kExpm1, + kFloor, + kLgamma, + kLog, + kLog2, + kLog10, + kLog1p, + kLogSigmoid, + kNegative, + kReciprocal, + kReciprocalNoNan, + kRint, + kRound, + kRsqrt, + kSigmoid, + kSign, + kSin, + kSinh, + kSqrt, + kSquare, + kTan, + kTrunc, + kNotEqualZero, // logical op kLogicalNot, // utils op kIsInf, kIsNan, + kIsFinite, + kNanAssign, }; } diff --git a/oneflow/core/ep/include/stream.h b/oneflow/core/ep/include/stream.h index c64779ea5e851f7efdc44ca0122b97cb6a3945e4..e57bb02e7cb88048c4e5cd1e596063501c99162f 100644 --- a/oneflow/core/ep/include/stream.h +++ b/oneflow/core/ep/include/stream.h @@ -20,6 +20,7 @@ limitations under the License. #include "oneflow/core/common/device_type.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/ep/include/event.h" +#include "oneflow/core/ep/include/gpu_macro.h" namespace oneflow { @@ -37,6 +38,7 @@ class Stream { virtual Device* device() const = 0; virtual Maybe Sync() = 0; virtual void RecordEvent(Event* event) = 0; + virtual Maybe GetAsyncError() { return Maybe::Ok(); } virtual Maybe OnExecutionContextSetup() { return Maybe::Ok(); } virtual Maybe OnExecutionContextTeardown() { return Maybe::Ok(); } diff --git a/oneflow/core/ep/rocm/cuda_device.cpp b/oneflow/core/ep/rocm/cuda_device.cpp deleted file mode 100644 index 850a4902a0cccb915793e2b3ca587580762506a1..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_device.cpp +++ /dev/null @@ -1,179 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/cuda_device.h" -#include "oneflow/core/ep/rocm/cuda_event.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#ifdef WITH_ROCM - -#include -#include - -// #if CUDA_VERSION >= 11000 -// #include -// #endif - -namespace oneflow { - -namespace ep { - -namespace { - -constexpr size_t kDefaultConstBufElementCount = 1024 * 1024; - -template -void CreateConstBuffer(void** buf, T value, size_t n) { - OF_CUDA_CHECK(hipMalloc(buf, n * sizeof(T))); - std::vector host(n, value); - OF_CUDA_CHECK(hipMemcpy(*buf, host.data(), n * sizeof(T), hipMemcpyDefault)); -} - -} // namespace - -CudaDevice::CudaDevice(int device_index, DeviceManager* device_manager) - : device_index_(device_index), - event_flags_{}, - properties_{}, - device_manager_(device_manager), - const_buf_elem_cnt_(0), - const_zeros_buffer_(nullptr), - const_ones_buffer_fp32_(nullptr), - const_ones_buffer_fp16_(nullptr), - const_ones_buffer_bf16_(nullptr) { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipGetDeviceProperties(&properties_, device_index_)); - event_flags_ = hipEventDisableTiming; - if (ParseBooleanFromEnv("ONEFLOW_STREAM_CUDA_EVENT_FLAG_BLOCKING_SYNC", false)) { - event_flags_ |= hipEventBlockingSync; - } - const_buf_elem_cnt_ = ParseIntegerFromEnv("ONEFLOW_EP_CUDA_CONST_BUFFER_ELEMENT_COUNT", - kDefaultConstBufElementCount); - if (const_buf_elem_cnt_ > 0) { - CreateConstBuffer(&const_zeros_buffer_, static_cast(0), const_buf_elem_cnt_); - CreateConstBuffer(&const_ones_buffer_fp32_, static_cast(1.0), - const_buf_elem_cnt_); - CreateConstBuffer(&const_ones_buffer_fp16_, static_cast(1.0), const_buf_elem_cnt_); -// #if CUDA_VERSION >= 11000 -// CreateConstBuffer(&const_ones_buffer_bf16_, static_cast(1.0), -// const_buf_elem_cnt_); -// #endif - } -} - -CudaDevice::~CudaDevice() { - CudaCurrentDeviceGuard guard(device_index_); - for (auto* event : events_) { delete event; } - OF_CUDA_CHECK(hipFree(const_zeros_buffer_)); - OF_CUDA_CHECK(hipFree(const_ones_buffer_fp32_)); - OF_CUDA_CHECK(hipFree(const_ones_buffer_fp16_)); - OF_CUDA_CHECK(hipFree(const_ones_buffer_bf16_)); -} - -void CudaDevice::SetAsActiveDevice() { OF_CUDA_CHECK(hipSetDevice(device_index_)); } - -Stream* CudaDevice::CreateStream() { - CudaCurrentDeviceGuard guard(device_index_); - return new CudaStream(this); -} - -void CudaDevice::DestroyStream(Stream* stream) { - CudaCurrentDeviceGuard guard(device_index_); - delete stream; -} - -void CudaDevice::CreateEvents(Event** events, size_t count) { - size_t copied = 0; - { - std::lock_guard lock(events_mutex_); - copied = std::min(count, events_.size()); - size_t offset = events_.size() - copied; - std::copy(events_.begin() + offset, events_.end(), events); - events_.resize(offset); - } - if (copied != count) { - CudaCurrentDeviceGuard guard(device_index_); - for (size_t i = copied; i < count; ++i) { events[i] = new CudaEvent(event_flags_); } - } -} - -void CudaDevice::DestroyEvents(Event** events, size_t count) { - std::lock_guard lock(events_mutex_); - events_.insert(events_.end(), events, events + count); -} - -Maybe CudaDevice::Alloc(const AllocationOptions& options, void** ptr, size_t size) { - CudaCurrentDeviceGuard guard(device_index_); - CHECK(!options.HasPinnedDevice()); - hipError_t err = hipMalloc(ptr, size); - if (err != hipSuccess) { - return Error::RuntimeError() << hipGetErrorString(err); - } else { - return Maybe::Ok(); - } -} - -void CudaDevice::Free(const AllocationOptions& attr, void* ptr) { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipFree(ptr)); -} - -Maybe CudaDevice::AllocPinned(const AllocationOptions& options, void** ptr, size_t size) { - CudaCurrentDeviceGuard guard(device_index_); - hipError_t err = NumaAwareCudaMallocHost(device_index_, ptr, size); - if (err != hipSuccess) { - return Error::RuntimeError() << hipGetErrorString(err); - } else { - return Maybe::Ok(); - } -} - -void CudaDevice::FreePinned(const AllocationOptions& options, void* ptr) { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipHostFree(ptr)); -} - -const hipDeviceProp_t& CudaDevice::properties() const { return properties_; } - -const void* CudaDevice::GetConstZeros(DataType data_type, size_t n) const { - if (GetSizeOfDataType(data_type) * n - <= GetSizeOfDataType(DataType::kFloat) * const_buf_elem_cnt_) { - return const_zeros_buffer_; - } else { - return nullptr; - } -} - -const void* CudaDevice::GetConstOnes(DataType data_type, size_t n) const { - if (n <= const_buf_elem_cnt_) { - if (data_type == DataType::kFloat) { - return const_ones_buffer_fp32_; - } else if (data_type == DataType::kFloat16) { - return const_ones_buffer_fp16_; - } else if (data_type == DataType::kBFloat16) { - return const_ones_buffer_bf16_; - } else { - return nullptr; - } - } else { - return nullptr; - } -} - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/cuda_device.h b/oneflow/core/ep/rocm/cuda_device.h deleted file mode 100644 index 76e101530df5a1f36e3e9f0b2531983fb54762dc..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_device.h +++ /dev/null @@ -1,78 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_H_ -#define ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_H_ - -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/common/data_type.h" - -#ifdef WITH_ROCM - -#include - -namespace oneflow { - -namespace ep { - -class CudaDevice : public Device { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaDevice); - explicit CudaDevice(int device_index, DeviceManager* device_manager); - ~CudaDevice() override; - - void SetAsActiveDevice() override; - - DeviceType device_type() const override { return DeviceType::kCUDA; } - size_t device_index() const override { return device_index_; } - DeviceManager* device_manager() const override { return device_manager_; } - - Stream* CreateStream() override; - void DestroyStream(Stream* stream) override; - - void CreateEvents(Event** events, size_t count) override; - void DestroyEvents(Event** events, size_t count) override; - - Maybe Alloc(const AllocationOptions& options, void** ptr, size_t size) override; - void Free(const AllocationOptions& options, void* ptr) override; - Maybe AllocPinned(const AllocationOptions& options, void** ptr, size_t size) override; - void FreePinned(const AllocationOptions& options, void* ptr) override; - - const hipDeviceProp_t& properties() const; - - const void* GetConstZeros(DataType data_type, size_t n) const; - const void* GetConstOnes(DataType data_type, size_t n) const; - - private: - int device_index_; - std::mutex events_mutex_; - std::vector events_; - unsigned int event_flags_; - hipDeviceProp_t properties_; - DeviceManager* device_manager_; - int64_t const_buf_elem_cnt_; - void* const_zeros_buffer_; - void* const_ones_buffer_fp32_; - void* const_ones_buffer_fp16_; - void* const_ones_buffer_bf16_; -}; - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_H_ diff --git a/oneflow/core/ep/rocm/cuda_device_manager.cpp b/oneflow/core/ep/rocm/cuda_device_manager.cpp deleted file mode 100644 index 48664df749f194f8358c7ba28bbe109f49e5d84f..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_device_manager.cpp +++ /dev/null @@ -1,68 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/cuda_device_manager.h" -#include "oneflow/core/device/cuda_util.h" - -#ifdef WITH_ROCM - -namespace oneflow { - -namespace ep { - -CudaDeviceManager::CudaDeviceManager(DeviceManagerRegistry* registry) : registry_(registry) {} -CudaDeviceManager::~CudaDeviceManager() = default; - -DeviceManagerRegistry* CudaDeviceManager::registry() const { return registry_; } - -std::shared_ptr CudaDeviceManager::GetDevice(size_t device_index) { - std::lock_guard lock(devices_mutex_); - if (device_index < devices_.size() && devices_.at(device_index)) { - return devices_.at(device_index); - } - auto device = std::make_shared(device_index, this); - if (device_index >= devices_.size()) { devices_.resize(device_index + 1); } - devices_.at(device_index) = device; - return device; -} - -size_t CudaDeviceManager::GetDeviceCount(size_t primary_device_index) { - CudaCurrentDeviceGuard guard(primary_device_index); - return this->GetDeviceCount(); -} - -size_t CudaDeviceManager::GetDeviceCount() { - int count = 0; - hipError_t err = hipGetDeviceCount(&count); - if (err == hipErrorNoDevice || err == hipErrorInsufficientDriver) { return 0; } - OF_CUDA_CHECK(err); - return count; -} - -size_t CudaDeviceManager::GetActiveDeviceIndex() { - int device = 0; - OF_CUDA_CHECK(hipGetDevice(&device)); - return static_cast(device); -} - -void CudaDeviceManager::SetActiveDeviceByIndex(size_t device_index) { - OF_CUDA_CHECK(hipSetDevice(static_cast(device_index))); -} - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/cuda_device_manager.h b/oneflow/core/ep/rocm/cuda_device_manager.h deleted file mode 100644 index e1b948843fdad6381ad6da9eedec242f919db2dc..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_device_manager.h +++ /dev/null @@ -1,54 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_MANAGER_H_ -#define ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_MANAGER_H_ - -#include "oneflow/core/ep/include/device_manager.h" -#include "oneflow/core/ep/rocm/cuda_device.h" - -#ifdef WITH_ROCM - -namespace oneflow { -namespace ep { - -class CudaDevice; - -class CudaDeviceManager : public DeviceManager { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaDeviceManager); - CudaDeviceManager(DeviceManagerRegistry* registry); - ~CudaDeviceManager() override; - - DeviceManagerRegistry* registry() const override; - std::shared_ptr GetDevice(size_t device_index) override; - size_t GetDeviceCount(size_t primary_device_index) override; - size_t GetDeviceCount() override; - size_t GetActiveDeviceIndex() override; - void SetActiveDeviceByIndex(size_t device_index) override; - - private: - std::mutex devices_mutex_; - std::vector> devices_; - DeviceManagerRegistry* registry_; -}; - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_EP_ROCM_CUDA_DEVICE_MANAGER_H_ diff --git a/oneflow/core/ep/rocm/cuda_device_manager_factory.cpp b/oneflow/core/ep/rocm/cuda_device_manager_factory.cpp deleted file mode 100644 index fb8d15c3256df6b54547d3d397b59500f28b46e0..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_device_manager_factory.cpp +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/include/device_manager_factory.h" -#include "oneflow/core/ep/include/device_manager_registry.h" -#include "oneflow/core/ep/rocm/cuda_device_manager.h" - -#ifdef WITH_ROCM - -#include -#include -#include - -namespace oneflow { - -namespace ep { - -namespace { - -std::string GetCudaVersionString(int version) { - return std::to_string(version / 1000) + "." + std::to_string((version % 1000) / 10); -} - -bool GetCudnnVersion(size_t* major, size_t* minor, size_t* patch) { - miopenStatus_t status = miopenGetVersion(major, minor, patch); - if (status == miopenStatusSuccess) { - return true; - } else { - LOG(ERROR) << "Failed to get cuDNN version: " << miopenGetErrorString(status); - return false; - } -} - -bool GetCudnnVersionString(std::string* version) { - size_t version_major = 0; - size_t version_minor = 0; - size_t version_patch = 0; - if (!GetCudnnVersion(&version_major, &version_minor, &version_patch)) { return false; } - *version = std::to_string(version_major) + "." + std::to_string(version_minor) + "." - + std::to_string(version_patch); - return true; -} - -void CudaDumpVersionInfo() { - { - int cuda_runtime_version = 0; - hipError_t err = hipRuntimeGetVersion(&cuda_runtime_version); - if (err == hipSuccess) { - LOG(INFO) << "CUDA runtime version: " << GetCudaVersionString(cuda_runtime_version); - } else { - LOG(ERROR) << "Failed to get cuda runtime version: " << hipGetErrorString(err); - } - } - - { - std::string cudnn_version_string; - if (GetCudnnVersionString(&cudnn_version_string)) { - LOG(INFO) << "cuDNN version: " << cudnn_version_string; - } - } - - { - int nccl_version = 0; - ncclResult_t result = ncclGetVersion(&nccl_version); - if (result == ncclSuccess) { - int nccl_version_major = - (nccl_version >= 20900) ? (nccl_version / 10000) : (nccl_version / 1000); - int nccl_version_minor = - (nccl_version >= 20900) ? (nccl_version % 10000) / 100 : (nccl_version % 1000) / 100; - int nccl_version_patch = (nccl_version % 100); - LOG(INFO) << "NCCL version: " << nccl_version_major << "." << nccl_version_minor << "." - << nccl_version_patch; - } else { - LOG(ERROR) << "Failed to get NCCL version: " << ncclGetErrorString(result); - } - } -} - -class CudaDeviceManagerFactory : public DeviceManagerFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaDeviceManagerFactory); - CudaDeviceManagerFactory() = default; - ~CudaDeviceManagerFactory() override = default; - - std::unique_ptr NewDeviceManager(DeviceManagerRegistry* registry) override { - return std::make_unique(registry); - } - - DeviceType device_type() const override { return DeviceType::kCUDA; } - - std::string device_type_name() const override { return "cuda"; } - - void DumpVersionInfo() const override { CudaDumpVersionInfo(); } -}; - -COMMAND(DeviceManagerRegistry::RegisterDeviceManagerFactory( - std::make_unique())) - -} // namespace - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/cuda_event.cpp b/oneflow/core/ep/rocm/cuda_event.cpp deleted file mode 100644 index 20ce0f025820ef3c7392e3ab2318dac2b354ba74..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_event.cpp +++ /dev/null @@ -1,56 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/cuda_event.h" - -#ifdef WITH_ROCM - -namespace oneflow { - -namespace ep { - -CudaEvent::CudaEvent(unsigned int flags) : cuda_event_{} { - OF_CUDA_CHECK(hipEventCreateWithFlags(&cuda_event_, flags)); -} - -CudaEvent::~CudaEvent() { OF_CUDA_CHECK(hipEventDestroy(cuda_event_)); } - -Maybe CudaEvent::QueryDone() { - hipError_t err = hipEventQuery(cuda_event_); - if (err == hipSuccess) { - return Maybe(true); - } else if (err == hipErrorNotReady) { - return Maybe(false); - } else { - return Error::RuntimeError() << hipGetErrorString(err); - } -} - -Maybe CudaEvent::Sync() { - hipError_t err = hipEventSynchronize(cuda_event_); - if (err == hipSuccess) { - return Maybe::Ok(); - } else { - return Error::RuntimeError() << hipGetErrorString(err); - } -} - -hipEvent_t CudaEvent::cuda_event() { return cuda_event_; } - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/cuda_event.h b/oneflow/core/ep/rocm/cuda_event.h deleted file mode 100644 index 37a3379702c6d5bab4fd3d6135e31ce10e073c12..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_event.h +++ /dev/null @@ -1,50 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EP_ROCM_CUDA_EVENT_H_ -#define ONEFLOW_CORE_EP_ROCM_CUDA_EVENT_H_ - -#include "oneflow/core/ep/include/event.h" - -#ifdef WITH_ROCM - -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace ep { - -class CudaEvent : public Event { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaEvent); - explicit CudaEvent(unsigned int flags); - ~CudaEvent() override; - - Maybe QueryDone() override; - Maybe Sync() override; - - hipEvent_t cuda_event(); - - private: - hipEvent_t cuda_event_; -}; - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_EP_ROCM_CUDA_EVENT_H_ diff --git a/oneflow/core/ep/rocm/cuda_stream.cpp b/oneflow/core/ep/rocm/cuda_stream.cpp deleted file mode 100644 index 18f1870c9635c9ddf380ca25eef06c09b4b1b6e0..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_stream.cpp +++ /dev/null @@ -1,180 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/resource_desc.h" -#include "oneflow/core/hardware/node_device_descriptor_manager.h" -#include "oneflow/core/hardware/cuda_device_descriptor.h" -#include "oneflow/core/ep/rocm/cuda_event.h" -#include "oneflow/core/ep/rocm/cuda_device.h" - -#ifdef WITH_ROCM - -namespace oneflow { - -namespace ep { - -namespace { - -constexpr size_t kDefaultWorkspaceSize = 4 * 1024 * 1024; // 4M - -void SetAffinityByDevice(int dev_id) { - auto node_device_desc_mgr = Singleton::Get(); - if (node_device_desc_mgr == nullptr) { return; } - auto node_device_desc = node_device_desc_mgr->GetLocalNodeDeviceDescriptor(); - auto cuda_device = std::dynamic_pointer_cast( - node_device_desc->GetDevice(hardware::kCudaDeviceDescriptorClassName, dev_id)); - if (!cuda_device) { return; } - node_device_desc->Topology()->SetCPUAffinityByPCIBusID(cuda_device->PCIBusID()); - node_device_desc->Topology()->SetMemoryAffinityByPCIBusID(cuda_device->PCIBusID()); -} - -} // namespace - -#ifdef WITH_ROCM_GRAPHS - -CudaGraphExecutable::CudaGraphExecutable() : graph_exec_(nullptr), dev_(-1) {} - -CudaGraphExecutable::~CudaGraphExecutable() { Reset(); } - -void CudaGraphExecutable::Update(hipGraph_t graph) { - int dev = -1; - OF_CUDA_CHECK(hipGetDevice(&dev)); - if (dev != dev_) { Reset(); } - dev_ = dev; - if (graph_exec_ != nullptr) { - hipGraphExecUpdateResult update_result{}; - hipGraphNode_t error_node = nullptr; - OF_CUDA_CHECK(hipGraphExecUpdate(graph_exec_, graph, &error_node, &update_result)); - if (update_result == hipGraphExecUpdateSuccess) { return; } - } - Reset(); - OF_CUDA_CHECK(hipGraphInstantiate(&graph_exec_, graph, NULL, NULL, 0)); -} - -void CudaGraphExecutable::Launch(hipStream_t stream) const { - OF_CUDA_CHECK(hipGraphLaunch(graph_exec_, stream)); -} - -bool CudaGraphExecutable::IsInstantiated() const { return graph_exec_ != nullptr; } - -void CudaGraphExecutable::Reset() { - if (graph_exec_ != nullptr) { - CudaCurrentDeviceGuard guard(dev_); - OF_CUDA_CHECK(hipGraphExecDestroy(graph_exec_)); - } -} - -#endif // WITH_ROCM_GRAPHS - -CudaStream::CudaStream(CudaDevice* device) - : device_index_(device->device_index()), device_(device) { - CudaCurrentDeviceGuard guard(device_index_); - // cuda_stream - OF_CUDA_CHECK(hipStreamCreate(&cuda_stream_)); - // cublas_handle - OF_CUBLAS_CHECK(hipblasCreate(&cublas_handle_)); - OF_CUBLAS_CHECK(hipblasSetStream(cublas_handle_, cuda_stream_)); - - workspace_size_ = kDefaultWorkspaceSize; - OF_CUDA_CHECK(hipMalloc(&workspace_, workspace_size_)); - - OF_CUDNN_CHECK(hipdnnCreate(&cudnn_handle_)); - - OF_CUDNN_CHECK(hipdnnSetStream(cudnn_handle_, cuda_stream_)); -} - -CudaStream::~CudaStream() { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipStreamSynchronize(cuda_stream_)); - OF_CUDNN_CHECK(hipdnnDestroy(cudnn_handle_)); - OF_CUBLAS_CHECK(hipblasDestroy(cublas_handle_)); - - OF_CUDA_CHECK(hipStreamDestroy(cuda_stream_)); - OF_CUDA_CHECK(hipFree(workspace_)); -} - -Maybe CudaStream::OnExecutionContextSetup() { - OF_CUDA_CHECK(hipSetDevice(device_index_)); - SetAffinityByDevice(device_index_); - return Maybe::Ok(); -} - -Maybe CudaStream::OnExecutionContextTeardown() { return Maybe::Ok(); } - -DeviceType CudaStream::device_type() const { return DeviceType::kCUDA; } - -CudaDevice* CudaStream::device() const { return device_; } - -Maybe CudaStream::Sync() { - hipError_t err = hipStreamSynchronize(cuda_stream_); - if (err == hipSuccess) { - return Maybe::Ok(); - } else { - return Error::RuntimeError() << hipGetErrorString(err) << " (" << err << ") "; - } -} - -void CudaStream::RecordEvent(Event* event) { - auto* cuda_event = static_cast(event); // NOLINT - OF_CUDA_CHECK(hipEventRecord(cuda_event->cuda_event(), cuda_stream_)); -} - -hipStream_t CudaStream::cuda_stream() const { return cuda_stream_; } - -hipblasHandle_t CudaStream::cublas_handle() const { return cublas_handle_; } - -void* CudaStream::cublas_workspace() const { return workspace_; } - -size_t CudaStream::cublas_workspace_size() const { return workspace_size_; } - -hipdnnHandle_t CudaStream::cudnn_handle() const { return cudnn_handle_; } - -const hipDeviceProp_t& CudaStream::device_properties() const { return device_->properties(); } - -int CudaStream::cuda_arch() const { - return device_->properties().major * 100 + device_->properties().minor * 10; -} - -#ifdef WITH_ROCM_GRAPHS - -void CudaStream::BeginGraphCapture() { - CHECK(!is_graph_capturing_); - is_graph_capturing_ = true; - OF_CUDA_CHECK(hipStreamBeginCapture(cuda_stream_, hipStreamCaptureModeThreadLocal)); -} - -void CudaStream::EndGraphCapture(CudaGraphExecutable* executable) { - hipGraph_t graph = nullptr; - OF_CUDA_CHECK(hipStreamEndCapture(cuda_stream_, &graph)); - executable->Update(graph); - OF_CUDA_CHECK(hipGraphDestroy(graph)); - is_graph_capturing_ = false; -} - -bool CudaStream::IsGraphCapturing() const { return is_graph_capturing_; } - -void CudaStream::LaunchGraph(const CudaGraphExecutable* executable) { - executable->Launch(cuda_stream_); -} - -#endif // WITH_ROCM_GRAPHS - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/cuda_stream.h b/oneflow/core/ep/rocm/cuda_stream.h deleted file mode 100644 index b3149a7ed23bed687611c7aef675725a16f48f4b..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/cuda_stream.h +++ /dev/null @@ -1,168 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EP_ROCM_CUDA_STREAM_H_ -#define ONEFLOW_CORE_EP_ROCM_CUDA_STREAM_H_ - -#include "oneflow/core/ep/include/stream.h" -#include "oneflow/core/ep/rocm/cuda_device.h" - -#ifdef WITH_ROCM - -#include -#include "oneflow/core/hipdnn/hipdnn.h" - -// #if CUDA_VERSION >= 11000 -// #define WITH_ROCM_GRAPHS -// #endif // CUDA_VERSION >= 11000 - -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace ep { - -class CudaDevice; - -#ifdef WITH_ROCM_GRAPHS - -class CudaGraphExecutable { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaGraphExecutable); - CudaGraphExecutable(); - ~CudaGraphExecutable(); - - void Update(hipGraph_t graph); - void Launch(hipStream_t stream) const; - bool IsInstantiated() const; - - private: - void Reset(); - - hipGraphExec_t graph_exec_; - int dev_; -}; - -#endif // WITH_ROCM_GRAPHS - -struct CudaLaunchConfig { - dim3 grid_dim; - dim3 block_dim; - size_t shared_mem_size; - CudaLaunchConfig() : grid_dim{}, block_dim{}, shared_mem_size(0) {} - - CudaLaunchConfig(unsigned int grid_size, unsigned int block_size, size_t shared_mem_size) - : grid_dim(grid_size), block_dim(block_size), shared_mem_size(shared_mem_size) {} -}; - -class CudaStream : public Stream { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaStream); - explicit CudaStream(CudaDevice* device); - ~CudaStream() override; - - static constexpr uint32_t kDefaultBlockSize = 256; - - DeviceType device_type() const override; - CudaDevice* device() const override; - Maybe Sync() override; - void RecordEvent(Event* event) override; - - Maybe OnExecutionContextSetup() override; - Maybe OnExecutionContextTeardown() override; - - hipStream_t cuda_stream() const; - hipblasHandle_t cublas_handle() const; - -// #if CUDA_VERSION >= 10010 - -// cublasLtHandle_t cublas_lt_handle() const; - -// #endif - - hipdnnHandle_t cudnn_handle() const; - void* cublas_workspace() const; - size_t cublas_workspace_size() const; - const hipDeviceProp_t& device_properties() const; - int cuda_arch() const; - - void InitLaunchConfigWithWaves(CudaLaunchConfig* config, size_t elem_cnt, size_t block_size, - size_t max_waves) const { - const uint32_t max_grid_size = max_waves * device_properties().multiProcessorCount - * (device_properties().maxThreadsPerMultiProcessor / block_size); - const uint32_t grid_size = - std::min(max_grid_size, (elem_cnt + block_size - 1) / block_size); - config->grid_dim = dim3(grid_size); - config->block_dim = dim3(block_size); - config->shared_mem_size = 0; - } - -#ifdef __HIPCC__ - template - void LaunchKernel(void (*kernel)(Params...), const CudaLaunchConfig& launch_config, - Args... args) { - kernel<<>>(args...); - } - - template - void LaunchKernel(void (*kernel)(Params...), size_t elem_cnt, size_t max_waves, Args... args) { - constexpr uint32_t block_size = kDefaultBlockSize; - CudaLaunchConfig config{}; - InitLaunchConfigWithWaves(&config, elem_cnt, block_size, max_waves); - LaunchKernel(kernel, config, args...); - } - - template - void LaunchKernelDefaultWaves(void (*kernel)(Params...), size_t elem_cnt, Args... args) { - const size_t default_waves = 32; - LaunchKernel(kernel, elem_cnt, default_waves, args...); - } -#endif // __HIPCC__ - -#ifdef WITH_ROCM_GRAPHS - void BeginGraphCapture(); - void EndGraphCapture(CudaGraphExecutable* executable); - bool IsGraphCapturing() const; - void LaunchGraph(const CudaGraphExecutable* executable); -#endif // WITH_ROCM_GRAPHS - - private: - hipStream_t cuda_stream_{}; - hipblasHandle_t cublas_handle_{}; - -// #if CUDA_VERSION >= 10010 - -// cublasLtHandle_t cublas_lt_handle_{}; - -// #endif - - hipdnnHandle_t cudnn_handle_{}; - int device_index_; - void* workspace_{}; - size_t workspace_size_{}; -#ifdef WITH_ROCM_GRAPHS - bool is_graph_capturing_{}; -#endif // WITH_ROCM_GRAPHS - CudaDevice* device_; -}; - -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_EP_ROCM_CUDA_STREAM_H_ diff --git a/oneflow/core/ep/rocm/primitive/add.hip.cpp b/oneflow/core/ep/rocm/primitive/add.hip.cpp deleted file mode 100644 index 174cdbbd819e55c6da3de4630d565dbca7e89378..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/add.hip.cpp +++ /dev/null @@ -1,139 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/ep/include/primitive/add.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/device/cuda_pseudo_bfloat16.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -template -struct AddFunctor; - -template -struct AddFunctor { - __device__ T operator()(T x) const { return x; } -}; - -template -struct AddFunctor { - __device__ T operator()(T x0, U x1, Args... xs) const { - return x0 + AddFunctor()(x1, xs...); - } -}; - -template -__global__ void AddGpu(const Args*... srcs, T* dst, size_t count) { - CUDA_1D_KERNEL_LOOP_T(size_t, i, count) { dst[i] = AddFunctor()(srcs[i]...); } -} - -template -void LaunchAddGpu(hipStream_t stream, const Args*... srcs, T* dst, size_t count) { - AddGpu - <<>>(srcs..., dst, count); -} - -template -void DispatchLaunch(hipStream_t stream, const T* const* srcs, size_t arity, T* dst, size_t count) { - if (arity == 0) { - OF_CUDA_CHECK(hipMemsetAsync(dst, 0, count * sizeof(T), stream)); - } else if (arity == 1) { - OF_CUDA_CHECK(hipMemcpyAsync(dst, srcs[0], count * sizeof(T), hipMemcpyDefault, stream)); - } else if (arity == 2) { - OF_CUDA_CHECK((cuda::elementwise::Binary, T, T, T>( - AddFunctor(), count, dst, srcs[0], srcs[1], stream))); - } else if (arity == 3) { - OF_CUDA_CHECK((cuda::elementwise::Ternary, T, T, T, T>( - AddFunctor(), count, dst, srcs[0], srcs[1], srcs[2], stream))); - } else if (arity == 4) { - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], dst, count); - } else if (arity == 5) { - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], srcs[4], dst, count); - } else if (arity == 6) { - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], srcs[4], srcs[5], - dst, count); - } else if (arity == 7) { - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], srcs[4], - srcs[5], srcs[6], dst, count); - } else if (arity == 8) { - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], srcs[4], - srcs[5], srcs[6], srcs[7], dst, count); - } else { - DispatchLaunch(stream, srcs + 7, arity - 7, dst, count); - LaunchAddGpu(stream, srcs[0], srcs[1], srcs[2], srcs[3], srcs[4], - srcs[5], srcs[6], dst, dst, count); - } -} - -template -class AddImpl : public Add { - public: - OF_DISALLOW_COPY_AND_MOVE(AddImpl); - AddImpl() = default; - ~AddImpl() override = default; - - using Add::Launch; - void Launch(Stream* stream, const void* const* srcs, size_t arity, void* dst, - size_t count) override { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - DispatchLaunch(cuda_stream, reinterpret_cast(srcs), arity, - reinterpret_cast(dst), count); - } -}; - -template -std::unique_ptr NewAdd() { - return std::unique_ptr(new AddImpl()); -} - -class AddFactoryImpl : public AddFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(AddFactoryImpl); - AddFactoryImpl() = default; - ~AddFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type) override { -#define MAKE_NEW_ADD_ENTRY(type_cpp, type_proto) {type_proto, NewAdd}, - - static const std::map()>> new_add_handle{ - OF_PP_FOR_EACH_TUPLE(MAKE_NEW_ADD_ENTRY, CUDA_PRIMITIVE_ALL_TYPE_SEQ)}; - -#undef MAKE_NEW_ADD_ENTRY - - const auto it = new_add_handle.find(data_type); - if (it != new_add_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, AddFactory, AddFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/binary_functor.hip.h b/oneflow/core/ep/rocm/primitive/binary_functor.hip.h deleted file mode 100644 index 3dd42dcc90636eeae593f1d74348c6a412555e83..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/binary_functor.hip.h +++ /dev/null @@ -1,151 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/ep/common/primitive/binary_functor.h" - -namespace oneflow { -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -template -struct BinaryFunctor { - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC Dst operator()(Src src0, Src src1) const { return pow(src0, src1); } -}; - -template<> -struct BinaryFunctor { - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(bool src0, bool src1) const { - return static_cast(pow(static_cast(src0), static_cast(src1))); - } -}; - -template<> -struct BinaryFunctor { - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC half operator()(half src0, half src1) const { - return static_cast(pow(static_cast(src0), static_cast(src1))); - } -}; - -template -struct BinaryFunctor { - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) { -#if defined(__CUDA_ARCH__) - coef = sqrt(static_cast(2.0) / acos(static_cast(-1.0))); -#elif defined(__HIP_DEVICE_COMPILE__) - coef = sqrt(static_cast(2.0) / acos(static_cast(-1.0))); -#else - coef = std::sqrt(static_cast(2.0) / std::acos(static_cast(-1.0))); -#endif - } - - OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { - return static_cast(0.5) - * (static_cast(1.0) + erf(static_cast(M_SQRT1_2) * x) - + x * coef * exp(static_cast(-0.5) * x * x)) - * dy; - } - Src coef; -}; - -template -struct BinaryFunctor { - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC Dst operator()(Src dy, Src x) const { - Src tanh_val = tanh(x); - return static_cast(dy * (static_cast(1.0) - tanh_val * tanh_val)); - } -}; - -// /*********nv_bfloat16_kernel*******/ - -// #if CUDA_VERSION >= 11000 - -// template<> -// struct BinaryFunctor { -// OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) {} - -// OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src0, nv_bfloat16 src1) const { -// return static_cast(pow(static_cast(src0), static_cast(src1))); -// } -// }; - -// #define SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(op) \ -// template<> \ -// struct BinaryFunctor { \ -// OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ -// \ -// BinaryFunctor float_functor; \ -// OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src0, nv_bfloat16 src1) const { \ -// return __float2bfloat16(float_functor(__bfloat162float(src0), __bfloat162float(src1))); \ -// } \ -// }; - -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kEluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kCeluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kGeluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kHardswishBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kHardsigmoidBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kHardshrinkBackwardWithDyY); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kHardtanhBackwardWithDyY); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kLeakyReluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kMishBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kSeluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kSiluBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kSoftsignBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kSoftplusBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kSoftshrinkBackwardWithDyY); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kTanhBackwardWithDyX); -// SPECIALIZATION_PSEUDO_BFLOAT16_BINARY_FUNCTOR(BinaryOp::kThresholdBackwardWithDyX); - -// #endif // CUDA_VERSION >= 11000 - -#define SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(op) \ - template<> \ - struct BinaryFunctor { \ - OF_DEVICE_FUNC BinaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ - \ - BinaryFunctor float_functor; \ - OF_DEVICE_FUNC half operator()(half src0, half src1) const { \ - return __float2half(float_functor(__half2float(src0), __half2float(src1))); \ - } \ - }; - -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kEluBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kCeluBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kGeluBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kHardswishBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kHardshrinkBackwardWithDyY); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kMishBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSiluBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSeluBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSoftplusBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSoftsignBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kSoftshrinkBackwardWithDyY); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kThresholdBackwardWithDyX); -SPECIALIZATION_PSEUDO_HALF_BINARY_FUNCTOR(BinaryOp::kTanhBackwardWithDyX); - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.cpp b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.cpp deleted file mode 100644 index 25759ec4ee4abfc0282062eae653600fd9b5ac7a..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.cpp +++ /dev/null @@ -1,110 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" -#include "oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/primitive/binary_functor.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -template -std::unique_ptr NewBroadcastElementwiseBinary(Scalar attr0, - Scalar attr1); - -namespace { - -class BroadcastElementwiseBinaryFactoryImpl : public BroadcastElementwiseBinaryFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseBinaryFactoryImpl); - BroadcastElementwiseBinaryFactoryImpl() = default; - ~BroadcastElementwiseBinaryFactoryImpl() override = default; - - std::unique_ptr New(BinaryOp op, DataType src_type, DataType dst_type, - size_t max_num_dims) override { - return New(op, src_type, dst_type, max_num_dims, Scalar(), Scalar()); - } - - std::unique_ptr New(BinaryOp op, DataType src_type, DataType dst_type, - size_t max_num_dims, Scalar attr0) override { - return New(op, src_type, dst_type, max_num_dims, attr0, Scalar()); - } - - std::unique_ptr New(BinaryOp binary_op, DataType src_type, - DataType dst_type, size_t max_num_dims, - Scalar attr0, Scalar attr1) override { - if (max_num_dims > kMaxNumDims) { return nullptr; } -#define MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY(binary_op, data_type_pair) \ - {std::make_tuple(binary_op, OF_PP_PAIR_SECOND(data_type_pair), \ - OF_PP_PAIR_SECOND(data_type_pair)), \ - NewBroadcastElementwiseBinary}, - -#define MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_AND_LOGICAL_ENTRY( \ - binary_op, src_data_type_pair, dst_data_type_pair) \ - {std::make_tuple(binary_op, OF_PP_PAIR_SECOND(src_data_type_pair), \ - OF_PP_PAIR_SECOND(dst_data_type_pair)), \ - NewBroadcastElementwiseBinary}, - -#define MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY(binary_op, data_type_pair) \ - {std::make_tuple(binary_op, OF_PP_PAIR_SECOND(data_type_pair), \ - OF_PP_PAIR_SECOND(data_type_pair)), \ - NewBroadcastElementwiseBinary}, - - static const std::map< - std::tuple, - std::function(Scalar, Scalar)>> - new_broadcast_elementwise_binary_handle{ - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, - BINARY_MATH_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ) - - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_AND_LOGICAL_ENTRY, - BINARY_COMPARISION_OP_SEQ BINARY_LOGICAL_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ, - CUDA_PRIMITIVE_BOOL_TYPE_SEQ) - - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY, - BINARY_ACTIVATION_BACKWARD_OP_SEQ, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ)}; - -#undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_AND_LOGICAL_ENTRY -#undef MAKE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY - - const auto it = new_broadcast_elementwise_binary_handle.find( - std::make_tuple(binary_op, src_type, dst_type)); - if (it != new_broadcast_elementwise_binary_handle.end()) { - return it->second(attr0, attr1); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BroadcastElementwiseBinaryFactory, - BroadcastElementwiseBinaryFactoryImpl); -} // namespace -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h deleted file mode 100644 index 20b271780949885338bb78a3eaddea3b72d968a1..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h +++ /dev/null @@ -1,397 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" -#include "oneflow/core/ep/common/primitive/broadcast_elementwise_binary.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/primitive/binary_functor.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -namespace { - -template -struct GetPackType { - using type = typename std::aligned_storage::type; -}; - -template -using PackType = typename GetPackType::type; - -template -union Pack { - static_assert(sizeof(PackType) == sizeof(T) * N, ""); - OF_DEVICE_FUNC Pack() { - // do nothing - } - PackType storage; - T elem[N]; -}; - -template -struct BroadcastElementwiseBinaryParams { - NdIndexOffsetHelper src0_index_helper; - NdIndexOffsetHelper src1_index_helper; - NdIndexOffsetHelper dst_index_helper; - size_t num_dims; - IndexType src0_index_mask[max_dims]; - IndexType src1_index_mask[max_dims]; - IndexType count{}; - const void* src0{}; - const void* src1{}; - void* dst{}; - Scalar attr0; - Scalar attr1; -}; - -template -__global__ void BroadcastElementwiseBinaryGpu( - BroadcastElementwiseBinaryParams params) { - constexpr size_t dst_pack_size = - src0_pack_size > src1_pack_size ? src0_pack_size : src1_pack_size; - static_assert(src0_pack_size == dst_pack_size || src0_pack_size == 1, ""); - static_assert(src1_pack_size == dst_pack_size || src1_pack_size == 1, ""); - - const PackType* src0 = - reinterpret_cast*>(params.src0); - const PackType* src1 = - reinterpret_cast*>(params.src1); - PackType* dst = reinterpret_cast*>(params.dst); - - IndexType src0_index[max_dims]; - IndexType src1_index[max_dims]; - IndexType dst_index[max_dims]; - size_t num_dims = params.num_dims; - CUDA_1D_KERNEL_LOOP_T(IndexType, offset, params.count) { - params.dst_index_helper.OffsetToNdIndex(offset, dst_index, num_dims); -#pragma unroll - for (int i = 0; i < max_dims; ++i) { - if (i < num_dims) { - src0_index[i] = params.src0_index_mask[i] * dst_index[i]; - src1_index[i] = params.src1_index_mask[i] * dst_index[i]; - } else { - src0_index[i] = 0; - src1_index[i] = 0; - } - } - const IndexType src0_offset = params.src0_index_helper.NdIndexToOffset(src0_index, num_dims); - const IndexType src1_offset = params.src1_index_helper.NdIndexToOffset(src1_index, num_dims); - Pack src0_pack; - src0_pack.storage = src0[src0_offset]; - Pack src1_pack; - src1_pack.storage = src1[src1_offset]; - Pack dst_pack; - BinaryFunctor functor(params.attr0, params.attr1); -#pragma unroll - for (int j = 0; j < dst_pack_size; ++j) { - const Src src0_val = - (src0_pack_size == dst_pack_size) ? src0_pack.elem[j] : src0_pack.elem[0]; - const Src src1_val = - (src1_pack_size == dst_pack_size) ? src1_pack.elem[j] : src1_pack.elem[0]; - dst_pack.elem[j] = functor(src0_val, src1_val); - } - dst[offset] = dst_pack.storage; - } -} - -template -void LaunchKernel(Stream* stream, int num_dims, const int64_t* src0_dims, const void* src0, - const int64_t* src1_dims, const void* src1, const int64_t* dst_dims, void* dst, - size_t count, Scalar attr0, Scalar attr1) { - BroadcastElementwiseBinaryParams params; - for (size_t i = 0; i < num_dims; ++i) { - params.src0_index_mask[i] = (src0_dims[i] == 1) ? 0 : 1; - params.src1_index_mask[i] = (src1_dims[i] == 1) ? 0 : 1; - } - params.src0_index_helper = NdIndexOffsetHelper(src0_dims, num_dims); - params.src1_index_helper = NdIndexOffsetHelper(src1_dims, num_dims); - params.dst_index_helper = NdIndexOffsetHelper(dst_dims, num_dims); - params.num_dims = num_dims; - params.src0 = src0; - params.src1 = src1; - params.dst = dst; - params.count = static_cast(count); - params.attr0 = attr0; - params.attr1 = attr1; - auto* cuda_stream = stream->As(); - BroadcastElementwiseBinaryGpu - <<cuda_stream()>>>(params); -} - -template -void DispatchIndexType(Stream* stream, size_t num_dims, const int64_t* src0_dims, const void* src0, - const int64_t* src1_dims, const void* src1, const int64_t* dst_dims, - void* dst, Scalar attr0, Scalar attr1) { - size_t count = GetElementCount(num_dims, dst_dims); - if (count < GetMaxVal()) { - LaunchKernel( - stream, num_dims, src0_dims, src0, src1_dims, src1, dst_dims, dst, count, attr0, attr1); - } else { - LaunchKernel( - stream, num_dims, src0_dims, src0, src1_dims, src1, dst_dims, dst, count, attr0, attr1); - } -} - -template -void DispatchPackSize(Stream* stream, size_t src0_pack_size, size_t src1_pack_size, size_t num_dims, - const int64_t* src0_dims, const void* src0, const int64_t* src1_dims, - const void* src1, const int64_t* dst_dims, void* dst, Scalar attr0, - Scalar attr1) { - void (*func)(Stream* /*stream*/, size_t /*num_dims*/, const int64_t* /*src0_dims*/, - const void* /*src0*/, const int64_t* /*src1_dims*/, const void* /*src1*/, - const int64_t* /*dst_dims*/, void* /*dst*/, Scalar /*attr0*/, Scalar /*attr1*/) = - nullptr; - if (src0_pack_size == 1 && src1_pack_size == 1) { - func = DispatchIndexType; - } else if (src0_pack_size == 4 && src1_pack_size == 4) { - func = DispatchIndexType; - } else if (src0_pack_size == 1 && src1_pack_size == 4) { - func = DispatchIndexType; - } else if (src0_pack_size == 4 && src1_pack_size == 1) { - func = DispatchIndexType; - } else { - UNIMPLEMENTED(); - } - func(stream, num_dims, src0_dims, src0, src1_dims, src1, dst_dims, dst, attr0, attr1); -} - -template -void DispatchNumDims(Stream* stream, size_t src0_pack_size, size_t src1_pack_size, size_t num_dims, - const int64_t* src0_dims, const void* src0, const int64_t* src1_dims, - const void* src1, const int64_t* dst_dims, void* dst, Scalar attr0, - Scalar attr1) { - void (*func)(Stream* /*stream*/, size_t /*src0_pack_size*/, size_t /*src1_pack_size*/, - size_t /*num_dims*/, const int64_t* /*src0_dims*/, const void* /*src0*/, - const int64_t* /*src1_dims*/, const void* /*src1*/, const int64_t* /*dst_dims*/, - void* /*dst*/, Scalar /*attr0*/, Scalar /*attr1*/) = nullptr; - CHECK_NE(num_dims, 1); - if (num_dims == 2) { - func = DispatchPackSize; - } else if (num_dims == 3) { - func = DispatchPackSize; - } else if (num_dims == 4) { - func = DispatchPackSize; - } else if (num_dims <= 8) { - func = DispatchPackSize; - } else { - UNIMPLEMENTED(); - } - func(stream, src0_pack_size, src1_pack_size, num_dims, src0_dims, src0, src1_dims, src1, dst_dims, - dst, attr0, attr1); -} - -template -size_t GetPackSize(size_t num_src_dims, const int64_t* src0_dims, const void* src0, - const int64_t* src1_dims, const void* src1, void* dst) { - static_assert(max_pack_size > 0 && (max_pack_size & (max_pack_size - 1)) == 0, ""); - CHECK(src0_dims[num_src_dims - 1] != 1 || src1_dims[num_src_dims - 1] != 1); - auto dst_ptr = reinterpret_cast(dst); - for (size_t pack_size = max_pack_size; pack_size > 2; pack_size /= 2) { - bool is_src0_supported = (src0_dims[num_src_dims - 1] == 1) - || IsPackSizeSupported(pack_size, num_src_dims, src0_dims, src0); - bool is_src1_supported = (src1_dims[num_src_dims - 1] == 1) - || IsPackSizeSupported(pack_size, num_src_dims, src1_dims, src1); - if (is_src0_supported && is_src1_supported && (dst_ptr % (pack_size * sizeof(R))) == 0) { - return pack_size; - } - } - return 1; -} - -constexpr size_t kMaxPackSize = 4; - -template -void LaunchWithSimplified(Stream* stream, size_t simplified_num_dims, int64_t* simplified_src0_dims, - const void* src0, int64_t* simplified_src1_dims, const void* src1, - int64_t* simplified_dst_dims, void* dst, Scalar attr0, Scalar attr1) { - CHECK_LE(simplified_num_dims, kMaxNumDims); - size_t pack_size = GetPackSize(simplified_num_dims, simplified_src0_dims, - src0, simplified_src1_dims, src1, dst); - size_t src0_pack_size = 1; - size_t src1_pack_size = 1; - if (simplified_src0_dims[simplified_num_dims - 1] != 1) { - simplified_src0_dims[simplified_num_dims - 1] /= pack_size; - src0_pack_size = pack_size; - } - if (simplified_src1_dims[simplified_num_dims - 1] != 1) { - simplified_src1_dims[simplified_num_dims - 1] /= pack_size; - src1_pack_size = pack_size; - } - simplified_dst_dims[simplified_num_dims - 1] /= pack_size; - DispatchNumDims(stream, src0_pack_size, src1_pack_size, simplified_num_dims, - simplified_src0_dims, src0, simplified_src1_dims, src1, - simplified_dst_dims, dst, attr0, attr1); -} - -template -struct BinaryLhsScalarFunctor { - __host__ __device__ BinaryLhsScalarFunctor(Src scalar, Scalar attr0, Scalar attr1) - : scalar(scalar), functor(attr0, attr1) {} - __device__ Dst operator()(Src src) const { return functor(scalar, src); } - const Src scalar; - BinaryFunctor functor; -}; - -template -struct BinaryRhsScalarFunctor { - __host__ __device__ BinaryRhsScalarFunctor(Src scalar, Scalar attr0, Scalar attr1) - : scalar(scalar), functor(attr0, attr1) {} - __device__ Dst operator()(Src src) const { return functor(src, scalar); } - const Src scalar; - BinaryFunctor functor; -}; - -template -struct BinaryLhsScalarPtrFunctorFactory { - __host__ __device__ BinaryLhsScalarPtrFunctorFactory(const Src* scalar_ptr, Scalar attr0, - Scalar attr1) - : scalar_ptr(scalar_ptr), attr0(attr0), attr1(attr1) {} - __device__ BinaryLhsScalarFunctor operator()() const { - return BinaryLhsScalarFunctor(*scalar_ptr, attr0, attr1); - } - const Src* scalar_ptr; - Scalar attr0, attr1; -}; - -template -struct BinaryRhsScalarPtrFunctorFactory { - __host__ __device__ explicit BinaryRhsScalarPtrFunctorFactory(const Src* scalar_ptr, Scalar attr0, - Scalar attr1) - : scalar_ptr(scalar_ptr), attr0(attr0), attr1(attr1) {} - __device__ BinaryRhsScalarFunctor operator()() const { - return BinaryRhsScalarFunctor(*scalar_ptr, attr0, attr1); - } - const Src* scalar_ptr; - Scalar attr0, attr1; -}; - -template -void DispatchLaunch(Stream* stream, size_t num_src0_dims, const int64_t* src0_dims, const Src* src0, - size_t num_src1_dims, const int64_t* src1_dims, const Src* src1, Dst* dst, - Scalar attr0, Scalar attr1) { - auto* cuda_stream = stream->As(); - size_t simplified_num_dims = 0; - int64_t simplified_src0_dims[kMaxNumDims]; - int64_t simplified_src1_dims[kMaxNumDims]; - int64_t simplified_dst_dims[kMaxNumDims]; - SimplifyBroadcastDims(num_src0_dims, src0_dims, num_src1_dims, src1_dims, - &simplified_num_dims, simplified_src0_dims, - simplified_src1_dims, simplified_dst_dims); - CheckInplace(simplified_num_dims, simplified_src0_dims, src0, simplified_src1_dims, src1, - simplified_dst_dims, dst); - if (IsDimsEquals(simplified_num_dims, simplified_src0_dims, simplified_num_dims, - simplified_src1_dims)) { - const int64_t elem_cnt = GetElementCount(simplified_num_dims, simplified_src0_dims); - OF_CUDA_CHECK((cuda::elementwise::Binary( - BinaryFunctor(attr0, attr1), elem_cnt, dst, src0, - src1, cuda_stream->cuda_stream()))); - } else { - if (simplified_num_dims == 1 && simplified_src0_dims[0] == 1) { - OF_CUDA_CHECK((cuda::elementwise::UnaryWithFactory( - BinaryLhsScalarPtrFunctorFactory(src0, attr0, attr1), - simplified_src1_dims[0], dst, src1, cuda_stream->cuda_stream()))); - } else if (simplified_num_dims == 1 && simplified_src1_dims[0] == 1) { - OF_CUDA_CHECK((cuda::elementwise::UnaryWithFactory( - BinaryRhsScalarPtrFunctorFactory(src1, attr0, attr1), - simplified_src0_dims[0], dst, src0, cuda_stream->cuda_stream()))); - } else { - LaunchWithSimplified(stream, simplified_num_dims, simplified_src0_dims, - src0, simplified_src1_dims, src1, - simplified_dst_dims, dst, attr0, attr1); - } - } -} - -template -T GetValue(Scalar value) { - return value.Value(); -} - -template<> -half GetValue(Scalar value) { - return static_cast(GetValue(value)); -} - -// #if CUDA_VERSION >= 11000 - -// template<> -// nv_bfloat16 GetValue(Scalar value) { -// return static_cast(GetValue(value)); -// } - -// #endif // CUDA_VERSION >= 11000 - -template -class BroadcastElementwiseBinaryImpl : public BroadcastElementwiseBinary { - public: - OF_DISALLOW_COPY_AND_MOVE(BroadcastElementwiseBinaryImpl); - BroadcastElementwiseBinaryImpl(Scalar attr0, Scalar attr1) : attr0(attr0), attr1(attr1) {} - ~BroadcastElementwiseBinaryImpl() override = default; - - void Launch(Stream* stream, Scalar src0, size_t num_src1_dims, const int64_t* src1_dims, - const void* src1, void* dst) override { - auto* cuda_stream = stream->As(); - const size_t elem_cnt = GetElementCount(num_src1_dims, src1_dims); - OF_CUDA_CHECK((cuda::elementwise::Unary( - BinaryLhsScalarFunctor(GetValue(src0), attr0, attr1), elem_cnt, - reinterpret_cast(dst), reinterpret_cast(src1), - cuda_stream->cuda_stream()))); - } - void Launch(Stream* stream, size_t num_src0_dims, const int64_t* src0_dims, const void* src0, - Scalar src1, void* dst) override { - auto* cuda_stream = stream->As(); - const size_t elem_cnt = GetElementCount(num_src0_dims, src0_dims); - OF_CUDA_CHECK((cuda::elementwise::Unary( - BinaryRhsScalarFunctor(GetValue(src1), attr0, attr1), elem_cnt, - reinterpret_cast(dst), reinterpret_cast(src0), - cuda_stream->cuda_stream()))); - } - void Launch(Stream* stream, size_t num_src0_dims, const int64_t* src0_dims, const void* src0, - size_t num_src1_dims, const int64_t* src1_dims, const void* src1, - void* dst) override { - DispatchLaunch( - stream, num_src0_dims, src0_dims, reinterpret_cast(src0), num_src1_dims, - src1_dims, reinterpret_cast(src1), reinterpret_cast(dst), attr0, attr1); - } - - private: - Scalar attr0, attr1; -}; - -} // namespace - -template -std::unique_ptr NewBroadcastElementwiseBinary(Scalar attr0, - Scalar attr1) { - return std::unique_ptr( - new BroadcastElementwiseBinaryImpl(attr0, attr1)); -} - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_activation_grad.hip.cpp b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_activation_grad.hip.cpp deleted file mode 100644 index c6252be0da975a387864e619a82ce9958325247c..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_activation_grad.hip.cpp +++ /dev/null @@ -1,39 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY(binary_op, \ - data_type_pair) \ - template std::unique_ptr NewBroadcastElementwiseBinary< \ - binary_op, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(data_type_pair)>( \ - Scalar attr0, Scalar attr1); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_ACTIVATION_GRAD_ENTRY, - BINARY_ACTIVATION_BACKWARD_OP_SEQ, - CUDA_PRIMITIVE_FLOATING_TYPE_SEQ); - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow - diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_comparision.hip.cpp b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_comparision.hip.cpp deleted file mode 100644 index a7fc91ed08e72b48ecfcc76721e8d4b617f914d3..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_comparision.hip.cpp +++ /dev/null @@ -1,38 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_ENTRY( \ - binary_op, src_data_type_pair, dst_data_type_pair) \ - template std::unique_ptr NewBroadcastElementwiseBinary< \ - binary_op, OF_PP_PAIR_FIRST(src_data_type_pair), OF_PP_PAIR_FIRST(dst_data_type_pair)>( \ - Scalar attr0, Scalar attr1); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_COMPARASION_ENTRY, - BINARY_COMPARISION_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ, - CUDA_PRIMITIVE_BOOL_TYPE_SEQ); - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_logical.hip.cpp b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_logical.hip.cpp deleted file mode 100644 index ffa05eb78b83794dcb69625b809fc77dbd91ae4d..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_logical.hip.cpp +++ /dev/null @@ -1,38 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_LOGICAL_ENTRY(binary_op, src_data_type_pair, \ - dst_data_type_pair) \ - template std::unique_ptr NewBroadcastElementwiseBinary< \ - binary_op, OF_PP_PAIR_FIRST(src_data_type_pair), OF_PP_PAIR_FIRST(dst_data_type_pair)>( \ - Scalar attr0, Scalar attr1); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_LOGICAL_ENTRY, - BINARY_COMPARISION_OP_SEQ BINARY_LOGICAL_OP_SEQ, - CUDA_PRIMITIVE_ALL_TYPE_SEQ, CUDA_PRIMITIVE_BOOL_TYPE_SEQ); - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_math.hip.cpp b/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_math.hip.cpp deleted file mode 100644 index a2ca2bb2540f6e2d73030bf23b7184282b39fa4f..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary_math.hip.cpp +++ /dev/null @@ -1,36 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/primitive/broadcast_elementwise_binary.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { -namespace broadcast_elementwise_binary { - -#define INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY(binary_op, data_type_pair) \ - template std::unique_ptr NewBroadcastElementwiseBinary< \ - binary_op, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(data_type_pair)>( \ - Scalar attr0, Scalar attr1); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NEW_BROADCAST_ELEMENTWISE_BINARY_MATH_ENTRY, - BINARY_MATH_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ); - -} // namespace broadcast_elementwise_binary -} // namespace primitive -} // namespace ep - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/broadcast_matmul.cpp b/oneflow/core/ep/rocm/primitive/broadcast_matmul.cpp deleted file mode 100644 index d42ace21b03aa27ffdc7a24f19e3554024f2c5b2..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/broadcast_matmul.cpp +++ /dev/null @@ -1,237 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM - -#include "oneflow/core/ep/include/primitive/primitive.h" -#include "oneflow/core/ep/include/primitive/broadcast_matmul.h" -#include "oneflow/core/ep/common/primitive/broadcast_matmul.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include -#include -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace broadcast_matmul { - -namespace internal { - -namespace { - -constexpr size_t kMaxNumDims = 8; - -Optional OptCudaDataType(DataType data_type) { - switch (data_type) { - case kFloat: return HIPBLAS_R_32F; - case kDouble: return HIPBLAS_R_64F; - case kFloat16: return HIPBLAS_R_16F; -// #if CUDA_VERSION >= 11000 -// case kBFloat16: return CUDA_R_16BF; -// #endif // CUDA_VERSION >= 11000 - default: return NullOpt; - } -} - -hipblasDatatype_t GetCudaDataType(DataType data_type) { - auto cuda_data_type = OptCudaDataType(data_type); - CHECK(cuda_data_type.has_value()); - return cuda_data_type.value_or(HIPBLAS_R_32F); -} - -union CublasScalarParameter { - double d; - float s; -}; - -CublasScalarParameter GetCublasScalarParameter(Scalar scalar, hipblasDatatype_t compute_type) { - CublasScalarParameter sp{}; - if (compute_type == HIPBLAS_R_64F) { - sp.d = scalar.Value(); - } else if (compute_type == HIPBLAS_R_32F) { - sp.s = scalar.Value(); - } else if (compute_type == HIPBLAS_R_16F) { - sp.s = scalar.Value(); - } else { - UNIMPLEMENTED(); - } - return sp; -} - -hipblasDatatype_t GetComputeType(DataType data_type) { - switch (data_type) { - case kFloat: return HIPBLAS_R_32F; - case kDouble: return HIPBLAS_R_64F; - case kFloat16: return HIPBLAS_R_16F; -// #if CUDA_VERSION >= 11000 -// case kBFloat16: return HIPBLAS_R_32F; -// #endif // CUDA_VERSION >= 11000 - default: UNIMPLEMENTED(); return HIPBLAS_R_32F; - } -} - -void LaunchBroadcastMatmul(Stream* stream, DataType data_type, BlasTransposeType transpose_a, - BlasTransposeType transpose_b, int64_t num_batch_dims, - const int64_t* broadcast_batch_dims, const int64_t* a_batch_dims, - const int64_t* b_batch_dims, const int64_t* c_batch_dims, int64_t m, - int64_t n, int64_t k, Scalar alpha, const void* a, const void* b, - Scalar beta, void* c) { - auto* cuda_stream = stream->As(); - const auto cuda_data_type = GetCudaDataType(data_type); - const auto compute_type = GetComputeType(data_type); - const auto sp_alpha = GetCublasScalarParameter(alpha, compute_type); - __half h_alpha = 0; - if (compute_type == HIPBLAS_R_16F) { - h_alpha = __float2half(sp_alpha.s); - } - const auto GetCublasOperation = [](BlasTransposeType transpose_type) { - if (transpose_type == BlasTransposeType::N) { - return HIPBLAS_OP_N; - } else if (transpose_type == BlasTransposeType::T) { - return HIPBLAS_OP_T; - } else { - UNIMPLEMENTED(); - return HIPBLAS_OP_N; - } - }; - const hipblasOperation_t cublas_trans_a = GetCublasOperation(transpose_b); - const hipblasOperation_t cublas_trans_b = GetCublasOperation(transpose_a); - const int cublas_m = n; - const int cublas_n = m; - const int cublas_k = k; - int cublas_lda = 0; - if (transpose_b == BlasTransposeType::N) { - cublas_lda = n; - } else if (transpose_b == BlasTransposeType::T) { - cublas_lda = k; - } else { - UNIMPLEMENTED(); - } - int cublas_ldb = 0; - if (transpose_a == BlasTransposeType::N) { - cublas_ldb = k; - } else if (transpose_a == BlasTransposeType::T) { - cublas_ldb = m; - } else { - UNIMPLEMENTED(); - } - const int cublas_ldc = n; - // CublasMathModeGuard guard(cuda_stream->cublas_handle()); -// if (data_type == DataType::kFloat16) { -// #if CUDA_VERSION < 11000 -// guard.SetMathMode(CUBLAS_TENSOR_OP_MATH); -// #else -// guard.SetMathMode(CUBLAS_DEFAULT_MATH); -// #endif // CUDA_VERSION < 11000 -// } -// #if CUDA_VERSION >= 11000 -// hipblasGemmAlgo_t algo = HIPBLAS_GEMM_DEFAULT; - hipblasGemmAlgo_t algo = HIPBLAS_GEMM_DEFAULT; -// #else -// hipblasGemmAlgo_t algo = -// (data_type == DataType::kFloat16) ? CUBLAS_GEMM_DFALT_TENSOR_OP : HIPBLAS_GEMM_DEFAULT; -// #endif - if (num_batch_dims == 1 && c_batch_dims[0] != 1) { - const void* cublas_a = b; - const void* cublas_b = a; - void* cublas_c = c; - const int64_t a_batch_count = a_batch_dims[0]; - const int64_t b_batch_count = b_batch_dims[0]; - CHECK(a_batch_count == 1 || b_batch_count == 1 || a_batch_count == b_batch_count); - CHECK_GT(a_batch_count, 0); - CHECK_GT(b_batch_count, 0); - const int batch_count = std::max(a_batch_count, b_batch_count); - const long long int cublas_stride_a = b_batch_count == 1 ? 0 : cublas_m * cublas_k; - const long long int cublas_stride_b = a_batch_count == 1 ? 0 : cublas_k * cublas_n; - const long long int cublas_stride_c = cublas_m * cublas_n; - const auto sp_beta = GetCublasScalarParameter(beta, compute_type); - __half h_beta = 0; - if (compute_type == HIPBLAS_R_16F) { - h_beta = __float2half(sp_beta.s); - OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( - cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, cublas_k, - &h_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_stride_a, cublas_b, cuda_data_type, - cublas_ldb, cublas_stride_b, &h_beta, cublas_c, cuda_data_type, cublas_ldc, - cublas_stride_c, batch_count, compute_type, algo)); - } else { - OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( - cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, cublas_k, - &sp_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_stride_a, cublas_b, cuda_data_type, - cublas_ldb, cublas_stride_b, &sp_beta, cublas_c, cuda_data_type, cublas_ldc, - cublas_stride_c, batch_count, compute_type, algo)); - } - - } else { - auto func = [&](const void* batch_a, const void* batch_b, void* batch_c, Scalar batch_beta) { - const auto sp_beta = GetCublasScalarParameter(batch_beta, compute_type); - __half h_beta = 0; - const void* cublas_a = batch_b; - const void* cublas_b = batch_a; - void* cublas_c = batch_c; - if (compute_type == HIPBLAS_R_16F) { - h_beta = __float2half(sp_beta.s); - OF_CUBLAS_CHECK(hipblasGemmEx( - cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, - cublas_k, &h_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_b, cuda_data_type, - cublas_ldb, &h_beta, cublas_c, cuda_data_type, cublas_ldc, compute_type, algo)); - } else { - OF_CUBLAS_CHECK(hipblasGemmEx( - cuda_stream->cublas_handle(), cublas_trans_a, cublas_trans_b, cublas_m, cublas_n, - cublas_k, &sp_alpha, cublas_a, cuda_data_type, cublas_lda, cublas_b, cuda_data_type, - cublas_ldb, &sp_beta, cublas_c, cuda_data_type, cublas_ldc, compute_type, algo)); - } - - }; - ForEachMatmul(data_type, m, n, k, beta, num_batch_dims, broadcast_batch_dims, - a_batch_dims, b_batch_dims, c_batch_dims, a, b, c, func); - } -} - -class BroadcastMatmulFactoryImpl : public BroadcastMatmulFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(BroadcastMatmulFactoryImpl); - BroadcastMatmulFactoryImpl() = default; - ~BroadcastMatmulFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type, BlasTransposeType transpose_a, - BlasTransposeType transpose_b, - size_t max_num_dims) override { - auto cuda_data_type = OptCudaDataType(data_type); - if (max_num_dims <= kMaxNumDims && cuda_data_type.has_value()) { - return std::make_unique>(data_type, transpose_a, - transpose_b); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, BroadcastMatmulFactory, BroadcastMatmulFactoryImpl); - -} // namespace - -} // namespace internal - -} // namespace broadcast_matmul - -} // namespace primitive -} // namespace ep - -} // namespace oneflow - -#endif // WITH_ROCM diff --git a/oneflow/core/ep/rocm/primitive/cast.hip.cpp b/oneflow/core/ep/rocm/primitive/cast.hip.cpp deleted file mode 100644 index d65d12658dcd5552fd8c4244999fc74bede3d909..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/cast.hip.cpp +++ /dev/null @@ -1,148 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/ep/include/primitive/cast.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -template -struct CastFunctor { - __device__ To operator()(From from) const { return static_cast(from); } -}; - -template -struct CastFunctor::value>::type> { - __device__ To operator()(half from) const { return static_cast(static_cast(from)); } - - __device__ void Apply2(To* to, const half* from) const { - const float2 f2 = __half22float2(*reinterpret_cast(from)); - to[0] = static_cast(f2.x); - to[1] = static_cast(f2.y); - } -}; - -template -struct CastFunctor::value>::type> { - __device__ half operator()(From from) const { - return static_cast(static_cast(from)); - } - - __device__ void Apply2(half* to, const From* from) const { - float2 f2; - f2.x = static_cast(from[0]); - f2.y = static_cast(from[1]); - *reinterpret_cast(to) = __float22half2_rn(f2); - } -}; - -// #if CUDA_VERSION >= 11000 - -// template -// struct CastFunctor::value -// || std::is_same::value)>::type> { -// __device__ To operator()(nv_bfloat16 from) const { -// return static_cast(static_cast(from)); -// } -// }; - -// template -// struct CastFunctor::value -// || std::is_same::value)>::type> { -// __device__ nv_bfloat16 operator()(From from) const { -// return static_cast(static_cast(from)); -// } -// }; - -// #endif // CUDA_VERSION >= 11000 - -template -class CastImpl : public Cast { - public: - OF_DISALLOW_COPY_AND_MOVE(CastImpl); - explicit CastImpl() = default; - ~CastImpl() override = default; - - void Launch(Stream* stream, const void* from, void* to, size_t count) override { - auto* cuda_stream = stream->As(); - OF_CUDA_CHECK((cuda::elementwise::Unary, To, From>( - CastFunctor(), count, reinterpret_cast(to), - reinterpret_cast(from), cuda_stream->cuda_stream()))); - } -}; - -template -std::unique_ptr NewCast() { - return std::unique_ptr(new CastImpl()); -} - -#define CUDA_PRIMITIVE_CAST_TYPE_SEQ \ - CUDA_PRIMITIVE_BOOL_TYPE_SEQ \ - CUDA_PRIMITIVE_CHAR_TYPE_SEQ \ - CUDA_PRIMITIVE_INT8_TYPE_SEQ \ - CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ - CUDA_PRIMITIVE_INT32_TYPE_SEQ \ - CUDA_PRIMITIVE_UINT32_TYPE_SEQ \ - CUDA_PRIMITIVE_INT64_TYPE_SEQ \ - CUDA_PRIMITIVE_UINT64_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ - CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ - CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ - -class CastFactoryImpl : public CastFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(CastFactoryImpl); - CastFactoryImpl() = default; - ~CastFactoryImpl() override = default; - - std::unique_ptr New(DataType from, DataType to) override { -#define MAKE_NEW_CAST_ENTRY(from_pair, to_pair) \ - {std::make_pair(OF_PP_PAIR_SECOND(from_pair), OF_PP_PAIR_SECOND(to_pair)), \ - NewCast}, - - static const std::map, std::function()>> - new_cast_handle{OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - MAKE_NEW_CAST_ENTRY, CUDA_PRIMITIVE_CAST_TYPE_SEQ, CUDA_PRIMITIVE_CAST_TYPE_SEQ)}; - -#undef MAKE_NEW_CAST_ENTRY - - const auto it = new_cast_handle.find(std::make_pair(from, to)); - if (it != new_cast_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, CastFactory, CastFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/constant_pad.hip.cpp b/oneflow/core/ep/rocm/primitive/constant_pad.hip.cpp deleted file mode 100644 index fd0d037d78d5bbdefa723a2e8ffd1fe78ab3ee2e..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/constant_pad.hip.cpp +++ /dev/null @@ -1,255 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/include/primitive/constant_pad.h" -#include "oneflow/core/ep/common/primitive/constant_pad.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace ep { - -namespace primitive { - -namespace { - -template -__global__ void ConstantPadKernel(ConstantPadParams params, - StorageType packed_pad_val) { - const StorageType* src = reinterpret_cast(params.src); - StorageType* dst = reinterpret_cast(params.dst); - IndexType src_index[num_dims]; - IndexType dst_index[num_dims]; - CUDA_1D_KERNEL_LOOP_T(IndexType, linear_index, params.elem_cnt) { - params.dst_index_helper.OffsetToNdIndex(linear_index, dst_index); - bool if_pad = false; -#pragma unroll - for (int i = 0; i < num_dims; i++) { - if (dst_index[i] >= params.valid_start[i] && dst_index[i] < params.valid_end[i]) { - src_index[i] = dst_index[i] - params.valid_start[i]; - } else { - if_pad = true; - break; - } - } - StorageType dst_val = packed_pad_val; - if (!if_pad) { - const IndexType src_offset = params.src_index_helper.NdIndexToOffset(src_index); - dst_val = src[src_offset]; - } - dst[linear_index] = dst_val; - } -} - -template<> -half GetValue(Scalar value) { - return static_cast(GetValue(value)); -} - -// #if CUDA_VERSION >= 11000 - -// template<> -// nv_bfloat16 GetValue(Scalar value) { -// return static_cast(GetValue(value)); -// } - -// #endif // CUDA_VERSION >= 11000 - -template -void LaunchKernel(Stream* stream, ConstantPadParams params, - StorageType packed_pad_val, size_t elem_cnt) { - stream->As()->LaunchKernelDefaultWaves( - (ConstantPadKernel), elem_cnt, params, packed_pad_val); -} - -template -void LaunchKernel(Stream* stream, void* dst, const int64_t* dst_dims, const void* src, - const int64_t* src_dims, const int64_t* padding_before, - const int64_t* padding_after, StorageType packed_pad_val, size_t elem_cnt) { - ConstantPadParams params; - params.dst_index_helper = OffsetToIndexCalculator(dst_dims); - params.src_index_helper = NdIndexOffsetHelper(src_dims); - params.dst = dst; - params.src = src; - for (int i = 0; i < num_dims; i++) { - params.valid_start[i] = padding_before[i]; - params.valid_end[i] = dst_dims[i] - padding_after[i]; - } - params.elem_cnt = elem_cnt; - LaunchKernel(stream, params, packed_pad_val, elem_cnt); -} - -template -void DispatchIndexType(Stream* stream, void* dst, const int64_t* dst_dims, const void* src, - const int64_t* src_dims, const int64_t* padding_before, - const int64_t* padding_after, StorageType packed_pad_val, size_t elem_cnt) { - if (elem_cnt < GetMaxVal()) { - LaunchKernel(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, packed_pad_val, - elem_cnt); - } else { - LaunchKernel(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, packed_pad_val, - elem_cnt); - } -} - -template -void DispatchPackSize(Stream* stream, void* dst, int64_t* dst_dims, const void* src, - int64_t* src_dims, int64_t* padding_before, int64_t* padding_after, - T pad_val) { - constexpr int32_t max_packsize = GetMaxPackSize(); - size_t launch_pack_size = GetLaunchPackSize(num_dims, dst, dst_dims, src, src_dims, - padding_before, padding_after); - - dst_dims[num_dims - 1] /= launch_pack_size; - src_dims[num_dims - 1] /= launch_pack_size; - padding_before[num_dims - 1] /= launch_pack_size; - padding_after[num_dims - 1] /= launch_pack_size; - - size_t elem_cnt = 1; - for (int i = 0; i < num_dims; i++) { elem_cnt *= dst_dims[i]; } - if (launch_pack_size == 1) { - Pack packed_pad_val(pad_val); - DispatchIndexType>(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, - packed_pad_val.storage, elem_cnt); - } else if (launch_pack_size == 2) { - Pack packed_pad_val(pad_val); - DispatchIndexType>(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, - packed_pad_val.storage, elem_cnt); - } else if (launch_pack_size == 4) { - Pack packed_pad_val(pad_val); - DispatchIndexType>(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, - packed_pad_val.storage, elem_cnt); - } else if (launch_pack_size == 8) { - Pack packed_pad_val(pad_val); - DispatchIndexType>(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, - packed_pad_val.storage, elem_cnt); - } else if (launch_pack_size == 16) { - Pack packed_pad_val(pad_val); - DispatchIndexType>(stream, dst, dst_dims, src, src_dims, - padding_before, padding_after, - packed_pad_val.storage, elem_cnt); - } else { - UNIMPLEMENTED(); - } -} - -template -void LaunchWithSimplified(Stream* stream, size_t num_dims, void* dst, int64_t* dst_dims, - const void* src, int64_t* src_dims, int64_t* padding_before, - int64_t* padding_after, T pad_val) { - void (*func)(Stream* /*stream*/, void* /*dst*/, int64_t* /*dst_dims*/, const void* /*src*/, - int64_t* /*src_dims*/, int64_t* /*padding_before*/, int64_t* /*padding_after*/, T) = - nullptr; - if (num_dims == 1) { - func = DispatchPackSize<1, T>; - } else if (num_dims == 2) { - func = DispatchPackSize<2, T>; - } else if (num_dims == 3) { - func = DispatchPackSize<3, T>; - } else if (num_dims == 4) { - func = DispatchPackSize<4, T>; - } else if (num_dims == 5) { - func = DispatchPackSize<5, T>; - } else if (num_dims == 6) { - func = DispatchPackSize<6, T>; - } else if (num_dims == 7) { - func = DispatchPackSize<7, T>; - } else if (num_dims == 8) { - func = DispatchPackSize<8, T>; - } else { - UNIMPLEMENTED(); - } - func(stream, dst, dst_dims, src, src_dims, padding_before, padding_after, pad_val); -} - -template -void SimplifyThenLaunch(Stream* stream, size_t num_dims, const int64_t* src_dims, const void* src, - const int64_t* padding_before, const int64_t* padding_after, T pad_val, - void* dst) { - CHECK_LE(num_dims, kMaxNumDims); - int64_t simplified_dst_dims[kMaxNumDims]; - int64_t simplified_src_dims[kMaxNumDims]; - int64_t simplified_padding_before[kMaxNumDims]; - int64_t simplified_padding_after[kMaxNumDims]; - size_t simplified_num_dims = 1; - SimplifyPadDims(num_dims, src_dims, padding_before, padding_after, &simplified_num_dims, - simplified_dst_dims, simplified_src_dims, simplified_padding_before, - simplified_padding_after); - LaunchWithSimplified(stream, simplified_num_dims, dst, simplified_dst_dims, src, - simplified_src_dims, simplified_padding_before, simplified_padding_after, - pad_val); -} - -template -class ConstantPadImpl : public ConstantPad { - public: - OF_DISALLOW_COPY_AND_MOVE(ConstantPadImpl); - ConstantPadImpl() = default; - ~ConstantPadImpl() override = default; - - void Launch(Stream* stream, size_t num_dims, const int64_t* src_dims, const void* src, - const int64_t* padding_before, const int64_t* padding_after, Scalar pad_val, - void* dst) override { - SimplifyThenLaunch(stream, num_dims, src_dims, src, padding_before, padding_after, - GetValue(pad_val), dst); - } -}; - -template -std::unique_ptr NewConstantPad() { - return std::unique_ptr(new ConstantPadImpl()); -} - -class ConstantPadFactoryImpl : public ConstantPadFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(ConstantPadFactoryImpl); - ConstantPadFactoryImpl() = default; - ~ConstantPadFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type) override { -#define MAKE_NEW_CONSTANT_PAD_ENTRY(type_cpp, type_proto) {type_proto, NewConstantPad}, - - static const std::map()>> - new_constant_pad_handle{ - OF_PP_FOR_EACH_TUPLE(MAKE_NEW_CONSTANT_PAD_ENTRY, CUDA_PRIMITIVE_ALL_TYPE_SEQ)}; - -#undef MAKE_NEW_CONSTANT_PAD_ENTRY - - const auto it = new_constant_pad_handle.find(data_type); - if (it != new_constant_pad_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, ConstantPadFactory, ConstantPadFactoryImpl); - -} // namespace - -} // namespace primitive - -} // namespace ep - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/copy_nd.hip.cpp b/oneflow/core/ep/rocm/primitive/copy_nd.hip.cpp deleted file mode 100644 index 8dc4589c2e04f7d019ca6e584993212453031074..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/copy_nd.hip.cpp +++ /dev/null @@ -1,95 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/ep/include/primitive/copy_nd.h" -#include "oneflow/core/ep/common/primitive/copy_nd.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -template -__global__ void CopyNdKernel(CopyNdKernelParams params) { - using T = typename std::aligned_storage::type; - const T* src = reinterpret_cast(params.src); - T* dst = reinterpret_cast(params.dst); - IndexType copy_index[num_dims]; - IndexType src_index[num_dims]; - IndexType dst_index[num_dims]; - CUDA_1D_KERNEL_LOOP_T(IndexType, i, params.count) { - params.copy_index_helper.OffsetToNdIndex(i, copy_index); -#pragma unroll - for (size_t j = 0; j < num_dims; ++j) { - src_index[j] = params.src_pos[j] + copy_index[j]; - dst_index[j] = params.dst_pos[j] + copy_index[j]; - } - const IndexType src_offset = params.src_index_helper.NdIndexToOffset(src_index); - const IndexType dst_offset = params.dst_index_helper.NdIndexToOffset(dst_index); - dst[dst_offset] = src[src_offset]; - } -} - -template -void LaunchKernel(Stream* stream, CopyNdKernelParams params) { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - CopyNdKernel - <<>>(params); -} - -class CopyNdImpl : public CopyNd { - public: - OF_DISALLOW_COPY_AND_MOVE(CopyNdImpl); - CopyNdImpl() = default; - ~CopyNdImpl() override = default; - - void Launch(Stream* stream, DataType data_type, size_t num_dims, void* dst, - const int64_t* dst_dims, const int64_t* dst_pos, const void* src, - const int64_t* src_dims, const int64_t* src_pos, - const int64_t* extent) const override { - SimplifyThenLaunch(stream, data_type, num_dims, dst, dst_dims, dst_pos, src, src_dims, src_pos, - extent); - } -}; - -class CopyNdFactoryImpl : public CopyNdFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(CopyNdFactoryImpl); - CopyNdFactoryImpl() = default; - ~CopyNdFactoryImpl() override = default; - - std::unique_ptr New(size_t max_num_dims) override { - if (max_num_dims <= kMaxNumDims) { - return std::unique_ptr(new CopyNdImpl()); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, CopyNdFactory, CopyNdFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/elementwise_unary.hip.cpp b/oneflow/core/ep/rocm/primitive/elementwise_unary.hip.cpp deleted file mode 100644 index 1f0c93b2efc14ec93b7d67cf0d1486bddf5abe7a..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/elementwise_unary.hip.cpp +++ /dev/null @@ -1,117 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/common/primitive/elementwise_unary.h" -#include "oneflow/core/ep/rocm/primitive/unary_functor.hip.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -template -class ElementwiseUnaryImpl : public ElementwiseUnary { - public: - OF_DISALLOW_COPY_AND_MOVE(ElementwiseUnaryImpl); - ElementwiseUnaryImpl(Scalar attr0, Scalar attr1) : attr0(attr0), attr1(attr1) {} - ~ElementwiseUnaryImpl() override = default; - - void Launch(Stream* stream, const void* src, void* dst, size_t count) override { - auto* cuda_stream = stream->As(); - auto functor = UnaryFunctor(attr0, attr1); - OF_CUDA_CHECK((cuda::elementwise::Unary( - functor, count, reinterpret_cast(dst), reinterpret_cast(src), - cuda_stream->cuda_stream()))); - } - - protected: - Scalar attr0, attr1; -}; - -template -std::unique_ptr NewElementwiseUnary(Scalar attr0, Scalar attr1) { - return std::unique_ptr( - new ElementwiseUnaryImpl(attr0, attr1)); -} - -class ElementwiseUnaryFactoryImpl : public ElementwiseUnaryFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(ElementwiseUnaryFactoryImpl); - ElementwiseUnaryFactoryImpl() = default; - ~ElementwiseUnaryFactoryImpl() override = default; - - std::unique_ptr New(UnaryOp unary_op, DataType src_type, - DataType dst_dtype) override { - return New(unary_op, src_type, dst_dtype, Scalar(), Scalar()); - } - - std::unique_ptr New(UnaryOp unary_op, DataType src_type, DataType dst_dtype, - Scalar attr0) override { - return New(unary_op, src_type, dst_dtype, attr0, Scalar()); - } - - std::unique_ptr New(UnaryOp unary_op, DataType src_type, DataType dst_dtype, - Scalar attr0, Scalar attr1) override { -#define MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY(unary_op, dtype_pair) \ - {std::make_tuple(unary_op, OF_PP_PAIR_SECOND(dtype_pair), OF_PP_PAIR_SECOND(dtype_pair)), \ - NewElementwiseUnary}, - -#define MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY(unary_op, src_type_pair, dst_dtype_pair) \ - {std::make_tuple(unary_op, OF_PP_PAIR_SECOND(src_type_pair), OF_PP_PAIR_SECOND(dst_dtype_pair)), \ - NewElementwiseUnary}, - - static const std::map, - std::function(Scalar, Scalar)>> - new_elementwise_unary_handle{ - // For All Type OP - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, - UNARY_MATH_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ) - // For Float Type OP - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY, - UNARY_FLOATING_MATH_OP_SEQ, - CUDA_PRIMITIVE_FLOATING_TYPE_SEQ) - - // For Utils OP - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY, - UNARY_UTILS_OP_SEQ, UTIL_OPS_DATA_TYPE_SEQ, - CUDA_PRIMITIVE_BOOL_TYPE_SEQ) - - // For Logical OP - OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY, - UNARY_LOGICAL_OP_SEQ, CUDA_PRIMITIVE_ALL_TYPE_SEQ, - CUDA_PRIMITIVE_BOOL_TYPE_SEQ)}; - -#undef MAKE_NEW_DIFFERENT_DTYPE_ELEMENTWISE_UNARY_ENTRY - -#undef MAKE_NEW_SAME_DTYPE_ELEMENTWISE_UNARY_ENTRY - const auto it = - new_elementwise_unary_handle.find(std::make_tuple(unary_op, src_type, dst_dtype)); - if (it != new_elementwise_unary_handle.end()) { - return it->second(attr0, attr1); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, ElementwiseUnaryFactory, ElementwiseUnaryFactoryImpl); - -} // namespace -} // namespace primitive -} // namespace ep -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/fill.hip.cpp b/oneflow/core/ep/rocm/primitive/fill.hip.cpp deleted file mode 100644 index c77b25171909f3ca6cbf4d44d1974fc793964f72..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/fill.hip.cpp +++ /dev/null @@ -1,151 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/include/primitive/fill.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -template -using Storage = typename std::aligned_storage::type; - -template -union Pack { - static constexpr size_t size = sizeof(T) * pack; - explicit __device__ __host__ Pack(T value) { - static_assert(sizeof(Pack) == size, ""); - static_assert(alignof(Pack) == size, ""); -#pragma unroll - for (size_t i = 0; i < pack; ++i) { elem[i] = value; } - } - T elem[pack]; - Storage storage; -}; - -template -__global__ void FillGpu(T* dst, T value, size_t count) { - const size_t pack_count = count / pack; - Pack pack_value(value); - auto* pack_dst = reinterpret_cast(dst); - CUDA_1D_KERNEL_LOOP_T(size_t, i, pack_count) { pack_dst[i] = pack_value.storage; } - T* tail_dst = dst + pack_count * pack; - const size_t tail_count = count - pack_count * pack; - CUDA_1D_KERNEL_LOOP_T(size_t, i, tail_count) { tail_dst[i] = value; } -} - -template -T GetValue(Scalar value) { - return value.Value(); -} - -template<> -half GetValue(Scalar value) { - return static_cast(GetValue(value)); -} - -// #if CUDA_VERSION >= 11000 - -// template<> -// nv_bfloat16 GetValue(Scalar value) { -// return static_cast(GetValue(value)); -// } - -// #endif // CUDA_VERSION >= 11000 - -template -typename std::enable_if<(pack != 0), void>::type LaunchPackFill(hipStream_t stream, T* dst, - T value, size_t count) { - FillGpu - <<>>(dst, value, count); -} - -template -typename std::enable_if<(pack == 0), void>::type LaunchPackFill(hipStream_t stream, T* dst, - T value, size_t count) { - LOG(FATAL) << "wrong alignment"; -} - -template -void LaunchFill(hipStream_t stream, T* dst, T value, size_t count) { - auto uintptr = reinterpret_cast(dst); - if (uintptr % 16 == 0) { - LaunchPackFill(stream, dst, value, count); - } else if (uintptr % 8 == 0) { - LaunchPackFill(stream, dst, value, count); - } else if (uintptr % 4 == 0) { - LaunchPackFill(stream, dst, value, count); - } else if (uintptr % 2 == 0) { - LaunchPackFill(stream, dst, value, count); - } else { - LaunchPackFill(stream, dst, value, count); - } -} - -template -class FillImpl : public Fill { - public: - OF_DISALLOW_COPY_AND_MOVE(FillImpl); - FillImpl() = default; - ~FillImpl() override = default; - - void Launch(Stream* stream, void* dst, Scalar value, size_t count) override { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - LaunchFill(cuda_stream, reinterpret_cast(dst), GetValue(value), count); - } -}; - -template -std::unique_ptr NewFill() { - return std::unique_ptr(new FillImpl()); -} - -class FillFactoryImpl : public FillFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(FillFactoryImpl); - FillFactoryImpl() = default; - ~FillFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type) override { -#define MAKE_NEW_FILL_ENTRY(type_cpp, type_proto) {type_proto, NewFill}, - - static const std::map()>> new_fill_handle{ - OF_PP_FOR_EACH_TUPLE(MAKE_NEW_FILL_ENTRY, CUDA_PRIMITIVE_ALL_TYPE_SEQ)}; - -#undef MAKE_NEW_FILL_ENTRY - - const auto it = new_fill_handle.find(data_type); - if (it != new_fill_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, FillFactory, FillFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/memcpy.cpp b/oneflow/core/ep/rocm/primitive/memcpy.cpp deleted file mode 100644 index 19624e428e3f13823731280c5f681ee8d3c5ecb3..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/memcpy.cpp +++ /dev/null @@ -1,62 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM - -#include "oneflow/core/ep/include/primitive/memcpy.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -class MemcpyImpl : public Memcpy { - public: - OF_DISALLOW_COPY_AND_MOVE(MemcpyImpl); - MemcpyImpl() = default; - ~MemcpyImpl() override = default; - - void Launch(Stream* stream, void* dst, const void* src, size_t count) override { - if (dst == src) { return; } - auto* cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemcpyAsync(dst, src, count, hipMemcpyDefault, cuda_stream->cuda_stream())); - } -}; - -class MemcpyFactoryImpl : public MemcpyFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(MemcpyFactoryImpl); - MemcpyFactoryImpl() = default; - ~MemcpyFactoryImpl() override = default; - - std::unique_ptr New(MemcpyKind kind) override { - return std::unique_ptr(new MemcpyImpl()); - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemcpyFactory, MemcpyFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow - -#endif diff --git a/oneflow/core/ep/rocm/primitive/memset.cpp b/oneflow/core/ep/rocm/primitive/memset.cpp deleted file mode 100644 index f92fc4d02a05e405129f325171110b87d09dc1ae..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/memset.cpp +++ /dev/null @@ -1,59 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM - -#include "oneflow/core/ep/include/primitive/memset.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -class MemsetImpl : public Memset { - public: - OF_DISALLOW_COPY_AND_MOVE(MemsetImpl); - MemsetImpl() = default; - ~MemsetImpl() override = default; - - void Launch(Stream* stream, void* ptr, int value, size_t count) override { - auto* cuda_stream = stream->As(); - OF_CUDA_CHECK(hipMemsetAsync(ptr, value, count, cuda_stream->cuda_stream())); - } -}; - -class MemsetFactoryImpl : public MemsetFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(MemsetFactoryImpl); - MemsetFactoryImpl() = default; - ~MemsetFactoryImpl() override = default; - - std::unique_ptr New() override { return std::unique_ptr(new MemsetImpl()); } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, MemsetFactory, MemsetFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow - -#endif diff --git a/oneflow/core/ep/rocm/primitive/permute.hip.cpp b/oneflow/core/ep/rocm/primitive/permute.hip.cpp deleted file mode 100644 index afc26a353efd1570089dc09505130b7f57e52f49..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/permute.hip.cpp +++ /dev/null @@ -1,333 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/include/primitive/permute.h" -#include "oneflow/core/ep/common/primitive/permute_impl.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace permute { - -namespace internal { - -namespace { - -constexpr int32_t kMov4TileSize = 32; -constexpr int32_t kMov2TileSize = 64; -constexpr int32_t kBlockRows = 8; - -template -__global__ void PermuteKernel(PermuteKernelParams params) { - using T = typename std::aligned_storage::type; - const T* src = reinterpret_cast(params.src); - T* dst = reinterpret_cast(params.dst); - IndexType src_index[num_dims]; - IndexType dst_index[num_dims]; - CUDA_1D_KERNEL_LOOP_T(IndexType, i, params.count) { - params.dst_index_helper.OffsetToNdIndex(i, dst_index); -#pragma unroll - for (size_t dim = 0; dim < num_dims; ++dim) { - src_index[params.permutation[dim]] = dst_index[dim]; - } - IndexType src_offset = params.src_index_helper.NdIndexToOffset(src_index); - dst[i] = src[src_offset]; - } -} - -// (B, X, Y) -> (B, Y, X) -// refer from https://developer.nvidia.com/blog/efficient-matrix-transpose-cuda-cc/ -template -__global__ void BatchTransposeKernel(const void* src_ptr, void* dst_ptr, IndexType rows, - IndexType cols, IndexType num_tile_rows, - IndexType num_tile_cols, int32_t block_nums) { - const IndexType src_rows = rows; - const IndexType src_cols = cols; - const IndexType dst_rows = cols; - const IndexType dst_cols = rows; - - using T = typename std::aligned_storage::type; - __shared__ T tile[tile_size][tile_size + 1]; // To avoid bank conflict. - - const T* src = reinterpret_cast(src_ptr); - T* dst = reinterpret_cast(dst_ptr); - - IndexType batch_num_tile = num_tile_rows * num_tile_cols; - for (int i = blockIdx.x, step = gridDim.x; i < block_nums; i += step) { - const IndexType batch_index = i / batch_num_tile; // the index of batch. - const IndexType tile_index = - i - batch_index * batch_num_tile; // equal to i % (num_tile_rows*num_tile_cols). the - // flatten index of tile in a batch. - - const IndexType tile_row_index = - tile_index / num_tile_cols; // the row index of tile in a batch. - const IndexType tile_col_index = - tile_index - - tile_row_index - * num_tile_cols; // equal to k % num_tile_cols. the col index of tile in a batch. - - const IndexType offset = batch_index * src_rows * src_cols; - { - IndexType col_in_tile = threadIdx.x; - IndexType col_in_matrix = tile_col_index * tile_size + threadIdx.x; -#pragma unroll - for (IndexType row_in_tile = threadIdx.y; row_in_tile < tile_size; - row_in_tile += kBlockRows) { - IndexType row_in_matrix = row_in_tile + tile_row_index * tile_size; - if (col_in_matrix < src_cols && row_in_matrix < src_rows) { - tile[row_in_tile][col_in_tile] = src[offset + row_in_matrix * src_cols + col_in_matrix]; - } - } - } - __syncthreads(); - { - IndexType col_in_tile = threadIdx.x; - IndexType col_in_matrix = tile_row_index * tile_size + threadIdx.x; -#pragma unroll - for (IndexType row_in_tile = threadIdx.y; row_in_tile < tile_size; - row_in_tile += kBlockRows) { - IndexType row_in_matrix = row_in_tile + tile_col_index * tile_size; - if (col_in_matrix < dst_cols && row_in_matrix < dst_rows) { - dst[offset + row_in_matrix * dst_cols + col_in_matrix] = tile[col_in_tile][row_in_tile]; - } - } - } - __syncthreads(); - } -} - -/* -Here is a Movementsie=2 version of Batch Transpose. -When the H W can be divided by 2. we can read data use movementsize=4, and write back as -movementsize=4. -*/ -template -__global__ void BatchTransposeMovement2Kernel(const void* src_ptr, void* dst_ptr, IndexType rows, - IndexType cols, IndexType num_tile_rows, - IndexType num_tile_cols, int32_t block_nums) { - const IndexType src_rows = rows; - const IndexType src_cols = cols; - const IndexType dst_rows = cols; - const IndexType dst_cols = rows; - - static_assert(tile_size % 2 == 0, ""); - using T_MOV2 = typename std::aligned_storage<2, 2>::type; - using T_MOV4 = typename std::aligned_storage<4, 4>::type; - - const T_MOV4* src = reinterpret_cast(src_ptr); - T_MOV4* dst = reinterpret_cast(dst_ptr); - - // Use union structure to process Load and Store. - __shared__ union { - T_MOV2 tile_m2[tile_size][tile_size + 2]; // half [64][66] - T_MOV4 tile_m4[tile_size][tile_size / 2 + 1]; // half2 [64][33] - } tile_mem; - - IndexType batch_num_tile = num_tile_rows * num_tile_cols; - for (int i = blockIdx.x, step = gridDim.x; i < block_nums; i += step) { - const IndexType batch_index = i / batch_num_tile; // the index of batch. - const IndexType tile_index = - i - batch_index * batch_num_tile; // equal to i % (num_tile_rows*num_tile_cols). the - // flatten index of tile in a batch. - - const IndexType tile_row_index = - tile_index / num_tile_cols; // the row index of tile in a batch. - const IndexType tile_col_index = - tile_index - - tile_row_index - * num_tile_cols; // equal to k % num_tile_cols. the col index of tile in a batch. - - const IndexType offset = batch_index * src_rows * src_cols; - { - IndexType col_in_tile = threadIdx.x; - IndexType col_in_matrix = tile_col_index * tile_size + threadIdx.x * 2; -#pragma unroll - for (IndexType row_in_tile = threadIdx.y; row_in_tile < tile_size; - row_in_tile += kBlockRows) { - IndexType row_in_matrix = row_in_tile + tile_row_index * tile_size; - if (col_in_matrix < src_cols && row_in_matrix < src_rows) { - tile_mem.tile_m4[row_in_tile][col_in_tile] = - src[(offset + row_in_matrix * src_cols + col_in_matrix) / 2]; - } - } - } - __syncthreads(); - { - IndexType col_in_tile = threadIdx.x; - IndexType col_in_matrix = tile_row_index * tile_size + threadIdx.x * 2; -#pragma unroll - for (IndexType row_in_tile = threadIdx.y; row_in_tile < tile_size; - row_in_tile += kBlockRows) { - IndexType row_in_matrix = row_in_tile + tile_col_index * tile_size; - union { - T_MOV4 m4; - T_MOV2 m2[2]; - } tmp_storage; - - if (col_in_matrix < dst_cols && row_in_matrix < dst_rows) { - tmp_storage.m2[0] = tile_mem.tile_m2[col_in_tile * 2][row_in_tile]; - tmp_storage.m2[1] = tile_mem.tile_m2[col_in_tile * 2 + 1][row_in_tile]; - dst[(offset + row_in_matrix * dst_cols + col_in_matrix) / 2] = tmp_storage.m4; - } - } - } - __syncthreads(); - } -} - -template -void LaunchBatchTransposeKernel(hipStream_t& cuda_stream, - const PermuteKernelParams& params, - const IndexType& num_batches, const IndexType& rows, - const IndexType& cols) { - IndexType num_tile_rows = (rows + tile_size - 1) / tile_size; - IndexType num_tile_cols = (cols + tile_size - 1) / tile_size; - const int32_t block_nums = num_batches * num_tile_rows * num_tile_cols; - int32_t launched_block_nums = std::min(block_nums, kCudaMaxBlocksNum); - if (tile_size == kMov2TileSize) { - const int32_t half2_thread = tile_size / 2; // cause each thread process two half elements. - BatchTransposeMovement2Kernel - <<>>( - params.src, params.dst, rows, cols, num_tile_rows, num_tile_cols, - block_nums); // Set threads num as 32x8 cause each threads - // process 4 elements to 64x66 half share memory. - } else { - BatchTransposeKernel - <<>>( - params.src, params.dst, rows, cols, num_tile_rows, num_tile_cols, block_nums); - } -} - -template -bool CheckIfGreaterEqualThanTileSize(const IndexType& rows, const IndexType& cols) { - if (rows < tile_size || cols < tile_size) { return false; } - return true; -} - -template -bool CheckLaunchBatchTranspose(const int* permutation, const IndexType& num_batches, - const IndexType& rows, const IndexType& cols) { - if (CheckIfGreaterEqualThanTileSize(rows, cols)) { - if (num_batches == 1 && permutation[1] == 0 && permutation[0] == 1) { - // 2d tensor case: (0, 1) -> (1, 0) - return true; - } else if (num_dims == 3 && permutation[2] == 1 && permutation[1] == 2) { - // 3d tensor case: (0, 1, 2) -> (0, 2, 1) - return true; - } else { - return false; - } - } - return false; -} - -template -bool CheckUseMov2(const IndexType& rows, const IndexType& cols, const void* src, void* dst) { - auto src_ptr = reinterpret_cast(src); - auto dst_ptr = reinterpret_cast(dst); - return (movement_size == 2) && (rows % 2 == 0) && (cols % 2 == 0) && (src_ptr % 4 == 0) - && (dst_ptr % 4 == 0); -} - -template -void InferBatchTransposeShape(const int64_t* src_dims, IndexType* num_batches, IndexType* rows, - IndexType* cols) { - if (num_dims == 2) { - *num_batches = 1; - *rows = src_dims[0]; - *cols = src_dims[1]; - } else { - *num_batches = src_dims[0]; - *rows = src_dims[1]; - *cols = src_dims[2]; - } -} - -template -void LaunchKernel(Stream* stream, const int64_t* src_dims, const void* src, const int* permutation, - void* dst, size_t count) { - PermuteKernelParams params = - MakePermuteParams(src_dims, src, permutation, dst, count); - hipStream_t cuda_stream = stream->As()->cuda_stream(); - - if (num_dims == 2 || num_dims == 3) { - IndexType num_batches; - IndexType rows; - IndexType cols; - InferBatchTransposeShape(src_dims, &num_batches, &rows, &cols); - if (CheckLaunchBatchTranspose(params.permutation, num_batches, rows, - cols)) { - if (CheckUseMov2(rows, cols, src, dst)) { - LaunchBatchTransposeKernel(cuda_stream, params, - num_batches, rows, cols); - } else { - LaunchBatchTransposeKernel( - cuda_stream, params, num_batches, rows, cols); - } - } else { - PermuteKernel - <<>>(params); - } - } else { - PermuteKernel - <<>>(params); - } -} - -class PermuteImpl : public Permute { - public: - OF_DISALLOW_COPY_AND_MOVE(PermuteImpl); - PermuteImpl() = default; - ~PermuteImpl() override = default; - - using Permute::Launch; - void Launch(Stream* stream, DataType data_type, size_t num_dims, const int64_t* src_dims, - const void* src, const int* permutation, void* dst) override { - SimplifyThenLaunch(stream, data_type, num_dims, src_dims, src, permutation, dst); - } -}; - -class PermuteFactoryImpl : public PermuteFactory { - public: - OF_DISALLOW_COPY_AND_MOVE(PermuteFactoryImpl); - PermuteFactoryImpl() = default; - ~PermuteFactoryImpl() override = default; - - std::unique_ptr New(size_t max_num_dims) override { - if (max_num_dims <= kMaxNumDims) { - return std::unique_ptr(new PermuteImpl()); - } else { - return nullptr; - } - } -}; - -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, PermuteFactory, PermuteFactoryImpl); - -} // namespace - -} // namespace internal - -} // namespace permute - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/softmax.hip.cpp b/oneflow/core/ep/rocm/primitive/softmax.hip.cpp deleted file mode 100644 index c1d2d006b01f1e2af2954d3edaa4e7170aee2891..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/softmax.hip.cpp +++ /dev/null @@ -1,107 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/ep/include/primitive/softmax.h" -#include "oneflow/core/ep/include/primitive/log_softmax.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -enum class Algorithm { - kSoftmax, - kLogSoftmax, -}; - -template -void SoftmaxGpu(hipStream_t cuda_stream, size_t rows, size_t cols, const T* x, T* y) { - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - oneflow::cuda::softmax::DirectLoad load(x, cols); - oneflow::cuda::softmax::DirectStore store(y, cols); - if (algorithm == Algorithm::kSoftmax) { - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - cuda_stream, load, store, rows, cols))); - } else if (algorithm == Algorithm::kLogSoftmax) { - OF_CUDA_CHECK((cuda::softmax::DispatchLogSoftmax( - cuda_stream, load, store, rows, cols))); - } else { - UNIMPLEMENTED(); - } -} - -template -class SoftmaxImpl : public SoftmaxBase { - public: - OF_DISALLOW_COPY_AND_MOVE(SoftmaxImpl); - SoftmaxImpl() = default; - ~SoftmaxImpl() override = default; - - void Launch(Stream* stream, size_t rows, size_t cols, const void* x, void* y) override { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - SoftmaxGpu(cuda_stream, rows, cols, reinterpret_cast(x), - reinterpret_cast(y)); - } -}; - -template -std::unique_ptr NewSoftmax() { - return std::unique_ptr(new SoftmaxImpl()); -} - -template -class GenericSoftmaxFactoryImpl : public FactoryBase { - public: - OF_DISALLOW_COPY_AND_MOVE(GenericSoftmaxFactoryImpl); - GenericSoftmaxFactoryImpl() = default; - ~GenericSoftmaxFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type) override { -#define MAKE_NEW_SOFTMAX_ENTRY(type_cpp, type_proto) \ - {type_proto, NewSoftmax}, - - static const std::map()>> - new_softmax_handle{ - OF_PP_FOR_EACH_TUPLE(MAKE_NEW_SOFTMAX_ENTRY, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ)}; - -#undef MAKE_NEW_SOFTMAX_ENTRY - - const auto it = new_softmax_handle.find(data_type); - if (it != new_softmax_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -using SoftmaxFactoryImpl = GenericSoftmaxFactoryImpl; -using LogSoftmaxFactoryImpl = - GenericSoftmaxFactoryImpl; -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, SoftmaxFactory, SoftmaxFactoryImpl); -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, LogSoftmaxFactory, LogSoftmaxFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/softmax_backward.hip.cpp b/oneflow/core/ep/rocm/primitive/softmax_backward.hip.cpp deleted file mode 100644 index d1848907d6b61c04278a1db122e59d6c36318a72..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/softmax_backward.hip.cpp +++ /dev/null @@ -1,116 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/ep/include/primitive/softmax_backward.h" -#include "oneflow/core/ep/include/primitive/log_softmax_backward.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace ep { -namespace primitive { - -namespace { - -enum class Algorithm { - kSoftmax, - kLogSoftmax, -}; - -template -void SoftmaxBackwardGpu(hipStream_t cuda_stream, size_t rows, size_t cols, const T* y, const T* dy, - T* dx) { - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - cuda::softmax::DirectLoad load_y(y, cols); - cuda::softmax::DirectLoad load_dy(dy, cols); - cuda::softmax::DirectStore store(dx, cols); - if (algorithm == Algorithm::kSoftmax) { - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( - cuda_stream, load_y, load_dy, store, rows, cols))); - } else if (algorithm == Algorithm::kLogSoftmax) { - OF_CUDA_CHECK((cuda::softmax::DispatchLogSoftmaxGrad( - cuda_stream, load_y, load_dy, store, rows, cols))); - } else { - UNIMPLEMENTED(); - } -} - -template -class SoftmaxBackwardImpl : public SoftmaxBackwardBase { - public: - OF_DISALLOW_COPY_AND_MOVE(SoftmaxBackwardImpl); - SoftmaxBackwardImpl() = default; - ~SoftmaxBackwardImpl() override = default; - - void Launch(Stream* stream, size_t rows, size_t cols, const void* y, const void* dy, - void* dx) override { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - SoftmaxBackwardGpu(cuda_stream, rows, cols, reinterpret_cast(y), - reinterpret_cast(dy), reinterpret_cast(dx)); - } -}; - -template -std::unique_ptr NewSoftmaxBackward() { - return std::unique_ptr( - new SoftmaxBackwardImpl()); -} - -template -class GenericSoftmaxBackwardFactoryImpl : public BackwardFactoryBase { - public: - OF_DISALLOW_COPY_AND_MOVE(GenericSoftmaxBackwardFactoryImpl); - GenericSoftmaxBackwardFactoryImpl() = default; - ~GenericSoftmaxBackwardFactoryImpl() override = default; - - std::unique_ptr New(DataType data_type) override { -#define MAKE_NEW_SOFTMAX_ENTRY(type_cpp, type_proto) \ - {type_proto, NewSoftmaxBackward}, - - static const std::map()>> - new_softmax_backward_handle{ - OF_PP_FOR_EACH_TUPLE(MAKE_NEW_SOFTMAX_ENTRY, CUDA_PRIMITIVE_FLOATING_TYPE_SEQ)}; - -#undef MAKE_NEW_SOFTMAX_ENTRY - - const auto it = new_softmax_backward_handle.find(data_type); - if (it != new_softmax_backward_handle.end()) { - return it->second(); - } else { - return nullptr; - } - } -}; - -using SoftmaxBackwardFactoryImpl = - GenericSoftmaxBackwardFactoryImpl; -using LogSoftmaxBackwardFactoryImpl = - GenericSoftmaxBackwardFactoryImpl; -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, SoftmaxBackwardFactory, SoftmaxBackwardFactoryImpl); -REGISTER_PRIMITIVE_FACTORY(DeviceType::kCUDA, LogSoftmaxBackwardFactory, - LogSoftmaxBackwardFactoryImpl); - -} // namespace - -} // namespace primitive -} // namespace ep - -} // namespace oneflow diff --git a/oneflow/core/ep/rocm/primitive/type_seq.h b/oneflow/core/ep/rocm/primitive/type_seq.h deleted file mode 100644 index d82aa05fdec1d0a620204a65dfdf930a9c76efea..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/type_seq.h +++ /dev/null @@ -1,78 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EP_CUDA_PRIMITIVE_TYPE_SEQ_H_ -#define ONEFLOW_CORE_EP_CUDA_PRIMITIVE_TYPE_SEQ_H_ - -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/common/data_type.h" - -#ifdef WITH_ROCM -#include -#include - -// #if CUDA_VERSION >= 11000 -// #include -// #endif // CUDA_VERSION >= 11000 - -#define CUDA_PRIMITIVE_BOOL_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(bool, DataType::kBool) -#define CUDA_PRIMITIVE_CHAR_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(char, DataType::kChar) -#define CUDA_PRIMITIVE_INT8_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) -#define CUDA_PRIMITIVE_UINT8_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) -#define CUDA_PRIMITIVE_INT32_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) -#define CUDA_PRIMITIVE_UINT32_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) -#define CUDA_PRIMITIVE_INT64_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) -#define CUDA_PRIMITIVE_UINT64_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) -#define CUDA_PRIMITIVE_FLOAT_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) -#define CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) -#define CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) - -// #if CUDA_VERSION >= 11000 -// #define CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) -// #else -#define CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ -// #endif // CUDA_VERSION >= 11000 - -#define CUDA_PRIMITIVE_ALL_TYPE_SEQ \ - CUDA_PRIMITIVE_BOOL_TYPE_SEQ \ - CUDA_PRIMITIVE_CHAR_TYPE_SEQ \ - CUDA_PRIMITIVE_INT8_TYPE_SEQ \ - CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ - CUDA_PRIMITIVE_INT32_TYPE_SEQ \ - CUDA_PRIMITIVE_INT64_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ - CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ - CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ - -#define CUDA_PRIMITIVE_FLOATING_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ - CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ - CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ - -#define UTIL_OPS_DATA_TYPE_SEQ \ - CUDA_PRIMITIVE_INT8_TYPE_SEQ \ - CUDA_PRIMITIVE_UINT8_TYPE_SEQ \ - CUDA_PRIMITIVE_INT32_TYPE_SEQ \ - CUDA_PRIMITIVE_INT64_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT_TYPE_SEQ \ - CUDA_PRIMITIVE_DOUBLE_TYPE_SEQ \ - CUDA_PRIMITIVE_FLOAT16_TYPE_SEQ \ - CUDA_PRIMITIVE_BFLOAT16_TYPE_SEQ - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_EP_CUDA_PRIMITIVE_TYPE_SEQ_H_ \ No newline at end of file diff --git a/oneflow/core/ep/rocm/primitive/unary_functor.hip.h b/oneflow/core/ep/rocm/primitive/unary_functor.hip.h deleted file mode 100644 index f3ff3958f7a2b489d9203249cd9585bea281958d..0000000000000000000000000000000000000000 --- a/oneflow/core/ep/rocm/primitive/unary_functor.hip.h +++ /dev/null @@ -1,170 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/common/primitive/unary_functor.h" -#include "oneflow/core/ep/rocm/primitive/type_seq.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace ep { -namespace primitive { - -template -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC Dst operator()(Src src) const { - return static_cast(0.5) * src - * (static_cast(1.0) + erf(static_cast(M_SQRT1_2) * src)); - } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC float operator()(float src) const { return tanhf(src); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC double operator()(double src) const { return tanh(src); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC half operator()(half src) const { return __float2half(tanhf(__half2float(src))); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(half src) const { return isinf(__half2float(src)); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(float src) const { return isinf(src); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(double src) const { return isinf(src); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(half src) const { return isnan(__half2float(src)); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(float src) const { return isnan(src); } -}; - -template<> -struct UnaryFunctor { - UnaryFunctor(Scalar attr0, Scalar attr1) {} - - OF_DEVICE_FUNC bool operator()(double src) const { return isnan(src); } -}; - -#define SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(op) \ - template<> \ - struct UnaryFunctor { \ - UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ - \ - UnaryFunctor float_functor; \ - OF_DEVICE_FUNC half operator()(half src) const { \ - return __float2half(float_functor(__half2float(src))); \ - } \ - }; - -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kElu); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kCelu); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kGelu); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kMish); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSelu); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSilu); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSoftSign); -SPECIALIZATION_PSEUDO_HALF_UNARY_FUNCTOR(UnaryOp::kSoftPlus); - -// /*********nv_bfloat16_kernel*******/ - -// #if CUDA_VERSION >= 11000 - -// #define SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(op) \ -// template<> \ -// struct UnaryFunctor { \ -// UnaryFunctor(Scalar attr0, Scalar attr1) : float_functor(attr0, attr1) {} \ -// \ -// UnaryFunctor float_functor; \ -// OF_DEVICE_FUNC nv_bfloat16 operator()(nv_bfloat16 src) const { \ -// return __float2bfloat16(float_functor(__bfloat162float(src))); \ -// } \ -// }; - -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kElu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kCelu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kGelu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardSwish); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardSigmoid); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardShrink); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kHardTanh); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kLeakyRelu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kMish); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSelu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSilu); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftShrink); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftSign); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kSoftPlus); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kTanh); -// SPECIALIZATION_PSEUDO_BFLOAT16_UNARY_FUNCTOR(UnaryOp::kThreshold); - -// template<> -// struct UnaryFunctor { -// UnaryFunctor(Scalar attr0, Scalar attr1) {} - -// OF_DEVICE_FUNC bool operator()(nv_bfloat16 src) const { return isinf(__bfloat162float(src)); } -// }; - -// template<> -// struct UnaryFunctor { -// UnaryFunctor(Scalar attr0, Scalar attr1) {} - -// OF_DEVICE_FUNC bool operator()(nv_bfloat16 src) const { return isnan(__bfloat162float(src)); } -// }; - -// #endif - -} // namespace primitive -} // namespace ep -} // namespace oneflow - - diff --git a/oneflow/core/ep/test/primitive/fill_test.cpp b/oneflow/core/ep/test/primitive/fill_test.cpp index c204a47823cdd40653ad9a302b4a947cfe38eaa3..cb5fa7d67cb235d3424bb0898a2296b70b22c3f8 100644 --- a/oneflow/core/ep/test/primitive/fill_test.cpp +++ b/oneflow/core/ep/test/primitive/fill_test.cpp @@ -92,9 +92,9 @@ TEST_F(PrimitiveTest, TestFill) { 1024); #endif // CUDA_VERSION >= 11000 #endif // WITH_CUDA -// #ifdef WITH_ROCM -// TestFill(&device_manager_registry_, available_device_types_, 1024); -// #endif // WITH_ROCM +#ifdef WITH_ROCM + // TestFill(&device_manager_registry_, available_device_types_, 1024); +#endif // WITH_ROCM TestFill(&device_manager_registry_, available_device_types_, 1024); } diff --git a/oneflow/core/ep/test/primitive/softmax_backward_test.cpp b/oneflow/core/ep/test/primitive/softmax_backward_test.cpp index 77d3c20e42705847aa74b3b1ca1a137bc15ed1f5..28f24b46076a0bbab98d085aca2575ccf9b32c9e 100644 --- a/oneflow/core/ep/test/primitive/softmax_backward_test.cpp +++ b/oneflow/core/ep/test/primitive/softmax_backward_test.cpp @@ -115,10 +115,10 @@ void TestSoftmaxBackward(DeviceManagerRegistry* registry, const std::set(registry, device_types, num_rows, num_cols, false); - // TestSoftmaxBackward(registry, device_types, num_rows, num_cols, - // true); - // TestSoftmaxBackward(registry, device_types, num_rows, num_cols, - // false); + TestSoftmaxBackward(registry, device_types, num_rows, num_cols, + true); + TestSoftmaxBackward(registry, device_types, num_rows, num_cols, + false); TestSoftmaxBackward(registry, device_types, num_rows, num_cols, true); TestSoftmaxBackward(registry, device_types, num_rows, diff --git a/oneflow/core/ep/test/primitive/softmax_test.cpp b/oneflow/core/ep/test/primitive/softmax_test.cpp index a42968b1028c501ef7897a0f0ef4539a77b5a210..79eb56806230c9962f166f03d3663cc36577dc7f 100644 --- a/oneflow/core/ep/test/primitive/softmax_test.cpp +++ b/oneflow/core/ep/test/primitive/softmax_test.cpp @@ -101,8 +101,8 @@ void TestSoftmax(DeviceManagerRegistry* registry, const std::set& de int num_rows, int num_cols) { TestSoftmax(registry, device_types, num_rows, num_cols, true); TestSoftmax(registry, device_types, num_rows, num_cols, false); - // TestSoftmax(registry, device_types, num_rows, num_cols, true); - // TestSoftmax(registry, device_types, num_rows, num_cols, false); + TestSoftmax(registry, device_types, num_rows, num_cols, true); + TestSoftmax(registry, device_types, num_rows, num_cols, false); TestSoftmax(registry, device_types, num_rows, num_cols, true); TestSoftmax(registry, device_types, num_rows, num_cols, false); } diff --git a/oneflow/core/framework/arg_tuple.cpp b/oneflow/core/framework/arg_tuple.cpp index 9cb4fa4bbf12e1fea36505e5caafff722381c7bf..6ca5b9f030e466213be8c2fc7ffb74198d8e906e 100644 --- a/oneflow/core/framework/arg_tuple.cpp +++ b/oneflow/core/framework/arg_tuple.cpp @@ -37,7 +37,8 @@ void InitArgName2BnIndex2TensorTupleIndex( const int32_t bn_index = pair.second; // vector is auto created by [] if arg_name doesn't exist in map auto* bn_index2tensor_tuple_index = &(*arg_name2bn_index2tensor_tuple_index)[arg_name]; - CHECK_EQ(bn_index2tensor_tuple_index->size(), bn_index); + CHECK_EQ(bn_index2tensor_tuple_index->size(), bn_index) + << "Duplicate index of " << arg_name << ": " << bn_index; bn_index2tensor_tuple_index->emplace_back(i); } } diff --git a/oneflow/core/framework/attr_map.cpp b/oneflow/core/framework/attr_map.cpp index 9a6a71b6ae88481835ae46fe7d17d18d8ae86641..f93fa51ab31af1ace8883b2901c8e7d2ca4f016f 100644 --- a/oneflow/core/framework/attr_map.cpp +++ b/oneflow/core/framework/attr_map.cpp @@ -19,124 +19,143 @@ limitations under the License. #include "oneflow/core/framework/attr_value_accessor.h" #include "oneflow/core/framework/user_op_attr.pb.h" #include "oneflow/core/operator/op_conf.pb.h" +#include "oneflow/core/framework/mutable_attr_map.h" namespace oneflow { -namespace { - -size_t HashAttrName2AttrValWrapper(const oneflow::AttrName2AttrValWrapper& attr_name2attr_val) { - size_t hash_value = 0; - for (const auto& pair : attr_name2attr_val) { - hash_value ^= std::hash()(pair.first); - hash_value ^= pair.second->hash_value(); +AttrMap::AttrInternal::AttrInternal() + : max_size(0), + size(0), + hash_value(0), + ordered_attr_names(std::make_shared>()) {} + +AttrMap::AttrInternal::AttrInternal( + size_t _max_size, size_t _size, size_t _hash_value, + const std::shared_ptr>& _ordered_attr_names) + : max_size(_max_size), + size(_size), + hash_value(_hash_value), + ordered_attr_names(_ordered_attr_names) {} + +AttrMap::AttrMap() : internal_(std::make_shared()) {} + +AttrMap::AttrMap(const MutableAttrMap& other) + : internal_(std::make_shared(other.max_size(), /*size*/ 0, + /*hash_value*/ 0, + other.ordered_attr_names())) { + internal_->attrs.resize(internal_->max_size); + for (int i = 0; i < internal_->max_size; ++i) { + internal_->attrs[i].second = other.valid_masks()[i]; + if (other.valid_masks()[i]) { + ++(internal_->size); + internal_->attrs[i].first = other.attrs()[i]; + // compute hash code + HashCombine(&internal_->hash_value, other.attrs()[i]->hash_value()); + } } - return hash_value; -} - -const AttrName2AttrValWrapper& EmptyAttrName2AttrVal() { - static const auto empty = std::make_shared(); - static const AttrName2AttrValWrapper empty_symbol(empty); - return empty_symbol; -} - -} // namespace - -AttrName2AttrValWrapper::AttrName2AttrValWrapper( - const std::shared_ptr& attrs) - : attrs_(attrs) { - hash_value_ = HashAttrName2AttrValWrapper(*this); } -bool AttrName2AttrValWrapper::operator==(const AttrName2AttrValWrapper& other) const { - if (this->size() != other.size()) { return false; } - for (const_iterator this_iter = this->begin(), that_iter = other.begin(); - this_iter != this->end(); ++this_iter, ++that_iter) { - if (this_iter->first != that_iter->first) { return false; } - if (*this_iter->second != *that_iter->second) { return false; } +AttrMap::AttrMap(const UserOpConf& user_conf) + : internal_(std::make_shared()) { + for (const auto& kv : user_conf.attr()) { + auto cpp_attr_value = user_op::AttrValueUtil::ToCppAttrValue(kv.second); + if (cpp_attr_value.IsOk()) { + ++(internal_->size); + internal_->ordered_attr_names->emplace_back(kv.first); + internal_->attrs.emplace_back(CHECK_JUST(cpp_attr_value), true); + // compute hash code + HashCombine(&internal_->hash_value, internal_->attrs.back().first->hash_value()); + } else { + LOG(ERROR) << user_conf.DebugString() + << " failed to convert to cpp attr value, key: " << kv.first; + } } - return true; + internal_->max_size = internal_->size; } -AttrMap::AttrMap() : attrs_(EmptyAttrName2AttrVal()) {} - -AttrMap::AttrMap(const std::shared_ptr& attrs) : attrs_(attrs) {} - -namespace { - -AttrName2AttrValWrapper MakeAttrName2AttrValWrapper( - const std::initializer_list& init) { - const auto& attrs = std::make_shared(); - for (const auto& pair : init) { attrs->emplace(pair.first, pair.second); } - return AttrName2AttrValWrapper(attrs); -} - -AttrName2AttrValWrapper MakeAttrName2AttrValWrapper(const MutableAttrMap& other) { - const auto& attrs = std::make_shared(); - for (const auto& pair : other) { attrs->emplace(pair.first, pair.second); } - return AttrName2AttrValWrapper(attrs); -} - -} // namespace - -AttrMap::AttrMap(std::initializer_list init) - : attrs_(MakeAttrName2AttrValWrapper(init)) {} - -AttrMap::AttrMap(const MutableAttrMap& other) : attrs_(MakeAttrName2AttrValWrapper(other)) {} - AttrMap& AttrMap::operator=(const AttrMap& other) { - attrs_ = other.attrs_; + internal_ = other.internal_; return *this; } -bool AttrMap::operator==(const AttrMap& other) const { return attrs_ == other.attrs_; } +bool AttrMap::operator==(const AttrMap& other) const { + if (internal_->size != other.internal_->size + || internal_->hash_value != other.internal_->hash_value) { + return false; + } + for (int i = 0; i < std::min(internal_->size, other.internal_->size); ++i) { + if (internal_->attrs[i].second != other.internal_->attrs[i].second) { return false; } + if (internal_->attrs[i].second) { + if ((*internal_->ordered_attr_names)[i] != (*other.internal_->ordered_attr_names)[i]) { + return false; + } + if (*(internal_->attrs[i].first) != *(other.internal_->attrs[i].first)) { return false; } + } + } + return true; +} template Maybe AttrMap::GetAttr(const std::string& attr_name) const { - const auto& it = this->find(attr_name); - CHECK_OR_RETURN(it != this->end()) << attr_name << " not found"; - const auto* ptr = dynamic_cast*>(it->second.get()); - CHECK_NOTNULL_OR_RETURN(ptr); + const auto& attr = Attr4Name(attr_name); + CHECK_OR_RETURN(attr) << Error::InvalidValueError() + << "no attribute found. attribute name: " << attr_name; + const auto* ptr = dynamic_cast*>(attr.get()); + CHECK_NOTNULL_OR_RETURN(ptr) << Error::RuntimeError() << "Ptr should be non-null"; return ptr->val(); } const std::shared_ptr& AttrMap::Attr4Name( const std::string& attr_name) const { - const auto& iter = find(attr_name); - if (iter != end()) { return iter->second; } + int idx = internal_->ordered_attr_names->order(attr_name); + if (idx >= 0) { return internal_->attrs[idx].first; } static const std::shared_ptr none; return none; } -AttrMap MakeAttrMapFromUserOpConf(const UserOpConf& user_op_conf) { - const auto& attrs = std::make_shared(); - for (const auto& kv : user_op_conf.attr()) { - auto cpp_attr_value = user_op::AttrValueUtil::ToCppAttrValue(kv.second); - if (cpp_attr_value.IsOk()) { - attrs->emplace(kv.first, CHECK_JUST(cpp_attr_value)); - } else { - LOG(ERROR) << user_op_conf.DebugString() - << " failed to convert to cpp attr value, key: " << kv.first; - } +bool AttrMap::Has(const std::string& attr_name) const { return Attr4Name(attr_name) != nullptr; } + +AttrMap::const_iterator::const_iterator(size_t pos, const AttrMap::AttrInternal* internal) + : pos_(pos), internal_(internal) { + UpdateKV(); +} + +AttrMap::const_iterator& AttrMap::const_iterator::operator++() { + ++pos_; + UpdateKV(); + return *this; +} + +void AttrMap::const_iterator::UpdateKV() { + while (pos_ < internal_->max_size) { + if (internal_->attrs[pos_].second) { break; } + ++pos_; + } + if (pos_ < internal_->max_size) { + kv_.first = (*internal_->ordered_attr_names)[pos_]; + kv_.second = internal_->attrs[pos_].first; } - return AttrMap(attrs); } +AttrMap MakeAttrMapFromUserOpConf(const UserOpConf& user_conf) { return AttrMap(user_conf); } + template Maybe ComposedAttrMap::GetAttr(const std::string& attr_name) const { const auto& attr = Attr4Name(attr_name); - CHECK_NOTNULL_OR_RETURN(attr.get()) - << Error::InvalidValueError(std::string("no attribute found. attribute name: ") + attr_name); + CHECK_OR_RETURN(attr) << Error::InvalidValueError() + << "no attribute found. attribute name: " << attr_name; return dynamic_cast*>(attr.get())->val(); } const std::shared_ptr& ComposedAttrMap::Attr4Name( const std::string& attr_name) const { - const auto& prior_iter = prior_.find(attr_name); - if (prior_iter != prior_.end()) { return prior_iter->second; } - const auto& base_iter = base_.find(attr_name); - if (base_iter != base_.end()) { return base_iter->second; } - static const std::shared_ptr none; - return none; + const auto& prior_attr = prior_.Attr4Name(attr_name); + if (prior_attr) { return prior_attr; } + return base_.Attr4Name(attr_name); +} + +bool ComposedAttrMap::Has(const std::string& attr_name) const { + return Attr4Name(attr_name) != nullptr; } #define DEFINE_ATTR_VALUE_MAP_GET_ATTR(field, T, attr_type) \ @@ -146,23 +165,4 @@ const std::shared_ptr& ComposedAttrMap::Attr4Name( OF_PP_FOR_EACH_TUPLE(DEFINE_ATTR_VALUE_MAP_GET_ATTR, ATTR_SEQ); #undef DEFINE_ATTR_VALUE_MAP_GET_ATTR -template<> -Maybe MutableAttrMap::SetAttr(const std::string& attr_name, - const std::shared_ptr& attr_val) { - (*this)[attr_name] = attr_val; - return Maybe::Ok(); -} - -template -Maybe MutableAttrMap::SetAttr(const std::string& attr_name, const T& attr_val) { - (*this)[attr_name] = std::make_shared>(attr_val); - return Maybe::Ok(); -} - -#define DEFINE_ATTR_VALUE_MAP_SET_ATTR(field, T, attr_type) \ - template Maybe MutableAttrMap::SetAttr(const std::string& attr_name, const T& attr_val); - -OF_PP_FOR_EACH_TUPLE(DEFINE_ATTR_VALUE_MAP_SET_ATTR, ATTR_SEQ); -#undef DEFINE_ATTR_VALUE_MAP_SET_ATTR - } // namespace oneflow diff --git a/oneflow/core/framework/attr_map.h b/oneflow/core/framework/attr_map.h index 87d0e88483ee3303cffad2e93f634a001f4ff79d..f9f1a8eda9c1a33102b1cb41f61e7eee783aad76 100644 --- a/oneflow/core/framework/attr_map.h +++ b/oneflow/core/framework/attr_map.h @@ -17,7 +17,7 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_ATTR_MAP_H_ #include "oneflow/core/common/util.h" -#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/small_vector.h" namespace oneflow { @@ -26,81 +26,81 @@ class AttrVal; } class AttrValue; class MutableAttrMap; +class UserOpConf; -// Make sure AttrName2AttrVal is a ordered map. -using AttrName2AttrVal = std::map>; - -class AttrName2AttrValWrapper { - public: - AttrName2AttrValWrapper(const std::shared_ptr& attrs); - AttrName2AttrValWrapper(const AttrName2AttrValWrapper&) = default; - AttrName2AttrValWrapper(AttrName2AttrValWrapper&&) = default; - ~AttrName2AttrValWrapper() = default; - - size_t size() const { return attrs_->size(); } - bool empty() const { return attrs_->empty(); } - - AttrName2AttrValWrapper& operator=(const AttrName2AttrValWrapper& other) { - attrs_ = other.attrs_; - hash_value_ = other.hash_value_; - return *this; - } - - bool operator==(const AttrName2AttrValWrapper& other) const; - - using const_iterator = typename AttrName2AttrVal::const_iterator; - const_iterator begin() const { return attrs_->begin(); } - const_iterator end() const { return attrs_->end(); } - - const_iterator find(const std::string& attr_name) const { return attrs_->find(attr_name); } - - size_t hash_value() const { return hash_value_; } - - private: - std::shared_ptr attrs_; - size_t hash_value_; -}; +template +class OrderedStringList; class AttrMap final { public: AttrMap(); - explicit AttrMap(const std::shared_ptr& attrs); - - using value_type = typename AttrName2AttrVal::value_type; - AttrMap(std::initializer_list init); - - AttrMap(const MutableAttrMap& other); // without coping AttrVal + AttrMap(const MutableAttrMap& other); + AttrMap(const UserOpConf& user_conf); AttrMap(const AttrMap&) = default; AttrMap(AttrMap&&) = default; ~AttrMap() = default; - AttrMap& operator=(const AttrMap& other); - - bool operator==(const AttrMap& other) const; + bool Has(const std::string& attr_name) const; template Maybe GetAttr(const std::string& attr_name) const; const std::shared_ptr& Attr4Name(const std::string& attr_name) const; - size_t size() const { return attrs_.size(); } - bool empty() const { return attrs_.empty(); } + AttrMap& operator=(const AttrMap& other); - using const_iterator = typename AttrName2AttrVal::const_iterator; - const_iterator begin() const { return attrs_.begin(); } - const_iterator end() const { return attrs_.end(); } + bool operator==(const AttrMap& other) const; - const_iterator find(const std::string& attr_name) const { return attrs_.find(attr_name); } + size_t size() const { return internal_->size; } + bool empty() const { return internal_->size > 0; } - size_t hash_value() const { return attrs_.hash_value(); } + size_t hash_value() const { return internal_->hash_value; } + + struct AttrInternal { + AttrInternal(); + AttrInternal(size_t max_size, size_t size, size_t hash_value, + const std::shared_ptr>& ordered_attr_names); + size_t max_size; + size_t size; + size_t hash_value; + std::shared_ptr> ordered_attr_names; + small_vector, bool>, 8> attrs; + }; + + class const_iterator { + public: + using const_reference = const std::pair>&; + using const_pointer = const std::pair>*; + + const_iterator(size_t pos, const AttrInternal* internal); + ~const_iterator() = default; + + const_reference operator*() const { return kv_; } + const_pointer operator->() const { return &kv_; } + + const_iterator& operator++(); + bool operator==(const const_iterator& x) const { + return pos_ == x.pos_ && internal_ == x.internal_; + } + bool operator!=(const const_iterator& x) const { return !(*this == x); } + + private: + void UpdateKV(); + + size_t pos_; + const AttrInternal* internal_; + std::pair> kv_; + }; + + const_iterator begin() const { return const_iterator(0, internal_.get()); } + const_iterator end() const { return const_iterator(internal_->max_size, internal_.get()); } private: - AttrName2AttrValWrapper attrs_; + std::shared_ptr internal_; }; -class UserOpConf; -AttrMap MakeAttrMapFromUserOpConf(const UserOpConf& user_op_conf); +AttrMap MakeAttrMapFromUserOpConf(const UserOpConf& user_conf); class ComposedAttrMap final { public: @@ -114,6 +114,8 @@ class ComposedAttrMap final { const std::shared_ptr& Attr4Name(const std::string& attr_name) const; + bool Has(const std::string& attr_name) const; + void ResetPrior(const AttrMap& prior) { prior_ = prior; } void ResetBase(const AttrMap& base) { base_ = base; } @@ -122,25 +124,10 @@ class ComposedAttrMap final { AttrMap base_; }; -class MutableAttrMap : public std::map> { - public: - using std::map>::map; - - template - Maybe SetAttr(const std::string& attr_name, const T& attr_val); -}; - } // namespace oneflow namespace std { -template<> -struct hash final { - size_t operator()(const oneflow::AttrName2AttrValWrapper& attr_name2attr_val) const { - return attr_name2attr_val.hash_value(); - } -}; - template<> struct hash final { size_t operator()(const oneflow::AttrMap& attr_map) const { return attr_map.hash_value(); } diff --git a/oneflow/core/framework/attr_map_test.cpp b/oneflow/core/framework/attr_map_test.cpp index c627b486628321451d0af514ed2870de9aa3fd57..2637691e168927f3474d2c57ef22c6978de52d1a 100644 --- a/oneflow/core/framework/attr_map_test.cpp +++ b/oneflow/core/framework/attr_map_test.cpp @@ -16,16 +16,15 @@ limitations under the License. #include "gtest/gtest.h" #include "oneflow/core/framework/attr_map.h" #include "oneflow/core/framework/attr_value.h" +#include "oneflow/core/framework/mutable_attr_map.h" namespace oneflow { namespace test { TEST(AttrMap, basic) { - MutableAttrMap mut_attr_map{}; - CHECK_JUST(mut_attr_map.SetAttr("zero", 0)); - CHECK_JUST(mut_attr_map.SetAttr("one", 1)); - CHECK_JUST(mut_attr_map.SetAttr>("zeros", std::vector{0})); - CHECK_JUST(mut_attr_map.SetAttr>("ones", std::vector{1})); + auto& mut_attr_map = THREAD_CACHED_MUTABLE_ATTR_MAP("zero", "one", "zeros", "ones"); + mut_attr_map.SetAllAttrs(static_cast(0), static_cast(1), + std::vector{0}, std::vector{1}); AttrMap attr_map(mut_attr_map); { const auto& val = CHECK_JUST(attr_map.GetAttr("zero")); @@ -55,30 +54,28 @@ TEST(AttrMap, basic) { TEST(AttrMap, hash_value) { HashMap attr_map2int_value; - MutableAttrMap mut_attr_map{}; - CHECK_JUST(mut_attr_map.SetAttr("zero", 0)); - CHECK_JUST(mut_attr_map.SetAttr("one", 1)); - CHECK_JUST(mut_attr_map.SetAttr>("zeros", std::vector{0})); - CHECK_JUST(mut_attr_map.SetAttr>("ones", std::vector{1})); + auto& mut_attr_map = THREAD_CACHED_MUTABLE_ATTR_MAP("zero", "one", "zeros", "ones"); + mut_attr_map.SetAllAttrs(static_cast(0), static_cast(1), + std::vector{0}, std::vector{1}); ASSERT_EQ(AttrMap(mut_attr_map).hash_value(), AttrMap(mut_attr_map).hash_value()); ASSERT_TRUE(AttrMap(mut_attr_map) == AttrMap(mut_attr_map)); } TEST(AttrMap, hash_map) { HashMap attr_map2int_value; - MutableAttrMap mut_attr_map{}; + auto& mut_attr_map = THREAD_CACHED_MUTABLE_ATTR_MAP("zero", "one", "zeros", "ones"); attr_map2int_value[AttrMap(mut_attr_map)] = 0; ASSERT_EQ(attr_map2int_value.at(AttrMap(mut_attr_map)), 0); - CHECK_JUST(mut_attr_map.SetAttr("zero", 0)); + mut_attr_map.SetAttr<0>(static_cast(0)); attr_map2int_value[AttrMap(mut_attr_map)] = 1; ASSERT_EQ(attr_map2int_value.at(AttrMap(mut_attr_map)), 1); - CHECK_JUST(mut_attr_map.SetAttr("one", 1)); + mut_attr_map.SetAttr<1>(static_cast(1)); attr_map2int_value[AttrMap(mut_attr_map)] = 2; ASSERT_EQ(attr_map2int_value.at(AttrMap(mut_attr_map)), 2); - CHECK_JUST(mut_attr_map.SetAttr>("zeros", std::vector{0})); + mut_attr_map.SetAttr<2>(std::vector{0}); attr_map2int_value[AttrMap(mut_attr_map)] = 3; ASSERT_EQ(attr_map2int_value.at(AttrMap(mut_attr_map)), 3); - CHECK_JUST(mut_attr_map.SetAttr>("ones", std::vector{1})); + mut_attr_map.SetAttr<3>(std::vector{1}); attr_map2int_value[AttrMap(mut_attr_map)] = 4; ASSERT_EQ(attr_map2int_value.at(AttrMap(mut_attr_map)), 4); } diff --git a/oneflow/core/framework/attr_value.h b/oneflow/core/framework/attr_value.h index afe8b390bf4d66b8df38a6a1035242836a5b09e7..023d4b679708d39f144e6aca977cc146e528e42e 100644 --- a/oneflow/core/framework/attr_value.h +++ b/oneflow/core/framework/attr_value.h @@ -92,7 +92,10 @@ class AttrVal { AttrVal() = default; virtual ~AttrVal() = default; + virtual AttrType type() const = 0; virtual size_t hash_value() const = 0; + + virtual const void* Ptr() const = 0; virtual bool operator==(const AttrVal& other) const = 0; bool operator!=(const AttrVal& other) const { return !(*this == other); } @@ -104,12 +107,12 @@ template class TypedAttrValIf : public AttrVal { public: virtual const T& val() const = 0; - size_t hash_value() const override { return std::hash()(val()); } + + AttrType type() const override { return GetAttrType::value; } bool operator==(const AttrVal& other) const override { - auto* that = dynamic_cast*>(&other); - if (that == nullptr) { return false; } - return this->val() == that->val(); + if (other.type() != GetAttrType::value) { return false; } + return *static_cast(Ptr()) == *static_cast(other.Ptr()); } }; @@ -120,6 +123,9 @@ class TypedAttrVal final : public TypedAttrValIf { ~TypedAttrVal() = default; const T& val() const override { return val_; } + const void* Ptr() const override { return static_cast(&val_); } + + size_t hash_value() const override { return std::hash()(val_); } private: OF_DISALLOW_COPY_AND_MOVE(TypedAttrVal); @@ -134,6 +140,9 @@ class TypedAttrValRef final : public TypedAttrValIf { ~TypedAttrValRef() = default; const T& val() const override { return *val_; } + const void* Ptr() const override { return static_cast(val_); } + + size_t hash_value() const override { return std::hash()(*val_); } private: OF_DISALLOW_COPY_AND_MOVE(TypedAttrValRef); diff --git a/oneflow/core/framework/attr_value_accessor.cpp b/oneflow/core/framework/attr_value_accessor.cpp index 9b438ed9c37bbc88dc2a6c3b6af726c080777308..1cc2eeb78fcd4a8895b941486d422e553e1612f4 100644 --- a/oneflow/core/framework/attr_value_accessor.cpp +++ b/oneflow/core/framework/attr_value_accessor.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/common/shape.h" #include "oneflow/core/common/stride.h" #include "oneflow/core/common/protobuf.h" +#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/framework/user_op_conf.h" namespace oneflow { diff --git a/oneflow/core/framework/attr_value_accessor.h b/oneflow/core/framework/attr_value_accessor.h index d5f711f6b97b0810fae4fcb1999c431605071523..3ea43431b93c9e44d1d7ffd8c54100fbe996664a 100644 --- a/oneflow/core/framework/attr_value_accessor.h +++ b/oneflow/core/framework/attr_value_accessor.h @@ -16,11 +16,12 @@ limitations under the License. #ifndef ONEFLOW_CORE_FRAMEWORK_ATTR_VAL_ACCESSOR_H_ #define ONEFLOW_CORE_FRAMEWORK_ATTR_VAL_ACCESSOR_H_ -#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/common/maybe.h" namespace oneflow { +class AttrValue; + namespace user_op { template diff --git a/oneflow/core/framework/autocast.cpp b/oneflow/core/framework/autocast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..aaaff0f61462782a2a5ecedce14441fad5823440 --- /dev/null +++ b/oneflow/core/framework/autocast.cpp @@ -0,0 +1,149 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/autocast.h" +#include "oneflow/core/job_rewriter/auto_mixed_precision.h" +#include "oneflow/core/job_rewriter/auto_mixed_precision_lists.h" + +namespace oneflow { +namespace autocast { + +namespace { + +bool* autocast_enabled() { + static thread_local bool autocast_enabled = false; + return &autocast_enabled; +} +DeviceType* autocast_device_type() { + static thread_local DeviceType autocast_device_type = kCUDA; + return &autocast_device_type; +} +Symbol* autocast_dtype() { + static thread_local Symbol autocast_dtype = DType::Float16(); + return &autocast_dtype; +} +Symbol* autocast_cpu_dtype() { + static thread_local Symbol autocast_cpu_dtype = DType::BFloat16(); + return &autocast_cpu_dtype; +} +Symbol* autocast_gpu_dtype() { + static thread_local Symbol autocast_gpu_dtype = DType::Float16(); + return &autocast_gpu_dtype; +} +bool* cache_enabled() { + static thread_local bool cache_enabled = true; + return &cache_enabled; +} + +} // namespace + +bool is_enabled() { return *autocast_enabled(); } +void set_enabled(bool enabled) { *autocast_enabled() = enabled; } + +DeviceType get_autocast_device_type() { return *autocast_device_type(); } +void set_autocast_device_type(DeviceType device_type) { *autocast_device_type() = device_type; } + +Symbol get_autocast_dtype() { return *autocast_dtype(); } +Symbol get_autocast_cpu_dtype() { return *autocast_cpu_dtype(); } +Symbol get_autocast_gpu_dtype() { return *autocast_gpu_dtype(); } + +void set_autocast_dtype(Symbol dtype) { *autocast_dtype() = dtype; } +void set_autocast_cpu_dtype(Symbol dtype) { *autocast_cpu_dtype() = dtype; } +void set_autocast_gpu_dtype(Symbol dtype) { *autocast_gpu_dtype() = dtype; } + +bool is_autocast_cache_enabled() { return *cache_enabled(); } +void set_autocast_cache_enabled(bool enabled) { *cache_enabled() = enabled; } +void clear_cache() { + // TODO(hjchen2) +} + +AutoCastColor AutoCastMeta::autocast_color() const { return autocast_color_; } + +void AutoCastMeta::set_autocast_color(AutoCastColor color) { autocast_color_ = color; } + +bool AutoCastMeta::is_autocast_eligible(DeviceType device_type, Symbol dtype) const { + int device_index = static_cast(device_type); + if (is_autocast_eligible_.size() > device_index) { + int dtype_index = static_cast(dtype->data_type()); + if (is_autocast_eligible_[device_index].size() > dtype_index) { + return is_autocast_eligible_[device_index][dtype_index]; + } + } + return false; +} + +void AutoCastMeta::set_autocast_eligible(DeviceType device_type, Symbol dtype) { + int device_index = static_cast(device_type); + while (is_autocast_eligible_.size() <= device_index) { + is_autocast_eligible_.resize(device_index + 1); + } + int dtype_index = static_cast(dtype->data_type()); + while (is_autocast_eligible_[device_index].size() <= dtype_index) { + is_autocast_eligible_[device_index].resize(dtype_index + 1); + } + is_autocast_eligible_[device_index][dtype_index] = true; +} + +bool AutoCastMeta::is_args_autocast_eligible(int arg_index) const { + CHECK_LT_OR_THROW(arg_index, is_args_autocast_eligible_.size()); // NOLINT + return is_args_autocast_eligible_[arg_index]; +} + +const std::vector& AutoCastMeta::is_args_autocast_eligible() const { + return is_args_autocast_eligible_; +} + +void AutoCastMeta::set_arg_autocast_eligible(int arg_index) { + CHECK_LT_OR_THROW(arg_index, is_args_autocast_eligible_.size()); // NOLINT + is_args_autocast_eligible_[arg_index] = true; +} + +std::shared_ptr MakeAutoCastMeta( + const std::string& op_type_name, + const std::vector>& input_args) { + auto autocast_meta = std::make_shared(input_args.size()); + if (AutoMixedPrecisionLists::WhiteList().count(op_type_name)) { + autocast_meta->set_autocast_color(kWhite); + } else if (AutoMixedPrecisionLists::GrayList().count(op_type_name)) { + autocast_meta->set_autocast_color(kGray); + } else if (AutoMixedPrecisionLists::ClearList().count(op_type_name)) { + autocast_meta->set_autocast_color(kClear); + } else { + autocast_meta->set_autocast_color(kBlack); + } + for (int i = 0; i < input_args.size(); ++i) { + if (!amp::IsNoCast(op_type_name, input_args[i])) { + autocast_meta->set_arg_autocast_eligible(i); + } + } + // autocast only supports the following device type(s) and low precision type(s): + // - device type: CUDA + // - low precision type: half, bfloat16 + static std::vector autocast_device_types{kCUDA}; + static std::vector> autocast_dtypes{DType::Float16(), DType::BFloat16()}; + + if (autocast_meta->autocast_color() != kBlack) { + for (auto device_type : autocast_device_types) { + for (auto dtype : autocast_dtypes) { + autocast_meta->set_autocast_eligible(device_type, dtype); + } + } + } + return autocast_meta; +} + +} // namespace autocast +} // namespace oneflow diff --git a/oneflow/core/framework/autocast.h b/oneflow/core/framework/autocast.h new file mode 100644 index 0000000000000000000000000000000000000000..6495c620c05f6a00e1643ba686bbc00e7c346853 --- /dev/null +++ b/oneflow/core/framework/autocast.h @@ -0,0 +1,75 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_AUTOCAST_H_ +#define ONEFLOW_CORE_FRAMEWORK_AUTOCAST_H_ + +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/dtype.h" + +namespace oneflow { +namespace autocast { + +bool is_enabled(); +void set_enabled(bool enabled); + +DeviceType get_autocast_device_type(); +void set_autocast_device_type(DeviceType device_type); + +Symbol get_autocast_dtype(); +Symbol get_autocast_cpu_dtype(); +Symbol get_autocast_gpu_dtype(); + +void set_autocast_dtype(Symbol dtype); +void set_autocast_cpu_dtype(Symbol dtype); +void set_autocast_gpu_dtype(Symbol dtype); + +bool is_autocast_cache_enabled(); +void set_autocast_cache_enabled(bool enabled); +void clear_cache(); + +enum AutoCastColor { kNoColor, kWhite, kGray, kClear, kBlack }; + +class AutoCastMeta final { + public: + AutoCastMeta() : AutoCastMeta(0) {} + explicit AutoCastMeta(int args_num) + : autocast_color_(kNoColor), is_args_autocast_eligible_(args_num, false) {} + + AutoCastColor autocast_color() const; + + bool is_autocast_eligible(DeviceType device_type, Symbol dtype) const; + + bool is_args_autocast_eligible(int arg_index) const; + const std::vector& is_args_autocast_eligible() const; + + void set_autocast_color(AutoCastColor color); + void set_autocast_eligible(DeviceType device_type, Symbol dtype); + void set_arg_autocast_eligible(int arg_index); + + private: + AutoCastColor autocast_color_; + std::vector> is_autocast_eligible_; + std::vector is_args_autocast_eligible_; +}; + +std::shared_ptr MakeAutoCastMeta( + const std::string& op_type_name, + const std::vector>& input_args); + +} // namespace autocast +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_AUTOCAST_H_ diff --git a/oneflow/core/framework/compute_complexity_fn_context.h b/oneflow/core/framework/compute_complexity_fn_context.h new file mode 100644 index 0000000000000000000000000000000000000000..11ba56cef1e0e7b27af4026303daae3c124c935d --- /dev/null +++ b/oneflow/core/framework/compute_complexity_fn_context.h @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_COMPUTE_COMPLEXITY_FN_CONTEXT_H_ +#define ONEFLOW_CORE_FRAMEWORK_COMPUTE_COMPLEXITY_FN_CONTEXT_H_ + +#include "oneflow/core/framework/user_op_conf.h" +#include "oneflow/core/job/parallel_desc.h" + +namespace oneflow { + +class Shape; + +namespace user_op { + +class UserOpDefWrapper; + +class ComputeComplexityFnContext { + public: + virtual ~ComputeComplexityFnContext() = default; + + virtual TensorDesc* TensorDesc4ArgNameAndIndex(const std::string&, int32_t) = 0; + virtual const Shape& Shape4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual DataType Dtype4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual const std::vector>& inputs() const = 0; + virtual const std::vector>& outputs() const = 0; + virtual const NdSbp NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const = 0; + virtual const NdSbpSignature* GetNdSbpSignature() const = 0; + + template + T Attr(const std::string& attr_name) const { + return conf_.attr(attr_name); + } + + virtual const ParallelDesc& parallel_desc() const = 0; + virtual bool IsDynamic4ArgNameAndIndex(const std::string&, int32_t) const = 0; + + const UserOpConfWrapper& user_op_conf() const { return conf_; } + + protected: + explicit ComputeComplexityFnContext(UserOpConfWrapper&& conf) : conf_(std::move(conf)) {} + + private: + UserOpConfWrapper conf_; +}; + +} // namespace user_op + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_COMPUTE_COMPLEXITY_FN_CONTEXT_H_ diff --git a/oneflow/core/framework/config_def.cpp b/oneflow/core/framework/config_def.cpp index 786bd1c6947a4c7c628ed67730abae9ff3e4e919..7d2c2891bb7256d170ec36403156ce7e2589f70a 100644 --- a/oneflow/core/framework/config_def.cpp +++ b/oneflow/core/framework/config_def.cpp @@ -30,7 +30,7 @@ ConfigDef* MutGlobalConfigDef() { template AttrValue* AddAttrDef(const std::string& name, const std::string& description) { auto* name2flag_def = MutGlobalConfigDef()->mutable_attr_name2attr_def(); - CHECK(name2flag_def->find(name) == name2flag_def->end()); + CHECK(name2flag_def->find(name) == name2flag_def->end()) << "Duplicate attribute: " << name; auto* flag_def = &(*name2flag_def)[name]; flag_def->set_name(name); flag_def->set_description(description); diff --git a/oneflow/core/framework/consistency_check.cpp b/oneflow/core/framework/consistency_check.cpp index 4e277da4ebc0f064b474d3d2abd8210d9eb61832..0f44e15b1811746fb5f36d086c33da9afbc5c54c 100644 --- a/oneflow/core/framework/consistency_check.cpp +++ b/oneflow/core/framework/consistency_check.cpp @@ -24,7 +24,7 @@ limitations under the License. #include "oneflow/core/framework/sync_symbol_parallel_desc.h" #include "oneflow/core/common/constant.h" #include "oneflow/core/common/check_level.h" -#include "oneflow/core/framework/sync_symbol_consistent_tensor_meta.h" +#include "oneflow/core/framework/sync_symbol_global_tensor_meta.h" namespace oneflow { @@ -82,22 +82,22 @@ FLAT_MSG_BEGIN(FlatMetaInfoConsistency); const auto& this_placement = JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( this->placement_symbol_id())); - CHECK_OR_RETURN(this_placement == placement) << "Each rank must have same input placement"; - CHECK_EQ_OR_RETURN(nd_sbp.has_value(), this->has_nd_sbp_symbol_id()); + CHECK_OR_RETURN(this_placement == placement) << Error::RuntimeError() << "Each rank must have the same input placement"; + CHECK_EQ_OR_RETURN(nd_sbp.has_value(), this->has_nd_sbp_symbol_id()) << Error::RuntimeError() << "Either all ranks have sbp or not"; if (this->has_nd_sbp_symbol_id()) { const auto& that_nd_sbp = JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( this->nd_sbp_symbol_id())); const auto& this_nd_sbp = JUST(nd_sbp); - CHECK_OR_RETURN(this_nd_sbp == that_nd_sbp) << "Each rank must have same input sbp"; + CHECK_OR_RETURN(this_nd_sbp == that_nd_sbp) << Error::RuntimeError() << "Each rank must have the same input sbp"; } - CHECK_EQ_OR_RETURN(grad_nd_sbp.has_value(), this->has_grad_nd_sbp_symbol_id()); + CHECK_EQ_OR_RETURN(grad_nd_sbp.has_value(), this->has_grad_nd_sbp_symbol_id()) << Error::RuntimeError() << "Either all ranks have grad sbp or not"; if (this->has_grad_nd_sbp_symbol_id()) { const auto& that_grad_nd_sbp = JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( this->grad_nd_sbp_symbol_id())); const auto& this_grad_nd_sbp = JUST(grad_nd_sbp); - CHECK_OR_RETURN(this_grad_nd_sbp == that_grad_nd_sbp) << "Each rank must have same input grad sbp"; + CHECK_OR_RETURN(this_grad_nd_sbp == that_grad_nd_sbp)<< Error::RuntimeError() << "Each rank must have same input grad sbp"; } return Maybe::Ok(); } @@ -181,7 +181,7 @@ Maybe DataConsistencyCheck(const void* buffer_ptr, size_t buffer_size, JUST_MSG(ctx.WaitDone(), kAsymmetricCodeErrorMsg); CHECK_OR_RETURN(std::memcmp(buffer_ptr, reinterpret_cast(recv_ptr), buffer_size) == 0) - << "Each rank must have same input sequence or numpy array"; + << Error::RuntimeError() << "Each rank must have same input sequence or numpy array"; return Maybe::Ok(); } diff --git a/oneflow/core/framework/consistency_check.h b/oneflow/core/framework/consistency_check.h index 10934b5ba1101aa95ae796589d600180e8d5578d..3729a63fb193eddd100de36d69df8b87a3772a51 100644 --- a/oneflow/core/framework/consistency_check.h +++ b/oneflow/core/framework/consistency_check.h @@ -20,7 +20,7 @@ limitations under the License. #include "oneflow/core/common/symbol.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/tensor_meta.h" namespace oneflow { diff --git a/oneflow/core/framework/consistent_tensor_infer_cache.cpp b/oneflow/core/framework/consistent_tensor_infer_cache.cpp deleted file mode 100644 index d131513bbcfdae2c999ec48a791044c582c2f659..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/consistent_tensor_infer_cache.cpp +++ /dev/null @@ -1,377 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/consistent_tensor_infer_cache.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/framework/to_string.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/common/container_util.h" - -namespace oneflow { -namespace one { - -namespace { - -bool OptionalEqual(const Optional>& lhs, const Optional>& rhs) { - if (lhs.has_value() != rhs.has_value()) { return false; } - if (!lhs.has_value()) { return true; } - return CHECK_JUST(lhs) == CHECK_JUST(rhs); -} - -} // namespace - -size_t InputConsistentTensorMeta::hash_value() const { - size_t hash_value = std::hash>()(tensor_meta()); - if (consumer_nd_sbp_constraint().has_value()) { - hash_value ^= std::hash>()(CHECK_JUST(consumer_nd_sbp_constraint())); - } - return hash_value; -} - -bool InputConsistentTensorMeta::operator==(const InputConsistentTensorMeta& other) const { - return this->tensor_meta() == other.tensor_meta() - && OptionalEqual(this->consumer_nd_sbp_constraint(), other.consumer_nd_sbp_constraint()); -} - -void InputConsistentTensorMeta::assign(Symbol tensor_meta, - const Optional>& consumer_nd_sbp_constraint) { - tensor_meta_ = tensor_meta; - consumer_nd_sbp_constraint_ = consumer_nd_sbp_constraint; -} - -size_t ConsistentTensorMetaInferArgs::hash_value() const { - size_t hash_value = std::hash()(attrs_); - const auto& tensor_meta_hash_functor = std::hash(); - for (const auto& tensor_meta : input_consistent_tensor_metas_) { - HashCombine(&hash_value, tensor_meta_hash_functor(tensor_meta)); - } - return hash_value; -} - -size_t SrcOpConsistentTensorMetaInferArgs::hash_value() const { - size_t hash_value = std::hash()(attrs_); - hash_value ^= std::hash>()(parallel_desc_); - hash_value ^= std::hash>()(nd_sbp_); - return hash_value; -} - -bool ConsistentTensorMetaInferArgs::operator==(const ConsistentTensorMetaInferArgs& other) const { - return this->attrs_ == other.attrs_ - && this->input_consistent_tensor_metas_ == other.input_consistent_tensor_metas_; -} - -bool SrcOpConsistentTensorMetaInferArgs::operator==( - const SrcOpConsistentTensorMetaInferArgs& other) const { - return this->attrs_ == other.attrs_ && this->parallel_desc_ == other.parallel_desc_ - && this->nd_sbp_ == other.nd_sbp_; -} - -Maybe ConsistentTensorMetaInferArgs::MakeNdSbpConstraints( - const UserOpExpr& user_op_expr, NdSbpSignature* nd_sbp_signature) const { - const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); - auto* map = nd_sbp_signature->mutable_bn_in_op2nd_sbp(); - for (int i = 0; i < input_arg_tuple.size(); ++i) { - const auto& constaint = input_consistent_tensor_metas_.at(i).consumer_nd_sbp_constraint(); - if (constaint.has_value()) { (*map)[input_arg_tuple.indexed_bns().at(i)] = *JUST(constaint); } - } - return Maybe::Ok(); -} - -Maybe ConsistentTensorMetaInferArgs::MakeInputBlobDescs( - const UserOpExpr& user_op_expr, std::vector* blob_descs) const { - CHECK_OR_RETURN(blob_descs->empty()); - const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); - blob_descs->reserve(input_arg_tuple.size()); - for (int i = 0; i < input_arg_tuple.size(); ++i) { - const auto& tensor_meta = *input_consistent_tensor_metas_.at(i).tensor_meta(); - const auto& shape = std::const_pointer_cast(tensor_meta.shape_ptr()); - const auto& stride = std::const_pointer_cast(tensor_meta.stride_ptr()); - blob_descs->emplace_back(shape, stride, tensor_meta.data_type()); - } - return Maybe::Ok(); -} - -Maybe ConsistentTensorMetaInferArgs::MakeNdSbpInferHints( - const UserOpExpr& user_op_expr, const std::vector& blob_descs, - std::vector* hints) const { - CHECK_OR_RETURN(hints->empty()); - const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); - hints->reserve(input_arg_tuple.size()); - for (int i = 0; i < input_arg_tuple.size(); ++i) { - const auto& tensor_meta = *input_consistent_tensor_metas_.at(i).tensor_meta(); - const auto* parallel_desc = &*tensor_meta.parallel_desc(); - const auto* blob_desc = &blob_descs.at(i); - const auto* nd_sbp = &*tensor_meta.nd_sbp(); - hints->emplace_back(parallel_desc, blob_desc, nd_sbp); - } - return Maybe::Ok(); -} - -Maybe ConsistentTensorMetaInferArgs::New( - const AttrMap& attrs, const TensorTuple& input_tensors) { - std::shared_ptr infer_args(new ConsistentTensorMetaInferArgs()); - infer_args->attrs_ = attrs; - infer_args->input_consistent_tensor_metas_.resize(input_tensors.size()); - JUST(infer_args->InitInputConsistentTensorMetas(input_tensors)); - return infer_args; -} - -Maybe SrcOpConsistentTensorMetaInferArgs::New( - const AttrMap& attrs, Symbol parallel_desc, Symbol nd_sbp) { - std::shared_ptr infer_args( - new SrcOpConsistentTensorMetaInferArgs()); - infer_args->attrs_ = attrs; - infer_args->parallel_desc_ = parallel_desc; - infer_args->nd_sbp_ = nd_sbp; - return infer_args; -} - -Maybe ConsistentTensorMetaInferArgs::InitInputConsistentTensorMetas( - const TensorTuple& input_tensors) { - for (int i = 0; i < input_tensors.size(); ++i) { - const auto& tensor = *input_tensors.at(i); - const auto& tensor_meta = JUST(tensor.consistent_tensor_meta()); - const auto& constraint = JUST(tensor.consumer_nd_sbp_constraint()); - input_consistent_tensor_metas_.at(i).assign(tensor_meta, constraint); - } - return Maybe::Ok(); -} - -namespace { - -Maybe MakeOp(const UserOpExpr& user_op_expr, const AttrMap& attrs, - const std::string& device_tag) { - OperatorConf op_conf; - JUST(user_op_expr.BuildOpConf(&op_conf, attrs)); - DeviceType device_type = JUST(DeviceType4DeviceTag(device_tag)); - return JUST(ConstructOp(op_conf, device_type)); -} - -Maybe CheckInputParallelDescIdentical(const ConsistentTensorMetaInferArgs& infer_args) { - if (infer_args.input_consistent_tensor_metas().empty()) { return Maybe::Ok(); } - const auto& first_parallel_desc = - infer_args.input_consistent_tensor_metas().begin()->tensor_meta()->parallel_desc(); - for (int i = 0; i < infer_args.input_consistent_tensor_metas().size(); ++i) { - CHECK_OR_RETURN(first_parallel_desc - == JUST(VectorAt(infer_args.input_consistent_tensor_metas(), i)) - .tensor_meta() - ->parallel_desc()) - << Error::RuntimeError() - << "Expected all tensors to be on the same placement, but found " - "at least two placements, " - << *JUST(PlacementToString(first_parallel_desc)) << " (positional 0) and " - << *JUST(PlacementToString(JUST(VectorAt(infer_args.input_consistent_tensor_metas(), i)) - .tensor_meta() - ->parallel_desc())) - << " (positional " << i << ")!"; - } - return Maybe::Ok(); -} - -Maybe CheckIsDeviceSupportedByOp(const ParallelDesc& parallel_desc, - const std::string& op_type_name) { - if (IsCpuOnly(op_type_name)) { CHECK_EQ_OR_RETURN(parallel_desc.device_tag(), "cpu"); } - return Maybe::Ok(); -} - -class UserOpExprDeviceAndStreamInferContext final : public user_op::DeviceAndStreamInferContext { - public: - UserOpExprDeviceAndStreamInferContext(const UserOpExpr* user_op_expr, - const ConsistentTensorMetaInferArgs* infer_args) - : user_op_expr_(user_op_expr), - composed_attrs_(infer_args->attrs(), user_op_expr->base_attrs()), - in_tensor_devices_(user_op_expr_->input_size()), - out_tensor_devices_(user_op_expr_->output_size()) { - for (int i = 0; i < user_op_expr_->input_size(); ++i) { - const auto& parallel_desc = - infer_args->input_consistent_tensor_metas().at(i).tensor_meta()->parallel_desc(); - in_tensor_devices_.at(i) = CHECK_JUST(GetTensorDevice(parallel_desc)); - } - } - - const std::vector>& inputs() const override { - return user_op_expr_->indexed_input_pairs(); - } - - const std::vector>& outputs() const override { - return user_op_expr_->indexed_output_pairs(); - } - - Symbol* OutputTensorDevice4ArgNameAndIndex(const std::string& name, - int64_t index) override { - const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); - int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); - CHECK_GE(tuple_index, 0); - CHECK_LT(tuple_index, user_op_expr_->output_size()); - return &out_tensor_devices_.at(tuple_index); - } - - Symbol InputTensorDevice4ArgNameAndIndex(const std::string& name, - int64_t index) const override { - const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); - int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); - CHECK_GE(tuple_index, 0); - CHECK_LT(tuple_index, user_op_expr_->input_size()); - return in_tensor_devices_.at(tuple_index); - } - - private: - const std::shared_ptr& Attr4Name( - const std::string& attr_name) const override { - return composed_attrs_.Attr4Name(attr_name); - } - const UserOpExpr* user_op_expr_; - const ComposedAttrMap composed_attrs_; - std::vector> in_tensor_devices_; - std::vector> out_tensor_devices_; -}; - -} // namespace - -/* static */ Maybe> ConsistentTensorInferCache::InferDeviceAndStream( - const UserOpExpr& user_op_expr, const ConsistentTensorMetaInferArgs& infer_args) { - if (!user_op_expr.device_and_stream_infer_fn()) { - Symbol parallel_desc = - infer_args.input_consistent_tensor_metas().at(0).tensor_meta()->parallel_desc(); - return GetDefaultStreamByPlacement(parallel_desc); - } else { - UserOpExprDeviceAndStreamInferContext device_and_stream_ctx(&user_op_expr, &infer_args); - return TRY(user_op_expr.device_and_stream_infer_fn()(&device_and_stream_ctx)); - } -} - -/* static */ Maybe ConsistentTensorInferCache::Infer( - const UserOpExpr& user_op_expr, const ConsistentTensorMetaInferArgs& infer_args) { - CHECK_GT_OR_RETURN(infer_args.input_consistent_tensor_metas().size(), 0); - Symbol parallel_desc = - infer_args.input_consistent_tensor_metas().at(0).tensor_meta()->parallel_desc(); - JUST(CheckInputParallelDescIdentical(infer_args)); - JUST(CheckIsDeviceSupportedByOp(*parallel_desc, user_op_expr.op_type_name())); - std::vector output_mut_metas(user_op_expr.output_size()); - { - // Infer OpArgMutConsistentTensorMeta. - const auto& input_metas = infer_args.input_consistent_tensor_metas(); - JUST(user_op_expr.InferLogicalTensorDesc( - infer_args.attrs(), parallel_desc, - [&](int32_t i) { return &*input_metas.at(i).tensor_meta(); }, - [&](int32_t i) { return output_mut_metas.at(i).mut_tensor_meta(); })); - } - const auto& op = JUST(MakeOp(user_op_expr, infer_args.attrs(), parallel_desc->device_tag())); - JUST(op->FillOpParallelDesc(parallel_desc.shared_from_symbol())); - JUST(op->InferParallelSignatureIf()); - { - // Infer parallel distribution. - NdSbpSignature nd_sbp_constraints; - JUST(infer_args.MakeNdSbpConstraints(user_op_expr, &nd_sbp_constraints)); - std::vector blob_descs; - JUST(infer_args.MakeInputBlobDescs(user_op_expr, &blob_descs)); - std::vector pd_infer_hints; - JUST(infer_args.MakeNdSbpInferHints(user_op_expr, blob_descs, &pd_infer_hints)); - const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); - const auto& NdSbpInferHint4Ibn = [&](const std::string& ibn) -> Maybe { - int32_t input_index = input_arg_tuple.bn_in_op2tensor_tuple_index().at(ibn); - CHECK_GE_OR_RETURN(input_index, 0); - CHECK_LT_OR_RETURN(input_index, pd_infer_hints.size()); - return &pd_infer_hints.at(input_index); - }; - // The inferred results can be retrieved by op->NdSbp4BnInOp(obn). - JUST(op->InferNdSbpSignatureIf(nd_sbp_constraints, *parallel_desc, NdSbpInferHint4Ibn)); - } - auto result = std::make_unique(user_op_expr.input_size(), - user_op_expr.output_size()); - auto* input_metas = result->mut_input_tensor_metas(); - for (int32_t i = 0; i < user_op_expr.input_size(); ++i) { - const auto& old_consistent_tensor_meta = - infer_args.input_consistent_tensor_metas().at(i).tensor_meta(); - const auto& ibn = user_op_expr.input_arg_tuple()->indexed_bns().at(i); - const auto& nd_sbp = SymbolOf(*JUST(op->NdSbp4BnInOp(ibn))); - ConsistentTensorMeta consistent_tensor_meta(old_consistent_tensor_meta->shape_ptr(), - old_consistent_tensor_meta->dtype(), nd_sbp, - old_consistent_tensor_meta->parallel_desc()); - input_metas->at(i) = SymbolOf(consistent_tensor_meta); - } - auto* output_metas = result->mut_output_tensor_metas(); - for (int32_t i = 0; i < user_op_expr.output_size(); ++i) { - const auto& output_mut_meta = output_mut_metas.at(i); - const auto& shape = output_mut_meta.tensor_meta().shape_ptr(); - DataType data_type = output_mut_meta.tensor_meta().data_type(); - const auto& obn = user_op_expr.output_arg_tuple()->indexed_bns().at(i); - const auto& nd_sbp = SymbolOf(*JUST(op->NdSbp4BnInOp(obn))); - ConsistentTensorMeta tensor_meta(shape, data_type, nd_sbp, parallel_desc); - output_metas->at(i) = SymbolOf(tensor_meta); - } - result->set_stream(JUST(InferDeviceAndStream(user_op_expr, infer_args))); - return std::shared_ptr(std::move(result)); -} - -/* static */ Maybe ConsistentTensorInferCache::Infer( - const UserOpExpr& user_op_expr, const SrcOpConsistentTensorMetaInferArgs& infer_args) { - Symbol parallel_desc = infer_args.parallel_desc(); - JUST(CheckIsDeviceSupportedByOp(*parallel_desc, user_op_expr.op_type_name())); - std::vector output_mut_metas(user_op_expr.output_size()); - { - // Infer OpArgMutConsistentTensorMeta. - const auto& GetInputTensorMeta = [](int32_t i) { - UNIMPLEMENTED(); - return nullptr; - }; - JUST(user_op_expr.InferLogicalTensorDesc( - infer_args.attrs(), parallel_desc, GetInputTensorMeta, - [&](int32_t i) { return output_mut_metas.at(i).mut_tensor_meta(); })); - } - auto result = std::make_unique(user_op_expr.input_size(), - user_op_expr.output_size()); - auto* output_metas = result->mut_output_tensor_metas(); - for (int32_t i = 0; i < user_op_expr.output_size(); ++i) { - const auto& output_mut_meta = output_mut_metas.at(i); - const auto& shape = output_mut_meta.tensor_meta().shape_ptr(); - DataType data_type = output_mut_meta.tensor_meta().data_type(); - const auto& nd_sbp = infer_args.nd_sbp(); - ConsistentTensorMeta tensor_meta(shape, data_type, nd_sbp, parallel_desc); - output_metas->at(i) = SymbolOf(tensor_meta); - } - result->set_stream(JUST(GetDefaultStreamByPlacement(parallel_desc))); - return std::shared_ptr(std::move(result)); -} - -Maybe ConsistentTensorInferCache::GetOrInfer( - const ConsistentTensorMetaInferArgs& infer_args) { - auto iter = cache_.find(infer_args); - if (iter == cache_.end()) { - const auto& user_op_expr = user_op_expr_.lock(); - CHECK_OR_RETURN(static_cast(user_op_expr)); - const auto& output_tensor_metas = JUST(Infer(*user_op_expr, infer_args)); - iter = cache_.emplace(infer_args, output_tensor_metas).first; - } - return iter->second; -} - -Maybe ConsistentTensorInferCache::GetOrInfer( - const SrcOpConsistentTensorMetaInferArgs& infer_args) { - auto iter = src_op_cache_.find(infer_args); - if (iter == src_op_cache_.end()) { - const auto& user_op_expr = user_op_expr_.lock(); - CHECK_OR_RETURN(static_cast(user_op_expr)); - const auto& output_tensor_metas = JUST(Infer(*user_op_expr, infer_args)); - iter = src_op_cache_.emplace(infer_args, output_tensor_metas).first; - } - return iter->second; -} - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/framework/consistent_tensor_infer_cache.h b/oneflow/core/framework/consistent_tensor_infer_cache.h deleted file mode 100644 index 8cbaf94a271c5c6ab6542e774786734c9b2aa100..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/consistent_tensor_infer_cache.h +++ /dev/null @@ -1,238 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_CONSISTENT_TENSOR_INFER_CACHE_H_ -#define ONEFLOW_CORE_FRAMEWORK_CONSISTENT_TENSOR_INFER_CACHE_H_ - -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/framework/attr_map.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/stream.h" -#include "oneflow/core/framework/tensor_meta.h" -#include "oneflow/core/register/blob_desc.h" -#include "oneflow/core/job/nd_sbp_infer_hint.h" - -namespace oneflow { - -class NdSbp; - -class ParallelDesc; - -namespace one { - -class ConsistentTensorMeta; - -class InputConsistentTensorMeta final { - public: - InputConsistentTensorMeta() : tensor_meta_(), consumer_nd_sbp_constraint_() {} - InputConsistentTensorMeta(Symbol tensor_meta, - const Optional>& consumer_nd_sbp_constraint) - : tensor_meta_(tensor_meta), consumer_nd_sbp_constraint_(consumer_nd_sbp_constraint) {} - - InputConsistentTensorMeta(const InputConsistentTensorMeta&) = default; - InputConsistentTensorMeta(InputConsistentTensorMeta&&) = default; - ~InputConsistentTensorMeta() = default; - - size_t hash_value() const; - bool operator==(const InputConsistentTensorMeta& other) const; - Symbol tensor_meta() const { return tensor_meta_; } - const Optional>& consumer_nd_sbp_constraint() const { - return consumer_nd_sbp_constraint_; - } - void assign(Symbol tensor_meta, - const Optional>& consumer_nd_sbp_constraint); - - private: - Symbol tensor_meta_; - Optional> consumer_nd_sbp_constraint_; -}; - -class TensorTuple; -class UserOpExpr; - -class ConsistentTensorMetaInferArgs final { - public: - ConsistentTensorMetaInferArgs(const ConsistentTensorMetaInferArgs&) = default; - ConsistentTensorMetaInferArgs(ConsistentTensorMetaInferArgs&&) = default; - ~ConsistentTensorMetaInferArgs() = default; - - const std::vector& input_consistent_tensor_metas() const { - return input_consistent_tensor_metas_; - } - const AttrMap& attrs() const { return attrs_; } - - size_t hash_value() const; - - bool operator==(const ConsistentTensorMetaInferArgs& other) const; - - Maybe MakeNdSbpConstraints(const UserOpExpr& user_op_expr, - NdSbpSignature* nd_sbp_signature) const; - - Maybe MakeInputBlobDescs(const UserOpExpr& user_op_expr, - std::vector* blob_descs) const; - - Maybe MakeNdSbpInferHints(const UserOpExpr& user_op_expr, - const std::vector& blob_descs, - std::vector* hints) const; - - static Maybe New(const AttrMap& attrs, - const TensorTuple& input_tensors); - - private: - ConsistentTensorMetaInferArgs() = default; - Maybe InitInputConsistentTensorMetas(const TensorTuple& input_tensors); - - AttrMap attrs_; - std::vector input_consistent_tensor_metas_; -}; - -class SrcOpConsistentTensorMetaInferArgs final { - public: - SrcOpConsistentTensorMetaInferArgs(const SrcOpConsistentTensorMetaInferArgs&) = default; - SrcOpConsistentTensorMetaInferArgs(SrcOpConsistentTensorMetaInferArgs&&) = default; - ~SrcOpConsistentTensorMetaInferArgs() = default; - - Symbol parallel_desc() const { return parallel_desc_; } - Symbol nd_sbp() const { return nd_sbp_; } - const AttrMap& attrs() const { return attrs_; } - - size_t hash_value() const; - - bool operator==(const SrcOpConsistentTensorMetaInferArgs& other) const; - - static Maybe New(const AttrMap& attrs, - Symbol parallel_desc, - Symbol nd_sbp); - - private: - SrcOpConsistentTensorMetaInferArgs() = default; - - AttrMap attrs_; - Symbol parallel_desc_; - Symbol nd_sbp_; -}; - -class OpArgMutConsistentTensorMeta final { - public: - OpArgMutConsistentTensorMeta() - : tensor_meta_(std::make_shared(), DataType::kInvalidDataType) {} - - OpArgMutConsistentTensorMeta(const OpArgMutConsistentTensorMeta&) = default; - OpArgMutConsistentTensorMeta(OpArgMutConsistentTensorMeta&&) = default; - ~OpArgMutConsistentTensorMeta() = default; - - const TensorMeta& tensor_meta() const { return tensor_meta_; } - - TensorMeta* mut_tensor_meta() { return &tensor_meta_; } - - private: - TensorMeta tensor_meta_; -}; - -} // namespace one -} // namespace oneflow - -namespace std { - -template<> -struct hash final { - size_t operator()(const oneflow::one::InputConsistentTensorMeta& val) const { - return val.hash_value(); - } -}; - -template<> -struct hash final { - size_t operator()(const oneflow::one::ConsistentTensorMetaInferArgs& val) const { - return val.hash_value(); - } -}; - -template<> -struct hash final { - size_t operator()(const oneflow::one::SrcOpConsistentTensorMetaInferArgs& val) const { - return val.hash_value(); - } -}; - -} // namespace std - -namespace oneflow { -namespace one { - -class ConsistentTensorInferResult final { - public: - ConsistentTensorInferResult(size_t input_size, size_t output_size) - : input_tensor_metas_(input_size), output_tensor_metas_(output_size) {} - ConsistentTensorInferResult(const ConsistentTensorInferResult&) = delete; - ConsistentTensorInferResult(ConsistentTensorInferResult&&) = delete; - ~ConsistentTensorInferResult() = default; - - const std::vector>& input_tensor_metas() const { - return input_tensor_metas_; - } - const std::vector>& output_tensor_metas() const { - return output_tensor_metas_; - } - - std::vector>* mut_input_tensor_metas() { - return &input_tensor_metas_; - } - std::vector>* mut_output_tensor_metas() { - return &output_tensor_metas_; - } - - const Symbol& stream() const { return stream_; } - void set_stream(const Symbol& stream) { stream_ = stream; } - - private: - std::vector> input_tensor_metas_; - std::vector> output_tensor_metas_; - Symbol stream_; -}; - -class ConsistentTensorInferCache final { - public: - ConsistentTensorInferCache(const std::shared_ptr& user_op_expr) - : user_op_expr_(user_op_expr) {} - - Maybe GetOrInfer( - const ConsistentTensorMetaInferArgs& infer_args); - - static Maybe Infer( - const UserOpExpr& user_op_expr, const ConsistentTensorMetaInferArgs& infer_args); - - Maybe GetOrInfer( - const SrcOpConsistentTensorMetaInferArgs& infer_args); - - static Maybe Infer( - const UserOpExpr& user_op_expr, const SrcOpConsistentTensorMetaInferArgs& infer_args); - - private: - static Maybe> InferDeviceAndStream( - const UserOpExpr& user_op_expr, const ConsistentTensorMetaInferArgs& infer_args); - - std::weak_ptr user_op_expr_; - HashMap> cache_; - HashMap> - src_op_cache_; -}; - -} // namespace one -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_CONSISTENT_TENSOR_INFER_CACHE_H_ diff --git a/oneflow/core/framework/device.cpp b/oneflow/core/framework/device.cpp index 11deac86b72b3ca85e18650f451576892ac8c1ad..1304578095d9378bf0f8e4c0925fa2598e6da7f3 100644 --- a/oneflow/core/framework/device.cpp +++ b/oneflow/core/framework/device.cpp @@ -31,7 +31,7 @@ namespace oneflow { namespace { inline size_t HashDevice(const std::string& type, int64_t device_id) { - return std::hash()(type) ^ std::hash()(device_id); + return Hash(type, device_id); } void CheckDeviceType(const std::string& type) { @@ -56,7 +56,7 @@ Maybe Device::Init() { { DeviceType dev_type = enum_type_; if (dev_type == kMockDevice) { dev_type = DeviceType::kCPU; } - mem_case_ = MemoryCaseUtil::MakeMemCase(dev_type, device_id_); + mem_case_ = memory::MakeMemCaseShared(enum_type_, device_id_); } return Maybe::Ok(); } @@ -67,7 +67,8 @@ Maybe Device::Init() { /* static */ Maybe> Device::ThreadLocalGetOrNew(const std::string& type, int64_t device_id) { - CHECK_GE_OR_RETURN(device_id, 0); + CHECK_GE_OR_RETURN(device_id, 0) + << Error::InvalidValueError() << "Device ID should be non-negative"; static thread_local HashMap>> map; auto* device_id2symbol = &map[type]; auto iter = device_id2symbol->find(device_id); @@ -79,21 +80,30 @@ Maybe Device::Init() { return iter->second; } +/* static */ Maybe> Device::ThreadLocalGetOrNew( + const std::string& type_or_type_with_device_id) { + static thread_local HashMap> map; + auto iter = map.find(type_or_type_with_device_id); + if (iter == map.end()) { + std::string type; + int device_id = -1; + JUST(ParsingDeviceTag(type_or_type_with_device_id, &type, &device_id)); + CheckDeviceType(type); + if (device_id == -1) { device_id = GlobalProcessCtx::LocalRank(); } + Device device(type, device_id); + JUST(device.Init()); + iter = map.emplace(type_or_type_with_device_id, SymbolOf(device)).first; + } + return iter->second; +} + /* static */ Maybe> Device::New(const std::string& type) { return New(type, GlobalProcessCtx::LocalRank()); } /* static */ Maybe> Device::ParseAndNew( const std::string& type_or_type_with_device_id) { - std::string type; - int device_id = -1; - JUST(ParsingDeviceTag(type_or_type_with_device_id, &type, &device_id)); - CheckDeviceType(type); - if (device_id == -1) { - return Device::New(type); - } else { - return Device::New(type, device_id); - } + return ThreadLocalGetOrNew(type_or_type_with_device_id); } std::string Device::ToRepr() const { @@ -120,13 +130,17 @@ Maybe> Device::MakeDeviceByParallelDesc(const ParallelDesc& paral for (const auto& item : parallel_desc.parallel_conf().device_name()) { machine_device_ids.emplace_back(item); } - CHECK_EQ_OR_RETURN(machine_device_ids.size(), 1); + CHECK_EQ_OR_RETURN(machine_device_ids.size(), 1) + << Error::InvalidValueError() << "Number of machine device should be one"; const std::string& machine_device_id = machine_device_ids.at(0); size_t pos = machine_device_id.find(':'); - CHECK_NE_OR_RETURN(pos, std::string::npos) << "device_name: " << machine_device_id; + CHECK_NE_OR_RETURN(pos, std::string::npos) + << Error::InvalidValueError() << "Invalid device ID: " << machine_device_id; std::string device_id = machine_device_id.substr(pos + 1); - CHECK_EQ_OR_RETURN(device_id.find('-'), std::string::npos); - CHECK_OR_RETURN(IsStrInt(device_id)); + CHECK_EQ_OR_RETURN(device_id.find('-'), std::string::npos) + << Error::InvalidValueError() << "Device ID should be non-negative"; + CHECK_OR_RETURN(IsStrInt(device_id)) + << Error::InvalidValueError() << "Device ID is not integer: " << device_id; return Device::New(type, std::stoi(device_id)); } @@ -159,7 +173,8 @@ Maybe ParsingDeviceTag(const std::string& device_tag, std::string* device_ *device_index = -1; } else { std::string index_str = device_tag.substr(pos + 1); - CHECK_OR_RETURN(IsStrInt(index_str)) << "Invalid device " << device_tag; + CHECK_OR_RETURN(IsStrInt(index_str)) + << Error::InvalidValueError() << "Invalid device tag " << device_tag; *device_name = device_tag.substr(0, pos); *device_index = std::stoi(index_str); } diff --git a/oneflow/core/framework/device.h b/oneflow/core/framework/device.h index 38dd62bfadd173c64fa98016ec751c90470ec013..028d55019601b1946565d5c3cbad7e457681585e 100644 --- a/oneflow/core/framework/device.h +++ b/oneflow/core/framework/device.h @@ -53,6 +53,7 @@ class Device final { const std::shared_ptr& mem_case() const { return mem_case_; } static Maybe> ThreadLocalGetOrNew(const std::string& type, int64_t device_id); + static Maybe> ThreadLocalGetOrNew(const std::string& type_or_type_with_device_id); static Maybe> New(const std::string& type, int64_t device_id); static Maybe> New(const std::string& type); static Maybe> ParseAndNew(const std::string& type_or_type_with_device_id); diff --git a/oneflow/core/framework/dtype.cpp b/oneflow/core/framework/dtype.cpp index 44ca536e521da7bd138420d9599af361c0d91730..a7ea89cbe294db6bbb61b10f462d0a75e8afaa9c 100644 --- a/oneflow/core/framework/dtype.cpp +++ b/oneflow/core/framework/dtype.cpp @@ -37,9 +37,11 @@ DEFINE_STATIC_SWITCH_FUNC(std::size_t, GetDataTypeBytes, MAKE_DATA_TYPE_BYTES_SW class DTypeMeta final { public: - DTypeMeta(const std::string& name, bool is_signed, bool is_floating_point, bool is_complex) + DTypeMeta(const std::string& name, bool is_signed, bool is_integer, bool is_floating_point, + bool is_complex) : name_(name), is_signed_(is_signed), + is_integer_(is_integer), is_floating_point_(is_floating_point), is_complex_(is_complex) {} DTypeMeta(const DTypeMeta&) = default; @@ -48,40 +50,43 @@ class DTypeMeta final { const std::string& name() const { return name_; } bool is_signed() const { return is_signed_; } + bool is_integer() const { return is_integer_; } bool is_floating_point() const { return is_floating_point_; } bool is_complex() const { return is_complex_; } private: const std::string name_; const bool is_signed_; + const bool is_integer_; const bool is_floating_point_; const bool is_complex_; }; Maybe DTypeMeta4DataType(DataType data_type) { static const HashMap data_type2dtype_meta{ - {DataType::kInvalidDataType, DTypeMeta("oneflow.invalid_data_type", false, false, false)}, - {DataType::kChar, DTypeMeta("oneflow.char", false, false, false)}, - {DataType::kFloat16, DTypeMeta("oneflow.float16", true, true, false)}, - {DataType::kFloat, DTypeMeta("oneflow.float32", true, true, false)}, - {DataType::kDouble, DTypeMeta("oneflow.float64", true, true, false)}, - {DataType::kInt8, DTypeMeta("oneflow.int8", true, false, false)}, - {DataType::kInt16, DTypeMeta("oneflow.int16", true, false, false)}, - {DataType::kInt32, DTypeMeta("oneflow.int32", true, false, false)}, - {DataType::kInt64, DTypeMeta("oneflow.int64", true, false, false)}, - {DataType::kInt128, DTypeMeta("oneflow.int128", true, false, false)}, - {DataType::kUInt8, DTypeMeta("oneflow.uint8", false, false, false)}, - {DataType::kUInt16, DTypeMeta("oneflow.uint16", false, false, false)}, - {DataType::kUInt32, DTypeMeta("oneflow.uint32", false, false, false)}, - {DataType::kUInt64, DTypeMeta("oneflow.uint64", false, false, false)}, - {DataType::kUInt128, DTypeMeta("oneflow.uint128", false, false, false)}, - {DataType::kOFRecord, DTypeMeta("oneflow.of_record", false, false, false)}, - {DataType::kTensorBuffer, DTypeMeta("oneflow.tensor_buffer", false, false, false)}, - {DataType::kBFloat16, DTypeMeta("oneflow.bfloat16", true, true, false)}, - {DataType::kBool, DTypeMeta("oneflow.bool", false, false, false)}, - {DataType::kComplex32, DTypeMeta("oneflow.complex32", false, false, true)}, - {DataType::kComplex64, DTypeMeta("oneflow.complex64", false, false, true)}, - {DataType::kComplex128, DTypeMeta("oneflow.complex128", false, false, true)}, + {DataType::kInvalidDataType, + DTypeMeta("oneflow.invalid_data_type", false, false, false, false)}, + {DataType::kChar, DTypeMeta("oneflow.char", false, false, false, false)}, + {DataType::kFloat16, DTypeMeta("oneflow.float16", true, false, true, false)}, + {DataType::kFloat, DTypeMeta("oneflow.float32", true, false, true, false)}, + {DataType::kDouble, DTypeMeta("oneflow.float64", true, false, true, false)}, + {DataType::kInt8, DTypeMeta("oneflow.int8", true, true, false, false)}, + {DataType::kInt16, DTypeMeta("oneflow.int16", true, true, false, false)}, + {DataType::kInt32, DTypeMeta("oneflow.int32", true, true, false, false)}, + {DataType::kInt64, DTypeMeta("oneflow.int64", true, true, false, false)}, + {DataType::kInt128, DTypeMeta("oneflow.int128", true, true, false, false)}, + {DataType::kUInt8, DTypeMeta("oneflow.uint8", false, true, false, false)}, + {DataType::kUInt16, DTypeMeta("oneflow.uint16", false, true, false, false)}, + {DataType::kUInt32, DTypeMeta("oneflow.uint32", false, true, false, false)}, + {DataType::kUInt64, DTypeMeta("oneflow.uint64", false, true, false, false)}, + {DataType::kUInt128, DTypeMeta("oneflow.uint128", false, true, false, false)}, + {DataType::kOFRecord, DTypeMeta("oneflow.of_record", false, false, false, false)}, + {DataType::kTensorBuffer, DTypeMeta("oneflow.tensor_buffer", false, false, false, false)}, + {DataType::kBFloat16, DTypeMeta("oneflow.bfloat16", true, false, true, false)}, + {DataType::kBool, DTypeMeta("oneflow.bool", false, false, false, false)}, + {DataType::kComplex32, DTypeMeta("oneflow.complex32", false, false, false, true)}, + {DataType::kComplex64, DTypeMeta("oneflow.complex64", false, false, false, true)}, + {DataType::kComplex128, DTypeMeta("oneflow.complex128", false, false, false, true)}, }; return MapAt(data_type2dtype_meta, data_type); }; @@ -140,6 +145,8 @@ const int DType::priority_order[DataType_ARRAYSIZE] = {0, /*kInvalid*/ 16, /*kComplex64*/ 17 /*kComplex128*/}; +bool DType::is_integer() const { return CHECK_JUST(DTypeMeta4DataType(data_type_)).is_integer(); } + bool DType::is_floating_point() const { return CHECK_JUST(DTypeMeta4DataType(data_type_)).is_floating_point(); } @@ -242,4 +249,27 @@ Symbol promoteTypes(const Symbol a, const Symbol b) { return _promoteTypesLookup[static_cast(a->data_type())][static_cast(b->data_type())]; } +namespace { + +std::mutex default_dtype_mutex; +Symbol* GetMutDefaultDTypeSymbol() { + static Symbol default_dtype = CHECK_JUST(DType::Get(DataType::kFloat)); + return &default_dtype; +} + +} // namespace + +Maybe SetDefaultDType(const Symbol& dtype) { + std::lock_guard lock(default_dtype_mutex); + CHECK_OR_RETURN(dtype->is_floating_point()) + << "only floating-point types are supported as the default type"; + *GetMutDefaultDTypeSymbol() = dtype; + return Maybe::Ok(); +} + +Symbol GetDefaultDType() { + std::lock_guard lock(default_dtype_mutex); + return *GetMutDefaultDTypeSymbol(); +} + } // namespace oneflow diff --git a/oneflow/core/framework/dtype.h b/oneflow/core/framework/dtype.h index 39934075bbfc9f8275b8e7ca8f774794f7010714..b3f396085d0e480a7f6135bc266546ea97b9b615 100644 --- a/oneflow/core/framework/dtype.h +++ b/oneflow/core/framework/dtype.h @@ -59,6 +59,7 @@ class DType final { DataType data_type() const { return data_type_; } bool is_signed() const; bool is_complex() const; + bool is_integer() const; bool is_floating_point() const; const std::string& name() const; Maybe bytes() const; @@ -76,6 +77,9 @@ class DType final { Symbol promoteTypes(const Symbol a, const Symbol b); +Maybe SetDefaultDType(const Symbol& dtype); +Symbol GetDefaultDType(); + } // namespace oneflow namespace std { diff --git a/oneflow/core/framework/framework.h b/oneflow/core/framework/framework.h index cb62c9281311e9030d6de23fe7c7cfd5954f5b62..a33dc7607137585b36d0953ce5caa6f30cd846c1 100644 --- a/oneflow/core/framework/framework.h +++ b/oneflow/core/framework/framework.h @@ -24,9 +24,11 @@ limitations under the License. #include "oneflow/core/framework/sbp_context.h" #include "oneflow/core/framework/infer_output_blob_time_shape_fn_context.h" #include "oneflow/core/framework/infer_nd_sbp_fn_context.h" +#include "oneflow/core/framework/compute_complexity_fn_context.h" +#include "oneflow/core/framework/get_nd_sbp_signature_list_context.h" #include "oneflow/core/framework/user_op_hob.h" -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/framework/op_kernel.h" #include "oneflow/core/framework/user_op_def.h" #include "oneflow/core/framework/multi_thread.h" diff --git a/oneflow/core/framework/get_nd_sbp_signature_list_context.h b/oneflow/core/framework/get_nd_sbp_signature_list_context.h new file mode 100644 index 0000000000000000000000000000000000000000..21c445c702661c5b6cb6a05b906a65f2db0dcd6d --- /dev/null +++ b/oneflow/core/framework/get_nd_sbp_signature_list_context.h @@ -0,0 +1,57 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_COMPUTE_GET_ND_SBP_SIGNATURE_LIST_CONTEXT_H_ +#define ONEFLOW_CORE_FRAMEWORK_COMPUTE_GET_ND_SBP_SIGNATURE_LIST_CONTEXT_H_ + +#include "oneflow/core/framework/user_op_conf.h" +#include "oneflow/core/framework/user_op_registry.h" +#include "oneflow/core/job/parallel_desc.h" + +namespace oneflow { + +class Shape; + +namespace user_op { + +class UserOpDefWrapper; + +class GetNdSbpSignatureListContext { + public: + virtual ~GetNdSbpSignatureListContext() = default; + + virtual void AddNdSbpSignature(NdSbpSignature&) = 0; + virtual const Shape& parallel_hierarchy() = 0; + virtual const Shape& BlobShape4InputArgNameAndIndex(const std::string& arg_name, + int32_t index) const = 0; + template + T Attr(const std::string& attr_name) const { + return conf_.attr(attr_name); + } + + const UserOpConfWrapper& user_op_conf() const { return conf_; } + + protected: + explicit GetNdSbpSignatureListContext(UserOpConfWrapper&& conf) : conf_(std::move(conf)) {} + + private: + UserOpConfWrapper conf_; +}; + +} // namespace user_op + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_COMPUTE_GET_ND_SBP_SIGNATURE_LIST_CONTEXT_H_ diff --git a/oneflow/core/framework/global_tensor_infer_cache.cpp b/oneflow/core/framework/global_tensor_infer_cache.cpp new file mode 100644 index 0000000000000000000000000000000000000000..365d76fe0b61ab36cffbc64135fe40140da8d80a --- /dev/null +++ b/oneflow/core/framework/global_tensor_infer_cache.cpp @@ -0,0 +1,379 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/global_tensor_infer_cache.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/framework/to_string.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/user_op_registry_manager.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/env_var/eager.h" + +namespace oneflow { +namespace one { + +namespace { + +bool OptionalEqual(const Optional>& lhs, const Optional>& rhs) { + if (lhs.has_value() != rhs.has_value()) { return false; } + if (!lhs.has_value()) { return true; } + return CHECK_JUST(lhs) == CHECK_JUST(rhs); +} + +} // namespace + +size_t InputGlobalTensorMeta::hash_value() const { + size_t hash_value = std::hash>()(tensor_meta()); + if (consumer_nd_sbp_constraint().has_value()) { + AddHash(&hash_value, CHECK_JUST(consumer_nd_sbp_constraint())); + } + return hash_value; +} + +bool InputGlobalTensorMeta::operator==(const InputGlobalTensorMeta& other) const { + return this->tensor_meta() == other.tensor_meta() + && OptionalEqual(this->consumer_nd_sbp_constraint(), other.consumer_nd_sbp_constraint()); +} + +void InputGlobalTensorMeta::assign(Symbol tensor_meta, + const Optional>& consumer_nd_sbp_constraint) { + tensor_meta_ = tensor_meta; + consumer_nd_sbp_constraint_ = consumer_nd_sbp_constraint; +} + +size_t GlobalTensorMetaInferArgs::hash_value() const { + size_t hash_value = std::hash()(attrs_); + const auto& tensor_meta_hash_functor = std::hash(); + for (const auto& tensor_meta : input_global_tensor_metas_) { + HashCombine(&hash_value, tensor_meta_hash_functor(tensor_meta)); + } + return hash_value; +} + +size_t SrcOpGlobalTensorMetaInferArgs::hash_value() const { + size_t hash_value = std::hash()(attrs_); + AddHash(&hash_value, parallel_desc_); + AddHash(&hash_value, nd_sbp_); + return hash_value; +} + +bool GlobalTensorMetaInferArgs::operator==(const GlobalTensorMetaInferArgs& other) const { + return this->attrs_ == other.attrs_ + && this->input_global_tensor_metas_ == other.input_global_tensor_metas_; +} + +bool SrcOpGlobalTensorMetaInferArgs::operator==(const SrcOpGlobalTensorMetaInferArgs& other) const { + return this->attrs_ == other.attrs_ && this->parallel_desc_ == other.parallel_desc_ + && this->nd_sbp_ == other.nd_sbp_; +} + +Maybe GlobalTensorMetaInferArgs::MakeNdSbpConstraints( + const UserOpExpr& user_op_expr, NdSbpSignature* nd_sbp_signature) const { + const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); + auto* map = nd_sbp_signature->mutable_bn_in_op2nd_sbp(); + for (int i = 0; i < input_arg_tuple.size(); ++i) { + const auto& constaint = input_global_tensor_metas_[i].consumer_nd_sbp_constraint(); + if (constaint.has_value()) { (*map)[input_arg_tuple.indexed_bns().at(i)] = *JUST(constaint); } + } + return Maybe::Ok(); +} + +Maybe GlobalTensorMetaInferArgs::MakeInputBlobDescs(const UserOpExpr& user_op_expr, + std::vector* blob_descs) const { + CHECK_OR_RETURN(blob_descs->empty()); + const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); + blob_descs->reserve(input_arg_tuple.size()); + for (int i = 0; i < input_arg_tuple.size(); ++i) { + const auto& tensor_meta = *input_global_tensor_metas_[i].tensor_meta(); + blob_descs->emplace_back(tensor_meta.shape(), tensor_meta.stride(), tensor_meta.data_type()); + } + return Maybe::Ok(); +} + +Maybe GlobalTensorMetaInferArgs::MakeNdSbpInferHints( + const UserOpExpr& user_op_expr, const std::vector& blob_descs, + std::vector* hints) const { + CHECK_OR_RETURN(hints->empty()); + const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); + hints->reserve(input_arg_tuple.size()); + for (int i = 0; i < input_arg_tuple.size(); ++i) { + const auto& tensor_meta = *input_global_tensor_metas_[i].tensor_meta(); + const auto* parallel_desc = &*tensor_meta.parallel_desc(); + const auto* blob_desc = &blob_descs.at(i); + const auto* nd_sbp = &*tensor_meta.nd_sbp(); + hints->emplace_back(parallel_desc, blob_desc, nd_sbp); + } + return Maybe::Ok(); +} + +Maybe GlobalTensorMetaInferArgs::New(const AttrMap& attrs, + const TensorTuple& input_tensors) { + std::shared_ptr infer_args(new GlobalTensorMetaInferArgs()); + infer_args->attrs_ = attrs; + infer_args->input_global_tensor_metas_.resize(input_tensors.size()); + JUST(infer_args->InitInputGlobalTensorMetas(input_tensors)); + return infer_args; +} + +Maybe SrcOpGlobalTensorMetaInferArgs::New( + const AttrMap& attrs, Symbol parallel_desc, Symbol nd_sbp) { + std::shared_ptr infer_args(new SrcOpGlobalTensorMetaInferArgs()); + infer_args->attrs_ = attrs; + infer_args->parallel_desc_ = parallel_desc; + infer_args->nd_sbp_ = nd_sbp; + return infer_args; +} + +Maybe GlobalTensorMetaInferArgs::InitInputGlobalTensorMetas( + const TensorTuple& input_tensors) { + for (int i = 0; i < input_tensors.size(); ++i) { + const auto& tensor = *input_tensors.at(i); + const auto& tensor_meta = JUST(tensor.global_tensor_meta()); + const auto& constraint = JUST(tensor.consumer_nd_sbp_constraint()); + input_global_tensor_metas_[i].assign(tensor_meta, constraint); + } + return Maybe::Ok(); +} + +namespace { + +Maybe MakeOp(const UserOpExpr& user_op_expr, const AttrMap& attrs, + const std::string& device_tag) { + OperatorConf op_conf; + JUST(user_op_expr.BuildOpConf(&op_conf, attrs)); + DeviceType device_type = JUST(DeviceType4DeviceTag(device_tag)); + return JUST(ConstructOp(op_conf, device_type)); +} + +Maybe CheckInputParallelDescIdentical(const GlobalTensorMetaInferArgs& infer_args) { + if (infer_args.input_global_tensor_metas().empty()) { return Maybe::Ok(); } + const auto& first_parallel_desc = + infer_args.input_global_tensor_metas().begin()->tensor_meta()->parallel_desc(); + for (int i = 0; i < infer_args.input_global_tensor_metas().size(); ++i) { + CHECK_OR_RETURN( + first_parallel_desc + == JUST(VectorAt(infer_args.input_global_tensor_metas(), i)).tensor_meta()->parallel_desc()) + << Error::RuntimeError() + << "Expected all tensors to be on the same placement, but found " + "at least two placements, " + << *JUST(PlacementToString(first_parallel_desc)) << " (positional 0) and " + << *JUST(PlacementToString(JUST(VectorAt(infer_args.input_global_tensor_metas(), i)) + .tensor_meta() + ->parallel_desc())) + << " (positional " << i << ")!"; + } + return Maybe::Ok(); +} + +Maybe CheckIsDeviceSupportedByOp(const ParallelDesc& parallel_desc, + const std::string& op_type_name) { + if (IsCpuOnly(op_type_name)) { CHECK_EQ_OR_RETURN(parallel_desc.device_tag(), "cpu"); } + return Maybe::Ok(); +} + +class UserOpExprDeviceAndStreamInferContext final : public user_op::DeviceAndStreamInferContext { + public: + UserOpExprDeviceAndStreamInferContext(const UserOpExpr* user_op_expr, + const GlobalTensorMetaInferArgs* infer_args) + : user_op_expr_(user_op_expr), + composed_attrs_(infer_args->attrs(), user_op_expr->base_attrs()), + in_tensor_devices_(user_op_expr_->input_size()), + out_tensor_devices_(user_op_expr_->output_size()) { + for (int i = 0; i < user_op_expr_->input_size(); ++i) { + const auto& parallel_desc = + infer_args->input_global_tensor_metas().at(i).tensor_meta()->parallel_desc(); + in_tensor_devices_.at(i) = CHECK_JUST(GetTensorDevice(parallel_desc)); + } + } + + const std::vector>& inputs() const override { + return user_op_expr_->indexed_input_pairs(); + } + + const std::vector>& outputs() const override { + return user_op_expr_->indexed_output_pairs(); + } + + Symbol* OutputTensorDevice4ArgNameAndIndex(const std::string& name, + int64_t index) override { + const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0); + CHECK_LT(tuple_index, user_op_expr_->output_size()); + return &out_tensor_devices_.at(tuple_index); + } + + Symbol InputTensorDevice4ArgNameAndIndex(const std::string& name, + int64_t index) const override { + const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0); + CHECK_LT(tuple_index, user_op_expr_->input_size()); + return in_tensor_devices_.at(tuple_index); + } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return composed_attrs_.Attr4Name(attr_name); + } + const UserOpExpr* user_op_expr_; + const ComposedAttrMap composed_attrs_; + std::vector> in_tensor_devices_; + std::vector> out_tensor_devices_; +}; + +} // namespace + +/* static */ Maybe> GlobalTensorInferCache::InferDeviceAndStream( + const UserOpExpr& user_op_expr, const GlobalTensorMetaInferArgs& infer_args) { + if (!user_op_expr.device_and_stream_infer_fn()) { + Symbol parallel_desc = + infer_args.input_global_tensor_metas()[0].tensor_meta()->parallel_desc(); + return GetDefaultStreamByPlacement(parallel_desc); + } else { + UserOpExprDeviceAndStreamInferContext device_and_stream_ctx(&user_op_expr, &infer_args); + return TRY(user_op_expr.device_and_stream_infer_fn()(&device_and_stream_ctx)); + } +} + +/* static */ Maybe GlobalTensorInferCache::Infer( + const UserOpExpr& user_op_expr, const GlobalTensorMetaInferArgs& infer_args) { + CHECK_GT_OR_RETURN(infer_args.input_global_tensor_metas().size(), 0); // NOLINT + Symbol parallel_desc = + infer_args.input_global_tensor_metas()[0].tensor_meta()->parallel_desc(); + JUST(CheckInputParallelDescIdentical(infer_args)); + JUST(CheckIsDeviceSupportedByOp(*parallel_desc, user_op_expr.op_type_name())); + std::vector output_mut_metas(user_op_expr.output_size()); + { + // Infer OpArgMutGlobalTensorMeta. + const auto& input_metas = infer_args.input_global_tensor_metas(); + JUST(user_op_expr.InferLogicalTensorDesc( + infer_args.attrs(), parallel_desc, + [&](int32_t i) { return &*input_metas.at(i).tensor_meta(); }, + [&](int32_t i) { return output_mut_metas.at(i).mut_tensor_meta(); })); + } + const auto& op = JUST(MakeOp(user_op_expr, infer_args.attrs(), parallel_desc->device_tag())); + JUST(op->FillOpParallelDesc(parallel_desc.shared_from_symbol())); + JUST(op->InferParallelSignatureIf()); + { + // Infer parallel distribution. + NdSbpSignature nd_sbp_constraints; + JUST(infer_args.MakeNdSbpConstraints(user_op_expr, &nd_sbp_constraints)); + std::vector blob_descs; + JUST(infer_args.MakeInputBlobDescs(user_op_expr, &blob_descs)); + std::vector pd_infer_hints; + JUST(infer_args.MakeNdSbpInferHints(user_op_expr, blob_descs, &pd_infer_hints)); + const auto& input_arg_tuple = *user_op_expr.input_arg_tuple(); + const auto& NdSbpInferHint4Ibn = [&](const std::string& ibn) -> Maybe { + int32_t input_index = input_arg_tuple.bn_in_op2tensor_tuple_index().at(ibn); + CHECK_GE_OR_RETURN(input_index, 0); + CHECK_LT_OR_RETURN(input_index, pd_infer_hints.size()); + return &pd_infer_hints.at(input_index); + }; + // The inferred results can be retrieved by op->NdSbp4BnInOp(obn). + JUST(op->InferNdSbpSignatureIf(nd_sbp_constraints, *parallel_desc, NdSbpInferHint4Ibn)); + } + auto result = std::make_unique(user_op_expr.input_size(), + user_op_expr.output_size()); + auto* input_metas = result->mut_input_tensor_metas(); + for (int32_t i = 0; i < user_op_expr.input_size(); ++i) { + const auto& old_global_tensor_meta = infer_args.input_global_tensor_metas()[i].tensor_meta(); + const auto& ibn = user_op_expr.input_arg_tuple()->indexed_bns().at(i); + const auto& nd_sbp = SymbolOf(*JUST(op->NdSbp4BnInOp(ibn))); + GlobalTensorMeta global_tensor_meta(old_global_tensor_meta->shape(), + old_global_tensor_meta->dtype(), nd_sbp, + old_global_tensor_meta->parallel_desc()); + (*input_metas)[i] = SymbolOf(global_tensor_meta); + } + auto* output_metas = result->mut_output_tensor_metas(); + for (int32_t i = 0; i < user_op_expr.output_size(); ++i) { + const auto& output_mut_meta = output_mut_metas.at(i); + const auto& shape = output_mut_meta.tensor_meta().shape(); + DataType data_type = output_mut_meta.tensor_meta().data_type(); + const auto& obn = user_op_expr.output_arg_tuple()->indexed_bns().at(i); + const auto& nd_sbp = SymbolOf(*JUST(op->NdSbp4BnInOp(obn))); + GlobalTensorMeta tensor_meta(shape, data_type, nd_sbp, parallel_desc); + output_metas->at(i) = SymbolOf(tensor_meta); + } + result->set_stream(JUST(InferDeviceAndStream(user_op_expr, infer_args))); + return std::shared_ptr(std::move(result)); +} + +/* static */ Maybe GlobalTensorInferCache::Infer( + const UserOpExpr& user_op_expr, const SrcOpGlobalTensorMetaInferArgs& infer_args) { + Symbol parallel_desc = infer_args.parallel_desc(); + JUST(CheckIsDeviceSupportedByOp(*parallel_desc, user_op_expr.op_type_name())); + std::vector output_mut_metas(user_op_expr.output_size()); + { + // Infer OpArgMutGlobalTensorMeta. + const auto& GetInputTensorMeta = [](int32_t i) { + UNIMPLEMENTED(); + return nullptr; + }; + JUST(user_op_expr.InferLogicalTensorDesc( + infer_args.attrs(), parallel_desc, GetInputTensorMeta, + [&](int32_t i) { return output_mut_metas.at(i).mut_tensor_meta(); })); + } + auto result = std::make_unique(user_op_expr.input_size(), + user_op_expr.output_size()); + auto* output_metas = result->mut_output_tensor_metas(); + for (int32_t i = 0; i < user_op_expr.output_size(); ++i) { + const auto& output_mut_meta = output_mut_metas.at(i); + const auto& shape = output_mut_meta.tensor_meta().shape(); + DataType data_type = output_mut_meta.tensor_meta().data_type(); + const auto& nd_sbp = infer_args.nd_sbp(); + GlobalTensorMeta tensor_meta(shape, data_type, nd_sbp, parallel_desc); + output_metas->at(i) = SymbolOf(tensor_meta); + } + result->set_stream(JUST(GetDefaultStreamByPlacement(parallel_desc))); + return std::shared_ptr(std::move(result)); +} + +Maybe GlobalTensorInferCache::GetOrInfer( + const GlobalTensorMetaInferArgs& infer_args) { + auto iter = cache_.find(infer_args); + if (iter == cache_.end()) { + if (unlikely(cache_.size() >= ThreadLocalEnvInteger())) { + cache_.clear(); + } + const auto& user_op_expr = user_op_expr_.lock(); + CHECK_OR_RETURN(static_cast(user_op_expr)); + const auto& output_tensor_metas = JUST(Infer(*user_op_expr, infer_args)); + iter = cache_.emplace(infer_args, output_tensor_metas).first; + } + return iter->second; +} + +Maybe GlobalTensorInferCache::GetOrInfer( + const SrcOpGlobalTensorMetaInferArgs& infer_args) { + auto iter = src_op_cache_.find(infer_args); + if (iter == src_op_cache_.end()) { + if (unlikely(src_op_cache_.size() + >= ThreadLocalEnvInteger())) { + src_op_cache_.clear(); + } + const auto& user_op_expr = user_op_expr_.lock(); + CHECK_OR_RETURN(static_cast(user_op_expr)); + const auto& output_tensor_metas = JUST(Infer(*user_op_expr, infer_args)); + iter = src_op_cache_.emplace(infer_args, output_tensor_metas).first; + } + return iter->second; +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/global_tensor_infer_cache.h b/oneflow/core/framework/global_tensor_infer_cache.h new file mode 100644 index 0000000000000000000000000000000000000000..773ac205486267f377e60f19430176ad0b864120 --- /dev/null +++ b/oneflow/core/framework/global_tensor_infer_cache.h @@ -0,0 +1,232 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_GLOBAL_TENSOR_INFER_CACHE_H_ +#define ONEFLOW_CORE_FRAMEWORK_GLOBAL_TENSOR_INFER_CACHE_H_ + +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/common/tensor_meta.h" +#include "oneflow/core/register/blob_desc.h" +#include "oneflow/core/job/nd_sbp_infer_hint.h" + +namespace oneflow { + +class NdSbp; + +class ParallelDesc; + +namespace one { + +class GlobalTensorMeta; + +class InputGlobalTensorMeta final { + public: + InputGlobalTensorMeta() : tensor_meta_(), consumer_nd_sbp_constraint_() {} + InputGlobalTensorMeta(Symbol tensor_meta, + const Optional>& consumer_nd_sbp_constraint) + : tensor_meta_(tensor_meta), consumer_nd_sbp_constraint_(consumer_nd_sbp_constraint) {} + + InputGlobalTensorMeta(const InputGlobalTensorMeta&) = default; + InputGlobalTensorMeta(InputGlobalTensorMeta&&) = default; + ~InputGlobalTensorMeta() = default; + + size_t hash_value() const; + bool operator==(const InputGlobalTensorMeta& other) const; + Symbol tensor_meta() const { return tensor_meta_; } + const Optional>& consumer_nd_sbp_constraint() const { + return consumer_nd_sbp_constraint_; + } + void assign(Symbol tensor_meta, + const Optional>& consumer_nd_sbp_constraint); + + private: + Symbol tensor_meta_; + Optional> consumer_nd_sbp_constraint_; +}; + +class TensorTuple; +class UserOpExpr; + +class GlobalTensorMetaInferArgs final { + public: + GlobalTensorMetaInferArgs(const GlobalTensorMetaInferArgs&) = default; + GlobalTensorMetaInferArgs(GlobalTensorMetaInferArgs&&) = default; + ~GlobalTensorMetaInferArgs() = default; + + const std::vector& input_global_tensor_metas() const { + return input_global_tensor_metas_; + } + const AttrMap& attrs() const { return attrs_; } + + size_t hash_value() const; + + bool operator==(const GlobalTensorMetaInferArgs& other) const; + + Maybe MakeNdSbpConstraints(const UserOpExpr& user_op_expr, + NdSbpSignature* nd_sbp_signature) const; + + Maybe MakeInputBlobDescs(const UserOpExpr& user_op_expr, + std::vector* blob_descs) const; + + Maybe MakeNdSbpInferHints(const UserOpExpr& user_op_expr, + const std::vector& blob_descs, + std::vector* hints) const; + + static Maybe New(const AttrMap& attrs, + const TensorTuple& input_tensors); + + private: + GlobalTensorMetaInferArgs() = default; + Maybe InitInputGlobalTensorMetas(const TensorTuple& input_tensors); + + AttrMap attrs_; + std::vector input_global_tensor_metas_; +}; + +class SrcOpGlobalTensorMetaInferArgs final { + public: + SrcOpGlobalTensorMetaInferArgs(const SrcOpGlobalTensorMetaInferArgs&) = default; + SrcOpGlobalTensorMetaInferArgs(SrcOpGlobalTensorMetaInferArgs&&) = default; + ~SrcOpGlobalTensorMetaInferArgs() = default; + + Symbol parallel_desc() const { return parallel_desc_; } + Symbol nd_sbp() const { return nd_sbp_; } + const AttrMap& attrs() const { return attrs_; } + + size_t hash_value() const; + + bool operator==(const SrcOpGlobalTensorMetaInferArgs& other) const; + + static Maybe New(const AttrMap& attrs, + Symbol parallel_desc, + Symbol nd_sbp); + + private: + SrcOpGlobalTensorMetaInferArgs() = default; + + AttrMap attrs_; + Symbol parallel_desc_; + Symbol nd_sbp_; +}; + +class OpArgMutGlobalTensorMeta final { + public: + OpArgMutGlobalTensorMeta() + : tensor_meta_(std::make_shared(), DataType::kInvalidDataType) {} + + OpArgMutGlobalTensorMeta(const OpArgMutGlobalTensorMeta&) = default; + OpArgMutGlobalTensorMeta(OpArgMutGlobalTensorMeta&&) = default; + ~OpArgMutGlobalTensorMeta() = default; + + const TensorMeta& tensor_meta() const { return tensor_meta_; } + + TensorMeta* mut_tensor_meta() { return &tensor_meta_; } + + private: + MutTensorMeta tensor_meta_; +}; + +} // namespace one +} // namespace oneflow + +namespace std { + +template<> +struct hash final { + size_t operator()(const oneflow::one::InputGlobalTensorMeta& val) const { + return val.hash_value(); + } +}; + +template<> +struct hash final { + size_t operator()(const oneflow::one::GlobalTensorMetaInferArgs& val) const { + return val.hash_value(); + } +}; + +template<> +struct hash final { + size_t operator()(const oneflow::one::SrcOpGlobalTensorMetaInferArgs& val) const { + return val.hash_value(); + } +}; + +} // namespace std + +namespace oneflow { +namespace one { + +class GlobalTensorInferResult final { + public: + GlobalTensorInferResult(size_t input_size, size_t output_size) + : input_tensor_metas_(input_size), output_tensor_metas_(output_size) {} + GlobalTensorInferResult(const GlobalTensorInferResult&) = delete; + GlobalTensorInferResult(GlobalTensorInferResult&&) = delete; + ~GlobalTensorInferResult() = default; + + const std::vector>& input_tensor_metas() const { + return input_tensor_metas_; + } + const std::vector>& output_tensor_metas() const { + return output_tensor_metas_; + } + + std::vector>* mut_input_tensor_metas() { return &input_tensor_metas_; } + std::vector>* mut_output_tensor_metas() { return &output_tensor_metas_; } + + const Symbol& stream() const { return stream_; } + void set_stream(const Symbol& stream) { stream_ = stream; } + + private: + std::vector> input_tensor_metas_; + std::vector> output_tensor_metas_; + Symbol stream_; +}; + +class GlobalTensorInferCache final { + public: + GlobalTensorInferCache(const std::shared_ptr& user_op_expr) + : user_op_expr_(user_op_expr) {} + + Maybe GetOrInfer(const GlobalTensorMetaInferArgs& infer_args); + + static Maybe Infer(const UserOpExpr& user_op_expr, + const GlobalTensorMetaInferArgs& infer_args); + + Maybe GetOrInfer(const SrcOpGlobalTensorMetaInferArgs& infer_args); + + static Maybe Infer( + const UserOpExpr& user_op_expr, const SrcOpGlobalTensorMetaInferArgs& infer_args); + + private: + static Maybe> InferDeviceAndStream(const UserOpExpr& user_op_expr, + const GlobalTensorMetaInferArgs& infer_args); + + std::weak_ptr user_op_expr_; + HashMap> cache_; + HashMap> + src_op_cache_; +}; + +} // namespace one +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_GLOBAL_TENSOR_INFER_CACHE_H_ diff --git a/oneflow/core/framework/infer_util.cpp b/oneflow/core/framework/infer_util.cpp index 599f6a9070d7a63cbaca3f024549b5ae70d3b186..5d977601925d7cbdbb82983d3b50108c73d587e7 100644 --- a/oneflow/core/framework/infer_util.cpp +++ b/oneflow/core/framework/infer_util.cpp @@ -32,15 +32,18 @@ Maybe TensorDescInferFnUtil::Unchanged(InferContext* ctx) { const std::pair& input_arg = ctx->inputs().at(i); if (first_tensor_desc) { const TensorDesc& tensor_desc = ctx->InputTensorDesc(input_arg.first, input_arg.second); - CHECK_EQ_OR_RETURN(tensor_desc.shape(), first_tensor_desc->shape()); + CHECK_EQ_OR_RETURN(tensor_desc.shape(), first_tensor_desc->shape()) + << Error::RuntimeError() << "Tensor descriptions should have the same shape: expected " + << first_tensor_desc->shape() << " but got " << tensor_desc.shape(); } else { first_tensor_desc = &ctx->InputTensorDesc(input_arg.first, input_arg.second); } } for (size_t i = 0; i < ctx->outputs().size(); ++i) { const std::pair& output_arg = ctx->outputs().at(i); - *ctx->OutputIsDynamic(output_arg.first, output_arg.second) = first_tensor_desc->is_dynamic(); - *ctx->OutputShape(output_arg.first, output_arg.second) = first_tensor_desc->shape(); + ctx->SetOutputIsDynamic(output_arg.first, output_arg.second, // NOLINT + first_tensor_desc->is_dynamic()); // NOLINT + ctx->SetOutputShape(output_arg.first, output_arg.second, first_tensor_desc->shape()); // NOLINT } return Maybe::Ok(); } @@ -51,24 +54,31 @@ Maybe TensorDescInferFnUtil::UnchangedDataType(InferContext* ctx) { const std::pair& input_arg = ctx->inputs().at(i); if (first_tensor_desc) { const TensorDesc& tensor_desc = ctx->InputTensorDesc(input_arg.first, input_arg.second); - CHECK_EQ_OR_RETURN(tensor_desc.data_type(), first_tensor_desc->data_type()); + CHECK_EQ_OR_RETURN(tensor_desc.data_type(), first_tensor_desc->data_type()) + << Error::TypeError() << "Tensor descriptions should have the same type. Expected " + << DataType_Name(first_tensor_desc->data_type()) << ", but got " + << DataType_Name(tensor_desc.data_type()); } else { first_tensor_desc = &ctx->InputTensorDesc(input_arg.first, input_arg.second); } } for (size_t i = 0; i < ctx->outputs().size(); ++i) { const std::pair& output_arg = ctx->outputs().at(i); - *ctx->OutputDType(output_arg.first, output_arg.second) = first_tensor_desc->data_type(); + ctx->SetOutputDType(output_arg.first, output_arg.second, // NOLINT + first_tensor_desc->data_type()); // NOLINT } return Maybe::Ok(); } Maybe TensorDescInferFnUtil::InOutCorrespond(InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->inputs().size(), ctx->outputs().size()); + CHECK_EQ_OR_RETURN(ctx->inputs().size(), ctx->outputs().size()) + << Error::InvalidValueError() + << "Different input and output size. Input size :" << ctx->inputs().size() + << ", output size: " << ctx->outputs().size(); for (size_t i = 0; i < ctx->inputs().size(); ++i) { const auto& input_arg = ctx->inputs().at(i); const auto& output_arg = ctx->outputs().at(i); - *ctx->OutputTensorDesc(output_arg.first, output_arg.second) = + *ctx->MutOutputTensorDesc(output_arg.first, output_arg.second) = ctx->InputTensorDesc(input_arg.first, input_arg.second); } return Maybe::Ok(); diff --git a/oneflow/core/framework/infer_util.h b/oneflow/core/framework/infer_util.h index 5b32ea318444811c4d7ef802b7113b19f35d8f97..17aeb0211294636fdb4c36cd0ad7a4e02c533505 100644 --- a/oneflow/core/framework/infer_util.h +++ b/oneflow/core/framework/infer_util.h @@ -18,8 +18,7 @@ limitations under the License. #include "oneflow/core/common/maybe.h" #include "oneflow/core/framework/user_op_conf.h" -#include "oneflow/core/framework/tensor_desc.h" -#include "oneflow/core/framework/attr_value.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/job/placement.pb.h" #include "oneflow/core/job/sbp_parallel.h" #include "oneflow/core/job/parallel_desc.h" @@ -30,6 +29,13 @@ class Shape; class JobDesc; class Device; +namespace user_op { +class AttrVal; +} // namespace user_op + +template +extern const T& AttrValueCast(const user_op::AttrVal& val); + namespace user_op { class UserOpDefWrapper; @@ -39,18 +45,25 @@ class InferContext { virtual ~InferContext() = default; virtual const TensorDesc& InputTensorDesc(const std::string&, int32_t) const = 0; - virtual TensorDesc* OutputTensorDesc(const std::string&, int32_t) = 0; + virtual const TensorDesc& OutputTensorDesc(const std::string&, int32_t) const = 0; + virtual TensorDesc* MutOutputTensorDesc(const std::string&, int32_t) = 0; virtual const TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string&, int32_t) const = 0; virtual const Shape& InputShape(const std::string&, int32_t) const = 0; - virtual Shape* OutputShape(const std::string&, int32_t) = 0; - virtual Shape* Shape4ArgNameAndIndex(const std::string&, int32_t) = 0; + virtual const Shape& OutputShape(const std::string&, int32_t) const = 0; + virtual void SetOutputShape(const std::string&, int32_t, const Shape&) = 0; + virtual const Shape& Shape4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual void SetShape4ArgNameAndIndex(const std::string&, int32_t, const Shape&) = 0; virtual const Stride& InputStride(const std::string&, int32_t) const = 0; - virtual Stride* OutputStride(const std::string&, int32_t) = 0; - virtual Stride* Stride4ArgNameAndIndex(const std::string&, int32_t) = 0; - virtual const DataType& InputDType(const std::string&, int32_t) const = 0; - virtual DataType* OutputDType(const std::string&, int32_t) = 0; - virtual DataType* Dtype4ArgNameAndIndex(const std::string&, int32_t) = 0; + virtual const Stride& OutputStride(const std::string&, int32_t) const = 0; + virtual void SetOutputStride(const std::string&, int32_t, const Stride&) = 0; + virtual const Stride& Stride4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual void SetStride4ArgNameAndIndex(const std::string&, int32_t, const Stride&) = 0; + virtual DataType InputDType(const std::string&, int32_t) const = 0; + virtual DataType OutputDType(const std::string&, int32_t) const = 0; + virtual void SetOutputDType(const std::string&, int32_t, DataType) = 0; + virtual DataType Dtype4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual void SetDtype4ArgNameAndIndex(const std::string&, int32_t, DataType) = 0; virtual const std::vector>& inputs() const = 0; virtual const std::vector>& outputs() const = 0; virtual const std::string& input(const std::string& arg_name, int32_t index) const = 0; @@ -80,8 +93,10 @@ class InferContext { virtual const NdSbp& NdSbp4ArgNameAndIndex(const std::string&, int32_t) const = 0; virtual bool InputIsDynamic(const std::string&, int32_t) const = 0; - virtual bool* OutputIsDynamic(const std::string&, int32_t) = 0; - virtual bool* IsDynamic4ArgNameAndIndex(const std::string&, int32_t) = 0; + virtual bool OutputIsDynamic(const std::string&, int32_t) const = 0; + virtual void SetOutputIsDynamic(const std::string&, int32_t, bool) = 0; + virtual bool IsDynamic4ArgNameAndIndex(const std::string&, int32_t) const = 0; + virtual void SetIsDynamic4ArgNameAndIndex(const std::string&, int32_t, bool) = 0; virtual int64_t parallel_num() const = 0; diff --git a/oneflow/core/framework/instructions_builder.cpp b/oneflow/core/framework/instructions_builder.cpp index 27271ba8be0a7dbf8ef4c4223955ca308eb521d0..b7086596025061abb00da4f2041b9e19219ff503 100644 --- a/oneflow/core/framework/instructions_builder.cpp +++ b/oneflow/core/framework/instructions_builder.cpp @@ -14,7 +14,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +#include +#include #include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/core/framework/stream_guard.h" #include "oneflow/core/framework/symbol_storage_util.h" #include "oneflow/core/device/event_record.h" #include "oneflow/core/framework/parallel_conf_util.h" @@ -27,43 +30,51 @@ limitations under the License. #include "oneflow/core/common/decorator.h" #include "oneflow/core/common/blocking_counter.h" #include "oneflow/core/common/singleton_ptr.h" +#include "oneflow/core/common/env_var/vm.h" #include "oneflow/core/rpc/include/global_process_ctx.h" -#include "oneflow/core/vm/barrier_phy_instr_operand.h" -#include "oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h" -#include "oneflow/core/vm/consume_local_dep_object_phy_instr_operand.h" -#include "oneflow/core/eager/release_tensor_instruction_type.h" -#include "oneflow/core/vm/touch_tensors_instruction_type.h" -#include "oneflow/core/eager/blob_instruction_type.h" -#include "oneflow/core/eager/op_call_instruction_type.h" -#include "oneflow/core/vm/barrier_instruction_type.h" +#include "oneflow/core/vm/access_blob_arg_cb_instruction_policy.h" +#include "oneflow/core/vm/ep_record_event_instruction_policy.h" +#include "oneflow/core/vm/op_call_instruction_policy.h" +#include "oneflow/core/vm/barrier_instruction_policy.h" +#include "oneflow/core/vm/critical_section_instruction_policy.h" +#include "oneflow/core/vm/release_tensor_instruction_policy.h" +#include "oneflow/core/vm/lazy_job_instruction_policy.h" +#include "oneflow/core/vm/global_sync_instruction_policy.h" +#include "oneflow/core/vm/op_call_instruction_policy.h" +#include "oneflow/core/vm/stream_wait_instruction_policy.h" +#include "oneflow/core/vm/stream_record_event_instruction_policy.h" +#include "oneflow/core/vm/stream_wait_event_instruction_policy.h" +#include "oneflow/core/vm/sync_access_instruction_policy.h" +#include "oneflow/core/vm/touch_tensors_instruction_policy.h" #include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/vm/vm_util.h" -#include "oneflow/core/framework/consistent_tensor_infer_cache.h" +#include "oneflow/core/framework/global_tensor_infer_cache.h" #include "oneflow/core/eager/local_dep_object.h" -#include "oneflow/core/eager/critical_section_instruction_type.h" -#include "oneflow/core/eager/lazy_job_instruction_type.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/stream_need_soft_sync.h" #include "oneflow/core/framework/stream_is_comm_net_stream.h" +#include "oneflow/core/framework/stream_support_stream_wait.h" +#include "oneflow/core/framework/stream_on_independent_thread.h" #include "oneflow/core/job/env_desc.h" #include "oneflow/core/profiler/profiler.h" #include "oneflow/core/platform/include/pthread_fork.h" +#include "oneflow/core/vm/allocate_tensor_instruction_policy.h" namespace oneflow { namespace { Maybe> RawGetCriticalSectionStream() { - return Stream::New(JUST(Device::New("cpu")), StreamRole::kCriticalSection); + return Stream::New(JUST(Device::New("cpu")), StreamType::kCriticalSection); } static constexpr auto* GetCriticalSectionStream = DECORATE(&RawGetCriticalSectionStream, ThreadLocal); Maybe> RawGetLazyJobLauncherStream() { - return Stream::New(JUST(Device::New("cpu")), StreamRole::kLazyJobLauncher); + return Stream::New(JUST(Device::New("cpu")), StreamType::kLazyJobLauncher); } static constexpr auto* GetLazyJobLauncherStream = @@ -71,24 +82,6 @@ static constexpr auto* GetLazyJobLauncherStream = } // namespace -template -Maybe InstructionsBuilder::MakeCriticalSectionBegin( - vm::Stream* vm_stream, const std::shared_ptr& phy_instr_operand) { - auto instruction = intrusive::make_shared( - vm_stream, SingletonPtr(), phy_instr_operand); - instruction_list_->EmplaceBack(std::move(instruction)); - return Maybe::Ok(); -} - -template -Maybe InstructionsBuilder::MakeCriticalSectionEnd( - vm::Stream* vm_stream, const std::shared_ptr& phy_instr_operand) { - auto instruction = intrusive::make_shared( - vm_stream, SingletonPtr(), phy_instr_operand); - instruction_list_->EmplaceBack(std::move(instruction)); - return Maybe::Ok(); -} - // clang-format off // Job e.g.: // [wait_and_send_ids] @@ -130,9 +123,9 @@ Maybe InstructionsBuilder::MakeCriticalSectionEnd( // CriticalSectionBegin. // critical_section_callback is a non-blocking opkernel which notifies instruction // CriticalSectionEnd done. -Maybe InstructionsBuilder::LaunchLazyJob(const one::EagerBlobObjectListPtr& inputs, - const one::EagerBlobObjectListPtr& outputs, - const one::EagerBlobObjectListPtr& parameters, +Maybe InstructionsBuilder::LaunchLazyJob(const vm::EagerBlobObjectListPtr& inputs, + const vm::EagerBlobObjectListPtr& outputs, + const vm::EagerBlobObjectListPtr& parameters, const std::shared_ptr& nn_graph) { JUST(SoftSyncNNGraphBuffers(inputs, nn_graph)); JUST(SoftSyncNNGraphBuffers(outputs, nn_graph)); @@ -146,37 +139,37 @@ Maybe InstructionsBuilder::LaunchLazyJob(const one::EagerBlobObjectListPtr { for (const auto& op_name : nn_graph->inputs_op_names()) { const auto& event_record = std::make_shared(); - CHECK_OR_RETURN(input_op_name2end_event_record->emplace(op_name, event_record).second); + CHECK_OR_RETURN(input_op_name2end_event_record->emplace(op_name, event_record).second) + << Error::RuntimeError() << "Duplicate Op name " << op_name; } auto stream = JUST(GetCriticalSectionStream()); auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); - const auto& phy_instr_operand = - std::make_shared( - nn_graph, inputs, input_op_name2end_event_record, vm_stream); - JUST(MakeCriticalSectionBegin(vm_stream, phy_instr_operand)); + auto instruction = intrusive::make_shared( + vm_stream, std::make_shared( + nn_graph, inputs, input_op_name2end_event_record, vm_stream)); + instruction_list_->EmplaceBack(std::move(instruction)); } const auto& output_op_name2end_event_record = std::make_shared>>(); { for (const auto& op_name : nn_graph->outputs_op_names()) { const auto& event_record = std::make_shared(); - CHECK_OR_RETURN(output_op_name2end_event_record->emplace(op_name, event_record).second); + CHECK_OR_RETURN(output_op_name2end_event_record->emplace(op_name, event_record).second) + << Error::RuntimeError() << "Duplicate Op name " << op_name; } auto stream = JUST(GetCriticalSectionStream()); auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); - const auto& phy_instr_operand = - std::make_shared( - nn_graph, outputs, output_op_name2end_event_record, vm_stream); - JUST(MakeCriticalSectionBegin(vm_stream, phy_instr_operand)); + auto instruction = intrusive::make_shared( + vm_stream, std::make_shared( + nn_graph, outputs, output_op_name2end_event_record, vm_stream)); + instruction_list_->EmplaceBack(std::move(instruction)); } { - const auto& phy_instr_operand = - std::make_shared(nn_graph, parameters); auto stream = JUST(GetLazyJobLauncherStream()); auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); auto instruction = intrusive::make_shared( - vm_stream, SingletonPtr(), phy_instr_operand); + vm_stream, std::make_shared(nn_graph, parameters)); instruction_list_->EmplaceBack(std::move(instruction)); } auto stream = JUST(GetCriticalSectionStream()); @@ -185,27 +178,29 @@ Maybe InstructionsBuilder::LaunchLazyJob(const one::EagerBlobObjectListPtr const auto& eager_blob_object = inputs->at(i); const auto& op_name = nn_graph->inputs_op_names().at(i); const auto& event_record = JUST(MapAt(*input_op_name2end_event_record, op_name)); - const auto& phy_instr_operand = std::make_shared( - eager_blob_object, event_record, vm_stream); - JUST(MakeCriticalSectionEnd(vm_stream, phy_instr_operand)); + auto instruction = intrusive::make_shared( + vm_stream, std::make_shared( + eager_blob_object, event_record, vm_stream)); + instruction_list_->EmplaceBack(std::move(instruction)); } for (int i = 0; i < nn_graph->outputs_op_names().size(); ++i) { const auto& eager_blob_object = outputs->at(i); const auto& op_name = nn_graph->outputs_op_names().at(i); const auto& event_record = JUST(MapAt(*output_op_name2end_event_record, op_name)); - const auto& phy_instr_operand = std::make_shared( - eager_blob_object, event_record, vm_stream); - JUST(MakeCriticalSectionEnd(vm_stream, phy_instr_operand)); + auto instruction = intrusive::make_shared( + vm_stream, std::make_shared( + eager_blob_object, event_record, vm_stream)); + instruction_list_->EmplaceBack(std::move(instruction)); } } return Maybe::Ok(); } Maybe InstructionsBuilder::SoftSyncNNGraphBuffers( - const one::EagerBlobObjectListPtr& eager_blob_objects, + const vm::EagerBlobObjectListPtr& eager_blob_objects, const std::shared_ptr& nn_graph) { const auto& stream = JUST(GetCriticalSectionStream()); - JUST(SoftSyncStream(eager_blob_objects, stream)); + JUST(SoftSyncStream(*eager_blob_objects, stream)); return Maybe::Ok(); } @@ -237,7 +232,7 @@ Maybe InstructionsBuilder::GetOpConfSymbol(const OperatorCon Maybe InstructionsBuilder::BuildInitialScope( int64_t session_id, const JobConfigProto& job_conf, const std::string& device_tag, const std::vector& machine_device_ids, const std::shared_ptr& hierarchy, - bool is_mirrored) { + bool is_local) { ScopeProto scope_proto; scope_proto.set_session_id(session_id); std::shared_ptr job_conf_sym = JUST(GetJobConfSymbol(job_conf)); @@ -251,10 +246,10 @@ Maybe InstructionsBuilder::BuildInitialScope( std::shared_ptr host_parallel_desc_sym = JUST(GetParallelDescSymbol(*parallel_conf)); scope_proto.set_host_parallel_desc_symbol_id(JUST(host_parallel_desc_sym->symbol_id())); - if (is_mirrored) { - scope_proto.mutable_opt_mirrored_parallel_conf()->mutable_mirrored_parallel(); + if (is_local) { + scope_proto.mutable_opt_local_parallel_conf()->mutable_local_parallel(); } else { - scope_proto.mutable_opt_mirrored_parallel_conf()->clear_mirrored_parallel(); + scope_proto.mutable_opt_local_parallel_conf()->clear_local_parallel(); } return GetScopeSymbol(scope_proto); } @@ -262,7 +257,7 @@ Maybe InstructionsBuilder::BuildInitialScope( Maybe InstructionsBuilder::BuildInitialScopeWithPlacement(int64_t session_id, const JobConfigProto& job_conf, Symbol placement, - bool is_mirrored) { + bool is_local) { ScopeProto scope_proto; scope_proto.set_session_id(session_id); std::shared_ptr job_conf_sym = JUST(GetJobConfSymbol(job_conf)); @@ -276,10 +271,10 @@ Maybe InstructionsBuilder::BuildInitialScopeWithPlacement(int64_t session std::shared_ptr host_parallel_desc_sym = JUST(GetParallelDescSymbol(new_placement->parallel_conf())); scope_proto.set_host_parallel_desc_symbol_id(JUST(host_parallel_desc_sym->symbol_id())); - if (is_mirrored) { - scope_proto.mutable_opt_mirrored_parallel_conf()->mutable_mirrored_parallel(); + if (is_local) { + scope_proto.mutable_opt_local_parallel_conf()->mutable_local_parallel(); } else { - scope_proto.mutable_opt_mirrored_parallel_conf()->clear_mirrored_parallel(); + scope_proto.mutable_opt_local_parallel_conf()->clear_local_parallel(); } return GetScopeSymbol(scope_proto); } @@ -317,13 +312,13 @@ Maybe InstructionsBuilder::BuildScopeWithNewParallelConf(const std::share std::get<1>(*tag_and_dev_ids_and_hierarchy), hierarchy); } -Maybe InstructionsBuilder::BuildScopeWithNewIsMirrored(const std::shared_ptr& scope, - bool is_mirrored) { - const auto SetScopeProto = [is_mirrored](const std::shared_ptr& scope_proto) { - if (is_mirrored) { - scope_proto->mutable_opt_mirrored_parallel_conf()->mutable_mirrored_parallel(); +Maybe InstructionsBuilder::BuildScopeWithNewIsLocal(const std::shared_ptr& scope, + bool is_local) { + const auto SetScopeProto = [is_local](const std::shared_ptr& scope_proto) { + if (is_local) { + scope_proto->mutable_opt_local_parallel_conf()->mutable_local_parallel(); } else { - scope_proto->mutable_opt_mirrored_parallel_conf()->clear_mirrored_parallel(); + scope_proto->mutable_opt_local_parallel_conf()->clear_local_parallel(); } }; @@ -359,31 +354,55 @@ Maybe InstructionsBuilder::BuildScopeByProtoStrSetter( } Maybe InstructionsBuilder::Call(const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& input_eager_blob_objects, - const one::EagerBlobObjectListPtr& output_eager_blob_objects, + vm::EagerBlobObjectList&& input_eager_blob_objects, + vm::EagerBlobObjectList&& output_eager_blob_objects, const one::OpExprInterpContext& ctx, Symbol stream) { - return Call(opkernel, input_eager_blob_objects, output_eager_blob_objects, nullptr, ctx, stream); + return Call(opkernel, std::move(input_eager_blob_objects), std::move(output_eager_blob_objects), + nullptr, ctx, stream); +} + +Maybe InstructionsBuilder::AllocateTensors(const vm::EagerBlobObjectList& eager_blob_objects, + Symbol stream) { + // try soft sync eager blob objects which have memory allocated. + JUST(SoftSyncStream(eager_blob_objects, stream)); + auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); + const auto& instruction_policy = + std::make_shared(eager_blob_objects, vm_stream); + auto instruction = intrusive::make_shared(vm_stream, instruction_policy); + instruction_list_->EmplaceBack(std::move(instruction)); + for (const auto& eager_blob_object : eager_blob_objects) { + if (!eager_blob_object->producer_stream().has_value()) { + JUST(eager_blob_object->init_producer_stream(stream)); + } + eager_blob_object->set_last_used_stream(stream); + } + return Maybe::Ok(); } Maybe InstructionsBuilder::Call( const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& input_eager_blob_objects, - const one::EagerBlobObjectListPtr& output_eager_blob_objects, - const std::shared_ptr& consistent_tensor_infer_result, + vm::EagerBlobObjectList&& input_eager_blob_objects, + vm::EagerBlobObjectList&& output_eager_blob_objects, + const std::shared_ptr& global_tensor_infer_result, const one::OpExprInterpContext& ctx, Symbol stream) { + stream = JUST(StreamGuard::TryConvertStream(stream)); + Symbol allocator_stream = JUST(GetAllocatorStream(stream)); + if (stream != allocator_stream) { + JUST(AllocateTensors(output_eager_blob_objects, allocator_stream)); + } JUST(SoftSyncStream(output_eager_blob_objects, stream)); JUST(SoftSyncStream(input_eager_blob_objects, stream)); - auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); - auto phy_instr_operand = JUST(vm::OpCallPhyInstrOperand::New( - vm_stream, opkernel, input_eager_blob_objects, output_eager_blob_objects, - consistent_tensor_infer_result, ctx, *one::CurrentDevVmDepObjectConsumeMode())); - auto instruction = intrusive::make_shared( - vm_stream, SingletonPtr(), phy_instr_operand); - instruction_list_->EmplaceBack(std::move(instruction)); - for (const auto& output : *output_eager_blob_objects) { + for (const auto& output : output_eager_blob_objects) { if (!output->producer_stream().has_value()) { JUST(output->init_producer_stream(stream)); } output->set_last_used_stream(stream); } + auto* vm_stream = JUST(Singleton::Get()->GetVmStream(stream)); + auto instruction = intrusive::make_shared( + vm_stream, JUST(vm::OpCallInstructionPolicy::New( + vm_stream, opkernel, std::move(input_eager_blob_objects), + std::move(output_eager_blob_objects), global_tensor_infer_result, ctx, + *one::CurrentDevVmDepObjectConsumeMode()))); + instruction_list_->EmplaceBack(std::move(instruction)); return Maybe::Ok(); } @@ -395,89 +414,214 @@ Maybe InstructionsBuilder::ReleaseTensor( && producer_stream->device()->enum_type() != DeviceType::kCPU) { return Maybe::Ok(); } - if (last_used_stream != producer_stream) { - JUST(SoftSyncStream({JUST(eager_blob_object->compute_local_dep_object())}, "mut", - last_used_stream)); - } Optional> stream{}; if (*one::CurrentDevVmDepObjectConsumeMode() == one::DevVmDepObjectConsumeMode::NONE) { stream = Optional>(NullOpt); - } else if (IsCommNetStream::Visit(last_used_stream->stream_role())) { + } else if (IsCommNetStream::Visit(last_used_stream->stream_type())) { // Disable inter-device instruction sequential for tensor used by communicative stream. // It's not acceptable for us that cuda compute stream is blocked by cuda nccl stream. stream = Optional>(NullOpt); - } else if (IsCommNetStream::Visit(producer_stream->stream_role())) { + } else if (IsCommNetStream::Visit(producer_stream->stream_type())) { // Disable inter-device instruction sequential for tensor produced by communicative stream. stream = Optional>(NullOpt); } else { stream = producer_stream; } + struct EnableStreamWaitOnReleaseTensor final + : public StreamTypeVisitor { + static bool VisitCompute() { return true; } + static bool VisitHost2Device() { return true; } + static bool VisitDevice2Host() { return true; } + static bool VisitCcl() { return false; } + static bool VisitBarrier() { return false; } + static bool VisitCriticalSection() { return false; } + static bool VisitLazyJobLauncher() { return false; } + static bool VisitPinnedCompute() { return VisitCompute(); } + }; + const auto& EnableStreamWait = [&] { + if (last_used_stream->device() != producer_stream->device()) { return false; } + if (last_used_stream->stream_type() == producer_stream->stream_type()) { return true; } + return EnableStreamWaitOnReleaseTensor::Visit(last_used_stream->stream_type()) + && EnableStreamWaitOnReleaseTensor::Visit(producer_stream->stream_type()); + }; + if (last_used_stream != producer_stream) { + if (stream.has_value() && EnableStreamWait()) { + JUST(SoftSyncStreamBetween({JUST(eager_blob_object->compute_local_dep_object())}, + last_used_stream, JUST(stream))); + } else { + JUST(RecordEvent({JUST(eager_blob_object->compute_local_dep_object())}, last_used_stream)); + } + eager_blob_object->set_last_used_stream(producer_stream); + } auto vm_stream = stream.map([](Symbol stream) -> vm::Stream* { return CHECK_JUST(Singleton::Get()->GetVmStream(stream)); }); - const auto& phy_instr_operand = - std::make_shared(eager_blob_object, vm_stream); - StreamRole stream_role = producer_stream->stream_role(); - DeviceType device_type = producer_stream->device()->enum_type(); + StreamType stream_type = producer_stream->stream_type(); auto instruction = intrusive::make_shared( JUST(Singleton::Get()->GetVmStream(producer_stream)), - JUST(GetReleaseInstructionType::Visit(stream_role, device_type)), phy_instr_operand); + JUST(vm::MakeReleaseTensorInstructionPolicy::Visit(stream_type, eager_blob_object, + vm_stream))); instruction_list_->EmplaceBack(std::move(instruction)); + return Maybe::Ok(); } Maybe InstructionsBuilder::TouchTensors( - const one::EagerBlobObjectListPtr& eager_blob_object) { - const auto& phy_instr_operand = - std::make_shared(*eager_blob_object); + const vm::EagerBlobObjectListPtr& eager_blob_objects) { Symbol device = JUST(Device::New("cpu")); Symbol stream = JUST(GetDefaultStreamByDevice(device)); + return TouchTensors(eager_blob_objects, stream); +} + +Maybe InstructionsBuilder::TouchTensors(const vm::EagerBlobObjectListPtr& eager_blob_objects, + Symbol stream) { + JUST(SoftSyncStream(*eager_blob_objects, stream)); auto instruction = intrusive::make_shared( JUST(Singleton::Get()->GetVmStream(stream)), - SingletonPtr(), phy_instr_operand); + std::make_unique(*eager_blob_objects)); instruction_list_->EmplaceBack(std::move(instruction)); return Maybe::Ok(); } -Maybe InstructionsBuilder::SoftSyncStream( - const one::EagerBlobObjectListPtr& eager_blob_objects, Symbol stream) { - SmallSet> last_used_streams; - for (const auto& eager_blob_object : *eager_blob_objects) { - const auto& opt_last_used_stream = eager_blob_object->last_used_stream(); - if (unlikely(!opt_last_used_stream.has_value())) { continue; } - const auto& last_used_stream = JUST(opt_last_used_stream); - if (last_used_stream != stream) { SmallSetInsert(&last_used_streams, last_used_stream); } +namespace { + +template +using SmallSet = small_vector; + +template +std::pair::iterator, bool> SmallSetInsert(SmallSet* vec, const T& elem) { + for (auto iter = vec->begin(); iter != vec->end(); ++iter) { + if (*iter == elem) { return std::make_pair(iter, false); } } - for (const auto& last_used_stream : last_used_streams) { - std::vector> dep_objects; - dep_objects.reserve(eager_blob_objects->size()); - for (const auto& eager_blob_object : *eager_blob_objects) { + vec->push_back(elem); + return std::make_pair(vec->end() - 1, true); +} + +template +Maybe ForEachEagerBlobObjectsNeedingSoftSync( + const vm::EagerBlobObjectList& eager_blob_objects, Symbol stream, + const DoEachT& DoEach) { + if (eager_blob_objects.size() <= kOpArgsReservedSize) { + for (const auto& eager_blob_object : eager_blob_objects) { const auto& opt_last_used_stream = eager_blob_object->last_used_stream(); if (unlikely(!opt_last_used_stream.has_value())) { continue; } - if (JUST(opt_last_used_stream) == last_used_stream) { - dep_objects.emplace_back(JUST(eager_blob_object->compute_local_dep_object())); + const auto& last_used_stream = JUST(opt_last_used_stream); + if (last_used_stream != stream) { + small_vector, kOpArgsReservedSize> dep_objects{ + intrusive::shared_ptr( + JUST(eager_blob_object->compute_local_dep_object()))}; + JUST(DoEach(last_used_stream, std::move(dep_objects))); + } + } + } else { + SmallSet> last_used_streams; + for (const auto& eager_blob_object : eager_blob_objects) { + const auto& opt_last_used_stream = eager_blob_object->last_used_stream(); + if (unlikely(!opt_last_used_stream.has_value())) { continue; } + const auto& last_used_stream = JUST(opt_last_used_stream); + if (last_used_stream != stream) { SmallSetInsert(&last_used_streams, last_used_stream); } + } + for (const auto& last_used_stream : last_used_streams) { + small_vector, kOpArgsReservedSize> dep_objects{}; + for (const auto& eager_blob_object : eager_blob_objects) { + const auto& opt_stream = eager_blob_object->last_used_stream(); + if (unlikely(!opt_stream.has_value())) { continue; } + if (JUST(opt_stream) == last_used_stream) { + dep_objects.emplace_back(JUST(eager_blob_object->compute_local_dep_object())); + } } - eager_blob_object->set_last_used_stream(stream); + JUST(DoEach(last_used_stream, std::move(dep_objects))); } - JUST(SoftSyncStream(std::move(dep_objects), "mut", last_used_stream)); } return Maybe::Ok(); } -Maybe InstructionsBuilder::SoftSyncStream( - std::vector>&& compute_local_dep_objects, - const std::string& modifier, Symbol last_used_stream) { +} // namespace + +Maybe InstructionsBuilder::SoftSyncStream(const vm::EagerBlobObjectList& eager_blob_objects, + Symbol stream) { + JUST(ForEachEagerBlobObjectsNeedingSoftSync( + eager_blob_objects, stream, + [&](Symbol last_used_stream, auto&& dep_objects) -> Maybe { + return SoftSyncStreamBetween(std::move(dep_objects), last_used_stream, stream); + })); + for (const auto& eager_blob_object : eager_blob_objects) { + eager_blob_object->set_last_used_stream(stream); + } + return Maybe::Ok(); +} + +namespace { + +bool SupportingStreamWait(Symbol from_stream, Symbol to_stream) { + if (from_stream->device() == to_stream->device() + && from_stream->stream_type() == to_stream->stream_type() + && from_stream->thread_uid() == to_stream->thread_uid()) { + CHECK(from_stream == to_stream); + } + if (unlikely(!ThreadLocalEnvBool())) { return false; } + DeviceType from_device_type = from_stream->device()->enum_type(); + DeviceType to_device_type = from_stream->device()->enum_type(); + return from_stream->device() == to_stream->device() && from_device_type == DeviceType::kCUDA + && StreamSupportStreamWait::Visit(from_stream->stream_type(), from_device_type) + && StreamSupportStreamWait::Visit(to_stream->stream_type(), to_device_type) + && !StreamOnIndependentThread::Visit(from_stream->stream_type()) + && !StreamOnIndependentThread::Visit(to_stream->stream_type()); +} + +} // namespace + +Maybe InstructionsBuilder::SoftSyncStreamBetween( + small_vector, kOpArgsReservedSize>&& dependences, + Symbol from_stream, Symbol to_stream) { + CHECK(from_stream != to_stream) << "synchronization is unnecessary"; + if (SupportingStreamWait(from_stream, to_stream)) { + JUST(StreamWait(std::move(dependences), from_stream, to_stream)); + } else { + JUST(RecordEvent(std::move(dependences), from_stream)); + } + return Maybe::Ok(); +} + +Maybe InstructionsBuilder::StreamWait( + small_vector, kOpArgsReservedSize>&& dependences, + Symbol from_stream, Symbol to_stream) { + auto* from_vm_stream = JUST(Singleton::Get()->GetVmStream(from_stream)); + auto* to_vm_stream = JUST(Singleton::Get()->GetVmStream(to_stream)); + if (from_vm_stream->mut_thread_ctx() != to_vm_stream->mut_thread_ctx()) { + auto stream_record_event = + std::make_shared(dependences); + auto record_instruction = + intrusive::make_shared(from_vm_stream, stream_record_event); + instruction_list_->EmplaceBack(std::move(record_instruction)); + auto stream_wait_event = + std::make_shared(dependences, stream_record_event); + auto wait_instruction = + intrusive::make_shared(to_vm_stream, stream_wait_event); + instruction_list_->EmplaceBack(std::move(wait_instruction)); + } else { + auto instruction = intrusive::make_shared( + to_vm_stream, std::make_unique( + std::move(dependences), from_vm_stream, to_vm_stream)); + instruction_list_->EmplaceBack(std::move(instruction)); + } + return Maybe::Ok(); +} + +Maybe InstructionsBuilder::RecordEvent( + small_vector, kOpArgsReservedSize>&& + compute_local_dep_objects, + Symbol last_used_stream) { DeviceType device_type = last_used_stream->device()->enum_type(); - if (!NeedSoftSync::Visit(last_used_stream->stream_role(), device_type)) { + if (!NeedSoftSync::Visit(last_used_stream->stream_type(), device_type)) { return Maybe::Ok(); } - OF_PROFILER_RANGE_GUARD("SoftStream"); - const auto& phy_instr_operand = std::make_shared( - std::move(compute_local_dep_objects), modifier); - StreamRole stream_role = last_used_stream->stream_role(); + std::string modifier = "mut"; + StreamType stream_type = last_used_stream->stream_type(); auto instruction = intrusive::make_shared( JUST(Singleton::Get()->GetVmStream(last_used_stream)), - JUST(GetRecordEventInstructionType::Visit(stream_role, device_type)), phy_instr_operand); + JUST(GetRecordEventInstructionPolicy::Visit(stream_type, device_type, + std::move(compute_local_dep_objects), modifier))); instruction_list_->EmplaceBack(std::move(instruction)); return Maybe::Ok(); } @@ -485,7 +629,8 @@ Maybe InstructionsBuilder::SoftSyncStream( template Maybe InstructionsBuilder::SyncAccessBlobByCallback( const T tensor, const std::shared_ptr& btb, - const std::function& Callback, const std::string& modifier) { + const std::function&)>& Callback, + const std::string& modifier) { // We want balance the cpu overhead and notification latency. // // balanced timeline here: @@ -518,43 +663,39 @@ Maybe InstructionsBuilder::SyncAccessBlobByCallback( // | | | // main thread: |<---------------------------- S ----------------------------->| - const auto& CallbackWrapper = [btb, Callback](uint64_t ofblob_ptr) { - btb->mut_blocking_counter()->Decrease(); - Callback(ofblob_ptr); + const auto& CallbackWrapper = [btb, Callback]( + ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + btb->mut_notifier()->Notify(); + Callback(stream, eager_blob_object); btb->mut_spin_counter()->Decrease(); }; return AccessBlobByCallback(tensor, CallbackWrapper, modifier); } template Maybe InstructionsBuilder::SyncAccessBlobByCallback( - const std::shared_ptr tensor, const std::shared_ptr& btb, - const std::function& Callback, const std::string& modifier); + const std::shared_ptr tensor, const std::shared_ptr& btb, + const std::function&)>& Callback, + const std::string& modifier); template Maybe InstructionsBuilder::SyncAccessBlobByCallback( - const one::EagerMirroredTensorImpl* tensor, const std::shared_ptr& btb, - const std::function& Callback, const std::string& modifier); + const one::EagerLocalTensorImpl* tensor, const std::shared_ptr& btb, + const std::function&)>& Callback, + const std::string& modifier); namespace { -Maybe> GetDevice(const std::shared_ptr& tensor) { +Maybe> GetDevice(const std::shared_ptr& tensor) { return tensor->device(); // return Maybe> } -Maybe> GetDevice(const one::EagerMirroredTensorImpl* tensor) { +Maybe> GetDevice(const one::EagerLocalTensorImpl* tensor) { return tensor->device(); // return const Symbol& } -} // namespace - template -Maybe InstructionsBuilder::AccessBlobByCallback(const T tensor, - const std::function& callback, - const std::string& modifier) { - const std::shared_ptr& eager_blob_object = JUST(tensor->eager_blob_object()); - const auto& phy_instr_operand = - std::make_shared(eager_blob_object, callback, modifier); +Maybe> GetAccessStream(const T tensor) { Symbol device = JUST(GetDevice(tensor)); - Symbol stream = JUST(GetDefaultStreamByDevice(device)); // Do not use producer_stream or last_used_stream. // Bug case when using producer_stream or last_used_stream: // @@ -565,57 +706,137 @@ Maybe InstructionsBuilder::AccessBlobByCallback(const T tensor, // ``` // `ndarray` may not be ones because instruction AccessBlobByCallback is prescheduled before // oneflow.ones actually finished. + Symbol stream = JUST(GetDefaultStreamByDevice(device)); + return StreamGuard::TryConvertStream(stream); +} + +} // namespace + +template +Maybe InstructionsBuilder::AccessBlobByCallback( + const T tensor, + const std::function&)>& callback, + const std::string& modifier) { + const std::shared_ptr& eager_blob_object = JUST(tensor->eager_blob_object()); + Symbol stream = JUST(GetAccessStream(tensor)); + JUST(SoftSyncStream({eager_blob_object}, stream)); auto instruction = intrusive::make_shared( // Never replace `stream` with producer_stream or last_used_stream. JUST(Singleton::Get()->GetVmStream(stream)), - SingletonPtr(), phy_instr_operand); + std::make_shared(eager_blob_object, callback, + modifier)); instruction_list_->EmplaceBack(std::move(instruction)); return Maybe::Ok(); } template Maybe InstructionsBuilder::AccessBlobByCallback( - const std::shared_ptr tensor, - const std::function& callback, const std::string& modifier); + const std::shared_ptr tensor, + const std::function&)>& callback, + const std::string& modifier); template Maybe InstructionsBuilder::AccessBlobByCallback( - const one::EagerMirroredTensorImpl* tensor, const std::function& callback, + const one::EagerLocalTensorImpl* tensor, + const std::function&)>& callback, const std::string& modifier); namespace { Maybe> GetBarrierStream() { auto device = JUST(Device::New("cpu")); - return Stream::New(device, StreamRole::kBarrier); + return Stream::New(device, StreamType::kBarrier); } } // namespace Maybe InstructionsBuilder::GlobalSync() { - const auto& phy_instr_operand = std::make_shared([]() {}); auto stream = JUST(GetBarrierStream()); auto instruction = intrusive::make_shared( JUST(Singleton::Get()->GetVmStream(stream)), - SingletonPtr(), phy_instr_operand); + std::make_shared()); instruction_list_->PushBack(instruction.Mutable()); return Maybe::Ok(); } Maybe InstructionsBuilder::Barrier(const std::function& Callback) { - const auto& phy_instr_operand = std::make_shared(Callback); auto stream = JUST(GetBarrierStream()); auto instruction = intrusive::make_shared( JUST(Singleton::Get()->GetVmStream(stream)), - SingletonPtr(), phy_instr_operand); + std::make_shared(Callback)); instruction_list_->PushBack(instruction.Mutable()); return Maybe::Ok(); } -Maybe PhysicalRun(const std::function(InstructionsBuilder*)>& Build) { - vm::InstructionList instruction_list; - InstructionsBuilder instructions_builder(&instruction_list); - JUST(Build(&instructions_builder)); - JUST(vm::Run(instructions_builder.mut_instruction_list())); +namespace { + +template +Maybe MutThreadLocalInstruction(Symbol stream) { + static thread_local std::vector> vec; + if (unlikely(stream->unique_stream_id() >= vec.size())) { + vec.resize(stream->unique_stream_id() + 1); + } + auto* instruction_ptr = &vec[stream->unique_stream_id()]; + if (static_cast(*instruction_ptr) && (*instruction_ptr)->ref_cnt() != 1) { + // This instruction should not be reusd because of being hold by other threads. + instruction_ptr->Reset(); + } + if (unlikely(!static_cast(*instruction_ptr))) { + *instruction_ptr = intrusive::make_shared( + JUST(Singleton::Get()->GetVmStream(stream)), + std::make_shared()); + } + return instruction_ptr->Mutable(); +} + +} // namespace + +template +Maybe SyncAccessSmallMem(char* mem_ptr, size_t bytes, const T tensor) { + static thread_local vm::InstructionList instruction_list; + static thread_local InstructionsBuilder instructions_builder(&instruction_list); + const std::shared_ptr& eager_blob_object = JUST(tensor->eager_blob_object()); + const Symbol stream = JUST(GetAccessStream(tensor)); + if (eager_blob_object->last_used_stream().has_value() + && stream != JUST(eager_blob_object->last_used_stream())) { + // Synchronize stream. + JUST(instructions_builder.SoftSyncStream({eager_blob_object}, stream)); + } + InstructionPolicyT* instruction_policy = nullptr; + { + // Construct instruction. + auto* instruction = JUST(MutThreadLocalInstruction(stream)); + instruction_policy = + static_cast(instruction->mut_instruction_policy()); // NOLINT + instruction_policy->Reset(mem_ptr, bytes, eager_blob_object.get()); + instruction_list.PushBack(instruction); + } + // Dispatch instructions. + JUST(vm::Run(&instruction_list)); + { + // This thread should blocking wait if and only if there is a lot of workload on worker thread. + // When workload is small, we want better performance by skipping cond_.notify_xxx which costs + // about 2us to 3us. + auto* virtual_machine = JUST(SingletonMaybe()); + static constexpr int kSkipBlockingThreshold = 2; + if (virtual_machine->flying_instruction_cnt() < kSkipBlockingThreshold) { + // skip pthread_cond_broadcast on worker thread. + instruction_policy->mut_btb()->mut_notifier()->Notify(); + } + } + // wait until done. + JUST(instruction_policy->mut_btb()->WaitUntilCntEqualZero( + VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); return Maybe::Ok(); } +template +Maybe SyncReadSmallMem(char* mem_ptr, size_t bytes, const T tensor) { + return SyncAccessSmallMem(mem_ptr, bytes, tensor); +} + +template Maybe SyncReadSmallMem(char* mem_ptr, size_t bytes, + const std::shared_ptr tensor); + +template Maybe SyncReadSmallMem(char* mem_ptr, size_t bytes, + const one::EagerLocalTensorImpl* tensor); + } // namespace oneflow diff --git a/oneflow/core/framework/instructions_builder.h b/oneflow/core/framework/instructions_builder.h index f9eba9fa9fe07e76c0846810675d353d535445da..ed8e0375e05bb90b7b9843b2521ff64776a1805e 100644 --- a/oneflow/core/framework/instructions_builder.h +++ b/oneflow/core/framework/instructions_builder.h @@ -16,8 +16,9 @@ limitations under the License. #ifndef ONEFLOW_CORE_FRAMEWORK_INSTRUCTIONS_BUILDER_H_ #define ONEFLOW_CORE_FRAMEWORK_INSTRUCTIONS_BUILDER_H_ -#include "oneflow/core/eager/op_call_phy_instr_operand.h" -#include "oneflow/core/eager/lazy_job_phy_instr_operand.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/framework/op_interpreter.h" #include "oneflow/core/vm/instruction.h" #include "oneflow/core/job/job_desc.h" #include "oneflow/core/job/parallel_desc.h" @@ -28,14 +29,15 @@ limitations under the License. #include "oneflow/core/common/shape.h" #include "oneflow/core/common/blocking_then_busy.h" #include "oneflow/core/operator/op_conf_symbol.h" +#include "oneflow/core/vm/vm_util.h" namespace oneflow { namespace one { class StatefulOpKernel; class TensorTuple; -class MirroredTensor; -class ConsistentTensorInferResult; +class LocalTensor; +class GlobalTensorInferResult; } // namespace one class NNGraphIf; @@ -55,13 +57,13 @@ class InstructionsBuilder : public std::enable_shared_from_this LaunchLazyJob(const one::EagerBlobObjectListPtr& inputs, - const one::EagerBlobObjectListPtr& outputs, - const one::EagerBlobObjectListPtr& parameters, + Maybe LaunchLazyJob(const vm::EagerBlobObjectListPtr& inputs, + const vm::EagerBlobObjectListPtr& outputs, + const vm::EagerBlobObjectListPtr& parameters, const std::shared_ptr& nn_graph); // soft sync for inputs/outputs buffers of NNGraph - Maybe SoftSyncNNGraphBuffers(const one::EagerBlobObjectListPtr& eager_blob_objects, + Maybe SoftSyncNNGraphBuffers(const vm::EagerBlobObjectListPtr& eager_blob_objects, const std::shared_ptr& nn_graph); Maybe GetJobConfSymbol(const JobConfigProto& job_conf); @@ -74,16 +76,22 @@ class InstructionsBuilder : public std::enable_shared_from_this ReleaseTensor(const std::shared_ptr& eager_blob_object); - Maybe TouchTensors(const one::EagerBlobObjectListPtr& eager_blob_object); + Maybe TouchTensors(const vm::EagerBlobObjectListPtr& eager_blob_objects); + + Maybe TouchTensors(const vm::EagerBlobObjectListPtr& eager_blob_objects, + Symbol stream); template - Maybe SyncAccessBlobByCallback(const T tensor, const std::shared_ptr& btb, - const std::function& Callback, - const std::string& modifier); + Maybe SyncAccessBlobByCallback( + const T tensor, const std::shared_ptr& btb, + const std::function&)>& Callback, + const std::string& modifier); template - Maybe AccessBlobByCallback(const T tensor, const std::function& callback, - const std::string& modifier); + Maybe AccessBlobByCallback( + const T tensor, + const std::function&)>& callback, + const std::string& modifier); Maybe GlobalSync(); Maybe Barrier(const std::function& callback); @@ -91,10 +99,10 @@ class InstructionsBuilder : public std::enable_shared_from_this BuildInitialScope(int64_t session_id, const JobConfigProto& job_conf, const std::string& device_tag, const std::vector& machine_device_ids, - const std::shared_ptr& hierarchy, bool is_mirrored); + const std::shared_ptr& hierarchy, bool is_local); Maybe BuildInitialScopeWithPlacement(int64_t session_id, const JobConfigProto& job_conf, - Symbol placement, bool is_mirrored); + Symbol placement, bool is_local); Maybe BuildScopeWithNewParallelDesc(const std::shared_ptr& scope, const std::string& device_tag, @@ -104,7 +112,7 @@ class InstructionsBuilder : public std::enable_shared_from_this BuildScopeWithNewParallelConf(const std::shared_ptr& scope, const ParallelConf& parallel_conf); - Maybe BuildScopeWithNewIsMirrored(const std::shared_ptr& scope, bool is_mirrored); + Maybe BuildScopeWithNewIsLocal(const std::shared_ptr& scope, bool is_local); Maybe BuildScopeWithNewScopeName(const std::shared_ptr& scope, const std::string& scope_name); @@ -118,38 +126,51 @@ class InstructionsBuilder : public std::enable_shared_from_this& StrSetter); Maybe Call(const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& input_eager_blob_objects, - const one::EagerBlobObjectListPtr& output_eager_blob_objects, + vm::EagerBlobObjectList&& input_eager_blob_objects, + vm::EagerBlobObjectList&& output_eager_blob_objects, const one::OpExprInterpContext& ctx, Symbol stream); Maybe Call( const std::shared_ptr& opkernel, - const one::EagerBlobObjectListPtr& input_eager_blob_objects, - const one::EagerBlobObjectListPtr& output_eager_blob_objects, - const std::shared_ptr& consistent_tensor_infer_result, + vm::EagerBlobObjectList&& input_eager_blob_objects, + vm::EagerBlobObjectList&& output_eager_blob_objects, + const std::shared_ptr& global_tensor_infer_result, const one::OpExprInterpContext& ctx, Symbol stream); - private: - Maybe SoftSyncStream(const one::EagerBlobObjectListPtr& eager_blob_objects, + Maybe SoftSyncStream(const vm::EagerBlobObjectList& eager_blob_objects, Symbol stream); - Maybe SoftSyncStream( - std::vector>&& compute_local_dep_objects, - const std::string& modifier, Symbol stream); private: - template - Maybe MakeCriticalSectionBegin(vm::Stream* vm_stream, - const std::shared_ptr& phy_instr_operand); + Maybe AllocateTensors(const vm::EagerBlobObjectList& eager_blob_objects, + Symbol stream); + + Maybe SoftSyncStreamBetween( + small_vector, kOpArgsReservedSize>&& dependences, + Symbol from_stream, Symbol to_stream); + + Maybe StreamWait( + small_vector, kOpArgsReservedSize>&& dependences, + Symbol from_stream, Symbol to_stream); - template - Maybe MakeCriticalSectionEnd(vm::Stream* vm_stream, - const std::shared_ptr& phy_instr_operand); + Maybe RecordEvent(small_vector, kOpArgsReservedSize>&& + compute_local_dep_objects, + Symbol stream); vm::InstructionList* instruction_list_; }; // Make VM instructions with instruction builder and run instructions with physical/local view. -Maybe PhysicalRun(const std::function(InstructionsBuilder*)>& Build); +template +Maybe PhysicalRun(const CallbackT& Build) { + vm::InstructionList instruction_list; + InstructionsBuilder instructions_builder(&instruction_list); + JUST(Build(&instructions_builder)); + JUST(vm::Run(instructions_builder.mut_instruction_list())); + return Maybe::Ok(); +} + +template +Maybe SyncReadSmallMem(char* mem_ptr, size_t bytes, const T tensor); } // namespace oneflow diff --git a/oneflow/core/framework/local_tensor_infer_cache.cpp b/oneflow/core/framework/local_tensor_infer_cache.cpp new file mode 100644 index 0000000000000000000000000000000000000000..51752d324124701c8e1a6df811f71a1f7229e794 --- /dev/null +++ b/oneflow/core/framework/local_tensor_infer_cache.cpp @@ -0,0 +1,218 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/local_tensor_infer_cache.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/env_var/eager.h" +#include "oneflow/core/framework/infer_util.h" + +namespace oneflow { +namespace one { + +namespace { + +Maybe CheckIsDeviceSupportedByOp(const Device& device, const std::string& op_type_name) { + if (IsCpuOnly(op_type_name)) { CHECK_EQ_OR_RETURN(device.type(), "cpu"); } // NOLINT + return Maybe::Ok(); +} + +Maybe CheckInputDeviceIdentical(const LocalTensorMetaInferArgs& infer_args, + Symbol default_device) { + for (int i = 0; i < infer_args.input_local_tensor_metas().size(); ++i) { + CHECK_OR_RETURN(default_device + == JUST(VectorAt(infer_args.input_local_tensor_metas(), i))->device()) + << Error::RuntimeError() + << "Expected all tensors to be on the same device, but found " + "at least two devices, " + << default_device->ToString() << " (positional 0) and " + << JUST(VectorAt(infer_args.input_local_tensor_metas(), i))->device()->ToString() + << " (positional " << i << ")!"; + } + return Maybe::Ok(); +} + +class UserOpExprDeviceAndStreamInferContext final : public user_op::DeviceAndStreamInferContext { + public: + UserOpExprDeviceAndStreamInferContext(const UserOpExpr* user_op_expr, + const LocalTensorMetaInferArgs& infer_args, + OpArgsVector* output_tensor_metas) + : user_op_expr_(user_op_expr), + composed_attrs_(infer_args.attrs(), user_op_expr->base_attrs()), + infer_args_(infer_args), + output_tensor_metas_(output_tensor_metas) {} + + const std::vector>& inputs() const override { + return user_op_expr_->indexed_input_pairs(); + } + + const std::vector>& outputs() const override { + return user_op_expr_->indexed_output_pairs(); + } + + Symbol* OutputTensorDevice4ArgNameAndIndex(const std::string& name, + int64_t index) override { + const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0) << "tuple index should be non-negative, but got " << tuple_index; + CHECK_LT(tuple_index, user_op_expr_->output_size()) + << "tuple index " << tuple_index << " should be less than output size " + << user_op_expr_->output_size(); + return output_tensor_metas_->at(tuple_index).mut_device(); + } + + Symbol InputTensorDevice4ArgNameAndIndex(const std::string& name, + int64_t index) const override { + const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0) << "tuple index should be non-negative, but got " << tuple_index; + CHECK_LT(tuple_index, user_op_expr_->input_size()) + << "tuple index " << tuple_index << " should be less than input size " + << user_op_expr_->input_size(); + return infer_args_.input_local_tensor_metas().at(tuple_index)->device(); + } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return composed_attrs_.Attr4Name(attr_name); + } + const UserOpExpr* user_op_expr_; + const ComposedAttrMap composed_attrs_; + const LocalTensorMetaInferArgs& infer_args_; + OpArgsVector* output_tensor_metas_; +}; + +Maybe> InferDeviceAndStream(const UserOpExpr& user_op_expr, + const Symbol& default_device, + const LocalTensorMetaInferArgs& infer_args, + OpArgsVector* output_tensor_metas) { + Symbol stream; + if (!user_op_expr.has_device_and_stream_infer_fn()) { + stream = JUST(GetDefaultStreamByDevice(default_device)); + for (int i = 0; i < user_op_expr.output_size(); i++) { + auto& tensor_meta = output_tensor_metas->at(i); + *tensor_meta.mut_device() = default_device; + } + } else { + if (!user_op_expr.device_and_stream_infer_fn()) { + Symbol device = infer_args.input_local_tensor_metas().at(0)->device(); + stream = JUST(GetDefaultStreamByDevice(device)); + } else { + UserOpExprDeviceAndStreamInferContext device_and_stream_ctx(&user_op_expr, infer_args, + output_tensor_metas); + stream = JUST(user_op_expr.device_and_stream_infer_fn()(&device_and_stream_ctx)); + } + } + return stream; +} + +} // namespace + +size_t LocalTensorMetaInferArgs::hash_value() const { + size_t hash_value = std::hash()(attrs_); + HashCombine(&hash_value, std::hash>()(default_device_)); + const auto& tensor_meta_hash_functor = std::hash>(); + for (const auto& tensor_meta : input_local_tensor_metas_) { + HashCombine(&hash_value, tensor_meta_hash_functor(tensor_meta)); + } + return hash_value; +} + +bool LocalTensorMetaInferArgs::operator==(const LocalTensorMetaInferArgs& other) const { + return this->attrs_ == other.attrs_ && this->default_device_ == other.default_device_ + && this->input_local_tensor_metas_ == other.input_local_tensor_metas_; +} + +Maybe LocalTensorMetaInferArgs::Init(const AttrMap& attrs, Symbol default_device, + const TensorTuple& input_tensors) { + this->attrs_ = attrs; + this->default_device_ = default_device; + this->input_local_tensor_metas_.resize(input_tensors.size()); + JUST(this->InitInputLocalTensorMetas(input_tensors)); + return Maybe::Ok(); +} + +Maybe LocalTensorMetaInferArgs::InitInputLocalTensorMetas(const TensorTuple& input_tensors) { + for (int i = 0; i < input_tensors.size(); ++i) { + input_local_tensor_metas_.at(i) = JUST(input_tensors.at(i)->local_tensor_meta()); + } + return Maybe::Ok(); +} + +/* static */ Maybe LocalTensorInferCache::Infer( + const UserOpExpr& user_op_expr, const LocalTensorMetaInferArgs& infer_args) { + const auto& default_device = infer_args.default_device(); + JUST(CheckInputDeviceIdentical(infer_args, default_device)); + JUST(CheckIsDeviceSupportedByOp(*default_device, user_op_expr.op_type_name())); + + auto result = std::make_unique(user_op_expr.output_size()); + + OpArgsVector output_mut_metas(user_op_expr.output_size()); + // Infer devices + Symbol stream = + JUST(InferDeviceAndStream(user_op_expr, default_device, infer_args, &output_mut_metas)); + result->set_stream(stream); + + { + const auto& GetInputTensorMeta = [&](int32_t i) -> const TensorMeta* { + return infer_args.input_local_tensor_metas().at(i).shared_from_symbol().get(); + }; + JUST(user_op_expr.InferPhysicalTensorDesc( + infer_args.attrs(), stream->device()->type(), GetInputTensorMeta, + [&](int32_t i) -> TensorMeta* { return &output_mut_metas.at(i); })); + } + + auto* mut_output_tensor_metas = result->mut_output_tensor_metas(); + for (int32_t i = 0; i < user_op_expr.output_size(); ++i) { + if (!JUST(user_op_expr.SupportNonContiguous())) { + Stride stride(output_mut_metas.at(i).shape()); + output_mut_metas.at(i).set_stride(stride); + } + CHECK_OR_RETURN(static_cast(output_mut_metas.at(i).device())) + << Error::RuntimeError() << "device not infered"; + mut_output_tensor_metas->at(i) = SymbolOf( + LocalTensorMeta(output_mut_metas.at(i).shape(), output_mut_metas.at(i).stride(), + output_mut_metas.at(i).data_type(), output_mut_metas.at(i).device())); + } + return std::shared_ptr(std::move(result)); +} + +Maybe LocalTensorInferCache::GetOrInfer( + const LocalTensorMetaInferArgs& infer_args) { + if (ThreadLocalEnvBool()) { + auto iter = cache_.find(infer_args); + if (iter == cache_.end()) { + if (unlikely(cache_.size() + >= ThreadLocalEnvInteger())) { + cache_.clear(); + } + const auto& user_op_expr = user_op_expr_.lock(); + CHECK_OR_RETURN(static_cast(user_op_expr)); // NOLINT + const auto& output_tensor_metas = JUST(Infer(*user_op_expr, infer_args)); + iter = cache_.emplace(infer_args, output_tensor_metas).first; + } + return iter->second; + } else { + const auto& user_op_expr = user_op_expr_.lock(); + return JUST(Infer(*user_op_expr, infer_args)); + } +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/local_tensor_infer_cache.h b/oneflow/core/framework/local_tensor_infer_cache.h new file mode 100644 index 0000000000000000000000000000000000000000..45a0eb6dde906e15945383d5ca8c9e8827312665 --- /dev/null +++ b/oneflow/core/framework/local_tensor_infer_cache.h @@ -0,0 +1,120 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_LOCAL_TENSOR_INFER_CACHE_H_ +#define ONEFLOW_CORE_FRAMEWORK_LOCAL_TENSOR_INFER_CACHE_H_ + +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/op_args_vector.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/common/tensor_meta.h" + +namespace oneflow { + +class Device; + +namespace one { + +class TensorTuple; +class UserOpExpr; + +class LocalTensorMetaInferArgs final { + public: + LocalTensorMetaInferArgs() = default; + LocalTensorMetaInferArgs(const LocalTensorMetaInferArgs&) = default; + LocalTensorMetaInferArgs(LocalTensorMetaInferArgs&&) = default; + ~LocalTensorMetaInferArgs() = default; + + const OpArgsVector>& input_local_tensor_metas() const { + return input_local_tensor_metas_; + } + const AttrMap& attrs() const { return attrs_; } + + const Symbol& default_device() const { return default_device_; } + + size_t hash_value() const; + + bool operator==(const LocalTensorMetaInferArgs& other) const; + + Maybe Init(const AttrMap& attrs, Symbol default_device, + const TensorTuple& input_tensors); + + private: + Maybe InitInputLocalTensorMetas(const TensorTuple& input_tensors); + + AttrMap attrs_; + Symbol default_device_; + OpArgsVector> input_local_tensor_metas_; +}; + +} // namespace one +} // namespace oneflow + +namespace std { + +template<> +struct hash final { + size_t operator()(const oneflow::one::LocalTensorMetaInferArgs& val) const { + return val.hash_value(); + } +}; + +} // namespace std + +namespace oneflow { +namespace one { + +class LocalTensorInferResult final { + public: + LocalTensorInferResult(size_t output_size) : output_tensor_metas_(output_size) {} + LocalTensorInferResult(const LocalTensorInferResult&) = delete; + LocalTensorInferResult(LocalTensorInferResult&&) = delete; + ~LocalTensorInferResult() = default; + + const OpArgsVector>& output_tensor_metas() const { + return output_tensor_metas_; + } + OpArgsVector>* mut_output_tensor_metas() { return &output_tensor_metas_; } + + const Symbol& stream() const { return stream_; } + void set_stream(const Symbol& stream) { stream_ = stream; } + + private: + OpArgsVector> output_tensor_metas_; + Symbol stream_; +}; + +class LocalTensorInferCache final { + public: + LocalTensorInferCache(const std::shared_ptr& user_op_expr) + : user_op_expr_(user_op_expr) {} + + Maybe GetOrInfer(const LocalTensorMetaInferArgs& infer_args); + + private: + static Maybe Infer(const UserOpExpr& user_op_expr, + const LocalTensorMetaInferArgs& infer_args); + + std::weak_ptr user_op_expr_; + HashMap> cache_; +}; + +} // namespace one +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_LOCAL_TENSOR_INFER_CACHE_H_ diff --git a/oneflow/core/framework/multi_client_session_context.cpp b/oneflow/core/framework/multi_client_session_context.cpp index f65f373b0be45da90c03fb8740ad3b81735aff30..50e6083eaf7f4315b8d6e6b177c07ae7ee2b1f26 100644 --- a/oneflow/core/framework/multi_client_session_context.cpp +++ b/oneflow/core/framework/multi_client_session_context.cpp @@ -43,7 +43,7 @@ limitations under the License. #endif // WITH_CUDA #ifdef WITH_ROCM #include -#endif // WITH_ROCM +#endif namespace oneflow { @@ -56,7 +56,8 @@ int32_t GetCpuDeviceNum() { return std::thread::hardware_concurrency(); } MultiClientSessionContext::MultiClientSessionContext( const std::shared_ptr& env_ctx) : env_ctx_(env_ctx) { - CHECK(Singleton::Get() == nullptr); + CHECK(Singleton::Get() == nullptr) + << "Duplicate multi client session context"; Singleton::SetAllocated(this); } @@ -65,6 +66,7 @@ MultiClientSessionContext::~MultiClientSessionContext() { if (Singleton::Get() != nullptr) { Singleton::SetAllocated(nullptr); } + env_ctx_.reset(); } Maybe MultiClientSessionContext::TryInit(const ConfigProto& config_proto) { @@ -123,14 +125,15 @@ Maybe MultiClientSessionContext::TryInit(const ConfigProto& config_proto) Maybe MultiClientSessionContext::TryInit(const std::string& config_proto_str) { ConfigProto config_proto; CHECK_OR_RETURN(TxtString2PbMessage(config_proto_str, &config_proto)) - << "failed to parse config_proto: " << config_proto_str; + << Error::RuntimeError() << "failed to parse config_proto: " << config_proto_str; return TryInit(config_proto); } Maybe MultiClientSessionContext::UpdateResource(const Resource& reso_proto) { - CHECK_OR_RETURN(is_inited_) << " session must be inited when updating resource."; + CHECK_OR_RETURN(is_inited_) << Error::RuntimeError() + << " session must be inited when updating resource."; CHECK_NOTNULL_OR_RETURN((Singleton::Get())) - << "ResourceDesc get failed!"; + << Error::RuntimeError() << "ResourceDesc get failed!"; Singleton::Get()->Update(reso_proto); return Maybe::Ok(); } @@ -138,7 +141,7 @@ Maybe MultiClientSessionContext::UpdateResource(const Resource& reso_proto Maybe MultiClientSessionContext::UpdateResource(const std::string& reso_proto_str) { Resource reso_proto; CHECK_OR_RETURN(TxtString2PbMessage(reso_proto_str, &reso_proto)) - << "failed to parse config_proto: " << reso_proto_str; + << Error::RuntimeError() << "failed to parse config_proto: " << reso_proto_str; return UpdateResource(reso_proto); } @@ -171,7 +174,6 @@ Maybe MultiClientSessionContext::TryClose() { Singleton::New(Singleton::Get()->resource(), GlobalProcessCtx::NumOfProcessPerNode()); VLOG(1) << "Finish delete multi client session context." << std::endl; - env_ctx_.reset(); is_inited_ = false; } return Maybe::Ok(); diff --git a/oneflow/core/framework/multi_client_session_context.h b/oneflow/core/framework/multi_client_session_context.h index 8fbd2c5c64f628e563d1ef2b8cf1bb3e68a06f00..7e534c34050cc4660da4596afb5e7a719523fb1e 100644 --- a/oneflow/core/framework/multi_client_session_context.h +++ b/oneflow/core/framework/multi_client_session_context.h @@ -16,7 +16,6 @@ limitations under the License. #ifndef ONEFLOW_CORE_FRAMEWORK_MULTI_CLIENT_SESSION_CONTEXT_H_ #define ONEFLOW_CORE_FRAMEWORK_MULTI_CLIENT_SESSION_CONTEXT_H_ -#include #include "oneflow/core/common/util.h" #include "oneflow/core/job/job_set.pb.h" #include "oneflow/core/common/maybe.h" diff --git a/oneflow/core/framework/mutable_attr_map.h b/oneflow/core/framework/mutable_attr_map.h new file mode 100644 index 0000000000000000000000000000000000000000..2e48167c82d2e8d4a275104ba478d8cefedb7cf1 --- /dev/null +++ b/oneflow/core/framework/mutable_attr_map.h @@ -0,0 +1,126 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_CACHED_ATTR_MAP_H_ +#define ONEFLOW_CORE_FRAMEWORK_CACHED_ATTR_MAP_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/attr_value.h" +#include "oneflow/core/framework/attr_value_accessor.h" +#include "oneflow/core/framework/ordered_string_list.h" +#include "oneflow/core/framework/user_op_attr.pb.h" +#include "oneflow/core/operator/op_conf.pb.h" + +namespace oneflow { + +class MutableAttrMap { + public: + OF_DISALLOW_COPY_AND_MOVE(MutableAttrMap); + + explicit MutableAttrMap(const std::vector& attr_names) + : max_size_(attr_names.size()), + valid_masks_(max_size_, 0), + ordered_attr_names_(std::make_shared>()) { + for (const auto& attr_name : attr_names) { ordered_attr_names_->emplace_back(attr_name); } + attrs_.resize(max_size_); + } + + ~MutableAttrMap() = default; + + size_t max_size() const { return max_size_; } + + const std::shared_ptr>& ordered_attr_names() const { + return ordered_attr_names_; + } + const small_vector& valid_masks() const { return valid_masks_; } + const small_vector, 8>& attrs() const { return attrs_; } + + inline void reset() { + // mark all cached attributes as illegal values + memset(valid_masks_.data(), 0, max_size_); + } + + template + inline void SetAttr(const char* attr_name, const T& attr_val) { + auto idx = ordered_attr_names_->order(attr_name); + CHECK_OR_THROW(idx != -1) << "has no attribute named " << attr_name; + SetAttrNoThrow(idx, attr_val); + } + + template + inline void SetAttr(const T& attr_val) { + CHECK_LT_OR_THROW(I, max_size_) + << "index " << I << " is out of bound, and the max size is " << max_size_; + SetAttrNoThrow(I, attr_val); + } + + template + inline void SetAllAttrs(Args&&... args) { + CHECK_EQ_OR_THROW(sizeof...(args), max_size_) + << "requires " << max_size_ << " arguments, but gives " << sizeof...(args); + SetAttrNoThrow(std::forward(args)..., + std::make_index_sequence{}); + } + + private: + template::value + && !internal::IsOptional::value, + int>::type = 0> + inline void SetAttrNoThrow(int idx, const T& attr_val) { + valid_masks_[idx] = true; + if (!attrs_[idx] /*|| attrs_[idx]->type() != user_op::GetAttrType::value*/ + || *static_cast(attrs_[idx]->Ptr()) != attr_val) { + attrs_[idx] = std::make_shared>(attr_val); + } + } + + template::value, int>::type = 0> + inline void SetAttrNoThrow(int idx, const T& attr_val) { + if (attr_val) { + using U = typename T::value_type; + SetAttrNoThrow(idx, attr_val.value_or(U())); + } + } + + template::value, int>::type = 0> + inline void SetAttrNoThrow(int idx, const T&) {} + + template + inline void SetAttrNoThrow(Args&&... args, std::index_sequence) { + __attribute__((__unused__)) int dummy[] = {(SetAttrNoThrow(I, std::forward(args)), 0)...}; + } + + // The actually count of all attributes + size_t max_size_; + small_vector valid_masks_; + small_vector, 8> attrs_; + // The ordered attribute names is determined and should be shared + // between other AttrMap + std::shared_ptr> ordered_attr_names_; +}; + +#define THREAD_CACHED_MUTABLE_ATTR_MAP(...) \ + []() -> MutableAttrMap& { \ + thread_local static MutableAttrMap attrs(std::vector{__VA_ARGS__}); \ + attrs.reset(); \ + return attrs; \ + }() + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_CACHED_ATTR_MAP_H_ diff --git a/oneflow/core/framework/nd_sbp.cpp b/oneflow/core/framework/nd_sbp.cpp index ad715093e6a3d1b974e2487daa80aebeb33a42d2..f1f9dca51eedf4a494d7ddda4b567dc8f6ea305e 100644 --- a/oneflow/core/framework/nd_sbp.cpp +++ b/oneflow/core/framework/nd_sbp.cpp @@ -80,7 +80,8 @@ Maybe> GetDualNdSbpStrList(Symbol nd_sbp) { namespace private_details { Maybe> RawGetNdSbp(const std::vector>& sbp_list) { - CHECK_OR_RETURN(!sbp_list.empty()); + CHECK_OR_RETURN(!sbp_list.empty()) + << Error::InvalidValueError() << "sbp_list should be non-empty"; NdSbp nd_sbp; for (const auto& sbp : sbp_list) { *(nd_sbp.mutable_sbp_parallel()->Add()) = *sbp; } return SymbolOf(nd_sbp); @@ -88,7 +89,8 @@ Maybe> RawGetNdSbp(const std::vector>& sbp_lis Maybe>> RawGetSbpList(Symbol nd_sbp) { const auto& vec = std::make_shared>>(); - CHECK_OR_RETURN(!nd_sbp->sbp_parallel().empty()); + CHECK_OR_RETURN(!nd_sbp->sbp_parallel().empty()) + << Error::InvalidValueError() << "sbp_parallel should be non-empty"; for (const auto& sbp_parallel : nd_sbp->sbp_parallel()) { vec->emplace_back(SymbolOf(sbp_parallel)); } @@ -102,6 +104,20 @@ bool RawContainSplitSbp(Symbol nd_sbp) { return false; } +Maybe>> RawNdSbpReplacePartialByBroadcast( + const std::vector>& sbp_list) { + auto result = std::make_shared>>(sbp_list.size()); + for (int i = 0; i < sbp_list.size(); ++i) { + const auto& sbp = sbp_list[i]; + if (sbp->has_partial_sum_parallel()) { + (*result)[i] = JUST(MakeBroadcastSbpParallel()); + } else { + (*result)[i] = sbp; + } + } + return result; +} + } // namespace private_details const std::vector>& GetNoneSbpList() { diff --git a/oneflow/core/framework/nd_sbp.h b/oneflow/core/framework/nd_sbp.h index 77e19d3c9b166b938c4a13a670fd261c575d98cf..36383ac53bdc927f7a339c4c5bd254b44c48e052 100644 --- a/oneflow/core/framework/nd_sbp.h +++ b/oneflow/core/framework/nd_sbp.h @@ -43,6 +43,9 @@ Maybe> RawGetNdSbp(const std::vector>& sbp_lis Maybe>> RawGetSbpList(Symbol nd_sbp); bool RawContainSplitSbp(Symbol nd_sbp); +Maybe>> RawNdSbpReplacePartialByBroadcast( + const std::vector>& sbp_list); + } // namespace private_details static constexpr auto* GetNdSbp = DECORATE(&private_details::RawGetNdSbp, ThreadLocalCopiable); @@ -51,6 +54,9 @@ static constexpr auto* ContainSplitSbp = DECORATE(&private_details::RawContainSplitSbp, ThreadLocal); const std::vector>& GetNoneSbpList(); +static constexpr auto* NdSbpReplacePartialByBroadcast = + DECORATE(&private_details::RawNdSbpReplacePartialByBroadcast, ThreadLocalCachedCopiable); + std::string SbpToString(Symbol sbp_sym); std::string NdSbpToString(Symbol nd_sbp_sym); std::string SbpToString(const SbpParallel& sbp); diff --git a/oneflow/core/framework/nn_graph.cpp b/oneflow/core/framework/nn_graph.cpp index e38ca2747995e73116353a51e73d38b8a7f8c10e..5c4150b29eda80db6588d1341fbcd2f6be386f58 100644 --- a/oneflow/core/framework/nn_graph.cpp +++ b/oneflow/core/framework/nn_graph.cpp @@ -17,6 +17,7 @@ limitations under the License. #include "oneflow/core/common/buffer_manager.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/scalar.h" +#include "oneflow/core/common/cost_util.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/control/ctrl_client.h" @@ -24,6 +25,7 @@ limitations under the License. #include "oneflow/core/eager/eager_blob_object.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/framework/scope_util.h" #include "oneflow/core/framework/tensor_name_scope.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/graph/op_graph.h" @@ -34,6 +36,7 @@ limitations under the License. #include "oneflow/core/job/critical_section_instance.h" #include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/job/plan_util.h" +#include "oneflow/core/job/utils/progress_bar.h" #include "oneflow/core/job_rewriter/job_completer.h" #include "oneflow/core/persistence/tee_persistent_log_stream.h" #include "oneflow/core/vm/virtual_machine.h" @@ -46,7 +49,7 @@ namespace oneflow { namespace { Maybe GetTensorValidInCurRank(const std::shared_ptr& tensor) { - if (tensor->is_consistent()) { + if (tensor->is_global()) { const auto& parallel_id = JUST(GetParallelId4CurrentProcessCtx(JUST(tensor->parallel_desc()))); if (parallel_id->has_value()) { return true; @@ -60,7 +63,7 @@ Maybe GetTensorValidInCurRank(const std::shared_ptr& tensor) Maybe GetTensorMetaString(const std::shared_ptr& tensor) { std::string ret = "shape=" + tensor->shape()->ToString() + ", dtype=" + tensor->dtype()->name(); - if (tensor->is_consistent()) { + if (tensor->is_global()) { ret += ", placement=" + *JUST(PlacementToString(JUST(tensor->parallel_desc()))); ret += ", nd_sbp=" + NdSbpToString(JUST(tensor->nd_sbp())); } else { @@ -111,15 +114,21 @@ int64_t NNGraph::variable_op_size() const { return variable_op_names_.size(); } Maybe NNGraph::RegisterAdditionalVarOpNamesAndTensorsToBeLoaded( const std::vector& additional_var_names, const std::vector>& additional_var_tensors) { - CHECK_EQ_OR_RETURN(additional_var_names.size(), additional_var_tensors.size()); + CHECK_EQ_OR_RETURN(additional_var_names.size(), additional_var_tensors.size()) + << Error::RuntimeError() + << "Number of additional variable names and tensors mismatch. " + "Size of variable names: " + << additional_var_names.size() << ", size of tensors: " << additional_var_tensors.size(); CHECK_OR_RETURN(additional_variable_op_tobe_loaded_name2tensor_.empty()) - << " The additional variables (states in Optimizer or LRScheduler) of nn.Graph " << name_ - << " are register repeatedly."; + << Error::RuntimeError() + << "The additional variables (states in Optimizer or LRScheduler) of nn.Graph " << name_ + << " are registered repeatedly."; FOR_RANGE(size_t, i, 0, additional_var_names.size()) { CHECK_OR_RETURN(additional_variable_op_tobe_loaded_name2tensor_ .emplace(JUST(VectorAt(additional_var_names, i)), JUST(VectorAt(additional_var_tensors, i))) - .second); + .second) + << Error::RuntimeError() << "Duplicate variable name: " << additional_var_names[i]; } return Maybe::Ok(); } @@ -127,11 +136,20 @@ Maybe NNGraph::RegisterAdditionalVarOpNamesAndTensorsToBeLoaded( Maybe NNGraph::RegisterInputOpNamesAndTensors( const std::vector& inputs_op_names, const std::vector>& input_tensors) { - CHECK_EQ_OR_RETURN(inputs_op_names.size(), input_tensors.size()); + CHECK_EQ_OR_RETURN(inputs_op_names.size(), input_tensors.size()) + << Error::RuntimeError() + << "Number of input op names and tensors mismatch. " + "Size of op names: " + << inputs_op_names.size() << ", size of tensors: " << input_tensors.size(); CHECK_OR_RETURN(inputs_op_names_.empty()) - << " The input tensors of nn.Graph " << name_ << " are register repeatedly."; - CHECK_OR_RETURN(input_tensors_valid_.empty()); - CHECK_OR_RETURN(inputs_tensor_meta_str_.empty()); + << Error::RuntimeError() << "The input tensors of nn.Graph " << name_ + << " are registered repeatedly."; + CHECK_OR_RETURN(input_tensors_valid_.empty()) + << Error::RuntimeError() << "The input tensors of nn.Graph " << name_ + << " are registered repeatedly."; + CHECK_OR_RETURN(inputs_tensor_meta_str_.empty()) + << Error::RuntimeError() << "The input tensors of nn.Graph " << name_ + << " are registered repeatedly."; inputs_op_names_.assign(inputs_op_names.begin(), inputs_op_names.end()); input_tensors_valid_.reserve(input_tensors.size()); inputs_tensor_meta_str_.reserve(input_tensors.size()); @@ -139,18 +157,26 @@ Maybe NNGraph::RegisterInputOpNamesAndTensors( input_tensors_valid_.emplace_back(JUST(GetTensorValidInCurRank(input_tensor))); inputs_tensor_meta_str_.emplace_back(*JUST(GetTensorMetaString(input_tensor))); } - CHECK_EQ_OR_RETURN(input_tensors_valid_.size(), input_tensors.size()); + CHECK_EQ_OR_RETURN(input_tensors_valid_.size(), input_tensors.size()); // NOLINE return Maybe::Ok(); } Maybe NNGraph::RegisterOutputOpNamesAndTensors( const std::vector& outputs_op_names, const std::vector>& output_tensors) { - CHECK_EQ_OR_RETURN(outputs_op_names.size(), output_tensors.size()); + CHECK_EQ_OR_RETURN(outputs_op_names.size(), output_tensors.size()) + << "Number of output op names and tensors mismatch " + "Size of op names: " + << outputs_op_names.size() << ", size of tensors: " << output_tensors.size(); CHECK_OR_RETURN(outputs_op_names_.empty()) - << " The output tensors of nn.Graph " << name_ << " are register repeatedly."; - CHECK_OR_RETURN(output_tensors_valid_.empty()); - CHECK_OR_RETURN(outputs_tensor_meta_str_.empty()); + << Error::RuntimeError() << "The output tensors of nn.Graph " << name_ + << " are registered repeatedly."; + CHECK_OR_RETURN(output_tensors_valid_.empty()) + << Error::RuntimeError() << "The output tensors of nn.Graph " << name_ + << " are registered repeatedly."; + CHECK_OR_RETURN(outputs_tensor_meta_str_.empty()) + << Error::RuntimeError() << "The output tensors of nn.Graph " << name_ + << " are registered repeatedly."; outputs_op_names_.assign(outputs_op_names.begin(), outputs_op_names.end()); output_tensors_valid_.reserve(output_tensors.size()); outputs_tensor_meta_str_.reserve(output_tensors.size()); @@ -158,7 +184,7 @@ Maybe NNGraph::RegisterOutputOpNamesAndTensors( output_tensors_valid_.emplace_back(JUST(GetTensorValidInCurRank(output_tensor))); outputs_tensor_meta_str_.emplace_back(*JUST(GetTensorMetaString(output_tensor))); } - CHECK_EQ_OR_RETURN(output_tensors_valid_.size(), output_tensors.size()); + CHECK_EQ_OR_RETURN(output_tensors_valid_.size(), output_tensors.size()); // NOLINT return Maybe::Ok(); } @@ -166,14 +192,20 @@ Maybe NNGraph::RegisterVariableOpNamesAndTensors( const std::vector& variable_op_names, const std::vector>& variable_tensors) { JUST(vm::CurrentRankSync()); - CHECK_EQ_OR_RETURN(variable_op_names.size(), variable_tensors.size()); + CHECK_EQ_OR_RETURN(variable_op_names.size(), variable_tensors.size()) + << "Number of variable names and tensors mismatch. " + "Size of variable names: " + << variable_op_names.size() << ", size of tensors: " << variable_tensors.size(); for (int32_t i = 0; i < variable_op_names.size(); ++i) { - const std::shared_ptr& var = variable_tensors.at(i); - CHECK_OR_RETURN(var->is_eager()); + const std::shared_ptr& var = variable_tensors[i]; + CHECK_OR_RETURN(var->is_eager()) + << Error::InvalidValueError() << "Tensor variable to register in nn.Graph should be eager"; const std::string& var_name = variable_op_names.at(i); - CHECK_OR_RETURN(!var_name.empty()); - CHECK_OR_RETURN(variable_op_name2tensor_.emplace(var_name, var).second); - CHECK_OR_RETURN(variable_op_names_.insert(var_name).second); + CHECK_OR_RETURN(!var_name.empty()) << Error::InvalidValueError() << "Empty variable name"; + CHECK_OR_RETURN(variable_op_name2tensor_.emplace(var_name, var).second) + << Error::RuntimeError() << "Duplicate variable name: " << var_name; + CHECK_OR_RETURN(variable_op_names_.insert(var_name).second) + << Error::RuntimeError() << "Duplicate variable name: " << var_name; } return Maybe::Ok(); } @@ -184,11 +216,15 @@ Maybe NNGraph::RegisterFreeEagerTensorsToVariableOpNames() { for (const auto& pair : free_eager_tensors) { const std::string& var_name = pair.first; const std::shared_ptr& var = pair.second; - CHECK_OR_RETURN(var->is_eager()); - CHECK_OR_RETURN(!var_name.empty()); - CHECK_OR_RETURN(variable_op_name2tensor_.emplace(var_name, var).second); - CHECK_OR_RETURN(additional_variable_op_name_.insert(var_name).second); - CHECK_OR_RETURN(variable_op_names_.insert(var_name).second); + CHECK_OR_RETURN(var->is_eager()) + << Error::RuntimeError() << "Free tensor variable to register in nn.Graph should be eager"; + CHECK_OR_RETURN(!var_name.empty()) << Error::RuntimeError() << "Empty variable name"; + CHECK_OR_RETURN(variable_op_name2tensor_.emplace(var_name, var).second) + << Error::RuntimeError() << "Duplicate variable name: " << var_name; + CHECK_OR_RETURN(additional_variable_op_name_.insert(var_name).second) + << Error::RuntimeError() << "Duplicate variable name: " << var_name; + CHECK_OR_RETURN(variable_op_names_.insert(var_name).second) + << Error::RuntimeError() << "Duplicate variable name: " << var_name; } return Maybe::Ok(); } @@ -203,7 +239,8 @@ Maybe>> NNGraph::GetAdditionalVarOpTens std::vector> tensors; for (const auto& iter : additional_variable_op_name_) { auto find_iter = variable_op_name2tensor_.find(iter); - CHECK_OR_RETURN(find_iter != variable_op_name2tensor_.end()); + CHECK_OR_RETURN(find_iter != variable_op_name2tensor_.end()) + << Error::RuntimeError() << "Additional variable op name " << iter << " not found."; tensors.push_back(find_iter->second); } return tensors; @@ -217,31 +254,34 @@ Maybe NNGraph::RegisterNewVariableOpInJobPass() { const VariableOpConf& var_conf = variable_op.op_conf().variable_conf(); const std::string& var_name = variable_op.op_name(); CHECK_OR_RETURN(var_conf.has_initializer()) - << " nn.Graph ONLY support variable op with initializer conf."; + << Error::RuntimeError() << "nn.Graph ONLY support variable op with initializer conf."; if (var_conf.initializer().has_constant_conf() || var_conf.initializer().has_constant_int_conf() /* vairable ops inserted by system */) { CHECK_OR_RETURN(variable_op_names_.insert(var_name).second) - << " ERROR! variable_op_name: " << var_name << " has been add in nn.Graph: " << name_; + << Error::RuntimeError() << "Variable_op_name: " << var_name + << " has been added in nn.Graph: " << name_; CHECK_OR_RETURN( variable_op_name2tensor_.insert({var_name, std::shared_ptr()}).second) - << " ERROR! variable Tensor with op_name: " << var_name + << Error::RuntimeError() << "Variable Tensor with op_name: " << var_name << " has been add in nn.Graph: " << name_; CHECK_OR_RETURN(additional_variable_op_name_.insert(var_name).second) - << " ERROR! variable Tensor with op_name: " << var_name + << Error::RuntimeError() << "Variable Tensor with op_name: " << var_name << " has been add in nn.Graph: " << name_; } else /* vairable ops from user code */ { CHECK_OR_RETURN(var_conf.initializer().has_empty_conf()) - << " nn.Graph ONLY support variable_op with empty conf," - << " because variable is inited by eager tensor." - << " This error variable conf is : " << variable_op.op_conf().DebugString() + << Error::RuntimeError() << "nn.Graph ONLY support variable_op with empty conf, " + << "because variable is inited by eager tensor. " + << "This error variable conf is: " << variable_op.op_conf().DebugString() << " in nn.Graph " << name_; CHECK_OR_RETURN(variable_op_names_.find(var_name) != variable_op_names_.end()) - << " ERROR! " << var_name << " must be a variable created in nn.Graph: " << name_; + << Error::RuntimeError() << var_name + << " must be a variable created in nn.Graph: " << name_; } return Maybe::Ok(); })); return Maybe::Ok(); } + Maybe NNGraph::DeleteOutdatedVariableInVariableTensorMgr() { std::set variable_names = [&]() -> Maybe> { std::set variable_names_; @@ -263,13 +303,17 @@ Maybe NNGraph::DeleteOutdatedVariableInVariableTensorMgr() { } Maybe NNGraph::CompileAndInitRuntime() { - CHECK_OR_RETURN(!runtime_inited_); + auto compile_tc = std::make_unique>(true, true); + CHECK_OR_RETURN(!runtime_inited_) + << Error::RuntimeError() << "nn.Graph runtime is already initialized"; JUST(RegisterFreeEagerTensorsToVariableOpNames()); JUST(RegisterNewVariableOpInJobPass()); JUST(DeleteOutdatedVariableInVariableTensorMgr()); // NOTE(chengcheng): TensorNameScope need to be cleared after current graph is built. one::TensorNameScope::Global()->Clear(); + // Clear all backward pass scope + ClearAllBackwardPassScope(); // NOTE(chengcheng): Singleton need be clear before GlobalJobDescScope construct. if (Singleton::Get() != nullptr) { Singleton::Delete(); } @@ -278,29 +322,33 @@ Maybe NNGraph::CompileAndInitRuntime() { // NOTE(chengcheng): do job compeleter for each rank. JUST(JobCompleter().Complete(&job_)); + compile_tc->Count("[GraphCompile]" + name_ + " CompleteJob", 0); if (GlobalProcessCtx::IsThisProcessMaster()) { - double start = GetCurTime(); // TODO(chengcheng): new memory reused by chunk Compiler().Compile(&job_, &plan_); + auto sub_compile_tc = std::make_unique>(true, true); PlanUtil::GenMemBlockAndChunkWithVariableOpNames4Plan(&plan_, variable_op_names_); - - VLOG(1) << "Graph name: " << name_ << " compile time: " << (GetCurTime() - start) / 1000000000.0 - << " seconds."; + sub_compile_tc->Count("[GraphCompile]" + name_ + " GenMemBlockAndChunk", 1, true); if (Singleton::Get()->enable_debug_mode()) { TeePersistentLogStream::Create("job_" + name_ + "_plan")->Write(plan_); PlanUtil::ToDotFile(plan_, "job_" + name_ + "_plan.dot"); } + sub_compile_tc->Count("[GraphCompile]" + name_ + " LogPlan", 1, true); PlanUtil::GenRegisterHint(&plan_); + sub_compile_tc->Count("[GraphCompile]" + name_ + " GenRegisterHint", 1, true); // TODO(chengcheng): test collective boxing for multi-job. PlanUtil::GenCollectiveBoxingPlan(&job_, &plan_); - // PlanUtil::SetForceInplaceMemBlock(&plan_); NOTE(chengcheng): only for ssp. + sub_compile_tc->Count("[GraphCompile]" + name_ + " GenCollectiveBoxingPlan", 1, true); PlanUtil::DumpCtrlRegstInfoToPlan(&plan_); + sub_compile_tc->Count("[GraphCompile]" + name_ + " DumpCtrlRegstInfoToPlan", 1, true); PlanUtil::PlanMemoryLog(&plan_, name_); if (Singleton::Get()->enable_debug_mode()) { PlanUtil::GenLightPlan(&plan_, name_); } + sub_compile_tc->Count("[GraphCompile]" + name_ + " GenMemAndLightPlanLog", 1, true); } + compile_tc->Count("[GraphCompile]" + name_ + " CompilePlan", 0); if (GlobalProcessCtx::WorldSize() > 1) { std::string plan_name = "plan:" + job_name(); if (GlobalProcessCtx::IsThisProcessMaster()) { @@ -316,6 +364,7 @@ Maybe NNGraph::CompileAndInitRuntime() { Singleton::Get()->ClearKV(plan_name); } } + compile_tc->Count("[GraphCompile]" + name_ + " SyncPlan", 0, true); // NOTE(chengcheng): recovery op_attr PlanUtil::PopulateOpAttribute(&plan_, plan_.job_id2op_attribute_ref_table()); @@ -329,27 +378,31 @@ Maybe NNGraph::CompileAndInitRuntime() { JUST(vm->ShrinkAllMem()); runtime_.reset(new Runtime(plan_, variable_op_name2eager_blob_object_)); + compile_tc->Count("[GraphCompile]" + name_ + " InitRuntime", 0, true); + JUST(LogProgress("[GraphCompile]" + name_ + " Done", true)); runtime_inited_ = true; return Maybe::Ok(); } Maybe NNGraph::GetVariableRealBlobAfterSyncPlan() { - CHECK_OR_RETURN(variable_op_name2eager_blob_object_.empty()) << kOfBugIssueUploadPrompt; + CHECK_OR_RETURN(variable_op_name2eager_blob_object_.empty()) + << Error::RuntimeError() << kOfBugIssueUploadPrompt; JUST(vm::CurrentRankSync()); // Create or Rebuild variable, then get the real blob. for (const std::string& var_name : variable_op_names_) { auto iter = variable_op_name2tensor_.find(var_name); - CHECK_OR_RETURN(iter != variable_op_name2tensor_.end()) << var_name << " not found."; + CHECK_OR_RETURN(iter != variable_op_name2tensor_.end()) + << Error::RuntimeError() << "variable op name " << var_name << " not found."; std::shared_ptr tensor = iter->second; vm::EagerBlobObject* var_blob = nullptr; if (plan_.job_id2op_attribute_ref_table().at(job_id_).op_name2op_attribute().find(var_name) == plan_.job_id2op_attribute_ref_table().at(job_id_).op_name2op_attribute().end()) { // Deal with variable tensor not used in nn.Graph build. - CHECK(tensor != NULL) - << "the tensor of " << var_name - << " is not existed in job, so it's not created in nn.Graph and cannot be NULL."; - if (tensor->is_consistent()) { - const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); + CHECK_OR_RETURN(tensor != NULL) + << Error::RuntimeError() << "The tensor of " << var_name + << " does not exist in the job, so it's not created in nn.Graph and cannot be NULL."; + if (tensor->is_global()) { + const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); var_blob = JUST(local_var->eager_blob_object()).get(); } else { var_blob = JUST(tensor->eager_blob_object()).get(); @@ -382,8 +435,8 @@ Maybe NNGraph::GetVariableRealBlobAfterSyncPlan() { } // NOTE(chengcheng): New EagerTensor need set LazyMode false. auto lazy_mode_disabled_guard = LazyMode::Guard(/*is_enabled*/ false); - tensor = JUST(one::functional::ConsistentConstant( - blob_desc.shape(), value, Symbol(dtype), placement, *sbp_tuple)); + tensor = JUST(one::functional::GlobalConstant(blob_desc.shape(), value, + Symbol(dtype), placement, *sbp_tuple)); JUST(vm::CurrentRankSync()); VLOG(2) << "Lazy nn.Graph name " << name_ << " op: " << op_attribute.op_conf().name() << " created in JobPass, nn.Graph has created a eager tensor for this variable.\n"; @@ -391,10 +444,10 @@ Maybe NNGraph::GetVariableRealBlobAfterSyncPlan() { // Load a additional variable tensor auto lazy_mode_disabled_guard = LazyMode::Guard(/*is_enabled*/ false); std::vector> grad_sbp_tuple; - // To consistent from a local or consistent tensor. - bool check_meta = load_tensor_iter->second->is_consistent() ? false : true; - tensor = JUST(one::functional::ToConsistent(load_tensor_iter->second, placement, *sbp_tuple, - grad_sbp_tuple, check_meta)); + // To consistent from a local or global tensor. + bool check_meta = load_tensor_iter->second->is_global() ? false : true; + tensor = JUST(one::functional::ToGlobal(load_tensor_iter->second, placement, *sbp_tuple, + grad_sbp_tuple, check_meta, /*copy=*/false)); JUST(vm::CurrentRankSync()); VLOG(2) << "Lazy nn.Graph name " << name_ << " op: " << op_attribute.op_conf().name() << " created in JobPass, nn.Graph has loaded the tensor from state dict for this " @@ -406,9 +459,9 @@ Maybe NNGraph::GetVariableRealBlobAfterSyncPlan() { // valid. session_ctx_->StoreFreeEagerTensorWithNameByGraphName(name_, tensor, var_name); - const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); + const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); var_blob = JUST(local_var->eager_blob_object()).get(); - } else if (tensor->is_consistent()) { + } else if (tensor->is_global()) { // Deal with tensors which need to change sbp. NdSbpSignature var_nd_sbp_signature = NdSbpSignature(plan_.job_id2op_attribute_ref_table() .at(job_id_) @@ -427,26 +480,26 @@ Maybe NNGraph::GetVariableRealBlobAfterSyncPlan() { } { auto lazy_mode_disabled_guard = LazyMode::Guard(/* is_enabled */ false); - const auto& new_tensor = JUST( - one::functional::ToConsistent(tensor, JUST(tensor->parallel_desc()), - optimized_sbp_parallels, {}, /* check_meta */ false)); + const auto& new_tensor = JUST(one::functional::ToGlobal( + tensor, JUST(tensor->parallel_desc()), optimized_sbp_parallels, {}, + /* check_meta */ false, /*copy=*/false)); JUST(vm::CurrentRankSync()); // Use tensor.set_data inferface and make new TensorImpl instead of the old one. JUST(tensor->set_data(new_tensor)); } } - const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); + const std::shared_ptr local_var = JUST(tensor->cur_rank_phy_tensor()); var_blob = JUST(local_var->eager_blob_object()).get(); } else { var_blob = JUST(tensor->eager_blob_object()).get(); } - CHECK_OR_RETURN(var_blob != nullptr) << kOfBugIssueUploadPrompt; + CHECK_OR_RETURN(var_blob != nullptr) << Error::RuntimeError() << kOfBugIssueUploadPrompt; CHECK_OR_RETURN(variable_op_name2eager_blob_object_.emplace(var_name, var_blob).second) - << kOfBugIssueUploadPrompt; + << Error::RuntimeError() << kOfBugIssueUploadPrompt; } // Initialize or check mem_ptr_for_allocation_computation_pipelining by TouchTensors instruction. JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - auto eager_blob_objects = std::make_shared>>(); + auto eager_blob_objects = std::make_shared(); for (const auto& pair : variable_op_name2eager_blob_object_) { eager_blob_objects->push_back(pair.second->shared_from_this()); } @@ -508,12 +561,13 @@ void NNGraph::CloseRuntimeBuffers() { namespace { -Maybe MakeEagerBlobObjectList(std::vector>* blob_list, +Maybe MakeEagerBlobObjectList(vm::EagerBlobObjectList* blob_list, const one::TensorTuple& tensor_list) { blob_list->reserve(tensor_list.size()); for (const auto& tensor : tensor_list) { - CHECK_OR_RETURN(tensor->is_eager()); - if (tensor->is_consistent()) { + CHECK_OR_RETURN(tensor->is_eager()) + << Error::RuntimeError() << "Tensors in nn.Graph should be eager"; + if (tensor->is_global()) { blob_list->emplace_back(JUST(JUST(tensor->cur_rank_phy_tensor())->eager_blob_object())); } else { blob_list->emplace_back(JUST(tensor->eager_blob_object())); @@ -527,43 +581,53 @@ Maybe MakeEagerBlobObjectList(std::vector RunLazyNNGraph(const one::TensorTuple& inputs, const one::TensorTuple& outputs, const one::TensorTuple& parameters, const std::shared_ptr& nn_graph) { - CHECK_EQ_OR_RETURN(inputs.size(), nn_graph->inputs_op_names().size()); - CHECK_EQ_OR_RETURN(outputs.size(), nn_graph->outputs_op_names().size()); + CHECK_EQ_OR_RETURN(inputs.size(), nn_graph->inputs_op_names().size()) + << Error::RuntimeError() + << "Number of inputs and NNGraph::inputs_op_names mismatch. " + "Size of inputs: " + << inputs.size() + << ", size of NNGraph::inputs_op_names: " << nn_graph->inputs_op_names().size(); + CHECK_EQ_OR_RETURN(outputs.size(), nn_graph->outputs_op_names().size()) + << Error::RuntimeError() + << "Number of outputs and NNGraph::outputs_op_names mismatch. " + "Size of outputs: " + << outputs.size() + << ", size of NNGraph::outputs_op_names: " << nn_graph->outputs_op_names().size(); // NOTE(chengcheng): // parameters not used in LaunchLazyJobInstrucntion; // the args: parameters is all variable tensor hold by nn.Graph // but the NNGraph::variable_op_size may has FreeEagerTensor as sepcial variable op. - CHECK_LE_OR_RETURN(parameters.size(), nn_graph->variable_op_size()); + CHECK_LE_OR_RETURN(parameters.size(), nn_graph->variable_op_size()) + << Error::RuntimeError() << "Parameter size should be less than or equal to variable size"; for (int i = 0; i < inputs.size(); ++i) { // TODO(chengcheng, liufengwei): // use TensorMeta.to_string and equal. std::string tensor_meta_str = *JUST(GetTensorMetaString(inputs.at(i))); const std::string& static_meta_str = nn_graph->inputs_tensor_meta_str().at(i); CHECK_OR_RETURN(static_meta_str == tensor_meta_str) - << "\n nn.Graph ONLY accepts static inputs tensor meta, please check whether your input " - << "tensor meta each step is the same as the input of first call graph. \n The excepted " - << "tensor meta is : ( \n " << static_meta_str - << " \n) , but the actual tensor meta is : ( \n " << tensor_meta_str << " \n)"; + << Error::RuntimeError() + << "nn.Graph ONLY accepts static inputs tensor meta, please check whether your input " + << "tensor meta each step is the same as the input of first call graph.\nThe excepted " + << "tensor meta is: " << static_meta_str + << ", but the actual tensor meta is: " << tensor_meta_str; } for (int i = 0; i < outputs.size(); ++i) { CHECK_OR_RETURN(nn_graph->outputs_tensor_meta_str().at(i) - == *JUST(GetTensorMetaString(outputs.at(i)))); + == *JUST(GetTensorMetaString(outputs.at(i)))) + << Error::RuntimeError() << "Output tensor meta string mismatch"; } - std::vector> input_blobs; - std::vector> output_blobs; - std::vector> var_blobs; + vm::EagerBlobObjectList input_blobs; + vm::EagerBlobObjectList output_blobs; + vm::EagerBlobObjectList var_blobs; JUST(MakeEagerBlobObjectList(&input_blobs, inputs)); JUST(MakeEagerBlobObjectList(&output_blobs, outputs)); JUST(MakeEagerBlobObjectList(&var_blobs, parameters)); const auto& input_blob_list_ptr = - std::make_shared>>( - std::move(input_blobs)); + std::make_shared(std::move(input_blobs)); const auto& output_blob_list_ptr = - std::make_shared>>( - std::move(output_blobs)); + std::make_shared(std::move(output_blobs)); const auto& var_blob_list_ptr = - std::make_shared>>( - std::move(var_blobs)); + std::make_shared(std::move(var_blobs)); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->LaunchLazyJob(input_blob_list_ptr, output_blob_list_ptr, var_blob_list_ptr, nn_graph); @@ -573,8 +637,7 @@ Maybe RunLazyNNGraph(const one::TensorTuple& inputs, const one::TensorTupl Maybe SoftSyncNNGraphBuffers(const one::TensorTuple& buffers, const std::shared_ptr& nn_graph) { - const auto& eager_blob_objects = - std::make_shared>>(); + const auto& eager_blob_objects = std::make_shared(); JUST(MakeEagerBlobObjectList(eager_blob_objects.get(), buffers)); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->SoftSyncNNGraphBuffers(eager_blob_objects, nn_graph); diff --git a/oneflow/core/framework/op_attrs.cpp b/oneflow/core/framework/op_attrs.cpp deleted file mode 100644 index 7ae08477890942464b821c4f82320ff3cc53e69f..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_attrs.cpp +++ /dev/null @@ -1,58 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/op_attrs.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/framework/op_interp_ctx.h" - -namespace oneflow { - -size_t OpAttrs::count(const std::string& attr_name) const { - return ctx_->AttrNames().count(attr_name); -} - -Maybe OpAttrs::at(const std::string& attr_name) const { return ctx_->GetAttr(attr_name); } -Maybe OpAttrs::operator[](const std::string& attr_name) const { - return ctx_->GetAttr(attr_name); -} - -OpAttrs::const_iterator OpAttrs::begin() const { - const auto& attrs = ctx_->AttrNames(); - return const_iterator(attrs.cbegin(), attrs.cend(), this); -} -OpAttrs::const_iterator OpAttrs::end() const { - const auto& attrs = ctx_->AttrNames(); - return const_iterator(attrs.cend(), attrs.cend(), this); -} - -bool OpAttrs::operator==(const OpAttrs& other) const { - // TODO(hjchen2): Compare each attribute - return ctx_ == other.ctx_; -} - -} // namespace oneflow - -namespace std { - -size_t hash::operator()(const oneflow::OpAttrs& attrs) const { - size_t hash_val = 0; - for (const auto& it : attrs) { - oneflow::AddHash(&hash_val, it.first); - oneflow::HashCombine(&hash_val, it.second->hash_value()); - } - return hash_val; -} - -} // namespace std diff --git a/oneflow/core/framework/op_attrs.h b/oneflow/core/framework/op_attrs.h deleted file mode 100644 index 46f6df71f18b4d6ae790193e6f7d52d08abadd8c..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_attrs.h +++ /dev/null @@ -1,102 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_OP_ATTRS_H_ -#define ONEFLOW_CORE_FRAMEWORK_OP_ATTRS_H_ - -#include -#include - -#include "oneflow/core/common/hash_container.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/framework/attr_value.h" - -namespace oneflow { - -using user_op::AttrVal; - -class OpInterpCtx; - -class OpAttrs { - public: - explicit OpAttrs(const OpInterpCtx* ctx) : ctx_(ctx) {} - - size_t count(const std::string& attr_name) const; - - template - Maybe at(const std::string& attr_name) { - return AttrValueCast(*JUST(this->at(attr_name))); - } - Maybe at(const std::string& attr_name) const; - Maybe operator[](const std::string& attr_name) const; - - class const_iterator { - public: - using bucket_iter = HashSet::const_iterator; - using reference = const std::pair>&; - using pointer = const std::pair>*; - - const_iterator() = default; - const_iterator(bucket_iter pos, bucket_iter limit, const OpAttrs* self) - : pos_(pos), limit_(limit), self_(self) { - CHECK_JUST(UpdateKV()); - } - reference operator*() const { return kv_; } - pointer operator->() const { return &kv_; } - - const_iterator& operator++() { - pos_++; - CHECK_JUST(UpdateKV()); - return *this; - } - bool operator==(const const_iterator& x) const { return pos_ == x.pos_ && self_ == x.self_; } - bool operator!=(const const_iterator& x) const { return !(*this == x); } - - private: - Maybe UpdateKV() { - if (pos_ != limit_) { - kv_.first = *pos_; - kv_.second = JUST(self_->at(*pos_)); - } - return Maybe::Ok(); - } - - bucket_iter pos_; - bucket_iter limit_; - const OpAttrs* self_; - std::pair> kv_; - }; - - const_iterator begin() const; - const_iterator end() const; - - bool operator==(const OpAttrs& other) const; - - private: - const OpInterpCtx* ctx_; -}; - -} // namespace oneflow - -namespace std { - -template<> -struct hash { - size_t operator()(const oneflow::OpAttrs& attrs) const; -}; - -} // namespace std - -#endif // ONEFLOW_CORE_FRAMEWORK_OP_ATTRS_H_ diff --git a/oneflow/core/framework/op_base.h b/oneflow/core/framework/op_base.h deleted file mode 100644 index 0f74faf5153f8e53727215aade0f9b2e627d63b5..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_base.h +++ /dev/null @@ -1,55 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_OP_BASE_H_ -#define ONEFLOW_CORE_FRAMEWORK_OP_BASE_H_ - -#include - -#include "oneflow/core/common/hash_container.h" -#include "oneflow/core/common/maybe.h" - -namespace oneflow { - -namespace user_op { -class AttrVal; -} // namespace user_op -using AttrVal = user_op::AttrVal; - -class OpBase { - public: - virtual ~OpBase() = default; - - virtual Maybe GetAttr(const std::string& attr_name) const = 0; - - virtual const HashSet& AttrNames() const { - static const HashSet attr_names; - return attr_names; - } - - protected: - OpBase() = default; -}; - -class FakeOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override { - return Error::RuntimeError() << "`FakeOp` has no attribute."; - } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_OP_BASE_H_ diff --git a/oneflow/core/framework/op_builder.cpp b/oneflow/core/framework/op_builder.cpp index ab6a8e2e982cefebb81d50d5a84d1c3860b38ea7..39cc92b0ea450f66dc146b1b1a1732b0ad97e651 100644 --- a/oneflow/core/framework/op_builder.cpp +++ b/oneflow/core/framework/op_builder.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/common/protobuf.h" +#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/framework/attr_value_accessor.h" #include "oneflow/core/framework/id_util.h" diff --git a/oneflow/core/framework/op_definition.h b/oneflow/core/framework/op_definition.h new file mode 100644 index 0000000000000000000000000000000000000000..94b00942f3fcf5d8ed82e364c3799fc862994374 --- /dev/null +++ b/oneflow/core/framework/op_definition.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_OP_DEFINITION_H_ +#define ONEFLOW_CORE_FRAMEWORK_OP_DEFINITION_H_ +#include + +#include "oneflow/core/common/hash_container.h" +#include "oneflow/core/common/maybe.h" + +namespace oneflow { + +namespace user_op { +class AttrVal; +} // namespace user_op +using AttrVal = user_op::AttrVal; + +class OpDefinitionBase { + public: + virtual ~OpDefinitionBase() = default; + virtual Maybe Attr(const std::string& attr_name) const = 0; + virtual const HashSet& AttributeNames() const = 0; + + protected: + OpDefinitionBase() = default; +}; + +template +class OpDefinition : public OpDefinitionBase { + public: + virtual ~OpDefinition() = default; + const HashSet& AttributeNames() const override { return Derived::AttrNames(); } + + protected: + OpDefinition() : OpDefinitionBase() {} +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_OP_DEFINITION_H_ diff --git a/oneflow/core/framework/op_expr.cpp b/oneflow/core/framework/op_expr.cpp index b914f4f0d6bacc3a030c3baec5a5e17c3efe95bc..b105a27ce0e6cc3afaa32f7d79a196aa60594cb4 100644 --- a/oneflow/core/framework/op_expr.cpp +++ b/oneflow/core/framework/op_expr.cpp @@ -22,13 +22,19 @@ limitations under the License. #include "oneflow/core/framework/op_expr_grad_function.h" #include "oneflow/core/framework/op_interpreter/dispatch_frame.h" #include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/framework/consistent_tensor_infer_cache.h" +#include "oneflow/core/framework/local_tensor_infer_cache.h" +#include "oneflow/core/framework/global_tensor_infer_cache.h" #include "oneflow/core/operator/op_conf.pb.h" #include "oneflow/user/kernels/stateful_opkernel.h" namespace oneflow { namespace one { +Maybe OpExpr::GetOrCreateAutoCastMeta() const { + static auto autocast_meta = std::make_shared(); + return autocast_meta; +} + BuiltinOpExpr::BuiltinOpExpr(const std::string& op_name, const std::vector& indexed_ibns, const std::vector& indexed_obns) @@ -36,77 +42,59 @@ BuiltinOpExpr::BuiltinOpExpr(const std::string& op_name, input_arg_tuple_(new ArgTuple(indexed_ibns)), output_arg_tuple_(new ArgTuple(indexed_obns)) {} -#define DEFINE_OPEXPR_OP_TYPE_NAME(_T, _op_type_name) \ - template<> \ - const std::string& BuiltinOpExprImpl<_T>::op_type_name() const { \ - static const std::string& name(_op_type_name); \ - return name; \ - } - -DEFINE_OPEXPR_OP_TYPE_NAME(FeedInputOpConf, "feed_input"); -DEFINE_OPEXPR_OP_TYPE_NAME(FeedVariableOpConf, "feed_variable"); -DEFINE_OPEXPR_OP_TYPE_NAME(FetchOutputOpConf, "fetch_output"); -DEFINE_OPEXPR_OP_TYPE_NAME(ImageDecoderRandomCropResizeOpConf, "image_gpu_decode"); -DEFINE_OPEXPR_OP_TYPE_NAME(VariableOpConf, "variable"); -DEFINE_OPEXPR_OP_TYPE_NAME(CastToMirroredOpConf, "cast_to_mirrored"); -DEFINE_OPEXPR_OP_TYPE_NAME(CastFromMirroredOpConf, "cast_from_mirrored"); -DEFINE_OPEXPR_OP_TYPE_NAME(DistributeSplitOpConf, "distribute_split"); -DEFINE_OPEXPR_OP_TYPE_NAME(DistributeCloneOpConf, "distribute_clone"); -DEFINE_OPEXPR_OP_TYPE_NAME(DistributeConcatOpConf, "distribute_concat"); -DEFINE_OPEXPR_OP_TYPE_NAME(DistributeAddOpConf, "distribute_add"); - -#undef DEFINE_OPEXPR_OP_TYPE_NAME +#define DEFINE_BUILTIN_OPEXPR_OP(T, op_type, disable_grad, support_non_contiguous) \ + template<> \ + const std::string& BuiltinOpExprImpl::op_type_name() const { \ + static const std::string& name(op_type); \ + return name; \ + } \ + template<> \ + Maybe BuiltinOpExprImpl::IsGradDisabled() const { \ + return disable_grad; \ + } \ + template<> \ + Maybe BuiltinOpExprImpl::SupportNonContiguous() const { \ + return support_non_contiguous; \ + } \ + template<> \ + Maybe BuiltinOpExprImpl::GetOrCreateAutoCastMeta() const { \ + return OpExpr::GetOrCreateAutoCastMeta(); \ + } + +DEFINE_BUILTIN_OPEXPR_OP(FeedInputOpConf, "feed_input", false, false); +DEFINE_BUILTIN_OPEXPR_OP(FeedVariableOpConf, "feed_variable", false, false); +DEFINE_BUILTIN_OPEXPR_OP(FetchOutputOpConf, "fetch_output", false, false); +DEFINE_BUILTIN_OPEXPR_OP(ImageDecoderRandomCropResizeOpConf, "image_gpu_decode", true, false); +DEFINE_BUILTIN_OPEXPR_OP(VariableOpConf, "variable", true, false); +DEFINE_BUILTIN_OPEXPR_OP(CastToLocalOpConf, "cast_to_local", false, false); +DEFINE_BUILTIN_OPEXPR_OP(CastFromLocalOpConf, "cast_from_local", false, false); +DEFINE_BUILTIN_OPEXPR_OP(DistributeSplitOpConf, "distribute_split", false, false); +DEFINE_BUILTIN_OPEXPR_OP(DistributeCloneOpConf, "distribute_clone", false, false); +DEFINE_BUILTIN_OPEXPR_OP(DistributeConcatOpConf, "distribute_concat", false, false); +DEFINE_BUILTIN_OPEXPR_OP(DistributeAddOpConf, "distribute_add", false, false); + +#undef DEFINE_BUILTIN_OPEXPR_OP template<> const std::string& BuiltinOpExprImpl::op_type_name() const { return op_proto_.op_type_name(); } -const std::string& ConsistentToConsistentOpExpr::op_type_name() const { - static const std::string kOpTypeName = "consistent_to_consistent"; +const std::string& GlobalToGlobalOpExpr::op_type_name() const { + static const std::string kOpTypeName = "global_to_global"; return kOpTypeName; } -const std::string& CastToConsistentOpExpr::op_type_name() const { - static const std::string kOpTypeName = "cast_to_consistent"; +const std::string& LocalToGlobalOpExpr::op_type_name() const { + static const std::string kOpTypeName = "local_to_global"; return kOpTypeName; } -const std::string& CastFromConsistentOpExpr::op_type_name() const { - static const std::string kOpTypeName = "cast_from_consistent"; +const std::string& GlobalToLocalOpExpr::op_type_name() const { + static const std::string kOpTypeName = "global_to_local"; return kOpTypeName; } -#define DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(_T, flag) \ - template<> \ - Maybe BuiltinOpExprImpl<_T>::IsGradDisabled() const { \ - return flag; \ - } \ - template<> \ - Maybe BuiltinOpExprImpl<_T>::SupportNonContiguous() const { \ - return false; \ - } - -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(FeedInputOpConf, true); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(FeedVariableOpConf, true); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(FetchOutputOpConf, true); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(VariableOpConf, true); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE( - ImageDecoderRandomCropResizeOpConf, true); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(CastToMirroredOpConf, - false); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(CastFromMirroredOpConf, - false); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(DistributeSplitOpConf, - false); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(DistributeCloneOpConf, - false); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(DistributeConcatOpConf, - false); -DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE(DistributeAddOpConf, false); - -#undef DEFINE_OPEXPR_IS_GRAD_DISABLED_AND_SUPPORT_NON_CONTIGUOUS_DEFAULT_VALUE - template<> Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_conf, const AttrMap& attrs) const { @@ -166,6 +154,15 @@ Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure return std::make_shared(op_grad_func_); } +template<> +Maybe BuiltinOpExprImpl::GetOrCreateAutoCastMeta() const { + if (!autocast_meta_) { + autocast_meta_ = + autocast::MakeAutoCastMeta(proto().op_type_name(), this->indexed_input_pairs()); + } + return autocast_meta_; +} + namespace { class UserOpExprInferContext : public user_op::InferContext { @@ -192,14 +189,18 @@ class UserOpExprInferContext : public user_op::InferContext { const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->TensorDesc4ArgNameAndIndex(arg_name, index); + return *TensorDesc4ArgNameAndIndex(arg_name, index); } - - user_op::TensorDesc* OutputTensorDesc(const std::string& name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(name, index); + const user_op::TensorDesc& OutputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return *TensorDesc4ArgNameAndIndex(arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(const std::string& name, int32_t index) override { + return MutTensorDesc4ArgNameAndIndex(name, index); } - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& name, int32_t index) { + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& name, + int32_t index) const { { const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); @@ -208,10 +209,31 @@ class UserOpExprInferContext : public user_op::InferContext { { const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + if (tuple_index >= 0) { return tensor_meta4input_index_(tuple_index); } + } + return nullptr; + } + + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(const std::string& name, int32_t index) { + { + const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); if (tuple_index >= 0) { - return const_cast(tensor_meta4input_index_(tuple_index)); + TensorMeta* tensor_meta_ptr = tensor_meta4output_index_(tuple_index); + CHECK_NOTNULL(dynamic_cast(tensor_meta_ptr)); + return tensor_meta_ptr; } } + { + const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + if (tuple_index >= 0) { + const TensorMeta* tensor_meta_ptr = tensor_meta4input_index_(tuple_index); + CHECK_NOTNULL(dynamic_cast(tensor_meta_ptr)); + return const_cast(tensor_meta_ptr); + } + } + PRINT_BUG_PROMPT_AND_ABORT(); return nullptr; } @@ -222,15 +244,29 @@ class UserOpExprInferContext : public user_op::InferContext { return tensor_meta4input_index_(tuple_index)->shape(); } - Shape* OutputShape(const std::string& name, int32_t index) override { + const Shape& OutputShape(const std::string& name, int32_t index) const override { const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); CHECK_GE(tuple_index, 0); - return tensor_meta4output_index_(tuple_index)->mut_shape(); + return tensor_meta4input_index_(tuple_index)->shape(); } - Shape* Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_shape(); + void SetOutputShape(const std::string& name, int32_t index, const Shape& shape) override { + const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0); + TensorMeta* tensor_meta_ptr = tensor_meta4output_index_(tuple_index); + CHECK_NOTNULL(dynamic_cast(tensor_meta_ptr)); + return tensor_meta_ptr->set_shape(shape); + } + + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->shape(); + } + + void SetShape4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Shape& shape) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_shape(shape); } const Stride& InputStride(const std::string& name, int32_t index) const override { @@ -240,34 +276,62 @@ class UserOpExprInferContext : public user_op::InferContext { return tensor_meta4input_index_(tuple_index)->stride(); } - Stride* OutputStride(const std::string& name, int32_t index) override { + const Stride& OutputStride(const std::string& name, int32_t index) const override { + const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); + int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); + CHECK_GE(tuple_index, 0); + return tensor_meta4output_index_(tuple_index)->stride(); + } + + void SetOutputStride(const std::string& name, int32_t index, const Stride& stride) override { const auto& arg_tuple = *user_op_expr_->output_arg_tuple(); int32_t tuple_index = arg_tuple.TensorTupleIndex4ArgNameAndIndex(name, index); CHECK_GE(tuple_index, 0); - return tensor_meta4output_index_(tuple_index)->mut_stride(); + TensorMeta* tensor_meta_ptr = tensor_meta4output_index_(tuple_index); + CHECK_NOTNULL(dynamic_cast(tensor_meta_ptr)); + return tensor_meta_ptr->set_stride(stride); } - Stride* Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_stride(); + const Stride& Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->stride(); } - const DataType& InputDType(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Dtype4ArgNameAndIndex(arg_name, index); + void SetStride4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Stride& stride) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_stride(stride); } - DataType* OutputDType(const std::string& arg_name, int32_t index) override { + + DataType InputDType(const std::string& arg_name, int32_t index) const override { return Dtype4ArgNameAndIndex(arg_name, index); } - DataType* Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_data_type(); + DataType OutputDType(const std::string& arg_name, int32_t index) const override { + return Dtype4ArgNameAndIndex(arg_name, index); + } + void SetOutputDType(const std::string& arg_name, int32_t index, DataType data_type) override { + return SetDtype4ArgNameAndIndex(arg_name, index, data_type); + } + DataType Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->data_type(); + } + void SetDtype4ArgNameAndIndex(const std::string& arg_name, int32_t index, + DataType data_type) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_data_type(data_type); } bool InputIsDynamic(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->IsDynamic4ArgNameAndIndex(arg_name, index); + return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* OutputIsDynamic(const std::string& arg_name, int32_t index) override { + bool OutputIsDynamic(const std::string& arg_name, int32_t index) const override { return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_is_dynamic(); + void SetOutputIsDynamic(const std::string& arg_name, int32_t index, bool is_dynamic) override { + return SetIsDynamic4ArgNameAndIndex(arg_name, index, is_dynamic); + } + bool IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->is_dynamic(); + } + void SetIsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index, + bool is_dynamic) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_is_dynamic(is_dynamic); } const std::string& input(const std::string& arg_name, int32_t index) const override { const auto& arg_tuple = *user_op_expr_->input_arg_tuple(); @@ -315,34 +379,55 @@ class UserOpExprInferContext : public user_op::InferContext { std::string loc_; }; +namespace { + +Symbol Get1DBroadcastNdSbp() { + NdSbp broadcast_nd_sbp; + broadcast_nd_sbp.mutable_sbp_parallel()->Add()->mutable_broadcast_parallel(); + return SymbolOf(broadcast_nd_sbp); +} + +auto* CachedGet1DBroadcastNdSbp = DECORATE(&Get1DBroadcastNdSbp, ThreadLocalCached); + +} // namespace + class UserOpExprPhysicalInferContext final : public UserOpExprInferContext { public: - using UserOpExprInferContext::UserOpExprInferContext; + UserOpExprPhysicalInferContext( + const UserOpExpr* user_op_expr, const AttrMap& attrs, const std::string& device_tag, + const std::function& TensorMeta4InputIndex, + const std::function& TensorMeta4OutputIndex) + : UserOpExprInferContext(user_op_expr, attrs, device_tag, TensorMeta4InputIndex, + TensorMeta4OutputIndex), + parallel_desc_(CHECK_JUST(GetParallelDescOfThisRank(device_tag))) { + parallel_ctx_.set_parallel_id(0); + parallel_ctx_.set_parallel_num(1); + } ~UserOpExprPhysicalInferContext() override = default; const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& name, int32_t index) const override { - UNIMPLEMENTED(); + PRINT_BUG_PROMPT_AND_ABORT(); return nullptr; } - const ParallelContext& parallel_ctx() const override { - UNIMPLEMENTED(); - return *(const ParallelContext*)nullptr; - } - const ParallelDesc& parallel_desc() const override { - UNIMPLEMENTED(); - return *(const ParallelDesc*)nullptr; - } - const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string&, int32_t) const override { - UNIMPLEMENTED(); - return *(const SbpParallel*)nullptr; + const ParallelContext& parallel_ctx() const override { return parallel_ctx_; } + const ParallelDesc& parallel_desc() const override { return *parallel_desc_; } + const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& name, + int32_t index) const override { + CHECK_NOTNULL(TensorDesc4ArgNameAndIndex(name, index)); + return CachedGet1DBroadcastNdSbp()->sbp_parallel(0); } - const NdSbp& NdSbp4ArgNameAndIndex(const std::string&, int32_t) const override { - UNIMPLEMENTED(); - return *(const NdSbp*)nullptr; + const NdSbp& NdSbp4ArgNameAndIndex(const std::string& name, int32_t index) const override { + CHECK_NOTNULL(TensorDesc4ArgNameAndIndex(name, index)); + return *(CachedGet1DBroadcastNdSbp()); } int64_t parallel_num() const override { return 1; } + + private: + // these member vars just used for physical infer + Symbol parallel_desc_; + ParallelContext parallel_ctx_; }; class UserOpExprLogicalInferContext final : public UserOpExprInferContext { @@ -366,24 +451,23 @@ class UserOpExprLogicalInferContext final : public UserOpExprInferContext { const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& name, int32_t index) const override { - UNIMPLEMENTED(); + PRINT_BUG_PROMPT_AND_ABORT(); + return nullptr; } const ParallelContext& parallel_ctx() const override { return parallel_ctx_; } const ParallelDesc& parallel_desc() const override { return *parallel_desc_; } const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& name, int32_t index) const override { - auto* tensor_meta = dynamic_cast( - const_cast(this)->TensorDesc4ArgNameAndIndex(name, index)); - CHECK_NOTNULL(tensor_meta); + const GlobalTensorMeta* tensor_meta = + dynamic_cast(TensorDesc4ArgNameAndIndex(name, index)); Symbol nd_sbp = tensor_meta->nd_sbp(); CHECK_EQ(nd_sbp->sbp_parallel_size(), 1); return nd_sbp->sbp_parallel(0); } const NdSbp& NdSbp4ArgNameAndIndex(const std::string& name, int32_t index) const override { - auto* tensor_meta = dynamic_cast( - const_cast(this)->TensorDesc4ArgNameAndIndex(name, index)); - CHECK_NOTNULL(tensor_meta); + const GlobalTensorMeta* tensor_meta = + dynamic_cast(TensorDesc4ArgNameAndIndex(name, index)); return *tensor_meta->nd_sbp(); } int64_t parallel_num() const override { return parallel_desc_->parallel_num(); } @@ -450,8 +534,11 @@ Maybe UserOpExpr::Init(const std::shared_ptr& self) { const auto* registry = user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_proto_.op_type_name()); CHECK_NOTNULL_OR_RETURN(registry); - tensor_desc_infer_fn_ = registry->logical_tensor_desc_infer_fn; - CHECK_OR_RETURN(static_cast(tensor_desc_infer_fn_)) + logical_tensor_desc_infer_fn_ = registry->logical_tensor_desc_infer_fn; + CHECK_OR_RETURN(static_cast(logical_tensor_desc_infer_fn_)) + << Error::RuntimeError() << "registry->logical_tensor_desc_infer_fn failed."; + physical_tensor_desc_infer_fn_ = registry->physical_tensor_desc_infer_fn; + CHECK_OR_RETURN(static_cast(physical_tensor_desc_infer_fn_)) << Error::RuntimeError() << "registry->logical_tensor_desc_infer_fn failed."; dtype_infer_fn_ = registry->data_type_infer_fn; CHECK_OR_RETURN(static_cast(dtype_infer_fn_)) @@ -459,7 +546,8 @@ Maybe UserOpExpr::Init(const std::shared_ptr& self) { if (registry->device_and_stream_infer_fn) { device_and_stream_infer_fn_ = registry->device_and_stream_infer_fn; } - consistent_tensor_infer_cache_.reset(new ConsistentTensorInferCache(self)); + local_tensor_infer_cache_.reset(new LocalTensorInferCache(self)); + global_tensor_infer_cache_.reset(new GlobalTensorInferCache(self)); return Maybe::Ok(); } @@ -480,7 +568,7 @@ Maybe UserOpExpr::InferPhysicalTensorDesc( const std::function& TensorMeta4OutputIndex) const { UserOpExprPhysicalInferContext infer_ctx(this, attrs, device_tag, TensorMeta4InputIndex, TensorMeta4OutputIndex); - JUST(tensor_desc_infer_fn_(&infer_ctx)); + JUST(physical_tensor_desc_infer_fn_(&infer_ctx)); JUST(dtype_infer_fn_(&infer_ctx)); return Maybe::Ok(); } @@ -491,7 +579,7 @@ Maybe UserOpExpr::InferLogicalTensorDesc( const std::function& TensorMeta4OutputIndex) const { UserOpExprLogicalInferContext infer_ctx(this, attrs, parallel_desc, TensorMeta4InputIndex, TensorMeta4OutputIndex); - JUST(tensor_desc_infer_fn_(&infer_ctx)); + JUST(logical_tensor_desc_infer_fn_(&infer_ctx)); JUST(dtype_infer_fn_(&infer_ctx)); return Maybe::Ok(); } @@ -505,31 +593,27 @@ Maybe> UserOpExpr::InferDeviceAndStream(const AttrMap& attrs, return TRY(device_and_stream_infer_fn_(&device_infer_ctx)); } -ConsistentToConsistentOpExpr::ConsistentToConsistentOpExpr( - const Optional>& grad_nd_sbp) +GlobalToGlobalOpExpr::GlobalToGlobalOpExpr(const Optional>& grad_nd_sbp) : grad_nd_sbp_(grad_nd_sbp) {} -/* static */ Maybe ConsistentToConsistentOpExpr::New( +/* static */ Maybe GlobalToGlobalOpExpr::New( const Optional>& grad_nd_sbp) { - auto* ptr = new ConsistentToConsistentOpExpr(grad_nd_sbp); - return std::shared_ptr(ptr); + auto* ptr = new GlobalToGlobalOpExpr(grad_nd_sbp); + return std::shared_ptr(ptr); } -CastConsistentOpExpr::CastConsistentOpExpr(const std::string& op_name) : op_name_(op_name) {} +CastGlobalOpExpr::CastGlobalOpExpr(const std::string& op_name) : op_name_(op_name) {} -CastToConsistentOpExpr::CastToConsistentOpExpr(const std::string& op_name) - : CastConsistentOpExpr(op_name) {} +LocalToGlobalOpExpr::LocalToGlobalOpExpr(const std::string& op_name) : CastGlobalOpExpr(op_name) {} -/* static */ Maybe CastToConsistentOpExpr::New(const std::string& op_name) { - return std::shared_ptr(new CastToConsistentOpExpr(op_name)); +/* static */ Maybe LocalToGlobalOpExpr::New(const std::string& op_name) { + return std::shared_ptr(new LocalToGlobalOpExpr(op_name)); } -CastFromConsistentOpExpr::CastFromConsistentOpExpr(const std::string& op_name) - : CastConsistentOpExpr(op_name) {} +GlobalToLocalOpExpr::GlobalToLocalOpExpr(const std::string& op_name) : CastGlobalOpExpr(op_name) {} -/* static */ Maybe CastFromConsistentOpExpr::New( - const std::string& op_name) { - return std::shared_ptr(new CastFromConsistentOpExpr(op_name)); +/* static */ Maybe GlobalToLocalOpExpr::New(const std::string& op_name) { + return std::shared_ptr(new GlobalToLocalOpExpr(op_name)); } template<> @@ -544,7 +628,12 @@ Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_con template<> Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { - UNIMPLEMENTED_THEN_RETURN(); + if (!op_grad_func_.get()) { + op_grad_func_.reset(NewObj("graph_feed_and_fetch")); + CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); // NOLINT + JUST(op_grad_func_->Init(*this)); + } + return std::make_shared(op_grad_func_); } template<> @@ -558,7 +647,12 @@ Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_ template<> Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { - UNIMPLEMENTED_THEN_RETURN(); + if (!op_grad_func_.get()) { + op_grad_func_.reset(NewObj("graph_feed_and_fetch")); + CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); // NOLINT + JUST(op_grad_func_->Init(*this)); + } + return std::make_shared(op_grad_func_); } template<> @@ -573,7 +667,12 @@ Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_c template<> Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { - UNIMPLEMENTED_THEN_RETURN(); + if (!op_grad_func_.get()) { + op_grad_func_.reset(NewObj("graph_feed_and_fetch")); + CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); // NOLINT + JUST(op_grad_func_->Init(*this)); + } + return std::make_shared(op_grad_func_); } template<> @@ -619,57 +718,56 @@ Maybe BuiltinOpExprImpl::GetOrCreateOpGradClo } template<> -Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_conf, - const AttrMap& attrs) const { +Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_conf, + const AttrMap& attrs) const { CHECK_EQ_OR_RETURN(attrs.size(), 0); *(op_conf->mutable_name()) = op_name_; - *(op_conf->mutable_cast_to_mirrored_conf()) = op_proto_; + *(op_conf->mutable_cast_to_local_conf()) = op_proto_; *(op_conf->mutable_loc()) = DispatchFrame::get_str(); return Maybe::Ok(); } template<> -Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { +Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { UNIMPLEMENTED_THEN_RETURN(); } template<> -Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_conf, - const AttrMap& attrs) const { +Maybe BuiltinOpExprImpl::BuildOpConf(OperatorConf* op_conf, + const AttrMap& attrs) const { CHECK_EQ_OR_RETURN(attrs.size(), 0); *(op_conf->mutable_name()) = op_name_; - *(op_conf->mutable_cast_from_mirrored_conf()) = op_proto_; + *(op_conf->mutable_cast_from_local_conf()) = op_proto_; *(op_conf->mutable_loc()) = DispatchFrame::get_str(); return Maybe::Ok(); } template<> -Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() - const { +Maybe BuiltinOpExprImpl::GetOrCreateOpGradClosure() const { UNIMPLEMENTED_THEN_RETURN(); } -Maybe ConsistentToConsistentOpExpr::GetOrCreateOpGradClosure() const { +Maybe GlobalToGlobalOpExpr::GetOrCreateOpGradClosure() const { if (!op_grad_func_.get()) { - op_grad_func_.reset(NewObj("consistent_to_consistent")); + op_grad_func_.reset(NewObj("global_to_global")); CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); JUST(op_grad_func_->Init(*this)); } return std::make_shared(op_grad_func_); } -Maybe CastToConsistentOpExpr::GetOrCreateOpGradClosure() const { +Maybe LocalToGlobalOpExpr::GetOrCreateOpGradClosure() const { if (!op_grad_func_.get()) { - op_grad_func_.reset(NewObj("cast_to_consistent")); + op_grad_func_.reset(NewObj("local_to_global")); CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); JUST(op_grad_func_->Init(*this)); } return std::make_shared(op_grad_func_); } -Maybe CastFromConsistentOpExpr::GetOrCreateOpGradClosure() const { +Maybe GlobalToLocalOpExpr::GetOrCreateOpGradClosure() const { if (!op_grad_func_.get()) { - op_grad_func_.reset(NewObj("cast_from_consistent")); + op_grad_func_.reset(NewObj("global_to_local")); CHECK_NOTNULL_OR_RETURN(op_grad_func_.get()); JUST(op_grad_func_->Init(*this)); } @@ -751,7 +849,9 @@ Maybe SelectTopNOpExpr::GetOrCreateOpGradClosure() const { void FunctionOpExpr::reset_state() const { state_.reset(new FunctionAutoGradCaptureState); } Maybe FunctionOpExpr::GetOrCreateOpGradClosure() const { - if (!op_grad_func_) { op_grad_func_.reset(new FunctionOpExprGradFunction(backward_fn_)); } + if (!op_grad_func_) { + op_grad_func_.reset(new FunctionOpExprGradFunction(func_name_, backward_fn_)); + } return std::make_shared(op_grad_func_, state_); } diff --git a/oneflow/core/framework/op_expr.h b/oneflow/core/framework/op_expr.h index 3806724c408eea95b644f3c747e44dd786684492..fd0450a8f529cf704a32b23e38e23b459610077e 100644 --- a/oneflow/core/framework/op_expr.h +++ b/oneflow/core/framework/op_expr.h @@ -23,6 +23,7 @@ limitations under the License. #include "oneflow/core/job/sbp_parallel.h" #include "oneflow/core/operator/op_conf.pb.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/autocast.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/tensor_tuple.h" @@ -52,6 +53,8 @@ class OpExpr { virtual Maybe GetOrCreateOpGradClosure() const = 0; + virtual Maybe GetOrCreateAutoCastMeta() const; + protected: OpExpr() = default; }; @@ -113,6 +116,8 @@ class BuiltinOpExprImpl : public BuiltinOpExpr { Maybe GetOrCreateOpGradClosure() const override; + Maybe GetOrCreateAutoCastMeta() const override; + Maybe BuildOpConf(OperatorConf* op_conf, const AttrMap& attrs) const override; protected: @@ -123,10 +128,12 @@ class BuiltinOpExprImpl : public BuiltinOpExpr { ProtoType op_proto_; mutable std::shared_ptr op_grad_func_; + mutable std::shared_ptr autocast_meta_; }; class StatefulOpKernel; -class ConsistentTensorInferCache; +class LocalTensorInferCache; +class GlobalTensorInferCache; class UserOpExpr final : public BuiltinOpExprImpl { public: @@ -159,8 +166,11 @@ class UserOpExpr final : public BuiltinOpExprImpl { const std::function& TensorMeta4OutputIndex) const; Maybe> InferDeviceAndStream(const AttrMap& attrs, const TensorTuple& inputs, TensorTuple* outputs) const; - ConsistentTensorInferCache* mut_consistent_tensor_infer_cache() const { - return consistent_tensor_infer_cache_.get(); + LocalTensorInferCache* mut_local_tensor_infer_cache() const { + return local_tensor_infer_cache_.get(); + } + GlobalTensorInferCache* mut_global_tensor_infer_cache() const { + return global_tensor_infer_cache_.get(); } private: @@ -169,18 +179,20 @@ class UserOpExpr final : public BuiltinOpExprImpl { const std::vector& indexed_obns); Maybe Init(const std::shared_ptr& self); AttrMap base_attrs_; - user_op::TensorDescInferFn tensor_desc_infer_fn_; + user_op::TensorDescInferFn logical_tensor_desc_infer_fn_; + user_op::TensorDescInferFn physical_tensor_desc_infer_fn_; user_op::DataTypeInferFn dtype_infer_fn_; user_op::DeviceAndStreamInferFn device_and_stream_infer_fn_; mutable HashMap, std::shared_ptr> stream2kernel_; - std::shared_ptr consistent_tensor_infer_cache_; + std::shared_ptr local_tensor_infer_cache_; + std::shared_ptr global_tensor_infer_cache_; }; -class ConsistentToConsistentOpExpr : public OpExpr { +class GlobalToGlobalOpExpr : public OpExpr { public: - virtual ~ConsistentToConsistentOpExpr() = default; + virtual ~GlobalToGlobalOpExpr() = default; - static Maybe New(const Optional>& grad_nd_sbp); + static Maybe New(const Optional>& grad_nd_sbp); const Optional>& grad_nd_sbp() const { return grad_nd_sbp_; } const std::string& op_type_name() const override; @@ -192,15 +204,15 @@ class ConsistentToConsistentOpExpr : public OpExpr { Maybe GetOrCreateOpGradClosure() const override; protected: - ConsistentToConsistentOpExpr(const Optional>& grad_nd_sbp); + GlobalToGlobalOpExpr(const Optional>& grad_nd_sbp); Optional> grad_nd_sbp_; // Reserved for configuring grad sbp mutable std::shared_ptr op_grad_func_; }; -class CastConsistentOpExpr : public OpExpr { +class CastGlobalOpExpr : public OpExpr { public: - virtual ~CastConsistentOpExpr() = default; + virtual ~CastGlobalOpExpr() = default; const std::string& op_name() const { return op_name_; } int input_size() const override { return 1; } @@ -210,36 +222,36 @@ class CastConsistentOpExpr : public OpExpr { Maybe SupportNonContiguous() const override { return false; } protected: - CastConsistentOpExpr(const std::string& op_name); + CastGlobalOpExpr(const std::string& op_name); std::string op_name_; mutable std::shared_ptr op_grad_func_; }; -class CastToConsistentOpExpr final : public CastConsistentOpExpr { +class LocalToGlobalOpExpr final : public CastGlobalOpExpr { public: - ~CastToConsistentOpExpr() = default; + ~LocalToGlobalOpExpr() = default; - static Maybe New(const std::string& op_name); + static Maybe New(const std::string& op_name); const std::string& op_type_name() const override; Maybe GetOrCreateOpGradClosure() const override; private: - CastToConsistentOpExpr(const std::string& op_name); + LocalToGlobalOpExpr(const std::string& op_name); }; -class CastFromConsistentOpExpr final : public CastConsistentOpExpr { +class GlobalToLocalOpExpr final : public CastGlobalOpExpr { public: - ~CastFromConsistentOpExpr() = default; + ~GlobalToLocalOpExpr() = default; - static Maybe New(const std::string& op_name); + static Maybe New(const std::string& op_name); const std::string& op_type_name() const override; Maybe GetOrCreateOpGradClosure() const override; private: - CastFromConsistentOpExpr(const std::string& op_name); + GlobalToLocalOpExpr(const std::string& op_name); }; // NOTE(chengcheng): For Lazy nn.Graph Feed/Fetch EagerTensor to/from LazyTensor. @@ -251,8 +263,8 @@ using FetchOutputOpExpr = BuiltinOpExprImpl; using ImageDecoderRandomCropResizeOpExpr = BuiltinOpExprImpl; using VariableOpExpr = BuiltinOpExprImpl; -using CastToMirroredOpExpr = BuiltinOpExprImpl; -using CastFromMirroredOpExpr = BuiltinOpExprImpl; +using CastToLocalOpExpr = BuiltinOpExprImpl; +using CastFromLocalOpExpr = BuiltinOpExprImpl; using DistributeSplitOpExpr = BuiltinOpExprImpl; using DistributeCloneOpExpr = BuiltinOpExprImpl; using DistributeConcatOpExpr = BuiltinOpExprImpl; diff --git a/oneflow/core/framework/op_expr_grad_function.cpp b/oneflow/core/framework/op_expr_grad_function.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d4a2ea36e61cf7073349503f0e244e094623f4e3 --- /dev/null +++ b/oneflow/core/framework/op_expr_grad_function.cpp @@ -0,0 +1,50 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/op_expr_grad_function.h" + +#include "oneflow/core/framework/saved_tensor_hooks.h" + +namespace oneflow { +namespace one { + +void AutoGradCaptureState::unpack() { + if (saved_tensors_.empty() && !hooks_.empty()) { + for (const auto& hook : hooks_) { saved_tensors_.push_back(hook->unpack()); } + hooks_.clear(); + } +} + +size_t AutoGradCaptureState::SaveTensorForBackward(const std::shared_ptr& tensor) { + auto hook = []() -> std::unique_ptr { + if (auto* hook_creator = Singleton::Get()) { + return hook_creator->new_saved_tensor_hook(); + } + return nullptr; + }(); + if (hook) { + hook->pack(tensor); + size_t offset = hooks_.size(); + hooks_.push_back(std::move(hook)); + return offset; + } else { + size_t offset = saved_tensors_.size(); + saved_tensors_.emplace_back(tensor); + return offset; + } +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/op_expr_grad_function.h b/oneflow/core/framework/op_expr_grad_function.h index 02dacf23ebcf1af76a5c6afd5ef8cf65edfad162..3e23b7b81053e4243cb9d9b786051f9b9b341ccb 100644 --- a/oneflow/core/framework/op_expr_grad_function.h +++ b/oneflow/core/framework/op_expr_grad_function.h @@ -19,7 +19,10 @@ limitations under the License. #include "oneflow/core/autograd/autograd_captured_tensor.h" #include "oneflow/core/common/auto_registration_factory.h" +#include "oneflow/core/common/op_args_vector.h" #include "oneflow/core/framework/op_interpreter.h" +#include "oneflow/core/profiler/profiler.h" +#include "oneflow/core/framework/saved_tensor_hooks.h" namespace oneflow { namespace one { @@ -31,23 +34,25 @@ class AutoGradCaptureState { AutoGradCaptureState() = default; virtual ~AutoGradCaptureState() = default; + void unpack(); + const TensorTuple& SavedTensors() const { return saved_tensors_; } - size_t SaveTensorForBackward(const std::shared_ptr& tensor) { - size_t offset = saved_tensors_.size(); - saved_tensors_.emplace_back(tensor); - return offset; - } + size_t SaveTensorForBackward(const std::shared_ptr& tensor); + + public: + std::vector input_requires_grad; protected: TensorTuple saved_tensors_; + small_vector, TensorTuple::kInitialSize> hooks_; }; class FunctionAutoGradCaptureState final : public AutoGradCaptureState, public std::enable_shared_from_this { public: - FunctionAutoGradCaptureState() = default; + FunctionAutoGradCaptureState() : pyobj_ptr_(nullptr, [](void*) {}) {} using AutoGradCaptureState::SavedTensors; using AutoGradCaptureState::SaveTensorForBackward; @@ -59,8 +64,19 @@ class FunctionAutoGradCaptureState final std::shared_ptr GetSharedFromThis() { return shared_from_this(); } + // NOTE(wyg): Hold PyOjbect ptr to ensure getting the same object when casting to python. + // And decrease the reference count when C++ object is destructed to avoid memory leaking. + void* pyobject() const { return pyobj_ptr_.get(); } + void set_pyobject_ptr(std::unique_ptr&& pyobj_ptr) { + pyobj_ptr_ = std::move(pyobj_ptr); + } + + public: + std::vector input_requires_grad; + private: HashSet non_differentiable_tensors_; + std::unique_ptr pyobj_ptr_; }; // Stateless container base of the backward op exprs. @@ -96,14 +112,19 @@ class OpExprGradFunction : public OpExprGradFunctionIf { CHECK_NOTNULL_OR_RETURN(state); // Convert outputs from `Tensor` to `AutogradCapturedTensor` to avoid // circular reference between `Tensor` and `FunctionNode`. + OF_PROFILER_RANGE_PUSH("init inputs"); TensorTuple captured_inputs(inputs.size()); for (int i = 0; i < inputs.size(); ++i) { captured_inputs[i] = JUST(AutogradCapturedTensor::MakeTensor(inputs.at(i))); } + OF_PROFILER_RANGE_POP(); + OF_PROFILER_RANGE_PUSH("init outputs"); TensorTuple captured_outputs(outputs.size()); for (int i = 0; i < outputs.size(); ++i) { captured_outputs[i] = JUST(AutogradCapturedTensor::MakeTensor(outputs.at(i))); } + OF_PROFILER_RANGE_POP(); + OF_PROFILER_RANGE_GUARD("Capture"); return Capture(state, captured_inputs, captured_outputs, interp_ctx); } @@ -136,7 +157,8 @@ class OpExprGradFunction : public OpExprGradFunctionIf { class FunctionOpExprGradFunction final : public OpExprGradFunctionIf { public: using FType = AutogradFunctionBase::FType; - explicit FunctionOpExprGradFunction(const FType& backward_fn) : backward_fn_(backward_fn) {} + FunctionOpExprGradFunction(const std::string& func_name, const FType& backward_fn) + : backward_fn_(backward_fn), op_name_(func_name) {} std::shared_ptr MakeCustomState() const override { PRINT_BUG_PROMPT_AND_ABORT() @@ -152,7 +174,11 @@ class FunctionOpExprGradFunction final : public OpExprGradFunctionIf { Maybe CaptureIf(AutoGradCaptureState* ctx, const TensorTuple& inputs, const TensorTuple& outputs, const OpExprInterpContext& interp_ctx) const override { - // do nothing + FunctionAutoGradCaptureState* func_ctx = dynamic_cast(ctx); + func_ctx->input_requires_grad.resize(inputs.size()); + for (int i = 0; i < inputs.size(); ++i) { + func_ctx->input_requires_grad[i] = inputs.at(i)->requires_grad(); + } return Maybe::Ok(); } @@ -163,12 +189,28 @@ class FunctionOpExprGradFunction final : public OpExprGradFunctionIf { CHECK_NOTNULL_OR_RETURN(func_ctx); const std::shared_ptr& out = backward_fn_( const_cast(func_ctx)->GetSharedFromThis(), out_grads); - in_grads->assign(out->begin(), out->end()); + in_grads->resize(func_ctx->input_requires_grad.size()); + CHECK_EQ_OR_RETURN(out->size(), in_grads->size()) + << "RuntimeError: function " << op_name_ + << " returned an incorrect number of gradients (expected " << in_grads->size() << ", got " + << out->size() << ")"; + for (int i = 0; i < in_grads->size(); ++i) { + if (func_ctx->input_requires_grad[i]) { + if (!out->at(i)) { + return Error::RuntimeError() + << "autograd.Function named " << op_name_ << "'s inputs[" << i + << "] requires grad but got None grad. Please use Tensor.detach() for this " + "input."; + } + in_grads->at(i) = out->at(i); + } + } return Maybe::Ok(); } protected: FType backward_fn_; + std::string op_name_; }; // Stateful wrapper of the `OpExprGradFunction`. @@ -176,7 +218,7 @@ class OpExprGradClosure { public: // Use `shared_ptr` in order to keep `impl` alive even if the forward op has been released. explicit OpExprGradClosure(const std::shared_ptr& impl) - : impl_(impl), state_(impl->MakeCustomState()) {} + : OpExprGradClosure(impl, impl->MakeCustomState()) {} explicit OpExprGradClosure(const std::shared_ptr& impl, const std::shared_ptr& state) : impl_(impl), state_(state) {} @@ -189,6 +231,7 @@ class OpExprGradClosure { } Maybe Apply(const TensorTuple& out_grads, TensorTuple* in_grads) const { + state_->unpack(); return impl_->ApplyIf(state_.get(), out_grads, in_grads); } diff --git a/oneflow/core/framework/op_interp_ctx.cpp b/oneflow/core/framework/op_interp_ctx.cpp deleted file mode 100644 index 8e950fbf78f28d95f1b6d467c0fbdab4d6e78abc..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_interp_ctx.cpp +++ /dev/null @@ -1,66 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/op_interp_ctx.h" -#include "oneflow/core/framework/attr_value.h" - -namespace oneflow { - -Maybe OpInterpCtx::GetAttr(const std::string& attr_name) const { - return op_->GetAttr(attr_name); -} - -template -Maybe OpInterpCtx::GetAttr(const std::string& attr_name) const { - const auto& attr_val = JUST(this->GetAttr(attr_name)); - if (const auto* ptr = dynamic_cast*>(attr_val.get())) { - return ptr->val(); - } - return Error::RuntimeError() << "Invalid type for attribute " << attr_name; -} - -OpAttrs OpInterpCtx::GetAttrs() const { return OpAttrs(this); } - -template -Maybe OpInterpCtx::SetAttr(const std::string& attr_name, const T& attr_val) { - *const_cast(&JUST(this->GetAttr(attr_name))) = attr_val; - return Maybe::Ok(); -} - -#define INSTANCE_ATTR_GETTER_AND_SETTER(field, T, attr_type) \ - template Maybe OpInterpCtx::GetAttr(const std::string& attr_name) const; \ - template Maybe OpInterpCtx::SetAttr(const std::string& attr_name, const T& attr_val); - -OF_PP_FOR_EACH_TUPLE(INSTANCE_ATTR_GETTER_AND_SETTER, ATTR_SEQ) -#undef INSTANCE_ATTR_GETTER_AND_SETTER - -Maybe OpInterpCtx::SetAttr(const std::string& attr_name, const AttrVal& attr_val) { -#define MAKE_ENTRY(field, cpp_type, attr_type) \ - if (const auto* ptr = dynamic_cast*>(&attr_val)) { \ - return this->SetAttr(attr_name, ptr->val()); \ - } - - OF_PP_FOR_EACH_TUPLE(MAKE_ENTRY, ATTR_SEQ); -#undef MAKE_ENTRY - return Error::RuntimeError() << "Invalid type for attribute " << attr_name; -} - -bool OpInterpCtx::HasAttr(const std::string& attr_name) const { - return AttrNames().count(attr_name) > 0; -} - -const HashSet& OpInterpCtx::AttrNames() const { return op_->AttrNames(); } - -} // namespace oneflow diff --git a/oneflow/core/framework/op_interp_ctx.h b/oneflow/core/framework/op_interp_ctx.h deleted file mode 100644 index f6944e7591c9a65f980e78266e2e5bb7e949bf81..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_interp_ctx.h +++ /dev/null @@ -1,73 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_OP_INTERP_CTX_H_ -#define ONEFLOW_CORE_FRAMEWORK_OP_INTERP_CTX_H_ - -#include - -#include "oneflow/core/common/hash_container.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/framework/attr_value.h" -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/framework/op_attrs.h" -#include "oneflow/core/framework/op_base.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/job/sbp_parallel.h" - -namespace oneflow { - -using user_op::AttrVal; -template -using TypedAttrValRef = user_op::TypedAttrValRef; - -namespace user_op { -class OpKernelState; -} // namespace user_op - -class OpInterpCtx { - public: - explicit OpInterpCtx(const std::shared_ptr& op) : op_(op) {} - virtual ~OpInterpCtx() = default; - - template - Maybe GetAttr(const std::string& attr_name) const; - - Maybe GetAttr(const std::string& attr_name) const; - - OpAttrs GetAttrs() const; - - template - Maybe SetAttr(const std::string& attr_name, const T& attr_val); - - Maybe SetAttr(const std::string& attr_name, const AttrVal& attr_val); - - bool HasAttr(const std::string& attr_name) const; - - const HashSet& AttrNames() const; - - public: - std::shared_ptr op_; - - Optional> device; // for local op - Optional> parallel_desc; // for consistent op - Optional> sbp; // for consistent op - Optional state; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_OP_INTERP_CTX_H_ diff --git a/oneflow/core/framework/op_interpreter.h b/oneflow/core/framework/op_interpreter.h index 250f3bd76ad9397571c769564c2dc74f907490e4..d56fd3ad8c277cd7231f118c6061f822bd6d03e3 100644 --- a/oneflow/core/framework/op_interpreter.h +++ b/oneflow/core/framework/op_interpreter.h @@ -33,8 +33,6 @@ namespace one { struct OpExprInterpContext { OpExprInterpContext(const AttrMap& attrs_arg) : attrs(attrs_arg) {} - OpExprInterpContext(const AttrMap& attrs_arg, const bool inplace) - : attrs(attrs_arg), inplace(inplace) {} OpExprInterpContext(const AttrMap& attrs_arg, Symbol device_arg) : attrs(attrs_arg), device(device_arg) {} OpExprInterpContext(const AttrMap& attrs_arg, std::shared_ptr state_arg) @@ -53,9 +51,8 @@ struct OpExprInterpContext { AttrMap attrs; Optional> device; // for local op - Optional> parallel_desc; // for consistent op - Optional> nd_sbp; // for consistent op - Optional inplace; // for inplace operation op + Optional> parallel_desc; // for global op + Optional> nd_sbp; // for global op std::shared_ptr state; }; @@ -81,11 +78,11 @@ class OpExprInterpreter { _macro(UserOp); \ _macro(SelectTopNOp); \ _macro(VariableOp); \ - _macro(CastToMirroredOp); \ - _macro(CastFromMirroredOp); \ - _macro(ConsistentToConsistentOp); \ - _macro(CastToConsistentOp); \ - _macro(CastFromConsistentOp); \ + _macro(CastToLocalOp); \ + _macro(CastFromLocalOp); \ + _macro(GlobalToGlobalOp); \ + _macro(LocalToGlobalOp); \ + _macro(GlobalToLocalOp); \ _macro(DistributeSplitOp); \ _macro(DistributeCloneOp); \ _macro(DistributeConcatOp); \ @@ -120,7 +117,7 @@ class LazyInterpreter : public OpExprInterpreter { DECLARE_NORMAL_APPLY_FUNC(FeedVariableOp); DECLARE_NORMAL_APPLY_FUNC(FetchOutputOp); DECLARE_NORMAL_APPLY_FUNC(FunctionOp); - DECLARE_NORMAL_APPLY_FUNC(ConsistentToConsistentOp); + DECLARE_NORMAL_APPLY_FUNC(GlobalToGlobalOp); DECLARE_NORMAL_APPLY_FUNC(ImageDecoderRandomCropResizeOp); }; @@ -142,19 +139,19 @@ class EagerInterpreter : public OpExprInterpreter { DECLARE_NORMAL_APPLY_FUNC(FunctionOp); }; -class EagerConsistentInterpreter : public EagerInterpreter { +class EagerGlobalInterpreter : public EagerInterpreter { public: - EagerConsistentInterpreter() : EagerInterpreter() {} - virtual ~EagerConsistentInterpreter() = default; + EagerGlobalInterpreter() : EagerInterpreter() {} + virtual ~EagerGlobalInterpreter() = default; private: FOR_EACH_BUILTIN_OPS(DECLARE_OVERRIDE_APPLY_FUNC); }; -class EagerMirroredInterpreter : public EagerInterpreter { +class EagerLocalInterpreter : public EagerInterpreter { public: - EagerMirroredInterpreter() : EagerInterpreter() {} - virtual ~EagerMirroredInterpreter() = default; + EagerLocalInterpreter() : EagerInterpreter() {} + virtual ~EagerLocalInterpreter() = default; private: FOR_EACH_BUILTIN_OPS(DECLARE_OVERRIDE_APPLY_FUNC); diff --git a/oneflow/core/framework/op_interpreter/eager_consistent_op_interpreter.cpp b/oneflow/core/framework/op_interpreter/eager_consistent_op_interpreter.cpp deleted file mode 100644 index 7e8b5f4b97bc06544bba76636bc67d111e014faf..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_interpreter/eager_consistent_op_interpreter.cpp +++ /dev/null @@ -1,319 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include -#include "oneflow/core/framework/to_string.h" -#include "oneflow/core/framework/op_interpreter.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/instructions_builder.h" -#include "oneflow/core/framework/scope_util.h" -#include "oneflow/core/framework/session_util.h" -#include "oneflow/core/framework/symbol_storage_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_name_scope.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/framework/consistent_tensor_infer_cache.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/autograd/autograd_mode.h" -#include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" -#include "oneflow/user/kernels/stateful_opkernel.h" -#include "oneflow/core/framework/consistency_check.h" -#include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/framework/tensor_consistent_id.h" -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/boxing/eager_boxing_logger.h" -#include "oneflow/core/common/cpp_attribute.h" - -namespace oneflow { -namespace one { - -namespace { - -Maybe> GetParallelDesc(const TensorTuple& inputs, - const OpExprInterpContext& ctx) { - if (!inputs.empty()) { return inputs.at(0)->parallel_desc(); } - return JUST(ctx.parallel_desc); -} - -std::string GetDynamicOpConsistentFailedDebugString(const UserOpExpr& user_op_expr, - const StatefulOpKernel& kernel) { - CHECK(!kernel.output_tuple_indexes4mut2_obns().empty()); - std::string plentysuffix = kernel.output_tuple_indexes4mut2_obns().size() == 1 ? "s" : ""; - std::stringstream ss; - ss << "operator `" << user_op_expr.op_type_name() << "`" - << " does not support consistent mode because the shape" << plentysuffix << " of output tensor" - << plentysuffix << " "; - int i = 0; - for (const auto& out_index : kernel.output_tuple_indexes4mut2_obns()) { - if (i++ > 0) { ss << ", "; } - ss << out_index; - } - ss << " are not infered before op computation."; - return ss.str(); -} - -Maybe IsAllZeroSizeTensorMeta(const std::vector>& tensor_metas) { - if (tensor_metas.empty()) { return false; } - for (const auto& tensor_meta : tensor_metas) { - if (tensor_meta->shape().elem_cnt() != 0) { return false; } - } - return true; -} - -constexpr auto* CachedIsAllZeroSizeTensorMeta = - DECORATE(&IsAllZeroSizeTensorMeta, ThreadLocalCopiable); - -Maybe CalcBoxingOutput(const std::shared_ptr& input, Symbol out_nd_sbp, - Symbol out_parallel_desc, - bool current_rank_local_is_valid) { - const auto& logical_shape = input->shape(); - // If the input is a tensor of size 0, construct the output directly. - if (unlikely(logical_shape->elem_cnt() == 0)) { - ConsistentTensorMeta tensor_meta(logical_shape, input->dtype()->data_type(), out_nd_sbp, - out_parallel_desc); - const auto& tensor_impl = - JUST(EagerConsistentTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); - std::shared_ptr output = std::make_shared(tensor_impl); - return output; - } - const auto* mgr = Singleton::Get(); - // Eager boxing - const auto& in_nd_sbp = JUST(input->nd_sbp()); - const auto& in_parallel_desc = JUST(input->parallel_desc()); - const auto& boxing_interpreter = JUST(mgr->GetEagerBoxingInterpreter( - in_nd_sbp, out_nd_sbp, in_parallel_desc, out_parallel_desc, *logical_shape)); - Singleton::Get()->Log( - *JUST(boxing_interpreter->boxing_interpreter_status()), /* prefix */ ""); - if (!current_rank_local_is_valid) { return input; } - const auto& output = JUST(boxing_interpreter->Interpret(input, in_nd_sbp, out_nd_sbp, - in_parallel_desc, out_parallel_desc)); - return output; -} - -auto* GetBoxingOutput = - DECORATE(DECORATE(&CalcBoxingOutput, CheckConsistentTensorMeta), DisableRecusiveBoxingCall); - -Maybe Interpret(const UserOpExpr& user_op_expr, const TensorTuple& inputs, - TensorTuple* outputs, const OpExprInterpContext& ctx) { - CHECK_EQ_OR_RETURN(outputs->size(), user_op_expr.output_size()); - const auto& parallel_desc = JUST(GetParallelDesc(inputs, ctx)); - std::shared_ptr result; - NonRecursiveMetaInfoConsistencyCheckScope scope; - if (inputs.empty()) { - // check consistency placement and nd_sbp, do not check in non-src op because it is assumed that - // InferSbp in op is a deterministic algorithm - JUST(MetaInfoConsistencyCheck(parallel_desc, ctx.nd_sbp, 1, /* force_check */ false)); - const auto& infer_args = - JUST(SrcOpConsistentTensorMetaInferArgs::New(ctx.attrs, parallel_desc, JUST(ctx.nd_sbp))); - result = JUST(user_op_expr.mut_consistent_tensor_infer_cache()->GetOrInfer(*infer_args)); - } else { - for (int i = 0; i < outputs->size(); ++i) { - if ((*outputs)[i]) { - const auto& nd_sbp = JUST((*outputs)[i]->nd_sbp()); - JUST((*outputs)[i]->set_consumer_nd_sbp_constraint(nd_sbp)); - } - } - const auto& infer_args = JUST(ConsistentTensorMetaInferArgs::New(ctx.attrs, inputs)); - result = JUST(user_op_expr.mut_consistent_tensor_infer_cache()->GetOrInfer(*infer_args)); - } - const auto& output_tensor_metas = result->output_tensor_metas(); - Optional parallel_id; - const auto& tensor_device = JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); - for (int i = 0; i < outputs->size(); ++i) { - if (!outputs->at(i)) { - const auto& tensor_impl = JUST(EagerConsistentTensorImpl::New( - output_tensor_metas.at(i), tensor_device, parallel_id, false, false)); - (*outputs)[i].reset(new ConsistentTensor(tensor_impl)); - } else { - JUST((*outputs)[i]->set_consumer_nd_sbp_constraint(NullOpt)); - } - } - // Do nothing if output_tensors has 0-size shape. Since the input of some ops is 0-size but the - // output is not 0-size, it cannot be judged based on the input, such as flow.cat - if (unlikely(JUST(CachedIsAllZeroSizeTensorMeta(output_tensor_metas)))) { - return Maybe::Ok(); - } - // Run instruction Call - const auto& kernel = JUST(user_op_expr.MutKernel4Stream(result->stream())); - CHECK_EQ_OR_RETURN(kernel->output_tuple_indexes4mut2_obns().size(), 0) - << Error::UnimplementedError() - << GetDynamicOpConsistentFailedDebugString(user_op_expr, *kernel); - std::shared_ptr input_eager_blob_objects = - std::make_shared(inputs.size()); - // expand lifetime of boxing outputs to the end of this function - TensorTuple boxing_outputs; - for (int i = 0; i < inputs.size(); ++i) { - std::shared_ptr input = inputs.at(i); - const auto& infered_input_meta = result->input_tensor_metas().at(i); - const auto& input_parallel_desc = JUST(input->parallel_desc()); - CHECK_OR_RETURN(input_parallel_desc == infered_input_meta->parallel_desc()); - if (input_parallel_desc->parallel_num() != 1 - && infered_input_meta->nd_sbp() != JUST(input->nd_sbp())) { - input = JUST(GetBoxingOutput(input, infered_input_meta->nd_sbp(), - infered_input_meta->parallel_desc(), parallel_id.has_value())); - boxing_outputs.emplace_back(input); - } - const auto& local_tensor = JUST(input->cur_rank_phy_tensor()); - input_eager_blob_objects->at(i) = JUST(local_tensor->eager_blob_object()); - } - // Do nothing if the `parallel_desc` doesn't cover current ProcessCtx. - if (!parallel_id.has_value()) { return Maybe::Ok(); } - std::shared_ptr output_eager_blob_objects = - std::make_shared(outputs->size()); - for (int i = 0; i < outputs->size(); ++i) { - const auto& local_tensor = JUST(outputs->at(i)->cur_rank_phy_tensor()); - output_eager_blob_objects->at(i) = JUST(local_tensor->eager_blob_object()); - } - JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - return builder->Call(kernel, input_eager_blob_objects, output_eager_blob_objects, result, ctx, - result->stream()); - })); - return Maybe::Ok(); -} - -auto* InterpretThenInitConsistentId = DECORATE(&Interpret, NonRecursiveInitConsistentId); - -} // namespace - -Maybe EagerConsistentInterpreter::ApplyImpl(const UserOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return InterpretThenInitConsistentId(op_expr, inputs, outputs, ctx); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const VariableOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -namespace { - -static constexpr auto* RecursiveGetBoxingOutput = - DECORATE(&CalcBoxingOutput, CheckConsistentTensorMeta); - -Maybe RawConsistentToConsistent(const ConsistentToConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) { - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(outputs->size(), 1); - const auto& input = inputs.at(0); - CHECK_OR_RETURN(input->is_consistent()); - CHECK_OR_RETURN(ctx.parallel_desc.has_value()); - CHECK_OR_RETURN(ctx.nd_sbp.has_value()); - const auto& in_parallel_desc = JUST(input->parallel_desc()); - const auto& out_nd_sbp = JUST(ctx.nd_sbp); - const auto& out_parallel_desc = JUST(ctx.parallel_desc); - const auto& in_parallel_id = JUST(GetParallelId4CurrentProcessCtx(in_parallel_desc)); - const auto& out_parallel_id = JUST(GetParallelId4CurrentProcessCtx(out_parallel_desc)); - const auto& tensor = - JUST(RecursiveGetBoxingOutput(input, out_nd_sbp, out_parallel_desc, - in_parallel_id->has_value() || out_parallel_id->has_value())); - CHECK_OR_RETURN(tensor); - if (out_parallel_id->has_value()) { - const auto& nd_sbp = JUST(tensor->nd_sbp()); - const auto& parallel_desc = JUST(tensor->parallel_desc()); - CHECK_OR_RETURN(nd_sbp == out_nd_sbp) - << ". nd_sbp: " << NdSbpToString(nd_sbp) << ", out_nd_sbp" << NdSbpToString(out_nd_sbp); - CHECK_OR_RETURN(parallel_desc == out_parallel_desc); - outputs->at(0) = tensor; - } else { - ConsistentTensorMeta tensor_meta(tensor->shape(), tensor->dtype()->data_type(), out_nd_sbp, - out_parallel_desc); - const auto& tensor_impl = - JUST(EagerConsistentTensorImpl::New(SymbolOf(tensor_meta), tensor->requires_grad(), false)); - outputs->at(0).reset(new ConsistentTensor(tensor_impl)); - } - CHECK_OR_RETURN(outputs->at(0)); - return Maybe::Ok(); -} - -static constexpr auto* ConsistentToConsistent = - DECORATE(&RawConsistentToConsistent, NonRecursiveInitConsistentId); - -} // namespace - -Maybe EagerConsistentInterpreter::ApplyImpl(const ConsistentToConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - JUST(ConsistentToConsistent(op_expr, inputs, outputs, ctx)); - return Maybe::Ok(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const CastToConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const CastFromConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - CHECK_EQ_OR_RETURN(inputs.size(), 1); - const auto& input_tensor = inputs.at(0); - const auto& mirrored_tensor = JUST(JUST(input_tensor->cur_rank_phy_tensor())->detach()); - bool requires_grad = autograd::GradMode::is_enabled() && input_tensor->requires_grad(); - JUST(mirrored_tensor->set_requires_grad(requires_grad)); - mirrored_tensor->set_is_leaf(!requires_grad); - outputs->at(0) = mirrored_tensor; - return Maybe::Ok(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const CastToMirroredOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const CastFromMirroredOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const DistributeSplitOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const DistributeCloneOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const DistributeConcatOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const DistributeAddOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerConsistentInterpreter::ApplyImpl(const SelectTopNOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/eager_global_op_interpreter.cpp b/oneflow/core/framework/op_interpreter/eager_global_op_interpreter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..bdc307ea1a0b766ba1f0ddb94ac43e77311c6087 --- /dev/null +++ b/oneflow/core/framework/op_interpreter/eager_global_op_interpreter.cpp @@ -0,0 +1,314 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include +#include "oneflow/core/framework/to_string.h" +#include "oneflow/core/framework/op_interpreter.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/core/framework/scope_util.h" +#include "oneflow/core/framework/session_util.h" +#include "oneflow/core/framework/symbol_storage_util.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_name_scope.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/framework/global_tensor_infer_cache.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/autograd/autograd_mode.h" +#include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" +#include "oneflow/user/kernels/stateful_opkernel.h" +#include "oneflow/core/framework/consistency_check.h" +#include "oneflow/core/framework/tensor_rpc_util.h" +#include "oneflow/core/framework/tensor_global_id.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/boxing/eager_boxing_logger.h" +#include "oneflow/core/common/cpp_attribute.h" + +namespace oneflow { +namespace one { + +namespace { + +Maybe> GetParallelDesc(const TensorTuple& inputs, + const OpExprInterpContext& ctx) { + if (!inputs.empty()) { return inputs.at(0)->parallel_desc(); } + return JUST(ctx.parallel_desc); +} + +std::string GetDynamicOpGlobalFailedDebugString(const UserOpExpr& user_op_expr, + const StatefulOpKernel& kernel) { + CHECK(!kernel.output_tuple_indexes4mut2_obns().empty()); + std::string plentysuffix = kernel.output_tuple_indexes4mut2_obns().size() == 1 ? "s" : ""; + std::stringstream ss; + ss << "operator `" << user_op_expr.op_type_name() << "`" + << " does not support global mode because the shape" << plentysuffix << " of output tensor" + << plentysuffix << " "; + int i = 0; + for (const auto& out_index : kernel.output_tuple_indexes4mut2_obns()) { + if (i++ > 0) { ss << ", "; } + ss << out_index; + } + ss << " are not infered before op computation."; + return ss.str(); +} + +Maybe IsAllZeroSizeTensorMeta(const std::vector>& tensor_metas) { + if (tensor_metas.empty()) { return false; } + for (const auto& tensor_meta : tensor_metas) { + if (tensor_meta->shape().elem_cnt() != 0) { return false; } + } + return true; +} + +constexpr auto* CachedIsAllZeroSizeTensorMeta = + DECORATE(&IsAllZeroSizeTensorMeta, ThreadLocalCopiable); + +Maybe CalcBoxingOutput(const std::shared_ptr& input, Symbol out_nd_sbp, + Symbol out_parallel_desc, + bool current_rank_local_is_valid) { + const auto& logical_shape = input->shape(); + // If the input is a tensor of size 0, construct the output directly. + if (unlikely(logical_shape->elem_cnt() == 0)) { + GlobalTensorMeta tensor_meta(*logical_shape, input->dtype()->data_type(), out_nd_sbp, + out_parallel_desc); + const auto& tensor_impl = + JUST(EagerGlobalTensorImpl::New(SymbolOf(tensor_meta), input->requires_grad(), false)); + std::shared_ptr output = std::make_shared(tensor_impl); + return output; + } + const auto* mgr = Singleton::Get(); + // Eager boxing + const auto& in_nd_sbp = JUST(input->nd_sbp()); + const auto& in_parallel_desc = JUST(input->parallel_desc()); + const auto& boxing_interpreter = JUST(mgr->GetEagerBoxingInterpreter( + in_nd_sbp, out_nd_sbp, in_parallel_desc, out_parallel_desc, *logical_shape)); + Singleton::Get()->Log( + *JUST(boxing_interpreter->boxing_interpreter_status()), /* prefix */ ""); + if (!current_rank_local_is_valid) { return input; } + const auto& output = JUST(boxing_interpreter->Interpret(input, in_nd_sbp, out_nd_sbp, + in_parallel_desc, out_parallel_desc)); + return output; +} + +auto* GetBoxingOutput = + DECORATE(DECORATE(&CalcBoxingOutput, CheckGlobalTensorMeta), DisableRecusiveBoxingCall); + +Maybe Interpret(const UserOpExpr& user_op_expr, const TensorTuple& inputs, + TensorTuple* outputs, const OpExprInterpContext& ctx) { + CHECK_EQ_OR_RETURN(outputs->size(), user_op_expr.output_size()); + const auto& parallel_desc = JUST(GetParallelDesc(inputs, ctx)); + std::shared_ptr result; + NonRecursiveMetaInfoConsistencyCheckScope scope; + if (inputs.empty()) { + // check consistency placement and nd_sbp, do not check in non-src op because it is assumed that + // InferSbp in op is a deterministic algorithm + JUST(MetaInfoConsistencyCheck(parallel_desc, ctx.nd_sbp, 1, /* force_check */ false)); + const auto& infer_args = + JUST(SrcOpGlobalTensorMetaInferArgs::New(ctx.attrs, parallel_desc, JUST(ctx.nd_sbp))); + result = JUST(user_op_expr.mut_global_tensor_infer_cache()->GetOrInfer(*infer_args)); + } else { + for (int i = 0; i < outputs->size(); ++i) { + if ((*outputs)[i]) { + const auto& nd_sbp = JUST((*outputs)[i]->nd_sbp()); + JUST((*outputs)[i]->set_consumer_nd_sbp_constraint(nd_sbp)); + } + } + const auto& infer_args = JUST(GlobalTensorMetaInferArgs::New(ctx.attrs, inputs)); + result = JUST(user_op_expr.mut_global_tensor_infer_cache()->GetOrInfer(*infer_args)); + } + const auto& output_tensor_metas = result->output_tensor_metas(); + Optional parallel_id; + const auto& tensor_device = JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); + for (int i = 0; i < outputs->size(); ++i) { + if (!outputs->at(i)) { + const auto& tensor_impl = JUST(EagerGlobalTensorImpl::New( + output_tensor_metas[i], tensor_device, parallel_id, false, false)); + (*outputs)[i].reset(new GlobalTensor(tensor_impl)); + } else { + JUST((*outputs)[i]->set_consumer_nd_sbp_constraint(NullOpt)); + } + } + // Do nothing if output_tensors has 0-size shape. Since the input of some ops is 0-size but the + // output is not 0-size, it cannot be judged based on the input, such as flow.cat + if (unlikely(JUST(CachedIsAllZeroSizeTensorMeta(output_tensor_metas)))) { + return Maybe::Ok(); + } + // Run instruction Call + const auto& kernel = JUST(user_op_expr.MutKernel4Stream(result->stream())); + CHECK_EQ_OR_RETURN(kernel->output_tuple_indexes4mut2_obns().size(), 0) + << Error::UnimplementedError() << GetDynamicOpGlobalFailedDebugString(user_op_expr, *kernel); + vm::EagerBlobObjectList input_eager_blob_objects(inputs.size()); + // expand lifetime of boxing outputs to the end of this function + TensorTuple boxing_outputs; + for (int i = 0; i < inputs.size(); ++i) { + std::shared_ptr input = inputs.at(i); + const auto& infered_input_meta = result->input_tensor_metas().at(i); + const auto& input_parallel_desc = JUST(input->parallel_desc()); + CHECK_OR_RETURN(input_parallel_desc == infered_input_meta->parallel_desc()); + if (input_parallel_desc->parallel_num() != 1 + && infered_input_meta->nd_sbp() != JUST(input->nd_sbp())) { + input = JUST(GetBoxingOutput(input, infered_input_meta->nd_sbp(), + infered_input_meta->parallel_desc(), parallel_id.has_value())); + boxing_outputs.emplace_back(input); + } + const auto& local_tensor = JUST(input->cur_rank_phy_tensor()); + input_eager_blob_objects.at(i) = JUST(local_tensor->eager_blob_object()); + } + // Do nothing if the `parallel_desc` doesn't cover current ProcessCtx. + if (!parallel_id.has_value()) { return Maybe::Ok(); } + vm::EagerBlobObjectList output_eager_blob_objects(outputs->size()); + for (int i = 0; i < outputs->size(); ++i) { + const auto& local_tensor = JUST(outputs->at(i)->cur_rank_phy_tensor()); + output_eager_blob_objects.at(i) = JUST(local_tensor->eager_blob_object()); + } + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + return builder->Call(kernel, std::move(input_eager_blob_objects), + std::move(output_eager_blob_objects), result, ctx, result->stream()); + })); + return Maybe::Ok(); +} + +auto* InterpretThenInitGlobalId = DECORATE(&Interpret, NonRecursiveInitGlobalId); + +} // namespace + +Maybe EagerGlobalInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTuple& inputs, + TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return InterpretThenInitGlobalId(op_expr, inputs, outputs, ctx); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const VariableOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +namespace { + +static constexpr auto* RecursiveGetBoxingOutput = + DECORATE(&CalcBoxingOutput, CheckGlobalTensorMeta); + +Maybe RawGlobalToGlobal(const GlobalToGlobalOpExpr& op_expr, const TensorTuple& inputs, + TensorTuple* outputs, const OpExprInterpContext& ctx) { + CHECK_EQ_OR_RETURN(inputs.size(), 1); + CHECK_EQ_OR_RETURN(outputs->size(), 1); + const auto& input = inputs.at(0); + CHECK_OR_RETURN(input->is_global()); // NOLINT + CHECK_OR_RETURN(ctx.parallel_desc.has_value()); + CHECK_OR_RETURN(ctx.nd_sbp.has_value()); + const auto& in_parallel_desc = JUST(input->parallel_desc()); + const auto& out_nd_sbp = JUST(ctx.nd_sbp); + const auto& out_parallel_desc = JUST(ctx.parallel_desc); + const auto& in_parallel_id = JUST(GetParallelId4CurrentProcessCtx(in_parallel_desc)); + const auto& out_parallel_id = JUST(GetParallelId4CurrentProcessCtx(out_parallel_desc)); + const auto& tensor = + JUST(RecursiveGetBoxingOutput(input, out_nd_sbp, out_parallel_desc, + in_parallel_id->has_value() || out_parallel_id->has_value())); + CHECK_OR_RETURN(tensor); + if (out_parallel_id->has_value()) { + const auto& nd_sbp = JUST(tensor->nd_sbp()); + const auto& parallel_desc = JUST(tensor->parallel_desc()); + CHECK_OR_RETURN(nd_sbp == out_nd_sbp) + << ". nd_sbp: " << NdSbpToString(nd_sbp) << ", out_nd_sbp" << NdSbpToString(out_nd_sbp); + CHECK_OR_RETURN(parallel_desc == out_parallel_desc); + outputs->at(0) = tensor; + } else { + GlobalTensorMeta tensor_meta(*tensor->shape(), tensor->dtype()->data_type(), out_nd_sbp, + out_parallel_desc); + const auto& tensor_impl = + JUST(EagerGlobalTensorImpl::New(SymbolOf(tensor_meta), tensor->requires_grad(), false)); + (*outputs)[0].reset(new GlobalTensor(tensor_impl)); + } + CHECK_OR_RETURN(outputs->at(0)); + return Maybe::Ok(); +} + +static constexpr auto* GlobalToGlobal = DECORATE(&RawGlobalToGlobal, NonRecursiveInitGlobalId); + +} // namespace + +Maybe EagerGlobalInterpreter::ApplyImpl(const GlobalToGlobalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + JUST(GlobalToGlobal(op_expr, inputs, outputs, ctx)); + return Maybe::Ok(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const LocalToGlobalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const GlobalToLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + CHECK_EQ_OR_RETURN(inputs.size(), 1); + const auto& input_tensor = inputs.at(0); + const auto& local_tensor = JUST(JUST(input_tensor->cur_rank_phy_tensor())->detach()); + bool requires_grad = autograd::GradMode::is_enabled() && input_tensor->requires_grad(); + JUST(local_tensor->set_requires_grad(requires_grad)); + local_tensor->set_is_leaf(!requires_grad); + (*outputs)[0] = local_tensor; + return Maybe::Ok(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const CastToLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const CastFromLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const DistributeSplitOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const DistributeCloneOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const DistributeConcatOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const DistributeAddOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerGlobalInterpreter::ApplyImpl(const SelectTopNOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.cpp b/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9c774d46ca990e9d45a347010ffa63ff2384bbf2 --- /dev/null +++ b/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.cpp @@ -0,0 +1,425 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/mutable_attr_map.h" +#include "oneflow/core/framework/op_interpreter.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/core/framework/scope_util.h" +#include "oneflow/core/framework/session_util.h" +#include "oneflow/core/framework/symbol_storage_util.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_name_scope.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/framework/local_tensor_infer_cache.h" +#include "oneflow/core/common/stride.h" +#include "oneflow/core/memory/memory_case_util.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/user/kernels/stateful_opkernel.h" +#include "oneflow/core/vm/vm_util.h" +#include "oneflow/core/vm/virtual_machine.h" +#include "oneflow/core/autograd/autograd_mode.h" +#include "oneflow/core/framework/placement_sbp_util.h" +#include "oneflow/core/framework/tensor_rpc_util.h" +#include "oneflow/core/framework/tensor_global_id.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/id_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" +#include "oneflow/core/profiler/profiler.h" + +namespace oneflow { +namespace one { + +namespace { + +Maybe> RawGetDefaultCpuDevice() { return Device::New("cpu"); } + +constexpr auto* GetDefaultCpuDevice = DECORATE(&RawGetDefaultCpuDevice, ThreadLocal); + +Maybe> GetDefaultDevice(const TensorTuple& inputs, const OpExprInterpContext& ctx) { + if (inputs.empty()) { + if (ctx.device.has_value()) { + return JUST(ctx.device); + } else { + return GetDefaultCpuDevice(); + } + } + return JUST(inputs.at(0)->device()); +} + +Maybe TensorImpl4Tensor(const std::shared_ptr& tensor) { + CHECK_OR_RETURN(static_cast(tensor)); + return tensor->mut_eager_local_tensor_impl(); +} + +} // namespace + +Maybe NaiveInterpret(const UserOpExpr& user_op_expr, const TensorTuple& inputs, + TensorTuple* outputs, const OpExprInterpContext& ctx) { + OF_PROFILER_RANGE_GUARD("NaiveInterpret"); + CHECK_EQ_OR_RETURN(outputs->size(), user_op_expr.output_size()); // NOLINT + Symbol default_device = JUST(GetDefaultDevice(inputs, ctx)); + const std::shared_ptr result = + JUST([&]() -> Maybe { + LocalTensorMetaInferArgs infer_args; + JUST(infer_args.Init(ctx.attrs, default_device, inputs)); + return JUST(user_op_expr.mut_local_tensor_infer_cache()->GetOrInfer(infer_args)); + }()); + + vm::EagerBlobObjectList input_eager_blob_objects(inputs.size()); + for (int i = 0; i < inputs.size(); i++) { + input_eager_blob_objects.at(i) = JUST(inputs.at(i)->eager_blob_object()); + } + + const auto& output_tensor_metas = result->output_tensor_metas(); + vm::EagerBlobObjectList output_eager_blob_objects(outputs->size()); + + const auto& kernel = JUST(user_op_expr.MutKernel4Stream(result->stream())); + + for (int i = 0; i < outputs->size(); i++) { + if (!outputs->at(i)) { + // NOTE: if op support stride(non-contiguous input), then output tensor's stride + // should be inferred in InferLogicalTensorDesc. + // otherwise, it will be set here(according to shape). + std::shared_ptr mut_tensor_meta; + { + if (kernel->output_is_mut2_type(i)) { + mut_tensor_meta = std::make_shared( + output_tensor_metas.at(i)->shape(), output_tensor_metas.at(i)->stride(), + output_tensor_metas.at(i)->dtype(), output_tensor_metas.at(i)->device()); + } + } + std::shared_ptr tensor_impl = + std::make_shared(false, false); + const auto& dep_object = NewLocalDepObject(); + JUST( + tensor_impl->InitEagerBlobObject(output_tensor_metas.at(i), mut_tensor_meta, dep_object)); + output_eager_blob_objects.at(i) = JUST(tensor_impl->eager_blob_object()); + (*outputs)[i] = std::make_shared(tensor_impl); + } else { + const auto* tensor_impl = JUST(TensorImpl4Tensor(outputs->at(i))); + // output i is inplaced. + // check TensorMeta of infer result and TensorMeta of output i. + CHECK_OR_RETURN(tensor_impl->tensor_meta()->shape() // NOLINT + == output_tensor_metas.at(i)->shape()) // NOLINT + << Error::RuntimeError() << tensor_impl->tensor_meta()->shape().ToString() // NOLINT + << " .vs " // NOLINT + << output_tensor_metas.at(i)->shape().ToString(); // NOLINT + CHECK_OR_RETURN(tensor_impl->tensor_meta()->dtype() // NOLINT + == output_tensor_metas.at(i)->dtype()) // NOLINT + << Error::RuntimeError() << DataType_Name(tensor_impl->tensor_meta()->dtype()) // NOLINT + << " .vs " // NOLINT + << DataType_Name(output_tensor_metas.at(i)->dtype()); // NOLINT + bool has_eager_blob_object = JUST(outputs->at(i)->has_eager_blob_object()); + CHECK_OR_RETURN(has_eager_blob_object); // NOLINT + output_eager_blob_objects.at(i) = JUST(outputs->at(i)->eager_blob_object()); + // TODO(zhaoluyang):(thread_local TensorMeta set stride then check) + // CHECK_OR_RETURN(tensor_impl->tensor_meta()->stride() == + // output_tensor_metas->at(i)->stride()); + } + } + + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + return builder->Call(kernel, std::move(input_eager_blob_objects), + std::move(output_eager_blob_objects), ctx, result->stream()); + })); + for (int64_t index : kernel->output_tuple_indexes4mut2_obns()) { + const auto* tensor_impl = JUST(TensorImpl4Tensor(outputs->at(index))); + auto btb = std::make_shared(); + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + return builder->SyncAccessBlobByCallback( + tensor_impl, btb, [](ep::Stream* stream, const std::shared_ptr&) {}, + "const"); + })); + JUST(btb->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); + const auto& mut_tensor_meta = const_cast(tensor_impl)->mut_tensor_meta(); + Symbol new_tensor_meta = + SymbolOf(LocalTensorMeta(mut_tensor_meta->shape(), mut_tensor_meta->stride(), + mut_tensor_meta->dtype(), mut_tensor_meta->device())); + std::shared_ptr final_tensor_impl = + std::make_shared(JUST(tensor_impl->tensor_storage()), + JUST(tensor_impl->storage_offset()), false, false); + JUST(final_tensor_impl->InitEagerBlobObject( + new_tensor_meta, + JUST(JUST(outputs->at(index)->eager_blob_object())->compute_local_dep_object()))); + JUST(JUST(outputs->at(index)->AsLocalTensor())->set_impl(final_tensor_impl)); + } + + return Maybe::Ok(); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTuple& inputs, + TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return NaiveInterpret(op_expr, inputs, outputs, ctx); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const VariableOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +static Maybe BuildAndRunLocalCastInstruction(const BuiltinOpExpr& op_expr, + const TensorTuple& inputs, + TensorTuple* outputs) { + // TODO() + OF_UNIMPLEMENTED(); +} + +namespace { + +Maybe EagerCclBroadcast(Symbol parallel_desc, int64_t root, + size_t size, const std::vector& shape_list) { + return one::OpBuilder("eager_ccl_broadcast", *JUST(UniqueStr("eager_ccl_broadcast"))) + .Input("in", size) + .Output("out", size) + .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Attr>("shape_list", shape_list) + .Attr("root", root) + .Build(); +} + +auto* CachedEagerCclBroadcastOpExpr = DECORATE(&EagerCclBroadcast, ThreadLocalCachedCopiable); + +} // namespace + +Maybe Broadcast(const std::shared_ptr& tensor, int64_t src_rank, + Symbol parallel_desc, bool inplace) { + CHECK_OR_RETURN(parallel_desc->containing_current_rank()); + if (parallel_desc->parallel_num() == 1 /* no broadcast */) { return tensor; } + std::shared_ptr op_expr = + JUST(CachedEagerCclBroadcastOpExpr(parallel_desc, src_rank, 1, {*tensor->shape()})); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("root"); + attrs.SetAllAttrs(src_rank); + if (src_rank == GlobalProcessCtx::Rank() || inplace) { + TensorTuple outputs{tensor}; + JUST(OpInterpUtil::Dispatch(*op_expr, {tensor}, &outputs, + one::OpExprInterpContext(attrs, parallel_desc))); + return tensor; + } else { + return JUST(OpInterpUtil::Dispatch( + *op_expr, {tensor}, one::OpExprInterpContext(attrs, parallel_desc))); + } +} + +Maybe Broadcast(const TensorTuple& inputs, int64_t src_rank, + Symbol parallel_desc, bool inplace) { + CHECK_OR_RETURN(parallel_desc->containing_current_rank()) + << "Current rank are not contained in the placement arguement"; + if (parallel_desc->parallel_num() == 1 /* no broadcast */) { return inputs; } + std::vector shape_list; + for (const auto& tensor : inputs) { shape_list.emplace_back(*tensor->shape()); } + std::shared_ptr op_expr = + JUST(CachedEagerCclBroadcastOpExpr(parallel_desc, src_rank, inputs.size(), shape_list)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("root"); + attrs.SetAllAttrs(src_rank); + if (src_rank == GlobalProcessCtx::Rank() || inplace) { + auto outputs = std::make_shared(inputs); + JUST(OpInterpUtil::Dispatch(*op_expr, inputs, outputs.get(), + one::OpExprInterpContext(attrs, parallel_desc))); + return outputs; + } else { + return JUST(OpInterpUtil::Dispatch( + *op_expr, inputs, one::OpExprInterpContext(attrs, parallel_desc))); + } +} + +namespace { + +Maybe GetSyncedTensorIfBroadcast(const std::shared_ptr& tensor, + Symbol parallel_desc, Symbol nd_sbp) { + Optional parallel_id; + JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); + if (!parallel_id.has_value()) { return tensor; } + const auto& broadcast_parallel_desc = JUST(GetBroadcastSubParallelDesc(parallel_desc, nd_sbp)); + int64_t root = JUST(broadcast_parallel_desc->MachineId4ParallelId(0)); + return Broadcast(tensor, root, broadcast_parallel_desc, false); +} + +Maybe CalcPhysicalShape(Symbol global_tensor_meta) { + const auto& opt_parallel_id = + JUST(GetParallelId4CurrentProcessCtx(global_tensor_meta->parallel_desc())); + int64_t parallel_id = JUST(*opt_parallel_id); + return GetPhysicalShape(global_tensor_meta->shape(), *global_tensor_meta->nd_sbp(), + *global_tensor_meta->parallel_desc(), parallel_id); +} + +static constexpr auto* GetPhysicalShape = DECORATE(&CalcPhysicalShape, ThreadLocal); + +Maybe TryReshapeTensor(const std::shared_ptr& tensor, + Symbol global_tensor_meta) { + CHECK_OR_RETURN(tensor->is_local()); + const auto& physical_shape = JUST(GetPhysicalShape(global_tensor_meta)); + if (*physical_shape == *tensor->shape()) { return tensor; } + CHECK_EQ_OR_RETURN(physical_shape->elem_cnt(), tensor->shape()->elem_cnt()); + // TODO(lixinqi) inplace reshape. + return tensor; +} + +} // namespace + +Maybe EagerLocalInterpreter::ApplyImpl(const GlobalToGlobalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +namespace { + +Maybe RawLocalToGlobal(const LocalToGlobalOpExpr& op_expr, const TensorTuple& inputs, + TensorTuple* outputs, const OpExprInterpContext& ctx) { + std::shared_ptr input_local_tensor; + { + CHECK_EQ_OR_RETURN(inputs.size(), 1); + CHECK_OR_RETURN(!inputs[0]->is_global()); // NOLINT + const auto& input_tensor = JUST(inputs.at(0)->detach()); + input_local_tensor = JUST(input_tensor->AsLocalTensor()); + CHECK_OR_RETURN(input_local_tensor) + << Error::InvalidValueError() << "Tensor Cast Error"; // NOLINT + bool requires_grad = autograd::GradMode::is_enabled() && inputs.at(0)->requires_grad(); + JUST(input_local_tensor->set_requires_grad(requires_grad)); + input_local_tensor->set_is_leaf(!requires_grad); + } + std::shared_ptr global_tensor; + { + CHECK_OR_RETURN(ctx.parallel_desc.has_value()); + CHECK_OR_RETURN(ctx.nd_sbp.has_value()); + const auto& nd_sbp = JUST(ctx.nd_sbp); + const auto& parallel_desc = JUST(ctx.parallel_desc); + const auto& logical_shape = JUST(ctx.attrs.GetAttr("shape")); + DataType dtype = JUST(ctx.attrs.GetAttr("dtype")); + GlobalTensorMeta tensor_meta(logical_shape, dtype, nd_sbp, parallel_desc); + Optional parallel_id{}; + const auto& device = JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); + const auto& global_tensor_impl = JUST(EagerGlobalTensorImpl::New( + SymbolOf(tensor_meta), device, parallel_id, input_local_tensor->requires_grad(), + !input_local_tensor->requires_grad())); + global_tensor = std::make_shared(global_tensor_impl); + if (parallel_id.has_value()) { + const auto& pyhsical_shape = JUST(GetPhysicalShape(tensor_meta)); + const auto& input_local_tensor_shape = input_local_tensor->shape(); + CHECK_EQ_OR_RETURN(*pyhsical_shape, *input_local_tensor_shape); // NOLINT + CHECK_OR_RETURN(dtype == input_local_tensor->dtype()->data_type()); // NOLINT + global_tensor_impl->reset_cur_rank_phy_tensor(input_local_tensor); + } + } + (*outputs)[0] = global_tensor; + return Maybe::Ok(); +} + +static constexpr auto* LocalToGlobal = DECORATE(&RawLocalToGlobal, NonRecursiveInitGlobalId); + +} // namespace + +Maybe EagerLocalInterpreter::ApplyImpl(const LocalToGlobalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + bool sync_data = JUST(ctx.attrs.GetAttr("sync_data")); + JUST(LocalToGlobal(op_expr, inputs, outputs, ctx)); + const auto& global_tensor = JUST((*outputs)[0]->AsGlobalTensor()); + JUST(WithConsistencyChecked(global_tensor, [&]() -> Maybe { + if (IsGlobalTensorMetaCheckDisabled()) { return Maybe::Ok(); } + const auto& parallel_desc = JUST(ctx.parallel_desc); + const auto& parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); + if (!parallel_id->has_value()) { return Maybe::Ok(); } + const auto& nd_sbp = JUST(ctx.nd_sbp); + const auto& tensor_meta = JUST(global_tensor->global_tensor_meta()); + const auto& local_tensor = JUST(global_tensor->cur_rank_phy_tensor()); + const auto& reshaped_tensor = JUST(TryReshapeTensor(local_tensor, tensor_meta)); + std::shared_ptr synced_tensor = reshaped_tensor; + if (sync_data) { + synced_tensor = JUST(GetSyncedTensorIfBroadcast(reshaped_tensor, parallel_desc, nd_sbp)); + } + auto* global_tensor_impl = reinterpret_cast(global_tensor->mut_impl()); + CHECK_NOTNULL_OR_RETURN(global_tensor_impl); + global_tensor_impl->reset_cur_rank_phy_tensor(JUST(synced_tensor->AsLocalTensor())); + return Maybe::Ok(); + })); + return Maybe::Ok(); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const GlobalToLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + OF_UNIMPLEMENTED(); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const CastToLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunLocalCastInstruction(op_expr, inputs, outputs); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const CastFromLocalOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunLocalCastInstruction(op_expr, inputs, outputs); +} + +static Maybe BuildAndRunDistributeSplitOrCloneInstruction(const BuiltinOpExpr& op_expr, + const TensorTuple& inputs, + TensorTuple* outputs) { + // TODO() + OF_UNIMPLEMENTED(); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const DistributeSplitOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunDistributeSplitOrCloneInstruction(op_expr, inputs, outputs); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const DistributeCloneOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunDistributeSplitOrCloneInstruction(op_expr, inputs, outputs); +} + +static Maybe BuildAndRunDistributeConcatAndAddInstruction(const BuiltinOpExpr& op_expr, + const TensorTuple& inputs, + TensorTuple* outputs) { + // TODO() + OF_UNIMPLEMENTED(); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const DistributeConcatOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunDistributeConcatAndAddInstruction(op_expr, inputs, outputs); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const DistributeAddOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + return BuildAndRunDistributeConcatAndAddInstruction(op_expr, inputs, outputs); +} + +Maybe EagerLocalInterpreter::ApplyImpl(const SelectTopNOpExpr& op_expr, + const TensorTuple& inputs, TensorTuple* outputs, + const OpExprInterpContext& ctx) const { + int top_n = JUST(ctx.attrs.GetAttr("top_n")); + outputs->resize(top_n); + for (int i = 0; i < top_n; ++i) { (*outputs)[i] = JUST(JUST(VectorAt(inputs, i))->detach()); } + return Maybe::Ok(); +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.h b/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.h new file mode 100644 index 0000000000000000000000000000000000000000..5320ddbf28e30c276378e51731a5b629307725b9 --- /dev/null +++ b/oneflow/core/framework/op_interpreter/eager_local_op_interpreter.h @@ -0,0 +1,35 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/eager/eager_blob_object.h" + +namespace oneflow { + +class Device; +class TensorTuple; +class ParallelDesc; + +namespace one { + +class Tensor; + +Maybe Broadcast(const std::shared_ptr& tensor, int64_t src_rank, + Symbol parallel_desc, bool inplace); + +Maybe Broadcast(const TensorTuple& inputs, int64_t src_rank, + Symbol parallel_desc, bool inplace); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.cpp b/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.cpp deleted file mode 100644 index 357d563acaaeba844663b8dfe40d6f00951cbea4..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.cpp +++ /dev/null @@ -1,426 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/op_interpreter.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/instructions_builder.h" -#include "oneflow/core/framework/scope_util.h" -#include "oneflow/core/framework/session_util.h" -#include "oneflow/core/framework/symbol_storage_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_name_scope.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/common/stride.h" -#include "oneflow/core/memory/memory_case_util.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/user/kernels/stateful_opkernel.h" -#include "oneflow/core/vm/vm_util.h" -#include "oneflow/core/autograd/autograd_mode.h" -#include "oneflow/core/framework/placement_sbp_util.h" -#include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/framework/tensor_consistent_id.h" -#include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/id_util.h" -#include "oneflow/core/functional/functional.h" -#include "oneflow/core/rpc/include/global_process_ctx.h" - -namespace oneflow { -namespace one { - -namespace { - -Maybe> GetDefaultDevice(const OpExprInterpContext& ctx) { - if (ctx.device.has_value()) { return JUST(ctx.device); } - return Device::New("cpu", 0); -} - -Maybe TensorImpl4Tensor(const std::shared_ptr& tensor) { - CHECK_OR_RETURN(static_cast(tensor)); - return tensor->mut_eager_mirrored_tensor_impl(); -} - -class MutMirroredTensorMeta : public TensorMeta { - public: - MutMirroredTensorMeta() - : TensorMeta(std::make_shared(), std::make_shared(), - kInvalidDataType) {} - MutMirroredTensorMeta(const MutMirroredTensorMeta&) = default; - MutMirroredTensorMeta(MutMirroredTensorMeta&&) = default; - ~MutMirroredTensorMeta() override = default; -}; - -std::vector* ThreadLocalDefaultOutputMutTensorMetas(int64_t size) { - static thread_local std::vector struct_vec; - static thread_local std::vector ptr_vec; - struct_vec.resize(size); - ptr_vec.resize(size); - if (size == 1) { - ptr_vec.at(0) = &struct_vec.at(0); // unfold loop - } else if (size == 2) { - ptr_vec.at(0) = &struct_vec.at(0); // unfold loop - ptr_vec.at(1) = &struct_vec.at(1); // unfold loop - } else { - for (int i = 0; i < size; ++i) { ptr_vec.at(i) = &struct_vec.at(i); } - } - return &ptr_vec; -} - -} // namespace - -Maybe NaiveInterpret(const UserOpExpr& user_op_expr, const TensorTuple& inputs, - const Symbol& default_device, TensorTuple* outputs, - const OpExprInterpContext& ctx) { - const auto& attrs = ctx.attrs; - std::shared_ptr input_eager_blob_objects = - std::make_shared(inputs.size()); - for (int i = 0; i < inputs.size(); i++) { - const auto& input_device = JUST(inputs.at(i)->device()); - if (i > 0) { - CHECK_OR_RETURN(*default_device == *input_device) - << Error::RuntimeError() - << "Expected all tensors to be on the same device, but found at least two devices, " - << default_device->ToString() << " (positional 0) and " << input_device->ToString() - << " (positional " << i << ")!"; - } - input_eager_blob_objects->at(i) = JUST(inputs.at(i)->eager_blob_object()); - } - std::shared_ptr output_eager_blob_objects = - std::make_shared(outputs->size()); - auto* output_tensor_metas = ThreadLocalDefaultOutputMutTensorMetas(outputs->size()); - for (int i = 0; i < outputs->size(); i++) { - if (!outputs->at(i)) { - const auto& tensor_impl = std::make_shared(); - outputs->at(i) = std::make_shared(tensor_impl); - output_tensor_metas->at(i) = tensor_impl->mut_tensor_meta(); - } else { - bool has_eager_blob_object = JUST(outputs->at(i)->has_eager_blob_object()); - CHECK_OR_RETURN(has_eager_blob_object); - output_eager_blob_objects->at(i) = JUST(outputs->at(i)->eager_blob_object()); - } - } - Symbol stream; - bool need_check_mem_case = true; - - // Infer devices - if (!user_op_expr.has_device_and_stream_infer_fn()) { - stream = JUST(GetDefaultStreamByDevice(default_device)); - for (int i = 0; i < outputs->size(); i++) { - auto* tensor_impl = JUST(TensorImpl4Tensor(outputs->at(i))); - *JUST(tensor_impl->mut_device()) = default_device; - } - } else { - need_check_mem_case = false; - stream = JUST(user_op_expr.InferDeviceAndStream(attrs, inputs, outputs)); - } - - // Infer shapes and dtypes - const auto& device_tag = stream->device()->type(); - JUST(user_op_expr.InferPhysicalTensorDesc( - attrs, device_tag, - [&](int32_t i) -> const TensorMeta* { - return CHECK_JUST(TensorImpl4Tensor(inputs[i]))->mut_tensor_meta(); - }, - [&](int32_t i) -> TensorMeta* { - // using thread_local TensorMeta pointer if inplace. - // using tensor_impl TensorMeta pointer if not inplace. - return output_tensor_metas->at(i); - })); - - for (int i = 0; i < output_eager_blob_objects->size(); i++) { - auto* tensor_impl = JUST(TensorImpl4Tensor(outputs->at(i))); - if (!output_eager_blob_objects->at(i)) { - // NOTE: if op support stride(non-contiguous input), then output tensor's stride - // should be inferred in InferLogicalTensorDesc. - // otherwise, it will be set here(according to shape). - if (!JUST(user_op_expr.SupportNonContiguous())) { - std::shared_ptr stride(new Stride(*tensor_impl->shape())); - tensor_impl->mut_tensor_meta()->set_stride(stride); - } - const auto& dep_object = NewLocalDepObject(); - JUST(tensor_impl->InitEagerBlobObject(dep_object)); - output_eager_blob_objects->at(i) = JUST(tensor_impl->eager_blob_object()); - } else { - // output i is inplaced. - // check thread_local TensorMeta and tensor_impl TensorMeta. - CHECK_OR_RETURN(tensor_impl->tensor_meta()->shape() == output_tensor_metas->at(i)->shape()); - // TODO:(thread_local TensorMeta set stride then check) - // CHECK_OR_RETURN(tensor_impl->tensor_meta()->stride() == - // output_tensor_metas->at(i)->stride()); - CHECK_OR_RETURN(tensor_impl->tensor_meta()->dtype() == output_tensor_metas->at(i)->dtype()); - } - } - - const auto& kernel = JUST(user_op_expr.MutKernel4Stream(stream)); - kernel->set_need_check_mem_case(need_check_mem_case); - - for (int64_t index : kernel->output_tuple_indexes4mut2_obns()) { - output_eager_blob_objects->at(index)->set_is_shape_synced(false); - } - - JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - return builder->Call(kernel, input_eager_blob_objects, output_eager_blob_objects, ctx, stream); - })); - return Maybe::Ok(); -} - -static Maybe NaiveInterpret(const UserOpExpr& user_op_expr, const TensorTuple& inputs, - TensorTuple* outputs, const OpExprInterpContext& ctx) { - CHECK_EQ_OR_RETURN(outputs->size(), user_op_expr.output_size()); - Symbol default_device; - if (inputs.empty()) { - default_device = JUST(GetDefaultDevice(ctx)); - } else { - default_device = JUST(inputs.at(0)->device()); - } - return NaiveInterpret(user_op_expr, inputs, default_device, outputs, ctx); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const UserOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return NaiveInterpret(op_expr, inputs, outputs, ctx); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const VariableOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -static Maybe BuildAndRunMirroredCastInstruction(const BuiltinOpExpr& op_expr, - const TensorTuple& inputs, - TensorTuple* outputs) { - // TODO() - OF_UNIMPLEMENTED(); -} - -namespace { - -Maybe EagerNcclBroadcast(Symbol parallel_desc, int64_t root) { - return one::OpBuilder("eager_nccl_broadcast", *JUST(UniqueStr("eager_nccl_broadcast"))) - .Input("in") - .Output("out") - .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) - .Attr("root", root) - .Build(); -} - -auto* CachedEagerNcclBroadcastOpExpr = DECORATE(&EagerNcclBroadcast, ThreadLocal); - -} // namespace - -Maybe Broadcast(const std::shared_ptr& tensor, int64_t src_rank, - Symbol parallel_desc, bool inplace) { - CHECK_OR_RETURN(parallel_desc->containing_current_rank()); - if (parallel_desc->parallel_num() == 1 /* no broadcast */) { return tensor; } - std::shared_ptr op_expr = - JUST(CachedEagerNcclBroadcastOpExpr(parallel_desc, src_rank)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("root", src_rank)); - if (src_rank == GlobalProcessCtx::Rank() || inplace) { - TensorTuple outputs{tensor}; - JUST(OpInterpUtil::Dispatch(*op_expr, {tensor}, &outputs, - one::OpExprInterpContext(attrs, parallel_desc))); - return tensor; - } else { - return JUST(OpInterpUtil::Dispatch( - *op_expr, {tensor}, one::OpExprInterpContext(attrs, parallel_desc))); - } -} - -namespace { - -Maybe GetSyncedTensorIfBroadcast(const std::shared_ptr& tensor, - Symbol parallel_desc, Symbol nd_sbp) { - Optional parallel_id; - JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); - if (!parallel_id.has_value()) { return tensor; } - const auto& broadcast_parallel_desc = JUST(GetBroadcastSubParallelDesc(parallel_desc, nd_sbp)); - int64_t root = JUST(broadcast_parallel_desc->MachineId4ParallelId(0)); - return Broadcast(tensor, root, broadcast_parallel_desc, false); -} - -Maybe CalcPhysicalShape(Symbol consistent_tensor_meta) { - const auto& opt_parallel_id = - JUST(GetParallelId4CurrentProcessCtx(consistent_tensor_meta->parallel_desc())); - int64_t parallel_id = JUST(*opt_parallel_id); - return GetPhysicalShape(consistent_tensor_meta->shape(), *consistent_tensor_meta->nd_sbp(), - *consistent_tensor_meta->parallel_desc(), parallel_id); -} - -static constexpr auto* GetPhysicalShape = DECORATE(&CalcPhysicalShape, ThreadLocal); - -Maybe TryReshapeTensor(const std::shared_ptr& tensor, - Symbol consistent_tensor_meta) { - CHECK_OR_RETURN(tensor->is_local()); - const auto& physical_shape = JUST(GetPhysicalShape(consistent_tensor_meta)); - if (*physical_shape == *tensor->shape()) { return tensor; } - CHECK_EQ_OR_RETURN(physical_shape->elem_cnt(), tensor->shape()->elem_cnt()); - // TODO(lixinqi) inplace reshape. - return tensor; -} - -} // namespace - -Maybe EagerMirroredInterpreter::ApplyImpl(const ConsistentToConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -namespace { - -Maybe RawLocalToConsistent(const CastToConsistentOpExpr& op_expr, const TensorTuple& inputs, - TensorTuple* outputs, const OpExprInterpContext& ctx) { - std::shared_ptr input_mirrored_tensor; - { - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_OR_RETURN(!inputs.at(0)->is_consistent()); - const auto& input_tensor = JUST(inputs.at(0)->detach()); - input_mirrored_tensor = JUST(input_tensor->AsMirroredTensor()); - CHECK_OR_RETURN(input_mirrored_tensor) << Error::InvalidValueError("Tensor Cast Error"); - bool requires_grad = autograd::GradMode::is_enabled() && inputs.at(0)->requires_grad(); - JUST(input_mirrored_tensor->set_requires_grad(requires_grad)); - input_mirrored_tensor->set_is_leaf(!requires_grad); - } - std::shared_ptr consistent_tensor; - { - CHECK_OR_RETURN(ctx.parallel_desc.has_value()); - CHECK_OR_RETURN(ctx.nd_sbp.has_value()); - const auto& nd_sbp = JUST(ctx.nd_sbp); - const auto& parallel_desc = JUST(ctx.parallel_desc); - const auto& logical_shape = JUST(ctx.attrs.GetAttr("shape")); - DataType dtype = JUST(ctx.attrs.GetAttr("dtype")); - ConsistentTensorMeta tensor_meta(std::make_shared(logical_shape), dtype, nd_sbp, - parallel_desc); - Optional parallel_id{}; - const auto& device = JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); - const auto& consistent_tensor_impl = JUST(EagerConsistentTensorImpl::New( - SymbolOf(tensor_meta), device, parallel_id, input_mirrored_tensor->requires_grad(), - !input_mirrored_tensor->requires_grad())); - consistent_tensor = std::make_shared(consistent_tensor_impl); - if (parallel_id.has_value()) { - const auto& pyhsical_shape = JUST(GetPhysicalShape(tensor_meta)); - const auto& input_mirrored_tensor_shape = input_mirrored_tensor->shape(); - CHECK_EQ_OR_RETURN(*pyhsical_shape, *input_mirrored_tensor_shape); - CHECK_OR_RETURN(dtype == input_mirrored_tensor->dtype()->data_type()); - consistent_tensor_impl->reset_cur_rank_phy_tensor(input_mirrored_tensor); - } - } - outputs->at(0) = consistent_tensor; - return Maybe::Ok(); -} - -static constexpr auto* LocalToConsistent = - DECORATE(&RawLocalToConsistent, NonRecursiveInitConsistentId); - -} // namespace - -Maybe EagerMirroredInterpreter::ApplyImpl(const CastToConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - JUST(LocalToConsistent(op_expr, inputs, outputs, ctx)); - const auto& consistent_tensor = JUST(outputs->at(0)->AsConsistentTensor()); - JUST(WithConsistencyChecked(consistent_tensor, [&]() -> Maybe { - if (IsConsistentTensorMetaCheckDisabled()) { return Maybe::Ok(); } - const auto& parallel_desc = JUST(ctx.parallel_desc); - const auto& parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); - if (!parallel_id->has_value()) { return Maybe::Ok(); } - const auto& nd_sbp = JUST(ctx.nd_sbp); - const auto& tensor_meta = JUST(consistent_tensor->consistent_tensor_meta()); - const auto& local_tensor = JUST(consistent_tensor->cur_rank_phy_tensor()); - const auto& reshaped_tensor = JUST(TryReshapeTensor(local_tensor, tensor_meta)); - const auto& synced_tensor = - JUST(GetSyncedTensorIfBroadcast(reshaped_tensor, parallel_desc, nd_sbp)); - auto* consistent_tensor_impl = - reinterpret_cast(consistent_tensor->mut_impl()); - CHECK_NOTNULL_OR_RETURN(consistent_tensor_impl); - consistent_tensor_impl->reset_cur_rank_phy_tensor(JUST(synced_tensor->AsMirroredTensor())); - return Maybe::Ok(); - })); - return Maybe::Ok(); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const CastFromConsistentOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - OF_UNIMPLEMENTED(); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const CastToMirroredOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunMirroredCastInstruction(op_expr, inputs, outputs); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const CastFromMirroredOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunMirroredCastInstruction(op_expr, inputs, outputs); -} - -static Maybe BuildAndRunDistributeSplitOrCloneInstruction(const BuiltinOpExpr& op_expr, - const TensorTuple& inputs, - TensorTuple* outputs) { - // TODO() - OF_UNIMPLEMENTED(); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const DistributeSplitOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunDistributeSplitOrCloneInstruction(op_expr, inputs, outputs); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const DistributeCloneOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunDistributeSplitOrCloneInstruction(op_expr, inputs, outputs); -} - -static Maybe BuildAndRunDistributeConcatAndAddInstruction(const BuiltinOpExpr& op_expr, - const TensorTuple& inputs, - TensorTuple* outputs) { - // TODO() - OF_UNIMPLEMENTED(); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const DistributeConcatOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunDistributeConcatAndAddInstruction(op_expr, inputs, outputs); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const DistributeAddOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - return BuildAndRunDistributeConcatAndAddInstruction(op_expr, inputs, outputs); -} - -Maybe EagerMirroredInterpreter::ApplyImpl(const SelectTopNOpExpr& op_expr, - const TensorTuple& inputs, TensorTuple* outputs, - const OpExprInterpContext& ctx) const { - int top_n = JUST(ctx.attrs.GetAttr("top_n")); - outputs->resize(top_n); - for (int i = 0; i < top_n; ++i) { (*outputs)[i] = JUST(JUST(VectorAt(inputs, i))->detach()); } - return Maybe::Ok(); -} - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.h b/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.h deleted file mode 100644 index ffd6d47d124142ee78f1827e121836bfa74dae69..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.h +++ /dev/null @@ -1,32 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/eager/eager_blob_object.h" - -namespace oneflow { - -class Device; -class TensorTuple; -class ParallelDesc; - -namespace one { - -class Tensor; - -Maybe Broadcast(const std::shared_ptr& tensor, int64_t src_rank, - Symbol parallel_desc, bool inplace); - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/lazy_op_interpreter.cpp b/oneflow/core/framework/op_interpreter/lazy_op_interpreter.cpp index 1b773dc8cd2cbaec2450ea25b90fc786e9559c43..ab00fd922bdcd681e1720de0f07436f16f866050 100644 --- a/oneflow/core/framework/op_interpreter/lazy_op_interpreter.cpp +++ b/oneflow/core/framework/op_interpreter/lazy_op_interpreter.cpp @@ -13,6 +13,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/framework/op_interpreter/lazy_op_interpreter.h" + #include #include "oneflow/core/common/cpp_attribute.h" #include "oneflow/core/common/maybe.h" @@ -34,14 +36,15 @@ limitations under the License. #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/framework/user_op_registry.h" #include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/job/job_desc.h" #include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/operator/operator.h" #include "oneflow/core/job/sbp_parallel.h" #include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/functional/functional.h" -namespace oneflow { +namespace oneflow { namespace one { namespace { @@ -49,7 +52,7 @@ namespace { Maybe BuildTensor(const OpAttribute& op_attribute, const std::string& bn_in_op, const std::shared_ptr& parallel_desc, const bool is_lazy, const bool is_local) { - CHECK_OR_RETURN(op_attribute.has_logical_blob_desc_signature()); + CHECK_OR_RETURN(op_attribute.has_logical_blob_desc_signature()); // NOLINT(maybe-need-error-msg) const auto& blob_desc_sign_map = op_attribute.logical_blob_desc_signature().bn_in_op2blob_desc(); auto blob_desc_it = blob_desc_sign_map.find(bn_in_op); CHECK_OR_RETURN(blob_desc_it != blob_desc_sign_map.end()) @@ -61,8 +64,8 @@ Maybe BuildTensor(const OpAttribute& op_attribute, const std::string& bn if (is_local) { const auto& device = JUST(Device::MakeDeviceByParallelDesc(*parallel_desc)); const auto& tensor = - JUST(MirroredTensor::MakeTensor(shape, stride, dtype, device, is_lazy, - /* requires_grad= */ false, /* is_leaf= */ true)); + JUST(LocalTensor::MakeTensor(shape, stride, dtype, device, is_lazy, + /* requires_grad= */ false, /* is_leaf= */ true)); return static_cast>(tensor); } else { const auto& nd_sbp_sign_map = op_attribute.nd_sbp_signature().bn_in_op2nd_sbp(); @@ -70,9 +73,9 @@ Maybe BuildTensor(const OpAttribute& op_attribute, const std::string& bn CHECK_OR_RETURN(nd_sbp_it != nd_sbp_sign_map.end()) << "nd_sbp of " << bn_in_op << " not found in op " << op_attribute.op_conf().name(); NdSbp nd_sbp(nd_sbp_it->second); - const auto& tensor = JUST(ConsistentTensor::MakeTensor( - shape, dtype, SymbolOf(nd_sbp), SymbolOf(*parallel_desc), is_lazy, - /*requires_grad=*/false, /*is_leaf=*/true)); + const auto& tensor = JUST(GlobalTensor::MakeTensor(shape, dtype, SymbolOf(nd_sbp), + SymbolOf(*parallel_desc), is_lazy, + /*requires_grad=*/false, /*is_leaf=*/true)); return static_cast>(tensor); } } @@ -81,9 +84,9 @@ Maybe CheckTensorMatchAttr(const std::shared_ptr& tensor, const OpAttribute& op_attribute, const std::string& bn_in_op, const std::shared_ptr& parallel_desc, const bool is_local) { - CHECK_EQ_OR_RETURN(tensor->is_local(), is_local); + CHECK_EQ_OR_RETURN(tensor->is_local(), is_local); // NOLINT(maybe-need-error-msg) - CHECK_OR_RETURN(op_attribute.has_logical_blob_desc_signature()); + CHECK_OR_RETURN(op_attribute.has_logical_blob_desc_signature()); // NOLINT(maybe-need-error-msg) const auto& blob_desc_sign_map = op_attribute.logical_blob_desc_signature().bn_in_op2blob_desc(); auto blob_desc_it = blob_desc_sign_map.find(bn_in_op); CHECK_OR_RETURN(blob_desc_it != blob_desc_sign_map.end()) @@ -91,33 +94,39 @@ Maybe CheckTensorMatchAttr(const std::shared_ptr& tensor, auto shape = std::make_shared(blob_desc_it->second.shape()); auto dtype = blob_desc_it->second.data_type(); - CHECK_EQ_OR_RETURN(*tensor->shape(), *shape); - CHECK_EQ_OR_RETURN(tensor->dtype()->data_type(), dtype); + CHECK_EQ_OR_RETURN(*tensor->shape(), *shape); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(tensor->dtype()->data_type(), dtype); // NOLINT(maybe-need-error-msg) if (is_local) { const auto& device = JUST(Device::MakeDeviceByParallelDesc(*parallel_desc)); - CHECK_OR_RETURN(JUST(tensor->device()) == device); + CHECK_OR_RETURN(JUST(tensor->device()) == device); // NOLINT(maybe-need-error-msg) } else { const auto& nd_sbp_sign_map = op_attribute.nd_sbp_signature().bn_in_op2nd_sbp(); auto nd_sbp_it = nd_sbp_sign_map.find(bn_in_op); CHECK_OR_RETURN(nd_sbp_it != nd_sbp_sign_map.end()) << "nd_sbp of " << bn_in_op << " not found in op " << op_attribute.op_conf().name(); - NdSbp nd_sbp(nd_sbp_it->second); - CHECK_OR_RETURN(JUST(tensor->nd_sbp()) == SymbolOf(nd_sbp)) - << "The input sbp is not valid for an inplace operation, please try to use non-inplace. " - << NdSbpToString(JUST(tensor->nd_sbp())) << " vs " << NdSbpToString(nd_sbp); - CHECK_OR_RETURN(JUST(tensor->parallel_desc()) == SymbolOf(*parallel_desc)); + // Only check the nd_sbp if auto parallel is not enable, + // since the semi-auto parallellism rule might have inconsistency with the auto-parallel + // strategy. + if (!GlobalJobDesc().enable_auto_parallel()) { + NdSbp nd_sbp(nd_sbp_it->second); + CHECK_OR_RETURN(JUST(tensor->nd_sbp()) == SymbolOf(nd_sbp)) + << "The input sbp is not valid for an inplace operation, please try to use non-inplace. " + << NdSbpToString(JUST(tensor->nd_sbp())) << " vs " << NdSbpToString(nd_sbp); + } + CHECK_OR_RETURN(JUST(tensor->parallel_desc()) // NOLINT(maybe-need-error-msg) + == SymbolOf(*parallel_desc)); // NOLINT(maybe-need-error-msg) } return Maybe::Ok(); } Maybe GetDeviceTagOfTensor(const std::shared_ptr& tensor) { - if (tensor->is_consistent()) { return JUST(tensor->parallel_desc())->device_tag(); } + if (tensor->is_global()) { return JUST(tensor->parallel_desc())->device_tag(); } return JUST(tensor->device())->type(); } bool GetIsDynamicOfTensor(const std::shared_ptr& tensor) { - if (tensor->is_consistent()) { + if (tensor->is_global()) { return false; } else { return true; @@ -128,8 +137,8 @@ Maybe GenNdSbpByTensor(NdSbp* nd_sbp, const std::shared_ptr& tenso nd_sbp->clear_sbp_parallel(); if (tensor->is_local()) { // NOTE(chengcheng): - // OneFlow Lazy is always consistent. LocalTensor is a special case of ConsistentTensor which - // placement is only this rank, and SbpParallel is Broadcast. + // OneFlow Lazy is always global. LocalTensor is a special case of GlobalTensor + // which placement is only this rank, and SbpParallel is Broadcast. nd_sbp->add_sbp_parallel()->mutable_broadcast_parallel(); } else { *nd_sbp = *JUST(tensor->nd_sbp()); @@ -172,7 +181,7 @@ Maybe NewScopeWithParallelConfAndCurScope(const ParallelConf& parallel_co })); // NOTE(chengcheng): need sync vm for get scope right now JUST(vm::CurrentRankSync()); - CHECK_OR_RETURN(new_scope); + CHECK_OR_RETURN(new_scope); // NOLINT(maybe-need-error-msg) return new_scope; } @@ -181,7 +190,9 @@ Maybe NewScopeWithParallelDescByTensor(const std::shared_ptr& ten JUST(GetParallelDescOfTensor(tensor))->parallel_conf()); } -int32_t GetGradAccStep(const JobConfigProto& job_conf) { +Maybe GetGradAccStep() { + const auto& infer_ctx = JUST(GetCurInferCtx()); + const auto& job_conf = infer_ctx->job().job_conf(); if (job_conf.has_train_conf() && job_conf.has_num_gradient_accumulation_steps() && job_conf.num_gradient_accumulation_steps() > 1) { return job_conf.num_gradient_accumulation_steps(); @@ -190,11 +201,73 @@ int32_t GetGradAccStep(const JobConfigProto& job_conf) { } } -Maybe GradAccTryInsertUnpackAfterInput( - const OperatorConf& input_conf, const std::shared_ptr& blob_parallel_desc, - const std::shared_ptr& input_tensor) { +Maybe AddFreeEagerTensorToVariableOp(const std::shared_ptr& input_tensor) { + if (!input_tensor->is_contiguous()) { + LazyMode::Guard lazy_mode_disabled_guard(false); + JUST(functional::InplaceToContiguous(input_tensor)); + JUST(vm::CurrentRankSync()); + } + + CHECK_OR_RETURN(input_tensor->is_eager()); // NOLINT(maybe-need-error-msg) + const std::string& empty_lbn = TensorNameScope::Global()->Lookup(input_tensor); + CHECK_OR_RETURN(empty_lbn.empty()); // NOLINT(maybe-need-error-msg) + std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(input_tensor)); + OperatorConf op_conf; + op_conf.set_scope_symbol_id(JUST(scope->symbol_id())); + op_conf.set_device_tag(JUST(GetDeviceTagOfTensor(input_tensor))); + VariableOpConf* var_conf = op_conf.mutable_variable_conf(); + var_conf->set_out("out"); + input_tensor->shape()->ToProto(var_conf->mutable_shape()); + var_conf->set_data_type(input_tensor->dtype()->data_type()); + // NOTE(chengcheng): VariableOpConf initializer_conf is useless because variable is inited + // by EagerTensor. + var_conf->mutable_initializer()->mutable_empty_conf(); + JUST(GenVariableOpConfNdSbpStringByTensor(var_conf, input_tensor)); + // NOTE(chengcheng): Free EagerTensor not trainable + var_conf->set_trainable(false); + auto infer_ctx = JUST(GetCurInferCtx()); - int64_t grad_acc_step = GetGradAccStep(infer_ctx->job().job_conf()); + // NOTE(chengcheng): MUST reset unique op name before InferCtx::AddOp, FreeEagerTensor has no + // name so just new a unique name for it. + const std::string new_op_name = *JUST(infer_ctx->NewUniqueOpNameByFunctionalOpConf(op_conf)); + op_conf.set_name(new_op_name); + + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n" + << op_conf.DebugString() << std::endl; + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(op_conf)); + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" + << op_conf.name() << " for FreeEagerTensor.\n"; + VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() + << " infer and and op attr : \n" + << op_attr.DebugString() << " for FreeEagerTensor.\n"; + + // NOTE(chengcheng): MUST store this tensor to MultiClientSessionContext for graph runtime bind. + const std::string graph_name = *JUST(JUST(GlobalJobBuildAndInferCtxMgr())->GetCurrentJobName()); + const std::string lbn = GenLogicalBlobName(new_op_name, "out"); + Singleton::Get()->StoreFreeEagerTensorWithNameByGraphName( + graph_name, input_tensor, new_op_name); + + int64_t parallel_desc_sym_id = JUST(scope->GetParallelDescSymbolId(op_conf)); + auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); + + auto var_tensor = JUST(BuildTensor(op_attr, "out", blob_parallel_desc, /* is_lazy= */ true, + /* is_local= */ input_tensor->is_local())); + TensorNameScope::Global()->Record(var_tensor, lbn); + + // NOTE(chengcheng): MUST record this eager_tensor name as new variable output lbn. + // NOTE(chengcheng): in GradAcc FreeEagerTensor need insert repeat op, but there is no need to + // create a new tensor for repeat op out. We just set repeat lbn as this free eager tensor's lbn. + auto repeat_tensor = JUST(GradAccTryInsertRepeatAfterVar(var_tensor)); + const std::string& repeat_tensor_name = TensorNameScope::Global()->Lookup(repeat_tensor); + CHECK_OR_RETURN(!repeat_tensor_name.empty()); // NOLINT(maybe-need-error-msg) + TensorNameScope::Global()->Record(input_tensor, repeat_tensor_name); + return Maybe::Ok(); +} + +} // namespace + +Maybe GradAccTryInsertUnpackAfterInput(const std::shared_ptr& input) { + int32_t grad_acc_step = JUST(GetGradAccStep()); if (grad_acc_step > 1) { // NOTE(chengcheng): // We assume that the input data is one mini-batch which containing multi micro-batches. @@ -204,41 +277,18 @@ Maybe GradAccTryInsertUnpackAfterInput( << " Once call nn.Graph in OneFlow, it indicates a mini-batch. When grad acc steps > 1, \n" << " the input tensor of nn.Graph will be unpacked by 0th dim into multiple micro-batches " << " and exec them in order.\n"; - - user_op::UserOpConfWrapperBuilder unpack_builder("System-GradientAccumulation-InputUnpack-" - + input_conf.name() + "-" + NewUniqueId()); - const std::string input_tensor_lbn = GenLogicalBlobName(input_conf.name(), "out"); - const auto unpack_op = unpack_builder.OpTypeName("unpack") - .Input("in", input_tensor_lbn) - .Output("out") - .Attr("unpack_num", grad_acc_step) - .ScopeSymbolId(input_conf.scope_symbol_id()) - .DeviceTag(input_conf.device_tag()) - .Build(); - - OpAttribute unpack_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(unpack_op.op_conf())); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" - << unpack_op.op_conf().DebugString() << std::endl; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << unpack_op_attr.DebugString() << std::endl; - - const std::string unpack_lbn = unpack_op.output("out", 0); - auto unpack_input = - JUST(BuildTensor(unpack_op_attr, "out_0", blob_parallel_desc, /* is_lazy= */ true, - /* is_local= */ input_tensor->is_local())); - TensorNameScope::Global()->Record(unpack_input, unpack_lbn); - return unpack_input; + const auto& infer_ctx = JUST(GetCurInferCtx()); + const auto& input_lbn = TensorNameScope::Global()->Lookup(input); + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() + << " add grad acc unpack op after input " << input_lbn << std::endl; + return functional::GradAccUnpack(input, grad_acc_step); } else { - return input_tensor; + return input; } } -Maybe GradAccTryInsertRepeatAfterVar( - const OperatorConf& var_conf, const std::shared_ptr& blob_parallel_desc, - const std::shared_ptr& var_tensor) { - auto infer_ctx = JUST(GetCurInferCtx()); - int64_t grad_acc_step = GetGradAccStep(infer_ctx->job().job_conf()); +Maybe GradAccTryInsertRepeatAfterVar(const std::shared_ptr& variable) { + int32_t grad_acc_step = JUST(GetGradAccStep()); if (grad_acc_step > 1) { // NOTE(chengcheng): // We assume that the nn.Graph once call is one mini-batch which containing multi @@ -247,42 +297,18 @@ Maybe GradAccTryInsertRepeatAfterVar( << " Current OneFlow nn.Graph grad acc semantics is different from Torch. \n" << " Once call nn.Graph in OneFlow, it indicates a mini-batch. When grad acc steps > 1, \n" << " the var tensor of nn.Graph will be repeated exec for multiple micro-batches. \n"; - - const std::string var_tensor_lbn = GenLogicalBlobName(var_conf.name(), "out"); - user_op::UserOpConfWrapperBuilder repeat_builder("System-GradientAccumulation-VariableRepeat-" - + var_conf.name() + "-" + NewUniqueId()); - const auto repeat_op = repeat_builder.OpTypeName("repeat") - .Input("in", var_tensor_lbn) - .Output("out") - .Attr("repeat_num", grad_acc_step) - .ScopeSymbolId(var_conf.scope_symbol_id()) - .DeviceTag(var_conf.device_tag()) - .Build(); - - OpAttribute repeat_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(repeat_op.op_conf())); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" - << repeat_op.op_conf().DebugString() << std::endl; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << repeat_op_attr.DebugString() << std::endl; - - const std::string repeat_lbn = repeat_op.output("out", 0); - auto repeat_var = - JUST(BuildTensor(repeat_op_attr, "out_0", blob_parallel_desc, /* is_lazy= */ true, - /* is_local= */ var_tensor->is_local())); - TensorNameScope::Global()->Record(repeat_var, repeat_lbn); - return repeat_var; + const auto& infer_ctx = JUST(GetCurInferCtx()); + const auto& variable_lbn = TensorNameScope::Global()->Lookup(variable); + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() + << " add grad acc repeat op after variable " << variable_lbn << std::endl; + return functional::GradAccRepeat(variable, grad_acc_step); } else { - return var_tensor; + return variable; } } -Maybe GradAccTryInsertPackBeforeOutput(const std::shared_ptr& scope, - const std::string& output_in_lbn, - const std::string& output_op_name, - const std::shared_ptr& output_tensor) { - auto infer_ctx = JUST(GetCurInferCtx()); - int64_t grad_acc_step = GetGradAccStep(infer_ctx->job().job_conf()); +Maybe GradAccTryInsertPackBeforeOutput(const std::shared_ptr& output) { + int32_t grad_acc_step = JUST(GetGradAccStep()); if (grad_acc_step > 1) { // NOTE(chengcheng): // We assume that the nn.Graph once call is one mini-batch which containing multi @@ -292,42 +318,19 @@ Maybe GradAccTryInsertPackBeforeOutput(const std::shared_ptr& sco << " Once call nn.Graph in OneFlow, it indicates a mini-batch. When grad acc steps > 1, \n" << " the output tensor of nn.Graph will be packed to a big tensor by 0th dim, after exec \n" << " for multiple micro-batches. \n"; - - user_op::UserOpConfWrapperBuilder pack_builder("System-GradientAccumulation-OutputPack-" - + output_op_name); - const auto output_pack_op = pack_builder.OpTypeName("pack") - .Input("in", output_in_lbn) - .Output("out") - .Attr("pack_num", grad_acc_step) - .ScopeSymbolId(JUST(scope->symbol_id())) - .DeviceTag(JUST(GetDeviceTagOfTensor(output_tensor))) - .Build(); - - int64_t parallel_desc_sym_id = JUST(scope->GetParallelDescSymbolId(output_pack_op.op_conf())); - auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); - - OpAttribute pack_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(output_pack_op.op_conf())); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" - << output_pack_op.op_conf().DebugString() << std::endl; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << pack_op_attr.DebugString() << std::endl; - - const std::string pack_lbn = output_pack_op.output("out", 0); - auto packed_output = - JUST(BuildTensor(pack_op_attr, "out_0", blob_parallel_desc, /* is_lazy= */ true, - /* is_local= */ output_tensor->is_local())); - TensorNameScope::Global()->Record(packed_output, pack_lbn); - return packed_output; + const auto& infer_ctx = JUST(GetCurInferCtx()); + const auto& output_lbn = TensorNameScope::Global()->Lookup(output); + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() + << " add grad acc pack op before output " << output_lbn << std::endl; + return functional::GradAccPack(output, grad_acc_step); } else { - return output_tensor; + return output; } } Maybe GradAccTryInsertRepeatTickBeforeSource( - const std::shared_ptr& source_op_conf) { - auto infer_ctx = JUST(GetCurInferCtx()); - int64_t grad_acc_step = GetGradAccStep(infer_ctx->job().job_conf()); + const std::shared_ptr& source_op_conf, bool is_local) { + int32_t grad_acc_step = JUST(GetGradAccStep()); if (grad_acc_step > 1) { // NOTE(chengcheng): // We assume that the nn.Graph once call is one mini-batch which containing multi @@ -336,145 +339,50 @@ Maybe GradAccTryInsertRepeatTickBeforeSource( << " Current OneFlow nn.Graph grad acc semantics is different from Torch. \n" << " Once call nn.Graph in OneFlow, it indicates a mini-batch. When grad acc steps > 1, \n" << " the source op of nn.Graph will be repeated exec n-times for multiple micro-batches.\n"; - + const auto& infer_ctx = JUST(GetCurInferCtx()); // Insert Tick OperatorConf tick_conf{}; - tick_conf.set_name("System-GradientAccumulation-RepeatTick-DeviceTick-" - + source_op_conf->name()); + tick_conf.set_name("Sys-GradAcc-RepeatTick-DeviceTick-" + source_op_conf->name()); tick_conf.set_device_tag(source_op_conf->device_tag()); tick_conf.mutable_device_tick_conf()->set_out("out"); tick_conf.set_scope_symbol_id(source_op_conf->scope_symbol_id()); auto tick_lbn = GenLogicalBlobName(tick_conf.name(), tick_conf.device_tick_conf().out()); - OpAttribute tick_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(tick_conf)); + OpAttribute tick_op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(tick_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" << tick_conf.DebugString() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " infer and and op attr : \n" << tick_op_attr.DebugString() << std::endl; - user_op::UserOpConfWrapperBuilder repeat_builder( - "System-GradientAccumulation-RepeatTick-Repeat-" + source_op_conf->name()); - const auto repeat_op = repeat_builder.OpTypeName("repeat") - .Input("in", tick_lbn) - .Output("out") - .Attr("repeat_num", grad_acc_step) - .ScopeSymbolId(source_op_conf->scope_symbol_id()) - .DeviceTag(source_op_conf->device_tag()) - .Build(); - - OpAttribute repeat_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(repeat_op.op_conf())); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" - << repeat_op.op_conf().DebugString() << std::endl; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << repeat_op_attr.DebugString() << std::endl; + const auto& scope = + Singleton>::Get()->Get(source_op_conf->scope_symbol_id()); + int64_t parallel_desc_sym_id = JUST(scope.GetParallelDescSymbolId(tick_conf)); + auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); - const std::string repeat_tick_lbn = repeat_op.output("out", 0); + auto tick_tensor = JUST(BuildTensor(tick_op_attr, tick_conf.device_tick_conf().out(), + blob_parallel_desc, /* is_lazy= */ true, + /* is_local= */ is_local)); + TensorNameScope::Global()->Record(tick_tensor, tick_lbn); + + VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() + << " add grad acc repeat op after tick op " << tick_conf.name() + << " and before source op" << source_op_conf->name(); + auto repeat_tensor = JUST(functional::GradAccRepeat(tick_tensor, grad_acc_step)); + const std::string& repeat_tensor_name = TensorNameScope::Global()->Lookup(repeat_tensor); + CHECK_OR_RETURN(!repeat_tensor_name.empty()); // NOLINT(maybe-need-error-msg) (*source_op_conf->mutable_user_conf()->mutable_input())[user_op::kUserSourceOpTickInputArgName] - .add_s(repeat_op.output("out", 0)); - } - return Maybe::Ok(); -} - -Maybe GradAccTryInsertRepeatAfterFreeVar(const OperatorConf& var_conf) { - const std::string var_tensor_lbn = GenLogicalBlobName(var_conf.name(), "out"); - auto infer_ctx = JUST(GetCurInferCtx()); - int64_t grad_acc_step = GetGradAccStep(infer_ctx->job().job_conf()); - if (grad_acc_step > 1) { - // NOTE(chengcheng): - // We assume that the nn.Graph once call is one mini-batch which containing multi - // micro-batches. So we just repeat variable tensor for each micro-batch. - VLOG(2) - << " Current OneFlow nn.Graph grad acc semantics is different from Torch. \n" - << " Once call nn.Graph in OneFlow, it indicates a mini-batch. When grad acc steps > 1, \n" - << " the free var tensor of nn.Graph will be repeated exec for multiple micro-batches. \n"; - - user_op::UserOpConfWrapperBuilder repeat_builder("System-GradientAccumulation-VariableRepeat-" - + var_conf.name() + "-" + NewUniqueId()); - const auto repeat_op = repeat_builder.OpTypeName("repeat") - .Input("in", var_tensor_lbn) - .Output("out") - .Attr("repeat_num", grad_acc_step) - .ScopeSymbolId(var_conf.scope_symbol_id()) - .DeviceTag(var_conf.device_tag()) - .Build(); - - OpAttribute repeat_op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(repeat_op.op_conf())); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op: \n" - << repeat_op.op_conf().DebugString() << std::endl; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << repeat_op_attr.DebugString() << std::endl; - - const std::string repeat_lbn = repeat_op.output("out", 0); - return repeat_lbn; - } else { - return var_tensor_lbn; - } -} - -Maybe AddFreeEagerTensorToVariableOp(const std::shared_ptr& input_tensor) { - if (!input_tensor->is_contiguous()) { - LazyMode::Guard lazy_mode_disabled_guard(false); - JUST(functional::InplaceToContiguous(input_tensor)); - JUST(vm::CurrentRankSync()); + .add_s(repeat_tensor_name); } - - CHECK_OR_RETURN(input_tensor->is_eager()); - const std::string& empty_lbn = TensorNameScope::Global()->Lookup(input_tensor); - CHECK_OR_RETURN(empty_lbn.empty()); - std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(input_tensor)); - OperatorConf op_conf; - op_conf.set_scope_symbol_id(JUST(scope->symbol_id())); - op_conf.set_device_tag(JUST(GetDeviceTagOfTensor(input_tensor))); - VariableOpConf* var_conf = op_conf.mutable_variable_conf(); - var_conf->set_out("out"); - input_tensor->shape()->ToProto(var_conf->mutable_shape()); - var_conf->set_data_type(input_tensor->dtype()->data_type()); - // NOTE(chengcheng): VariableOpConf initializer_conf is useless because variable is inited - // by EagerTensor. - var_conf->mutable_initializer()->mutable_empty_conf(); - JUST(GenVariableOpConfNdSbpStringByTensor(var_conf, input_tensor)); - // NOTE(chengcheng): Free EagerTensor not trainable - var_conf->set_trainable(false); - - auto infer_ctx = JUST(GetCurInferCtx()); - // NOTE(chengcheng): MUST reset unique op name before InferCtx::AddOp, FreeEagerTensor has no - // name so just new a unique name for it. - const std::string new_op_name = *JUST(infer_ctx->NewUniqueOpNameByFunctionalOpConf(op_conf)); - op_conf.set_name(new_op_name); - - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n" - << op_conf.DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(op_conf)); - VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" - << op_conf.name() << " for FreeEagerTensor.\n"; - VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() - << " infer and and op attr : \n" - << op_attr.DebugString() << " for FreeEagerTensor.\n"; - - // NOTE(chengcheng): MUST store this tensor to MultiClientSessionContext for graph runtime bind. - const std::string graph_name = *JUST(JUST(GlobalJobBuildAndInferCtxMgr())->GetCurrentJobName()); - const std::string lbn = GenLogicalBlobName(new_op_name, "out"); - Singleton::Get()->StoreFreeEagerTensorWithNameByGraphName( - graph_name, input_tensor, new_op_name); - // NOTE(chengcheng): MUST record this eager_tensor name as new variable output lbn. - // NOTE(chengcheng): in GradAcc FreeEagerTensor need insert repeat op, but there is no need to - // create a new tensor for repeat op out. We just set repeat lbn as this free eager tensor's lbn. - TensorNameScope::Global()->Record(input_tensor, - *JUST(GradAccTryInsertRepeatAfterFreeVar(op_conf))); return Maybe::Ok(); } -} // namespace - Maybe LazyInterpreter::ApplyImpl(const FeedInputOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { // NOTE(chengcheng): inputs[0] is the EagerTensor - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) const std::shared_ptr& input_tensor = inputs.at(0); - CHECK_OR_RETURN(input_tensor->is_eager()); + CHECK_OR_RETURN(input_tensor->is_eager()); // NOLINT(maybe-need-error-msg) std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(input_tensor)); @@ -491,7 +399,7 @@ Maybe LazyInterpreter::ApplyImpl(const FeedInputOpExpr& op_expr, const Ten input_tensor->shape()->ToProto(blob_conf->mutable_shape()); blob_conf->set_data_type(input_tensor->dtype()->data_type()); - // NOTE(chengcheng): is_dynamic true has conflict in consistent lazy job even if world size 1. + // NOTE(chengcheng): is_dynamic true has conflict in global lazy job even if world size 1. // this flag will be removed in the future. // blob_conf->set_is_dynamic(GetIsDynamicOfTensor(input_tensor)); blob_conf->set_is_dynamic(false); @@ -500,7 +408,7 @@ Maybe LazyInterpreter::ApplyImpl(const FeedInputOpExpr& op_expr, const Ten auto infer_ctx = JUST(GetCurInferCtx()); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n: " << op_conf.DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(op_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" << op_conf.name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() @@ -511,26 +419,28 @@ Maybe LazyInterpreter::ApplyImpl(const FeedInputOpExpr& op_expr, const Ten auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); - CHECK_OR_RETURN(!(*outputs)[0]); + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!(*outputs)[0]); // NOLINT(maybe-need-error-msg) const std::string obn = "out"; // NOTE(chengcheng): obn is NOT op_expr.indexed_obns auto origin_input = JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ true, /* is_local= */ input_tensor->is_local())); TensorNameScope::Global()->Record(origin_input, GenLogicalBlobName(op_conf.name(), obn)); + TensorNameScope::Global()->Record(input_tensor, GenLogicalBlobName(op_conf.name(), obn)); - // NOTE(chengcheng): Do GradAcc pass when add input op. - (*outputs)[0] = JUST(GradAccTryInsertUnpackAfterInput(op_conf, blob_parallel_desc, origin_input)); + // NOTE: The input will then be unpacked in DispatchFeedInputOpExprFunctor + // if GradAcc is enabled + (*outputs)[0] = origin_input; return Maybe::Ok(); } Maybe LazyInterpreter::ApplyImpl(const FeedVariableOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { // NOTE(chengcheng): inputs[0] is the EagerTensor - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) const std::shared_ptr& input_tensor = inputs.at(0); - CHECK_OR_RETURN(input_tensor->is_eager()); + CHECK_OR_RETURN(input_tensor->is_eager()); // NOLINT(maybe-need-error-msg) std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(input_tensor)); @@ -558,7 +468,7 @@ Maybe LazyInterpreter::ApplyImpl(const FeedVariableOpExpr& op_expr, const auto infer_ctx = JUST(GetCurInferCtx()); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n: " << op_conf.DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(op_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" << op_conf.name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() @@ -569,48 +479,45 @@ Maybe LazyInterpreter::ApplyImpl(const FeedVariableOpExpr& op_expr, const auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); - CHECK_OR_RETURN(!(*outputs)[0]); + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!(*outputs)[0]); // NOLINT(maybe-need-error-msg) const std::string obn = "out"; // NOTE(chengcheng): obn is NOT op_expr.indexed_obns auto origin_var = JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ true, /* is_local */ input_tensor->is_local())); - // NOTE(chengcheng): Record variable op output LazyTenosr TensorNameScope::Global()->Record(origin_var, GenLogicalBlobName(op_conf.name(), obn)); // NOTE(chengcheng): Record EagerTensor as variable tensor name TensorNameScope::Global()->Record(input_tensor, GenLogicalBlobName(op_conf.name(), obn)); - (*outputs)[0] = JUST(GradAccTryInsertRepeatAfterVar(op_conf, blob_parallel_desc, origin_var)); + // NOTE: The output variable will then be repeat in DispatchFeedVariableOpExprFunctor + // if GradAcc is enabled + (*outputs)[0] = origin_var; return Maybe::Ok(); } Maybe LazyInterpreter::ApplyImpl(const FetchOutputOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { + // NOTE: The input has been packed in DispatchFetchOutputOpExprFunctor + // if GradAcc is enabled // NOTE(chengcheng): inputs[0] is the LazyTensor - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) const std::shared_ptr& input_tensor = inputs.at(0); std::string input_lbn = TensorNameScope::Global()->Lookup(input_tensor); // Lazy tensor must has lbn. // Eager tensor may has lbn if it has already been treated as an output of a variable op // or an output of an inplace op. if (input_lbn.empty()) { - CHECK_OR_RETURN(input_tensor->is_eager()); + CHECK_OR_RETURN(input_tensor->is_eager()); // NOLINT(maybe-need-error-msg) // This output tensor is a new free eager tensor, so treat it as a new variable op output. JUST(AddFreeEagerTensorToVariableOp(input_tensor)); input_lbn = TensorNameScope::Global()->Lookup(input_tensor); + CHECK_OR_RETURN(!input_lbn.empty()); // NOLINT(maybe-need-error-msg) } - CHECK_OR_RETURN(!input_lbn.empty()); // lbn must exist. - std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(input_tensor)); - std::shared_ptr output_tensor = - JUST(GradAccTryInsertPackBeforeOutput(scope, input_lbn, op_expr.op_name(), input_tensor)); - - const std::string output_lbn = TensorNameScope::Global()->Lookup(output_tensor); - OperatorConf op_conf; op_conf.set_name(op_expr.op_name()); // construct by python nn.Graph op_conf.set_scope_symbol_id(JUST(scope->symbol_id())); @@ -619,21 +526,21 @@ Maybe LazyInterpreter::ApplyImpl(const FetchOutputOpExpr& op_expr, const T // We contruct OutputOpConf instead of FetchOutputOpConf because FetchOutputOpExpr JUST // for get nn.Graph output LazyTensor. OutputOpConf* output_conf = op_conf.mutable_output_conf(); - output_conf->set_in(output_lbn); + output_conf->set_in(input_lbn); output_conf->set_out("out"); InterfaceBlobConf* blob_conf = output_conf->mutable_blob_conf(); - output_tensor->shape()->ToProto(blob_conf->mutable_shape()); - blob_conf->set_data_type(output_tensor->dtype()->data_type()); - // NOTE(chengcheng): is_dynamic true has conflict in consistent lazy job even if world size 1. + input_tensor->shape()->ToProto(blob_conf->mutable_shape()); + blob_conf->set_data_type(input_tensor->dtype()->data_type()); + // NOTE(chengcheng): is_dynamic true has conflict in global lazy job even if world size 1. // this flag will be removed in the future. - // blob_conf->set_is_dynamic(GetIsDynamicOfTensor(output_tensor)); + // blob_conf->set_is_dynamic(GetIsDynamicOfTensor(input_tensor)); blob_conf->set_is_dynamic(false); - JUST(GenNdSbpByTensor(blob_conf->mutable_nd_sbp(), output_tensor)); + JUST(GenNdSbpByTensor(blob_conf->mutable_nd_sbp(), input_tensor)); auto infer_ctx = JUST(GetCurInferCtx()); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n" << op_conf.DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(op_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" << op_conf.name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() @@ -644,23 +551,23 @@ Maybe LazyInterpreter::ApplyImpl(const FetchOutputOpExpr& op_expr, const T auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); - CHECK_OR_RETURN(!(*outputs)[0]); + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!(*outputs)[0]); // NOLINT(maybe-need-error-msg) const std::string obn = "out"; // NOTE(chengcheng): obn is NOT op_expr.indexed_obns (*outputs)[0] = JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ false, - /* is_local= */ output_tensor->is_local())); + /* is_local= */ input_tensor->is_local())); return Maybe::Ok(); } Maybe LazyInterpreter::ApplyImpl(const ImageDecoderRandomCropResizeOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) const std::shared_ptr& input_tensor = inputs.at(0); const std::string& input_lbn = TensorNameScope::Global()->Lookup(input_tensor); - CHECK_OR_RETURN(!input_lbn.empty()); // lbn must exist. + CHECK_OR_RETURN(!input_lbn.empty()); // NOLINT(maybe-need-error-msg) auto op_conf = JUST(OpInterpUtil::GenBuiltinOpConf(op_expr, ctx.attrs)); std::string device_tag; @@ -687,7 +594,7 @@ Maybe LazyInterpreter::ApplyImpl(const ImageDecoderRandomCropResizeOpExpr& op_conf->set_name(new_op_name); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n" << op_conf->DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(*op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(*op_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" << op_conf->name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() @@ -698,9 +605,9 @@ Maybe LazyInterpreter::ApplyImpl(const ImageDecoderRandomCropResizeOpExpr& auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); - CHECK_OR_RETURN(!(*outputs)[0]); + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!(*outputs)[0]); // NOLINT(maybe-need-error-msg) const std::string obn = "out"; // NOTE(chengcheng): obn is NOT op_expr.indexed_obns (*outputs)[0] = JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ true, /* is_local= */ input_tensor->is_local())); @@ -717,15 +624,15 @@ Maybe LazyInterpreterApplyImplForSourceUserOpExpr(const UserOpExpr& op_exp bool is_local; std::shared_ptr parallel_desc; if (ctx.parallel_desc.has_value()) { - // NOTE(chengcheng): consistent - CHECK_OR_RETURN(!ctx.device.has_value()); + // NOTE(chengcheng): global + CHECK_OR_RETURN(!ctx.device.has_value()); // NOLINT(maybe-need-error-msg) const auto& parallel_desc_sym = JUST(ctx.parallel_desc); parallel_desc = parallel_desc_sym.shared_from_symbol(); JUST(MetaInfoConsistencyCheck(parallel_desc_sym, ctx.nd_sbp, 1, /* force_check */ false)); is_local = false; } else { // NOTE(chengcheng): local - CHECK_OR_RETURN(!ctx.nd_sbp.has_value()); + CHECK_OR_RETURN(!ctx.nd_sbp.has_value()); // NOLINT(maybe-need-error-msg) if (ctx.device.has_value()) { const auto& device = JUST(ctx.device); const auto& placement = JUST(Placement4Device(device)); @@ -747,6 +654,7 @@ Maybe LazyInterpreterApplyImplForSourceUserOpExpr(const UserOpExpr& op_exp auto infer_ctx = JUST(GetCurInferCtx()); // NOTE(chengcheng): MUST reset unique op name before InferCtx::AddOp const std::string new_op_name = *JUST(infer_ctx->NewUniqueOpNameByFunctionalOpConf(*op_conf)); + const std::string graph_name = infer_ctx->job().job_conf().job_name(); // NOTE(chengcheng): for UserOp, NOT only reset op_name, but also the output values. op_conf->set_name(new_op_name); @@ -761,11 +669,11 @@ Maybe LazyInterpreterApplyImplForSourceUserOpExpr(const UserOpExpr& op_exp } } - JUST(GradAccTryInsertRepeatTickBeforeSource(op_conf)); + JUST(GradAccTryInsertRepeatTickBeforeSource(op_conf, is_local)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " try to add op: \n" << op_conf->DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(*op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(*op_conf)); VLOG(2) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() << " add op : \n" << op_conf->name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << infer_ctx->job().job_conf().job_name() @@ -776,12 +684,18 @@ Maybe LazyInterpreterApplyImplForSourceUserOpExpr(const UserOpExpr& op_exp auto blob_parallel_desc = JUST(GetSymbol(parallel_desc_sym_id)); // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), op_expr.output_size()); + CHECK_EQ_OR_RETURN(outputs->size(), op_expr.output_size()); // NOLINT(maybe-need-error-msg) for (int i = 0; i < op_expr.output_size(); ++i) { - CHECK_OR_RETURN(!(*outputs)[i]); const std::string& obn = op_expr.indexed_obns().at(i); - (*outputs)[i] = - JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ true, is_local)); + if (!(*outputs)[i]) { + (*outputs)[i] = + JUST(BuildTensor(op_attr, obn, blob_parallel_desc, /* is_lazy= */ true, is_local)); + } else { + VLOG(2) << "Lazy nn.Graph name " << graph_name << " source op name " << new_op_name + << " run with inplace."; + const std::shared_ptr& inplace_out = (*outputs)[i]; + JUST(CheckTensorMatchAttr(inplace_out, op_attr, obn, blob_parallel_desc, is_local)); + } TensorNameScope::Global()->Record((*outputs)[i], GenLogicalBlobName(new_op_name, obn)); } return Maybe::Ok(); @@ -791,23 +705,23 @@ Maybe LazyInterpreterApplyImplForCopyUserOpExpr(const UserOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) { - CHECK_OR_RETURN(op_expr.op_type_name() == "copy"); - CHECK_EQ_OR_RETURN(inputs.size(), 1); - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); + CHECK_OR_RETURN(op_expr.op_type_name() == "copy"); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) const std::shared_ptr& input_tensor = inputs.at(0); std::string input_lbn = TensorNameScope::Global()->Lookup(input_tensor); if (input_lbn.empty()) { JUST(AddFreeEagerTensorToVariableOp(input_tensor)); input_lbn = TensorNameScope::Global()->Lookup(input_tensor); } - CHECK_OR_RETURN(!input_lbn.empty()); // lbn must exist. + CHECK_OR_RETURN(!input_lbn.empty()); // NOLINT(maybe-need-error-msg) std::string device_type = JUST(ctx.attrs.GetAttr("device_type")); int64_t device_id = JUST(ctx.attrs.GetAttr("device_id")); - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) if (input_tensor->is_local()) { - (*outputs)[0] = JUST(MirroredTensor::MakeTensor( + (*outputs)[0] = JUST(LocalTensor::MakeTensor( input_tensor->shape(), JUST(input_tensor->stride()), input_tensor->dtype()->data_type(), JUST(Device::New(device_type, device_id)), /* is_lazy= */ true, @@ -817,10 +731,10 @@ Maybe LazyInterpreterApplyImplForCopyUserOpExpr(const UserOpExpr& op_expr, parallel_conf.set_device_tag(device_type); ParallelDesc parallel_desc(parallel_conf); (*outputs)[0] = - JUST(ConsistentTensor::MakeTensor(input_tensor->shape(), input_tensor->dtype()->data_type(), - JUST(input_tensor->nd_sbp()), SymbolOf(parallel_desc), - /* is_lazy= */ true, - /*requires_grad=*/false, /*is_leaf=*/true)); + JUST(GlobalTensor::MakeTensor(input_tensor->shape(), input_tensor->dtype()->data_type(), + JUST(input_tensor->nd_sbp()), SymbolOf(parallel_desc), + /* is_lazy= */ true, + /*requires_grad=*/false, /*is_leaf=*/true)); } // NOTE(chengcheng): output tensor lbn is SAME with input tensor. TensorNameScope::Global()->Record(outputs->at(0), input_lbn); @@ -831,7 +745,7 @@ Maybe LazyInterpreterApplyImplForCopyUserOpExpr(const UserOpExpr& op_expr, Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { - CHECK_EQ_OR_RETURN(inputs.size(), op_expr.input_size()); + CHECK_EQ_OR_RETURN(inputs.size(), op_expr.input_size()); // NOLINT(maybe-need-error-msg) // NOTE(chengcheng): Handle special UserOp such as: // 1. [Source UserOp] : OFRecordReader, CoinFlip @@ -857,7 +771,7 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu // NOTE(chengcheng): // Normal UserOp inputs size >= 1 for infer parallel_desc. - CHECK_GE_OR_RETURN(inputs.size(), 1); + CHECK_GE_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) auto op_conf = JUST(OpInterpUtil::GenBuiltinOpConf(op_expr, ctx.attrs)); std::shared_ptr scope = JUST(NewScopeWithParallelDescByTensor(JUST(VectorAt(inputs, 0)))); op_conf->set_scope_symbol_id(JUST(scope->symbol_id())); @@ -874,7 +788,7 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu for (int i = 0; i < inputs.size(); ++i) { const auto& input_tensor = inputs.at(i); - CHECK_EQ_OR_RETURN(is_local, input_tensor->is_local()); + CHECK_EQ_OR_RETURN(is_local, input_tensor->is_local()); // NOLINT(maybe-need-error-msg) if (is_local) { CHECK_OR_RETURN(device_tag == JUST(GetDeviceTagOfTensor(input_tensor))) << Error::RuntimeError() << "Lazy nn.Graph name: " << graph_name @@ -903,7 +817,7 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu JUST(AddFreeEagerTensorToVariableOp(input_tensor)); lbn = TensorNameScope::Global()->Lookup(input_tensor); } - CHECK_OR_RETURN(!lbn.empty()); // NOTE(chengcheng): lbn must not empty now. + CHECK_OR_RETURN(!lbn.empty()); // NOLINT(maybe-need-error-msg) ReplaceInputLbnInOpCustomizedConf(op_conf.get(), ibn, lbn); } @@ -921,7 +835,7 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu } // Check outputs num and setup output tensor properties. - CHECK_EQ_OR_RETURN(outputs->size(), op_expr.output_size()); + CHECK_EQ_OR_RETURN(outputs->size(), op_expr.output_size()); // NOLINT(maybe-need-error-msg) // Disable boxing if the computation is inplace. for (int i = 0; i < op_expr.output_size(); ++i) { @@ -937,7 +851,7 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu } VLOG(2) << "Lazy nn.Graph name " << graph_name << " try to add op: \n" << op_conf->DebugString() << std::endl; - OpAttribute op_attr = *JUST(infer_ctx->AddAndInferConsistentOp(*op_conf)); + OpAttribute op_attr = *JUST(infer_ctx->AddAndInferGlobalOp(*op_conf)); VLOG(2) << "Lazy nn.Graph name " << graph_name << " add op : \n" << op_conf->name() << std::endl; VLOG(3) << "Lazy nn.Graph name " << graph_name << " infer and and op attr : \n" << op_attr.DebugString() << std::endl; @@ -961,31 +875,32 @@ Maybe LazyInterpreter::ApplyImpl(const UserOpExpr& op_expr, const TensorTu } Maybe LazyInterpreter::ApplyImpl(const FunctionOpExpr& op_expr, const TensorTuple& inputs, - TensorTuple* outputs, const OpExprInterpContext& ctx) const { - // TODO(hjchen2) - OF_UNIMPLEMENTED() << "The type " << op_expr.op_type_name() - << " has not been supported in LazyInterpreter::Apply."; + TensorTuple* outputs, const OpExprInterpContext&) const { + // Must reset ctx in each forward + op_expr.reset_state(); + std::shared_ptr ctx = op_expr.state(); + *outputs = *(op_expr.forward()(ctx, inputs)); return Maybe::Ok(); } -Maybe LazyInterpreter::ApplyImpl(const ConsistentToConsistentOpExpr& op_expr, +Maybe LazyInterpreter::ApplyImpl(const GlobalToGlobalOpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) const { - CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); - CHECK_EQ_OR_RETURN(inputs.size(), 1); + CHECK_EQ_OR_RETURN(op_expr.input_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(inputs.size(), 1); // NOLINT(maybe-need-error-msg) const auto& input_tensor = inputs[0]; - CHECK_OR_RETURN(input_tensor->is_consistent()); + CHECK_OR_RETURN(input_tensor->is_global()); // NOLINT(maybe-need-error-msg) - CHECK_OR_RETURN(ctx.parallel_desc.has_value()); + CHECK_OR_RETURN(ctx.parallel_desc.has_value()); // NOLINT(maybe-need-error-msg) const auto& parallel_desc_sym = JUST(ctx.parallel_desc); - CHECK_OR_RETURN(ctx.nd_sbp.has_value()); + CHECK_OR_RETURN(ctx.nd_sbp.has_value()); // NOLINT(maybe-need-error-msg) const auto& sbp_sym = JUST(ctx.nd_sbp); std::string input_lbn = TensorNameScope::Global()->Lookup(input_tensor); if (input_lbn.empty()) { JUST(AddFreeEagerTensorToVariableOp(input_tensor)); input_lbn = TensorNameScope::Global()->Lookup(input_tensor); - CHECK_OR_RETURN(!input_lbn.empty()); + CHECK_OR_RETURN(!input_lbn.empty()); // NOLINT(maybe-need-error-msg) } std::shared_ptr input_proxy; @@ -994,16 +909,16 @@ Maybe LazyInterpreter::ApplyImpl(const ConsistentToConsistentOpExpr& op_ex // NOTE(zwx): The input tensor's parallel_desc is not equal to that of op's, // create a proxy input with the parallel_desc that is the same as op's input_proxy = - JUST(ConsistentTensor::MakeTensor(input_tensor->shape(), input_tensor->dtype()->data_type(), - JUST(input_tensor->nd_sbp()), parallel_desc_sym, - /* is_lazy= */ true, - /*requires_grad=*/false, /*is_leaf=*/true)); + JUST(GlobalTensor::MakeTensor(input_tensor->shape(), input_tensor->dtype()->data_type(), + JUST(input_tensor->nd_sbp()), parallel_desc_sym, + /* is_lazy= */ true, + /*requires_grad=*/false, /*is_leaf=*/true)); TensorNameScope::Global()->Record(input_proxy, input_lbn); } - CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); - CHECK_EQ_OR_RETURN(outputs->size(), 1); - CHECK_OR_RETURN(!(*outputs)[0]); + CHECK_EQ_OR_RETURN(op_expr.output_size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(outputs->size(), 1); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!(*outputs)[0]); // NOLINT(maybe-need-error-msg) if (!op_expr.grad_nd_sbp().has_value() && sbp_sym == JUST(input_tensor->nd_sbp())) { // NOTE(chengcheng): if to_global ONLY change placement (nd_sbp and grad_nd_sbp is same), diff --git a/oneflow/core/framework/op_interpreter/lazy_op_interpreter.h b/oneflow/core/framework/op_interpreter/lazy_op_interpreter.h new file mode 100644 index 0000000000000000000000000000000000000000..7a5698cb279c2003afa98816e593ec1b8a61ec70 --- /dev/null +++ b/oneflow/core/framework/op_interpreter/lazy_op_interpreter.h @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_name_scope.h" +#include "oneflow/core/operator/operator.h" + +namespace oneflow { +namespace one { + +Maybe GradAccTryInsertUnpackAfterInput(const std::shared_ptr& input); +Maybe GradAccTryInsertRepeatAfterVar(const std::shared_ptr& variable); +Maybe GradAccTryInsertPackBeforeOutput(const std::shared_ptr& output); + +Maybe GradAccTryInsertRepeatTickBeforeSource( + const std::shared_ptr& source_op_conf, bool is_local); + +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/framework/op_interpreter/op_interpreter.cpp b/oneflow/core/framework/op_interpreter/op_interpreter.cpp index 6dea92f954cb82e971a9b3b6dce57dbc3187b665..3dfdd60c5645ca1262a4db72edc1eaf436c4dd55 100644 --- a/oneflow/core/framework/op_interpreter/op_interpreter.cpp +++ b/oneflow/core/framework/op_interpreter/op_interpreter.cpp @@ -23,6 +23,7 @@ limitations under the License. #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/profiler/profiler.h" namespace oneflow { namespace one { @@ -38,7 +39,7 @@ Maybe LazyInterpreter::Apply(const OpExpr& op_expr, const TensorTuple& inp APPLY_IF(FeedVariableOp); APPLY_IF(FetchOutputOp); APPLY_IF(UserOp); - APPLY_IF(ConsistentToConsistentOp); + APPLY_IF(GlobalToGlobalOp); APPLY_IF(FunctionOp); APPLY_IF(ImageDecoderRandomCropResizeOp); #undef APPLY_IF @@ -56,11 +57,11 @@ Maybe EagerInterpreter::Apply(const OpExpr& op_expr, const TensorTuple& in APPLY_IF(UserOp); APPLY_IF(VariableOp); - APPLY_IF(CastToMirroredOp); - APPLY_IF(CastFromMirroredOp); - APPLY_IF(ConsistentToConsistentOp); - APPLY_IF(CastToConsistentOp); - APPLY_IF(CastFromConsistentOp); + APPLY_IF(CastToLocalOp); + APPLY_IF(CastFromLocalOp); + APPLY_IF(GlobalToGlobalOp); + APPLY_IF(LocalToGlobalOp); + APPLY_IF(GlobalToLocalOp); APPLY_IF(DistributeSplitOp); APPLY_IF(DistributeCloneOp); APPLY_IF(DistributeConcatOp); @@ -90,28 +91,14 @@ Maybe AutogradInterpreter::Apply(const OpExpr& op_expr, const TensorTuple& std::any_of(inputs.begin(), inputs.end(), [](const std::shared_ptr& tensor) { return tensor->requires_grad(); }); } - -// NOTE: if this op not support stride, then need to tensor->contiguous() -#define HANDLE_NON_CONTIGUOUS_INPUT(tensor_tuple_ptr) \ - TensorTuple tmp_inputs; \ - if (!LazyMode::is_enabled() && !JUST(op_expr.SupportNonContiguous())) { \ - tmp_inputs.resize(inputs.size()); \ - for (size_t i = 0; i < inputs.size(); i++) { tmp_inputs[i] = inputs[i]->contiguous(); } \ - tensor_tuple_ptr = &tmp_inputs; \ - } - - const TensorTuple* inputs_ptr = &inputs; - HANDLE_NON_CONTIGUOUS_INPUT(inputs_ptr); - { autograd::AutoGradMode mode(false); - const bool inplace = ctx.inplace.value_or(false); - if (inplace) { *outputs = *inputs_ptr; } - JUST(internal_->Apply(op_expr, *inputs_ptr, outputs, ctx)); + JUST(internal_->Apply(op_expr, inputs, outputs, ctx)); } // Lazy mode will construct backward compute graph in passes, so disable autograd if lazy mode. std::shared_ptr grad_closure(nullptr); - if (requires_grad && !LazyMode::is_enabled()) { + if (requires_grad) { + OF_PROFILER_RANGE_PUSH("autograd.GetOrCreateOpGradClosure"); grad_closure = JUST(op_expr.GetOrCreateOpGradClosure()); auto backward_fn = std::make_shared(); backward_fn->body = [=](const TensorTuple& out_grads, TensorTuple* in_grads, @@ -121,15 +108,25 @@ Maybe AutogradInterpreter::Apply(const OpExpr& op_expr, const TensorTuple& return Maybe::Ok(); }; backward_fn->status = [=]() { return grad_closure->state()->SavedTensors().size() > 0; }; + OF_PROFILER_RANGE_POP(); + OF_PROFILER_RANGE_PUSH("autograd.AddNode"); JUST(GetThreadLocalAutogradEngine()->AddNode(op_expr.op_type_name() + "_backward", backward_fn, - *inputs_ptr, outputs)); + inputs, outputs)); + OF_PROFILER_RANGE_POP(); + } + + if (requires_grad) { + OF_PROFILER_RANGE_GUARD("autograd.Capture"); + // Capture inputs and outputs after `AddNode` because of that grad function + // node has been attached to them. + JUST(grad_closure->Capture(inputs, *outputs, ctx)); } // Update outputs autograd meta // Note: if requires_grad is True, we will create a new autograd meta for each output - // in `AddBackwardFuncPtr` to support inplace operation, so the update should after - // `AddBackwardFuncPtr` + // in `AddNode` to support inplace operation, so the update should after + // `AddNode` for (auto& output : *outputs) { - output->set_is_leaf(inputs_ptr->size() == 0 || !requires_grad); + output->set_is_leaf(inputs.size() == 0 || !requires_grad); // If the output `requires_grad` is true, it means that the output is inplaced. // The output `requires_grad` should be determined by this: // - If the inplaced output `requires_grad` is true, then the autograd must be disabled, @@ -155,12 +152,6 @@ Maybe AutogradInterpreter::Apply(const OpExpr& op_expr, const TensorTuple& requires_grad && IsSupportRequireGradDataType(output->dtype()->data_type()))); } } - - if (requires_grad && !LazyMode::is_enabled()) { - // Capture inputs and outputs after `AddBackwardFuncPtr` because of that grad function - // node has been attached to them. - JUST(grad_closure->Capture(*inputs_ptr, *outputs, ctx)); - } return Maybe::Ok(); } diff --git a/oneflow/core/framework/op_interpreter/op_interpreter_util.cpp b/oneflow/core/framework/op_interpreter/op_interpreter_util.cpp index f9eff347004f6622262294b409a4ce3f1bfa2f9c..a079398075c657e96b5a6e2c13ae3b30abd9fd9e 100644 --- a/oneflow/core/framework/op_interpreter/op_interpreter_util.cpp +++ b/oneflow/core/framework/op_interpreter/op_interpreter_util.cpp @@ -14,7 +14,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include #include #include "oneflow/core/common/maybe.h" @@ -22,21 +21,23 @@ limitations under the License. #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/tensor_impl.h" +#include "oneflow/core/functional/tensor_processor.h" #include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" #include "oneflow/core/operator/operator.h" +#include "oneflow/core/profiler/profiler.h" namespace oneflow { namespace one { namespace { -std::shared_ptr BuildEagerInterpreter(const bool& is_mirrored) { +std::shared_ptr BuildEagerInterpreter(const bool& is_local) { std::shared_ptr internal; - if (is_mirrored) { - internal = std::make_shared(); + if (is_local) { + internal = std::make_shared(); } else { - internal = std::make_shared(); + internal = std::make_shared(); } return std::make_shared(internal); } @@ -56,7 +57,7 @@ std::string ErrorString4Inputs(const TensorTuple& inputs, const OpExpr& op_expr) if (tensor->is_local()) { error_str << "local"; } else { - error_str << "consistent"; + error_str << "global"; } if (++idx != inputs.size()) { error_str << ", "; } } @@ -66,58 +67,62 @@ std::string ErrorString4Inputs(const TensorTuple& inputs, const OpExpr& op_expr) Maybe GetInterpreter(const TensorTuple& inputs, const OpExprInterpContext& ctx, const OpExpr& op_expr) { static const auto& g_lazy_interpreter = BuildLazyInterpreter(); - static const auto& g_eager_consistent_interpreter = BuildEagerInterpreter(/*is_mirrored=*/false); - static const auto& g_eager_mirrored_interpreter = BuildEagerInterpreter(/*is_mirrored=*/true); - if (!LazyMode::is_enabled()) { - if (inputs.empty()) { - if (ctx.parallel_desc.has_value()) { - JUST(ctx.nd_sbp); - CHECK_OR_RETURN(!ctx.device.has_value()); - return g_eager_consistent_interpreter; + static const auto& g_eager_global_interpreter = BuildEagerInterpreter(/*is_local=*/false); + static const auto& g_eager_local_interpreter = BuildEagerInterpreter(/*is_local=*/true); + bool is_local = true; + if (inputs.empty()) { + if (ctx.parallel_desc.has_value()) { + JUST(ctx.nd_sbp); + CHECK_OR_RETURN(!ctx.device.has_value()); + is_local = false; + } else { + CHECK_OR_RETURN(!ctx.nd_sbp.has_value()); + } + } else { + if (inputs[0]->is_global()) { + if (inputs.size() == 1) { + // do nothing + } else if (inputs.size() == 2) { + CHECK_OR_RETURN(inputs[1]->is_global()) // NOLINT + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency + } else if (inputs.size() == 3) { + CHECK_OR_RETURN(inputs[1]->is_global()) + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency + CHECK_OR_RETURN(inputs[2]->is_global()) + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency } else { - CHECK_OR_RETURN(!ctx.nd_sbp.has_value()); - return g_eager_mirrored_interpreter; + for (const auto& tensor : inputs) { + CHECK_OR_RETURN(tensor->is_global()) << ErrorString4Inputs(inputs, op_expr); + } } + is_local = false; } else { - if (inputs.at(0)->is_consistent()) { - if (inputs.size() == 1) { - // do nothing - } else if (inputs.size() == 2) { - CHECK_OR_RETURN(inputs.at(1)->is_consistent()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - } else if (inputs.size() == 3) { - CHECK_OR_RETURN(inputs.at(1)->is_consistent()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - CHECK_OR_RETURN(inputs.at(2)->is_consistent()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - } else { - for (const auto& tensor : inputs) { - CHECK_OR_RETURN(tensor->is_consistent()) << ErrorString4Inputs(inputs, op_expr); - } - } - return g_eager_consistent_interpreter; + if (inputs.size() == 1) { + // do nothing + } else if (inputs.size() == 2) { + CHECK_OR_RETURN(inputs.at(1)->is_local()) + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency + } else if (inputs.size() == 3) { + CHECK_OR_RETURN(inputs.at(1)->is_local()) + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency + CHECK_OR_RETURN(inputs.at(2)->is_local()) + << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency } else { - if (inputs.size() == 1) { - // do nothing - } else if (inputs.size() == 2) { - CHECK_OR_RETURN(inputs.at(1)->is_local()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - } else if (inputs.size() == 3) { - CHECK_OR_RETURN(inputs.at(1)->is_local()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - CHECK_OR_RETURN(inputs.at(2)->is_local()) - << ErrorString4Inputs(inputs, op_expr); // unroll loop for efficiency - } else { - for (const auto& tensor : inputs) { - CHECK_OR_RETURN(tensor->is_local()) << ErrorString4Inputs(inputs, op_expr); - } + for (const auto& tensor : inputs) { + CHECK_OR_RETURN(tensor->is_local()) << ErrorString4Inputs(inputs, op_expr); } - return g_eager_mirrored_interpreter; } } - UNIMPLEMENTED_THEN_RETURN(); } - return g_lazy_interpreter; + if (!LazyMode::is_enabled()) { + if (is_local) { + return g_eager_local_interpreter; + } else { + return g_eager_global_interpreter; + } + } else { + return g_lazy_interpreter; + } } } // namespace @@ -125,6 +130,7 @@ Maybe GetInterpreter(const TensorTuple& inputs, const OpExp template<> /* static */ Maybe OpInterpUtil::Dispatch( const OpExpr& op_expr, const TensorTuple& inputs, const OpExprInterpContext& ctx) { + OF_PROFILER_RANGE_GUARD("Dispatch"); auto outputs = std::make_shared(op_expr.output_size()); JUST(Dispatch(op_expr, inputs, outputs.get(), ctx)); return outputs; @@ -134,23 +140,32 @@ template<> /* static */ Maybe OpInterpUtil::Dispatch(const OpExpr& op_expr, const TensorTuple& inputs, const OpExprInterpContext& ctx) { + OF_PROFILER_RANGE_GUARD("Dispatch"); return JUST(Dispatch(op_expr, inputs, ctx))->at(0); } /* static */ Maybe OpInterpUtil::Dispatch(const OpExpr& op_expr, const TensorTuple& inputs, TensorTuple* outputs, const OpExprInterpContext& ctx) { - return JUST(GetInterpreter(inputs, ctx, op_expr))->Apply(op_expr, inputs, outputs, ctx); + OF_PROFILER_RANGE_GUARD("Dispatch"); + functional::TensorProcessorPipe processor(inputs, outputs); + if (autocast::is_enabled()) { + JUST(processor.Apply( + *JUST(op_expr.GetOrCreateAutoCastMeta()))); + } + JUST(processor.Apply(JUST(op_expr.SupportNonContiguous()))); + return JUST(GetInterpreter(processor.inputs(), ctx, op_expr)) + ->Apply(op_expr, processor.inputs(), processor.outputs(), ctx); } /* static */ Maybe OpInterpUtil::AddOpAndInferOpAttribute( - const OperatorConf& op_conf, const bool is_mirrored_strategy_enabled) { + const OperatorConf& op_conf, const bool is_local_strategy_enabled) { std::shared_ptr op_attribute = JUST([&]() -> Maybe { auto infer_ctx = JUST(GetCurInferCtx()); - if (is_mirrored_strategy_enabled) { - return infer_ctx->AddAndInferMirroredOp(op_conf); + if (is_local_strategy_enabled) { + return infer_ctx->AddAndInferLocalOp(op_conf); } else { - return infer_ctx->AddAndInferConsistentOp(op_conf); + return infer_ctx->AddAndInferGlobalOp(op_conf); } }()); return op_attribute; diff --git a/oneflow/core/framework/op_interpreter/op_interpreter_util.h b/oneflow/core/framework/op_interpreter/op_interpreter_util.h index f2eb53b55e79c04bfef48bd46ed37208d58fa081..789b3a6f8fb71e5948bf348de08e96dce97f0bb2 100644 --- a/oneflow/core/framework/op_interpreter/op_interpreter_util.h +++ b/oneflow/core/framework/op_interpreter/op_interpreter_util.h @@ -58,7 +58,7 @@ class OpInterpUtil { TensorTuple* outputs, const OpExprInterpContext& ctx); static Maybe AddOpAndInferOpAttribute(const OperatorConf& op_conf, - const bool is_mirrored_strategy_enabled); + const bool is_local_strategy_enabled); static Maybe GenBuiltinOpConf(const BuiltinOpExpr& op_expr, const AttrMap& attrs); }; diff --git a/oneflow/core/framework/op_kernel.cpp b/oneflow/core/framework/op_kernel.cpp index 73add18775fa1f53d01f68c00408dcb9cdff4078..cbbfc59f2d783772d3b9f5a08275a6ffe91b5f9f 100644 --- a/oneflow/core/framework/op_kernel.cpp +++ b/oneflow/core/framework/op_kernel.cpp @@ -25,7 +25,7 @@ void OpKernel::InferShape(KernelInferContext* ctx) const { CHECK_NOTNULL(op_infer_ctx); ctx->GetOpInferFn()(op_infer_ctx); for (const auto& arg_pair : ctx->outputs()) { - const Shape& shape = *op_infer_ctx->OutputShape(arg_pair.first, arg_pair.second); + const Shape& shape = op_infer_ctx->OutputShape(arg_pair.first, arg_pair.second); auto mut_shape_view = ctx->MutShapeView4ArgNameAndIndex(arg_pair.first, arg_pair.second); mut_shape_view.set_shape(shape); } diff --git a/oneflow/core/framework/op_kernel.h b/oneflow/core/framework/op_kernel.h index cbb5dd7ec804be962d912b57f0e2110e7bdfb078..4332576590ae1d4221398bac7768e0bdfca53199 100644 --- a/oneflow/core/framework/op_kernel.h +++ b/oneflow/core/framework/op_kernel.h @@ -23,7 +23,6 @@ limitations under the License. #include "oneflow/core/framework/util.h" #include "oneflow/core/framework/user_op_tensor.h" #include "oneflow/core/framework/user_op_conf.h" -#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/framework/user_op_registry.h" #include "oneflow/core/framework/infer_util.h" #include "oneflow/core/ep/include/stream.h" @@ -306,7 +305,9 @@ class OpKernel { virtual void Compute(KernelComputeContext* ctx, OpKernelState*, const OpKernelCache*) const { Compute(ctx); } - virtual void Compute(KernelComputeContext*) const { LOG(WARNING) << "UNIMPLEMENTED"; } + virtual void Compute(KernelComputeContext* ctx) const { + LOG(WARNING) << ctx->op_name() << " :UNIMPLEMENTED"; + } virtual void InferShape(KernelInferContext* ctx) const; virtual bool AlwaysComputeWhenAllOutputsEmpty() const = 0; virtual bool IsKernelLaunchSynchronized() const { return true; } diff --git a/oneflow/core/framework/ordered_string_list.h b/oneflow/core/framework/ordered_string_list.h new file mode 100644 index 0000000000000000000000000000000000000000..28c5d2f6913c219b1a52966a24c85107faa2bb80 --- /dev/null +++ b/oneflow/core/framework/ordered_string_list.h @@ -0,0 +1,60 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_ORDERED_STRING_LIST_H_ +#define ONEFLOW_CORE_FRAMEWORK_ORDERED_STRING_LIST_H_ + +#include "llvm/ADT/StringRef.h" + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/small_vector.h" + +namespace oneflow { + +template +class OrderedStringList { + public: + OrderedStringList() = default; + + size_t size() const { return strings_.size(); } + + void emplace_back(llvm::StringRef s) { + strings_.emplace_back(std::make_shared(s.str())); + order_.emplace(*strings_.back(), order_.size()); + } + + int order(llvm::StringRef s) const { + const auto& it = order_.find(s); + if (it == order_.end()) { return -1; } + return it->second; + } + + const std::string& operator[](int idx) { return *(strings_[idx]); } + + private: + struct Hash { + size_t operator()(llvm::StringRef val) const { + return HashCombine(val.size(), val.size() > 0 ? static_cast(val.data()[0] - '0') : 0); + } + }; + HashMap order_; + // Use shared_ptr to prevent the appended element from being freed when the + // vector increases + small_vector, N> strings_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_ORDERED_STRING_LIST_H_ diff --git a/oneflow/core/framework/placement_sbp_util.cpp b/oneflow/core/framework/placement_sbp_util.cpp index dd4cb6b6ebdf4bd5de49fe9334578ae51a162bd8..8d3d383b64a7841e94be5bf4ba84ab0e5e755c0a 100644 --- a/oneflow/core/framework/placement_sbp_util.cpp +++ b/oneflow/core/framework/placement_sbp_util.cpp @@ -17,7 +17,7 @@ limitations under the License. #include #include "oneflow/core/framework/placement_sbp_util.h" #include "oneflow/core/framework/placed_nd_sbp.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/tensor_meta.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/common/util.h" @@ -329,16 +329,16 @@ namespace { // 3) (S1, S1) is not decomposable. // although `nd_sbp (S0, S0) on shape (4, 4)` is not decomposable, they could be transformed into a // decomposable form: `n_sbp (S0, S1) on shape (2, 2, 4)`. -Maybe, Symbol>> CalcDecomposableEquivalent( - Symbol tensor_meta, Symbol dst_nd_sbp) { +Maybe, Symbol>> CalcDecomposableEquivalent( + Symbol tensor_meta, Symbol dst_nd_sbp) { std::shared_ptr shape = tensor_meta->shape_ptr(); Symbol src_nd_sbp = tensor_meta->nd_sbp(); const auto& hierarchy = tensor_meta->parallel_desc()->hierarchy(); std::tie(shape, src_nd_sbp, dst_nd_sbp) = *JUST( CalcDecomposableEquivalentShapeAndNdSbpPair(*shape, *hierarchy, src_nd_sbp, dst_nd_sbp)); - one::ConsistentTensorMeta decomposible_tensor_meta(shape, tensor_meta->dtype(), src_nd_sbp, - tensor_meta->parallel_desc()); + one::GlobalTensorMeta decomposible_tensor_meta(*shape, tensor_meta->dtype(), src_nd_sbp, + tensor_meta->parallel_desc()); return std::make_pair(SymbolOf(decomposible_tensor_meta), dst_nd_sbp); } @@ -508,7 +508,7 @@ Maybe GetPhysicalShape(const Shape& shape, Symbol nd_sbp, return GetPhysicalShape(shape, *nd_sbp, *parallel_desc, JUST(*parallel_id)); } -Maybe GetSubLogicalShape(Symbol tensor_meta, +Maybe GetSubLogicalShape(Symbol tensor_meta, Symbol sub_parallel_desc, Symbol sub_nd_sbp) { CHECK_EQ_OR_RETURN(sub_nd_sbp->sbp_parallel_size(), 1); // NOLINT(maybe-need-error-msg) const auto& logical_shape = tensor_meta->shape(); @@ -523,18 +523,17 @@ Maybe GetSubLogicalShape(Symbol tensor_meta, return sub_logical_shape; } -Maybe> CalcSubConsistentTensorMeta( - Symbol tensor_meta, Symbol sub_parallel_desc, +Maybe> CalcSubGlobalTensorMeta( + Symbol tensor_meta, Symbol sub_parallel_desc, Symbol sub_nd_sbp) { CHECK_EQ_OR_RETURN(sub_nd_sbp->sbp_parallel_size(), 1); // NOLINT(maybe-need-error-msg) const auto& logical_shape = JUST(GetSubLogicalShape(tensor_meta, sub_parallel_desc, sub_nd_sbp)); - one::ConsistentTensorMeta sub_consistent_tensor_meta(logical_shape, tensor_meta->dtype(), - sub_nd_sbp, sub_parallel_desc); - return SymbolOf(sub_consistent_tensor_meta); + one::GlobalTensorMeta sub_global_tensor_meta(*logical_shape, tensor_meta->dtype(), sub_nd_sbp, + sub_parallel_desc); + return SymbolOf(sub_global_tensor_meta); } -static constexpr auto* GetSubConsistentTensorMeta = - DECORATE(&CalcSubConsistentTensorMeta, ThreadLocal); +static constexpr auto* GetSubGlobalTensorMeta = DECORATE(&CalcSubGlobalTensorMeta, ThreadLocal); Maybe> ReplaceNdSbpComponent(Symbol nd_sbp, int64_t axis, Symbol component) { @@ -546,15 +545,15 @@ Maybe> ReplaceNdSbpComponent(Symbol nd_sbp, int64_t axis, return SymbolOf(new_nd_sbp); } -Maybe> ReplaceNdSbp(Symbol tensor_meta, - Symbol nd_sbp) { - one::ConsistentTensorMeta new_tensor_meta(tensor_meta->shape_ptr(), tensor_meta->dtype(), nd_sbp, - tensor_meta->parallel_desc()); +Maybe> ReplaceNdSbp(Symbol tensor_meta, + Symbol nd_sbp) { + one::GlobalTensorMeta new_tensor_meta(tensor_meta->shape(), tensor_meta->dtype(), nd_sbp, + tensor_meta->parallel_desc()); return SymbolOf(new_tensor_meta); } Maybe> DecomposeIntoNaiveTransformations( - Symbol tensor_meta, Symbol dst_nd_sbp) { + Symbol tensor_meta, Symbol dst_nd_sbp) { std::tie(tensor_meta, dst_nd_sbp) = *JUST(GetDecomposableEquivalent(tensor_meta, dst_nd_sbp)); const auto& parallel_desc = tensor_meta->parallel_desc(); const auto& src_nd_sbp = tensor_meta->nd_sbp(); @@ -583,13 +582,13 @@ Maybe> DecomposeIntoNaiveTransformations( JUST(GetSelectedSubParallelDesc(parallel_desc, SymbolOf(axis2selected))); const auto& sub_src_nd_sbp = JUST(MakeNdSbp(src_sbp)); const auto& sub_dst_nd_sbp = JUST(MakeNdSbp(dst_sbp)); - const auto& sub_consistent_tensor_meta = - JUST(GetSubConsistentTensorMeta(tensor_meta, sub_parallel_desc, sub_src_nd_sbp)); + const auto& sub_global_tensor_meta = + JUST(GetSubGlobalTensorMeta(tensor_meta, sub_parallel_desc, sub_src_nd_sbp)); const auto& new_src_nd_sbp = JUST(ReplaceNdSbpComponent(tensor_meta->nd_sbp(), axis, sub_dst_nd_sbp)); tensor_meta = JUST(ReplaceNdSbp(tensor_meta, new_src_nd_sbp)); transformations->emplace_back(NaiveBoxingTransformation{ - .consistent_tensor_meta = sub_consistent_tensor_meta, + .global_tensor_meta = sub_global_tensor_meta, .dst_nd_sbp = sub_dst_nd_sbp, }); } diff --git a/oneflow/core/framework/placement_sbp_util.h b/oneflow/core/framework/placement_sbp_util.h index 425f969a565d5f837d5a5b912dde5ab759b471f3..8b31cca1b03125a01513fd60a882cf4359a9082d 100644 --- a/oneflow/core/framework/placement_sbp_util.h +++ b/oneflow/core/framework/placement_sbp_util.h @@ -32,14 +32,14 @@ class PlacedNdSbp; namespace one { -class ConsistentTensorMeta; +class GlobalTensorMeta; } // 1) src_nd_sbp.sbp_parallel_size() == 1 // 2) dst_nd_sbp.sbp_parallel_size() == 1 struct NaiveBoxingTransformation { - Symbol consistent_tensor_meta; + Symbol global_tensor_meta; Symbol dst_nd_sbp; }; @@ -57,15 +57,15 @@ Maybe> GetBroadcastSubParallelDesc(Symbol par Symbol nd_sbp); Maybe> DecomposeIntoNaiveTransformations( - Symbol tensor_meta, Symbol dst_nd_sbp); + Symbol tensor_meta, Symbol dst_nd_sbp); Maybe IsNdSbpBoxingAcyclic(Symbol src_nd_sbp, Symbol dst_nd_sbp); Maybe> GetNdSbpValidTransformationAxisSequence(Symbol src_nd_sbp, Symbol dst_nd_sbp); -Maybe> CalcSubConsistentTensorMeta( - Symbol tensor_meta, Symbol sub_parallel_desc, +Maybe> CalcSubGlobalTensorMeta( + Symbol tensor_meta, Symbol sub_parallel_desc, Symbol sub_nd_sbp); Maybe> CalcSubParallelDesc4Axis(Symbol parallel_desc, int axis); @@ -80,8 +80,8 @@ extern Maybe (*CheckIsNdSbpBoxingAcyclicWithDecompose)(Symbol int64_t CalcIndex4Axis(int64_t offset, const Stride& stride, int axis); -static constexpr auto* GetSubConsistentTensorMeta = - DECORATE(&private_details::CalcSubConsistentTensorMeta, ThreadLocal); +static constexpr auto* GetSubGlobalTensorMeta = + DECORATE(&private_details::CalcSubGlobalTensorMeta, ThreadLocal); static constexpr auto* GetBroadcastSubParallelDesc = DECORATE(&private_details::GetBroadcastSubParallelDesc, ThreadLocal); diff --git a/oneflow/core/framework/placement_sbp_util_test.cpp b/oneflow/core/framework/placement_sbp_util_test.cpp index e6a9db28b2f534be58de3e76d6d7877efa25dbf1..4c7d953db6377f8ac0e8e531f2f2b1653893a0c2 100644 --- a/oneflow/core/framework/placement_sbp_util_test.cpp +++ b/oneflow/core/framework/placement_sbp_util_test.cpp @@ -15,7 +15,7 @@ limitations under the License. */ #include "gtest/gtest.h" #include "oneflow/core/framework/placement_sbp_util.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/tensor_meta.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/shape.h" @@ -151,10 +151,10 @@ Symbol GetNdSbp(Args... sbps) { return SymbolOf(nd_sbp); } -Symbol MakeConsistentTensorMeta(Symbol parallel_desc, - Symbol nd_sbp) { - const auto& shape = std::make_shared(DimVector{256, 256}); - one::ConsistentTensorMeta tensor_meta(shape, DataType::kInt32, nd_sbp, parallel_desc); +Symbol MakeGlobalTensorMeta(Symbol parallel_desc, + Symbol nd_sbp) { + auto shape = Shape(DimVector{256, 256}); + one::GlobalTensorMeta tensor_meta(shape, DataType::kInt32, nd_sbp, parallel_desc); return SymbolOf(tensor_meta); } @@ -171,7 +171,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_axis0) { const auto& parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); const auto& src_nd_sbp = GetNdSbp("P", "B"); const auto& dst_nd_sbp = GetNdSbp("S0", "B"); - const auto& tensor_meta = MakeConsistentTensorMeta(parallel_desc, src_nd_sbp); + const auto& tensor_meta = MakeGlobalTensorMeta(parallel_desc, src_nd_sbp); const auto& transformations = CHECK_JUST(private_details::DecomposeIntoNaiveTransformations(tensor_meta, dst_nd_sbp)); ASSERT_EQ(transformations->size(), 1); @@ -180,7 +180,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_axis0) { expected_parallel_conf.add_device_name(std::string("0:0")); expected_parallel_conf.add_device_name(std::string("1:0")); const auto& expected_parallel_desc = SymbolOf(ParallelDesc(expected_parallel_conf)); - const auto& ctensor_meta = transformations->at(0).consistent_tensor_meta; + const auto& ctensor_meta = transformations->at(0).global_tensor_meta; ASSERT_TRUE(ctensor_meta->parallel_desc() == expected_parallel_desc); ASSERT_EQ(ctensor_meta->nd_sbp()->sbp_parallel_size(), 1); ASSERT_EQ(transformations->at(0).dst_nd_sbp->sbp_parallel_size(), 1); @@ -200,7 +200,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_axis1) { const auto& parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); const auto& src_nd_sbp = GetNdSbp("S0", "P"); const auto& dst_nd_sbp = GetNdSbp("S0", "S1"); - const auto& tensor_meta = MakeConsistentTensorMeta(parallel_desc, src_nd_sbp); + const auto& tensor_meta = MakeGlobalTensorMeta(parallel_desc, src_nd_sbp); const auto& transformations = CHECK_JUST(private_details::DecomposeIntoNaiveTransformations(tensor_meta, dst_nd_sbp)); ASSERT_EQ(transformations->size(), 1); @@ -208,7 +208,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_axis1) { expected_parallel_conf.set_device_tag("cpu"); expected_parallel_conf.add_device_name("0:0-3"); const auto& expected_parallel_desc = SymbolOf(ParallelDesc(expected_parallel_conf)); - const auto& ctensor_meta = transformations->at(0).consistent_tensor_meta; + const auto& ctensor_meta = transformations->at(0).global_tensor_meta; ASSERT_TRUE(ctensor_meta->parallel_desc() == expected_parallel_desc); ASSERT_EQ(ctensor_meta->nd_sbp()->sbp_parallel_size(), 1); ASSERT_EQ(transformations->at(0).dst_nd_sbp->sbp_parallel_size(), 1); @@ -228,7 +228,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_two_axes) { const auto& parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); const auto& src_nd_sbp = GetNdSbp("S0", "P"); const auto& dst_nd_sbp = GetNdSbp("B", "S0"); - const auto& tensor_meta = MakeConsistentTensorMeta(parallel_desc, src_nd_sbp); + const auto& tensor_meta = MakeGlobalTensorMeta(parallel_desc, src_nd_sbp); const auto& transformations = CHECK_JUST(private_details::DecomposeIntoNaiveTransformations(tensor_meta, dst_nd_sbp)); ASSERT_EQ(transformations->size(), 2); @@ -238,7 +238,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_two_axes) { expected_parallel_conf.add_device_name(std::string("0:0")); expected_parallel_conf.add_device_name(std::string("1:0")); const auto& expected_parallel_desc = SymbolOf(ParallelDesc(expected_parallel_conf)); - const auto& ctensor_meta = transformations->at(0).consistent_tensor_meta; + const auto& ctensor_meta = transformations->at(0).global_tensor_meta; ASSERT_TRUE(ctensor_meta->parallel_desc() == expected_parallel_desc); ASSERT_EQ(ctensor_meta->nd_sbp()->sbp_parallel_size(), 1); ASSERT_EQ(transformations->at(0).dst_nd_sbp->sbp_parallel_size(), 1); @@ -251,7 +251,7 @@ TEST(DecomposeIntoNaiveTransformations, decompose_two_axes) { expected_parallel_conf.set_device_tag("cpu"); expected_parallel_conf.add_device_name("0:0-1"); const auto& expected_parallel_desc = SymbolOf(ParallelDesc(expected_parallel_conf)); - const auto& ctensor_meta = transformations->at(1).consistent_tensor_meta; + const auto& ctensor_meta = transformations->at(1).global_tensor_meta; ASSERT_TRUE(ctensor_meta->parallel_desc() == expected_parallel_desc); ASSERT_EQ(ctensor_meta->nd_sbp()->sbp_parallel_size(), 1); ASSERT_EQ(transformations->at(1).dst_nd_sbp->sbp_parallel_size(), 1); diff --git a/oneflow/core/framework/placement_utils.cpp b/oneflow/core/framework/placement_utils.cpp index 343b7030dee8797018da98f28bf1c5d5d64c3b8c..685c2419ecb0187fbb8fe0ef37c06a5b06177395 100644 --- a/oneflow/core/framework/placement_utils.cpp +++ b/oneflow/core/framework/placement_utils.cpp @@ -34,8 +34,8 @@ Maybe> ReplacePlacementDeviceTag(Symbol paral return SymbolOf(*out_parallel_desc); } -Maybe TouchConsistentTensor(const std::shared_ptr& tensor) { - CHECK_OR_RETURN(tensor->is_consistent()); +Maybe TouchGlobalTensor(const std::shared_ptr& tensor) { + CHECK_OR_RETURN(tensor->is_global()); // NOLINT return Maybe::Ok(); } diff --git a/oneflow/core/framework/placement_utils.h b/oneflow/core/framework/placement_utils.h index 4cb9f7b210a865626053453897cdcefa350681b4..dc3ccc68a838cc4d1ef0cea9009516092884640d 100644 --- a/oneflow/core/framework/placement_utils.h +++ b/oneflow/core/framework/placement_utils.h @@ -26,9 +26,9 @@ namespace oneflow { Maybe> ReplacePlacementDeviceTag(Symbol parallel_desc, const std::string& device_type); -Maybe TouchConsistentTensor(const std::shared_ptr& tensor); +Maybe TouchGlobalTensor(const std::shared_ptr& tensor); -constexpr auto* CheckMetaConsistency = DECORATE(&TouchConsistentTensor, CheckConsistentTensorMeta); +constexpr auto* CheckMetaConsistency = DECORATE(&TouchGlobalTensor, CheckGlobalTensorMeta); } // namespace oneflow diff --git a/oneflow/core/framework/random_generator.cpp b/oneflow/core/framework/random_generator.cpp index 9dbe7c7045c53357a6141e681b0eb4c1ee3a81c8..3c45ea134954b6d407c5da5b0baf7bc949bc7fae 100644 --- a/oneflow/core/framework/random_generator.cpp +++ b/oneflow/core/framework/random_generator.cpp @@ -18,12 +18,9 @@ limitations under the License. #include #include "oneflow/core/control/global_process_ctx.h" -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/device/cuda_util.h" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/device/cuda_util.h" -#endif // WITH_ROCM namespace oneflow { namespace one { @@ -38,16 +35,11 @@ Maybe ManualSeed(uint64_t seed, const std::string& device, int device_inde if (device == "cpu") { JUST(DefaultCPUGenerator())->set_current_seed(seed); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) else if (device == "cuda") { JUST(DefaultCUDAGenerator(device_index))->set_current_seed(seed); } #endif // WITH_CUDA -#ifdef WITH_ROCM - else if (device == "cuda") { - JUST(DefaultCUDAGenerator(device_index))->set_current_seed(seed); - } -#endif // WITH_ROCM else if (device == "auto") { JUST(DefaultAutoGenerator())->set_current_seed(seed); } else { @@ -97,7 +89,7 @@ Maybe DefaultCPUGenerator() { return default_cpu_generator; } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) Maybe DefaultCUDAGenerator(int device_index) { static int device_count = GetCudaDeviceCount(); static std::vector init_flags(device_count); @@ -113,23 +105,6 @@ Maybe DefaultCUDAGenerator(int device_index) { return default_cuda_generator.at(device_index); } #endif // WITH_CUDA -#ifdef WITH_ROCM -Maybe DefaultCUDAGenerator(int device_index) { - static int device_count = GetCudaDeviceCount(); - static std::vector init_flags(device_count); - static std::vector> default_cuda_generator(device_count); - - if (device_index == -1) { device_index = GetCudaDeviceIndex(); } - CHECK_OR_RETURN(device_index >= 0 && device_index < device_count) - << "Invalid device index " << device_index; - std::call_once(init_flags[device_index], [&]() { - default_cuda_generator[device_index] = std::make_shared( - CHECK_JUST(CHECK_JUST(DefaultAutoGenerator())->Get(device_index))); - }); - return default_cuda_generator.at(device_index); -} -#endif // WITH_ROCM - Maybe MakeAutoGenerator() { return std::make_shared(std::make_shared(default_rng_seed_val)); @@ -139,7 +114,7 @@ Maybe MakeCPUGenerator() { return std::make_shared(std::make_shared(default_rng_seed_val)); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) Maybe MakeCUDAGenerator(int device_index) { if (device_index == -1) { device_index = GetCudaDeviceIndex(); } CHECK_OR_RETURN(device_index >= 0 && device_index < GetCudaDeviceCount()) @@ -147,32 +122,16 @@ Maybe MakeCUDAGenerator(int device_index) { return std::make_shared( std::make_shared(default_rng_seed_val, device_index)); } -#endif // -#ifdef WITH_ROCM -Maybe MakeCUDAGenerator(int device_index) { - if (device_index == -1) { device_index = GetCudaDeviceIndex(); } - CHECK_OR_RETURN(device_index >= 0 && device_index < GetCudaDeviceCount()) - << "Invalid device index " << device_index; - return std::make_shared( - std::make_shared(default_rng_seed_val, device_index)); -} -#endif // WITH_ROCM +#endif // WITH_CUDA Maybe ManualSeedAllCudaGenerator(uint64_t seed) { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) static int device_count = GetCudaDeviceCount(); FOR_RANGE(int, device_id, 0, device_count) { const auto& cuda_gen = JUST(DefaultCUDAGenerator(device_id)); cuda_gen->set_current_seed(seed); } #endif // WITH_CUDA -#ifdef WITH_ROCM - static int device_count = GetCudaDeviceCount(); - FOR_RANGE(int, device_id, 0, device_count) { - const auto& cuda_gen = JUST(DefaultCUDAGenerator(device_id)); - cuda_gen->set_current_seed(seed); - } -#endif // WITH_ROCM return Maybe::Ok(); } @@ -180,16 +139,11 @@ Maybe MakeGenerator(const std::string& device, int device_index) { if (device == "cpu") { return MakeCPUGenerator(); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) else if (device == "cuda") { return MakeCUDAGenerator(device_index); } #endif // WITH_CUDA -#ifdef WITH_ROCM - else if (device == "cuda") { - return MakeCUDAGenerator(device_index); - } -#endif // WITH_ROCM else if (device == "auto") { return MakeAutoGenerator(); } else { @@ -203,16 +157,11 @@ Maybe DefaultGenerator(const std::string& device, int device_index) { if (device == "cpu") { return DefaultCPUGenerator(); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) else if (device == "cuda") { return DefaultCUDAGenerator(device_index); } -#endif // -#ifdef WITH_ROCM - else if (device == "cuda") { - return DefaultCUDAGenerator(device_index); - } -#endif // WITH_ROCM +#endif // WITH_CUDA else if (device == "auto") { return DefaultAutoGenerator(); } else { diff --git a/oneflow/core/framework/random_generator.h b/oneflow/core/framework/random_generator.h index 0aafab716a89c92a7bf8e90d6d91c58f1c910a48..3ac55919ae7131f7ab7311fc0fecb3d15fb33931 100644 --- a/oneflow/core/framework/random_generator.h +++ b/oneflow/core/framework/random_generator.h @@ -83,14 +83,10 @@ Maybe MakeAutoGenerator(); Maybe DefaultCPUGenerator(); Maybe MakeCPUGenerator(); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) Maybe DefaultCUDAGenerator(int device_index = -1); Maybe MakeCUDAGenerator(); #endif // WITH_CUDA -#ifdef WITH_ROCM -Maybe DefaultCUDAGenerator(int device_index = -1); -Maybe MakeCUDAGenerator(); -#endif // WITH_ROCM Maybe ManualSeedAllCudaGenerator(uint64_t seed); } // namespace one diff --git a/oneflow/core/framework/random_generator_impl.cpp b/oneflow/core/framework/random_generator_impl.cpp index bccb23b7f72ebf24feef02c0bd7ae1a2a10ab18b..7a28f8b92e1084e0067ff0234ffc885e5b255f88 100644 --- a/oneflow/core/framework/random_generator_impl.cpp +++ b/oneflow/core/framework/random_generator_impl.cpp @@ -17,12 +17,12 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/common/cpp_attribute.h" +#include "oneflow/core/common/str_util.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/vm/virtual_machine.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/platform/include/pthread_fork.h" #ifdef WITH_CUDA @@ -58,6 +58,7 @@ void CPUGeneratorImpl::set_current_seed(uint64_t seed) { CHECK_JUST(CPUSynchronize()); seed_ = seed; engine_.seed(seed_); + torch_engine_ = pytorch_mt19937_engine(seed); } Maybe CPUGeneratorImpl::GetState() const { @@ -82,9 +83,9 @@ Maybe CPUGeneratorImpl::GetState() const { } state.seed = current_seed(); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(of_blob->mut_blob()->mut_dptr(), &state, sizeof(state)); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(eager_blob_object->mut_dptr(), &state, sizeof(state)); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "mut")); return tensor_state; @@ -105,9 +106,9 @@ Maybe CPUGeneratorImpl::SetState(const std::shared_ptr& tensor_sta << sizeof(state) << ", but got " << tensor_state->shape()->elem_cnt(); } - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(reinterpret_cast(&state), of_blob->blob().dptr(), sizeof(state)); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(reinterpret_cast(&state), eager_blob_object->dptr(), sizeof(state)); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "const")); @@ -178,6 +179,21 @@ void CUDAGeneratorImpl::set_current_seed(uint64_t seed) { CHECK_JUST(CUDASynchronize()); seed_ = seed; detail::InitCurandStates(seed_, max_block_num_, max_thread_num_, curand_states_, cuda_gen_state_); + philox_offset_per_thread_ = 0; +} + +// NOTE(Liang Depeng): The implementation of ` CUDAGeneratorImpl::get_philox_offset` is modified +// from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/CUDAGeneratorImpl.cpp#L269 +// in order to make distribution related cuda kernels to have the same output as pytorch +// when setting the same seed. +uint64_t CUDAGeneratorImpl::get_philox_offset(uint64_t increment) { + // rounds increment up to the nearest multiple of 4 + increment = ((increment + 3) / 4) * 4; + CHECK_EQ(this->philox_offset_per_thread_ % 4, 0); + uint64_t offset = this->philox_offset_per_thread_; + this->philox_offset_per_thread_ += increment; + return offset; } Maybe CUDAGeneratorImpl::GetState() const { @@ -189,11 +205,12 @@ Maybe CUDAGeneratorImpl::GetState() const { const auto& tensor_state = JUST(functional::Empty(Shape{total_size}, DType::UInt8(), device, /*pin_memory=*/false)); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - OF_CUDA_CHECK(cudaMemcpy(of_blob->mut_blob()->mut_dptr(), curand_states_, state_size, - cudaMemcpyDefault)); - memcpy(of_blob->mut_blob()->mut_dptr() + state_size, &seed_, sizeof(int64_t)); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + OF_CUDA_CHECK( + cudaMemcpy(eager_blob_object->mut_dptr(), curand_states_, state_size, cudaMemcpyDefault)); + memcpy(static_cast(eager_blob_object->mut_dptr()) + state_size, &seed_, + sizeof(int64_t)); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "mut")); return tensor_state; @@ -216,9 +233,9 @@ Maybe CUDAGeneratorImpl::SetState(const std::shared_ptr& tensor_st CudaCurrentDeviceGuard dev_guard(this->device_index()); JUST(CUDASynchronize()); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - const uint8_t* data = of_blob->blob().dptr(); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + const uint8_t* data = static_cast(eager_blob_object->dptr()); // Do not use set_current_seed() since synchronization will lead to deadlock. seed_ = *((uint64_t*)(data + state_size)); OF_CUDA_CHECK(cudaMemcpy(curand_states_, data, state_size, cudaMemcpyDefault)); @@ -286,6 +303,21 @@ void CUDAGeneratorImpl::set_current_seed(uint64_t seed) { CHECK_JUST(CUDASynchronize()); seed_ = seed; detail::InitCurandStates(seed_, max_block_num_, max_thread_num_, curand_states_, cuda_gen_state_); + philox_offset_per_thread_ = 0; +} + +// NOTE(Liang Depeng): The implementation of ` CUDAGeneratorImpl::get_philox_offset` is modified +// from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/CUDAGeneratorImpl.cpp#L269 +// in order to make distribution related cuda kernels to have the same output as pytorch +// when setting the same seed. +uint64_t CUDAGeneratorImpl::get_philox_offset(uint64_t increment) { + // rounds increment up to the nearest multiple of 4 + increment = ((increment + 3) / 4) * 4; + CHECK_EQ(this->philox_offset_per_thread_ % 4, 0); + uint64_t offset = this->philox_offset_per_thread_; + this->philox_offset_per_thread_ += increment; + return offset; } Maybe CUDAGeneratorImpl::GetState() const { @@ -297,11 +329,12 @@ Maybe CUDAGeneratorImpl::GetState() const { const auto& tensor_state = JUST(functional::Empty(Shape{total_size}, DType::UInt8(), device, /*pin_memory=*/false)); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - OF_CUDA_CHECK(hipMemcpy(of_blob->mut_blob()->mut_dptr(), curand_states_, state_size, - hipMemcpyDefault)); - memcpy(of_blob->mut_blob()->mut_dptr() + state_size, &seed_, sizeof(int64_t)); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + OF_CUDA_CHECK( + hipMemcpy(eager_blob_object->mut_dptr(), curand_states_, state_size, hipMemcpyDefault)); + memcpy(static_cast(eager_blob_object->mut_dptr()) + state_size, &seed_, + sizeof(int64_t)); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "mut")); return tensor_state; @@ -324,9 +357,9 @@ Maybe CUDAGeneratorImpl::SetState(const std::shared_ptr& tensor_st CudaCurrentDeviceGuard dev_guard(this->device_index()); JUST(CUDASynchronize()); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - const uint8_t* data = of_blob->blob().dptr(); + const auto& callback = [&](ep::Stream*, + const std::shared_ptr& eager_blob_object) { + const uint8_t* data = static_cast(eager_blob_object->dptr()); // Do not use set_current_seed() since synchronization will lead to deadlock. seed_ = *((uint64_t*)(data + state_size)); OF_CUDA_CHECK(hipMemcpy(curand_states_, data, state_size, hipMemcpyDefault)); @@ -398,9 +431,10 @@ Maybe AutoGeneratorImpl::GetState() const { data += state.device_tag_length; for (int i = 0; i < tensor_states.size(); ++i) { const auto& tensor = tensor_states.at(i); - const auto& callback = [&data, &state_sizes, i](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(data, of_blob->blob().dptr(), state_sizes.at(i)); + const auto& callback = [&data, &state_sizes, i]( + ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(data, eager_blob_object->dptr(), state_sizes.at(i)); }; JUST(SyncAccessTensorWithTimeOut(tensor, callback, "const")); data += state_sizes.at(i); @@ -409,9 +443,10 @@ Maybe AutoGeneratorImpl::GetState() const { const auto& device = JUST(Device::New("cpu")); const auto& tensor_state = JUST(functional::Empty(Shape{total_size}, DType::UInt8(), device, /*pin_memory=*/false)); - const auto& callback = [&buffer, &total_size](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(of_blob->mut_blob()->mut_dptr(), buffer.data(), total_size); + const auto& callback = [&buffer, &total_size]( + ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(eager_blob_object->mut_dptr(), buffer.data(), total_size); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "mut")); return tensor_state; @@ -428,9 +463,10 @@ Maybe AutoGeneratorImpl::SetState(const std::shared_ptr& tensor_st AutoGeneratorState state; int64_t total_size = tensor_state->shape()->elem_cnt(); std::vector buffer(total_size); - const auto& callback = [&buffer, &total_size](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(buffer.data(), of_blob->blob().dptr(), total_size); + const auto& callback = [&buffer, &total_size]( + ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(buffer.data(), eager_blob_object->dptr(), total_size); }; JUST(SyncAccessTensorWithTimeOut(tensor_state, callback, "const")); @@ -454,9 +490,10 @@ Maybe AutoGeneratorImpl::SetState(const std::shared_ptr& tensor_st int64_t state_size = state_sizes.at(i); tensor_states[i] = JUST(functional::Empty(Shape{state_size}, DType::UInt8(), device, /*pin_memory=*/false)); - const auto& callback = [&data, &state_size](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - memcpy(of_blob->mut_blob()->mut_dptr(), data, state_size); + const auto& callback = [&data, &state_size]( + ep::Stream*, + const std::shared_ptr& eager_blob_object) { + memcpy(eager_blob_object->mut_dptr(), data, state_size); }; JUST(SyncAccessTensorWithTimeOut(tensor_states[i], callback, "mut")); data += state_size; diff --git a/oneflow/core/framework/random_generator_impl.cu b/oneflow/core/framework/random_generator_impl.cu index 52762526cfea12fc9f337d145f0d47f8d8548174..e03cf51e275523df9f2adc65ee554af285cba6f5 100644 --- a/oneflow/core/framework/random_generator_impl.cu +++ b/oneflow/core/framework/random_generator_impl.cu @@ -21,12 +21,12 @@ namespace one { namespace { -__global__ void InitCurandStatesKernel(uint64_t seed, curandState* states, +__global__ void InitCurandStatesKernel(uint64_t seed, GPURAND(State)* states, CUDAGeneratorState* cuda_gen_state) { const int id = blockIdx.x * blockDim.x + threadIdx.x; size_t local_seed = (static_cast(seed) + 0x9e3779b9U + (static_cast(id) << 6U) + (static_cast(id) >> 2U)); - curand_init(local_seed, 0, 0, &states[id]); + GPURAND(_init)(local_seed, 0, 0, &states[id]); cuda_gen_state->dev_counter = static_cast(0); cuda_gen_state->dev_offset = static_cast(0); } @@ -35,7 +35,7 @@ __global__ void InitCurandStatesKernel(uint64_t seed, curandState* states, namespace detail { -void InitCurandStates(uint64_t seed, int32_t block_num, int32_t thread_num, curandState* states, +void InitCurandStates(uint64_t seed, int32_t block_num, int32_t thread_num, GPURAND(State)* states, CUDAGeneratorState* cuda_gen_state) { InitCurandStatesKernel<<>>(seed, states, cuda_gen_state); } diff --git a/oneflow/core/framework/random_generator_impl.h b/oneflow/core/framework/random_generator_impl.h index 2350fc857b6daba66d8a05df34577f35187dccc7..f82548a2a93fd15bc8546012d29e48be3c96e46b 100644 --- a/oneflow/core/framework/random_generator_impl.h +++ b/oneflow/core/framework/random_generator_impl.h @@ -16,6 +16,10 @@ limitations under the License. #ifndef ONEFLOW_CORE_FRAMEWORK_RANDOM_GENERATOR_IMPL_H_ #define ONEFLOW_CORE_FRAMEWORK_RANDOM_GENERATOR_IMPL_H_ +#include +#include +#include +#include #include #include #include @@ -23,6 +27,7 @@ limitations under the License. #include "oneflow/core/common/device_type.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/framework/device.h" +#include "oneflow/core/ep/include/gpu_macro.h" #ifdef WITH_CUDA #include #include @@ -90,15 +95,103 @@ class DeviceGeneratorImpl : public GeneratorImpl { const DeviceType& device_type() const { return device_type_; } int device_index() const { return device_index_; } + std::mutex mutex_; + protected: DeviceType device_type_; int device_index_; }; +// NOTE(Liang Depeng): The following implementation of mt19937 is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/core/MT19937RNGEngine.h +// in order to make distribution related cpu kernels to have the same output as +// pytorch when setting the same seed. +constexpr int MERSENNE_STATE_N = 624; +constexpr int MERSENNE_STATE_M = 397; +constexpr uint32_t MATRIX_A = 0x9908b0df; +constexpr uint32_t UMASK = 0x80000000; +constexpr uint32_t LMASK = 0x7fffffff; + +struct pytorch_mt19937_data_pod { + uint64_t seed_; + int left_; + bool seeded_; + uint32_t next_; + std::array state_; +}; + +class pytorch_mt19937_engine { + public: + inline explicit pytorch_mt19937_engine(uint64_t seed = 5489) { init_with_uint32(seed); } + + inline pytorch_mt19937_data_pod data() const { return data_; } + + inline void set_data(pytorch_mt19937_data_pod data) { data_ = data; } + + inline uint64_t seed() const { return data_.seed_; } + + inline bool is_valid() { + if ((data_.seeded_ == true) && (data_.left_ > 0 && data_.left_ <= MERSENNE_STATE_N) + && (data_.next_ <= MERSENNE_STATE_N)) { + return true; + } + return false; + } + + inline uint32_t operator()() { + uint32_t y; + + if (--(data_.left_) == 0) { next_state(); } + y = *(data_.state_.data() + data_.next_++); + y ^= (y >> 11); + y ^= (y << 7) & 0x9d2c5680; + y ^= (y << 15) & 0xefc60000; + y ^= (y >> 18); + + return y; + } + + private: + pytorch_mt19937_data_pod data_; + + inline void init_with_uint32(uint64_t seed) { + data_.seed_ = seed; + data_.seeded_ = true; + data_.state_[0] = seed & 0xffffffff; + for (int j = 1; j < MERSENNE_STATE_N; ++j) { + data_.state_[j] = (1812433253 * (data_.state_[j - 1] ^ (data_.state_[j - 1] >> 30)) + j); + } + data_.left_ = 1; + data_.next_ = 0; + } + + inline uint32_t mix_bits(uint32_t u, uint32_t v) { return (u & UMASK) | (v & LMASK); } + + inline uint32_t twist(uint32_t u, uint32_t v) { + return (mix_bits(u, v) >> 1) ^ (v & 1 ? MATRIX_A : 0); + } + + inline void next_state() { + uint32_t* p = data_.state_.data(); + data_.left_ = MERSENNE_STATE_N; + data_.next_ = 0; + + for (int j = MERSENNE_STATE_N - MERSENNE_STATE_M + 1; --j; p++) { + *p = p[MERSENNE_STATE_M] ^ twist(p[0], p[1]); + } + + for (int j = MERSENNE_STATE_M; --j; p++) { + *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], p[1]); + } + + *p = p[MERSENNE_STATE_M - MERSENNE_STATE_N] ^ twist(p[0], data_.state_[0]); + } +}; + class CPUGeneratorImpl : public DeviceGeneratorImpl { public: explicit CPUGeneratorImpl(uint64_t seed) - : DeviceGeneratorImpl(seed, DeviceType::kCPU, 0), engine_(seed) {} + : DeviceGeneratorImpl(seed, DeviceType::kCPU, 0), engine_(seed), torch_engine_(seed) {} virtual ~CPUGeneratorImpl() = default; @@ -106,6 +199,8 @@ class CPUGeneratorImpl : public DeviceGeneratorImpl { std::mt19937& engine() { return engine_; } + pytorch_mt19937_engine& torch_engine() { return torch_engine_; } + Maybe> device() const override { return Device::New("cpu", device_index()); } Maybe GetState() const override; @@ -113,6 +208,10 @@ class CPUGeneratorImpl : public DeviceGeneratorImpl { public: std::mt19937 engine_; + // TODO(Liang Depeng): needed to implement the get_state/set_state of pytorch_mt_19937_engine + // refer to + // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/CPUGeneratorImpl.cpp#L206 + pytorch_mt19937_engine torch_engine_; }; #ifdef WITH_CUDA @@ -139,11 +238,14 @@ class CUDAGeneratorImpl : public DeviceGeneratorImpl { Maybe GetState() const override; Maybe SetState(const std::shared_ptr& tensor_state) override; + uint64_t get_philox_offset(uint64_t increment); + private: int32_t max_block_num_; int32_t max_thread_num_; curandState* curand_states_; CUDAGeneratorState* cuda_gen_state_; + uint64_t philox_offset_per_thread_ = 0; }; namespace detail { @@ -178,11 +280,15 @@ class CUDAGeneratorImpl : public DeviceGeneratorImpl { Maybe GetState() const override; Maybe SetState(const std::shared_ptr& tensor_state) override; + uint64_t get_philox_offset(uint64_t increment); + private: int32_t max_block_num_; int32_t max_thread_num_; hiprandState* curand_states_; CUDAGeneratorState* cuda_gen_state_; + uint64_t philox_offset_per_thread_ = 0; + }; namespace detail { diff --git a/oneflow/core/framework/random_generator_impl.hip.cpp b/oneflow/core/framework/random_generator_impl.hip.cpp deleted file mode 100644 index d0285cc7edf54ba08307aa48b077e28aa976e2c6..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/random_generator_impl.hip.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/random_generator_impl.h" - -namespace oneflow { -namespace one { - -namespace { - -__global__ void InitCurandStatesKernel(uint64_t seed, hiprandState* states, - CUDAGeneratorState* cuda_gen_state) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - size_t local_seed = (static_cast(seed) + 0x9e3779b9U + (static_cast(id) << 6U) - + (static_cast(id) >> 2U)); - hiprand_init(local_seed, 0, 0, &states[id]); - cuda_gen_state->dev_counter = static_cast(0); - cuda_gen_state->dev_offset = static_cast(0); -} - -} // namespace - -namespace detail { - -void InitCurandStates(uint64_t seed, int32_t block_num, int32_t thread_num, hiprandState* states, - CUDAGeneratorState* cuda_gen_state) { - hipLaunchKernelGGL(InitCurandStatesKernel, block_num, thread_num, 0, 0, seed, states, cuda_gen_state); -} - -} // namespace detail - -} // namespace one -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/framework/rank_group_rpc_util.cpp b/oneflow/core/framework/rank_group_rpc_util.cpp index 84c7c2808fba1e315c2f0517a57353a086b55386..89577e01357d611ce6cb851c42dcec0e7207d427 100644 --- a/oneflow/core/framework/rank_group_rpc_util.cpp +++ b/oneflow/core/framework/rank_group_rpc_util.cpp @@ -22,7 +22,7 @@ limitations under the License. #include "oneflow/core/job/rank_group.h" #include "oneflow/core/job/rank_group_scope.h" #include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/thread/thread_consistent_id.h" +#include "oneflow/core/thread/thread_global_id.h" #include "oneflow/core/rpc/include/global_process_ctx.h" namespace oneflow { diff --git a/oneflow/core/framework/saved_tensor_hooks.h b/oneflow/core/framework/saved_tensor_hooks.h new file mode 100644 index 0000000000000000000000000000000000000000..78e3927ea77f2e390844b2cf7ae720719348f4c5 --- /dev/null +++ b/oneflow/core/framework/saved_tensor_hooks.h @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_SAVED_TENSOR_HOOKS_H_ +#define ONEFLOW_CORE_FRAMEWORK_SAVED_TENSOR_HOOKS_H_ + +#include "oneflow/core/framework/tensor.h" + +namespace oneflow { +namespace one { +class SavedTensorHook { + public: + virtual ~SavedTensorHook() = default; + virtual void pack(const std::shared_ptr& tensor) = 0; + virtual std::shared_ptr unpack() = 0; +}; + +class SavedTensorHookCreator { + public: + virtual ~SavedTensorHookCreator() = default; + virtual std::unique_ptr new_saved_tensor_hook() const = 0; +}; + +} // namespace one +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_SAVED_TENSOR_HOOKS_H_ diff --git a/oneflow/core/framework/sbp_context.cpp b/oneflow/core/framework/sbp_context.cpp index 1446db2a439693ab3ae3cf41def032233bca6201..007206e1e239a4ab14c149258be7996ad69980a9 100644 --- a/oneflow/core/framework/sbp_context.cpp +++ b/oneflow/core/framework/sbp_context.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/sbp_context.h" #include "oneflow/core/job/sbp_signature_builder.h" #include "oneflow/core/operator/operator.h" +#include "oneflow/core/framework/nd_sbp.h" namespace oneflow { @@ -148,6 +149,25 @@ Maybe InferNdSbp4SrcOp(user_op::InferNdSbpFnContext* ctx, const SbpParalle return Maybe::Ok(); } +Maybe SetSrcOpNdSbp(const NdSbpSignature& nd_sbp_sig, const std::string& blob_name, + OperatorConf* op_conf) { + CHECK_OR_RETURN(nd_sbp_sig.bn_in_op2nd_sbp().find(blob_name) + != nd_sbp_sig.bn_in_op2nd_sbp().end()) + << "blob `" << blob_name << "` can't found in NdSBP signature: " << nd_sbp_sig.DebugString(); + const auto& nd_sbp = nd_sbp_sig.bn_in_op2nd_sbp().at(blob_name); + std::vector nd_sbp_str_list = *JUST(GetNdSbpStrList(nd_sbp)); + CHECK_OR_RETURN(op_conf->has_user_conf()) + << "user_op::SetSrcOpNdSbp function only used to set user op conf"; + CHECK_OR_RETURN(op_conf->user_conf().attr().find("nd_sbp") != op_conf->user_conf().attr().end()) + << op_conf->name() << " has no attr named `nd_sbp`"; + *op_conf->mutable_user_conf() + ->mutable_attr() + ->at("nd_sbp") + .mutable_at_list_string() + ->mutable_val() = {nd_sbp_str_list.begin(), nd_sbp_str_list.end()}; + return Maybe::Ok(); +} + } // namespace user_op } // namespace oneflow diff --git a/oneflow/core/framework/sbp_context.h b/oneflow/core/framework/sbp_context.h index 1566f4df7153ab560aaea238f7de9f797b7ba38d..bac1db925f6a5d933c565ef00c2f5510d9bee607 100644 --- a/oneflow/core/framework/sbp_context.h +++ b/oneflow/core/framework/sbp_context.h @@ -76,6 +76,12 @@ class SbpContext : public SbpContextBase { SbpContext() = default; ~SbpContext() override = default; + // hierarchy value is the value at the dimension corresponding to the current SBP + // For example, 2 machines, 4 gpus per machine, hierarchy = [2, 4] + // Suppose we have nd_sbp = (S0, B) + // The hierarchy value corresponding to S0 is 2 + // The hierarchy value corresponding to B is 4. + virtual int64_t hierarchy_value() const = 0; virtual UserOpSbpSignatureBuilder NewBuilder() = 0; }; @@ -96,6 +102,8 @@ struct GetSbpFnUtil { }; Maybe InferNdSbp4SrcOp(user_op::InferNdSbpFnContext* ctx, const SbpParallel& default_sbp); +Maybe SetSrcOpNdSbp(const NdSbpSignature& nd_sbp_sig, const std::string& blob_name, + OperatorConf* op_conf); } // namespace user_op diff --git a/oneflow/core/framework/sbp_infer_util.cpp b/oneflow/core/framework/sbp_infer_util.cpp index 97a9e6425880d1b3cca8a8195f5a04a904ce9131..ef3edcfe96f1228591efd0d09f4b00199e182b0d 100644 --- a/oneflow/core/framework/sbp_infer_util.cpp +++ b/oneflow/core/framework/sbp_infer_util.cpp @@ -17,9 +17,15 @@ limitations under the License. #include "oneflow/core/framework/sbp_infer_util.h" #include "oneflow/core/auto_parallel/boxing_collector.h" #include "oneflow/core/boxing/eager_boxing_interpreter_mgr.h" +#include "oneflow/core/common/device_type.pb.h" +#include "oneflow/core/common/nd_index_offset_helper.h" #include "oneflow/core/common/util.h" +#include "oneflow/core/job/global_for.h" #include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/job/resource_desc.h" +#include "oneflow/core/job/sbp_parallel.pb.h" namespace oneflow { @@ -55,11 +61,21 @@ double Penalty4PartialInConsumer(double logical_blob_size, int32_t producer_para } } +int32_t Ratio4Sbp(const NdSbp& nd_sbp, const ParallelDesc& parallel_desc, + const std::function& classifier) { + int32_t ratio = 1; + for (int32_t sbp_id = 0; sbp_id < nd_sbp.sbp_parallel_size(); sbp_id++) { + if (classifier(nd_sbp.sbp_parallel(sbp_id))) { ratio *= parallel_desc.hierarchy()->At(sbp_id); } + } + return ratio; +} + Maybe ComputCopyCostBetweenTwoSbpParallel(const SbpParallel& producer_sbp_parallel, const SbpParallel& consumer_sbp_parallel, const BlobDesc& logical_blob_desc, - const ParallelDesc& producer_parallel_desc, - const ParallelDesc& consumer_parallel_desc) { + bool on_same_devices, + int32_t producer_parallel_num, + int32_t consumer_parallel_num) { if (!(CheckSbpParallel(producer_sbp_parallel) && CheckSbpParallel(consumer_sbp_parallel))) { return Error::RuntimeError() << "Illegal sbp parallel has been found."; } @@ -74,7 +90,7 @@ Maybe ComputCopyCostBetweenTwoSbpParallel(const SbpParallel& producer_sb // NOTE: A tensor placed on cpu with a consumer operator that accepts cuda inputs would be // transfered to cuda later. We might not have correct parallel description at this moment. - if (producer_parallel_desc == consumer_parallel_desc) { + if (on_same_devices && producer_parallel_num == consumer_parallel_num) { // Same sbp, no cost: S->S, B->B, P->P if (producer_sbp_parallel == consumer_sbp_parallel) { return 0.0; } double logical_blob_size = @@ -84,8 +100,8 @@ Maybe ComputCopyCostBetweenTwoSbpParallel(const SbpParallel& producer_sb // arbitrary i. // ? -> P if (consumer_sbp_parallel.has_partial_sum_parallel()) { - return Penalty4PartialInConsumer(logical_blob_size, producer_parallel_desc.parallel_num(), - consumer_parallel_desc.parallel_num()); + return Penalty4PartialInConsumer(logical_blob_size, producer_parallel_num, + consumer_parallel_num); } // B->S if (producer_sbp_parallel.has_broadcast_parallel()) { return 1.0; } @@ -95,15 +111,14 @@ Maybe ComputCopyCostBetweenTwoSbpParallel(const SbpParallel& producer_sb if (consumer_sbp_parallel.has_split_parallel() && producer_sbp_parallel.has_split_parallel()) { // S(0)->S(1), S(1)->S(0), etc. - return logical_blob_size * (producer_parallel_desc.parallel_num() - 1) - / producer_parallel_desc.parallel_num(); + return logical_blob_size * (producer_parallel_num - 1) / producer_parallel_num; } else { // P->S, S->B/P - return logical_blob_size * (producer_parallel_desc.parallel_num() - 1); + return logical_blob_size * (producer_parallel_num - 1); } } // P->B - return 2 * logical_blob_size * (producer_parallel_desc.parallel_num() - 1); + return 2 * logical_blob_size * (producer_parallel_num - 1); } else { // Not supporting P->P for different placement if (LazyMode::is_enabled()) { @@ -118,17 +133,16 @@ Maybe ComputCopyCostBetweenTwoSbpParallel(const SbpParallel& producer_sb double overall_cost = logical_blob_size; // ? -> B if (consumer_sbp_parallel.has_broadcast_parallel()) { - overall_cost += (consumer_parallel_desc.parallel_num() - 1) * logical_blob_size; + overall_cost += (consumer_parallel_num - 1) * logical_blob_size; } // P -> ? if (producer_sbp_parallel.has_partial_sum_parallel()) { - overall_cost += (producer_parallel_desc.parallel_num() - 1) * logical_blob_size; + overall_cost += (producer_parallel_num - 1) * logical_blob_size; } // ? -> P if (consumer_sbp_parallel.has_partial_sum_parallel()) { - overall_cost += - Penalty4PartialInConsumer(logical_blob_size, producer_parallel_desc.parallel_num(), - consumer_parallel_desc.parallel_num()); + overall_cost += Penalty4PartialInConsumer(logical_blob_size, producer_parallel_num, + consumer_parallel_num); } // For B->S, S->S, overall_cost == logical_blob_size; return overall_cost; @@ -187,9 +201,8 @@ double ComputCopyCostBetweenTwoDiffSbpParallel(const SbpParallel& producer_sbp_p Maybe ComputCopyCostBetweenTwoNdSbp(const NdSbp& producer_nd_sbp, const NdSbp& consumer_nd_sbp, double logical_blob_size, - const std::shared_ptr& hierarchy, - bool on_same_devices) { - if (hierarchy->NumAxes() != 2) { return kUnsupportedBoxing; } + const Shape& hierarchy, bool on_same_devices) { + if (hierarchy.NumAxes() != 2) { return kUnsupportedBoxing; } const auto& producer_sbp_size = producer_nd_sbp.sbp_parallel_size(); const auto& consumer_sbp_size = consumer_nd_sbp.sbp_parallel_size(); // One of the SBP should have size 2 @@ -206,7 +219,7 @@ Maybe ComputCopyCostBetweenTwoNdSbp(const NdSbp& producer_nd_sbp, // The SBP parallel are the same at dimension (dim_same_sbp) if (producer_nd_sbp.sbp_parallel(dim_producer) == consumer_nd_sbp.sbp_parallel(dim_consumer)) { if (!producer_nd_sbp.sbp_parallel(dim_producer).has_split_parallel()) { - logical_blob_size *= hierarchy->At(dim_same_sbp); + logical_blob_size *= hierarchy.At(dim_same_sbp); } // The SBP parallel are different at dimension (dim_diff_sbp) int32_t dim_diff_sbp = 1 - dim_same_sbp; @@ -226,7 +239,7 @@ Maybe ComputCopyCostBetweenTwoNdSbp(const NdSbp& producer_nd_sbp, } return ComputCopyCostBetweenTwoDiffSbpParallel( producer_nd_sbp.sbp_parallel(dim_producer), consumer_nd_sbp.sbp_parallel(dim_consumer), - logical_blob_size, hierarchy->At(dim_diff_sbp), on_same_devices); + logical_blob_size, hierarchy.At(dim_diff_sbp), on_same_devices); } } return kUnsupportedBoxing; @@ -250,33 +263,36 @@ Maybe ComputeEagerCopyCostBetweenNdSbp(const NdSbp& producer_sbp_paralle return kUnsupportedBoxing; } - ParallelDesc reduced_in_parallel_desc = producer_parallel_desc; - ParallelDesc reduced_out_parallel_desc = consumer_parallel_desc; + bool on_same_devices = producer_parallel_desc.EqualsIgnoringHierarchy(consumer_parallel_desc); + + // Reduce before cost computation + Shape reduced_in_hierarchy; NdSbp reduced_in_nd_sbp; + Shape reduced_out_hierarchy; NdSbp reduced_out_nd_sbp; - InOutParallelDimReduce(producer_parallel_desc, consumer_parallel_desc, producer_sbp_parallel, - consumer_sbp_parallel, &reduced_in_parallel_desc, - &reduced_out_parallel_desc, &reduced_in_nd_sbp, &reduced_out_nd_sbp); - - const auto& in_hierarchy = reduced_in_parallel_desc.hierarchy(); - const auto& out_hierarchy = reduced_out_parallel_desc.hierarchy(); + InOutParallelDimReduce(*producer_parallel_desc.hierarchy(), *consumer_parallel_desc.hierarchy(), + producer_sbp_parallel, consumer_sbp_parallel, &reduced_in_hierarchy, + &reduced_out_hierarchy, &reduced_in_nd_sbp, &reduced_out_nd_sbp, + logical_blob_desc.shape()); bool same_nd_sbp = reduced_in_nd_sbp == reduced_out_nd_sbp; // Same sbp is always supported. - if (same_nd_sbp && reduced_in_parallel_desc == reduced_out_parallel_desc) { return 0.0; } + if (same_nd_sbp && on_same_devices && reduced_in_hierarchy == reduced_out_hierarchy) { + return 0.0; + } if (requires_same_sbp) { return kUnsupportedBoxing; } - int32_t in_dim = in_hierarchy->NumAxes(); - int32_t out_dim = out_hierarchy->NumAxes(); + int32_t in_dim = reduced_in_hierarchy.NumAxes(); + int32_t out_dim = reduced_out_hierarchy.NumAxes(); // We support different hierarchy for 1D sbp if (in_dim == 1 && out_dim == 1) { return ComputCopyCostBetweenTwoSbpParallel( reduced_in_nd_sbp.sbp_parallel(0), reduced_out_nd_sbp.sbp_parallel(0), logical_blob_desc, - reduced_in_parallel_desc, reduced_out_parallel_desc); + on_same_devices, reduced_in_hierarchy.elem_cnt(), reduced_out_hierarchy.elem_cnt()); } double total_cost = 1.0; - if (reduced_in_parallel_desc == reduced_out_parallel_desc) { + if (on_same_devices && reduced_in_hierarchy == reduced_out_hierarchy) { // NOTE: After analysis, transfer cost increase if spliting the same dimension. // Example 1: (S(1), S(0), S(1), S(0)) -> (S(0), S(0), S(0), S(0)) // Example 2: (B, S(0)) -> (S(0), S(0)) @@ -285,14 +301,15 @@ Maybe ComputeEagerCopyCostBetweenNdSbp(const NdSbp& producer_sbp_paralle // simplification. bool normal_case = true; // nd to nd - for (int32_t i = 0; i < reduced_in_parallel_desc.hierarchy()->NumAxes(); ++i) { + for (int32_t i = 0; i < in_dim; ++i) { const auto& in_sbp = reduced_in_nd_sbp.sbp_parallel(i); const auto& out_sbp = reduced_out_nd_sbp.sbp_parallel(i); // Have bugs here. (B, S0) -> (S0, S0) will give a cost 0. // Actually it is (1-1/m)T for hierarchy (n, m) // TODO: Fix that after support all sbp combination for eager. total_cost += JUST(ComputCopyCostBetweenTwoSbpParallel( - in_sbp, out_sbp, logical_blob_desc, reduced_in_parallel_desc, reduced_out_parallel_desc)); + in_sbp, out_sbp, logical_blob_desc, on_same_devices, reduced_in_hierarchy.elem_cnt(), + reduced_out_hierarchy.elem_cnt())); // Add the penalty for P in the consumer if (out_sbp.has_partial_sum_parallel() && (in_sbp != out_sbp)) { total_cost += Penalty4PartialInConsumer( @@ -322,20 +339,20 @@ Maybe ComputeEagerCopyCostBetweenNdSbp(const NdSbp& producer_sbp_paralle logical_blob_desc.shape().elem_cnt() * GetSizeOfDataType(logical_blob_desc.data_type()); { double in_cost = 1.0; - for (int32_t i = 0; i < reduced_in_parallel_desc.hierarchy()->NumAxes(); ++i) { + for (int32_t i = 0; i < in_dim; ++i) { // P -> ? if (reduced_in_nd_sbp.sbp_parallel(i).has_partial_sum_parallel()) { - in_cost *= reduced_in_parallel_desc.hierarchy()->At(i); + in_cost *= reduced_in_hierarchy.At(i); } } total_cost += logical_blob_size * in_cost; } { double out_cost = 1.0; - for (int32_t i = 0; i < reduced_out_parallel_desc.hierarchy()->NumAxes(); ++i) { + for (int32_t i = 0; i < out_dim; ++i) { // ? -> B if (reduced_out_nd_sbp.sbp_parallel(i).has_broadcast_parallel()) { - out_cost *= reduced_out_parallel_desc.hierarchy()->At(i); + out_cost *= reduced_out_hierarchy.At(i); } // Add the penalty for P in the consumer if (reduced_out_nd_sbp.sbp_parallel(i).has_partial_sum_parallel()) { @@ -360,100 +377,258 @@ Maybe GetComputeCopyCostFunc() { } } -void CollaborativeParallelDimReduce(const ParallelDesc& in_parallel_desc, - const ParallelDesc& out_parallel_desc, const NdSbp& in_nd_sbp, - const NdSbp& out_nd_sbp, ParallelDesc* reduced_in_parallel_desc, - ParallelDesc* reduced_out_parallel_desc, - NdSbp* reduced_in_nd_sbp, NdSbp* reduced_out_nd_sbp) { - const auto& in_hierarchy = in_parallel_desc.hierarchy(); - const auto& out_hierarchy = out_parallel_desc.hierarchy(); - CHECK_EQ(in_hierarchy->NumAxes(), out_hierarchy->NumAxes()); - - DimVector reduced_in_hierarchy; - DimVector reduced_out_hierarchy; - FOR_RANGE(int64_t, i, 0, in_hierarchy->NumAxes()) { - if (in_hierarchy->At(i) != 1 || out_hierarchy->At(i) != 1) { - if (reduced_in_nd_sbp->sbp_parallel().empty() - || (in_nd_sbp.sbp_parallel(i) - != reduced_in_nd_sbp->sbp_parallel(reduced_in_nd_sbp->sbp_parallel_size() - 1) - || out_nd_sbp.sbp_parallel(i) - != reduced_out_nd_sbp->sbp_parallel(reduced_out_nd_sbp->sbp_parallel_size() - - 1))) { - reduced_in_hierarchy.emplace_back(in_hierarchy->At(i)); - *reduced_in_nd_sbp->add_sbp_parallel() = in_nd_sbp.sbp_parallel(i); - - reduced_out_hierarchy.emplace_back(out_hierarchy->At(i)); - *reduced_out_nd_sbp->add_sbp_parallel() = out_nd_sbp.sbp_parallel(i); - } else { - reduced_in_hierarchy.back() *= in_hierarchy->At(i); - reduced_out_hierarchy.back() *= out_hierarchy->At(i); - } - } +// Replace the hierarchy and then create a new parallel description +void ReplaceHierarchy4ParallelDesc(const ParallelDesc& old_parallel_desc, + const Shape& new_hierarchy, ParallelDesc* new_parallel_desc) { + if (*old_parallel_desc.hierarchy() == new_hierarchy) { + *new_parallel_desc = old_parallel_desc; + } else { + ParallelConf new_parallel_conf = old_parallel_desc.parallel_conf(); + new_hierarchy.ToProto(new_parallel_conf.mutable_hierarchy()); + *new_parallel_desc = ParallelDesc(new_parallel_conf); } - if (reduced_in_hierarchy.empty()) { - reduced_in_hierarchy.emplace_back(in_hierarchy->At(0)); - *reduced_in_nd_sbp->add_sbp_parallel() = in_nd_sbp.sbp_parallel(0); +} - reduced_out_hierarchy.emplace_back(out_hierarchy->At(0)); - *reduced_out_nd_sbp->add_sbp_parallel() = out_nd_sbp.sbp_parallel(0); +// We can not just simply merging two same split +// For example, shape = [6], we are trying to merge [2, 2]: (S0, S0) -> [4]: S0 +// For each rank, [4]: S0 has number of data: 2, 2, 1, 1 +// For each rank, [2]: S0 has number of data: 3, 3 +// For each rank, [2, 2]: (S0, S0) has number of data: 2, 1, 2, 1 +// Thus {[2, 2]: (S0, S0)} != {[4]: S0} for shape [6] +// However {[2, 2]: (S0, S0)} == {[4]: S0} for shape [4], [5], [7], [8] +// More specifically, {[a, b]: (Si, Si)} == {[a*b]: Si} if and only if +// shape value % (a * b) == 0, 1, a*b - 1 +bool CanMergeSplit(int32_t shape_value, int32_t merged_split_hierarchy_value) { + int32_t remainder = shape_value % merged_split_hierarchy_value; + if (remainder <= 1 || remainder == merged_split_hierarchy_value - 1) { + return true; + } else { + return false; } +} - ParallelConf reduced_in_parallel_conf = in_parallel_desc.parallel_conf(); - Shape(reduced_in_hierarchy).ToProto(reduced_in_parallel_conf.mutable_hierarchy()); - *reduced_in_parallel_desc = ParallelDesc(reduced_in_parallel_conf); +} // namespace - ParallelConf reduced_out_parallel_conf = out_parallel_desc.parallel_conf(); - Shape(reduced_out_hierarchy).ToProto(reduced_out_parallel_conf.mutable_hierarchy()); - *reduced_out_parallel_desc = ParallelDesc(reduced_out_parallel_conf); +int32_t PartialRatio4Producer(const NdSbp& sbp_producer, + const ParallelDesc& producer_parallel_desc) { + return Ratio4Sbp(sbp_producer, producer_parallel_desc, &SbpParallel::has_partial_sum_parallel); } -} // namespace +int32_t BroadcastRatio4Consumer(const NdSbp& sbp_consumer, + const ParallelDesc& consumer_parallel_desc) { + return Ratio4Sbp(sbp_consumer, consumer_parallel_desc, &SbpParallel::has_broadcast_parallel); +} -void NdSbpDimReduce(const ParallelDesc& parallel_desc, const NdSbp& nd_sbp, - ParallelDesc* reduced_parallel_desc, NdSbp* reduced_nd_sbp) { - const auto& hierarchy = parallel_desc.hierarchy(); - DimVector reduced_hierarchy; - FOR_RANGE(int64_t, i, 0, hierarchy->NumAxes()) { - if (hierarchy->At(i) != 1) { - if (reduced_nd_sbp->sbp_parallel().empty() - || (nd_sbp.sbp_parallel(i) - != reduced_nd_sbp->sbp_parallel(reduced_nd_sbp->sbp_parallel_size() - 1))) { - reduced_hierarchy.emplace_back(hierarchy->At(i)); - *reduced_nd_sbp->add_sbp_parallel() = nd_sbp.sbp_parallel(i); +void NdSbpDimReduce(const Shape& hierarchy, const NdSbp& nd_sbp, Shape* reduced_hierarchy, + NdSbp* reduced_nd_sbp, const Shape& logical_shape) { + NdSbpsDimReduce(hierarchy, {&nd_sbp}, reduced_hierarchy, {reduced_nd_sbp}, logical_shape); +} + +void NdSbpsDimReduce(const Shape& hierarchy, const std::vector& nd_sbps, + Shape* reduced_hierarchy, const std::vector& reduced_nd_sbps, + const Shape& logical_shape) { + int32_t sbp_num = nd_sbps.size(); + // Speed up for 1d sbp + if (hierarchy.NumAxes() == 1) { + *reduced_hierarchy = hierarchy; + for (int32_t index = 0; index < sbp_num; index++) { + if (hierarchy.elem_cnt() == 1) { + reduced_nd_sbps[index]->add_sbp_parallel()->mutable_broadcast_parallel(); + } else { + *reduced_nd_sbps[index] = *nd_sbps[index]; + } + } + return; + } + reduced_hierarchy->clear(); + for (auto& reduced_nd_sbp : reduced_nd_sbps) { reduced_nd_sbp->clear_sbp_parallel(); } + // At this moment, if we have [2, 4, 3, 7]: (S0, S1, S0, S0) for logical shape [601, 301, 999] + // We hold the split when accessing the current dimension + // Do the true splitting until we reach the next step + // dim = 0, split_axis2holding_reduced_shapes: {(0: 601)}, last split axis = -1 + // dim = 1, split_axis2holding_reduced_shapes: {(0: 300, 301), (1: 301)}, last split axis = 0 + // dim = 2, split_axis2holding_reduced_shapes: {(0: 300, 301), (1: 75, 76)}, last split axis = 1 + // dim = 3, at this moment, last split axis (0) == current split axis (0), + // dim = 3, but judging 300 % (3 * 7) = 6 fails the CanMergeSplit(), not merging + // dim = 3, split_axis2holding_reduced_shapes: {(0: 100, 101), (1: 75, 76)}, last split axis = 0 + std::vector>> index2split_axis2holding_reduced_shapes(sbp_num); + std::vector> index2last_holding_reduced_shapes(sbp_num); + std::vector last_split_axises(sbp_num, -1); + std::vector indexes(sbp_num); + for (int32_t index = 0; index < sbp_num; index++) { indexes[index] = index; } + auto add_to_reduced_sbp_hierarchy = [&](int32_t hierarchy_dim) { + // Clear the last holding split axis + for (int32_t index = 0; index < sbp_num; index++) { + auto& split_axis2holding_reduced_shapes = index2split_axis2holding_reduced_shapes[index]; + auto& last_holding_reduced_shapes = index2last_holding_reduced_shapes[index]; + auto& last_split_axis = last_split_axises[index]; + auto& nd_sbp = nd_sbps[index]; + auto& reduced_nd_sbp = reduced_nd_sbps[index]; + if (last_split_axis >= 0) { + auto& holding_reduced_shapes = split_axis2holding_reduced_shapes[last_split_axis]; + holding_reduced_shapes.clear(); + for (int32_t last_holding_reduced_shape : last_holding_reduced_shapes) { + int32_t quotient = last_holding_reduced_shape / reduced_hierarchy->back(); + if (last_holding_reduced_shape % reduced_hierarchy->back() != 0) { + holding_reduced_shapes.insert(quotient + 1); + } + holding_reduced_shapes.insert(quotient); + } + } + // Add a new sbp_parallel and a new hierarchy dimension + const auto& curr_sbp_parallel = nd_sbp->sbp_parallel(hierarchy_dim); + *reduced_nd_sbp->add_sbp_parallel() = curr_sbp_parallel; + // Hold the current split shape + if (curr_sbp_parallel.has_split_parallel()) { + last_holding_reduced_shapes.clear(); + last_split_axis = curr_sbp_parallel.split_parallel().axis(); + auto it = split_axis2holding_reduced_shapes.find(last_split_axis); + if (it == split_axis2holding_reduced_shapes.end()) { + // Looking at a dimension which is never splitted before + // Shape: [601, ...], sbp: (S0, ...) + last_holding_reduced_shapes.push_back(logical_shape.At(last_split_axis)); + } else { + // This dimension is splitted before + // Shape: [601, 301, ...], sbp: (S0, S1, B, S0, ...), hierarchy: [2, 3, 100, 7, ...] + // Looking at i = 3, we hold the second S0, but 601 is already splitted by the first S0. + // split_axis2holding_reduced_shapes: {(0: 300, 301), (1: 100, 101)} + last_holding_reduced_shapes.assign(it->second.begin(), it->second.end()); + } } else { - reduced_hierarchy.back() *= hierarchy->At(i); + last_split_axis = -1; } } + // Add a new hierarchy dimension + reduced_hierarchy->emplace_back(hierarchy.At(hierarchy_dim)); + }; + for (int32_t hierarchy_dim = 0; hierarchy_dim < hierarchy.NumAxes(); hierarchy_dim++) { + // Shrink those dimension with hierarchy value = 1 + if (hierarchy.At(hierarchy_dim) == 1) { continue; } + if (reduced_hierarchy->empty()) { + // Empty hierarchy, add to the back + add_to_reduced_sbp_hierarchy(hierarchy_dim); + continue; + } + if (std::all_of(indexes.begin(), indexes.end(), [&](int32_t index) { + // reduced_hierarchy->size() == reduced_nd_sbps[index]->sbp_parallel_size() + // Basically, current nd sbp == reduced nd sbp.back() + return nd_sbps[index]->sbp_parallel(hierarchy_dim) + == reduced_nd_sbps[index]->sbp_parallel(reduced_hierarchy->size() - 1); + })) { + int32_t merged_hierarchy_value = reduced_hierarchy->back() * hierarchy.At(hierarchy_dim); + // You can merge two sbp with B or P. + // If sbp = S, then you need to make sure that all the shape value can be splitted + if (std::all_of(indexes.begin(), indexes.end(), [&](int32_t index) { + return !nd_sbps[index]->sbp_parallel(hierarchy_dim).has_split_parallel() + || std::all_of(index2last_holding_reduced_shapes[index].begin(), + index2last_holding_reduced_shapes[index].end(), [&](int32_t i) { + return CanMergeSplit(i, merged_hierarchy_value); + }); + })) { + // Merge sbp and hierarchy + reduced_hierarchy->back() = merged_hierarchy_value; + continue; + } + } + // Can not merge, add to the back + add_to_reduced_sbp_hierarchy(hierarchy_dim); } // [1, 1, ..., 1]: Any --> [1]: (B) - if (reduced_hierarchy.empty()) { - reduced_hierarchy.emplace_back(hierarchy->At(0)); - reduced_nd_sbp->add_sbp_parallel()->mutable_broadcast_parallel(); + if (reduced_hierarchy->empty()) { + reduced_hierarchy->emplace_back(hierarchy.At(0)); + for (auto& reduced_nd_sbp : reduced_nd_sbps) { + reduced_nd_sbp->add_sbp_parallel()->mutable_broadcast_parallel(); + } + } +} + +void NdSbpDimReduce(const ParallelDesc& parallel_desc, const NdSbp& nd_sbp, + ParallelDesc* reduced_parallel_desc, NdSbp* reduced_nd_sbp, + const Shape& logical_shape) { + // Speed up for 1d sbp + if (parallel_desc.hierarchy()->NumAxes() == 1) { + *reduced_parallel_desc = parallel_desc; + *reduced_nd_sbp = nd_sbp; + return; + } + Shape reduced_hierarchy; + NdSbpDimReduce(*parallel_desc.hierarchy(), nd_sbp, &reduced_hierarchy, reduced_nd_sbp, + logical_shape); + + ReplaceHierarchy4ParallelDesc(parallel_desc, reduced_hierarchy, reduced_parallel_desc); +} + +void InOutParallelDimReduce(const Shape& in_hierarchy, const Shape& out_hierarchy, + const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, + Shape* reduced_in_hierarchy, Shape* reduced_out_hierarchy, + NdSbp* reduced_in_nd_sbp, NdSbp* reduced_out_nd_sbp, + const Shape& logical_shape) { + if (in_hierarchy == out_hierarchy) { + // [2, 4]: (S0, S0) -> [2, 4]: (S0, S1) + NdSbpsDimReduce(in_hierarchy, {&in_nd_sbp, &out_nd_sbp}, reduced_in_hierarchy, + {reduced_in_nd_sbp, reduced_out_nd_sbp}, logical_shape); + *reduced_out_hierarchy = *reduced_in_hierarchy; + } else { + // [2, 4]: (S0, S0) -> [4, 2]: (S0, S1) + // [2, 4]: (S0, S0) -> [3, 3]: (S0, S1) + NdSbpDimReduce(in_hierarchy, in_nd_sbp, reduced_in_hierarchy, reduced_in_nd_sbp, logical_shape); + NdSbpDimReduce(out_hierarchy, out_nd_sbp, reduced_out_hierarchy, reduced_out_nd_sbp, + logical_shape); + + // Sbp of 3d or higher dimension would use general basic communication + // Only looks at 1d to 2d or 2d to 1d + if (reduced_in_hierarchy->NumAxes() + reduced_out_hierarchy->NumAxes() == 3 + && reduced_in_hierarchy->elem_cnt() == reduced_out_hierarchy->elem_cnt()) { + if (reduced_in_hierarchy->NumAxes() == 1) { + // [8]: S0 -> [4, 2]: (S0, S1) + // [8]: B -> [2, 4]: (S0, S1) + const auto& in_sbp_parallel = reduced_in_nd_sbp->sbp_parallel(0); + if (!in_sbp_parallel.has_split_parallel() + || CanMergeSplit(logical_shape.At(in_sbp_parallel.split_parallel().axis()), + reduced_in_hierarchy->elem_cnt())) { + // Change [8]: S0 -> [4, 2]: (S0, S1) to [4, 2]: (S0, S0) -> [4, 2]: (S0, S1) + // Change [8]: B -> [2, 4]: (S0, S1) to [2, 4]: (B, B) -> [2, 4]: (S0, S1) + *reduced_in_nd_sbp->add_sbp_parallel() = in_sbp_parallel; + *reduced_in_hierarchy = *reduced_out_hierarchy; + } + } else { + // [2, 3]: (S0, P) -> [6]: S0 + // [3, 4]: (B, S1) -> [12]: B + const auto& out_sbp_parallel = reduced_out_nd_sbp->sbp_parallel(0); + if (!out_sbp_parallel.has_split_parallel() + || CanMergeSplit(logical_shape.At(out_sbp_parallel.split_parallel().axis()), + reduced_out_hierarchy->elem_cnt())) { + // Change [2, 3]: (S0, P) -> [6]: S0 to [2, 3]: (S0, P) -> [2, 3]: (S0, S0) + // Change [3, 4]: (B, S1) -> [12]: B to [3, 4]: (B, S1) -> [3, 4]: (B, B) + *reduced_out_nd_sbp->add_sbp_parallel() = out_sbp_parallel; + *reduced_out_hierarchy = *reduced_in_hierarchy; + } + } + } } - ParallelConf reduced_parallel_conf = parallel_desc.parallel_conf(); - Shape(reduced_hierarchy).ToProto(reduced_parallel_conf.mutable_hierarchy()); - *reduced_parallel_desc = ParallelDesc(reduced_parallel_conf); } void InOutParallelDimReduce(const ParallelDesc& in_parallel_desc, const ParallelDesc& out_parallel_desc, const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, ParallelDesc* reduced_in_parallel_desc, ParallelDesc* reduced_out_parallel_desc, NdSbp* reduced_in_nd_sbp, - NdSbp* reduced_out_nd_sbp) { - const int64_t in_hierarchy_axes = in_parallel_desc.hierarchy()->NumAxes(); - const int64_t out_hierarchy_axes = out_parallel_desc.hierarchy()->NumAxes(); - if (in_hierarchy_axes == 1 && out_hierarchy_axes == 1) { + NdSbp* reduced_out_nd_sbp, const Shape& logical_shape) { + // Speed up for 1d sbp + if (in_parallel_desc.hierarchy()->NumAxes() == 1 + && out_parallel_desc.hierarchy()->NumAxes() == 1) { *reduced_in_parallel_desc = in_parallel_desc; *reduced_out_parallel_desc = out_parallel_desc; *reduced_in_nd_sbp = in_nd_sbp; *reduced_out_nd_sbp = out_nd_sbp; - } else if (in_hierarchy_axes != out_hierarchy_axes) { - NdSbpDimReduce(in_parallel_desc, in_nd_sbp, reduced_in_parallel_desc, reduced_in_nd_sbp); - NdSbpDimReduce(out_parallel_desc, out_nd_sbp, reduced_out_parallel_desc, reduced_out_nd_sbp); } else { - CollaborativeParallelDimReduce(in_parallel_desc, out_parallel_desc, in_nd_sbp, out_nd_sbp, - reduced_in_parallel_desc, reduced_out_parallel_desc, - reduced_in_nd_sbp, reduced_out_nd_sbp); + Shape reduced_in_hierarchy; + Shape reduced_out_hierarchy; + InOutParallelDimReduce(*in_parallel_desc.hierarchy(), *out_parallel_desc.hierarchy(), in_nd_sbp, + out_nd_sbp, &reduced_in_hierarchy, &reduced_out_hierarchy, + reduced_in_nd_sbp, reduced_out_nd_sbp, logical_shape); + ReplaceHierarchy4ParallelDesc(in_parallel_desc, reduced_in_hierarchy, reduced_in_parallel_desc); + ReplaceHierarchy4ParallelDesc(out_parallel_desc, reduced_out_hierarchy, + reduced_out_parallel_desc); } } @@ -466,18 +641,19 @@ Maybe ComputeLazyCopyCostBetweenNdSbp(const NdSbp& producer_sbp_parallel if (!(CheckNdSbp(producer_sbp_parallel) && CheckNdSbp(consumer_sbp_parallel))) { return Error::RuntimeError() << "Illegal sbp parallel has been found."; } - ParallelDesc reduced_in_parallel_desc = producer_parallel_desc; - ParallelDesc reduced_out_parallel_desc = consumer_parallel_desc; + bool on_same_devices = producer_parallel_desc.EqualsIgnoringHierarchy(consumer_parallel_desc); + + // Reduce before cost computation + Shape reduced_in_hierarchy; NdSbp reduced_in_nd_sbp; + Shape reduced_out_hierarchy; NdSbp reduced_out_nd_sbp; - InOutParallelDimReduce(producer_parallel_desc, consumer_parallel_desc, producer_sbp_parallel, - consumer_sbp_parallel, &reduced_in_parallel_desc, - &reduced_out_parallel_desc, &reduced_in_nd_sbp, &reduced_out_nd_sbp); - - const auto& in_hierarchy = reduced_in_parallel_desc.hierarchy(); - const auto& out_hierarchy = reduced_out_parallel_desc.hierarchy(); - int32_t in_dim = in_hierarchy->NumAxes(); - int32_t out_dim = out_hierarchy->NumAxes(); + InOutParallelDimReduce(*producer_parallel_desc.hierarchy(), *consumer_parallel_desc.hierarchy(), + producer_sbp_parallel, consumer_sbp_parallel, &reduced_in_hierarchy, + &reduced_out_hierarchy, &reduced_in_nd_sbp, &reduced_out_nd_sbp, + logical_blob_desc.shape()); + int32_t in_dim = reduced_in_hierarchy.NumAxes(); + int32_t out_dim = reduced_out_hierarchy.NumAxes(); // Not supporting n-D sbp with n >= 3 // TODO: Support it in the future if (std::min(in_dim, out_dim) <= 0 || std::max(in_dim, out_dim) >= 3) { @@ -486,7 +662,9 @@ Maybe ComputeLazyCopyCostBetweenNdSbp(const NdSbp& producer_sbp_parallel bool same_nd_sbp = reduced_in_nd_sbp == reduced_out_nd_sbp; // Same sbp is always supported. - if (same_nd_sbp && reduced_in_parallel_desc == reduced_out_parallel_desc) { return 0.0; } + if (same_nd_sbp && on_same_devices && reduced_in_hierarchy == reduced_out_hierarchy) { + return 0.0; + } if (requires_same_sbp) { return kUnsupportedBoxing; } // We support different hierarchy for 1D sbp @@ -494,37 +672,58 @@ Maybe ComputeLazyCopyCostBetweenNdSbp(const NdSbp& producer_sbp_parallel return GetTransferCost() + JUST(ComputCopyCostBetweenTwoSbpParallel( reduced_in_nd_sbp.sbp_parallel(0), reduced_out_nd_sbp.sbp_parallel(0), - logical_blob_desc, reduced_in_parallel_desc, reduced_out_parallel_desc)); + logical_blob_desc, on_same_devices, reduced_in_hierarchy.elem_cnt(), + reduced_out_hierarchy.elem_cnt())); + } + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + static const bool enable_general_basic_communication = + ParseBooleanFromEnv("ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION", false); + // Use a general basic communication if no P in the consumer + if ((((Singleton::Get()->nccl_use_compute_stream() + && producer_parallel_desc == consumer_parallel_desc) + || enable_general_basic_communication) + && !NdSbpHasPartialParallel(consumer_sbp_parallel)) + && producer_parallel_desc.device_type() == DeviceType::kCUDA + && consumer_parallel_desc.device_type() == DeviceType::kCUDA) { + return Cost4GeneralBasicCommunication(producer_sbp_parallel, consumer_sbp_parallel, + logical_blob_desc, producer_parallel_desc, + consumer_parallel_desc) + + GetTransferCost(); + } +#endif // WITH_CUDA + + // Not supporting different hierarchy without general basic communication + if (reduced_in_hierarchy.elem_cnt() != reduced_out_hierarchy.elem_cnt()) { + return kUnsupportedBoxing; } - // Not supporting different hierarchy - // TODO: Support it in the future - if (in_hierarchy->elem_cnt() != out_hierarchy->elem_cnt()) { return kUnsupportedBoxing; } double logical_blob_size = logical_blob_desc.shape().elem_cnt() * GetSizeOfDataType(logical_blob_desc.data_type()); - bool on_same_devices = - reduced_in_parallel_desc.EqualsIgnoringHierarchy(reduced_out_parallel_desc); if (in_dim == 2 && out_dim == 2) { // Not supporting different hierarchy // TODO: Support it in the future - if (*in_hierarchy != *out_hierarchy) { return kUnsupportedBoxing; } + if (reduced_in_hierarchy != reduced_out_hierarchy) { return kUnsupportedBoxing; } return GetTransferCost() + JUST(ComputCopyCostBetweenTwoNdSbp(reduced_in_nd_sbp, reduced_out_nd_sbp, - logical_blob_size, in_hierarchy, on_same_devices)); + logical_blob_size, reduced_in_hierarchy, + on_same_devices)); } // (in_dim == 2 && out_dim == 1) || (in_dim == 1 && out_dim == 2) if (in_dim == 2 && out_dim == 1) { return GetTransferCost() + JUST(ComputCopyCostBetweenTwoNdSbp(reduced_in_nd_sbp, reduced_out_nd_sbp, - logical_blob_size, in_hierarchy, on_same_devices)); + logical_blob_size, reduced_in_hierarchy, + on_same_devices)); } if (in_dim == 1 && out_dim == 2) { return GetTransferCost() + JUST(ComputCopyCostBetweenTwoNdSbp(reduced_in_nd_sbp, reduced_out_nd_sbp, - logical_blob_size, out_hierarchy, on_same_devices)); + logical_blob_size, reduced_out_hierarchy, + on_same_devices)); } return Error::RuntimeError() @@ -541,7 +740,7 @@ double GetValidMaxCopyCost() { double GetTransferCost() { // Each transfer would have cost. // Except for same parallel description and sbp - static const double kTransferCost = ParseFloatFromEnv("AUTO_PARALLEL_TRANSFER_COST", 1.65e8); + static const double kTransferCost = ParseFloatFromEnv("AUTO_PARALLEL_TRANSFER_COST", 1.65e4); return kTransferCost; } @@ -561,14 +760,17 @@ void SetNdSbpSignature(NdSbpSignature* nd_sbp_signature, const SbpSignature& sbp } void DfsGetNdSbpSignature(NdSbpSignature& nd_sbp_sig, int32_t depth, int32_t dims, - const SbpSignatureList& sbp_sig_list, + const Shape& hierarchy, + const HashMap& hierarchy_value2sbp_sig_list, std::vector* nd_sbp_sig_list) { if (depth == dims) { nd_sbp_sig_list->push_back(nd_sbp_sig); } else { - for (const auto& sbp_signature : sbp_sig_list.sbp_signature()) { + for (const auto& sbp_signature : + hierarchy_value2sbp_sig_list.at(hierarchy.At(depth)).sbp_signature()) { SetNdSbpSignature(&nd_sbp_sig, sbp_signature, depth); - DfsGetNdSbpSignature(nd_sbp_sig, depth + 1, dims, sbp_sig_list, nd_sbp_sig_list); + DfsGetNdSbpSignature(nd_sbp_sig, depth + 1, dims, hierarchy, hierarchy_value2sbp_sig_list, + nd_sbp_sig_list); } } } @@ -594,10 +796,7 @@ double Storage4NdSbp(const NdSbp& nd_sbp, Shape& logical_shape, const Shape& par const int64_t axis = sbp_parallel.split_parallel().axis(); if (axis >= logical_shape.NumAxes()) { return kUnsupportedBoxing; } // Use completely average split to count the storage - if (logical_shape.At(axis) <= 0 - || (logical_shape.At(axis) % parallel_hierarchy.At(dim_sbp) > 0)) { - return kUnsupportedBoxing; - } + if (logical_shape.At(axis) < parallel_hierarchy.At(dim_sbp)) { return kUnsupportedBoxing; } logical_shape.Set(axis, logical_shape.At(axis) / parallel_hierarchy.At(dim_sbp)); } } @@ -629,6 +828,47 @@ Maybe ComputeCopyCostWithMiddleNodes(const NdSbp& producer_sbp_parallel, const ParallelDesc& producer_parallel_desc, const ParallelDesc& consumer_parallel_desc, bool requires_same_sbp) { + // In 90% of the transfer, we would have the same parallel description for producer and consumer + // We need to speed it up and give an approximation of the cost + if (producer_parallel_desc.EqualsIgnoringHierarchy(consumer_parallel_desc)) { + // [2, 2]: (S0, S1) -> [2, 2]: (S0, S1) + if (*producer_parallel_desc.hierarchy() == *consumer_parallel_desc.hierarchy() + && producer_sbp_parallel == consumer_sbp_parallel) { + return 0.0; + } + // Reduce before cost computation + Shape reduced_in_hierarchy; + NdSbp reduced_in_nd_sbp; + Shape reduced_out_hierarchy; + NdSbp reduced_out_nd_sbp; + InOutParallelDimReduce(*producer_parallel_desc.hierarchy(), *consumer_parallel_desc.hierarchy(), + producer_sbp_parallel, consumer_sbp_parallel, &reduced_in_hierarchy, + &reduced_out_hierarchy, &reduced_in_nd_sbp, &reduced_out_nd_sbp, + logical_blob_desc.shape()); + + // [2, 2]: (B, B) -> [4]: B + if (reduced_in_hierarchy == reduced_out_hierarchy && reduced_in_nd_sbp == reduced_out_nd_sbp) { + return 1.0; + } + } + if (requires_same_sbp) { return kUnsupportedBoxing; } +#if defined(WITH_CUDA) || defined(WITH_ROCM) + static const bool enable_general_basic_communication = + ParseBooleanFromEnv("ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION", false); + // Use a general basic communication if no P in the consumer + if ((((Singleton::Get()->nccl_use_compute_stream() + && producer_parallel_desc == consumer_parallel_desc) + || enable_general_basic_communication) + && !NdSbpHasPartialParallel(consumer_sbp_parallel)) + && producer_parallel_desc.device_type() == DeviceType::kCUDA + && consumer_parallel_desc.device_type() == DeviceType::kCUDA) { + return Cost4GeneralBasicCommunication(producer_sbp_parallel, consumer_sbp_parallel, + logical_blob_desc, producer_parallel_desc, + consumer_parallel_desc) + + GetTransferCost(); + } +#endif // WITH_CUDA + // Initialize boxing collector constexpr int32_t kRegularMaxSplitAxes = 6; static thread_local BoxingCollector boxing_collector(kRegularMaxSplitAxes); @@ -672,40 +912,152 @@ Maybe ComputeCopyCostWithMiddleNodes(const NdSbp& producer_sbp_parallel, } // Decide the priority to infer sbp -double ComputeSbpInferPriority(const NdSbp& producer_sbp_parallel, - const NdSbp& consumer_sbp_parallel, +double ComputeSbpInferPriority(const NdSbp& producer_nd_sbp, const NdSbp& consumer_nd_sbp, const ParallelDesc& producer_parallel_desc, - const ParallelDesc& consumer_parallel_desc, bool requires_same_sbp) { - ParallelDesc reduced_in_parallel_desc = producer_parallel_desc; - ParallelDesc reduced_out_parallel_desc = consumer_parallel_desc; + const ParallelDesc& consumer_parallel_desc, bool requires_same_sbp, + const Shape& logical_shape) { + if (producer_nd_sbp == consumer_nd_sbp && producer_parallel_desc == consumer_parallel_desc) { + // Highest priority: this blob have the same placement and sbp on both the producer and + // consumer + return 0.0; + } + // Reduce before cost computation + Shape reduced_in_hierarchy; NdSbp reduced_in_nd_sbp; + Shape reduced_out_hierarchy; NdSbp reduced_out_nd_sbp; - InOutParallelDimReduce(producer_parallel_desc, consumer_parallel_desc, producer_sbp_parallel, - consumer_sbp_parallel, &reduced_in_parallel_desc, - &reduced_out_parallel_desc, &reduced_in_nd_sbp, &reduced_out_nd_sbp); + InOutParallelDimReduce(*producer_parallel_desc.hierarchy(), *consumer_parallel_desc.hierarchy(), + producer_nd_sbp, consumer_nd_sbp, &reduced_in_hierarchy, + &reduced_out_hierarchy, &reduced_in_nd_sbp, &reduced_out_nd_sbp, + logical_shape); if (requires_same_sbp) { // This blob does not support boxing - if (reduced_in_nd_sbp == reduced_out_nd_sbp - && reduced_in_parallel_desc == reduced_out_parallel_desc) { - // Highest priority: this blob have the same placement and sbp on both the producer and - // consumer - return 0.0; + if (reduced_in_nd_sbp == reduced_out_nd_sbp && reduced_in_hierarchy == reduced_out_hierarchy + && producer_parallel_desc.EqualsIgnoringHierarchy(consumer_parallel_desc)) { + // Normal priority: No transfer occurs but we have different sbp + // For example: [1]:S0 -> [1]:B + // [1, 2]:(P, S0) -> [1, 2]:(S0, S0) + return 1.0; } else { // Penality: this blob have different placements and sbps but it does not support boxing return 2.0; } } else { // This blob supports boxing - if (reduced_in_nd_sbp == reduced_out_nd_sbp) { - // Highest priority: this blob have the same sbp on both the producer and consumer - // Not just [0-3] -> [4-7], but also cpu:[0] -> cuda:[0-3] - return 0.0; + if (producer_nd_sbp.sbp_parallel_size() == consumer_nd_sbp.sbp_parallel_size()) { + if (producer_nd_sbp == consumer_nd_sbp) { + // Highest priority: this blob have the same sbp on both the producer and consumer + // Not just [0-3] -> [4-7], but also cpu:[0] -> cuda:[0-3] + return 0.0; + } } else { - // Normal priority: transfer occurs - return 1.0; + if (reduced_in_nd_sbp == reduced_out_nd_sbp) { + // Highest priority: this blob have the same sbp on both the producer and consumer + // [2, 2]: (S0, S0) -> [2]: S0 + // (learning rate) [1]: B -> [2, 2]: (B, B) + return 0.0; + } + } + // Normal priority: transfer might occurs + // Or might not: [1, 2]: (P, S0) -> [1, 2]: (B, S0) + // No transfer but not highest priority + return 1.0; + } +} + +// The transfer ratio for general basic communication +// Cost = ratio * data amount +// When we get the this function, either producer_sbp_parallel != consumer_sbp_parallel +// or producer_parallel_desc != consumer_parallel_desc +double Cost4GeneralBasicCommunication(const NdSbp& producer_sbp_parallel, + const NdSbp& consumer_sbp_parallel, + const BlobDesc& logical_blob_desc, + const ParallelDesc& producer_parallel_desc, + const ParallelDesc& consumer_parallel_desc) { + // The upper bound of the amount of the transferred data + int32_t producer_partial_ratio = + PartialRatio4Producer(producer_sbp_parallel, producer_parallel_desc); + int32_t consumer_broadcast_ratio = + BroadcastRatio4Consumer(consumer_sbp_parallel, consumer_parallel_desc); + // More intersection on the same devices + bool on_same_devices = producer_parallel_desc.EqualsIgnoringHierarchy(consumer_parallel_desc); + // approximate intersection ratio + double intersection_ratio = 1.0; + // (?, P, ?)->(Si, Sj)->(?, B, ?), two-step transfer + if (producer_partial_ratio > 1 && consumer_broadcast_ratio > 1) { + if (on_same_devices) { + // Pure P in the producer or B in the consumer + // (P, P, P) -> ? or ? -> (B, B) + if (producer_partial_ratio == producer_parallel_desc.parallel_num() + || consumer_broadcast_ratio == consumer_parallel_desc.parallel_num()) { + // There some cases which is not applicable to this ratio + // We just take the one with the largest possibility + // For example: (P, S0) -> (B, B) for 1-D blob with machine hierarchy [n, m] + // The path should be (P, S0) -> (S0, S0) -> (B, B) + // true intersection ratio = 1/m + 1 + intersection_ratio = 2.0; + } else { + // sbp_consumer = (B, Si) or (Si, B) + for (int32_t sbp_id = 0; sbp_id < std::min(producer_sbp_parallel.sbp_parallel_size(), + consumer_sbp_parallel.sbp_parallel_size()); + sbp_id++) { + if (consumer_sbp_parallel.sbp_parallel(sbp_id).has_split_parallel()) { + const auto& producer_sbp4sbp_id = producer_sbp_parallel.sbp_parallel(sbp_id); + // (B, P) or (Si, P) -> (Si, B) + // (P, B) or (P, Si) -> (B, Si) + if (producer_sbp4sbp_id.has_broadcast_parallel() + || producer_sbp4sbp_id == consumer_sbp_parallel.sbp_parallel(sbp_id)) { + intersection_ratio = 2.0; + break; + } + } + } + // Judge whether the intersection ratio is given a value (2.0) + if (intersection_ratio == 1.0) { + // The true intersection ratio range from 0 to 2, + // we just take a middle point of the range as the approximation + // For example: (P, S0) -> (S0, B), Path: (P, S0) -> (S1, S0) -> (S0, B) + // true intersection ratio = 1 + 1/m + // For example: (P, S0) -> (S1, B), Path: (P, S0) -> (S1, S0) -> (S1, B) + // true intersection ratio = 1 + 1 + // For example: (P, S0) -> (B, S0), with a 1D blob + // true intersection ratio = (n+p-1)/nm + (n+p-1)/nm + // For example: (S0, P) -> (B, S0), Path: (S0, P) -> (S0, S1) -> (B, S0) + // true intersection ratio = 1 + 1/n + + // We use the approximation 1 + (1/n + 1/m)/2 + intersection_ratio = 1.0 + 0.5 / producer_parallel_desc.hierarchy()->At(0) + + 0.5 / producer_parallel_desc.hierarchy()->At(1); + } + } + } + // Otherwise, on different devices + // intersection_ratio = 1.0; + } else { + // No P in the producer or no B in the consumer, one-step transfer + if (on_same_devices) { + // We use simulation for nD sbp with n=1,2,3,... + TensorSliceView in_second_slice = + GetTensorSliceView4ParallelId(*producer_parallel_desc.hierarchy(), producer_sbp_parallel, + logical_blob_desc.shape(), /*parallel_id=*/1); + TensorSliceView out_second_slice = + GetTensorSliceView4ParallelId(*consumer_parallel_desc.hierarchy(), consumer_sbp_parallel, + logical_blob_desc.shape(), /*parallel_id=*/1); + const TensorSliceView& intersection = in_second_slice.Intersect(out_second_slice); + // The intersection ratio is design for two steps. + // However, we only have one step here, we would increase the ratio by 1.0 + // to eliminate the unused step + intersection_ratio += std::min( + 1.0, (double)(intersection.shape().elem_cnt() * producer_parallel_desc.parallel_num()) + / logical_blob_desc.shape().elem_cnt()); } + // Otherwise, on different devices + // intersection_ratio = 1.0; } + // Subtract the intersection part + return (producer_partial_ratio + consumer_broadcast_ratio - intersection_ratio) + * logical_blob_desc.shape().elem_cnt() * GetSizeOfDataType(logical_blob_desc.data_type()); } } // namespace oneflow diff --git a/oneflow/core/framework/sbp_infer_util.h b/oneflow/core/framework/sbp_infer_util.h index 6af5f84faabaa95b89b8bb3c71872a545ae0a63c..d4dca3cc71ebb7d08c97508a19303307e818e8f6 100644 --- a/oneflow/core/framework/sbp_infer_util.h +++ b/oneflow/core/framework/sbp_infer_util.h @@ -33,14 +33,35 @@ enum Penalty4PartialInConsumerTag : int { kStrict = 3 // Not allow a transfer to P }; +// [2, 3, 4, 5, 9, 100, 8]: (P, S0, P, P, B, S1, P) +// partial ratio = 2 * 4 * 5 * 8 +int32_t PartialRatio4Producer(const NdSbp& sbp_producer, + const ParallelDesc& producer_parallel_desc); + +// [2, 3, 4, 5, 9, 100, 8]: (P, S0, B, P, B, S1, P) +// broadcast ratio = 4 * 9 +int32_t BroadcastRatio4Consumer(const NdSbp& sbp_consumer, + const ParallelDesc& consumer_parallel_desc); + +void NdSbpDimReduce(const Shape& hierarchy, const NdSbp& nd_sbp, Shape* reduced_hierarchy, + NdSbp* reduced_nd_sbp, const Shape& logical_shape); +void NdSbpsDimReduce(const Shape& hierarchy, const std::vector& nd_sbps, + Shape* reduced_hierarchy, const std::vector& reduced_nd_sbps, + const Shape& logical_shape); void NdSbpDimReduce(const ParallelDesc& parallel_desc, const NdSbp& nd_sbp, - ParallelDesc* reduced_parallel_desc, NdSbp* reduced_nd_sbp); - + ParallelDesc* reduced_parallel_desc, NdSbp* reduced_nd_sbp, + const Shape& logical_shape); + +void InOutParallelDimReduce(const Shape& in_hierarchy, const Shape& out_hierarchy, + const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, + Shape* reduced_in_hierarchy, Shape* reduced_out_hierarchy, + NdSbp* reduced_in_nd_sbp, NdSbp* reduced_out_nd_sbp, + const Shape& logical_shape); void InOutParallelDimReduce(const ParallelDesc& in_parallel_desc, const ParallelDesc& out_parallel_desc, const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, ParallelDesc* reduced_in_parallel_desc, ParallelDesc* reduced_out_parallel_desc, NdSbp* reduced_in_nd_sbp, - NdSbp* reduced_out_nd_sbp); + NdSbp* reduced_out_nd_sbp, const Shape& logical_shape); double GetValidMaxCopyCost(); @@ -52,7 +73,8 @@ void SetNdSbpSignature(NdSbpSignature* nd_sbp_signature, const SbpSignature& sbp int32_t sbp_axis); void DfsGetNdSbpSignature(NdSbpSignature& nd_sbp_sig, int32_t depth, int32_t dims, - const SbpSignatureList& sbp_sig_list, + const Shape& hierarchy, + const HashMap& hierarchy_value2sbp_sig_list, std::vector* nd_sbp_sig_list); // Compute storage for given NdSbp @@ -94,7 +116,16 @@ Maybe ComputeCopyCostWithMiddleNodes(const NdSbp& producer_sbp_parallel, double ComputeSbpInferPriority(const NdSbp& producer_sbp_parallel, const NdSbp& consumer_sbp_parallel, const ParallelDesc& producer_parallel_desc, - const ParallelDesc& consumer_parallel_desc, bool requires_same_sbp); + const ParallelDesc& consumer_parallel_desc, bool requires_same_sbp, + const Shape& logical_shape); + +// The transfer ratio for general basic communication +// Cost = ratio * data amount +double Cost4GeneralBasicCommunication(const NdSbp& producer_sbp_parallel, + const NdSbp& consumer_sbp_parallel, + const BlobDesc& logical_blob_desc, + const ParallelDesc& producer_parallel_desc, + const ParallelDesc& consumer_parallel_desc); } // namespace oneflow diff --git a/oneflow/core/framework/scope_util.cpp b/oneflow/core/framework/scope_util.cpp index bd1c638f495f3a38903db6f600c6e04dca7fd325..9794ba472c71630d47a922c33315197bbf990ba6 100644 --- a/oneflow/core/framework/scope_util.cpp +++ b/oneflow/core/framework/scope_util.cpp @@ -21,6 +21,7 @@ limitations under the License. #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/session_util.h" #include "oneflow/core/job/job_conf.pb.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { @@ -59,13 +60,13 @@ Maybe MakeScope(const JobConfigProto& config_proto, const Device& device) } Maybe MakeInitialScope(const JobConfigProto& job_conf, Symbol placement, - bool is_mirrored) { + bool is_local) { std::shared_ptr scope; JUST(PhysicalRun([&scope, &job_conf, placement, - is_mirrored](InstructionsBuilder* builder) -> Maybe { + is_local](InstructionsBuilder* builder) -> Maybe { int64_t session_id = JUST(GetDefaultSessionId()); scope = - JUST(builder->BuildInitialScopeWithPlacement(session_id, job_conf, placement, is_mirrored)); + JUST(builder->BuildInitialScopeWithPlacement(session_id, job_conf, placement, is_local)); return Maybe::Ok(); })); return scope; @@ -96,4 +97,63 @@ Maybe ThreadLocalScopeStackPop() { return Maybe::Ok(); } +BackwardPassScopeGuard::BackwardPassScopeGuard() { + if (LazyMode::is_enabled()) { + const auto& scope = CHECK_JUST(GetCurrentScope()); + if (scope) { + backward_pass_scope_ = CHECK_JUST(FindOrCreateBackwardPassScope(scope)); + CHECK_JUST(ThreadLocalScopeStackPush(backward_pass_scope_)); + } + } +} + +BackwardPassScopeGuard::BackwardPassScopeGuard(const std::shared_ptr& scope) { + if (scope && LazyMode::is_enabled()) { + backward_pass_scope_ = CHECK_JUST(FindOrCreateBackwardPassScope(scope)); + CHECK_JUST(ThreadLocalScopeStackPush(backward_pass_scope_)); + } +} + +BackwardPassScopeGuard::~BackwardPassScopeGuard() { + if (backward_pass_scope_) { CHECK_JUST(ThreadLocalScopeStackPop()); } +} + +class BackwardPassScopeStorage { + public: + std::mutex mutex; + + static BackwardPassScopeStorage* Global() { + static BackwardPassScopeStorage instance; + return &instance; + } + HashMap>& get() { return scopes_; } + + private: + HashMap> scopes_; +}; + +extern const std::string kBackwardPass; +Maybe FindOrCreateBackwardPassScope(const std::shared_ptr& scope) { + auto* storage = BackwardPassScopeStorage::Global(); + auto& scopes = storage->get(); + std::lock_guard lock(storage->mutex); + auto it = scopes.find(JUST(scope->symbol_id())); + if (it != scopes.end()) { return it->second; } + auto scope_proto = JUST((scope->MakeChildScopeProto())); + scope_proto->set_calculation_pass_name(kBackwardPass); + std::shared_ptr backward_pass_scope; + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + backward_pass_scope = JUST(builder->GetScopeSymbol(*scope_proto)); + return Maybe::Ok(); + })); + scopes.emplace(JUST(scope->symbol_id()), backward_pass_scope); + return backward_pass_scope; +} + +void ClearAllBackwardPassScope() { + auto* storage = BackwardPassScopeStorage::Global(); + std::lock_guard lock(storage->mutex); + storage->get().clear(); +} + } // namespace oneflow diff --git a/oneflow/core/framework/scope_util.h b/oneflow/core/framework/scope_util.h index 89ab60e138cf587b4e362e1f305714a6dc5897ad..55ee3b0bc0c5a07cfeef6b7254d361f96abb8bd1 100644 --- a/oneflow/core/framework/scope_util.h +++ b/oneflow/core/framework/scope_util.h @@ -24,7 +24,7 @@ namespace oneflow { Maybe MakeScope(const JobConfigProto& config_proto, const Device& device); Maybe MakeInitialScope(const JobConfigProto& job_conf, Symbol placement, - bool is_mirrored); + bool is_local); Maybe GetCurrentScope(); @@ -34,6 +34,19 @@ Maybe ThreadLocalScopeStackPush(const std::shared_ptr& scope); Maybe ThreadLocalScopeStackPop(); +class BackwardPassScopeGuard { + public: + BackwardPassScopeGuard(); + explicit BackwardPassScopeGuard(const std::shared_ptr& scope); + ~BackwardPassScopeGuard(); + + private: + std::shared_ptr backward_pass_scope_; +}; + +Maybe FindOrCreateBackwardPassScope(const std::shared_ptr& scope); +void ClearAllBackwardPassScope(); + } // namespace oneflow #endif // ONEFLOW_CORE_FRAMEWORK_SCOPE_UTIL_H_ diff --git a/oneflow/core/framework/session_util.cpp b/oneflow/core/framework/session_util.cpp index 0478d30dbfb7fdab37694776179665a0936cab29..73946dbc22f3adacf6138aaa9955e4abb5726ee5 100644 --- a/oneflow/core/framework/session_util.cpp +++ b/oneflow/core/framework/session_util.cpp @@ -13,8 +13,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include -#include "oneflow/core/common/util.h" #include "oneflow/core/framework/session_util.h" namespace oneflow { @@ -26,11 +24,6 @@ std::mutex* GlobalSessionUtilMutex() { return &global_id2session_map_mutex; } -HashMap>* GlobalId2SessionMap() { - static HashMap> id2session_map; - return &id2session_map; -} - std::vector* RegsiteredSessionIds() { static std::vector default_sess_id; return &default_sess_id; @@ -44,35 +37,6 @@ Maybe SetDefaultSessionId(int64_t val) { } // namespace -Session::Session(int64_t id) - : id_(id), is_mirrored_strategy_enabled_stack_(new std::vector()) { - instruction_list_.reset(new vm::InstructionList()); -} - -int64_t Session::id() const { return id_; } - -const std::shared_ptr& Session::instruction_list() const { - return instruction_list_; -} - -Maybe Session::PushMirroredStrategyEnabled(bool is_mirrored) { - is_mirrored_strategy_enabled_stack_->emplace_back(is_mirrored); - return Maybe::Ok(); -} -Maybe Session::PopMirroredStrategyEnabled() { - is_mirrored_strategy_enabled_stack_->pop_back(); - return Maybe::Ok(); -} - -Maybe Session::IsMirroredStrategyEnabled() const { - return is_mirrored_strategy_enabled_stack_->size() > 0 - && is_mirrored_strategy_enabled_stack_->back(); -} -Maybe Session::IsConsistentStrategyEnabled() const { - return is_mirrored_strategy_enabled_stack_->size() > 0 - && !is_mirrored_strategy_enabled_stack_->back(); -} - Maybe GetDefaultSessionId() { std::unique_lock lock(*GlobalSessionUtilMutex()); const auto& regsitered_ids = *(RegsiteredSessionIds()); @@ -80,38 +44,22 @@ Maybe GetDefaultSessionId() { return regsitered_ids.back(); } -Maybe RegsiterSession(int64_t id) { - std::shared_ptr sess = std::make_shared(id); - std::unique_lock lock(*GlobalSessionUtilMutex()); - auto* id2session_map = GlobalId2SessionMap(); - CHECK_OR_RETURN(id2session_map->find(id) == id2session_map->end()); - (*id2session_map)[id] = sess; - JUST(SetDefaultSessionId(id)); - return id2session_map->at(id); -} - -Maybe GetDefaultSession() { +bool RegsterSessionId(int64_t session_id) { std::unique_lock lock(*GlobalSessionUtilMutex()); - const auto& regsitered_ids = *(RegsiteredSessionIds()); - CHECK_GT_OR_RETURN(regsitered_ids.size(), 0); - int64_t default_sess_id = regsitered_ids.back(); - auto* id2session_map = GlobalId2SessionMap(); - CHECK_OR_RETURN(id2session_map->find(default_sess_id) != id2session_map->end()); - return id2session_map->at(default_sess_id); + auto* regsitered_ids = RegsiteredSessionIds(); + auto itor = std::find(regsitered_ids->begin(), regsitered_ids->end(), session_id); + if (itor != regsitered_ids->end()) { return false; } + regsitered_ids->push_back(session_id); + return true; } -Maybe ClearSessionById(int64_t id) { +bool ClearSessionId(int64_t session_id) { std::unique_lock lock(*GlobalSessionUtilMutex()); - auto* id2session_map = GlobalId2SessionMap(); - CHECK_OR_RETURN(id2session_map->find(id) != id2session_map->end()); - id2session_map->erase(id); - auto* sess_ids = RegsiteredSessionIds(); - int32_t i = 0; - for (; i < sess_ids->size(); ++i) { - if (sess_ids->at(i) == id) { break; } - } - sess_ids->erase(sess_ids->begin() + i); - return Maybe::Ok(); + auto* regsitered_ids = RegsiteredSessionIds(); + auto itor = std::find(regsitered_ids->begin(), regsitered_ids->end(), session_id); + if (itor == regsitered_ids->end()) { return false; } + regsitered_ids->erase(itor); + return true; } } // namespace oneflow diff --git a/oneflow/core/framework/session_util.h b/oneflow/core/framework/session_util.h index 26139c51f77fc0fcfb0f33b6aa4efef1bb64f6b7..274cfa77cb10df22024e8b49fbb529f923fa65ff 100644 --- a/oneflow/core/framework/session_util.h +++ b/oneflow/core/framework/session_util.h @@ -17,38 +17,12 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_SESSION_UTIL_H_ #include "oneflow/core/common/maybe.h" -#include "oneflow/core/vm/instruction.h" namespace oneflow { -class Session { - public: - Session(int64_t id); - Session(const Session&) = delete; - Session(Session&&) = delete; - ~Session() = default; - - int64_t id() const; - const std::shared_ptr& instruction_list() const; - - std::shared_ptr> is_mirrored_strategy_enabled_stack() const { - return is_mirrored_strategy_enabled_stack_; - } - Maybe PushMirroredStrategyEnabled(bool is_mirrored); - Maybe PopMirroredStrategyEnabled(); - Maybe IsMirroredStrategyEnabled() const; - Maybe IsConsistentStrategyEnabled() const; - - private: - int64_t id_; - std::shared_ptr instruction_list_; - std::shared_ptr> is_mirrored_strategy_enabled_stack_; -}; - Maybe GetDefaultSessionId(); -Maybe RegsiterSession(int64_t id); -Maybe GetDefaultSession(); -Maybe ClearSessionById(int64_t id); +bool RegsterSessionId(int64_t session_id); +bool ClearSessionId(int64_t session_id); } // namespace oneflow diff --git a/oneflow/core/framework/stream.cpp b/oneflow/core/framework/stream.cpp index e0e6c8bfb1361e2300d47adb4840ef64302e8077..837fe8e6ec493bc286eb373e3e9af2bbff2506f3 100644 --- a/oneflow/core/framework/stream.cpp +++ b/oneflow/core/framework/stream.cpp @@ -15,24 +15,27 @@ limitations under the License. */ #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/stream_is_comm_net_stream.h" +#include "oneflow/core/thread/thread_global_id.h" #include "oneflow/core/common/decorator.h" #include "oneflow/core/common/static_global.h" #include "oneflow/core/common/singleton.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/framework/stream_mgr.h" +#include "oneflow/core/vm/stream_get_allocator_stream_type.h" namespace oneflow { -Stream::Stream(Symbol device, StreamRole stream_role) - : device_(device), stream_role_(stream_role), unique_stream_id_(-1) {} +Stream::Stream(Symbol device, StreamType stream_type, size_t thread_uid) + : device_(device), stream_type_(stream_type), thread_uid_(thread_uid), unique_stream_id_(-1) {} Maybe Stream::Init(size_t unique_stream_id) { unique_stream_id_ = unique_stream_id; return Maybe::Ok(); } -/*static*/ Maybe> Stream::RawNew(Symbol device, StreamRole stream_role) { - std::shared_ptr stream(new Stream(device, stream_role)); +/*static*/ Maybe> Stream::RawNew(Symbol device, StreamType stream_type, + size_t thread_uid) { + std::shared_ptr stream(new Stream(device, stream_type, thread_uid)); return JUST(SingletonMaybe()) ->AddStreamSymbol(*stream, [&](size_t unique_stream_id) -> Maybe> { JUST(stream->Init(unique_stream_id)); @@ -40,27 +43,38 @@ Maybe Stream::Init(size_t unique_stream_id) { }); } -/*static*/ Maybe> Stream::New(Symbol device, StreamRole stream_role) { - constexpr auto* Make = DECORATE(&Stream::RawNew, ThreadLocal); - return Make(device, stream_role); +/*static*/ Maybe> Stream::New(Symbol device, StreamType stream_type, + size_t thread_uid) { + constexpr auto* Make = DECORATE(&Stream::RawNew, ThreadLocalCopiable); + return Make(device, stream_type, thread_uid); } namespace { Maybe> RawGetDefaultStreamByDevice(Symbol device) { - return Stream::New(device, StreamRole::kCompute); + return Stream::New(device, StreamType::kCompute); } Maybe> RawGetDefaultStreamByPlacement(Symbol parallel_desc) { return RawGetDefaultStreamByDevice(JUST(GetTensorDevice(parallel_desc))); } +Maybe> RawGetAllocatorStream(Symbol stream) { + StreamType allocator_stream_type = JUST(GetAllocatorStreamType::Visit(stream->stream_type())); + if (allocator_stream_type == stream->stream_type()) { return stream; } + return Stream::New(stream->device(), allocator_stream_type, stream->thread_uid()); +} + } // namespace +int64_t Stream::kDefaultStreamThreadUid = 0; + decltype(GetDefaultStreamByDevice) GetDefaultStreamByDevice = DECORATE(&RawGetDefaultStreamByDevice, ThreadLocal); decltype(GetDefaultStreamByPlacement) GetDefaultStreamByPlacement = DECORATE(&RawGetDefaultStreamByPlacement, ThreadLocal); +decltype(GetAllocatorStream) GetAllocatorStream = DECORATE(&RawGetAllocatorStream, ThreadLocal); + } // namespace oneflow diff --git a/oneflow/core/framework/stream.h b/oneflow/core/framework/stream.h index e851eb1e8e68b2cea7f902aafdd57c7d0e163e0a..67059ba3ed9d5940e644e3412ff7fd0239968dea 100644 --- a/oneflow/core/framework/stream.h +++ b/oneflow/core/framework/stream.h @@ -17,7 +17,7 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_STREAM_H_ #include -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" #include "oneflow/core/common/symbol.h" #include "oneflow/core/common/optional.h" #include "oneflow/core/common/maybe.h" @@ -32,25 +32,35 @@ class Stream final { ~Stream() = default; bool operator==(const Stream& that) const { - return this->device() == that.device() && this->stream_role() == that.stream_role(); + return this->device() == that.device() && this->stream_type() == that.stream_type() + && this->thread_uid() == that.thread_uid(); } bool operator!=(const Stream& that) const { return !(*this == that); } - static Maybe> New(Symbol device, StreamRole stream_role); + static Maybe> New(Symbol device, StreamType stream_type) { + return New(device, stream_type, kDefaultStreamThreadUid); + } + static Maybe> New(Symbol device, StreamType stream_type, + size_t thread_uid); Symbol device() const { return device_; } - StreamRole stream_role() const { return stream_role_; } + StreamType stream_type() const { return stream_type_; } + size_t thread_uid() const { return thread_uid_; } size_t unique_stream_id() const { return unique_stream_id_; } + static int64_t kDefaultStreamThreadUid; + private: - Stream(Symbol device, StreamRole stream_role); + Stream(Symbol device, StreamType stream_type, size_t thread_uid); - static Maybe> RawNew(Symbol device, StreamRole stream_role); + static Maybe> RawNew(Symbol device, StreamType stream_type, + size_t thread_uid); Maybe Init(size_t unique_stream_id); Symbol device_; - StreamRole stream_role_; + StreamType stream_type_; + size_t thread_uid_; size_t unique_stream_id_; }; @@ -58,6 +68,8 @@ extern Maybe> (*GetDefaultStreamByDevice)(Symbol); class ParallelDesc; extern Maybe> (*GetDefaultStreamByPlacement)(Symbol); +extern Maybe> (*GetAllocatorStream)(Symbol); + } // namespace oneflow namespace std { @@ -65,8 +77,7 @@ template<> struct hash final { size_t operator()(const oneflow::Stream& stream) const { using namespace oneflow; - return std::hash>()(stream.device()) - ^ std::hash()(stream.stream_role()); + return Hash(stream.device(), stream.stream_type(), stream.thread_uid()); } }; diff --git a/oneflow/core/framework/stream_allocator_is_pinned.h b/oneflow/core/framework/stream_allocator_is_pinned.h index 6c10cbad444e2f9e1a6e65184c1c14e08a0710b1..be49b1d450ffdeaa3647742bf668315aeb723149 100644 --- a/oneflow/core/framework/stream_allocator_is_pinned.h +++ b/oneflow/core/framework/stream_allocator_is_pinned.h @@ -17,16 +17,15 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_STREAM_ALLOCATOR_IS_PINNED_H_ #include -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" namespace oneflow { -struct IsStreamAllocatorPinned : public StreamRoleVisitor { +struct IsStreamAllocatorPinned : public StreamTypeVisitor { static bool VisitCompute() { return false; } static bool VisitHost2Device() { return false; } static bool VisitDevice2Host() { return false; } - static bool VisitSyncedLaunchedCommNet() { return false; } - static bool VisitAsyncedLaunchedCommNet() { return false; } + static bool VisitCcl() { return false; } static bool VisitBarrier() { return false; } static bool VisitCriticalSection() { return false; } static bool VisitLazyJobLauncher() { return false; } diff --git a/oneflow/core/framework/stream_get_stream_role_name.h b/oneflow/core/framework/stream_get_stream_role_name.h deleted file mode 100644 index e3a5036d3335b0b242bcd8cb93ecf7acb3293d58..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/stream_get_stream_role_name.h +++ /dev/null @@ -1,41 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_ROLE_NAME_H_ -#define ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_ROLE_NAME_H_ - -#include -#include -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/common/device_type.h" -#include "oneflow/core/framework/to_string.h" - -namespace oneflow { - -struct GetStreamRoleName : public StreamRoleVisitor { - static const char* VisitCompute() { return "compute"; } - static const char* VisitHost2Device() { return "h2d"; } - static const char* VisitDevice2Host() { return "d2h"; } - static const char* VisitSyncedLaunchedCommNet() { return "synced_launched_comm_net"; } - static const char* VisitAsyncedLaunchedCommNet() { return "asynced_launched_comm_net"; } - static const char* VisitBarrier() { return "barrier"; } - static const char* VisitCriticalSection() { return "critical_section"; } - static const char* VisitLazyJobLauncher() { return "lazy_job_launcher"; } - static const char* VisitPinnedCompute() { return "pin_memory"; } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_ROLE_NAME_H_ diff --git a/oneflow/core/framework/stream_get_stream_type_name.h b/oneflow/core/framework/stream_get_stream_type_name.h new file mode 100644 index 0000000000000000000000000000000000000000..1eadfc5b818b84f4817fd71e2e802477310674e2 --- /dev/null +++ b/oneflow/core/framework/stream_get_stream_type_name.h @@ -0,0 +1,40 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_TYPE_NAME_H_ +#define ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_TYPE_NAME_H_ + +#include +#include +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/common/device_type.h" +#include "oneflow/core/framework/to_string.h" + +namespace oneflow { + +struct GetStreamTypeName : public StreamTypeVisitor { + static const char* VisitCompute() { return "compute"; } + static const char* VisitHost2Device() { return "h2d"; } + static const char* VisitDevice2Host() { return "d2h"; } + static const char* VisitCcl() { return "ccl"; } + static const char* VisitBarrier() { return "barrier"; } + static const char* VisitCriticalSection() { return "critical_section"; } + static const char* VisitLazyJobLauncher() { return "lazy_job_launcher"; } + static const char* VisitPinnedCompute() { return "pinned_compute"; } +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_STREAM_GET_STREAM_TYPE_NAME_H_ diff --git a/oneflow/core/framework/stream_guard.cpp b/oneflow/core/framework/stream_guard.cpp new file mode 100644 index 0000000000000000000000000000000000000000..10a48bd0d7884393ea2c7a3b6e17b0cc7d87cc50 --- /dev/null +++ b/oneflow/core/framework/stream_guard.cpp @@ -0,0 +1,25 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/stream_guard.h" + +namespace oneflow { + +/*static*/ Optional* StreamGuard::MutCurrent() { + static thread_local Optional current; + return ¤t; +} + +} // namespace oneflow diff --git a/oneflow/core/framework/stream_guard.h b/oneflow/core/framework/stream_guard.h new file mode 100644 index 0000000000000000000000000000000000000000..14cb9aa3bc8230a84d22aaaabfd8eb78e5a5f1c9 --- /dev/null +++ b/oneflow/core/framework/stream_guard.h @@ -0,0 +1,65 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_STREAM_GUARD_H_ +#define ONEFLOW_CORE_FRAMEWORK_STREAM_GUARD_H_ + +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/common/env_var/stream.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/framework/stream_set.h" +#include "oneflow/core/framework/stream_is_comm_net_stream.h" +#include "oneflow/core/thread/thread_global_id.h" + +namespace oneflow { + +class StreamConverter final { + public: + explicit StreamConverter(const std::shared_ptr& stream_set) + : stream_set_(stream_set) {} + + Maybe> TryConvertStream(Symbol stream) { + size_t thread_uid = stream_set_->worker_thread_id(); + return Stream::New(stream->device(), stream->stream_type(), thread_uid); + } + + private: + const std::shared_ptr stream_set_; +}; + +class StreamGuard final { + public: + explicit StreamGuard(const std::shared_ptr& stream_converter) { + old_value_ = Current(); + *MutCurrent() = stream_converter; + } + ~StreamGuard() { *MutCurrent() = old_value_; } + + static Maybe> TryConvertStream(Symbol stream) { + if (!Current().has_value()) { return stream; } + return JUST(Current())->TryConvertStream(stream); + } + + private: + static const Optional& Current() { return *MutCurrent(); } + static Optional* MutCurrent(); + + Optional old_value_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_STREAM_GUARD_H_ diff --git a/oneflow/core/framework/stream_is_comm_net_stream.h b/oneflow/core/framework/stream_is_comm_net_stream.h index 4ac2e91c9c1cb287e91e4b9b975cb614b4784a0d..8f88ca1f95ea5a65c0fe4a7792fe117189042450 100644 --- a/oneflow/core/framework/stream_is_comm_net_stream.h +++ b/oneflow/core/framework/stream_is_comm_net_stream.h @@ -17,16 +17,15 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_STREAM_IS_COMM_NET_STREAM_H_ #include -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" namespace oneflow { -struct IsCommNetStream final : public StreamRoleVisitor { +struct IsCommNetStream final : public StreamTypeVisitor { static bool VisitCompute() { return false; } static bool VisitHost2Device() { return false; } static bool VisitDevice2Host() { return false; } - static bool VisitSyncedLaunchedCommNet() { return true; } - static bool VisitAsyncedLaunchedCommNet() { return true; } + static bool VisitCcl() { return true; } static bool VisitBarrier() { return false; } static bool VisitCriticalSection() { return false; } static bool VisitLazyJobLauncher() { return false; } diff --git a/oneflow/core/framework/stream_need_soft_sync.h b/oneflow/core/framework/stream_need_soft_sync.h index 3e4ccdb744b07d431f978045ffb6a5e43859a7a4..3cf43b17d5403dc0141230b0061eaad16c3c79d9 100644 --- a/oneflow/core/framework/stream_need_soft_sync.h +++ b/oneflow/core/framework/stream_need_soft_sync.h @@ -18,16 +18,15 @@ limitations under the License. #include #include "oneflow/core/common/device_type.h" -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" namespace oneflow { -struct NeedSoftSync : public StreamRoleVisitor { +struct NeedSoftSync : public StreamTypeVisitor { static bool VisitCompute(DeviceType device_type) { return device_type != kCPU; } static bool VisitHost2Device(DeviceType) { return false; } static bool VisitDevice2Host(DeviceType) { return false; } - static bool VisitSyncedLaunchedCommNet(DeviceType device_type) { return false; } - static bool VisitAsyncedLaunchedCommNet(DeviceType) { return false; } + static bool VisitCcl(DeviceType device_type) { return false; } static bool VisitBarrier(DeviceType) { return false; } static bool VisitCriticalSection(DeviceType) { return false; } static bool VisitLazyJobLauncher(DeviceType) { return false; } diff --git a/oneflow/core/framework/stream_on_independent_thread.h b/oneflow/core/framework/stream_on_independent_thread.h index 099b3003063b47db50c37321f12a890f3d4e521f..96d8b061e96e4ac97fc9ee7d51ff2ff6e7b78c32 100644 --- a/oneflow/core/framework/stream_on_independent_thread.h +++ b/oneflow/core/framework/stream_on_independent_thread.h @@ -17,16 +17,15 @@ limitations under the License. #define ONEFLOW_CORE_FRAMEWORK_STREAM_ON_INDEPENDENT_THREAD_H_ #include -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" namespace oneflow { -struct StreamOnIndependentThread : public StreamRoleVisitor { +struct StreamOnIndependentThread : public StreamTypeVisitor { static bool VisitCompute() { return false; } static bool VisitHost2Device() { return false; } static bool VisitDevice2Host() { return false; } - static bool VisitSyncedLaunchedCommNet() { return false; } - static bool VisitAsyncedLaunchedCommNet() { return false; } + static bool VisitCcl() { return false; } static bool VisitBarrier() { return false; } static bool VisitCriticalSection() { return true; } static bool VisitLazyJobLauncher() { return true; } diff --git a/oneflow/core/framework/stream_set.cpp b/oneflow/core/framework/stream_set.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8db5887e9f3aa2c5873fc879f1a3239822078720 --- /dev/null +++ b/oneflow/core/framework/stream_set.cpp @@ -0,0 +1,35 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include +#include +#include "oneflow/core/framework/stream_set.h" +#include "oneflow/core/thread/thread_global_id.h" +#include "oneflow/core/common/env_var/stream.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { + +StreamSet::StreamSet(int64_t worker_thread_id) : worker_thread_id_(worker_thread_id) {} + +StreamSet::~StreamSet() {} + +/*static*/ Maybe StreamSet::New(int64_t worker_thread_id) { + return std::shared_ptr(new StreamSet(worker_thread_id)); +} + +} // namespace oneflow diff --git a/oneflow/core/framework/stream_set.h b/oneflow/core/framework/stream_set.h new file mode 100644 index 0000000000000000000000000000000000000000..971985bff20c0ec9643bf20700feb6e465f6d332 --- /dev/null +++ b/oneflow/core/framework/stream_set.h @@ -0,0 +1,42 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_STREAM_SET_H_ +#define ONEFLOW_CORE_FRAMEWORK_STREAM_SET_H_ + +#include +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/framework/stream.h" + +namespace oneflow { + +class StreamSet final { + public: + ~StreamSet(); + + static Maybe New(int64_t worker_thread_id); + + int64_t worker_thread_id() const { return worker_thread_id_; } + + private: + StreamSet(int64_t worker_thread_id); + + int64_t worker_thread_id_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_STREAM_SET_H_ diff --git a/oneflow/core/framework/stream_support_stream_wait.h b/oneflow/core/framework/stream_support_stream_wait.h new file mode 100644 index 0000000000000000000000000000000000000000..5188a06efa877c2346fdfabb69238c46e6d0affa --- /dev/null +++ b/oneflow/core/framework/stream_support_stream_wait.h @@ -0,0 +1,40 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_STREAM_SUPPORT_STREAM_WAIT_H_ +#define ONEFLOW_CORE_FRAMEWORK_STREAM_SUPPORT_STREAM_WAIT_H_ + +#include +#include "oneflow/core/common/stream_type.h" + +namespace oneflow { + +struct StreamSupportStreamWait : public StreamTypeVisitor { + static bool VisitCompute(DeviceType device_type) { return Supported(device_type); } + static bool VisitHost2Device(DeviceType device_type) { return Supported(device_type); } + static bool VisitDevice2Host(DeviceType device_type) { return Supported(device_type); } + static bool VisitCcl(DeviceType device_type) { return Supported(device_type); } + static bool VisitBarrier(DeviceType device_type) { return false; } + static bool VisitCriticalSection(DeviceType device_type) { return false; } + static bool VisitLazyJobLauncher(DeviceType device_type) { return false; } + static bool VisitPinnedCompute(DeviceType device_type) { return VisitCompute(device_type); } + + private: + static bool Supported(DeviceType device_type) { return device_type == kCUDA; } +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_STREAM_SUPPORT_STREAM_WAIT_H_ diff --git a/oneflow/core/framework/sync_symbol_consistent_tensor_meta.cpp b/oneflow/core/framework/sync_symbol_consistent_tensor_meta.cpp deleted file mode 100644 index af26e21185e051748fffe706c806b10e2bf87a4a..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/sync_symbol_consistent_tensor_meta.cpp +++ /dev/null @@ -1,96 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/sync_symbol_consistent_tensor_meta.h" -#include "oneflow/core/framework/sync_symbol_parallel_desc.h" -#include "oneflow/core/framework/sync_symbol_nd_sbp.h" -#include "oneflow/core/framework/rank_group_rpc_util.h" -#include "oneflow/core/framework/tensor_meta.h" -#include "oneflow/core/framework/synced_symbol_map.h" -#include "oneflow/core/common/flat_shape.h" - -namespace oneflow { - -struct FlatConsistentTensorMeta final { - static Maybe New( - uint64_t symbol_id, Symbol consistent_tensor_meta) { - const auto& meta = std::make_shared(); - JUST(meta->Init(symbol_id, consistent_tensor_meta)); - return meta; - } - - Maybe Init(uint64_t symbol_id, Symbol consistent_tensor_meta) { - this->symbol_id = symbol_id; - JUST(this->shape.Init(consistent_tensor_meta->shape())); - this->dtype = static_cast(consistent_tensor_meta->dtype()); - this->is_dynamic = consistent_tensor_meta->is_dynamic(); - this->nd_sbp = JUST( - SyncedSymbolMap::FindOrSync(consistent_tensor_meta->nd_sbp(), &SyncSymbolNdSbp)); - this->parallel_desc = JUST(SyncedSymbolMap::FindOrSync( - consistent_tensor_meta->parallel_desc(), &SyncSymbolParallelDesc)); - return Maybe::Ok(); - } - - Maybe Check(uint64_t symbol_id, Symbol consistent_tensor_meta) { - CHECK_EQ_OR_RETURN(this->symbol_id, symbol_id); - JUST(this->shape.Check(consistent_tensor_meta->shape())); - CHECK_EQ_OR_RETURN(static_cast(this->dtype), consistent_tensor_meta->dtype()); - CHECK_EQ_OR_RETURN(this->is_dynamic, consistent_tensor_meta->is_dynamic()); - const auto& nd_sbp = JUST(SyncedSymbolMap::Symbol4SyncedSymbolId(this->nd_sbp)); - CHECK_OR_RETURN(nd_sbp == consistent_tensor_meta->nd_sbp()); - const auto& parallel_desc = - JUST(SyncedSymbolMap::Symbol4SyncedSymbolId(this->parallel_desc)); - CHECK_OR_RETURN(parallel_desc == consistent_tensor_meta->parallel_desc()); - return Maybe::Ok(); - } - - uint64_t symbol_id; - FlatShape shape; - int32_t dtype; - bool is_dynamic; - uint64_t nd_sbp; - uint64_t parallel_desc; -}; - -Maybe SyncSymbolConsistentTensorMeta( - uint64_t symbol_id, Symbol consistent_tensor_meta) { - const auto& transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncSymbolConsistentTensorMeta)); - const auto& recv_buffer = std::make_shared(); - NaiveAsyncTransportCtx ctx( - transport_token, - [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - const auto& send_buffer = - JUST(FlatConsistentTensorMeta::New(symbol_id, consistent_tensor_meta)); - *buffer = send_buffer.get(); - *size = sizeof(FlatConsistentTensorMeta); - *Cb = [send_buffer] {}; - return Maybe::Ok(); - }, - [recv_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = recv_buffer.get(); - *size = sizeof(FlatConsistentTensorMeta); - *Cb = [recv_buffer] {}; - return Maybe::Ok(); - }); - const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - JUST(ctx.WaitDone()); - JUST(recv_buffer->Check(symbol_id, consistent_tensor_meta)); - return Maybe::Ok(); -} - -} // namespace oneflow diff --git a/oneflow/core/framework/sync_symbol_consistent_tensor_meta.h b/oneflow/core/framework/sync_symbol_consistent_tensor_meta.h deleted file mode 100644 index 16b4f998824415062ec2029fb0153dbfc523ac57..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/sync_symbol_consistent_tensor_meta.h +++ /dev/null @@ -1,34 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_CONSISTENT_TENSOR_META_H_ -#define ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_CONSISTENT_TENSOR_META_H_ - -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/framework/transport_util.h" -#include "oneflow/core/framework/transport_token.h" - -namespace oneflow { - -namespace one { -class ConsistentTensorMeta; -} - -Maybe SyncSymbolConsistentTensorMeta(uint64_t symbol_id, Symbol); - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_CONSISTENT_TENSOR_META_H_ diff --git a/oneflow/core/framework/sync_symbol_global_tensor_meta.cpp b/oneflow/core/framework/sync_symbol_global_tensor_meta.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cea91be4d1c3927fa749ed5b528c83e52444f60b --- /dev/null +++ b/oneflow/core/framework/sync_symbol_global_tensor_meta.cpp @@ -0,0 +1,95 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/sync_symbol_global_tensor_meta.h" +#include "oneflow/core/framework/sync_symbol_parallel_desc.h" +#include "oneflow/core/framework/sync_symbol_nd_sbp.h" +#include "oneflow/core/framework/rank_group_rpc_util.h" +#include "oneflow/core/common/tensor_meta.h" +#include "oneflow/core/framework/synced_symbol_map.h" +#include "oneflow/core/common/flat_shape.h" + +namespace oneflow { + +struct FlatGlobalTensorMeta final { + static Maybe New(uint64_t symbol_id, + Symbol global_tensor_meta) { + const auto& meta = std::make_shared(); + JUST(meta->Init(symbol_id, global_tensor_meta)); + return meta; + } + + Maybe Init(uint64_t symbol_id, Symbol global_tensor_meta) { + this->symbol_id = symbol_id; + JUST(this->shape.Init(global_tensor_meta->shape())); + this->dtype = static_cast(global_tensor_meta->dtype()); + this->is_dynamic = global_tensor_meta->is_dynamic(); + this->nd_sbp = + JUST(SyncedSymbolMap::FindOrSync(global_tensor_meta->nd_sbp(), &SyncSymbolNdSbp)); + this->parallel_desc = JUST(SyncedSymbolMap::FindOrSync( + global_tensor_meta->parallel_desc(), &SyncSymbolParallelDesc)); + return Maybe::Ok(); + } + + Maybe Check(uint64_t symbol_id, Symbol global_tensor_meta) { + CHECK_EQ_OR_RETURN(this->symbol_id, symbol_id); + JUST(this->shape.Check(global_tensor_meta->shape())); + CHECK_EQ_OR_RETURN(static_cast(this->dtype), global_tensor_meta->dtype()); // NOLINT + CHECK_EQ_OR_RETURN(this->is_dynamic, global_tensor_meta->is_dynamic()); // NOLINT + const auto& nd_sbp = JUST(SyncedSymbolMap::Symbol4SyncedSymbolId(this->nd_sbp)); + CHECK_OR_RETURN(nd_sbp == global_tensor_meta->nd_sbp()); // NOLINT + const auto& parallel_desc = + JUST(SyncedSymbolMap::Symbol4SyncedSymbolId(this->parallel_desc)); + CHECK_OR_RETURN(parallel_desc == global_tensor_meta->parallel_desc()); // NOLINT + return Maybe::Ok(); + } + + uint64_t symbol_id; + FlatShape shape; + int32_t dtype; + bool is_dynamic; + uint64_t nd_sbp; + uint64_t parallel_desc; +}; + +Maybe SyncSymbolGlobalTensorMeta(uint64_t symbol_id, + Symbol global_tensor_meta) { + const auto& transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncSymbolGlobalTensorMeta)); + const auto& recv_buffer = std::make_shared(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + const auto& send_buffer = JUST(FlatGlobalTensorMeta::New(symbol_id, global_tensor_meta)); + *buffer = send_buffer.get(); + *size = sizeof(FlatGlobalTensorMeta); + *Cb = [send_buffer] {}; + return Maybe::Ok(); + }, + [recv_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_buffer.get(); + *size = sizeof(FlatGlobalTensorMeta); + *Cb = [recv_buffer] {}; + return Maybe::Ok(); + }); + const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + JUST(ctx.WaitDone()); + JUST(recv_buffer->Check(symbol_id, global_tensor_meta)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/core/framework/sync_symbol_global_tensor_meta.h b/oneflow/core/framework/sync_symbol_global_tensor_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..773355883c18fdd30d2cf015639bb456af0b58d4 --- /dev/null +++ b/oneflow/core/framework/sync_symbol_global_tensor_meta.h @@ -0,0 +1,34 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_GLOBAL_TENSOR_META_H_ +#define ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_GLOBAL_TENSOR_META_H_ + +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/core/framework/transport_token.h" + +namespace oneflow { + +namespace one { +class GlobalTensorMeta; +} + +Maybe SyncSymbolGlobalTensorMeta(uint64_t symbol_id, Symbol); + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_SYNC_SYMBOL_GLOBAL_TENSOR_META_H_ diff --git a/oneflow/core/framework/sync_symbol_parallel_desc.cpp b/oneflow/core/framework/sync_symbol_parallel_desc.cpp index 5a8642a106364f9c11cad5925f96b57a3bb705ea..75519dcc8a2c49b41b17418a08067088e474250b 100644 --- a/oneflow/core/framework/sync_symbol_parallel_desc.cpp +++ b/oneflow/core/framework/sync_symbol_parallel_desc.cpp @@ -26,8 +26,9 @@ namespace { static const int kLimitParallelConfString = 1024 * 64; struct FlatParallelConf { size_t available_size() const { - CHECK_GE(this->buffer_size, 0); - CHECK_LT(this->buffer_size, kLimitParallelConfString); + CHECK_GE(this->buffer_size, 0) << "Buffer size should be non-negative"; + CHECK_LT(this->buffer_size, kLimitParallelConfString) + << "Buffer size should be less than " << kLimitParallelConfString; return sizeof(FlatParallelConf) - kLimitParallelConfString + this->buffer_size; } @@ -42,22 +43,33 @@ struct FlatParallelConf { Maybe Init(uint64_t symbol_id, Symbol parallel_desc) { const auto& parallel_conf = parallel_desc->parallel_conf(); int64_t byte_size = parallel_conf.ByteSize(); - CHECK_LE_OR_RETURN(byte_size, kLimitParallelConfString); + CHECK_LE_OR_RETURN(byte_size, kLimitParallelConfString) + << Error::InvalidValueError() << "Byte size of parallel description should be less than " + << kLimitParallelConfString << ", but got " << byte_size; this->symbol_id = symbol_id; this->buffer_size = byte_size; - CHECK_OR_RETURN(parallel_conf.SerializeToArray(this->buffer, kLimitParallelConfString)); + CHECK_OR_RETURN(parallel_conf.SerializeToArray(this->buffer, kLimitParallelConfString)) + << Error::RuntimeError() + << "Error serializing parallel description: " << parallel_conf.ShortDebugString(); return Maybe::Ok(); } Maybe Check(uint64_t symbol_id, Symbol parallel_desc) const { const auto& parallel_conf = parallel_desc->parallel_conf(); int64_t byte_size = parallel_conf.ByteSize(); - CHECK_LE_OR_RETURN(byte_size, kLimitParallelConfString); - CHECK_EQ_OR_RETURN(this->symbol_id, symbol_id); - CHECK_EQ_OR_RETURN(this->buffer_size, byte_size); + const auto& debugString = parallel_conf.ShortDebugString(); + CHECK_LE_OR_RETURN(byte_size, kLimitParallelConfString) + << Error::InvalidValueError() << "Byte size of parallel description should be less than " + << kLimitParallelConfString << ", but got " << byte_size; + CHECK_EQ_OR_RETURN(this->symbol_id, symbol_id) << Error::RuntimeError() << "expected symbol id " + << symbol_id << ", but got " << this->symbol_id; + CHECK_EQ_OR_RETURN(this->buffer_size, byte_size) + << Error::RuntimeError() << "Inconsistent parallel description: " << debugString; std::vector serialized(byte_size); - CHECK_OR_RETURN(parallel_conf.SerializeToArray(serialized.data(), kLimitParallelConfString)); - CHECK_EQ_OR_RETURN(std::memcmp(serialized.data(), this->buffer, byte_size), 0); + CHECK_OR_RETURN(parallel_conf.SerializeToArray(serialized.data(), kLimitParallelConfString)) + << Error::RuntimeError() << "Error serializing parallel description: " << debugString; + CHECK_EQ_OR_RETURN(std::memcmp(serialized.data(), this->buffer, byte_size), 0) + << Error::RuntimeError() << "Inconsistent parallel description: " << debugString; return Maybe::Ok(); } diff --git a/oneflow/core/framework/system_ops.cpp b/oneflow/core/framework/system_ops.cpp deleted file mode 100644 index 44b449fe5ec299cfa344ed7a4e9a21a9107cfac6..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/system_ops.cpp +++ /dev/null @@ -1,115 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/system_ops.h" -#include "oneflow/core/framework/attr_value.h" - -namespace oneflow { -namespace schema { - -Maybe CastToConsistentOp::GetAttr(const std::string& attr_name) const { - if (attr_name == "shape") { - return CastAttrValue(&shape); - } else if (attr_name == "dtype") { - return CastAttrValue(&dtype); - } else { - return Error::RuntimeError() << "CastToConsistent op has no attribute named " << attr_name; - } -} - -const HashSet& CastToConsistentOp::AttrNames() const { - static HashSet attr_names{"shape", "dtype"}; - return attr_names; -} - -Maybe SelectTopNOp::GetAttr(const std::string& attr_name) const { - if (attr_name == "top_n") { - return CastAttrValue(&top_n); - } else { - return Error::RuntimeError() << "SelectTopN op has no attribute named " << attr_name; - } -} - -const HashSet& SelectTopNOp::AttrNames() const { - static HashSet attr_names{"top_n"}; - return attr_names; -} - -Maybe FeedInputOp::GetAttr(const std::string& attr_name) const { - return Error::RuntimeError() << "FeedInput op has no attribute named " << attr_name; -} - -Maybe FetchOutputOp::GetAttr(const std::string& attr_name) const { - return Error::RuntimeError() << "FetchOutput op has no attribute named " << attr_name; -} - -Maybe FeedVariableOp::GetAttr(const std::string& attr_name) const { - if (attr_name == "_l2") { - return CastAttrValue(&_l2); - } else { - return Error::RuntimeError() << "FeedVariable op has no attribute named " << attr_name; - } -} - -const HashSet& FeedVariableOp::AttrNames() const { - static HashSet attr_names{"_l2"}; - return attr_names; -} - -Maybe ImageDecoderRandomCropResizeOp::GetAttr(const std::string& attr_name) const { - if (attr_name == "target_width") { - return CastAttrValue(&target_width); - } else if (attr_name == "target_height") { - return CastAttrValue(&target_height); - } else if (attr_name == "num_workers") { - return CastAttrValue(&num_workers); - } else if (attr_name == "max_num_pixels") { - return CastAttrValue(&max_num_pixels); - } else if (attr_name == "warmup_size") { - return CastAttrValue(&warmup_size); - } else if (attr_name == "seed") { - return CastAttrValue(&seed); - } else if (attr_name == "num_attempts") { - return CastAttrValue(&num_attempts); - } else if (attr_name == "random_area_min") { - return CastAttrValue(&random_area_min); - } else if (attr_name == "random_area_max") { - return CastAttrValue(&random_area_max); - } else if (attr_name == "random_aspect_ratio_min") { - return CastAttrValue(&random_aspect_ratio_min); - } else if (attr_name == "random_aspect_ratio_max") { - return CastAttrValue(&random_aspect_ratio_max); - } else { - return Error::RuntimeError() << "FeedVariable op has no attribute named " << attr_name; - } -} - -const HashSet& ImageDecoderRandomCropResizeOp::AttrNames() const { - static HashSet attr_names{"target_width", - "target_height", - "num_workers", - "max_num_pixels", - "warmup_size", - "seed", - "num_attempts", - "random_area_min", - "random_area_max", - "random_aspect_ratio_min", - "random_aspect_ratio_max"}; - return attr_names; -} - -} // namespace schema -} // namespace oneflow diff --git a/oneflow/core/framework/system_ops.h b/oneflow/core/framework/system_ops.h deleted file mode 100644 index 69b1fad6858bfa5b5497cf437c9e1b96fccdfd6f..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/system_ops.h +++ /dev/null @@ -1,89 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_SYSTEM_OPS_H_ -#define ONEFLOW_CORE_FRAMEWORK_SYSTEM_OPS_H_ - -#include "oneflow/core/framework/op_base.h" - -#include "oneflow/core/common/data_type.pb.h" -#include "oneflow/core/common/hash_container.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/shape.h" - -namespace oneflow { -namespace schema { - -class CastToConsistentOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; - const HashSet& AttrNames() const override; - - public: - Shape shape; - DataType dtype; -}; - -class SelectTopNOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; - const HashSet& AttrNames() const override; - - public: - int32_t top_n; -}; - -class FeedInputOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; -}; - -class FetchOutputOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; -}; - -class FeedVariableOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; - const HashSet& AttrNames() const override; - - public: - double _l2; -}; - -class ImageDecoderRandomCropResizeOp : public OpBase { - public: - Maybe GetAttr(const std::string& attr_name) const override; - const HashSet& AttrNames() const override; - - public: - int64_t target_width; - int64_t target_height; - int64_t num_workers; - int64_t max_num_pixels; - int64_t warmup_size; - int64_t seed; - int64_t num_attempts; - float random_area_min; - float random_area_max; - float random_aspect_ratio_min; - float random_aspect_ratio_max; -}; - -} // namespace schema -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_SYSTEM_OPS_H_ diff --git a/oneflow/core/framework/tensor.cpp b/oneflow/core/framework/tensor.cpp index 40f131ae3990fad99e1ad9b27811108ddbffe215..9a5d5aa4843b796cd3eca07ae16a8724ffaeb43e 100644 --- a/oneflow/core/framework/tensor.cpp +++ b/oneflow/core/framework/tensor.cpp @@ -26,7 +26,7 @@ limitations under the License. #include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/autograd/autograd_engine.h" -#include "oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.h" +#include "oneflow/core/framework/op_interpreter/eager_local_op_interpreter.h" #include "oneflow/core/functional/functional.h" namespace oneflow { @@ -42,9 +42,9 @@ Maybe Tensor::BorrowTensorName(const Tensor* other) const { return Maybe::Ok(); } -Maybe StaticZerosTensor::AsMirroredTensor() { - CHECK_OR_RETURN(is_local()); - return std::dynamic_pointer_cast( +Maybe StaticZerosTensor::AsLocalTensor() { + CHECK_OR_RETURN(is_local()); // NOLINT + return std::dynamic_pointer_cast( JUST(functional::Constant(*shape_, Scalar(0), CHECK_JUST(DType::Get(dtype_)), device_))); } @@ -59,52 +59,53 @@ std::shared_ptr Parameter::pin_memory() const { return CHECK_JUST(functional::PinMemory(tensor)); } -/* static */ Maybe MirroredTensor::MakeTensor( - const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype, const Symbol& device, bool is_lazy, bool requires_grad, bool is_leaf) { - const auto& tensor_meta = - std::make_shared(std::make_shared(*shape), dtype, device); +/* static */ Maybe LocalTensor::MakeTensor(const std::shared_ptr& shape, + const std::shared_ptr& stride, + DataType dtype, + const Symbol& device, bool is_lazy, + bool requires_grad, bool is_leaf) { + const auto& tensor_meta = SymbolOf(LocalTensorMeta(*shape, dtype, device)); if (is_lazy) { - const auto& impl = - std::make_shared(tensor_meta, requires_grad, is_leaf); - return std::make_shared(impl); + const auto& impl = std::make_shared(tensor_meta, requires_grad, is_leaf); + return std::make_shared(impl); } else { - const auto& impl = - std::make_shared(tensor_meta, requires_grad, is_leaf); - return std::make_shared(impl); + const auto& impl = std::make_shared(requires_grad, is_leaf); + const auto& dep_object = NewLocalDepObject(); + JUST(impl->InitEagerBlobObject(tensor_meta, dep_object)); + return std::make_shared(impl); } } -bool MirroredTensor::is_cuda() const { return CHECK_JUST(device())->type() == "cuda"; } +bool LocalTensor::is_cuda() const { return CHECK_JUST(device())->type() == "cuda"; } -Maybe MirroredTensor::detach() const { - std::shared_ptr tensor = std::make_shared(JUST(impl_->detach())); +Maybe LocalTensor::detach() const { + std::shared_ptr tensor = std::make_shared(JUST(impl_->detach())); if (this->is_lazy()) { JUST(tensor->BorrowTensorName(this)); } return tensor; } -std::shared_ptr MirroredTensor::contiguous() const { +std::shared_ptr LocalTensor::contiguous() const { std::shared_ptr tensor = std::const_pointer_cast(shared_from_this()); if (tensor->is_contiguous()) { return tensor; } return CHECK_JUST(functional::ToContiguous(tensor)); } -std::shared_ptr MirroredTensor::pin_memory() const { +std::shared_ptr LocalTensor::pin_memory() const { std::shared_ptr tensor = std::const_pointer_cast(shared_from_this()); return CHECK_JUST(functional::PinMemory(tensor)); } -Maybe MirroredTensor::clone() const { +Maybe LocalTensor::clone() const { const auto& device_type = JUST(this->device())->type(); int64_t device_id = JUST(this->device())->device_id(); std::shared_ptr input = std::const_pointer_cast(shared_from_this()); - const bool pin_memory = JUST(JUST(input->AsMirroredTensor())->is_pinned()); + const bool pin_memory = JUST(JUST(input->AsLocalTensor())->is_pinned()); return JUST(functional::Copy(input, device_type, device_id, /*pin_memory=*/pin_memory)); } -Maybe MirroredTensor::set_data(const std::shared_ptr& other) { +Maybe LocalTensor::set_data(const std::shared_ptr& other) { CHECK_OR_RETURN(this->is_leaf()) << "Can only set leaf tensor's data."; - const auto& mirrored_tensor = std::dynamic_pointer_cast(JUST(other->detach())); + const auto& mirrored_tensor = std::dynamic_pointer_cast(JUST(other->detach())); CHECK_NOTNULL_OR_RETURN(mirrored_tensor) << "Can not set a global tensor to the data of a local tensor"; bool old_requires_grad = requires_grad(); @@ -115,67 +116,59 @@ Maybe MirroredTensor::set_data(const std::shared_ptr& other) { return Maybe::Ok(); } -std::shared_ptr ConsistentTensor::contiguous() const { +std::shared_ptr GlobalTensor::contiguous() const { std::shared_ptr tensor = std::const_pointer_cast(shared_from_this()); if (tensor->is_contiguous()) { return tensor; } return CHECK_JUST(functional::ToContiguous(tensor)); } -std::shared_ptr ConsistentTensor::pin_memory() const { +std::shared_ptr GlobalTensor::pin_memory() const { std::shared_ptr tensor = std::const_pointer_cast(shared_from_this()); return CHECK_JUST(functional::PinMemory(tensor)); } -Maybe ConsistentTensor::clone() const { - const auto& local_tensor = JUST(cur_rank_phy_tensor()); - const auto& device_type = JUST(local_tensor->device())->type(); - int64_t device_id = JUST(local_tensor->device())->device_id(); - const auto& cloned_local_tensor = - JUST(functional::Copy(local_tensor, device_type, device_id, /*pin_memory=*/false)); - DisableCheckConsistentTensorMetaScope disable_meta_check{}; - return functional::LocalToConsistent(cloned_local_tensor, JUST(parallel_desc()), - *JUST(GetSbpList(JUST(nd_sbp()))), *shape(), dtype()); -} - -Maybe ConsistentTensor::MakeTensor(const std::shared_ptr& shape, - DataType dtype, Symbol nd_sbp, - Symbol parallel_desc, - bool is_lazy, bool requires_grad, - bool is_leaf) { - std::shared_ptr impl; - Symbol consistent_tensor_meta( - ConsistentTensorMeta(shape, dtype, nd_sbp, parallel_desc)); +Maybe GlobalTensor::clone() const { + std::shared_ptr input = std::const_pointer_cast(shared_from_this()); + DisableCheckGlobalTensorMetaScope disable_meta_check{}; + return JUST(functional::ToGlobal(input, JUST(parallel_desc()), *JUST(GetSbpList(JUST(nd_sbp()))), + /*grad_sbp_parallels=*/{}, /* sync_data */ true, /*copy=*/true)); +} + +Maybe GlobalTensor::MakeTensor(const std::shared_ptr& shape, + DataType dtype, Symbol nd_sbp, + Symbol parallel_desc, bool is_lazy, + bool requires_grad, bool is_leaf) { + std::shared_ptr impl; + Symbol global_tensor_meta( + GlobalTensorMeta(*shape, dtype, nd_sbp, parallel_desc)); if (is_lazy) { - impl = - std::make_shared(consistent_tensor_meta, requires_grad, is_leaf); + impl = std::make_shared(global_tensor_meta, requires_grad, is_leaf); } else { - impl = JUST(EagerConsistentTensorImpl::New(consistent_tensor_meta, requires_grad, is_leaf)); + impl = JUST(EagerGlobalTensorImpl::New(global_tensor_meta, requires_grad, is_leaf)); } - return std::make_shared(impl); + return std::make_shared(impl); } -bool ConsistentTensor::is_cuda() const { +bool GlobalTensor::is_cuda() const { return CHECK_JUST(parallel_desc())->device_type() == DeviceType::kCUDA; } -Maybe ConsistentTensor::detach() const { - std::shared_ptr tensor = std::make_shared(JUST(impl_->detach())); +Maybe GlobalTensor::detach() const { + std::shared_ptr tensor = std::make_shared(JUST(impl_->detach())); if (this->is_lazy()) { JUST(tensor->BorrowTensorName(this)); } return tensor; } -Maybe ConsistentTensor::set_data(const std::shared_ptr& other) { +Maybe GlobalTensor::set_data(const std::shared_ptr& other) { CHECK_OR_RETURN(this->is_leaf()) << "Only leaf tensor's data can be set, because non-leaf tensor's data has been captured in " "the backward graph in autograd."; - const auto& consistent_tensor = - std::dynamic_pointer_cast(JUST(other->detach())); - CHECK_NOTNULL_OR_RETURN(consistent_tensor); - JUST(WithConsistencyChecked(consistent_tensor, - [&]() -> Maybe { return Maybe::Ok(); })); + const auto& global_tensor = std::dynamic_pointer_cast(JUST(other->detach())); + CHECK_NOTNULL_OR_RETURN(global_tensor); // NOLINT + JUST(WithConsistencyChecked(global_tensor, [&]() -> Maybe { return Maybe::Ok(); })); bool old_requires_grad = requires_grad(); - impl_ = consistent_tensor->impl_; + impl_ = global_tensor->impl_; JUST(set_requires_grad(old_requires_grad)); grad_fn_node_ = nullptr; if (other->is_lazy()) { JUST(this->BorrowTensorName(other.get())); } diff --git a/oneflow/core/framework/tensor.h b/oneflow/core/framework/tensor.h index 0c76ae532ee5cd06fd4a714826e49916ef4f3edc..7802781371d91947840ff7e79689b696bbd26b87 100644 --- a/oneflow/core/framework/tensor.h +++ b/oneflow/core/framework/tensor.h @@ -25,6 +25,8 @@ limitations under the License. #include "oneflow/core/framework/tensor_impl.h" #include "oneflow/core/framework/transport_token.h" #include "oneflow/core/common/error.h" +#include "oneflow/core/autograd/autograd_engine.h" +#include "oneflow/core/job/global_mode.h" namespace oneflow { @@ -35,8 +37,8 @@ namespace one { class FunctionNode; -class ConsistentTensor; -class MirroredTensor; +class GlobalTensor; +class LocalTensor; class Tensor : public std::enable_shared_from_this { public: @@ -55,8 +57,8 @@ class Tensor : public std::enable_shared_from_this { virtual Maybe> device() const = 0; virtual Maybe*> mut_device() = 0; virtual bool is_cuda() const = 0; - virtual bool is_consistent() const = 0; - virtual bool is_local() const { return !is_consistent(); } + virtual bool is_global() const = 0; + virtual bool is_local() const { return !is_global(); } virtual bool is_lazy() const = 0; virtual bool is_eager() const { return !is_lazy(); } virtual bool is_contiguous() const = 0; @@ -64,10 +66,11 @@ class Tensor : public std::enable_shared_from_this { virtual const TensorMeta& tensor_meta() const = 0; virtual Maybe data() = 0; virtual std::shared_ptr pin_memory() const = 0; - virtual Maybe> consistent_tensor_meta() const { OF_UNIMPLEMENTED(); } + virtual Maybe> local_tensor_meta() const { OF_UNIMPLEMENTED(); } + virtual Maybe> global_tensor_meta() const { OF_UNIMPLEMENTED(); } - // Getters valid only for EagerMirroredTensor - virtual Maybe mut_eager_mirrored_tensor_impl() { OF_UNIMPLEMENTED(); } + // Getters valid only for EagerLocalTensor + virtual Maybe mut_eager_local_tensor_impl() { OF_UNIMPLEMENTED(); } virtual Maybe eager_blob_object() const = 0; virtual Maybe compute_local_dep_object() const = 0; virtual Maybe has_eager_blob_object() const = 0; @@ -75,11 +78,11 @@ class Tensor : public std::enable_shared_from_this { virtual Maybe stride() const { OF_UNIMPLEMENTED(); } virtual Maybe storage_offset() const { OF_UNIMPLEMENTED(); } - // Getters/Setters valid only for EagerConsistentTensor + // Getters/Setters valid only for EagerGlobalTensor virtual Maybe>&> consumer_nd_sbp_constraint() const { OF_UNIMPLEMENTED(); } - virtual Maybe cur_rank_phy_tensor() const { OF_UNIMPLEMENTED(); } + virtual Maybe cur_rank_phy_tensor() const { OF_UNIMPLEMENTED(); } virtual Maybe set_consumer_nd_sbp_constraint(const Optional>& val) { OF_UNIMPLEMENTED(); } @@ -89,6 +92,7 @@ class Tensor : public std::enable_shared_from_this { virtual bool is_leaf() const = 0; virtual bool retain_grad() const = 0; virtual std::shared_ptr grad_fn_node() const = 0; + virtual int32_t get_grad_fn_output_index() const = 0; virtual Maybe acc_grad() const = 0; virtual Maybe current_grad() const = 0; virtual Maybe detach() const = 0; @@ -100,6 +104,7 @@ class Tensor : public std::enable_shared_from_this { virtual Maybe set_retain_grad(bool retain_grad) = 0; virtual void set_grad_fn_node(const std::shared_ptr& grad_fn_node) = 0; virtual std::shared_ptr mut_grad_fn_node() = 0; + virtual void set_grad_fn_output_index(int32_t idx) = 0; virtual Maybe set_acc_grad(const std::shared_ptr& grad) = 0; virtual Maybe mut_acc_grad() = 0; virtual void set_is_leaf(bool is_leaf) = 0; @@ -113,22 +118,27 @@ class Tensor : public std::enable_shared_from_this { virtual Maybe RegisterStorageDeleteHook(const std::function& hook) { OF_UNIMPLEMENTED(); }; - virtual Maybe AsMirroredTensor() = 0; - virtual Maybe AsConsistentTensor() = 0; + virtual Maybe AsLocalTensor() = 0; + virtual Maybe AsGlobalTensor() = 0; Maybe BorrowTensorName(const Tensor* other) const; // The same tensor instance should share the python object to ensure that // their id are consistent in Python. That is if x and y are hold the same tensor, // then `id(x)` should equal to `id(y)` - void* pyobject() const { return pyobject_; } - void set_pyobject(void* object) { pyobject_ = object; } + void* pyobject() const { return pyobj_ptr_.get(); } + void set_pyobject_ptr(std::unique_ptr&& pyobj_ptr) { + pyobj_ptr_ = std::move(pyobj_ptr); + } + bool owns_pyobj() const { return owns_pyobj_; } + void set_owns_pyobj(bool owns_pyobj) { owns_pyobj_ = owns_pyobj; } protected: - Tensor() : pyobject_(nullptr) {} + Tensor() : pyobj_ptr_(nullptr, [](void*) {}), owns_pyobj_(false) {} private: - void* pyobject_; + std::unique_ptr pyobj_ptr_; + bool owns_pyobj_; }; class StaticZerosTensor final : public Tensor { @@ -149,8 +159,8 @@ class StaticZerosTensor final : public Tensor { PRINT_BUG_PROMPT_AND_ABORT(); return false; } - bool is_consistent() const override { return false; } - bool is_local() const override { return !is_consistent(); } + bool is_global() const override { return false; } + bool is_local() const override { return !is_global(); } bool is_lazy() const override { PRINT_BUG_PROMPT_AND_ABORT(); return false; @@ -164,12 +174,15 @@ class StaticZerosTensor final : public Tensor { std::shared_ptr pin_memory() const override { return std::const_pointer_cast(shared_from_this()); } - Maybe> consistent_tensor_meta() const override { + Maybe> local_tensor_meta() const override { + RETURN_ERROR_WITH_BUG_PROMPT(); + } + Maybe> global_tensor_meta() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } - // Getters valid only for EagerMirroredTensor - Maybe mut_eager_mirrored_tensor_impl() override { + // Getters valid only for EagerLocalTensor + Maybe mut_eager_local_tensor_impl() override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe eager_blob_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } @@ -181,11 +194,11 @@ class StaticZerosTensor final : public Tensor { Maybe stride() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe storage_offset() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } - // Getters/Setters valid only for EagerConsistentTensor + // Getters/Setters valid only for EagerGlobalTensor Maybe>&> consumer_nd_sbp_constraint() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } - Maybe cur_rank_phy_tensor() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } + Maybe cur_rank_phy_tensor() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe set_consumer_nd_sbp_constraint(const Optional>& val) override { RETURN_ERROR_WITH_BUG_PROMPT(); } @@ -212,6 +225,10 @@ class StaticZerosTensor final : public Tensor { PRINT_BUG_PROMPT_AND_ABORT(); return nullptr; } + int32_t get_grad_fn_output_index() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + return 0; + } Maybe acc_grad() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe current_grad() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe detach() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } @@ -232,6 +249,7 @@ class StaticZerosTensor final : public Tensor { void set_grad_fn_node(const std::shared_ptr& grad_fn_node) override { PRINT_BUG_PROMPT_AND_ABORT(); } + void set_grad_fn_output_index(int32_t idx) override { PRINT_BUG_PROMPT_AND_ABORT(); } std::shared_ptr mut_grad_fn_node() override { PRINT_BUG_PROMPT_AND_ABORT(); return *(std::shared_ptr*)nullptr; @@ -260,8 +278,8 @@ class StaticZerosTensor final : public Tensor { RETURN_ERROR_WITH_BUG_PROMPT(); } - Maybe AsMirroredTensor() override; - Maybe AsConsistentTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } + Maybe AsLocalTensor() override; + Maybe AsGlobalTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } private: StaticZerosTensor(const std::shared_ptr& shape, DataType dtype, @@ -281,16 +299,19 @@ class TensorIf : public Tensor { // acc_grad is tensor's accumulated grad in more than once backward operation, // and current_grad is temporary grad to shared data with different FunctionNode std::shared_ptr grad_fn_node() const override { return grad_fn_node_; } + int32_t get_grad_fn_output_index() const override { return grad_fn_output_index_; } // Setters for autograd void set_grad_fn_node(const std::shared_ptr& grad_fn_node) override { grad_fn_node_ = grad_fn_node; } std::shared_ptr mut_grad_fn_node() override { return grad_fn_node_; } + void set_grad_fn_output_index(int32_t idx) override { grad_fn_output_index_ = idx; } protected: TensorIf() = default; std::shared_ptr grad_fn_node_; + int32_t grad_fn_output_index_ = -1; }; template @@ -310,13 +331,16 @@ class ProxyTensor : public TensorIf { virtual Maybe> device() const override { return tensor_->device(); } virtual Maybe*> mut_device() override { return tensor_->mut_device(); } virtual bool is_cuda() const override { return tensor_->is_cuda(); } - virtual bool is_consistent() const override { return tensor_->is_consistent(); } + virtual bool is_global() const override { return tensor_->is_global(); } virtual bool is_local() const override { return tensor_->is_local(); } virtual bool is_lazy() const override { return tensor_->is_lazy(); } virtual bool is_eager() const override { return tensor_->is_eager(); } virtual const TensorMeta& tensor_meta() const override { return tensor_->tensor_meta(); } - virtual Maybe> consistent_tensor_meta() const override { - return tensor_->consistent_tensor_meta(); + virtual Maybe> local_tensor_meta() const override { + return tensor_->local_tensor_meta(); + } + virtual Maybe> global_tensor_meta() const override { + return tensor_->global_tensor_meta(); } virtual Maybe data() override { return tensor_->detach(); } virtual std::shared_ptr pin_memory() const override { return tensor_->pin_memory(); } @@ -333,8 +357,8 @@ class ProxyTensor : public TensorIf { return tensor_->mut_grad_fn_node(); } - virtual Maybe mut_eager_mirrored_tensor_impl() override { - return tensor_->mut_eager_mirrored_tensor_impl(); + virtual Maybe mut_eager_local_tensor_impl() override { + return tensor_->mut_eager_local_tensor_impl(); } virtual Maybe eager_blob_object() const override { return tensor_->eager_blob_object(); @@ -355,7 +379,7 @@ class ProxyTensor : public TensorIf { virtual Maybe transport_token() const override { return tensor_->transport_token(); } - virtual Maybe cur_rank_phy_tensor() const override { + virtual Maybe cur_rank_phy_tensor() const override { return tensor_->cur_rank_phy_tensor(); } virtual Maybe set_consumer_nd_sbp_constraint(const Optional>& val) override { @@ -402,16 +426,16 @@ class ProxyTensor : public TensorIf { return Maybe::Ok(); } - virtual Maybe AsMirroredTensor() override { - if (const auto& mirrored_tensor = std::dynamic_pointer_cast(tensor_)) { - return mirrored_tensor; + virtual Maybe AsLocalTensor() override { + if (const auto& local_tensor = std::dynamic_pointer_cast(tensor_)) { + return local_tensor; } RETURN_ERROR_WITH_BUG_PROMPT(); } - virtual Maybe AsConsistentTensor() override { - if (const auto& consistent_tensor = std::dynamic_pointer_cast(tensor_)) { - return consistent_tensor; + virtual Maybe AsGlobalTensor() override { + if (const auto& global_tensor = std::dynamic_pointer_cast(tensor_)) { + return global_tensor; } RETURN_ERROR_WITH_BUG_PROMPT(); } @@ -436,12 +460,12 @@ class Parameter final : public ProxyTensor { } }; -class MirroredTensor final : public TensorIf { +class LocalTensor final : public TensorIf { public: - OF_DISALLOW_COPY_AND_MOVE(MirroredTensor); - MirroredTensor() = default; - explicit MirroredTensor(const std::shared_ptr& impl) { impl_ = impl; } - ~MirroredTensor() override = default; + OF_DISALLOW_COPY_AND_MOVE(LocalTensor); + LocalTensor() = default; + explicit LocalTensor(const std::shared_ptr& impl) { impl_ = impl; } + ~LocalTensor() override = default; // Getters std::shared_ptr shape() const override { return impl_->shape(); } @@ -454,7 +478,7 @@ class MirroredTensor final : public TensorIf { OF_RUNTIME_ERROR() << "Local tensor has no sbp property. " "sbp is the description in the oneflow distributed case, you can refer to " - "https://docs.oneflow.org/master/parallelism/03_consistent_tensor.html; " + "https://docs.oneflow.org/master/parallelism/03_global_tensor.html; " "For example, create a global tensor like this : 'x = oneflow.tensor((2,3, " "placement=oneflow.placement(\"cuda\", {0: 0}), sbp=oneflow.sbp.broadcast))', then " "'x.sbp' is 'oneflow.sbp.broadcast'"; @@ -467,7 +491,7 @@ class MirroredTensor final : public TensorIf { Maybe> device() const override { return impl_->device(); } Maybe*> mut_device() override { return impl_->mut_device(); } bool is_lazy() const override { return impl_->is_lazy(); } - bool is_consistent() const override { return false; } + bool is_global() const override { return false; } bool is_cuda() const override; std::shared_ptr contiguous() const override; @@ -475,7 +499,7 @@ class MirroredTensor final : public TensorIf { Maybe data() override { return this->detach(); } std::shared_ptr pin_memory() const override; - // Getters valid only for EagerMirroredTensor + // Getters valid only for EagerLocalTensor Maybe eager_blob_object() const override { return impl_->eager_blob_object(); } @@ -496,8 +520,14 @@ class MirroredTensor final : public TensorIf { bool is_contiguous() const override { return impl_->is_contiguous(); } Maybe is_pinned() const override { return impl_->is_pinned(); }; + Maybe> local_tensor_meta() const override { return impl_->tensor_meta(); } + // Setters for autograd Maybe set_acc_grad(const std::shared_ptr& grad) override { + if (!grad_fn_node_ && requires_grad()) { + CHECK_OR_RETURN(is_leaf()) << "only leaf tensor may have no grad_fn"; + AddAccumulateFunctionNode(shared_from_this()); + } return impl_->set_acc_grad(grad); } Maybe set_requires_grad(bool requires_grad) override { @@ -522,36 +552,43 @@ class MirroredTensor final : public TensorIf { Maybe detach() const override; Maybe clone() const override; - static Maybe MakeTensor(const std::shared_ptr& shape, - const std::shared_ptr& stride, - DataType dtype, const Symbol& device, - bool is_lazy, bool requires_grad, bool is_leaf); - MirroredTensorImpl* mut_impl() { return impl_.get(); } - Maybe mut_eager_mirrored_tensor_impl() override { - return impl_->mut_eager_mirrored_tensor_impl(); + static Maybe MakeTensor(const std::shared_ptr& shape, + const std::shared_ptr& stride, DataType dtype, + const Symbol& device, bool is_lazy, + bool requires_grad, bool is_leaf); + LocalTensorImpl* mut_impl() { return impl_.get(); } + Maybe mut_eager_local_tensor_impl() override { + return impl_->mut_eager_local_tensor_impl(); + } + user_op::TensorDesc* mut_tensor_meta() override { + return std::const_pointer_cast(impl_->mut_tensor_meta()).get(); } - user_op::TensorDesc* mut_tensor_meta() override { return impl_->mut_tensor_meta(); } Maybe set_data(const std::shared_ptr& other) override; + Maybe set_impl(std::shared_ptr impl) { + impl_ = impl; + return Maybe::Ok(); + } + Maybe RegisterStorageDeleteHook(const std::function& hook) override { return impl_->RegisterStorageDeleteHook(hook); } - Maybe AsMirroredTensor() override { - return std::dynamic_pointer_cast(shared_from_this()); + Maybe AsLocalTensor() override { + return std::dynamic_pointer_cast(shared_from_this()); } - Maybe AsConsistentTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } + Maybe AsGlobalTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } private: - std::shared_ptr impl_; + std::shared_ptr impl_; }; -class ConsistentTensor final : public TensorIf { +class GlobalTensor final : public TensorIf { public: - OF_DISALLOW_COPY_AND_MOVE(ConsistentTensor); - ConsistentTensor() = default; - explicit ConsistentTensor(const std::shared_ptr& impl) { impl_ = impl; } - ~ConsistentTensor() override = default; + OF_DISALLOW_COPY_AND_MOVE(GlobalTensor); + GlobalTensor() = default; + explicit GlobalTensor(const std::shared_ptr& impl) { impl_ = impl; } + ~GlobalTensor() override = default; // Getters std::shared_ptr shape() const override { return impl_->shape(); } @@ -560,27 +597,29 @@ class ConsistentTensor final : public TensorIf { Maybe> nd_sbp() const override { return impl_->nd_sbp(); } Maybe> parallel_desc() const override { return impl_->parallel_desc(); } Maybe> device() const override { + if (GlobalMode::is_enabled()) { + const auto& device_tag = JUST(parallel_desc())->device_tag(); + return JUST(Device::New(device_tag)); + } OF_RUNTIME_ERROR() << "Only local tensors have 'device'. Please use " "'.placement' for global tensors."; } Maybe*> mut_device() override { - OF_RUNTIME_ERROR() << "ConsistentTensor has no mut_device property"; + OF_RUNTIME_ERROR() << "GlobalTensor has no mut_device property"; } bool is_lazy() const override { return impl_->is_lazy(); } - bool is_consistent() const override { return true; } + bool is_global() const override { return true; } Maybe>&> consumer_nd_sbp_constraint() const override { return impl_->consumer_nd_sbp_constraint(); } - Maybe cur_rank_phy_tensor() const override { - return impl_->cur_rank_phy_tensor(); - } + Maybe cur_rank_phy_tensor() const override { return impl_->cur_rank_phy_tensor(); } bool is_cuda() const override; std::shared_ptr contiguous() const override; Maybe data() override { return this->detach(); } Maybe stride() const override { return impl_->stride(); } std::shared_ptr pin_memory() const override; - // Getters valid only for EagerMirroredTensor + // Getters valid only for EagerLocalTensor Maybe eager_blob_object() const override { return impl_->eager_blob_object(); } @@ -610,6 +649,10 @@ class ConsistentTensor final : public TensorIf { // Setters for autograd Maybe set_acc_grad(const std::shared_ptr& grad) override { + if (!grad_fn_node_ && requires_grad()) { + CHECK_OR_RETURN(is_leaf()) << "only leaf tensor may have no grad_fn"; + AddAccumulateFunctionNode(shared_from_this()); + } return impl_->set_acc_grad(grad); } Maybe mut_acc_grad() override { return impl_->mut_acc_grad(); } @@ -634,27 +677,26 @@ class ConsistentTensor final : public TensorIf { Maybe detach() const override; Maybe clone() const override; - static Maybe MakeTensor(const std::shared_ptr& shape, - DataType dtype, Symbol nd_sbp, - Symbol parallel_desc, bool is_lazy, - bool requires_grad, bool is_leaf); + static Maybe MakeTensor(const std::shared_ptr& shape, DataType dtype, + Symbol nd_sbp, Symbol parallel_desc, + bool is_lazy, bool requires_grad, bool is_leaf); - ConsistentTensorImpl* mut_impl() { return impl_.get(); } + GlobalTensorImpl* mut_impl() { return impl_.get(); } - Maybe> consistent_tensor_meta() const override { + Maybe> global_tensor_meta() const override { return impl_->tensor_meta(); } user_op::TensorDesc* mut_tensor_meta() override { return impl_->mut_tensor_meta(); } Maybe set_data(const std::shared_ptr& other) override; - Maybe AsMirroredTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } - Maybe AsConsistentTensor() override { - return std::dynamic_pointer_cast(shared_from_this()); + Maybe AsLocalTensor() override { RETURN_ERROR_WITH_BUG_PROMPT(); } + Maybe AsGlobalTensor() override { + return std::dynamic_pointer_cast(shared_from_this()); } private: - std::shared_ptr impl_; + std::shared_ptr impl_; }; } // namespace one diff --git a/oneflow/core/framework/tensor_arg.cpp b/oneflow/core/framework/tensor_arg.cpp index f80ac72025e7a2142b96a8486155c8ab6b1af96e..a6ee08f33892292ed722a3e6d0159bc8854ac4f8 100644 --- a/oneflow/core/framework/tensor_arg.cpp +++ b/oneflow/core/framework/tensor_arg.cpp @@ -42,14 +42,8 @@ Maybe TensorArg::PushPartialTensor(const std::shared_ptr& partial_ return Maybe::Ok(); } -Maybe TensorArg::GetAccTensor(const std::vector& hooks) { +Maybe TensorArg::GetAccTensor() const { CHECK_OR_RETURN(Empty() == false) << "Can not GetAccTensor because it is empty"; - if (!hooks.empty()) { - for (const auto& hook : hooks) { - auto new_grad = hook(acc_tensor_); - if (new_grad) { acc_tensor_ = new_grad; } - } - } return acc_tensor_; } diff --git a/oneflow/core/framework/tensor_arg.h b/oneflow/core/framework/tensor_arg.h index 3f60db4facdb45b56baafa734aa003f9b3e0a48b..0baf1866a0a6b530a02665be6d08d47c471358a9 100644 --- a/oneflow/core/framework/tensor_arg.h +++ b/oneflow/core/framework/tensor_arg.h @@ -38,7 +38,7 @@ class TensorArg final { bool Empty() const; void Release(); Maybe PushPartialTensor(const std::shared_ptr& partial_tensor); - Maybe GetAccTensor(const std::vector& hooks); + Maybe GetAccTensor() const; private: std::shared_ptr acc_tensor_; diff --git a/oneflow/core/framework/tensor_consistent_id.cpp b/oneflow/core/framework/tensor_consistent_id.cpp deleted file mode 100644 index f004f81c4640da0bac3309a9f682a758fd8cacf6..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/tensor_consistent_id.cpp +++ /dev/null @@ -1,57 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/framework/transport_token.h" -#include "oneflow/core/framework/tensor_consistent_id.h" - -namespace oneflow { - -namespace { - -Maybe> RawGetMetaTransportToken() { - const auto& token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeMeta)); - return std::make_shared(token); -} -static constexpr auto* GetMetaTransportToken = DECORATE(&RawGetMetaTransportToken, ThreadLocal); - -} // namespace - -Maybe NewTensorConsistentId() { return ++**JUST(GetMetaTransportToken()); } - -namespace one { - -int64_t* MutThreadLocalConsistentIdDepth() { - static thread_local int64_t recursive_depth = 0; - return &recursive_depth; -} - -Maybe InitConsistentId(TensorTuple* outputs) { - for (const auto& output : *outputs) { - CHECK_OR_RETURN(output); - const auto& consistent_tensor = JUST(output->AsConsistentTensor()); - CHECK_OR_RETURN(consistent_tensor) - << Error::UnimplementedError() << "consistent tensors suppported only."; - const auto& transport_token = JUST(NewTensorConsistentId()); - JUST(consistent_tensor->mut_impl()->set_transport_token(transport_token)); - } - return Maybe::Ok(); -} - -} // namespace one - -} // namespace oneflow diff --git a/oneflow/core/framework/tensor_consistent_id.h b/oneflow/core/framework/tensor_consistent_id.h deleted file mode 100644 index 96cf8e28b88d9c5dfb9aff5d295d6aa09ccf9c7a..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/tensor_consistent_id.h +++ /dev/null @@ -1,52 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_TENSOR_CONSISTENT_ID_ -#define ONEFLOW_CORE_FRAMEWORK_TENSOR_CONSISTENT_ID_ - -#include "oneflow/core/common/maybe.h" - -namespace oneflow { - -Maybe NewTensorConsistentId(); - -namespace one { - -class TensorTuple; - -int64_t* MutThreadLocalConsistentIdDepth(); -Maybe InitConsistentId(TensorTuple* outputs); - -template -struct NonRecursiveInitConsistentId; - -template -struct NonRecursiveInitConsistentId, Arg0, Arg1, TensorTuple*, Args...> { - template (*func)(Arg0, Arg1, TensorTuple*, Args...)> - static Maybe Call(Arg0 arg0, Arg1 arg1, TensorTuple* outputs, Args... args) { - auto* recursive_depth = MutThreadLocalConsistentIdDepth(); - ++*recursive_depth; - Maybe ret = func(arg0, arg1, outputs, args...); - --*recursive_depth; - if (*recursive_depth == 0 && ret.IsOk()) { JUST(InitConsistentId(outputs)); } - return ret; - } -}; - -} // namespace one - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_TENSOR_CONSISTENT_ID_ diff --git a/oneflow/core/framework/tensor_desc.h b/oneflow/core/framework/tensor_desc.h deleted file mode 100644 index c22e92aa12a8c4be60c85249e42fad945a2c3e51..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/tensor_desc.h +++ /dev/null @@ -1,80 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_TENSOR_DESC_H_ -#define ONEFLOW_CORE_FRAMEWORK_TENSOR_DESC_H_ - -#include "oneflow/core/common/util.h" -#include "oneflow/core/register/blob_desc.pb.h" -#include "oneflow/core/common/shape.h" -#include "oneflow/core/common/stride.h" - -namespace oneflow { - -namespace user_op { - -class TensorDesc { - public: - virtual ~TensorDesc() = default; - TensorDesc& operator=(const TensorDesc& rhs); - bool operator==(const TensorDesc&) const; - - virtual const Shape& shape() const = 0; - virtual Shape* mut_shape() = 0; - virtual const Stride& stride() const = 0; - virtual Stride* mut_stride() = 0; - virtual DataType data_type() const = 0; - virtual DataType* mut_data_type() = 0; - - virtual bool is_dynamic() const = 0; - virtual bool* mut_is_dynamic() = 0; - virtual void set_is_dynamic(bool val) = 0; - - protected: - TensorDesc() = default; -}; - -class NaiveTensorDesc final : public TensorDesc { - public: - NaiveTensorDesc() = default; - ~NaiveTensorDesc() override = default; - NaiveTensorDesc(const NaiveTensorDesc&); - NaiveTensorDesc(const BlobDescProto&); - - NaiveTensorDesc& operator=(const BlobDescProto&); - - const Shape& shape() const override { return shape_; } - Shape* mut_shape() override { return &shape_; } - const Stride& stride() const override { return stride_; } - Stride* mut_stride() override { return &stride_; } - DataType data_type() const override { return data_type_; } - DataType* mut_data_type() override { return &data_type_; } - - bool is_dynamic() const override { return is_dynamic_; } - bool* mut_is_dynamic() override { return &is_dynamic_; } - void set_is_dynamic(bool val) override { is_dynamic_ = val; } - - private: - Shape shape_; - Stride stride_; - DataType data_type_; - bool is_dynamic_; -}; - -} // namespace user_op - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_TENSOR_DESC_H_ diff --git a/oneflow/core/framework/tensor_global_id.cpp b/oneflow/core/framework/tensor_global_id.cpp new file mode 100644 index 0000000000000000000000000000000000000000..006320762b5fb3f935eacef0996e295b4b98b443 --- /dev/null +++ b/oneflow/core/framework/tensor_global_id.cpp @@ -0,0 +1,57 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/framework/transport_token.h" +#include "oneflow/core/framework/tensor_global_id.h" + +namespace oneflow { + +namespace { + +Maybe> RawGetMetaTransportToken() { + const auto& token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeMeta)); + return std::make_shared(token); +} +static constexpr auto* GetMetaTransportToken = DECORATE(&RawGetMetaTransportToken, ThreadLocal); + +} // namespace + +Maybe NewTensorGlobalId() { return ++**JUST(GetMetaTransportToken()); } + +namespace one { + +int64_t* MutThreadLocalGlobalIdDepth() { + static thread_local int64_t recursive_depth = 0; + return &recursive_depth; +} + +Maybe InitGlobalId(TensorTuple* outputs) { + for (const auto& output : *outputs) { + CHECK_OR_RETURN(output); + const auto& global_tensor = JUST(output->AsGlobalTensor()); + CHECK_OR_RETURN(global_tensor) + << Error::UnimplementedError() << "global tensors suppported only."; + const auto& transport_token = JUST(NewTensorGlobalId()); + JUST(global_tensor->mut_impl()->set_transport_token(transport_token)); + } + return Maybe::Ok(); +} + +} // namespace one + +} // namespace oneflow diff --git a/oneflow/core/framework/tensor_global_id.h b/oneflow/core/framework/tensor_global_id.h new file mode 100644 index 0000000000000000000000000000000000000000..ddc9c3005c8a6a71c2fff862edac6544c1fcf124 --- /dev/null +++ b/oneflow/core/framework/tensor_global_id.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_FRAMEWORK_TENSOR_GLOBAL_ID_ +#define ONEFLOW_CORE_FRAMEWORK_TENSOR_GLOBAL_ID_ + +#include "oneflow/core/common/maybe.h" + +namespace oneflow { + +Maybe NewTensorGlobalId(); + +namespace one { + +class TensorTuple; + +int64_t* MutThreadLocalGlobalIdDepth(); +Maybe InitGlobalId(TensorTuple* outputs); + +template +struct NonRecursiveInitGlobalId; + +template +struct NonRecursiveInitGlobalId, Arg0, Arg1, TensorTuple*, Args...> { + template (*func)(Arg0, Arg1, TensorTuple*, Args...)> + static Maybe Call(Arg0 arg0, Arg1 arg1, TensorTuple* outputs, Args... args) { + auto* recursive_depth = MutThreadLocalGlobalIdDepth(); + ++*recursive_depth; + Maybe ret = func(arg0, arg1, outputs, args...); + --*recursive_depth; + if (*recursive_depth == 0 && ret.IsOk()) { JUST(InitGlobalId(outputs)); } + return ret; + } +}; + +} // namespace one + +} // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_TENSOR_GLOBAL_ID_ diff --git a/oneflow/core/framework/tensor_impl.cpp b/oneflow/core/framework/tensor_impl.cpp index b7f68d41601a718a5cf35bd8093c5a633cf8634f..e2b5d14d7e8c0a66dbb661121c431dfefef0d729 100644 --- a/oneflow/core/framework/tensor_impl.cpp +++ b/oneflow/core/framework/tensor_impl.cpp @@ -15,8 +15,8 @@ limitations under the License. */ #include #include "oneflow/core/common/blocking_then_busy.h" -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/common/tensor_meta.h" #include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/tensor_impl.h" @@ -31,7 +31,6 @@ limitations under the License. #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/operator/operator.h" #include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/framework/stream_allocator_is_pinned.h" namespace oneflow { @@ -59,30 +58,27 @@ Maybe TensorImpl::set_acc_grad(const std::shared_ptr& grad) { Maybe TensorImpl::mut_acc_grad() { return autograd_meta_->mut_acc_grad(); } Maybe TensorImpl::set_retain_grad(bool retain_grad) { - autograd_meta_->set_retain_grad(retain_grad); + if (!requires_grad() && retain_grad) { + return Error::RuntimeError() << "Can't retain_grad on Tensor that has requires_grad=False"; + } + if (!is_leaf() && retain_grad) { autograd_meta_->set_retain_grad(retain_grad); } return Maybe::Ok(); } -Maybe LazyMirroredTensorImpl::detach() const { - auto detached_impl = std::make_shared(tensor_meta_, false, true); - return std::shared_ptr(detached_impl); +Maybe LazyLocalTensorImpl::detach() const { + auto detached_impl = std::make_shared(tensor_meta_, false, true); + return std::shared_ptr(detached_impl); } -EagerMirroredTensorImpl::EagerMirroredTensorImpl() - : MirroredTensorImpl(std::make_shared(), false, false) {} - -EagerMirroredTensorImpl::EagerMirroredTensorImpl( - const std::shared_ptr& tensor_meta, bool requires_grad, bool is_leaf) - : MirroredTensorImpl(tensor_meta, requires_grad, is_leaf) {} +EagerLocalTensorImpl::EagerLocalTensorImpl(const std::shared_ptr& tensor_storage, + int64_t storage_offset, bool requires_grad, bool is_leaf) + : LocalTensorImpl(requires_grad, is_leaf), + tensor_storage_(tensor_storage), + storage_offset_(storage_offset) {} -EagerMirroredTensorImpl::~EagerMirroredTensorImpl() {} +EagerLocalTensorImpl::~EagerLocalTensorImpl() {} -EagerMirroredTensorImpl::EagerMirroredTensorImpl( - const std::shared_ptr& tensor_meta, - const std::shared_ptr& tensor_storage, bool requires_grad, bool is_leaf) - : MirroredTensorImpl(tensor_meta, requires_grad, is_leaf), tensor_storage_(tensor_storage) {} - -Maybe EagerMirroredTensorImpl::UpdateTensorStorage() { +Maybe EagerLocalTensorImpl::UpdateTensorStorage() { const auto& eager_blob_object = eager_blob_object_; tensor_storage_ = std::make_shared(eager_blob_object->tensor_storage()); tensor_storage_->set_releaser_hook( @@ -97,99 +93,96 @@ Maybe EagerMirroredTensorImpl::UpdateTensorStorage() { return Maybe::Ok(); } -Maybe EagerMirroredTensorImpl::compute_local_dep_object() const { +const std::shared_ptr& EagerLocalTensorImpl::mut_tensor_meta() { + return eager_blob_object_->mut_tensor_meta(); +} +// Getters +const Symbol& EagerLocalTensorImpl::tensor_meta() const { + return eager_blob_object_->tensor_meta(); +} + +Maybe EagerLocalTensorImpl::compute_local_dep_object() const { return JUST(eager_blob_object())->compute_local_dep_object(); } -Maybe EagerMirroredTensorImpl::InitEagerBlobObject( +Maybe EagerLocalTensorImpl::InitEagerBlobObject( + const Symbol& local_tensor_meta, + const std::shared_ptr& mut_local_tensor_meta, const intrusive::shared_ptr& dep_object) { - CHECK_OR_RETURN(static_cast(device())); - const auto& mem_case = device()->mem_case(); - const auto& mut_shape = std::const_pointer_cast(tensor_meta()->shape_ptr()); - const auto& mut_stride = std::const_pointer_cast(tensor_meta()->stride_ptr()); + CHECK_OR_RETURN(static_cast(local_tensor_meta->device())); // NOLINT + const auto& mem_case = local_tensor_meta->device()->mem_case(); if (tensor_storage_) { auto tensor_storage = tensor_storage_->storage(); - eager_blob_object_ = std::make_shared(mem_case, mut_shape, mut_stride, - dtype(), tensor_storage, dep_object); + eager_blob_object_ = std::make_shared( + mem_case, local_tensor_meta, mut_local_tensor_meta, local_tensor_meta->dtype(), + tensor_storage, dep_object); } else { - const auto& eager_blob_object = - std::make_shared(mem_case, mut_shape, mut_stride, dtype(), - std::make_shared(), dep_object); + const auto& eager_blob_object = std::make_shared( + mem_case, local_tensor_meta, mut_local_tensor_meta, local_tensor_meta->dtype(), + std::make_shared(), dep_object); JUST(set_eager_blob_object(eager_blob_object)); } return Maybe::Ok(); } -Maybe EagerMirroredTensorImpl::is_pinned() const { +Maybe EagerLocalTensorImpl::is_pinned() const { if (!eager_blob_object_) { return false; } - return IsStreamAllocatorPinned::Visit(JUST(eager_blob_object_->producer_stream())->stream_role()); + return IsStreamAllocatorPinned::Visit(JUST(eager_blob_object_->producer_stream())->stream_type()); } -Maybe EagerMirroredTensorImpl::set_eager_blob_object( +Maybe EagerLocalTensorImpl::set_eager_blob_object( std::shared_ptr eager_blob_object) { eager_blob_object_ = eager_blob_object; - CHECK_OR_RETURN(eager_blob_object_->shape_ptr().get() == tensor_meta()->shape_ptr().get()) - << kOfBugIssueUploadPrompt; + CHECK_OR_RETURN(eager_blob_object_->shape() == tensor_meta()->shape()) << kOfBugIssueUploadPrompt; CHECK_OR_RETURN(eager_blob_object_->data_type() == tensor_meta()->dtype()) << kOfBugIssueUploadPrompt; JUST(UpdateTensorStorage()); return Maybe::Ok(); } -std::shared_ptr EagerMirroredTensorImpl::shape() const { +std::shared_ptr EagerLocalTensorImpl::shape() const { if (!eager_blob_object_) { return tensor_meta()->shape_ptr(); } - if (!eager_blob_object_->is_shape_synced()) { - auto btb = std::make_shared(1); - CHECK_JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - return builder->SyncAccessBlobByCallback( - this, btb, [](uint64_t) {}, "const"); - })); - TRY(btb->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())) - .GetOrThrow(); - eager_blob_object_->set_is_shape_synced(true); - } return eager_blob_object_->shape_ptr(); } -std::shared_ptr EagerMirroredTensorImpl::stride() const { +std::shared_ptr EagerLocalTensorImpl::stride() const { if (!eager_blob_object_) { return tensor_meta()->stride_ptr(); } return eager_blob_object_->stride_ptr(); ; } -Maybe EagerMirroredTensorImpl::detach() const { - auto detached_impl = - std::make_shared(tensor_meta_, tensor_storage_, false, true); +Maybe EagerLocalTensorImpl::detach() const { + auto detached_impl = std::make_shared(tensor_storage_, false, true); detached_impl->eager_blob_object_ = eager_blob_object_; - return std::shared_ptr(detached_impl); + return std::shared_ptr(detached_impl); } -Maybe EagerMirroredTensorImpl::RegisterStorageDeleteHook(const std::function& hook) { +Maybe EagerLocalTensorImpl::RegisterStorageDeleteHook(const std::function& hook) { CHECK_OR_RETURN(eager_blob_object_) << "EagerBlobObject has not initialized"; eager_blob_object_->RegisterStorageDeleteHook(hook); return Maybe::Ok(); } -Maybe LazyConsistentTensorImpl::detach() const { - auto detached_impl = std::make_shared(tensor_meta_, false, true); - return std::shared_ptr(detached_impl); +Maybe LazyGlobalTensorImpl::detach() const { + auto detached_impl = std::make_shared(tensor_meta_, false, true); + return std::shared_ptr(detached_impl); } -EagerConsistentTensorImpl::EagerConsistentTensorImpl( - Symbol consistent_tensor_meta, bool requires_grad, bool is_leaf, - const std::shared_ptr& cur_rank_phy_tensor) - : ConsistentTensorImpl(consistent_tensor_meta, cur_rank_phy_tensor->requires_grad(), - cur_rank_phy_tensor->is_leaf()), +EagerGlobalTensorImpl::EagerGlobalTensorImpl( + Symbol global_tensor_meta, + const std::shared_ptr& cur_rank_phy_tensor) + : GlobalTensorImpl(global_tensor_meta, cur_rank_phy_tensor->requires_grad(), + cur_rank_phy_tensor->is_leaf()), cur_rank_phy_tensor_(cur_rank_phy_tensor) {} -/* static */ Maybe EagerConsistentTensorImpl::New( - Symbol consistent_tensor_meta, bool requires_grad, bool is_leaf) { - const auto& parallel_desc = consistent_tensor_meta->parallel_desc(); +/* static */ Maybe EagerGlobalTensorImpl::New( + Symbol global_tensor_meta, bool requires_grad, bool is_leaf) { + const auto& parallel_desc = global_tensor_meta->parallel_desc(); Optional parallel_id; const auto& device = JUST(parallel_desc->GetTensorDevice4CurrentProcessCtx(¶llel_id)); - return EagerConsistentTensorImpl::New(consistent_tensor_meta, device, parallel_id, requires_grad, - is_leaf); + return EagerGlobalTensorImpl::New(global_tensor_meta, device, parallel_id, requires_grad, + is_leaf); } namespace { @@ -206,50 +199,47 @@ Maybe GetPhysicalShape(const Shape& logical_shape, const NdSbp& nd_sbp, } // namespace -/* static */ Maybe EagerConsistentTensorImpl::New( - Symbol consistent_tensor_meta, Symbol device, +/* static */ Maybe EagerGlobalTensorImpl::New( + Symbol global_tensor_meta, Symbol device, const Optional& parallel_id, bool requires_grad, bool is_leaf) { - const auto& shape = consistent_tensor_meta->shape_ptr(); - const auto& dtype = consistent_tensor_meta->dtype(); - const auto& nd_sbp = consistent_tensor_meta->nd_sbp(); - const auto& parallel_desc = consistent_tensor_meta->parallel_desc(); + const auto& shape = global_tensor_meta->shape_ptr(); + const auto& dtype = global_tensor_meta->dtype(); + const auto& nd_sbp = global_tensor_meta->nd_sbp(); + const auto& parallel_desc = global_tensor_meta->parallel_desc(); const auto& cur_rank_phy_shape = JUST(GetPhysicalShape(*shape, *nd_sbp, *parallel_desc, parallel_id)); - std::shared_ptr cur_rank_phy_tensor; + std::shared_ptr cur_rank_phy_tensor; // If the `'parallel_desc` doesn't cover current ProcessCtx or the tensor has 0-size shape, there // is no need to compute through the corresponding opkernel, and can be obtained directly through // empty op. if (parallel_id.has_value() && shape->elem_cnt() != 0) { const auto& cur_rank_phy_tensor_meta = - std::make_shared(cur_rank_phy_shape, dtype, device); - auto cur_rank_phy_tensor_impl = - std::make_shared(cur_rank_phy_tensor_meta, requires_grad, is_leaf); + SymbolOf(LocalTensorMeta(*cur_rank_phy_shape, dtype, device)); + auto cur_rank_phy_tensor_impl = std::make_shared(requires_grad, is_leaf); const auto& dep_object = NewLocalDepObject(); - JUST(cur_rank_phy_tensor_impl->InitEagerBlobObject(dep_object)); - cur_rank_phy_tensor = std::make_shared(cur_rank_phy_tensor_impl); + JUST(cur_rank_phy_tensor_impl->InitEagerBlobObject(cur_rank_phy_tensor_meta, dep_object)); + cur_rank_phy_tensor = std::make_shared(cur_rank_phy_tensor_impl); } else { const auto& dtype_symbol = JUST(DType::Get(dtype)); const auto& empty = JUST(functional::Empty(*cur_rank_phy_shape, dtype_symbol, device, /*pin_memory=*/false)); - cur_rank_phy_tensor = JUST(empty->AsMirroredTensor()); + cur_rank_phy_tensor = JUST(empty->AsLocalTensor()); JUST(cur_rank_phy_tensor->set_requires_grad(requires_grad)); cur_rank_phy_tensor->set_is_leaf(is_leaf); } - auto* tensor_impl = - new EagerConsistentTensorImpl(consistent_tensor_meta, cur_rank_phy_tensor->requires_grad(), - cur_rank_phy_tensor->is_leaf(), cur_rank_phy_tensor); - return std::shared_ptr(tensor_impl); + auto* tensor_impl = new EagerGlobalTensorImpl(global_tensor_meta, cur_rank_phy_tensor); + return std::shared_ptr(tensor_impl); } -Maybe EagerConsistentTensorImpl::detach() const { - auto detached_impl = JUST(EagerConsistentTensorImpl::New(tensor_meta_, false, true)); - detached_impl->cur_rank_phy_tensor_ = cur_rank_phy_tensor_; +Maybe EagerGlobalTensorImpl::detach() const { + auto detached_impl = std::shared_ptr(new EagerGlobalTensorImpl( + tensor_meta_, JUST(JUST(cur_rank_phy_tensor_->detach())->AsLocalTensor()))); detached_impl->consumer_nd_sbp_constraint_ = consumer_nd_sbp_constraint_; detached_impl->transport_token_ = transport_token_; - return std::shared_ptr(detached_impl); + return std::shared_ptr(detached_impl); } -std::shared_ptr EagerConsistentTensorImpl::stride() const { +std::shared_ptr EagerGlobalTensorImpl::stride() const { if (!cur_rank_phy_tensor_) { return tensor_meta()->stride_ptr(); } const auto& stride_ptr = cur_rank_phy_tensor_->tensor_meta().stride_ptr(); return stride_ptr; diff --git a/oneflow/core/framework/tensor_impl.h b/oneflow/core/framework/tensor_impl.h index 0ddcda5a52719d3040868ef2d4171d39ecfb70de..86713eb7a5e3de0b7f5c0fab4313a68be523ea18 100644 --- a/oneflow/core/framework/tensor_impl.h +++ b/oneflow/core/framework/tensor_impl.h @@ -21,8 +21,8 @@ limitations under the License. #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/optional.h" #include "oneflow/core/framework/tensor_storage.h" -#include "oneflow/core/framework/tensor_desc.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/tensor_desc.h" +#include "oneflow/core/common/tensor_meta.h" #include "oneflow/core/framework/transport_token.h" #include "oneflow/core/autograd/autograd_meta.h" #include "oneflow/core/common/symbol.h" @@ -57,7 +57,7 @@ class TensorImpl { virtual DataType dtype() const = 0; virtual bool is_lazy() const = 0; - // Getters valid only for EagerMirroredTensorImpl + // Getters valid only for EagerLocalTensorImpl virtual Maybe eager_blob_object() const = 0; virtual Maybe compute_local_dep_object() const = 0; virtual Maybe tensor_storage() const { OF_UNIMPLEMENTED(); } @@ -99,41 +99,37 @@ class TensorImpl { std::shared_ptr autograd_meta_; }; -class EagerMirroredTensorImpl; -class MirroredTensorImpl : public TensorImpl { +class EagerLocalTensorImpl; +class LocalTensorImpl : public TensorImpl { public: - virtual ~MirroredTensorImpl() = default; + virtual ~LocalTensorImpl() = default; // Getters - DataType dtype() const override { return tensor_meta_->dtype(); } - const Symbol& device() const { return tensor_meta_->device(); } - const std::shared_ptr& tensor_meta() const { return tensor_meta_; } - bool is_contiguous() const override { return tensor_meta_->is_contiguous(); } + DataType dtype() const override { return tensor_meta()->dtype(); } + const Symbol& device() const { return tensor_meta()->device(); } + bool is_contiguous() const override { return tensor_meta()->is_contiguous(); } + virtual const Symbol& tensor_meta() const = 0; // Setters - MirroredTensorMeta* mut_tensor_meta() { - return const_cast(tensor_meta_.get()); + virtual const std::shared_ptr& mut_tensor_meta() = 0; + Maybe*> mut_device() { + return std::const_pointer_cast(mut_tensor_meta())->mut_device(); } - Maybe*> mut_device() { return mut_tensor_meta()->mut_device(); } - virtual Maybe mut_eager_mirrored_tensor_impl() { + virtual Maybe mut_eager_local_tensor_impl() { RETURN_ERROR_WITH_BUG_PROMPT(); } - virtual Maybe detach() const { RETURN_ERROR_WITH_BUG_PROMPT(); } + virtual Maybe detach() const { RETURN_ERROR_WITH_BUG_PROMPT(); } protected: - MirroredTensorImpl(const std::shared_ptr& tensor_meta, - bool requires_grad, bool is_leaf) - : TensorImpl(requires_grad, is_leaf), tensor_meta_(tensor_meta) {} - - std::shared_ptr tensor_meta_; + LocalTensorImpl(bool requires_grad, bool is_leaf) : TensorImpl(requires_grad, is_leaf) {} }; -class MirroredTensor; +class LocalTensor; -class ConsistentTensorImpl : public TensorImpl { +class GlobalTensorImpl : public TensorImpl { public: - virtual ~ConsistentTensorImpl() = default; + virtual ~GlobalTensorImpl() = default; // Getters std::shared_ptr shape() const override { return tensor_meta_->shape_ptr(); } @@ -144,10 +140,10 @@ class ConsistentTensorImpl : public TensorImpl { const Optional>& consumer_nd_sbp_constraint() const { return consumer_nd_sbp_constraint_; } - virtual Maybe cur_rank_phy_tensor() const { RETURN_ERROR_WITH_BUG_PROMPT(); } - Symbol tensor_meta() const { return tensor_meta_; } + virtual Maybe cur_rank_phy_tensor() const { RETURN_ERROR_WITH_BUG_PROMPT(); } + Symbol tensor_meta() const { return tensor_meta_; } - // Getters valid only for EagerMirroredTensorImpl + // Getters valid only for EagerLocalTensorImpl Maybe eager_blob_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe compute_local_dep_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); @@ -159,7 +155,7 @@ class ConsistentTensorImpl : public TensorImpl { consumer_nd_sbp_constraint_ = val; } - ConsistentTensorMeta* mut_tensor_meta() { + GlobalTensorMeta* mut_tensor_meta() { PRINT_BUG_PROMPT_AND_ABORT(); return nullptr; } @@ -171,69 +167,82 @@ class ConsistentTensorImpl : public TensorImpl { return Maybe::Ok(); } - virtual Maybe detach() const { RETURN_ERROR_WITH_BUG_PROMPT(); } + virtual Maybe detach() const { RETURN_ERROR_WITH_BUG_PROMPT(); } protected: - ConsistentTensorImpl(Symbol tensor_meta, bool requires_grad, bool is_leaf) + GlobalTensorImpl(Symbol tensor_meta, bool requires_grad, bool is_leaf) : TensorImpl(requires_grad, is_leaf), tensor_meta_(tensor_meta), consumer_nd_sbp_constraint_(), transport_token_() {} - Symbol tensor_meta_; + Symbol tensor_meta_; Optional> consumer_nd_sbp_constraint_; Optional transport_token_; }; -class LazyMirroredTensorImpl final : public MirroredTensorImpl { +class LazyLocalTensorImpl final : public LocalTensorImpl { public: - OF_DISALLOW_COPY_AND_MOVE(LazyMirroredTensorImpl); - LazyMirroredTensorImpl(const std::shared_ptr& tensor_meta, - bool requires_grad, bool is_leaf) - : MirroredTensorImpl(tensor_meta, requires_grad, is_leaf) {} - ~LazyMirroredTensorImpl() override = default; + OF_DISALLOW_COPY_AND_MOVE(LazyLocalTensorImpl); + LazyLocalTensorImpl(const Symbol& tensor_meta, bool requires_grad, bool is_leaf) + : LocalTensorImpl(requires_grad, is_leaf), tensor_meta_(tensor_meta) {} + ~LazyLocalTensorImpl() override = default; // Getters + const Symbol& tensor_meta() const override { return tensor_meta_; } std::shared_ptr shape() const override { return tensor_meta()->shape_ptr(); } std::shared_ptr stride() const override { return tensor_meta()->stride_ptr(); } bool is_lazy() const override { return true; } bool is_contiguous() const override { // TODO:(zhaoluyang) default return true for now, - // but should return real status while stride/view mechanism is ready in lazy-mirrored mode + // but should return real status while stride/view mechanism is ready in lazy-local mode return true; } Maybe is_pinned() const override { return false; } - // Getters valid only for EagerMirroredTensorImpl + const std::shared_ptr& mut_tensor_meta() override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + + // Getters valid only for EagerLocalTensorImpl Maybe eager_blob_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe compute_local_dep_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe tensor_storage() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } Maybe has_eager_blob_object() const override { RETURN_ERROR_WITH_BUG_PROMPT(); } - Maybe detach() const override; + Maybe detach() const override; + + private: + Symbol tensor_meta_; }; -class EagerMirroredTensorImpl final : public MirroredTensorImpl { +class EagerLocalTensorImpl final : public LocalTensorImpl { public: - OF_DISALLOW_COPY_AND_MOVE(EagerMirroredTensorImpl); - EagerMirroredTensorImpl(); - EagerMirroredTensorImpl(const std::shared_ptr& tensor_meta, - bool requires_grad, bool is_leaf); - EagerMirroredTensorImpl(const std::shared_ptr& tensor_meta, - const std::shared_ptr& tensor_storage, bool requires_grad, - bool is_leaf); - ~EagerMirroredTensorImpl() override; - + OF_DISALLOW_COPY_AND_MOVE(EagerLocalTensorImpl); + EagerLocalTensorImpl() + : EagerLocalTensorImpl(std::shared_ptr(), 0, false, false) {} + EagerLocalTensorImpl(const std::shared_ptr& tensor_storage, bool requires_grad, + bool is_leaf) + : EagerLocalTensorImpl(tensor_storage, 0, requires_grad, is_leaf) {} + EagerLocalTensorImpl(const std::shared_ptr& tensor_storage, int64_t storage_offset, + bool requires_grad, bool is_leaf); + + EagerLocalTensorImpl(bool requires_grad, bool is_leaf) + : EagerLocalTensorImpl(std::shared_ptr(), 0, requires_grad, is_leaf) {} + ~EagerLocalTensorImpl() override; + + const std::shared_ptr& mut_tensor_meta() override; // Getters + const Symbol& tensor_meta() const override; std::shared_ptr shape() const override; std::shared_ptr stride() const override; - Maybe detach() const override; + Maybe detach() const override; bool is_lazy() const override { return false; } - bool is_contiguous() const override { return tensor_meta_->is_contiguous(); } + bool is_contiguous() const override { return tensor_meta()->is_contiguous(); } Maybe is_pinned() const override; - // Getters valid only for EagerMirroredTensorImpl + // Getters valid only for EagerLocalTensorImpl Maybe eager_blob_object() const override { CHECK_OR_RETURN(eager_blob_object_); return eager_blob_object_; @@ -244,13 +253,23 @@ class EagerMirroredTensorImpl final : public MirroredTensorImpl { return tensor_storage_; } Maybe has_eager_blob_object() const override { return eager_blob_object_.get(); } - Maybe storage_offset() const override { return tensor_meta_->storage_offset(); } - + Maybe storage_offset() const override { return storage_offset_; } // Setters TensorStorage* mut_tensor_storage() { return tensor_storage_.get(); } + void set_storage_offset(int64_t offset) { storage_offset_ = offset; } + + Maybe InitEagerBlobObject( + const Symbol& local_tensor_meta, + const std::shared_ptr& mut_local_tensor_meta, + const intrusive::shared_ptr& dep_object); + Maybe InitEagerBlobObject(const Symbol& local_tensor_meta, + const intrusive::shared_ptr& dep_object) { + JUST(InitEagerBlobObject(local_tensor_meta, std::shared_ptr(), + dep_object)); + return Maybe::Ok(); + } - Maybe InitEagerBlobObject(const intrusive::shared_ptr& dep_object); - Maybe mut_eager_mirrored_tensor_impl() override { return this; } + Maybe mut_eager_local_tensor_impl() override { return this; } Maybe RegisterStorageDeleteHook(const std::function& hook) override; @@ -259,33 +278,34 @@ class EagerMirroredTensorImpl final : public MirroredTensorImpl { Maybe set_eager_blob_object(std::shared_ptr eager_blob_object); std::shared_ptr tensor_storage_; + int64_t storage_offset_; std::shared_ptr eager_blob_object_; }; -class LazyConsistentTensorImpl final : public ConsistentTensorImpl { +class LazyGlobalTensorImpl final : public GlobalTensorImpl { public: - OF_DISALLOW_COPY_AND_MOVE(LazyConsistentTensorImpl); - LazyConsistentTensorImpl(Symbol consistent_tensor_meta, bool requires_grad, - bool is_leaf) - : ConsistentTensorImpl(consistent_tensor_meta, requires_grad, is_leaf) {} - ~LazyConsistentTensorImpl() override = default; + OF_DISALLOW_COPY_AND_MOVE(LazyGlobalTensorImpl); + LazyGlobalTensorImpl(Symbol global_tensor_meta, bool requires_grad, + bool is_leaf) + : GlobalTensorImpl(global_tensor_meta, requires_grad, is_leaf) {} + ~LazyGlobalTensorImpl() override = default; // Getters bool is_lazy() const override { return true; } bool is_contiguous() const override { // TODO:(zhaoluyang) default return true for now, - // but should return real status while stride/view mechanism is ready in lazy-consistent mode + // but should return real status while stride/view mechanism is ready in lazy-global mode return true; } - Maybe detach() const override; + Maybe detach() const override; }; -class EagerConsistentTensorImpl final : public ConsistentTensorImpl { +class EagerGlobalTensorImpl final : public GlobalTensorImpl { public: - OF_DISALLOW_COPY_AND_MOVE(EagerConsistentTensorImpl); - ~EagerConsistentTensorImpl() override = default; + OF_DISALLOW_COPY_AND_MOVE(EagerGlobalTensorImpl); + ~EagerGlobalTensorImpl() override = default; // Getters std::shared_ptr stride() const override; @@ -293,31 +313,30 @@ class EagerConsistentTensorImpl final : public ConsistentTensorImpl { bool is_contiguous() const override { // TODO:(zhaoluyang) default return true for now, - // but should return real status while stride/view mechanism is ready in eager-consistent mode + // but should return real status while stride/view mechanism is ready in eager-global mode return true; } - Maybe cur_rank_phy_tensor() const override { return cur_rank_phy_tensor_; } - void reset_cur_rank_phy_tensor(const std::shared_ptr& val) { + Maybe cur_rank_phy_tensor() const override { return cur_rank_phy_tensor_; } + void reset_cur_rank_phy_tensor(const std::shared_ptr& val) { cur_rank_phy_tensor_ = val; } - static Maybe New(Symbol consistent_tensor_meta, - bool requires_grad, bool is_leaf); + static Maybe New(Symbol global_tensor_meta, + bool requires_grad, bool is_leaf); - static Maybe New(Symbol consistent_tensor_meta, - Symbol device, - const Optional& parallel_id, - bool requires_grad, bool is_leaf); + static Maybe New(Symbol global_tensor_meta, + Symbol device, + const Optional& parallel_id, bool requires_grad, + bool is_leaf); - Maybe detach() const override; + Maybe detach() const override; private: - EagerConsistentTensorImpl(Symbol consistent_tensor_meta, bool requires_grad, - bool is_leaf, - const std::shared_ptr& cur_rank_phy_tensor); + EagerGlobalTensorImpl(Symbol global_tensor_meta, + const std::shared_ptr& cur_rank_phy_tensor); - std::shared_ptr cur_rank_phy_tensor_; + std::shared_ptr cur_rank_phy_tensor_; }; } // namespace one diff --git a/oneflow/core/framework/tensor_meta.cpp b/oneflow/core/framework/tensor_meta.cpp deleted file mode 100644 index ede1e574023a7eb3e127760584ffe1ec677c1cec..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/tensor_meta.cpp +++ /dev/null @@ -1,83 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/tensor_meta.h" -#include "oneflow/core/common/stride.h" -#include "oneflow/core/framework/device.h" - -namespace oneflow { -namespace one { - -MirroredTensorMeta::MirroredTensorMeta() - : TensorMeta(std::make_shared(), std::make_shared(), - DataType::kInvalidDataType), - device_(Symbol()), - storage_offset_(0) {} - -MirroredTensorMeta::MirroredTensorMeta(const std::shared_ptr& shape, DataType dtype, - Symbol device) - : TensorMeta(shape, std::make_shared(*shape), dtype), - device_(device), - storage_offset_(0) {} - -MirroredTensorMeta::MirroredTensorMeta(const std::shared_ptr& shape, - const std::shared_ptr& stride, DataType dtype, - Symbol device, int64_t storage_offset) - : TensorMeta(shape, stride, dtype), device_(device), storage_offset_(storage_offset) {} - -bool MirroredTensorMeta::operator==(const MirroredTensorMeta& other) const { - // It's correct to ignore is_dynamic_ field. - return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() - && *this->device() == *other.device() && this->stride() == other.stride() - && this->storage_offset() == other.storage_offset(); -} - -size_t MirroredTensorMeta::CalcHashValue() const { - // It's correct to ignore is_dynamic_ field. - return std::hash()(*shape_ptr()) ^ std::hash()(dtype()) - ^ std::hash()(*device()) ^ std::hash()(stride()) ^ storage_offset(); -} - -bool ConsistentTensorMeta::operator==(const ConsistentTensorMeta& other) const { - // It's correct to ignore is_dynamic_ field. - return *this->shape_ptr() == *other.shape_ptr() && this->dtype() == other.dtype() - && this->nd_sbp() == other.nd_sbp() && this->parallel_desc() == other.parallel_desc(); -} - -size_t ConsistentTensorMeta::CalcHashValue() const { - return std::hash()(*shape_ptr()) ^ std::hash()(dtype()) - ^ std::hash>()(nd_sbp()) - ^ std::hash>()(parallel_desc()); -} - -bool IsContiguous(const Shape& shape, const Stride& stride) { - if (!shape.is_initialized() || shape.NumAxes() < 1 || shape.elem_cnt() <= 1) { return true; } - int64_t dim = shape.NumAxes(); - int64_t expected_stride = 1; - bool contig_if_nonempty = true; - for (int64_t i = dim - 1; i >= 0; --i) { - // Contiguous by default when any dim is equal to zero - // https://stackoverflow.com/questions/31681324/identify-contiguous-segments-of-a-non-contiguous-numpy-array - if (shape.At(i) == 0) { return true; } - if (contig_if_nonempty && shape.At(i) != 1) { - if (stride.at(i) != expected_stride) { contig_if_nonempty = false; } - expected_stride *= shape.At(i); - } - } - return contig_if_nonempty; -} - -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/framework/tensor_meta.h b/oneflow/core/framework/tensor_meta.h deleted file mode 100644 index dc8664d68929f5c74ea3bbdf3f1f976838eb7654..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/tensor_meta.h +++ /dev/null @@ -1,140 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_FRAMEWORK_TENSOR_META_H_ -#define ONEFLOW_FRAMEWORK_TENSOR_META_H_ - -#include -#include "oneflow/core/framework/tensor_desc.h" -#include "oneflow/core/common/symbol.h" - -namespace oneflow { - -class NdSbp; -class Shape; -class Stride; -class Device; -class ParallelDesc; - -namespace one { - -bool IsContiguous(const Shape& shape, const Stride& stride); - -class TensorMeta : public user_op::TensorDesc { - public: - TensorMeta(const std::shared_ptr& shape, DataType dtype) - : shape_(shape), - stride_(std::shared_ptr(new Stride(*shape))), - data_type_(dtype), - is_dynamic_(false) {} - TensorMeta(const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype) - : shape_(shape), stride_(stride), data_type_(dtype), is_dynamic_(false) {} - TensorMeta(const TensorMeta&) = default; - TensorMeta(TensorMeta&&) = default; - virtual ~TensorMeta() = default; - - const std::shared_ptr& shape_ptr() const { return shape_; } - const std::shared_ptr& stride_ptr() const { return stride_; } - - const Shape& shape() const override { return *shape_; } - const Stride& stride() const override { return *stride_; } - DataType dtype() const { return data_type_; } - DataType data_type() const override { return data_type_; } - bool is_dynamic() const override { return is_dynamic_; } - bool is_contiguous() const { return IsContiguous(shape(), *stride_); } - - void set_shape(const std::shared_ptr& val) { shape_ = val; } - Shape* mut_shape() override { return const_cast(shape_.get()); } - void set_stride(const std::shared_ptr& val) { stride_ = val; } - Stride* mut_stride() override { return const_cast(stride_.get()); } - DataType* mut_dtype() { return &data_type_; } - void set_dtype(DataType data_type) { data_type_ = data_type; } - DataType* mut_data_type() override { return &data_type_; } - bool* mut_is_dynamic() override { return &is_dynamic_; } - void set_is_dynamic(bool val) override { is_dynamic_ = val; } - - private: - std::shared_ptr shape_; - std::shared_ptr stride_; - DataType data_type_; - bool is_dynamic_; -}; - -class MirroredTensorMeta : public TensorMeta { - public: - // uninitialized MirroredTensorMeta. - MirroredTensorMeta(); - MirroredTensorMeta(const std::shared_ptr& shape, DataType dtype, - Symbol device); - MirroredTensorMeta(const std::shared_ptr& shape, - const std::shared_ptr& stride, DataType dtype, - Symbol device, int64_t storage_offset); - virtual ~MirroredTensorMeta() = default; - - const Symbol& device() const { return device_; } - int64_t storage_offset() const { return storage_offset_; } - - Symbol* mut_device() { return &device_; } - void set_storage_offset(int64_t offset) { storage_offset_ = offset; } - - bool operator==(const MirroredTensorMeta& other) const; - size_t CalcHashValue() const; - - private: - Symbol device_; - int64_t storage_offset_; -}; - -class ConsistentTensorMeta : public TensorMeta { - public: - ConsistentTensorMeta(const std::shared_ptr& shape, DataType dtype, - Symbol nd_sbp, Symbol parallel_desc) - : TensorMeta(shape, dtype), nd_sbp_(nd_sbp), parallel_desc_(parallel_desc) {} - ConsistentTensorMeta(const ConsistentTensorMeta&) = default; - ConsistentTensorMeta(ConsistentTensorMeta&&) = default; - virtual ~ConsistentTensorMeta() = default; - - bool operator==(const ConsistentTensorMeta& other) const; - - Symbol nd_sbp() const { return nd_sbp_; } - Symbol parallel_desc() const { return parallel_desc_; } - - void set_nd_sbp(Symbol val) { nd_sbp_ = val; } - - void set_parallel_desc(Symbol val) { parallel_desc_ = val; } - - size_t CalcHashValue() const; - - private: - Symbol nd_sbp_; - Symbol parallel_desc_; -}; - -} // namespace one -} // namespace oneflow - -namespace std { - -template<> -struct hash final { - size_t operator()(const oneflow::one::ConsistentTensorMeta& consistent_tensor_meta) const { - return consistent_tensor_meta.CalcHashValue(); - } -}; - -} // namespace std - -#endif // ONEFLOW_FRAMEWORK_TENSOR_META_H_ diff --git a/oneflow/core/framework/tensor_methods.cpp b/oneflow/core/framework/tensor_methods.cpp index 3572e6ab27ecc6b6611d11b32904a77f69bd518d..fe0b87a3bf7b27dab6d05eeac283ea42f85c761e 100644 --- a/oneflow/core/framework/tensor_methods.cpp +++ b/oneflow/core/framework/tensor_methods.cpp @@ -17,14 +17,16 @@ limitations under the License. #include "oneflow/core/autograd/autograd_engine.h" #include "oneflow/core/autograd/autograd_mode.h" #include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/scalar.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/eager/eager_blob_object.h" #include "oneflow/core/common/stride.h" +#include "oneflow/core/framework/dtype.h" #include "oneflow/core/functional/functional.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/ep/include/device_manager_registry.h" #include "oneflow/core/common/wrap_dim_utils.h" +#include "oneflow/core/functional/functional_api.yaml.h" namespace oneflow { namespace one { @@ -47,6 +49,36 @@ bool IsViewApplicable(const std::shared_ptr& input) { return false; } +static bool IsOverlappingMemorys(const std::vector& sizes, + const std::vector& strides) { + // reference: torch/csrc/autograd/FunctionsManual.cpp _maybe_overlapping_memory() + if (sizes.size() > 0) { + std::vector argsort(sizes.size()); + std::iota(argsort.begin(), argsort.end(), 0); + std::sort(argsort.begin(), argsort.end(), + [&](std::size_t i, std::size_t j) { return strides[i] < strides[j]; }); + int64_t max_index_in_slice = 0; + for (auto i : argsort) { + auto stride_ = strides[i]; + if (stride_ <= max_index_in_slice) { return true; } + max_index_in_slice += stride_ * (sizes[i] - 1); + } + } + return false; +} + +static int64_t MinStorageSize(const std::vector& sizes, + const std::vector& strides, int64_t storage_offset) { + int64_t storage_size = storage_offset + 1; + int64_t ndim = sizes.size(); + for (size_t i = 0; i < ndim; i++) { + auto size_i = sizes[i]; + if (size_i == 0) { return storage_offset; } + storage_size += (size_i - 1) * strides[i]; + } + return storage_size; +} + Maybe BasicView(const std::shared_ptr& input, const Shape& target_shape, int64_t storage_offset) { /** @@ -64,28 +96,44 @@ Maybe BasicView(const std::shared_ptr& input, const Shape& targe const Stride& target_stride, int64_t storage_offset) { // TODO(): Check shape compatible. auto device = JUST(input->device()); - auto tensor_meta = std::make_shared( - std::make_shared(target_shape), std::make_shared(target_stride), - input->dtype()->data_type(), device, storage_offset); + auto tensor_meta = + SymbolOf(LocalTensorMeta(target_shape, target_stride, input->dtype()->data_type(), device)); CHECK_OR_RETURN(JUST(input->has_eager_blob_object())); // new output tensor const auto& blob_object = JUST(input->eager_blob_object()); bool requires_grad = (autograd::GradMode::is_enabled() && input->requires_grad()); - auto tensor_impl = std::make_shared( - tensor_meta, JUST(input->tensor_storage()), requires_grad, - /*is_leaf=*/!requires_grad); - JUST(tensor_impl->InitEagerBlobObject(JUST(blob_object->compute_local_dep_object()))); + auto tensor_impl = std::make_shared(JUST(input->tensor_storage()), + storage_offset, requires_grad, + /*is_leaf=*/!requires_grad); + JUST( + tensor_impl->InitEagerBlobObject(tensor_meta, JUST(blob_object->compute_local_dep_object()))); - auto view_tensor = std::make_shared(tensor_impl); + auto view_tensor = std::make_shared(tensor_impl); const std::shared_ptr& view_eager_blob_object = JUST(view_tensor->eager_blob_object()); view_eager_blob_object->set_storage_offset(JUST(view_tensor->storage_offset())); - view_eager_blob_object->set_is_shape_synced(true); return std::static_pointer_cast(view_tensor); } +Maybe InplaceView(const std::shared_ptr& input, const Shape& target_shape, + const Stride& target_stride, int64_t storage_offset) { + const auto* tensor_impl = JUST(input->mut_eager_local_tensor_impl()); + const auto& mut_tensor_meta = const_cast(tensor_impl)->mut_tensor_meta(); + Symbol new_tensor_meta = SymbolOf(LocalTensorMeta( + target_shape, target_stride, input->dtype()->data_type(), JUST(input->device()))); + + bool requires_grad = (autograd::GradMode::is_enabled() && input->requires_grad()); + std::shared_ptr new_tensor_impl = std::make_shared( + JUST(input->tensor_storage()), storage_offset, /*requires_grad=*/requires_grad, + /*is_leaf=*/!requires_grad); + JUST(new_tensor_impl->InitEagerBlobObject( + new_tensor_meta, JUST(JUST(input->eager_blob_object())->compute_local_dep_object()))); + JUST(JUST(input->AsLocalTensor())->set_impl(new_tensor_impl)); + return Maybe::Ok(); +} + Maybe Reshape(const std::shared_ptr& input, const Shape& target_shape) { Stride target_stride(target_shape); return Reshape(input, target_shape, target_stride); @@ -93,7 +141,7 @@ Maybe Reshape(const std::shared_ptr& input, const Shape& target_ Maybe Reshape(const std::shared_ptr& input, const Shape& target_shape, const Stride& target_stride) { - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); std::shared_ptr output = JUST(BasicView(input, target_shape, target_stride, storage_offset)); @@ -133,7 +181,7 @@ Maybe Slice(const std::shared_ptr& input, const std::vectorAsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); for (int i = 0; i < ndim; ++i) { int64_t step = std::min(steps[i], shape->At(i)); CHECK_OR_RETURN(step >= 0) << Error::RuntimeError() << "Step must be greater than zero."; @@ -189,7 +237,7 @@ Maybe Unsqueeze(const std::shared_ptr& input, const int32_t& exp target_stride_vec[expand_dim] = expand_dim < ndim ? strides->at(expand_dim) : 1; } - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); std::shared_ptr output = JUST(BasicView(input, Shape(target_dim_vec), target_stride_vec, storage_offset)); @@ -212,6 +260,48 @@ Maybe Unsqueeze(const std::shared_ptr& input, const int32_t& exp return output; } +Maybe InplaceUnsqueeze(const std::shared_ptr& input, const int32_t& expand_dim) { + const auto& shape = input->shape(); + const auto& strides = JUST(input->stride()); + const auto& ndim = shape->NumAxes(); + + DimVector target_dim_vec(ndim + 1); + Stride target_stride_vec(ndim + 1); + + { + int cnt = 0; + for (int i = 0; i < ndim; i++) { + if (i == expand_dim) { cnt++; } + target_dim_vec[cnt] = shape->At(i); + target_stride_vec[cnt] = strides->at(i); + cnt++; + } + target_dim_vec[expand_dim] = 1; + target_stride_vec[expand_dim] = expand_dim < ndim ? strides->at(expand_dim) : 1; + } + + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); + JUST(view::InplaceView(input, Shape(target_dim_vec), target_stride_vec, storage_offset)); + + if (autograd::GradMode::is_enabled() && input->requires_grad()) { + auto backward_fn = std::make_shared(); + backward_fn->body = [=](const TensorTuple& out_grads, TensorTuple* in_grads, + bool create_graph) -> Maybe { + autograd::AutoGradMode mode(create_graph); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + JUST(oneflow::VectorAt(*in_grads, 0)) = + JUST(functional::Reshape(JUST(oneflow::VectorAt(out_grads, 0)), *shape)); + return Maybe::Ok(); + }; + backward_fn->status = []() { return false; }; + TensorTuple outputs{input}; + JUST(GetThreadLocalAutogradEngine()->AddNode("view::inplace_unsqueeze_backward", backward_fn, + {input}, &outputs)); + } + return Maybe::Ok(); +} + Maybe Squeeze(const std::shared_ptr& input, const std::vector& squeeze_dims) { const auto& shape = input->shape(); @@ -233,7 +323,7 @@ Maybe Squeeze(const std::shared_ptr& input, } } - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); std::shared_ptr output = JUST(BasicView(input, Shape(target_dim_vec), target_stride_vec, storage_offset)); @@ -256,49 +346,82 @@ Maybe Squeeze(const std::shared_ptr& input, return output; } -Maybe Expand(const std::shared_ptr& input, const std::vector& in_shape, - const std::vector& expand_shape) { +Maybe InplaceSqueeze(const std::shared_ptr& input, + const std::vector& squeeze_dims) { const auto& shape = input->shape(); const auto& strides = JUST(input->stride()); - const int64_t ndim = in_shape.size(); + const int64_t ndim = shape->NumAxes(); - const int64_t target_ndim = expand_shape.size(); + const int target_ndim = ndim - squeeze_dims.size(); DimVector target_dim_vec(target_ndim); Stride target_stride_vec(target_ndim); - for (int i = 0; i < target_ndim; i++) { - if (i < ndim) { - if (expand_shape[target_ndim - 1 - i] == -1) { - target_dim_vec[target_ndim - 1 - i] = in_shape[ndim - 1 - i]; - target_stride_vec[target_ndim - 1 - i] = strides->at(ndim - 1 - i); - } else if (in_shape[ndim - 1 - i] - == 1) { // TODO (bowen): what if dim is 1, should stride be set to 0? - target_dim_vec[target_ndim - 1 - i] = expand_shape[target_ndim - 1 - i]; - target_stride_vec[target_ndim - 1 - i] = 0; - } else { - if (expand_shape[target_ndim - 1 - i] != in_shape[ndim - 1 - i]) { - return Error::RuntimeError() - << "The expanded size of the tensor (" << expand_shape[target_ndim - 1 - i] << ")" - << "must match the existing size (" << in_shape[ndim - 1 - i] - << ") at non-singleton dimension " << ndim - i << ". Target sizes: " - << ". Tensor sizes: " << shape->ToString(); - } - target_dim_vec[target_ndim - 1 - i] = in_shape[ndim - 1 - i]; - target_stride_vec[target_ndim - 1 - i] = strides->at(ndim - 1 - i); + { + int cnt = 0; + for (int i = 0; i < ndim; i++) { + if (find(squeeze_dims.begin(), squeeze_dims.end(), i) == squeeze_dims.end()) { + target_dim_vec[cnt] = shape->At(i); + target_stride_vec[cnt] = strides->at(i); + cnt++; } - } else { - if (expand_shape[target_ndim - 1 - i] == -1) { - return Error::RuntimeError() << "The expanded size of the tensor (-1) " - << "isn't allowed in a leading, non-existing dimension 0"; + } + } + + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); + JUST(view::InplaceView(input, Shape(target_dim_vec), target_stride_vec, storage_offset)); + + if (autograd::GradMode::is_enabled() && input->requires_grad()) { + auto backward_fn = std::make_shared(); + backward_fn->body = [=](const TensorTuple& out_grads, TensorTuple* in_grads, + bool create_graph) -> Maybe { + autograd::AutoGradMode mode(create_graph); + CHECK_EQ_OR_RETURN(out_grads.size(), 1); // NOLINT(maybe-need-error-msg) + in_grads->resize(1); + JUST(oneflow::VectorAt(*in_grads, 0)) = JUST(functional::Reshape( + JUST(oneflow::VectorAt(out_grads, 0)), Shape(input->shape()->dim_vec()))); + return Maybe::Ok(); + }; + backward_fn->status = []() { return true; }; + TensorTuple outputs{input}; + JUST(GetThreadLocalAutogradEngine()->AddNode("view::inplace_squeeze_backward", backward_fn, + {input}, &outputs)); + } + return Maybe::Ok(); +} + +Maybe Expand(const std::shared_ptr& input, const Shape& expand_shape) { + const Shape& input_shape = *input->shape(); + const Stride& input_stride = *JUST(input->stride()); + size_t lpad = expand_shape.size() - input_shape.size(); + CHECK_GE_OR_RETURN(lpad, 0); // NOLINT(maybe-need-error-msg) + + Stride expand_stride(expand_shape.size(), 0); + std::vector reduce_dims; + reduce_dims.reserve(expand_shape.size()); + + for (int i = expand_shape.size() - 1; i >= 0; --i) { + int64_t dim = i < lpad ? 1 : input_shape[i - lpad]; + if (dim == expand_shape[i]) { + if (i >= lpad) { + expand_stride[i] = input_stride[i - lpad]; + } else if (i < expand_shape.size() - 1) { + expand_stride[i] = expand_stride[i + 1] * expand_shape[i + 1]; } - target_dim_vec[target_ndim - 1 - i] = expand_shape[target_ndim - 1 - i]; - target_stride_vec[target_ndim - 1 - i] = 0; + } else { + CHECK_EQ_OR_RETURN(dim, 1); // NOLINT(maybe-need-error-msg) + reduce_dims.push_back(i); } } - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + if (input_shape.size() == 0) { + // handle scalar expand backward reduce dims + reduce_dims.clear(); + for (int32_t axis = 0; axis < expand_shape.size(); ++axis) { reduce_dims.push_back(axis); } + } + + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); std::shared_ptr output = - JUST(BasicView(input, Shape(target_dim_vec), target_stride_vec, storage_offset)); + JUST(BasicView(input, expand_shape, expand_stride, storage_offset)); if (autograd::GradMode::is_enabled() && input->requires_grad()) { auto backward_fn = std::make_shared(); @@ -308,7 +431,14 @@ Maybe Expand(const std::shared_ptr& input, const std::vectorresize(1); - (*in_grads)[0] = JUST(functional::ExpandGrad(out_grads[0], in_shape, expand_shape)); + in_grads->at(0) = out_grads[0]; + bool keep_dims = (input_shape.size() > 0); + if (reduce_dims.size() > 0) { + in_grads->at(0) = JUST(functional::ReduceSum(in_grads->at(0), reduce_dims, keep_dims)); + } + if (lpad > 0 && keep_dims) { + in_grads->at(0) = JUST(functional::Flatten(in_grads->at(0), 0, lpad)); + } return Maybe::Ok(); }; backward_fn->status = []() { return true; }; @@ -329,7 +459,7 @@ Maybe Narrow(const std::shared_ptr& input, const int64_t& dim, c dim_vec.insert(dim_vec.end(), length); dim_vec.insert(dim_vec.end(), shape->dim_vec().cbegin() + dim + 1, shape->dim_vec().end()); - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); Shape target_shape(dim_vec); Stride stride(ndim); @@ -360,12 +490,136 @@ Maybe Narrow(const std::shared_ptr& input, const int64_t& dim, c return output; } -Maybe AsStrided(const std::shared_ptr& input, const std::vector& size, - const std::vector& stride_vec, const int32_t& storage_offset) { +Maybe AsStridedGrad(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset) { + CHECK_OR_RETURN(input->is_local()) << "input must be local tensor."; + // reference: torch/csrc/autograd/FunctionsManual.cpp + const size_t odim = dy->ndim(); + std::vector out_sizes_, out_strides_; + out_sizes_.reserve(odim); + out_strides_.reserve(odim); + auto grad = dy; + for (int64_t i = odim - 1; i >= 0; i--) { + auto size_i = sizes[i]; + auto stride_i = strides[i]; + if (size_i == 0) { + return functional::Constant(*dy->shape(), 0, grad->dtype(), JUST(grad->device())); + } else if (size_i == 1) { + grad = JUST(functional::Squeeze(grad, std::vector{int(i)})); + } else if (stride_i == 0) { + grad = JUST(functional::ReduceSum(grad, std::vector{int(i)}, false)); + } else { + out_sizes_.insert(out_sizes_.begin(), size_i); + out_strides_.insert(out_strides_.begin(), stride_i); + } + } + + // Step (2)~(4) for the algorithm in NOTE [ Detecting Memory Overlap Within A + // Strided Tensor ] + // on output geometry + const bool out_maybe_overlap = IsOverlappingMemorys(out_sizes_, out_strides_); + + // For input geometry, + // check for size 0 dimensions, + // skip size 1 dimensions, + // Step (0)~(1) for the algorithm in NOTE [ Detecting Memory Overlap Within A + // Strided Tensor ] + // on input geometry + auto idim = input->ndim(); + std::vector inp_sizes(input->shape()->begin(), input->shape()->end()); + std::vector inp_strides(JUST(input->stride())->begin(), JUST(input->stride())->end()); + std::vector inp_sizes_, inp_strides_; + inp_sizes_.reserve(idim); + inp_strides_.reserve(idim); + for (int64_t i = idim - 1; i >= 0; i--) { + auto size_i = inp_sizes[i]; + auto stride_i = inp_strides[i]; + if (size_i == 0) { + return functional::Constant(*input->shape(), 0, grad->dtype(), JUST(grad->device())); + } else if (size_i != 1) { + inp_sizes_.insert(inp_sizes_.begin(), size_i); + inp_strides_.insert(inp_strides_.begin(), stride_i); + } + } + // Step (1)~(4) for the algorithm in NOTE [ Detecting Memory Overlap Within A + // Strided Tensor ] + // on input geometry + const bool inp_maybe_overlap = IsOverlappingMemorys(inp_sizes_, inp_strides_); + + // Rest of this function implements + // Step (1)~(4) for the algorithm in NOTE [ as_strided Backward and + // layout-aware/agnostic autograd ] + // TODO: Raise if not all output values are visible in input geometry. + // Technically speaking, if you treat those values as constants, not + // raising is fine, and mathematically correct. However, these values + // really are contained in some base tensor, and by treating them as + // constants we are ignoring this tight dependency. Therefore, it is + // more sensible to raise here. + + // Step (1): create underlying tensor as "storage" + auto input_storage_offset = JUST(input->storage_offset()); + auto shared_offset = std::min(input_storage_offset, storage_offset); + auto inp_effective_offset = input_storage_offset - shared_offset; + auto out_effective_offset = storage_offset - shared_offset; + auto base_size = std::max(MinStorageSize(inp_sizes_, inp_strides_, inp_effective_offset), + MinStorageSize(out_sizes_, out_strides_, out_effective_offset)); + auto storage = + JUST(functional::Constant(Shape({base_size}), 0, grad->dtype(), JUST(grad->device()))); + + std::shared_ptr flatten_full_indices; + if (inp_maybe_overlap || out_maybe_overlap) { + flatten_full_indices = JUST(functional::Arange(Scalar(0), Scalar(base_size), Scalar(1), + DType::Int64(), JUST(grad->device()))); + } + + // Step (2): use output geometry to scatter gradients into storage + if (out_maybe_overlap) { + auto out_indices = JUST(functional::AsStrided(flatten_full_indices, out_sizes_, out_strides_, + out_effective_offset)); + storage = JUST(functional::IndexAddInplace( + storage, 0, + JUST(functional::Reshape(out_indices, Shape({out_indices->shape()->elem_cnt()}))), + JUST(functional::Reshape(grad, Shape({grad->shape()->elem_cnt()}))), Scalar(1.0))); + } else { + // assume that new tensors have 0 storage offset + // torch impl: storage.as_strided(out_sizes_, out_strides_, out_effective_offset) + // .copy_(grad); + // TODO(wangyinggang): use functional::copy_ replace this TensorSetItem + storage = JUST(functional::AsStrided(storage, out_sizes_, out_strides_, out_effective_offset)); + functional::TensorIndex ellipsis_index; + ellipsis_index.emplace_back(functional::detail::EllipsisIndex()); + JUST(functional::TensorSetItem(storage, ellipsis_index, grad)); + } + + // Step (3): if input tensor has overlapping memory, divide scattered gradient + // at storage[i] by the number of times i shows up in input geometry + if (inp_maybe_overlap) { + auto count = + JUST(functional::Constant(*storage->shape(), 0, storage->dtype(), JUST(storage->device()))); + flatten_full_indices = JUST(functional::AsStrided(flatten_full_indices, inp_sizes_, + inp_strides_, inp_effective_offset)); + auto inp_indices = JUST(functional::Reshape( + flatten_full_indices, Shape({flatten_full_indices->shape()->elem_cnt()}))); + + auto ones = JUST(functional::Constant(Shape({1}), 0, grad->dtype(), JUST(grad->device()))); + count = JUST(functional::IndexAddInplace(count, 0, inp_indices, ones, Scalar(1.0))); + count = JUST(functional::Expand(count, *inp_indices->shape())); + storage = JUST(functional::Div(storage, count)); // this will give nan outside visible range + } + + // Step (4): return as_strided view of the storage tensor with input geometry + return functional::AsStrided(storage, inp_sizes, inp_strides, inp_effective_offset); +} + +Maybe AsStrided(const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset) { DimVector dim_vec; - dim_vec.insert(dim_vec.end(), size.begin(), size.end()); + dim_vec.insert(dim_vec.end(), sizes.begin(), sizes.end()); Shape target_shape(dim_vec); - Stride stride(stride_vec.begin(), stride_vec.end()); + Stride stride(strides.begin(), strides.end()); auto output = JUST(view::BasicView(input, target_shape, stride, storage_offset)); if (autograd::GradMode::is_enabled() && input->requires_grad()) { auto backward_fn = std::make_shared(); @@ -374,11 +628,8 @@ Maybe AsStrided(const std::shared_ptr& input, const std::ve autograd::AutoGradMode mode(create_graph); CHECK_EQ_OR_RETURN(out_grads.size(), 1) << "out grad size should be 1, but got " << out_grads.size(); - auto like = JUST(functional::Empty(Shape(input->shape()->dim_vec()), input->dtype(), - JUST(input->device()), /*pin_memory=*/false)); in_grads->resize(1); - (*in_grads)[0] = - JUST(functional::AsStridedGrad(out_grads[0], like, size, stride_vec, storage_offset)); + (*in_grads)[0] = JUST(AsStridedGrad(out_grads[0], input, sizes, strides, storage_offset)); return Maybe::Ok(); }; backward_fn->status = []() { return true; }; @@ -389,16 +640,45 @@ Maybe AsStrided(const std::shared_ptr& input, const std::ve return output; } +Maybe InplaceAsStrided(const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset) { + DimVector dim_vec; + dim_vec.insert(dim_vec.end(), sizes.begin(), sizes.end()); + Shape target_shape(dim_vec); + Stride stride(strides.begin(), strides.end()); + JUST(view::InplaceView(input, target_shape, stride, storage_offset)); + if (autograd::GradMode::is_enabled() && input->requires_grad()) { + auto backward_fn = std::make_shared(); + backward_fn->body = [=](const TensorTuple& out_grads, TensorTuple* in_grads, + bool create_graph) -> Maybe { + autograd::AutoGradMode mode(create_graph); + CHECK_EQ_OR_RETURN(out_grads.size(), 1) + << "out grad size should be 1, but got " << out_grads.size(); + in_grads->resize(1); + (*in_grads)[0] = JUST(AsStridedGrad(out_grads[0], input, sizes, strides, storage_offset)); + return Maybe::Ok(); + }; + backward_fn->status = []() { return true; }; + TensorTuple outputs{input}; + JUST(GetThreadLocalAutogradEngine()->AddNode("view::inplace_as_strided_backward", backward_fn, + {input}, &outputs)); + } + return Maybe::Ok(); +} + Maybe Transpose(const std::shared_ptr& input, const std::vector& permute) { const auto& shape = input->shape(); const auto& strides = JUST(input->stride()); const int64_t ndim = shape->NumAxes(); - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); CHECK_EQ_OR_RETURN(permute.size(), ndim) << "permute size should be equal to input tensor's ndim, but got " << permute.size(); auto positive_perm = permute; - for (auto i = 0; i < positive_perm.size(); i++) { JUST(maybe_wrap_dim(positive_perm[i], ndim)); } + for (auto i = 0; i < positive_perm.size(); i++) { + positive_perm[i] = JUST(maybe_wrap_dim(positive_perm[i], ndim)); + } DimVector target_dims(ndim); Stride stride(ndim); @@ -435,7 +715,7 @@ Maybe UnfoldTensor(const std::shared_ptr& input, const int32_t& const auto& shape = input->shape(); const auto& stride = JUST(input->stride()); const int64_t ndim = shape->NumAxes(); - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); CHECK_GE_OR_RETURN(dimension, 0) << "attibute dimension should be >= 0, but got " << dimension; CHECK_LE_OR_RETURN(dimension, ndim) @@ -489,7 +769,7 @@ Maybe Diagonal(const std::shared_ptr& input, const int32_t offse const auto& shape = input->shape(); const auto& stride = JUST(input->stride()); const int64_t ndim = shape->NumAxes(); - int64_t storage_offset = JUST(JUST(input->AsMirroredTensor())->storage_offset()); + int64_t storage_offset = JUST(JUST(input->AsLocalTensor())->storage_offset()); // infer output storage_offset int64_t diag_size = 0; @@ -547,5 +827,16 @@ Maybe Diagonal(const std::shared_ptr& input, const int32_t offse } } // namespace view + +Maybe Touch(std::shared_ptr input, Symbol stream) { + auto eager_blob_objects = std::make_shared(); + if (input->is_global()) { input = JUST(input->cur_rank_phy_tensor()); } + if (input) { eager_blob_objects->push_back(JUST(input->eager_blob_object())); } + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + return builder->TouchTensors(eager_blob_objects, stream); + })); + return Maybe::Ok(); +} + } // namespace one } // namespace oneflow diff --git a/oneflow/core/framework/tensor_methods.h b/oneflow/core/framework/tensor_methods.h index 2f6cab7df10f1c1ca86bb06466d836658e653d51..722715c9e203dcc594d49f2dbb72559286f814ee 100644 --- a/oneflow/core/framework/tensor_methods.h +++ b/oneflow/core/framework/tensor_methods.h @@ -20,6 +20,9 @@ limitations under the License. #include "oneflow/core/framework/tensor.h" namespace oneflow { + +class Stream; + namespace one { class Tensor; @@ -30,12 +33,21 @@ bool IsEnvViewDisabled(); bool IsViewApplicable(const std::shared_ptr& input); +static bool IsOverlappingMemorys(const std::vector& sizes, + const std::vector& strides); + +static int64_t MinStorageSize(const std::vector& sizes, + const std::vector& strides, int64_t storage_offset); + Maybe BasicView(const std::shared_ptr& input, const Shape& target_shape, int64_t storage_offset); Maybe BasicView(const std::shared_ptr& input, const Shape& target_shape, const Stride& target_stride, int64_t storage_offset); +Maybe InplaceView(const std::shared_ptr& input, const Shape& target_shape, + const Stride& target_stride, int64_t storage_offset); + Maybe Reshape(const std::shared_ptr& input, const Shape& target_shape); Maybe Reshape(const std::shared_ptr& input, const Shape& target_shape, @@ -46,17 +58,31 @@ Maybe Slice(const std::shared_ptr& input, const std::vector Unsqueeze(const std::shared_ptr& input, const int32_t& expand_dim); +Maybe InplaceUnsqueeze(const std::shared_ptr& input, const int32_t& expand_dim); + Maybe Squeeze(const std::shared_ptr& input, const std::vector& squeeze_dims); -Maybe Expand(const std::shared_ptr& input, const std::vector& in_shape, - const std::vector& expand_shape); +Maybe InplaceSqueeze(const std::shared_ptr& input, + const std::vector& squeeze_dims); + +Maybe Expand(const std::shared_ptr& input, const Shape& expand_shape); Maybe Narrow(const std::shared_ptr& input, const int64_t& dim, const int64_t& start, const int64_t& length); -Maybe AsStrided(const std::shared_ptr& input, const std::vector& size, - const std::vector& stride, const int32_t& storage_offset); +Maybe AsStridedGrad(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset); + +Maybe AsStrided(const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset); + +Maybe InplaceAsStrided(const std::shared_ptr& input, + const std::vector& sizes, const std::vector& strides, + const int64_t& storage_offset); Maybe Transpose(const std::shared_ptr& input, const std::vector& permute); @@ -67,7 +93,11 @@ Maybe Diagonal(const std::shared_ptr& input, const int32_t offse const int32_t dim1, const int32_t dim2); } // namespace view + +Maybe Touch(std::shared_ptr input, Symbol stream); + } // namespace one + } // namespace oneflow #endif // ONEFLOW_CORE_FRAMEWORK_TENSOR_METHOD_H_ diff --git a/oneflow/core/framework/tensor_name_scope.cpp b/oneflow/core/framework/tensor_name_scope.cpp index 9c8d9ee72a6566dcebb7a7a8e87443fe5775de7a..7b5f13e1c0e4ba41dc021808e5902628d4a70279 100644 --- a/oneflow/core/framework/tensor_name_scope.cpp +++ b/oneflow/core/framework/tensor_name_scope.cpp @@ -26,9 +26,13 @@ namespace one { const std::string& TensorNameScope::Lookup(const Tensor* tensor) const { uint64_t key = reinterpret_cast(tensor); + const auto* tensor_names = [&]() { + if (tensor->is_lazy()) { return &lazy_tensor_names_; } + return &eager_tensor_names_; + }(); std::lock_guard lock(mutex_); - const auto& it = tensor_names_.find(key); - if (it != tensor_names_.end()) { + const auto& it = tensor_names->find(key); + if (it != tensor_names->end()) { return it->second; } else { return default_tensor_name_; @@ -40,10 +44,14 @@ const std::string& TensorNameScope::Lookup(const std::shared_ptr& tensor } void TensorNameScope::Record(const Tensor* tensor, const std::string& name) { - std::lock_guard lock(mutex_); uint64_t key = reinterpret_cast(tensor); + auto* tensor_names = [&]() { + if (tensor->is_lazy()) { return &lazy_tensor_names_; } + return &eager_tensor_names_; + }(); + std::lock_guard lock(mutex_); // We assume that the name of the tensor will be update more than once. - tensor_names_[key] = name; + (*tensor_names)[key] = name; } void TensorNameScope::Record(const std::shared_ptr& tensor, const std::string& name) { @@ -52,7 +60,8 @@ void TensorNameScope::Record(const std::shared_ptr& tensor, const std::s void TensorNameScope::Clear() { std::lock_guard lock(mutex_); - tensor_names_.clear(); + lazy_tensor_names_.clear(); + eager_tensor_names_.clear(); } } // namespace one diff --git a/oneflow/core/framework/tensor_name_scope.h b/oneflow/core/framework/tensor_name_scope.h index 2636745e15d8f453d7217ba695a4c546bd2ff567..11a319a9a1381add53cf980ef95a15bc5d8b5039 100644 --- a/oneflow/core/framework/tensor_name_scope.h +++ b/oneflow/core/framework/tensor_name_scope.h @@ -44,7 +44,8 @@ class TensorNameScope { std::string default_tensor_name_; // uint64_t(Tensor*) -> the name of the tensor. - std::unordered_map tensor_names_; + std::unordered_map lazy_tensor_names_; + std::unordered_map eager_tensor_names_; }; } // namespace one diff --git a/oneflow/core/framework/tensor_rpc_util.cpp b/oneflow/core/framework/tensor_rpc_util.cpp index 029b897c63bd4147f32c60935c6168988a259569..1f8f1938bcecc602bf5441e93912480967fac2bb 100644 --- a/oneflow/core/framework/tensor_rpc_util.cpp +++ b/oneflow/core/framework/tensor_rpc_util.cpp @@ -15,7 +15,7 @@ limitations under the License. */ #include #include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/framework/sync_symbol_consistent_tensor_meta.h" +#include "oneflow/core/framework/sync_symbol_global_tensor_meta.h" #include "oneflow/core/framework/sync_symbol_nd_sbp.h" #include "oneflow/core/framework/synced_symbol_map.h" #include "oneflow/core/framework/rank_group_rpc_util.h" @@ -35,7 +35,7 @@ struct FlatTensorConsistency; class CheckConsistencyAsyncTransportCtx : public AsyncTransportCtx { public: CheckConsistencyAsyncTransportCtx(const TransportToken& transport_token, - Symbol tensor_meta, + Symbol tensor_meta, const Optional>& consumer_nd_sbp_constraint, const TransportToken& tensor_transport_token) : AsyncTransportCtx(transport_token), @@ -54,7 +54,7 @@ class CheckConsistencyAsyncTransportCtx : public AsyncTransportCtx { Maybe Check() const; private: - Symbol tensor_meta_; + Symbol tensor_meta_; Optional> consumer_nd_sbp_constraint_; TransportToken tensor_transport_token_; std::shared_ptr flat_tensor_consistency_; @@ -69,7 +69,7 @@ FLAT_MSG_BEGIN(FlatTensorConsistency); return consistency; } static Maybe New( - Symbol tensor_meta, + Symbol tensor_meta, const Optional>& consumer_nd_sbp_constraint, const TransportToken& tensor_transport_token) { const auto& consistency = std::make_shared(); @@ -78,18 +78,18 @@ FLAT_MSG_BEGIN(FlatTensorConsistency); return consistency; } - Maybe Check(Symbol tensor_meta, + Maybe Check(Symbol tensor_meta, const Optional>& consumer_nd_sbp_constraint, const TransportToken& tensor_transport_token) { const auto& this_synced_tensor_meta = - JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( + JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( this->synced_tensor_meta_symbol_id())); CHECK_OR_RETURN(this_synced_tensor_meta == tensor_meta); CHECK_EQ_OR_RETURN(consumer_nd_sbp_constraint.has_value(), this->has_consumer_nd_sbp_constraint_symbol_id()); if (this->has_consumer_nd_sbp_constraint_symbol_id()) { const auto& that_rank_constaint = - JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( + JUST(SyncedSymbolMap::Symbol4SyncedSymbolId( this->consumer_nd_sbp_constraint_symbol_id()))->nd_sbp(); const auto& this_rank_constaint = JUST(consumer_nd_sbp_constraint); CHECK_OR_RETURN(this_rank_constaint == that_rank_constaint); @@ -99,11 +99,11 @@ FLAT_MSG_BEGIN(FlatTensorConsistency); } private: - Maybe Init(Symbol tensor_meta, + Maybe Init(Symbol tensor_meta, const Optional>& consumer_nd_sbp_constraint, const TransportToken& tensor_transport_token) { - this->set_synced_tensor_meta_symbol_id(JUST(SyncedSymbolMap::FindOrSync( - tensor_meta, &SyncSymbolConsistentTensorMeta))); + this->set_synced_tensor_meta_symbol_id(JUST(SyncedSymbolMap::FindOrSync( + tensor_meta, &SyncSymbolGlobalTensorMeta))); if (consumer_nd_sbp_constraint.has_value()) { const auto& this_rank_constaint = JUST(consumer_nd_sbp_constraint); this->set_consumer_nd_sbp_constraint_symbol_id( @@ -161,7 +161,7 @@ Maybe LaunchTensorMetaConsistencyCheck( const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); const auto& transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeCheckTensorConsistency)); - const auto& tensor_meta = JUST(tensor.consistent_tensor_meta()); + const auto& tensor_meta = JUST(tensor.global_tensor_meta()); const auto& constaint = JUST(tensor.consumer_nd_sbp_constraint()); const TransportToken& tensor_transport_token = JUST(tensor.transport_token()); const auto& ctx = std::make_shared( diff --git a/oneflow/core/framework/tensor_rpc_util.h b/oneflow/core/framework/tensor_rpc_util.h index deb36bc45d905d55f9ba6af8ec81486cfb547c6d..5bc8d14f67c5b60418323cb3eb1bec1cee4fcf32 100644 --- a/oneflow/core/framework/tensor_rpc_util.h +++ b/oneflow/core/framework/tensor_rpc_util.h @@ -41,15 +41,15 @@ Maybe RunCallback(const std::shared_ptr& tensor, } // namespace private_details -inline bool IsConsistentTensorMetaCheckDisabled() { +inline bool IsGlobalTensorMetaCheckDisabled() { return *private_details::MutThreadLocalTensorMetaCheckDepth() > 1; } template -struct CheckConsistentTensorMeta; +struct CheckGlobalTensorMeta; template -struct CheckConsistentTensorMeta&, Args...> { +struct CheckGlobalTensorMeta&, Args...> { static_assert(is_maybe::value, "returned value type must be Maybe."); template&, Args...)> static RetT Call(const std::shared_ptr& tensor, Args... args) { @@ -62,23 +62,19 @@ struct CheckConsistentTensorMeta&, Args ++*depth; RetT ret = func(tensor, args...); --*depth; - // Always synchronize consistent tensor meta even if `func` failed. + // Always synchronize global tensor meta even if `func` failed. if (*depth == 0 && is_env_enabled_check) { JUST(private_details::BusyWaitAndCheck(ctx)); } return ret; } }; -struct DisableCheckConsistentTensorMetaScope final { - DisableCheckConsistentTensorMetaScope() { - ++*private_details::MutThreadLocalTensorMetaCheckDepth(); - } - ~DisableCheckConsistentTensorMetaScope() { - --*private_details::MutThreadLocalTensorMetaCheckDepth(); - } +struct DisableCheckGlobalTensorMetaScope final { + DisableCheckGlobalTensorMetaScope() { ++*private_details::MutThreadLocalTensorMetaCheckDepth(); } + ~DisableCheckGlobalTensorMetaScope() { --*private_details::MutThreadLocalTensorMetaCheckDepth(); } }; static constexpr auto* WithConsistencyChecked = - DECORATE(&private_details::RunCallback, CheckConsistentTensorMeta); + DECORATE(&private_details::RunCallback, CheckGlobalTensorMeta); } // namespace oneflow diff --git a/oneflow/core/framework/tensor_storage.cpp b/oneflow/core/framework/tensor_storage.cpp index c85aafacf68d1f07711ac49b88bab01cb88a548d..71369fc756fc4212b95742e50e59963c2d6e7772 100644 --- a/oneflow/core/framework/tensor_storage.cpp +++ b/oneflow/core/framework/tensor_storage.cpp @@ -22,7 +22,7 @@ namespace oneflow { namespace one { TensorStorage::TensorStorage(const std::shared_ptr& parallel_desc) - : storage_(std::make_shared()) {} + : storage_(std::make_shared()) {} TensorStorage::TensorStorage(const std::shared_ptr& tensor_storage) : storage_(tensor_storage) {} diff --git a/oneflow/core/framework/tensor_tuple.h b/oneflow/core/framework/tensor_tuple.h index b996aa5b080e899ed1cef1aa617960bc4665f8f5..51b8c947f8f298febffb18134de194fc129c40ef 100644 --- a/oneflow/core/framework/tensor_tuple.h +++ b/oneflow/core/framework/tensor_tuple.h @@ -19,13 +19,15 @@ limitations under the License. #include #include +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/common/op_args_reserved_size.h" namespace oneflow { namespace one { class Tensor; -class TensorTuple final : public std::vector>, +class TensorTuple final : public small_vector, kOpArgsReservedSize>, public std::enable_shared_from_this { public: // TensorTuple(const TensorTuple&) = delete; diff --git a/oneflow/core/framework/tensor_util.cpp b/oneflow/core/framework/tensor_util.cpp index 769225c02054bb07f90e4f0186a38c8ceef3e54a..c4ef23554ccdba3bffc275f6dd6a6f2d13f20ca2 100644 --- a/oneflow/core/framework/tensor_util.cpp +++ b/oneflow/core/framework/tensor_util.cpp @@ -14,19 +14,24 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/tensor_util.h" - #include "oneflow/core/common/blocking_then_busy.h" -#include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/core/framework/tensor_name_scope.h" +#include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/vm/virtual_machine.h" +#include "oneflow/core/vm/symbol_storage.h" +#include "oneflow/core/functional/functional.h" namespace oneflow { namespace one { -Maybe SyncAccessTensorWithTimeOut(const std::shared_ptr& tensor, - const std::function& Callback, - const std::string& modifier) { - auto btb = std::make_shared(1); - auto local_tensor = JUST(tensor->AsMirroredTensor()); +Maybe SyncAccessTensorWithTimeOut( + const std::shared_ptr& tensor, + const std::function&)>& Callback, + const std::string& modifier) { + auto btb = std::make_shared(); + auto local_tensor = JUST(tensor->AsLocalTensor()); JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { return builder->SyncAccessBlobByCallback(local_tensor, btb, Callback, modifier); })); @@ -34,5 +39,69 @@ Maybe SyncAccessTensorWithTimeOut(const std::shared_ptr& tensor, return Maybe::Ok(); } +Maybe CopyLocalTensorDataTo(const std::shared_ptr& input, void* mem_ptr, + size_t size) { + CHECK_OR_RETURN(input->is_local()); // NOLINT + CHECK_OR_RETURN(input->is_contiguous()) << Error::RuntimeError() << kOfBugIssueUploadPrompt; + CHECK_EQ_OR_RETURN(input->shape()->elem_cnt() * JUST(input->dtype()->bytes()), size) + << Error::RuntimeError() << kOfBugIssueUploadPrompt; + if (input->nelement() == 1) { return GetItemInScalarTensor(input, mem_ptr, size); } + std::shared_ptr local_tensor = JUST(input->AsLocalTensor()); + const auto& Callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, mem_ptr, eager_blob_object->dptr(), size, memory::MakeHostMemCase(), + eager_blob_object->mem_case()); + }; + auto btb = std::make_shared(); + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + return builder->SyncAccessBlobByCallback(local_tensor, btb, Callback, "const"); + })); + JUST(btb->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); + return Maybe::Ok(); +} + +Maybe GetTensorScope(const std::shared_ptr& tensor) { + CHECK_OR_RETURN(LazyMode::is_enabled()) + << "it's not allowed to access tensor scope in eager mode"; + const auto& lbn = TensorNameScope::Global()->Lookup(tensor); + CHECK_OR_RETURN(!lbn.empty()) << "can not access tensor scope since it is not a lazy tensor or a " + "captured eager tensor in graph"; + const auto& infer_ctx = JUST(GetCurInferCtx()); + auto lbi = GenLogicalBlobId(lbn); + const auto* op = JUST(infer_ctx->Op4OpName(lbi.op_name())); + return Singleton>::Get()->MaybeGetPtr(op->op_conf().scope_symbol_id()); +} + +Maybe GetItemInScalarTensor(const std::shared_ptr& scalar_tensor, void* scalar_ptr, + size_t size) { + CHECK_EQ_OR_RETURN(GetSizeOfDataType(scalar_tensor->dtype()->data_type()), size) + << "invalid size"; + CHECK_OR_RETURN(scalar_tensor->is_eager()) << "Only eager scalar tensor support GetItem."; + CHECK_EQ_OR_RETURN(scalar_tensor->nelement(), 1) + << "can only convert a tensor of size 1 to a Python scalar"; + std::shared_ptr local_tensor; + { + auto tensor = scalar_tensor; + if (tensor->is_global()) { + Symbol parallel_desc; + { + const ParallelConf parallel_conf = GenParallelConfOfCpuOnAllRanks(); + JUST(PhysicalRun( + [¶llel_desc, ¶llel_conf](InstructionsBuilder* builder) -> Maybe { + parallel_desc = SymbolOf(*JUST(builder->GetParallelDescSymbol(parallel_conf))); + return Maybe::Ok(); + })); + } + const auto& broadcast_sbp = JUST(MakeBroadcastSbpParallel()); + tensor = JUST(functional::ToGlobal(tensor, parallel_desc, {broadcast_sbp}, /*grad_sbp=*/{}, + /*check_meta=*/false, /*copy=*/false)); + tensor = JUST(functional::GlobalToLocal(tensor, /*copy=*/false)); + } + local_tensor = JUST(tensor->AsLocalTensor()); + } + JUST(SyncReadSmallMem(reinterpret_cast(scalar_ptr), size, local_tensor)); + return Maybe::Ok(); +} + } // namespace one } // namespace oneflow diff --git a/oneflow/core/framework/tensor_util.h b/oneflow/core/framework/tensor_util.h index 8154ab66e92c8b8140390eb544f92ec35dea668e..17b16732456d9334bf396d1133856974e7153b61 100644 --- a/oneflow/core/framework/tensor_util.h +++ b/oneflow/core/framework/tensor_util.h @@ -13,18 +13,49 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_CORE_FRAMEWORK_TENSOR_UTIL_H_ +#define ONEFLOW_CORE_FRAMEWORK_TENSOR_UTIL_H_ + #include #include #include "oneflow/core/common/maybe.h" +#include "oneflow/core/job/scope.h" +#include "oneflow/core/common/data_type.h" namespace oneflow { + +namespace ep { +class Stream; +} + +namespace vm { +class EagerBlobObject; +} + namespace one { class Tensor; -Maybe SyncAccessTensorWithTimeOut(const std::shared_ptr& tensor, - const std::function& callback, - const std::string& modifier); +Maybe SyncAccessTensorWithTimeOut( + const std::shared_ptr& tensor, + const std::function&)>& callback, + const std::string& modifier); + +Maybe CopyLocalTensorDataTo(const std::shared_ptr& input, void* mem_ptr, size_t size); + +Maybe GetTensorScope(const std::shared_ptr& tensor); + +Maybe GetItemInScalarTensor(const std::shared_ptr& scalar_tensor, void* scalar_ptr, + size_t size); +template +Maybe GetItemInScalarTensor(const std::shared_ptr& scalar_tensor) { + T scalar{0}; + JUST(GetItemInScalarTensor(scalar_tensor, &scalar, sizeof(T))); + return scalar; +} + } // namespace one } // namespace oneflow + +#endif // ONEFLOW_CORE_FRAMEWORK_TENSOR_UTIL_H_ diff --git a/oneflow/core/framework/transport_token.cpp b/oneflow/core/framework/transport_token.cpp index 1176d45d6d52e292365e11f307f21fcf79667f35..49c6b587333805fb81995cb79391fcb0402fa453 100644 --- a/oneflow/core/framework/transport_token.cpp +++ b/oneflow/core/framework/transport_token.cpp @@ -17,21 +17,21 @@ limitations under the License. #include "oneflow/core/framework/transport_token.h" #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/data_type.h" -#include "oneflow/core/thread/thread_consistent_id.h" +#include "oneflow/core/thread/thread_global_id.h" #include "oneflow/core/framework/rank_group_rpc_util.h" namespace oneflow { /*static*/ Maybe TransportToken::NewTransportToken(TransportTokenType type) { - int32_t thread_consistent_id = JUST(GetThisThreadConsistentId()); - CHECK_GE_OR_RETURN(thread_consistent_id, 0); - CHECK_LT_OR_RETURN(thread_consistent_id, MaxNumberOfThreadConsistentUId()); - return TransportToken(type, thread_consistent_id); + int32_t thread_global_id = JUST(GetThisThreadGlobalId()); + CHECK_GE_OR_RETURN(thread_global_id, 0); // NOLINT + CHECK_LT_OR_RETURN(thread_global_id, MaxNumberOfThreadGlobalUId()); // NOLINT + return TransportToken(type, thread_global_id); } -Maybe TransportToken::CheckThreadConsistentId() const { - int32_t thread_consistent_id = JUST(GetThisThreadConsistentId()); - CHECK_EQ_OR_RETURN(thread_consistent_id, this->thread_consistent_id()); +Maybe TransportToken::CheckThreadGlobalId() const { + int32_t thread_global_id = JUST(GetThisThreadGlobalId()); + CHECK_EQ_OR_RETURN(thread_global_id, this->thread_global_id()); // NOLINT return Maybe::Ok(); } diff --git a/oneflow/core/framework/transport_token.h b/oneflow/core/framework/transport_token.h index fe9a6ed9a630dd0d42374cfb9e777ae451239c99..b15973046737fde6f954e3f20cacfe879e0d1ea3 100644 --- a/oneflow/core/framework/transport_token.h +++ b/oneflow/core/framework/transport_token.h @@ -24,7 +24,7 @@ limitations under the License. namespace oneflow { const static int kTransportTokenTypeBit = 5; -const static int kTransportTokenThreadConsistentIdBit = 3; +const static int kTransportTokenThreadGlobalIdBit = 3; enum TransportTokenType { // Begin @@ -33,7 +33,7 @@ enum TransportTokenType { kTransportTokenTypeMeta, // e.g. for consistent id generating kTransportTokenTypeSyncSymbolParallelDesc, kTransportTokenTypeSyncSymbolNdSbp, - kTransportTokenTypeSyncSymbolConsistentTensorMeta, + kTransportTokenTypeSyncSymbolGlobalTensorMeta, kTransportTokenTypeCheckRankGroupConsistency, kTransportTokenTypeCheckTensorConsistency, kTransportTokenTypeSyncLocalShapeDtype, @@ -59,18 +59,18 @@ class TransportToken final { static Maybe NewTransportToken(TransportTokenType type); - static constexpr size_t MaxNumberOfThreadConsistentUId() { - return (1 << kTransportTokenThreadConsistentIdBit); + static constexpr size_t MaxNumberOfThreadGlobalUId() { + return (1 << kTransportTokenThreadGlobalIdBit); } - Maybe CheckThreadConsistentId() const; + Maybe CheckThreadGlobalId() const; bool operator==(const TransportToken& other) const { return static_cast(*this) == static_cast(other); } // Getters TransportTokenType type() const { return static_cast(type_); } - int thread_consistent_id() const { return thread_consistent_id_; } + int thread_global_id() const { return thread_global_id_; } int32_t seq_id() const { return seq_id_; } // Setters @@ -85,18 +85,18 @@ class TransportToken final { } private: - TransportToken(TransportTokenType type, uint8_t thread_consistent_id) + TransportToken(TransportTokenType type, uint8_t thread_global_id) : src_rank_(0), dst_rank_(0), type_(static_cast(type)), - thread_consistent_id_(thread_consistent_id), + thread_global_id_(thread_global_id), seq_id_(0) {} uint16_t src_rank_; uint16_t dst_rank_; uint8_t type_ : kTransportTokenTypeBit; // TransportTokenType - uint8_t thread_consistent_id_ : kTransportTokenThreadConsistentIdBit; - uint32_t seq_id_ : (32 - kTransportTokenTypeBit - kTransportTokenThreadConsistentIdBit); + uint8_t thread_global_id_ : kTransportTokenThreadGlobalIdBit; + uint32_t seq_id_ : (32 - kTransportTokenTypeBit - kTransportTokenThreadGlobalIdBit); }; static_assert(sizeof(TransportToken) == sizeof(uint64_t), ""); diff --git a/oneflow/core/framework/transport_util.cpp b/oneflow/core/framework/transport_util.cpp index 75c0f6c5e00a1adcffa70fe335f19726aa067b68..a6b13d9e65d12582fd9a4c2fec7dc0f878d7c7d5 100644 --- a/oneflow/core/framework/transport_util.cpp +++ b/oneflow/core/framework/transport_util.cpp @@ -19,7 +19,7 @@ limitations under the License. #include "oneflow/core/framework/transport_util.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/transport/transport.h" -#include "oneflow/core/thread/thread_consistent_id.h" +#include "oneflow/core/thread/thread_global_id.h" #include "oneflow/core/job/rank_group.h" #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/spin_counter.h" @@ -83,7 +83,7 @@ namespace { Maybe> RawGetTransportToken(const TransportToken& token) { CHECK_EQ_OR_RETURN(token.seq_id(), 0); - JUST(token.CheckThreadConsistentId()); + JUST(token.CheckThreadGlobalId()); auto auto_token = std::make_shared(token); return auto_token; } diff --git a/oneflow/core/framework/user_op_conf.cpp b/oneflow/core/framework/user_op_conf.cpp index b74ba0a4e66a6df196269b6236a62a7d992c7f34..e48ba6704cd7b459bcca212c376a22f58c1e8e58 100644 --- a/oneflow/core/framework/user_op_conf.cpp +++ b/oneflow/core/framework/user_op_conf.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/operator/operator.h" #include "oneflow/core/register/blob_desc.h" #include "oneflow/core/framework/user_op_def.h" +#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/framework/attr_value_accessor.h" namespace oneflow { @@ -123,47 +124,6 @@ UserOpWrapper::UserOpWrapper( InitTensorDescFromOpArgs(op.user_conf().output()); } -bool UserOpWrapper::NeedGenGradTensor4OpInput(const std::string& input_arg_name, - int32_t index) const { - auto it = op_conf().user_conf().input().find(input_arg_name); - CHECK(it != op_conf().user_conf().input().end()) - << "arg_name: " << input_arg_name << ", index: " << index; - CHECK(index >= 0 && index < it->second.s_size()) - << "arg_name: " << input_arg_name << ", index: " << index; - return diff_fn_(GenRepeatedBn(input_arg_name, index)) != nullptr; -} - -bool UserOpWrapper::HasGradTensor4OpOutput(const std::string& output_arg_name, - int32_t index) const { - auto it = op_conf().user_conf().output().find(output_arg_name); - CHECK(it != op_conf().user_conf().output().end()) - << "arg_name: " << output_arg_name << ", index: " << index; - CHECK(index >= 0 && index < it->second.s_size()) - << "arg_name: " << output_arg_name << ", index: " << index; - return diff_fn_(GenRepeatedBn(output_arg_name, index)) != nullptr; -} - -std::string UserOpWrapper::output_grad(const std::string& output_arg_name, int32_t index) const { - auto it = op_conf().user_conf().output().find(output_arg_name); - CHECK(it != op_conf().user_conf().output().end()) - << "arg_name: " << output_arg_name << ", index: " << index; - CHECK(index >= 0 && index < it->second.s_size()) - << "arg_name: " << output_arg_name << ", index: " << index; - return GenLogicalBlobName(*diff_fn_(GenRepeatedBn(output_arg_name, index))); -} - -std::string UserOpWrapper::GetGradTensorWithOpOutput(const std::string& output_arg_name, - int32_t index) const { - return output_grad(output_arg_name, index); -} - -void UserOpWrapper::BindGradTensorWithOpInput(const std::string& logical_grad_blob_name, - const std::string& input_arg_name, - int32_t index) const { - CHECK(NeedGenGradTensor4OpInput(input_arg_name, index)); - *diff_fn_(GenRepeatedBn(input_arg_name, index)) = GenLogicalBlobId(logical_grad_blob_name); -} - const TensorDesc& UserOpWrapper::arg_tensor_desc(const std::string& arg_name, int32_t index) const { std::string bn = GenRepeatedBn(arg_name, index); CHECK(bn2tensor_desc_.find(bn) != bn2tensor_desc_.end()); @@ -175,13 +135,6 @@ const TensorDesc& UserOpWrapper::TensorDesc4ArgNameAndIndex(const std::string& a return arg_tensor_desc(arg_name, index); } -void UserOpWrapper::InputGradBind(const user_op::OpArg& input, - const UserOpInputGradGetFn& grad_fn) { - if (NeedGenGradTensor4OpInput(input.name(), input.index())) { - BindGradTensorWithOpInput(grad_fn(), input.name(), input.index()); - } -} - UserOpConfWrapperBuilder& UserOpConfWrapperBuilder::InputBind( const std::string& arg_name, const std::string& logical_blob_name) { if (input_.find(arg_name) == input_.end()) { input_order_.emplace_back(arg_name); } @@ -244,33 +197,6 @@ UserOpConfWrapper UserOpConfWrapperBuilder::Build() { return wrapper_; } -void BackwardOpConfContext::DefineOp(const std::string& op_name, const BackwardOpBuilderFn& fn) { - auto it = op_builder_fns_.find(op_name); - CHECK(it == op_builder_fns_.end()) << " op_name " << op_name << " has been defined."; - op_builder_fns_[op_name] = fn; -} - -UserOpConfWrapper& BackwardOpConfContext::GetOp(const std::string& op_name) { - auto it = op_builder_results_.find(op_name); - if (it != op_builder_results_.end()) { - // return result from cache - return it->second; - } else { - // build and put result into cache - auto fn_it = op_builder_fns_.find(op_name); - CHECK(fn_it != op_builder_fns_.end()) << " op_name " << op_name << " has no builder function."; - CHECK(fn_it->second != nullptr) << " op_name " << op_name << " builder function is null."; - UserOpConfWrapperBuilder builder(op_name); - auto ret = op_builder_results_.emplace(std::make_pair(op_name, fn_it->second(builder))); - CHECK(ret.second == true) << " op_name " << op_name << " build result insert failed."; - - // add new op conf - bw_op_confs_->emplace_back(ret.first->second.op_conf()); - - return ret.first->second; - } -} - } // namespace user_op Maybe CheckArgDefIsValidInUserOpConf( diff --git a/oneflow/core/framework/user_op_conf.h b/oneflow/core/framework/user_op_conf.h index 706ca3efcf991aa0066345eca35a10d2e382976e..f414fb6fa52095b80b5efa54be8da79be9634a49 100644 --- a/oneflow/core/framework/user_op_conf.h +++ b/oneflow/core/framework/user_op_conf.h @@ -18,7 +18,7 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/common/maybe.h" -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/framework/user_op_def.pb.h" #include "oneflow/core/framework/user_op_attr.pb.h" #include "oneflow/core/framework/user_op_conf.pb.h" @@ -67,8 +67,7 @@ class UserOpConfWrapper final { template const T& attr_or_default(const std::string& attr_name, const T& default_val) const { - const auto& it = attrs_.find(attr_name); - if (it != attrs_.end()) { + if (attrs_.Has(attr_name)) { return CHECK_JUST(attrs_.GetAttr(attr_name)); } else { return default_val; @@ -85,7 +84,6 @@ class UserOpConfWrapper final { AttrMap attrs_; }; -using UserOpInputGradGetFn = std::function; class UserOpWrapper final { public: UserOpWrapper(const OperatorConf& op, const std::function&, @@ -106,8 +104,6 @@ class UserOpWrapper final { const std::string& output(const std::string& arg_name, int32_t index) const { return conf_.output(arg_name, index); } - std::string output_grad(const std::string& output_arg_name, int32_t index) const; - std::string GetGradTensorWithOpOutput(const std::string& output_arg_name, int32_t index) const; template T attr(const std::string& attr_name) const { @@ -122,13 +118,6 @@ class UserOpWrapper final { const TensorDesc& arg_tensor_desc(const std::string& arg_name, int32_t index) const; const TensorDesc& TensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) const; - public: - void InputGradBind(const user_op::OpArg& input, const UserOpInputGradGetFn& grad_fn); - void BindGradTensorWithOpInput(const std::string& logical_grad_blob_name, - const std::string& input_arg_name, int32_t index) const; - bool NeedGenGradTensor4OpInput(const std::string& input_arg_name, int32_t index) const; - bool HasGradTensor4OpOutput(const std::string& output_arg_name, int32_t index) const; - private: UserOpConfWrapper conf_; std::function diff_fn_; @@ -174,25 +163,6 @@ class UserOpConfWrapperBuilder final { std::string device_tag_; }; -using BackwardOpBuilder = UserOpConfWrapperBuilder; -using BackwardOpBuilderFn = std::function; -class BackwardOpConfContext final { - public: - BackwardOpConfContext(const UserOpWrapper& fw_op_wp, std::vector* bw_op_confs) - : fw_op_wp_(fw_op_wp), bw_op_confs_(bw_op_confs) {} - - public: - UserOpWrapper& FwOp() { return fw_op_wp_; } - void DefineOp(const std::string& op_name, const BackwardOpBuilderFn& fn); - UserOpConfWrapper& GetOp(const std::string& op_name); - - private: - UserOpWrapper fw_op_wp_; - HashMap op_builder_fns_; - HashMap op_builder_results_; - std::vector* bw_op_confs_; -}; - } // namespace user_op Maybe GetAttrTypeImpl(const std::string& op_type_name, const std::string& attr_name); diff --git a/oneflow/core/framework/user_op_def.cpp b/oneflow/core/framework/user_op_def.cpp index def5da20d92291e321f88487dffc02c88c25f31d..959ff21d859c9bb673c04435c01b0c2b808b4528 100644 --- a/oneflow/core/framework/user_op_def.cpp +++ b/oneflow/core/framework/user_op_def.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/user_op_def.h" +#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/framework/attr_value_accessor.h" namespace oneflow { diff --git a/oneflow/core/framework/user_op_grad_registry.cpp b/oneflow/core/framework/user_op_grad_registry.cpp deleted file mode 100644 index e7bd71c67a79ca63008232aaeac975cf99a63515..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/user_op_grad_registry.cpp +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/user_op_grad_registry.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { - -namespace user_op { - -OpGradRegistry& OpGradRegistry::Name(const std::string& op_type_name) { - CHECK(!op_type_name.empty()); - result_.op_type_name = op_type_name; - return *this; -} - -OpGradRegistry& OpGradRegistry::SetGenBackwardOpConfFn(GenBackwardOpConfFn fn) { - result_.gen_bw_fn = std::move(fn); - return *this; -} - -OpGradRegistry& OpGradRegistry::SetBackwardOpConfGenFn(BackwardOpConfGenFn fn) { - result_.bw_gen_fn = std::move(fn); - return *this; -} - -Maybe OpGradRegistry::Finish() { - CHECK_OR_RETURN((result_.gen_bw_fn != nullptr) || (result_.bw_gen_fn != nullptr)) - << "No BackwardOpConf generate function for " << result_.op_type_name; - return *this; -} - -} // namespace user_op - -} // namespace oneflow diff --git a/oneflow/core/framework/user_op_grad_registry.h b/oneflow/core/framework/user_op_grad_registry.h deleted file mode 100644 index 6a3027aba340c6c318e1784590743346f7c9d053..0000000000000000000000000000000000000000 --- a/oneflow/core/framework/user_op_grad_registry.h +++ /dev/null @@ -1,55 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_FRAMEWORK_USER_OP_GRAD_REGISTRY_H_ -#define ONEFLOW_CORE_FRAMEWORK_USER_OP_GRAD_REGISTRY_H_ - -#include "oneflow/core/framework/user_op_conf.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { - -namespace user_op { - -using AddOpFn = std::function; -using GenBackwardOpConfFn = std::function(const UserOpWrapper&, AddOpFn)>; -using BackwardOpConfGenFn = std::function(BackwardOpConfContext*)>; - -struct OpGradRegistryResult { - std::string op_type_name; - GenBackwardOpConfFn gen_bw_fn; - BackwardOpConfGenFn bw_gen_fn; -}; - -class OpGradRegistry final { - public: - OpGradRegistry& Name(const std::string& op_type_name); - // old - OpGradRegistry& SetGenBackwardOpConfFn(GenBackwardOpConfFn fn); - // new - OpGradRegistry& SetBackwardOpConfGenFn(BackwardOpConfGenFn fn); - - Maybe Finish(); - OpGradRegistryResult GetResult() { return result_; } - - private: - OpGradRegistryResult result_; -}; - -} // namespace user_op - -} // namespace oneflow - -#endif // ONEFLOW_CORE_FRAMEWORK_USER_OP_GRAD_REGISTRY_H_ diff --git a/oneflow/core/framework/user_op_hob.h b/oneflow/core/framework/user_op_hob.h index 390f81899d1e07ce58749a93bab622cd05e32ff9..2d1ec1f4152241e2da3c6af7990750e64496e1d0 100644 --- a/oneflow/core/framework/user_op_hob.h +++ b/oneflow/core/framework/user_op_hob.h @@ -51,6 +51,15 @@ ALWAYS_INLINE inline auto HobDataType(const std::string& tensor_name, int tensor }); } +ALWAYS_INLINE inline auto HobInputSize(const std::string& tensor_name) { + std::ostringstream string_stream; + string_stream << "size of input \'" << tensor_name << "\'"; + return hob::make_custom(string_stream.str(), + [tensor_name](const KernelRegContext& ctx) -> int32_t { + return ctx.user_op_conf().input_size(tensor_name); + }); +} + template ALWAYS_INLINE inline auto HobAttr(const std::string& attr_name) { return hob::make_custom(attr_name, [attr_name](const user_op::KernelRegContext& ctx) -> const T& { diff --git a/oneflow/core/framework/user_op_kernel_registry.cpp b/oneflow/core/framework/user_op_kernel_registry.cpp index 711fe321c55a4a0a6d0fb4b770a3709a9b0cdbf5..9901afaa5935f620d0a49e0ac752cb43628a2dda 100644 --- a/oneflow/core/framework/user_op_kernel_registry.cpp +++ b/oneflow/core/framework/user_op_kernel_registry.cpp @@ -39,6 +39,11 @@ OpKernelRegistry& OpKernelRegistry::SetInplaceProposalFn(InplaceProposalFn fn) { return *this; } +OpKernelRegistry& OpKernelRegistry::SetPriority(int32_t priority) { + result_.priority = priority; + return *this; +} + Maybe OpKernelRegistry::Finish() { CHECK_OR_RETURN(result_.create_fn != nullptr) << "No Create function for " << result_.op_type_name; diff --git a/oneflow/core/framework/user_op_kernel_registry.h b/oneflow/core/framework/user_op_kernel_registry.h index 79446d821e3f41bc5c0f4b5570364eeac5328bff..022e07ad509e68a895c8d39d9f53535d3d8179d1 100644 --- a/oneflow/core/framework/user_op_kernel_registry.h +++ b/oneflow/core/framework/user_op_kernel_registry.h @@ -64,6 +64,11 @@ using AddInplaceArgPair = std::function( using InplaceProposalFn = std::function(const InferContext&, AddInplaceArgPair)>; using IsMatchedHob = std::shared_ptr>; +constexpr int kKernelPriorityFallback = -10; +constexpr int kKernelPriorityDefault = 0; +constexpr int kKernelPriorityOptimized = 10; +constexpr int kKernelPriorityExperimental = 100; + struct OpKernelRegistryResult { std::string op_type_name; @@ -72,6 +77,7 @@ struct OpKernelRegistryResult { InferTmpSizeFn infer_tmp_size_fn; InplaceProposalFn inplace_proposal_fn; IsMatchedHob is_matched_hob; + int32_t priority = kKernelPriorityDefault; }; class OpKernelRegistry final { @@ -94,6 +100,7 @@ class OpKernelRegistry final { OpKernelRegistryResult GetResult() { return result_; } OpKernelRegistry& SetCreateFn(OpKernelCreateFn fn); + OpKernelRegistry& SetPriority(int32_t priority); private: OpKernelRegistryResult result_; diff --git a/oneflow/core/framework/user_op_registry.cpp b/oneflow/core/framework/user_op_registry.cpp index 886b3084cee6ab6d082e6037b6913913eaafeb21..c5959d57102ae756d7c1c5a8b1b341b044b51e0c 100644 --- a/oneflow/core/framework/user_op_registry.cpp +++ b/oneflow/core/framework/user_op_registry.cpp @@ -208,6 +208,22 @@ OpRegistry& OpRegistry::SetDeviceAndStreamInferFn( return *this; } +OpRegistry& OpRegistry::SetComputeComplexityFn(ComputeComplexityFn compute_complexity_fn) { + result_.compute_complexity_fn = std::move(compute_complexity_fn); + return *this; +} + +OpRegistry& OpRegistry::SetGetNdSbpSignatureListFn(GetNdSbpSignatureListFn get_nd_sbp_list_fn) { + result_.get_nd_sbp_list_fn = std::move(get_nd_sbp_list_fn); + return *this; +} + +OpRegistry& OpRegistry::SetDumpNdSbpSignatureForOpConfFn( + Operator::DumpNdSbpSignatureForOpConfFn fn) { + result_.dump_nd_sbp_signature_for_op_conf_fn = std::move(fn); + return *this; +} + Maybe OpRegistry::Finish() { CHECK_OR_RETURN(result_.logical_tensor_desc_infer_fn != nullptr) << "No TensorDescInfer function for " << result_.op_type_name; @@ -228,12 +244,12 @@ Maybe OpRegistry::Finish() { == in_physical.shape()); } for (const auto& pair : ctx->outputs()) { - TensorDesc* desc = ctx->OutputTensorDesc(pair.first, pair.second); + TensorDesc* desc = ctx->MutOutputTensorDesc(pair.first, pair.second); *desc = *ctx->LogicalTensorDesc4ArgNameAndIndex(pair.first, pair.second); const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex(pair.first, pair.second); - *desc->mut_shape() = *JUST( - GetPhysicalShape(desc->shape(), nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx())); - *desc->mut_stride() = Stride(desc->shape()); + desc->set_shape(*JUST( + GetPhysicalShape(desc->shape(), nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx()))); + desc->set_stride(Stride(desc->shape())); } } return Maybe::Ok(); @@ -261,7 +277,7 @@ Maybe OpRegistry::Finish() { for (const auto& pair : ctx->outputs()) { *ctx->OutputTensorDevice4ArgNameAndIndex(pair.first, pair.second) = default_device; } - return Stream::New(default_device, StreamRole::kCompute); + return Stream::New(default_device, StreamType::kCompute); }; } return *this; diff --git a/oneflow/core/framework/user_op_registry.h b/oneflow/core/framework/user_op_registry.h index d122cb8cd13327d79b92e41534054d0c7f4fe190..8bdeb97bf63848ef15c23e1ac0455c0d6bdae401 100644 --- a/oneflow/core/framework/user_op_registry.h +++ b/oneflow/core/framework/user_op_registry.h @@ -23,6 +23,7 @@ limitations under the License. #include "oneflow/core/framework/user_op_attr.pb.h" #include "oneflow/core/framework/user_op_conf.pb.h" #include "oneflow/core/operator/op_attribute.pb.h" +#include "oneflow/core/operator/operator.h" namespace oneflow { @@ -39,6 +40,8 @@ class InferSbpSignatureFnContext; class InferOutputBlobTimeShapeFnContext; class InferNdSbpFnContext; class DeviceAndStreamInferContext; +class ComputeComplexityFnContext; +class GetNdSbpSignatureListContext; using CheckAttrFn = std::function(const UserOpDefWrapper&, const UserOpConfWrapper&)>; using TensorDescInferFn = std::function(InferContext*)>; @@ -58,6 +61,9 @@ using OutputArgModifyFn = std::function(const GetOutputArgModifier&, const UserOpConfWrapper&)>; using OutputBlobTimeShapeInferFn = std::function(InferOutputBlobTimeShapeFnContext*)>; using NdSbpInferFn = std::function(InferNdSbpFnContext*)>; +using ComputeComplexityFn = std::function(ComputeComplexityFnContext*)>; +// TODO: set up another context +using GetNdSbpSignatureListFn = std::function(GetNdSbpSignatureListContext*)>; struct OpRegistryResult { OpRegistryResult() @@ -86,6 +92,9 @@ struct OpRegistryResult { OutputArgModifyFn output_arg_modify_fn; OutputBlobTimeShapeInferFn output_blob_time_shape_infer_fn; NdSbpInferFn nd_sbp_infer_fn; + ComputeComplexityFn compute_complexity_fn; + GetNdSbpSignatureListFn get_nd_sbp_list_fn; + Operator::DumpNdSbpSignatureForOpConfFn dump_nd_sbp_signature_for_op_conf_fn; }; class OpRegistry final { @@ -132,6 +141,9 @@ class OpRegistry final { OpRegistry& SetCheckAttrFn(CheckAttrFn fn); OpRegistry& SetDataTypeInferFn(DataTypeInferFn fn); OpRegistry& SetDeviceAndStreamInferFn(DeviceAndStreamInferFn fn); + OpRegistry& SetComputeComplexityFn(ComputeComplexityFn fn); + OpRegistry& SetGetNdSbpSignatureListFn(GetNdSbpSignatureListFn fn); + OpRegistry& SetDumpNdSbpSignatureForOpConfFn(Operator::DumpNdSbpSignatureForOpConfFn fn); Maybe Finish(); OpRegistryResult GetResult() { return result_; } diff --git a/oneflow/core/framework/user_op_registry_manager.cpp b/oneflow/core/framework/user_op_registry_manager.cpp index f573ee9442c1e04d770abfda1bc263c003c7064f..25a378dca7641e4f413d3ef73dec29113c3b0aba 100644 --- a/oneflow/core/framework/user_op_registry_manager.cpp +++ b/oneflow/core/framework/user_op_registry_manager.cpp @@ -17,12 +17,15 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/framework/infer_util.h" -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/kernel/kernel.pb.h" #include "oneflow/core/operator/operator.h" +#include "oneflow/core/common/env_var/env_var.h" namespace oneflow { +DEFINE_ENV_BOOL(ONEFLOW_KERNEL_ENABLE_PRIORITY_EXPERIMENTAL, false); + namespace user_op { UserOpRegistryMgr& UserOpRegistryMgr::Get() { @@ -49,25 +52,6 @@ const OpRegistryResult* UserOpRegistryMgr::GetOpRegistryResult(const std::string return nullptr; } -OpGradRegistry UserOpRegistryMgr::CheckAndGetOpGradRegistry(const std::string& op_type_name) { - CHECK(!op_type_name.empty()); - auto it = op_grad_reg_result_.find(op_type_name); - CHECK(it == op_grad_reg_result_.end()); - return OpGradRegistry().Name(op_type_name); -} - -Maybe UserOpRegistryMgr::Register(OpGradRegistryResult result) { - CHECK_OR_RETURN(op_grad_reg_result_.emplace(result.op_type_name, result).second); - return Maybe::Ok(); -} - -const OpGradRegistryResult* UserOpRegistryMgr::GetOpGradRegistryResult( - const std::string& op_type_name) { - auto it = op_grad_reg_result_.find(op_type_name); - if (it != op_grad_reg_result_.end()) { return &(it->second); } - return nullptr; -} - OpKernelRegistry UserOpRegistryMgr::CheckAndGetOpKernelRegistry(const std::string& op_type_name) { CHECK(!op_type_name.empty()); return OpKernelRegistry().Name(op_type_name); @@ -103,41 +87,59 @@ Maybe UserOpRegistryMgr::GetOpKernelRegistryResul const std::string& op_type_name, const KernelRegContext& ctx) { auto it = op_kernel_reg_result_.find(op_type_name); if (it == op_kernel_reg_result_.end()) { - return Error::OpKernelNotFoundError("There is no kernel registered for Current OperatorConf. ", - {}) + return Error::OpKernelNotFoundError({}) + << "There is no kernel registered for Current OperatorConf. " << GetErrorMsgOfSearchedOp(ctx); } const OpKernelRegistryResult* ret = nullptr; + int32_t cur_priority = kKernelPriorityFallback; + const bool enable_priority_experimental = EnvBool(); for (const auto& reg_val : it->second) { + if (reg_val.priority >= kKernelPriorityExperimental && (!enable_priority_experimental)) { + continue; + } if (reg_val.is_matched_hob->get(ctx)) { - if (ret != nullptr) { - std::vector debug_msgs; - for (const auto& local_reg_val : it->second) { - if (local_reg_val.is_matched_hob->get(ctx)) { - debug_msgs.emplace_back(local_reg_val.is_matched_hob->DebugStr(ctx)); - } - } - return Error::MultipleOpKernelsMatchedError( - "There are more than one kernels matching Current OperatorConf. ", debug_msgs) - << GetErrorMsgOfSearchedOp(ctx); + if (ret == nullptr || reg_val.priority > cur_priority) { + ret = ®_val; + cur_priority = reg_val.priority; + } else if (ret != nullptr && reg_val.priority == cur_priority) { + LOG(WARNING) + << "There are more than one kernels with same priority matching Current OperatorConf. " + << GetErrorMsgOfSearchedOp(ctx); + } else { + // do nothing } - ret = ®_val; } } + if (ret == nullptr) { std::vector debug_msgs; for (const auto& reg_val : it->second) { debug_msgs.emplace_back(reg_val.is_matched_hob->DebugStr(ctx)); } - return Error::OpKernelNotFoundError("Cannot find the kernel matching Current OperatorConf. ", - debug_msgs) + return Error::OpKernelNotFoundError(debug_msgs) + << "Cannot find the kernel matching Current OperatorConf. " << GetErrorMsgOfSearchedOp(ctx); } return ret; } +Maybe UserOpRegistryMgr::IsOpKernelRegistered(const std::string& op_type_name, + const KernelRegContext& ctx) { + auto it = op_kernel_reg_result_.find(op_type_name); + if (it == op_kernel_reg_result_.end()) { return false; } + const bool enable_priority_experimental = EnvBool(); + for (const auto& reg_val : it->second) { + if (reg_val.priority >= kKernelPriorityExperimental && (!enable_priority_experimental)) { + continue; + } + if (reg_val.is_matched_hob->get(ctx)) { return true; } + } + return false; +} + } // namespace user_op } // namespace oneflow diff --git a/oneflow/core/framework/user_op_registry_manager.h b/oneflow/core/framework/user_op_registry_manager.h index 1d76747f84e551ac38d5478e51bce03b98713c70..e46a7c13b757b4e4ebd9c8ecf98bff6a98de17ed 100644 --- a/oneflow/core/framework/user_op_registry_manager.h +++ b/oneflow/core/framework/user_op_registry_manager.h @@ -18,7 +18,6 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/framework/user_op_registry.h" -#include "oneflow/core/framework/user_op_grad_registry.h" #include "oneflow/core/framework/user_op_kernel_registry.h" #include "oneflow/core/common/registry_error.h" @@ -40,14 +39,11 @@ class UserOpRegistryMgr final { Maybe Register(OpRegistryResult result); const OpRegistryResult* GetOpRegistryResult(const std::string& op_type_name); - OpGradRegistry CheckAndGetOpGradRegistry(const std::string& op_type_name); - Maybe Register(OpGradRegistryResult result); - const OpGradRegistryResult* GetOpGradRegistryResult(const std::string& op_type_name); - OpKernelRegistry CheckAndGetOpKernelRegistry(const std::string& op_type_name); Maybe Register(OpKernelRegistryResult result); Maybe GetOpKernelRegistryResult(const std::string& op_type_name, const KernelRegContext& ctx); + Maybe IsOpKernelRegistered(const std::string& op_type_name, const KernelRegContext& ctx); const HashMap& GetAllOpRegistryResults() { return op_reg_result_; @@ -55,7 +51,6 @@ class UserOpRegistryMgr final { private: HashMap op_reg_result_; - HashMap op_grad_reg_result_; HashMap> op_kernel_reg_result_; }; @@ -83,11 +78,6 @@ struct UserOpRegisterTrigger final { #define REGISTER_NO_GRAD_CPU_ONLY_USER_OP(name) REGISTER_NO_GRAD_USER_OP(name).SupportCpuOnly() -#define REGISTER_USER_OP_GRAD(name) \ - static ::oneflow::user_op::UserOpRegisterTrigger<::oneflow::user_op::OpGradRegistry> OF_PP_CAT( \ - g_register_trigger, __COUNTER__) = \ - ::oneflow::user_op::UserOpRegistryMgr::Get().CheckAndGetOpGradRegistry(name) - #define REGISTER_USER_KERNEL(name) \ static ::oneflow::user_op::UserOpRegisterTrigger<::oneflow::user_op::OpKernelRegistry> \ OF_PP_CAT(g_register_trigger, __COUNTER__) = \ diff --git a/oneflow/core/framework/user_op_tensor.h b/oneflow/core/framework/user_op_tensor.h index f974ace9cf6a98f8a067d5682cc370e74ba47654..cce7d5ee5c5f4f3a5f9e3aba28da9e9f76897c46 100644 --- a/oneflow/core/framework/user_op_tensor.h +++ b/oneflow/core/framework/user_op_tensor.h @@ -61,7 +61,7 @@ class Tensor { protected: template void CheckDataType() const { - LOG_IF(FATAL, (std::is_same::value == false && std::is_same::value == false && std::is_same::value == false + LOG_IF(FATAL, (std::is_same::value == false && std::is_same::value == false && data_type() != DataType::kChar && data_type() != GetDataType::value)) << "tensor data_type mismatched. value: " << DataType_Name(data_type()) << ", template T:" << DataType_Name(GetDataType::value); diff --git a/oneflow/core/framework/variable_tensor_mgr.cpp b/oneflow/core/framework/variable_tensor_mgr.cpp index 1b7e528c9c5ae72e6f8d09fca9e62b2d04d9592d..94c11a178a02a66e097308e47f492d78600c21ea 100644 --- a/oneflow/core/framework/variable_tensor_mgr.cpp +++ b/oneflow/core/framework/variable_tensor_mgr.cpp @@ -13,25 +13,38 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include -#include -#include +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/variable_tensor_mgr.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/common/just.h" #include "oneflow/core/common/throw.h" #include "oneflow/core/framework/tensor.h" +#include "oneflow/core/functional/functional.h" namespace oneflow { -void VariableTensorMgr::Set(const std::string& variable_op_name, - const std::shared_ptr& variable_tensor) { - variables_[variable_op_name] = variable_tensor; +Maybe VariableTensorMgr::Set(const std::string& variable_op_name, + const std::shared_ptr& variable_tensor, + const Symbol& dtype) { + if (dtype && variable_tensor->dtype() != dtype) { + LazyMode::Guard guard{false}; + variables_[variable_op_name] = JUST(one::functional::Cast(variable_tensor, dtype, false)); + } else { + variables_[variable_op_name] = variable_tensor; + } + return Maybe::Ok(); } -std::shared_ptr VariableTensorMgr::Get(const std::string& variable_op_name) { +Maybe VariableTensorMgr::Get(const std::string& variable_op_name, + const Symbol& dtype) { if (variables_.find(variable_op_name) != variables_.end()) { - return variables_[variable_op_name]; + const auto variable_tensor = variables_[variable_op_name]; + if (dtype && variable_tensor->dtype() != dtype) { + LazyMode::Guard guard{false}; + return JUST(one::functional::Cast(variable_tensor, dtype, false)); + } + return variable_tensor; } return std::shared_ptr(nullptr); } @@ -45,9 +58,10 @@ Maybe VariableTensorMgr::Fill( const std::vector>& variable_tensors) { CHECK_EQ_OR_THROW(variable_op_names.size(), variable_tensors.size()) << "The number of variable op names is not equal with the number of variable tensors."; + for (size_t i = 0; i < variable_op_names.size(); ++i) { - Set(JUST(oneflow::VectorAt(variable_op_names, i)), - JUST(oneflow::VectorAt(variable_tensors, i))); + JUST(Set(JUST(oneflow::VectorAt(variable_op_names, i)), + JUST(oneflow::VectorAt(variable_tensors, i)))); } return Maybe::Ok(); } @@ -63,7 +77,7 @@ VariableTensorMgr::Dump() { return std::make_tuple(variable_op_names, variable_tensors); } -void VariableTensorMgr::Clear() { +void VariableTensorMgr::Reset() { std::map>().swap(variables_); } diff --git a/oneflow/core/framework/variable_tensor_mgr.h b/oneflow/core/framework/variable_tensor_mgr.h index b5925f66287ea9bc9ec823025edf868ba81d7b1a..c8d352bbf991380d569db3d9bc39f06f7239bc41 100644 --- a/oneflow/core/framework/variable_tensor_mgr.h +++ b/oneflow/core/framework/variable_tensor_mgr.h @@ -13,15 +13,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef ONEFLOW_CORE_OPERATOR_VARIABLE_TENSOR_MGR_H_ -#define ONEFLOW_CORE_OPERATOR_VARIABLE_TENSOR_MGR_H_ +#ifndef ONEFLOW_CORE_FRAMEWORK_VARIABLE_TENSOR_MGR_H_ +#define ONEFLOW_CORE_FRAMEWORK_VARIABLE_TENSOR_MGR_H_ #include #include #include #include "oneflow/core/common/just.h" #include "oneflow/core/common/util.h" - +#include "oneflow/core/framework/dtype.h" namespace oneflow { template @@ -37,15 +37,18 @@ class VariableTensorMgr final { OF_DISALLOW_COPY_AND_MOVE(VariableTensorMgr); ~VariableTensorMgr() = default; - void Set(const std::string& variable_op_name, - const std::shared_ptr& variable_tensor); - std::shared_ptr Get(const std::string& variable_op_name); + Maybe Set(const std::string& variable_op_name, + const std::shared_ptr& variable_tensor, + const Symbol& dtype = Symbol()); + Maybe Get(const std::string& variable_op_name, + const Symbol& dtype = Symbol()); + void Delete(const std::string& variable_op_name); Maybe Fill(const std::vector& variable_op_names, const std::vector>& variable_tensors); std::tuple, std::vector>> Dump(); std::vector DumpNames(); - void Clear(); + void Reset(); private: friend class Singleton; @@ -56,4 +59,4 @@ class VariableTensorMgr final { } // namespace oneflow -#endif // ONEFLOW_CORE_OPERATOR_VARIABLE_TENSOR_MGR_H_ +#endif // ONEFLOW_CORE_FRAMEWORK_VARIABLE_TENSOR_MGR_H_ diff --git a/oneflow/core/functional/function_library.h b/oneflow/core/functional/function_library.h index 570edffb3bcf5fa88a9629b1ae7c23698c356749..158fce88e05a03c024e28df2b8f6c441132d6c93 100644 --- a/oneflow/core/functional/function_library.h +++ b/oneflow/core/functional/function_library.h @@ -21,6 +21,7 @@ limitations under the License. #include "oneflow/core/functional/packed_functor.h" #include "oneflow/core/common/stride.h" #include "oneflow/core/framework/tensor_methods.h" +#include "oneflow/core/common/throw.h" namespace oneflow { namespace one { @@ -72,7 +73,7 @@ class FunctionLibrary { auto* functors = PackedFuncCreatorMap::FType>::Get(); const auto& it = functors->find(func_name); CHECK_OR_RETURN(it != functors->end()) - << "Functor was not found for \"" << func_name + << Error::RuntimeError() << "Functor was not found for \"" << func_name << "\", please check whether the functor has been registered correctly or not."; return it->second(); } @@ -89,8 +90,9 @@ class FunctionLibrary { void add_functor_creator(const std::string& func_name, Creator creator) { using func_type = typename function_traits::func_type; auto* functors = PackedFuncCreatorMap::FType>::Get(); - CHECK_EQ(functors->count(func_name), 0) - << "The functor with name " << func_name << " has been registered more than once."; + CHECK_OR_THROW(functors->count(func_name) == 0) + << Error::RuntimeError() << "The functor with name " << func_name + << " has been registered more than once."; functors->emplace(func_name, creator); } }; diff --git a/oneflow/core/functional/functional_api.yaml b/oneflow/core/functional/functional_api.yaml index 5d4976d92d1a11d1f3910b1367e93c0665ff518e..a5ccb1017e32328318b8e16f945026912ae5028d 100644 --- a/oneflow/core/functional/functional_api.yaml +++ b/oneflow/core/functional/functional_api.yaml @@ -68,6 +68,14 @@ signature: "Tensor (Tensor input, Tensor tensor1, Tensor tensor2, *, Scalar value=1) => InplaceAddcmul" bind_python: true +- name: "addcdiv" + signature: "Tensor (Tensor input, Tensor tensor1, Tensor tensor2, *, Scalar value=1) => AddCDiv" + bind_python: true + +- name: "addcdiv_" + signature: "Tensor (Tensor input, Tensor tensor1, Tensor tensor2, *, Scalar value=1) => InplaceAddCDiv" + bind_python: true + - name: "div" signature: [ @@ -86,11 +94,15 @@ bind_python: true - name: "div_grad" - signature: "Tensor (Tensor y, Tensor z, Tensor dz) => DivGrad" + signature: "Tensor (Tensor dz, Tensor z, Tensor y) => DivGrad" bind_python: False - name: "equal" - signature: + signature: "Bool (Tensor input, Tensor other) => Equal" + bind_python: true + +- name: "broadcast_equal" + signature: [ "Tensor (Tensor input, Tensor other) => BroadcastEqual", "Tensor (Tensor input, Scalar other) => ScalarLogicalEqual", @@ -185,18 +197,18 @@ bind_python: True - name: "pow_x_grad" - signature: "Tensor (Tensor dz, Tensor x, Tensor y) => PowXGrad" + signature: "Tensor (Tensor x, Tensor y, Tensor dz) => PowXGrad" bind_python: False - name: "pow_y_grad" - signature: "Tensor (Tensor dz, Tensor x, Tensor y) => PowYGrad" + signature: "Tensor (Tensor x, Tensor y, Tensor dz) => PowYGrad" bind_python: False - name: "searchsorted" signature: [ - 'Tensor (Tensor sorted_sequence, Tensor values, Bool out_int32=False, Bool right=False) => SearchSorted', - 'Tensor (Tensor sorted_sequence, Scalar values, Bool out_int32=False, Bool right=False) => SearchSortedScalar', + "Tensor (Tensor sorted_sequence, Tensor values, Bool out_int32=False, Bool right=False) => SearchSorted", + "Tensor (Tensor sorted_sequence, Scalar values, Bool out_int32=False, Bool right=False) => SearchSortedScalar", ] bind_python: True @@ -213,11 +225,11 @@ bind_python: False - name: "broadcast_pow_x_grad" - signature: "Tensor (Tensor dz, Tensor x, Tensor y, Tensor z) => BroadcastPowXGrad" + signature: "Tensor (Tensor x, Tensor y, Tensor dz) => BroadcastPowXGrad" bind_python: False - name: "broadcast_pow_y_grad" - signature: "Tensor (Tensor dz, Tensor x, Tensor y, Tensor z) => BroadcastPowYGrad" + signature: "Tensor (Tensor x, Tensor y, Tensor dz) => BroadcastPowYGrad" bind_python: False - name: "floor_divide" @@ -237,6 +249,22 @@ signature: "Tensor (Tensor dz, Tensor x, Tensor y) => FloorDivYGrad" bind_python: False +- name: "trunc_divide" + signature: + [ + "Tensor (Tensor input, Tensor other) => TruncDiv", + "Tensor (Tensor input, Scalar other, *, Bool inplace=False) => ScalarTruncDiv", + ] + bind_python: True + +- name: "truncdiv_x_grad" + signature: "Tensor (Tensor dz, Tensor x, Tensor y) => TruncDivXGrad" + bind_python: False + +- name: "truncdiv_y_grad" + signature: "Tensor (Tensor dz, Tensor x, Tensor y) => TruncDivYGrad" + bind_python: False + - name: "xdivy_x_grad" signature: "Tensor (Tensor dz, Tensor x, Tensor y) => XdivyXGrad" bind_python: False @@ -254,26 +282,29 @@ bind_python: False - name: "max" - signature: [ - "Tensor (Tensor input) => Max", - "TensorTuple (Tensor input, Int32 dim, Bool keepdim=False) => Max", - "Tensor (Tensor input, Tensor other) => Max" - ] + signature: + [ + "Tensor (Tensor input) => Max", + "TensorTuple[values, indices] (Tensor input, Int32 dim, Bool keepdim=False) => Max", + "Tensor (Tensor input, Tensor other) => Max", + ] bind_python: True - name: "min" - signature: [ - "Tensor (Tensor input) => Min", - "TensorTuple (Tensor input, Int32 dim, Bool keepdim=False) => Min", - "Tensor (Tensor input, Tensor other) => Min" - ] + signature: + [ + "Tensor (Tensor input) => Min", + "TensorTuple[values, indices] (Tensor input, Int32 dim, Bool keepdim=False) => Min", + "Tensor (Tensor input, Tensor other) => Min", + ] bind_python: True - name: "median" - signature: [ - "Tensor (Tensor input) => Median", - "TensorTuple (Tensor input, Int32 dim=-1, Bool keepdim=False) => MedianWithIndices", - ] + signature: + [ + "Tensor (Tensor input) => Median", + "TensorTuple[values, indices] (Tensor input, Int32 dim=-1, Bool keepdim=False) => MedianWithIndices", + ] bind_python: True - name: "reduce_max" @@ -285,38 +316,50 @@ bind_python: True - name: "reduce_sum" + signature: + [ + "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceSum", + "Tensor (Tensor x) => ReduceSumWhole", + ] + bind_python: True + +- name: "reduce_nansum" signature: [ - "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceSum", - "Tensor (Tensor x) => ReduceSumWhole" + "Tensor (Tensor input, Int32List[1] dim, Bool keepdim=False, *, DataType dtype=None) => ReduceNanSum", + "Tensor (Tensor input, *, DataType dtype=None) => ReduceNanSumWhole" ] bind_python: True - name: "reduce_mean" - signature: [ - "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceMean", - "Tensor (Tensor x) => ReduceMeanWhole" - ] + signature: + [ + "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceMean", + "Tensor (Tensor x) => ReduceMeanWhole", + ] bind_python: True - name: "reduce_all" - signature: [ - "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceAll", - "Tensor (Tensor x) => ReduceAllWhole" - ] + signature: + [ + "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceAll", + "Tensor (Tensor x) => ReduceAllWhole", + ] bind_python: True - name: "reduce_any" - signature: [ - "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceAny", - "Tensor (Tensor x) => ReduceAnyWhole" - ] + signature: + [ + "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => ReduceAny", + "Tensor (Tensor x) => ReduceAnyWhole", + ] bind_python: True - name: "reduce_prod" - signature: [ - "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False, *, DataType dtype=None) => ReduceProd", - "Tensor (Tensor x, *, DataType dtype=None) => ReduceProdWhole", - ] + signature: + [ + "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False, *, DataType dtype=None) => ReduceProd", + "Tensor (Tensor x, *, DataType dtype=None) => ReduceProdWhole", + ] bind_python: True - name: "reduce_min_device_stage" @@ -351,6 +394,18 @@ signature: "Tensor (Tensor out_diff, Tensor mask, Tensor device_count, Int32List axis, Bool keepdims=False) => ReduceMaxGlobalStageGrad" bind_python: False +- name: "logsumexp" + signature: "Tensor (Tensor x, Int32List[1] dim, Bool keepdim=False) => LogSumExp" + bind_python: True + +- name: "logaddexp" + signature: "Tensor (Tensor x, Tensor y) => LogAddExp" + bind_python: True + +- name: "quantile" + signature: 'Tensor (Tensor x, Scalar q, Int32 dim, Bool keepdim=False, String interpolation="linear", Tensor out=None) => Quantile' + bind_python: True + - name: "transpose" signature: [ @@ -360,13 +415,17 @@ bind_python: True - name: "as_strided" - signature: "Tensor (Tensor input, Int32List size, Int32List stride, Int32 storage_offset=0) => AsStrided" + signature: "Tensor (Tensor input, Int64List size, Int64List stride, Int64 storage_offset=0) => AsStrided" bind_python: True - name: "as_strided_grad" - signature: "Tensor (Tensor dy, Tensor input, Int32List size, Int32List stride, Int32 storage_offset=0) => AsStridedGrad" + signature: "Tensor (Tensor dy, Tensor input, Int64List size, Int64List stride, Int64 storage_offset=0) => AsStridedGrad" bind_python: False +- name: "as_strided_" + signature: "Tensor (Tensor input, Int64List size, Int64List stride, Int64 storage_offset=0) => InplaceAsStrided" + bind_python: True + - name: "select" signature: "Tensor (Tensor input, Int32 dim, Int32 index) => Select" bind_python: True @@ -393,7 +452,7 @@ - name: "t" signature: "Tensor (Tensor input) => TransposeAllDimFunction" - bind_python: True + bind_python: True - name: "not_equal_zero" signature: "Tensor (Tensor x) => NotEqualZero" @@ -484,6 +543,14 @@ signature: "Tensor (Tensor x, Tensor dy) => Log2Grad" bind_python: False +- name: "log10" + signature: "Tensor (Tensor x) => Log10" + bind_python: True + +- name: "log10_grad" + signature: "Tensor (Tensor x, Tensor dy) => Log10Grad" + bind_python: False + - name: "sqrt" signature: "Tensor (Tensor x) => Sqrt" bind_python: True @@ -520,6 +587,10 @@ signature: "Tensor (Tensor x, Int32List[1] dim=None, Bool unbiased=None, Bool keepdim=None) => Variance" bind_python: True +- name: "rms_layer_norm" + signature: "Tensor (Tensor hidden_states, Tensor weight, Float variance_epsilon) => RMSLayerNormalization" + bind_python: True + - name: "relu" signature: "Tensor (Tensor x, Bool inplace=False) => Relu" bind_python: True @@ -528,6 +599,15 @@ signature: "Tensor (Tensor dy, Tensor y) => ReluGrad" bind_python: False +- name: "hann_window" + signature: [ + "Tensor (Int64 window_length, Bool periodic=True, *, Device device=None, DataType dtype=None, + Bool requires_grad=False) => HannWindow", + "Tensor (Int64 window_length, Bool periodic=True, *, Placement placement, SbpList sbp, DataType dtype=None, + Bool requires_grad=False) => GlobalHannWindow", + ] + bind_python: True + - name: "hardtanh" signature: "Tensor (Tensor x, Double min_val, Double max_val) => HardTanh" bind_python: True @@ -584,16 +664,40 @@ signature: "Tensor (Tensor dy, Tensor x) => GeluGrad" bind_python: False +- name: "fast_gelu" + signature: "Tensor (Tensor x) => FastGelu" + bind_python: True + +- name: "fast_gelu_grad" + signature: "Tensor (Tensor dy, Tensor x) => FastGeluGrad" + bind_python: False + +- name: "quick_gelu" + signature: "Tensor (Tensor x) => QuickGelu" + bind_python: True + +- name: "quick_gelu_grad" + signature: "Tensor (Tensor dy, Tensor x) => QuickGeluGrad" + bind_python: False + +- name: "gelu_with_approximate" + signature: 'Tensor (Tensor x, String approximate="none") => GeluWithApproximate' + bind_python: True + - name: "glu" signature: "Tensor (Tensor input, Int64 dim=-1) => Glu" bind_python: True +- name: "fused_glu" + signature: "Tensor (Tensor x, Tensor w, Tensor b, Tensor v=None, Tensor c=None, String activation=\"none\") => FusedGlu" + bind_python: True + - name: "sigmoid" signature: "Tensor (Tensor x) => Sigmoid" bind_python: True - name: "sigmoid_grad" - signature: "Tensor (Tensor x, Tensor dy) => SigmoidGrad" + signature: "Tensor (Tensor y, Tensor dy) => SigmoidGrad" bind_python: True - name: "hardsigmoid" @@ -620,10 +724,18 @@ signature: "Tensor (Tensor dy, Tensor y) => SoftmaxGrad" bind_python: False +- name: "gumbel_softmax" + signature: "Tensor (Tensor x, Double tau=1., Int64 dim=None, Bool hard=False, Generator generator=None) => GumbelSoftmax" + bind_python: True + - name: "log_softmax" signature: "Tensor (Tensor x, Int64 dim=None) => LogSoftmax" bind_python: True +- name: "log_softmax_grad" + signature: "Tensor (Tensor dy, Tensor y) => LogSoftmaxGrad" + bind_python: False + - name: "hardswish" signature: "Tensor (Tensor x) => HardSwish" bind_python: True @@ -642,14 +754,14 @@ - name: "normal" signature: [ - "Tensor (Float mean, Float std, Shape size, *, Tensor out=None, DataType dtype=None, Device device=None, - Generator generator=None, Bool requires_grad=False) => Normal", - "Tensor (Float mean, Float std, Int32 size, *, Tensor out=None, DataType dtype=None, Device device=None, - Generator generator=None, Bool requires_grad=False) => Normal2", - "Tensor (Float mean, Float std, Shape size, *, Tensor out=None, Placement placement, SbpList sbp, DataType dtype=None, - Generator generator=None, Bool requires_grad=False) => ConsistentNormal", - "Tensor (Float mean, Float std, Int32 size, *, Tensor out=None, Placement placement, SbpList sbp, DataType dtype=None, - Generator generator=None, Bool requires_grad=False) => ConsistentNormal2", + "Tensor (Float mean, Float std, Shape size, *, Tensor out=None, DataType dtype=None, Device device=None, + Generator generator=None, Bool requires_grad=False) => Normal", + "Tensor (Float mean, Float std, Int32 size, *, Tensor out=None, DataType dtype=None, Device device=None, + Generator generator=None, Bool requires_grad=False) => Normal2", + "Tensor (Float mean, Float std, Shape size, *, Tensor out=None, Placement placement, SbpList sbp, DataType dtype=None, + Generator generator=None, Bool requires_grad=False) => GlobalNormal", + "Tensor (Float mean, Float std, Int32 size, *, Tensor out=None, Placement placement, SbpList sbp, DataType dtype=None, + Generator generator=None, Bool requires_grad=False) => GlobalNormal2", ] bind_python: True @@ -677,19 +789,24 @@ - name: "normalization_add_relu_grad" signature: "TensorTuple (Tensor x, Tensor dy, Tensor moving_mean, Tensor moving_variance, - Tensor gamma, Tensor beta, Tensor reserve_space, Tensor y, Int32 axis=1, + Tensor gamma, Tensor beta, Tensor reserve_space, Tensor y, Int32 axis=1, Float epsilon=1e-5, Bool has_addend) => NormalizationAddReluGrad" bind_python: False - name: "eye" - signature: [ - "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Device device=None, Bool requires_grad=False) => Eye", - "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, String device, Bool requires_grad=False) => Eye", - "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Bool requires_grad=False, Placement placement, SbpList sbp) => Eye", - "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Bool requires_grad=False, Placement placement, Sbp sbp) => Eye", + signature: + [ + "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Device device=None, Bool requires_grad=False) => Eye", + "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, String device, Bool requires_grad=False) => Eye", + "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Bool requires_grad=False, Placement placement, SbpList sbp) => Eye", + "Tensor (Scalar n, Scalar m=None, *, DataType dtype=kFloat, Bool requires_grad=False, Placement placement, Sbp sbp) => Eye", ] bind_python: True +- name: "eye_" + signature: "Tensor (Tensor x) => EyeInplace" + bind_python: True + - name: "erfinv" signature: "Tensor (Tensor x) => Erfinv" bind_python: True @@ -709,8 +826,8 @@ - name: "global_arange" signature: [ "Tensor (Scalar start, Scalar end, Scalar step=1, *, DataType dtype=None, - Placement placement, SbpList sbp) => ConsistentArange", - "Tensor (Scalar end, *, DataType dtype=None, Placement placement, SbpList sbp) => ConsistentArange", + Placement placement, SbpList sbp) => GlobalArange", + "Tensor (Scalar end, *, DataType dtype=None, Placement placement, SbpList sbp) => GlobalArange", ] bind_python: True @@ -730,6 +847,10 @@ signature: "TensorTuple (Tensor x, DataType dtype=kInt32) => ArgWhere" bind_python: True +- name: "nonzero" + signature: "TensorTuple (Tensor x, Bool as_tuple=False) => NonZero" + bind_python: True + - name: "broadcast_like" signature: "Tensor (Tensor x, Tensor like, Int32List broadcast_axes=[]) => BroadcastLike" bind_python: True @@ -738,6 +859,14 @@ signature: "Tensor (Tensor x, DataType dtype, Bool pin_memory=False) => Cast" bind_python: True +- name: "global_tensor_constant" + signature: "Tensor (Shape shape, Tensor value, *, DataType dtype, Placement placement, SbpList sbp) => GlobalTensorConstant" + bind_python: True + +- name: "tensor_constant" + signature: "Tensor (Shape shape, Tensor value, *, DataType dtype, Device device=None) => TensorConstant" + bind_python: True + - name: "constant" signature: [ @@ -748,7 +877,7 @@ - name: "global_constant" signature: [ - "Tensor (Shape shape, Scalar value, *, DataType dtype, Placement placement, SbpList sbp) => ConsistentConstant", + "Tensor (Shape shape, Scalar value, *, DataType dtype, Placement placement, SbpList sbp) => GlobalConstant", ] bind_python: True @@ -759,22 +888,31 @@ - name: "global_empty" signature: [ - "Tensor (Shape shape, *, DataType dtype, Placement placement, SbpList sbp) => ConsistentEmpty", + "Tensor (Shape shape, *, DataType dtype, Placement placement, SbpList sbp) => GlobalEmpty", ] bind_python: True - name: "zeros_like" signature: "Tensor (Tensor x) => ZerosLike" - bind_python: True + bind_python: False - name: "ones_like" signature: "Tensor (Tensor x) => OnesLike" - bind_python: True + bind_python: False - name: "bernoulli" signature: [ - "Tensor (Tensor x, *, DataType dtype=kFloat, Generator generator=None) => Bernoulli", + "Tensor (Tensor input, *, DataType dtype=kFloat, Generator generator=None, Bool inplace=False) => Bernoulli", + "Tensor (Tensor input, Double p, *, DataType dtype=kFloat, Generator generator=None, Bool inplace=False) => BernoulliProb", + ] + bind_python: True + +- name: "bernoulli_" + signature: + [ + "Tensor (Tensor input, *, DataType dtype=kFloat, Generator generator=None) => BernoulliInplace", + "Tensor (Tensor input, Double p, *, DataType dtype=kFloat, Generator generator=None) => BernoulliProbInplace", ] bind_python: True @@ -788,23 +926,23 @@ - name: "conv1d" signature: - "Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[1] stride=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[1] stride=1, Int32List[1] padding=0, Int32List[1] dilation=1, Int32 groups=1, - String channel_pos=\"channels_first\") => Conv1d" + String channel_pos="channels_first") => Conv1d' bind_python: True - name: "conv2d" signature: - "Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[2] stride=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[2] stride=1, Int32List[2] padding=0, Int32List[2] dilation=1, Int32 groups=1, - String channel_pos=\"channels_first\") => Conv2d" + String channel_pos="channels_first") => Conv2d' bind_python: True - name: "conv3d" signature: - "Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[3] stride=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[3] stride=1, Int32List[3] padding=0, Int32List[3] dilation=1, Int32 groups=1, - String channel_pos=\"channels_first\") => Conv3d" + String channel_pos="channels_first") => Conv3d' bind_python: True - name: "fake_quantization" @@ -828,7 +966,7 @@ - name: "moving_average_min_max_observer" signature: "TensorTuple (Tensor in, Tensor current_train_step, Tensor moving_max, Tensor moving_min, - Bool training, Int64 stop_update_after_iters, String quantization_formula, + Bool training, Int64 stop_update_after_iters, String quantization_formula, Int32 quantization_bit, String quantization_scheme, Float momentum) => MovingAverageMinMaxObserver" bind_python: True @@ -854,22 +992,22 @@ - name: "deconv1d" signature: - 'Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[1] stride=1, - Int32List[1] padding=0, Int32List[1] output_padding=0, Int32 groups=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[1] stride=1, + Int32List[1] padding=0, Int32List[1] output_padding=0, Int32 groups=1, Int32List[1] dilation=1, String data_format="channels_first") => Deconv1d' bind_python: True - name: "deconv2d" signature: - 'Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[2] stride=1, - Int32List[2] padding=0, Int32List[2] output_padding=0, Int32 groups=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[2] stride=1, + Int32List[2] padding=0, Int32List[2] output_padding=0, Int32 groups=1, Int32List[2] dilation=1, String data_format="channels_first") => Deconv2d' bind_python: True - name: "deconv3d" signature: - 'Tensor (Tensor x, Tensor weight, Tensor bias=None, Int32List[3] stride=1, - Int32List[3] padding=0, Int32List[3] output_padding=0, Int32 groups=1, + 'Tensor (Tensor input, Tensor weight, Tensor bias=None, Int32List[3] stride=1, + Int32List[3] padding=0, Int32List[3] output_padding=0, Int32 groups=1, Int32List[3] dilation=1, String data_format="channels_first") => Deconv3d' bind_python: True @@ -877,10 +1015,6 @@ signature: "Tensor (Tensor x, Shape shape) => Expand" bind_python: True -- name: "expand_grad" - signature: "Tensor (Tensor dy, Int32List out, Int32List expand) => ExpandGrad" - bind_python: False - - name: "repeat" signature: "Tensor (Tensor input, Shape repeat_shape) => Repeat" bind_python: True @@ -890,10 +1024,11 @@ bind_python: False - name: "repeat_interleave" - signature: [ - "Tensor (Tensor input, Int32 repeats, Int32 dim=None) => RepeatInterLeaveInt", - "Tensor (Tensor input, Tensor repeats, Int32 dim, Int32 output_size=None) => RepeatInterLeaveTensor", - ] + signature: + [ + "Tensor (Tensor input, Int32 repeats, Int32 dim=None) => RepeatInterLeaveInt", + "Tensor (Tensor input, Tensor repeats, Int32 dim, Int32 output_size=None) => RepeatInterLeaveTensor", + ] bind_python: True - name: "tile" @@ -912,10 +1047,20 @@ signature: "Tensor (Tensor input, Int32 dim) => Unsqueeze" bind_python: True +- name: "unsqueeze_multiple" + signature: "Tensor (Tensor input, Int32List dim, Int32 dims) => UnsqueezeMultiple" + bind_python: False + +- name: "unsqueeze_" + signature: "Tensor (Tensor input, Int32 dim) => InplaceUnsqueeze" + bind_python: True + - name: "squeeze" - signature: [ - "Tensor (Tensor x, Int32List[1] dim=None) => Squeeze", - ] + signature: "Tensor (Tensor x, Int32List[1] dim=None) => Squeeze" + bind_python: True + +- name: "squeeze_" + signature: "Tensor (Tensor x, Int32List[1] dim=None) => InplaceSqueeze" bind_python: True - name: "exp" @@ -973,38 +1118,35 @@ bind_python: True - name: "mm" - signature: - "Tensor (Tensor input, Tensor mat2) => MatMulNoBroadCast" + signature: "Tensor (Tensor input, Tensor mat2) => MatMulNoBroadCast" bind_python: True -- name: "mv" - signature: - "Tensor (Tensor input, Tensor vec) => Mv" +- name: "fused_mlp" + signature: "Tensor (Tensor x, TensorTuple weights, TensorTuple biases, Bool skip_final_activation) => FusedMLP" bind_python: True -- name: "fused_mlp" - signature: - "Tensor (Tensor x, TensorTuple weights, TensorTuple biases, Bool skip_final_activation) => FusedMLP" +- name: "fused_matmul_bias" + signature: "Tensor (Tensor x, Tensor weight, Tensor bias, Tensor _add_to_output=None) => FusedMatmulBias" bind_python: True +- name: "fused_mlp_grad" + signature: "TensorTuple (Tensor dy, Tensor x, TensorTuple weights, TensorTuple cublas_aux, TensorTuple hidden, FloatList alpha_list) => FusedMLPGrad" + bind_python: False + - name: "cublas_bias_add_relu_matmul_grad" - signature: - "TensorTuple (Tensor dy, Tensor weight, Tensor aux, Double alpha=1.0) => CublasBiasAddReluMatmulGrad" + signature: "TensorTuple (Tensor dy, Tensor weight, Tensor aux, Double alpha=1.0) => CublasBiasAddReluMatmulGrad" bind_python: False - name: "cublas_matmul_bias_add_grad" - signature: - "TensorTuple (Tensor dy, Tensor x) => CublasMatmulBiasAddGrad" + signature: "TensorTuple (Tensor dy, Tensor x) => CublasMatmulBiasAddGrad" bind_python: False - name: "fused_matmul_bias_add_relu_dropout" - signature: - "Tensor (Tensor x, TensorTuple weights, TensorTuple biases, Bool skip_final_activation, FloatList dropout_rate_list, Generator generator=None) => FusedMatmulBiasAddReluDropout" + signature: "Tensor (Tensor x, TensorTuple weights, TensorTuple biases, Bool skip_final_activation, FloatList dropout_rate_list, Generator generator=None) => FusedMatmulBiasAddReluDropout" bind_python: True - name: "fused_relu_dropout_grad" - signature: - "Tensor (Tensor dy, Tensor mask, Float scale) => FusedReluDropoutGrad" + signature: "Tensor (Tensor dy, Tensor mask, Float scale) => FusedReluDropoutGrad" bind_python: False - name: "broadcast_matmul_grad_b" @@ -1017,29 +1159,63 @@ Double alpha=1.0) => BatchMatMul" bind_python: True +- name: "baddbmm" + signature: + "Tensor (Tensor input, Tensor batch1, Tensor batch2, *, Double beta=1.0, Double alpha=1.0) => BaddBmm" + bind_python: True + +- name: "matrix_vector_product" + signature: "Tensor (Tensor input, Tensor vec) => MatrixVectorProduct" + bind_python: True + +- name: "matrix_vector_product_grad_a" + signature: "Tensor (Tensor dy, Tensor b) => MatrixVectorProductGradA" + bind_python: False + +- name: "matrix_vector_product_grad_b" + signature: "Tensor (Tensor dy, Tensor a) => MatrixVectorProductGradB" + bind_python: False + +- name: "vector_matrix_product" + signature: "Tensor (Tensor vec, Tensor input) => VectorMatrixProduct" + bind_python: False + +- name: "vector_matrix_product_grad_a" + signature: "Tensor (Tensor dy, Tensor b) => VectorMatrixProductGradA" + bind_python: False + +- name: "vector_matrix_product_grad_b" + signature: "Tensor (Tensor dy, Tensor a) => VectorMatrixProductGradB" + bind_python: False + - name: "tensordot" - signature: [ - "Tensor (Tensor a, Tensor b, Int32List dims_a, Int32List dims_b) => TensorDot", - "Tensor (Tensor a, Tensor b, Int32 dims) => TensorDotIntDims" - ] + signature: + [ + "Tensor (Tensor a, Tensor b, Int32List dims_a, Int32List dims_b) => TensorDot", + "Tensor (Tensor a, Tensor b, Int32 dims) => TensorDotIntDims", + ] bind_python: True - name: "l1_loss" - signature: "Tensor(Tensor input, Tensor target, String reduction) => L1Loss" + signature: 'Tensor(Tensor input, Tensor target, String reduction="mean") => L1Loss' bind_python: True - name: "mse_loss" - signature: "Tensor(Tensor input, Tensor target, String reduction) => MseLoss" + signature: 'Tensor(Tensor input, Tensor target, String reduction="mean") => MseLoss' bind_python: True - name: "kl_div_loss" - signature: "Tensor(Tensor input, Tensor target, Bool log_target, String reduction) => KLDivLoss" + signature: 'Tensor(Tensor input, Tensor target, Bool log_target=False, String reduction="mean") => KLDivLoss' bind_python: True - name: "kl_div_loss_grad" signature: "Tensor(Tensor dy, Tensor input, Tensor target, Bool log_target) => KLDivLossGrad" bind_python: False +- name: "kl_div_loss_target_grad" + signature: "Tensor(Tensor dy, Tensor input, Tensor target, Bool log_target) => KLDivLossTargetGrad" + bind_python: False + - name: "nll_loss" signature: "Tensor(Tensor input, Tensor target, Tensor weight=None, Int64 ignore_index, String reduction) => NLLLoss" bind_python: True @@ -1049,25 +1225,37 @@ bind_python: False - name: "binary_cross_entropy_loss" - signature: "Tensor(Tensor input, Tensor target, Tensor weight=None, String reduction) => BinaryCrossEntropyLoss" + signature: 'Tensor(Tensor input, Tensor target, Tensor weight=None, String reduction="mean") => BinaryCrossEntropyLoss' bind_python: True - name: "binary_cross_entropy_loss_grad" signature: "Tensor(Tensor dy, Tensor input, Tensor target, Tensor weight=None) => BinaryCrossEntropyLossGrad" bind_python: False +- name: "binary_cross_entropy_loss_target_grad" + signature: "Tensor(Tensor dy, Tensor input, Tensor target, Tensor weight=None) => BinaryCrossEntropyLossTargetGrad" + bind_python: False + - name: "binary_cross_entropy_with_logits_loss" - signature: "Tensor(Tensor input, Tensor target, Tensor weight=None, Tensor pos_weight=None, String reduction) => BinaryCrossEntropyWithLogitsLoss" + signature: 'Tensor(Tensor input, Tensor target, Tensor weight=None, Tensor pos_weight=None, String reduction="mean") => BinaryCrossEntropyWithLogitsLoss' bind_python: True - name: "binary_cross_entropy_with_logits_loss_grad" signature: "Tensor(Tensor dy, Tensor input, Tensor target, Tensor weight=None, Tensor pos_weight=None) => BinaryCrossEntropyWithLogitsLossGrad" bind_python: True +- name: "binary_cross_entropy_with_logits_loss_target_grad" + signature: "Tensor(Tensor dy, Tensor input, Tensor target, Tensor weight=None, Tensor pos_weight=None) => BinaryCrossEntropyWithLogitsLossTargetGrad" + bind_python: False + - name: "binary_cross_entropy_with_logits_reduce_mean_loss_grad" signature: "Tensor(Tensor dy, Tensor input, Tensor target) => BinaryCrossEntropyWithLogitsReduceMeanLossGrad" bind_python: False +- name: "binary_cross_entropy_with_logits_reduce_mean_loss_target_grad" + signature: "Tensor(Tensor dy, Tensor input, Tensor target) => BinaryCrossEntropyWithLogitsReduceMeanLossTargetGrad" + bind_python: False + - name: "sparse_cross_entropy" signature: "Tensor (Tensor prediction, Tensor label, Int64 depth) => SparseCrossEntropy" bind_python: True @@ -1081,9 +1269,17 @@ bind_python: True - name: "cross_entropy" - signature: "Tensor(Tensor input, Tensor target, Tensor weight=None, Int64 ignore_index=-100, String reduction=\"mean\") => CrossEntropy" + signature: 'Tensor(Tensor input, Tensor target, Tensor weight=None, Int64 ignore_index=-100, String reduction="mean", Double label_smoothing=0.0) => CrossEntropy' bind_python: True +- name: "cross_entropy_label_smoothing" + signature: 'Tensor(Tensor input, Tensor target, Tensor weight=None, Int64 ignore_index=-100, String reduction="mean", Double label_smoothing=0.0) => CrossEntropyLabelSmoothing' + bind_python: False + +- name: "cross_entropy_prob" + signature: 'Tensor(Tensor input, Tensor target, Tensor weight=None, String reduction="mean", Double label_smoothing=0.0) => CrossEntropyProb' + bind_python: False + - name: "distributed_sparse_cross_entropy_grad" signature: "Tensor (Tensor prediction, Tensor label, Tensor dy, Int64 depth) => SparseCrossEntropyMsGrad" bind_python: False @@ -1096,6 +1292,10 @@ signature: "Tensor (Tensor dy, Tensor prob, Tensor label, Int64 depth) => SparseSoftmaxCrossEntropyGrad" bind_python: False +- name: "sparse_softmax_cross_entropy_ms_grad" + signature: "Tensor (Tensor dy, Tensor prob, Tensor label, Int64 depth) => SparseSoftmaxCrossEntropyMsGrad" + bind_python: False + - name: "softmax_cross_entropy" signature: "Tensor (Tensor logits, Tensor label) => SoftmaxCrossEntropy" bind_python: True @@ -1120,17 +1320,16 @@ signature: "Tensor (Tensor dy, Tensor label, Tensor theta, Float m1, Float m2, Float m3, Int64 depth) => CombinedMarginLossGrad" bind_python: False - - name: "triplet_margin_loss" signature: "Tensor (Tensor anchor, Tensor positive, Tensor negative, *, Float margin, Float p, Float eps, Bool swap, String reduction) => TripletMarginLoss" bind_python: True - + - name: "margin_ranking_loss" signature: "Tensor (Tensor input_1, Tensor input_2, Tensor target, Float margin, String reduction) => MarginRankingLoss" bind_python: True - name: "ctc_loss" - signature: "Tensor (Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Int64 max_target_length, Int32 blank, Bool zero_infinity, String reduction) => CtcLoss" + signature: "Tensor (Tensor log_probs, Tensor targets, Tensor input_lengths, Tensor target_lengths, Int64 max_target_length, Int64 blank, Bool zero_infinity, String reduction) => CtcLoss" bind_python: True - name: "affine_grid" @@ -1160,45 +1359,49 @@ bind_python: true - name: "masked_fill" - signature: "Tensor (Tensor x, Tensor mask, Scalar value) => MaskedFill" + signature: "Tensor (Tensor input, Tensor mask, Scalar value) => MaskedFill" + bind_python: true + +- name: "masked_fill_" + signature: "Tensor (Tensor input, Tensor mask, Scalar value) => MaskedFillInplace" bind_python: true - name: "movedim" - signature: [ - "Tensor (Tensor input, Int32 source, Int32 destination) => MovedimInt", - "Tensor (Tensor input, Int32List source, Int32List destination) => MovedimVec", - ] + signature: + [ + "Tensor (Tensor input, Int32 source, Int32 destination) => MovedimInt", + "Tensor (Tensor input, Int32List source, Int32List destination) => MovedimVec", + ] bind_python: True - name: "tensor_split" - signature: [ - "TensorTuple (Tensor input, Int32 indices_or_sections, Int32 dim=0) => TensorSplitInt", - "TensorTuple (Tensor input, Int32List indices_or_sections, Int32 dim=0) => TensorSplitVec", - ] + signature: + [ + "TensorTuple (Tensor input, Int32 indices_or_sections, Int32 dim=0) => TensorSplitInt", + "TensorTuple (Tensor input, Int32List indices_or_sections, Int32 dim=0) => TensorSplitVec", + ] bind_python: True - name: "hsplit" - signature: [ - "TensorTuple (Tensor input, Int32 indices_or_sections) => HsplitInt", - "TensorTuple (Tensor input, Int32List indices_or_sections) => HsplitVec", - ] + signature: + [ + "TensorTuple (Tensor input, Int32 indices_or_sections) => HsplitInt", + "TensorTuple (Tensor input, Int32List indices_or_sections) => HsplitVec", + ] bind_python: True - name: "vsplit" - signature: [ - "TensorTuple (Tensor input, Int32 indices_or_sections) => VsplitInt", - "TensorTuple (Tensor input, Int32List indices_or_sections) => VsplitVec", - ] + signature: + [ + "TensorTuple (Tensor input, Int32 indices_or_sections) => VsplitInt", + "TensorTuple (Tensor input, Int32List indices_or_sections) => VsplitVec", + ] bind_python: True - name: "negative" signature: "Tensor (Tensor x) => Negative" bind_python: True -- name: "negative_grad" - signature: "Tensor (Tensor x, Tensor dy) => NegativeGrad" - bind_python: False - - name: "layer_norm_affine" signature: "Tensor (Tensor x, Tensor gamma, Tensor beta, Int64 begin_norm_axis, @@ -1218,7 +1421,28 @@ bind_python: False - name: "layer_norm_param_grad" - signature: "TensorTuple (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Int64 begin_params_axis, Double epsilon) => LayerNormParamGrad" + signature: "TensorTuple (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Int64 begin_params_axis) => LayerNormParamGrad" + bind_python: False + +- name: "rms_norm" + signature: "Tensor (Tensor x, Tensor weight=None, Shape normalized_shape, Float epsilon=1e-6) => RMSNorm" + bind_python: True + +- name: "rms_norm_grad" + signature: "Tensor (Tensor dy, Tensor x, Tensor inv_rms, Tensor weight=None, Bool param_grad) => RMSNormGrad" + bind_python: False + +- name: "group_norm" + signature: + 'Tensor (Tensor x, Tensor gamma=None, Tensor beta=None, Bool affine, Int32 num_groups, Double epsilon, String data_format="channels_first", String activation="none") => GroupNorm' + bind_python: True + +- name: "group_norm_grad" + signature: "Tensor (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Tensor gamma=None, Int32 num_groups, Double epsilon) => GroupNormGrad" + bind_python: False + +- name: "group_norm_param_grad" + signature: "TensorTuple (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance) => GroupNormParamGrad" bind_python: False - name: "avg_pool2d_nhwc" @@ -1229,9 +1453,8 @@ bind_python: True - name: "ctc_loss_grad" - signature: - "Tensor (Tensor loss_grad, Tensor log_probs, Tensor targets, - Tensor input_lengths, Tensor target_lengths, Tensor loss, Tensor alpha, Int32 blank, Bool zero_infinity, Int64 max_target_length) => CtcLossGrad" + signature: "Tensor (Tensor loss_grad, Tensor log_probs, Tensor targets, + Tensor input_lengths, Tensor target_lengths, Tensor loss, Tensor alpha, Int64 blank, Bool zero_infinity, Int64 max_target_length) => CtcLossGrad" bind_python: False - name: "adaptive_avg_pool1d" @@ -1281,12 +1504,41 @@ bind_python: True - name: "max_pool_grad" - signature: - "Tensor (Tensor x, Tensor indice, Tensor dy, Int32 ndims, + signature: "Tensor (Tensor x, Tensor indice, Tensor dy, Int32 ndims, String data_format, Int32List padding, Int32List kernel_size, Int32List stride, Int32List dilation, Bool return_indices, Bool ceil_mode) => MaxPoolNdGrad" bind_python: False +- name: "max_unpool1d" + signature: + 'Tensor (Tensor input, Tensor indices, Int32List[1] kernel_size, Int32List[1] stride=None, + Int32List[1] padding=0, Shape output_size=None) => MaxUnpool1D' + bind_python: True + +- name: "max_unpool2d" + signature: + 'Tensor (Tensor input, Tensor indices, Int32List[2] kernel_size, Int32List[2] stride=None, + Int32List[2] padding=0, Shape output_size=None) => MaxUnpool2D' + bind_python: True + +- name: "max_unpool3d" + signature: + 'Tensor (Tensor input, Tensor indices, Int32List[3] kernel_size, Int32List[3] stride=None, + Int32List[3] padding=0, Shape output_size=None) => MaxUnpool3D' + bind_python: True + +- name: "max_unpool1d_grad" + signature: "Tensor (Tensor x, Tensor indice, Tensor dy) => MaxUnpool1dGrad" + bind_python: False + +- name: "max_unpool2d_grad" + signature: "Tensor (Tensor x, Tensor indice, Tensor dy) => MaxUnpool2dGrad" + bind_python: False + +- name: "max_unpool3d_grad" + signature: "Tensor (Tensor x, Tensor indice, Tensor dy) => MaxUnpool3dGrad" + bind_python: False + - name: "prelu" signature: "Tensor (Tensor x, Tensor alpha) => PRelu" bind_python: True @@ -1341,16 +1593,17 @@ - name: "to" signature: [ - # type of device must be string for global tensor to perform argument validation - "Tensor (Tensor x, String device=None, DataType dtype=None, Bool copy=False) => To", - "Tensor (Tensor x, Device device=None, DataType dtype=None, Bool copy=False) => To", - "Tensor (Tensor x, DataType dtype=None, Bool copy=False) => To", - "Tensor (Tensor x, Tensor other, Bool copy=False) => To" - ] + # type of device must be string for global tensor to perform argument validation + "Tensor (Tensor x, String device=None, DataType dtype=None, Bool copy=False) => To", + "Tensor (Tensor x, Device device=None, DataType dtype=None, Bool copy=False) => To", + "Tensor (Tensor x, DataType dtype=None, Bool copy=False) => To", + "Tensor (Tensor x, Tensor other, Bool copy=False) => To", + "Tensor (Tensor x, String device=None) => To", + ] bind_python: True - name: "flip" - signature: "Tensor (Tensor x, Int32List dims) => Flip" + signature: "Tensor (Tensor x, Int32List[1] dims) => Flip" bind_python: True - name: "upsample" @@ -1361,81 +1614,85 @@ - name: "upsample_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double height_scale, Double width_scale, Bool align_corners, - String data_format, String interpolation) => UpsampleGrad' + "Tensor (Tensor dy, Tensor x, Double height_scale, Double width_scale, Bool align_corners, + String data_format, String interpolation) => UpsampleGrad" bind_python: False - name: "upsample_linear_1d" - signature: 'Tensor (Tensor x, Double scale_factor=0.0, Bool align_corners=False, Int64List[1] output_size=None, + signature: + 'Tensor (Tensor x, Double scale_factor=0.0, Bool align_corners=False, Int64List[1] output_size=None, String data_format="channels_first") => UpsampleLinear1D' bind_python: True - name: "upsample_linear_1d_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double scale_factor=0.0, Bool align_corners=False, Int64List[1] output_size=None, + 'Tensor (Tensor dy, Tensor x, Double scale_factor=0.0, Bool align_corners=False, Int64List[1] output_size=None, String data_format="channels_first") => UpsampleLinear1DGrad' bind_python: False - name: "upsample_nearest_1d" - signature: 'Tensor (Tensor x, Double scale_factor=0.0, Int64List[1] output_size=None, + signature: + 'Tensor (Tensor x, Double scale_factor=0.0, Int64List[1] output_size=None, String data_format="channels_first") => UpsampleNearest1D' bind_python: True - name: "upsample_nearest_1d_grad" - signature: 'Tensor (Tensor dy, Tensor x, Double scale_factor=0.0, Int64List[1] output_size=None, + signature: + 'Tensor (Tensor dy, Tensor x, Double scale_factor=0.0, Int64List[1] output_size=None, String data_format="channels_first") => UpsampleNearest1DGrad' bind_python: False - name: "upsample_nearest_2d" - signature: 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Int64List[2] output_size=None, + signature: + 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleNearest2D' bind_python: True - name: "upsample_nearest_2d_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Int64List[2] output_size=None, + 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleNearest2DGrad' bind_python: False - name: "upsample_bilinear_2d" signature: - 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, + 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleBilinear2D' bind_python: True - name: "upsample_bilinear_2d_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, + 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleBilinear2DGrad' bind_python: False - name: "upsample_bicubic_2d" signature: - 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, + 'Tensor (Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleBicubic2D' bind_python: True - name: "upsample_bicubic_2d_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, + 'Tensor (Tensor dy, Tensor x, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[2] output_size=None, String data_format="channels_first") => UpsampleBicubic2DGrad' bind_python: False - name: "upsample_nearest_3d" signature: - 'Tensor (Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Int64List[3] output_size=None, + 'Tensor (Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Int64List[3] output_size=None, String data_format="channels_first") => UpsampleNearest3D' bind_python: True - name: "upsample_nearest_3d_grad" signature: - 'Tensor (Tensor dy, Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Int64List[3] output_size=None, + 'Tensor (Tensor dy, Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Int64List[3] output_size=None, String data_format="channels_first") => UpsampleNearest3DGrad' bind_python: False - name: "upsample_trilinear_3d" signature: - 'Tensor (Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, + 'Tensor (Tensor x, Double depth_scale=0.0, Double height_scale=0.0, Double width_scale=0.0, Bool align_corners=False, Int64List[3] output_size=None, String data_format="channels_first") => UpsampleTrilinear3D' bind_python: True @@ -1445,6 +1702,30 @@ Bool align_corners=False, Int64List[3] output_size=None, String data_format="channels_first") => UpsampleTrilinear3DGrad' bind_python: False +- name: "fused_get_boundding_boxes_coord" + signature: "TensorTuple (Tensor x1, Tensor y1, Tensor w1, Tensor h1, Tensor x2, Tensor y2, Tensor w2, Tensor h2) => FusedGetBounddingBoxesCoord" + bind_python: True + +- name: "fused_get_boundding_boxes_coord_grad" + signature: "TensorTuple (Tensor b1_x1_diff, Tensor b1_x2_diff, Tensor b1_y1_diff, Tensor b1_y2_diff, Tensor b2_x1_diff, Tensor b2_x2_diff, Tensor b2_y1_diff, Tensor b2_y2_diff) => FusedGetBounddingBoxesCoordGrad" + bind_python: False + +- name: "fused_get_ciou_result" + signature: "TensorTuple (Tensor v, Tensor iou, Tensor rho2, Tensor c2, Float eps) => FusedGetCiouResult" + bind_python: True + +- name: "fused_get_ciou_result_grad" + signature: "TensorTuple (Tensor dy ,Tensor alpha, Tensor rho2, Tensor c2) => FusedGetCiouResultGrad" + bind_python: False + +- name: "fused_get_iou" + signature: "Tensor (Tensor w1, Tensor h1, Tensor w2, Tensor h2, Tensor inter, Float eps) => FusedGetIou" + bind_python: True + +- name: "fused_get_iou_grad" + signature: "TensorTuple (Tensor diou, Tensor w1, Tensor h1, Tensor w2, Tensor h2, Tensor inter, Float eps) => FusedGetIouGrad" + bind_python: False + - name: "abs" signature: "Tensor (Tensor x) => Abs" bind_python: True @@ -1637,6 +1918,10 @@ signature: "Tensor (Tensor x, Tensor segment_ids, Tensor like, Int64 axis) => UnsortedSegmentSumLike" bind_python: False +- name: "unsorted_segment_sum" + signature: "Tensor (Tensor x, Tensor segment_ids, Int64 axis, Int64 num_segments) => UnsortedSegmentSum" + bind_python: True + - name: "tril" signature: "Tensor (Tensor x, Int64 diagonal=0) => Tril" bind_python: True @@ -1645,24 +1930,41 @@ signature: "Tensor (Tensor x, Int64 diagonal=0) => Triu" bind_python: True +- name: "triu_" + signature: "Tensor (Tensor x, Int64 diagonal=0) => InplaceTriu" + bind_python: True + - name: "clamp" - signature: - ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => Clamp"] + signature: "Tensor (Tensor input, Scalar min=None, Scalar max=None) => Clamp" bind_python: true - name: "clamp_" - signature: - ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => ClampInplace"] + signature: "Tensor (Tensor input, Scalar min=None, Scalar max=None) => ClampInplace" bind_python: true -- name: "clip" - signature: - ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => Clip"] +- name: "clamp_min" + signature: "Tensor (Tensor input, Scalar min) => ClampMin" bind_python: true -- name: "clip_" - signature: - ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => ClipInplace"] +- name: "clamp_min_" + signature: "Tensor (Tensor input, Scalar min) => ClampMinInplace" + bind_python: true + +- name: "clamp_max" + signature: "Tensor (Tensor input, Scalar max) => ClampMax" + bind_python: true + +- name: "clamp_max_" + signature: "Tensor (Tensor input, Scalar min) => ClampMaxInplace" + bind_python: true + +- name: "clip" + signature: ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => Clip"] + bind_python: true + +- name: "clip_" + signature: + ["Tensor (Tensor input, Scalar min=None, Scalar max=None) => ClipInplace"] bind_python: true - name: "clamp_grad" @@ -1670,7 +1972,7 @@ bind_python: False - name: "vector_norm" - signature: + signature: [ "Tensor (Tensor input, Scalar ord=2, Int32List dim=None, Bool keepdim=False, *, DataType dtype=None) => VectorNorm", "Tensor (Tensor input, Scalar ord=2, Scalar dim, Bool keepdim=False, *, DataType dtype=None) => VectorNorm", @@ -1678,7 +1980,7 @@ bind_python: True - name: "matrix_norm" - signature: + signature: [ "Tensor (Tensor input, Scalar ord, Int32List dim, Bool keepdim=False, *, DataType dtype=None) => MatrixNorm", "Tensor (Tensor input, String ord, Int32List dim, Bool keepdim=False, *, DataType dtype=None) => MatrixNorm", @@ -1686,15 +1988,23 @@ bind_python: True - name: "norm" - signature: + signature: [ - "Tensor (Tensor input, Scalar ord=None, Int32List dim=None, Bool keepdim=False, *, DataType dtype=None) => Norm", + "Tensor (Tensor input, Scalar ord=None, Int32List dim=None, Bool keepdim=False, *, DataType dtype=None, Bool for_norm=False) => Norm", "Tensor (Tensor input, String ord, Int32List dim=None, Bool keepdim=False, *, DataType dtype=None) => Norm", "Tensor (Tensor input, Scalar ord=None, Scalar dim, Bool keepdim=False, *, DataType dtype=None) => ScalarNorm", "Tensor (Tensor input, String ord, Scalar dim, Bool keepdim=False, *, DataType dtype=None) => ScalarNorm", ] bind_python: True +- name: "inv" + signature: "Tensor (Tensor x) => Inv" + bind_python: True + +- name: "linalg_cross" + signature: "Tensor (Tensor input, Tensor other, Int64 dim=None) => LinalgCross" + bind_python: True + - name: "dropout" signature: "Tensor (Tensor input, Float p=0.5, Bool training=True, Bool inplace=False, Generator generator=None, *, Tensor addend=None) => Dropout" bind_python: True @@ -1703,6 +2013,30 @@ signature: "Tensor (Tensor dy, Tensor mask, Float scale) => DropoutGrad" bind_python: False +- name: "dropout1d" + signature: "Tensor (Tensor input, Float p=0.5, Bool training=True) => Dropout1d" + bind_python: True + +- name: "dropout2d" + signature: "Tensor (Tensor input, Float p=0.5, Bool training=True) => Dropout2d" + bind_python: True + +- name: "dropout3d" + signature: "Tensor (Tensor input, Float p=0.5, Bool training=True) => Dropout3d" + bind_python: True + +- name: "constant_pad" + signature: "Tensor (Tensor x, Int64List pad, Scalar value=0) => ConstantPad" + bind_python: False + +- name: "reflection_pad" + signature: "Tensor (Tensor x, Int64List pad) => ReflectionPad" + bind_python: False + +- name: "replication_pad" + signature: "Tensor (Tensor x, Int64List pad) => ReplicationPad" + bind_python: False + - name: "pad" signature: 'Tensor (Tensor x, Int64List pad, String mode="constant", Scalar value=0) => Pad' bind_python: True @@ -1716,7 +2050,7 @@ bind_python: True - name: "silu_grad" - signature: "Tensor (Tensor x, Tensor dy) => SiluGrad" + signature: "Tensor (Tensor dy, Tensor x) => SiluGrad" bind_python: False - name: "mish" @@ -1724,7 +2058,7 @@ bind_python: True - name: "mish_grad" - signature: "Tensor (Tensor x, Tensor dy) => MishGrad" + signature: "Tensor (Tensor dy, Tensor x) => MishGrad" bind_python: False - name: "selu" @@ -1732,7 +2066,7 @@ bind_python: True - name: "selu_grad" - signature: "Tensor (Tensor x, Tensor dy) => SeluGrad" + signature: "Tensor (Tensor dy, Tensor x) => SeluGrad" bind_python: False - name: "softsign" @@ -1740,7 +2074,7 @@ bind_python: True - name: "softsign_grad" - signature: "Tensor (Tensor x, Tensor dy) => SoftSignGrad" + signature: "Tensor (Tensor dy, Tensor x) => SoftSignGrad" bind_python: False - name: "diag" @@ -1766,19 +2100,35 @@ - name: "scatter" signature: [ - "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src) => DimScatter", - "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src) => DimScatterUpdateScalar", + "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src, *, String reduce=None, Bool inplace=False) => DimScatter", + "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src, *, String reduce=None, Bool inplace=False) => DimScatterScalar", ] bind_python: True +- name: "scatter_update" + signature: + [ + "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src, *, Bool inplace=False) => DimScatterUpdate", + "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src, *, Bool inplace=False) => DimScatterUpdateScalar", + ] + bind_python: False + - name: "scatter_add" signature: [ - "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src) => DimScatterAdd", - "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src) => DimScatterAddScalar", + "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src, *, Bool inplace=False) => DimScatterAdd", + "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src, *, Bool inplace=False) => DimScatterAddScalar", ] bind_python: True +- name: "scatter_mul" + signature: + [ + "Tensor (Tensor input, Int32 dim, Tensor index, Tensor src, *, Bool inplace=False) => DimScatterMul", + "Tensor (Tensor input, Int32 dim, Tensor index, Scalar src, *, Bool inplace=False) => DimScatterMulScalar", + ] + bind_python: False + - name: "scatter_add_like" signature: "Tensor (Tensor like, Int32 dim, Tensor index, Tensor src) => DimScatterAddLike" bind_python: False @@ -1789,23 +2139,23 @@ - name: "avg_pool1d" signature: - 'Tensor (Tensor input, Int32List[1] kernel_size, Int32List[1] stride=None, - Int32List[1] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, - Int32 divisor_override=0, String data_format="channels_first") => AvgPool1D' + 'Tensor (Tensor input, Int32List[1] kernel_size, Int32List[1] stride=None, + Int32List[1] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, + Int32 divisor_override=0, String data_format="channels_first") => AvgPool1D' bind_python: True - name: "avg_pool2d" signature: - 'Tensor (Tensor input, Int32List[2] kernel_size, Int32List[2] stride=None, - Int32List[2] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, - Int32 divisor_override=0, String data_format="channels_first") => AvgPool2D' + 'Tensor (Tensor input, Int32List[2] kernel_size, Int32List[2] stride=None, + Int32List[2] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, + Int32 divisor_override=0, String data_format="channels_first") => AvgPool2D' bind_python: True - name: "avg_pool3d" signature: - 'Tensor (Tensor input, Int32List[3] kernel_size, Int32List[3] stride=None, - Int32List[3] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, - Int32 divisor_override=0, String data_format="channels_first") => AvgPool3D' + 'Tensor (Tensor input, Int32List[3] kernel_size, Int32List[3] stride=None, + Int32List[3] padding=0, Bool ceil_mode=False, Bool count_include_pad=True, + Int32 divisor_override=0, String data_format="channels_first") => AvgPool3D' bind_python: True - name: "avg_pool_grad" @@ -1839,30 +2189,86 @@ signature: "TensorTuple (Tensor x, TensorTuple like, Int64 axis) => StackGrad" bind_python: False +- name: "atleast_1d" + signature: + [ + "Tensor (Tensor input) => AtLeast1D", + "TensorTuple (TensorTuple tensors) => AtLeast1D", + ] + bind_python: True + +- name: "atleast_2d" + signature: + [ + "Tensor (Tensor input) => AtLeast2D", + "TensorTuple (TensorTuple tensors) => AtLeast2D", + ] + bind_python: True + +- name: "atleast_3d" + signature: + [ + "Tensor (Tensor input) => AtLeast3D", + "TensorTuple (TensorTuple tensors) => AtLeast3D", + ] + bind_python: True + +- name: "hstack" + signature: "Tensor (TensorTuple tensors) => HStack" + bind_python: True + +- name: "vstack" + signature: "Tensor (TensorTuple tensors) => VStack" + bind_python: True + +- name: "dstack" + signature: "Tensor (TensorTuple tensors) => DStack" + bind_python: True + +- name: "column_stack" + signature: "Tensor (TensorTuple tensors) => ColumnStack" + bind_python: True + +- name: "row_stack" + signature: "Tensor (TensorTuple tensors) => RowStack" + bind_python: True + - name: "local_to_global" - signature: "Tensor (Tensor x, Placement placement, SbpList sbp, Shape shape, DataType dtype) => LocalToConsistent" + signature: "Tensor (Tensor x, Placement placement, SbpList sbp, Shape shape, DataType dtype, Bool sync_data, Bool copy=False) => LocalToGlobal" bind_python: False - name: "to_global" - signature: "Tensor (Tensor x, Placement placement, SbpList sbp, SbpList grad_sbp, Bool check_meta) => ToConsistent" + signature: "Tensor (Tensor x, Placement placement, SbpList sbp, SbpList grad_sbp, Bool check_meta, Bool copy=False) => ToGlobal" bind_python: True - name: "to_local" - signature: "Tensor (Tensor x) => ConsistentToLocal" + signature: "Tensor (Tensor x, Bool copy=False) => GlobalToLocal" bind_python: True - name: "stream_touch" signature: "Void (TensorTuple x) => StreamTouch" bind_python: True -- name: "broadcast" - signature: "Tensor (Tensor x, *, Int64 src_rank=0, Bool inplace=True) => Broadcast" +- name: "comm_broadcast" + signature: + [ + "Tensor (Tensor x, *, Int64 src_rank=0, Bool inplace=True) => CommBroadcast", + "TensorTuple (TensorTuple inputs, *, Int64 src_rank=0, Bool inplace=True) => CommBroadcastTensors", + ] bind_python: True - name: "local_all_reduce" signature: "Tensor (Tensor x, Bool inplace=False) => LocalAllReduce" bind_python: True +- name: "local_all_gather" + signature: "Tensor (Tensor output, Tensor input) => LocalAllGather" + bind_python: True + +- name: "local_reduce_scatter" + signature: "Tensor (Tensor output, Tensor input) => LocalReduceScatter" + bind_python: True + - name: "local_reduce" signature: "Tensor (Tensor x, *, Int64 dst=0, Bool inplace=True) => LocalReduce" bind_python: True @@ -1892,19 +2298,19 @@ bind_python: False - name: "global_all_reduce" - signature: "Tensor (Tensor x) => ConsistentAllReduce" + signature: "Tensor (Tensor x) => GlobalAllReduce" bind_python: False - name: "global_reduce_scatter" - signature: "Tensor (Tensor x, String op_type) => ConsistentReduceScatter" + signature: "Tensor (Tensor x, String op_type) => GlobalReduceScatter" bind_python: False - name: "global_all_gather" - signature: "Tensor (Tensor x) => ConsistentAllGather" + signature: "Tensor (Tensor x) => GlobalAllGather" bind_python: False - name: "global_s2s" - signature: "Tensor (Tensor x, SbpList out_sbp) => ConsistentS2S" + signature: "Tensor (Tensor x, SbpList out_sbp) => GlobalS2S" bind_python: False - name: "select_top_n" @@ -1923,6 +2329,10 @@ signature: "Tensor (Tensor in) => AmpWhiteIdentity" bind_python: True +- name: "amp_black_identity" + signature: "Tensor (Tensor in) => AmpBlackIdentity" + bind_python: True + - name: "reshape_like" signature: "Tensor (Tensor in, Tensor like) => ReshapeLike" bind_python: True @@ -1940,7 +2350,7 @@ "Tensor (Shape size, *, DataType dtype=None, Device device=None, Generator generator=None, Bool requires_grad=False) => Rand", "Tensor (Shape size, *, Placement placement, SbpList sbp, DataType dtype=None, - Generator generator=None, Bool requires_grad=False) => ConsistentRand", + Generator generator=None, Bool requires_grad=False) => GlobalRand", ] bind_python: True @@ -1949,7 +2359,16 @@ "Tensor (Shape size, *, DataType dtype=None, Device device=None, Generator generator=None, Bool requires_grad=False) => RandN", "Tensor (Shape size, *, Placement placement, SbpList sbp, DataType dtype=None, - Generator generator=None, Bool requires_grad=False) => ConsistentRandN", + Generator generator=None, Bool requires_grad=False) => GlobalRandN", + ] + bind_python: True + +- name: "randn_like" + signature: [ + "Tensor (Tensor input, *, DataType dtype=None, Device device=None, + Generator generator=None, Bool requires_grad=False) => RandnLike", + "Tensor (Tensor input, *, Placement placement, SbpList sbp, DataType dtype=None, + Generator generator=None, Bool requires_grad=False) => GlobalRandnLike", ] bind_python: True @@ -1960,9 +2379,22 @@ "Tensor (Int64 high, Shape size, *, DataType dtype=None, Device device=None, Generator generator=None, Bool requires_grad=False)=> RandInt", "Tensor (Int64 low, Int64 high, Shape size, *, Placement placement, SbpList sbp, - DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> ConsistentRandInt", + DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> GlobalRandInt", "Tensor (Int64 high, Shape size, *, Placement placement, SbpList sbp, - DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> ConsistentRandInt", + DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> GlobalRandInt", + ] + bind_python: True + +- name: "randint_like" + signature: [ + "Tensor (Tensor x, Int64 low, Int64 high, *, DataType dtype=None, + Device device=None, Generator generator=None, Bool requires_grad=False)=> RandIntLike", + "Tensor (Tensor x, Int64 high, *, DataType dtype=None, + Device device=None, Generator generator=None, Bool requires_grad=False)=> RandIntLike", + "Tensor (Tensor x, Int64 low, Int64 high, *, Placement placement, SbpList sbp, + DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> GlobalRandIntLike", + "Tensor (Tensor x, Int64 high, *, Placement placement, SbpList sbp, + DataType dtype=None, Generator generator=None, Bool requires_grad=False)=> GlobalRandIntLike", ] bind_python: True @@ -1970,7 +2402,7 @@ signature: [ "Tensor (Int32 n, *, Generator generator=None, DataType dtype=kInt64, Device device=None, Bool requires_grad=False) => RandPerm", - "Tensor (Int32 n, *, Placement placement, SbpList sbp, Generator generator=None, DataType dtype=kInt64, Bool requires_grad=False) => ConsistentRandPerm", + "Tensor (Int32 n, *, Placement placement, SbpList sbp, Generator generator=None, DataType dtype=kInt64, Bool requires_grad=False) => GlobalRandPerm", ] bind_python: True @@ -1984,41 +2416,40 @@ - name: "unfold" signature: - 'Tensor (Tensor x, String data_format="channels_first", Int32List kernel_size, - Int32List dilation_rate, Int32List padding, - Int32List strides) => Unfold' + 'Tensor (Tensor x, Int32List[2] kernel_size, Int32List[2] dilation=1, Int32List[2] padding=0, + Int32List[2] stride=1, String data_format="channels_first") => Unfold' bind_python: True - name: "fold" - signature: 'Tensor (Tensor x, String data_format="channels_first", - Int32List output_size, Int32List kernel_size, - Int32List dilation_rate, Int32List padding, - Int32List strides) => Fold' + signature: + 'Tensor (Tensor x, Int32List[1] output_size, Int32List[2] kernel_size, Int32List[2] dilation=1, + Int32List[2] padding=0, Int32List[2] stride=1, String data_format="channels_first") => Fold' bind_python: True - name: "split" - signature: [ - "TensorTuple (Tensor x, Int64 split_size_or_sections, Int64 dim=0) => Split", - "TensorTuple (Tensor x, Int64List split_size_or_sections, Int64 dim=0) => SplitWithSize", - ] + signature: + [ + "TensorTuple (Tensor x, Int64 split_size_or_sections, Int64 dim=0) => Split", + "TensorTuple (Tensor x, Int64List split_size_or_sections, Int64 dim=0) => SplitWithSize", + ] bind_python: True - name: "unbind" - signature: [ - "TensorTuple (Tensor x, Int64 dim=0) => Unbind", - ] + signature: ["TensorTuple (Tensor x, Int64 dim=0) => Unbind"] bind_python: True - name: "chunk" - signature: [ - "TensorTuple (Tensor x, Int64 chunks, Int64 dim=0) => Chunk", - ] + signature: ["TensorTuple (Tensor x, Int64 chunks, Int64 dim=0) => Chunk"] bind_python: True - name: "split_like" signature: "TensorTuple (Tensor x, TensorTuple like, Int64 axis) => SplitLike" bind_python: True +- name: "pairwise_distance" + signature: "Tensor (Tensor x1, Tensor x2, Float p=2.0, Double eps=1e-6, Bool keepdim=False) => PairwiseDistance" + bind_python: True + - name: "cosine_similarity" signature: "Tensor (Tensor x, Tensor y, Int32 dim=1, Double eps=1e-8) => CosineSimilarity" bind_python: True @@ -2076,13 +2507,61 @@ bind_python: False - name: "fused_scale_tril_softmax_mask_scale" - signature: "TensorTuple (Tensor a, *, Float p=0.5, Int64 diagonal, Float tril_scale_value, Generator generator=None) => FusedScaleTrilSoftmaxMaskScale" + signature: "TensorTuple (Tensor a, *, Float p=0.5, Int64 diagonal, Float tril_scale_value, Float tril_fill_value=0.0, Generator generator=None) => FusedScaleTrilSoftmaxMaskScale" bind_python: True - name: "fused_scale_tril_softmax_mask_scale_grad" signature: "Tensor (Tensor softmax_y, Tensor dy, Tensor mask, Int64 diagonal, Float tril_scale_value, Float mask_scale_value) => FusedScaleTrilSoftmaxMaskScaleGrad" bind_python: False +- name: "fused_bias_add_scale_mask_softmax_dropout" + signature: "TensorTuple (Tensor x, Tensor bias, Tensor mask, *, Float fill_value=0.0, Float scale=1.0, Float p=0.5, Bool training=True, Generator generator=None) => FusedBiasAddScaleMaskSoftmaxDropout" + bind_python: True + +- name: "fused_multi_head_attention_inference" + signature: "Tensor (Tensor query, Tensor key, Tensor value, Int64 num_heads, Bool causal=False, Int64 query_hidden_slice_start=0, Int64 query_hidden_slice_end=-1, Int64 key_hidden_slice_start=0, Int64 key_hidden_slice_end=-1, Int64 value_hidden_slice_start=0, Int64 value_hidden_slice_end=-1) => FusedMultiHeadAttentionInference" + bind_python: True + +- name: "fused_get_center_dist" + signature: "Tensor (Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2) => FusedCenter" + bind_python: True + +- name: "fused_get_center_dist_grad" + signature: "TensorTuple (Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2, Tensor rho2_diff) => FusedCenterGrad" + bind_python: False + +- name: "fused_get_intersection_area" + signature: "Tensor (Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2) => FusedGetIntersectionArea" + bind_python: True + +- name: "fused_get_intersection_area_grad" + signature: "TensorTuple (Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2, Tensor inter_diff) => FusedGetIntersectionAreaGrad" + bind_python: False + +- name: "fused_get_ciou_diagonal_angle" + signature: "Tensor (Tensor w1, Tensor h1, Tensor w2, Tensor h2, Float eps) => FusedGetCiouDiagonalAngle" + bind_python: True + +- name: "fused_get_ciou_diagonal_angle_grad" + signature: "TensorTuple (Tensor w1, Tensor h1, Tensor w2, Tensor h2, Tensor v_diff, Float eps) => FusedGetCiouDiagonalAngleGrad" + bind_python: False + +- name: "fused_get_convex_diagonal_squared" + signature: "Tensor (Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2, Float eps) => FusedGetConvexDiagonalSquared" + bind_python: True + +- name: "fused_get_convex_diagonal_squared_grad" + signature: "TensorTuple (Tensor c2_diff, Tensor b1_x1, Tensor b1_x2, Tensor b2_x1, Tensor b2_x2, Tensor b1_y1, Tensor b1_y2, Tensor b2_y1, Tensor b2_y2, Float eps) => FusedGetConvexDiagonalSquaredGrad" + bind_python: False + +- name: "grouped_matmul_bias" + signature: "TensorTuple (TensorTuple xs, TensorTuple weights, TensorTuple biases) => GroupedMatmulBias" + bind_python: True + +- name: "grouped_matmul" + signature: "TensorTuple (TensorTuple xs, TensorTuple weights) => GroupedMatmul" + bind_python: True + - name: "send" signature: "Void (Tensor input, Int64 dst, Bool send_meta=True) => Send" bind_python: True @@ -2104,23 +2583,19 @@ bind_python: True - name: "distributed_partial_fc_sample_disable_boxing" - signature: - "TensorTuple (Tensor sampled_weight_diff, Tensor sampled_label) => DistributedPariticalFCSampleDisableBoxing" + signature: "TensorTuple (Tensor sampled_weight_diff, Tensor sampled_label) => DistributedPariticalFCSampleDisableBoxing" bind_python: False - name: "nms" - signature: - "Tensor (Tensor x, Float iou_threshold, Int32 keep_n=-1) => Nms" + signature: "Tensor (Tensor x, Float iou_threshold, Int32 keep_n=-1) => Nms" bind_python: True - name: "roi_align" - signature: - "Tensor (Tensor x, Tensor rois, Float spatial_scale, Int32 pooled_h, Int32 pooled_w, Int32 sampling_ratio, Bool aligned) => RoiAlign" + signature: "Tensor (Tensor x, Tensor rois, Float spatial_scale, Int32 pooled_h, Int32 pooled_w, Int32 sampling_ratio, Bool aligned) => RoiAlign" bind_python: True - name: "roi_align_grad" - signature: - "Tensor (Tensor dy, Tensor x_like, Tensor rois, Float spatial_scale, Int32 pooled_h, Int32 pooled_w, Int32 sampling_ratio, Bool aligned) => RoiAlignGrad" + signature: "Tensor (Tensor dy, Tensor x_like, Tensor rois, Float spatial_scale, Int32 pooled_h, Int32 pooled_w, Int32 sampling_ratio, Bool aligned) => RoiAlignGrad" bind_python: False - name: "meshgrid" @@ -2128,8 +2603,7 @@ bind_python: True - name: "index_select" - signature: - "Tensor (Tensor input, Int64 dim, Tensor index) => IndexSelect" + signature: "Tensor (Tensor input, Int64 dim, Tensor index) => IndexSelect" bind_python: True - name: "decode_onerec" @@ -2141,11 +2615,11 @@ bind_python: True - name: "fused_dot_feature_interaction" - signature: "Tensor (TensorTuple features, Tensor output_concat=None, Bool self_interaction=False, Int32 output_padding=0, String pooling=\"none\") => FusedDotFeatureInteraction" + signature: 'Tensor (TensorTuple features, Tensor output_concat=None, Bool self_interaction=False, Int32 output_padding=0, String pooling="none") => FusedDotFeatureInteraction' bind_python: True - name: "fused_dot_feature_interaction_grad" - signature: "TensorTuple (Tensor dy, TensorTuple features, Bool has_output_concat_grad=False, Bool self_interaction=False, Int32 output_concat_grad_dim=0, String pooling=\"none\") => FusedDotFeatureInteractionGrad" + signature: 'TensorTuple (Tensor dy, TensorTuple features, Bool has_output_concat_grad=False, Bool self_interaction=False, Int32 output_concat_grad_dim=0, String pooling="none") => FusedDotFeatureInteractionGrad' bind_python: False - name: "fused_cross_feature_interaction" @@ -2172,8 +2646,8 @@ signature: "Tensor (Shape shape, ShapeList shape_list, FloatList value_list, DataType data_type, Bool dynamic_out) => GenTensorBuffer" bind_python: True -- name: "top_k" - signature: "Tensor (Tensor input, Int32 k, Bool sorted=True) => TopK" +- name: "topk" + signature: "TensorTuple[values, indices] (Tensor input, Int32 k, Int32 dim=None, Bool largest=True, Bool sorted=True) => TopK" bind_python: True - name: "in_top_k" @@ -2193,39 +2667,51 @@ bind_python: False - name: "one_embedding_id_shuffle" - signature: "TensorTuple (Tensor ids, Tensor table_ids=None, Int32 num_tables=1) => OneEmbeddingIdShuffle" + signature: "TensorTuple (Tensor ids, Tensor table_ids=None, Int32 num_tables=1, String embedding_name) => OneEmbeddingIdShuffle" bind_python: True - name: "one_embedding_embedding_shuffle" - signature: "Tensor (Tensor cur_rank_embeddings, Tensor num_unique_matrix, Tensor cur_rank_inverse_indices, Tensor inverse_unique_partition_indices) => OneEmbeddingEmbeddingShuffle" + signature: "Tensor (Tensor cur_rank_embeddings, Tensor num_unique_matrix, Tensor cur_rank_inverse_indices, Tensor inverse_unique_partition_indices, String embedding_name) => OneEmbeddingEmbeddingShuffle" bind_python: True - name: "one_embedding_embedding_gradient_shuffle" - signature: "Tensor (Tensor embedding_grad, Tensor num_unique_matrix, Tensor cur_rank_inverse_indices, Tensor inverse_unique_partition_indices) => OneEmbeddingEmbeddingGradientShuffle" + signature: "Tensor (Tensor embedding_grad, Tensor num_unique_matrix, Tensor cur_rank_inverse_indices, Tensor inverse_unique_partition_indices, String embedding_name) => OneEmbeddingEmbeddingGradientShuffle" bind_python: True - name: "one_embedding_lookup" - signature: "Tensor (Tensor shadow, Tensor ids, Tensor table_ids=None, DataType dtype, Int64 embedding_size, Int32 num_tables, String embedding_tables, String embedding_store_options) => OneEmbeddingLookup" + signature: "Tensor (Tensor num_unique_ids, Tensor unique_ids, Tensor table_ids, DataType dtype, DataType embedding_dtype, Int64 line_size, Int64 embedding_size, String embedding_name, String embedding_tables, String state_initializer, Int64 seed=0) => OneEmbeddingLookup" + bind_python: True + +- name: "one_embedding_fused_lookup" + signature: "Tensor (Tensor shadow, Tensor ids, Tensor table_ids=None, DataType dtype, String embedding_name, Int64 line_size, Int64 embedding_size, Bool is_full_cache, Int32 num_tables, String embedding_tables, Int64 padding_idx=None, Int64 seed=0) => OneEmbeddingFusedLookup" + bind_python: True + +- name: "one_embedding_fused_lookup_grad" + signature: "Void (Tensor ids, Tensor embedding_grad, String embedding_name, Int64 line_size, Int64 embedding_size) => OneEmbeddingFusedLookupGrad" bind_python: True - name: "one_embedding_unique_key_value_pair" - signature: "TensorTuple (Tensor keys, Tensor values=None, Int32 num_tables) => OneEmbeddingUniqueKeyValuePair" + signature: "TensorTuple (Tensor keys, Tensor values=None, Int32 num_tables, String embedding_name) => OneEmbeddingUniqueKeyValuePair" + bind_python: True + +- name: "one_embedding_embedding_put" + signature: "Void (Tensor num_unique_ids, Tensor unique_ids, Tensor unique_embeddings, String embedding_name, Int64 line_size) => OneEmbeddingEmbeddingPut" bind_python: True - name: "one_embedding_sgd_update" - signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate, Tensor down_scale_by_tensor, Tensor skip_if, Double scale, Float weight_decay, Float momentum) => OneEmbeddingSgdUpdate" + signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate=None, Tensor down_scale_by_tensor=None, Tensor skip_if=None, Float learning_rate_val, Double scale, Float weight_decay, Float momentum, Int64 line_size, Int64 embedding_size, String embedding_name) => OneEmbeddingSgdUpdate" bind_python: True - name: "one_embedding_adam_update" - signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate, Tensor down_scale_by_tensor, Tensor skip_if, Tensor bias_correction1=None, Tensor bias_correction2=None, Double scale=1.0, Float weight_decay=0.0, Float beta1=0.9, Float beta2=0.999, Float epsilon=0, Bool do_bias_correction=True) => OneEmbeddingAdamUpdate" + signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate=None, Tensor down_scale_by_tensor=None, Tensor skip_if=None, Tensor bias_correction1=None, Tensor bias_correction2=None, Float learning_rate_val, Double scale, Float weight_decay, Float beta1, Float beta2, Float bias_correction1_val, Float bias_correction2_val, Float epsilon, Bool do_bias_correction, Int64 line_size, Int64 embedding_size, String embedding_name) => OneEmbeddingAdamUpdate" bind_python: True - name: "one_embedding_adagrad_update" - signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate, Tensor down_scale_by_tensor, Tensor skip_if, Tensor train_step, Double scale=1.0, Float weight_decay=0.0, Float lr_decay=0.0, Float epsilon=0) => OneEmbeddingAdagradUpdate" + signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate=None, Tensor down_scale_by_tensor=None, Tensor skip_if=None, Tensor train_step=None, Int64 train_step_val, Float learning_rate_val, Double scale, Float weight_decay, Float lr_decay, Float epsilon, Int64 line_size, Int64 embedding_size, String embedding_name) => OneEmbeddingAdagradUpdate" bind_python: True - name: "one_embedding_ftrl_update" - signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate, Tensor down_scale_by_tensor, Tensor skip_if, Double scale, Float weight_decay, Float lr_power, Float lambda1, Float lambda2, Float beta) => OneEmbeddingFtrlUpdate" + signature: "Tensor (Tensor num_unique_ids, Tensor unique_embeddings, Tensor embedding_grad, Tensor learning_rate=None, Tensor down_scale_by_tensor=None, Tensor skip_if=None, Float learning_rate_val, Double scale, Float weight_decay, Float lr_power, Float lambda1, Float lambda2, Float beta, Int64 line_size, Int64 embedding_size, String embedding_name) => OneEmbeddingFtrlUpdate" bind_python: True - name: "einsum" @@ -2244,6 +2730,10 @@ signature: "Tensor (Tensor input) => IsInf" bind_python: True +- name: "isfinite" + signature: "Tensor (Tensor input) => IsFinite" + bind_python: True + - name: "roc_auc_score" signature: "Tensor (Tensor label, Tensor pred) => RocAucScore" bind_python: True @@ -2253,12 +2743,21 @@ bind_python: True - name: "fill_" - signature: [ - "Tensor (Tensor in, Scalar value) => Fill", - "Tensor (Tensor in, Tensor value) => FillTensor" + signature: + [ + "Tensor (Tensor in, Tensor value) => FillTensor", + "Tensor (Tensor in, Scalar value) => Fill", ] bind_python: True +- name: "index_add" + signature: "Tensor (Tensor input, Int64 dim, Tensor index, Tensor source, Scalar alpha=1.0) => IndexAdd" + bind_python: True + +- name: "index_add_" + signature: "Tensor (Tensor input, Int64 dim, Tensor index, Tensor source, Scalar alpha=1.0) => IndexAddInplace" + bind_python: True + - name: "rnn_tanh_cell" signature: "Tensor (Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih=None, Tensor b_hh=None) => RnnTanhCell" bind_python: True @@ -2295,7 +2794,7 @@ signature: [ "TensorTuple (Tensor input, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional, Bool batch_first) => RnnTanhInput", - "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => RnnTanhData" + "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => RnnTanhData", ] bind_python: True @@ -2303,7 +2802,7 @@ signature: [ "TensorTuple (Tensor input, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional, Bool batch_first) => RnnReluInput", - "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => RnnReluData" + "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => RnnReluData", ] bind_python: True @@ -2311,7 +2810,7 @@ signature: [ "TensorTuple (Tensor input, TensorTuple hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional, Bool batch_first) => LstmInput", - "TensorTuple (Tensor data, Tensor batch_sizes, TensorTuple hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => LstmData" + "TensorTuple (Tensor data, Tensor batch_sizes, TensorTuple hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => LstmData", ] bind_python: True @@ -2319,10 +2818,311 @@ signature: [ "TensorTuple (Tensor input, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional, Bool batch_first) => GruInput", - "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => GruData" + "TensorTuple (Tensor data, Tensor batch_sizes, Tensor hx, TensorTuple params, Bool has_biases, Int32 num_layers, Float dropout, Bool train, Bool bidirectional) => GruData", ] bind_python: True - name: "pack_padded_sequence" signature: "TensorTuple (Tensor input, Tensor lengths, Bool batch_first) => PackPaddedSequence" bind_python: True + +- name: "multi_tensor_sgd_update" + signature: "Void (TensorTuple model, TensorTuple model_diff, Double scale, Float weight_decay, Float learning_rate_val) => MultiTensorSgdUpdate" + bind_python: True + +- name: "multi_tensor_yolov5_weight_update" + signature: "Void (TensorTuple model, TensorTuple model_update, Float d) => MultiTensorYoloV5WeightUpdate" + bind_python: True + +- name: "multi_tensor_momentum_update" + signature: "Void (TensorTuple model, TensorTuple model_diff, TensorTuple momentum_buf, Double scale, Float weight_decay, Float learning_rate_val, Float momentum, Float dampening, Bool nesterov, Bool maximize) => MultiTensorMomentumUpdate" + bind_python: True + +- name: "multi_tensor_adam_update" + signature: "Void (TensorTuple model, TensorTuple model_diff, TensorTuple m, TensorTuple v, Float learning_rate_val, Float l2, Float beta1, Float beta2, Float bias_correction1_val, Float bias_correction2_val, Bool do_bias_correction, Double scale, Float weight_decay, Float epsilon) => MultiTensorAdamUpdate" + bind_python: True + +- name: "grad_acc_repeat" + signature: "Tensor (Tensor input, Int32 repeat_num) => GradAccRepeat" + bind_python: False + +- name: "grad_acc_collect" + signature: "Tensor (Tensor input, Int32 collect_num) => GradAccCollect" + bind_python: False + +- name: "grad_acc_pack" + signature: "Tensor (Tensor input, Int32 pack_num) => GradAccPack" + bind_python: False + +- name: "grad_acc_unpack" + signature: "Tensor (Tensor input, Int32 unpack_num) => GradAccUnpack" + bind_python: False + +- name: "trunc" + signature: "Tensor (Tensor input) => Trunc" + bind_python: True + +- name: "silu_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SiluGradGrad" + bind_python: False + +- name: "mish_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => MishGradGrad" + bind_python: False + +- name: "selu_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SeluGradGrad" + bind_python: False + +- name: "softsign_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SoftSignGradGrad" + bind_python: False + +- name: "gelu_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => GeluGradGrad" + bind_python: False + +- name: "hardsigmoid_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => HardSigmoidGradGrad" + bind_python: False + +- name: "hardswish_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => HardSwishGradGrad" + bind_python: False + +- name: "softplus_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx, Double beta=1.0, Double threshold=20.0) => SoftplusGradGrad" + bind_python: False + +- name: "elu_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx, Double alpha) => EluGradGrad" + bind_python: False + +- name: "celu_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx, Double alpha) => CeluGradGrad" + bind_python: False + +- name: "batch_norm_stats" + signature: "TensorTuple (Tensor input, Int32 axis, Float eps) => BatchNormStats" + bind_python: True + +- name: "batch_norm_gather_stats_with_counts" + signature: "TensorTuple (Tensor input, Tensor mean, Tensor invstd, Tensor running_mean=None, Tensor running_var=None, Float momentum, Float eps, Tensor counts) => BatchNormGatherStatsWithCounts" + bind_python: True + +- name: "batch_norm_elemt" + signature: "Tensor (Tensor input, Tensor weight, Tensor bias, Tensor mean, Tensor invstd, Int32 axis, Float eps) => BatchNormElemt" + bind_python: True + +- name: "batch_norm_backward_reduce" + signature: "TensorTuple (Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Int32 axis) => BatchNormBackwardReduce" + bind_python: True + +- name: "batch_norm_backward_elemt" + signature: "Tensor (Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor weight, Tensor sum_dy, Tensor sum_dy_xmu, Tensor count, Int32 axis) => BatchNormBackwardElemt" + bind_python: True + +- name: "adaptive_max_pool1d" + signature: "TensorTuple (Tensor input, Int64List[1] output_size) => AdaptiveMaxPool1D" + bind_python: True + +- name: "adaptive_max_pool2d" + signature: "TensorTuple (Tensor input, Int64List[2] output_size) => AdaptiveMaxPool2D" + bind_python: True + +- name: "adaptive_max_pool3d" + signature: "TensorTuple (Tensor input, Int64List[3] output_size) => AdaptiveMaxPool3D" + bind_python: True + +- name: "adaptive_max_pool_grad" + signature: "Tensor (Tensor x, Tensor index, Tensor dy, Int32 ndims) => AdaptiveMaxPoolNdGrad" + bind_python: False + +- name: "tan_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => TanGradGrad" + bind_python: False + +- name: "sinh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SinhGradGrad" + bind_python: False + +- name: "cosh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => CoshGradGrad" + bind_python: False + +- name: "tanh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => TanhGradGrad" + bind_python: False + +- name: "acos_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AcosGradGrad" + bind_python: False + +- name: "asin_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AsinGradGrad" + bind_python: False + +- name: "atan_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AtanGradGrad" + bind_python: False + +- name: "asinh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AsinhGradGrad" + bind_python: False + +- name: "acosh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AcoshGradGrad" + bind_python: False + +- name: "atanh_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => AtanhGradGrad" + bind_python: False + +- name: "erf_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => ErfGradGrad" + bind_python: False + +- name: "erfc_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => ErfcGradGrad" + bind_python: False + +- name: "exp_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => ExpGradGrad" + bind_python: False + +- name: "expm1_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => Expm1GradGrad" + bind_python: False + +- name: "log_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => LogGradGrad" + bind_python: False + +- name: "logsigmoid_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => LogSigmoidGradGrad" + bind_python: False + +- name: "log2_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => Log2GradGrad" + bind_python: False + +- name: "log10_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => Log10GradGrad" + bind_python: False + +- name: "log1p_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => Log1pGradGrad" + bind_python: False + +- name: "reciprocal_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => ReciprocalGradGrad" + bind_python: False + +- name: "reciprocal_no_nan_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => ReciprocalNoNanGradGrad" + bind_python: False + +- name: "rsqrt_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => RsqrtGradGrad" + bind_python: False + +- name: "sqrt_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SqrtGradGrad" + bind_python: False + +- name: "square_grad_grad" + signature: "Tensor (Tensor x, Tensor dydx) => SquareGradGrad" + bind_python: False + +- name: "sigmoid_grad_grad" + signature: "Tensor (Tensor y, Tensor dydx) => SigmoidGradGrad" + bind_python: False + +- name: "max_pool_grad_grad" + signature: "Tensor (Tensor dydx, Tensor indices, Int32 ndims) => MaxPoolNdGradGrad" + bind_python: False + +- name: "exponential_" + signature: "Tensor (Tensor x, Float lambd=1.0, Generator generator=None) => Exponential" + bind_python: True + +- name: "multinomial" + signature: "Tensor (Tensor x, Int32 num_samples, Bool replacement=False, Generator generator=None) => Multinomial" + bind_python: True + +- name: "max_pool_grad_grad" + signature: "Tensor (Tensor dydx, Tensor indices, Int32 ndims) => MaxPoolNdGradGrad" + bind_python: False + +- name: "deform_conv2d" + signature: + "Tensor (Tensor input,Tensor weight,Tensor offset,Tensor mask,Tensor bias=None, Int32 stride_h,Int32 stride_w,Int32 pad_h, + Int32 pad_w,Int32 dilation_h,Int32 dilation_w,Int32 groups,Int32 offset_groups,Bool use_mask) => DeformConv2d" + bind_python: True + +- name: "deform_conv2d_input_grad" + signature: + "TensorTuple (Tensor output_grad,Tensor input,Tensor weight,Tensor offset,Tensor mask=None, Int32 stride_h,Int32 stride_w,Int32 pad_h, + Int32 pad_w,Int32 dilation_h,Int32 dilation_w,Int32 groups,Int32 offset_groups,Bool use_mask) => DeformConv2dInputGrad" + bind_python: False + +- name: "deform_conv2d_param_grad" + signature: + "Tensor (Tensor output_grad,Tensor input,Tensor weight,Tensor offset,Tensor mask, Int32 stride_h,Int32 stride_w,Int32 pad_h, + Int32 pad_w,Int32 dilation_h,Int32 dilation_w,Int32 groups,Int32 offset_groups,Bool use_mask) => DeformConv2dParamGrad" + bind_python: False + +- name: "broadcast_shapes" + signature: "Shape (ShapeList shapes) => BroadcastShapes" + bind_python: True + +- name: "broadcast_tensors" + signature: "TensorTuple (TensorTuple tensors) => BroadcastTensors" + bind_python: True + +- name: "broadcast_to" + signature: "Tensor (Tensor x, Shape shape) => BroadcastTo" + bind_python: True +- name: "bincount" + signature: "Tensor (Tensor input, Tensor weights=None, Int64 minlength=None) => BinCount" + bind_python: True + +- name: "stft" + signature: + 'Tensor (Tensor input, Int64 n_fft,Int64 hop_length=None, Int64 win_length=None, Tensor window=None,Bool center=True,String pad_mode="reflect",Bool normalized=False,Bool onesided=True,Bool return_complex=False) =>Stft' + bind_python: True + +- name: "isclose" + signature: "Tensor (Tensor input, Tensor other, Float atol=1e-08, Float rtol=1e-05, Bool equal_nan=False) => IsClose" + bind_python: True + +- name: "uniform_" + signature: "Tensor (Tensor x,Scalar from, Scalar to) => InplaceUniform" + bind_python: True + +- name: "fused_fast_gelu_mul" + signature: "Tensor (Tensor x, Tensor multiplier) => FusedFastGeluMul" + bind_python: True + +- name: "fused_fast_gelu_mul_grad" + signature: "TensorTuple (Tensor dy, Tensor x, Tensor multiplier) => FusedFastGeluMulGrad" + bind_python: False + +- name: "unique" + signature: [ + "Tensor (Tensor x, Bool sorted=True, DataType dtype=kInt32) => Unique", + "TensorTuple (Tensor x, Bool sorted=True, Bool return_inverse=False, Bool return_counts=False, DataType dtype=kInt32) => UniqueWithCounts" + ] + bind_python: True + +- name: "fused_weighted_sum" + signature: "Tensor (TensorTuple in, FloatList weights, Float alpha=1.0) => FusedWeightedSum" + bind_python: True + +- name: "sort" + signature: "TensorTuple[values, indices] (Tensor input, Int32 dim=-1, Bool descending=False) => Sort" + bind_python: True + +- name: "throw_error" + signature: "Tensor (Tensor input) => ThrowError" + bind_python: True diff --git a/oneflow/core/functional/impl/activation_functor.cpp b/oneflow/core/functional/impl/activation_functor.cpp index 3a604b3ebf1a5570e25cd52c46205ac750ba0e3e..87a6090e373f9973fdbffe719c2d68a4bee5041c 100644 --- a/oneflow/core/functional/impl/activation_functor.cpp +++ b/oneflow/core/functional/impl/activation_functor.cpp @@ -17,17 +17,21 @@ limitations under the License. #include "oneflow/core/common/error.h" #include "oneflow/core/common/scalar.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/functional_api.yaml.h" #include "oneflow/core/functional/impl/unary_functor.h" #include "oneflow/core/functional/impl/binary_functor.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/functional/function_library.h" #include "oneflow/core/autograd/autograd_mode.h" #include "oneflow/core/functional/sequence_function.h" +#include "oneflow/core/kernel/kernel_util.h" namespace oneflow { namespace one { @@ -92,12 +96,8 @@ class PReluGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& x, const std::shared_ptr& alpha) const { - MutableAttrMap attrs; - if (alpha->requires_grad()) { - JUST(attrs.SetAttr("alpha_requires_grad", true)); - } else { - JUST(attrs.SetAttr("alpha_requires_grad", false)); - } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha_requires_grad"); + attrs.SetAllAttrs(alpha->requires_grad()); return OpInterpUtil::Dispatch(*op_, {dy, x, alpha}, attrs); } @@ -112,9 +112,8 @@ class HardTanhFunctor { } Maybe operator()(const std::shared_ptr& x, const double& min_val, const double& max_val) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("min_val", min_val)); - JUST(attrs.SetAttr("max_val", max_val)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("min_val", "max_val"); + attrs.SetAllAttrs(min_val, max_val); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -130,9 +129,8 @@ class HardTanhGradFunctor { Maybe operator()(const std::shared_ptr& y, const std::shared_ptr& dy, const double& min_val, const double& max_val) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("min_val", min_val)); - JUST(attrs.SetAttr("max_val", max_val)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("min_val", "max_val"); + attrs.SetAllAttrs(min_val, max_val); return OpInterpUtil::Dispatch(*op_, {y, dy}, attrs); } @@ -144,8 +142,8 @@ class EluFunctor { public: EluFunctor() { op_ = CHECK_JUST(one::OpBuilder("elu").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const double& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -160,8 +158,8 @@ class EluGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const double& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {x, dy}, attrs); } @@ -174,8 +172,8 @@ class CeluFunctor { CeluFunctor() { op_ = CHECK_JUST(one::OpBuilder("celu").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const double& alpha, bool inplace) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); if (inplace) { JUST(CheckInplaceValid(x)); std::shared_ptr outputs = std::make_shared(1); @@ -198,8 +196,8 @@ class CeluGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const double& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {x, dy}, attrs); } @@ -219,6 +217,34 @@ class GeluGradFunctor : public BinaryFunctor { } }; +class FastGeluFunctor : public UnaryFunctor { + public: + FastGeluFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fast_gelu").Input("in").Output("out").Build()); + } +}; + +class FastGeluGradFunctor : public BinaryFunctor { + public: + FastGeluGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fast_gelu_grad").Input("dy").Input("x").Output("dx").Build()); + } +}; + +class QuickGeluFunctor : public UnaryFunctor { + public: + QuickGeluFunctor() { + op_ = CHECK_JUST(one::OpBuilder("quick_gelu").Input("x").Output("y").Build()); + } +}; + +class QuickGeluGradFunctor : public BinaryFunctor { + public: + QuickGeluGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("quick_gelu_grad").Input("dy").Input("x").Output("dx").Build()); + } +}; + class GluFunctor { public: GluFunctor() {} @@ -278,8 +304,8 @@ class HardShrinkFunctor { Maybe operator()(const std::shared_ptr& x, const double& lambd, bool inplace) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("lambd", lambd)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("lambd"); + attrs.SetAllAttrs(lambd); if (inplace) { JUST(CheckInplaceValid(x)); std::shared_ptr outputs = std::make_shared(1); @@ -298,13 +324,13 @@ class HardShrinkFunctor { class HardShrinkGradFunctor { public: HardShrinkGradFunctor() { - op_ = CHECK_JUST(one::OpBuilder("hardshrink_grad").Input("dy").Input("y").Output("dx").Build()); + op_ = CHECK_JUST(one::OpBuilder("hardshrink_grad").Input("y").Input("dy").Output("dx").Build()); } Maybe operator()(const std::shared_ptr& y, const std::shared_ptr& dy, const double& lambd) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("lambd", lambd)); - return OpInterpUtil::Dispatch(*op_, {dy, y}, attrs); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("lambd"); + attrs.SetAllAttrs(lambd); + return OpInterpUtil::Dispatch(*op_, {y, dy}, attrs); } private: @@ -328,8 +354,6 @@ class SoftmaxFunctorBase { }; int64_t dim_ = dim ? JUST(dim) : get_dim(); - if (dim_ < 0) { dim_ += num_axes; } - dim_ = JUST(maybe_wrap_dim(dim_, num_axes)); if (dim_ != num_axes - 1) { std::vector input_perm(input_shape->dim_vec().size(), 0); @@ -384,6 +408,78 @@ class LogSoftmaxFunctor : public SoftmaxFunctorBase { } }; +class LogSoftmaxGradFunctor { + public: + LogSoftmaxGradFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("log_softmax_grad").Input("prob").Input("dy").Output("dx").Build()); + } + + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& y) const { + return OpInterpUtil::Dispatch(*op_, {y, dy}); + } + + private: + std::shared_ptr op_; +}; + +class GumbelSoftmaxFunctor { + public: + Maybe operator()(const std::shared_ptr& in, const double& tau, + const Optional& dim, bool hard, + const Optional& generator) const { + auto in_shape = in->shape(); + auto device = JUST(in->device()); + auto dtype = in->dtype(); + const int64_t num_axes = in_shape->NumAxes(); + + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("tau", "seed", "hard"); + attrs.SetAllAttrs(tau, static_cast(gen->current_seed()), hard); + + auto random_tensor = + JUST(functional::Rand(*in_shape.get(), dtype, device, gen, /*requires_grad=*/false)); + auto gumbel_noise_tensor = JUST(functional::ScalarSub( + Scalar(0.0), + JUST(functional::Log(JUST(functional::ScalarSub( + Scalar(0.0), JUST(functional::Log(random_tensor)), /*alpha=*/1.0)))), + /*alpha=*/1.0)); + auto gumbel_in_tensor = JUST(functional::ScalarDiv( + JUST(functional::Add(in, gumbel_noise_tensor, /*alpha=*/1.0, /*inplace=*/false)), + Scalar(tau))); + + auto out_soft = JUST(functional::Softmax(gumbel_in_tensor, dim)); + if (hard) { + const auto get_dim = [num_axes]() -> int64_t { + const int64_t ndim = num_axes; + if (ndim == 0 || ndim == 1 || ndim == 3) { + return 0; + } else { + return 1; + } + }; + + int64_t dim_ = dim ? JUST(dim) : get_dim(); + dim_ = JUST(maybe_wrap_dim(dim_, num_axes)); + auto out_max = JUST(functional::ArgMax(out_soft, dim_, /*keepdim=*/true, dtype)); + auto index = + JUST(functional::To(out_max, JUST(DType::Get(DataType::kInt64)), /*copy=*/false)); + auto zero = JUST(functional::ZerosLike(out_soft)); + auto out_hard = + JUST(functional::DimScatterUpdateScalar(zero, dim_, index, 1.0, /*inplace=*/false)); + + auto out_hard_has_grad = + functional::Add(JUST(functional::Sub(out_hard, JUST(out_soft->detach()), /*alpha=*/1.0, + /*inplace=*/false)), + out_soft, /*alpha=*/1.0, /*inplace=*/false); + return out_hard_has_grad; + } else { + return out_soft; + } + } +}; + class HardSwishFunctor : public UnaryFunctor { public: HardSwishFunctor() { @@ -405,8 +501,8 @@ class LeakyReluFunctor { } Maybe operator()(const std::shared_ptr& x, const float& alpha, bool inplace) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); if (inplace) { JUST(CheckInplaceValid(x)); std::shared_ptr outputs = std::make_shared(1); @@ -429,8 +525,8 @@ class LeakyReluGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const float& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {x, dy}, attrs); } @@ -446,9 +542,8 @@ class SoftplusFunctor { Maybe operator()(const std::shared_ptr& x, const double& beta, const double& threshold) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("beta", beta)); - JUST(attrs.SetAttr("threshold", threshold)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("beta", "threshold"); + attrs.SetAllAttrs(beta, threshold); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -463,9 +558,8 @@ class SoftplusGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const double& beta, const double& threshold) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("beta", beta)); - JUST(attrs.SetAttr("threshold", threshold)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("beta", "threshold"); + attrs.SetAllAttrs(beta, threshold); return OpInterpUtil::Dispatch(*op_, {x, dy}, attrs); } @@ -531,11 +625,11 @@ class SoftShrinkFunctor { Maybe operator()(const std::shared_ptr& x, const double& alpha, bool inplace) const { - MutableAttrMap attrs; CHECK_GE_OR_RETURN(alpha, 0) << Error::RuntimeError() << "alpha must be greater or equal to 0, but found to be " << alpha << "."; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); if (inplace) { JUST(CheckInplaceValid(x)); std::shared_ptr outputs = std::make_shared(1); @@ -559,9 +653,8 @@ class ThresholdFunctor { Maybe operator()(const std::shared_ptr& x, const double& threshold, const double& value) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("threshold_val", threshold)); - JUST(attrs.SetAttr("value", value)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("threshold_val", "value"); + attrs.SetAllAttrs(threshold, value); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -577,8 +670,8 @@ class ThresholdGradFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const double& threshold) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("threshold_val", threshold)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("threshold_val"); + attrs.SetAllAttrs(threshold); return OpInterpUtil::Dispatch(*op_, {x, dy}, attrs); } @@ -589,13 +682,13 @@ class ThresholdGradFunctor { class SoftShrinkGradFunctor { public: SoftShrinkGradFunctor() { - op_ = CHECK_JUST(one::OpBuilder("softshrink_grad").Input("dy").Input("y").Output("dx").Build()); + op_ = CHECK_JUST(one::OpBuilder("softshrink_grad").Input("y").Input("dy").Output("dx").Build()); } Maybe operator()(const std::shared_ptr& y, const std::shared_ptr& dy, const double& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); - return OpInterpUtil::Dispatch(*op_, {dy, y}, attrs); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); + return OpInterpUtil::Dispatch(*op_, {y, dy}, attrs); } private: @@ -617,6 +710,10 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("CeluGrad"); m.add_functor("Gelu"); m.add_functor("GeluGrad"); + m.add_functor("FastGelu"); + m.add_functor("FastGeluGrad"); + m.add_functor("QuickGelu"); + m.add_functor("QuickGeluGrad"); m.add_functor("Glu"); m.add_functor("HardSigmoid"); m.add_functor("HardSigmoidGrad"); @@ -625,6 +722,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Softmax"); m.add_functor("SoftmaxGrad"); m.add_functor("LogSoftmax"); + m.add_functor("LogSoftmaxGrad"); + m.add_functor("GumbelSoftmax"); m.add_functor("HardSwish"); m.add_functor("HardSwishGrad"); m.add_functor("LeakyRelu"); diff --git a/oneflow/core/functional/impl/array_functor.cpp b/oneflow/core/functional/impl/array_functor.cpp index 045c4dd154571def7068a69cb6467d0101c2043e..7eced83018faad377106acfd2d203cc2d8e5f077 100644 --- a/oneflow/core/functional/impl/array_functor.cpp +++ b/oneflow/core/functional/impl/array_functor.cpp @@ -13,40 +13,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ - #include "oneflow/core/autograd/autograd_mode.h" -#include "oneflow/core/common/data_type.pb.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/scalar.h" -#include "oneflow/core/common/singleton.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/common/protobuf.h" #include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/symbol.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/attr_map.h" -#include "oneflow/core/framework/device.h" -#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/framework/placement_utils.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/framework/random_generator_impl.h" -#include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/function_library.h" +#include "oneflow/core/functional/functional_api.yaml.h" #include "oneflow/core/functional/sequence_function.h" -#include "oneflow/core/functional/impl/common.h" #include "oneflow/core/functional/impl/unary_functor.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/job/sbp_parallel.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/ep/include/device_manager_registry.h" -#include "oneflow/api/common/ofblob.h" +#include "oneflow/core/job/global_mode.h" +#include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/framework/tensor_util.h" +#include "oneflow/core/job/nd_sbp_util.h" namespace oneflow { namespace one { @@ -117,41 +99,139 @@ class ArgMinFunctor { Maybe operator()(const std::shared_ptr& input, const Optional& dim, const Optional& keepdim, const Optional>& dtype) const { + TensorProcessor tensor_processor; + JUST(tensor_processor.AddInputs({input}, DType::Float()).Apply()); + const auto x = JUST(tensor_processor.GetInputs()).at(0); return sequence_function(Negative) .then(std::bind(ArgMax, std::placeholders::_1, dim, keepdim, dtype)) - .call(input); + .call(x); + } +}; + +class GlobalTensorConstantFunctor { + public: + GlobalTensorConstantFunctor() { + op_ = CHECK_JUST(one::OpBuilder("tensor_constant").Input("in").Output("out").Build()); + } + Maybe operator()(const Shape& shape, const std::shared_ptr& value, + const Symbol& dtype, const Symbol& placement, + const std::vector>& sbp_tuple) const { + CHECK_OR_RETURN(value->ndim() <= 1 && value->nelement() == 1) + << "Only tensor with single element or scalar tensor are supported as value!"; + CHECK_OR_RETURN(value->is_global()) << "The value tensor should be global tensor"; + // NOTE: this op is an source op, so the value(scalar tensor) should not have autograd status. + autograd::AutoGradMode mode(false); + JUST(CheckDeviceIdsIsValid(placement)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "nd_sbp"); + attrs.SetAllAttrs(shape, dtype->data_type(), NullOpt); + + auto dispatch_constant = + [&](const std::vector>& sbp_tuple) -> Maybe { + std::vector nd_sbp(sbp_tuple.size()); + { + for (int i = 0; i < sbp_tuple.size(); ++i) { + nd_sbp[i] = SbpParallelToString(*sbp_tuple[i]); + } + } + attrs.SetAttr<2>(nd_sbp); + return OpInterpUtil::Dispatch(*op_, {value}, attrs); + }; + bool has_partial_parallel = + std::any_of(sbp_tuple.begin(), sbp_tuple.end(), + [](const Symbol& sbp) { return sbp->has_partial_sum_parallel(); }); + // The source op does not support Partial + if (has_partial_parallel) { + const auto& fixed_sbp_tuple = JUST(NdSbpReplacePartialByBroadcast(sbp_tuple)); + const auto& tensor = JUST(dispatch_constant(*fixed_sbp_tuple)); + return functional::ToGlobal(tensor, placement, sbp_tuple, {}, /* check_meta */ false, + /*copy*/ false); + } else { + return dispatch_constant(sbp_tuple); + } } + + private: + std::shared_ptr op_; }; -class ConsistentConstantFunctor { + +class TensorConstantFunctor { public: - ConsistentConstantFunctor() { - op_ = CHECK_JUST(one::OpBuilder("constant").Output("out").Build()); + TensorConstantFunctor() { + op_ = CHECK_JUST(one::OpBuilder("tensor_constant").Input("in").Output("out").Build()); + } + Maybe operator()(const Shape& shape, const std::shared_ptr& value, + const Symbol& dtype, + const Optional>& device) const { + CHECK_OR_RETURN(value->ndim() <= 1 && value->nelement() == 1) + << "Only tensor with single element or scalar tensor are supported as value!"; + // NOTE: this op is an source op, so the value(scalar tensor) should not have autograd status. + autograd::AutoGradMode mode(false); + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalTensorConstant(shape, value, dtype, + GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())))); + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype"); + attrs.SetAllAttrs(shape, dtype->data_type()); + if (device.has_value()) { + Symbol device_symbol = JUST(device); + return OpInterpUtil::Dispatch(*op_, {value}, + OpExprInterpContext(attrs, device_symbol)); + } else { + return OpInterpUtil::Dispatch(*op_, {value}, attrs); + } } + + private: + std::shared_ptr op_; +}; + +class GlobalConstantFunctor { + public: + GlobalConstantFunctor() { op_ = CHECK_JUST(one::OpBuilder("constant").Output("out").Build()); } Maybe operator()(const Shape& shape, const Scalar& value, const Symbol& dtype, const Symbol& placement, const std::vector>& sbp_tuple) const { JUST(CheckDeviceIdsIsValid(placement)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "floating_value", + "is_floating_value", "integer_value", "nd_sbp"); if (IsIntegralDataType(dtype->data_type())) { - JUST(attrs.SetAttr("is_floating_value", false)); - JUST(attrs.SetAttr("integer_value", value.As())); + attrs.SetAllAttrs(shape, dtype->data_type(), NullOpt, false, value.As(), NullOpt); } else { - JUST(attrs.SetAttr("is_floating_value", true)); - JUST(attrs.SetAttr("floating_value", value.As())); + attrs.SetAllAttrs(shape, dtype->data_type(), value.As(), true, NullOpt, NullOpt); } - if (LazyMode::is_enabled()) { - std::vector nd_sbp(sbp_tuple.size()); - { - for (int i = 0; i < sbp_tuple.size(); ++i) { - nd_sbp.at(i) = SbpParallelToString(*sbp_tuple.at(i)); + + auto dispatch_constant = + [&](const std::vector>& sbp_tuple) -> Maybe { + if (LazyMode::is_enabled()) { + std::vector nd_sbp(sbp_tuple.size()); + { + for (int i = 0; i < sbp_tuple.size(); ++i) { + nd_sbp[i] = SbpParallelToString(*sbp_tuple[i]); + } } + attrs.SetAttr<5>(nd_sbp); + } + const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); + return OpInterpUtil::Dispatch(*op_, {}, + OpExprInterpContext(attrs, placement, nd_sbp)); + }; + bool has_partial_parallel = [&]() { + for (const auto& sbp : sbp_tuple) { + if (sbp->has_partial_sum_parallel()) { return true; } } - JUST(attrs.SetAttr>("nd_sbp", nd_sbp)); + return false; + }(); + // Since the source op does not support Partial, it is necessary to replace Partial + // with Broadcast, and then convert it to Partial + if (has_partial_parallel) { + const auto& fixed_sbp_tuple = JUST(NdSbpReplacePartialByBroadcast(sbp_tuple)); + const auto& tensor = JUST(dispatch_constant(*fixed_sbp_tuple)); + return functional::ToGlobal(tensor, placement, sbp_tuple, {}, /* check_meta */ false, + /*copy*/ false); + } else { + return dispatch_constant(sbp_tuple); } - const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); - return OpInterpUtil::Dispatch(*op_, {}, OpExprInterpContext(attrs, placement, nd_sbp)); } private: @@ -163,15 +243,17 @@ class ConstantFunctor { ConstantFunctor() { op_ = CHECK_JUST(one::OpBuilder("constant").Output("out").Build()); } Maybe operator()(const Shape& shape, const Scalar& value, const Symbol& dtype, const Optional>& device) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalConstant(shape, value, dtype, + GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())))); + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "floating_value", + "is_floating_value", "integer_value"); if (IsIntegralDataType(dtype->data_type())) { - JUST(attrs.SetAttr("is_floating_value", false)); - JUST(attrs.SetAttr("integer_value", value.As())); + attrs.SetAllAttrs(shape, dtype->data_type(), NullOpt, false, value.As()); } else { - JUST(attrs.SetAttr("is_floating_value", true)); - JUST(attrs.SetAttr("floating_value", value.As())); + attrs.SetAllAttrs(shape, dtype->data_type(), value.As(), true, NullOpt); } if (device.has_value()) { Symbol device_symbol = JUST(device); @@ -190,30 +272,35 @@ class EmptyFunctor { EmptyFunctor() { op_ = CHECK_JUST(one::OpBuilder("empty").Output("out").Build()); } Maybe operator()(const Shape& shape, const Symbol& dtype, const Optional>& device, const bool pin_memory) const { - MutableAttrMap attrs; + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalEmpty(shape, dtype, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())))); + } Symbol device_symbol = device.value_or(JUST(Device::New("cpu", 0))); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("pin_memory", pin_memory)); - JUST(attrs.SetAttr("device_type", device_symbol->type())); - JUST(attrs.SetAttr("device_id", device_symbol->device_id())); - return OpInterpUtil::Dispatch(*op_, {}, attrs); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "pin_memory", "device_type", "device_id"); + attrs.SetAllAttrs(shape, dtype->data_type(), pin_memory, device_symbol->type(), + device_symbol->device_id()); + if (device.has_value()) { + Symbol device_symbol = JUST(device); + return OpInterpUtil::Dispatch(*op_, {}, OpExprInterpContext(attrs, device_symbol)); + } else { + return OpInterpUtil::Dispatch(*op_, {}, attrs); + } } private: std::shared_ptr op_; }; -class ConsistentEmptyFunctor { +class GlobalEmptyFunctor { public: - ConsistentEmptyFunctor() { op_ = CHECK_JUST(one::OpBuilder("empty").Output("out").Build()); } + GlobalEmptyFunctor() { op_ = CHECK_JUST(one::OpBuilder("empty").Output("out").Build()); } Maybe operator()(const Shape& shape, const Symbol& dtype, const Symbol& placement, const std::vector>& sbp_tuple) const { JUST(CheckDeviceIdsIsValid(placement)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "nd_sbp"); if (LazyMode::is_enabled()) { std::vector nd_sbp(sbp_tuple.size()); { @@ -221,7 +308,9 @@ class ConsistentEmptyFunctor { nd_sbp.at(i) = SbpParallelToString(*sbp_tuple.at(i)); } } - JUST(attrs.SetAttr>("nd_sbp", nd_sbp)); + attrs.SetAllAttrs(shape, dtype->data_type(), nd_sbp); + } else { + attrs.SetAllAttrs(shape, dtype->data_type(), NullOpt); } const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch(*op_, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -247,29 +336,45 @@ class OnesLikeFunctor : public UnaryFunctor { class FlattenFunctor { public: - FlattenFunctor() { - op_ = CHECK_JUST(one::OpBuilder("flatten").Input("in").Output("out").Build()); - } + FlattenFunctor() = default; + Maybe operator()(const std::shared_ptr& x, const int32_t& start_dim, const int32_t& end_dim) const { - const auto& x_shape = x->shape(); - const int32_t x_dim = x_shape->dim_vec().size(); + const Shape& in_shape = *x->shape(); + int32_t ndim = in_shape.size(); + + auto CheckAndWrapDim = [&](int32_t dim) -> Maybe { + // handle scalar + if (ndim == 0 && (dim == 0 || dim == -1)) { return 0; } + if (dim < -ndim || dim >= ndim) { + return Error::IndexError() << "Dimension out of range (expected to be in range of [" + << -ndim << ", " << ndim - 1 << "], but got " << dim << ")"; + } + return dim >= 0 ? dim : dim + ndim; + }; - int new_start_dim = start_dim; - int new_end_dim = end_dim; - if (start_dim < 0) { new_start_dim += x_dim; } - if (end_dim < 0) { new_end_dim += x_dim; } - if (new_start_dim == new_end_dim) { return x; } + // -n dim (negative dim) indicate ndim-n + // for example, when ndim == 3, (-3) == (0), (-2) == (1), (-1) == (2) + int32_t true_start_dim = JUST(CheckAndWrapDim(start_dim)); + int32_t true_end_dim = JUST(CheckAndWrapDim(end_dim)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("start_dim", start_dim)); - JUST(attrs.SetAttr("end_dim", end_dim)); + if (true_start_dim > true_end_dim) { + return Error::RuntimeError() << "flatten() has invalid args: start_dim (" << start_dim + << ") cannot come after end_dim (" << end_dim << ")"; + } - return OpInterpUtil::Dispatch(*op_, {x}, attrs); - } + // identity when start_dim == end_dim + if (true_start_dim == true_end_dim) { return x; } - private: - std::shared_ptr op_; + DimVector dim_vec{in_shape.begin(), in_shape.begin() + true_start_dim + 1}; + for (int i = true_start_dim + 1; i <= true_end_dim; ++i) { dim_vec.back() *= in_shape[i]; } + dim_vec.insert(dim_vec.end(), in_shape.begin() + true_end_dim + 1, in_shape.end()); + Shape reshape_shape{dim_vec}; + CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), reshape_shape.elem_cnt()) + << Error::RuntimeError() << "invalid reshape from " << in_shape.ToString() << " to " + << reshape_shape.ToString(); + return JUST(Reshape(x, reshape_shape)); + } }; class WhereFunctor { @@ -296,26 +401,21 @@ class WhereScalarXFunctor { } Maybe operator()(const std::shared_ptr& condition, const Scalar& scalar, const std::shared_ptr& y) const { - MutableAttrMap attrs; + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("bool_operand", "has_bool_operand", "float_operand", + "has_float_operand", "int_operand", "has_int_operand"); + auto input = y; if (scalar.IsBool()) { - JUST(attrs.SetAttr("bool_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", true)); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", false)); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false, NullOpt, false); } else if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("float_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", false)); - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); + input = JUST(functional::Cast(y, DType::Double(), /*pin_memory=*/false)); + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true, NullOpt, false); } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("int_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", false)); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); + attrs.SetAllAttrs(NullOpt, false, NullOpt, false, scalar.As(), true); } else { UNIMPLEMENTED_THEN_RETURN() << "The scalar in Where shoule be float or int."; } - return OpInterpUtil::Dispatch(*op_, {condition, y}, attrs); + return OpInterpUtil::Dispatch(*op_, {condition, input}, attrs); } private: @@ -330,26 +430,21 @@ class WhereScalarYFunctor { } Maybe operator()(const std::shared_ptr& condition, const std::shared_ptr& x, const Scalar& scalar) const { - MutableAttrMap attrs; + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("bool_operand", "has_bool_operand", "float_operand", + "has_float_operand", "int_operand", "has_int_operand"); + auto input = x; if (scalar.IsBool()) { - JUST(attrs.SetAttr("bool_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", true)); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", false)); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false, NullOpt, false); } else if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("float_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", false)); - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); + input = JUST(functional::Cast(x, DType::Double(), /*pin_memory=*/false)); + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true, NullOpt, false); } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("int_operand", scalar.As())); - JUST(attrs.SetAttr("has_bool_operand", false)); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); + attrs.SetAllAttrs(NullOpt, false, NullOpt, false, scalar.As(), true); } else { UNIMPLEMENTED_THEN_RETURN() << "The scalar in Where shoule be bool, float or int."; } - return OpInterpUtil::Dispatch(*op_, {condition, x}, attrs); + return OpInterpUtil::Dispatch(*op_, {condition, input}, attrs); } private: @@ -363,34 +458,19 @@ class WhereScalarXYFunctor { } Maybe operator()(const std::shared_ptr& condition, const Scalar& x_scalar, const Scalar& y_scalar) const { - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "x_bool_operand", "y_bool_operand", "has_x_bool_operand", "has_y_bool_operand", + "x_float_operand", "y_float_operand", "has_x_float_operand", "has_y_float_operand", + "x_int_operand", "y_int_operand", "has_x_int_operand", "has_y_int_operand"); if (x_scalar.IsBool() && y_scalar.IsBool()) { - JUST(attrs.SetAttr("x_bool_operand", x_scalar.As())); - JUST(attrs.SetAttr("y_bool_operand", y_scalar.As())); - JUST(attrs.SetAttr("has_x_bool_operand", true)); - JUST(attrs.SetAttr("has_y_bool_operand", true)); - JUST(attrs.SetAttr("has_x_float_operand", false)); - JUST(attrs.SetAttr("has_y_float_operand", false)); - JUST(attrs.SetAttr("has_x_int_operand", false)); - JUST(attrs.SetAttr("has_y_int_operand", false)); + attrs.SetAllAttrs(x_scalar.As(), y_scalar.As(), true, true, NullOpt, NullOpt, + false, false, NullOpt, NullOpt, false, false); } else if (x_scalar.IsFloatingPoint() && y_scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("x_float_operand", x_scalar.As())); - JUST(attrs.SetAttr("y_float_operand", y_scalar.As())); - JUST(attrs.SetAttr("has_x_bool_operand", false)); - JUST(attrs.SetAttr("has_y_bool_operand", false)); - JUST(attrs.SetAttr("has_x_float_operand", true)); - JUST(attrs.SetAttr("has_y_float_operand", true)); - JUST(attrs.SetAttr("has_x_int_operand", false)); - JUST(attrs.SetAttr("has_y_int_operand", false)); + attrs.SetAllAttrs(NullOpt, NullOpt, false, false, x_scalar.As(), + y_scalar.As(), true, true, NullOpt, NullOpt, false, false); } else if (x_scalar.IsIntegral() && y_scalar.IsIntegral()) { - JUST(attrs.SetAttr("x_int_operand", x_scalar.As())); - JUST(attrs.SetAttr("y_int_operand", y_scalar.As())); - JUST(attrs.SetAttr("has_x_bool_operand", false)); - JUST(attrs.SetAttr("has_y_bool_operand", false)); - JUST(attrs.SetAttr("has_x_float_operand", false)); - JUST(attrs.SetAttr("has_y_float_operand", false)); - JUST(attrs.SetAttr("has_x_int_operand", true)); - JUST(attrs.SetAttr("has_y_int_operand", true)); + attrs.SetAllAttrs(NullOpt, NullOpt, false, false, NullOpt, NullOpt, false, false, + x_scalar.As(), y_scalar.As(), true, true); } else { UNIMPLEMENTED_THEN_RETURN() << "The scalar in Where shoule be bool, float or int."; } @@ -409,8 +489,8 @@ class ArgWhereFunctor { } Maybe operator()(const std::shared_ptr& x, const Symbol& dtype) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype"); + attrs.SetAllAttrs(dtype->data_type()); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -418,6 +498,48 @@ class ArgWhereFunctor { std::shared_ptr op_; }; +class NonZeroFunctor { + public: + NonZeroFunctor() {} + Maybe operator()(const std::shared_ptr& x, bool as_tuple) const { + std::shared_ptr input = x; + if (as_tuple && input->ndim() == 0) { input = JUST(functional::Unsqueeze(input, 0)); } + int64_t ndim = input->ndim(); + const auto& output_tuple = + JUST(functional::ArgWhere(input, JUST(DType::Get(DataType::kInt64)))); + const std::shared_ptr& size = JUST(VectorAt(*output_tuple, 1)); + CHECK_EQ_OR_RETURN(size->shape()->elem_cnt(), 1) + << Error::RuntimeError() << kOfBugIssueUploadPrompt; + CHECK_OR_RETURN(size->dtype() == JUST(DType::Get(DataType::kInt64))) + << Error::RuntimeError() << kOfBugIssueUploadPrompt; + int64_t size_val = -1; + { + if (size->is_global()) { + CHECK_OR_RETURN(JUST(size->parallel_desc())->parallel_num() == 1 // NOLINT + || NdSbpIsAllBroadcast(*JUST(size->nd_sbp()))); // NOLINT + } + JUST(GetItemInScalarTensor(size->is_local() ? size : JUST(size->cur_rank_phy_tensor()), + &size_val, sizeof(size_val))); + } + std::vector start{0, 0}; + std::vector stop{size_val, ndim}; + std::vector step{1, 1}; + const auto& output = JUST( + functional::Slice(output_tuple->at(0), start, stop, step, /*enable_view_slice=*/false)); + std::shared_ptr outputs = std::make_shared(); + if (as_tuple) { + const auto& transposed_output = JUST(functional::Transpose2dim(output, 1, 0)); + for (int64_t i = 0; i < ndim; ++i) { + outputs->emplace_back( + JUST(functional::TensorGetItem(transposed_output, {functional::detail::IndexItem(i)}))); + } + } else { + outputs->emplace_back(output); + } + return outputs; + } +}; + class BroadcastLikeFunctor { public: BroadcastLikeFunctor() { @@ -429,7 +551,12 @@ class BroadcastLikeFunctor { const Shape& x_shape = *x->shape(); const Shape& like_shape = *like->shape(); if (x_shape == like_shape) { return x; } - MutableAttrMap attrs; + CHECK_GE_OR_RETURN(like_shape.NumAxes(), x_shape.NumAxes()) + << Error::RuntimeError() << "The number of sizes provided (" << like_shape.NumAxes() + << ") must be greater or equal to the number of dimensions in the tensor (" + << x_shape.NumAxes() << ")" + << ". Target sizes: " << like_shape.ToString() << ". Tensor sizes: " << x_shape.ToString(); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("broadcast_axes"); if (broadcast_axes.empty()) { int64_t like_ndim = like_shape.NumAxes(); int64_t x_ndim = x_shape.NumAxes(); @@ -452,9 +579,9 @@ class BroadcastLikeFunctor { } } } - JUST(attrs.SetAttr>("broadcast_axes", broadcast_axes)); + attrs.SetAllAttrs(broadcast_axes); } else { - JUST(attrs.SetAttr>("broadcast_axes", broadcast_axes)); + attrs.SetAllAttrs(broadcast_axes); } return OpInterpUtil::Dispatch(*op_, {x, JUST(like->detach())}, attrs); } @@ -475,37 +602,49 @@ class ConcatFunctor { const int64_t ninput = inputs.size(); int64_t axis = dim; int64_t ndim = inputs[0]->ndim(); + int64_t nelement = inputs[0]->nelement(); int64_t max_dim_size = 0; CHECK_GE_OR_RETURN(ninput, 1) << Error::RuntimeError() << "inputs size must greater than 0"; axis = JUST(maybe_wrap_dim(axis, ndim)); const std::shared_ptr& shape = inputs[0]->shape(); for (const auto& input : inputs) { - CHECK_OR_RETURN(input->ndim() == ndim) - << Error::RuntimeError() << "Tensors must have same number of dimensions: got " - << input->ndim() << " and " << ndim << " is expected."; + if (nelement == 0 and ndim == 1) { + if (input->nelement() != 0 or input->ndim() != 1) { + ndim = input->ndim(); + nelement = input->nelement(); + } else { + continue; + } + } else if (input->nelement() != 0 or input->ndim() != 1) { + CHECK_OR_RETURN(input->ndim() == ndim) + << Error::RuntimeError() << "Tensors must have same number of dimensions: got " << ndim + << " and " << input->ndim() << " is expected."; + } for (int i = 0; i < ndim; ++i) { + if (input->nelement() == 0 and input->ndim() == 1) { continue; } if (axis == i) { max_dim_size += input->shape()->At(i); - } else { + } else if (inputs[0]->nelement() != 0) { CHECK_OR_RETURN(input->shape()->At(i) == shape->At(i)) << Error::RuntimeError() << "Sizes of tensors must match except in dimension " << axis << ". Got " << input->shape()->At(i) << " and " << shape->At(i) - << " is expected in dimension 1."; + << " is expected in dimension " << i << "."; } } } - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); - JUST(attrs.SetAttr("max_dim_size", max_dim_size)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "max_dim_size"); + attrs.SetAllAttrs(axis, max_dim_size); TensorTuple outputs; for (int i = 0; i < ninput; i += kMaxInputCount) { size_t size = (i + kMaxInputCount) < ninput ? kMaxInputCount : ninput - i; TensorTuple partial_inputs(size); TensorProcessor tensor_processor; for (int j = 0; j < size; ++j) { partial_inputs[j] = inputs[i + j]; } - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs(partial_inputs).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true) + .AddInputs(partial_inputs, inputs.at(i)->dtype()) + .Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); outputs.emplace_back( JUST(OpInterpUtil::Dispatch(*ops_[size - 1], input_tuple, attrs))); @@ -532,7 +671,6 @@ class StackFunctor { int64_t ndims = inputs[0]->ndim(); int64_t stack_dim = dim; stack_dim = JUST(maybe_wrap_dim(stack_dim, ndims + 1)); - if (ninput == 1) { return ExpandDims(inputs[0], dim); } const std::shared_ptr& first_in_shape = inputs[0]->shape(); for (const auto& input : inputs) { for (int i = 0; i < ndims; ++i) { @@ -543,16 +681,20 @@ class StackFunctor { } } int64_t max_dim_size = ninput; - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", stack_dim)); - JUST(attrs.SetAttr("max_dim_size", max_dim_size)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "max_dim_size"); + attrs.SetAllAttrs(stack_dim, max_dim_size); TensorTuple outputs; for (int i = 0; i < ninput; i += kMaxInputCount) { size_t size = (i + kMaxInputCount) < ninput ? kMaxInputCount : ninput - i; TensorTuple partial_inputs(size); for (int j = 0; j < size; ++j) { partial_inputs[j] = inputs[i + j]; } - outputs.emplace_back( - JUST(OpInterpUtil::Dispatch(*ops_.at(size - 1), partial_inputs, attrs))); + if (partial_inputs.size() == 1) { + // Use ExpandDims functor for only one input + outputs.emplace_back(JUST(functional::ExpandDims(partial_inputs[0], dim))); + } else { + outputs.emplace_back( + JUST(OpInterpUtil::Dispatch(*ops_[size - 1], partial_inputs, attrs))); + } } if (outputs.size() == 1) { return outputs.at(0); } return Concat(outputs, stack_dim); @@ -581,8 +723,8 @@ class StackGradFunctor { CHECK_LE_OR_RETURN(like.size(), kMaxInputCount) << Error::RuntimeError() << "like.size() must not greater than " << kMaxInputCount << ", but got " << like.size(); - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); TensorTuple inputs(like.size() + 1); inputs[0] = x; for (int i = 0; i < like.size(); ++i) { inputs[i + 1] = like[i]; } @@ -593,67 +735,168 @@ class StackGradFunctor { std::vector> ops_; }; -class ExpandFunctor { +class AtLeast1DFunctor { public: - ExpandFunctor() { op_ = CHECK_JUST(one::OpBuilder("expand").Input("in").Output("out").Build()); } - Maybe operator()(const std::shared_ptr& x, const Shape& shape) const { - CHECK_GE_OR_RETURN(shape.NumAxes(), x->shape()->NumAxes()) - << Error::RuntimeError() << "expand(tensor{" << x->shape()->ToString() - << "}, size=" << x->shape()->NumAxes() << "): the number of sizes provided (" - << shape.NumAxes() << ") " - << "must be greater or equal to the number of dimensions in the tensor (" - << x->shape()->NumAxes() << ")"; - std::vector in_shape(x->shape()->NumAxes()); - for (int i = 0; i < in_shape.size(); ++i) { in_shape[i] = x->shape()->At(i); } - - // check the parameters - int shift = shape.NumAxes() - in_shape.size(); - for (int i = shape.NumAxes() - 1; i >= 0; --i) { - int index = i - shift; - if (index >= 0) { - if (shape.At(i) != -1 && shape.At(i) != in_shape[index]) { - CHECK_OR_RETURN(shape.At(i) >= 0 && in_shape[index] == 1) - << Error::RuntimeError() << "The expanded size of the tensor (" << shape.At(i) - << ") must match the existing size (" << in_shape[index] - << ") at non-singleton dimension " << i << ". Target sizes: " << shape.ToString() - << ". Tensor sizes: " << x->shape()->ToString(); - } - } else { - CHECK_GE_OR_RETURN(shape.At(i), 0) - << Error::RuntimeError() << "The expanded size of the tensor (" << shape.At(i) - << ") isn't allowed in a leading, non-existing dimension " << i - << " .Target size: " << shape.ToString(); - } + Maybe operator()(const std::shared_ptr& x) const { + if (x->ndim() == 0) { + return JUST(Reshape(x, {1})); + } else + return x; + } +}; + +class AtLeast1DListFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + TensorTuple result = TensorTuple(inputs.size()); + for (int32_t i = 0; i < inputs.size(); i++) { + result.at(i) = JUST(AtLeast1D(JUST(VectorAt(inputs, i)))); } + return result; + } +}; - std::vector expand_shape(shape.NumAxes()); - for (int i = 0; i < shape.NumAxes(); ++i) { expand_shape[i] = shape.dim_vec().at(i); } +class AtLeast2DFunctor { + public: + Maybe operator()(const std::shared_ptr& x) const { + if (x->ndim() == 0) { + return JUST(Reshape(x, {1, 1})); + } else if (x->ndim() == 1) { + return JUST(Unsqueeze(x, 0)); + } else + return x; + } +}; - MutableAttrMap attrs; - JUST(attrs.SetAttr>("logical_in_shape", in_shape)); - JUST(attrs.SetAttr>("logical_expand_shape", expand_shape)); +class AtLeast2DListFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + TensorTuple result = TensorTuple(inputs.size()); + for (int32_t i = 0; i < inputs.size(); i++) { + result.at(i) = JUST(AtLeast2D(JUST(VectorAt(inputs, i)))); + } + return result; + } +}; - // if input tensor is eager local, then try return tensor's view - if (view::IsViewApplicable(x)) { return view::Expand(x, in_shape, expand_shape); } - return OpInterpUtil::Dispatch(*op_, {x}, attrs); +class AtLeast3DFunctor { + public: + Maybe operator()(const std::shared_ptr& x) const { + if (x->ndim() == 0) { + return JUST(Reshape(x, {1, 1, 1})); + } else if (x->ndim() == 1) { + return JUST(Reshape(x, {1, x->shape()->At(0), 1})); + } else if (x->ndim() == 2) { + return JUST(Unsqueeze(x, -1)); + } else + return x; } +}; - private: - std::shared_ptr op_; +class AtLeast3DListFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + TensorTuple result = TensorTuple(inputs.size()); + for (int32_t i = 0; i < inputs.size(); i++) { + result.at(i) = JUST(AtLeast3D(JUST(VectorAt(inputs, i)))); + } + return result; + } }; -class ExpandGradFunctor { +class ColumnStackFunctor { public: - ExpandGradFunctor() { - op_ = CHECK_JUST(one::OpBuilder("expand_grad").Input("in").Output("out").Build()); + Maybe operator()(const TensorTuple& inputs) const { + std::shared_ptr new_inputs = std::make_shared(inputs.size()); + for (int32_t i = 0; i < inputs.size(); i++) { + const auto& t = JUST(VectorAt(inputs, i)); + if (t->ndim() <= 1) + new_inputs->at(i) = JUST(Reshape(t, {t->nelement(), 1})); + else + new_inputs->at(i) = t; + } + return HStack(*new_inputs); } - Maybe operator()(const std::shared_ptr& dy, - const std::vector& logical_in_shape, - const std::vector& logical_expand_shape) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("logical_out_shape", logical_in_shape)); - JUST(attrs.SetAttr>("logical_expand_shape", logical_expand_shape)); - return OpInterpUtil::Dispatch(*op_, {dy}, attrs); +}; + +class HStackFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + std::shared_ptr new_inputs = JUST(AtLeast1D(inputs)); + if (new_inputs->at(0)->ndim() == 1) + return Concat(*new_inputs, 0); + else + return Concat(*new_inputs, 1); + } +}; + +class VStackFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + std::shared_ptr new_inputs = JUST(AtLeast2D(inputs)); + return Concat(*new_inputs, 0); + } +}; + +class RowStackFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { return VStack(inputs); } +}; + +class DStackFunctor { + public: + Maybe operator()(const TensorTuple& inputs) const { + std::shared_ptr new_inputs = JUST(AtLeast3D(inputs)); + return Concat(*new_inputs, 2); + } +}; + +class ExpandFunctor { + public: + ExpandFunctor() { op_ = CHECK_JUST(one::OpBuilder("expand").Input("in").Output("out").Build()); } + Maybe operator()(const std::shared_ptr& x, const Shape& shape) const { + const Shape& in_shape = *x->shape(); + int lpad = shape.size() - in_shape.size(); + if (lpad < 0) { + return Error::RuntimeError() + << "expand(tensor{" << in_shape.ToString() << "}, size=" << in_shape.size() + << "): the number of sizes provided (" << shape.size() << ") " + << "must be greater or equal to the number of dimensions in the tensor (" + << in_shape.size() << ")"; + } + + DimVector expand_shape_vec = shape.dim_vec(); + for (size_t i = 0; i < shape.size(); ++i) { + const auto& t_dim = shape[i]; + if (t_dim < -1) { + return Error::RuntimeError() << "Trying to create tensor with negative dimension " << t_dim; + } + if (i >= lpad) { + const auto& dim = in_shape[i - lpad]; + if (dim != 1 && t_dim != -1 && t_dim != dim) { + return Error::RuntimeError() + << "The expanded size of the tensor (" << t_dim + << ") must match the existing size (" << dim << ") at non-singleton dimension " + << i << ". Target sizes: " << shape.ToString() + << ". Tensor sizes: " << in_shape.ToString(); + } + if (t_dim == -1) { expand_shape_vec[i] = dim; } + } else { + if (t_dim == -1) { + return Error::RuntimeError() << "The expanded size of the tensor (-1) isn't allowed in a " + "leading, non-existing dimension " + << i; + } + } + } + + // if input tensor is eager local, then try return tensor's view + Shape expand_shape(expand_shape_vec); + if (view::IsViewApplicable(x)) { return view::Expand(x, expand_shape); } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("expand_shape"); + attrs.SetAllAttrs(expand_shape); + return OpInterpUtil::Dispatch(*op_, {x}, attrs); } private: @@ -668,13 +911,12 @@ class ExpandDimsFunctor { Maybe operator()(const std::shared_ptr& input, const int32_t& dim) const { int32_t expand_dim = dim; const int32_t ndim = input->shape()->NumAxes(); - JUST(maybe_wrap_dim(dim, ndim + 1)); - if (dim < 0) { expand_dim = dim + ndim + 1; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", expand_dim)); + expand_dim = JUST(maybe_wrap_dim(dim, ndim + 1)); if (view::IsViewApplicable(input)) { return view::Unsqueeze(input, expand_dim); } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(expand_dim); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -682,6 +924,58 @@ class ExpandDimsFunctor { std::shared_ptr op_; }; +class UnsqueezeMultipleFunctor { + public: + UnsqueezeMultipleFunctor() {} + Maybe operator()(const std::shared_ptr& x, const std::vector& dim, + const int32_t& n_dims) const { + if (dim.size() == 0 || x->ndim() == n_dims) { + return x; + } else if (dim.size() == 1) { + return JUST(functional::Unsqueeze(x, JUST(VectorAt(dim, 0)))); + } else { + std::shared_ptr tensor = x; + const auto& dims_to_unsqueeze = JUST(dim_list_to_bitset(dim, n_dims)); + + // Unsqueeze is called several times to extend the dimension when the View mechanism is + // enabled. Otherwise, calculate the target shape and call reshape. + if (view::IsViewApplicable(tensor)) { + for (int32_t i = 0; i < n_dims; i++) { + if ((*dims_to_unsqueeze)[i]) { tensor = JUST(view::Unsqueeze(tensor, i)); } + } + } else { + std::vector target_dims(n_dims, 0); + int32_t tensor_index = 0; + for (int32_t i = 0; i < n_dims; i++) { + if ((*dims_to_unsqueeze)[i]) { + target_dims[i] = 1; + } else { + CHECK_LT_OR_RETURN(tensor_index, tensor->ndim()); // NOLINT(maybe-need-error-msg) + target_dims[i] = tensor->shape()->at(tensor_index); + tensor_index++; + } + } + Shape infered_shape(DimVector(target_dims.begin(), target_dims.end())); + tensor = JUST(functional::Reshape(tensor, infered_shape)); + } + return tensor; + } + } +}; + +class InplaceUnsqueezeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const int32_t& dim) const { + JUST(CheckInplaceValid(input)); + const int64_t expand_dim = JUST(maybe_wrap_dim(dim, input->shape()->NumAxes() + 1)); + CHECK_OR_RETURN(view::IsViewApplicable(input)) + << "inplace unsqueeze(tensor.unsqueeze_) only support in eager local mode!"; + + JUST(view::InplaceUnsqueeze(input, expand_dim)); + return input; + } +}; + class SqueezeFunctor { public: SqueezeFunctor() { @@ -704,11 +998,10 @@ class SqueezeFunctor { } } - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axes", squeeze_dims)); - if (view::IsViewApplicable(x)) { return view::Squeeze(x, squeeze_dims); } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axes"); + attrs.SetAllAttrs(squeeze_dims); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -716,15 +1009,43 @@ class SqueezeFunctor { std::shared_ptr op_; }; +class InplaceSqueezeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const Optional>& dim) const { + JUST(CheckInplaceValid(input)); + const int32_t ndim = input->shape()->NumAxes(); + std::vector squeeze_dims; + squeeze_dims.reserve(ndim); + if (dim.has_value()) { + std::vector dims = *JUST(dim); + for (int32_t dim_i : dims) { + dim_i = JUST(maybe_wrap_dim(dim_i, ndim)); + if (input->shape()->At(dim_i) == 1) { squeeze_dims.emplace_back(dim_i); } + } + } else { + for (int i = 0; i < ndim; ++i) { + if (input->shape()->At(i) == 1) { squeeze_dims.emplace_back(i); } + } + } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axes"); + attrs.SetAllAttrs(squeeze_dims); + + CHECK_OR_RETURN(view::IsViewApplicable(input)) + << "inplace squeeze(tensor.squeeze_) only support in eager local mode!"; + + JUST(view::InplaceSqueeze(input, squeeze_dims)); + return input; + } +}; + class RollFunctor { public: RollFunctor() { op_ = CHECK_JUST(one::OpBuilder("roll").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const std::vector& shifts, const Optional>& dims) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("shifts", shifts)); - std::vector actual_dims; if (dims.has_value()) { actual_dims = *JUST(dims); @@ -734,8 +1055,9 @@ class RollFunctor { CHECK_EQ_OR_RETURN(shifts.size(), actual_dims.size()) << Error::RuntimeError() << "shifts and dimensions must align. shifts: " << shifts.size() << ", dims: " << actual_dims.size(); - JUST(attrs.SetAttr>("dims", actual_dims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shifts", "dims"); + attrs.SetAllAttrs(shifts, actual_dims); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -750,8 +1072,8 @@ class GatherFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& indices, const int64_t& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {x, indices}, attrs); } @@ -774,7 +1096,7 @@ class DimGatherFunctor { CHECK_EQ_OR_RETURN(sparse_grad, false) << Error::RuntimeError() << "Only support bool = False for now!"; - JUST(maybe_wrap_dim(dim, index->ndim())); + int64_t new_dim = JUST(maybe_wrap_dim(dim, index->ndim())); if (input->ndim() > 0 && index->ndim() > 0) { CHECK_EQ_OR_RETURN(input->ndim(), index->ndim()) << Error::RuntimeError() @@ -790,17 +1112,17 @@ class DimGatherFunctor { } if (input->ndim() > 0 && index->ndim() > 0) { FOR_RANGE(int32_t, i, 0, input->ndim()) { - if (i != dim) { + if (i != new_dim) { CHECK_LE_OR_RETURN(index->shape()->At(i), input->shape()->At(i)) << Error::RuntimeError() << "Size does not match at dimension " << i << " expected index " << *(index->shape()) << " to be smaller than self " - << *(input->shape()) << " apart from dimension " << dim; + << *(input->shape()) << " apart from dimension " << new_dim; } } } - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + attrs.SetAllAttrs(static_cast(new_dim)); return OpInterpUtil::Dispatch(*op_, {input, index}, attrs); } @@ -808,44 +1130,41 @@ class DimGatherFunctor { std::shared_ptr op_; }; -class DimScatterFunctor { - public: - DimScatterFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_update") - .Input("input") - .Input("index") - .Input("src") - .Output("output") - .Build()); - } - Maybe operator()(const std::shared_ptr& input, const int32_t& dim, - const std::shared_ptr& index, - const std::shared_ptr& src) const { - MutableAttrMap attrs; - const int32_t ndim = input->shape()->NumAxes(); - JUST(attrs.SetAttr("dim", dim < 0 ? dim + ndim : dim)); - return OpInterpUtil::Dispatch(*op_, {input, index, src}, attrs); - } +enum class DimScatterType { kUpdate, kAdd, kMultiply }; - private: - std::shared_ptr op_; -}; +template +std::string DimScatterTypeToString() { + switch (T) { + case DimScatterType::kUpdate: return "_update"; + case DimScatterType::kAdd: return "_add"; + case DimScatterType::kMultiply: return "_mul"; + } + return ""; +} -class DimScatterAddFunctor { +template +class DimScatterFunctorImpl { public: - DimScatterAddFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_add") - .Input("input") - .Input("index") - .Input("src") - .Output("output") - .Build()); - } + DimScatterFunctorImpl() + : op_(CHECK_JUST(one::OpBuilder("dim_scatter" + DimScatterTypeToString()) + .Input("input") + .Input("index") + .Input("src") + .Output("output") + .Build())) {} Maybe operator()(const std::shared_ptr& input, const int32_t& dim, const std::shared_ptr& index, - const std::shared_ptr& src) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); + const std::shared_ptr& src, bool inplace) const { + const int32_t ndim = input->shape()->NumAxes(); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + attrs.SetAllAttrs(static_cast(JUST(maybe_wrap_dim(dim, ndim)))); + if (inplace) { + JUST(CheckInplaceValid(input)); + auto outputs = std::make_shared(1); + outputs->at(0) = input; + JUST(OpInterpUtil::Dispatch(*op_, {input, index, src}, outputs.get(), attrs)); + return outputs->at(0); + } return OpInterpUtil::Dispatch(*op_, {input, index, src}, attrs); } @@ -853,65 +1172,48 @@ class DimScatterAddFunctor { std::shared_ptr op_; }; -class DimScatterAddLikeFunctor { - public: - DimScatterAddLikeFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_add_like") - .Input("like") - .Input("index") - .Input("src") - .Output("output") - .Build()); - } - Maybe operator()(const std::shared_ptr& like, const int32_t& dim, - const std::shared_ptr& index, - const std::shared_ptr& src) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); - return OpInterpUtil::Dispatch(*op_, {like, index, src}, attrs); - } - - private: - std::shared_ptr op_; -}; - -class DimScatterMulFunctor { +class DimScatterFunctor { public: - DimScatterMulFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_mul") - .Input("input") - .Input("index") - .Input("src") - .Output("output") - .Build()); - } Maybe operator()(const std::shared_ptr& input, const int32_t& dim, const std::shared_ptr& index, - const std::shared_ptr& src) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); - return OpInterpUtil::Dispatch(*op_, {input, index, src}, attrs); + const std::shared_ptr& src, + const Optional& reduce, bool inplace) const { + if (reduce.has_value()) { + const std::string& reduce_str = *JUST(reduce); + if (reduce_str == "add") { + return DimScatterAdd(input, dim, index, src, inplace); + } else if (reduce_str == "multiply") { + return DimScatterMul(input, dim, index, src, inplace); + } else { + CHECK_OR_RETURN(false) << Error::RuntimeError() << "Invalid reduce type: " << reduce_str; + } + } + return functional::DimScatterUpdate(input, dim, index, src, inplace); } - - private: - std::shared_ptr op_; }; -class DimScatterUpdateScalarFunctor { +template +class DimScatterScalarFunctorImpl { public: - DimScatterUpdateScalarFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_update_scalar") - .Input("input") - .Input("index") - .Output("output") - .Build()); - } + DimScatterScalarFunctorImpl() + : op_(CHECK_JUST(one::OpBuilder("dim_scatter" + DimScatterTypeToString() + "_scalar") + .Input("input") + .Input("index") + .Output("output") + .Build())) {} Maybe operator()(const std::shared_ptr& input, const int32_t& dim, - const std::shared_ptr& index, const Scalar& src) const { - MutableAttrMap attrs; + const std::shared_ptr& index, const Scalar& src, + bool inplace) const { const int32_t ndim = input->shape()->NumAxes(); - JUST(attrs.SetAttr("dim", dim < 0 ? dim + ndim : dim)); - JUST(attrs.SetAttr("src_scalar", src.As())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim", "src_scalar"); + attrs.SetAllAttrs(static_cast(JUST(maybe_wrap_dim(dim, ndim))), src.As()); + if (inplace) { + JUST(CheckInplaceValid(input)); + auto outputs = std::make_shared(1); + outputs->at(0) = input; + JUST(OpInterpUtil::Dispatch(*op_, {input, index}, outputs.get(), attrs)); + return outputs->at(0); + } return OpInterpUtil::Dispatch(*op_, {input, index}, attrs); } @@ -919,42 +1221,41 @@ class DimScatterUpdateScalarFunctor { std::shared_ptr op_; }; -class DimScatterAddScalarFunctor { +class DimScatterScalarFunctor { public: - DimScatterAddScalarFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_add_scalar") - .Input("input") - .Input("index") - .Output("output") - .Build()); - } Maybe operator()(const std::shared_ptr& input, const int32_t& dim, - const std::shared_ptr& index, const Scalar& src) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); - JUST(attrs.SetAttr("src_scalar", src.As())); - return OpInterpUtil::Dispatch(*op_, {input, index}, attrs); + const std::shared_ptr& index, const Scalar& src, + const Optional& reduce, bool inplace) const { + if (reduce.has_value()) { + const std::string& reduce_str = *JUST(reduce); + if (reduce_str == "add") { + return DimScatterAddScalar(input, dim, index, src, inplace); + } else if (reduce_str == "multiply") { + return DimScatterMulScalar(input, dim, index, src, inplace); + } else { + CHECK_OR_RETURN(false) << Error::RuntimeError() << "Invalid reduce type: " << reduce_str; + } + } + return functional::DimScatterUpdateScalar(input, dim, index, src, inplace); } - - private: - std::shared_ptr op_; }; -class DimScatterMulScalarFunctor { +class DimScatterAddLikeFunctor { public: - DimScatterMulScalarFunctor() { - op_ = CHECK_JUST(one::OpBuilder("dim_scatter_mul_scalar") - .Input("input") + DimScatterAddLikeFunctor() { + op_ = CHECK_JUST(one::OpBuilder("dim_scatter_add_like") + .Input("like") .Input("index") + .Input("src") .Output("output") .Build()); } - Maybe operator()(const std::shared_ptr& input, const int32_t& dim, - const std::shared_ptr& index, const Scalar& src) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); - JUST(attrs.SetAttr("src_scalar", src.As())); - return OpInterpUtil::Dispatch(*op_, {input, index}, attrs); + Maybe operator()(const std::shared_ptr& like, const int32_t& dim, + const std::shared_ptr& index, + const std::shared_ptr& src) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + attrs.SetAllAttrs(dim); + return OpInterpUtil::Dispatch(*op_, {like, index, src}, attrs); } private: @@ -968,8 +1269,8 @@ class ArgSortFunctor { } Maybe operator()(const std::shared_ptr& in, const std::string& direction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("direction", direction)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("direction"); + attrs.SetAllAttrs(direction); CHECK_OR_RETURN(direction == "ASCENDING" || direction == "DESCENDING") << Error::RuntimeError() << "expected the input direction parameter value is \"ASCENDING\" or \"DESCENDING\", " @@ -1010,9 +1311,8 @@ class SearchSortedFunctor { << "for searchsorted op, the size of input sorted_sequence' last dimension should " << "be less than " << INT32_MAX; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("out_int32", out_int32)); - JUST(attrs.SetAttr("right", right)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("out_int32", "right"); + attrs.SetAllAttrs(out_int32, right); return OpInterpUtil::Dispatch(*op_, {sorted_sequence, values}, attrs); } @@ -1039,17 +1339,8 @@ class SearchSortedScalarFunctor { << "for searchsorted op, the size of input sorted_sequence' last dimension should " << "be less than " << INT32_MAX; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("out_int32", out_int32)); - JUST(attrs.SetAttr("right", right)); - bool is_values_float = values.IsFloatingPoint(); - if (is_values_float) { - double_t values_tmp = values.As(); - JUST(attrs.SetAttr("values", values_tmp)); - } else { - int64_t values_tmp = values.As(); - JUST(attrs.SetAttr("values", values_tmp)); - } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("out_int32", "right", "values"); + attrs.SetAllAttrs(out_int32, right, values.As()); return OpInterpUtil::Dispatch(*op_, {sorted_sequence}, attrs); } @@ -1080,8 +1371,8 @@ class ScatterNdFunctor { } Maybe operator()(const std::shared_ptr& indices, const std::shared_ptr& updates, const Shape& shape) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape"); + attrs.SetAllAttrs(shape); return OpInterpUtil::Dispatch(*op_, {indices, updates}, attrs); } @@ -1105,14 +1396,28 @@ class TensorScatterNdUpdateFunctor { const std::shared_ptr& updates, bool inplace) const { CHECK_OR_RETURN(*tensor->dtype() == *updates->dtype()) << Error::RuntimeError() << "The dtype of tensor and updates must be same."; + std::shared_ptr contiguous_index = JUST(functional::ToContiguous(indices)); if (inplace) { - JUST(CheckInplaceValid(tensor)); - auto outputs = std::make_shared(1); - outputs->at(0) = tensor; - JUST(OpInterpUtil::Dispatch(*op_, {tensor, indices, updates}, outputs.get())); - return outputs->at(0); + if (tensor->is_global()) { + // NOTE: global tensor_scatter_nd_update inplace must calculate on another tensor and assign + // back because of input's sbp limited + auto output = + JUST(OpInterpUtil::Dispatch(*op_, {tensor, contiguous_index, updates})); + int64_t ndim = tensor->shape()->NumAxes(); + // TODO: use inplace copy op to write back to origin tensor + std::vector start(ndim, 0); + std::vector stop(tensor->shape()->begin(), tensor->shape()->end()); + std::vector step(ndim, 1); + return functional::SliceUpdate(tensor, output, start, stop, step, /*inplace=*/true); + } else { + JUST(CheckInplaceValid(tensor)); + auto outputs = std::make_shared(1); + (*outputs)[0] = tensor; + JUST(OpInterpUtil::Dispatch(*op_, {tensor, contiguous_index, updates}, outputs.get())); + return (*outputs)[0]; + } } else { - return OpInterpUtil::Dispatch(*op_, {tensor, indices, updates}); + return OpInterpUtil::Dispatch(*op_, {tensor, contiguous_index, updates}); } } @@ -1146,7 +1451,7 @@ class ReshapeFunctor { op_ = CHECK_JUST(one::OpBuilder("reshape").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const Shape& shape) const { - Shape infered_shape = *JUST(InferShape(x, shape)); + Shape infered_shape = *JUST(InferShapeUnspecifiedDim(x->shape()->Count(0), shape)); if (view::IsViewApplicable(x)) { Optional infered_stride = @@ -1155,8 +1460,8 @@ class ReshapeFunctor { return view::Reshape(x, infered_shape, *JUST(infered_stride)); } } - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", infered_shape)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape"); + attrs.SetAllAttrs(infered_shape); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1168,20 +1473,18 @@ class ViewFunctor { public: ViewFunctor() { op_ = CHECK_JUST(one::OpBuilder("reshape").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const Shape& shape) const { - Shape infered_shape = *JUST(InferShape(x, shape)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", infered_shape)); - + Shape infered_shape = *JUST(InferShapeUnspecifiedDim(x->shape()->Count(0), shape)); if (view::IsViewApplicable(x)) { Optional infered_stride = ComputeStride(*(x->shape()), *JUST(x->stride()), infered_shape); - CHECK_OR_RETURN(infered_stride.has_value()) + CHECK_OR_RETURN_ERROR(infered_stride.has_value()) << Error::RuntimeError() << "view size is not compatible with input tensor's size and stride (at least one " "dimension spans across two contiguous subspaces). Use .reshape(...) instead."; return view::Reshape(x, infered_shape, *JUST(infered_stride)); } - + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape"); + attrs.SetAllAttrs(infered_shape); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1195,7 +1498,7 @@ class ToContiguousFunctor { op_ = CHECK_JUST(one::OpBuilder("to_contiguous").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& input) const { - if (input->is_consistent() || input->is_lazy()) { return input; } + if (input->is_global() || input->is_lazy()) { return input; } return OpInterpUtil::Dispatch(*op_, {input}); } @@ -1215,13 +1518,23 @@ class InplaceToContiguousFunctor { auto contiguous_tensor = JUST(functional::ToContiguous(input)); CHECK_OR_RETURN(input->is_local() && contiguous_tensor->is_local()) << "Both ref and value must be local tensor."; - std::shared_ptr stride(new Stride(*input->shape())); + const Stride stride(*input->shape()); // update stride - JUST(input->mut_eager_mirrored_tensor_impl())->mut_tensor_meta()->set_stride(stride); const auto& blob_object = JUST(input->eager_blob_object()); - // update eager_blob_object - JUST(JUST(input->mut_eager_mirrored_tensor_impl()) - ->InitEagerBlobObject(JUST(blob_object->compute_local_dep_object()))); + Symbol old_tensor_meta = JUST(input->local_tensor_meta()); + + Symbol new_tensor_meta = SymbolOf(LocalTensorMeta( + old_tensor_meta->shape(), stride, old_tensor_meta->dtype(), old_tensor_meta->device())); + + std::shared_ptr final_tensor_impl = + std::make_shared(JUST(input->tensor_storage()), + JUST(input->storage_offset()), + input->requires_grad(), input->is_leaf()); + JUST(final_tensor_impl->set_retain_grad(input->retain_grad())); + JUST(final_tensor_impl->InitEagerBlobObject(new_tensor_meta, + JUST(blob_object->compute_local_dep_object()))); + JUST(JUST(input->AsLocalTensor())->set_impl(final_tensor_impl)); + // assign contiguous tensor data JUST(OpInterpUtil::Dispatch(*assign_op_, {input, contiguous_tensor})); return input; @@ -1254,10 +1567,8 @@ class NarrowFunctor { if (view::IsViewApplicable(input)) { return JUST(view::Narrow(input, narrow_dim, narrow_start, length)); } - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", narrow_dim)); - JUST(attrs.SetAttr("start", start)); - JUST(attrs.SetAttr("length", length)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim", "start", "length"); + attrs.SetAllAttrs(narrow_dim, start, length); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -1273,10 +1584,8 @@ class NarrowGradFunctor { Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& like, const int64_t& dim, const int64_t& start, const int64_t& length) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); - JUST(attrs.SetAttr("start", start)); - JUST(attrs.SetAttr("length", length)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim", "start", "length"); + attrs.SetAllAttrs(dim, start, length); return OpInterpUtil::Dispatch(*op_, {dy, like}, attrs); } @@ -1294,10 +1603,8 @@ class SliceFunctor { return view::Slice(x, start, stop, step); } - MutableAttrMap attrs; - JUST(attrs.SetAttr>("start", start)); - JUST(attrs.SetAttr>("stop", stop)); - JUST(attrs.SetAttr>("step", step)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("start", "stop", "step"); + attrs.SetAllAttrs(start, stop, step); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1315,19 +1622,22 @@ class SliceUpdateFunctor { const std::shared_ptr& value, const std::vector& start, const std::vector& stop, const std::vector& step, bool inplace) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("start", start)); - JUST(attrs.SetAttr>("stop", stop)); - JUST(attrs.SetAttr>("step", step)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("start", "stop", "step"); + attrs.SetAllAttrs(start, stop, step); + + TensorProcessor tensor_processor; + JUST(tensor_processor.AddInputs({ref, value}) + .PromoteInputsToCommonDtype(true, ref->dtype()) + .Apply()); if (inplace) { auto outputs = std::make_shared(1); JUST(CheckInplaceValid(ref)); JUST(VectorAt(*outputs, 0)) = ref; - JUST(OpInterpUtil::Dispatch(*op_, {ref, value}, outputs.get(), attrs)); + JUST(OpInterpUtil::Dispatch(*op_, JUST(tensor_processor.GetInputs()), outputs.get(), attrs)); return JUST(VectorAt(*outputs, 0)); } else { - return OpInterpUtil::Dispatch(*op_, {ref, value}, attrs); + return OpInterpUtil::Dispatch(*op_, JUST(tensor_processor.GetInputs()), attrs); } } @@ -1343,11 +1653,8 @@ class SliceGradFunctor { Maybe operator()(const std::shared_ptr& dy, const Shape& like_shape, const std::vector& start, const std::vector& stop, const std::vector& step) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("like_shape", like_shape)); - JUST(attrs.SetAttr>("start", start)); - JUST(attrs.SetAttr>("stop", stop)); - JUST(attrs.SetAttr>("step", step)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("like_shape", "start", "stop", "step"); + attrs.SetAllAttrs(like_shape, start, stop, step); return OpInterpUtil::Dispatch(*op_, {dy}, attrs); } @@ -1364,12 +1671,9 @@ class UpsampleGradFunctor { const std::shared_ptr& x, const double& height_scale, const double& width_scale, const bool& align_corners, const std::string& data_format, const std::string& interpolation) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - JUST(attrs.SetAttr("interpolation", interpolation)); - JUST(attrs.SetAttr("data_format", data_format)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "align_corners", + "interpolation", "data_format"); + attrs.SetAllAttrs(height_scale, width_scale, align_corners, interpolation, data_format); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1382,15 +1686,10 @@ class CopyFunctor { CopyFunctor() { op_ = CHECK_JUST(one::OpBuilder("copy").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const std::string& device_type, const int64_t& device_id, const bool pin_memory) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("device_type", device_type)); - JUST(attrs.SetAttr("device_id", device_id)); - JUST(attrs.SetAttr("pin_memory", pin_memory)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("device_type", "device_id", "pin_memory"); + attrs.SetAllAttrs(device_type, device_id, pin_memory); -#ifdef WITH_CUDA - if (device_type == "cuda") { InitCudaContextOnce(device_id); } -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) if (device_type == "cuda") { InitCudaContextOnce(device_id); } #endif return OpInterpUtil::Dispatch(*op_, {x}, attrs); @@ -1405,8 +1704,13 @@ class FlipFunctor { FlipFunctor() { op_ = CHECK_JUST(one::OpBuilder("flip").Input("x").Output("y").Build()); } Maybe operator()(const std::shared_ptr& x, const std::vector& dims) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("dims", dims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dims"); + if (dims.empty()) { + attrs.SetAllAttrs(dims); + } else { + std::vector flip_dims = *JUST(CheckAxis(dims, x->ndim())); + attrs.SetAllAttrs(flip_dims); + } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1421,12 +1725,11 @@ class UnfoldTensorFunctor { } Maybe operator()(const std::shared_ptr& x, const int32_t& dimension, const int32_t& size, const int32_t& step) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dimension", dimension)); - JUST(attrs.SetAttr("size", size)); - JUST(attrs.SetAttr("step", step)); // if input tensor is eager local, than try return tensor's view if (view::IsViewApplicable(x)) { return view::UnfoldTensor(x, dimension, size, step); } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dimension", "size", "step"); + attrs.SetAllAttrs(dimension, size, step); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1443,10 +1746,8 @@ class UnfoldTensorGradFunctor { Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& x, const int32_t& dimension, const int32_t& size, const int32_t& step) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dimension", dimension)); - JUST(attrs.SetAttr("size", size)); - JUST(attrs.SetAttr("step", step)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dimension", "size", "step"); + attrs.SetAllAttrs(dimension, size, step); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1463,12 +1764,12 @@ class UpsampleLinear1DFunctor { const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale_factor", scale_factor)); - JUST(attrs.SetAttr("align_corners", align_corners)); - JUST(attrs.SetAttr("data_format", data_format)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_factor", "align_corners", "data_format", + "output_size"); if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + attrs.SetAllAttrs(scale_factor, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(scale_factor, align_corners, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1488,13 +1789,13 @@ class UpsampleLinear1DGradFunctor { const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale_factor", scale_factor)); - JUST(attrs.SetAttr("align_corners", align_corners)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_factor", "align_corners", "data_format", + "output_size"); if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + attrs.SetAllAttrs(scale_factor, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(scale_factor, align_corners, data_format, NullOpt); } - JUST(attrs.SetAttr("data_format", data_format)); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1510,11 +1811,11 @@ class UpsampleNearest1DFunctor { Maybe operator()(const std::shared_ptr& x, const double& scale_factor, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale_factor", scale_factor)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_factor", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(scale_factor, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(scale_factor, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1533,11 +1834,11 @@ class UpsampleNearest1DGradFunctor { const std::shared_ptr& x, const double& scale_factor, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale_factor", scale_factor)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_factor", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(scale_factor, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(scale_factor, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1555,12 +1856,12 @@ class UpsampleNearest2DFunctor { const double& width_scale, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1580,12 +1881,12 @@ class UpsampleNearest2DGradFunctor { const double& width_scale, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1603,13 +1904,12 @@ class UpsampleBilinear2DFunctor { const double& width_scale, const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "align_corners", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1629,14 +1929,13 @@ class UpsampleBilinear2DGradFunctor { const double& width_scale, const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "align_corners", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, NullOpt); } - JUST(attrs.SetAttr("data_format", data_format)); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1653,13 +1952,12 @@ class UpsampleBicubic2DFunctor { const double& width_scale, const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "align_corners", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1679,14 +1977,13 @@ class UpsampleBicubic2DGradFunctor { const double& width_scale, const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("height_scale", "width_scale", "align_corners", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(height_scale, width_scale, align_corners, data_format, NullOpt); } - JUST(attrs.SetAttr("data_format", data_format)); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1703,13 +2000,12 @@ class UpsampleNearest3DFunctor { const double& height_scale, const double& width_scale, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth_scale", depth_scale)); - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth_scale", "height_scale", "width_scale", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, data_format, NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1729,14 +2025,13 @@ class UpsampleNearest3DGradFunctor { const double& height_scale, const double& width_scale, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth_scale", depth_scale)); - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth_scale", "height_scale", "width_scale", + "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, data_format, *JUST(output_size)); + } else { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, data_format, NullOpt); } - JUST(attrs.SetAttr("data_format", data_format)); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1754,14 +2049,14 @@ class UpsampleTrilinear3DFunctor { const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth_scale", depth_scale)); - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - JUST(attrs.SetAttr("data_format", data_format)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth_scale", "height_scale", "width_scale", + "align_corners", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, align_corners, data_format, + *JUST(output_size)); + } else { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, align_corners, data_format, + NullOpt); } return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1782,15 +2077,15 @@ class UpsampleTrilinear3DGradFunctor { const bool& align_corners, const Optional>& output_size, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth_scale", depth_scale)); - JUST(attrs.SetAttr("height_scale", height_scale)); - JUST(attrs.SetAttr("width_scale", width_scale)); - JUST(attrs.SetAttr("align_corners", align_corners)); - if (output_size.has_value()) { - JUST(attrs.SetAttr>("output_size", *JUST(output_size))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth_scale", "height_scale", "width_scale", + "align_corners", "data_format", "output_size"); + if (output_size) { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, align_corners, data_format, + *JUST(output_size)); + } else { + attrs.SetAllAttrs(depth_scale, height_scale, width_scale, align_corners, data_format, + NullOpt); } - JUST(attrs.SetAttr("data_format", data_format)); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1811,8 +2106,8 @@ class UnsortedSegmentSumLikeFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& segment_ids, const std::shared_ptr& like, const int64_t& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {x, segment_ids, like}, attrs); } @@ -1820,14 +2115,34 @@ class UnsortedSegmentSumLikeFunctor { std::shared_ptr op_; }; +class UnsortedSegmentSumFunctor { + public: + UnsortedSegmentSumFunctor() { + op_ = CHECK_JUST(one::OpBuilder("unsorted_segment_sum") + .Input("data") + .Input("segment_ids") + .Output("out") + .Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& segment_ids, const int64_t& axis, + const int64_t& num_segments) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "num_segments"); + attrs.SetAllAttrs(axis, num_segments); + return OpInterpUtil::Dispatch(*op_, {x, segment_ids}, attrs); + } + + private: + std::shared_ptr op_; +}; + class TrilFunctor { public: TrilFunctor() { op_ = CHECK_JUST(one::OpBuilder("tril").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const int64_t& diagonal) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("diagonal", diagonal)); - JUST(attrs.SetAttr("is_floating_fill_value", false)); - JUST(attrs.SetAttr("integer_fill_value", 0)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal", "is_floating_fill_value", "integer_fill_value"); + attrs.SetAllAttrs(diagonal, false, static_cast(0)); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1839,8 +2154,8 @@ class TriuFunctor { public: TriuFunctor() { op_ = CHECK_JUST(one::OpBuilder("triu").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const int64_t& diagonal) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("diagonal", diagonal)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal"); + attrs.SetAllAttrs(diagonal); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1848,12 +2163,31 @@ class TriuFunctor { std::shared_ptr op_; }; +class InplaceTriuFunctor { + public: + InplaceTriuFunctor() { + op_ = CHECK_JUST(one::OpBuilder("triu").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& x, const int64_t& diagonal) const { + JUST(CheckInplaceValid(x)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal"); + attrs.SetAllAttrs(diagonal); + std::shared_ptr outputs = std::make_shared(1); + outputs->at(0) = x; + JUST(OpInterpUtil::Dispatch(*op_, {x}, outputs.get(), attrs)); + return outputs->at(0); + } + + private: + std::shared_ptr op_; +}; + class DiagFunctor { public: DiagFunctor() { op_ = CHECK_JUST(one::OpBuilder("diag").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const int32_t& diagonal) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("diagonal", diagonal)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal"); + attrs.SetAllAttrs(diagonal); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1868,8 +2202,8 @@ class DiagGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& x, const int32_t& diagonal) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("diagonal", diagonal)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal"); + attrs.SetAllAttrs(diagonal); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1892,13 +2226,11 @@ class DiagonalFunctor { CHECK_NE_OR_RETURN(p_dim1, p_dim2) << Error::RuntimeError() << "diagonal dimensions cannot be identical " << dim1 << ", " << dim2; - - MutableAttrMap attrs; - JUST(attrs.SetAttr("offset", offset)); - if (view::IsViewApplicable(x)) { return view::Diagonal(x, offset, p_dim1, p_dim2); } else { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("offset"); + attrs.SetAllAttrs(offset); std::vector input_index{p_dim1, p_dim2}; for (int32_t i = 0; i < ndims; i++) { if (i != p_dim1 && i != p_dim2) { input_index.push_back(i); } @@ -1919,8 +2251,8 @@ class DiagonalGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& x, const int32_t& offset) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("offset", offset)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("offset"); + attrs.SetAllAttrs(offset); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -1994,12 +2326,9 @@ class TensorGetItemFunctor { Shape shape(DimVector(target_dims.begin(), target_dims.end())); if (shape != *(result->shape())) { result = JUST(Reshape(result, shape)); } if (!tensor_indices.empty()) { - JUST(UnifyLocalTensorAndIndicesOnDevice(x, tensor_indices)); + JUST(UnifyInputAndIndicesOnDevice(x, tensor_indices)); result = JUST(ApplyAdvancedIndexing(result, tensor_indices)); } - - // TODO(): Returns a view of tensor `x`. - if (result == x) { result = JUST(Identity(x)); } return result; } }; @@ -2015,24 +2344,21 @@ class TensorSetItemFunctor { std::vector target_dims; JUST(PrepareSliceIndices(index, *(x->shape()), &slice_indices, &tensor_indices, &expand_dims, &target_dims)); - if (expand_dims.size()) { - slice_indices = *JUST(RemoveExpandDimSlice(slice_indices, expand_dims)); + auto expand_input = x; + if (!expand_dims.empty()) { + CHECK_OR_RETURN(view::IsViewApplicable(x)) << "expand dims must enable view, " + "please try to set ONEFLOW_DISABLE_VIEW=0"; + for (int i = 0; i < expand_dims.size(); ++i) { + int64_t dim = expand_dims[i]; + expand_input = JUST(functional::ExpandDims(expand_input, dim + i)); + } } - int64_t ndims = x->shape()->NumAxes(); + int64_t ndims = expand_input->shape()->NumAxes(); CHECK_EQ_OR_RETURN(slice_indices.size(), ndims) << Error::RuntimeError() << "Failed to prepare slice indices."; - // Not support combined indexing now - if (!tensor_indices.empty()) { - CHECK_OR_RETURN(tensor_indices.size() == ndims - && std::all_of(tensor_indices.begin(), tensor_indices.end(), - [](const std::shared_ptr& index) { return index; })) - << Error::RuntimeError() - << "Combining indexing is not support for tensor setitem currently"; - } Shape target_shape(DimVector(target_dims.begin(), target_dims.end())); if (target_shape.Count(0) == 0) { return Maybe::Ok(); } - const auto& value_shape = value->shape(); bool matched = [&]() { for (int i = 0; i < value_shape->NumAxes() - target_shape.NumAxes(); ++i) { @@ -2047,51 +2373,85 @@ class TensorSetItemFunctor { // TODO: replace reshape by unsqueeze with view mechanism. // after here, each scalar tensor will be one with one dimension. for (auto& tensor : tensor_indices) { - if (tensor->ndim() == 0) { tensor = JUST(functional::Reshape(tensor, Shape({1}))); } - } - if (tensor_indices.size() == ndims) { // advance indexing - if (ndims == 0 && index[0].IsEllipsis()) { - // for scalar input tensor setitem, only support ellipsis indexing type - Shape tmp_shape{1}; - const auto& value_tensor = JUST(functional::View(value, tmp_shape)); - const auto& input_tensor = JUST(functional::View(x, tmp_shape)); - std::vector starts(1, 0); - std::vector stops(1, 1); - std::vector steps(1, 1); - JUST(SliceUpdate(input_tensor, value_tensor, starts, stops, steps, /*inplace=*/true)); - } else { - // advance indexing - std::shared_ptr indices = JUST(functional::Stack(tensor_indices, 0)); - if (indices->shape()->elem_cnt() == 0) { return Maybe::Ok(); } - indices = JUST(functional::Transpose(indices, {1, 0})); - value_tensor = JUST(functional::Expand(value_tensor, {indices->shape()->At(0)})); - JUST(functional::TensorScatterNdUpdate(x, indices, value_tensor, /*inplace=*/true)); + if (tensor && tensor->ndim() == 0) { tensor = JUST(functional::Reshape(tensor, Shape({1}))); } + } + + DimVector slice_dims(ndims); + std::vector start(ndims), end(ndims), step(ndims); + for (int i = 0; i < ndims; ++i) { + const auto& slice = slice_indices[i]; + start[i] = slice.start(); + end[i] = slice.end(); + step[i] = slice.step(); + slice_dims[i] = (end[i] - start[i] + step[i] - 1) / step[i]; + } + if (tensor_indices.empty()) { + Shape slice_shape(slice_dims); + if (slice_shape != *(value_tensor->shape())) { + // NOTE: + // 1. The value shape must can be broadcasted to the target shape. + // 2. The slice shape must have equal element count with the target shape. + // + // So, we should be expand to target_shape and then reshape to slice_shape. + // + // For example: + // x = flow.rand(2, 3, 4) + // y = flow.rand(3) + // x[:, :, 1] = y + // + // value_shape = (3,), target_shape = (2, 3), slice_shape = (2, 3, 1) + // We must change value shape to slice_shape if it uses SliceUpdate op. + if (target_shape != *(value_tensor->shape()) && target_shape.NumAxes() > 0) { + value_tensor = JUST(Expand(value_tensor, target_shape)); + } + if (slice_shape != *(value_tensor->shape())) { + value_tensor = JUST(Reshape(value_tensor, slice_shape)); + } } - } else { // slice update - if (target_shape.NumAxes() != 0 && // NOLINT - /*need_expand=*/value_shape->Count(0) != target_shape.Count(0)) { - // Remove the beginning redundant 1-dimensions. - if (value_shape->NumAxes() > target_shape.NumAxes()) { - int64_t start_axis = value_shape->NumAxes() - target_shape.NumAxes(); - const auto& shape = JUST(value_shape->Slice(start_axis, value_shape->NumAxes())); - value_tensor = JUST(Reshape(value, *shape)); + JUST(SliceUpdate(expand_input, value_tensor, start, end, step, /*inplace=*/true)); + } else { + bool is_identity = [&]() { + if (target_shape.NumAxes() == 0) { return false; } + for (int i = 0; i < ndims; ++i) { + if (start[i] != 0 || end[i] != expand_input->shape()->At(i) || step[i] != 1) { + return false; + } + } + return true; + }(); + std::shared_ptr result; + if (is_identity) { + result = expand_input; + } else { + if (expand_input->is_local()) { + CHECK_OR_RETURN(view::IsViewApplicable(expand_input)) + << "combined slice setitem must enable view, please try to set " + "ONEFLOW_DISABLE_VIEW=0"; + result = JUST(Slice(expand_input, start, end, step, /*enable_view_slice=*/true)); + } else { + // global tensor + result = JUST(Slice(expand_input, start, end, step, /*enable_view_slice=*/false)); } - value_tensor = JUST(Expand(value_tensor, target_shape)); } - std::vector start(ndims), end(ndims), step(ndims); - DimVector slice_dims(ndims); - for (int i = 0; i < ndims; ++i) { - const auto& slice = slice_indices.at(i); - start[i] = slice.start(); - end[i] = slice.end(); - step[i] = slice.step(); - slice_dims[i] = (end[i] - start[i] + step[i] - 1) / step[i]; + const Shape& slice_result_shape = *(result->shape()); + if (target_shape != slice_result_shape) { + result = JUST(functional::View(result, target_shape)); } - Shape slice_shape(slice_dims); - if (slice_shape != *(value_tensor->shape())) { - value_tensor = JUST(Reshape(value_tensor, slice_shape)); + + JUST(UnifyInputAndIndicesOnDevice(result, tensor_indices)); + result = JUST(ApplyAdvancedIndexingUpdate(result, tensor_indices, value)); + + // Write the sliced tensor back to the original tensor. + if (result->is_global()) { + if (*result->shape() != slice_result_shape) { + CHECK_EQ_OR_RETURN(result->shape()->elem_cnt(), slice_result_shape.elem_cnt()) + << Error::RuntimeError() + << "The global tensor size mismatch. Target sizes: " << slice_result_shape.ToString() + << ", value sizes: " << result->shape()->ToString(); + result = JUST(functional::View(result, slice_result_shape)); + } + JUST(SliceUpdate(expand_input, result, start, end, step, /*inplace=*/true)); } - JUST(SliceUpdate(x, value_tensor, start, end, step, /*inplace=*/true)); } return Maybe::Ok(); } @@ -2176,46 +2536,32 @@ class DivGradFunctor { class BroadcastPowXGradFunctor { public: - BroadcastPowXGradFunctor() { - op_ = CHECK_JUST(one::OpBuilder("broadcast_pow_x_grad") - .Input("dz") - .Input("x") - .Input("y") - .Input("z") - .Output("dx") - .Build()); - } - Maybe operator()(const std::shared_ptr& dz, - const std::shared_ptr& x, + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y, - const std::shared_ptr& z) const { - return OpInterpUtil::Dispatch(*op_, {dz, x, y, z}); + const std::shared_ptr& dz) const { + auto y_sub_one = JUST(functional::ScalarSub(y, 1, /*alpha=*/1, /*inplace=*/false)); + auto result = functional::sequence_function(functional::BroadcastPow) + .then(std::bind(functional::Mul, std::placeholders::_1, y)) + .then(std::bind(functional::Mul, std::placeholders::_1, dz)) + .then(std::bind(functional::BroadcastReduceSumLike, std::placeholders::_1, x)) + .call(x, y_sub_one); + return result; } - - private: - std::shared_ptr op_; }; class BroadcastPowYGradFunctor { public: - BroadcastPowYGradFunctor() { - op_ = CHECK_JUST(one::OpBuilder("broadcast_pow_y_grad") - .Input("dz") - .Input("x") - .Input("y") - .Input("z") - .Output("dy") - .Build()); - } - Maybe operator()(const std::shared_ptr& dz, - const std::shared_ptr& x, + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y, - const std::shared_ptr& z) const { - return OpInterpUtil::Dispatch(*op_, {dz, x, y, z}); + const std::shared_ptr& dz) const { + auto result = + functional::sequence_function(functional::BroadcastPow) + .then(std::bind(functional::Mul, std::placeholders::_1, JUST(functional::Log(x)))) + .then(std::bind(functional::Mul, std::placeholders::_1, dz)) + .then(std::bind(functional::BroadcastReduceSumLike, std::placeholders::_1, y)) + .call(x, y); + return result; } - - private: - std::shared_ptr op_; }; class IdentityFunctor { @@ -2244,6 +2590,19 @@ class AmpWhiteIdentityFunctor { std::shared_ptr op_; }; +class AmpBlackIdentityFunctor { + public: + AmpBlackIdentityFunctor() { + op_ = CHECK_JUST(one::OpBuilder("amp_black_identity").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& in) const { + return OpInterpUtil::Dispatch(*op_, {in}); + } + + private: + std::shared_ptr op_; +}; + class ReduceSumLikeFunctor { public: ReduceSumLikeFunctor() { @@ -2253,9 +2612,9 @@ class ReduceSumLikeFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& like, const std::vector& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axis", axis)); - return OpInterpUtil::Dispatch(*op_, {x, like}, attrs); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); + return OpInterpUtil::Dispatch(*op_, {x, JUST(like->detach())}, attrs); } private: @@ -2371,8 +2730,8 @@ class SplitLikeFunctor { CHECK_LE_OR_RETURN(like.size(), kMaxInputCount) << Error::RuntimeError() << "like.size() must not greater than " << kMaxInputCount << ", but got " << like.size(); - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); TensorTuple inputs(like.size() + 1); inputs[0] = x; for (int i = 0; i < like.size(); ++i) { inputs[i + 1] = JUST(like[i]->detach()); } @@ -2439,8 +2798,8 @@ class UnsortedBatchSegmentSumFunctor { Maybe operator()(const std::shared_ptr& data, const std::shared_ptr& segment_ids, const int64_t& num_segments) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_segments", num_segments)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_segments"); + attrs.SetAllAttrs(num_segments); return OpInterpUtil::Dispatch(*op_, {data, segment_ids}, attrs); } @@ -2448,6 +2807,7 @@ class UnsortedBatchSegmentSumFunctor { std::shared_ptr op_; }; +template class MaskedFillFunctor { public: MaskedFillFunctor() { @@ -2455,27 +2815,28 @@ class MaskedFillFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& mask, const Scalar& value) const { - MutableAttrMap attrs; - if (IsFloatingDataType(x->dtype()->data_type())) { - JUST(attrs.SetAttr("float_operand", value.As())); - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); - JUST(attrs.SetAttr("has_bool_operand", false)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("float_operand", "has_float_operand", "int_operand", + "has_int_operand", "bool_operand", "has_bool_operand"); + if (IsFloatingDataType(x->dtype()->data_type()) + || x->dtype()->data_type() == DataType::kFloat16) { + attrs.SetAllAttrs(value.As(), true, NullOpt, false, NullOpt, false); } else if (IsIntegralDataType(x->dtype()->data_type())) { - JUST(attrs.SetAttr("int_operand", value.As())); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); - JUST(attrs.SetAttr("has_bool_operand", false)); + attrs.SetAllAttrs(NullOpt, false, value.As(), true, NullOpt, false); } else if (IsBoolDataType(x->dtype()->data_type())) { - JUST(attrs.SetAttr("bool_operand", value.As())); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", false)); - JUST(attrs.SetAttr("has_bool_operand", true)); + attrs.SetAllAttrs(NullOpt, false, NullOpt, false, value.As(), true); } else { UNIMPLEMENTED_THEN_RETURN() << "Only support floating or integral data type."; } const auto& x_shape = *(x->shape()); const auto& mask_shape = *(mask->shape()); + + std::shared_ptr outputs = std::make_shared(1); + if (inplace) { + JUST(CheckInplaceValid(x)); + (*outputs)[0] = x; + } + if (x_shape != mask_shape) { Shape max_shape = Shape::Ones(std::max(x_shape.NumAxes(), mask_shape.NumAxes())); const Shape& x_extend_shape = @@ -2485,10 +2846,13 @@ class MaskedFillFunctor { FOR_RANGE(int64_t, i, 0, max_shape.NumAxes()) { max_shape.Set(i, std::max(x_extend_shape.At(i), mask_extend_shape.At(i))); } - return OpInterpUtil::Dispatch( - *op_, {JUST(Expand(x, max_shape)), JUST(Expand(mask, max_shape))}, attrs); + JUST(OpInterpUtil::Dispatch(*op_, {JUST(Expand(x, max_shape)), JUST(Expand(mask, max_shape))}, + outputs.get(), attrs)); + return outputs->at(0); } - return OpInterpUtil::Dispatch(*op_, {x, mask}, attrs); + + JUST(OpInterpUtil::Dispatch(*op_, {x, mask}, outputs.get(), attrs)); + return outputs->at(0); } private: @@ -2598,8 +2962,8 @@ Maybe LocalTensorTo(const std::shared_ptr& x, const std::string& return tensor; } -Maybe ConsistentTensorTo(const std::shared_ptr& x, const std::string& device_type, - const Symbol& dtype, const bool& copy) { +Maybe GlobalTensorTo(const std::shared_ptr& x, const std::string& device_type, + const Symbol& dtype, const bool& copy) { std::shared_ptr tensor; auto input_placement = JUST(x->parallel_desc()); std::string input_device_tag = input_placement->device_tag(); @@ -2623,11 +2987,12 @@ Maybe ConsistentTensorTo(const std::shared_ptr& x, const std::st auto nd_sbp = JUST(x->nd_sbp()); std::vector> sbp_tuple(nd_sbp->sbp_parallel().size()); for (int i = 0; i < sbp_tuple.size(); ++i) { sbp_tuple[i] = nd_sbp->sbp_parallel().Get(i); } - tensor = JUST(ConsistentToLocal(x)); + tensor = JUST(GlobalToLocal(x, /*copy=*/false)); Symbol device = JUST(Device::New(device_type)); tensor = JUST(LocalTensorTo(tensor, device->type(), device->device_id(), dtype, copy)); JUST(tensor->set_requires_grad(x->requires_grad())); - return JUST(LocalToConsistent(tensor, placement, sbp_tuple, *(x->shape()), dtype)); + return JUST(LocalToGlobal(tensor, placement, sbp_tuple, *(x->shape()), dtype, + /* sync_data */ true, /*copy=*/false)); } } @@ -2639,14 +3004,14 @@ class ToFunctor { const Optional& device_, const Optional>& dtype_, bool copy) const { Symbol dtype = dtype_.value_or(input->dtype()); - if (input->is_consistent()) { + if (input->is_global()) { std::string device_type = device_.value_or(JUST(input->parallel_desc())->device_tag()); CHECK_OR_RETURN(ep::DeviceManagerRegistry::GetDeviceTypeByDeviceTypeName(device_type) != DeviceType::kInvalidDevice) << Error::RuntimeError() << "Only string device without device id (eg. \"cpu\" or \"cuda\") is expected " - << "for consistent tensor, but got " << device_.value_or(""); - return JUST(ConsistentTensorTo(input, device_type, dtype, copy)); + << "for global tensor, but got " << device_.value_or(""); + return JUST(GlobalTensorTo(input, device_type, dtype, copy)); } else { std::string device_name = ""; int device_id = 0; @@ -2668,13 +3033,20 @@ class To2Functor { Maybe operator()(const std::shared_ptr& input, const Optional>& device_, const Optional>& dtype_, bool copy) const { - CHECK_OR_RETURN(!(input->is_consistent() && device_.has_value())) - << Error::RuntimeError() - << "Only string device without device id (eg. \"cpu\" or \"cuda\") is expected " - << "for consistent tensor, but got " << device_.value_or(Symbol())->ToRepr(); - if (input->is_consistent()) { - std::string device_type = JUST(input->parallel_desc())->device_tag(); - return JUST(ConsistentTensorTo(input, device_type, dtype_.value_or(input->dtype()), copy)); + if (input->is_global()) { + if (!device_.has_value()) { + std::string device_type = JUST(input->parallel_desc())->device_tag(); + return JUST(GlobalTensorTo(input, device_type, dtype_.value_or(input->dtype()), copy)); + } else { + if (!GlobalMode::is_enabled()) { + CHECK_OR_RETURN(!device_.has_value()) + << Error::RuntimeError() + << "Only string device without device id (eg. \"cpu\" or \"cuda\") is expected " + << "for global tensor, but got " << device_.value_or(Symbol())->ToRepr(); + } + std::string device_type = device_.value_or(Symbol())->type(); + return JUST(GlobalTensorTo(input, device_type, dtype_.value_or(input->dtype()), copy)); + } } else { auto dtype = dtype_.value_or(input->dtype()); auto device = @@ -2689,8 +3061,8 @@ class To3Functor { Maybe operator()(const std::shared_ptr& input, const Optional>& dtype_, bool copy) const { Symbol dtype = dtype_.value_or(input->dtype()); - if (input->is_consistent()) { - return ConsistentTensorTo(input, JUST(input->parallel_desc())->device_tag(), dtype, copy); + if (input->is_global()) { + return GlobalTensorTo(input, JUST(input->parallel_desc())->device_tag(), dtype, copy); } else { auto device = JUST(input->device()); return LocalTensorTo(input, device->type(), device->device_id(), dtype, copy); @@ -2702,7 +3074,7 @@ class To4Functor { public: Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& other, bool copy) const { - CHECK_OR_RETURN(!input->is_consistent() && !other->is_consistent()) + CHECK_OR_RETURN(!input->is_global() && !other->is_global()) << Error::RuntimeError() << "tensor.to(other) can only be called when tensor and other are local tensors"; Symbol dtype = other->dtype(); @@ -2713,14 +3085,77 @@ class To4Functor { } }; +class ToDeviceFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const Optional& device_) const { + Symbol dtype = input->dtype(); + const bool copy = false; + if (input->is_global()) { + std::string device_type = device_.value_or(JUST(input->parallel_desc())->device_tag()); + CHECK_OR_RETURN(ep::DeviceManagerRegistry::GetDeviceTypeByDeviceTypeName(device_type) + != DeviceType::kInvalidDevice) + << Error::RuntimeError() + << "Only string device without device id (eg. \"cpu\" or \"cuda\") is expected " + << "for global tensor, but got " << device_.value_or(""); + return JUST(GlobalTensorTo(input, device_type, dtype, copy)); + } else { + std::string device_name = ""; + int device_id = 0; + if (device_.has_value()) { + JUST(ParsingDeviceTag(device_.value_or(""), &device_name, &device_id)); + if (device_id == -1) { device_id = GlobalProcessCtx::LocalRank(); } + } else { + Symbol device = JUST(input->device()); + device_name = device->type(); + device_id = device->device_id(); + } + return JUST(LocalTensorTo(input, device_name, device_id, dtype, copy)); + } + } +}; + class TopKFunctor { public: TopKFunctor() { op_ = CHECK_JUST(one::OpBuilder("top_k").Input("in").Output("out").Build()); } - Maybe operator()(const std::shared_ptr& input, int32_t k, bool sorted) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("k", k)); - JUST(attrs.SetAttr("sorted", sorted)); - return OpInterpUtil::Dispatch(*op_, {input}, attrs); + Maybe operator()(const std::shared_ptr& input, const int32_t k, + const Optional& dim, const bool largest, + const bool sorted) const { + auto outputs = std::make_shared(2); + std::shared_ptr values; + std::shared_ptr indices; + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("k", "sorted"); + attrs.SetAllAttrs(k, sorted); + + int32_t dim_value = dim.value_or(-1); + int32_t axis = dim_value; + axis = JUST(maybe_wrap_dim(axis, input->ndim())); + if (axis == input->ndim() - 1) { + if (largest) { + indices = JUST(OpInterpUtil::Dispatch(*op_, {input}, attrs)); + } else { + auto neg_input = JUST(ScalarMul(input, -1, false)); + indices = JUST(OpInterpUtil::Dispatch(*op_, {neg_input}, attrs)); + } + values = JUST(DimGather(input, axis, indices, false)); + + } else { + auto perm = JUST(GetPermWhenTransposeAxisToLastDim(input->ndim(), dim_value)); + auto x = JUST(Transpose(input, *perm)); + if (largest) { + indices = JUST(OpInterpUtil::Dispatch(*op_, {x}, attrs)); + } else { + auto neg_input = JUST(ScalarMul(x, -1, false)); + indices = JUST(OpInterpUtil::Dispatch(*op_, {neg_input}, attrs)); + } + auto inversed_perm = JUST(GetInversedPerm(*perm)); + indices = JUST(Transpose(indices, *inversed_perm)); + values = JUST(DimGather(input, axis, indices, false)); + } + (*outputs)[0] = values; + (*outputs)[1] = indices; + return outputs; } private: @@ -2741,8 +3176,8 @@ class InTopKFunctor { << Error::RuntimeError() << "The dimension of targets must be 1"; CHECK_EQ_OR_RETURN(predictions->ndim(), 2) << Error::RuntimeError() << "The dimension of predictions must be 2"; - MutableAttrMap attrs; - JUST(attrs.SetAttr("k", k)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("k"); + attrs.SetAllAttrs(k); return OpInterpUtil::Dispatch(*op_, {targets, predictions}, attrs); } @@ -2757,9 +3192,8 @@ class TensorBufferToTensorFunctor { } Maybe operator()(const std::shared_ptr& input, const Shape& instance_shape, const Symbol& dtype) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("instance_shape", instance_shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("instance_shape", "dtype"); + attrs.SetAllAttrs(instance_shape, dtype->data_type()); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -2773,8 +3207,8 @@ class TensorToTensorBufferFunctor { op_ = CHECK_JUST(one::OpBuilder("tensor_to_tensor_buffer").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& input, int32_t instance_dims) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("instance_dims", instance_dims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("instance_dims"); + attrs.SetAllAttrs(instance_dims); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -2790,12 +3224,9 @@ class GenTensorBufferFunctor { Maybe operator()(const Shape& shape, const std::vector& shape_list, const std::vector& value_list, const Symbol& dtype, bool dynamic_out) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr>("shape_list", shape_list)); - JUST(attrs.SetAttr>("value_list", value_list)); - JUST(attrs.SetAttr("data_type", dtype->data_type())); - JUST(attrs.SetAttr("dynamic_out", dynamic_out)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "shape_list", "value_list", "data_type", + "dynamic_out"); + attrs.SetAllAttrs(shape, shape_list, value_list, dtype->data_type(), dynamic_out); return OpInterpUtil::Dispatch(*op_, {}, attrs); } @@ -2837,8 +3268,14 @@ class RepeatFunctor { } } else { input_reshape_vec.insert(input_reshape_vec.begin(), input_shape_val); - expand_shape_vec.insert(expand_shape_vec.begin(), input_shape_val); - output_reshape_vec.insert(output_reshape_vec.begin(), input_shape_val); + // For 0-size tensor, align with PyTorch. + if (repeat_shape_val == 0) { + expand_shape_vec.insert(expand_shape_vec.begin(), 0); + output_reshape_vec.insert(output_reshape_vec.begin(), 0); + } else { + expand_shape_vec.insert(expand_shape_vec.begin(), input_shape_val); + output_reshape_vec.insert(output_reshape_vec.begin(), input_shape_val); + } } } else { expand_shape_vec.insert(expand_shape_vec.begin(), repeat_shape.At(i)); @@ -2851,7 +3288,7 @@ class RepeatFunctor { std::shared_ptr reshaped_tensor = JUST(Reshape(input, input_reshape)); std::shared_ptr expanded_tensor = JUST(Expand(reshaped_tensor, expand_shape)); std::shared_ptr result = JUST(Reshape(expanded_tensor, output_reshape)); - return result; + return result->contiguous(); } }; @@ -2864,8 +3301,8 @@ class RepeatInterLeaveIndexFunctor { Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& cumsum, const int32_t& repeat_num) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("repeat_num", repeat_num)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("repeat_num"); + attrs.SetAllAttrs(static_cast(repeat_num)); return OpInterpUtil::Dispatch(*op_, {input, cumsum}, attrs); } @@ -2926,9 +3363,11 @@ class RepeatInterLeaveTensorFunctor { std::vector repeats_value(repeats_shape->elem_cnt()); if (!output_size.has_value()) { - const auto& callback = [&](uint64_t ofblob_ptr) { - CHECK_JUST(BlobBufferCopyUtil::To(ofblob_ptr, repeats_value.data(), - repeats_value.size())); + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, repeats_value.data(), eager_blob_object->dptr(), + repeats_value.size() * sizeof(int64_t), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); }; SyncAccessTensorWithTimeOut(repeats, callback, "const").GetOrThrow(); for (const auto x : repeats_value) { @@ -3017,11 +3456,11 @@ class PinMemoryFunctor { CHECK_JUST(one::OpBuilder("slice_update").Input("ref").Input("value").Output("y").Build()); } Maybe operator()(const std::shared_ptr& input) const { - // TODO:(zhaoluyang) support consistent tensor.pin_memory() + // TODO:(zhaoluyang) support global tensor.pin_memory() CHECK_OR_RETURN(input->is_local() && !(LazyMode::is_enabled())) << Error::RuntimeError() << "Tensor.pin_memory() only support local tensor for now!"; // if tensor already pinned, then just return - if (JUST(JUST(input->AsMirroredTensor())->is_pinned())) { return input; } + if (JUST(JUST(input->AsLocalTensor())->is_pinned())) { return input; } auto shape = input->shape(); auto device = JUST(input->device()); const bool requires_grad = input->requires_grad(); @@ -3033,21 +3472,22 @@ class PinMemoryFunctor { // TODO: remove this requires_grad JUST(empty->set_requires_grad(requires_grad)); const int32_t ndim = input->ndim(); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("start", "stop", "step"); if (ndim == 0) { + // TODO(wyg): use TensorSetItem after supporting non-requires_grad tensor inplace // for 0-dim tensor - TensorIndex tensor_index; - tensor_index.emplace_back(functional::detail::IndexItem(functional::detail::EllipsisIndex{})); - JUST(functional::TensorSetItem(empty, tensor_index, input)); - return empty; + empty = JUST(functional::ExpandDims(empty, 0)); // expand to [1, ] + auto expand_input = JUST(functional::ExpandDims(input, 0)); // expand to [1, ] + attrs.SetAllAttrs(std::vector{0}, std::vector{1}, std::vector{1}); + auto outputs = TensorTuple{empty}; + JUST(OpInterpUtil::Dispatch(*op_, TensorTuple{empty, expand_input}, &outputs, attrs)); + return outputs[0]; } else { - MutableAttrMap attrs; std::vector starts(ndim, 0); std::vector stops(ndim); std::vector steps(ndim, 1); for (int i = 0; i < ndim; ++i) { stops[i] = input->shape()->At(i); } - JUST(attrs.SetAttr>("start", starts)); - JUST(attrs.SetAttr>("stop", stops)); - JUST(attrs.SetAttr>("step", steps)); + attrs.SetAllAttrs(starts, stops, steps); JUST(empty->set_requires_grad(requires_grad)); auto outputs = TensorTuple{empty}; JUST(OpInterpUtil::Dispatch(*op_, TensorTuple{empty, input}, &outputs, attrs)); @@ -3064,13 +3504,13 @@ class FillFunctor { FillFunctor() { op_ = CHECK_JUST(one::OpBuilder("fill_").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& in, const Scalar& value) const { JUST(CheckInplaceValid(in)); - MutableAttrMap attrs; - if (IsFloatingDataType(in->dtype()->data_type())) { - JUST(attrs.SetAttr("floating_value", value.As())); - JUST(attrs.SetAttr("is_floating_value", true)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("floating_value", "is_floating_value", "integral_value"); + if (IsFloatingDataType(in->dtype()->data_type()) + || in->dtype()->data_type() == DataType::kFloat16) { + attrs.SetAllAttrs(value.As(), true, NullOpt); } else if (IsIntegralDataType(in->dtype()->data_type())) { - JUST(attrs.SetAttr("integral_value", value.As())); - JUST(attrs.SetAttr("is_floating_value", false)); + attrs.SetAllAttrs(NullOpt, false, value.As()); } else { UNIMPLEMENTED_THEN_RETURN() << "Only support floating or integral data type."; } @@ -3099,7 +3539,9 @@ class FillTensorFunctor { << "fill_ only supports 0-dimension value tensor but got tensor with " << ndim << " dimensions."; TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({in, value}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true, in->dtype()) + .AddInputs({in, value}) + .Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); auto outputs = std::make_shared(1); (*outputs)[0] = in; @@ -3111,14 +3553,344 @@ class FillTensorFunctor { std::shared_ptr op_; }; +class IndexAddFunctor { + public: + IndexAddFunctor() { + op_ = CHECK_JUST(one::OpBuilder("index_add") + .Input("input") + .Input("index") + .Input("source") + .Output("output") + .Build()); + } + Maybe operator()(const std::shared_ptr& input, const int64_t& dim, + const std::shared_ptr& index, + const std::shared_ptr& source, const Scalar& alpha) const { + CHECK_OR_RETURN(source->ndim() == 0 || index->shape()->Count(0) == source->shape()->At(dim)) + << "index_copy_(): Number of indices (," << index->shape()->Count(0) + << ", \") should be equal to source.size(dim) (," << source->shape()->At(dim) << ", \")"; + CHECK_OR_RETURN(index->dtype()->data_type() != DataType::kInt32 + || index->dtype()->data_type() != DataType::kInt64) + << "Input(Index) holds the wrong type, it holds " + << DataType_Name(index->dtype()->data_type()) + << " , but " + "desires to be int32_t or int64_t"; + const float alpha_value = alpha.As(); + int64_t dim_ = dim; + dim_ = JUST(maybe_wrap_dim(dim_, input->ndim())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim", "alpha"); + attrs.SetAllAttrs(dim_, alpha_value); + TensorProcessor tensor_processor; + JUST(tensor_processor.PromoteInputsToCommonDtype(true, input->dtype()) + .AddInputs({input, source}) + .Apply()); + TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); + return OpInterpUtil::Dispatch(*op_, {input, index, input_tuple.at(1)}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class IndexAddInplaceFunctor { + public: + IndexAddInplaceFunctor() { + op_ = CHECK_JUST(one::OpBuilder("index_add") + .Input("input") + .Input("index") + .Input("source") + .Output("output") + .Build()); + } + Maybe operator()(const std::shared_ptr& input, const int64_t& dim, + const std::shared_ptr& index, + const std::shared_ptr& source, const Scalar& alpha) const { + CHECK_OR_RETURN(source->ndim() == 0 || index->shape()->Count(0) == source->shape()->At(dim)) + << "index_copy_(): Number of indices (," << index->shape()->Count(0) + << ", \") should be equal to source.size(dim) (," << source->shape()->At(dim) << ", \")"; + CHECK_OR_RETURN(index->dtype()->data_type() != DataType::kInt32 + || index->dtype()->data_type() != DataType::kInt64) + << "Input(Index) holds the wrong type, it holds " + << DataType_Name(index->dtype()->data_type()) + << " , but " + "desires to be int32_t or int64_t"; + const float alpha_value = alpha.As(); + int64_t dim_ = dim; + dim_ = JUST(maybe_wrap_dim(dim_, input->ndim())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim", "alpha"); + attrs.SetAllAttrs(dim_, alpha_value); + JUST(CheckInplaceValid(input)); + std::shared_ptr outputs = std::make_shared(1); + outputs->at(0) = input; + TensorProcessor tensor_processor; + JUST(tensor_processor.PromoteInputsToCommonDtype(true, input->dtype()) + .AddInputs({input, source}) + .Apply()); + TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); + JUST(OpInterpUtil::Dispatch(*op_, {input, index, input_tuple.at(1)}, outputs.get(), attrs)); + return outputs->at(0); + } + + private: + std::shared_ptr op_; +}; + +class BroadcastShapesFunctor { + public: + Maybe operator()(const std::vector& shapes) const { + return InferUnifiedShapeForBroadcasting(shapes); + } +}; + +class BroadcastTensorsFunctor { + public: + Maybe operator()(const TensorTuple& tensors) const { + if (tensors.empty()) { return Error::RuntimeError() << "tensors should not be empty."; } + + Shape shape_to_broadcast; + std::deque need_to_broadcast; + + std::tie(shape_to_broadcast, need_to_broadcast) = + *JUST(InferUnifiedShapeForBroadcastingWithInfo([&tensors]() { + std::vector shapes; + for (auto& x : tensors) { shapes.push_back(*x->shape()); } + return shapes; + }())); + + std::shared_ptr outputs = std::make_shared(); + for (size_t i = 0; i < tensors.size(); ++i) { + outputs->emplace_back(need_to_broadcast.at(i) // NOLINT + ? JUST(functional::Expand(tensors.at(i), shape_to_broadcast)) + : tensors.at(i)); + } + return outputs; + } +}; +class BinCountFunctor { + public: + BinCountFunctor() { + op_ = CHECK_JUST(OpBuilder("bincount").Input("in").Output("out").Build()); + weight_op_ = + CHECK_JUST(OpBuilder("bincount").Input("in").Input("weight").Output("out").Build()); + } + + Maybe operator()(const std::shared_ptr& input, const Optional& weight, + const Optional& minlength) const { + CHECK_OR_RETURN(!input->dtype()->is_floating_point()) << "bincount can only support int tensor"; + TensorProcessor tensor_processor; + JUST(tensor_processor.AddInputs({input}, DType::Int64()).Apply()); + const auto x = JUST(tensor_processor.GetInputs()).at(0); + std::shared_ptr local_tensor = x; + int64_t max = 0; + + // check min value + { + if (x->is_global()) { local_tensor = JUST(GlobalToLocal(x, false)); } + auto tensor_min = JUST(functional::Min(local_tensor)); + int64_t min = 0; + const auto& callback_min = + [&](ep::Stream* stream, const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, &min, eager_blob_object->dptr(), sizeof(min), + memory::MakeHostMemCase(), eager_blob_object->mem_case()); + }; + JUST(SyncAccessTensorWithTimeOut(tensor_min, callback_min, "const")); + CHECK_GE_OR_RETURN(min, 0) << "bincount only supports 1-d non-negative integral inputs."; + + auto tensor_max = JUST(functional::Max(local_tensor)); + const auto& callback_max = + [&](ep::Stream* stream, const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, &max, eager_blob_object->dptr(), sizeof(max), + memory::MakeHostMemCase(), eager_blob_object->mem_case()); + }; + JUST(SyncAccessTensorWithTimeOut(tensor_max, callback_max, "const")); + max += 1; + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("size"); + if (minlength) { + CHECK_GE_OR_RETURN(JUST(minlength), 0) << "minlength should be >= 0"; + max = std::max(JUST(minlength), max); + } + attrs.SetAllAttrs(max); + if (weight) { + CHECK_EQ_OR_RETURN(JUST(weight)->nelement(), x->nelement()) + << "input and weights should have the same length"; + return OpInterpUtil::Dispatch(*weight_op_, {x, JUST(weight)}, attrs); + } else { + return OpInterpUtil::Dispatch(*op_, {x}, attrs); + } + } + + private: + std::shared_ptr op_; + std::shared_ptr weight_op_; +}; + +class UniqueFunctor { + public: + UniqueFunctor() { + op_ = CHECK_JUST( + OpBuilder("unique").Input("x").Output("y").Output("idx").Output("num_unique").Build()); + }; + Maybe operator()(const std::shared_ptr& x, const bool sorted, + const Symbol& dtype) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("out_idx", "sorted"); + DataType out_idx = dtype->data_type(); + attrs.SetAllAttrs(out_idx, sorted); + std::shared_ptr output = JUST( + OpInterpUtil::Dispatch(*op_, {JUST(functional::Flatten(x, 0, -1))}, attrs)); + int64_t num_unique = 0; + std::shared_ptr num_unique_tensor = output->at(2); + { + if (num_unique_tensor->is_global()) { + num_unique_tensor = JUST(GlobalToLocal(num_unique_tensor, false)); + } + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, &num_unique, eager_blob_object->dptr(), + GetSizeOfDataType(dtype->data_type()), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); + }; + JUST(SyncAccessTensorWithTimeOut(num_unique_tensor, callback, "const")); + } + return functional::Slice(output->at(0), /*start=*/{0}, /*end=*/{num_unique}, /*step=*/{1}, + false); + } + + private: + std::shared_ptr op_; +}; + +class UniqueWithCountsFunctor { + public: + UniqueWithCountsFunctor() { + unique_op_ = CHECK_JUST( + OpBuilder("unique").Input("x").Output("y").Output("idx").Output("num_unique").Build()); + unique_with_counts_op_ = CHECK_JUST(OpBuilder("unique_with_counts") + .Input("x") + .Output("y") + .Output("idx") + .Output("num_unique") + .Output("count") + .Build()); + }; + Maybe operator()(const std::shared_ptr& x, const bool sorted, + const bool return_inverse, const bool return_counts, + const Symbol& dtype) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("out_idx", "sorted"); + attrs.SetAllAttrs(dtype->data_type(), sorted); + std::shared_ptr output; + if (return_counts) { + output = JUST(OpInterpUtil::Dispatch( + *unique_with_counts_op_, {JUST(functional::Flatten(x, 0, -1))}, attrs)); + } else { + output = JUST(OpInterpUtil::Dispatch( + *unique_op_, {JUST(functional::Flatten(x, 0, -1))}, attrs)); + } + + int64_t num_unique = 0; + std::shared_ptr num_unique_tensor = output->at(2); + { + if (num_unique_tensor->is_global()) { + num_unique_tensor = JUST(GlobalToLocal(num_unique_tensor, false)); + } + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, &num_unique, eager_blob_object->dptr(), + GetSizeOfDataType(dtype->data_type()), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); + }; + JUST(SyncAccessTensorWithTimeOut(num_unique_tensor, callback, "const")); + } + auto result = std::make_shared(); + const auto& y = JUST( + functional::Slice(output->at(0), /*start=*/{0}, /*end=*/{num_unique}, /*step=*/{1}, false)); + result->emplace_back(y); + if (return_inverse) { + result->emplace_back(JUST(functional::Reshape(output->at(1), *x->shape()))); + } + if (return_counts) { + const auto count = JUST(functional::Slice(output->at(3), /*start=*/{0}, /*end=*/{num_unique}, + /*step=*/{1}, false)); + result->emplace_back(count); + } + return result; + } + + private: + std::shared_ptr unique_op_; + std::shared_ptr unique_with_counts_op_; +}; + +class BaddBmmFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& batch1, + const std::shared_ptr& batch2, const double& beta, + const double& alpha) const { + const int32_t batch1_ndim = batch1->ndim(); + const int32_t batch2_ndim = batch2->ndim(); + CHECK_EQ_OR_RETURN(batch1_ndim, 3) << Error::RuntimeError() << "batch1 must be a 3D tensor"; + CHECK_EQ_OR_RETURN(batch2_ndim, 3) << Error::RuntimeError() << "batch2 must be a 3D tensor"; + CHECK_EQ_OR_RETURN(batch1->dim(0), batch2->dim(0)) + << Error::RuntimeError() << "batch1 and batch2 must have same number of batches, got ," + << batch1->dim(0) << " and " << batch2->dim(0); + CHECK_EQ_OR_RETURN(batch1->dim(2), batch2->dim(1)) + << "Incompatible matrix sizes for bmm (" << batch1->dim(1) << "x" << batch1->dim(2) + << " and " << batch2->dim(1) << "x" << batch2->dim(2) << ")"; + + if (beta == 0.0) { + // In stable diffsion, the beta param is always 0.0, so we can avoid use add and mul op to + // optimize speed and bandwidth in cuda. + return JUST(functional::BatchMatMul(batch1, batch2, false, false, alpha)); + } else { + // TODO(add a fuse kernel to optimize speed and bancwidth in cuda) + return JUST( + functional::Add(JUST(functional::ScalarMul(beta, input)), + JUST(functional::BatchMatMul(batch1, batch2, false, false, alpha)), + /*alpha=*/1.0, /*inplace=*/false)); + } + } +}; + +class SortFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const int32_t& dim, + const bool descending) const { + auto outputs = std::make_shared(2); + std::shared_ptr values; + std::shared_ptr indices; + int32_t axis = dim; + axis = JUST(maybe_wrap_dim(axis, input->ndim())); + std::string direction("ASCENDING"); + if (descending) { direction.assign("DESCENDING"); } + if (axis == input->ndim() - 1) { + indices = JUST(ArgSort(input, direction)); + values = JUST(DimGather(input, axis, indices, false)); + } else { + std::shared_ptr> perm = + JUST(GetPermWhenTransposeAxisToLastDim(input->ndim(), dim)); + auto x = JUST(Transpose(input, *perm)); + auto indices_temp = JUST(ArgSort(x, direction)); + auto inversed_perm = JUST(GetInversedPerm(*perm)); + indices = JUST(Transpose(indices_temp, *inversed_perm)); + values = JUST(DimGather(input, axis, indices, false)); + } + (*outputs)[0] = values; + (*outputs)[1] = indices; + return outputs; + } +}; + } // namespace impl ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ArgMax"); m.add_functor("ArgMin"); - m.add_functor("ConsistentConstant"); + m.add_functor("GlobalTensorConstant"); + m.add_functor("TensorConstant"); + m.add_functor("GlobalConstant"); m.add_functor("Constant"); - m.add_functor("ConsistentEmpty"); + m.add_functor("GlobalEmpty"); m.add_functor("Empty"); m.add_functor("ZerosLike"); m.add_functor("OnesLike"); @@ -3130,15 +3902,29 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("WhereScalarY"); m.add_functor("WhereScalarXY"); m.add_functor("ArgWhere"); + m.add_functor("NonZero"); m.add_functor("BroadcastLike"); m.add_functor("Concat"); m.add_functor("Stack"); m.add_functor("StackGrad"); + m.add_functor("AtLeast1D"); + m.add_functor("AtLeast1D"); + m.add_functor("AtLeast2D"); + m.add_functor("AtLeast2D"); + m.add_functor("AtLeast3D"); + m.add_functor("AtLeast3D"); + m.add_functor("HStack"); + m.add_functor("ColumnStack"); + m.add_functor("VStack"); + m.add_functor("RowStack"); + m.add_functor("DStack"); m.add_functor("Expand"); - m.add_functor("ExpandGrad"); m.add_functor("ExpandDims"); m.add_functor("Unsqueeze"); + m.add_functor("UnsqueezeMultiple"); + m.add_functor("InplaceUnsqueeze"); m.add_functor("Squeeze"); + m.add_functor("InplaceSqueeze"); m.add_functor("Roll"); m.add_functor("Gather"); m.add_functor("DimGather"); @@ -3179,20 +3965,28 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("UpsampleTrilinear3D"); m.add_functor("UpsampleTrilinear3DGrad"); m.add_functor("UnsortedSegmentSumLike"); + m.add_functor("UnsortedSegmentSum"); m.add_functor("Tril"); m.add_functor("Triu"); + m.add_functor("InplaceTriu"); m.add_functor("Diag"); m.add_functor("DiagGrad"); m.add_functor("Diagonal"); m.add_functor("DiagonalGrad"); m.add_functor("TensorGetItem"); + m.add_functor>("DimScatterUpdate"); + m.add_functor>("DimScatterAdd"); + m.add_functor>("DimScatterMul"); m.add_functor("DimScatter"); - m.add_functor("DimScatterAdd"); - m.add_functor("DimScatterMul"); - m.add_functor("DimScatterUpdateScalar"); - m.add_functor("DimScatterAddScalar"); + m.add_functor>( + "DimScatterUpdateScalar"); + m.add_functor>( + "DimScatterAddScalar"); + m.add_functor>( + "DimScatterMulScalar"); + m.add_functor("DimScatterScalar"); m.add_functor("DimScatterAddLike"); - m.add_functor("DimScatterMulScalar"); + m.add_functor("TensorSetItem"); m.add_functor("CastLike"); m.add_functor("ElementwiseMinGrad"); @@ -3202,6 +3996,7 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("DivGrad"); m.add_functor("Identity"); m.add_functor("AmpWhiteIdentity"); + m.add_functor("AmpBlackIdentity"); m.add_functor("ReduceSumLike"); m.add_functor("BroadcastReduceSumLike"); m.add_functor("Split"); @@ -3211,10 +4006,12 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("SplitWithSize"); m.add_functor("BatchGather"); m.add_functor("UnsortedBatchSegmentSum"); - m.add_functor("MaskedFill"); + m.add_functor>("MaskedFill"); + m.add_functor>("MaskedFillInplace"); m.add_functor("Meshgrid"); m.add_functor("IndexSelect"); - m.add_functor("To"); + m.add_functor("To"); m.add_functor("TopK"); m.add_functor("InTopK"); m.add_functor("TensorToTensorBuffer"); @@ -3229,6 +4026,16 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("TransposeAllDimFunction"); m.add_functor("ReshapeLike"); m.add_functor("PinMemory"); + m.add_functor("BroadcastShapes"); + m.add_functor("BroadcastTensors"); + m.add_functor("BroadcastTo"); // BroadcastTo is an alias of Expand + m.add_functor("BinCount"); + m.add_functor("IndexAdd"); + m.add_functor("IndexAddInplace"); + m.add_functor("Unique"); + m.add_functor("UniqueWithCounts"); + m.add_functor("BaddBmm"); + m.add_functor("Sort"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/binary_functor.cpp b/oneflow/core/functional/impl/binary_functor.cpp index f8cb7128691d4978c0e0153f0dc66ab60339c8df..ba37adf47cafedcf2969606893a382a5b37942c8 100644 --- a/oneflow/core/functional/impl/binary_functor.cpp +++ b/oneflow/core/functional/impl/binary_functor.cpp @@ -16,15 +16,20 @@ limitations under the License. #include "oneflow/core/functional/impl/binary_functor.h" +#include "oneflow/core/common/error.h" +#include "oneflow/core/common/maybe.h" #include "oneflow/core/common/scalar.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/function_library.h" +#include "oneflow/core/functional/functional_api.yaml.h" #include "oneflow/core/functional/sequence_function.h" namespace oneflow { @@ -33,6 +38,32 @@ namespace functional { namespace impl { +namespace { + +bool IsCPUScalarTensor(const std::shared_ptr& tensor) { + return tensor->shape()->NumAxes() == 0 + && TensorDeviceToString(tensor).find("cpu") != std::string::npos; +} + +} // namespace + +std::string TensorDeviceToString(const std::shared_ptr& tensor) { + if (tensor->is_global()) { return CHECK_JUST(tensor->parallel_desc())->device_tag(); } + return CHECK_JUST(tensor->device())->ToString(); +} + +Maybe CastDeviceForCPUScalarTensor(std::shared_ptr& tensor, + std::shared_ptr& other, bool inplace) { + if (TensorDeviceToString(tensor) != TensorDeviceToString(other)) { + if (IsCPUScalarTensor(other)) { + other = JUST(functional::To(other, TensorDeviceToString(tensor))); + } else if (!inplace && IsCPUScalarTensor(tensor)) { + tensor = JUST(functional::To(tensor, TensorDeviceToString(other))); + } + } + return Maybe::Ok(); +} + class AddFunctor { public: AddFunctor() { @@ -44,21 +75,22 @@ class AddFunctor { Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& other, const Scalar& alpha, bool inplace) const { - if (IsIntegralDataType(input->dtype()->data_type()) + auto input_tensor = input; + if (IsIntegralDataType(input_tensor->dtype()->data_type()) && IsIntegralDataType(other->dtype()->data_type()) && alpha.IsFloatingPoint()) { return Error::RuntimeError() << "For integral input tensors, argument alpha must not be a floating point number."; } - bool input_static_zeros = IsStaticZerosTensor(input); + bool input_static_zeros = IsStaticZerosTensor(input_tensor); if (input_static_zeros || IsStaticZerosTensor(other)) { - CHECK_OR_RETURN(JUST(input->device()) == JUST(other->device())) + CHECK_OR_RETURN(JUST(input_tensor->device()) == JUST(other->device())) << Error::RuntimeError() << "Expected all tensors to be on the same device, but found at least two devices, " - << JUST(input->device())->ToString() << " and " << JUST(other->device())->ToString() - << "!"; - CHECK_OR_RETURN(*input->shape() == *other->shape()) - << Error::RuntimeError() << "The size of tensor a " << input->shape()->ToString() + << JUST(input_tensor->device())->ToString() << " and " + << JUST(other->device())->ToString() << "!"; + CHECK_OR_RETURN(*input_tensor->shape() == *other->shape()) + << Error::RuntimeError() << "The size of tensor a " << input_tensor->shape()->ToString() << " must match the size of tensor b " << other->shape(); if (input_static_zeros) { if ((alpha.IsIntegral() && alpha.Value() == 1) @@ -70,24 +102,29 @@ class AddFunctor { return JUST(functional::ScalarMul(alpha, other)); } } - return input; + return input_tensor; } const OpExpr* op = nullptr; + Optional> promote_dtype; + if (inplace) { promote_dtype = input_tensor->dtype(); } TensorProcessor tensor_processor; if ((alpha.IsIntegral() && alpha.Value() == 1) || (alpha.IsFloatingPoint() && std::fabs(alpha.Value() - 1.0) < std::numeric_limits::epsilon())) { - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({input, other}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true, promote_dtype) + .AddInputs({input_tensor, other}) + .Apply()); } else { - JUST(tensor_processor.PromoteInputsToCommonDtype(true) - .AddInputs({input, JUST(functional::ScalarMul(alpha, other))}) + JUST(tensor_processor.PromoteInputsToCommonDtype(true, promote_dtype) + .AddInputs({input_tensor, JUST(functional::ScalarMul(alpha, other))}) .Apply()); } TensorTuple input_vec = JUST(tensor_processor.GetInputs()); const std::shared_ptr& input_cast = input_vec[0]; const std::shared_ptr& other_cast = input_vec[1]; + JUST(CastDeviceForCPUScalarTensor(input_vec[0], input_vec[1], inplace)); if (*input_cast->shape() == *other_cast->shape()) { op = add_op_.get(); @@ -95,9 +132,9 @@ class AddFunctor { op = broadcast_add_op_.get(); } if (inplace) { - JUST(CheckInplaceCastValid(input, input_cast)); - JUST(CheckInplaceValid(input)); - JUST(CheckShapeCanExpandTo(*other_cast->shape(), *input_cast->shape())); + JUST(CheckInplaceCastValid(input_tensor, input_cast)); + JUST(CheckInplaceValid(input_tensor)); + JUST(CheckInplaceShapeCanExpandTo(*other_cast->shape(), *input_cast->shape())); std::shared_ptr outputs = std::make_shared(1); outputs->at(0) = input_cast; JUST(OpInterpUtil::Dispatch(*op, input_vec, outputs.get())); @@ -150,8 +187,11 @@ class MulFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/false)); TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({tensor_x, tensor_y}).Apply()); TensorTuple input_vec = JUST(tensor_processor.GetInputs()); return OpInterpUtil::Dispatch(*broadcast_mul_op_, input_vec); @@ -182,7 +222,7 @@ class InplaceMulFunctor { const std::shared_ptr& y_cast = input_vec.at(1); JUST(CheckInplaceValid(x)); JUST(CheckInplaceCastValid(x, x_cast)); - JUST(CheckShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); + JUST(CheckInplaceShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); std::shared_ptr outputs = std::make_shared(1); outputs->at(0) = x; JUST(OpInterpUtil::Dispatch(*broadcast_mul_op_, input_vec, outputs.get())); @@ -242,20 +282,35 @@ class InplaceDivFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/true)); + + // NOTE: div operator will cast inputs to float when dtype is integral TensorProcessor tensor_processor; - if (y->requires_grad()) { - JUST(tensor_processor.PromoteInputsToCommonDtype(true) - .AddInputs({JUST(Identity(x)), y}) - .Apply()); + TensorTuple tensor_processor_inputs; + { + if (tensor_y->requires_grad()) { + tensor_processor_inputs.assign({JUST(Identity(tensor_x)), tensor_y}); + } else { + tensor_processor_inputs.assign({tensor_x, tensor_y}); + } + } + if (promoteTypes(tensor_x->dtype(), tensor_y->dtype())->is_integer()) { + tensor_processor.AddInputs(tensor_processor_inputs, DType::Float()); } else { - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + tensor_processor.AddInputs(tensor_processor_inputs) + .PromoteInputsToCommonDtype(true) + .PromoteIntegerInputsToFloatDtype(true); } + JUST(tensor_processor.Apply()); + const TensorTuple& input_vec = JUST(tensor_processor.GetInputs()); const std::shared_ptr& x_cast = input_vec.at(0); const std::shared_ptr& y_cast = input_vec.at(1); JUST(CheckInplaceValid(x)); JUST(CheckInplaceCastValid(x, x_cast)); - JUST(CheckShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); + JUST(CheckInplaceShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); std::shared_ptr outputs = std::make_shared(1); outputs->at(0) = x; JUST(OpInterpUtil::Dispatch(*broadcast_div_op_, input_vec, outputs.get())); @@ -265,11 +320,42 @@ class InplaceDivFunctor { private: std::shared_ptr broadcast_div_op_; }; + class Atan2Functor : public BinaryFloatFunctor { public: Atan2Functor() { op_ = CHECK_JUST(one::OpBuilder("atan2").Input("x").Input("y").Output("z").Build()); } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& y) const { + const int64_t x_element = x->nelement(); + const int64_t y_element = y->nelement(); + CHECK_GT_OR_RETURN(x_element, 0) + << Error::RuntimeError() << "the size of input should be > 0, but got " << x_element; + CHECK_GT_OR_RETURN(y_element, 0) + << Error::RuntimeError() << "the size of input should be > 0, but got " << y_element; + + if ((x_element != 1 && y_element != 1) && (x->shape()->NumAxes() == y->shape()->NumAxes())) { + return BinaryFloatFunctor::operator()(x, y); + } + + auto broad_x_ = x; + auto broad_y_ = y; + if (x_element == 1) { + broad_x_ = JUST(functional::Expand(x, *y->shape())); + } else if (y_element == 1) { + broad_y_ = JUST(functional::Expand(y, *x->shape())); + } else if (x->shape()->NumAxes() != y->shape()->NumAxes()) { + return Error::RuntimeError() << "The size of tensor a (" << x->shape()->NumAxes() + << ") must match the size of tensor b " + "(" + << y->shape()->NumAxes() << ") at non-singleton dimension 1"; + } else { + return Error::RuntimeError() << ""; + } + + return BinaryFloatFunctor::operator()(broad_x_, broad_y_); + } }; class PowFunctor : public BinaryFloatFunctor { @@ -295,6 +381,17 @@ class FloorDivFunctor : public BinaryFunctor { } }; +class TruncDivFunctor : public BinaryFunctor { + public: + TruncDivFunctor() { + op_ = CHECK_JUST(one::OpBuilder("truncdiv").Input("x").Input("y").Output("z").Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& y) const { + return BinaryFunctor::operator()(x, y); + } +}; + class BroadcastFModFunctor : public BinaryFunctor { public: BroadcastFModFunctor() { @@ -309,6 +406,27 @@ class BroadcastEqualFunctor : public BinaryFunctor { } }; +class EqualFunctor { + public: + EqualFunctor() { + broadcast_equal_op_ = + CHECK_JUST(one::OpBuilder("broadcast_equal").Input("x").Input("y").Output("z").Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& y) const { + if (*x->shape() != *y->shape()) { return false; } + if (x->nelement() == 0) { return true; } + + std::shared_ptr output = JUST( + ReduceAllWhole(JUST(OpInterpUtil::Dispatch(*broadcast_equal_op_, {x, y}, {})))); + bool status = JUST(GetItemInScalarTensor(output)); + return status; + } + + private: + std::shared_ptr broadcast_equal_op_; +}; + class BroadcastNotEqualFunctor : public BinaryFunctor { public: BroadcastNotEqualFunctor() { @@ -371,6 +489,31 @@ class BroadcastLessEqualFunctor : public BinaryFunctor { } }; +class BroadcastIsCloseFunctor { + public: + BroadcastIsCloseFunctor() { + eq_nan_op_ = CHECK_JUST( + one::OpBuilder("broadcast_isclose_eq_nan").Input("x").Input("y").Output("z").Build()); + neq_nan_op_ = CHECK_JUST( + one::OpBuilder("broadcast_isclose_neq_nan").Input("x").Input("y").Output("z").Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& y, const float atol, + const float rtol, const bool equal_nan) const { + auto& attr = THREAD_CACHED_MUTABLE_ATTR_MAP("atol", "rtol", "equal_nan"); + attr.SetAllAttrs(atol, rtol, equal_nan); + if (equal_nan) { + return OpInterpUtil::Dispatch(*eq_nan_op_, {x, y}, attr); + } else { + return OpInterpUtil::Dispatch(*neq_nan_op_, {x, y}, attr); + } + } + + private: + std::shared_ptr eq_nan_op_; + std::shared_ptr neq_nan_op_; +}; + class ScalarAddByTensorFunctor : public InplaceableBinaryFunctor { public: ScalarAddByTensorFunctor() { @@ -418,6 +561,7 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Pow"); m.add_functor("BroadcastPow"); m.add_functor("BroadcastEqual"); + m.add_functor("Equal"); m.add_functor("BroadcastNotEqual"); m.add_functor("BroadcastGreater"); m.add_functor("BroadcastGreaterEqual"); @@ -432,6 +576,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ScalarDivByTensor"); m.add_functor("BroadcastFMod"); m.add_functor("FloorDiv"); + m.add_functor("TruncDiv"); + m.add_functor("IsClose"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/binary_functor.h b/oneflow/core/functional/impl/binary_functor.h index 99424b81dfe1a093dcba7e0c5800badb0fc35b3c..ffd98ada44fc3b608a4b465471252b704b969df6 100644 --- a/oneflow/core/functional/impl/binary_functor.h +++ b/oneflow/core/functional/impl/binary_functor.h @@ -22,6 +22,7 @@ limitations under the License. #include "oneflow/core/framework/tensor.h" #include "oneflow/core/functional/impl/common.h" #include "oneflow/core/functional/tensor_processor.h" +#include "oneflow/core/functional/functional.h" namespace oneflow { namespace one { @@ -29,12 +30,20 @@ namespace functional { namespace impl { +std::string TensorDeviceToString(const std::shared_ptr& tensor); + +Maybe CastDeviceForCPUScalarTensor(std::shared_ptr& tensor, + std::shared_ptr& other, bool inplace); + class BinaryFunctor { public: Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/false)); TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({tensor_x, tensor_y}).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); return OpInterpUtil::Dispatch(*op_, input_tuple); } @@ -50,8 +59,18 @@ class BinaryFloatFunctor { public: Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/false)); TensorProcessor tensor_processor; - JUST(tensor_processor.AddInputs({x, y}, DType::Float()).Apply()); + if (promoteTypes(tensor_x->dtype(), tensor_y->dtype())->is_integer()) { + tensor_processor.AddInputs({tensor_x, tensor_y}, DType::Float()); + } else { + tensor_processor.AddInputs({tensor_x, tensor_y}) + .PromoteInputsToCommonDtype(true) + .PromoteIntegerInputsToFloatDtype(true); + } + JUST(tensor_processor.Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); return OpInterpUtil::Dispatch(*op_, input_tuple); } @@ -85,14 +104,17 @@ class InplaceableBinaryFunctor { public: Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y, bool inplace) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, inplace)); TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({tensor_x, tensor_y}).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); if (inplace) { std::shared_ptr& x_cast = input_tuple.at(0); std::shared_ptr& y_cast = input_tuple.at(1); JUST(CheckInplaceCastValid(x, x_cast)); - JUST(CheckShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); + JUST(CheckInplaceShapeCanExpandTo(*y_cast->shape(), *x_cast->shape())); std::shared_ptr outputs = std::make_shared(1); outputs->at(0) = x_cast; JUST(OpInterpUtil::Dispatch(*op_, input_tuple, outputs.get())); diff --git a/oneflow/core/functional/impl/binary_grad_functor.cpp b/oneflow/core/functional/impl/binary_grad_functor.cpp index 1738b205796bd4b691001f41a38e8f2a4c17443e..ef471f6e4a19d7be1e540028fa5c350e4500a701 100644 --- a/oneflow/core/functional/impl/binary_grad_functor.cpp +++ b/oneflow/core/functional/impl/binary_grad_functor.cpp @@ -66,6 +66,7 @@ ONEFLOW_FUNCTION_LIBRARY(m) { ADD_BINARY_GRAD_FUNCTOR(Pow, "Pow"); ADD_BINARY_GRAD_FUNCTOR(Atan2, "Atan2"); ADD_BINARY_GRAD_FUNCTOR(FloorDiv, "FloorDiv"); + ADD_BINARY_GRAD_FUNCTOR(TruncDiv, "TruncDiv"); ADD_BINARY_GRAD_FUNCTOR(Xdivy, "Xdivy"); ADD_BINARY_GRAD_FUNCTOR(Xlogy, "Xlogy"); }; diff --git a/oneflow/core/functional/impl/comm_functor.cpp b/oneflow/core/functional/impl/comm_functor.cpp index e080e59691923c89012aff269e452777e85dc011..bd903caab3845bbff294dbfdcaa032624ef24e70 100644 --- a/oneflow/core/functional/impl/comm_functor.cpp +++ b/oneflow/core/functional/impl/comm_functor.cpp @@ -13,15 +13,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/id_util.h" #include "oneflow/core/framework/attr_map.h" #include "oneflow/core/framework/attr_value.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/op_interpreter/eager_mirrored_op_interpreter.h" +#include "oneflow/core/framework/op_interpreter/eager_local_op_interpreter.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/functional/functional.h" @@ -32,6 +34,7 @@ limitations under the License. #include "oneflow/core/job/rank_group_scope.h" #include "oneflow/core/rpc/include/global_process_ctx.h" #include "oneflow/core/common/flat_shape.h" +#include "oneflow/core/framework/user_op_registry_manager.h" namespace oneflow { namespace one { @@ -41,39 +44,90 @@ namespace impl { namespace { +#define OF_KERNEL_NOT_SUPPORT_ERROR(op_type, device_type) \ + Error::RuntimeError() << op_type << " not suport for the device (" \ + << DeviceType_Name(device_type) << ") because eager kernel of " << op_type \ + << " is not registered" + +class EagerCclKernelRegContext final : public user_op::KernelRegContext { + public: + explicit EagerCclKernelRegContext(DeviceType device_type) : device_type_(device_type) {} + ~EagerCclKernelRegContext() = default; + + DeviceType device_type() const override { return device_type_; } + const ParallelContext& parallel_ctx() const override { PRINT_BUG_PROMPT_AND_ABORT(); } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + const std::vector>& inputs() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + const std::vector>& outputs() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + + const user_op::UserOpConfWrapper& user_op_conf() const override { PRINT_BUG_PROMPT_AND_ABORT(); } + + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + PRINT_BUG_PROMPT_AND_ABORT(); + } + + private: + DeviceType device_type_; +}; + +Maybe RawCheckCclKernelRegistered(const std::string& op_type_name, DeviceType device_type) { + EagerCclKernelRegContext reg_ctx(device_type); + return user_op::UserOpRegistryMgr::Get().IsOpKernelRegistered(op_type_name, reg_ctx); +} + +static constexpr auto* CheckCclKernelRegistered = + DECORATE(&RawCheckCclKernelRegistered, ThreadLocalCachedCopiable); + bool IsSplitSbp(Symbol sbp_parallel) { return sbp_parallel->has_split_parallel(); } -Maybe EagerNcclAllReduce(Symbol parallel_desc) { - return one::OpBuilder("eager_nccl_all_reduce", *JUST(UniqueStr("eager_nccl_all_reduce"))) +Maybe EagerCclAllReduce(Symbol parallel_desc) { + CHECK_OR_RETURN( + JUST(CheckCclKernelRegistered("eager_ccl_all_reduce", parallel_desc->device_type()))) + << OF_KERNEL_NOT_SUPPORT_ERROR("AllReduce", parallel_desc->device_type()); + return one::OpBuilder("eager_ccl_all_reduce", *JUST(UniqueStr("eager_ccl_all_reduce"))) .Input("in") .Output("out") .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) .Build(); } -static constexpr auto* CachedEagerNcclAllReduceOpExpr = DECORATE(&EagerNcclAllReduce, ThreadLocal); +static constexpr auto* CachedEagerCclAllReduceOpExpr = DECORATE(&EagerCclAllReduce, ThreadLocal); -Maybe EagerNcclReduceScatter(Symbol parallel_desc, - const std::string& op_type) { - return one::OpBuilder("eager_nccl_reduce_scatter", *JUST(UniqueStr("eager_nccl_reduce_scatter"))) +Maybe EagerCclReduceScatter(Symbol parallel_desc, + const std::string& op_type) { + CHECK_OR_RETURN( + JUST(CheckCclKernelRegistered("eager_ccl_reduce_scatter", parallel_desc->device_type()))) + << OF_KERNEL_NOT_SUPPORT_ERROR("ReduceScatter", parallel_desc->device_type()); + return one::OpBuilder("eager_ccl_reduce_scatter", *JUST(UniqueStr("eager_ccl_reduce_scatter"))) .Input("in") .Output("out") .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) .Attr("op_type", op_type) .Build(); } -static constexpr auto* CachedNcclReduceScatterOpExpr = - DECORATE(&EagerNcclReduceScatter, ThreadLocalCopiable); - -Maybe EagerNcclAllGather(Symbol parallel_desc) { - return one::OpBuilder("eager_nccl_all_gather", *JUST(UniqueStr("eager_nccl_all_gather"))) +static constexpr auto* CachedCclReduceScatterOpExpr = + DECORATE(&EagerCclReduceScatter, ThreadLocalCopiable); + +Maybe EagerCclAllGather(Symbol parallel_desc) { + CHECK_OR_RETURN( + JUST(CheckCclKernelRegistered("eager_ccl_all_gather", parallel_desc->device_type()))) + << OF_KERNEL_NOT_SUPPORT_ERROR("AllGather", parallel_desc->device_type()); + return one::OpBuilder("eager_ccl_all_gather", *JUST(UniqueStr("eager_ccl_all_gather"))) .Input("in") .Output("out") .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) .Build(); } -static constexpr auto* CachedEagerNcclAllGatherOpExpr = DECORATE(&EagerNcclAllGather, ThreadLocal); +static constexpr auto* CachedEagerCclAllGatherOpExpr = DECORATE(&EagerCclAllGather, ThreadLocal); Maybe EagerNcclS2S(Symbol parallel_desc, Symbol src_sbp, Symbol dst_sbp) { @@ -88,8 +142,10 @@ Maybe EagerNcclS2S(Symbol parallel_desc, Symbol EagerNcclReduce(Symbol parallel_desc, int64_t root) { - return one::OpBuilder("eager_nccl_reduce", *JUST(UniqueStr("eager_nccl_reduce"))) +Maybe EagerCclReduce(Symbol parallel_desc, int64_t root) { + CHECK_OR_RETURN(JUST(CheckCclKernelRegistered("eager_ccl_reduce", parallel_desc->device_type()))) + << OF_KERNEL_NOT_SUPPORT_ERROR("Reduce", parallel_desc->device_type()); + return one::OpBuilder("eager_ccl_reduce", *JUST(UniqueStr("eager_ccl_reduce"))) .Input("in") .Output("out") .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) @@ -97,13 +153,60 @@ Maybe EagerNcclReduce(Symbol parallel_desc, int64 .Build(); } -auto* CachedEagerNcclReduceOpExpr = DECORATE(&EagerNcclReduce, ThreadLocal); +auto* CachedEagerCclReduceOpExpr = DECORATE(&EagerCclReduce, ThreadLocal); + +Maybe RankGroupAndDeviceType2AllReduceOpExpr(Symbol rank_group, + DeviceType device_type) { + CHECK_OR_RETURN(JUST(CheckCclKernelRegistered("eager_ccl_all_reduce", device_type))) + << OF_KERNEL_NOT_SUPPORT_ERROR("AllReduce", device_type); + const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(device_type, rank_group)); + return one::OpBuilder("eager_ccl_all_reduce") + .Input("in") + .Output("out") + .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Build(); +} + +auto* CachedRankGroupAndDeviceType2AllReduceOpExpr = + DECORATE(&RankGroupAndDeviceType2AllReduceOpExpr, ThreadLocal); + +Maybe RankGroupAndDeviceType2AllGatherOpExpr(Symbol rank_group, + DeviceType device_type) { + CHECK_OR_RETURN(JUST(CheckCclKernelRegistered("eager_ccl_all_gather", device_type))) + << OF_KERNEL_NOT_SUPPORT_ERROR("AllGather", device_type); + const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(device_type, rank_group)); + return one::OpBuilder("eager_ccl_all_gather") + .Input("in") + .Output("out") + .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Build(); +} + +auto* CachedRankGroupAndDeviceType2AllGatherOpExpr = + DECORATE(&RankGroupAndDeviceType2AllGatherOpExpr, ThreadLocal); + +Maybe RankGroupAndDeviceType2ReduceScatterOpExpr(Symbol rank_group, + DeviceType device_type) { + CHECK_OR_RETURN(JUST(CheckCclKernelRegistered("eager_ccl_reduce_scatter", device_type))) + << OF_KERNEL_NOT_SUPPORT_ERROR("ReduceScatter", device_type); + const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(device_type, rank_group)); + return one::OpBuilder("eager_ccl_reduce_scatter", *JUST(UniqueStr("eager_ccl_reduce_scatter"))) + .Input("in") + .Output("out") + .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) + .Build(); +} + +auto* CachedRankGroupAndDeviceType2ReduceScatterOpExpr = + DECORATE(&RankGroupAndDeviceType2ReduceScatterOpExpr, ThreadLocal); + +#undef OF_KERNEL_NOT_SUPPORT_ERROR } // namespace -class BroadcastFunctor { +class CommBroadcastFunctor { public: - BroadcastFunctor() = default; + CommBroadcastFunctor() = default; Maybe operator()(const std::shared_ptr& x, int64_t src_rank, bool inplace) const { const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); @@ -115,6 +218,20 @@ class BroadcastFunctor { } }; +class CommBroadcastTensorsFunctor { + public: + CommBroadcastTensorsFunctor() = default; + Maybe operator()(const one::TensorTuple& inputs, int64_t src_rank, + bool inplace) const { + if (inputs.empty()) { return inputs; } + const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); + const auto& x = JUST(VectorAt(inputs, 0)); + DeviceType device_type = JUST(x->device())->enum_type(); + const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(device_type, rank_group)); + return one::Broadcast(inputs, src_rank, parallel_desc, inplace); + } +}; + namespace { Maybe RawStreamTouchFunctorOpExpr(size_t input_size) { @@ -140,24 +257,6 @@ class StreamTouchFunctor { } }; -namespace { - -Maybe RankGroupAndDeviceType2AllReduceOpExpr(Symbol rank_group, - DeviceType device_type) { - const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(device_type, rank_group)); - return one::OpBuilder("eager_nccl_all_reduce") - .Input("in") - .Output("out") - .Attr("parallel_conf", PbMessage2TxtString(parallel_desc->parallel_conf())) - .Attr("async_launch", true) - .Build(); -} - -auto* CachedRankGroupAndDeviceType2AllReduceOpExpr = - DECORATE(&RankGroupAndDeviceType2AllReduceOpExpr, ThreadLocal); - -} // namespace - class LocalAllReduceFunctor { public: LocalAllReduceFunctor() = default; @@ -165,14 +264,12 @@ class LocalAllReduceFunctor { const auto& device = JUST(x->device()); CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()); const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); - const std::string& device_type_str = device->type(); - CHECK_OR_RETURN(device_type_str == "cuda" || device_type_str == "cpu"); - DeviceType device_type = device_type_str == "cuda" ? DeviceType::kCUDA : DeviceType::kCPU; + DeviceType device_type = device->enum_type(); std::shared_ptr op_expr = JUST(CachedRankGroupAndDeviceType2AllReduceOpExpr(rank_group, device_type)); auto op_input = x; if (const auto& static_zeros_tensor = std::dynamic_pointer_cast(x)) { - op_input = std::dynamic_pointer_cast(JUST(static_zeros_tensor->AsMirroredTensor())); + op_input = std::dynamic_pointer_cast(JUST(static_zeros_tensor->AsLocalTensor())); } if (inplace) { JUST(CheckInplaceValid(op_input)); @@ -185,28 +282,27 @@ class LocalAllReduceFunctor { } }; -class ConsistentAllReduceFunctor { +class GlobalAllReduceFunctor { public: - ConsistentAllReduceFunctor() = default; + GlobalAllReduceFunctor() = default; Maybe operator()(const std::shared_ptr& x) const { { - CHECK_OR_RETURN(x->is_consistent()) << "Tensor is not consistent"; + CHECK_OR_RETURN(x->is_global()) << "Tensor is not global"; CHECK_OR_RETURN(NdSbpIsAllPartialSum(*JUST(x->nd_sbp()))) << "Tensor's sbp must be partial_sum"; } - std::shared_ptr op_expr = - JUST(CachedEagerNcclAllReduceOpExpr(JUST(x->parallel_desc()))); + std::shared_ptr op_expr = JUST(CachedEagerCclAllReduceOpExpr(JUST(x->parallel_desc()))); return JUST(OpInterpUtil::Dispatch(*op_expr, {x})); } }; -class ConsistentReduceScatterFunctor { +class GlobalReduceScatterFunctor { public: - ConsistentReduceScatterFunctor() = default; + GlobalReduceScatterFunctor() = default; Maybe operator()(const std::shared_ptr& x, const std::string& op_type) const { { - CHECK_OR_RETURN(x->is_consistent()); + CHECK_OR_RETURN(x->is_global()); // NOLINT if (op_type == "max") { CHECK_OR_RETURN(NdSbpIsAllBroadcast(*JUST(x->nd_sbp()))) << "Tensor's sbp must be broadcast to get reduce_max"; @@ -220,35 +316,98 @@ class ConsistentReduceScatterFunctor { } } std::shared_ptr op_expr = - JUST(CachedNcclReduceScatterOpExpr(JUST(x->parallel_desc()), op_type)); + JUST(CachedCclReduceScatterOpExpr(JUST(x->parallel_desc()), op_type)); return JUST(OpInterpUtil::Dispatch(*op_expr, {x})); } }; -class ConsistentAllGatherFunctor { +class LocalReduceScatterFunctor { + public: + LocalReduceScatterFunctor() = default; + Maybe operator()(const std::shared_ptr& output, + const std::shared_ptr& input) const { + DataType dtype_val = input->dtype()->data_type(); + CHECK_EQ_OR_RETURN(input->shape()->elem_cnt(), + output->nelement() * GlobalProcessCtx::WorldSize()) + << Error::RuntimeError() + << "output tensor size must be equal to world_size times input tensor size"; + CHECK_EQ_OR_RETURN(dtype_val, output->dtype()->data_type()) + << Error::RuntimeError() << Error::RuntimeError() + << "output tensor must have the same type as input tensor"; + const Shape& shape = *output->shape(); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("output_shape", "output_dtype"); + attrs.SetAllAttrs(shape, dtype_val); + const auto& device = JUST(input->device()); + CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()); + const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); + DeviceType device_type = device->enum_type(); + std::shared_ptr op_expr = + JUST(CachedRankGroupAndDeviceType2ReduceScatterOpExpr(rank_group, device_type)); + auto op_input = input; + if (const auto& static_zeros_tensor = std::dynamic_pointer_cast(input)) { + op_input = std::dynamic_pointer_cast(JUST(static_zeros_tensor->AsLocalTensor())); + } + TensorTuple outputs{output}; + JUST(OpInterpUtil::Dispatch(*op_expr, {op_input}, &outputs, attrs)); + return outputs[0]; + } +}; + +class GlobalAllGatherFunctor { public: - ConsistentAllGatherFunctor() = default; + GlobalAllGatherFunctor() = default; Maybe operator()(const std::shared_ptr& x) const { { - CHECK_OR_RETURN(x->is_consistent()) << "Tensor is not consistent"; + CHECK_OR_RETURN(x->is_global()) << "Tensor is not global"; CHECK_OR_RETURN(NdSbpIsAllSplit(*JUST(x->nd_sbp()), 0)) << "Tensor's sbp must be split to get all_gather"; } - std::shared_ptr op_expr = - JUST(CachedEagerNcclAllGatherOpExpr(JUST(x->parallel_desc()))); + std::shared_ptr op_expr = JUST(CachedEagerCclAllGatherOpExpr(JUST(x->parallel_desc()))); return JUST(OpInterpUtil::Dispatch(*op_expr, {x})); } }; -class ConsistentS2SFunctor { +class LocalAllGatherFunctor { + public: + LocalAllGatherFunctor() = default; + Maybe operator()(const std::shared_ptr& output, + const std::shared_ptr& input) const { + DataType dtype_val = input->dtype()->data_type(); + CHECK_EQ_OR_RETURN(input->shape()->elem_cnt() * GlobalProcessCtx::WorldSize(), + output->nelement()) + << Error::RuntimeError() + << "output tensor size must be equal to world_size times input tensor size"; + CHECK_EQ_OR_RETURN(dtype_val, output->dtype()->data_type()) + << Error::RuntimeError() << Error::RuntimeError() + << "output tensor must have the same type as input tensor"; + const Shape& shape = *output->shape(); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("output_shape", "output_dtype"); + attrs.SetAllAttrs(shape, dtype_val); + const auto& device = JUST(input->device()); + CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()); + const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); + DeviceType device_type = device->enum_type(); + std::shared_ptr op_expr = + JUST(CachedRankGroupAndDeviceType2AllGatherOpExpr(rank_group, device_type)); + auto op_input = input; + if (const auto& static_zeros_tensor = std::dynamic_pointer_cast(input)) { + op_input = std::dynamic_pointer_cast(JUST(static_zeros_tensor->AsLocalTensor())); + } + TensorTuple outputs{output}; + JUST(OpInterpUtil::Dispatch(*op_expr, {op_input}, &outputs, attrs)); + return outputs[0]; + } +}; + +class GlobalS2SFunctor { public: - ConsistentS2SFunctor() = default; + GlobalS2SFunctor() = default; Maybe operator()(const std::shared_ptr& x, const std::vector>& sbp_parallels) const { Symbol in_nd_sbp = JUST(x->nd_sbp()); Symbol out_nd_sbp = JUST(GetNdSbp(sbp_parallels)); { - CHECK_OR_RETURN(x->is_consistent()); + CHECK_OR_RETURN(x->is_global()); // NOLINT CHECK_EQ_OR_RETURN(in_nd_sbp->sbp_parallel_size(), 1); CHECK_OR_RETURN(IsSplitSbp(in_nd_sbp->sbp_parallel(0))); CHECK_EQ_OR_RETURN(out_nd_sbp->sbp_parallel_size(), 1); @@ -267,19 +426,17 @@ class SendFunctor { public: SendFunctor() { op_expr_ = CHECK_JUST(one::OpBuilder("send").Input("in").Build()); } Maybe operator()(const std::shared_ptr& x, int64_t dst, bool send_meta) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dst_process_id", dst)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dst_process_id"); + attrs.SetAllAttrs(dst); if (send_meta) { std::shared_ptr flat_shape = JUST(FlatShape::New(*x->shape())); - JUST(ccl::Send(flat_shape.get(), sizeof(*flat_shape), DataType::kChar, dst, - nullptr)); + JUST(ccl::CpuSend(flat_shape.get(), sizeof(*flat_shape), dst)); DataType dtype = x->dtype()->data_type(); - JUST(ccl::Send(&dtype, sizeof(dtype), DataType::kChar, dst, nullptr)); + JUST(ccl::CpuSend(&dtype, sizeof(dtype), dst)); DeviceType device_type = JUST(Device::GetPlacement(*JUST(x->device())))->device_type(); - JUST(ccl::Send(&device_type, sizeof(device_type), DataType::kChar, dst, - nullptr)); + JUST(ccl::CpuSend(&device_type, sizeof(device_type), dst)); } JUST(OpInterpUtil::Dispatch(*op_expr_, {x}, attrs)); return Maybe::Ok(); @@ -296,8 +453,6 @@ class RecvFunctor { const Optional>& optional_dtype, const Optional>& optional_device, const Optional& out) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("src_process_id", src)); Shape shape; DataType data_type = DataType::kInvalidDataType; Symbol device; @@ -308,25 +463,21 @@ class RecvFunctor { } else if (!optional_shape.has_value() && !optional_dtype.has_value() && !optional_device.has_value()) { FlatShape flat_shape{}; - JUST(ccl::Recv(&flat_shape, sizeof(flat_shape), DataType::kChar, src, - nullptr)); + JUST(ccl::CpuRecv(&flat_shape, sizeof(flat_shape), src)); shape = *JUST(flat_shape.ToShape()); - JUST(ccl::Recv(&data_type, sizeof(data_type), DataType::kChar, src, - nullptr)); + JUST(ccl::CpuRecv(&data_type, sizeof(data_type), src)); DeviceType device_type = DeviceType::kInvalidDevice; - JUST(ccl::Recv(&device_type, sizeof(device_type), DataType::kChar, src, - nullptr)); + JUST(ccl::CpuRecv(&device_type, sizeof(device_type), src)); device = JUST(Device::New(*JUST(DeviceTag4DeviceType(device_type)))); } else { UNIMPLEMENTED_THEN_RETURN() << "All or none of shape, dtype and device should have value."; } - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", data_type)); - JUST(attrs.SetAttr("device_type", device->type())); - JUST(attrs.SetAttr("device_id", device->device_id())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("src_process_id", "shape", "dtype", "device_type", + "device_id"); + attrs.SetAllAttrs(src, shape, data_type, device->type(), device->device_id()); OpExprInterpContext op_expr_interp_context(attrs, device); if (out.has_value()) { @@ -351,12 +502,13 @@ class LocalReduceFunctor { Maybe operator()(const std::shared_ptr& x, int64_t dst, bool inplace) const { const auto& device = JUST(x->device()); { CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()); } - static thread_local std::unordered_map, Symbol> - rank_group2parallel_desc; + static thread_local std::unordered_map, Symbol>, + Symbol> + rank_group_with_device2parallel_desc; const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); - auto iter = rank_group2parallel_desc.find(rank_group); + auto iter = rank_group_with_device2parallel_desc.find({rank_group, device}); Symbol parallel_desc; - if (iter == rank_group2parallel_desc.end()) { + if (iter == rank_group_with_device2parallel_desc.end()) { ParallelConf parallel_conf; parallel_conf.set_device_tag(device->type()); JUST(rank_group->ForEachRank([¶llel_conf](int64_t rank) -> Maybe { @@ -365,11 +517,11 @@ class LocalReduceFunctor { return Maybe::Ok(); })); parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); - rank_group2parallel_desc[rank_group] = parallel_desc; + rank_group_with_device2parallel_desc[{rank_group, device}] = parallel_desc; } else { parallel_desc = iter->second; } - std::shared_ptr op_expr = JUST(CachedEagerNcclReduceOpExpr(parallel_desc, dst)); + std::shared_ptr op_expr = JUST(CachedEagerCclReduceOpExpr(parallel_desc, dst)); if (inplace) { TensorTuple outputs{x}; JUST(OpInterpUtil::Dispatch(*op_expr, {x}, &outputs)); @@ -384,12 +536,15 @@ class LocalReduceFunctor { ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("StreamTouch"); - m.add_functor("Broadcast"); + m.add_functor("CommBroadcast"); + m.add_functor("CommBroadcastTensors"); m.add_functor("LocalAllReduce"); - m.add_functor("ConsistentAllReduce"); - m.add_functor("ConsistentReduceScatter"); - m.add_functor("ConsistentAllGather"); - m.add_functor("ConsistentS2S"); + m.add_functor("LocalAllGather"); + m.add_functor("LocalReduceScatter"); + m.add_functor("GlobalAllReduce"); + m.add_functor("GlobalReduceScatter"); + m.add_functor("GlobalAllGather"); + m.add_functor("GlobalS2S"); m.add_functor("Send"); m.add_functor("Recv"); m.add_functor("LocalReduce"); diff --git a/oneflow/core/functional/impl/common.cpp b/oneflow/core/functional/impl/common.cpp index 79ddc6bad62ccd123c493856a402097777206522..62bb724e457f408ca370dc9cf786252d22d967d6 100644 --- a/oneflow/core/functional/impl/common.cpp +++ b/oneflow/core/functional/impl/common.cpp @@ -13,13 +13,81 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "fmt/core.h" #include "oneflow/core/functional/impl/common.h" #include "oneflow/core/autograd/autograd_mode.h" #include "oneflow/core/common/wrap_dim_utils.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/ccl/ccl.h" +#include "oneflow/core/job/rank_group.h" namespace oneflow { namespace one { namespace functional { +namespace { + +Maybe InferUnifiedShapeForBroadcasting(const Shape& input_shape, const Shape& other_shape) { + // same shapes need no broadcasting + if (input_shape == other_shape) { return input_shape; } + + const auto unify_shapes_with_same_num_axes = [](const Shape& input_shape, + const Shape& other_shape) -> Maybe { + // num_axes.first == num_axes.second + Shape target; + for (size_t i = 0; i < input_shape.NumAxes() /* both input_shape and other_shape are ok */; + ++i) { + const auto num_in_curr_dim = std::make_pair(input_shape.At(i), other_shape.At(i)); + + // A = (2, ), B = (2, ), A[0] == B[0], so C = (2, ) + if (num_in_curr_dim.first == num_in_curr_dim.second) { + target.push_back(num_in_curr_dim.first); + continue; + } + + // A = (2, ), B = (3, ), A[0] != B[0] and A[0] != 1 and B[0] != 1, so raise RuntimeError + if (num_in_curr_dim.first != 1 && num_in_curr_dim.second != 1) { + return Error::RuntimeError() + << fmt::format("input and other can't be broadcasted to a single shape. [input's " + "shape: {}, other's shape: {}].", + input_shape.ToString(), other_shape.ToString()); + } + + // A = (2, ), B = (1, ), A[0] != B[0] but B[0] == 1, so C = (2, ) + target.push_back( + num_in_curr_dim.first == 1 + ? num_in_curr_dim.second + : num_in_curr_dim.first); // num_in_curr_dim.first and num_in_curr_dim.second can't + // be 1 at the same time + } + return target; + }; + + const int64_t input_num_axes = input_shape.NumAxes(); + const int64_t other_num_axes = other_shape.NumAxes(); + + if (input_num_axes == other_num_axes) { + return unify_shapes_with_same_num_axes(input_shape, other_shape); + } + + const int64_t unified_num_axes = std::max(input_num_axes, other_num_axes); + + // shape = (3, 4) and unified_num_axes = 3 ==> shape will be (1, 3, 4) + const auto expand_shape_if_necessary = [unified_num_axes](const Shape& shape_to_expand) { + const int64_t shape_to_expand_num_axes = shape_to_expand.NumAxes(); + if (shape_to_expand_num_axes < unified_num_axes) { + auto new_shape = Shape::Ones(unified_num_axes); + std::copy(shape_to_expand.begin(), shape_to_expand.end(), + new_shape.begin() + (unified_num_axes - shape_to_expand_num_axes)); + return new_shape; + } + return shape_to_expand; + }; + + return unify_shapes_with_same_num_axes(expand_shape_if_necessary(input_shape), + expand_shape_if_necessary(other_shape)); +} + +} // namespace bool IsStaticZerosTensor(const std::shared_ptr& x) { return nullptr != std::dynamic_pointer_cast(x); @@ -29,25 +97,24 @@ bool IsInplaceValid(const std::shared_ptr& x) { return !autograd::GradMode::is_enabled() || !(x->is_leaf() && x->requires_grad()); } +bool IsScalarTensor(const std::shared_ptr& x) { + return x->shape()->NumAxes() == 0 && x->shape()->elem_cnt() == 1; +} + Maybe> CheckAxis(const std::vector& axis, const int32_t& ndim) { const int32_t naxis = axis.size(); - + int32_t reduce_ndim = naxis; + if (naxis == 0 || ndim == 0) { reduce_ndim = ndim; }; + std::vector reduce_axis(reduce_ndim); if (naxis == 0) { - std::vector reduce_axis(ndim); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - return reduce_axis; } else { - std::vector reduce_axis(naxis); - std::vector axis_num(ndim); + JUST(dim_list_to_bitset(axis, ndim)); // checking axis[dim]'s validation for (int32_t i = 0; i < naxis; i++) { - reduce_axis[i] = JUST(maybe_wrap_dim(axis[i], ndim)); - axis_num[reduce_axis[i]]++; - CHECK_OR_RETURN(axis_num[reduce_axis[i]] < 2) - << Error::RuntimeError() << "dim " << reduce_axis[i] - << " appears multiple times in the list of dims"; + if (i < reduce_ndim) { reduce_axis[i] = JUST(maybe_wrap_dim(axis[i], ndim)); }; } - return reduce_axis; } + return reduce_axis; } Maybe CheckInplaceValid(const std::shared_ptr& x) { @@ -65,27 +132,30 @@ Maybe CheckInplaceCastValid(const std::shared_ptr& x, return Maybe::Ok(); } -bool IsShapeCanExpandTo(const Shape& shape, const Shape& expand_shape) { - if (shape == expand_shape) { return true; } - if (expand_shape.NumAxes() < shape.NumAxes()) { return false; } +Maybe CheckInplaceShapeCanExpandTo(const Shape& shape, const Shape& expand_shape) { + if (shape == expand_shape) { return Maybe::Ok(); } + + CHECK_OR_RETURN(expand_shape.NumAxes() >= shape.NumAxes()) + << Error::RuntimeError() << "Can not expand origin shape " << shape.ToString() << " to " + << expand_shape.ToString() << " in an inplace operation"; + int shift = expand_shape.NumAxes() - shape.NumAxes(); for (int i = expand_shape.NumAxes() - 1; i >= 0; --i) { int index = i - shift; if (index >= 0) { int dim_a = expand_shape.At(i); int dim_b = shape.At(index); - if (dim_a != dim_b && (dim_a <= 0 || dim_b != 1)) { return false; } + // NOTE(lixiang): When a dimension of tensor a and tensor b are not equal in size, dim_a needs + // to be greater than 0, and dim_b should be equal to 1. + CHECK_OR_RETURN(!(dim_a != dim_b && (dim_a <= 0 || dim_b != 1))) + << Error::RuntimeError() << "Tensor with shape " << expand_shape.ToString() + << " doesn't match the broadcast shape in an inplace operation"; } else { - if (expand_shape.At(i) <= 0) { return false; } + // For 0-size tensor, expand_shape.At(i) can equal to 0. + CHECK_OR_RETURN(expand_shape.At(i) >= 0); // NOLINT(maybe-need-error-msg) } } - return true; -} -Maybe CheckShapeCanExpandTo(const Shape& shape, const Shape& expand_shape) { - CHECK_OR_RETURN(IsShapeCanExpandTo(shape, expand_shape)) - << Error::RuntimeError() << "Can not expand shape " << shape.ToString() << " to " - << expand_shape.ToString(); return Maybe::Ok(); } @@ -136,35 +206,104 @@ Optional ComputeStride(const Shape& shape, const Stride& stride, return target_stride; } -Maybe InferShape(const std::shared_ptr& x, const Shape& shape) { +Maybe InferShapeUnspecifiedDim(const int64_t& elem_count, const Shape& shape) { int need_infer_axis = -1; - size_t count = 1; + int64_t target_elem_count = 1; for (int i = 0; i < shape.NumAxes(); ++i) { if (shape.At(i) < -1) { return Error::RuntimeError() << "Invalid shape dimension " << shape.At(i); } else if (shape.At(i) == -1) { - CHECK_EQ_OR_RETURN(need_infer_axis, -1) + CHECK_OR_RETURN_ERROR(need_infer_axis == -1) << Error::RuntimeError() << "only one dimension can be inferred"; need_infer_axis = i; } else { - count *= shape.At(i); + target_elem_count *= shape.At(i); } } - size_t x_count = x->shape()->Count(0); Shape infered_shape = shape; if (need_infer_axis == -1) { - CHECK_EQ_OR_RETURN(shape.Count(0), x_count) - << Error::RuntimeError() << "shape '" << shape.ToString() - << "' is invalid for input of size " << x->nelement(); + if (elem_count > 0) { + // For 0-size tensor, we don't need to check the element size. + CHECK_OR_RETURN_ERROR(target_elem_count == elem_count) + << Error::RuntimeError() << "shape '" << shape.ToString() + << "' is invalid for input of size " << elem_count; + } } else { - infered_shape.Set(need_infer_axis, x_count / count); - CHECK_EQ_OR_RETURN(infered_shape.Count(0), x_count) + infered_shape.Set(need_infer_axis, elem_count / target_elem_count); + CHECK_OR_RETURN_ERROR(target_elem_count * infered_shape.At(need_infer_axis) == elem_count) << Error::RuntimeError() << "shape '" << shape.ToString() - << "' is invalid for input of size " << x->nelement(); + << "' is invalid for input of size " << elem_count; } return infered_shape; } +Maybe InferUnifiedShapeForBroadcasting(const std::vector& shapes) { + if (shapes.empty()) { return Error::RuntimeError() << "shapes should not be empty."; } + if (shapes.size() == 1) { return JUST(VectorAt(shapes, 0)); } + + auto result = + *JUST(InferUnifiedShapeForBroadcasting(JUST(VectorAt(shapes, 0)), JUST(VectorAt(shapes, 1)))); + + // (1, 2) vs (3, 2) => (3, 2) + if (shapes.size() == 2) { return result; } + + /* + (1, 3) vs (3, 1) vs (3, 1, 1) + + 1. (1, 3) vs (3, 1) => (3, 3) + 2. (3, 3) vs (3, 1, 1) => (3, 3, 3) + 3. (3, 3, 3) is the final result + */ + for (auto iter = shapes.begin() + 2; iter != shapes.end(); ++iter) { + result = *JUST(InferUnifiedShapeForBroadcasting(result, *iter)); + } + return result; +} + +/* + if input shapes are [(1, 3), (3, 1), (3, 1, 1)] + will return ((3, 3, 3), [true, true, true]) + means the shape to broadcast to is (3, 3, 3) and all three shapes need broadcasting +*/ +Maybe>> InferUnifiedShapeForBroadcastingWithInfo( + const std::vector& shapes) { + const auto unified_shape = *JUST(InferUnifiedShapeForBroadcasting(shapes)); + std::deque need_to_broadcast; + for (const auto& x : shapes) { need_to_broadcast.emplace_back(x != unified_shape); } + return std::make_tuple(unified_shape, need_to_broadcast); +} + +Maybe BroadcastSeedToAllRanks(uint64_t* seed, int64_t root) { + CHECK_NOTNULL_OR_RETURN(seed) << "seed is not allowed to be nullptr"; + const auto& rank_group = JUST(RankGroup::DefaultRankGroup()); + const auto& parallel_desc = JUST(RankGroup::GetDefaultParallelDesc(DeviceType::kCPU, rank_group)); + const auto& meta_transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeMeta)); + JUST(ccl::CpuBroadcast(seed, seed, sizeof(*seed), root, parallel_desc, meta_transport_token)); + return Maybe::Ok(); +} + +Maybe> GetPermWhenTransposeAxisToLastDim(const int32_t& ndim, + const int32_t& axis) { + auto wrap_dim = JUST(maybe_wrap_dim(axis, ndim)); + std::vector perm(ndim); + for (int i = 0; i < ndim - 1; i++) { + if (i < wrap_dim) { + perm[i] = i; + } else { + perm[i] = i + 1; + } + } + perm[ndim - 1] = wrap_dim; + return perm; +} + +Maybe> GetInversedPerm(const std::vector& perm) { + std::vector inversed_perm(perm.size()); + for (int i = 0; i < perm.size(); i++) { inversed_perm[perm[i]] = i; } + return inversed_perm; +} + } // namespace functional } // namespace one } // namespace oneflow diff --git a/oneflow/core/functional/impl/common.h b/oneflow/core/functional/impl/common.h index 935d0e9e5cd222b4a65182d15ae41e2eed675407..243e29eac3f59c1d0e0df14703c060168f3f422a 100644 --- a/oneflow/core/functional/impl/common.h +++ b/oneflow/core/functional/impl/common.h @@ -29,16 +29,27 @@ static constexpr size_t kMaxOutputCount = 128; bool IsStaticZerosTensor(const std::shared_ptr& x); bool IsInplaceValid(const std::shared_ptr& x); -bool IsShapeCanExpandTo(const Shape& shape, const Shape& expand_shape); +bool IsScalarTensor(const std::shared_ptr& x); Maybe> CheckAxis(const std::vector& axis, const int32_t& ndim); Maybe CheckInplaceValid(const std::shared_ptr& x); Maybe CheckInplaceCastValid(const std::shared_ptr& x, const std::shared_ptr& x_cast); -Maybe CheckShapeCanExpandTo(const Shape& shape, const Shape& expand_shape); +Maybe CheckInplaceShapeCanExpandTo(const Shape& shape, const Shape& expand_shape); Optional ComputeStride(const Shape& shape, const Stride& stride, const Shape& target_shape); -Maybe InferShape(const std::shared_ptr& x, const Shape& shape); +Maybe InferShapeUnspecifiedDim(const int64_t& elem_count, const Shape& shape); +// returns unified_shape +Maybe InferUnifiedShapeForBroadcasting(const std::vector& shapes); +// returns tuple +Maybe>> InferUnifiedShapeForBroadcastingWithInfo( + const std::vector& shapes); + +Maybe BroadcastSeedToAllRanks(uint64_t* seed, int64_t root = 0); + +Maybe> GetPermWhenTransposeAxisToLastDim(const int32_t& ndim, + const int32_t& axis); +Maybe> GetInversedPerm(const std::vector& perm); } // namespace functional } // namespace one } // namespace oneflow diff --git a/oneflow/core/functional/impl/consistent_cast.cpp b/oneflow/core/functional/impl/consistent_cast.cpp deleted file mode 100644 index 2af4f1b0d0e03c335954cc4a8c82f755f16995b0..0000000000000000000000000000000000000000 --- a/oneflow/core/functional/impl/consistent_cast.cpp +++ /dev/null @@ -1,563 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/framework/consistency_check.h" -#include "oneflow/core/functional/function_library.h" -#include "oneflow/core/framework/id_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/functional/functional.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/autograd/autograd_mode.h" -#include "oneflow/core/autograd/autograd_engine.h" -#include "oneflow/core/framework/tensor_rpc_util.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/resource_desc.h" -#include "oneflow/core/job/rank_group_scope.h" -#include "oneflow/core/job/lazy_mode.h" -#include "oneflow/core/framework/transport_token.h" -#include "oneflow/core/framework/transport_util.h" -#include "oneflow/core/framework/placement_sbp_util.h" -#include "oneflow/core/intrusive/flat_msg.h" -#include "oneflow/core/common/flat_shape.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/common/decorator.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/common/cpp_attribute.h" -#include "oneflow/core/ccl/ccl.h" -#include "oneflow/core/common/constant.h" -#include "oneflow/core/common/env_var/debug_mode.h" - -namespace oneflow { -namespace one { -namespace functional { - -namespace impl { - -namespace { - -// NOTE: use env variable 'ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE' indicate whether the -// shape and dtype of input tensor on each rank is the same when cast local tensor to global tensor. -// If set true, there will be no meta-information synchronization on each rank. -Optional ParseEagerLocalToGlobalBalancedOverride() { - const char* env_p = std::getenv("ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE"); - if (env_p == nullptr) { - return Optional(); - } else { - return ParseBooleanFromEnv("ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE", false); - } -} - -bool NeedSyncAndCheckShapeAndDtype(bool check_meta_hint) { - thread_local Optional eager_local_to_global_balanced_override = - ParseEagerLocalToGlobalBalancedOverride(); - if (eager_local_to_global_balanced_override.has_value()) { - return IsInDebugMode() || !CHECK_JUST(eager_local_to_global_balanced_override); - } else { - return IsInDebugMode() || check_meta_hint; - } -} - -// clang-format off -FLAT_MSG_BEGIN(FlatShapeAndDataType); - // Methods - static Maybe New() { - const auto& flat_shape_dtype = std::make_shared(); - flat_shape_dtype->clear(); - return flat_shape_dtype; - } - static Maybe New(const Shape& shape, DataType dtype) { - const auto& flat_shape_dtype = JUST(New()); - JUST(flat_shape_dtype->mutable_shape()->Init(shape)); - flat_shape_dtype->set_dtype(dtype); - return flat_shape_dtype; - } - Maybe Check(const Shape& shape, DataType dtype) const { - JUST(this->shape().Check(shape)); - CHECK_EQ_OR_RETURN(this->dtype(), dtype) << Error::RuntimeError() - << "Expected all tensors on each rank to be the same dtype, but found " - "at least two dtypes, " << DType(this->dtype()).name() << " and " - << DType(dtype).name() << "!"; - return Maybe::Ok(); - } - Maybe Check(const FlatShapeAndDataType& flat_shape_dtype) const { - JUST(this->shape().Check(flat_shape_dtype.shape())); - CHECK_EQ_OR_RETURN(this->dtype(), flat_shape_dtype.dtype()) - << Error::RuntimeError() - << "Expected input of each rank must have the same dtype, but got at least two dtypes, " - << DType(this->dtype()).name() << " and " << DType(flat_shape_dtype.dtype()).name(); - return Maybe::Ok(); - } - Maybe ToShape(Shape* shape) const { return this->shape().ToShape(shape); } - Maybe ToShape() const { return shape().ToShape(); } - int64_t At(int i) const { return shape().At(i); } - int64_t NumAxes() const { return shape().NumAxes(); } - - private: - // Fields - FLAT_MSG_DEFINE_OPTIONAL(FlatShape, shape); - FLAT_MSG_DEFINE_OPTIONAL(DataType, dtype); -FLAT_MSG_END(FlatShapeAndDataType); -// clang-format on - -Maybe ShapeAndDataTypeConsistencyCheck(const Symbol& placement, - const Shape& shape, DataType dtype) { - if (!placement->containing_current_rank() || placement->parallel_num() == 1) { - return Maybe::Ok(); - } - - const auto& transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); - const auto& send_buffer = JUST(FlatShapeAndDataType::New(shape, dtype)); - const auto& recv_buffer = JUST(FlatShapeAndDataType::New()); - recv_buffer->clear(); - - NaiveAsyncTransportCtx ctx( - transport_token, - [send_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = send_buffer.get(); - *size = sizeof(FlatShapeAndDataType); - *Cb = [send_buffer] {}; - return Maybe::Ok(); - }, - [recv_buffer](int64_t rank, void** buffer, std::size_t* size, - std::function* Cb) -> Maybe { - *buffer = recv_buffer.get(); - *size = sizeof(FlatShapeAndDataType); - *Cb = [recv_buffer] {}; - return Maybe::Ok(); - }); - const auto& rank_group = JUST(RankGroup::New(placement)); - JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); - JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); - JUST_MSG(ctx.WaitDone(), kAsymmetricCodeErrorMsg); - JUST(send_buffer->Check(*recv_buffer)); - return Maybe::Ok(); -} - -Maybe>> BroadcastGatherShapeAndDataType( - const Shape& shape, DataType dtype, Symbol parallel_desc) { - const auto& transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); - const auto& send_buffer = JUST(FlatShapeAndDataType::New(shape, dtype)); - const auto& map = std::make_shared>>(); - map->emplace(GlobalProcessCtx::Rank(), send_buffer); - NaiveAsyncTransportCtx ctx( - transport_token, - [send_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { - *buffer = send_buffer.get(); - *size = sizeof(FlatShapeAndDataType); - *Cb = [send_buffer] {}; - return Maybe::Ok(); - }, - [map](int64_t rank, void** buffer, std::size_t* size, - std::function* Cb) -> Maybe { - const auto& recv_buffer = JUST(FlatShapeAndDataType::New()); - recv_buffer->clear(); - *buffer = recv_buffer.get(); - *size = sizeof(FlatShapeAndDataType); - *Cb = [recv_buffer] {}; - CHECK_OR_RETURN(map->emplace(rank, recv_buffer).second); // NOLINT(maybe-need-error-msg) - return Maybe::Ok(); - }); - const auto& rank_group = JUST(RankGroup::New(parallel_desc)); - JUST(TransportUtil::BroadcastToOtherRanks(rank_group, rank_group, transport_token, &ctx)); - JUST(TransportUtil::CollectFromOtherRanks(rank_group, rank_group, transport_token, &ctx)); - JUST_MSG(ctx.WaitDone(), kAsymmetricCodeErrorMsg); - return map; -} - -Maybe FindRoot(Symbol broadcast_parallel_desc, - Symbol src_parallel_desc) { - for (int64_t process_id : broadcast_parallel_desc->sorted_machine_ids()) { - if (src_parallel_desc->ContainingMachineId(process_id)) { return process_id; } - } - UNIMPLEMENTED_THEN_RETURN(); -} - -auto* CachedFindRoot = DECORATE(&FindRoot, ThreadLocal); - -Maybe BroadcastShapeAndDtype(const Shape& shape, DataType dtype, - Symbol parallel_desc) { - const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); - const auto& rank_group_parallel_desc = - JUST(RankGroup::GetDefaultParallelDesc(parallel_desc->device_type(), rank_group)); - const auto& process_id2broadcast_group = - JUST(GetBroadcastGroup(parallel_desc, rank_group_parallel_desc)); - const auto& broadcast_parallel_desc = - JUST(MapAt(*process_id2broadcast_group, GlobalProcessCtx::Rank())); - - const auto& in_flat_shape_dtype = JUST(FlatShapeAndDataType::New(shape, dtype)); - const auto& out_flat_shape_dtype = JUST(FlatShapeAndDataType::New()); - int64_t root = JUST(CachedFindRoot(broadcast_parallel_desc, parallel_desc)); - const auto& transport_token = - JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); - JUST(ccl::CpuBroadcast(in_flat_shape_dtype.get(), out_flat_shape_dtype.get(), - sizeof(FlatShapeAndDataType), root, broadcast_parallel_desc, - transport_token)); - return out_flat_shape_dtype; -} - -Maybe GetConcatenatedShapeAndCheckDtype( - Shape* logical_shape, DataType* dtype, - const HashMap>& rank2flat_shape_dtype, - Symbol parallel_desc, Symbol nd_sbp) { - *dtype = rank2flat_shape_dtype.begin()->second->dtype(); - HashMap> rank2logical_shape; - for (const auto& pair : rank2flat_shape_dtype) { - rank2logical_shape.emplace(pair.first, JUST(pair.second->ToShape())); - CHECK_EQ_OR_RETURN(*dtype, pair.second->dtype()) - << Error::RuntimeError() - << "Expected all tensors on each rank to be the same dtype, but found " - "at least two dtypes, " - << DType(*dtype).name() << "(rank " << rank2flat_shape_dtype.begin()->first << ") and " - << DType(pair.second->dtype()).name() << "(rank " << pair.first << ")!"; - } - const auto& GetRankPhyShapeByParallelId = [&](Symbol parallel_desc, - int64_t parallel_id) -> Maybe { - int64_t machine_id = JUST(parallel_desc->MachineId4ParallelId(parallel_id)); - return JUST(MapAt(rank2logical_shape, machine_id)); - }; - const auto& parallel_hierarchy = parallel_desc->hierarchy(); - Stride parallel_stride(*parallel_hierarchy); - for (int32_t i = nd_sbp->sbp_parallel_size() - 1; i >= 0; --i) { - if (nd_sbp->sbp_parallel(i).has_split_parallel()) { - int64_t concat_axis = nd_sbp->sbp_parallel(i).split_parallel().axis(); - int64_t group_size = parallel_hierarchy->Count(0, i); - int64_t stride = parallel_stride.at(i); - for (int group_id = 0; group_id < group_size; ++group_id) { - int64_t parallel_num_in_group = parallel_hierarchy->At(i); - for (int64_t stride_id = 0; stride_id < stride; ++stride_id) { - ParallelConf parallel_conf; - parallel_conf.set_device_tag(parallel_desc->device_tag()); - int64_t start_parallel_id = group_id * parallel_num_in_group + stride_id; - for (int64_t parallel_id_in_group = 0; parallel_id_in_group < parallel_num_in_group; - ++parallel_id_in_group) { - int64_t id = start_parallel_id + parallel_id_in_group * stride; - int64_t machine_id = JUST(parallel_desc->MachineId4ParallelId(id)); - int64_t device_id = JUST(parallel_desc->DeviceId4ParallelId(id)); - parallel_conf.add_device_name(std::string("@") + std::to_string(machine_id) + ":" - + std::to_string(device_id)); - } - Symbol sub_parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); - std::shared_ptr first_shape = - JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, 0)); - CHECK_GE_OR_RETURN(concat_axis, 0) - << Error::RuntimeError() << "Split axis must not be negative, but got " << concat_axis - << "!"; - CHECK_LT_OR_RETURN(concat_axis, first_shape->NumAxes()) - << Error::RuntimeError() << "Split axis out of range (expected to be in range of [" - << 0 << ", " << first_shape->NumAxes() << "), but got " << concat_axis << "!)"; - - int64_t logical_concat_dim = first_shape->At(concat_axis); - for (int parallel_id = 1; parallel_id < sub_parallel_desc->parallel_num(); - ++parallel_id) { - const auto& rank_shape = - JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, parallel_id)); - CHECK_EQ_OR_RETURN(rank_shape->NumAxes(), first_shape->NumAxes()) - << Error::RuntimeError() << "Sizes of tensors must match except in dimension " - << concat_axis << ", but found " << first_shape->ToString() << "(rank " - << JUST(sub_parallel_desc->MachineId4ParallelId(0)) << ") and " - << rank_shape->ToString() << "(rank " - << JUST(sub_parallel_desc->MachineId4ParallelId(parallel_id)) << ")!"; - logical_concat_dim += rank_shape->At(concat_axis); - } - - BalancedSplitter bs(logical_concat_dim, sub_parallel_desc->parallel_num()); - CHECK_EQ_OR_RETURN(first_shape->At(concat_axis), bs.At(0).size()) - << Error::RuntimeError() << "Sizes of tensors in dimension " << concat_axis - << " must be same or match balanced split distribution. See " - "https://github.com/Oneflow-Inc/oneflow/blob/master/oneflow/core/common/" - "balanced_splitter.h " - "for details of balanced split"; - first_shape->Set(concat_axis, logical_concat_dim); - - for (int parallel_id = 1; parallel_id < sub_parallel_desc->parallel_num(); - ++parallel_id) { - std::shared_ptr rank_shape = - JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, parallel_id)); - for (int i = 0; i < first_shape->NumAxes(); ++i) { - if (i == concat_axis) { - CHECK_EQ_OR_RETURN(rank_shape->At(i), bs.At(parallel_id).size()) - << Error::RuntimeError() << "Sizes of tensors in dimension " << concat_axis - << " must be same or match balanced split distribution. See " - "https://github.com/Oneflow-Inc/oneflow/blob/master/oneflow/core/common/" - "balanced_splitter.h " - "for details of balanced split"; - } else { - CHECK_EQ_OR_RETURN(rank_shape->At(i), first_shape->At(i)) - << Error::RuntimeError() << "Sizes of tensors must match except in dimension " - << concat_axis << ". Expected size " << first_shape->At(i) << " but got size " - << rank_shape->At(i) << " for tensor on rank " - << JUST(sub_parallel_desc->MachineId4ParallelId(parallel_id)) << "!"; - } - } - rank_shape->Set(concat_axis, logical_concat_dim); - } - } - } - } - } - *logical_shape = *JUST(GetRankPhyShapeByParallelId(parallel_desc, 0)); - return Maybe::Ok(); -} - -Maybe GetLogicalShapeAndDataType(Shape* logical_shape, DataType* /* in and out */ dtype, - std::shared_ptr physical_shape, - Symbol parallel_desc, Symbol nd_sbp, - bool sync_and_check_meta) { - if (!sync_and_check_meta) { - *logical_shape = *JUST(GetLogicalShape(*physical_shape, *nd_sbp, *parallel_desc)); - } else { - if (ContainSplitSbp(nd_sbp)) { - *logical_shape = *physical_shape; - if (parallel_desc->containing_current_rank()) { - const auto& rank2flat_shape_dtype = - JUST(BroadcastGatherShapeAndDataType(*logical_shape, *dtype, parallel_desc)); - JUST(GetConcatenatedShapeAndCheckDtype(logical_shape, dtype, *rank2flat_shape_dtype, - parallel_desc, nd_sbp)); - } - } else { - *logical_shape = *physical_shape; - JUST(ShapeAndDataTypeConsistencyCheck(parallel_desc, *logical_shape, *dtype)); - } - } - if (JUST(RankGroup::New(parallel_desc)) != JUST(RankGroupScope::CurrentRankGroup())) { - const auto& flat_shape_dtype = - JUST(BroadcastShapeAndDtype(*logical_shape, *dtype, parallel_desc)); - *logical_shape = *JUST(flat_shape_dtype->ToShape()); - *dtype = flat_shape_dtype->dtype(); - } - return Maybe::Ok(); -} - -Maybe CheckNdSbpValid(Symbol nd_sbp, const Shape& logical_shape) { - for (int i = 0; i < nd_sbp->sbp_parallel_size(); ++i) { - const auto& sbp_parallel = nd_sbp->sbp_parallel(i); - if (sbp_parallel.has_split_parallel()) { - CHECK_LT_OR_RETURN(sbp_parallel.split_parallel().axis(), logical_shape.NumAxes()) - << Error::RuntimeError() << "Split axis out of range (expected to be in range of [" << 0 - << ", " << logical_shape.NumAxes() << "), but got " - << sbp_parallel.split_parallel().axis() << "!)"; - } - } - return Maybe::Ok(); -} - -namespace { - -Maybe RawGetConsistentToConsistentOpExpr( - const std::vector>& grad_sbp_parallels) { - Optional> grad_nd_sbp; - if (!grad_sbp_parallels.empty()) { grad_nd_sbp = JUST(GetNdSbp(grad_sbp_parallels)); } - std::shared_ptr op_expr = JUST(one::ConsistentToConsistentOpExpr::New(grad_nd_sbp)); - return op_expr; -} - -} // namespace - -static constexpr auto* GetConsistentToConsistentOpExpr = - DECORATE(&RawGetConsistentToConsistentOpExpr, ThreadLocalCopiable); - -Maybe ConsistentToConsistent(const std::shared_ptr& x, - Symbol parallel_desc, - const std::vector>& sbp_parallels, - const std::vector>& grad_sbp_parallels) { - const auto& consistent_tensor = JUST(x->AsConsistentTensor()); - CHECK_NOTNULL_OR_RETURN(consistent_tensor) << "consistent tensors supported only"; - const auto& nd_sbp = JUST(GetNdSbp(sbp_parallels)); - JUST(CheckNdSbpValid(nd_sbp, *x->shape())); - std::shared_ptr op; - if (unlikely(!LazyMode::is_enabled() - && JUST(x->parallel_desc())->hierarchy()->NumAxes() - != parallel_desc->hierarchy()->NumAxes() - && grad_sbp_parallels.size() == 0)) { - op = JUST(GetConsistentToConsistentOpExpr(*JUST(GetSbpList(JUST(x->nd_sbp()))))); - } else { - op = JUST(GetConsistentToConsistentOpExpr(grad_sbp_parallels)); - } - if (!LazyMode::is_enabled() && JUST(x->nd_sbp()) == nd_sbp - && JUST(x->parallel_desc()) == parallel_desc && grad_sbp_parallels.size() == 0) { - return x; - } - const auto& tensor = JUST(OpInterpUtil::Dispatch( - *op, {consistent_tensor}, OpExprInterpContext(AttrMap{}, parallel_desc, nd_sbp))); - if (!LazyMode::is_enabled() && tensor != x && !IsConsistentTensorMetaCheckDisabled()) { - const auto& input_consistent_id = JUST(x->transport_token()); - const auto& output_consistend_id = JUST(tensor->transport_token()); - CHECK_NE_OR_RETURN(input_consistent_id, output_consistend_id); // NOLINT(maybe-need-error-msg) - } - return tensor; -} - -Maybe LocalToConsistent(const std::shared_ptr& x, - Symbol parallel_desc, - const std::vector>& sbp_parallels, - const std::shared_ptr& op, bool check_meta_hint) { - CHECK_OR_RETURN(!x->is_lazy()) - << Error::RuntimeError() - << "local_tensor.to_global() is not supported within nn.Graph for now"; - CHECK_OR_RETURN(x->is_local()) << Error::RuntimeError() << "local tensors supported only"; - std::shared_ptr input = x; - // copy to right device first if input's device type is wrong - if (JUST(input->device())->type() != parallel_desc->device_tag()) { - VLOG(2) << "The device_type of the input tensor is different from placement, now copy it to " - << parallel_desc->device_tag(); - input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), - /*pin_memory=*/false)); - } - // copy to default device of the current rank if input's device type is right but not on default - // device - if (JUST(input->device())->device_id() != GlobalProcessCtx::LocalRank()) { - VLOG(2) << "The tensor isn't on default device of the current rank., now copy it to " - << parallel_desc->device_tag() << ": " << GlobalProcessCtx::LocalRank(); - input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), - /*pin_memory=*/false)); - } - const auto& device = JUST(input->device()); - CHECK_EQ_OR_RETURN(device->type(), parallel_desc->device_tag()) - << Error::UnimplementedError() << "tensor' device type must be same with placement."; - CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()) - << Error::UnimplementedError() << "tensor must be on default device of the current rank."; - Symbol nd_sbp = JUST(GetNdSbp(sbp_parallels)); - const auto& shape = std::make_shared(); - DataType dtype = x->dtype()->data_type(); - bool sync_and_check_meta = NeedSyncAndCheckShapeAndDtype(check_meta_hint); - JUST(GetLogicalShapeAndDataType(shape.get(), &dtype, x->shape(), parallel_desc, nd_sbp, - sync_and_check_meta)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", *shape)); - JUST(attrs.SetAttr("dtype", dtype)); - const auto& output = JUST(OpInterpUtil::Dispatch( - *op, {input}, OpExprInterpContext(attrs, parallel_desc, nd_sbp))); - return output; -} - -} // namespace - -class LocalToConsistentFunctor { - public: - LocalToConsistentFunctor() { - op_ = - CHECK_JUST(one::CastToConsistentOpExpr::New(*CHECK_JUST(UniqueStr("cast_to_consistent")))); - } - - Maybe operator()(const std::shared_ptr& x, - Symbol parallel_desc, - const std::vector>& sbp_parallels, - const Shape& shape, const Symbol& dtype) const { - JUST(CheckDeviceIdsIsValid(parallel_desc)); - NonRecursiveMetaInfoConsistencyCheckScope no_recursive_meta_info_conisitency_check_scope; - JUST(MetaInfoConsistencyCheck(parallel_desc, sbp_parallels, 1, /* force_check */ false)); - CHECK_OR_RETURN(x->is_local()) - << Error::RuntimeError() - << "Expected local tensor for local_to_global but got global tensor!"; - std::shared_ptr input = x->contiguous(); - // copy to right device first if input's device type is wrong - if (JUST(input->device())->type() != parallel_desc->device_tag()) { - VLOG(2) << "The device_type of the input tensor is different from placement, now copy it to " - << parallel_desc->device_tag(); - input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), - /*pin_memory=*/false)); - } - // copy to default device of the current rank if input's device type is right but not on default - // device - if (JUST(input->device())->device_id() != GlobalProcessCtx::LocalRank()) { - VLOG(2) << "The tensor isn't on default device of the current rank., now copy it to " - << parallel_desc->device_tag() << ": " << GlobalProcessCtx::LocalRank(); - input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), - /*pin_memory=*/false)); - } - Symbol nd_sbp = JUST(GetNdSbp(sbp_parallels)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - DisableCheckConsistentTensorMetaScope scope{}; - const auto& tensor = JUST(OpInterpUtil::Dispatch( - *op_, {input}, OpExprInterpContext(attrs, parallel_desc, nd_sbp))); - return tensor; - } - - private: - std::shared_ptr op_; -}; - -class ToConsistentFunctor { - public: - ToConsistentFunctor() { - local_to_consistent_op_ = - CHECK_JUST(one::CastToConsistentOpExpr::New(*CHECK_JUST(UniqueStr("cast_to_consistent")))); - } - - Maybe operator()(const std::shared_ptr& x, - Symbol parallel_desc, - const std::vector>& sbp_parallels, - const std::vector>& grad_sbp_parallels, - bool check_meta) const { - JUST(CheckDeviceIdsIsValid(parallel_desc)); - NonRecursiveMetaInfoConsistencyCheckScope scope; - JUST(MetaInfoConsistencyCheck(parallel_desc, sbp_parallels, grad_sbp_parallels, 1, - /* force_check */ check_meta)); - std::shared_ptr tensor; - if (x->is_consistent()) { - tensor = JUST(ConsistentToConsistent(x, parallel_desc, sbp_parallels, grad_sbp_parallels)); - } else { - tensor = JUST( - LocalToConsistent(x, parallel_desc, sbp_parallels, local_to_consistent_op_, check_meta)); - } - return tensor; - } - - private: - std::shared_ptr local_to_consistent_op_; -}; - -class ConsistentToLocalFunctor { - public: - ConsistentToLocalFunctor() { - op_ = CHECK_JUST( - one::CastFromConsistentOpExpr::New(*CHECK_JUST(UniqueStr("consistent_to_local")))); - } - - Maybe operator()(const std::shared_ptr& x) const { - CHECK_OR_RETURN(!x->is_lazy()) - << Error::RuntimeError() - << "consistent_tensor.to_local() is not supported within nn.Graph for now"; - CHECK_OR_RETURN(x->is_consistent()) - << Error::RuntimeError() << "Expected global tensor for to_local but got local tensor!"; - return JUST(OpInterpUtil::Dispatch(*op_, {x})); - } - - private: - std::shared_ptr op_; -}; - -} // namespace impl - -ONEFLOW_FUNCTION_LIBRARY(m) { - m.add_functor("LocalToConsistent"); - m.add_functor("ToConsistent"); - m.add_functor("ConsistentToLocal"); -}; - -} // namespace functional -} // namespace one -} // namespace oneflow diff --git a/oneflow/core/functional/impl/dataset_functor.cpp b/oneflow/core/functional/impl/dataset_functor.cpp index f06bb98bfc01ee2b9a1841961753a86db3d3e3aa..093f7c11e7262643ee571feda1468f836e149f0e 100644 --- a/oneflow/core/functional/impl/dataset_functor.cpp +++ b/oneflow/core/functional/impl/dataset_functor.cpp @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter.h" @@ -53,32 +54,14 @@ class DecodeOneRecFunctor { const Symbol& dtype, const Shape& shape, const bool is_dynamic, const Optional& reshape, const Optional& batch_padding) const { - MutableAttrMap attrs; - bool has_reshape = false; - bool has_batch_padding = false; - - if (reshape.has_value()) { - has_reshape = true; - JUST(attrs.SetAttr("reshape", *JUST(reshape))); - } else { - has_reshape = false; - JUST(attrs.SetAttr("reshape", shape)); - } - - if (batch_padding.has_value()) { - has_batch_padding = true; - JUST(attrs.SetAttr("batch_padding", *JUST(batch_padding))); - } else { - has_batch_padding = false; - JUST(attrs.SetAttr("batch_padding", shape)); - } - JUST(attrs.SetAttr("key", key)); - JUST(attrs.SetAttr("data_type", dtype->data_type())); - JUST(attrs.SetAttr("static_shape", shape)); - JUST(attrs.SetAttr("is_dynamic", is_dynamic)); - JUST(attrs.SetAttr("has_reshape", has_reshape)); - JUST(attrs.SetAttr("has_batch_padding", has_batch_padding)); - + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("reshape", "batch_padding", "key", "data_type", + "static_shape", "is_dynamic", "has_reshape", + "has_batch_padding"); + bool has_reshape = reshape.has_value(); + bool has_batch_padding = batch_padding.has_value(); + attrs.SetAllAttrs(has_reshape ? *JUST(reshape) : shape, + has_batch_padding ? *JUST(batch_padding) : shape, key, dtype->data_type(), + shape, is_dynamic, has_reshape, has_batch_padding); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } diff --git a/oneflow/core/functional/impl/eye_functor.cpp b/oneflow/core/functional/impl/eye_functor.cpp index 8c8ded73d46377af4ddc09a31918a77828ae5b69..ff36bab74d577f6b1df08da03de9bd4a3fd1d399 100644 --- a/oneflow/core/functional/impl/eye_functor.cpp +++ b/oneflow/core/functional/impl/eye_functor.cpp @@ -21,6 +21,7 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" @@ -46,10 +47,8 @@ class EyeDevcieFunctor { Maybe operator()(const Scalar& rows, const Optional& cols, const Symbol& dtype, const Optional>& device, const bool& requires_grad) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("rows", rows.As())); - JUST(attrs.SetAttr("cols", cols.value_or(rows).As())); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rows", "cols", "dtype"); + attrs.SetAllAttrs(rows.As(), cols.value_or(rows).As(), dtype->data_type()); OpExprInterpContext ctx(attrs); ctx.device = device; auto res = JUST(OpInterpUtil::Dispatch(*op_, {}, ctx)); @@ -71,14 +70,13 @@ class EyeDeviceStrFunctor { } }; -class ConsistentEyeSbpListFunctor { +class GlobalEyeSbpListFunctor { public: - ConsistentEyeSbpListFunctor() { op_ = CHECK_JUST(one::OpBuilder("eye").Output("out").Build()); } + GlobalEyeSbpListFunctor() { op_ = CHECK_JUST(one::OpBuilder("eye").Output("out").Build()); } Maybe operator()(const Scalar& rows, const Optional& cols, const Symbol& dtype, const bool& requires_grad, const Symbol& placement, const std::vector>& sbp_tuple) const { - MutableAttrMap attrs; CHECK_EQ_OR_RETURN(sbp_tuple.size(), placement->hierarchy()->NumAxes()) << "len(sbp) == len(placement.hierarchy) required, but " << "len(sbp)==" << sbp_tuple.size() << ", " @@ -88,10 +86,7 @@ class ConsistentEyeSbpListFunctor { CHECK_OR_RETURN(sbp_tuple.at(i)->has_broadcast_parallel()) << "sbp of eye should be broadcast only"; } - - JUST(attrs.SetAttr("rows", rows.As())); - JUST(attrs.SetAttr("cols", cols.value_or(rows).As())); - JUST(attrs.SetAttr("dtype", dtype->data_type())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rows", "cols", "dtype", "nd_sbp"); if (LazyMode::is_enabled()) { std::vector nd_sbp(sbp_tuple.size()); { @@ -99,8 +94,13 @@ class ConsistentEyeSbpListFunctor { nd_sbp.at(i) = SbpParallelToString(*sbp_tuple.at(i)); } } - JUST(attrs.SetAttr>("nd_sbp", nd_sbp)); + attrs.SetAllAttrs(rows.As(), cols.value_or(rows).As(), dtype->data_type(), + nd_sbp); + } else { + attrs.SetAllAttrs(rows.As(), cols.value_or(rows).As(), dtype->data_type(), + NullOpt); } + const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); auto res = JUST( OpInterpUtil::Dispatch(*op_, {}, OpExprInterpContext(attrs, placement, nd_sbp))); @@ -112,7 +112,7 @@ class ConsistentEyeSbpListFunctor { std::shared_ptr op_; }; -class ConsistentEyeSbpFunctor { +class GlobalEyeSbpFunctor { public: Maybe operator()(const Scalar& rows, const Optional& cols, const Symbol& dtype, const bool& requires_grad, @@ -125,11 +125,31 @@ class ConsistentEyeSbpFunctor { } // namespace impl +class EyeInplaceFunctor { + public: + EyeInplaceFunctor() { op_ = CHECK_JUST(one::OpBuilder("eye").Output("out").Build()); } + Maybe operator()(const std::shared_ptr& x) const { + JUST(CheckInplaceValid(x)); + std::shared_ptr outputs = std::make_shared(1); + outputs->at(0) = x; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rows", "cols", "dtype"); + attrs.SetAllAttrs(x->shape()->At(0), x->shape()->At(1), x->dtype()->data_type()); + OpExprInterpContext ctx(attrs); + ctx.device = JUST(x->device()); + JUST(OpInterpUtil::Dispatch(*op_, {}, outputs.get(), ctx)); + return outputs->at(0); + } + + private: + std::shared_ptr op_; +}; + using namespace impl; ONEFLOW_FUNCTION_LIBRARY(m) { - m.add_functor("Eye"); + m.add_functor("Eye"); + m.add_functor("EyeInplace"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/global_cast.cpp b/oneflow/core/functional/impl/global_cast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ccea55e1a2666e19d4053e4494afd4e10e915394 --- /dev/null +++ b/oneflow/core/functional/impl/global_cast.cpp @@ -0,0 +1,584 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/consistency_check.h" +#include "oneflow/core/functional/function_library.h" +#include "oneflow/core/framework/id_util.h" +#include "oneflow/core/framework/mutable_attr_map.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/autograd/autograd_mode.h" +#include "oneflow/core/autograd/autograd_engine.h" +#include "oneflow/core/framework/tensor_rpc_util.h" +#include "oneflow/core/control/global_process_ctx.h" +#include "oneflow/core/job/global_for.h" +#include "oneflow/core/job/resource_desc.h" +#include "oneflow/core/job/rank_group_scope.h" +#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/framework/transport_token.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/core/framework/placement_sbp_util.h" +#include "oneflow/core/intrusive/flat_msg.h" +#include "oneflow/core/common/flat_shape.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/balanced_splitter.h" +#include "oneflow/core/common/decorator.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/common/cpp_attribute.h" +#include "oneflow/core/ccl/ccl.h" +#include "oneflow/core/common/constant.h" +#include "oneflow/core/common/env_var/debug_mode.h" + +namespace oneflow { +namespace one { +namespace functional { + +namespace impl { + +namespace { + +// NOTE: use env variable 'ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE' indicate whether the +// shape and dtype of input tensor on each rank is the same when cast local tensor to global tensor. +// If set true, there will be no meta-information synchronization on each rank. +Optional ParseEagerLocalToGlobalBalancedOverride() { + const char* env_p = std::getenv("ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE"); + if (env_p == nullptr) { + return Optional(); + } else { + return ParseBooleanFromEnv("ONEFLOW_EAGER_LOCAL_TO_GLOBAL_BALANCED_OVERRIDE", false); + } +} + +bool NeedSyncAndCheckShapeAndDtype(bool check_meta_hint) { + thread_local Optional eager_local_to_global_balanced_override = + ParseEagerLocalToGlobalBalancedOverride(); + if (eager_local_to_global_balanced_override.has_value()) { + return IsInDebugMode() || !CHECK_JUST(eager_local_to_global_balanced_override); + } else { + return IsInDebugMode() || check_meta_hint; + } +} + +// clang-format off +FLAT_MSG_BEGIN(FlatShapeAndDataType); + // Methods + static Maybe New() { + const auto& flat_shape_dtype = std::make_shared(); + flat_shape_dtype->clear(); + return flat_shape_dtype; + } + static Maybe New(const Shape& shape, DataType dtype) { + const auto& flat_shape_dtype = JUST(New()); + JUST(flat_shape_dtype->mutable_shape()->Init(shape)); + flat_shape_dtype->set_dtype(dtype); + return flat_shape_dtype; + } + Maybe Check(const Shape& shape, DataType dtype) const { + JUST(this->shape().Check(shape)); + CHECK_EQ_OR_RETURN(this->dtype(), dtype) << Error::RuntimeError() + << "Expected all tensors on each rank to be the same dtype, but found " + "at least two dtypes, " << DType(this->dtype()).name() << " and " + << DType(dtype).name() << "!"; + return Maybe::Ok(); + } + Maybe Check(const FlatShapeAndDataType& flat_shape_dtype) const { + JUST(this->shape().Check(flat_shape_dtype.shape())); + CHECK_EQ_OR_RETURN(this->dtype(), flat_shape_dtype.dtype()) + << Error::RuntimeError() + << "Expected input of each rank must have the same dtype, but got at least two dtypes, " + << DType(this->dtype()).name() << " and " << DType(flat_shape_dtype.dtype()).name(); + return Maybe::Ok(); + } + Maybe ToShape(Shape* shape) const { return this->shape().ToShape(shape); } + Maybe ToShape() const { return shape().ToShape(); } + int64_t At(int i) const { return shape().At(i); } + int64_t NumAxes() const { return shape().NumAxes(); } + + private: + // Fields + FLAT_MSG_DEFINE_OPTIONAL(FlatShape, shape); + FLAT_MSG_DEFINE_OPTIONAL(DataType, dtype); +FLAT_MSG_END(FlatShapeAndDataType); +// clang-format on + +Maybe ShapeAndDataTypeConsistencyCheck(const Symbol& placement, + const Shape& shape, DataType dtype) { + if (!placement->containing_current_rank() || placement->parallel_num() == 1) { + return Maybe::Ok(); + } + + const auto& transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); + const auto& send_buffer = JUST(FlatShapeAndDataType::New(shape, dtype)); + const auto& recv_buffer = JUST(FlatShapeAndDataType::New()); + recv_buffer->clear(); + + NaiveAsyncTransportCtx ctx( + transport_token, + [send_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = send_buffer.get(); + *size = sizeof(FlatShapeAndDataType); + *Cb = [send_buffer] {}; + return Maybe::Ok(); + }, + [recv_buffer](int64_t rank, void** buffer, std::size_t* size, + std::function* Cb) -> Maybe { + *buffer = recv_buffer.get(); + *size = sizeof(FlatShapeAndDataType); + *Cb = [recv_buffer] {}; + return Maybe::Ok(); + }); + const auto& rank_group = JUST(RankGroup::New(placement)); + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + JUST_MSG(ctx.WaitDone(), kAsymmetricCodeErrorMsg); + JUST(send_buffer->Check(*recv_buffer)); + return Maybe::Ok(); +} + +Maybe>> BroadcastGatherShapeAndDataType( + const Shape& shape, DataType dtype, Symbol parallel_desc) { + const auto& transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); + const auto& send_buffer = JUST(FlatShapeAndDataType::New(shape, dtype)); + const auto& map = std::make_shared>>(); + map->emplace(GlobalProcessCtx::Rank(), send_buffer); + NaiveAsyncTransportCtx ctx( + transport_token, + [send_buffer](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = send_buffer.get(); + *size = sizeof(FlatShapeAndDataType); + *Cb = [send_buffer] {}; + return Maybe::Ok(); + }, + [map](int64_t rank, void** buffer, std::size_t* size, + std::function* Cb) -> Maybe { + const auto& recv_buffer = JUST(FlatShapeAndDataType::New()); + recv_buffer->clear(); + *buffer = recv_buffer.get(); + *size = sizeof(FlatShapeAndDataType); + *Cb = [recv_buffer] {}; + CHECK_OR_RETURN(map->emplace(rank, recv_buffer).second); // NOLINT(maybe-need-error-msg) + return Maybe::Ok(); + }); + const auto& rank_group = JUST(RankGroup::New(parallel_desc)); + JUST(TransportUtil::BroadcastToOtherRanks(rank_group, rank_group, transport_token, &ctx)); + JUST(TransportUtil::CollectFromOtherRanks(rank_group, rank_group, transport_token, &ctx)); + JUST_MSG(ctx.WaitDone(), kAsymmetricCodeErrorMsg); + return map; +} + +Maybe FindRoot(Symbol broadcast_parallel_desc, + Symbol src_parallel_desc) { + for (int64_t process_id : broadcast_parallel_desc->sorted_machine_ids()) { + if (src_parallel_desc->ContainingMachineId(process_id)) { return process_id; } + } + UNIMPLEMENTED_THEN_RETURN(); +} + +auto* CachedFindRoot = DECORATE(&FindRoot, ThreadLocal); + +Maybe BroadcastShapeAndDtype(const Shape& shape, DataType dtype, + Symbol parallel_desc) { + const auto& rank_group = JUST(RankGroupScope::CurrentRankGroup()); + const auto& rank_group_parallel_desc = + JUST(RankGroup::GetDefaultParallelDesc(parallel_desc->device_type(), rank_group)); + const auto& process_id2broadcast_group = + JUST(GetBroadcastGroup(parallel_desc, rank_group_parallel_desc)); + const auto& broadcast_parallel_desc = + JUST(MapAt(*process_id2broadcast_group, GlobalProcessCtx::Rank())); + + const auto& in_flat_shape_dtype = JUST(FlatShapeAndDataType::New(shape, dtype)); + const auto& out_flat_shape_dtype = JUST(FlatShapeAndDataType::New()); + int64_t root = JUST(CachedFindRoot(broadcast_parallel_desc, parallel_desc)); + const auto& transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeSyncLocalShapeDtype)); + JUST(ccl::CpuBroadcast(in_flat_shape_dtype.get(), out_flat_shape_dtype.get(), + sizeof(FlatShapeAndDataType), root, broadcast_parallel_desc, + transport_token)); + return out_flat_shape_dtype; +} + +Maybe GetConcatenatedShapeAndCheckDtype( + Shape* logical_shape, DataType* dtype, + const HashMap>& rank2flat_shape_dtype, + Symbol parallel_desc, Symbol nd_sbp) { + *dtype = rank2flat_shape_dtype.begin()->second->dtype(); + HashMap> rank2logical_shape; + for (const auto& pair : rank2flat_shape_dtype) { + rank2logical_shape.emplace(pair.first, JUST(pair.second->ToShape())); + CHECK_EQ_OR_RETURN(*dtype, pair.second->dtype()) + << Error::RuntimeError() + << "Expected all tensors on each rank to be the same dtype, but found " + "at least two dtypes, " + << DType(*dtype).name() << "(rank " << rank2flat_shape_dtype.begin()->first << ") and " + << DType(pair.second->dtype()).name() << "(rank " << pair.first << ")!"; + } + const auto& GetRankPhyShapeByParallelId = [&](Symbol parallel_desc, + int64_t parallel_id) -> Maybe { + int64_t machine_id = JUST(parallel_desc->MachineId4ParallelId(parallel_id)); + return JUST(MapAt(rank2logical_shape, machine_id)); + }; + const auto& parallel_hierarchy = parallel_desc->hierarchy(); + Stride parallel_stride(*parallel_hierarchy); + for (int32_t i = nd_sbp->sbp_parallel_size() - 1; i >= 0; --i) { + if (nd_sbp->sbp_parallel(i).has_split_parallel()) { + int64_t concat_axis = nd_sbp->sbp_parallel(i).split_parallel().axis(); + int64_t group_size = parallel_hierarchy->Count(0, i); + int64_t stride = parallel_stride.at(i); + for (int group_id = 0; group_id < group_size; ++group_id) { + int64_t parallel_num_in_group = parallel_hierarchy->At(i); + for (int64_t stride_id = 0; stride_id < stride; ++stride_id) { + ParallelConf parallel_conf; + parallel_conf.set_device_tag(parallel_desc->device_tag()); + int64_t start_parallel_id = group_id * parallel_num_in_group + stride_id; + for (int64_t parallel_id_in_group = 0; parallel_id_in_group < parallel_num_in_group; + ++parallel_id_in_group) { + int64_t id = start_parallel_id + parallel_id_in_group * stride; + int64_t machine_id = JUST(parallel_desc->MachineId4ParallelId(id)); + int64_t device_id = JUST(parallel_desc->DeviceId4ParallelId(id)); + parallel_conf.add_device_name(std::string("@") + std::to_string(machine_id) + ":" + + std::to_string(device_id)); + } + Symbol sub_parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); + std::shared_ptr first_shape = + JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, 0)); + CHECK_GE_OR_RETURN(concat_axis, 0) + << Error::RuntimeError() << "Split axis must not be negative, but got " << concat_axis + << "!"; + CHECK_LT_OR_RETURN(concat_axis, first_shape->NumAxes()) + << Error::RuntimeError() << "Split axis out of range (expected to be in range of [" + << 0 << ", " << first_shape->NumAxes() << "), but got " << concat_axis << "!)"; + + int64_t logical_concat_dim = first_shape->At(concat_axis); + for (int parallel_id = 1; parallel_id < sub_parallel_desc->parallel_num(); + ++parallel_id) { + const auto& rank_shape = + JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, parallel_id)); + CHECK_EQ_OR_RETURN(rank_shape->NumAxes(), first_shape->NumAxes()) + << Error::RuntimeError() << "Sizes of tensors must match except in dimension " + << concat_axis << ", but found " << first_shape->ToString() << "(rank " + << JUST(sub_parallel_desc->MachineId4ParallelId(0)) << ") and " + << rank_shape->ToString() << "(rank " + << JUST(sub_parallel_desc->MachineId4ParallelId(parallel_id)) << ")!"; + logical_concat_dim += rank_shape->At(concat_axis); + } + + BalancedSplitter bs(logical_concat_dim, sub_parallel_desc->parallel_num()); + CHECK_EQ_OR_RETURN(first_shape->At(concat_axis), bs.At(0).size()) + << Error::RuntimeError() << "Sizes of tensors in dimension " << concat_axis + << " must be same or match balanced split distribution. See " + "https://github.com/Oneflow-Inc/oneflow/blob/master/oneflow/core/common/" + "balanced_splitter.h " + "for details of balanced split"; + first_shape->Set(concat_axis, logical_concat_dim); + + for (int parallel_id = 1; parallel_id < sub_parallel_desc->parallel_num(); + ++parallel_id) { + std::shared_ptr rank_shape = + JUST(GetRankPhyShapeByParallelId(sub_parallel_desc, parallel_id)); + for (int i = 0; i < first_shape->NumAxes(); ++i) { + if (i == concat_axis) { + CHECK_EQ_OR_RETURN(rank_shape->At(i), bs.At(parallel_id).size()) + << Error::RuntimeError() << "Sizes of tensors in dimension " << concat_axis + << " must be same or match balanced split distribution. See " + "https://github.com/Oneflow-Inc/oneflow/blob/master/oneflow/core/common/" + "balanced_splitter.h " + "for details of balanced split"; + } else { + CHECK_EQ_OR_RETURN(rank_shape->At(i), first_shape->At(i)) + << Error::RuntimeError() << "Sizes of tensors must match except in dimension " + << concat_axis << ". Expected size " << first_shape->At(i) << " but got size " + << rank_shape->At(i) << " for tensor on rank " + << JUST(sub_parallel_desc->MachineId4ParallelId(parallel_id)) << "!"; + } + } + rank_shape->Set(concat_axis, logical_concat_dim); + } + } + } + } + } + *logical_shape = *JUST(GetRankPhyShapeByParallelId(parallel_desc, 0)); + return Maybe::Ok(); +} + +Maybe GetLogicalShapeAndDataType(Shape* logical_shape, DataType* /* in and out */ dtype, + std::shared_ptr physical_shape, + Symbol parallel_desc, Symbol nd_sbp, + bool sync_and_check_meta) { + if (!sync_and_check_meta) { + *logical_shape = *JUST(GetLogicalShape(*physical_shape, *nd_sbp, *parallel_desc)); + } else { + if (ContainSplitSbp(nd_sbp)) { + *logical_shape = *physical_shape; + if (parallel_desc->containing_current_rank()) { + const auto& rank2flat_shape_dtype = + JUST(BroadcastGatherShapeAndDataType(*logical_shape, *dtype, parallel_desc)); + JUST(GetConcatenatedShapeAndCheckDtype(logical_shape, dtype, *rank2flat_shape_dtype, + parallel_desc, nd_sbp)); + } + } else { + *logical_shape = *physical_shape; + JUST(ShapeAndDataTypeConsistencyCheck(parallel_desc, *logical_shape, *dtype)); + } + } + if (JUST(RankGroup::New(parallel_desc)) != JUST(RankGroupScope::CurrentRankGroup())) { + const auto& flat_shape_dtype = + JUST(BroadcastShapeAndDtype(*logical_shape, *dtype, parallel_desc)); + *logical_shape = *JUST(flat_shape_dtype->ToShape()); + *dtype = flat_shape_dtype->dtype(); + } + return Maybe::Ok(); +} + +Maybe CheckNdSbpValid(Symbol nd_sbp, const Shape& logical_shape) { + for (int i = 0; i < nd_sbp->sbp_parallel_size(); ++i) { + const auto& sbp_parallel = nd_sbp->sbp_parallel(i); + if (sbp_parallel.has_split_parallel()) { + CHECK_LT_OR_RETURN(sbp_parallel.split_parallel().axis(), logical_shape.NumAxes()) + << Error::RuntimeError() << "Split axis out of range (expected to be in range of [" << 0 + << ", " << logical_shape.NumAxes() << "), but got " + << sbp_parallel.split_parallel().axis() << "!)"; + } + } + return Maybe::Ok(); +} + +namespace { + +Maybe RawGetGlobalToGlobalOpExpr( + const std::vector>& grad_sbp_parallels) { + Optional> grad_nd_sbp; + if (!grad_sbp_parallels.empty()) { grad_nd_sbp = JUST(GetNdSbp(grad_sbp_parallels)); } + std::shared_ptr op_expr = JUST(one::GlobalToGlobalOpExpr::New(grad_nd_sbp)); + return op_expr; +} + +} // namespace + +static constexpr auto* GetGlobalToGlobalOpExpr = + DECORATE(&RawGetGlobalToGlobalOpExpr, ThreadLocalCopiable); + +Maybe GlobalToGlobal(const std::shared_ptr& x, Symbol parallel_desc, + const std::vector>& sbp_parallels, + const std::vector>& grad_sbp_parallels, + bool copy) { + const auto& global_tensor = JUST(x->AsGlobalTensor()); + CHECK_NOTNULL_OR_RETURN(global_tensor) << "global tensors supported only"; + const auto& nd_sbp = JUST(GetNdSbp(sbp_parallels)); + JUST(CheckNdSbpValid(nd_sbp, *x->shape())); + std::shared_ptr op; + if (unlikely(!LazyMode::is_enabled() + && JUST(x->parallel_desc())->hierarchy()->NumAxes() + != parallel_desc->hierarchy()->NumAxes() + && grad_sbp_parallels.size() == 0)) { + op = JUST(GetGlobalToGlobalOpExpr(*JUST(GetSbpList(JUST(x->nd_sbp()))))); + } else { + op = JUST(GetGlobalToGlobalOpExpr(grad_sbp_parallels)); + } + if (!LazyMode::is_enabled() && JUST(x->nd_sbp()) == nd_sbp + && JUST(x->parallel_desc()) == parallel_desc && grad_sbp_parallels.size() == 0) { + if (copy) { return functional::Identity(x); } + return x; + } + const auto& tensor = JUST(OpInterpUtil::Dispatch( + *op, {global_tensor}, OpExprInterpContext(AttrMap{}, parallel_desc, nd_sbp))); + if (!LazyMode::is_enabled() && tensor != x && !IsGlobalTensorMetaCheckDisabled()) { + const auto& input_global_id = JUST(x->transport_token()); + const auto& output_consistend_id = JUST(tensor->transport_token()); + CHECK_NE_OR_RETURN(input_global_id, output_consistend_id); // NOLINT(maybe-need-error-msg) + } + return tensor; +} + +Maybe LocalToGlobal(const std::shared_ptr& x, Symbol parallel_desc, + const std::vector>& sbp_parallels, + const std::shared_ptr& op, bool check_meta_hint, bool copy) { + CHECK_OR_RETURN(!x->is_lazy()) + << Error::RuntimeError() + << "local_tensor.to_global() is not supported within nn.Graph for now"; + CHECK_OR_RETURN(x->is_local()) << Error::RuntimeError() << "local tensors supported only"; + std::shared_ptr input = x; + // copy to right device first if input's device type is wrong + if (JUST(input->device())->type() != parallel_desc->device_tag()) { + VLOG(2) << "The device_type of the input tensor is different from placement, now copy it to " + << parallel_desc->device_tag(); + input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), + /*pin_memory=*/false)); + } + // copy to default device of the current rank if input's device type is right but not on default + // device + bool device_mismatch = JUST(input->device())->device_id() != GlobalProcessCtx::LocalRank(); + if (copy || device_mismatch) { + if (device_mismatch) { + VLOG(2) << "The tensor isn't on default device of the current rank, now copy it to " + << parallel_desc->device_tag() << ": " << GlobalProcessCtx::LocalRank(); + } + input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), + /*pin_memory=*/false)); + } + const auto& device = JUST(input->device()); + CHECK_EQ_OR_RETURN(device->type(), parallel_desc->device_tag()) + << Error::UnimplementedError() << "tensor' device type must be same with placement."; + CHECK_EQ_OR_RETURN(device->device_id(), GlobalProcessCtx::LocalRank()) + << Error::UnimplementedError() << "tensor must be on default device of the current rank."; + Symbol nd_sbp = JUST(GetNdSbp(sbp_parallels)); + const auto& shape = std::make_shared(); + DataType dtype = x->dtype()->data_type(); + bool sync_and_check_meta = NeedSyncAndCheckShapeAndDtype(check_meta_hint); + JUST(GetLogicalShapeAndDataType(shape.get(), &dtype, x->shape(), parallel_desc, nd_sbp, + sync_and_check_meta)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "sync_data"); + attrs.SetAllAttrs(*shape, dtype, true); + const auto& output = JUST(OpInterpUtil::Dispatch( + *op, {input}, OpExprInterpContext(attrs, parallel_desc, nd_sbp))); + return output; +} + +} // namespace + +class LocalToGlobalFunctor { + public: + LocalToGlobalFunctor() { + op_ = CHECK_JUST(one::LocalToGlobalOpExpr::New(*CHECK_JUST(UniqueStr("local_to_global")))); + } + + Maybe operator()(const std::shared_ptr& x, + Symbol parallel_desc, + const std::vector>& sbp_parallels, + const Shape& shape, const Symbol& dtype, bool sync_data, + bool copy) const { + JUST(CheckDeviceIdsIsValid(parallel_desc)); + NonRecursiveMetaInfoConsistencyCheckScope no_recursive_meta_info_conisitency_check_scope; + JUST(MetaInfoConsistencyCheck(parallel_desc, sbp_parallels, 1, /* force_check */ false)); + CHECK_OR_RETURN(x->is_local()) + << Error::RuntimeError() + << "Expected local tensor for local_to_global but got global tensor!"; + std::shared_ptr input = x->contiguous(); + // copy to right device first if input's device type is wrong + if (JUST(input->device())->type() != parallel_desc->device_tag()) { + VLOG(2) << "The device_type of the input tensor is different from placement, now copy it to " + << parallel_desc->device_tag(); + input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), + /*pin_memory=*/false)); + } + // copy to default device of the current rank if input's device type is right but not on default + // device + bool device_mismatch = JUST(input->device())->device_id() != GlobalProcessCtx::LocalRank(); + if (copy || device_mismatch) { + if (device_mismatch) { + VLOG(2) << "The tensor isn't on default device of the current rank, now copy it to " + << parallel_desc->device_tag() << ": " << GlobalProcessCtx::LocalRank(); + } + input = JUST(functional::Copy(x, parallel_desc->device_tag(), GlobalProcessCtx::LocalRank(), + /*pin_memory=*/false)); + } + Symbol nd_sbp = JUST(GetNdSbp(sbp_parallels)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "dtype", "sync_data"); + attrs.SetAllAttrs(shape, dtype->data_type(), sync_data); + DisableCheckGlobalTensorMetaScope scope{}; + const auto& tensor = JUST(OpInterpUtil::Dispatch( + *op_, {input}, OpExprInterpContext(attrs, parallel_desc, nd_sbp))); + return tensor; + } + + private: + std::shared_ptr op_; +}; + +class ToGlobalFunctor { + public: + ToGlobalFunctor() { + local_to_global_op_ = + CHECK_JUST(one::LocalToGlobalOpExpr::New(*CHECK_JUST(UniqueStr("local_to_global")))); + } + + Maybe operator()(const std::shared_ptr& x, + Symbol parallel_desc, + const std::vector>& sbp_parallels, + const std::vector>& grad_sbp_parallels, + bool check_meta, bool copy) const { + JUST(CheckDeviceIdsIsValid(parallel_desc)); + NonRecursiveMetaInfoConsistencyCheckScope scope; + JUST(MetaInfoConsistencyCheck(parallel_desc, sbp_parallels, grad_sbp_parallels, 1, + /* force_check */ check_meta)); + std::shared_ptr tensor; + if (x->is_global()) { + tensor = JUST(GlobalToGlobal(x, parallel_desc, sbp_parallels, grad_sbp_parallels, copy)); + } else { + DeviceType device_type = parallel_desc->device_type(); + if (device_type == DeviceType::kCPU || device_type == DeviceType::kCUDA) { + tensor = JUST( + LocalToGlobal(x, parallel_desc, sbp_parallels, local_to_global_op_, check_meta, copy)); + } else { + // Assuming that the newly adapted hardware device does not support collective + // communication, since local to global may need to synchronize data (through the + // broadcast API), if device_type is neither cpu nor cuda, generate global tensor + // with the corresponding cpu placement first, then convert the cpu global tensor + // to the desired placement. + Symbol cpu_parallel_desc = + JUST(ReplaceDeviceType(parallel_desc, DeviceType::kCPU)); + std::shared_ptr cpu_tensor = JUST(LocalToGlobal( + x, cpu_parallel_desc, sbp_parallels, local_to_global_op_, check_meta, copy)); + tensor = + JUST(GlobalToGlobal(cpu_tensor, parallel_desc, sbp_parallels, GetNoneSbpList(), copy)); + } + } + return tensor; + } + + private: + std::shared_ptr local_to_global_op_; +}; + +class GlobalToLocalFunctor { + public: + GlobalToLocalFunctor() { + op_ = CHECK_JUST(one::GlobalToLocalOpExpr::New(*CHECK_JUST(UniqueStr("global_to_local")))); + } + + Maybe operator()(const std::shared_ptr& x, bool copy) const { + CHECK_OR_RETURN(!x->is_lazy()) + << Error::RuntimeError() + << "global_tensor.to_local() is not supported within nn.Graph for now"; + CHECK_OR_RETURN(x->is_global()) + << Error::RuntimeError() << "Expected global tensor for to_local but got local tensor!"; + const auto& local_tensor = JUST(OpInterpUtil::Dispatch(*op_, {x})); + if (copy) { return local_tensor->clone(); } + return local_tensor; + } + + private: + std::shared_ptr op_; +}; + +} // namespace impl + +ONEFLOW_FUNCTION_LIBRARY(m) { + m.add_functor("LocalToGlobal"); + m.add_functor("ToGlobal"); + m.add_functor("GlobalToLocal"); +}; + +} // namespace functional +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/functional/impl/gradient_accumulation_functor.cpp b/oneflow/core/functional/impl/gradient_accumulation_functor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..99c7c08f0be49530c06f3864f19717df79159db6 --- /dev/null +++ b/oneflow/core/functional/impl/gradient_accumulation_functor.cpp @@ -0,0 +1,102 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/mutable_attr_map.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/functional/function_library.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/impl/common.h" + +namespace oneflow { +namespace one { +namespace functional { + +namespace impl { + +class GradAccRepeatFunctor { + public: + GradAccRepeatFunctor() { + op_ = CHECK_JUST(one::OpBuilder("repeat").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& in, int32_t repeat_num) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("repeat_num"); + attrs.SetAllAttrs(repeat_num); + return OpInterpUtil::Dispatch(*op_, {in}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class GradAccCollectFunctor { + public: + GradAccCollectFunctor() { + op_ = CHECK_JUST(one::OpBuilder("acc").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& in, int32_t collect_num) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("max_acc_num"); + attrs.SetAllAttrs(collect_num); + return OpInterpUtil::Dispatch(*op_, {in}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class GradAccPackFunctor { + public: + GradAccPackFunctor() { + op_ = CHECK_JUST(one::OpBuilder("pack").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& in, int32_t pack_num) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("pack_num"); + attrs.SetAllAttrs(pack_num); + return OpInterpUtil::Dispatch(*op_, {in}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class GradAccUnpackFunctor { + public: + GradAccUnpackFunctor() { + op_ = CHECK_JUST(one::OpBuilder("unpack").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& in, int32_t unpack_num) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("unpack_num"); + attrs.SetAllAttrs(unpack_num); + return OpInterpUtil::Dispatch(*op_, {in}, attrs); + } + + private: + std::shared_ptr op_; +}; + +} // namespace impl + +ONEFLOW_FUNCTION_LIBRARY(m) { + m.add_functor("GradAccRepeat"); + m.add_functor("GradAccCollect"); + m.add_functor("GradAccPack"); + m.add_functor("GradAccUnpack"); +} + +} // namespace functional +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/functional/impl/higher_derivative_functor.cpp b/oneflow/core/functional/impl/higher_derivative_functor.cpp index c0bb87e5d2f0c1e6abf41480c6489ce6d0bcd8e6..ff79508385f8b2d4679d2e15158dfef07a76e6a9 100644 --- a/oneflow/core/functional/impl/higher_derivative_functor.cpp +++ b/oneflow/core/functional/impl/higher_derivative_functor.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include #include "oneflow/core/common/scalar.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/attr_map.h" @@ -22,6 +23,7 @@ limitations under the License. #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/functional/functional_api.yaml.h" #include "oneflow/core/functional/sequence_function.h" #include "oneflow/core/functional/function_library.h" #include "oneflow/core/functional/impl/common.h" @@ -57,11 +59,535 @@ class CosGradGradFunctor { } }; +class TanGradGradFunctor { + public: + // dx = 1/cos^2(x), ddx = 2*sinx/cos^3(x) = tan_grad(x)*tan(x)*2 + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Mul) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(2), input); + }) + .call(JUST(functional::Tan(x)), JUST(functional::TanGrad(x, dydx))); + return r; + } +}; + +class SinhGradGradFunctor { + public: + // dx = cosh(x), ddx = sinh(x) = cosh_grad(x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::CoshGrad(x, dydx); + } +}; + +class CoshGradGradFunctor { + public: + // dx = sinh(x), ddx = cosh(x) = sinh_grad(x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::SinhGrad(x, dydx); + } +}; + +class TanhGradGradFunctor { + public: + // dx = sech^2(x), ddx = -2*sech^2(x)*tanh(x) = tan_grad(x)*tanh(x)*(-2) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Mul) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(-2), input); + }) + .call(JUST(functional::Tanh(x)), JUST(functional::TanhGrad(x, dydx))); + return r; + } +}; + +class AsinGradGradFunctor { + public: + // dx = 1/sqrt(1-x*x)=rsqrt(1-x*x), ddx = rsqrt_grad(1-x*x)*(-2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarSub(Scalar(1), input, /*alpha=*/1.0); + }) + .then(std::bind(functional::RsqrtGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(-2), input); + }) + .call(x); + return r; + } +}; +class AcosGradGradFunctor { + public: + // dx = -1/sqrt(1-x*x)=-rsqrt(1-x*x), ddx = rsqrt_grad(1-x*x)*(2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarSub(Scalar(1), input, /*alpha=*/1.0); + }) + .then(std::bind(functional::RsqrtGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(2), input); + }) + .call(x); + return r; + } +}; + +class AtanGradGradFunctor { + public: + // dx = 1/(1+x*x), ddx = reci_grad(1+x*x)*(2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(Scalar(1), input, /*alpha=*/1.0); + }) + .then(std::bind(functional::ReciprocalGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(2), input); + }) + .call(x); + return r; + } +}; + +class AsinhGradGradFunctor { + public: + // dx = 1/sqrt(1+x*x)=rsqrt(1+x*x), ddx = rsqrt_grad(1+x*x)*(2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(Scalar(1), input, /*alpha=*/1.0); + }) + .then(std::bind(functional::RsqrtGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(2), input); + }) + .call(x); + return r; + } +}; + +class AcoshGradGradFunctor { + public: + // dx = 1/sqrt(x*x-1)=rsqrt(x*x-1), ddx = rsqrt_grad(x*x-1)*(2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarSub(input, Scalar(1), /*alpha=*/1.0, + /*inplace=*/false); + }) + .then(std::bind(functional::RsqrtGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(2), input); + }) + .call(x); + + return r; + } +}; + +class AtanhGradGradFunctor { + public: + // dx = 1/(1-x*x), ddx = reci_grad(1-x*x)*(-2x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto r = sequence_function(functional::Square) + .then([](const std::shared_ptr& input) { + return functional::ScalarSub(Scalar(1), input, /*alpha=*/1.0); + }) + .then(std::bind(functional::ReciprocalGrad, std::placeholders::_1, dydx)) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(-2), input); + }) + .call(x); + return r; + } +}; + +class ErfGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(-2), + JUST(functional::Mul(x, JUST(functional::ErfGrad(x, dydx))))); + } +}; + +class ErfcGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(-2), + JUST(functional::Mul(x, JUST(functional::ErfcGrad(x, dydx))))); + } +}; + +class ExpGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ExpGrad(x, dydx); + } +}; + +class Expm1GradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ExpGrad(x, dydx); + } +}; + +class LogGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ReciprocalGrad(x, dydx); + } +}; + +class Log2GradGradFunctor { + public: + // dx = 1/(x*ln2), ddx = 1/ln2 * -1/(x*x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(1.0 / std::log(2.0f)), + JUST(functional::ReciprocalGrad(x, dydx))); + } +}; + +class Log10GradGradFunctor { + public: + // dx = 1/(x*ln10), ddx = 1/ln10 * -1/(x*x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(1.0 / std::log(10.0f)), + JUST(functional::ReciprocalGrad(x, dydx))); + } +}; + +class Log1pGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ReciprocalGrad( + JUST(functional::ScalarAdd(Scalar(1), x, /*alpha=*/Scalar(1))), dydx); + } +}; + +class LogSigmoidGradGradFunctor { + public: + // dx = exp(-x)/(1+exp(-x)), ddx = -exp(-x)/(1+exp(-x))^2 = -sigmoid_grad(x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::Negative(JUST(functional::SigmoidGrad(JUST(functional::Sigmoid(x)), dydx))); + } +}; + +class ReciprocalGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::Negative(JUST(functional::ScalarPowGrad(x, dydx, Scalar(-2)))); + } +}; + +class ReciprocalNoNanGradGradFunctor { + public: + // dx = -pow(x,-2), ddx = -pow_grad(x,-2) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::Negative(JUST(functional::ScalarPowGrad(x, dydx, Scalar(-2)))); + } +}; + +class RsqrtGradGradFunctor { + public: + // dx = -0.5*pow(x,-1.5), ddx = -0.5*pow_grad(x,-1.5) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(-0.5), + JUST(functional::ScalarPowGrad(x, dydx, Scalar(-1.5)))); + } +}; + +class SqrtGradGradFunctor { + public: + // dx = 0.5*pow(x,-0.5), ddx = -0.25*pow(x,-1.5) = 0.5*rsqrt_grad(x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(Scalar(0.5), JUST(functional::RsqrtGrad(x, dydx))); + } +}; + +class SquareGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ScalarMul(2, dydx); + } +}; + +class SigmoidGradGradFunctor { + public: + // dy = y * (1 - y), ddy = 1 - 2*y + Maybe operator()(const std::shared_ptr& y, + const std::shared_ptr& dydx) const { + return functional::Mul(JUST(functional::ScalarSub(1, y, /*alpha=*/2)), dydx); + } +}; + +class SiluGradGradFunctor { + public: + // y = x ∗ sigmoid(x) + // y' = (sig(x) + x * sig_grad(x)) + // y'' = (sig(x) + x*sig_grad(x))' = sig_grad(x)*(x+2-2*silu(x)) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto res = functional::sequence_function(functional::Silu) + .then([](const std::shared_ptr& input) { + return functional::ScalarSub(Scalar(2.0), input, /*alpha=*/Scalar(2.0)); + }) + .then([&x](const std::shared_ptr& input) { + return functional::Add(x, input, /*alpha=*/Scalar(1.0), /*inplace=*/false); + }) + // Since we use y to compute SigmoidGrad, here we need to use sigmoid with x to + // compute x first. + // TODO(zzk): Implement SigmoidGradXDy func. + .then(std::bind(functional::SigmoidGrad, JUST(functional::Sigmoid(x)), + std::placeholders::_1)) + .then(std::bind(functional::Mul, dydx, std::placeholders::_1)) + .call(x); + return res; + } +}; + +class SeluGradGradFunctor { + public: + // y'' = scale * alpha * exp(x) (x < 0) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto condition = JUST(functional::ScalarLogicalLess(x, Scalar(0.0))); + auto res = functional::Where(condition, JUST(functional::SeluGrad(dydx, x)), + JUST(functional::ZerosLike(x))); + return res; + } +}; + +class SoftSignGradGradFunctor { + public: + // y = x/(1+abs(x)), y' = 1/(1+abs(x))^2, y'' = -2/(1+abs(x))^3*abs_grad(x) + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto res = functional::sequence_function(functional::Abs) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(Scalar(1.0), input, /*alpha=*/Scalar(1)); + }) + .then([](const std::shared_ptr& input) { + return functional::ScalarPow(input, Scalar(-3), /*inplace=*/false); + }) + .then([](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(-2), input); + }) + .then(std::bind(functional::AbsGrad, x, std::placeholders::_1)) + .then(std::bind(functional::Mul, dydx, std::placeholders::_1)) + .call(x); + return res; + } +}; + +class HardSigmoidGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + return functional::ZerosLike(x); + } +}; + +class HardSwishGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + auto condition = JUST(functional::ScalarLogicalGreater( + (JUST(functional::ScalarLogicalLess(x, Scalar(3.0)))), Scalar(-3.0))); + return functional::Where(condition, JUST(functional::ScalarDiv(dydx, Scalar(3.0))), + JUST(functional::ZerosLike(x))); + } +}; + +class SoftplusGradGradFunctor { + public: + // beta*x <= threshold: + // y = 1/beta*ln(1+exp(beta*x)), y' = 1/(1+exp(beta*x))*exp(beta*x) + // y'' = beta*exp(beta*x)/(1+exp(beta*x))^2 = beta*sig(beta*x)(1-sig(beta*x)) + // = beta*sig_grad(beta*x) + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dydx, + const double& beta, const double& threshold) const { + auto beta_x = JUST(functional::ScalarMul(x, beta, /*inplace=*/false)); + auto condition = JUST(functional::ScalarLogicalLess(beta_x, Scalar(threshold))); + auto zero_out = JUST(functional::ZerosLike(x)); + auto res = functional::sequence_function(functional::Sigmoid) + .then(std::bind(functional::SigmoidGrad, std::placeholders::_1, dydx)) + .then([&beta](const std::shared_ptr& input) { + return functional::ScalarMul(Scalar(beta), input); + }) + .then(std::bind(functional::Where, condition, std::placeholders::_1, zero_out)) + .call(beta_x); + + return res; + } +}; + +class EluGradGradFunctor { + public: + // y = max(0,x) + min(0,alpha∗(exp(x)−1)) + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dydx, + const double& alpha) const { + auto condition = JUST(functional::ScalarLogicalLess(x, Scalar(0.0))); + return functional::Where(condition, JUST(functional::EluGrad(x, dydx, alpha)), + JUST(functional::ZerosLike(x))); + } +}; + +class CeluGradGradFunctor { + public: + // y = max(0,x) + min(0,alpha∗(exp(x/alpha)−1)) + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dydx, + const double& alpha) const { + auto condition = JUST(functional::ScalarLogicalLess(x, Scalar(0))); + auto a = JUST(functional::CeluGrad(x, dydx, alpha)); + auto b = JUST(functional::ScalarDiv(a, Scalar(alpha))); + auto r = functional::Where(condition, b, JUST(functional::ZerosLike(x))); + return r; + } +}; + +class MaxPoolNdGradGradFunctor { + public: + Maybe operator()(const std::shared_ptr& dydx, + const std::shared_ptr& indices, const int ndims) const { + if (indices->nelement()) { + Shape view_shape(indices->shape()->begin(), indices->shape()->end() - ndims); + view_shape.push_back(-1); + auto indices_view = JUST(functional::Reshape(indices, view_shape)); + auto outgrad_view = JUST(functional::Reshape(dydx, view_shape)); + return functional::sequence_function(functional::DimGather) + .then(std::bind(functional::Reshape, std::placeholders::_1, *indices->shape())) + .call(outgrad_view, -1, indices_view, /*sparse_grad=*/false); + } else { + // empty inputs, return 0size tensor + return functional::ZerosLike(indices); + } + } +}; + +class MishGradGradFunctor { + public: + // y = x ∗ tanh(softplus(x)) + // ddx = grad_tsp * sig * (2 + x * (1 + (-1 - 2 * tsp) * sig)), sig equal grad_sp here + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + const auto sig = JUST(functional::Sigmoid(x)); + const auto sp = JUST(functional::Log1p(JUST(functional::Exp(x)))); + const auto grad_tsp = JUST(functional::TanhGrad(sp, dydx)); + + auto r = functional::sequence_function(functional::Tanh) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(-1, input, /*alpha=*/-2); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, sig)) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/1); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, x)) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(2, input, /*alpha=*/1); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, sig)) + .then(std::bind(functional::Mul, std::placeholders::_1, grad_tsp)) + .call(sp); + return r; + } +}; + +class GeluGradGradFunctor { + public: + // y = gussian(x) = 0.5 * x * (1.0 + erf(sqrt(0.5) * x)); + // dx = 0.5 * (1.0 + erf(sqrt(0.5)*x) + x * coef * exp(-0.5*x*x)) * dy), coef = sqrt(-2.0/pi) + // ddx = coef * grad1 * grad2 * flow.exp(t) * (1+t), t = -0.5*x*x + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dydx) const { + const auto& tmp = JUST(functional::ScalarMul(-0.5, JUST(functional::Square(x)))); + const auto& tmp_add_one = JUST(functional::ScalarAdd(1, tmp, 1)); + const Scalar coef = std::sqrt(2.0 / std::acos(-1.0)); + + auto r = functional::sequence_function(functional::Exp) + .then(std::bind(functional::Mul, std::placeholders::_1, tmp_add_one)) + .then(std::bind(functional::Mul, std::placeholders::_1, dydx)) + .then([&coef](const std::shared_ptr& input) { + return functional::ScalarMul(coef, input); + }) + .call(tmp); + return r; + } +}; } // namespace impl ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("SinGradGrad"); m.add_functor("CosGradGrad"); + m.add_functor("TanGradGrad"); + m.add_functor("SinhGradGrad"); + m.add_functor("CoshGradGrad"); + m.add_functor("TanhGradGrad"); + m.add_functor("AsinGradGrad"); + m.add_functor("AcosGradGrad"); + m.add_functor("AtanGradGrad"); + m.add_functor("AsinhGradGrad"); + m.add_functor("AcoshGradGrad"); + m.add_functor("AtanhGradGrad"); + m.add_functor("ErfGradGrad"); + m.add_functor("ErfcGradGrad"); + m.add_functor("ExpGradGrad"); + m.add_functor("Expm1GradGrad"); + m.add_functor("LogGradGrad"); + m.add_functor("Log2GradGrad"); + m.add_functor("Log10GradGrad"); + m.add_functor("Log1pGradGrad"); + m.add_functor("LogSigmoidGradGrad"); + m.add_functor("ReciprocalGradGrad"); + m.add_functor("ReciprocalNoNanGradGrad"); + m.add_functor("RsqrtGradGrad"); + m.add_functor("SqrtGradGrad"); + m.add_functor("SquareGradGrad"); + m.add_functor("SigmoidGradGrad"); + m.add_functor("SiluGradGrad"); + m.add_functor("SeluGradGrad"); + m.add_functor("SoftSignGradGrad"); + m.add_functor("HardSigmoidGradGrad"); + m.add_functor("HardSwishGradGrad"); + m.add_functor("SoftplusGradGrad"); + m.add_functor("EluGradGrad"); + m.add_functor("CeluGradGrad"); + m.add_functor("MaxPoolNdGradGrad"); + m.add_functor("MishGradGrad"); + m.add_functor("GeluGradGrad"); } } // namespace functional diff --git a/oneflow/core/functional/impl/linalg_functor.cpp b/oneflow/core/functional/impl/linalg_functor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..71dd7a399aa8203e7e1d97966b0eaf8d0a050ab8 --- /dev/null +++ b/oneflow/core/functional/impl/linalg_functor.cpp @@ -0,0 +1,136 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "fmt/core.h" +#include "oneflow/core/common/device_type.pb.h" +#include "oneflow/core/common/error.h" +#include "oneflow/core/common/error.pb.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/tensor_desc.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/function_library.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/impl/common.h" + +namespace oneflow { +namespace one { +namespace functional { + +namespace impl { +namespace linalg { + +class CrossFunctor { + public: + CrossFunctor() { + op_ = CHECK_JUST(OpBuilder("linalg_cross").Input("input").Input("other").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& other, + const Optional& dim) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + + const auto do_dispatch_base_on_device = [&attrs, this]( + const std::shared_ptr& input, + const std::shared_ptr& other, + const int64_t dim) -> Maybe { + DeviceType device{}; + + if (input->is_global()) { + device = JUST(input->parallel_desc())->device_type(); + } else { + device = JUST(input->device())->enum_type(); + } + + const int64_t final_dim = input->ndim() - 1; + + if (device == DeviceType::kCUDA && dim != final_dim) { + attrs.SetAllAttrs(final_dim); + + std::vector perm(input->ndim(), 0); + for (size_t i = 0; i < perm.size(); ++i) { perm[i] = static_cast(i); } + std::swap(perm[dim], perm[final_dim]); + return functional::Transpose( + JUST(OpInterpUtil::Dispatch(*op_, + {JUST(functional::Transpose(input, perm)), + JUST(functional::Transpose(other, perm))}, + attrs)), + perm); + } + + attrs.SetAllAttrs(dim); + return OpInterpUtil::Dispatch(*op_, {input, other}, attrs); + }; + + Shape shape_to_broadcast; + std::deque need_to_broadcast; + + std::tie(shape_to_broadcast, need_to_broadcast) = + *JUST(InferUnifiedShapeForBroadcastingWithInfo({*input->shape(), *other->shape()})); + CHECK_EQ_OR_RETURN(need_to_broadcast.size(), 2) + << fmt::format("The number of boolean values to determine if the tensor is to be broadcast " + "should be 2 (which is {})", + need_to_broadcast.size()); + const auto new_input = + need_to_broadcast[0] ? JUST(functional::Expand(input, shape_to_broadcast)) : input; + const auto new_other = + need_to_broadcast[1] ? JUST(functional::Expand(other, shape_to_broadcast)) : other; + + if (!dim.has_value()) { + return do_dispatch_base_on_device(new_input, new_other, + JUST(FindValidDim(shape_to_broadcast))); + } + + int64_t new_dim = JUST(dim); + if (new_dim < 0) { new_dim += shape_to_broadcast.NumAxes(); } + CHECK_EQ_OR_RETURN(shape_to_broadcast.At(new_dim), 3) + << Error::RuntimeError() + << fmt::format("the size of the specified dimension(which is {}) is not 3.", JUST(dim)); + + return do_dispatch_base_on_device(new_input, new_other, new_dim); + } + + private: + Maybe FindValidDim(const Shape& shape) const { + int64_t valid_dim = -1; + const auto& dim_vec = shape.dim_vec(); + for (size_t i = 0; i < dim_vec.size(); ++i) { + if (dim_vec[i] == 3) { + valid_dim = i; + break; + } + } + if (valid_dim == -1) { return Error::RuntimeError() << "no dimension of size 3 in input."; } + return valid_dim; + } + + std::shared_ptr op_; +}; + +} // namespace linalg +} // namespace impl + +using namespace impl::linalg; + +ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("LinalgCross"); } + +} // namespace functional +} // namespace one +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/functional/impl/math_functor.cpp b/oneflow/core/functional/impl/math_functor.cpp index ebf4b3bd7de6920bd012c520176896eaaf0fcd3d..e2bae5661d895e26d48c2054dac2e6008e0e0d8c 100644 --- a/oneflow/core/functional/impl/math_functor.cpp +++ b/oneflow/core/functional/impl/math_functor.cpp @@ -14,23 +14,21 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/autograd/autograd_mode.h" #include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/error.h" #include "oneflow/core/common/scalar.h" -#include "oneflow/core/framework/attr_map.h" -#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/function_library.h" -#include "oneflow/core/functional/impl/common.h" -#include "oneflow/core/functional/impl/unary_functor.h" +#include "oneflow/core/functional/impl/binary_functor.h" #include "oneflow/core/job/lazy_mode.h" -#include "oneflow/core/job/sbp_parallel.h" #include "oneflow/core/functional/tensor_processor.h" +#include "oneflow/core/profiler/profiler.h" #include #include @@ -85,13 +83,12 @@ class ScalarMathBaseFunctor { if (std::dynamic_pointer_cast(x) && op_->op_type_name() == "scalar_mul") { return x; } - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("float_operand", "has_float_operand", + "int_operand", "has_int_operand"); TensorProcessor tensor_processor; Symbol lowest_dtype; if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("float_operand", scalar.As())); - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false); // Only promote type to Float32 when tensor is Int type but scalar is float type. if (DType::priority_order[x->dtype()->data_type()] < DType::priority_order[DType::Float16()->data_type()]) { @@ -100,13 +97,14 @@ class ScalarMathBaseFunctor { lowest_dtype = x->dtype(); } } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("int_operand", scalar.As())); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); - // Only promote type to Int64 when tensor is Bool type but scalar is int type. + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true); + // Promote type to Int64 when tensor is Bool type but scalar is int type. + // Promote type to Float32 when op is scalar_div. if (DType::priority_order[x->dtype()->data_type()] == DType::priority_order[DType::Bool()->data_type()]) { lowest_dtype = DType::Int64(); + } else if (op_->op_type_name() == "scalar_div") { + lowest_dtype = x->dtype() == DType::Float16() ? DType::Float16() : DType::Float(); } else { lowest_dtype = x->dtype(); } @@ -122,13 +120,7 @@ class ScalarMathBaseFunctor { std::shared_ptr outputs = std::make_shared(1); (*outputs)[0] = x; - // TODO:(zhaoluyang) - // If the op need inplace operaton, and input tensor is non-contiguous, - // the interpreter will do input->contiguous() operaton for geting the correct result, - // therefore, output tensor and input will not inplaced. When scalar_math op/kernel - // support strided tensor as input, the problem above will be solved! - JUST(OpInterpUtil::Dispatch(*op_, {x}, outputs.get(), - OpExprInterpContext(attrs, /*inplace=*/true))); + JUST(OpInterpUtil::Dispatch(*op_, {x}, outputs.get(), OpExprInterpContext(attrs))); return outputs->at(0); } else { return OpInterpUtil::Dispatch(*op_, casted_vec, attrs); @@ -193,7 +185,7 @@ class ScalarSub2Functor { public: Maybe operator()(const Scalar& scalar, const std::shared_ptr& input, const Scalar& alpha) const { - return ScalarAdd(scalar, JUST(ScalarMul(input, Scalar(-1), false)), alpha); + return ScalarAdd(scalar, input, Scalar(-1) * alpha); } }; @@ -228,7 +220,7 @@ class ScalarDivFunctor : public ScalarMathBaseFunctor { class ScalarDiv2Functor { public: Maybe operator()(const Scalar& scalar, const std::shared_ptr& x) const { - return functional::ScalarMul(JUST(functional::ReciprocalNoNan(x)), scalar, /*inplace=*/false); + return functional::ScalarMul(JUST(functional::Reciprocal(x)), scalar, /*inplace=*/false); } }; @@ -252,15 +244,12 @@ class ScalarPowGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const Scalar& scalar) const { - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("float_operand", "has_float_operand", + "int_operand", "has_int_operand"); if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); - JUST(attrs.SetAttr("float_operand", scalar.As())); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false); } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); - JUST(attrs.SetAttr("int_operand", scalar.As())); + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true); } else { UNIMPLEMENTED_THEN_RETURN() << "The scalar in ScalarPowGrad should be float or int."; } @@ -287,15 +276,12 @@ class ScalarReversePowGradFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& dy, const Scalar& scalar) const { - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("float_operand", "has_float_operand", + "int_operand", "has_int_operand"); if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); - JUST(attrs.SetAttr("float_operand", scalar.As())); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false); } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); - JUST(attrs.SetAttr("int_operand", scalar.As())); + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true); } else { UNIMPLEMENTED_THEN_RETURN() << "The scalar in ScalarTensorPowGrad should be float or int."; } @@ -311,6 +297,11 @@ class ScalarFloorDivFunctor : public ScalarMathBaseFunctor { ScalarFloorDivFunctor() : ScalarMathBaseFunctor(/*op_name=*/"scalar_floordiv") {} }; +class ScalarTruncDivFunctor : public ScalarMathBaseFunctor { + public: + ScalarTruncDivFunctor() : ScalarMathBaseFunctor(/*op_name=*/"scalar_truncdiv") {} +}; + class ScalarFModFunctor : public ScalarMathBaseFunctor { public: ScalarFModFunctor() : ScalarMathBaseFunctor(/*op_name=*/"scalar_fmod") {} @@ -324,15 +315,14 @@ class ReduceMaxFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); if (axis.empty()) { std::vector reduce_axis(x->ndim()); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - JUST(attrs.SetAttr>("axis", reduce_axis)); + attrs.SetAllAttrs(reduce_axis, keepdims); } else { - JUST(attrs.SetAttr>("axis", axis)); + attrs.SetAllAttrs(axis, keepdims); } - JUST(attrs.SetAttr("keepdims", keepdims)); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -348,15 +338,14 @@ class ReduceMinFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); if (axis.empty()) { std::vector reduce_axis(x->ndim()); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - JUST(attrs.SetAttr>("axis", reduce_axis)); + attrs.SetAllAttrs(reduce_axis, keepdims); } else { - JUST(attrs.SetAttr>("axis", axis)); + attrs.SetAllAttrs(axis, keepdims); } - JUST(attrs.SetAttr("keepdims", keepdims)); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -441,13 +430,13 @@ class ReduceSumWholeFunctor { one::OpBuilder("reduce_sum").Input("input_tensor").Output("output_tensor").Build()); } Maybe operator()(const std::shared_ptr& x) const { - MutableAttrMap attrs; const int32_t naxis = x->ndim(); if (naxis == 0) { return x; } // for 0-dim Tensor std::vector axis(naxis); std::iota(axis.begin(), axis.end(), 0); - JUST(attrs.SetAttr>("axis", axis)); - JUST(attrs.SetAttr("keepdims", false)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(axis, false); TensorProcessor tensor_processor; JUST(tensor_processor.AddInputs({x}, /*lowest_dtype=*/DType::Int64()).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); @@ -466,11 +455,11 @@ class ReduceSumFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; std::vector reduce_axis = *JUST(CheckAxis(axis, x->ndim())); if (reduce_axis.size() == 0) { return x; } - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, keepdims); TensorProcessor tensor_processor; JUST(tensor_processor.AddInputs({x}, /*lowest_dtype=*/DType::Int64()).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); @@ -481,6 +470,64 @@ class ReduceSumFunctor { std::shared_ptr op_; }; +class ReduceNanSumFunctor { + public: + ReduceNanSumFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("reduce_nansum").Input("input_tensor").Output("output_tensor").Build()); + } + Maybe operator()(const std::shared_ptr& x, const std::vector& axis, + const bool& keepdims, const Optional>& dtype) const { + std::shared_ptr tensor = x; + if (dtype.has_value() && (dtype != x->dtype())) { + tensor = JUST(Cast(x, JUST(dtype), /*pin_memory=*/false)); + } + + std::vector reduce_axis = *JUST(CheckAxis(axis, tensor->ndim())); + if (reduce_axis.size() == 0) { return tensor; } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, keepdims); + TensorProcessor tensor_processor; + JUST(tensor_processor.AddInputs({tensor}, /*lowest_dtype=*/DType::Int64()).Apply()); + TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); + return OpInterpUtil::Dispatch(*op_, input_tuple, attrs); + } + + private: + std::shared_ptr op_; +}; + +class ReduceNanSumWholeFunctor { + public: + ReduceNanSumWholeFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("reduce_nansum").Input("input_tensor").Output("output_tensor").Build()); + } + Maybe operator()(const std::shared_ptr& x, + const Optional>& dtype) const { + std::shared_ptr tensor = x; + if (dtype.has_value() && (dtype != x->dtype())) { + tensor = JUST(Cast(x, JUST(dtype), /*pin_memory=*/false)); + } + + const int32_t ndim = tensor->ndim(); + if (ndim == 0) { return tensor; } // for 0-dim Tensor + std::vector axis(ndim); + std::iota(axis.begin(), axis.end(), 0); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(axis, false); + TensorProcessor tensor_processor; + JUST(tensor_processor.AddInputs({tensor}, /*lowest_dtype=*/DType::Int64()).Apply()); + TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); + return OpInterpUtil::Dispatch(*op_, input_tuple, attrs); + } + + private: + std::shared_ptr op_; +}; + class ReduceAllWholeFunctor { public: ReduceAllWholeFunctor() { @@ -488,21 +535,10 @@ class ReduceAllWholeFunctor { one::OpBuilder("reduce_all").Input("input_tensor").Output("output_tensor").Build()); } Maybe operator()(const std::shared_ptr& x) const { - bool IsZeroSize = [&]() { - for (int i = 0; i < x->shape()->NumAxes(); i++) { - if (x->shape()->at(i) == 0) return true; - } - return false; - }(); - if (x->shape()->NumAxes() == 0 || IsZeroSize) { - return JUST(Squeeze(JUST(Constant(Shape{1}, Scalar(1), DType::Bool(), JUST(x->device()))), - std::vector({0}))); - } - MutableAttrMap attrs; std::vector reduce_axis(x->ndim()); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", false)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, false); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -518,11 +554,10 @@ class ReduceAllFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; std::vector reduce_axis = *JUST(CheckAxis(axis, x->ndim())); if (reduce_axis.size() == 0) { return x; } - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, keepdims); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -537,11 +572,10 @@ class ReduceAnyWholeFunctor { one::OpBuilder("reduce_any").Input("input_tensor").Output("output_tensor").Build()); } Maybe operator()(const std::shared_ptr& x) const { - MutableAttrMap attrs; std::vector reduce_axis(x->ndim()); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", false)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, false); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -557,11 +591,10 @@ class ReduceAnyFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; std::vector reduce_axis = *JUST(CheckAxis(axis, x->ndim())); if (reduce_axis.size() == 0) { return x; } - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, keepdims); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -581,8 +614,8 @@ class ReduceDeviceStageBaseFunctor { .Build())) {} Maybe operator()(const std::shared_ptr& in, const std::vector& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {in}, attrs); } virtual ~ReduceDeviceStageBaseFunctor() = default; @@ -605,8 +638,8 @@ class ReduceDeviceStageGradBaseFunctor { const std::shared_ptr& mask, const std::shared_ptr& count, const std::vector& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {out_diff, mask, count}, attrs); } virtual ~ReduceDeviceStageGradBaseFunctor() = default; @@ -652,9 +685,8 @@ class ReduceGlobalStageBaseFunctor { Maybe operator()(const std::shared_ptr& in, const std::shared_ptr& device_count, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axis", axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(axis, keepdims); return OpInterpUtil::Dispatch(*op_, {in, device_count}, attrs); } virtual ~ReduceGlobalStageBaseFunctor() = default; @@ -677,9 +709,8 @@ class ReduceGlobalStageGradBaseFunctor { const std::shared_ptr& mask, const std::shared_ptr& device_count, const std::vector& axis, const bool& keepdims) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("axis", axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(axis, keepdims); return OpInterpUtil::Dispatch(*op_, {out_diff, mask, device_count}, attrs); } virtual ~ReduceGlobalStageGradBaseFunctor() = default; @@ -717,7 +748,8 @@ class ReduceMeanWholeFunctor { ReduceMeanWholeFunctor() {} Maybe operator()(const std::shared_ptr& x) const { // ReduceMean only calculate floating values. - CHECK_OR_RETURN(IsFloatingDataType(x->dtype()->data_type())) + CHECK_OR_RETURN(IsFloatingDataType(x->dtype()->data_type()) + || x->dtype()->data_type() == DataType::kFloat16) << "RuntimeError: Can only calculate the mean of floating types."; size_t reduce_count = 1; reduce_count = x->shape()->Count(0); @@ -733,8 +765,12 @@ class ReduceMeanFunctor { Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims) const { // ReduceMean only calculate floating values. - CHECK_OR_RETURN(IsFloatingDataType(x->dtype()->data_type())) + // NOTE: Should use original reduce_mean op/kernel rather than current way(ReduceSum / + // reduce_count) because it could encounter precision problem(like overflow) in float16 case. + CHECK_OR_RETURN(IsFloatingDataType(x->dtype()->data_type()) + || x->dtype()->data_type() == DataType::kFloat16) << "RuntimeError: Can only calculate the mean of floating types."; + const auto& sum = JUST(functional::ReduceSum(x, axis, keepdims)); size_t reduce_count = 1; if (axis.empty()) { @@ -756,7 +792,6 @@ class ReduceProdWholeFunctor { } Maybe operator()(const std::shared_ptr& x, const Optional>& dtype) const { - MutableAttrMap attrs; std::shared_ptr tensor = x; if (dtype.has_value() && (dtype != x->dtype())) { tensor = JUST(Cast(tensor, JUST(dtype), /*pin_memory=*/false)); @@ -773,8 +808,9 @@ class ReduceProdWholeFunctor { TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); std::vector reduce_axis(tensor->ndim()); std::iota(reduce_axis.begin(), reduce_axis.end(), 0); - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", false)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, false); return JUST(OpInterpUtil::Dispatch(*op_, input_tuple, attrs)); } @@ -813,7 +849,6 @@ class MedianWithIndicesFunctor { } Maybe operator()(const std::shared_ptr& x, const int32_t& dim, const bool& keepdim) const { - MutableAttrMap attrs; int32_t axis = dim; const int64_t ndim = x->ndim(); axis = JUST(maybe_wrap_dim(axis, ndim)); @@ -848,7 +883,6 @@ class ReduceProdFunctor { } Maybe operator()(const std::shared_ptr& x, const std::vector& axis, const bool& keepdims, const Optional>& dtype) const { - MutableAttrMap attrs; std::shared_ptr tensor = x; if (dtype.has_value() && (dtype != x->dtype())) { tensor = JUST(Cast(tensor, JUST(dtype), /*pin_memory=*/false)); @@ -865,8 +899,9 @@ class ReduceProdFunctor { TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); std::vector reduce_axis = *JUST(CheckAxis(axis, x->ndim())); if (reduce_axis.size() == 0) { return x; } - JUST(attrs.SetAttr>("axis", reduce_axis)); - JUST(attrs.SetAttr("keepdims", keepdims)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + attrs.SetAllAttrs(reduce_axis, keepdims); return JUST(OpInterpUtil::Dispatch(*op_, input_tuple, attrs)); } @@ -874,6 +909,99 @@ class ReduceProdFunctor { std::shared_ptr op_; }; +class LogSumExpFunctor { + public: + LogSumExpFunctor() {} + Maybe operator()(const std::shared_ptr& x, const std::vector& axis, + const bool& keepdims) const { + if (x->ndim() == 0) { + // can't take amax of 0-dim tensor + return To(x, JUST(DType::Get(DataType::kFloat)), false); + } else if (x->nelement() == 0) { + // can't take amax of empty tensor + std::shared_ptr exp_out = JUST(Exp(x)); + return Log(JUST(ReduceSum(exp_out, axis, keepdims))); + } else { + const std::shared_ptr& maxes = JUST(Amax(x, axis, true)); + const std::shared_ptr& maxes_squeezed = + (keepdims ? maxes : JUST(SqueezeMultiple(maxes, axis))); + JUST(MaskedFillInplace(maxes_squeezed, + JUST(ScalarLogicalEqual(JUST(Abs(maxes_squeezed)), INFINITY)), 0)); + std::shared_ptr exp_out = JUST(Exp(JUST(Sub(x, maxes, 1, false)))); + return Add(JUST(Log(JUST(ReduceSum(exp_out, axis, keepdims)))), maxes_squeezed, 1, false); + } + } + + private: + Maybe SqueezeMultiple(const std::shared_ptr& x, + const std::vector& axis) const { + int ndims = x->ndim(); + const auto& dims_to_squeeze = JUST(dim_list_to_bitset(axis, ndims)); + std::shared_ptr result = x; + for (int i = ndims - 1; i >= 0; --i) { + if ((*dims_to_squeeze)[i]) { + std::vector dims = {i}; + result = JUST(Squeeze(result, dims)); + } + } + return result; + } +}; + +class LogAddExpFunctor { + public: + LogAddExpFunctor() {} + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& y) const { + CHECK_OR_RETURN(x->nelement() > 0 && y->nelement() > 0) + << "logaddexp do not support 0-size tensor."; + const std::shared_ptr& maxes = JUST(Maximum(x, y)); + std::shared_ptr exp_out = + JUST(Exp(JUST(Negative(JUST(Abs(JUST(Sub(x, y, 1, false)))))))); + std::shared_ptr add_out = JUST(ScalarAdd(1.0, exp_out, 1)); + return Add(maxes, JUST(Log(add_out)), 1, false); + } +}; + +class QuantileFunctor { + public: + QuantileFunctor() {} + Maybe operator()(const std::shared_ptr& x, const Scalar& q, + const int32_t& dim, const bool& keepdim, + const std::string& interpolation, + const Optional& out) const { + // TODO(Liang Depeng): refine the implementaion of quantile to have the full funcitonality + // later. + CHECK_EQ_OR_RETURN(x->ndim(), 2) + << "for now oneflow.quantile only support `input` tensor with 2 dims."; + double qf = 0; + if (q.IsIntegral()) { + qf = static_cast(q.As()); + } else { + qf = q.As(); + } + CHECK_EQ_OR_RETURN(dim, 1) << "for now oneflow.quantile only support `dim=1`."; + CHECK_OR_RETURN(interpolation == "linear") + << "for now oneflow.quantile only support `interpolation=linear`."; + + double qn = qf * (x->dim(1) - 1); + std::shared_ptr sorted_index = JUST(ArgSort(x, "ASCENDING")); + std::shared_ptr sorted_x = JUST(DimGather(x, dim, sorted_index, false)); + std::shared_ptr ranks = + JUST(Constant(Shape({x->dim(0), 1}), qn, DType::Double(), JUST(x->device()))); + std::shared_ptr ranks_below = JUST(Floor(ranks)); + std::shared_ptr weights = JUST(Sub(ranks, ranks_below, 1, false)); + ranks_below = + JUST(DimGather(sorted_x, dim, JUST(Cast(ranks_below, DType::Int64(), false)), false)); + std::shared_ptr ranks_above = + JUST(DimGather(sorted_x, dim, JUST(Cast(JUST(Ceil(ranks)), DType::Int64(), false)), false)); + std::shared_ptr res = JUST(Add( + ranks_below, JUST(Mul(JUST(Sub(ranks_above, ranks_below, 1, false)), weights)), 1, false)); + std::vector dims = {-1}; + return Squeeze(res, dims); + } +}; + class TransposeFunctor { public: TransposeFunctor() { @@ -881,7 +1009,6 @@ class TransposeFunctor { } Maybe operator()(const std::shared_ptr& input, const std::vector& permute) const { - MutableAttrMap attrs; auto ndim = input->ndim(); CHECK_EQ_OR_RETURN(ndim, permute.size()) << "number of dims don't match in permute"; @@ -893,7 +1020,9 @@ class TransposeFunctor { } // currently, view only support eager and local mode if (view::IsViewApplicable(input)) { return JUST(view::Transpose(input, positive_perm)); } - JUST(attrs.SetAttr>("perm", positive_perm)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("perm"); + attrs.SetAllAttrs(positive_perm); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -908,7 +1037,6 @@ class Transpose2dimFunctor { } Maybe operator()(const std::shared_ptr& input, const int32_t dim0, const int32_t dim1) const { - MutableAttrMap attrs; const int64_t ndim = input->ndim(); std::vector permute; permute.reserve(ndim); @@ -921,7 +1049,9 @@ class Transpose2dimFunctor { std::swap(permute[dim_0], permute[dim_1]); Shape shape(DimVector(permute.begin(), permute.end())); if (view::IsViewApplicable(input)) { return JUST(view::Transpose(input, permute)); } - JUST(attrs.SetAttr>("perm", permute)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("perm"); + attrs.SetAllAttrs(permute); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -935,8 +1065,8 @@ class AsStridedFunctor { op_ = CHECK_JUST(one::OpBuilder("as_strided").Input("input").Output("output").Build()); } Maybe operator()(const std::shared_ptr& input, - const std::vector& size, const std::vector& stride, - const int32_t& storage_offset) const { + const std::vector& size, const std::vector& stride, + const int64_t& storage_offset) const { CHECK_OR_RETURN(size.size() == stride.size()) << "mismatch in length of strides and shape"; for (size_t i = 0; i < size.size(); i++) { CHECK_OR_RETURN(size[i] >= 0) << "Trying to create tensor with negative dimension" << size[i]; @@ -947,10 +1077,8 @@ class AsStridedFunctor { if (view::IsViewApplicable(input)) { return JUST(view::AsStrided(input, size, stride, storage_offset)); } - MutableAttrMap attrs; - JUST(attrs.SetAttr>("size", size)); - JUST(attrs.SetAttr>("stride", stride)); - JUST(attrs.SetAttr("storage_offset", storage_offset)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("size", "stride", "storage_offset"); + attrs.SetAllAttrs(size, stride, storage_offset); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -966,12 +1094,13 @@ class AsStridedGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& input, - const std::vector& size, const std::vector& stride, - const int32_t& storage_offset) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("size", size)); - JUST(attrs.SetAttr>("stride", stride)); - JUST(attrs.SetAttr("storage_offset", storage_offset)); + const std::vector& size, const std::vector& stride, + const int64_t& storage_offset) const { + if (view::IsViewApplicable(input)) { + return JUST(view::AsStridedGrad(dy, input, size, stride, storage_offset)); + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("size", "stride", "storage_offset"); + attrs.SetAllAttrs(size, stride, storage_offset); return OpInterpUtil::Dispatch(*op_, {dy, input}, attrs); } @@ -979,37 +1108,55 @@ class AsStridedGradFunctor { std::shared_ptr op_; }; +class InplaceAsStridedFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const std::vector& size, const std::vector& stride, + const int64_t& storage_offset) const { + JUST(CheckInplaceValid(input)); + CHECK_OR_RETURN(size.size() == stride.size()) << "mismatch in length of strides and shape"; + for (size_t i = 0; i < size.size(); i++) { + CHECK_OR_RETURN(size[i] >= 0) << "Trying to create tensor with negative dimension" << size[i]; + CHECK_OR_RETURN(stride[i] >= 0) + << "as_strided: Negative strides are not supported at the moment, got strides:" + << stride[i]; + } + CHECK_OR_RETURN(view::IsViewApplicable(input)) + << "Only support as_strided_ in eager local mode"; + JUST(view::InplaceAsStrided(input, size, stride, storage_offset)); + return input; + } +}; class ArangeFunctor { public: ArangeFunctor() { op_ = CHECK_JUST(one::OpBuilder("arange").Output("out").Build()); } Maybe operator()(const Scalar& start, const Scalar& limit, const Scalar& delta, const Optional>& dtype, const Optional>& device) const { - MutableAttrMap attrs; + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalArange(start, limit, delta, dtype, + GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())))); + } + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("integer_start", "integer_limit", "integer_delta", + "float_start", "float_limit", "float_delta", "dtype"); if (dtype.has_value()) { const DataType range_dtype = JUST(dtype)->data_type(); if (IsIntegralDataType(range_dtype)) { - JUST(attrs.SetAttr("integer_start", start.As())); - JUST(attrs.SetAttr("integer_limit", limit.As())); - JUST(attrs.SetAttr("integer_delta", delta.As())); - JUST(attrs.SetAttr("dtype", range_dtype)); + attrs.SetAllAttrs(start.As(), limit.As(), delta.As(), NullOpt, + NullOpt, NullOpt, range_dtype); } else { - JUST(attrs.SetAttr("float_start", start.As())); - JUST(attrs.SetAttr("float_limit", limit.As())); - JUST(attrs.SetAttr("float_delta", delta.As())); - JUST(attrs.SetAttr("dtype", range_dtype)); + attrs.SetAllAttrs(NullOpt, NullOpt, NullOpt, start.As(), limit.As(), + delta.As(), range_dtype); } } else { if (start.IsIntegral() && limit.IsIntegral() && delta.IsIntegral()) { - JUST(attrs.SetAttr("integer_start", start.As())); - JUST(attrs.SetAttr("integer_limit", limit.As())); - JUST(attrs.SetAttr("integer_delta", delta.As())); - JUST(attrs.SetAttr("dtype", DType::Int64()->data_type())); + attrs.SetAllAttrs(start.As(), limit.As(), delta.As(), NullOpt, + NullOpt, NullOpt, DType::Int64()->data_type()); } else { - JUST(attrs.SetAttr("float_start", start.As())); - JUST(attrs.SetAttr("float_limit", limit.As())); - JUST(attrs.SetAttr("float_delta", delta.As())); - JUST(attrs.SetAttr("dtype", DType::Float()->data_type())); + attrs.SetAllAttrs(NullOpt, NullOpt, NullOpt, start.As(), limit.As(), + delta.As(), DType::Float()->data_type()); } } OpExprInterpContext ctx(attrs); @@ -1029,39 +1176,33 @@ class Arange2Functor { } }; -class ConsistentArangeFunctor { +class GlobalArangeFunctor { public: - ConsistentArangeFunctor() { op_ = CHECK_JUST(one::OpBuilder("arange").Output("out").Build()); } + GlobalArangeFunctor() { op_ = CHECK_JUST(one::OpBuilder("arange").Output("out").Build()); } Maybe operator()(const Scalar& start, const Scalar& limit, const Scalar& delta, const Optional>& dtype, const Symbol& placement, const std::vector>& sbp_tuple) const { JUST(CheckDeviceIdsIsValid(placement)); - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("integer_start", "integer_limit", "integer_delta", + "float_start", "float_limit", "float_delta", + "dtype", "nd_sbp"); if (dtype.has_value()) { const DataType range_dtype = JUST(dtype)->data_type(); if (IsIntegralDataType(range_dtype)) { - JUST(attrs.SetAttr("integer_start", start.As())); - JUST(attrs.SetAttr("integer_limit", limit.As())); - JUST(attrs.SetAttr("integer_delta", delta.As())); - JUST(attrs.SetAttr("dtype", range_dtype)); + attrs.SetAllAttrs(start.As(), limit.As(), delta.As(), NullOpt, + NullOpt, NullOpt, range_dtype, NullOpt); } else { - JUST(attrs.SetAttr("float_start", start.As())); - JUST(attrs.SetAttr("float_limit", limit.As())); - JUST(attrs.SetAttr("float_delta", delta.As())); - JUST(attrs.SetAttr("dtype", range_dtype)); + attrs.SetAllAttrs(NullOpt, NullOpt, NullOpt, start.As(), limit.As(), + delta.As(), range_dtype, NullOpt); } } else { if (start.IsIntegral() && limit.IsIntegral() && delta.IsIntegral()) { - JUST(attrs.SetAttr("integer_start", start.As())); - JUST(attrs.SetAttr("integer_limit", limit.As())); - JUST(attrs.SetAttr("integer_delta", delta.As())); - JUST(attrs.SetAttr("dtype", DType::Int64()->data_type())); + attrs.SetAllAttrs(start.As(), limit.As(), delta.As(), NullOpt, + NullOpt, NullOpt, DType::Int64()->data_type(), NullOpt); } else { - JUST(attrs.SetAttr("float_start", start.As())); - JUST(attrs.SetAttr("float_limit", limit.As())); - JUST(attrs.SetAttr("float_delta", delta.As())); - JUST(attrs.SetAttr("dtype", DType::Float()->data_type())); + attrs.SetAllAttrs(NullOpt, NullOpt, NullOpt, start.As(), limit.As(), + delta.As(), DType::Float()->data_type(), NullOpt); } } if (LazyMode::is_enabled()) { @@ -1071,7 +1212,7 @@ class ConsistentArangeFunctor { nd_sbp.at(i) = SbpParallelToString(*sbp_tuple.at(i)); } } - JUST(attrs.SetAttr>("nd_sbp", nd_sbp)); + attrs.SetAttr<7>(nd_sbp); } const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); return OpInterpUtil::Dispatch(*op_, {}, OpExprInterpContext(attrs, placement, nd_sbp)); @@ -1081,13 +1222,80 @@ class ConsistentArangeFunctor { std::shared_ptr op_; }; -class ConsistentArange2Functor { +class GlobalArange2Functor { public: - Maybe operator()(const Scalar& limit, const Symbol& dtype, + Maybe operator()(const Scalar& limit, const Optional>& dtype, const Symbol& placement, const std::vector>& sbp_tuple) const { JUST(CheckDeviceIdsIsValid(placement)); - return ConsistentArange(Scalar(0), limit, Scalar(1), dtype, placement, sbp_tuple); + return GlobalArange(Scalar(0), limit, Scalar(1), dtype, placement, sbp_tuple); + } +}; + +class HannWindowFunctor { + public: + Maybe operator()(const int64_t window_length, const bool& periodic, + const Optional>& device, + const Optional>& dtype, const bool& requires_grad) const { + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalHannWindow( + window_length, periodic, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), dtype, requires_grad)); + } + autograd::AutoGradMode mode(false); + if (dtype.has_value() && !IsFloatingDataType(JUST(dtype)->data_type())) { + return Error::RuntimeError() + << "hann_window expects floating point dtypes, got: " << JUST(dtype)->name(); + } + // TODO: speedup + auto result = JUST(Arange(1, 2, 1, dtype, device)); + if (window_length != 1) { + if (periodic) { + const auto indice = JUST(Arange(window_length + 1, dtype, device)); + const auto div_result = JUST(ScalarDiv(JUST(ScalarMul(2 * M_PI, indice)), window_length)); + result = JUST(Slice(JUST(ScalarDiv(JUST(ScalarSub(1, JUST(Cos(div_result)), 1)), 2)), {0}, + {window_length}, {1}, /*enable_view_slice=*/false)); + } else { + const auto indice = JUST(Arange(window_length, dtype, device)); + const auto div_result = + JUST(ScalarDiv(JUST(ScalarMul(2 * M_PI, indice)), window_length - 1)); + result = JUST(ScalarDiv(JUST(ScalarSub(1, JUST(Cos(div_result)), 1)), 2)); + } + } + JUST(result->set_requires_grad(requires_grad)); + return result; + } +}; + +class GlobalHannWindowFunctor { + public: + Maybe operator()(const int64_t window_length, const bool& periodic, + const Symbol& placement, + const std::vector>& sbp, + const Optional>& dtype, const bool& requires_grad) const { + autograd::AutoGradMode mode(false); + JUST(CheckDeviceIdsIsValid(placement)); + if (dtype.has_value() && !IsFloatingDataType(JUST(dtype)->data_type())) { + return Error::RuntimeError() + << "hann_window expects floating point dtypes, got: " << JUST(dtype)->name(); + } + auto result = JUST(GlobalArange(1, 1 + window_length, 1, dtype, placement, sbp)); + if (window_length != 1) { + if (periodic) { + const auto indice = JUST(GlobalArange(window_length + 8, dtype, placement, sbp)); + const auto div_result = JUST(ScalarDiv(JUST(ScalarMul(2 * M_PI, indice)), window_length)); + result = JUST(Slice(JUST(ScalarDiv(JUST(ScalarSub(1, JUST(Cos(div_result)), 1)), 2)), {0}, + {window_length}, {1}, /*enable_view_slice=*/false)); + } else { + const auto indice = JUST(GlobalArange(window_length, dtype, placement, sbp)); + const auto div_result = + JUST(ScalarDiv(JUST(ScalarMul(2 * M_PI, indice)), window_length - 1)); + result = JUST(ScalarDiv(JUST(ScalarSub(1, JUST(Cos(div_result)), 1)), 2)); + } + } + result = JUST(ToGlobal(result, placement, sbp, {}, true, /*copy=*/false)); + JUST(result->set_requires_grad(requires_grad)); + return result; } }; @@ -1097,9 +1305,8 @@ class CastFunctor { Maybe operator()(const std::shared_ptr& x, const Symbol& dtype, const bool pin_memory) const { if (x->dtype() == dtype) { return x; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("pin_memory", pin_memory)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype", "pin_memory"); + attrs.SetAllAttrs(dtype->data_type(), pin_memory); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -1118,28 +1325,30 @@ class ClampBaseFunctor { const Optional& max, bool inplace) const { CHECK_OR_RETURN(min.has_value() || max.has_value()) << "Requires one of argument `min` and `max` at least in clip."; - MutableAttrMap attrs; - if (IsFloatingDataType(x->dtype()->data_type())) { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("floating_min", "integral_min", "floating_max", + "integral_max"); + if (IsFloatingDataType(x->dtype()->data_type()) + || x->dtype()->data_type() == DataType::kFloat16) { if (min.has_value()) { const auto& min_val = JUST(min); - JUST(attrs.SetAttr("floating_min", min_val->As())); - JUST(attrs.SetAttr("integral_min", 0)); + attrs.SetAttr<0>(min_val->As()); + attrs.SetAttr<1>(static_cast(0)); } if (max.has_value()) { const auto& max_val = JUST(max); - JUST(attrs.SetAttr("floating_max", max_val->As())); - JUST(attrs.SetAttr("integral_max", 0)); + attrs.SetAttr<2>(max_val->As()); + attrs.SetAttr<3>(static_cast(0)); } } else if (IsIntegralDataType(x->dtype()->data_type())) { if (min.has_value()) { const auto& min_val = JUST(min); - JUST(attrs.SetAttr("floating_min", 0)); - JUST(attrs.SetAttr("integral_min", min_val->As())); + attrs.SetAttr<0>(static_cast(0)); + attrs.SetAttr<1>(min_val->As()); } if (max.has_value()) { const auto& max_val = JUST(max); - JUST(attrs.SetAttr("floating_max", 0)); - JUST(attrs.SetAttr("integral_max", max_val->As())); + attrs.SetAttr<2>(static_cast(0)); + attrs.SetAttr<3>(max_val->As()); } } else { UNIMPLEMENTED_THEN_RETURN() << "Only support floating or integral data type."; @@ -1177,7 +1386,21 @@ class ClampFunctor : public ClampBaseFunctor { public: Maybe operator()(const std::shared_ptr& x, const Optional& min, const Optional& max) const { - return ClampBaseFunctor::operator()(x, min, max, false); + return ClampBaseFunctor::operator()(x, min, max, /* inplace=*/false); + } +}; + +class ClampMinFunctor : public ClampBaseFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const Scalar& min) const { + return ClampBaseFunctor::operator()(x, min, NullOpt, /* inplace=*/false); + } +}; + +class ClampMaxFunctor : public ClampBaseFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const Scalar& max) const { + return ClampBaseFunctor::operator()(x, NullOpt, max, /* inplace=*/false); } }; @@ -1185,7 +1408,21 @@ class ClampInplaceFunctor : public ClampBaseFunctor { public: Maybe operator()(const std::shared_ptr& x, const Optional& min, const Optional& max) const { - return ClampBaseFunctor::operator()(x, min, max, true); + return ClampBaseFunctor::operator()(x, min, max, /* inplace=*/true); + } +}; + +class ClampMinInplaceFunctor : public ClampBaseFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const Scalar& min) const { + return ClampBaseFunctor::operator()(x, min, NullOpt, /* inplace=*/true); + } +}; + +class ClampMaxInplaceFunctor : public ClampBaseFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const Scalar& max) const { + return ClampBaseFunctor::operator()(x, NullOpt, max, /* inplace=*/true); } }; @@ -1270,10 +1507,7 @@ class VectorNormFunctor { res = JUST(ReduceMin(JUST(Abs(x)), dim, keepdim)); } else if (ord_val == 2.0 && keepdim == false && full_dim_flag && x->requires_grad() == false) { - // res = JUST(SqrtSquareSum(x)); - res = - JUST(ScalarPow(JUST(ReduceSum(JUST(ScalarPow(JUST(Abs(x)), ord, false)), dim, keepdim)), - Scalar(1.0) / ord, false)); + res = JUST(SqrtSquareSum(x)); } else { res = JUST(ScalarPow(JUST(ReduceSum(JUST(ScalarPow(JUST(Abs(x)), ord, false)), dim, keepdim)), @@ -1439,7 +1673,8 @@ class NormFunctor { NormFunctor() {} Maybe operator()(const std::shared_ptr& x, const Optional& ord, const Optional>& input_dim, const bool& keepdim, - const Optional>& dtype) const { + const Optional>& dtype, const bool& for_norm) const { + // If for_norm, the functor will be used to oneflow.norm. std::shared_ptr res; if (dtype) { Symbol dtype_val = JUST(dtype); @@ -1457,8 +1692,9 @@ class NormFunctor { } } Scalar ord_sca; + bool ord_type = false; if (ord.has_value()) { - auto ord_type = (*JUST(ord)).IsIntegral(); + ord_type = (*JUST(ord)).IsIntegral(); if (ord_type) { ord_sca = Scalar((*JUST(ord)).As()); } else { @@ -1488,6 +1724,17 @@ class NormFunctor { if (ord.has_value()) { CHECK_OR_RETURN(x->ndim() <= 2) << "linalg.norm(): input must be 1-D or 2-D when dim is None and ord is not None"; + if (ord_type) { + const double ord_double = (*JUST(ord)).As(); + if (for_norm && (ord_double >= 2 || ord_double <= -2)) { + const int32_t num_axes = x->shape()->NumAxes(); + std::vector axes_vec(num_axes); + std::iota(axes_vec.begin(), axes_vec.end(), 0); + return ScalarPow(JUST(ReduceSum(JUST(ScalarPow(JUST(Abs(x)), ord_sca, false)), axes_vec, + /*keepdims=*/false)), + 1 / ord_double, false); + } + } if (x->ndim() == 1) { res = JUST(VectorNorm(x, ord_sca, input_dim, keepdim, dtype)); } else { @@ -1558,7 +1805,7 @@ class ScalarNormFunctor { } if (input_dim.IsIntegral()) { std::vector dim(1, input_dim.As()); - return functional::Norm(x, ord, dim, keepdim, dtype); + return functional::Norm(x, ord, dim, keepdim, dtype, /*for_norm=*/false); } else { UNIMPLEMENTED_THEN_RETURN() << "linalg_norm(): only supports int dim."; } @@ -1595,6 +1842,25 @@ class ScalarNorm2Functor { } }; +class InvFunctor { + public: + InvFunctor() { op_ = CHECK_JUST(one::OpBuilder("inv").Input("x").Output("y").Build()); } + Maybe operator()(const std::shared_ptr& x) const { + if (x->ndim() < 2) { + return Error::RuntimeError() << "linalg.inv: The input tensor must be at least 2 dimensions."; + } + if (x->dim(x->ndim() - 1) != x->dim(x->ndim() - 2)) { + return Error::RuntimeError() << "linalg.inv: A must be batches of square matrices, " + << "but they are " << x->dim(x->ndim() - 2) << " by " + << x->dim(x->ndim() - 1) << " matrices"; + } + return OpInterpUtil::Dispatch(*op_, {x}, {}); + } + + private: + std::shared_ptr op_; +}; + class ClampGradFunctor { public: ClampGradFunctor() { @@ -1610,28 +1876,29 @@ class ClampGradFunctor { const Optional& max) const { CHECK_OR_RETURN(min.has_value() || max.has_value()) << "Requires one of argument `min` and `max` at least in clip_grad."; - MutableAttrMap attrs; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("floating_min", "integral_min", "floating_max", + "integral_max"); if (IsFloatingDataType(x->dtype()->data_type())) { if (min.has_value()) { const auto& min_val = JUST(min); - JUST(attrs.SetAttr("floating_min", min_val->As())); - JUST(attrs.SetAttr("integral_min", 0)); + attrs.SetAttr<0>(min_val->As()); + attrs.SetAttr<1>(static_cast(0)); } if (max.has_value()) { const auto& max_val = JUST(max); - JUST(attrs.SetAttr("floating_max", max_val->As())); - JUST(attrs.SetAttr("integral_max", 0)); + attrs.SetAttr<2>(max_val->As()); + attrs.SetAttr<3>(static_cast(0)); } } else if (IsIntegralDataType(x->dtype()->data_type())) { if (min.has_value()) { const auto& min_val = JUST(min); - JUST(attrs.SetAttr("integral_min", min_val->As())); - JUST(attrs.SetAttr("floating_min", 0)); + attrs.SetAttr<0>(static_cast(0)); + attrs.SetAttr<1>(min_val->As()); } if (max.has_value()) { const auto& max_val = JUST(max); - JUST(attrs.SetAttr("floating_max", 0)); - JUST(attrs.SetAttr("integral_max", max_val->As())); + attrs.SetAttr<2>(static_cast(0)); + attrs.SetAttr<3>(max_val->As()); } } else { UNIMPLEMENTED_THEN_RETURN() << "Only support floating or integral data type."; @@ -1668,9 +1935,9 @@ class SelectFunctor { << "], but got " << index << ")"; int32_t pos_index = index >= 0 ? index : index + size; - std::vector sizes(input->shape()->dim_vec().begin(), input->shape()->dim_vec().end()); + std::vector sizes(input->shape()->dim_vec().begin(), input->shape()->dim_vec().end()); const auto& stride = *JUST(input->stride()); - std::vector strides(stride.begin(), stride.end()); + std::vector strides(stride.begin(), stride.end()); auto storage_offset = JUST(input->storage_offset()) + pos_index * strides[pos_dim]; sizes.erase(sizes.begin() + pos_dim); @@ -1685,11 +1952,11 @@ class SelectTopNFunctor { SelectTopNFunctor() { op_ = CHECK_JUST(one::SelectTopNOpExpr::New()); } Maybe operator()(const TensorTuple& inputs, int32_t n) const { - MutableAttrMap attr; - JUST(attr.SetAttr("top_n", n)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("top_n"); + attrs.SetAllAttrs(n); std::vector require_grad(n); for (int i = 0; i < n; ++i) { require_grad[i] = JUST(VectorAt(inputs, i))->requires_grad(); } - const auto& output = JUST(OpInterpUtil::Dispatch(*op_, inputs, attr)); + const auto& output = JUST(OpInterpUtil::Dispatch(*op_, inputs, attrs)); for (int i = 0; i < output->size(); ++i) { (*output)[i]->set_is_leaf(false); JUST((*output)[i]->set_requires_grad(require_grad[i])); @@ -1712,8 +1979,11 @@ class MinimumFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/false)); TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({tensor_x, tensor_y}).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); if (*x->shape() == *y->shape()) { return OpInterpUtil::Dispatch(*elementwise_minimum_op_, @@ -1740,8 +2010,11 @@ class MaximumFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y) const { + auto tensor_x = x; + auto tensor_y = y; + JUST(CastDeviceForCPUScalarTensor(tensor_x, tensor_y, /*inplace=*/false)); TensorProcessor tensor_processor; - JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({x, y}).Apply()); + JUST(tensor_processor.PromoteInputsToCommonDtype(true).AddInputs({tensor_x, tensor_y}).Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); if (*x->shape() == *y->shape()) { return OpInterpUtil::Dispatch(*elementwise_maximum_op_, @@ -1764,14 +2037,13 @@ class ScalarLogicalBaseFunctor { } virtual ~ScalarLogicalBaseFunctor() = default; Maybe operator()(const std::shared_ptr& x, const Scalar& scalar) const { - MutableAttrMap attrs; TensorProcessor tensor_processor; Symbol lowest_dtype; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("float_operand", "has_float_operand", + "int_operand", "has_int_operand"); if (scalar.IsFloatingPoint()) { - JUST(attrs.SetAttr("float_operand", scalar.As())); - JUST(attrs.SetAttr("has_float_operand", true)); - JUST(attrs.SetAttr("has_int_operand", false)); + attrs.SetAllAttrs(scalar.As(), true, NullOpt, false); // Only promote type to Float32 when tensor is Int type but scalar is float type. if (DType::priority_order[x->dtype()->data_type()] < DType::priority_order[DType::Float16()->data_type()]) { @@ -1779,10 +2051,8 @@ class ScalarLogicalBaseFunctor { } else { lowest_dtype = x->dtype(); } - } else if (scalar.IsIntegral()) { - JUST(attrs.SetAttr("int_operand", scalar.As())); - JUST(attrs.SetAttr("has_float_operand", false)); - JUST(attrs.SetAttr("has_int_operand", true)); + } else if (scalar.IsIntegral() || scalar.IsBool()) { + attrs.SetAllAttrs(NullOpt, false, scalar.As(), true); // Only promote type to Int64 when tensor is Bool type but scalar is int type. if (DType::priority_order[x->dtype()->data_type()] == DType::priority_order[DType::Bool()->data_type()]) { @@ -2012,12 +2282,10 @@ class VarianceFunctor { Maybe operator()(const std::shared_ptr& input, const Optional>& dim, const Optional& unbiased, const Optional& keepdim) const { - if (!IsFloatingDataType(input->dtype()->data_type())) { + if (!(IsFloatingDataType(input->dtype()->data_type()) + || IsHalfDataType(input->dtype()->data_type()))) { return Error::RuntimeError() << "var only support floating point dtypes"; } - MutableAttrMap attrs; - if (unbiased) { JUST(attrs.SetAttr("unbiased", JUST(unbiased))); } - if (keepdim) { JUST(attrs.SetAttr("keepdim", JUST(keepdim))); } std::vector axis; const int ndim = input->ndim(); axis.reserve(ndim); @@ -2025,16 +2293,15 @@ class VarianceFunctor { for (int i = 0; i < ndim; i++) { axis.emplace_back(i); } } else { std::vector& dims = *JUST(dim); - JUST(maybe_wrap_dim(dims.size(), ndim)); + JUST(maybe_wrap_dim(dims.size(), ndim)); // only check validation std::sort(dims.begin(), dims.end()); axis.assign(dims.begin(), dims.end()); } for (size_t i = 0; i < axis.size(); i++) { if (axis[i] < 0) { axis[i] += ndim; } } - JUST(attrs.SetAttr>("dim", axis)); - JUST(attrs.SetAttr("dtype", input->dtype()->data_type())); - + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("unbiased", "keepdim", "dim", "dtype"); + attrs.SetAllAttrs(unbiased, keepdim, axis, input->dtype()->data_type()); return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -2042,6 +2309,29 @@ class VarianceFunctor { std::shared_ptr op_; }; +class RMSLayerNormalizationFunctor { + public: + Maybe operator()(const std::shared_ptr& hidden_states, + const std::shared_ptr& weight, + const float& variance_epsilon) const { + std::shared_ptr cast_hidden_states = hidden_states; + if (hidden_states->dtype() != DType::Float()) { + cast_hidden_states = + JUST(functional::Cast(hidden_states, DType::Float(), /*pin_memory=*/false)); + } + std::shared_ptr normalized_hidden_states = JUST(functional::Mul( + cast_hidden_states, JUST(functional::Rsqrt(JUST(functional::ScalarAdd( + JUST(functional::ReduceMean(JUST(Square(hidden_states)), + std::vector{-1}, true)), + Scalar(variance_epsilon), 1.0, false)))))); + if (weight->dtype() == DType::Float16()) { + normalized_hidden_states = + JUST(functional::Cast(normalized_hidden_states, weight->dtype(), /*pin_memory=*/false)); + } + return JUST(functional::Mul(normalized_hidden_states, weight)); + } +}; + class DotFunctor { public: DotFunctor() { @@ -2276,6 +2566,18 @@ class ErfinvInplaceFunctor { std::shared_ptr op_; }; +class GeluWithApproximateFunctor { + public: + Maybe operator()(const std::shared_ptr& x, + const std::string& approximate) const { + if (approximate != "none" && approximate != "tanh") { + return Error::RuntimeError() << "the approximate argument should be 'none' or 'tanh'"; + } + if (approximate == "tanh") { return FastGelu(x); } + return Gelu(x); + } +}; + class CumBaseFunctor { public: explicit CumBaseFunctor(std::string op_name) { @@ -2286,8 +2588,8 @@ class CumBaseFunctor { auto ndim = input->ndim(); dim = JUST(maybe_wrap_dim(dim, ndim)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + attrs.SetAllAttrs(dim); TensorProcessor tensor_processor; if (dtype) { JUST(tensor_processor.AddInputs({input}, JUST(dtype)).Apply()); @@ -2331,8 +2633,8 @@ class CumProdGradFunctor : public CumGradBaseFunctor { const std::shared_ptr& y, const std::shared_ptr& x, int64_t dim) const { // No need to check dim validation here, while CumProdFunctor handled already - MutableAttrMap attrs; - JUST(attrs.SetAttr("dim", dim)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dim"); + attrs.SetAllAttrs(dim); return OpInterpUtil::Dispatch(*op_, {dy, y, x}, attrs); } }; @@ -2667,7 +2969,7 @@ class EinSumFunctor { CHECK_OR_RETURN( // Ensure ellipsis is correct i + 2 < rhs.length() && rhs[++i] == '.' && rhs[++i] == '.') - "einsum(): found \'.\' for output that is not part of any ellipsis (...)"; + << "einsum(): found \'.\' for output that is not part of any ellipsis (...)"; ell_index = perm_index; perm_index += ell_num_dim; found_ell = true; @@ -2907,6 +3209,623 @@ class EinSumFunctor { } }; +class TruncFunctor { + public: + TruncFunctor() { op_ = CHECK_JUST(one::OpBuilder("trunc").Input("in").Output("out").Build()); } + Maybe operator()(const std::shared_ptr& x) const { + return OpInterpUtil::Dispatch(*op_, {x}); + } + + private: + std::shared_ptr op_; +}; +class AddCDivFunctor { + public: + AddCDivFunctor() {} + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& tensor1, + const std::shared_ptr& tensor2, const Scalar& value) const { + return JUST(Add(input, JUST(ScalarMul(JUST(Div(tensor1, tensor2)), value, false)), 1, false)); + } +}; + +class InplaceAddCDivFunctor { + public: + InplaceAddCDivFunctor() {} + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& tensor1, + const std::shared_ptr& tensor2, const Scalar& value) const { + JUST(CheckInplaceValid(input)); + std::shared_ptr outputs = std::make_shared(1); + JUST(VectorAt(*outputs, 0)) = input; + JUST(Add(input, JUST(ScalarMul(JUST(Div(tensor1, tensor2)), value, false)), 1, true)); + return JUST(VectorAt(*outputs, 0)); + } +}; + +class StftFunctor { + public: + StftFunctor() { + op_ = CHECK_JUST(one::OpBuilder("stft").Input("input").Output("output").Build()); + } + + Maybe operator()(const std::shared_ptr& input, const int64_t n_fft, + const Optional& hop_length, const Optional& win_length, + const Optional& window, const bool center, + const std::string& mode, const bool normalized, const bool onesided, + const bool return_complex) const { + int64_t new_hop_length = hop_length.has_value() == true ? JUST(hop_length) : n_fft / 4; + int64_t new_win_length = win_length.has_value() == true ? JUST(win_length) : n_fft; + auto input_tensor = input; + + // TODO(yzm):Remove this line when complex numbers are supported + CHECK_OR_RETURN(return_complex == false) + << Error::RuntimeError() << "return_complex parameter is not supported at this time"; + + const auto& NumAxes = input_tensor->shape()->NumAxes(); + CHECK_OR_RETURN(NumAxes == 2 || NumAxes == 1) + << Error::RuntimeError() << "Expected a 1D or 2D tensor,but got " << NumAxes << "D"; + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("normalized", "onesided", "return_complex"); + attrs.SetAllAttrs(normalized, onesided, return_complex); + + if (NumAxes == 1) { input_tensor = JUST(functional::Unsqueeze(input_tensor, 0)); } + if (center) { + const auto& input_shape = input_tensor->shape(); + const auto input_dim = input_tensor->shape()->NumAxes(); + + const auto extra_dims = std::max(size_t{3}, (size_t)input_dim) - input_dim; + const auto pad_amount = n_fft / 2; + + DimVector extended_shape(extra_dims, 1); + extended_shape.append(input_shape->begin(), input_shape->end()); + input_tensor = + JUST(functional::Pad(JUST(functional::View(input_tensor, Shape(extended_shape))), + {pad_amount, pad_amount}, mode, Scalar(0))); + + DimVector view_shape; + if (input_dim == 1) { + view_shape = {input_tensor->shape()->back()}; + } else { + view_shape = {input_shape->at(0), input_tensor->shape()->back()}; + } + input_tensor = JUST(functional::View(input_tensor, Shape(view_shape))); + } + + int32_t batch = input_tensor->shape()->At(0); + int32_t len = input_tensor->shape()->At(1); + int32_t n_frames = 1 + (len - n_fft) / new_hop_length; + int32_t fft_size = static_cast(n_fft); + CHECK_OR_RETURN(n_fft > 0 && n_fft <= len) + << Error::RuntimeError() << "Expected 0 < n_fft < " << len << " ,but got " << n_fft; + CHECK_GT_OR_RETURN(new_hop_length, 0) + << Error::RuntimeError() << "Expected hop_length > 0, but got " << new_hop_length; + CHECK_OR_RETURN(new_win_length > 0 && new_win_length <= n_fft) + << Error::RuntimeError() << "Expected 0 < win_length <=n_fft ,but got " << new_win_length; + const auto& stride = *JUST(input_tensor->stride()); + std::vector strides(stride.begin(), stride.end()); + input_tensor = + JUST(view::AsStrided(input_tensor, {batch, n_frames, fft_size}, + {JUST(VectorAt(strides, 0)), + static_cast(new_hop_length) * JUST(VectorAt(strides, 1)), + JUST(VectorAt(strides, 1))}, + 0)); + + std::shared_ptr temp_tensor; + if (window.has_value()) { + temp_tensor = JUST(window); + CHECK_OR_RETURN(temp_tensor->shape()->NumAxes() == 1 + && temp_tensor->shape()->at(0) == new_win_length) + << Error::RuntimeError() + << "Expected a 1D window tensor of size equal to win_length=" << new_win_length + << ", but got window with size " << temp_tensor->shape()->ToString(); + } + if (new_win_length < n_fft) { + temp_tensor = JUST(functional::Fill(temp_tensor, 0)); + const int64_t left = (n_fft - new_win_length) / 2; + + if (window.has_value()) { + // TODO(yzm):Copy the window matrix to the defined range,such as + //''' + // functional::AssignLocalTensor(JUST(functional::Narrow(temp_tensor, 0, + // left,new_win_length)), window); + //''' + // Remove the following check after support + CHECK_OR_RETURN(false) << Error::RuntimeError() + << "The following conditions are not currently supported: " + "win_length( + *op_, {JUST(functional::ToContiguous(input_tensor))}, attrs)); + if (NumAxes == 2 && input->shape()->At(0) == 1) { + output = JUST(functional::Unsqueeze(output, 0)); + } + return output; + } + + private: + std::shared_ptr op_; +}; +class FusedWeightedSumFunctor { + public: + FusedWeightedSumFunctor() { + op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 1; n < op_.size(); ++n) { + op_[n] = + CHECK_JUST(one::OpBuilder("fused_weighted_sum").Input("in", n).Output("out").Build()); + } + } + Maybe operator()(const TensorTuple& in, const std::vector& weights, + const float& alpha) const { + CHECK_GE_OR_RETURN(in.size(), 1); + CHECK_LT_OR_RETURN(in.size(), kMaxInputCount); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("weights", "alpha"); + attrs.SetAllAttrs(weights, alpha); + return JUST(OpInterpUtil::Dispatch(*op_[in.size()], in, attrs)); + } + + private: + std::vector> op_; +}; + +class FusedCenterFunctor { + public: + FusedCenterFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_center_dist") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Output("rho2") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& b1_x1, const std::shared_ptr& b1_x2, + const std::shared_ptr& b2_x1, const std::shared_ptr& b2_x2, + const std::shared_ptr& b1_y1, const std::shared_ptr& b1_y2, + const std::shared_ptr& b2_y1, const std::shared_ptr& b2_y2) const { + return OpInterpUtil::Dispatch( + *op_, {b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedCenterGradFunctor { + public: + FusedCenterGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_center_dist_grad") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Input("rho2_diff") + .Output("b1_x1_diff") + .Output("b1_x2_diff") + .Output("b2_x1_diff") + .Output("b2_x2_diff") + .Output("b1_y1_diff") + .Output("b1_y2_diff") + .Output("b2_y1_diff") + .Output("b2_y2_diff") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& b1_x1, const std::shared_ptr& b1_x2, + const std::shared_ptr& b2_x1, const std::shared_ptr& b2_x2, + const std::shared_ptr& b1_y1, const std::shared_ptr& b1_y2, + const std::shared_ptr& b2_y1, const std::shared_ptr& b2_y2, + const std::shared_ptr& rho2_diff) const { + return OpInterpUtil::Dispatch( + *op_, {b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, rho2_diff}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetIntersectionAreaFunctor { + public: + FusedGetIntersectionAreaFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_intersection_area") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Output("inter") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& b1_x1, const std::shared_ptr& b1_x2, + const std::shared_ptr& b2_x1, const std::shared_ptr& b2_x2, + const std::shared_ptr& b1_y1, const std::shared_ptr& b1_y2, + const std::shared_ptr& b2_y1, const std::shared_ptr& b2_y2) const { + return OpInterpUtil::Dispatch( + *op_, {b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetIntersectionAreaGradFunctor { + public: + FusedGetIntersectionAreaGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_intersection_area_grad") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Input("inter_diff") + .Output("b1_x1_diff") + .Output("b1_x2_diff") + .Output("b2_x1_diff") + .Output("b2_x2_diff") + .Output("b1_y1_diff") + .Output("b1_y2_diff") + .Output("b2_y1_diff") + .Output("b2_y2_diff") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& b1_x1, const std::shared_ptr& b1_x2, + const std::shared_ptr& b2_x1, const std::shared_ptr& b2_x2, + const std::shared_ptr& b1_y1, const std::shared_ptr& b1_y2, + const std::shared_ptr& b2_y1, const std::shared_ptr& b2_y2, + const std::shared_ptr& inter_diff) const { + return OpInterpUtil::Dispatch( + *op_, {b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, inter_diff}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetBounddingBoxesCoordFunctor { + public: + FusedGetBounddingBoxesCoordFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_boundding_boxes_coord") + .Input("x1") + .Input("y1") + .Input("w1") + .Input("h1") + .Input("x2") + .Input("y2") + .Input("w2") + .Input("h2") + .Output("b1_x1") + .Output("b1_x2") + .Output("b1_y1") + .Output("b1_y2") + .Output("b2_x1") + .Output("b2_x2") + .Output("b2_y1") + .Output("b2_y2") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& x1, const std::shared_ptr& y1, + const std::shared_ptr& w1, const std::shared_ptr& h1, + const std::shared_ptr& x2, const std::shared_ptr& y2, + const std::shared_ptr& w2, const std::shared_ptr& h2) const { + return OpInterpUtil::Dispatch(*op_, {x1, y1, w1, h1, x2, y2, w2, h2}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetBounddingBoxesCoordGradFunctor { + public: + FusedGetBounddingBoxesCoordGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_boundding_boxes_coord_grad") + .Input("b1_x1_diff") + .Input("b1_x2_diff") + .Input("b1_y1_diff") + .Input("b1_y2_diff") + .Input("b2_x1_diff") + .Input("b2_x2_diff") + .Input("b2_y1_diff") + .Input("b2_y2_diff") + .Output("x1_diff") + .Output("y1_diff") + .Output("w1_diff") + .Output("h1_diff") + .Output("x2_diff") + .Output("y2_diff") + .Output("w2_diff") + .Output("h2_diff") + .Build()); + } + + Maybe operator()(const std::shared_ptr& b1_x1_diff, + const std::shared_ptr& b1_x2_diff, + const std::shared_ptr& b1_y1_diff, + const std::shared_ptr& b1_y2_diff, + const std::shared_ptr& b2_x1_diff, + const std::shared_ptr& b2_x2_diff, + const std::shared_ptr& b2_y1_diff, + const std::shared_ptr& b2_y2_diff) const { + return OpInterpUtil::Dispatch(*op_, + {b1_x1_diff, b1_x2_diff, b1_y1_diff, b1_y2_diff, + b2_x1_diff, b2_x2_diff, b2_y1_diff, b2_y2_diff}, + {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetCiouDiagonalAngleFunctor { + public: + FusedGetCiouDiagonalAngleFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_ciou_diagonal_angle") + .Input("w1") + .Input("h1") + .Input("w2") + .Input("h2") + .Output("v") + .Build()); + } + + Maybe operator()(const std::shared_ptr& w1, + const std::shared_ptr& h1, + const std::shared_ptr& w2, + const std::shared_ptr& h2, const float eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch(*op_, {w1, h1, w2, h2}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetCiouResultFunctor { + public: + FusedGetCiouResultFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_ciou_result") + .Input("v") + .Input("iou") + .Input("rho2") + .Input("c2") + .Output("y") + .Output("alpha") + .Build()); + } + + Maybe operator()(const std::shared_ptr& v, + const std::shared_ptr& iou, + const std::shared_ptr& rho2, + const std::shared_ptr& c2, const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch(*op_, {v, iou, rho2, c2}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetCiouDiagonalAngleGradFunctor { + public: + FusedGetCiouDiagonalAngleGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_ciou_diagonal_angle_grad") + .Input("w1") + .Input("h1") + .Input("w2") + .Input("h2") + .Input("v_diff") + .Output("w1_diff") + .Output("h1_diff") + .Output("w2_diff") + .Output("h2_diff") + .Build()); + } + + Maybe operator()(const std::shared_ptr& w1, + const std::shared_ptr& h1, + const std::shared_ptr& w2, + const std::shared_ptr& h2, + const std::shared_ptr& v_diff, const float eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch(*op_, {w1, h1, w2, h2, v_diff}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetCiouResultGradFunctor { + public: + FusedGetCiouResultGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_ciou_result_grad") + .Input("dy") + .Input("alpha") + .Input("rho2") + .Input("c2") + .Output("dv") + .Output("diou") + .Output("drho2") + .Output("dc2") + .Build()); + } + + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& alpha, + const std::shared_ptr& rho2, + const std::shared_ptr& c2) const { + return OpInterpUtil::Dispatch(*op_, {dy, alpha, rho2, c2}, {}); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetIouFunctor { + public: + FusedGetIouFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_iou") + .Input("w1") + .Input("h1") + .Input("w2") + .Input("h2") + .Input("inter") + .Output("iou") + .Build()); + } + + Maybe operator()(const std::shared_ptr& w1, + const std::shared_ptr& h1, + const std::shared_ptr& w2, + const std::shared_ptr& h2, + const std::shared_ptr& inter, const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch(*op_, {w1, h1, w2, h2, inter}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetIouGradFunctor { + public: + FusedGetIouGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_iou_grad") + .Input("diou") + .Input("w1") + .Input("h1") + .Input("w2") + .Input("h2") + .Input("inter") + .Output("dw1") + .Output("dh1") + .Output("dinter") + .Build()); + } + + Maybe operator()(const std::shared_ptr& diou, + const std::shared_ptr& w1, + const std::shared_ptr& h1, + const std::shared_ptr& w2, + const std::shared_ptr& h2, + const std::shared_ptr& inter, const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch(*op_, {diou, w1, h1, w2, h2, inter}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetConvexDiagonalSquaredFunctor { + public: + FusedGetConvexDiagonalSquaredFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_convex_diagonal_squared") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Output("c2") + .Build()); + } + + Maybe operator()(const std::shared_ptr& b1_x1, + const std::shared_ptr& b1_x2, + const std::shared_ptr& b2_x1, + const std::shared_ptr& b2_x2, + const std::shared_ptr& b1_y1, + const std::shared_ptr& b1_y2, + const std::shared_ptr& b2_y1, + const std::shared_ptr& b2_y2, const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch( + *op_, {b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedGetConvexDiagonalSquaredGradFunctor { + public: + FusedGetConvexDiagonalSquaredGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_get_convex_diagonal_squared_grad") + .Input("c2_diff") + .Input("b1_x1") + .Input("b1_x2") + .Input("b2_x1") + .Input("b2_x2") + .Input("b1_y1") + .Input("b1_y2") + .Input("b2_y1") + .Input("b2_y2") + .Output("b1_x1_diff") + .Output("b1_x2_diff") + .Output("b2_x1_diff") + .Output("b2_x2_diff") + .Output("b1_y1_diff") + .Output("b1_y2_diff") + .Output("b2_y1_diff") + .Output("b2_y2_diff") + .Build()); + } + + Maybe operator()( + const std::shared_ptr& c2_diff, const std::shared_ptr& b1_x1, + const std::shared_ptr& b1_x2, const std::shared_ptr& b2_x1, + const std::shared_ptr& b2_x2, const std::shared_ptr& b1_y1, + const std::shared_ptr& b1_y2, const std::shared_ptr& b2_y1, + const std::shared_ptr& b2_y2, const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps"); + attrs.SetAllAttrs(eps); + return OpInterpUtil::Dispatch( + *op_, {c2_diff, b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2}, attrs); + } + + private: + std::shared_ptr op_; +}; + } // namespace impl using namespace impl; @@ -2917,6 +3836,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ScalarSub"); m.add_functor("ScalarMul"); m.add_functor("InplaceScalarMul"); + m.add_functor("AddCDiv"); + m.add_functor("InplaceAddCDiv"); m.add_functor("ScalarDiv"); m.add_functor("InplaceScalarDiv"); m.add_functor("ScalarPow"); @@ -2935,6 +3856,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Amax"); m.add_functor("ReduceSum"); m.add_functor("ReduceSumWhole"); + m.add_functor("ReduceNanSum"); + m.add_functor("ReduceNanSumWhole"); m.add_functor("ReduceAll"); m.add_functor("ReduceAllWhole"); m.add_functor("ReduceAny"); @@ -2949,18 +3872,28 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ReduceMaxDeviceStageGrad"); m.add_functor("ReduceMinGlobalStageGrad"); m.add_functor("ReduceMaxGlobalStageGrad"); + m.add_functor("LogSumExp"); + m.add_functor("LogAddExp"); + m.add_functor("Quantile"); m.add_functor("Transpose"); m.add_functor("Transpose2dim"); m.add_functor("Permute"); m.add_functor("AsStrided"); m.add_functor("AsStridedGrad"); + m.add_functor("InplaceAsStrided"); m.add_functor("Swapaxes"); m.add_functor("Swapdims"); m.add_functor("Arange"); - m.add_functor("ConsistentArange"); + m.add_functor("GlobalArange"); + m.add_functor("HannWindow"); + m.add_functor("GlobalHannWindow"); m.add_functor("Cast"); m.add_functor("Clamp"); + m.add_functor("ClampMin"); + m.add_functor("ClampMax"); m.add_functor("ClampInplace"); + m.add_functor("ClampMinInplace"); + m.add_functor("ClampMaxInplace"); m.add_functor("Clip"); m.add_functor("ClipInplace"); m.add_functor("SqrtSquareSum"); @@ -2977,6 +3910,7 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Max"); m.add_functor("ScalarFMod"); m.add_functor("ScalarFloorDiv"); + m.add_functor("ScalarTruncDiv"); m.add_functor("ScalarLogicalEqual"); m.add_functor( "ScalarLogicalNotEqual"); @@ -2991,6 +3925,7 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ScalarLogicalXor"); m.add_functor("StandardDeviation"); m.add_functor("Variance"); + m.add_functor("RMSLayerNormalization"); m.add_functor("Dot"); m.add_functor("MovedimVec"); m.add_functor("MovedimInt"); @@ -3006,6 +3941,26 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Cumprod"); m.add_functor("CumprodGrad"); m.add_functor("EinSum"); + m.add_functor("Inv"); + m.add_functor("GeluWithApproximate"); + m.add_functor("Trunc"); + m.add_functor("Stft"); + m.add_functor("FusedWeightedSum"); + m.add_functor("FusedCenter"); + m.add_functor("FusedCenterGrad"); + m.add_functor("FusedGetBounddingBoxesCoord"); + m.add_functor("FusedGetBounddingBoxesCoordGrad"); + m.add_functor("FusedGetCiouDiagonalAngle"); + m.add_functor("FusedGetCiouDiagonalAngleGrad"); + m.add_functor("FusedGetCiouResult"); + m.add_functor("FusedGetCiouResultGrad"); + m.add_functor("FusedGetIntersectionArea"); + m.add_functor("FusedGetIntersectionAreaGrad"); + m.add_functor("FusedGetIou"); + m.add_functor("FusedGetIouGrad"); + m.add_functor("FusedGetConvexDiagonalSquared"); + m.add_functor( + "FusedGetConvexDiagonalSquaredGrad"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/nn_functor.cpp b/oneflow/core/functional/impl/nn_functor.cpp index 2816e61ad26abf9ca962e4bb1060921d8ab1ec94..58c7f07b9c1fbfdd789702c522a5f39994949cab 100644 --- a/oneflow/core/functional/impl/nn_functor.cpp +++ b/oneflow/core/functional/impl/nn_functor.cpp @@ -14,33 +14,19 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/common/data_type.pb.h" -#include "oneflow/core/common/error.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/common/scalar.h" -#include "oneflow/core/framework/attr_map.h" +#include "fmt/core.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/framework/tensor_util.h" -#include "oneflow/core/framework/op_interpreter.h" -#include "oneflow/core/framework/random_generator.h" -#include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/function_library.h" #include "oneflow/core/functional/sequence_function.h" #include "oneflow/core/functional/impl/common.h" #include "oneflow/core/functional/impl/unary_functor.h" -#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/kernel/kernel_util.h" #include "oneflow/user/kernels/random_mask_like_kernel.h" #include "oneflow/user/kernels/dropout_kernel.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/common/container_util.h" #include "oneflow/user/kernels/distributions/common.h" -#include "oneflow/core/framework/nd_sbp.h" namespace oneflow { namespace one { @@ -55,7 +41,6 @@ class BiasAddFunctor { } Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& bias, const int32_t& axis) const { - MutableAttrMap attrs; int32_t axis_val = axis; if (axis_val < 0) { const int64_t num_axes = x->shape()->NumAxes(); @@ -69,7 +54,8 @@ class BiasAddFunctor { << Error::RuntimeError() << "The size of tensor x " << x->shape()->ToString() << " must match the size of tensor b " << bias->shape()->ToString() << " at dimension " << axis_val; - JUST(attrs.SetAttr("axis", axis_val)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis_val); return OpInterpUtil::Dispatch(*op_, {x, bias}, attrs); } @@ -81,15 +67,15 @@ class ConvBaseFunctor { public: explicit ConvBaseFunctor(const int& num_spatial_dims) : num_spatial_dims_(num_spatial_dims) { bias_op_ = CHECK_JUST(one::OpBuilder("bias_add").Input("a").Input("b").Output("out").Build()); + enable_fused_conv_bias_ = ParseBooleanFromEnv("ONEFLOW_KERNEL_ENABLE_FUSED_CONV_BIAS", false); } virtual ~ConvBaseFunctor() = default; - Maybe operator()(const std::shared_ptr& x, + Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& weight, const Optional& bias, const std::vector& stride, const std::vector& padding, const std::vector& dilation, const int32_t& groups, const std::string& channel_pos) const { - MutableAttrMap conv_attrs; std::vector kernel_size_vec(num_spatial_dims_); int32_t channel_idx = 1; int32_t kernel_idx_offset = 2; @@ -101,15 +87,17 @@ class ConvBaseFunctor { for (int i = 0; i < num_spatial_dims_; i++) { kernel_size_vec.at(i) = ((weight->shape())->At(i + kernel_idx_offset)); } - JUST(conv_attrs.SetAttr("filters", (weight->shape())->At(0))); - JUST(conv_attrs.SetAttr>("padding_before", padding)); - JUST(conv_attrs.SetAttr>("kernel_size", kernel_size_vec)); - JUST(conv_attrs.SetAttr>("strides", stride)); - JUST(conv_attrs.SetAttr>("dilation_rate", dilation)); - JUST(conv_attrs.SetAttr("groups", groups)); - JUST(conv_attrs.SetAttr("data_format", channel_pos)); + auto& conv_attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("filters", "kernel_size", "padding_before", "strides", + "dilation_rate", "groups", "data_format"); + conv_attrs.SetAllAttrs(static_cast(weight->shape()->At(0)), kernel_size_vec, padding, + stride, dilation, groups, channel_pos); + if (bias && enable_fused_conv_bias_) { + return OpInterpUtil::Dispatch(*conv_bias_op_, {input, weight, JUST(bias)}, + conv_attrs); + } const std::shared_ptr& conv_out = - JUST(OpInterpUtil::Dispatch(*conv_op_, {x, weight}, conv_attrs)); + JUST(OpInterpUtil::Dispatch(*conv_op_, {input, weight}, conv_attrs)); if (bias) { return functional::BiasAdd(conv_out, JUST(bias), channel_idx); } else { @@ -120,7 +108,9 @@ class ConvBaseFunctor { protected: std::shared_ptr conv_op_; std::shared_ptr bias_op_; + std::shared_ptr conv_bias_op_; int32_t num_spatial_dims_; + bool enable_fused_conv_bias_; }; class Conv1dFunctor : public ConvBaseFunctor { @@ -128,6 +118,8 @@ class Conv1dFunctor : public ConvBaseFunctor { Conv1dFunctor() : ConvBaseFunctor(/*num_spatial_dims_=*/1) { conv_op_ = CHECK_JUST(one::OpBuilder("conv1d").Input("in").Input("weight").Output("out").Build()); + conv_bias_op_ = CHECK_JUST( + one::OpBuilder("conv1d").Input("in").Input("weight").Input("bias").Output("out").Build()); } }; @@ -136,6 +128,8 @@ class Conv2dFunctor : public ConvBaseFunctor { Conv2dFunctor() : ConvBaseFunctor(/*num_spatial_dims_=*/2) { conv_op_ = CHECK_JUST(one::OpBuilder("conv2d").Input("in").Input("weight").Output("out").Build()); + conv_bias_op_ = CHECK_JUST( + one::OpBuilder("conv2d").Input("in").Input("weight").Input("bias").Output("out").Build()); } }; @@ -144,6 +138,8 @@ class Conv3dFunctor : public ConvBaseFunctor { Conv3dFunctor() : ConvBaseFunctor(/*num_spatial_dims_=*/3) { conv_op_ = CHECK_JUST(one::OpBuilder("conv3d").Input("in").Input("weight").Output("out").Build()); + conv_bias_op_ = CHECK_JUST( + one::OpBuilder("conv3d").Input("in").Input("weight").Input("bias").Output("out").Build()); } }; @@ -153,14 +149,13 @@ class DeConvBaseFunctor { bias_op_ = CHECK_JUST(one::OpBuilder("bias_add").Input("a").Input("b").Output("out").Build()); } virtual ~DeConvBaseFunctor() = default; - Maybe operator()(const std::shared_ptr& x, + Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& weight, const Optional& bias, const std::vector& stride, const std::vector& padding, const std::vector& output_padding, const int32_t& groups, const std::vector& dilation, const std::string& data_format) const { - MutableAttrMap deconv_attrs; std::vector kernel_size_vec(num_spatial_dims_); int32_t kernel_idx_offset = 2; if (data_format == "channels_last") { kernel_idx_offset = 1; } @@ -168,19 +163,16 @@ class DeConvBaseFunctor { kernel_size_vec[i] = ((weight->shape())->At(i + kernel_idx_offset)); } - JUST(deconv_attrs.SetAttr("filters", (weight->shape())->At(1) * groups)); - JUST(deconv_attrs.SetAttr>("padding_before", padding)); - JUST(deconv_attrs.SetAttr>("kernel_size", kernel_size_vec)); - JUST(deconv_attrs.SetAttr>("output_padding", output_padding)); - JUST(deconv_attrs.SetAttr>("strides", stride)); - JUST(deconv_attrs.SetAttr>("dilation_rate", dilation)); - JUST(deconv_attrs.SetAttr("groups", groups)); - JUST(deconv_attrs.SetAttr("data_format", data_format)); - std::shared_ptr deconv_out = nullptr; - deconv_out = JUST(OpInterpUtil::Dispatch(*deconv_op_, {x, weight}, deconv_attrs)); + auto& deconv_attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("filters", "kernel_size", "padding_before", "output_padding", + "strides", "dilation_rate", "groups", "data_format"); + deconv_attrs.SetAllAttrs(static_cast(weight->shape()->At(1) * groups), kernel_size_vec, + padding, output_padding, stride, dilation, groups, data_format); + std::shared_ptr deconv_out = + JUST(OpInterpUtil::Dispatch(*deconv_op_, {input, weight}, deconv_attrs)); if (bias) { - MutableAttrMap bias_attrs; - JUST(bias_attrs.SetAttr("axis", 1)); + auto& bias_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + bias_attrs.SetAllAttrs(static_cast(1)); return OpInterpUtil::Dispatch(*bias_op_, {deconv_out, JUST(bias)}, bias_attrs); } else { return deconv_out; @@ -232,9 +224,8 @@ class EmbeddingReNormFunctor { std::shared_ptr outputs = std::make_shared(1); JUST(oneflow::VectorAt(*outputs, 0)) = in; - MutableAttrMap attrs; - JUST(attrs.SetAttr("max_norm", max_norm)); - JUST(attrs.SetAttr("norm_type", norm_type)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("max_norm", "norm_type"); + attrs.SetAllAttrs(max_norm, norm_type); JUST(OpInterpUtil::Dispatch(*op_, {in, indices}, outputs.get(), attrs)); return JUST(oneflow::VectorAt(*outputs, 0)); @@ -257,9 +248,8 @@ class EmbeddingFunctor { CHECK_EQ_OR_RETURN(weight->ndim(), 2) << "The dimension of weight should be 2"; int64_t new_padding_idx = -1; if (padding_idx.has_value()) { new_padding_idx = JUST(padding_idx); } - MutableAttrMap attrs; - JUST(attrs.SetAttr("padding_idx", new_padding_idx)); - JUST(attrs.SetAttr("scale_grad_by_freq", scale_grad_by_freq)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding_idx", "scale_grad_by_freq"); + attrs.SetAllAttrs(new_padding_idx, scale_grad_by_freq); return OpInterpUtil::Dispatch(*op_, {weight, indices}, attrs); } @@ -300,27 +290,57 @@ class MatMulFunctor { const bool& transpose_b, const double& alpha) const { const auto& a_shape = a->shape(); const auto& b_shape = b->shape(); + CHECK_GE_OR_RETURN(a_shape->NumAxes(), 1) + << Error::RuntimeError() << "Tensor a's dim should >= 1"; + CHECK_GE_OR_RETURN(b_shape->NumAxes(), 1) + << Error::RuntimeError() << "Tensor b's dim should >= 1"; - // TODO(): Support 1-d tensor by dot. - CHECK_GE_OR_RETURN(a_shape->NumAxes(), 2) - << Error::RuntimeError() << "Tensor a's dim should >= 2"; - CHECK_GE_OR_RETURN(b_shape->NumAxes(), 2) - << Error::RuntimeError() << "Tensor b's dim should >= 2"; - - MutableAttrMap attrs; - JUST(attrs.SetAttr("transpose_a", transpose_a)); - JUST(attrs.SetAttr("transpose_b", transpose_b)); - JUST(attrs.SetAttr("alpha", alpha)); - if (a_shape->NumAxes() != b_shape->NumAxes()) { - CHECK_EQ_OR_RETURN(b_shape->NumAxes(), 2) - << Error::RuntimeError() - << "Not support number of dimensions of a being less than number of dimensions of b!"; - return OpInterpUtil::Dispatch(*bcast_matmul_op_, {a, b}, attrs); + DeviceType device_type{}; + if (a->is_global()) { + device_type = JUST(a->parallel_desc())->device_type(); + } else { + device_type = JUST(a->device())->enum_type(); + } + std::shared_ptr cast_a = a; + std::shared_ptr cast_b = b; + std::shared_ptr result; + if ((cast_a->dtype()->is_integer()) && (device_type == DeviceType::kCPU)) { + cast_a = JUST(functional::Cast(a, JUST(DType::Get(DataType::kFloat)), /*pin_memory=*/false)); + cast_b = JUST(functional::Cast(b, JUST(DType::Get(DataType::kFloat)), /*pin_memory=*/false)); + } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("transpose_a", "transpose_b", "alpha"); + attrs.SetAllAttrs(transpose_a, transpose_b, alpha); + const int64_t a_num_axes = a_shape->NumAxes(); + const int64_t b_num_axes = b_shape->NumAxes(); + if (a_num_axes == 1 && b_num_axes == 2) { + result = JUST(VectorMatrixProduct(cast_a, cast_b)); + } else if (a_num_axes == 2 && b_num_axes == 1) { + result = JUST(MatrixVectorProduct(cast_a, cast_b)); + } else if (a_num_axes == 2 && b_num_axes == 2) { + result = JUST(OpInterpUtil::Dispatch(*matmul_op_, {cast_a, cast_b}, attrs)); + } else if (a_num_axes == b_num_axes) { + bool if_batch_matmul = true; + for (int i = 0; i < a_num_axes - 2; ++i) { + if (a_shape->At(i) != b_shape->At(i)) { + if_batch_matmul = false; + break; + } + } + if (if_batch_matmul) { + result = JUST(OpInterpUtil::Dispatch(*batch_matmul_op_, {cast_a, cast_b}, attrs)); + } else { + result = JUST(OpInterpUtil::Dispatch(*bcast_matmul_op_, {cast_a, cast_b}, attrs)); + } + } else { + result = JUST(OpInterpUtil::Dispatch(*bcast_matmul_op_, {cast_a, cast_b}, attrs)); } - if (a_shape->NumAxes() > 2) { - return OpInterpUtil::Dispatch(*batch_matmul_op_, {a, b}, attrs); + + if ((a->dtype()->is_integer()) && (device_type == DeviceType::kCPU)) { + return JUST(functional::Cast(result, a->dtype(), /*pin_memory=*/false)); + } else { + return result; } - return OpInterpUtil::Dispatch(*matmul_op_, {a, b}, attrs); } private: @@ -348,19 +368,63 @@ class BatchMatMulFunctor { << "-dimensional tensor for argument #2"; CHECK_EQ_OR_RETURN(a_shape->At(0), b_shape->At(0)) << Error::RuntimeError() << "Batch dim not match, please check input!"; - CHECK_EQ_OR_RETURN(a_shape->At(2), b_shape->At(1)) - << Error::RuntimeError() << "Matmul dim not match, please check input!"; - MutableAttrMap attrs; - JUST(attrs.SetAttr("transpose_a", transpose_a)); - JUST(attrs.SetAttr("transpose_b", transpose_b)); - JUST(attrs.SetAttr("alpha", alpha)); - return OpInterpUtil::Dispatch(*batch_matmul_op_, {a, b}, attrs); + const int64_t matmul_dim_a = transpose_a ? a_shape->At(1) : a_shape->At(2); + const int64_t matmul_dim_b = transpose_b ? b_shape->At(2) : b_shape->At(1); + CHECK_EQ_OR_RETURN(matmul_dim_a, matmul_dim_b) + << Error::RuntimeError() << "Matmul dim not match, got " << matmul_dim_a << " of mat1 and " + << matmul_dim_b << " of mat2, please check input!"; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("transpose_a", "transpose_b", "alpha"); + attrs.SetAllAttrs(transpose_a, transpose_b, alpha); + + DeviceType device_type{}; + if (a->is_global()) { + device_type = JUST(a->parallel_desc())->device_type(); + } else { + device_type = JUST(a->device())->enum_type(); + } + std::shared_ptr cast_a = a; + std::shared_ptr cast_b = b; + if ((a->dtype()->is_integer()) && (device_type == DeviceType::kCPU)) { + cast_a = JUST(functional::Cast(a, JUST(DType::Get(DataType::kFloat)), /*pin_memory=*/false)); + cast_b = JUST(functional::Cast(b, JUST(DType::Get(DataType::kFloat)), /*pin_memory=*/false)); + } + + auto result = JUST(OpInterpUtil::Dispatch(*batch_matmul_op_, {cast_a, cast_b}, attrs)); + if ((a->dtype()->is_integer()) && (device_type == DeviceType::kCPU)) { + return JUST(functional::Cast(result, a->dtype(), /*pin_memory=*/false)); + } else { + return result; + } } private: std::shared_ptr batch_matmul_op_; }; +class VectorMatrixProductFunctor { + public: + VectorMatrixProductFunctor() { + vector_matrix_product_op_ = CHECK_JUST( + one::OpBuilder("vector_matrix_product").Input("a").Input("b").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& vec, + const std::shared_ptr& input) const { + const auto& vec_shape = vec->shape(); + const auto& input_shape = input->shape(); + CHECK_OR_RETURN(input_shape->NumAxes() == 2 && vec_shape->NumAxes() == 1) + << Error::RuntimeError() << "vector @ matrix expected, got " + << "1, " << input_shape->NumAxes() << ", " << vec_shape->NumAxes(); + CHECK_EQ_OR_RETURN(vec_shape->at(0), input_shape->at(0)) + << Error::RuntimeError() << "size mismatch, got " << 1 << ", " + << std::to_string(vec_shape->at(0)) << " x " << std::to_string(input_shape->at(0)) << ", " + << std::to_string(input_shape->at(1)); + return OpInterpUtil::Dispatch(*vector_matrix_product_op_, {vec, input}); + } + + private: + std::shared_ptr vector_matrix_product_op_; +}; + class TensorDotIntDimsFunctor { public: Maybe operator()(const std::shared_ptr& a, const std::shared_ptr& b, @@ -544,7 +608,7 @@ class FusedMLPFunctor { #if CUDA_VERSION >= 11060 DeviceType device_type{}; - if (x->is_consistent()) { + if (x->is_global()) { device_type = JUST(x->parallel_desc())->device_type(); } else { device_type = JUST(x->device())->enum_type(); @@ -557,8 +621,8 @@ class FusedMLPFunctor { std::copy(weights.begin(), weights.end(), input.begin() + 1); std::copy(biases.begin(), biases.end(), input.begin() + 1 + weight_size); - MutableAttrMap attrs; - JUST(attrs.SetAttr("skip_final_activation", skip_final_activation)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("skip_final_activation"); + attrs.SetAllAttrs(skip_final_activation); return OpInterpUtil::Dispatch(*fused_op_[weight_size], input, attrs); } #endif // CUDA_VERSION >= 11060 @@ -586,6 +650,79 @@ class FusedMLPFunctor { #endif }; +class FusedMatmulBiasFunctor { + public: + FusedMatmulBiasFunctor() { + _with_add_to_output_op = CHECK_JUST(one::OpBuilder("fused_matmul_bias") + .Input("x") + .Input("weight") + .Input("bias") + .Input("_add_to_output") + .Output("out") + .Build()); + _without_add_to_output_op = CHECK_JUST(one::OpBuilder("fused_matmul_bias") + .Input("x") + .Input("weight") + .Input("bias") + .Output("out") + .Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& weight, + const std::shared_ptr& bias, + const Optional& _add_to_output) const { + /* + x: (m_i, ... m_0, k) + weight: (n, k) need transpose + bias: (n) + */ + const auto& x_shape = x->shape(); + const int64_t k = x_shape->At(x->shape()->NumAxes() - 1); + + const auto& weight_shape = weight->shape(); + const auto& bias_shape = bias->shape(); + + CHECK_EQ_OR_RETURN(weight_shape->NumAxes(), 2) + << Error::RuntimeError() << "Weight's dim size should == 2"; + CHECK_EQ_OR_RETURN(bias_shape->NumAxes(), 1) + << Error::RuntimeError() << "Bias's dim size should == 1"; + + const int64_t n = weight_shape->At(0); + CHECK_EQ_OR_RETURN(bias_shape->At(0), n) + << Error::RuntimeError() << "Bias's dim is not equal to weight's first dim. "; + CHECK_EQ_OR_RETURN(weight_shape->At(1), k) + << Error::RuntimeError() << "weight's second dim should be equal to input's second dim. "; + +#if CUDA_VERSION >= 10200 + DeviceType device_type{}; + if (x->is_global()) { + device_type = JUST(x->parallel_desc())->device_type(); + } else { + device_type = JUST(x->device())->enum_type(); + } + + if (device_type == DeviceType::kCUDA) { + if (_add_to_output) { + return OpInterpUtil::Dispatch(*_with_add_to_output_op, + {x, weight, bias, JUST(_add_to_output)}); + } + return OpInterpUtil::Dispatch(*_without_add_to_output_op, {x, weight, bias}); + } +#endif // CUDA_VERSION >= 10200 + + auto matmul_bias = JUST(functional::BiasAdd( + JUST(functional::MatMul(x, weight, false, true, 1.0)), bias, x->shape()->NumAxes() - 1)); + if (_add_to_output) { + return JUST(functional::Add({matmul_bias, JUST(_add_to_output)}, false)); + } + return matmul_bias; + } + + private: + std::shared_ptr _with_add_to_output_op; + std::shared_ptr _without_add_to_output_op; +}; + class FusedMatmulBiasAddReluDropoutFunctor { public: FusedMatmulBiasAddReluDropoutFunctor() { @@ -646,7 +783,7 @@ class FusedMatmulBiasAddReluDropoutFunctor { #if CUDA_VERSION >= 11060 DeviceType device_type{}; - if (x->is_consistent()) { + if (x->is_global()) { device_type = JUST(x->parallel_desc())->device_type(); } else { device_type = JUST(x->device())->enum_type(); @@ -658,9 +795,8 @@ class FusedMatmulBiasAddReluDropoutFunctor { input[0] = x; std::copy(weights.begin(), weights.end(), input.begin() + 1); std::copy(biases.begin(), biases.end(), input.begin() + 1 + weight_size); - MutableAttrMap attrs; - JUST(attrs.SetAttr("skip_final_activation", skip_final_activation)); - JUST(attrs.SetAttr>("dropout_rate_list", dropout_rate_list)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("skip_final_activation", "dropout_rate_list"); + attrs.SetAllAttrs(skip_final_activation, dropout_rate_list); return OpInterpUtil::Dispatch(*fused_op_[weight_size], input, OpExprInterpContext(attrs, dropout_state)); } @@ -706,12 +842,9 @@ class LayerNormFunctor { } Maybe operator()(const std::shared_ptr& x, const int64_t& begin_norm_axis, const int64_t& begin_params_axis, const double& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("begin_norm_axis", begin_norm_axis)); - JUST(attrs.SetAttr("begin_params_axis", begin_params_axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("center", false)); - JUST(attrs.SetAttr("scale", false)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "begin_params_axis", "epsilon", + "center", "scale"); + attrs.SetAllAttrs(begin_norm_axis, begin_params_axis, epsilon, false, false); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -735,12 +868,9 @@ class LayerNormAffineFunctor { const std::shared_ptr& gamma, const std::shared_ptr& beta, const int64_t& begin_norm_axis, const int64_t& begin_params_axis, const double& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("begin_norm_axis", begin_norm_axis)); - JUST(attrs.SetAttr("begin_params_axis", begin_params_axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("center", true)); - JUST(attrs.SetAttr("scale", true)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "begin_params_axis", "epsilon", + "center", "scale"); + attrs.SetAllAttrs(begin_norm_axis, begin_params_axis, epsilon, true, true); return OpInterpUtil::Dispatch(*op_, {x, gamma, beta}, attrs); } @@ -748,6 +878,104 @@ class LayerNormAffineFunctor { std::shared_ptr op_; }; +class GroupNormFunctor { + public: + GroupNormFunctor() { + op_ = CHECK_JUST(one::OpBuilder("group_norm") + .Input("x") + .Output("y") + .Output("mean") + .Output("inv_variance") + .Attr("affine", false) + .Build()); + affine_op_ = CHECK_JUST(one::OpBuilder("group_norm") + .Input("x") + .Input("gamma") + .Input("beta") + .Output("y") + .Output("mean") + .Output("inv_variance") + .Attr("affine", true) + .Build()); + } + Maybe operator()(const std::shared_ptr& x, + const Optional& gamma, const Optional& beta, + const bool affine, const int32_t num_groups, const double& epsilon, + const std::string& data_format, const std::string& activation) const { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("num_groups", "epsilon", "data_format", "activation"); + attrs.SetAllAttrs(num_groups, epsilon, data_format, activation); + if (affine) { + return OpInterpUtil::Dispatch(*affine_op_, {x, JUST(gamma), JUST(beta)}, attrs); + } else { + return OpInterpUtil::Dispatch(*op_, {x}, attrs); + } + } + + private: + std::shared_ptr op_; + std::shared_ptr affine_op_; +}; + +bool CheckNormShape(const Shape& x_shape, const Shape& normalized_shape) { + if (x_shape.size() < normalized_shape.size()) { return false; } + size_t b_ndim = x_shape.size() - normalized_shape.size(); + for (int i = 0; i < x_shape.size(); ++i) { + if (i >= b_ndim) { + if (x_shape[i] != normalized_shape[i - b_ndim]) { return false; } + } + } + return true; +} + +class RMSNormFunctor { + public: + RMSNormFunctor() { + op_ = CHECK_JUST(one::OpBuilder("rms_norm").Input("x").Output("y").Output("inv_rms").Build()); + op_affine_ = CHECK_JUST(one::OpBuilder("rms_norm") + .Input("x") + .Input("weight") + .Output("y") + .Output("inv_rms") + .Build()); + } + + Maybe operator()(const std::shared_ptr& x, const Optional& weight, + const Shape& normalized_shape, const float epsilon) const { + const Shape& x_shape = *x->shape(); + if (weight) { + const Shape& w_shape = *JUST(weight)->shape(); + CHECK_EQ_OR_RETURN(w_shape, normalized_shape) + << "Expected weight be the same shape with normalized_shape " + << normalized_shape.ToString() << ", but got " << w_shape.ToString(); + } + if (!CheckNormShape(x_shape, normalized_shape)) { + auto shape_str_without_parentheses = + x_shape.ToString().substr(1, x_shape.ToString().size() - 2); + return Error::RuntimeError() + << "Given normalized_shape=" << normalized_shape.ToString() + << ", expected input with shape (*, " << shape_str_without_parentheses + << "), but got input of " << x_shape.ToString(); + } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("normalized_shape", "epsilon"); + attrs.SetAllAttrs(normalized_shape, epsilon); + if (weight) { + const DataType dtype = x->dtype()->data_type(); + if (JUST(weight)->dtype()->data_type() != dtype) { + auto weight_cast = JUST(functional::Cast(JUST(weight), DType{dtype}, /*pin_memory=*/false)); + return OpInterpUtil::Dispatch(*op_affine_, {x, weight_cast}, attrs); + } + return OpInterpUtil::Dispatch(*op_affine_, {x, JUST(weight)}, attrs); + } + return OpInterpUtil::Dispatch(*op_, {x}, attrs); + } + + private: + std::shared_ptr op_; + std::shared_ptr op_affine_; +}; + class PixelShuffleFunctor { public: PixelShuffleFunctor() {} @@ -792,14 +1020,11 @@ class TFPoolNDFunctor { const std::vector& padding_before, const std::vector& padding_after, const std::string& data_format, const bool& ceil_mode) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("pool_size", kernel_size)); - JUST(attrs.SetAttr>("strides", strides)); - JUST(attrs.SetAttr("padding", padding)); - JUST(attrs.SetAttr>("padding_before", padding_before)); - JUST(attrs.SetAttr>("padding_after", padding_after)); - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("pool_size", "strides", "padding", "padding_before", + "padding_after", "data_format", "ceil_mode"); + attrs.SetAllAttrs(kernel_size, strides, padding, padding_before, padding_after, data_format, + ceil_mode); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -821,40 +1046,26 @@ class MaxPoolNDFunctor { if (!return_indices && dilation.at(0) == 1 && dilation.at(1) == 1) { // legacy tf style maxpool2d , use cudnn implementation // with high performance but do not support dilation/return_indices - MutableAttrMap attrs; std::vector padding_before{padding.at(0), padding.at(1)}; std::vector padding_after{padding.at(0), padding.at(1)}; - JUST(attrs.SetAttr>("pool_size", kernel_size)); - if (stride.has_value()) { - JUST(attrs.SetAttr>("strides", *JUST(stride))); - } else { - JUST(attrs.SetAttr>("strides", kernel_size)); - } - JUST(attrs.SetAttr("padding", "customized")); - JUST(attrs.SetAttr>("padding_before", padding_before)); - JUST(attrs.SetAttr>("padding_after", padding_after)); - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("pool_size", "strides", "padding", "padding_before", + "padding_after", "data_format", "ceil_mode"); + attrs.SetAllAttrs(kernel_size, stride ? *JUST(stride) : kernel_size, + std::string("customized"), padding_before, padding_after, data_format, + ceil_mode); TensorTuple output; output.emplace_back(JUST(OpInterpUtil::Dispatch(*tf_maxpool_op_, {x}, attrs))); return output; } } - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - if (stride.has_value()) { - JUST(attrs.SetAttr>("stride", *JUST(stride))); - } else { - JUST(attrs.SetAttr>( - "stride", kernel_size)); // If stride is None, we set it as kernel_size to align Pytorch. - } - JUST(attrs.SetAttr>("dilation", dilation)); - JUST(attrs.SetAttr("return_indices", return_indices)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("kernel_size", "padding", "stride", "dilation", + "data_format", "return_indices", "ceil_mode"); + // If stride is None, we set it as kernel_size to align Pytorch. + attrs.SetAllAttrs(kernel_size, padding, stride ? *JUST(stride) : kernel_size, dilation, + data_format, return_indices, ceil_mode); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -892,14 +1103,64 @@ class MaxPool3DFunctor : public MaxPoolNDFunctor { } }; +template +class MaxUnpoolNDFunctor { + public: + MaxUnpoolNDFunctor() + : op_(CHECK_JUST(one::OpBuilder(fmt::format("max_unpool_{}d", N)) + .Input("x") + .Input("indices") + .Output("y") + .Build())){}; + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& indices, + const std::vector& kernel_size, + const Optional>& stride, + const std::vector& padding, + const Optional& output_size) const { + const auto fmt_error_msg = [](const std::string& name, int32_t num, bool check_element) { + if (check_element) { + return fmt::format("each element in `{}` must be greater than 0, got {}", name, num); + } + return fmt::format("`{}` must be an integer or a list of {} integers", name, N); + }; + + CHECK_EQ_OR_RETURN(kernel_size.size(), N) << fmt_error_msg("kernel_size", N, false); + for (int32_t pool_dim : kernel_size) { + CHECK_GT_OR_RETURN(pool_dim, 0) << fmt_error_msg("kernel_size", pool_dim, true); + } + + if (stride) { + CHECK_EQ_OR_RETURN(JUST(stride)->size(), N) << fmt_error_msg("stride", N, false); + for (int32_t stride_dim : *JUST(stride)) { + CHECK_GT_OR_RETURN(stride_dim, 0) << fmt_error_msg("stride", stride_dim, true); + } + } + for (int32_t i = 0; i < padding.size(); i++) { + CHECK_GE_OR_RETURN(kernel_size[i], 2 * padding[i]) + << "pad should be smaller than half of kernel size"; + } + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("kernel_size", "padding", "stride", + "has_output_size", "output_size"); + attrs.SetAllAttrs(kernel_size, padding, stride ? *JUST(stride) : kernel_size, + output_size.has_value(), + output_size.has_value() ? *JUST(output_size) : Shape()); + return OpInterpUtil::Dispatch(*op_, {x, indices}, attrs); + } + + protected: + std::shared_ptr op_; +}; + class AdaptivePoolNDFunctor { public: AdaptivePoolNDFunctor() = default; virtual ~AdaptivePoolNDFunctor() = default; Maybe operator()(const std::shared_ptr& x, const std::vector& output_size) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("output_size", output_size)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("output_size"); + attrs.SetAllAttrs(output_size); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -928,6 +1189,44 @@ class AdaptiveAvgPool3DFunctor : public AdaptivePoolNDFunctor { } }; +class AdaptiveMaxPoolBaseFunctor { + public: + AdaptiveMaxPoolBaseFunctor() = default; + virtual ~AdaptiveMaxPoolBaseFunctor() = default; + Maybe operator()(const std::shared_ptr& x, + const std::vector& output_size) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("output_size"); + attrs.SetAllAttrs(output_size); + return OpInterpUtil::Dispatch(*op_, {x}, attrs); + } + + protected: + std::shared_ptr op_; +}; + +class AdaptiveMaxPool1DFunctor : public AdaptiveMaxPoolBaseFunctor { + public: + AdaptiveMaxPool1DFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("adaptive_max_pool1d").Input("x").Output("y").Output("index").Build()); + } +}; + +class AdaptiveMaxPool2DFunctor : public AdaptiveMaxPoolBaseFunctor { + public: + AdaptiveMaxPool2DFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("adaptive_max_pool2d").Input("x").Output("y").Output("index").Build()); + } +}; + +class AdaptiveMaxPool3DFunctor : public AdaptiveMaxPoolBaseFunctor { + public: + AdaptiveMaxPool3DFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("adaptive_max_pool3d").Input("x").Output("y").Output("index").Build()); + } +}; class LossFunctorBase { public: Maybe apply_reduction(const Maybe& x, const std::string& reduction) const { @@ -978,8 +1277,8 @@ class SmoothL1LossFunctor : LossFunctorBase { Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& target, const float& beta, const std::string& reduction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("beta", beta)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("beta"); + attrs.SetAllAttrs(beta); return apply_reduction(OpInterpUtil::Dispatch(*op_, {input, target}, attrs), reduction); } @@ -996,9 +1295,16 @@ class KLDivLossFunctor : public LossFunctorBase { Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& target, const bool log_target, const std::string& reduction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("log_target", log_target)); - return apply_reduction(OpInterpUtil::Dispatch(*op_, {input, target}, attrs), reduction); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("log_target"); + attrs.SetAllAttrs(log_target); + if (reduction == "batchmean" && input->ndim() != 0) { + const auto& result = JUST( + apply_reduction(OpInterpUtil::Dispatch(*op_, {input, target}, attrs), "sum")); + return ScalarDiv(result, input->shape()->At(0)); + } else { + return apply_reduction(OpInterpUtil::Dispatch(*op_, {input, target}, attrs), + reduction); + } } private: @@ -1043,10 +1349,8 @@ class BinaryCrossEntropyLossFunctor : public LossFunctorBase { const std::shared_ptr& target, const Optional& weight, const std::string& reduction) const { - MutableAttrMap attrs; - auto out = - weight ? OpInterpUtil::Dispatch(*op_weight_, {input, target, JUST(weight)}, attrs) - : OpInterpUtil::Dispatch(*op_, {input, target}, attrs); + auto out = weight ? OpInterpUtil::Dispatch(*op_weight_, {input, target, JUST(weight)}) + : OpInterpUtil::Dispatch(*op_, {input, target}); return apply_reduction(out, reduction); } @@ -1093,9 +1397,21 @@ class BinaryCrossEntropyWithLogitsLossFunctor : public LossFunctorBase { const Optional& weight, const Optional& pos_weight, const std::string& reduction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("has_pos_weight", pos_weight.has_value())); + if (pos_weight) { + const auto pos_weight_shape = JUST(pos_weight)->shape(); + // pos weight shape = (), (1,), (1,1)... or (input/target.shape[-1],) + const bool is_pos_weight_shape_valid = + (pos_weight_shape->elem_cnt() == 1) + || (pos_weight_shape->NumAxes() == 1 + && pos_weight_shape->At(0) == target->shape()->back()); + + CHECK_OR_RETURN(is_pos_weight_shape_valid) + << Error::RuntimeError() + << "pos_weight must be a vector with length equal to the number of classes."; + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("has_pos_weight"); + attrs.SetAllAttrs(pos_weight.has_value()); std::shared_ptr out; if (weight) { if (pos_weight) { @@ -1191,8 +1507,8 @@ class NLLLossFunctor { target_ = target; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("ignore_index", ignore_index)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("ignore_index"); + attrs.SetAllAttrs(ignore_index); std::shared_ptr nll_result; if (weight) { @@ -1243,7 +1559,18 @@ class CrossEntropyFunctor { Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& target, const Optional& weight, const int64_t& ignore_index, - const std::string& reduction) const { + const std::string& reduction, const double& label_smoothing) const { + if (input->shape() == target->shape()) { + CHECK_OR_RETURN(target->dtype()->is_floating_point()) + << "Expected floating point type for target with class probabilities, got " + << target->dtype()->name(); + CHECK_LT_OR_RETURN(ignore_index, 0) + << "ignore_index is not supported for floating point targe"; + return CrossEntropyProb(input, target, weight, reduction, label_smoothing); + } + if (label_smoothing > 0.0) + return CrossEntropyLabelSmoothing(input, target, weight, ignore_index, reduction, + label_smoothing); CHECK_OR_RETURN(reduction == "none" || reduction == "sum" || reduction == "mean") << Error::RuntimeError() << "Reduction should be none, sum or mean."; const auto& input_shape = input->shape(); @@ -1263,8 +1590,8 @@ class CrossEntropyFunctor { const auto target_ = JUST(functional::Flatten(target, 0, target->shape()->NumAxes() - 1)); - MutableAttrMap attrs; - JUST(attrs.SetAttr("ignore_index", ignore_index)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("ignore_index"); + attrs.SetAllAttrs(ignore_index); std::shared_ptr nll_result; if (weight) { @@ -1291,6 +1618,153 @@ class CrossEntropyFunctor { std::shared_ptr op_nll_weight_; }; +class CrossEntropyLabelSmoothingFunctor { + public: + CrossEntropyLabelSmoothingFunctor() { + op_log_softmax_ = CHECK_JUST(one::OpBuilder("log_softmax").Input("in").Output("prob").Build()); + + op_nll_ = CHECK_JUST(one::OpBuilder("nll") + .Input("input") + .Input("target") + .Output("output") + .Output("out_weight") + .Build()); + + op_nll_weight_ = CHECK_JUST(one::OpBuilder("nll") + .Input("input") + .Input("target") + .Input("weight") + .Output("output") + .Output("out_weight") + .Build()); + } + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& target, + const Optional& weight, const int64_t& ignore_index, + const std::string& reduction, const double& label_smoothing) const { + CHECK_OR_RETURN(reduction == "none" || reduction == "sum" || reduction == "mean") + << Error::RuntimeError() << "Reduction should be none, sum or mean."; + const auto& input_shape = input->shape(); + const auto& target_shape = target->shape(); + + std::vector input_perm(input_shape->dim_vec().size(), 0); + input_perm[input_perm.size() - 1] = 1; + for (size_t i = 1; i < input_perm.size() - 1; ++i) { input_perm[i] = i + 1; } + CHECK_OR_RETURN(label_smoothing > 0.0 && label_smoothing <= 1.0) + << "label_smoothing must be between 0.0 and 1.0. Got: " << label_smoothing; + + const auto& input_ = JUST(sequence_function(functional::Transpose) + .then(std::bind(functional::Reshape, std::placeholders::_1, + Shape({-1, input_shape->At(1)}))) + .then([this](const std::shared_ptr& x) { + return OpInterpUtil::Dispatch(*op_log_softmax_, {x}); + }) + .call(input, input_perm)); + const auto& target_ = JUST(functional::Flatten(target, 0, target->shape()->NumAxes() - 1)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("ignore_index"); + attrs.SetAllAttrs(ignore_index); + + std::shared_ptr nll_result; + if (weight) { + nll_result = JUST(OpInterpUtil::Dispatch( + *op_nll_weight_, {input_, target_, JUST(weight)}, attrs)); + } else { + nll_result = JUST(OpInterpUtil::Dispatch(*op_nll_, {input_, target_}, attrs)); + } + + const auto& ignore_mask = JUST(Reshape(JUST(ScalarLogicalEqual(target_, ignore_index)), {-1})); + + // smooth_loss = (-(input_ * weight.reshape(1, -1)).sum(1) * ~ignore_mask).reshape_as(target) + std::shared_ptr smooth_loss = input_; + if (weight) { + const auto& weight_2d = JUST(Reshape(JUST(weight), {1, -1})); + smooth_loss = JUST(Mul(smooth_loss, weight_2d)); + } + smooth_loss = JUST(Negative(JUST(ReduceSum(smooth_loss, {1}, false)))); + smooth_loss = JUST(MaskedFill(smooth_loss, ignore_mask, 0.0)); + smooth_loss = JUST(Reshape(smooth_loss, *target_shape)); + + int64_t n_classes = input->shape()->At(1); + auto nll_loss = JUST(VectorAt(*nll_result, 0)); + nll_loss = JUST(functional::Reshape(nll_loss, *target_shape)); + + // loss = nll_loss * (1 - label_smoothing) + smooth_loss * label_smoothing / num_classes + if (reduction == "none") { + return JUST(Add(JUST(ScalarMul(nll_loss, 1 - label_smoothing, false)), + JUST(ScalarMul(smooth_loss, label_smoothing / n_classes, false)), 1, false)); + } + + const auto& nll_loss_sum = JUST(ReduceSum(nll_loss, {}, false)); + const auto& smooth_loss_sum = JUST(ReduceSum(smooth_loss, {}, false)); + const auto& cross_entropy_loss_sum = + JUST(Add(JUST(ScalarMul(nll_loss_sum, 1 - label_smoothing, false)), + JUST(ScalarMul(smooth_loss_sum, label_smoothing / n_classes, false)), 1, false)); + if (reduction == "sum") { return cross_entropy_loss_sum; } + + const auto& total_weight = JUST(ReduceSum(JUST(VectorAt(*nll_result, 1)), {}, false)); + return Div(cross_entropy_loss_sum, total_weight); + } + + private: + std::shared_ptr op_log_softmax_; + std::shared_ptr op_nll_; + std::shared_ptr op_nll_weight_; +}; + +class CrossEntropyProbFunctor : public LossFunctorBase { + public: + CrossEntropyProbFunctor() { + op_log_softmax_ = CHECK_JUST(one::OpBuilder("log_softmax").Input("in").Output("prob").Build()); + } + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& target, + const Optional& weight, const std::string& reduction, + const double& label_smoothing) const { + const auto& input_shape = input->shape(); + const auto& target_shape = target->shape(); + + std::vector input_perm(input_shape->NumAxes(), 0); + input_perm[input_perm.size() - 1] = 1; + for (size_t i = 1; i < input_perm.size() - 1; ++i) { input_perm[i] = i + 1; } + + const auto input_ = JUST(sequence_function(functional::Transpose) + .then(std::bind(functional::Reshape, std::placeholders::_1, + Shape({-1, input_shape->At(1)}))) + .then([this](const std::shared_ptr& x) { + return OpInterpUtil::Dispatch(*op_log_softmax_, {x}); + }) + .call(input, input_perm)); + std::shared_ptr target_ = + JUST(sequence_function(functional::Transpose) + .then(std::bind(functional::Reshape, std::placeholders::_1, + Shape({-1, target_shape->At(1)}))) + .call(target, input_perm)); + if (label_smoothing > 0) { + int32_t num_classes = input_->shape()->At(1); + target_ = + JUST(ScalarAdd(JUST(ScalarMul(target_, static_cast(1) - label_smoothing, false)), + label_smoothing / static_cast(num_classes), 1, false)); + } + + auto nll_result = JUST(Negative(JUST(Mul(input_, target_)))); + if (weight) { + const auto& weight_expand = JUST(Unsqueeze(JUST(weight), 0)); + nll_result = JUST(Mul(nll_result, weight_expand)); + } + DimVector target_reshape_(input->ndim() - 1); + for (size_t i = 0; i < target_reshape_.size(); ++i) { + target_reshape_[i] = input_shape->At(input_perm[i]); + } + nll_result = JUST(ReduceSum(nll_result, {-1}, false)); + nll_result = JUST(Reshape(nll_result, Shape(target_reshape_))); + return apply_reduction(nll_result, reduction); + } + + private: + std::shared_ptr op_log_softmax_; +}; + class SparseCrossEntropyFunctor { public: SparseCrossEntropyFunctor() { @@ -1302,8 +1776,8 @@ class SparseCrossEntropyFunctor { } Maybe operator()(const std::shared_ptr& prediction, const std::shared_ptr& label, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); return OpInterpUtil::Dispatch(*op_, {prediction, label}, attrs); } @@ -1323,8 +1797,8 @@ class SparseCrossEntropyMsFunctor { } Maybe operator()(const std::shared_ptr& prediction, const std::shared_ptr& label, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); return OpInterpUtil::Dispatch(*op_, {prediction, label}, attrs); } @@ -1392,7 +1866,7 @@ class SparseSoftmaxCrossEntropyFunctor { Maybe RunWithMsVersion(const std::shared_ptr& logits, const std::shared_ptr& label) const { - if (!(logits->is_consistent() && label->is_consistent())) { return false; } + if (!(logits->is_global() && label->is_global())) { return false; } if (JUST(logits->parallel_desc())->parallel_num() == 1) { return false; } @@ -1416,9 +1890,9 @@ class SparseSoftmaxCrossEntropyFunctor { Maybe SparseSoftmaxCrossEntropyOperator(const std::shared_ptr& logits, const std::shared_ptr& label) const { - MutableAttrMap attrs; int64_t depth = logits->shape()->At(logits->shape()->NumAxes() - 1); - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); const auto& result = JUST(OpInterpUtil::Dispatch(*op_sparse_softmax_cross_entropy_, {logits, label}, attrs)); return result->at(1); @@ -1426,9 +1900,9 @@ class SparseSoftmaxCrossEntropyFunctor { Maybe LazySparseSoftmaxCrossEntropyMsOperator( const std::shared_ptr& logits, const std::shared_ptr& label) const { - MutableAttrMap attrs; int64_t depth = logits->shape()->At(logits->shape()->NumAxes() - 1); - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); const auto& result = JUST(OpInterpUtil::Dispatch( *op_sparse_softmax_cross_entropy_ms_, {logits, label}, attrs)); return result->at(1); @@ -1437,10 +1911,11 @@ class SparseSoftmaxCrossEntropyFunctor { Maybe EagerSparseSoftmaxCrossEntropyMsOperator( const std::shared_ptr& logits, const std::shared_ptr& label) const { // op_reduce_max_device_stage_ - MutableAttrMap attrs; int64_t depth = logits->shape()->At(logits->shape()->NumAxes() - 1); int32_t axis = logits->shape()->NumAxes() - 1; - JUST(attrs.SetAttr>("axis", {axis})); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(std::vector{axis}); const auto& max_device_stage = JUST(OpInterpUtil::Dispatch(*op_reduce_max_device_stage_, {logits}, attrs)); std::shared_ptr max_global_stage_input0 = max_device_stage->at(0); @@ -1470,52 +1945,51 @@ class SparseSoftmaxCrossEntropyFunctor { s0s1_sbp_parallels.emplace_back(logits_nd_sbp.sbp_parallel(0)); s0s1_sbp_parallels.emplace_back(logits_nd_sbp.sbp_parallel(1)); - max_global_stage_input0 = JUST(functional::ToConsistent( - max_device_stage->at(0), JUST(max_device_stage->at(0)->parallel_desc()), - new_sbp_parallels, s0s1_sbp_parallels, /* check_meta */ false)); - max_global_stage_input1 = JUST(functional::ToConsistent( - max_device_stage->at(2), JUST(max_device_stage->at(0)->parallel_desc()), - new_sbp_parallels, s0s1_sbp_parallels, /* check_meta */ false)); + max_global_stage_input0 = JUST(functional::ToGlobal( + (*max_device_stage)[0], JUST((*max_device_stage)[0]->parallel_desc()), new_sbp_parallels, + s0s1_sbp_parallels, /* check_meta */ false, /*copy=*/false)); + max_global_stage_input1 = JUST(functional::ToGlobal( + (*max_device_stage)[2], JUST((*max_device_stage)[0]->parallel_desc()), new_sbp_parallels, + s0s1_sbp_parallels, /* check_meta */ false, /*copy=*/false)); } // op_reduce_max_global_stage_ - attrs.clear(); - JUST(attrs.SetAttr>("axis", {axis})); - JUST(attrs.SetAttr("keepdims", true)); + auto& reduce_max_global_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + reduce_max_global_attrs.SetAllAttrs(std::vector{axis}, true); const auto& max_global_stage = JUST(OpInterpUtil::Dispatch( - *op_reduce_max_global_stage_, {max_global_stage_input0, max_global_stage_input1}, attrs)); + *op_reduce_max_global_stage_, {max_global_stage_input0, max_global_stage_input1}, + reduce_max_global_attrs)); auto& broadcast_sub_input = max_global_stage->at(0); if (logits_nd_sbp.sbp_parallel_size() == 2) { - broadcast_sub_input = JUST(functional::ToConsistent( - broadcast_sub_input, JUST(max_device_stage->at(0)->parallel_desc()), new_sbp_parallels, - new_sbp_parallels, /* check_meta */ false)); + broadcast_sub_input = JUST(functional::ToGlobal( + broadcast_sub_input, JUST((*max_device_stage)[0]->parallel_desc()), new_sbp_parallels, + new_sbp_parallels, /* check_meta */ false, /*copy=*/false)); } // op_broadcast_sub_ - attrs.clear(); - const auto& output_broadcast_sub = JUST(OpInterpUtil::Dispatch( - *op_broadcast_sub_, {logits, broadcast_sub_input}, attrs)); + const auto& output_broadcast_sub = JUST( + OpInterpUtil::Dispatch(*op_broadcast_sub_, {logits, broadcast_sub_input})); // op_exp_ const auto& output_exp = - JUST(OpInterpUtil::Dispatch(*op_exp_, {(*output_broadcast_sub)[0]}, attrs)); + JUST(OpInterpUtil::Dispatch(*op_exp_, {(*output_broadcast_sub)[0]})); // op_reduce_sum_ - JUST(attrs.SetAttr>("axis", {axis})); - JUST(attrs.SetAttr("keepdims", true)); - const auto& output_reduce_sum = - JUST(OpInterpUtil::Dispatch(*op_reduce_sum_, {(*output_exp)[0]}, attrs)); + auto& reduce_sum_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "keepdims"); + reduce_sum_attrs.SetAllAttrs(std::vector{axis}, true); + const auto& output_reduce_sum = JUST( + OpInterpUtil::Dispatch(*op_reduce_sum_, {(*output_exp)[0]}, reduce_sum_attrs)); std::shared_ptr broadcast_div_input1 = output_reduce_sum->at(0); if (logits_nd_sbp.sbp_parallel_size() == 2) { std::vector> empty_grad_sbp_parallels; - broadcast_div_input1 = JUST(functional::ToConsistent( - output_reduce_sum->at(0), JUST(output_reduce_sum->at(0)->parallel_desc()), - new_sbp_parallels, new_sbp_parallels, /* check_meta */ false)); + broadcast_div_input1 = JUST(functional::ToGlobal( + (*output_reduce_sum)[0], JUST((*output_reduce_sum)[0]->parallel_desc()), + new_sbp_parallels, new_sbp_parallels, /* check_meta */ false, /*copy=*/false)); } // op_broadcast_div_ - attrs.clear(); const auto& predictions = JUST(OpInterpUtil::Dispatch( - *op_broadcast_div_, {(*output_exp)[0], broadcast_div_input1}, attrs)); + *op_broadcast_div_, {(*output_exp)[0], broadcast_div_input1})); // op_sparse_cross_entropy_ms_ - JUST(attrs.SetAttr("depth", depth)); - const auto& output = JUST(OpInterpUtil::Dispatch(*op_sparse_cross_entropy_ms_, - {(*predictions)[0], label}, attrs)); + auto& sparse_cross_entropy_ms_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + sparse_cross_entropy_ms_attrs.SetAllAttrs(depth); + const auto& output = JUST(OpInterpUtil::Dispatch( + *op_sparse_cross_entropy_ms_, {(*predictions)[0], label}, sparse_cross_entropy_ms_attrs)); return output; } @@ -1587,11 +2061,8 @@ class CombinedMarginLossFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& label, const float& m1, const float& m2, const float& m3) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("m1", m1)); - JUST(attrs.SetAttr("m2", m2)); - JUST(attrs.SetAttr("m3", m3)); - JUST(attrs.SetAttr("depth", x->shape()->At(1))); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("m1", "m2", "m3", "depth"); + attrs.SetAllAttrs(m1, m2, m3, x->shape()->At(1)); return OpInterpUtil::Dispatch(*op_, {x, label}, attrs); } @@ -1616,25 +2087,47 @@ class CtcLossFunctor { const std::shared_ptr& targets, const std::shared_ptr& input_lengths, const std::shared_ptr& target_lengths, - const int64_t& max_target_length, const int& blank, + const int64_t& max_target_length, const int64_t& blank, const bool& zero_infinity, const std::string& reduction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("max_target_length", max_target_length)); - JUST(attrs.SetAttr("blank", blank)); - JUST(attrs.SetAttr("zero_infinity", zero_infinity)); - auto out = JUST(OpInterpUtil::Dispatch( - *op_, {log_probs, targets, input_lengths, target_lengths}, attrs)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("max_target_length", "blank", "zero_infinity"); + attrs.SetAllAttrs(max_target_length, blank, zero_infinity); + std::shared_ptr out; + if (targets->dtype()->data_type() == DataType::kInt32) { + out = JUST(OpInterpUtil::Dispatch( + *op_, {log_probs, targets, input_lengths, target_lengths}, attrs)); + } else { + out = JUST(OpInterpUtil::Dispatch( + *op_, + {log_probs, JUST(functional::Cast(targets, DType::Int64(), false)), input_lengths, + target_lengths}, + attrs)); + } if (zero_infinity) { - const auto create_constant = [&](const Scalar& scalar) -> Maybe { - return functional::Constant(*out->shape(), scalar, out->dtype(), JUST(out->device())); - }; - - out = JUST(sequence_function(functional::Constant) - .then(std::bind(functional::BroadcastEqual, out, std::placeholders::_1)) - .then(std::bind(functional::Where, std::placeholders::_1, - JUST(create_constant(Scalar(0))), out)) - .call(*out->shape(), Scalar(std::numeric_limits::infinity()), - out->dtype(), JUST(out->device()))); + if (out->is_local()) { + const auto create_constant = [&](const Scalar& scalar) -> Maybe { + return functional::Constant(*out->shape(), scalar, out->dtype(), JUST(out->device())); + }; + + out = JUST(sequence_function(functional::Constant) + .then(std::bind(functional::BroadcastEqual, out, std::placeholders::_1)) + .then(std::bind(functional::Where, std::placeholders::_1, + JUST(create_constant(Scalar(0))), out)) + .call(*out->shape(), Scalar(std::numeric_limits::infinity()), + out->dtype(), JUST(out->device()))); + } else { + const auto& placement = JUST(out->parallel_desc()); + const auto& nd_sbp = *JUST(GetSbpList(JUST(out->nd_sbp()))); + const auto create_constant = [&](const Scalar& scalar) -> Maybe { + return functional::GlobalConstant(*out->shape(), scalar, out->dtype(), placement, nd_sbp); + }; + + out = JUST(sequence_function(functional::GlobalConstant) + .then(std::bind(functional::BroadcastEqual, out, std::placeholders::_1)) + .then(std::bind(functional::Where, std::placeholders::_1, + JUST(create_constant(Scalar(0))), out)) + .call(*out->shape(), Scalar(std::numeric_limits::infinity()), + out->dtype(), placement, nd_sbp)); + } } CHECK_OR_RETURN([&]() -> bool { if ((reduction != "none") && (reduction != "sum") && (reduction != "mean")) return false; @@ -1718,9 +2211,8 @@ class AffineGridFunctor { } Maybe operator()(const std::shared_ptr& theta, const Shape& size, const bool& align_corners) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("size", size)); - JUST(attrs.SetAttr("align_corners", align_corners)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("size", "align_corners"); + attrs.SetAllAttrs(size, align_corners); return OpInterpUtil::Dispatch(*op_, {theta}, attrs); } @@ -1738,10 +2230,9 @@ class GridSampleFunctor { const std::shared_ptr& grid, const std::string& interpolation_mode, const std::string& padding_mode, const bool& align_corners) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("interpolation_mode", interpolation_mode)); - JUST(attrs.SetAttr("padding_mode", padding_mode)); - JUST(attrs.SetAttr("align_corners", align_corners)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("interpolation_mode", "padding_mode", "align_corners"); + attrs.SetAllAttrs(interpolation_mode, padding_mode, align_corners); return OpInterpUtil::Dispatch(*op_, {input, grid}, attrs); } @@ -1749,161 +2240,6 @@ class GridSampleFunctor { std::shared_ptr op_; }; -class NormalFunctor { - public: - NormalFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } - Maybe operator()(const float& mean, const float& std, const Shape& shape, - const Optional& out, - const Optional>& optional_dtype, - const Optional>& optional_device, - const Optional& optional_generator, - const bool& requires_grad) const { - Symbol dtype = DType::Float(); - if (optional_dtype.has_value()) { - dtype = JUST(optional_dtype); - if (dtype->data_type() != DataType::kFloat && dtype->data_type() != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in normal()."; - } - } - Symbol device = JUST(Device::New("cpu")); - if (optional_device.has_value()) { device = JUST(optional_device); } - - if (out.has_value()) { - auto out_tensor = JUST(out); - Symbol output_tensor_dtype = out_tensor->dtype(); - if (optional_dtype.has_value()) { - CHECK_OR_RETURN(output_tensor_dtype == dtype) - << Error::RuntimeError() << "data type " << dtype->name() - << " does not match data type of out parameter " << output_tensor_dtype->name(); - } - dtype = output_tensor_dtype; - Symbol out_tensor_device = JUST(out_tensor->device()); - if (optional_device.has_value()) { - CHECK_OR_RETURN(out_tensor_device == JUST(optional_device)) - << Error::RuntimeError() << "device type " << device->ToString() - << " does not match device type of out parameter " << out_tensor_device->ToString(); - } - device = out_tensor_device; - } - - const auto gen = optional_generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("mean", mean)); - JUST(attrs.SetAttr("std", std)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("seed", gen->current_seed())); - - const auto& distribution_state = std::make_shared(gen); - OpExprInterpContext ctx(attrs, distribution_state); - ctx.device = device; - if (out.has_value()) { - std::shared_ptr outputs = std::make_shared(1); - (*outputs)[0] = JUST(out); - JUST(OpInterpUtil::Dispatch(*op_, {}, outputs.get(), ctx)); - return (*outputs)[0]; - } - - auto result = JUST(OpInterpUtil::Dispatch(*op_, {}, ctx)); - JUST(result->set_requires_grad(requires_grad)); - return result; - } - - private: - std::shared_ptr op_; -}; - -class Normal2Functor { - public: - Maybe operator()(const float& mean, const float& std, const int32_t& shape, - const Optional& out, - const Optional>& optional_dtype, - const Optional>& optional_device, - const Optional& optional_generator, - const bool& requires_grad) const { - const Shape size = Shape({shape}); - return Normal(mean, std, size, out, optional_dtype, optional_device, optional_generator, - requires_grad); - } -}; - -class ConsistentNormalFunctor { - public: - ConsistentNormalFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } - Maybe operator()(const float& mean, const float& std, const Shape& shape, - const Optional& out, const Symbol& placement, - const std::vector>& sbp_tuple, - const Optional>& optional_dtype, - const Optional& optional_generator, - const bool& requires_grad) const { - JUST(CheckDeviceIdsIsValid(placement)); - - Symbol dtype = DType::Float(); - if (optional_dtype.has_value()) { - dtype = JUST(optional_dtype); - if (dtype->data_type() != DataType::kFloat && dtype->data_type() != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in normal()."; - } - } - - if (out.has_value()) { - auto out_tensor = JUST(out); - Symbol output_tensor_dtype = out_tensor->dtype(); - if (optional_dtype.has_value()) { - CHECK_OR_RETURN(output_tensor_dtype == dtype) - << Error::RuntimeError() << "data type " << dtype->name() - << " does not match data type of out parameter (" << output_tensor_dtype->name(); - } - dtype = output_tensor_dtype; - } - - const auto gen = optional_generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("mean", mean)); - JUST(attrs.SetAttr("std", std)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("seed", gen->current_seed())); - - const auto& distribution_state = std::make_shared(gen); - const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); - if (LazyMode::is_enabled()) { - JUST(attrs.SetAttr>("nd_sbp", *JUST(GetNdSbpStrList(nd_sbp)))); - } - - if (out.has_value()) { - std::shared_ptr outputs = std::make_shared(1); - (*outputs)[0] = JUST(out); - JUST(OpInterpUtil::Dispatch( - *op_, {}, outputs.get(), - OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); - return (*outputs)[0]; - } - - auto result = JUST(OpInterpUtil::Dispatch( - *op_, {}, OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); - JUST(result->set_requires_grad(requires_grad)); - return result; - } - - private: - std::shared_ptr op_; -}; - -class ConsistentNormal2Functor { - public: - Maybe operator()(const float& mean, const float& std, const int32_t& shape, - const Optional& out, const Symbol& placement, - const std::vector>& sbp_tuple, - const Optional>& optional_dtype, - const Optional& optional_generator, - const bool& requires_grad) const { - const Shape size = Shape({shape}); - return ConsistentNormal(mean, std, size, out, placement, sbp_tuple, optional_dtype, - optional_generator, requires_grad); - } -}; - class NormalizationFunctor { public: NormalizationFunctor() { @@ -1936,6 +2272,7 @@ class NormalizationFunctor { .Output("inv_variance") .Attr("training", true) .Build()); + cast_op_ = CHECK_JUST(one::OpBuilder("cast").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const Optional& moving_mean, @@ -1943,15 +2280,15 @@ class NormalizationFunctor { const Optional& gamma, const Optional& beta, const int32_t& axis, const float& epsilon, const float& momentum, const bool& training) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "epsilon", "momentum"); // convert torch momentum to tensorflow momentum - JUST(attrs.SetAttr("momentum", 1.0 - momentum)); + attrs.SetAllAttrs(axis, epsilon, static_cast(1.0 - momentum)); CHECK_OR_RETURN((moving_mean && moving_variance) || (!moving_mean && !moving_variance)) << Error::RuntimeError() - << "Both moving_mean and moving_variance should be None or Tensor."; + << "Both running_mean and running_variance should be None or Tensor."; + + const DataType dtype = x->dtype()->data_type(); std::shared_ptr gamma_val; std::shared_ptr beta_val; @@ -1967,26 +2304,71 @@ class NormalizationFunctor { beta_val = JUST(functional::Constant(gamma_beta_shape, 0.0, x->dtype(), JUST(x->device()))); } + const DataType gamma_dtype = gamma_val->dtype()->data_type(); + const DataType beta_dtype = beta_val->dtype()->data_type(); + CHECK_EQ_OR_RETURN(gamma_dtype, beta_dtype) + << Error::RuntimeError() << "gamma and beta have different data types."; + if (gamma_dtype != dtype) { + gamma_val = JUST(functional::Cast(gamma_val, DType{dtype}, /*pin_memory=*/false)); + beta_val = JUST(functional::Cast(beta_val, DType{dtype}, /*pin_memory=*/false)); + } + + std::shared_ptr moving_mean_val; + std::shared_ptr moving_variance_val; + bool need_cast_moving_stats = false; + if (moving_mean) { + const DataType moving_mean_dtype = JUST(moving_mean)->dtype()->data_type(); + CHECK_EQ_OR_RETURN(JUST(moving_variance)->dtype()->data_type(), moving_mean_dtype) + << Error::RuntimeError() << "moving_mean and moving_variance have different data types."; + need_cast_moving_stats = (moving_mean_dtype != dtype); + if (need_cast_moving_stats) { + moving_mean_val = + JUST(functional::Cast(JUST(moving_mean), DType{dtype}, /*pin_memory=*/false)); + moving_variance_val = + JUST(functional::Cast(JUST(moving_variance), DType{dtype}, /*pin_memory=*/false)); + } else { + moving_mean_val = JUST(moving_mean); + moving_variance_val = JUST(moving_variance); + } + } + + std::shared_ptr res; + if (!training) { CHECK_OR_RETURN(moving_mean && moving_variance) << Error::RuntimeError() << "Must have moving_mean and moving_variance in eval mode."; - return OpInterpUtil::Dispatch( - *norm_eval_op_, {x, JUST(moving_mean), JUST(moving_variance), gamma_val, beta_val}, - attrs); + res = JUST(OpInterpUtil::Dispatch( + *norm_eval_op_, {x, moving_mean_val, moving_variance_val, gamma_val, beta_val}, attrs)); + } else if (moving_mean) { + res = JUST(OpInterpUtil::Dispatch( + *norm_training_stats_op_, {x, moving_mean_val, moving_variance_val, gamma_val, beta_val}, + attrs)); + } else { + res = JUST(OpInterpUtil::Dispatch(*norm_training_no_stats_op_, + {x, gamma_val, beta_val}, attrs)); } - if (moving_mean) { - return OpInterpUtil::Dispatch( - *norm_training_stats_op_, - {x, JUST(moving_mean), JUST(moving_variance), gamma_val, beta_val}, attrs); + + if (need_cast_moving_stats) { + // For inplace update moving_mean and moving_variance + JUST(CheckInplaceValid(JUST(moving_mean))); + std::shared_ptr outputs = std::make_shared(1); + outputs->at(0) = JUST(moving_mean); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype", "pin_memory"); + attrs.SetAllAttrs(JUST(moving_mean)->dtype()->data_type(), false); + JUST(OpInterpUtil::Dispatch(*cast_op_, {moving_mean_val}, outputs.get(), attrs)); + JUST(CheckInplaceValid(JUST(moving_variance))); + outputs->at(0) = JUST(moving_variance); + JUST(OpInterpUtil::Dispatch(*cast_op_, {moving_variance_val}, outputs.get(), attrs)); } - return OpInterpUtil::Dispatch(*norm_training_no_stats_op_, - {x, gamma_val, beta_val}, attrs); + + return res; } private: std::shared_ptr norm_eval_op_; std::shared_ptr norm_training_stats_op_; std::shared_ptr norm_training_no_stats_op_; + std::shared_ptr cast_op_; }; class NormalizationAddReluFunctor { @@ -2058,11 +2440,9 @@ class NormalizationAddReluFunctor { const std::shared_ptr& beta, const int32_t& axis, const float& epsilon, const float& momentum, const bool& is_training) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "epsilon", "momentum"); // convert torch momentum to tensorflow momentum - JUST(attrs.SetAttr("momentum", 1.0f - momentum)); + attrs.SetAllAttrs(axis, epsilon, static_cast(1.0 - momentum)); CHECK_OR_RETURN((moving_mean && moving_variance) || (!moving_mean && !moving_variance)) << Error::RuntimeError() @@ -2110,65 +2490,254 @@ class NormalizationAddReluFunctor { std::shared_ptr fused_addend_norm_training_no_stats_op_; }; -class PadFunctor { +class ConstantPadFunctor { public: - PadFunctor() { - pad_ = CHECK_JUST(one::OpBuilder("pad").Input("x").Output("y").Build()); - reflect_pad_ = CHECK_JUST(one::OpBuilder("reflection_pad2d").Input("x").Output("y").Build()); - replicate_pad_ = CHECK_JUST(one::OpBuilder("replication_pad2d").Input("x").Output("y").Build()); + ConstantPadFunctor() { + constant_pad_ = CHECK_JUST(one::OpBuilder("pad").Input("x").Output("y").Build()); } - Maybe operator()(const std::shared_ptr& x, const std::vector& pad, - const std::string& mode, const Scalar& value) const { - const int64_t ndim = x->shape()->NumAxes(); - CHECK_LE_OR_RETURN(pad.size(), 2 * ndim) + Maybe operator()(const std::shared_ptr& input, + const std::vector& pad, const Scalar& value) const { + const int64_t ndim = input->shape()->NumAxes(); + const int64_t pad_size = pad.size(); + CHECK_LE_OR_RETURN(pad_size, 2 * ndim) << Error::RuntimeError() << "Pad size should less than or equal to input axes * 2."; - MutableAttrMap attrs; - JUST(attrs.SetAttr>("padding", pad)); - if (mode == "constant") { - CHECK_EQ_OR_RETURN(pad.size() % 2, 0) - << Error::RuntimeError() << "Length of pad must be even but instead it equals " - << pad.size(); - if (IsFloatingDataType(x->dtype()->data_type()) - || x->dtype()->data_type() == DataType::kFloat16) { - JUST(attrs.SetAttr("floating_constant_value", value.As())); - JUST(attrs.SetAttr("integral_constant_value", 0)); - } else if (IsIntegralDataType(x->dtype()->data_type())) { - JUST(attrs.SetAttr("floating_constant_value", 0)); - JUST(attrs.SetAttr("integral_constant_value", value.As())); - } else { - UNIMPLEMENTED_THEN_RETURN() << "Data type should be floating or integral type."; + CHECK_EQ_OR_RETURN(pad_size % 2, 0) + << Error::RuntimeError() << "Length of pad must be even but instead it equals " << pad_size; + + std::vector pad_before(ndim, 0); + std::vector pad_after(ndim, 0); + const int64_t pad_pair = pad_size / 2; + for (int64_t i = 0; i < pad_pair; ++i) { + pad_before[ndim - i - 1] = pad[2 * i]; + pad_after[ndim - i - 1] = pad[2 * i + 1]; + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding", "floating_constant_value", + "integral_constant_value", "padding_before", + "padding_after"); + if (IsFloatingDataType(input->dtype()->data_type()) + || input->dtype()->data_type() == DataType::kFloat16) { + attrs.SetAllAttrs(pad, value.As(), static_cast(0), pad_before, pad_after); + } else if (IsIntegralDataType(input->dtype()->data_type())) { + attrs.SetAllAttrs(pad, static_cast(0), value.As(), pad_before, pad_after); + } else if (input->dtype() == DType::Bool()) { + int64_t bool_value = value.As(); + CHECK_OR_RETURN(bool_value == 1 || bool_value == 0) + << "value must be 1/0 or True/False for bool Tensor"; + attrs.SetAllAttrs(pad, static_cast(0), value.As(), pad_before, pad_after); + } else { + UNIMPLEMENTED_THEN_RETURN() << "Data type should be floating, bool or integral type."; + } + return OpInterpUtil::Dispatch(*constant_pad_, {input}, attrs); + } + + private: + std::shared_ptr constant_pad_; +}; + +class ReflectionPadFunctor { + public: + ReflectionPadFunctor() { + reflect_pad1d_ = CHECK_JUST(one::OpBuilder("reflection_pad1d").Input("x").Output("y").Build()); + reflect_pad2d_ = CHECK_JUST(one::OpBuilder("reflection_pad2d").Input("x").Output("y").Build()); + } + Maybe operator()(const std::shared_ptr& input, + const std::vector& pad) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding"); + attrs.SetAllAttrs(pad); + const int64_t pad_size = pad.size(); + const size_t ndim = input->ndim(); + CHECK_LE_OR_RETURN(pad_size, 2 * ndim) + << Error::RuntimeError() << "Pad size should less than or equal to input axes * 2."; + + if (pad_size == 2) { + // 2D/3D reflect padding + CHECK_OR_RETURN((ndim == 2 && input->shape()->At(1) != 0) + || (ndim == 3 && input->shape()->At(1) != 0 && input->shape()->At(2) != 0)) + << "2D or 3D (batch mode) tensor expected for input, but got: " << ndim; + const int64_t pad_left = pad[0]; + const int64_t pad_right = pad[1]; + const int64_t dim_w = (ndim == 3) ? 2 : 1; + const int64_t input_width = input->shape()->At(dim_w); + const int64_t output_w = input_width + pad_left + pad_right; + CHECK_OR_RETURN(pad_left < input_width && pad_right < input_width) + << "Padding size should be less than the corresponding input dimension, but got: " + "padding (" + << pad_left << ", " << pad_right << ") at dimension " << dim_w << " of input " + << input->shape()->ToString(); + CHECK_OR_RETURN(output_w >= 1) + << "input (W: " << input_width << ")is too small. Calculated output W: " << output_w; + + if (ndim == 2) { + // for 2D input + auto unsqueezed_input = JUST(functional::Unsqueeze(input, 0)); + auto unsqueezed_output = + JUST(OpInterpUtil::Dispatch(*reflect_pad1d_, {unsqueezed_input}, attrs)); + return JUST(functional::Squeeze(unsqueezed_output, std::vector{0})); + } + return OpInterpUtil::Dispatch(*reflect_pad1d_, {input}, attrs); + } else if (pad_size == 4) { + // 3D/4D reflect padding + bool valid_dims = input->shape()->At(1) != 0 && input->shape()->At(2) != 0; + CHECK_OR_RETURN((ndim == 3 && valid_dims) + || (ndim == 4 && valid_dims && input->shape()->At(3) != 0)) + << "3D or 4D (batch mode) tensor expected for input, but got: " << ndim; + + int dim_h = 1; + int dim_w = 2; + if (ndim == 4) { + dim_w++; + dim_h++; } - std::vector pad_before(ndim, 0); - std::vector pad_after(ndim, 0); - const int64_t pad_pair = pad.size() / 2; - for (int64_t i = 0; i < pad_pair; ++i) { - pad_before[ndim - i - 1] = pad[2 * i]; - pad_after[ndim - i - 1] = pad[2 * i + 1]; + const int64_t pad_left = pad[0]; + const int64_t pad_right = pad[1]; + const int64_t pad_top = pad[2]; + const int64_t pad_bottom = pad[3]; + + const int64_t input_h = input->shape()->At(dim_h); + const int64_t input_w = input->shape()->At(dim_w); + const int64_t output_h = input_h + pad_top + pad_bottom; + const int64_t output_w = input_w + pad_left + pad_right; + CHECK_OR_RETURN(pad_left < input_w && pad_right < input_w) + << Error::RuntimeError() + << "Padding size should be less than the corresponding input " + "dimension, but got: padding (" + << pad_left << ", " << pad_right << ") at dimension " << dim_w << " of input " << ndim; + + CHECK_OR_RETURN(pad_top < input_h && pad_bottom < input_h) + << Error::RuntimeError() + << "Padding size should be less than the corresponding input " + "dimension, but got: padding (" + << pad_top << ", " << pad_bottom << ") at dimension " << dim_h << " of input " << ndim; + + CHECK_OR_RETURN(output_w >= 1 || output_h >= 1) + << Error::RuntimeError() << "input (H: " << input_h << ", W: " << input_w + << ")is too small. Calculated output H: " << output_h << " W: " << output_w; + + if (ndim == 3) { + // for 3D input + auto unsqueezed_input = JUST(functional::Unsqueeze(input, 0)); + auto unsqueezed_output = + JUST(OpInterpUtil::Dispatch(*reflect_pad2d_, {unsqueezed_input}, attrs)); + return JUST(functional::Squeeze(unsqueezed_output, std::vector{0})); } - JUST(attrs.SetAttr>("padding_before", pad_before)); - JUST(attrs.SetAttr>("padding_after", pad_after)); - return OpInterpUtil::Dispatch(*pad_, {x}, attrs); + return OpInterpUtil::Dispatch(*reflect_pad2d_, {input}, attrs); + } else if (pad_size == 6) { + UNIMPLEMENTED_THEN_RETURN() << "5D reflect padding are not supported for now"; + } else { + UNIMPLEMENTED_THEN_RETURN() + << "Only 2D, 3D, 4D, 5D padding with non-constant padding are supported for now"; + } + } + + private: + std::shared_ptr reflect_pad1d_; + std::shared_ptr reflect_pad2d_; +}; +class ReplicationPadFunctor { + public: + ReplicationPadFunctor() { + replicate_pad1d_ = + CHECK_JUST(one::OpBuilder("replication_pad1d").Input("x").Output("y").Build()); + replicate_pad2d_ = + CHECK_JUST(one::OpBuilder("replication_pad2d").Input("x").Output("y").Build()); + } + Maybe operator()(const std::shared_ptr& input, + const std::vector& pad) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding"); + attrs.SetAllAttrs(pad); + const int64_t pad_size = pad.size(); + const size_t ndim = input->ndim(); + CHECK_LE_OR_RETURN(pad_size, 2 * ndim) + << Error::RuntimeError() << "Pad size should less than or equal to input axes * 2."; + if (pad_size == 2) { + // 2D/3D replicate padding + CHECK_OR_RETURN((ndim == 2 && input->shape()->At(0) != 0 && input->shape()->At(1) != 0) + || (ndim == 3 && input->shape()->At(1) != 0 && input->shape()->At(2) != 0)) + << "Expected 2D or 3D (batch mode) tensor with possibly 0 batch size and other " + "non-zero dimensions for input, but got: " + << ndim; + const int64_t pad_left = pad[0]; + const int64_t pad_right = pad[1]; + const int64_t dim_w = (ndim == 3) ? 2 : 1; + const int64_t input_width = input->shape()->At(dim_w); + const int64_t output_w = input_width + pad_left + pad_right; + CHECK_OR_RETURN(output_w >= 1) + << "input (W: " << input_width << ")is too small. Calculated output W: " << output_w; + + if (ndim == 2) { + // for 2D input + auto unsqueezed_input = JUST(functional::Unsqueeze(input, 0)); + auto unsqueezed_output = + JUST(OpInterpUtil::Dispatch(*replicate_pad1d_, {unsqueezed_input}, attrs)); + return JUST(functional::Squeeze(unsqueezed_output, std::vector{0})); + } + return OpInterpUtil::Dispatch(*replicate_pad1d_, {input}, attrs); + } else if (pad_size == 4) { + // 3D/4D replicate padding + bool valid_dims = input->shape()->At(1) != 0 && input->shape()->At(2) != 0; + CHECK_OR_RETURN((ndim == 3 && valid_dims) + || (ndim == 4 && valid_dims && input->shape()->At(3) != 0)) + << "3D or 4D (batch mode) tensor expected for input, but got: " << ndim; + + int dim_h = 1; + int dim_w = 2; + if (ndim == 4) { + dim_w++; + dim_h++; + } + + const int64_t pad_left = pad[0]; + const int64_t pad_right = pad[1]; + const int64_t pad_top = pad[2]; + const int64_t pad_bottom = pad[3]; + + const int64_t input_h = input->shape()->At(dim_h); + const int64_t input_w = input->shape()->At(dim_w); + const int64_t output_h = input_h + pad_top + pad_bottom; + const int64_t output_w = input_w + pad_left + pad_right; + CHECK_OR_RETURN(output_w >= 1 || output_h >= 1) + << Error::RuntimeError() << "input (H: " << input_h << ", W: " << input_w + << ")is too small. Calculated output H: " << output_h << " W: " << output_w; + + if (ndim == 3) { + // for 3D input + auto unsqueezed_input = JUST(functional::Unsqueeze(input, 0)); + auto unsqueezed_output = + JUST(OpInterpUtil::Dispatch(*replicate_pad2d_, {unsqueezed_input}, attrs)); + return JUST(functional::Squeeze(unsqueezed_output, std::vector{0})); + } + return OpInterpUtil::Dispatch(*replicate_pad2d_, {input}, attrs); + } else if (pad_size == 6) { + UNIMPLEMENTED_THEN_RETURN() << "5D replicate padding are not supported for now"; + } else { + UNIMPLEMENTED_THEN_RETURN() + << "Only 2D, 3D, 4D, 5D padding with non-constant padding are supported for now"; + } + } + + private: + std::shared_ptr replicate_pad1d_; + std::shared_ptr replicate_pad2d_; +}; + +class PadFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const std::vector& pad, const std::string& mode, + const Scalar& value) const { + if (mode == "constant") { + return functional::ConstantPad(input, pad, value); } else if (mode == "reflect") { - const int64_t pad_h = x->shape()->dim_vec().at(2); - const int64_t pad_w = x->shape()->dim_vec().at(3); - CHECK_OR_RETURN(pad[2] < pad_h && pad[3] < pad_h && pad[0] < pad_w && pad[1] < pad_w) - << Error::RuntimeError() - << "padding size should be less than the corresponding input dimension!"; - return OpInterpUtil::Dispatch(*reflect_pad_, {x}, attrs); + return functional::ReflectionPad(input, pad); } else if (mode == "replicate") { - return OpInterpUtil::Dispatch(*replicate_pad_, {x}, attrs); + return functional::ReplicationPad(input, pad); } else { UNIMPLEMENTED_THEN_RETURN() << "Pad mode is " << mode << ", but only constant, reflect and replicate are valid."; } } - - private: - std::shared_ptr pad_; - std::shared_ptr reflect_pad_; - std::shared_ptr replicate_pad_; }; class DropoutFunctor { @@ -2195,8 +2764,8 @@ class DropoutFunctor { } const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); const auto& dropout_state = std::make_shared(gen); - MutableAttrMap dropout_attrs; - JUST(dropout_attrs.SetAttr("rate", p)); + auto& dropout_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rate"); + dropout_attrs.SetAllAttrs(p); if (addend) { if ((!training) || p == 0.0) { JUST(OpInterpUtil::Dispatch(*add_op_, {x, JUST(addend)}, outputs.get())); @@ -2223,6 +2792,104 @@ class DropoutFunctor { std::shared_ptr add_op_; }; +namespace { +Maybe MakeFeatureNoise(const std::shared_ptr& x) { + const int64_t ndim = x->ndim(); + CHECK_GE_OR_RETURN(ndim, 2) << Error::RuntimeError() + << "Feature dropout requires at least 2 dimensions in the input"; + std::vector sizes; + sizes.reserve(ndim); + sizes.push_back(x->shape()->At(0)); + sizes.push_back(x->shape()->At(1)); + for (int i = 2; i < ndim; i++) { sizes.push_back(1); } + return JUST(Empty(Shape(sizes), x->dtype(), JUST(x->device()), false)); +} + +Maybe DropoutImpl(const std::shared_ptr& input, const float& p, + const bool& train) { + CHECK_EQ_OR_RETURN(p >= 0 && p <= 1, true) + << "dropout probability has to be between 0 and 1, but got " << p; + if (p == 0 || !train || input->shape()->elem_cnt() == 0) { return input; } + if (p == 1) { + std::shared_ptr other = + JUST(Constant(*input->shape(), Scalar(0.0), input->dtype(), JUST(input->device()))); + return InplaceMul(input, other); + } + std::shared_ptr noise = JUST(MakeFeatureNoise(input)); + noise = + JUST(BernoulliProb(noise, 1.0 - p, noise->dtype(), JUST(one::DefaultAutoGenerator()), false)); + noise = JUST(InplaceScalarDiv(noise, Scalar(1.0 - p))); + noise = JUST(InplaceMul(input, noise)); + return noise; +} +} // namespace + +class Dropout1dFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const float& p, + const bool& training) const { + CHECK_EQ_OR_RETURN(p < 0 || p > 1.0, true) + << "dropout probability has to be between 0 and 1, but got " << p; + const int input_dim = input->ndim(); + CHECK_EQ_OR_RETURN(input_dim != 2 && input_dim != 3, true) + << "dropout1d: Expected 2D or 3D input, but received a {inp_dim}D input. " + "Note that dropout1d exists to provide channel-wise dropout on inputs with 1 " + "spatial dimension, a channel dimension, and an optional batch dimension " + "(i.e. 2D or 3D inputs)."; + bool is_batched = (input_dim == 3); + std::shared_ptr result; + if (!is_batched) { result = JUST(Unsqueeze(input, 0)); } + result = JUST(DropoutImpl(result, p, training)); + if (!is_batched) { result = JUST(Squeeze(result, std::vector{0})); } + return result; + } +}; + +class Dropout2dFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const float& p, + const bool& training) const { + CHECK_EQ_OR_RETURN(p < 0 || p > 1.0, true) + << "dropout probability has to be between 0 and 1, but got " << p; + const int input_dim = input->ndim(); + CHECK_EQ_OR_RETURN(input_dim != 3 && input_dim != 4, true) + << "dropout2d: Received a {inp_dim}-D input to dropout2d, which is deprecated " + "and will result in an error in a future release. To retain the behavior " + "and silence this warning, please use dropout instead. Note that dropout2d " + "exists to provide channel-wise dropout on inputs with 2 spatial dimensions, " + "a channel dimension, and an optional batch dimension (i.e. 3D or 4D inputs)."; + CHECK_EQ_OR_RETURN(input_dim == 3, true) + << "dropout2d: Received a 3D input to dropout2d and assuming that channel-wise " + "1D dropout behavior is desired - input is interpreted as shape (N, C, L), where C " + "is the channel dim. This behavior will change in a future release to interpret the " + "input as one without a batch dimension, i.e. shape (C, H, W). To maintain the 1D " + "channel-wise dropout behavior, please switch to using dropout1d instead."; + return JUST(DropoutImpl(input, p, training)); + } +}; + +class Dropout3dFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const float& p, + const bool& training) const { + CHECK_EQ_OR_RETURN(p < 0 || p > 1.0, true) + << "dropout probability has to be between 0 and 1, but got " << p; + const int input_dim = input->ndim(); + CHECK_EQ_OR_RETURN(input_dim != 4 && input_dim != 5, true) + << "dropout3d: Received a {inp_dim}-D input to dropout3d, which is deprecated " + "and will result in an error in a future release. To retain the behavior " + "and silence this warning, please use dropout instead. Note that dropout3d " + "exists to provide channel-wise dropout on inputs with 3 spatial dimensions, " + "a channel dimension, and an optional batch dimension (i.e. 4D or 5D inputs)."; + bool is_batched = (input_dim == 5); + std::shared_ptr result; + if (!is_batched) { result = JUST(Unsqueeze(input, 0)); } + result = JUST(DropoutImpl(result, p, training)); + if (!is_batched) { result = JUST(Squeeze(result, std::vector{0})); } + return result; + } +}; + class DropoutGradFunctor { public: DropoutGradFunctor() { @@ -2231,8 +2898,8 @@ class DropoutGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& mask, const float& scale) const { - MutableAttrMap dropout_grad_attrs; - JUST(dropout_grad_attrs.SetAttr("scale", scale)); + auto& dropout_grad_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale"); + dropout_grad_attrs.SetAllAttrs(scale); return OpInterpUtil::Dispatch(*dropout_grad_op_, {dy, mask}, dropout_grad_attrs); } @@ -2250,19 +2917,12 @@ class AvgPoolNDFunctor { const std::vector& padding, const bool& ceil_mode, const bool& count_include_pad, const int32_t& divisor_override, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - if (stride.has_value()) { - JUST(attrs.SetAttr>("stride", *JUST(stride))); - } else { - JUST(attrs.SetAttr>( - "stride", kernel_size)); // If stride is None, we set it as kernel_size to align Pytorch. - } - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); - JUST(attrs.SetAttr("count_include_pad", count_include_pad)); - JUST(attrs.SetAttr("divisor_override", divisor_override)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("kernel_size", "padding", "stride", "data_format", + "ceil_mode", "count_include_pad", "divisor_override"); + // If stride is None, we set it as kernel_size to align Pytorch. + attrs.SetAllAttrs(kernel_size, padding, stride ? *JUST(stride) : kernel_size, data_format, + ceil_mode, count_include_pad, divisor_override); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -2296,22 +2956,18 @@ class UnfoldFunctor { UnfoldFunctor() { unfold_op_ = CHECK_JUST(one::OpBuilder("unfold").Input("x").Output("y").Build()); } - Maybe operator()(const std::shared_ptr& x, const std::string& data_format, + Maybe operator()(const std::shared_ptr& x, const std::vector& kernel_size, const std::vector& dilation_rate, - const std::vector& padding, - const std::vector& strides) const { + const std::vector& padding, const std::vector& strides, + const std::string& data_format) const { const auto& x_shape = x->shape(); // Only Support 4d tensor now. CHECK_EQ_OR_RETURN(x_shape->NumAxes(), 4) << Error::RuntimeError() << "Input Tensor dim should == 4"; - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("dilation_rate", dilation_rate)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("strides", strides)); - + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("kernel_size", "dilation_rate", "padding", + "strides", "data_format"); + attrs.SetAllAttrs(kernel_size, dilation_rate, padding, strides, data_format); return OpInterpUtil::Dispatch(*unfold_op_, {x}, attrs); } @@ -2322,24 +2978,19 @@ class UnfoldFunctor { class FoldFunctor { public: FoldFunctor() { fold_op_ = CHECK_JUST(one::OpBuilder("fold").Input("x").Output("y").Build()); } - Maybe operator()(const std::shared_ptr& x, const std::string& data_format, + Maybe operator()(const std::shared_ptr& x, const std::vector& output_size, const std::vector& kernel_size, const std::vector& dilation_rate, - const std::vector& padding, - const std::vector& strides) const { + const std::vector& padding, const std::vector& strides, + const std::string& data_format) const { const auto& x_shape = x->shape(); // Only Support 3d tensor fold now. format is (N, C*K*K, L) CHECK_EQ_OR_RETURN(x_shape->NumAxes(), 3) << Error::RuntimeError() << "Input Tensor dim should == 3"; - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("output_size", output_size)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("dilation_rate", dilation_rate)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("strides", strides)); - + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("output_size", "kernel_size", "dilation_rate", + "padding", "strides", "data_format"); + attrs.SetAllAttrs(output_size, kernel_size, dilation_rate, padding, strides, data_format); return OpInterpUtil::Dispatch(*fold_op_, {x}, attrs); } @@ -2356,38 +3007,33 @@ class OneHotFunctor { const Scalar& on_value, const Scalar& off_value) const { CHECK_OR_RETURN(!IsFloatingDataType(input->dtype()->data_type())) << Error::RuntimeError() << "one_hot is only applicable to index tensor."; - MutableAttrMap attrs; + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("depth", "dtype", "floating_on_value", "floating_off_value", + "integer_on_value", "integer_off_value"); + int64_t depth = num_classes; if (num_classes == -1) { std::vector axis(input->ndim()); std::iota(axis.begin(), axis.end(), 0); auto tensor_max = JUST(functional::ReduceMax(input, axis, false)); int64_t max = 0; - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(&max, 1); // copy 1 scalar(int64_t) tensor's value to max + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, &max, eager_blob_object->dptr(), sizeof(max), + memory::MakeHostMemCase(), eager_blob_object->mem_case()); }; JUST(SyncAccessTensorWithTimeOut(tensor_max, callback, "const")); - JUST(attrs.SetAttr("depth", max + 1)); - - } else { - JUST(attrs.SetAttr("depth", num_classes)); + depth = max + 1; } // Refer to: https://github.com/Oneflow-Inc/oneflow/pull/5315/files#r755823506 bool is_on_value_double = on_value.IsFloatingPoint(); bool is_off_value_double = off_value.IsFloatingPoint(); if (is_on_value_double || is_off_value_double) { - JUST(attrs.SetAttr("dtype", kFloat)); - JUST(attrs.SetAttr("floating_on_value", on_value.As())); - JUST(attrs.SetAttr("floating_off_value", off_value.As())); - JUST(attrs.SetAttr("integer_on_value", 0)); - JUST(attrs.SetAttr("integer_off_value", 0)); + attrs.SetAllAttrs(depth, kFloat, on_value.As(), off_value.As(), + static_cast(0), static_cast(0)); } else { - JUST(attrs.SetAttr("dtype", kInt64)); - JUST(attrs.SetAttr("floating_on_value", 0)); - JUST(attrs.SetAttr("floating_off_value", 0)); - JUST(attrs.SetAttr("integer_on_value", on_value.As())); - JUST(attrs.SetAttr("integer_off_value", off_value.As())); + attrs.SetAllAttrs(depth, kInt64, static_cast(0), static_cast(0), + on_value.As(), off_value.As()); } return OpInterpUtil::Dispatch(*one_hot_op_, {input}, attrs); } @@ -2396,6 +3042,18 @@ class OneHotFunctor { std::shared_ptr one_hot_op_; }; +class PairwiseDistanceFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& y, + const float& p, const double& eps, bool keepdim) const { + const int64_t xdim = x->ndim(); + const int64_t ydim = y->ndim(); + const int64_t output_dim = xdim > ydim ? xdim : ydim; + const auto& sub = JUST(ScalarAdd(JUST(Sub(x, y, 1, false)), eps, 1, false)); + return ScalarNorm(sub, p, output_dim - 1, keepdim, NullOpt); + } +}; + class CosineSimilarityFunctor { public: Maybe operator()(const std::shared_ptr& x, @@ -2411,8 +3069,8 @@ class CosineSimilarityFunctor { int64_t offset = max_shape.NumAxes() - 1 - i; int64_t dim_x = x_shape.NumAxes() - 1 - offset; int64_t dim_y = y_shape.NumAxes() - 1 - offset; - int64_t size_x = (dim_x >= 0) ? x_shape.At(i) : 1; - int64_t size_y = (dim_y >= 0) ? y_shape.At(i) : 1; + int64_t size_x = (dim_x >= 0) ? x_shape.At(dim_x) : 1; + int64_t size_y = (dim_y >= 0) ? y_shape.At(dim_y) : 1; if (!(size_x == size_y || size_x == 1 || size_y == 1)) { return Error::RuntimeError() << "The size of tensor a (" << size_x << ") must match the size of tensor b (" @@ -2454,8 +3112,8 @@ class L2NormalizeFunctor { } Maybe operator()(const std::shared_ptr& input, const int32_t& axis, const float& epsilon) const { - const auto ndims = input->shape()->NumAxes(); - const auto final_dim = ndims - 1; + const int32_t ndims = input->shape()->NumAxes(); + const int32_t final_dim = ndims - 1; auto axis_ = axis >= 0 ? axis : axis + ndims; CHECK_GE_OR_RETURN(axis_, 0) << Error::RuntimeError() << "Axis should >=0 but axis is " << axis_ @@ -2463,9 +3121,8 @@ class L2NormalizeFunctor { CHECK_LE_OR_RETURN(axis_, final_dim) << Error::RuntimeError() << "Axis should < " << ndims << " but axis is " << axis_ << " now."; - MutableAttrMap attrs; - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("axis", final_dim)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("epsilon", "axis"); + attrs.SetAllAttrs(epsilon, final_dim); if (axis_ == final_dim) { return OpInterpUtil::Dispatch(*op_, {input}, attrs); } @@ -2512,9 +3169,8 @@ class FusedSelfAttentionFunctor { } Maybe operator()(const std::shared_ptr& hidden_states, const int64_t& head_size, const float& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("head_size", head_size)); - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("head_size", "alpha"); + attrs.SetAllAttrs(head_size, alpha); return OpInterpUtil::Dispatch(*op_, {hidden_states}, attrs); } @@ -2536,8 +3192,8 @@ class FusedSelfAttentionGradFunctor { const std::shared_ptr& value_grad, const std::shared_ptr& hidden_states, const float& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {query_mul_key_grad, value_grad, hidden_states}, attrs); } @@ -2560,11 +3216,11 @@ class FusedScaleTrilSoftmaxMaskScaleFunctor { } Maybe operator()(const std::shared_ptr& x, const float p, const int64_t diagonal, const float tril_scale_value, + const float tril_fill_value, const Optional& generator) const { const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap random_mask_like_attrs; - JUST(random_mask_like_attrs.SetAttr("rate", p)); - JUST(random_mask_like_attrs.SetAttr("seed", gen->current_seed())); + auto& random_mask_like_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rate", "seed"); + random_mask_like_attrs.SetAllAttrs(p, static_cast(gen->current_seed())); const auto& random_mask_like_state = std::make_shared(gen); const auto& mask = JUST(OpInterpUtil::Dispatch( @@ -2573,11 +3229,9 @@ class FusedScaleTrilSoftmaxMaskScaleFunctor { float mask_scale_value = 1.0; if (p != 1.0) { mask_scale_value = 1.0 / (1.0 - p); } - MutableAttrMap fused_attrs; - JUST(fused_attrs.SetAttr("diagonal", diagonal)); - JUST(fused_attrs.SetAttr("tril_scale_value", tril_scale_value)); - JUST(fused_attrs.SetAttr("mask_scale_value", mask_scale_value)); - + auto& fused_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal", "tril_scale_value", + "mask_scale_value", "tril_fill_value"); + fused_attrs.SetAllAttrs(diagonal, tril_scale_value, mask_scale_value, tril_fill_value); return OpInterpUtil::Dispatch(*fused_op_, {x, mask}, fused_attrs); } @@ -2600,9 +3254,8 @@ class L2NormalizeGradFunctor { const std::shared_ptr& y, const std::shared_ptr& square_x_sum, const int32_t& axis, const float& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "epsilon"); + attrs.SetAllAttrs(axis, epsilon); return OpInterpUtil::Dispatch(*op_, {dy, y, square_x_sum}, attrs); } @@ -2618,8 +3271,8 @@ class FusedBiasAddGeluFunctor { } Maybe operator()(const std::shared_ptr& a, const std::shared_ptr& b, const int32_t& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {a, b}, attrs); } @@ -2640,8 +3293,8 @@ class FusedBiasAddGeluGradFunctor { Maybe operator()(const std::shared_ptr& a, const std::shared_ptr& b, const std::shared_ptr& dy, const int32_t& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); return OpInterpUtil::Dispatch(*op_, {a, b, dy}, attrs); } @@ -2664,23 +3317,22 @@ class FusedBiasAddDropoutFunctor { Maybe operator()(const std::shared_ptr& a, const std::shared_ptr& b, const float& p, const int32_t& axis, const Optional& generator) const { - const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap random_mask_like_attrs; - JUST(random_mask_like_attrs.SetAttr("rate", p)); - JUST(random_mask_like_attrs.SetAttr("seed", gen->current_seed())); - const auto& random_mask_like_state = std::make_shared(gen); - - float scale = 0.0; - if (p != 1.0) { scale = 1.0 / (1.0 - p); } - MutableAttrMap fused_bias_add_mask_attrs; - JUST(fused_bias_add_mask_attrs.SetAttr("scale", scale)); int32_t axis_val = axis; if (axis_val < 0) { const int64_t num_axes = a->shape()->NumAxes(); axis_val += num_axes; } - JUST(fused_bias_add_mask_attrs.SetAttr("axis", axis_val)); if (p > 0.0) { + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + auto& random_mask_like_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rate", "seed"); + random_mask_like_attrs.SetAllAttrs(p, static_cast(gen->current_seed())); + const auto& random_mask_like_state = std::make_shared(gen); + + float scale = 0.0; + if (p != 1.0) { scale = 1.0 / (1.0 - p); } + auto& fused_bias_add_mask_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale", "axis"); + fused_bias_add_mask_attrs.SetAllAttrs(scale, axis_val); + return SequenceFunction()>([&]() -> Maybe { return OpInterpUtil::Dispatch( *random_mask_like_op_, {a}, @@ -2701,6 +3353,100 @@ class FusedBiasAddDropoutFunctor { std::shared_ptr fused_bias_add_mask_scale_op_; }; +class FusedGluFunctor { + public: + FusedGluFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_glu") + .Input("x") + .Input("w") + .Input("b") + .Output("y") + .Output("matmul_wx") + .Build()); + + split_op_ = CHECK_JUST(one::OpBuilder("fused_glu") + .Input("x") + .Input("w") + .Input("b") + .Input("v") + .Input("c") + .Output("y") + .Output("matmul_wx") + .Output("matmul_vx") + .Build()); + } + + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& w, + const std::shared_ptr& b, const Optional& v, + const Optional& c, const std::string& activation) const { + // check whether the user provide splited tensors + bool is_split_mode = false; + if (v && c) { + is_split_mode = true; + } else if (!v && !c) { + is_split_mode = false; + } else { + return Error::RuntimeError() << "expected consistant existance of tensor v and c"; + } + + // obtain input shape + const auto& x_shape = *(x->shape()); + const auto& w_shape = *(w->shape()); + const auto& b_shape = *(b->shape()); + + // check number of axes of x, w and b + CHECK_GE_OR_RETURN(x_shape.NumAxes(), 2) + << "number of axes of \'x\' should have be greater than 1, yet get " << x_shape.NumAxes(); + CHECK_EQ_OR_RETURN(w_shape.NumAxes(), 2) + << "number of axes of \'w\' should have be equal to 2, yet get " << w_shape.NumAxes(); + CHECK_EQ_OR_RETURN(b_shape.NumAxes(), 1) + << "number of axes of \'b\' should have be equal to 1, yet get " << b_shape.NumAxes(); + + // check input shapes of w and b + size_t x_num_axes = x_shape.NumAxes(); + CHECK_EQ_OR_RETURN(w_shape.At(1), x_shape.At(x_num_axes - 1)) + << "dimension 1 of \'w\'(" << w_shape.At(1) + << ") is not consistant with the last dimension of \'x\'(" << x_shape.At(x_num_axes - 1) + << ")"; + CHECK_EQ_OR_RETURN(b_shape.At(0), w_shape.At(0)) + << "dimension 0 of \'b\'(" << b_shape.At(0) + << ") is not consistant with dimension 0 of \'w\'(" << w_shape.At(0) << ")"; + if (!is_split_mode) { + CHECK_EQ_OR_RETURN(w_shape.At(1) % 2, 0) << "dimension 1 of \'w\' is not divisible by 2"; + } + + // check both dimensions and input shapes of v and c (optional) + if (is_split_mode) { + const auto& v_shape = *(JUST(v)->shape()); + const auto& c_shape = *(JUST(c)->shape()); + + CHECK_EQ_OR_RETURN(v_shape.NumAxes(), 2) + << "number of axes of \'v\' should have be equal to 2, yet get " << v_shape.NumAxes(); + CHECK_EQ_OR_RETURN(c_shape.NumAxes(), 1) + << "number of axes of \'c\' should have be equal to 1, yet get " << c_shape.NumAxes(); + + CHECK_OR_RETURN(v_shape == w_shape) << "the shape of \'v\' is not consistant with \'w\'"; + CHECK_OR_RETURN(c_shape == b_shape) << "the shape of \'c\' is not consistant with \'b\'"; + } + + // set activation attribute + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("activation"); + attrs.SetAllAttrs(activation); + + // dispatch corresponding operator + if (is_split_mode) { + return OpInterpUtil::Dispatch(*split_op_, {x, w, b, JUST(v), JUST(c)}, attrs); + } else { + return OpInterpUtil::Dispatch(*op_, {x, w, b}, attrs); + } + } + + private: + std::shared_ptr op_; + std::shared_ptr split_op_; +}; + class FusedScaleTrilFunctor { public: FusedScaleTrilFunctor() { @@ -2709,29 +3455,28 @@ class FusedScaleTrilFunctor { Maybe operator()(const std::shared_ptr& x, const int64_t& diagonal, const Scalar& fill_value, const Scalar& scale) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("diagonal", diagonal)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "diagonal", "floating_fill_value", "is_floating_fill_value", "integer_fill_value", + "floating_scale_value", "is_floating_scale_value", "integer_scale_value"); bool is_fill_value_double = fill_value.IsFloatingPoint(); bool is_scale_double = scale.IsFloatingPoint(); + + double floating_fill_value = 0; + int64_t integer_fill_value = 0; if (is_fill_value_double) { - JUST(attrs.SetAttr("floating_fill_value", fill_value.As())); - JUST(attrs.SetAttr("integer_fill_value", 0)); - JUST(attrs.SetAttr("is_floating_fill_value", true)); + floating_fill_value = fill_value.As(); } else { - JUST(attrs.SetAttr("floating_fill_value", 0)); - JUST(attrs.SetAttr("integer_fill_value", fill_value.As())); - JUST(attrs.SetAttr("is_floating_fill_value", false)); + integer_fill_value = fill_value.As(); } - + double floating_scale_value = 0; + int64_t integer_scale_value = 0; if (is_scale_double) { - JUST(attrs.SetAttr("floating_scale_value", scale.As())); - JUST(attrs.SetAttr("integer_scale_value", 0)); - JUST(attrs.SetAttr("is_floating_scale_value", true)); + floating_scale_value = scale.As(); } else { - JUST(attrs.SetAttr("floating_scale_value", 0)); - JUST(attrs.SetAttr("integer_scale_value", scale.As())); - JUST(attrs.SetAttr("is_floating_scale_value", false)); + integer_scale_value = scale.As(); } + attrs.SetAllAttrs(diagonal, floating_fill_value, is_fill_value_double, integer_fill_value, + floating_scale_value, is_scale_double, integer_scale_value); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -2748,10 +3493,9 @@ class FusedScaleMaskSoftmaxFunctor { Maybe operator()(const std::shared_ptr& x, const std::shared_ptr& mask, const float& fill_value, const float& scale) const { - MutableAttrMap attrs_; - JUST(attrs_.SetAttr("scale_value", scale)); - JUST(attrs_.SetAttr("mask_fill_value", fill_value)); - return OpInterpUtil::Dispatch(*op_, {x, mask}, attrs_); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_value", "mask_fill_value"); + attrs.SetAllAttrs(scale, fill_value); + return OpInterpUtil::Dispatch(*op_, {x, mask}, attrs); } private: @@ -2779,9 +3523,8 @@ class FusedScaleMaskSoftmaxDropoutFunctor { float rate = p; if (!training) rate = 0.0; const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap random_mask_like_attrs; - JUST(random_mask_like_attrs.SetAttr("rate", rate)); - JUST(random_mask_like_attrs.SetAttr("seed", gen->current_seed())); + auto& random_mask_like_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rate", "seed"); + random_mask_like_attrs.SetAllAttrs(rate, static_cast(gen->current_seed())); const auto& random_mask_like_state = std::make_shared(gen); const auto& dropout_mask = JUST(OpInterpUtil::Dispatch( @@ -2790,12 +3533,9 @@ class FusedScaleMaskSoftmaxDropoutFunctor { float dropout_scale = 0.0; if (rate != 1.0) { dropout_scale = 1.0 / (1.0 - rate); } - MutableAttrMap fused_scale_mask_softmax_dropout_attrs; - JUST(fused_scale_mask_softmax_dropout_attrs.SetAttr("scale_value", scale)); - JUST(fused_scale_mask_softmax_dropout_attrs.SetAttr("mask_fill_value", fill_value)); - JUST(fused_scale_mask_softmax_dropout_attrs.SetAttr("dropout_scale_value", - dropout_scale)); - + auto& fused_scale_mask_softmax_dropout_attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("scale_value", "mask_fill_value", "dropout_scale_value"); + fused_scale_mask_softmax_dropout_attrs.SetAllAttrs(scale, fill_value, dropout_scale); return OpInterpUtil::Dispatch(*fused_scale_mask_softmax_dropout_op_, {x, mask, dropout_mask}, fused_scale_mask_softmax_dropout_attrs); @@ -2806,6 +3546,56 @@ class FusedScaleMaskSoftmaxDropoutFunctor { std::shared_ptr fused_scale_mask_softmax_dropout_op_; }; +// Equivalent to +// masked = (x + bias) * mask * scale_value +// unmask = (1 - mask).bool() +// masked.masked_fill_(unmask, mask_fill_value) +// softmax_y = softmax(masked, dim=-1) +// y = dropout(softmax_y, p) +class FusedBiasAddScaleMaskSoftmaxDropoutFunctor { + public: + FusedBiasAddScaleMaskSoftmaxDropoutFunctor() { + random_mask_op_ = + CHECK_JUST(one::OpBuilder("random_mask_like").Input("like").Output("out").Build()); + fused_op_ = CHECK_JUST(one::OpBuilder("fused_bias_add_scale_mask_softmax_dropout") + .Input("x") + .Input("bias") + .Input("mask") + .Input("dropout_mask") + .Output("y") + .Output("softmax_y") + .Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& bias, + const std::shared_ptr& mask, const float& fill_value, + const float& scale, const float& p, const bool& training, + const Optional& generator) const { + float rate = p; + if (!training) rate = 0.0; + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + auto& random_mask_like_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("rate", "seed"); + random_mask_like_attrs.SetAllAttrs(rate, static_cast(gen->current_seed())); + const auto& random_mask_like_state = std::make_shared(gen); + + const auto& dropout_mask = JUST(OpInterpUtil::Dispatch( + *random_mask_op_, {x}, + OpExprInterpContext(random_mask_like_attrs, random_mask_like_state))); + + float dropout_scale = 0.0; + if (rate != 1.0) { dropout_scale = 1.0 / (1.0 - rate); } + auto& fused_scale_mask_softmax_dropout_attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("scale_value", "mask_fill_value", "dropout_scale_value"); + fused_scale_mask_softmax_dropout_attrs.SetAllAttrs(scale, fill_value, dropout_scale); + return OpInterpUtil::Dispatch(*fused_op_, {x, bias, mask, dropout_mask}, + fused_scale_mask_softmax_dropout_attrs); + } + + private: + std::shared_ptr random_mask_op_; + std::shared_ptr fused_op_; +}; + class CtcGreedyDecoderFunctor { public: CtcGreedyDecoderFunctor() { @@ -2819,8 +3609,8 @@ class CtcGreedyDecoderFunctor { Maybe operator()(const std::shared_ptr& log_probs, const std::shared_ptr& input_lengths, const bool& merge_repeated) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("merge_repeated", merge_repeated)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("merge_repeated"); + attrs.SetAllAttrs(merge_repeated); return OpInterpUtil::Dispatch(*op_, {log_probs, input_lengths}, attrs); } @@ -2853,9 +3643,8 @@ class NmsFunctor { Maybe operator()(const std::shared_ptr& x, const float& iou_threshold, const int32_t& keep_n) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("iou_threshold", iou_threshold)); - JUST(attrs.SetAttr("keep_n", keep_n)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("iou_threshold", "keep_n"); + attrs.SetAllAttrs(iou_threshold, keep_n); return OpInterpUtil::Dispatch(*op_, {x}, attrs); } @@ -2873,12 +3662,9 @@ class RoiAlignFunctor { const std::shared_ptr& rois, const float& spatial_scale, const int32_t& pooled_h, const int32_t& pooled_w, const int32_t& sampling_ratio, const bool& aligned) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("spatial_scale", spatial_scale)); - JUST(attrs.SetAttr("pooled_h", pooled_h)); - JUST(attrs.SetAttr("pooled_w", pooled_w)); - JUST(attrs.SetAttr("sampling_ratio", sampling_ratio)); - JUST(attrs.SetAttr("aligned", aligned)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("spatial_scale", "pooled_h", "pooled_w", + "sampling_ratio", "aligned"); + attrs.SetAllAttrs(spatial_scale, pooled_h, pooled_w, sampling_ratio, aligned); return OpInterpUtil::Dispatch(*op_, {x, rois}, attrs); } @@ -2902,12 +3688,9 @@ class RoiAlignGradFunctor { const std::shared_ptr& rois, const float& spatial_scale, const int32_t& pooled_h, const int32_t& pooled_w, const int32_t& sampling_ratio, const bool& aligned) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("spatial_scale", spatial_scale)); - JUST(attrs.SetAttr("pooled_h", pooled_h)); - JUST(attrs.SetAttr("pooled_w", pooled_w)); - JUST(attrs.SetAttr("sampling_ratio", sampling_ratio)); - JUST(attrs.SetAttr("aligned", aligned)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("spatial_scale", "pooled_h", "pooled_w", + "sampling_ratio", "aligned"); + attrs.SetAllAttrs(spatial_scale, pooled_h, pooled_w, sampling_ratio, aligned); return OpInterpUtil::Dispatch(*op_, {dy, x_like, rois}, attrs); } @@ -2938,10 +3721,6 @@ class FusedDotFeatureInteractionFunctor { Maybe operator()(const TensorTuple& features, const Optional& output_concat, const bool& self_interaction, const int32_t& output_padding, const std::string& pooling) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("self_interaction", self_interaction)); - JUST(attrs.SetAttr("output_padding", output_padding)); - JUST(attrs.SetAttr("pooling", pooling)); const int64_t n_features = features.size(); TensorTuple inputs; if (n_features > kMaxInputCount) { @@ -2952,23 +3731,25 @@ class FusedDotFeatureInteractionFunctor { CHECK_OR_RETURN(pooling == "sum" || pooling == "none") << Error::RuntimeError() << "pooling should be sum or none, but get " << pooling; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("self_interaction", "output_padding", "pooling", + "has_output_concat"); if (pooling == "sum") { CHECK_EQ_OR_RETURN(output_padding, 0) << Error::RuntimeError() << "output_padding should be equal to 0. "; CHECK_OR_RETURN(!output_concat) << Error::RuntimeError() << "output_concat should not exist"; - JUST(attrs.SetAttr("has_output_concat", false)); + attrs.SetAllAttrs(self_interaction, output_padding, pooling, false); const std::shared_ptr& bi_interaction = JUST(OpInterpUtil::Dispatch( *JUST(oneflow::VectorAt(ops_no_output_concat_, n_features - 1)), inputs, attrs)); std::vector reduce_axes_vec = {1}; return functional::ReduceSum(bi_interaction, reduce_axes_vec, true); } if (output_concat) { - JUST(attrs.SetAttr("has_output_concat", true)); + attrs.SetAllAttrs(self_interaction, output_padding, pooling, true); inputs.push_back(JUST(output_concat)); return OpInterpUtil::Dispatch( *JUST(oneflow::VectorAt(ops_has_output_concat_, n_features - 1)), inputs, attrs); } else { - JUST(attrs.SetAttr("has_output_concat", false)); + attrs.SetAllAttrs(self_interaction, output_padding, pooling, false); return OpInterpUtil::Dispatch( *JUST(oneflow::VectorAt(ops_no_output_concat_, n_features - 1)), inputs, attrs); } @@ -3001,8 +3782,8 @@ class FusedCrossFeatureInteractionFunctor { UNIMPLEMENTED_THEN_RETURN() << "Fused Cross Interaction mode only support `vector` and `matrix`. "; } - MutableAttrMap attrs; - JUST(attrs.SetAttr("interaction_mode", interaction_mode)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("interaction_mode"); + attrs.SetAllAttrs(interaction_mode); return OpInterpUtil::Dispatch(*op_, {x, weight, x0, bias}, attrs); } @@ -3035,10 +3816,10 @@ class OneEmbeddingIdShuffleFunctor { } Maybe operator()(const std::shared_ptr& ids, - const Optional& table_ids, - const int32_t& num_tables) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_tables", num_tables)); + const Optional& table_ids, const int32_t& num_tables, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_tables", "embedding_name"); + attrs.SetAllAttrs(num_tables, embedding_name); if (table_ids) { return OpInterpUtil::Dispatch(*op_table_ids_has_in_out_, {ids, JUST(table_ids)}, attrs); @@ -3064,14 +3845,19 @@ class OneEmbeddingEmbeddingShuffleFunctor { .Build()); } - Maybe operator()( - const std::shared_ptr& cur_rank_embeddings, - const std::shared_ptr& num_unique_matrix, - const std::shared_ptr& cur_rank_inverse_indices, - const std::shared_ptr& inverse_unique_partition_indices) const { + Maybe operator()(const std::shared_ptr& cur_rank_embeddings, + const std::shared_ptr& num_unique_matrix, + const std::shared_ptr& cur_rank_inverse_indices, + const std::shared_ptr& inverse_unique_partition_indices, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("embedding_size", "embedding_name"); + const int64_t num_axes = cur_rank_embeddings->shape()->NumAxes(); + attrs.SetAllAttrs(cur_rank_embeddings->shape()->At(num_axes - 1), embedding_name); return OpInterpUtil::Dispatch( - *op_, {cur_rank_embeddings, num_unique_matrix, cur_rank_inverse_indices, - inverse_unique_partition_indices}); + *op_, + {cur_rank_embeddings, num_unique_matrix, cur_rank_inverse_indices, + inverse_unique_partition_indices}, + attrs); } private: @@ -3090,14 +3876,19 @@ class OneEmbeddingEmbeddingGradientShuffleFunctor { .Build()); } - Maybe operator()( - const std::shared_ptr& embedding_grad, - const std::shared_ptr& num_unique_matrix, - const std::shared_ptr& cur_rank_inverse_indices, - const std::shared_ptr& inverse_unique_partition_indices) const { + Maybe operator()(const std::shared_ptr& embedding_grad, + const std::shared_ptr& num_unique_matrix, + const std::shared_ptr& cur_rank_inverse_indices, + const std::shared_ptr& inverse_unique_partition_indices, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("embedding_size", "embedding_name"); + const int64_t num_axes = embedding_grad->shape()->NumAxes(); + attrs.SetAllAttrs(embedding_grad->shape()->At(num_axes - 1), embedding_name); return OpInterpUtil::Dispatch( - *op_, {embedding_grad, num_unique_matrix, cur_rank_inverse_indices, - inverse_unique_partition_indices}); + *op_, + {embedding_grad, num_unique_matrix, cur_rank_inverse_indices, + inverse_unique_partition_indices}, + attrs); } private: @@ -3107,13 +3898,43 @@ class OneEmbeddingEmbeddingGradientShuffleFunctor { class OneEmbeddingLookupFunctor { public: OneEmbeddingLookupFunctor() { - op_has_table_ids_ = CHECK_JUST(one::OpBuilder("embedding_lookup_placeholder") + op_ = CHECK_JUST(one::OpBuilder("embedding_lookup") + .Input("num_unique_ids") + .Input("unique_ids") + .Input("table_ids") + .Output("unique_values") + .Build()); + } + + Maybe operator()(const std::shared_ptr& num_unique_ids, + const std::shared_ptr& unique_ids, + const std::shared_ptr& table_ids, + const Symbol& dtype, const Symbol& embedding_dtype, + const int64_t line_size, const int64_t embedding_size, + const std::string& embedding_name, const std::string& embedding_tables, + const std::string& state_initializer, const int64_t seed) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype", "embedding_dtype", "line_size", + "embedding_size", "embedding_name", + "embedding_tables", "state_initializer", "seed"); + attrs.SetAllAttrs(dtype->data_type(), embedding_dtype->data_type(), line_size, embedding_size, + embedding_name, embedding_tables, state_initializer, seed); + return OpInterpUtil::Dispatch(*op_, {num_unique_ids, unique_ids, table_ids}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class OneEmbeddingFusedLookupFunctor { + public: + OneEmbeddingFusedLookupFunctor() { + op_has_table_ids_ = CHECK_JUST(one::OpBuilder("one_embedding_fused_lookup") .Input("shadow") .Input("ids") .Input("table_ids") .Output("embeddings") .Build()); - op_no_table_ids_ = CHECK_JUST(one::OpBuilder("embedding_lookup_placeholder") + op_no_table_ids_ = CHECK_JUST(one::OpBuilder("one_embedding_fused_lookup") .Input("shadow") .Input("ids") .Output("embeddings") @@ -3123,17 +3944,42 @@ class OneEmbeddingLookupFunctor { Maybe operator()(const std::shared_ptr& shadow, const std::shared_ptr& ids, const Optional& table_ids, const Symbol& dtype, - const int64_t embedding_size, const int32_t num_tables, - const std::string& embedding_tables, - const std::string& key_value_store_options) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("dtype", dtype->data_type())); - JUST(attrs.SetAttr("embedding_size", embedding_size)); - JUST(attrs.SetAttr("num_tables", num_tables)); - JUST(attrs.SetAttr("embedding_tables", embedding_tables)); - JUST(attrs.SetAttr("key_value_store_options", key_value_store_options)); + const std::string& embedding_name, const int64_t line_size, + const int64_t embedding_size, const bool is_full_cache, + const int32_t num_tables, const std::string& embedding_tables, + const Optional& padding_idx, const int64_t seed) const { + int64_t padding_idx_val = -1; + bool has_padding_idx = false; + if (padding_idx.has_value()) { + padding_idx_val = JUST(padding_idx); + has_padding_idx = true; + } + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "dtype", "embedding_name", "line_size", "embedding_size", "is_full_cache", "num_tables", + "embedding_tables", "seed", "padding_idx", "has_padding_idx"); + attrs.SetAllAttrs(dtype->data_type(), embedding_name, line_size, embedding_size, is_full_cache, + num_tables, embedding_tables, seed, padding_idx_val, has_padding_idx); if (table_ids) { - return OpInterpUtil::Dispatch(*op_has_table_ids_, {shadow, ids, JUST(table_ids)}, + const auto& table_ids_shape = *(JUST(table_ids)->shape()); + const auto& ids_shape = *(ids->shape()); + auto broadcast_table_ids = JUST(table_ids); + if (table_ids_shape != ids_shape) { + CHECK_LE_OR_RETURN(table_ids_shape.NumAxes(), ids_shape.NumAxes()) + << "table_ids num_axes should be less equal to ids num_axes, but got table_ids " + "num_axes " + << table_ids_shape.NumAxes() << " and ids num_axes " << ids_shape.NumAxes(); + const int64_t left_extend_dims = ids_shape.NumAxes() - table_ids_shape.NumAxes(); + for (int64_t i = 0; i < table_ids_shape.NumAxes(); i++) { + CHECK_EQ_OR_RETURN(table_ids_shape.at(i), ids_shape.at(left_extend_dims + i)) + << "when table_ids's shape not equals ids shape, table_ids must be able to be " + "broadcast to ids_shape " + "but got table_ids_shape: " + << table_ids_shape.DebugStr() << ", ids_shape: " << ids_shape.DebugStr(); + } + broadcast_table_ids = + JUST(functional::BroadcastLike(JUST(table_ids), ids, std::vector{})); + } + return OpInterpUtil::Dispatch(*op_has_table_ids_, {shadow, ids, broadcast_table_ids}, attrs); } else { return OpInterpUtil::Dispatch(*op_no_table_ids_, {shadow, ids}, attrs); @@ -3145,6 +3991,54 @@ class OneEmbeddingLookupFunctor { std::shared_ptr op_no_table_ids_; }; +class OneEmbeddingFusedLookupGradFunctor { + public: + OneEmbeddingFusedLookupGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("one_embedding_fused_lookup_grad") + .Input("ids") + .Input("embedding_grad") + .Build()); + } + + Maybe operator()(const std::shared_ptr& ids, + const std::shared_ptr& embedding_grad, + const std::string& embedding_name, const int64_t line_size, + const int64_t embedding_size) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("embedding_name", "line_size", "embedding_size"); + attrs.SetAllAttrs(embedding_name, line_size, embedding_size); + JUST(OpInterpUtil::Dispatch(*op_, {ids, embedding_grad}, attrs)); + return Maybe::Ok(); + } + + private: + std::shared_ptr op_; +}; + +class OneEmbeddingEmbeddingPutFunctor { + public: + OneEmbeddingEmbeddingPutFunctor() { + op_ = CHECK_JUST(one::OpBuilder("embedding_put") + .Input("num_unique_ids") + .Input("unique_ids") + .Input("unique_embeddings") + .Build()); + } + + Maybe operator()(const std::shared_ptr& num_unique_ids, + const std::shared_ptr& unique_ids, + const std::shared_ptr& unique_embeddings, + const std::string& embedding_name, const int64_t line_size) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("embedding_name", "line_size"); + attrs.SetAllAttrs(embedding_name, line_size); + JUST(OpInterpUtil::Dispatch(*op_, {num_unique_ids, unique_ids, unique_embeddings}, + attrs)); + return Maybe::Ok(); + } + + private: + std::shared_ptr op_; +}; + class OneEmbeddingUniqueKeyValuePairFunctor { public: OneEmbeddingUniqueKeyValuePairFunctor() { @@ -3166,10 +4060,10 @@ class OneEmbeddingUniqueKeyValuePairFunctor { } Maybe operator()(const std::shared_ptr& keys, - const Optional& values, - const int32_t num_tables) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_tables", num_tables)); + const Optional& values, const int32_t num_tables, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_tables", "embedding_name"); + attrs.SetAllAttrs(num_tables, embedding_name); if (values) { return OpInterpUtil::Dispatch(*op_has_input_value_, {keys, JUST(values)}, attrs); } else { @@ -3185,8 +4079,24 @@ class OneEmbeddingUniqueKeyValuePairFunctor { class OneEmbeddingSgdUpdateFunctor { public: OneEmbeddingSgdUpdateFunctor() { + // This functor is only used in one_embedding eager mode with lr passed by attr and no optional + // input, we also define functor with all optional input just for unittest. when the optional + // input learning_rate tensor has passed in, we think all optional input are not None and check + // them. + sgd_no_optional_input_op_ = CHECK_JUST(one::OpBuilder("one_embedding_sgd_update") + .Input("num_unique_ids") + .Input("unique_embeddings") + .Input("embedding_grad") + .Output("updated_unique_embeddings") + .Build()); + momentum_no_optional_input_op_ = CHECK_JUST(one::OpBuilder("one_embedding_momentum_update") + .Input("num_unique_ids") + .Input("unique_embeddings") + .Input("embedding_grad") + .Output("updated_unique_embeddings") + .Build()); // This functor is just for unittest - sgd_op_ = CHECK_JUST(one::OpBuilder("sgd_embedding_update") + sgd_op_ = CHECK_JUST(one::OpBuilder("one_embedding_sgd_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3195,7 +4105,7 @@ class OneEmbeddingSgdUpdateFunctor { .Input("skip_if") .Output("updated_unique_embeddings") .Build()); - momentum_op_ = CHECK_JUST(one::OpBuilder("momentum_embedding_update") + momentum_op_ = CHECK_JUST(one::OpBuilder("one_embedding_momentum_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3209,37 +4119,76 @@ class OneEmbeddingSgdUpdateFunctor { Maybe operator()(const std::shared_ptr& num_unique_ids, const std::shared_ptr& unique_embeddings, const std::shared_ptr& embedding_grad, - const std::shared_ptr& learning_rate, - const std::shared_ptr& down_scale_by_tensor, - const std::shared_ptr& skip_if, const double scale, - const float weight_decay, const float momentum) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); + const Optional& learning_rate, + const Optional& down_scale_by_tensor, + const Optional& skip_if, const float learning_rate_val, + const double scale, const float weight_decay, const float momentum, + const int64_t line_size, const int64_t embedding_size, + const std::string& embedding_name) const { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "weight_decay", "line_size", + "embedding_size", "embedding_name", "beta"); if (momentum == 0) { - return OpInterpUtil::Dispatch(*sgd_op_, - {num_unique_ids, unique_embeddings, embedding_grad, - learning_rate, down_scale_by_tensor, skip_if}, - attrs); + attrs.SetAllAttrs(learning_rate_val, scale, weight_decay, line_size, embedding_size, + embedding_name, NullOpt); + + if (learning_rate) { + CHECK(down_scale_by_tensor); + CHECK(skip_if); + return OpInterpUtil::Dispatch( + *sgd_op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if)}, + attrs); + } else { + CHECK(!down_scale_by_tensor); + CHECK(!skip_if); + return OpInterpUtil::Dispatch( + *sgd_no_optional_input_op_, {num_unique_ids, unique_embeddings, embedding_grad}, attrs); + } } else { - JUST(attrs.SetAttr("beta", momentum)); - return OpInterpUtil::Dispatch(*momentum_op_, - {num_unique_ids, unique_embeddings, embedding_grad, - learning_rate, down_scale_by_tensor, skip_if}, - attrs); + attrs.SetAllAttrs(learning_rate_val, scale, weight_decay, line_size, embedding_size, + embedding_name, momentum); + if (learning_rate) { + CHECK(down_scale_by_tensor); + CHECK(skip_if); + return OpInterpUtil::Dispatch( + *momentum_op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if)}, + attrs); + } else { + CHECK(!down_scale_by_tensor); + CHECK(!skip_if); + return OpInterpUtil::Dispatch(*momentum_no_optional_input_op_, + {num_unique_ids, unique_embeddings, embedding_grad}, + attrs); + } } } private: + std::shared_ptr sgd_no_optional_input_op_; std::shared_ptr sgd_op_; + std::shared_ptr momentum_no_optional_input_op_; std::shared_ptr momentum_op_; }; class OneEmbeddingAdamUpdateFunctor { public: OneEmbeddingAdamUpdateFunctor() { + // This functor is only used in one_embedding eager mode with lr passed by attr and no optional + // input, we also define functor with all optional input just for unittest. when the optional + // input learning_rate tensor has passed in, we think all optional input are not None and check + // them. + no_optional_input_op_ = CHECK_JUST(one::OpBuilder("one_embedding_adam_update") + .Input("num_unique_ids") + .Input("unique_embeddings") + .Input("embedding_grad") + .Output("updated_unique_embeddings") + .Build()); // This functor is just for unittest - no_bias_correction_op_ = CHECK_JUST(one::OpBuilder("adam_embedding_update") + no_bias_correction_op_ = CHECK_JUST(one::OpBuilder("one_embedding_adam_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3248,7 +4197,7 @@ class OneEmbeddingAdamUpdateFunctor { .Input("skip_if") .Output("updated_unique_embeddings") .Build()); - do_bias_correction_op_ = CHECK_JUST(one::OpBuilder("adam_embedding_update") + do_bias_correction_op_ = CHECK_JUST(one::OpBuilder("one_embedding_adam_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3261,49 +4210,74 @@ class OneEmbeddingAdamUpdateFunctor { .Build()); } - Maybe operator()(const std::shared_ptr& num_unique_ids, - const std::shared_ptr& unique_embeddings, - const std::shared_ptr& embedding_grad, - const std::shared_ptr& learning_rate, - const std::shared_ptr& down_scale_by_tensor, - const std::shared_ptr& skip_if, - const Optional& bias_correction1, - const Optional& bias_correction2, const double scale, - const float weight_decay, const float beta1, const float beta2, - const float epsilon, const bool do_bias_correction) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("beta1", beta1)); - JUST(attrs.SetAttr("beta2", beta2)); - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("do_bias_correction", do_bias_correction)); - if (do_bias_correction) { - CHECK(bias_correction1); - CHECK(bias_correction2); - return OpInterpUtil::Dispatch( - *do_bias_correction_op_, - {num_unique_ids, unique_embeddings, embedding_grad, learning_rate, down_scale_by_tensor, - skip_if, JUST(bias_correction1), JUST(bias_correction2)}, - attrs); + Maybe operator()( + const std::shared_ptr& num_unique_ids, + const std::shared_ptr& unique_embeddings, + const std::shared_ptr& embedding_grad, + const Optional& learning_rate, const Optional& down_scale_by_tensor, + const Optional& skip_if, const Optional& bias_correction1, + const Optional& bias_correction2, const float learning_rate_val, + const double scale, const float weight_decay, const float beta1, const float beta2, + const float& bias_correction1_val, const float& bias_correction2_val, const float epsilon, + const bool do_bias_correction, const int64_t line_size, const int64_t embedding_size, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "learning_rate_val", "scale", "weight_decay", "beta1", "beta2", "epsilon", + "bias_correction1_val", "bias_correction2_val", "do_bias_correction", "line_size", + "embedding_size", "embedding_name"); + attrs.SetAllAttrs(learning_rate_val, scale, weight_decay, beta1, beta2, epsilon, + bias_correction1_val, bias_correction2_val, do_bias_correction, line_size, + embedding_size, embedding_name); + if (learning_rate) { + CHECK(down_scale_by_tensor); + CHECK(skip_if); + if (do_bias_correction) { + CHECK(bias_correction1); + CHECK(bias_correction2); + return OpInterpUtil::Dispatch( + *do_bias_correction_op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if), JUST(bias_correction1), + JUST(bias_correction2)}, + attrs); + } else { + return OpInterpUtil::Dispatch( + *no_bias_correction_op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if)}, + attrs); + } } else { - return OpInterpUtil::Dispatch(*no_bias_correction_op_, - {num_unique_ids, unique_embeddings, embedding_grad, - learning_rate, down_scale_by_tensor, skip_if}, - attrs); + CHECK(!down_scale_by_tensor); + CHECK(!skip_if); + CHECK(!bias_correction1); + CHECK(!bias_correction2); + return OpInterpUtil::Dispatch( + *no_optional_input_op_, {num_unique_ids, unique_embeddings, embedding_grad}, attrs); } } private: std::shared_ptr no_bias_correction_op_; std::shared_ptr do_bias_correction_op_; + std::shared_ptr no_optional_input_op_; }; class OneEmbeddingAdagradUpdateFunctor { public: OneEmbeddingAdagradUpdateFunctor() { + // This functor is only used in one_embedding eager mode with lr passed by attr and no optional + // input, we also define functor with all optional input just for unittest. when the optional + // input learning_rate tensor has passed in, we think all optional input are not None and check + // them. + op_no_optional_input_ = CHECK_JUST(one::OpBuilder("one_embedding_adagrad_update") + .Input("num_unique_ids") + .Input("unique_embeddings") + .Input("embedding_grad") + .Output("updated_unique_embeddings") + .Build()); // This functor is just for unittest - op_ = CHECK_JUST(one::OpBuilder("adagrad_embedding_update") + op_ = CHECK_JUST(one::OpBuilder("one_embedding_adagrad_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3318,33 +4292,57 @@ class OneEmbeddingAdagradUpdateFunctor { Maybe operator()(const std::shared_ptr& num_unique_ids, const std::shared_ptr& unique_embeddings, const std::shared_ptr& embedding_grad, - const std::shared_ptr& learning_rate, - const std::shared_ptr& down_scale_by_tensor, - const std::shared_ptr& skip_if, - const std::shared_ptr& train_step, const double scale, - const float weight_decay, const float lr_decay, - const float epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("lr_decay", lr_decay)); - JUST(attrs.SetAttr("epsilon", epsilon)); - return OpInterpUtil::Dispatch( - *op_, - {num_unique_ids, unique_embeddings, embedding_grad, learning_rate, down_scale_by_tensor, - skip_if, train_step}, - attrs); + const Optional& learning_rate, + const Optional& down_scale_by_tensor, + const Optional& skip_if, + const Optional& train_step, const int64_t train_step_val, + const float learning_rate_val, const double scale, + const float weight_decay, const float lr_decay, const float epsilon, + const int64_t line_size, const int64_t embedding_size, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("train_step_val", "learning_rate_val", "scale", + "weight_decay", "lr_decay", "epsilon", "line_size", + "embedding_size", "embedding_name"); + attrs.SetAllAttrs(train_step_val, learning_rate_val, scale, weight_decay, lr_decay, epsilon, + line_size, embedding_size, embedding_name); + if (learning_rate) { + CHECK(down_scale_by_tensor); + CHECK(skip_if); + CHECK(train_step); + return OpInterpUtil::Dispatch( + *op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if), JUST(train_step)}, + attrs); + } else { + CHECK(!down_scale_by_tensor); + CHECK(!skip_if); + CHECK(!train_step); + return OpInterpUtil::Dispatch( + *op_no_optional_input_, {num_unique_ids, unique_embeddings, embedding_grad}, attrs); + } } private: std::shared_ptr op_; + std::shared_ptr op_no_optional_input_; }; class OneEmbeddingFtrlUpdateFunctor { public: OneEmbeddingFtrlUpdateFunctor() { + // This functor is only used in one_embedding eager mode with lr passed by attr and no optional + // input, we also define functor with all optional input just for unittest. when the optional + // input learning_rate tensor has passed in, we think all optional input are not None and check + // them. + op_no_optional_input_ = CHECK_JUST(one::OpBuilder("one_embedding_ftrl_update") + .Input("num_unique_ids") + .Input("unique_embeddings") + .Input("embedding_grad") + .Output("updated_unique_embeddings") + .Build()); // This functor is just for unittest - op_ = CHECK_JUST(one::OpBuilder("ftrl_embedding_update") + op_ = CHECK_JUST(one::OpBuilder("one_embedding_ftrl_update") .Input("num_unique_ids") .Input("unique_embeddings") .Input("embedding_grad") @@ -3358,26 +4356,80 @@ class OneEmbeddingFtrlUpdateFunctor { Maybe operator()(const std::shared_ptr& num_unique_ids, const std::shared_ptr& unique_embeddings, const std::shared_ptr& embedding_grad, - const std::shared_ptr& learning_rate, - const std::shared_ptr& down_scale_by_tensor, - const std::shared_ptr& skip_if, const double scale, - const float weight_decay, const float lr_power, const float lambda1, - const float lambda2, const float beta) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("scale", scale)); - JUST(attrs.SetAttr("weight_decay", weight_decay)); - JUST(attrs.SetAttr("lr_power", lr_power)); - JUST(attrs.SetAttr("lambda1", lambda1)); - JUST(attrs.SetAttr("lambda2", lambda2)); - JUST(attrs.SetAttr("beta", beta)); - return OpInterpUtil::Dispatch(*op_, - {num_unique_ids, unique_embeddings, embedding_grad, - learning_rate, down_scale_by_tensor, skip_if}, - attrs); + const Optional& learning_rate, + const Optional& down_scale_by_tensor, + const Optional& skip_if, const float learning_rate_val, + const double scale, const float weight_decay, const float lr_power, + const float lambda1, const float lambda2, const float beta, + const int64_t line_size, const int64_t embedding_size, + const std::string& embedding_name) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("learning_rate_val", "scale", "weight_decay", + "lr_power", "lambda1", "lambda2", "beta", + "line_size", "embedding_size", "embedding_name"); + attrs.SetAllAttrs(learning_rate_val, scale, weight_decay, lr_power, lambda1, lambda2, beta, + line_size, embedding_size, embedding_name); + if (learning_rate) { + CHECK(down_scale_by_tensor); + CHECK(skip_if); + return OpInterpUtil::Dispatch( + *op_, + {num_unique_ids, unique_embeddings, embedding_grad, JUST(learning_rate), + JUST(down_scale_by_tensor), JUST(skip_if)}, + attrs); + } else { + CHECK(!down_scale_by_tensor); + CHECK(!skip_if); + return OpInterpUtil::Dispatch( + *op_no_optional_input_, {num_unique_ids, unique_embeddings, embedding_grad}, attrs); + } } private: std::shared_ptr op_; + std::shared_ptr op_no_optional_input_; +}; + +class DeformConv2dFunctor { + public: + DeformConv2dFunctor() { + bias_op_ = CHECK_JUST(one::OpBuilder("bias_add").Input("a").Input("b").Output("out").Build()); + deformconv2d_op_ = CHECK_JUST(one::OpBuilder("deform_conv2d") + .Input("input") + .Input("weight") + .Input("offset") + .Input("mask") + .Output("output") + .Build()); + } + + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& weight, + const std::shared_ptr& offset, + const std::shared_ptr& mask, + const Optional& bias, const int32_t& stride_h, + const int32_t& stride_w, const int32_t& pad_h, const int32_t& pad_w, + const int32_t& dilation_h, const int32_t& dilation_w, + const int32_t& groups, const int32_t& offset_groups, + const bool& use_mask) const { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("stride_h", "stride_w", "pad_h", "pad_w", "dilation_h", + "dilation_w", "groups", "offset_groups", "use_mask"); + attrs.SetAllAttrs(stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, groups, + offset_groups, use_mask); + const std::shared_ptr& deformconv2d_out = JUST( + OpInterpUtil::Dispatch(*deformconv2d_op_, {input, weight, offset, mask}, attrs)); + if (bias) { + auto bias_shape = JUST(bias)->shape(); + auto& bias_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + bias_attrs.SetAllAttrs(static_cast(1)); + return OpInterpUtil::Dispatch(*bias_op_, {deformconv2d_out, JUST(bias)}, bias_attrs); + } + return deformconv2d_out; + } + + private: + std::shared_ptr deformconv2d_op_; + std::shared_ptr bias_op_; }; class RocAucScoreFunctor { @@ -3396,8 +4448,127 @@ class RocAucScoreFunctor { std::shared_ptr op_; }; -class MvFunctor { +class MultiTensorSgdUpdateFunctor { + public: + MultiTensorSgdUpdateFunctor() { + op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 0; n < op_.size(); ++n) { + op_[n] = CHECK_JUST(one::OpBuilder("multi_tensor_sgd_update") + .Input("model", n + 1) + .Input("model_diff", n + 1) + .Build()); + } + } + + Maybe operator()(const TensorTuple& model, const TensorTuple& model_diff, + const double& scale, const float& weight_decay, + const float& learning_rate_val) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale", "weight_decay", "learning_rate_val"); + attrs.SetAllAttrs(scale, weight_decay, learning_rate_val); + const int64_t weight_size = model.size(); + for (int i = 0; i < weight_size; i += kMaxInputCount) { + size_t size = (i + kMaxInputCount) < weight_size ? kMaxInputCount : weight_size - i; + TensorTuple input(2 * size); + std::copy(model.begin() + i, model.begin() + i + size, input.begin()); + std::copy(model_diff.begin() + i, model_diff.begin() + i + size, input.begin() + size); + JUST(OpInterpUtil::Dispatch(*op_[size - 1], input, attrs)); + } + return Maybe::Ok(); + } + + private: + std::vector> op_; +}; + +class MultiTensorMomentumUpdateFunctor { + public: + MultiTensorMomentumUpdateFunctor() { + op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 0; n < op_.size(); ++n) { + op_[n] = CHECK_JUST(one::OpBuilder("multi_tensor_momentum_update") + .Input("model", n + 1) + .Input("model_diff", n + 1) + .Input("momentum_buf", n + 1) + .Build()); + } + } + + Maybe operator()(const TensorTuple& model, const TensorTuple& model_diff, + const TensorTuple& momentum_buf, const double& scale, + const float& weight_decay, const float& learning_rate_val, + const float& momentum, const float& dampening, const bool& nesterov, + const bool& maximize) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale", "weight_decay", "learning_rate_val", + "momentum", "dampening", "nesterov", "maximize"); + attrs.SetAllAttrs(scale, weight_decay, learning_rate_val, momentum, dampening, nesterov, + maximize); + const int64_t weight_size = model.size(); + for (int i = 0; i < weight_size; i += kMaxInputCount) { + size_t size = (i + kMaxInputCount) < weight_size ? kMaxInputCount : weight_size - i; + TensorTuple input(3 * size); + std::copy(model.begin() + i, model.begin() + i + size, input.begin()); + std::copy(model_diff.begin() + i, model_diff.begin() + i + size, input.begin() + size); + std::copy(momentum_buf.begin() + i, momentum_buf.begin() + i + size, + input.begin() + 2 * size); + JUST(OpInterpUtil::Dispatch(*op_[size - 1], input, attrs)); + } + return Maybe::Ok(); + } + + private: + std::vector> op_; +}; + +class MultiTensorAdamUpdateFunctor { + public: + MultiTensorAdamUpdateFunctor() { + op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 0; n < op_.size(); ++n) { + op_[n] = CHECK_JUST(one::OpBuilder("multi_tensor_adam_update") + .Input("model", n + 1) + .Input("model_diff", n + 1) + .Input("m", n + 1) + .Input("v", n + 1) + .Build()); + } + } + + Maybe operator()(const TensorTuple& model, const TensorTuple& model_diff, + const TensorTuple& m, const TensorTuple& v, const float& learning_rate_val, + const float& l2, const float& beta1, const float& beta2, + const float& bias_correction1_val, const float& bias_correction2_val, + const bool& do_bias_correction, const double& scale, + const float& weight_decay, const float& epsilon) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP( + "scale", "weight_decay", "beta1", "beta2", "bias_correction1_val", "bias_correction2_val", + "do_bias_correction", "learning_rate_val", "l2", "epsilon"); + attrs.SetAllAttrs(scale, weight_decay, beta1, beta2, bias_correction1_val, bias_correction2_val, + do_bias_correction, learning_rate_val, l2, epsilon); + + const int64_t weight_size = model.size(); + for (int i = 0; i < weight_size; i += kMaxInputCount) { + size_t size = (i + kMaxInputCount) < weight_size ? kMaxInputCount : weight_size - i; + TensorTuple input(4 * size); + std::copy(model.begin() + i, model.begin() + i + size, input.begin()); + std::copy(model_diff.begin() + i, model_diff.begin() + i + size, input.begin() + size); + std::copy(m.begin() + i, m.begin() + i + size, input.begin() + 2 * size); + std::copy(v.begin() + i, v.begin() + i + size, input.begin() + 3 * size); + JUST(OpInterpUtil::Dispatch(*op_[size - 1], input, attrs)); + } + return Maybe::Ok(); + } + + private: + std::vector> op_; +}; + +class MatrixVectorProductFunctor { public: + MatrixVectorProductFunctor() { + matrix_vector_product_op_ = CHECK_JUST( + one::OpBuilder("matrix_vector_product").Input("a").Input("b").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& input, const std::shared_ptr& vec) const { const auto& input_shape = input->shape(); @@ -3409,14 +4580,364 @@ class MvFunctor { << Error::RuntimeError() << "size mismatch, got " << std::to_string(input_shape->at(0)) << ", " << std::to_string(input_shape->at(0)) << "x" << std::to_string(input_shape->at(1)) << ", " << std::to_string(vec_shape->at(0)); - // TODO(zhongshsh): speedup - const std::shared_ptr reshape_vec = - JUST(Reshape(vec, Shape(DimVector{vec_shape->at(0), 1}))); - std::shared_ptr out = JUST(MatMul(input, reshape_vec, false, false, 1.0)); - std::shared_ptr reshape_out = JUST(Squeeze( - JUST(Reshape(out, Shape(DimVector{1, input_shape->at(0)}))), std::vector({0}))); - return reshape_out; + return OpInterpUtil::Dispatch(*matrix_vector_product_op_, {input, vec}); + } + + private: + std::shared_ptr matrix_vector_product_op_; +}; + +class BatchNormStatsFunctor { + public: + BatchNormStatsFunctor() { + op_ = CHECK_JUST( + one::OpBuilder("batch_norm_stats").Input("input").Output("mean").Output("invstd").Build()); + } + + Maybe operator()(const std::shared_ptr& input, const int& axis, + const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "eps"); + attrs.SetAllAttrs(axis, eps); + return OpInterpUtil::Dispatch(*op_, {input}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class BatchNormGatherStatsWithCountsFunctor { + public: + BatchNormGatherStatsWithCountsFunctor() { + op_with_running_mean_and_var_ = CHECK_JUST(one::OpBuilder("batch_norm_gather_stats_with_counts") + .Input("input") + .Input("mean") + .Input("invstd") + .Input("counts") + .Input("running_mean") + .Input("running_var") + .Output("global_mean") + .Output("global_invstd") + .Build()); + op_without_running_mean_and_var_ = + CHECK_JUST(one::OpBuilder("batch_norm_gather_stats_with_counts") + .Input("input") + .Input("mean") + .Input("invstd") + .Input("counts") + .Output("global_mean") + .Output("global_invstd") + .Build()); + } + + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& mean, + const std::shared_ptr& invstd, + const Optional& running_mean, + const Optional& running_var, const float& momentum, + const float& eps, + const std::shared_ptr& counts) const { + CHECK_OR_RETURN((running_mean && running_var) || (!running_mean && !running_var)) + << Error::RuntimeError() + << "Both running_mean and running_var should be None or Tensor at the same time."; + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("eps", "momentum"); + attrs.SetAllAttrs(eps, momentum); + + if (running_mean) { + return OpInterpUtil::Dispatch( + *op_with_running_mean_and_var_, + {input, mean, invstd, counts, JUST(running_mean), JUST(running_var)}, attrs); + } + return OpInterpUtil::Dispatch(*op_without_running_mean_and_var_, + {input, mean, invstd, counts}, attrs); + } + + private: + std::shared_ptr op_with_running_mean_and_var_; + std::shared_ptr op_without_running_mean_and_var_; +}; + +class BatchNormElemtFunctor { + public: + BatchNormElemtFunctor() { + op_ = CHECK_JUST(one::OpBuilder("batch_norm_elemt") + .Input("input") + .Input("weight") + .Input("bias") + .Input("mean") + .Input("invstd") + .Output("output") + .Build()); + } + + Maybe operator()(const std::shared_ptr& input, + const std::shared_ptr& weight, + const std::shared_ptr& bias, + const std::shared_ptr& mean, + const std::shared_ptr& invstd, const int& axis, + const float& eps) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "eps"); + attrs.SetAllAttrs(axis, eps); + return OpInterpUtil::Dispatch(*op_, {input, weight, bias, mean, invstd}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class BatchNormBackwardReduceFunctor { + public: + BatchNormBackwardReduceFunctor() { + op_ = CHECK_JUST(one::OpBuilder("batch_norm_backward_reduce") + .Input("grad_out") + .Input("input") + .Input("mean") + .Input("invstd") + .Output("sum_dy") + .Output("sum_dy_xmu") + .Output("grad_weight") + .Output("grad_bias") + .Build()); + } + + Maybe operator()(const std::shared_ptr& grad_out, + const std::shared_ptr& input, + const std::shared_ptr& mean, + const std::shared_ptr& invstd, const int& axis) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); + return OpInterpUtil::Dispatch(*op_, {grad_out, input, mean, invstd}, attrs); } + + private: + std::shared_ptr op_; +}; + +class BatchNormBackwardElemtFunctor { + public: + BatchNormBackwardElemtFunctor() { + op_ = CHECK_JUST(one::OpBuilder("batch_norm_backward_elemt") + .Input("grad_out") + .Input("input") + .Input("mean") + .Input("invstd") + .Input("weight") + .Input("sum_dy") + .Input("sum_dy_xmu") + .Input("count") + .Output("grad_in") + .Build()); + } + + Maybe operator()(const std::shared_ptr& grad_out, + const std::shared_ptr& input, + const std::shared_ptr& mean, + const std::shared_ptr& invstd, + const std::shared_ptr& weight, + const std::shared_ptr& sum_dy, + const std::shared_ptr& sum_dy_xmu, + const std::shared_ptr& count, const int& axis) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis"); + attrs.SetAllAttrs(axis); + return OpInterpUtil::Dispatch( + *op_, {grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedMultiHeadAttentionInferenceFunctor { + public: + FusedMultiHeadAttentionInferenceFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_multi_head_attention_inference") + .Input("query") + .Input("key") + .Input("value") + .Output("out") + .Build()); + } + Maybe operator()( + const std::shared_ptr& query, const std::shared_ptr& key, + const std::shared_ptr& value, const int64_t& num_heads, const bool& causal, + const int64_t& query_hidden_slice_start, const int64_t& query_hidden_slice_end, + const int64_t& key_hidden_slice_start, const int64_t& key_hidden_slice_end, + const int64_t& value_hidden_slice_start, const int64_t& value_hidden_slice_end) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_heads", "causal", "query_hidden_slice_start", + "query_hidden_slice_end", "key_hidden_slice_start", + "key_hidden_slice_end", "value_hidden_slice_start", + "value_hidden_slice_end"); + attrs.SetAllAttrs(num_heads, causal, query_hidden_slice_start, query_hidden_slice_end, + key_hidden_slice_start, key_hidden_slice_end, value_hidden_slice_start, + value_hidden_slice_end); + return OpInterpUtil::Dispatch(*op_, {query, key, value}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedFastGeluMulFunctor { + public: + FusedFastGeluMulFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_fast_gelu_mul") + .Input("in") + .Input("multiplier") + .Output("out") + .Build()); + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& multiplier) const { + return OpInterpUtil::Dispatch(*op_, {x, multiplier}); + } + + private: + std::shared_ptr op_; +}; + +class FusedFastGeluMulGradFunctor { + public: + FusedFastGeluMulGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("fused_fast_gelu_mul_grad") + .Input("out_diff") + .Input("in") + .Input("multiplier") + .Output("in_diff") + .Output("multiplier_diff") + .Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& x, + const std::shared_ptr& multiplier) const { + return OpInterpUtil::Dispatch(*op_, {dy, x, multiplier}); + } + + private: + std::shared_ptr op_; +}; + +class GroupedMatmulBiasFunctor { + public: + GroupedMatmulBiasFunctor() { + fused_op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 1; n < fused_op_.size(); ++n) { + fused_op_[n] = CHECK_JUST(one::OpBuilder("grouped_matmul_bias") + .Input("xs", n) + .Input("weights", n) + .Input("biases", n) + .Output("ys", n) + .Build()); + } + } + Maybe operator()(const TensorTuple& xs, const TensorTuple& weights, + const TensorTuple& biases) const { + const int64_t input_size = xs.size(); + const int64_t weight_size = weights.size(); + const int64_t bias_size = biases.size(); + CHECK_GE_OR_RETURN(input_size, 1) + << Error::RuntimeError() << "The number of xs should be greater equal than 1."; + CHECK_EQ_OR_RETURN(weight_size, input_size) + << Error::RuntimeError() << "The number of weights should be equal to xs."; + CHECK_EQ_OR_RETURN(bias_size, input_size) + << Error::RuntimeError() << "The number of bias should be equal to xs."; + for (int64_t i = 0; i < input_size; ++i) { + const auto& input_shape = xs[i]->shape(); + const auto& weight_shape = weights[i]->shape(); + const auto& bias_shape = biases[i]->shape(); + CHECK_GE_OR_RETURN(input_shape->NumAxes(), 2) + << Error::RuntimeError() << "x's dim size should greater equal than 2."; + CHECK_EQ_OR_RETURN(weight_shape->NumAxes(), 2) + << Error::RuntimeError() << "Weight's dim size should == 2"; + CHECK_EQ_OR_RETURN(bias_shape->NumAxes(), 1) + << Error::RuntimeError() << "Bias's dim size should == 1"; + const int64_t k = input_shape->At(input_shape->NumAxes() - 1); + CHECK_EQ_OR_RETURN(weight_shape->At(1), k) + << Error::RuntimeError() << "weight's second dim should be equal to input's last dim. "; + const int64_t n = weight_shape->At(0); + CHECK_EQ_OR_RETURN(bias_shape->At(0), n) + << Error::RuntimeError() << "Bias's dim is not equal to weight's first dim. "; + } + TensorTuple input(3 * input_size); + std::copy(xs.begin(), xs.end(), input.begin() + 0 * input_size); + std::copy(weights.begin(), weights.end(), input.begin() + 1 * input_size); + std::copy(biases.begin(), biases.end(), input.begin() + 2 * input_size); + return OpInterpUtil::Dispatch(*fused_op_[input_size], input); + } + + private: + std::vector> fused_op_; +}; + +class GroupedMatmulFunctor { + public: + GroupedMatmulFunctor() { + fused_op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 1; n < fused_op_.size(); ++n) { + fused_op_[n] = CHECK_JUST(one::OpBuilder("grouped_matmul_bias") + .Input("xs", n) + .Input("weights", n) + .Output("ys", n) + .Build()); + } + } + Maybe operator()(const TensorTuple& xs, const TensorTuple& weights) const { + const int64_t input_size = xs.size(); + const int64_t weight_size = weights.size(); + CHECK_GE_OR_RETURN(input_size, 1) + << Error::RuntimeError() << "The number of xs should be greater equal than 1."; + CHECK_EQ_OR_RETURN(weight_size, input_size) + << Error::RuntimeError() << "The number of weights should be equal to xs."; + for (int64_t i = 0; i < input_size; ++i) { + const auto& input_shape = xs[i]->shape(); + const auto& weight_shape = weights[i]->shape(); + CHECK_GE_OR_RETURN(input_shape->NumAxes(), 2) + << Error::RuntimeError() << "x's dim size should greater equal than 2."; + CHECK_EQ_OR_RETURN(weight_shape->NumAxes(), 2) + << Error::RuntimeError() << "Weight's dim size should == 2"; + const int64_t k = input_shape->At(input_shape->NumAxes() - 1); + CHECK_EQ_OR_RETURN(weight_shape->At(1), k) + << Error::RuntimeError() << "weight's second dim should be equal to input's last dim. "; + } + TensorTuple input(2 * input_size); + std::copy(xs.begin(), xs.end(), input.begin() + 0 * input_size); + std::copy(weights.begin(), weights.end(), input.begin() + 1 * input_size); + return OpInterpUtil::Dispatch(*fused_op_[input_size], input); + } + + private: + std::vector> fused_op_; +}; + +class MultiTensorYoloV5WeightUpdateFunctor { + public: + MultiTensorYoloV5WeightUpdateFunctor() { + op_.resize(kMaxInputCount /*the maximum number of inputs*/); + for (int n = 0; n < op_.size(); ++n) { + op_[n] = CHECK_JUST(one::OpBuilder("multi_tensor_yolov5_weight_update") + .Input("model", n + 1) + .Input("model_update", n + 1) + .Build()); + } + } + + Maybe operator()(const TensorTuple& model, const TensorTuple& model_update, + const float& d) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("d"); + attrs.SetAllAttrs(d); + const int64_t weight_size = model.size(); + for (int i = 0; i < weight_size; i += kMaxInputCount) { + size_t size = (i + kMaxInputCount) < weight_size ? kMaxInputCount : weight_size - i; + TensorTuple input(size * 2); + std::copy(model.begin() + i, model.begin() + i + size, input.begin()); + std::copy(model_update.begin() + i, model_update.begin() + i + size, + input.begin() + 1 * size); + JUST(OpInterpUtil::Dispatch(*op_[size - 1], input, attrs)); + } + return Maybe::Ok(); + } + + private: + std::vector> op_; }; } // namespace impl @@ -3433,21 +4954,30 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Embedding"); m.add_functor("MatMul"); m.add_functor("MatMulNoBroadCast"); - m.add_functor("Mv"); m.add_functor("BatchMatMul"); + m.add_functor("MatrixVectorProduct"); + m.add_functor("VectorMatrixProduct"); m.add_functor("TensorDot"); m.add_functor("TensorDotIntDims"); m.add_functor("FusedMLP"); + m.add_functor("FusedMatmulBias"); m.add_functor("FusedMatmulBiasAddReluDropout"); m.add_functor("LayerNorm"); m.add_functor("LayerNormAffine"); + m.add_functor("GroupNorm"); m.add_functor("TFAvgPool2D"); m.add_functor("MaxPool1D"); m.add_functor("MaxPool2D"); m.add_functor("MaxPool3D"); + m.add_functor>("MaxUnpool1D"); + m.add_functor>("MaxUnpool2D"); + m.add_functor>("MaxUnpool3D"); m.add_functor("AdaptiveAvgPool1D"); m.add_functor("AdaptiveAvgPool2D"); m.add_functor("AdaptiveAvgPool3D"); + m.add_functor("AdaptiveMaxPool1D"); + m.add_functor("AdaptiveMaxPool2D"); + m.add_functor("AdaptiveMaxPool3D"); m.add_functor("L1Loss"); m.add_functor("MseLoss"); m.add_functor("KLDivLoss"); @@ -3457,6 +4987,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("SparseCrossEntropy"); m.add_functor("SparseCrossEntropyMs"); m.add_functor("CrossEntropy"); + m.add_functor("CrossEntropyLabelSmoothing"); + m.add_functor("CrossEntropyProb"); m.add_functor("SparseSoftmaxCrossEntropy"); m.add_functor("SoftmaxCrossEntropy"); m.add_functor("SoftmaxCrossEntropyGrad"); @@ -3469,9 +5001,15 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("GridSample"); m.add_functor("Normalization"); m.add_functor("NormalizationAddRelu"); + m.add_functor("ConstantPad"); + m.add_functor("ReflectionPad"); + m.add_functor("ReplicationPad"); m.add_functor("Pad"); m.add_functor("Dropout"); m.add_functor("DropoutGrad"); + m.add_functor("Dropout1d"); + m.add_functor("Dropout2d"); + m.add_functor("Dropout3d"); m.add_functor("PixelShuffle"); m.add_functor("AvgPool1D"); m.add_functor("AvgPool2D"); @@ -3481,15 +5019,19 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("OneHot"); m.add_functor("FusedSelfAttention"); m.add_functor("FusedSelfAttentionGrad"); + m.add_functor("PairwiseDistance"); m.add_functor("CosineSimilarity"); m.add_functor("Normalize"); m.add_functor("L2Normalize"); m.add_functor("L2NormalizeGrad"); m.add_functor("FusedBiasAddGelu"); m.add_functor("FusedBiasAddGeluGrad"); + m.add_functor("FusedGlu"); m.add_functor("FusedBiasAddDropout"); m.add_functor("FusedScaleMaskSoftmax"); m.add_functor("FusedScaleMaskSoftmaxDropout"); + m.add_functor( + "FusedBiasAddScaleMaskSoftmaxDropout"); m.add_functor("FusedScaleTrilSoftmaxMaskScale"); m.add_functor("FusedScaleTril"); m.add_functor("CtcGreedyDecoder"); @@ -3504,16 +5046,31 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor( "OneEmbeddingEmbeddingGradientShuffle"); m.add_functor("OneEmbeddingLookup"); + m.add_functor("OneEmbeddingFusedLookup"); + m.add_functor("OneEmbeddingFusedLookupGrad"); + m.add_functor("OneEmbeddingEmbeddingPut"); m.add_functor("OneEmbeddingUniqueKeyValuePair"); - m.add_functor("Normal"); - m.add_functor("Normal2"); - m.add_functor("ConsistentNormal"); - m.add_functor("ConsistentNormal2"); m.add_functor("OneEmbeddingSgdUpdate"); m.add_functor("OneEmbeddingAdamUpdate"); m.add_functor("OneEmbeddingAdagradUpdate"); m.add_functor("OneEmbeddingFtrlUpdate"); m.add_functor("RocAucScore"); + m.add_functor("MultiTensorSgdUpdate"); + m.add_functor("MultiTensorMomentumUpdate"); + m.add_functor("MultiTensorAdamUpdate"); + m.add_functor("DeformConv2d"); + m.add_functor("BatchNormStats"); + m.add_functor("BatchNormGatherStatsWithCounts"); + m.add_functor("BatchNormElemt"); + m.add_functor("BatchNormBackwardReduce"); + m.add_functor("BatchNormBackwardElemt"); + m.add_functor("FusedMultiHeadAttentionInference"); + m.add_functor("FusedFastGeluMul"); + m.add_functor("FusedFastGeluMulGrad"); + m.add_functor("GroupedMatmulBias"); + m.add_functor("GroupedMatmul"); + m.add_functor("RMSNorm"); + m.add_functor("MultiTensorYoloV5WeightUpdate"); } } // namespace functional diff --git a/oneflow/core/functional/impl/nn_grad_functor.cpp b/oneflow/core/functional/impl/nn_grad_functor.cpp index 5307d0b0e26e3cf6b140f8baf15ee770c4fc3f4d..43747b28bf877a3d931dae6e445538ec100af8a0 100644 --- a/oneflow/core/functional/impl/nn_grad_functor.cpp +++ b/oneflow/core/functional/impl/nn_grad_functor.cpp @@ -13,16 +13,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ - -#include "oneflow/core/common/scalar.h" -#include "oneflow/core/framework/attr_map.h" +#include "fmt/core.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" #include "oneflow/core/functional/function_library.h" -#include "oneflow/core/functional/impl/common.h" +#include "oneflow/core/functional/sequence_function.h" #include "oneflow/core/functional/impl/unary_functor.h" #include "oneflow/core/common/container_util.h" @@ -39,9 +34,8 @@ class ConvBiasGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const int32_t& num_spatial_dims, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_spatial_dims", num_spatial_dims)); - JUST(attrs.SetAttr("data_format", data_format)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_spatial_dims", "data_format"); + attrs.SetAllAttrs(num_spatial_dims, data_format); return OpInterpUtil::Dispatch(*op_, {dy}, attrs); } @@ -62,14 +56,11 @@ class ConvFilterGradFunctor { const std::vector& padding_before, const std::vector& dilation_rate, const int32_t& groups, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_spatial_dims", num_spatial_dims)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("strides", strides)); - JUST(attrs.SetAttr>("padding_before", padding_before)); - JUST(attrs.SetAttr>("dilation_rate", dilation_rate)); - JUST(attrs.SetAttr("groups", groups)); - JUST(attrs.SetAttr("data_format", data_format)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("num_spatial_dims", "kernel_size", "strides", + "padding_before", "dilation_rate", "groups", "data_format"); + attrs.SetAllAttrs(num_spatial_dims, kernel_size, strides, padding_before, dilation_rate, groups, + data_format); return OpInterpUtil::Dispatch(*op_, {dy, x}, attrs); } @@ -95,15 +86,12 @@ class ConvDataGradFunctor { const std::vector& padding_before, const std::vector& dilation_rate, const int32_t& groups, const std::string& data_format) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("num_spatial_dims", num_spatial_dims)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("strides", strides)); - JUST(attrs.SetAttr>("padding_before", padding_before)); - JUST(attrs.SetAttr>("dilation_rate", dilation_rate)); - JUST(attrs.SetAttr("groups", groups)); - JUST(attrs.SetAttr("data_format", data_format)); - return OpInterpUtil::Dispatch(*op_, {dy, weight, x}, attrs); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("num_spatial_dims", "kernel_size", "strides", + "padding_before", "dilation_rate", "groups", "data_format"); + attrs.SetAllAttrs(num_spatial_dims, kernel_size, strides, padding_before, dilation_rate, groups, + data_format); + return OpInterpUtil::Dispatch(*op_, {dy, weight, JUST(x->detach())}, attrs); } private: @@ -124,9 +112,8 @@ class EmbeddingGradFunctor { const std::shared_ptr& weight, const std::shared_ptr& indices, const int64_t& padding_idx, const bool& scale_grad_by_freq) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("padding_idx", padding_idx)); - JUST(attrs.SetAttr("scale_grad_by_freq", scale_grad_by_freq)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding_idx", "scale_grad_by_freq"); + attrs.SetAllAttrs(padding_idx, scale_grad_by_freq); return OpInterpUtil::Dispatch(*op_, {dy, weight, indices}, attrs); } @@ -140,7 +127,7 @@ class MaxPoolNdGradFunctor { for (int ndims = 1; ndims <= 3; ++ndims) { const auto& op_type_name = GetOpTypeName(ndims); op_expr_map_[op_type_name] = CHECK_JUST( - one::OpBuilder(op_type_name).Input("x").Input("indice").Input("dy").Output("dx").Build()); + one::OpBuilder(op_type_name).Input("dy").Input("x").Input("indice").Output("dx").Build()); } } static std::string GetOpTypeName(const int32_t& ndims) { @@ -153,21 +140,65 @@ class MaxPoolNdGradFunctor { const std::vector& kernel_size, const std::vector& stride, const std::vector& dilation, const bool& return_indices, const bool& ceil_mode) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("stride", stride)); - JUST(attrs.SetAttr>("dilation", dilation)); - JUST(attrs.SetAttr("return_indices", return_indices)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("data_format", "padding", "kernel_size", "stride", + "dilation", "return_indices", "ceil_mode"); + attrs.SetAllAttrs(data_format, padding, kernel_size, stride, dilation, return_indices, + ceil_mode); const auto& op_type_name = GetOpTypeName(ndims); const auto& it = op_expr_map_.find(op_type_name); CHECK_OR_RETURN(it != op_expr_map_.end()) << Error::RuntimeError() << "Encounter unsupported op " << op_type_name << " in MaxPoolNdGradFunctor."; CHECK_NOTNULL_OR_RETURN(it->second); // NOLINT(maybe-need-error-msg) - return OpInterpUtil::Dispatch(*it->second, {x, indice, dy}, attrs); + return OpInterpUtil::Dispatch(*it->second, {dy, x, indice}, attrs); + } + + protected: + std::unordered_map> op_expr_map_; +}; + +template +class MaxUnpoolNdGradFunctor { + public: + MaxUnpoolNdGradFunctor() + : op_(CHECK_JUST(one::OpBuilder(fmt::format("max_unpool_{}d_grad", N)) + .Input("dy") + .Input("x") + .Input("indices") + .Output("dx") + .Build())) {} + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& indice, + const std::shared_ptr& dy) const { + return OpInterpUtil::Dispatch(*op_, {dy, x, indice}); + } + + protected: + std::shared_ptr op_; +}; + +class AdaptiveMaxPoolNdGradFunctor { + public: + AdaptiveMaxPoolNdGradFunctor() { + for (int ndims = 1; ndims <= 3; ++ndims) { + const auto& op_type_name = GetOpTypeName(ndims); + op_expr_map_[op_type_name] = CHECK_JUST( + one::OpBuilder(op_type_name).Input("dy").Input("x").Input("index").Output("dx").Build()); + } + } + static std::string GetOpTypeName(const int32_t& ndims) { + return "adaptive_max_pool" + std::to_string(ndims) + "d_grad"; + } + Maybe operator()(const std::shared_ptr& x, + const std::shared_ptr& dy, + const std::shared_ptr& index, const int32_t& ndims) const { + const auto& op_type_name = GetOpTypeName(ndims); + const auto& it = op_expr_map_.find(op_type_name); + CHECK_OR_RETURN(it != op_expr_map_.end()) + << Error::RuntimeError() << "Encounter unsupported op " << op_type_name + << " in AdaptiveMaxPoolNdGradFunctor."; + CHECK_NOTNULL_OR_RETURN(it->second); // NOLINT(maybe-need-error-msg) + return OpInterpUtil::Dispatch(*it->second, {dy, x, index}); } protected: @@ -196,14 +227,11 @@ class TFPoolNdGradFunctor { const std::vector& padding_after, const std::vector& pool_size, const std::vector& strides, const bool& ceil_mode) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr("padding", padding)); - JUST(attrs.SetAttr>("padding_before", padding_before)); - JUST(attrs.SetAttr>("padding_after", padding_after)); - JUST(attrs.SetAttr>("pool_size", pool_size)); - JUST(attrs.SetAttr>("strides", strides)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("data_format", "padding", "padding_before", "padding_after", + "pool_size", "strides", "ceil_mode"); + attrs.SetAllAttrs(data_format, padding, padding_before, padding_after, pool_size, strides, + ceil_mode); const auto& op_type_name = GetOpTypeName(mode, ndims); const auto& it = op_expr_map_.find(op_type_name); CHECK_OR_RETURN(it != op_expr_map_.end()) @@ -224,7 +252,7 @@ class AdaptivePoolNdGradFunctor { for (int ndims = 1; ndims <= 3; ++ndims) { const auto& op_type_name = GetOpTypeName(mode, ndims); op_expr_map_[op_type_name] = - CHECK_JUST(one::OpBuilder(op_type_name).Input("x").Input("dy").Output("dx").Build()); + CHECK_JUST(one::OpBuilder(op_type_name).Input("dy").Input("x").Output("dx").Build()); } } } @@ -240,7 +268,7 @@ class AdaptivePoolNdGradFunctor { << Error::RuntimeError() << "Encounter unsupported op " << op_type_name << " in AdaptivePoolNdGradFunctor."; CHECK_NOTNULL_OR_RETURN(it->second); // NOLINT(maybe-need-error-msg) - return OpInterpUtil::Dispatch(*it->second, {x, dy}); + return OpInterpUtil::Dispatch(*it->second, {dy, x}); } protected: @@ -260,8 +288,8 @@ class SparseCrossEntropyGradFunctor { Maybe operator()(const std::shared_ptr& prediction, const std::shared_ptr& label, const std::shared_ptr& dy, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); return OpInterpUtil::Dispatch(*op_, {prediction, label, dy}, attrs); } @@ -283,8 +311,8 @@ class SparseCrossEntropyMsGradFunctor { Maybe operator()(const std::shared_ptr& prediction, const std::shared_ptr& label, const std::shared_ptr& dy, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); return OpInterpUtil::Dispatch(*op_, {prediction, label, dy}, attrs); } @@ -307,8 +335,31 @@ class SparseSoftmaxCrossEntropyGrad { Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& prob, const std::shared_ptr& label, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); + return OpInterpUtil::Dispatch(*op_, {prob, label, dy}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class SparseSoftmaxCrossEntropyMsGrad { + public: + SparseSoftmaxCrossEntropyMsGrad() { + op_ = CHECK_JUST(one::OpBuilder("sparse_softmax_cross_entropy_ms_grad") + .Input("prob") + .Input("label") + .Input("dy") + .Output("prediction_diff") + .Build()); + } + + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& prob, + const std::shared_ptr& label, const int64_t& depth) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("depth"); + attrs.SetAllAttrs(depth); return OpInterpUtil::Dispatch(*op_, {prob, label, dy}, attrs); } @@ -329,8 +380,8 @@ class SmoothL1LossGradFunctor { Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& input, const std::shared_ptr& target, const float& beta) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("beta", beta)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("beta"); + attrs.SetAllAttrs(beta); return OpInterpUtil::Dispatch(*op_, {dy, input, target}, attrs); } @@ -343,9 +394,9 @@ class KLDivLossGradFunctor { public: KLDivLossGradFunctor() { op_ = CHECK_JUST(one::OpBuilder("kl_div_loss_grad") + .Input("dy") .Input("input") .Input("target") - .Input("dy") .Output("dx") .Build()); } @@ -353,16 +404,43 @@ class KLDivLossGradFunctor { const std::shared_ptr& input, const std::shared_ptr& target, const bool log_target) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("log_target", log_target)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("log_target"); + attrs.SetAllAttrs(log_target); - return OpInterpUtil::Dispatch(*op_, {input, target, dy}, attrs); + return OpInterpUtil::Dispatch(*op_, {dy, input, target}, attrs); } private: std::shared_ptr op_; }; +class KLDivLossTargetGradFunctor { + public: + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::shared_ptr& target, + const bool log_target) const { + if (log_target) { + return functional::sequence_function(functional::Sub) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/Scalar(1)); + }) + .then(std::bind(functional::Mul, std::placeholders::_1, JUST(functional::Exp(target)))) + .then(std::bind(functional::Mul, std::placeholders::_1, dy)) + .call(target, input, /*alpha=*/1, /*inplace=*/false); + } else { + return functional::sequence_function(functional::Log) + .then([](const std::shared_ptr& input) { + return functional::ScalarAdd(1, input, /*alpha=*/Scalar(1)); + }) + .then(std::bind(functional::Sub, std::placeholders::_1, input, /*alpha=*/1, + /*inplace=*/false)) + .then(std::bind(functional::Mul, std::placeholders::_1, dy)) + .call(target); + } + } +}; + class NLLGradFunctor { public: NLLGradFunctor() { @@ -386,12 +464,12 @@ class NLLGradFunctor { const std::shared_ptr& input, const std::shared_ptr& target, const Optional& weight, const int64_t ignore_index) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("ignore_index", ignore_index)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("ignore_index"); + attrs.SetAllAttrs(ignore_index); if (weight) { - return OpInterpUtil::Dispatch(*op_weight_, - {out_grad, input, target, JUST(weight)}, attrs); + return OpInterpUtil::Dispatch( + *op_weight_, {out_grad, input, target, JUST(JUST(weight)->detach())}, attrs); } else { return OpInterpUtil::Dispatch(*op_, {out_grad, input, target}, attrs); } @@ -406,16 +484,16 @@ class BinaryCrossEntropyLossGradFunctor { public: BinaryCrossEntropyLossGradFunctor() { op_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_grad") + .Input("dy") .Input("input") .Input("target") - .Input("dy") .Output("dx") .Build()); op_weight_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_grad") + .Input("dy") .Input("input") .Input("target") .Input("weight") - .Input("dy") .Output("dx") .Build()); } @@ -423,13 +501,10 @@ class BinaryCrossEntropyLossGradFunctor { const std::shared_ptr& input, const std::shared_ptr& target, const Optional& weight) const { - MutableAttrMap attrs; - if (weight) { - return OpInterpUtil::Dispatch(*op_weight_, {input, target, JUST(weight), dy}, - attrs); + return OpInterpUtil::Dispatch(*op_weight_, {dy, input, target, JUST(weight)}); } else { - return OpInterpUtil::Dispatch(*op_, {input, target, dy}, attrs); + return OpInterpUtil::Dispatch(*op_, {dy, input, target}); } } @@ -438,35 +513,51 @@ class BinaryCrossEntropyLossGradFunctor { std::shared_ptr op_weight_; }; +class BinaryCrossEntropyLossTargetGradFunctor { + public: + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::shared_ptr& target, + const Optional& weight) const { + auto log_one_sub_input = JUST(functional::Log(JUST(ScalarSub(1, input, /*alpha=*/1)))); + auto grad = functional::sequence_function(functional::Log) + .then(std::bind(functional::Sub, log_one_sub_input, std::placeholders::_1, + /*alpha=*/1, /*inplace=*/false)) + .then(std::bind(functional::Mul, dy, std::placeholders::_1)) + .call(input); + return weight ? Mul(JUST(grad), JUST(weight)) : grad; + } +}; + class BinaryCrossEntropyWithLogitsLossGradFunctor { public: BinaryCrossEntropyWithLogitsLossGradFunctor() { op_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_with_logits_grad") + .Input("dy") .Input("input") .Input("target") - .Input("dy") .Output("dx") .Build()); op_weight_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_with_logits_grad") + .Input("dy") .Input("input") .Input("target") .Input("weight") - .Input("dy") .Output("dx") .Build()); op_pos_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_with_logits_grad") + .Input("dy") .Input("input") .Input("target") .Input("pos_weight") - .Input("dy") .Output("dx") .Build()); op_weight_pos_ = CHECK_JUST(one::OpBuilder("binary_cross_entropy_with_logits_grad") + .Input("dy") .Input("input") .Input("target") .Input("weight") .Input("pos_weight") - .Input("dy") .Output("dx") .Build()); } @@ -475,24 +566,23 @@ class BinaryCrossEntropyWithLogitsLossGradFunctor { const std::shared_ptr& target, const Optional& weight, const Optional& pos_weight) const { - MutableAttrMap attrs; - - JUST(attrs.SetAttr("has_pos_weight", pos_weight.has_value())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("has_pos_weight"); + attrs.SetAllAttrs(pos_weight.has_value()); if (weight) { if (pos_weight) { return OpInterpUtil::Dispatch( - *op_weight_pos_, {input, target, JUST(weight), JUST(pos_weight), dy}, attrs); + *op_weight_pos_, {dy, input, target, JUST(weight), JUST(pos_weight)}, attrs); } else { - return OpInterpUtil::Dispatch(*op_weight_, {input, target, JUST(weight), dy}, + return OpInterpUtil::Dispatch(*op_weight_, {dy, input, target, JUST(weight)}, attrs); } } else { if (pos_weight) { - return OpInterpUtil::Dispatch(*op_pos_, {input, target, JUST(pos_weight), dy}, + return OpInterpUtil::Dispatch(*op_pos_, {dy, input, target, JUST(pos_weight)}, attrs); } else { - return OpInterpUtil::Dispatch(*op_, {input, target, dy}, attrs); + return OpInterpUtil::Dispatch(*op_, {dy, input, target}, attrs); } } } @@ -504,6 +594,33 @@ class BinaryCrossEntropyWithLogitsLossGradFunctor { std::shared_ptr op_weight_pos_; }; +class BinaryCrossEntropyWithLogitsLossTargetGradFunctor { + public: + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::shared_ptr& target, + const Optional& weight, + const Optional& pos_weight) const { + if (pos_weight) { + auto sig = JUST(functional::Sigmoid(input)); + auto log_one_sub_sig = + JUST(functional::Log(JUST(functional::ScalarSub(1, sig, /*alpha=*/1)))); + auto grad = functional::sequence_function(functional::Log) + .then(std::bind(functional::Mul, std::placeholders::_1, JUST(pos_weight))) + .then(std::bind(functional::Sub, log_one_sub_sig, std::placeholders::_1, + /*alpha=*/1, false)) + .call(sig); + + return weight ? functional::Mul(JUST(grad), JUST(weight)) : grad; + } else { + auto grad = functional::sequence_function(functional::Negative) + .then(std::bind(functional::Mul, std::placeholders::_1, dy)) + .call(input); + return weight ? functional::Mul(JUST(grad), JUST(weight)) : grad; + } + } +}; + class BinaryCrossEntropyWithLogitsReduceMeanLossGradFunctor { public: BinaryCrossEntropyWithLogitsReduceMeanLossGradFunctor() { @@ -526,6 +643,17 @@ class BinaryCrossEntropyWithLogitsReduceMeanLossGradFunctor { std::shared_ptr op_pos_; std::shared_ptr op_weight_pos_; }; + +class BinaryCrossEntropyWithLogitsReduceMeanLossTargetGradFunctor { + public: + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& input, + const std::shared_ptr& target) const { + auto neg_mean_dy = JUST(functional::ScalarMul(-1.0 / input->nelement(), dy)); + return functional::Mul(input, neg_mean_dy); + } +}; + class CombinedMarginLossGradFunctor { public: CombinedMarginLossGradFunctor() { @@ -540,11 +668,8 @@ class CombinedMarginLossGradFunctor { const std::shared_ptr& label, const std::shared_ptr& theta, const float& m1, const float& m2, const float& m3, const int64_t& depth) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("m1", m1)); - JUST(attrs.SetAttr("m2", m2)); - JUST(attrs.SetAttr("m3", m3)); - JUST(attrs.SetAttr("depth", depth)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("m1", "m2", "m3", "depth"); + attrs.SetAllAttrs(m1, m2, m3, depth); return OpInterpUtil::Dispatch(*op_, {dy, label, theta}, attrs); } @@ -559,9 +684,8 @@ class AffineGridGradFunctor { } Maybe operator()(const std::shared_ptr& dgrid, const Shape& size, const bool& align_corners) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("size", size)); - JUST(attrs.SetAttr("align_corners", align_corners)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("size", "align_corners"); + attrs.SetAllAttrs(size, align_corners); return OpInterpUtil::Dispatch(*op_, {dgrid}, attrs); } @@ -585,10 +709,9 @@ class GridSampleGradFunctor { const std::shared_ptr& grid, const std::string& interpolation_mode, const std::string& padding_mode, const bool& align_corners) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("interpolation_mode", interpolation_mode)); - JUST(attrs.SetAttr("padding_mode", padding_mode)); - JUST(attrs.SetAttr("align_corners", align_corners)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("interpolation_mode", "padding_mode", "align_corners"); + attrs.SetAllAttrs(interpolation_mode, padding_mode, align_corners); return OpInterpUtil::Dispatch(*op_, {doutput, input, grid}, attrs); } @@ -616,12 +739,20 @@ class CtcLossGradFunctor { const std::shared_ptr& input_lengths, const std::shared_ptr& target_lengths, const std::shared_ptr& loss, - const std::shared_ptr& alpha, const int32_t& blank, + const std::shared_ptr& alpha, const int64_t& blank, const bool& zero_infinity, const int64_t& max_target_length) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("blank", blank)); - JUST(attrs.SetAttr("zero_infinity", zero_infinity)); - JUST(attrs.SetAttr("max_target_length", max_target_length)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("blank", "zero_infinity", "max_target_length"); + attrs.SetAllAttrs(blank, zero_infinity, max_target_length); + if (targets->dtype()->data_type() == DataType::kInt32) { + return OpInterpUtil::Dispatch( + *op_, {grad_out, log_probs, targets, input_lengths, target_lengths, loss, alpha}, attrs); + } else { + return OpInterpUtil::Dispatch( + *op_, + {grad_out, log_probs, JUST(functional::Cast(targets, DType::Int64(), false)), + input_lengths, target_lengths, loss, alpha}, + attrs); + } return OpInterpUtil::Dispatch( *op_, {grad_out, log_probs, targets, input_lengths, target_lengths, loss, alpha}, attrs); } @@ -633,23 +764,37 @@ class CtcLossGradFunctor { class PadGradFunctor { public: PadGradFunctor() { - reflect_pad_grad_ = + reflect_pad1d_grad_ = + CHECK_JUST(one::OpBuilder("reflection_pad1d_grad").Input("dy").Output("dx").Build()); + reflect_pad2d_grad_ = CHECK_JUST(one::OpBuilder("reflection_pad2d_grad").Input("dy").Output("dx").Build()); - replicate_pad_grad_ = + replicate_pad1d_grad_ = + CHECK_JUST(one::OpBuilder("replication_pad1d_grad").Input("dy").Output("dx").Build()); + replicate_pad2d_grad_ = CHECK_JUST(one::OpBuilder("replication_pad2d_grad").Input("dy").Output("dx").Build()); } Maybe operator()(const std::shared_ptr& dy, const std::vector& pad, const std::string& mode, const Scalar& value) const { const int64_t ndim = dy->shape()->NumAxes(); - size_t padding_size = 2 * ndim; - CHECK_LE_OR_RETURN(pad.size(), padding_size) - << Error::RuntimeError() << "Pad size should less than or equal to input axes * 2."; - MutableAttrMap attrs; - JUST(attrs.SetAttr>("padding", pad)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("padding"); + attrs.SetAllAttrs(pad); if (mode == "reflect") { - return OpInterpUtil::Dispatch(*reflect_pad_grad_, {dy}, attrs); + if (ndim == 3) { + return OpInterpUtil::Dispatch(*reflect_pad1d_grad_, {dy}, attrs); + } else if (ndim == 4) { + return OpInterpUtil::Dispatch(*reflect_pad2d_grad_, {dy}, attrs); + } else { + UNIMPLEMENTED_THEN_RETURN() << "only 3D/4D reflect padding are supported for now"; + } + } else if (mode == "replicate") { - return OpInterpUtil::Dispatch(*replicate_pad_grad_, {dy}, attrs); + if (ndim == 3) { + return OpInterpUtil::Dispatch(*replicate_pad1d_grad_, {dy}, attrs); + } else if (ndim == 4) { + return OpInterpUtil::Dispatch(*replicate_pad2d_grad_, {dy}, attrs); + } else { + UNIMPLEMENTED_THEN_RETURN() << "only 3D/4D replicate padding are supported for now"; + } } else { UNIMPLEMENTED_THEN_RETURN() << "Pad mode is " << mode << ", but only constant, reflect and replicate are valid."; @@ -657,8 +802,10 @@ class PadGradFunctor { } private: - std::shared_ptr reflect_pad_grad_; - std::shared_ptr replicate_pad_grad_; + std::shared_ptr reflect_pad1d_grad_; + std::shared_ptr reflect_pad2d_grad_; + std::shared_ptr replicate_pad1d_grad_; + std::shared_ptr replicate_pad2d_grad_; }; class AvgPoolNdGradFunctor { @@ -667,7 +814,7 @@ class AvgPoolNdGradFunctor { for (int ndims = 1; ndims <= 3; ++ndims) { const auto& op_type_name = GetOpTypeName(ndims); op_expr_map_[op_type_name] = - CHECK_JUST(one::OpBuilder(op_type_name).Input("x").Input("dy").Output("dx").Build()); + CHECK_JUST(one::OpBuilder(op_type_name).Input("dy").Input("x").Output("dx").Build()); } } static std::string GetOpTypeName(const int32_t& ndims) { @@ -679,21 +826,18 @@ class AvgPoolNdGradFunctor { const std::vector& kernel_size, const std::vector& stride, const bool& ceil_mode, const bool& count_include_pad, const int32_t& divisor_override) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("data_format", data_format)); - JUST(attrs.SetAttr>("padding", padding)); - JUST(attrs.SetAttr>("kernel_size", kernel_size)); - JUST(attrs.SetAttr>("stride", stride)); - JUST(attrs.SetAttr("ceil_mode", ceil_mode)); - JUST(attrs.SetAttr("count_include_pad", count_include_pad)); - JUST(attrs.SetAttr("divisor_override", divisor_override)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("data_format", "padding", "kernel_size", "stride", + "ceil_mode", "count_include_pad", "divisor_override"); + attrs.SetAllAttrs(data_format, padding, kernel_size, stride, ceil_mode, count_include_pad, + divisor_override); const auto& op_type_name = GetOpTypeName(ndims); const auto& it = op_expr_map_.find(op_type_name); CHECK_OR_RETURN(it != op_expr_map_.end()) << Error::RuntimeError() << "Encounter unsupported op " << op_type_name << " in AvgPoolNdGradFunctor."; CHECK_NOTNULL_OR_RETURN(it->second); // NOLINT(maybe-need-error-msg) - return OpInterpUtil::Dispatch(*it->second, {x, dy}, attrs); + return OpInterpUtil::Dispatch(*it->second, {dy, x}, attrs); } protected: @@ -720,9 +864,8 @@ class NormalizationGradFunctor { const std::shared_ptr& inv_variance, const std::shared_ptr& gamma, const float& epsilon, const int32_t& axis) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("epsilon", epsilon)); - JUST(attrs.SetAttr("axis", axis)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("epsilon", "axis"); + attrs.SetAllAttrs(epsilon, axis); return OpInterpUtil::Dispatch(*op_, {grad, x, mean, inv_variance, gamma}, attrs); } @@ -767,9 +910,8 @@ class NormalizationAddReluGradFunctor { const std::shared_ptr& gamma, const std::shared_ptr& beta, const std::shared_ptr& reserve_space, const std::shared_ptr& y, const int32_t& axis, const float& epsilon, bool has_addend) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("axis", axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("axis", "epsilon"); + attrs.SetAllAttrs(axis, epsilon); if (has_addend) { return OpInterpUtil::Dispatch( *addend_op_, {x, grad, mean, inv_variance, gamma, beta, reserve_space, y}, attrs); @@ -800,9 +942,8 @@ class LayerNormGradFunctor { const std::shared_ptr& mean, const std::shared_ptr& inv_variance, const int64_t& begin_norm_axis, const double& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("begin_norm_axis", begin_norm_axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "epsilon"); + attrs.SetAllAttrs(begin_norm_axis, epsilon); return OpInterpUtil::Dispatch(*op_, {dy, x, mean, inv_variance}, attrs); } @@ -828,9 +969,8 @@ class LayerNormAffineGradFunctor { const std::shared_ptr& inv_variance, const std::shared_ptr& gamma, const int64_t& begin_norm_axis, const double& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("begin_norm_axis", begin_norm_axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "epsilon"); + attrs.SetAllAttrs(begin_norm_axis, epsilon); return OpInterpUtil::Dispatch(*op_, {dy, x, mean, inv_variance, gamma}, attrs); } @@ -854,10 +994,9 @@ class LayerNormParamGradFunctor { const std::shared_ptr& x, const std::shared_ptr& mean, const std::shared_ptr& inv_variance, - const int64_t& begin_params_axis, const double& epsilon) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("begin_params_axis", begin_params_axis)); - JUST(attrs.SetAttr("epsilon", epsilon)); + const int64_t& begin_params_axis) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_params_axis"); + attrs.SetAllAttrs(begin_params_axis); return OpInterpUtil::Dispatch(*op_, {dy, x, mean, inv_variance}, attrs); } @@ -865,6 +1004,113 @@ class LayerNormParamGradFunctor { std::shared_ptr op_; }; +class GroupNormGradFunctor { + public: + GroupNormGradFunctor() { + affine_grad_op_ = CHECK_JUST(one::OpBuilder("group_norm_grad") + .Input("dy") + .Input("x") + .Input("mean") + .Input("inv_variance") + .Input("gamma") + .Output("dx") + .Build()); + grad_op_ = CHECK_JUST(one::OpBuilder("group_norm_grad") + .Input("dy") + .Input("x") + .Input("mean") + .Input("inv_variance") + .Output("dx") + .Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& x, + const std::shared_ptr& mean, + const std::shared_ptr& inv_variance, + const Optional& gamma, const int32_t& num_groups, + const double& epsilon) const { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("num_groups", "epsilon"); + attrs.SetAttr("num_groups", num_groups); + attrs.SetAttr("epsilon", epsilon); + if (gamma) { + return OpInterpUtil::Dispatch(*affine_grad_op_, + {dy, x, mean, inv_variance, JUST(gamma)}, attrs); + } else { + return OpInterpUtil::Dispatch(*grad_op_, {dy, x, mean, inv_variance}, attrs); + } + } + + private: + std::shared_ptr affine_grad_op_; + std::shared_ptr grad_op_; +}; + +class GroupNormParamGradFunctor { + public: + GroupNormParamGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("group_norm_param_grad") + .Input("dy") + .Input("x") + .Input("mean") + .Input("inv_variance") + .Output("dgamma") + .Output("dbeta") + .Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& x, + const std::shared_ptr& mean, + const std::shared_ptr& inv_variance) const { + return OpInterpUtil::Dispatch(*op_, {dy, x, mean, inv_variance}); + } + + private: + std::shared_ptr op_; +}; + +class RMSNormGradFunctor { + public: + RMSNormGradFunctor() { + grad_op_ = CHECK_JUST(one::OpBuilder("rms_norm_grad") + .Input("dy") + .Input("x") + .Input("inv_rms") + .Output("dx") + .Build()); + affine_grad_op_ = CHECK_JUST(one::OpBuilder("rms_norm_grad") + .Input("dy") + .Input("x") + .Input("inv_rms") + .Input("weight") + .Output("dx") + .Build()); + param_grad_op_ = CHECK_JUST(one::OpBuilder("rms_norm_param_grad") + .Input("dy") + .Input("x") + .Input("inv_rms") + .Output("weight_grad") + .Build()); + } + + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& x, + const std::shared_ptr& inv_rms, + const Optional& weight, const bool param_grad) const { + if (param_grad) { + return OpInterpUtil::Dispatch(*param_grad_op_, {dy, x, inv_rms}); + } else if (weight) { + return OpInterpUtil::Dispatch(*affine_grad_op_, {dy, x, inv_rms, JUST(weight)}); + } else { + return OpInterpUtil::Dispatch(*grad_op_, {dy, x, inv_rms}); + } + } + + private: + std::shared_ptr grad_op_; + std::shared_ptr affine_grad_op_; + std::shared_ptr param_grad_op_; +}; + class BroadcastMatmulGradBFunctor { public: BroadcastMatmulGradBFunctor() { @@ -873,9 +1119,9 @@ class BroadcastMatmulGradBFunctor { } Maybe operator()(const std::shared_ptr& a, const std::shared_ptr& b, double alpha) const { - MutableAttrMap attr; - JUST(attr.SetAttr("alpha", alpha)); - return OpInterpUtil::Dispatch(*op_, {a, b}, attr); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); + return OpInterpUtil::Dispatch(*op_, {a, b}, attrs); } private: @@ -896,10 +1142,9 @@ class FusedScaleTrilSoftmaxMaskScaleGradFunctor { const std::shared_ptr& dy, const std::shared_ptr& mask, const int64_t diagonal, const float tril_scale_value, const float mask_scale_value) const { - MutableAttrMap fused_attrs; - JUST(fused_attrs.SetAttr("diagonal", diagonal)); - JUST(fused_attrs.SetAttr("tril_scale_value", tril_scale_value)); - JUST(fused_attrs.SetAttr("mask_scale_value", mask_scale_value)); + auto& fused_attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("diagonal", "tril_scale_value", "mask_scale_value"); + fused_attrs.SetAllAttrs(diagonal, tril_scale_value, mask_scale_value); return OpInterpUtil::Dispatch(*fused_op_, {softmax_y, dy, mask}, fused_attrs); } @@ -920,9 +1165,9 @@ class FusedScaleMaskSoftmaxGradFunctor { Maybe operator()(const std::shared_ptr& y, const std::shared_ptr& dy, const std::shared_ptr& mask, const float& scale) const { - MutableAttrMap attrs_; - JUST(attrs_.SetAttr("scale_value", scale)); - return OpInterpUtil::Dispatch(*op_, {y, dy, mask}, attrs_); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_value"); + attrs.SetAllAttrs(scale); + return OpInterpUtil::Dispatch(*op_, {y, dy, mask}, attrs); } private: @@ -945,11 +1190,9 @@ class FusedScaleMaskSoftmaxDropoutGradFunctor { const std::shared_ptr& mask, const std::shared_ptr& dropout_mask, const float& scale, const float& dropout_scale) const { - MutableAttrMap attrs_; - JUST(attrs_.SetAttr("scale_value", scale)); - JUST(attrs_.SetAttr("dropout_scale_value", dropout_scale)); - - return OpInterpUtil::Dispatch(*op_, {softmax_y, dy, mask, dropout_mask}, attrs_); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale_value", "dropout_scale_value"); + attrs.SetAllAttrs(scale, dropout_scale); + return OpInterpUtil::Dispatch(*op_, {softmax_y, dy, mask, dropout_mask}, attrs); } private: @@ -971,8 +1214,8 @@ class CublasBiasAddReluMatmulGradFunctor { const std::shared_ptr& weight, const std::shared_ptr& aux, const double& alpha) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("alpha", alpha)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha"); + attrs.SetAllAttrs(alpha); return OpInterpUtil::Dispatch(*op_, {dy, weight, aux}, attrs); } @@ -1007,9 +1250,9 @@ class FusedReluDropoutGradFunctor { } Maybe operator()(const std::shared_ptr& dy, const std::shared_ptr& mask, const float& scale) const { - MutableAttrMap attr_map; - JUST(attr_map.SetAttr("scale", scale)); - return OpInterpUtil::Dispatch(*op_, {dy, mask}, attr_map); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("scale"); + attrs.SetAllAttrs(scale); + return OpInterpUtil::Dispatch(*op_, {dy, mask}, attrs); } private: @@ -1044,10 +1287,9 @@ class FusedDotFeatureInteractionGradFunctor { const bool& has_output_concat, const bool& self_interaction, const int32_t& output_concat_grad_dim, const std::string& pooling) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("self_interaction", self_interaction)); - JUST(attrs.SetAttr("output_concat_grad_dim", output_concat_grad_dim)); - JUST(attrs.SetAttr("pooling", pooling)); + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("self_interaction", "output_concat_grad_dim", "pooling"); + attrs.SetAllAttrs(self_interaction, output_concat_grad_dim, pooling); CHECK_OR_RETURN(pooling == "sum" || pooling == "none") << Error::RuntimeError() << "pooling should be sum or none, but get " << pooling << ". "; const int64_t n_features_grad = features.size(); @@ -1130,6 +1372,196 @@ class FusedCrossFeatureInteractionV2GradFunctor { std::shared_ptr v2_grad_op_; }; +class MatrixVectorProductGradAFunctor { + public: + MatrixVectorProductGradAFunctor() { + matrix_vector_product_grad_a_op_ = CHECK_JUST( + one::OpBuilder("matrix_vector_product_grad_a").Input("dy").Input("b").Output("dx").Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& b) const { + return OpInterpUtil::Dispatch(*matrix_vector_product_grad_a_op_, {dy, b}); + } + + private: + std::shared_ptr matrix_vector_product_grad_a_op_; +}; + +class MatrixVectorProductGradBFunctor { + public: + MatrixVectorProductGradBFunctor() { + matrix_vector_product_grad_b_op_ = CHECK_JUST( + one::OpBuilder("matrix_vector_product_grad_b").Input("dy").Input("a").Output("dx").Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& a) const { + return OpInterpUtil::Dispatch(*matrix_vector_product_grad_b_op_, {dy, a}); + } + + private: + std::shared_ptr matrix_vector_product_grad_b_op_; +}; + +class VectorMatrixProductGradAFunctor { + public: + VectorMatrixProductGradAFunctor() { + vector_matrix_product_grad_a_op_ = CHECK_JUST( + one::OpBuilder("vector_matrix_product_grad_a").Input("dy").Input("b").Output("dx").Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& b) const { + return OpInterpUtil::Dispatch(*vector_matrix_product_grad_a_op_, {dy, b}); + } + + private: + std::shared_ptr vector_matrix_product_grad_a_op_; +}; + +class VectorMatrixProductGradBFunctor { + public: + VectorMatrixProductGradBFunctor() { + vector_matrix_product_grad_b_op_ = CHECK_JUST( + one::OpBuilder("vector_matrix_product_grad_b").Input("dy").Input("a").Output("dx").Build()); + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& a) const { + return OpInterpUtil::Dispatch(*vector_matrix_product_grad_b_op_, {dy, a}); + } + + private: + std::shared_ptr vector_matrix_product_grad_b_op_; +}; +class DeformConv2dInputGradFunctor { + public: + DeformConv2dInputGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("deform_conv2d_input_grad") + .Input("output_grad") + .Input("input") + .Input("weight") + .Input("offset") + .Output("input_grad") + .Output("offset_grad") + .Build()); + + mask_op_ = CHECK_JUST(one::OpBuilder("deform_conv2d_input_grad") + .Input("output_grad") + .Input("input") + .Input("weight") + .Input("offset") + .Input("mask") + .Output("input_grad") + .Output("offset_grad") + .Output("mask_grad") + .Build()); + } + + Maybe operator()(const std::shared_ptr& output_grad, + const std::shared_ptr& input, + const std::shared_ptr& weight, + const std::shared_ptr& offset, + const Optional& mask, const int32_t& stride_h, + const int32_t& stride_w, const int32_t& pad_h, const int32_t& pad_w, + const int32_t& dilation_h, const int32_t& dilation_w, + const int32_t& groups, const int32_t& offset_groups, + const bool& use_mask) const { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("stride_h", "stride_w", "pad_h", "pad_w", "dilation_h", + "dilation_w", "groups", "offset_groups", "use_mask"); + attrs.SetAllAttrs(stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, groups, + offset_groups, use_mask); + if (mask) { + return OpInterpUtil::Dispatch( + *mask_op_, {output_grad, input, weight, offset, JUST(mask)}, attrs); + } else { + return OpInterpUtil::Dispatch(*op_, {output_grad, input, weight, offset}, attrs); + } + } + + private: + std::shared_ptr op_; + std::shared_ptr mask_op_; +}; + +class DeformConv2dParamGradFunctor { + public: + DeformConv2dParamGradFunctor() { + op_ = CHECK_JUST(one::OpBuilder("deform_conv2d_param_grad") + .Input("output_grad") + .Input("input") + .Input("weight") + .Input("offset") + .Input("mask") + .Output("weight_grad") + .Build()); + } + + Maybe operator()(const std::shared_ptr& output_grad, + const std::shared_ptr& input, + const std::shared_ptr& weight, + const std::shared_ptr& offset, + const std::shared_ptr& mask, const int32_t& stride_h, + const int32_t& stride_w, const int32_t& pad_h, const int32_t& pad_w, + const int32_t& dilation_h, const int32_t& dilation_w, + const int32_t& groups, const int32_t& offset_groups, + const bool& use_mask) const { + auto& attrs = + THREAD_CACHED_MUTABLE_ATTR_MAP("stride_h", "stride_w", "pad_h", "pad_w", "dilation_h", + "dilation_w", "groups", "offset_groups", "use_mask"); + attrs.SetAllAttrs(stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, groups, + offset_groups, use_mask); + return OpInterpUtil::Dispatch(*op_, {output_grad, input, weight, offset, mask}, attrs); + } + + private: + std::shared_ptr op_; +}; + +class FusedMLPGradFunctor { + public: + FusedMLPGradFunctor() { +#if CUDA_VERSION >= 11060 + fused_op_.resize(kMaxInputCount /*the maximum number of layers*/); + for (int n = 1; n < fused_op_.size(); ++n) { + fused_op_[n] = CHECK_JUST(one::OpBuilder("cublas_fused_mlp_grad") + .Input("dy") + .Input("x") + .Input("weights", n) + .Input("cublas_aux", n) + .Input("hidden", n) + .Output("d_x") + .Output("d_biases", n) + .Output("d_weights", n) + .Build()); + } +#endif + } + Maybe operator()(const std::shared_ptr& dy, + const std::shared_ptr& x, const TensorTuple& weights, + const TensorTuple& cublas_aux, const TensorTuple& hidden, + const std::vector& alpha_list) const { + const int64_t weight_size = weights.size(); + CHECK_EQ_OR_RETURN(alpha_list.size(), weight_size - 1) + << "Alpha list size should be equal to weight_size - 1. "; + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("alpha_list"); + attrs.SetAllAttrs(alpha_list); + TensorTuple input(2 + 3 * weight_size); + input[0] = dy; + input[1] = x; + std::copy(weights.begin(), weights.end(), input.begin() + 2); + std::copy(cublas_aux.begin(), cublas_aux.end(), input.begin() + 2 + weight_size); + std::copy(hidden.begin(), hidden.end(), input.begin() + 2 + 2 * weight_size); +#if CUDA_VERSION >= 11060 + return OpInterpUtil::Dispatch(*fused_op_[weight_size], input, attrs); +#endif + UNIMPLEMENTED_THEN_RETURN() << "Only Support in CUDA_VERSION >= 11060"; + } + + private: +#if CUDA_VERSION >= 11060 + std::vector> fused_op_; +#endif +}; + } // namespace impl ONEFLOW_FUNCTION_LIBRARY(m) { @@ -1140,18 +1572,27 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("TFPoolNdGrad"); m.add_functor("AdaptivePoolNdGrad"); m.add_functor("KLDivLossGrad"); + m.add_functor("KLDivLossTargetGrad"); m.add_functor("NLLGrad"); m.add_functor("BinaryCrossEntropyLossGrad"); + m.add_functor("BinaryCrossEntropyLossTargetGrad"); m.add_functor( "BinaryCrossEntropyWithLogitsLossGrad"); + m.add_functor( + "BinaryCrossEntropyWithLogitsLossTargetGrad"); m.add_functor("SparseCrossEntropyGrad"); m.add_functor("SparseCrossEntropyMsGrad"); m.add_functor("SparseSoftmaxCrossEntropyGrad"); + m.add_functor("SparseSoftmaxCrossEntropyMsGrad"); m.add_functor("SmoothL1LossGrad"); m.add_functor("CombinedMarginLossGrad"); m.add_functor("AffineGridGrad"); m.add_functor("GridSampleGrad"); m.add_functor("MaxPoolNdGrad"); + m.add_functor>("MaxUnpool1dGrad"); + m.add_functor>("MaxUnpool2dGrad"); + m.add_functor>("MaxUnpool3dGrad"); + m.add_functor("AdaptiveMaxPoolNdGrad"); m.add_functor("PadGrad"); m.add_functor("AvgPoolNdGrad"); m.add_functor("NormalizationGrad"); @@ -1159,6 +1600,8 @@ ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("LayerNormGrad"); m.add_functor("LayerNormAffineGrad"); m.add_functor("LayerNormParamGrad"); + m.add_functor("GroupNormGrad"); + m.add_functor("GroupNormParamGrad"); m.add_functor("BroadcastMatmulGradB"); m.add_functor("CtcLossGrad"); m.add_functor( @@ -1173,8 +1616,18 @@ ONEFLOW_FUNCTION_LIBRARY(m) { "FusedCrossFeatureInteractionV1Grad"); m.add_functor( "FusedCrossFeatureInteractionV2Grad"); + m.add_functor("FusedMLPGrad"); m.add_functor( "BinaryCrossEntropyWithLogitsReduceMeanLossGrad"); + m.add_functor( + "BinaryCrossEntropyWithLogitsReduceMeanLossTargetGrad"); + m.add_functor("MatrixVectorProductGradA"); + m.add_functor("MatrixVectorProductGradB"); + m.add_functor("VectorMatrixProductGradA"); + m.add_functor("VectorMatrixProductGradB"); + m.add_functor("DeformConv2dInputGrad"); + m.add_functor("DeformConv2dParamGrad"); + m.add_functor("RMSNormGrad"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/quantization.cpp b/oneflow/core/functional/impl/quantization.cpp index 3bcceb62c09ca5a9fe148eac2672e4ffe9583cc4..5458685d2da1f11e6ec463d6ec5faf1bfe8de720 100644 --- a/oneflow/core/functional/impl/quantization.cpp +++ b/oneflow/core/functional/impl/quantization.cpp @@ -17,6 +17,7 @@ limitations under the License. #include "oneflow/core/functional/impl/binary_functor.h" #include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_expr.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" @@ -44,11 +45,10 @@ class MinMaxObserverFunctor { const int32_t& quantization_bit, const std::string& quantization_scheme, const bool& per_layer_quantization) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("quantization_formula", quantization_formula)); - JUST(attrs.SetAttr("quantization_bit", quantization_bit)); - JUST(attrs.SetAttr("quantization_scheme", quantization_scheme)); - JUST(attrs.SetAttr("per_layer_quantization", per_layer_quantization)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("quantization_formula", "quantization_bit", + "quantization_scheme", "per_layer_quantization"); + attrs.SetAllAttrs(quantization_formula, quantization_bit, quantization_scheme, + per_layer_quantization); return OpInterpUtil::Dispatch(*op_, {in}, attrs); } @@ -77,13 +77,11 @@ class MovingAverageMinMaxObserverFunctor { const int32_t& quantization_bit, const std::string& quantization_scheme, const float& momentum) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("training", training)); - JUST(attrs.SetAttr("quantization_formula", quantization_formula)); - JUST(attrs.SetAttr("stop_update_after_iters", stop_update_after_iters)); - JUST(attrs.SetAttr("quantization_bit", quantization_bit)); - JUST(attrs.SetAttr("quantization_scheme", quantization_scheme)); - JUST(attrs.SetAttr("momentum", momentum)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("training", "quantization_formula", + "stop_update_after_iters", "quantization_bit", + "quantization_scheme", "momentum"); + attrs.SetAllAttrs(training, quantization_formula, stop_update_after_iters, quantization_bit, + quantization_scheme, momentum); return OpInterpUtil::Dispatch( *op_, {in, current_train_step, moving_max, moving_min}, attrs); } @@ -107,10 +105,9 @@ class FakeQuantizationFunctor { const std::shared_ptr& zero_point, const std::string& quantization_formula, const int32_t& quantization_bit, const std::string& quantization_scheme) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("quantization_formula", quantization_formula)); - JUST(attrs.SetAttr("quantization_bit", quantization_bit)); - JUST(attrs.SetAttr("quantization_scheme", quantization_scheme)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("quantization_formula", "quantization_bit", + "quantization_scheme"); + attrs.SetAllAttrs(quantization_formula, quantization_bit, quantization_scheme); return OpInterpUtil::Dispatch(*op_, {in, scale, zero_point}, attrs); } @@ -133,10 +130,9 @@ class QuantizationFunctor { const std::shared_ptr& zero_point, const std::string quantization_formula, const int32_t& quantization_bit, const std::string quantization_scheme) const { - MutableAttrMap attrs; - JUST(attrs.SetAttr("quantization_formula", quantization_formula)); - JUST(attrs.SetAttr("quantization_bit", quantization_bit)); - JUST(attrs.SetAttr("quantization_scheme", quantization_scheme)); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("quantization_formula", "quantization_bit", + "quantization_scheme"); + attrs.SetAllAttrs(quantization_formula, quantization_bit, quantization_scheme); return OpInterpUtil::Dispatch(*op_, {in, scale, zero_point}, attrs); } diff --git a/oneflow/core/functional/impl/random_functor.cpp b/oneflow/core/functional/impl/random_functor.cpp index d9276e876abdb427b8ef0e0ed0c12d5fa09b1568..ea2bb475bc6856823b9d85d3a972c266fd599ddc 100644 --- a/oneflow/core/functional/impl/random_functor.cpp +++ b/oneflow/core/functional/impl/random_functor.cpp @@ -13,27 +13,16 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/common/singleton.h" -#include "oneflow/core/common/optional.h" -#include "oneflow/core/common/protobuf.h" -#include "oneflow/core/framework/attr_map.h" -#include "oneflow/core/framework/op_builder.h" -#include "oneflow/core/framework/op_expr.h" -#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" -#include "oneflow/core/framework/tensor.h" -#include "oneflow/core/framework/tensor_tuple.h" -#include "oneflow/core/framework/op_interpreter.h" -#include "oneflow/core/framework/random_generator.h" +#include "oneflow/core/framework/mutable_attr_map.h" #include "oneflow/core/framework/nd_sbp.h" -#include "oneflow/core/functional/functional.h" +#include "oneflow/core/framework/op_builder.h" #include "oneflow/core/functional/function_library.h" -#include "oneflow/core/functional/impl/common.h" #include "oneflow/core/functional/impl/unary_functor.h" +#include "oneflow/core/job/global_mode.h" #include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/sbp_parallel.h" -#include "oneflow/core/job/lazy_mode.h" #include "oneflow/user/kernels/distributions/common.h" +#include "oneflow/user/kernels/random_seed_util.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" namespace oneflow { namespace one { @@ -47,22 +36,164 @@ class BernoulliFunctor { bernoulli_op_ = CHECK_JUST(one::OpBuilder("bernoulli").Input("in").Output("out").Build()); } Maybe operator()(const std::shared_ptr& x, const Symbol& dtype, - const Optional& generator) const { + const Optional& generator, const bool& inplace) const { const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap bernoulli_attrs; - JUST(bernoulli_attrs.SetAttr("dtype", dtype->data_type())); - JUST(bernoulli_attrs.SetAttr("seed", gen->current_seed())); + auto& bernoulli_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype", "seed", "p"); + // p == -1 means bernoulli op doesn't use p to generate random number + bernoulli_attrs.SetAllAttrs(dtype->data_type(), static_cast(gen->current_seed()), + static_cast(-1)); const auto& distribution_state = std::make_shared(gen); + if (inplace) { + auto outputs = std::make_shared(1); + JUST(CheckInplaceValid(x)); + (*outputs)[0] = x; + JUST(OpInterpUtil::Dispatch(*bernoulli_op_, {x}, outputs.get(), + OpExprInterpContext(bernoulli_attrs, distribution_state))); + return outputs->at(0); + } else { + return OpInterpUtil::Dispatch( + *bernoulli_op_, {x}, OpExprInterpContext(bernoulli_attrs, distribution_state)); + } + } + + private: + std::shared_ptr bernoulli_op_; +}; + +class BernoulliInplaceFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const Symbol& dtype, + const Optional& generator) const { + return Bernoulli(x, dtype, generator, true); + } +}; - return OpInterpUtil::Dispatch(*bernoulli_op_, {x}, - OpExprInterpContext(bernoulli_attrs, distribution_state)); +class BernoulliProbFunctor { + public: + BernoulliProbFunctor() { + bernoulli_op_ = CHECK_JUST(one::OpBuilder("bernoulli").Input("in").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& x, const double& p, + const Symbol& dtype, const Optional& generator, + const bool& inplace) const { + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + const auto& distribution_state = std::make_shared(gen); + CHECK_OR_THROW(p >= 0.0 && p <= 1.0) << "bernoulli expects p to be in [0, 1], but got p=" << p; + + auto& bernoulli_attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("dtype", "seed", "p"); + bernoulli_attrs.SetAllAttrs(dtype->data_type(), static_cast(gen->current_seed()), p); + if (inplace) { + auto outputs = std::make_shared(1); + JUST(CheckInplaceValid(x)); + (*outputs)[0] = x; + JUST(OpInterpUtil::Dispatch(*bernoulli_op_, {x}, outputs.get(), + OpExprInterpContext(bernoulli_attrs, distribution_state))); + return outputs->at(0); + } else { + return OpInterpUtil::Dispatch( + *bernoulli_op_, {x}, OpExprInterpContext(bernoulli_attrs, distribution_state)); + } } private: std::shared_ptr bernoulli_op_; }; +class BernoulliProbInplaceFunctor { + public: + Maybe operator()(const std::shared_ptr& x, const double& p, + const Symbol& dtype, + const Optional& generator) const { + return BernoulliProb(x, p, dtype, generator, true); + } +}; + +class InplaceUniformFunctor { + public: + InplaceUniformFunctor() { + uniform_op_ = CHECK_JUST(one::OpBuilder("uniform").Output("out").Build()); + uniform_int_op_ = CHECK_JUST(one::OpBuilder("uniform_int").Output("out").Build()); + } + Maybe operator()(const std::shared_ptr& x, const Scalar& from, + const Scalar& to) const { + JUST(CheckInplaceValid(x)); + const Shape& shape = *(x->shape()); + std::shared_ptr exec_op; + const auto& dtype = x->dtype(); + bool IsInteger = false; + + if (dtype->is_floating_point()) { + exec_op = uniform_op_; + } else if (dtype->is_integer()) { + exec_op = uniform_int_op_; + IsInteger = true; + } else { + OF_UNIMPLEMENTED() << "Only support floating and int dtype."; + } + DataType dtype_val = dtype->data_type(); + + auto gen = JUST(one::DefaultAutoGenerator()); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("from", "to", "shape", "dtype", "seed", "nb_sbp"); + auto outputs = std::make_shared(1); + (*outputs)[0] = x; + if (x->is_global()) // global + { + const auto& nb_sbp = JUST(x->nd_sbp()); + const auto& placement = JUST(x->parallel_desc()); + JUST(CheckDeviceIdsIsValid(placement)); + uint64_t init_seed = JUST(gen->Get(0))->engine()(); + if (LazyMode::is_enabled()) // lazy + { + if (IsInteger) { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(init_seed), *JUST(GetNdSbpStrList(nb_sbp))); + } else { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(init_seed), *JUST(GetNdSbpStrList(nb_sbp))); + } + } else // eager + { + uint64_t rank_seed = 0; + { + JUST(BroadcastSeedToAllRanks(&init_seed, /*root=*/0)); + rank_seed = + JUST(GetRandomSeedForRank(*placement, *nb_sbp, init_seed, GlobalProcessCtx::Rank())); + } + if (IsInteger) { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(rank_seed), NullOpt); + } else { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(rank_seed), NullOpt); + } + gen = JUST(MakeGenerator(placement->device_type())); + gen->set_current_seed(rank_seed); + } + const auto& distribution_state = std::make_shared(gen); + OpExprInterpContext ctx(attrs, placement, nb_sbp, distribution_state); + JUST(OpInterpUtil::Dispatch(*exec_op, {}, outputs.get(), ctx)); + } else { // local + if (IsInteger) { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(gen->current_seed()), NullOpt); + } else { + attrs.SetAllAttrs(from.Value(), to.Value(), shape, dtype_val, + static_cast(gen->current_seed()), NullOpt); + } + const auto& distribution_state = std::make_shared(gen); + OpExprInterpContext ctx(attrs, distribution_state); + ctx.device = JUST(x->device()); + JUST(OpInterpUtil::Dispatch(*exec_op, {}, outputs.get(), ctx)); + } + return outputs->at(0); + } + + private: + std::shared_ptr uniform_op_; + std::shared_ptr uniform_int_op_; +}; + class RandFunctor { public: RandFunctor() { op_ = CHECK_JUST(one::OpBuilder("uniform").Output("out").Build()); } @@ -70,21 +201,23 @@ class RandFunctor { const Optional>& device, const Optional& generator, const bool& requires_grad) const { - DataType dtype_val = DataType::kFloat; + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalRand(shape, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), dtype, generator, + requires_grad)); + } + DataType dtype_val = GetDefaultDType()->data_type(); if (dtype.has_value()) { dtype_val = JUST(dtype)->data_type(); - if (dtype_val != DataType::kFloat && dtype_val != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in rand()."; + if (!JUST(dtype)->is_floating_point()) { + OF_UNIMPLEMENTED() << "Only support floating dtype in rand()."; } } const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("from", 0)); - JUST(attrs.SetAttr("to", 1)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("from", "to", "shape", "dtype", "seed"); + attrs.SetAllAttrs(static_cast(0), static_cast(1), shape, dtype_val, + static_cast(gen->current_seed())); const auto& distribution_state = std::make_shared(gen); @@ -99,37 +232,46 @@ class RandFunctor { std::shared_ptr op_; }; -class ConsistentRandFunctor { +class GlobalRandFunctor { public: - ConsistentRandFunctor() { op_ = CHECK_JUST(one::OpBuilder("uniform").Output("out").Build()); } + GlobalRandFunctor() { op_ = CHECK_JUST(one::OpBuilder("uniform").Output("out").Build()); } Maybe operator()(const Shape& shape, const Symbol& placement, const std::vector>& sbp_tuple, const Optional>& dtype, const Optional& generator, const bool& requires_grad) const { JUST(CheckDeviceIdsIsValid(placement)); - DataType dtype_val = DataType::kFloat; + DataType dtype_val = GetDefaultDType()->data_type(); if (dtype.has_value()) { dtype_val = JUST(dtype)->data_type(); if (dtype_val != DataType::kFloat && dtype_val != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in rand()."; + OF_UNIMPLEMENTED() << "Only support floating dtype in rand()."; } } - const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("from", 0)); - JUST(attrs.SetAttr("to", 1)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); - - const auto& distribution_state = std::make_shared(gen); + auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + uint64_t init_seed = JUST(gen->Get(0))->engine()(); const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("from", "to", "shape", "dtype", "seed", "nd_sbp"); if (LazyMode::is_enabled()) { - JUST(attrs.SetAttr>("nd_sbp", *JUST(GetNdSbpStrList(nd_sbp)))); + attrs.SetAllAttrs(static_cast(0), static_cast(1), shape, dtype_val, + static_cast(init_seed), *JUST(GetNdSbpStrList(nd_sbp))); + } else { + uint64_t rank_seed = 0; + { + JUST(BroadcastSeedToAllRanks(&init_seed, /*root=*/0)); + rank_seed = + JUST(GetRandomSeedForRank(*placement, *nd_sbp, init_seed, GlobalProcessCtx::Rank())); + } + attrs.SetAllAttrs(static_cast(0), static_cast(1), shape, dtype_val, + static_cast(rank_seed), NullOpt); + gen = JUST(MakeGenerator(placement->device_type())); + gen->set_current_seed(rank_seed); } + const auto& distribution_state = std::make_shared(gen); + auto result = JUST(OpInterpUtil::Dispatch( *op_, {}, OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); JUST(result->set_requires_grad(requires_grad)); @@ -139,32 +281,93 @@ class ConsistentRandFunctor { private: std::shared_ptr op_; }; - class RandNFunctor { public: - RandNFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } Maybe operator()(const Shape& shape, const Optional>& dtype, const Optional>& device, const Optional& generator, const bool& requires_grad) const { - DataType dtype_val = DataType::kFloat; - if (dtype) { dtype_val = JUST(dtype)->data_type(); } - if (dtype_val != DataType::kFloat && dtype_val != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in randn()."; + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalRandN(shape, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), dtype, generator, + requires_grad)); } + if (dtype.has_value() && !JUST(dtype)->is_floating_point()) { + OF_UNIMPLEMENTED() << "Only support floating dtype in randn()."; + } + const auto& out = Optional(); + return Normal(static_cast(0), static_cast(1), shape, out, dtype, device, + generator, requires_grad); + } +}; - const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("mean", 0)); - JUST(attrs.SetAttr("std", 1)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); +class GlobalRandNFunctor { + public: + Maybe operator()(const Shape& shape, const Symbol& placement, + const std::vector>& sbp_tuple, + const Optional>& dtype, + const Optional& generator, + const bool& requires_grad) const { + if (dtype.has_value() && !JUST(dtype)->is_floating_point()) { + OF_UNIMPLEMENTED() << "Only support floating dtype in randn()."; + } + const auto& out = Optional(); + return GlobalNormal(static_cast(0), static_cast(1), shape, out, placement, + sbp_tuple, dtype, generator, requires_grad); + } +}; - const auto& distribution_state = std::make_shared(gen); +class NormalFunctor { + public: + NormalFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } + Maybe operator()(const float& mean, const float& std, const Shape& shape, + const Optional& out, + const Optional>& optional_dtype, + const Optional>& optional_device, + const Optional& optional_generator, + const bool& requires_grad) const { + Symbol dtype = GetDefaultDType(); + if (optional_dtype.has_value()) { + if (!JUST(optional_dtype)->is_floating_point()) { + OF_UNIMPLEMENTED() << "Only support float and double in normal()."; + } + dtype = JUST(optional_dtype); + } + Symbol device = JUST(Device::New("cpu")); + if (optional_device.has_value()) { device = JUST(optional_device); } + + if (out.has_value()) { + auto out_tensor = JUST(out); + Symbol output_tensor_dtype = out_tensor->dtype(); + if (optional_dtype.has_value()) { + CHECK_OR_RETURN(output_tensor_dtype == dtype) + << Error::RuntimeError() << "data type " << dtype->name() + << " does not match data type of out parameter " << output_tensor_dtype->name(); + } + dtype = output_tensor_dtype; + Symbol out_tensor_device = JUST(out_tensor->device()); + if (optional_device.has_value()) { + CHECK_OR_RETURN(out_tensor_device == JUST(optional_device)) + << Error::RuntimeError() << "device type " << device->ToString() + << " does not match device type of out parameter " << out_tensor_device->ToString(); + } + device = out_tensor_device; + } + const auto gen = optional_generator.value_or(JUST(one::DefaultAutoGenerator())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("mean", "std", "shape", "dtype", "seed"); + attrs.SetAllAttrs(static_cast(mean), static_cast(std), shape, + dtype->data_type(), static_cast(gen->current_seed())); + const auto& distribution_state = std::make_shared(gen); OpExprInterpContext ctx(attrs, distribution_state); ctx.device = device; + if (out.has_value()) { + std::shared_ptr outputs = std::make_shared(1); + (*outputs)[0] = JUST(out); + JUST(OpInterpUtil::Dispatch(*op_, {}, outputs.get(), ctx)); + return (*outputs)[0]; + } + auto result = JUST(OpInterpUtil::Dispatch(*op_, {}, ctx)); JUST(result->set_requires_grad(requires_grad)); return result; @@ -174,35 +377,84 @@ class RandNFunctor { std::shared_ptr op_; }; -class ConsistentRandNFunctor { +class Normal2Functor { public: - ConsistentRandNFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } - Maybe operator()(const Shape& shape, const Symbol& placement, + Maybe operator()(const float& mean, const float& std, const int32_t& shape, + const Optional& out, + const Optional>& optional_dtype, + const Optional>& optional_device, + const Optional& optional_generator, + const bool& requires_grad) const { + const Shape size = Shape({shape}); + return Normal(mean, std, size, out, optional_dtype, optional_device, optional_generator, + requires_grad); + } +}; + +class GlobalNormalFunctor { + public: + GlobalNormalFunctor() { op_ = CHECK_JUST(one::OpBuilder("normal").Output("out").Build()); } + Maybe operator()(const float& mean, const float& std, const Shape& shape, + const Optional& out, const Symbol& placement, const std::vector>& sbp_tuple, - const Optional>& dtype, - const Optional& generator, + const Optional>& optional_dtype, + const Optional& optional_generator, const bool& requires_grad) const { JUST(CheckDeviceIdsIsValid(placement)); - DataType dtype_val = DataType::kFloat; - if (dtype) { dtype_val = JUST(dtype)->data_type(); } - if (dtype_val != DataType::kFloat && dtype_val != DataType::kDouble) { - OF_UNIMPLEMENTED() << "Only support float and double in randn()."; + + Symbol dtype = DType::Float(); + if (optional_dtype.has_value()) { + if (!JUST(optional_dtype)->is_floating_point()) { + OF_UNIMPLEMENTED() << "Only support float and double in normal()."; + } + dtype = JUST(optional_dtype); } - const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("mean", 0)); - JUST(attrs.SetAttr("std", 1)); - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); + if (out.has_value()) { + auto out_tensor = JUST(out); + Symbol output_tensor_dtype = out_tensor->dtype(); + if (optional_dtype.has_value()) { + CHECK_OR_RETURN(output_tensor_dtype == dtype) + << Error::RuntimeError() << "data type " << dtype->name() + << " does not match data type of out parameter (" << output_tensor_dtype->name(); + } + dtype = output_tensor_dtype; + } - const auto& distribution_state = std::make_shared(gen); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("mean", "std", "shape", "dtype", "seed", "nd_sbp"); const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); + + std::shared_ptr gen = optional_generator.value_or(JUST(one::DefaultAutoGenerator())); + uint64_t init_seed = JUST(gen->Get(0))->engine()(); + if (LazyMode::is_enabled()) { - JUST(attrs.SetAttr>("nd_sbp", *JUST(GetNdSbpStrList(nd_sbp)))); + attrs.SetAllAttrs(static_cast(mean), static_cast(std), shape, + dtype->data_type(), static_cast(init_seed), + *JUST(GetNdSbpStrList(nd_sbp))); + } else { + uint64_t rank_seed = 0; + { + JUST(BroadcastSeedToAllRanks(&init_seed, /*root=*/0)); + rank_seed = + JUST(GetRandomSeedForRank(*placement, *nd_sbp, init_seed, GlobalProcessCtx::Rank())); + } + attrs.SetAllAttrs(static_cast(mean), static_cast(std), shape, + dtype->data_type(), static_cast(rank_seed), NullOpt); + gen = JUST(MakeGenerator(placement->device_type())); + gen->set_current_seed(rank_seed); } + const auto& distribution_state = std::make_shared(gen); + + if (out.has_value()) { + std::shared_ptr outputs = std::make_shared(1); + (*outputs)[0] = JUST(out); + JUST(OpInterpUtil::Dispatch( + *op_, {}, outputs.get(), + OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); + return (*outputs)[0]; + } + auto result = JUST(OpInterpUtil::Dispatch( *op_, {}, OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); JUST(result->set_requires_grad(requires_grad)); @@ -213,6 +465,45 @@ class ConsistentRandNFunctor { std::shared_ptr op_; }; +class GlobalNormal2Functor { + public: + Maybe operator()(const float& mean, const float& std, const int32_t& shape, + const Optional& out, const Symbol& placement, + const std::vector>& sbp_tuple, + const Optional>& optional_dtype, + const Optional& optional_generator, + const bool& requires_grad) const { + const Shape size = Shape({shape}); + return GlobalNormal(mean, std, size, out, placement, sbp_tuple, optional_dtype, + optional_generator, requires_grad); + } +}; + +class RandnLikeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const Optional>& dtype, + const Optional>& device, + const Optional& generator, + const bool& requires_grad) const { + return RandN(*input->shape(), dtype.value_or(input->dtype()), + device.value_or(JUST(input->device())), generator, requires_grad); + } +}; + +class GlobalRandnLikeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, + const Symbol& placement, + const std::vector>& sbp, + const Optional>& dtype, + const Optional& generator, + const bool& requires_grad) const { + return GlobalRandN(*input->shape(), placement, sbp, dtype.value_or(input->dtype()), generator, + requires_grad); + } +}; + class RandIntFunctor { public: RandIntFunctor() { op_ = CHECK_JUST(one::OpBuilder("uniform_int").Output("out").Build()); } @@ -222,16 +513,17 @@ class RandIntFunctor { const Optional>& device, const Optional& generator, const bool& requires_grad) const { + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalRandInt( + low, high, shape, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), dtype, generator, requires_grad)); + } DataType dtype_val = DataType::kInt64; if (dtype) { dtype_val = JUST(dtype)->data_type(); } const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("from", low)); - JUST(attrs.SetAttr("to", high)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "from", "to", "dtype", "seed"); + attrs.SetAllAttrs(shape, low, high, dtype_val, static_cast(gen->current_seed())); const auto& distribution_state = std::make_shared(gen); @@ -258,11 +550,33 @@ class RandInt2Functor { } }; -class ConsistentRandIntFunctor { +class RandIntLikeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const int64_t low, + const int64_t high, const Optional>& dtype, + const Optional>& device, + const Optional& generator, + const bool& requires_grad) const { + const Shape shape = *input->shape(); + return RandInt(low, high, shape, dtype, device, generator, requires_grad); + } +}; + +class RandIntLike2Functor { public: - ConsistentRandIntFunctor() { - op_ = CHECK_JUST(one::OpBuilder("uniform_int").Output("out").Build()); + Maybe operator()(const std::shared_ptr& input, const int64_t high, + const Optional>& dtype, + const Optional>& device, + const Optional& generator, + const bool& requires_grad) const { + const Shape shape = *input->shape(); + return RandInt(/*low*/ 0, high, shape, dtype, device, generator, requires_grad); } +}; + +class GlobalRandIntFunctor { + public: + GlobalRandIntFunctor() { op_ = CHECK_JUST(one::OpBuilder("uniform_int").Output("out").Build()); } Maybe operator()(const int64_t low, const int64_t high, const Shape& shape, const Symbol& placement, @@ -274,20 +588,27 @@ class ConsistentRandIntFunctor { DataType dtype_val = DataType::kInt64; if (dtype) { dtype_val = JUST(dtype)->data_type(); } - const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("shape", shape)); - JUST(attrs.SetAttr("from", low)); - JUST(attrs.SetAttr("to", high)); - JUST(attrs.SetAttr("dtype", dtype_val)); - JUST(attrs.SetAttr("seed", gen->current_seed())); - - const auto& distribution_state = std::make_shared(gen); - + auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); const auto& nd_sbp = JUST(GetNdSbp(sbp)); + uint64_t init_seed = JUST(gen->Get(0))->engine()(); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("shape", "from", "to", "dtype", "seed", "nd_sbp"); if (LazyMode::is_enabled()) { - JUST(attrs.SetAttr>("nd_sbp", *JUST(GetNdSbpStrList(nd_sbp)))); + attrs.SetAllAttrs(shape, low, high, dtype_val, static_cast(init_seed), + *JUST(GetNdSbpStrList(nd_sbp))); + } else { + uint64_t rank_seed = 0; + { + JUST(BroadcastSeedToAllRanks(&init_seed, /*root=*/0)); + rank_seed = + JUST(GetRandomSeedForRank(*placement, *nd_sbp, init_seed, GlobalProcessCtx::Rank())); + } + attrs.SetAllAttrs(shape, low, high, dtype_val, static_cast(rank_seed), NullOpt); + gen = JUST(MakeGenerator(placement->device_type())); + gen->set_current_seed(rank_seed); } + const auto& distribution_state = std::make_shared(gen); + auto result = JUST(OpInterpUtil::Dispatch( *op_, {}, OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); @@ -299,7 +620,7 @@ class ConsistentRandIntFunctor { std::shared_ptr op_; }; -class ConsistentRandInt2Functor { +class GlobalRandInt2Functor { public: Maybe operator()(const int64_t high, const Shape& shape, const Symbol& placement, @@ -308,8 +629,33 @@ class ConsistentRandInt2Functor { const Optional& generator, const bool& requires_grad) const { JUST(CheckDeviceIdsIsValid(placement)); - return ConsistentRandInt(/*low*/ 0, high, shape, placement, sbp, dtype, generator, - requires_grad); + return GlobalRandInt(/*low*/ 0, high, shape, placement, sbp, dtype, generator, requires_grad); + } +}; + +class GlobalRandIntLikeFunctor { + public: + Maybe operator()(const std::shared_ptr& input, const int64_t low, + const int64_t high, const Symbol& placement, + const std::vector>& sbp, + const Optional>& dtype, + const Optional& generator, + const bool& requires_grad) const { + const Shape shape = *input->shape(); + return GlobalRandInt(low, high, shape, placement, sbp, dtype, generator, requires_grad); + } +}; + +class GlobalRandIntLike2Functor { + public: + Maybe operator()(const std::shared_ptr& input, const int64_t high, + const Symbol& placement, + const std::vector>& sbp, + const Optional>& dtype, + const Optional& generator, + const bool& requires_grad) const { + const Shape shape = *input->shape(); + return GlobalRandInt(/*low*/ 0, high, shape, placement, sbp, dtype, generator, requires_grad); } }; @@ -319,10 +665,14 @@ class RandPermFunctor { Maybe operator()(const int32_t n, const Optional& generator, const Symbol& dtype, const Optional>& device, const bool& requires_grad) const { + if (GlobalMode::is_enabled()) { + return JUST(functional::GlobalRandPerm(n, GetGlobalParallelDescFromDevice(device), + *JUST(GetSbpList(GlobalMode::nd_sbp())), generator, + dtype, requires_grad)); + } const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("n", n)); - JUST(attrs.SetAttr("seed", gen->current_seed())); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("n", "seed"); + attrs.SetAllAttrs(n, static_cast(gen->current_seed())); const auto& distribution_state = std::make_shared(gen); @@ -338,9 +688,9 @@ class RandPermFunctor { std::shared_ptr randperm_op_; }; -class ConsistentRandPermFunctor { +class GlobalRandPermFunctor { public: - ConsistentRandPermFunctor() { + GlobalRandPermFunctor() { randperm_op_ = CHECK_JUST(one::OpBuilder("randperm").Output("out").Build()); } Maybe operator()(const int32_t n, const Symbol& placement, @@ -349,16 +699,17 @@ class ConsistentRandPermFunctor { const bool& requires_grad) const { JUST(CheckDeviceIdsIsValid(placement)); const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); - MutableAttrMap attrs; - JUST(attrs.SetAttr("n", n)); - JUST(attrs.SetAttr("seed", gen->current_seed())); - const auto& distribution_state = std::make_shared(gen); - const auto& nd_sbp = JUST(GetNdSbp(sbp_tuple)); + + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("n", "seed", "nd_sbp"); if (LazyMode::is_enabled()) { - JUST(attrs.SetAttr>("nd_sbp", *JUST(GetNdSbpStrList(nd_sbp)))); + attrs.SetAllAttrs(n, static_cast(gen->current_seed()), + *JUST(GetNdSbpStrList(nd_sbp))); + } else { + attrs.SetAllAttrs(n, static_cast(gen->current_seed()), NullOpt); } + auto result = JUST(OpInterpUtil::Dispatch( *randperm_op_, {}, OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); @@ -369,20 +720,161 @@ class ConsistentRandPermFunctor { private: std::shared_ptr randperm_op_; }; + +class ExponentialFunctor { + public: + ExponentialFunctor() { op_ = CHECK_JUST(one::OpBuilder("exponential").Output("out").Build()); } + Maybe operator()(const std::shared_ptr& x, const float& lambd, + const Optional& generator) const { + DataType dtype_val = x->dtype()->data_type(); + const Shape& out_shape = *(x->shape()); + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + const auto& distribution_state = std::make_shared(gen); + + std::shared_ptr outputs = std::make_shared(1); + outputs->at(0) = x; + if (x->is_global()) { + const auto& placement = JUST(x->parallel_desc()); + const auto& nd_sbp = JUST(x->nd_sbp()); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("seed", "lambd", "dtype", "out_shape", "nd_sbp"); + if (LazyMode::is_enabled()) { + attrs.SetAllAttrs(static_cast(gen->current_seed()), lambd, dtype_val, out_shape, + *JUST(GetNdSbpStrList(nd_sbp))); + } else { + attrs.SetAllAttrs(static_cast(gen->current_seed()), lambd, dtype_val, out_shape, + NullOpt); + } + JUST(OpInterpUtil::Dispatch( + *op_, {}, outputs.get(), + OpExprInterpContext(attrs, placement, nd_sbp, distribution_state))); + } else { + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("seed", "lambd", "dtype", "out_shape"); + attrs.SetAllAttrs(static_cast(gen->current_seed()), lambd, dtype_val, out_shape); + OpExprInterpContext ctx(attrs, distribution_state); + ctx.device = JUST(x->device()); + JUST(OpInterpUtil::Dispatch(*op_, {}, outputs.get(), ctx)); + } + return outputs->at(0); + } + + private: + std::shared_ptr op_; +}; + +// NOTE(Liang Depeng): The implementation of MultinomialFunctor is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/Distributions.cpp#L548 +class MultinomialFunctor { + public: + MultinomialFunctor() { + op_cpu_ = + CHECK_JUST(one::OpBuilder("multinomial_with_replacement").Input("x").Output("out").Build()); + op_gpu_ = CHECK_JUST(one::OpBuilder("multinomial_with_replacement") + .Input("x") + .Input("prefix_sum") + .Output("out") + .Build()); + } + Maybe operator()(const std::shared_ptr& x, const int& num_samples, + const bool& replacement, + const Optional& generator) const { + CHECK_OR_RETURN(x->ndim() > 0 && x->ndim() <= 2) + << "The input probability tensor must be 1 or 2 dim, " + << "but got: " << x->ndim(); + CHECK_OR_RETURN(x->dtype()->is_floating_point()) + << "multinomial only supports floating-point dtypes for input, but got: " + << x->dtype()->name(); + CHECK_OR_RETURN(num_samples > 0) << "cannot sample num_samples <= 0 samples"; + int64_t num_categories = x->dim(x->ndim() - 1); + CHECK_OR_RETURN(replacement || num_samples <= num_categories) + << "cannot sample num_samples > prob_dist.size(-1) samples without replacement"; + + /* The largest consecutive integer representable in float32 (2^24) */ + constexpr int64_t FLOAT32_MAX_CONSECUTIVE_INT = 1 << (FLT_MANT_DIG); + // Since the index tensor is float, numCategories cannot exceed max float integer precision + CHECK_OR_RETURN(num_categories <= FLOAT32_MAX_CONSECUTIVE_INT) + << "number of categories cannot exceed 2^24"; + // Fast-path for no replacement. + // Reference: + // https://github.com/pytorch/pytorch/issues/11931#issuecomment-625882503 + if (!replacement) { + // The algorithm is from gumbel softmax. + // s = argmax( logp - log(-log(eps)) ) where eps ~ U(0, 1) + // Here we can apply exp to the formula which will not affect result of + // argmax or topk. Then we have + // s = argmax( p / (-log(eps)) ) where eps ~ U(0, 1). + // We can also simplify the formula above by + // s = argmax( p / q ) where q ~ Exp(1) + std::shared_ptr q = + JUST(functional::Empty(*(x->shape()), x->dtype(), JUST(x->device()), false)); + q = JUST(functional::Exponential(q, 1, generator)); + // In theory the probability to generate 0 from exponential distribution is + // 0. However, on CUDA side there is a protection to avoid 0s, but on CPU + // side, there is a very low probability to generate 0 from + // exponential. The probability is about 2^(-DBL_MANT_DIG). We just + // ignore it here, but there may be some risk to get invalid output on CPU. + q = JUST(functional::Div(x, q)); + std::shared_ptr result; + if (num_samples == 1) { + result = JUST(functional::ArgMax(q, -1, true, JUST(DType::Get(DataType::kInt64)))); + } else { + std::shared_ptr temp = + JUST(functional::TopK(q, num_samples, -1, + /*largest=*/true, /*sorted=*/true)); + result = (*temp)[1]; + } + return result; + } + + const auto gen = generator.value_or(JUST(one::DefaultAutoGenerator())); + const auto& distribution_state = std::make_shared(gen); + auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("seed", "num_samples"); + attrs.SetAllAttrs(static_cast(gen->current_seed()), num_samples); + OpExprInterpContext ctx(attrs, distribution_state); + ctx.device = JUST(x->device()); + DeviceType input_device = JUST(x->device())->enum_type(); + if (input_device == DeviceType::kCPU) { + return OpInterpUtil::Dispatch(*op_cpu_, {x}, ctx); + } else { + std::shared_ptr sum_last_dim = JUST(functional::ReduceSum(x, {-1}, true)); + std::shared_ptr norm_dist = JUST(functional::Div(x, sum_last_dim)); + std::shared_ptr prefix_sum = JUST(functional::Cumsum(norm_dist, -1, x->dtype())); + return OpInterpUtil::Dispatch(*op_gpu_, {norm_dist, prefix_sum}, ctx); + } + } + + private: + std::shared_ptr op_cpu_; + std::shared_ptr op_gpu_; +}; + } // namespace impl using namespace impl; ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("Bernoulli"); + m.add_functor("BernoulliInplace"); + m.add_functor("BernoulliProb"); + m.add_functor("BernoulliProbInplace"); m.add_functor("RandPerm"); - m.add_functor("ConsistentRandPerm"); + m.add_functor("GlobalRandPerm"); m.add_functor("Rand"); - m.add_functor("ConsistentRand"); + m.add_functor("GlobalRand"); m.add_functor("RandN"); - m.add_functor("ConsistentRandN"); + m.add_functor("GlobalRandN"); + m.add_functor("Normal"); + m.add_functor("Normal2"); + m.add_functor("GlobalNormal"); + m.add_functor("GlobalNormal2"); + m.add_functor("RandnLike"); + m.add_functor("GlobalRandnLike"); m.add_functor("RandInt"); - m.add_functor("ConsistentRandInt"); + m.add_functor("GlobalRandInt"); + m.add_functor("RandIntLike"); + m.add_functor("GlobalRandIntLike"); + m.add_functor("Exponential"); + m.add_functor("Multinomial"); + m.add_functor("InplaceUniform"); }; } // namespace functional diff --git a/oneflow/core/functional/impl/rnn_functor.cpp b/oneflow/core/functional/impl/rnn_functor.cpp index 39bf67976aa718fed30035b3069fc7b6aca27962..9739328e4549310a522b4c5478f77ba134b2c144 100644 --- a/oneflow/core/functional/impl/rnn_functor.cpp +++ b/oneflow/core/functional/impl/rnn_functor.cpp @@ -34,8 +34,8 @@ limitations under the License. #include "oneflow/core/functional/impl/common.h" #include "oneflow/core/functional/impl/unary_functor.h" #include "oneflow/core/job/lazy_mode.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/common/container_util.h" +#include "oneflow/core/kernel/kernel_util.h" #include "oneflow/user/kernels/distributions/common.h" #include "oneflow/core/framework/nd_sbp.h" @@ -81,7 +81,7 @@ Maybe check_rnn_cell_forward_hidden(const std::shared_ptr& in Maybe check_attributes(const std::shared_ptr& input, const TensorTuple& params, const TensorTuple& hiddens, bool check_dtype = false) { DeviceType input_device{}; - if (input->is_consistent()) { + if (input->is_global()) { input_device = JUST(input->parallel_desc())->device_type(); } else { input_device = JUST(input->device())->enum_type(); @@ -92,7 +92,7 @@ Maybe check_attributes(const std::shared_ptr& input, const Te auto check_tensors = [&](const std::string& name, const std::shared_ptr& t) -> Maybe { DeviceType t_device{}; - if (t->is_consistent()) { + if (t->is_global()) { t_device = JUST(t->parallel_desc())->device_type(); } else { t_device = JUST(t->device())->enum_type(); @@ -225,7 +225,7 @@ struct GRUCell { const std::shared_ptr& hidden, const cell_params& params, bool pre_compute_input = false) const { DeviceType input_device{}; - if (input->is_consistent()) { + if (input->is_global()) { input_device = JUST(input->parallel_desc())->device_type(); } else { input_device = JUST(input->device())->enum_type(); @@ -278,7 +278,7 @@ struct LSTMCell { const std::shared_ptr& cx = hidden[1]; DeviceType input_device{}; - if (input->is_consistent()) { + if (input->is_global()) { input_device = JUST(input->parallel_desc())->device_type(); } else { input_device = JUST(input->device())->enum_type(); @@ -773,9 +773,11 @@ Maybe _rnn_pack_sequence_impl(const std::shared_ptr& i std::vector batch_sizes_vec; batch_sizes_vec.resize(batch_sizes->nelement()); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(batch_sizes_vec.data(), batch_sizes_vec.size()); + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, batch_sizes_vec.data(), eager_blob_object->dptr(), + batch_sizes_vec.size() * sizeof(int64_t), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); }; JUST(SyncAccessTensorWithTimeOut(batch_sizes, callback, "const")); int64_t num_steps = batch_sizes->shape()->At(0); @@ -1071,9 +1073,11 @@ Maybe _lstm_pack_sequence_impl(const std::shared_ptr& std::vector batch_sizes_vec; batch_sizes_vec.resize(batch_sizes->nelement()); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(batch_sizes_vec.data(), batch_sizes_vec.size()); + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, batch_sizes_vec.data(), eager_blob_object->dptr(), + batch_sizes_vec.size() * sizeof(int64_t), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); }; JUST(SyncAccessTensorWithTimeOut(batch_sizes, callback, "const")); int64_t num_steps = batch_sizes->shape()->At(0); @@ -1313,10 +1317,11 @@ class PackPaddedSequenceFunctor { int64_t batch_size = new_input->shape()->At(1); std::vector lengths_vec; lengths_vec.resize(lengths->nelement()); - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo( - lengths_vec.data(), lengths_vec.size()); // copy 1 scalar(int64_t) tensor's value to max + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, lengths_vec.data(), eager_blob_object->dptr(), + lengths_vec.size() * sizeof(int64_t), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); }; JUST(SyncAccessTensorWithTimeOut(lengths, callback, "const")); @@ -1394,10 +1399,11 @@ class PackPaddedSequenceFunctor { const Shape ls(lsv); std::shared_ptr batch_sizes_t = JUST(functional::Empty(ls, lengths->dtype(), JUST(lengths->device()), false)); - const auto& callback2 = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyFrom( - batch_sizes.data(), batch_sizes.size()); // copy 1 scalar(int64_t) tensor's value to max + const auto& callback2 = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + SyncAutoMemcpy(stream, eager_blob_object->mut_dptr(), batch_sizes.data(), + batch_sizes.size() * sizeof(int64_t), eager_blob_object->mem_case(), + memory::MakeHostMemCase()); // copy 1 scalar(int64_t) tensor's value to max }; JUST(SyncAccessTensorWithTimeOut(batch_sizes_t, callback2, "const")); diff --git a/oneflow/core/functional/impl/slice_boxing_functor.cpp b/oneflow/core/functional/impl/slice_boxing_functor.cpp index 08e24a5c9dedf5f3e9bc3da658f7dfb70c9819f6..56861f0f0105e76a24e9318964e02ad92104c44f 100644 --- a/oneflow/core/functional/impl/slice_boxing_functor.cpp +++ b/oneflow/core/functional/impl/slice_boxing_functor.cpp @@ -23,7 +23,6 @@ limitations under the License. #include "oneflow/core/functional/functional.h" #include "oneflow/core/functional/function_library.h" #include "oneflow/core/functional/impl/common.h" -#include "oneflow/core/ccl/ccl.h" namespace oneflow { namespace one { diff --git a/oneflow/core/functional/impl/test_functor.cpp b/oneflow/core/functional/impl/test_functor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3e7c1021dbe20056e278119a1c878acf06bf95cd --- /dev/null +++ b/oneflow/core/functional/impl/test_functor.cpp @@ -0,0 +1,57 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/framework/op_builder.h" +#include "oneflow/core/framework/op_expr.h" +#include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/framework/tensor.h" +#include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/functional/functional.h" +#include "oneflow/core/functional/function_library.h" +#include "oneflow/core/functional/functional_api.yaml.h" +#include "oneflow/core/functional/impl/common.h" + +namespace oneflow { +namespace one { +namespace functional { + +namespace impl { + +class ThrowErrorFunctor final { + public: + ThrowErrorFunctor() { + op_ = CHECK_JUST(one::OpBuilder("throw_error").Input("x").Output("y").Build()); + } + Maybe operator()(const std::shared_ptr& input) const { + return JUST(OpInterpUtil::Dispatch(*op_, {input})); + } + + protected: + std::shared_ptr op_; +}; + +} // namespace impl + +using namespace impl; + +ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("ThrowError"); }; + +} // namespace functional +} // namespace one +} // namespace oneflow diff --git a/oneflow/core/functional/impl/unary_functor.cpp b/oneflow/core/functional/impl/unary_functor.cpp index 936506d88ee74eebafdf86c29aea5c1154cf9c7b..0fe0058f4b716fdb0aa414dd816be10bb548ebb9 100644 --- a/oneflow/core/functional/impl/unary_functor.cpp +++ b/oneflow/core/functional/impl/unary_functor.cpp @@ -31,43 +31,48 @@ namespace impl { OF_PP_MAKE_TUPLE_SEQ("sin", InplaceSin) \ OF_PP_MAKE_TUPLE_SEQ("floor", InplaceFloor) -#define UNARY_FUNC_SEQ \ - OF_PP_MAKE_TUPLE_SEQ("abs", Abs) \ - OF_PP_MAKE_TUPLE_SEQ("acos", Acos) \ - OF_PP_MAKE_TUPLE_SEQ("ceil", Ceil) \ - OF_PP_MAKE_TUPLE_SEQ("cosh", Cosh) \ - OF_PP_MAKE_TUPLE_SEQ("floor", Floor) \ - OF_PP_MAKE_TUPLE_SEQ("lgamma", Lgamma) \ - OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", LogSigmoid) \ - OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan", ReciprocalNoNan) \ - OF_PP_MAKE_TUPLE_SEQ("rint", Rint) \ - OF_PP_MAKE_TUPLE_SEQ("round", Round) - -#define FLOAT_UNARY_FUNC_SEQ \ - OF_PP_MAKE_TUPLE_SEQ("acosh", Acosh) \ - OF_PP_MAKE_TUPLE_SEQ("asin", Asin) \ - OF_PP_MAKE_TUPLE_SEQ("asinh", Asinh) \ - OF_PP_MAKE_TUPLE_SEQ("atan", Atan) \ - OF_PP_MAKE_TUPLE_SEQ("atanh", Atanh) \ - OF_PP_MAKE_TUPLE_SEQ("sin", Sin) \ - OF_PP_MAKE_TUPLE_SEQ("cos", Cos) \ - OF_PP_MAKE_TUPLE_SEQ("erf", Erf) \ - OF_PP_MAKE_TUPLE_SEQ("erfc", Erfc) \ - OF_PP_MAKE_TUPLE_SEQ("exp", Exp) \ - OF_PP_MAKE_TUPLE_SEQ("expm1", Expm1) \ - OF_PP_MAKE_TUPLE_SEQ("log", Log) \ - OF_PP_MAKE_TUPLE_SEQ("log2", Log2) \ - OF_PP_MAKE_TUPLE_SEQ("log1p", Log1p) \ - OF_PP_MAKE_TUPLE_SEQ("negative", Negative) \ - OF_PP_MAKE_TUPLE_SEQ("reciprocal", Reciprocal) \ - OF_PP_MAKE_TUPLE_SEQ("rsqrt", Rsqrt) \ - OF_PP_MAKE_TUPLE_SEQ("sigmoid_v2", Sigmoid) \ - OF_PP_MAKE_TUPLE_SEQ("sign", Sign) \ - OF_PP_MAKE_TUPLE_SEQ("sinh", Sinh) \ - OF_PP_MAKE_TUPLE_SEQ("sqrt", Sqrt) \ - OF_PP_MAKE_TUPLE_SEQ("square", Square) \ - OF_PP_MAKE_TUPLE_SEQ("tan", Tan) \ - OF_PP_MAKE_TUPLE_SEQ("tanh", Tanh) \ +#define UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("abs", Abs) \ + OF_PP_MAKE_TUPLE_SEQ("acos", Acos) \ + OF_PP_MAKE_TUPLE_SEQ("cosh", Cosh) \ + OF_PP_MAKE_TUPLE_SEQ("lgamma", Lgamma) \ + OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", LogSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan", ReciprocalNoNan) + +#define FLOAT_UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("acosh", Acosh) \ + OF_PP_MAKE_TUPLE_SEQ("asin", Asin) \ + OF_PP_MAKE_TUPLE_SEQ("asinh", Asinh) \ + OF_PP_MAKE_TUPLE_SEQ("atan", Atan) \ + OF_PP_MAKE_TUPLE_SEQ("atanh", Atanh) \ + OF_PP_MAKE_TUPLE_SEQ("sin", Sin) \ + OF_PP_MAKE_TUPLE_SEQ("cos", Cos) \ + OF_PP_MAKE_TUPLE_SEQ("erf", Erf) \ + OF_PP_MAKE_TUPLE_SEQ("erfc", Erfc) \ + OF_PP_MAKE_TUPLE_SEQ("exp", Exp) \ + OF_PP_MAKE_TUPLE_SEQ("expm1", Expm1) \ + OF_PP_MAKE_TUPLE_SEQ("log", Log) \ + OF_PP_MAKE_TUPLE_SEQ("log2", Log2) \ + OF_PP_MAKE_TUPLE_SEQ("log10", Log10) \ + OF_PP_MAKE_TUPLE_SEQ("log1p", Log1p) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal", Reciprocal) \ + OF_PP_MAKE_TUPLE_SEQ("rsqrt", Rsqrt) \ + OF_PP_MAKE_TUPLE_SEQ("sinh", Sinh) \ + OF_PP_MAKE_TUPLE_SEQ("sqrt", Sqrt) \ + OF_PP_MAKE_TUPLE_SEQ("square", Square) \ + OF_PP_MAKE_TUPLE_SEQ("tan", Tan) \ + OF_PP_MAKE_TUPLE_SEQ("tanh", Tanh) + +#define FLOAT_UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_Y_SEQ OF_PP_MAKE_TUPLE_SEQ("sigmoid", Sigmoid) + +#define UNARY_FUNC_BWD_WITH_FILL_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("rint", Rint) \ + OF_PP_MAKE_TUPLE_SEQ("round", Round) \ + OF_PP_MAKE_TUPLE_SEQ("floor", Floor) \ + OF_PP_MAKE_TUPLE_SEQ("ceil", Ceil) + +#define FLOAT_UNARY_FUNC_BWD_WITH_FILL_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("sign", Sign) \ OF_PP_MAKE_TUPLE_SEQ("not_equal_zero", NotEqualZero) #define LOGICAL_FLOAT_UNARY_FUNC_SEQ OF_PP_MAKE_TUPLE_SEQ("logical_not", LogicalNot) @@ -80,10 +85,10 @@ namespace impl { } \ }; -#define UNARY_ELEMENTWISE_GRAD_FUNCTOR(op_type_name, class_name, base) \ - class class_name##GradFunctor : public base { \ +#define UNARY_ELEMENTWISE_BWD_WITH_DY_X_FUNCTOR(op_type_name, class_name, base) \ + class class_name##WithDyXGradFunctor : public base { \ public: \ - class_name##GradFunctor() { \ + class_name##WithDyXGradFunctor() { \ op_ = CHECK_JUST(one::OpBuilder(std::string("") + op_type_name + "_grad") \ .Input("x") \ .Input("dy") \ @@ -92,73 +97,117 @@ namespace impl { } \ }; -#define INPLACE_UNARY_FUNCOTRS(op_type_name, class_name) \ +#define UNARY_ELEMENTWISE_BWD_WITH_DY_Y_FUNCTOR(op_type_name, class_name, base) \ + class class_name##WithDyYGradFunctor : public base { \ + public: \ + class_name##WithDyYGradFunctor() { \ + op_ = CHECK_JUST(one::OpBuilder(std::string("") + op_type_name + "_grad") \ + .Input("y") \ + .Input("dy") \ + .Output("dx") \ + .Build()); \ + } \ + }; + +#define INPLACE_UNARY_FUNCTORS(op_type_name, class_name) \ UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, InplaceUnaryFunctor) -#define INPLACE_FLOAT_UNARY_FUNCOTRS(op_type_name, class_name) \ +#define INPLACE_FLOAT_UNARY_FUNCTORS(op_type_name, class_name) \ UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, InplaceFloatUnaryFunctor) #define LOGICAL_FLOAT_UNARY_FUNCTORS(op_type_name, class_name) \ UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, FloatUnaryFunctor) -#define UNARY_FUNCOTRS(op_type_name, class_name) \ +#define UNARY_FUNCTORS(op_type_name, class_name) \ UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, UnaryFunctor) \ - UNARY_ELEMENTWISE_GRAD_FUNCTOR(op_type_name, class_name, BinaryFunctor) -#define FLOAT_UNARY_FUNCOTRS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_BWD_WITH_DY_X_FUNCTOR(op_type_name, class_name, BinaryFunctor) +#define FLOAT_UNARY_FUNCTORS(op_type_name, class_name) \ UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, FloatUnaryFunctor) \ - UNARY_ELEMENTWISE_GRAD_FUNCTOR(op_type_name, class_name, BinaryFunctor) + UNARY_ELEMENTWISE_BWD_WITH_DY_X_FUNCTOR(op_type_name, class_name, BinaryFunctor) -OF_PP_FOR_EACH_TUPLE(INPLACE_FLOAT_UNARY_FUNCOTRS, INPLACE_UNARY_FLOAT_FUNC_SEQ); -OF_PP_FOR_EACH_TUPLE(UNARY_FUNCOTRS, UNARY_FUNC_SEQ); -OF_PP_FOR_EACH_TUPLE(FLOAT_UNARY_FUNCOTRS, FLOAT_UNARY_FUNC_SEQ); +#define UNARY_BWD_WITH_DY_X_FUNCTORS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, UnaryFunctor) \ + UNARY_ELEMENTWISE_BWD_WITH_DY_X_FUNCTOR(op_type_name, class_name, BinaryFunctor) + +#define FLOAT_UNARY_BWD_WITH_DY_X_FUNCTORS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, FloatUnaryFunctor) \ + UNARY_ELEMENTWISE_BWD_WITH_DY_X_FUNCTOR(op_type_name, class_name, BinaryFunctor) + +#define FLOAT_UNARY_WITH_DY_Y_FUNCTORS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, FloatUnaryFunctor) \ + UNARY_ELEMENTWISE_BWD_WITH_DY_Y_FUNCTOR(op_type_name, class_name, BinaryFunctor) + +#define FLOAT_UNARY_BWD_WITH_FILL_FUNCTORS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, FloatUnaryFunctor) + +#define UNARY_BWD_WITH_FILL_FUNCTORS(op_type_name, class_name) \ + UNARY_ELEMENTWISE_FUNCTOR(op_type_name, class_name, UnaryFunctor) + +OF_PP_FOR_EACH_TUPLE(INPLACE_FLOAT_UNARY_FUNCTORS, INPLACE_UNARY_FLOAT_FUNC_SEQ); OF_PP_FOR_EACH_TUPLE(LOGICAL_FLOAT_UNARY_FUNCTORS, LOGICAL_FLOAT_UNARY_FUNC_SEQ); +OF_PP_FOR_EACH_TUPLE(UNARY_BWD_WITH_DY_X_FUNCTORS, UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ); +OF_PP_FOR_EACH_TUPLE(FLOAT_UNARY_BWD_WITH_DY_X_FUNCTORS, + FLOAT_UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ); + +OF_PP_FOR_EACH_TUPLE(FLOAT_UNARY_WITH_DY_Y_FUNCTORS, FLOAT_UNARY_PRIMITIVE_FUNC_BWD_WITH_DY_Y_SEQ); +OF_PP_FOR_EACH_TUPLE(UNARY_BWD_WITH_FILL_FUNCTORS, UNARY_FUNC_BWD_WITH_FILL_SEQ); +OF_PP_FOR_EACH_TUPLE(FLOAT_UNARY_BWD_WITH_FILL_FUNCTORS, FLOAT_UNARY_FUNC_BWD_WITH_FILL_SEQ); + +UNARY_ELEMENTWISE_FUNCTOR("negative", Negative, FloatUnaryFunctor) + } // namespace impl using namespace impl; -#define ADD_UNARY_FUNCTOR(class_name, functor_name) \ - m.add_functor(functor_name); \ - m.add_functor(std::string("") + functor_name + "Grad"); +#define ADD_UNARY_FUNCTOR_WITH_DY_X(class_name, functor_name) \ + m.add_functor(functor_name); \ + m.add_functor(std::string("") + functor_name + "Grad"); + +#define ADD_UNARY_FUNCTOR_WITH_DY_Y(class_name, functor_name) \ + m.add_functor(functor_name); \ + m.add_functor(std::string("") + functor_name + "Grad"); ONEFLOW_FUNCTION_LIBRARY(m) { - ADD_UNARY_FUNCTOR(Abs, "Abs"); - ADD_UNARY_FUNCTOR(Acos, "Acos"); - ADD_UNARY_FUNCTOR(Acosh, "Acosh"); - ADD_UNARY_FUNCTOR(Asin, "Asin"); - ADD_UNARY_FUNCTOR(Asinh, "Asinh"); - ADD_UNARY_FUNCTOR(Atan, "Atan"); - ADD_UNARY_FUNCTOR(Atanh, "Atanh"); - ADD_UNARY_FUNCTOR(Ceil, "Ceil"); - ADD_UNARY_FUNCTOR(Cos, "Cos"); - ADD_UNARY_FUNCTOR(Cosh, "Cosh"); - ADD_UNARY_FUNCTOR(Erf, "Erf"); - ADD_UNARY_FUNCTOR(Erfc, "Erfc"); - ADD_UNARY_FUNCTOR(Exp, "Exp"); - ADD_UNARY_FUNCTOR(Expm1, "Expm1"); - ADD_UNARY_FUNCTOR(Floor, "Floor"); - ADD_UNARY_FUNCTOR(Lgamma, "Lgamma"); - ADD_UNARY_FUNCTOR(Log, "Log"); - ADD_UNARY_FUNCTOR(Log2, "Log2"); - ADD_UNARY_FUNCTOR(Log1p, "Log1p"); - ADD_UNARY_FUNCTOR(LogSigmoid, "LogSigmoid"); - ADD_UNARY_FUNCTOR(Negative, "Negative"); - ADD_UNARY_FUNCTOR(Reciprocal, "Reciprocal"); - ADD_UNARY_FUNCTOR(ReciprocalNoNan, "ReciprocalNoNan"); - ADD_UNARY_FUNCTOR(Rint, "Rint"); - ADD_UNARY_FUNCTOR(Round, "Round"); - ADD_UNARY_FUNCTOR(Rsqrt, "Rsqrt"); - ADD_UNARY_FUNCTOR(Sigmoid, "Sigmoid"); - ADD_UNARY_FUNCTOR(Sign, "Sign"); - ADD_UNARY_FUNCTOR(Sin, "Sin"); - ADD_UNARY_FUNCTOR(Sinh, "Sinh"); - ADD_UNARY_FUNCTOR(Sqrt, "Sqrt"); - ADD_UNARY_FUNCTOR(Square, "Square"); - ADD_UNARY_FUNCTOR(Tan, "Tan"); - ADD_UNARY_FUNCTOR(Tanh, "Tanh"); - ADD_UNARY_FUNCTOR(NotEqualZero, "NotEqualZero") + ADD_UNARY_FUNCTOR_WITH_DY_X(Abs, "Abs"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Acos, "Acos"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Acosh, "Acosh"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Asin, "Asin"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Asinh, "Asinh"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Atan, "Atan"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Atanh, "Atanh"); + m.add_functor("Ceil"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Cos, "Cos"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Cosh, "Cosh"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Erf, "Erf"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Erfc, "Erfc"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Exp, "Exp"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Expm1, "Expm1"); + m.add_functor("Floor"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Lgamma, "Lgamma"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Log, "Log"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Log2, "Log2"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Log10, "Log10"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Log1p, "Log1p"); + ADD_UNARY_FUNCTOR_WITH_DY_X(LogSigmoid, "LogSigmoid"); + m.add_functor("Negative"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Reciprocal, "Reciprocal"); + ADD_UNARY_FUNCTOR_WITH_DY_X(ReciprocalNoNan, "ReciprocalNoNan"); + m.add_functor("Rint"); + m.add_functor("Round"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Rsqrt, "Rsqrt"); + ADD_UNARY_FUNCTOR_WITH_DY_Y(Sigmoid, "Sigmoid"); + m.add_functor("Sign"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Sin, "Sin"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Sinh, "Sinh"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Sqrt, "Sqrt"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Square, "Square"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Tan, "Tan"); + ADD_UNARY_FUNCTOR_WITH_DY_X(Tanh, "Tanh"); + m.add_functor("NotEqualZero"); m.add_functor("LogicalNot"); m.add_functor("Sin_"); m.add_functor("Floor_"); }; -#undef ADD_UNARY_FUNCTOR +#undef ADD_UNARY_FUNCTOR_WITH_DY_X +#undef ADD_UNARY_FUNCTOR_WITH_DY_Y } // namespace functional } // namespace one diff --git a/oneflow/core/functional/impl/unary_functor.h b/oneflow/core/functional/impl/unary_functor.h index 5a289d997e3bdb16920976ccd0b4252e08870bdd..2a3a3ff545d39ee2cca0d04b354774406ff5582e 100644 --- a/oneflow/core/functional/impl/unary_functor.h +++ b/oneflow/core/functional/impl/unary_functor.h @@ -69,7 +69,10 @@ class FloatUnaryFunctor { Maybe operator()(const std::shared_ptr& x) const { // The functor lowest Dtype is Float32. (For sigmoid, tanh and etc. ) TensorProcessor tensor_processor; - JUST(tensor_processor.AddInputs({x}, DType::Float()).Apply()); + JUST(tensor_processor.AddInputs({x}, DType::Float()) + .PromoteInputsToCommonDtype(true) + .PromoteIntegerInputsToFloatDtype(true) + .Apply()); TensorTuple input_tuple = JUST(tensor_processor.GetInputs()); return OpInterpUtil::Dispatch(*op_, input_tuple); } diff --git a/oneflow/core/functional/impl/util_ops_functor.cpp b/oneflow/core/functional/impl/util_ops_functor.cpp index 2b298f3c28c612c0920973d5668552719ae0ba1a..a54276c70a7db8533497f9cddfadce00334b36e2 100644 --- a/oneflow/core/functional/impl/util_ops_functor.cpp +++ b/oneflow/core/functional/impl/util_ops_functor.cpp @@ -53,12 +53,20 @@ class IsInfFunctor final : public UtilOpsFunctor { IsInfFunctor() { op_ = CHECK_JUST(one::OpBuilder("isinf").Input("in").Output("out").Build()); } }; +class IsFiniteFunctor final : public UtilOpsFunctor { + public: + IsFiniteFunctor() { + op_ = CHECK_JUST(one::OpBuilder("isfinite").Input("in").Output("out").Build()); + } +}; + } // namespace impl using namespace impl; ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("IsNan"); }; ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("IsInf"); }; +ONEFLOW_FUNCTION_LIBRARY(m) { m.add_functor("IsFinite"); }; } // namespace functional } // namespace one diff --git a/oneflow/core/functional/tensor_index.cpp b/oneflow/core/functional/tensor_index.cpp index b73564164ab611f6b69524d3bed1d43e4a4e5033..3aa54a1bf976b4af9af73b1356ce7a5a1f76365b 100644 --- a/oneflow/core/functional/tensor_index.cpp +++ b/oneflow/core/functional/tensor_index.cpp @@ -23,10 +23,10 @@ limitations under the License. #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/functional/functional.h" #include "oneflow/core/job/sbp_parallel.h" -#include "oneflow/core/register/ofblob.h" #include "oneflow/core/common/stride.h" #include "oneflow/core/framework/op_builder.h" #include "oneflow/core/framework/op_interpreter/op_interpreter_util.h" +#include "oneflow/core/kernel/kernel_util.h" namespace oneflow { namespace one { @@ -42,7 +42,7 @@ int64_t CountSpecifiedDims(const TensorIndex& index) { specified_ndims++; } else if (index_item.IsTensor()) { const auto& tensor = index_item.tensor(); - if (tensor->dtype() == DType::Int8() || tensor->dtype() == DType::UInt8()) { + if (IsMaskTensor(tensor)) { specified_ndims += tensor->ndim(); } else { specified_ndims++; @@ -63,14 +63,15 @@ Maybe ExpandMaskIndex(const std::shared_ptr& index) { return Error::RuntimeError() << "Advanced indexing by boolean(mask) tensor only valid in eager mode."; } - if (size_tensor->is_consistent()) { + if (size_tensor->is_global()) { // TODO(): check size_tensor sbp is broadcast. - size_tensor = JUST(functional::ConsistentToLocal(size_tensor)); + size_tensor = JUST(functional::GlobalToLocal(size_tensor, /*copy=*/false)); } int64_t size = 0; - const auto& callback = [&](uint64_t of_blob_ptr) { - auto* of_blob = reinterpret_cast(of_blob_ptr); - of_blob->AutoMemCopyTo(&size, 1); + const auto& callback = [&](ep::Stream* stream, + const std::shared_ptr& eager_blob_object) { + AutoMemcpy(stream, &size, eager_blob_object->dptr(), sizeof(size), memory::MakeHostMemCase(), + eager_blob_object->mem_case()); }; JUST(SyncAccessTensorWithTimeOut(size_tensor, callback, "const")); @@ -83,36 +84,39 @@ Maybe ExpandMaskIndex(const std::shared_ptr& index) { return indices; } +// NOTE: expand each non-empty indice to same shape. Maybe ExpandIndices(const TensorTuple& indices) { - bool first = true; std::shared_ptr expanded_shape; - for (int i = 0; i < indices.size(); ++i) { - if (!indices.at(i)) { continue; } - if (first) { - expanded_shape = indices.at(i)->shape(); - first = false; - } else { - const auto& shape = indices.at(i)->shape(); - int ndims = std::max(shape->NumAxes(), expanded_shape->NumAxes()); - DimVector sizes(ndims); - for (int j = ndims - 1; j >= 0; --j) { - int dim = j - (ndims - shape->NumAxes()); - int expanded_dim = j - (ndims - expanded_shape->NumAxes()); - if (dim < 0) { - sizes[j] = expanded_shape->At(expanded_dim); - } else if (expanded_dim < 0) { - sizes[j] = shape->At(dim); - } else { - int size = shape->At(dim); - int expanded_size = expanded_shape->At(expanded_dim); - CHECK_OR_RETURN(size == expanded_size || size == 1 || expanded_size == 1) - << Error::RuntimeError() << "The size of tensor a (" << size - << ") must match the size of tensor b (" << expanded_size - << ") at non-singleton dimension " << i; - sizes[j] = size == 1 ? expanded_size : size; + { + bool first = true; + for (int i = 0; i < indices.size(); ++i) { + if (!indices.at(i)) { continue; } + if (first) { + expanded_shape = indices.at(i)->shape(); + first = false; + } else { + const auto& shape = indices.at(i)->shape(); + int ndims = std::max(shape->NumAxes(), expanded_shape->NumAxes()); + DimVector sizes(ndims); + for (int j = ndims - 1; j >= 0; --j) { + int dim = j - (ndims - shape->NumAxes()); + int expanded_dim = j - (ndims - expanded_shape->NumAxes()); + if (dim < 0) { + sizes[j] = expanded_shape->At(expanded_dim); + } else if (expanded_dim < 0) { + sizes[j] = shape->At(dim); + } else { + int size = shape->At(dim); + int expanded_size = expanded_shape->At(expanded_dim); + CHECK_OR_RETURN(size == expanded_size || size == 1 || expanded_size == 1) + << Error::RuntimeError() << "The size of tensor a (" << size + << ") must match the size of tensor b (" << expanded_size + << ") at non-singleton dimension " << i; + sizes[j] = size == 1 ? expanded_size : size; + } } + expanded_shape.reset(new Shape(sizes)); } - expanded_shape.reset(new Shape(sizes)); } } auto expanded_indices = std::make_shared(indices.size()); @@ -127,6 +131,11 @@ Maybe ExpandIndices(const TensorTuple& indices) { return expanded_indices; } +// NOTE(wyg): +// Judge whether all index dims are contiguous. +// e.g. [:, index0, index1, :] -> True +// [index0, :, index1] -> False +// [index0, index1, :] -> True Maybe IsContinuousSubspace(const TensorTuple& indices) { int token = 0; for (int i = 0; i < indices.size(); ++i) { @@ -141,8 +150,12 @@ Maybe IsContinuousSubspace(const TensorTuple& indices) { return true; } -Maybe TransposeFront(const std::shared_ptr& input, const TensorTuple& indices, - std::shared_ptr* output, TensorTuple* valid_indices) { +// NOTE(wyg): +// Move indices subspace to be contiguous and ahead. +// e.g. [:, index0, index1] -> [index0, index1, :] +Maybe> TransposeFront(const std::shared_ptr& input, + const TensorTuple& indices, std::shared_ptr* output, + TensorTuple* valid_indices) { std::vector permute; permute.reserve(input->ndim()); for (int i = 0; i < input->ndim(); ++i) { @@ -165,11 +178,11 @@ Maybe TransposeFront(const std::shared_ptr& input, const TensorTup } else { *output = input; } - return Maybe::Ok(); + return permute; } Maybe AdjustSubspace(const std::shared_ptr& input, const TensorTuple& indices, - const int& index_ndim) { + const int& index_ndim, bool reverse = false) { int index_subspace_pos = -1; for (int i = 0; i < indices.size(); ++i) { if (indices.at(i)) { @@ -183,10 +196,17 @@ Maybe AdjustSubspace(const std::shared_ptr& input, const TensorT << Error::IndexError() << "Failed to adjust subspace since the index is out of bounds for tensor dimension " << ndim; std::vector permute; - permute.reserve(ndim); - for (int i = 0; i < index_subspace_pos; ++i) { permute.emplace_back(i + index_ndim); } - for (int i = 0; i < index_ndim; ++i) { permute.emplace_back(i); } - for (int i = permute.size(); i < ndim; ++i) { permute.emplace_back(i); } + { + permute.reserve(ndim); + if (reverse) { + for (int i = 0; i < index_ndim; ++i) { permute.emplace_back(index_subspace_pos + i); } + for (int i = 0; i < index_subspace_pos; ++i) { permute.emplace_back(i); } + } else { + for (int i = 0; i < index_subspace_pos; ++i) { permute.emplace_back(i + index_ndim); } + for (int i = 0; i < index_ndim; ++i) { permute.emplace_back(i); } + } + for (int i = permute.size(); i < ndim; ++i) { permute.emplace_back(i); } + } return Transpose(input, permute); } @@ -196,8 +216,42 @@ Maybe HasFalseIndex(const TensorIndex& index) { }); } +bool IsValidScalarTensorIndex(const std::shared_ptr& tensor) { + if (!(tensor->dtype()->is_integer() || tensor->dtype() == DType::Bool())) { return false; } + return tensor->shape()->NumAxes() == 0 && tensor->shape()->elem_cnt() == 1; +} + +// Permute back for global tensor which transpose dims to front +Maybe PermuteBackForGlobalTensor(const std::shared_ptr& result, + const std::vector& permute) { + CHECK_OR_RETURN(result->is_global()); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(result->ndim(), permute.size()); // NOLINT(maybe-need-error-msg) + std::vector inv_permute(permute.size()); + for (int32_t i = 0; i < permute.size(); ++i) { inv_permute[permute[i]] = i; } + + bool not_permute = true; + { + for (int32_t i = 0; i < permute.size(); ++i) { + if (inv_permute[i] != i) { + not_permute = false; + break; + } + } + } + if (!not_permute) { + return Transpose(result, inv_permute); + } else { + return result; + } +} + } // namespace +bool IsMaskTensor(const std::shared_ptr& tensor) { + return tensor->dtype() == DType::Int8() || tensor->dtype() == DType::UInt8() + || tensor->dtype() == DType::Bool(); +} + Maybe PrepareSliceIndices(const TensorIndex& index, const Shape& shape, std::vector* slice_indices, TensorTuple* tensor_indices, std::vector* expand_dims, @@ -267,27 +321,49 @@ Maybe PrepareSliceIndices(const TensorIndex& index, const Shape& shape, dim++; } else if (index_item.IsTensor()) { const auto& tensor = index_item.tensor(); - auto indices = std::make_shared(); - if (tensor->dtype() == DType::Int8() || tensor->dtype() == DType::UInt8() - || tensor->dtype() == DType::Bool()) { - for (int j = 0; j < tensor->ndim(); ++j) { - if (tensor->shape()->At(j) != shape.At(dim + j)) { + if (IsValidScalarTensorIndex(tensor) && !LazyMode::is_enabled()) { + if (tensor->dtype()->is_integer() && tensor->dtype()->data_type() != DataType::kUInt8) { + int64_t integer = JUST(GetItemInScalarTensor(tensor)); + if (integer < 0) { integer += shape.At(dim); } + if (integer < 0 || integer >= shape.At(dim)) { return Error::IndexError() - << "The shape of the mask " << tensor->shape()->ToString() << " at index " << j - << " does not match the shape of the indexed tensor " << shape.ToString() - << " at index " << dim + j; + << "Index " << index_item.integer() << " is out of bounds for dimension " << dim + << " with size " << shape.At(dim); + } + slice_indices->emplace_back(integer, integer + 1, 1); + dim++; + } else { + bool boolean_index = JUST(GetItemInScalarTensor(tensor)); + if (!has_expand_boolean_dim) { + expand_dims->emplace_back(dim); + slice_indices->emplace_back(0, boolean_index, 1); + target_dims->emplace_back(boolean_index); + has_expand_boolean_dim = true; } } - indices = JUST(ExpandMaskIndex(tensor)); } else { - indices->emplace_back(tensor); - } - for (int j = 0; j < indices->size(); ++j) { - slice_indices->emplace_back(0, shape.At(dim), 1); - tensor_indices->resize(target_dims->size()); - tensor_indices->emplace_back(indices->at(j)); - target_dims->emplace_back(shape.At(dim)); - dim++; + auto indices = std::make_shared(); + if (tensor->dtype() == DType::Int8() || tensor->dtype() == DType::UInt8() + || tensor->dtype() == DType::Bool()) { + for (int j = 0; j < tensor->ndim(); ++j) { + if (tensor->shape()->At(j) != shape.At(dim + j)) { + return Error::IndexError() + << "The shape of the mask " << tensor->shape()->ToString() << " at index " << j + << " does not match the shape of the indexed tensor " << shape.ToString() + << " at index " << dim + j; + } + } + indices = JUST(ExpandMaskIndex(tensor)); + } else { + indices->emplace_back(tensor); + } + for (int j = 0; j < indices->size(); ++j) { + slice_indices->emplace_back(0, shape.At(dim), 1); + tensor_indices->resize(target_dims->size()); + tensor_indices->emplace_back(indices->at(j)); + target_dims->emplace_back(shape.At(dim)); + dim++; + } } } } @@ -323,46 +399,125 @@ Maybe ApplyAdvancedIndexing(const std::shared_ptr& input, bool is_continuous_subspace = JUST(IsContinuousSubspace(indices)); // Since the start dimension cannot be specified for `gather_nd`, so we should - // transpose the input as long as the first indice is null. + // transpose the input as long as the first index is null. std::shared_ptr transposed_input; TensorTuple valid_indices; JUST(TransposeFront(input, *expanded_indices, &transposed_input, &valid_indices)); if (valid_indices.empty()) { return input; } int index_ndim = valid_indices.at(0)->ndim(); - auto packed_indices = JUST(Stack(valid_indices, 0)); - int packed_ndim = packed_indices->ndim(); - CHECK_GT_OR_RETURN(packed_ndim, 0) - << Error::RuntimeError() << "Index array dimension should be greater than 0."; - std::vector permute(packed_ndim); - permute[packed_ndim - 1] = 0; - std::iota(permute.begin(), permute.end() - 1, 1); - packed_indices = JUST(Transpose(packed_indices, permute))->contiguous(); - - if (transposed_input->is_consistent()) { - const auto& placement = JUST(transposed_input->parallel_desc()); - const auto& broadcast_sbp = JUST(MakeBroadcastSbpParallel()); - int n = JUST(input->nd_sbp())->sbp_parallel_size(); - std::vector> grad_sbp_tuple; - packed_indices = JUST(ToConsistent(packed_indices, placement, - std::vector>(n, broadcast_sbp), - grad_sbp_tuple, /* check_meta */ false)); + std::shared_ptr packed_indices; + if (valid_indices.size() == 1) { + packed_indices = JUST(functional::Unsqueeze(valid_indices.at(0), -1)); } else { - Symbol device = JUST(transposed_input->device()); - if (JUST(packed_indices->device()) != device) { - packed_indices = - JUST(Copy(packed_indices, device->type(), device->device_id(), /*pin_memory=*/false)); - } + packed_indices = JUST(Stack(valid_indices, 0)); + int packed_ndim = packed_indices->ndim(); + CHECK_GT_OR_RETURN(packed_ndim, 0) + << Error::RuntimeError() << "Index array dimension should be greater than 0."; + std::vector permute(packed_ndim); + permute[packed_ndim - 1] = 0; + std::iota(permute.begin(), permute.end() - 1, 1); + packed_indices = JUST(Transpose(packed_indices, permute))->contiguous(); } + + CHECK_EQ_OR_RETURN(transposed_input->is_local(), packed_indices->is_local()) + << Error::RuntimeError() << "The input and indices must be both local or global."; + auto result = JUST(GatherNd(transposed_input, packed_indices)); int required_ndim = input->ndim() - valid_indices.size() + index_ndim; CHECK_EQ_OR_RETURN(result->ndim(), required_ndim) << Error::RuntimeError() << "The indexing result dimension is " << result->ndim() << ", but shoule be " << required_ndim; - if (is_continuous_subspace) { result = JUST(AdjustSubspace(result, indices, index_ndim)); } + if (is_continuous_subspace) { + result = JUST(AdjustSubspace(result, indices, index_ndim, /*reverse*/ false)); + } return result; } +Maybe ApplyAdvancedIndexingUpdate(const std::shared_ptr& input, + const TensorTuple& indices, + const std::shared_ptr& value) { + CHECK_GE_OR_RETURN(input->ndim(), indices.size()) + << Error::IndexError() << "Too many indices for tensor of dimension " << input->ndim(); + const auto& expanded_indices = JUST(ExpandIndices(indices)); + bool is_continuous_subspace = JUST(IsContinuousSubspace(indices)); + + // Since the start dimension cannot be specified for `scatter_nd`, so we should + // transpose the input as long as the first index is null. + std::shared_ptr transposed_input; + TensorTuple valid_indices; + const auto& transposed_input_permute = + JUST(TransposeFront(input, *expanded_indices, &transposed_input, &valid_indices)); + // NOTE: For local tensor, we make sure that transposed_input is a view of input. + // Therefore we need not transpose it back because we update the value in a same memory + // by tensor_scatter_nd_update operator. + if (input->is_local()) { + CHECK_EQ_OR_RETURN(JUST(transposed_input->tensor_storage()), JUST(input->tensor_storage())) + << Error::RuntimeError() + << "This setitem operator must enable view mechanism, please try to set " + "ONEFLOW_DISABLE_VIEW=0"; + } + + if (valid_indices.empty()) { + CHECK_EQ_OR_RETURN(value->nelement(), 0) << Error::IndexError() << "invalid indices"; + return input; + } + int index_ndim = valid_indices[0]->ndim(); + auto packed_indices = JUST(Stack(valid_indices, 0)); + { + int packed_ndim = packed_indices->ndim(); + CHECK_GT_OR_RETURN(packed_ndim, 0) + << Error::RuntimeError() << "Index array dimension should be greater than 0."; + std::vector permute(packed_ndim); + permute[packed_ndim - 1] = 0; + std::iota(permute.begin(), permute.end() - 1, 1); + packed_indices = JUST(Transpose(packed_indices, permute))->contiguous(); + } + + CHECK_EQ_OR_RETURN(transposed_input->is_local(), packed_indices->is_local()) + << Error::RuntimeError() << "The input and indices must be both local or global."; + + Shape expand_shape; + { + if (is_continuous_subspace) { + bool index_subspace_begin = true; + for (int i = 0; i < indices.size(); ++i) { + // if the index is the first not-null index + if (indices[i]) { + if (!index_subspace_begin) { continue; } + for (int j = 0; j < index_ndim; ++j) { + expand_shape.emplace_back(valid_indices[0]->shape()->At(j)); + } + index_subspace_begin = false; + } else { + expand_shape.emplace_back(input->shape()->At(i)); + } + } + } else { + expand_shape = *(valid_indices[0]->shape()); + for (int i = 0; i < indices.size(); ++i) { + if (!indices[i]) { expand_shape.emplace_back(input->shape()->At(i)); } + } + } + for (int i = indices.size(); i < input->ndim(); ++i) { + expand_shape.emplace_back(input->shape()->At(i)); + } + } + std::shared_ptr expand_value = JUST(Expand(value, expand_shape)); + + // reverse adjust value if index subspace is continuous but transposed since the start + // dimension cannot be specified for `scatter_nd` + if (is_continuous_subspace) { + expand_value = JUST(AdjustSubspace(expand_value, indices, index_ndim, /*reverse*/ true)); + } + JUST(TensorScatterNdUpdate(transposed_input, packed_indices, expand_value, /*inplace=*/true)); + // Global tensor is not support view, so we should permute back and copy to origin input if need + if (transposed_input->is_global()) { + return PermuteBackForGlobalTensor(transposed_input, *transposed_input_permute); + } + return transposed_input; +} + Maybe ApplySelectIndexing(const std::shared_ptr& input, const TensorIndex& tensor_index) { const int32_t index = tensor_index[0].integer(); @@ -375,33 +530,22 @@ Maybe ApplySelectIndexing(const std::shared_ptr& input, << Error::IndexError() << "Index out of range (expected to be in range of [" << -size << "," << size - 1 << "], but got " << index << ")"; int32_t pos_index = index >= 0 ? index : index + size; - std::vector sizes(input->shape()->dim_vec().begin() + 1, + std::vector sizes(input->shape()->dim_vec().begin() + 1, input->shape()->dim_vec().end()); const auto& stride = *JUST(input->stride()); - const int32_t storage_offset = JUST(input->storage_offset()) + pos_index * stride[pos_dim]; - std::vector strides(stride.begin() + 1, stride.end()); - - if (view::IsViewApplicable(input)) { - return view::AsStrided(input, sizes, strides, storage_offset); - } else { - MutableAttrMap attrs; - JUST(attrs.SetAttr>("size", sizes)); - JUST(attrs.SetAttr>("stride", strides)); - JUST(attrs.SetAttr("storage_offset", storage_offset)); - std::shared_ptr op_ = - JUST(one::OpBuilder("as_strided").Input("input").Output("output").Build()); - return one::OpInterpUtil::Dispatch(*op_, {input}, attrs); - } + const int64_t storage_offset = JUST(input->storage_offset()) + pos_index * stride[pos_dim]; + std::vector strides(stride.begin() + 1, stride.end()); + return functional::AsStrided(input, sizes, strides, storage_offset); } -Maybe UnifyLocalTensorAndIndicesOnDevice(const std::shared_ptr& x, - TensorTuple& tensor_indices) { - if (!x->is_consistent()) { +Maybe UnifyInputAndIndicesOnDevice(const std::shared_ptr& x, + TensorTuple& tensor_indices) { + if (x->is_local()) { const auto x_device = JUST(x->device()); for (int64_t i = 0; i < tensor_indices.size(); ++i) { const auto tensor_index = tensor_indices[i]; if (tensor_index == nullptr) { continue; } - if (tensor_index->is_consistent()) { return Maybe::Ok(); } + if (tensor_index->is_global()) { return Maybe::Ok(); } const auto tensor_index_device = JUST(tensor_index->device()); if ((tensor_index_device->type() != x_device->type()) || (tensor_index_device->device_id() != x_device->device_id())) { @@ -409,6 +553,23 @@ Maybe UnifyLocalTensorAndIndicesOnDevice(const std::shared_ptr& x, JUST(Copy(tensor_index, x_device->type(), x_device->device_id(), /*pin_memory=*/false)); } } + } else { + // global tensor + const auto& placement = JUST(x->parallel_desc()); + const auto& broadcast_sbp = JUST(MakeBroadcastSbpParallel()); + int n = JUST(x->nd_sbp())->sbp_parallel_size(); + std::vector> grad_sbp_tuple; + for (int64_t i = 0; i < tensor_indices.size(); ++i) { + const auto tensor_index = tensor_indices[i]; + if (tensor_index == nullptr) { continue; } + if (tensor_index->is_local()) { + // NOTE: LocalToGlobal should be called in eager mode + LazyMode::Guard lazy_mode_disabled_guard(/*is_enabled*/ false); + tensor_indices[i] = JUST(ToGlobal(tensor_index, placement, + std::vector>(n, broadcast_sbp), + grad_sbp_tuple, /*check_meta=*/false, /*copy=*/false)); + } + } } return Maybe::Ok(); } diff --git a/oneflow/core/functional/tensor_index.h b/oneflow/core/functional/tensor_index.h index 72a60339c2a8dc5eaf60b7eb38d958241ed8fbce..073d5da272d311f3bfe70fb1f8f28bf358bb351b 100644 --- a/oneflow/core/functional/tensor_index.h +++ b/oneflow/core/functional/tensor_index.h @@ -109,6 +109,8 @@ class TensorIndex : public std::vector { using std::vector::vector; }; +bool IsMaskTensor(const std::shared_ptr& tensor); + Maybe PrepareSliceIndices(const TensorIndex& index, const Shape& shape, std::vector* slice_indices, TensorTuple* tensor_indices, std::vector* expand_dims, @@ -123,8 +125,12 @@ Maybe ApplyAdvancedIndexing(const std::shared_ptr& input, Maybe ApplySelectIndexing(const std::shared_ptr& input, const TensorIndex& index); -Maybe UnifyLocalTensorAndIndicesOnDevice(const std::shared_ptr& x, - TensorTuple& tensor_indices); +Maybe UnifyInputAndIndicesOnDevice(const std::shared_ptr& x, + TensorTuple& tensor_indices); + +Maybe ApplyAdvancedIndexingUpdate(const std::shared_ptr& input, + const TensorTuple& indices, + const std::shared_ptr& value); } // namespace functional } // namespace one diff --git a/oneflow/core/functional/tensor_processor.cpp b/oneflow/core/functional/tensor_processor.cpp index 6e8b56f8bebc23cc14fb6a0a91dd7a0a435863b4..38d4c44fe52725a0fca747702c36c1a7a23c0a7a 100644 --- a/oneflow/core/functional/tensor_processor.cpp +++ b/oneflow/core/functional/tensor_processor.cpp @@ -13,10 +13,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include +#include #include "oneflow/core/functional/tensor_processor.h" #include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/throw.h" #include "oneflow/core/framework/dtype.h" #include "oneflow/core/functional/functional.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { namespace one { @@ -26,20 +30,22 @@ namespace { Symbol ComputeCommonDType(const TensorTuple& tensor_tuple) { Symbol common_dtype = DType::InvalidDataType(); + bool all_scalar_tensors = std::all_of( + tensor_tuple.begin(), tensor_tuple.end(), + [](const std::shared_ptr& tensor) { return tensor->shape()->NumAxes() == 0; }); for (auto& tensor_ptr : tensor_tuple) { + // skip scalar tensor + if (!all_scalar_tensors && tensor_ptr->shape()->NumAxes() == 0) { continue; } common_dtype = promoteTypes(tensor_ptr->dtype(), common_dtype); } return common_dtype; } bool CheckHasDifferentInputDType(const TensorTuple& tensor_tuple) { - Symbol common_dtype = DType::InvalidDataType(); + if (tensor_tuple.size() <= 1) { return false; } + Symbol common_dtype = tensor_tuple[0]->dtype(); for (auto& tensor_ptr : tensor_tuple) { - if (common_dtype == DType::InvalidDataType()) { - common_dtype = tensor_ptr->dtype(); // Initialize the common_dtype_ - } else { - return true; - } + if (common_dtype != tensor_ptr->dtype()) { return true; } } return false; } @@ -77,12 +83,43 @@ TensorProcessor& TensorProcessor::PromoteInputsToCommonDtype(bool is_promote) { return *this; } +TensorProcessor& TensorProcessor::PromoteInputsToCommonDtype( + bool is_promote, const Optional>& promote_dtype) { + promote_inputs_to_common_dtype_ = is_promote; + promote_dtype_ = promote_dtype; + return *this; +} + +TensorProcessor& TensorProcessor::PromoteIntegerInputsToFloatDtype(bool is_promote) { + promote_integer_inputs_to_float_ = is_promote; + CHECK_OR_THROW(!promote_integer_inputs_to_float_ || promote_inputs_to_common_dtype_) + << "when set promote_integer_inputs_to_float to 'True', then promote_inputs_to_common_dtype " + "should be set to 'True' first!"; + return *this; +} + Maybe TensorProcessor::Apply() { if (promote_inputs_to_common_dtype_) { bool has_different_input_dtype = CheckHasDifferentInputDType(tensor_tuple_); if (has_different_input_dtype) { - common_dtype_ = ComputeCommonDType(tensor_tuple_); + if (promote_dtype_.has_value()) { + common_dtype_ = CHECK_JUST(promote_dtype_); + } else { + common_dtype_ = ComputeCommonDType(tensor_tuple_); + } + if (promote_integer_inputs_to_float_ && common_dtype_->is_integer()) { + // Promotes common dtype to the default float scalar type, if needed. + // same to pytorch's computeTypes() in torch/csrc/jit/codegen/cuda/type_promotion.cpp + common_dtype_ = DType::Float(); + } JUST(CastToSameType(tensor_tuple_, common_dtype_)); + } else { + if (tensor_tuple_.size() == 1 && !tensor_tuple_[0]->dtype()->is_floating_point()) { + Symbol cast_dtype = (inputs_lowest_dtype_vec_[0] == DType::InvalidDataType()) + ? DType::Float() + : inputs_lowest_dtype_vec_[0]; + JUST(CastToSameType(tensor_tuple_, cast_dtype)); + } } } else { for (int i = 0; i < tensor_tuple_.size(); ++i) { @@ -100,6 +137,113 @@ Maybe TensorProcessor::Apply() { return Maybe::Ok(); } +static bool IsAllContiguous(const TensorTuple& tensors) { + for (const auto& t : tensors) { + if (t && !t->is_contiguous()) { return false; } + } + return true; +} + +Maybe TensorLayoutProcessor::Apply() { + if (LazyMode::is_enabled()) { return Maybe::Ok(); } + if (!non_contiguous_enabled_ && !IsAllContiguous(inputs_)) { + contiguous_inputs_.resize(inputs_.size()); + for (int i = 0; i < inputs_.size(); ++i) { contiguous_inputs_[i] = inputs_[i]->contiguous(); } + } + // inplace operation is not allowed if input is non-contiguous and non-contiguous is + // not supported for this operation + if (!non_contiguous_enabled_ && outputs_ && !IsAllContiguous(*outputs_)) { + post_process_outputs_.reserve(outputs_->size()); + post_process_output_indices_.reserve(outputs_->size()); + for (int i = 0; i < outputs_->size(); ++i) { + if ((*outputs_)[i] && !(*outputs_)[i]->is_contiguous()) { + post_process_outputs_.emplace_back((*outputs_)[i]); + post_process_output_indices_.emplace_back(i); + (*outputs_)[i] = nullptr; + } + } + } + return Maybe::Ok(); +} + +TensorLayoutProcessor::~TensorLayoutProcessor() { + for (int i = 0; i < post_process_output_indices_.size(); ++i) { + int output_index = post_process_output_indices_[i]; + CHECK_OR_THROW((*outputs_)[output_index]) + << "the output which index is " << i << " should not be nullptr"; + functional::TensorIndex ellipsis_index; + ellipsis_index.emplace_back(functional::detail::EllipsisIndex()); + CHECK_JUST(functional::TensorSetItem(post_process_outputs_[i], ellipsis_index, + (*outputs_)[output_index])); + (*outputs_)[output_index] = post_process_outputs_[i]; + } +} + +Maybe TensorAutoCastProcessor::Apply() { + if (!autocast::is_enabled()) { return Maybe::Ok(); } + if (autocast_meta_.autocast_color() == autocast::kNoColor) { return Maybe::Ok(); } + auto autocast_device_type = autocast::get_autocast_device_type(); + auto autocast_dtype = autocast::get_autocast_dtype(); + auto IsDeviceType = [](const std::shared_ptr& tensor, + DeviceType device_type) -> Maybe { + return tensor->is_local() ? JUST(tensor->device())->enum_type() == device_type + : JUST(tensor->parallel_desc())->device_type() == device_type; + }; + bool is_autocast_eligible = [&]() { + if (!autocast_meta_.is_autocast_eligible(autocast_device_type, autocast_dtype)) { + return false; + } + // Skip autocast if output data type is float32 + if (outputs_) { + for (const auto& output : *outputs_) { + if (output && output->dtype() != autocast_dtype) { return false; } + } + } + // Skip autocast if any input is float32 for gray or clear list + if (autocast_meta_.autocast_color() != autocast::kWhite) { + for (int i = 0; i < inputs_.size(); ++i) { + if (autocast_meta_.is_args_autocast_eligible(i) && inputs_[i]->dtype()->is_floating_point() + && inputs_[i]->dtype() != autocast_dtype) { + return false; + } + } + } + return true; + }(); + // Disable autocast temporarily to avoid going into a dead loop + autocast::set_enabled(false); + if (is_autocast_eligible) { + const auto& args_eligible = autocast_meta_.is_args_autocast_eligible(); + CHECK_EQ_OR_RETURN(args_eligible.size(), inputs_.size()) + << Error::RuntimeError() << "argument autocast eligible size should equal to input size"; + autocast_inputs_.resize(inputs_.size()); + for (int i = 0; i < inputs_.size(); ++i) { + if (args_eligible[i] && JUST(IsDeviceType(inputs_[i], autocast_device_type)) + && inputs_[i]->dtype()->is_floating_point() && inputs_[i]->dtype() != autocast_dtype) { + autocast_inputs_[i] = JUST(functional::To(inputs_[i], autocast_dtype, /*copy*/ false)); + } else { + autocast_inputs_[i] = inputs_[i]; + } + } + } else { + // Fallback to float32 + auto common_dtype = ComputeCommonDType(inputs_); + auto promote_dtype = promoteTypes(common_dtype, DType::Float()); + autocast_inputs_.resize(inputs_.size()); + for (int i = 0; i < inputs_.size(); ++i) { + if (JUST(IsDeviceType(inputs_[i], autocast_device_type)) + && inputs_[i]->dtype()->is_floating_point() && inputs_[i]->dtype() != promote_dtype) { + autocast_inputs_[i] = JUST(functional::To(inputs_[i], promote_dtype, /*copy*/ false)); + } else { + autocast_inputs_[i] = inputs_[i]; + } + } + } + // Enable autocast to restore autocast state + autocast::set_enabled(true); + return Maybe::Ok(); +} + } // namespace functional } // namespace one } // namespace oneflow diff --git a/oneflow/core/functional/tensor_processor.h b/oneflow/core/functional/tensor_processor.h index 64a68cfccece5006b7e9fd92eec76e653f402606..72894df05659e6476fb009aa856ddf721d41fb3f 100644 --- a/oneflow/core/functional/tensor_processor.h +++ b/oneflow/core/functional/tensor_processor.h @@ -16,9 +16,16 @@ limitations under the License. #ifndef ONEFLOW_CORE_FUNCTIONAL_TENSOR_PROCESSOR_H_ #define ONEFLOW_CORE_FUNCTIONAL_TENSOR_PROCESSOR_H_ +#include +#include +#include +#include + #include "oneflow/core/common/symbol.h" #include "oneflow/core/functional/impl/common.h" +#include "oneflow/core/framework/autocast.h" #include "oneflow/core/framework/tensor_tuple.h" +#include "oneflow/core/common/optional.h" namespace oneflow { namespace one { @@ -27,20 +34,158 @@ namespace functional { class TensorProcessor final { public: TensorProcessor() - : common_dtype_(DType::InvalidDataType()), promote_inputs_to_common_dtype_(false){}; + : common_dtype_(DType::InvalidDataType()), + promote_dtype_(NullOpt), + promote_inputs_to_common_dtype_(false), + promote_integer_inputs_to_float_(false){}; TensorProcessor& AddInputs(const TensorTuple& init_list); TensorProcessor& AddInputs(const TensorTuple& init_list, Symbol tensor_lowest_dtype); Maybe Apply(); TensorProcessor& PromoteInputsToCommonDtype(bool is_promote); + TensorProcessor& PromoteInputsToCommonDtype(bool is_promote, + const Optional>& promote_dtype); + TensorProcessor& PromoteIntegerInputsToFloatDtype(bool is_promote); Maybe GetInputs() { return tensor_tuple_; }; private: TensorTuple tensor_tuple_; Symbol common_dtype_; + Optional> promote_dtype_; std::vector> inputs_lowest_dtype_vec_; bool promote_inputs_to_common_dtype_; + bool promote_integer_inputs_to_float_; +}; + +class TensorLayoutProcessor final { + public: + TensorLayoutProcessor(const TensorTuple& inputs, bool non_contiguous_enabled) + : TensorLayoutProcessor(inputs, nullptr, non_contiguous_enabled) {} + TensorLayoutProcessor(const TensorTuple& inputs, TensorTuple* outputs, + bool non_contiguous_enabled) + : inputs_(inputs), outputs_(outputs), non_contiguous_enabled_(non_contiguous_enabled) {} + + ~TensorLayoutProcessor(); + + Maybe Apply(); + + const TensorTuple& inputs() const { + if (!contiguous_inputs_.empty()) { return contiguous_inputs_; } + return inputs_; + } + TensorTuple* outputs() const { return outputs_; } + + private: + const TensorTuple& inputs_; + TensorTuple* outputs_; + bool non_contiguous_enabled_; + TensorTuple contiguous_inputs_; + std::vector post_process_output_indices_; + TensorTuple post_process_outputs_; +}; + +class TensorAutoCastProcessor final { + public: + TensorAutoCastProcessor(const TensorTuple& inputs, const autocast::AutoCastMeta& autocast_meta) + : TensorAutoCastProcessor(inputs, nullptr, autocast_meta) {} + TensorAutoCastProcessor(const TensorTuple& inputs, TensorTuple* outputs, + const autocast::AutoCastMeta& autocast_meta) + : inputs_(inputs), outputs_(outputs), autocast_meta_(autocast_meta) {} + + ~TensorAutoCastProcessor() = default; + + Maybe Apply(); + + const TensorTuple& inputs() const { + if (!autocast_inputs_.empty()) { return autocast_inputs_; } + return inputs_; + } + + TensorTuple* outputs() const { return outputs_; } + + private: + const TensorTuple& inputs_; + TensorTuple* outputs_; + const autocast::AutoCastMeta& autocast_meta_; + TensorTuple autocast_inputs_; +}; + +template +struct TupleTrait { + constexpr static size_t size = sizeof...(TPArgs); + constexpr static size_t max_storage_size = std::max({sizeof(TPArgs)...}); + constexpr static size_t alignment = std::max({alignof(TPArgs)...}); + using type = std::tuple; +}; + +struct TensorProcessorTuple { + using trait = TupleTrait; + constexpr static size_t size = trait::size; + constexpr static size_t max_storage_size = trait::max_storage_size; + constexpr static size_t alignment = trait::alignment; + using type = typename trait::type; +}; + +class TensorProcessorStorage { + public: + constexpr static size_t TPMaxStorageSize = TensorProcessorTuple::max_storage_size; + + TensorProcessorStorage() = default; + TensorProcessorStorage(TensorProcessorStorage&& other) = default; + + ~TensorProcessorStorage() { + if (deleter_) { deleter_(buffer_); } + } + + template + void New(Args&&... args) { + static_assert(sizeof(TP) <= TPMaxStorageSize, "Insufficient buffer size"); + new (buffer_) TP(std::forward(args)...); + deleter_ = [](char* buffer) { reinterpret_cast(buffer)->~TP(); }; + } + + template + TP* As() { + return reinterpret_cast(buffer_); + } + + private: + alignas(TensorProcessorTuple::alignment) char buffer_[TPMaxStorageSize]; + std::function deleter_; +}; + +class TensorProcessorPipe final { + public: + constexpr static size_t TPSize = TensorProcessorTuple::size; + + TensorProcessorPipe(const TensorTuple& inputs) : TensorProcessorPipe(inputs, nullptr) {} + TensorProcessorPipe(const TensorTuple& inputs, TensorTuple* outputs) + : inputs_(&inputs), outputs_(outputs), index_(0) {} + + template + Maybe Apply(Args&&... args) { + CHECK_LT_OR_RETURN(index_, static_cast(TPSize)) + << Error::RuntimeError() << "The tensor processor pipe can only be applied up to " + << static_cast(TPSize) << " times"; + processors_[index_].New(*inputs_, outputs_, std::forward(args)...); + auto* processor = processors_[index_].As(); + JUST(processor->Apply()); + inputs_ = &(processor->inputs()); + outputs_ = processor->outputs(); + ++index_; + return Maybe::Ok(); + } + + const TensorTuple& inputs() const { return *inputs_; } + + TensorTuple* outputs() const { return outputs_; } + + private: + const TensorTuple* inputs_; + TensorTuple* outputs_; + int index_; + TensorProcessorStorage processors_[TPSize]; }; } // namespace functional diff --git a/oneflow/core/graph/boxing/boxing_logger.cpp b/oneflow/core/graph/boxing/boxing_logger.cpp index f4504fc0ee515dffeb6ea46436db7cdc9d24fce3..f2b11621a7579d3c1c23ffab49a37431bc338da0 100644 --- a/oneflow/core/graph/boxing/boxing_logger.cpp +++ b/oneflow/core/graph/boxing/boxing_logger.cpp @@ -57,6 +57,17 @@ std::string ParallelDescToString(const ParallelDesc& parallel_desc) { return serialized_parallel_desc; } +std::string NdSbpToCsvString(const NdSbp& nd_sbp) { + std::ostringstream ss; + ss << "("; + for (size_t i = 0; i < nd_sbp.sbp_parallel_size(); ++i) { + if (i > 0) { ss << " "; } + ss << SbpToString(nd_sbp.sbp_parallel(i)); + } + ss << ")"; + return ss.str(); +} + std::string MakeBoxingLoggerCsvRow(const SubTskGphBuilderStatus& status, const std::string& src_op_name, const std::string& dst_op_name, const ParallelDesc& src_parallel_desc, @@ -68,8 +79,8 @@ std::string MakeBoxingLoggerCsvRow(const SubTskGphBuilderStatus& status, serialized_status += dst_op_name + ","; serialized_status += ParallelDescToString(src_parallel_desc) + ","; serialized_status += ParallelDescToString(dst_parallel_desc) + ","; - serialized_status += NdSbpToString(src_nd_sbp) + ","; - serialized_status += NdSbpToString(dst_nd_sbp) + ","; + serialized_status += NdSbpToCsvString(src_nd_sbp) + ","; + serialized_status += NdSbpToCsvString(dst_nd_sbp) + ","; serialized_status += GenLogicalBlobName(lbi) + ","; serialized_status += DataType_Name(logical_blob_desc.data_type()) + ","; serialized_status += ShapeToString(logical_blob_desc.shape()) + ","; diff --git a/oneflow/core/graph/boxing/hierarchical_sub_task_graph_builder_impl.cpp b/oneflow/core/graph/boxing/hierarchical_sub_task_graph_builder_impl.cpp index 618db1e23c4272f756a96cb08875f764cdd77ff8..d19cad3c2059c5aee8aa8d5914872f4af485e22c 100644 --- a/oneflow/core/graph/boxing/hierarchical_sub_task_graph_builder_impl.cpp +++ b/oneflow/core/graph/boxing/hierarchical_sub_task_graph_builder_impl.cpp @@ -27,6 +27,10 @@ limitations under the License. #include "oneflow/core/graph/boxing/sub_task_graph_builder_util.h" #include "oneflow/core/framework/sbp_infer_util.h" #include "oneflow/core/job/sbp_parallel.h" +#include "oneflow/core/graph/nccl_send_recv_boxing_task_node.h" +#include "oneflow/core/job/nd_sbp_util.h" +#include "oneflow/core/graph/task_stream_id.h" +#include "oneflow/core/job/job_desc.h" namespace oneflow { @@ -46,6 +50,37 @@ std::shared_ptr Make1DSubTskGphBuilder() { return std::make_shared(builders); } +void MergeParallelConf(const ParallelDesc& parallel_desc_0, const ParallelDesc& parallel_desc_1, + ParallelConf* parallel_conf) { + CHECK_EQ(parallel_desc_0.device_tag(), parallel_desc_1.device_tag()); + std::set> machine_device_ids; + for (int64_t machine_id : parallel_desc_0.sorted_machine_ids()) { + for (int64_t device_id : parallel_desc_0.sorted_dev_phy_ids(machine_id)) { + machine_device_ids.insert(std::make_pair(machine_id, device_id)); + } + } + for (int64_t machine_id : parallel_desc_1.sorted_machine_ids()) { + for (int64_t device_id : parallel_desc_1.sorted_dev_phy_ids(machine_id)) { + machine_device_ids.insert(std::make_pair(machine_id, device_id)); + } + } + parallel_conf->set_device_tag(parallel_desc_0.device_tag()); + for (const auto& pair : machine_device_ids) { + parallel_conf->add_device_name("@" + std::to_string(pair.first) + ":" + + std::to_string(pair.second)); + } +} + +inline std::string NewUniqueIdGbc() { + static std::atomic counter(0); + static std::atomic curr_job_id(0); + if (curr_job_id != GlobalJobDesc().job_id()) { + curr_job_id = GlobalJobDesc().job_id(); + counter = 0; + } + return std::to_string(counter.fetch_add(1, std::memory_order_relaxed)); +} + } // namespace class FlatSubTskGphBuilder final : public HierarchicalSubTskGphBuilder { @@ -78,6 +113,68 @@ class FlatSubTskGphBuilder final : public HierarchicalSubTskGphBuilder { std::shared_ptr sub_tsk_gph_builder_; }; +class NDNcclSendRecvBoxingSubTskGphBuilder final : public HierarchicalSubTskGphBuilder { + public: + OF_DISALLOW_COPY_AND_MOVE(NDNcclSendRecvBoxingSubTskGphBuilder); + NDNcclSendRecvBoxingSubTskGphBuilder() {} + ~NDNcclSendRecvBoxingSubTskGphBuilder() override = default; + + Maybe Build(SubTskGphBuilderCtx* ctx, + const std::vector& sorted_in_tasks, + std::vector* sorted_out_tasks, + std::vector>* sorted_ctrl_tasks, + const ParallelDesc& in_parallel_desc, + const ParallelDesc& out_parallel_desc, + const LogicalBlobId& lbi, const BlobDesc& logical_blob_desc, + const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, + const Shape& time_shape) const override { + if (in_parallel_desc.device_type() == DeviceType::kCUDA + && out_parallel_desc.device_type() == DeviceType::kCUDA + && !NdSbpHasPartialParallel(out_nd_sbp)) { +#if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) + ParallelConf merged_parallel_conf; + MergeParallelConf(in_parallel_desc.parallel_conf(), out_parallel_desc.parallel_conf(), + &merged_parallel_conf); + ParallelDesc merged_parallel_desc(merged_parallel_conf); + TaskNode* first_in_node = sorted_in_tasks.front(); + sorted_ctrl_tasks->resize(out_parallel_desc.parallel_num()); + std::string stream_name = "NCCL_SEND_RECV_BOXING" + NewUniqueIdGbc(); + FOR_RANGE(int64_t, id, 0, merged_parallel_desc.parallel_num()) { + NcclSendRecvBoxingTaskNode* node = ctx->task_graph()->NewNode(); + const int64_t machine_id = JUST(merged_parallel_desc.MachineId4ParallelId(id)); + int64_t device_index = JUST(merged_parallel_desc.DeviceId4ParallelId(id)); + int64_t thrd_id = EncodeStreamIdToInt64(GenerateNamedTaskStreamId( + machine_id, merged_parallel_desc.device_type(), device_index, stream_name)); + bool has_input = in_parallel_desc.Containing(machine_id, device_index); + bool has_output = out_parallel_desc.Containing(machine_id, device_index); + node->Init(machine_id, thrd_id, lbi, logical_blob_desc.shape(), + logical_blob_desc.data_type(), in_nd_sbp, out_nd_sbp, in_parallel_desc, + out_parallel_desc, id, merged_parallel_desc, has_input, has_output, stream_name); + if (has_input) { + int64_t in_id = + JUST(in_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + ctx->task_graph()->ConnectWithLbi(sorted_in_tasks.at(in_id), node, lbi); + } else { + // TODO: find nearest + std::string regst_desc_name; + first_in_node->BuildCtrlRegstDesc(node, ®st_desc_name); + TaskEdge* edge = ctx->task_graph()->NewEdge(); + Connect(first_in_node, edge, node); + first_in_node->BindEdgeWithProducedRegst(edge, regst_desc_name); + } + if (has_output) { sorted_out_tasks->push_back(node); } + } + return BuildSubTskGphBuilderStatus("NDNcclSendRecvBoxingSubTskGphBuilder", ""); +#else + return Error::BoxingNotSupportedError() << "No CUDA or low NCCL version"; +#endif + } else { + return Error::BoxingNotSupportedError() + << "Partial SBP in the consumer or not running on CUDA"; + } + } +}; + class IntraGroupSubTskGphBuilder final : public HierarchicalSubTskGphBuilder { public: OF_DISALLOW_COPY_AND_MOVE(IntraGroupSubTskGphBuilder); @@ -257,21 +354,22 @@ class Dim0NdSbpMismatchedSubTskGphBuilder final : public HierarchicalSubTskGphBu if (in_parallel_desc.hierarchy()->NumAxes() == 2 && (*in_parallel_desc.hierarchy() == *out_parallel_desc.hierarchy()) && in_nd_sbp.sbp_parallel(0) != out_nd_sbp.sbp_parallel(0) - && in_nd_sbp.sbp_parallel(1) == out_nd_sbp.sbp_parallel(1)) { - if (!(NdSbpAllSameSplitParallel(in_nd_sbp) || NdSbpAllSameSplitParallel(out_nd_sbp))) { - return inter_group_sub_tsk_gph_builder_->Build( - ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, - out_parallel_desc, lbi, logical_blob_desc, in_nd_sbp, out_nd_sbp, time_shape); - } else { - return Error::BoxingNotSupportedError(); - } + && in_nd_sbp.sbp_parallel(1) == out_nd_sbp.sbp_parallel(1) + && !(NdSbpAllSameSplitParallel(in_nd_sbp) || NdSbpAllSameSplitParallel(out_nd_sbp))) { + return inter_group_sub_tsk_gph_builder_->Build( + ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, + out_parallel_desc, lbi, logical_blob_desc, in_nd_sbp, out_nd_sbp, time_shape); } else { - return Error::BoxingNotSupportedError(); + return nd_nccl_send_recv_boxing_sub_tsk_gph_builder_->Build( + ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, + out_parallel_desc, lbi, logical_blob_desc, in_nd_sbp, out_nd_sbp, time_shape); } } private: std::unique_ptr inter_group_sub_tsk_gph_builder_; + std::unique_ptr + nd_nccl_send_recv_boxing_sub_tsk_gph_builder_; }; class Same2DHierarchySubTskGphBuilder final : public HierarchicalSubTskGphBuilder { @@ -298,12 +396,10 @@ class Same2DHierarchySubTskGphBuilder final : public HierarchicalSubTskGphBuilde return intra_group_sub_tsk_gph_builder_->Build( ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, out_parallel_desc, lbi, logical_blob_desc, in_nd_sbp, out_nd_sbp, time_shape); - } else if (in_nd_sbp.sbp_parallel(1) == out_nd_sbp.sbp_parallel(1)) { + } else { return dim0_nd_sbp_mismatched_sub_tsk_gph_builder_->Build( ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, out_parallel_desc, lbi, logical_blob_desc, in_nd_sbp, out_nd_sbp, time_shape); - } else { - return Error::BoxingNotSupportedError(); } } else { return Error::BoxingNotSupportedError(); @@ -315,69 +411,18 @@ class Same2DHierarchySubTskGphBuilder final : public HierarchicalSubTskGphBuilde std::unique_ptr dim0_nd_sbp_mismatched_sub_tsk_gph_builder_; }; -class ExpandToSame2DHierarchySubTskGphBuilder final : public HierarchicalSubTskGphBuilder { - public: - OF_DISALLOW_COPY_AND_MOVE(ExpandToSame2DHierarchySubTskGphBuilder); - ExpandToSame2DHierarchySubTskGphBuilder() { - same_2d_hierarchy_sub_tsk_gph_builder_.reset(new Same2DHierarchySubTskGphBuilder()); - } - ~ExpandToSame2DHierarchySubTskGphBuilder() override = default; - - Maybe Build(SubTskGphBuilderCtx* ctx, - const std::vector& sorted_in_tasks, - std::vector* sorted_out_tasks, - std::vector>* sorted_ctrl_tasks, - const ParallelDesc& in_parallel_desc, - const ParallelDesc& out_parallel_desc, - const LogicalBlobId& lbi, const BlobDesc& logical_blob_desc, - const NdSbp& in_nd_sbp, const NdSbp& out_nd_sbp, - const Shape& time_shape) const override { - if (in_parallel_desc.hierarchy()->elem_cnt() == out_parallel_desc.hierarchy()->elem_cnt() - && in_parallel_desc.hierarchy()->NumAxes() == 1 - && out_parallel_desc.hierarchy()->NumAxes() == 2) { - ParallelConf intermediate_parallel_conf = in_parallel_desc.parallel_conf(); - out_parallel_desc.hierarchy()->ToProto(intermediate_parallel_conf.mutable_hierarchy()); - NdSbp intermediate_nd_sbp; - *intermediate_nd_sbp.add_sbp_parallel() = in_nd_sbp.sbp_parallel(0); - *intermediate_nd_sbp.add_sbp_parallel() = in_nd_sbp.sbp_parallel(0); - return same_2d_hierarchy_sub_tsk_gph_builder_->Build( - ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, - ParallelDesc(intermediate_parallel_conf), out_parallel_desc, lbi, logical_blob_desc, - intermediate_nd_sbp, out_nd_sbp, time_shape); - } else if (in_parallel_desc.hierarchy()->elem_cnt() == out_parallel_desc.hierarchy()->elem_cnt() - && in_parallel_desc.hierarchy()->NumAxes() == 2 - && out_parallel_desc.hierarchy()->NumAxes() == 1) { - ParallelConf intermediate_parallel_conf = out_parallel_desc.parallel_conf(); - in_parallel_desc.hierarchy()->ToProto(intermediate_parallel_conf.mutable_hierarchy()); - NdSbp intermediate_nd_sbp; - *intermediate_nd_sbp.add_sbp_parallel() = out_nd_sbp.sbp_parallel(0); - *intermediate_nd_sbp.add_sbp_parallel() = out_nd_sbp.sbp_parallel(0); - return same_2d_hierarchy_sub_tsk_gph_builder_->Build( - ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, in_parallel_desc, - ParallelDesc(intermediate_parallel_conf), lbi, logical_blob_desc, in_nd_sbp, - intermediate_nd_sbp, time_shape); - } else { - return Error::BoxingNotSupportedError(); - } - } - - private: - std::unique_ptr same_2d_hierarchy_sub_tsk_gph_builder_; -}; - struct DispatchHierarchicalSubTskGphBuilder::Impl { Impl(); std::unique_ptr flat_sub_tsk_gph_builder_; std::unique_ptr same_2d_hierarchy_sub_tsk_gph_builder_; - std::unique_ptr - expand_to_same_2d_hierarchy_sub_tsk_gph_builder_; + std::unique_ptr + nd_nccl_send_recv_boxing_sub_tsk_gph_builder_; }; DispatchHierarchicalSubTskGphBuilder::Impl::Impl() { flat_sub_tsk_gph_builder_.reset(new FlatSubTskGphBuilder()); same_2d_hierarchy_sub_tsk_gph_builder_.reset(new Same2DHierarchySubTskGphBuilder()); - expand_to_same_2d_hierarchy_sub_tsk_gph_builder_.reset( - new ExpandToSame2DHierarchySubTskGphBuilder()); + nd_nccl_send_recv_boxing_sub_tsk_gph_builder_.reset(new NDNcclSendRecvBoxingSubTskGphBuilder()); } DispatchHierarchicalSubTskGphBuilder::DispatchHierarchicalSubTskGphBuilder() { @@ -397,11 +442,22 @@ Maybe DispatchHierarchicalSubTskGphBuilder::Build( ParallelDesc reduced_out_parallel_desc = out_parallel_desc; NdSbp reduced_in_nd_sbp; NdSbp reduced_out_nd_sbp; + // The 1d to 2d and 2d to 1d cases are consider in this function + // If it gives out 1d sbp and 2d sbp simultaneously, then that the 2d sbp can not be converted + // to 1d sbp and 1d sbp can not be expanded to 2d sbp. InOutParallelDimReduce(in_parallel_desc, out_parallel_desc, in_nd_sbp, out_nd_sbp, &reduced_in_parallel_desc, &reduced_out_parallel_desc, &reduced_in_nd_sbp, - &reduced_out_nd_sbp); + &reduced_out_nd_sbp, logical_blob_desc.shape()); const auto& in_hierarchy = reduced_in_parallel_desc.hierarchy(); const auto& out_hierarchy = reduced_out_parallel_desc.hierarchy(); + if ((in_hierarchy->NumAxes() > 2 || out_hierarchy->NumAxes() > 2) + && reduced_in_parallel_desc.device_type() == DeviceType::kCUDA + && reduced_out_parallel_desc.device_type() == DeviceType::kCUDA) { + return impl_->nd_nccl_send_recv_boxing_sub_tsk_gph_builder_->Build( + ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, reduced_in_parallel_desc, + reduced_out_parallel_desc, lbi, logical_blob_desc, reduced_in_nd_sbp, reduced_out_nd_sbp, + time_shape); + } if (in_hierarchy->NumAxes() <= 2 && out_hierarchy->NumAxes() <= 2) { if (in_hierarchy->NumAxes() == 1 && out_hierarchy->NumAxes() == 1) { return impl_->flat_sub_tsk_gph_builder_->Build( @@ -413,10 +469,9 @@ Maybe DispatchHierarchicalSubTskGphBuilder::Build( ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, reduced_in_parallel_desc, reduced_out_parallel_desc, lbi, logical_blob_desc, reduced_in_nd_sbp, reduced_out_nd_sbp, time_shape); - } else if (in_hierarchy->elem_cnt() == out_hierarchy->elem_cnt() - && ((in_hierarchy->NumAxes() == 1 && out_hierarchy->NumAxes() == 2) - || (in_hierarchy->NumAxes() == 2 && out_hierarchy->NumAxes() == 1))) { - return impl_->expand_to_same_2d_hierarchy_sub_tsk_gph_builder_->Build( + } else if (reduced_in_parallel_desc.device_type() == DeviceType::kCUDA + && reduced_out_parallel_desc.device_type() == DeviceType::kCUDA) { + return impl_->nd_nccl_send_recv_boxing_sub_tsk_gph_builder_->Build( ctx, sorted_in_tasks, sorted_out_tasks, sorted_ctrl_tasks, reduced_in_parallel_desc, reduced_out_parallel_desc, lbi, logical_blob_desc, reduced_in_nd_sbp, reduced_out_nd_sbp, time_shape); diff --git a/oneflow/core/graph/boxing/slice_boxing_sub_task_graph_builder.cpp b/oneflow/core/graph/boxing/slice_boxing_sub_task_graph_builder.cpp index e7698a3848f520c22bd998b2be9ef527fc10cb78..079d220b255c133d002e4dfa46f04141e64859be 100644 --- a/oneflow/core/graph/boxing/slice_boxing_sub_task_graph_builder.cpp +++ b/oneflow/core/graph/boxing/slice_boxing_sub_task_graph_builder.cpp @@ -234,7 +234,7 @@ Maybe SliceBoxingSubTskGphBuilder::Build( comment = "BuildSubTaskGphP2B"; } else { BlobDesc flat_blob_desc(logical_blob_desc.data_type()); - flat_blob_desc.mut_shape() = Shape({logical_blob_desc.shape().elem_cnt()}); + flat_blob_desc.set_shape(Shape({logical_blob_desc.shape().elem_cnt()})); std::vector middle_nodes; SbpParallel middle_sbp; middle_sbp.mutable_split_parallel()->set_axis(0); diff --git a/oneflow/core/graph/compute_task_node.h b/oneflow/core/graph/compute_task_node.h index 9ff29eef5978eb16f53f9206425f24d6802479a0..5c78a487f53507f80c5666197524fe48f17759e6 100644 --- a/oneflow/core/graph/compute_task_node.h +++ b/oneflow/core/graph/compute_task_node.h @@ -89,6 +89,10 @@ class FnOpCompTaskNodeCreator : public OpCompTaskNodeCreator { REGISTER_CLASS_CREATOR(std::string, op_type_name, OpCompTaskNodeCreator, \ ([] { return new StaticOpCompTaskNodeCreator(); })); +#define REGISTER_USER_OP_COMP_TASK_NODE_TYPE_WITH_FUNC(op_type_name, func) \ + REGISTER_CLASS_CREATOR(std::string, op_type_name, OpCompTaskNodeCreator, \ + ([] { return new FnOpCompTaskNodeCreator(func); })); + #define REGISTER_SYSTEM_OP_COMP_TASK_NODE_TYPE(op_type_case, comp_task_node_type) \ REGISTER_CLASS_CREATOR(int32_t, op_type_case, OpCompTaskNodeCreator, \ ([] { return new StaticOpCompTaskNodeCreator(); })); diff --git a/oneflow/core/graph/copy_task_node.cpp b/oneflow/core/graph/copy_task_node.cpp index 2ab5b557983cf784f2b4b8485cf0ccd6774d6ae1..bf26e1883dad310497c12cd121e484003f7ce489 100644 --- a/oneflow/core/graph/copy_task_node.cpp +++ b/oneflow/core/graph/copy_task_node.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/graph/copy_task_node.h" #include "oneflow/core/graph/task_stream_id.h" +#include "oneflow/core/framework/user_op_registry_manager.h" namespace oneflow { @@ -30,21 +31,31 @@ void CopyTaskNode::BuildExecGphAndRegst() { auto in_regst = GetSoleConsumedRegst("copy_in"); out_regst->CopyBlobDescFrom(in_regst.get()); ExecNode* node = mut_exec_gph().NewNode(); - node->mut_op() = CHECK_JUST(ConstructOp(NewCopyOpConf())); + auto constructed = CHECK_JUST(ConstructOp(NewCopyOpConf())); + + // prevent filling parallel desc for copy commnet + if (constructed->op_conf().has_user_conf()) { + std::shared_ptr hierarchy = std::make_shared(Shape({1})); + auto parallel_desc = + ParallelDesc::New(constructed->op_conf().device_tag(), {"0:0-0"}, hierarchy).GetOrThrow(); + CHECK_JUST(constructed->FillOpParallelDesc(parallel_desc)); + } + + node->mut_op() = constructed; node->BindBnWithRegst(node->op()->SoleIbn(), in_regst); node->BindBnWithRegst(node->op()->SoleObn(), out_regst); } void CopyTaskNode::InferProducedDataRegstTimeShape() { NaiveInferProducedDataRegstTimeShape(); } -void CopyHdTaskNode::Init(CopyHdOpConf::Type copy_type, const DeviceId& device_id, +void CopyHdTaskNode::Init(CopyHdType copy_type, const DeviceId& device_id, const LogicalBlobId& lbi) { copy_type_ = copy_type; set_machine_id(device_id.rank()); int64_t thrd_id = -1; - if (copy_type == CopyHdOpConf::H2D) { + if (copy_type == CopyHdType::H2D) { thrd_id = EncodeStreamIdToInt64(GenerateNamedTaskStreamId(device_id, "H2D")); - } else if (copy_type == CopyHdOpConf::D2H) { + } else if (copy_type == CopyHdType::D2H) { thrd_id = EncodeStreamIdToInt64(GenerateNamedTaskStreamId(device_id, "D2H")); } else { UNIMPLEMENTED(); @@ -54,11 +65,13 @@ void CopyHdTaskNode::Init(CopyHdOpConf::Type copy_type, const DeviceId& device_i } void CopyHdTaskNode::InitProducedRegstMemCase(MemoryCase* mem_case) { - if (copy_type_ == CopyHdOpConf::H2D) { + if (copy_type_ == CopyHdType::H2D) { TaskNode::InitProducedRegstMemCase(mem_case); - } else if (copy_type_ == CopyHdOpConf::D2H) { - mem_case->mutable_host_mem()->mutable_cuda_pinned_mem()->set_device_id( - stream_id().device_id().device_index()); + } else if (copy_type_ == CopyHdType::D2H) { + mem_case->set_device_type(DeviceType::kCPU); + mem_case->set_device_id(0); + mem_case->set_pinned_device_type(device_type()); + mem_case->set_pinned_device_id(stream_id().device_id().device_index()); } else { UNIMPLEMENTED(); } @@ -66,14 +79,23 @@ void CopyHdTaskNode::InitProducedRegstMemCase(MemoryCase* mem_case) { OperatorConf CopyHdTaskNode::NewCopyOpConf() { OperatorConf conf; - conf.set_name("copy_hd_" + NewUniqueId()); conf.set_device_tag(*CHECK_JUST(DeviceTag4DeviceType(device_type()))); - conf.mutable_copy_hd_conf()->set_type(copy_type_); + auto copy_type_name = "undefined"; + if (copy_type_ == CopyHdType::D2H) { + copy_type_name = "copy_d2h"; + } else if (copy_type_ == CopyHdType::H2D) { + copy_type_name = "copy_h2d"; + } else { + LOG(FATAL) << "unknow copy type: " << copy_type_; + } + conf.set_name(std::string(copy_type_name) + "_" + NewUniqueId()); + *conf.mutable_user_conf()->mutable_op_type_name() = copy_type_name; auto in_regst = GetSoleConsumedRegst("copy_in"); CHECK_EQ(in_regst->NumOfLbi(), 1); in_regst->ForEachLbi([&](const LogicalBlobId& lbi) { - *conf.mutable_copy_hd_conf()->mutable_lbi() = lbi; - CHECK(lbi == this->lbi()); + (*conf.mutable_user_conf()->mutable_input())["in"].add_s(GenLogicalBlobName(lbi)); + (*conf.mutable_user_conf()->mutable_output())["out"].add_s( + GenLogicalBlobName(conf.name(), GenRepeatedBn("out", 0))); }); return conf; } diff --git a/oneflow/core/graph/copy_task_node.h b/oneflow/core/graph/copy_task_node.h index 59cc53018a155322403c83ad5d8db281076fb7b2..251e22a92ac96402dace44d4cc505cdb7872c664 100644 --- a/oneflow/core/graph/copy_task_node.h +++ b/oneflow/core/graph/copy_task_node.h @@ -37,6 +37,8 @@ class CopyTaskNode : public TransportTaskNode { void InferProducedDataRegstTimeShape() final; }; +enum CopyHdType { H2D = 0, D2H = 1 }; + class CopyHdTaskNode final : public CopyTaskNode { public: OF_DISALLOW_COPY_AND_MOVE(CopyHdTaskNode); @@ -45,13 +47,13 @@ class CopyHdTaskNode final : public CopyTaskNode { TaskType GetTaskType() const override { return TaskType::kCopyHd; } - void Init(CopyHdOpConf::Type, const DeviceId& device_id, const LogicalBlobId& lbi); + void Init(CopyHdType, const DeviceId& device_id, const LogicalBlobId& lbi); - CopyHdOpConf::Type copy_type() const { return copy_type_; } + CopyHdType copy_type() const { return copy_type_; } MemZoneId MemZoneId121() const override { - if (copy_type_ == CopyHdOpConf::H2D) { + if (copy_type_ == CopyHdType::H2D) { return TaskNode::MemZoneId121(); - } else if (copy_type_ == CopyHdOpConf::D2H) { + } else if (copy_type_ == CopyHdType::D2H) { return GetNodeCPUMemZoneId(this->machine_id()); } else { UNIMPLEMENTED(); @@ -63,7 +65,7 @@ class CopyHdTaskNode final : public CopyTaskNode { void InitProducedRegstMemCase(MemoryCase*) override; OperatorConf NewCopyOpConf() override; - CopyHdOpConf::Type copy_type_; + CopyHdType copy_type_; }; class CopyCommNetTaskNode final : public CopyTaskNode { diff --git a/oneflow/core/graph/inplace_lbi_graph.cpp b/oneflow/core/graph/inplace_lbi_graph.cpp index f1fc4320e64198272f026adf93b4120bc27403e1..16a4363f32bc548669d2260ad70433d9b817259c 100644 --- a/oneflow/core/graph/inplace_lbi_graph.cpp +++ b/oneflow/core/graph/inplace_lbi_graph.cpp @@ -26,6 +26,9 @@ bool IsSourceNode(const Operator& op) { && op_conf.user_conf().output().size() == 1) { return true; } + if (op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == "mutable_cast_once") { + return true; + } if (op_conf.has_variable_conf()) { return true; } if (op_conf.has_distribute_clone_conf() && op_conf.distribute_clone_conf().is_variable_ref()) { return true; diff --git a/oneflow/core/graph/nccl_send_recv_boxing_task_node.cpp b/oneflow/core/graph/nccl_send_recv_boxing_task_node.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e6ab2530c368521b9a25596772ab3a6e6a7e8e29 --- /dev/null +++ b/oneflow/core/graph/nccl_send_recv_boxing_task_node.cpp @@ -0,0 +1,96 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/to_string.h" +#include "oneflow/core/graph/nccl_send_recv_boxing_task_node.h" + +namespace oneflow { + +void NcclSendRecvBoxingTaskNode::Init(int64_t machine_id, int64_t thrd_id, const LogicalBlobId& lbi, + const Shape& logical_shape, const DataType& data_type, + const NdSbp& src_nd_sbp, const NdSbp& dst_nd_sbp, + const ParallelDesc& src_parallel_desc, + const ParallelDesc& dst_parallel_desc, + const int64_t parallel_id, const ParallelDesc& parallel_desc, + const bool has_input, const bool has_output, + const std::string& stream_name) { + set_machine_id(machine_id); + set_thrd_id(thrd_id); + set_lbi(lbi); + logical_shape_ = logical_shape; + src_nd_sbp_ = src_nd_sbp; + dst_nd_sbp_ = dst_nd_sbp; + src_parallel_conf_ = src_parallel_desc.parallel_conf(); + dst_parallel_conf_ = dst_parallel_desc.parallel_conf(); + parallel_conf_ = parallel_desc.parallel_conf(); + parallel_ctx_.set_parallel_id(parallel_id); + parallel_ctx_.set_parallel_num(parallel_desc.parallel_num()); + has_input_ = has_input; + has_output_ = has_output; + data_type_ = data_type; + stream_name_ = stream_name; +} + +void NcclSendRecvBoxingTaskNode::ProduceAllRegstsAndBindEdges() { + if (has_output_) { + std::shared_ptr out_regst = ProduceRegst("out", true, 1, 1); + this->ForEachOutDataEdge([&](TaskEdge* out_dege) { out_dege->AddRegst("out", out_regst); }); + } + ProduceRegst("tmp", true); +} + +void NcclSendRecvBoxingTaskNode::ConsumeAllRegsts() { + this->ForEachInDataEdge( + [&](TaskEdge* in_edge) { ConsumeRegst("in", SoleInDataEdge()->GetSoleRegst()); }); +} + +void NcclSendRecvBoxingTaskNode::BuildExecGphAndRegst() { + ExecNode* node = mut_exec_gph().NewNode(); + OperatorConf op_conf; + op_conf.set_name("System-Nccl-Send-Recv-Boxing-" + NewUniqueId()); + op_conf.set_device_tag(*CHECK_JUST(DeviceTag4DeviceType(this->device_type()))); + op_conf.set_stream_name_hint(stream_name_); + auto* nccl_send_recv_boxing_conf = op_conf.mutable_nccl_send_recv_boxing_conf(); + *nccl_send_recv_boxing_conf->mutable_lbi() = lbi(); + logical_shape_.ToProto(nccl_send_recv_boxing_conf->mutable_logical_shape()); + nccl_send_recv_boxing_conf->set_data_type(data_type_); + *nccl_send_recv_boxing_conf->mutable_src_nd_sbp() = src_nd_sbp_; + *nccl_send_recv_boxing_conf->mutable_dst_nd_sbp() = dst_nd_sbp_; + *nccl_send_recv_boxing_conf->mutable_parallel_conf() = parallel_conf_; + *nccl_send_recv_boxing_conf->mutable_src_parallel_conf() = src_parallel_conf_; + *nccl_send_recv_boxing_conf->mutable_dst_parallel_conf() = dst_parallel_conf_; + nccl_send_recv_boxing_conf->set_has_input(has_input_); + nccl_send_recv_boxing_conf->set_has_output(has_output_); + std::shared_ptr sole_op = CHECK_JUST(ConstructOp(op_conf)); + node->mut_op() = sole_op; + CHECK_JUST(sole_op->FillOpParallelDesc(parallel_conf_)); + if (has_input_) { node->BindBnWithRegst(sole_op->SoleIbn(), GetSoleConsumedRegst("in")); } + if (has_output_) { + std::shared_ptr out_regst = GetProducedRegst("out"); + out_regst->AddLbi(sole_op->BnInOp2Lbi(sole_op->SoleObn())); + node->BindBnWithRegst(sole_op->SoleObn(), out_regst); + } + node->AddBnToRegstAndBindIt(&Operator::tmp_bns, GetProducedRegst("tmp")); + node->InferBlobDescs(parallel_ctx()); +} + +void NcclSendRecvBoxingTaskNode::InferProducedDataRegstTimeShape() { + auto out_regst = GetProducedRegst("out"); + if (out_regst != nullptr) { out_regst->mut_data_regst_time_shape()->reset(new Shape({1, 1})); } + auto tmp_regst = GetProducedRegst("tmp"); + tmp_regst->mut_data_regst_time_shape()->reset(new Shape({1, 1})); +} + +} // namespace oneflow diff --git a/oneflow/core/graph/nccl_send_recv_boxing_task_node.h b/oneflow/core/graph/nccl_send_recv_boxing_task_node.h new file mode 100644 index 0000000000000000000000000000000000000000..1fcc4482f0e936c77c7ff973806b79770c7591e2 --- /dev/null +++ b/oneflow/core/graph/nccl_send_recv_boxing_task_node.h @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_GRAPH_NCCL_SEND_RECV_BOXING_TASK_NODE_H_ +#define ONEFLOW_CORE_GRAPH_NCCL_SEND_RECV_BOXING_TASK_NODE_H_ + +#include "oneflow/core/graph/transport_task_node.h" + +namespace oneflow { + +class NcclSendRecvBoxingTaskNode : public TransportTaskNode { + public: + OF_DISALLOW_COPY_AND_MOVE(NcclSendRecvBoxingTaskNode); + NcclSendRecvBoxingTaskNode() = default; + ~NcclSendRecvBoxingTaskNode() override = default; + + void Init(int64_t machine_id, int64_t thrd_id, const LogicalBlobId& lbi, + const Shape& logical_shape, const DataType& data_type, const NdSbp& src_nd_sbp, + const NdSbp& dst_nd_sbp, const ParallelDesc& src_parallel_desc, + const ParallelDesc& dst_parallel_desc, const int64_t parallel_id, + const ParallelDesc& parallel_desc, const bool has_input, const bool has_output, + const std::string& stream_name); + TaskType GetTaskType() const override { return TaskType::kNcclSendRecvBoxing; } + const ParallelContext* parallel_ctx() const override { return ¶llel_ctx_; } + + private: + void BuildExecGphAndRegst() override; + void ProduceAllRegstsAndBindEdges() override; + void ConsumeAllRegsts() final; + void InferProducedDataRegstTimeShape() final; + + Shape logical_shape_; + DataType data_type_; + NdSbp src_nd_sbp_; + NdSbp dst_nd_sbp_; + ParallelConf src_parallel_conf_; + ParallelConf dst_parallel_conf_; + ParallelConf parallel_conf_; + ParallelContext parallel_ctx_; + bool has_input_; + bool has_output_; + std::string stream_name_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_GRAPH_NCCL_SEND_RECV_BOXING_TASK_NODE_H_ diff --git a/oneflow/core/graph/op_graph.cpp b/oneflow/core/graph/op_graph.cpp index 45e5eba9166622d3d1fbfb0122b8ce78e3f37c05..74978f7bef5ff871e8821447ccc9ff2be57b86e4 100644 --- a/oneflow/core/graph/op_graph.cpp +++ b/oneflow/core/graph/op_graph.cpp @@ -13,11 +13,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include #include "oneflow/core/graph/op_graph.h" #include "oneflow/core/job/job_builder.h" -#include "oneflow/core/job/mirrored_sig_infer_hint.h" +#include "oneflow/core/job/local_sig_infer_hint.h" #include "oneflow/core/job/lazy_mode.h" +#include "oneflow/core/auto_parallel/algorithm_util.h" +#include "oneflow/core/framework/nd_sbp.h" namespace oneflow { @@ -322,27 +323,25 @@ void OpGraph::InferOpNodeNdSbpSignature(OpNode* op_node, op_node->InitLbi2NdSbp(); } -Maybe OpGraph::InferOpNodeMirroredSignature(OpNode* op_node, bool is_mirrored_conf) const { - HashMap ibn2mirrored_sig_infer_hint; +Maybe OpGraph::InferOpNodeLocalSignature(OpNode* op_node, bool is_local_conf) const { + HashMap ibn2local_sig_infer_hint; for (const std::string& ibn : op_node->op().input_bns()) { const LogicalBlobId& lbi = op_node->op().BnInOp2Lbi(ibn); const auto* producer = op_node->MutSrcNode4Ibn(ibn); const ParallelDesc* parallel_desc = &producer->parallel_desc(); const auto& producer_obn = *JUST(producer->op().obn4lbi(lbi)); - const auto& opt_mirrored_parallel = - *JUST(producer->op().OptMirroredParallel4BnInOp(producer_obn)); - MirroredSigInferHint infer_ctx(parallel_desc, opt_mirrored_parallel.has_mirrored_parallel()); - ibn2mirrored_sig_infer_hint.emplace(ibn, infer_ctx); + const auto& opt_local_parallel = *JUST(producer->op().OptLocalParallel4BnInOp(producer_obn)); + LocalSigInferHint infer_ctx(parallel_desc, opt_local_parallel.has_local_parallel()); + ibn2local_sig_infer_hint.emplace(ibn, infer_ctx); } - const auto& MirroredSigInferHint4Ibn = - [&](const std::string& ibn) -> Maybe { - const auto& iter = ibn2mirrored_sig_infer_hint.find(ibn); - CHECK_OR_RETURN(iter != ibn2mirrored_sig_infer_hint.end()) - << "input blob not found. ibn: " << ibn; + const auto& LocalSigInferHint4Ibn = + [&](const std::string& ibn) -> Maybe { + const auto& iter = ibn2local_sig_infer_hint.find(ibn); + CHECK_OR_RETURN(iter != ibn2local_sig_infer_hint.end()) << "input blob not found. ibn: " << ibn; return &iter->second; }; - JUST(op_node->mut_op()->InferMirroredSignatureIf(MirroredSigInferHint4Ibn, is_mirrored_conf, - op_node->parallel_desc())); + JUST(op_node->mut_op()->InferLocalSignatureIf(LocalSigInferHint4Ibn, is_local_conf, + op_node->parallel_desc())); return Maybe::Ok(); } @@ -363,14 +362,14 @@ Maybe OpGraph::InferLogicalBlobDesc(const Job& job) const { JUST(op_node->mut_op()->FillLogicalInBlobDesc(LogicalBlobDesc4InputIndex)); // Infer ParallelSignature JUST(op_node->mut_op()->InferParallelSignatureIf()); - // Infer mirrored_signature - bool is_mirrored_conf = false; + // Infer local_signature + bool is_local_conf = false; { - const auto& op_name2is_mirrored = job_parallel_view_conf.op_name2is_mirrored_parallel_view(); - const auto& iter = op_name2is_mirrored.find(op_node->op().op_name()); - if (iter != op_name2is_mirrored.end()) { is_mirrored_conf = iter->second; } + const auto& op_name2is_local = job_parallel_view_conf.op_name2is_local_parallel_view(); + const auto& iter = op_name2is_local.find(op_node->op().op_name()); + if (iter != op_name2is_local.end()) { is_local_conf = iter->second; } } - JUST(InferOpNodeMirroredSignature(op_node, is_mirrored_conf)); + JUST(InferOpNodeLocalSignature(op_node, is_local_conf)); NdSbpSignature nd_sbp_sig_conf; { const auto& op_name2nd_sbp_sig_conf = job_parallel_view_conf.op_name2nd_sbp_signature_conf(); @@ -567,4 +566,62 @@ Maybe OpGraph::ForEachOpNode(const std::function(const OpNode& return Maybe::Ok(); } +// Print the graph with SBP in order +void OpGraph::PrintSBPGraphDebugInfo() const { + // test debug + std::cout << "Get Into Print Op Graph" << std::endl; + // Collect op_node + std::vector NodeList; + ForEachNode([&](OpNode* op_node) { NodeList.push_back(op_node); }); + + // test debug + std::cout << "Deciding order" << std::endl; + // Decide the order to vist the op + std::vector order; + auto_parallel::DecideOrder(NodeList, order, [&](OpNode* a, OpNode* b) { + return a->op().op_name().compare(b->op().op_name()) > 0; + }); + std::vector str_order; + + // test debug + std::cout << "Finish deciding order" << std::endl; + + for (int32_t i = 0; i < NodeList.size(); i++) { + OpNode* op_node = NodeList[order[i]]; + std::cout << op_node->op().op_name() << " (^_^):" << std::endl; + // Sort before printing + const auto& op_input_bns = op_node->op().input_bns(); + auto comp = [](const std::string& a, const std::string& b) { return a.compare(b) > 0; }; + auto_parallel::DecideOrder(op_input_bns, str_order, comp); + // Print out SBP information for input operator + for (int32_t j : str_order) { + const auto& ibn = op_input_bns[j]; + auto producer_node = op_node->MutSrcNode4Ibn(ibn); + std::cout << "Pre Op:" << producer_node->op().op_name() << ": " << ibn; + const auto& this_sbp_parallel = op_node->NdSbp4BnInOp(ibn); + std::cout << ", " << NdSbpToString(this_sbp_parallel); + const auto input_blob_modifier_ = op_node->op().InputBlobModifier4Ibn(ibn); + bool is_same_sbp = input_blob_modifier_.has_is_mutable() && input_blob_modifier_.is_mutable(); + if (is_same_sbp) std::cout << ", same SBP"; + std::cout << ", " + << op_node->LogicalBlobDesc4Lbi(op_node->op().BnInOp2Lbi(ibn)).shape().elem_cnt(); + std::cout << std::endl; + } + // Sort before printing + const auto& op_output_bns = op_node->op().output_bns(); + auto_parallel::DecideOrder(op_output_bns, str_order, comp); + // Print out SBP information for output blobs + for (int32_t j : str_order) { + const auto& obn = op_output_bns[j]; + std::cout << "Out Op:" << obn; + const auto& this_sbp_parallel = op_node->NdSbp4BnInOp(obn); + std::cout << ", " << NdSbpToString(this_sbp_parallel); + std::cout << ", " + << op_node->LogicalBlobDesc4Lbi(op_node->op().BnInOp2Lbi(obn)).shape().elem_cnt(); + std::cout << std::endl; + } + std::cout << std::endl; + } +} + } // namespace oneflow diff --git a/oneflow/core/graph/op_graph.h b/oneflow/core/graph/op_graph.h index fd47030e3b94bbb14f823972b3ff3999ae779f4d..f36acf04ada85acecb4b102105de9c11c94ccd93 100644 --- a/oneflow/core/graph/op_graph.h +++ b/oneflow/core/graph/op_graph.h @@ -19,11 +19,14 @@ limitations under the License. #include "oneflow/core/graph/graph.h" #include "oneflow/core/job/job_desc.h" #include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/job/mirrored_parallel.pb.h" +#include "oneflow/core/job/local_parallel.pb.h" #include "oneflow/core/operator/operator.h" #include "oneflow/core/common/balanced_splitter.h" namespace oneflow { +namespace auto_parallel { +class SbpConstructor; +} class OpEdge; class OpGraph; @@ -55,6 +58,7 @@ class OpNode final : public Node { private: friend class OpGraph; friend class OpEdge; + friend class auto_parallel::SbpConstructor; // Setters Operator* mut_op() { return op_.get(); } @@ -126,7 +130,12 @@ class OpGraph final : public Graph { Maybe Init(const Job& job); + // Print the graph with SBP in order + void PrintSBPGraphDebugInfo() const; + private: + friend class auto_parallel::SbpConstructor; + void InitNodes(const Job& job); void InitEdges(); void InitProducerOpName2CtrlConsumerOpNames(const Job& job); @@ -134,7 +143,7 @@ class OpGraph final : public Graph { void InferBlobLastUsed() const; void InferTimeShape() const; void InferOpNodeNdSbpSignature(OpNode* op_node, const NdSbpSignature& nd_sbp_sig_conf) const; - Maybe InferOpNodeMirroredSignature(OpNode* op_node, bool is_mirrored_conf) const; + Maybe InferOpNodeLocalSignature(OpNode* op_node, bool is_local_conf) const; Maybe InferLogicalBlobDesc(const Job& job) const; std::string GetOpNameKey(const std::string& op_name, const LogicalBlobId& lbi) const; LogicalBlobId GetLogicalBlobIdKey(const std::string& op_name, const LogicalBlobId& lbi) const; diff --git a/oneflow/core/graph/plan_task_graph.cpp b/oneflow/core/graph/plan_task_graph.cpp index 02079dbc82df8fec990457f905ddddb83b7969c3..95b9cbff48f68d90bf2b5a5c233297cd7023b25a 100644 --- a/oneflow/core/graph/plan_task_graph.cpp +++ b/oneflow/core/graph/plan_task_graph.cpp @@ -19,7 +19,6 @@ namespace oneflow { int64_t PlanTaskNode::chain_id() const { int64_t chain_id = task_proto_->task_set_info().chain_id(); - CHECK_NE(chain_id, -1); return chain_id; } diff --git a/oneflow/core/graph/sharable_mem_block_graph.cpp b/oneflow/core/graph/sharable_mem_block_graph.cpp deleted file mode 100644 index 26b4c9303ebdcdf44008ce7e001ea83053e5a7de..0000000000000000000000000000000000000000 --- a/oneflow/core/graph/sharable_mem_block_graph.cpp +++ /dev/null @@ -1,97 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/graph/sharable_mem_block_graph.h" -#include "oneflow/core/register/register_desc.h" -#include "oneflow/core/register/runtime_register_desc.h" -#include "oneflow/core/graph/inplace_regst_graph.h" - -namespace oneflow { - -namespace { - -bool IsConsumersAndProducerInSameChain(const RegstDescProto& regst_desc, - const PlanTaskGraph& plan_task_graph) { - auto ChainId4TaskId = [&](int64_t task_id) { - return plan_task_graph.TaskProto4TaskId(task_id)->task_set_info().chain_id(); - }; - int64_t producer_chain_id = ChainId4TaskId(regst_desc.producer_task_id()); - for (int64_t consumer_task_id : regst_desc.consumer_task_id()) { - if (ChainId4TaskId(consumer_task_id) != producer_chain_id) { return false; } - } - return true; -} -void ForEachInplacedRegstDescs( - const HashSet regst_desc, - const std::function&)>& Handler) { - InplaceRegstGraph inplace_gph(regst_desc); - inplace_gph.ForEachConnectedComponent([&](const HashSet& nodes) { - if (nodes.size() == 1) { return; } - HashSet regst_descs; - for (const auto* node : nodes) { CHECK(regst_descs.emplace(node->regst_desc()).second); } - Handler(regst_descs); - }); -} - -} // namespace - -SharableMemBlockNode::SharableMemBlockNode(int64_t chain_id, - const HashSet& regst_descs) - : chain_id_(chain_id), regst_descs_(regst_descs.begin(), regst_descs.end()) {} - -SharableMemBlockGraph::SharableMemBlockGraph( - const PlanTaskGraph& plan_task_gph, - const std::function& IsSharable) { - HashMap> chain_id2regst_descs; - for (const TaskProto& task : plan_task_gph.plan().task()) { - for (const auto& pair : task.produced_regst_desc()) { - if (IsConsumersAndProducerInSameChain(pair.second, plan_task_gph) - && IsSharable(pair.second)) { - CHECK(chain_id2regst_descs[task.task_set_info().chain_id()].emplace(&pair.second).second); - } - } - } - for (const auto& pair : chain_id2regst_descs) { - HashMap regst_desc2node; - for (const auto* regst_desc : pair.second) { - auto* node = new SharableMemBlockNode(pair.first, {regst_desc}); - AddAllocatedNode(node); - CHECK(regst_desc2node.emplace(regst_desc, node).second); - } - ForEachInplacedRegstDescs(pair.second, [&](const HashSet& regst_descs) { - auto* parent = new SharableMemBlockNode(pair.first, regst_descs); - AddAllocatedNode(parent); - for (const RegstDescProto* regst_desc : regst_descs) { - auto* edge = new SharableMemBlockEdge(); - AddAllocatedEdge(edge); - Connect(parent, edge, regst_desc2node.at(regst_desc)); - } - }); - } -} - -void SharableMemBlockGraph::ForEachSourceNodeGroup( - const std::function& GroupBy, - const std::function&)>& Handler) const { - HashMap> group_key2source_nodes; - for (const SharableMemBlockNode* source : source_nodes()) { - group_key2source_nodes[GroupBy(source)].emplace_back(source); - } - for (const auto& pair : group_key2source_nodes) { - if (pair.second.size() > 1) { Handler(pair.second); } - } -} - -} // namespace oneflow diff --git a/oneflow/core/graph/sharable_mem_block_graph.h b/oneflow/core/graph/sharable_mem_block_graph.h deleted file mode 100644 index 7f4b1c6186a2bff7126ccce1e8948152a34acb93..0000000000000000000000000000000000000000 --- a/oneflow/core/graph/sharable_mem_block_graph.h +++ /dev/null @@ -1,64 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_GRAPH_SHARABLE_MEM_BLOCK_GRAPH_H_ -#define ONEFLOW_CORE_GRAPH_SHARABLE_MEM_BLOCK_GRAPH_H_ - -#include "oneflow/core/graph/graph.h" -#include "oneflow/core/register/register_desc.pb.h" -#include "oneflow/core/graph/plan_task_graph.h" - -namespace oneflow { - -class SharableMemBlockEdge; - -class SharableMemBlockNode final : public Node { - public: - OF_DISALLOW_COPY_AND_MOVE(SharableMemBlockNode); - SharableMemBlockNode(int64_t chain_id, const HashSet& regst_descs); - - ~SharableMemBlockNode() = default; - - int64_t chain_id() const { return chain_id_; } - const std::vector& regst_descs() const { return regst_descs_; } - - private: - const int64_t chain_id_; - const std::vector regst_descs_; -}; - -class SharableMemBlockEdge final : public Edge { - public: - OF_DISALLOW_COPY_AND_MOVE(SharableMemBlockEdge); - SharableMemBlockEdge() = default; - ~SharableMemBlockEdge() = default; -}; - -class SharableMemBlockGraph final - : public Graph { - public: - OF_DISALLOW_COPY_AND_MOVE(SharableMemBlockGraph); - SharableMemBlockGraph(const PlanTaskGraph& plan_task_gph, - const std::function& IsSharable); - ~SharableMemBlockGraph() = default; - - void ForEachSourceNodeGroup( - const std::function& GroupBy, - const std::function&)>& Handler) const; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_GRAPH_SHARABLE_MEM_BLOCK_GRAPH_H_ diff --git a/oneflow/core/graph/straighten_nodes.cpp b/oneflow/core/graph/straighten_nodes.cpp index c6f27d73d152a3c62f640723c7e3e6f63c336cdf..1aa043ecd8362a3dd1b2797d00dc5b67b28efe72 100644 --- a/oneflow/core/graph/straighten_nodes.cpp +++ b/oneflow/core/graph/straighten_nodes.cpp @@ -13,33 +13,62 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include +#include "oneflow/core/common/util.h" +#include "oneflow/core/graph/compute_task_node.h" #include "oneflow/core/graph/straighten_nodes.h" +#include "oneflow/core/common/shape.h" #include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/graph/task_graph.h" #include "oneflow/core/graph/task_node.h" +#include "oneflow/core/job/job_conf.pb.h" #include "oneflow/core/job/job_desc.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/job/task.pb.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "oneflow/core/register/runtime_register_desc.h" namespace oneflow { namespace { enum TaskClassifier : int { - kWaitingTransfer = 0, - kWaitingComputation = 1, + kWaitingOverlapNode = 0, + kWaitingMainComputation = 1, kRunASAP = 2, kRunALAP = 3 }; +// deciding parameter +// The sorting order of nodes for the straighten algorithm +enum StraightenOrder : int { + kTributaryLayerAscend = 0, // small tributary layers go first + kDistanceToOverlapAscend = 1, // small minimum distance to overlap go first + kLayerAscend = 2, // first in first out + kMemoryIncrementAscend = 3, // small memory increment go first + kExceedTimeAscend = 4, // small exceed time go first + + kTributaryLayerDescend = 100, // large tributary layers go first + kDistanceToOverlapDescend = 101, // long distance to overlap go first + kLayerDescend = 102, // last in first out + kMemoryIncrementDescend = 103, // large memory increment go first + kExceedTimeDescend = 104, // large exceed time go first +}; + +// The difference between a descending order and its corresponding ascending order +const int kDiff4AscendDescend = 100; + class TopoStruct { public: TaskNode* node = nullptr; int32_t min_layer = -1; int32_t tributary_layer = -1; - bool on_mainstem = false; + bool on_trunk = false; int32_t counter = 0; - int32_t min_distance2transfer = -1; + int32_t min_distance2overlap = -1; + int64_t memory_increment = -1; TopoStruct* next_same_node = nullptr; + int32_t exceed_time = -1; // We can have some other nodes in it for example // SbpNode* node; // SbpEdge* node; @@ -50,21 +79,64 @@ class TopoStruct { void SpreadTributaryLayer(HashMap* task_node2topo_struct); - void SpreadMainstem(HashMap* task_node2topo_struct); + void SpreadTrunk(HashMap* task_node2topo_struct); + + // The minimum computation distance from the beginning of this op to the next overlap node + int32_t GetMinDistance2Overlap(HashMap* task_node2topo_struct); - // The minimum computation distance from the beginning of this op to the next transfer - int32_t GetMinDistance2Transfer(HashMap* task_node2topo_struct); + // Memory increment = (memory of out registers) - (memory of in registers) + void ComputeMeomoryIncrement(); + + // Exceed time = time of cpu - time of gpu + // For most operators, the execution time on gpu exceed the execution time on cpu. + // However, overlap is needed if time of cpu > time of gpu. + void ComputeExceedTime(); + + // TODO: We might design more deciding parameter and choose a right combination of them in the + // future. // deciding parameter - // i = 0: those with small tributary layers go first - // i = 1: those with small minimum distance to transfer go first - // i = 2: first in first out - // i = 3: those with large tributary layers go first - // i = 4: those with long distance to transfer go first - // i = 5: last in first out - int32_t GetDecidingParameter(int32_t i) const; + // kTributaryLayerAscend = 0, // small tributary layers go first + // kDistanceToOverlapAscend = 1, // small minimum distance to overlap go first + // kLayerAscend = 2, // first in first out + // kMemoryIncrementAscend = 3, // small memory increment go first + // kExceedTimeAscend = 4, // small exceed time go first + // kTributaryLayerDescend = 100, // large tributary layers go first + // kDistanceToOverlapDescend = 101, // long distance to overlap go first + // kLayerDescend = 102, // last in first out + // kMemoryIncrementDescend = 103, // large memory increment go first + // kExceedTimeDescend = 104, // large exceed time go first + int64_t GetDecidingParameter(StraightenOrder so) const; }; +StraightenAlgorithmTag sat; + +// NOTE: Leave these code for debugging in the future +// static std::vector decide_parameters({ParseIntegerFromEnv("Parameter0", 3), +// ParseIntegerFromEnv("Parameter1", 0), +// ParseIntegerFromEnv("Parameter2", 3)}); +// The best parameter set for saving time is {102, 100} +// The best parameter set for saving memory is {3, 0} +static std::vector decide_parameters; + +// SAT, a.k.a. Scholastic Aptitude Test, +// is the college admission test in the United States of America. +void InitDecideParameters(StraightenAlgorithmTag sat) { + decide_parameters.clear(); + if (sat == StraightenAlgorithmTag::kCompressMemory) { + decide_parameters.push_back(StraightenOrder::kMemoryIncrementAscend); + decide_parameters.push_back(StraightenOrder::kTributaryLayerAscend); + } else if (sat == StraightenAlgorithmTag::kOverlap4Transfer) { + decide_parameters.push_back(StraightenOrder::kLayerDescend); + decide_parameters.push_back(StraightenOrder::kTributaryLayerDescend); + } else { + // sat==StraightenAlgorithmTag::kOverlap4CpuGpu + decide_parameters.push_back(StraightenOrder::kExceedTimeDescend); + decide_parameters.push_back(StraightenOrder::kLayerDescend); + decide_parameters.push_back(StraightenOrder::kMemoryIncrementAscend); + } +} + // move the head from source to target void MoveFrontBetweenMaps(std::map& source, std::map& target) { @@ -91,6 +163,7 @@ bool ShouldRunASAP(TaskType task_type) { case TaskType::kAcc: // 0 case TaskType::kSourceTick: // 0 case TaskType::kAccTick: // 0 + case TaskType::kAccCtrlTick: // ? case TaskType::kCase: // 0 case TaskType::kEsac: // 0 case TaskType::kReentrantLock: return true; // 0 @@ -104,14 +177,13 @@ bool IsTransferNode(TaskType task_type) { switch (task_type) { // We mark the number of occurrences in bert case TaskType::kCollectiveBoxingGeneric: // 76 + case TaskType::kNcclSendRecvBoxing: // ? case TaskType::kCopyHd: // 27 case TaskType::kSliceBoxing: // 16 case TaskType::kCopyCommNet: // 12 case TaskType::kCollectiveBoxingPack: // 8 case TaskType::kCollectiveBoxingUnpack: // 8 case TaskType::kBoxingZeros: // 3 - case TaskType::kForeignInput: // 0 - case TaskType::kForeignOutput: // 0 case TaskType::kDistributeConcat: // 0 case TaskType::kDistributeSplit: // 0 case TaskType::kBoxingIdentity: // 0 @@ -121,14 +193,44 @@ bool IsTransferNode(TaskType task_type) { } } +// Some operators have longer time in cpu and less time in gpu. +// Running those operators without overlap would cause large gap during each iteration. +// For example, expand dims would not execute any kernel on gpu but still need 10us to execute some +// functions on cpu. +bool ShortGpuTime(const OperatorConf& op_conf) { + if (op_conf.has_variable_conf()) { + // Variable operators would not be run. They just create tensors. + // We do not visualize any execution in NVTX. (Even a tick operator has something in NVTX.) + return true; + } + if (op_conf.has_user_conf()) { + const auto& op_type_name = op_conf.user_conf().op_type_name(); + // They are sorted according to frequency of occurrences in stable diffusion + if (op_type_name == "expand_dims" // 90 + || op_type_name == "cast" // 16 + || op_type_name == "expand" // 2 + ) { + return true; + } + } + return false; +} + // Classifier for the set according to the task type TaskClassifier GetTaskClassifier(const TaskNode* node) { // Check task.pb.h for detail // They are sorted according to frequency of judgement // frequency of judgement = the number of occurrences / the times of judgement TaskType task_type = node->GetTaskType(); - if (task_type == TaskType::kNormalForward) { return TaskClassifier::kWaitingComputation; } - if (IsTransferNode(task_type)) { return TaskClassifier::kWaitingTransfer; } + if (task_type == TaskType::kNormalForward) { + const auto& op_conf = dynamic_cast(node)->op()->op_conf(); + if (sat == StraightenAlgorithmTag::kOverlap4CpuGpu && ShortGpuTime(op_conf)) { + return TaskClassifier::kWaitingOverlapNode; + } else { + return TaskClassifier::kWaitingMainComputation; + } + } + if (IsTransferNode(task_type)) { return TaskClassifier::kWaitingOverlapNode; } if (task_type == TaskType::kCallbackNotify) { return TaskClassifier::kRunALAP; } if (ShouldRunASAP(task_type)) { return TaskClassifier::kRunASAP; } CHECK(false) << "Unclassified or invalid task type (" << task_type << ") showing up"; @@ -136,7 +238,7 @@ TaskClassifier GetTaskClassifier(const TaskNode* node) { return TaskClassifier::kRunASAP; } -// Drop down the maximum layer with the minimum layer form consumer +// Drop down the maximum layer with the minimum layer from consumer void TopoStruct::DropTributaryLayer(int32_t upper_bound) { if (upper_bound < tributary_layer || tributary_layer < 0) { tributary_layer = upper_bound; } } @@ -146,7 +248,7 @@ void TopoStruct::DropTributaryLayer(int32_t upper_bound) { void TopoStruct::SpreadTributaryLayer(HashMap* task_node2topo_struct) { if (counter || min_layer <= 0) { return; } int32_t producer_max_lay = 0; - if (on_mainstem) { + if (on_trunk) { producer_max_lay = min_layer - 1; } else { // On a tributary, the operator could be run later. @@ -162,15 +264,15 @@ void TopoStruct::SpreadTributaryLayer(HashMap* task_node2 counter--; } -// Judge if this node is on the mainstem +// Judge if this node is on the trunk // If so, judge it for its producer/upstream nodes -void TopoStruct::SpreadMainstem(HashMap* task_node2topo_struct) { +void TopoStruct::SpreadTrunk(HashMap* task_node2topo_struct) { // Skip it if this node is already judged. - if (on_mainstem) { return; } + if (on_trunk) { return; } CHECK_GE(min_layer, 0) << "TopoStruct not initialized!"; - on_mainstem = true; - // If I am in the mainstem, then all the children with (min_layer >= my layer id - 1) would be - // considered as in the mainstem + on_trunk = true; + // If I am in the trunk, then all the children with (min_layer >= my layer id - 1) would be + // considered as in the trunk node->ForEachNodeOnInEdge([&](TaskNode* in) { auto& topo_struct_in = task_node2topo_struct->at(in); if (topo_struct_in.min_layer == min_layer - 1) { @@ -179,73 +281,131 @@ void TopoStruct::SpreadMainstem(HashMap* task_node2topo_s }); } -// The minimum computation distance from the beginning of this op to the next transfer -int32_t TopoStruct::GetMinDistance2Transfer(HashMap* task_node2topo_struct) { - if (min_distance2transfer >= 0) { return min_distance2transfer; } - // if this node is a transfer node - if (IsTransferNode(node->GetTaskType())) { - min_distance2transfer = 0; - return min_distance2transfer; +// The minimum computation distance from the beginning of this op to the next overlap +int32_t TopoStruct::GetMinDistance2Overlap(HashMap* task_node2topo_struct) { + if (min_distance2overlap >= 0) { return min_distance2overlap; } + // if this node should be overlapped by main computation nodes + if (GetTaskClassifier(node) == TaskClassifier::kWaitingOverlapNode) { + min_distance2overlap = 0; + return min_distance2overlap; } // Otherwise, initialize it with a large number // Well, the total number in the task graph is large enough - min_distance2transfer = task_node2topo_struct->size(); + min_distance2overlap = task_node2topo_struct->size(); node->ForEachNodeOnOutEdge([&](TaskNode* out) { - min_distance2transfer = - std::min(min_distance2transfer, - task_node2topo_struct->at(out).GetMinDistance2Transfer(task_node2topo_struct)); + min_distance2overlap = + std::min(min_distance2overlap, + task_node2topo_struct->at(out).GetMinDistance2Overlap(task_node2topo_struct)); }); - ++min_distance2transfer; - return min_distance2transfer; + ++min_distance2overlap; + return min_distance2overlap; +} + +// Memory increment = (memory of out registers) - (memory of in registers) +void TopoStruct::ComputeMeomoryIncrement() { + if (memory_increment < 0) { + memory_increment = 0; + for (const auto& produced_register : node->produced_regsts()) { + if (produced_register.second->enable_reuse_mem()) { + RegstDescProto temp_proto; + produced_register.second->ToProto(&temp_proto); + memory_increment += RtRegstDesc(temp_proto).TotalMainByteSize4AllRegst(); + } + } + for (const auto& consumed_register_list : node->consumed_regsts()) { + for (const auto& consumed_register : consumed_register_list.second) { + if (consumed_register->enable_reuse_mem()) { + RegstDescProto temp_proto; + consumed_register->ToProto(&temp_proto); + memory_increment -= RtRegstDesc(temp_proto).TotalMainByteSize4AllRegst() + / consumed_register->consumers().size(); + } + } + } + } +} + +// Exceed time = time of cpu - time of gpu +void TopoStruct::ComputeExceedTime() { + if (node->GetTaskType() == TaskType::kNormalForward + && ShortGpuTime(dynamic_cast(node)->op()->op_conf())) { + exceed_time = 1; + } else { + exceed_time = 0; + } } // deciding parameter -// i = 0: those with small tributary layers go first -// i = 1: those with small minimum distance to transfer go first -// i = 2: first in first out -// i = 3: those with large tributary layers go first -// i = 4: those with long distance to transfer go first -// i = 5: last in first out -int32_t TopoStruct::GetDecidingParameter(int32_t i) const { - int32_t sign = 1; - if (i >= 3) { - i -= 3; +// kTributaryLayerAscend = 0, // small tributary layers go first +// kDistanceToOverlapAscend = 1, // small minimum distance to overlap go first +// kLayerAscend = 2, // first in first out +// kMemoryIncrementAscend = 3, // small memory increment go first +// kExceedTimeAscend = 4, // small exceed time go first +// kTributaryLayerDescend = 100, // large tributary layers go first +// kDistanceToOverlapDescend = 101, // long distance to overlap go first +// kLayerDescend = 102, // last in first out +// kMemoryIncrementDescend = 103, // large memory increment go first +// kExceedTimeDescend = 104, // large exceed time go first +int64_t TopoStruct::GetDecidingParameter(StraightenOrder so) const { + int64_t sign = 1; + if (so >= kDiff4AscendDescend) { + so = StraightenOrder(int(so) - kDiff4AscendDescend); sign = -1; } - switch (i) { - case 0: return sign * tributary_layer; - case 1: return sign * min_distance2transfer; - case 2: return sign * min_layer; + switch (so) { + case StraightenOrder::kTributaryLayerAscend: return sign * tributary_layer; + case StraightenOrder::kDistanceToOverlapAscend: return sign * min_distance2overlap; + case StraightenOrder::kLayerAscend: return sign * min_layer; + case StraightenOrder::kMemoryIncrementAscend: return sign * memory_increment; + case StraightenOrder::kExceedTimeAscend: return sign * exceed_time; + default: return 0; } - return 0; } -// Find the mainstem of the task graph, then reduce the wait time for tributaries -void FindMainstem(HashMap* task_node2topo_struct) { +// Find the trunk of the task graph, then reduce the wait time for tributaries +void FindTrunk(HashMap* task_node2topo_struct) { // Find the maximum layer number int32_t max_min_layer = -1; for (const auto& pair : *task_node2topo_struct) { if (max_min_layer < pair.second.min_layer) { max_min_layer = pair.second.min_layer; } } - // All the nodes with min_layer>=mainstem_end_id would be considered as mainstem nodes - // The last 5 layers would be considered as in mainstem anyway. - int32_t mainstem_end_id = max_min_layer - 4; + // All the nodes with min_layer>=trunk_end_id would be considered as trunk nodes + // The last 5 layers would be considered as in trunk anyway. + int32_t trunk_end_id = max_min_layer - 4; for (auto& pair : *task_node2topo_struct) { auto& topo_struct = pair.second; // Initialize the counter and Tributary Layer topo_struct.counter = pair.first->out_edges().size(); topo_struct.tributary_layer = max_min_layer; - // Find out all the nodes on the mainstem. - if (topo_struct.min_layer >= mainstem_end_id) { - topo_struct.SpreadMainstem(task_node2topo_struct); - } + // Find out all the nodes on the trunk. + if (topo_struct.min_layer >= trunk_end_id) { topo_struct.SpreadTrunk(task_node2topo_struct); } } for (auto& pair : *task_node2topo_struct) { // Compute maximum layer for tributaries pair.second.SpreadTributaryLayer(task_node2topo_struct); - // Set the min_distance2transfer for each topological structure - pair.second.GetMinDistance2Transfer(task_node2topo_struct); + // Set the min_distance2overlap for each topological structure + pair.second.GetMinDistance2Overlap(task_node2topo_struct); + } +} + +void UpdateSat(const HashMap& task_node2topo_struct) { + sat = GlobalJobDesc().job_conf().straighten_algorithm_tag_in_task_graph(); + if (sat == StraightenAlgorithmTag::kOverlap4CpuGpu) { + // If not cpu nodes, then the overlap strategy between cpu and gpu might consume large memory + bool exist_cpu_nodes = false; + for (const auto& pair : task_node2topo_struct) { + // Found a cpu node + if (pair.second.exceed_time == 1) { + exist_cpu_nodes = true; + break; + } + } + if (!exist_cpu_nodes) { + // Switch to the compress memory strategy, the default one + // Since the overlap strategy for transfer might not be working on 1n1d. + sat = StraightenAlgorithmTag::kCompressMemory; + } } } @@ -265,6 +425,8 @@ void StraightenNodes(TaskGraph* task_graph, std::vector* ordered_task task_graph->TopoForEachNode([&](TaskNode* node) { auto& topo_struct = task_node2topo_struct[node]; topo_struct.node = node; + topo_struct.ComputeMeomoryIncrement(); + topo_struct.ComputeExceedTime(); if (node->in_edges().empty()) { topo_struct.min_layer = 0; } else { @@ -322,23 +484,21 @@ void StraightenNodes(TaskGraph* task_graph, std::vector* ordered_task }); // Generate other parameters in the topological data structure - FindMainstem(&task_node2topo_struct); + FindTrunk(&task_node2topo_struct); - VLOG(3) << "Straightening order: " << 5 << ", " << 3; + // Update sat, since sat might be changed in previous jobs + UpdateSat(task_node2topo_struct); + // Decide which node should run first + InitDecideParameters(sat); + VLOG(3) << "Straightening order: "; + for (int32_t decide_parameter : decide_parameters) { VLOG(3) << decide_parameter; } // Order in the waiting sets - // Decide which node should run first struct comp { bool operator()(const TopoStruct* a, const TopoStruct* b) const { - // NOTE: Leave these code for debugging in the future - // static std::vector decide_parameters({ParseIntegerFromEnv("Parameter0", 5), - // ParseIntegerFromEnv("Parameter1", 3), - // ParseIntegerFromEnv("Parameter2", 5)}); - // The best parameter set is {5, 3} - static std::vector decide_parameters({5, 3}); - for (int32_t decide_parameter : decide_parameters) { - int32_t decide_parameter_a = a->GetDecidingParameter(decide_parameter); - int32_t decide_parameter_b = b->GetDecidingParameter(decide_parameter); + for (auto decide_parameter : decide_parameters) { + auto decide_parameter_a = a->GetDecidingParameter(decide_parameter); + auto decide_parameter_b = b->GetDecidingParameter(decide_parameter); if (decide_parameter_a != decide_parameter_b) { return decide_parameter_a < decide_parameter_b; } @@ -348,10 +508,15 @@ void StraightenNodes(TaskGraph* task_graph, std::vector* ordered_task }; // Classify sets for the task nodes - // std::set waiting_transfer; // 0, TaskClassifier::kWaitingTransfer - // std::set waiting_computation; // 1, TaskClassifier::kWaitingComputation - // std::set run_asap; // 2, TaskClassifier::kRunASAP , run as soon as possible - // std::set run_alap; // 3, TaskClassifier::kRunALAP , run as late as possible + // 0, TaskClassifier::kWaitingOverlapNode + // It contains transfer nodes, and those with less time in gpu if request. + // std::set waiting_overlap_node; + // 1, TaskClassifier::kWaitingMainComputation + // std::set waiting_main_computation; + // 2, TaskClassifier::kRunASAP , run as soon as possible + // std::set run_asap; + // 3, TaskClassifier::kRunALAP , run as late as possible + // std::set run_alap; const int32_t num_classifier = 4; std::vector> waiting_lists(num_classifier); @@ -445,8 +610,8 @@ void StraightenNodes(TaskGraph* task_graph, std::vector* ordered_task // straightening while (true) { if (waiting_lists[TaskClassifier::kRunASAP].empty()) { - if (waiting_lists[TaskClassifier::kWaitingTransfer].empty()) { - if (waiting_lists[TaskClassifier::kWaitingComputation].empty()) { + if (waiting_lists[TaskClassifier::kWaitingOverlapNode].empty()) { + if (waiting_lists[TaskClassifier::kWaitingMainComputation].empty()) { if (waiting_lists[TaskClassifier::kRunALAP].empty()) { // All the waiting lists are empty break; @@ -456,25 +621,25 @@ void StraightenNodes(TaskGraph* task_graph, std::vector* ordered_task } } else { // Execute one computation node - execute(TaskClassifier::kWaitingComputation, 1); + execute(TaskClassifier::kWaitingMainComputation, 1); } } else { int32_t computation_num = - std::min(int32_t(waiting_lists[TaskClassifier::kWaitingComputation].size() - / (waiting_lists[TaskClassifier::kWaitingTransfer].size())), - remain_task_nums[TaskClassifier::kWaitingComputation] - / remain_task_nums[TaskClassifier::kWaitingTransfer]); - // Holding the transfer - std::vector transfer_execution_list; - move2execution_list(waiting_lists[TaskClassifier::kWaitingTransfer], - transfer_execution_list); - remain_task_nums[TaskClassifier::kWaitingTransfer] -= transfer_execution_list.size(); - for (auto* transfer_node : transfer_execution_list) { SetOrderInGraph(transfer_node); } - // Overlap transfer with computation - execute(TaskClassifier::kWaitingComputation, computation_num); - - // Release the transfer - for (auto* transfer_node : transfer_execution_list) { finish_execution(transfer_node); } + std::min(int32_t(waiting_lists[TaskClassifier::kWaitingMainComputation].size() + / (waiting_lists[TaskClassifier::kWaitingOverlapNode].size())), + remain_task_nums[TaskClassifier::kWaitingMainComputation] + / remain_task_nums[TaskClassifier::kWaitingOverlapNode]); + // Holding the node to be overlapped + std::vector overlap_execution_list; + move2execution_list(waiting_lists[TaskClassifier::kWaitingOverlapNode], + overlap_execution_list); + remain_task_nums[TaskClassifier::kWaitingOverlapNode] -= overlap_execution_list.size(); + for (auto* overlap_node : overlap_execution_list) { SetOrderInGraph(overlap_node); } + // Overlap the node with computation from the trunk + execute(TaskClassifier::kWaitingMainComputation, computation_num); + + // Release the overlap node + for (auto* overlap_node : overlap_execution_list) { finish_execution(overlap_node); } } } else { execute(TaskClassifier::kRunASAP, waiting_lists[TaskClassifier::kRunASAP].size()); diff --git a/oneflow/core/graph/task_graph.cpp b/oneflow/core/graph/task_graph.cpp index 70a7cd34343bebfd85eb8754e0acbf1fddc47152..8cf05e8871e2129fc8209bf730aa48a7b9c54a49 100644 --- a/oneflow/core/graph/task_graph.cpp +++ b/oneflow/core/graph/task_graph.cpp @@ -15,7 +15,10 @@ limitations under the License. */ #include "oneflow/core/graph/task_graph.h" #include "oneflow/core/common/util.h" +#include "oneflow/core/common/env_var/debug_mode.h" #include "oneflow/core/graph/inplace_lbi_graph.h" +#include "oneflow/core/job/job_conf.pb.h" +#include "oneflow/core/job/job_desc.h" #include "oneflow/core/register/blob_desc.h" #include "oneflow/core/job/global_for.h" #include "oneflow/core/operator/variable_op.h" @@ -31,9 +34,14 @@ limitations under the License. #include "oneflow/core/graph/task_stream_index_manager.h" #include "oneflow/core/ep/include/primitive/memcpy.h" #include "oneflow/core/graph/straighten_nodes.h" +#include "oneflow/core/register/runtime_register_desc.h" +#include "oneflow/core/common/env_var/env_var.h" namespace oneflow { +// TODO(Chengcheng): default false. +DEFINE_ENV_BOOL(ONEFLOW_ENABLE_OUTDATED_OPT_FW_CHAIN_MERGE, true); + namespace { bool IsMemcpyPrimitiveSupported(DeviceType device_type, ep::primitive::MemcpyKind kind) { @@ -57,6 +65,14 @@ bool IsConnectToTickOp(const TaskNode* node) { return false; } +bool IsSubsetTickOpConf(const OperatorConf& op_conf) { + return op_conf.has_src_subset_tick_conf() || op_conf.has_dst_subset_tick_conf(); +} + +bool IsTickOpConf(const OperatorConf& conf) { + return IsClassRegistered(conf.op_type_case()); +} + std::string GetOpConfCalculationPassName(const OperatorConf& op_conf) { CHECK(op_conf.has_scope_symbol_id()); int64_t scope_symbol_id = op_conf.scope_symbol_id(); @@ -79,14 +95,6 @@ bool IsOptimizerPassOp(const Operator* op) { return GetOpConfCalculationPassName(op->op_conf()) == kOptimizerPass; } -bool IsSubsetTickOpConf(const OperatorConf& op_conf) { - return op_conf.has_src_subset_tick_conf() || op_conf.has_dst_subset_tick_conf(); -} - -bool IsTickOpConf(const OperatorConf& conf) { - return IsClassRegistered(conf.op_type_case()); -} - bool IsSpecialOpNotConsiderMergeInChain(const Operator* op) { const OperatorConf& op_conf = op->op_conf(); if (op_conf.has_variable_conf() || op_conf.has_tick_conf() || op_conf.has_device_tick_conf() @@ -104,7 +112,7 @@ bool IsSpecialOpNotConsiderMergeInChain(const Operator* op) { } // NOTE(chengcheng): ONLY nccl_use_compute_stream = false will exclude optimizer pass ops if (!Singleton::Get()->nccl_use_compute_stream() - && IsOptimizerPassOp(op)) { + && IsOptimizerPassOp(op) && EnvBool()) { return true; } return false; @@ -135,8 +143,8 @@ std::shared_ptr GetTaskNodeTimeShape(const TaskNode* node) { } void TraverseConnectedSubGraphMergeInThisChain(TaskNode* this_node, const int64_t this_chain_id) { - CHECK_NE(this_chain_id, -1); - CHECK_EQ(this_node->chain_id(), -1); + CHECK(IsValidChainId(this_chain_id)); + CHECK(!IsValidChainId(this_node->chain_id())); // bfs search all node can be merged in this chain std::shared_ptr seed_time_shape = GetTaskNodeTimeShape(this_node); HashSet visited_nodes; @@ -147,14 +155,14 @@ void TraverseConnectedSubGraphMergeInThisChain(TaskNode* this_node, const int64_ TaskNode* cur_node = queued_nodes.front(); queued_nodes.pop(); - CHECK_EQ(cur_node->chain_id(), -1); + CHECK(!IsValidChainId(cur_node->chain_id())); cur_node->set_chain_id(this_chain_id); cur_node->ForEachNodeOnInOutDataEdge([&](TaskNode* next_node) { if (visited_nodes.find(next_node) == visited_nodes.end() && CanBeMergedInChain(next_node) && this_node->thrd_id() == next_node->thrd_id() && (*GetTaskNodeTimeShape(next_node)) == (*seed_time_shape)) { - if (next_node->chain_id() == -1) { + if (!IsValidChainId(next_node->chain_id())) { queued_nodes.push(next_node); visited_nodes.insert(next_node); } else { @@ -196,7 +204,8 @@ MakePredicatorIsLbiAllConsumersReachable( IsOpNameDataOrCtrlReachable) { auto IsDataOrCtrlReachable = [IsOpNameDataOrCtrlReachable](const TaskNode* src_node, const TaskNode* dst_node) -> bool { - if (src_node->chain_id() == dst_node->chain_id() + if (IsValidChainId(src_node->chain_id()) && IsValidChainId(dst_node->chain_id()) + && src_node->chain_id() == dst_node->chain_id() && src_node->order_in_graph() <= dst_node->order_in_graph()) { return true; } @@ -412,7 +421,17 @@ void ForEachOpGraphNecessaryCtrlEdge( if (dst_time_shape == nullptr) { dst_time_shape = CHECK_JUST(dst->op().GetOpTimeShape()).get(); } - CHECK_EQ(src_time_shape->elem_cnt(), dst_time_shape->elem_cnt()); + if (src_time_shape->elem_cnt() != dst_time_shape->elem_cnt()) { + // NOTE(chengcheng): acc / pack op node can be merged and add ctrl edge. + CHECK(src->op().op_conf().has_user_conf()); + const std::string& op_type_name = src->op().op_conf().user_conf().op_type_name(); + CHECK(op_type_name == "acc" || op_type_name == "pack"); + const Shape* src_input_time_shape = + CHECK_JUST(src->op().GetInputBlobFastestTimeShape()).get(); + CHECK_EQ(src_input_time_shape->elem_cnt(), dst_time_shape->elem_cnt()); + } else { + CHECK_EQ(src_time_shape->elem_cnt(), dst_time_shape->elem_cnt()); + } Handler(src, dst); } } @@ -421,7 +440,7 @@ void ForEachOpGraphNecessaryCtrlEdge( } // namespace -TaskGraph::TaskGraph(bool enable_straighten_algorithm) { +TaskGraph::TaskGraph() { OpGraph* op_graph = Singleton::Get(); sub_tsk_gph_builder_ctx_.reset(new SubTskGphBuilderCtx(this)); boxing_logger_ = CreateBoxingLogger(); @@ -452,11 +471,6 @@ TaskGraph::TaskGraph(bool enable_straighten_algorithm) { } }); - if (enable_straighten_algorithm && GlobalProcessCtx::WorldSize() > 1) { - StraightenNodes(this, &ordered_task_nodes_); - } else { - SetOrderInGraphForEachNode(); - } if (Singleton::Get()->enable_debug_mode()) { ToDotWithAutoFilePath(); } } @@ -493,7 +507,7 @@ TaskNode* TaskGraph::GetProxyNode(TaskNode* src_node, const LogicalBlobId& lbi, // src must be not on the cpu mem zone, copy d2h first CHECK(IsMemcpyDtoHSupported(src_mem_zone_id.device_type())); CopyHdTaskNode* copy_task = NewNode(); - copy_task->Init(CopyHdOpConf::D2H, src_mem_zone_id, lbi); + copy_task->Init(CopyHdType::D2H, src_mem_zone_id, lbi); Connect(src_node, NewTaskEdgeWithLbi(lbi), copy_task); proxy2node[key] = copy_task; return copy_task; @@ -513,7 +527,7 @@ TaskNode* TaskGraph::GetProxyNode(TaskNode* src_node, const LogicalBlobId& lbi, GetProxyNode(src_node, lbi, GetNodeCPUMemZoneId(dst_mem_zone_id.rank())); CHECK(IsMemcpyHtoDSupported(dst_mem_zone_id.device_type())); CopyHdTaskNode* copy_task = NewNode(); - copy_task->Init(CopyHdOpConf::H2D, dst_mem_zone_id, lbi); + copy_task->Init(CopyHdType::H2D, dst_mem_zone_id, lbi); Connect(proxy_on_dst_host, NewTaskEdgeWithLbi(lbi), copy_task); proxy2node[key] = copy_task; return copy_task; @@ -554,7 +568,12 @@ void TaskGraph::RemoveEmptyRegsts() { } void TaskGraph::MergeChainAndAddOrderingCtrlEdgeInSameChain() { - MergeChain(); + if (EnableLogicalChain()) { + MergeChainByLogicalChainId(); + } else { + // TODO(chengcheng): erase old chain version in the future. + MergeChainByPhysicalTaskGraph(); + } BuildCtrlRegstDescInSameChain(); } @@ -568,13 +587,12 @@ void TaskGraph::SetOrderInGraphForEachNode() { TopoForEachNode(SetOrderInGraph); } -void TaskGraph::MergeChain() { +void TaskGraph::MergeChainByPhysicalTaskGraph() { int64_t chain_id = 0; for (auto* this_node : ordered_task_nodes_) { // skip if this node has been set in a chain. - if (this_node->chain_id() != -1) { continue; } + if (IsValidChainId(this_node->chain_id())) { continue; } - CHECK_EQ(this_node->chain_id(), -1); if (CanBeMergedInChain(this_node)) { TraverseConnectedSubGraphMergeInThisChain(this_node, chain_id); } else { @@ -583,17 +601,32 @@ void TaskGraph::MergeChain() { ++chain_id; } - for (auto* node : ordered_task_nodes_) { CHECK_NE(node->chain_id(), -1); } + for (auto* node : ordered_task_nodes_) { CHECK(IsValidChainId(node->chain_id())); } +} + +void TaskGraph::MergeChainByLogicalChainId() { + for (TaskNode* this_node : ordered_task_nodes_) { + CompTaskNode* comp_node = dynamic_cast(this_node); + if (!comp_node) { continue; } + const int64_t logical_chain_id = comp_node->op()->op_conf().logical_chain_id(); + if (IsValidChainId(logical_chain_id)) { this_node->set_chain_id(logical_chain_id); } + } } void TaskGraph::BuildCtrlRegstDescInSameChain() { - HashMap chain_id2node; + auto GenPhysicalChainId = [](TaskNode* node) { + // NOTE(chengcheng): different rank cannot use same chain id for bad ctrl link. + return (node->chain_id() << 31) | (node->machine_id()); + }; + HashMap physical_chain_id2node; for (auto* node : ordered_task_nodes_) { if (IsConnectToTickOp(node)) { continue; } - int64_t chain_id = node->chain_id(); - auto iter = chain_id2node.find(chain_id); - if (iter == chain_id2node.end()) { - CHECK(chain_id2node.emplace(chain_id, node).second); + // NOTE(chengcheng): skip invalid chain id + if (!IsValidChainId(node->chain_id())) { continue; } + int64_t physical_chain_id = GenPhysicalChainId(node); + auto iter = physical_chain_id2node.find(physical_chain_id); + if (iter == physical_chain_id2node.end()) { + CHECK(physical_chain_id2node.emplace(physical_chain_id, node).second); } else { TaskNode* src_node = iter->second; TaskNode* dst_node = node; @@ -727,6 +760,12 @@ DEFINE_BLD_SUB_TASK_GRAPH_METHOD(BldSubTskGphByBoxing) { const ParallelDesc& src_parallel_desc = src_op_node->parallel_desc(); const ParallelDesc& dst_parallel_desc = dst_op_node->parallel_desc(); const BlobDesc& blob_desc = src_op_node->LogicalBlobDesc4Lbi(lbi); + VLOG(3) << "src op: " << src_op_node->op().op_name() + << " dst op: " << dst_op_node->op().op_name() + << " src_parallel_conf: " << src_parallel_desc.parallel_conf().DebugString() + << " dst parallel conf: " << dst_parallel_desc.parallel_conf().DebugString() + << " src_nd_sbp " << src_nd_sbp.DebugString() << " dst nd_sbp " + << dst_nd_sbp.DebugString(); auto status = CHECK_JUST(hierarchical_sub_tsk_gph_builder_->Build( sub_tsk_gph_builder_ctx_.get(), in_nodes, &out_nodes, &sorted_ctrl_tasks, src_parallel_desc, dst_parallel_desc, lbi, blob_desc, src_nd_sbp, dst_nd_sbp, @@ -854,4 +893,18 @@ void TaskGraph::BuildTaskPath(TaskNode* src_node, TaskNode* dst_node, const Logi ConnectWithLbi(proxy_node, dst_node, lbi); } +void TaskGraph::DecideExecutionOrder() { + // For one machine with no transfer available, the straighten algorithm for overlaps consume a lot + // of memory + StraightenAlgorithmTag straighten_algorithm_tag = + GlobalJobDesc().job_conf().straighten_algorithm_tag_in_task_graph(); + if (straighten_algorithm_tag == StraightenAlgorithmTag::kDisable + || (straighten_algorithm_tag == StraightenAlgorithmTag::kOverlap4Transfer + && GlobalProcessCtx::WorldSize() == 1)) { + SetOrderInGraphForEachNode(); + } else { + StraightenNodes(this, &ordered_task_nodes_); + } +} + } // namespace oneflow diff --git a/oneflow/core/graph/task_graph.h b/oneflow/core/graph/task_graph.h index cddf049bccde3f2f90620de8f85a8d73d64b65a7..69643213aa74458916d8b3af41770db590a47243 100644 --- a/oneflow/core/graph/task_graph.h +++ b/oneflow/core/graph/task_graph.h @@ -43,11 +43,12 @@ class TaskGraph final : public Graph { OF_DISALLOW_COPY_AND_MOVE(TaskGraph); ~TaskGraph() override; - explicit TaskGraph(bool enable_straighten_algorithm); + explicit TaskGraph(); const char* TypeName() const override { return "TaskGraph"; } void RemoveEmptyRegsts(); void MergeChainAndAddOrderingCtrlEdgeInSameChain(); + void DecideExecutionOrder(); void EnableInplaceMemSharing(const std::function& IsOpNameDataOrCtrlReachable); @@ -81,7 +82,8 @@ class TaskGraph final : public Graph { const std::vector& dst_task_nodes); void SetOrderInGraphForEachNode(); - void MergeChain(); + void MergeChainByPhysicalTaskGraph(); + void MergeChainByLogicalChainId(); void BuildCtrlRegstDescInSameChain(); // inplace @@ -117,8 +119,7 @@ class TaskGraph final : public Graph { struct Hasher { inline size_t operator()(const ProxyKey& key) const { - return std::hash{}(key.src_node) ^ std::hash{}(key.lbi) - ^ key.dst_mem_zone_id.hash(); + return Hash(key.src_node, key.lbi, key.dst_mem_zone_id.hash()); } }; }; diff --git a/oneflow/core/graph/task_node.cpp b/oneflow/core/graph/task_node.cpp index a918b39290b0c5809a3557f3429c04083e2cf8df..1ea81b6c39eba8882b95fcb531920f50af828253 100644 --- a/oneflow/core/graph/task_node.cpp +++ b/oneflow/core/graph/task_node.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/graph/task_node.h" #include "oneflow/core/job/id_manager.h" +#include "oneflow/core/memory/memory_case_util.h" namespace oneflow { @@ -83,7 +84,7 @@ void TaskNode::set_thrd_id(int64_t val) { } void TaskNode::set_chain_id(int64_t val) { - CHECK_EQ(chain_id_, -1); + CHECK(!IsValidChainId(chain_id_)); chain_id_ = val; } @@ -203,7 +204,6 @@ bool TaskNode::IsMeaningLess() { return produced_regsts_.empty() && consumed_reg void TaskNode::ToProto(TaskProto* task_proto) const { // Step1: process some scalar items. - CHECK_NE(chain_id_, -1); task_proto->set_task_type(GetTaskType()); task_proto->set_machine_id(machine_id_); task_proto->set_thrd_id(thrd_id_); @@ -308,20 +308,19 @@ void TaskNode::InitProducedRegstMemCase(RegstDesc* regst) { } void TaskNode::InitProducedRegstMemCase(MemoryCase* mem_case) { - if (device_type() == DeviceType::kCPU) { - mem_case->mutable_host_mem(); - } else if (device_type() == DeviceType::kCUDA) { - mem_case->mutable_device_cuda_mem()->set_device_id(stream_id().device_id().device_index()); - } else { - UNIMPLEMENTED(); - } + mem_case->set_device_type(device_type()); + mem_case->set_device_id(stream_id().device_id().device_index()); } void TaskNode::PinConsumedRegstMemCase(MemoryCase* mem_case) { - if (mem_case->has_host_mem() && device_type() == DeviceType::kCUDA) { - mem_case->mutable_host_mem()->mutable_cuda_pinned_mem()->set_device_id( - stream_id().device_id().device_index()); - } + // When a node located on non-cpu device consumes a cpu regst, + // the regst memory should be pinned on host memory (locked page memory). + // When the regst is not on host, skip pinning + if (!memory::IsHostMem(*mem_case)) { return; } + // When the node is located on host, skip pinning + if (device_type() == DeviceType::kCPU) { return; } + mem_case->set_pinned_device_type(device_type()); + mem_case->set_pinned_device_id(stream_id().device_id().device_index()); } void TaskNode::ConsumeRegst(const std::string& name) { diff --git a/oneflow/core/graph/task_node.h b/oneflow/core/graph/task_node.h index f35db587bf280cb536b47e304fd1492521bea654..c25e6261f9660c63bb44518252aee1667293a460 100644 --- a/oneflow/core/graph/task_node.h +++ b/oneflow/core/graph/task_node.h @@ -40,6 +40,8 @@ RegstDescProto* FindOrCreateProducedCtrlRegstDesc(TaskProto* task_proto, RegstDescIdSet* FindOrCreateConsumedCtrlRegstDescIdSet(TaskProto* task_proto, const std::string& regst_desc_name); +bool inline IsValidChainId(int64_t val) { return val >= 0; } + class TaskEdge; class TaskNode : public Node { diff --git a/oneflow/core/graph_impl/acc_ctrl_tick_compute_task_node.cpp b/oneflow/core/graph_impl/acc_ctrl_tick_compute_task_node.cpp new file mode 100644 index 0000000000000000000000000000000000000000..adf7b2b89d17a3513ab338c0264b631259cee8d7 --- /dev/null +++ b/oneflow/core/graph_impl/acc_ctrl_tick_compute_task_node.cpp @@ -0,0 +1,57 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/graph/compute_task_node.h" +#include "oneflow/core/graph/task_stream_index_manager.h" + +namespace oneflow { + +class AccCtrlTickCompTaskNode final : public CompTaskNode { + public: + OF_DISALLOW_COPY_AND_MOVE(AccCtrlTickCompTaskNode); + AccCtrlTickCompTaskNode() = default; + ~AccCtrlTickCompTaskNode() = default; + TaskType GetTaskType() const override { return TaskType::kAccCtrlTick; } + void ProduceAllRegstsAndBindEdges() override; + void ConsumeAllRegsts() override; + void BuildExecGphAndRegst() override; +}; + +void AccCtrlTickCompTaskNode::ProduceAllRegstsAndBindEdges() { + std::shared_ptr regst = ProduceRegst("out", false); + ForEachOutDataEdge([&](TaskEdge* edge) { edge->AddRegst("out", regst); }); +} + +void AccCtrlTickCompTaskNode::ConsumeAllRegsts() { + ConsumeRegst("in", SoleInDataEdge()->GetSoleRegst()); +} + +void AccCtrlTickCompTaskNode::BuildExecGphAndRegst() { + std::shared_ptr in_regst = GetSoleConsumedRegst("in"); + std::shared_ptr out_regst = GetProducedRegst("out"); + std::shared_ptr op = this->op(); + ExecNode* exec_node = mut_exec_gph().NewNode(); + exec_node->mut_op() = op; + exec_node->BindBnWithRegst(op->SoleIbn(), in_regst); + out_regst->AddLbi(op->BnInOp2Lbi(op->SoleObn())); + exec_node->BindBnWithRegst(op->SoleObn(), out_regst); + exec_node->InferBlobDescs(parallel_ctx()); +} + +REGISTER_COMP_TASK_STREAM_INDEX_GETTER(TaskType::kAccCtrlTick); + +REGISTER_USER_OP_COMP_TASK_NODE_TYPE("acc_ctrl_tick", AccCtrlTickCompTaskNode); + +} // namespace oneflow diff --git a/oneflow/core/graph_impl/decode_h2d_compute_task_node.cpp b/oneflow/core/graph_impl/decode_h2d_compute_task_node.cpp index d3dd3cac3205fd7f82f7ef2cc7a806ac67206008..e24b67bd309137f0d63467b267d85c01be0b9a47 100644 --- a/oneflow/core/graph_impl/decode_h2d_compute_task_node.cpp +++ b/oneflow/core/graph_impl/decode_h2d_compute_task_node.cpp @@ -39,7 +39,8 @@ void DecodeH2DCompTaskNode::ConsumeAllRegsts() { } void DecodeH2DCompTaskNode::ProduceAllRegstsAndBindEdges() { - std::shared_ptr out_regst = ProduceRegst("out", false, 2, 2); + auto regst_num = ParseIntegerFromEnv("ONEFLOW_DECODE_H2D_REGST_NUM", 2); + std::shared_ptr out_regst = ProduceRegst("out", false, regst_num, regst_num); ForEachOutDataEdge([&](TaskEdge* edge) { edge->AddRegst("out", out_regst); }); ProduceRegst("tmp", false); } @@ -56,18 +57,8 @@ void DecodeH2DCompTaskNode::BuildExecGphAndRegst() { node->InferBlobDescs(parallel_ctx()); } -#ifdef WITH_CUDA - -REGISTER_NAMED_TASK_STREAM_INDEX_GETTER(DeviceType::kCUDA, TaskType::kDecodeH2D, "DECODE_H2D") - -#endif - -#ifdef WITH_ROCM - REGISTER_NAMED_TASK_STREAM_INDEX_GETTER(DeviceType::kCUDA, TaskType::kDecodeH2D, "DECODE_H2D") -#endif - namespace { CompTaskNode* CreateCompTaskNodeByOpDeviceType(const OperatorConf& op_conf) { @@ -82,5 +73,6 @@ CompTaskNode* CreateCompTaskNodeByOpDeviceType(const OperatorConf& op_conf) { REGISTER_SYSTEM_OP_COMP_TASK_NODE_TYPE_WITH_FUNC(OperatorConf::kImageDecoderRandomCropResizeConf, CreateCompTaskNodeByOpDeviceType); +REGISTER_USER_OP_COMP_TASK_NODE_TYPE_WITH_FUNC("raw_reader", CreateCompTaskNodeByOpDeviceType); } // namespace oneflow diff --git a/oneflow/core/graph_impl/foreign_io_compute_task_node.cpp b/oneflow/core/graph_impl/foreign_io_compute_task_node.cpp deleted file mode 100644 index afcfe2b948fb4f805ba02a20b5761d2597ac31e5..0000000000000000000000000000000000000000 --- a/oneflow/core/graph_impl/foreign_io_compute_task_node.cpp +++ /dev/null @@ -1,99 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/graph/compute_task_node.h" -#include "oneflow/core/graph/task_stream_index_manager.h" - -namespace oneflow { - -class ForeignIOCompTaskNode : public CompTaskNode { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignIOCompTaskNode); - ForeignIOCompTaskNode() = default; - virtual ~ForeignIOCompTaskNode() override = default; - - void ProduceAllRegstsAndBindEdges() override; - void ConsumeAllRegsts() override; - void BuildExecGphAndRegst() override; - bool IsMeaningLess() override { return false; } - - private: - void InferProducedDataRegstTimeShape() override; -}; - -void ForeignIOCompTaskNode::ProduceAllRegstsAndBindEdges() { - std::shared_ptr out_regst = ProduceRegst("out", false, 1, 1); - ForEachOutDataEdge([&](TaskEdge* edge) { edge->AddRegst("out", out_regst); }); -} - -void ForeignIOCompTaskNode::ConsumeAllRegsts() { - ConsumeRegst("in"); - ForEachInDataEdge([&](TaskEdge* edge) { ConsumeRegst("in", edge->GetSoleRegst()); }); -} - -void ForeignIOCompTaskNode::BuildExecGphAndRegst() { - ExecNode* node = mut_exec_gph().NewNode(); - node->mut_op() = this->op(); - const std::list>& in_regsts = GetConsumedRegst("in"); - for (const std::string& ibn : node->op()->input_bns()) { - node->BindBnWithOneOfTheRegsts(ibn, in_regsts); - } - std::shared_ptr out_regst = GetProducedRegst("out"); - for (const std::string& obn : node->op()->output_bns()) { - const LogicalBlobId& lbi = node->op()->BnInOp2Lbi(obn); - out_regst->AddLbi(lbi); - node->BindBnWithRegst(obn, out_regst); - } - node->InferBlobDescs(parallel_ctx()); -} - -void ForeignIOCompTaskNode::InferProducedDataRegstTimeShape() { - auto time_shape = (*in_edges().begin())->src_node()->GetFastestInputOutputTimeShape(); - for (TaskEdge* edge : in_edges()) { - CHECK(time_shape->elem_cnt() == edge->src_node()->GetFastestInputOutputTimeShape()->elem_cnt()); - } - ForEachProducedDataRegst([time_shape](const std::string& name, RegstDesc* regst) { - *regst->mut_data_regst_time_shape() = time_shape; - }); -} - -class ForeignInputCompTaskNode final : public ForeignIOCompTaskNode { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignInputCompTaskNode); - ForeignInputCompTaskNode() = default; - ~ForeignInputCompTaskNode() override = default; - - TaskType GetTaskType() const override { return TaskType::kForeignInput; } -}; - -class ForeignOutputCompTaskNode final : public ForeignIOCompTaskNode { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignOutputCompTaskNode); - ForeignOutputCompTaskNode() = default; - ~ForeignOutputCompTaskNode() override = default; - - TaskType GetTaskType() const override { return TaskType::kForeignOutput; } -}; - -REGISTER_NAMED_TASK_STREAM_INDEX_GETTER(DeviceType::kCPU, TaskType::kForeignInput, "FOREIGN_INPUT"); - -REGISTER_SYSTEM_OP_COMP_TASK_NODE_TYPE(OperatorConf::kForeignInputConf, ForeignInputCompTaskNode); - -REGISTER_NAMED_TASK_STREAM_INDEX_GETTER(DeviceType::kCPU, TaskType::kForeignOutput, - "FOREIGN_OUTPUT"); - -REGISTER_SYSTEM_OP_COMP_TASK_NODE_TYPE(OperatorConf::kForeignOutputConf, ForeignOutputCompTaskNode); - -} // namespace oneflow diff --git a/oneflow/core/graph_impl/repeat_compute_task_node.cpp b/oneflow/core/graph_impl/repeat_compute_task_node.cpp index 9ff9073b88abe54e0efd69724ea659a6c28d15af..8c887688d75f7b4a2d30f24c4d83a7d742549d09 100644 --- a/oneflow/core/graph_impl/repeat_compute_task_node.cpp +++ b/oneflow/core/graph_impl/repeat_compute_task_node.cpp @@ -43,14 +43,23 @@ void RepeatCompTaskNode::ProduceAllRegstsAndBindEdges() { } void RepeatCompTaskNode::BuildExecGphAndRegst() { + std::shared_ptr in_regst = GetSoleConsumedRegst("in"); ExecNode* node = mut_exec_gph().NewNode(); std::shared_ptr sole_op = op(); node->mut_op() = sole_op; - node->BindBnWithRegst(sole_op->SoleIbn(), GetSoleConsumedRegst("in")); + node->BindBnWithRegst(sole_op->SoleIbn(), in_regst); std::shared_ptr out_regst = GetProducedRegst("out"); out_regst->AddLbi(sole_op->BnInOp2Lbi(sole_op->SoleObn())); node->BindBnWithRegst(sole_op->SoleObn(), out_regst); node->InferBlobDescs(parallel_ctx()); + + // NOTE(chengcheng): force inplace + CHECK_EQ(in_regst->NumOfLbi(), 1); + CHECK_EQ(out_regst->NumOfLbi(), 1); + CHECK_EQ(in_regst->min_register_num(), 1); + // NOTE(chengcheng): input need unreused mem + in_regst->set_enable_reuse_mem(false); + out_regst->set_force_inplace_consumed_regst_desc_id(in_regst->regst_desc_id()); } REGISTER_COMP_TASK_STREAM_INDEX_GETTER(TaskType::kRepeat); diff --git a/oneflow/core/hardware/cuda_device_descriptor.cpp b/oneflow/core/hardware/cuda_device_descriptor.cpp index a2a23353a6d3dd5a57e76382a3e685198eb59994..5d6bc6eb56fe8df59a1b791de5f8a4f7f39c4717 100644 --- a/oneflow/core/hardware/cuda_device_descriptor.cpp +++ b/oneflow/core/hardware/cuda_device_descriptor.cpp @@ -83,6 +83,20 @@ std::shared_ptr CudaDeviceDescriptor::Query(int32_t cudaDeviceProp prop{}; const cudaError_t err = cudaGetDeviceProperties(&prop, ordinal); CHECK(err == cudaSuccess); + static const std::set compiled_archs{CUDA_REAL_ARCHS}; + if (compiled_archs.find(prop.major * 10 + prop.minor) == compiled_archs.cend() + && compiled_archs.find(prop.major * 10) == compiled_archs.cend()) { + static std::atomic once_flag(false); + if (!once_flag.exchange(true)) { + LOG(WARNING) + << "The CUDA device '" << prop.name << "' with capability " + << prop.major * 10 + prop.minor + << " is not compatible with the current OneFlow installation. The current program " + "may throw a 'no kernel image is available for execution " + "on the device' error or hang for a long time. Please reinstall OneFlow " + "compiled with a newer version of CUDA."; + } + } auto* desc = new CudaDeviceDescriptor(); desc->impl_->ordinal = ordinal; desc->impl_->name = prop.name; @@ -208,6 +222,20 @@ std::shared_ptr CudaDeviceDescriptor::Query(int32_t hipDeviceProp_t prop{}; const hipError_t err = hipGetDeviceProperties(&prop, ordinal); CHECK(err == hipSuccess); + // static const std::set compiled_archs{CUDA_REAL_ARCHS}; + // if (compiled_archs.find(prop.major * 10 + prop.minor) == compiled_archs.cend() + // && compiled_archs.find(prop.major * 10) == compiled_archs.cend()) { + // static std::atomic once_flag(false); + // if (!once_flag.exchange(true)) { + // LOG(WARNING) + // << "The CUDA device '" << prop.name << "' with capability " + // << prop.major * 10 + prop.minor + // << " is not compatible with the current OneFlow installation. The current program " + // "may throw a 'no kernel image is available for execution " + // "on the device' error or hang for a long time. Please reinstall OneFlow " + // "compiled with a newer version of CUDA."; + // } + // } auto* desc = new CudaDeviceDescriptor(); desc->impl_->ordinal = ordinal; desc->impl_->name = prop.name; diff --git a/oneflow/core/hardware/cuda_device_descriptor.h b/oneflow/core/hardware/cuda_device_descriptor.h index 880a5f882e88290486ec051aa1c7b511440a7af3..ae5f83d1c880681496d7329f5533b96c09430b3a 100644 --- a/oneflow/core/hardware/cuda_device_descriptor.h +++ b/oneflow/core/hardware/cuda_device_descriptor.h @@ -21,7 +21,7 @@ limitations under the License. #include #include -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) namespace oneflow { @@ -60,43 +60,4 @@ class CudaDeviceDescriptor : public DeviceDescriptor { #endif // WITH_CUDA -#ifdef WITH_ROCM - -namespace oneflow { - -namespace hardware { - -constexpr char kCudaDeviceDescriptorClassName[] = "cuda"; - -class CudaDeviceDescriptor : public DeviceDescriptor { - public: - OF_DISALLOW_COPY_AND_MOVE(CudaDeviceDescriptor); - ~CudaDeviceDescriptor() override; - - int32_t Ordinal() const; - const std::string& Name() const; - size_t GlobalMemorySizeBytes() const; - int32_t ClockRateKHz() const; - int32_t ComputeCapabilityMajor() const; - int32_t ComputeCapabilityMinor() const; - int32_t MemoryClockRateKHz() const; - int32_t MemoryBusWidthBit() const; - const std::string& PCIBusID() const; - void Serialize(std::string* serialized) const; - static std::shared_ptr Query(int32_t ordinal); - static std::shared_ptr Deserialize(const std::string& serialized); - - private: - CudaDeviceDescriptor(); - - struct Impl; - std::unique_ptr impl_; -}; - -} // namespace hardware - -} // namespace oneflow - -#endif // WITH_ROCM - #endif // ONEFLOW_CORE_HARDWARE_CUDA_DEVICE_DESCRIPTOR_H_ diff --git a/oneflow/core/hip/atomic.hip.h b/oneflow/core/hip/atomic.hip.h deleted file mode 100644 index cf1e00161a3f565dbdd1f7a952be8a4734dce727..0000000000000000000000000000000000000000 --- a/oneflow/core/hip/atomic.hip.h +++ /dev/null @@ -1,214 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_HIP_ATOMIC_H_ -#define ONEFLOW_CORE_HIP_ATOMIC_H_ - -#if defined(__HIPCC__) - -#include -#include -#include - -namespace oneflow { - -namespace cuda { - -namespace atomic { - -namespace internal { - -template -__device__ __forceinline__ T CastCASImpl(T* address, T compare, T val) { - static_assert(sizeof(T) == sizeof(U), ""); - U ret = atomicCAS(reinterpret_cast(address), *(reinterpret_cast(&compare)), - *(reinterpret_cast(&val))); - return *(reinterpret_cast(&ret)); -} - -template -__device__ __forceinline__ typename std::enable_if::type -CASImpl(T* address, T compare, T val) { - return CastCASImpl(address, compare, val); -} - -template -__device__ __forceinline__ - typename std::enable_if::type - CASImpl(T* address, T compare, T val) { - return CastCASImpl(address, compare, val); -} - -__device__ __forceinline__ int CASImpl(int* address, int compare, int val) { - return atomicCAS(address, compare, val); -} - -__device__ __forceinline__ unsigned int CASImpl(unsigned int* address, unsigned int compare, - unsigned int val) { - return atomicCAS(address, compare, val); -} - -__device__ __forceinline__ unsigned long long int CASImpl(unsigned long long int* address, - unsigned long long int compare, - unsigned long long int val) { - return atomicCAS(address, compare, val); -} - -// #if __CUDA_ARCH__ >= 700 - -// __device__ __forceinline__ unsigned short int CASImpl(unsigned short int* address, -// unsigned short int compare, -// unsigned short int val) { -// return atomicCAS(address, compare, val); -// } - -// #endif // __CUDA_ARCH__ >= 700 - -template -struct AddOp { - __device__ __forceinline__ T operator()(T a, T b) { return a + b; } -}; - -template class BinaryOp> -__device__ __forceinline__ T AtomicCASBinaryImpl(T* address, T val) { - T old = *address; - T assumed; - do { - assumed = old; - old = CASImpl(address, assumed, BinaryOp()(old, val)); - } while (old != assumed); - return old; -} - -template -__device__ __forceinline__ T AddImpl(T* address, T val) { - return AtomicCASBinaryImpl(address, val); -} - -__device__ __forceinline__ int AddImpl(int* address, int val) { return atomicAdd(address, val); } - -__device__ __forceinline__ unsigned int AddImpl(unsigned int* address, unsigned int val) { - return atomicAdd(address, val); -} - -__device__ __forceinline__ unsigned long long int AddImpl(unsigned long long int* address, - unsigned long long int val) { - return atomicAdd(address, val); -} - -__device__ __forceinline__ uint64_t AddImpl(uint64_t* address, uint64_t val) { - static_assert(sizeof(uint64_t) == sizeof(unsigned long long int), ""); - return static_cast(atomicAdd(reinterpret_cast(address), - static_cast(val))); -} - -__device__ __forceinline__ float AddImpl(float* address, float val) { - return atomicAdd(address, val); -} - -// #if __CUDA_ARCH__ >= 600 - -__device__ __forceinline__ double AddImpl(double* address, double val) { - return atomicAdd(address, val); -} - -// __device__ __forceinline__ half2 AddImpl(half2* address, half2 val) { -// return atomicAdd(address, val); -// } - -// #endif // __CUDA_ARCH__ >= 600 - -// #if __CUDA_ARCH__ >= 700 - -__device__ __forceinline__ half AddImpl(half* address, half val) { - float address_value = __half2float(*address); - return __float2half(atomicAdd(&address_value, __half2float(val))); } - -// #endif // __CUDA_ARCH__ >= 700 - -// #if __CUDA_ARCH__ >= 800 - -// __device__ __forceinline__ nv_bfloat16 AddImpl(nv_bfloat16* address, nv_bfloat16 val) { -// return atomicAdd(address, val); -// } - -// #endif // __CUDA_ARCH__ >= 800 - -// #if __CUDA_ARCH__ < 530 - -__device__ __forceinline__ half2 AddImpl(half2* address, half2 val) { - half2 res; - float2 address_value = __half22float2(*address); - res.data.x = __float2half(atomicAdd(&address_value.x, __half2float(val.data.x))); - res.data.y = __float2half(atomicAdd(&address_value.y, __half2float(val.data.y))); - return res; -} - -// #endif // __CUDA_ARCH__ < 530 - -} // namespace internal - -template -__device__ __forceinline__ typename std::enable_if::value, T>::type Cast(U v) { - return static_cast(v); -} - -template -__device__ __forceinline__ typename std::enable_if::value, T>::type Cast(U v) { - return v; -} - -template -__device__ __forceinline__ T CAS(T* address, U compare, V val) { - return internal::CASImpl(address, Cast(compare), Cast(val)); -} - -template -__device__ __forceinline__ T Add(T* address, U val) { - return internal::AddImpl(address, Cast(val)); -} - -__device__ __forceinline__ float Max(float* address, const float val) { - int* address_as_i = (int*)address; - int old = *address_as_i; - int assumed = 0; - do { - assumed = old; - old = atomicCAS(address_as_i, assumed, __float_as_int(fmaxf(val, __int_as_float(assumed)))); - } while (assumed != old); - return __int_as_float(old); -} - -__device__ __forceinline__ double Max(double* address, const double val) { - unsigned long long int* address_as_i = (unsigned long long int*)address; - unsigned long long int old = *address_as_i; - unsigned long long int assumed = 0; - do { - assumed = old; - old = atomicCAS(address_as_i, assumed, - __double_as_longlong(fmax(val, __longlong_as_double(assumed)))); - } while (assumed != old); - return __longlong_as_double(old); -} - -} // namespace atomic - -} // namespace cuda - -} // namespace oneflow - -#endif // defined(__HIPCC__) - -#endif // ONEFLOW_CORE_CUDA_ATOMIC_H_ diff --git a/oneflow/core/hip/elementwise.hip.h b/oneflow/core/hip/elementwise.hip.h deleted file mode 100644 index 29b51fd067d1839aee26743e9a9b741878b28f05..0000000000000000000000000000000000000000 --- a/oneflow/core/hip/elementwise.hip.h +++ /dev/null @@ -1,243 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_HIP_ELEMENTWISE_H_ -#define ONEFLOW_CORE_HIP_ELEMENTWISE_H_ - -#ifdef WITH_ROCM - -#include -#include -#include -#include - -namespace oneflow { - -namespace cuda { - -namespace elementwise { - -constexpr int kBlockSize = 256; -constexpr int kNumWaves = 32; - -inline hipError_t GetNumBlocks(int64_t n, int* num_blocks) { - int dev; - { - hipError_t err = hipGetDevice(&dev); - if (err != hipSuccess) { return err; } - } - int sm_count; - { - hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev); - if (err != hipSuccess) { return err; } - } - int tpm; - { - hipError_t err = hipDeviceGetAttribute(&tpm, hipDeviceAttributeMaxThreadsPerMultiProcessor, dev); - if (err != hipSuccess) { return err; } - } - *num_blocks = std::max(1, std::min((n + kBlockSize - 1) / kBlockSize, - sm_count * tpm / kBlockSize * kNumWaves)); - return hipSuccess; -} - -template -struct GetPackType { - using type = typename std::aligned_storage::type; -}; - -template -using PackType = typename GetPackType::type; - -template -union Pack { - static_assert(sizeof(PackType) == sizeof(T) * pack_size, ""); - __device__ Pack() { - // do nothing - } - PackType storage; - T elem[pack_size]; -}; - -template -struct alignas(sizeof(T) * pack_size) Packed { - __device__ Packed() { - // do nothing - } - union { - T elem[pack_size]; - }; -}; - -constexpr int kMaxPackBytes = 128 / 8; -constexpr int kMaxPackSize = 8; - -constexpr int Min(int a, int b) { return a < b ? a : b; } - -template -constexpr int PackSize() { - return Min(kMaxPackBytes / sizeof(T), kMaxPackSize); -} - -template -constexpr int PackSize() { - return Min(PackSize(), PackSize()); -} - -template -class HasApply2 { - typedef char one; - struct two { - char x[2]; - }; - - template - static one test(decltype(&C::Apply2)); - template - static two test(...); - - public: - enum { value = sizeof(test(0)) == sizeof(char) }; -}; - -template -__device__ typename std::enable_if::value == true && pack_size % 2 == 0, - Packed>::type -ApplyPack(const FunctorT& functor, const IN... in[pack_size]) { - Packed ret; -#pragma unroll - for (int j = 0; j < pack_size; j += 2) { functor.Apply2(ret.elem + j, (in + j)...); } - return ret; -} - -template -__device__ typename std::enable_if::value == false || pack_size % 2 != 0, - Packed>::type -ApplyPack(const FunctorT& functor, const IN... in[pack_size]) { - Packed ret; -#pragma unroll - for (int j = 0; j < pack_size; ++j) { ret.elem[j] = functor((in[j])...); } - return ret; -} - -template -__global__ void __launch_bounds__(kBlockSize) - ApplyGeneric(FactoryT factory, int64_t n_pack, Packed* pack_r, - const Packed*... pack_in, int64_t n_tail, R* tail_r, - const IN*... tail_in) { - auto functor = factory(); - const int global_tid = blockIdx.x * kBlockSize + threadIdx.x; - for (int64_t i = global_tid; i < n_pack; i += blockDim.x * gridDim.x) { - pack_r[i] = ApplyPack(functor, (pack_in[i].elem)...); - } - if (tail && global_tid < n_tail) { tail_r[global_tid] = functor((tail_in[global_tid])...); } -} - -template -struct SimpleFactory { - explicit SimpleFactory(FunctorT functor) : tpl(functor) {} - __device__ FunctorT operator()() const { return tpl; } - - private: - FunctorT tpl; -}; - -template -bool IsAligendForPack() { - return true; -} - -template -bool IsAligendForPack(const T* ptr, const Args*... others) { - return reinterpret_cast(ptr) % sizeof(Pack) == 0 - && IsAligendForPack(others...); -} - -template -hipError_t LaunchKernel(FactoryT factory, int64_t n, R* r, const IN*... in, hipStream_t stream) { - const int64_t n_pack = n / pack_size; - const int64_t tail_offset = n_pack * pack_size; - const int64_t n_tail = n - tail_offset; - int num_blocks; - { - hipError_t err = GetNumBlocks(n_pack, &num_blocks); - if (err != hipSuccess) { return err; } - } - auto func = n_tail > 0 ? ApplyGeneric - : ApplyGeneric; - hipLaunchKernelGGL(func, num_blocks, kBlockSize, 0, stream, - factory, n_pack, reinterpret_cast*>(r), - (reinterpret_cast*>(in))..., n_tail, r + tail_offset, - (in + tail_offset)...); - return hipPeekAtLastError(); -} - -template -struct GenericLauncher { - static hipError_t Launch(FactoryT factory, int64_t n, R* r, const IN*... in, - hipStream_t stream) { - constexpr int max_pack_size = PackSize(); - if (IsAligendForPack(r, in...)) { - return LaunchKernel(factory, n, r, in..., stream); - } else { - return LaunchKernel<1, FactoryT, R, IN...>(factory, n, r, in..., stream); - } - } -}; - -template -inline hipError_t UnaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, - hipStream_t stream) { - return GenericLauncher::Launch(factory, n, r, a, stream); -} - -template -inline hipError_t Unary(FunctorT functor, int64_t n, R* r, const A* a, hipStream_t stream) { - return UnaryWithFactory(SimpleFactory(functor), n, r, a, stream); -} - -template -inline hipError_t BinaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, - hipStream_t stream) { - return GenericLauncher::Launch(factory, n, r, a, b, stream); -} - -template -inline hipError_t Binary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, - hipStream_t stream) { - return BinaryWithFactory(SimpleFactory(functor), n, r, a, b, stream); -} - -template -inline hipError_t TernaryWithFactory(FactoryT factory, int64_t n, R* r, const A* a, const B* b, - const C* c, hipStream_t stream) { - return GenericLauncher::Launch(factory, n, r, a, b, c, stream); -} - -template -inline hipError_t Ternary(FunctorT functor, int64_t n, R* r, const A* a, const B* b, const C* c, - hipStream_t stream) { - return TernaryWithFactory(SimpleFactory(functor), n, r, a, b, c, stream); -} - -} // namespace elementwise - -} // namespace cuda - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_CUDA_ELEMENTWISE_H_ diff --git a/oneflow/core/hip/layer_norm.hip.h b/oneflow/core/hip/layer_norm.hip.h deleted file mode 100644 index 97891c3a9b0fe0769bcb082373281fb93c912b85..0000000000000000000000000000000000000000 --- a/oneflow/core/hip/layer_norm.hip.h +++ /dev/null @@ -1,1606 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifndef ONEFLOW_CORE_HIP_LAYER_NORM_H_ -#define ONEFLOW_CORE_HIP_LAYER_NORM_H_ - -#ifdef WITH_ROCM - -#include "hip/hip_runtime.h" -#include -// #include -#include - -namespace oneflow { - -namespace cuda { - -namespace layer_norm { - -constexpr int kWarpSize = 64; - -template -struct SumOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return a + b; } -}; - -template -struct MaxOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return max(a, b); } -}; - -template class ReductionOp, typename T, int thread_group_width = kWarpSize> -__inline__ __device__ T WarpAllReduce(T val) { - for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { - // val = ReductionOp()(val, __shfl_xor(0xffffffff, val, mask, thread_group_width)); - val = ReductionOp()(val, __shfl_xor(val, mask, thread_group_width)); - } - return val; -} - -template class ReductionOp, typename T, int block_size> -__inline__ __device__ T BlockAllReduce(T val) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - __shared__ T result_broadcast; - T result = BlockReduce(temp_storage).Reduce(val, ReductionOp()); - if (threadIdx.x == 0) { result_broadcast = result; } - __syncthreads(); - return result_broadcast; -} - -template -__inline__ __device__ T Div(T a, T b); - -template<> -__inline__ __device__ float Div(float a, float b) { -#ifdef OF_LAYER_NORM_USE_FAST_MATH - return __fdividef(a, b); -#else - return a / b; -#endif -} - -template<> -__inline__ __device__ double Div(double a, double b) { - return a / b; -} - -template -__inline__ __device__ T Rsqrt(T x); - -template<> -__inline__ __device__ float Rsqrt(float x) { -#ifdef OF_LAYER_NORM_USE_FAST_MATH - return __frsqrt_rn(x); -#else - return rsqrt(x); -#endif -} - -template<> -__inline__ __device__ double Rsqrt(double x) { - return rsqrt(x); -} - -template -inline hipError_t GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, - int64_t max_blocks, int64_t waves, int* num_blocks) { - int dev; - { - hipError_t err = hipGetDevice(&dev); - if (err != hipSuccess) { return err; } - } - int sm_count; - { - hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev); - if (err != hipSuccess) { return err; } - } - int max_active_blocks; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor(&max_active_blocks, func, - block_size, dynamic_smem_size); - } - *num_blocks = - std::max(1, std::min(max_blocks, sm_count * max_active_blocks * waves)); - return hipSuccess; -} - -template -struct DefaultComputeType { - using type = T; -}; - -template<> -struct DefaultComputeType { - using type = float; -}; - -// #if CUDA_VERSION >= 11000 -// template<> -// struct DefaultComputeType { -// using type = float; -// }; -// #endif // CUDA_VERSION >= 11000 - -template -struct GetPackType { - using type = typename std::aligned_storage::type; -}; - -template -using PackType = typename GetPackType::type; - -template -union Pack { - static_assert(sizeof(PackType) == sizeof(T) * N, ""); - __device__ Pack() { - // do nothing - } - PackType storage; - T elem[N]; -}; - -template -struct DirectLoad { - DirectLoad(const SRC* src, int64_t row_size) : src(src), row_size(row_size) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - Pack pack; - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { dst[i] = static_cast(pack.elem[i]); } - } - const SRC* src; - int64_t row_size; -}; - -template -struct DirectStore { - DirectStore(DST* dst, int64_t row_size) : dst(dst), row_size(row_size) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - Pack pack; - const int64_t offset = (row * row_size + col) / N; -#pragma unroll - for (int i = 0; i < N; ++i) { pack.elem[i] = static_cast(src[i]); } - *(reinterpret_cast*>(dst) + offset) = pack.storage; - } - DST* dst; - int64_t row_size; -}; - -template -inline __device__ void WelfordCombine(T val, T* mean, T* m2, T* count) { - // Use Welford Online algorithem to compute mean and variance - // For more details you can refer to: - // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm - *count += 1; - T delta1 = val - *mean; - *mean += Div(delta1, *count); - T delta2 = val - *mean; - *m2 += delta1 * delta2; -} - -template -inline __device__ void WelfordCombine(T b_mean, T b_m2, T b_count, T* mean, T* m2, T* count) { - if (b_count == 0) { return; } - T new_count = *count + b_count; - T nb_over_n = Div(b_count, new_count); - T delta = b_mean - *mean; - *mean += delta * nb_over_n; - *m2 += b_m2 + delta * delta * (*count) * nb_over_n; - *count = new_count; -} - -template -__inline__ __device__ void WelfordWarpReduce(T thread_mean, T thread_m2, T thread_count, T* mean, - T* m2, T* count) { - *mean = thread_mean; - *m2 = thread_m2; - *count = thread_count; - for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { - // T b_mean = __shfl_down(0xffffffff, *mean, mask, thread_group_width); - // T b_m2 = __shfl_down(0xffffffff, *m2, mask, thread_group_width); - // T b_count = __shfl_down(0xffffffff, *count, mask, thread_group_width); - T b_mean = __shfl_down(*mean, mask, thread_group_width); - T b_m2 = __shfl_down(*m2, mask, thread_group_width); - T b_count = __shfl_down(*count, mask, thread_group_width); - WelfordCombine(b_mean, b_m2, b_count, mean, m2, count); - } -} - -template -__inline__ __device__ void WelfordWarpAllReduce(T thread_mean, T thread_m2, T thread_count, T* mean, - T* m2, T* count) { - WelfordWarpReduce(thread_mean, thread_m2, thread_count, mean, m2, count); - // *mean = __shfl(0xffffffff, *mean, 0, thread_group_width); - // *m2 = __shfl(0xffffffff, *m2, 0, thread_group_width); - // *count = __shfl(0xffffffff, *count, 0, thread_group_width); - *mean = __shfl(*mean, 0, thread_group_width); - *m2 = __shfl(*m2, 0, thread_group_width); - *count = __shfl(*count, 0, thread_group_width); -} - -template -__inline__ __device__ void WelfordBlockAllReduce(T thread_mean, T thread_m2, T thread_count, - T* result_mean, T* result_m2, T* result_count) { - __shared__ T mean_shared[kWarpSize]; - __shared__ T m2_shared[kWarpSize]; - __shared__ T count_shared[kWarpSize]; - __shared__ T mean_result_broadcast; - __shared__ T m2_result_broadcast; - __shared__ T count_result_broadcast; - const int lid = threadIdx.x % kWarpSize; - const int wid = threadIdx.x / kWarpSize; - T warp_mean = 0; - T warp_m2 = 0; - T warp_count = 0; - WelfordWarpReduce(thread_mean, thread_m2, thread_count, &warp_mean, &warp_m2, &warp_count); - __syncthreads(); - if (lid == 0) { - mean_shared[wid] = warp_mean; - m2_shared[wid] = warp_m2; - count_shared[wid] = warp_count; - } - __syncthreads(); - if (wid == 0) { - if (threadIdx.x < blockDim.x / kWarpSize) { - warp_mean = mean_shared[lid]; - warp_m2 = m2_shared[lid]; - warp_count = count_shared[lid]; - } else { - warp_mean = static_cast(0); - warp_m2 = static_cast(0); - warp_count = static_cast(0); - } - __syncthreads(); - T block_mean = 0; - T block_m2 = 0; - T block_count = 0; - WelfordWarpReduce(warp_mean, warp_m2, warp_count, &block_mean, &block_m2, &block_count); - if (lid == 0) { - mean_result_broadcast = block_mean; - m2_result_broadcast = block_m2; - count_result_broadcast = block_count; - } - } - __syncthreads(); - *result_mean = mean_result_broadcast; - *result_m2 = m2_result_broadcast; - *result_count = count_result_broadcast; -} - -template -__global__ void LayerNormWarpImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - static_assert(cols_per_thread % pack_size == 0, ""); - static_assert(thread_group_width <= kWarpSize, ""); - static_assert(kWarpSize % thread_group_width == 0, ""); - constexpr int num_packs = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - ComputeType buf[rows_per_access][cols_per_thread]; - const int64_t global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; - const int64_t num_global_thread_group = gridDim.x * blockDim.y; - const int64_t lane_id = threadIdx.x; - const int64_t step = num_global_thread_group * rows_per_access; - for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) { - ComputeType thread_mean[rows_per_access]; - ComputeType thread_m2[rows_per_access]; - ComputeType thread_count[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - thread_mean[row_id] = 0; - thread_m2[row_id] = 0; - thread_count[row_id] = 0; - ComputeType* row_buf = buf[row_id]; -#pragma unroll - for (int pack_id = 0; pack_id < num_packs; ++pack_id) { - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - const int pack_offset = pack_id * pack_size; - if (!padding || col < cols) { - load.template load(row_buf + pack_offset, row + row_id, col); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - WelfordCombine(row_buf[pack_offset + i], thread_mean + row_id, thread_m2 + row_id, - thread_count + row_id); - } - } else { -#pragma unroll - for (int i = 0; i < pack_size; ++i) { row_buf[pack_offset + i] = 0; } - } - } - } - ComputeType warp_mean[rows_per_access]; - ComputeType warp_m2[rows_per_access]; - ComputeType warp_count[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - int global_row_id = row + row_id; - ComputeType* row_buf = buf[row_id]; - WelfordWarpAllReduce( - thread_mean[row_id], thread_m2[row_id], thread_count[row_id], warp_mean + row_id, - warp_m2 + row_id, warp_count + row_id); - ComputeType row_mean = warp_mean[row_id]; - ComputeType row_variance = - max(Div(warp_m2[row_id], warp_count[row_id]), static_cast(0.0)); - ComputeType row_inv_var = Rsqrt(row_variance + static_cast(epsilon)); - if (lane_id == 0) { - mean[global_row_id] = row_mean; - inv_variance[global_row_id] = row_inv_var; - } -#pragma unroll - for (int i = 0; i < cols_per_thread; ++i) { - row_buf[i] = (row_buf[i] - row_mean) * row_inv_var; - } -#pragma unroll - for (int i = 0; i < num_packs; ++i) { - const int col = (i * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - store.template store(row_buf + i * pack_size, global_row_id, col); - } - } - } - } -} - -template -inline hipError_t LaunchLayerNormWarpImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - constexpr int block_size = 128; - constexpr int waves = 32; - static_assert(block_size % thread_group_width == 0, ""); - constexpr int thread_groups_per_block = block_size / thread_group_width; - dim3 block_dim(thread_group_width, thread_groups_per_block); - const int64_t num_blocks = - (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; - int grid_dim_x; - { - hipError_t err = - GetNumBlocks(LayerNormWarpImpl, - block_size, 0, num_blocks, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormWarpImpl - <<>>(load, store, rows, cols, epsilon, mean, inv_variance); - return hipPeekAtLastError(); -} - -template -inline hipError_t DispatchLayerNormWarpImplPadding(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchLayerNormWarpImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else { - return LaunchLayerNormWarpImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } -} - -template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(5) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(7) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(9) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(11) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(13) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(15) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(17) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(19) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(21) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(23) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(25) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(27) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(29) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(31) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} -template -typename std::enable_if::type DispatchLayerNormWarpImplCols( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, ComputeType* inv_variance) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } else { \ - return DispatchLayerNormWarpImplPadding( \ - stream, load, store, rows, cols, epsilon, mean, inv_variance); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormWarpImplPadding(stream, load, store, rows, cols, epsilon, mean, \ - inv_variance); \ - } - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -struct DispatchLayerNormWarpImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - if (cols % 4 == 0) { - return DispatchLayerNormWarpImplCols( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else if (cols % 2 == 0) { - return DispatchLayerNormWarpImplCols( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else { - return DispatchLayerNormWarpImplCols( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } - } -}; - -template -inline hipError_t DispatchLayerNormWarpImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - return DispatchLayerNormWarpImplPackSize()( - stream, load, store, rows, cols, epsilon, mean, inv_variance); -} - -template -__global__ void LayerNormBlockSMemImpl(LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[]; - auto* buf = reinterpret_cast(shared_buf); - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_mean = 0; - ComputeType thread_m2 = 0; - ComputeType thread_count = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - buf[i * num_packs + pack_id] = pack[i]; - WelfordCombine(pack[i], &thread_mean, &thread_m2, &thread_count); - } - } - ComputeType row_mean = 0; - ComputeType row_m2 = 0; - ComputeType row_count = 0; - WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, &row_mean, &row_m2, - &row_count); - ComputeType row_variance = max(Div(row_m2, row_count), static_cast(0.0)); - ComputeType row_inv_var = Rsqrt(row_variance + static_cast(epsilon)); - if (threadIdx.x == 0) { - mean[row] = row_mean; - inv_variance[row] = row_inv_var; - } - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - pack[i] = (buf[i * num_packs + pack_id] - row_mean) * row_inv_var; - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -__global__ void LayerNormBlockSMemImpl_1024(LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[]; - auto* buf = reinterpret_cast(shared_buf); - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_mean = 0; - ComputeType thread_m2 = 0; - ComputeType thread_count = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - buf[i * num_packs + pack_id] = pack[i]; - WelfordCombine(pack[i], &thread_mean, &thread_m2, &thread_count); - } - } - ComputeType row_mean = 0; - ComputeType row_m2 = 0; - ComputeType row_count = 0; - WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, &row_mean, &row_m2, - &row_count); - ComputeType row_variance = max(Div(row_m2, row_count), static_cast(0.0)); - ComputeType row_inv_var = Rsqrt(row_variance + static_cast(epsilon)); - if (threadIdx.x == 0) { - mean[row] = row_mean; - inv_variance[row] = row_inv_var; - } - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - pack[i] = (buf[i * num_packs + pack_id] - row_mean) * row_inv_var; - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - - -template -inline hipError_t LaunchLayerNormBlockSMemImpl(hipStream_t stream, LOAD load, STORE store, - int smem, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = - GetNumBlocks(LayerNormBlockSMemImpl, - block_size, smem, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormBlockSMemImpl - <<>>(load, store, rows, cols, epsilon, mean, - inv_variance); - return hipPeekAtLastError(); -} - -template -inline hipError_t LaunchLayerNormBlockSMemImpl_1024(hipStream_t stream, LOAD load, STORE store, - int smem, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = - GetNumBlocks(LayerNormBlockSMemImpl_1024, - block_size, smem, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormBlockSMemImpl_1024 - <<>>(load, store, rows, cols, epsilon, mean, - inv_variance); - return hipPeekAtLastError(); -} - -template -inline hipError_t TryDispatchLayerNormBlockSMemImplBlockSize( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, ComputeType* inv_variance, bool* success) { - constexpr int block_size_conf_1 = 128; - constexpr int block_size_conf_2 = 256; - constexpr int block_size_conf_3 = 512; - constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType); - int max_active_blocks_conf_1; - - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_1, - LayerNormBlockSMemImpl, - block_size_conf_1, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_1 <= 0) { - *success = false; - return hipSuccess; - } - int max_active_blocks_conf_4; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_4, - LayerNormBlockSMemImpl_1024, - block_size_conf_4, smem); - if (err != hipSuccess) { return err; } - } - - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormBlockSMemImpl_1024( - stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); - } - int max_active_blocks_conf_3; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_3, - LayerNormBlockSMemImpl, - block_size_conf_3, smem); - if (err != hipSuccess) { return err; } - } - - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormBlockSMemImpl( - stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); - } - int max_active_blocks_conf_2; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_2, - LayerNormBlockSMemImpl, - block_size_conf_2, smem); - if (err != hipSuccess) { return err; } - } - - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormBlockSMemImpl( - stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); - } - *success = true; - return LaunchLayerNormBlockSMemImpl( - stream, load, store, smem, rows, cols, epsilon, mean, inv_variance); -} - -template -struct TryDispatchLayerNormBlockSMemImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance, bool* success) { - if (cols % 4 == 0) { - return TryDispatchLayerNormBlockSMemImplBlockSize( - stream, load, store, rows, cols, epsilon, mean, inv_variance, success); - } else if (cols % 2 == 0) { - return TryDispatchLayerNormBlockSMemImplBlockSize( - stream, load, store, rows, cols, epsilon, mean, inv_variance, success); - } else { - return TryDispatchLayerNormBlockSMemImplBlockSize( - stream, load, store, rows, cols, epsilon, mean, inv_variance, success); - } - } -}; - -template -inline hipError_t TryDispatchLayerNormBlockSMemImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance, bool* success) { - return TryDispatchLayerNormBlockSMemImplPackSize()( - stream, load, store, rows, cols, epsilon, mean, inv_variance, success); -} - -template -__global__ void LayerNormBlockUncachedImpl(LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, - ComputeType* mean, ComputeType* inv_variance) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_mean = 0; - ComputeType thread_m2 = 0; - ComputeType thread_count = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - WelfordCombine(pack[i], &thread_mean, &thread_m2, &thread_count); - } - } - ComputeType row_mean = 0; - ComputeType row_m2 = 0; - ComputeType row_count = 0; - WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, &row_mean, &row_m2, - &row_count); - ComputeType row_variance = max(Div(row_m2, row_count), static_cast(0.0)); - ComputeType row_inv_var = Rsqrt(row_variance + static_cast(epsilon)); - if (threadIdx.x == 0) { - mean[row] = row_mean; - inv_variance[row] = row_inv_var; - } - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - const int pack_offset = pack_id * pack_size; - load.template load(pack, row, pack_offset); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { pack[i] = (pack[i] - row_mean) * row_inv_var; } - store.template store(pack, row, pack_offset); - } - } -} - -template -inline hipError_t LaunchLayerNormBlockUncachedImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - constexpr int block_size = 1024; - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = - GetNumBlocks(LayerNormBlockUncachedImpl, - block_size, 0, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormBlockUncachedImpl - <<>>(load, store, rows, cols, epsilon, mean, inv_variance); - return hipPeekAtLastError(); -} - -template -struct DispatchLayerNormBlockUncachedImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - if (cols % 4 == 0) { - return LaunchLayerNormBlockUncachedImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else if (cols % 2 == 0) { - return LaunchLayerNormBlockUncachedImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } else { - return LaunchLayerNormBlockUncachedImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } - } -}; - -template -inline hipError_t DispatchLayerNormBlockUncachedImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - return DispatchLayerNormBlockUncachedImplPackSize()( - stream, load, store, rows, cols, epsilon, mean, inv_variance); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLayerNorm(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - if (cols <= 1024) { - return DispatchLayerNormWarpImpl(stream, load, store, rows, cols, - epsilon, mean, inv_variance); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = TryDispatchLayerNormBlockSMemImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance, - &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchLayerNormBlockUncachedImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLayerNorm(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, const double epsilon, ComputeType* mean, - ComputeType* inv_variance) { - return DispatchLayerNormBlockUncachedImpl( - stream, load, store, rows, cols, epsilon, mean, inv_variance); -} - -/* -LayerNormGrad dx: -normalized = (x - mean) * inv_var -sum_stats1 = sum(scaled_dy) -sum_stats2 = sum(scaled_dy * normalized) -dx = cols * dy - sum_stats1 - normalized * sum_stats2 -dx *= inv_var / cols -*/ -template -__global__ void LayerNormGradWarpImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - static_assert(cols_per_thread % pack_size == 0, ""); - constexpr int pack_per_thread = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - static_assert(thread_group_width <= kWarpSize, ""); - static_assert(kWarpSize % thread_group_width == 0, ""); - ComputeType normalized_buf[rows_per_access][cols_per_thread]; - ComputeType dy_buf[rows_per_access][cols_per_thread]; - const ComputeType one_over_cols = static_cast(1.0) / static_cast(cols); - const int64_t global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; - const int64_t num_global_thread_group = gridDim.x * blockDim.y; - const int lane_id = threadIdx.x; - const int64_t step = num_global_thread_group * rows_per_access; - for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) { - ComputeType sum_stats1[rows_per_access]; - ComputeType sum_stats2[rows_per_access]; - ComputeType inv_variance_buf[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - const int global_row_id = row + row_id; - ComputeType mean_val = mean[global_row_id]; - inv_variance_buf[row_id] = inv_variance[global_row_id]; - sum_stats1[row_id] = 0; - sum_stats2[row_id] = 0; - ComputeType* row_normalized_buf = normalized_buf[row_id]; - ComputeType* row_dy_buf = dy_buf[row_id]; -#pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - const int pack_offset = pack_id * pack_size; - if (!padding || col < cols) { - load_x.template load(row_normalized_buf + pack_offset, global_row_id, col); - load_scaled_dy.template load(row_dy_buf + pack_offset, global_row_id, col); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - const int col_id = pack_offset + i; - // row_normalized_buf store x - row_normalized_buf[col_id] = - (row_normalized_buf[col_id] - mean_val) * inv_variance_buf[row_id]; - sum_stats1[row_id] += row_dy_buf[col_id]; - sum_stats2[row_id] += row_dy_buf[col_id] * row_normalized_buf[col_id]; - } - } - } - } - ComputeType warp_sum_stats1[rows_per_access]; - ComputeType warp_sum_stats2[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - warp_sum_stats1[row_id] = - WarpAllReduce(sum_stats1[row_id]); - warp_sum_stats2[row_id] = - WarpAllReduce(sum_stats2[row_id]); - } -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - const int global_row_id = row + row_id; - ComputeType* row_normalized_buf = normalized_buf[row_id]; - ComputeType* row_dy_buf = dy_buf[row_id]; - const ComputeType inv_variance_over_cols = inv_variance_buf[row_id] * one_over_cols; -#pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - for (int i = 0; i < pack_size; ++i) { - const int col_id = pack_id * pack_size + i; - row_dy_buf[col_id] = (cols * row_dy_buf[col_id] - warp_sum_stats1[row_id] - - row_normalized_buf[col_id] * warp_sum_stats2[row_id]) - * inv_variance_over_cols; - } - store.template store(row_dy_buf + pack_id * pack_size, global_row_id, col); - } - } - } - } -} - -template -inline hipError_t LaunchLayerNormGradWarpImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) { - constexpr int block_size = 128; - constexpr int waves = 32; - static_assert(block_size % thread_group_width == 0, ""); - constexpr int thread_groups_per_block = block_size / thread_group_width; - dim3 block_dim(thread_group_width, thread_groups_per_block); - const int64_t num_blocks = - (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; - int grid_dim_x; - { - hipError_t err = GetNumBlocks( - LayerNormGradWarpImpl, - block_size, 0, num_blocks, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormGradWarpImpl - <<>>(load_x, load_scaled_dy, store, mean, inv_variance, - rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t DispatchLayerNormGradWarpImplPadding(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchLayerNormGradWarpImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } else { - return LaunchLayerNormGradWarpImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } -} - -template -typename std::enable_if::type DispatchLayerNormGradWarpImplCols( - hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } else { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(5) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(7) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(9) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(11) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(13) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(15) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(17) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(19) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(21) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(23) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(25) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(27) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(29) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(31) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -typename std::enable_if::type DispatchLayerNormGradWarpImplCols( - hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } else { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchLayerNormGradWarpImplPadding( \ - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -struct DispatchLayerNormGradWarpImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - if (cols % 2 == 0) { - return DispatchLayerNormGradWarpImplCols( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } else { - return DispatchLayerNormGradWarpImplCols( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } - } -}; - -template -inline hipError_t DispatchLayerNormGradWarpImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - return DispatchLayerNormGradWarpImplPackSize()( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); -} - -template -__global__ void LayerNormGradBlockSMemImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, - const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) { - extern __shared__ __align__(sizeof(double)) unsigned char grad_shared_buf[]; - auto* normalized_buf = reinterpret_cast(grad_shared_buf); - auto* dy_buf = normalized_buf + cols; - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - const ComputeType one_over_cols = static_cast(1.0) / static_cast(cols); - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType sum_stats1 = 0; - ComputeType sum_stats2 = 0; - const ComputeType mean_val = mean[row]; - const ComputeType inv_variance_val = inv_variance[row]; - const ComputeType inv_variance_over_cols = inv_variance_val * one_over_cols; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - const int buf_offset = i * num_packs + pack_id; - ComputeType normalized = (x_pack[i] - mean_val) * inv_variance_val; - normalized_buf[buf_offset] = normalized; - dy_buf[buf_offset] = dy_pack[i]; - sum_stats1 += dy_pack[i]; - sum_stats2 += dy_pack[i] * normalized; - } - } - const ComputeType row_sum_stats1 = BlockAllReduce(sum_stats1); - const ComputeType row_sum_stats2 = BlockAllReduce(sum_stats2); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - const int buf_offset = i * num_packs + pack_id; - pack[i] = (cols * dy_buf[buf_offset] - row_sum_stats1 - - normalized_buf[buf_offset] * row_sum_stats2) - * inv_variance_over_cols; - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -__global__ void LayerNormGradBlockSMemImpl_1024(LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, - const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - extern __shared__ __align__(sizeof(double)) unsigned char grad_shared_buf[]; - auto* normalized_buf = reinterpret_cast(grad_shared_buf); - auto* dy_buf = normalized_buf + cols; - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - const ComputeType one_over_cols = static_cast(1.0) / static_cast(cols); - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType sum_stats1 = 0; - ComputeType sum_stats2 = 0; - const ComputeType mean_val = mean[row]; - const ComputeType inv_variance_val = inv_variance[row]; - const ComputeType inv_variance_over_cols = inv_variance_val * one_over_cols; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - const int buf_offset = i * num_packs + pack_id; - ComputeType normalized = (x_pack[i] - mean_val) * inv_variance_val; - normalized_buf[buf_offset] = normalized; - dy_buf[buf_offset] = dy_pack[i]; - sum_stats1 += dy_pack[i]; - sum_stats2 += dy_pack[i] * normalized; - } - } - const ComputeType row_sum_stats1 = BlockAllReduce(sum_stats1); - const ComputeType row_sum_stats2 = BlockAllReduce(sum_stats2); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - const int buf_offset = i * num_packs + pack_id; - pack[i] = (cols * dy_buf[buf_offset] - row_sum_stats1 - - normalized_buf[buf_offset] * row_sum_stats2) - * inv_variance_over_cols; - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchLayerNormGradBlockSMemImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, int smem, - const int64_t rows, const int64_t cols) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(LayerNormGradBlockSMemImpl, - block_size, smem, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormGradBlockSMemImpl - <<>>(load_x, load_scaled_dy, store, mean, inv_variance, - rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t LaunchLayerNormGradBlockSMemImpl_1024(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, int smem, - const int64_t rows, const int64_t cols) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(LayerNormGradBlockSMemImpl_1024, - block_size, smem, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormGradBlockSMemImpl_1024 - <<>>(load_x, load_scaled_dy, store, mean, inv_variance, - rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t TryDispatchLayerNormGradBlockSMemImplBlockSize( - hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, const ComputeType* inv_variance, const int64_t rows, - const int64_t cols, bool* success) { - constexpr int block_size_conf_1 = 128; - constexpr int block_size_conf_2 = 256; - constexpr int block_size_conf_3 = 512; - constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType) * 2; - int max_active_blocks_conf_1; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_1, - LayerNormGradBlockSMemImpl, - block_size_conf_1, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_1 <= 0) { - *success = false; - return hipSuccess; - } - int max_active_blocks_conf_4; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_4, - LayerNormGradBlockSMemImpl_1024, - block_size_conf_4, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormGradBlockSMemImpl_1024( - stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); - } - int max_active_blocks_conf_3; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_3, - LayerNormGradBlockSMemImpl, - block_size_conf_3, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormGradBlockSMemImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); - } - int max_active_blocks_conf_2; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_2, - LayerNormGradBlockSMemImpl, - block_size_conf_2, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { - *success = true; - return LaunchLayerNormGradBlockSMemImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, smem, rows, cols); - } - *success = true; - return LaunchLayerNormGradBlockSMemImpl(stream, load_x, load_scaled_dy, store, - mean, inv_variance, smem, rows, cols); -} - -template -struct TryDispatchLayerNormGradBlockSMemImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols, bool* success) { - if (cols % 2 == 0) { - return TryDispatchLayerNormGradBlockSMemImplBlockSize( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, success); - } else { - return TryDispatchLayerNormGradBlockSMemImplBlockSize( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, success); - } - } -}; - -template -inline hipError_t TryDispatchLayerNormGradBlockSMemImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, - const int64_t rows, const int64_t cols, - bool* success) { - return TryDispatchLayerNormGradBlockSMemImplPackSize()( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, success); -} - -template -__global__ void LayerNormGradBlockUncachedImpl(LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, - const ComputeType* inv_variance, const int64_t rows, - const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = static_cast(cols) / pack_size; - const ComputeType one_over_cols = static_cast(1.0) / static_cast(cols); - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - const ComputeType mean_val = mean[row]; - const ComputeType inv_variance_val = inv_variance[row]; - const ComputeType inv_variance_over_cols = inv_variance_val * one_over_cols; - ComputeType sum_stats1 = 0; - ComputeType sum_stats2 = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); - -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - sum_stats1 += dy_pack[i]; - sum_stats2 += dy_pack[i] * (x_pack[i] - mean_val) * inv_variance_val; - } - } - const ComputeType row_sum_stats1 = BlockAllReduce(sum_stats1); - const ComputeType row_sum_stats2 = BlockAllReduce(sum_stats2); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType x_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_x.template load(x_pack, row, pack_id * pack_size); - load_scaled_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - dy_pack[i] = (cols * dy_pack[i] - row_sum_stats1 - - (x_pack[i] - mean_val) * inv_variance_val * row_sum_stats2) - * inv_variance_over_cols; - } - store.template store(dy_pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchLayerNormGradBlockUncachedImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, STORE store, - const ComputeType* mean, - const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - constexpr int block_size = 1024; - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = - GetNumBlocks(LayerNormGradBlockUncachedImpl, - block_size, 0, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - LayerNormGradBlockUncachedImpl - <<>>(load_x, load_scaled_dy, store, mean, inv_variance, - rows, cols); - return hipPeekAtLastError(); -} - -template -struct DispatchLayerNormGradBlockUncachedImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - if (cols % 2 == 0 && cols > kWarpSize) { - return LaunchLayerNormGradBlockUncachedImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } else { - return LaunchLayerNormGradBlockUncachedImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } - } -}; - -template -inline hipError_t DispatchLayerNormGradBlockUncachedImpl(hipStream_t stream, LOAD_X load_x, - LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, - const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - return DispatchLayerNormGradBlockUncachedImplPackSize()( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLayerNormGrad(hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - if (cols <= 1024) { - return DispatchLayerNormGradWarpImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = - TryDispatchLayerNormGradBlockSMemImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols, - &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchLayerNormGradBlockUncachedImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLayerNormGrad(hipStream_t stream, LOAD_X load_x, LOAD_SCALED_DY load_scaled_dy, - STORE store, const ComputeType* mean, const ComputeType* inv_variance, - const int64_t rows, const int64_t cols) { - return DispatchLayerNormGradBlockUncachedImpl( - stream, load_x, load_scaled_dy, store, mean, inv_variance, rows, cols); -} - -} // namespace layer_norm - -} // namespace cuda - -} // namespace oneflow - - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_CUDA_LAYER_NORM_H_ diff --git a/oneflow/core/hip/softmax.hip.h b/oneflow/core/hip/softmax.hip.h deleted file mode 100644 index 5cf7f058702157e8fa929431c696a0144a13206f..0000000000000000000000000000000000000000 --- a/oneflow/core/hip/softmax.hip.h +++ /dev/null @@ -1,1499 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifndef ONEFLOW_CORE_HIP_SOFTMAX_H_ -#define ONEFLOW_CORE_HIP_SOFTMAX_H_ - -#ifdef WITH_ROCM - -#include -// #include -#include -#include - -// #if CUDA_VERSION >= 11000 -// #include -// #endif // CUDA_VERSION >= 11000 - -namespace oneflow { - -namespace cuda { - -namespace softmax { - -constexpr int kWarpSize = 64; - -template -struct SumOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return a + b; } -}; - -template -struct MaxOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return max(a, b); } -}; - -template class ReductionOp, typename T, int thread_group_width = kWarpSize> -__inline__ __device__ T WarpAllReduce(T val) { - for (int mask = thread_group_width / 2; mask > 0; mask /= 2) { - // val = ReductionOp()(val, __shfl_xor(0xffffffff, val, mask)); - val = ReductionOp()(val, __shfl_xor(val, mask, kWarpSize)); - } - return val; -} - -template class ReductionOp, typename T, int block_size> -__inline__ __device__ T BlockAllReduce(T val) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - __shared__ T result_broadcast; - T result = BlockReduce(temp_storage).Reduce(val, ReductionOp()); - if (threadIdx.x == 0) { result_broadcast = result; } - __syncthreads(); - return result_broadcast; -} - -template -__inline__ __device__ T Inf(); - -template<> -__inline__ __device__ float Inf() { - return __int_as_float(0x7f800000U); -} - -template<> -__inline__ __device__ double Inf() { - return __longlong_as_double(0x7ff0000000000000ULL); -} - -template -__inline__ __device__ T Exp(T x); - -template<> -__inline__ __device__ float Exp(float x) { -#ifdef OF_SOFTMAX_USE_FAST_MATH - return __expf(x); -#else - return exp(x); -#endif -} - -template<> -__inline__ __device__ double Exp(double x) { - return exp(x); -} - -template -__inline__ __device__ T Div(T a, T b); - -template<> -__inline__ __device__ float Div(float a, float b) { -#ifdef OF_SOFTMAX_USE_FAST_MATH - return __fdividef(a, b); -#else - return a / b; -#endif -} - -template<> -__inline__ __device__ double Div(double a, double b) { - return a / b; -} - -template -__inline__ __device__ T Log(T x); - -template<> -__inline__ __device__ float Log(float x) { -#ifdef OF_SOFTMAX_USE_FAST_MATH - return __logf(x); -#else - return log(x); -#endif -} -template<> -__inline__ __device__ double Log(double x) { - return log(x); -} - -inline hipError_t GetNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, - int* num_blocks) { - int dev; - { - hipError_t err = hipGetDevice(&dev); - if (err != hipSuccess) { return err; } - } - int sm_count; - { - hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev); - if (err != hipSuccess) { return err; } - } - int tpm; - { - hipError_t err = hipDeviceGetAttribute(&tpm, hipDeviceAttributeMaxThreadsPerMultiProcessor, dev); - if (err != hipSuccess) { return err; } - } - *num_blocks = - std::max(1, std::min(max_blocks, sm_count * tpm / block_size * waves)); - return hipSuccess; -} - -template -struct DefaultComputeType { - using type = T; -}; - -template<> -struct DefaultComputeType { - using type = float; -}; - -// #if CUDA_VERSION >= 11000 -// template<> -// struct DefaultComputeType { -// using type = float; -// }; -// #endif // CUDA_VERSION >= 11000 - -template -struct GetPackType { - using type = typename std::aligned_storage::type; -}; - -template -using PackType = typename GetPackType::type; - -template -union Pack { - static_assert(sizeof(PackType) == sizeof(T) * N, ""); - __device__ Pack() { - // do nothing - } - PackType storage; - T elem[N]; -}; - -template -struct DirectLoad { - DirectLoad(const SRC* src, int64_t row_size) : src(src), row_size(row_size) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - Pack pack; - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { dst[i] = static_cast(pack.elem[i]); } - } - const SRC* src; - int64_t row_size; -}; - -template -struct DirectStore { - DirectStore(DST* dst, int64_t row_size) : dst(dst), row_size(row_size) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - Pack pack; - const int64_t offset = (row * row_size + col) / N; -#pragma unroll - for (int i = 0; i < N; ++i) { pack.elem[i] = static_cast(src[i]); } - *(reinterpret_cast*>(dst) + offset) = pack.storage; - } - DST* dst; - int64_t row_size; -}; - -enum class Algorithm { - kSoftmax = 0, - kLogSoftmax = 1, -}; - -template -__global__ void SoftmaxWarpImpl(LOAD load, STORE store, const int64_t rows, const int64_t cols) { - static_assert(cols_per_thread % pack_size == 0, ""); - static_assert(thread_group_width <= kWarpSize, ""); - static_assert(kWarpSize % thread_group_width == 0, ""); - constexpr int num_packs = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - ComputeType buf[rows_per_access][cols_per_thread]; - const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; - const int num_global_thread_group = gridDim.x * blockDim.y; - const int lane_id = threadIdx.x; - const int64_t step = num_global_thread_group * rows_per_access; - for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) { - ComputeType thread_max[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - thread_max[row_id] = -Inf(); - ComputeType* row_buf = buf[row_id]; -#pragma unroll - for (int pack_id = 0; pack_id < num_packs; ++pack_id) { - const int pack_offset = pack_id * pack_size; - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - load.template load(row_buf + pack_offset, row + row_id, col); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - thread_max[row_id] = max(thread_max[row_id], row_buf[pack_offset + i]); - } - } else { -#pragma unroll - for (int i = 0; i < pack_size; ++i) { row_buf[pack_offset + i] = -Inf(); } - } - } - } - ComputeType warp_max[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - warp_max[row_id] = WarpAllReduce(thread_max[row_id]); - } - ComputeType thread_sum[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - thread_sum[row_id] = 0; - ComputeType* row_buf = buf[row_id]; -#pragma unroll - for (int i = 0; i < cols_per_thread; ++i) { - if (algorithm == Algorithm::kSoftmax) { - row_buf[i] = Exp(row_buf[i] - warp_max[row_id]); - thread_sum[row_id] += row_buf[i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - row_buf[i] -= warp_max[row_id]; - thread_sum[row_id] += Exp(row_buf[i]); - } else { - asm volatile("s_trap 0;"); - } - } - } - ComputeType warp_sum[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - warp_sum[row_id] = WarpAllReduce(thread_sum[row_id]); - } -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - ComputeType* row_buf = buf[row_id]; -#pragma unroll - for (int i = 0; i < cols_per_thread; ++i) { - if (algorithm == Algorithm::kSoftmax) { - row_buf[i] = Div(row_buf[i], warp_sum[row_id]); - } else if (algorithm == Algorithm::kLogSoftmax) { - row_buf[i] -= Log(warp_sum[row_id]); - } else { - asm volatile("s_trap 0;"); - } - } -#pragma unroll - for (int i = 0; i < num_packs; ++i) { - const int col = (i * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - store.template store(row_buf + i * pack_size, row + row_id, col); - } - } - } - } -} - -template -inline hipError_t LaunchSoftmaxWarpImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols) { - // std::cout << "LaunchSoftmaxWarpImpl" << std::endl; - constexpr int block_size = 128; - constexpr int waves = 32; - static_assert(block_size % thread_group_width == 0, ""); - constexpr int thread_groups_per_block = block_size / thread_group_width; - dim3 block_dim(thread_group_width, thread_groups_per_block); - const int64_t num_blocks = - (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxWarpImpl - <<>>(load, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t DispatchSoftmaxWarpImplPadding(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchSoftmaxWarpImpl( - stream, load, store, rows, cols); - } else { - return LaunchSoftmaxWarpImpl( - stream, load, store, rows, cols); - } -} - -template -typename std::enable_if::type DispatchSoftmaxWarpImplCols( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, \ - rows, cols); \ - } else { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, \ - rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, rows, cols); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(5) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(7) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(9) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(11) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(13) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(15) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(17) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(19) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(21) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(23) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(25) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(27) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(29) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(31) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -typename std::enable_if::type DispatchSoftmaxWarpImplCols( - hipStream_t stream, LOAD load, STORE store, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, \ - rows, cols); \ - } else { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, \ - rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchSoftmaxWarpImplPadding(stream, load, store, rows, cols); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -struct DispatchSoftmaxWarpImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - if (cols % 2 == 0) { - return DispatchSoftmaxWarpImplCols(stream, load, - store, rows, cols); - } else { - return DispatchSoftmaxWarpImplCols(stream, load, - store, rows, cols); - } - } -}; - -template -inline hipError_t DispatchSoftmaxWarpImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols) { - return DispatchSoftmaxWarpImplPackSize()(stream, load, store, - rows, cols); -} - -template -__global__ void SoftmaxBlockSMemImpl(LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[]; - auto* buf = reinterpret_cast(shared_buf); - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_max = -Inf(); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - buf[i * num_packs + pack_id] = pack[i]; - thread_max = max(thread_max, pack[i]); - } - } - const ComputeType row_max = BlockAllReduce(thread_max); - ComputeType thread_sum = 0; - for (int col = tid; col < cols; col += block_size) { - if (algorithm == Algorithm::kSoftmax) { - const ComputeType exp_x = Exp(buf[col] - row_max); - buf[col] = exp_x; - thread_sum += exp_x; - } else { - const ComputeType x = buf[col] - row_max; - buf[col] = x; - thread_sum += Exp(x); - } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - pack[i] = Div(buf[i * num_packs + pack_id], row_sum); - } else if (algorithm == Algorithm::kLogSoftmax) { - pack[i] = buf[i * num_packs + pack_id] - Log(row_sum); - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -__global__ void SoftmaxBlockSMemImpl_1024(LOAD load, STORE store, const int64_t rows, - const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - extern __shared__ __align__(sizeof(double)) unsigned char shared_buf[]; - auto* buf = reinterpret_cast(shared_buf); - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_max = -Inf(); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - buf[i * num_packs + pack_id] = pack[i]; - thread_max = max(thread_max, pack[i]); - } - } - const ComputeType row_max = BlockAllReduce(thread_max); - ComputeType thread_sum = 0; - for (int col = tid; col < cols; col += block_size) { - if (algorithm == Algorithm::kSoftmax) { - const ComputeType exp_x = Exp(buf[col] - row_max); - buf[col] = exp_x; - thread_sum += exp_x; - } else { - const ComputeType x = buf[col] - row_max; - buf[col] = x; - thread_sum += Exp(x); - } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - pack[i] = Div(buf[i * num_packs + pack_id], row_sum); - } else if (algorithm == Algorithm::kLogSoftmax) { - pack[i] = buf[i * num_packs + pack_id] - Log(row_sum); - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchSoftmaxBlockSMemImpl(hipStream_t stream, LOAD load, STORE store, int smem, - const int64_t rows, const int64_t cols) { - - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxBlockSMemImpl - <<>>(load, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t LaunchSoftmaxBlockSMemImpl_1024(hipStream_t stream, LOAD load, STORE store, int smem, - const int64_t rows, const int64_t cols) { - - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxBlockSMemImpl_1024 - <<>>(load, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t TryDispatchSoftmaxBlockSMemImplBlockSize(hipStream_t stream, LOAD load, - STORE store, const int64_t rows, - const int64_t cols, bool* success) { - - constexpr int block_size_conf_1 = 128; - constexpr int block_size_conf_2 = 256; - constexpr int block_size_conf_3 = 512; - constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType); - int max_active_blocks_conf_1; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_1, - SoftmaxBlockSMemImpl, - block_size_conf_1, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_1 <= 0) { - *success = false; - return hipSuccess; - } - int max_active_blocks_conf_4; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_4, - SoftmaxBlockSMemImpl_1024, - block_size_conf_4, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxBlockSMemImpl_1024(stream, load, store, smem, rows, cols); - } - int max_active_blocks_conf_3; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_3, - SoftmaxBlockSMemImpl, - block_size_conf_3, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxBlockSMemImpl(stream, load, store, smem, rows, cols); - } - int max_active_blocks_conf_2; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_2, - SoftmaxBlockSMemImpl, - block_size_conf_2, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxBlockSMemImpl(stream, load, store, smem, rows, cols); - } - *success = true; - return LaunchSoftmaxBlockSMemImpl(stream, load, store, smem, rows, cols); -} - -template -struct TryDispatchSoftmaxBlockSMemImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols, bool* success) { - if (cols % 2 == 0) { - return TryDispatchSoftmaxBlockSMemImplBlockSize( - stream, load, store, rows, cols, success); - } else { - return TryDispatchSoftmaxBlockSMemImplBlockSize( - stream, load, store, rows, cols, success); - } - } -}; - -template -inline hipError_t TryDispatchSoftmaxBlockSMemImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols, - bool* success) { - return TryDispatchSoftmaxBlockSMemImplPackSize()( - stream, load, store, rows, cols, success); -} - -template -__global__ void SoftmaxBlockUncachedImpl(LOAD load, STORE store, const int64_t rows, - const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_max = -Inf(); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { thread_max = max(thread_max, pack[i]); } - } - const ComputeType row_max = BlockAllReduce(thread_max); - ComputeType thread_sum = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { thread_sum += Exp(pack[i] - row_max); } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; - load.template load(pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - pack[i] = Div(Exp(pack[i] - row_max), row_sum); - } else if (algorithm == Algorithm::kLogSoftmax) { - pack[i] = (pack[i] - row_max) - Log(row_sum); - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchSoftmaxBlockUncachedImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols) { - // std::cout << "LaunchSoftmaxBlockUncachedImpl" << std::endl; - constexpr int block_size = 1024; - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxBlockUncachedImpl - <<>>(load, store, rows, cols); - return hipPeekAtLastError(); -} - -template -struct DispatchSoftmaxBlockUncachedImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - if (cols % 2 == 0) { - return LaunchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); - } else { - return LaunchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); - } - } -}; - -template -inline hipError_t DispatchSoftmaxBlockUncachedImpl(hipStream_t stream, LOAD load, STORE store, - const int64_t rows, const int64_t cols) { - return DispatchSoftmaxBlockUncachedImplPackSize()( - stream, load, store, rows, cols); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchSoftmax(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - if (cols < 1024) { - return DispatchSoftmaxWarpImpl( - stream, load, store, rows, cols); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = - TryDispatchSoftmaxBlockSMemImpl( - stream, load, store, rows, cols, &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchSoftmax(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - return DispatchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLogSoftmax(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - if (cols <= 1024) { - return DispatchSoftmaxWarpImpl( - stream, load, store, rows, cols); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = - TryDispatchSoftmaxBlockSMemImpl( - stream, load, store, rows, cols, &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLogSoftmax(hipStream_t stream, LOAD load, STORE store, const int64_t rows, - const int64_t cols) { - return DispatchSoftmaxBlockUncachedImpl( - stream, load, store, rows, cols); -} - -template -__global__ void SoftmaxGradWarpImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, - const int64_t cols) { - static_assert(cols_per_thread % pack_size == 0, ""); - constexpr int pack_per_thread = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - static_assert(thread_group_width <= kWarpSize, ""); - static_assert(kWarpSize % thread_group_width == 0, ""); - ComputeType y_buf[rows_per_access][cols_per_thread]; - ComputeType dy_buf[rows_per_access][cols_per_thread]; - const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; - const int num_global_thread_group = gridDim.x * blockDim.y; - const int lane_id = threadIdx.x; - const int64_t step = num_global_thread_group * rows_per_access; - for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) { - ComputeType thread_sum[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - thread_sum[row_id] = 0; - ComputeType* row_y_buf = y_buf[row_id]; - ComputeType* row_dy_buf = dy_buf[row_id]; -#pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { - const int pack_offset = pack_id * pack_size; - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - load_y.template load(row_y_buf + pack_offset, row + row_id, col); - load_dy.template load(row_dy_buf + pack_offset, row + row_id, col); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - thread_sum[row_id] += row_y_buf[pack_offset + i] * row_dy_buf[pack_offset + i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - thread_sum[row_id] += row_dy_buf[pack_offset + i]; - } else { - asm volatile("s_trap 0;"); - } - } - } - } - } - ComputeType warp_sum[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - warp_sum[row_id] = WarpAllReduce(thread_sum[row_id]); - } -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; ++row_id) { - ComputeType* row_y_buf = y_buf[row_id]; - ComputeType* row_dy_buf = dy_buf[row_id]; -#pragma unroll - for (int pack_id = 0; pack_id < pack_per_thread; ++pack_id) { - const int pack_offset = pack_id * pack_size; - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - if (!padding || col < cols) { - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - row_dy_buf[pack_offset + i] = - (row_dy_buf[pack_offset + i] - warp_sum[row_id]) * row_y_buf[pack_offset + i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - row_dy_buf[pack_offset + i] -= Exp(row_y_buf[pack_offset + i]) * warp_sum[row_id]; - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(row_dy_buf + pack_offset, row + row_id, col); - } - } - } - } -} - -template -inline hipError_t LaunchSoftmaxGradWarpImpl(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, - STORE store, const int64_t rows, const int64_t cols) { - constexpr int block_size = 128; - constexpr int waves = 32; - static_assert(block_size % thread_group_width == 0, ""); - constexpr int thread_groups_per_block = block_size / thread_group_width; - dim3 block_dim(thread_group_width, thread_groups_per_block); - const int64_t num_blocks = - (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxGradWarpImpl - <<>>(load_y, load_dy, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t DispatchSoftmaxGradWarpImplPadding(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchSoftmaxGradWarpImpl(stream, load_y, load_dy, store, rows, cols); - } else { - return LaunchSoftmaxGradWarpImpl(stream, load_y, load_dy, store, rows, cols); - } -} - -template -typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( - hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchSoftmaxGradWarpImplPadding( \ - stream, load_y, load_dy, store, rows, cols); \ - } else { \ - return DispatchSoftmaxGradWarpImplPadding( \ - stream, load_y, load_dy, store, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchSoftmaxGradWarpImplPadding(stream, load_y, load_dy, \ - store, rows, cols); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(5) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(7) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(9) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(11) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(13) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(15) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(17) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(19) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(21) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(23) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(25) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(27) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(29) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(31) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -typename std::enable_if::type DispatchSoftmaxGradWarpImplCols( - hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchSoftmaxGradWarpImplPadding( \ - stream, load_y, load_dy, store, rows, cols); \ - } else { \ - return DispatchSoftmaxGradWarpImplPadding( \ - stream, load_y, load_dy, store, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchSoftmaxGradWarpImplPadding(stream, load_y, load_dy, \ - store, rows, cols); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -struct DispatchSoftmaxGradWarpImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - if (cols % 2 == 0) { - return DispatchSoftmaxGradWarpImplCols( - stream, load_y, load_dy, store, rows, cols); - } else { - return DispatchSoftmaxGradWarpImplCols( - stream, load_y, load_dy, store, rows, cols); - } - } -}; - -template -inline hipError_t DispatchSoftmaxGradWarpImpl(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, - STORE store, const int64_t rows, - const int64_t cols) { - return DispatchSoftmaxGradWarpImplPackSize()( - stream, load_y, load_dy, store, rows, cols); -} - -template -__global__ void SoftmaxGradBlockSMemImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - extern __shared__ __align__(sizeof(double)) unsigned char grad_shared_buf[]; - auto* y_buf = reinterpret_cast(grad_shared_buf); - auto* dy_buf = y_buf + cols; - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_sum = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType y_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_y.template load(y_pack, row, pack_id * pack_size); - load_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - y_buf[i * num_packs + pack_id] = y_pack[i]; - dy_buf[i * num_packs + pack_id] = dy_pack[i]; - if (algorithm == Algorithm::kSoftmax) { - thread_sum += y_pack[i] * dy_pack[i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - thread_sum += dy_pack[i]; - } else { - asm volatile("s_trap 0;"); - } - } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - pack[i] = (dy_buf[i * num_packs + pack_id] - row_sum) * y_buf[i * num_packs + pack_id]; - } else if (algorithm == Algorithm::kLogSoftmax) { - pack[i] = dy_buf[i * num_packs + pack_id] - Exp(y_buf[i * num_packs + pack_id]) * row_sum; - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -__global__ void SoftmaxGradBlockSMemImpl_1024(LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - extern __shared__ __align__(sizeof(double)) unsigned char grad_shared_buf[]; - auto* y_buf = reinterpret_cast(grad_shared_buf); - auto* dy_buf = y_buf + cols; - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_sum = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType y_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_y.template load(y_pack, row, pack_id * pack_size); - load_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - y_buf[i * num_packs + pack_id] = y_pack[i]; - dy_buf[i * num_packs + pack_id] = dy_pack[i]; - if (algorithm == Algorithm::kSoftmax) { - thread_sum += y_pack[i] * dy_pack[i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - thread_sum += dy_pack[i]; - } else { - asm volatile("s_trap 0;"); - } - } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType pack[pack_size]; -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - pack[i] = (dy_buf[i * num_packs + pack_id] - row_sum) * y_buf[i * num_packs + pack_id]; - } else if (algorithm == Algorithm::kLogSoftmax) { - pack[i] = dy_buf[i * num_packs + pack_id] - Exp(y_buf[i * num_packs + pack_id]) * row_sum; - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchSoftmaxGradBlockSMemImpl(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, int smem, - const int64_t rows, const int64_t cols) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxGradBlockSMemImpl - <<>>(load_y, load_dy, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t LaunchSoftmaxGradBlockSMemImpl_1024(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, int smem, - const int64_t rows, const int64_t cols) { - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxGradBlockSMemImpl_1024 - <<>>(load_y, load_dy, store, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t TryDispatchSoftmaxGradBlockSMemImplBlockSize(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, - const int64_t rows, - const int64_t cols, bool* success) { - constexpr int block_size_conf_1 = 128; - constexpr int block_size_conf_2 = 256; - constexpr int block_size_conf_3 = 512; - constexpr int block_size_conf_4 = 1024; - const size_t smem = cols * sizeof(ComputeType) * 2; - int max_active_blocks_conf_1; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_1, - SoftmaxGradBlockSMemImpl, - block_size_conf_1, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_1 <= 0) { - *success = false; - return hipSuccess; - } - int max_active_blocks_conf_4; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_4, - SoftmaxGradBlockSMemImpl_1024, - block_size_conf_4, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_4 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxGradBlockSMemImpl_1024(stream, load_y, load_dy, - store, smem, rows, cols); - } - int max_active_blocks_conf_3; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_3, - SoftmaxGradBlockSMemImpl, - block_size_conf_3, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_3 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxGradBlockSMemImpl(stream, load_y, load_dy, - store, smem, rows, cols); - } - int max_active_blocks_conf_2; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks_conf_2, - SoftmaxGradBlockSMemImpl, - block_size_conf_2, smem); - if (err != hipSuccess) { return err; } - } - if (max_active_blocks_conf_2 == max_active_blocks_conf_1) { - *success = true; - return LaunchSoftmaxGradBlockSMemImpl(stream, load_y, load_dy, - store, smem, rows, cols); - } - *success = true; - return LaunchSoftmaxGradBlockSMemImpl(stream, load_y, load_dy, - store, smem, rows, cols); -} - -template -struct TryDispatchSoftmaxGradBlockSMemImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols, bool* success) { - if (cols % 2 == 0) { - return TryDispatchSoftmaxGradBlockSMemImplBlockSize(stream, load_y, load_dy, store, - rows, cols, success); - } else { - return TryDispatchSoftmaxGradBlockSMemImplBlockSize(stream, load_y, load_dy, store, - rows, cols, success); - } - } -}; - -template -inline hipError_t TryDispatchSoftmaxGradBlockSMemImpl(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols, - bool* success) { - return TryDispatchSoftmaxGradBlockSMemImplPackSize()(stream, load_y, load_dy, store, - rows, cols, success); -} - -template -__global__ void SoftmaxGradBlockUncachedImpl(LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) __attribute__((amdgpu_flat_work_group_size(1,1024))) { - const int tid = threadIdx.x; - assert(cols % pack_size == 0); - const int num_packs = cols / pack_size; - for (int64_t row = blockIdx.x; row < rows; row += gridDim.x) { - ComputeType thread_sum = 0; - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType y_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_y.template load(y_pack, row, pack_id * pack_size); - load_dy.template load(dy_pack, row, pack_id * pack_size); - -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - thread_sum += y_pack[i] * dy_pack[i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - thread_sum += dy_pack[i]; - } else { - asm volatile("s_trap 0;"); - } - } - } - const ComputeType row_sum = BlockAllReduce(thread_sum); - for (int pack_id = tid; pack_id < num_packs; pack_id += block_size) { - ComputeType y_pack[pack_size]; - ComputeType dy_pack[pack_size]; - load_y.template load(y_pack, row, pack_id * pack_size); - load_dy.template load(dy_pack, row, pack_id * pack_size); -#pragma unroll - for (int i = 0; i < pack_size; ++i) { - if (algorithm == Algorithm::kSoftmax) { - dy_pack[i] = (dy_pack[i] - row_sum) * y_pack[i]; - } else if (algorithm == Algorithm::kLogSoftmax) { - dy_pack[i] -= Exp(y_pack[i]) * row_sum; - } else { - asm volatile("s_trap 0;"); - } - } - store.template store(dy_pack, row, pack_id * pack_size); - } - } -} - -template -inline hipError_t LaunchSoftmaxGradBlockUncachedImpl(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - constexpr int block_size = 1024; - constexpr int waves = 32; - int grid_dim_x; - { - hipError_t err = GetNumBlocks(block_size, rows, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - } - SoftmaxGradBlockUncachedImpl - <<>>(load_y, load_dy, store, rows, cols); - return hipPeekAtLastError(); -} - -template -struct DispatchSoftmaxGradBlockUncachedImplPackSize { - hipError_t operator()(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - if (cols % 2 == 0 && cols > kWarpSize) { - return LaunchSoftmaxGradBlockUncachedImpl( - stream, load_y, load_dy, store, rows, cols); - } else { - return LaunchSoftmaxGradBlockUncachedImpl( - stream, load_y, load_dy, store, rows, cols); - } - } -}; - -template -inline hipError_t DispatchSoftmaxGradBlockUncachedImpl(hipStream_t stream, LOAD_Y load_y, - LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - return DispatchSoftmaxGradBlockUncachedImplPackSize()(stream, load_y, load_dy, store, - rows, cols); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchSoftmaxGrad(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - if (cols <= 1024) { - return DispatchSoftmaxGradWarpImpl( - stream, load_y, load_dy, store, rows, cols); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = TryDispatchSoftmaxGradBlockSMemImpl( - stream, load_y, load_dy, store, rows, cols, &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, - store, rows, cols); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchSoftmaxGrad(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, store, - rows, cols); -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLogSoftmaxGrad(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - if (cols <= 1024) { - return DispatchSoftmaxGradWarpImpl( - stream, load_y, load_dy, store, rows, cols); - } else { - bool dispatch_smem_impl_success; - { - hipError_t err = TryDispatchSoftmaxGradBlockSMemImpl( - stream, load_y, load_dy, store, rows, cols, &dispatch_smem_impl_success); - if (err != hipSuccess) { return err; } - } - if (!dispatch_smem_impl_success) { - return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, - store, rows, cols); - } - return hipSuccess; - } -} - -template -inline typename std::enable_if::value, hipError_t>::type -DispatchLogSoftmaxGrad(hipStream_t stream, LOAD_Y load_y, LOAD_DY load_dy, STORE store, - const int64_t rows, const int64_t cols) { - return DispatchSoftmaxGradBlockUncachedImpl(stream, load_y, load_dy, - store, rows, cols); -} - -} // namespace softmax - -} // namespace cuda - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_CUDA_SOFTMAX_H_ diff --git a/oneflow/core/hip/unique.hip.h b/oneflow/core/hip/unique.hip.h deleted file mode 100644 index 6e7e6714d6437a077a86551e0ba2034b387c8cc9..0000000000000000000000000000000000000000 --- a/oneflow/core/hip/unique.hip.h +++ /dev/null @@ -1,251 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_HIP_UNIQUE_H_ -#define ONEFLOW_CORE_HIP_UNIQUE_H_ - -#ifdef WITH_ROCM - -#include -#include "hip/hip_runtime.h" -// #include -#include "hip/hip_runtime.h" -#include "oneflow/core/common/permutation_iterator.h" -#include "oneflow/core/common/not_equal_to_previous_adjacent_iterator.h" - -namespace oneflow { - -namespace cuda { - -namespace unique { - -using Flag = uint32_t; -static constexpr Flag kDefault = 0x0; -static constexpr Flag kInputSorted = 0x1; -static constexpr Flag kOutputInverseIndices = 0x1 << 1; -static constexpr Flag kOutputCounts = 0x1 << 2; - -namespace { - -constexpr size_t kCudaAlignSize = 512; - -__device__ __host__ __forceinline__ size_t GetCudaAlignedSize(size_t size) { - return (size + kCudaAlignSize - 1) / kCudaAlignSize * kCudaAlignSize; -} - -template -__device__ __host__ __forceinline__ T* PtrOffset(void* ptr, size_t offset) { - return reinterpret_cast(reinterpret_cast(ptr) + offset); -} - -__device__ __host__ __forceinline__ size_t max(size_t a, size_t b) { return a > b ? a : b; } - -template -hipError_t DoUnique(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, - void* workspace, size_t* workspace_size, hipStream_t stream) { - size_t ws = *workspace_size; - hipError_t err = hipcub::DeviceSelect::Unique( - workspace, ws, sorted_in, unique, num_unique, n, stream); - if (err != hipSuccess) { return err; } - if (*workspace_size == 0) { *workspace_size = ws; } - return hipSuccess; -} - -template -hipError_t DoUniqueWithCounts(size_t n, const Key* sorted_in, Key* unique, Index* num_unique, - Index* counts, void* workspace, size_t* workspace_size, - hipStream_t stream) { - size_t ws = *workspace_size; - hipError_t err = hipcub::DeviceRunLengthEncode::Encode( - workspace, ws, sorted_in, unique, counts, num_unique, n, stream); - if (err != hipSuccess) { return err; } - if (*workspace_size == 0) { *workspace_size = ws; } - return hipSuccess; -} - -template -hipError_t DispatchOutputCounts(Flag flag, size_t n, const Key* sorted_in, Key* unique, - Index* num_unique, Index* counts, void* workspace, - size_t* workspace_size, hipStream_t stream) { - size_t ws = *workspace_size; - if ((flag & kOutputCounts) != 0) { - hipError_t err = DoUniqueWithCounts(n, sorted_in, unique, num_unique, counts, - workspace, &ws, stream); - if (err != hipSuccess) { return err; } - } else { - hipError_t err = - DoUnique(n, sorted_in, unique, num_unique, workspace, &ws, stream); - if (err != hipSuccess) { return err; } - } - if (*workspace_size == 0) { *workspace_size = ws; } - return hipSuccess; -} - -template -hipError_t DoGenInverseIndices(size_t n, const Key* sorted_in, - InverseIndicesIter inverse_indices_iter, void* workspace, - size_t* workspace_size, hipStream_t stream) { - size_t ws = *workspace_size; - NotEqualToPreviousAdjacentIterator unique_counting_iter(sorted_in, 0); - hipError_t err = - hipcub::DeviceScan::InclusiveSum( - workspace, ws, unique_counting_iter, inverse_indices_iter, n, stream); - if (err != hipSuccess) { return err; } - if (*workspace_size == 0) { *workspace_size = ws; } - return hipSuccess; -} - -template -hipError_t DispatchOutputInverseIndices(Flag flag, size_t n, const Key* sorted_in, Key* unique, - Index* num_unique, InverseIndicesIter inverse_indices_iter, - Index* counts, void* workspace, size_t* workspace_size, - hipStream_t stream) { - size_t dispatch_with_counts_ws = *workspace_size; - size_t do_gen_inverse_indices_ws = *workspace_size; - { - hipError_t err = - DispatchOutputCounts(flag, n, sorted_in, unique, num_unique, counts, workspace, - &dispatch_with_counts_ws, stream); - if (err != hipSuccess) { return err; } - } - if ((flag & kOutputInverseIndices) != 0) { - hipError_t err = DoGenInverseIndices( - n, sorted_in, inverse_indices_iter, workspace, &do_gen_inverse_indices_ws, stream); - if (err != hipSuccess) { return err; } - } - if (*workspace_size == 0) { - *workspace_size = max(dispatch_with_counts_ws, do_gen_inverse_indices_ws); - } - return hipSuccess; -} - -template -__global__ void IotaKernel(size_t n, T* out) { - for (T i = blockIdx.x * blockDim.x + threadIdx.x, step = blockDim.x * gridDim.x; i < n; - i += step) { - out[i] = i; - } -} - -template -hipError_t DoSort(size_t n, const Key* in, Key* sorted, Index* sorted_indices, void* workspace, - size_t* workspace_size, hipStream_t stream) { - Index* indices; - const size_t indices_size = GetCudaAlignedSize(n * sizeof(Index)); - void* sort_workspace; - size_t sort_ws; - if (*workspace_size == 0) { - indices = nullptr; - sort_workspace = nullptr; - sort_ws = 0; - } else { - if (*workspace_size <= indices_size) { return hipErrorInvalidValue; } - indices = PtrOffset(workspace, 0); - sort_workspace = PtrOffset(workspace, indices_size); - sort_ws = *workspace_size - indices_size; - } - if (*workspace_size != 0) { - const int block_size = 1024; - const int num_blocks = static_cast((n + block_size - 1) / block_size); - IotaKernel<<>>(n, indices); - } - hipError_t err = hipcub::DeviceRadixSort::SortPairs( - sort_workspace, sort_ws, in, sorted, indices, sorted_indices, n, 0, sizeof(Key) * 8, stream); - if (err != hipSuccess) { return err; } - if (*workspace_size == 0) { *workspace_size = indices_size + sort_ws; } - return hipSuccess; -} - -template -hipError_t DispatchInputSorted(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, - Index* inverse_indices, Index* counts, void* workspace, - size_t* workspace_size, hipStream_t stream) { - if ((flag & kInputSorted) != 0) { - return DispatchOutputInverseIndices(flag, n, in, unique, num_unique, - inverse_indices, counts, workspace, - workspace_size, stream); - } else { - const size_t sorted_in_size = GetCudaAlignedSize(n * sizeof(Key)); - const size_t sorted_indices_size = GetCudaAlignedSize(n * sizeof(Index)); - const size_t sort_buffer_size = sorted_in_size + sorted_indices_size; - Key* sorted_in; - Index* sorted_indices; - size_t do_sort_ws; - void* do_sort_workspace; - size_t do_inverse_indices_ws; - void* do_inverse_indices_workspace; - if (*workspace_size == 0) { - sorted_in = nullptr; - sorted_indices = nullptr; - do_sort_ws = 0; - do_sort_workspace = nullptr; - do_inverse_indices_ws = 0; - do_inverse_indices_workspace = nullptr; - } else { - if (*workspace_size <= sort_buffer_size) { return hipErrorInvalidValue; } - sorted_in = PtrOffset(workspace, 0); - sorted_indices = PtrOffset(workspace, sorted_in_size); - do_sort_ws = *workspace_size - sort_buffer_size; - do_sort_workspace = PtrOffset(workspace, sort_buffer_size); - do_inverse_indices_ws = do_sort_ws; - do_inverse_indices_workspace = do_sort_workspace; - } - { - hipError_t err = DoSort(n, in, sorted_in, sorted_indices, do_sort_workspace, - &do_sort_ws, stream); - if (err != hipSuccess) { return err; } - } - PermutationIterator inverse_indices_iter(inverse_indices, - sorted_indices); - { - hipError_t err = DispatchOutputInverseIndices( - flag, n, sorted_in, unique, num_unique, inverse_indices_iter, counts, - do_inverse_indices_workspace, &do_inverse_indices_ws, stream); - if (err != hipSuccess) { return err; } - } - if (*workspace_size == 0) { - *workspace_size = sort_buffer_size + max(do_sort_ws, do_inverse_indices_ws); - } - return hipSuccess; - } -} - -} // namespace - -template -hipError_t Launch(Flag flag, size_t n, const Key* in, Key* unique, Index* num_unique, - Index* inverse_indices, Index* counts, void* workspace, size_t workspace_size, - hipStream_t stream) { - if (workspace_size == 0) { return hipErrorInvalidValue; } - return DispatchInputSorted(flag, n, in, unique, num_unique, inverse_indices, counts, - workspace, &workspace_size, stream); -} - -template -hipError_t GetWorkspaceSize(Flag flag, size_t n, size_t* workspace_size) { - *workspace_size = 0; - return DispatchInputSorted(flag, n, nullptr, nullptr, nullptr, nullptr, nullptr, - nullptr, workspace_size, 0); -} - -} // namespace unique - -} // namespace cuda - -} // namespace oneflow - -#endif // WITH_ROCM - -#endif // ONEFLOW_CORE_CUDA_UNIQUE_H_ diff --git a/oneflow/core/hipdnn/hipdnn.h b/oneflow/core/hipdnn/hipdnn.h index 09b0046120bf455f0145a0d209a61a72e26296c7..ad77f474e7d1a08ad6145b76eabb7a0b3bb7e5d5 100644 --- a/oneflow/core/hipdnn/hipdnn.h +++ b/oneflow/core/hipdnn/hipdnn.h @@ -28,6 +28,7 @@ #define HIPDNN_BN_MIN_EPSILON 1e-05 #include +#include #define HIPDNN_VERSION 7000 diff --git a/oneflow/core/hipdnn/hipdnn_miopen.cpp b/oneflow/core/hipdnn/hipdnn_miopen.cpp index 8b6f6b991de9a7b3aa9838bcc990f4a84d903e1f..1d33266929c3ecebf16afde8fadc1e5e375bbafb 100644 --- a/oneflow/core/hipdnn/hipdnn_miopen.cpp +++ b/oneflow/core/hipdnn/hipdnn_miopen.cpp @@ -165,9 +165,17 @@ hipdnnStatus_t hipTomiopenDataType(hipdnnDataType_t in, miopenDataType_t *out) { *out = miopenHalf; break; case HIPDNN_DATA_DOUBLE: + *out = miopenDouble; + break; case HIPDNN_DATA_INT8: + *out = miopenInt8; + break; case HIPDNN_DATA_INT32: + *out = miopenInt32; + break; case HIPDNN_DATA_INT8x4: + *out = miopenInt8x4; + break; default: HIPDNN_OPEN_LOG_M("hipTomiopenDataType " << in << ": NOT SUPPORTED." << std::flush); @@ -1059,7 +1067,7 @@ hipdnnStatus_t hipdnnSetTensor4dDescriptor(hipdnnTensorDescriptor_t tensorDesc, int c, int h, int w) { miopenDataType_t miDT; - CHECK_HIPDNN(hipTensorFormatSupported(format)); + //CHECK_HIPDNN(hipTensorFormatSupported(format)); CHECK_HIPDNN(hipTomiopenDataType(dataType, &miDT)); CHECK_MIO(miopenSet4dTensorDescriptor((miopenTensorDescriptor_t)tensorDesc, miDT, n, c, h, w)); @@ -1287,7 +1295,7 @@ hipdnnStatus_t hipdnnSetFilter4dDescriptor(hipdnnFilterDescriptor_t filterDesc, int k, int c, int h, int w) { miopenDataType_t miDT; - CHECK_HIPDNN(hipTensorFormatSupported(format)); + //CHECK_HIPDNN(hipTensorFormatSupported(format)); CHECK_HIPDNN(hipTomiopenDataType(dataType, &miDT)); CHECK_MIO(miopenSet4dTensorDescriptor((miopenTensorDescriptor_t)filterDesc, miDT, k, c, h, w)); @@ -1389,6 +1397,8 @@ hipdnnStatus_t hipdnnSetConvolutionNdDescriptor( << arrayLength << std::flush); int pad_h, pad_w, u, v, d_h, d_w; + miopenConvolutionMode_t miConvMode; + CHECK_HIPDNN(hipTomiopenConvolutionMode(mode,&miConvMode)); hipdnnConvolutionDescriptor_t convDesc_cast = ((structConvDesc_t*)(convDesc))->descriptor; @@ -1418,10 +1428,15 @@ hipdnnStatus_t hipdnnSetConvolutionNdDescriptor( v = filterStrideA[1]; d_h = dilationA[0]; d_w = dilationA[1]; - CHECK_MIO(miopenInitConvolutionDescriptor( - (miopenConvolutionDescriptor_t)convDesc_cast, - miopenConvolution, pad_h, - pad_w, u, v, d_h, d_w) ); + // CHECK_MIO(miopenInitConvolutionDescriptor( + // (miopenConvolutionDescriptor_t)convDesc_cast, + // miopenConvolution, pad_h, + // pad_w, u, v, d_h, d_w) ); + + CHECK_MIO(miopenInitConvolutionNdDescriptor((miopenConvolutionDescriptor_t)convDesc_cast, + arrayLength, const_cast(padA), const_cast(filterStrideA), + const_cast(dilationA), miConvMode)); + // Populate the map container with key being newly created 2Ddescriptor // and value a 3 dim array with index mapping as // 0-pad, 1-stride and 2-dilation @@ -2994,13 +3009,17 @@ hipdnnStatus_t hipdnnSetFilterNdDescriptor( HIPDNN_OPEN_LOG_C("ENTER hipdnnSetFilterNdDescriptor " << filterDesc << std::flush); - int strideA[nbDims - 1]; + // int strideA[nbDims - 1]; + int strideA[nbDims]; for (int k = nbDims - 1; k >= 0; k--) { strideA[k] = (k != nbDims - 1) ? strideA[k + 1] * filterDimA[k + 1] : 1; } CHECK_HIPDNN(hipTomiopenDataType(dataType, &moDT)); - int strideDimA[nbDims - 1]; + // int strideDimA[nbDims - 1]; + + int strideDimA[nbDims]; + for (int k = nbDims - 1; k >= 0; k--) { strideDimA[k] = (k != nbDims - 1) ? strideDimA[k + 1] * filterDimA[k + 1] : 1; diff --git a/oneflow/core/intrusive/dss.h b/oneflow/core/intrusive/dss.h index 30566dc9761da50f4295f0f0fde4347f101f9a6f..e0a56be4a861439aabbe959b877e0636b65b2837 100644 --- a/oneflow/core/intrusive/dss.h +++ b/oneflow/core/intrusive/dss.h @@ -317,9 +317,9 @@ namespace oneflow { struct __DssFieldType__ { \ using type = field_type; \ }; \ - static const int OF_PP_CAT(field, kDssFieldNumber) = field_counter; \ + [[maybe_unused]] static const int OF_PP_CAT(field, kDssFieldNumber) = field_counter; \ using OF_PP_CAT(field, DssFieldType) = field_type; \ - static const int OF_PP_CAT(field, kDssFieldOffset) = \ + [[maybe_unused]] static const int OF_PP_CAT(field, kDssFieldOffset) = \ __DssAccumulatedAlignedSize4Counter__::value; #define _DSS_END(field_counter, dss_type, type) \ diff --git a/oneflow/core/intrusive/flat_msg.h b/oneflow/core/intrusive/flat_msg.h index 60370dc51168212118118d718763bb0eb428e297..895c35bc99d5d63504512e8761d72af1baae7fb2 100644 --- a/oneflow/core/intrusive/flat_msg.h +++ b/oneflow/core/intrusive/flat_msg.h @@ -37,16 +37,16 @@ namespace oneflow { FLAT_MSG_DEFINE_BASIC_METHODS(struct_name); \ FLAT_MSG_DEFINE_DEFAULT(struct_name); -#define FLAT_MSG_END(struct_name) \ - static_assert(__is_flat_message_type__, "this struct is not a flat message"); \ - \ - public: \ - static const int __NumberOfFields__ = STATIC_COUNTER(field_counter); \ - \ - public: \ - INCREASE_STATIC_COUNTER(field_counter); \ - DSS_END(STATIC_COUNTER(field_counter), "flat message", struct_name); \ - } \ +#define FLAT_MSG_END(struct_name) \ + static_assert(__is_flat_message_type__, "this struct is not a flat message"); \ + \ + public: \ + [[maybe_unused]] static const int __NumberOfFields__ = STATIC_COUNTER(field_counter); \ + \ + public: \ + INCREASE_STATIC_COUNTER(field_counter); \ + DSS_END(STATIC_COUNTER(field_counter), "flat message", struct_name); \ + } \ ; #define FLAT_MSG_DEFINE_OPTIONAL(field_type, field_name) \ diff --git a/oneflow/core/intrusive/shared_ptr.h b/oneflow/core/intrusive/shared_ptr.h index f96e6eb80dd1d2b57f43488415f9f14b562e31a4..0eb0e82d97a26b0965cacfb45f26e63e2f0abc6f 100644 --- a/oneflow/core/intrusive/shared_ptr.h +++ b/oneflow/core/intrusive/shared_ptr.h @@ -32,10 +32,12 @@ class shared_ptr final { ptr_ = nullptr; Reset(obj_ptr.ptr_); } - shared_ptr(shared_ptr&& obj_ptr) { + shared_ptr(shared_ptr&& obj_ptr) noexcept { ptr_ = obj_ptr.ptr_; obj_ptr.ptr_ = nullptr; } + // NOLINTNEXTLINE(google-explicit-constructor) + operator shared_ptr() const { return shared_ptr(ptr_); } ~shared_ptr() { Clear(); } template @@ -49,13 +51,11 @@ class shared_ptr final { explicit operator bool() const { return ptr_ != nullptr; } value_type* get() const { return ptr_; } const value_type& Get() const { return *ptr_; } - const value_type* operator->() const { return ptr_; } - const value_type& operator*() const { return *ptr_; } + value_type* operator->() const { return ptr_; } + value_type& operator*() const { return *ptr_; } bool operator==(const shared_ptr& rhs) const { return this->ptr_ == rhs.ptr_; } value_type* Mutable() { return ptr_; } - value_type* operator->() { return ptr_; } // NOLINT - value_type& operator*() { return *ptr_; } void Reset() { Reset(nullptr); } @@ -71,6 +71,12 @@ class shared_ptr final { return *this; } + shared_ptr& operator=(shared_ptr&& rhs) noexcept { + ptr_ = rhs.ptr_; + rhs.ptr_ = nullptr; + return *this; + } + static shared_ptr __UnsafeMove__(value_type* ptr) { shared_ptr ret; ret.ptr_ = ptr; diff --git a/oneflow/core/job/cluster.cpp b/oneflow/core/job/cluster.cpp deleted file mode 100644 index 8df53113d1907f2de85c4841d733bb8a542dae16..0000000000000000000000000000000000000000 --- a/oneflow/core/job/cluster.cpp +++ /dev/null @@ -1,90 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job/cluster.h" -#include "oneflow/core/job/cluster_instruction.pb.h" -#include "oneflow/core/job/cluster_instruction.h" -#include "oneflow/core/control/ctrl_client.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/job/oneflow.h" -#include "oneflow/core/job/session_global_objects_scope.h" -#include "oneflow/core/job/env_global_objects_scope.h" -#include "oneflow/core/job/job_set.pb.h" -#include "oneflow/core/thread/thread_pool.h" - -namespace oneflow { - -namespace { - -void AsyncRunLazyJobSet(ThreadPool* lazy_runtime_thread, - std::shared_ptr wait_session_init) { - lazy_runtime_thread->AddWork([wait_session_init] { - ConfigProto config_proto; - Singleton::Get()->PullKV("config_proto", &config_proto); - CHECK_NOTNULL(Singleton::Get()); - int32_t machine_num = Singleton::Get()->TotalMachineNum(); - // do nothing if it's not my business - if (GlobalProcessCtx::Rank() >= machine_num) { return; } - Singleton::New(); - CHECK_JUST(Singleton::Get()->Init(config_proto)); - wait_session_init->Decrease(); - JobSet job_set; - Singleton::Get()->PullKV("session_job_set", &job_set); - { - Oneflow oneflow; - CHECK_JUST(oneflow.Init(job_set)); - } - Singleton::Delete(); - }); -} - -} // namespace - -Maybe Cluster::WorkerLoop() { - // The reason why excluding master machine is that - // eager instruction for compile-time symbol constructing must be done synchronously - CHECK_OR_RETURN(!GlobalProcessCtx::IsThisProcessMaster()); - { - // Oneflow::~Oneflow blocking in current thread is not acceptable - // Two reasons why `lazy_runtime_thread` is needed: - // 1. making current thread non-block by - // taking over the execution of Oneflow::~Oneflow - // 2. as a Synchronizing guard for all unfinished Oneflow::~Oneflow - // - // thread_num must be 1. - ThreadPool lazy_runtime_thread(1); - std::list> wait_session_init_list; - while (true) { - auto mut_cluster_instruction = std::make_shared(); - ClusterInstruction::WorkerReceiveInstruction(mut_cluster_instruction.get()); - if (mut_cluster_instruction->has_cluster_ctrl_halt()) { - break; - } else if (mut_cluster_instruction->has_cluster_ctrl_abort()) { - LOG(FATAL) << "received abort instruction"; - } else if (mut_cluster_instruction->has_cluster_ctrl_session_start()) { - ClusterInstruction::NewSessionBarrier(); - auto wait_session_init = std::make_shared(1); - wait_session_init_list.emplace_back(wait_session_init); - AsyncRunLazyJobSet(&lazy_runtime_thread, wait_session_init); - } else { - OF_UNIMPLEMENTED(); - } - } - } - ClusterInstruction::HaltBarrier(); - return Maybe::Ok(); -} - -} // namespace oneflow diff --git a/oneflow/core/job/cluster.h b/oneflow/core/job/cluster.h deleted file mode 100644 index 354e99acb2d938ebba042d1f1145676e1fc60eb0..0000000000000000000000000000000000000000 --- a/oneflow/core/job/cluster.h +++ /dev/null @@ -1,29 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_JOB_CLUSTER_H_ -#define ONEFLOW_CORE_JOB_CLUSTER_H_ - -#include "oneflow/core/common/maybe.h" - -namespace oneflow { - -struct Cluster final { - static Maybe WorkerLoop(); -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_JOB_CLUSTER_H_ diff --git a/oneflow/core/job/collective_boxing.zip b/oneflow/core/job/collective_boxing.zip deleted file mode 100644 index 4a3040360a6e3aacad033dacb5d708c3aa45463e..0000000000000000000000000000000000000000 Binary files a/oneflow/core/job/collective_boxing.zip and /dev/null differ diff --git a/oneflow/core/job/collective_boxing/nccl_executor_backend.cu b/oneflow/core/job/collective_boxing/nccl_executor_backend.cu index 0281ff657b6e0c94c0f06c12b4169fed117ba7fb..b48128cf6da23da6c0b63b3f09d19f455a4033d1 100644 --- a/oneflow/core/job/collective_boxing/nccl_executor_backend.cu +++ b/oneflow/core/job/collective_boxing/nccl_executor_backend.cu @@ -24,7 +24,11 @@ limitations under the License. #include "oneflow/core/thread/thread_pool.h" #include "oneflow/core/device/cuda_util.h" +#ifdef WITH_ROCM +#include +#else #include +#endif #include #include @@ -95,7 +99,7 @@ __global__ void MultiCopyGpu(MultiCopyParams multi_params) { } } -void MultiCopy(cudaStream_t stream, const MultiCopyParams& multi_params) { +void MultiCopy(GPU(Stream_t) stream, const MultiCopyParams& multi_params) { if (multi_params.count <= 0) { return; } CHECK_LE(multi_params.count, kMultiCopyParamsMaxSize); int64_t max_count = multi_params.params[0].count; @@ -183,7 +187,7 @@ class CommGroup final { for (int32_t local_rank = 0; local_rank < local_ranks.size(); ++local_rank) { const int32_t global_rank = local_ranks.at(local_rank); const int32_t device_id = device_set.device(global_rank).device_id(); - OF_CUDA_CHECK(cudaSetDevice(device_id)); + OF_CUDA_CHECK(GPU(SetDevice)(device_id)); rank_vec_.emplace_back(device_id, global_rank, global_rank_count_, local_rank, local_rank_count); rank_vec_.at(local_rank).InitRank(nccl_unique_id, global_rank_count_); @@ -209,43 +213,43 @@ class StreamCtx { : device_id_(device_id), fusion_buffer_size_(fusion_buffer_size) { CudaCurrentDeviceGuard guard(device_id_); int priority; - OF_CUDA_CHECK(cudaDeviceGetStreamPriorityRange(nullptr, &priority)); - OF_CUDA_CHECK(cudaStreamCreateWithPriority(&stream_, cudaStreamNonBlocking, priority)); - OF_CUDA_CHECK(cudaMalloc(&fusion_buffer_, fusion_buffer_size_)); + OF_CUDA_CHECK(GPU(DeviceGetStreamPriorityRange)(nullptr, &priority)); + OF_CUDA_CHECK(GPU(StreamCreateWithPriority)(&stream_, GPU(StreamNonBlocking), priority)); + OF_CUDA_CHECK(GPU(Malloc)(&fusion_buffer_, fusion_buffer_size_)); cb_event_poller_ = std::thread(&StreamCtx::PollEvent, this); } ~StreamCtx() { cb_event_chan_.Close(); cb_event_poller_.join(); CudaCurrentDeviceGuard guard(device_id_); - OF_CUDA_CHECK(cudaStreamSynchronize(stream_)); - OF_CUDA_CHECK(cudaStreamDestroy(stream_)); - OF_CUDA_CHECK(cudaFree(fusion_buffer_)); + OF_CUDA_CHECK(GPU(StreamSynchronize)(stream_)); + OF_CUDA_CHECK(GPU(StreamDestroy)(stream_)); + OF_CUDA_CHECK(GPU(Free)(fusion_buffer_)); } void PollEvent() { CudaCurrentDeviceGuard guard(device_id_); while (true) { - std::pair> cb_event; + std::pair> cb_event; ChannelStatus status = cb_event_chan_.Receive(&cb_event); if (status == kChannelStatusErrorClosed) { break; } CHECK_EQ(status, kChannelStatusSuccess); - OF_CUDA_CHECK(cudaEventSynchronize(cb_event.first)); + OF_CUDA_CHECK(GPU(EventSynchronize)(cb_event.first)); cb_event.second(); - OF_CUDA_CHECK(cudaEventDestroy(cb_event.first)); + OF_CUDA_CHECK(GPU(EventDestroy)(cb_event.first)); } } void AddCallback(const std::function& callback) { - cudaEvent_t event; - OF_CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming)); - OF_CUDA_CHECK(cudaEventRecord(event, stream_)); + GPU(Event_t) event; + OF_CUDA_CHECK(GPU(EventCreateWithFlags)(&event, GPU(EventDisableTiming))); + OF_CUDA_CHECK(GPU(EventRecord)(event, stream_)); CHECK_EQ(cb_event_chan_.Send(std::make_pair(event, callback)), kChannelStatusSuccess); } int32_t device_id() const { return device_id_; } - cudaStream_t stream() const { return stream_; } + GPU(Stream_t) stream() const { return stream_; } size_t fusion_buffer_size() const { return fusion_buffer_size_; } @@ -253,10 +257,10 @@ class StreamCtx { private: int32_t device_id_; - cudaStream_t stream_ = nullptr; + GPU(Stream_t) stream_ = nullptr; size_t fusion_buffer_size_; char* fusion_buffer_ = nullptr; - Channel>> cb_event_chan_; + Channel>> cb_event_chan_; std::thread cb_event_poller_; }; @@ -292,7 +296,7 @@ void LaunchFusedAllReduce(const CommGroup& comm_group, request_entry->GetRuntimeRequest(local_rank)->send_buff, request_entry->size_in_bytes()); }); - OF_CUDA_CHECK(cudaSetDevice(comm_rank.device_id())); + OF_CUDA_CHECK(GPU(SetDevice)(comm_rank.device_id())); MultiCopy(stream_ctx->stream(), copy_in_params); } @@ -300,7 +304,7 @@ void LaunchFusedAllReduce(const CommGroup& comm_group, for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { const CommRank& comm_rank = comm_group.GetCommRank(local_rank); const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - OF_CUDA_CHECK(cudaSetDevice(comm_rank.device_id())); + OF_CUDA_CHECK(GPU(SetDevice)(comm_rank.device_id())); OF_NCCL_CHECK(ncclAllReduce(stream_ctx->fusion_buffer(), stream_ctx->fusion_buffer(), elem_cnt, nccl_data_type, nccl_reduce_op, comm_rank.nccl_comm(), stream_ctx->stream())); @@ -317,7 +321,7 @@ void LaunchFusedAllReduce(const CommGroup& comm_group, stream_ctx->fusion_buffer() + offset_vec.at(i), request_entry->size_in_bytes()); }); - OF_CUDA_CHECK(cudaSetDevice(comm_rank.device_id())); + OF_CUDA_CHECK(GPU(SetDevice)(comm_rank.device_id())); MultiCopy(stream_ctx->stream(), copy_out_params); } } @@ -331,7 +335,7 @@ void LaunchAggregatedOps(const CommGroup& comm_group, const CommRank& comm_rank = comm_group.GetCommRank(local_rank); const auto comm = comm_rank.nccl_comm(); const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - OF_CUDA_CHECK(cudaSetDevice(comm_rank.device_id())); + OF_CUDA_CHECK(GPU(SetDevice)(comm_rank.device_id())); request_store->ForEachMutRequestEntryForIdsInJob( request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { const auto& op_desc = request_entry->desc().op_desc(); @@ -410,7 +414,7 @@ void AddCallbackAndResetRuntimeRequest( runtime_request_info_vec->emplace_back( std::move(saved_runtime_request_info.at(i).at(local_rank))); }); - OF_CUDA_CHECK(cudaSetDevice(comm_rank.device_id())); + OF_CUDA_CHECK(GPU(SetDevice)(comm_rank.device_id())); stream_ctx->AddCallback([runtime_request_info_vec]() { for (auto& runtime_request_info : *runtime_request_info_vec) { runtime_request_info->callback(Maybe::Ok()); @@ -478,7 +482,7 @@ struct NcclExecutorBackend::Impl { void InitStreamCtx() { int32_t num_devices; - OF_CUDA_CHECK(cudaGetDeviceCount(&num_devices)); + OF_CUDA_CHECK(GPU(GetDeviceCount)(&num_devices)); stream_id2device_id2stream_ctx.resize(num_streams); for (int64_t stream_id = 0; stream_id < num_streams; ++stream_id) { stream_id2device_id2stream_ctx.at(stream_id).resize(num_devices); diff --git a/oneflow/core/job/collective_boxing/nccl_executor_backend.h b/oneflow/core/job/collective_boxing/nccl_executor_backend.h index 435a0d2a159593d633be73ab3b18b307b350534b..911b45975ec2dd2019c19a7ef2cdd74c7320d056 100644 --- a/oneflow/core/job/collective_boxing/nccl_executor_backend.h +++ b/oneflow/core/job/collective_boxing/nccl_executor_backend.h @@ -54,6 +54,6 @@ class NcclExecutorBackend : public ExecutorBackend { } // namespace oneflow -#endif // WITH_CUDA || WITH_ROCM +#endif // WITH_CUDA #endif // ONEFLOW_CORE_JOB_COLLECTIVE_BOXING_NCCL_EXECUTOR_BACKEND_H_ diff --git a/oneflow/core/job/collective_boxing/nccl_executor_backend.hip.cpp b/oneflow/core/job/collective_boxing/nccl_executor_backend.hip.cpp deleted file mode 100644 index cab89de6ea5cd9ff8c299909ca046eff14e86e46..0000000000000000000000000000000000000000 --- a/oneflow/core/job/collective_boxing/nccl_executor_backend.hip.cpp +++ /dev/null @@ -1,665 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/job/collective_boxing/nccl_executor_backend.h" -#include "oneflow/core/job/collective_boxing/request_store.h" -#include "oneflow/core/device/nccl_util.h" -#include "oneflow/core/graph/boxing/collective_boxing_util.h" -#include "oneflow/core/job/resource_desc.h" -#include "oneflow/core/control/ctrl_client.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/thread/thread_pool.h" -#include "oneflow/core/device/cuda_util.h" - -#include - -#include -#include - -namespace oneflow { - -namespace boxing { - -namespace collective { - -namespace { - -ncclRedOp_t GetNcclReduceOp(ReduceMethod reduce_method) { - if (reduce_method == kReduceMethodSum) { - return ncclRedOp_t::ncclSum; - } else { - UNIMPLEMENTED(); - return ncclRedOp_t{}; - } -} - -std::string GetNcclUniqueIdRpcKey(const std::string& name, int64_t stream_id) { - return "CollectiveBoxingExecutorNcclUniqueIdRpcKey-" + name + "-" + std::to_string(stream_id); -} - -struct CopyParams { - void* dst; - const void* src; - int64_t count; -}; - -constexpr int64_t kMultiCopyParamsMaxSize = 128; -constexpr int64_t kMultiCopyAlignSize = 32; - -int64_t GetMultiCopyAlignedSize(int64_t size) { - return ((size + kMultiCopyAlignSize - 1) / kMultiCopyAlignSize) * kMultiCopyAlignSize; -} - -struct MultiCopyParams { - CopyParams params[kMultiCopyParamsMaxSize]; - int64_t count; - - MultiCopyParams() : count(0), params{} {} - - void Add(void* dst, const void* src, int64_t count) { - CHECK_LT(this->count, kMultiCopyParamsMaxSize); - params[this->count].dst = dst; - params[this->count].src = src; - params[this->count].count = count; - this->count += 1; - } -}; - -using BulkType = ulonglong2; - -__global__ void MultiCopyGpu(MultiCopyParams multi_params) { - for (int64_t p = 0; p < multi_params.count; ++p) { - const CopyParams params = multi_params.params[p]; - auto* bulk_dst = reinterpret_cast(params.dst); - const auto* bulk_src = reinterpret_cast(params.src); - const int64_t bulk_count = params.count / sizeof(BulkType); - CUDA_1D_KERNEL_LOOP_T(int64_t, i, bulk_count) { bulk_dst[i] = bulk_src[i]; } - const int64_t tail_offset = bulk_count * sizeof(BulkType); - auto* tail_dst = reinterpret_cast(params.dst) + tail_offset; - const auto* tail_src = reinterpret_cast(params.src) + tail_offset; - const int64_t tail_count = params.count - tail_offset; - CUDA_1D_KERNEL_LOOP_T(int64_t, i, tail_count) { tail_dst[i] = tail_src[i]; } - } -} - -void MultiCopy(hipStream_t stream, const MultiCopyParams& multi_params) { - if (multi_params.count <= 0) { return; } - CHECK_LE(multi_params.count, kMultiCopyParamsMaxSize); - int64_t max_count = multi_params.params[0].count; - for (int64_t i = 0; i < multi_params.count; ++i) { - max_count = std::max(max_count, multi_params.params[i].count); - } - hipLaunchKernelGGL(MultiCopyGpu, BlocksNum4ThreadsNum(max_count), kCudaThreadsNumPerBlock, 0, stream, - multi_params); -} - -class CommRank final { - public: - OF_DISALLOW_COPY(CommRank); - CommRank(int32_t device_id, int32_t global_rank, int32_t global_rank_count, int32_t local_rank, - int32_t local_rank_count) - : device_id_(device_id), - global_rank_(global_rank), - local_rank_(local_rank), - nccl_comm_(nullptr) {} - - CommRank(CommRank&& rhs) noexcept { - this->device_id_ = rhs.device_id_; - this->global_rank_ = rhs.global_rank_; - this->local_rank_ = rhs.local_rank_; - this->nccl_comm_ = rhs.nccl_comm_; - rhs.nccl_comm_ = nullptr; - } - - ~CommRank() { - if (nccl_comm_ != nullptr) { - CudaCurrentDeviceGuard guard(device_id_); - OF_NCCL_CHECK(ncclCommDestroy(nccl_comm_)); - } - } - - int32_t device_id() const { return device_id_; } - - ncclComm_t nccl_comm() const { return nccl_comm_; } - - void InitRank(ncclUniqueId unique_id, int32_t global_rank_count) { - CudaCurrentDeviceGuard guard(device_id_); - OF_NCCL_CHECK(ncclCommInitRank(&nccl_comm_, global_rank_count, unique_id, global_rank_)); - } - - private: - int32_t device_id_; - int32_t global_rank_; - int32_t local_rank_; - ncclComm_t nccl_comm_; -}; - -class CommGroup final { - public: - OF_DISALLOW_COPY(CommGroup); - CommGroup() = default; - ~CommGroup() = default; - CommGroup(CommGroup&& rhs) noexcept { - rank_vec_.swap(rhs.rank_vec_); - global_rank_count_ = rhs.global_rank_count_; - } - - void InitGroup(const DeviceSet& device_set, const std::string& unique_name) { - CudaCurrentDeviceGuard guard; - const int64_t this_machine_id = GlobalProcessCtx::Rank(); - global_rank_count_ = device_set.device_size(); - std::vector local_ranks; - for (int32_t i = 0; i < global_rank_count_; ++i) { - if (device_set.device(i).machine_id() == this_machine_id) { local_ranks.emplace_back(i); } - } - const int32_t local_rank_count = local_ranks.size(); - CHECK_GT(local_rank_count, 0); - ncclUniqueId nccl_unique_id{}; - if (local_ranks.front() == 0) { - OF_NCCL_CHECK(ncclGetUniqueId(&nccl_unique_id)); - if (local_rank_count != global_rank_count_) { - Singleton::Get()->PushKV(unique_name, NcclUniqueIdToString(nccl_unique_id)); - } - } else { - Singleton::Get()->PullKV(unique_name, [&nccl_unique_id](const std::string& val) { - NcclUniqueIdFromString(val, &nccl_unique_id); - }); - } - rank_vec_.reserve(local_rank_count); - OF_NCCL_CHECK(ncclGroupStart()); - for (int32_t local_rank = 0; local_rank < local_ranks.size(); ++local_rank) { - const int32_t global_rank = local_ranks.at(local_rank); - const int32_t device_id = device_set.device(global_rank).device_id(); - OF_CUDA_CHECK(hipSetDevice(device_id)); - rank_vec_.emplace_back(device_id, global_rank, global_rank_count_, local_rank, - local_rank_count); - rank_vec_.at(local_rank).InitRank(nccl_unique_id, global_rank_count_); - } - OF_NCCL_CHECK(ncclGroupEnd()); - } - - int32_t global_rank_count() const { return global_rank_count_; } - - int32_t local_rank_count() const { return rank_vec_.size(); } - - const CommRank& GetCommRank(int32_t local_rank) const { return rank_vec_.at(local_rank); } - - private: - std::vector rank_vec_; - int32_t global_rank_count_ = 0; -}; - -class StreamCtx { - public: - OF_DISALLOW_COPY(StreamCtx); - StreamCtx(int32_t device_id, size_t fusion_buffer_size) - : device_id_(device_id), fusion_buffer_size_(fusion_buffer_size) { - CudaCurrentDeviceGuard guard(device_id_); - int priority; - OF_CUDA_CHECK(hipDeviceGetStreamPriorityRange(nullptr, &priority)); - OF_CUDA_CHECK(hipStreamCreateWithPriority(&stream_, hipStreamNonBlocking, priority)); - OF_CUDA_CHECK(hipMalloc(&fusion_buffer_, fusion_buffer_size_)); - cb_event_poller_ = std::thread(&StreamCtx::PollEvent, this); - } - ~StreamCtx() { - cb_event_chan_.Close(); - cb_event_poller_.join(); - CudaCurrentDeviceGuard guard(device_id_); - OF_CUDA_CHECK(hipStreamSynchronize(stream_)); - OF_CUDA_CHECK(hipStreamDestroy(stream_)); - OF_CUDA_CHECK(hipFree(fusion_buffer_)); - } - - void PollEvent() { - CudaCurrentDeviceGuard guard(device_id_); - while (true) { - std::pair> cb_event; - ChannelStatus status = cb_event_chan_.Receive(&cb_event); - if (status == kChannelStatusErrorClosed) { break; } - CHECK_EQ(status, kChannelStatusSuccess); - OF_CUDA_CHECK(hipEventSynchronize(cb_event.first)); - cb_event.second(); - OF_CUDA_CHECK(hipEventDestroy(cb_event.first)); - } - } - - void AddCallback(const std::function& callback) { - hipEvent_t event; - OF_CUDA_CHECK(hipEventCreateWithFlags(&event, hipEventDisableTiming)); - OF_CUDA_CHECK(hipEventRecord(event, stream_)); - CHECK_EQ(cb_event_chan_.Send(std::make_pair(event, callback)), kChannelStatusSuccess); - } - - int32_t device_id() const { return device_id_; } - - hipStream_t stream() const { return stream_; } - - size_t fusion_buffer_size() const { return fusion_buffer_size_; } - - char* fusion_buffer() const { return fusion_buffer_; } - - private: - int32_t device_id_; - hipStream_t stream_ = nullptr; - size_t fusion_buffer_size_; - char* fusion_buffer_ = nullptr; - Channel>> cb_event_chan_; - std::thread cb_event_poller_; -}; - -void LaunchFusedAllReduce(const CommGroup& comm_group, - const std::vector>& device_id2stream_ctx, - const std::shared_ptr& request_store, - const std::vector& request_ids) { - CHECK_LE(request_ids.size(), kMultiCopyParamsMaxSize); - RequestEntry* first_request_entry = request_store->MutRequestEntry(request_ids.front()); - const ncclDataType_t nccl_data_type = - GetNcclDataType(first_request_entry->desc().op_desc().data_type()); - const ncclRedOp_t nccl_reduce_op = - GetNcclReduceOp(first_request_entry->desc().op_desc().reduce_method()); - const int64_t size_of_data_type = - GetSizeOfDataType(first_request_entry->desc().op_desc().data_type()); - std::vector offset_vec; - offset_vec.reserve(request_ids.size()); - int64_t offset = 0; - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - offset_vec.emplace_back(offset); - offset += GetMultiCopyAlignedSize(request_entry->size_in_bytes()); - }); - const int64_t elem_cnt = offset / size_of_data_type; - for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { - MultiCopyParams copy_in_params; - const CommRank& comm_rank = comm_group.GetCommRank(local_rank); - const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - CHECK_LE(offset, stream_ctx->fusion_buffer_size()); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - copy_in_params.Add(stream_ctx->fusion_buffer() + offset_vec.at(i), - request_entry->GetRuntimeRequest(local_rank)->send_buff, - request_entry->size_in_bytes()); - }); - OF_CUDA_CHECK(hipSetDevice(comm_rank.device_id())); - MultiCopy(stream_ctx->stream(), copy_in_params); - } - - OF_NCCL_CHECK(ncclGroupStart()); - for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { - const CommRank& comm_rank = comm_group.GetCommRank(local_rank); - const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - OF_CUDA_CHECK(hipSetDevice(comm_rank.device_id())); - OF_NCCL_CHECK(ncclAllReduce(stream_ctx->fusion_buffer(), stream_ctx->fusion_buffer(), elem_cnt, - nccl_data_type, nccl_reduce_op, comm_rank.nccl_comm(), - stream_ctx->stream())); - } - OF_NCCL_CHECK(ncclGroupEnd()); - - for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { - MultiCopyParams copy_out_params; - const CommRank& comm_rank = comm_group.GetCommRank(local_rank); - const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - copy_out_params.Add(request_entry->GetRuntimeRequest(local_rank)->recv_buff, - stream_ctx->fusion_buffer() + offset_vec.at(i), - request_entry->size_in_bytes()); - }); - OF_CUDA_CHECK(hipSetDevice(comm_rank.device_id())); - MultiCopy(stream_ctx->stream(), copy_out_params); - } -} - -void LaunchAggregatedOps(const CommGroup& comm_group, - const std::vector>& device_id2stream_ctx, - const std::shared_ptr& request_store, - const std::vector& request_ids) { - OF_NCCL_CHECK(ncclGroupStart()); - for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { - const CommRank& comm_rank = comm_group.GetCommRank(local_rank); - const auto comm = comm_rank.nccl_comm(); - const StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - OF_CUDA_CHECK(hipSetDevice(comm_rank.device_id())); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - const auto& op_desc = request_entry->desc().op_desc(); - const std::shared_ptr& runtime_request_info = - request_entry->GetRuntimeRequest(local_rank); - const OpType op_type = op_desc.op_type(); - const void* send_buff = runtime_request_info->send_buff; - void* recv_buff = runtime_request_info->recv_buff; - const int64_t elem_cnt = request_entry->elem_cnt(); - const ncclDataType_t nccl_data_type = GetNcclDataType(op_desc.data_type()); - const int32_t num_ranks = comm_group.global_rank_count(); - if (op_type == OpType::kOpTypeAllReduce) { - OF_NCCL_CHECK(ncclAllReduce(send_buff, recv_buff, elem_cnt, nccl_data_type, - GetNcclReduceOp(op_desc.reduce_method()), comm, - stream_ctx->stream())); - } else if (op_type == OpType::kOpTypeAllGather) { - CHECK_EQ(elem_cnt % num_ranks, 0); - OF_NCCL_CHECK(ncclAllGather(send_buff, recv_buff, elem_cnt / num_ranks, nccl_data_type, - comm, stream_ctx->stream())); - } else if (op_type == OpType::kOpTypeReduceScatter) { - CHECK_EQ(elem_cnt % num_ranks, 0); - OF_NCCL_CHECK(ncclReduceScatter( - send_buff, recv_buff, elem_cnt / num_ranks, nccl_data_type, - GetNcclReduceOp(op_desc.reduce_method()), comm, stream_ctx->stream())); - } else if (op_type == OpType::kOpTypeReduce) { - OF_NCCL_CHECK(ncclReduce(send_buff, recv_buff, elem_cnt, nccl_data_type, - GetNcclReduceOp(op_desc.reduce_method()), op_desc.root(), comm, - stream_ctx->stream())); - } else if (op_type == OpType::kOpTypeBroadcast) { - OF_NCCL_CHECK(ncclBroadcast(send_buff, recv_buff, elem_cnt, nccl_data_type, - op_desc.root(), comm, stream_ctx->stream())); - } else if (op_type == OpType::kOpTypeAll2All) { -#if NCCL_VERSION_CODE > 2700 - const int64_t elem_per_rank = elem_cnt / num_ranks; - const int64_t elem_per_chunk = elem_per_rank / num_ranks; - const int64_t dtype_size = GetSizeOfDataType(op_desc.data_type()); - const int64_t chunk_size = elem_per_chunk * dtype_size; - for (int64_t j = 0; j < num_ranks; ++j) { - OF_NCCL_CHECK(ncclSend(reinterpret_cast( - reinterpret_cast(send_buff) + j * chunk_size), - elem_per_chunk, nccl_data_type, j, comm, - stream_ctx->stream())); - OF_NCCL_CHECK(ncclRecv( - reinterpret_cast(reinterpret_cast(recv_buff) + j * chunk_size), - elem_per_chunk, nccl_data_type, j, comm, stream_ctx->stream())); - } -#else - UNIMPLEMENTED(); -#endif - } else { - UNIMPLEMENTED(); - } - }); - } - OF_NCCL_CHECK(ncclGroupEnd()); -} - -void AddCallbackAndResetRuntimeRequest( - const CommGroup& comm_group, - const std::vector>& device_id2stream_ctx, - const std::shared_ptr& request_store, const std::vector& request_ids) { - std::vector>> saved_runtime_request_info( - request_ids.size()); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - saved_runtime_request_info.at(i) = std::move(request_entry->ResetRuntimeRequest()); - }); - for (int32_t local_rank = 0; local_rank < comm_group.local_rank_count(); ++local_rank) { - const CommRank& comm_rank = comm_group.GetCommRank(local_rank); - StreamCtx* stream_ctx = device_id2stream_ctx.at(comm_rank.device_id()).get(); - auto runtime_request_info_vec = - std::make_shared>>(); - runtime_request_info_vec->reserve(request_ids.size()); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - runtime_request_info_vec->emplace_back( - std::move(saved_runtime_request_info.at(i).at(local_rank))); - }); - OF_CUDA_CHECK(hipSetDevice(comm_rank.device_id())); - stream_ctx->AddCallback([runtime_request_info_vec]() { - for (auto& runtime_request_info : *runtime_request_info_vec) { - runtime_request_info->callback(Maybe::Ok()); - } - }); - } -} - -} // namespace - -struct NcclExecutorBackend::Impl { - Impl(const CollectiveBoxingConf& conf, std::shared_ptr request_store) - : conf(conf), request_store(std::move(request_store)) { - CHECK_GT(conf.nccl_num_streams(), 0); - CHECK_GE(conf.nccl_fusion_threshold_mb(), 0); - fusion_threshold = conf.nccl_fusion_threshold_mb() * 1024 * 1024; - num_streams = conf.nccl_num_streams(); - current_stream_id = 0; - enable_mixed_fusion = - (!conf.nccl_fusion_all_reduce_use_buffer()) && conf.nccl_enable_mixed_fusion(); - int nccl_version; - OF_NCCL_CHECK(ncclGetVersion(&nccl_version)); - if (nccl_version == 21003) { - LOG(WARNING) - << "Current nccl version is 2.10.3, in this version, ncclGroup() with mixed " - "datatype/element/collective could induce crash or corruption, so we will not " - "fuse any request."; - } - InitStreamCtx(); - InitIsOpTypeFusionEnabled(); - } - ~Impl() { - stream_id2device_id2stream_ctx.clear(); - device_set2stream_id2comm_group.clear(); - } - - void InitCommGroup(int64_t job_id) { - std::set local_device_ids; - request_store->ForEachMutRequestEntryInJob( - job_id, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - const auto& request = request_entry->desc(); - if (request.op_desc().backend() != Backend::kBackendNCCL) { return; } - if (!request_entry->HasRankOnThisNode()) { return; } - const DeviceSet& device_set = request.device_set(); - if (device_set2stream_id2comm_group.count(device_set) > 0) { return; } - auto& stream_id2comm_group = device_set2stream_id2comm_group[device_set]; - stream_id2comm_group.resize(num_streams); - for (int32_t stream_id = 0; stream_id < num_streams; ++stream_id) { - stream_id2comm_group.at(stream_id).InitGroup( - device_set, GetNcclUniqueIdRpcKey(request.op_desc().name(), stream_id)); - } - for (int32_t j = 0; j < stream_id2comm_group.at(0).local_rank_count(); ++j) { - local_device_ids.emplace(stream_id2comm_group.at(0).GetCommRank(j).device_id()); - } - }); - for (int32_t stream_id = 0; stream_id < num_streams; ++stream_id) { - for (const int64_t device_id : local_device_ids) { - if (stream_id2device_id2stream_ctx.at(stream_id).at(device_id) == nullptr) { - stream_id2device_id2stream_ctx.at(stream_id).at(device_id) = - std::make_unique(device_id, fusion_threshold); - } - } - } - } - - void InitStreamCtx() { - int32_t num_devices; - OF_CUDA_CHECK(hipGetDeviceCount(&num_devices)); - stream_id2device_id2stream_ctx.resize(num_streams); - for (int64_t stream_id = 0; stream_id < num_streams; ++stream_id) { - stream_id2device_id2stream_ctx.at(stream_id).resize(num_devices); - } - } - - void InitIsOpTypeFusionEnabled() { - op_type2fusion_enabled.resize(OpType_ARRAYSIZE, false); - op_type2fusion_enabled.at(OpType::kOpTypeAllReduce) = conf.nccl_fusion_all_reduce(); - op_type2fusion_enabled.at(OpType::kOpTypeAllGather) = conf.nccl_fusion_all_gather(); - op_type2fusion_enabled.at(OpType::kOpTypeReduceScatter) = conf.nccl_fusion_reduce_scatter(); - op_type2fusion_enabled.at(OpType::kOpTypeReduce) = conf.nccl_fusion_reduce(); - op_type2fusion_enabled.at(OpType::kOpTypeBroadcast) = conf.nccl_fusion_broadcast(); - op_type2fusion_enabled.at(OpType::kOpTypeAll2All) = false; - } - - int32_t NextStreamId() { - const int32_t stream_id = current_stream_id; - current_stream_id = (current_stream_id + 1) % num_streams; - return stream_id; - } - - bool IsOpTypeFusionEnabled(OpType op_type) const { return op_type2fusion_enabled.at(op_type); } - - bool IsRequestEntryFusionEnabled(const RequestEntry* entry) const { - return IsOpTypeFusionEnabled(entry->desc().op_desc().op_type()); - } - - bool CanRequestEntryFuse(const RequestEntry* lhs, const RequestEntry* rhs) const { - { - int nccl_version; - OF_NCCL_CHECK(ncclGetVersion(&nccl_version)); - // Workaround for https://github.com/NVIDIA/nccl/issues/560 - if (nccl_version == 21003) { return false; } - } - if (lhs->device_set_symbol() != rhs->device_set_symbol()) { return false; } - if ((!IsRequestEntryFusionEnabled(lhs)) || (!IsRequestEntryFusionEnabled(rhs))) { - return false; - } - if ((!enable_mixed_fusion) - && lhs->desc().op_desc().op_type() != rhs->desc().op_desc().op_type()) { - return false; - } - if (conf.nccl_fusion_all_reduce_use_buffer()) { - if (lhs->desc().op_desc().op_type() == OpType::kOpTypeAllReduce - && rhs->desc().op_desc().op_type() == OpType::kOpTypeAllReduce) { - CHECK(lhs->desc().op_desc().has_reduce_method()); - CHECK(rhs->desc().op_desc().has_reduce_method()); - return lhs->desc().op_desc().reduce_method() == rhs->desc().op_desc().reduce_method() - && lhs->desc().op_desc().data_type() == rhs->desc().op_desc().data_type(); - } else if (lhs->desc().op_desc().op_type() == OpType::kOpTypeAllReduce - || rhs->desc().op_desc().op_type() == OpType::kOpTypeAllReduce) { - return false; - } else { - return true; - } - } else { - return true; - } - } - - void GroupRequests(const std::vector& request_ids, - const std::function&&, void*)>& Handler) { - std::vector group; - int64_t group_size = 0; - const int64_t fusion_max_ops = std::min(conf.nccl_fusion_max_ops(), kMultiCopyParamsMaxSize); - request_store->ForEachMutRequestEntryForIdsInJob( - request_ids, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - const auto& request = request_entry->desc(); - const int64_t size = GetMultiCopyAlignedSize(request_entry->size_in_bytes()); - if (group.empty() - || !CanRequestEntryFuse(request_store->MutRequestEntry(group.back()), request_entry) - || group_size + size > fusion_threshold || group.size() >= fusion_max_ops) { - if (!group.empty()) { - void* token = CreateGroupToken(group); - Handler(std::move(group), token); - group.clear(); - group_size = 0; - } - } - group.emplace_back(request_id); - group_size += size; - }); - if (!group.empty()) { - void* token = CreateGroupToken(group); - Handler(std::move(group), token); - } - } - - struct GroupToken { - GroupToken(const std::vector& group, std::vector* stream_id2comm_group) - : request_ids(group), stream_id2comm_group(stream_id2comm_group) {} - std::vector request_ids; - std::vector* stream_id2comm_group; - }; - - void* CreateGroupToken(const std::vector& group) { - CHECK_GT(group.size(), 0); - void* group_token; - const DeviceSet& first_device_set = - request_store->MutRequestEntry(group.front())->desc().device_set(); - auto it = device_set2stream_id2comm_group.find(first_device_set); - CHECK(it != device_set2stream_id2comm_group.end()); - group_token = new GroupToken(group, &it->second); - request_store->ForEachMutRequestEntryForIdsInJob( - group, [&](RequestEntry* request_entry, int32_t i, const RequestId& request_id) { - const DeviceSet& device_set = request_entry->desc().device_set(); - CHECK(first_device_set == device_set); - }); - return group_token; - } - - void DestroyGroupToken(void* group_token) { - GroupToken* token = static_cast(group_token); - delete token; - } - - void ExecuteGroup(void* group_token) { - GroupToken* token = static_cast(group_token); - const std::vector& request_ids = token->request_ids; - if (request_ids.empty()) { return; } - const int32_t stream_id = NextStreamId(); - CudaCurrentDeviceGuard device_guard; - const auto& comm_group = token->stream_id2comm_group->at(stream_id); - auto& device_id2stream_ctx = stream_id2device_id2stream_ctx.at(stream_id); - if (request_store->MutRequestEntry(request_ids.front())->desc().op_desc().op_type() - == OpType::kOpTypeAllReduce - && conf.nccl_fusion_all_reduce_use_buffer() && request_ids.size() > 1) { - LaunchFusedAllReduce(comm_group, device_id2stream_ctx, request_store, request_ids); - } else { - LaunchAggregatedOps(comm_group, device_id2stream_ctx, request_store, request_ids); - } - AddCallbackAndResetRuntimeRequest(comm_group, device_id2stream_ctx, request_store, request_ids); - } - - CollectiveBoxingConf conf; - int64_t fusion_threshold; - int32_t num_streams; - int32_t current_stream_id; - bool enable_mixed_fusion; - std::vector op_type2fusion_enabled; - std::shared_ptr request_store; - HashMap> device_set2stream_id2comm_group; - std::vector>> stream_id2device_id2stream_ctx; -}; - -NcclExecutorBackend::NcclExecutorBackend() = default; - -NcclExecutorBackend::~NcclExecutorBackend() = default; - -void NcclExecutorBackend::Init(std::shared_ptr request_store) { - impl_ = std::make_unique( - Singleton::Get()->collective_boxing_conf(), request_store); -} - -void NcclExecutorBackend::InitJob(int64_t job_id) { - CudaCurrentDeviceGuard guard; - impl_->InitCommGroup(job_id); -} - -void NcclExecutorBackend::DeinitJob(int64_t job_id) {} - -void NcclExecutorBackend::GroupRequests( - const std::vector& request_ids, - const std::function&&, void*)>& Handler) { - impl_->GroupRequests(request_ids, Handler); -} - -void* NcclExecutorBackend::CreateGroupToken(const std::vector& group) { - return impl_->CreateGroupToken(group); -} - -void NcclExecutorBackend::DestroyGroupToken(void* group_token) { - return impl_->DestroyGroupToken(group_token); -} - -void NcclExecutorBackend::ExecuteGroup(void* group_token) { impl_->ExecuteGroup(group_token); } - -} // namespace collective - -} // namespace boxing - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/job/collective_boxing/scheduler.cpp b/oneflow/core/job/collective_boxing/scheduler.cpp index 8e582c777d626f2500e24799419d769b0a10e8e3..f4f8ae7ccdfa8a5a7ad3840034af8baadbc3dbef 100644 --- a/oneflow/core/job/collective_boxing/scheduler.cpp +++ b/oneflow/core/job/collective_boxing/scheduler.cpp @@ -142,21 +142,13 @@ void ExecutorImpl::Init(std::shared_ptr request_store) { } void ExecutorImpl::InitJob(int64_t job_id) { -#ifdef WITH_CUDA - if (backends_.at(Backend::kBackendNCCL)) { backends_.at(Backend::kBackendNCCL)->InitJob(job_id); } -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) if (backends_.at(Backend::kBackendNCCL)) { backends_.at(Backend::kBackendNCCL)->InitJob(job_id); } #endif } void ExecutorImpl::DeinitJob(int64_t job_id) { -#ifdef WITH_CUDA - if (backends_.at(Backend::kBackendNCCL)) { - backends_.at(Backend::kBackendNCCL)->DeinitJob(job_id); - } -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) if (backends_.at(Backend::kBackendNCCL)) { backends_.at(Backend::kBackendNCCL)->DeinitJob(job_id); } diff --git a/oneflow/core/job/compiler.cpp b/oneflow/core/job/compiler.cpp index 4fc11e0eb877372a1699cd59698f149b6b91dc52..c782fd541e6cd265da5f5bd75baddddc4d8f272a 100644 --- a/oneflow/core/job/compiler.cpp +++ b/oneflow/core/job/compiler.cpp @@ -22,6 +22,8 @@ limitations under the License. #include "oneflow/core/job_rewriter/job_completer.h" #include "oneflow/core/thread/thread_pool.h" #include "oneflow/core/common/blocking_counter.h" +#include "oneflow/core/common/cost_util.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { @@ -46,31 +48,35 @@ void CreateOpAttributeRef(Plan* plan, int64_t job_id, TaskProto* task_proto) { } void Compiler::Compile(Job* job, Plan* plan) const { + const auto& job_name = job->job_conf().job_name(); + auto compile_tc = std::make_unique>(true, true); // Step1: new Singleton and set log configs. Singleton::New(*job); const JobDesc& job_desc = GlobalJobDesc(); - if (Singleton::Get()->enable_debug_mode() - || Singleton::Get()->enable_dry_run()) { + if (Singleton::Get()->enable_debug_mode()) { TeePersistentLogStream::Create(StrCat("optimized_job", job_desc.job_id()))->Write(*job); Singleton::Get()->ToDotWithFilePath( "optimized_dlnet_" + std::to_string(job_desc.job_id()) + "_op_graph.dot"); } + compile_tc->Count("[GraphCompile]" + job_name + " NewOpGraph", 1); // Step2: build task_gph. // TODO(levi): we can rewrite this part of code in visitor pattern. - auto task_gph = - std::make_unique(job->job_conf().enable_straighten_algorithm_in_task_graph()); + auto task_gph = std::make_unique(); using std::placeholders::_1; + LazyMode::Guard guard(true); task_gph->ForEachNode(std::bind(&TaskNode::ProduceAllRegstsAndBindEdges, _1)); task_gph->ForEachNode(std::bind(&TaskNode::ConsumeAllRegsts, _1)); task_gph->ForEachNode(std::bind(&TaskNode::PinConsumedRegst, _1)); task_gph->TopoForEachNode(&TaskNode::Build); task_gph->RemoveEmptyRegsts(); + task_gph->TopoForEachNode(&TaskNode::InferTimeShapeIfMeaningful); + task_gph->DecideExecutionOrder(); task_gph->MergeChainAndAddOrderingCtrlEdgeInSameChain(); auto IsReachable = Singleton::Get()->MakePredicatorIsOpNameDataOrCtrlReachable(); if (job_desc.enable_inplace()) { task_gph->EnableInplaceMemSharing(IsReachable); } - task_gph->TopoForEachNode(&TaskNode::InferTimeShapeIfMeaningful); task_gph->ForEachEdge([&](TaskEdge* task_edge) { task_edge->CheckRegstLbiValid(); }); + compile_tc->Count("[GraphCompile]" + job_name + " BuildTaskGraph", 1, true); // Step3: put infomation from task_gph into plan. const int64_t node_num = task_gph->node_num(); @@ -99,13 +105,18 @@ void Compiler::Compile(Job* job, Plan* plan) const { counter.WaitForeverUntilCntEqualZero(); // NOTE(levi): release task_gph here to decrise memory peak. task_gph.reset(); + compile_tc->Count("[GraphCompile]" + job_name + " AddTaskToPlan", 1, true); // Step4: post-process for plan and delete Singleton. auto* job_id2job_conf = plan->mutable_job_confs()->mutable_job_id2job_conf(); (*job_id2job_conf)[GlobalJobDesc().job_id()] = GlobalJobDesc().job_conf(); // NOTE(chengcheng): infer mem blob id & set inplace & add ctrl + // TODO(chengcheng): set inplace hint for cpu regst IntraJobMemSharingUtil::InferMemBlockId4MemReusedRegst(plan, IsReachable); + PlanUtil::MergeMemBlockIdByLogicalChainId(plan, *job); PlanUtil::SetUniqueMemBlockId4UnreusedMemRegst(plan); + PlanUtil::SetForceInplaceMemBlock(plan); + compile_tc->Count("[GraphCompile]" + job_name + " InferMemShare", 1, true); Singleton::Delete(); } diff --git a/oneflow/core/job/critical_section_instance.h b/oneflow/core/job/critical_section_instance.h index 765056d8864a0597a17e044080bcb760024494af..18be6b43dc9ef747db04cedb048536cf07bd816f 100644 --- a/oneflow/core/job/critical_section_instance.h +++ b/oneflow/core/job/critical_section_instance.h @@ -16,10 +16,17 @@ limitations under the License. #ifndef ONEFLOW_CORE_JOB_CRITICAL_SECTION_INSTANCE_H_ #define ONEFLOW_CORE_JOB_CRITICAL_SECTION_INSTANCE_H_ -#include "oneflow/core/register/ofblob.h" +#include +#include "oneflow/core/common/util.h" namespace oneflow { +class Blob; + +namespace ep { +class Stream; +} + class CriticalSectionInstance { public: CriticalSectionInstance() = default; @@ -28,7 +35,8 @@ class CriticalSectionInstance { virtual ~CriticalSectionInstance() = default; - virtual void AccessBlobByOpName(uint64_t ofblob_ptr, const std::string& op_name) const { + virtual void AccessBlobByOpName(ep::Stream* stream, Blob* blob, + const std::string& op_name) const { UNIMPLEMENTED(); } virtual void Finish() const { UNIMPLEMENTED(); } diff --git a/oneflow/core/job/eager_nccl_comm_manager.cpp b/oneflow/core/job/eager_nccl_comm_manager.cpp index 13a77a7507c9d83f740e4440c75a1015355f4485..27db1a39630e4f99a06a7a777cbae0415f712477 100644 --- a/oneflow/core/job/eager_nccl_comm_manager.cpp +++ b/oneflow/core/job/eager_nccl_comm_manager.cpp @@ -14,12 +14,14 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +#include #include "oneflow/core/control/ctrl_client.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/job/eager_nccl_comm_manager.h" #include "oneflow/core/device/nccl_util.h" #include "oneflow/core/job/id_manager.h" #include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/operator/op_conf.pb.h" #include "oneflow/core/vm/vm_util.h" #ifdef WITH_CUDA @@ -78,8 +80,15 @@ void CreateNcclComm(ncclComm_t* comm, const int dev, const std::string& key, << ", key = {" << key << "}\n"; } -bool NeedUnifiedNcclCommInit(const std::string& op_type_name) { - return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered(op_type_name); +bool NeedUnifiedNcclCommInit(const OperatorConf& op_conf) { + if (op_conf.has_user_conf()) { + return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered( + op_conf.user_conf().op_type_name()); + } else { + // Please check the .h file for hard-coding of the name + return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered( + kSystemOpPrefix + std::to_string(op_conf.op_type_case())); + } } } // namespace @@ -169,9 +178,7 @@ void EagerNcclCommMgr::CreateCommFromPlan(const Plan& plan) { continue; } const auto& op_conf = op_attr->op_conf(); - if (!op_conf.has_user_conf()) { continue; } - if (!NeedUnifiedNcclCommInit(op_conf.user_conf().op_type_name())) { continue; } - + if (!NeedUnifiedNcclCommInit(op_conf)) { continue; } if (!op_attr->has_parallel_conf_signature()) { continue; } if (!op_attr->parallel_conf_signature().has_op_parallel_conf()) { continue; } @@ -270,8 +277,15 @@ void CreateNcclComm(ncclComm_t* comm, const int dev, const std::string& key, << ", key = {" << key << "}\n"; } -bool NeedUnifiedNcclCommInit(const std::string& op_type_name) { - return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered(op_type_name); +bool NeedUnifiedNcclCommInit(const OperatorConf& op_conf) { + if (op_conf.has_user_conf()) { + return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered( + op_conf.user_conf().op_type_name()); + } else { + // Please check the .h file for hard-coding of the name + return UserKernelUnifiedNcclCommInitRegistry::Instance().IsRegistered( + kSystemOpPrefix + std::to_string(op_conf.op_type_case())); + } } } // namespace @@ -362,8 +376,7 @@ void EagerNcclCommMgr::CreateCommFromPlan(const Plan& plan) { } const auto& op_conf = op_attr->op_conf(); if (!op_conf.has_user_conf()) { continue; } - if (!NeedUnifiedNcclCommInit(op_conf.user_conf().op_type_name())) { continue; } - + if (!NeedUnifiedNcclCommInit(op_conf)) { continue; } if (!op_attr->has_parallel_conf_signature()) { continue; } if (!op_attr->parallel_conf_signature().has_op_parallel_conf()) { continue; } diff --git a/oneflow/core/job/eager_nccl_comm_manager.h b/oneflow/core/job/eager_nccl_comm_manager.h index aff0fa2bf366ef3b83acb06cfe51b58d534b899e..2e3187fe9924f0754d0c8222d7f7e973713bfe79 100644 --- a/oneflow/core/job/eager_nccl_comm_manager.h +++ b/oneflow/core/job/eager_nccl_comm_manager.h @@ -83,12 +83,19 @@ class UserKernelUnifiedNcclCommInitRegistry final { std::set reg_set_; }; +static const std::string kSystemOpPrefix = "sys_op_"; + } // namespace oneflow #define REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT(op_type_name) \ static auto OF_PP_CAT(g_nccl_comm_reg_, __COUNTER__) = \ ::oneflow::UserKernelUnifiedNcclCommInitRegistry::Trigger(op_type_name) +#define REGISTER_SYSTEM_OP_KERNEL_UNIFIED_NCCL_COMM_INIT(op_type_case) \ + static auto OF_PP_CAT(g_nccl_comm_reg_, __COUNTER__) = \ + ::oneflow::UserKernelUnifiedNcclCommInitRegistry::Trigger(::oneflow::kSystemOpPrefix \ + + std::to_string(op_type_case)) + #endif // WITH_CUDA #endif // ONEFLOW_CORE_JOB_EAGER_NCCL_COMM_MANAGER_H_ diff --git a/oneflow/core/job/env.proto b/oneflow/core/job/env.proto index e0de1932c3de5402c14c909da749a1187ab653a7..796568196d51110f5dbcb827c3e21a37b9f096ca 100644 --- a/oneflow/core/job/env.proto +++ b/oneflow/core/job/env.proto @@ -12,8 +12,9 @@ message Machine { message CppLoggingConf { optional string log_dir = 1 [default = "./log"]; - optional int32 logtostderr = 2 [default = 0]; + optional int32 logtostderr = 2 [default = 1]; optional int32 logbuflevel = 3 [default = -1]; + optional int32 minloglevel = 4 [default = 1]; } message EnvProto { diff --git a/oneflow/core/job/env_global_objects_scope.cpp b/oneflow/core/job/env_global_objects_scope.cpp index 670349bd0e781ce2d77914562540d6b136918db6..5717ba1b1091bf1ffcc927f88ba694d28f28294f 100644 --- a/oneflow/core/job/env_global_objects_scope.cpp +++ b/oneflow/core/job/env_global_objects_scope.cpp @@ -41,6 +41,7 @@ limitations under the License. #include "oneflow/core/hardware/node_device_descriptor_manager.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/framework/multi_client_session_context.h" +#include "oneflow/core/framework/scope_util.h" #include "oneflow/core/operator/op_node_signature.pb.h" #include "oneflow/core/comm_network/comm_network.h" #include "oneflow/core/comm_network/epoll/epoll_comm_network.h" @@ -52,9 +53,11 @@ limitations under the License. #include "oneflow/core/embedding/embedding_manager.h" #ifdef WITH_RDMA #include "oneflow/core/platform/include/ibv.h" +#include "oneflow/core/comm_network/ibverbs/ibverbs_comm_network.h" #endif // WITH_RDMA #include "oneflow/core/ep/include/device_manager_registry.h" #include "oneflow/core/ep/cpu/cpu_device_manager.h" +#include "oneflow/core/common/env_var/debug_mode.h" namespace oneflow { @@ -71,9 +74,15 @@ void InitLogging(const CppLoggingConf& logging_conf) { FLAGS_log_dir = LogDir(logging_conf.log_dir()); FLAGS_logtostderr = logging_conf.logtostderr(); FLAGS_logbuflevel = logging_conf.logbuflevel(); + FLAGS_minloglevel = logging_conf.minloglevel(); FLAGS_stderrthreshold = 1; // 1=WARNING google::InitGoogleLogging("oneflow"); - LocalFS()->RecursivelyCreateDirIfNotExist(FLAGS_log_dir); + if (IsInDebugMode()) { + // record all level logs to file in debug mode + FLAGS_logtostderr = 0; + FLAGS_minloglevel = 0; // 0=INFO + } + if (!FLAGS_logtostderr) { LocalFS()->RecursivelyCreateDirIfNotExist(FLAGS_log_dir); } } int32_t GetDefaultCpuDeviceNum() { return std::thread::hardware_concurrency(); } @@ -109,7 +118,19 @@ void ClearAllSymbol() { #if defined(WITH_RDMA) && defined(OF_PLATFORM_POSIX) -bool CommNetIBEnabled() { return ibv::IsAvailable(); } +bool CommNetIBEnabled() { + if (!ibv::IsAvailable()) { return false; } + const auto* node_manager = Singleton::Get(); + if (node_manager == nullptr) { return false; } + for (int64_t rank = 0; rank < GlobalProcessCtx::WorldSize(); ++rank) { + const auto& node = node_manager->GetNodeDeviceDescriptor(rank); + if (!node) { return false; } + const auto& list = node->GetDeviceDescriptorList("net_ib"); + if (!list) { return false; } + if (list->DeviceCount() == 0) { return false; } + } + return true; +} #endif // WITH_RDMA && OF_PLATFORM_POSIX @@ -135,16 +156,10 @@ Maybe EnvGlobalObjectsScope::Init(const EnvProto& env_proto) { Singleton::New(); // Avoid dead lock by using CHECK_JUST instead of JUST. because it maybe be blocked in // ~CtrlBootstrap. - if (Singleton::Get()->enable_dry_run()) { -#ifdef RPC_BACKEND_LOCAL - LOG(INFO) << "Using rpc backend: dry-run"; - Singleton::SetAllocated(new DryRunRpcManager()); -#else - static_assert(false, "Requires rpc backend dry-run to dry run oneflow"); -#endif // RPC_BACKEND_LOCAL - } else if ((env_proto.machine_size() == 1 && env_proto.has_ctrl_bootstrap_conf() == false) - || (env_proto.has_ctrl_bootstrap_conf() - && env_proto.ctrl_bootstrap_conf().world_size() == 1)) /*single process*/ { + + if ((env_proto.machine_size() == 1 && env_proto.has_ctrl_bootstrap_conf() == false) + || (env_proto.has_ctrl_bootstrap_conf() + && env_proto.ctrl_bootstrap_conf().world_size() == 1)) /*single process*/ { #ifdef RPC_BACKEND_LOCAL LOG(INFO) << "Using rpc backend: local"; Singleton::SetAllocated(new LocalRpcManager()); @@ -175,27 +190,20 @@ Maybe EnvGlobalObjectsScope::Init(const EnvProto& env_proto) { Singleton::New(); Singleton::New(Singleton::Get()->ComputeThreadPoolSize()); SetCpuDeviceManagerNumThreads(); -#ifdef WITH_CUDA - Singleton::New(); - Singleton::New(); - Singleton::New(); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) Singleton::New(); Singleton::New(); + Singleton::New(); Singleton::New(); #endif Singleton::New(Singleton::Get()->resource()); - Singleton::New(); - if (!Singleton::Get()->enable_dry_run()) { #ifdef __linux__ - Singleton::New(); - Singleton::New(); - if (Singleton::Get()->process_ranks().size() > 1) { - Singleton::SetAllocated(Singleton::Get()); - } -#endif // __linux__ + Singleton::New(); + Singleton::New(); + if (Singleton::Get()->process_ranks().size() > 1) { + Singleton::SetAllocated(Singleton::Get()); } +#endif // __linux__ { std::vector> kernel_observers; if (ParseBooleanFromEnv("ONEFLOW_DEBUG_KERNEL_SYNC_CHECK", false)) { @@ -220,28 +228,20 @@ EnvGlobalObjectsScope::~EnvGlobalObjectsScope() { if (is_normal_exit_.has_value() && !CHECK_JUST(is_normal_exit_)) { return; } TensorBufferPool::Delete(); Singleton::Delete(); - if (!Singleton::Get()->enable_dry_run()) { #ifdef __linux__ - if (Singleton::Get()->process_ranks().size() > 1) { - if (Singleton::Get() - != dynamic_cast(Singleton::Get())) { - Singleton::Delete(); - } + if (Singleton::Get()->process_ranks().size() > 1) { + if (Singleton::Get() != dynamic_cast(Singleton::Get())) { + Singleton::Delete(); } - Singleton::Delete(); - Singleton::Delete(); -#endif // __linux__ } - Singleton::Delete(); + Singleton::Delete(); + Singleton::Delete(); +#endif // __linux__ Singleton::Delete(); -#ifdef WITH_CUDA - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) Singleton::Delete(); Singleton::Delete(); + Singleton::Delete(); Singleton::Delete(); #endif Singleton::Delete(); @@ -257,6 +257,7 @@ EnvGlobalObjectsScope::~EnvGlobalObjectsScope() { Singleton::Delete(); Singleton::Delete(); ClearAllSymbol(); + ClearAllBackwardPassScope(); if (Singleton::Get() != nullptr) { Singleton::SetAllocated(nullptr); } @@ -265,30 +266,26 @@ EnvGlobalObjectsScope::~EnvGlobalObjectsScope() { } Maybe InitRDMA() { - if (!Singleton::Get()->enable_dry_run()) { #ifdef __linux__ - if (Singleton::Get()->process_ranks().size() > 1) { + if (Singleton::Get()->process_ranks().size() > 1) { #if defined(WITH_RDMA) && defined(OF_PLATFORM_POSIX) - if (CommNetIBEnabled()) { - if (Singleton::Get() == nullptr) { - Singleton::New(); - Singleton::SetAllocated(Singleton::Get()); - } else { - LOG(WARNING) << "Skip init RDMA because RDMA is already initialized!"; - } + if (CommNetIBEnabled()) { + if (Singleton::Get() == nullptr) { + Singleton::New(); + Singleton::SetAllocated(Singleton::Get()); } else { - LOG(WARNING) << "Skip init RDMA because RDMA is unavailable!"; + LOG(INFO) << "Skip init RDMA because RDMA is already initialized!"; } -#else - LOG(WARNING) << "Skip init RDMA because RDMA is not compiled!"; -#endif // WITH_RDMA && OF_PLATFORM_POSIX } else { - LOG(WARNING) << "Skip init RDMA because only one process in this group!"; + LOG(WARNING) << "Skip init RDMA because RDMA is unavailable!"; } -#endif // __linux__ +#else + LOG(WARNING) << "Skip init RDMA because RDMA is not compiled!"; +#endif // WITH_RDMA && OF_PLATFORM_POSIX } else { - LOG(WARNING) << "Skip init RDMA in dry run mode!"; + LOG(INFO) << "Skip init RDMA because only one process in this group!"; } +#endif // __linux__ return Maybe::Ok(); } @@ -300,4 +297,18 @@ Maybe RDMAIsInitialized() { #endif // WITH_RDMA && OF_PLATFORM_POSIX } +Maybe DestoryRDMA() { +#if defined(WITH_RDMA) && defined(OF_PLATFORM_POSIX) + if (JUST(RDMAIsInitialized())) { + CHECK_NOTNULL(Singleton::Get()); + CHECK_NOTNULL(Singleton::Get()); + Singleton::Delete(); + if (Singleton::Get()) { + Singleton::SetAllocated(Singleton::Get()); + } + } +#endif // WITH_RDMA && OF_PLATFORM_POSIX + return Maybe::Ok(); +} + } // namespace oneflow diff --git a/oneflow/core/job/env_global_objects_scope.h b/oneflow/core/job/env_global_objects_scope.h index ff17a05573f0ef47853d3b89440af99b03be3051..3495642d85bbe9fa37357629a8b2142e7f04baa4 100644 --- a/oneflow/core/job/env_global_objects_scope.h +++ b/oneflow/core/job/env_global_objects_scope.h @@ -49,6 +49,8 @@ Maybe InitRDMA(); Maybe RDMAIsInitialized(); +Maybe DestoryRDMA(); + } // namespace oneflow #endif // ONEFLOW_CORE_JOB_CLUSTER_OBJECTS_SCOPE_H_ diff --git a/oneflow/core/job/foreign_callback.h b/oneflow/core/job/foreign_callback.h deleted file mode 100644 index d75a0f51cd29981e39d3bce6e68e300c47f171f6..0000000000000000000000000000000000000000 --- a/oneflow/core/job/foreign_callback.h +++ /dev/null @@ -1,45 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_JOB_FOREIGN_CALLBACK_H_ -#define ONEFLOW_CORE_JOB_FOREIGN_CALLBACK_H_ - -#include "oneflow/core/job/placement.pb.h" -#include "oneflow/core/operator/op_attribute.pb.h" - -namespace oneflow { - -class ForeignCallback { - public: - ForeignCallback() = default; - virtual ~ForeignCallback() = default; - - virtual void EagerMirroredCast(const OpAttribute& op_attribute, - const ParallelConf& parallel_conf) const { - UNIMPLEMENTED(); - } - virtual void EagerInterpretCompletedOp(const OpAttribute& op_attribute, - const ParallelConf& parallel_conf) const { - UNIMPLEMENTED(); - } - - virtual void OfBlobCall(int64_t unique_id, int64_t ofblob_ptr) const { UNIMPLEMENTED(); } - - virtual void RemoveForeignCallback(int64_t unique_id) const { UNIMPLEMENTED(); } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_JOB_FOREIGN_CALLBACK_H_ diff --git a/oneflow/core/job/foreign_watcher.h b/oneflow/core/job/foreign_watcher.h deleted file mode 100644 index 314b6d5120b3e64d0eb572b656b7fb4c84600397..0000000000000000000000000000000000000000 --- a/oneflow/core/job/foreign_watcher.h +++ /dev/null @@ -1,33 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_JOB_FOREIGN_WATCHER_H_ -#define ONEFLOW_CORE_JOB_FOREIGN_WATCHER_H_ - -#include "oneflow/core/register/ofblob.h" - -namespace oneflow { - -class ForeignWatcher { - public: - ForeignWatcher() = default; - virtual ~ForeignWatcher() = default; - - virtual void Call(const std::string& handler_uuid, int64_t ofblob_ptr) const { UNIMPLEMENTED(); } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_JOB_FOREIGN_WATCHER_H_ diff --git a/oneflow/core/job/global_for.cpp b/oneflow/core/job/global_for.cpp index 51c965e888e81014f966a46016f249bad7858928..28092b818228f6724c2ffd0122845feaf56d56fb 100644 --- a/oneflow/core/job/global_for.cpp +++ b/oneflow/core/job/global_for.cpp @@ -21,7 +21,6 @@ limitations under the License. namespace oneflow { -COMMAND(Singleton::SetAllocated(new bool(false))); COMMAND(Singleton, MultiClient>::SetAllocated(new Optional())); } // namespace oneflow diff --git a/oneflow/core/job/global_for.h b/oneflow/core/job/global_for.h index 7a7319aee6183bb76c6748881717a9e0b1e6d796..f657c2795fa590788d29506ce058952d5f9dc2ae 100644 --- a/oneflow/core/job/global_for.h +++ b/oneflow/core/job/global_for.h @@ -23,8 +23,6 @@ namespace oneflow { class ForSession {}; class ForEnv {}; -class EagerExecution {}; - class MultiClient {}; } // namespace oneflow diff --git a/oneflow/core/job/global_mode.cpp b/oneflow/core/job/global_mode.cpp new file mode 100644 index 0000000000000000000000000000000000000000..700319ac7c0abb4ac7f08bee45400e9571435940 --- /dev/null +++ b/oneflow/core/job/global_mode.cpp @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/job/global_mode.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/framework/device.h" + +namespace oneflow { + +Symbol GetGlobalParallelDescFromDevice(const Optional>& device) { + auto parallel_desc = GlobalMode::parallel_desc(); + if (device.has_value()) { + const auto& device_type = device.value_or(Symbol())->type(); + if (parallel_desc->parallel_conf().device_tag() != device_type) { + ParallelConf parallel_conf = parallel_desc->parallel_conf(); + parallel_conf.set_device_tag(device_type); + parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); + } + } + return parallel_desc; +} + +/* static */ bool* GlobalMode::get_mode_ptr() { + thread_local bool mode = false; + return &mode; +} +/* static */ bool GlobalMode::is_enabled() { return *get_mode_ptr(); } +/* static */ void GlobalMode::set_enabled(bool enabled) { *get_mode_ptr() = enabled; } + +/* static */ Symbol* GlobalMode::get_nd_sbp_ptr() { + thread_local Symbol nd_sbp; + return &nd_sbp; +} +/* static */ Symbol GlobalMode::nd_sbp() { return *get_nd_sbp_ptr(); } +/* static */ void GlobalMode::set_nd_sbp(Symbol nd_sbp) { *get_nd_sbp_ptr() = nd_sbp; } + +/* static */ Symbol* GlobalMode::get_parallel_desc_ptr() { + thread_local Symbol parallel_desc; + return ¶llel_desc; +} +/* static */ Symbol GlobalMode::parallel_desc() { return *get_parallel_desc_ptr(); } +/* static */ void GlobalMode::set_parallel_desc(Symbol parallel_desc) { + *get_parallel_desc_ptr() = parallel_desc; +} + +} // namespace oneflow diff --git a/oneflow/core/job/global_mode.h b/oneflow/core/job/global_mode.h new file mode 100644 index 0000000000000000000000000000000000000000..8fcac7f2e4da8d68f3745abb51c68358981b2c51 --- /dev/null +++ b/oneflow/core/job/global_mode.h @@ -0,0 +1,81 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_JOB_GLOBAL_MODE_H_ +#define ONEFLOW_CORE_JOB_GLOBAL_MODE_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/job/sbp_parallel.pb.h" + +namespace oneflow { + +Symbol GetGlobalParallelDescFromDevice(const Optional>& device); +class GlobalMode { + public: + OF_DISALLOW_COPY_AND_MOVE(GlobalMode); + GlobalMode() = default; + ~GlobalMode() = default; + + static bool is_enabled(); + static Symbol nd_sbp(); + static Symbol parallel_desc(); + + class Guard { + public: + explicit Guard(bool enabled) + : prev_mode_(GlobalMode::is_enabled()), + prev_nd_sbp_(GlobalMode::nd_sbp()), + prev_parallel_desc_(GlobalMode::parallel_desc()) { + CHECK(!enabled); + GlobalMode::set_enabled(enabled); + } + explicit Guard(bool enabled, Symbol nd_sbp, Symbol parallel_desc) + : prev_mode_(GlobalMode::is_enabled()), + prev_nd_sbp_(GlobalMode::nd_sbp()), + prev_parallel_desc_(GlobalMode::parallel_desc()) { + GlobalMode::set_enabled(enabled); + if (enabled) { + GlobalMode::set_nd_sbp(nd_sbp); + GlobalMode::set_parallel_desc(parallel_desc); + } + } + ~Guard() { + GlobalMode::set_enabled(prev_mode_); + GlobalMode::set_nd_sbp(prev_nd_sbp_); + GlobalMode::set_parallel_desc(prev_parallel_desc_); + } + + private: + bool prev_mode_; + Symbol prev_nd_sbp_; + Symbol prev_parallel_desc_; + }; + + private: + static bool* get_mode_ptr(); + static Symbol* get_nd_sbp_ptr(); + static Symbol* get_parallel_desc_ptr(); + + static void set_enabled(bool enabled); + static void set_nd_sbp(Symbol nd_sbp); + static void set_parallel_desc(Symbol parallel_desc); +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_GLOBAL_MODE_H_ diff --git a/oneflow/core/job/graph_scope_vars.cpp b/oneflow/core/job/graph_scope_vars.cpp index 758e7ca1b35fa13b5d5d668a9cf843e42938967c..f4c126884ffc318099af945929e4625d0a83dd0b 100644 --- a/oneflow/core/job/graph_scope_vars.cpp +++ b/oneflow/core/job/graph_scope_vars.cpp @@ -14,11 +14,22 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/job/graph_scope_vars.h" +#include namespace oneflow { namespace { +std::vector* GetPythonPathsToBeFilteredForDebuggingVar() { + static thread_local std::vector filtered_paths; + return &filtered_paths; +} + +std::vector* GetPythonPathsToBeKeptForDebuggingVar() { + static thread_local std::vector kept_paths; + return &kept_paths; +} + bool* GetGraphVerboseStepLr() { static thread_local bool graph_verbose_step_lr = false; return &graph_verbose_step_lr; @@ -33,6 +44,11 @@ bool* GetGraphDebugModeFlag() { static thread_local bool graph_debug_mode_flag = false; return &graph_debug_mode_flag; } + +bool* GetGraphDebugOnlyUserPyStackFlag() { + static thread_local bool graph_debug_only_user_py_stack = true; + return &graph_debug_only_user_py_stack; +} } // namespace bool IsOpenGraphVerboseStepLr() { @@ -46,9 +62,30 @@ void SetGraphVerboseStepLr(bool verbose) { *graph_verbose_step_lr = verbose; } +void InitPythonPathsToBeKeptAndFilteredForDebugging(const std::string& python_base_dir) { + std::vector* kept_paths = GetPythonPathsToBeKeptForDebuggingVar(); + kept_paths->clear(); + kept_paths->push_back(python_base_dir + "/test"); + kept_paths->push_back(python_base_dir + "/nn/modules"); + + std::vector* filtered_paths = GetPythonPathsToBeFilteredForDebuggingVar(); + filtered_paths->clear(); + filtered_paths->push_back(python_base_dir); +} + +const std::vector& GetPythonPathsToBeFilteredForDebugging() { + return *GetPythonPathsToBeFilteredForDebuggingVar(); +} +const std::vector& GetPythonPathsToBeKeptForDebugging() { + return *GetPythonPathsToBeKeptForDebuggingVar(); +} + void SetGraphDebugMaxPyStackDepth(int32_t depth) { *GetGraphDebugMaxPyStackDepthVar() = depth; } int32_t GetGraphDebugMaxPyStackDepth() { return *GetGraphDebugMaxPyStackDepthVar(); } void SetGraphDebugMode(bool mode) { *GetGraphDebugModeFlag() = mode; } bool GetGraphDebugMode() { return *GetGraphDebugModeFlag(); } + +void SetGraphDebugOnlyUserPyStack(bool flag) { *GetGraphDebugOnlyUserPyStackFlag() = flag; } +bool GetGraphDebugOnlyUserPyStack() { return *GetGraphDebugOnlyUserPyStackFlag(); } } // namespace oneflow diff --git a/oneflow/core/job/graph_scope_vars.h b/oneflow/core/job/graph_scope_vars.h index de6d39c63122371234a1a81d145bd0680b5057b6..8f1da27f66231c081b67d5c292c8601dfe252fef 100644 --- a/oneflow/core/job/graph_scope_vars.h +++ b/oneflow/core/job/graph_scope_vars.h @@ -17,6 +17,9 @@ limitations under the License. #define ONEFLOW_CORE_JOB_GRAPH_SCOPE_VARS_H_ #include +#include +#include + namespace oneflow { bool IsOpenGraphVerboseStepLr(); @@ -26,6 +29,12 @@ void SetGraphDebugMaxPyStackDepth(int32_t depth); int32_t GetGraphDebugMaxPyStackDepth(); void SetGraphDebugMode(bool mode); bool GetGraphDebugMode(); +void SetGraphDebugOnlyUserPyStack(bool flag); +bool GetGraphDebugOnlyUserPyStack(); +void InitPythonPathsToBeKeptAndFilteredForDebugging(const std::string& python_base_dir); +const std::vector& GetPythonPathsToBeFilteredForDebugging(); +const std::vector& GetPythonPathsToBeKeptForDebugging(); + } // namespace oneflow #endif // ONEFLOW_CORE_JOB_GRAPH_SCOPE_VARS_H_ diff --git a/oneflow/core/job/initializer_conf.proto b/oneflow/core/job/initializer_conf.proto index 677a4ef67ca82f5e52433e7d86ff6f7093665e35..5973dbab7cc19a03032bac4fa1036be4485ad2a7 100644 --- a/oneflow/core/job/initializer_conf.proto +++ b/oneflow/core/job/initializer_conf.proto @@ -9,48 +9,11 @@ message ConstantIntInitializerConf { optional int64 value = 1 [default = 0]; } -message RandomUniformInitializerConf { - optional float min = 1 [default = 0]; - optional float max = 2 [default = 1]; -} - -message RandomUniformIntInitializerConf { - optional int32 min = 1 [default = 0]; - optional int32 max = 2 [default = 1]; -} - message RandomNormalInitializerConf { optional float mean = 1 [default = 0]; optional float std = 2 [default = 1]; } -message TruncatedNormalInitializerConf { - optional float mean = 1 [default = 0.0]; - optional float std = 2 [default = 0.05]; -} - -enum VarianceNorm { - kFanIn = 0; - kFanOut = 1; - kAverage = 2; -} - -enum RandomDistribution { - kRandomUniform = 0; - kRandomNormal = 1; - kTruncatedNormal = 2; -} - -message XavierInitializerConf { - required VarianceNorm variance_norm = 1; - required string data_format = 2; -} - -message MsraInitializerConf { - required VarianceNorm variance_norm = 1; - required string data_format = 2; -} - //output[D_0 ... D_(axis - 1) i D_(axis + 1) ... D_n] = start + i * stride message RangeInitializerConf { optional double start = 1 [default = 0]; @@ -64,13 +27,6 @@ message IntRangeInitializerConf { optional int64 axis = 3 [default = -1]; } -message VarianceScalingInitializerConf { - required float scale = 1; - required VarianceNorm variance_norm = 2; - required RandomDistribution distribution = 3; - required string data_format = 4; -} - message EmptyInitializerConf { } @@ -78,16 +34,10 @@ message InitializerConf { oneof type { ConstantInitializerConf constant_conf = 1; ConstantIntInitializerConf constant_int_conf = 2; - RandomUniformInitializerConf random_uniform_conf = 3; - RandomUniformIntInitializerConf random_uniform_int_conf = 4; - RandomNormalInitializerConf random_normal_conf = 5; - TruncatedNormalInitializerConf truncated_normal_conf = 6; - XavierInitializerConf xavier_conf = 7; - MsraInitializerConf msra_conf = 8; - RangeInitializerConf range_conf = 9; - IntRangeInitializerConf int_range_conf = 10; - VarianceScalingInitializerConf variance_scaling_conf = 11; - EmptyInitializerConf empty_conf = 12; + RandomNormalInitializerConf random_normal_conf = 3; + RangeInitializerConf range_conf = 4; + IntRangeInitializerConf int_range_conf = 5; + EmptyInitializerConf empty_conf = 6; } } diff --git a/oneflow/core/job/inter_job_mem_sharing_util.cpp b/oneflow/core/job/inter_job_mem_sharing_util.cpp index 099c2afc00ed71bf069d2a70f5a97e17443caf60..8b2b52fba1fe9b623904995f16dd7da7a1eaddf3 100644 --- a/oneflow/core/job/inter_job_mem_sharing_util.cpp +++ b/oneflow/core/job/inter_job_mem_sharing_util.cpp @@ -179,9 +179,9 @@ void MergeReusedChunk(HashMap* chunk_id2chunk, for (const auto& pair : *chunk_id2chunk) { const ChunkProto& chunk = pair.second; const MemoryCase& mem_case = chunk.mem_case(); - // only reused mem in cuda device - if (mem_case.has_host_mem()) { continue; } - int64_t mzuid = MemoryCaseUtil::GenMemZoneUniqueId(chunk.machine_id(), mem_case); + // NOTE(zwx): do not reuse mem on cpu + if (memory::IsHostMem(mem_case)) { continue; } + int64_t mzuid = memory::GetUniqueMemCaseId(chunk.machine_id(), mem_case); CHECK_EQ(chunk.job_id_size(), 1); CHECK(job_id2mzuid2chunk_id[chunk.job_id(0)].emplace(mzuid, chunk.chunk_id()).second); } @@ -275,8 +275,7 @@ void MergeSharedMemBlockR2L(RegstDescProto* lhs, RegstDescProto* rhs, CHECK_EQ(separated_header_mem_size, right_rt_regst.TotalSeparatedHeaderByteSize4AllRegst()); int64_t merged_header_id = lhs->separated_header_mem_block_id(); int64_t erased_header_id = rhs->separated_header_mem_block_id(); - MemoryCase header_mem_case = - MemoryCaseUtil::GetHostMemoryCaseForRegstSeparatedHeader(lhs->mem_case()); + MemoryCase header_mem_case = memory::GetPinnedHostMemoryCase(lhs->mem_case()); MemBlockProto* merged_header_block = CheckValidAndGetMemBlock(merged_header_id, separated_header_mem_size, header_mem_case); MemBlockProto* erased_header_block = @@ -314,10 +313,8 @@ void MergeSharedInterfaceMemBlock(const std::vector>& jobs, MergeSharedMemBlockR2L(first_regst_desc, regst_desc, mem_block_id2mem_block); - MemoryCase common_mem_case; - CHECK(MemoryCaseUtil::GetCommonMemoryCase(common_mem_case_vec.at(i), regst_desc->mem_case(), - &common_mem_case)); - common_mem_case_vec[i] = common_mem_case; + CHECK(memory::EqualsIgnorePinnedDevice(common_mem_case_vec.at(i), regst_desc->mem_case())); + common_mem_case_vec[i] = regst_desc->mem_case(); } } for (const auto& pair : job_id2same_op_name_sorted_task_protos) { diff --git a/oneflow/core/job/intra_job_mem_sharing_util.cpp b/oneflow/core/job/intra_job_mem_sharing_util.cpp index fa1c8586c078f9f50acf7af6b822b1057254454f..46858f6c476c36ab2547852d09ec492b1e835265 100644 --- a/oneflow/core/job/intra_job_mem_sharing_util.cpp +++ b/oneflow/core/job/intra_job_mem_sharing_util.cpp @@ -15,9 +15,12 @@ limitations under the License. */ #include "oneflow/core/job/intra_job_mem_sharing_util.h" #include "oneflow/core/common/blocking_counter.h" +#include "oneflow/core/common/hash_container.h" #include "oneflow/core/common/str_util.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/job/id_manager.h" +#include "oneflow/core/job/job_desc.h" +#include "oneflow/core/job/memory_share_strategy.h" #include "oneflow/core/register/runtime_register_desc.h" #include "oneflow/core/thread/thread_pool.h" #include "oneflow/core/graph/task_node.h" @@ -57,18 +60,6 @@ int64_t GenDeviceUniqueId(int64_t machine_id, int64_t device_id) { return (machine_id << 32) | device_id; } -void GenRegstDescId2RegstDesc(Plan* plan, - HashMap* regst_desc_id2regst_desc) { - regst_desc_id2regst_desc->clear(); - for (int i = 0; i < plan->task_size(); i++) { - TaskProto* task = plan->mutable_task(i); - for (auto& pair : *task->mutable_produced_regst_desc()) { - int64_t regst_desc_id = pair.second.regst_desc_id(); - regst_desc_id2regst_desc->insert({regst_desc_id, &pair.second}); - } - } -} - void TryConnectWithMemSafeGuardCtrlRegstDesc(TaskProto* src_task_proto, TaskProto* dst_task_proto) { RegstDescProto* ctrl_regst_desc = FindOrCreateProducedCtrlRegstDesc(src_task_proto, "out_ctrl_shared_mem_safe_guard"); @@ -91,13 +82,16 @@ struct MemoryChain { }; void InitMemoryChains(Plan* plan, - HashMap>* device2chain2mem_chain) { + HashMap>* device2chain2mem_chain, + HashMap* mem_reused_regst2size) { for (int64_t i = 0; i < plan->task_size(); ++i) { TaskProto* task = plan->mutable_task(i); const StreamId stream_id = PlanUtil::GetStreamId(*task); int64_t machine_id = task->machine_id(); DeviceType device_type = stream_id.device_id().device_type(); - if (device_type != DeviceType::kCUDA) { continue; } + // TODO(zwx): eliminate this special 'is cpu' determine + if (device_type == DeviceType::kCPU) { continue; } + if (!IsValidChainId(task->task_set_info().chain_id())) { continue; } int64_t device_id = stream_id.device_id().device_index(); int64_t device_unique_id = GenDeviceUniqueId(machine_id, device_id); MemoryChain* mem_chain = @@ -105,13 +99,15 @@ void InitMemoryChains(Plan* plan, mem_chain->sorted_tasks.emplace_back(task); for (auto& pair : *(task->mutable_produced_regst_desc())) { RegstDescProto* regst_desc = &pair.second; - if (regst_desc->mem_case().has_device_cuda_mem() - && regst_desc->mem_case().device_cuda_mem().device_id() == device_id - && regst_desc->enable_reuse_mem() && regst_desc->register_num() == 1 - && regst_desc->mem_block_id() == -1 && regst_desc->mem_block_offset() == -1 - && regst_desc->regst_desc_type().has_data_regst_desc()) { + int64_t regst_total_main_size = RtRegstDesc(*regst_desc).TotalMainByteSize4AllRegst(); + if (regst_desc->mem_case().device_type() == device_type + && regst_desc->mem_case().device_id() == device_id && regst_desc->enable_reuse_mem() + && regst_desc->register_num() == 1 && regst_desc->mem_block_id() == -1 + && regst_desc->mem_block_offset() == -1 + && regst_desc->regst_desc_type().has_data_regst_desc() && regst_total_main_size > 0) { CHECK(mem_chain->mem_reused_regsts.insert(regst_desc).second); - mem_chain->total_mem_reused_size += RtRegstDesc(*regst_desc).TotalMainByteSize4AllRegst(); + (*mem_reused_regst2size)[regst_desc] = regst_total_main_size; + mem_chain->total_mem_reused_size += regst_total_main_size; // for time shape in mem chain Shape regst_time_shape = @@ -189,11 +185,13 @@ void GenMemChainTasksAndRegsts( Plan* plan, const std::function& IsOpNameDataOrCtrlReachable, HashMap>* mem_chain2sorted_tasks, - HashMap>* mem_chain2mem_reused_regsts) { + HashMap>* mem_chain2mem_reused_regsts, + HashMap>* mem_chain2regst_desc_id2reuse_regst_desc, + HashMap* mem_reused_regst2size) { mem_chain2sorted_tasks->clear(); mem_chain2mem_reused_regsts->clear(); HashMap> device2chain2mem_chain; - InitMemoryChains(plan, &device2chain2mem_chain); + InitMemoryChains(plan, &device2chain2mem_chain, mem_reused_regst2size); auto TryGetTaskNodeLogicalOpName = [&](const TaskProto* task_proto, std::string* op_name) -> bool { @@ -252,10 +250,18 @@ void GenMemChainTasksAndRegsts( CHECK(sorted_tasks->empty()); sorted_tasks->insert(sorted_tasks->end(), merged_chain->sorted_tasks.begin(), merged_chain->sorted_tasks.end()); - HashSet* mem_reused_regsts = &((*mem_chain2mem_reused_regsts)[mem_chain_id]); + std::vector* mem_reused_regsts = + &((*mem_chain2mem_reused_regsts)[mem_chain_id]); CHECK(mem_reused_regsts->empty()); - mem_reused_regsts->insert(merged_chain->mem_reused_regsts.begin(), + mem_reused_regsts->insert(mem_reused_regsts->end(), merged_chain->mem_reused_regsts.begin(), merged_chain->mem_reused_regsts.end()); + // Merge HashSet mem_chain2mem_reused_regsts and HashMap regst_desc_id2reuse_regst_desc + auto& regst_desc_id2reuse_regst_desc = + (*mem_chain2regst_desc_id2reuse_regst_desc)[mem_chain_id]; + CHECK(regst_desc_id2reuse_regst_desc.empty()); + for (auto& mem_reused_regst : merged_chain->mem_reused_regsts) { + regst_desc_id2reuse_regst_desc[mem_reused_regst->regst_desc_id()] = mem_reused_regst; + } ++mem_chain_id; } } @@ -272,7 +278,8 @@ void GenMemChainTasksAndRegsts( std::vector* sorted_tasks = &(pair.second); // NOTE(chengcheng): We CANNOT only add ctrl safe guard between first and last task, // because of the sorted_tasks may connected as a graph, has multi-tail tasks(sink task). - const HashSet& mem_reused_regsts = mem_chain2mem_reused_regsts->at(pair.first); + const std::vector& mem_reused_regsts = + mem_chain2mem_reused_regsts->at(pair.first); if (mem_reused_regsts.size() <= 1) { continue; } HashSet consumer_task_ids; @@ -300,17 +307,15 @@ void GenMemChainTasksAndRegsts( } void GenRegstAllocFreeTimeLineAndRegstMutualExclusions( - const std::vector& sorted_tasks, const HashSet& mem_reused_regsts, - const HashMap& regst_desc_id2regst_desc, - std::vector>* alloc_regsts_timeline, - std::vector>* free_regsts_timeline, - HashMap>* regst2mutual_exclusion_regsts, - HashMap* consumer2inplaced_regst) { - CHECK(alloc_regsts_timeline->empty() && free_regsts_timeline->empty()); - CHECK(regst2mutual_exclusion_regsts->empty()); + const std::vector& sorted_tasks, + const std::vector& mem_reused_regsts, + const HashMap& regst_desc_id2reuse_regst_desc, + const HashMap& mem_reused_regst2size, + HashMap>* regst2lifetime, + HashMap* consumer2inplaced_regst, size_t* peak_memory) { CHECK(consumer2inplaced_regst->empty()); - alloc_regsts_timeline->resize(sorted_tasks.size()); - free_regsts_timeline->resize(sorted_tasks.size()); + std::vector> alloc_regsts_timeline(sorted_tasks.size()); + std::vector> free_regsts_timeline(sorted_tasks.size()); HashMap task_id2sorted_id; for (int64_t i = 0; i < sorted_tasks.size(); ++i) { TaskProto* task = sorted_tasks.at(i); @@ -335,11 +340,11 @@ void GenRegstAllocFreeTimeLineAndRegstMutualExclusions( RegstDescProto* inplaced_regst = nullptr; while (consumer_regst->has_hint_inplace_consumed_regst_desc_id() && consumer_regst->hint_inplace_consumed_regst_desc_id() != -1) { - RegstDescProto* hint_inplaced_regst = - regst_desc_id2regst_desc.at(consumer_regst->hint_inplace_consumed_regst_desc_id()); - if (mem_reused_regsts.find(hint_inplaced_regst) != mem_reused_regsts.end()) { - inplaced_regst = hint_inplaced_regst; - consumer_regst = hint_inplaced_regst; + const auto& iterator_hint_inplaced_regst = regst_desc_id2reuse_regst_desc.find( + consumer_regst->hint_inplace_consumed_regst_desc_id()); + if (iterator_hint_inplaced_regst != regst_desc_id2reuse_regst_desc.end()) { + inplaced_regst = iterator_hint_inplaced_regst->second; + consumer_regst = iterator_hint_inplaced_regst->second; } else { break; } @@ -355,9 +360,8 @@ void GenRegstAllocFreeTimeLineAndRegstMutualExclusions( continue; } - CHECK(alloc_regsts_timeline->at(task_id2sorted_id.at(regst_desc->producer_task_id())) - .insert(regst_desc) - .second); + alloc_regsts_timeline[task_id2sorted_id.at(regst_desc->producer_task_id())].push_back( + regst_desc); CHECK(regst_desc_id2free_index .emplace(regst_desc->regst_desc_id(), FindLastFreeIndexInSortedTasks(regst_desc)) .second); @@ -372,361 +376,161 @@ void GenRegstAllocFreeTimeLineAndRegstMutualExclusions( FindLastFreeIndexInSortedTasks(consumer_regst_desc)); } for (const auto& pair : regst_desc_id2free_index) { - CHECK(free_regsts_timeline->at(pair.second) - .insert(regst_desc_id2regst_desc.at(pair.first)) - .second); + free_regsts_timeline[pair.second].push_back(regst_desc_id2reuse_regst_desc.at(pair.first)); } HashSet remain_regsts; + size_t remain_memory = 0; + *peak_memory = 0; for (int64_t i = 0; i < sorted_tasks.size(); ++i) { - for (RegstDescProto* alloc_regst : alloc_regsts_timeline->at(i)) { - CHECK(regst2mutual_exclusion_regsts->emplace(alloc_regst, std::vector()) - .second); - for (RegstDescProto* remain_regst : remain_regsts) { - regst2mutual_exclusion_regsts->at(alloc_regst).emplace_back(remain_regst); - regst2mutual_exclusion_regsts->at(remain_regst).emplace_back(alloc_regst); - } + for (RegstDescProto* alloc_regst : alloc_regsts_timeline.at(i)) { + // Record the born time + (*regst2lifetime)[alloc_regst].first = i; CHECK(remain_regsts.insert(alloc_regst).second); + remain_memory += mem_reused_regst2size.at(alloc_regst); // NOTE(chengcheng): insert time line to regst proto alloc_regst->set_mem_block_total_actor_count(sorted_tasks.size()); alloc_regst->set_alloc_before_actor(i); } - for (RegstDescProto* free_regst : free_regsts_timeline->at(i)) { + // Update the peak of memory during execution + if (*peak_memory < remain_memory) { *peak_memory = remain_memory; } + for (RegstDescProto* free_regst : free_regsts_timeline.at(i)) { CHECK_EQ(remain_regsts.erase(free_regst), 1); free_regst->set_free_after_actor(i); + remain_memory -= mem_reused_regst2size.at(free_regst); + // Record the die time + (*regst2lifetime)[free_regst].second = i + 1; } } + // Make sure that every register has a die time CHECK(remain_regsts.empty()); } -struct Piece { - int64_t begin; - int64_t end; - bool is_free; -}; -using PieceIt = std::list::iterator; - -class MemBlockBuffer final { - public: - MemBlockBuffer(size_t size) : buffer_size_(size) { - Piece start_piece; - start_piece.begin = 0; - start_piece.end = size; - start_piece.is_free = true; - piece_list_.emplace_back(start_piece); - }; - ~MemBlockBuffer() = default; - - void Occupy(int64_t begin, int64_t end); - void FindFreeOffsetAndNewBufferSize(int64_t size, int64_t* offset, size_t* new_buffer_size); - - private: - void CheckValid() { - CHECK(piece_list_.size() >= 1); - CHECK(piece_list_.begin()->begin == 0); - CHECK(std::prev(piece_list_.end())->end == buffer_size_); - for (auto it = std::next(piece_list_.begin()); it != piece_list_.end(); ++it) { - auto pre_it = std::prev(it); - CHECK(pre_it->begin < pre_it->end && pre_it->end == it->begin); - } - } - - void MergePieceAndCheckValid() { - CheckValid(); - for (auto it = std::next(piece_list_.begin()); it != piece_list_.end(); ++it) { - auto pre_it = std::prev(it); - if (it->is_free == pre_it->is_free) { - it->begin = pre_it->begin; - CHECK(piece_list_.erase(pre_it) == it); - } - } - CheckValid(); - } - - std::list piece_list_; - size_t buffer_size_; -}; - -void MemBlockBuffer::Occupy(int64_t begin, int64_t end) { - CHECK(begin <= end && end <= buffer_size_); - for (auto it = piece_list_.begin(); it != piece_list_.end(); ++it) { - if (it->end <= begin) { continue; } - if (end <= it->begin) { break; } - if (it->is_free) { - if (begin != it->begin) { - CHECK(it->begin < begin); - CHECK(begin < it->end); - Piece free_piece; - free_piece.begin = it->begin; - free_piece.end = begin; - free_piece.is_free = true; - it->begin = begin; - it = piece_list_.insert(it, free_piece); - } else if (end < it->end) { - Piece busy_piece; - busy_piece.begin = it->begin; - busy_piece.end = end; - busy_piece.is_free = false; - it->begin = end; - it = piece_list_.insert(it, busy_piece); - begin = end; - } else { - it->is_free = false; - begin = it->end; - } - } else { - begin = it->end; - end = std::max(begin, end); - } - } - MergePieceAndCheckValid(); -} - -void MemBlockBuffer::FindFreeOffsetAndNewBufferSize(int64_t size, int64_t* offset, - size_t* new_buffer_size) { - CheckValid(); - for (auto it = piece_list_.begin(); it != piece_list_.end(); ++it) { - if (it->is_free && (it->end - it->begin) >= size) { - *offset = it->begin; - *new_buffer_size = buffer_size_; - return; - } - } - auto last_it = std::prev(piece_list_.end()); - if (last_it->is_free) { - *offset = last_it->begin; - *new_buffer_size = buffer_size_ + size - (last_it->end - last_it->begin); - } else { - *offset = buffer_size_; - *new_buffer_size = buffer_size_ + size; - } -} - -void MemReusedAlgorithm_AllocateByOrderAndMutualExclusion( +void MemReusedAlgorithmAllocateByOrder( const std::vector& order, - const HashMap& regst_desc2size, - const HashMap>& regst2mutual_exclusion_regsts, + const HashMap& regst_desc2size, + const HashMap>& regst2lifetime, MemBlockResultInfo* result) { HashMap* regst_desc2offset = &(result->regst_desc2offset); + // NOTE: It is important to make the variables local. + // It took me several days to find out that using passed-in vector for size, order, and lifetime + // would double the running time. Switch HashMap to vector + int32_t total_register_num = order.size(); + std::vector order2size(total_register_num); + std::vector> order2lifetime(total_register_num); + std::vector order2offset(total_register_num); + for (int32_t i = 0; i < total_register_num; i++) { + order2size[i] = regst_desc2size.at(order[i]); + order2lifetime[i] = regst2lifetime.at(order[i]); + } size_t buffer_size = 1; - for (RegstDescProto* regst_desc : order) { - MemBlockBuffer buffer(buffer_size); - for (RegstDescProto* mutual_regst : regst2mutual_exclusion_regsts.at(regst_desc)) { - if (regst_desc2offset->find(mutual_regst) != regst_desc2offset->end()) { - int64_t begin = regst_desc2offset->at(mutual_regst); - int64_t end = begin + regst_desc2size.at(mutual_regst); - buffer.Occupy(begin, end); + // Sort by offset + auto comp = [&order2offset](const auto& a, const auto& b) { + if (order2offset[a] != order2offset[b]) { return order2offset[a] < order2offset[b]; } + // Make sure we have a stable order even if we have the same offset for different registers + return a < b; + }; + std::set sorted_registers(comp); + // Decide offset following the given order + for (int32_t inserting_id = 0; inserting_id < total_register_num; inserting_id++) { + int64_t inserting_offset = 0; + int64_t inserting_end = inserting_offset + order2size[inserting_id]; + const auto& inserting_lifetime = order2lifetime[inserting_id]; + for (const auto& curr_register : sorted_registers) { + // i: inserting register, j: current register + // x: register offset, l: register size + // If x_i + l_i <= x_j, then the inserting register would be placed at x_i + if (order2offset[curr_register] >= inserting_end) { break; } + // If i and j are excluded, and x_i + l_i > x_j, + // then we try to place i at x_j + l_j and check the following registers + if (IsLifetimeExcluded(inserting_lifetime, order2lifetime[curr_register])) { + int64_t curr_end = order2offset[curr_register] + order2size[curr_register]; + // Can not set inserting offset = current end directly. + // We might have two excluded registers like this: + // register a: [100, 10000] + // register b: [500, 600] + if (inserting_offset < curr_end) { + inserting_offset = curr_end; + inserting_end = inserting_offset + order2size[inserting_id]; + } } } - int64_t offset = -1; - buffer.FindFreeOffsetAndNewBufferSize(regst_desc2size.at(regst_desc), &offset, &buffer_size); - CHECK(offset >= 0 && offset <= buffer_size); - CHECK(regst_desc2offset->emplace(regst_desc, offset).second); + // Either we break the loop or the loop terminated naturally, we can place i at inserting_offset + order2offset[inserting_id] = inserting_offset; + sorted_registers.insert(inserting_id); + // Update total size + if (inserting_end > buffer_size) { buffer_size = inserting_end; } } + result->mem_block_size = buffer_size; + // Switch vector to HashMap + for (int32_t i = 0; i < total_register_num; i++) { + (*regst_desc2offset)[order[i]] = order2offset[i]; + } } -void MemReusedAlgorithm_MemSizeFirstAlgo( - const HashMap>& regst2mutual_exclusion_regsts, - const HashMap& regst2alloc_order, MemBlockResultInfo* result) { +void MemReusedMemSizeFirstAlgo( + const HashMap>& regst2lifetime, + const HashMap& mem_reused_regst2size, MemBlockResultInfo* result) { std::vector order; - order.reserve(regst2mutual_exclusion_regsts.size()); - HashMap regst_desc2size; - for (const auto& pair : regst2mutual_exclusion_regsts) { - order.emplace_back(pair.first); - CHECK(regst_desc2size.emplace(pair.first, RtRegstDesc(*pair.first).TotalMainByteSize4AllRegst()) - .second); - } + order.reserve(regst2lifetime.size()); + for (const auto& pair : regst2lifetime) { order.emplace_back(pair.first); } std::sort(order.begin(), order.end(), [&](RegstDescProto* lhs, RegstDescProto* rhs) { - int64_t l_size = regst_desc2size.at(lhs); - int64_t r_size = regst_desc2size.at(rhs); - if (l_size == r_size) { return regst2alloc_order.at(lhs) < regst2alloc_order.at(rhs); } - return l_size > r_size; + size_t l_value = mem_reused_regst2size.at(lhs); + size_t r_value = mem_reused_regst2size.at(rhs); + if (l_value == r_value) { return regst2lifetime.at(lhs).first < regst2lifetime.at(rhs).first; } + return l_value > r_value; }); - MemReusedAlgorithm_AllocateByOrderAndMutualExclusion(order, regst_desc2size, - regst2mutual_exclusion_regsts, result); + MemReusedAlgorithmAllocateByOrder(order, mem_reused_regst2size, regst2lifetime, result); } -void MemReusedAlgorithm_MutualExclusionFirstAlgo( - const HashMap>& regst2mutual_exclusion_regsts, - const HashMap& regst2alloc_order, MemBlockResultInfo* result) { +void MemReusedMutualExclusionFirstAlgo( + const HashMap>& regst2lifetime, + const HashMap& mem_reused_regst2size, MemBlockResultInfo* result) { std::vector order; - order.reserve(regst2mutual_exclusion_regsts.size()); - HashMap regst_desc2size; - for (const auto& pair : regst2mutual_exclusion_regsts) { - order.emplace_back(pair.first); - CHECK(regst_desc2size.emplace(pair.first, RtRegstDesc(*pair.first).TotalMainByteSize4AllRegst()) - .second); - } + order.reserve(regst2lifetime.size()); + for (const auto& pair : regst2lifetime) { order.emplace_back(pair.first); } std::sort(order.begin(), order.end(), [&](RegstDescProto* lhs, RegstDescProto* rhs) { - int64_t l_size = regst2mutual_exclusion_regsts.at(lhs).size(); - int64_t r_size = regst2mutual_exclusion_regsts.at(rhs).size(); - if (l_size == r_size) { return regst2alloc_order.at(lhs) < regst2alloc_order.at(rhs); } - return l_size > r_size; + int64_t l_value = regst2lifetime.at(lhs).second - regst2lifetime.at(lhs).first; + int64_t r_value = regst2lifetime.at(rhs).second - regst2lifetime.at(rhs).first; + if (l_value == r_value) { return regst2lifetime.at(lhs).first < regst2lifetime.at(rhs).first; } + return l_value > r_value; }); - MemReusedAlgorithm_AllocateByOrderAndMutualExclusion(order, regst_desc2size, - regst2mutual_exclusion_regsts, result); + MemReusedAlgorithmAllocateByOrder(order, mem_reused_regst2size, regst2lifetime, result); } -class BfcAllocator final { - public: - BfcAllocator(int64_t size) : buffer_size_(size) { - Piece start_piece; - start_piece.begin = 0; - start_piece.end = size; - start_piece.is_free = true; - piece_list_.emplace_back(start_piece); - }; - ~BfcAllocator() = default; - - // Return offset of the buffer for this allocate size memory - int64_t AllocateRaw(int64_t size); - void FreeRaw(int64_t offset, int64_t size); - int64_t buffer_size() const { return buffer_size_; } - - private: - void CheckValid() { - CHECK(piece_list_.size() >= 1); - CHECK(piece_list_.front().begin == 0); - CHECK(piece_list_.back().end == buffer_size_); - for (auto it = std::next(piece_list_.begin()); it != piece_list_.end(); ++it) { - auto pre_it = std::prev(it); - CHECK(pre_it->begin < pre_it->end && pre_it->end == it->begin); - CHECK(!(pre_it->is_free && it->is_free)); - } - } - - void MergeFreePieceAndCheckValid() { - for (auto it = std::next(piece_list_.begin()); it != piece_list_.end(); ++it) { - auto pre_it = std::prev(it); - if (it->is_free && pre_it->is_free) { - it->begin = pre_it->begin; - CHECK(piece_list_.erase(pre_it) == it); - } - } - CheckValid(); - } - - std::list piece_list_; - int64_t buffer_size_; - HashMap offset2occupied_piece_; -}; - -int64_t BfcAllocator::AllocateRaw(int64_t size) { - int64_t offset = -1; - PieceIt candidate_piece = piece_list_.end(); - for (auto it = piece_list_.begin(); it != piece_list_.end(); ++it) { - int64_t piece_size = it->end - it->begin; - if (it->is_free && piece_size >= size) { - if (candidate_piece == piece_list_.end() - || piece_size < (candidate_piece->end - candidate_piece->begin)) { - candidate_piece = it; - } - } - } - if (candidate_piece == piece_list_.end()) { - auto last_it = std::prev(piece_list_.end()); - if (last_it->is_free) { - offset = last_it->begin; - buffer_size_ += size - (last_it->end - last_it->begin); - last_it->end = buffer_size_; - last_it->is_free = false; - CHECK(offset2occupied_piece_.emplace(offset, last_it).second); - } else { - offset = last_it->end; - buffer_size_ += size; - Piece new_piece; - new_piece.begin = last_it->end; - new_piece.end = buffer_size_; - new_piece.is_free = false; - piece_list_.emplace_back(new_piece); - CHECK(offset2occupied_piece_.emplace(offset, std::prev(piece_list_.end())).second); - } - } else { - int64_t piece_size = candidate_piece->end - candidate_piece->begin; - offset = candidate_piece->begin; - if (piece_size > size) { - Piece new_piece; - new_piece.begin = candidate_piece->begin; - new_piece.end = candidate_piece->begin + size; - new_piece.is_free = false; - candidate_piece->begin = new_piece.end; - PieceIt new_it = piece_list_.insert(candidate_piece, new_piece); - CHECK(offset2occupied_piece_.emplace(offset, new_it).second); - } else { - CHECK_EQ(size, piece_size); - candidate_piece->is_free = false; - CHECK(offset2occupied_piece_.emplace(offset, candidate_piece).second); - } - } - CheckValid(); - CHECK_NE(offset, -1); - CHECK(offset2occupied_piece_.find(offset) != offset2occupied_piece_.end()); - return offset; -} - -void BfcAllocator::FreeRaw(int64_t offset, int64_t size) { - CHECK(offset2occupied_piece_.find(offset) != offset2occupied_piece_.end()); - PieceIt occupied_piece = offset2occupied_piece_.at(offset); - CHECK(occupied_piece->is_free == false); - CHECK_EQ((occupied_piece->end - occupied_piece->begin), size); - occupied_piece->is_free = true; - CHECK(offset2occupied_piece_.erase(offset) == 1); - MergeFreePieceAndCheckValid(); -} - -void MemReusedAlgorithm_TimeLineAlgo( - const std::vector>& alloc_regsts_timeline, - const std::vector>& free_regsts_timeline, MemBlockResultInfo* result) { - HashMap* regst_desc2offset = &(result->regst_desc2offset); - regst_desc2offset->clear(); - int64_t buffer_size = 1; - BfcAllocator bfc_allocator(buffer_size); - - auto GetRegstSize = [](const RegstDescProto* regst) -> int64_t { - return RtRegstDesc(*regst).TotalMainByteSize4AllRegst(); - }; - - CHECK_EQ(alloc_regsts_timeline.size(), free_regsts_timeline.size()); - for (int64_t i = 0; i < alloc_regsts_timeline.size(); ++i) { - for (RegstDescProto* alloc_regst : alloc_regsts_timeline.at(i)) { - CHECK(regst_desc2offset - ->emplace(alloc_regst, bfc_allocator.AllocateRaw(GetRegstSize(alloc_regst))) - .second); - } - for (RegstDescProto* free_regst : free_regsts_timeline.at(i)) { - CHECK(regst_desc2offset->find(free_regst) != regst_desc2offset->end()); - bfc_allocator.FreeRaw(regst_desc2offset->at(free_regst), GetRegstSize(free_regst)); +void MemReusedTimeLineAlgo( + const HashMap>& regst2lifetime, + const HashMap& mem_reused_regst2size, MemBlockResultInfo* result) { + std::vector order; + order.reserve(regst2lifetime.size()); + for (const auto& pair : regst2lifetime) { order.emplace_back(pair.first); } + std::sort(order.begin(), order.end(), [&](RegstDescProto* lhs, RegstDescProto* rhs) { + int64_t l_value = regst2lifetime.at(lhs).first; + int64_t r_value = regst2lifetime.at(rhs).first; + if (l_value == r_value) { + return regst2lifetime.at(lhs).second > regst2lifetime.at(rhs).second; } - } - result->mem_block_size = bfc_allocator.buffer_size(); + return l_value > r_value; + }); + MemReusedAlgorithmAllocateByOrder(order, mem_reused_regst2size, regst2lifetime, result); } void SelectAlgorithmGenMemBlockOffset4Regsts( - MemAllocAlgoType algo_id, const std::vector>& alloc_regsts_timeline, - const std::vector>& free_regsts_timeline, - const HashMap>& regst2mutual_exclusion_regsts, - MemBlockResultInfo* result) { + MemAllocAlgoType algo_id, + const HashMap>& regst2lifetime, + const HashMap& mem_reused_regst2size, MemBlockResultInfo* result) { CHECK_EQ(result->mem_block_size, 0); CHECK(result->regst_desc2offset.empty()); - // NOTE(chengcheng): When mem size or exclusion num equal, there need second order by allocate. - HashMap regst2alloc_order; - for (int64_t i = 0; i < alloc_regsts_timeline.size(); ++i) { - const auto& regsts = alloc_regsts_timeline.at(i); - for (RegstDescProto* regst : regsts) { CHECK(regst2alloc_order.emplace(regst, i).second); } - } switch (algo_id) { case kMemSizeFirstAlgo: - MemReusedAlgorithm_MemSizeFirstAlgo(regst2mutual_exclusion_regsts, regst2alloc_order, result); + MemReusedMemSizeFirstAlgo(regst2lifetime, mem_reused_regst2size, result); break; case kMutualExclusionFirstAlgo: - MemReusedAlgorithm_MutualExclusionFirstAlgo(regst2mutual_exclusion_regsts, regst2alloc_order, - result); - break; - case kTimeLineAlgo: - MemReusedAlgorithm_TimeLineAlgo(alloc_regsts_timeline, free_regsts_timeline, result); + MemReusedMutualExclusionFirstAlgo(regst2lifetime, mem_reused_regst2size, result); break; + case kTimeLineAlgo: MemReusedTimeLineAlgo(regst2lifetime, mem_reused_regst2size, result); break; default: UNIMPLEMENTED(); } CHECK_GT(result->mem_block_size, 0); @@ -748,6 +552,7 @@ void InitAlgo2Result(HashMap* algo2result) CHECK(algo2result->empty()); const MemoryAllocationAlgorithmConf& mem_alloc_algo_conf = GlobalJobDesc().job_conf().memory_allocation_algorithm_conf(); + // NOTE: Experiments show that memory first might be good enough for some cases. if (mem_alloc_algo_conf.use_mem_size_first_algo()) { CHECK(algo2result->emplace(kMemSizeFirstAlgo, MemBlockResultInfo()).second); } @@ -766,35 +571,37 @@ void IntraJobMemSharingUtil::InferMemBlockId4MemReusedRegst( IsOpNameDataOrCtrlReachable) { // 1 device 1 mem chain HashMap> mem_chain2sorted_tasks; - HashMap> mem_chain2mem_reused_regsts; + HashMap> mem_chain2mem_reused_regsts; + // NOTE: We only store those reusable registers in mem_chain2regst_desc_id2reuse_regst_desc. + // There are no duplicated registers in different memory chains. + HashMap> mem_chain2regst_desc_id2reuse_regst_desc; + HashMap mem_reused_regst2size; GenMemChainTasksAndRegsts(plan, IsOpNameDataOrCtrlReachable, &mem_chain2sorted_tasks, - &mem_chain2mem_reused_regsts); + &mem_chain2mem_reused_regsts, &mem_chain2regst_desc_id2reuse_regst_desc, + &mem_reused_regst2size); if (mem_chain2mem_reused_regsts.empty()) { return; } HashSet mem_chains; for (const auto& pair : mem_chain2mem_reused_regsts) { mem_chains.insert(pair.first); } - HashMap regst_desc_id2regst_desc; - GenRegstDescId2RegstDesc(plan, ®st_desc_id2regst_desc); - // info for algorithm - HashMap>> mem_chain2task2alloc_regsts; - HashMap>> mem_chain2task2free_regsts; - HashMap>> - mem_chain2regst2mutual_exclusion_regsts; + // register lifetime + HashMap>> mem_chain2regst2lifetime; // info for inplace HashMap> mem_chain2consumer2inplaced_regst; + // info for straighten + HashMap mem_chain2peak_memory; // step 1: generate regst alloc/free queue AND regst mutual exclusions for (const auto& pair : mem_chain2mem_reused_regsts) { GenRegstAllocFreeTimeLineAndRegstMutualExclusions( - mem_chain2sorted_tasks.at(pair.first), pair.second, regst_desc_id2regst_desc, - &mem_chain2task2alloc_regsts[pair.first], &mem_chain2task2free_regsts[pair.first], - &mem_chain2regst2mutual_exclusion_regsts[pair.first], - &mem_chain2consumer2inplaced_regst[pair.first]); + mem_chain2sorted_tasks.at(pair.first), pair.second, + mem_chain2regst_desc_id2reuse_regst_desc.at(pair.first), mem_reused_regst2size, + &mem_chain2regst2lifetime[pair.first], &mem_chain2consumer2inplaced_regst[pair.first], + &mem_chain2peak_memory[pair.first]); } // step 2: multi-thread run several algorithm for each mem chain HashMap> mem_chain2algo2result; { - int64_t work_size = mem_chain2mem_reused_regsts.size() * CountMemAllocAlgoNum(); + int64_t work_size = mem_chain2mem_reused_regsts.size(); int64_t thread_pool_size = std::min(work_size, std::thread::hardware_concurrency()); BlockingCounter counter(work_size); ThreadPool thread_pool(thread_pool_size); @@ -803,13 +610,10 @@ void IntraJobMemSharingUtil::InferMemBlockId4MemReusedRegst( for (auto& pair : mem_chain2algo2result.at(mem_chain_id)) { MemAllocAlgoType algo_id = pair.first; MemBlockResultInfo* result = &pair.second; - thread_pool.AddWork([algo_id, mem_chain_id, &mem_chain2task2alloc_regsts, - &mem_chain2task2free_regsts, &mem_chain2regst2mutual_exclusion_regsts, - result, &counter]() { + thread_pool.AddWork([algo_id, mem_chain_id, &mem_chain2regst2lifetime, + &mem_reused_regst2size, result, &counter]() { SelectAlgorithmGenMemBlockOffset4Regsts( - algo_id, mem_chain2task2alloc_regsts.at(mem_chain_id), - mem_chain2task2free_regsts.at(mem_chain_id), - mem_chain2regst2mutual_exclusion_regsts.at(mem_chain_id), result); + algo_id, mem_chain2regst2lifetime.at(mem_chain_id), mem_reused_regst2size, result); counter.Decrease(); }); } @@ -818,14 +622,24 @@ void IntraJobMemSharingUtil::InferMemBlockId4MemReusedRegst( } // step 3: choose best one for each mem chain and set offset for inplace consumer regst - for (const auto& pair : mem_chain2algo2result) { - const MemBlockResultInfo* best_result = nullptr; - for (const auto& algo_result_pair : pair.second) { + for (auto& pair : mem_chain2algo2result) { + MemBlockResultInfo* best_result = nullptr; + for (auto& algo_result_pair : pair.second) { if (!best_result || algo_result_pair.second.mem_block_size < best_result->mem_block_size) { best_result = &algo_result_pair.second; } } CHECK(best_result != nullptr); + + // Update the offset with a smaller total memory size if the current size is greater than the + // lower bound + if (GlobalJobDesc().job_conf().enable_compress_memory()) { + MemoryShareStrategy mss; + mss.AdaptivelyUpdateOffset(mem_reused_regst2size, mem_chain2regst2lifetime.at(pair.first), + mem_chain2peak_memory[pair.first], &best_result->mem_block_size, + &best_result->regst_desc2offset); + } + int64_t mem_block_id = Singleton::Get()->NewMemBlockId(); CHECK_EQ(mem_chain2mem_reused_regsts.at(pair.first).size(), (best_result->regst_desc2offset.size() @@ -848,16 +662,19 @@ void IntraJobMemSharingUtil::InferMemBlockId4MemReusedRegst( } // set inplace hint and check + const auto& regst_desc_id2reuse_regst_desc = + mem_chain2regst_desc_id2reuse_regst_desc.at(pair.first); for (auto& consumer_inplace_pair : mem_chain2consumer2inplaced_regst.at(pair.first)) { RegstDescProto* consumer_regst_desc = consumer_inplace_pair.first; RegstDescProto* inplaced_regst_desc = consumer_inplace_pair.second; CHECK(consumer_regst_desc->has_inplace_consumed_regst_desc_id() == false); CHECK(consumer_regst_desc->has_hint_inplace_consumed_regst_desc_id()); int64_t hint = consumer_regst_desc->hint_inplace_consumed_regst_desc_id(); - // NOTE(chengcheng): hint regst desc id may NOT the inplaced_regst_desc_id + // NOTE(chengcheng): hint regst desc id may NOT be the inplaced_regst_desc_id // because of nest inplace. - auto hint_it = regst_desc_id2regst_desc.find(hint); - CHECK(hint_it != regst_desc_id2regst_desc.end()); + // NOTE: All the registers in mem_chain2consumer2inplaced_regst are reusable + auto hint_it = regst_desc_id2reuse_regst_desc.find(hint); + CHECK(hint_it != regst_desc_id2reuse_regst_desc.end()); RegstDescProto* in_regst_desc = hint_it->second; CHECK_EQ(consumer_regst_desc->mem_block_id(), in_regst_desc->mem_block_id()); CHECK_EQ(consumer_regst_desc->mem_block_offset(), in_regst_desc->mem_block_offset()); diff --git a/oneflow/core/job/job.proto b/oneflow/core/job/job.proto index 709cf80ac9037580976be39939af4417ecf6366a..c6551ee51ccb1838f59a465b11d884fbda9499b6 100644 --- a/oneflow/core/job/job.proto +++ b/oneflow/core/job/job.proto @@ -9,12 +9,11 @@ import "oneflow/core/register/op_blob_arg.proto"; import "oneflow/core/register/blob_desc.proto"; import "oneflow/core/operator/op_conf.proto"; import "oneflow/core/job/sbp_parallel.proto"; -import "oneflow/core/job/lbi_diff_watcher_info.proto"; import "oneflow/core/job/module_conf.proto"; message JobParallelViewConf { map op_name2sbp_signature_conf = 1; - map op_name2is_mirrored_parallel_view = 2; + map op_name2is_local_parallel_view = 2; map op_name2nd_sbp_signature_conf = 3; } @@ -23,10 +22,13 @@ message JobHelperConf { map tag2op_name_relations = 2; map lbn2logical_blob_desc = 4; map lbn2logical_object_id = 5; - optional LbiDiffWatcherInfo lbi_diff_watcher_info = 8; map op_name2arg_signature = 9; } +message MergedLogicalChainIdGroup { + repeated int64 logical_chain_id_list = 1; +} + message Job { optional DLNetConf net = 1; optional Placement placement = 2; @@ -34,4 +36,5 @@ message Job { optional JobParallelViewConf job_parallel_view_conf = 4; optional JobHelperConf helper = 5; map module_name2module_conf = 6; + repeated MergedLogicalChainIdGroup logical_chain_groups = 7; } diff --git a/oneflow/core/job/job_build_and_infer_ctx.cpp b/oneflow/core/job/job_build_and_infer_ctx.cpp index f3b8273c5ed11e95cf61b392d5cd4471bb40cd74..558dfbe8c952b3907794ac3aa71a0b3cd58a62e0 100644 --- a/oneflow/core/job/job_build_and_infer_ctx.cpp +++ b/oneflow/core/job/job_build_and_infer_ctx.cpp @@ -15,13 +15,14 @@ limitations under the License. */ #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/protobuf.h" +#include "oneflow/core/common/cost_util.h" +#include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/framework/config_def.h" #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/scope_util.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/job/job_build_and_infer_ctx.h" -#include "oneflow/core/job/mirrored_sig_infer_hint.h" +#include "oneflow/core/job/local_sig_infer_hint.h" #include "oneflow/core/job/scope.h" #include "oneflow/core/job_rewriter/autograd.h" #include "oneflow/core/job_rewriter/job_pass.h" @@ -32,8 +33,8 @@ limitations under the License. namespace oneflow { -static const std::string kAutoMirroredBlobNamePrefix = - "System-Mirrored-Blob-Auto-Converted-From-Consistent-Blob"; +static const std::string kAutoLocalBlobNamePrefix = + "System-Local-Blob-Auto-Converted-From-Global-Blob"; namespace { @@ -60,22 +61,6 @@ Maybe GetOpNames(const Job& job, HashSet* op_names) { return Maybe::Ok(); } -Maybe EagerRunOps(const Job& job, HashSet* op_names, - void (ForeignCallback::*interpret)(const OpAttribute& op_attribute, - const ParallelConf& parallel_conf) - const) { - const auto& op_graph = JUST(OpGraph::New(job)); - const auto* foreign_callback = JUST(SingletonMaybe>()); - JUST(op_graph->ForEachOpNode([&](const OpNode& op_node) -> Maybe { - if (!op_names->insert(op_node.op().op_name()).second) { return Maybe::Ok(); } - const auto& op_attribute = op_node.op().GetOpAttributeWithoutOpNameAndLbn(); - const auto& parallel_conf = op_node.parallel_desc().parallel_conf(); - (foreign_callback->get()->*interpret)(*op_attribute, parallel_conf); - return Maybe::Ok(); - })); - return Maybe::Ok(); -} - void UpdateOpName2AncestorsNeedNoGrad( const Operator& op, const std::function& Op4OpName, const bool is_train, HashMap* op_name2ancestors_need_no_grad) { @@ -140,8 +125,6 @@ Maybe JobBuildAndInferCtx::AddLbiParallelConf2BlobPlacement( iter = parallel_desc2blob_placement_group_.emplace(parallel_desc, blob_pg).first; } const auto& lbi = op->BnInOp2Lbi(obn); - CHECK_OR_RETURN(std::find(iter->second->lbi().begin(), iter->second->lbi().end(), lbi) - == iter->second->lbi().end()); *iter->second->add_lbi() = lbi; } return Maybe::Ok(); @@ -168,7 +151,7 @@ Maybe JobBuildAndInferCtx::DecodeLbiHintAndReturnNewOpConf( void JobBuildAndInferCtx::AddOpAndUpdateJobParallelViewConf(const OperatorConf& operator_conf, const ParallelDesc& parallel_desc, const NdSbpSignature& nd_sbp_signature, - bool is_mirrored_parallel_view) const { + bool is_local_parallel_view) const { auto* op_name2sbp_sig = job_->mutable_job_parallel_view_conf()->mutable_op_name2sbp_signature_conf(); auto* op_name2nd_sbp_sig = @@ -181,11 +164,9 @@ void JobBuildAndInferCtx::AddOpAndUpdateJobParallelViewConf(const OperatorConf& (*op_name2sbp_sig)[operator_conf.name()] = sbp_signature; } } - auto* op_name2is_mirrored_parallel_view = - job_->mutable_job_parallel_view_conf()->mutable_op_name2is_mirrored_parallel_view(); - if (is_mirrored_parallel_view) { - (*op_name2is_mirrored_parallel_view)[operator_conf.name()] = true; - } + auto* op_name2is_local_parallel_view = + job_->mutable_job_parallel_view_conf()->mutable_op_name2is_local_parallel_view(); + if (is_local_parallel_view) { (*op_name2is_local_parallel_view)[operator_conf.name()] = true; } job_->mutable_net()->add_op()->CopyFrom(operator_conf); // set up the module config @@ -202,10 +183,9 @@ void JobBuildAndInferCtx::AddOpAndUpdateJobParallelViewConf(const OperatorConf& } } -Maybe JobBuildAndInferCtx::InferMirroredSignature(Operator* op, - bool is_mirrored_parallel_view_conf, - const ParallelDesc& parallel_desc) { - HashMap ibn2mirrored_sig_infer_hint; +Maybe JobBuildAndInferCtx::InferLocalSignature(Operator* op, bool is_local_parallel_view_conf, + const ParallelDesc& parallel_desc) { + HashMap ibn2local_sig_infer_hint; for (const std::string& ibn : op->input_bns()) { const LogicalBlobId& lbi = op->BnInOp2Lbi(ibn); CHECK_OR_RETURN(lbi2logical_blob_desc_.find(lbi) != lbi2logical_blob_desc_.end()) @@ -215,20 +195,19 @@ Maybe JobBuildAndInferCtx::InferMirroredSignature(Operator* op, const ParallelDesc* pd = &lbi2parallel_desc_from_producer_view_.at(lbi); const auto* producer_op = op_name2op_.at(lbi.op_name()).get(); const auto& producer_obn = *JUST(producer_op->obn4lbi(lbi)); - const auto& opt_mirrored_parallel = - *CHECK_JUST(producer_op->OptMirroredParallel4BnInOp(producer_obn)); - ibn2mirrored_sig_infer_hint.emplace( - ibn, MirroredSigInferHint(pd, opt_mirrored_parallel.has_mirrored_parallel())); + const auto& opt_local_parallel = + *CHECK_JUST(producer_op->OptLocalParallel4BnInOp(producer_obn)); + ibn2local_sig_infer_hint.emplace( + ibn, LocalSigInferHint(pd, opt_local_parallel.has_local_parallel())); } - const auto& MirroredSigInferHint4Ibn = - [&](const std::string& ibn) -> Maybe { - const auto& iter = ibn2mirrored_sig_infer_hint.find(ibn); - CHECK_OR_RETURN(iter != ibn2mirrored_sig_infer_hint.end()) - << "input blob not found. ibn: " << ibn; + const auto& LocalSigInferHint4Ibn = + [&](const std::string& ibn) -> Maybe { + const auto& iter = ibn2local_sig_infer_hint.find(ibn); + CHECK_OR_RETURN(iter != ibn2local_sig_infer_hint.end()) << "input blob not found. ibn: " << ibn; return &iter->second; }; - JUST(op->InferMirroredSignatureIf(MirroredSigInferHint4Ibn, is_mirrored_parallel_view_conf, - parallel_desc)); + JUST( + op->InferLocalSignatureIf(LocalSigInferHint4Ibn, is_local_parallel_view_conf, parallel_desc)); return Maybe::Ok(); } @@ -312,7 +291,7 @@ Maybe JobBuildAndInferCtx::CheckOpBlobSplitability(Operator* op, int64_t p }; for (const auto& pair : JUST(op->sbp_signature())->bn_in_op2sbp_parallel()) { if (!pair.second.has_split_parallel()) { continue; } - if (JUST(op->OptMirroredParallel4BnInOp(pair.first))->has_mirrored_parallel()) { continue; } + if (JUST(op->OptLocalParallel4BnInOp(pair.first))->has_local_parallel()) { continue; } int64_t axis = pair.second.split_parallel().axis(); const LogicalBlobId& lbi = op->BnInOp2Lbi(pair.first); int64_t blob_parallel_num = GetParallelNum(pair.first); @@ -326,12 +305,13 @@ Maybe JobBuildAndInferCtx::CheckOpBlobSplitability(Operator* op, int64_t p if (logical_blob_desc.shape().NumAxes() > 0) { CHECK_GE_OR_RETURN(logical_blob_desc.shape().At(axis), blob_parallel_num) << "op_name: " << lbi.op_name() << " blob_name: " << lbi.blob_name() - << " cannot split blob by parallel_num: " << std::to_string(blob_parallel_num); + << " shape: " << logical_blob_desc.shape() + << " cannot be splitted by parallel_num: " << blob_parallel_num << " at axis " << axis; } } } else { for (const auto& pair : JUST(op->nd_sbp_signature())->bn_in_op2nd_sbp()) { - if (JUST(op->OptMirroredParallel4BnInOp(pair.first))->has_mirrored_parallel()) { continue; } + if (JUST(op->OptLocalParallel4BnInOp(pair.first))->has_local_parallel()) { continue; } const LogicalBlobId& lbi = op->BnInOp2Lbi(pair.first); const BlobDesc& logical_blob_desc = *(lbi2logical_blob_desc_.at(lbi).get()); Shape current_shape = logical_blob_desc.shape(); @@ -340,10 +320,13 @@ Maybe JobBuildAndInferCtx::CheckOpBlobSplitability(Operator* op, int64_t p if (sbp_parallel.has_split_parallel()) { const int64_t axis = sbp_parallel.split_parallel().axis(); CHECK_GT_OR_RETURN(current_shape.At(axis), 0); - CHECK_EQ_OR_RETURN(current_shape.At(axis) % parallel_hierarchy->At(i), 0) + // Support unbalanced splitting + CHECK_GE_OR_RETURN(current_shape.At(axis), parallel_hierarchy->At(i)) << "op_name: " << lbi.op_name() << " blob_name: " << lbi.blob_name() - << " cannot split blob by parallel_hierarchy: " - << std::to_string(parallel_hierarchy->At(i)); + << " shape: " << logical_blob_desc.shape() + << " cannot be splitted by nd sbp: " << NdSbpToString(pair.second) << " at axis " + << axis << " with parallel_hierarchy: " << *parallel_hierarchy; + // Split and take the minimum one current_shape.Set(axis, current_shape.At(axis) / parallel_hierarchy->At(i)); } } @@ -398,10 +381,10 @@ Maybe JobBuildAndInferCtx::InitConstraitNdSbpSignature( return nd_sbp_sig; } -bool JobBuildAndInferCtx::HasAnyMirroredBlobInput(const Operator& op) const { +bool JobBuildAndInferCtx::HasAnyLocalBlobInput(const Operator& op) const { for (const auto& ibn : op.input_bns()) { const auto& lbi = op.BnInOp2Lbi(ibn); - if (mirrored_lbi2sub_lbis_.find(lbi) != mirrored_lbi2sub_lbis_.end()) { return true; } + if (local_lbi2sub_lbis_.find(lbi) != local_lbi2sub_lbis_.end()) { return true; } } return false; } @@ -424,8 +407,8 @@ Maybe JobBuildAndInferCtx::ParallelDesc4Lbi(const LogicalBl Maybe JobBuildAndInferCtx::AllInputsBroadcastParallel(const Operator& op) const { for (const auto& ibn : op.input_bns()) { const LogicalBlobId& lbi = op.BnInOp2Lbi(ibn); - const auto& iter = mirrored_lbi2sbp_parallel_.find(lbi); - if (iter != mirrored_lbi2sbp_parallel_.end()) { + const auto& iter = local_lbi2sbp_parallel_.find(lbi); + if (iter != local_lbi2sbp_parallel_.end()) { if (!iter->second.has_broadcast_parallel()) { return false; } } else { if (!JUST(SbpParallel4Lbi(lbi))->has_broadcast_parallel()) { return false; } @@ -438,16 +421,15 @@ bool JobBuildAndInferCtx::IsVariableLbi(const LogicalBlobId& lbi) const { return op_name2op_.at(lbi.op_name())->op_conf().has_variable_conf(); } -Maybe JobBuildAndInferCtx::CheckAllInputsConvertableToMirroredBlob(const Operator& op) const { +Maybe JobBuildAndInferCtx::CheckAllInputsConvertableToLocalBlob(const Operator& op) const { for (const auto& ibn : op.input_bns()) { const auto& lbi = op.BnInOp2Lbi(ibn); - if (mirrored_lbi2sub_lbis_.find(lbi) != mirrored_lbi2sub_lbis_.end()) { continue; } + if (local_lbi2sub_lbis_.find(lbi) != local_lbi2sub_lbis_.end()) { continue; } const auto& sbp = *JUST(SbpParallel4Lbi(lbi)); if (sbp.has_broadcast_parallel()) { continue; } if (sbp.has_split_parallel() && sbp.split_parallel().axis() == 0) { continue; } const std::string& lbn = GenLogicalBlobName(lbi); - return Error::CheckFailedError() - << "input lbn: " << lbn << " is not convertable to mirrored blob"; + return Error::CheckFailedError() << "input lbn: " << lbn << " is not convertable to local blob"; } return Maybe::Ok(); } @@ -456,9 +438,9 @@ Maybe LazyJobBuildAndInferCtx::CheckAllInputsWithSameParallelNum(const Ope int32_t parallel_num) const { for (const auto& ibn : op.input_bns()) { const auto& lbi = op.BnInOp2Lbi(ibn); - const auto& iter = mirrored_lbi2sub_lbis().find(lbi); + const auto& iter = local_lbi2sub_lbis().find(lbi); int32_t ibn_parallel_num = 0; - if (iter != mirrored_lbi2sub_lbis().end()) { + if (iter != local_lbi2sub_lbis().end()) { ibn_parallel_num = iter->second.size(); } else { ibn_parallel_num = JUST(ParallelDesc4Lbi(lbi))->parallel_num(); @@ -470,49 +452,18 @@ Maybe LazyJobBuildAndInferCtx::CheckAllInputsWithSameParallelNum(const Ope return Maybe::Ok(); } -Maybe EagerJobBuildAndInferCtx::CheckAllInputsWithSameParallelNum( - const Operator& op, int32_t parallel_num) const { - for (const auto& ibn : op.input_bns()) { - const auto& lbi = op.BnInOp2Lbi(ibn); - int32_t ibn_parallel_num = JUST(ParallelDesc4Lbi(lbi))->parallel_num(); - CHECK_EQ_OR_RETURN(ibn_parallel_num, parallel_num) - << "the parallel_num of input lbn: " << GenLogicalBlobName(lbi) - << "is not equals to op' parallel_num"; - } - return Maybe::Ok(); -} - -Maybe JobBuildAndInferCtx::AddLbiAndDiffWatcherUuidPair( - const LbiAndDiffWatcherUuidPair& lbi_uuid_pair) { - const auto& job_name = job_->job_conf().job_name(); - auto* job_helper = job_->mutable_helper(); - auto* job_name2pairs = - job_helper->mutable_lbi_diff_watcher_info()->mutable_job_name2lbi_and_watcher_uuids(); - LbiAndDiffWatcherUuidPairList* pairs = &(*job_name2pairs)[job_name]; - auto PairFoundCond = [&](const LbiAndDiffWatcherUuidPair& x) { - return x.lbi() == lbi_uuid_pair.lbi() && x.watcher_uuid() == lbi_uuid_pair.watcher_uuid(); - }; - auto found_iter = std::find_if(pairs->lbi_and_uuid_pair().begin(), - pairs->lbi_and_uuid_pair().end(), PairFoundCond); - CHECK_OR_RETURN(found_iter == pairs->lbi_and_uuid_pair().end()) - << "diff blob has been watched. (logical_blob_name: " - << GenLogicalBlobName(lbi_uuid_pair.lbi()) << ", job_name: " << job_name << ")"; - *pairs->mutable_lbi_and_uuid_pair()->Add() = lbi_uuid_pair; - return Maybe::Ok(); -} - -Maybe JobBuildAndInferCtx::AddAndInferMirroredOp(const OperatorConf& op_conf) { +Maybe JobBuildAndInferCtx::AddAndInferLocalOp(const OperatorConf& op_conf) { CHECK_OR_RETURN(op_conf.has_scope_symbol_id()); const auto& scope = Singleton>::Get()->Get(op_conf.scope_symbol_id()); const auto* job_desc = JUST(scope.job_desc()); const auto& parallel_desc = *JUST(scope.GetParallelDesc(op_conf)); auto op = JUST(ConstructOp(op_conf, parallel_desc.device_type())); - JUST(CheckAllInputsConvertableToMirroredBlob(*op)); + JUST(CheckAllInputsConvertableToLocalBlob(*op)); int32_t parallel_num = parallel_desc.parallel_num(); JUST(CheckAllInputsWithSameParallelNum(*op, parallel_num)); - auto GetSubOpName = [&](int index) { return GetMirroredOpName(op_conf.name(), index); }; + auto GetSubOpName = [&](int index) { return GetLocalOpName(op_conf.name(), index); }; OperatorConf sub_op_conf(op_conf); - int64_t sub_op_list_size = SizeOfSubConsistentOpList(parallel_num); + int64_t sub_op_list_size = SizeOfSubGlobalOpList(parallel_num); auto last_op_attribute = std::make_shared(); FOR_RANGE(int32_t, i, 0, sub_op_list_size) { ResetOpConfName(&sub_op_conf, GetSubOpName(i)); @@ -520,19 +471,19 @@ Maybe JobBuildAndInferCtx::AddAndInferMirroredOp(const OperatorConf const auto& lbi = *JUST(GetSubLbi(op_conf.scope_symbol_id(), op->BnInOp2Lbi(ibn), i)); ReplaceInputLbnInOpCustomizedConf(&sub_op_conf, ibn, GenLogicalBlobName(lbi)); } - const ParallelConf& parallel_conf = GetMirroredOpParallelConf(parallel_desc, i); - bool is_mirrored_parallel_view = GetIsMirroredParallelView(); + const ParallelConf& parallel_conf = GetLocalOpParallelConf(parallel_desc, i); + bool is_local_parallel_view = GetIsLocalParallelView(); last_op_attribute = - JUST(AddAndInferOp(sub_op_conf, parallel_conf, job_desc, is_mirrored_parallel_view)); + JUST(AddAndInferOp(sub_op_conf, parallel_conf, job_desc, is_local_parallel_view)); } bool is_broadcast = JUST(AllInputsBroadcastParallel(*op)); for (const auto& obn : op->output_bns()) { const auto& lbi = op->BnInOp2Lbi(obn); - auto* sub_lbis = &mirrored_lbi2sub_lbis_[lbi]; + auto* sub_lbis = &local_lbi2sub_lbis_[lbi]; sub_lbis->resize(sub_op_list_size, op->BnInOp2Lbi(obn)); FOR_RANGE(int32_t, i, 0, sub_op_list_size) { sub_lbis->at(i).set_op_name(GetSubOpName(i)); } - CHECK(mirrored_lbi2parallel_desc_.emplace(lbi, parallel_desc).second); - auto* sbp_parallel = &mirrored_lbi2sbp_parallel_[lbi]; + CHECK(local_lbi2parallel_desc_.emplace(lbi, parallel_desc).second); + auto* sbp_parallel = &local_lbi2sbp_parallel_[lbi]; if (is_broadcast) { sbp_parallel->mutable_broadcast_parallel(); } else { @@ -545,17 +496,16 @@ Maybe JobBuildAndInferCtx::AddAndInferMirroredOp(const OperatorConf Maybe JobBuildAndInferCtx::GetSubLbi(int64_t scope_symbol_id, const LogicalBlobId& lbi, int32_t index) { - auto lbi_vec_iter = mirrored_lbi2sub_lbis_.find(lbi); - if (lbi_vec_iter == mirrored_lbi2sub_lbis_.end()) { - const auto& new_lbi = - JUST(FindOrCreateMirroredLbiFromCompatibleConsistentBlob(scope_symbol_id, lbi)); - lbi_vec_iter = mirrored_lbi2sub_lbis_.find(*new_lbi); - CHECK(lbi_vec_iter != mirrored_lbi2sub_lbis_.end()); + auto lbi_vec_iter = local_lbi2sub_lbis_.find(lbi); + if (lbi_vec_iter == local_lbi2sub_lbis_.end()) { + const auto& new_lbi = JUST(FindOrCreateLocalLbiFromCompatibleGlobalBlob(scope_symbol_id, lbi)); + lbi_vec_iter = local_lbi2sub_lbis_.find(*new_lbi); + CHECK(lbi_vec_iter != local_lbi2sub_lbis_.end()); } return &lbi_vec_iter->second.at(index); } -Maybe JobBuildAndInferCtx::AddAndInferConsistentOp(const OperatorConf& op_conf) { +Maybe JobBuildAndInferCtx::AddAndInferGlobalOp(const OperatorConf& op_conf) { CHECK_OR_RETURN(op_conf.has_scope_symbol_id()); const auto& scope = Singleton>::Get()->Get(op_conf.scope_symbol_id()); const auto& parallel_desc = *JUST(scope.GetParallelDesc(op_conf)); @@ -567,7 +517,7 @@ Maybe JobBuildAndInferCtx::AddAndInferConsistentOp(const OperatorCo Maybe JobBuildAndInferCtx::AddAndInferOp(const OperatorConf& op_conf, const ParallelConf& origin_parallel_conf, const JobDesc* job_desc, - bool is_mirrored_parallel_view) { + bool is_local_parallel_view) { CHECK_OR_RETURN(has_job_conf_) << Error::JobConfNotSetError(); if (!is_job_conf_frozen_) { is_job_conf_frozen_ = true; } const std::string& op_name = op_conf.name(); @@ -599,17 +549,22 @@ Maybe JobBuildAndInferCtx::AddAndInferOp(const OperatorConf& op_con JUST(op->FillLogicalInBlobDesc(GetBlobDesc4BnInOp)); JUST(op->InferParallelSignatureIf()); - // infer mirrored signature - JUST(InferMirroredSignature(op, is_mirrored_parallel_view, parallel_desc)); + // infer local signature + JUST(InferLocalSignature(op, is_local_parallel_view, parallel_desc)); // infer nd_sbp signature - NdSbpSignature nd_sbp_sig_conf = *JUST(InitConstraitNdSbpSignature(*op, ibn2disable_boxing)); + NdSbpSignature nd_sbp_sig_conf; + // Only infer nd_sbp signature if auto parallel is not enable, + // since the semi-auto parallellism rule might have inconsistency with the auto-parallel strategy. + if (!job_desc->enable_auto_parallel()) { + nd_sbp_sig_conf = *JUST(InitConstraitNdSbpSignature(*op, ibn2disable_boxing)); + } // Override constrait nd_sbp if sbp hint is given if (!sbp_sig_conf.bn_in_op2sbp_parallel().empty()) { SbpSignatureToNdSbpSignature(sbp_sig_conf, &nd_sbp_sig_conf); } AddOpAndUpdateJobParallelViewConf(*new_op_conf, parallel_desc, nd_sbp_sig_conf, - is_mirrored_parallel_view); + is_local_parallel_view); JUST(InferOpOutNdSbp(op, nd_sbp_sig_conf, parallel_desc)); // infer logical blob desc @@ -632,8 +587,6 @@ Maybe JobBuildAndInferCtx::AddAndInferOp(const OperatorConf& op_con *JUST(op->GetParallelDesc4BnInOp(bn))); } JUST(AddLbiParallelConf2BlobPlacement(op, ParallelDesc4Obn)); - // Infer whether input/output blobs are backward used - JUST(InferBlobBackwardSignature(op)); // Check splitability JUST(CheckOpBlobSplitability(op, parallel_desc.parallel_num())); @@ -648,11 +601,11 @@ Maybe JobBuildAndInferCtx::SetTrainConf(const TrainConf& train_conf) { } Maybe JobBuildAndInferCtx::AddLossLogicalBlobName(const std::string& lbn) { - if (IsMirroredBlob(lbn)) { return AddLossMirroredBlobName(lbn); } - return AddLossConsistentBlobName(lbn); + if (IsLocalBlob(lbn)) { return AddLossLocalBlobName(lbn); } + return AddLossGlobalBlobName(lbn); } -Maybe JobBuildAndInferCtx::AddLossConsistentBlobName(const std::string& lbn) { +Maybe JobBuildAndInferCtx::AddLossGlobalBlobName(const std::string& lbn) { JUST(CheckLbnValidAndExist(lbn)); CHECK_OR_RETURN(job_->job_conf().has_train_conf()) << Error::UnknownJobBuildAndInferError() @@ -661,6 +614,42 @@ Maybe JobBuildAndInferCtx::AddLossConsistentBlobName(const std::string& lb return Maybe::Ok(); } +Maybe JobBuildAndInferCtx::MarkVariableGradientBlobNames( + const HashMap& variable_grad_lbns) { + CHECK_OR_RETURN(job_->job_conf().has_train_conf()) + << Error::UnknownJobBuildAndInferError() + << "job has no TrainConf when add variable gradient logical blob name"; + auto* train_conf = job_->mutable_job_conf()->mutable_train_conf(); + for (int i = 0; i < train_conf->optimizer_conf_size(); ++i) { + auto* optimizer_conf = train_conf->mutable_optimizer_conf(i); + for (const auto& variable_op_name : optimizer_conf->variable_op_names()) { + const auto& it = variable_grad_lbns.find(variable_op_name + "/out"); + if (it != variable_grad_lbns.end()) { + optimizer_conf->add_variable_grad_lbns(it->second); + } else { + // add an empty gradient lbn for variable that has no gradient + optimizer_conf->add_variable_grad_lbns(""); + } + } + } + return Maybe::Ok(); +} + +Maybe JobBuildAndInferCtx::MarkOutputGradientBlobNames( + const HashMap& output_gradient_lbns) { + CHECK_OR_RETURN(job_->job_conf().has_train_conf()) + << Error::UnknownJobBuildAndInferError() + << "job has no TrainConf when add variable gradient logical blob name"; + auto* train_conf = job_->mutable_job_conf()->mutable_train_conf(); + for (const auto& loss_lbn : train_conf->loss_lbn()) { + const auto& it = output_gradient_lbns.find(loss_lbn); + CHECK_OR_RETURN(it != output_gradient_lbns.end()) + << Error::UnknownJobBuildAndInferError() << "gradient is missing for loss " << loss_lbn; + train_conf->add_loss_grad_lbn(it->second); + } + return Maybe::Ok(); +} + Maybe JobBuildAndInferCtx::GetStaticShape(const std::string& lbn) const { JUST(CheckLbnValidAndExist(lbn)); return lbi2logical_blob_desc_.at(GenLogicalBlobId(lbn))->shape(); @@ -715,65 +704,63 @@ Maybe JobBuildAndInferCtx::GetParallelDescFromProducerView( return &(lbi2parallel_desc_from_producer_view_.at(GenLogicalBlobId(lbn))); } -Maybe JobBuildAndInferCtx::AddLossMirroredBlobName(const std::string& lbn) { - const auto& mirrored_lbi = JUST(GetMirroredLbi(lbn)); +Maybe JobBuildAndInferCtx::AddLossLocalBlobName(const std::string& lbn) { + const auto& local_lbi = JUST(GetLocalLbi(lbn)); CHECK_OR_RETURN(job_->job_conf().has_train_conf()) << Error::UnknownJobBuildAndInferError() << "job has no TrainConf when adding loss logical blob name"; - for (const auto& lbi : mirrored_lbi2sub_lbis_.at(*mirrored_lbi)) { + for (const auto& lbi : local_lbi2sub_lbis_[*local_lbi]) { job_->mutable_job_conf()->mutable_train_conf()->add_loss_lbn(GenLogicalBlobName(lbi)); } return Maybe::Ok(); } -Maybe JobBuildAndInferCtx::GetMirroredLbi(const std::string& lbn_with_hint) const { +Maybe JobBuildAndInferCtx::GetLocalLbi(const std::string& lbn_with_hint) const { const LogicalBlobId& lbi = GenLogicalBlobId(lbn_with_hint); - if (mirrored_lbi2sub_lbis_.find(lbi) != mirrored_lbi2sub_lbis_.end()) { return lbi; } - return Error::CheckFailedError() << lbn_with_hint << " is not a mirrored blob name"; + if (local_lbi2sub_lbis_.find(lbi) != local_lbi2sub_lbis_.end()) { return lbi; } + return Error::CheckFailedError() << lbn_with_hint << " is not a local blob name"; } -Maybe JobBuildAndInferCtx::MirroredBlobGetNumSubLbi(const std::string& lbn_with_hint) const { - const auto& mirrored_lbi = JUST(GetMirroredLbi(lbn_with_hint)); - return mirrored_lbi2sub_lbis_.at(*mirrored_lbi).size(); +Maybe JobBuildAndInferCtx::LocalBlobGetNumSubLbi(const std::string& lbn_with_hint) const { + const auto& local_lbi = JUST(GetLocalLbi(lbn_with_hint)); + return local_lbi2sub_lbis_.at(*local_lbi).size(); // NOLINT } -Maybe JobBuildAndInferCtx::MirroredBlobGetSubLbi( +Maybe JobBuildAndInferCtx::LocalBlobGetSubLbi( const std::string& lbn_with_hint, int index) const { - const auto& mirrored_lbi = JUST(GetMirroredLbi(lbn_with_hint)); - const auto& vec = mirrored_lbi2sub_lbis_.at(*mirrored_lbi); + const auto& local_lbi = JUST(GetLocalLbi(lbn_with_hint)); + const auto& vec = local_lbi2sub_lbis_.at(*local_lbi); // NOLINT CHECK_GE_OR_RETURN(index, 0); CHECK_LT_OR_RETURN(index, vec.size()); return &vec.at(index); } -bool JobBuildAndInferCtx::IsMirroredBlob(const std::string& lbn) const { - bool is_mirrored_blob = TRY(GetMirroredLbi(lbn)).IsOk(); - if (is_mirrored_blob) { return is_mirrored_blob; } +bool JobBuildAndInferCtx::IsLocalBlob(const std::string& lbn) const { + bool is_local_blob = TRY(GetLocalLbi(lbn)).IsOk(); + if (is_local_blob) { return is_local_blob; } const LogicalBlobId& lbi = GenLogicalBlobId(lbn); CHECK(lbi2logical_blob_desc_.find(lbi) != lbi2logical_blob_desc_.end()) << "lbn: " << lbn; return false; } -Maybe JobBuildAndInferCtx::MirroredBlobGetStaticShape( - const std::string& lbn_with_hint) const { - const auto& lbi = *JUST(MirroredBlobGetSubLbi(lbn_with_hint, 0)); +Maybe JobBuildAndInferCtx::LocalBlobGetStaticShape(const std::string& lbn_with_hint) const { + const auto& lbi = *JUST(LocalBlobGetSubLbi(lbn_with_hint, 0)); return lbi2logical_blob_desc_.at(lbi)->shape(); } -Maybe JobBuildAndInferCtx::MirroredBlobGetDataType( - const std::string& lbn_with_hint) const { - const auto& lbi = *JUST(MirroredBlobGetSubLbi(lbn_with_hint, 0)); +Maybe JobBuildAndInferCtx::LocalBlobGetDataType(const std::string& lbn_with_hint) const { + const auto& lbi = *JUST(LocalBlobGetSubLbi(lbn_with_hint, 0)); return lbi2logical_blob_desc_.at(lbi)->data_type(); } -Maybe JobBuildAndInferCtx::MirroredBlobIsDynamic(const std::string& lbn_with_hint) const { - const auto& lbi = *JUST(MirroredBlobGetSubLbi(lbn_with_hint, 0)); +Maybe JobBuildAndInferCtx::LocalBlobIsDynamic(const std::string& lbn_with_hint) const { + const auto& lbi = *JUST(LocalBlobGetSubLbi(lbn_with_hint, 0)); return lbi2logical_blob_desc_.at(lbi)->is_dynamic(); } -Maybe JobBuildAndInferCtx::MirroredBlobGetSplitAxisFromProducerView( +Maybe JobBuildAndInferCtx::LocalBlobGetSplitAxisFromProducerView( const std::string& lbn_with_hint) const { - const auto& lbi = *JUST(MirroredBlobGetSubLbi(lbn_with_hint, 0)); + const auto& lbi = *JUST(LocalBlobGetSubLbi(lbn_with_hint, 0)); OptInt64 ret; const auto& nd_sbp = lbi2nd_sbp_from_producer_view_.at(lbi); CHECK_EQ_OR_RETURN(nd_sbp.sbp_parallel_size(), 1); @@ -782,10 +769,10 @@ Maybe JobBuildAndInferCtx::MirroredBlobGetSplitAxisFromProducerView( return ret; } -Maybe JobBuildAndInferCtx::MirroredBlobGetParallelDescFromProducerView( +Maybe JobBuildAndInferCtx::LocalBlobGetParallelDescFromProducerView( const std::string& lbn_with_hint) const { - const auto& lbi = JUST(GetMirroredLbi(lbn_with_hint)); - return &(mirrored_lbi2parallel_desc_.at(*lbi)); + const auto& lbi = JUST(GetLocalLbi(lbn_with_hint)); + return &(local_lbi2parallel_desc_.at(*lbi)); // NOLINT } Maybe JobBuildAndInferCtx::CheckJob() const { @@ -859,38 +846,28 @@ Maybe JobBuildAndInferCtx::CheckLbnValidAndExist(const std::string& lbn) c const Job& JobBuildAndInferCtx::job() const { return *job_; } -std::string LazyJobBuildAndInferCtx::GetMirroredOpName(const std::string& op_name, - int64_t parallel_id) const { +std::string LazyJobBuildAndInferCtx::GetLocalOpName(const std::string& op_name, + int64_t parallel_id) const { return op_name + "_" + std::to_string(parallel_id); } -std::string EagerJobBuildAndInferCtx::GetMirroredOpName(const std::string& op_name, - int64_t parallel_id) const { - return op_name; -} - -ParallelConf LazyJobBuildAndInferCtx::GetMirroredOpParallelConf(const ParallelDesc& parallel_desc, - int64_t parallel_id) const { +ParallelConf LazyJobBuildAndInferCtx::GetLocalOpParallelConf(const ParallelDesc& parallel_desc, + int64_t parallel_id) const { return parallel_desc.GetParallelIdOnlyParallelConf(parallel_id); } -ParallelConf EagerJobBuildAndInferCtx::GetMirroredOpParallelConf(const ParallelDesc& parallel_desc, - int64_t parallel_id) const { - return parallel_desc.parallel_conf(); -} - -Maybe LazyJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompatibleConsistentBlob( +Maybe LazyJobBuildAndInferCtx::FindOrCreateLocalLbiFromCompatibleGlobalBlob( int64_t scope_symbol_id, const LogicalBlobId& lbi) { const std::string& lbn = GenLogicalBlobName(lbi); - const auto& sbn_it = mut_consistent_lbi2mirrored_lbi()->find(lbi); - if (sbn_it != mut_consistent_lbi2mirrored_lbi()->end()) { return sbn_it->second; } + const auto& sbn_it = mut_global_lbi2local_lbi()->find(lbi); + if (sbn_it != mut_global_lbi2local_lbi()->end()) { return sbn_it->second; } const SbpParallel& sbp = *JUST(SbpParallel4Lbi(lbi)); const ParallelDesc& parallel_desc = *JUST(ParallelDesc4Lbi(lbi)); - LogicalBlobId mirrored_lbi; - mirrored_lbi.set_op_name(kAutoMirroredBlobNamePrefix + NewUniqueId()); - mirrored_lbi.set_blob_name("out"); - (*mut_consistent_lbi2mirrored_lbi())[lbi] = mirrored_lbi; - auto* lbi_vec = &(*mut_mirrored_lbi2sub_lbis())[mirrored_lbi]; + LogicalBlobId local_lbi; + local_lbi.set_op_name(kAutoLocalBlobNamePrefix + NewUniqueId()); + local_lbi.set_blob_name("out"); + (*mut_global_lbi2local_lbi())[lbi] = local_lbi; + auto* lbi_vec = &(*mut_local_lbi2sub_lbis())[local_lbi]; lbi_vec->reserve(parallel_desc.parallel_num()); auto PushBackSubLbi = [&](const std::string& op_name, const std::string& blob_name) { LogicalBlobId sub_lbi; @@ -902,7 +879,7 @@ Maybe LazyJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompati op_conf.set_scope_symbol_id(scope_symbol_id); op_conf.set_device_tag(*JUST(DeviceTag4DeviceType(parallel_desc.device_type()))); if (sbp.has_broadcast_parallel()) { - op_conf.set_name(kAutoMirroredBlobNamePrefix + "-DistributeClone-" + NewUniqueId()); + op_conf.set_name(kAutoLocalBlobNamePrefix + "-DistributeClone-" + NewUniqueId()); auto* distribute_clone = op_conf.mutable_distribute_clone_conf(); distribute_clone->set_in(lbn); FOR_RANGE(int32_t, i, 0, parallel_desc.parallel_num()) { @@ -913,8 +890,8 @@ Maybe LazyJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompati } } else if (sbp.has_split_parallel()) { CHECK_EQ_OR_RETURN(sbp.split_parallel().axis(), 0) - << "only `S(0)' consistent blob is compatible to mirrored blob"; - op_conf.set_name(kAutoMirroredBlobNamePrefix + "-DistributeSplit-" + NewUniqueId()); + << "only `S(0)' global blob is compatible to local blob"; + op_conf.set_name(kAutoLocalBlobNamePrefix + "-DistributeSplit-" + NewUniqueId()); auto* distribute_split = op_conf.mutable_distribute_split_conf(); distribute_split->set_in(lbn); distribute_split->set_axis(0); @@ -925,7 +902,7 @@ Maybe LazyJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompati PushBackSubLbi(op_conf.name(), blob_name); } } else { - OF_UNIMPLEMENTED() << "`P' consistant blob is not compatible to mirrored blob"; + OF_UNIMPLEMENTED() << "`P' global blob is not compatible to local blob"; } { const auto& producer_op_conf = JUST(Op4OpName(lbi.op_name()))->op_conf(); @@ -934,52 +911,18 @@ Maybe LazyJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompati const auto* job_desc = JUST(scope.job_desc()); JUST(AddAndInferOp(op_conf, parallel_desc.parallel_conf(), job_desc, false)); } - return mirrored_lbi; -} - -Maybe EagerJobBuildAndInferCtx::FindOrCreateMirroredLbiFromCompatibleConsistentBlob( - int64_t scope_symbol_id, const LogicalBlobId& lbi) { - const std::string& lbn = GenLogicalBlobName(lbi); - const auto& sbn_it = mut_consistent_lbi2mirrored_lbi()->find(lbi); - if (sbn_it != mut_consistent_lbi2mirrored_lbi()->end()) { return sbn_it->second; } - const SbpParallel& sbp = *JUST(SbpParallel4Lbi(lbi)); - CHECK_OR_RETURN(!sbp.has_partial_sum_parallel()) - << "`P' consistant blob is not compatible to mirrored blob"; - const ParallelDesc& parallel_desc = *JUST(ParallelDesc4Lbi(lbi)); - OperatorConf op_conf; - { - // inherit scope_symbol_id from producer - const auto& producer_op_conf = JUST(Op4OpName(lbi.op_name()))->op_conf(); - CHECK_OR_RETURN(producer_op_conf.has_scope_symbol_id()); - op_conf.set_scope_symbol_id(producer_op_conf.scope_symbol_id()); - } - op_conf.set_scope_symbol_id(scope_symbol_id); - op_conf.set_device_tag(*JUST(DeviceTag4DeviceType(parallel_desc.device_type()))); - op_conf.set_name(kAutoMirroredBlobNamePrefix + "-CastToMirrored-" + NewUniqueId()); - auto* cast_to_mirrored_conf = op_conf.mutable_cast_to_mirrored_conf(); - cast_to_mirrored_conf->set_in(lbn); - cast_to_mirrored_conf->set_out("out"); - *cast_to_mirrored_conf->mutable_sbp_parallel() = sbp; - LogicalBlobId mirrored_lbi; - mirrored_lbi.set_op_name(op_conf.name()); - mirrored_lbi.set_blob_name("out"); - (*mut_consistent_lbi2mirrored_lbi())[lbi] = mirrored_lbi; - (*mut_mirrored_lbi2sub_lbis())[mirrored_lbi].emplace_back(mirrored_lbi); - const auto& parallel_conf = parallel_desc.parallel_conf(); - const auto& op_attribute = JUST(AddAndInferConsistentOp(op_conf)); - (*JUST(SingletonMaybe>())) - ->EagerMirroredCast(*op_attribute, parallel_conf); - return mirrored_lbi; + return local_lbi; } Maybe LazyJobBuildAndInferCtx::Complete() { CHECK_GT_OR_RETURN(job().net().op_size(), 0) << " Sorry, nn.Graph need at least 1 op in net, but get 0 now."; + auto compile_tc = std::make_unique>(true, true); CHECK_NOTNULL(Singleton::Get()); Singleton::Delete(); auto scope = std::make_unique(mut_job()->job_conf(), job_id()); JobPassCtx job_pass_ctx(GlobalJobDesc()); - const auto& job_name = job().job_conf().job_name(); + const auto job_name = job().job_conf().job_name(); auto LogJob = [&](const std::string& name_suffix) -> void { std::string full_log_name = job_name + "-job_id_" + std::to_string(job_id()) + "-" + name_suffix; @@ -1001,6 +944,7 @@ Maybe LazyJobBuildAndInferCtx::Complete() { int32_t pass_cnt = 0; const int64_t prev_v = FLAGS_v; auto DoPass = [&](const std::string& pass_name, int32_t cnt = 0) -> Maybe { + auto pass_tc = std::make_unique>(true, true); VLOG(1) << job_name << " start compiling with pass" << " pass_cnt_" + std::to_string(pass_cnt) + "-" + pass_name << (cnt > 0 ? std::to_string(cnt) : ""); @@ -1018,12 +962,12 @@ Maybe LazyJobBuildAndInferCtx::Complete() { VLOG(1) << job_name << " finish compiling with pass" << " pass_cnt_" + std::to_string(pass_cnt) + "-" + pass_name << (cnt > 0 ? std::to_string(cnt) : ""); + pass_tc->Count("[GraphCompile]" + job_name + " " + pass_name, 1, true); ++pass_cnt; return Maybe::Ok(); }; - if (Singleton::Get()->enable_debug_mode() - || Singleton::Get()->enable_dry_run()) { + if (Singleton::Get()->enable_debug_mode()) { TeePersistentLogStream::Create(StrCat("forward_graph", job_id()))->Write(job()); Singleton::New(job()); Singleton::Get()->ToDotWithFilePath("forward_dlnet_" + std::to_string(job_id()) @@ -1032,24 +976,39 @@ Maybe LazyJobBuildAndInferCtx::Complete() { } if (GlobalJobDesc().Bool("__is_user_function__")) { - JUST(DoPass("ModelUpdateConfCompatiblePass")); - JUST(DoPass("AddInputOutputOpsPass")); + // insert pinned identity to prevent the loss, loss initial gradient and + // variable gradient from being eliminated by IRRoundTripBeforeAD pass + JUST(DoPass("InsertPinnedIdentityOpPass")); + // prune the dangling constant which are the 0 gradients initialized by + // the autograd engine for those tensors that have no gradients + JUST(DoPass("EliminateDeadNodesPass")); JUST(DoPass("NormalizationExponentialAverageAutoTickPass")); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) JUST(DoPass("AutoMixedPrecision")); - JUST(DoPass("PruneAmpWhiteIdentityOpPass")); #endif + JUST(DoPass("PruneAmpWhiteIdentityOpPass")); JUST(DoPass("OptimizerPlacementOptimizationPass")); + // run FuseAddToOutputPass before IRRoundTripBeforeAD since add_2 maybe + // fused as add_n in IRRoundTripBeforeAD pass + JUST(DoPass("FuseAddToOutputPass")); +#ifdef WITH_MLIR + JUST(DoPass("IRRoundTripBeforeAD")); +#endif // WITH_MLIR + // run DynamicLossScaleSchedulePass, AutoTrainStep and AutoLearningRate + // after IRRoundTripBeforeAD since IRRoundTripBeforeAD will do DCE + // optimization which could eliminate the nodes inserted by them JUST(DoPass("DynamicLossScaleSchedulePass")); JUST(DoPass("AutoTrainStep")); JUST(DoPass("AutoLearningRate")); JUST(DoPass("QuantAwareTraining")); -#ifdef WITH_MLIR - JUST(DoPass("IRRoundTripBeforeAD")); -#endif // WITH_MLIR - JUST(DoPass("GenerateBackwardAndOptimizerOpConfs")); + JUST(DoPass("GenerateOptimizerOpConfs")); + // pinned identity can be pruned since GenerateOptimizerOpConfs pass has + // already construct a complete computational graph + JUST(DoPass("PrunePinnedIdentityOpPass")); JUST(DoPass("ReplaceEmbeddingOps")); JUST(DoPass("SequentialOneEmbeddingOpsPass")); + JUST(DoPass("FuseEmbeddingShuffleInteractionPass")); + JUST(DoPass("FuseBCEReduceMeanFwBwPass")); JUST(DoPass("AddSspVariableProxy")); JUST(DoPass("CheckpointingPass")); JUST(DoPass("CudnnFusedNormalizationAddReluPass")); @@ -1057,116 +1016,29 @@ Maybe LazyJobBuildAndInferCtx::Complete() { #ifdef WITH_MLIR JUST(DoPass("IRRoundTrip")); #endif // WITH_MLIR - JUST(DoPass("FuseAddToOutputPass")); // run this pass again to fuse ops created in the first run. // TODO(guoran): loop multiple times inside the pass JUST(DoPass("FuseAddToOutputPass", 1)); + JUST(DoPass("FuseConsecutiveAddPass")); JUST(DoPass("IndexedSlicesOptimizerRewritePass")); JUST(DoPass("SplitSparseSoftmaxCrossEntropyOpPass")); JUST(DoPass("DoParallelCastBeforeWideningTypeCast")); - JUST(DoPass("AddLbiDiffWatcherOpConfs")); JUST(DoPass("FuseCastScalePass")); JUST(DoPass("PruneParallelCastOpsPass")); JUST(DoPass("FuseUpdateOpsPass")); + JUST(DoPass("FuseModelUpdateCastOpsPass")); + JUST(DoPass("MultiTensorModelUpdatePass")); JUST(DoPass("FixPipelineStageIdPass")); JUST(DoPass("PipelineBufferPass")); + JUST(DoPass("AutoParallelPass")); +#ifdef WITH_CUTLASS + JUST(DoPass("CutlassConvTuningWarmupPass")); +#endif // WITH_CUTLASS JUST(DoPass("DumpVariableInfoPass")); } JUST(DoPass("DumpBlobParallelConfPass")); JUST(CheckJob()); - return Maybe::Ok(); -} - -Maybe EagerJobBuildAndInferCtx::Complete() { - CHECK_NOTNULL(Singleton::Get()); - Singleton::Delete(); - JUST(GetOpNames(job(), &executed_op_names_)); - auto scope = std::make_unique(mut_job()->job_conf(), job_id()); - JobPassCtx job_pass_ctx(GlobalJobDesc()); - auto DoPass = [&](const std::string& pass_name) -> Maybe { - return JobPass4Name(pass_name)(mut_job(), &job_pass_ctx); - }; - JUST(DoPass("AutoTrainStep")); - JUST(DoPass("AutoLearningRate")); - JUST(DoPass("GenerateBackwardAndOptimizerOpConfs")); - JUST(DoPass("AddLbiDiffWatcherOpConfs")); - JUST(EagerRunOps(job(), &executed_op_names_, &ForeignCallback::EagerInterpretCompletedOp)); - return Maybe::Ok(); -} - -Maybe JobBuildAndInferCtx::InferBlobBackwardSignature(Operator* op) { - std::function IsLbiBackwardUsed; - JUST(InferBlobBackwardSignature(*op, &IsLbiBackwardUsed)); - auto* map = op->mut_blob_backward_used_signature()->mutable_bn_in_op2blob_backward_used(); - const auto& SetIsBlobBackwardUsed = [&](const std::string& bn_in_op) { - (*map)[bn_in_op] = IsLbiBackwardUsed(op->BnInOp2Lbi(bn_in_op)); - }; - for (const auto& ibn : op->input_bns()) { SetIsBlobBackwardUsed(ibn); } - for (const auto& obn : op->output_bns()) { SetIsBlobBackwardUsed(obn); } - return Maybe::Ok(); -} - -Maybe JobBuildAndInferCtx::InferBlobBackwardSignature( - const Operator& op, std::function* IsLbiBackwardUsed) { - const bool is_train = job().job_conf().has_train_conf(); - if (!is_train) { - *IsLbiBackwardUsed = [](const LogicalBlobId&) { return false; }; - return Maybe::Ok(); - } - const auto& Op4Name = [&](const std::string& op_name) { return CHECK_JUST(Op4OpName(op_name)); }; - UpdateOpName2AncestorsNeedNoGrad(op, Op4Name, is_train, &op_name2ancestors_need_no_grad_); - // always return true if output_size > 1 - if (op.output_bns().size() > 1) { - *IsLbiBackwardUsed = [](const LogicalBlobId&) { return true; }; - return Maybe::Ok(); - } - std::vector bw_op_confs; - LogicalBlobId fake_diff_lbi; - fake_diff_lbi.set_op_name("fake_op_name"); - fake_diff_lbi.set_blob_name("fake_blob_name"); - HashMap in_diff2lbi; - const auto& DiffLbi4BnInOp = [&](const std::string& bn) -> LogicalBlobId* { - const auto& input_bns = op.input_bns(); - const auto& output_bns = op.output_bns(); - if (std::find(input_bns.begin(), input_bns.end(), bn) != input_bns.end()) { - const auto& lbi = op.BnInOp2Lbi(bn); - if (op_name2ancestors_need_no_grad_.at(lbi.op_name())) { return nullptr; } - if (op.InputBlobModifier4Ibn(bn).requires_grad() == false) { return nullptr; } - return &in_diff2lbi[bn]; - } else if (std::find(output_bns.begin(), output_bns.end(), bn) != output_bns.end()) { - return &fake_diff_lbi; - } else { - LOG(FATAL) << "diff lbi for bn in op not found, bn: " << op.op_name() << "/" << bn; - } - return nullptr; - }; - const auto& FwLogicalBlobDescPtr4Lbi = [&](const LogicalBlobId& lbi) -> const BlobDesc* { - const auto& iter = lbi2logical_blob_desc_.find(lbi); - if (iter != lbi2logical_blob_desc_.end()) { return iter->second.get(); } - return nullptr; - }; - const auto& LogicalBlobDesc4BnInOp = [&](const std::string& bn) -> const BlobDesc& { - const LogicalBlobId& lbi = op.BnInOp2Lbi(bn); - const auto* logical_blob_desc = FwLogicalBlobDescPtr4Lbi(lbi); - CHECK_NOTNULL(logical_blob_desc); - return *logical_blob_desc; - }; - const auto& maybe_ok = - TRY(GenerateBackwardOpConfIf(op, &bw_op_confs, DiffLbi4BnInOp, LogicalBlobDesc4BnInOp)); - CHECK_OR_RETURN(maybe_ok.IsOk() || maybe_ok.error()->has_gradient_function_not_found_error()) - << GetFormatedSerializedError(::oneflow::private_details::JustGetError(maybe_ok)); - // find backward used logical blob ids - auto backward_used_lbis = std::make_shared>(); - for (const auto& bw_op_conf : bw_op_confs) { - const auto& bw_op = JUST(ConstructOp(bw_op_conf, op.device_type())); - for (const auto& ibn : bw_op->input_bns()) { - const auto& lbi = bw_op->BnInOp2Lbi(ibn); - if (FwLogicalBlobDescPtr4Lbi(lbi) != nullptr) { backward_used_lbis->insert(lbi); } - } - } - *IsLbiBackwardUsed = [backward_used_lbis](const LogicalBlobId& lbi) { - return backward_used_lbis->find(lbi) != backward_used_lbis->end(); - }; + compile_tc->Count("[GraphCompile]" + job_name + " OptimizationLogicalGraph", 0); return Maybe::Ok(); } @@ -1312,23 +1184,22 @@ Maybe JobBuildAndInferCtx::Rebuild() { op_name2op_.clear(); parallel_desc2placement_group_.clear(); parallel_desc2blob_placement_group_.clear(); - consistent_lbi2mirrored_lbi_.clear(); - mirrored_lbi2sub_lbis_.clear(); - mirrored_lbi2parallel_desc_.clear(); - mirrored_lbi2sbp_parallel_.clear(); + global_lbi2local_lbi_.clear(); + local_lbi2sub_lbis_.clear(); + local_lbi2parallel_desc_.clear(); + local_lbi2sbp_parallel_.clear(); op_name2ancestors_need_no_grad_.clear(); // record op mirror view - HashMap op_name2is_mirrored; + HashMap op_name2is_local; CHECK_OR_RETURN(job_->has_job_parallel_view_conf()); for (const auto& op_conf : job_->net().op()) { const auto& op_name = op_conf.name(); - CHECK_OR_RETURN(op_name2is_mirrored.find(op_name) == op_name2is_mirrored.end()); - op_name2is_mirrored[op_name] = false; - const auto& op_name2is_mirrored_parallel_view = - job_->job_parallel_view_conf().op_name2is_mirrored_parallel_view(); - if (op_name2is_mirrored_parallel_view.find(op_name) - != op_name2is_mirrored_parallel_view.end()) { - if (op_name2is_mirrored_parallel_view.at(op_name)) { op_name2is_mirrored[op_name] = true; } + CHECK_OR_RETURN(op_name2is_local.find(op_name) == op_name2is_local.end()); // NOLINT + op_name2is_local[op_name] = false; + const auto& op_name2is_local_parallel_view = + job_->job_parallel_view_conf().op_name2is_local_parallel_view(); + if (op_name2is_local_parallel_view.find(op_name) != op_name2is_local_parallel_view.end()) { + if (op_name2is_local_parallel_view.at(op_name)) { op_name2is_local[op_name] = true; } } } // build op graph @@ -1347,12 +1218,12 @@ Maybe JobBuildAndInferCtx::Rebuild() { // topo traverse op_graph to AddAndInferOp op_graph.TopoForEachNode([&](OpNode* node) -> void { const auto& op_conf = node->op().op_conf(); - CHECK(op_name2is_mirrored.find(op_conf.name()) != op_name2is_mirrored.end()); - bool is_mirrored = op_name2is_mirrored.at(op_conf.name()); - if (is_mirrored) { - CHECK_JUST(AddAndInferMirroredOp(op_conf)); + CHECK(op_name2is_local.find(op_conf.name()) != op_name2is_local.end()); + bool is_local = op_name2is_local.at(op_conf.name()); + if (is_local) { + CHECK_JUST(AddAndInferLocalOp(op_conf)); } else { - CHECK_JUST(AddAndInferConsistentOp(op_conf)); + CHECK_JUST(AddAndInferGlobalOp(op_conf)); } }); // updata job_helper diff --git a/oneflow/core/job/job_build_and_infer_ctx.h b/oneflow/core/job/job_build_and_infer_ctx.h index 13f259e7ea670d002ca1c9efe9ad7f3b7ccc036c..6fffceb52947b884e77e62cab4e8c26465d36014 100644 --- a/oneflow/core/job/job_build_and_infer_ctx.h +++ b/oneflow/core/job/job_build_and_infer_ctx.h @@ -35,11 +35,14 @@ class JobBuildAndInferCtx { virtual ~JobBuildAndInferCtx() = default; Maybe SetJobConf(const JobConfigProto& job_conf); - Maybe AddLbiAndDiffWatcherUuidPair(const LbiAndDiffWatcherUuidPair& lbi_uuid_pair); - Maybe AddAndInferConsistentOp(const OperatorConf& op_conf); - Maybe AddAndInferMirroredOp(const OperatorConf& op_conf); + Maybe AddAndInferGlobalOp(const OperatorConf& op_conf); + Maybe AddAndInferLocalOp(const OperatorConf& op_conf); Maybe AddLossLogicalBlobName(const std::string& lbn); Maybe SetTrainConf(const TrainConf& train_conf); + Maybe MarkVariableGradientBlobNames( + const HashMap& variable_grad_lbns); + Maybe MarkOutputGradientBlobNames( + const HashMap& output_gradient_lbns); bool HasJobConf() const; Maybe GetStaticShape(const std::string& lbn) const; @@ -50,15 +53,15 @@ class JobBuildAndInferCtx { Maybe GetSplitAxisFromProducerView(const std::string& lbn) const; Maybe GetParallelDescFromProducerView(const std::string& lbn) const; - bool IsMirroredBlob(const std::string& lbn) const; - Maybe MirroredBlobGetNumSubLbi(const std::string& lbn) const; - Maybe MirroredBlobGetSubLbi(const std::string& lbn, int index) const; + bool IsLocalBlob(const std::string& lbn) const; + Maybe LocalBlobGetNumSubLbi(const std::string& lbn) const; + Maybe LocalBlobGetSubLbi(const std::string& lbn, int index) const; - Maybe MirroredBlobGetStaticShape(const std::string& lbn_with_hint) const; - Maybe MirroredBlobGetDataType(const std::string& lbn_with_hint) const; - Maybe MirroredBlobIsDynamic(const std::string& lbn_with_hint) const; - Maybe MirroredBlobGetSplitAxisFromProducerView(const std::string& lbn_with_hint) const; - Maybe MirroredBlobGetParallelDescFromProducerView( + Maybe LocalBlobGetStaticShape(const std::string& lbn_with_hint) const; + Maybe LocalBlobGetDataType(const std::string& lbn_with_hint) const; + Maybe LocalBlobIsDynamic(const std::string& lbn_with_hint) const; + Maybe LocalBlobGetSplitAxisFromProducerView(const std::string& lbn_with_hint) const; + Maybe LocalBlobGetParallelDescFromProducerView( const std::string& lbn_with_hint) const; const Job& job() const; @@ -71,36 +74,35 @@ class JobBuildAndInferCtx { // NOTE(chengcheng): Only used in multi-client. Maybe NewUniqueOpNameByFunctionalOpConf(const OperatorConf& op_conf); + Maybe Op4OpName(const std::string& op_name) const; virtual Maybe Complete() = 0; protected: virtual Maybe CheckAllInputsWithSameParallelNum(const Operator& op, int32_t parallel_num) const = 0; - virtual std::string GetMirroredOpName(const std::string& op_name, int64_t parallel_id) const = 0; - virtual int64_t SizeOfSubConsistentOpList(int64_t parallel_num) const = 0; - virtual ParallelConf GetMirroredOpParallelConf(const ParallelDesc&, - int64_t parallel_id) const = 0; - virtual bool GetIsMirroredParallelView() const = 0; - virtual Maybe FindOrCreateMirroredLbiFromCompatibleConsistentBlob( + virtual std::string GetLocalOpName(const std::string& op_name, int64_t parallel_id) const = 0; + virtual int64_t SizeOfSubGlobalOpList(int64_t parallel_num) const = 0; + virtual ParallelConf GetLocalOpParallelConf(const ParallelDesc&, int64_t parallel_id) const = 0; + virtual bool GetIsLocalParallelView() const = 0; + virtual Maybe FindOrCreateLocalLbiFromCompatibleGlobalBlob( int64_t scope_symbol_id, const LogicalBlobId& lbn) = 0; Job* mut_job() const { return job_; } - const HashMap>& mirrored_lbi2sub_lbis() const { - return mirrored_lbi2sub_lbis_; + const HashMap>& local_lbi2sub_lbis() const { + return local_lbi2sub_lbis_; } - HashMap>* mut_mirrored_lbi2sub_lbis() { - return &mirrored_lbi2sub_lbis_; + HashMap>* mut_local_lbi2sub_lbis() { + return &local_lbi2sub_lbis_; } Maybe ParallelDesc4Lbi(const LogicalBlobId& lbi) const; - HashMap* mut_consistent_lbi2mirrored_lbi() { - return &consistent_lbi2mirrored_lbi_; + HashMap* mut_global_lbi2local_lbi() { + return &global_lbi2local_lbi_; } Maybe SbpParallel4Lbi(const LogicalBlobId& lbi) const; bool IsVariableLbi(const LogicalBlobId& lbi) const; - Maybe Op4OpName(const std::string& op_name) const; Maybe AddAndInferOp(const OperatorConf& op_conf, const ParallelConf& parallel_conf, - const JobDesc* job_desc, bool is_mirrored_parallel_view); + const JobDesc* job_desc, bool is_local_parallel_view); private: Maybe InferOpParallelConf( @@ -118,26 +120,22 @@ class JobBuildAndInferCtx { void AddOpAndUpdateJobParallelViewConf(const OperatorConf& operator_conf, const ParallelDesc& parallel_desc, const NdSbpSignature& nd_sbp_signature, - bool is_mirrored_parallel_view) const; - Maybe InferMirroredSignature(Operator*, bool is_mirrored_parallel_view_conf, - const ParallelDesc&); + bool is_local_parallel_view) const; + Maybe InferLocalSignature(Operator*, bool is_local_parallel_view_conf, const ParallelDesc&); Maybe InferOpOutNdSbp(Operator*, const NdSbpSignature&, const ParallelDesc&); Maybe GenOpProducedEmptyLogicalBlobDesc(Operator* op); Maybe CheckOpBlobSplitability(Operator*, int64_t parallel_num); Maybe CheckPlacement() const; Maybe CheckJobConf() const; Maybe CheckOpScope() const; - Maybe GetMirroredLbi(const std::string& lbn_with_hint) const; - bool HasAnyMirroredBlobInput(const Operator& op) const; - Maybe CheckAllInputsConvertableToMirroredBlob(const Operator& op) const; - Maybe AddLossConsistentBlobName(const std::string& lbn); - Maybe AddLossMirroredBlobName(const std::string& lbn); + Maybe GetLocalLbi(const std::string& lbn_with_hint) const; + bool HasAnyLocalBlobInput(const Operator& op) const; + Maybe CheckAllInputsConvertableToLocalBlob(const Operator& op) const; + Maybe AddLossGlobalBlobName(const std::string& lbn); + Maybe AddLossLocalBlobName(const std::string& lbn); Maybe GetSubLbi(int64_t scope_symbol_id, const LogicalBlobId& lbi, int32_t index); Maybe AllInputsBroadcastParallel(const Operator& op) const; - Maybe InferBlobBackwardSignature(Operator* op); - Maybe InferBlobBackwardSignature( - const Operator& op, std::function* IsLbiBackwardUsed); Job* job_; int64_t job_id_; @@ -148,10 +146,10 @@ class JobBuildAndInferCtx { HashMap> op_name2op_; HashMap parallel_desc2placement_group_; HashMap parallel_desc2blob_placement_group_; - HashMap consistent_lbi2mirrored_lbi_; - HashMap> mirrored_lbi2sub_lbis_; - HashMap mirrored_lbi2parallel_desc_; - HashMap mirrored_lbi2sbp_parallel_; + HashMap global_lbi2local_lbi_; + HashMap> local_lbi2sub_lbis_; + HashMap local_lbi2parallel_desc_; + HashMap local_lbi2sbp_parallel_; bool is_job_conf_frozen_; bool has_job_conf_; HashMap op_name2ancestors_need_no_grad_; @@ -168,32 +166,12 @@ class LazyJobBuildAndInferCtx : public JobBuildAndInferCtx { Maybe Complete() override; Maybe CheckAllInputsWithSameParallelNum(const Operator& op, int32_t parallel_num) const override; - std::string GetMirroredOpName(const std::string& op_name, int64_t parallel_id) const override; - int64_t SizeOfSubConsistentOpList(int64_t parallel_num) const override { return parallel_num; } - ParallelConf GetMirroredOpParallelConf(const ParallelDesc&, int64_t parallel_id) const override; - bool GetIsMirroredParallelView() const override { return false; } - Maybe FindOrCreateMirroredLbiFromCompatibleConsistentBlob( - int64_t scope_symbol_id, const LogicalBlobId& lbn) override; -}; - -class EagerJobBuildAndInferCtx : public JobBuildAndInferCtx { - public: - OF_DISALLOW_COPY_AND_MOVE(EagerJobBuildAndInferCtx); - EagerJobBuildAndInferCtx(Job* job, int64_t job_id) : JobBuildAndInferCtx(job, job_id) {} - virtual ~EagerJobBuildAndInferCtx() = default; - - private: - Maybe Complete() override; - Maybe CheckAllInputsWithSameParallelNum(const Operator& op, - int32_t parallel_num) const override; - std::string GetMirroredOpName(const std::string& op_name, int64_t parallel_id) const override; - int64_t SizeOfSubConsistentOpList(int64_t parallel_num) const override { return 1; } - ParallelConf GetMirroredOpParallelConf(const ParallelDesc&, int64_t parallel_id) const override; - bool GetIsMirroredParallelView() const override { return true; } - Maybe FindOrCreateMirroredLbiFromCompatibleConsistentBlob( + std::string GetLocalOpName(const std::string& op_name, int64_t parallel_id) const override; + int64_t SizeOfSubGlobalOpList(int64_t parallel_num) const override { return parallel_num; } + ParallelConf GetLocalOpParallelConf(const ParallelDesc&, int64_t parallel_id) const override; + bool GetIsLocalParallelView() const override { return false; } + Maybe FindOrCreateLocalLbiFromCompatibleGlobalBlob( int64_t scope_symbol_id, const LogicalBlobId& lbn) override; - - HashSet executed_op_names_; }; } // namespace oneflow diff --git a/oneflow/core/job/job_build_and_infer_ctx_mgr.cpp b/oneflow/core/job/job_build_and_infer_ctx_mgr.cpp index 041d4f5419234ca750f1797cabfb7482708f2bec..d87b403e5db4db358cb04bb60f602310ec6c0301 100644 --- a/oneflow/core/job/job_build_and_infer_ctx_mgr.cpp +++ b/oneflow/core/job/job_build_and_infer_ctx_mgr.cpp @@ -43,11 +43,6 @@ JobBuildAndInferCtx* LazyJobBuildAndInferCtxMgr::NewJobBuildAndInferCtx(Job* job return new LazyJobBuildAndInferCtx(job, job_id); } -JobBuildAndInferCtx* EagerJobBuildAndInferCtxMgr::NewJobBuildAndInferCtx(Job* job, - int64_t job_id) const { - return new EagerJobBuildAndInferCtx(job, job_id); -} - Maybe JobBuildAndInferCtxMgr::FindJobBuildAndInferCtx( const std::string& job_name) { CHECK_OR_RETURN(job_name2infer_ctx_.find(job_name) != job_name2infer_ctx_.end()) @@ -90,20 +85,6 @@ Maybe LazyJobBuildAndInferCtxMgr::VirtualCloseJob() { return Maybe::Ok(); } -Maybe EagerJobBuildAndInferCtxMgr::VirtualCloseJob() { - const JobDesc* job_desc = Singleton::Get(); - if (job_desc != nullptr) { - CHECK_EQ_OR_RETURN(job_desc->job_name(), *JUST(GetCurrentJobName())); - CHECK_EQ_OR_RETURN(job_desc->job_id(), mut_job_set()->job_size() - 1); - Singleton::Delete(); - } - mut_job_set()->clear_job(); - clear_job_name2infer_ctx(); - return Maybe::Ok(); -} - -bool EagerExecutionEnabled() { return *Singleton::Get(); } - Maybe GlobalJobBuildAndInferCtxMgr() { return JUST(SingletonMaybe()); } diff --git a/oneflow/core/job/job_build_and_infer_ctx_mgr.h b/oneflow/core/job/job_build_and_infer_ctx_mgr.h index 27f9c3150ccd2a470e4555c6756fceb84bcf0ea5..399842bd12e8e9c4bbe862e80ca94bd0ae855dbd 100644 --- a/oneflow/core/job/job_build_and_infer_ctx_mgr.h +++ b/oneflow/core/job/job_build_and_infer_ctx_mgr.h @@ -21,7 +21,6 @@ limitations under the License. #include "oneflow/core/job/job.pb.h" #include "oneflow/core/job/job_set.pb.h" #include "oneflow/core/job/job_build_and_infer_ctx.h" -#include "oneflow/core/job/lbi_diff_watcher_info.pb.h" namespace oneflow { @@ -66,21 +65,6 @@ class LazyJobBuildAndInferCtxMgr : public JobBuildAndInferCtxMgr { JobBuildAndInferCtx* NewJobBuildAndInferCtx(Job* job, int64_t job_id) const override; }; -class EagerJobBuildAndInferCtxMgr : public JobBuildAndInferCtxMgr { - public: - OF_DISALLOW_COPY_AND_MOVE(EagerJobBuildAndInferCtxMgr); - EagerJobBuildAndInferCtxMgr() : JobBuildAndInferCtxMgr() {} - ~EagerJobBuildAndInferCtxMgr() override = default; - - private: - friend class Singleton; - - Maybe VirtualCloseJob() override; - JobBuildAndInferCtx* NewJobBuildAndInferCtx(Job* job, int64_t job_id) const override; -}; - -bool EagerExecutionEnabled(); - Maybe GlobalJobBuildAndInferCtxMgr(); Maybe GetJobBuildAndInferCtx(const std::string& job_name); Maybe GetCurInferCtx(); diff --git a/oneflow/core/job/job_builder.h b/oneflow/core/job/job_builder.h index a954d12ed7e1e353b7e59bc94542ba261d8dd15e..f3c78d671a59bcaaca66657fd58e6001d64e98f9 100644 --- a/oneflow/core/job/job_builder.h +++ b/oneflow/core/job/job_builder.h @@ -43,6 +43,8 @@ class JobBuilder final { return job_->mutable_job_parallel_view_conf(); } + MergedLogicalChainIdGroup* add_logical_chain_groups() { return job_->add_logical_chain_groups(); } + Maybe OpConf4OpName(const std::string& op_name) const; Maybe MutableOpConf4OpName(const std::string& op_name); diff --git a/oneflow/core/job/job_conf.proto b/oneflow/core/job/job_conf.proto index 2ebe5dfbb499b1cfc9d14cc644bd7ba34d8fb6ce..f13a4340ff8d9c341d63c0e86982fc5c4824f38a 100644 --- a/oneflow/core/job/job_conf.proto +++ b/oneflow/core/job/job_conf.proto @@ -16,6 +16,9 @@ message NaiveModelUpdateConf { message MomentumModelUpdateConf { optional float beta = 1 [default = 0.9]; + optional float dampening = 2 [default = 0.0]; + optional bool nesterov = 3 [default = false]; + optional bool maximize = 4 [default = false]; } message RMSPropModelUpdateConf { @@ -36,6 +39,7 @@ message AdamModelUpdateConf { optional float epsilon = 3 [default = 1e-8]; optional bool do_bias_correction = 4 [default = true]; optional bool amsgrad = 5 [default = false]; + optional bool smart_decay = 6 [default = false]; } message LazyAdamModelUpdateConf { @@ -67,6 +71,12 @@ message FtrlModelUpdateConf { optional float beta = 5 [default = 0.0]; } +message AdadeltaModelUpdateConf { + required float rho = 1 [default = 0.9]; + required float epsilon = 2 [default = 1e-6]; + required bool maximize = 3 [default = false]; +} + message ClipByGlobalNormConf { optional float max_norm = 1 [default = 1.0]; optional double norm_type = 2 [default = 2.0]; @@ -93,10 +103,12 @@ message WeightDecayConf { message OptimizerConf { repeated string variable_op_names = 1; optional float base_learning_rate = 2; + repeated string variable_grad_lbns = 3; optional LearningRateDecayConf learning_rate_decay = 4; optional string learning_rate_lbn = 5; optional ClipConf clip_conf = 6; optional WeightDecayConf weight_decay_conf = 7; + optional float lr_scale = 8 [default = 1.0]; oneof normal_mdupdt { NaiveModelUpdateConf naive_conf = 1000; MomentumModelUpdateConf momentum_conf = 1001; @@ -107,6 +119,7 @@ message OptimizerConf { LambModelUpdateConf lamb_conf = 1006; AdagradModelUpdateConf adagrad_conf = 1007; FtrlModelUpdateConf ftrl_conf = 1008; + AdadeltaModelUpdateConf adadelta_conf = 1009; } } @@ -136,6 +149,7 @@ message DynamicLossScalePolicy { message TrainConf { repeated OptimizerConf optimizer_conf = 1; repeated string loss_lbn = 2; + repeated string loss_grad_lbn = 6; optional string train_step_lbn = 3; oneof loss_scale_policy { float loss_scale_factor = 4 [default = 1]; @@ -154,7 +168,7 @@ message PredictConf { message MemoryAllocationAlgorithmConf { optional bool use_mem_size_first_algo = 1 [default = true]; - optional bool use_mutual_exclusion_first_algo = 2 [default = true]; + optional bool use_mutual_exclusion_first_algo = 2 [default = false]; optional bool use_time_line_algo = 3 [default = false]; } @@ -191,6 +205,13 @@ message JobSignatureDef { map outputs = 2; } +enum StraightenAlgorithmTag { + kDisable = 1; + kOverlap4Transfer = 2; + kCompressMemory = 3; + kOverlap4CpuGpu = 4; +} + message JobConfigProto { required string job_name = 1; @@ -240,9 +261,20 @@ message JobConfigProto { optional bool cudnn_conv_enable_pseudo_half = 600 [default = true]; optional bool enable_auto_mixed_precision = 602 [default = false]; optional bool enable_quantization_aware_training = 603 [default = false]; - - optional bool enable_straighten_algorithm_in_task_graph = 700 [default = false]; + optional DataType mixed_precision_data_type = 604 [default = kFloat16]; // kFloat16 or kBFloat16 + optional bool enable_multi_tensor_update = 605 [default = false]; + optional bool enable_fused_model_update_cast = 606 [default = false]; + + optional bool enable_auto_parallel = 700 [default = false]; + optional double auto_parallel_computation_cost_ratio = 701 [default = 0.05]; + optional double auto_parallel_wait_time = 702 [default = 1.65e4]; + optional bool enable_auto_parallel_trunk_algo = 703 [default = true]; + optional bool enable_auto_parallel_sbp_collector = 704 [default = false]; + optional bool enable_auto_parallel_ignore_user_sbp_config = 705 [default = false]; + optional StraightenAlgorithmTag straighten_algorithm_tag_in_task_graph = 800 [default = kCompressMemory]; + optional bool enable_compress_memory = 801 [default = false]; + optional int64 concurrency_width = 1000 [default = 128]; map flag_name2flag_value = 2000; diff --git a/oneflow/core/job/job_desc.cpp b/oneflow/core/job/job_desc.cpp index 7a66100f054043fbad8fd1dd89dce14af9912d3e..5b37403a6af78974d3b8d7af1569a02bbcab8aea 100644 --- a/oneflow/core/job/job_desc.cpp +++ b/oneflow/core/job/job_desc.cpp @@ -79,21 +79,4 @@ GlobalJobDescScope::~GlobalJobDescScope() { Singleton::Delete(); } const JobDesc& GlobalJobDesc() { return *Singleton::Get(); } -bool IsPullJob(const std::string& job_name, const InterUserJobInfo& inter_user_job_info) { - for (const auto& pair : inter_user_job_info.output_or_var_op_name2pull_job_name()) { - if (pair.second == job_name) { return true; } - } - return false; -} - -bool IsPushJob(const std::string& job_name, const InterUserJobInfo& inter_user_job_info) { - for (const auto& pair : inter_user_job_info.input_or_var_op_name2push_job_name()) { - if (pair.second == job_name) { return true; } - } - if (job_name == inter_user_job_info.global_model_init_job_name()) { return true; } - if (job_name == inter_user_job_info.global_model_load_job_name()) { return true; } - if (job_name == inter_user_job_info.global_model_save_job_name()) { return true; } - return false; -} - } // namespace oneflow diff --git a/oneflow/core/job/job_desc.h b/oneflow/core/job/job_desc.h index f0eba0786f95c780d7c4c246e2d0deaf2be3c1d3..4d3446608afd124ae08778d8911464abe3aa293b 100644 --- a/oneflow/core/job/job_desc.h +++ b/oneflow/core/job/job_desc.h @@ -53,12 +53,16 @@ class JobDesc final { bool enable_reuse_mem() const { return job_conf_.enable_reuse_mem(); } bool enable_inplace() const { return job_conf_.enable_inplace(); } bool enable_auto_mixed_precision() const { return job_conf_.enable_auto_mixed_precision(); } + bool enable_multi_tensor_update() const { return job_conf_.enable_multi_tensor_update(); } + bool enable_fused_model_update_cast() const { return job_conf_.enable_fused_model_update_cast(); } + DataType mixed_precision_data_type() const { return job_conf_.mixed_precision_data_type(); } bool do_parallel_cast_before_widening_type_cast() const { return job_conf_.do_parallel_cast_before_widening_type_cast(); }; bool prune_parallel_cast_ops() const { return job_conf_.prune_parallel_cast_ops(); } bool prune_cast_to_static_shape_ops() const { return job_conf_.prune_cast_to_static_shape_ops(); } bool prune_amp_white_identity_ops() const { return job_conf_.prune_amp_white_identity_ops(); } + bool enable_auto_parallel() const { return job_conf_.enable_auto_parallel(); } int64_t cudnn_buf_limit_mbyte() const { return job_conf_.cudnn_buf_limit_mbyte(); } #define DEFINE_FUNCTION_CONFIG_GETTER(T, func_name, field_name) \ diff --git a/oneflow/core/job/job_instance.h b/oneflow/core/job/job_instance.h index 5be11bd8a2667f3b2ac2ffa962a32db2ab39eee0..903caa0b02b21f122cae53f24b80d9a9ddacdd7c 100644 --- a/oneflow/core/job/job_instance.h +++ b/oneflow/core/job/job_instance.h @@ -16,7 +16,8 @@ limitations under the License. #ifndef ONEFLOW_CORE_JOB_JOB_INSTANCE_H_ #define ONEFLOW_CORE_JOB_JOB_INSTANCE_H_ -#include "oneflow/core/register/ofblob.h" +#include +#include "oneflow/core/common/util.h" namespace oneflow { @@ -27,10 +28,6 @@ class JobInstance { virtual ~JobInstance() = default; virtual std::string job_name() const { UNIMPLEMENTED(); } - virtual std::string sole_input_op_name_in_user_job() const { UNIMPLEMENTED(); } - virtual std::string sole_output_op_name_in_user_job() const { UNIMPLEMENTED(); } - virtual void PushBlob(uint64_t ofblob_ptr) const { UNIMPLEMENTED(); } - virtual void PullBlob(uint64_t ofblob_ptr) const { UNIMPLEMENTED(); } virtual void Finish() const { UNIMPLEMENTED(); } }; diff --git a/oneflow/core/job/job_ir.cpp b/oneflow/core/job/job_ir.cpp index f5552b925149aad5dab96dd8511d1d98dad9c340..8e459c04408d69bd56e8d0a56e5943e84257772e 100644 --- a/oneflow/core/job/job_ir.cpp +++ b/oneflow/core/job/job_ir.cpp @@ -27,6 +27,10 @@ Maybe SaveJobToIR(Job* job, const std::string& path) { UNIMPLEMENTED_THEN_RETURN() << "SaveJobToIR is only supported WITH_MLIR"; } +Maybe ConvertJobToIR(Job* job) { + UNIMPLEMENTED_THEN_RETURN() << "ConvertJobToIR is only supported WITH_MLIR"; +} + Maybe LoadJobFromIR(Job* job, const std::string& path) { UNIMPLEMENTED_THEN_RETURN() << "LoadJobFromIR is only supported WITH_MLIR"; } diff --git a/oneflow/core/job/job_ir.h b/oneflow/core/job/job_ir.h index 7dbd8da0c316ef790617d7878428864e7a7a577b..5bf122c6265b46079f7c7abeed976867df469d2a 100644 --- a/oneflow/core/job/job_ir.h +++ b/oneflow/core/job/job_ir.h @@ -22,6 +22,7 @@ limitations under the License. namespace oneflow { Maybe ConvertJobToTosaIR(Job* job); +Maybe ConvertJobToIR(Job* job); Maybe SaveJobToIR(Job* job, const std::string& path); Maybe LoadJobFromIR(Job* job, const std::string& path); diff --git a/oneflow/core/job/lbi_diff_watcher_info.proto b/oneflow/core/job/lbi_diff_watcher_info.proto deleted file mode 100644 index fa5151ebd5c7044d5b98703775d74bebb4eb0eb8..0000000000000000000000000000000000000000 --- a/oneflow/core/job/lbi_diff_watcher_info.proto +++ /dev/null @@ -1,17 +0,0 @@ -syntax = "proto2"; -package oneflow; - -import "oneflow/core/register/logical_blob_id.proto"; - -message LbiAndDiffWatcherUuidPair { - required LogicalBlobId lbi = 1; - required string watcher_uuid = 2; -} - -message LbiAndDiffWatcherUuidPairList { - repeated LbiAndDiffWatcherUuidPair lbi_and_uuid_pair = 1; -} - -message LbiDiffWatcherInfo { - map job_name2lbi_and_watcher_uuids = 1; -} diff --git a/oneflow/core/job/local_parallel.proto b/oneflow/core/job/local_parallel.proto new file mode 100644 index 0000000000000000000000000000000000000000..bcc8c0898c2508164ebb9e1a0276eb414aec3751 --- /dev/null +++ b/oneflow/core/job/local_parallel.proto @@ -0,0 +1,13 @@ +syntax = "proto2"; +package oneflow; + +message LocalParallel { +} + +message OptLocalParallel { + optional LocalParallel local_parallel = 1; +} + +message LocalSignature { + map bn_in_op2opt_local_parallel = 1; +} diff --git a/oneflow/core/job/local_sig_infer_hint.h b/oneflow/core/job/local_sig_infer_hint.h new file mode 100644 index 0000000000000000000000000000000000000000..f49ba5f3e2fe2f5fc31c7729742af1e6534627f6 --- /dev/null +++ b/oneflow/core/job/local_sig_infer_hint.h @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ +#define ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ + +#include "oneflow/core/job/parallel_desc.h" + +namespace oneflow { + +class LocalSigInferHint final { + public: + LocalSigInferHint(const ParallelDesc* parallel_desc, bool is_local_parallel_view) + : parallel_desc_(parallel_desc), is_local_parallel_view_(is_local_parallel_view) {} + + const ParallelDesc& parallel_desc() const { return *parallel_desc_; } + bool is_local_parallel_view() const { return is_local_parallel_view_; } + + private: + const ParallelDesc* parallel_desc_; + bool is_local_parallel_view_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ diff --git a/oneflow/core/job/memory_share_strategy.cpp b/oneflow/core/job/memory_share_strategy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5ed722ef9861573904e3eff3f747932893680b9a --- /dev/null +++ b/oneflow/core/job/memory_share_strategy.cpp @@ -0,0 +1,452 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/job/memory_share_strategy.h" +#include +#include +#include "oneflow/core/common/hash_container.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/register/runtime_register_desc.h" + +namespace oneflow { + +namespace { +constexpr int32_t kMaxIterStep = 100; +} // anonymous namespace + +bool IsLifetimeExcluded(const std::pair& a, + const std::pair& b) { + return a.first < b.second && b.first < a.second; +} + +// Initialization +void MemoryShareStrategy::InitRegister( + const HashMap>& register2lifetime) { + total_register_num_ = register2lifetime.size(); + index2register_.resize(total_register_num_); + int32_t register_id = 0; + for (const auto& pair : register2lifetime) { + index2register_[register_id] = pair.first; + register_id++; + } +} + +void MemoryShareStrategy::InitRegisterInformation( + const HashMap& mem_reused_regst2size) { + total_register_num_ = index2register_.size(); + register_size_.resize(total_register_num_); + for (int32_t register_id = 0; register_id < total_register_num_; register_id++) { + const auto& register_ = index2register_[register_id]; + int64_t register_size = mem_reused_regst2size.at(register_); + register_size_[register_id] = register_size; + register2index_[register_] = register_id; + } + order_.resize(total_register_num_); + for (int32_t i = 0; i < total_register_num_; i++) { order_[i] = i; } +} + +// Steal a compact position as the initial strategy +void MemoryShareStrategy::StealCompactPosition( + const HashMap& regst_desc2offset, + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime) { + // Initialization + InitRegister(register2lifetime); + + // Sort index2register_ + std::sort(index2register_.begin(), index2register_.end(), + [&](RegstDescProto* i, RegstDescProto* j) { + return regst_desc2offset.at(i) < regst_desc2offset.at(j); + }); + // Update other information + InitRegisterInformation(mem_reused_regst2size); + + left_registers_.clear(); + left_registers_.resize(total_register_num_); + excluded_registers_.clear(); + excluded_registers_.resize(total_register_num_); + // should_visit_[i] indicates whether we should visit register[i]. + // should_visit_[i] = 0: should not visit i, or have already visited i.. + // should_visit_[i] = 1: should visit i, i is excluded with j + // should_visit_[i] = 2: should visit i, i is not excluded with j + should_visit_.clear(); + should_visit_.resize(total_register_num_, 0); + register_offset_.resize(total_register_num_); + // Generate a compact relationship of position + // For example we have 3 relationship: x1 < x2, x2 < x3, x1 < x3 + // We would delete the redundant relationship (x1 < x3) + for (int32_t j = 0; j < total_register_num_; j++) { + const auto& register_j = index2register_[j]; + register_offset_[j] = regst_desc2offset.at(register_j); + auto& excluded_register_j = excluded_registers_[j]; + const auto& lifetime_j = register2lifetime.at(register_j); + // Init should visit with all orders of the excluded register + for (int32_t i = j + 1; i < total_register_num_; i++) { + if (IsLifetimeExcluded(lifetime_j, register2lifetime.at(index2register_[i]))) { + // Copy the data to excluded registers + excluded_register_j.insert(i); + excluded_registers_[i].insert(j); + } + } + } + + for (int32_t j = 0; j < total_register_num_; j++) { ResetCompactPosition(j); } +} + +// Generate a compact position with the order of occurrence +// Not recommended +void MemoryShareStrategy::GenerateCompactPosition( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime) { + HashMap regst_desc2offset; + int64_t offset = 0; + for (const auto& pair : register2lifetime) { + regst_desc2offset[pair.first] = offset; + offset++; + } + StealCompactPosition(regst_desc2offset, mem_reused_regst2size, register2lifetime); +} + +// Compute optimal cost with compact relationship +size_t MemoryShareStrategy::ComputeOptimalCost4CompactRelationship() { + int64_t mem_block_size = 0; + for (int32_t i = 0; i < total_register_num_; i++) { + mem_block_size = + std::max(mem_block_size, ComputeOffset4CompactRelationship(i) + register_size_[i]); + } + mem_block_size_ = size_t(mem_block_size); + return mem_block_size_; +} + +// Compute offset with compact relationship +int64_t MemoryShareStrategy::ComputeOffset4CompactRelationship(int32_t i) { + if (register_offset_[i] < 0) { + // An initial value x would be store as -x - 1. + register_offset_[i] = -register_offset_[i] - 1; + for (int32_t j : left_registers_[i]) { + register_offset_[i] = + std::max(register_offset_[i], ComputeOffset4CompactRelationship(j) + register_size_[j]); + } + } + return register_offset_[i]; +} + +size_t MemoryShareStrategy::ComputeOptimalAdjustedCost() { + // Initial optimal cost + size_t optimal_cost = ComputeOptimalCostFrom0(); + // All the registers excluded with register i are sorted from left to right + // std::vector order_; + // auto CompareRegisterPosition = [&](int32_t i, int32_t j) { + // return register_offset_[i] < register_offset_[j]; + // }; + backup_registers_.clear(); + backup_registers_.resize(total_register_num_); + // The number of steps that the optimal cost does not decrease + int32_t step_no_decrease = 0; + for (int32_t m = 0; m < max_iteration_step_; m++) { + for (int32_t i = 0; i < total_register_num_; i++) { + EliminateRegister(i); + size_t cost_without_i = ComputeOptimalCostFrom0(); + // Find the offset of i which has the minimum cost + int64_t min_x_i = -1; + if (cost_without_i < optimal_cost) { + // Find the minimum cost + int64_t min_cost = optimal_cost; + // Back up the current register offset with elimination of i + auto register_offset_backup = register_offset_; + // Try to insert the register i into the sorted excluded registers + HashSet all_x_i; + for (int32_t j : excluded_registers_[i]) { + // Insert i before j + all_x_i.insert(register_offset_backup[j]); + // Insert i after j + all_x_i.insert(register_offset_backup[j] + register_size_[j]); + } + + for (int64_t x_i : all_x_i) { + int64_t cost_insert_i = ComputeOptimalCostWithOccupation(i, x_i, register_offset_backup); + // Check if we found a smaller cost + if (cost_insert_i < min_cost) { + min_cost = cost_insert_i; + min_x_i = x_i; + if (min_cost <= cost_without_i) { break; } + } + } + // Found a smaller cost + if (min_x_i >= 0) { + InsertRegister(i, min_x_i, register_offset_backup); + optimal_cost = ComputeOptimalCostFrom0(); + } + } + // Found a smaller cost + if (min_x_i >= 0) { + // Move to a new status with smaller cost, dump the backup of the offset. + ClearBackup(); + step_no_decrease = 0; + } else { + // Recover to the original status + RecoverFromBackup(i); + // Adjust the offset after recovery + ComputeOptimalCostFrom0(); + // Terminate it if no cost reduce for any of the adjustment. + step_no_decrease++; + if (step_no_decrease >= total_register_num_) { break; } + } + } + if (step_no_decrease >= total_register_num_) { break; } + } + CHECK_JUST(CheckConflict()); + return optimal_cost; +} + +// Let x_i occupy some space [x_i, x_i + l_i), then we recompute the optimal cost +size_t MemoryShareStrategy::ComputeOptimalCostWithOccupation( + int32_t i, int64_t x_i, const std::vector& register_offset_backup) { + // The end of register i. + int64_t e_i = x_i + register_size_[i]; + register_offset_.clear(); + register_offset_.resize(total_register_num_, -1); + for (int32_t k : excluded_registers_[i]) { + // x_k + l_k > x_i + // k is behind i + if (register_offset_backup[k] + register_size_[k] > x_i) { + register_offset_[k] = -e_i - 1; + } else { + register_offset_[k] = register_offset_backup[k]; + } + } + register_offset_[i] = x_i; + return ComputeOptimalCost4CompactRelationship(); +} + +// Eliminate one register +void MemoryShareStrategy::EliminateRegister(int32_t i) { + // Init back up registers + backup_registers_[i] = left_registers_[i]; + for (auto j : excluded_registers_[i]) { + if (register_offset_[i] < register_offset_[j]) { + should_visit_.clear(); + should_visit_.resize(total_register_num_, 0); + // should_visit_[i] = 0: should not visit i, or have already visited i.. + // should_visit_[i] = 1: should visit i, i is excluded with j + // should_visit_[i] = 2: should visit i, i is not excluded with j + // should_visit_[i] = -1: i is visited, i is excluded with j + // should_visit_[i] = -2: i is visited, i is not excluded with j + for (int32_t k = 0; k < total_register_num_; k++) { + if (register_offset_[k] < register_offset_[j]) { + if (Exclude(k, j)) { + should_visit_[k] = 1; + } else { + should_visit_[k] = 2; + } + } + } + // Eliminate all the grandsons of the excluded registers + for (int32_t k : excluded_registers_[j]) { + if (should_visit_[k] == 1) { EliminateRedundantRelationshipIgnore(i, k); } + } + for (int32_t k : excluded_registers_[j]) { + if (should_visit_[k] == -1) { + if (left_registers_[j].insert(k).second) { backup_registers_[j].insert(k); } + } + } + if (left_registers_[j].erase(i)) { backup_register_behind_i_.insert(j); } + } + } + left_registers_[i].clear(); +} + +// Whether i and j occurs simultaneously +bool MemoryShareStrategy::Exclude(int32_t i, int32_t j) { + return excluded_registers_[i].find(j) != excluded_registers_[i].end(); +} + +// If the previous strategy has fewer cost, recover to the previous one from the backup. +void MemoryShareStrategy::RecoverFromBackup(int32_t i) { + for (int32_t j = 0; j < total_register_num_; j++) { + if (i == j) { + left_registers_[i] = backup_registers_[i]; + } else { + for (int32_t k : backup_registers_[j]) { left_registers_[j].erase(k); } + } + } + for (int32_t j : backup_register_behind_i_) { left_registers_[j].insert(i); } + ClearBackup(); +} + +// Clear backup +void MemoryShareStrategy::ClearBackup() { + for (auto& backup_register : backup_registers_) { backup_register.clear(); } + backup_register_behind_i_.clear(); +} + +size_t MemoryShareStrategy::ComputeOptimalCostFrom0() { + register_offset_.clear(); + register_offset_.resize(total_register_num_, -1); + return ComputeOptimalCost4CompactRelationship(); +} + +// Insert register i at position [x_i, x_i + l_i) +void MemoryShareStrategy::InsertRegister(int32_t i, int64_t x_i, + const std::vector& original_register_offset) { + ComputeOptimalCostWithOccupation(i, x_i, original_register_offset); + std::sort(order_.begin(), order_.end(), + [&](int32_t k, int32_t j) { return register_offset_[k] < register_offset_[j]; }); + for (int32_t j : order_) { + if (register_offset_[i] <= register_offset_[j]) { ResetCompactPosition(j); } + } +} + +// Eliminate children of j but ignore i. +void MemoryShareStrategy::EliminateRedundantRelationshipIgnore(int32_t i, int32_t j) { + // Ignore i + if (i == j) { return; } + if (should_visit_[j] > 0) { + // Do not look into it again + should_visit_[j] = -should_visit_[j]; + for (int32_t k : left_registers_[j]) { + EliminateRedundantRelationshipIgnore(i, k); + should_visit_[k] = 0; + } + } +} + +// Check whether the current offset does not introduce any conflict +Maybe MemoryShareStrategy::CheckConflict() { + CHECK_EQ_OR_RETURN(index2register_.size(), register_offset_.size()) + << "Not equal size, we might be calling CheckConflict() at a wrong time."; + for (int32_t i = 0; i < total_register_num_; i++) { + CHECK_GE_OR_RETURN(register_offset_[i], 0) << "Register offset is not computed."; + for (int32_t j : excluded_registers_[i]) { + CHECK_OR_RETURN(register_offset_[i] + register_size_[i] <= register_offset_[j] + || register_offset_[j] + register_size_[j] <= register_offset_[i]) + << "Two registers overlap"; + } + } + return Maybe::Ok(); +} + +// Update the offset with the adjusted strategy +void MemoryShareStrategy::UpdateOffset(size_t* mem_block_size, + HashMap* regst_desc2offset) { + size_t optimal_cost = ComputeOptimalAdjustedCost(); + if (optimal_cost < *mem_block_size) { + VLOG(3) << "Original cost: " << *mem_block_size << ", updated cost: " << optimal_cost; + *mem_block_size = optimal_cost; + for (auto& pair : *regst_desc2offset) { + pair.second = register_offset_[register2index_[pair.first]]; + } + } +} + +// Find all the k < i, eliminates k < j, +// since k < i and i < j have already implied that. +void MemoryShareStrategy::EliminateRedundantRelationship(int32_t i) { + // If i is already eliminate, skip it. + if (should_visit_[i]) { + for (int32_t k : left_registers_[i]) { + // Eliminate all the k < i + EliminateRedundantRelationship(k); + // Eliminate left[i] + should_visit_[k] = 0; + } + } +} + +// Reset the compact position for the registers +void MemoryShareStrategy::ResetCompactPosition(int32_t j) { + left_registers_[j].clear(); + // Mark all the registers on the left + for (int32_t i = 0; i < total_register_num_; i++) { + if (register_offset_[i] < register_offset_[j]) { + if (Exclude(i, j)) { + should_visit_[i] = 1; + } else { + should_visit_[i] = 2; + } + } else { + // Might be unnecessary since we clear up should_visit_ before. + should_visit_[i] = 0; + } + } + + for (int32_t i = 0; i < total_register_num_; i++) { + if (should_visit_[i] == 1) { + // Find all the k < i, eliminates k < j, + // since k < i and i < j have already implied that. + // Also reset should_visit_[i] to false, + // since we have already visited i. + EliminateRedundantRelationship(i); + } + } + + for (int32_t i = 0; i < total_register_num_; i++) { + if (should_visit_[i] == 1) { + // i < j + left_registers_[j].insert(i); + } + // Might be unnecessary since we clear up should_visit_ before. + should_visit_[i] = 0; + } +} + +// Update the maximum iteration step with the current size and lower bound +void MemoryShareStrategy::UpdateMaxIteration(size_t mem_block_size, size_t lower_bound) { + if (lower_bound > 0) { + max_iteration_step_ = ((mem_block_size - lower_bound) * 100) / lower_bound; + } else { + // A graph only containing several 0 size tensors might have lower bound = 0. + // Check test_div.py::TestDiv::test_0_size_div for example. + max_iteration_step_ = 0; + } + // if mem_block_size is closed to the maximum number of type size_t, then we might have a negative + // value for (mem_block_size - lower_bound) * 100 + // In this case, we just set a large max_iteration_step_ + if (max_iteration_step_ < 0) { max_iteration_step_ = kMaxIterStep; } +} + +// Adaptively update the offset of registers to minimize the total memory +void MemoryShareStrategy::AdaptivelyUpdateOffset( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime, + size_t lower_bound, size_t* mem_block_size, + HashMap* regst_desc2offset) { + if (*mem_block_size > lower_bound) { + VLOG(3) << "Current memory size: " << *mem_block_size << ", lower bound : " << lower_bound; + UpdateMaxIteration(*mem_block_size, lower_bound); + VLOG(3) << "max iteration step: " << max_iteration_step_; + if (max_iteration_step_ > 0) { + StealCompactPosition(*regst_desc2offset, mem_reused_regst2size, register2lifetime); + UpdateOffset(mem_block_size, regst_desc2offset); + } + } +} + +// Set the offset of registers to minimize the total memory +// Iterating from a random order might take a lot of steps to reach the optimal cost. +// Therefore, this function is not recommended with an initial offset provided. +void MemoryShareStrategy::GenerateOffset( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime, + size_t* mem_block_size, HashMap* regst_desc2offset) { + max_iteration_step_ = kMaxIterStep; + VLOG(3) << "max iteration step: " << max_iteration_step_; + GenerateCompactPosition(mem_reused_regst2size, register2lifetime); + UpdateOffset(mem_block_size, regst_desc2offset); +} + +} // namespace oneflow diff --git a/oneflow/core/job/memory_share_strategy.h b/oneflow/core/job/memory_share_strategy.h new file mode 100644 index 0000000000000000000000000000000000000000..c584b2a9f538fa1a23f003bfa8f436618a0e45fa --- /dev/null +++ b/oneflow/core/job/memory_share_strategy.h @@ -0,0 +1,129 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_JOB_MEMORY_SHARE_STRATEGY_H_ +#define ONEFLOW_CORE_JOB_MEMORY_SHARE_STRATEGY_H_ + +#include +#include "oneflow/core/common/hash_container.h" +#include "oneflow/core/register/register_desc.pb.h" +#include "oneflow/core/common/maybe.h" + +namespace oneflow { + +// NOTE: Another trick to save times. +// Comparing two numbers is faster than asking the existence in a HashSet. +bool IsLifetimeExcluded(const std::pair& a, const std::pair& b); + +class MemoryShareStrategy { + public: + // Adaptively update the offset of registers to minimize the total memory + void AdaptivelyUpdateOffset( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime, + size_t lower_bound, size_t* mem_block_size, + HashMap* regst_desc2offset); + + // Set the offset of registers to minimize the total memory + // Iterating from a random order might take a lot of steps to reach the optimal cost. + // Therefore, this function is not recommended with an initial offset provided. + void GenerateOffset( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime, + size_t* mem_block_size, HashMap* regst_desc2offset); + + private: + size_t mem_block_size_; + int32_t max_iteration_step_; + std::vector register_offset_; + std::vector register_size_; + HashMap register2index_; + std::vector index2register_; + // left registers store the first registers on the left, which have smaller offsets. + // For example, 1 < 2 < 3 < 5 + // 2 < 4 < 5 + // Then + // left_registers_[1] = {} + // left_registers_[2] = {1} + // left_registers_[3] = {2} + // left_registers_[4] = {2} + // left_registers_[5] = {3, 4} + // We know that 1 < 3, but 1 is not in left_registers_[3], + // since we only store the first registers. + std::vector> left_registers_; + // Store all the registers which exist simultaneously. + std::vector> excluded_registers_; + // Back up the changes + std::vector> backup_registers_; + HashSet backup_register_behind_i_; + // A buffer which implies whether we should visit a register + std::vector should_visit_; + int32_t total_register_num_; + std::vector order_; + + // Mid-level interfaces + // Steal a compact position as the initial strategy + void StealCompactPosition( + const HashMap& regst_desc2offset, + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime); + // Generate a compact position with the order of occurrence + void GenerateCompactPosition( + const HashMap& mem_reused_regst2size, + const HashMap>& register2lifetime); + // Update the offset with the adjusted strategy + void UpdateOffset(size_t* mem_block_size, HashMap* regst_desc2offset); + // Update the maximum iteration step with the current size and lower bound + void UpdateMaxIteration(size_t mem_block_size, size_t lower_bound); + + // Initialization + void InitRegister(const HashMap>& register2lifetime); + void InitRegisterInformation(const HashMap& mem_reused_regst2size); + // Adjust the original strategy, return the updated optimal cost + size_t ComputeOptimalAdjustedCost(); + // Eliminate one register + void EliminateRegister(int32_t i); + // Eliminate children of j but ignore i. + void EliminateRedundantRelationshipIgnore(int32_t i, int32_t j); + // Whether i and j occurs simultaneously + bool Exclude(int32_t i, int32_t j); + // If the previous strategy without the elimination of i has fewer cost, recover to the previous + // one from the backup. + void RecoverFromBackup(int32_t i); + // Clear backup + void ClearBackup(); + // Let x_i occupy some space [x_i, x_i + l_i), then we recompute the optimal cost + size_t ComputeOptimalCostWithOccupation(int32_t i, int64_t x_i, + const std::vector& register_offset_backup); + // Insert register i at position [x_i, x_i + l_i) + void InsertRegister(int32_t i, int64_t x_i, const std::vector& original_register_offset); + + // Compute optimal cost with compact relationship + size_t ComputeOptimalCost4CompactRelationship(); + size_t ComputeOptimalCostFrom0(); + // Compute offset with compact relationship + int64_t ComputeOffset4CompactRelationship(int32_t i); + // Check whether the current offset does not introduce any conflict + Maybe CheckConflict(); + // Reset the compact position for the registers with should_visit_ = 0 + void ResetCompactPosition(int32_t j); + // Find all the k < i, eliminates k < j, + // since k < i and i < j have already implied that. + void EliminateRedundantRelationship(int32_t i); +}; +} // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_MEMORY_SHARE_STRATEGY_H_ diff --git a/oneflow/core/job/mirrored_parallel.proto b/oneflow/core/job/mirrored_parallel.proto deleted file mode 100644 index cfa27e08ec5ea6e0a983fd78f93e80fce4333233..0000000000000000000000000000000000000000 --- a/oneflow/core/job/mirrored_parallel.proto +++ /dev/null @@ -1,13 +0,0 @@ -syntax = "proto2"; -package oneflow; - -message MirroredParallel { -} - -message OptMirroredParallel { - optional MirroredParallel mirrored_parallel = 1; -} - -message MirroredSignature { - map bn_in_op2opt_mirrored_parallel = 1; -} diff --git a/oneflow/core/job/mirrored_sig_infer_hint.h b/oneflow/core/job/mirrored_sig_infer_hint.h deleted file mode 100644 index 1c5d12bba89ab3006dc277e9d00a44e7278201c6..0000000000000000000000000000000000000000 --- a/oneflow/core/job/mirrored_sig_infer_hint.h +++ /dev/null @@ -1,38 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ -#define ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ - -#include "oneflow/core/job/parallel_desc.h" - -namespace oneflow { - -class MirroredSigInferHint final { - public: - MirroredSigInferHint(const ParallelDesc* parallel_desc, bool is_mirrored_parallel_view) - : parallel_desc_(parallel_desc), is_mirrored_parallel_view_(is_mirrored_parallel_view) {} - - const ParallelDesc& parallel_desc() const { return *parallel_desc_; } - bool is_mirrored_parallel_view() const { return is_mirrored_parallel_view_; } - - private: - const ParallelDesc* parallel_desc_; - bool is_mirrored_parallel_view_; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_JOB_MIRRORED_SIG_INFER_HINT_H_ diff --git a/oneflow/core/job/nd_sbp_util.cpp b/oneflow/core/job/nd_sbp_util.cpp index 9726e5e902b83bcbc05d10800373a58044676577..78409fbf140395ff34a9a9365806043af14b3917 100644 --- a/oneflow/core/job/nd_sbp_util.cpp +++ b/oneflow/core/job/nd_sbp_util.cpp @@ -19,48 +19,6 @@ limitations under the License. #include "oneflow/core/common/nd_index_offset_helper.h" namespace oneflow { -namespace { -// Go through all the ranks while transfer between two nd sbps with no PartialSum under the same -// placement. -// NOTE: We need to make sure no partial sums in the sbps of the producer and consumer. -void DfsTraverseRanks4NdSbp( - int32_t depth, std::vector& in_parallel_ids, - const std::vector& out_parallel_ids, const Shape& parallel_hierarchy, - const NdIndexOffsetHelper& hierarchy_index_helper, - const NdSbp& in_nd_sbp, const std::function& visit) { - if (depth >= parallel_hierarchy.NumAxes()) { - visit(hierarchy_index_helper.NdIndexToOffset(in_parallel_ids.data(), - parallel_hierarchy.NumAxes())); - return; - } - if (in_nd_sbp.sbp_parallel(depth).has_broadcast_parallel()) { - // If Broadcast in the sbp of the producer, only visit those ranks with the same id as the - // current rank along the depth-dimension. - in_parallel_ids[depth] = out_parallel_ids[depth]; - DfsTraverseRanks4NdSbp(depth + 1, in_parallel_ids, out_parallel_ids, parallel_hierarchy, - hierarchy_index_helper, in_nd_sbp, visit); - } else { - // If Split or PartialSum, go through all the ranks along the depth-dimension. - for (int64_t i = 0; i < parallel_hierarchy.dim_vec().at(depth); i++) { - in_parallel_ids[depth] = i; - DfsTraverseRanks4NdSbp(depth + 1, in_parallel_ids, out_parallel_ids, parallel_hierarchy, - hierarchy_index_helper, in_nd_sbp, visit); - } - } -} - -void DfsTraverse4NdSbp(int64_t recv_id, const std::shared_ptr& parallel_hierarchy, - const NdSbp& in_nd_sbp, const std::function& visit) { - int32_t hierarchy_dimension = parallel_hierarchy->NumAxes(); - const NdIndexOffsetHelper hierarchy_index_helper( - parallel_hierarchy->dim_vec().data(), hierarchy_dimension); - std::vector in_parallel_ids(hierarchy_dimension); - std::vector out_parallel_ids(hierarchy_dimension); - hierarchy_index_helper.OffsetToNdIndex(recv_id, out_parallel_ids.data(), hierarchy_dimension); - DfsTraverseRanks4NdSbp(0, in_parallel_ids, out_parallel_ids, *parallel_hierarchy, - hierarchy_index_helper, in_nd_sbp, visit); -} -} // namespace std::vector GetTensorSliceView(const int64_t parallel_num, const SbpParallel& sbp_parallel, @@ -105,10 +63,6 @@ TensorSliceView GetTensorSliceView4ParallelRank(const Shape& parallel_hierarchy, ranges[i].mut_begin() = 0; ranges[i].mut_end() = logical_shape.At(i); } - if (logical_shape.NumAxes() == 0) { - // NOTE(chengcheng): For Scalar Tensor. - ranges.emplace_back(0, 1); - } if (parallel_hierarchy.elem_cnt() == 1) { return TensorSliceView(ranges); } if (parallel_hierarchy.NumAxes() == 1) { const SbpParallel& sbp_parallel = nd_sbp.sbp_parallel(0); @@ -124,15 +78,19 @@ TensorSliceView GetTensorSliceView4ParallelRank(const Shape& parallel_hierarchy, ranges[split_axis] = bs.At(id); } } else { + Shape physical_shape(logical_shape); FOR_RANGE(int64_t, i, 0, parallel_hierarchy.NumAxes()) { const SbpParallel& sbp_parallel = nd_sbp.sbp_parallel(i); if (sbp_parallel.has_split_parallel()) { const int64_t split_axis = sbp_parallel.split_parallel().axis(); CHECK_GE(split_axis, 0); CHECK_LT(split_axis, ranges.size()); - CHECK_EQ(ranges[split_axis].size() % parallel_hierarchy.At(i), 0); - const int64_t range_size = ranges[split_axis].size() / parallel_hierarchy.At(i); - const int64_t dim_start = ranges[split_axis].begin() + parallel_rank.at(i) * range_size; + CHECK_GE(ranges[split_axis].size(), parallel_hierarchy.At(i)); + const BalancedSplitter bs(physical_shape.At(split_axis), parallel_hierarchy.At(i)); + const auto& range = bs.At(parallel_rank.at(i)); + const int64_t range_size = range.size(); + const int64_t dim_start = ranges[split_axis].begin() + range.begin(); + physical_shape.Set(split_axis, range_size); ranges[split_axis].mut_begin() = dim_start; ranges[split_axis].mut_end() = dim_start + range_size; } @@ -203,45 +161,4 @@ bool NdSbpIsAllSplit(const NdSbp& nd_sbp, int64_t axis) { return true; } -void GetRankSendRecvIntersection(int64_t parallel_id, - const std::shared_ptr& parallel_hierarchy, - const NdSbp& src_nd_sbp, const NdSbp& dst_nd_sbp, - const Shape& logical_shape, - std::vector* send_intersections, - std::vector* recv_intersections) { - CHECK(parallel_hierarchy != nullptr); - const int64_t parallel_num = parallel_hierarchy->elem_cnt(); - CHECK_LT(parallel_id, parallel_num); - - const std::vector& in_slices = - GetTensorSliceView(*parallel_hierarchy, src_nd_sbp, logical_shape); - const std::vector& out_slices = - GetTensorSliceView(*parallel_hierarchy, dst_nd_sbp, logical_shape); - - // cur rank recv from - recv_intersections->resize(parallel_num); - const TensorSliceView& cur_rank_out_slice = out_slices.at(parallel_id); - const auto& add_to_recv_intersections = [&](int32_t send_id) { - const TensorSliceView& in_slice = in_slices.at(send_id); - const TensorSliceView& intersection = cur_rank_out_slice.Intersect(in_slice); - if (intersection.IsEmpty()) { return; } - recv_intersections->at(send_id) = intersection; - }; - DfsTraverse4NdSbp(parallel_id, parallel_hierarchy, src_nd_sbp, add_to_recv_intersections); - - // cur rank send to - send_intersections->resize(parallel_num); - const TensorSliceView& cur_rank_in_slice = in_slices.at(parallel_id); - for (int64_t recv_i = 0; recv_i < parallel_num; ++recv_i) { - const auto& add_to_send_intersections = [&](int32_t send_id) { - if (send_id != parallel_id) { return; } - const TensorSliceView& out_slice = out_slices.at(recv_i); - const TensorSliceView& intersection = out_slice.Intersect(cur_rank_in_slice); - if (intersection.IsEmpty()) { return; } - send_intersections->at(recv_i) = intersection; - }; - DfsTraverse4NdSbp(recv_i, parallel_hierarchy, src_nd_sbp, add_to_send_intersections); - } -} - } // namespace oneflow diff --git a/oneflow/core/job/nd_sbp_util.h b/oneflow/core/job/nd_sbp_util.h index 7eac44a52fc1456deb3572b61c75bc8c86950a5f..be8b72c774651d56e144d881cd127d3117310a09 100644 --- a/oneflow/core/job/nd_sbp_util.h +++ b/oneflow/core/job/nd_sbp_util.h @@ -39,12 +39,6 @@ bool NdSbpIsAllSplit(const NdSbp& nd_sbp, int64_t axis); bool NdSbpHasPartialParallel(const NdSbp& nd_sbp); bool NdSbpHasBroadcastParallel(const NdSbp& nd_sbp); -void GetRankSendRecvIntersection(int64_t parallel_id, - const std::shared_ptr& parallel_hierarchy, - const NdSbp& src_nd_sbp, const NdSbp& dst_nd_sbp, - const Shape& logical_shape, - std::vector* send_intersections, - std::vector* recv_intersections); } // namespace oneflow #endif // ONEFLOW_CORE_JOB_SBP_PARALLEL_H_ diff --git a/oneflow/core/job/oneflow.cpp b/oneflow/core/job/oneflow.cpp index 66d2923c5bb18ad1c778ecb72c4e42f6bbe2bc70..6ba4871fa2d395695fbd97843fcd92bd6196c9d7 100644 --- a/oneflow/core/job/oneflow.cpp +++ b/oneflow/core/job/oneflow.cpp @@ -817,82 +817,6 @@ void FinishGlobalCriticalSectionDesc(const Plan& plan, int64_t job_size) { critical_section_desc->Done(); } -void MakePullJob(const std::string& job_name, const std::string& op_name, - const ParallelBlobConf& parallel_blob_conf, Job* job) { - auto* flag_name2flag_value = job->mutable_job_conf()->mutable_flag_name2flag_value(); - (*flag_name2flag_value)["__is_user_function__"].set_at_bool(false); - auto* op_name2job_name = - Singleton::Get()->mutable_output_or_var_op_name2pull_job_name(); - CHECK(op_name2job_name->find(op_name) == op_name2job_name->end()); - (*op_name2job_name)[op_name] = job_name; - DataType data_type; - JobBuilder job_builder(job); - OperatorConf input_op_conf; - { - input_op_conf.set_name(op_name); - auto* input_conf = input_op_conf.mutable_input_conf(); - input_conf->set_out("out"); - auto* blob_conf = input_conf->mutable_blob_conf(); - CHECK_JUST(InterfaceOpUtil::InitBlobConf(blob_conf, parallel_blob_conf)); - data_type = blob_conf->data_type(); - job_builder.AddOps(parallel_blob_conf.parallel_conf(), {input_op_conf}); - } - OperatorConf foreign_output_op_conf; - { - foreign_output_op_conf.set_name(std::string("System-Pull-ForeignOutput_") + NewUniqueId()); - auto* foreign_output_conf = foreign_output_op_conf.mutable_foreign_output_conf(); - foreign_output_conf->set_in(input_op_conf.name() + "/out"); - foreign_output_conf->set_ofblob_buffer_name(GetForeignOutputBufferName(job_name)); - ParallelConf parallel_conf; - parallel_conf.set_device_tag("cpu"); - parallel_conf.add_device_name("0:0"); - job_builder.AddOps(parallel_conf, {foreign_output_op_conf}); - } - auto* job_conf = job->mutable_job_conf(); - job_conf->set_job_name(job_name); - job_conf->mutable_predict_conf(); - job_conf->set_default_data_type(data_type); -} - -void MakePushJob(const std::string& job_name, const std::string& op_name, - const ParallelBlobConf& parallel_blob_conf, Job* job) { - auto* flag_name2flag_value = job->mutable_job_conf()->mutable_flag_name2flag_value(); - (*flag_name2flag_value)["__is_user_function__"].set_at_bool(false); - auto* op_name2job_name = - Singleton::Get()->mutable_input_or_var_op_name2push_job_name(); - CHECK(op_name2job_name->find(op_name) == op_name2job_name->end()); - (*op_name2job_name)[op_name] = job_name; - DataType data_type; - JobBuilder job_builder(job); - OperatorConf foreign_input_op_conf; - { - foreign_input_op_conf.set_name(std::string("System-Push-ForeignInput_") + NewUniqueId()); - auto* foreign_input_conf = foreign_input_op_conf.mutable_foreign_input_conf(); - foreign_input_conf->set_out("out"); - foreign_input_conf->set_ofblob_buffer_name(GetForeignInputBufferName(job_name)); - auto* blob_conf = foreign_input_conf->mutable_blob_conf(); - CHECK_JUST(InterfaceOpUtil::InitBlobConf(blob_conf, parallel_blob_conf)); - data_type = blob_conf->data_type(); - ParallelConf parallel_conf; - parallel_conf.set_device_tag("cpu"); - parallel_conf.add_device_name("0:0"); - job_builder.AddOps(parallel_conf, {foreign_input_op_conf}); - } - OperatorConf output_op_conf; - { - output_op_conf.set_name(op_name); - auto* output_conf = output_op_conf.mutable_output_conf(); - output_conf->set_in(foreign_input_op_conf.name() + "/out"); - output_conf->set_out("out"); - CHECK_JUST(InterfaceOpUtil::InitBlobConf(output_conf->mutable_blob_conf(), parallel_blob_conf)); - job_builder.AddOps(parallel_blob_conf.parallel_conf(), {output_op_conf}); - } - auto* job_conf = job->mutable_job_conf(); - job_conf->set_job_name(job_name); - job_conf->mutable_predict_conf(); - job_conf->set_default_data_type(data_type); -} - REGISTER_FUNCTION_CONFIG_DEF().Bool("__is_user_function__", true, "is user defined function"); Maybe CompileJobsAndMergePlans(const PbRpf& job_confs, Plan& plan) { @@ -910,22 +834,6 @@ Maybe CompileJobsAndMergePlans(const PbRpf& job_confs, Plan& plan) { JobDesc job_desc(jobs.at(i)->job_conf(), i); if (job_desc.Bool("__is_user_function__")) { function_jobs.emplace_back(jobs.at(i)); } } - HashMap push_op_name2parallel_blob_conf; - FilterOpName2ParallelBlobConf({OperatorConf::kInputConf}, function_jobs, - &push_op_name2parallel_blob_conf); - HashMap pull_op_name2parallel_blob_conf; - FilterOpName2ParallelBlobConf({OperatorConf::kReturnConf}, function_jobs, - &pull_op_name2parallel_blob_conf); - for (const auto& pair : push_op_name2parallel_blob_conf) { - auto push_job = std::make_shared(); - MakePushJob(std::string("System-Push-") + pair.first, pair.first, pair.second, push_job.get()); - jobs.emplace_back(push_job); - } - for (const auto& pair : pull_op_name2parallel_blob_conf) { - auto pull_job = std::make_shared(); - MakePullJob(std::string("System-Pull-") + pair.first, pair.first, pair.second, pull_job.get()); - jobs.emplace_back(pull_job); - } std::vector sub_plans(jobs.size()); FOR_RANGE(int64_t, i, 0, jobs.size()) { @@ -991,10 +899,6 @@ Maybe Oneflow::Init(const oneflow::JobSet& job_set) { runtime_buffers_scope_.reset(new RuntimeBuffersScope(plan_.job_confs())); } OF_PROFILER_RANGE_PUSH("new Runtime"); - if (Singleton::Get()->enable_dry_run()) { - LOG(ERROR) << "this is dry run, exiting"; - exit(0); - } HashMap variable_op_name2eager_blob_object; runtime_.reset(new Runtime(plan_, variable_op_name2eager_blob_object)); diff --git a/oneflow/core/job/parallel_desc.cpp b/oneflow/core/job/parallel_desc.cpp index b0c20c2c0561fa0e5b968885babed4cd37dd72eb..574bdafab4f402ec7196fd42f1577d32f2344bf6 100644 --- a/oneflow/core/job/parallel_desc.cpp +++ b/oneflow/core/job/parallel_desc.cpp @@ -385,6 +385,17 @@ ParallelConf GenParallelConfOfCpuZeroOnAllMachines() { return parallel_conf; } +ParallelConf GenParallelConfOfCpuOnAllRanks() { + ParallelConf parallel_conf; + parallel_conf.set_device_tag("cpu"); + int64_t node_size = GlobalProcessCtx::NodeSize(); + int64_t device_num = GlobalProcessCtx::NumOfProcessPerNode(); + for (int64_t node_id = 0; node_id < node_size; ++node_id) { + parallel_conf.add_device_name(std::to_string(node_id) + ":0-" + std::to_string(device_num - 1)); + } + return parallel_conf; +} + namespace { Maybe> CalcParallelId4CurrentProcessCtx(Symbol parallel_desc) { @@ -477,6 +488,14 @@ Maybe RawCheckDeviceIdsIsValid(Symbol placement) { return Maybe::Ok(); } +Maybe> RawGetParallelDescOfThisRank(const std::string& device_tag) { + ParallelConf parallel_conf; + parallel_conf.set_device_tag(device_tag); + parallel_conf.add_device_name(std::to_string(GlobalProcessCtx::Rank()) + ":" + + std::to_string(GlobalProcessCtx::LocalRank())); + return SymbolOf(ParallelDesc(parallel_conf)); +} + } // namespace decltype(GetParallelId4CurrentProcessCtx) GetParallelId4CurrentProcessCtx = @@ -488,6 +507,8 @@ decltype(PlacementToString) PlacementToString = DECORATE(&RawPlacementToString, decltype(GetTensorDevice) GetTensorDevice = DECORATE(&RawGetTensorDevice, ThreadLocal); decltype(TxtStringToPlacement) TxtStringToPlacement = DECORATE(&RawTxtStringToPlacement, ThreadLocalCopiable); +decltype(GetParallelDescOfThisRank) GetParallelDescOfThisRank = + DECORATE(&RawGetParallelDescOfThisRank, ThreadLocalCopiable); decltype(CheckDeviceIdsIsValid) CheckDeviceIdsIsValid = DECORATE(&RawCheckDeviceIdsIsValid, ThreadLocal); diff --git a/oneflow/core/job/parallel_desc.h b/oneflow/core/job/parallel_desc.h index 3e9f1b964c0e43137ca7a7f2c7e5df5ed8015fa0..f343ac3d083a2f8382740625af3187459018905c 100644 --- a/oneflow/core/job/parallel_desc.h +++ b/oneflow/core/job/parallel_desc.h @@ -146,6 +146,8 @@ extern Maybe> (*GetTensorDevice)(Symbol parallel_de extern Maybe> (*TxtStringToPlacement)(const std::string& parallel_conf_str); extern Maybe (*CheckDeviceIdsIsValid)(Symbol placement); +extern Maybe> (*GetParallelDescOfThisRank)(const std::string& device_tag); + inline bool operator==(const ParallelConf& lhs, const ParallelConf& rhs) { return ParallelDesc(lhs) == ParallelDesc(rhs); } @@ -159,6 +161,7 @@ std::tuple GetPartIdAndPartNumFromParallelCtx( ParallelConf GenParallelConfOfCpuZeroOnMaster(); ParallelConf GenParallelConfOfCpuZeroOnAllMachines(); +ParallelConf GenParallelConfOfCpuOnAllRanks(); namespace private_details { @@ -176,16 +179,16 @@ namespace std { template<> struct hash { size_t operator()(const oneflow::ParallelDesc& pr) const { + using namespace oneflow; size_t ret = 0; int i = 0; int shift_roundtrip = (sizeof(size_t) / 2); for (int machine_id : pr.sorted_machine_ids()) { int shift = i++ % shift_roundtrip; - ret ^= machine_id << shift_roundtrip << shift; - ret ^= pr.sorted_dev_phy_ids(machine_id).size() << shift; + AddHash(&ret, machine_id << shift_roundtrip << shift); + AddHash(&ret, pr.sorted_dev_phy_ids(machine_id).size() << shift); } - const auto& shape_hash = std::hash(); - ret ^= shape_hash(*pr.hierarchy()); + AddHash(&ret, *pr.hierarchy()); return hash()(ret); } }; diff --git a/oneflow/core/job/placement_scope.h b/oneflow/core/job/placement_scope.h index 130955c501d1b2d4b92f909568f52b6f688706b7..caf480177fd100b877af4a3b9fc37042c1c8fedd 100644 --- a/oneflow/core/job/placement_scope.h +++ b/oneflow/core/job/placement_scope.h @@ -29,10 +29,7 @@ class PlacementScope final { PlacementScope(Symbol device_parallel_desc, Symbol host_parallel_desc) : device_parallel_desc_(device_parallel_desc), host_parallel_desc_(host_parallel_desc) {} - size_t hash_value() const { - const auto& hash_functor = std::hash>(); - return hash_functor(device_parallel_desc_) ^ hash_functor(host_parallel_desc_); - } + size_t hash_value() const { return Hash(device_parallel_desc_, host_parallel_desc_); } bool operator==(const PlacementScope& other) const { return this->device_parallel_desc_ == other.device_parallel_desc_ diff --git a/oneflow/core/job/plan_util.cpp b/oneflow/core/job/plan_util.cpp index 8d0f3bb647ba24a2f2e40dcfaf65e6f31cbe8178..c35db55162ed766fd03406c1c600e8aeddb0f7b5 100644 --- a/oneflow/core/job/plan_util.cpp +++ b/oneflow/core/job/plan_util.cpp @@ -26,6 +26,8 @@ limitations under the License. #include "oneflow/core/register/runtime_register_desc.h" #include "oneflow/core/persistence/tee_persistent_log_stream.h" #include "oneflow/core/ep/include/device_manager_registry.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/graph/task_node.h" namespace oneflow { @@ -51,9 +53,50 @@ std::function PlanUtil::MakeGetterTaskProto4TaskId(co return [task_id2task_proto](int64_t task_id) { return task_id2task_proto->at(task_id); }; } +namespace { + +void SetVariableOpNamesForVariableAndRepeatRegst(Plan* plan) { + // NOTE(chengcheng): set variable_op_name before set separated header because var regst alway + // separated. + HashMap regst_id2var_name; + for (int i = 0; i < plan->task_size(); i++) { + TaskProto* task = plan->mutable_task(i); + if (task->exec_sequence().exec_node_size() == 1) { + const auto& op_conf = + PlanUtil::GetOpAttribute(plan, task->job_id(), + task->exec_sequence().exec_node(0).kernel_conf()) + .op_conf(); + if (op_conf.has_variable_conf()) { + RegstDescProto* regst = PlanUtil::GetSoleProducedDataRegst(task); + regst_id2var_name.emplace(regst->regst_desc_id(), op_conf.name()); + regst->set_variable_op_name(op_conf.name()); + } + } + } + + for (int i = 0; i < plan->task_size(); i++) { + TaskProto* task = plan->mutable_task(i); + if (task->task_type() == TaskType::kRepeat) { + RegstDescProto* regst = PlanUtil::GetSoleProducedDataRegst(task); + CHECK(regst->has_force_inplace_consumed_regst_desc_id()); + int64_t force_inplace_regst_id = regst->force_inplace_consumed_regst_desc_id(); + auto var_name_it = regst_id2var_name.find(force_inplace_regst_id); + if (var_name_it != regst_id2var_name.end()) { + regst->set_variable_op_name(var_name_it->second); + VLOG(3) << " set var op name to repeat regst : " << regst->DebugString(); + } + } + } +} + +} // namespace + void PlanUtil::SetUniqueMemBlockId4UnreusedMemRegst(Plan* plan) { + SetVariableOpNamesForVariableAndRepeatRegst(plan); + for (int i = 0; i < plan->task_size(); i++) { TaskProto* task = plan->mutable_task(i); + for (auto& pair : *task->mutable_produced_regst_desc()) { RegstDescProto* regst_desc = &pair.second; if (regst_desc->mem_block_id() == -1) { @@ -61,6 +104,13 @@ void PlanUtil::SetUniqueMemBlockId4UnreusedMemRegst(Plan* plan) { regst_desc->set_mem_block_id(Singleton::Get()->NewMemBlockId()); regst_desc->set_mem_block_offset(0); } + + RtRegstDesc rt_regst_desc(*regst_desc); + int64_t regst_separated_size = rt_regst_desc.TotalSeparatedHeaderByteSize4AllRegst(); + if (regst_separated_size > 0) { + int64_t separated_mem_block_id = Singleton::Get()->NewMemBlockId(); + regst_desc->set_separated_header_mem_block_id(separated_mem_block_id); + } } } } @@ -85,9 +135,9 @@ void GenChunkForMultiNNGraphMemoryReuseInMultiClient( // NOTE(chengcheng): // only reused mem in cuda device. // special cpu memory like OFRecord pb and TensorBuffer CANNOT reused by another plan. - if (mem_block->mem_case().has_host_mem()) { continue; } + if (memory::IsHostMem(mem_block->mem_case())) { continue; } int64_t mem_zone_uid = - MemoryCaseUtil::GenMemZoneUniqueId(mem_block->machine_id(), mem_block->mem_case()); + memory::GetUniqueMemCaseId(mem_block->machine_id(), mem_block->mem_case()); auto it = mzuid2mem_blocks.find(mem_zone_uid); if (it == mzuid2mem_blocks.end()) { it = mzuid2mem_blocks.emplace(mem_zone_uid, HashSet()).first; @@ -187,6 +237,94 @@ void GenChunkForMultiNNGraphMemoryReuseInMultiClient( } // namespace +void PlanUtil::MergeMemBlockIdByLogicalChainId(Plan* plan, const Job& job) { + if (job.logical_chain_groups_size() == 0) { return; } + HashMap> logical_chain_id2machine_id2mem_block_id; + + for (int64_t i = 0; i < plan->task_size(); ++i) { + TaskProto* task = plan->mutable_task(i); + const StreamId stream_id = PlanUtil::GetStreamId(*task); + int64_t machine_id = task->machine_id(); + DeviceType device_type = stream_id.device_id().device_type(); + // TODO(zwx): eliminate this special 'is cpu' determine + if (device_type == DeviceType::kCPU) { continue; } + if (!IsValidChainId(task->task_set_info().chain_id())) { continue; } + int64_t logical_chain_id = task->task_set_info().chain_id(); + + for (auto& pair : *(task->mutable_produced_regst_desc())) { + RegstDescProto* regst_desc = &pair.second; + if (regst_desc->mem_block_id() != -1 && regst_desc->enable_reuse_mem() + && regst_desc->mem_case().device_type() == device_type + && regst_desc->regst_desc_type().has_data_regst_desc()) { + int64_t mem_block_id = regst_desc->mem_block_id(); + auto* rank2blocks = &(logical_chain_id2machine_id2mem_block_id[logical_chain_id]); + if (rank2blocks->find(machine_id) == rank2blocks->end()) { + rank2blocks->emplace(machine_id, mem_block_id); + } else { + CHECK_EQ(rank2blocks->at(machine_id), mem_block_id); + } + } + } + } + + HashMap mem_block_id2merged_mem_block_id; + for (const auto& logical_chain_group : job.logical_chain_groups()) { + CHECK_GE(logical_chain_group.logical_chain_id_list_size(), 2); + int64_t merged_logical_chain_id = logical_chain_group.logical_chain_id_list(0); + CHECK(logical_chain_id2machine_id2mem_block_id.find(merged_logical_chain_id) + != logical_chain_id2machine_id2mem_block_id.end()); + const auto& merged_rank2block = + logical_chain_id2machine_id2mem_block_id.at(merged_logical_chain_id); + for (int64_t i = 1; i < logical_chain_group.logical_chain_id_list_size(); ++i) { + int64_t this_logical_chain_id = logical_chain_group.logical_chain_id_list(i); + // NOTE(chengcheng): merge mem block id by each rank + CHECK(logical_chain_id2machine_id2mem_block_id.find(this_logical_chain_id) + != logical_chain_id2machine_id2mem_block_id.end()); + const auto& this_rank2block = + logical_chain_id2machine_id2mem_block_id.at(this_logical_chain_id); + for (const auto& pair : this_rank2block) { + int64_t this_machine_id = pair.first; + int64_t this_mem_block_id = pair.second; + CHECK(merged_rank2block.find(this_machine_id) != merged_rank2block.end()); + int64_t merged_mem_block_id = merged_rank2block.at(this_machine_id); + CHECK(mem_block_id2merged_mem_block_id.emplace(this_mem_block_id, merged_mem_block_id) + .second); + VLOG(2) << " merge mem_block_id: " << this_mem_block_id << " to " << merged_mem_block_id; + } + } + } + + for (int64_t i = 0; i < plan->task_size(); ++i) { + TaskProto* task = plan->mutable_task(i); + const StreamId stream_id = PlanUtil::GetStreamId(*task); + DeviceType device_type = stream_id.device_id().device_type(); + // TODO(zwx): eliminate this special 'is cpu' determine + if (device_type == DeviceType::kCPU) { continue; } + if (!IsValidChainId(task->task_set_info().chain_id())) { continue; } + + for (auto& pair : *(task->mutable_produced_regst_desc())) { + RegstDescProto* regst_desc = &pair.second; + if (regst_desc->mem_block_id() != -1 && regst_desc->enable_reuse_mem() + && regst_desc->mem_case().device_type() == device_type + && regst_desc->regst_desc_type().has_data_regst_desc()) { + int64_t mem_block_id = regst_desc->mem_block_id(); + if (mem_block_id2merged_mem_block_id.find(mem_block_id) + != mem_block_id2merged_mem_block_id.end()) { + // merge mem_block_id + int64_t merged_mem_block_id = mem_block_id2merged_mem_block_id.at(mem_block_id); + regst_desc->set_mem_block_id(merged_mem_block_id); + const auto& data_regst = regst_desc->regst_desc_type().data_regst_desc(); + CHECK_GE(data_regst.lbi2blob_desc_size(), 1); + const auto& lbi2blob_desc_pair = data_regst.lbi2blob_desc(0); + std::string tensor_name = GenLogicalBlobName(lbi2blob_desc_pair.lbi()); + VLOG(3) << " regst: " << tensor_name << " merge mem block id " << mem_block_id << " to " + << merged_mem_block_id; + } + } + } + } +} + void PlanUtil::GenMemBlockAndChunkWithVariableOpNames4Plan( Plan* plan, const HashSet& variable_op_names) { HashMap> mem_block_id2mem_block; @@ -217,7 +355,6 @@ void PlanUtil::GenMemBlockAndChunkWithVariableOpNames4Plan( int64_t mem_block_offset = regst_desc->mem_block_offset(); CHECK_NE(mem_block_id, -1); CHECK_NE(mem_block_offset, -1); - CHECK_EQ(regst_desc->separated_header_mem_block_id(), -1); std::string var_name; bool is_variable_regst = IsVariableRegst(task, &var_name); @@ -234,7 +371,8 @@ void PlanUtil::GenMemBlockAndChunkWithVariableOpNames4Plan( int64_t regst_main_size = rt_regst_desc.TotalMainByteSize4AllRegst(); int64_t regst_separated_size = rt_regst_desc.TotalSeparatedHeaderByteSize4AllRegst(); - if (mem_block_id2mem_block.find(mem_block_id) == mem_block_id2mem_block.end()) { + auto mem_block_it = mem_block_id2mem_block.find(mem_block_id); + if (mem_block_it == mem_block_id2mem_block.end()) { MemBlockProto mem_block; mem_block.set_mem_block_id(mem_block_id); mem_block.add_job_id(job_id); @@ -251,34 +389,56 @@ void PlanUtil::GenMemBlockAndChunkWithVariableOpNames4Plan( .emplace(mem_block.mem_block_id(), std::make_unique(mem_block)) .second); } else { - MemBlockProto* mem_block = mem_block_id2mem_block.at(mem_block_id).get(); - CHECK(!mem_block->has_variable_op_name()); // variable regst mem block is unique. + MemBlockProto* mem_block = mem_block_it->second.get(); CHECK_EQ(mem_block->job_id(0), job_id); CHECK_EQ(mem_block->machine_id(), machine_id); CHECK(mem_block->mem_case() == regst_desc->mem_case()); CHECK_EQ(mem_block->enable_reuse_mem(), regst_desc->enable_reuse_mem()); - mem_block->set_mem_size(std::max(mem_block->mem_size(), regst_main_size + mem_block_offset)); + if (mem_block->enable_reuse_mem()) { + mem_block->set_mem_size( + std::max(mem_block->mem_size(), regst_main_size + mem_block_offset)); + } else { + CHECK_EQ(mem_block->mem_size(), regst_main_size); + CHECK_EQ(mem_block_offset, 0); + } + if (is_variable_regst) { + mem_block->set_variable_op_name(var_name); + mem_block->set_is_separated_header(false); + } } if (regst_separated_size > 0) { - int64_t separated_mem_block_id = Singleton::Get()->NewMemBlockId(); - regst_desc->set_separated_header_mem_block_id(separated_mem_block_id); - MemBlockProto mem_block; - mem_block.set_mem_block_id(separated_mem_block_id); - mem_block.add_job_id(job_id); - mem_block.set_machine_id(machine_id); - *(mem_block.mutable_mem_case()) = - MemoryCaseUtil::GetHostMemoryCaseForRegstSeparatedHeader(regst_desc->mem_case()); - mem_block.set_enable_reuse_mem(false); - mem_block.set_mem_size(regst_separated_size); - mem_block.set_thrd_id_hint(thrd_id); - if (is_variable_regst) { - mem_block.set_variable_op_name(var_name); - mem_block.set_is_separated_header(true); + CHECK(regst_desc->has_separated_header_mem_block_id()) << regst_desc->DebugString(); + int64_t separated_mem_block_id = regst_desc->separated_header_mem_block_id(); + CHECK_NE(separated_mem_block_id, -1); + if (mem_block_id2mem_block.find(separated_mem_block_id) == mem_block_id2mem_block.end()) { + MemBlockProto mem_block; + mem_block.set_mem_block_id(separated_mem_block_id); + mem_block.add_job_id(job_id); + mem_block.set_machine_id(machine_id); + *(mem_block.mutable_mem_case()) = memory::GetPinnedHostMemoryCase(regst_desc->mem_case()); + mem_block.set_enable_reuse_mem(false); + mem_block.set_mem_size(regst_separated_size); + mem_block.set_thrd_id_hint(thrd_id); + if (is_variable_regst) { + mem_block.set_variable_op_name(var_name); + mem_block.set_is_separated_header(true); + } + CHECK(mem_block_id2mem_block + .emplace(mem_block.mem_block_id(), std::make_unique(mem_block)) + .second); + } else { + MemBlockProto* mem_block = mem_block_id2mem_block.at(separated_mem_block_id).get(); + CHECK_EQ(mem_block->job_id(0), job_id); + CHECK_EQ(mem_block->machine_id(), machine_id); + CHECK(mem_block->mem_case() == memory::GetPinnedHostMemoryCase(regst_desc->mem_case())); + CHECK_EQ(mem_block->enable_reuse_mem(), false); + CHECK_EQ(mem_block->mem_size(), regst_separated_size); + if (is_variable_regst) { + mem_block->set_variable_op_name(var_name); + mem_block->set_is_separated_header(true); + } } - CHECK(mem_block_id2mem_block - .emplace(mem_block.mem_block_id(), std::make_unique(mem_block)) - .second); } }; @@ -345,8 +505,7 @@ void PlanUtil::CleanUselessMemBlockAndCheckValid(Plan* plan) { const MemBlockProto& header_mem_block = mem_block_id2mem_block.at(header_block_id); CHECK_EQ(header_mem_block.mem_size(), separated_header_mem_size); CHECK_EQ(task.machine_id(), header_mem_block.machine_id()); - CHECK(header_mem_block.mem_case() - == MemoryCaseUtil::GetHostMemoryCaseForRegstSeparatedHeader(regst.mem_case())); + CHECK(header_mem_block.mem_case() == memory::GetPinnedHostMemoryCase(regst.mem_case())); CHECK(header_mem_block.enable_reuse_mem() == false); const auto& header_block_job_ids = mem_block_id2job_ids[header_block_id]; CHECK(header_block_job_ids.find(task.job_id()) != header_block_job_ids.end()); @@ -657,8 +816,22 @@ void PlanUtil::SetForceInplaceMemBlock(Plan* plan) { CHECK_EQ(in_regst_desc->mem_block_offset(), 0); CHECK_EQ(regst_desc->mem_block_offset(), 0); CHECK_EQ(in_regst_desc->register_num(), regst_desc->register_num()); + CHECK(in_regst_desc->mem_case() == regst_desc->mem_case()); + RtRegstDesc in_regst_rt(*in_regst_desc); + RtRegstDesc regst_rt(*regst_desc); + CHECK_EQ(in_regst_rt.TotalByteSize4AllRegst(), regst_rt.TotalByteSize4AllRegst()); + CHECK_EQ(in_regst_rt.TotalMainByteSize4AllRegst(), regst_rt.TotalMainByteSize4AllRegst()); + CHECK_EQ(in_regst_rt.TotalSeparatedHeaderByteSize4AllRegst(), + regst_rt.TotalSeparatedHeaderByteSize4AllRegst()); regst_desc->set_mem_block_id(in_regst_desc->mem_block_id()); regst_desc->set_inplace_consumed_regst_desc_id(force_id); + if (in_regst_desc->has_separated_header_mem_block_id()) { + CHECK(regst_desc->has_separated_header_mem_block_id()); + regst_desc->set_separated_header_mem_block_id( + in_regst_desc->separated_header_mem_block_id()); + } + VLOG(3) << " set force inplace from " << regst_desc->DebugString() << " to " + << in_regst_desc->DebugString(); } } } @@ -861,9 +1034,9 @@ namespace { struct MemBlockMemoryInfo { int64_t mem_block_id; int64_t mem_block_mem_size; - bool is_reused; - std::vector ordered_op_names; - MemBlockMemoryInfo() : mem_block_id(-1), mem_block_mem_size(-1), is_reused(false) {} + int64_t regst_num; + std::vector ordered_regst_desc_id; + MemBlockMemoryInfo() : mem_block_id(-1), mem_block_mem_size(-1), regst_num(-1) {} }; struct ChunkMemoryInfo { @@ -879,6 +1052,7 @@ struct RankDeviceMemoryInfo { ChunkMemoryInfo chunk_info; int64_t total_mem_size; int64_t not_reused_mem_size; + std::vector not_reused_mem_block_ids; int64_t eager_variable_total_mem_size; std::vector eager_variable_mem_block_ids; RankDeviceMemoryInfo() @@ -902,14 +1076,13 @@ void PlanUtil::PlanMemoryLog(Plan* plan, const std::string& plan_name) { std::vector rank_device_memory_infos(GlobalProcessCtx::WorldSize(), RankDeviceMemoryInfo()); HashMap mem_block_id2info; + HashMap regst_desc_id2regst; for (const ChunkProto& chunk : plan->block_chunk_list().chunk()) { int64_t rank_id = chunk.machine_id(); auto& info = rank_device_memory_infos[rank_id]; info.rank_id = rank_id; - if (chunk.mem_case().has_device_cuda_mem()) { - info.device_id = chunk.mem_case().device_cuda_mem().device_id(); - } + if (!memory::IsHostMem(chunk.mem_case())) { info.device_id = chunk.mem_case().device_id(); } info.total_mem_size += chunk.mem_size(); info.chunk_info.chunk_id = chunk.chunk_id(); info.chunk_info.chunk_mem_size = chunk.mem_size(); @@ -922,19 +1095,18 @@ void PlanUtil::PlanMemoryLog(Plan* plan, const std::string& plan_name) { info.mem_block_id = mem_block_id; info.mem_block_mem_size = mem_block.mem_size(); auto& rank_memory_info = rank_device_memory_infos.at(mem_block.machine_id()); - if (mem_block.mem_case().has_device_cuda_mem()) { + if (!memory::IsHostMem(mem_block.mem_case())) { if (mem_block.has_chunk_id()) { rank_memory_info.chunk_info.mem_block_ids.push_back(mem_block_id); - info.is_reused = true; } else { - rank_memory_info.chunk_info.mem_block_ids.push_back(mem_block_id); - info.is_reused = false; - rank_memory_info.not_reused_mem_size += mem_block.mem_size(); - rank_memory_info.total_mem_size += mem_block.mem_size(); if (mem_block.has_variable_op_name()) { rank_memory_info.eager_variable_mem_block_ids.push_back(mem_block_id); rank_memory_info.eager_variable_total_mem_size += mem_block.mem_size(); + } else { + rank_memory_info.not_reused_mem_block_ids.push_back(mem_block_id); + rank_memory_info.not_reused_mem_size += mem_block.mem_size(); } + rank_memory_info.total_mem_size += mem_block.mem_size(); } } } @@ -944,9 +1116,9 @@ void PlanUtil::PlanMemoryLog(Plan* plan, const std::string& plan_name) { const auto& regst = pair.second; if (regst.regst_desc_type().has_data_regst_desc() && mem_block_id2info.find(regst.mem_block_id()) != mem_block_id2info.end()) { - const auto data_regst = regst.regst_desc_type().data_regst_desc(); - std::string op_name = data_regst.lbi2blob_desc(0).lbi().op_name(); - mem_block_id2info.at(regst.mem_block_id()).ordered_op_names.push_back(op_name); + mem_block_id2info.at(regst.mem_block_id()) + .ordered_regst_desc_id.push_back(regst.regst_desc_id()); + regst_desc_id2regst.emplace(regst.regst_desc_id(), ®st); } } } @@ -960,40 +1132,84 @@ void PlanUtil::PlanMemoryLog(Plan* plan, const std::string& plan_name) { for (auto& rank_memory_info : rank_device_memory_infos) { std::sort(rank_memory_info.chunk_info.mem_block_ids.begin(), rank_memory_info.chunk_info.mem_block_ids.end(), CompMemBlock); - LOG(INFO) << " Graph name " << plan_name << " in Rank: " << rank_memory_info.rank_id + std::sort(rank_memory_info.not_reused_mem_block_ids.begin(), + rank_memory_info.not_reused_mem_block_ids.end(), CompMemBlock); + std::sort(rank_memory_info.eager_variable_mem_block_ids.begin(), + rank_memory_info.eager_variable_mem_block_ids.end(), CompMemBlock); + LOG(INFO) << "\n Graph name " << plan_name << " in Rank: " << rank_memory_info.rank_id << ", Device: " << rank_memory_info.device_id << " needs to allocate [ " << B2MiB(rank_memory_info.total_mem_size) - << " MiB ] device memory. \n In general, Chunk id: " + << " MiB ] device memory. \n In general, Chunk id: " << rank_memory_info.chunk_info.chunk_id << " memory is [ " << B2MiB(rank_memory_info.chunk_info.chunk_mem_size) - << " MiB ]; \n Memory out of Chunk is [ " + << " MiB ] with mem_block_num = " << rank_memory_info.chunk_info.mem_block_ids.size() + << "\n Unreused memory not eager var is [ " << B2MiB(rank_memory_info.not_reused_mem_size) - << " MiB ]; and in particular: Eager Variable Tensor total memory is [ " - << B2MiB(rank_memory_info.eager_variable_total_mem_size) << " MiB ]."; + << " MiB ] with mem_block_num = " << rank_memory_info.not_reused_mem_block_ids.size() + << "\n Eager Variable Tensor total memory is [ " + << B2MiB(rank_memory_info.eager_variable_total_mem_size) + << " MiB ] with mem_block_num = " + << rank_memory_info.eager_variable_mem_block_ids.size() << "\n"; } + auto Vlog3ForMemBlockDetails = [&](int64_t device_id, const std::vector& mem_block_ids, + const std::string& prefix) { + for (int64_t mem_block_id : mem_block_ids) { + CHECK(mem_block_id2info.find(mem_block_id) != mem_block_id2info.end()); + const auto& mem_block_info = mem_block_id2info.at(mem_block_id); + if (mem_block_info.ordered_regst_desc_id.size() != 1) { continue; } + const auto* regst = regst_desc_id2regst.at(mem_block_info.ordered_regst_desc_id.at(0)); + const auto& data_regst = regst->regst_desc_type().data_regst_desc(); + const auto& lbi2blob_desc_pair = data_regst.lbi2blob_desc(0); + std::string tensor_name = GenLogicalBlobName(lbi2blob_desc_pair.lbi()); + const auto& blob_desc = lbi2blob_desc_pair.blob_desc(); + VLOG(3) << "In Device: " << device_id << " Memblock id: " << mem_block_id << prefix + << " size: " << B2MiB(mem_block_info.mem_block_mem_size) + << " MiB, name: " << tensor_name << "\nshape: " << Shape(blob_desc.shape()).ToString() + << " ,dtype: " << DataType_Name(blob_desc.data_type()); + } + }; + for (const auto& rank_memory_info : rank_device_memory_infos) { int64_t chunk_id = rank_memory_info.chunk_info.chunk_id; int64_t device_id = rank_memory_info.device_id; - int64_t not_reuse_size = rank_memory_info.not_reused_mem_size; - VLOG(2) << " For detail: Chunk id: " << chunk_id << " has " - << rank_memory_info.chunk_info.mem_block_ids.size() << " MemBlocks" - << " not reused size = " << B2MiB(not_reuse_size); + VLOG(2) << "========================= " + << "In Device : " << device_id << " Chunk Memory info details:"; for (int64_t mem_block_id : rank_memory_info.chunk_info.mem_block_ids) { CHECK(mem_block_id2info.find(mem_block_id) != mem_block_id2info.end()); const auto& mem_block_info = mem_block_id2info.at(mem_block_id); VLOG(2) << " In Device: " << device_id << " Chunk id: " << chunk_id << " MemBlock id: " << mem_block_id - << " has num = " << mem_block_info.ordered_op_names.size() - << " ops with mem size = " << B2MiB(mem_block_info.mem_block_mem_size) - << " is reused " << mem_block_info.is_reused; - for (int64_t i = 0; i < mem_block_info.ordered_op_names.size(); ++i) { - VLOG(3) << " In Device: " << device_id << " Chunk id: " << chunk_id - << " In MemBlock id: " << mem_block_id << " order: " << i << " is reused " - << mem_block_info.is_reused - << " op_name: " << mem_block_info.ordered_op_names.at(i); + << " has num = " << mem_block_info.ordered_regst_desc_id.size() + << " tensor with mem size = " << B2MiB(mem_block_info.mem_block_mem_size); + for (int64_t i = 0; i < mem_block_info.ordered_regst_desc_id.size(); ++i) { + const auto* regst = regst_desc_id2regst.at(mem_block_info.ordered_regst_desc_id.at(i)); + const auto& data_regst = regst->regst_desc_type().data_regst_desc(); + const auto& lbi2blob_desc_pair = data_regst.lbi2blob_desc(0); + std::string tensor_name = GenLogicalBlobName(lbi2blob_desc_pair.lbi()); + const auto& blob_desc = lbi2blob_desc_pair.blob_desc(); + std::string alloc_order = "inplaced"; + if (regst->has_alloc_before_actor()) { + alloc_order = std::to_string(regst->alloc_before_actor()); + } + std::string free_order = "inplaced"; + if (regst->has_free_after_actor()) { + free_order = std::to_string(regst->free_after_actor()); + } + VLOG(3) << "In Chunk id: " << chunk_id << ", MemBlock id: " << mem_block_id + << " Order: " << i + << " ,duration: " << (regst->free_after_actor() - regst->alloc_before_actor() + 1) + << " ,size: " << B2MiB(BlobDesc(blob_desc).AlignedTotalByteSize()) + << " MiB, name: " << tensor_name + << "\nshape: " << Shape(blob_desc.shape()).ToString() + << " ,dtype: " << DataType_Name(blob_desc.data_type()) + << " ,alloc_order: " << alloc_order << " ,free_order: " << free_order; } } + + Vlog3ForMemBlockDetails(device_id, rank_memory_info.not_reused_mem_block_ids, " Unreused "); + Vlog3ForMemBlockDetails(device_id, rank_memory_info.eager_variable_mem_block_ids, + " EagerVariable "); } } @@ -1038,8 +1254,20 @@ void PlanUtil::GenLightPlan(Plan* plan, const std::string& plan_name) { << " regst_id2proto cannot find: " << regst_id; const RegstDescProto& regst = regst_id2proto.at(regst_id); ret += " regst_num: " + std::to_string(regst.register_num()); - std::string mem = ", cpu "; - if (regst.mem_case().has_device_cuda_mem()) { mem = ", cuda "; } + std::string mem; + switch (regst.mem_case().device_type()) { + case DeviceType::kCPU: { + mem = ", cpu "; + break; + } + case DeviceType::kCUDA: { + mem = ", cuda "; + break; + } + default: { + UNIMPLEMENTED() << "Unsupported device_type " << regst.mem_case().device_type(); + } + } ret += mem; if (regst.regst_desc_type().has_data_regst_desc()) { const DataRegstDesc& data = regst.regst_desc_type().data_regst_desc(); diff --git a/oneflow/core/job/plan_util.h b/oneflow/core/job/plan_util.h index 6c45db300e0a46f40204fe3e1e75cf7cc1d444a1..9de7142a629545ec5a77af9ed26c94f5d8219a9b 100644 --- a/oneflow/core/job/plan_util.h +++ b/oneflow/core/job/plan_util.h @@ -28,6 +28,7 @@ namespace oneflow { struct PlanUtil { static RegstDescProto* GetSoleProducedDataRegst(TaskProto* task_proto); static std::function MakeGetterTaskProto4TaskId(const Plan& plan); + static void MergeMemBlockIdByLogicalChainId(Plan* plan, const Job& job); static void SetUniqueMemBlockId4UnreusedMemRegst(Plan* plan); static void GenMemBlockAndChunk4Plan(Plan* plan); static void GenMemBlockAndChunkWithVariableOpNames4Plan( diff --git a/oneflow/core/job/resource_desc.cpp b/oneflow/core/job/resource_desc.cpp index 92cd3562e36c41155b650c2dc31fc49bf8b9353f..6a89cf4a985f2ced60ebaa0720c1528bc968a473 100644 --- a/oneflow/core/job/resource_desc.cpp +++ b/oneflow/core/job/resource_desc.cpp @@ -65,8 +65,6 @@ bool ResourceDesc::enable_debug_mode() const { return IsInDebugMode() || resource_.enable_debug_mode(); } -bool ResourceDesc::enable_dry_run() const { return std::getenv("ONEFLOW_DRY_RUN") != nullptr; } - CollectiveBoxingConf ResourceDesc::collective_boxing_conf() const { if (resource_.has_collective_boxing_conf()) { return resource_.collective_boxing_conf(); diff --git a/oneflow/core/job/resource_desc.h b/oneflow/core/job/resource_desc.h index 2fbc7043424a259efd5daaf290a485aad8f83ffd..dd469f3276f66d93723435245a14af214e3379ad 100644 --- a/oneflow/core/job/resource_desc.h +++ b/oneflow/core/job/resource_desc.h @@ -45,7 +45,6 @@ class ResourceDesc final { size_t thread_local_cache_max_size() const { return resource_.thread_local_cache_max_size(); } int32_t ComputeThreadPoolSize() const; bool enable_debug_mode() const; - bool enable_dry_run() const; CollectiveBoxingConf collective_boxing_conf() const; bool nccl_use_compute_stream() const; diff --git a/oneflow/core/job/runtime.cpp b/oneflow/core/job/runtime.cpp index d25f3e39af23cdb8aefde2cbec867e6d0e05a599..c4896741837b1684e566661976048c04d336612d 100644 --- a/oneflow/core/job/runtime.cpp +++ b/oneflow/core/job/runtime.cpp @@ -70,12 +70,9 @@ Runtime::Runtime( Singleton::Get()->AddPlan(plan); collective_boxing_scheduler_plan_token_ = Singleton::Get()->AddPlan(plan); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) Singleton::Get()->CreateCommFromPlan(plan); #endif // WITH_CUDA -#ifdef WITH_ROCM - Singleton::Get()->CreateCommFromPlan(plan); -#endif // WITH_ROCM } std::vector source_tasks; source_tasks.reserve(plan.task().size()); diff --git a/oneflow/core/job/runtime_buffers_scope.cpp b/oneflow/core/job/runtime_buffers_scope.cpp index 2c1a68a3aa5ec442a59d007cec5332b64cc741b7..f48f97b7c58062cfd664a2d9b83109aaa052e947 100644 --- a/oneflow/core/job/runtime_buffers_scope.cpp +++ b/oneflow/core/job/runtime_buffers_scope.cpp @@ -27,8 +27,6 @@ RuntimeBuffersScope::RuntimeBuffersScope(const JobConfs& job_confs) { for (const auto& pair : job_confs.job_id2job_conf()) { const auto& job_name = pair.second.job_name(); CHECK_EQ(pair.first, Singleton::Get()->at(job_name)); - buffer_mgr->NewBuffer(GetForeignInputBufferName(job_name), 2); - buffer_mgr->NewBuffer(GetForeignOutputBufferName(job_name), 2); size_t concurrency_width = pair.second.concurrency_width(); buffer_mgr->NewBuffer(GetCallbackNotifierBufferName(job_name), concurrency_width); } @@ -39,8 +37,6 @@ RuntimeBuffersScope::~RuntimeBuffersScope() { for (const auto& pair : *Singleton::Get()) { const auto& job_name = pair.first; buffer_mgr->Get(GetCallbackNotifierBufferName(job_name))->Close(); - buffer_mgr->Get(GetForeignOutputBufferName(job_name))->Close(); - buffer_mgr->Get(GetForeignInputBufferName(job_name))->Close(); } Singleton>::Get()->Get(kBufferNameGlobalWaitJobId)->Close(); } diff --git a/oneflow/core/job/sbp_parallel.cpp b/oneflow/core/job/sbp_parallel.cpp index fe21d115d0aef89be41743ba0a7a4642b3438f3e..288141544101818e67046ac80835c642bf563b89 100644 --- a/oneflow/core/job/sbp_parallel.cpp +++ b/oneflow/core/job/sbp_parallel.cpp @@ -270,9 +270,62 @@ void CheckSbpSignatureAndNdSbpEquals(const SbpSignature& sbp_sig, } } -Maybe NdSbpSignatureListAsString(const std::vector& nd_sbp_sig_list, - const PbRpf& inputs, - const PbRpf& outputs) { +bool NdSbpAllSameSplitParallel(const NdSbp& nd_sbp) { + CHECK_GT(nd_sbp.sbp_parallel_size(), 0); + const SbpParallel& first_sbp = nd_sbp.sbp_parallel(0); + if (!first_sbp.has_split_parallel()) { return false; } + FOR_RANGE(int64_t, i, 1, nd_sbp.sbp_parallel_size()) { + if (nd_sbp.sbp_parallel(i) != first_sbp) { return false; } + } + return true; +} + +Maybe NdSbpSignatureToString(const NdSbpSignature& nd_sbp_signature, + const std::vector& inputs, + const std::vector& outputs) { + std::ostringstream ss; + + auto AppendBnNdSbpString = [&](const std::string& bn) -> Maybe { + auto iter = nd_sbp_signature.bn_in_op2nd_sbp().find(bn); + if (iter == nd_sbp_signature.bn_in_op2nd_sbp().end()) { + return Error::RuntimeError() + << "can't find " << bn << " in NdSbpSignature: " << nd_sbp_signature.DebugString(); + } + ss << " " << NdSbpToString(iter->second); + return Maybe::Ok(); + }; + + int bn_index = 0; + for (const auto& ibn : inputs) { + if (bn_index > 0) { ss << ", "; } + ss << ibn; + JUST(AppendBnNdSbpString(ibn)); + bn_index++; + } + + ss << " -> "; + bn_index = 0; + for (const auto& obn : outputs) { + if (bn_index > 0) { ss << ", "; } + ss << obn; + JUST(AppendBnNdSbpString(obn)); + bn_index++; + } + + return ss.str(); +} + +Maybe NdSbpSignatureToString(const NdSbpSignature& nd_sbp_signature, + const PbRpf& inputs, + const PbRpf& outputs) { + return NdSbpSignatureToString(nd_sbp_signature, + std::vector{inputs.begin(), inputs.end()}, + std::vector{outputs.begin(), outputs.end()}); +} + +Maybe NdSbpSignatureListToString(const std::vector& nd_sbp_sig_list, + const std::vector& inputs, + const std::vector& outputs) { std::ostringstream ss; if (nd_sbp_sig_list.empty()) { return ss.str(); } @@ -292,6 +345,7 @@ Maybe NdSbpSignatureListAsString(const std::vector& return Maybe::Ok(); }; + ss << "\n"; JUST(WalkIO([](const std::string& bn) -> Maybe { return bn; })); ss << ": "; @@ -302,7 +356,7 @@ Maybe NdSbpSignatureListAsString(const std::vector& auto it = nd_sbp_sig.bn_in_op2nd_sbp().find(bn); if (it == nd_sbp_sig.bn_in_op2nd_sbp().end()) { return Error::RuntimeError() - << "can't find " << bn << "in NdSbpSignature: " << nd_sbp_sig.DebugString(); + << "can't find " << bn << " in NdSbpSignature: " << nd_sbp_sig.DebugString(); } return NdSbpToString(it->second); })); @@ -312,14 +366,12 @@ Maybe NdSbpSignatureListAsString(const std::vector& return ss.str(); } -bool NdSbpAllSameSplitParallel(const NdSbp& nd_sbp) { - CHECK_GT(nd_sbp.sbp_parallel_size(), 0); - const SbpParallel& first_sbp = nd_sbp.sbp_parallel(0); - if (!first_sbp.has_split_parallel()) { return false; } - FOR_RANGE(int64_t, i, 1, nd_sbp.sbp_parallel_size()) { - if (nd_sbp.sbp_parallel(i) != first_sbp) { return false; } - } - return true; +Maybe NdSbpSignatureListToString(const std::vector& nd_sbp_sig_list, + const PbRpf& inputs, + const PbRpf& outputs) { + return NdSbpSignatureListToString(nd_sbp_sig_list, + std::vector{inputs.begin(), inputs.end()}, + std::vector{outputs.begin(), outputs.end()}); } } // namespace oneflow diff --git a/oneflow/core/job/sbp_parallel.h b/oneflow/core/job/sbp_parallel.h index c693c9d9bf5238b665e964d7c5881005bd3f8b5e..e75f08b3bc160943377bfd061dd802fac50c181d 100644 --- a/oneflow/core/job/sbp_parallel.h +++ b/oneflow/core/job/sbp_parallel.h @@ -80,13 +80,29 @@ void SbpSignatureToNdSbpSignature(const SbpSignature& sbp_signature, void NdSbpSignatureToSbpSignature(const NdSbpSignature& nd_sbp_signature, SbpSignature* sbp_signature); + void CheckSbpSignatureAndNdSbpEquals(const SbpSignature& sbp_sig, const NdSbpSignature& nd_sbp_sig); -Maybe NdSbpSignatureListAsString(const std::vector& nd_sbp_sig_list, - const PbRpf& inputs, - const PbRpf& outputs); bool NdSbpAllSameSplitParallel(const NdSbp& nd_sbp); +// Print functions + +Maybe NdSbpSignatureToString(const NdSbpSignature& nd_sbp_signature, + const std::vector& inputs, + const std::vector& outputs); + +Maybe NdSbpSignatureToString(const NdSbpSignature& nd_sbp_signature, + const PbRpf& inputs, + const PbRpf& outputs); + +Maybe NdSbpSignatureListToString(const std::vector& nd_sbp_sig_list, + const std::vector& inputs, + const std::vector& outputs); + +Maybe NdSbpSignatureListToString(const std::vector& nd_sbp_sig_list, + const PbRpf& inputs, + const PbRpf& outputs); + } // namespace oneflow #endif // ONEFLOW_CORE_JOB_SBP_PARALLEL_H_ diff --git a/oneflow/core/job/scope.h b/oneflow/core/job/scope.h index 207a951c3d01ef1f66c26ccb4da876cfc7ae20f7..f36edb3d1e990ad14d7830693e7fa9aef21081da 100644 --- a/oneflow/core/job/scope.h +++ b/oneflow/core/job/scope.h @@ -20,7 +20,6 @@ limitations under the License. #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/job/placement_scope.h" #include "oneflow/core/job/job_desc.h" -#include "oneflow/core/framework/attr_value.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/optional.h" #include "oneflow/core/common/symbol.h" @@ -52,8 +51,8 @@ class Scope final { Maybe GetParallelDescSymbolId(const OperatorConf& op_conf) const; Maybe> GetParallelDesc(const OperatorConf& op_conf) const; - const OptMirroredParallel& opt_mirrored_parallel_conf() const { - return scope_proto_.opt_mirrored_parallel_conf(); + const OptLocalParallel& opt_local_parallel_conf() const { + return scope_proto_.opt_local_parallel_conf(); } const ScopeProto& scope_proto() const { return scope_proto_; } const ScopeProto& data() const { return scope_proto_; } diff --git a/oneflow/core/job/scope.proto b/oneflow/core/job/scope.proto index 61f9026cd0980e044c8ba298e89f6d7b78deac25..961a914acfb86aae3e86b83c673418384f37b515 100644 --- a/oneflow/core/job/scope.proto +++ b/oneflow/core/job/scope.proto @@ -1,7 +1,7 @@ syntax = "proto2"; package oneflow; -import "oneflow/core/job/mirrored_parallel.proto"; +import "oneflow/core/job/local_parallel.proto"; import "oneflow/core/framework/user_op_attr.proto"; import "oneflow/core/job/module_conf.proto"; @@ -10,7 +10,7 @@ message ScopeProto { required int64 device_parallel_desc_symbol_id = 30; required int64 host_parallel_desc_symbol_id = 40; optional bool enable_cpu_alternative_op = 41 [default = true]; - required OptMirroredParallel opt_mirrored_parallel_conf = 50; + required OptLocalParallel opt_local_parallel_conf = 50; repeated string scope_op_name_prefixes = 60; optional int64 parent_scope_symbol_id = 70; required int64 session_id = 80; diff --git a/oneflow/core/job/session.cpp b/oneflow/core/job/session.cpp index 7585783af76ef4a252a22b0a9ac6fc38b7320bdd..5525746c1d334ff938e22db4c10b3357ac578b10 100644 --- a/oneflow/core/job/session.cpp +++ b/oneflow/core/job/session.cpp @@ -40,7 +40,7 @@ LogicalConfigProtoContext::LogicalConfigProtoContext(const std::string& config_p LogicalConfigProtoContext::~LogicalConfigProtoContext() { config_proto_ctx_.reset(); - // TODO(hanbinbin): destory ConfigProtoContext of worker machines + // TODO(hanbinbin): destroy ConfigProtoContext of worker machines } } // namespace oneflow diff --git a/oneflow/core/job/session_global_objects_scope.cpp b/oneflow/core/job/session_global_objects_scope.cpp deleted file mode 100644 index ecf360c84cae669351264df589f59416c2331b5f..0000000000000000000000000000000000000000 --- a/oneflow/core/job/session_global_objects_scope.cpp +++ /dev/null @@ -1,115 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job/session_global_objects_scope.h" -#include "oneflow/core/control/ctrl_server.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/framework/load_library.h" -#include "oneflow/core/job/collective_boxing/scheduler.h" -#include "oneflow/core/job/critical_section_desc.h" -#include "oneflow/core/job/global_for.h" -#include "oneflow/core/job/id_manager.h" -#include "oneflow/core/job/inter_user_job_info.pb.h" -#include "oneflow/core/job/job_build_and_infer_ctx_mgr.h" -#include "oneflow/core/job/job_set_compile_ctx.h" -#include "oneflow/core/job/job_desc.h" -#include "oneflow/core/job/job_instance.h" -#include "oneflow/core/job/resource_desc.h" -#include "oneflow/core/job/runtime_context.h" -#include "oneflow/core/job/runtime_buffer_managers_scope.h" -#include "oneflow/core/job/runtime_job_descs.h" -#include "oneflow/core/job/version.h" -#include "oneflow/core/memory/chunk_manager.h" -#include "oneflow/core/memory/memory_allocator.h" -#include "oneflow/core/register/register_manager.h" -#include "oneflow/user/summary/events_writer.h" -#include "oneflow/core/thread/thread_manager.h" -#include "oneflow/core/graph/task_stream_index_manager.h" - -namespace oneflow { - -SessionGlobalObjectsScope::SessionGlobalObjectsScope() {} - -Maybe SessionGlobalObjectsScope::Init(const ConfigProto& config_proto) { - session_id_ = config_proto.session_id(); - Singleton::Delete(); - DumpVersionInfo(); - Singleton::New(config_proto.resource(), - GlobalProcessCtx::NumOfProcessPerNode()); - Singleton::New(); - Singleton::New(); - if (GlobalProcessCtx::IsThisProcessMaster()) { - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - } - - { - // NOTE(chengcheng): Init Global(singleton) Runtime objects. - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - Singleton::New(); - } - - return Maybe::Ok(); -} - -Maybe SessionGlobalObjectsScope::EagerInit(const ConfigProto& config_proto) { - session_id_ = config_proto.session_id(); - Singleton::Delete(); - DumpVersionInfo(); - Singleton::New(config_proto.resource()); - return Maybe::Ok(); -} - -SessionGlobalObjectsScope::~SessionGlobalObjectsScope() { - { - // NOTE(chengcheng): Delete Global(singleton) Runtime objects. - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - } - - if (GlobalProcessCtx::IsThisProcessMaster()) { - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - } - Singleton::Delete(); - Singleton::Delete(); - Singleton::Delete(); - Singleton::New(Singleton::Get()->resource(), - GlobalProcessCtx::NumOfProcessPerNode()); -} - -} // namespace oneflow diff --git a/oneflow/core/job/session_global_objects_scope.h b/oneflow/core/job/session_global_objects_scope.h deleted file mode 100644 index 99cb05e8b349a259a552e85a1a037e138a838b83..0000000000000000000000000000000000000000 --- a/oneflow/core/job/session_global_objects_scope.h +++ /dev/null @@ -1,40 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_JOB_ENVIRONMENT_OBJECTS_SCOPE_H_ -#define ONEFLOW_CORE_JOB_ENVIRONMENT_OBJECTS_SCOPE_H_ - -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/job_set.pb.h" -#include "oneflow/core/common/maybe.h" - -namespace oneflow { - -class SessionGlobalObjectsScope final { - public: - OF_DISALLOW_COPY_AND_MOVE(SessionGlobalObjectsScope); - SessionGlobalObjectsScope(); - ~SessionGlobalObjectsScope(); - - Maybe Init(const ConfigProto& config_proto); - Maybe EagerInit(const ConfigProto& config_proto); - - private: - int64_t session_id_; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_JOB_ENVIRONMENT_OBJECTS_SCOPE_H_ diff --git a/oneflow/core/job/task.proto b/oneflow/core/job/task.proto index e4df1c4a0db67a41b6d8535283e2f67e934eedb8..6432c5629c4a47d371916b60d9751982e763c170 100644 --- a/oneflow/core/job/task.proto +++ b/oneflow/core/job/task.proto @@ -12,9 +12,10 @@ enum TaskType { kCopyCommNet = 13; kDeviceTick = 27; kPack = 30; - kUnpack = 32; - kRepeat = 34; - kAcc = 37; + kUnpack = 31; + kRepeat = 32; + kAcc = 33; + kAccCtrlTick = 34; kSrcSubsetTick = 38; kDstSubsetTick = 39; kSourceTick = 40; @@ -25,8 +26,6 @@ enum TaskType { kWaitAndSendIds = 45; kReentrantLock = 46; kCallbackNotify = 47; - kForeignInput = 48; - kForeignOutput = 49; kDistributeConcat = 55; kDistributeSplit = 56; kSliceBoxing = 57; @@ -38,6 +37,7 @@ enum TaskType { kSspVariableProxy = 63; kBoxingZeros = 64; kCriticalSectionWaitTick = 65; + kNcclSendRecvBoxing = 66; }; message RegstDescIdSet { diff --git a/oneflow/core/job/utils/progress_bar.cpp b/oneflow/core/job/utils/progress_bar.cpp new file mode 100644 index 0000000000000000000000000000000000000000..753616bbc8999340f3fb2f2c0ebb36504537510c --- /dev/null +++ b/oneflow/core/job/utils/progress_bar.cpp @@ -0,0 +1,47 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/job/utils/progress_bar.h" +#include "oneflow/core/job/graph_scope_vars.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" + +namespace oneflow { + +Maybe LogProgress(const std::string& task_name, bool is_end) { + const bool log_progress = + GetGraphDebugMode() || ThreadLocalEnvBool(); + if (!log_progress || OF_PREDICT_FALSE(GlobalProcessCtx::Rank() != 0)) { + return Maybe::Ok(); + } + + const static thread_local uint64_t progress_total_num = 60; + static thread_local uint64_t progress_cnt = 1; + static constexpr char clear_line[] = + " \r"; + + auto const& limited_str = task_name.size() > 60 ? task_name.substr(0, 60) : task_name; + std::cout << clear_line << "[" << progress_cnt << "/" << progress_total_num << "]" << limited_str + << "\r" << std::flush; + if (is_end) { + progress_cnt = 0; + std::cout << clear_line << std::endl << std::flush; + } + ++progress_cnt; + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/core/job/utils/progress_bar.h b/oneflow/core/job/utils/progress_bar.h new file mode 100644 index 0000000000000000000000000000000000000000..504fecaf25a299eb2c21bec34080747f18367ce8 --- /dev/null +++ b/oneflow/core/job/utils/progress_bar.h @@ -0,0 +1,31 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_JOB_UTILS_PROGRESS_BAR_H_ +#define ONEFLOW_CORE_JOB_UTILS_PROGRESS_BAR_H_ + +#include +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/env_var/env_var.h" + +namespace oneflow { + +DEFINE_THREAD_LOCAL_ENV_BOOL(ONEFLOW_NNGRAPH_ENABLE_PROGRESS_BAR, false); + +Maybe LogProgress(const std::string& task_name = "", bool is_end = false); + +} // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_UTILS_PROGRESS_BAR_H_ diff --git a/oneflow/core/job_rewriter/adadelta_optim.cpp b/oneflow/core/job_rewriter/adadelta_optim.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e8fc386e665803795bfbbecb4143b063782ce77b --- /dev/null +++ b/oneflow/core/job_rewriter/adadelta_optim.cpp @@ -0,0 +1,92 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/user_op_conf.h" +#include "oneflow/core/job/initializer_conf.pb.h" +#include "oneflow/core/job/job_builder.h" +#include "oneflow/core/job/job_conf.pb.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/job_rewriter/optimizer.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/operator/variable_op.h" + +namespace oneflow { + +namespace { + +std::string GenVariableOutputLbn(const OperatorConf& op_conf) { + CHECK(op_conf.has_variable_conf()); + return GenLogicalBlobName(op_conf.name(), op_conf.variable_conf().out()); +} + +OperatorConf GenerateAdadeltaHelperVariableConf(const VariableOp& op, const std::string& name) { + OperatorConf helper_variable_op(op.op_conf()); + helper_variable_op.set_name(op.op_name() + "-" + name); + helper_variable_op.mutable_variable_conf()->set_out("out"); + InitializerConf constant_initializer; + constant_initializer.mutable_constant_conf()->set_value(0.0f); + *(helper_variable_op.mutable_variable_conf()->mutable_initializer()) = constant_initializer; + helper_variable_op.set_scope_symbol_id(op.op_conf().scope_symbol_id()); + return helper_variable_op; +} + +void GenerateAdadeltaOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, + const std::string& model_diff_lbn, + const OptimizerConf& optimizer_conf, JobBuilder* job_builder) { + const VariableOp* var_op = dynamic_cast(&var_op_node.op()); + CHECK_NOTNULL(var_op); + + user_op::UserOpConfWrapperBuilder adadelta_update_op_builder(var_op->op_name() + "_optimizer"); + float rho = 0.0; + float epsilon = 0.0; + bool maximize = false; + + const AdadeltaModelUpdateConf& adadelta_conf = optimizer_conf.adadelta_conf(); + rho = adadelta_conf.rho(); + epsilon = adadelta_conf.epsilon(); + maximize = adadelta_conf.maximize(); + const std::string& learning_rate_lbn = optimizer_conf.learning_rate_lbn(); + + OperatorConf square_avgs_var(GenerateAdadeltaHelperVariableConf(*var_op, "square_avgs")); + OperatorConf acc_deltas_var(GenerateAdadeltaHelperVariableConf(*var_op, "acc_deltas")); + job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), + {square_avgs_var, acc_deltas_var}); + + adadelta_update_op_builder.OpTypeName("adadelta_update") + .Input("model", GenLogicalBlobName(var_op->BnInOp2Lbi("out"))) + .Input("model_diff", model_diff_lbn) + .Input("learning_rate", learning_rate_lbn) + .Input("square_avgs", GenVariableOutputLbn(square_avgs_var)) + .Input("acc_deltas", GenVariableOutputLbn(acc_deltas_var)) + .Attr("rho", rho) + .Attr("epsilon", epsilon) + .Attr("maximize", maximize) + .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) + .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + if (optimizer_conf.has_lr_scale()) { + adadelta_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } + SetDynamicLossScaleSkipIf(ctx, &adadelta_update_op_builder); + const auto adadelta_update_op = adadelta_update_op_builder.Build(); + job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {adadelta_update_op.op_conf()}); +} + +} // namespace + +REGISTER_OPTIMIZER(OptimizerConf::kAdadeltaConf, &GenerateAdadeltaOptimizerOpConf); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/adagrad_optm.cpp b/oneflow/core/job_rewriter/adagrad_optm.cpp index 3f6f03ee090a4e67371ec8e9f8d740cf466884dd..8d00a9541b943f00102e017001cb8b0dfe13b7df 100644 --- a/oneflow/core/job_rewriter/adagrad_optm.cpp +++ b/oneflow/core/job_rewriter/adagrad_optm.cpp @@ -78,6 +78,9 @@ void GenerateAdagradOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Attr("lr_decay", lr_decay) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + if (optimizer_conf.has_lr_scale()) { + adagrad_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &adagrad_update_op_builder); const auto adagrad_update_op = adagrad_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {adagrad_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/adam_optm.cpp b/oneflow/core/job_rewriter/adam_optm.cpp index e69bde9b4b8ce21a588ac7083b1648927f10136d..b88ce5d96499dfa8923177a0c260e5ed7f6ebd7b 100644 --- a/oneflow/core/job_rewriter/adam_optm.cpp +++ b/oneflow/core/job_rewriter/adam_optm.cpp @@ -34,9 +34,8 @@ namespace std { template<> struct hash { size_t operator()(const oneflow::BiasCorrectionFactorCacheKey& key) const { - const auto& float_hash = std::hash(); - const auto& parallel_conf_hash = std::hash(); - return float_hash(key.beta) ^ parallel_conf_hash(key.parallel_conf); + using namespace oneflow; + return Hash(key.beta, key.parallel_conf); } }; @@ -117,7 +116,6 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, } else { UNIMPLEMENTED(); } - OperatorConf m_var(GenerateAdamHelperVariableOpConf(*var_op, "m", 0.f)); OperatorConf v_var(GenerateAdamHelperVariableOpConf(*var_op, "v", 0.f)); OperatorConf max_v_var{}; @@ -131,6 +129,19 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, const std::string& train_step_lbn = job_builder->job().job_conf().train_conf().train_step_lbn(); const std::string& learning_rate_lbn = optimizer_conf.learning_rate_lbn(); + adam_update_op_builder.OpTypeName("adam_update") + .Input("model", GenLogicalBlobName(var_op->BnInOp2Lbi("out"))) + .Input("model_diff", model_diff_lbn) + .Input("learning_rate", learning_rate_lbn) + .Input("m", GenVariableOutputLbn(m_var)) + .Input("v", GenVariableOutputLbn(v_var)) + .Attr("beta1", beta1) + .Attr("beta2", beta2) + .Attr("epsilon", epsilon) + .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) + .Attr("amsgrad", amsgrad) + .Attr("do_bias_correction", do_bias_correction) + .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); if (do_bias_correction) { const std::string& job_pass_state_key = "adam_bias_correction_factor"; const bool has_state = CHECK_JUST(ctx->HasState(job_pass_state_key)); @@ -170,38 +181,13 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, const std::string bias_correction2_lbn = state->GetLbn(beta2, "adam_bias_correction_factor2", bias_correction_parallel_conf, AddAdamBiasCorrectionFactorOp); - adam_update_op_builder.OpTypeName("adam_update") - .Input("model", GenLogicalBlobName(var_op->BnInOp2Lbi("out"))) - .Input("model_diff", model_diff_lbn) - .Input("learning_rate", learning_rate_lbn) - .Input("bias_correction1", bias_correction1_lbn) - .Input("bias_correction2", bias_correction2_lbn) - .Input("m", GenVariableOutputLbn(m_var)) - .Input("v", GenVariableOutputLbn(v_var)) - .Attr("beta1", beta1) - .Attr("beta2", beta2) - .Attr("epsilon", epsilon) - .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) - .Attr("amsgrad", amsgrad) - .Attr("do_bias_correction", true) - .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); - } else { - adam_update_op_builder.OpTypeName("adam_update") - .Input("model", GenLogicalBlobName(var_op->BnInOp2Lbi("out"))) - .Input("model_diff", model_diff_lbn) - .Input("learning_rate", learning_rate_lbn) - .Input("m", GenVariableOutputLbn(m_var)) - .Input("v", GenVariableOutputLbn(v_var)) - .Attr("beta1", beta1) - .Attr("beta2", beta2) - .Attr("epsilon", epsilon) - .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) - .Attr("amsgrad", amsgrad) - .Attr("do_bias_correction", false) - .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + adam_update_op_builder.Input("bias_correction1", bias_correction1_lbn) + .Input("bias_correction2", bias_correction2_lbn); } - if (amsgrad) { adam_update_op_builder.Input("max_v", GenVariableOutputLbn(max_v_var)); } + if (optimizer_conf.has_lr_scale()) { + adam_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &adam_update_op_builder); const auto adam_update_op = adam_update_op_builder.Build(); diff --git a/oneflow/core/job_rewriter/add_input_output_ops_pass.cpp b/oneflow/core/job_rewriter/add_input_output_ops_pass.cpp deleted file mode 100644 index 6d139496bf737e0b6fc0aac898971e437d328705..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/add_input_output_ops_pass.cpp +++ /dev/null @@ -1,182 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/util.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/job_rewriter/job_pass.h" -#include "oneflow/core/job/job.pb.h" -#include "oneflow/core/job/placement.pb.h" -#include "oneflow/core/operator/op_conf.pb.h" -#include "oneflow/core/register/logical_blob_id.pb.h" - -namespace oneflow { - -namespace { - -std::string MakeInputOpConf(const std::string& input_op_name, const int64_t scope_sym_id, - const InterfaceBlobConf& blob_conf, OperatorConf* input_op_conf) { - input_op_conf->set_name(input_op_name); - input_op_conf->set_scope_symbol_id(scope_sym_id); - auto* input_conf = input_op_conf->mutable_input_conf(); - input_conf->set_out("out"); - input_conf->mutable_blob_conf()->CopyFrom(blob_conf); - return GenLogicalBlobName(input_op_name, "out"); -} - -std::string MakeOutputOpConf(const std::string& output_op_name, const int64_t scope_sym_id, - const LogicalBlobId& lbi, OperatorConf* output_op_conf) { - output_op_conf->set_name(output_op_name); - output_op_conf->set_scope_symbol_id(scope_sym_id); - auto* return_conf = output_op_conf->mutable_return_conf(); - return_conf->set_in(GenLogicalBlobName(lbi)); - return_conf->set_out("out"); - return GenLogicalBlobName(output_op_name, "out"); -} - -class AddInputOutputOpsPass final : public JobPass { - public: - OF_DISALLOW_COPY_AND_MOVE(AddInputOutputOpsPass); - AddInputOutputOpsPass() = default; - ~AddInputOutputOpsPass() override = default; - bool IsEnabled(const JobPassCtx& ctx) const { return true; } - Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - JobBuilder job_builder(job); - return Apply(op_graph, &job_builder); - } -}; - -Maybe AddInputOutputOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { - if (!job_builder->job().job_conf().has_signature()) { return Maybe::Ok(); } - const auto& job_sig = job_builder->job().job_conf().signature(); - auto IsInputLbi = [&](const LogicalBlobId& lbi) -> bool { - for (const auto& pair : job_sig.inputs()) { - if (pair.second.lbi() == lbi) { return true; } - } - return false; - }; - - HashMap inferface_lbi2scope_sym_id; - auto RecordScopeSymbolId = [&](const LogicalBlobId& lbi) -> void { - const auto* op_node = op_graph.OpNode4OpName(lbi.op_name()); - inferface_lbi2scope_sym_id.emplace(lbi, op_node->op().op_conf().scope_symbol_id()); - }; - - HashSet keep_op_names; - HashSet traced_lbi; - // TODO: This search way only support stateless subgraph. - // Control edge and mutable input need to be considered when supporting side-effect subgraph. - std::function(const LogicalBlobId&)> SearchConstSrcAndTrace; - SearchConstSrcAndTrace = [&](const LogicalBlobId& lbi) -> Maybe { - CHECK(traced_lbi.insert(lbi).second); - keep_op_names.insert(lbi.op_name()); - const auto* op_node = op_graph.OpNode4OpName(lbi.op_name()); - if (op_node->in_edges().empty()) { return Maybe::Ok(); } - for (const auto& ibn : op_node->op().input_bns()) { - CHECK_OR_RETURN(!op_node->op().InputBlobModifier4Ibn(ibn).is_mutable()); - const auto& src_lbi = op_node->op().BnInOp2Lbi(ibn); - if (IsInputLbi(src_lbi)) { - RecordScopeSymbolId(src_lbi); - } else if (traced_lbi.find(src_lbi) == traced_lbi.end()) { - SearchConstSrcAndTrace(src_lbi); - } else { - // pass - } - } - return Maybe::Ok(); - }; - for (const auto& pair : job_sig.outputs()) { - const auto& lbi = pair.second.lbi(); - RecordScopeSymbolId(lbi); - SearchConstSrcAndTrace(lbi); - } - - std::vector drop_op_names; - drop_op_names.reserve(op_graph.node_num()); - op_graph.ForEachNode([&](const OpNode* op_node) { - const auto& op_name = op_node->op().op_name(); - if (keep_op_names.find(op_name) == keep_op_names.end()) { drop_op_names.emplace_back(op_name); } - }); - for (const auto& op_name : keep_op_names) { - const auto* op_node = op_graph.OpNode4OpName(op_name); - for (const auto& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { - // keep op can't include ctrl_in edge of drop op - CHECK_OR_RETURN(std::find(drop_op_names.begin(), drop_op_names.end(), ctrl_in_op_name) - == drop_op_names.end()); - } - } - - HashMap io_op_name2op_conf; - HashMap io_op_name2parallel_conf; - HashSet input_consumer_op_names; - std::vector input_consumer_op_confs; - for (const auto& pair : job_sig.inputs()) { - const auto& input_name = pair.first; - const auto& input_def = pair.second; - auto it = inferface_lbi2scope_sym_id.find(input_def.lbi()); - if (it == inferface_lbi2scope_sym_id.end()) { continue; } - const auto* op_node = op_graph.OpNode4OpName(input_def.lbi().op_name()); - CHECK_OR_RETURN(io_op_name2op_conf.emplace(input_name, OperatorConf()).second); - int64_t scope_sym_id = it->second; - std::string input_lbn = MakeInputOpConf(input_name, scope_sym_id, input_def.blob_conf(), - &io_op_name2op_conf[input_name]); - CHECK_OR_RETURN( - io_op_name2parallel_conf.emplace(input_name, &op_node->parallel_desc().parallel_conf()) - .second); - - for (const OpEdge* out_edge : op_node->out_edges()) { - auto iter = out_edge->lbi2ibns().find(input_def.lbi()); - if (iter == out_edge->lbi2ibns().end()) { continue; } - const auto* consumer_op_node = out_edge->dst_node(); - const auto& consumer_op_name = consumer_op_node->op().op_name(); - CHECK_OR_RETURN(input_consumer_op_names.insert(consumer_op_name).second); - input_consumer_op_confs.emplace_back(consumer_op_node->op().op_conf()); - auto* consumer_op_conf = &input_consumer_op_confs.back(); - for (const auto& ibn : iter->second) { - const auto& old_lbn = ReplaceInputLbnInOpCustomizedConf(consumer_op_conf, ibn, input_lbn); - CHECK_EQ(old_lbn, GenLogicalBlobName(input_def.lbi())); - } - } - } - for (const auto& pair : job_sig.outputs()) { - const auto& output_name = pair.first; - const auto& output_def = pair.second; - const auto* op_node = op_graph.OpNode4OpName(output_def.lbi().op_name()); - CHECK_OR_RETURN(io_op_name2op_conf.emplace(output_name, OperatorConf()).second); - int64_t scope_sym_id = inferface_lbi2scope_sym_id.at(output_def.lbi()); - MakeOutputOpConf(output_name, scope_sym_id, output_def.lbi(), &io_op_name2op_conf[output_name]); - CHECK_OR_RETURN( - io_op_name2parallel_conf.emplace(output_name, &op_node->parallel_desc().parallel_conf()) - .second); - } - - for (const auto& pair : io_op_name2op_conf) { - const auto* parallel_conf = io_op_name2parallel_conf.at(pair.first); - job_builder->AddOps(*parallel_conf, {pair.second}); - } - job_builder->MutOpsOnlyOnce(input_consumer_op_confs); - job_builder->DelOps(drop_op_names); - return Maybe::Ok(); -} - -} // namespace - -REGISTER_JOB_PASS("AddInputOutputOpsPass", AddInputOutputOpsPass); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/add_lbi_diff_watcher.cpp b/oneflow/core/job_rewriter/add_lbi_diff_watcher.cpp deleted file mode 100644 index 067381a0661f60565eb846c51d3c35e98c6650e7..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/add_lbi_diff_watcher.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/job_pass.h" -#include "oneflow/core/job/lbi_diff_watcher_info.pb.h" -#include "oneflow/core/operator/operator.h" - -namespace oneflow { - -namespace { - -class AddLbiDiffWatcherOpConfs final : public JobPass { - public: - bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } - - Maybe Apply(Job* job) const; - - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - return Apply(job); - } -}; - -Maybe AddLbiDiffWatcherOpConfs::Apply(Job* job) const { - JobBuilder job_builder(job); - const auto& map = job->helper().lbi_diff_watcher_info().job_name2lbi_and_watcher_uuids(); - if (map.find(GlobalJobDesc().job_name()) == map.end()) { return Maybe::Ok(); } - const auto& tag2lbi_relations = job->helper().tag2lbi_relations(); - const auto& conf_iter = tag2lbi_relations.find(kProducedLbi2ConsumedDiffLbi); - if (conf_iter == tag2lbi_relations.end()) { return Maybe::Ok(); } - HashMap lbi2diff_lbi; - for (const auto& pair : conf_iter->second.pair()) { - CHECK(lbi2diff_lbi.emplace(pair.first(), pair.second()).second); - } - const auto& pair_list = map.at(GlobalJobDesc().job_name()).lbi_and_uuid_pair(); - for (const LbiAndDiffWatcherUuidPair& pair : pair_list) { - if (lbi2diff_lbi.find(pair.lbi()) == lbi2diff_lbi.end()) { continue; } - const auto& diff_lbi = lbi2diff_lbi.at(pair.lbi()); - const auto& diff_lbi_op_conf = JUST(job_builder.OpConf4OpName(diff_lbi.op_name())); - OperatorConf foreign_watcher_op; - foreign_watcher_op.set_name("System-LbiDiffWatcher-ForeignWatcher-" + NewUniqueId()); - foreign_watcher_op.set_scope_symbol_id(diff_lbi_op_conf.scope_symbol_id()); - auto* foreign_watcher_conf = foreign_watcher_op.mutable_foreign_watch_conf(); - foreign_watcher_conf->set_in(GenLogicalBlobName(diff_lbi)); - foreign_watcher_conf->set_handler_uuid(pair.watcher_uuid()); - job_builder.AddOps(JUST(job_builder.ParallelConf4Lbi(pair.lbi())), {foreign_watcher_op}); - } - return Maybe::Ok(); -} - -REGISTER_JOB_PASS("AddLbiDiffWatcherOpConfs", AddLbiDiffWatcherOpConfs); - -} // namespace - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/add_ssp_variable_proxy.cpp b/oneflow/core/job_rewriter/add_ssp_variable_proxy.cpp index c4901e1f2eab014c47315432f6ca2f1487f8634b..bb4aad9add9b3e56b4add2bc936c7886c6dc2792 100644 --- a/oneflow/core/job_rewriter/add_ssp_variable_proxy.cpp +++ b/oneflow/core/job_rewriter/add_ssp_variable_proxy.cpp @@ -17,7 +17,6 @@ limitations under the License. #include "oneflow/core/job/job.pb.h" #include "oneflow/core/job/scope.h" #include "oneflow/core/job_rewriter/calculation_pass.h" -#include "oneflow/core/job_rewriter/autograd.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/framework/framework.h" @@ -46,7 +45,18 @@ class AddSspVariableProxyPass final : public JobPass { Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { HashMap> var2ref_value_pair; HashSet var_consumers; - JUST(ForEachTrainableVarOpNode(op_graph, [&](OpNode* op_node) -> Maybe { + HashSet trainable_variable_op_names; + const Job& job = job_builder->job(); + for (const auto& optimizer_conf : job.job_conf().train_conf().optimizer_conf()) { + for (const auto& variable_op_name : optimizer_conf.variable_op_names()) { + trainable_variable_op_names.insert(variable_op_name); + } + } + auto IsTrainableVarOp = [&](const OperatorConf& op_conf) { + if (!op_conf.has_variable_conf()) { return false; } + return trainable_variable_op_names.count(op_conf.name()) > 0; + }; + JUST(ForEachTrainableVarOpNode(op_graph, IsTrainableVarOp, [&](OpNode* op_node) -> Maybe { op_node->ForEachNodeOnOutEdge([&](OpNode* consumer) { var_consumers.insert(consumer); }); const auto& old_var_out_lbi = op_node->op().BnInOp2Lbi("out"); return AddSspVarProxyOp(op_node, job_builder, &var2ref_value_pair[old_var_out_lbi].first, @@ -69,17 +79,16 @@ class AddSspVariableProxyPass final : public JobPass { return Maybe::Ok(); } - Maybe ForEachTrainableVarOpNode(const OpGraph& op_graph, - const std::function(OpNode*)>& DoEach) const { - std::function NeedBackwardOp; - JUST(MakePredicatorNeedBackwardOp(op_graph, &NeedBackwardOp)); + Maybe ForEachTrainableVarOpNode( + const OpGraph& op_graph, const std::function& IsTrainableVarOp, + const std::function(OpNode*)>& DoEach) const { const auto& IsSspVarProxy = [](const OperatorConf& op_conf) { return op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == "ssp_variable_proxy"; }; JUST(op_graph.MaybeForEachNode([&](OpNode* op_node) -> Maybe { const auto& op_conf = op_node->op().op_conf(); CHECK_OR_RETURN(!IsSspVarProxy(op_conf)) << "AddSspVariableProxy can not be applied twice"; - if (op_conf.has_variable_conf() && NeedBackwardOp(op_node)) { return DoEach(op_node); } + if (IsTrainableVarOp(op_conf)) { return DoEach(op_node); } return Maybe::Ok(); })); return Maybe::Ok(); diff --git a/oneflow/core/job_rewriter/auto_mixed_precision.cpp b/oneflow/core/job_rewriter/auto_mixed_precision.cpp index ac6dd5a4249b73658c86bf510a75d2f8ae6756ef..b8df42827fd4d7d88da47d818d654fae2e03c834 100644 --- a/oneflow/core/job_rewriter/auto_mixed_precision.cpp +++ b/oneflow/core/job_rewriter/auto_mixed_precision.cpp @@ -14,13 +14,11 @@ See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA - +#include "oneflow/core/job_rewriter/auto_mixed_precision.h" #include "oneflow/core/job_rewriter/auto_mixed_precision_lists.h" #include -#include "oneflow/core/device/cuda_util.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/job_rewriter/job_pass.h" #include "oneflow/core/job_rewriter/pass_util.h" @@ -37,7 +35,6 @@ void VerifyAMPList(const AMPList& amp_list) { } } -using OpArg = std::pair; using NoCastRegistry = std::multimap; NoCastRegistry* GetNoCastRegistry() { @@ -57,13 +54,16 @@ std::function MakePredicatorIsAllowedToRunWithHalf(const OpGraph& auto allowed_set = std::make_shared>(); op_graph.ForEachNode([&](OpNode* node) { if (node->parallel_desc().device_type() != DeviceType::kCUDA) { return; } - if (node->op().output_bns().size() > 0) { INSERT_CHECK(allowed_set->insert(node)); } + if (node->op().output_bns().size() > 0 + || IsUserOpWithTypeName(node->op().op_conf(), "one_embedding_fused_lookup_grad")) { + INSERT_CHECK(allowed_set->insert(node)); + } }); return [allowed_set](OpNode* node) -> bool { return IsKeyFound(*allowed_set, node); }; } void InsertCastOpImpl(bool f2h, const OpGraph& op_graph, const HashSet& white_set, - JobBuilder* job_builder) { + const DataType mixed_precision_data_type, JobBuilder* job_builder) { HashSet white_set_edges; { std::function&(OpNode*)> Node2Edges = @@ -89,22 +89,24 @@ void InsertCastOpImpl(bool f2h, const OpGraph& op_graph, const HashSet& HashMap> edges_group_by_lbn; { for (OpEdge* edge : white_set_edges) { - CHECK_EQ(1, edge->lbis().size()); - std::string lbn = GenLogicalBlobName(edge->lbis().front()); - edges_group_by_lbn[lbn].emplace_back(edge); + for (const auto& lbi : edge->lbis()) { + std::string lbn = GenLogicalBlobName(lbi); + edges_group_by_lbn[lbn].emplace_back(edge); + } } } HashMap dst_op_name2dst_op_confs; for (auto& pair : edges_group_by_lbn) { const std::string& lbn = pair.first; + LogicalBlobId cur_lbi = GenLogicalBlobId(lbn); OpNode* src_node = pair.second.front()->src_node(); - const BlobDesc& blob_desc = src_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(lbn)); + const BlobDesc& blob_desc = src_node->LogicalBlobDesc4Lbi(cur_lbi); if (blob_desc.data_type() != DataType::kFloat) { continue; } std::string cast_suffix = f2h ? "-cast_f2h" : "-cast_h2f"; - DataType cast_data_type = f2h ? DataType::kFloat16 : DataType::kFloat; + DataType cast_data_type = f2h ? mixed_precision_data_type : DataType::kFloat; auto cast_op = user_op::UserOpConfWrapperBuilder(ReplaceSlashToDash4Lbn(lbn) + cast_suffix) .Op("cast") .Input("in", lbn) @@ -117,27 +119,23 @@ void InsertCastOpImpl(bool f2h, const OpGraph& op_graph, const HashSet& for (OpEdge* edge : pair.second) { CHECK(src_node == edge->src_node()); OpNode* dst_node = edge->dst_node(); - LogicalBlobId cur_lbi = edge->lbis().front(); - CHECK_EQ(lbn, GenLogicalBlobName(cur_lbi)); - CHECK_EQ(1, edge->lbi2ibns().at(cur_lbi).size()); - const std::string& dst_ibn = edge->lbi2ibns().at(cur_lbi).front(); - - if (dst_node->op().op_conf().has_user_conf()) { - const std::string& op_type = dst_node->op().op_conf().user_conf().op_type_name(); - const auto& op_arg = GenUnRepeatedBn(dst_ibn); - if (FindInNoCastRegisry(op_type, op_arg)) { continue; } - } - - cast_is_consumed = true; - - const std::string& dst_op_name = dst_node->op().op_name(); - if (!IsKeyFound(dst_op_name2dst_op_confs, dst_op_name)) { - INSERT_CHECK( - dst_op_name2dst_op_confs.insert(std::make_pair(dst_op_name, dst_node->op().op_conf()))); + const auto& dst_ibns = edge->lbi2ibns().at(cur_lbi); + for (const auto& dst_ibn : dst_ibns) { + if (dst_node->op().op_conf().has_user_conf()) { + const std::string& op_type = dst_node->op().op_conf().user_conf().op_type_name(); + const auto& op_arg = GenUnRepeatedBn(dst_ibn); + if (FindInNoCastRegisry(op_type, op_arg)) { continue; } + } + cast_is_consumed = true; + const std::string& dst_op_name = dst_node->op().op_name(); + if (!IsKeyFound(dst_op_name2dst_op_confs, dst_op_name)) { + INSERT_CHECK(dst_op_name2dst_op_confs.insert( + std::make_pair(dst_op_name, dst_node->op().op_conf()))); + } + OperatorConf& dst_op_conf = dst_op_name2dst_op_confs.at(dst_op_name); + std::string new_lbn = cast_op.op_name() + "/out_0"; + CHECK_EQ(lbn, ReplaceInputLbnInOpCustomizedConf(&dst_op_conf, dst_ibn, new_lbn)); } - OperatorConf& dst_op_conf = dst_op_name2dst_op_confs.at(dst_op_name); - std::string new_lbn = cast_op.op_name() + "/out_0"; - CHECK_EQ(lbn, ReplaceInputLbnInOpCustomizedConf(&dst_op_conf, dst_ibn, new_lbn)); } if (cast_is_consumed) { @@ -167,15 +165,7 @@ class AutoMixedPrecision final : public JobPass { bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().enable_auto_mixed_precision(); } - - Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; - - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - JobBuilder job_builder(job); - return Apply(op_graph, &job_builder); - } + Maybe Apply(Job* job, JobPassCtx* ctx) const override; private: void FillBlackSet(const OpGraph& op_graph, HashSet* black_set) const; @@ -186,7 +176,7 @@ class AutoMixedPrecision final : public JobPass { const HashSet& black_set, HashSet* white_set) const; void InsertCastOp(const OpGraph& op_graph, const HashSet& white_set, - JobBuilder* job_builder) const; + const DataType mixed_precision_data_type, JobBuilder* job_builder) const; const AMPList& white_list_; const AMPList& black_list_; @@ -194,8 +184,10 @@ class AutoMixedPrecision final : public JobPass { const AMPList& clear_list_; }; -Maybe AutoMixedPrecision::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { - CHECK_GE(CUDA_VERSION, 10000); +Maybe AutoMixedPrecision::Apply(Job* job, JobPassCtx* ctx) const { + if (!ctx->job_desc().enable_auto_mixed_precision()) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); CHECK(GlobalJobDesc().DefaultDataType() == DataType::kFloat); VerifyAMPList(white_list_); @@ -220,8 +212,10 @@ Maybe AutoMixedPrecision::Apply(const OpGraph& op_graph, JobBuilder* job_b PropagateWhiteThroughClearNodes(op_graph, IsAllowedToRunWithHalf, black_set, &white_set); VLOG(2) << "WhiteSet include: " << Container2Str, OpNode*>(white_set, OpName4Node); - - InsertCastOp(op_graph, white_set, job_builder); + const DataType mixed_precision_data_type = ctx->job_desc().mixed_precision_data_type(); + CHECK(mixed_precision_data_type == DataType::kFloat16 + || mixed_precision_data_type == DataType::kBFloat16); + InsertCastOp(op_graph, white_set, mixed_precision_data_type, &job_builder); return Maybe::Ok(); } @@ -255,12 +249,15 @@ void AutoMixedPrecision::FillWhiteSet(const OpGraph& op_graph, std::function IsAllowedToRunWithHalf, const HashSet& black_set, HashSet* white_set) const { - HashSet upstream_or_part_of_white; - auto IsWhiteAndAllowedToRunHalf = [&](OpNode* node) { - return IsAllowedToRunWithHalf(node) && IsNodeInList(white_list_, node); + auto IsWhiteOrSinkAndAllowedToRunHalf = [&](OpNode* node) { + return IsAllowedToRunWithHalf(node) + && (IsNodeInList(white_list_, node) + || (node->out_edges().empty() + && (IsNodeInList(gray_list_, node) || IsNodeInList(clear_list_, node)))); }; + HashSet upstream_or_part_of_white; DfsTopoGraphTraversal( - op_graph, true, IsWhiteAndAllowedToRunHalf, + op_graph, true, IsWhiteOrSinkAndAllowedToRunHalf, [&](OpNode* node) { return !IsKeyFound(black_set, node) && IsAllowedToRunWithHalf(node) && (IsNodeInList(gray_list_, node) || IsNodeInList(clear_list_, node)); @@ -272,6 +269,9 @@ void AutoMixedPrecision::FillWhiteSet(const OpGraph& op_graph, << " to upstream_or_part_of_white"; }); + auto IsWhiteAndAllowedToRunHalf = [&](OpNode* node) { + return IsAllowedToRunWithHalf(node) && IsNodeInList(white_list_, node); + }; DfsTopoGraphTraversal( op_graph, false, IsWhiteAndAllowedToRunHalf, [&](OpNode* node) { return IsKeyFound(upstream_or_part_of_white, node); }, @@ -304,12 +304,15 @@ void AutoMixedPrecision::PropagateWhiteThroughClearNodes( } void AutoMixedPrecision::InsertCastOp(const OpGraph& op_graph, const HashSet& white_set, + const DataType mixed_precision_data_type, JobBuilder* job_builder) const { - InsertCastOpImpl(true, op_graph, white_set, job_builder); - InsertCastOpImpl(false, op_graph, white_set, job_builder); + InsertCastOpImpl(true, op_graph, white_set, mixed_precision_data_type, job_builder); + InsertCastOpImpl(false, op_graph, white_set, mixed_precision_data_type, job_builder); } +#if (defined(WITH_CUDA) && defined(CUDA_VERSION) && CUDA_VERSION >= 10000) || defined(WITH_ROCM) REGISTER_JOB_PASS("AutoMixedPrecision", AutoMixedPrecision); +#endif // WITH_CUDA } // namespace @@ -335,13 +338,32 @@ REGISTER_NO_CAST_REGISTRY("normalization", "moving_variance", 0) REGISTER_NO_CAST_REGISTRY("normalization", "gamma", 0) REGISTER_NO_CAST_REGISTRY("normalization", "beta", 0) +REGISTER_NO_CAST_REGISTRY("normalization_grad", "gamma", 0) + REGISTER_NO_CAST_REGISTRY("normalization_add_relu", "moving_mean", 0) REGISTER_NO_CAST_REGISTRY("normalization_add_relu", "moving_variance", 0) REGISTER_NO_CAST_REGISTRY("normalization_add_relu", "gamma", 0) REGISTER_NO_CAST_REGISTRY("normalization_add_relu", "beta", 0) +REGISTER_NO_CAST_REGISTRY("normalization_add_relu_grad", "gamma", 0) +REGISTER_NO_CAST_REGISTRY("normalization_add_relu_grad", "beta", 0) +REGISTER_NO_CAST_REGISTRY("normalization_add_relu_grad", "mean", 0) +REGISTER_NO_CAST_REGISTRY("normalization_add_relu_grad", "inv_variance", 0) +REGISTER_NO_CAST_REGISTRY("normalization_add_relu_grad", "reserve_space", 0) + +REGISTER_NO_CAST_REGISTRY("layer_norm_grad", "mean", 0) +REGISTER_NO_CAST_REGISTRY("layer_norm_grad", "inv_variance", 0) +REGISTER_NO_CAST_REGISTRY("layer_norm_param_grad", "mean", 0) +REGISTER_NO_CAST_REGISTRY("layer_norm_param_grad", "inv_variance", 0) + } // namespace -} // namespace oneflow +namespace amp { -#endif // WITH_CUDA +bool IsNoCast(const std::string& op_type, const OpArg& op_arg) { + return FindInNoCastRegisry(op_type, op_arg); +} + +} // namespace amp + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/auto_mixed_precision.h b/oneflow/core/job_rewriter/auto_mixed_precision.h new file mode 100644 index 0000000000000000000000000000000000000000..a51265cde94501b4e523f9e9730138646113649a --- /dev/null +++ b/oneflow/core/job_rewriter/auto_mixed_precision.h @@ -0,0 +1,33 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_JOB_REWRITER_AUTO_MIXED_PRECISION_H_ +#define ONEFLOW_CORE_JOB_REWRITER_AUTO_MIXED_PRECISION_H_ + +#include + +namespace oneflow { + +using OpArg = std::pair; + +namespace amp { + +bool IsNoCast(const std::string& op_type, const OpArg& op_arg); + +} // namespace amp + +} // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_REWRITER_AUTO_MIXED_PRECISION_H_ diff --git a/oneflow/core/job_rewriter/auto_mixed_precision_lists.cpp b/oneflow/core/job_rewriter/auto_mixed_precision_lists.cpp index 9dfec399ca2ac98cd3831167c990823a1176abd1..2fb6c74bc21bb00fe9ef4377964147a52731d27b 100644 --- a/oneflow/core/job_rewriter/auto_mixed_precision_lists.cpp +++ b/oneflow/core/job_rewriter/auto_mixed_precision_lists.cpp @@ -15,83 +15,363 @@ limitations under the License. */ #include "oneflow/core/job_rewriter/auto_mixed_precision_lists.h" +#ifdef WITH_ROCM + namespace oneflow { const AMPList& AutoMixedPrecisionLists::WhiteList() { static AMPList white_list = {"matmul", "batch_matmul", "conv2d", + "conv_data_grad", + "conv_filter_grad", + "conv_bias_grad", "amp_white_identity", "broadcast_matmul", + "broadcast_matmul_grad_b", "fused_self_attention_query_mul_key_and_value", + "fused_self_attention_query_mul_key_and_value_grad", "prelu", + "prelu_grad", "tf_prelu", + "tf_prelu_grad", "cublas_fused_mlp", + "cublas_fused_mlp_grad", + "fused_matmul_bias", + "cublas_bias_add_relu_matmul_grad", + "fused_glu", "fused_matmul_bias_add_relu_dropout", + "fused_relu_dropout_grad", "fused_dot_feature_interaction", - "embedding_lookup_placeholder", - "binary_cross_entropy_with_logits_reduce_mean"}; + "fused_dot_feature_interaction_grad", + "one_embedding_fused_lookup", + "one_embedding_fused_lookup_grad", + "binary_cross_entropy_with_logits_reduce_mean", + "binary_cross_entropy_with_logits_reduce_mean_grad", + "fused_cross_feature_interaction", + "fused_cross_feature_interaction_v1_grad", + "fused_cross_feature_interaction_v2_grad", + "fused_multi_head_attention_inference" + }; return white_list; } const AMPList& AutoMixedPrecisionLists::BlackList() { // TODO(niuchong): reduce_mean? - static AMPList black_list = {}; + static AMPList black_list = {"amp_black_identity", + "grouped_matmul_bias", + "reduce_sum", + "reduce_sum_like", + "layer_norm_grad", + "layer_norm", + "layer_norm_param_grad" + }; + return black_list; +} + +const AMPList& AutoMixedPrecisionLists::GrayList() { + static AMPList gray_list = {"add_n", + "tf_avg_pool_1d", + "tf_avg_pool_1d_grad", + "tf_avg_pool_2d", + "tf_avg_pool_2d_grad", + "tf_avg_pool_3d", + "tf_avg_pool_3d_grad", + "avg_pool_1d", + "avg_pool_1d_grad", + "avg_pool_2d", + "avg_pool_2d_grad", + "avg_pool_3d", + "avg_pool_3d_grad", + "bias_add", + "sigmoid_grad", + "tanh", + "tanh_grad", + "sqrt", + "sqrt_grad", + "scalar_mul", + "scalar_mul_by_tensor", + "scalar_add", + "scalar_div", + "scalar_pow", + "broadcast_add", + "broadcast_sub", + "broadcast_mul", + "broadcast_div", + "rms_norm", + "rms_norm_grad", + "rms_norm_param_grad", + "dropout", + "dropout_grad", + "softmax", + "softmax_grad", + "log_softmax", + "log_softmax_grad", + "gelu", + "gelu_grad", + "fast_gelu", + "fast_gelu_grad", + "normalization", + "normalization_grad", + "normalization_add_relu", + "normalization_add_relu_grad", + "sparse_softmax_cross_entropy", + "sparse_softmax_cross_entropy_grad", + "nll", + "nll_grad", + "fused_tril_scale_softmax_mask_scale", + "fused_tril_scale_softmax_mask_scale_grad", + "fused_scale_mask_softmax_dropout", + "fused_scale_mask_softmax_dropout_grad", + "fused_scale_mask_softmax", + "fused_scale_mask_softmax_grad", + "fused_bias_add_scale_mask_softmax_dropout", + "fused_bias_add_gelu", + "fused_bias_add_gelu_grad", + "fused_bias_add_mask_scale", + "fused_fast_gelu_mul", + "fused_fast_gelu_mul_grad", + "acc", + "reciprocal", + "reciprocal_no_nan", + "group_norm", + "group_norm_param_grad", + "group_norm_grad", + "silu", + "silu_grad", + "fused_weighted_sum"}; + return gray_list; +} + +const AMPList& AutoMixedPrecisionLists::ClearList() { + // TODO(niuchong): tuple_identity + static AMPList clear_list = {"broadcast_like", + "gather", + "gather_nd", + "scatter_nd", + "scatter_nd_like", + "unsorted_segment_sum_like", + "tf_max_pool_1d", + "tf_max_pool_1d_grad", + "tf_max_pool_2d", + "tf_max_pool_2d_grad", + "tf_max_pool_3d", + "tf_max_pool_3d_grad", + "max_pool_1d", + "max_pool_1d_grad", + "max_pool_2d", + "max_pool_2d_grad", + "max_pool_3d", + "max_pool_3d_grad", + "reshape", + "reshape_like", + "relu", + "relu_grad", + "transpose", + "random_mask_like", + "concat", + "split_like", + "pad", + "same_padding", + "same_padding_grad", + "tril", + "slice", + "slice_grad", + "fused_scale_tril", + "identity", + "squeeze", + "embedding", + "embedding_grad", + "expand", + "expand_dims", + "cast_to_static_shape", + "parallel_cast", + "hierarchical_parallel_cast", + "hierarchical_parallel_cast_like", + "repeat", + "unpack", + "pack", + "nvtx_start", + "nvtx_end", + "narrow", + "narrow_grad", + "ones_like", + "pinned_identity", + "to_contiguous", + "copy", + "upsample_nearest_2d"}; + + return clear_list; +} + +} // namespace oneflow + + +#else +namespace oneflow { + +const AMPList& AutoMixedPrecisionLists::WhiteList() { + static AMPList white_list = {"matmul", + "batch_matmul", + "conv2d", + "conv_data_grad", + "conv_filter_grad", + "conv_bias_grad", + "amp_white_identity", + "broadcast_matmul", + "broadcast_matmul_grad_b", + "fused_self_attention_query_mul_key_and_value", + "fused_self_attention_query_mul_key_and_value_grad", + "prelu", + "prelu_grad", + "tf_prelu", + "tf_prelu_grad", + "cublas_fused_mlp", + "cublas_fused_mlp_grad", + "fused_matmul_bias", + "cublas_bias_add_relu_matmul_grad", + "fused_glu", + "fused_matmul_bias_add_relu_dropout", + "fused_relu_dropout_grad", + "fused_dot_feature_interaction", + "fused_dot_feature_interaction_grad", + "one_embedding_fused_lookup", + "one_embedding_fused_lookup_grad", + "binary_cross_entropy_with_logits_reduce_mean", + "binary_cross_entropy_with_logits_reduce_mean_grad", + "fused_cross_feature_interaction", + "fused_cross_feature_interaction_v1_grad", + "fused_cross_feature_interaction_v2_grad", + "fused_multi_head_attention_inference", + "grouped_matmul_bias"}; + return white_list; +} + +const AMPList& AutoMixedPrecisionLists::BlackList() { + // TODO(niuchong): reduce_mean? + static AMPList black_list = {"amp_black_identity"}; return black_list; } const AMPList& AutoMixedPrecisionLists::GrayList() { static AMPList gray_list = {"add_n", "tf_avg_pool_1d", + "tf_avg_pool_1d_grad", "tf_avg_pool_2d", + "tf_avg_pool_2d_grad", "tf_avg_pool_3d", + "tf_avg_pool_3d_grad", + "avg_pool_1d", + "avg_pool_1d_grad", + "avg_pool_2d", + "avg_pool_2d_grad", + "avg_pool_3d", + "avg_pool_3d_grad", "bias_add", - "sigmoid_v2", + "reduce_sum", + "reduce_sum_like", + "sigmoid_grad", "tanh", + "tanh_grad", "sqrt", + "sqrt_grad", "scalar_mul", + "scalar_mul_by_tensor", "scalar_add", "scalar_div", + "scalar_pow", "broadcast_add", "broadcast_sub", "broadcast_mul", "broadcast_div", "layer_norm", + "layer_norm_param_grad", + "layer_norm_grad", + "rms_norm", + "rms_norm_grad", + "rms_norm_param_grad", "dropout", + "dropout_grad", "softmax", + "softmax_grad", + "log_softmax", + "log_softmax_grad", "gelu", + "gelu_grad", + "fast_gelu", + "fast_gelu_grad", "normalization", + "normalization_grad", "normalization_add_relu", + "normalization_add_relu_grad", "sparse_softmax_cross_entropy", + "sparse_softmax_cross_entropy_grad", + "nll", + "nll_grad", "fused_tril_scale_softmax_mask_scale", + "fused_tril_scale_softmax_mask_scale_grad", "fused_scale_mask_softmax_dropout", + "fused_scale_mask_softmax_dropout_grad", "fused_scale_mask_softmax", + "fused_scale_mask_softmax_grad", + "fused_bias_add_scale_mask_softmax_dropout", "fused_bias_add_gelu", + "fused_bias_add_gelu_grad", "fused_bias_add_mask_scale", - "acc"}; + "fused_fast_gelu_mul", + "fused_fast_gelu_mul_grad", + "acc", + "reciprocal", + "reciprocal_no_nan", + "group_norm", + "group_norm_param_grad", + "group_norm_grad", + "silu", + "silu_grad", + "fused_weighted_sum"}; return gray_list; } const AMPList& AutoMixedPrecisionLists::ClearList() { // TODO(niuchong): tuple_identity - static AMPList clear_list = {"gather", + static AMPList clear_list = {"broadcast_like", + "gather", + "gather_nd", + "scatter_nd", + "scatter_nd_like", + "unsorted_segment_sum_like", "tf_max_pool_1d", + "tf_max_pool_1d_grad", "tf_max_pool_2d", + "tf_max_pool_2d_grad", "tf_max_pool_3d", + "tf_max_pool_3d_grad", + "max_pool_1d", + "max_pool_1d_grad", + "max_pool_2d", + "max_pool_2d_grad", + "max_pool_3d", + "max_pool_3d_grad", "reshape", + "reshape_like", "relu", + "relu_grad", "transpose", "random_mask_like", "concat", + "split_like", "pad", "same_padding", + "same_padding_grad", "tril", "slice", + "slice_grad", "fused_scale_tril", "identity", - "flatten", "squeeze", "embedding", + "embedding_grad", + "expand", "expand_dims", "cast_to_static_shape", "parallel_cast", @@ -102,9 +382,17 @@ const AMPList& AutoMixedPrecisionLists::ClearList() { "pack", "nvtx_start", "nvtx_end", - "narrow"}; + "narrow", + "narrow_grad", + "ones_like", + "pinned_identity", + "to_contiguous", + "copy", + "upsample_nearest_2d"}; return clear_list; } } // namespace oneflow + +#endif // WITH_ROCM diff --git a/oneflow/core/job_rewriter/auto_parallel.cpp b/oneflow/core/job_rewriter/auto_parallel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8cf97b3ff01c2789907cf2ffbf97bed87ceb42c1 --- /dev/null +++ b/oneflow/core/job_rewriter/auto_parallel.cpp @@ -0,0 +1,171 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/common/hash_container.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/job/job.pb.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/auto_parallel/sbp_constructor.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" + +namespace oneflow { + +namespace { + +class AutoParallelPass final : public JobPass { + public: + OF_DISALLOW_COPY_AND_MOVE(AutoParallelPass); + AutoParallelPass() = default; + ~AutoParallelPass() override = default; + + Maybe Apply(const OpGraph& op_graph, Job* job) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!job->job_conf().enable_auto_parallel()) { return Maybe::Ok(); } + VLOG(3) << "=== Enable AutoParallel ==="; + if (job->job_conf().enable_auto_parallel_ignore_user_sbp_config()) { + JUST(RemoveParallelCastOps(job)); + } + const OpGraph op_graph(*job); + return Apply(op_graph, job); + } + + private: + Maybe RemoveParallelCastOps(Job* job) const; +}; + +Maybe AutoParallelPass::Apply(const OpGraph& op_graph, Job* job) const { + // auto-parallel + LOG(INFO) << "Start Auto Parallel"; + auto time_begin = std::chrono::high_resolution_clock::now(); + + auto_parallel::SbpConstructor sbp_constructor(op_graph, job); + JUST(sbp_constructor.FindBestSbpSignature()); + JUST(sbp_constructor.DumpNdSbpSignatureForJob(op_graph, job)); + auto time_end = std::chrono::high_resolution_clock::now(); + VLOG(2) << "Auto parallel took " + << std::chrono::duration_cast(time_end - time_begin).count() + << " ms\n"; + if (GlobalProcessCtx::Rank() == 0) { + // sbp_constructor.PrintSBPGraphDebugInfo(); + JUST(sbp_constructor.CheckSbpAgreement(*job)); + } + return Maybe::Ok(); +} + +REGISTER_JOB_PASS("AutoParallelPass", AutoParallelPass); + +Maybe AutoParallelPass::RemoveParallelCastOps(Job* job) const { + VLOG(3) << "Remove parallel cast ops for auto_parallel:"; + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + HashMap op_name2op_conf; + HashMap op_name2nd_sbp_signature; + HashSet ctrl_in_op_names; + op_graph.ForEachNode([&](const OpNode* op_node) { + for (const std::string& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { + ctrl_in_op_names.insert(ctrl_in_op_name); + } + }); + const auto IsParallelCastOp = [](const OperatorConf& op_conf) -> bool { + return op_conf.has_user_conf() + && (op_conf.user_conf().op_type_name() == "parallel_cast" + || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast" + || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast_like"); + }; + std::vector del_op_names; + HashSet del_op_name_set; + std::function Try2Delete = [&](const OpNode* op_node) { + if (del_op_name_set.find(op_node->op().op_name()) != del_op_name_set.end()) { return; } + const OperatorConf& op_conf = op_node->op().op_conf(); + if (!IsParallelCastOp(op_conf)) { return; } + if (!op_conf.ctrl_in_op_name().empty()) { + VLOG(3) << "Skip " << op_conf.name() << ", because it has ctrl edge."; + return; + } + if (ctrl_in_op_names.find(op_conf.name()) != ctrl_in_op_names.end()) { + VLOG(3) << "Skip " << op_conf.name() << ", because it is a ctrl edge."; + return; + } + if (op_node->in_edges().size() != 1) { return; } + + // Find the first op which won't be deleted + const OpNode* source_op = op_node; + const OpNode* producer = op_node->SoleInEdge()->src_node(); + while (IsParallelCastOp(producer->op().op_conf())) { + Try2Delete(producer); + if (del_op_name_set.find(producer->op().op_name()) == del_op_name_set.end()) { break; } + source_op = producer; + producer = source_op->SoleInEdge()->src_node(); + } + user_op::UserOpConfWrapper conf_wrapper_in(source_op->op().op_conf()); + const LogicalBlobId& parallel_cast_in_lbi = GenLogicalBlobId(conf_wrapper_in.input("in", 0)); + + user_op::UserOpConfWrapper conf_wrapper_out(op_conf); + const LogicalBlobId& parallel_cast_out_lbi = + GenLogicalBlobId(conf_wrapper_out.output("out", 0)); + if (op_node->parallel_desc() != producer->parallel_desc()) { + VLOG(3) << "Skip " << op_node->op().op_name() << "(with placement: " + << *CHECK_JUST(PlacementToString(SymbolOf(op_node->parallel_desc()))) + << "), because producer " << producer->op().op_name() << "'s placement is " + << *CHECK_JUST(PlacementToString(SymbolOf(producer->parallel_desc()))); + return; + } + for (const OpEdge* out_edge : op_node->out_edges()) { + const OpNode* consumer = out_edge->dst_node(); + if (consumer->parallel_desc() != op_node->parallel_desc()) { + VLOG(3) << "Skip " << op_node->op().op_name() << "(with placement: " + << *CHECK_JUST(PlacementToString(SymbolOf(op_node->parallel_desc()))) + << "), because consumer " << consumer->op().op_name() << "'s placement is " + << *CHECK_JUST(PlacementToString(SymbolOf(consumer->parallel_desc()))); + return; + } + } + op_name2nd_sbp_signature[producer->op().op_name()] = producer->nd_sbp_signature(); + for (const OpEdge* out_edge : op_node->out_edges()) { + const OpNode* consumer = out_edge->dst_node(); + const std::string& consumer_op_name = consumer->op().op_name(); + op_name2nd_sbp_signature[consumer_op_name] = consumer->nd_sbp_signature(); + if (op_name2op_conf.find(consumer_op_name) == op_name2op_conf.end()) { + op_name2op_conf[consumer_op_name] = consumer->op().op_conf(); + } + OperatorConf& consumer_op_conf = op_name2op_conf.at(consumer_op_name); + for (const std::string& ibn : consumer->op().input_bns()) { + if (consumer->op().BnInOp2Lbi(ibn) == parallel_cast_out_lbi) { + const auto& new_val = GenLogicalBlobName(parallel_cast_in_lbi); + const auto& old_val = ReplaceInputLbnInOpCustomizedConf(&consumer_op_conf, ibn, new_val); + CHECK_EQ(GenLogicalBlobName(parallel_cast_out_lbi), old_val); + } + } + } + del_op_names.emplace_back(op_conf.name()); + del_op_name_set.insert(op_conf.name()); + VLOG(3) << "\tremove " << op_conf.name(); + }; + op_graph.ForEachNode(Try2Delete); + for (const auto& pair : op_name2op_conf) { job_builder.MutOpsOnlyOnce({pair.second}); } + for (const auto& pair : op_name2nd_sbp_signature) { + job_builder.AddNdSbpSignature4OpName(pair.first, pair.second); + } + job_builder.DelOps(del_op_names); + return Maybe::Ok(); +} + +} // namespace + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/auto_train_step.cpp b/oneflow/core/job_rewriter/auto_train_step.cpp index 28991f9db66bda5894d80418b849eb76f18056db..17e8693cb32abdefa5036945d17e41257238ea45 100644 --- a/oneflow/core/job_rewriter/auto_train_step.cpp +++ b/oneflow/core/job_rewriter/auto_train_step.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/job_rewriter/job_pass.h" #include "oneflow/core/job/job.pb.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/job_rewriter/dynamic_loss_scale_job_pass_state.h" #include "oneflow/core/framework/scope_util.h" @@ -59,12 +58,18 @@ Maybe AutoTrainStep::Apply(Job* job, JobPassCtx* ctx) const { GenLogicalBlobName(identity_op_conf.name(), identity_conf->out()); JobBuilder job_builder(job); - const ParallelConf& parallel_conf = GenParallelConfOfCpuZeroOnMaster(); + ParallelConf parallel_conf; + if (ParseBooleanFromEnv("ONEFLOW_GRAPH_PLACE_TRAINING_STATE_ON_ALL_RANKS", false)) { + parallel_conf = GenParallelConfOfCpuOnAllRanks(); + + } else { + parallel_conf = GenParallelConfOfCpuZeroOnMaster(); + } int64_t scope_symbol_id = 0; { const auto& opt_scope_symbol_id = JUST(MakeInitialScope(job->job_conf(), SymbolOf(ParallelDesc(parallel_conf)), - /* is_mirrored */ false)) + /* is_local */ false)) ->symbol_id(); CHECK_OR_RETURN(opt_scope_symbol_id.has_value()) << Error::RuntimeError() << "symbol_id not initialized"; diff --git a/oneflow/core/job_rewriter/autograd.cpp b/oneflow/core/job_rewriter/autograd.cpp index 4fdf6f3b50d416cdbf1d2d6b92cbab97cc5a05cb..1cf84fab360763ad20b548f11642de61e90f207d 100644 --- a/oneflow/core/job_rewriter/autograd.cpp +++ b/oneflow/core/job_rewriter/autograd.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/job_rewriter/autograd.h" #include "oneflow/core/job/job_builder.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/job_rewriter/clone_grad.h" #include "oneflow/core/operator/variable_op.h" #include "oneflow/core/register/op_blob_arg.pb.h" @@ -27,6 +26,7 @@ limitations under the License. #include "oneflow/core/job_rewriter/dynamic_loss_scale_job_pass_state.h" #include "oneflow/core/framework/scope_util.h" #include "oneflow/core/job_rewriter/clip_by_global_norm_job_pass_state.h" +#include "oneflow/core/job_rewriter/pass_util.h" namespace oneflow { @@ -82,7 +82,7 @@ Maybe GetLossOpNodes(const OpGraph& op_graph, std::list* loss_op_ loss_op_nodes->emplace_back(op_node); } }); - if (loss_op_nodes->empty()) { return Error::LossBlobNotFoundError("Loss blob not found."); } + if (loss_op_nodes->empty()) { return Error::LossBlobNotFoundError() << "Loss blob not found."; } return Maybe::Ok(); } @@ -99,117 +99,6 @@ Maybe GetLossOpNodesAndAscendants(const OpGraph& op_graph, HashSet::Ok(); } -std::function MakePredicatorHasDiff4LbiOpName( - const OpGraph& op_graph, const std::function& NeedBackwardOp) { - auto lbis2ops_with_in_diff = std::make_shared>>(); - op_graph.ForEachEdge([&](OpEdge* edge) { - if (NeedBackwardOp(edge->src_node()) && NeedBackwardOp(edge->dst_node())) { - for (const auto& lbi : edge->lbis()) { - const auto& obn = edge->lbi2obn().at(lbi); - if (edge->src_node()->op().OutputBlobModifier4Obn(obn).requires_grad()) { - (*lbis2ops_with_in_diff)[lbi].emplace(edge->dst_node()->op().op_name()); - } - } - } - }); - return [lbis2ops_with_in_diff](const LogicalBlobId& lbi, const std::string& op_name) { - if (lbis2ops_with_in_diff->find(lbi) == lbis2ops_with_in_diff->end()) { return false; } - const auto& op_names = lbis2ops_with_in_diff->at(lbi); - return op_names.find(op_name) != op_names.end(); - }; -} - -void GenerateOriginDiffLbi(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, - const LogicalBlobId& lbi, const ParallelDesc& parallel_desc, - const std::function& AddOp, - LogicalBlobId* out_diff_lbi) { - const TrainConf& train_conf = ctx->job_desc().job_conf().train_conf(); - OperatorConf constant_like_op{}; - constant_like_op.set_name(lbi.op_name() + "_" + lbi.blob_name() + "_grad_ConstantLike"); - ConstantLikeOpConf* constant_like_conf = constant_like_op.mutable_constant_like_conf(); - constant_like_conf->set_like(GenLogicalBlobName(lbi)); - constant_like_conf->set_out("out"); - { - float origin_grad; - if (train_conf.has_loss_scale_factor()) { - origin_grad = train_conf.loss_scale_factor(); - } else { - origin_grad = 1.0; - } - constant_like_conf->set_float_operand(origin_grad); - } - AddOp(constant_like_op); - NdSbp broadcast_nd_sbp; - for (int32_t i = 0; i < parallel_desc.hierarchy()->NumAxes(); ++i) { - broadcast_nd_sbp.add_sbp_parallel()->mutable_broadcast_parallel(); - } - if (train_conf.has_dynamic_loss_scale_policy()) { - const auto& dynamic_loss_scale_state = - CHECK_JUST(ctx->GetState("dynamic_loss_scale_state")); - std::string loss_scale_val_lbn; - const DataType data_type = op_graph.GetLogicalBlobDesc(lbi).data_type(); - if (data_type == DataType::kFloat) { - loss_scale_val_lbn = dynamic_loss_scale_state.loss_scale_val_lbn(); - } else { - auto cast_op = - user_op::UserOpConfWrapperBuilder(lbi.op_name() + "_" + lbi.blob_name() + "_grad_Cast") - .Op("cast") - .Input("in", dynamic_loss_scale_state.loss_scale_val_lbn()) - .Output("out") - .Attr("dtype", data_type) - .ScopeSymbolId(ScopeSymbolId4Lbi(op_graph, lbi)) - .Build(); - AddOp(cast_op.op_conf()); - OpBlobArg cast_in_op_blob_arg; - cast_in_op_blob_arg.set_op_name(cast_op.op_name()); - cast_in_op_blob_arg.set_bn_in_op(GenRepeatedBn("in", 0)); - job_builder->SetNdSbp4Oba(cast_in_op_blob_arg, broadcast_nd_sbp); - OpBlobArg cast_out_op_blob_arg; - cast_out_op_blob_arg.set_op_name(cast_op.op_name()); - cast_out_op_blob_arg.set_bn_in_op(GenRepeatedBn("out", 0)); - job_builder->SetNdSbp4Oba(cast_out_op_blob_arg, broadcast_nd_sbp); - loss_scale_val_lbn = cast_op.output("out", 0); - } - { - const OpNode* loss_node = op_graph.OpNode4OpName(lbi.op_name()); - const int64_t time_shape_elem_cnt = - CHECK_JUST(loss_node->op().GetInputBlobFastestTimeShape())->elem_cnt(); - if (time_shape_elem_cnt != 1) { - const auto repeat_op = user_op::UserOpConfWrapperBuilder(lbi.op_name() + "_" - + lbi.blob_name() + "_grad_Repeat") - .OpTypeName("repeat") - .Input("in", loss_scale_val_lbn) - .Output("out") - .Attr("repeat_num", time_shape_elem_cnt) - .Build(); - AddOp(repeat_op.op_conf()); - OpBlobArg repeat_in_op_blob_arg; - repeat_in_op_blob_arg.set_op_name(repeat_op.op_name()); - repeat_in_op_blob_arg.set_bn_in_op(GenRepeatedBn("in", 0)); - job_builder->SetNdSbp4Oba(repeat_in_op_blob_arg, broadcast_nd_sbp); - OpBlobArg repeat_out_op_blob_arg; - repeat_out_op_blob_arg.set_op_name(repeat_op.op_name()); - repeat_out_op_blob_arg.set_bn_in_op(GenRepeatedBn("out", 0)); - job_builder->SetNdSbp4Oba(repeat_out_op_blob_arg, broadcast_nd_sbp); - loss_scale_val_lbn = repeat_op.output("out", 0); - } - } - auto scalar_mul_op = - user_op::UserOpConfWrapperBuilder(lbi.op_name() + "_" + lbi.blob_name() + "_grad_Scale") - .Op("scalar_mul_by_tensor") - .Input("x", GenLogicalBlobName(constant_like_op.name(), constant_like_conf->out())) - .Input("scalar", loss_scale_val_lbn) - .Output("y") - .ScopeSymbolId(ScopeSymbolId4Lbi(op_graph, lbi)) - .Build(); - AddOp(scalar_mul_op.op_conf()); - *out_diff_lbi = GenLogicalBlobId(scalar_mul_op.output("y", 0)); - } else { - out_diff_lbi->set_op_name(constant_like_op.name()); - out_diff_lbi->set_blob_name(constant_like_conf->out()); - } -} - const ParallelConf& ProducerParallelConf4Lbi(const OpGraph& op_graph, const LogicalBlobId& lbi) { return op_graph.OpNode4OpName(lbi.op_name())->parallel_desc().parallel_conf(); } @@ -223,7 +112,8 @@ void ScaleModelDiffByConstantLossInstanceNum(const OpGraph& op_graph, JobBuilder const LogicalBlobId& lbi = pair.first; LogicalBlobId& diff_lbi = pair.second; auto scalar_mul_op = - user_op::UserOpConfWrapperBuilder("System-ModelDiffScale-ScalarMul_" + NewUniqueId()) + user_op::UserOpConfWrapperBuilder("Sys-DiffScale-ScalarMul-" + lbi.op_name() + "_" + + lbi.blob_name() + "-" + NewUniqueId()) .Op("scalar_mul") .Input("in", GenLogicalBlobName(diff_lbi)) .Output("out") @@ -238,33 +128,33 @@ void ScaleModelDiffByConstantLossInstanceNum(const OpGraph& op_graph, JobBuilder } } -Maybe TryMirroredCastTotalLossInstanceNum( +Maybe TryLocalCastTotalLossInstanceNum( JobBuilder* job_builder, const HashMap& loss_lbi2loss_node, LogicalBlobId* total_loss_instance_num_lbi) { - auto IsMirrored4Lbi = [](const LogicalBlobId& lbi, OpNode* op_node) -> Maybe { + auto IsLocal4Lbi = [](const LogicalBlobId& lbi, OpNode* op_node) -> Maybe { const auto& obn = *JUST(op_node->op().obn4lbi(lbi)); - const auto& opt_mirrored_parallel = *JUST(op_node->op().OptMirroredParallel4BnInOp(obn)); - return opt_mirrored_parallel.has_mirrored_parallel(); + const auto& opt_local_parallel = *JUST(op_node->op().OptLocalParallel4BnInOp(obn)); + return opt_local_parallel.has_local_parallel(); }; const auto& begin = *loss_lbi2loss_node.begin(); - bool is_mirrored = JUST(IsMirrored4Lbi(begin.first, begin.second)); + bool is_local = JUST(IsLocal4Lbi(begin.first, begin.second)); for (const auto& pair : loss_lbi2loss_node) { - bool is_other_mirrored = JUST(IsMirrored4Lbi(pair.first, pair.second)); - CHECK_EQ_OR_RETURN(is_mirrored, is_other_mirrored); + bool is_other_local = JUST(IsLocal4Lbi(pair.first, pair.second)); + CHECK_EQ_OR_RETURN(is_local, is_other_local); // NOLINT } - if (is_mirrored) { + if (is_local) { OperatorConf op_conf; - op_conf.set_name("System-Cast-Mirrored-TotalLossInstanceNum" + NewUniqueId()); - CastFromMirroredOpConf* cast_from_mirrored = op_conf.mutable_cast_from_mirrored_conf(); - cast_from_mirrored->set_in(GenLogicalBlobName(*total_loss_instance_num_lbi)); - cast_from_mirrored->set_out("out"); - cast_from_mirrored->mutable_sbp_parallel()->mutable_partial_sum_parallel(); + op_conf.set_name("System-Cast-Local-TotalLossInstanceNum" + NewUniqueId()); + CastFromLocalOpConf* cast_from_local = op_conf.mutable_cast_from_local_conf(); + cast_from_local->set_in(GenLogicalBlobName(*total_loss_instance_num_lbi)); + cast_from_local->set_out("out"); + cast_from_local->mutable_sbp_parallel()->mutable_partial_sum_parallel(); const auto& parallel_conf = JUST(job_builder->ParallelConf4Lbi(*total_loss_instance_num_lbi)); int64_t scope_symbol_id = 0; { const auto& opt_scope_symbol_id = JUST(MakeInitialScope(job_builder->job().job_conf(), SymbolOf(ParallelDesc(parallel_conf)), - /* is_mirrored */ false)) + /* is_local */ false)) ->symbol_id(); CHECK_OR_RETURN(opt_scope_symbol_id.has_value()) << Error::RuntimeError() << "symbol_id not initialized"; @@ -322,7 +212,7 @@ void ScaleModelDiffByDynamicLossInstanceNum( const auto& opt_scope_symbol_id = CHECK_JUST(MakeInitialScope(job_builder->job().job_conf(), SymbolOf(ParallelDesc(parallel_conf)), - /* is_mirrored */ false)) + /* is_local */ false)) ->symbol_id(); if (!opt_scope_symbol_id.has_value()) { THROW(RuntimeError) << "symbol_id not initialized"; } scope_symbol_id = CHECK_JUST(opt_scope_symbol_id); @@ -335,13 +225,14 @@ void ScaleModelDiffByDynamicLossInstanceNum( } else { UNIMPLEMENTED(); } - CHECK_JUST(TryMirroredCastTotalLossInstanceNum(job_builder, loss_lbi2loss_node, - &total_loss_instance_num_lbi)); + CHECK_JUST(TryLocalCastTotalLossInstanceNum(job_builder, loss_lbi2loss_node, + &total_loss_instance_num_lbi)); for (auto& pair : *lbi2diff_lbi) { const LogicalBlobId& lbi = pair.first; LogicalBlobId& diff_lbi = pair.second; auto scalar_div_op = - user_op::UserOpConfWrapperBuilder("System-ModelDiffScale-ScalarDiv_" + NewUniqueId()) + user_op::UserOpConfWrapperBuilder("Sys-DiffScale-ScalarDiv-" + lbi.op_name() + "_" + + lbi.blob_name() + "-" + NewUniqueId()) .Op("scalar_div_by_tensor") .Input("x", GenLogicalBlobName(diff_lbi)) .Input("scalar", GenLogicalBlobName(total_loss_instance_num_lbi)) @@ -360,49 +251,6 @@ bool AllSplitDistribution(const NdSbp& nd_sbp) { return true; } -void InitOutOba2OutDiffLbi(JobPassCtx* ctx, const OpGraph& op_graph, - const std::list& loss_nodes, - HashMap* out_oba2out_diff_lbi, - JobBuilder* job_builder) { - for (const std::string& loss_lbn : ctx->job_desc().job_conf().train_conf().loss_lbn()) { - const LogicalBlobId loss_lbi = GenLogicalBlobId(loss_lbn); - const auto loss_node_it = std::find_if( - loss_nodes.cbegin(), loss_nodes.cend(), - [&](const OpNode* node) { return node->op().op_name() == loss_lbi.op_name(); }); - CHECK(loss_node_it != loss_nodes.cend()); - const OpNode* loss_op_node = *loss_node_it; - const auto bn_it = std::find_if( - loss_op_node->op().output_bns().cbegin(), loss_op_node->op().output_bns().cend(), - [&](const std::string& obn) { return loss_lbi == loss_op_node->op().BnInOp2Lbi(obn); }); - CHECK(bn_it != loss_op_node->op().output_bns().cend()); - LogicalBlobId* out_diff_lbi = - &(*out_oba2out_diff_lbi)[GenOpBlobArg(loss_op_node->op().op_name(), *bn_it)]; - int64_t scope_symbol_id = loss_op_node->op().op_conf().scope_symbol_id(); - const auto AddOp = [&](const OperatorConf& op_conf) { - OperatorConf new_op_conf = op_conf; - new_op_conf.set_scope_symbol_id(scope_symbol_id); - job_builder->AddOps(loss_op_node->parallel_desc().parallel_conf(), {new_op_conf}); - }; - GenerateOriginDiffLbi(ctx, op_graph, job_builder, loss_lbi, loss_op_node->parallel_desc(), - AddOp, out_diff_lbi); - } -} - -void CalcOutLbi2OutDiffLbi(const OpGraph& op_graph, - const HashMap& out_oba2out_diff_lbi, - HashMap* out_lbi2out_diff_lbi) { - op_graph.ForEachNode([&](OpNode* op_node) { - for (const auto& obn : op_node->op().output_bns()) { - const auto& lbi = op_node->op().BnInOp2Lbi(obn); - const auto& oba = GenOpBlobArg(op_node->op().op_name(), obn); - const auto& out_diff_lbi_it = out_oba2out_diff_lbi.find(oba); - if (out_diff_lbi_it != out_oba2out_diff_lbi.end()) { - CHECK(out_lbi2out_diff_lbi->emplace(lbi, out_diff_lbi_it->second).second); - } - } - }); -} - void ForEachAggregatedParamGroup( const OpGraph& op_graph, const HashMap& lbi2diff_lbi, const std::functionsymbol_id(); if (!opt_scope_symbol_id.has_value()) { THROW(RuntimeError) << "symbol_id not initialized"; } return CHECK_JUST(opt_scope_symbol_id); @@ -807,150 +655,6 @@ Maybe MakeGetterLossOpNode4OpName( return Maybe::Ok(); } -Maybe MakePredicatorNeedBackwardOp(const OpGraph& op_graph, - std::function* NeedBackwardOp) { - auto var_op_nodes_and_descendants = std::make_shared>(); - GetVariableOpNodesAndDescendants(op_graph, var_op_nodes_and_descendants.get()); - auto loss_op_nodes_and_ascendants = std::make_shared>(); - JUST(GetLossOpNodesAndAscendants(op_graph, loss_op_nodes_and_ascendants.get())); - *NeedBackwardOp = [var_op_nodes_and_descendants, loss_op_nodes_and_ascendants](OpNode* op_node) { - if (var_op_nodes_and_descendants->find(op_node) == var_op_nodes_and_descendants->end()) { - return false; - } - if (loss_op_nodes_and_ascendants->find(op_node) == loss_op_nodes_and_ascendants->end()) { - return false; - } - for (const auto& ibn : op_node->op().input_bns()) { - if (op_node->op().InputBlobModifier4Ibn(ibn).requires_grad()) { return true; } - } - for (const auto& obn : op_node->op().output_bns()) { - if (op_node->op().OutputBlobModifier4Obn(obn).requires_grad()) { return true; } - } - return false; - }; - return Maybe::Ok(); -} - -void GetVariableOpNodesAndDescendants(const OpGraph& op_graph, HashSet* op_nodes) { - std::list starts; - op_graph.ForEachNode([&](OpNode* op_node) { - const auto& op_conf = op_node->op().op_conf(); - if (op_conf.has_variable_conf()) { starts.emplace_back(op_node); } - if (op_conf.has_user_conf() - && op_conf.user_conf().op_type_name() == "embedding_lookup_placeholder") { - starts.push_back(op_node); - } - }); - auto ForEachNextNode = [&](OpNode* op_node, const std::function& Handler) { - for (OpEdge* edge : op_node->out_edges()) { - if (AnyLbiWithDiffLbi(edge)) { Handler(edge->dst_node()); } - } - }; - op_graph.BfsForEachNode(starts, ForEachNextNode, - [&](OpNode* op_node) { op_nodes->emplace(op_node); }); -} - -Maybe GenerateBackwardOpConfWrapperStruct::Call( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) const { - if (naive_func_) { - (*naive_func_)(op, op_confs, DiffLbi4BnInOp); - } else if (maybe_func_) { - JUST((*maybe_func_)(op, op_confs, DiffLbi4BnInOp, LogicalBlobDesc4BnInOp)); - } else { - UNIMPLEMENTED_THEN_RETURN() << "\nNo gradient function found\n" - << PbMessage2TxtString(op.op_conf()); - } - return Maybe::Ok(); -} - -Maybe GenerateBackwardOpConfIf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - std::unique_ptr obj; - const auto& op_type_case = op.op_conf().op_type_case(); - if (!IsClassRegistered(op_type_case)) { - return Error::GradientFunctionNotFoundError() << PbMessage2TxtString(op.op_conf()); - } - obj.reset(NewObj(op_type_case)); - return obj->Call(op, op_confs, DiffLbi4BnInOp, LogicalBlobDesc4BnInOp); -} - -Maybe AutoGrad(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, - HashMap* out_lbi2out_diff_lbi, - OpBlobArgPairs* identical_sbp_oba_pairs) { - std::function NeedBackwardOp; - JUST(MakePredicatorNeedBackwardOp(op_graph, &NeedBackwardOp)); - std::list loss_nodes; - JUST(GetLossOpNodes(op_graph, &loss_nodes)); - CheckNotReachableAmongOpNodes(op_graph, loss_nodes); - for (OpNode* loss_node : loss_nodes) { - CHECK(NeedBackwardOp(loss_node)) << loss_node->op().op_name(); - } - - // generate ones lbi as loss's diff - HashMap out_oba2out_diff_lbi; - InitOutOba2OutDiffLbi(ctx, op_graph, loss_nodes, &out_oba2out_diff_lbi, job_builder); - - // generate backward ops - auto ForEachInNode = [&](OpNode* op_node, const std::function& Handler) { - op_node->ForEachNodeOnInEdge([&](OpNode* in_node) { - if (NeedBackwardOp(in_node)) { Handler(in_node); } - }); - }; - auto ForEachOutNode = [&](OpNode* op_node, const std::function& Handler) { - op_node->ForEachNodeOnOutEdge([&](OpNode* out_node) { - if (NeedBackwardOp(out_node)) { Handler(out_node); } - }); - }; - auto HasDiff4LbiOpName = MakePredicatorHasDiff4LbiOpName(op_graph, NeedBackwardOp); - HashMap in_oba2in_diff_lbi; - HashMap out_oba2clone_bw_add_out_lbi; - std::list topo_nodes; - op_graph.TopoForEachNode(loss_nodes, ForEachOutNode, ForEachInNode, - [&](OpNode* op_node) { topo_nodes.emplace_back(op_node); }); - for (OpNode* op_node : topo_nodes) { - const auto& op_name = op_node->op().op_name(); - auto DiffLbi4BnInOp = [&](const std::string& bn) -> LogicalBlobId* { - const auto& input_bns = op_node->op().input_bns(); - const auto& output_bns = op_node->op().output_bns(); - if (std::find(input_bns.begin(), input_bns.end(), bn) != input_bns.end()) { - if (HasDiff4LbiOpName(op_node->op().BnInOp2Lbi(bn), op_name) == false) { return nullptr; } - if (op_node->op().InputBlobModifier4Ibn(bn).requires_grad() == false) { return nullptr; } - return &in_oba2in_diff_lbi[GenOpBlobArg(op_name, bn)]; - } else if (std::find(output_bns.begin(), output_bns.end(), bn) != output_bns.end()) { - if (op_node->op().OutputBlobModifier4Obn(bn).requires_grad() == false) { return nullptr; } - const auto& out_diff_lbi_it = out_oba2out_diff_lbi.find(GenOpBlobArg(op_name, bn)); - if (out_diff_lbi_it == out_oba2out_diff_lbi.end()) { return nullptr; } - return &out_diff_lbi_it->second; - } else { - LOG(FATAL) << "diff lbi for bn in op not found, bn: " << op_name << "/" << bn; - } - return nullptr; - }; - auto LogicalBlobDesc4BnInOp = [&](const std::string& bn) -> const BlobDesc& { - return op_graph.GetLogicalBlobDesc(op_node->op().BnInOp2Lbi(bn)); - }; - JUST(GenerateCloneGradOpIfNeed(*op_node, job_builder, in_oba2in_diff_lbi, &out_oba2out_diff_lbi, - &out_oba2clone_bw_add_out_lbi)); - std::vector ops; - JUST(GenerateBackwardOpConfIf(op_node->op(), &ops, DiffLbi4BnInOp, LogicalBlobDesc4BnInOp)); - int64_t scope_symbol_id = op_node->op().op_conf().scope_symbol_id(); - for (auto& op_conf : ops) { op_conf.set_scope_symbol_id(scope_symbol_id); } - if (op_node->op().op_conf().has_user_conf() - && op_node->op().op_conf().user_conf().op_type_name() == "hierarchical_parallel_cast") { - const auto& producer_node = op_node->ProducerOpNode4Lbi(op_node->op().BnInOp2Lbi("in_0")); - job_builder->AddOps(producer_node.parallel_desc().parallel_conf(), ops); - } else { - job_builder->AddOps(op_node->parallel_desc().parallel_conf(), ops); - } - } - CalcOutLbi2OutDiffLbi(op_graph, out_oba2out_diff_lbi, out_lbi2out_diff_lbi); - return Maybe::Ok(); -} - Maybe ScaleModelDiffByLossInstanceNum(const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi) { std::function LossOpNode4OpName; @@ -1008,6 +712,123 @@ Maybe ScaleModelDiffByLossInstanceNum(const OpGraph& op_graph, JobBuilder* return Maybe::Ok(); } +Maybe ScaleInitialDiffByLossScale( + JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, + HashMap* loss_lbi2initial_diff_lbi) { + const TrainConf& train_conf = ctx->job_desc().job_conf().train_conf(); + if (!train_conf.has_dynamic_loss_scale_policy() && !train_conf.has_loss_scale_factor()) { + return Maybe::Ok(); + } + for (auto& it : *loss_lbi2initial_diff_lbi) { + const auto& loss_lbi = it.first; + const auto& initial_diff_lbi = it.second; + const OpNode* initial_diff_node = op_graph.OpNode4OpName(initial_diff_lbi.op_name()); + int64_t scope_symbol_id = initial_diff_node->op().op_conf().scope_symbol_id(); + const auto& parallel_conf = initial_diff_node->parallel_desc().parallel_conf(); + + std::string loss_diff_lbn = GenLogicalBlobName(initial_diff_lbi); + const DataType init_diff_data_type = op_graph.GetLogicalBlobDesc(initial_diff_lbi).data_type(); + // cast loss init diff from float16 to float32 since we need do loss scale (float32 multiply) + // later + if (init_diff_data_type != DataType::kFloat) { + std::string cast_op_name = + initial_diff_lbi.op_name() + "_" + initial_diff_lbi.blob_name() + "_loss_scale-cast_h2f"; + auto cast_op = user_op::UserOpConfWrapperBuilder(cast_op_name) + .Op("cast") + .Input("in", loss_diff_lbn) + .Output("out") + .Attr("dtype", DataType::kFloat) + .ScopeSymbolId(scope_symbol_id) + .Build(); + job_builder->AddOps(parallel_conf, {cast_op.op_conf()}); + loss_diff_lbn = cast_op.output("out", 0); + } + + std::string loss_scale_val_lbn; + if (train_conf.has_dynamic_loss_scale_policy()) { + const auto& dynamic_loss_scale_state = + JUST(ctx->GetState("dynamic_loss_scale_state")); + loss_scale_val_lbn = dynamic_loss_scale_state.loss_scale_val_lbn(); + } else if (train_conf.has_loss_scale_factor()) { + OperatorConf constant_like_op{}; + constant_like_op.set_name(loss_lbi.op_name() + "_" + loss_lbi.blob_name() + + "_constant_like_loss_scale"); + constant_like_op.set_scope_symbol_id(scope_symbol_id); + ConstantLikeOpConf* constant_like_conf = constant_like_op.mutable_constant_like_conf(); + constant_like_conf->set_like(loss_diff_lbn); + constant_like_conf->set_out("out"); + constant_like_conf->set_float_operand(train_conf.loss_scale_factor()); + job_builder->AddOps(parallel_conf, {constant_like_op}); + loss_scale_val_lbn = GenLogicalBlobName(constant_like_op.name(), constant_like_conf->out()); + } else { + UNIMPLEMENTED_THEN_RETURN() << "dynamic or static loss scale must be config"; + } + + const int64_t time_shape_elem_cnt = + JUST(initial_diff_node->op().GetInputBlobFastestTimeShape())->elem_cnt(); + if (time_shape_elem_cnt != 1) { + const auto repeat_op = + user_op::UserOpConfWrapperBuilder(loss_lbi.op_name() + "_" + loss_lbi.blob_name() + + "_loss_scale-repeat") + .OpTypeName("repeat") + .Input("in", loss_scale_val_lbn) + .Output("out") + .Attr("repeat_num", time_shape_elem_cnt) + .ScopeSymbolId(scope_symbol_id) + .Build(); + job_builder->AddOps(parallel_conf, {repeat_op.op_conf()}); + loss_scale_val_lbn = repeat_op.output("out", 0); + } + + auto scalar_mul_op = + user_op::UserOpConfWrapperBuilder(initial_diff_lbi.op_name() + "_" + + initial_diff_lbi.blob_name() + "_scale_initial_diff") + .Op("scalar_mul_by_tensor") + .Input("x", loss_diff_lbn) + .Input("scalar", loss_scale_val_lbn) + .Output("y") + .ScopeSymbolId(scope_symbol_id) + .Build(); + job_builder->AddOps(parallel_conf, {scalar_mul_op.op_conf()}); + std::string scaled_initial_diff_lbn = scalar_mul_op.output("y", 0); + + // cast loss initial diff back to float16 + if (init_diff_data_type != DataType::kFloat) { + std::string cast_op_name = + initial_diff_lbi.op_name() + "_" + initial_diff_lbi.blob_name() + "_loss_scale-cast_f2h"; + auto cast_op = user_op::UserOpConfWrapperBuilder(cast_op_name) + .Op("cast") + .Input("in", scaled_initial_diff_lbn) + .Output("out") + .Attr("dtype", init_diff_data_type) + .ScopeSymbolId(scope_symbol_id) + .Build(); + job_builder->AddOps(parallel_conf, {cast_op.op_conf()}); + scaled_initial_diff_lbn = cast_op.output("out", 0); + } + + // update consumer input by scalar_mul_op output + initial_diff_node->ForEachNodeOnOutEdge([&](const OpNode* out_node) { + for (const std::string& ibn : out_node->op().input_bns()) { + if (out_node->op().BnInOp2Lbi(ibn) == initial_diff_lbi) { + if (!CHECK_JUST(job_builder->IsInMutOpTransaction(out_node->op().op_name()))) { + CHECK_JUST(job_builder->MutOpTransactionMut(out_node->op().op_conf())); + } + OperatorConf& mut_consumer_op = + CHECK_JUST(job_builder->MutOpTransactionGet(out_node->op().op_name())); + const auto& old_lbn = + ReplaceInputLbnInOpCustomizedConf(&mut_consumer_op, ibn, scaled_initial_diff_lbn); + CHECK_EQ(old_lbn, GenLogicalBlobName(initial_diff_lbi)); + } + } + }); + // update initial diff lbi + it.second = GenLogicalBlobId(scaled_initial_diff_lbn); + } + JUST(job_builder->MutOpTransactionCommit()); + return Maybe::Ok(); +} + void ScaleModelDiffByLossScale(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi) { auto ProducerOpNode4Lbi = [&](const LogicalBlobId& lbi) { @@ -1063,7 +884,8 @@ void ScaleModelDiffByLossScale(JobPassCtx* ctx, const OpGraph& op_graph, JobBuil const LogicalBlobId& lbi = pair.first; LogicalBlobId& diff_lbi = pair.second; auto scalar_mul_op = - user_op::UserOpConfWrapperBuilder("System-ModelDiffScale-ScalarMul-" + NewUniqueId()) + user_op::UserOpConfWrapperBuilder("Sys-DiffScale-ScalarMul-" + lbi.op_name() + "_" + + lbi.blob_name() + "-" + NewUniqueId()) .Op("scalar_mul_by_tensor") .Input("x", GenLogicalBlobName(diff_lbi)) .Input("scalar", LossScale4DataType(op_graph.GetLogicalBlobDesc(lbi).data_type())) @@ -1081,7 +903,8 @@ void ScaleModelDiffByLossScale(JobPassCtx* ctx, const OpGraph& op_graph, JobBuil const LogicalBlobId& lbi = pair.first; LogicalBlobId& diff_lbi = pair.second; auto scalar_mul_op = - user_op::UserOpConfWrapperBuilder("System-ModelDiffScale-ScalarMul-" + NewUniqueId()) + user_op::UserOpConfWrapperBuilder("Sys-DiffScale-ScalarMul-" + lbi.op_name() + "_" + + lbi.blob_name() + "-" + NewUniqueId()) .Op("scalar_mul") .Input("in", GenLogicalBlobName(diff_lbi)) .Output("out") @@ -1170,6 +993,28 @@ void AddDiffParallelCast(const OpGraph& op_graph, JobBuilder* job_builder, } } +void AddDiffHalf2FloatCast(const OpGraph& op_graph, JobBuilder* job_builder, + HashMap* lbi2diff_lbi) { + for (auto& pair : *lbi2diff_lbi) { + LogicalBlobId& diff_lbi = pair.second; + auto data_type = op_graph.GetLogicalBlobDesc(diff_lbi).data_type(); + if (data_type != DataType::kFloat) { + std::string lbn = GenLogicalBlobName(diff_lbi); + const OpNode* op_node = op_graph.OpNode4OpName(diff_lbi.op_name()); + int64_t scope_symbol_id = op_node->op().op_conf().scope_symbol_id(); + auto cast_op = user_op::UserOpConfWrapperBuilder(ReplaceSlashToDash4Lbn(lbn) + "-cast_h2f") + .Op("cast") + .Input("in", lbn) + .Output("out") + .Attr("dtype", DataType::kFloat) + .ScopeSymbolId(scope_symbol_id) + .Build(); + job_builder->AddOps(op_node->parallel_desc().parallel_conf(), {cast_op.op_conf()}); + diff_lbi = GenLogicalBlobId(cast_op.output("out", 0)); + } + } +} + void AddDiffStaticShapeCast(const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi) { for (auto& pair : *lbi2diff_lbi) { diff --git a/oneflow/core/job_rewriter/autograd.h b/oneflow/core/job_rewriter/autograd.h index 3f6b3ffb029ff98d068a947a8fe799198deebe70..b0be476689292561ee582fc8d17f69e48de533af 100644 --- a/oneflow/core/job_rewriter/autograd.h +++ b/oneflow/core/job_rewriter/autograd.h @@ -24,11 +24,8 @@ namespace oneflow { class JobPassCtx; -Maybe MakePredicatorNeedBackwardOp(const OpGraph& op_graph, - std::function* NeedBackwardOp); -Maybe AutoGrad(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, - HashMap* out_lbi2out_diff_lbi, - OpBlobArgPairs* identical_sbp_oba_pairs); +void AddDiffHalf2FloatCast(const OpGraph& op_graph, JobBuilder* job_builder, + HashMap* lbi2diff_lbi); void AddDiffParallelCast(const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi); void AddDiffStaticShapeCast(const OpGraph& op_graph, JobBuilder* job_builder, @@ -40,43 +37,17 @@ Maybe MakeGetterLossOpNode4OpName( const OpGraph& op_graph, std::function* LossOpNode4OpName); Maybe ScaleModelDiffByLossInstanceNum(const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi); + +Maybe ScaleInitialDiffByLossScale( + JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, + HashMap* loss_lbi2initial_diff_lbi); + void ScaleModelDiffByLossScale(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi); void RegularizeGradient(const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi); void ClipGradient(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, HashMap* lbi2diff_lbi, const ClipConf& clip_conf); -Maybe GenerateBackwardOpConfIf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp); -void GetVariableOpNodesAndDescendants(const OpGraph& op_graph, HashSet* op_nodes); - -class GenerateBackwardOpConfWrapperStruct final { - public: - using NaiveFunc = std::function*, - const std::function&)>; - using MaybeFunc = - std::function(const Operator&, std::vector*, - const std::function&, - const std::function&)>; - GenerateBackwardOpConfWrapperStruct(const NaiveFunc& f) - : naive_func_(std::make_unique(f)) {} - GenerateBackwardOpConfWrapperStruct(const MaybeFunc& f) - : maybe_func_(std::make_unique(f)) {} - Maybe Call(const Operator&, std::vector*, - const std::function&, - const std::function&) const; - - private: - const std::unique_ptr naive_func_; - const std::unique_ptr maybe_func_; -}; - -#define REGISTER_OP_GRAD(op_type_case, gen_grad_func) \ - REGISTER_CLASS_CREATOR(int32_t, op_type_case, GenerateBackwardOpConfWrapperStruct, \ - ([] { return new GenerateBackwardOpConfWrapperStruct(gen_grad_func); })) - } // namespace oneflow #endif // ONEFLOW_CORE_JOB_REWRITER_AUTOGRAD_H_ diff --git a/oneflow/core/job_rewriter/autotick.cpp b/oneflow/core/job_rewriter/autotick.cpp index 6bac104b19ef4bf98be31a49cd77982f9a4ac20b..7a58866b42107bf6fda44d6315b01e53443bf446 100644 --- a/oneflow/core/job_rewriter/autotick.cpp +++ b/oneflow/core/job_rewriter/autotick.cpp @@ -15,6 +15,8 @@ limitations under the License. */ #include "oneflow/core/common/container_util.h" #include "oneflow/core/common/protobuf.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" #include "oneflow/core/job_rewriter/autotick.h" #include "oneflow/core/job/job_builder.h" #include "oneflow/core/job/critical_section_desc.h" @@ -22,6 +24,8 @@ limitations under the License. #include "oneflow/core/common/container_util.h" #include "oneflow/core/common/buffer_manager.h" #include "oneflow/core/job/global_for.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "oneflow/core/operator/operator.h" namespace oneflow { @@ -449,8 +453,8 @@ Maybe AddGlobalInputOutputCriticalSection( return Maybe::Ok(); } -Maybe MultiClientAddWaitAndSendIds(JobBuilder* job_builder, int64_t machine_id, - const std::string& src_op_name) { +Maybe MultiClientAddOneWaitAndSendIdsOp(JobBuilder* job_builder, int64_t machine_id, + const OperatorConf& src_op_consumer) { ParallelConf parallel_conf; { parallel_conf.set_device_tag("cpu"); @@ -472,30 +476,54 @@ Maybe MultiClientAddWaitAndSendIds(JobBuilder* job_builder, int64_t machin // connect wait_and_send_ids to tick op which was connected to the src tick op OperatorConf tick_op_conf; - bool find_src_tick_consumer_tick = false; + tick_op_conf.CopyFrom(src_op_consumer); + CHECK_OR_RETURN(tick_op_conf.has_tick_conf()); + CHECK_EQ_OR_RETURN(tick_op_conf.tick_conf().tick_size(), 1); + tick_op_conf.mutable_tick_conf()->clear_tick(); + tick_op_conf.mutable_tick_conf()->add_tick( + GenLogicalBlobName(wait_and_send_ids_op_conf.name(), "out")); + JUST(job_builder->MutOpOnlyOnce(tick_op_conf)); + + return Maybe::Ok(); +} + +Maybe MultiClientAddWaitAndSendIds( + JobBuilder* job_builder, const HashMap& machine_id2src_op_name) { + // Prepare the consumer tick op for each Source op + HashMap src_op_name2solo_consumer_tick_op; + HashSet src_op_names; + for (const auto& pair : machine_id2src_op_name) { + CHECK_OR_RETURN(src_op_names.insert(pair.second).second) + << " duplicated src op name " << pair.second; + } JUST(job_builder->ForEachOperator([&](const Operator& op) -> Maybe { // skip if the op is not a tick op if (!op.op_conf().has_tick_conf()) { return Maybe::Ok(); } for (const auto& ibn : op.input_bns()) { const auto& input_lbi = op.BnInOp2Lbi(ibn); - if (input_lbi.op_name() == src_op_name) { - CHECK_OR_RETURN(!find_src_tick_consumer_tick); - tick_op_conf.CopyFrom(op.op_conf()); - find_src_tick_consumer_tick = true; - } + if (src_op_names.count(input_lbi.op_name()) == 0) { continue; } + auto insert_pair = + src_op_name2solo_consumer_tick_op.emplace(input_lbi.op_name(), op.op_conf()); + CHECK_OR_RETURN(insert_pair.second) + << " Duplicated src op name " << input_lbi.op_name() << " old op " + << insert_pair.first->second.DebugString() << " new op " << op.op_conf().DebugString(); } return Maybe::Ok(); })); - CHECK_OR_RETURN(find_src_tick_consumer_tick); - CHECK_OR_RETURN(tick_op_conf.has_tick_conf()); - CHECK_EQ_OR_RETURN(tick_op_conf.tick_conf().tick_size(), 1); - tick_op_conf.mutable_tick_conf()->clear_tick(); - tick_op_conf.mutable_tick_conf()->add_tick( - GenLogicalBlobName(wait_and_send_ids_op_conf.name(), "out")); - JUST(job_builder->MutOpOnlyOnce(tick_op_conf)); - // erase the src tick op - job_builder->DelOps({src_op_name}); + // Replace Source op with WaitAndSendIds op + for (const auto& pair : machine_id2src_op_name) { + auto tick_op_iter = src_op_name2solo_consumer_tick_op.find(pair.second); + CHECK_OR_RETURN(tick_op_iter != src_op_name2solo_consumer_tick_op.end()) + << "Can't find consumer tick op of source op name " << pair.second << " machine id " + << pair.first; + JUST(MultiClientAddOneWaitAndSendIdsOp(job_builder, pair.first, tick_op_iter->second)); + } + + // Delete Source op + std::vector src_op_name_vec{src_op_names.begin(), src_op_names.end()}; + job_builder->DelOps(src_op_name_vec); + return Maybe::Ok(); } @@ -586,9 +614,8 @@ Maybe MultiClientAutoSourceAndSinkTick(const OpGraph& op_graph, Job* job) } { JobBuilder job_builder(job); - for (const auto& pair : machine_id2src_op_name) { - JUST(MultiClientAddWaitAndSendIds(&job_builder, pair.first, pair.second)); - } + JUST(MultiClientAddWaitAndSendIds(&job_builder, machine_id2src_op_name)); + for (const auto& pair : machine_id2sink_op_name) { JUST(MultiClientAddCallbackNotifier(&job_builder, pair.first, pair.second)); } diff --git a/oneflow/core/job_rewriter/boxing_with_middle_nodes.cpp b/oneflow/core/job_rewriter/boxing_with_middle_nodes.cpp index 91ed0f77f8742aacafba01728b07036e99e04135..79fb1fb429dd6540a0ff556343a1d372b44bcf40 100644 --- a/oneflow/core/job_rewriter/boxing_with_middle_nodes.cpp +++ b/oneflow/core/job_rewriter/boxing_with_middle_nodes.cpp @@ -14,8 +14,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/job_rewriter/boxing_with_middle_nodes.h" +#include "oneflow/core/common/just.h" #include "oneflow/core/common/util.h" #include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/framework/sbp_infer_util.h" #include "oneflow/core/job/job_desc.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/auto_parallel/boxing_collector.h" @@ -30,10 +32,6 @@ Maybe BoxingWithMiddleNodes(const OpGraph& op_graph, JobBuilder* job_build } // Initialize boxing collector BoxingCollector boxing_collector; - // We assemble the boxing table from S(0) to S(5). - // Those splitting in higher axes are considered in the customized boxing. - constexpr int32_t kRegularMaxSplitAxes = 6; - JUST(boxing_collector.Init(kRegularMaxSplitAxes)); std::vector middle_sbps; HashMap op_node2op_conf; // Fill other unsupported combinations diff --git a/oneflow/core/job_rewriter/broadcast_to_compatible_with_grad.cpp b/oneflow/core/job_rewriter/broadcast_to_compatible_with_grad.cpp deleted file mode 100644 index 8f36105c76582f95bf9d5bc5588d3111cec66bab..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/broadcast_to_compatible_with_grad.cpp +++ /dev/null @@ -1,62 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autograd.h" -#include "oneflow/core/common/shape_view.h" -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -namespace { - -Maybe GenBroadcastToCompatibleWithGradOpConf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK(op.op_conf().has_broadcast_to_compatible_with_conf()); - if (DiffLbi4BnInOp("x") != nullptr) { - const Shape& x_shape = LogicalBlobDesc4BnInOp("x").shape(); - const Shape& y_shape = LogicalBlobDesc4BnInOp("y").shape(); - Shape x_extend_shape = CreateLeftExtendedShape(ShapeView(x_shape), y_shape.NumAxes()); - std::vector reduced_axes(x_extend_shape.NumAxes() - x_shape.NumAxes()); - std::iota(reduced_axes.begin(), reduced_axes.end(), 0); - FOR_RANGE(int64_t, i, reduced_axes.size(), y_shape.NumAxes()) { - if (x_extend_shape.At(i) == 1 && y_shape.At(i) != 1) { - reduced_axes.emplace_back(i); - } else { - CHECK_EQ(x_extend_shape.At(i), y_shape.At(i)); - } - } - const auto reduce_sum_like_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name()) - .Op("reduce_sum_like") - .Input("x", GenLogicalBlobName(*DiffLbi4BnInOp("y"))) - .Input("like", GenLogicalBlobName(op.BnInOp2Lbi("x"))) - .Attr>("axis", reduced_axes) - .Output("y") - .ScopeSymbolId(op.op_conf().scope_symbol_id()) - .Build(); - op_confs->emplace_back(reduce_sum_like_op.op_conf()); - *DiffLbi4BnInOp("x") = GenLogicalBlobId(reduce_sum_like_op.output("y", 0)); - } - return Maybe::Ok(); -} - -} // namespace - -REGISTER_OP_GRAD(OperatorConf::kBroadcastToCompatibleWithConf, - &GenBroadcastToCompatibleWithGradOpConf); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/checkpointing_pass.cpp b/oneflow/core/job_rewriter/checkpointing_pass.cpp index 43ed3a9d3634283d6ef70a579c1c845ed5bb0514..630992909ea37ae4cc52d76cbec6901b71afb6f8 100644 --- a/oneflow/core/job_rewriter/checkpointing_pass.cpp +++ b/oneflow/core/job_rewriter/checkpointing_pass.cpp @@ -19,8 +19,10 @@ limitations under the License. #include "oneflow/core/job_rewriter/calculation_pass.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/operator/operator.h" #include "oneflow/core/rpc/include/global_process_ctx.h" +#include "oneflow/core/common/env_var/debug_mode.h" namespace oneflow { @@ -45,8 +47,8 @@ class CheckpointingPass final : public JobPass { Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; }; -const std::string kCheckpointingFakeOpNamePrefix = "OneFlow-System-Checkpointing-Fake-Fw-Op_"; -const std::string kCheckpointingBadOpName = "OneFlow-System-CheckpointPassBadEndOpName"; +const std::string kCheckpointingFakeOpNamePrefix = "Sys-Checkpointing-Fake-Fw-Op_"; +const std::string kCheckpointingBadOpName = "Sys-CheckpointPassBadEndOpName"; const Scope& Scope4OpNode(const OpNode* op_node) { int64_t scope_symbol_id = op_node->op().op_conf().scope_symbol_id(); @@ -148,6 +150,7 @@ Maybe CheckpointingPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu // so we need collect bw consumer between subgraphs, and update them in job builder only once. HashMap total_bw_consumers_op_name2conf; + int32_t subgraph_id = 0; for (auto& subgraph : checkpointing_subgraphs) { // step 3.1 ignore this subgraph if there is no direct edge to backward pass op. HashSet bw_consumers; @@ -161,6 +164,8 @@ Maybe CheckpointingPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu } if (bw_consumers.empty()) { continue; } + HashSet checkpointing_tensor; + HashMap subgraph_op_name2op_node; ParallelConf parallel_conf; for (const OpNode* node : subgraph) { @@ -216,6 +221,7 @@ Maybe CheckpointingPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu list_s.set_s(i, kCheckpointingFakeOpNamePrefix + old_lbn); } else { source_node_in_fake_subgraph.insert(fake_op_name); + checkpointing_tensor.insert(old_lbi); } } } @@ -290,6 +296,22 @@ Maybe CheckpointingPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu std::vector fake_op_confs; for (auto& pair : fake_op_name2conf) { fake_op_confs.emplace_back(pair.second); } job_builder->AddOps(parallel_conf, fake_op_confs); + + // step 3.6 log checkpointing tensor flow debug. + if (IsInDebugMode()) { + VLOG(2) << " In subgraph: " << subgraph_id + << " has checkpointing tensor num = " << checkpointing_tensor.size(); + for (const auto& lbi : checkpointing_tensor) { + const OpNode* node = op_graph.OpNode4OpName(lbi.op_name()); + const BlobDesc& blob = node->LogicalBlobDesc4Lbi(lbi); + VLOG(2) << "Checkpointing tensor: " << GenLogicalBlobName(lbi) + << " ,shape: " << blob.shape().ToString() + << " ,dtype: " << DataType_Name(blob.data_type()) + << " ,placement: " << *JUST(PlacementToString(SymbolOf(node->parallel_desc()))) + << " ,sbp: " << NdSbpToString(node->NdSbp4Lbi(lbi)); + } + subgraph_id++; + } } // step 4. update bw consumers in job builder only once diff --git a/oneflow/core/job_rewriter/cudnn_fused_normalization_add_relu_pass.cpp b/oneflow/core/job_rewriter/cudnn_fused_normalization_add_relu_pass.cpp index 2cda407c634790fed92c58d3efc7e7769b6dd8ea..0a07f3a43c24399568994cc2b6745f45429a6a7b 100644 --- a/oneflow/core/job_rewriter/cudnn_fused_normalization_add_relu_pass.cpp +++ b/oneflow/core/job_rewriter/cudnn_fused_normalization_add_relu_pass.cpp @@ -32,6 +32,16 @@ bool IsFusedBnAddReluSupported() { #endif } +bool IsNormalizationAddReluOp(const OperatorConf& op) { + return op.has_user_conf() + && (op.user_conf().op_type_name() == "normalization_add_relu" + || op.user_conf().op_type_name() == "normalization_add_relu_grad"); +} + +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), IsNormalizationAddReluOp); +} + } // namespace class CudnnFusedNormalizationAddReluPass final : public JobPass { @@ -50,30 +60,24 @@ class CudnnFusedNormalizationAddReluPass final : public JobPass { return IsFusedBnAddReluSupported(); } } - Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; - - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - JobBuilder job_builder(job); - return Apply(op_graph, &job_builder); - } + Maybe Apply(Job* job, JobPassCtx* ctx) const override; }; -Maybe CudnnFusedNormalizationAddReluPass::Apply(const OpGraph& op_graph, - JobBuilder* job_builder) const { +Maybe CudnnFusedNormalizationAddReluPass::Apply(Job* job, JobPassCtx* ctx) const { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + if (!NeedDoPass(*job)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + const DataType mixed_precision_data_type = ctx->job_desc().mixed_precision_data_type(); op_graph.ForEachNode([&](const OpNode* op_node) { const OperatorConf& op_conf = op_node->op().op_conf(); - if (!op_conf.has_user_conf()) { return; } + if (!IsNormalizationAddReluOp(op_conf)) { return; } const std::string& op_type_name = op_conf.user_conf().op_type_name(); - if (op_type_name != "normalization_add_relu" && op_type_name != "normalization_add_relu_grad") { - return; - } const user_op::UserOpConfWrapper user_op_conf(op_conf); const BlobDesc& x_desc = op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(user_op_conf.input("x", 0))); const int32_t axis = user_op_conf.attr("axis"); - if (x_desc.data_type() != DataType::kFloat16) { return; } + if (x_desc.data_type() != mixed_precision_data_type) { return; } const Shape& x_shape = x_desc.shape(); if (x_shape.Count(axis + 1) != 1) { return; } if (x_shape.At(axis) % 4 != 0) { return; } @@ -82,7 +86,7 @@ Maybe CudnnFusedNormalizationAddReluPass::Apply(const OpGraph& op_graph, auto training_it = mute_attrs->find("training"); if (training_it != mute_attrs->end()) { mute_attrs->erase(training_it); } new_op_conf.mutable_user_conf()->set_op_type_name("cudnn_fused_" + op_type_name); - job_builder->MutOpsOnlyOnce({new_op_conf}); + job_builder.MutOpsOnlyOnce({new_op_conf}); }); return Maybe::Ok(); } diff --git a/oneflow/core/job_rewriter/cutlass_conv_tuning_warmup_pass.cpp b/oneflow/core/job_rewriter/cutlass_conv_tuning_warmup_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..da51ab85e95abb79efa023f57dff8d4a6346f751 --- /dev/null +++ b/oneflow/core/job_rewriter/cutlass_conv_tuning_warmup_pass.cpp @@ -0,0 +1,187 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifdef WITH_CUTLASS + +#include "oneflow/core/framework/to_string.h" +#include "oneflow/core/job/global_for.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/user/kernels/cutlass_conv_tuner.h" +#include "oneflow/core/ep/include/device_manager_registry.h" +#include "oneflow/core/framework/user_op_conf.h" + +namespace oneflow { + +namespace { + +constexpr size_t kMaxWorkspaceSize = 128 * 1024 * 1024; // 128MB +constexpr size_t kBufferMallocAlign = 128 * 1024 * 1024; // 128MB + +class CutlassConvTuningWarmupPass final : public JobPass { + public: + CutlassConvTuningWarmupPass() = default; + ~CutlassConvTuningWarmupPass() override = default; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override; +}; + +Maybe CutlassConvTuningWarmupPass::Apply(Job* job, JobPassCtx* ctx) const { + if (!ParseBooleanFromEnv("ONEFLOW_KERENL_CONV_ENABLE_CUTLASS_IMPL", false)) { + return Maybe::Ok(); + } + if (!ParseBooleanFromEnv("ONEFLOW_KERENL_CONV_CUTLASS_IMPL_ENABLE_TUNING_WARMUP", false)) { + return Maybe::Ok(); + } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + + auto device = Singleton::Get()->GetDevice(DeviceType::kCUDA, 0); + ep::Stream* stream = device->CreateStream(); + void* workspace = nullptr; + char* buffer = nullptr; + size_t buffer_size = 0; + OF_CUDA_CHECK(cudaMalloc(&workspace, kMaxWorkspaceSize)); + op_graph.ForEachNode([&](const OpNode* node) { + const OperatorConf& op_conf = node->op().op_conf(); + if (!op_conf.has_user_conf()) { return; } + if (op_conf.user_conf().op_type_name() != "conv2d") { return; } + if (node->parallel_desc().device_type() != DeviceType::kCUDA) { return; } + if (node->parallel_desc().parallel_num() != 1) { return; } + if (!node->parallel_desc().containing_current_rank()) { return; } + user_op::UserOpConfWrapper conv2d_op(op_conf); + if (conv2d_op.attr("data_format") != "channels_last") { return; } + if (conv2d_op.attr("groups") != 1) { return; } + VLOG(3) << "Tuning " << op_conf.name(); + const auto& in_desc = node->LogicalBlobDesc4Lbi(GenLogicalBlobId(conv2d_op.input("in", 0))); + if (in_desc.data_type() != DataType::kFloat16) { return; } + const auto& weight_desc = + node->LogicalBlobDesc4Lbi(GenLogicalBlobId(conv2d_op.input("weight", 0))); + const auto& out_desc = node->LogicalBlobDesc4Lbi(GenLogicalBlobId(conv2d_op.output("out", 0))); + + const auto& padding_before = conv2d_op.attr>("padding_before"); + const auto& dilation_rate = conv2d_op.attr>("dilation_rate"); + const auto& strides = conv2d_op.attr>("strides"); + + const int n = in_desc.shape().At(0); + const int h = in_desc.shape().At(1); + const int w = in_desc.shape().At(2); + const int c = in_desc.shape().At(3); + + const int k = weight_desc.shape().At(0); + const int r = weight_desc.shape().At(1); + const int s = weight_desc.shape().At(2); + CHECK_EQ(weight_desc.shape().At(3), c); + + const int p = out_desc.shape().At(1); + const int q = out_desc.shape().At(2); + + cutlass::library::ConvFunctionalKey key( + cutlass::library::Provider::kCUTLASS, cutlass::library::ConvKind::kFprop, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF32, cutlass::library::NumericTypeID::kF32); + + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_CONV_ALLOW_HALF_PRECISION_ACCUMULATION", false); + + if (allow_half_accumulation) { + key.element_accumulator = cutlass::library::NumericTypeID::kF16; + key.element_compute = cutlass::library::NumericTypeID::kF16; + } + + const size_t x_size = GetCudaAlignedSize(in_desc.ByteSizeOfBlobBody()); + const size_t w_size = GetCudaAlignedSize(weight_desc.ByteSizeOfBlobBody()); + const size_t y_size = GetCudaAlignedSize(out_desc.ByteSizeOfBlobBody()); + size_t bias_size = 0; + if (conv2d_op.has_input("bias", 0)) { + bias_size = + GetCudaAlignedSize(node->LogicalBlobDesc4Lbi(GenLogicalBlobId(conv2d_op.input("bias", 0))) + .ByteSizeOfBlobBody()); + } + const size_t total_buf_size = x_size + w_size + y_size + bias_size; + if (total_buf_size > buffer_size) { + size_t malloc_size = RoundUp(total_buf_size, kBufferMallocAlign); + OF_CUDA_CHECK(cudaFree(buffer)); + OF_CUDA_CHECK(cudaMalloc(&buffer, malloc_size)); + buffer_size = malloc_size; + } + void* x_ptr = buffer; + void* w_ptr = buffer + x_size; + void* y_ptr = buffer + x_size + w_size; + void* bias_ptr = nullptr; + if (bias_size != 0) { bias_ptr = buffer + x_size + w_size + y_size; } + + cutlass::conv::Conv2dProblemSize problem_size( + n, h, w, c, k, r, s, p, q, padding_before.at(0), padding_before.at(1), strides.at(0), + strides.at(1), dilation_rate.at(0), dilation_rate.at(1), + cutlass::conv::Mode::kCrossCorrelation); + cutlass::library::Conv2dConfiguration configuraion; + configuraion.split_k_mode = cutlass::conv::SplitKMode::kSerial; + configuraion.problem_size = problem_size; + configuraion.stride_a = {c, w * c, h * w * c}; + configuraion.stride_b = {c, s * c, r * s * c}; + configuraion.stride_c = {0, 0, 0}; + cutlass::library::ConvArguments arguments; + arguments.A = x_ptr; + arguments.B = w_ptr; + arguments.reordered_B = nullptr; + arguments.C = bias_ptr; + arguments.D = y_ptr; + union SP { + float f{}; + half h; + }; + + SP alpha; + SP beta; + + if (allow_half_accumulation) { + alpha.h = static_cast(1.0F); + if (bias_ptr == nullptr) { + beta.h = static_cast(0.0F); + } else { + beta.h = static_cast(1.0F); + } + } else { + alpha.f = 1.0F; + if (bias_ptr == nullptr) { + beta.f = 0.0F; + } else { + beta.f = 1.0F; + } + } + arguments.alpha = α + arguments.beta = β + arguments.pointer_mode = cutlass::library::ScalarPointerMode::kHost; + + const cutlass::library::Operation* operation = CutlassConvTuner::Get().FindConv2dOperation( + stream->As(), key, configuraion, arguments, workspace, kMaxWorkspaceSize); + if (operation != nullptr) { VLOG(3) << "Fastest operation: " << operation->description().name; } + }); + OF_CUDA_CHECK(cudaFree(workspace)); + OF_CUDA_CHECK(cudaFree(buffer)); + device->DestroyStream(stream); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("CutlassConvTuningWarmupPass", CutlassConvTuningWarmupPass); + +} // namespace oneflow + +#endif // WITH_CUTLASS diff --git a/oneflow/core/job_rewriter/distribute_grad.cpp b/oneflow/core/job_rewriter/distribute_grad.cpp deleted file mode 100644 index a2d292b528c0bd60534daf656ae13930115505f9..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/distribute_grad.cpp +++ /dev/null @@ -1,133 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autograd.h" - -namespace oneflow { - -namespace { - -Maybe GenerateBackwardOpConf4Concat( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK(op.op_conf().has_distribute_concat_conf()); - const DistributeConcatOpConf& distribute_concat_conf = op.op_conf().distribute_concat_conf(); - OperatorConf split_op; - split_op.set_name(op.op_conf().name() + "_grad"); - DistributeSplitOpConf* split_op_conf = split_op.mutable_distribute_split_conf(); - split_op_conf->set_in(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - split_op_conf->set_axis(distribute_concat_conf.axis()); - FOR_RANGE(int32_t, i, 0, distribute_concat_conf.in_size()) { - const std::string& ibn_of_distribute_concat_op = op.input_bns().Get(i); - const std::string& obn = "out_" + std::to_string(i); - split_op_conf->add_out(obn); - if (DiffLbi4BnInOp(ibn_of_distribute_concat_op) != nullptr) { - DiffLbi4BnInOp(ibn_of_distribute_concat_op)->set_op_name(split_op.name()); - DiffLbi4BnInOp(ibn_of_distribute_concat_op)->set_blob_name(obn); - } - } - op_confs->emplace_back(split_op); - return Maybe::Ok(); -} - -Maybe GenerateBackwardOpConf4Split( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK(op.op_conf().has_distribute_split_conf()); - const DistributeSplitOpConf& distribute_split_conf = op.op_conf().distribute_split_conf(); - OperatorConf concat_op; - concat_op.set_name(op.op_conf().name() + "_grad"); - DistributeConcatOpConf* concat_op_conf = concat_op.mutable_distribute_concat_conf(); - concat_op_conf->set_axis(distribute_split_conf.axis()); - const bool has_diff_0 = DiffLbi4BnInOp(op.output_bns().Get(0)) != nullptr; - FOR_RANGE(int32_t, i, 0, distribute_split_conf.out_size()) { - const std::string& obn_of_distribute_split_op = op.output_bns().Get(i); - const bool has_diff_i = DiffLbi4BnInOp(obn_of_distribute_split_op) != nullptr; - CHECK_EQ(has_diff_i, has_diff_0); - if (has_diff_i) { - concat_op_conf->add_in(GenLogicalBlobName(*DiffLbi4BnInOp(obn_of_distribute_split_op))); - } - } - concat_op_conf->set_out("out"); - if (DiffLbi4BnInOp("in") != nullptr) { - CHECK_EQ(concat_op_conf->in_size(), distribute_split_conf.out_size()); - DiffLbi4BnInOp("in")->set_op_name(concat_op.name()); - DiffLbi4BnInOp("in")->set_blob_name("out"); - op_confs->emplace_back(concat_op); - } - return Maybe::Ok(); -} - -Maybe GenerateBackwardOpConf4Clone( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK(op.op_conf().has_distribute_clone_conf()); - const DistributeCloneOpConf& conf = op.op_conf().distribute_clone_conf(); - OperatorConf partial_op; - partial_op.set_name(op.op_conf().name() + "_grad"); - DistributeAddOpConf* partial_op_conf = partial_op.mutable_distribute_add_conf(); - const bool has_diff_0 = DiffLbi4BnInOp(op.output_bns().Get(0)) != nullptr; - FOR_RANGE(int32_t, i, 0, conf.out_size()) { - const std::string& obn_of_distribute_clone_op = op.output_bns().Get(i); - const bool has_diff_i = DiffLbi4BnInOp(obn_of_distribute_clone_op) != nullptr; - CHECK_EQ(has_diff_i, has_diff_0); - if (has_diff_i) { - partial_op_conf->add_in(GenLogicalBlobName(*DiffLbi4BnInOp(obn_of_distribute_clone_op))); - } - } - partial_op_conf->set_out("out"); - if (DiffLbi4BnInOp("in") != nullptr) { - CHECK_EQ(partial_op_conf->in_size(), conf.out_size()); - DiffLbi4BnInOp("in")->set_op_name(partial_op.name()); - DiffLbi4BnInOp("in")->set_blob_name("out"); - op_confs->emplace_back(partial_op); - } - return Maybe::Ok(); -} - -Maybe GenerateBackwardOpConf4Add( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK(op.op_conf().has_distribute_add_conf()); - const auto& distribute_add_conf = op.op_conf().distribute_add_conf(); - OperatorConf broadcast_op; - broadcast_op.set_name(op.op_conf().name() + "_grad"); - DistributeCloneOpConf* broadcast_op_conf = broadcast_op.mutable_distribute_clone_conf(); - broadcast_op_conf->set_in(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - FOR_RANGE(int32_t, i, 0, distribute_add_conf.in_size()) { - const std::string& ibn_of_distribute_add_op = op.input_bns().Get(i); - const std::string& obn = "out_" + std::to_string(i); - broadcast_op_conf->add_out(obn); - if (DiffLbi4BnInOp(ibn_of_distribute_add_op) != nullptr) { - DiffLbi4BnInOp(ibn_of_distribute_add_op)->set_op_name(broadcast_op.name()); - DiffLbi4BnInOp(ibn_of_distribute_add_op)->set_blob_name(obn); - } - } - op_confs->emplace_back(broadcast_op); - return Maybe::Ok(); -} - -} // namespace - -REGISTER_OP_GRAD(OperatorConf::kDistributeConcatConf, &GenerateBackwardOpConf4Concat); -REGISTER_OP_GRAD(OperatorConf::kDistributeSplitConf, &GenerateBackwardOpConf4Split); -REGISTER_OP_GRAD(OperatorConf::kDistributeCloneConf, &GenerateBackwardOpConf4Clone); -REGISTER_OP_GRAD(OperatorConf::kDistributeAddConf, &GenerateBackwardOpConf4Add); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/do_parallel_cast_before_widening_type_cast_pass.cpp b/oneflow/core/job_rewriter/do_parallel_cast_before_widening_type_cast_pass.cpp index 82adf9e6f15300b78c0076c815ed9144ebf62574..0b923bca8061bc61231fe235111f2b2169eddfbe 100644 --- a/oneflow/core/job_rewriter/do_parallel_cast_before_widening_type_cast_pass.cpp +++ b/oneflow/core/job_rewriter/do_parallel_cast_before_widening_type_cast_pass.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/user_op_conf.h" #include "oneflow/core/job_rewriter/job_pass.h" #include "oneflow/core/job_rewriter/pass_util.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" namespace oneflow { @@ -33,6 +34,7 @@ class DoParallelCastBeforeWideningTypeCast final : public JobPass { Maybe Apply(Job* job, JobPassCtx* ctx) const override { if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + if (GlobalProcessCtx::WorldSize() == 1) { return Maybe::Ok(); } const OpGraph op_graph(*job); JobBuilder job_builder(job); return Apply(op_graph, &job_builder); @@ -62,7 +64,7 @@ Maybe DoParallelCastBeforeWideningTypeCast::Apply(const OpGraph& op_graph, const auto cast_in_lbi = cast_node->SoleInEdge()->lbis().front(); const auto cast_in_dtype = cast_node->LogicalBlobDesc4Lbi(cast_in_lbi).data_type(); const auto cast_out_dtype = cast_conf_wrapper.attr("dtype"); - if (!(cast_in_dtype == DataType::kFloat16 + if (!((cast_in_dtype == DataType::kFloat16 || cast_in_dtype == DataType::kBFloat16) && (cast_out_dtype == DataType::kFloat || cast_out_dtype == DataType::kDouble))) { return; } diff --git a/oneflow/core/job_rewriter/dynamic_loss_scale_schedule_pass.cpp b/oneflow/core/job_rewriter/dynamic_loss_scale_schedule_pass.cpp index ccd3e8eba0e4b261eab7beb350d285f6d7c0147f..68596b4f40d30a1f0b34c899da6484cd35a1a8db 100644 --- a/oneflow/core/job_rewriter/dynamic_loss_scale_schedule_pass.cpp +++ b/oneflow/core/job_rewriter/dynamic_loss_scale_schedule_pass.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/job_rewriter/job_pass.h" #include "oneflow/core/job/job.pb.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/job_rewriter/dynamic_loss_scale_job_pass_state.h" #include "oneflow/core/framework/scope_util.h" @@ -45,7 +44,7 @@ Maybe DynamicLossScaleSchedulePass::Apply(Job* job, JobPassCtx* ctx) const { const auto& opt_scope_symbol_id = JUST(MakeInitialScope(job->job_conf(), SymbolOf(ParallelDesc(parallel_conf)), - /* is_mirrored */ false)) + /* is_local */ false)) ->symbol_id(); CHECK_OR_RETURN(opt_scope_symbol_id.has_value()) << Error::RuntimeError() << "symbol_id not initialized"; diff --git a/oneflow/core/job_rewriter/dynamic_reshape_grad.cpp b/oneflow/core/job_rewriter/dynamic_reshape_grad.cpp deleted file mode 100644 index 70c5c935fd3ebb7086d8d3450529bc643d890a9b..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/dynamic_reshape_grad.cpp +++ /dev/null @@ -1,44 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autograd.h" - -namespace oneflow { - -namespace { - -void GenerateBackwardOpConf4DynamicReshape( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp) { - CHECK(op.op_conf().has_dynamic_reshape_conf()); - if (DiffLbi4BnInOp("in") != nullptr) { - OperatorConf reverse_reshape_op; - reverse_reshape_op.set_name(op.op_name() + "_grad"); - DynamicReshapeLikeOpConf* reshape_like_op_conf = - reverse_reshape_op.mutable_dynamic_reshape_like_conf(); - reshape_like_op_conf->set_x(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - reshape_like_op_conf->set_like(GenLogicalBlobName(op.BnInOp2Lbi("in"))); - reshape_like_op_conf->set_y("y"); - op_confs->emplace_back(reverse_reshape_op); - DiffLbi4BnInOp("in")->set_op_name(reverse_reshape_op.name()); - DiffLbi4BnInOp("in")->set_blob_name("y"); - } -} - -} // namespace - -REGISTER_OP_GRAD(OperatorConf::kDynamicReshapeConf, GenerateBackwardOpConf4DynamicReshape); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/eliminate_dead_nodes_pass.cpp b/oneflow/core/job_rewriter/eliminate_dead_nodes_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9db9d060719b6d7f1d6cbdfed7c65bfce5e01871 --- /dev/null +++ b/oneflow/core/job_rewriter/eliminate_dead_nodes_pass.cpp @@ -0,0 +1,70 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +class EliminateDeadNodesPass final : public JobPass { + public: + EliminateDeadNodesPass() = default; + ~EliminateDeadNodesPass() override = default; + + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +static bool IsNoSideEffect(const OpNode* op_node) { + static HashSet no_side_effect_ops = { + "constant", "zeros_like", "ones_like", "repeat", "acc", "pack", "unpack", + }; + static HashSet no_side_effect_system_ops = { + OperatorConf::kDeviceTickConf, + }; + const auto& op_conf = op_node->op().op_conf(); + if (!op_conf.has_user_conf()) { return no_side_effect_system_ops.count(op_conf.op_type_case()); } + return no_side_effect_ops.count(op_conf.user_conf().op_type_name()); +} + +Maybe EliminateDeadNodesPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { + HashSet delete_ops; + std::vector delete_op_confs; + op_graph.ReverseTopoForEachNode([&](const OpNode* op_node) { + if (!IsNoSideEffect(op_node)) { return; } + for (const auto* out_edge : op_node->out_edges()) { + if (!delete_ops.count(out_edge->dst_node())) { return; } + } + VLOG(3) << "Eliminate dead node: " << op_node->op().op_name(); + delete_ops.insert(op_node); + delete_op_confs.emplace_back(op_node->op().op_conf()); + }); + + job_builder->DelOps(delete_op_confs); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("EliminateDeadNodesPass", EliminateDeadNodesPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/fix_pipeline_stage_id_pass.cpp b/oneflow/core/job_rewriter/fix_pipeline_stage_id_pass.cpp index 69056c34f5b49d8e3f8afbb19f8b49bf59648fc5..744e3b8f14e675da9219e03451b69fabba0dd1af 100644 --- a/oneflow/core/job_rewriter/fix_pipeline_stage_id_pass.cpp +++ b/oneflow/core/job_rewriter/fix_pipeline_stage_id_pass.cpp @@ -41,7 +41,10 @@ class FixPipelineStageIdPass final : public JobPass { return Apply(op_graph, &job_builder); } - bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } + bool IsEnabled(const JobPassCtx& ctx) const { + return ctx.job_desc().IsTrain() + && ctx.job_desc().job_conf().num_gradient_accumulation_steps() > 1; + } Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; }; @@ -85,9 +88,6 @@ Maybe NewScopeWithStageId(int64_t old_scope_symbol_id, int64_t stage_id } Maybe FixPipelineStageIdPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { - if (GlobalJobDesc().job_conf().num_gradient_accumulation_steps() <= 1) { - return Maybe::Ok(); - } int64_t max_stage_id = 0; op_graph.ForEachNode([&](const OpNode* this_node) { if (!OpNodeHasScope(this_node)) { diff --git a/oneflow/core/job_rewriter/foreign_input_autotick.cpp b/oneflow/core/job_rewriter/foreign_input_autotick.cpp deleted file mode 100644 index 305d88b99dca27d6c6eafed71192bf918473cbbd..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/foreign_input_autotick.cpp +++ /dev/null @@ -1,41 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autotick.h" - -namespace oneflow { - -namespace { - -class MutForeignInputOpConTickInputHelper final : public MutOpConTickInputHelper { - public: - MutForeignInputOpConTickInputHelper() : MutOpConTickInputHelper() {} - - bool VirtualIsTickInputBound() const override { - return op_conf().foreign_input_conf().has_tick(); - } - - OperatorConf NewTickInputBoundOpConf(const std::string& lbn) const override { - OperatorConf ret(op_conf()); - ret.mutable_foreign_input_conf()->set_tick(lbn); - return ret; - } -}; - -} // namespace - -REGISTER_AUTO_TICK(OperatorConf::kForeignInputConf, MutForeignInputOpConTickInputHelper); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/ftrl_optm.cpp b/oneflow/core/job_rewriter/ftrl_optm.cpp index d31459f7828e87d3bedc14d57d057837bb345544..4e0f149b83c37d2f2029dbb3cf42614e7f6a273d 100644 --- a/oneflow/core/job_rewriter/ftrl_optm.cpp +++ b/oneflow/core/job_rewriter/ftrl_optm.cpp @@ -83,7 +83,9 @@ void GenerateFtrlOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Attr("beta", beta) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); - + if (optimizer_conf.has_lr_scale()) { + ftrl_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &ftrl_update_op_builder); const auto ftrl_update_op = ftrl_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {ftrl_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/fuse_add_to_output_pass.cpp b/oneflow/core/job_rewriter/fuse_add_to_output_pass.cpp index c7333135c02bf7ea5039eb19dddff0ea66ccf85a..43682983a4371c33ea56331a3291f85a8becd35c 100644 --- a/oneflow/core/job_rewriter/fuse_add_to_output_pass.cpp +++ b/oneflow/core/job_rewriter/fuse_add_to_output_pass.cpp @@ -42,13 +42,13 @@ class FuseAddToOutputPass final : public JobPass { Maybe FuseAddToOutputPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { const HashMap supported_op_type_name2output_arg( - {{"conv_data_grad", user_op::OpArg("dx", 0)}, - {"normalization", user_op::OpArg("y", 0)}, + {{"normalization", user_op::OpArg("y", 0)}, {"dropout", user_op::OpArg("out", 0)}, {"matmul", user_op::OpArg("out", 0)}, {"layer_norm_grad", user_op::OpArg("dx", 0)}, {"batch_matmul", user_op::OpArg("out", 0)}, {"fused_bias_add_mask_scale", user_op::OpArg("out", 0)}, + {"fused_matmul_bias", user_op::OpArg("out", 0)}, {"broadcast_matmul", user_op::OpArg("out", 0)}, {"broadcast_matmul_grad_b", user_op::OpArg("out", 0)}}); HashSet consumer_op_names; diff --git a/oneflow/core/job_rewriter/fuse_bce_reduce_mean_fw_bw_pass.cpp b/oneflow/core/job_rewriter/fuse_bce_reduce_mean_fw_bw_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f568aa24fde4da73c2aac69ef33fd36c1530892e --- /dev/null +++ b/oneflow/core/job_rewriter/fuse_bce_reduce_mean_fw_bw_pass.cpp @@ -0,0 +1,142 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +void UpdateConsumerOpConf(const OpNode* consumer, const LogicalBlobId& out, + const std::string& new_out_lbn, + HashMap* op_name2op_conf) { + const std::string& consumer_op_name = consumer->op().op_name(); + if (op_name2op_conf->find(consumer_op_name) == op_name2op_conf->end()) { + (*op_name2op_conf)[consumer_op_name] = consumer->op().op_conf(); + } + for (const std::string& ibn : consumer->op().input_bns()) { + if (consumer->op().BnInOp2Lbi(ibn) == out) { + OperatorConf& consumer_op_conf = op_name2op_conf->at(consumer_op_name); + const auto& new_val = new_out_lbn; + const auto& old_val = ReplaceInputLbnInOpCustomizedConf(&consumer_op_conf, ibn, new_val); + CHECK_EQ(GenLogicalBlobName(out), old_val); + } + } +} + +class FuseBCEReduceMeanFwBwPass final : public JobPass { + public: + FuseBCEReduceMeanFwBwPass() = default; + ~FuseBCEReduceMeanFwBwPass() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { + return ParseBooleanFromEnv("ONEFLOW_FUSE_BCE_REDUCE_MEAN_FW_BW", false); + } + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +Maybe FuseBCEReduceMeanFwBwPass::Apply(const OpGraph& op_graph, + JobBuilder* job_builder) const { + // This pass fuse binary_cross_entropy_with_logits_reduce_mean and + // binary_cross_entropy_with_logits_reduce_mean_grad. delete the h2f cast to loss, and the + // constant_like of dy. + const auto IsSafeToDelete = MakePredicatorIsSafeToDelete(op_graph); + HashMap op_name2op_conf; + std::vector delete_ops; + op_graph.ForEachNode([&](const OpNode* op_node) { + if (!IsUserOpWithTypeName(op_node->op().op_conf(), + "binary_cross_entropy_with_logits_reduce_mean")) { + return; + } + if (op_node->out_edges().size() > 2) { return; } + bool find_grad_op = false; + for (const OpEdge* out_edge : op_node->out_edges()) { + const OpNode* consumer = out_edge->dst_node(); + if (!IsSafeToDelete(consumer)) { return; } + if (!(IsUserOpWithTypeName(consumer->op().op_conf(), "cast") + || consumer->op().op_conf().has_constant_like_conf() + || consumer->op().op_conf().has_output_conf())) { + return; + } + if (consumer->op().op_conf().has_constant_like_conf()) { + const OpNode* grad_node = consumer->SoleOutEdge()->dst_node(); + if (!IsUserOpWithTypeName(grad_node->op().op_conf(), + "binary_cross_entropy_with_logits_reduce_mean_grad")) { + return; + } + find_grad_op = true; + if (!IsSafeToDelete(grad_node)) { return; } + } + } + if (!find_grad_op) { return; } + const user_op::UserOpConfWrapper bce_op_conf(op_node->op().op_conf()); + user_op::UserOpConfWrapperBuilder fused_op_builder(bce_op_conf.op_name()); + fused_op_builder.OpTypeName("fused_bce_reduce_mean_fw_bw") + .Input("input", bce_op_conf.input("input", 0)) + .Input("target", bce_op_conf.input("target", 0)) + .Output("out") + .Output("dx"); + for (const OpEdge* out_edge : op_node->out_edges()) { + const OpNode* consumer = out_edge->dst_node(); + if (IsUserOpWithTypeName(consumer->op().op_conf(), "cast")) { + const user_op::UserOpConfWrapper cast_conf(consumer->op().op_conf()); + fused_op_builder.Attr("out_dtype", cast_conf.attr("dtype")); + // delete cast and update cast consumer's in. + delete_ops.push_back(consumer->op().op_conf()); + for (const OpEdge* cast_out_edge : consumer->out_edges()) { + const OpNode* cast_consumer = cast_out_edge->dst_node(); + UpdateConsumerOpConf(cast_consumer, GenLogicalBlobId(cast_conf.output("out", 0)), + GenLogicalBlobName(bce_op_conf.op_name(), "out_0"), + &op_name2op_conf); + } + } else if (consumer->op().op_conf().has_constant_like_conf()) { + fused_op_builder.Attr( + "constant_value", consumer->op().op_conf().constant_like_conf().float_operand()); + const OpNode* grad_node = consumer->SoleOutEdge()->dst_node(); + // delete constant_like and grad op, update consumer + delete_ops.push_back(grad_node->op().op_conf()); + delete_ops.push_back(consumer->op().op_conf()); + const user_op::UserOpConfWrapper grad_conf(grad_node->op().op_conf()); + for (const OpEdge* grad_out_edge : grad_node->out_edges()) { + const OpNode* grad_consumer = grad_out_edge->dst_node(); + UpdateConsumerOpConf(grad_consumer, GenLogicalBlobId(grad_conf.output("dx", 0)), + GenLogicalBlobName(bce_op_conf.op_name(), "dx_0"), &op_name2op_conf); + } + } else { + continue; + } + } + user_op::UserOpConfWrapper fused_op = + fused_op_builder.ScopeSymbolId(bce_op_conf.op_conf().scope_symbol_id()).Build(); + job_builder->MutOpsOnlyOnce({fused_op.op_conf()}); + }); + job_builder->DelOps(delete_ops); + for (const auto& pair : op_name2op_conf) { job_builder->MutOpsOnlyOnce({pair.second}); } + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("FuseBCEReduceMeanFwBwPass", FuseBCEReduceMeanFwBwPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/fuse_cast_scale_pass.cpp b/oneflow/core/job_rewriter/fuse_cast_scale_pass.cpp index 13e0744bfca7096f3d6079455c7c4563513f9c21..1d6016a35e2dbcb1d5eb9e8f1b03e01faa8df351 100644 --- a/oneflow/core/job_rewriter/fuse_cast_scale_pass.cpp +++ b/oneflow/core/job_rewriter/fuse_cast_scale_pass.cpp @@ -20,27 +20,6 @@ namespace oneflow { namespace { -std::function MakePredicatorIsSafeToDelete(const OpGraph& op_graph) { - HashSet ctrl_in_op_names; - op_graph.ForEachNode([&](const OpNode* op_node) { - for (const std::string& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { - ctrl_in_op_names.insert(ctrl_in_op_name); - } - }); - return [=](const OpNode* op_node) { - if (op_node->out_edges().size() > 1) { return false; } - if (!op_node->op().op_conf().ctrl_in_op_name().empty()) { return false; } - if (ctrl_in_op_names.find(op_node->op().op_conf().name()) != ctrl_in_op_names.end()) { - return false; - } - return true; - }; -} - -bool IsUserOpWithTypeName(const OperatorConf& op_conf, const std::string& op_type_name) { - return op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == op_type_name; -}; - class FuseCastScalePass final : public JobPass { public: FuseCastScalePass() = default; @@ -78,7 +57,9 @@ Maybe FuseCastScalePass::Apply(const OpGraph& op_graph, JobBuilder* job_bu } const user_op::UserOpConfWrapper cast_user_conf(op_node->op().op_conf()); if (op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(cast_user_conf.input("in", 0))).data_type() - != DataType::kFloat16) { + != DataType::kFloat16 + && op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(cast_user_conf.input("in", 0))).data_type() + != DataType::kBFloat16) { return; } if (op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(cast_user_conf.output("out", 0))).data_type() diff --git a/oneflow/core/job_rewriter/fuse_consecutive_add_pass.cpp b/oneflow/core/job_rewriter/fuse_consecutive_add_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d481d802a817539a095c928fb8f866f6c248b861 --- /dev/null +++ b/oneflow/core/job_rewriter/fuse_consecutive_add_pass.cpp @@ -0,0 +1,103 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/graph/op_graph.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/common/cost_util.h" +#include "oneflow/core/operator/operator.h" + +namespace oneflow { + +namespace { + +class FuseConsecutiveAddPass final : public JobPass { + public: + FuseConsecutiveAddPass() = default; + ~FuseConsecutiveAddPass() override = default; + + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + JUST(Apply(op_graph, &job_builder)); + return Maybe::Ok(); + } +}; + +Maybe FuseConsecutiveAddPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { + const auto IsSafeToDelete = MakePredicatorIsSafeToDelete(op_graph); + std::vector delete_ops; + op_graph.TopoForEachNode([&](const OpNode* op_node) { + if (!IsUserOpWithTypeName(op_node->op().op_conf(), "add_n") || !IsSafeToDelete(op_node) + || op_node->out_edges().size() != 1) { + return; + } + OpNode* sole_dst_node = op_node->SoleOutEdge()->dst_node(); + if (!IsUserOpWithTypeName(sole_dst_node->op().op_conf(), "add_n") + || !IsSafeToDelete(sole_dst_node)) { + return; + } + + const std::string this_op_name = op_node->op().op_name(); + + const auto& GetCurOpConf = [&](const OpNode& cur_op) -> OperatorConf { + const std::string& cur_op_name = cur_op.op().op_name(); + if (!CHECK_JUST(job_builder->IsInMutOpTransaction(cur_op_name))) { + return cur_op.op().op_conf(); + } else { + return CHECK_JUST(job_builder->MutOpTransactionGet(cur_op_name)); + } + }; + + int64_t fused_cnt = 0; + auto fused_op_conf = GetCurOpConf(*sole_dst_node); + auto in_it = fused_op_conf.mutable_user_conf()->mutable_input()->find("in"); + CHECK(in_it != fused_op_conf.mutable_user_conf()->mutable_input()->end()); + auto* in_lbns = in_it->second.mutable_s(); + auto in_lbn_it = in_lbns->begin(); + while (in_lbn_it != in_lbns->end()) { + const auto lbi = GenLogicalBlobId(*in_lbn_it); + if (lbi.op_name() == this_op_name) { + in_lbn_it = in_lbns->erase(in_lbn_it); + ++fused_cnt; + } else { + ++in_lbn_it; + } + } + + const auto& this_op_conf = GetCurOpConf(*op_node); + auto this_in_it = this_op_conf.user_conf().input().find("in"); + CHECK(this_in_it != this_op_conf.user_conf().input().end()); + for (int64_t fuse_i = 0; fuse_i < fused_cnt; ++fuse_i) { + for (const auto& this_in_lbn : this_in_it->second.s()) { *(in_lbns->Add()) = this_in_lbn; } + } + + CHECK_JUST(job_builder->MutOpTransactionMut(fused_op_conf)); + delete_ops.emplace_back(this_op_name); + }); + + if (delete_ops.empty()) { return Maybe::Ok(); } + JUST(job_builder->MutOpTransactionCommit()); + job_builder->DelOps(delete_ops); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("FuseConsecutiveAddPass", FuseConsecutiveAddPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/fuse_embedding_interaction_pass.cpp b/oneflow/core/job_rewriter/fuse_embedding_interaction_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d3366d5a3eee4382f629bbc59eb0283d6572b318 --- /dev/null +++ b/oneflow/core/job_rewriter/fuse_embedding_interaction_pass.cpp @@ -0,0 +1,165 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +class FuseEmbeddingShuffleInteractionPass final : public JobPass { + public: + FuseEmbeddingShuffleInteractionPass() = default; + ~FuseEmbeddingShuffleInteractionPass() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { + // if enable quantize, not support fuse kernel. + bool enable_quantized_comm = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); + bool enable_fuse_embedding_interaction = + ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSE_EMBEDDING_INTERACTION", false); + return (!enable_quantized_comm && enable_fuse_embedding_interaction); + } + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +Maybe FuseEmbeddingShuffleInteractionPass::Apply(const OpGraph& op_graph, + JobBuilder* job_builder) const { + op_graph.ForEachNode([&](const OpNode* op_node) { + if (!IsUserOpWithTypeName(op_node->op().op_conf(), "embedding_shuffle")) { return; } + if (op_node->out_edges().size() > 2) { return; } + const user_op::UserOpConfWrapper embedding_shuffle_conf(op_node->op().op_conf()); + const std::string& embeddings_lbn = embedding_shuffle_conf.output("embeddings", 0); + const std::string& indices_lbn = + embedding_shuffle_conf.input("inverse_unique_partition_indices", 0); + const std::string& num_unique_matrix_lbn = embedding_shuffle_conf.input("num_unique_matrix", 0); + if (op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(embeddings_lbn)).data_type() + != DataType::kFloat16 + || embedding_shuffle_conf.attr("embedding_size") % 2 != 0) { + // only support half and embedding_size % 2 == 0 fuse, because atomicAdd half is slow. + return; + } + if (op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(indices_lbn)).data_type() + != DataType::kUInt32) { + // only support indices with uint32_t dtype + return; + } + if (op_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(num_unique_matrix_lbn)).data_type() + != DataType::kUInt32) { + // only support num_unique with uint32_t dtype + return; + } + for (const OpEdge* out_edge : op_node->out_edges()) { + const OpNode* consumer = out_edge->dst_node(); + if (!consumer->op().op_conf().has_user_conf()) { return; } + const user_op::UserOpConfWrapper consumer_op_conf(consumer->op().op_conf()); + if (!(consumer_op_conf.op_type_name() == "fused_dot_feature_interaction" + || consumer_op_conf.op_type_name() == "fused_dot_feature_interaction_grad")) { + return; + } + if (consumer_op_conf.attr("pooling") != "none") { return; } + int input_size = consumer_op_conf.input_size("features"); + CHECK_GT(input_size, 0) << input_size; + if (consumer_op_conf.input("features", input_size - 1) != embeddings_lbn) { + // only support embeddings as last feature + return; + } + user_op::UserOpConfWrapperBuilder fused_op_builder(consumer_op_conf.op_name()); + const std::string& op_type_name = consumer_op_conf.op_type_name(); + fused_op_builder.OpTypeName(op_type_name) + .Input("sparse_feature", embeddings_lbn) + .Input("sparse_indices", indices_lbn) + .Input("num_valid_sparse_feature", num_unique_matrix_lbn) + .Attr("self_interaction", consumer_op_conf.attr("self_interaction")) + .Attr("pooling", consumer_op_conf.attr("pooling")); + for (int i = 0; i < input_size - 1; ++i) { + fused_op_builder.Input("features", consumer_op_conf.input("features", i)); + } + OperatorConf new_op_conf = consumer->op().op_conf(); + if (op_type_name == "fused_dot_feature_interaction") { + if (consumer_op_conf.has_input("output_concat", 0)) { + fused_op_builder.Input("output_concat", consumer_op_conf.input("output_concat", 0)); + } + fused_op_builder.Output("out") + .Attr("has_output_concat", consumer_op_conf.attr("has_output_concat")) + .Attr("output_padding", consumer_op_conf.attr("output_padding")); + *new_op_conf.mutable_user_conf() = fused_op_builder.Build().op_conf().user_conf(); + } else { + // fused_dot_feature_interaction_grad + fused_op_builder.Input("dy", consumer_op_conf.input("dy", 0)) + .Output("features_grad", input_size - 1) + .Output("sparse_feature_grad") + .Attr("output_concat_grad_dim", + consumer_op_conf.attr("output_concat_grad_dim")); + if (consumer_op_conf.has_output("output_concat_grad", 0)) { + fused_op_builder.Output("output_concat_grad"); + } + user_op::UserOpConfWrapper fused_dot_feature_interaction_grad_op = fused_op_builder.Build(); + *new_op_conf.mutable_user_conf() = + fused_dot_feature_interaction_grad_op.op_conf().user_conf(); + const LogicalBlobId last_feature_grad_lbi = + GenLogicalBlobId(consumer_op_conf.output("features_grad", input_size - 1)); + std::string sparse_feature_grad_lbn = + fused_dot_feature_interaction_grad_op.output("sparse_feature_grad", 0); + for (const OpEdge* out_edge : consumer->out_edges()) { + const OpNode* grad_out_node = out_edge->dst_node(); + if (out_edge->lbis().size() == 1 && out_edge->lbis().front() == last_feature_grad_lbi) { + if (!IsUserOpWithTypeName(grad_out_node->op().op_conf(), + "embedding_gradient_shuffle")) { + return; + } + OperatorConf new_embedding_gradient_shuffle_conf = grad_out_node->op().op_conf(); + for (const std::string& ibn : grad_out_node->op().input_bns()) { + if (grad_out_node->op().BnInOp2Lbi(ibn) == last_feature_grad_lbi) { + const auto& new_val = sparse_feature_grad_lbn; + const auto& old_val = ReplaceInputLbnInOpCustomizedConf( + &new_embedding_gradient_shuffle_conf, ibn, new_val); + CHECK_EQ(GenLogicalBlobName(last_feature_grad_lbi), old_val); + } + } + auto bool_attr = ::oneflow::AttrValue(); + bool_attr.set_at_bool(true); + (*(new_embedding_gradient_shuffle_conf.mutable_user_conf() + ->mutable_attr()))["skip_first_scatter"] = bool_attr; + job_builder->MutOpsOnlyOnce({new_embedding_gradient_shuffle_conf}); + } + } + } + job_builder->MutOpsOnlyOnce({new_op_conf}); + } + auto bool_attr = ::oneflow::AttrValue(); + bool_attr.set_at_bool(true); + OperatorConf new_embedding_shuffle_conf = op_node->op().op_conf(); + (*(new_embedding_shuffle_conf.mutable_user_conf()->mutable_attr()))["skip_last_gather"] = + bool_attr; + job_builder->MutOpsOnlyOnce({new_embedding_shuffle_conf}); + }); + + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("FuseEmbeddingShuffleInteractionPass", FuseEmbeddingShuffleInteractionPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/fuse_model_update_cast_pass.cpp b/oneflow/core/job_rewriter/fuse_model_update_cast_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..325477a037ef3166d4b615a927d22dd943ab54cd --- /dev/null +++ b/oneflow/core/job_rewriter/fuse_model_update_cast_pass.cpp @@ -0,0 +1,168 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +class FuseModelUpdateCastOpsPass final : public JobPass { + public: + FuseModelUpdateCastOpsPass() = default; + ~FuseModelUpdateCastOpsPass() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { + return (ctx.job_desc().enable_fused_model_update_cast() + || ParseBooleanFromEnv("ONEFLOW_FUSE_MODEL_UPDATE_CAST", false)) + && ctx.job_desc().enable_auto_mixed_precision(); + } + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + LOG(INFO) << "Enable fuse model update cast pass. "; + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +Maybe FuseModelUpdateCastOpsPass::Apply(const OpGraph& op_graph, + JobBuilder* job_builder) const { + op_graph.ForEachNode([&](OpNode* op_node) { + const auto& op_conf = op_node->op().op_conf(); + if (!op_conf.has_variable_conf()) { return; } + LogicalBlobId model_copy_lbi; + + for (OpEdge* find_cast_edge : op_node->out_edges()) { + OpNode* find_cast_node = find_cast_edge->dst_node(); + if (!IsUserOpWithTypeName(find_cast_node->op().op_conf(), "cast")) { continue; } + const user_op::UserOpConfWrapper cast_user_conf(find_cast_node->op().op_conf()); + if (find_cast_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(cast_user_conf.input("in", 0))) + .data_type() + != DataType::kFloat) { + continue; + } + if (find_cast_node->LogicalBlobDesc4Lbi(GenLogicalBlobId(cast_user_conf.output("out", 0))) + .data_type() + != DataType::kFloat16) { + continue; + } + // Currently only support for cuda, maybe remove this limit. + if (find_cast_node->parallel_desc().device_type() != DeviceType::kCUDA) { continue; } + + for (OpEdge* find_model_update_edge : op_node->out_edges()) { + OpNode* find_model_update_update_node = find_model_update_edge->dst_node(); + if (!IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), "sgd_update") + && !IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), + "adam_update")) { + continue; + } + + // Currently only support for cuda, maybe remove this limit. + if (find_model_update_update_node->parallel_desc().device_type() != DeviceType::kCUDA) { + continue; + } + + const user_op::UserOpConfWrapper model_update_user_conf( + find_model_update_update_node->op().op_conf()); + + // Here we find cast and model_update node, Replace cast as mutable_cast_once, and add + // model_copy to model_update node. + user_op::UserOpConfWrapperBuilder fused_cast_op_builder(cast_user_conf.op_name()); + fused_cast_op_builder.OpTypeName("mutable_cast_once") + .Input("in", cast_user_conf.input("in", 0)) + .Attr("dtype", cast_user_conf.attr("dtype")) + .Output("out"); + + CHECK(cast_user_conf.op_conf().has_scope_symbol_id()); + fused_cast_op_builder.ScopeSymbolId(cast_user_conf.op_conf().scope_symbol_id()); + + OperatorConf new_cast_op_conf = cast_user_conf.op_conf(); + *new_cast_op_conf.mutable_user_conf() = fused_cast_op_builder.Build().op_conf().user_conf(); + job_builder->MutOpsOnlyOnce({new_cast_op_conf}); + + const user_op::UserOpConfWrapper new_cast_user_conf(new_cast_op_conf); + model_copy_lbi = GenLogicalBlobId(new_cast_user_conf.output("out", 0)); + user_op::UserOpConfWrapperBuilder fused_model_update_op_builder( + model_update_user_conf.op_name()); + if (IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), "sgd_update")) { + fused_model_update_op_builder.OpTypeName("sgd_update") + .Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)) + .Input("learning_rate", model_update_user_conf.input("learning_rate", 0)) + .Attr("scale", model_update_user_conf.attr("scale")) + .Attr("l1", model_update_user_conf.attr("l1")) + .Attr("l2", model_update_user_conf.attr("l2")) + .Attr("weight_decay", model_update_user_conf.attr("weight_decay")) + .Attr("learning_rate_scale", + model_update_user_conf.attr("learning_rate_scale")); + } else if (IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), + "adam_update")) { + fused_model_update_op_builder.OpTypeName("adam_update") + .Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)) + .Input("m", model_update_user_conf.input("m", 0)) + .Input("v", model_update_user_conf.input("v", 0)) + .Input("learning_rate", model_update_user_conf.input("learning_rate", 0)) + .Attr("scale", model_update_user_conf.attr("scale")) + .Attr("l1", model_update_user_conf.attr("l1")) + .Attr("l2", model_update_user_conf.attr("l2")) + .Attr("weight_decay", model_update_user_conf.attr("weight_decay")) + .Attr("beta1", model_update_user_conf.attr("beta1")) + .Attr("beta2", model_update_user_conf.attr("beta2")) + .Attr("epsilon", model_update_user_conf.attr("epsilon")) + .Attr("amsgrad", model_update_user_conf.attr("amsgrad")) + .Attr("do_bias_correction", + model_update_user_conf.attr("do_bias_correction")) + .Attr("learning_rate_scale", + model_update_user_conf.attr("learning_rate_scale")); + ; + if (model_update_user_conf.attr("do_bias_correction")) { + fused_model_update_op_builder.Input( + "bias_correction1", model_update_user_conf.input("bias_correction1", 0)); + fused_model_update_op_builder.Input( + "bias_correction2", model_update_user_conf.input("bias_correction2", 0)); + } + if (model_update_user_conf.attr("amsgrad")) { + fused_model_update_op_builder.Input("max_v", model_update_user_conf.input("max_v", 0)); + } + } else { + UNIMPLEMENTED() << "Need support more optimizers. "; + } + fused_model_update_op_builder.Input("model_copy", GenLogicalBlobName(model_copy_lbi)); + CHECK(model_update_user_conf.op_conf().has_scope_symbol_id()); + fused_model_update_op_builder.ScopeSymbolId( + model_update_user_conf.op_conf().scope_symbol_id()); + + OperatorConf new_model_update_op_conf = model_update_user_conf.op_conf(); + *new_model_update_op_conf.mutable_user_conf() = + fused_model_update_op_builder.Build().op_conf().user_conf(); + job_builder->MutOpsOnlyOnce({new_model_update_op_conf}); + break; + } + break; + } + }); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("FuseModelUpdateCastOpsPass", FuseModelUpdateCastOpsPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/fuse_update_ops_pass.cpp b/oneflow/core/job_rewriter/fuse_update_ops_pass.cpp index 004e00ef6769fbed84ae90d5f2619deb3f9aeae1..d08f2e22cc6bece48aa17de9c72faccb2777381f 100644 --- a/oneflow/core/job_rewriter/fuse_update_ops_pass.cpp +++ b/oneflow/core/job_rewriter/fuse_update_ops_pass.cpp @@ -20,27 +20,6 @@ namespace oneflow { namespace { -std::function MakePredicatorIsSafeToDelete(const OpGraph& op_graph) { - HashSet ctrl_in_op_names; - op_graph.ForEachNode([&](const OpNode* op_node) { - for (const std::string& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { - ctrl_in_op_names.insert(ctrl_in_op_name); - } - }); - return [=](const OpNode* op_node) { - if (op_node->out_edges().size() > 1) { return false; } - if (!op_node->op().op_conf().ctrl_in_op_name().empty()) { return false; } - if (ctrl_in_op_names.find(op_node->op().op_conf().name()) != ctrl_in_op_names.end()) { - return false; - } - return true; - }; -} - -bool IsUserOpWithTypeName(const OperatorConf& op_conf, const std::string& op_type_name) { - return op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == op_type_name; -}; - class FuseUpdateOpsPass final : public JobPass { public: FuseUpdateOpsPass() = default; @@ -72,7 +51,8 @@ Maybe FuseUpdateOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu && user_op_conf.op_type_name() != "lars_update" && user_op_conf.op_type_name() != "adagrad_update" && user_op_conf.op_type_name() != "lamb_update" - && user_op_conf.op_type_name() != "ftrl_update") { + && user_op_conf.op_type_name() != "ftrl_update" + && user_op_conf.op_type_name() != "adadelta_update") { return; } if (user_op_conf.attr("scale") != 1.0 || user_op_conf.attr("l1") != 0.0f @@ -159,7 +139,8 @@ Maybe FuseUpdateOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu .Attr("scale", scale) .Attr("l1", l1) .Attr("l2", l2) - .Attr("weight_decay", user_op_conf.attr("weight_decay")); + .Attr("weight_decay", user_op_conf.attr("weight_decay")) + .Attr("learning_rate_scale", user_op_conf.attr("learning_rate_scale")); if (scale_by_tensor_lbn != "") { fused_op_builder.Input("scale_by_tensor", scale_by_tensor_lbn); } @@ -170,13 +151,18 @@ Maybe FuseUpdateOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu // do nothing } else if (user_op_conf.op_type_name() == "momentum_update") { fused_op_builder.Input("momentum", user_op_conf.input("momentum", 0)) - .Attr("beta", user_op_conf.attr("beta")); + .Attr("beta", user_op_conf.attr("beta")) + .Attr("dampening", user_op_conf.attr("dampening")) + .Attr("nesterov", user_op_conf.attr("nesterov")) + .Attr("maximize", user_op_conf.attr("maximize")); } else if (user_op_conf.op_type_name() == "adam_update") { fused_op_builder.Input("m", user_op_conf.input("m", 0)) .Input("v", user_op_conf.input("v", 0)) .Attr("beta1", user_op_conf.attr("beta1")) .Attr("beta2", user_op_conf.attr("beta2")) - .Attr("epsilon", user_op_conf.attr("epsilon")); + .Attr("epsilon", user_op_conf.attr("epsilon")) + .Attr("amsgrad", user_op_conf.attr("amsgrad")) + .Attr("do_bias_correction", user_op_conf.attr("do_bias_correction")); if (user_op_conf.has_input("max_v", 0)) { fused_op_builder.Input("max_v", user_op_conf.input("max_v", 0)); } @@ -210,7 +196,8 @@ Maybe FuseUpdateOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu .Input("v", user_op_conf.input("v", 0)) .Attr("beta1", user_op_conf.attr("beta1")) .Attr("beta2", user_op_conf.attr("beta2")) - .Attr("epsilon", user_op_conf.attr("epsilon")); + .Attr("epsilon", user_op_conf.attr("epsilon")) + .Attr("do_bias_correction", user_op_conf.attr("do_bias_correction")); if (user_op_conf.has_input("bias_correction1", 0)) { fused_op_builder.Input("bias_correction1", user_op_conf.input("bias_correction1", 0)); } @@ -224,6 +211,12 @@ Maybe FuseUpdateOpsPass::Apply(const OpGraph& op_graph, JobBuilder* job_bu .Attr("lambda1", user_op_conf.attr("lambda1")) .Attr("lambda2", user_op_conf.attr("lambda2")) .Attr("beta", user_op_conf.attr("beta")); + } else if (user_op_conf.op_type_name() == "adadelta_update") { + fused_op_builder.Input("square_avgs", user_op_conf.input("square_avgs", 0)) + .Input("acc_deltas", user_op_conf.input("acc_deltas", 0)) + .Attr("rho", user_op_conf.attr("rho")) + .Attr("epsilon", user_op_conf.attr("epsilon")) + .Attr("maximize", user_op_conf.attr("maximize")); } else { UNIMPLEMENTED(); } diff --git a/oneflow/core/job_rewriter/generate_backward_and_optimizer_op_confs.cpp b/oneflow/core/job_rewriter/generate_backward_and_optimizer_op_confs.cpp deleted file mode 100644 index 2462a6c6199e51eb926d9ae2779e1e7d43517d9d..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/generate_backward_and_optimizer_op_confs.cpp +++ /dev/null @@ -1,172 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/job_pass.h" -#include "oneflow/core/job_rewriter/autograd.h" -#include "oneflow/core/job_rewriter/optimizer.h" -#include "oneflow/core/job_rewriter/calculation_pass.h" -#include "oneflow/core/job/scope.h" -#include "oneflow/core/job/scope.pb.h" -#include "oneflow/core/job/foreign_callback.h" -#include "oneflow/core/vm/symbol_storage.h" -#include "oneflow/core/framework/instructions_builder.h" - -namespace oneflow { - -namespace { - -void UpdateJobHelperConfProducedLbi2ConsumedDiffLbi( - const HashMap& lbi2diff_lbi, JobBuilder* job_builder) { - auto& mut_pairs = - (*job_builder->mutable_helper()->mutable_tag2lbi_relations())[kProducedLbi2ConsumedDiffLbi]; - for (const auto& pair : lbi2diff_lbi) { - auto* mut_pair = mut_pairs.add_pair(); - *mut_pair->mutable_first() = pair.first; - *mut_pair->mutable_second() = pair.second; - } -} - -class GenerateBackwardAndOptimizerOpConfs final : public JobPass { - public: - OF_DISALLOW_COPY_AND_MOVE(GenerateBackwardAndOptimizerOpConfs); - GenerateBackwardAndOptimizerOpConfs() = default; - ~GenerateBackwardAndOptimizerOpConfs() override = default; - - bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } - - Maybe Apply(Job* job, JobPassCtx* ctx) const override; -}; - -void FilterModelLbi2ModelDiffLbiByOpConf( - const OpGraph& op_graph, const HashMap& lbi2diff_lbi, - HashMap* model_lbi2model_diff_lbi) { - for (const auto& pair : lbi2diff_lbi) { - const LogicalBlobId& lbi = pair.first; - const LogicalBlobId& diff_lbi = pair.second; - const OpNode* producer = op_graph.OpNode4OpName(lbi.op_name()); - if (producer->op().op_conf().has_variable_conf()) { - (*model_lbi2model_diff_lbi)[lbi] = diff_lbi; - } - } -} - -void FilterCurModelLbi2ModelDiffLbiByName( - const ::google::protobuf::RepeatedPtrField& variables, - const HashMap& model_lbi2model_diff_lbi, - HashMap* cur_model_lbi2model_diff_lbi) { - for (const std::string& variable : variables) { - const LogicalBlobId& lbi = GenLogicalBlobId(variable + "/out"); - if (model_lbi2model_diff_lbi.find(lbi) != model_lbi2model_diff_lbi.end()) { - (*cur_model_lbi2model_diff_lbi)[lbi] = model_lbi2model_diff_lbi.at(lbi); - } - } -} - -Maybe WithCalculationPassScope(const std::string& pass_name, Job* job, - const std::function()>& Handler) { - HashSet exists_op_names; - for (const auto& op_conf : job->net().op()) { - CHECK_OR_RETURN(exists_op_names.emplace(op_conf.name()).second); - } - JUST(Handler()); - // using a new JobBuilder to avoid bugs caused by MutOnlyOnce - auto new_job_builder = std::make_shared(job); - HashMap> scope_id2op_names; - const auto& scope_storage = *Singleton>::Get(); - for (const auto& op_conf : job->net().op()) { - if (exists_op_names.count(op_conf.name()) > 0) { continue; } - CHECK_OR_RETURN(op_conf.has_scope_symbol_id()); - OF_RETURN_IF_ERROR(scope_storage.MaybeGet(op_conf.scope_symbol_id())) << op_conf.DebugString(); - scope_id2op_names[op_conf.scope_symbol_id()].emplace_back(&op_conf); - } - const auto& GetNewScopeSymbolId = [&](int64_t old_scope_symbol_id) -> Maybe { - const auto& old_scope = JUST(scope_storage.MaybeGet(old_scope_symbol_id)); - std::shared_ptr new_scope = std::make_shared(old_scope.scope_proto()); - new_scope->set_parent_scope_symbol_id(old_scope_symbol_id); - new_scope->set_calculation_pass_name(pass_name); - std::shared_ptr new_scope_symbol; - JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { - new_scope_symbol = JUST(builder->GetScopeSymbol(*new_scope)); - return Maybe::Ok(); - })); - return JUST(new_scope_symbol->symbol_id()); - }; - for (const auto& pair : scope_id2op_names) { - int64_t new_scope_symbol_id = JUST(GetNewScopeSymbolId(pair.first)); - std::vector op_confs(pair.second.size()); - for (int i = 0; i < pair.second.size(); ++i) { - op_confs.at(i).CopyFrom(*pair.second.at(i)); - op_confs.at(i).set_scope_symbol_id(new_scope_symbol_id); - } - new_job_builder->MutOpsOnlyOnce(op_confs); - } - return new_job_builder; -} - -Maybe GenerateBackwardAndOptimizerOpConfs::Apply(Job* job, JobPassCtx* ctx) const { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - auto job_builder = std::make_shared(job); - const JobBuilder* old_job_builder = job_builder.get(); - LogicalBlobId total_loss_instance_num; - HashMap lbi2diff_lbi; - OpBlobArgPairs identical_sbp_oba_pairs; - job_builder = JUST(WithCalculationPassScope(kBackwardPass, job, [&]() -> Maybe { - CHECK(old_job_builder == job_builder.get()); // Check this lambda never been async called - JUST(AutoGrad(ctx, op_graph, job_builder.get(), &lbi2diff_lbi, &identical_sbp_oba_pairs)); - return Maybe::Ok(); - })); - HashMap model_lbi2model_diff_lbi; - FilterModelLbi2ModelDiffLbiByOpConf(op_graph, lbi2diff_lbi, &model_lbi2model_diff_lbi); - old_job_builder = job_builder.get(); - job_builder = JUST(WithCalculationPassScope(kOptimizerPass, job, [&]() -> Maybe { - CHECK(old_job_builder == job_builder.get()); // Check this lambda never been async called - AddDiffStaticShapeCast(op_graph, job_builder.get(), &model_lbi2model_diff_lbi); - AddDiffParallelCast(op_graph, job_builder.get(), &model_lbi2model_diff_lbi); - JUST(ScaleModelDiffByLossInstanceNum(op_graph, job_builder.get(), &model_lbi2model_diff_lbi)); - ScaleModelDiffByLossScale(ctx, op_graph, job_builder.get(), &model_lbi2model_diff_lbi); - JUST(CountNotFiniteIfNeeded(ctx, op_graph, job_builder.get(), model_lbi2model_diff_lbi)); - for (const auto& optimizer_conf : job->job_conf().train_conf().optimizer_conf()) { - HashMap cur_model_lbi2model_diff_lbi; - FilterCurModelLbi2ModelDiffLbiByName(optimizer_conf.variable_op_names(), - model_lbi2model_diff_lbi, &cur_model_lbi2model_diff_lbi); - if (optimizer_conf.has_clip_conf()) { - ClipGradient(ctx, op_graph, job_builder.get(), &cur_model_lbi2model_diff_lbi, - optimizer_conf.clip_conf()); - } - RegularizeGradient(op_graph, job_builder.get(), &cur_model_lbi2model_diff_lbi); - op_graph.ForEachNode([&](OpNode* op_node) { - const VariableOp* var_op = dynamic_cast(&op_node->op()); - if (var_op == nullptr - || cur_model_lbi2model_diff_lbi.find(var_op->BnInOp2Lbi(var_op->SoleObn())) - == cur_model_lbi2model_diff_lbi.end()) { - return; - } - const std::string& model_diff_lbn = GenLogicalBlobName( - cur_model_lbi2model_diff_lbi.at(var_op->BnInOp2Lbi(var_op->SoleObn()))); - AddOptimizerOp(ctx, *op_node, model_diff_lbn, optimizer_conf, job_builder.get()); - }); - } - return Maybe::Ok(); - })); - UpdateJobHelperConfProducedLbi2ConsumedDiffLbi(lbi2diff_lbi, job_builder.get()); - return Maybe::Ok(); -} - -REGISTER_JOB_PASS("GenerateBackwardAndOptimizerOpConfs", GenerateBackwardAndOptimizerOpConfs); - -} // namespace - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/generate_optimizer_op_confs.cpp b/oneflow/core/job_rewriter/generate_optimizer_op_confs.cpp new file mode 100644 index 0000000000000000000000000000000000000000..aa61e59d3a277e469415eea069e460b75d98f66a --- /dev/null +++ b/oneflow/core/job_rewriter/generate_optimizer_op_confs.cpp @@ -0,0 +1,162 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/job_rewriter/autograd.h" +#include "oneflow/core/job_rewriter/optimizer.h" +#include "oneflow/core/job_rewriter/calculation_pass.h" +#include "oneflow/core/job/scope.h" +#include "oneflow/core/job/scope.pb.h" +#include "oneflow/core/vm/symbol_storage.h" +#include "oneflow/core/framework/instructions_builder.h" + +namespace oneflow { + +namespace { + +class GenerateOptimizerOpConfs final : public JobPass { + public: + OF_DISALLOW_COPY_AND_MOVE(GenerateOptimizerOpConfs); + GenerateOptimizerOpConfs() = default; + ~GenerateOptimizerOpConfs() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } + + Maybe Apply(Job* job, JobPassCtx* ctx) const override; +}; + +void FilterCurModelLbi2ModelDiffLbiByName( + const ::google::protobuf::RepeatedPtrField& variables, + const HashMap& model_lbi2model_diff_lbi, + HashMap* cur_model_lbi2model_diff_lbi) { + for (const std::string& variable : variables) { + const LogicalBlobId& lbi = GenLogicalBlobId(variable + "/out"); + if (model_lbi2model_diff_lbi.find(lbi) != model_lbi2model_diff_lbi.end()) { + (*cur_model_lbi2model_diff_lbi)[lbi] = model_lbi2model_diff_lbi.at(lbi); + } + } +} + +Maybe WithCalculationPassScope(const std::string& pass_name, Job* job, + const std::function()>& Handler) { + HashSet exists_op_names; + for (const auto& op_conf : job->net().op()) { + CHECK_OR_RETURN(exists_op_names.emplace(op_conf.name()).second); + } + JUST(Handler()); + // using a new JobBuilder to avoid bugs caused by MutOnlyOnce + auto new_job_builder = std::make_shared(job); + HashMap> scope_id2op_names; + const auto& scope_storage = *Singleton>::Get(); + for (const auto& op_conf : job->net().op()) { + if (exists_op_names.count(op_conf.name()) > 0) { continue; } + CHECK_OR_RETURN(op_conf.has_scope_symbol_id()); + OF_RETURN_IF_ERROR(scope_storage.MaybeGet(op_conf.scope_symbol_id())) << op_conf.DebugString(); + scope_id2op_names[op_conf.scope_symbol_id()].emplace_back(&op_conf); + } + const auto& GetNewScopeSymbolId = [&](int64_t old_scope_symbol_id) -> Maybe { + const auto& old_scope = JUST(scope_storage.MaybeGet(old_scope_symbol_id)); + std::shared_ptr new_scope = std::make_shared(old_scope.scope_proto()); + new_scope->set_parent_scope_symbol_id(old_scope_symbol_id); + new_scope->set_calculation_pass_name(pass_name); + std::shared_ptr new_scope_symbol; + JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe { + new_scope_symbol = JUST(builder->GetScopeSymbol(*new_scope)); + return Maybe::Ok(); + })); + return JUST(new_scope_symbol->symbol_id()); + }; + for (const auto& pair : scope_id2op_names) { + int64_t new_scope_symbol_id = JUST(GetNewScopeSymbolId(pair.first)); + std::vector op_confs(pair.second.size()); + for (int i = 0; i < pair.second.size(); ++i) { + op_confs.at(i).CopyFrom(*pair.second.at(i)); + op_confs.at(i).set_scope_symbol_id(new_scope_symbol_id); + } + new_job_builder->MutOpsOnlyOnce(op_confs); + } + return new_job_builder; +} + +Maybe GenerateOptimizerOpConfs::Apply(Job* job, JobPassCtx* ctx) const { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const auto& train_conf = job->job_conf().train_conf(); + // loss initial gradients + HashMap loss_lbi2initial_diff_lbi; + CHECK_OR_RETURN(train_conf.loss_lbn_size() == train_conf.loss_grad_lbn_size()) + << "loss_lbn and loss_grad_lbn size mismatch"; + for (int i = 0; i < train_conf.loss_lbn_size(); ++i) { + auto loss_lbi = GenLogicalBlobId(train_conf.loss_lbn(i)); + auto loss_grad_lbi = GenLogicalBlobId(train_conf.loss_grad_lbn(i)); + loss_lbi2initial_diff_lbi.emplace(loss_lbi, loss_grad_lbi); + } + // variable gradients + HashMap model_lbi2model_diff_lbi; + for (const auto& optimizer_conf : train_conf.optimizer_conf()) { + CHECK_OR_RETURN(optimizer_conf.variable_op_names_size() + == optimizer_conf.variable_grad_lbns_size()) + << "variable_op_names and variable_grad_lbns size mismatch"; + for (int i = 0; i < optimizer_conf.variable_op_names_size(); ++i) { + auto model_lbi = GenLogicalBlobId(optimizer_conf.variable_op_names(i) + "/out"); + const auto& model_diff_lbn = optimizer_conf.variable_grad_lbns(i); + // variable maybe has no gradient, so skip it if model_diff_lbn is empty + if (!model_diff_lbn.empty()) { + model_lbi2model_diff_lbi.emplace(model_lbi, GenLogicalBlobId(model_diff_lbn)); + } + } + } + const OpGraph op_graph(*job); + auto job_builder = std::make_shared(job); + const JobBuilder* old_job_builder = job_builder.get(); + job_builder = JUST(WithCalculationPassScope(kOptimizerPass, job, [&]() -> Maybe { + CHECK(old_job_builder == job_builder.get()); // Check this lambda never been async called + AddDiffHalf2FloatCast(op_graph, job_builder.get(), &model_lbi2model_diff_lbi); + AddDiffStaticShapeCast(op_graph, job_builder.get(), &model_lbi2model_diff_lbi); + AddDiffParallelCast(op_graph, job_builder.get(), &model_lbi2model_diff_lbi); + JUST(ScaleModelDiffByLossInstanceNum(op_graph, job_builder.get(), &model_lbi2model_diff_lbi)); + JUST(ScaleInitialDiffByLossScale(ctx, op_graph, job_builder.get(), &loss_lbi2initial_diff_lbi)); + ScaleModelDiffByLossScale(ctx, op_graph, job_builder.get(), &model_lbi2model_diff_lbi); + JUST(CountNotFiniteIfNeeded(ctx, op_graph, job_builder.get(), model_lbi2model_diff_lbi)); + for (const auto& optimizer_conf : job->job_conf().train_conf().optimizer_conf()) { + HashMap cur_model_lbi2model_diff_lbi; + FilterCurModelLbi2ModelDiffLbiByName(optimizer_conf.variable_op_names(), + model_lbi2model_diff_lbi, &cur_model_lbi2model_diff_lbi); + if (optimizer_conf.has_clip_conf()) { + ClipGradient(ctx, op_graph, job_builder.get(), &cur_model_lbi2model_diff_lbi, + optimizer_conf.clip_conf()); + } + RegularizeGradient(op_graph, job_builder.get(), &cur_model_lbi2model_diff_lbi); + op_graph.ForEachNode([&](OpNode* op_node) { + const VariableOp* var_op = dynamic_cast(&op_node->op()); + if (var_op == nullptr + || cur_model_lbi2model_diff_lbi.find(var_op->BnInOp2Lbi(var_op->SoleObn())) + == cur_model_lbi2model_diff_lbi.end()) { + return; + } + const std::string& model_diff_lbn = GenLogicalBlobName( + cur_model_lbi2model_diff_lbi.at(var_op->BnInOp2Lbi(var_op->SoleObn()))); + AddOptimizerOp(ctx, *op_node, model_diff_lbn, optimizer_conf, job_builder.get()); + }); + } + return Maybe::Ok(); + })); + return Maybe::Ok(); +} + +REGISTER_JOB_PASS("GenerateOptimizerOpConfs", GenerateOptimizerOpConfs); + +} // namespace + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/group_boxing_by_dst_parallel.cpp b/oneflow/core/job_rewriter/group_boxing_by_dst_parallel.cpp index dd3e8039af7b123d76c21fb9a0e56a476fa067b8..92f4326df877a369e6fb825d5eb3f2adc4265b3c 100644 --- a/oneflow/core/job_rewriter/group_boxing_by_dst_parallel.cpp +++ b/oneflow/core/job_rewriter/group_boxing_by_dst_parallel.cpp @@ -66,6 +66,7 @@ Maybe GroupBoxingByDstParallel(const OpGraph& op_graph, JobBuilder* job_bu if (blob_modifier_.has_is_mutable() && blob_modifier_.is_mutable()) { continue; } const LogicalBlobId& lbi = node->op().BnInOp2Lbi(ibn); const OpNode& producer = node->ProducerOpNode4Lbi(lbi); + const auto& logical_shape = node->LogicalBlobDesc4Lbi(lbi).shape(); const NdSbp& producer_nd_sbp = producer.NdSbp4Lbi(lbi); const std::string& producer_lbn = *CHECK_JUST(producer.op().obn4lbi(lbi)); const ParallelDesc& producer_parallel_desc = @@ -73,7 +74,7 @@ Maybe GroupBoxingByDstParallel(const OpGraph& op_graph, JobBuilder* job_bu ParallelDesc reduced_in_parallel_desc = producer_parallel_desc; NdSbp reduced_in_nd_sbp; NdSbpDimReduce(producer_parallel_desc, producer_nd_sbp, &reduced_in_parallel_desc, - &reduced_in_nd_sbp); + &reduced_in_nd_sbp, logical_shape); const NdSbp& consumer_nd_sbp = node->NdSbp4BnInOp(ibn); const ParallelDesc& consumer_parallel_desc = @@ -81,7 +82,7 @@ Maybe GroupBoxingByDstParallel(const OpGraph& op_graph, JobBuilder* job_bu ParallelDesc reduced_out_parallel_desc = consumer_parallel_desc; NdSbp reduced_out_nd_sbp; NdSbpDimReduce(consumer_parallel_desc, consumer_nd_sbp, &reduced_out_parallel_desc, - &reduced_out_nd_sbp); + &reduced_out_nd_sbp, logical_shape); if (reduced_in_parallel_desc == reduced_out_parallel_desc && reduced_in_nd_sbp == reduced_out_nd_sbp) { @@ -101,7 +102,8 @@ Maybe GroupBoxingByDstParallel(const OpGraph& op_graph, JobBuilder* job_bu const ParallelDesc& dst_parallel_desc = parallel7group.first.first; const NdSbp& dst_nd_sbp = parallel7group.first.second; OperatorConf identity_op_conf{}; - identity_op_conf.set_name("System-Boxing-Identity-" + NewUniqueId()); + identity_op_conf.set_name("Sys-Boxing-GroupIdentity-" + lbi.op_name() + "_" + lbi.blob_name() + + "-" + NewUniqueId()); IdentityOpConf* identity_conf = identity_op_conf.mutable_identity_conf(); identity_conf->set_in(GenLogicalBlobName(lbi)); identity_conf->set_out("out"); diff --git a/oneflow/core/job_rewriter/identity_grad.cpp b/oneflow/core/job_rewriter/identity_grad.cpp deleted file mode 100644 index 8ddac5c9afe9038191318b208bb68e579811a1a2..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/identity_grad.cpp +++ /dev/null @@ -1,104 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autograd.h" - -namespace oneflow { - -namespace { - -void GenerateIdentityBackwardOpConf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp) { - if (DiffLbi4BnInOp("in") != nullptr) { - OperatorConf grad_op{}; - grad_op.set_name("System-AutoGrad-" + op.op_name()); - IdentityOpConf* identity_op_conf = grad_op.mutable_identity_conf(); - identity_op_conf->set_in(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - identity_op_conf->set_out("out"); - op_confs->emplace_back(grad_op); - DiffLbi4BnInOp("in")->set_op_name(grad_op.name()); - DiffLbi4BnInOp("in")->set_blob_name(identity_op_conf->out()); - } -} - -REGISTER_OP_GRAD(OperatorConf::kIdentityConf, &GenerateIdentityBackwardOpConf); -REGISTER_OP_GRAD(OperatorConf::kCopyConf, &GenerateIdentityBackwardOpConf); - -} // namespace - -void GenerateBwSbpParallel(SbpParallel* bw_sbp_parallel, const SbpParallel& fw_sbp_parallel) { - if (fw_sbp_parallel.has_split_parallel()) { - *bw_sbp_parallel = fw_sbp_parallel; - } else if (fw_sbp_parallel.has_broadcast_parallel()) { - bw_sbp_parallel->mutable_partial_sum_parallel(); - } else if (fw_sbp_parallel.has_partial_sum_parallel()) { - bw_sbp_parallel->mutable_broadcast_parallel(); - } else { - UNIMPLEMENTED(); - } -} - -namespace { - -void GenerateCastToMirroredBackwardOpConf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp) { - CHECK(op.op_conf().has_cast_to_mirrored_conf()); - const auto& fw_op_conf = op.op_conf().cast_to_mirrored_conf(); - if (DiffLbi4BnInOp("in") != nullptr) { - OperatorConf grad_op{}; - grad_op.set_name("System-AutoGrad-" + op.op_name()); - grad_op.set_scope_symbol_id(op.op_conf().scope_symbol_id()); - CastFromMirroredOpConf* bw_op_conf = grad_op.mutable_cast_from_mirrored_conf(); - bw_op_conf->set_in(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - bw_op_conf->set_out("out"); - GenerateBwSbpParallel(bw_op_conf->mutable_sbp_parallel(), fw_op_conf.sbp_parallel()); - op_confs->emplace_back(grad_op); - DiffLbi4BnInOp("in")->set_op_name(grad_op.name()); - DiffLbi4BnInOp("in")->set_blob_name(bw_op_conf->out()); - } -} - -REGISTER_OP_GRAD(OperatorConf::kCastToMirroredConf, &GenerateCastToMirroredBackwardOpConf); - -} // namespace - -namespace { - -void GenerateCastFromMirroredBackwardOpConf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp) { - CHECK(op.op_conf().has_cast_from_mirrored_conf()); - const auto& fw_op_conf = op.op_conf().cast_from_mirrored_conf(); - if (DiffLbi4BnInOp("in") != nullptr) { - OperatorConf grad_op{}; - grad_op.set_name("System-AutoGrad-" + op.op_name()); - grad_op.set_scope_symbol_id(op.op_conf().scope_symbol_id()); - CastToMirroredOpConf* bw_op_conf = grad_op.mutable_cast_to_mirrored_conf(); - bw_op_conf->set_in(GenLogicalBlobName(*DiffLbi4BnInOp("out"))); - bw_op_conf->set_out("out"); - GenerateBwSbpParallel(bw_op_conf->mutable_sbp_parallel(), fw_op_conf.sbp_parallel()); - op_confs->emplace_back(grad_op); - DiffLbi4BnInOp("in")->set_op_name(grad_op.name()); - DiffLbi4BnInOp("in")->set_blob_name(bw_op_conf->out()); - } -} - -REGISTER_OP_GRAD(OperatorConf::kCastFromMirroredConf, &GenerateCastFromMirroredBackwardOpConf); - -} // namespace - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/indexed_slices_optimizer_rewrite_pass.cpp b/oneflow/core/job_rewriter/indexed_slices_optimizer_rewrite_pass.cpp index b092f1f8f2261c985bc9d41bb49c35c9c4eb849d..82351f83b64ff1df6fd761b81286e847d81d1dab 100644 --- a/oneflow/core/job_rewriter/indexed_slices_optimizer_rewrite_pass.cpp +++ b/oneflow/core/job_rewriter/indexed_slices_optimizer_rewrite_pass.cpp @@ -107,13 +107,17 @@ Maybe IndexedSlicesOptimizerRewritePass::Apply(const OpGraph& op_graph, indexed_slices_op_builder.OpTypeName("indexed_slices_" + user_op_conf.op_type_name()) .Input("model", user_op_conf.input("model", 0)) .Input("learning_rate", user_op_conf.input("learning_rate", 0)) - .Attr("weight_decay", user_op_conf.attr("weight_decay")); + .Attr("weight_decay", user_op_conf.attr("weight_decay")) + .Attr("learning_rate_scale", user_op_conf.attr("learning_rate_scale")); if (user_op_conf.op_type_name() == "sgd_update") { // do nothing } else if (user_op_conf.op_type_name() == "momentum_update") { indexed_slices_op_builder.Input("momentum", user_op_conf.input("momentum", 0)) - .Attr("beta", user_op_conf.attr("beta")); + .Attr("beta", user_op_conf.attr("beta")) + .Attr("dampening", user_op_conf.attr("dampening")) + .Attr("nesterov", user_op_conf.attr("nesterov")) + .Attr("maximize", user_op_conf.attr("maximize")); } else if (user_op_conf.op_type_name() == "adam_update") { indexed_slices_op_builder.Input("m", user_op_conf.input("m", 0)) .Input("v", user_op_conf.input("v", 0)) diff --git a/oneflow/core/job_rewriter/insert_nccl_logical_op_pass.cpp b/oneflow/core/job_rewriter/insert_nccl_logical_op_pass.cpp index b2ab4cbda975dfb35f0a027344e242a5328d1a3b..e5c16137120c7b08d31486a4e0ebe9999cc507f5 100644 --- a/oneflow/core/job_rewriter/insert_nccl_logical_op_pass.cpp +++ b/oneflow/core/job_rewriter/insert_nccl_logical_op_pass.cpp @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/job/nd_sbp_util.h" -#if defined(WITH_CUDA) || defined(ROCM) +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/framework/instructions_builder.h" @@ -33,7 +33,6 @@ namespace oneflow { namespace { -// Do InsertNcclLogicalOpPass will use backward recomputation for sublinear memory cost. class InsertNcclLogicalOpPass final : public JobPass { public: OF_DISALLOW_COPY_AND_MOVE(InsertNcclLogicalOpPass); @@ -103,6 +102,17 @@ bool SharedPtrShapeEqual(const std::shared_ptr& lhs, void FindAllConnectedSubgraphForGpuExecOrder(std::vector>* ret, const OpGraph& op_graph, const std::vector& order) { + // NOTE(chengcheng): acc subgraph may greater than fw/bw subgraph. we need use max time shape. + std::shared_ptr seed_time_shape = std::make_shared(Shape({1, 1})); + op_graph.ForEachNode([&](const OpNode* node) { + std::shared_ptr this_time_shape = GetOpNodeTimeShape(node); + if (this_time_shape->elem_cnt() > seed_time_shape->elem_cnt()) { + seed_time_shape = this_time_shape; + } + }); + + VLOG(2) << " seed time shape = " << seed_time_shape->ToString(); + HashSet visited; for (const OpNode* seed_node : order) { @@ -112,6 +122,7 @@ void FindAllConnectedSubgraphForGpuExecOrder(std::vector> // NOTE(chengcheng): ONLY consider GPU op and parallel num > 1. if (seed_parallel_desc.device_type() != DeviceType::kCUDA) { continue; } if (seed_parallel_desc.parallel_num() <= 1) { continue; } + if (!SharedPtrShapeEqual(GetOpNodeTimeShape(seed_node), seed_time_shape)) { continue; } if (IsBreakpointOpNode(seed_node)) { continue; } HashSet this_subgraph; @@ -422,7 +433,7 @@ bool TryBuildNcclLogicalOpConf(OperatorConf* ret, const OpNode* src_node, const InOutParallelDimReduce(src_node->parallel_desc(), dst_node->parallel_desc(), src_node->NdSbp4Lbi(lbi), dst_node->NdSbp4Lbi(lbi), src_reduced_parallel_desc, dst_reduced_parallel_desc, src_reduced_nd_sbp, - dst_reduced_nd_sbp); + dst_reduced_nd_sbp, logical_blob_desc.shape()); CHECK_EQ(src_reduced_parallel_desc->parallel_num(), dst_reduced_parallel_desc->parallel_num()); std::shared_ptr src_reduced_hierarchy = src_reduced_parallel_desc->hierarchy(); @@ -746,6 +757,8 @@ void InsertNcclLogicalOpsAfterAcc(const OpGraph& op_graph, } } + if (nccl_op_infos.empty()) { return; } + for (const auto* node : after_acc_subgraph_nodes) { ordered_after_acc_subgraph.push_back(node); } CHECK_EQ(after_acc_subgraph_nodes.size(), ordered_after_acc_subgraph.size()); @@ -947,7 +960,9 @@ void InsertBwSinkAccTickAndNcclLogicalOpsInPlacementGroupAfterAcc( << ", we will try insert special identity and ctrl for " << " UNSAFE handle ALL nccl ops between different time shape: " << time_shape_before_acc->DebugStr() << "->acc->" << time_shape_after_acc->DebugStr() - << "\n\n"; + << "\n\n" + << " Debug: before acc op: " << bw_sink_op->op().op_conf().DebugString() + << " -> after acc op: " << first_acc_op->op().op_conf().DebugString(); CHECK_GT(time_shape_before_acc->elem_cnt(), time_shape_after_acc->elem_cnt()); CHECK_EQ(time_shape_before_acc->elem_cnt() % time_shape_after_acc->elem_cnt(), 0); @@ -969,10 +984,12 @@ void InsertBwSinkAccTickAndNcclLogicalOpsInPlacementGroupAfterAcc( .OpTypeName("cast_to_tick") .Input("in", bw_sink_op_out_lbn) .Output("out") + .ScopeSymbolId(bw_sink_op->op().op_conf().scope_symbol_id()) .Build(); OperatorConf bw_sink_acc_tick_conf; bw_sink_acc_tick_conf.set_name(std::string("System-BwSinkTick-AccTick_") + NewUniqueId()); + bw_sink_acc_tick_conf.set_scope_symbol_id(bw_sink_op->op().op_conf().scope_symbol_id()); auto* acc_conf = bw_sink_acc_tick_conf.mutable_acc_tick_conf(); acc_conf->set_one(cast_to_tick_op.output("out", 0)); acc_conf->set_acc("acc"); @@ -981,6 +998,7 @@ void InsertBwSinkAccTickAndNcclLogicalOpsInPlacementGroupAfterAcc( OperatorConf bw_sink_final_tick_conf; bw_sink_final_tick_conf.set_name(std::string("System-BwSinkFinalTick-DeviceTick_") + NewUniqueId()); + bw_sink_final_tick_conf.set_scope_symbol_id(bw_sink_op->op().op_conf().scope_symbol_id()); auto* tick_conf = bw_sink_final_tick_conf.mutable_device_tick_conf(); tick_conf->add_tick(GenLogicalBlobName(bw_sink_acc_tick_conf.name(), "acc")); tick_conf->set_out("out"); diff --git a/oneflow/core/job_rewriter/insert_pinned_identity_op_pass.cpp b/oneflow/core/job_rewriter/insert_pinned_identity_op_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6e04d5fa979bd84a0745bd631c63ff9dc8c99a1d --- /dev/null +++ b/oneflow/core/job_rewriter/insert_pinned_identity_op_pass.cpp @@ -0,0 +1,113 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/job_rewriter/job_pass.h" + +namespace oneflow { + +namespace { + +class InsertPinnedIdentityOpPass final : public JobPass { + public: + InsertPinnedIdentityOpPass() = default; + ~InsertPinnedIdentityOpPass() override = default; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override; +}; + +Maybe InsertPinnedIdentityOp(JobBuilder* job_builder, const OpGraph& op_graph, + const std::string& lbn) { + auto lbi = GenLogicalBlobId(lbn); + const OpNode* node = op_graph.OpNode4OpName(lbi.op_name()); + auto pinned_identity_op = + user_op::UserOpConfWrapperBuilder(lbi.op_name() + "_" + lbi.blob_name() + "_pinned_identity") + .Op("pinned_identity") + .Input("in", lbn) + .Output("out") + .ScopeSymbolId(node->op().op_conf().scope_symbol_id()) + .Build(); + const auto& parallel_conf = node->parallel_desc().parallel_conf(); + job_builder->AddOps(parallel_conf, {pinned_identity_op.op_conf()}); + + node->ForEachNodeOnOutEdge([&](const OpNode* out_node) { + for (const std::string& ibn : out_node->op().input_bns()) { + if (out_node->op().BnInOp2Lbi(ibn) == lbi) { + if (!CHECK_JUST(job_builder->IsInMutOpTransaction(out_node->op().op_name()))) { + CHECK_JUST(job_builder->MutOpTransactionMut(out_node->op().op_conf())); + } + OperatorConf& mut_consumer_op = + CHECK_JUST(job_builder->MutOpTransactionGet(out_node->op().op_name())); + const auto& old_lbn = ReplaceInputLbnInOpCustomizedConf( + &mut_consumer_op, ibn, pinned_identity_op.output("out", 0)); + CHECK_EQ(old_lbn, GenLogicalBlobName(lbi)); + } + } + }); + return pinned_identity_op.output("out", 0); +} + +Maybe InsertPinnedIdentityOpPass::Apply(Job* job, JobPassCtx* ctx) const { + if (!ctx->job_desc().IsTrain()) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + + HashMap pinned_lbns; + TrainConf* train_conf = job->mutable_job_conf()->mutable_train_conf(); + // insert after loss + for (int i = 0; i < train_conf->loss_lbn_size(); ++i) { + const auto& loss_lbn = train_conf->loss_lbn(i); + auto it = pinned_lbns.find(loss_lbn); + if (it == pinned_lbns.end()) { + const auto& pinned_loss_lbn = JUST(InsertPinnedIdentityOp(&job_builder, op_graph, loss_lbn)); + it = pinned_lbns.emplace(loss_lbn, *pinned_loss_lbn).first; + } + train_conf->set_loss_lbn(i, it->second); + } + // insert after loss initial gradient + for (int i = 0; i < train_conf->loss_grad_lbn_size(); ++i) { + const auto& loss_grad_lbn = train_conf->loss_grad_lbn(i); + auto it = pinned_lbns.find(loss_grad_lbn); + if (it == pinned_lbns.end()) { + const auto& pinned_loss_grad_lbn = + JUST(InsertPinnedIdentityOp(&job_builder, op_graph, loss_grad_lbn)); + it = pinned_lbns.emplace(loss_grad_lbn, *pinned_loss_grad_lbn).first; + } + train_conf->set_loss_grad_lbn(i, it->second); + } + // insert after variable gradient + for (int i = 0; i < train_conf->optimizer_conf_size(); ++i) { + auto* optimizer_conf = train_conf->mutable_optimizer_conf(i); + for (int j = 0; j < optimizer_conf->variable_grad_lbns_size(); ++j) { + const auto& variable_grad_lbn = optimizer_conf->variable_grad_lbns(j); + if (variable_grad_lbn.empty()) { continue; } + auto it = pinned_lbns.find(variable_grad_lbn); + if (it == pinned_lbns.end()) { + const auto& pinned_variable_grad_lbn = + JUST(InsertPinnedIdentityOp(&job_builder, op_graph, variable_grad_lbn)); + it = pinned_lbns.emplace(variable_grad_lbn, *pinned_variable_grad_lbn).first; + } + optimizer_conf->set_variable_grad_lbns(j, it->second); + } + } + JUST(job_builder.MutOpTransactionCommit()); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("InsertPinnedIdentityOpPass", InsertPinnedIdentityOpPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/job_completer.cpp b/oneflow/core/job_rewriter/job_completer.cpp index 750ae0060aae9f371489f131f349f0190db723c3..0b1a835d2565ddc2eee74e1ff613379c0e5ef5eb 100644 --- a/oneflow/core/job_rewriter/job_completer.cpp +++ b/oneflow/core/job_rewriter/job_completer.cpp @@ -22,6 +22,8 @@ limitations under the License. #include "oneflow/core/job_rewriter/group_boxing_by_dst_parallel.h" #include "oneflow/core/framework/config_def.h" #include "oneflow/core/job_rewriter/boxing_with_middle_nodes.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" +#include "oneflow/core/common/cost_util.h" namespace oneflow { @@ -104,32 +106,49 @@ Maybe SetCtrlInOpName4VariableOp(const OpGraph& op_graph, JobBuilder* job_ } // namespace Maybe JobCompleter::Complete(Job* job) const { + const auto& job_name = job->job_conf().job_name(); JobPassCtx job_pass_ctx(GlobalJobDesc()); - JUST(JobPass4Name("DumpBlobParallelConfPass")(job, &job_pass_ctx)); // NOTE(chengcheng): disable this pass for reduce boxing memory life cycle to memory cost. + auto compile_tc = std::make_unique>(true, true); if (!Singleton::Get() ->resource() .disable_group_boxing_by_dst_parallel()) { JUST(WithOpGraphAndMutJobBuilder(job, &GroupBoxingByDstParallel)); } - JUST(WithOpGraphAndMutJobBuilder(job, &BoxingWithMiddleNodes)); + compile_tc->Count("[GraphCompile]" + job_name + " GroupBoxingByDstParallel", 1, true); + if (GlobalProcessCtx::WorldSize() > 1) { + JUST(WithOpGraphAndMutJobBuilder(job, &BoxingWithMiddleNodes)); + } + compile_tc->Count("[GraphCompile]" + job_name + " BoxingWithMiddleNodes", 1, true); JUST(WithOpGraphAndMutJobBuilder(job, &SetCtrlInOpName4VariableOp)); + compile_tc->Count("[GraphCompile]" + job_name + " SetCtrl", 1, true); // complete tick ops JUST(WithOpGraphAndMutJobBuilder(job, &AutoPrependTick)); + compile_tc->Count("[GraphCompile]" + job_name + " AutoPrependTick", 1, true); JUST(WithOpGraphAndMutJobBuilder(job, &AddTickForTimeShape)); + compile_tc->Count("[GraphCompile]" + job_name + " AddTickForTimeShape", 1, true); JUST(WithOpGraphAndMutJob(job, &MultiClientAutoSourceAndSinkTick)); + compile_tc->Count("[GraphCompile]" + job_name + " AutoSourceAndSinkTick", 1, true); JUST(WithOpGraphAndMutJob(job, &MultiClientAutoInterfaceCriticalSectionTick)); + compile_tc->Count("[GraphCompile]" + job_name + " CriticalSectionTick", 1, true); JUST(JobPass4Name("SystemOpFillJobNamePass")(job, &job_pass_ctx)); + compile_tc->Count("[GraphCompile]" + job_name + " SystemOpFillJobNamePass", 1, true); JUST(JobPass4Name("DumpBlobParallelConfPass")(job, &job_pass_ctx)); + compile_tc->Count("[GraphCompile]" + job_name + " DumpBlobParallelConfPass", 1, true); #if defined(WITH_CUDA) || defined(WITH_ROCM) if (Singleton::Get()->nccl_use_compute_stream()) { // NOTE(chengcheng): this pass need as last pass for insert correct op with nccl boxing. JUST(JobPass4Name("InsertNcclLogicalOpPass")(job, &job_pass_ctx)); - // NOTE(chengcheng): Becasue insert new logical nccl op, MUST dump time shape, sbp again. + compile_tc->Count("[GraphCompile]" + job_name + " InsertNcclLogicalOpPass", 1, true); + // NOTE(chengcheng): Because insert new logical nccl op, MUST dump time shape, sbp again. JUST(JobPass4Name("DumpBlobParallelConfPass")(job, &job_pass_ctx)); + compile_tc->Count("[GraphCompile]" + job_name + " DumpBlobParallelConfPass", 1, true); } #endif // WITH_CUDA + JUST(JobPass4Name("LogicalChainPass")(job, &job_pass_ctx)); + JUST(JobPass4Name("DumpBlobParallelConfPass")(job, &job_pass_ctx)); JUST(CheckOpGraph(OpGraph(*job))); + compile_tc->Count("[GraphCompile]" + job_name + " CheckOpGraph", 1, true); return Maybe::Ok(); } diff --git a/oneflow/core/job_rewriter/job_pass.h b/oneflow/core/job_rewriter/job_pass.h index 8f44c86e2ab5a0df61fa86257ff88107c87d5271..1770cd7db0f212f381abf7f7b6569e5c8f099823 100644 --- a/oneflow/core/job_rewriter/job_pass.h +++ b/oneflow/core/job_rewriter/job_pass.h @@ -16,9 +16,10 @@ limitations under the License. #ifndef ONEFLOW_CORE_JOB_REWRITER_JOB_PASS_H_ #define ONEFLOW_CORE_JOB_REWRITER_JOB_PASS_H_ +#include "oneflow/core/common/util.h" #include "oneflow/core/graph/op_graph.h" #include "oneflow/core/job/job_builder.h" -#include "oneflow/core/common/util.h" +#include "oneflow/core/job_rewriter/pass_util.h" namespace oneflow { diff --git a/oneflow/core/job_rewriter/lamb_optm.cpp b/oneflow/core/job_rewriter/lamb_optm.cpp index 999f6d34d10d471c8935ffe6232d5317025f22f5..914a7bb90b5ecaf77d917a0b9455d8f20bcfba72 100644 --- a/oneflow/core/job_rewriter/lamb_optm.cpp +++ b/oneflow/core/job_rewriter/lamb_optm.cpp @@ -31,9 +31,8 @@ namespace std { template<> struct hash { size_t operator()(const oneflow::BiasCorrectionFactorCacheKey& key) const { - const auto& float_hash = std::hash(); - const auto& parallel_conf_hash = std::hash(); - return float_hash(key.beta) ^ parallel_conf_hash(key.parallel_conf); + using namespace oneflow; + return Hash(key.beta, key.parallel_conf); } }; @@ -177,6 +176,10 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); } + if (optimizer_conf.has_lr_scale()) { + lamb_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } + SetDynamicLossScaleSkipIf(ctx, &lamb_update_op_builder); const auto lamb_update_op = lamb_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {lamb_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/lars_optm.cpp b/oneflow/core/job_rewriter/lars_optm.cpp index 95a794e3d96bf43b7f0e5c207044fc4f51ebd3d8..0f5fc7dd189ec9cbaa89b9a59bd19cf405368b37 100644 --- a/oneflow/core/job_rewriter/lars_optm.cpp +++ b/oneflow/core/job_rewriter/lars_optm.cpp @@ -47,6 +47,9 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Attr("lars_coefficient", optimizer_conf.lars_conf().lars_coefficient()) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + if (optimizer_conf.has_lr_scale()) { + lars_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &lars_update_op_builder); user_op::UserOpConfWrapper lars_update_op = lars_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {lars_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/logical_chain_pass.cpp b/oneflow/core/job_rewriter/logical_chain_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3f06410ab627d7cb221eb6d33acdfaf899a90cee --- /dev/null +++ b/oneflow/core/job_rewriter/logical_chain_pass.cpp @@ -0,0 +1,664 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/nd_sbp.h" +#include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/core/framework/sbp_infer_util.h" +#include "oneflow/core/job/scope.h" +#include "oneflow/core/job/sbp_parallel.h" +#include "oneflow/core/job/job.pb.h" +#include "oneflow/core/job/nd_sbp_util.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/job_rewriter/calculation_pass.h" +#include "oneflow/core/vm/vm_util.h" +#include "oneflow/core/vm/symbol_storage.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/common/env_var/env_var.h" +#include "oneflow/core/common/env_var/debug_mode.h" + +namespace oneflow { + +DEFINE_ENV_BOOL(ENABLE_ACC_CHAIN_MERGE, true); + +namespace { + +class LogicalChainPass final : public JobPass { + public: + OF_DISALLOW_COPY_AND_MOVE(LogicalChainPass); + LogicalChainPass() = default; + ~LogicalChainPass() = default; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } + + bool IsEnabled(const JobPassCtx& ctx) const { return EnableLogicalChain(); } + + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; +}; + +bool IsTickOpConf(const OperatorConf& op_conf) { + if (IsClassRegistered(op_conf.op_type_case())) { return true; } + if (op_conf.has_user_conf()) { + const std::string& user_type_name = op_conf.user_conf().op_type_name(); + if (user_type_name == "cast_to_tick" || user_type_name == "acc_ctrl_tick") { return true; } + } + return false; +} + +bool IsBreakpointOpNode(const OpNode* node) { + // NOTE(chengcheng): breakpoint op is special which CANNOT merge in chain such as: + // variable, tick, repeat/acc/pack/unpack change timeshape + const Operator& op = node->op(); + const OperatorConf& op_conf = op.op_conf(); + + // TODO(chengcheng): filter ops which has special type + // TODO(chengcheng): get stream by op type + if (op_conf.has_variable_conf() /* varialbe */ + || IsTickOpConf(op_conf) /* tick */ + || op_conf.has_input_conf() || op_conf.has_output_conf() /* io */ + || op_conf.has_wait_and_send_ids_conf() || op_conf.has_callback_notify_conf() /* ctrl */ + || op_conf.has_image_decoder_random_crop_resize_conf() /* gpu decode */) { + return true; + } + + if (op_conf.has_user_conf()) { + const std::string& user_type_name = op_conf.user_conf().op_type_name(); + if (user_type_name == "repeat" || user_type_name == "unpack" + || user_type_name == "identity_buffer" || user_type_name == "copy_h2d" + || user_type_name == "copy_d2h") { + return true; + } + } + return false; +} + +bool IsAccOrPackOpNode(const OpNode* node) { + const auto& op_conf = node->op().op_conf(); + return op_conf.has_user_conf() + && (op_conf.user_conf().op_type_name() == "acc" + || op_conf.user_conf().op_type_name() == "pack"); +} + +bool IsAccOpNode(const OpNode* node) { + return node->op().op_conf().has_user_conf() + && node->op().op_conf().user_conf().op_type_name() == "acc"; +} + +bool IsRepeatOpNode(const OpNode* node) { + return node->op().op_conf().has_user_conf() + && node->op().op_conf().user_conf().op_type_name() == "repeat"; +} + +std::shared_ptr GetOpNodeFastestTimeShape(const OpNode* op_node) { + return CHECK_JUST(op_node->op().GetInputOutputFastestTimeShape()); +} + +std::shared_ptr GetOpNodeInputTimeShape(const OpNode* op_node) { + return CHECK_JUST(op_node->op().GetInputBlobFastestTimeShape()); +} + +bool SharedPtrShapeEqual(const std::shared_ptr& lhs, + const std::shared_ptr& rhs) { + return (*lhs) == (*rhs); +} + +bool NeedInsertBoxingBetweenOpNodes(const OpNode* a_node, const OpNode* b_node, + const OpEdge* edge) { + CHECK(a_node != b_node && (edge->src_node() == a_node || edge->src_node() == b_node) + && (edge->dst_node() == a_node || edge->dst_node() == b_node)); + if (a_node->parallel_desc().parallel_num() > 1) { + for (const auto& lbi : edge->lbis()) { + if (a_node->NdSbp4Lbi(lbi) != b_node->NdSbp4Lbi(lbi)) { return true; } + } + } + return false; +} + +void GetLogicalChainsWithTimeShape(std::vector>* ret, + const std::vector& order, + const std::shared_ptr& seed_time_shape) { + HashSet visited; + for (const OpNode* seed_node : order) { + if (visited.find(seed_node) != visited.end()) { continue; } + CHECK(visited.insert(seed_node).second); + const ParallelDesc& seed_parallel_desc = seed_node->parallel_desc(); + // TODO(chengcheng): support cpu chain. + if (seed_parallel_desc.device_type() == DeviceType::kCPU) { continue; } + if (!SharedPtrShapeEqual(GetOpNodeFastestTimeShape(seed_node), seed_time_shape)) { continue; } + if (IsBreakpointOpNode(seed_node)) { continue; } + + HashSet this_subgraph; + std::queue queued_nodes; + + queued_nodes.push(seed_node); + while (!queued_nodes.empty()) { + const OpNode* cur_node = queued_nodes.front(); + queued_nodes.pop(); + + CHECK(cur_node->parallel_desc().EqualsIgnoringHierarchy(seed_parallel_desc)); + CHECK(this_subgraph.insert(cur_node).second); + + auto SearchToNextNode = [&](const OpNode* cur_node, const OpNode* next_node, + const OpEdge* edge) { + if (visited.find(next_node) == visited.end() && (!IsBreakpointOpNode(next_node)) + && next_node->parallel_desc().EqualsIgnoringHierarchy(seed_parallel_desc) + && SharedPtrShapeEqual(GetOpNodeFastestTimeShape(next_node), seed_time_shape) + && !NeedInsertBoxingBetweenOpNodes(cur_node, next_node, edge)) { + CHECK(visited.insert(next_node).second); + queued_nodes.push(next_node); + } + }; + + for (const OpEdge* in_edge : cur_node->in_edges()) { + SearchToNextNode(cur_node, in_edge->src_node(), in_edge); + } + for (const OpEdge* out_edge : cur_node->out_edges()) { + SearchToNextNode(cur_node, out_edge->dst_node(), out_edge); + } + } + + if (this_subgraph.size() > 1) { + ret->emplace_back(HashSet()); + ret->back().swap(this_subgraph); + } + } +} + +struct LogicalChain { + int64_t logical_chain_id; + std::vector ordered_op_nodes; + LogicalChain() : logical_chain_id(-1) {} +}; + +struct PlacementLogicalChainsInfo { + std::vector> ordered_logical_chains; + std::vector ordered_acc_op_nodes; + std::shared_ptr after_acc_logical_chain; + const ParallelDesc* seed_parallel_desc; + std::shared_ptr seed_time_shape; + PlacementLogicalChainsInfo() : seed_parallel_desc(nullptr) {} +}; + +std::string GenParallelConfKey(const ParallelConf& conf) { + std::string ret = conf.device_tag(); + for (const auto& name : conf.device_name()) { ret += ("-" + name); } + return ret; +} + +void InitPlacementLogicalChainsInfoFromSet( + const std::shared_ptr& logical_chain, + const HashSet& origin_logical_chain, + const HashMap& op_node2global_order, + const std::function& CmpOpNodeOrder) { + auto* logical_chain_ordered_nodes = &logical_chain->ordered_op_nodes; + CHECK(logical_chain_ordered_nodes->empty()); + logical_chain_ordered_nodes->assign(origin_logical_chain.begin(), origin_logical_chain.end()); + std::sort(logical_chain_ordered_nodes->begin(), logical_chain_ordered_nodes->end(), + CmpOpNodeOrder); + const OpNode* begin_op = logical_chain_ordered_nodes->front(); + const OpNode* end_op = logical_chain_ordered_nodes->back(); + int64_t begin_op_global_order = op_node2global_order.at(begin_op); + int64_t end_op_global_order = op_node2global_order.at(end_op); + CHECK(begin_op != end_op); + CHECK_LT(begin_op_global_order, end_op_global_order); +} + +void CreateAfterAccLogicalChain(const std::shared_ptr& after_acc_logical_chain, + const std::vector& ordered_acc_op_nodes, + const ParallelDesc& seed_parallel_desc) { + // Meta time shape (1, 1) + std::shared_ptr seed_time_shape = std::make_shared(Shape({1, 1})); + HashSet visited; + HashSet after_acc_chain_ops; + std::queue queued_nodes; + auto SearchToNextNode = [&](const OpNode* cur_node, const OpNode* next_node, const OpEdge* edge) { + if (visited.find(next_node) == visited.end() && (!IsBreakpointOpNode(next_node)) + && next_node->parallel_desc().EqualsIgnoringHierarchy(seed_parallel_desc) + && SharedPtrShapeEqual(GetOpNodeFastestTimeShape(next_node), seed_time_shape) + && !NeedInsertBoxingBetweenOpNodes(cur_node, next_node, edge)) { + CHECK(visited.insert(next_node).second); + queued_nodes.push(next_node); + } + }; + + for (const OpNode* acc_node : ordered_acc_op_nodes) { + for (const OpEdge* out_edge : acc_node->out_edges()) { + const OpNode* seed_node = out_edge->dst_node(); + SearchToNextNode(acc_node, seed_node, out_edge); + } + } + + while (!queued_nodes.empty()) { + const OpNode* cur_node = queued_nodes.front(); + queued_nodes.pop(); + + CHECK(after_acc_chain_ops.insert(cur_node).second); + + for (const OpEdge* in_edge : cur_node->in_edges()) { + SearchToNextNode(cur_node, in_edge->src_node(), in_edge); + } + for (const OpEdge* out_edge : cur_node->out_edges()) { + SearchToNextNode(cur_node, out_edge->dst_node(), out_edge); + } + } + + if (after_acc_chain_ops.size() > 1) { + for (const OpNode* node : after_acc_chain_ops) { + after_acc_logical_chain->ordered_op_nodes.push_back(node); + } + CHECK_EQ(after_acc_logical_chain->ordered_op_nodes.size(), after_acc_chain_ops.size()); + } +} + +void TryMergeAfterAccLogicalChainToLastLogicalChain( + PlacementLogicalChainsInfo* info, HashMap* mut_op_name2conf, + JobBuilder* job_builder, + const std::function& IsReachable) { + if (!EnvBool()) { return; } + + const int64_t acc_chain_id = info->after_acc_logical_chain->logical_chain_id; + auto& acc_chain_order_ops = info->after_acc_logical_chain->ordered_op_nodes; + const auto& last_chain = info->ordered_logical_chains.back(); + const OpNode* last_chain_src_op = last_chain->ordered_op_nodes.front(); + const OpNode* last_chain_sink_op = last_chain->ordered_op_nodes.back(); + HashSet last_chain_ops(last_chain->ordered_op_nodes.begin(), + last_chain->ordered_op_nodes.end()); + + const OpNode* acc_chain_src_op = acc_chain_order_ops.front(); + const OpNode* acc_chain_sink_op = acc_chain_order_ops.back(); + // NOTE(chengcheng): find all nontrivial sink consumer ops + HashSet nontrivial_sink_consumers; + for (const OpNode* chain_op : last_chain->ordered_op_nodes) { + chain_op->ForEachNodeOnOutEdge([&](const OpNode* out_node) { + if (last_chain_ops.find(out_node) == last_chain_ops.end() + && !IsTickOpConf(out_node->op().op_conf()) + && SharedPtrShapeEqual(GetOpNodeFastestTimeShape(out_node), info->seed_time_shape)) { + nontrivial_sink_consumers.insert(out_node); + } + }); + } + + // NOTE(chengcheng): find last op can insert acc ctrl tick. + while ((!acc_chain_sink_op->op().op_conf().has_user_conf()) + || IsReachable(acc_chain_sink_op->op().op_name(), last_chain_src_op->op().op_name())) { + VLOG(3) << " cannot insert acc ctrl edge between: [" << last_chain_src_op->op().op_name() + << "] -> [" << acc_chain_sink_op->op().op_name() << "] , debug info :\n" + << last_chain_src_op->op().op_conf().DebugString() << "\n" + << acc_chain_sink_op->op().op_conf().DebugString() << "\n"; + + VLOG(3) << "remove op : " << acc_chain_sink_op->op().op_name() + << " from after acc logical chain: " << acc_chain_id; + acc_chain_order_ops.pop_back(); + if (acc_chain_order_ops.size() > 1) { + acc_chain_sink_op = acc_chain_order_ops.back(); + } else { + acc_chain_sink_op = nullptr; + break; + } + } + if (acc_chain_sink_op == nullptr) { return; } + + // NOTE(chengcheng): find last op can insert acc tick. + while (IsReachable(acc_chain_src_op->op().op_name(), last_chain_sink_op->op().op_name())) { + VLOG(3) << " cannot insert acc tick edge between: [" << last_chain_sink_op->op().op_name() + << "] -> [" << acc_chain_src_op->op().op_name() << "] , debug info :\n" + << last_chain_sink_op->op().op_conf().DebugString() << "\n" + << acc_chain_src_op->op().op_conf().DebugString() << "\n"; + + VLOG(3) << "remove op : " << acc_chain_src_op->op().op_name() + << " from after acc logical chain: " << acc_chain_id; + acc_chain_order_ops.erase(acc_chain_order_ops.begin()); + if (acc_chain_order_ops.size() > 1) { + acc_chain_src_op = acc_chain_order_ops.front(); + } else { + acc_chain_src_op = nullptr; + break; + } + } + if (acc_chain_src_op == nullptr) { return; } + + // NOTE(chengcheng): + // 1.add acc ctrl tick between last chain src to acc chain sink for memory lock. + const int64_t acc_num = job_builder->job().job_conf().num_gradient_accumulation_steps(); + CHECK_GT(acc_num, 1); + const auto& fc_src_obns = last_chain_src_op->op().output_bns(); + CHECK(!fc_src_obns.empty()); + const std::string& last_chain_src_out_lbn = + GenLogicalBlobName(last_chain_src_op->op().BnInOp2Lbi(fc_src_obns.Get(0))); + + VLOG(3) << " last_chain_src_out_lbn : " << last_chain_src_out_lbn; + user_op::UserOpConfWrapper acc_ctrl_tick_op = + user_op::UserOpConfWrapperBuilder("Sys-AccCtrlTick4MergeLastAccChain-" + NewUniqueId()) + .OpTypeName("acc_ctrl_tick") + .Input("in", last_chain_src_out_lbn) + .Output("out") + .ScopeSymbolId(last_chain_src_op->op().op_conf().scope_symbol_id()) + .Attr("max_acc_num", acc_num) + .Build(); + + OperatorConf& acc_chain_sink_op_conf = + CHECK_JUST(MapAt(*mut_op_name2conf, acc_chain_sink_op->op().op_name())); + CHECK(acc_chain_sink_op_conf.has_user_conf()); + (*acc_chain_sink_op_conf.mutable_user_conf() + ->mutable_input())[user_op::kUserSourceOpTickInputArgName] + .add_s(acc_ctrl_tick_op.output("out", 0)); + CHECK_JUST(job_builder->AddOp(last_chain_src_op->parallel_desc().parallel_conf(), + acc_ctrl_tick_op.op_conf())); + VLOG(3) << " Insert acc ctrl tick between: [" << last_chain_src_op->op().op_name() << "] -> [" + << acc_chain_sink_op->op().op_name() << "]"; + + // NOTE(chengcheng): + // 2.add acc tick between last chain sink to acc chain src for strict exec order. + const auto& fc_sink_obns = last_chain_sink_op->op().output_bns(); + CHECK(!fc_sink_obns.empty()); + const std::string last_chain_sink_lbn = + GenLogicalBlobName(last_chain_sink_op->op().BnInOp2Lbi(fc_sink_obns.Get(0))); + VLOG(3) << " last_chain_sink_lbn : " << last_chain_sink_lbn; + + user_op::UserOpConfWrapper cast_to_tick_op = + user_op::UserOpConfWrapperBuilder("Sys-LogicalChainSink-CastToTick-" + NewUniqueId()) + .OpTypeName("cast_to_tick") + .Input("in", last_chain_sink_lbn) + .Output("out") + .ScopeSymbolId(last_chain_sink_op->op().op_conf().scope_symbol_id()) + .Build(); + + CHECK_JUST(job_builder->AddOp(last_chain_sink_op->parallel_desc().parallel_conf(), + cast_to_tick_op.op_conf())); + + std::string acc_tick_output_lbn = cast_to_tick_op.output("out", 0); + if (!IsAccOrPackOpNode(last_chain_sink_op)) { + // NOTE(chengcheng): Acc Op can be merged in fw/bw chain, if the last op is acc op, + // there is no need and CANNOT insert acc tick op. + + OperatorConf sink_acc_tick_conf; + sink_acc_tick_conf.set_name(std::string("Sys-LogicalChainSink-AccTick_") + NewUniqueId()); + sink_acc_tick_conf.set_scope_symbol_id(last_chain_sink_op->op().op_conf().scope_symbol_id()); + auto* acc_conf = sink_acc_tick_conf.mutable_acc_tick_conf(); + acc_conf->set_one(cast_to_tick_op.output("out", 0)); + acc_conf->set_acc("acc"); + acc_conf->set_max_acc_num(acc_num); + acc_tick_output_lbn = GenLogicalBlobName(sink_acc_tick_conf.name(), "acc"); + + VLOG(3) << " insert acc tick op : " << sink_acc_tick_conf.name() + << " of last op in fw/bw chain."; + + CHECK_JUST(job_builder->AddOp(last_chain_sink_op->parallel_desc().parallel_conf(), + sink_acc_tick_conf)); + } + + OperatorConf sink_final_tick_conf; + sink_final_tick_conf.set_name(std::string("Sys-LogicalChainSink-FinalTick-DeviceTick_") + + NewUniqueId()); + sink_final_tick_conf.set_scope_symbol_id(last_chain_sink_op->op().op_conf().scope_symbol_id()); + auto* tick_conf = sink_final_tick_conf.mutable_device_tick_conf(); + tick_conf->add_tick(acc_tick_output_lbn); + tick_conf->set_out("out"); + + // NOTE(chengcheng): + // 3. Important Tips: If there have nontrivial_sink_consumers, there must insert ctrl + // between sink consumer with acc chain for exec order. + for (const OpNode* sink_consumer : nontrivial_sink_consumers) { + VLOG(2) << " insert acc tick between nontrivial_sink_consumer: [" + << sink_consumer->op().op_name() << "] -> [" << sink_final_tick_conf.name() + << "] for mem safe guard."; + CHECK(!IsReachable(acc_chain_src_op->op().op_name(), sink_consumer->op().op_name())); + const auto& sink_consumer_obns = sink_consumer->op().output_bns(); + CHECK(!sink_consumer_obns.empty()); + + std::string sink_consumer_output_lbn = + GenLogicalBlobName(sink_consumer->op().BnInOp2Lbi(sink_consumer_obns.Get(0))); + user_op::UserOpConfWrapper sink_consumer_cast_to_tick_op = + user_op::UserOpConfWrapperBuilder("Sys-LogicalChainSinkConsumer-CastToTick-" + + NewUniqueId()) + .OpTypeName("cast_to_tick") + .Input("in", sink_consumer_output_lbn) + .Output("out") + .ScopeSymbolId(sink_consumer->op().op_conf().scope_symbol_id()) + .Build(); + + CHECK_JUST(job_builder->AddOp(sink_consumer->parallel_desc().parallel_conf(), + sink_consumer_cast_to_tick_op.op_conf())); + + std::string sink_consumer_acc_tick_lbn = sink_consumer_cast_to_tick_op.output("out", 0); + if (!IsAccOrPackOpNode(sink_consumer)) { + OperatorConf sink_consumer_acc_tick_conf; + sink_consumer_acc_tick_conf.set_name(std::string("Sys-LogicalChainSinkConsumer-AccTick_") + + NewUniqueId()); + sink_consumer_acc_tick_conf.set_scope_symbol_id( + acc_chain_src_op->op().op_conf().scope_symbol_id()); + auto* acc_conf = sink_consumer_acc_tick_conf.mutable_acc_tick_conf(); + acc_conf->set_one(sink_consumer_acc_tick_lbn); + acc_conf->set_acc("acc"); + acc_conf->set_max_acc_num(acc_num); + sink_consumer_acc_tick_lbn = GenLogicalBlobName(sink_consumer_acc_tick_conf.name(), "acc"); + + VLOG(3) << " insert acc tick op : " << sink_consumer_acc_tick_conf.name() + << " of nontrivial_sink_consumer in fw/bw chain."; + + CHECK_JUST(job_builder->AddOp(sink_consumer->parallel_desc().parallel_conf(), + sink_consumer_acc_tick_conf)); + } + tick_conf->add_tick(sink_consumer_acc_tick_lbn); + } + + CHECK_JUST(job_builder->AddOp(last_chain_sink_op->parallel_desc().parallel_conf(), + sink_final_tick_conf)); + + CHECK_JUST(MapAt(*mut_op_name2conf, acc_chain_src_op->op().op_name())) + .add_ctrl_in_op_name(sink_final_tick_conf.name()); + + VLOG(3) << " Insert acc tick between: [" << last_chain_sink_op->op().op_name() << "] -> [" + << acc_chain_src_op->op().op_name() << "]"; + + // NOTE(chengcheng): + // 4. merge last chain and acc chain + MergedLogicalChainIdGroup* group = job_builder->add_logical_chain_groups(); + group->add_logical_chain_id_list(last_chain->logical_chain_id); + group->add_logical_chain_id_list(acc_chain_id); + VLOG(3) << " Merge acc chain : " << acc_chain_id + << " to last logical chain : " << last_chain->logical_chain_id; +} + +Maybe LogicalChainPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { + std::vector ordered_op_nodes; + HashMap op_node2global_order; + HashMap mut_op_name2conf; + // TODO(chengcheng) : better order for memory. + std::shared_ptr seed_time_shape = std::make_shared(Shape({1, 1})); + op_graph.TopoForEachNodeWithCtrlEdge([&](const OpNode* node) { + ordered_op_nodes.emplace_back(node); + op_node2global_order.emplace(node, ordered_op_nodes.size() - 1); + std::shared_ptr this_time_shape = GetOpNodeFastestTimeShape(node); + if (this_time_shape->elem_cnt() > seed_time_shape->elem_cnt()) { + seed_time_shape = this_time_shape; + } + mut_op_name2conf.emplace(node->op().op_name(), node->op().op_conf()); + }); + + VLOG(2) << " seed time shape = " << seed_time_shape->ToString(); + + std::vector> logical_chains; + GetLogicalChainsWithTimeShape(&logical_chains, ordered_op_nodes, seed_time_shape); + if (logical_chains.size() == 0) { return Maybe::Ok(); } + + int64_t logical_chain_id = 0; + auto NewLogicalChainId = [&]() { return logical_chain_id++; }; + + auto CmpOpNodeOrder = [&](const OpNode* lhs, const OpNode* rhs) { + return op_node2global_order.at(lhs) < op_node2global_order.at(rhs); + }; + auto CmpLogicalChainOrder = [&](const std::shared_ptr& lhs, + const std::shared_ptr& rhs) { + int64_t lhs_begin_op_global_order = op_node2global_order.at(lhs->ordered_op_nodes.front()); + int64_t rhs_begin_op_global_order = op_node2global_order.at(rhs->ordered_op_nodes.front()); + return lhs_begin_op_global_order < rhs_begin_op_global_order; + }; + auto IsReachable = op_graph.MakePredicatorIsOpNameDataOrCtrlReachable(); + + HashMap placement2logical_chains; + for (const auto& origin_logical_chain : logical_chains) { + const OpNode* rand_node = *origin_logical_chain.begin(); + const ParallelDesc& this_parallel_desc = rand_node->parallel_desc(); + std::string key = GenParallelConfKey(this_parallel_desc.parallel_conf()); + auto it = placement2logical_chains.find(key); + if (it == placement2logical_chains.end()) { + it = placement2logical_chains.emplace(key, PlacementLogicalChainsInfo()).first; + it->second.seed_parallel_desc = &this_parallel_desc; + it->second.seed_time_shape = seed_time_shape; + } + auto& info = it->second; + info.ordered_logical_chains.emplace_back(std::make_shared()); + InitPlacementLogicalChainsInfoFromSet(info.ordered_logical_chains.back(), origin_logical_chain, + op_node2global_order, CmpOpNodeOrder); + } + + for (auto& pair : placement2logical_chains) { + std::sort(pair.second.ordered_logical_chains.begin(), pair.second.ordered_logical_chains.end(), + CmpLogicalChainOrder); + } + + for (const OpNode* this_node : ordered_op_nodes) { + if (IsAccOpNode(this_node)) { + const ParallelDesc& this_parallel_desc = this_node->parallel_desc(); + std::string key = GenParallelConfKey(this_parallel_desc.parallel_conf()); + auto it = placement2logical_chains.find(key); + if (it != placement2logical_chains.end()) { + it->second.ordered_acc_op_nodes.emplace_back(this_node); + } + } + } + + auto InsertCtrlEdgeInChain = [&](const std::vector& ordered_op_nodes) { + for (int64_t i = 1; i < ordered_op_nodes.size(); ++i) { + const OpNode* this_node = CHECK_JUST(VectorAt(ordered_op_nodes, i)); + const OpNode* prev_node = CHECK_JUST(VectorAt(ordered_op_nodes, i - 1)); + const std::string& this_op_name = this_node->op().op_name(); + const std::string& prev_op_name = prev_node->op().op_name(); + if (!IsReachable(prev_op_name, this_op_name)) { + CHECK_JUST(MapAt(mut_op_name2conf, this_op_name)).add_ctrl_in_op_name(prev_op_name); + } + } + }; + + auto InsertLogicalChainId = [&](const std::vector& ordered_op_nodes, + const int64_t logical_chain_id) { + for (const OpNode* op_node : ordered_op_nodes) { + CHECK_JUST(MapAt(mut_op_name2conf, op_node->op().op_name())) + .set_logical_chain_id(logical_chain_id); + } + }; + + auto FixLogicalChainOpStreamHint = [&](const std::vector& ordered_op_nodes) { + std::string stream_index_name = ""; + for (const OpNode* op_node : ordered_op_nodes) { + const OperatorConf& op_conf = op_node->op().op_conf(); + if (op_conf.has_stream_name_hint() && !op_conf.stream_name_hint().empty()) { + if (stream_index_name.empty()) { + stream_index_name = op_conf.stream_name_hint(); + } else { + CHECK_EQ(stream_index_name, op_conf.stream_name_hint()); + } + } + } + + if (!stream_index_name.empty()) { + for (const OpNode* op_node : ordered_op_nodes) { + OperatorConf& op_conf = CHECK_JUST(MapAt(mut_op_name2conf, op_node->op().op_name())); + if (!op_conf.has_stream_name_hint()) { + op_conf.set_stream_name_hint(stream_index_name); + VLOG(3) << " Op: " << op_conf.name() << " fix stream name hint : " << stream_index_name; + } + } + } + }; + + for (auto& pair : placement2logical_chains) { + const auto& placement = pair.first; + auto& info = pair.second; + CHECK_GE(info.ordered_logical_chains.size(), 1); + + // NOTE(chengcheng): set logical chain id for each op in each logical chain, and insert ctrl + // edge for order. + for (auto& logical_chain : info.ordered_logical_chains) { + logical_chain->logical_chain_id = NewLogicalChainId(); + InsertLogicalChainId(logical_chain->ordered_op_nodes, logical_chain->logical_chain_id); + // TODO(chengcheng): rm fix hint and use thrd id in logical op node. + FixLogicalChainOpStreamHint(logical_chain->ordered_op_nodes); + InsertCtrlEdgeInChain(logical_chain->ordered_op_nodes); + } + + for (const auto& logical_chain : info.ordered_logical_chains) { + VLOG(3) << " In placement: " << placement + << " logical_chain_id: " << logical_chain->logical_chain_id + << " has op num = " << logical_chain->ordered_op_nodes.size(); + + for (int i = 0; i < logical_chain->ordered_op_nodes.size(); ++i) { + const OpNode* ordered_op = JUST(VectorAt(logical_chain->ordered_op_nodes, i)); + VLOG(3) << " ChainId: " << logical_chain_id << " order: " << i + << " op_name: " << ordered_op->op().op_name() + << " global_order: " << JUST(MapAt(op_node2global_order, ordered_op)); + } + } + + // NOTE(chengcheng): create logical chain after acc, and merge with last logical chain. + const std::vector& ordered_acc_op_nodes = info.ordered_acc_op_nodes; + if (!ordered_acc_op_nodes.empty()) { + info.after_acc_logical_chain = std::make_shared(); + CreateAfterAccLogicalChain(info.after_acc_logical_chain, ordered_acc_op_nodes, + *info.seed_parallel_desc); + auto& acc_chain_order_ops = info.after_acc_logical_chain->ordered_op_nodes; + if (acc_chain_order_ops.size() > 1) { + info.after_acc_logical_chain->logical_chain_id = NewLogicalChainId(); + std::sort(acc_chain_order_ops.begin(), acc_chain_order_ops.end(), CmpOpNodeOrder); + + TryMergeAfterAccLogicalChainToLastLogicalChain(&info, &mut_op_name2conf, job_builder, + IsReachable); + + if (acc_chain_order_ops.size() <= 1) { continue; } + + VLOG(3) << " In placement: " << placement + << " AccLogicalChain: " << info.after_acc_logical_chain->logical_chain_id + << " has op num = " << acc_chain_order_ops.size(); + + for (int i = 0; i < acc_chain_order_ops.size(); ++i) { + const OpNode* ordered_op = JUST(VectorAt(acc_chain_order_ops, i)); + VLOG(3) << " AfterAccChainId: " << info.after_acc_logical_chain->logical_chain_id + << " order: " << i << " op_name: " << ordered_op->op().op_name() + << " global_order: " << JUST(MapAt(op_node2global_order, ordered_op)); + } + + InsertLogicalChainId(acc_chain_order_ops, info.after_acc_logical_chain->logical_chain_id); + InsertCtrlEdgeInChain(acc_chain_order_ops); + } + } + } + + // NOTE(chengcheng): update global order and chain id for ops. + for (const auto& pair : mut_op_name2conf) { JUST(job_builder->MutOpOnlyOnce(pair.second)); } + + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("LogicalChainPass", LogicalChainPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/model_update_conf_compatible_pass.cpp b/oneflow/core/job_rewriter/model_update_conf_compatible_pass.cpp deleted file mode 100644 index e5d2828be8070f86c27524d1c27ee742dfc40679..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/model_update_conf_compatible_pass.cpp +++ /dev/null @@ -1,104 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/util.h" -#include "oneflow/core/job_rewriter/job_pass.h" -#include "oneflow/core/job/job.pb.h" -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -namespace { - -class ModelUpdateConfCompatiblePass final : public JobPass { - public: - OF_DISALLOW_COPY_AND_MOVE(ModelUpdateConfCompatiblePass); - ModelUpdateConfCompatiblePass() = default; - ~ModelUpdateConfCompatiblePass() override = default; - - bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } - - Maybe Apply(const OpGraph& op_graph, Job* job) const; - - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - return Apply(op_graph, job); - } -}; - -Maybe ModelUpdateConfCompatiblePass::Apply(const OpGraph& op_graph, Job* job) const { - const TrainConf& train_conf = job->job_conf().train_conf(); - const bool use_model_update_conf = - train_conf.has_model_update_conf() || train_conf.has_primary_lr() - || train_conf.has_primary_lr_lbn() || train_conf.has_secondary_lr() - || train_conf.has_secondary_lr_lbn(); - if (!use_model_update_conf) { return Maybe::Ok(); } - CHECK_OR_RETURN(train_conf.optimizer_conf_size() == 0) - << "Use model update conf and optimizer conf at same time"; - const NormalModelUpdateOpUserConf& model_update_conf = train_conf.model_update_conf(); - TrainConf* mutable_train_conf = job->mutable_job_conf()->mutable_train_conf(); - OptimizerConf* optimizer_conf = mutable_train_conf->add_optimizer_conf(); - op_graph.ForEachNode([&](OpNode* op_node) { - if (op_node->op().op_conf().has_variable_conf()) { - optimizer_conf->add_variable_op_names(op_node->op().op_name()); - } - }); - if (model_update_conf.has_learning_rate_decay()) { - optimizer_conf->mutable_learning_rate_decay()->CopyFrom( - model_update_conf.learning_rate_decay()); - } - if (model_update_conf.has_clip_conf()) { - optimizer_conf->mutable_clip_conf()->CopyFrom(model_update_conf.clip_conf()); - } - if (model_update_conf.has_weight_decay_conf()) { - optimizer_conf->mutable_weight_decay_conf()->CopyFrom(model_update_conf.weight_decay_conf()); - } - if (train_conf.has_primary_lr()) { - optimizer_conf->set_base_learning_rate(train_conf.primary_lr()); - } - if (train_conf.has_primary_lr_lbn()) { - optimizer_conf->set_learning_rate_lbn(train_conf.primary_lr_lbn()); - } - if (model_update_conf.has_naive_conf()) { - optimizer_conf->mutable_naive_conf()->CopyFrom(model_update_conf.naive_conf()); - } else if (model_update_conf.has_momentum_conf()) { - optimizer_conf->mutable_momentum_conf()->CopyFrom(model_update_conf.momentum_conf()); - } else if (model_update_conf.has_rmsprop_conf()) { - optimizer_conf->mutable_rmsprop_conf()->CopyFrom(model_update_conf.rmsprop_conf()); - } else if (model_update_conf.has_lars_conf()) { - optimizer_conf->mutable_lars_conf()->CopyFrom(model_update_conf.lars_conf()); - } else if (model_update_conf.has_adam_conf()) { - optimizer_conf->mutable_adam_conf()->CopyFrom(model_update_conf.adam_conf()); - } else if (model_update_conf.has_lazy_adam_conf()) { - optimizer_conf->mutable_lazy_adam_conf()->CopyFrom(model_update_conf.lazy_adam_conf()); - } else if (model_update_conf.has_lamb_conf()) { - optimizer_conf->mutable_lamb_conf()->CopyFrom(model_update_conf.lamb_conf()); - } else { - UNIMPLEMENTED(); - } - mutable_train_conf->clear_model_update_conf(); - mutable_train_conf->clear_primary_lr(); - mutable_train_conf->clear_primary_lr_lbn(); - mutable_train_conf->clear_secondary_lr(); - mutable_train_conf->clear_secondary_lr_lbn(); - return Maybe::Ok(); -} - -REGISTER_JOB_PASS("ModelUpdateConfCompatiblePass", ModelUpdateConfCompatiblePass); - -} // namespace - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/momentum_optm.cpp b/oneflow/core/job_rewriter/momentum_optm.cpp index 8d5f264241b64a8fcaeb33b1a1efca70c104f6a1..89e862aa936f02dbcb792049f2bfc7608451c97b 100644 --- a/oneflow/core/job_rewriter/momentum_optm.cpp +++ b/oneflow/core/job_rewriter/momentum_optm.cpp @@ -58,8 +58,14 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Input("learning_rate", optimizer_conf.learning_rate_lbn()) .Input("momentum", GenLogicalBlobName(op_name, momentum_var.variable_conf().out())) .Attr("beta", optimizer_conf.momentum_conf().beta()) + .Attr("dampening", optimizer_conf.momentum_conf().dampening()) + .Attr("nesterov", optimizer_conf.momentum_conf().nesterov()) + .Attr("maximize", optimizer_conf.momentum_conf().maximize()) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + if (optimizer_conf.has_lr_scale()) { + momentum_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &momentum_update_op_builder); user_op::UserOpConfWrapper momentum_update_op = momentum_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {momentum_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/multi_tensor_model_update.cpp b/oneflow/core/job_rewriter/multi_tensor_model_update.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a2d4e83ad83b20692dc1053223d94a5606e9bf73 --- /dev/null +++ b/oneflow/core/job_rewriter/multi_tensor_model_update.cpp @@ -0,0 +1,413 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/util.h" +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +struct SGDOptimizerKey { + std::string learning_rate; + std::string scale_by_tensor_lbn; + std::string skip_if_lbn; + double scale; + float l1; + float l2; + float weight_decay; + ParallelConf parallel_conf; + bool has_model_copy; + /* + In fuse_model_update_cast pass, not all the cast fp16 model_diff kernel can be fused, + it may cause some model diff type is float16, some is float32. + So here we need to use model_diff datatype as key to group. + */ + DataType model_diff_dtype; +}; + +bool operator==(const SGDOptimizerKey& lhs, const SGDOptimizerKey& rhs) { + return (lhs.learning_rate == rhs.learning_rate) + && (lhs.scale_by_tensor_lbn == rhs.scale_by_tensor_lbn) + && (lhs.skip_if_lbn == rhs.skip_if_lbn) && (lhs.scale == rhs.scale) && (lhs.l1 == rhs.l1) + && (lhs.l2 == rhs.l2) && (lhs.weight_decay == rhs.weight_decay) + && (lhs.parallel_conf == rhs.parallel_conf) && (lhs.has_model_copy == rhs.has_model_copy) + && (lhs.model_diff_dtype == rhs.model_diff_dtype); +} + +struct AdamOptimizerKey { + std::string learning_rate; + std::string scale_by_tensor_lbn; + std::string skip_if_lbn; + double scale; + float l1; + float l2; + float beta1; + float beta2; + float epsilon; + float weight_decay; + bool amsgrad; + bool do_bias_correction; + ParallelConf parallel_conf; + bool has_model_copy; + DataType model_diff_dtype; +}; + +bool operator==(const AdamOptimizerKey& lhs, const AdamOptimizerKey& rhs) { + return (lhs.learning_rate == rhs.learning_rate) + && (lhs.scale_by_tensor_lbn == rhs.scale_by_tensor_lbn) + && (lhs.skip_if_lbn == rhs.skip_if_lbn) && (lhs.scale == rhs.scale) && (lhs.l1 == rhs.l1) + && (lhs.l2 == rhs.l2) && (lhs.beta1 == rhs.beta1) && (lhs.beta2 == rhs.beta2) + && (lhs.epsilon == rhs.epsilon) && (lhs.weight_decay == rhs.weight_decay) + && (lhs.amsgrad == rhs.amsgrad) && (lhs.do_bias_correction == rhs.do_bias_correction) + && (lhs.parallel_conf == rhs.parallel_conf) && (lhs.has_model_copy == rhs.has_model_copy) + && (lhs.model_diff_dtype == rhs.model_diff_dtype); +} + +} // namespace oneflow + +namespace std { + +template<> +struct hash { + size_t operator()(const oneflow::SGDOptimizerKey& key) const { + const auto float_hash = std::hash(); + const auto double_hash = std::hash(); + const auto& string_hash = std::hash(); + const auto& parallel_conf_hash = std::hash(); + const auto& bool_hash = std::hash(); + const auto& dtype_hash = std::hash(); + + size_t hash = string_hash(key.learning_rate); + oneflow::HashCombine(&hash, string_hash(key.scale_by_tensor_lbn)); + oneflow::HashCombine(&hash, string_hash(key.skip_if_lbn)); + oneflow::HashCombine(&hash, double_hash(key.scale)); + oneflow::HashCombine(&hash, float_hash(key.l1)); + oneflow::HashCombine(&hash, float_hash(key.l2)); + oneflow::HashCombine(&hash, float_hash(key.weight_decay)); + oneflow::HashCombine(&hash, parallel_conf_hash(key.parallel_conf)); + oneflow::HashCombine(&hash, bool_hash(key.has_model_copy)); + oneflow::HashCombine(&hash, dtype_hash(key.model_diff_dtype)); + return hash; + } +}; + +template<> +struct hash { + size_t operator()(const oneflow::AdamOptimizerKey& key) const { + const auto& float_hash = std::hash(); + const auto& double_hash = std::hash(); + const auto& string_hash = std::hash(); + const auto& bool_hash = std::hash(); + const auto& parallel_conf_hash = std::hash(); + const auto& dtype_hash = std::hash(); + + size_t hash = string_hash(key.learning_rate); + oneflow::HashCombine(&hash, string_hash(key.scale_by_tensor_lbn)); + oneflow::HashCombine(&hash, string_hash(key.skip_if_lbn)); + oneflow::HashCombine(&hash, double_hash(key.scale)); + oneflow::HashCombine(&hash, float_hash(key.l1)); + oneflow::HashCombine(&hash, float_hash(key.l2)); + oneflow::HashCombine(&hash, float_hash(key.beta1)); + oneflow::HashCombine(&hash, float_hash(key.beta2)); + oneflow::HashCombine(&hash, float_hash(key.epsilon)); + oneflow::HashCombine(&hash, float_hash(key.weight_decay)); + oneflow::HashCombine(&hash, bool_hash(key.amsgrad)); + oneflow::HashCombine(&hash, bool_hash(key.do_bias_correction)); + oneflow::HashCombine(&hash, parallel_conf_hash(key.parallel_conf)); + oneflow::HashCombine(&hash, bool_hash(key.has_model_copy)); + oneflow::HashCombine(&hash, dtype_hash(key.model_diff_dtype)); + return hash; + } +}; + +} // namespace std + +namespace oneflow { + +namespace { + +void AddScaleAndSkipLbn(user_op::UserOpConfWrapperBuilder& multi_tensor_model_update_op_builder, + const user_op::UserOpConfWrapper& model_update_user_conf) { + if (model_update_user_conf.has_input("scale_by_tensor", 0)) { + multi_tensor_model_update_op_builder.Input("scale_by_tensor", + model_update_user_conf.input("scale_by_tensor", 0)); + } + if (model_update_user_conf.has_input("skip_if", 0)) { + multi_tensor_model_update_op_builder.Input("skip_if", + model_update_user_conf.input("skip_if", 0)); + } +} + +void AddProcessedVariable(HashSet& processed_variable_list, + const user_op::UserOpConfWrapper& model_update_user_conf) { + /* + Since each variable op will be processed in pass, for example, Adam optimizer has 3 variables: + model, m, v. We replace to multi tensor optimizer and processed 3 variables at once, if we don't + filter these variables, these variables will be repeated 3 times in multi_tensor_update kernel. + + Here we use a HashSet to sign if the variable has been processed. + */ + processed_variable_list.emplace(model_update_user_conf.input("model", 0)); + if (model_update_user_conf.op_type_name() == "adam_update") { + processed_variable_list.emplace(model_update_user_conf.input("m", 0)); + processed_variable_list.emplace(model_update_user_conf.input("v", 0)); + } +} + +bool IfVariableProcessed(const HashSet& processed_variable_list, + const user_op::UserOpConfWrapper& model_update_user_conf) { + if (model_update_user_conf.op_type_name() == "sgd_update") { + const auto& processed_model_iter = + processed_variable_list.find(model_update_user_conf.input("model", 0)); + if (processed_model_iter != processed_variable_list.end()) { return true; } + } else if (model_update_user_conf.op_type_name() == "adam_update") { + const auto& processed_model_iter = + processed_variable_list.find(model_update_user_conf.input("model", 0)); + const auto& processed_m_iter = + processed_variable_list.find(model_update_user_conf.input("m", 0)); + const auto& processed_v_iter = + processed_variable_list.find(model_update_user_conf.input("v", 0)); + if (processed_model_iter != processed_variable_list.end() + && processed_m_iter != processed_variable_list.end() + && processed_v_iter != processed_variable_list.end()) { + return true; + } + } else { + UNIMPLEMENTED() << "Current Optimizer do not support multi tensor update. "; + } + return false; +} + +class MultiTensorModelUpdatePass final : public JobPass { + public: + MultiTensorModelUpdatePass() = default; + ~MultiTensorModelUpdatePass() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { + return ctx.job_desc().enable_multi_tensor_update() + || ParseBooleanFromEnv("ONEFLOW_ENABLE_MULTI_TENSOR_MODEL_UPDATE", false); + } + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +Maybe MultiTensorModelUpdatePass::Apply(const OpGraph& op_graph, + JobBuilder* job_builder) const { + if (!job_builder->job().job_conf().has_train_conf()) { return Maybe::Ok(); } + std::vector delete_ops; + ParallelConf parallel_conf{}; + HashMap multi_tensor_sgd_update_hashmap; + HashMap multi_tensor_adam_update_hashmap; + HashSet processed_variable_list{}; + + op_graph.ForEachNode([&](OpNode* op_node) { + const auto& op_conf = op_node->op().op_conf(); + if (!op_conf.has_variable_conf()) { return; } + LogicalBlobId model_copy_lbi; + + for (OpEdge* find_model_update_edge : op_node->out_edges()) { + OpNode* find_model_update_update_node = find_model_update_edge->dst_node(); + if (!IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), "sgd_update") + && !IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), "adam_update")) { + continue; + } + const user_op::UserOpConfWrapper model_update_user_conf( + find_model_update_update_node->op().op_conf()); + // Multi tensor update pass only support for CUDA currently. + if (find_model_update_update_node->parallel_desc().device_type() != DeviceType::kCUDA) { + continue; + } + + // Multi tensor update pass only support Data Parallel. + bool if_data_parallel = true; + for (const auto& pair : + find_model_update_update_node->sbp_signature().bn_in_op2sbp_parallel()) { + if (!pair.second.has_broadcast_parallel()) { + if_data_parallel = false; + break; + } + } + if (!if_data_parallel) { continue; } + + // Check the variable has been processed before. + if (IfVariableProcessed(processed_variable_list, model_update_user_conf)) { continue; } + + delete_ops.emplace_back(find_model_update_update_node->op().op_conf()); + parallel_conf = find_model_update_update_node->parallel_desc().parallel_conf(); + + std::string scale_by_tensor_lbn = ""; + std::string skip_if_lbn = ""; + bool has_model_copy = false; + if (model_update_user_conf.has_input("scale_by_tensor", 0)) { + scale_by_tensor_lbn = model_update_user_conf.input("scale_by_tensor", 0); + } + if (model_update_user_conf.has_input("skip_if", 0)) { + skip_if_lbn = model_update_user_conf.input("skip_if", 0); + } + if (model_update_user_conf.has_input("model_copy", 0)) { has_model_copy = true; } + + const BlobDesc& model_diff_blob_desc = op_graph.GetLogicalBlobDesc( + GenLogicalBlobId(model_update_user_conf.input("model_diff", 0))); + const DataType model_diff_dtype = model_diff_blob_desc.data_type(); + + if (IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), "sgd_update")) { + SGDOptimizerKey key{model_update_user_conf.input("learning_rate", 0), + scale_by_tensor_lbn, + skip_if_lbn, + model_update_user_conf.attr("scale"), + model_update_user_conf.attr("l1"), + model_update_user_conf.attr("l2"), + model_update_user_conf.attr("weight_decay"), + parallel_conf, + has_model_copy, + model_diff_dtype}; + const auto& iter = multi_tensor_sgd_update_hashmap.find(key); + + if (iter != multi_tensor_sgd_update_hashmap.end()) { + iter->second.Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)); + if (has_model_copy) { + iter->second.Input("model_copy", model_update_user_conf.input("model_copy", 0)); + } + } else { + user_op::UserOpConfWrapperBuilder multi_tensor_sgd_update_op_builder( + "multi_tensor_model_update" + NewUniqueId()); + std::string op_type_name = "multi_tensor_sgd_update"; + if (has_model_copy) { op_type_name = "multi_tensor_sgd_update_with_cast"; } + + multi_tensor_sgd_update_op_builder.OpTypeName(op_type_name) + .Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)) + .Input("learning_rate", model_update_user_conf.input("learning_rate", 0)) + .Attr("scale", model_update_user_conf.attr("scale")) + .Attr("l1", model_update_user_conf.attr("l1")) + .Attr("l2", model_update_user_conf.attr("l2")) + .Attr("weight_decay", model_update_user_conf.attr("weight_decay")); + if (has_model_copy) { + multi_tensor_sgd_update_op_builder.Input("model_copy", + model_update_user_conf.input("model_copy", 0)); + } + + AddScaleAndSkipLbn(multi_tensor_sgd_update_op_builder, model_update_user_conf); + + CHECK(model_update_user_conf.op_conf().has_scope_symbol_id()); + multi_tensor_sgd_update_op_builder.ScopeSymbolId( + model_update_user_conf.op_conf().scope_symbol_id()); + multi_tensor_sgd_update_hashmap.emplace(key, multi_tensor_sgd_update_op_builder); + } + } else if (IsUserOpWithTypeName(find_model_update_update_node->op().op_conf(), + "adam_update")) { + AdamOptimizerKey key{model_update_user_conf.input("learning_rate", 0), + scale_by_tensor_lbn, + skip_if_lbn, + model_update_user_conf.attr("scale"), + model_update_user_conf.attr("l1"), + model_update_user_conf.attr("l2"), + model_update_user_conf.attr("beta1"), + model_update_user_conf.attr("beta2"), + model_update_user_conf.attr("epsilon"), + model_update_user_conf.attr("weight_decay"), + model_update_user_conf.attr("amsgrad"), + model_update_user_conf.attr("do_bias_correction"), + parallel_conf, + has_model_copy, + model_diff_dtype}; + if (key.amsgrad) { + UNIMPLEMENTED() << "Multi Tensor Adam update do not support amsgrad = True. "; + } + const auto& iter = multi_tensor_adam_update_hashmap.find(key); + + if (iter != multi_tensor_adam_update_hashmap.end()) { + iter->second.Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)) + .Input("m", model_update_user_conf.input("m", 0)) + .Input("v", model_update_user_conf.input("v", 0)); + if (has_model_copy) { + iter->second.Input("model_copy", model_update_user_conf.input("model_copy", 0)); + } + if (model_update_user_conf.attr("do_bias_correction")) { + iter->second + .Input("bias_correction1", model_update_user_conf.input("bias_correction1", 0)) + .Input("bias_correction2", model_update_user_conf.input("bias_correction2", 0)); + } + } else { + user_op::UserOpConfWrapperBuilder multi_tensor_adam_update_op_builder( + "multi_tensor_model_update" + NewUniqueId()); + std::string op_type_name = "multi_tensor_adam_update"; + if (has_model_copy) { op_type_name = "multi_tensor_adam_update_with_cast"; } + multi_tensor_adam_update_op_builder.OpTypeName(op_type_name) + .Input("model", model_update_user_conf.input("model", 0)) + .Input("model_diff", model_update_user_conf.input("model_diff", 0)) + .Input("m", model_update_user_conf.input("m", 0)) + .Input("v", model_update_user_conf.input("v", 0)) + .Input("learning_rate", model_update_user_conf.input("learning_rate", 0)) + .Attr("scale", model_update_user_conf.attr("scale")) + .Attr("l1", model_update_user_conf.attr("l1")) + .Attr("l2", model_update_user_conf.attr("l2")) + .Attr("beta1", model_update_user_conf.attr("beta1")) + .Attr("beta2", model_update_user_conf.attr("beta2")) + .Attr("epsilon", model_update_user_conf.attr("epsilon")) + .Attr("weight_decay", model_update_user_conf.attr("weight_decay")) + .Attr("amsgrad", model_update_user_conf.attr("amsgrad")) + .Attr("do_bias_correction", + model_update_user_conf.attr("do_bias_correction")); + + if (model_update_user_conf.attr("do_bias_correction")) { + multi_tensor_adam_update_op_builder + .Input("bias_correction1", model_update_user_conf.input("bias_correction1", 0)) + .Input("bias_correction2", model_update_user_conf.input("bias_correction2", 0)); + } + if (has_model_copy) { + multi_tensor_adam_update_op_builder.Input( + "model_copy", model_update_user_conf.input("model_copy", 0)); + } + AddScaleAndSkipLbn(multi_tensor_adam_update_op_builder, model_update_user_conf); + + CHECK(model_update_user_conf.op_conf().has_scope_symbol_id()); + multi_tensor_adam_update_op_builder.ScopeSymbolId( + model_update_user_conf.op_conf().scope_symbol_id()); + multi_tensor_adam_update_hashmap.emplace(key, multi_tensor_adam_update_op_builder); + } + } else { + UNIMPLEMENTED() << "Current Optimizer do not support multi tensor update. "; + } + + AddProcessedVariable(processed_variable_list, model_update_user_conf); + break; + } + }); + for (auto& op : multi_tensor_sgd_update_hashmap) { + auto multi_tensor_model_update_sgd_op = op.second.Build(); + job_builder->AddOps(parallel_conf, {multi_tensor_model_update_sgd_op.op_conf()}); + } + for (auto& op : multi_tensor_adam_update_hashmap) { + auto multi_tensor_model_update_adam_op = op.second.Build(); + job_builder->AddOps(parallel_conf, {multi_tensor_model_update_adam_op.op_conf()}); + } + job_builder->DelOps(delete_ops); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("MultiTensorModelUpdatePass", MultiTensorModelUpdatePass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/optimizer_placement_optimization_pass.cpp b/oneflow/core/job_rewriter/optimizer_placement_optimization_pass.cpp index 40c6dd84197364126dd7d2de4fc98a69e2bc59da..a1ffc59bf193500146de1e806a7963ae67812dad 100644 --- a/oneflow/core/job_rewriter/optimizer_placement_optimization_pass.cpp +++ b/oneflow/core/job_rewriter/optimizer_placement_optimization_pass.cpp @@ -62,8 +62,8 @@ class DataParallelNodeSequence final { int64_t len() const { return len_; } void resize(const int64_t size) { - CHECK(size <= len_); - CHECK(size > 1); + CHECK_LE(size, len_); + CHECK_GE(size, 1); nodes_.resize(size); len_ = nodes().size(); } @@ -297,7 +297,8 @@ bool IsS0SignatureSupported(const OpNode* node) { auto LogicalBlobDesc4Ibn = [&](const std::string& bn) -> Maybe { return Maybe(node->LogicalBlobDesc4Lbi(node->op().BnInOp2Lbi(bn))); }; - CHECK_JUST(node->op().GetSbpSignaturesIf(LogicalBlobDesc4Ibn, node->parallel_desc(), &list)); + CHECK_JUST(node->op().GetSbpSignaturesIf(LogicalBlobDesc4Ibn, + node->parallel_desc().parallel_num(), &list)); const auto IsInOutS0Parallel = [&](const SbpSignature& signature) { return IsS0Parallel(signature, node->op().SoleIbn()) && IsS0Parallel(signature, node->op().SoleObn()); @@ -337,8 +338,7 @@ bool IsSplitValid(const Shape& shape, const NdSbp& nd_sbp, const Shape& hierachy if (sbp.has_split_parallel()) { const int64_t dim = sbp.split_parallel().axis(); if (dim >= cur_shape.NumAxes()) { return false; } - // Evenly split. - if (cur_shape.At(dim) % hierachy.At(i) != 0) { return false; } + // Unbalanced split and take the minimum cur_shape.Set(dim, cur_shape.At(dim) / hierachy.At(i)); // Larger then min size. if (cur_shape.elem_cnt() < min_size) { return false; } @@ -379,8 +379,8 @@ void GenerateSplitSignature(const NdSbp& var_nd_sbp, const OperatorConf& new_var } void ShardSequence(JobBuilder* builder, const int64_t threshold, const ParallelDesc& pd, std::vector&& sorted_sequences) { - // For all sorted sequnence, set the variable op in the sequence to S - // and add ctrl edge to control the exectuion order between variable ops. + // For all sorted sequence, set the variable op in the sequence to S + // and add ctrl edge to control the execution order between variable ops. // A sequence is a variable op and its cast(fp32 to fp16) op. This is because the forward pass // consume the fp16 variable and the optimizer consume the fp32 variable. std::string prev_allowed_op_name = ""; @@ -401,11 +401,11 @@ void ShardSequence(JobBuilder* builder, const int64_t threshold, const ParallelD } bool split_is_allowed = true; - if (split_is_allowed) { + { NdSbp new_nd_sbp; std::vector nd_sbp_str_vec; for (const auto& sbp_str : new_var_op_conf.variable_conf().nd_sbp()) { - nd_sbp_str_vec.push_back(sbp_str); + nd_sbp_str_vec.emplace_back(sbp_str); } ParseNdSbpFromStringList(nd_sbp_str_vec, &new_nd_sbp); // check allowed by min shard size and evenly split @@ -429,7 +429,7 @@ void ShardSequence(JobBuilder* builder, const int64_t threshold, const ParallelD } } if (!split_is_allowed) { - VLOG(3) << var_node->op().op_name() << " failed to change form B to S " + VLOG(3) << var_node->op().op_name() << " failed to change from B to S " << " with op conf " << new_var_op_conf.variable_conf().DebugString(); continue; } @@ -442,7 +442,7 @@ void ShardSequence(JobBuilder* builder, const int64_t threshold, const ParallelD SetNdSbp4Consumers(builder, sorted_sequences.at(i), var_nd_sbp); } prev_allowed_op_name = var_node->op().op_name(); - VLOG(3) << var_node->op().op_name() << " succeed to change form B to " << new_split_signature + VLOG(3) << var_node->op().op_name() << " succeed to change from B to " << new_split_signature << " on ranks dim " << split_dim << " with op conf " << new_var_op_conf.variable_conf().DebugString(); } @@ -521,6 +521,12 @@ class OptimizerPlacementOptimizationPass final : public JobPass { && ctx->job_desc().job_conf().optimizer_placement_optimization_mode() != "none")) { return Maybe::Ok(); } + if (job->job_conf().enable_auto_parallel() + && job->job_conf().enable_auto_parallel_ignore_user_sbp_config()) { + LOG(WARNING) << "ZeRO optimization will be ignored when enabling AutoParallel to ignore user " + "sbp configuration"; + return Maybe::Ok(); + } const std::string& mode = ctx->job_desc().job_conf().optimizer_placement_optimization_mode(); const OpGraph op_graph(*job); JobBuilder job_builder(job); diff --git a/oneflow/core/job_rewriter/pass_util.cpp b/oneflow/core/job_rewriter/pass_util.cpp index 7157c7006a76f687bbef81c6fbb929b53b6a61c9..ded6173be124420cd919a7657ae5140ca163d067 100644 --- a/oneflow/core/job_rewriter/pass_util.cpp +++ b/oneflow/core/job_rewriter/pass_util.cpp @@ -54,4 +54,25 @@ void DfsTopoGraphTraversal(const OpGraph& graph, bool reversed, }); } +std::function MakePredicatorIsSafeToDelete(const OpGraph& op_graph) { + HashSet ctrl_in_op_names; + op_graph.ForEachNode([&](const OpNode* op_node) { + for (const std::string& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { + ctrl_in_op_names.insert(ctrl_in_op_name); + } + }); + return [=](const OpNode* op_node) { + if (op_node->out_edges().size() > 1) { return false; } + if (!op_node->op().op_conf().ctrl_in_op_name().empty()) { return false; } + if (ctrl_in_op_names.find(op_node->op().op_conf().name()) != ctrl_in_op_names.end()) { + return false; + } + return true; + }; +} + +bool IsUserOpWithTypeName(const OperatorConf& op_conf, const std::string& op_type_name) { + return op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == op_type_name; +} + } // namespace oneflow diff --git a/oneflow/core/job_rewriter/pass_util.h b/oneflow/core/job_rewriter/pass_util.h index 149f7c94adbced33e244a56ddf23b3a8f607b1f5..79d884b7aa7bc3a0d1aec985ecfd16d6c1d3cc9c 100644 --- a/oneflow/core/job_rewriter/pass_util.h +++ b/oneflow/core/job_rewriter/pass_util.h @@ -13,6 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_CORE_JOB_REWRITER_PASS_UTIL_H_ +#define ONEFLOW_CORE_JOB_REWRITER_PASS_UTIL_H_ + #include #include @@ -72,4 +75,9 @@ class OpConfCache { } }; +std::function MakePredicatorIsSafeToDelete(const OpGraph& op_graph); +bool IsUserOpWithTypeName(const OperatorConf& op_conf, const std::string& op_type_name); + } // namespace oneflow + +#endif // ONEFLOW_CORE_JOB_REWRITER_PASS_UTIL_H_ diff --git a/oneflow/core/job_rewriter/pipeline_buffer_pass.cpp b/oneflow/core/job_rewriter/pipeline_buffer_pass.cpp index bb9a62b340c08df458e98d735feae9993aac6a2a..7df20c8ff514f51ca5ae9dc09d1654acce24bbc8 100644 --- a/oneflow/core/job_rewriter/pipeline_buffer_pass.cpp +++ b/oneflow/core/job_rewriter/pipeline_buffer_pass.cpp @@ -39,7 +39,11 @@ class PipelineBufferPass final : public JobPass { return Apply(op_graph, &job_builder); } - bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } + bool IsEnabled(const JobPassCtx& ctx) const { + // Pipeline optimization depends on gradient accumulatioin. + return ctx.job_desc().IsTrain() + && ctx.job_desc().job_conf().num_gradient_accumulation_steps() > 1; + } Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; }; @@ -224,11 +228,6 @@ void TryInsertOrUseBufferOpBothSrcDst( } Maybe PipelineBufferPass::Apply(const OpGraph& op_graph, JobBuilder* job_builder) const { - // Pipeline optimization depends on gradient accumulatioin. - if (GlobalJobDesc().job_conf().num_gradient_accumulation_steps() <= 1) { - return Maybe::Ok(); - } - int64_t max_stage_id = 0; op_graph.ForEachNode([&](const OpNode* this_node) { if (!OpNodeHasScope(this_node)) { diff --git a/oneflow/core/job_rewriter/prune_amp_white_identity_op_pass.cpp b/oneflow/core/job_rewriter/prune_amp_white_identity_op_pass.cpp index 16dab2eba810395a7674796c5e7a8f684308d537..fd49e2aad7433ee1b543b43c8b4dc46921f5ac74 100644 --- a/oneflow/core/job_rewriter/prune_amp_white_identity_op_pass.cpp +++ b/oneflow/core/job_rewriter/prune_amp_white_identity_op_pass.cpp @@ -20,6 +20,16 @@ namespace oneflow { namespace { +bool IsAmpIdentityOp(const OperatorConf& op) { + return op.has_user_conf() + && (op.user_conf().op_type_name() == "amp_white_identity" + || op.user_conf().op_type_name() == "amp_black_identity"); +} + +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), IsAmpIdentityOp); +} + class PruneAmpWhiteIdentityOpPass final : public JobPass { public: PruneAmpWhiteIdentityOpPass() = default; @@ -30,46 +40,73 @@ class PruneAmpWhiteIdentityOpPass final : public JobPass { Maybe PruneAmpWhiteIdentityOpPass::Apply(Job* job, JobPassCtx* ctx) const { if (!ctx->job_desc().prune_amp_white_identity_ops()) { return Maybe::Ok(); } + if (!NeedDoPass(*job)) { return Maybe::Ok(); } const OpGraph op_graph(*job); - JobBuilder job_builder(job); - HashMap op_name2op_conf; + HashSet ctrl_in_op_names; op_graph.ForEachNode([&](const OpNode* op_node) { for (const std::string& ctrl_in_op_name : op_node->op().op_conf().ctrl_in_op_name()) { ctrl_in_op_names.insert(ctrl_in_op_name); } }); - std::vector del_op_names; + + HashSet del_nodes; op_graph.ForEachNode([&](const OpNode* op_node) { + const std::string& op_name = op_node->op().op_name(); const OperatorConf& op_conf = op_node->op().op_conf(); - if (!op_conf.has_user_conf()) { return; } - const std::string& op_type_name = op_conf.user_conf().op_type_name(); - if (op_type_name != "amp_white_identity") { return; } + // not amp identity op + if (!IsAmpIdentityOp(op_conf)) { return; } + // has ctrl in if (!op_conf.ctrl_in_op_name().empty()) { return; } - if (ctrl_in_op_names.find(op_conf.name()) != ctrl_in_op_names.end()) { return; } + // is ctrl in of another op + if (ctrl_in_op_names.find(op_name) != ctrl_in_op_names.end()) { return; } + // not sole in if (op_node->in_edges().size() != 1) { return; } - const user_op::UserOpConfWrapper user_op_conf(op_conf); - const LogicalBlobId& in_lbi = GenLogicalBlobId(user_op_conf.input("in", 0)); - const LogicalBlobId& out_lbi = GenLogicalBlobId(user_op_conf.output("out", 0)); + + del_nodes.insert(op_node); + }); + + HashMap to_update_op_confs; + std::vector del_op_names; + del_op_names.reserve(del_nodes.size()); + for (const OpNode* op_node : del_nodes) { + del_op_names.emplace_back(op_node->op().op_name()); + + // find first node not deleted + const OpNode* first = op_node; + const OpNode* producer = op_node->SoleInEdge()->src_node(); + while (del_nodes.find(producer) != del_nodes.end()) { + first = producer; + producer = producer->SoleInEdge()->src_node(); + } + + const auto& old_lbi = op_node->op().BnInOp2Lbi(op_node->op().SoleObn()); + const auto& new_lbi = first->op().BnInOp2Lbi(first->op().SoleIbn()); + for (const OpEdge* out_edge : op_node->out_edges()) { const OpNode* consumer = out_edge->dst_node(); - const std::string& consumer_op_name = consumer->op().op_name(); - if (op_name2op_conf.find(consumer_op_name) == op_name2op_conf.end()) { - op_name2op_conf[consumer_op_name] = consumer->op().op_conf(); - } - OperatorConf& consumer_op_conf = op_name2op_conf.at(consumer_op_name); - for (const std::string& ibn : consumer->op().input_bns()) { - if (consumer->op().BnInOp2Lbi(ibn) == out_lbi) { - const auto& old_val = - ReplaceInputLbnInOpCustomizedConf(&consumer_op_conf, ibn, GenLogicalBlobName(in_lbi)); - CHECK_EQ(GenLogicalBlobName(out_lbi), old_val); + if (del_nodes.find(consumer) == del_nodes.end()) { + const Operator& op = consumer->op(); + for (const std::string& ibn : op.input_bns()) { + if (op.BnInOp2Lbi(ibn) == old_lbi) { + auto iter = to_update_op_confs.find(op.op_name()); + if (iter == to_update_op_confs.end()) { + iter = to_update_op_confs.emplace(op.op_name(), op.op_conf()).first; + } + OperatorConf& op_conf = iter->second; + const auto& old_val = + ReplaceInputLbnInOpCustomizedConf(&op_conf, ibn, GenLogicalBlobName(new_lbi)); + CHECK_EQ_OR_RETURN(GenLogicalBlobName(old_lbi), old_val); + } } } } - del_op_names.emplace_back(op_conf.name()); - }); - for (const auto& pair : op_name2op_conf) { job_builder.MutOpsOnlyOnce({pair.second}); } + } + + JobBuilder job_builder(job); + for (const auto& pair : to_update_op_confs) { job_builder.MutOpsOnlyOnce({pair.second}); } job_builder.DelOps(del_op_names); + return Maybe::Ok(); } diff --git a/oneflow/core/job_rewriter/prune_cast_to_static_shape_op_pass.cpp b/oneflow/core/job_rewriter/prune_cast_to_static_shape_op_pass.cpp index 4141a427041341aebe47c979bcca9db5647ae759..2b968686c0e62707f79e0d40c88adf874fcb03ed 100644 --- a/oneflow/core/job_rewriter/prune_cast_to_static_shape_op_pass.cpp +++ b/oneflow/core/job_rewriter/prune_cast_to_static_shape_op_pass.cpp @@ -20,6 +20,14 @@ namespace oneflow { namespace { +bool IsRelatedOp(const OperatorConf& op) { + return op.has_user_conf() && (op.user_conf().op_type_name() == "cast_to_static_shape"); +} + +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), IsRelatedOp); +} + class PruneCastToStaticShapeOpsPass final : public JobPass { public: PruneCastToStaticShapeOpsPass() = default; @@ -32,6 +40,7 @@ class PruneCastToStaticShapeOpsPass final : public JobPass { Maybe Apply(Job* job, JobPassCtx* ctx) const override { if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + if (!NeedDoPass(*job)) { return Maybe::Ok(); } const OpGraph op_graph(*job); JobBuilder job_builder(job); return Apply(op_graph, &job_builder); diff --git a/oneflow/core/job_rewriter/prune_parallel_cast_op_pass.cpp b/oneflow/core/job_rewriter/prune_parallel_cast_op_pass.cpp index cd6b198487c43bfd39ea68a61d6562a7840dae4a..e3a42e09950ae2e3e8fe32c27d2fe4e952f84816 100644 --- a/oneflow/core/job_rewriter/prune_parallel_cast_op_pass.cpp +++ b/oneflow/core/job_rewriter/prune_parallel_cast_op_pass.cpp @@ -20,6 +20,17 @@ namespace oneflow { namespace { +bool IsParallelCastOp(const OperatorConf& op_conf) { + return op_conf.has_user_conf() + && (op_conf.user_conf().op_type_name() == "parallel_cast" + || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast" + || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast_like"); +} + +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), IsParallelCastOp); +} + class PruneParallelCastOpsPass final : public JobPass { public: PruneParallelCastOpsPass() = default; @@ -30,6 +41,7 @@ class PruneParallelCastOpsPass final : public JobPass { Maybe Apply(Job* job, JobPassCtx* ctx) const override { if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + if (!NeedDoPass(*job)) { return Maybe::Ok(); } const OpGraph op_graph(*job); JobBuilder job_builder(job); return Apply(op_graph, &job_builder); @@ -46,12 +58,6 @@ Maybe PruneParallelCastOpsPass::Apply(const OpGraph& op_graph, ctrl_in_op_names.insert(ctrl_in_op_name); } }); - const auto IsParallelCastOp = [](const OperatorConf& op_conf) -> bool { - return op_conf.has_user_conf() - && (op_conf.user_conf().op_type_name() == "parallel_cast" - || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast" - || op_conf.user_conf().op_type_name() == "hierarchical_parallel_cast_like"); - }; std::vector del_op_names; op_graph.ForEachNode([&](const OpNode* op_node) { const OperatorConf& op_conf = op_node->op().op_conf(); diff --git a/oneflow/core/job_rewriter/prune_pinned_identity_op_pass.cpp b/oneflow/core/job_rewriter/prune_pinned_identity_op_pass.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b32ac2d95fb96428b227aea3e4d59d822e6aa8f7 --- /dev/null +++ b/oneflow/core/job_rewriter/prune_pinned_identity_op_pass.cpp @@ -0,0 +1,120 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/job_rewriter/job_pass.h" + +namespace oneflow { + +namespace { + +class PrunePinnedIdentityOpPass final : public JobPass { + public: + PrunePinnedIdentityOpPass() = default; + ~PrunePinnedIdentityOpPass() override = default; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override; +}; + +Maybe PrunePinnedIdentityOp(JobBuilder* job_builder, + std::vector* outdated_ops, + const OpGraph& op_graph, const std::string& lbn) { + auto lbi = GenLogicalBlobId(lbn); + const OpNode* op_node = op_graph.OpNode4OpName(lbi.op_name()); + CHECK_EQ_OR_RETURN(op_node->in_edges().size(), 1); // NOLINT + const OperatorConf& op_conf = op_node->op().op_conf(); + CHECK_OR_RETURN(op_conf.has_user_conf()); // NOLINT + const std::string& op_type_name = op_conf.user_conf().op_type_name(); + CHECK_OR_RETURN(op_type_name == "pinned_identity"); // NOLINT + + // skip prune if the pinned identity has `ctrl_in_op` + if (!op_conf.ctrl_in_op_name().empty()) { return lbn; } + + const user_op::UserOpConfWrapper user_op_conf(op_conf); + const LogicalBlobId& in_lbi = GenLogicalBlobId(user_op_conf.input("in", 0)); + const LogicalBlobId& out_lbi = GenLogicalBlobId(user_op_conf.output("out", 0)); + + op_node->ForEachNodeOnOutEdge([&](const OpNode* out_node) { + for (const std::string& ibn : out_node->op().input_bns()) { + if (out_node->op().BnInOp2Lbi(ibn) == out_lbi) { + if (!CHECK_JUST(job_builder->IsInMutOpTransaction(out_node->op().op_name()))) { + CHECK_JUST(job_builder->MutOpTransactionMut(out_node->op().op_conf())); + } + OperatorConf& mut_consumer_op = + CHECK_JUST(job_builder->MutOpTransactionGet(out_node->op().op_name())); + const auto& old_lbn = + ReplaceInputLbnInOpCustomizedConf(&mut_consumer_op, ibn, GenLogicalBlobName(in_lbi)); + CHECK_EQ(old_lbn, GenLogicalBlobName(out_lbi)); + } + } + }); + outdated_ops->push_back(op_conf.name()); + return GenLogicalBlobName(in_lbi); +} + +Maybe PrunePinnedIdentityOpPass::Apply(Job* job, JobPassCtx* ctx) const { + if (!job->job_conf().has_train_conf()) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + HashMap pruned_lbns; + std::vector outdated_ops; + TrainConf* train_conf = job->mutable_job_conf()->mutable_train_conf(); + // prune loss pinned identity + for (int i = 0; i < train_conf->loss_lbn_size(); ++i) { + const auto& pinned_loss_lbn = train_conf->loss_lbn(i); + auto it = pruned_lbns.find(pinned_loss_lbn); + if (it == pruned_lbns.end()) { + const auto& loss_lbn = + JUST(PrunePinnedIdentityOp(&job_builder, &outdated_ops, op_graph, pinned_loss_lbn)); + it = pruned_lbns.emplace(pinned_loss_lbn, *loss_lbn).first; + } + train_conf->set_loss_lbn(i, it->second); + } + // prune loss initial gradient pinned identity + for (int i = 0; i < train_conf->loss_grad_lbn_size(); ++i) { + const auto& pinned_loss_grad_lbn = train_conf->loss_grad_lbn(i); + auto it = pruned_lbns.find(pinned_loss_grad_lbn); + if (it == pruned_lbns.end()) { + const auto& loss_grad_lbn = + JUST(PrunePinnedIdentityOp(&job_builder, &outdated_ops, op_graph, pinned_loss_grad_lbn)); + it = pruned_lbns.emplace(pinned_loss_grad_lbn, *loss_grad_lbn).first; + } + train_conf->set_loss_grad_lbn(i, it->second); + } + // prune variable gradient pinned identity + for (int i = 0; i < train_conf->optimizer_conf_size(); ++i) { + auto* optimizer_conf = train_conf->mutable_optimizer_conf(i); + for (int j = 0; j < optimizer_conf->variable_grad_lbns_size(); ++j) { + const auto& pinned_variable_grad_lbn = optimizer_conf->variable_grad_lbns(j); + if (pinned_variable_grad_lbn.empty()) { continue; } + auto it = pruned_lbns.find(pinned_variable_grad_lbn); + if (it == pruned_lbns.end()) { + const auto& variable_grad_lbn = JUST( + PrunePinnedIdentityOp(&job_builder, &outdated_ops, op_graph, pinned_variable_grad_lbn)); + it = pruned_lbns.emplace(pinned_variable_grad_lbn, *variable_grad_lbn).first; + } + optimizer_conf->set_variable_grad_lbns(j, it->second); + } + } + job_builder.DelOps(outdated_ops); + JUST(job_builder.MutOpTransactionCommit()); + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("PrunePinnedIdentityOpPass", PrunePinnedIdentityOpPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/replace_embedding_ops_pass.cpp b/oneflow/core/job_rewriter/replace_embedding_ops_pass.cpp index 904a20919c2beb9c29469ce6cb91c1392ebcc024..7e3e415735570c7897acc790e7fff9f844ce72d1 100644 --- a/oneflow/core/job_rewriter/replace_embedding_ops_pass.cpp +++ b/oneflow/core/job_rewriter/replace_embedding_ops_pass.cpp @@ -17,9 +17,9 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/job_rewriter/dynamic_loss_scale_job_pass_state.h" #include "oneflow/core/job_rewriter/autograd.h" -#include "oneflow/core/embedding/key_value_store_options.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/job_rewriter/clip_by_global_norm_job_pass_state.h" +#include "oneflow/core/embedding/embedding_manager.h" namespace oneflow { @@ -103,17 +103,9 @@ Maybe DynamicLossScaleAddGradient( return Maybe::Ok(); } -// void BuildEmbeddingLookup(JobPassCtx* ctx, JobBuilder* job_builder, const int64_t embedding_size, -// const int64_t line_size, const std::string& embedding_name, -// bool has_embedding_prefetch, const ParallelConf& parallel_conf, -// const user_op::UserOpConfWrapper& embedding_op, -// const std::string& num_unique_ids_lbn, const std::string& unique_ids_lbn, -// const std::string& unique_table_ids_lbn, std::string* embedding_lbn, -// std::string* unique_values_lbn, OperatorConf* embedding_prefetch_op_conf, -// OperatorConf* embedding_lookup_op_conf) { void BuildEmbeddingLookup( JobPassCtx* ctx, JobBuilder* job_builder, const int64_t embedding_size, const int64_t line_size, - const std::string& embedding_name, bool has_embedding_prefetch, + const std::string& embedding_name, const int64_t seed, bool has_embedding_prefetch, const ParallelConf& parallel_conf, const user_op::UserOpConfWrapper& embedding_op, const std::string& prefetch_num_unique_ids_lbn, const std::string& prefetch_unique_ids_lbn, const std::string& prefetch_unique_table_ids_lbn, const std::string& num_unique_ids_lbn, @@ -123,13 +115,10 @@ void BuildEmbeddingLookup( std::string context_lbn; if (has_embedding_prefetch) { // embedding prefetch op - user_op::UserOpConfWrapperBuilder embedding_prefetch_op_builder(embedding_op.op_name() - + "_embedding_prefetch"); + user_op::UserOpConfWrapperBuilder embedding_prefetch_op_builder( + embedding_op.op_name() + "_embedding_prefetch" + NewUniqueId()); user_op::UserOpConfWrapper embedding_prefetch_op = embedding_prefetch_op_builder.OpTypeName("embedding_prefetch") - // .Input("num_unique_ids", num_unique_ids_lbn) - // .Input("unique_ids", unique_ids_lbn) - // .Input("table_ids", unique_table_ids_lbn) .Input("num_unique_ids", prefetch_num_unique_ids_lbn) .Input("unique_ids", prefetch_unique_ids_lbn) .Input("table_ids", prefetch_unique_table_ids_lbn) @@ -139,10 +128,10 @@ void BuildEmbeddingLookup( .Attr("embedding_tables", embedding_op.attr("embedding_tables")) .Attr("embedding_name", embedding_name) + .Attr("seed", seed) .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()) .Build(); *embedding_prefetch_op_conf = embedding_prefetch_op.op_conf(); - // embedding_prefetch_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false)) { embedding_prefetch_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); } @@ -150,8 +139,8 @@ void BuildEmbeddingLookup( } // embedding lookup op - user_op::UserOpConfWrapperBuilder embedding_lookup_op_builder(embedding_op.op_name() - + "_embedding_lookup"); + user_op::UserOpConfWrapperBuilder embedding_lookup_op_builder( + embedding_op.op_name() + "_embedding_lookup" + NewUniqueId()); embedding_lookup_op_builder.OpTypeName("embedding_lookup") .Input("num_unique_ids", num_unique_ids_lbn) .Input("unique_ids", unique_ids_lbn) @@ -162,6 +151,7 @@ void BuildEmbeddingLookup( .Attr("line_size", line_size) .Attr("embedding_tables", embedding_op.attr("embedding_tables")) .Attr("embedding_name", embedding_name) + .Attr("seed", seed) .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()); if (has_embedding_prefetch) { embedding_lookup_op_builder.Input("context", context_lbn); } bool has_embeddings_output = @@ -175,7 +165,6 @@ void BuildEmbeddingLookup( } user_op::UserOpConfWrapper embedding_lookup_op = embedding_lookup_op_builder.Build(); *embedding_lookup_op_conf = embedding_lookup_op.op_conf(); - // embedding_lookup_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false)) { embedding_lookup_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); } @@ -188,29 +177,32 @@ void BuildEmbeddingLookup( } void BuildEmbeddingShuffle(JobBuilder* job_builder, const std::string& embedding_name, - const ParallelConf& parallel_conf, + int64_t embedding_size, const ParallelConf& parallel_conf, const user_op::UserOpConfWrapper& embedding_op, const std::string& inverse_indices_lbn, const std::string& inner_inverse_unique_partition_indices_lbn, const std::string& num_unique_matrix_lbn, const std::string& embedding_lbn, std::vector* add_ops, std::string* new_embeddings_lbn) { - user_op::UserOpConfWrapperBuilder embedding_shuffle_op_builder(embedding_op.op_name() - + "_embedding_shuffle"); + const bool is_train_job = job_builder->job().job_conf().has_train_conf(); + user_op::UserOpConfWrapperBuilder embedding_shuffle_op_builder( + embedding_op.op_name() + "_embedding_shuffle" + NewUniqueId()); user_op::UserOpConfWrapper embedding_shuffle_op = embedding_shuffle_op_builder.OpTypeName("embedding_shuffle") .Input("cur_rank_embeddings", embedding_lbn) .Input("cur_rank_inverse_indices", inverse_indices_lbn) .Input("inverse_unique_partition_indices", inner_inverse_unique_partition_indices_lbn) .Input("num_unique_matrix", num_unique_matrix_lbn) + .Attr("embedding_name", embedding_name) + .Attr("embedding_size", embedding_size) + .Attr("is_train", is_train_job) .Output("embeddings") .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()) .Build(); OperatorConf embedding_shuffle_new_op_conf = embedding_shuffle_op.op_conf(); - // if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_EMBEDDING_SHUFFLE_INDEPENTENT_STREAM", false)) { if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false) && ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_EMBEDDING_SHUFFLE_INDEPENTENT_STREAM", true)) { - embedding_shuffle_new_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING_SHUFFLE"); + embedding_shuffle_new_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING"); } add_ops->push_back(embedding_shuffle_new_op_conf); *new_embeddings_lbn = embedding_shuffle_op.output("embeddings", 0); @@ -218,7 +210,7 @@ void BuildEmbeddingShuffle(JobBuilder* job_builder, const std::string& embedding void BuildEmbeddingGradientShuffle( JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, const OpNode* op_node, - const std::string& embedding_name, const bool use_system_gather, + const std::string& embedding_name, int64_t embedding_size, const bool use_system_gather, const ParallelConf& embedding_parallel_conf, const int64_t embedding_scope_symbol_id, const user_op::UserOpConfWrapper& embedding_op, const std::string& inverse_indices_lbn, const std::string& inner_inverse_unique_partition_indices_lbn, @@ -255,8 +247,13 @@ void BuildEmbeddingGradientShuffle( *cur_rank_unique_embedding_grad_lbn = unsorted_segment_sum_op.output("out", 0); } else { // embedding_gradient_shuffle op + // if no dynamic loss scale or no clip_grad, we think gradient shuffle grad's invalid buffer + // need not to be memset. + const bool has_dynamic_loss_scale = + job_builder->job().job_conf().train_conf().has_dynamic_loss_scale_policy(); + const bool only_zero_valid_grad = (!has_clip_grad) && (!has_dynamic_loss_scale); user_op::UserOpConfWrapperBuilder embedding_gradient_shuffle_op_builder( - embedding_op.op_name() + "_embedding_gradient_shuffle"); + embedding_op.op_name() + "_embedding_gradient_shuffle" + NewUniqueId()); user_op::UserOpConfWrapper embedding_gradient_shuffle_op = embedding_gradient_shuffle_op_builder.OpTypeName("embedding_gradient_shuffle") .Input("cur_rank_inverse_indices", inverse_indices_lbn) @@ -264,15 +261,16 @@ void BuildEmbeddingGradientShuffle( .Input("embedding_grad", update_embedding_grad_lbn) .Input("num_unique_matrix", num_unique_matrix_lbn) .Output("cur_rank_unique_embedding_grad") + .Attr("embedding_name", embedding_name) + .Attr("embedding_size", embedding_size) + .Attr("only_zero_valid_grad", only_zero_valid_grad) .ScopeSymbolId(embedding_scope_symbol_id) .Build(); OperatorConf embedding_gradient_shuffle_new_op_conf = embedding_gradient_shuffle_op.op_conf(); - // if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_EMBEDDING_SHUFFLE_INDEPENTENT_STREAM", false)) { if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false) && ParseBooleanFromEnv( "ONEFLOW_ONE_EMBEDDING_EMBEDDING_GRADIENT_SHUFFLE_INDEPENTENT_STREAM", true)) { - embedding_gradient_shuffle_new_op_conf.set_stream_name_hint(embedding_name - + "_EMBEDDING_SHUFFLE"); + embedding_gradient_shuffle_new_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING"); } job_builder->AddOps(embedding_parallel_conf, {embedding_gradient_shuffle_new_op_conf}); *cur_rank_unique_embedding_grad_lbn = @@ -350,7 +348,6 @@ double GetLossInstanceNumScaleFactor(const OpGraph& op_graph, JobBuilder* job_bu void BuildIdShuffle(bool use_system_gather, const std::string& embedding_name, const user_op::UserOpConfWrapper& embedding_op, - // std::vector* add_ops, std::vector* add_ops, std::string* prefetch_num_unique_lbn, std::string* prefetch_unique_ids_lbn, std::string* prefetch_unique_table_ids_lbn, @@ -359,9 +356,10 @@ void BuildIdShuffle(bool use_system_gather, const std::string& embedding_name, std::string* unique_table_ids_lbn, std::string* inverse_indices_lbn, std::string* num_unique_matrix_lbn) { const int32_t num_tables = embedding_op.attr("num_tables"); + const int64_t padding_idx = embedding_op.attr("padding_idx"); + const int64_t has_padding_idx = embedding_op.attr("has_padding_idx"); bool enable_pipelined_execution = !ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false); - if (use_system_gather) { user_op::UserOpConfWrapperBuilder unique_op_builder(embedding_op.op_name() + "_unique_ids_and_tables"); @@ -372,13 +370,15 @@ void BuildIdShuffle(bool use_system_gather, const std::string& embedding_name, .Output("unique_values") .Output("inverse_indices") .Attr("num_tables", num_tables) + .Attr("padding_idx", padding_idx) + .Attr("has_padding_idx", has_padding_idx) + .Attr("embedding_name", embedding_name) .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()); if (embedding_op.has_input("table_ids", 0)) { unique_op_builder.Input("values", embedding_op.input("table_ids", 0)); } user_op::UserOpConfWrapper unique_op = unique_op_builder.Build(); OperatorConf unique_new_op_conf = unique_op.op_conf(); - // unique_new_op_conf.set_stream_name_hint(embedding_name + "_ID_SHUFFLE"); if (enable_pipelined_execution) { unique_new_op_conf.set_stream_name_hint(embedding_name + "_ID_SHUFFLE"); } @@ -391,7 +391,8 @@ void BuildIdShuffle(bool use_system_gather, const std::string& embedding_name, *prefetch_unique_ids_lbn = *unique_ids_lbn; *prefetch_unique_table_ids_lbn = *unique_table_ids_lbn; } else { - user_op::UserOpConfWrapperBuilder id_shuffle_op_builder(embedding_op.op_name() + "_id_shuffle"); + user_op::UserOpConfWrapperBuilder id_shuffle_op_builder(embedding_op.op_name() + "_id_shuffle" + + NewUniqueId()); id_shuffle_op_builder.OpTypeName("id_shuffle") .Input("ids", embedding_op.input("ids", 0)) .Output("inverse_unique_partition_indices") @@ -401,24 +402,64 @@ void BuildIdShuffle(bool use_system_gather, const std::string& embedding_name, .Output("cur_rank_inverse_indices") .Output("num_unique_matrix") .Attr("num_tables", num_tables) + .Attr("padding_idx", padding_idx) + .Attr("has_padding_idx", has_padding_idx) + .Attr("embedding_name", embedding_name) .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()); if (embedding_op.has_input("table_ids", 0)) { id_shuffle_op_builder.Input("table_ids", embedding_op.input("table_ids", 0)); } user_op::UserOpConfWrapper id_shuffle_op = id_shuffle_op_builder.Build(); OperatorConf id_shuffle_new_op_conf = id_shuffle_op.op_conf(); - // id_shuffle_new_op_conf.set_stream_name_hint(embedding_name + "_ID_SHUFFLE"); if (enable_pipelined_execution) { id_shuffle_new_op_conf.set_stream_name_hint(embedding_name + "_ID_SHUFFLE"); } add_ops->push_back(id_shuffle_new_op_conf); - *inner_inverse_unique_partition_indices_lbn = - id_shuffle_op.output("inverse_unique_partition_indices", 0); - *num_unique_ids_lbn = id_shuffle_op.output("cur_rank_num_unique", 0); - *unique_ids_lbn = id_shuffle_op.output("cur_rank_unique_ids", 0); - *unique_table_ids_lbn = id_shuffle_op.output("cur_rank_unique_table_ids", 0); - *inverse_indices_lbn = id_shuffle_op.output("cur_rank_inverse_indices", 0); - *num_unique_matrix_lbn = id_shuffle_op.output("num_unique_matrix", 0); + if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ADD_ID_SHUFFLE_COPY_OUT", true)) { + // add id_shuffle_copy_out, so the consumer can use light_actor and cuda_graph. + user_op::UserOpConfWrapperBuilder identity_op_builder( + embedding_op.op_name() + "_id_shuffle_copy_out_" + NewUniqueId()); + user_op::UserOpConfWrapper identity_op = + identity_op_builder.OpTypeName("id_shuffle_copy_out") + .Attr("embedding_name", embedding_name) + .Input("inverse_unique_partition_indices", + id_shuffle_op.output("inverse_unique_partition_indices", 0)) + .Input("cur_rank_num_unique", id_shuffle_op.output("cur_rank_num_unique", 0)) + .Input("cur_rank_unique_ids", id_shuffle_op.output("cur_rank_unique_ids", 0)) + .Input("cur_rank_unique_table_ids", + id_shuffle_op.output("cur_rank_unique_table_ids", 0)) + .Input("cur_rank_inverse_indices", + id_shuffle_op.output("cur_rank_inverse_indices", 0)) + .Input("num_unique_matrix", id_shuffle_op.output("num_unique_matrix", 0)) + .Output("out_inverse_unique_partition_indices") + .Output("out_cur_rank_num_unique") + .Output("out_cur_rank_unique_ids") + .Output("out_cur_rank_unique_table_ids") + .Output("out_cur_rank_inverse_indices") + .Output("out_num_unique_matrix") + .ScopeSymbolId(embedding_op.op_conf().scope_symbol_id()) + .Build(); + OperatorConf identity_op_conf = identity_op.op_conf(); + if (enable_pipelined_execution) { + identity_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING"); + } + add_ops->push_back(identity_op_conf); + *inner_inverse_unique_partition_indices_lbn = + identity_op.output("out_inverse_unique_partition_indices", 0); + *num_unique_ids_lbn = identity_op.output("out_cur_rank_num_unique", 0); + *unique_ids_lbn = identity_op.output("out_cur_rank_unique_ids", 0); + *unique_table_ids_lbn = identity_op.output("out_cur_rank_unique_table_ids", 0); + *inverse_indices_lbn = identity_op.output("out_cur_rank_inverse_indices", 0); + *num_unique_matrix_lbn = identity_op.output("out_num_unique_matrix", 0); + } else { + *inner_inverse_unique_partition_indices_lbn = + id_shuffle_op.output("inverse_unique_partition_indices", 0); + *num_unique_ids_lbn = id_shuffle_op.output("cur_rank_num_unique", 0); + *unique_ids_lbn = id_shuffle_op.output("cur_rank_unique_ids", 0); + *unique_table_ids_lbn = id_shuffle_op.output("cur_rank_unique_table_ids", 0); + *inverse_indices_lbn = id_shuffle_op.output("cur_rank_inverse_indices", 0); + *num_unique_matrix_lbn = id_shuffle_op.output("num_unique_matrix", 0); + } *prefetch_num_unique_lbn = id_shuffle_op.output("cur_rank_num_unique", 0); *prefetch_unique_ids_lbn = id_shuffle_op.output("cur_rank_unique_ids", 0); *prefetch_unique_table_ids_lbn = id_shuffle_op.output("cur_rank_unique_table_ids", 0); @@ -506,21 +547,71 @@ void ScaleGrad(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder } } -void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, - const ParallelConf& embedding_parallel_conf, - const int64_t embedding_scope_symbol_id, const int64_t embedding_size, - const int64_t line_size, const float l1, const float l2, - const std::string& embedding_name, const OptimizerConf& optimizer_conf, - const user_op::UserOpConfWrapper& embedding_op, - const std::string& num_unique_ids_lbn, const std::string& unique_ids_lbn, - const std::string& unique_values_lbn, - const std::string& embedding_grad_lbn, - const std::string& learning_rate_lbn, std::string* new_embedding_grad_lbn, - std::string* state_initializer, - OperatorConf* embedding_update_new_op_conf) { +bool IsSupportFusedUpdatePut(const bool is_full_cache, const bool enable_auto_mixed_precision, + const bool is_sgd, const std::string& down_scale_by_lbn, + const std::string& skip_if_lbn, const float l1, const float l2, + const float weight_decay) { + if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSE_UPDATE_PUT", true)) { return false; } + if (!is_full_cache) { return false; } + if (!enable_auto_mixed_precision) { return false; } + if (!is_sgd) { return false; } + if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_GRADIENT_SHUFFLE_USE_FP16", true)) { + return false; + } + if (!down_scale_by_lbn.empty()) { return false; } + if (!skip_if_lbn.empty()) { return false; } + if (l1 != 0) { return false; } + if (l2 != 0) { return false; } + if (weight_decay != 0) { return false; } + return true; +} + +void BuildEmbeddingUpdate( + JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder, + const ParallelConf& embedding_parallel_conf, const int64_t embedding_scope_symbol_id, + const bool is_full_cache, const int64_t embedding_size, const int64_t line_size, const float l1, + const float l2, const std::string& embedding_name, const OptimizerConf& optimizer_conf, + const user_op::UserOpConfWrapper& embedding_op, const std::string& num_unique_ids_lbn, + const std::string& unique_ids_lbn, const std::string& unique_values_lbn, + const std::string& embedding_grad_lbn, const std::string& learning_rate_lbn, + std::string* new_embedding_grad_lbn, std::string* state_initializer, + OperatorConf* embedding_update_new_op_conf) { const TrainConf& train_conf = job_builder->job().job_conf().train_conf(); const bool has_clip_grad = optimizer_conf.has_clip_conf(); *new_embedding_grad_lbn = embedding_grad_lbn; + std::string update_skip_if_lbn; + std::string fuse_to_update_down_scale_by_lbn; + double fuse_to_update_scale = 1.0; + ScaleGrad(ctx, op_graph, job_builder, embedding_parallel_conf, embedding_scope_symbol_id, + has_clip_grad, embedding_grad_lbn, new_embedding_grad_lbn, &update_skip_if_lbn, + &fuse_to_update_down_scale_by_lbn, &fuse_to_update_scale); + + if (IsSupportFusedUpdatePut(is_full_cache, ctx->job_desc().enable_auto_mixed_precision(), + optimizer_conf.has_naive_conf(), fuse_to_update_down_scale_by_lbn, + update_skip_if_lbn, l1, l2, + optimizer_conf.weight_decay_conf().weight_decay_rate())) { + user_op::UserOpConfWrapperBuilder fused_embedding_update_put_op_builder( + embedding_op.op_name() + "_fused_embedding_update_put" + NewUniqueId()); + user_op::UserOpConfWrapper fused_embedding_update_put_op = + fused_embedding_update_put_op_builder.OpTypeName("one_embedding_fused_sgd_update_put") + .Input("num_unique_ids", num_unique_ids_lbn) + .Input("unique_ids", unique_ids_lbn) + .Input("unique_embeddings", unique_values_lbn) + .Input("embedding_grad", *new_embedding_grad_lbn) + .Input("learning_rate", learning_rate_lbn) + .Attr("scale", fuse_to_update_scale) + .Attr("embedding_name", embedding_name) + .Attr("embedding_size", embedding_size) + .Attr("line_size", line_size) + .ScopeSymbolId(embedding_scope_symbol_id) + .Build(); + *embedding_update_new_op_conf = fused_embedding_update_put_op.op_conf(); + if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false)) { + embedding_update_new_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); + } + return; + } + auto AddAdamBiasCorrectionFactorOp = [&](float beta_val, const std::string& op_name) -> std::string { user_op::UserOpConfWrapperBuilder op_builder(embedding_op.op_name() + op_name); @@ -534,33 +625,45 @@ void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* job_builder->AddOps(embedding_parallel_conf, {adam_bias_correction_factor_op.op_conf()}); return adam_bias_correction_factor_op.output("out", 0); }; - user_op::UserOpConfWrapperBuilder embedding_update_op_builder(embedding_op.op_name() - + "_embedding_update"); + user_op::UserOpConfWrapperBuilder embedding_update_op_builder( + embedding_op.op_name() + "_embedding_update" + NewUniqueId()); std::vector state_constant_init_values; if (optimizer_conf.has_naive_conf()) { - embedding_update_op_builder.OpTypeName("sgd_embedding_update"); + embedding_update_op_builder.OpTypeName("one_embedding_sgd_update"); } else if (optimizer_conf.has_momentum_conf()) { - embedding_update_op_builder.OpTypeName("momentum_embedding_update") + embedding_update_op_builder.OpTypeName("one_embedding_momentum_update") .Attr("beta", optimizer_conf.momentum_conf().beta()); } else if (optimizer_conf.has_adam_conf()) { const AdamModelUpdateConf& adam_conf = optimizer_conf.adam_conf(); - embedding_update_op_builder.OpTypeName("adam_embedding_update") - .Attr("beta1", adam_conf.beta1()) - .Attr("beta2", adam_conf.beta2()) - .Attr("epsilon", adam_conf.epsilon()) - .Attr("do_bias_correction", adam_conf.do_bias_correction()); - if (adam_conf.do_bias_correction()) { - const std::string bias_correction1_lbn = - AddAdamBiasCorrectionFactorOp(adam_conf.beta1(), "adam_bias_correction_factor1"); - const std::string bias_correction2_lbn = - AddAdamBiasCorrectionFactorOp(adam_conf.beta2(), "adam_bias_correction_factor2"); - embedding_update_op_builder.Input("bias_correction1", bias_correction1_lbn) - .Input("bias_correction2", bias_correction2_lbn); + if (adam_conf.smart_decay()) { + CHECK(adam_conf.do_bias_correction()) + << "when use smart decay adam, do_bias_correction should be true. but got " + << adam_conf.do_bias_correction(); + embedding_update_op_builder.OpTypeName("one_embedding_smart_decay_sparse_adam_update") + .Input("train_step", train_conf.train_step_lbn()) + .Attr("beta1", adam_conf.beta1()) + .Attr("beta2", adam_conf.beta2()) + .Attr("epsilon", adam_conf.epsilon()) + .Attr("do_bias_correction", adam_conf.do_bias_correction()); + } else { + embedding_update_op_builder.OpTypeName("one_embedding_adam_update") + .Attr("beta1", adam_conf.beta1()) + .Attr("beta2", adam_conf.beta2()) + .Attr("epsilon", adam_conf.epsilon()) + .Attr("do_bias_correction", adam_conf.do_bias_correction()); + if (adam_conf.do_bias_correction()) { + const std::string bias_correction1_lbn = + AddAdamBiasCorrectionFactorOp(adam_conf.beta1(), "adam_bias_correction_factor1"); + const std::string bias_correction2_lbn = + AddAdamBiasCorrectionFactorOp(adam_conf.beta2(), "adam_bias_correction_factor2"); + embedding_update_op_builder.Input("bias_correction1", bias_correction1_lbn) + .Input("bias_correction2", bias_correction2_lbn); + } } } else if (optimizer_conf.has_adagrad_conf()) { const AdagradModelUpdateConf& adagrad_conf = optimizer_conf.adagrad_conf(); state_constant_init_values.push_back(adagrad_conf.initial_accumulator_value()); - embedding_update_op_builder.OpTypeName("adagrad_embedding_update") + embedding_update_op_builder.OpTypeName("one_embedding_adagrad_update") .Input("train_step", train_conf.train_step_lbn()) .Attr("lr_decay", adagrad_conf.lr_decay()) .Attr("epsilon", adagrad_conf.epsilon()); @@ -569,7 +672,7 @@ void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* state_constant_init_values.push_back(ftrl_conf.initial_accumulator_value()); // For `z`, its init value is 0.0. state_constant_init_values.push_back(0.0); - embedding_update_op_builder.OpTypeName("ftrl_embedding_update") + embedding_update_op_builder.OpTypeName("one_embedding_ftrl_update") .Attr("lr_power", ftrl_conf.lr_power()) .Attr("lambda1", ftrl_conf.lambda1()) .Attr("lambda2", ftrl_conf.lambda2()) @@ -580,12 +683,6 @@ void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* MakeConstantInitializerAttr(embedding_size, line_size, state_constant_init_values, state_initializer); - std::string update_skip_if_lbn; - std::string fuse_to_update_down_scale_by_lbn; - double fuse_to_update_scale = 1.0; - ScaleGrad(ctx, op_graph, job_builder, embedding_parallel_conf, embedding_scope_symbol_id, - has_clip_grad, embedding_grad_lbn, new_embedding_grad_lbn, &update_skip_if_lbn, - &fuse_to_update_down_scale_by_lbn, &fuse_to_update_scale); embedding_update_op_builder.Input("num_unique_ids", num_unique_ids_lbn) .Input("unique_embeddings", unique_values_lbn) .Input("learning_rate", learning_rate_lbn) @@ -603,16 +700,18 @@ void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* user_op::UserOpConfWrapper embedding_update_op = embedding_update_op_builder.Input("embedding_grad", *new_embedding_grad_lbn) .Attr("scale", fuse_to_update_scale) + .Attr("embedding_name", embedding_name) + .Attr("embedding_size", embedding_size) + .Attr("line_size", line_size) .ScopeSymbolId(embedding_scope_symbol_id) .Build(); *embedding_update_new_op_conf = embedding_update_op.op_conf(); - // embedding_update_new_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false)) { - embedding_update_new_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); + embedding_update_new_op_conf->set_stream_name_hint(embedding_name + "_EMBEDDING"); } user_op::UserOpConfWrapperBuilder embedding_put_op_builder(embedding_op.op_name() - + "_embedding_put"); + + "_embedding_put" + NewUniqueId()); user_op::UserOpConfWrapper embedding_put_op = embedding_put_op_builder.OpTypeName("embedding_put") .Input("num_unique_ids", num_unique_ids_lbn) @@ -622,7 +721,6 @@ void BuildEmbeddingUpdate(JobPassCtx* ctx, const OpGraph& op_graph, JobBuilder* .ScopeSymbolId(embedding_scope_symbol_id) .Build(); OperatorConf embedding_put_new_op_conf = embedding_put_op.op_conf(); - // embedding_put_new_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING"); if (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false)) { embedding_put_new_op_conf.set_stream_name_hint(embedding_name + "_EMBEDDING"); } @@ -932,6 +1030,14 @@ void FilterEmbeddingGradients(JobPassCtx* ctx, const OpGraph& op_graph, JobBuild } } +bool IsRelatedOp(const OperatorConf& op) { + return op.has_user_conf() && (op.user_conf().op_type_name() == "one_embedding_fused_lookup"); +} + +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), IsRelatedOp); +} + } // namespace class ReplaceEmbeddingOps final : public JobPass { @@ -939,11 +1045,12 @@ class ReplaceEmbeddingOps final : public JobPass { ReplaceEmbeddingOps() = default; ~ReplaceEmbeddingOps() override = default; - bool IsEnabled(const JobPassCtx& ctx) const { return true; } + bool IsEnabled(const JobPassCtx& ctx) const { return ctx.job_desc().IsTrain(); } Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder, JobPassCtx* ctx) const; Maybe Apply(Job* job, JobPassCtx* ctx) const override { if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + if (!NeedDoPass(*job)) { return Maybe::Ok(); } const OpGraph op_graph(*job); JobBuilder job_builder(job); return Apply(op_graph, &job_builder, ctx); @@ -960,7 +1067,9 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ op_graph.ForEachNode([&](const OpNode* op_node) { const OperatorConf& op_conf = op_node->op().op_conf(); if (!op_conf.has_user_conf()) { return; } - if (!(op_conf.user_conf().op_type_name() == "embedding_lookup_placeholder")) { return; } + if (!(op_conf.user_conf().op_type_name() == "one_embedding_fused_lookup")) { return; } + std::vector add_ops; + std::vector delete_op_names; const user_op::UserOpConfWrapper embedding_op(op_node->op().op_conf()); const OpNode* shadow_producer = op_graph.OpNode4OpName(GenLogicalBlobId(embedding_op.input("shadow", 0)).op_name()); @@ -974,21 +1083,21 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ op_graph.OpNode4OpName(GenLogicalBlobId(shadow_cast_op.input("in", 0)).op_name()); CHECK(cast_producer->op().op_conf().has_variable_conf()) << cast_producer->op().op_name(); shadow_op_name = cast_producer->op().op_name(); + delete_op_names.push_back(shadow_cast_op.op_name()); } else { UNIMPLEMENTED() << "shadow must be variable or variable and cast"; } // assume all embeddings have same placement embedding_scope_symbol_id = embedding_op.op_conf().scope_symbol_id(); embedding_parallel_conf = op_node->parallel_desc().parallel_conf(); - - embedding::KeyValueStoreOptions options( - embedding_op.attr("key_value_store_options")); + const std::string& embedding_name = embedding_op.attr("embedding_name"); + const int64_t line_size = embedding_op.attr("line_size"); const int64_t embedding_size = embedding_op.attr("embedding_size"); + const bool is_full_cache = embedding_op.attr("is_full_cache"); + const int64_t seed = embedding_op.attr("seed"); const int64_t parallel_num = op_node->parallel_desc().parallel_num(); const bool use_system_gather = (parallel_num == 1 && ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_USE_SYSTEM_GATHER", true)); - std::vector add_ops; - std::vector delete_op_names; std::string new_embeddings_lbn; // prefetch can not exec in advance when it consume id_shuffle_copy_out, because @@ -997,7 +1106,6 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ std::string prefetch_num_unique_ids_lbn; std::string prefetch_unique_ids_lbn; std::string prefetch_unique_table_ids_lbn; - std::string inner_inverse_unique_partition_indices_lbn; std::string num_unique_ids_lbn; std::string unique_ids_lbn; @@ -1005,49 +1113,45 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ std::string inverse_indices_lbn; std::string num_unique_matrix_lbn; - BuildIdShuffle(use_system_gather, options.Name(), embedding_op, &add_ops, - // &inner_inverse_unique_partition_indices_lbn, &num_unique_ids_lbn, - // &unique_ids_lbn, &unique_table_ids_lbn, &inverse_indices_lbn, - // &num_unique_matrix_lbn); - &prefetch_num_unique_ids_lbn, &prefetch_unique_ids_lbn, + BuildIdShuffle(use_system_gather, embedding_name, embedding_op, &add_ops, + &prefetch_num_unique_ids_lbn, &prefetch_unique_ids_lbn, &prefetch_unique_table_ids_lbn, &inner_inverse_unique_partition_indices_lbn, &num_unique_ids_lbn, &unique_ids_lbn, &unique_table_ids_lbn, &inverse_indices_lbn, &num_unique_matrix_lbn); const bool is_train_job = job_builder->job().job_conf().has_train_conf(); - const bool no_optimizer_states = (embedding_size == options.LineSize()); - const bool has_embedding_prefetch = - (!options.IsFullCache()) && (is_train_job || no_optimizer_states); + const bool no_optimizer_states = (embedding_size == line_size); + const bool has_embedding_prefetch = (!is_full_cache) && (is_train_job || no_optimizer_states); OperatorConf embedding_prefetch_op_conf; OperatorConf embedding_lookup_op_conf; // embedding lookup op std::string embedding_lbn, unique_values_lbn; - // BuildEmbeddingLookup(ctx, job_builder, embedding_size, options.LineSize(), options.Name(), - // has_embedding_prefetch, embedding_parallel_conf, embedding_op, - // num_unique_ids_lbn, unique_ids_lbn, unique_table_ids_lbn, &embedding_lbn, - // &unique_values_lbn, &embedding_prefetch_op_conf, - // &embedding_lookup_op_conf); BuildEmbeddingLookup( - ctx, job_builder, embedding_size, options.LineSize(), options.Name(), has_embedding_prefetch, - embedding_parallel_conf, embedding_op, prefetch_num_unique_ids_lbn, prefetch_unique_ids_lbn, - prefetch_unique_table_ids_lbn, num_unique_ids_lbn, unique_ids_lbn, unique_table_ids_lbn, - &embedding_lbn, &unique_values_lbn, &embedding_prefetch_op_conf, &embedding_lookup_op_conf); + ctx, job_builder, embedding_size, line_size, embedding_name, seed, has_embedding_prefetch, + embedding_parallel_conf, embedding_op, prefetch_num_unique_ids_lbn, prefetch_unique_ids_lbn, + prefetch_unique_table_ids_lbn, num_unique_ids_lbn, unique_ids_lbn, unique_table_ids_lbn, + &embedding_lbn, &unique_values_lbn, &embedding_prefetch_op_conf, &embedding_lookup_op_conf); if (use_system_gather) { - user_op::UserOpConfWrapperBuilder gather_op_builder(embedding_op.op_name() + "_gather"); - user_op::UserOpConfWrapper gather_op = gather_op_builder.OpTypeName("gather") - .Input("in", embedding_lbn) - .Input("indices", inverse_indices_lbn) - .Output("out") - .ScopeSymbolId(embedding_scope_symbol_id) - .Build(); + user_op::UserOpConfWrapperBuilder gather_op_builder(embedding_op.op_name() + + "_one_embedding_gather"); + user_op::UserOpConfWrapper gather_op = + gather_op_builder.OpTypeName("one_embedding_gather") + .Input("in", embedding_lbn) + .Input("indices", inverse_indices_lbn) + .Output("out") + .Attr("embedding_size", embedding_size) + .Attr("embedding_name", embedding_name) + .ScopeSymbolId(embedding_scope_symbol_id) + .Build(); add_ops.push_back(gather_op.op_conf()); new_embeddings_lbn = gather_op.output("out", 0); } else { // embedding shuffle op - BuildEmbeddingShuffle(job_builder, options.Name(), embedding_parallel_conf, embedding_op, - inverse_indices_lbn, inner_inverse_unique_partition_indices_lbn, - num_unique_matrix_lbn, embedding_lbn, &add_ops, &new_embeddings_lbn); + BuildEmbeddingShuffle(job_builder, embedding_name, embedding_size, embedding_parallel_conf, + embedding_op, inverse_indices_lbn, + inner_inverse_unique_partition_indices_lbn, num_unique_matrix_lbn, + embedding_lbn, &add_ops, &new_embeddings_lbn); } delete_op_names.push_back(embedding_op.op_name()); @@ -1064,9 +1168,9 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ const OpNode* consumer = edge->dst_node(); if (consumer->op().op_conf().has_user_conf()) { const user_op::UserOpConfWrapper update_op_conf(consumer->op().op_conf()); - if (update_op_conf.op_type_name() != "embedding_update_placeholder") { continue; } - if (update_op_conf.attr("key_value_store_options") - != embedding_op.attr("key_value_store_options")) { + if (update_op_conf.op_type_name() != "one_embedding_fused_lookup_grad") { continue; } + if (update_op_conf.attr("embedding_name") + != embedding_op.attr("embedding_name")) { continue; } delete_op_names.push_back(update_op_conf.op_name()); @@ -1088,7 +1192,7 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ std::string embedding_grad_lbn; BuildEmbeddingGradientShuffle( - ctx, op_graph, job_builder, op_node, options.Name(), use_system_gather, + ctx, op_graph, job_builder, op_node, embedding_name, embedding_size, use_system_gather, embedding_parallel_conf, embedding_scope_symbol_id, embedding_op, inverse_indices_lbn, inner_inverse_unique_partition_indices_lbn, num_unique_matrix_lbn, update_op_conf.input("embedding_grad", 0), embedding_optimizer_conf.has_clip_conf(), @@ -1110,8 +1214,8 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ std::string new_embedding_grad_lbn; OperatorConf embedding_update_op_conf; BuildEmbeddingUpdate(ctx, op_graph, job_builder, embedding_parallel_conf, - embedding_scope_symbol_id, embedding_size, options.LineSize(), l1, l2, - options.Name(), embedding_optimizer_conf, embedding_op, + embedding_scope_symbol_id, is_full_cache, embedding_size, line_size, + l1, l2, embedding_name, embedding_optimizer_conf, embedding_op, num_unique_ids_lbn, unique_ids_lbn, unique_values_lbn, embedding_grad_lbn, learning_rate_lbn, &new_embedding_grad_lbn, &state_initializer, &embedding_update_op_conf); @@ -1121,7 +1225,7 @@ Maybe ReplaceEmbeddingOps::Apply(const OpGraph& op_graph, JobBuilder* job_ } if ((state_initializer.empty()) && !no_optimizer_states) { CHECK(!is_train_job) << "train job must have set state initializer"; - MakeConstantInitializerAttr(embedding_size, options.LineSize(), {}, &state_initializer); + MakeConstantInitializerAttr(embedding_size, line_size, {}, &state_initializer); } auto state_initializer_attr = ::oneflow::AttrValue(); state_initializer_attr.set_at_string(state_initializer); diff --git a/oneflow/core/job_rewriter/rmsprop_optm.cpp b/oneflow/core/job_rewriter/rmsprop_optm.cpp index dfb79fde8e903172af648253e92bd200d5e633fc..37e359ccf322ada26e5333d74a3a324a6b6a623d 100644 --- a/oneflow/core/job_rewriter/rmsprop_optm.cpp +++ b/oneflow/core/job_rewriter/rmsprop_optm.cpp @@ -38,7 +38,7 @@ OperatorConf GenerateRmspropHelperVariableOpConf(const VariableOp& op, const std } void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, - const std::string& model_diff_lbn, const OptimizerConf optimizer_conf, + const std::string& model_diff_lbn, const OptimizerConf& optimizer_conf, JobBuilder* job_builder) { const VariableOp* var_op = dynamic_cast(&var_op_node.op()); CHECK_NOTNULL(var_op); @@ -58,6 +58,11 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Attr("decay_rate", rmsprop_conf.decay_rate()) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + + if (optimizer_conf.has_lr_scale()) { + rmsprop_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } + SetDynamicLossScaleSkipIf(ctx, &rmsprop_update_op_builder); if (centered) { diff --git a/oneflow/core/job_rewriter/sequential_one_embedding_shuffle_ops_pass.cpp b/oneflow/core/job_rewriter/sequential_one_embedding_shuffle_ops_pass.cpp index 961e203c73cea96605d413c0dfcbc16ccd7c3bb7..292670fa1c63c59ac0fd8ebd6f48ea1ae1b26538 100644 --- a/oneflow/core/job_rewriter/sequential_one_embedding_shuffle_ops_pass.cpp +++ b/oneflow/core/job_rewriter/sequential_one_embedding_shuffle_ops_pass.cpp @@ -1,80 +1,79 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/job_rewriter/job_pass.h" -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -namespace { - -class SequentialOneEmbeddingOpsPass final : public JobPass { - public: - SequentialOneEmbeddingOpsPass() = default; - ~SequentialOneEmbeddingOpsPass() override = default; - - bool IsEnabled(const JobPassCtx& ctx) const { - return ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false); - } - Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; - - Maybe Apply(Job* job, JobPassCtx* ctx) const override { - if (!IsEnabled(*ctx)) { return Maybe::Ok(); } - const OpGraph op_graph(*job); - JobBuilder job_builder(job); - return Apply(op_graph, &job_builder); - } -}; - -bool IsUserOpWithTypeName(const OperatorConf& op_conf, const std::string& op_type_name) { - return op_conf.has_user_conf() && op_conf.user_conf().op_type_name() == op_type_name; -}; - -Maybe SequentialOneEmbeddingOpsPass::Apply(const OpGraph& op_graph, - JobBuilder* job_builder) const { - HashMap> stream_name_hint2shuffle_op_names; - op_graph.TopoForEachNode([&](const OpNode* op_node) { - if (!(IsUserOpWithTypeName(op_node->op().op_conf(), "id_shuffle") - || IsUserOpWithTypeName(op_node->op().op_conf(), "embedding_shuffle") - || IsUserOpWithTypeName(op_node->op().op_conf(), "embedding_gradient_shuffle"))) { - return; - } - OperatorConf op_conf = op_node->op().op_conf(); - std::string stream_name; - if (op_conf.has_stream_name_hint()) { - stream_name = op_conf.stream_name_hint(); - } else { - stream_name = "DEFAULT"; - } - const auto& it = stream_name_hint2shuffle_op_names.find(stream_name); - if (it != stream_name_hint2shuffle_op_names.end()) { - if (it->second.size() > 0) { - std::string pre_shuffle_op_name = it->second.back(); - op_conf.add_ctrl_in_op_name(pre_shuffle_op_name); - job_builder->MutOpsOnlyOnce({op_conf}); - } - it->second.push_back(op_conf.name()); - } else { - std::vector shuffle_ops{op_conf.name()}; - CHECK(stream_name_hint2shuffle_op_names.emplace(stream_name, shuffle_ops).second); - } - }); - - return Maybe::Ok(); -} - -} // namespace - -REGISTER_JOB_PASS("SequentialOneEmbeddingOpsPass", SequentialOneEmbeddingOpsPass); - -} // namespace oneflow \ No newline at end of file +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/job_rewriter/job_pass.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +class SequentialOneEmbeddingOpsPass final : public JobPass { + public: + SequentialOneEmbeddingOpsPass() = default; + ~SequentialOneEmbeddingOpsPass() override = default; + + bool IsEnabled(const JobPassCtx& ctx) const { + return ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_DISABLE_PIPELINED_EXECUTION", false); + } + Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; + + Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!IsEnabled(*ctx)) { return Maybe::Ok(); } + const OpGraph op_graph(*job); + JobBuilder job_builder(job); + return Apply(op_graph, &job_builder); + } +}; + +Maybe SequentialOneEmbeddingOpsPass::Apply(const OpGraph& op_graph, + JobBuilder* job_builder) const { + HashMap> stream_name_hint2shuffle_op_names; + op_graph.TopoForEachNode([&](const OpNode* op_node) { + if (!(IsUserOpWithTypeName(op_node->op().op_conf(), "id_shuffle") + || IsUserOpWithTypeName(op_node->op().op_conf(), "embedding_shuffle") + || IsUserOpWithTypeName(op_node->op().op_conf(), "embedding_gradient_shuffle"))) { + return; + } + OperatorConf op_conf = op_node->op().op_conf(); + std::string stream_name; + if (op_conf.has_stream_name_hint()) { + stream_name = op_conf.stream_name_hint(); + } else { + stream_name = "DEFAULT"; + } + const auto& it = stream_name_hint2shuffle_op_names.find(stream_name); + if (it != stream_name_hint2shuffle_op_names.end()) { + if (it->second.size() > 0) { + std::string pre_shuffle_op_name = it->second.back(); + op_conf.add_ctrl_in_op_name(pre_shuffle_op_name); + job_builder->MutOpsOnlyOnce({op_conf}); + } + it->second.push_back(op_conf.name()); + } else { + std::vector shuffle_ops{op_conf.name()}; + CHECK(stream_name_hint2shuffle_op_names.emplace(stream_name, shuffle_ops).second); + } + }); + + return Maybe::Ok(); +} + +} // namespace + +REGISTER_JOB_PASS("SequentialOneEmbeddingOpsPass", SequentialOneEmbeddingOpsPass); + +} // namespace oneflow diff --git a/oneflow/core/job_rewriter/sgd_optm.cpp b/oneflow/core/job_rewriter/sgd_optm.cpp index c0a19fb64c95b36ba37fb16faa7f183aa90db578..0c9037fa77f5679b1c59701340e4889d5eb6fe05 100644 --- a/oneflow/core/job_rewriter/sgd_optm.cpp +++ b/oneflow/core/job_rewriter/sgd_optm.cpp @@ -32,6 +32,9 @@ void GenerateOptimizerOpConf(JobPassCtx* ctx, const OpNode& var_op_node, .Input("learning_rate", optimizer_conf.learning_rate_lbn()) .Attr("weight_decay", GetOptimizerWeightDecayRate(optimizer_conf, *var_op)) .ScopeSymbolId(var_op->op_conf().scope_symbol_id()); + if (optimizer_conf.has_lr_scale()) { + sgd_update_op_builder.Attr("learning_rate_scale", optimizer_conf.lr_scale()); + } SetDynamicLossScaleSkipIf(ctx, &sgd_update_op_builder); user_op::UserOpConfWrapper sgd_update_op = sgd_update_op_builder.Build(); job_builder->AddOps(var_op_node.parallel_desc().parallel_conf(), {sgd_update_op.op_conf()}); diff --git a/oneflow/core/job_rewriter/split_sparse_softmax_cross_entropy_op_pass.cpp b/oneflow/core/job_rewriter/split_sparse_softmax_cross_entropy_op_pass.cpp index e9a0211ea623ad0244939d39854cdfdb8950397d..482bd8e9d92624f070366d54c4579545dc73a184 100644 --- a/oneflow/core/job_rewriter/split_sparse_softmax_cross_entropy_op_pass.cpp +++ b/oneflow/core/job_rewriter/split_sparse_softmax_cross_entropy_op_pass.cpp @@ -20,6 +20,12 @@ namespace oneflow { namespace { +bool NeedDoPass(const Job& job) { + return std::any_of(job.net().op().cbegin(), job.net().op().cend(), [&](const OperatorConf& op) { + return op.has_user_conf() && op.user_conf().op_type_name() == "sparse_softmax_cross_entropy_ms"; + }); +} + void UpdateProbConsumerOpConf(const std::string& new_prob_lbn, const OpNode* op_node, JobBuilder* job_builder) { for (const OpEdge* edge : op_node->out_edges()) { @@ -42,6 +48,7 @@ class SplitSparseSoftmaxCrossEntropyOpPass final : public JobPass { Maybe Apply(const OpGraph& op_graph, JobBuilder* job_builder) const; Maybe Apply(Job* job, JobPassCtx* ctx) const override { + if (!NeedDoPass(*job)) { return Maybe::Ok(); } const OpGraph op_graph(*job); JobBuilder job_builder(job); return Apply(op_graph, &job_builder); diff --git a/oneflow/core/job_rewriter/user_grad.cpp b/oneflow/core/job_rewriter/user_grad.cpp deleted file mode 100644 index 5b7ee236522adc655213512ff27239fe8ebe5d25..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/user_grad.cpp +++ /dev/null @@ -1,65 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/job_rewriter/autograd.h" -#include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/framework/user_op_conf.h" - -namespace oneflow { - -namespace { - -Maybe GenerateBackwardOpConf( - const Operator& fw_op, std::vector* bw_op_confs, - const std::function& DiffLbi4BnInOp, - const std::function& LogicalBlobDesc4BnInOp) { - CHECK_OR_RETURN(fw_op.op_conf().has_user_conf()); - const UserOpConf& user_conf = fw_op.op_conf().user_conf(); - const user_op::OpGradRegistryResult* val = - user_op::UserOpRegistryMgr::Get().GetOpGradRegistryResult(user_conf.op_type_name()); - CHECK_NOTNULL_OR_RETURN(val) << Error::GradientFunctionNotFoundError() - << " op cannot find backward op in autograd, forward op: " - << PbMessage2TxtString(fw_op.op_conf()); - - user_op::UserOpWrapper fw_user_op(fw_op.op_conf(), LogicalBlobDesc4BnInOp, DiffLbi4BnInOp); - if (nullptr != val->bw_gen_fn) { - // new refined interface - user_op::BackwardOpConfContext ctx(fw_user_op, bw_op_confs); - JUST(val->bw_gen_fn(&ctx)); - } else if (nullptr != val->gen_bw_fn) { - // old interface, will be removed when all backward gradient configs are using new interface - auto AddOp = [&](const user_op::UserOpConfWrapper& wrapper) { - bw_op_confs->emplace_back(wrapper.op_conf()); - }; - JUST(val->gen_bw_fn(fw_user_op, AddOp)); - } - - for (const std::string& ibn : fw_op.input_bns()) { - LogicalBlobId* lbi = DiffLbi4BnInOp(ibn); - if (lbi != nullptr) { - CHECK_OR_RETURN(lbi->has_op_name() && lbi->has_blob_name()) - << " user_op: " << fw_op.op_name() << " op_type_name: " << user_conf.op_type_name() - << " 's input blob " << ibn << " has not generate input diff blob !"; - } - } - return Maybe::Ok(); -} - -} // namespace - -REGISTER_OP_GRAD(OperatorConf::kUserConf, &GenerateBackwardOpConf); - -} // namespace oneflow diff --git a/oneflow/core/job_rewriter/variable_grad.cpp b/oneflow/core/job_rewriter/variable_grad.cpp deleted file mode 100644 index 112aacc2140d786c61be7414aadbb72077c89060..0000000000000000000000000000000000000000 --- a/oneflow/core/job_rewriter/variable_grad.cpp +++ /dev/null @@ -1,32 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/job_rewriter/autograd.h" - -namespace oneflow { - -namespace { - -void GenerateBackwardOpConf( - const Operator& op, std::vector* op_confs, - const std::function& DiffLbi4BnInOp) { - // do nothing -} - -} // namespace - -REGISTER_OP_GRAD(OperatorConf::kVariableConf, &GenerateBackwardOpConf); - -} // namespace oneflow diff --git a/oneflow/core/kernel/broadcast_to_compatible_with_kernel.cpp b/oneflow/core/kernel/broadcast_to_compatible_with_kernel.cpp index b1d3e84508ca58a17e72f921ebcd47b0979332b9..e0db3f8e7c76bbdbd805578355b0eccdcaaa5590 100644 --- a/oneflow/core/kernel/broadcast_to_compatible_with_kernel.cpp +++ b/oneflow/core/kernel/broadcast_to_compatible_with_kernel.cpp @@ -56,10 +56,7 @@ void BroadcastToCompatibleWithKernel::ForwardDataContent(KernelC OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTTER_BROADCAST_TO_COMPATIBLE_WITH_KERNEL, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -#if defined(WITH_CUDA) -REGISTTER_BROADCAST_TO_COMPATIBLE_WITH_KERNEL(DeviceType::kCUDA, (float16, DataType::kFloat16)) -#endif -#if defined(WITH_ROCM) +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTTER_BROADCAST_TO_COMPATIBLE_WITH_KERNEL(DeviceType::kCUDA, (float16, DataType::kFloat16)) #endif diff --git a/oneflow/core/kernel/callback_notify_kernel.cpp b/oneflow/core/kernel/callback_notify_kernel.cpp index 0245b6772c55b7dfe55124fa3ba77fa33ccd3794..107255ef52c771656e3b54ae97ec9c89f74bce79 100644 --- a/oneflow/core/kernel/callback_notify_kernel.cpp +++ b/oneflow/core/kernel/callback_notify_kernel.cpp @@ -39,10 +39,10 @@ void CallbackNotifyKernel::ForwardDataContent(KernelContext* ctx) const { std::string buffer_name; CHECK(this->op_conf().callback_notify_conf().has_job_name()); buffer_name = GetCallbackNotifierBufferName(this->op_conf().callback_notify_conf().job_name()); - std::shared_ptr foreign_job_instance; - BufferStatus buffer_status = buffer_mgr->Get(buffer_name)->TryReceive(&foreign_job_instance); + std::shared_ptr job_instance; + BufferStatus buffer_status = buffer_mgr->Get(buffer_name)->TryReceive(&job_instance); CHECK_NE(buffer_status, kBufferStatusEmpty); - if (buffer_status == kBufferStatusSuccess) { foreign_job_instance->Finish(); } + if (buffer_status == kBufferStatusSuccess) { job_instance->Finish(); } } ADD_CPU_DEFAULT_KERNEL_CREATOR(OperatorConf::kCallbackNotifyConf, CallbackNotifyKernel, diff --git a/oneflow/core/kernel/collective_boxing_unpack_kernel.cpp b/oneflow/core/kernel/collective_boxing_unpack_kernel.cpp index 2b81fc17868a9e7e917e5ba51585ba531f0d9abb..c9962a7e1ae0c54c3f0e39b3b698466356970cf6 100644 --- a/oneflow/core/kernel/collective_boxing_unpack_kernel.cpp +++ b/oneflow/core/kernel/collective_boxing_unpack_kernel.cpp @@ -36,6 +36,8 @@ void CollectiveBoxingUnpackKernel::ForwardDataContent(KernelContext* ctx) const const CollectiveBoxingUnpackOpConf& unpack_conf = this->op_conf().collective_boxing_unpack_conf(); const int64_t num_ranks = unpack_conf.num_ranks(); const Shape logical_shape(unpack_conf.logical_shape()); + // skip 0size tensor boxing + if (logical_shape.elem_cnt() == 0) { return; } const bool need_transpose = !((unpack_conf.src_sbp_parallel().has_split_parallel() && unpack_conf.src_sbp_parallel().split_parallel().axis() == 0) || unpack_conf.src_sbp_parallel().has_broadcast_parallel() diff --git a/oneflow/core/kernel/copy_hd_kernel.cpp b/oneflow/core/kernel/copy_hd_kernel.cpp deleted file mode 100644 index 69db03c23b8d2ba9488bf0ee04b37484ad8a3ea5..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/copy_hd_kernel.cpp +++ /dev/null @@ -1,65 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/ep/include/primitive/memcpy.h" - -namespace oneflow { - -class CopyHdKernel final : public Kernel { - public: - OF_DISALLOW_COPY_AND_MOVE(CopyHdKernel); - CopyHdKernel() = default; - ~CopyHdKernel() = default; - - private: - void VirtualKernelInit(KernelContext* ctx) override; - void ForwardDataContent(KernelContext* ctx) const override; - void ForwardHeader(KernelContext* ctx) const override; - - std::unique_ptr primitive_; -}; - -void CopyHdKernel::VirtualKernelInit(KernelContext* ctx) { - CHECK(this->op_conf().has_copy_hd_conf()); - const CopyHdOpConf& copy_hd_conf = this->op_conf().copy_hd_conf(); - ep::primitive::MemcpyKind kind{}; - if (copy_hd_conf.type() == CopyHdOpConf::H2D) { - kind = ep::primitive::MemcpyKind::kHtoD; - } else if (copy_hd_conf.type() == CopyHdOpConf::D2H) { - kind = ep::primitive::MemcpyKind::kDtoH; - } else { - UNIMPLEMENTED(); - } - primitive_ = - ep::primitive::NewPrimitive(ctx->stream()->device_type(), kind); - CHECK(primitive_); -} - -void CopyHdKernel::ForwardDataContent(KernelContext* ctx) const { - const Blob* in_blob = ctx->BnInOp2Blob(op_attribute().input_bns(0)); - Blob* out_blob = ctx->BnInOp2Blob(op_attribute().output_bns(0)); - const size_t body_byte_size = in_blob->ByteSizeOfBlobBody(); - CHECK_EQ(out_blob->ByteSizeOfBlobBody(), body_byte_size); - primitive_->Launch(ctx->stream(), out_blob->mut_dptr(), in_blob->dptr(), body_byte_size); -} - -void CopyHdKernel::ForwardHeader(KernelContext* ctx) const { - ctx->BnInOp2Blob("out")->CopyHeaderFrom(ctx->BnInOp2Blob("in")); -} - -REGISTER_KERNEL(OperatorConf::kCopyHdConf, CopyHdKernel); - -} // namespace oneflow diff --git a/oneflow/core/kernel/critical_section_callback_tick_kernel.cpp b/oneflow/core/kernel/critical_section_callback_tick_kernel.cpp index 775a9c53814caeb67b0d082f8cb46c181be5426f..9c11fde87abc08c78ef5009ebbbcbc1a400c35fb 100644 --- a/oneflow/core/kernel/critical_section_callback_tick_kernel.cpp +++ b/oneflow/core/kernel/critical_section_callback_tick_kernel.cpp @@ -36,11 +36,10 @@ void CriticalSectionCallbackTickKernel::ForwardDataContent(KernelContext* ctx) c auto* buffer_mgr = Singleton>>::Get(); CHECK(op_conf().has_critical_section_callback_tick_conf()); const std::string& buffer_name = op_conf().critical_section_callback_tick_conf().buffer_name(); - std::shared_ptr foreign_critical_section_instance; - BufferStatus buffer_status = - buffer_mgr->Get(buffer_name)->TryReceive(&foreign_critical_section_instance); + std::shared_ptr critical_section_instance; + BufferStatus buffer_status = buffer_mgr->Get(buffer_name)->TryReceive(&critical_section_instance); CHECK_EQ(buffer_status, kBufferStatusSuccess); - foreign_critical_section_instance->Finish(); + critical_section_instance->Finish(); } REGISTER_KERNEL(OperatorConf::kCriticalSectionCallbackTickConf, CriticalSectionCallbackTickKernel); diff --git a/oneflow/core/kernel/critical_section_wait_tick_kernel.cpp b/oneflow/core/kernel/critical_section_wait_tick_kernel.cpp index 5bebe4bc20222c2265f2bbd778f01fa85382f7b3..f6a9995673554e138a78d672aaf9e75cb9f8572e 100644 --- a/oneflow/core/kernel/critical_section_wait_tick_kernel.cpp +++ b/oneflow/core/kernel/critical_section_wait_tick_kernel.cpp @@ -36,9 +36,8 @@ void CriticalSectionWaitTickKernel::ForwardDataContent(KernelContext* ctx) const auto* buffer_mgr = Singleton>>::Get(); CHECK(this->op_conf().has_critical_section_wait_tick_conf()); const std::string& buffer_name = this->op_conf().critical_section_wait_tick_conf().buffer_name(); - std::shared_ptr foreign_critical_section_instance; - BufferStatus buffer_status = - buffer_mgr->Get(buffer_name)->Pull(&foreign_critical_section_instance); + std::shared_ptr critical_section_instance; + BufferStatus buffer_status = buffer_mgr->Get(buffer_name)->Pull(&critical_section_instance); CHECK_EQ(buffer_status, kBufferStatusSuccess); } diff --git a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.cu b/oneflow/core/kernel/cuda_check_numerics_kernel_observer.cu index 1d7a032e46c2b82747fc2c9c84dd77c9e23aaa6a..71180ef5d0ef5ac6651cbe8c0e28e8d36e657904 100644 --- a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.cu +++ b/oneflow/core/kernel/cuda_check_numerics_kernel_observer.cu @@ -30,8 +30,10 @@ template<> __device__ bool IsNotFinite(half x) { #if __CUDA_ARCH__ >= 530 return (__hisinf(x) || __hisnan(x)); +#elif defined(__HIP_DEVICE_COMPILE__) + return (__hisinf(x) || __hisnan(x)); #else - __trap(); + TRAP(); return true; #endif } @@ -50,14 +52,14 @@ __global__ void HasNotFiniteGpuKernel(const int64_t n, const T* x, volatile bool template bool HasNotFinite(ep::Stream* stream, const int64_t elem_cnt, const T* data_ptr, bool* has_not_finite_host, bool* has_not_finite_device) { - OF_CUDA_CHECK(cudaMemsetAsync(has_not_finite_device, 0, sizeof(bool), + OF_CUDA_CHECK(GPU(MemsetAsync)(has_not_finite_device, 0, sizeof(bool), stream->As()->cuda_stream())); HasNotFiniteGpuKernel <<As()->cuda_stream()>>>(elem_cnt, data_ptr, has_not_finite_device); - OF_CUDA_CHECK(cudaMemcpyAsync(has_not_finite_host, has_not_finite_device, sizeof(bool), - cudaMemcpyDefault, stream->As()->cuda_stream())); - OF_CUDA_CHECK(cudaStreamSynchronize(stream->As()->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(has_not_finite_host, has_not_finite_device, sizeof(bool), + GPU(MemcpyDefault), stream->As()->cuda_stream())); + OF_CUDA_CHECK(GPU(StreamSynchronize)(stream->As()->cuda_stream())); return *has_not_finite_host; } @@ -91,8 +93,8 @@ void DumpBlob(KernelContext* ctx, const std::string& bn) { if (blob != nullptr) { std::vector buffer(blob->ByteSizeOfBlobBody()); OF_CUDA_CHECK( - cudaMemcpy(buffer.data(), blob->dptr(), blob->ByteSizeOfBlobBody(), cudaMemcpyDefault)); - OF_CUDA_CHECK(cudaDeviceSynchronize()); + GPU(Memcpy)(buffer.data(), blob->dptr(), blob->ByteSizeOfBlobBody(), GPU(MemcpyDefault))); + OF_CUDA_CHECK(GPU(DeviceSynchronize)()); std::ofstream ofs(bn); ofs.write(buffer.data(), blob->ByteSizeOfBlobBody()); } @@ -107,15 +109,19 @@ void DumpBlobs(KernelContext* ctx, const Kernel* kernel) { CudaCheckNumericsKernelObserver::CudaCheckNumericsKernelObserver() : has_not_finite_host_(nullptr), has_not_finite_device_(nullptr) { - OF_CUDA_CHECK(cudaGetDevice(&device_id_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_id_)); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&has_not_finite_host_), sizeof(bool))); +#else OF_CUDA_CHECK(cudaMallocHost(&has_not_finite_host_, sizeof(bool))); - OF_CUDA_CHECK(cudaMalloc(&has_not_finite_device_, sizeof(bool))); +#endif + OF_CUDA_CHECK(GPU(Malloc)(&has_not_finite_device_, sizeof(bool))); } CudaCheckNumericsKernelObserver::~CudaCheckNumericsKernelObserver() { CudaCurrentDeviceGuard guard(device_id_); - OF_CUDA_CHECK(cudaFreeHost(has_not_finite_host_)); - OF_CUDA_CHECK(cudaFree(has_not_finite_device_)); + OF_CUDA_CHECK(GPU(FreeHost)(has_not_finite_host_)); + OF_CUDA_CHECK(GPU(Free)(has_not_finite_device_)); } void CudaCheckNumericsKernelObserver::DidForwardDataContent(KernelContext* ctx, diff --git a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.h b/oneflow/core/kernel/cuda_check_numerics_kernel_observer.h index 27269e78285650672657350660ec24bf72af3963..77fb55debfb8ea4e2a6eac6edae60b545b4bd217 100644 --- a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.h +++ b/oneflow/core/kernel/cuda_check_numerics_kernel_observer.h @@ -38,6 +38,6 @@ class CudaCheckNumericsKernelObserver final : public KernelObserver { } // namespace oneflow -#endif // WITH_CUDA || WITH_ROCM +#endif // WITH_CUDA #endif // ONEFLOW_CORE_KERNEL_CUDA_CHECK_NUMERICS_KERNEL_OBSERVER_H_ diff --git a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.hip.cpp b/oneflow/core/kernel/cuda_check_numerics_kernel_observer.hip.cpp deleted file mode 100644 index b0c81a678f237d4e25e04302e267877d7190bdd3..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/cuda_check_numerics_kernel_observer.hip.cpp +++ /dev/null @@ -1,133 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/kernel/cuda_check_numerics_kernel_observer.h" -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__device__ bool IsNotFinite(T x) { - return !isfinite(x); -} - -template<> -__device__ bool IsNotFinite(half x) { - return (__hisinf(x) || __hisnan(x)); -} - -template -__global__ void HasNotFiniteGpuKernel(const int64_t n, const T* x, volatile bool* has_not_finite) { - if (*has_not_finite) { return; } - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { - if (IsNotFinite(x[i])) { - *has_not_finite = true; - return; - } - } -} - -template -bool HasNotFinite(ep::Stream* stream, const int64_t elem_cnt, const T* data_ptr, - bool* has_not_finite_host, bool* has_not_finite_device) { - OF_CUDA_CHECK(hipMemsetAsync(has_not_finite_device, 0, sizeof(bool), - stream->As()->cuda_stream())); - HasNotFiniteGpuKernel - <<As()->cuda_stream()>>>(elem_cnt, data_ptr, has_not_finite_device); - OF_CUDA_CHECK(hipMemcpyAsync(has_not_finite_host, has_not_finite_device, sizeof(bool), - hipMemcpyDefault, stream->As()->cuda_stream())); - OF_CUDA_CHECK(hipStreamSynchronize(stream->As()->cuda_stream())); - return *has_not_finite_host; -} - -bool HasNotFiniteGpu(ep::Stream* stream, const Blob* blob, bool* has_not_finite_host, - bool* has_not_finite_device) { - auto* cuda_stream = stream->As(); - const DataType dtype = blob->data_type(); - const int64_t elem_cnt = blob->shape().elem_cnt(); - if (elem_cnt == 0) { return false; } - if (dtype == kFloat) { - return HasNotFinite(stream, elem_cnt, blob->dptr(), has_not_finite_host, - has_not_finite_device); - } else if (dtype == kDouble) { - return HasNotFinite(stream, elem_cnt, blob->dptr(), has_not_finite_host, - has_not_finite_device); - } else if (dtype == kFloat16) { - if (cuda_stream->cuda_arch() >= 530) { - return HasNotFinite(stream, elem_cnt, blob->dptr(), has_not_finite_host, - has_not_finite_device); - } else { - LOG(FATAL) << "use half need nvcc arch >= 530"; - return true; - } - } else { - return false; - } -} - -void DumpBlob(KernelContext* ctx, const std::string& bn) { - Blob* blob = ctx->BnInOp2Blob(bn); - if (blob != nullptr) { - std::vector buffer(blob->ByteSizeOfBlobBody()); - OF_CUDA_CHECK( - hipMemcpy(buffer.data(), blob->dptr(), blob->ByteSizeOfBlobBody(), hipMemcpyDefault)); - OF_CUDA_CHECK(hipDeviceSynchronize()); - std::ofstream ofs(bn); - ofs.write(buffer.data(), blob->ByteSizeOfBlobBody()); - } -} - -void DumpBlobs(KernelContext* ctx, const Kernel* kernel) { - for (const auto& obn : kernel->op_attribute().output_bns()) { DumpBlob(ctx, obn); } - for (const auto& ibn : kernel->op_attribute().input_bns()) { DumpBlob(ctx, ibn); } -} - -} // namespace - -CudaCheckNumericsKernelObserver::CudaCheckNumericsKernelObserver() - : has_not_finite_host_(nullptr), has_not_finite_device_(nullptr) { - OF_CUDA_CHECK(hipGetDevice(&device_id_)); - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&has_not_finite_host_), sizeof(bool))); - OF_CUDA_CHECK(hipMalloc(&has_not_finite_device_, sizeof(bool))); -} - -CudaCheckNumericsKernelObserver::~CudaCheckNumericsKernelObserver() { - CudaCurrentDeviceGuard guard(device_id_); - OF_CUDA_CHECK(hipHostFree(has_not_finite_host_)); - OF_CUDA_CHECK(hipFree(has_not_finite_device_)); -} - -void CudaCheckNumericsKernelObserver::DidForwardDataContent(KernelContext* ctx, - const Kernel* kernel) { - for (const auto& obn : kernel->op_attribute().output_bns()) { - Blob* blob = ctx->BnInOp2Blob(obn); - if (blob != nullptr) { - bool has_not_finite = - HasNotFiniteGpu(ctx->stream(), blob, has_not_finite_host_, has_not_finite_device_); - if (has_not_finite - && ParseBooleanFromEnv("ONEFLOW_DEBUG_KERNEL_SYNC_CHECK_NUMERICS_DUMP", false)) { - DumpBlobs(ctx, kernel); - } - CHECK(!has_not_finite) << kernel->op_conf().name() << " : " << obn << " has nan or inf"; - } - } -} - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/kernel/cuda_graph_support.h b/oneflow/core/kernel/cuda_graph_support.h index 2ec118c3a1d5d6e624a36f203fe7603fc0e7b1ea..d5ff492d9957bfd33990ab28e473ac482f3af9f9 100644 --- a/oneflow/core/kernel/cuda_graph_support.h +++ b/oneflow/core/kernel/cuda_graph_support.h @@ -14,6 +14,9 @@ See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_CORE_KERNEL_CUDA_GRAPH_SUPPORT_H_ +#define ONEFLOW_CORE_KERNEL_CUDA_GRAPH_SUPPORT_H_ + namespace oneflow { namespace user_op { @@ -41,3 +44,5 @@ class CudaGraphSupport { } // namespace user_op } // namespace oneflow + +#endif // ONEFLOW_CORE_KERNEL_CUDA_GRAPH_SUPPORT_H_ diff --git a/oneflow/core/kernel/eager_kernel.h b/oneflow/core/kernel/eager_kernel.h deleted file mode 100644 index 19c3f4a026802c8b6d3cf05743e2034db8c0db10..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/eager_kernel.h +++ /dev/null @@ -1,45 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_KERNEL_EAGER_KERNEL_H_ -#define ONEFLOW_CORE_KERNEL_EAGER_KERNEL_H_ - -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/framework/op_kernel.h" - -namespace oneflow { - -class EagerKernel final : public Kernel { - public: - OF_DISALLOW_COPY_AND_MOVE(EagerKernel); - explicit EagerKernel(const KernelConf& kernel_conf); - ~EagerKernel() = default; - - void Infer(std::function BnInOp2Blob) const; - - std::shared_ptr EagerForward( - const std::shared_ptr& old_opkernel_state, DeviceCtx* device_ctx, - std::function BnInOp2Blob) const; - - private: - void InitOpKernel(const KernelConf& kernel_conf); - void ForwardDataContent(KernelContext* kernel_ctx) const override { UNIMPLEMENTED(); } - std::unique_ptr kernel_; - mutable std::shared_ptr cache_; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_KERNEL_EAGER_KERNEL_H_ diff --git a/oneflow/core/kernel/foreign_input_kernel.cpp b/oneflow/core/kernel/foreign_input_kernel.cpp deleted file mode 100644 index 9aa8a17af450ed1cd9a702e7dafbbd946a47cf26..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/foreign_input_kernel.cpp +++ /dev/null @@ -1,50 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/common/buffer_manager.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/job/job_instance.h" - -namespace oneflow { - -class ForeignInputKernel final : public Kernel { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignInputKernel); - ForeignInputKernel() = default; - ~ForeignInputKernel() = default; - - void Forward(KernelContext* ctx) const override { ForwardDataContent(ctx); } - - private: - void ForwardDataContent(KernelContext* ctx) const override; -}; - -void ForeignInputKernel::ForwardDataContent(KernelContext* ctx) const { - const auto& buffer_name = op_conf().foreign_input_conf().ofblob_buffer_name(); - std::shared_ptr foreign_job_instance; - BufferStatus buffer_status = Singleton>>::Get() - ->Get(buffer_name) - ->TryReceive(&foreign_job_instance); - CHECK_NE(buffer_status, kBufferStatusEmpty); - if (buffer_status == kBufferStatusSuccess) { - OfBlob ofblob(ctx->stream(), ctx->BnInOp2Blob("out")); - foreign_job_instance->PushBlob(reinterpret_cast(&ofblob)); - } -} - -REGISTER_KERNEL(OperatorConf::kForeignInputConf, ForeignInputKernel); - -} // namespace oneflow diff --git a/oneflow/core/kernel/foreign_output_kernel.cpp b/oneflow/core/kernel/foreign_output_kernel.cpp deleted file mode 100644 index b81492ee6a12cc00119b6927f57cb38fd84d927c..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/foreign_output_kernel.cpp +++ /dev/null @@ -1,49 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/common/buffer_manager.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/job/job_instance.h" - -namespace oneflow { - -class ForeignOutputKernel final : public Kernel { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignOutputKernel); - ForeignOutputKernel() = default; - ~ForeignOutputKernel() = default; - - private: - bool IsStateless() const override { return false; } - void ForwardDataContent(KernelContext* ctx) const override; -}; - -void ForeignOutputKernel::ForwardDataContent(KernelContext* ctx) const { - const auto& buffer_name = op_conf().foreign_output_conf().ofblob_buffer_name(); - std::shared_ptr foreign_job_instance; - BufferStatus buffer_status = Singleton>>::Get() - ->Get(buffer_name) - ->TryReceive(&foreign_job_instance); - CHECK_NE(buffer_status, kBufferStatusEmpty); - if (buffer_status == kBufferStatusSuccess) { - OfBlob ofblob(ctx->stream(), ctx->BnInOp2Blob("in")); - foreign_job_instance->PullBlob(reinterpret_cast(&ofblob)); - } -} - -REGISTER_KERNEL(OperatorConf::kForeignOutputConf, ForeignOutputKernel); - -} // namespace oneflow diff --git a/oneflow/core/kernel/foreign_watch_kernel.cpp b/oneflow/core/kernel/foreign_watch_kernel.cpp deleted file mode 100644 index e958a21927bf3186affc74c86d6505e304c30e50..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/foreign_watch_kernel.cpp +++ /dev/null @@ -1,56 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/common/buffer_manager.h" -#include "oneflow/core/common/protobuf.h" -#include "oneflow/core/register/ofblob.h" -#include "oneflow/core/job/foreign_watcher.h" - -namespace oneflow { - -template -class ForeignWatchKernel final : public Kernel { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignWatchKernel); - ForeignWatchKernel() = default; - ~ForeignWatchKernel() = default; - - private: - bool IsStateless() const override { return false; } - void ForwardDataContent(KernelContext* ctx) const override; -}; - -template -void ForeignWatchKernel::ForwardDataContent(KernelContext* ctx) const { - OfBlob of_blob(ctx->stream(), ctx->BnInOp2Blob("in")); - (*Singleton>::Get()) - ->Call(this->op_conf().foreign_watch_conf().handler_uuid(), - reinterpret_cast(&of_blob)); -} - -REGISTER_KERNEL_WITH_DEVICE(OperatorConf::kForeignWatchConf, DeviceType::kCPU, - ForeignWatchKernel); - -#ifdef WITH_CUDA -REGISTER_KERNEL_WITH_DEVICE(OperatorConf::kForeignWatchConf, DeviceType::kCUDA, - ForeignWatchKernel); -#endif -#ifdef WITH_ROCM -REGISTER_KERNEL_WITH_DEVICE(OperatorConf::kForeignWatchConf, DeviceType::kCUDA, - ForeignWatchKernel); -#endif - -} // namespace oneflow diff --git a/oneflow/core/kernel/identity_kernel.cpp b/oneflow/core/kernel/identity_kernel.cpp index 60f4d3b77868040bb19089beb28028aa7088feb0..193531232455c320446a42127113dd4e18a382ec 100644 --- a/oneflow/core/kernel/identity_kernel.cpp +++ b/oneflow/core/kernel/identity_kernel.cpp @@ -42,8 +42,8 @@ void IdentityKernel::ForwardHeader(KernelContext* ctx) const { REGISTER_KERNEL(OperatorConf::kIdentityConf, IdentityKernel); REGISTER_KERNEL(OperatorConf::kCopyConf, IdentityKernel); -REGISTER_KERNEL(OperatorConf::kCastToMirroredConf, IdentityKernel); -REGISTER_KERNEL(OperatorConf::kCastFromMirroredConf, IdentityKernel); +REGISTER_KERNEL(OperatorConf::kCastToLocalConf, IdentityKernel); +REGISTER_KERNEL(OperatorConf::kCastFromLocalConf, IdentityKernel); REGISTER_KERNEL(OperatorConf::kBoxingIdentityConf, IdentityKernel); } // namespace oneflow diff --git a/oneflow/core/kernel/image_decoder_random_crop_resize_kernel.cpp b/oneflow/core/kernel/image_decoder_random_crop_resize_kernel.cpp index 4e48c1b89de4f5913126e9cdafdc4cd189905633..6718c2060ec86b2f20d5efcff498a13e64e2b6c2 100644 --- a/oneflow/core/kernel/image_decoder_random_crop_resize_kernel.cpp +++ b/oneflow/core/kernel/image_decoder_random_crop_resize_kernel.cpp @@ -23,7 +23,6 @@ limitations under the License. #include "oneflow/user/image/random_crop_generator.h" #include "oneflow/user/image/jpeg_decoder.h" #include -#include #ifdef WITH_CUDA @@ -76,7 +75,8 @@ class ROIGenerator { class RandomCropROIGenerator : public ROIGenerator { public: - RandomCropROIGenerator(RandomCropGenerator* crop_generator) : crop_generator_(crop_generator) {} + explicit RandomCropROIGenerator(RandomCropGenerator* crop_generator) + : crop_generator_(crop_generator) {} ~RandomCropROIGenerator() override = default; void Generate(int width, int height, ROI* roi) const override { @@ -224,7 +224,7 @@ void InitNppStreamContext(NppStreamContext* ctx, int dev, cudaStream_t stream) { cudaDevAttrMaxThreadsPerMultiProcessor, dev)); OF_CUDA_CHECK( cudaDeviceGetAttribute(&ctx->nMaxThreadsPerBlock, cudaDevAttrMaxThreadsPerBlock, dev)); - int smem_per_block; + int smem_per_block = 0; OF_CUDA_CHECK(cudaDeviceGetAttribute(&smem_per_block, cudaDevAttrMaxSharedMemoryPerBlock, dev)); ctx->nSharedMemPerBlock = smem_per_block; OF_CUDA_CHECK(cudaDeviceGetAttribute(&ctx->nCudaDevAttrComputeCapabilityMajor, @@ -269,7 +269,7 @@ class GpuDecodeHandle final : public DecodeHandle { nvjpegDevAllocator_t dev_allocator_{}; nvjpegPinnedAllocator_t pinned_allocator_{}; CpuDecodeHandle fallback_handle_; - unsigned char* fallback_buffer_; + unsigned char* fallback_buffer_{}; size_t fallback_buffer_size_; bool warmup_done_; bool use_hardware_acceleration_; @@ -328,10 +328,10 @@ void GpuDecodeHandle::DecodeRandomCrop(const unsigned char* data, size_t length, size_t dst_max_length, int* dst_width, int* dst_height) { // https://docs.nvidia.com/cuda/archive/10.2/nvjpeg/index.html#nvjpeg-decoupled-decode-api OF_NVJPEG_CHECK(nvjpegJpegStreamParse(jpeg_handle_, data, length, 0, 0, jpeg_stream_)); - unsigned int orig_width; - unsigned int orig_height; + unsigned int orig_width = 0; + unsigned int orig_height = 0; OF_NVJPEG_CHECK(nvjpegJpegStreamGetFrameDimensions(jpeg_stream_, &orig_width, &orig_height)); - ROI roi; + ROI roi{}; roi_generator->Generate(static_cast(orig_width), static_cast(orig_height), &roi); CHECK_LE(roi.w * roi.h * kNumChannels, dst_max_length); nvjpegImage_t image; @@ -339,8 +339,8 @@ void GpuDecodeHandle::DecodeRandomCrop(const unsigned char* data, size_t length, image.pitch[0] = roi.w * kNumChannels; OF_NVJPEG_CHECK(nvjpegDecodeParamsSetOutputFormat(jpeg_decode_params_, NVJPEG_OUTPUT_RGBI)); - nvjpegJpegDecoder_t jpeg_decoder; - nvjpegJpegState_t jpeg_state; + nvjpegJpegDecoder_t jpeg_decoder = nullptr; + nvjpegJpegState_t jpeg_state = nullptr; int is_hardware_acceleration_supported = -1; if (use_hardware_acceleration_) { nvjpegDecoderJpegSupported(hw_jpeg_decoder_, jpeg_stream_, jpeg_decode_params_, @@ -356,6 +356,8 @@ void GpuDecodeHandle::DecodeRandomCrop(const unsigned char* data, size_t length, if (roi.x != 0 || roi.y != 0 || roi.w != orig_width || roi.h != orig_height) { // hardware_acceleration not support nvjpegDecodeParamsSetROI OF_NVJPEG_CHECK(nvjpegDecodeParamsSetROI(jpeg_decode_params_, roi.x, roi.y, roi.w, roi.h)); + } else { + OF_NVJPEG_CHECK(nvjpegDecodeParamsSetROI(jpeg_decode_params_, 0, 0, -1, -1)); } OF_NVJPEG_CHECK(nvjpegStateAttachPinnedBuffer(jpeg_state, jpeg_pinned_buffer_)); OF_NVJPEG_CHECK(nvjpegStateAttachDeviceBuffer(jpeg_state, jpeg_device_buffer_)); @@ -379,7 +381,7 @@ void GpuDecodeHandle::Decode(const unsigned char* data, size_t length, unsigned void GpuDecodeHandle::CropResize(const unsigned char* src, int src_width, int src_height, ROIGenerator* roi_generator, unsigned char* dst, int dst_width, int dst_height) { - ROI roi; + ROI roi{}; roi_generator->Generate(static_cast(src_width), static_cast(src_height), &roi); const NppiSize src_size{ .width = src_width, @@ -412,12 +414,12 @@ void GpuDecodeHandle::DecodeRandomCropResize(const unsigned char* data, size_t l unsigned char* workspace, size_t workspace_size, unsigned char* dst, int target_width, int target_height) { - int width; - int height; - nvjpegChromaSubsampling_t subsampling; - int num_components; - nvjpegStatus_t status = nvjpegGetImageInfo(jpeg_handle_, data, length, &num_components, - &subsampling, &width, &height); + int width[NVJPEG_MAX_COMPONENT]; + int height[NVJPEG_MAX_COMPONENT]; + nvjpegChromaSubsampling_t subsampling{}; + int num_components = 0; + nvjpegStatus_t status = + nvjpegGetImageInfo(jpeg_handle_, data, length, &num_components, &subsampling, width, height); if (status != NVJPEG_STATUS_SUCCESS) { CHECK_LE(target_width * target_height * kNumChannels, fallback_buffer_size_); fallback_handle_.DecodeRandomCropResize(data, length, crop_generator, nullptr, 0, @@ -430,15 +432,15 @@ void GpuDecodeHandle::DecodeRandomCropResize(const unsigned char* data, size_t l NoChangeROIGenerator no_change_roi_generator; RandomCropROIGenerator random_crop_roi_generator(crop_generator); if (use_hardware_acceleration_) { - DecodeRandomCrop(data, length, &no_change_roi_generator, workspace, workspace_size, &width, - &height); - CropResize(workspace, width, height, &random_crop_roi_generator, dst, target_width, - target_height); + int w = 0; + int h = 0; + DecodeRandomCrop(data, length, &no_change_roi_generator, workspace, workspace_size, &w, &h); + CropResize(workspace, w, h, &random_crop_roi_generator, dst, target_width, target_height); } else { - DecodeRandomCrop(data, length, &random_crop_roi_generator, workspace, workspace_size, &width, - &height); - CropResize(workspace, width, height, &no_change_roi_generator, dst, target_width, - target_height); + int w = 0; + int h = 0; + DecodeRandomCrop(data, length, &random_crop_roi_generator, workspace, workspace_size, &w, &h); + CropResize(workspace, w, h, &no_change_roi_generator, dst, target_width, target_height); } } @@ -449,11 +451,11 @@ void GpuDecodeHandle::WarmupOnce(int warmup_size, unsigned char* workspace, size cv::randu(image, cv::Scalar(0, 0, 0), cv::Scalar(255, 255, 255)); std::vector data; cv::imencode(".jpg", image, data, {}); - int decoded_width; - int decoded_height; + int decoded_width = 0; + int decoded_height = 0; Decode(data.data(), data.size(), workspace, workspace_size, &decoded_width, &decoded_height); Synchronize(); - if (use_hardware_acceleration_ == true) { + if (use_hardware_acceleration_) { // Note(guoran): hardware acceleration jpeg decoder support baseline decoding only, use // progressive to warmup jpeg decoder. cv::imencode(".jpg", image, data, {cv::IMWRITE_JPEG_PROGRESSIVE, 1}); @@ -468,7 +470,7 @@ void GpuDecodeHandle::Synchronize() { OF_CUDA_CHECK(cudaStreamSynchronize(cuda_s template<> DecodeHandleFactory CreateDecodeHandleFactory(int target_width, int target_height) { - int dev; + int dev = 0; OF_CUDA_CHECK(cudaGetDevice(&dev)); return [dev, target_width, target_height]() -> std::shared_ptr { OF_CUDA_CHECK(cudaSetDevice(dev)); diff --git a/oneflow/core/kernel/input_kernel.cpp b/oneflow/core/kernel/input_kernel.cpp index 59218295bafd09ce38b835c5ab7a162d912738fa..fbc817fb57c000faf616551f150942d0bfc7cb86 100644 --- a/oneflow/core/kernel/input_kernel.cpp +++ b/oneflow/core/kernel/input_kernel.cpp @@ -40,8 +40,8 @@ class InputKernel final : public Kernel { BufferStatus buffer_status = buffer->TryReceive(&critical_section_instance); CHECK_NE(buffer_status, kBufferStatusEmpty); if (buffer_status == kBufferStatusSuccess) { - OfBlob ofblob(ctx->stream(), ctx->BnInOp2Blob("out")); - critical_section_instance->AccessBlobByOpName(reinterpret_cast(&ofblob), op_name); + critical_section_instance->AccessBlobByOpName(ctx->stream(), ctx->BnInOp2Blob("out"), + op_name); } } void ForwardHeader(KernelContext* ctx) const override {} diff --git a/oneflow/core/kernel/kernel.cpp b/oneflow/core/kernel/kernel.cpp index 995faeadaa5c1f78c46294c3615972e570a60632..a6673e0860708e7bc26d32712a0514b0beb58deb 100644 --- a/oneflow/core/kernel/kernel.cpp +++ b/oneflow/core/kernel/kernel.cpp @@ -44,6 +44,7 @@ void Kernel::InitBase(const KernelConf& kernel_conf) { } void Kernel::Init(const KernelConf& kernel_conf, KernelContext* ctx) { + SyncVmModeGuard guard(SyncVmMode::kEnable); InitBase(kernel_conf); VirtualKernelInit(ctx); } diff --git a/oneflow/core/kernel/kernel_util.cpp b/oneflow/core/kernel/kernel_util.cpp index 7a204bc6e192e1c872af91c1c23794518d52d3c9..3209c0d67814f75b6b02608ace1aa93a78c12a30 100644 --- a/oneflow/core/kernel/kernel_util.cpp +++ b/oneflow/core/kernel/kernel_util.cpp @@ -13,11 +13,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/kernel/kernel.h" #include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/common/balanced_splitter.h" #include "oneflow/core/register/register_manager.h" -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/memory/memory_case.pb.h" +#include "oneflow/core/memory/memory_case_util.h" #include "oneflow/core/ep/include/primitive/memcpy.h" #include "oneflow/core/ep/include/primitive/memset.h" @@ -27,15 +27,15 @@ void AutoMemcpy(ep::Stream* stream, void* dst, const void* src, size_t sz, const MemoryCase& dst_mem_case, const MemoryCase& src_mem_case) { ep::primitive::MemcpyKind kind{}; if (stream->device_type() == DeviceType::kCPU) { - CHECK(src_mem_case.has_host_mem()); - CHECK(dst_mem_case.has_host_mem()); + CHECK(memory::IsHostMem(src_mem_case)); + CHECK(memory::IsHostMem(dst_mem_case)); kind = ep::primitive::MemcpyKind::kDtoD; } else { - if (src_mem_case.has_host_mem()) { - CHECK(!dst_mem_case.has_host_mem()); + if (memory::IsHostMem(src_mem_case)) { + CHECK(!memory::IsHostMem(dst_mem_case)); kind = ep::primitive::MemcpyKind::kHtoD; - } else if (dst_mem_case.has_host_mem()) { - CHECK(!src_mem_case.has_host_mem()); + } else if (memory::IsHostMem(dst_mem_case)) { + CHECK(!memory::IsHostMem(src_mem_case)); kind = ep::primitive::MemcpyKind::kDtoH; } else { kind = ep::primitive::MemcpyKind::kDtoD; diff --git a/oneflow/core/kernel/kernel_util.cuh b/oneflow/core/kernel/kernel_util.cuh index b24eb5bd36b5d4fc6853f2672990a6dc45c845d4..73fc59c8b0bad29062a8dd201c7096537fd60027 100644 --- a/oneflow/core/kernel/kernel_util.cuh +++ b/oneflow/core/kernel/kernel_util.cuh @@ -15,8 +15,10 @@ limitations under the License. */ #ifndef ONEFLOW_CORE_KERNEL_KERNEL_UTIL_CUH_ #define ONEFLOW_CORE_KERNEL_KERNEL_UTIL_CUH_ +#include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/device/cuda_pseudo_half.h" #include "oneflow/core/common/data_type.h" +#include "oneflow/core/device/cuda_util.h" namespace oneflow { @@ -31,7 +33,7 @@ OF_DEVICE_FUNC T MaxWithLogThreshold(T x) { return x; } -#if defined(__CUDACC__) +#if defined(__CUDACC__) || defined(__HIPCC__) __device__ __forceinline__ half MaxWithLogThreshold(half x) { half threshold = hexp2(__float2half(-14.0)); if (__hgt(x, threshold)) { return x; } @@ -44,10 +46,75 @@ OF_DEVICE_FUNC T SafeLog(T x) { return logf(MaxWithLogThreshold(x)); } -#if defined(__CUDACC__) +#if defined(__CUDACC__) || defined(__HIPCC__) __device__ __forceinline__ half SafeLog(half x) { return hlog(MaxWithLogThreshold(x)); } #endif +// FastAtomicAdd is referenced from +// https://github.com/pytorch/pytorch/blob/396c3b1d88d7624938a2bb0b287f2a19f1e89bb4/aten/src/ATen/native/cuda/KernelUtils.cuh#L29 +#if defined(__CUDACC__) || defined(__HIPCC__) +template::value>::type* = nullptr> +__device__ __forceinline__ void fastSpecializedAtomicAdd(T* base, IndexT offset, + const IndexT length, T value) { +#if ((defined(CUDA_VERSION) && (CUDA_VERSION < 10000)) \ + || (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 700))) + cuda::atomic::Add(reinterpret_cast(base) + offset, static_cast(value)); +#else + // Accounts for the chance base falls on an odd 16 bit alignment (ie, not 32 bit aligned) + __half* target_addr = reinterpret_cast<__half*>(base + offset); + bool low_byte = (reinterpret_cast(target_addr) % sizeof(__half2) == 0); + +#ifdef WITH_ROCM + if (low_byte && offset < (length - 1)) { + __half2 value2; + value2.data.x = value; + value2.data.y = __float2half_rz(0); + cuda::atomic::Add(reinterpret_cast<__half2*>(target_addr), value2); + + } else if (!low_byte && offset > 0) { + __half2 value2; + value2.data.x = __float2half_rz(0); + value2.data.y = value; + cuda::atomic::Add(reinterpret_cast<__half2*>(target_addr - 1), value2); + + } else { + cuda::atomic::Add(reinterpret_cast<__half*>(base) + offset, static_cast<__half>(value)); + } +#else + if (low_byte && offset < (length - 1)) { + __half2 value2; + value2.x = value; + value2.y = __float2half_rz(0); + cuda::atomic::Add(reinterpret_cast<__half2*>(target_addr), value2); + + } else if (!low_byte && offset > 0) { + __half2 value2; + value2.x = __float2half_rz(0); + value2.y = value; + cuda::atomic::Add(reinterpret_cast<__half2*>(target_addr - 1), value2); + + } else { + cuda::atomic::Add(reinterpret_cast<__half*>(base) + offset, static_cast<__half>(value)); + } +#endif +#endif +} + +template::value>::type* = nullptr> +__device__ __forceinline__ void fastSpecializedAtomicAdd(T* base, IndexT offset, + const IndexT length, T value) { + cuda::atomic::Add(base + offset, value); +} + +template +__device__ __forceinline__ void FastAtomicAdd(T* base, IndexT offset, const IndexT length, + T value) { + fastSpecializedAtomicAdd(base, offset, length, value); +} +#endif + } // namespace oneflow #endif // ONEFLOW_CORE_KERNEL_KERNEL_UTIL_CUH_ diff --git a/oneflow/core/kernel/kernel_util.hip.h b/oneflow/core/kernel/kernel_util.hip.h deleted file mode 100644 index fc466e8cddae16dee92773cef027844873926607..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/kernel_util.hip.h +++ /dev/null @@ -1,53 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_KERNEL_KERNEL_UTIL_HIP_H_ -#define ONEFLOW_CORE_KERNEL_KERNEL_UTIL_HIP_H_ -#include "oneflow/core/device/cuda_pseudo_half.h" -#include "oneflow/core/common/data_type.h" - -namespace oneflow { - -template::value>::type* = nullptr> -OF_DEVICE_FUNC T MaxWithLogThreshold(T x) { - const T threshold = 1e-20; - return x > threshold ? x : threshold; -} - -template::value>::type* = nullptr> -OF_DEVICE_FUNC T MaxWithLogThreshold(T x) { - return x; -} - -#if defined(__CUDACC__) || defined(__HIPCC__) -__device__ __forceinline__ half MaxWithLogThreshold(half x) { - half threshold = hexp2(__float2half(-14.0)); - if (__hgt(x, threshold)) { return x; } - return threshold; -} -#endif - -template -OF_DEVICE_FUNC T SafeLog(T x) { - return logf(MaxWithLogThreshold(x)); -} - -#if defined(__CUDACC__) || defined(__HIPCC__) -__device__ __forceinline__ half SafeLog(half x) { return hlog(MaxWithLogThreshold(x)); } -#endif - -} // namespace oneflow - -#endif // ONEFLOW_CORE_KERNEL_KERNEL_UTIL_HIP_H_ diff --git a/oneflow/core/kernel/learning_rate_schedule_kernel.cpp b/oneflow/core/kernel/learning_rate_schedule_kernel.cpp index 62c4fe6b5f84f648ccefc7d1a348535d9d1bfe8d..9a8710aa3736782855469ab2968eeb87f8dcc817 100644 --- a/oneflow/core/kernel/learning_rate_schedule_kernel.cpp +++ b/oneflow/core/kernel/learning_rate_schedule_kernel.cpp @@ -13,6 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include +#include + #include "oneflow/core/kernel/kernel.h" #include "oneflow/core/job/resource_desc.h" #include "oneflow/core/job/global_for.h" @@ -30,7 +33,8 @@ class LearningRateScheduleKernel final : public Kernel { private: void VirtualKernelInit(KernelContext* ctx) override { if (Singleton::Get()->enable_debug_mode()) { - log_stream_ = TeePersistentLogStream::Create("train_step2lr.csv"); + pid_t pid = getpid(); + log_stream_ = TeePersistentLogStream::Create(std::to_string(pid) + "-train_step2lr.csv"); (*log_stream_) << "train_step, lr\n"; } if (IsOpenGraphVerboseStepLr()) { print_step_lr_ = true; } @@ -60,7 +64,7 @@ double LinearLearningRate(double base_lr, double start_factor, double end_factor CHECK_LE(start_factor, 1.0); CHECK_GE(end_factor, 0.0); CHECK_LE(end_factor, 1.0); - double multiplier = 1.0; + double multiplier = end_factor; double c_step_f = float(cur_step); double t_step_f = float(total_step); if (cur_step < total_step) { diff --git a/oneflow/core/kernel/nccl_send_recv_boxing_kernel.cpp b/oneflow/core/kernel/nccl_send_recv_boxing_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d6a47b7a124ae39cb9b6ce152a169fcb55f77168 --- /dev/null +++ b/oneflow/core/kernel/nccl_send_recv_boxing_kernel.cpp @@ -0,0 +1,256 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/kernel/kernel.h" +#include "oneflow/core/device/nccl_util.h" +#include "oneflow/core/job/eager_nccl_comm_manager.h" +#include "oneflow/core/register/tensor_slice_copier.h" +#include "oneflow/core/ep/include/primitive/memset.h" +#include "oneflow/core/ep/include/primitive/add.h" +#include "oneflow/core/operator/nccl_send_recv_boxing_op_util.h" + +#if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) + +namespace oneflow { + +class NcclSendRecvBoxingKernel final : public Kernel { + public: + OF_DISALLOW_COPY_AND_MOVE(NcclSendRecvBoxingKernel); + NcclSendRecvBoxingKernel() = default; + ~NcclSendRecvBoxingKernel() override = default; + + const std::vector>& in_tensor_slice_copier_vec() const { + return in_tensor_slice_copier_vec_; + } + const std::vector>& out_tensor_slice_copier_vec() const { + return out_tensor_slice_copier_vec_; + } + const std::vector& send_elem_cnts() const { return send_elem_cnts_; } + const std::vector& recv_elem_cnts() const { return recv_elem_cnts_; } + const bool has_input() const { return has_input_; } + const bool has_output() const { return has_output_; } + ncclComm_t comm() const { return GetOrCreate().comm; } + + private: + struct Comm { + Comm(ncclComm_t comm) : comm(comm) {} + ncclComm_t comm; + }; + + void Init() const { + ParallelDesc parallel_desc(parallel_conf_); + std::set> device_set; + for (int64_t parallel_id = 0; parallel_id < parallel_desc.parallel_num(); ++parallel_id) { + int64_t machine_id = CHECK_JUST(parallel_desc.MachineId4ParallelId(parallel_id)); + int64_t device_id = CHECK_JUST(parallel_desc.DeviceId4ParallelId(parallel_id)); + device_set.emplace(std::make_pair(machine_id, device_id)); + } + EagerNcclCommMgr* comm_mgr = CHECK_NOTNULL(Singleton::Get()); + ncclComm_t comm = comm_mgr->GetCommForDeviceAndStreamName(device_set, stream_name_); + comm_.reset(new Comm(comm)); + } + + const Comm& GetOrCreate() const { + if (!comm_) { Init(); } + return *comm_; + } + + void VirtualKernelInit(KernelContext* ctx) override; + void ForwardDataContent(KernelContext* ctx) const override; + + std::string stream_name_; + ParallelConf parallel_conf_; + mutable std::unique_ptr comm_; + bool src_nd_sbp_no_partial_parallel_; + std::vector> in_tensor_slice_copier_vec_; + std::vector> out_tensor_slice_copier_vec_; + std::vector send_elem_cnts_; + std::vector recv_elem_cnts_; + bool has_input_; + bool has_output_; +}; + +void NcclSendRecvBoxingKernel::ForwardDataContent(KernelContext* ctx) const { + Blob* buf = ctx->BnInOp2Blob("buf"); + ncclComm_t comm = this->comm(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + const std::vector& send_elem_cnts = this->send_elem_cnts(); + const std::vector& recv_elem_cnts = this->recv_elem_cnts(); + const int64_t parallel_num = this->kernel_conf().parallel_ctx().parallel_num(); + const DataType data_type = buf->data_type(); + std::vector send_in_ptr; + std::vector recv_out_ptr; + char* buf_ptr = buf->mut_dptr(); + int64_t offset = 0; + if (this->has_input()) { + for (int64_t i = 0; i < parallel_num; ++i) { + void* send_ptr = reinterpret_cast(buf_ptr + offset); + send_in_ptr.push_back(send_ptr); + offset += send_elem_cnts.at(i) * GetSizeOfDataType(data_type); + } + } + if (this->has_output()) { + for (int64_t i = 0; i < parallel_num; ++i) { + void* recv_ptr = reinterpret_cast(buf_ptr + offset); + recv_out_ptr.push_back(recv_ptr); + offset += recv_elem_cnts.at(i) * GetSizeOfDataType(data_type); + } + } + if (this->has_input()) { + const Blob* in = ctx->BnInOp2Blob("in"); + const std::vector>& in_tensor_slice_copier_vec = + this->in_tensor_slice_copier_vec(); + for (int64_t i = 0; i < parallel_num; ++i) { + if (in_tensor_slice_copier_vec.at(i)) { + in_tensor_slice_copier_vec.at(i)->Copy(ctx->stream(), send_in_ptr.at(i), in->dptr()); + } + } + } + OF_NCCL_CHECK(ncclGroupStart()); + for (int64_t i = 0; i < parallel_num; ++i) { + if (this->has_input() && send_elem_cnts.at(i) != 0) { + OF_NCCL_CHECK(ncclSend(send_in_ptr.at(i), send_elem_cnts.at(i), GetNcclDataType(data_type), i, + comm, cuda_stream)); + } + if (this->has_output() && recv_elem_cnts.at(i) != 0) { + OF_NCCL_CHECK(ncclRecv(recv_out_ptr.at(i), recv_elem_cnts.at(i), GetNcclDataType(data_type), + i, comm, cuda_stream)); + } + } + OF_NCCL_CHECK(ncclGroupEnd()); + if (!this->has_output()) { return; } + Blob* out = ctx->BnInOp2Blob("out"); + const std::vector>& out_tensor_slice_copier_vec = + this->out_tensor_slice_copier_vec(); + + if (src_nd_sbp_no_partial_parallel_) { + for (int64_t i = 0; i < parallel_num; ++i) { + if (out_tensor_slice_copier_vec.at(i)) { + out_tensor_slice_copier_vec.at(i)->Copy(ctx->stream(), out->mut_dptr(), recv_out_ptr.at(i)); + } + } + } else { + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type(), + out->data_type()); + CHECK(primitive); + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + CHECK(memset_primitive); + bool is_first_slice = true; + for (int64_t i = 0; i < parallel_num; ++i) { + if (out_tensor_slice_copier_vec.at(i)) { + if (is_first_slice) { + is_first_slice = false; + if (recv_elem_cnts.at(i) != out->shape().elem_cnt()) { + // if not same shape, memset out + memset_primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + out->shape().elem_cnt() * GetSizeOfDataType(data_type)); + } + out_tensor_slice_copier_vec.at(i)->Copy(ctx->stream(), out->mut_dptr(), + recv_out_ptr.at(i)); + } else { + if (recv_elem_cnts.at(i) == out->shape().elem_cnt()) { + primitive->Launch(ctx->stream(), out->dptr(), recv_out_ptr.at(i), out->mut_dptr(), + out->shape().elem_cnt()); + } else { + void* out_buf = reinterpret_cast(buf_ptr + offset); + memset_primitive->Launch(ctx->stream(), out_buf, 0, + out->shape().elem_cnt() * GetSizeOfDataType(data_type)); + out_tensor_slice_copier_vec.at(i)->Copy(ctx->stream(), out_buf, recv_out_ptr.at(i)); + primitive->Launch(ctx->stream(), out->dptr(), out_buf, out->mut_dptr(), + out->shape().elem_cnt()); + } + } + } + } + } +} + +void NcclSendRecvBoxingKernel::VirtualKernelInit(KernelContext* ctx) { + const NcclSendRecvBoxingOpConf& conf = this->op_conf().nccl_send_recv_boxing_conf(); + if (this->op_conf().has_stream_name_hint()) { + stream_name_ = this->op_conf().stream_name_hint(); + } else { + stream_name_ = EagerNcclCommMgr::kDefaultStreamName; + } + parallel_conf_ = conf.parallel_conf(); + const int64_t parallel_id = this->kernel_conf().parallel_ctx().parallel_id(); + ParallelDesc parallel_desc(parallel_conf_); + ParallelDesc src_parallel_desc(conf.src_parallel_conf()); + ParallelDesc dst_parallel_desc(conf.dst_parallel_conf()); + const NdSbp& src_nd_sbp = conf.src_nd_sbp(); + const NdSbp& dst_nd_sbp = conf.dst_nd_sbp(); + has_input_ = conf.has_input(); + has_output_ = conf.has_output(); + src_nd_sbp_no_partial_parallel_ = !NdSbpHasPartialParallel(src_nd_sbp); + const DataType data_type = this->kernel_conf().data_type(); + const DeviceType device_type = parallel_desc.device_type(); + const Shape& logical_shape = Shape(conf.logical_shape()); + const int64_t parallel_num = parallel_desc.parallel_num(); + + std::vector src_send_intersections; + std::vector dst_recv_intersections; + GetRankSendRecvIntersection(parallel_id, parallel_desc, src_parallel_desc, dst_parallel_desc, + src_nd_sbp, dst_nd_sbp, logical_shape, &src_send_intersections, + &dst_recv_intersections); + // if parallel_id exists in src parallel desc, has send + int64_t src_parallel_id = GetMappedParallelId(parallel_id, parallel_desc, src_parallel_desc); + if (src_parallel_id != -1) { + CHECK_EQ(src_send_intersections.size(), parallel_num); + send_elem_cnts_.resize(parallel_num); + in_tensor_slice_copier_vec_.resize(parallel_num); + const TensorSliceView& cur_rank_in_slice = GetTensorSliceView4ParallelId( + *src_parallel_desc.hierarchy(), src_nd_sbp, logical_shape, src_parallel_id); + for (int64_t i = 0; i < parallel_num; ++i) { + const TensorSliceView& intersection = src_send_intersections.at(i); + if (!intersection.IsEmpty()) { + send_elem_cnts_.at(i) = intersection.shape().elem_cnt(); + in_tensor_slice_copier_vec_.at(i).reset( + new TensorSliceCopier(intersection, cur_rank_in_slice, data_type, device_type)); + } + } + } else { + CHECK_EQ(src_send_intersections.size(), 0); + } + + // if parallel_id exists in src parallel desc, has send + int64_t dst_parallel_id = GetMappedParallelId(parallel_id, parallel_desc, dst_parallel_desc); + if (dst_parallel_id != -1) { + CHECK_EQ(dst_recv_intersections.size(), parallel_num); + recv_elem_cnts_.resize(parallel_num); + out_tensor_slice_copier_vec_.resize(parallel_num); + const TensorSliceView& cur_rank_out_slice = GetTensorSliceView4ParallelId( + *dst_parallel_desc.hierarchy(), dst_nd_sbp, logical_shape, dst_parallel_id); + for (int64_t i = 0; i < parallel_num; ++i) { + const TensorSliceView& intersection = dst_recv_intersections.at(i); + if (!intersection.IsEmpty()) { + recv_elem_cnts_.at(i) = intersection.shape().elem_cnt(); + out_tensor_slice_copier_vec_.at(i).reset( + new TensorSliceCopier(cur_rank_out_slice, intersection, data_type, device_type)); + } + } + } else { + CHECK_EQ(dst_recv_intersections.size(), 0); + } +} + +REGISTER_KERNEL(OperatorConf::kNcclSendRecvBoxingConf, NcclSendRecvBoxingKernel); + +REGISTER_SYSTEM_OP_KERNEL_UNIFIED_NCCL_COMM_INIT(OperatorConf::kNcclSendRecvBoxingConf); + +} // namespace oneflow + +#endif // WITH_CUDA && NCCL_VERSION_CODE > 2700 diff --git a/oneflow/core/kernel/output_kernel.cpp b/oneflow/core/kernel/output_kernel.cpp index 63d5a2f1527fef94e899506ecba7c78ac8dbf79f..737f9cc2e05df24c23b1db75f274a044efdb8cea 100644 --- a/oneflow/core/kernel/output_kernel.cpp +++ b/oneflow/core/kernel/output_kernel.cpp @@ -41,8 +41,7 @@ void OutputKernel::ForwardDataContent(KernelContext* ctx) const { BufferStatus buffer_status = buffer->TryReceive(&critical_section_instance); CHECK_NE(buffer_status, kBufferStatusEmpty); if (buffer_status == kBufferStatusSuccess) { - OfBlob ofblob(ctx->stream(), ctx->BnInOp2Blob("in")); - critical_section_instance->AccessBlobByOpName(reinterpret_cast(&ofblob), op_name); + critical_section_instance->AccessBlobByOpName(ctx->stream(), ctx->BnInOp2Blob("in"), op_name); } } diff --git a/oneflow/core/kernel/random_generator.cu b/oneflow/core/kernel/random_generator.cu index a27c168a0c78cefe3983c8dfe4e6f8359aa43e57..a546b99a31c5b72287cf697e8dd950f08408bcce 100644 --- a/oneflow/core/kernel/random_generator.cu +++ b/oneflow/core/kernel/random_generator.cu @@ -16,6 +16,8 @@ limitations under the License. #include "oneflow/core/kernel/random_generator.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_CUDA + namespace oneflow { namespace { @@ -56,3 +58,50 @@ void RandomGenerator::Uniform(const int64_t elem_cnt, T* dptr OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_RANDOM_GENERATOR_UNIFORM, FLOATING_DATA_TYPE_SEQ); } // namespace oneflow + +#endif // WITH_CUDA + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace { + +template +void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, T* ret); + +template<> +void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, float* ret) { + OF_CURAND_CHECK(hiprandGenerateUniform(gen, ret, n)); +} + +template<> +void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, double* ret) { + OF_CURAND_CHECK(hiprandGenerateUniformDouble(gen, ret, n)); +} + +} // namespace + +RandomGenerator::RandomGenerator(int64_t seed, ep::Stream* stream) { + OF_CURAND_CHECK(hiprandCreateGenerator(&curand_generator_, HIPRAND_RNG_PSEUDO_DEFAULT)); + OF_CURAND_CHECK(hiprandSetPseudoRandomGeneratorSeed(curand_generator_, seed)); + OF_CURAND_CHECK(hiprandSetStream(curand_generator_, stream->As()->cuda_stream())); +} + +RandomGenerator::~RandomGenerator() { + OF_CURAND_CHECK(hiprandDestroyGenerator(curand_generator_)); +} + +template +void RandomGenerator::Uniform(const int64_t elem_cnt, T* dptr) { + RngUniformGpu(curand_generator_, elem_cnt, dptr); +} + +#define INITIATE_CUDA_RANDOM_GENERATOR_UNIFORM(T, typeproto) \ + template void RandomGenerator::Uniform(const int64_t elem_cnt, T* dptr); + +OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_RANDOM_GENERATOR_UNIFORM, FLOATING_DATA_TYPE_SEQ); + +} // namespace oneflow + +#endif // WITH_ROCM diff --git a/oneflow/core/kernel/random_generator.hip.cpp b/oneflow/core/kernel/random_generator.hip.cpp deleted file mode 100644 index e35a1c62697f2fcd87f631e321921305204565d4..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/random_generator.hip.cpp +++ /dev/null @@ -1,59 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/random_generator.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - - -namespace oneflow { - -namespace { - -template -void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, T* ret); - -template<> -void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, float* ret) { - OF_CURAND_CHECK(hiprandGenerateUniform(gen, ret, n)); -} - -template<> -void RngUniformGpu(const hiprandGenerator_t& gen, int64_t n, double* ret) { - OF_CURAND_CHECK(hiprandGenerateUniformDouble(gen, ret, n)); -} - -} // namespace - -RandomGenerator::RandomGenerator(int64_t seed, ep::Stream* stream) { - OF_CURAND_CHECK(hiprandCreateGenerator(&curand_generator_, HIPRAND_RNG_PSEUDO_DEFAULT)); - OF_CURAND_CHECK(hiprandSetPseudoRandomGeneratorSeed(curand_generator_, seed)); - OF_CURAND_CHECK(hiprandSetStream(curand_generator_, stream->As()->cuda_stream())); -} - -RandomGenerator::~RandomGenerator() { - OF_CURAND_CHECK(hiprandDestroyGenerator(curand_generator_)); -} - -template -void RandomGenerator::Uniform(const int64_t elem_cnt, T* dptr) { - RngUniformGpu(curand_generator_, elem_cnt, dptr); -} - -#define INITIATE_CUDA_RANDOM_GENERATOR_UNIFORM(T, typeproto) \ - template void RandomGenerator::Uniform(const int64_t elem_cnt, T* dptr); - -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_RANDOM_GENERATOR_UNIFORM, FLOATING_DATA_TYPE_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/kernel/return_kernel.cpp b/oneflow/core/kernel/return_kernel.cpp index 107df7a7a4c5154297c4891415cdeef8e682baed..283fe8564311e4b491c0710cd85bc093193a1ea8 100644 --- a/oneflow/core/kernel/return_kernel.cpp +++ b/oneflow/core/kernel/return_kernel.cpp @@ -41,8 +41,7 @@ void ReturnKernel::ForwardDataContent(KernelContext* ctx) const { BufferStatus buffer_status = buffer->TryReceive(&critical_section_instance); CHECK_NE(buffer_status, kBufferStatusEmpty); if (buffer_status == kBufferStatusSuccess) { - OfBlob ofblob(ctx->stream(), ctx->BnInOp2Blob("in")); - critical_section_instance->AccessBlobByOpName(reinterpret_cast(&ofblob), op_name); + critical_section_instance->AccessBlobByOpName(ctx->stream(), ctx->BnInOp2Blob("in"), op_name); } } diff --git a/oneflow/core/kernel/runtime_blob_shape_infer_helper.cpp b/oneflow/core/kernel/runtime_blob_shape_infer_helper.cpp index ed5bb17086bf88efcef7a434df504621fbda8a61..3f2aeb3caff20b2969f63f569571e17223b57f18 100644 --- a/oneflow/core/kernel/runtime_blob_shape_infer_helper.cpp +++ b/oneflow/core/kernel/runtime_blob_shape_infer_helper.cpp @@ -67,8 +67,12 @@ void RuntimeBlobShapeInferHelper::UpdateInputBlobDescs7OpInferCacheKey( const Blob* blob = BnInOp2Blob(ibn); if (blob == nullptr) { return Symbol(); } BlobDesc* blob_desc = BlobDesc4BnInOp(ibn, blob->blob_desc()); - blob_desc->mut_shape().LeftOnesExtendedAssign(blob->shape()); - blob_desc->mut_stride().CheckNumAxesIdenticalAndAssign(blob->stride()); + Shape blob_shape = blob_desc->shape(); + blob_shape.LeftOnesExtendedAssign(blob->shape()); + blob_desc->set_shape(blob_shape); + Stride blob_stride = blob_desc->stride(); + blob_stride.CheckNumAxesIdenticalAndAssign(blob->stride()); + blob_desc->set_stride(blob_stride); return SymbolOf(blob_desc->shape()); }; const auto& input_bns = op_->input_bns(); diff --git a/oneflow/core/kernel/slice_boxing_kernel.cpp b/oneflow/core/kernel/slice_boxing_kernel.cpp index 389866a129f3ce2f6cd1f0bace75aa3dc9ef70af..a29c442bed0376f33b6cc17b78d42694fd30fd97 100644 --- a/oneflow/core/kernel/slice_boxing_kernel.cpp +++ b/oneflow/core/kernel/slice_boxing_kernel.cpp @@ -63,6 +63,10 @@ class SliceBoxingAddKernel final : public SliceBoxingKernel { void SliceBoxingKernel::VirtualKernelInit(KernelContext* ctx) { const SliceBoxingConf& conf = GetCustomizedBoxingConf(); + if (/*is_0size_tensor=*/std::any_of(conf.out_shape().dim().begin(), conf.out_shape().dim().end(), + [](int64_t dim) { return dim == 0; })) { + return; + } const TensorSliceView out_slice(conf.out_slice()); for (const TensorSliceViewProto& in_slice_proto : conf.in_slice()) { const TensorSliceView in_slice(in_slice_proto); @@ -82,6 +86,7 @@ const SliceBoxingConf& SliceBoxingCopyKernel::GetCustomizedBoxingConf() const { void SliceBoxingCopyKernel::ForwardDataContent(KernelContext* ctx) const { Blob* out = ctx->BnInOp2Blob("out"); + if (out->shape_view().elem_cnt() == 0) { return; } FOR_RANGE(int64_t, i, 0, this->op_attribute().input_bns().size()) { const Blob* in_i = ctx->BnInOp2Blob(GenRepeatedBn("in", i)); this->tensor_slice_copier_vec().at(i)->Copy(ctx->stream(), out, in_i); @@ -94,6 +99,7 @@ const SliceBoxingConf& SliceBoxingAddKernel::GetCustomizedBoxingConf() const { void SliceBoxingAddKernel::ForwardDataContent(KernelContext* ctx) const { Blob* out = ctx->BnInOp2Blob("out"); + if (out->shape_view().elem_cnt() == 0) { return; } std::unique_ptr primitive = ep::primitive::NewPrimitive(ctx->stream()->device_type(), out->data_type()); diff --git a/oneflow/core/kernel/sync_dynamic_resize_kernel.cpp b/oneflow/core/kernel/sync_dynamic_resize_kernel.cpp index 8150d44ce3fc49fb352e1888f2e4f7fbb7dac5f2..359b34c53ca0dfc8382a8d1028aa944d7df283d7 100644 --- a/oneflow/core/kernel/sync_dynamic_resize_kernel.cpp +++ b/oneflow/core/kernel/sync_dynamic_resize_kernel.cpp @@ -19,6 +19,7 @@ limitations under the License. #include "oneflow/core/kernel/kernel.h" #include "oneflow/core/register/register_desc.h" #include "oneflow/core/lazy/actor/actor_context.h" +#include "oneflow/core/memory/memory_case_util.h" #include #include @@ -74,7 +75,7 @@ class SyncDynamicResizeGPUKernel final : public Kernel { AutoMemcpy(ctx->stream(), out->mut_dptr(), in->dptr(), in->ByteSizeOfBlobBody(), out->mem_case(), in->mem_case()); AutoMemcpy(ctx->stream(), cuda_host_mem_ptr->Ptr(), size->dptr(), sizeof(SizeType), - MakeHostMemCase(), size->mem_case()); + memory::MakeHostMemCase(), size->mem_case()); const auto& UpdateShape = [out, cuda_host_mem_ptr, conf, this]() { const int64_t new_size = *reinterpret_cast(cuda_host_mem_ptr->Ptr()); CHECK_GE(new_size, 0); @@ -160,7 +161,7 @@ class SyncDynamicResizeGPUKernel final : public Kernel { AutoMemcpy(ctx->stream(), out->mut_dptr(), in->dptr(), in->ByteSizeOfBlobBody(), out->mem_case(), in->mem_case()); AutoMemcpy(ctx->stream(), cuda_host_mem_ptr->Ptr(), size->dptr(), sizeof(SizeType), - MakeHostMemCase(), size->mem_case()); + memory::MakeHostMemCase(), size->mem_case()); const auto& UpdateShape = [out, cuda_host_mem_ptr, conf, this]() { const int64_t new_size = *reinterpret_cast(cuda_host_mem_ptr->Ptr()); CHECK_GE(new_size, 0); diff --git a/oneflow/core/kernel/user_kernel.cpp b/oneflow/core/kernel/user_kernel.cpp index d37f071efb21ca38cf78344b1b3361823ac3892b..4d3be21caa16df6fda250b9c5ddbe4fa09570309 100644 --- a/oneflow/core/kernel/user_kernel.cpp +++ b/oneflow/core/kernel/user_kernel.cpp @@ -22,7 +22,6 @@ limitations under the License. #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/user_op_conf.h" #include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/kernel/eager_kernel.h" #include "oneflow/core/kernel/kernel.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/operator/operator.h" @@ -55,8 +54,12 @@ void FillTensorDescWithBlob(const Blob* blob, user_op::NaiveTensorDesc* tensor_d proto.set_data_type(blob->blob_desc().data_type()); proto.set_is_dynamic(blob->blob_desc().is_dynamic()); *tensor_desc = proto; - tensor_desc->mut_shape()->CheckNumAxesIdenticalAndAssign(blob->shape()); - tensor_desc->mut_stride()->CheckNumAxesIdenticalAndAssign(blob->stride()); + Shape tensor_desc_shape = tensor_desc->shape(); + tensor_desc_shape.CheckNumAxesIdenticalAndAssign(blob->shape()); + tensor_desc->set_shape(tensor_desc_shape); + Stride tensor_desc_stride = tensor_desc->stride(); + tensor_desc_stride.CheckNumAxesIdenticalAndAssign(blob->stride()); + tensor_desc->set_stride(tensor_desc_stride); } } // namespace @@ -147,7 +150,11 @@ class UserKernelInitAndCacheContext final : public user_op::KernelInitContext, auto& tensor_desc = pair.second.second; Blob* blob = BnInOp2Blob(bn); CHECK(blob != nullptr) << "Blob " << bn << " is not found in cache context."; - if (blob->blob_desc().is_dynamic()) { blob->shape().ToShape(tensor_desc.mut_shape()); } + if (blob->blob_desc().is_dynamic()) { + Shape shape; + blob->shape().ToShape(&shape); + tensor_desc.set_shape(shape); + } } } @@ -249,52 +256,89 @@ class UserKernelOpInferContext : public user_op::InferContext { const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->TensorDesc4ArgNameAndIndex(arg_name, - index); + return *TensorDesc4ArgNameAndIndex(arg_name, index); + } + const user_op::TensorDesc& OutputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return *TensorDesc4ArgNameAndIndex(arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(const std::string& arg_name, int32_t index) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index); } - user_op::TensorDesc* OutputTensorDesc(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index); + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return nullptr; } + return it->second.get(); } - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); if (it == arg2tensor_desc_.end()) { return nullptr; } return it->second.get(); } const Shape& InputShape(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Shape4ArgNameAndIndex(arg_name, index); + return Shape4ArgNameAndIndex(arg_name, index); } - Shape* OutputShape(const std::string& arg_name, int32_t index) override { + const Shape& OutputShape(const std::string& arg_name, int32_t index) const override { return Shape4ArgNameAndIndex(arg_name, index); } - Shape* Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_shape(); + void SetOutputShape(const std::string& arg_name, int32_t index, const Shape& shape) override { + SetShape4ArgNameAndIndex(arg_name, index, shape); + } + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->shape(); + } + void SetShape4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Shape& shape) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_shape(shape); } const Stride& InputStride(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Stride4ArgNameAndIndex(arg_name, index); + return Stride4ArgNameAndIndex(arg_name, index); } - Stride* OutputStride(const std::string& arg_name, int32_t index) override { + const Stride& OutputStride(const std::string& arg_name, int32_t index) const override { return Stride4ArgNameAndIndex(arg_name, index); } - Stride* Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_stride(); + void SetOutputStride(const std::string& arg_name, int32_t index, const Stride& stride) override { + return SetStride4ArgNameAndIndex(arg_name, index, stride); + } + const Stride& Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->stride(); } - const DataType& InputDType(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Dtype4ArgNameAndIndex(arg_name, index); + void SetStride4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Stride& stride) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_stride(stride); + } + DataType InputDType(const std::string& arg_name, int32_t index) const override { + return Dtype4ArgNameAndIndex(arg_name, index); } - DataType* OutputDType(const std::string& arg_name, int32_t index) override { + DataType OutputDType(const std::string& arg_name, int32_t index) const override { return Dtype4ArgNameAndIndex(arg_name, index); } - DataType* Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_data_type(); + void SetOutputDType(const std::string& arg_name, int32_t index, DataType data_type) override { + return SetDtype4ArgNameAndIndex(arg_name, index, data_type); + } + DataType Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->data_type(); + } + void SetDtype4ArgNameAndIndex(const std::string& arg_name, int32_t index, + DataType data_type) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_data_type(data_type); } bool InputIsDynamic(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->IsDynamic4ArgNameAndIndex(arg_name, index); + return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* OutputIsDynamic(const std::string& arg_name, int32_t index) override { + bool OutputIsDynamic(const std::string& arg_name, int32_t index) const override { return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index)->mut_is_dynamic(); + void SetOutputIsDynamic(const std::string& arg_name, int32_t index, bool is_dynamic) override { + return SetIsDynamic4ArgNameAndIndex(arg_name, index, is_dynamic); + } + bool IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return TensorDesc4ArgNameAndIndex(arg_name, index)->is_dynamic(); + } + void SetIsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index, + bool is_dynamic) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index)->set_is_dynamic(is_dynamic); } const ArgVec& inputs() const override { return inputs_; } @@ -324,8 +368,12 @@ class UserKernelOpInferContext : public user_op::InferContext { Blob* blob = BnInOp2Blob(GenRepeatedBn(arg_pair.first, arg_pair.second)); CHECK_NOTNULL(blob); if (*arg_tensor_desc_ptr) { - (*arg_tensor_desc_ptr)->mut_shape()->CheckNumAxesIdenticalAndAssign(blob->shape()); - (*arg_tensor_desc_ptr)->mut_stride()->CheckNumAxesIdenticalAndAssign(blob->stride()); + Shape tensor_desc_shape = (*arg_tensor_desc_ptr)->shape(); + tensor_desc_shape.CheckNumAxesIdenticalAndAssign(blob->shape()); + (*arg_tensor_desc_ptr)->set_shape(tensor_desc_shape); + Stride tensor_desc_stride = (*arg_tensor_desc_ptr)->stride(); + tensor_desc_stride.CheckNumAxesIdenticalAndAssign(blob->stride()); + (*arg_tensor_desc_ptr)->set_stride(tensor_desc_stride); } else { arg_tensor_desc_ptr->reset(new user_op::NaiveTensorDesc()); FillTensorDescWithBlob(blob, arg_tensor_desc_ptr->get()); @@ -570,6 +618,7 @@ class UserKernelComputeContext final : public user_op::KernelComputeContext { UserKernelBaseContext base_ctx_; }; +// kernel registry context used in kernel creation class UserKernelRegContext final : public user_op::KernelRegContext { public: explicit UserKernelRegContext(const KernelConf& kernel_conf) @@ -656,6 +705,26 @@ void UserKernel::ForwardUserKernel(const std::function(ctx_->stream()); + if (!cuda_stream->IsGraphCapturing()) { + if (cuda_graph_exec_->IsInstantiated() && (!updated)) { + cuda_stream->LaunchGraph(cuda_graph_exec_.get()); + return; + } + const auto* cuda_graph_support = + CHECK_NOTNULL(dynamic_cast(kernel_.get())); + if (cuda_graph_support->IsReadyForCapture(ctx_.get(), opkernel_state, + opkernel_cache_.get())) { + current_scope_capturing = true; + cuda_stream->BeginGraphCapture(); + } + } + } +#endif // WITH_ROCM_GRAPHS + kernel_->Compute(ctx_.get(), opkernel_state, opkernel_cache_.get()); #ifdef WITH_CUDA_GRAPHS @@ -665,10 +734,18 @@ void UserKernel::ForwardUserKernel(const std::functionLaunchGraph(cuda_graph_exec_.get()); } #endif // WITH_CUDA_GRAPHS +#ifdef WITH_ROCM_GRAPHS + if (cuda_graph_exec_ && current_scope_capturing) { + auto* cuda_stream = dynamic_cast(ctx_->stream()); + cuda_stream->EndGraphCapture(cuda_graph_exec_.get()); + cuda_stream->LaunchGraph(cuda_graph_exec_.get()); + } +#endif // WITH_ROCM_GRAPHS + } bool UserKernel::IsCudaGraphSupported() const { -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) return cuda_graph_exec_.get() != nullptr; #else return false; @@ -706,6 +783,25 @@ void UserKernel::VirtualKernelInit(KernelContext* ctx) { } } #endif // WITH_CUDA_GRAPHS +#ifdef WITH_ROCM_GRAPHS + if (ParseBooleanFromEnv("ONEFLOW_KERNEL_ENABLE_CUDA_GRAPH", false)) { + UserKernelInitContext init_ctx(ctx->stream(), kernel_conf()); + auto* cuda_stream = dynamic_cast(ctx->stream()); + const auto* cuda_graph_support = dynamic_cast(kernel_.get()); + if (cuda_stream != nullptr) { + if (cuda_graph_support != nullptr + && cuda_graph_support->IsCudaGraphSupported(&init_ctx, opkernel_state_.get())) { + cuda_graph_exec_.reset(new ep::CudaGraphExecutable()); + VLOG(3) << "CUDA Graphs Kernel: " << op_conf().name() << " (" + << op_conf().user_conf().op_type_name() << ")"; + } else { + VLOG(3) << "CUDA Graphs not supported: " << op_conf().name() << " (" + << op_conf().user_conf().op_type_name() << ")"; + } + } + } +#endif // WITH_ROCM_GRAPHS + } void UserKernel::ForwardDataContent(KernelContext* ctx) const { @@ -751,52 +847,4 @@ NEW_REGISTER_KERNEL(OperatorConf::kUserConf, UserKernel).SetIsMatchedPred([](con return true; }); -EagerKernel::EagerKernel(const KernelConf& kernel_conf) { - InitBase(kernel_conf); - InitOpKernel(kernel_conf); -} - -void EagerKernel::InitOpKernel(const KernelConf& kernel_conf) { - const std::string& op_type_name = kernel_conf.op_attribute().op_conf().user_conf().op_type_name(); - auto kernel_reg_val = CHECK_JUST(user_op::UserOpRegistryMgr::Get().GetOpKernelRegistryResult( - op_type_name, UserKernelRegContext(kernel_conf))); - CHECK_NOTNULL(kernel_reg_val); - kernel_.reset(kernel_reg_val->create_fn()); -} - -void EagerKernel::Infer(std::function BnInOp2Blob) const { - if (kernel_conf().all_blobs_are_static()) { return; } - UserKernelInferContext infer_ctx(nullptr, kernel_conf()); - infer_ctx.UpdateArg2Tensor(BnInOp2Blob); - auto* op_infer_ctx = dynamic_cast(infer_ctx.MutOpInferContext()); - if (op_infer_ctx) { op_infer_ctx->UpdateArg2TensorDesc(BnInOp2Blob); } - kernel_->InferShape(&infer_ctx); -} - -std::shared_ptr EagerKernel::EagerForward( - const std::shared_ptr& old_opkernel_state, DeviceCtx* device_ctx, - std::function BnInOp2Blob) const { - std::shared_ptr new_opkernel_state; - CHECK_NOTNULL(device_ctx); - UserKernelInitAndCacheContext init_and_cache_ctx(device_ctx->stream(), kernel_conf()); - if (old_opkernel_state) { - new_opkernel_state = old_opkernel_state; - } else { - new_opkernel_state = kernel_->CreateOpKernelState(&init_and_cache_ctx); - } - kernel_->InitOpKernelCacheWithFlags(&init_and_cache_ctx, user_op::OpKernelCache::kAllMayChanged, - &cache_); - - if (IsAllBlobEmpty(op_attribute().output_bns(), BnInOp2Blob) - && !kernel_->AlwaysComputeWhenAllOutputsEmpty()) { - return new_opkernel_state; - } - - // TODO(lixinqi): refactor to a lightweight KernelComputeContext - UserKernelComputeContext compute_ctx(device_ctx->stream(), kernel_conf()); - compute_ctx.UpdateTensorWithCorrBlob(BnInOp2Blob); - kernel_->Compute(&compute_ctx, new_opkernel_state.get(), cache_.get()); - return new_opkernel_state; -} - } // namespace oneflow diff --git a/oneflow/core/kernel/user_kernel.h b/oneflow/core/kernel/user_kernel.h index 26c44676a3f9d49d1252762b3340886ae5782bf9..1819a55897bb97874bfaaef9e659c0247d10cc7f 100644 --- a/oneflow/core/kernel/user_kernel.h +++ b/oneflow/core/kernel/user_kernel.h @@ -20,21 +20,14 @@ limitations under the License. #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/user_op_conf.h" #include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/kernel/eager_kernel.h" #include "oneflow/core/kernel/kernel.h" -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/ep/cuda/cuda_stream.h" #endif // WITH_CUDA -#ifdef WITH_ROCM - -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#endif // WITH_ROCM - namespace oneflow { class UserKernelComputeContext; @@ -75,7 +68,7 @@ class UserKernel final : public Kernel { std::unique_ptr cache_ctx_; std::unique_ptr infer_ctx_; std::unique_ptr infer_cache_; -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) std::unique_ptr cuda_graph_exec_; #endif // WITH_CUDA_GRAPHS }; diff --git a/oneflow/core/kernel/util/numeric_limits.cuh b/oneflow/core/kernel/util/numeric_limits.cuh index 7ef9e9e3ee0e1f5fbcd4cccaf59898a5ad3609ad..bfdda96a419edd1c7161e06a1c0226d6ea6b69bf 100644 --- a/oneflow/core/kernel/util/numeric_limits.cuh +++ b/oneflow/core/kernel/util/numeric_limits.cuh @@ -32,7 +32,7 @@ limitations under the License. namespace oneflow { namespace detail { -#if defined(__CUDACC__) +#if defined(__CUDACC__) || defined(__HIPCC__) #define OF_NUMERICS_FUNC static inline __host__ __device__ #else #define OF_NUMERICS_FUNC static inline @@ -116,6 +116,49 @@ struct numeric_limits { OF_NUMERICS_FUNC float upper_bound() { return static_cast(inf); } }; +#if defined(__CUDACC__) || defined(__HIPCC__) +static __device__ unsigned short int HALF_LOWEST = 0xfbff; +static __device__ unsigned short int HALF_MAX = 0x7bff; +static __device__ unsigned short int HALF_LOWER_BOUND = 0xfc00; +static __device__ unsigned short int HALF_UPPER_BOUND = 0x7c00; +template<> +struct numeric_limits { + static inline __device__ half lowest() { return *reinterpret_cast(&HALF_LOWEST); } + static inline __device__ half max() { return *reinterpret_cast(&HALF_MAX); } + static inline __device__ half lower_bound() { + return *reinterpret_cast(&HALF_LOWER_BOUND); + } + static inline __device__ half upper_bound() { + return *reinterpret_cast(&HALF_UPPER_BOUND); + } +}; + +#if CUDA_VERSION >= 11000 + +static __device__ unsigned short int NV_BFLOAT16_LOWEST = 0xff7f; +static __device__ unsigned short int NV_BFLOAT16_MAX = 0x7f7f; +static __device__ unsigned short int NV_BFLOAT16_LOWER_BOUND = 0xff80; +static __device__ unsigned short int NV_BFLOAT16_UPPER_BOUND = 0x7f80; +template<> +struct numeric_limits { + static inline __device__ nv_bfloat16 lowest() { + return *reinterpret_cast(&NV_BFLOAT16_LOWEST); + } + static inline __device__ nv_bfloat16 max() { + return *reinterpret_cast(&NV_BFLOAT16_MAX); + } + static inline __device__ nv_bfloat16 lower_bound() { + return *reinterpret_cast(&NV_BFLOAT16_LOWER_BOUND); + } + static inline __device__ nv_bfloat16 upper_bound() { + return *reinterpret_cast(&NV_BFLOAT16_UPPER_BOUND); + } +}; + +#endif // CUDA_VERSION >= 11000 + +#endif // defined(__CUDACC__) + template<> struct numeric_limits { OF_NUMERICS_FUNC double lowest() { return -DBL_MAX; } diff --git a/oneflow/core/kernel/util/numeric_limits.hip.h b/oneflow/core/kernel/util/numeric_limits.hip.h deleted file mode 100644 index 96a9b102a1ce70edae29b0daf6bc7f58bb3904a4..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/util/numeric_limits.hip.h +++ /dev/null @@ -1,128 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -// reference: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/NumericLimits.cuh -#pragma once -#include -#include -#include - -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" - -// numeric_limits.cuh is a holder for numeric limits definitions of commonly used -// types. This header is very specific to ROCm HIP and may be removed in the future. - -// The lower_bound and upper_bound constants are same as lowest and max for -// integral types, but are -inf and +inf for floating point types. They are -// useful in implementing min, max, etc. - -namespace oneflow { -namespace detail { - -#if defined(__HIPCC__) -#define OF_NUMERICS_FUNC static inline __host__ __device__ -#else -#define OF_NUMERICS_FUNC static inline -#endif - -template -struct numeric_limits {}; - -// WARNING: the following oneflow::numeric_limits definitions are there only to support -// HIP compilation for the moment. Use std::numeric_limits if you are not -// compiling for ROCm. -// from @colesbury: "The functions on numeric_limits aren't marked with -// __device__ which is why they don't work with ROCm. CUDA allows them -// because they're constexpr." - -namespace { -// ROCm doesn't like INFINITY too. -constexpr double inf = INFINITY; -} // namespace - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC bool lowest() { return false; } - OF_NUMERICS_FUNC bool max() { return true; } - OF_NUMERICS_FUNC bool lower_bound() { return false; } - OF_NUMERICS_FUNC bool upper_bound() { return true; } -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC uint8_t lowest() { return 0; } - OF_NUMERICS_FUNC uint8_t max() { return UINT8_MAX; } - OF_NUMERICS_FUNC uint8_t lower_bound() { return 0; } - OF_NUMERICS_FUNC uint8_t upper_bound() { return UINT8_MAX; } -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC int8_t lowest() { return INT8_MIN; } - OF_NUMERICS_FUNC int8_t max() { return INT8_MAX; } - OF_NUMERICS_FUNC int8_t lower_bound() { return INT8_MIN; } - OF_NUMERICS_FUNC int8_t upper_bound() { return INT8_MAX; } -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC int16_t lowest() { return INT16_MIN; } - OF_NUMERICS_FUNC int16_t max() { return INT16_MAX; } - OF_NUMERICS_FUNC int16_t lower_bound() { return INT16_MIN; } - OF_NUMERICS_FUNC int16_t upper_bound() { return INT16_MAX; } -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC int32_t lowest() { return INT32_MIN; } - OF_NUMERICS_FUNC int32_t max() { return INT32_MAX; } - OF_NUMERICS_FUNC int32_t lower_bound() { return INT32_MIN; } - OF_NUMERICS_FUNC int32_t upper_bound() { return INT32_MAX; } -}; - -template<> -struct numeric_limits { -#ifdef _MSC_VER - OF_NUMERICS_FUNC int64_t lowest() { return _I64_MIN; } - OF_NUMERICS_FUNC int64_t max() { return _I64_MAX; } - OF_NUMERICS_FUNC int64_t lower_bound() { return _I64_MIN; } - OF_NUMERICS_FUNC int64_t upper_bound() { return _I64_MAX; } -#else - OF_NUMERICS_FUNC int64_t lowest() { return INT64_MIN; } - OF_NUMERICS_FUNC int64_t max() { return INT64_MAX; } - OF_NUMERICS_FUNC int64_t lower_bound() { return INT64_MIN; } - OF_NUMERICS_FUNC int64_t upper_bound() { return INT64_MAX; } -#endif -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC float lowest() { return -FLT_MAX; } - OF_NUMERICS_FUNC float max() { return FLT_MAX; } - OF_NUMERICS_FUNC float lower_bound() { return -static_cast(inf); } - OF_NUMERICS_FUNC float upper_bound() { return static_cast(inf); } -}; - -template<> -struct numeric_limits { - OF_NUMERICS_FUNC double lowest() { return -DBL_MAX; } - OF_NUMERICS_FUNC double max() { return DBL_MAX; } - OF_NUMERICS_FUNC double lower_bound() { return -inf; } - OF_NUMERICS_FUNC double upper_bound() { return inf; } -}; - -} // namespace detail -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/kernel/util/numerics.cuh b/oneflow/core/kernel/util/numerics.cuh index d0e46faa070feebded6603961dbe58bf87254199..98f827d31c61120b3fa54159dd59193ad46e9f41 100644 --- a/oneflow/core/kernel/util/numerics.cuh +++ b/oneflow/core/kernel/util/numerics.cuh @@ -218,6 +218,20 @@ struct numerics { OF_NUMERICS_FUNC bool isinf(float a) { return ::isinf(a); } }; +#if defined(__CUDACC__) +template<> +struct numerics { + OF_NUMERICS_FUNC bool isnan(half a) { return ::isnan((float)a); } +}; +#endif + +#if defined(__HIPCC__) +template<> +struct numerics { + OF_NUMERICS_FUNC bool isnan(half a) { return ::isnan(__half2float(a)); } +}; +#endif + template<> struct numerics { OF_NUMERICS_FUNC double min() { return detail::numeric_limits::lowest(); } diff --git a/oneflow/core/kernel/util/numerics.hip.h b/oneflow/core/kernel/util/numerics.hip.h deleted file mode 100644 index 5b1bad8d308e2c9c9494331e7e7c0d4da0676501..0000000000000000000000000000000000000000 --- a/oneflow/core/kernel/util/numerics.hip.h +++ /dev/null @@ -1,250 +0,0 @@ - -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -// reference: https://github.com/pytorch/pytorch/blob/master/aten/src/THC/THCNumerics.cuh -#ifndef ONEFLOW_CORE_KERNEL_UTIL_NUMERICS_HIP_H -#define ONEFLOW_CORE_KERNEL_UTIL_NUMERICS_HIP_H -#pragma once - -#include -#include -#include -#include -#include - -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/util/numeric_limits.hip.h" - -namespace oneflow { -namespace detail { - -template -struct numerics {}; - -template -OF_NUMERICS_FUNC T powi(T a, T b) { - assert(numerics::ge(b, 0)); - T result = 1; - while (b) { - if (b & 1) { result *= a; } - b /= 2; - a *= a; - } - return result; -} - -template<> -struct numerics { - OF_NUMERICS_FUNC uint8_t min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC uint8_t max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC uint8_t lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC uint8_t upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(uint8_t a, uint8_t b) { return a < b; } - OF_NUMERICS_FUNC bool le(uint8_t a, uint8_t b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(uint8_t a, uint8_t b) { return a > b; } - OF_NUMERICS_FUNC bool ge(uint8_t a, uint8_t b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(uint8_t a, uint8_t b) { return a == b; } - OF_NUMERICS_FUNC bool ne(uint8_t a, uint8_t b) { return a != b; } - - OF_NUMERICS_FUNC uint8_t add(uint8_t a, uint8_t b) { return a + b; } - OF_NUMERICS_FUNC uint8_t mul(uint8_t a, uint8_t b) { return a * b; } - OF_NUMERICS_FUNC uint8_t sub(uint8_t a, uint8_t b) { return a - b; } - OF_NUMERICS_FUNC uint8_t div(uint8_t a, uint8_t b) { return a / b; } - OF_NUMERICS_FUNC uint8_t pow(uint8_t a, uint8_t b) { return powi(a, b); } - OF_NUMERICS_FUNC bool isnan(uint8_t a) { return false; } - OF_NUMERICS_FUNC bool isinf(uint8_t a) { return false; } -}; - -#ifdef _MSC_VER -// Suppress warning C4804: '/': unsafe use of type 'bool' in operation -#pragma warning(push) -#pragma warning(disable : 4804) -#endif - -template<> -struct numerics { - OF_NUMERICS_FUNC bool min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC bool max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC bool lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC bool upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(bool a, bool b) { return a < b; } - OF_NUMERICS_FUNC bool le(bool a, bool b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(bool a, bool b) { return a > b; } - OF_NUMERICS_FUNC bool ge(bool a, bool b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(bool a, bool b) { return a == b; } - OF_NUMERICS_FUNC bool ne(bool a, bool b) { return a != b; } - OF_NUMERICS_FUNC bool add(bool a, bool b) { return a + b; } - OF_NUMERICS_FUNC bool mul(bool a, bool b) { return a && b; } - OF_NUMERICS_FUNC bool sub(bool a, bool b) { return a - b; } - OF_NUMERICS_FUNC bool div(bool a, bool b) { return a / b; } - OF_NUMERICS_FUNC bool isnan(bool a) { return false; } - OF_NUMERICS_FUNC bool isinf(bool a) { return false; } -}; - -#ifdef _MSC_VER -#pragma warning(pop) -#endif - -template<> -struct numerics { - OF_NUMERICS_FUNC int8_t min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC int8_t max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC int8_t lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC int8_t upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(int8_t a, int8_t b) { return a < b; } - OF_NUMERICS_FUNC bool le(int8_t a, int8_t b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(int8_t a, int8_t b) { return a > b; } - OF_NUMERICS_FUNC bool ge(int8_t a, int8_t b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(int8_t a, int8_t b) { return a == b; } - OF_NUMERICS_FUNC bool ne(int8_t a, int8_t b) { return a != b; } - - OF_NUMERICS_FUNC int8_t add(int8_t a, int8_t b) { return a + b; } - OF_NUMERICS_FUNC int8_t mul(int8_t a, int8_t b) { return a * b; } - OF_NUMERICS_FUNC int8_t sub(int8_t a, int8_t b) { return a - b; } - OF_NUMERICS_FUNC int8_t div(int8_t a, int8_t b) { return a / b; } - OF_NUMERICS_FUNC int8_t pow(int8_t a, int8_t b) { return powi(a, b); } - OF_NUMERICS_FUNC bool isnan(int8_t a) { return false; } - OF_NUMERICS_FUNC bool isinf(int8_t a) { return false; } -}; - -template<> -struct numerics { - OF_NUMERICS_FUNC int16_t min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC int16_t max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC int16_t lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC int16_t upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(int16_t a, int16_t b) { return a < b; } - OF_NUMERICS_FUNC bool le(int16_t a, int16_t b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(int16_t a, int16_t b) { return a > b; } - OF_NUMERICS_FUNC bool ge(int16_t a, int16_t b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(int16_t a, int16_t b) { return a == b; } - OF_NUMERICS_FUNC bool ne(int16_t a, int16_t b) { return a != b; } - - OF_NUMERICS_FUNC int16_t add(int16_t a, int16_t b) { return a + b; } - OF_NUMERICS_FUNC int16_t mul(int16_t a, int16_t b) { return a * b; } - OF_NUMERICS_FUNC int16_t sub(int16_t a, int16_t b) { return a - b; } - OF_NUMERICS_FUNC int16_t div(int16_t a, int16_t b) { return a / b; } - OF_NUMERICS_FUNC int16_t pow(int16_t a, int16_t b) { return powi(a, b); } - OF_NUMERICS_FUNC bool isnan(int16_t a) { return false; } - OF_NUMERICS_FUNC bool isinf(int16_t a) { return false; } -}; - -template<> -struct numerics { - OF_NUMERICS_FUNC int32_t min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC int32_t max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC int32_t lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC int32_t upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(int32_t a, int32_t b) { return a < b; } - OF_NUMERICS_FUNC bool le(int32_t a, int32_t b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(int32_t a, int32_t b) { return a > b; } - OF_NUMERICS_FUNC bool ge(int32_t a, int32_t b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(int32_t a, int32_t b) { return a == b; } - OF_NUMERICS_FUNC bool ne(int32_t a, int32_t b) { return a != b; } - - OF_NUMERICS_FUNC int32_t add(int32_t a, int32_t b) { return a + b; } - OF_NUMERICS_FUNC int32_t mul(int32_t a, int32_t b) { return a * b; } - OF_NUMERICS_FUNC int32_t sub(int32_t a, int32_t b) { return a - b; } - OF_NUMERICS_FUNC int32_t div(int32_t a, int32_t b) { return a / b; } - OF_NUMERICS_FUNC int32_t pow(int32_t a, int32_t b) { return powi(a, b); } - OF_NUMERICS_FUNC bool isnan(int32_t a) { return false; } - OF_NUMERICS_FUNC bool isinf(int32_t a) { return false; } -}; - -template<> -struct numerics { - OF_NUMERICS_FUNC int64_t min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC int64_t max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC int64_t lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC int64_t upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(int64_t a, int64_t b) { return a < b; } - OF_NUMERICS_FUNC bool le(int64_t a, int64_t b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(int64_t a, int64_t b) { return a > b; } - OF_NUMERICS_FUNC bool ge(int64_t a, int64_t b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(int64_t a, int64_t b) { return a == b; } - OF_NUMERICS_FUNC bool ne(int64_t a, int64_t b) { return a != b; } - - OF_NUMERICS_FUNC int64_t add(int64_t a, int64_t b) { return a + b; } - OF_NUMERICS_FUNC int64_t mul(int64_t a, int64_t b) { return a * b; } - OF_NUMERICS_FUNC int64_t sub(int64_t a, int64_t b) { return a - b; } - OF_NUMERICS_FUNC int64_t div(int64_t a, int64_t b) { return a / b; }; - OF_NUMERICS_FUNC int64_t pow(int64_t a, int64_t b) { return powi(a, b); } - OF_NUMERICS_FUNC bool isnan(int64_t a) { return false; } - OF_NUMERICS_FUNC bool isinf(int64_t a) { return false; } -}; - -// DEPRECATED: use math functions from std and cuda math API (if needed) -template<> -struct numerics { - OF_NUMERICS_FUNC float min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC float max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC float lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC float upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(float a, float b) { return a < b; } - OF_NUMERICS_FUNC bool le(float a, float b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(float a, float b) { return a > b; } - OF_NUMERICS_FUNC bool ge(float a, float b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(float a, float b) { return a == b; } - OF_NUMERICS_FUNC bool ne(float a, float b) { return a != b; } - - OF_NUMERICS_FUNC float sqrt(float a) { return sqrtf(a); } - OF_NUMERICS_FUNC float atan(float a) { return atanf(a); } - OF_NUMERICS_FUNC float add(float a, float b) { return a + b; } - OF_NUMERICS_FUNC float div(float a, float b) { return a / b; } - OF_NUMERICS_FUNC float mul(float a, float b) { return a * b; } - OF_NUMERICS_FUNC float sub(float a, float b) { return a - b; } - OF_NUMERICS_FUNC float pow(float a, float b) { return powf(a, b); } - OF_NUMERICS_FUNC bool isnan(float a) { return ::isnan(a); } - OF_NUMERICS_FUNC bool isinf(float a) { return ::isinf(a); } -}; - -template<> -struct numerics { - OF_NUMERICS_FUNC double min() { return detail::numeric_limits::lowest(); } - OF_NUMERICS_FUNC double max() { return detail::numeric_limits::max(); } - OF_NUMERICS_FUNC double lower_bound() { return detail::numeric_limits::lower_bound(); } - OF_NUMERICS_FUNC double upper_bound() { return detail::numeric_limits::upper_bound(); } - - OF_NUMERICS_FUNC bool lt(double a, double b) { return a < b; } - OF_NUMERICS_FUNC bool le(double a, double b) { return a <= b; } - OF_NUMERICS_FUNC bool gt(double a, double b) { return a > b; } - OF_NUMERICS_FUNC bool ge(double a, double b) { return a >= b; } - OF_NUMERICS_FUNC bool eq(double a, double b) { return a == b; } - OF_NUMERICS_FUNC bool ne(double a, double b) { return a != b; } - - OF_NUMERICS_FUNC double sqrt(double a) { return ::sqrt(a); } - OF_NUMERICS_FUNC double atan(double a) { return ::atan(a); } - OF_NUMERICS_FUNC double add(double a, double b) { return a + b; } - OF_NUMERICS_FUNC double div(double a, double b) { return a / b; } - OF_NUMERICS_FUNC double mul(double a, double b) { return a * b; } - OF_NUMERICS_FUNC double sub(double a, double b) { return a - b; } - OF_NUMERICS_FUNC double pow(double a, double b) { return ::pow(a, b); } - OF_NUMERICS_FUNC bool isnan(double a) { return ::isnan(a); } - OF_NUMERICS_FUNC bool isinf(double a) { return ::isinf(a); } -}; - -} // namespace detail -} // namespace oneflow - -#endif // ONEFLOW_CORE_KERNEL_UTIL_NUMERICS_HIP_H \ No newline at end of file diff --git a/oneflow/core/lazy/actor/acc_ctrl_tick_actor.cpp b/oneflow/core/lazy/actor/acc_ctrl_tick_actor.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7e7aa4c38c5903406ce2cc74c0ffcde427a4f3f6 --- /dev/null +++ b/oneflow/core/lazy/actor/acc_ctrl_tick_actor.cpp @@ -0,0 +1,163 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/lazy/actor/actor.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +class AccCtrlTickActor : public Actor { + public: + OF_DISALLOW_COPY_AND_MOVE(AccCtrlTickActor); + AccCtrlTickActor() + : acc_cnt_(0), + max_acc_num_(0), + last_micro_batch_input_output_mutex_(false), + consumed_tick_regst_desc_id_(-1), + produced_tick_regst_desc_id_(-1){}; + virtual ~AccCtrlTickActor() = default; + + private: + // NOTE(chengcheng): Empty rs for naive and inplace regst, all regst is customized. + std::pair> GetNaiveOrCustomizedConsumedRegstDescName() + override { + return std::make_pair(RegstNameType::kNaive, HashSet{}); + } + std::pair> GetNaiveOrCustomizedProducedRegstDescName() + override { + return std::make_pair(RegstNameType::kNaive, HashSet{}); + } + + bool IsCustomizedReadReady() const override { + return (!last_micro_batch_input_output_mutex_) && consumed_tick_rs_.IsCurSlotReady(); + } + bool IsCustomizedWriteReady() const override { return produced_tick_rs_.IsCurSlotReady(); } + + void NormalProcessCustomizedEordMsg(const ActorMsg&) override {} + bool IsCustomizedReadAlwaysUnReadyFromNow() const override { + // all Messages are flushed + return ReceiveEordMsg(consumed_tick_regst_desc_id_); + } + + void VirtualActorInit(const TaskProto& proto) override; + void Act() override; + void AsyncSendCustomizedProducedRegstMsgToConsumer() override; + void AsyncSendCustomizedConsumedRegstMsgToProducer() override; + void UpdtStateAsCustomizedProducedRegst(Regst* regst) override; + void NormalProcessCustomizedReadableRegstMsg(const ActorMsg& msg) override; + + int32_t acc_cnt_; + int32_t max_acc_num_; + bool last_micro_batch_input_output_mutex_; + int64_t consumed_tick_regst_desc_id_; + int64_t produced_tick_regst_desc_id_; + RegstSlot consumed_tick_rs_; + RegstSlot produced_tick_rs_; +}; + +void AccCtrlTickActor::VirtualActorInit(const TaskProto& proto) { + acc_cnt_ = 0; + const OperatorConf& op_conf = + proto.exec_sequence().exec_node(0).kernel_conf().op_attribute().op_conf(); + max_acc_num_ = user_op::UserOpConfWrapper(op_conf).attr("max_acc_num"); + + // NOTE(chengcheng): check time shape equal max_acc_num + const Shape& in_time_shape = Singleton::Get() + ->RegstDesc4RegstDescId(Name2SoleRegstDescId("in")) + .data_regst_time_shape(); + const Shape& out_time_shape = Singleton::Get() + ->RegstDesc4RegstDescId(Name2SoleRegstDescId("out")) + .data_regst_time_shape(); + CHECK_EQ(in_time_shape.elem_cnt() % out_time_shape.elem_cnt(), 0); + CHECK_EQ(in_time_shape.elem_cnt() / out_time_shape.elem_cnt(), max_acc_num_); + CHECK_GT(max_acc_num_, 1); + + // input + const auto& consumed_ids = proto.consumed_regst_desc_id(); + CHECK_EQ(consumed_ids.size(), 1); + auto in_it = consumed_ids.find("in"); + CHECK(in_it != consumed_ids.end()); + CHECK_EQ(in_it->second.regst_desc_id_size(), 1); + consumed_tick_regst_desc_id_ = in_it->second.regst_desc_id(0); + consumed_tick_rs_.InsertRegstDescId(consumed_tick_regst_desc_id_); + consumed_tick_rs_.InitedDone(); + + // output + CHECK_EQ(proto.produced_regst_desc().size(), 1); + + const auto& produced_ids = proto.produced_regst_desc(); + CHECK_EQ(produced_ids.size(), 1); + auto out_it = produced_ids.find("out"); + CHECK(out_it != produced_ids.end()); + const RegstDescProto& out_regst_desc = out_it->second; + produced_tick_regst_desc_id_ = out_regst_desc.regst_desc_id(); + produced_tick_rs_.InsertRegstDescId(produced_tick_regst_desc_id_); + produced_tick_rs_.InitedDone(); + + ForEachProducedRegst([&](Regst* regst) { + CHECK_EQ(regst->regst_desc_id(), produced_tick_regst_desc_id_); + CHECK_EQ(0, produced_tick_rs_.TryPushBackRegst(regst)); + }); + + OF_SET_MSG_HANDLER(&AccCtrlTickActor::HandlerNormal); +} + +void AccCtrlTickActor::Act() { + acc_cnt_ += 1; + if (acc_cnt_ == max_acc_num_) { + CHECK(!last_micro_batch_input_output_mutex_); + last_micro_batch_input_output_mutex_ = true; + acc_cnt_ = 0; + } +} + +void AccCtrlTickActor::AsyncSendCustomizedProducedRegstMsgToConsumer() { + if (last_micro_batch_input_output_mutex_) { + CHECK(consumed_tick_rs_.IsCurSlotReady()); // inplace consume + CHECK(produced_tick_rs_.IsCurSlotReady()); + Regst* const tick_regst = produced_tick_rs_.Front(produced_tick_regst_desc_id_); + CHECK_GT(HandleRegstToConsumer(tick_regst), 0); + produced_tick_rs_.PopFrontRegsts({produced_tick_regst_desc_id_}); + } +} + +void AccCtrlTickActor::AsyncSendCustomizedConsumedRegstMsgToProducer() { + if (!last_micro_batch_input_output_mutex_) { + Regst* const tick_regst = consumed_tick_rs_.Front(consumed_tick_regst_desc_id_); + CHECK_NOTNULL(tick_regst); + AsyncSendRegstMsgToProducer(tick_regst); + CHECK_EQ(0, consumed_tick_rs_.TryPopFrontRegst(consumed_tick_regst_desc_id_)); + } +} + +void AccCtrlTickActor::UpdtStateAsCustomizedProducedRegst(Regst* regst) { + CHECK(last_micro_batch_input_output_mutex_); + CHECK_EQ(regst->regst_desc_id(), produced_tick_regst_desc_id_); + CHECK_EQ(produced_tick_rs_.TryPushBackRegst(regst), 0); + + Regst* in_regst = consumed_tick_rs_.Front(consumed_tick_regst_desc_id_); + CHECK(in_regst); + AsyncSendRegstMsgToProducer(in_regst); + CHECK_EQ(0, consumed_tick_rs_.TryPopFrontRegst(consumed_tick_regst_desc_id_)); + last_micro_batch_input_output_mutex_ = false; +} + +void AccCtrlTickActor::NormalProcessCustomizedReadableRegstMsg(const ActorMsg& msg) { + CHECK_EQ(0, consumed_tick_rs_.TryPushBackRegst(msg.regst())); +} + +REGISTER_ACTOR(TaskType::kAccCtrlTick, AccCtrlTickActor); + +} // namespace oneflow diff --git a/oneflow/core/lazy/actor/actor.cpp b/oneflow/core/lazy/actor/actor.cpp index ace3473b270392c87aeca60f8f0ba1460c4b29de..cd4d3807859fc47a73ceb62d525fb661e50f6d1c 100644 --- a/oneflow/core/lazy/actor/actor.cpp +++ b/oneflow/core/lazy/actor/actor.cpp @@ -16,7 +16,7 @@ limitations under the License. #include "oneflow/core/lazy/actor/actor.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/job/runtime_job_descs.h" -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" namespace oneflow { @@ -76,6 +76,7 @@ void KernelContextImpl::WillForward(KernelContext* kernel_ctx, const Kernel* ker } void KernelContextImpl::DidForward(KernelContext* kernel_ctx, const Kernel* kernel) { + CHECK_JUST_MSG(kernel_ctx->stream()->GetAsyncError(), kernel->op_conf().name()); Singleton::Get()->DidForward(kernel_ctx, kernel); if (stream_kernel_observer_ != nullptr) { stream_kernel_observer_->DidForward(kernel_ctx, kernel); diff --git a/oneflow/core/lazy/actor/actor_context.h b/oneflow/core/lazy/actor/actor_context.h index 1e9a80cfd1ca0beae16efefefe82fbd81e09e6db..70f204eadb288e38d7e14cd9dbb9176bc055844a 100644 --- a/oneflow/core/lazy/actor/actor_context.h +++ b/oneflow/core/lazy/actor/actor_context.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef ONEFLOW_CORE_LAZY_ACTOR_ACTOR_CONTEXT_H_ #define ONEFLOW_CORE_LAZY_ACTOR_ACTOR_CONTEXT_H_ -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" #include "oneflow/core/job/task.pb.h" namespace oneflow { diff --git a/oneflow/core/lazy/actor/light_actor.cpp b/oneflow/core/lazy/actor/light_actor.cpp index 7aa6bed4b1d551eb5a6eced6cc96b630b52e16a4..b3dde59cd45dba5c3919c8c60f9f5982d97ca06b 100644 --- a/oneflow/core/lazy/actor/light_actor.cpp +++ b/oneflow/core/lazy/actor/light_actor.cpp @@ -27,20 +27,14 @@ limitations under the License. #include "oneflow/core/job/runtime_job_descs.h" #include "oneflow/core/common/util.h" #include "oneflow/core/kernel/user_kernel.h" -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/ep/cuda/cuda_stream.h" #endif // WITH_CUDA -#ifdef WITH_ROCM - -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#endif // WITH_ROCM - namespace oneflow { namespace { @@ -197,7 +191,7 @@ size_t GetConsumerCount(const TaskProto& task) { return consumer_cnt; } -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) bool IsCUDAGraphSupported(const Kernel* kernel) { auto* user_kernel = dynamic_cast(kernel); @@ -235,7 +229,7 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr kernel_info_[0].reset(new KernelInfo()); const KernelConf& kernel_conf = task_proto.exec_sequence().exec_node(0).kernel_conf(); kernel_info_[0]->kernel = ConstructKernel(kernel_conf, this); -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) auto* cuda_stream = dynamic_cast(actor_ctx->stream_ctx()->stream()); if (cuda_stream != nullptr && kernel_conf.all_blobs_are_static() && IsCUDAGraphSupported(kernel_info_[0]->kernel.get())) { @@ -334,10 +328,16 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr Regst* regst = index2state_.Get(regst_desc_id_index_.Lookup(regst_desc_id_it->second)).regst; if (regst == nullptr) { + LOG(WARNING) << "null regst found, op:" + << node.kernel_conf().op_attribute().op_conf().name(); CHECK(kernel_info_[0]->bn_in_op2blob.emplace(bn, nullptr).second); continue; } Blob* blob = regst->GetBlobByLbi(pair.second); + if (!blob) { + LOG(WARNING) << "null blob found, op: " + << node.kernel_conf().op_attribute().op_conf().name(); + } CHECK(kernel_info_[0]->bn_in_op2blob.emplace(bn, blob).second); } } @@ -472,7 +472,7 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr } inline void LaunchKernel() { -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) bool is_capturing = false; if (cuda_graph_exec_[0]) { auto* cuda_stream = stream_ctx_->stream()->As(); @@ -489,7 +489,7 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr } #endif kernel_info_[0]->kernel->Launch(this); -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) if (cuda_graph_exec_[0] && is_capturing) { auto* cuda_stream = stream_ctx_->stream()->As(); cuda_stream->EndGraphCapture(cuda_graph_exec_[0].get()); @@ -551,6 +551,7 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr } void DidForward(KernelContext* kernel_ctx, const Kernel* kernel) override { + CHECK_JUST_MSG(kernel_ctx->stream()->GetAsyncError(), kernel->op_conf().name()); Singleton::Get()->DidForward(kernel_ctx, kernel); if (stream_kernel_observer_ != nullptr) { stream_kernel_observer_->DidForward(kernel_ctx, kernel); @@ -597,7 +598,7 @@ class LightActor : public ActorBase, public KernelContext, public ActorContextPr std::function return_inplace_consumed_fn_[inplace]; Thread* thread_; std::unique_ptr kernel_info_[exec_kernel]; -#ifdef WITH_CUDA_GRAPHS +#if defined(WITH_CUDA_GRAPHS) || defined(WITH_ROCM_GRAPHS) std::unique_ptr cuda_graph_exec_[exec_kernel]; #endif ActorContext* actor_ctx_; diff --git a/oneflow/core/lazy/actor/naive_actor.cpp b/oneflow/core/lazy/actor/naive_actor.cpp index 3ed41d9a594dc4df90294c2510194d574b91842a..01b7623c6770f00cb48d72c6ad591becbce7be68 100644 --- a/oneflow/core/lazy/actor/naive_actor.cpp +++ b/oneflow/core/lazy/actor/naive_actor.cpp @@ -26,21 +26,15 @@ void NaiveActor::VirtualActorInit(const TaskProto&) { } REGISTER_ACTOR(TaskType::kNormalForward, NaiveActor); -REGISTER_ACTOR(TaskType::kForeignInput, NaiveActor); -REGISTER_ACTOR(TaskType::kForeignOutput, NaiveActor); REGISTER_ACTOR(TaskType::kDistributeConcat, NaiveActor); REGISTER_ACTOR(TaskType::kDistributeSplit, NaiveActor); REGISTER_ACTOR(TaskType::kSliceBoxing, NaiveActor); REGISTER_ACTOR(TaskType::kBoxingIdentity, NaiveActor); REGISTER_ACTOR(TaskType::kCollectiveBoxingPack, NaiveActor); REGISTER_ACTOR(TaskType::kCollectiveBoxingUnpack, NaiveActor); +REGISTER_ACTOR(TaskType::kNcclSendRecvBoxing, NaiveActor); REGISTER_ACTOR(TaskType::kDecodeH2D, NaiveActor); REGISTER_ACTOR(TaskType::kCriticalSectionWaitTick, NaiveActor); -#ifdef WITH_CUDA REGISTER_ACTOR(TaskType::kCopyHd, NaiveActor); -#endif -#ifdef WITH_ROCM -REGISTER_ACTOR(TaskType::kCopyHd, NaiveActor); -#endif REGISTER_ACTOR(TaskType::kCollectiveBoxingGeneric, NaiveActor); } // namespace oneflow diff --git a/oneflow/core/lazy/actor/register_slot.cpp b/oneflow/core/lazy/actor/register_slot.cpp index 713dd0b6eaafac14cb4e8a39ba5709d246b24a7b..182710eaf92b3cc2f3b1c1a069bb8d16e787d1d4 100644 --- a/oneflow/core/lazy/actor/register_slot.cpp +++ b/oneflow/core/lazy/actor/register_slot.cpp @@ -17,6 +17,13 @@ limitations under the License. namespace oneflow { +int64_t RegstSlot::GetReadyRegstSize(int64_t regst_desc_id) const { + CHECK(is_inited_); + auto it = regst_desc_id2regsts_.find(regst_desc_id); + if (it == regst_desc_id2regsts_.end()) { return -1; } + return it->second.size(); +} + bool RegstSlot::HasRegstDescId(int64_t regst_desc_id) const { CHECK(is_inited_); return regst_desc_id2regsts_.find(regst_desc_id) != regst_desc_id2regsts_.end(); diff --git a/oneflow/core/lazy/actor/register_slot.h b/oneflow/core/lazy/actor/register_slot.h index a1db92c5e244476fe3c303c12047fed2b2d84e1c..12b67b9e47bcdc19f62d115223a68bf0fe0c9db4 100644 --- a/oneflow/core/lazy/actor/register_slot.h +++ b/oneflow/core/lazy/actor/register_slot.h @@ -30,6 +30,7 @@ class RegstSlot final { size_t total_regst_desc_cnt() const { return regst_desc_id2regsts_.size(); } size_t available_regst_desc_cnt() const { return available_regst_desc_cnt_; } + int64_t GetReadyRegstSize(int64_t regst_desc_id) const; bool IsCurSlotReady() const { return available_regst_desc_cnt() == total_regst_desc_cnt(); } bool HasRegstDescId(int64_t regst_desc_id) const; const std::deque& RegstDeq4RegstDescId(int64_t regst_desc_id) const; diff --git a/oneflow/core/lazy/actor/repeat_actor.cpp b/oneflow/core/lazy/actor/repeat_actor.cpp index c84643a719ecf8366659eb8ccb710a2e1bf37c85..a9f81f4394f553dfc55e842f6eea8f9538ea4348 100644 --- a/oneflow/core/lazy/actor/repeat_actor.cpp +++ b/oneflow/core/lazy/actor/repeat_actor.cpp @@ -14,28 +14,73 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/lazy/actor/actor.h" +#include "oneflow/core/framework/framework.h" namespace oneflow { class RepeatActor final : public Actor { public: OF_DISALLOW_COPY_AND_MOVE(RepeatActor); - RepeatActor() = default; + RepeatActor() + : repeat_count_(0), + repeat_num_(0), + wait_all_regst_return_(false), + consumed_var_regst_desc_id_(-1), + produced_repeat_var_regst_desc_id_(-1){}; ~RepeatActor() override = default; private: + // NOTE(chengcheng): Empty rs for naive and inplace regst, all regst is customized. + std::pair> GetNaiveOrCustomizedConsumedRegstDescName() + override { + return std::make_pair(RegstNameType::kNaive, HashSet{}); + } + std::pair> GetNaiveOrCustomizedProducedRegstDescName() + override { + return std::make_pair(RegstNameType::kNaive, HashSet{}); + } + void TakeOverInplaceConsumedAndProduced( + const PbMap& produced_ids) override { + // NOTE(chengcheng): all regst is customized. + inplace_consumed_rs_.InitedDone(); + inplace_produced_rs_.InitedDone(); + } + + bool IsCustomizedReadReady() const override { + return (!wait_all_regst_return_) && consumed_var_rs_.IsCurSlotReady(); + } + bool IsCustomizedWriteReady() const override { + return (!wait_all_regst_return_) && produced_repeat_var_rs_.IsCurSlotReady(); + } + + void NormalProcessCustomizedEordMsg(const ActorMsg&) override {} + bool IsCustomizedReadAlwaysUnReadyFromNow() const override { + // all Messages are flushed + return ReceiveEordMsg(consumed_var_regst_desc_id_); + } + void VirtualActorInit(const TaskProto& proto) override; void Act() override; - void VirtualAsyncSendNaiveConsumedRegstMsgToProducer() override; - void VirtualAsyncSendNaiveProducedRegstMsgToConsumer() override; - bool ConsumedCtrlRegstValid(int64_t regst_desc_id) const override; - bool IsCustomizedWriteReady() const override; - - int64_t repeat_num_; - int64_t repeat_count_; + void AsyncSendCustomizedProducedRegstMsgToConsumer() override; + void AsyncSendCustomizedConsumedRegstMsgToProducer() override; + void UpdtStateAsCustomizedProducedRegst(Regst* regst) override; + void NormalProcessCustomizedReadableRegstMsg(const ActorMsg& msg) override; + + int32_t repeat_count_; + int32_t repeat_num_; + bool wait_all_regst_return_; + int64_t consumed_var_regst_desc_id_; + int64_t produced_repeat_var_regst_desc_id_; + RegstSlot consumed_var_rs_; + RegstSlot produced_repeat_var_rs_; }; void RepeatActor::VirtualActorInit(const TaskProto& proto) { + repeat_count_ = 0; + const OperatorConf& op_conf = + proto.exec_sequence().exec_node(0).kernel_conf().op_attribute().op_conf(); + repeat_num_ = user_op::UserOpConfWrapper(op_conf).attr("repeat_num"); + const Shape& in_time_shape = Singleton::Get() ->RegstDesc4RegstDescId(Name2SoleRegstDescId("in")) .data_regst_time_shape(); @@ -47,63 +92,114 @@ void RepeatActor::VirtualActorInit(const TaskProto& proto) { FOR_RANGE(int64_t, i, 0, in_time_shape.NumAxes()) { CHECK_EQ(in_time_shape.At(i), out_time_shape.At(i)); } - repeat_num_ = out_time_shape.At(out_time_shape.NumAxes() - 1); - repeat_count_ = 0; - const RegstDescProto& out_regst_desc = proto.produced_regst_desc().at("out"); + CHECK_EQ(repeat_num_, out_time_shape.At(out_time_shape.NumAxes() - 1)); + + // input + const auto& consumed_ids = proto.consumed_regst_desc_id(); + auto in_it = consumed_ids.find("in"); + CHECK(in_it != consumed_ids.end()); + CHECK_EQ(in_it->second.regst_desc_id_size(), 1); + consumed_var_regst_desc_id_ = in_it->second.regst_desc_id(0); + consumed_var_rs_.InsertRegstDescId(consumed_var_regst_desc_id_); + consumed_var_rs_.InitedDone(); + + // output + const auto& produced_ids = proto.produced_regst_desc(); + auto out_it = produced_ids.find("out"); + CHECK(out_it != produced_ids.end()); + const RegstDescProto& out_regst_desc = out_it->second; CHECK(!out_regst_desc.enable_reuse_mem()); CHECK_EQ(out_regst_desc.register_num(), 1); + // check inplace + CHECK_EQ(out_regst_desc.inplace_consumed_regst_desc_id(), consumed_var_regst_desc_id_); + produced_repeat_var_regst_desc_id_ = out_regst_desc.regst_desc_id(); + produced_repeat_var_rs_.InsertRegstDescId(produced_repeat_var_regst_desc_id_); + produced_repeat_var_rs_.InitedDone(); - // Regst number hacking - if (naive_consumed_rs_.total_regst_desc_cnt() != 1) { - LOG(WARNING) - << "RepeatActor has more than one consumed register. This will impact performance."; - } - + // NOTE(chengcheng): repeat actor may has output ctrl regst. ctrl regst also need hack regst num. for (const auto& pair : proto.produced_regst_desc()) { const RegstDescProto& regst_desc = pair.second; int64_t regst_desc_id = regst_desc.regst_desc_id(); - // This itor begins from 1 because first regst was already inserted in TakeOverNaiveProduced + // This iter begins from 1 because first ctrl regst was already inserted in + // TakeOverNaiveProduced for (int64_t i = 1; i < repeat_num_; ++i) { Singleton::Get()->NewRegsts(regst_desc, [this, regst_desc_id](Regst* regst) { produced_regsts_[regst_desc_id].emplace_back(regst); produced_regst2reading_cnt_[regst] = 0; - naive_produced_rs_.TryPushBackRegst(regst); + if (regst_desc_id != produced_repeat_var_regst_desc_id_) { + CHECK_EQ(0, naive_produced_rs_.TryPushBackRegst(regst)); + } }); } } + ForEachProducedRegst([&](Regst* regst) { + if (regst->regst_desc_id() == produced_repeat_var_regst_desc_id_) { + CHECK_EQ(0, produced_repeat_var_rs_.TryPushBackRegst(regst)); + } + }); + + for (const auto& pair : proto.produced_regst_desc()) { + const RegstDescProto& regst_desc = pair.second; + int64_t regst_desc_id = regst_desc.regst_desc_id(); + if (regst_desc_id == produced_repeat_var_regst_desc_id_) { + CHECK_EQ(produced_repeat_var_rs_.GetReadyRegstSize(regst_desc_id), repeat_num_); + } else { + CHECK_EQ(naive_produced_rs_.GetReadyRegstSize(regst_desc_id), repeat_num_); + } + } + OF_SET_MSG_HANDLER(&RepeatActor::HandlerNormal); } void RepeatActor::Act() { - // reset repeat_count if need - if (repeat_count_ == repeat_num_) { repeat_count_ = 0; } - - if (repeat_count_ == 0) { AsyncLaunchKernel(); } - repeat_count_ += 1; -} -void RepeatActor::VirtualAsyncSendNaiveConsumedRegstMsgToProducer() { - if (repeat_count_ == repeat_num_) { HandleConsumedNaiveDataRegstToProducer(); } + if (repeat_count_ == repeat_num_) { + wait_all_regst_return_ = true; + repeat_count_ = 0; + } + + Regst* out_regst = produced_repeat_var_rs_.Front(produced_repeat_var_regst_desc_id_); + Regst* in_regst = consumed_var_rs_.Front(consumed_var_regst_desc_id_); + CHECK(out_regst && in_regst); + CHECK(out_regst->main_mem_ptr() == in_regst->main_mem_ptr()); + CHECK(out_regst->separated_header_mem_ptr() == in_regst->separated_header_mem_ptr()); + CHECK_EQ(out_regst->regst_desc()->MainByteSize4OneRegst(), + in_regst->regst_desc()->MainByteSize4OneRegst()); + CHECK_EQ(out_regst->regst_desc()->SeparatedHeaderByteSize4OneRegst(), + in_regst->regst_desc()->SeparatedHeaderByteSize4OneRegst()); } -void RepeatActor::VirtualAsyncSendNaiveProducedRegstMsgToConsumer() { - HandleProducedNaiveDataRegstToConsumer(); +void RepeatActor::AsyncSendCustomizedProducedRegstMsgToConsumer() { + CHECK(produced_repeat_var_rs_.IsCurSlotReady()); + Regst* const repeat_var_regst = produced_repeat_var_rs_.Front(produced_repeat_var_regst_desc_id_); + CHECK_GT(HandleRegstToConsumer(repeat_var_regst), 0); + produced_repeat_var_rs_.PopFrontRegsts({produced_repeat_var_regst_desc_id_}); } -bool RepeatActor::ConsumedCtrlRegstValid(int64_t regst_desc_id) const { - return repeat_count_ == repeat_num_; +void RepeatActor::AsyncSendCustomizedConsumedRegstMsgToProducer() { + // NOTE(chengcheng): do nothing. consumed var regst will return in inplace done. } -bool RepeatActor::IsCustomizedWriteReady() const { - if (repeat_count_ % repeat_num_ == 0) { - return total_reading_cnt_ == 0; - } else { - return true; +void RepeatActor::UpdtStateAsCustomizedProducedRegst(Regst* regst) { + CHECK_EQ(regst->regst_desc_id(), produced_repeat_var_regst_desc_id_); + CHECK_EQ(produced_repeat_var_rs_.TryPushBackRegst(regst), 0); + + if (wait_all_regst_return_ + && produced_repeat_var_rs_.GetReadyRegstSize(produced_repeat_var_regst_desc_id_) + == repeat_num_) { + Regst* in_regst = consumed_var_rs_.Front(consumed_var_regst_desc_id_); + CHECK(in_regst); + AsyncSendRegstMsgToProducer(in_regst); + CHECK_EQ(0, consumed_var_rs_.TryPopFrontRegst(consumed_var_regst_desc_id_)); + wait_all_regst_return_ = false; } } +void RepeatActor::NormalProcessCustomizedReadableRegstMsg(const ActorMsg& msg) { + CHECK_EQ(0, consumed_var_rs_.TryPushBackRegst(msg.regst())); +} REGISTER_ACTOR(TaskType::kRepeat, RepeatActor); } // namespace oneflow diff --git a/oneflow/core/lazy/stream_context/common/generic_stream_context.cpp b/oneflow/core/lazy/stream_context/common/generic_stream_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cccd36dcd3d30ffc98e5c01c17062cad5048e317 --- /dev/null +++ b/oneflow/core/lazy/stream_context/common/generic_stream_context.cpp @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/lazy/stream_context/include/generic_stream_context.h" +#include "oneflow/core/job/global_for.h" +#include "oneflow/core/ep/include/device_manager_registry.h" +#include "oneflow/core/ep/include/active_device_guard.h" + +namespace oneflow { + +GenericStreamContext::GenericStreamContext(const StreamId& stream_id) : stream_(nullptr) { + device_ = + std::dynamic_pointer_cast(Singleton::Get()->GetDevice( + stream_id.device_type(), stream_id.device_index())); + CHECK(device_); + ep::ActiveDeviceGuard guard(device_.get()); + stream_ = dynamic_cast(device_->CreateStream()); + CHECK(stream_ != nullptr); + poller_thread_ = std::thread([this]() { + CHECK_JUST(stream_->OnExecutionContextSetup()); + std::pair> cb_event; + while (cb_event_chan_.Receive(&cb_event) == kChannelStatusSuccess) { + CHECK_JUST(cb_event.first->Sync()); + cb_event.second(); + device_->DestroyEvent(cb_event.first); + } + CHECK_JUST(stream_->OnExecutionContextTeardown()); + }); +} + +GenericStreamContext::~GenericStreamContext() { + ep::ActiveDeviceGuard guard(device_.get()); + cb_event_chan_.Close(); + poller_thread_.join(); + device_->DestroyStream(stream_); +} + +Maybe GenericStreamContext::AddCallback(std::function callback) { + ep::Event* event = device_->CreateEvent(); + stream_->RecordEvent(event); + cb_event_chan_.Send(std::make_pair(event, std::move(callback))); + return Maybe::Ok(); +} + +ep::Stream* GenericStreamContext::stream() { return stream_; } + +} // namespace oneflow diff --git a/oneflow/core/stream/cpu/cpu_stream_context.cpp b/oneflow/core/lazy/stream_context/cpu/cpu_stream_context.cpp similarity index 96% rename from oneflow/core/stream/cpu/cpu_stream_context.cpp rename to oneflow/core/lazy/stream_context/cpu/cpu_stream_context.cpp index 15b2eec0651cabf0b26dad79a75b40e919900548..d0768fb8300a2460cbbdf2edaece7d2fc7e10a7f 100644 --- a/oneflow/core/stream/cpu/cpu_stream_context.cpp +++ b/oneflow/core/lazy/stream_context/cpu/cpu_stream_context.cpp @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/device_type.h" #include "oneflow/core/device/event_record.h" @@ -30,7 +30,7 @@ class CpuStreamContext : public StreamContext, public KernelObserverProvider { public: OF_DISALLOW_COPY_AND_MOVE(CpuStreamContext); CpuStreamContext(); - virtual ~CpuStreamContext(); + ~CpuStreamContext() override; ep::Stream* stream() override; Maybe AddCallback(std::function callback) override; diff --git a/oneflow/core/stream/cuda/cuda_stream_context.cpp b/oneflow/core/lazy/stream_context/cuda/cuda_stream_context.cpp similarity index 92% rename from oneflow/core/stream/cuda/cuda_stream_context.cpp rename to oneflow/core/lazy/stream_context/cuda/cuda_stream_context.cpp index 980e5fcbc2a9a3054aed02791c8f5c11ca4a63e7..c603741e06ecce959494ef82505bddaeef88b8ba 100644 --- a/oneflow/core/stream/cuda/cuda_stream_context.cpp +++ b/oneflow/core/lazy/stream_context/cuda/cuda_stream_context.cpp @@ -13,21 +13,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ - -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" #include "oneflow/core/profiler/profiler.h" #include "oneflow/core/job/global_for.h" #include "oneflow/core/common/device_type.h" #include "oneflow/core/kernel/chain_kernel_observer.h" #include "oneflow/core/kernel/cuda_check_numerics_kernel_observer.h" #include "oneflow/core/graph/stream_id.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_device.h" #include "oneflow/core/ep/include/device_manager_registry.h" #include "oneflow/core/common/channel.h" - #ifdef WITH_CUDA -#include "oneflow/core/ep/cuda/cuda_stream.h" -#include "oneflow/core/ep/cuda/cuda_device.h" #include namespace oneflow { @@ -38,7 +36,7 @@ class CudaStreamContext : public StreamContext, public KernelObserverProvider { public: OF_DISALLOW_COPY_AND_MOVE(CudaStreamContext); explicit CudaStreamContext(int device_index); - virtual ~CudaStreamContext(); + ~CudaStreamContext() override; Maybe AddCallback(std::function callback) override; DeviceType device_type() const override { return DeviceType::kCUDA; } @@ -55,8 +53,6 @@ class CudaStreamContext : public StreamContext, public KernelObserverProvider { std::shared_ptr device_; }; -} // namespace - CudaStreamContext::CudaStreamContext(int device_index) : stream_(nullptr), device_index_(device_index) { CudaCurrentDeviceGuard guard(device_index_); @@ -76,16 +72,16 @@ CudaStreamContext::CudaStreamContext(int device_index) kernel_observer_.reset(new ChainKernelObserver(kernel_observers)); poller_thread_ = std::thread([this]() { - stream_->OnExecutionContextSetup(); + CHECK_JUST(stream_->OnExecutionContextSetup()); OF_PROFILER_NAME_THIS_HOST_THREAD("_cuda" + std::to_string(device_index_) + " Poller : (" + std::to_string(device_index_) + ")"); std::pair> cb_event; while (cb_event_chan_.Receive(&cb_event) == kChannelStatusSuccess) { - cb_event.first->Sync(); + CHECK_JUST(cb_event.first->Sync()); cb_event.second(); device_->DestroyEvent(cb_event.first); } - stream_->OnExecutionContextTeardown(); + CHECK_JUST(stream_->OnExecutionContextTeardown()); }); } @@ -113,13 +109,15 @@ REGISTER_STREAM_CONTEXT_CREATOR_WITH_STREAM_ID( return new CudaStreamContext(stream_id.device_index()); })); +} // namespace + } // namespace oneflow #endif #ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/ep/rocm/cuda_device.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_device.h" #include namespace oneflow { @@ -130,7 +128,7 @@ class CudaStreamContext : public StreamContext, public KernelObserverProvider { public: OF_DISALLOW_COPY_AND_MOVE(CudaStreamContext); explicit CudaStreamContext(int device_index); - virtual ~CudaStreamContext(); + ~CudaStreamContext() override; Maybe AddCallback(std::function callback) override; DeviceType device_type() const override { return DeviceType::kCUDA; } @@ -147,8 +145,6 @@ class CudaStreamContext : public StreamContext, public KernelObserverProvider { std::shared_ptr device_; }; -} // namespace - CudaStreamContext::CudaStreamContext(int device_index) : stream_(nullptr), device_index_(device_index) { CudaCurrentDeviceGuard guard(device_index_); @@ -164,21 +160,20 @@ CudaStreamContext::CudaStreamContext(int device_index) "to a truthy " "value, it will impact performance"; kernel_observers.emplace_back(new CudaCheckNumericsKernelObserver()); - // LOG(WARNING) << "Environment variable ONEFLOW_DEBUG_KERNEL_SYNC_CHECK_NUMERICS has been not surpported for ROCM for now"; } kernel_observer_.reset(new ChainKernelObserver(kernel_observers)); poller_thread_ = std::thread([this]() { - stream_->OnExecutionContextSetup(); + CHECK_JUST(stream_->OnExecutionContextSetup()); OF_PROFILER_NAME_THIS_HOST_THREAD("_cuda" + std::to_string(device_index_) + " Poller : (" + std::to_string(device_index_) + ")"); std::pair> cb_event; while (cb_event_chan_.Receive(&cb_event) == kChannelStatusSuccess) { - cb_event.first->Sync(); + CHECK_JUST(cb_event.first->Sync()); cb_event.second(); device_->DestroyEvent(cb_event.first); } - stream_->OnExecutionContextTeardown(); + CHECK_JUST(stream_->OnExecutionContextTeardown()); }); } @@ -206,7 +201,8 @@ REGISTER_STREAM_CONTEXT_CREATOR_WITH_STREAM_ID( return new CudaStreamContext(stream_id.device_index()); })); +} // namespace + } // namespace oneflow #endif - diff --git a/oneflow/core/lazy/stream_context/include/generic_stream_context.h b/oneflow/core/lazy/stream_context/include/generic_stream_context.h new file mode 100644 index 0000000000000000000000000000000000000000..98463dcb16a88557f1d006036693d07b5ad00ce2 --- /dev/null +++ b/oneflow/core/lazy/stream_context/include/generic_stream_context.h @@ -0,0 +1,48 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_LAZY_STREAM_CONTEXT_GENERIC_STREAM_CONTEXT_H_ +#define ONEFLOW_CORE_LAZY_STREAM_CONTEXT_GENERIC_STREAM_CONTEXT_H_ + +#include "oneflow/core/lazy/stream_context/include/stream_context.h" +#include "oneflow/core/common/device_type.h" +#include "oneflow/core/graph/stream_id.h" +#include "oneflow/core/ep/include/stream.h" +#include "oneflow/core/ep/include/device.h" +#include "oneflow/core/common/channel.h" + +namespace oneflow { + +class GenericStreamContext : public StreamContext { + public: + OF_DISALLOW_COPY_AND_MOVE(GenericStreamContext); + explicit GenericStreamContext(const StreamId& stream_id); + ~GenericStreamContext() override; + + Maybe AddCallback(std::function callback) override; + DeviceType device_type() const override { return stream_->device_type(); } + + ep::Stream* stream() override; + + private: + ep::Stream* stream_; + Channel>> cb_event_chan_; + std::thread poller_thread_; + std::shared_ptr device_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_LAZY_STREAM_CONTEXT_GENERIC_STREAM_CONTEXT_H_ diff --git a/oneflow/core/stream/include/stream_context.h b/oneflow/core/lazy/stream_context/include/stream_context.h similarity index 87% rename from oneflow/core/stream/include/stream_context.h rename to oneflow/core/lazy/stream_context/include/stream_context.h index 1078755a527f4f90db7f98bc46572b09ee7ad210..3260fa4ee0d2b392b20faf8d5919d2624f0073d1 100644 --- a/oneflow/core/stream/include/stream_context.h +++ b/oneflow/core/lazy/stream_context/include/stream_context.h @@ -13,8 +13,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef ONEFLOW_CORE_STREAM_STREAM_CONTEXT_H_ -#define ONEFLOW_CORE_STREAM_STREAM_CONTEXT_H_ +#ifndef ONEFLOW_CORE_LAZY_STREAM_CONTEXT_STREAM_CONTEXT_H_ +#define ONEFLOW_CORE_LAZY_STREAM_CONTEXT_STREAM_CONTEXT_H_ #include "oneflow/core/common/util.h" #include "oneflow/core/common/auto_registration_factory.h" @@ -39,4 +39,4 @@ class StreamContext { } // namespace oneflow -#endif // ONEFLOW_CORE_STREAM_STREAM_CONTEXT_H_ +#endif // ONEFLOW_CORE_LAZY_STREAM_CONTEXT_STREAM_CONTEXT_H_ diff --git a/oneflow/core/memory/chunk_manager.cpp b/oneflow/core/memory/chunk_manager.cpp index 912a97258fd23b1f3901a9320aa34bde04e9730e..2943e2760ada8d779fce9e11503148421fe7a478 100644 --- a/oneflow/core/memory/chunk_manager.cpp +++ b/oneflow/core/memory/chunk_manager.cpp @@ -36,8 +36,7 @@ void ChunkMgr::GetChunkProtosByMemZoneUniqueId(int64_t mem_zone_uid, } void ChunkMgr::AddChunkProto(const ChunkProto& chunk) { - const int64_t mem_zone_uid = - MemoryCaseUtil::GenMemZoneUniqueId(chunk.machine_id(), chunk.mem_case()); + const int64_t mem_zone_uid = memory::GetUniqueMemCaseId(chunk.machine_id(), chunk.mem_case()); CHECK( chunk_id2chunk_proto_.emplace(chunk.chunk_id(), std::make_unique(chunk)).second); auto chunk_ids_it = mzuid2chunk_ids_.find(mem_zone_uid); diff --git a/oneflow/core/memory/memory_allocator.cpp b/oneflow/core/memory/memory_allocator.cpp index 41a801e862a243561fa7f04e2c19ef4285aefc5b..5d3d7a7a9ca4acf7f7f31e980dd01575c54c0b46 100644 --- a/oneflow/core/memory/memory_allocator.cpp +++ b/oneflow/core/memory/memory_allocator.cpp @@ -27,25 +27,16 @@ namespace oneflow { namespace { std::shared_ptr GetAllocationDevice(const MemoryCase& mem_case) { - DeviceType device_type = DeviceType::kInvalidDevice; - size_t device_index = 0; - if (mem_case.has_host_mem()) { - device_type = DeviceType::kCPU; - } else if (mem_case.has_device_cuda_mem()) { - device_type = DeviceType::kCUDA; - device_index = mem_case.device_cuda_mem().device_id(); - } else { - UNIMPLEMENTED(); - } - auto device = Singleton::Get()->GetDevice(device_type, device_index); + auto device = Singleton::Get()->GetDevice(mem_case.device_type(), + mem_case.device_id()); CHECK(device); return device; } ep::AllocationOptions GetAllocationOptions(const MemoryCase& mem_case) { ep::AllocationOptions options{}; - if (mem_case.has_host_mem() && mem_case.host_mem().has_cuda_pinned_mem()) { - options.SetPinnedDevice(DeviceType::kCUDA, mem_case.host_mem().cuda_pinned_mem().device_id()); + if (mem_case.has_pinned_device_type() && mem_case.has_pinned_device_id()) { + options.SetPinnedDevice(mem_case.pinned_device_type(), mem_case.pinned_device_id()); } return options; } @@ -81,23 +72,7 @@ MemoryAllocator::~MemoryAllocator() { } char* MemoryAllocator::Allocate(const MemoryCase& mem_case, std::size_t size) { - const int memset_val = 0; char* dptr = static_cast(MemoryAllocatorImpl::Allocate(mem_case, size)); - if (mem_case.has_host_mem()) { - memset(dptr, memset_val, size); - } else if (mem_case.has_device_cuda_mem()) { -#if defined(WITH_CUDA) - CudaCurrentDeviceGuard guard(mem_case.device_cuda_mem().device_id()); - OF_CUDA_CHECK(cudaMemset(dptr, memset_val, size)); -#elif defined(WITH_ROCM) - CudaCurrentDeviceGuard guard(mem_case.device_cuda_mem().device_id()); - OF_CUDA_CHECK(hipMemset(dptr, memset_val, size)); -#else - UNIMPLEMENTED(); -#endif - } else { - UNIMPLEMENTED(); - } deleters_.push_front(std::bind(&MemoryAllocator::Deallocate, this, dptr, mem_case)); return dptr; } diff --git a/oneflow/core/memory/memory_case.proto b/oneflow/core/memory/memory_case.proto index c60d60bf7c5f435901163d030f0f430e4d776395..78c0c9e8fe1044a7a8dbc16ea669fbde2afcd9bf 100644 --- a/oneflow/core/memory/memory_case.proto +++ b/oneflow/core/memory/memory_case.proto @@ -1,21 +1,11 @@ syntax = "proto2"; package oneflow; -message CudaPinnedMemory { - required int64 device_id = 1; -} - -message HostMemory { - optional CudaPinnedMemory cuda_pinned_mem = 1; -} - -message DeviceCudaMemory { - required int64 device_id = 1; -} +import "oneflow/core/common/device_type.proto"; message MemoryCase { - oneof case { - HostMemory host_mem = 1; - DeviceCudaMemory device_cuda_mem = 2; - } + required DeviceType device_type = 1; + required int64 device_id = 2; + optional DeviceType pinned_device_type = 3; + optional int64 pinned_device_id = 4; } diff --git a/oneflow/core/memory/memory_case_util.cpp b/oneflow/core/memory/memory_case_util.cpp index 9ef8f26d984f44d0f88918bcd630849b595fe9b5..f3ab8991013c9c13f884d4b8c883148ed4e68aff 100644 --- a/oneflow/core/memory/memory_case_util.cpp +++ b/oneflow/core/memory/memory_case_util.cpp @@ -15,71 +15,102 @@ limitations under the License. */ #include "oneflow/core/memory/memory_case_util.h" +#include + namespace oneflow { -bool MemoryCaseUtil::GetCommonMemoryCase(const MemoryCase& a, const MemoryCase& b, - MemoryCase* common) { - if (a.has_device_cuda_mem() && b.has_device_cuda_mem()) { - if (a.device_cuda_mem().device_id() == b.device_cuda_mem().device_id()) { - *common = a; - return true; - } else { - return false; - } - } else if (a.has_host_mem() && b.has_host_mem()) { - *common = a; - if (b.host_mem().has_cuda_pinned_mem()) { - *common->mutable_host_mem()->mutable_cuda_pinned_mem() = b.host_mem().cuda_pinned_mem(); - } - return true; - } else { - return false; +namespace memory { + +bool EqualsIgnorePinnedDevice(const MemoryCase& a, const MemoryCase& b) { + if (a.device_type() != b.device_type()) { return false; } + if (a.device_id() != b.device_id()) { return false; } + return true; +} + +void GetPinnedHostMemoryCase(const MemoryCase& mem_case, MemoryCase* ret) { + ret->set_device_type(DeviceType::kCPU); + ret->set_device_id(0); + if (!IsHostMem(mem_case)) { + ret->set_pinned_device_type(mem_case.device_type()); + ret->set_pinned_device_id(mem_case.device_id()); } } -MemoryCase MemoryCaseUtil::GetHostMemoryCaseForRegstSeparatedHeader(const MemoryCase& mem_case) { +MemoryCase GetPinnedHostMemoryCase(const MemoryCase& mem_case) { MemoryCase ret; - ret.mutable_host_mem(); - if (mem_case.has_device_cuda_mem()) { - ret.mutable_host_mem()->mutable_cuda_pinned_mem()->set_device_id( - mem_case.device_cuda_mem().device_id()); - } + GetPinnedHostMemoryCase(mem_case, &ret); return ret; } -int64_t MemoryCaseUtil::GenMemZoneId(const MemoryCase& mem_case) { - // [0, 127] = GPU device mem - // [128] = CPU host mem - // [129, 256] = CPU host mem used by CUDA with device id - // [257, ...] Other Device - if (mem_case.has_device_cuda_mem()) { - return mem_case.device_cuda_mem().device_id(); // GPU device mem +// clang-format off +// MemCaseId encoding (bits) +// | reserved | node_index | device_type | device_index | reserved | pinned_device_type | pinned_device_index | +// | --- 1 -- | --- 19 --- | ---- 5 ---- | ----- 7 ---- | -- 20 -- | ------- 5 -------- | ------- 7 --------- | +// | ---------------------- 32 ------------------------ | ---------------------- 32 ------------------------- | +// clang-format on + +namespace { + +constexpr size_t kDeviceIndexBits = 7; +constexpr size_t kDeviceTypeBits = 5; +constexpr size_t kDeviceTypeShift = kDeviceIndexBits; +constexpr size_t kNodeIndexShift = kDeviceTypeShift + kDeviceTypeBits; +constexpr size_t kPinnedDeviceShift = 32; + +} // namespace + +int64_t GetMemCaseId(const MemoryCase& mem_case) { + uint32_t high = 0; + high |= static_cast(mem_case.device_id()); + high |= static_cast(mem_case.device_type()) << kDeviceTypeShift; + uint32_t low = 0; + if (mem_case.has_pinned_device_id()) { + low |= static_cast(mem_case.pinned_device_id()); } - if (mem_case.has_host_mem()) { - if (mem_case.host_mem().has_cuda_pinned_mem()) { - return 129 + mem_case.host_mem().cuda_pinned_mem().device_id(); // Host mem used by GPU - } - return 128; // CPU host mem + if (mem_case.has_pinned_device_type()) { + low |= static_cast(mem_case.pinned_device_type()) << kDeviceTypeShift; } - UNIMPLEMENTED(); - return -1; + int64_t id = 0; + id |= static_cast(high) << kPinnedDeviceShift; + id |= static_cast(low); + return id; } -int64_t MemoryCaseUtil::GenMemZoneUniqueId(int64_t machine_id, const MemoryCase& mem_case) { - return (machine_id << 32) | (MemoryCaseUtil::GenMemZoneId(mem_case)); +int64_t GetUniqueMemCaseId(int64_t machine_id, const MemoryCase& mem_case) { + int64_t id = 0; + id |= (machine_id << kNodeIndexShift << kPinnedDeviceShift); + id |= GetMemCaseId(mem_case); + return id; } -std::shared_ptr MemoryCaseUtil::MakeMemCase(const DeviceType device_type, - const int64_t device_id) { - const auto& mem_case = std::make_shared(); +std::shared_ptr MakeMemCaseShared(const DeviceType device_type, + const int64_t device_id) { + auto mem_case_ptr = std::make_shared(); + mem_case_ptr->set_device_type(device_type); + // We consider that there is only one cpu physical device. + // As non-cpu devices, a logical device map to a physical device, + // however as cpu devices, all logical devices map to a single physical device. if (device_type == DeviceType::kCPU) { - mem_case->mutable_host_mem(); - } else if (device_type == DeviceType::kCUDA) { - mem_case->mutable_device_cuda_mem()->set_device_id(device_id); + mem_case_ptr->set_device_id(0); } else { - UNIMPLEMENTED(); + mem_case_ptr->set_device_id(device_id); } + return mem_case_ptr; +} + +MemoryCase MakeHostMemCase() { + MemoryCase mem_case; + mem_case.set_device_type(DeviceType::kCPU); + mem_case.set_device_id(0); return mem_case; } +bool IsHostMem(const MemoryCase& mem_case) { return mem_case.device_type() == DeviceType::kCPU; } + +} // namespace memory + +bool operator==(const MemoryCase& lhs, const MemoryCase& rhs) { + return google::protobuf::util::MessageDifferencer::Equals(lhs, rhs); +} + } // namespace oneflow diff --git a/oneflow/core/memory/memory_case_util.h b/oneflow/core/memory/memory_case_util.h index 25e8483430a208dc01d93fb64950f77aff2c250d..d79680e036bc4e4702b6db4f2173f41d51bb3b07 100644 --- a/oneflow/core/memory/memory_case_util.h +++ b/oneflow/core/memory/memory_case_util.h @@ -16,41 +16,27 @@ limitations under the License. #ifndef ONEFLOW_CORE_MEMORY_MEMORY_CASE_UTIL_H_ #define ONEFLOW_CORE_MEMORY_MEMORY_CASE_UTIL_H_ +#include "oneflow/core/common/util.h" #include "oneflow/core/common/device_type.h" #include "oneflow/core/memory/memory_case.pb.h" -#include "oneflow/core/common/util.h" namespace oneflow { -inline bool operator==(const MemoryCase& lhs, const MemoryCase& rhs) { - if (lhs.has_host_mem() && rhs.has_host_mem()) { - const HostMemory& lhs_host_mem = lhs.host_mem(); - const HostMemory& rhs_host_mem = rhs.host_mem(); - if (lhs_host_mem.has_cuda_pinned_mem() && rhs_host_mem.has_cuda_pinned_mem()) { - return lhs_host_mem.cuda_pinned_mem().device_id() - == rhs_host_mem.cuda_pinned_mem().device_id(); - } else { - return (!lhs_host_mem.has_cuda_pinned_mem()) && (!rhs_host_mem.has_cuda_pinned_mem()); - } - } - if (lhs.has_device_cuda_mem() && rhs.has_device_cuda_mem()) { - return lhs.device_cuda_mem().device_id() == rhs.device_cuda_mem().device_id(); - } - return false; -} - -struct MemoryCaseUtil { - static bool GetCommonMemoryCase(const MemoryCase& a, const MemoryCase& b, MemoryCase* common); - - static MemoryCase GetHostMemoryCaseForRegstSeparatedHeader(const MemoryCase& mem_case); - - static int64_t GenMemZoneUniqueId(int64_t machine_id, const MemoryCase& mem_case); - - static int64_t GenMemZoneId(const MemoryCase& mem_case); - - static std::shared_ptr MakeMemCase(const DeviceType device_type, - const int64_t device_id); -}; +namespace memory { + +bool EqualsIgnorePinnedDevice(const MemoryCase& a, const MemoryCase& b); +void GetPinnedHostMemoryCase(const MemoryCase& mem_case, MemoryCase* ret); +MemoryCase GetPinnedHostMemoryCase(const MemoryCase& mem_case); +int64_t GetMemCaseId(const MemoryCase& mem_case); +int64_t GetUniqueMemCaseId(int64_t machine_id, const MemoryCase& mem_case); +std::shared_ptr MakeMemCaseShared(const DeviceType device_type, + const int64_t device_id); +MemoryCase MakeHostMemCase(); +bool IsHostMem(const MemoryCase& mem_case); + +} // namespace memory + +bool operator==(const MemoryCase& lhs, const MemoryCase& rhs); } // namespace oneflow diff --git a/oneflow/core/ndarray/binary_func.h b/oneflow/core/ndarray/binary_func.h index 6beff4afefb37c36c997c6eea935a452f2d83aae..f0f5c14a1897be62a4006c4eaee542697ed711df 100644 --- a/oneflow/core/ndarray/binary_func.h +++ b/oneflow/core/ndarray/binary_func.h @@ -21,9 +21,8 @@ limitations under the License. #include #include -#if defined(__CUDACC__) -#include -#endif +#ifdef WITH_ROCM + #if defined(__HIPCC__) #include #endif @@ -49,9 +48,10 @@ namespace oneflow { OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, ARITHMETIC_REDUCE_BINARY_FUNC_NAME_SEQ) #define LOGICAL_REDUCE_BINARY_FUNC_SEQ \ OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, LOGICAL_REDUCE_BINARY_FUNC_NAME_SEQ) +#define NANSUM_REDUCE_BINARY_FUNC_SEQ OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, (NanSum)) #define NO_HALF_UTIL_FOUND \ - printf("cuda arch must >= 530"); \ + printf("rocm arch must >= 400"); \ assert(false); \ return __float2half(0.0) template class BinaryFunc, typename T> @@ -69,6 +69,20 @@ struct BinaryFuncTrait final { } \ } +template +struct BinaryFuncNanSum final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { +#if defined(__HIP_DEVICE_COMPILE__) + if (isnan(x)) return isnan(y) ? T{0} : y; + return isnan(y) ? x : x + y; +#else + if (std::isnan(x)) return std::isnan(y) ? T{0} : y; + return std::isnan(y) ? x : x + y; +#endif + } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncNanSum); + template struct BinaryFuncAdd final { static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x + y; } @@ -429,6 +443,16 @@ struct BinaryFuncLE final { }; SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncLE); +template +struct BinaryFuncIEN final { + // placeholder, no definition required, the type is only used to generate Op +}; + +template +struct BinaryFuncINN final { + // placeholder, no definition required, the type is only used to generate Op +}; + template struct BinaryFuncAND final { static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x && y; } @@ -534,6 +558,7 @@ struct UnitOfBinaryFunc; static OF_DEVICE_FUNC T Val() { return get_val(); } \ }; SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncAdd, GetZeroVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncNanSum, GetZeroVal); SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncSum, GetZeroVal); SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncMul, GetOneVal); SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncProd, GetOneVal); @@ -545,5 +570,548 @@ SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncAll, GetOneVal); } // namespace oneflow -#endif // ONEFLOW_CORE_NDARRAY_BINARY_FUNC_H_ +#else + +#if defined(__CUDACC__) +#include +#endif +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/common/util.h" +namespace oneflow { + +#define ARITHMETIC_BINARY_FUNC_NAME_SEQ (Add)(Sub)(Mul)(Div)(Min)(Max)(FloorMod)(FMod)(Pow) +#define LOGICAL_BINARY_FUNC_NAME_SEQ (EQ)(NE)(GT)(GE)(LT)(LE)(AND)(OR)(XOR) + +#define PREPEND_PREFIX_BINARY_FUNC(name) OF_PP_CAT(BinaryFunc, name) +#define ARITHMETIC_BINARY_FUNC_SEQ \ + OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, ARITHMETIC_BINARY_FUNC_NAME_SEQ) +#define LOGICAL_BINARY_FUNC_SEQ \ + OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, LOGICAL_BINARY_FUNC_NAME_SEQ) + +#define REDUCE_BINARY_FUNC_NAME_SEQ (Sum)(Max)(Min)(Prod)(Any)(All) +#define ARITHMETIC_REDUCE_BINARY_FUNC_NAME_SEQ (Sum)(Max)(Min)(Prod) +#define LOGICAL_REDUCE_BINARY_FUNC_NAME_SEQ (Any)(All) +#define REDUCE_BINARY_FUNC_SEQ \ + OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, REDUCE_BINARY_FUNC_NAME_SEQ) +#define ARITHMETIC_REDUCE_BINARY_FUNC_SEQ \ + OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, ARITHMETIC_REDUCE_BINARY_FUNC_NAME_SEQ) +#define LOGICAL_REDUCE_BINARY_FUNC_SEQ \ + OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, LOGICAL_REDUCE_BINARY_FUNC_NAME_SEQ) +#define NANSUM_REDUCE_BINARY_FUNC_SEQ OF_PP_SEQ_MAP(PREPEND_PREFIX_BINARY_FUNC, (NanSum)) + +#define NO_HALF_UTIL_FOUND \ + printf("cuda arch must >= 530"); \ + assert(false); \ + return __float2half(0.0) +template class BinaryFunc, typename T> +struct BinaryFuncTrait final { + typedef typename std::remove_const::Invoke(std::declval(), std::declval()))>::type return_type; +}; + +#define SPECIALIZE_CONST_TYPE_BINARY_FUNC(func_struct) \ + template \ + struct func_struct final { \ + static OF_DEVICE_FUNC const typename BinaryFuncTrait::return_type Invoke( \ + const T x, const T y) { \ + return func_struct::Invoke(x, y); \ + } \ + } + +template +struct BinaryFuncNanSum final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { +#if defined(__CUDACC__) + if (isnan(x)) return isnan(y) ? T{0} : y; + return isnan(y) ? x : x + y; +#else + if (std::isnan(x)) return std::isnan(y) ? T{0} : y; + return std::isnan(y) ? x : x + y; +#endif + } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncNanSum); + +template +struct BinaryFuncAdd final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x + y; } +}; +template +struct BinaryFuncSum final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return BinaryFuncAdd::Invoke(x, y); } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncAdd); + +template +struct BinaryFuncSub final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x - y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncSub); + +template +struct BinaryFuncMul final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x * y; } +}; +template<> +struct BinaryFuncMul final { + static OF_DEVICE_FUNC bool Invoke(const bool x, const bool y) { return x && y; } +}; +template +struct BinaryFuncProd final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return BinaryFuncMul::Invoke(x, y); } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncMul); + +template +struct BinaryFuncDiv final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x / y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncDiv); + +template +struct BinaryFuncFloorMod final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { +#if defined(__CUDACC__) + T trunc_mod = x % y; + return (trunc_mod != T(0)) && ((y < T(0)) != (trunc_mod < T(0))) ? trunc_mod + y : trunc_mod; +#else + T trunc_mod = x % y; + return (trunc_mod != T(0)) && ((y < T(0)) != (trunc_mod < T(0))) ? trunc_mod + y : trunc_mod; +#endif + } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncFloorMod); + +template<> +struct BinaryFuncFloorMod final { + static OF_DEVICE_FUNC uint8_t Invoke(const uint8_t x, const uint8_t y) { +#if defined(__CUDACC__) + uint8_t trunc_mod = x % y; + return trunc_mod; +#else + uint8_t trunc_mod = x % y; + return trunc_mod; +#endif + } +}; + +template +struct BinaryFuncFMod final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { +#if defined(__CUDACC__) + T trunc_mod = x % y; + return trunc_mod; +#else + T trunc_mod = x % y; + return trunc_mod; +#endif + } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncFMod); + +template +struct BinaryFuncPow final { + static OF_DEVICE_FUNC const T Invoke(const T x, const T y) { +#if defined(__CUDACC__) + return powf(x, y); +#else + return std::pow(x, y); +#endif + } +}; + +template<> +struct BinaryFuncPow final { + static OF_DEVICE_FUNC bool Invoke(const bool x, const bool y) { +#if defined(__CUDACC__) + return static_cast(powf(static_cast(x), static_cast(y))); +#else + return static_cast(std::pow(static_cast(x), static_cast(y))); +#endif + } +}; + +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncPow); + +template<> +struct BinaryFuncPow final { + static inline const float16 Invoke(const float16 x, const float16 y) { + return static_cast(std::pow(static_cast(x), static_cast(y))); + } +}; + +#if defined(__CUDACC__) +template<> +struct BinaryFuncPow final { + static OF_DEVICE_FUNC double Invoke(const double x, const double y) { return pow(x, y); } +}; + +template<> +struct BinaryFuncPow final { + static __device__ __forceinline__ float Invoke(const float x, const float y) { + return powf(x, y); + } +}; + +template<> +struct BinaryFuncPow final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530 + return __float2half(powf(__half2float(x), __half2float(y))); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; +#endif // defined(__CUDACC__) + +template +struct BinaryFuncFloorDiv final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { +#if defined(__CUDACC__) + return floor(fdividef(x, y)); +#else + return std::floor(x / y); +#endif + } +}; + +template +struct BinaryFuncMax final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x > y ? x : y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncMax); + +template +struct BinaryFuncMin final { + static OF_DEVICE_FUNC T Invoke(const T x, const T y) { return x < y ? x : y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncMin); + +template +struct BinaryFuncEQ final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x == y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncEQ); + +template +struct BinaryFuncNE final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x != y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncNE); + +template +struct BinaryFuncGT final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x > y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncGT); + +template +struct BinaryFuncGE final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x >= y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncGE); + +template +struct BinaryFuncLT final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x < y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncLT); + +template +struct BinaryFuncLE final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x <= y; } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncLE); + +template +struct BinaryFuncIEN final { + // placeholder, no definition required, the type is only used to generate Op +}; + +template +struct BinaryFuncINN final { + // placeholder, no definition required, the type is only used to generate Op +}; + +template +struct BinaryFuncAND final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x && y; } +}; +template +struct BinaryFuncAll final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return BinaryFuncAND::Invoke(x, y); } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncAND); + +template +struct BinaryFuncOR final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return x || y; } +}; +template +struct BinaryFuncAny final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return BinaryFuncOR::Invoke(x, y); } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncOR); + +template +struct BinaryFuncXOR final { + static OF_DEVICE_FUNC bool Invoke(const T x, const T y) { return (!x) != (!y); } +}; +SPECIALIZE_CONST_TYPE_BINARY_FUNC(BinaryFuncXOR); + +#if defined(__CUDACC__) +template<> +struct BinaryFuncAdd final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { return __hadd(x, y); } +}; + +template<> +struct BinaryFuncNanSum final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { + if (isnan(__half2float(x))) return isnan(__half2float(y)) ? half(0.0) : y; + return isnan(__half2float(y)) ? __hadd(x, y) : x; + } +}; + +template<> +struct BinaryFuncSub final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530 + return __hsub(x, y); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +template<> +struct BinaryFuncMul final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530 + return __hmul(x, y); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +template<> +struct BinaryFuncDiv final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if __CUDA_ARCH__ >= 530 + return __hdiv(x, y); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +template<> +struct BinaryFuncMax final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530 + return __hgt(x, y) ? x : y; +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +template<> +struct BinaryFuncMin final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530 + return __hlt(x, y) ? x : y; +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +#endif // defined(__CUDACC__) + +#if defined(__CUDACC__) + +template<> +struct BinaryFuncFloorMod final { + static __device__ __forceinline__ float Invoke(const float x, const float y) { + const float trunc_mod = fmodf(x, y); + return (trunc_mod != 0) && ((y < 0) != (trunc_mod < 0)) ? trunc_mod + y : trunc_mod; + } +}; + +template<> +struct BinaryFuncFloorMod final { + static __device__ __forceinline__ double Invoke(const double x, const double y) { + const double trunc_mod = fmod(x, y); + return (trunc_mod != 0) && ((y < 0) != (trunc_mod < 0)) ? trunc_mod + y : trunc_mod; + } +}; + +template<> +struct BinaryFuncFloorMod final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if __CUDA_ARCH__ >= 530 + const half trunc_mod = __float2half(fmodf(__half2float(x), __half2float(y))); + return __hne(trunc_mod, GetZeroVal()) + && __hlt(y, GetZeroVal()) != __hlt(trunc_mod, half(0)) + ? trunc_mod + y + : trunc_mod; +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; + +#else + +template<> +struct BinaryFuncFloorMod final { + static inline float Invoke(const float x, const float y) { + const float trunc_mod = std::fmod(x, y); + return (trunc_mod != 0) && ((y < 0) != (trunc_mod < 0)) ? trunc_mod + y : trunc_mod; + } +}; + +template<> +struct BinaryFuncFloorMod final { + static inline double Invoke(const double x, const double y) { + const double trunc_mod = std::fmod(x, y); + return (trunc_mod != 0) && ((y < 0) != (trunc_mod < 0)) ? trunc_mod + y : trunc_mod; + } +}; + +template<> +struct BinaryFuncFloorMod final { + static inline float16 Invoke(const float16 x, const float16 y) { + const float trunc_mod = std::fmod(static_cast(x), static_cast(y)); + return (trunc_mod != float(0)) && ((y < float(0)) != (trunc_mod < float(0))) + ? static_cast(trunc_mod + y) + : static_cast(trunc_mod); + } +}; + +#endif // defined(__CUDACC__) + +#if defined(__CUDACC__) + +template<> +struct BinaryFuncFMod final { + static __device__ __forceinline__ float Invoke(const float x, const float y) { + const float trunc_mod = fmodf(x, y); + return trunc_mod; + } +}; + +template<> +struct BinaryFuncFMod final { + static __device__ __forceinline__ double Invoke(const double x, const double y) { + const double trunc_mod = fmod(x, y); + return trunc_mod; + } +}; + +template<> +struct BinaryFuncFMod final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if __CUDA_ARCH__ >= 530 + const half trunc_mod = __float2half(fmodf(__half2float(x), __half2float(y))); + return trunc_mod; +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; +#else +template<> +struct BinaryFuncFMod final { + static inline float Invoke(const float x, const float y) { + const float trunc_mod = std::fmod(x, y); + return trunc_mod; + } +}; + +template<> +struct BinaryFuncFMod final { + static inline double Invoke(const double x, const double y) { + const double trunc_mod = std::fmod(x, y); + return trunc_mod; + } +}; +template<> +struct BinaryFuncFMod final { + static inline float16 Invoke(const float16 x, const float16 y) { + const float trunc_mod = std::fmod(static_cast(x), static_cast(y)); + return static_cast(trunc_mod); + } +}; + +#endif // defined(__CUDACC__) + +#if defined(__CUDACC__) + +template<> +struct BinaryFuncFloorDiv final { + static __device__ __forceinline__ uint8_t Invoke(uint8_t x, uint8_t y) { return x / y; } +}; + +template<> +struct BinaryFuncFloorDiv final { + static __device__ __forceinline__ int8_t Invoke(int8_t x, int8_t y) { return x / y; } +}; + +template<> +struct BinaryFuncFloorDiv final { + static __device__ __forceinline__ int32_t Invoke(int32_t x, int32_t y) { return x / y; } +}; + +template<> +struct BinaryFuncFloorDiv final { + static __device__ __forceinline__ int64_t Invoke(int64_t x, int64_t y) { return x / y; } +}; + +template<> +struct BinaryFuncFloorDiv final { + static __device__ __forceinline__ half Invoke(const half x, const half y) { +#if __CUDA_ARCH__ >= 530 + return __float2half(floor(fdividef(__half2float(x), __half2float(y)))); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; +#else +template<> +struct BinaryFuncFloorDiv final { + static inline float16 Invoke(float16 x, float16 y) { + return static_cast(std::floor(static_cast(x) / static_cast(y))); + } +}; + +#endif // defined(__CUDACC__) +template class binary_func> +struct UnitOfBinaryFunc; + +#define SPECIALIZE_UNIT_OF_BINARY_FUNC(binary_func, get_val) \ + template \ + struct UnitOfBinaryFunc final { \ + static OF_DEVICE_FUNC T Val() { return get_val(); } \ + }; +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncAdd, GetZeroVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncNanSum, GetZeroVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncSum, GetZeroVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncMul, GetOneVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncProd, GetOneVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncMax, GetMinVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncMin, GetMaxVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncAny, GetZeroVal); +SPECIALIZE_UNIT_OF_BINARY_FUNC(BinaryFuncAll, GetOneVal); +#undef SPECIALIZE_UNIT_OF_BINARY_FUNC + +} // namespace oneflow + +#endif //WITH_ROCM + +#endif // ONEFLOW_CORE_NDARRAY_BINARY_FUNC_H_ diff --git a/oneflow/core/ndarray/ndarray_apply_binary_core.cpp b/oneflow/core/ndarray/ndarray_apply_binary_core.cpp index 7e1b76acfa6afa0a464c32b972c70d22c9e86270..ee468ce9baf13606b9dcf70c42083875283ddb4a 100644 --- a/oneflow/core/ndarray/ndarray_apply_binary_core.cpp +++ b/oneflow/core/ndarray/ndarray_apply_binary_core.cpp @@ -31,16 +31,4 @@ struct NdarrayApplyBinaryCoreWrapper final { } }; -#define INSTANTIATE_NDARRAY_APPLY_BINARY_CORE(dtype_pair, binary_func) \ - template struct NdarrayApplyBinaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - ARITHMETIC_BINARY_FUNC_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - LOGICAL_BINARY_FUNC_SEQ) - } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_binary_core.cu b/oneflow/core/ndarray/ndarray_apply_binary_core.cu index 2acad9e6569d7d3ab00f007261fae62b1b4f4361..95cfcbfa8844d3e95745a0cb89a5ad2c7c15161e 100644 --- a/oneflow/core/ndarray/ndarray_apply_binary_core.cu +++ b/oneflow/core/ndarray/ndarray_apply_binary_core.cu @@ -53,15 +53,4 @@ struct NdarrayApplyBinaryCoreWrapper final { } }; -#define INSTANTIATE_NDARRAY_APPLY_BINARY_CORE(dtype_pair, binary_func) \ - template struct NdarrayApplyBinaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - LOGICAL_BINARY_FUNC_SEQ); } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_binary_core.hip.cpp b/oneflow/core/ndarray/ndarray_apply_binary_core.hip.cpp deleted file mode 100644 index 9ae4c6fde35b4d4b72a9e4398656dbeee153916a..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_apply_binary_core.hip.cpp +++ /dev/null @@ -1,68 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_apply_binary_core.h" -#include "oneflow/core/ndarray/binary_func.h" - -namespace oneflow { - -namespace { - -template class binary_func> -__global__ void NdarrayApplyBinaryApplyGpu(size_t n, - typename BinaryFuncTrait::return_type* y, - const T* a, const T* b) { - NdarrayApplyBinaryCore::Apply(n, y, a, b); -} - -template class binary_func> -__global__ void NdarrayApplyBinaryInplaceApplyGpu(size_t n, T* y, const T* x) { - NdarrayApplyBinaryCore::InplaceApply(n, y, x); -} - -} // namespace - -template class binary_func> -struct NdarrayApplyBinaryCoreWrapper final { - static void Apply(ep::Stream* stream, - const XpuVarNdarray::return_type>& y, - const XpuVarNdarray& a, const XpuVarNdarray& b) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayApplyBinaryApplyGpu), stream, n, n, y.host_ptr(), - a.host_ptr(), b.host_ptr()); - } - static void InplaceApply(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayApplyBinaryInplaceApplyGpu), stream, n, n, y.host_ptr(), - x.host_ptr()); - } -}; - -#define INSTANTIATE_NDARRAY_APPLY_BINARY_CORE(dtype_pair, binary_func) \ - template struct NdarrayApplyBinaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_BINARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - LOGICAL_BINARY_FUNC_SEQ); -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cpp b/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cpp index 5c3ae740c180aea3bb2880cca135cfac32d098ce..a3017dcaa5e9086a620d8ca9e6a137c8b7a6a15a 100644 --- a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cpp +++ b/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cpp @@ -35,26 +35,4 @@ struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - -#define INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC(dtype_pair, NDIMS, binary_func) \ - template struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper< \ - DeviceType::kCPU, OF_PP_PAIR_FIRST(dtype_pair), NDIMS, binary_func>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ((bool, DataType::kBool)), DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cu b/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cu index 3df617727fc273c99a0e59b9534884c0d4c2ac2a..0fea5aedc49fc8660351eeb6150f935b957d0889 100644 --- a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cu +++ b/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.cu @@ -164,26 +164,4 @@ struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - -#define INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC(dtype_pair, NDIMS, binary_func) \ - template struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(dtype_pair), NDIMS, binary_func>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ((bool, DataType::kBool)), DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.hip.cpp b/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.hip.cpp deleted file mode 100644 index 68e9d7b51a841cdbb28398c8a84753280b97e8a7..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.hip.cpp +++ /dev/null @@ -1,190 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_apply_broadcast_binary_core.h" - -namespace oneflow { - -namespace { - -template -struct XY2XFunctor final { - __host__ __device__ XY2XFunctor(Index dim_y) : dim_y_(dim_y) {} - - __host__ __device__ Index operator()(Index idx) const { return idx / dim_y_; } - - Index dim_y_; -}; - -template -struct XY2YFunctor final { - __host__ __device__ XY2YFunctor(Index dim_y) : dim_y_(dim_y) {} - - __host__ __device__ Index operator()(Index idx) const { return idx % dim_y_; } - - Index dim_y_; -}; - -template -struct XYZ2XZFunctor final { - __host__ __device__ XYZ2XZFunctor(Index dim_y, Index dim_z) - : dim_yz_(dim_y * dim_z), dim_z_(dim_z) {} - - __host__ __device__ Index operator()(Index idx) const { - const Index x = idx / dim_yz_; - const Index z = (idx % dim_yz_) % dim_z_; - return x * dim_z_ + z; - } - - Index dim_yz_; - Index dim_z_; -}; - -template -struct XYZ2YFunctor final { - __host__ __device__ XYZ2YFunctor(Index dim_y, Index dim_z) - : dim_yz_(dim_y * dim_z), dim_z_(dim_z) {} - - __host__ __device__ Index operator()(Index idx) const { return (idx % dim_yz_) / dim_z_; } - - Index dim_yz_; - Index dim_z_; -}; - -template class binary_func, typename OffsetFunctor> -__global__ void PartialBroadcastGpu(K n, typename BinaryFuncTrait::return_type* y, - const T* a, const T* b, OffsetFunctor offset_functor) { - CUDA_1D_KERNEL_LOOP_T(K, i, n) { y[i] = binary_func::Invoke(a[i], b[offset_functor(i)]); } -} - -template class binary_func> -__global__ void GpuBroadcastBinaryFunc( - const XpuVarNdarray::return_type> y, - const XpuVarNdarray a, const XpuVarNdarray b) { - NdarrayApplyBroadcastBinaryCore::Apply(y, a, b); -} -template class binary_func> -__global__ void GpuInplaceBroadcastBinaryFunc(const XpuVarNdarray y, - const XpuVarNdarray x) { - NdarrayApplyBroadcastBinaryCore::InplaceApply(y, x); -} - -} // namespace - -template class binary_func> -struct NdarrayApplyBroadcastBinaryCoreWrapper final { - static void Apply(ep::Stream* stream, - const XpuVarNdarray::return_type>& y, - const XpuVarNdarray& a, const XpuVarNdarray& b) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - if (IsKernelSafeInt32(n) && PartialBroadcast(stream, y, a, b)) { return; } - if (!IsKernelSafeInt32(n) && PartialBroadcast(stream, y, a, b)) { return; } - RUN_CUDA_KERNEL((GpuBroadcastBinaryFunc), stream, n, y, a, b); - } - - template - static bool PartialBroadcast( - ep::Stream* stream, - const XpuVarNdarray::return_type>& y, - const XpuVarNdarray& a, const XpuVarNdarray& b) { - size_t n = y.host_shape().HostElemNum(); - if (y.host_shape() == a.host_shape()) { - if (y.host_shape().NumAxes() == 2) { - const K y_dim0 = y.host_shape().At(0); - const K y_dim1 = y.host_shape().At(1); - const K b_dim0 = b.host_shape().At(0); - const K b_dim1 = b.host_shape().At(1); - if (b_dim0 == y_dim0 && b_dim1 == 1) { - XY2XFunctor xy2x(y_dim1); - RUN_CUDA_KERNEL((PartialBroadcastGpu>), stream, n, n, - y.host_ptr(), a.host_ptr(), b.host_ptr(), xy2x); - return true; - } - if (b_dim0 == 1 && b_dim1 == y_dim1) { - XY2YFunctor xy2y(y_dim1); - RUN_CUDA_KERNEL((PartialBroadcastGpu>), stream, n, n, - y.host_ptr(), a.host_ptr(), b.host_ptr(), xy2y); - return true; - } - } - if (y.host_shape().NumAxes() == 3) { - const K y_dim0 = y.host_shape().At(0); - const K y_dim1 = y.host_shape().At(1); - const K y_dim2 = y.host_shape().At(2); - const K b_dim0 = b.host_shape().At(0); - const K b_dim1 = b.host_shape().At(1); - const K b_dim2 = b.host_shape().At(2); - if (b_dim0 == y_dim0 && b_dim1 == 1 && b_dim2 == y_dim2) { - XYZ2XZFunctor xyz2xz(y_dim1, y_dim2); - RUN_CUDA_KERNEL((PartialBroadcastGpu>), stream, n, n, - y.host_ptr(), a.host_ptr(), b.host_ptr(), xyz2xz); - return true; - } - if (b_dim0 == 1 && b_dim1 == y_dim1 && b_dim2 == 1) { - XYZ2YFunctor xyz2y(y_dim1, y_dim2); - RUN_CUDA_KERNEL((PartialBroadcastGpu>), stream, n, n, - y.host_ptr(), a.host_ptr(), b.host_ptr(), xyz2y); - return true; - } - } - } - return false; - } -}; - -template class binary_func> -struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper - final { - static void InplaceApply(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x) { - size_t n = y.host_shape().HostElemNum(); - XpuVarNdarray a(y.host_shape(), y.host_ptr()); - using NBB = NdarrayApplyBroadcastBinaryCoreWrapper; - if (n == 0) { return; } - if (IsKernelSafeInt32(n) && NBB::template PartialBroadcast(stream, y, a, x)) { - return; - } - if (!IsKernelSafeInt32(n) && NBB::template PartialBroadcast(stream, y, a, x)) { - return; - } - RUN_CUDA_KERNEL((GpuInplaceBroadcastBinaryFunc), stream, n, y, x); - } -}; - -#define INSTANTIATE_BROADCAST_BINARY_FUNC(dtype_pair, NDIMS, binary_func) \ - template struct NdarrayApplyBroadcastBinaryCoreWrapper< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(dtype_pair), NDIMS, binary_func>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - -#define INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC(dtype_pair, NDIMS, binary_func) \ - template struct NdarrayApplyBroadcastInplaceBinaryCoreWrapper< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(dtype_pair), NDIMS, binary_func>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_INPLACE_BINARY_FUNC, - ((bool, DataType::kBool)), DIM_SEQ, LOGICAL_BINARY_FUNC_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/ndarray_apply_broadcast_unary_core.hip.cpp b/oneflow/core/ndarray/ndarray_apply_broadcast_unary_core.hip.cpp deleted file mode 100644 index 1b77803f32b38472b81dc6a504244a6035720e74..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_apply_broadcast_unary_core.hip.cpp +++ /dev/null @@ -1,46 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_apply_broadcast_unary_core.h" - -namespace oneflow { - -namespace { - -template class unary_func> -__global__ void GpuBroadcastUnaryFunc(const XpuVarNdarray y, const XpuVarNdarray x) { - NdarrayApplyBroadcastUnaryCore::Apply(y, x); -} - -} // namespace - -template class unary_func> -struct NdarrayApplyBroadcastUnaryCoreWrapper final { - static void Apply(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((GpuBroadcastUnaryFunc), stream, n, y, x); - } -}; - -#define INSTANTIATE_BROADCAST_UNARY_FUNC(dtype_pair, NDIMS, unary_func) \ - template struct NdarrayApplyBroadcastUnaryCoreWrapper< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(dtype_pair), NDIMS, unary_func>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BROADCAST_UNARY_FUNC, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_UNARY_FUNC_SEQ) -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/ndarray_apply_unary_core.cpp b/oneflow/core/ndarray/ndarray_apply_unary_core.cpp index c0ea2f92368ad815d79b930d987481685952df23..3900c2fc6c41c9768dfa47dffbf545fcf7cd110a 100644 --- a/oneflow/core/ndarray/ndarray_apply_unary_core.cpp +++ b/oneflow/core/ndarray/ndarray_apply_unary_core.cpp @@ -25,11 +25,4 @@ struct NdarrayApplyUnaryCoreWrapper final { } }; -#define INSTANTIATE_NDARRAY_APPLY_UNARY_CORE(dtype_pair, unary_func) \ - template struct NdarrayApplyUnaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_UNARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, - ARITHMETIC_UNARY_FUNC_SEQ) - } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_unary_core.cu b/oneflow/core/ndarray/ndarray_apply_unary_core.cu index 85f45203d6d2fb791a3e1b6f03c441659fff1675..81aba286b3e961160300be73e7042973ae5b074d 100644 --- a/oneflow/core/ndarray/ndarray_apply_unary_core.cu +++ b/oneflow/core/ndarray/ndarray_apply_unary_core.cu @@ -36,11 +36,4 @@ struct NdarrayApplyUnaryCoreWrapper final { } }; -#define INSTANTIATE_NDARRAY_APPLY_UNARY_CORE(dtype_pair, unary_func) \ - template struct NdarrayApplyUnaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_UNARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, - ARITHMETIC_UNARY_FUNC_SEQ); - } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_apply_unary_core.hip.cpp b/oneflow/core/ndarray/ndarray_apply_unary_core.hip.cpp deleted file mode 100644 index 41f68f46fba518ba4537f8bbe435929e423943d8..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_apply_unary_core.hip.cpp +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_apply_unary_core.h" -#include "oneflow/core/ndarray/unary_func.h" - -namespace oneflow { - -namespace { - -template class unary_func> -__global__ void NdarrayApplyUnaryInplaceApplyGpu(T* ptr, size_t n) { - NdarrayApplyUnaryCore::InplaceApply(ptr, n); -} - -} // namespace - -template class unary_func> -struct NdarrayApplyUnaryCoreWrapper final { - static void InplaceApply(ep::Stream* stream, const XpuVarNdarray& y) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayApplyUnaryInplaceApplyGpu), stream, n, y.host_ptr(), n); - } -}; - -#define INSTANTIATE_NDARRAY_APPLY_UNARY_CORE(dtype_pair, unary_func) \ - template struct NdarrayApplyUnaryCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_APPLY_UNARY_CORE, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, - ARITHMETIC_UNARY_FUNC_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/ndarray_assign_core.hip.cpp b/oneflow/core/ndarray/ndarray_assign_core.hip.cpp deleted file mode 100644 index 16dbfed82008fa5985ca27bd77a72d1f13672b96..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_assign_core.hip.cpp +++ /dev/null @@ -1,63 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_assign_core.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/kernel/kernel_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void NdarrayAssignReducedGpu(XpuVarNdarray y, - const XpuReducedNdarray reduced) { - NdarrayAssignCore::Assign(y, reduced); -} - -template -__global__ void NdarrayAssignGpu(XpuVarNdarray y, const XpuVarNdarray x) { - NdarrayAssignCore::Assign(y, x); -} - -} // namespace - -template -struct NdarrayAssignCoreWrapper final { - static void Assign(ep::Stream* ctx, const XpuVarNdarray& y, - const XpuReducedNdarray& reduced) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayAssignReducedGpu), ctx, n, y, reduced); - } - static void Assign(ep::Stream* ctx, const XpuVarNdarray& y, const XpuVarNdarray& x) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayAssignGpu), ctx, n, y, x); - } -}; - -#define INSTANTIATE_NDARRAY_ASSIGN(ret_dtype_pair, dtype_pair, NDIMS) \ - template struct NdarrayAssignCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - INSTANTIATE_NDARRAY_ASSIGN, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, DIM_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_ASSIGN, HALF_DATA_TYPE_SEQ, HALF_DATA_TYPE_SEQ, - DIM_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/ndarray_reduce.h b/oneflow/core/ndarray/ndarray_reduce.h index b169a112ad8349e7cb1c6482cae7fceaf7f4eaf1..445bf6b1cde21a3721f0fc9f8c38b44924f8517d 100644 --- a/oneflow/core/ndarray/ndarray_reduce.h +++ b/oneflow/core/ndarray/ndarray_reduce.h @@ -44,22 +44,16 @@ struct NdarrayReduce< CHECK_EQ(y.shape().NumAxes(), x.shape().NumAxes()); if (NdarrayNoReduce::Matched(y, x)) { NdarrayNoReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayNoReduce" << std::endl; } else if (NdarrayScalarReduce::Matched(y, x)) { NdarrayScalarReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayScalarReduce" << std::endl; } else if (NdarrayMatrixRowReduce::Matched(y, x)) { NdarrayMatrixRowReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayMatrixRowReduce" << std::endl; } else if (NdarrayMatrixColReduce::Matched(y, x)) { NdarrayMatrixColReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayMatrixColReduce" << std::endl; } else if (NdarrayXYZCubeXZReduce::Matched(y, x)) { NdarrayXYZCubeXZReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayXYZCubeXZReduce" << std::endl; } else { NdarrayDefaultReduce::Reduce(stream, y, x, tmp_storage); - // std::cout << "NdarrayDefaultReduce" << std::endl; } } diff --git a/oneflow/core/ndarray/ndarray_reduce_impl.cpp b/oneflow/core/ndarray/ndarray_reduce_impl.cpp index f508ee7cafd32668e7d489ea5f626905cf2b3c87..9c1a5d5ba14afd14883d9292502a126ba613e314 100644 --- a/oneflow/core/ndarray/ndarray_reduce_impl.cpp +++ b/oneflow/core/ndarray/ndarray_reduce_impl.cpp @@ -47,6 +47,8 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, FLOATING_DATA_TYPE_SEQ, + NANSUM_REDUCE_BINARY_FUNC_SEQ); template class binary_func> struct NdarrayReduceCoreWrapper final { @@ -64,4 +66,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, DIM_SEQ, REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, FLOATING_DATA_TYPE_SEQ, + DIM_SEQ, NANSUM_REDUCE_BINARY_FUNC_SEQ); + } // namespace oneflow diff --git a/oneflow/core/ndarray/ndarray_reduce_impl.cu b/oneflow/core/ndarray/ndarray_reduce_impl.cu index 1466a1a858cc7627fa4148a9e3f77a5fbc5371be..ae83ed103528ab5ef50ff55c16b56363eb15ab20 100644 --- a/oneflow/core/ndarray/ndarray_reduce_impl.cu +++ b/oneflow/core/ndarray/ndarray_reduce_impl.cu @@ -13,7 +13,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif +#include "oneflow/core/kernel/util/numerics.cuh" #include "oneflow/core/ndarray/ndarray_reduce_impl.h" #include "oneflow/core/ndarray/binary_func.h" #include "oneflow/core/common/preprocessor.h" @@ -21,6 +27,8 @@ limitations under the License. #include "oneflow/core/common/permutation_iterator.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_CUDA + namespace cub { struct Prod { template @@ -40,6 +48,15 @@ struct All { return a && b; } }; +struct NanSum { + template + __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { + if (oneflow::detail::numerics::isnan(a)) + return oneflow::detail::numerics::isnan(b) ? T{0} : b; + return oneflow::detail::numerics::isnan(b) ? a : a + b; + } +}; + } // namespace cub namespace oneflow { @@ -153,7 +170,7 @@ struct CubFunctor4BianryFunc; struct CubFunctor4BianryFunc final { \ using type = cub::func_name; \ }; -OF_PP_FOR_EACH_ATOMIC(SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC, REDUCE_BINARY_FUNC_NAME_SEQ); +OF_PP_FOR_EACH_ATOMIC(SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC, REDUCE_BINARY_FUNC_NAME_SEQ(NanSum)); #undef SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC struct RowOffsetFunctor final { @@ -358,10 +375,13 @@ struct NdarrayReduceCoreWrapper final template struct NdarrayMatrixRowReduce; \ template struct NdarrayMatrixColReduce; \ template struct NdarrayXYZCubeXZReduce; + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ, + UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, FLOATING_DATA_TYPE_SEQ, + NANSUM_REDUCE_BINARY_FUNC_SEQ); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, @@ -372,11 +392,395 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, binary_func>; OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ, + UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, DIM_SEQ, ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, FLOATING_DATA_TYPE_SEQ, + DIM_SEQ, NANSUM_REDUCE_BINARY_FUNC_SEQ); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, DIM_SEQ, LOGICAL_REDUCE_BINARY_FUNC_SEQ); } // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +namespace hipcub { +struct Prod { + template + __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { + return a * b; + } +}; +struct Any { + template + __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { + return a || b; + } +}; +struct All { + template + __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { + return a && b; + } +}; +struct NanSum { + template + __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { + if (oneflow::detail::numerics::isnan(a)) + return oneflow::detail::numerics::isnan(b) ? T{0} : b; + return oneflow::detail::numerics::isnan(b) ? a : a + b; + } +}; + +} // namespace hipcub + +namespace oneflow { + +namespace { + +template class R, typename T, typename K, typename RetT> +__global__ void MatrixColReduceBy1ThreadPerColumn(K num_elems, K num_cols, const T* in, RetT* out) { + CUDA_1D_KERNEL_LOOP_T(K, j, num_cols) { + K index = j; + T sum = in[index]; + for (index += num_cols; index < num_elems; index += num_cols) { + sum = R::Invoke(sum, in[index]); + } + out[j] = sum; + } +} + +template +struct WithAlign2 { + union { + T value; + int32_t padding; + }; +}; + +template class R, typename T, typename K, typename RetT> +__global__ void MatrixColReduceByWarpBlock(K num_elems, K num_cols, const T* in, RetT* out) { + const K thread_col = threadIdx.x % kCudaWarpSize; + const K thread_row = threadIdx.x / kCudaWarpSize; + const K thread_dim_row = blockDim.x / kCudaWarpSize; + const K num_valid_threads = thread_dim_row * num_cols; // ASSERT: always <= num_elems + const K col = blockIdx.x * kCudaWarpSize + thread_col; + __shared__ WithAlign2 partial_values[kCudaWarpSize * kCudaWarpSize]; + if (col < num_cols) { + K index = thread_row * num_cols + col; + T val = in[index]; + for (index += num_valid_threads; index < num_elems; index += num_valid_threads) { + val = R::Invoke(val, in[index]); + } + partial_values[threadIdx.x].value = val; + } + __syncthreads(); + if (col < num_cols && thread_row == 0) { + int index = thread_col; + T val = partial_values[index].value; + for (index += kCudaWarpSize; index < blockDim.x; index += kCudaWarpSize) { + val = R::Invoke(val, partial_values[index].value); + } + out[col] = val; + } +} + +template class R, typename T, typename K, typename RetT> +void MatrixColReduceBy1BlockLayer(ep::Stream* stream, K num_elems, K num_cols, const T* in, + RetT* out) { + CHECK_LE(num_cols, kCudaMaxBlocksNum * kCudaWarpSize); + const K num_rows = num_elems / num_cols; + CHECK_GT(num_rows, 0); + if (num_rows < kCudaWarpSize) { + RUN_CUDA_KERNEL((MatrixColReduceBy1ThreadPerColumn), stream, num_cols, num_elems, + num_cols, in, out); + } else { + const int num_blocks = (num_cols + kCudaWarpSize - 1) / kCudaWarpSize * 2; + const int num_threads = 1024; + auto Reduce = &MatrixColReduceByWarpBlock; + Reduce<<As()->cuda_stream()>>>( + num_elems, num_cols, in, out); + } +} + +const static int32_t kNumRows4OneBlockLayer = kCudaWarpSize * kCudaWarpSize; +const static int32_t kNumCols4OneBlockLayer = kCudaMaxBlocksNum * kCudaWarpSize / 2; + +template class R, typename T, typename K> +void MatrixColReduceK(ep::Stream* stream, K num_rows, K num_cols, const T* in, + typename BinaryFuncTrait::return_type* out, T* tmp) { + K num_elems = num_rows * num_cols; + if (num_rows < kNumRows4OneBlockLayer || num_cols > kNumCols4OneBlockLayer) { + MatrixColReduceBy1BlockLayer::return_type>( + stream, num_elems, num_cols, in, out); + } else { + int scale_shift = 1; + for (; true; ++scale_shift) { + if ((num_rows >> scale_shift) < kNumRows4OneBlockLayer) { break; } + if ((num_cols << scale_shift) > kNumCols4OneBlockLayer) { break; } + } + MatrixColReduceBy1BlockLayer(stream, num_elems, (num_cols << scale_shift), in, tmp); + // recursively calls MatrixColReduceK(...) log32(num_rows) times at most + MatrixColReduceK(stream, (1 << scale_shift), num_cols, tmp, out, tmp); + } +} + +template class R, typename T> +void MatrixColReduce(ep::Stream* stream, int64_t num_rows, int64_t num_cols, const T* in, + typename BinaryFuncTrait::return_type* out, T* tmp) { + if (IsKernelSafeInt32(num_rows * num_cols)) { + return MatrixColReduceK(stream, num_rows, num_cols, in, out, tmp); + } else { + return MatrixColReduceK(stream, num_rows, num_cols, in, out, tmp); + } +} + +} // namespace + +template class binary_func> +struct CubFunctor4BianryFunc; + +#define SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC(func_name) \ + template \ + struct CubFunctor4BianryFunc final { \ + using type = hipcub::func_name; \ + }; +OF_PP_FOR_EACH_ATOMIC(SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC, REDUCE_BINARY_FUNC_NAME_SEQ(NanSum)); +#undef SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC + +struct RowOffsetFunctor final { + OF_DEVICE_FUNC explicit RowOffsetFunctor(int32_t num_cols) : num_cols_(num_cols) {} + OF_DEVICE_FUNC int32_t operator()(const int32_t& x) const { return x * num_cols_; } + int32_t num_cols_; +}; + +template class binary_func> +struct NdarrayScalarReduce final { + using RetT = typename BinaryFuncTrait::return_type; + static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { + return y.shape().ElemNum() == 1; + } + + static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, + const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { + CHECK(Matched(y, x)); + size_t x_size = x.shape().ElemNum(); + size_t tmp_storage_bytes = 0; + auto DoReduce = [&](T* tmp_storage_ptr) { + int retcode = hipcub::DeviceReduce::Reduce( + tmp_storage_ptr, tmp_storage_bytes, x.ptr(), y.ptr(), x_size, + typename CubFunctor4BianryFunc::type(), + UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); + CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; + }; + DoReduce(nullptr); + //CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); + DoReduce(tmp_storage.ptr()); + } +}; + +template class binary_func> +struct NdarrayMatrixRowReduce final { + using RetT = typename BinaryFuncTrait::return_type; + static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { + if (y.shape().ElemNum() > GetMaxVal()) { return false; } + if (x.shape().NumAxes() != 2) { return false; } + if (y.shape().NumAxes() != 2) { return false; } + return x.shape().At(0) == y.shape().At(0) && y.shape().At(1) == 1; + } + + static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, + const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { + CHECK(Matched(y, x)); + int32_t num_rows = y.shape().ElemNum(); + int32_t num_cols = x.shape().ElemNum() / y.shape().ElemNum(); + RowOffsetFunctor get_row_offset(num_cols); + hipcub::CountingInputIterator counting_intput_it(0); + hipcub::TransformInputIterator> + transform_input_iter(counting_intput_it, get_row_offset); + size_t tmp_storage_bytes = 0; + auto DoReduce = [&](T* tmp_storage_ptr) { + int retcode = hipcub::DeviceSegmentedReduce::Reduce( + tmp_storage_ptr, tmp_storage_bytes, x.ptr(), y.ptr(), num_rows, transform_input_iter, + transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), + UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); + CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; + }; + DoReduce(nullptr); + CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); + DoReduce(tmp_storage.ptr()); + } +}; + +template class binary_func> +struct NdarrayMatrixColReduce final { + using RetT = typename BinaryFuncTrait::return_type; + static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { + if (y.shape().ElemNum() > GetMaxVal()) { return false; } + if (x.shape().NumAxes() != 2) { return false; } + if (y.shape().NumAxes() != 2) { return false; } + return y.shape().At(0) == 1 && x.shape().At(1) == y.shape().At(1); + } + + struct XY2YXFunctor final { + __host__ __device__ XY2YXFunctor(int32_t dim_x, int32_t dim_y) : dim_x_(dim_x), dim_y_(dim_y) {} + + __host__ __device__ int32_t operator()(const int32_t& idx) const { + const int32_t y = idx / dim_x_; + const int32_t x = idx % dim_x_; + return x * dim_y_ + y; + } + + int32_t dim_x_; + int32_t dim_y_; + }; + + static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, + const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { + CHECK(Matched(y, x)); + int64_t num_rows = x.shape().At(0); + int64_t num_cols = x.shape().At(1); + if (num_cols < kNumCols4OneBlockLayer) { + return MatrixColReduce(stream, num_rows, num_cols, x.host_ptr(), y.host_ptr(), + tmp_storage.host_ptr()); + } + RowOffsetFunctor get_row_offset(num_rows); + hipcub::CountingInputIterator counting_intput_it(0); + hipcub::TransformInputIterator> + transform_input_iter(counting_intput_it, get_row_offset); + + XY2YXFunctor xy2yx(x.shape().At(0), x.shape().At(1)); + using XY2YxIndexIter = + hipcub::TransformInputIterator>; + XY2YxIndexIter xy2yx_iter(counting_intput_it, xy2yx); + PermutationIterator x_iter(x.ptr(), xy2yx_iter); + size_t tmp_storage_bytes = 0; + auto DoReduce = [&](T* tmp_storage_ptr) { + int retcode = hipcub::DeviceSegmentedReduce::Reduce( + tmp_storage_ptr, tmp_storage_bytes, x_iter, y.ptr(), num_cols, transform_input_iter, + transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), + UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); + CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; + }; + DoReduce(nullptr); + CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); + DoReduce(tmp_storage.ptr()); + } +}; + +template class binary_func> +struct NdarrayXYZCubeXZReduce final { + using RetT = typename BinaryFuncTrait::return_type; + static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { + if (y.shape().ElemNum() > GetMaxVal()) { return false; } + if (x.shape().NumAxes() != 3) { return false; } + if (y.shape().NumAxes() != 3) { return false; } + return y.shape().At(0) == 1 && x.shape().At(1) == y.shape().At(1) && y.shape().At(2) == 1; + } + + struct XYZ2YxzFunctor final { + __host__ __device__ XYZ2YxzFunctor(int32_t dim_x, int32_t dim_y, int32_t dim_z) + : dim_z_(dim_z), dim_xz_(dim_x * dim_z), dim_yz_(dim_y * dim_z) {} + + __host__ __device__ int32_t operator()(const int32_t& idx) const { + const int32_t y = idx / dim_xz_; + const int32_t xz_idx = idx % dim_xz_; + const int32_t x = xz_idx / dim_z_; + const int32_t z = xz_idx % dim_z_; + return x * dim_yz_ + y * dim_z_ + z; + } + + int32_t dim_z_; + int32_t dim_xz_; + int32_t dim_yz_; + }; + + static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, + const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { + CHECK(Matched(y, x)); + int32_t num_rows = y.shape().ElemNum(); + int32_t num_cols = x.shape().ElemNum() / y.shape().ElemNum(); + + RowOffsetFunctor get_row_offset(num_cols); + hipcub::CountingInputIterator counting_intput_it(0); + hipcub::TransformInputIterator> + transform_input_iter(counting_intput_it, get_row_offset); + + XYZ2YxzFunctor xyz2yxz(x.shape().At(0), x.shape().At(1), x.shape().At(2)); + using XYZ2YxzIndexIter = + hipcub::TransformInputIterator>; + XYZ2YxzIndexIter xyz2yxz_iter(counting_intput_it, xyz2yxz); + PermutationIterator x_iter(x.ptr(), xyz2yxz_iter); + size_t tmp_storage_bytes = 0; + auto DoReduce = [&](T* tmp_storage_ptr) { + int retcode = hipcub::DeviceSegmentedReduce::Reduce( + tmp_storage_ptr, tmp_storage_bytes, x_iter, y.ptr(), num_rows, transform_input_iter, + transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), + UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); + CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; + }; + DoReduce(nullptr); + CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); + DoReduce(tmp_storage.ptr()); + } +}; + +namespace { + +template class binary_func> +__global__ void NdarrayReduceGpuInplaceReduceAxis(const XpuReducedNdarray dst_reduced, + const XpuReducedNdarray x, int axis) { + NdarrayReduceCore::ReduceAxis(dst_reduced, x, axis); +} + +} // namespace + +template class binary_func> +struct NdarrayReduceCoreWrapper final { + static void ReduceAxis(ep::Stream* stream, const XpuReducedNdarray& dst_reduced, + const XpuReducedNdarray& x, int axis) { + size_t n = x.host_shape().HostElemNum(); + RUN_CUDA_KERNEL((NdarrayReduceGpuInplaceReduceAxis), stream, n, + dst_reduced, x, axis); + } +}; + +#define INSTANTIATE_NDARRAY_REDUCE_IMPL(dtype, binary_func) \ + template struct NdarrayScalarReduce; \ + template struct NdarrayMatrixRowReduce; \ + template struct NdarrayMatrixColReduce; \ + template struct NdarrayXYZCubeXZReduce; + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, + ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ + UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, + ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, FLOATING_DATA_TYPE_SEQ, + NANSUM_REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ, + LOGICAL_REDUCE_BINARY_FUNC_SEQ); + +#define INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER(dtype_pair, NDIMS, binary_func) \ + template struct NdarrayReduceCoreWrapper; +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, + ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ + UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, + DIM_SEQ, ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, FLOATING_DATA_TYPE_SEQ, + DIM_SEQ, NANSUM_REDUCE_BINARY_FUNC_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ, + DIM_SEQ, LOGICAL_REDUCE_BINARY_FUNC_SEQ); + +} // namespace oneflow + +#endif diff --git a/oneflow/core/ndarray/ndarray_reduce_impl.h b/oneflow/core/ndarray/ndarray_reduce_impl.h index 44cca423129188806a2747c01968e0bbd28485db..346d0ca6e52fbd2bd46a4bd60ed6de955c0f6af4 100644 --- a/oneflow/core/ndarray/ndarray_reduce_impl.h +++ b/oneflow/core/ndarray/ndarray_reduce_impl.h @@ -56,7 +56,11 @@ struct NdarrayNoReduce& y, const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { - XpuNdarrayAssign::Assign(ctx, y, x); + if (std::is_same, BinaryFuncNanSum>()) { + XpuNdarrayAssign::AssignNanSum(ctx, y, x); + } else { + XpuNdarrayAssign::Assign(ctx, y, x); + } } }; diff --git a/oneflow/core/ndarray/ndarray_reduce_impl.hip.cpp b/oneflow/core/ndarray/ndarray_reduce_impl.hip.cpp deleted file mode 100644 index b651aa50f30307dad71ce09b3311bf43f2fc7cf5..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/ndarray_reduce_impl.hip.cpp +++ /dev/null @@ -1,383 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/ndarray/ndarray_reduce_impl.h" -#include "oneflow/core/ndarray/binary_func.h" -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/common/shape.h" -#include "oneflow/core/common/permutation_iterator.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace hipcub { -struct Prod { - template - __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { - return a * b; - } -}; -struct Any { - template - __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { - return a || b; - } -}; -struct All { - template - __host__ __device__ __forceinline__ T operator()(const T& a, const T& b) const { - return a && b; - } -}; -} // namespace hipcub - -namespace oneflow { - -namespace { - -template class R, typename T, typename K, typename RetT> -__global__ void MatrixColReduceBy1ThreadPerColumn(K num_elems, K num_cols, const T* in, RetT* out) { - CUDA_1D_KERNEL_LOOP_T(K, j, num_cols) { - K index = j; - T sum = in[index]; - for (index += num_cols; index < num_elems; index += num_cols) { - sum = R::Invoke(sum, in[index]); - } - out[j] = sum; - } -} - -template -struct WithAlign2 { - union { - T value; - int32_t padding; - }; -}; - -template class R, typename T, typename K, typename RetT> -__global__ void MatrixColReduceByWarpBlock(K num_elems, K num_cols, const T* in, RetT* out) { - const K thread_col = threadIdx.x % kCudaWarpSize; - const K thread_row = threadIdx.x / kCudaWarpSize; - const K thread_dim_row = blockDim.x / kCudaWarpSize; - const K num_valid_threads = thread_dim_row * num_cols; // ASSERT: always <= num_elems - const K col = blockIdx.x * kCudaWarpSize + thread_col; - __shared__ WithAlign2 partial_values[kCudaWarpSize * kCudaWarpSize]; - if (col < num_cols) { - K index = thread_row * num_cols + col; - T val = in[index]; - for (index += num_valid_threads; index < num_elems; index += num_valid_threads) { - val = R::Invoke(val, in[index]); - } - partial_values[threadIdx.x].value = val; - } - __syncthreads(); - if (col < num_cols && thread_row == 0) { - int index = thread_col; - T val = partial_values[index].value; - for (index += kCudaWarpSize; index < blockDim.x; index += kCudaWarpSize) { - val = R::Invoke(val, partial_values[index].value); - } - out[col] = val; - } -} - -template class R, typename T, typename K, typename RetT> -void MatrixColReduceBy1BlockLayer(ep::Stream* stream, K num_elems, K num_cols, const T* in, - RetT* out) { - CHECK_LE(num_cols, kCudaMaxBlocksNum * kCudaWarpSize); - const K num_rows = num_elems / num_cols; - CHECK_GT(num_rows, 0); - if (num_rows < kCudaWarpSize) { - RUN_CUDA_KERNEL((MatrixColReduceBy1ThreadPerColumn), stream, num_cols, num_elems, - num_cols, in, out); - } else { - const int num_blocks = (num_cols + kCudaWarpSize - 1) / kCudaWarpSize; - const int num_threads = kCudaWarpSize * kCudaWarpSize; - auto Reduce = &MatrixColReduceByWarpBlock; - Reduce<<As()->cuda_stream()>>>( - num_elems, num_cols, in, out); - } -} - -const static int32_t kNumRows4OneBlockLayer = kCudaWarpSize * kCudaWarpSize; -const static int32_t kNumCols4OneBlockLayer = kCudaMaxBlocksNum * kCudaWarpSize / 2; - -template class R, typename T, typename K> -void MatrixColReduceK(ep::Stream* stream, K num_rows, K num_cols, const T* in, - typename BinaryFuncTrait::return_type* out, T* tmp) { - K num_elems = num_rows * num_cols; - if (num_rows < kNumRows4OneBlockLayer || num_cols > kNumCols4OneBlockLayer) { - MatrixColReduceBy1BlockLayer::return_type>( - stream, num_elems, num_cols, in, out); - } else { - int scale_shift = 1; - for (; true; ++scale_shift) { - if ((num_rows >> scale_shift) < kNumRows4OneBlockLayer) { break; } - if ((num_cols << scale_shift) > kNumCols4OneBlockLayer) { break; } - } - MatrixColReduceBy1BlockLayer(stream, num_elems, (num_cols << scale_shift), in, tmp); - // recursively calls MatrixColReduceK(...) log32(num_rows) times at most - MatrixColReduceK(stream, (1 << scale_shift), num_cols, tmp, out, tmp); - } -} - -template class R, typename T> -void MatrixColReduce(ep::Stream* stream, int64_t num_rows, int64_t num_cols, const T* in, - typename BinaryFuncTrait::return_type* out, T* tmp) { - if (IsKernelSafeInt32(num_rows * num_cols)) { - return MatrixColReduceK(stream, num_rows, num_cols, in, out, tmp); - } else { - return MatrixColReduceK(stream, num_rows, num_cols, in, out, tmp); - } -} - -} // namespace - -template class binary_func> -struct CubFunctor4BianryFunc; - -#define SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC(func_name) \ - template \ - struct CubFunctor4BianryFunc final { \ - using type = hipcub::func_name; \ - }; -OF_PP_FOR_EACH_ATOMIC(SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC, REDUCE_BINARY_FUNC_NAME_SEQ); -#undef SPECIALIZE_CUB_FUNCTOR_4_BINARY_FUNC - -struct RowOffsetFunctor final { - OF_DEVICE_FUNC explicit RowOffsetFunctor(int32_t num_cols) : num_cols_(num_cols) {} - OF_DEVICE_FUNC int32_t operator()(const int32_t& x) const { return x * num_cols_; } - int32_t num_cols_; -}; - -template class binary_func> -struct NdarrayScalarReduce final { - using RetT = typename BinaryFuncTrait::return_type; - static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { - return y.shape().ElemNum() == 1; - } - - static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { - CHECK(Matched(y, x)); - size_t x_size = x.shape().ElemNum(); - size_t tmp_storage_bytes = 0; - auto DoReduce = [&](T* tmp_storage_ptr) { - int retcode = hipcub::DeviceReduce::Reduce( - tmp_storage_ptr, tmp_storage_bytes, x.ptr(), y.ptr(), x_size, - typename CubFunctor4BianryFunc::type(), - UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); - CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; - }; - DoReduce(nullptr); - // CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); - DoReduce(tmp_storage.ptr()); - } -}; - -template class binary_func> -struct NdarrayMatrixRowReduce final { - using RetT = typename BinaryFuncTrait::return_type; - static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { - if (y.shape().ElemNum() > GetMaxVal()) { return false; } - if (x.shape().NumAxes() != 2) { return false; } - if (y.shape().NumAxes() != 2) { return false; } - return x.shape().At(0) == y.shape().At(0) && y.shape().At(1) == 1; - } - - static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { - CHECK(Matched(y, x)); - int32_t num_rows = y.shape().ElemNum(); - int32_t num_cols = x.shape().ElemNum() / y.shape().ElemNum(); - RowOffsetFunctor get_row_offset(num_cols); - hipcub::CountingInputIterator counting_intput_it(0); - hipcub::TransformInputIterator> - transform_input_iter(counting_intput_it, get_row_offset); - size_t tmp_storage_bytes = 0; - auto DoReduce = [&](T* tmp_storage_ptr) { - int retcode = hipcub::DeviceSegmentedReduce::Reduce( - tmp_storage_ptr, tmp_storage_bytes, x.ptr(), y.ptr(), num_rows, transform_input_iter, - transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), - UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); - CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; - }; - DoReduce(nullptr); - CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); - DoReduce(tmp_storage.ptr()); - } -}; - -template class binary_func> -struct NdarrayMatrixColReduce final { - using RetT = typename BinaryFuncTrait::return_type; - static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { - if (y.shape().ElemNum() > GetMaxVal()) { return false; } - if (x.shape().NumAxes() != 2) { return false; } - if (y.shape().NumAxes() != 2) { return false; } - return y.shape().At(0) == 1 && x.shape().At(1) == y.shape().At(1); - } - - struct XY2YXFunctor final { - __host__ __device__ XY2YXFunctor(int32_t dim_x, int32_t dim_y) : dim_x_(dim_x), dim_y_(dim_y) {} - - __host__ __device__ int32_t operator()(const int32_t& idx) const { - const int32_t y = idx / dim_x_; - const int32_t x = idx % dim_x_; - return x * dim_y_ + y; - } - - int32_t dim_x_; - int32_t dim_y_; - }; - - static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { - CHECK(Matched(y, x)); - int64_t num_rows = x.shape().At(0); - int64_t num_cols = x.shape().At(1); - if (num_cols < kNumCols4OneBlockLayer) { - return MatrixColReduce(stream, num_rows, num_cols, x.host_ptr(), y.host_ptr(), - tmp_storage.host_ptr()); - } - RowOffsetFunctor get_row_offset(num_rows); - hipcub::CountingInputIterator counting_intput_it(0); - hipcub::TransformInputIterator> - transform_input_iter(counting_intput_it, get_row_offset); - - XY2YXFunctor xy2yx(x.shape().At(0), x.shape().At(1)); - using XY2YxIndexIter = - hipcub::TransformInputIterator>; - XY2YxIndexIter xy2yx_iter(counting_intput_it, xy2yx); - PermutationIterator x_iter(x.ptr(), xy2yx_iter); - size_t tmp_storage_bytes = 0; - auto DoReduce = [&](T* tmp_storage_ptr) { - int retcode = hipcub::DeviceSegmentedReduce::Reduce( - tmp_storage_ptr, tmp_storage_bytes, x_iter, y.ptr(), num_cols, transform_input_iter, - transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), - UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); - CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; - }; - DoReduce(nullptr); - CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); - DoReduce(tmp_storage.ptr()); - } -}; - -template class binary_func> -struct NdarrayXYZCubeXZReduce final { - using RetT = typename BinaryFuncTrait::return_type; - static bool Matched(const XpuVarNdarray& y, const XpuVarNdarray& x) { - if (y.shape().ElemNum() > GetMaxVal()) { return false; } - if (x.shape().NumAxes() != 3) { return false; } - if (y.shape().NumAxes() != 3) { return false; } - return y.shape().At(0) == 1 && x.shape().At(1) == y.shape().At(1) && y.shape().At(2) == 1; - } - - struct XYZ2YxzFunctor final { - __host__ __device__ XYZ2YxzFunctor(int32_t dim_x, int32_t dim_y, int32_t dim_z) - : dim_z_(dim_z), dim_xz_(dim_x * dim_z), dim_yz_(dim_y * dim_z) {} - - __host__ __device__ int32_t operator()(const int32_t& idx) const { - const int32_t y = idx / dim_xz_; - const int32_t xz_idx = idx % dim_xz_; - const int32_t x = xz_idx / dim_z_; - const int32_t z = xz_idx % dim_z_; - return x * dim_yz_ + y * dim_z_ + z; - } - - int32_t dim_z_; - int32_t dim_xz_; - int32_t dim_yz_; - }; - - static void Reduce(ep::Stream* stream, const XpuVarNdarray& y, - const XpuVarNdarray& x, const XpuVarNdarray& tmp_storage) { - CHECK(Matched(y, x)); - int32_t num_rows = y.shape().ElemNum(); - int32_t num_cols = x.shape().ElemNum() / y.shape().ElemNum(); - - RowOffsetFunctor get_row_offset(num_cols); - hipcub::CountingInputIterator counting_intput_it(0); - hipcub::TransformInputIterator> - transform_input_iter(counting_intput_it, get_row_offset); - - XYZ2YxzFunctor xyz2yxz(x.shape().At(0), x.shape().At(1), x.shape().At(2)); - using XYZ2YxzIndexIter = - hipcub::TransformInputIterator>; - XYZ2YxzIndexIter xyz2yxz_iter(counting_intput_it, xyz2yxz); - PermutationIterator x_iter(x.ptr(), xyz2yxz_iter); - size_t tmp_storage_bytes = 0; - auto DoReduce = [&](T* tmp_storage_ptr) { - int retcode = hipcub::DeviceSegmentedReduce::Reduce( - tmp_storage_ptr, tmp_storage_bytes, x_iter, y.ptr(), num_rows, transform_input_iter, - transform_input_iter + 1, typename CubFunctor4BianryFunc::type(), - UnitOfBinaryFunc::Val(), stream->As()->cuda_stream()); - CHECK_EQ(retcode, 0) << "hipcub::DeviceSegmentedReduce::Reduce error"; - }; - DoReduce(nullptr); - CHECK_GE(tmp_storage.shape().ElemNum() * sizeof(T), tmp_storage_bytes); - DoReduce(tmp_storage.ptr()); - } -}; - -namespace { - -template class binary_func> -__global__ void NdarrayReduceGpuInplaceReduceAxis(const XpuReducedNdarray dst_reduced, - const XpuReducedNdarray x, int axis) { - NdarrayReduceCore::ReduceAxis(dst_reduced, x, axis); -} - -} // namespace - -template class binary_func> -struct NdarrayReduceCoreWrapper final { - static void ReduceAxis(ep::Stream* stream, const XpuReducedNdarray& dst_reduced, - const XpuReducedNdarray& x, int axis) { - size_t n = x.host_shape().HostElemNum(); - RUN_CUDA_KERNEL((NdarrayReduceGpuInplaceReduceAxis), stream, n, - dst_reduced, x, axis); - } -}; - -#define INSTANTIATE_NDARRAY_REDUCE_IMPL(dtype, binary_func) \ - template struct NdarrayScalarReduce; \ - template struct NdarrayMatrixRowReduce; \ - template struct NdarrayMatrixColReduce; \ - template struct NdarrayXYZCubeXZReduce; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ, - ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_IMPL, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - BOOL_DATA_TYPE_SEQ, - LOGICAL_REDUCE_BINARY_FUNC_SEQ); - -#define INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER(dtype_pair, NDIMS, binary_func) \ - template struct NdarrayReduceCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, - ARITHMETIC_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ - UNSIGNED_INT_DATA_TYPE_SEQ, - DIM_SEQ, ARITHMETIC_REDUCE_BINARY_FUNC_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_REDUCE_CORE_WRAPPER, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - BOOL_DATA_TYPE_SEQ, - DIM_SEQ, LOGICAL_REDUCE_BINARY_FUNC_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/unary_func.h b/oneflow/core/ndarray/unary_func.h index 33828123e028aa37fa83197e03a152a693e0f949..a010813473e306ea8bdfc974863f800d2f55a66e 100644 --- a/oneflow/core/ndarray/unary_func.h +++ b/oneflow/core/ndarray/unary_func.h @@ -20,6 +20,7 @@ limitations under the License. #include #endif #if defined(__HIPCC__) +#include "hip/hip_runtime.h" #include #endif #include "oneflow/core/kernel/kernel_util.h" @@ -86,7 +87,7 @@ SPECIALIZE_CONST_TYPE_UNARY_FUNC(UnaryFuncExp); template<> struct UnaryFuncExp final { static OF_DEVICE_FUNC const float16 Invoke(const float16 x) { -#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) half res = static_cast(exp(static_cast(*reinterpret_cast(&x)))); return *reinterpret_cast(&res); #else diff --git a/oneflow/core/ndarray/xpu_ndarray_assign.h b/oneflow/core/ndarray/xpu_ndarray_assign.h index 96dfa5788e125e26e6ec850ca1eab8e1a4986f68..41026d227972d537df88a5aa3f892d988accde03 100644 --- a/oneflow/core/ndarray/xpu_ndarray_assign.h +++ b/oneflow/core/ndarray/xpu_ndarray_assign.h @@ -18,6 +18,8 @@ limitations under the License. #include "oneflow/core/ndarray/ndarray_assign_core.h" #include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/ep/include/primitive/elementwise_unary.h" +#include "oneflow/core/ep/include/primitive/unary_op.h" namespace oneflow { @@ -45,6 +47,17 @@ struct XpuNdarrayAssign< if (x.ptr() == y.ptr()) { return; } Memcpy(stream, y.ptr(), x.ptr(), y.shape().ElemNum() * sizeof(T)); } + + static void AssignNanSum(ep::Stream* stream, const XpuVarNdarray& y, + const XpuVarNdarray& x) { + CHECK(y.shape() == x.shape()); // NOLINT + CHECK_EQ(device_type, stream->device_type()) << "Device type mismatch"; + std::unique_ptr primitive = + ep::primitive::NewPrimitive( + device_type, ep::primitive::UnaryOp::kNanAssign, GetDataType(), GetDataType()); + CHECK(primitive) << "Can not create NanSum primitive for device type " << device_type; + primitive->Launch(stream, x.ptr(), y.ptr(), y.shape().ElemNum()); + } }; template diff --git a/oneflow/core/ndarray/xpu_ndarray_assign.hip.cpp b/oneflow/core/ndarray/xpu_ndarray_assign.hip.cpp deleted file mode 100644 index 693b39cb0a58bb17f68e0652d51af0a50f630c94..0000000000000000000000000000000000000000 --- a/oneflow/core/ndarray/xpu_ndarray_assign.hip.cpp +++ /dev/null @@ -1,62 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/ndarray/ndarray_assign_core.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/kernel/kernel_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void NdarrayAssignReducedGpu(XpuVarNdarray y, - const XpuReducedNdarray reduced) { - NdarrayAssignCore::Assign(y, reduced); -} - -template -__global__ void NdarrayAssignGpu(XpuVarNdarray y, const XpuVarNdarray x) { - NdarrayAssignCore::Assign(y, x); -} - -} // namespace - -template -struct NdarrayAssignCoreWrapper final { - static void Assign(ep::Stream* stream, XpuVarNdarray* y, - const XpuReducedNdarray& reduced) { - size_t n = y->host_shape().HostElemNum(); - RUN_CUDA_KERNEL((NdarrayAssignReducedGpu), stream, n, *y, reduced); - } - static void Assign(ep::Stream* ctx, const XpuVarNdarray& y, const XpuVarNdarray& x) { - size_t n = y.host_shape().HostElemNum(); - if (n == 0) { return; } - RUN_CUDA_KERNEL((NdarrayAssignGpu), ctx, n, y, x); - } -}; - -#define INSTANTIATE_NDARRAY_ASSIGN(ret_dtype_pair, dtype_pair, NDIMS) \ - template struct NdarrayAssignCoreWrapper; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - INSTANTIATE_NDARRAY_ASSIGN, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, DIM_SEQ); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_NDARRAY_ASSIGN, HALF_DATA_TYPE_SEQ, HALF_DATA_TYPE_SEQ, - DIM_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/ndarray/xpu_util.h b/oneflow/core/ndarray/xpu_util.h index 03dc098e47c98a48c3144d31e97fcd5835825d90..e7780b68e15cdfdcd83a5370aef00df3c2b3f83c 100644 --- a/oneflow/core/ndarray/xpu_util.h +++ b/oneflow/core/ndarray/xpu_util.h @@ -57,9 +57,9 @@ namespace oneflow { for (int64_t j = 0; j < (n); ++j) #endif -#if defined(__CUDACC__) +#if defined(__CUDACC__) #define OF_GLOBAL_FUNC __global__ -#elif defined(__HIP_DEVICE_COMPILE__) +#elif defined(__HIPCC__) #define OF_GLOBAL_FUNC __global__ #else #define OF_GLOBAL_FUNC diff --git a/oneflow/core/operator/acc_tick_op.cpp b/oneflow/core/operator/acc_tick_op.cpp index f27f55bc4e4000b50c537bd69418a934f3bdbda4..0c398ddad817a434f55dc76b4b2171aee7eca6f8 100644 --- a/oneflow/core/operator/acc_tick_op.cpp +++ b/oneflow/core/operator/acc_tick_op.cpp @@ -21,7 +21,7 @@ namespace { Maybe InferBlobDescs(const std::function& GetBlobDesc4BnInOp) { *GetBlobDesc4BnInOp("acc") = *GetBlobDesc4BnInOp("one"); - GetBlobDesc4BnInOp("acc")->mut_shape() = Shape({1LL}); + GetBlobDesc4BnInOp("acc")->set_shape(Shape({1LL})); return Maybe::Ok(); } diff --git a/oneflow/core/operator/boxing_op.cpp b/oneflow/core/operator/boxing_op.cpp index 49b06da73d079458b1c91f1315f5bce68f9eeed5..130998f36518ac75563ee5a28279691e2d7a822c 100644 --- a/oneflow/core/operator/boxing_op.cpp +++ b/oneflow/core/operator/boxing_op.cpp @@ -100,13 +100,13 @@ Maybe BoxingOp::InferBlobDescs( *out_blob_desc = *first_in_blob; CHECK_GT_OR_RETURN(split_conf.part_num(i), 0); data_tmp_blob_shape_vec[split_conf.axis()] = split_conf.part_num(i); - out_blob_desc->mut_shape() = Shape(data_tmp_blob_shape_vec); + out_blob_desc->set_shape(Shape(data_tmp_blob_shape_vec)); } } else if (conf.out_box_case() == BoxingOpConf::kCloneBox) { for (const std::string& obn : output_bns()) { BlobDesc* out_blob_desc = BlobDesc4BnInOp(obn); *out_blob_desc = *first_in_blob; - out_blob_desc->mut_shape() = Shape(data_tmp_blob_shape_vec); + out_blob_desc->set_shape(Shape(data_tmp_blob_shape_vec)); } } else { UNIMPLEMENTED_THEN_RETURN(); @@ -152,7 +152,7 @@ Maybe BoxingOp::InferTmpBlobDesc( && conf.out_box_case() == BoxingOpConf::kSplitBox) { if (!is_logical) { BlobDesc* data_tmp_blob_desc = GetBlobDesc4BnInOp(SoleTbn()); - data_tmp_blob_desc->mut_shape() = Shape(*data_tmp_vec_ptr); + data_tmp_blob_desc->set_shape(Shape(*data_tmp_vec_ptr)); data_tmp_blob_desc->set_data_type(GetBlobDesc4BnInOp(input_bns().Get(0))->data_type()); } } diff --git a/oneflow/core/operator/boxing_zeros_op.cpp b/oneflow/core/operator/boxing_zeros_op.cpp index c4bc585838afbdc3f85fb633e96f7d7f909bd6ea..c9067c6326f93b84ad22f9e470331edbd54d7ed4 100644 --- a/oneflow/core/operator/boxing_zeros_op.cpp +++ b/oneflow/core/operator/boxing_zeros_op.cpp @@ -58,7 +58,7 @@ Maybe BoxingZerosOp::InferOutBlobDescs( const BoxingZerosOpConf& conf = this->op_conf().boxing_zeros_conf(); BlobDesc* out = GetBlobDesc4BnInOp("out"); out->set_data_type(conf.data_type()); - out->mut_shape() = Shape(conf.shape()); + out->set_shape(Shape(conf.shape())); return Maybe::Ok(); } diff --git a/oneflow/core/operator/broadcast_to_compatible_with_op.cpp b/oneflow/core/operator/broadcast_to_compatible_with_op.cpp index ac7237800d9c961e51c69e136b9141ef5f06c66c..e377f1d8ca18acfd7773e3acc934d203dd4e46cc 100644 --- a/oneflow/core/operator/broadcast_to_compatible_with_op.cpp +++ b/oneflow/core/operator/broadcast_to_compatible_with_op.cpp @@ -46,7 +46,7 @@ Maybe InferBlobDescs(const OperatorConf& op_conf, } BlobDesc* y_desc = BlobDesc4BnInOp("y"); y_desc->CopyFrom(*x_desc); - y_desc->mut_shape() = broadcasted_shape; + y_desc->set_shape(broadcasted_shape); return Maybe::Ok(); } diff --git a/oneflow/core/operator/case_op.cpp b/oneflow/core/operator/case_op.cpp index 6037222bdbc0114f38b0d40c385c1bec3cab85c5..7d2bb1ff00809957da060aa23e891372cc2a19b7 100644 --- a/oneflow/core/operator/case_op.cpp +++ b/oneflow/core/operator/case_op.cpp @@ -34,7 +34,7 @@ Maybe InferBlobDescs(const Operator& op, CHECK_OR_RETURN(IsIntegralDataType(data_type)); for (const std::string& obn : op.output_bns()) { BlobDesc* out = BlobDesc4BnInOp(obn); - out->mut_shape() = Shape({1}); + out->set_shape(Shape({1})); out->set_data_type(data_type); } return Maybe::Ok(); diff --git a/oneflow/core/operator/collective_boxing_ops.cpp b/oneflow/core/operator/collective_boxing_ops.cpp index 791a806a38bdb1eb6f37e786a1d5b4bee418921c..4dc8b3af080019564fd471e23723bf8e38c35530 100644 --- a/oneflow/core/operator/collective_boxing_ops.cpp +++ b/oneflow/core/operator/collective_boxing_ops.cpp @@ -62,7 +62,7 @@ class CollectiveBoxingGenericOp : public Operator { if (GenericOpHasOutput(rank_desc)) { BlobDesc* out = GetBlobDesc4BnInOp("out"); out->set_data_type(data_type); - out->mut_shape() = GenericOpGetOutputShape(rank_desc); + out->set_shape(GenericOpGetOutputShape(rank_desc)); } return Maybe::Ok(); } diff --git a/oneflow/core/operator/collective_boxing_pack_op.cpp b/oneflow/core/operator/collective_boxing_pack_op.cpp index ca80c192aa7ac0e0697a4c6cc213f2ab5df1a94f..f490f1086ee9557079a238eb96f462e6d5d11b5f 100644 --- a/oneflow/core/operator/collective_boxing_pack_op.cpp +++ b/oneflow/core/operator/collective_boxing_pack_op.cpp @@ -61,7 +61,7 @@ Maybe CollectiveBoxingPackOp::InferOutBlobDescs( const BlobDesc* in_blob_desc = GetBlobDesc4BnInOp("in"); BlobDesc* out_blob_desc = GetBlobDesc4BnInOp("out"); *out_blob_desc = *in_blob_desc; - out_blob_desc->mut_shape() = Shape({in_blob_desc->shape().elem_cnt()}); + out_blob_desc->set_shape(Shape({in_blob_desc->shape().elem_cnt()})); return Maybe::Ok(); } diff --git a/oneflow/core/operator/collective_boxing_unpack_op.cpp b/oneflow/core/operator/collective_boxing_unpack_op.cpp index d06923b455ea4b7dc017d4aa419619093126dd1d..a1f3b6a821c3ebb428efc5fad5f60780757043d8 100644 --- a/oneflow/core/operator/collective_boxing_unpack_op.cpp +++ b/oneflow/core/operator/collective_boxing_unpack_op.cpp @@ -69,7 +69,7 @@ Maybe CollectiveBoxingUnpackOp::InferOutBlobDescs( out_shape.Set(dst_split_axis, out_shape.At(dst_split_axis) / unpack_conf.num_ranks()); } CHECK_EQ_OR_RETURN(out_shape.elem_cnt(), in_blob_desc->shape().elem_cnt()); - out_blob_desc->mut_shape() = out_shape; + out_blob_desc->set_shape(out_shape); return Maybe::Ok(); } diff --git a/oneflow/core/operator/copy_hd_op.cpp b/oneflow/core/operator/copy_hd_op.cpp deleted file mode 100644 index 860b01bc022329b00dee0d38c0092765261870e7..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/copy_hd_op.cpp +++ /dev/null @@ -1,75 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/operator/operator.h" - -namespace oneflow { - -class CopyHdOp final : public Operator { - public: - OF_DISALLOW_COPY_AND_MOVE(CopyHdOp); - CopyHdOp() = default; - ~CopyHdOp() override = default; - - Maybe InitFromOpConf() override; - Maybe InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const override { - UNIMPLEMENTED_THEN_RETURN(); - } - Maybe InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const override; - - private: - Maybe InferSbpSignature( - SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, - const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, - const ParallelDesc& parallel_desc) const override { - auto* bn2sbp = sbp_signature->mutable_bn_in_op2sbp_parallel(); - const SbpParallel& sbp_parallel = JUST(SbpInferHint4Ibn(input_bns().Get(0)))->sbp_parallel(); - (*bn2sbp)[input_bns().Get(0)] = sbp_parallel; - (*bn2sbp)[output_bns().Get(0)] = sbp_parallel; - return Maybe::Ok(); - } - LogicalBlobId lbi4ibn(const std::string& input_bn) const override; - LogicalBlobId lbi4obn(const std::string& output_bn) const override; -}; - -Maybe CopyHdOp::InitFromOpConf() { - EnrollInputBn("in", false); - EnrollOutputBn("out", false); - return Maybe::Ok(); -} - -Maybe CopyHdOp::InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const { - *GetBlobDesc4BnInOp("out") = *GetBlobDesc4BnInOp("in"); - return Maybe::Ok(); -} - -LogicalBlobId CopyHdOp::lbi4ibn(const std::string& input_bn) const { - return this->op_conf().copy_hd_conf().lbi(); -} - -LogicalBlobId CopyHdOp::lbi4obn(const std::string& output_bn) const { - return this->op_conf().copy_hd_conf().lbi(); -} - -REGISTER_OP(OperatorConf::kCopyHdConf, CopyHdOp); - -} // namespace oneflow diff --git a/oneflow/core/operator/critical_section_callback_tick_op.cpp b/oneflow/core/operator/critical_section_callback_tick_op.cpp index 1ad3099874e86cca576dffec8168164af0c4d945..4582747adca0ee17739b35c2c57ec3a28afa4f03 100644 --- a/oneflow/core/operator/critical_section_callback_tick_op.cpp +++ b/oneflow/core/operator/critical_section_callback_tick_op.cpp @@ -22,7 +22,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/critical_section_wait_tick_op.cpp b/oneflow/core/operator/critical_section_wait_tick_op.cpp index 3a72b0a111dbe5492511659dea2d74944e41d86f..d0b11f5029a71d20cd7ba3e2ceaa0b956c12cc6d 100644 --- a/oneflow/core/operator/critical_section_wait_tick_op.cpp +++ b/oneflow/core/operator/critical_section_wait_tick_op.cpp @@ -22,7 +22,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/device_tick_op.cpp b/oneflow/core/operator/device_tick_op.cpp index 226a919b4e6c03a9a33a359995aea7c0b491dc21..306371f6bfaafaec498bded3a742a2869e44ec30 100644 --- a/oneflow/core/operator/device_tick_op.cpp +++ b/oneflow/core/operator/device_tick_op.cpp @@ -29,7 +29,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } @@ -82,7 +82,6 @@ Maybe DeviceTickOp::InferOpTimeShape( return Maybe::Ok(); } -REGISTER_OP_SAME_OUTPUT_BLOB_REGST_NUM(OperatorConf::kDeviceTickConf, 2); REGISTER_OP(OperatorConf::kDeviceTickConf, DeviceTickOp); REGISTER_TICK_TOCK_OP(OperatorConf::kDeviceTickConf); diff --git a/oneflow/core/operator/distribute_add_op.cpp b/oneflow/core/operator/distribute_add_op.cpp index 04c19012c7317eab9deef16032ecedfb598f7366..054fe0d180009c82aee71b6946df38bf1b3ea90b 100644 --- a/oneflow/core/operator/distribute_add_op.cpp +++ b/oneflow/core/operator/distribute_add_op.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/operator/operator.h" #include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/job/scope.h" diff --git a/oneflow/core/operator/distribute_clone_op.cpp b/oneflow/core/operator/distribute_clone_op.cpp index 03f241d8c450beedff16a15844274aaeaa6cf276..50f9a44e45ef9b3ea8a355521540e0147e65a495 100644 --- a/oneflow/core/operator/distribute_clone_op.cpp +++ b/oneflow/core/operator/distribute_clone_op.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/operator/operator.h" #include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/job/scope.h" diff --git a/oneflow/core/operator/distribute_concat_op.cpp b/oneflow/core/operator/distribute_concat_op.cpp index dabb59c794de3f74de306a592c807deeed64cc12..28938e90c18f6482684d779e5dea67b8e29fa6e4 100644 --- a/oneflow/core/operator/distribute_concat_op.cpp +++ b/oneflow/core/operator/distribute_concat_op.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/operator/operator.h" #include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/job/scope.h" @@ -78,7 +77,9 @@ Maybe DistributeConcatOp::InferLogicalOutBlobDescs( } CHECK_EQ_OR_RETURN(in_i->data_type(), out->data_type()); } - out->mut_shape().Set(concat_axis, concat_dim_size); + Shape output = out->shape(); + output.Set(concat_axis, concat_dim_size); + out->set_shape(output); out->set_is_dynamic(false); return Maybe::Ok(); } @@ -121,7 +122,7 @@ Maybe DistributeConcatOp::InferOutBlobDescs( } BlobDesc* out_blob_desc = GetBlobDesc4BnInOp("out"); *out_blob_desc = *first_blob_desc; - out_blob_desc->mut_shape() = Shape(out_dim_vec); + out_blob_desc->set_shape(Shape(out_dim_vec)); out_blob_desc->set_is_dynamic(false); return Maybe::Ok(); } diff --git a/oneflow/core/operator/distribute_split_op.cpp b/oneflow/core/operator/distribute_split_op.cpp index 71013e93fd98f3b490c9c64166c1fd15415dab74..4e7a1164336a26ce2a3dfd1b1f887e2d01895a08 100644 --- a/oneflow/core/operator/distribute_split_op.cpp +++ b/oneflow/core/operator/distribute_split_op.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include "oneflow/core/operator/operator.h" #include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/vm/symbol_storage.h" #include "oneflow/core/job/scope.h" @@ -71,7 +70,9 @@ Maybe DistributeSplitOp::InferLogicalOutBlobDescs( FOR_RANGE(int, i, 0, parallel_desc.parallel_num()) { BlobDesc* out_blob_desc = BlobDesc4BnInOp(output_bns().Get(i)); *out_blob_desc = in_blob_desc; - out_blob_desc->mut_shape().Set(split_axis, bs.At(i).size()); + Shape output = out_blob_desc->shape(); + output.Set(split_axis, bs.At(i).size()); + out_blob_desc->set_shape(output); } return Maybe::Ok(); } @@ -97,7 +98,9 @@ Maybe DistributeSplitOp::InferOutBlobDescs( BalancedSplitter bs(in_blob_desc.shape().At(split_axis), out_blob_descs.size()); FOR_RANGE(int, i, 0, out_blob_descs.size()) { *out_blob_descs.at(i) = in_blob_desc; - out_blob_descs.at(i)->mut_shape().Set(split_axis, bs.At(i).size()); + Shape output = out_blob_descs.at(i)->shape(); // NOLINT + output.Set(split_axis, bs.At(i).size()); + out_blob_descs.at(i)->set_shape(output); // NOLINT } return Maybe::Ok(); } diff --git a/oneflow/core/operator/dst_subset_tick_op.cpp b/oneflow/core/operator/dst_subset_tick_op.cpp index c6d5743b1e33de2083c5a2f34ea065367afa9661..5152c049bc5770a29e11c8899d47c9944f230a1b 100644 --- a/oneflow/core/operator/dst_subset_tick_op.cpp +++ b/oneflow/core/operator/dst_subset_tick_op.cpp @@ -23,7 +23,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/dynamic_reshape_op.cpp b/oneflow/core/operator/dynamic_reshape_op.cpp index 110a949ead9662e93391dd1f2b4ca8a4ae450430..72ee5dc47c840db8d4dd9795998bd00f300eac8e 100644 --- a/oneflow/core/operator/dynamic_reshape_op.cpp +++ b/oneflow/core/operator/dynamic_reshape_op.cpp @@ -50,7 +50,7 @@ class DynamicReshapeOp final : public Operator { CHECK_EQ_OR_RETURN(in->shape().elem_cnt() % product, 0); out_dim_vec.at(inferred_axis) = in->shape().elem_cnt() / product; } - out->mut_shape() = Shape(out_dim_vec); + out->set_shape(Shape(out_dim_vec)); CHECK_EQ_OR_RETURN(in->shape().elem_cnt(), out->shape().elem_cnt()); return Maybe::Ok(); } @@ -65,7 +65,7 @@ class DynamicReshapeOp final : public Operator { *out = *in; DimVector out_dim_vec(conf.shape().dim().begin(), conf.shape().dim().end()); if (parallel_ctx->parallel_num() > 1) { - // consistent strategy + // global strategy // ONLY support sbp: S(0); and -1 must at axis 0 const auto& out_sbp_it = sbp_signature->bn_in_op2sbp_parallel().find("out"); CHECK_OR_RETURN(out_sbp_it != sbp_signature->bn_in_op2sbp_parallel().end()); @@ -96,7 +96,7 @@ class DynamicReshapeOp final : public Operator { CHECK_EQ_OR_RETURN(in->shape().elem_cnt() % product, 0); out_dim_vec.at(inferred_axis) = in->shape().elem_cnt() / product; } - out->mut_shape() = Shape(out_dim_vec); + out->set_shape(Shape(out_dim_vec)); CHECK_EQ_OR_RETURN(in->shape().elem_cnt(), out->shape().elem_cnt()); return Maybe::Ok(); } @@ -104,7 +104,7 @@ class DynamicReshapeOp final : public Operator { private: Maybe GetSbpSignatures( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const override { + SbpSignatureList* sbp_sig_list) const override { SbpSignatureBuilder() .Split(input_bns(), 0) .Split(output_bns(), 0) @@ -144,7 +144,7 @@ class DynamicReshapeLikeOp final : public Operator { private: Maybe GetSbpSignatures( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const override { + SbpSignatureList* sbp_sig_list) const override { SbpSignatureBuilder() .Split(input_bns(), 0) .Split(output_bns(), 0) diff --git a/oneflow/core/operator/esac_op.cpp b/oneflow/core/operator/esac_op.cpp index db6f19282279cc2ab18df95fe8b3d5ce6e49687c..e9b070897d12ae1b66109f2d7c0e3b73138c64c5 100644 --- a/oneflow/core/operator/esac_op.cpp +++ b/oneflow/core/operator/esac_op.cpp @@ -29,7 +29,7 @@ namespace { Maybe InferBlobDescs(const OperatorConf& op_conf, const std::function& BlobDesc4BnInOp) { BlobDesc* out = BlobDesc4BnInOp("out"); - out->mut_shape() = Shape({1}); + out->set_shape(Shape({1})); const DataType data_type = op_conf.esac_conf().data_type(); CHECK_OR_RETURN(IsIntegralDataType(data_type)); out->set_data_type(data_type); diff --git a/oneflow/core/operator/foreign_input_op.cpp b/oneflow/core/operator/foreign_input_op.cpp deleted file mode 100644 index 190c1beaf4316dd308e90b68d3f2e9669ae728dc..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_input_op.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/operator/foreign_input_op.h" -#include "oneflow/core/job/sbp_signature_builder.h" - -namespace oneflow { - -namespace { - -void CheckOpConf(const OperatorConf& op_conf) { CHECK(op_conf.ctrl_in_op_name().empty()); } - -Maybe InferBlobDescs(const OperatorConf& op_conf, - const std::function& BlobDesc4BnInOp) { - CheckOpConf(op_conf); - const auto& conf = op_conf.foreign_input_conf().blob_conf(); - BlobDesc* out_blob_desc = BlobDesc4BnInOp("out"); - out_blob_desc->mut_shape() = Shape(conf.shape()); - CHECK_OR_RETURN(conf.has_data_type()); - out_blob_desc->set_data_type(conf.data_type()); - out_blob_desc->set_is_dynamic(conf.is_dynamic()); - return Maybe::Ok(); -} - -} // namespace - -Maybe ForeignInputOp::InitFromOpConf() { - CHECK(op_conf().has_foreign_input_conf()); - if (op_conf().foreign_input_conf().has_tick()) { EnrollInputBn("tick", false); } - EnrollOutputBn("out", false); - return Maybe::Ok(); -} - -Maybe ForeignInputOp::InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const { - CHECK_EQ_OR_RETURN(parallel_desc.parallel_num(), 1); - return InferBlobDescs(op_conf(), BlobDesc4BnInOp); -} - -Maybe ForeignInputOp::InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const { - CHECK_EQ_OR_RETURN(parallel_ctx->parallel_num(), 1); - return InferBlobDescs(op_conf(), GetBlobDesc4BnInOp); -} - -Maybe ForeignInputOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { - return Maybe::Ok(); -} - -REGISTER_OP(OperatorConf::kForeignInputConf, ForeignInputOp); -REGISTER_OP_SAME_OUTPUT_BLOB_REGST_NUM(OperatorConf::kForeignInputConf, 1); - -} // namespace oneflow diff --git a/oneflow/core/operator/foreign_input_op.h b/oneflow/core/operator/foreign_input_op.h deleted file mode 100644 index 7e01e10a97f970c1bc3f79e812d0494795c47316..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_input_op.h +++ /dev/null @@ -1,43 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_OPERATOR_FOREIGN_INPUT_OP_H_ -#define ONEFLOW_CORE_OPERATOR_FOREIGN_INPUT_OP_H_ - -#include "oneflow/core/operator/operator.h" - -namespace oneflow { - -class ForeignInputOp final : public Operator { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignInputOp); - ForeignInputOp() : Operator() {} - ~ForeignInputOp() = default; - - Maybe InitFromOpConf() override; - Maybe InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const override; - Maybe InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const override; - - private: - Maybe GetSbpSignatures(SbpSignatureList* sbp_sig_list) const override; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_OPERATOR_FOREIGN_INPUT_OP_H_ diff --git a/oneflow/core/operator/foreign_output_op.cpp b/oneflow/core/operator/foreign_output_op.cpp deleted file mode 100644 index b2635527b886cfc145f6f86114964ac96d0b60df..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_output_op.cpp +++ /dev/null @@ -1,49 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/operator/foreign_output_op.h" -#include "oneflow/core/job/sbp_signature_builder.h" - -namespace oneflow { - -Maybe ForeignOutputOp::InitFromOpConf() { - CHECK(op_conf().has_foreign_output_conf()); - EnrollInputBn("in"); - return Maybe::Ok(); -} - -Maybe ForeignOutputOp::InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const { - CHECK_EQ_OR_RETURN(parallel_desc.parallel_num(), 1); - return Maybe::Ok(); -} - -Maybe ForeignOutputOp::InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const { - CHECK_EQ_OR_RETURN(parallel_ctx->parallel_num(), 1); - return Maybe::Ok(); -} - -Maybe ForeignOutputOp::GetSbpSignatures( - const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - SbpSignatureList* sbp_sig_list) const { - return Maybe::Ok(); -} - -REGISTER_OP(OperatorConf::kForeignOutputConf, ForeignOutputOp); - -} // namespace oneflow diff --git a/oneflow/core/operator/foreign_output_op.h b/oneflow/core/operator/foreign_output_op.h deleted file mode 100644 index cc9df8969b5c0c4bbc46692cf799c371b78bf3ac..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_output_op.h +++ /dev/null @@ -1,45 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_OPERATOR_FOREIGN_OUTPUT_OP_H_ -#define ONEFLOW_CORE_OPERATOR_FOREIGN_OUTPUT_OP_H_ - -#include "oneflow/core/operator/operator.h" - -namespace oneflow { - -class ForeignOutputOp final : public Operator { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignOutputOp); - ForeignOutputOp() = default; - ~ForeignOutputOp() override = default; - - Maybe InitFromOpConf() override; - Maybe InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const override; - Maybe InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const override; - - private: - Maybe GetSbpSignatures( - const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - SbpSignatureList* sbp_sig_list) const override; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_OPERATOR_FOREIGN_OUTPUT_OP_H_ diff --git a/oneflow/core/operator/foreign_watch_op.cpp b/oneflow/core/operator/foreign_watch_op.cpp deleted file mode 100644 index 13dff3aff9020c62b6018ba651b99acb2f39b51f..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_watch_op.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/operator/foreign_watch_op.h" -#include "oneflow/core/job/sbp_signature_builder.h" - -namespace oneflow { - -Maybe ForeignWatchOp::InitFromOpConf() { - CHECK(op_conf().has_foreign_watch_conf()); - EnrollInputBn("in"); - return Maybe::Ok(); -} - -Maybe ForeignWatchOp::InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const { - CHECK_EQ_OR_RETURN(parallel_desc.parallel_num(), 1); - return Maybe::Ok(); -} - -Maybe ForeignWatchOp::InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const { - CHECK_EQ_OR_RETURN(parallel_ctx->parallel_num(), 1); - return Maybe::Ok(); -} - -Maybe ForeignWatchOp::InferSbpSignature( - SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, - const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, - const ParallelDesc& parallel_desc) const { - CHECK_EQ_OR_RETURN(parallel_desc.parallel_num(), 1); - (*sbp_signature->mutable_bn_in_op2sbp_parallel())["in"].mutable_split_parallel()->set_axis(0); - return Maybe::Ok(); -} - -REGISTER_OP(OperatorConf::kForeignWatchConf, ForeignWatchOp); - -} // namespace oneflow diff --git a/oneflow/core/operator/foreign_watch_op.h b/oneflow/core/operator/foreign_watch_op.h deleted file mode 100644 index 1afca900d5e7493280b697f28340ebf1496c1e8d..0000000000000000000000000000000000000000 --- a/oneflow/core/operator/foreign_watch_op.h +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_OPERATOR_FOREIGN_WATCH_OP_H_ -#define ONEFLOW_CORE_OPERATOR_FOREIGN_WATCH_OP_H_ - -#include "oneflow/core/operator/operator.h" - -namespace oneflow { - -class ForeignWatchOp final : public Operator { - public: - OF_DISALLOW_COPY_AND_MOVE(ForeignWatchOp); - ForeignWatchOp() = default; - ~ForeignWatchOp() override = default; - - Maybe InitFromOpConf() override; - Maybe InferLogicalOutBlobDescs( - const std::function& BlobDesc4BnInOp, - const ParallelDesc& parallel_desc) const override; - Maybe InferOutBlobDescs( - const std::function& GetBlobDesc4BnInOp, - const ParallelContext* parallel_ctx) const override; - - private: - Maybe InferSbpSignature( - SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, - const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, - const ParallelDesc& parallel_desc) const override; -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_OPERATOR_FOREIGN_WATCH_OP_H_ diff --git a/oneflow/core/operator/identity_op.cpp b/oneflow/core/operator/identity_op.cpp index b08e2394869663657defb0702c077dbc1981c58b..5a9f4d401ceecaf655a64d54a96ad061e250f17c 100644 --- a/oneflow/core/operator/identity_op.cpp +++ b/oneflow/core/operator/identity_op.cpp @@ -15,7 +15,7 @@ limitations under the License. */ #include "oneflow/core/operator/operator.h" #include "oneflow/core/job/sbp_signature_builder.h" -#include "oneflow/core/job/mirrored_sig_infer_hint.h" +#include "oneflow/core/job/local_sig_infer_hint.h" #include "oneflow/core/common/protobuf.h" namespace oneflow { @@ -70,11 +70,11 @@ REGISTER_OP(OperatorConf::kIdentityConf, IdentityOpTpl); struct CopyOp {}; REGISTER_OP(OperatorConf::kCopyConf, IdentityOpTpl); -class MirroredCastOp : public Operator { +class LocalCastOp : public Operator { public: - OF_DISALLOW_COPY_AND_MOVE(MirroredCastOp); - MirroredCastOp() = default; - virtual ~MirroredCastOp() override = default; + OF_DISALLOW_COPY_AND_MOVE(LocalCastOp); + LocalCastOp() = default; + virtual ~LocalCastOp() override = default; Maybe InitFromOpConf() override { EnrollInputBn("in"); @@ -97,11 +97,11 @@ class MirroredCastOp : public Operator { namespace { -class CastToMirroredOp : public MirroredCastOp { +class CastToLocalOp : public LocalCastOp { public: - OF_DISALLOW_COPY_AND_MOVE(CastToMirroredOp); - CastToMirroredOp() = default; - virtual ~CastToMirroredOp() override = default; + OF_DISALLOW_COPY_AND_MOVE(CastToLocalOp); + CastToLocalOp() = default; + virtual ~CastToLocalOp() override = default; private: Maybe InferLogicalOutBlobDescs( @@ -109,7 +109,7 @@ class CastToMirroredOp : public MirroredCastOp { const ParallelDesc& parallel_desc) const override { BlobDesc* out = BlobDesc4BnInOp("out"); *out = *BlobDesc4BnInOp("in"); - const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_to_mirrored_conf().sbp_parallel()); + const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_to_local_conf().sbp_parallel()); if (conf_sbp.has_split_parallel()) { const int64_t axis = conf_sbp.split_parallel().axis(); CHECK_GE_OR_RETURN(axis, 0); @@ -117,7 +117,9 @@ class CastToMirroredOp : public MirroredCastOp { const int64_t dim_value = out->shape().At(axis); const int64_t parallel_num = parallel_desc.parallel_num(); CHECK_EQ_OR_RETURN(dim_value % parallel_num, 0); - out->mut_shape().Set(axis, dim_value / parallel_num); + Shape output = out->shape(); + output.Set(axis, dim_value / parallel_num); + out->set_shape(output); } return Maybe::Ok(); } @@ -127,43 +129,42 @@ class CastToMirroredOp : public MirroredCastOp { const std::function& CalcOrderValue4SbpSig, std::function(const std::string&)> SbpInferHint4Ibn, const ParallelDesc& parallel_desc) const override { - CHECK_NE_OR_RETURN(op_conf().cast_to_mirrored_conf().sbp_parallel().parallel_type_case(), + CHECK_NE_OR_RETURN(op_conf().cast_to_local_conf().sbp_parallel().parallel_type_case(), SbpParallel::PARALLEL_TYPE_NOT_SET) << "attribute sbp_parallel not set."; const auto& ibn_hint = *JUST(SbpInferHint4Ibn("in")); CHECK_EQ_OR_RETURN(ibn_hint.parallel_desc().parallel_num(), parallel_desc.parallel_num()); auto* map = sbp_signature->mutable_bn_in_op2sbp_parallel(); - const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_to_mirrored_conf().sbp_parallel()); + const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_to_local_conf().sbp_parallel()); CHECK_OR_RETURN(ibn_hint.sbp_parallel() == conf_sbp); (*map)["in"] = ibn_hint.sbp_parallel(); (*map)["out"] = conf_sbp; return Maybe::Ok(); } - Maybe InferMirroredSignature( - std::function(const std::string&)> - MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc) override { - const auto& in_infer_hint = *JUST(MirroredSigInferHint4Ibn("in")); - CHECK_OR_RETURN(!in_infer_hint.is_mirrored_parallel_view()) - << "error use of CastToMirroredOp. `in' shouldn't be a mirrored blob"; + Maybe InferLocalSignature( + std::function(const std::string&)> LocalSigInferHint4Ibn, + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc) override { + const auto& in_infer_hint = *JUST(LocalSigInferHint4Ibn("in")); + CHECK_OR_RETURN(!in_infer_hint.is_local_parallel_view()) + << "error use of CastToLocalOp. `in' shouldn't be a local blob"; CHECK_EQ_OR_RETURN(in_infer_hint.parallel_desc().parallel_num(), parallel_desc.parallel_num()); - MutOptMirroredParallel("in")->clear_mirrored_parallel(); - MutOptMirroredParallel("out")->mutable_mirrored_parallel(); + MutOptLocalParallel("in")->clear_local_parallel(); + MutOptLocalParallel("out")->mutable_local_parallel(); return Maybe::Ok(); } }; -REGISTER_OP(OperatorConf::kCastToMirroredConf, CastToMirroredOp); +REGISTER_OP(OperatorConf::kCastToLocalConf, CastToLocalOp); } // namespace namespace { -class CastFromMirroredOp : public MirroredCastOp { +class CastFromLocalOp : public LocalCastOp { public: - OF_DISALLOW_COPY_AND_MOVE(CastFromMirroredOp); - CastFromMirroredOp() = default; - virtual ~CastFromMirroredOp() override = default; + OF_DISALLOW_COPY_AND_MOVE(CastFromLocalOp); + CastFromLocalOp() = default; + virtual ~CastFromLocalOp() override = default; private: Maybe InferLogicalOutBlobDescs( @@ -171,12 +172,14 @@ class CastFromMirroredOp : public MirroredCastOp { const ParallelDesc& parallel_desc) const override { BlobDesc* out = BlobDesc4BnInOp("out"); *out = *BlobDesc4BnInOp("in"); - const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_from_mirrored_conf().sbp_parallel()); + const SbpParallel& conf_sbp = SbpParallel(op_conf().cast_from_local_conf().sbp_parallel()); if (conf_sbp.has_split_parallel()) { const int64_t axis = conf_sbp.split_parallel().axis(); CHECK_GE_OR_RETURN(axis, 0); CHECK_LT_OR_RETURN(axis, out->shape().NumAxes()); - out->mut_shape().Set(axis, out->shape().At(axis) * parallel_desc.parallel_num()); + Shape output = out->shape(); + output.Set(axis, out->shape().At(axis) * parallel_desc.parallel_num()); + out->set_shape(output); } return Maybe::Ok(); } @@ -185,31 +188,30 @@ class CastFromMirroredOp : public MirroredCastOp { const std::function& CalcOrderValue4SbpSig, std::function(const std::string&)> SbpInferHint4Ibn, const ParallelDesc& parallel_desc) const override { - CHECK_NE_OR_RETURN(op_conf().cast_from_mirrored_conf().sbp_parallel().parallel_type_case(), + CHECK_NE_OR_RETURN(op_conf().cast_from_local_conf().sbp_parallel().parallel_type_case(), SbpParallel::PARALLEL_TYPE_NOT_SET) << "attribute sbp_parallel not set."; const auto& ibn_hint = *JUST(SbpInferHint4Ibn("in")); CHECK_EQ_OR_RETURN(ibn_hint.parallel_desc().parallel_num(), parallel_desc.parallel_num()); auto* map = sbp_signature->mutable_bn_in_op2sbp_parallel(); (*map)["in"] = ibn_hint.sbp_parallel(); - (*map)["out"] = SbpParallel(op_conf().cast_from_mirrored_conf().sbp_parallel()); + (*map)["out"] = SbpParallel(op_conf().cast_from_local_conf().sbp_parallel()); return Maybe::Ok(); } - Maybe InferMirroredSignature( - std::function(const std::string&)> - MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc) override { - const auto& in_infer_hint = *JUST(MirroredSigInferHint4Ibn("in")); - CHECK_OR_RETURN(in_infer_hint.is_mirrored_parallel_view()) - << "error use of CastFromMirroredOp. `in' should be a mirrored blob"; + Maybe InferLocalSignature( + std::function(const std::string&)> LocalSigInferHint4Ibn, + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc) override { + const auto& in_infer_hint = *JUST(LocalSigInferHint4Ibn("in")); + CHECK_OR_RETURN(in_infer_hint.is_local_parallel_view()) + << "error use of CastFromLocalOp. `in' should be a local blob"; CHECK_EQ_OR_RETURN(in_infer_hint.parallel_desc().parallel_num(), parallel_desc.parallel_num()); - MutOptMirroredParallel("in")->mutable_mirrored_parallel(); - MutOptMirroredParallel("out")->clear_mirrored_parallel(); + MutOptLocalParallel("in")->mutable_local_parallel(); + MutOptLocalParallel("out")->clear_local_parallel(); return Maybe::Ok(); } }; -REGISTER_OP(OperatorConf::kCastFromMirroredConf, CastFromMirroredOp); +REGISTER_OP(OperatorConf::kCastFromLocalConf, CastFromLocalOp); } // namespace diff --git a/oneflow/core/operator/image_decoder_random_crop_resize_op.cpp b/oneflow/core/operator/image_decoder_random_crop_resize_op.cpp index 65330ebc7f76b261579f46af8f0cfc96c7366793..1ba2f8be6c0a275786aa08a90fe6a8945f6eb343 100644 --- a/oneflow/core/operator/image_decoder_random_crop_resize_op.cpp +++ b/oneflow/core/operator/image_decoder_random_crop_resize_op.cpp @@ -38,7 +38,7 @@ Maybe InferBlobDescs(const OperatorConf& op_conf, out_dim_vec.emplace_back(conf.target_height()); out_dim_vec.emplace_back(conf.target_width()); out_dim_vec.emplace_back(3); - out->mut_shape() = Shape(out_dim_vec); + out->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -77,7 +77,7 @@ class ImageDecoderRandomCropResizeOp final : public Operator { this->op_conf().image_decoder_random_crop_resize_conf(); BlobDesc* tmp = GetBlobDesc4BnInOp("tmp"); tmp->set_data_type(DataType::kUInt8); - tmp->mut_shape() = Shape({conf.max_num_pixels() * 3 * conf.num_workers()}); + tmp->set_shape(Shape({conf.max_num_pixels() * 3 * conf.num_workers()})); return Maybe::Ok(); } diff --git a/oneflow/core/operator/input_op.cpp b/oneflow/core/operator/input_op.cpp index a2eac5f299765e22a990e72ed85a00d2c0ef2ea5..6ccfbe59e2cb0b83b6386cb4ef4a2d1fe6df0b88 100644 --- a/oneflow/core/operator/input_op.cpp +++ b/oneflow/core/operator/input_op.cpp @@ -20,6 +20,25 @@ limitations under the License. namespace oneflow { +namespace { +Maybe InferInputOpNdSbpSignature(NdSbpSignature* nd_sbp_signature, + const ParallelDesc& parallel_desc, + const OperatorConf& op_conf) { + const auto& parallel_hierarchy = parallel_desc.hierarchy(); + const InterfaceBlobConf& blob_conf = op_conf.input_conf().blob_conf(); + if (op_conf.input_conf().has_tick()) { + NdSbp& tick_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["tick"]; + tick_nd_sbp.clear_sbp_parallel(); + FOR_RANGE(int64_t, i, 0, parallel_hierarchy->NumAxes()) { + tick_nd_sbp.mutable_sbp_parallel()->Add()->mutable_broadcast_parallel(); + } + } + NdSbp& out_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["out"]; + JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &out_nd_sbp)); + return Maybe::Ok(); +} +} // namespace + Maybe InputOp::InitFromOpConf() { CHECK(op_conf().has_input_conf()); if (op_conf().input_conf().has_tick()) { EnrollInputBn("tick", false); } @@ -64,21 +83,20 @@ Maybe InputOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { return Maybe::Ok(); } +Maybe InputOp::GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { + NdSbpSignature nd_sbp_signature; + JUST(InferInputOpNdSbpSignature(&nd_sbp_signature, parallel_desc, op_conf())); + nd_sbp_sig_list->emplace_back(nd_sbp_signature); + return Maybe::Ok(); +} + Maybe InputOp::InferNdSbpSignature( NdSbpSignature* nd_sbp_signature, const NdSbpSignature& nd_sbp_constraints, const ParallelDesc& parallel_desc, std::function(const std::string&)> NdSbpInferHint4Ibn) const { - const auto& parallel_hierarchy = parallel_desc.hierarchy(); - const InterfaceBlobConf& blob_conf = op_conf().input_conf().blob_conf(); - if (op_conf().input_conf().has_tick()) { - NdSbp& tick_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["tick"]; - tick_nd_sbp.clear_sbp_parallel(); - FOR_RANGE(int64_t, i, 0, parallel_hierarchy->NumAxes()) { - tick_nd_sbp.mutable_sbp_parallel()->Add()->mutable_broadcast_parallel(); - } - } - NdSbp& out_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["out"]; - JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &out_nd_sbp)); + JUST(InferInputOpNdSbpSignature(nd_sbp_signature, parallel_desc, op_conf())); return Maybe::Ok(); } diff --git a/oneflow/core/operator/input_op.h b/oneflow/core/operator/input_op.h index 501b567fb64bead6b725c579421ae18f1c489662..01a3f73c723ebb209434f39c9de00e62627eb46f 100644 --- a/oneflow/core/operator/input_op.h +++ b/oneflow/core/operator/input_op.h @@ -42,6 +42,10 @@ class InputOp final : public Operator { const ParallelDesc& parallel_desc) const override; Maybe GetSbpSignatures(SbpSignatureList* sbp_sig_list) const override; + Maybe GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, + std::vector* nd_sbp_sig_list) const override; Symbol GetOpConfWithoutOpNameAndLbn() const override; Maybe InferNdSbpSignature(NdSbpSignature* nd_sbp_signature, const NdSbpSignature& nd_sbp_constraints, diff --git a/oneflow/core/operator/interface_op_util.cpp b/oneflow/core/operator/interface_op_util.cpp index 01153d76e24a86ec2aa726efc9816d54c0d7e40a..cb127777b44f2ced04a0dcc5093491dddbf1f562 100644 --- a/oneflow/core/operator/interface_op_util.cpp +++ b/oneflow/core/operator/interface_op_util.cpp @@ -60,8 +60,8 @@ Maybe InterfaceOpUtil::InferOutBlobDesc(const InterfaceBlobConf& blob_conf const ParallelDesc& parallel_desc) { NdSbp nd_sbp; JUST(ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &nd_sbp)); - out_blob_desc->mut_shape() = - *JUST(GetPhysicalShape(Shape(blob_conf.shape()), nd_sbp, parallel_desc, *parallel_ctx)); + out_blob_desc->set_shape( + *JUST(GetPhysicalShape(Shape(blob_conf.shape()), nd_sbp, parallel_desc, *parallel_ctx))); out_blob_desc->set_data_type(blob_conf.data_type()); out_blob_desc->set_is_dynamic(blob_conf.is_dynamic()); return Maybe::Ok(); @@ -71,9 +71,9 @@ Maybe InterfaceOpUtil::InferLogicalOutBlobDesc(const InterfaceBlobConf& bl BlobDesc* out_blob_desc, const ParallelDesc& parallel_desc) { CHECK_OR_RETURN(blob_conf.has_shape()); - out_blob_desc->mut_shape() = Shape(blob_conf.shape()); + out_blob_desc->set_shape(Shape(blob_conf.shape())); CheckShape(out_blob_desc->shape()); - if (out_blob_desc->mut_shape().NumAxes() > 0) { CHECK_GT(out_blob_desc->mut_shape().At(0), 0); } + if (out_blob_desc->shape().NumAxes() > 0) { CHECK_GT(out_blob_desc->shape().At(0), 0); } CHECK_OR_RETURN(blob_conf.has_data_type()); out_blob_desc->set_data_type(blob_conf.data_type()); CHECK_OR_RETURN(blob_conf.has_is_dynamic()); diff --git a/oneflow/core/operator/learning_rate_schedule_op.cpp b/oneflow/core/operator/learning_rate_schedule_op.cpp index 6b926deeba780ba3bd2bd9ac20e93692473c3225..2e4dd9f0316434e1d9dd4353eb81c83d314d998a 100644 --- a/oneflow/core/operator/learning_rate_schedule_op.cpp +++ b/oneflow/core/operator/learning_rate_schedule_op.cpp @@ -51,7 +51,7 @@ Maybe InferBlobDescs(const std::function& B CHECK_EQ(train_step->shape().elem_cnt(), 1); CHECK_EQ(train_step->data_type(), DataType::kInt64); BlobDesc* out = BlobDesc4BnInOp("out"); - out->mut_shape() = Shape({1}); + out->set_shape(Shape({1})); out->set_data_type(DataType::kFloat); return Maybe::Ok(); } diff --git a/oneflow/core/operator/nccl_send_recv_boxing_op.cpp b/oneflow/core/operator/nccl_send_recv_boxing_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f27c506e00a28d8171e253ece23121915db223ff --- /dev/null +++ b/oneflow/core/operator/nccl_send_recv_boxing_op.cpp @@ -0,0 +1,142 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/common/protobuf.h" +#include "oneflow/core/operator/nccl_send_recv_boxing_op_util.h" + +namespace oneflow { + +class NcclSendRecvBoxingOp : public Operator { + public: + OF_DISALLOW_COPY_AND_MOVE(NcclSendRecvBoxingOp); + NcclSendRecvBoxingOp() = default; + ~NcclSendRecvBoxingOp() override = default; + + Maybe InitFromOpConf() override; + Maybe InferInternalBlobDescs( + const std::function& GetBlobDesc4BnInOp, + const ParallelContext* parallel_ctx, const JobDesc* job_desc) const override; + Maybe InferLogicalOutBlobDescs( + const std::function& BlobDesc4BnInOp, + const ParallelDesc& parallel_desc) const override { + UNIMPLEMENTED_THEN_RETURN(); + } + Maybe InferOutBlobDescs( + const std::function& GetBlobDesc4BnInOp, + const ParallelContext* parallel_ctx) const override; + + private: + LogicalBlobId lbi4ibn(const std::string& input_bn) const override; + LogicalBlobId lbi4obn(const std::string& output_bn) const override; +}; + +Maybe NcclSendRecvBoxingOp::InitFromOpConf() { + const NcclSendRecvBoxingOpConf& conf = this->op_conf().nccl_send_recv_boxing_conf(); + if (conf.has_input()) { EnrollInputBn("in", false); } + if (conf.has_output()) { EnrollOutputBn("out", false); } + EnrollTmpBn("buf"); + return Maybe::Ok(); +} + +Maybe NcclSendRecvBoxingOp::InferInternalBlobDescs( + const std::function& GetBlobDesc4BnInOp, + const ParallelContext* parallel_ctx, const JobDesc* job_desc) const { + BlobDesc* buf = GetBlobDesc4BnInOp("buf"); + const NcclSendRecvBoxingOpConf& conf = this->op_conf().nccl_send_recv_boxing_conf(); + const NdSbp& src_nd_sbp = conf.src_nd_sbp(); + const NdSbp& dst_nd_sbp = conf.dst_nd_sbp(); + ParallelDesc parallel_desc(conf.parallel_conf()); + ParallelDesc in_parallel_desc(conf.src_parallel_conf()); + ParallelDesc out_parallel_desc(conf.dst_parallel_conf()); + const int64_t parallel_num = parallel_desc.parallel_num(); + const int64_t parallel_id = parallel_ctx->parallel_id(); + const Shape& logical_shape = Shape(conf.logical_shape()); + std::vector src_send_intersections; + std::vector dst_recv_intersections; + GetRankSendRecvIntersection(parallel_id, parallel_desc, in_parallel_desc, out_parallel_desc, + src_nd_sbp, dst_nd_sbp, logical_shape, &src_send_intersections, + &dst_recv_intersections); + int64_t buf_count = 0; + if (conf.has_input()) { + const BlobDesc* in = GetBlobDesc4BnInOp("in"); + buf->set_data_type(in->data_type()); + CHECK_EQ(src_send_intersections.size(), parallel_num); + for (int64_t i = 0; i < parallel_num; ++i) { + const TensorSliceView& intersection = JUST(VectorAt(src_send_intersections, i)); + if (!intersection.IsEmpty()) { buf_count += intersection.shape().elem_cnt(); } + } + } + if (conf.has_output()) { + const BlobDesc* out = GetBlobDesc4BnInOp("out"); + buf->set_data_type(out->data_type()); + for (int64_t i = 0; i < parallel_num; ++i) { + const TensorSliceView& intersection = JUST(VectorAt(dst_recv_intersections, i)); + if (!intersection.IsEmpty()) { buf_count += intersection.shape().elem_cnt(); } + } + if (NdSbpHasPartialParallel(src_nd_sbp)) { + // Note: when src_nd_sbp has partial_sum, need a out_size buffer to copy and add to out. + buf_count += out->shape().elem_cnt(); + } + } + buf->set_shape(Shape({buf_count})); + return Maybe::Ok(); +} + +LogicalBlobId NcclSendRecvBoxingOp::lbi4ibn(const std::string& input_bn) const { + return this->op_conf().nccl_send_recv_boxing_conf().lbi(); +} + +LogicalBlobId NcclSendRecvBoxingOp::lbi4obn(const std::string& output_bn) const { + return this->op_conf().nccl_send_recv_boxing_conf().lbi(); +} + +Maybe NcclSendRecvBoxingOp::InferOutBlobDescs( + const std::function& GetBlobDesc4BnInOp, + const ParallelContext* parallel_ctx) const { + const NcclSendRecvBoxingOpConf& conf = this->op_conf().nccl_send_recv_boxing_conf(); + const Shape& logical_shape = Shape(conf.logical_shape()); + const ParallelDesc& parallel_desc = ParallelDesc(conf.parallel_conf()); + const int64_t machine_id = JUST(parallel_desc.MachineId4ParallelId(parallel_ctx->parallel_id())); + const int64_t device_index = JUST(parallel_desc.DeviceId4ParallelId(parallel_ctx->parallel_id())); + if (conf.has_input()) { + const BlobDesc* in_blob_desc = GetBlobDesc4BnInOp("in"); + const NdSbp& src_nd_sbp = conf.src_nd_sbp(); + const ParallelDesc& src_parallel_desc = ParallelDesc(conf.src_parallel_conf()); + int64_t src_parallel_id = + JUST(src_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + std::shared_ptr in_shape = + JUST(GetPhysicalShape(logical_shape, src_nd_sbp, src_parallel_desc, src_parallel_id)); + CHECK_EQ_OR_RETURN(*in_shape, in_blob_desc->shape()) + << "Non-matching shape of blobs for pieces of nccl send recv"; + } + if (conf.has_output()) { + BlobDesc* out_blob_desc = GetBlobDesc4BnInOp("out"); + const NdSbp& dst_nd_sbp = conf.dst_nd_sbp(); + const ParallelDesc& dst_parallel_desc = ParallelDesc(conf.dst_parallel_conf()); + int64_t dst_parallel_id = + JUST(dst_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + std::shared_ptr out_shape = + JUST(GetPhysicalShape(logical_shape, dst_nd_sbp, dst_parallel_desc, dst_parallel_id)); + out_blob_desc->set_shape(*out_shape); + out_blob_desc->set_data_type(conf.data_type()); + } + return Maybe::Ok(); +} + +REGISTER_OP(OperatorConf::kNcclSendRecvBoxingConf, NcclSendRecvBoxingOp); + +} // namespace oneflow diff --git a/oneflow/core/operator/nccl_send_recv_boxing_op_util.cpp b/oneflow/core/operator/nccl_send_recv_boxing_op_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a0be3320256605262bb01ead40ee4b6645220e12 --- /dev/null +++ b/oneflow/core/operator/nccl_send_recv_boxing_op_util.cpp @@ -0,0 +1,170 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/operator/nccl_send_recv_boxing_op_util.h" + +namespace oneflow { + +namespace { +// Go through all the ranks while transfer between two nd sbps with no PartialSum under the same +// placement. +// NOTE: We need to make sure no partial sums in the sbps of the producer and consumer. +void DfsTraverseRanks4NdSbp( + int32_t depth, std::vector& in_parallel_ids, + const std::vector& out_parallel_ids, const Shape& in_parallel_hierarchy, + const NdIndexOffsetHelper& in_hierarchy_index_helper, + const NdSbp& in_nd_sbp, const std::function& visit) { + if (depth >= in_parallel_hierarchy.NumAxes()) { + visit(in_hierarchy_index_helper.NdIndexToOffset(in_parallel_ids.data(), + in_parallel_hierarchy.NumAxes())); + return; + } + if (in_nd_sbp.sbp_parallel(depth).has_broadcast_parallel()) { + // If Broadcast in the sbp of the producer, only visit those ranks with the same id as the + // current rank along the depth-dimension. + in_parallel_ids[depth] = out_parallel_ids[depth]; + DfsTraverseRanks4NdSbp(depth + 1, in_parallel_ids, out_parallel_ids, in_parallel_hierarchy, + in_hierarchy_index_helper, in_nd_sbp, visit); + } else { + // If Split or PartialSum, go through all the ranks along the depth-dimension. + for (int64_t i = 0; i < in_parallel_hierarchy.dim_vec().at(depth); i++) { + in_parallel_ids[depth] = i; + DfsTraverseRanks4NdSbp(depth + 1, in_parallel_ids, out_parallel_ids, in_parallel_hierarchy, + in_hierarchy_index_helper, in_nd_sbp, visit); + } + } +} + +bool NdSbpNoPartialParallel(const NdSbp& nd_sbp) { + CHECK_GT(nd_sbp.sbp_parallel_size(), 0); + FOR_RANGE(int64_t, i, 0, nd_sbp.sbp_parallel_size()) { + if (nd_sbp.sbp_parallel(i).has_partial_sum_parallel()) { return false; } + } + return true; +} + +} // namespace + +int64_t GetMappedParallelId(const int64_t from_parallel_id, const ParallelDesc& from_parallel_desc, + const ParallelDesc& to_parallel_desc) { + const int64_t machine_id = CHECK_JUST(from_parallel_desc.MachineId4ParallelId(from_parallel_id)); + const int64_t device_index = CHECK_JUST(from_parallel_desc.DeviceId4ParallelId(from_parallel_id)); + if (to_parallel_desc.Containing(machine_id, device_index)) { + return CHECK_JUST(to_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + } else { + return -1; + } +} + +void GetRankSendRecvIntersection(int64_t parallel_id, const ParallelDesc& parallel_desc, + const ParallelDesc& in_parallel_desc, + const ParallelDesc& out_parallel_desc, const NdSbp& in_nd_sbp, + const NdSbp& out_nd_sbp, const Shape& logical_shape, + std::vector* send_intersections, + std::vector* recv_intersections) { + const int64_t parallel_num = parallel_desc.parallel_num(); + CHECK_LT(parallel_id, parallel_num); + + const std::vector& in_slices = + GetTensorSliceView(*in_parallel_desc.hierarchy(), in_nd_sbp, logical_shape); + const std::vector& out_slices = + GetTensorSliceView(*out_parallel_desc.hierarchy(), out_nd_sbp, logical_shape); + + const auto& in_parallel_hierarchy = in_parallel_desc.hierarchy(); + int32_t in_hierarchy_dimension = in_parallel_hierarchy->NumAxes(); + const NdIndexOffsetHelper in_hierarchy_index_helper( + in_parallel_hierarchy->dim_vec().data(), in_hierarchy_dimension); + + const int64_t machine_id = CHECK_JUST(parallel_desc.MachineId4ParallelId(parallel_id)); + const int64_t device_index = CHECK_JUST(parallel_desc.DeviceId4ParallelId(parallel_id)); + const int64_t in_parallel_num = in_parallel_desc.parallel_num(); + const int64_t out_parallel_num = out_parallel_desc.parallel_num(); + // cur rank recv from + // cur rank has output + if (out_parallel_desc.Containing(machine_id, device_index)) { + recv_intersections->resize(parallel_num); + int64_t out_id = + CHECK_JUST(out_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + const TensorSliceView& cur_rank_out_slice = out_slices.at(out_id); + const auto& add_to_recv_intersections = [&](int32_t send_id) { + const TensorSliceView& in_slice = in_slices.at(send_id); + const TensorSliceView& intersection = cur_rank_out_slice.Intersect(in_slice); + if (intersection.IsEmpty()) { return; } + const int64_t merged_id = GetMappedParallelId(send_id, in_parallel_desc, parallel_desc); + recv_intersections->at(merged_id) = intersection; + }; + int64_t corresponding_in_id = 0; + // For example [[0, 1], [2, 3]] -> [[1, 3], [5, 6]] + if (in_parallel_desc.Containing(machine_id, device_index)) { + // 1 and 3 are in [[0, 1], [2, 3]], use the same id in the producer parallel description + // The id of 1 is (0, 1), the id of 3 is (1, 1) + corresponding_in_id = + CHECK_JUST(in_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + } else { + // 5 and 7 are not in [[0, 1], [2, 3]] + // Then the id does not matter + corresponding_in_id = out_id % in_parallel_num; + } + std::vector in_parallel_ids(in_hierarchy_dimension); + // The corresponding parallel id of a consumer rank in the producer parallel description + std::vector out_parallel_ids(in_hierarchy_dimension); + in_hierarchy_index_helper.OffsetToNdIndex(corresponding_in_id, out_parallel_ids.data(), + in_hierarchy_dimension); + DfsTraverseRanks4NdSbp(0, in_parallel_ids, out_parallel_ids, *in_parallel_hierarchy, + in_hierarchy_index_helper, in_nd_sbp, add_to_recv_intersections); + } + + // cur rank send to + if (in_parallel_desc.Containing(machine_id, device_index)) { + send_intersections->resize(parallel_num); + int64_t in_id = + CHECK_JUST(in_parallel_desc.ParallelId4MachineDeviceId(machine_id, device_index)); + const TensorSliceView& cur_rank_in_slice = in_slices.at(in_id); + for (int64_t recv_i = 0; recv_i < out_parallel_num; ++recv_i) { + const auto& add_to_send_intersections = [&](int32_t send_id) { + if (send_id != in_id) { return; } + const TensorSliceView& out_slice = out_slices.at(recv_i); + const TensorSliceView& intersection = out_slice.Intersect(cur_rank_in_slice); + if (intersection.IsEmpty()) { return; } + const int64_t merged_id = GetMappedParallelId(recv_i, out_parallel_desc, parallel_desc); + send_intersections->at(merged_id) = intersection; + }; + int64_t out_device_id = CHECK_JUST(out_parallel_desc.DeviceId4ParallelId(recv_i)); + int64_t out_machine_id = CHECK_JUST(out_parallel_desc.MachineId4ParallelId(recv_i)); + int64_t corresponding_in_id = 0; + // For example [[0, 1], [2, 3]] -> [[1, 3], [5, 6]] + if (in_parallel_desc.Containing(out_machine_id, out_device_id)) { + // 1 and 3 are in [[0, 1], [2, 3]], use the same id in the producer parallel description + // The id of 1 is (0, 1), the id of 3 is (1, 1) + corresponding_in_id = + CHECK_JUST(in_parallel_desc.ParallelId4MachineDeviceId(out_machine_id, out_device_id)); + } else { + // 5 and 7 are not in [[0, 1], [2, 3]] + // Then the id does not matter + corresponding_in_id = recv_i % in_parallel_num; + } + std::vector in_parallel_ids(in_hierarchy_dimension); + // The corresponding parallel id of a consumer rank in the producer parallel description + std::vector out_parallel_ids(in_hierarchy_dimension); + in_hierarchy_index_helper.OffsetToNdIndex(corresponding_in_id, out_parallel_ids.data(), + in_hierarchy_dimension); + DfsTraverseRanks4NdSbp(0, in_parallel_ids, out_parallel_ids, *in_parallel_hierarchy, + in_hierarchy_index_helper, in_nd_sbp, add_to_send_intersections); + } + } +} + +} // namespace oneflow diff --git a/oneflow/core/operator/nccl_send_recv_boxing_op_util.h b/oneflow/core/operator/nccl_send_recv_boxing_op_util.h new file mode 100644 index 0000000000000000000000000000000000000000..f491a50e91b8bc8deb4039c9236e43be4f3153a1 --- /dev/null +++ b/oneflow/core/operator/nccl_send_recv_boxing_op_util.h @@ -0,0 +1,31 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/register/tensor_slice_view.h" +#include "oneflow/core/job/nd_sbp_util.h" + +namespace oneflow { + +int64_t GetMappedParallelId(const int64_t from_parallel_id, const ParallelDesc& from_parallel_desc, + const ParallelDesc& to_parallel_desc); + +void GetRankSendRecvIntersection(int64_t parallel_id, const ParallelDesc& parallel_desc, + const ParallelDesc& in_parallel_desc, + const ParallelDesc& out_parallel_desc, const NdSbp& in_nd_sbp, + const NdSbp& out_nd_sbp, const Shape& logical_shape, + std::vector* send_intersections, + std::vector* recv_intersections); + +} // namespace oneflow diff --git a/oneflow/core/operator/op_attribute.proto b/oneflow/core/operator/op_attribute.proto index e0e2c2b47411f248398da44f61774e5a8d767da0..548bec15790e0e7a3b926d297d9ba0e4b37f8733 100644 --- a/oneflow/core/operator/op_attribute.proto +++ b/oneflow/core/operator/op_attribute.proto @@ -6,7 +6,7 @@ import "oneflow/core/register/blob_desc.proto"; import "oneflow/core/operator/op_conf.proto"; import "oneflow/core/operator/arg_modifier_signature.proto"; import "oneflow/core/job/sbp_parallel.proto"; -import "oneflow/core/job/mirrored_parallel.proto"; +import "oneflow/core/job/local_parallel.proto"; import "oneflow/core/job/blob_lifetime_signature.proto"; import "oneflow/core/job/parallel_signature.proto"; import "oneflow/core/job/parallel_conf_signature.proto"; @@ -26,7 +26,7 @@ message OpAttribute { // op node signature optional SbpSignature sbp_signature = 104; - optional MirroredSignature mirrored_signature = 105; + optional LocalSignature local_signature = 105; optional BlobDescSignature logical_blob_desc_signature = 106; optional ParallelSignature parallel_signature = 108; optional ParallelConfSignature parallel_conf_signature = 109; diff --git a/oneflow/core/operator/op_conf.proto b/oneflow/core/operator/op_conf.proto index 4589ae3507eb2b855b818ba09873420cfcb013b1..162b43080389715124840a85b5ca964e21049840 100644 --- a/oneflow/core/operator/op_conf.proto +++ b/oneflow/core/operator/op_conf.proto @@ -13,6 +13,7 @@ import "oneflow/core/job/sbp_parallel.proto"; import "oneflow/core/graph/boxing/collective_boxing.proto"; import "oneflow/core/job/initializer_conf.proto"; import "oneflow/core/job/regularizer_conf.proto"; +import "oneflow/core/job/placement.proto"; import "oneflow/core/job/learning_rate_schedule_conf.proto"; import "oneflow/core/operator/interface_blob_conf.proto"; import "oneflow/core/register/blob_desc.proto"; @@ -53,15 +54,6 @@ message CopyCommNetOpConf { required LogicalBlobId lbi = 2; } -message CopyHdOpConf { - enum Type { - H2D = 0; - D2H = 1; - } - required Type type = 1; - required LogicalBlobId lbi = 2; -} - message BoxConcatConf { required int32 axis = 1; } @@ -127,13 +119,6 @@ message InputOpConf { optional string job_name = 4; } -message ForeignInputOpConf { - optional string tick = 1; - required string out = 2; - required InterfaceBlobConf blob_conf = 3; - required string ofblob_buffer_name = 4; -} - message ReturnOpConf { required string in = 1; required string out = 2; @@ -147,16 +132,6 @@ message OutputOpConf { optional string job_name = 4; } -message ForeignOutputOpConf { - required string in = 1; - required string ofblob_buffer_name = 3; -} - -message ForeignWatchOpConf { - required string in = 1; - required string handler_uuid = 2; -} - message VariableOpConf { optional string tick = 1; required string out = 2; @@ -280,13 +255,13 @@ message CopyOpConf { required string out = 2; } -message CastToMirroredOpConf { +message CastToLocalOpConf { required string in = 1; required string out = 2; required SbpParallel sbp_parallel = 3; } -message CastFromMirroredOpConf { +message CastFromLocalOpConf { required string in = 1; required string out = 2; required SbpParallel sbp_parallel = 3; @@ -401,6 +376,19 @@ message BoxingZerosOpConf { required DataType data_type = 3; } +message NcclSendRecvBoxingOpConf { + required LogicalBlobId lbi = 1; + required NdSbp src_nd_sbp = 2; + required NdSbp dst_nd_sbp = 3; + required ParallelConf parallel_conf = 4; + required ParallelConf src_parallel_conf = 5; + required ParallelConf dst_parallel_conf = 6; + required ShapeProto logical_shape = 7; + required DataType data_type = 8; + required bool has_input = 9; + required bool has_output = 10; +} + message OperatorConf { required string name = 1; optional string device_tag = 4 [default = "invalid_device"]; @@ -409,9 +397,9 @@ message OperatorConf { optional string stream_name_hint = 9; optional string pass_tag = 10; optional string loc = 11 [default = ""]; + optional int64 logical_chain_id = 12 [default = -1]; oneof op_type { // system op - CopyHdOpConf copy_hd_conf = 105; CopyCommNetOpConf copy_comm_net_conf = 106; BoxingOpConf boxing_conf = 108; VariableOpConf variable_conf = 122; @@ -429,11 +417,8 @@ message OperatorConf { WaitAndSendIdsOpConf wait_and_send_ids_conf = 139; ReentrantLockOpConf reentrant_lock_conf = 140; CallbackNotifyOpConf callback_notify_conf = 141; - ForeignInputOpConf foreign_input_conf = 142; - ForeignOutputOpConf foreign_output_conf = 143; AccTickOpConf acc_tick_conf = 144; ReturnOpConf return_conf = 146; - ForeignWatchOpConf foreign_watch_conf = 151; DistributeConcatOpConf distribute_concat_conf = 155; DistributeSplitOpConf distribute_split_conf = 156; DistributeCloneOpConf distribute_clone_conf = 157; @@ -446,6 +431,7 @@ message OperatorConf { CollectiveBoxingPackOpConf collective_boxing_pack_conf = 174; CollectiveBoxingUnpackOpConf collective_boxing_unpack_conf = 175; BoxingZerosOpConf boxing_zeros_conf = 176; + NcclSendRecvBoxingOpConf nccl_send_recv_boxing_conf = 177; UserOpConf user_conf = 199; // domain op @@ -459,15 +445,15 @@ message OperatorConf { ConstantLikeOpConf constant_like_conf = 339; SyncDynamicResizeOpConf sync_dynamic_resize_conf = 340; CopyOpConf copy_conf = 343; - CastToMirroredOpConf cast_to_mirrored_conf = 344; - CastFromMirroredOpConf cast_from_mirrored_conf = 345; + CastToLocalOpConf cast_to_local_conf = 344; + CastFromLocalOpConf cast_from_local_conf = 345; ImageDecoderRandomCropResizeOpConf image_decoder_random_crop_resize_conf = 349; // math op BroadcastToCompatibleWithOpConf broadcast_to_compatible_with_conf = 525; // NOTE(chengcheng): Lazy 1.0 system ops. - // Feed EagerTensor to interface op. + // Feed EagerTensor to interface op. // Note that FeedxxOp just for build CustomOpExpr, and has NO operator impl. FeedInputOpConf feed_input_conf = 600; FeedVariableOpConf feed_variable_conf = 601; diff --git a/oneflow/core/operator/op_infer_cache.h b/oneflow/core/operator/op_infer_cache.h index 58fbc948a5ef3d41784e51eb663a8bdcde4a0c91..c2148f85f12f0b398a90d45707dff566af5a845b 100644 --- a/oneflow/core/operator/op_infer_cache.h +++ b/oneflow/core/operator/op_infer_cache.h @@ -55,12 +55,10 @@ struct hash final { using namespace oneflow; size_t ibn_idx2shape_sym_hash_value = 0; for (const auto& shape_sym : op_infer_cache_key.ibn_idx2shape_sym) { - ibn_idx2shape_sym_hash_value ^= std::hash>()(shape_sym); + AddHash(&ibn_idx2shape_sym_hash_value, shape_sym); } - return std::hash()(op_infer_cache_key.scope) - ^ std::hash>()(op_infer_cache_key.op_conf_sym) - ^ ibn_idx2shape_sym_hash_value - ^ std::hash>()(op_infer_cache_key.dtype_signature_sym); + return Hash(op_infer_cache_key.scope, op_infer_cache_key.op_conf_sym, + ibn_idx2shape_sym_hash_value, op_infer_cache_key.dtype_signature_sym); } }; diff --git a/oneflow/core/operator/op_node_signature.proto b/oneflow/core/operator/op_node_signature.proto index 830d72871369f1cc53678d0ab1fa4c81145c21ad..11241a9323481ca761d5014198d87b276d845b9b 100644 --- a/oneflow/core/operator/op_node_signature.proto +++ b/oneflow/core/operator/op_node_signature.proto @@ -2,13 +2,13 @@ syntax = "proto2"; package oneflow; import "oneflow/core/job/sbp_parallel.proto"; -import "oneflow/core/job/mirrored_parallel.proto"; +import "oneflow/core/job/local_parallel.proto"; import "oneflow/core/register/blob_desc.proto"; import "oneflow/core/job/parallel_signature.proto"; message OpNodeSignature { optional SbpSignature sbp_signature = 1; - optional MirroredSignature mirrored_signature = 2; + optional LocalSignature local_signature = 2; optional BlobDescSignature logical_blob_desc_signature = 3; optional ParallelSignature parallel_signature = 5; } diff --git a/oneflow/core/operator/operator.cpp b/oneflow/core/operator/operator.cpp index 860e90c28880664ef1d9f1d30cece306728e8d82..54fdbe73502176d20c38b90d41feab644995d084 100644 --- a/oneflow/core/operator/operator.cpp +++ b/oneflow/core/operator/operator.cpp @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include #include "oneflow/core/common/balanced_splitter.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/common/decorator.h" @@ -20,7 +21,7 @@ limitations under the License. #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/job/mirrored_sig_infer_hint.h" +#include "oneflow/core/job/local_sig_infer_hint.h" #include "oneflow/core/job/sbp_signature_builder.h" #include "oneflow/core/job/scope.h" #include "oneflow/core/job/sbp_parallel.h" @@ -28,7 +29,6 @@ limitations under the License. #include "oneflow/core/operator/operator.h" #include "oneflow/core/operator/op_node_signature.pb.h" #include "oneflow/core/job/nd_sbp_infer_hint.h" -#include "oneflow/core/job/foreign_callback.h" #include "oneflow/core/framework/nd_sbp.h" #include "oneflow/core/framework/sbp_infer_util.h" #include "oneflow/core/framework/placement_sbp_util.h" @@ -221,6 +221,19 @@ Maybe FillLogicalBlobDescSignature( return Maybe::Ok(); } +Maybe SupportNonContiguous(const Operator* op) { + const auto& op_conf = op->op_conf(); + if (op_conf.has_user_conf()) { + const auto* registry = + user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_conf.user_conf().op_type_name()); + CHECK_NOTNULL_OR_RETURN(registry) + << "The op(operation) " << op_conf.user_conf().op_type_name() + << " is not found. Please check whether it has been registered correctly."; + return registry->non_contiguous_supported; + } + return false; +} + } // namespace Maybe Operator::FillLogicalInBlobDesc( @@ -316,7 +329,16 @@ Maybe Operator::InferLogicalOutBlobDescsIf() { output_index2logical_blob_desc_.reset(new std::vector>()); output_index2logical_blob_desc_->resize(output_bns().size()); for (int32_t i = 0; i < output_bns().size(); ++i) { - output_index2logical_blob_desc_->at(i) = output_logical_blob_desc_vec.at(i); + auto& out_blob_desc = output_logical_blob_desc_vec[i]; + // initialize stride by shape if the op does not support non-contiguous + if (!JUST(SupportNonContiguous(this))) { + out_blob_desc->set_stride(Stride(out_blob_desc->shape())); + } + CHECK_EQ_OR_RETURN(out_blob_desc->stride().size(), out_blob_desc->shape().size()) + << Error::RuntimeError() << "stride and shape size mismatch since stride is " + << out_blob_desc->stride().ToString() << " but shape is " + << out_blob_desc->shape().ToString(); + (*output_index2logical_blob_desc_)[i] = out_blob_desc; } return Maybe::Ok(); } @@ -332,7 +354,19 @@ Maybe Operator::InferBlobDescsIf( Maybe Operator::InferOutBlobDescsIf( std::function GetBlobDesc4BnInOp, const ParallelContext* parallel_ctx) const { - return InferOutBlobDescs(GetBlobDesc4BnInOp, parallel_ctx); + JUST(InferOutBlobDescs(GetBlobDesc4BnInOp, parallel_ctx)); + for (const auto& bn : output_bns()) { + BlobDesc* out_blob_desc = GetBlobDesc4BnInOp(bn); + // initialize stride by shape if the op does not support non-contiguous + if (!JUST(SupportNonContiguous(this))) { + out_blob_desc->set_stride(Stride(out_blob_desc->shape())); + } + CHECK_EQ_OR_RETURN(out_blob_desc->stride().size(), out_blob_desc->shape().size()) + << Error::RuntimeError() << "stride and shape size mismatch since stride is " + << out_blob_desc->stride().ToString() << " but shape is " + << out_blob_desc->shape().ToString(); + } + return Maybe::Ok(); } Maybe Operator::InferOutBlobDescs( @@ -354,8 +388,8 @@ Maybe Operator::InferOutBlobDescs( BlobDesc* desc = GetBlobDesc4BnInOp(bn); *desc = *JUST(GetLogicalBlobDesc4Obn(bn)); const auto& nd_sbp = nd_sbp_signature->bn_in_op2nd_sbp().at(bn); - desc->mut_shape() = - *JUST(GetPhysicalShape(desc->shape(), nd_sbp, *parallel_desc, *parallel_ctx)); + desc->set_shape( + *JUST(GetPhysicalShape(desc->shape(), nd_sbp, *parallel_desc, *parallel_ctx))); } } return Maybe::Ok(); @@ -463,8 +497,8 @@ Maybe Operator::GetInputOutputFastestTimeShape() const { Maybe Operator::GetSbpSignaturesIf( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const { - JUST(GetSbpSignatures(LogicalBlobDesc4Ibn, parallel_desc, sbp_sig_list)); + int32_t hierarchy_value, SbpSignatureList* sbp_sig_list) const { + JUST(GetSbpSignatures(LogicalBlobDesc4Ibn, hierarchy_value, sbp_sig_list)); SbpSignatureBuilder() .Broadcast(input_bns()) .Broadcast(output_bns()) @@ -476,32 +510,54 @@ Maybe Operator::GetNdSbpSignatureList( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { // Get 1D sbp signature list - SbpSignatureList sbp_sig_list; - JUST(GetSbpSignaturesIf(LogicalBlobDesc4Ibn, parallel_desc, &sbp_sig_list)); - CHECK_GT_OR_RETURN(sbp_sig_list.sbp_signature_size(), 0) - << op_name() << " gets no sbp signature from GetSbpSignaturesIf function!"; + HashMap hierarchy_value2sbp_sig_list; + // hierarchy value is the value at the dimension corresponding to the current SBP + // For example, 2 machines, 4 gpus per machine, hierarchy = [2, 4] + // Suppose we have nd_sbp = (S0, B) + // The hierarchy value corresponding to S0 is 2 + // The hierarchy value corresponding to B is 4. + for (int32_t hierarchy_value : *parallel_desc.hierarchy()) { + if (hierarchy_value2sbp_sig_list.find(hierarchy_value) == hierarchy_value2sbp_sig_list.end()) { + auto* sbp_sig_list = &hierarchy_value2sbp_sig_list[hierarchy_value]; + JUST(GetSbpSignaturesIf(LogicalBlobDesc4Ibn, hierarchy_value, sbp_sig_list)); + CHECK_GT_OR_RETURN(sbp_sig_list->sbp_signature_size(), 0) + << op_name() + << " gets no sbp signature from GetSbpSignaturesIf function for hierarchy value: " + << hierarchy_value; + } + } int32_t sbp_dimension = parallel_desc.hierarchy()->NumAxes(); NdSbpSignature nd_sbp_sig; - SbpSignatureToNdSbpSignature(sbp_sig_list.sbp_signature(0), &nd_sbp_sig); + SbpSignatureToNdSbpSignature(hierarchy_value2sbp_sig_list.begin()->second.sbp_signature(0), + &nd_sbp_sig); ResizeNdSbpSignature(nd_sbp_sig, sbp_dimension); // ND sbp signature list would be direct product of 1D sbp signatures CHECK_OR_RETURN(nd_sbp_sig_list->empty()); - DfsGetNdSbpSignature(nd_sbp_sig, 0, sbp_dimension, sbp_sig_list, nd_sbp_sig_list); + DfsGetNdSbpSignature(nd_sbp_sig, 0, sbp_dimension, *parallel_desc.hierarchy(), + hierarchy_value2sbp_sig_list, nd_sbp_sig_list); return Maybe::Ok(); } Maybe Operator::GetValidNdSbpSignatureList( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list, + bool check_output) const { JUST(GetNdSbpSignatureList(LogicalBlobDesc4Ibn, parallel_desc, nd_sbp_sig_list)); // Leave those valid Nd SBPs - JUST(FilterNdSbpSignatureListByLogicalShape(LogicalBlobDesc4Ibn, parallel_desc, nd_sbp_sig_list)); + JUST(FilterNdSbpSignatureListByLogicalShape(LogicalBlobDesc4Ibn, parallel_desc, nd_sbp_sig_list, + check_output)); CHECK_OR_RETURN(nd_sbp_sig_list->size() > 0) << "Empty sbp signature after filtering for " << op_name(); return Maybe::Ok(); } +Operator::DumpNdSbpSignatureForOpConfFn Operator::GetDumpNdSbpSignatureForOpConfFn() const { + return [](const NdSbpSignature& nd_sbp_sig, OperatorConf* op_conf) -> Maybe { + return Maybe::Ok(); + }; +} + void Operator::ForEachBnInOp(const std::function& Handler) const { for (const std::string& bn_in_op : input_bns()) { Handler(bn_in_op); } for (const std::string& bn_in_op : output_bns()) { Handler(bn_in_op); } @@ -531,19 +587,13 @@ Maybe Operator::FillNdSbpSignature(const NdSbpSignature& signature) { Maybe Operator::InferSbpSignatureIf( const SbpSignature& sbp_sig_conf, const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, + const std::function(const std::string&)>& SbpInferHint4Ibn, const ParallelDesc& parallel_desc) { SbpSignature signature; - if (parallel_desc.parallel_num() == 1) { - auto* bn2sbp = signature.mutable_bn_in_op2sbp_parallel(); - for (const auto& ibn : input_bns()) { (*bn2sbp)[ibn].mutable_broadcast_parallel(); } - for (const auto& obn : output_bns()) { (*bn2sbp)[obn].mutable_broadcast_parallel(); } - } else if (parallel_desc.parallel_num() > 1) { - JUST(InferSbpSignature(&signature, sbp_sig_conf, CalcOrderValue4SbpSig, SbpInferHint4Ibn, - parallel_desc)); - } else { - UNIMPLEMENTED(); - } + + JUST(InferSbpSignature(&signature, sbp_sig_conf, CalcOrderValue4SbpSig, SbpInferHint4Ibn, + parallel_desc)); + JUST(FillSbpSignature(signature)); return Maybe::Ok(); } @@ -597,16 +647,10 @@ Maybe Operator::InferSbpSignature( } else { CalcOrderValue4SbpSig = [](const SbpSignature&) -> int32_t { return 0; }; } - if (op_parallel_desc_->parallel_num() == 1) { - auto* bn2sbp = infered_sbp_signature->mutable_bn_in_op2sbp_parallel(); - for (const auto& ibn : input_bns()) { (*bn2sbp)[ibn].mutable_broadcast_parallel(); } - for (const auto& obn : output_bns()) { (*bn2sbp)[obn].mutable_broadcast_parallel(); } - } else if (op_parallel_desc_->parallel_num() > 1) { - JUST(InferSbpSignature(infered_sbp_signature, sbp_sig_conf, CalcOrderValue4SbpSig, - SbpInferHint4Ibn, *op_parallel_desc_)); - } else { - UNIMPLEMENTED(); - } + + JUST(InferSbpSignature(infered_sbp_signature, sbp_sig_conf, CalcOrderValue4SbpSig, + SbpInferHint4Ibn, *op_parallel_desc_)); + return Maybe::Ok(); } @@ -656,7 +700,8 @@ Maybe Operator::FilterAndCheckValidSbpSignatureListByLogicalShape( Maybe Operator::FilterNdSbpSignatureListByLogicalShape( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list, + bool check_output) const { auto FilterSbp4Blobs = [&](const PbRpf& bns, const NdSbpSignature& nd_sbp_sig) -> Maybe { // {in_0 : (S(6), B), in_1 : (S(0), S(1)), out : (B, S(1))} @@ -679,7 +724,9 @@ Maybe Operator::FilterNdSbpSignatureListByLogicalShape( }; // Go down from the tail to the head, since we might drop the tail. for (int32_t sbp_id = nd_sbp_sig_list->size() - 1; sbp_id >= 0; sbp_id--) { - if (JUST(FilterSbp4Blobs(input_bns(), JUST(VectorAt(*nd_sbp_sig_list, sbp_id))))) { + if (JUST(FilterSbp4Blobs(input_bns(), JUST(VectorAt(*nd_sbp_sig_list, sbp_id)))) + || (check_output + && JUST(FilterSbp4Blobs(output_bns(), JUST(VectorAt(*nd_sbp_sig_list, sbp_id)))))) { // Remove the Nd SBP candidate (*nd_sbp_sig_list)[sbp_id] = JUST(VectorAt(*nd_sbp_sig_list, nd_sbp_sig_list->size() - 1)); nd_sbp_sig_list->pop_back(); @@ -731,7 +778,7 @@ Maybe Operator::GreedilyFindMinCopyCostNdSbp( producer_infer_hint4ibn->nd_sbp(), JUST(VectorAt(nd_sbp_sig_list, i)).bn_in_op2nd_sbp().at(ibn), producer_infer_hint4ibn->parallel_desc(), *JUST(GetParallelDesc4BnInOp(ibn)), - requires_same_sbp[ibn_id]); + requires_same_sbp[ibn_id], producer_infer_hint4ibn->logical_blob_desc().shape()); sum_priority_ratio += priority_ratio; // We do not accept any blob which has a priority ratio greater than 1 if (priority_ratio > 1.5) { @@ -770,13 +817,13 @@ Maybe Operator::GreedilyFindMinCopyCostNdSbp( std::ostringstream err; err << "op: `" << op_name() << "` can't find available sbp signature." << std::endl; err << "candidate nd sbp signature are: " - << *JUST(NdSbpSignatureListAsString(nd_sbp_sig_list, input_bns(), output_bns())); + << *JUST(NdSbpSignatureListToString(nd_sbp_sig_list, input_bns(), output_bns())); err << ", but inputs sbp are:"; for (int32_t ibn_id = 0; ibn_id < input_bns().size(); ibn_id++) { const auto& ibn = input_bns().at(ibn_id); const NdSbp& nd_sbp = JUST(NdSbpInferHint4Ibn(ibn))->nd_sbp(); err << " " << ibn << ": " << NdSbpToString(nd_sbp); - if (!requires_same_sbp[ibn_id]) { err << " [ transfer disabled ]"; } + if (requires_same_sbp[ibn_id]) { err << " [ transfer disabled ]"; } err << ";"; } @@ -800,7 +847,9 @@ Maybe Operator::InferSbpSignature( SbpSignatureList valid_sbp_sig_list; { SbpSignatureList sbp_sig_candidates; - JUST(GetSbpSignaturesIf(LogicalBlobDesc4Ibn, parallel_desc, &sbp_sig_candidates)); + // For 1d sbp, hierarchy value = parallel num + JUST( + GetSbpSignaturesIf(LogicalBlobDesc4Ibn, parallel_desc.parallel_num(), &sbp_sig_candidates)); // filter sbp signatures by logical shape JUST(FilterAndCheckValidSbpSignatureListByLogicalShape(sbp_sig_candidates, LogicalBlobDesc4Ibn, parallel_desc, &valid_sbp_sig_list)); @@ -864,7 +913,8 @@ Maybe Operator::InferNdSbpSignature( return JUST(NdSbpInferHint4Ibn(ibn))->logical_blob_desc(); }; std::vector nd_sbp_sig_list; - JUST(GetValidNdSbpSignatureList(LogicalBlobDesc4Ibn, parallel_desc, &nd_sbp_sig_list)); + JUST(GetValidNdSbpSignatureList(LogicalBlobDesc4Ibn, parallel_desc, &nd_sbp_sig_list, + /*check_output=*/false)); // Filter nd_sbp according to `nd_sbp_constraints` for (int32_t i = nd_sbp_sig_list.size() - 1; i >= 0; --i) { // If any blob do not match nd_sbp_constraints, the candidate nd_sbp will be deleted. @@ -886,57 +936,98 @@ Maybe Operator::InferNdSbpSignature( } } -Maybe Operator::InferMirroredSignatureIf( - std::function(const std::string&)> MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc) { - return InferMirroredSignature(MirroredSigInferHint4Ibn, is_mirrored_parallel_view_conf, - parallel_desc); +Maybe Operator::InferLocalSignatureIf( + std::function(const std::string&)> LocalSigInferHint4Ibn, + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc) { + return InferLocalSignature(std::move(LocalSigInferHint4Ibn), is_local_parallel_view_conf, + parallel_desc); +} + +// Compute time complexity for given blob description and sbp signature. +// Use function to replace the HashMap from logical blob id to blob description pointer. +Maybe Operator::GetComputeComplexity( + NdSbpSignature* sbp_signature, + std::function logical_blob_desc4bn, + const ParallelDesc& parallel_desc) const { + const auto& sbp_bn_in_op2nd_sbp = sbp_signature->bn_in_op2nd_sbp(); + double complexity = 0; + const auto& parallel_hierarchy = *parallel_desc.hierarchy(); + + auto ComputeComplexity4Blobs = [&](const PbRpf& bns) -> Maybe { + for (const auto& bn : bns) { + const BlobDesc& logical_blob_desc = logical_blob_desc4bn(bn); + const NdSbp& nd_sbp = sbp_bn_in_op2nd_sbp.at(bn); + CHECK_EQ_OR_RETURN(nd_sbp.sbp_parallel_size(), parallel_hierarchy.NumAxes()) + << "At this moment, the dimension of nd SBP should be equal to the depth of hierarchy in " + << "parallel description."; + + double total_cost = logical_blob_desc.shape().elem_cnt(); + for (int32_t sbp_dim = 0; sbp_dim < nd_sbp.sbp_parallel_size(); sbp_dim++) { + const auto& sbp = nd_sbp.sbp_parallel(sbp_dim); + if (sbp.has_split_parallel()) { + const int64_t axis = sbp.split_parallel().axis(); + if (axis >= logical_blob_desc.shape().NumAxes() + || logical_blob_desc.shape().At(axis) < parallel_hierarchy.At(sbp_dim)) { + complexity = GetMaxVal(); + return Maybe::Ok(); + } else { + total_cost /= parallel_hierarchy.At(sbp_dim); + } + } + } + complexity += total_cost; + } + return Maybe::Ok(); + }; + JUST(ComputeComplexity4Blobs(input_bns())); + JUST(ComputeComplexity4Blobs(output_bns())); + return complexity; } -std::string DebugString4MirroredHint( - std::function(const std::string&)> MirroredSigInferHint4Ibn, +std::string DebugString4LocalHint( + std::function(const std::string&)> LocalSigInferHint4Ibn, const Operator& op) { std::string ret; for (const auto& ibn : op.input_bns()) { - const auto& infer_hint = *CHECK_JUST(MirroredSigInferHint4Ibn(ibn)); - bool is_mirrored = infer_hint.is_mirrored_parallel_view(); - ret += "arg: " + ibn + ", is_mirrored: " + (is_mirrored ? "true" : "false") + "\n"; + const auto& infer_hint = *CHECK_JUST(LocalSigInferHint4Ibn(ibn)); + bool is_local = infer_hint.is_local_parallel_view(); + ret += "arg: " + ibn + ", is_local: " + (is_local ? "true" : "false") + "\n"; } return ret; } -Maybe Operator::InferMirroredSignature( - std::function(const std::string&)> MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc) { - HashSet is_mirrored_parallel_view_values; +Maybe Operator::InferLocalSignature( + std::function(const std::string&)> + LocalSigInferHint4Ibn, // NOLINT + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc) { + HashSet is_local_parallel_view_values; for (const auto& ibn : input_bns()) { - const auto& infer_hint = *JUST(MirroredSigInferHint4Ibn(ibn)); - is_mirrored_parallel_view_values.insert(infer_hint.is_mirrored_parallel_view()); + const auto& infer_hint = *JUST(LocalSigInferHint4Ibn(ibn)); + is_local_parallel_view_values.insert(infer_hint.is_local_parallel_view()); } - CHECK_LE_OR_RETURN(is_mirrored_parallel_view_values.size(), 1) + CHECK_LE_OR_RETURN(is_local_parallel_view_values.size(), 1) << "mixed parallel_views are disallowed." << "\n=========== is_mirrrored_conf ===========\n" - << DebugString4MirroredHint(MirroredSigInferHint4Ibn, *this) - << "\n=========== op_cnf ===========\n" + << DebugString4LocalHint(LocalSigInferHint4Ibn, *this) << "\n=========== op_cnf ===========\n" << op_conf().DebugString(); - if (is_mirrored_parallel_view_values.size() == 1) { - is_mirrored_parallel_view_conf = *is_mirrored_parallel_view_values.begin(); + if (is_local_parallel_view_values.size() == 1) { + is_local_parallel_view_conf = *is_local_parallel_view_values.begin(); } - if (is_mirrored_parallel_view_conf) { + if (is_local_parallel_view_conf) { for (const auto& ibn : input_bns()) { - const auto& infer_hint = *JUST(MirroredSigInferHint4Ibn(ibn)); + const auto& infer_hint = *JUST(LocalSigInferHint4Ibn(ibn)); CHECK_EQ_OR_RETURN(infer_hint.parallel_desc().parallel_num(), parallel_desc.parallel_num()); } } - const auto SetIsMirroredParallel = [&](const std::string& bn_in_op) { - if (is_mirrored_parallel_view_conf) { - MutOptMirroredParallel(bn_in_op)->mutable_mirrored_parallel(); + const auto SetIsLocalParallel = [&](const std::string& bn_in_op) { + if (is_local_parallel_view_conf) { + MutOptLocalParallel(bn_in_op)->mutable_local_parallel(); } else { - MutOptMirroredParallel(bn_in_op)->clear_mirrored_parallel(); + MutOptLocalParallel(bn_in_op)->clear_local_parallel(); } }; - for (const auto& ibn : input_bns()) { SetIsMirroredParallel(ibn); } - for (const auto& obn : output_bns()) { SetIsMirroredParallel(obn); } + for (const auto& ibn : input_bns()) { SetIsLocalParallel(ibn); } + for (const auto& obn : output_bns()) { SetIsLocalParallel(obn); } return Maybe::Ok(); } @@ -974,24 +1065,23 @@ Maybe Operator::NdSbp4BnInOp(const std::string& bn_in_op) const { CHECK_OR_RETURN(nd_sbp_signature_) << "parallel distribution signature not infered"; const auto& map = nd_sbp_signature_->bn_in_op2nd_sbp(); const auto& iter = map.find(bn_in_op); - CHECK_OR_RETURN(iter != map.end()) - << "blob_name " << bn_in_op << " not found in parallel distribution"; + CHECK_OR_RETURN(iter != map.end()) << "op_name " << op_name() << " blob_name " << bn_in_op + << " not found in parallel distribution"; return &iter->second; } -Maybe Operator::OptMirroredParallel4BnInOp( +Maybe Operator::OptLocalParallel4BnInOp( const std::string& bn_in_op) const { - CHECK_OR_RETURN(mirrored_signature_) << "mirrored signature not infered"; - const auto& map = mirrored_signature_->bn_in_op2opt_mirrored_parallel(); + CHECK_OR_RETURN(local_signature_) << "local signature not infered"; + const auto& map = local_signature_->bn_in_op2opt_local_parallel(); const auto& iter = map.find(bn_in_op); - CHECK_OR_RETURN(iter != map.end()) - << "blob_name " << bn_in_op << " not found in mirrored signature"; + CHECK_OR_RETURN(iter != map.end()) << "blob_name " << bn_in_op << " not found in local signature"; return &iter->second; } -OptMirroredParallel* Operator::MutOptMirroredParallel(const std::string& bn_in_op) { - if (!mirrored_signature_) { mirrored_signature_.reset(new MirroredSignature()); } - auto* map = mirrored_signature_->mutable_bn_in_op2opt_mirrored_parallel(); +OptLocalParallel* Operator::MutOptLocalParallel(const std::string& bn_in_op) { + if (!local_signature_) { local_signature_.reset(new LocalSignature()); } + auto* map = local_signature_->mutable_bn_in_op2opt_local_parallel(); return &(*map)[bn_in_op]; } @@ -1282,10 +1372,10 @@ Maybe Operator::ToOpAttribute(OpAttribute* op_attribute) const { } else { op_attribute->clear_nd_sbp_signature(); } - if (mirrored_signature_) { - *op_attribute->mutable_mirrored_signature() = *mirrored_signature_; + if (local_signature_) { + *op_attribute->mutable_local_signature() = *local_signature_; } else { - op_attribute->clear_mirrored_signature(); + op_attribute->clear_local_signature(); } if (input_index2logical_blob_desc_) { JUST(FillLogicalBlobDescSignature( @@ -1322,7 +1412,7 @@ Maybe Operator::ToOpAttribute(OpAttribute* op_attribute) const { } else { ParallelConf parallel_conf = pair.second->parallel_conf(); const auto MakeParallelDescSymbol = [¶llel_conf]() -> Maybe { - int64_t symbol_id; + int64_t symbol_id = 0; const auto BuildInstruction = [&symbol_id, ¶llel_conf](InstructionsBuilder* builder) -> Maybe { symbol_id = JUST(JUST(builder->GetParallelDescSymbol(parallel_conf))->symbol_id()); @@ -1455,23 +1545,22 @@ Maybe InferOpOutSbpParallel( return Maybe::Ok(); } -Maybe InferMirroredSignature(Operator* op, const OpNodeSignature& upstream_signature, - bool is_mirrored, const ParallelDesc& parallel_desc) { - HashMap ibn2mirrored_sig_infer_hint; +Maybe InferLocalSignature(Operator* op, const OpNodeSignature& upstream_signature, + bool is_local, const ParallelDesc& parallel_desc) { + HashMap ibn2local_sig_infer_hint; for (const std::string& ibn : op->input_bns()) { - const auto& map = upstream_signature.mirrored_signature().bn_in_op2opt_mirrored_parallel(); - const auto& opt_mirrored_parallel = map.at(ibn); - ibn2mirrored_sig_infer_hint.emplace( - ibn, MirroredSigInferHint(¶llel_desc, opt_mirrored_parallel.has_mirrored_parallel())); + const auto& map = upstream_signature.local_signature().bn_in_op2opt_local_parallel(); + const auto& opt_local_parallel = map.at(ibn); + ibn2local_sig_infer_hint.emplace( + ibn, LocalSigInferHint(¶llel_desc, opt_local_parallel.has_local_parallel())); } - const auto& MirroredSigInferHint4Ibn = - [&](const std::string& ibn) -> Maybe { - const auto& iter = ibn2mirrored_sig_infer_hint.find(ibn); - CHECK_OR_RETURN(iter != ibn2mirrored_sig_infer_hint.end()) - << "input blob not found. ibn: " << ibn; + const auto& LocalSigInferHint4Ibn = + [&](const std::string& ibn) -> Maybe { + const auto& iter = ibn2local_sig_infer_hint.find(ibn); + CHECK_OR_RETURN(iter != ibn2local_sig_infer_hint.end()) << "input blob not found. ibn: " << ibn; return &iter->second; }; - JUST(op->InferMirroredSignatureIf(MirroredSigInferHint4Ibn, is_mirrored, parallel_desc)); + JUST(op->InferLocalSignatureIf(LocalSigInferHint4Ibn, is_local, parallel_desc)); return Maybe::Ok(); } @@ -1485,11 +1574,11 @@ Maybe CheckOpInputSignature(const Operator& op, const OpNodeSignature& ups { CHECK_OR_RETURN(upstream_signature.has_sbp_signature()); const auto& map = upstream_signature.sbp_signature().bn_in_op2sbp_parallel(); - CHECK_OR_RETURN(map.find(ibn) != map.end()); + CHECK_OR_RETURN(map.find(ibn) != map.end()); // NOLINT } { - CHECK_OR_RETURN(upstream_signature.has_mirrored_signature()); - const auto& map = upstream_signature.mirrored_signature().bn_in_op2opt_mirrored_parallel(); + CHECK_OR_RETURN(upstream_signature.has_local_signature()); // NOLINT + const auto& map = upstream_signature.local_signature().bn_in_op2opt_local_parallel(); CHECK_OR_RETURN(map.find(ibn) != map.end()); } } @@ -1501,7 +1590,7 @@ Maybe CheckOpInputSignature(const Operator& op, const OpNodeSignature& ups Maybe ConstructAndInferOp(const OperatorConf& op_conf, const OpNodeSignature& upstream_signature, const Scope& scope) { const auto& parallel_desc = *JUST(scope.GetParallelDesc(op_conf)); - bool is_mirrored = scope.opt_mirrored_parallel_conf().has_mirrored_parallel(); + bool is_local = scope.opt_local_parallel_conf().has_local_parallel(); const auto& op = JUST(ConstructOp(op_conf)); JUST(CheckOpInputSignature(*op, upstream_signature)); JUST(op->FillOpParallelDesc(parallel_desc)); @@ -1514,8 +1603,8 @@ Maybe ConstructAndInferOp(const OperatorConf& op_conf, return *bn_in_op2blob_desc.at(ibn); }; JUST(op->FillLogicalInBlobDesc(ConstBlobDesc4Ibn)); - // infer is_mirrored - JUST(InferMirroredSignature(op.get(), upstream_signature, is_mirrored, parallel_desc)); + // infer is_local + JUST(InferLocalSignature(op.get(), upstream_signature, is_local, parallel_desc)); SbpSignature sbp_sig_conf; // iner sbp JUST(InferOpOutSbpParallel(op.get(), upstream_signature, ConstBlobDesc4Ibn, sbp_sig_conf, @@ -1558,26 +1647,17 @@ Maybe GetNdHierarchyPhysicalShape(const Shape& logical_shape, const NdSbp const auto& sbp_parallel = nd_sbp.sbp_parallel(i); if (sbp_parallel.has_split_parallel()) { const int64_t split_axis = sbp_parallel.split_parallel().axis(); - if (LazyMode::is_enabled()) { - CHECK_EQ_OR_RETURN(physical->At(split_axis) % parallel_hierarchy.At(i), 0) - << Error::RuntimeError() << "In nn.Graph, expected size at split axis (" << split_axis - << ") of logical shape must be divisible by parallel num, but got logical_shape: " + // Both the lazy and eager mode support unbalanced splitting now + if (physical->At(split_axis) > 0) { + CHECK_GE_OR_RETURN(physical->At(split_axis), parallel_hierarchy.At(i)) + << Error::RuntimeError() << "Expected size at split axis (" << split_axis + << ") of logical shape must be be greater than or equal to parallel num, but got " + "logical_shape: " << logical_shape.ToString() << ", placement: " << *JUST(PlacementToString(SymbolOf(parallel_desc))) << ", nd_sbp: " << NdSbpToString(SymbolOf(nd_sbp)); - physical->Set(split_axis, physical->At(split_axis) / parallel_hierarchy.At(i)); - } else { - if (physical->At(split_axis) > 0) { - CHECK_GE_OR_RETURN(physical->At(split_axis), parallel_hierarchy.At(i)) - << Error::RuntimeError() << "Expected size at split axis (" << split_axis - << ") of logical shape must be be greater than or equal to parallel num, but got " - "logical_shape: " - << logical_shape.ToString() - << ", placement: " << *JUST(PlacementToString(SymbolOf(parallel_desc))) - << ", nd_sbp: " << NdSbpToString(SymbolOf(nd_sbp)); - const BalancedSplitter bs(physical->At(split_axis), parallel_hierarchy.At(i)); - physical->Set(split_axis, bs.At(CalcIndex4Axis(parallel_id, hierarch_stride, i)).size()); - } + const BalancedSplitter bs(physical->At(split_axis), parallel_hierarchy.At(i)); + physical->Set(split_axis, bs.At(CalcIndex4Axis(parallel_id, hierarch_stride, i)).size()); } } } diff --git a/oneflow/core/operator/operator.h b/oneflow/core/operator/operator.h index eb07fc32e63c48abff35abe7d0ea5ac9388db27b..64c82a9baf16557e61dfd4e9c345cdaa703522a9 100644 --- a/oneflow/core/operator/operator.h +++ b/oneflow/core/operator/operator.h @@ -24,7 +24,7 @@ limitations under the License. #include "oneflow/core/common/symbol.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/job/sbp_parallel.h" -#include "oneflow/core/job/mirrored_parallel.pb.h" +#include "oneflow/core/job/local_parallel.pb.h" #include "oneflow/core/operator/op_conf_util.h" #include "oneflow/core/register/blob_desc.h" #include "oneflow/core/job/job_builder.h" @@ -34,7 +34,7 @@ limitations under the License. namespace oneflow { -class MirroredSigInferHint; +class LocalSigInferHint; class OpNodeSignature; class Scope; @@ -146,33 +146,45 @@ class Operator { Maybe InferSbpSignatureIf( const SbpSignature& sbp_sig_conf, const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, + const std::function(const std::string&)>& SbpInferHint4Ibn, const ParallelDesc& parallel_desc); Maybe InferNdSbpSignatureIf( const NdSbpSignature& nd_sbp_constraints, const ParallelDesc& parallel_desc, std::function(const std::string&)> NdSbpInferHint4Ibn); - // Infer blob's MirroredSignature - Maybe InferMirroredSignatureIf( - std::function(const std::string&)> - MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc); + + // The function that how to dump nd_sbp for op_conf + using DumpNdSbpSignatureForOpConfFn = + std::function(const NdSbpSignature& nd_sbp_sig, OperatorConf* op_conf)>; + virtual DumpNdSbpSignatureForOpConfFn GetDumpNdSbpSignatureForOpConfFn() const; + + // Infer blob's LocalSignature + Maybe InferLocalSignatureIf( + std::function(const std::string&)> LocalSigInferHint4Ibn, + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc); + void GenKernelConf(const std::function& GetBlobDesc4BnInOp, const ParallelContext*, KernelConf*) const; const InputBlobModifier& InputBlobModifier4Ibn(const std::string& ibn) const; const OutputBlobModifier& OutputBlobModifier4Obn(const std::string& obn) const; Maybe SbpParallel4BnInOp(const std::string& bn_in_op) const; Maybe NdSbp4BnInOp(const std::string& bn_in_op) const; - Maybe OptMirroredParallel4BnInOp(const std::string& bn_in_op) const; + Maybe OptLocalParallel4BnInOp(const std::string& bn_in_op) const; Maybe GetSbpSignaturesIf( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const; + int32_t hierarchy_value, SbpSignatureList* sbp_sig_list) const; virtual Maybe GetNdSbpSignatureList( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const; + virtual Maybe GetComputeComplexity( + NdSbpSignature* sbp_signature, + std::function logical_blob_desc4bn, + const ParallelDesc& parallel_desc) const; + // TODO: Will infer blob shape before inferring sbp and delete the check_output later Maybe GetValidNdSbpSignatureList( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const; + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list, + bool check_output) const; void ForEachBnInOp(const std::function&) const; @@ -202,7 +214,7 @@ class Operator { const ParallelContext* parallel_ctx, const JobDesc* job_desc) const; virtual Maybe GetSbpSignatures( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const { + int32_t hierarchy_value, SbpSignatureList* sbp_sig_list) const { return GetSbpSignatures(LogicalBlobDesc4Ibn, sbp_sig_list); } virtual Maybe GetSbpSignatures( @@ -222,10 +234,9 @@ class Operator { virtual Maybe GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { OF_UNIMPLEMENTED() << " GetSbpSignatures unimplemented, op name: " << op_name(); } - virtual Maybe InferMirroredSignature( - std::function(const std::string&)> - MirroredSigInferHint4Ibn, - bool is_mirrored_parallel_view_conf, const ParallelDesc& parallel_desc); + virtual Maybe InferLocalSignature( + std::function(const std::string&)> LocalSigInferHint4Ibn, + bool is_local_parallel_view_conf, const ParallelDesc& parallel_desc); virtual Maybe InferInplaceObn2Ibn( HashMap* mut_inplace_obn2ibn, @@ -274,7 +285,7 @@ class Operator { InputBlobModifier* MutInputBlobModifier4Ibn(const std::string& ibn); OutputBlobModifier* MutOutputBlobModifier4Obn(const std::string& obn); - OptMirroredParallel* MutOptMirroredParallel(const std::string& bn_in_op); + OptLocalParallel* MutOptLocalParallel(const std::string& bn_in_op); private: enum BlobNameTag { @@ -288,7 +299,8 @@ class Operator { // TODO(wyg): 1d and nd sbp use this function to filter and check Maybe FilterNdSbpSignatureListByLogicalShape( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const; + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list, + bool check_output) const; Maybe GreedilyFindMinCopyCostNdSbp( NdSbpSignature* nd_sbp_signature, const std::function(const std::string&)>& NdSbpInferHint4Ibn, @@ -320,7 +332,7 @@ class Operator { ArgModifierSignature arg_modifier_signature_; std::unique_ptr blob_last_used_signature_; std::unique_ptr blob_backward_used_signature_; - std::unique_ptr mirrored_signature_; + std::unique_ptr local_signature_; HashMap> bn2index_pair_; HashMap lbi2output_index_; @@ -374,12 +386,12 @@ struct RuntimeRegstNum4OpSameOutputBlob final { ([] { return new RuntimeRegstNum4OpSameOutputBlob(num); })) #define REGISTER_USER_OP_SAME_OUTPUT_BLOB_REGST_NUM(op_type_name, num) \ -REGISTER_CLASS_CREATOR(std::string, op_type_name, RuntimeRegstNum4OpSameOutputBlob, \ + REGISTER_CLASS_CREATOR(std::string, op_type_name, RuntimeRegstNum4OpSameOutputBlob, \ ([] { return new RuntimeRegstNum4OpSameOutputBlob(num); })) #define REGISTER_USER_OP_SAME_OUTPUT_BLOB_REGST_NUM_WITH_FUNC(op_type_name, func) \ REGISTER_CLASS_CREATOR(std::string, op_type_name, RuntimeRegstNum4OpSameOutputBlob, \ - ([] { return new RuntimeRegstNum4OpSameOutputBlob(func); })); + ([] { return new RuntimeRegstNum4OpSameOutputBlob(func); })); struct IsInterfaceOpConf4OpTypeCase final {}; diff --git a/oneflow/core/operator/output_op.cpp b/oneflow/core/operator/output_op.cpp index f46ec4858d130f80df7937b6a24b18858b5489a1..588600dbfc8a2b6ad20350d27649aaeb99d0c948 100644 --- a/oneflow/core/operator/output_op.cpp +++ b/oneflow/core/operator/output_op.cpp @@ -20,6 +20,19 @@ limitations under the License. namespace oneflow { +namespace { +Maybe InferOutputOpNdSbpSignature(NdSbpSignature* nd_sbp_signature, + const ParallelDesc& parallel_desc, + const OperatorConf& op_conf) { + const InterfaceBlobConf& blob_conf = op_conf.output_conf().blob_conf(); + NdSbp& in_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["in"]; + NdSbp& out_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["out"]; + JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &in_nd_sbp)); + JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &out_nd_sbp)); + return Maybe::Ok(); +} +} // anonymous namespace + Maybe OutputOp::InitFromOpConf() { CHECK(op_conf().has_output_conf()); EnrollInputBn("in"); @@ -47,6 +60,37 @@ Maybe OutputOp::InferOutBlobDescs( return Maybe::Ok(); } +Maybe OutputOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { + SbpSignature* sbp = sbp_sig_list->mutable_sbp_signature()->Add(); + CHECK_EQ_OR_RETURN(JUST(GetOpParallelDesc())->hierarchy()->NumAxes(), 1) + << "Only support 1d sbp now."; + // Get sbp from BlobConf + const InterfaceBlobConf& blob_conf = op_conf().output_conf().blob_conf(); + // TODO: make sure blob_conf must set nd_sbp + CHECK_OR_RETURN(blob_conf.has_nd_sbp()); + const SbpParallel& sbp_parallel = SbpParallel(blob_conf.nd_sbp().sbp_parallel(0)); + if (sbp_parallel.has_broadcast_parallel()) { + SbpSignatureBuilder().Broadcast("in").Broadcast("out").Build(sbp); + } else if (sbp_parallel.has_partial_sum_parallel()) { + SbpSignatureBuilder().PartialSum("in").PartialSum("out").Build(sbp); + } else if (sbp_parallel.has_split_parallel()) { + int64_t split_axis = sbp_parallel.split_parallel().axis(); + SbpSignatureBuilder().Split("in", split_axis).Split("out", split_axis).Build(sbp); + } else { + UNIMPLEMENTED_THEN_RETURN(); + } + return Maybe::Ok(); +} + +Maybe OutputOp::GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { + NdSbpSignature nd_sbp_signature; + JUST(InferOutputOpNdSbpSignature(&nd_sbp_signature, parallel_desc, op_conf())); + nd_sbp_sig_list->emplace_back(nd_sbp_signature); + return Maybe::Ok(); +} + Maybe OutputOp::InferSbpSignature( SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, const std::function& CalcOrderValue4SbpSig, @@ -61,12 +105,7 @@ Maybe OutputOp::InferNdSbpSignature( NdSbpSignature* nd_sbp_signature, const NdSbpSignature& nd_sbp_constraints, const ParallelDesc& parallel_desc, std::function(const std::string&)> NdSbpInferHint4Ibn) const { - const InterfaceBlobConf& blob_conf = op_conf().output_conf().blob_conf(); - NdSbp& in_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["in"]; - NdSbp& out_nd_sbp = (*nd_sbp_signature->mutable_bn_in_op2nd_sbp())["out"]; - JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &in_nd_sbp)); - JUST(InterfaceOpUtil::ParseNdSbpFromBlobConf(blob_conf, parallel_desc, &out_nd_sbp)); - + JUST(InferOutputOpNdSbpSignature(nd_sbp_signature, parallel_desc, op_conf())); return Maybe::Ok(); } diff --git a/oneflow/core/operator/output_op.h b/oneflow/core/operator/output_op.h index 30ce7d84bad63626ff40bbf9f6a6b378a424553f..af8b006ebb63026e6b3734b0d8201cf080436026 100644 --- a/oneflow/core/operator/output_op.h +++ b/oneflow/core/operator/output_op.h @@ -41,6 +41,11 @@ class OutputOp final : public Operator { std::function(const std::string&)> SbpInferHint4Ibn, const ParallelDesc& parallel_desc) const override; Symbol GetOpConfWithoutOpNameAndLbn() const override; + Maybe GetSbpSignatures(SbpSignatureList* sbp_sig_list) const override; + Maybe GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, + std::vector* nd_sbp_sig_list) const override; Maybe InferNdSbpSignature(NdSbpSignature* nd_sbp_signature, const NdSbpSignature& nd_sbp_constraints, const ParallelDesc& parallel_desc, diff --git a/oneflow/core/operator/reentrant_lock_op.cpp b/oneflow/core/operator/reentrant_lock_op.cpp index f739ef1c5528ae0d291fd9a8154eac4a28a90d3b..280060ddc068a503008a6d477feab34be9187deb 100644 --- a/oneflow/core/operator/reentrant_lock_op.cpp +++ b/oneflow/core/operator/reentrant_lock_op.cpp @@ -32,7 +32,7 @@ Maybe InferBlobDescs(const std::function& B const DataType data_type = start->data_type(); CHECK_OR_RETURN(IsIntegralDataType(data_type)); BlobDesc* out = BlobDesc4BnInOp("out"); - out->mut_shape() = Shape({1}); + out->set_shape(Shape({1})); out->set_data_type(data_type); return Maybe::Ok(); } diff --git a/oneflow/core/operator/shape_elem_cnt_op.cpp b/oneflow/core/operator/shape_elem_cnt_op.cpp index 6d6d5d349b8f318377ac9d3bc04c39f4c5c7c1b4..74b8f8abd4a404c138ca074e1e01893c3ff8d4d6 100644 --- a/oneflow/core/operator/shape_elem_cnt_op.cpp +++ b/oneflow/core/operator/shape_elem_cnt_op.cpp @@ -60,7 +60,7 @@ namespace { Maybe InferBlobDescs(const OperatorConf& op_conf, const std::function& BlobDesc4BnInOp) { BlobDesc4BnInOp("y")->set_data_type(op_conf.shape_elem_cnt_conf().data_type()); - BlobDesc4BnInOp("y")->mut_shape() = Shape({}); + BlobDesc4BnInOp("y")->set_shape(Shape({})); return Maybe::Ok(); } diff --git a/oneflow/core/operator/sink_tick_op.cpp b/oneflow/core/operator/sink_tick_op.cpp index f2dee5018b6ed9552c37c6bd4203551a3d210608..785f7a141a544e8320120da1238af975a94bfee1 100644 --- a/oneflow/core/operator/sink_tick_op.cpp +++ b/oneflow/core/operator/sink_tick_op.cpp @@ -29,7 +29,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } @@ -49,7 +49,10 @@ Maybe SinkTickOp::InferOutBlobDescs( } Maybe SinkTickOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { - SbpSignatureBuilder().Broadcast(input_bns()).Build(sbp_sig_list->mutable_sbp_signature()->Add()); + SbpSignatureBuilder() + .Broadcast(input_bns()) + .Broadcast(output_bns()) + .Build(sbp_sig_list->mutable_sbp_signature()->Add()); return Maybe::Ok(); } diff --git a/oneflow/core/operator/slice_boxing_op.cpp b/oneflow/core/operator/slice_boxing_op.cpp index c2fc771fa452dea097cc9bfa6760e051752b3bd3..0bc097438b09736ba5b115c12f927590519d8e72 100644 --- a/oneflow/core/operator/slice_boxing_op.cpp +++ b/oneflow/core/operator/slice_boxing_op.cpp @@ -112,9 +112,9 @@ Maybe SliceBoxingOp::InferOutBlobDescs( if (slice_boxing_conf.has_out_shape()) { const Shape out_shape(slice_boxing_conf.out_shape()); CHECK_EQ(out_shape.elem_cnt(), out_slice.shape().elem_cnt()); - out->mut_shape() = out_shape; + out->set_shape(out_shape); } else { - out->mut_shape() = out_slice.shape(); + out->set_shape(out_slice.shape()); } return Maybe::Ok(); } diff --git a/oneflow/core/operator/source_tick_op.cpp b/oneflow/core/operator/source_tick_op.cpp index 4a1dd7b027160a993e4b5a8e3962f6a1ff2b11a4..a02d3295981c065309fe5b8b61f3e9110557d8bf 100644 --- a/oneflow/core/operator/source_tick_op.cpp +++ b/oneflow/core/operator/source_tick_op.cpp @@ -29,7 +29,7 @@ Maybe SourceTickOp::InferLogicalOutBlobDescs( const std::function& BlobDesc4BnInOp, const ParallelDesc& parallel_desc) const { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } @@ -39,7 +39,7 @@ Maybe SourceTickOp::InferOutBlobDescs( const ParallelContext* parallel_ctx) const { CHECK_EQ_OR_RETURN(parallel_ctx->parallel_num(), 1); BlobDesc* blob_desc = GetBlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/src_subset_tick_op.cpp b/oneflow/core/operator/src_subset_tick_op.cpp index a5bb5d06e01894774af2a9ba03ae7f90e71c4db6..5f44fe48a86640743b3ec2acc6565a4cda646269 100644 --- a/oneflow/core/operator/src_subset_tick_op.cpp +++ b/oneflow/core/operator/src_subset_tick_op.cpp @@ -48,7 +48,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/tick_op.cpp b/oneflow/core/operator/tick_op.cpp index 82686c76fbe40f5c7961e04f653f41dfe069b5c9..3ea960a54405050ed0852d55201be751482069b7 100644 --- a/oneflow/core/operator/tick_op.cpp +++ b/oneflow/core/operator/tick_op.cpp @@ -22,7 +22,7 @@ namespace { Maybe InferBlobDescs(const std::function& BlobDesc4BnInOp) { BlobDesc* blob_desc = BlobDesc4BnInOp("out"); - blob_desc->mut_shape() = Shape({1}); + blob_desc->set_shape(Shape({1})); blob_desc->set_data_type(DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/core/operator/user_op.cpp b/oneflow/core/operator/user_op.cpp index e7e9d8c2d2f7968b6f5df2dc14bd080347cf8cfe..3845da8f566a9e0bcfba74c3dc6339ae7f3f3263 100644 --- a/oneflow/core/operator/user_op.cpp +++ b/oneflow/core/operator/user_op.cpp @@ -15,11 +15,13 @@ limitations under the License. */ #include "oneflow/core/framework/infer_util.h" #include "oneflow/core/framework/sbp_context.h" -#include "oneflow/core/framework/tensor_desc.h" +#include "oneflow/core/common/tensor_desc.h" #include "oneflow/core/framework/to_string.h" #include "oneflow/core/operator/user_op.h" #include "oneflow/core/framework/infer_output_blob_time_shape_fn_context.h" #include "oneflow/core/framework/infer_nd_sbp_fn_context.h" +#include "oneflow/core/framework/compute_complexity_fn_context.h" +#include "oneflow/core/framework/get_nd_sbp_signature_list_context.h" namespace oneflow { @@ -44,15 +46,16 @@ BlobDesc* FindValidBlobDescOfBnsInOp( user_op::NaiveTensorDesc GenTensorDescFromBlobDesc(const BlobDesc* blob_desc) { user_op::NaiveTensorDesc tensor_desc; - *tensor_desc.mut_shape() = blob_desc->shape(); - *tensor_desc.mut_stride() = blob_desc->stride(); - *tensor_desc.mut_data_type() = blob_desc->data_type(); - *tensor_desc.mut_is_dynamic() = blob_desc->is_dynamic(); + tensor_desc.set_shape(blob_desc->shape()); + tensor_desc.set_stride(blob_desc->stride()); + tensor_desc.set_data_type(blob_desc->data_type()); + tensor_desc.set_is_dynamic(blob_desc->is_dynamic()); return tensor_desc; } } // namespace +// kernel registry context used in infer functions of user op class UserOpKernelRegContext final : public user_op::KernelRegContext { public: using ArgVec = std::vector>; @@ -147,12 +150,22 @@ class UserOpInferContext final : public user_op::InferContext { const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->TensorDesc4ArgNameAndIndex(arg_name, index); + return *TensorDesc4ArgNameAndIndex(arg_name, index); } - user_op::TensorDesc* OutputTensorDesc(const std::string& arg_name, int32_t index) override { - return TensorDesc4ArgNameAndIndex(arg_name, index); + const user_op::TensorDesc& OutputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return *TensorDesc4ArgNameAndIndex(arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(const std::string& arg_name, int32_t index) override { + return MutTensorDesc4ArgNameAndIndex(arg_name, index); } - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return nullptr; } + return &it->second; + } + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); if (it == arg2tensor_desc_.end()) { return nullptr; }; return &(it->second); @@ -171,48 +184,90 @@ class UserOpInferContext final : public user_op::InferContext { } } const Shape& InputShape(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Shape4ArgNameAndIndex(arg_name, index); + return Shape4ArgNameAndIndex(arg_name, index); } - Shape* OutputShape(const std::string& arg_name, int32_t index) override { + const Shape& OutputShape(const std::string& arg_name, int32_t index) const override { return Shape4ArgNameAndIndex(arg_name, index); } - Shape* Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { + void SetOutputShape(const std::string& arg_name, int32_t index, const Shape& shape) override { + SetShape4ArgNameAndIndex(arg_name, index, shape); + } + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); - if (it == arg2tensor_desc_.end()) { return nullptr; }; - return it->second.mut_shape(); + if (it == arg2tensor_desc_.end()) { + thread_local static Shape non_shape; + return non_shape; + }; + return it->second.shape(); + } + void SetShape4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Shape& shape) override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return; }; + return it->second.set_shape(shape); } const Stride& InputStride(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Stride4ArgNameAndIndex(arg_name, index); + return Stride4ArgNameAndIndex(arg_name, index); } - Stride* OutputStride(const std::string& arg_name, int32_t index) override { + const Stride& OutputStride(const std::string& arg_name, int32_t index) const override { return Stride4ArgNameAndIndex(arg_name, index); } - Stride* Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { + void SetOutputStride(const std::string& arg_name, int32_t index, const Stride& stride) override { + return SetStride4ArgNameAndIndex(arg_name, index, stride); + } + const Stride& Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); - if (it == arg2tensor_desc_.end()) { return nullptr; }; - return it->second.mut_stride(); + if (it == arg2tensor_desc_.end()) { + thread_local static Stride non_stride; + return non_stride; + }; + return it->second.stride(); } - const DataType& InputDType(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->Dtype4ArgNameAndIndex(arg_name, index); + void SetStride4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Stride& stride) override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return; }; + return it->second.set_stride(stride); } - DataType* OutputDType(const std::string& arg_name, int32_t index) override { + DataType InputDType(const std::string& arg_name, int32_t index) const override { return Dtype4ArgNameAndIndex(arg_name, index); } - DataType* Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { + DataType OutputDType(const std::string& arg_name, int32_t index) const override { + return Dtype4ArgNameAndIndex(arg_name, index); + } + void SetOutputDType(const std::string& arg_name, int32_t index, DataType data_type) override { + return SetDtype4ArgNameAndIndex(arg_name, index, data_type); + } + DataType Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); - if (it == arg2tensor_desc_.end()) { return nullptr; }; - return it->second.mut_data_type(); + if (it == arg2tensor_desc_.end()) { return DataType::kInvalidDataType; }; + return it->second.data_type(); + } + void SetDtype4ArgNameAndIndex(const std::string& arg_name, int32_t index, + DataType data_type) override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return; }; + return it->second.set_data_type(data_type); } bool InputIsDynamic(const std::string& arg_name, int32_t index) const override { - return *const_cast(this)->IsDynamic4ArgNameAndIndex(arg_name, index); + return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* OutputIsDynamic(const std::string& arg_name, int32_t index) override { + bool OutputIsDynamic(const std::string& arg_name, int32_t index) const override { return IsDynamic4ArgNameAndIndex(arg_name, index); } - bool* IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { + void SetOutputIsDynamic(const std::string& arg_name, int32_t index, bool is_dynamic) override { + return SetIsDynamic4ArgNameAndIndex(arg_name, index, is_dynamic); + } + bool IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); - if (it == arg2tensor_desc_.end()) { return nullptr; }; - return it->second.mut_is_dynamic(); + if (it == arg2tensor_desc_.end()) { return false; }; + return it->second.is_dynamic(); + } + void SetIsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index, + bool is_dynamic) override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return; }; + return it->second.set_is_dynamic(is_dynamic); } const ArgVec& inputs() const override { return op_->inputs(); } @@ -290,8 +345,9 @@ class UserOpSbpContext : public user_op::SbpContext { using ArgVec = std::vector>; UserOpSbpContext(const UserOp* op, SbpSignatureList* sbp_sig_list, - std::function(const std::string&)> LogicalBlobDesc4Ibn) - : op_(op), sbp_sig_list_(sbp_sig_list) { + std::function(const std::string&)> LogicalBlobDesc4Ibn, + int32_t hierarchy_value) + : op_(op), sbp_sig_list_(sbp_sig_list), hierarchy_value_(hierarchy_value) { const auto& user_op_conf = op->op_conf().user_conf(); for (auto it = user_op_conf.input().begin(); it != user_op_conf.input().end(); ++it) { const std::string& arg_name = it->first; @@ -324,10 +380,13 @@ class UserOpSbpContext : public user_op::SbpContext { return CHECK_JUST(op_->GetOpParallelDesc())->parallel_num(); } + int64_t hierarchy_value() const override { return hierarchy_value_; } + private: const UserOp* op_; SbpSignatureList* sbp_sig_list_; HashMap, user_op::NaiveTensorDesc> arg2tensor_desc_; + int32_t hierarchy_value_; }; class UserOpInferSbpSignatureFnContext : public user_op::InferSbpSignatureFnContext { @@ -487,6 +546,114 @@ class UserOpInferNdSbpFnContext : public user_op::InferNdSbpFnContext { std::function(const std::string&)> nd_sbp_infer_hint4ibn_fn_; }; +// Store information for computing computation cost +// TODO: Maybe this class could simplify +class UserOpComputeComplexityFnContext : public user_op::ComputeComplexityFnContext { + public: + using ArgVec = std::vector>; + + UserOpComputeComplexityFnContext( + const OperatorConf& op_conf, const ParallelDesc& parallel_desc, + const NdSbpSignature* sbp_signature, + std::function logical_blob_desc4bn) + : user_op::ComputeComplexityFnContext(user_op::UserOpConfWrapper(op_conf)), + parallel_desc_(parallel_desc), + sbp_signature_(sbp_signature) { + auto InitInOrOut = [&](const PbMap& arg_map, + ArgVec* arg_vec) { + for (auto it = arg_map.begin(); it != arg_map.end(); ++it) { + const std::string& arg_name = it->first; + for (int32_t i = 0; i < it->second.s_size(); ++i) { + const BlobDesc& blob = logical_blob_desc4bn(GenRepeatedBn(arg_name, i)); + auto key = std::make_pair(arg_name, i); + arg2tensor_desc_.emplace(key, GenTensorDescFromBlobDesc(&blob)); + arg_vec->emplace_back(std::make_pair(arg_name, i)); + } + } + }; + InitInOrOut(op_conf.user_conf().input(), &inputs_); + InitInOrOut(op_conf.user_conf().output(), &outputs_); + } + ~UserOpComputeComplexityFnContext() override = default; + + user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return nullptr; }; + return &(it->second); + } + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { + thread_local static Shape non_shape; + return non_shape; + }; + return it->second.shape(); + } + DataType Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return DataType::kInvalidDataType; }; + return it->second.data_type(); + } + bool IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return false; }; + return it->second.is_dynamic(); + } + + const NdSbp NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + const auto& bn2sbp = sbp_signature_->bn_in_op2nd_sbp(); + std::string bn = GenRepeatedBn(arg_name, index); + CHECK(bn2sbp.find(bn) != bn2sbp.end()); + return sbp_signature_->bn_in_op2nd_sbp().at(bn); + } + + const ArgVec& inputs() const override { return inputs_; } + const ArgVec& outputs() const override { return outputs_; } + const ParallelDesc& parallel_desc() const override { return parallel_desc_; }; + const NdSbpSignature* GetNdSbpSignature() const override { return sbp_signature_; } + + private: + ArgVec inputs_; + ArgVec outputs_; + const ParallelDesc parallel_desc_; + const NdSbpSignature* sbp_signature_; + HashMap, user_op::NaiveTensorDesc> arg2tensor_desc_; +}; + +class UserOpGetNdSbpSignatureListContext : public user_op::GetNdSbpSignatureListContext { + public: + UserOpGetNdSbpSignatureListContext( + const UserOp* op, + std::function(const std::string&)> LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) + : user_op::GetNdSbpSignatureListContext(user_op::UserOpConfWrapper(op->user_op_conf())), + op_(op), + logical_blob_desc4ibn_(std::move(LogicalBlobDesc4Ibn)), + parallel_desc_(parallel_desc), + nd_sbp_sig_list_(nd_sbp_sig_list) {} + ~UserOpGetNdSbpSignatureListContext() override = default; + + void AddNdSbpSignature(NdSbpSignature& nd_sbp_sig) override { + nd_sbp_sig_list_->emplace_back(nd_sbp_sig); + } + + const Shape& parallel_hierarchy() override { + return *(CHECK_JUST(op_->GetOpParallelDesc())->hierarchy()); + } + + const Shape& BlobShape4InputArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return CHECK_JUST(logical_blob_desc4ibn_(GenRepeatedBn(arg_name, index))).shape(); + } + + private: + const UserOp* op_; + std::function(const std::string&)> logical_blob_desc4ibn_; + const ParallelDesc parallel_desc_; + std::vector* nd_sbp_sig_list_; +}; + Maybe UserOp::InitFromOpConf() { CHECK_OR_RETURN(op_conf().has_user_conf()); for (const auto& pair : op_conf().user_conf().input()) { @@ -550,8 +717,8 @@ Maybe UserOp::InferInternalBlobDescs( BlobDesc* tmp_buffer_blob = GetBlobDesc4BnInOp(GenRepeatedBn("tmp_buffer", 0)); CHECK_NOTNULL_OR_RETURN(tmp_buffer_blob); tmp_buffer_blob->set_data_type(DataType::kChar); - tmp_buffer_blob->mut_shape() = Shape({static_cast(tmp_size)}); - tmp_buffer_blob->mut_stride() = Stride({static_cast(tmp_size)}); + tmp_buffer_blob->set_shape(Shape({static_cast(tmp_size)})); + tmp_buffer_blob->set_stride(Stride({static_cast(tmp_size)})); } return Maybe::Ok(); } @@ -578,11 +745,19 @@ Maybe UserOp::InferLogicalOutBlobDescs( JUST(val_->logical_tensor_desc_infer_fn(&infer_ctx)); for (const auto& pair : infer_ctx.outputs()) { BlobDesc* out_blob_desc = BlobDesc4BnInOp(GenRepeatedBn(pair.first, pair.second)); - user_op::TensorDesc* tensor_desc = infer_ctx.OutputTensorDesc(pair.first, pair.second); - out_blob_desc->set_data_type(tensor_desc->data_type()); - out_blob_desc->mut_shape() = tensor_desc->shape(); - out_blob_desc->mut_stride() = tensor_desc->stride(); - out_blob_desc->set_is_dynamic(tensor_desc->is_dynamic()); + const user_op::TensorDesc& tensor_desc = infer_ctx.OutputTensorDesc(pair.first, pair.second); + out_blob_desc->set_data_type(tensor_desc.data_type()); + out_blob_desc->set_shape(tensor_desc.shape()); + if (val_->non_contiguous_supported) { + out_blob_desc->set_stride(tensor_desc.stride()); + } else { + out_blob_desc->set_stride(Stride(out_blob_desc->shape())); + } + CHECK_EQ_OR_RETURN(out_blob_desc->stride().size(), out_blob_desc->shape().size()) + << Error::RuntimeError() << "stride and shape size mismatch since stride is " + << out_blob_desc->stride().ToString() << " but shape is " + << out_blob_desc->shape().ToString(); + out_blob_desc->set_is_dynamic(tensor_desc.is_dynamic()); } return Maybe::Ok(); } @@ -611,10 +786,18 @@ Maybe UserOp::InferOutBlobDescs( JUST(val_->physical_tensor_desc_infer_fn(&infer_ctx)); for (const auto& pair : infer_ctx.outputs()) { BlobDesc* out_blob_desc = GetBlobDesc4BnInOp(GenRepeatedBn(pair.first, pair.second)); - out_blob_desc->set_data_type(*(infer_ctx.OutputDType(pair.first, pair.second))); - out_blob_desc->mut_shape() = *(infer_ctx.OutputShape(pair.first, pair.second)); - out_blob_desc->mut_stride() = Stride(*(infer_ctx.OutputShape(pair.first, pair.second))); - out_blob_desc->set_is_dynamic(*infer_ctx.OutputIsDynamic(pair.first, pair.second)); + out_blob_desc->set_data_type(infer_ctx.OutputDType(pair.first, pair.second)); + out_blob_desc->set_shape(infer_ctx.OutputShape(pair.first, pair.second)); + if (val_->non_contiguous_supported) { + out_blob_desc->set_stride(infer_ctx.OutputStride(pair.first, pair.second)); + } else { + out_blob_desc->set_stride(Stride(out_blob_desc->shape())); + } + CHECK_EQ_OR_RETURN(out_blob_desc->stride().size(), out_blob_desc->shape().size()) + << Error::RuntimeError() << "stride and shape size mismatch since stride is " + << out_blob_desc->stride().ToString() << " but shape is " + << out_blob_desc->shape().ToString(); + out_blob_desc->set_is_dynamic(infer_ctx.OutputIsDynamic(pair.first, pair.second)); } return Maybe::Ok(); } @@ -670,6 +853,10 @@ LogicalBlobId UserOp::lbi4ibn(const std::string& input_bn) const { } LogicalBlobId UserOp::lbi4obn(const std::string& output_bn) const { + // TODO: remove this workaround and use different lbi for input and output + const bool is_copy_hd = op_conf().user_conf().op_type_name() == "copy_d2h" + || op_conf().user_conf().op_type_name() == "copy_h2d"; + if (is_copy_hd) { return GenLogicalBlobId(op_conf().user_conf().input().at("in").s(0)); } auto pair = GenUnRepeatedBn(output_bn); auto ret = GenLogicalBlobId(op_conf().user_conf().output().at(pair.first).s(pair.second)); CHECK_EQ(ret.op_name(), op_conf().name()); @@ -693,14 +880,15 @@ Maybe UserOp::InferSbpSignature( Maybe UserOp::GetSbpSignatures( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const { + int32_t hierarchy_value, SbpSignatureList* sbp_sig_list) const { CHECK_OR_RETURN(val_ != nullptr) << "cannot find op_type: " << op_conf().user_conf().op_type_name() << " in op registry!"; - UserOpSbpContext sbp_ctx(this, sbp_sig_list, LogicalBlobDesc4Ibn); + UserOpSbpContext sbp_ctx(this, sbp_sig_list, LogicalBlobDesc4Ibn, hierarchy_value); JUST(val_->get_sbp_fn(&sbp_ctx)); // Add Broadcast for source user op tick input - std::string tick_bn = GenRepeatedBn(user_op::kUserSourceOpTickInputArgName, 0); - if (val_->op_def.input_size() == 0 && input_bns().size() == 1) { + if (val_->op_def.input_size() == 1 && input_bns().size() == 1 + && val_->op_def.input(0).name() == user_op::kUserSourceOpTickInputArgName) { + std::string tick_bn = GenRepeatedBn(user_op::kUserSourceOpTickInputArgName, 0); CHECK_OR_RETURN(input_bns().Get(0) == tick_bn) << "user op_name: " << op_conf().name() << " op_type_name: " << op_conf().user_conf().op_type_name() @@ -735,6 +923,27 @@ Maybe UserOp::GetSbpSignatures( return Maybe::Ok(); } +Maybe UserOp::GetComputeComplexity( + NdSbpSignature* sbp_signature, + std::function logical_blob_desc4bn, + const ParallelDesc& parallel_desc) const { + if (val_->compute_complexity_fn) { + UserOpComputeComplexityFnContext user_op_compute_complexity_fn_context( + op_conf(), parallel_desc, sbp_signature, logical_blob_desc4bn); + return val_->compute_complexity_fn(&user_op_compute_complexity_fn_context); + } else { + return Operator::GetComputeComplexity(sbp_signature, logical_blob_desc4bn, parallel_desc); + } +} + +Operator::DumpNdSbpSignatureForOpConfFn UserOp::GetDumpNdSbpSignatureForOpConfFn() const { + if (val_->dump_nd_sbp_signature_for_op_conf_fn) { + return val_->dump_nd_sbp_signature_for_op_conf_fn; + } else { + return Operator::GetDumpNdSbpSignatureForOpConfFn(); + } +} + Maybe UserOp::InferOpTimeShape( const std::function(const std::string&)>& GetTimeShape4BnInOp, std::shared_ptr* time_shape) const { @@ -783,6 +992,20 @@ Maybe UserOp::InferNdSbpSignature( return Maybe::Ok(); } +Maybe UserOp::GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, std::vector* nd_sbp_sig_list) const { + if (val_->get_nd_sbp_list_fn) { + NdSbpSignature empty_sbp_signature; + UserOpGetNdSbpSignatureListContext user_op_get_nd_sbp_list_context( + this, LogicalBlobDesc4Ibn, parallel_desc, nd_sbp_sig_list); + return val_->get_nd_sbp_list_fn(&user_op_get_nd_sbp_list_context); + } else { + JUST(Operator::GetNdSbpSignatureList(LogicalBlobDesc4Ibn, parallel_desc, nd_sbp_sig_list)); + } + return Maybe::Ok(); +} + Symbol UserOp::GetOpConfWithoutOpNameAndLbn() const { OperatorConf op_conf(this->op_conf()); op_conf.set_name("undefined-op-name"); diff --git a/oneflow/core/operator/user_op.h b/oneflow/core/operator/user_op.h index 30a7d85684c0d2e361c98309ae1e20111fe8e25c..5399438fd4e7bc4733e91fb258c3d97243902767 100644 --- a/oneflow/core/operator/user_op.h +++ b/oneflow/core/operator/user_op.h @@ -44,6 +44,11 @@ class UserOp final : public Operator { HashMap* con_inplace_obn2ibn, const std::function& GetBlobDesc4BnInOp, const ParallelContext* parallel_ctx) const override; + Maybe GetComputeComplexity( + NdSbpSignature* sbp_signature, + std::function logical_blob_desc4bn, + const ParallelDesc& parallel_desc) const override; + Operator::DumpNdSbpSignatureForOpConfFn GetDumpNdSbpSignatureForOpConfFn() const override; Symbol GetOpConfWithoutOpNameAndLbn() const override; const user_op::UserOpConfWrapper& user_op_conf() const; const ArgVec& inputs() const { return inputs_; } @@ -59,7 +64,11 @@ class UserOp final : public Operator { const ParallelDesc& parallel_desc) const override; Maybe GetSbpSignatures( const std::function(const std::string&)>& LogicalBlobDesc4Ibn, - const ParallelDesc& parallel_desc, SbpSignatureList* sbp_sig_list) const override; + int32_t hierarchy_value, SbpSignatureList* sbp_sig_list) const override; + Maybe GetNdSbpSignatureList( + const std::function(const std::string&)>& LogicalBlobDesc4Ibn, + const ParallelDesc& parallel_desc, + std::vector* nd_sbp_sig_list) const override; Maybe InferOpTimeShape( const std::function(const std::string&)>& GetTimeShape4BnInOp, std::shared_ptr* time_shape) const override; diff --git a/oneflow/core/operator/variable_op.cpp b/oneflow/core/operator/variable_op.cpp index c6c8436df626a0526ac9d2508d163cb2acac608b..4f94a62d21b686a515e679d9443249d30e625d3e 100644 --- a/oneflow/core/operator/variable_op.cpp +++ b/oneflow/core/operator/variable_op.cpp @@ -52,7 +52,7 @@ Maybe VariableOp::InferLogicalOutBlobDescs( const ParallelDesc& parallel_desc) const { const VariableOpConf& variable_conf = op_conf().variable_conf(); BlobDesc* out_blob_desc = BlobDesc4BnInOp("out"); - out_blob_desc->mut_shape() = Shape(variable_conf.shape()); + out_blob_desc->set_shape(Shape(variable_conf.shape())); CHECK_OR_RETURN(variable_conf.has_data_type()); out_blob_desc->set_data_type(variable_conf.data_type()); return Maybe::Ok(); @@ -68,13 +68,28 @@ Maybe VariableOp::InferOutBlobDescs( out_blob_desc->set_data_type(variable_conf.data_type()); NdSbp nd_sbp; JUST(ParseNdSbpFromConf(variable_conf, parallel_desc, &nd_sbp)); - out_blob_desc->mut_shape() = - *JUST(GetPhysicalShape(Shape(variable_conf.shape()), nd_sbp, parallel_desc, *parallel_ctx)); + out_blob_desc->set_shape( + *JUST(GetPhysicalShape(Shape(variable_conf.shape()), nd_sbp, parallel_desc, *parallel_ctx))); return Maybe::Ok(); } Maybe VariableOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { - CHECK_EQ_OR_RETURN(JUST(GetOpParallelDesc())->hierarchy()->NumAxes(), 1); + int64_t num_axes = op_conf().variable_conf().shape().dim_size(); + for (int i = 0; i < num_axes; ++i) { + SbpSignatureBuilder() + .Broadcast(input_bns()) + .Split(output_bns(), i) + .Build(sbp_sig_list->mutable_sbp_signature()->Add()); + } + return Maybe::Ok(); +} + +Maybe VariableOp::InferSbpSignature( + SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, + const std::function& CalcOrderValue4SbpSig, + std::function(const std::string&)> SbpInferHint4Ibn, + const ParallelDesc& parallel_desc) const { + CHECK_EQ_OR_RETURN(parallel_desc.hierarchy()->NumAxes(), 1); SbpSignatureBuilder sbp_sig_builder; if (op_conf().variable_conf().nd_sbp_size() != 0) { CHECK_EQ_OR_RETURN(op_conf().variable_conf().nd_sbp_size(), 1); @@ -88,19 +103,7 @@ Maybe VariableOp::GetSbpSignatures(SbpSignatureList* sbp_sig_list) const { } else { sbp_sig_builder.Broadcast(output_bns()); } - sbp_sig_builder.Broadcast(input_bns()).Build(sbp_sig_list->mutable_sbp_signature()->Add()); - return Maybe::Ok(); -} - -Maybe VariableOp::InferSbpSignature( - SbpSignature* sbp_signature, const SbpSignature& sbp_sig_conf, - const std::function& CalcOrderValue4SbpSig, - std::function(const std::string&)> SbpInferHint4Ibn, - const ParallelDesc& parallel_desc) const { - CHECK_EQ_OR_RETURN(parallel_desc.hierarchy()->NumAxes(), 1); - SbpSignatureList sbp_sig_list; - JUST(GetSbpSignatures(&sbp_sig_list)); - *sbp_signature = sbp_sig_list.sbp_signature().Get(0); + sbp_sig_builder.Broadcast(input_bns()).Build(sbp_signature); return Maybe::Ok(); } @@ -125,6 +128,19 @@ Maybe VariableOp::InferNdSbpSignature( return Maybe::Ok(); } +Operator::DumpNdSbpSignatureForOpConfFn VariableOp::GetDumpNdSbpSignatureForOpConfFn() const { + return [](const NdSbpSignature& nd_sbp_sig, OperatorConf* op_conf) -> Maybe { + CHECK_OR_RETURN(op_conf->has_variable_conf()) << "VariableOp don't set variable op_conf"; + op_conf->mutable_variable_conf()->clear_nd_sbp(); + const auto& nd_sbp = nd_sbp_sig.bn_in_op2nd_sbp().at("out"); + + for (const auto& sbp_parallel : nd_sbp.sbp_parallel()) { + op_conf->mutable_variable_conf()->mutable_nd_sbp()->Add(SbpParallelToString(sbp_parallel)); + } + return Maybe::Ok(); + }; +} + REGISTER_OP(OperatorConf::kVariableConf, VariableOp); REGISTER_OP_SAME_OUTPUT_BLOB_REGST_NUM(OperatorConf::kVariableConf, 1); REGISTER_INTERFACE_OP(OperatorConf::kVariableConf); diff --git a/oneflow/core/operator/variable_op.h b/oneflow/core/operator/variable_op.h index 586de0fd8aab80dc669286d4ad51d1bfec7c85ff..9f8ecbb0a932702a6f944ad30e8226788dcc6826 100644 --- a/oneflow/core/operator/variable_op.h +++ b/oneflow/core/operator/variable_op.h @@ -47,6 +47,7 @@ class VariableOp final : public Operator { const ParallelDesc& parallel_desc, std::function(const std::string&)> NdSbpInferHint4Ibn) const override; + DumpNdSbpSignatureForOpConfFn GetDumpNdSbpSignatureForOpConfFn() const override; }; } // namespace oneflow diff --git a/oneflow/core/operator/wait_and_send_ids_op.cpp b/oneflow/core/operator/wait_and_send_ids_op.cpp index 40978e2fb897f58382747fb536a9190451bebae6..9a511fb5d44af92fb82f0502589336cfc1748149 100644 --- a/oneflow/core/operator/wait_and_send_ids_op.cpp +++ b/oneflow/core/operator/wait_and_send_ids_op.cpp @@ -28,7 +28,7 @@ namespace { Maybe InferBlobDescs(const OperatorConf& op_conf, const std::function& BlobDesc4BnInOp) { - BlobDesc4BnInOp("out")->mut_shape() = Shape({1}); + BlobDesc4BnInOp("out")->set_shape(Shape({1})); BlobDesc4BnInOp("out")->set_data_type(op_conf.wait_and_send_ids_conf().data_type()); return Maybe::Ok(); } diff --git a/oneflow/core/platform/include/ibv.h b/oneflow/core/platform/include/ibv.h index e6f8706e474260817d2fe0f871aaccdeb50e8141..83e40e6f0a0618f56a045f6cf7b5abd7e2470f8f 100644 --- a/oneflow/core/platform/include/ibv.h +++ b/oneflow/core/platform/include/ibv.h @@ -39,7 +39,8 @@ extern "C" typedef struct IBV { _(ibv_create_qp) \ _(ibv_dereg_mr) \ _(ibv_create_cq) \ - _(ibv_query_device) + _(ibv_query_device) \ + _(ibv_get_device_name) #define DECLARE_ONE(name) decltype(&name) name; IBV_APIS(DECLARE_ONE) diff --git a/oneflow/core/platform/lib/ibv_wrapper.cpp b/oneflow/core/platform/lib/ibv_wrapper.cpp index e38850ccf20a8d21a91156d0da3fc7757eabbe76..a810e9dcb9d9c446b03aeedf416906536a30bb71 100644 --- a/oneflow/core/platform/lib/ibv_wrapper.cpp +++ b/oneflow/core/platform/lib/ibv_wrapper.cpp @@ -117,6 +117,10 @@ int ibv_query_device(struct ibv_context* context, struct ibv_device_attr* device return LoadSymbol(__func__, &wrapper.ibv_query_device)(context, device_attr); } +const char* ibv_get_device_name(struct ibv_device* device) { + return LoadSymbol(__func__, &wrapper.ibv_get_device_name)(device); +} + } // namespace _stubs IBV wrapper = { diff --git a/oneflow/core/platform/lib/wrapper.cpp b/oneflow/core/platform/lib/wrapper.cpp index 0ff4c3911c571bcedae16b35934aecac1c3725a8..ff59c6e9f5961237bdde43dba960b1d3a46a7fb2 100644 --- a/oneflow/core/platform/lib/wrapper.cpp +++ b/oneflow/core/platform/lib/wrapper.cpp @@ -44,9 +44,6 @@ std::unique_ptr DynamicLibrary::Load(const std::vectorAbsolutePath() << "\n"; -#endif // __linux__ return std::unique_ptr(lib); } } diff --git a/oneflow/core/profiler/event.cpp b/oneflow/core/profiler/event.cpp index d56cebf4e53c1cfc40fcdad9bb90c47400608f6b..a4076d85b2983bd2e8ac9193813825d6aa9e12bf 100644 --- a/oneflow/core/profiler/event.cpp +++ b/oneflow/core/profiler/event.cpp @@ -1,91 +1,93 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "fmt/core.h" -#include "fmt/format.h" -#include "oneflow/core/profiler/event.h" -#include "oneflow/core/profiler/util.h" - -using json = nlohmann::json; - -namespace oneflow { - -namespace profiler { -nlohmann::json IEvent::ToJson() { - return json{{"name", name_}, {"time", GetDuration()}, {"input_shapes", "-"}}; -} - -void IEvent::SetStartedAt(double t) { started_at_ = t; } - -void IEvent::SetFinishedAt(double t) { finished_at_ = t; } - -void IEvent::Start() { SetStartedAt(GetTimeNow()); } - -void IEvent::Finish() { SetFinishedAt(GetTimeNow()); } - -bool IEvent::IsChildOf(const IEvent* e) { - if (!e) { return false; } - if (this == e) { return false; } - return GetStartedAt() >= e->GetStartedAt() - && GetFinishedAt() <= e->GetFinishedAt(); -} - -const std::string& IEvent::GetName() const { return name_; } - -std::string CustomEvent::Key() { return name_; } - -nlohmann::json CustomEvent::ToJson() { - auto j = IEvent::ToJson(); - j["type"] = EventType::kCustom; - j["custom_type"] = type_; - return j; -} - -std::shared_ptr CustomEvent::Create(const std::string& name, CustomEventType type) { - return std::shared_ptr(new CustomEvent(name, type)); -} - -std::string KernelEvent::Key() { return fmt::format("{}.{}", name_, GetFormatedInputShapes()); } - -nlohmann::json KernelEvent::ToJson() { - auto j = IEvent::ToJson(); - j["type"] = EventType::kOneflowKernel; - j["input_shapes"] = GetFormatedInputShapes(); -#if defined(WITH_CUDA) || defined(WITH_ROCM) - j["memory_size"] = memory_size_; - if (!children_.empty()) { j["children"] = children_; } -#endif // WITH_CUDA - return j; -} - -std::shared_ptr KernelEvent::Create( - const std::string& name, const std::function(void)>& shape_getter) { - return std::shared_ptr(new KernelEvent(name, shape_getter)); -} - -std::string KernelEvent::GetFormatedInputShapes(size_t max_num_to_format) { - if (input_shapes_.size() == 0) { return "-"; } - std::vector shapes_formated(std::min(input_shapes_.size(), max_num_to_format)); - for (auto i = 0; i < shapes_formated.size(); ++i) { - const std::string current_shape = input_shapes_[i].ToString(); - shapes_formated[i] = current_shape == "()" ? "scalar" : current_shape; - } - if (input_shapes_.size() > max_num_to_format) { shapes_formated.emplace_back("..."); } - return fmt::format("[{}]", fmt::join(shapes_formated, ", ")); -} - -} // namespace profiler +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "fmt/core.h" +#include "fmt/format.h" +#include "oneflow/core/profiler/event.h" +#include "oneflow/core/profiler/util.h" + +using json = nlohmann::json; + +namespace oneflow { + +namespace profiler { +nlohmann::json IEvent::ToJson() { + return json{{"name", name_}, {"time", GetDuration()}, {"input_shapes", "-"}}; +} + +void IEvent::SetStartedAt(double t) { started_at_ = t; } + +void IEvent::SetFinishedAt(double t) { finished_at_ = t; } + +void IEvent::Start() { SetStartedAt(GetTimeNow()); } + +void IEvent::Finish() { SetFinishedAt(GetTimeNow()); } + +bool IEvent::IsChildOf(const IEvent* e) { + if (!e) { return false; } + if (this == e) { return false; } + return GetStartedAt() >= e->GetStartedAt() + && GetFinishedAt() <= e->GetFinishedAt(); +} + +const std::string& IEvent::GetName() const { return name_; } + +std::string CustomEvent::Key() { return name_; } + +nlohmann::json CustomEvent::ToJson() { + auto j = IEvent::ToJson(); + j["type"] = EventType::kCustom; + j["custom_type"] = type_; + return j; +} + +std::shared_ptr CustomEvent::Create(const std::string& name, CustomEventType type) { + return std::shared_ptr(new CustomEvent(name, type)); +} + +std::string KernelEvent::Key() { return fmt::format("{}.{}", name_, GetFormatedInputShapes()); } + +nlohmann::json KernelEvent::ToJson() { + auto j = IEvent::ToJson(); + j["type"] = EventType::kOneflowKernel; + j["input_shapes"] = GetFormatedInputShapes(); +#if defined(WITH_CUDA) || defined(WITH_ROCM) + j["memory_size"] = memory_size_; + if (!children_.empty()) { j["children"] = children_; } +#endif // WITH_CUDA + return j; +} + +std::shared_ptr KernelEvent::Create( + const std::string& name, const std::function(void)>& shape_getter) { + return std::shared_ptr(new KernelEvent(name, shape_getter)); +} + +void KernelEvent::RecordShape(const ShapeView& shape) { input_shapes_.emplace_back(shape); } + +std::string KernelEvent::GetFormatedInputShapes(size_t max_num_to_format) { + if (input_shapes_.size() == 0) { return "-"; } + std::vector shapes_formated(std::min(input_shapes_.size(), max_num_to_format)); + for (auto i = 0; i < shapes_formated.size(); ++i) { + const std::string current_shape = input_shapes_[i].ToString(); + shapes_formated[i] = current_shape == "()" ? "scalar" : current_shape; + } + if (input_shapes_.size() > max_num_to_format) { shapes_formated.emplace_back("..."); } + return fmt::format("[{}]", fmt::join(shapes_formated, ", ")); +} + +} // namespace profiler } // namespace oneflow \ No newline at end of file diff --git a/oneflow/core/profiler/event.h b/oneflow/core/profiler/event.h index af60ff521c29ec96709fb4264f7cc848a72271e9..162f9c569dc27ff4bd869d358e82c0387e189407 100644 --- a/oneflow/core/profiler/event.h +++ b/oneflow/core/profiler/event.h @@ -1,186 +1,188 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_PROFILER_EVENT_H_ -#define ONEFLOW_CORE_PROFILER_EVENT_H_ - -#include -#include -#include -#include "nlohmann/json.hpp" -#include "oneflow/core/common/util.h" -#include "oneflow/core/common/shape_view.h" - -namespace oneflow { - -namespace profiler { - -class ProfileManager; - -enum class EventType { - kCustom, // has three kinds - kOneflowKernel // OneFlow cpu/cuda kernel -}; -enum class CustomEventType { - kDefault, // for record_function - kCudaKernel, // cuda kernel - kCudaRuntime // something like cudaLaunchKernel -}; -enum class EventTimeUnit { kNS, kUS }; - -class IEvent { - public: - OF_DISALLOW_COPY_AND_MOVE(IEvent); - - IEvent() = delete; - IEvent(const std::string& name, EventTimeUnit time_unit) : name_(name), time_unit_(time_unit) {} - - virtual std::string Key() = 0; - virtual nlohmann::json ToJson(); - virtual ~IEvent() = default; - - virtual void Start(); - virtual void Finish(); - bool IsChildOf(const IEvent* e); - - const std::string& GetName() const; - template - const T GetDuration(EventTimeUnit time_unit = EventTimeUnit::kUS) const; - template - const T GetStartedAt(EventTimeUnit time_unit = EventTimeUnit::kUS) const; - template - const T GetFinishedAt(EventTimeUnit time_unit = EventTimeUnit::kUS) const; - - protected: - virtual void SetStartedAt(double t); - virtual void SetFinishedAt(double t); - - std::string name_; - EventTimeUnit time_unit_; - double started_at_ = 0; - double finished_at_ = 0; -}; - -inline double ConvertTime(double time_, EventTimeUnit src_time_unit, EventTimeUnit dst_time_unit) { - if (src_time_unit == EventTimeUnit::kNS && dst_time_unit == EventTimeUnit::kUS) { - return time_ / 1000; - } - if (src_time_unit == EventTimeUnit::kUS && dst_time_unit == EventTimeUnit::kNS) { - return time_ * 1000; - } - return time_; -} - -template<> -const inline double IEvent::GetStartedAt(EventTimeUnit time_unit) const { - return ConvertTime(started_at_, time_unit_, time_unit); -} - -template<> -const inline time_t IEvent::GetStartedAt(EventTimeUnit time_unit) const { - return static_cast(GetStartedAt(time_unit)); -} - -template<> -const inline double IEvent::GetFinishedAt(EventTimeUnit time_unit) const { - return ConvertTime(finished_at_, time_unit_, time_unit); -} - -template<> -const inline time_t IEvent::GetFinishedAt(EventTimeUnit time_unit) const { - return static_cast(GetFinishedAt(time_unit)); -} - -template<> -const inline double IEvent::GetDuration(EventTimeUnit time_unit) const { - return GetFinishedAt(time_unit) - GetStartedAt(time_unit); -} - -template<> -const inline time_t IEvent::GetDuration(EventTimeUnit time_unit) const { - return static_cast(GetDuration(time_unit)); -} - -class CustomEvent final : public IEvent { - public: - friend class ProfileManager; - std::string Key() override; - - nlohmann::json ToJson() override; - - static std::shared_ptr Create(const std::string& name, - CustomEventType type = CustomEventType::kDefault); - - private: - CustomEventType type_; - CustomEvent(const std::string& custom_name, CustomEventType type) - : IEvent(custom_name, - type == CustomEventType::kDefault ? EventTimeUnit::kNS : EventTimeUnit::kUS), - type_(type) {} -}; - -class KernelEvent final : public IEvent { - public: - std::string Key() override; - - nlohmann::json ToJson() override; - - static std::shared_ptr Create( - const std::string& name, const std::function(void)>& shape_getter); - -#if defined(WITH_CUDA) || defined(WITH_ROCM) - void SetMemorySize(int64_t memory_size) { memory_size_ = memory_size; } - void AddChildEvent(const std::shared_ptr& e) { children_.emplace(e); } - bool AddChildEventIfSo(const std::shared_ptr& e) { - if (e->IsChildOf(dynamic_cast(this))) { - children_.emplace(e); - return true; - } - return false; - } - bool HasChildEvent(const std::shared_ptr& e) { return children_.count(e); } - void WalkAmongChildren(const std::function& e)>& f) const { - for (const auto& x : children_) { f(x); } - } -#endif // WITH_CUDA - - private: - KernelEvent(const std::string& kernel_name, - const std::function(void)>& shape_getter) - : IEvent(kernel_name, EventTimeUnit::kNS) { - if (shape_getter) { input_shapes_ = shape_getter(); } - } - -#if defined(WITH_CUDA) || defined(WITH_ROCM) - int64_t memory_size_ = -1; - std::set> children_; -#endif // WITH_CUDA - - std::vector input_shapes_; - std::string GetFormatedInputShapes(size_t max_num_to_format = 4); -}; - -} // namespace profiler -} // namespace oneflow - -namespace nlohmann { - -inline void to_json(json& j, const std::shared_ptr<::oneflow::profiler::IEvent>& event) { - j = event->ToJson(); -} - -} // namespace nlohmann - -#endif // ONEFLOW_CORE_PROFILER_EVENT_H_ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_PROFILER_EVENT_H_ +#define ONEFLOW_CORE_PROFILER_EVENT_H_ + +#include +#include +#include +#include "nlohmann/json.hpp" +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/shape_view.h" + +namespace oneflow { + +namespace profiler { + +class ProfileManager; + +enum class EventType { + kCustom, // has three kinds + kOneflowKernel // OneFlow cpu/cuda kernel +}; +enum class CustomEventType { + kDefault, // for record_function + kCudaKernel, // cuda kernel + kCudaRuntime // something like cudaLaunchKernel +}; +enum class EventTimeUnit { kNS, kUS }; + +class IEvent { + public: + OF_DISALLOW_COPY_AND_MOVE(IEvent); + + IEvent() = delete; + IEvent(const std::string& name, EventTimeUnit time_unit) : name_(name), time_unit_(time_unit) {} + + virtual std::string Key() = 0; + virtual nlohmann::json ToJson(); + virtual ~IEvent() = default; + + virtual void Start(); + virtual void Finish(); + bool IsChildOf(const IEvent* e); + + const std::string& GetName() const; + template + const T GetDuration(EventTimeUnit time_unit = EventTimeUnit::kUS) const; + template + const T GetStartedAt(EventTimeUnit time_unit = EventTimeUnit::kUS) const; + template + const T GetFinishedAt(EventTimeUnit time_unit = EventTimeUnit::kUS) const; + + protected: + virtual void SetStartedAt(double t); + virtual void SetFinishedAt(double t); + + std::string name_; + EventTimeUnit time_unit_; + double started_at_ = 0; + double finished_at_ = 0; +}; + +inline double ConvertTime(double time_, EventTimeUnit src_time_unit, EventTimeUnit dst_time_unit) { + if (src_time_unit == EventTimeUnit::kNS && dst_time_unit == EventTimeUnit::kUS) { + return time_ / 1000; + } + if (src_time_unit == EventTimeUnit::kUS && dst_time_unit == EventTimeUnit::kNS) { + return time_ * 1000; + } + return time_; +} + +template<> +const inline double IEvent::GetStartedAt(EventTimeUnit time_unit) const { + return ConvertTime(started_at_, time_unit_, time_unit); +} + +template<> +const inline time_t IEvent::GetStartedAt(EventTimeUnit time_unit) const { + return static_cast(GetStartedAt(time_unit)); +} + +template<> +const inline double IEvent::GetFinishedAt(EventTimeUnit time_unit) const { + return ConvertTime(finished_at_, time_unit_, time_unit); +} + +template<> +const inline time_t IEvent::GetFinishedAt(EventTimeUnit time_unit) const { + return static_cast(GetFinishedAt(time_unit)); +} + +template<> +const inline double IEvent::GetDuration(EventTimeUnit time_unit) const { + return GetFinishedAt(time_unit) - GetStartedAt(time_unit); +} + +template<> +const inline time_t IEvent::GetDuration(EventTimeUnit time_unit) const { + return static_cast(GetDuration(time_unit)); +} + +class CustomEvent final : public IEvent { + public: + friend class ProfileManager; + std::string Key() override; + + nlohmann::json ToJson() override; + + static std::shared_ptr Create(const std::string& name, + CustomEventType type = CustomEventType::kDefault); + + private: + CustomEventType type_; + CustomEvent(const std::string& custom_name, CustomEventType type) + : IEvent(custom_name, + type == CustomEventType::kDefault ? EventTimeUnit::kNS : EventTimeUnit::kUS), + type_(type) {} +}; + +class KernelEvent final : public IEvent { + public: + std::string Key() override; + + nlohmann::json ToJson() override; + + static std::shared_ptr Create( + const std::string& name, const std::function(void)>& shape_getter); + + void RecordShape(const ShapeView& shape); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + void SetMemorySize(int64_t memory_size) { memory_size_ = memory_size; } + void AddChildEvent(const std::shared_ptr& e) { children_.emplace(e); } + bool AddChildEventIfSo(const std::shared_ptr& e) { + if (e->IsChildOf(dynamic_cast(this))) { + children_.emplace(e); + return true; + } + return false; + } + bool HasChildEvent(const std::shared_ptr& e) { return children_.count(e); } + void WalkAmongChildren(const std::function& e)>& f) const { + for (const auto& x : children_) { f(x); } + } +#endif // WITH_CUDA + + private: + KernelEvent(const std::string& kernel_name, + const std::function(void)>& shape_getter) + : IEvent(kernel_name, EventTimeUnit::kNS) { + if (shape_getter) { input_shapes_ = shape_getter(); } + } + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + int64_t memory_size_ = -1; + std::set> children_; +#endif // WITH_CUDA + + std::vector input_shapes_; + std::string GetFormatedInputShapes(size_t max_num_to_format = 4); +}; + +} // namespace profiler +} // namespace oneflow + +namespace nlohmann { + +inline void to_json(json& j, const std::shared_ptr<::oneflow::profiler::IEvent>& event) { + j = event->ToJson(); +} + +} // namespace nlohmann + +#endif // ONEFLOW_CORE_PROFILER_EVENT_H_ diff --git a/oneflow/core/profiler/event_recorder.cpp b/oneflow/core/profiler/event_recorder.cpp index 82f1b41589acceef6940fe9c19b92712657e5c01..06483deb9e68ae9c7f0ecafdca9615b62718f822 100644 --- a/oneflow/core/profiler/event_recorder.cpp +++ b/oneflow/core/profiler/event_recorder.cpp @@ -46,7 +46,7 @@ Maybe EventRecorder::CreateKernelEventRecorder( } return std::make_shared(event); } -#else // WITH_CUDA +#else // WITH_CUDA if (pmgr->use_cpu_) { return std::make_shared( KernelEvent::Create(name, pmgr->record_shapes_ ? shape_getter : nullptr)); diff --git a/oneflow/core/profiler/event_recorder.h b/oneflow/core/profiler/event_recorder.h index 31b1c34e13abf7f1934d0746a1d238d56af04ddf..d194f0fa525d99da9a58f0462e88a6786eb8642c 100644 --- a/oneflow/core/profiler/event_recorder.h +++ b/oneflow/core/profiler/event_recorder.h @@ -1,60 +1,60 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ -#define ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ - -#include "oneflow/core/common/util.h" -#include "oneflow/core/profiler/event.h" - -namespace oneflow { -namespace profiler { - -class EventRecorder { - public: - using ShapeGetterFuncType = std::function(void)>; - - OF_DISALLOW_COPY_AND_MOVE(EventRecorder); - - explicit EventRecorder(const std::shared_ptr& event) : event_(event) { - CHECK_JUST(RegisterEventToProfileManager(event)); - event_->Start(); - } - - Maybe RegisterEventToProfileManager(const std::shared_ptr& event); - - ~EventRecorder() { - if (event_) { - event_->Finish(); - event_.reset(); - } - } - static std::shared_ptr CreateCustomEventRecorder(const std::string& name); - - static Maybe CreateKernelEventRecorder( - const std::string& name, -#if defined(WITH_CUDA) || defined(WITH_ROCM) - const std::function& memory_size_getter, -#endif - const ShapeGetterFuncType& shape_getter); - - private: - std::shared_ptr event_; -}; - -} // namespace profiler -} // namespace oneflow - -#endif // ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ +#define ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/profiler/event.h" + +namespace oneflow { +namespace profiler { + +class EventRecorder { + public: + using ShapeGetterFuncType = std::function(void)>; + + OF_DISALLOW_COPY_AND_MOVE(EventRecorder); + + explicit EventRecorder(const std::shared_ptr& event) : event_(event) { + CHECK_JUST(RegisterEventToProfileManager(event)); + event_->Start(); + } + + Maybe RegisterEventToProfileManager(const std::shared_ptr& event); + + ~EventRecorder() { + if (event_) { + event_->Finish(); + event_.reset(); + } + } + static std::shared_ptr CreateCustomEventRecorder(const std::string& name); + + static Maybe CreateKernelEventRecorder( + const std::string& name, +#if defined(WITH_CUDA) || defined(WITH_ROCM) + const std::function& memory_size_getter, +#endif + const ShapeGetterFuncType& shape_getter); + + private: + std::shared_ptr event_; +}; + +} // namespace profiler +} // namespace oneflow + +#endif // ONEFLOW_CORE_PROFILER_EVENT_RECORDER_H_ diff --git a/oneflow/core/profiler/kernel.cpp b/oneflow/core/profiler/kernel.cpp index a66055aa485a4f7b6409dddc64bb98cd2ddd69ff..dc8b8796653db62e0c0dc8d0fa32c7b9c56ccdde 100644 --- a/oneflow/core/profiler/kernel.cpp +++ b/oneflow/core/profiler/kernel.cpp @@ -17,11 +17,7 @@ limitations under the License. #include "oneflow/core/profiler/kernel.h" #include "oneflow/core/profiler/profiler.h" #include "oneflow/core/kernel/kernel.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif #include "oneflow/core/lazy/actor/actor_context.h" namespace oneflow { diff --git a/oneflow/core/profiler/profiler.cpp b/oneflow/core/profiler/profiler.cpp index 801d2330ccae0edd85fff33222aee99b467533db..d5da8757b278e54a5911c40994e64dc4228b0c8d 100644 --- a/oneflow/core/profiler/profiler.cpp +++ b/oneflow/core/profiler/profiler.cpp @@ -19,48 +19,45 @@ limitations under the License. #include "oneflow/core/profiler/kineto_shim.h" #include "oneflow/core/profiler/event_recorder.h" #include "oneflow/core/vm/vm_util.h" -#ifdef OF_ENABLE_PROFILER +#include "oneflow/core/device/cuda_util.h" +#ifdef WITH_CUDA +#include "oneflow/core/device/cuda_util.h" +#include +#include +#include +#include +#endif // WITH_CUDA #ifdef WITH_ROCM #include #include #include #include #include -#include "oneflow/core/device/cuda_util.h" -#else -#include -#include -#include -#include -#include "oneflow/core/device/cuda_util.h" -#endif -#endif // OF_ENABLE_PROFILER +#endif //WITH_ROCM namespace oneflow { namespace profiler { void NameThisHostThread(const std::string& name) { -#ifdef OF_ENABLE_PROFILER -#ifdef WITH_ROCM +#ifdef WITH_CUDA static thread_local std::unique_ptr thread_name_prefix; if (!thread_name_prefix) { thread_name_prefix.reset( new std::string(GetStringFromEnv("ONEFLOW_PROFILER_HOST_THREAD_NAME_PREFIX", ""))); } const std::string name_with_prefix = *thread_name_prefix + name; - // nvtxNameOsThreadA(syscall(SYS_gettid), name_with_prefix.c_str()); - roctxMarkA(name_with_prefix.c_str()); -#else + nvtxNameOsThreadA(syscall(SYS_gettid), name_with_prefix.c_str()); +#endif // WITH_CUDA +#ifdef WITH_ROCM static thread_local std::unique_ptr thread_name_prefix; if (!thread_name_prefix) { thread_name_prefix.reset( new std::string(GetStringFromEnv("ONEFLOW_PROFILER_HOST_THREAD_NAME_PREFIX", ""))); } const std::string name_with_prefix = *thread_name_prefix + name; - nvtxNameOsThreadA(syscall(SYS_gettid), name_with_prefix.c_str()); -#endif -#endif // OF_ENABLE_PROFILER + roctxMarkA(name_with_prefix.c_str()); +#endif //WITH_ROCM } void RangePush(const std::string& name) { diff --git a/oneflow/core/profiler/profiler.h b/oneflow/core/profiler/profiler.h index 5536448d4c53548f1bf6ee32d7d16eaa505cfbe7..d1dce4bc8ff2ca82eedb7520d3462f622cbc3e72 100644 --- a/oneflow/core/profiler/profiler.h +++ b/oneflow/core/profiler/profiler.h @@ -46,8 +46,9 @@ class RangeGuard final { std::shared_ptr ctx_; }; -#ifdef OF_ENABLE_PROFILER #define OF_PROFILER_NAME_THIS_HOST_THREAD(name) ::oneflow::profiler::NameThisHostThread(name) + +#ifdef OF_ENABLE_PROFILER #define OF_PROFILER_ONLY_CODE(...) __VA_ARGS__ #define OF_PROFILER_RANGE_PUSH(name) ::oneflow::profiler::RangePush(name) #define OF_PROFILER_RANGE_POP() ::oneflow::profiler::RangePop() @@ -59,7 +60,6 @@ class RangeGuard final { #define OF_PROFILER_RANGE_PUSH(name) #define OF_PROFILER_RANGE_POP() #define OF_PROFILER_RANGE_GUARD(name) -#define OF_PROFILER_NAME_THIS_HOST_THREAD(name) #define OF_PROFILER_LOG_HOST_MEMORY_USAGE(name) #endif diff --git a/oneflow/core/register/blob.h b/oneflow/core/register/blob.h index bea1635a93879db52f05d652fd4b0dde43d86082..56a80b8cbfadcd821b5ebddf083ab36bab8507f5 100644 --- a/oneflow/core/register/blob.h +++ b/oneflow/core/register/blob.h @@ -56,7 +56,12 @@ class Blob final { DataType data_type() const { return blob_desc_->data_type(); } const char* header_ptr() const { return header_ptr_; } - char* mut_header_ptr() { return header_ptr_; } + [[deprecated( + "\"mut_header_ptr\" will be removed in Bolb. Please avoid to use this method whenever " + "possible. Almost all methods of `mut_header_ptr` are also in `Blob`.")]] char* + mut_header_ptr() { + return header_ptr_; + } char* mut_contiguous_header_ptr(); const BlobDesc& blob_desc() const { return *blob_desc_; } const BlobDesc* blob_desc_ptr() const { return blob_desc_; } @@ -91,6 +96,7 @@ class Blob final { CheckDataType(data_type()); return static_cast(dptr_); } + // shape const Shape& static_shape() const { return blob_desc_->shape(); } const ShapeView& shape_view() const { return *shape_view_; } @@ -100,6 +106,7 @@ class Blob final { return mut_shape_view_.get(); } MutShapeView* ForceMutShapeView() { return mut_shape_view_.get(); } + // stride const Stride& stride() const { return blob_desc_->stride(); } diff --git a/oneflow/core/register/blob_desc.cpp b/oneflow/core/register/blob_desc.cpp index 57e97211016eb3ffd04edeffd09f30f25ec73647..ef201e5ee8cbb1e1c7d24af62b32d4f28193c564 100644 --- a/oneflow/core/register/blob_desc.cpp +++ b/oneflow/core/register/blob_desc.cpp @@ -22,40 +22,37 @@ bool CompareLbiBlobDescPair(const LbiBlobDescPair& lhs, const LbiBlobDescPair& r } BlobDesc::BlobDesc(const Shape& shape, DataType dtype, bool is_dynamic) - : shape_(std::make_shared(shape)), - stride_(std::shared_ptr(new Stride(shape))), + : shape_(SymbolOf(shape)), + stride_(SymbolOf(Stride(shape))), data_type_(dtype), is_dynamic_(is_dynamic) {} BlobDesc::BlobDesc(const Shape& shape, const Stride& stride, DataType dtype, bool is_dynamic) - : shape_(std::make_shared(shape)), - stride_(std::shared_ptr(new Stride(stride))), + : shape_(SymbolOf(shape)), + stride_(SymbolOf(stride)), data_type_(dtype), is_dynamic_(is_dynamic) {} -BlobDesc::BlobDesc(const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype, bool is_dynamic) +BlobDesc::BlobDesc(Symbol shape, Symbol stride, DataType dtype, bool is_dynamic) : shape_(shape), stride_(stride), data_type_(dtype), is_dynamic_(is_dynamic) {} BlobDesc::BlobDesc(const Shape& shape, DataType dtype) : BlobDesc(shape, Stride(shape), dtype, false) {} BlobDesc::BlobDesc(const Shape& shape, const Stride& stride, DataType dtype) : BlobDesc(shape, stride, dtype, false) {} -BlobDesc::BlobDesc(const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype) +BlobDesc::BlobDesc(Symbol shape, Symbol stride, DataType dtype) : BlobDesc(shape, stride, dtype, false) {} BlobDesc::BlobDesc(DataType dtype) : BlobDesc(Shape(), Stride(), dtype, false) {} BlobDesc::BlobDesc(const BlobDescProto& proto) { - shape_ = std::make_shared(proto.shape()); - stride_ = std::make_shared(proto.stride()); + shape_ = SymbolOf(Shape(proto.shape())); + stride_ = SymbolOf(Stride(proto.stride())); data_type_ = proto.data_type(); is_dynamic_ = proto.is_dynamic(); } -BlobDesc::BlobDesc(const BlobDesc& other) { - shape_ = std::make_shared(other.shape()); - stride_ = std::make_shared(other.stride()); - data_type_ = other.data_type(); - is_dynamic_ = other.is_dynamic(); -} +BlobDesc::BlobDesc(const BlobDesc& other) + : shape_(other.shape_), + stride_(other.stride_), + data_type_(other.data_type()), + is_dynamic_(other.is_dynamic()) {} void BlobDesc::ToProto(BlobDescProto* proto) const { shape().ToProto(proto->mutable_shape()); diff --git a/oneflow/core/register/blob_desc.h b/oneflow/core/register/blob_desc.h index 9aec27c1ce6713487416e6b98d9fdc2c227655bf..5aca886cb6db625b5407ea9a021c016cf98e3fb0 100644 --- a/oneflow/core/register/blob_desc.h +++ b/oneflow/core/register/blob_desc.h @@ -20,6 +20,7 @@ limitations under the License. #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/common/stride.h" +#include "oneflow/core/common/symbol.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/protobuf.h" #include "oneflow/core/register/blob_desc.pb.h" @@ -36,38 +37,36 @@ class BlobDesc final { // Segmentation fault with unknown reason. BlobDesc(const Shape& shape, DataType dtype, bool is_dynamic); BlobDesc(const Shape& shape, const Stride& stride, DataType dtype, bool is_dynamic); - BlobDesc(const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype, bool is_dynamic); + BlobDesc(Symbol shape, Symbol stride, DataType dtype, bool is_dynamic); BlobDesc(const Shape& shape, DataType dtype); BlobDesc(const Shape& shape, const Stride& stride, DataType dtype); - BlobDesc(const std::shared_ptr& shape, const std::shared_ptr& stride, - DataType dtype); + BlobDesc(Symbol shape, Symbol stride, DataType dtype); explicit BlobDesc(DataType dtype); explicit BlobDesc(const BlobDescProto& proto); explicit BlobDesc(const BlobDesc&); BlobDesc& operator=(const BlobDesc&); - const Shape& shape() const { return *CHECK_NOTNULL(shape_.get()); } - const Stride& stride() const { return *CHECK_NOTNULL(stride_.get()); } - const std::shared_ptr& shape_ptr() const { return shape_; } - const std::shared_ptr& stride_ptr() const { return stride_; } - Shape& mut_shape() { return *CHECK_NOTNULL(mut_shape_ptr().get()); } - Stride& mut_stride() { return *CHECK_NOTNULL(mut_stride_ptr().get()); } - void set_shape(const Shape& shape) { *CHECK_NOTNULL(mut_shape_ptr().get()) = shape; } - void set_stride(const Stride& stride) { - std::shared_ptr stride_ptr(new Stride(stride)); - this->stride_ = stride_ptr; + const Shape& shape() const { + CHECK(shape_.operator bool()); + return *shape_; } + const Stride& stride() const { + CHECK(stride_.operator bool()); + return *stride_; + } + const std::shared_ptr& shape_ptr() const { return shape_.shared_from_symbol(); } + const std::shared_ptr& stride_ptr() const { return stride_.shared_from_symbol(); } + + void set_shape(const Shape& shape) { this->shape_ = SymbolOf(shape); } + void set_stride(const Stride& stride) { this->stride_ = SymbolOf(stride); } DataType data_type() const { return data_type_; } - DataType* mut_data_type() { return &data_type_; } - void set_data_type(DataType val) { data_type_ = val; } + void set_data_type(DataType data_type) { data_type_ = data_type; } bool is_dynamic() const { return is_dynamic_; } - void set_is_dynamic(bool); - bool* mut_is_dynamic() { return &is_dynamic_; } + void set_is_dynamic(bool is_dynamic); bool operator==(const BlobDesc&) const; void ToProto(BlobDescProto*) const; @@ -81,12 +80,8 @@ class BlobDesc final { size_t AlignedTotalByteSize() const; private: - std::shared_ptr shape_; - std::shared_ptr stride_; - std::shared_ptr mut_shape_ptr() const { return std::const_pointer_cast(shape_); } - std::shared_ptr mut_stride_ptr() const { - return std::const_pointer_cast(stride_); - } + Symbol shape_; + Symbol stride_; DataType data_type_; bool is_dynamic_; }; diff --git a/oneflow/core/register/ofblob.h b/oneflow/core/register/ofblob.h deleted file mode 100644 index 7f0ddd551101deaee7aa4bf5e57637ee9822cadd..0000000000000000000000000000000000000000 --- a/oneflow/core/register/ofblob.h +++ /dev/null @@ -1,118 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_REGISTER_OFBLOB_H_ -#define ONEFLOW_CORE_REGISTER_OFBLOB_H_ - -#include "oneflow/core/register/blob.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/framework/dtype.h" - -namespace oneflow { - -class Blob; - -class OfBlob final { - public: - OF_DISALLOW_COPY_AND_MOVE(OfBlob); - OfBlob(ep::Stream* stream, Blob* blob) : stream_(stream), blob_(blob) { - mem_case_.mutable_host_mem(); - } - ~OfBlob() = default; - - const Blob& blob() const { return *blob_; } - int data_type() const { return blob_->data_type(); } - size_t NumAxes() const { return blob_->shape().NumAxes(); } - bool is_dynamic() const { return blob_->blob_desc().is_dynamic(); } - void CopyShapeTo(int64_t* ptr, int64_t num_axis) const; - void CopyStaticShapeTo(int64_t* ptr, int64_t num_axis) const; - void CopyShapeFrom(const int64_t* ptr, int64_t num_axis) const; - - template - inline void AutoMemCopyTo(T* ptr, int64_t len) const; - - template - inline void AutoMemCopyFrom(const T* ptr, int64_t len) const; - - void AsyncAutoMemset(const char value) const; - - Blob* mut_blob() { return blob_; } - ep::Stream* stream() { return stream_; } - - private: - ep::Stream* stream_; - Blob* blob_; - MemoryCase mem_case_; -}; - -inline void OfBlob::CopyShapeFrom(const int64_t* ptr, int64_t num_axis) const { - CHECK_EQ(num_axis, NumAxes()); - Shape shape(DimVector(ptr, ptr + num_axis)); - if (blob_->blob_desc().is_dynamic() == false) { - CHECK_EQ(shape, blob_->static_shape()); - return; - } - CHECK_LE(shape.elem_cnt(), blob_->static_shape().elem_cnt()); - blob_->mut_shape_view()->set_shape(shape); -} - -inline void OfBlob::CopyShapeTo(int64_t* ptr, int64_t num_axis) const { - CHECK_EQ(num_axis, NumAxes()); - FOR_RANGE(int32_t, i, 0, num_axis) { ptr[i] = blob_->shape().At(i); } -} - -inline void OfBlob::CopyStaticShapeTo(int64_t* ptr, int64_t num_axis) const { - CHECK_EQ(num_axis, NumAxes()); - FOR_RANGE(int32_t, i, 0, num_axis) { ptr[i] = blob_->static_shape().At(i); } -} - -template -inline void OfBlob::AutoMemCopyTo(T* ptr, int64_t len) const { - CHECK_EQ(blob_->shape().elem_cnt(), len); - CHECK(blob_->data_type() == GetDataType::value); - SyncAutoMemcpy(stream_, ptr, blob_->dptr(), len * sizeof(T), mem_case_, blob_->mem_case()); -} - -template<> -inline void OfBlob::AutoMemCopyTo(void* ptr, int64_t len) const { - CHECK_EQ(blob_->shape().elem_cnt() * (DType(blob_->data_type()).bytes().GetOrThrow()), len); - SyncAutoMemcpy(stream_, ptr, static_cast(blob_->dptr()), len * sizeof(char), - mem_case_, blob_->mem_case()); -} - -template -inline void OfBlob::AutoMemCopyFrom(const T* ptr, int64_t len) const { - blob_->blob_access_checker()->CheckBodyMutable(); - CHECK_EQ(blob_->shape().elem_cnt(), len); - CHECK(blob_->data_type() == GetDataType::value); - SyncAutoMemcpy(stream_, blob_->mut_dptr(), ptr, len * sizeof(T), blob_->mem_case(), mem_case_); -} -template<> -inline void OfBlob::AutoMemCopyFrom(const void* ptr, int64_t len) const { - blob_->blob_access_checker()->CheckBodyMutable(); - CHECK_EQ(blob_->shape().elem_cnt() * (DType(blob_->data_type()).bytes().GetOrThrow()), len); - SyncAutoMemcpy(stream_, static_cast(blob_->mut_dptr()), ptr, len * sizeof(char), - blob_->mem_case(), mem_case_); -} - -inline void OfBlob::AsyncAutoMemset(const char value) const { - ::oneflow::AutoMemset(stream_, blob_->mut_dptr(), value, - blob_->shape().elem_cnt() * GetSizeOfDataType(blob_->data_type()), - blob_->mem_case()); -} -} // namespace oneflow - -#endif // ONEFLOW_CORE_REGISTER_OFBLOB_H_ diff --git a/oneflow/core/register/register_desc.cpp b/oneflow/core/register/register_desc.cpp index d63c2d41e4c3df8f070c1193df940ce22d2736c5..b1defff2ebd5679a796c3dcd3d142cdef4668d04 100644 --- a/oneflow/core/register/register_desc.cpp +++ b/oneflow/core/register/register_desc.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/graph/copy_task_node.h" #include "oneflow/core/job/id_manager.h" #include "oneflow/core/register/runtime_register_desc.h" +#include "oneflow/core/memory/memory_case_util.h" namespace oneflow { @@ -179,16 +180,10 @@ void InitCtrlRegstDesc(int64_t producer_task_id, RegstDescProto* ctrl_regst_prot ctrl_regst_proto->set_max_register_num(1); ctrl_regst_proto->set_register_num(1); ctrl_regst_proto->mutable_regst_desc_type()->mutable_ctrl_regst_desc(); - ctrl_regst_proto->mutable_mem_case()->mutable_host_mem(); + *ctrl_regst_proto->mutable_mem_case() = memory::MakeHostMemCase(); ctrl_regst_proto->set_enable_reuse_mem(false); ctrl_regst_proto->set_mem_block_id(-1); ctrl_regst_proto->set_mem_block_offset(-1); } -MemoryCase MakeHostMemCase() { - MemoryCase mem_case; - mem_case.mutable_host_mem(); - return mem_case; -} - } // namespace oneflow diff --git a/oneflow/core/register/register_desc.h b/oneflow/core/register/register_desc.h index ea6982fe26cc5d681770f6c036f037d0fcd67bc4..1a57895dd16eaf26ad054ac581d23c71fbefaf41 100644 --- a/oneflow/core/register/register_desc.h +++ b/oneflow/core/register/register_desc.h @@ -24,7 +24,6 @@ namespace oneflow { const int32_t kMaxRegisterNum = std::numeric_limits::max(); void InitCtrlRegstDesc(int64_t producer_task_id, RegstDescProto* ctrl_regst_proto); -MemoryCase MakeHostMemCase(); class TaskNode; diff --git a/oneflow/core/register/register_manager.cpp b/oneflow/core/register/register_manager.cpp index a6ba064f25d047de35fc59176bf90005893eb68c..b70da6f7709699be42b50f5d8f4a92a8029a8ba5 100644 --- a/oneflow/core/register/register_manager.cpp +++ b/oneflow/core/register/register_manager.cpp @@ -84,7 +84,7 @@ void RegstMgr::AddPlan( CHECK_GE(var_blob->AlignedByteSizeOfBlobHeader(), mem_block.mem_size()); CHECK_GE(mem_block.mem_size(), var_blob->ByteSizeOfBlobHeader()); CHECK(mem_block_id2ptr_.emplace(mem_block_id, var_blob->mut_header_ptr()).second); - CHECK(mem_block.mem_case().has_host_mem()); + CHECK(memory::IsHostMem(mem_block.mem_case())); } else { CHECK_GE(var_blob->AlignedByteSizeOfBlobBody(), mem_block.mem_size()); CHECK_GE(mem_block.mem_size(), var_blob->ByteSizeOfBlobBody()); @@ -95,15 +95,14 @@ void RegstMgr::AddPlan( // blob has GPU op consume. We can JUST ignore this diff because it ONLY has little // perf loss but correct. // And this problem is NOT tensor.to("cuda") or tensor.to_global(). - CHECK((mem_block.mem_case().has_host_mem() && var_blob->mem_case().has_host_mem()) - || (mem_block.mem_case() == var_blob->mem_case())) + CHECK(memory::EqualsIgnorePinnedDevice(mem_block.mem_case(), var_blob->mem_case())) << " variable op name: " << var_name << " in rank: " << this_machine_id << " bind eager tensor failed. The eager var tensor mem_case is : " << var_blob->mem_case().DebugString() << " but graph expected_mem block mem_case is : " << mem_block.mem_case().DebugString(); } } else { - int64_t zone_id = MemoryCaseUtil::GenMemZoneId(mem_block.mem_case()); + int64_t zone_id = memory::GetMemCaseId(mem_block.mem_case()); if (zone_id2packed_chunk.find(zone_id) == zone_id2packed_chunk.end()) { zone_id2packed_chunk.emplace(zone_id, PackedChunkInfo(mem_block.mem_case())); } @@ -209,8 +208,7 @@ void RegstMgr::NewBlobsInOneRegst(const std::vector& lbis, Regs char* cur_body_pointer = nullptr; char* cur_header_pointer = nullptr; if (separated_header_mem_size > 0) { - MemoryCase host_mem_case; - host_mem_case.mutable_host_mem(); + MemoryCase host_mem_case = memory::MakeHostMemCase(); if (separated_header_mem_ptr == nullptr) { separated_header_mem_ptr = Singleton::Get()->Allocate(host_mem_case, separated_header_mem_size); diff --git a/oneflow/core/register/runtime_register_desc.cpp b/oneflow/core/register/runtime_register_desc.cpp index 6124ede1495debd737a40a971676b96b4df19aaf..b42da928cc507bb4858fd038ef16627a21542c31 100644 --- a/oneflow/core/register/runtime_register_desc.cpp +++ b/oneflow/core/register/runtime_register_desc.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/register/runtime_register_desc.h" +#include "oneflow/core/memory/memory_case_util.h" #include "oneflow/core/common/protobuf.h" namespace oneflow { @@ -45,7 +46,7 @@ RtRegstDesc::RtRegstDesc(const RegstDescProto& proto) { sorted_blob_desc_vec_.emplace_back(std::make_unique(BlobDesc(DataType::kChar))); } - if ((proto.mem_case().has_device_cuda_mem()) + if ((!memory::IsHostMem(proto.mem_case())) || (proto.has_variable_op_name() && !proto.variable_op_name().empty())) { // NOTE(chengcheng): When this regst is shared with EagerBlobObject, header is ALWAYS separated. has_separated_header_ = true; diff --git a/oneflow/core/rpc/lib/global_process_ctx.cpp b/oneflow/core/rpc/lib/global_process_ctx.cpp index d45d826fd667f1d2d19cf66b179e3cea3165d400..ab502c3b1b034695aa96751071e4b9e88ca63519 100644 --- a/oneflow/core/rpc/lib/global_process_ctx.cpp +++ b/oneflow/core/rpc/lib/global_process_ctx.cpp @@ -32,7 +32,10 @@ int64_t GlobalProcessCtx::Rank() { int64_t GlobalProcessCtx::LocalRank() { char* local_rank_env = std::getenv("LOCAL_RANK"); - if (!local_rank_env) { return 0; } + if (!local_rank_env) { + static int64_t local_rank = Rank() % NumOfProcessPerNode(); + return local_rank; + } CHECK(IsStrInt(local_rank_env)); static int64_t local_rank = std::stol(local_rank_env); return local_rank; diff --git a/oneflow/core/serving/saved_model.proto b/oneflow/core/serving/saved_model.proto deleted file mode 100644 index 1db0fbef97967e8768a09e1610de89b425f4420d..0000000000000000000000000000000000000000 --- a/oneflow/core/serving/saved_model.proto +++ /dev/null @@ -1,19 +0,0 @@ -syntax = "proto2"; -package oneflow; - -import "oneflow/core/operator/op_conf.proto"; -import "oneflow/core/job/job_conf.proto"; - -message SavedModel { - required string name = 1; - required int64 version = 2; - required string checkpoint_dir = 3; - map graphs = 4; - optional string default_graph_name = 5; -} - -message GraphDef { - repeated OperatorConf op_list = 1; - map signatures = 2; - optional string default_signature_name = 3; -} diff --git a/oneflow/core/thread/is_main_thread_test.cpp b/oneflow/core/thread/is_main_thread_test.cpp new file mode 100644 index 0000000000000000000000000000000000000000..91883c9d5f86b5989a7631ec45e7d13919d27383 --- /dev/null +++ b/oneflow/core/thread/is_main_thread_test.cpp @@ -0,0 +1,30 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include "oneflow/core/thread/thread_manager.h" + +namespace oneflow { +namespace test { + +TEST(IsMainThread, IsMainThread) { + EXPECT_TRUE(IsMainThread()); + auto non_main_thread = std::thread([&]() { EXPECT_FALSE(IsMainThread()); }); + non_main_thread.join(); +} + +} // namespace test +} // namespace oneflow diff --git a/oneflow/core/thread/thread.cpp b/oneflow/core/thread/thread.cpp index 7bf9365091fcbbd93efba1b306794eac425a635c..37a5909a7a33b4cfbca6071a9b81b697416f42f4 100644 --- a/oneflow/core/thread/thread.cpp +++ b/oneflow/core/thread/thread.cpp @@ -19,18 +19,26 @@ limitations under the License. #include "oneflow/core/lazy/actor/actor.h" #include "oneflow/core/lazy/actor/light_actor.h" #include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/stream/include/stream_context.h" +#include "oneflow/core/lazy/stream_context/include/stream_context.h" #include "oneflow/core/framework/to_string.h" +#include "oneflow/core/lazy/stream_context/include/generic_stream_context.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { Thread::Thread(const StreamId& stream_id) : thrd_id_(EncodeStreamIdToInt64(stream_id)) { local_msg_queue_enabled_ = ParseBooleanFromEnv("ONEFLOW_THREAD_ENABLE_LOCAL_MESSAGE_QUEUE", true); light_actor_enabled_ = ParseBooleanFromEnv("ONEFLOW_ACTOR_ENABLE_LIGHT_ACTOR", true); - StreamContext* stream_ctx = - NewObj(stream_id.device_id().device_type(), stream_id); - stream_ctx_.reset(stream_ctx); + if (IsClassRegistered(stream_id.device_id().device_type(), + stream_id)) { + stream_ctx_.reset(NewObj( + stream_id.device_id().device_type(), stream_id)); + } else { + stream_ctx_.reset(new GenericStreamContext(stream_id)); + } + actor_thread_ = std::thread([this, stream_id]() { + LazyMode::Guard guard(true); OF_PROFILER_NAME_THIS_HOST_THREAD("_" + ToString(stream_id.device_id().device_type()) + std::to_string(stream_id.device_id().device_index()) + "_actor"); diff --git a/oneflow/core/thread/thread_consistent_id.cpp b/oneflow/core/thread/thread_consistent_id.cpp deleted file mode 100644 index f208407ebe04220dc9ed7803b385383aabd4434d..0000000000000000000000000000000000000000 --- a/oneflow/core/thread/thread_consistent_id.cpp +++ /dev/null @@ -1,107 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/thread/thread_consistent_id.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/framework/transport_util.h" -#include "oneflow/core/common/container_util.h" - -namespace oneflow { - -namespace { - -class ConsistentIdStorage final { - public: - ConsistentIdStorage() = default; - ~ConsistentIdStorage() = default; - - static ConsistentIdStorage* Singleton() { - static auto* storage = new ConsistentIdStorage(); - return storage; - } - - size_t Size() const { - std::unique_lock lock(mutex_); - return id2debug_string_.size(); - } - - Maybe Emplace(int64_t id, const std::string& debug_string) { - std::unique_lock lock(mutex_); - for (const auto& pair : id2debug_string_) { CHECK_NE_OR_RETURN(debug_string, pair.second); } - CHECK_OR_RETURN(id2debug_string_.emplace(id, debug_string).second); - return Maybe::Ok(); - } - - Maybe TryEmplace(int64_t id, const std::string& debug_string) { - std::unique_lock lock(mutex_); - for (const auto& pair : id2debug_string_) { - if (pair.first == id) { CHECK_EQ_OR_RETURN(debug_string, pair.second); } - if (pair.second == debug_string) { CHECK_EQ_OR_RETURN(id, pair.first); } - } - id2debug_string_.emplace(id, debug_string); - return Maybe::Ok(); - } - - Maybe DebugString(int64_t id) const { - std::unique_lock lock(mutex_); - return MapAt(id2debug_string_, id); - } - - Maybe Reset() { - HashMap().swap(id2debug_string_); - return Maybe::Ok(); - } - - private: - mutable std::mutex mutex_; - HashMap id2debug_string_; -}; - -std::unique_ptr* MutThreadLocalUniqueConsistentId() { - static thread_local std::unique_ptr consistent_id; - return &consistent_id; -} - -} // namespace - -size_t GetThreadConsistentIdCount() { return ConsistentIdStorage::Singleton()->Size(); } - -Maybe InitThisThreadUniqueConsistentId(int64_t id, const std::string& debug_string) { - JUST(ConsistentIdStorage::Singleton()->Emplace(id, debug_string)); - auto* ptr = MutThreadLocalUniqueConsistentId(); - CHECK_ISNULL_OR_RETURN(ptr->get()); - ptr->reset(new int64_t(id)); - return Maybe::Ok(); -} - -Maybe InitThisThreadConsistentId(int64_t id, const std::string& debug_string) { - JUST(ConsistentIdStorage::Singleton()->TryEmplace(id, debug_string)); - auto* ptr = MutThreadLocalUniqueConsistentId(); - CHECK_ISNULL_OR_RETURN(ptr->get()); - ptr->reset(new int64_t(id)); - return Maybe::Ok(); -} - -Maybe GetThisThreadConsistentId() { - auto* ptr = MutThreadLocalUniqueConsistentId(); - CHECK_NOTNULL_OR_RETURN(ptr->get()); - return **ptr; -} - -Maybe ResetThisThreadUniqueConsistentId() { - return ConsistentIdStorage::Singleton()->Reset(); -} - -} // namespace oneflow diff --git a/oneflow/core/thread/thread_consistent_id.h b/oneflow/core/thread/thread_consistent_id.h deleted file mode 100644 index 2d9e513168a76e32d1997f4bccfe6bd95777cf2a..0000000000000000000000000000000000000000 --- a/oneflow/core/thread/thread_consistent_id.h +++ /dev/null @@ -1,39 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_THREAD_CONSISTENT_UNIQUE_ID_H_ -#define ONEFLOW_CORE_THREAD_CONSISTENT_UNIQUE_ID_H_ - -#include -#include "oneflow/core/common/maybe.h" - -namespace oneflow { - -const static int kThreadConsistentIdMain = 0; -const static int kThreadConsistentIdHook = 1; -const static int kThreadConsistentIdScheduler = 2; - -size_t GetThreadConsistentIdCount(); - -Maybe InitThisThreadUniqueConsistentId(int64_t thread_consistent_id, - const std::string& debug_string); -Maybe InitThisThreadConsistentId(int64_t thread_consistent_id, - const std::string& debug_string); -Maybe GetThisThreadConsistentId(); -Maybe ResetThisThreadUniqueConsistentId(); - -} // namespace oneflow - -#endif // ONEFLOW_CORE_THREAD_CONSISTENT_UNIQUE_ID_H_ diff --git a/oneflow/core/thread/thread_global_id.cpp b/oneflow/core/thread/thread_global_id.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d4c0a4f3f6f5aca75a8d4985d3cfb44a51993b95 --- /dev/null +++ b/oneflow/core/thread/thread_global_id.cpp @@ -0,0 +1,123 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/thread/thread_global_id.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/core/common/container_util.h" + +namespace oneflow { + +namespace { + +class GlobalIdStorage final { + public: + GlobalIdStorage() = default; + ~GlobalIdStorage() = default; + + static GlobalIdStorage* Singleton() { + static auto* storage = new GlobalIdStorage(); + return storage; + } + + size_t Size() const { + std::unique_lock lock(mutex_); + return id2debug_string_.size(); + } + + Maybe Emplace(int64_t id, const std::string& debug_string) { + std::unique_lock lock(mutex_); + for (const auto& pair : id2debug_string_) { CHECK_NE_OR_RETURN(debug_string, pair.second); } + CHECK_OR_RETURN(id2debug_string_.emplace(id, debug_string).second); + return Maybe::Ok(); + } + + bool IsInserted(int64_t id) { + std::unique_lock lock(mutex_); + return id2debug_string_.count(id) > 0; + } + + Maybe TryEmplace(int64_t id, const std::string& debug_string) { + std::unique_lock lock(mutex_); + for (const auto& pair : id2debug_string_) { + if (pair.first == id) { CHECK_EQ_OR_RETURN(debug_string, pair.second); } + if (pair.second == debug_string) { CHECK_EQ_OR_RETURN(id, pair.first); } + } + id2debug_string_.emplace(id, debug_string); + return Maybe::Ok(); + } + + Maybe DebugString(int64_t id) const { + std::unique_lock lock(mutex_); + return MapAt(id2debug_string_, id); + } + + Maybe Reset() { + HashMap().swap(id2debug_string_); + return Maybe::Ok(); + } + + private: + mutable std::mutex mutex_; + HashMap id2debug_string_; +}; + +Optional* MutThreadLocalUniqueGlobalId() { + static thread_local Optional global_id; + return &global_id; +} + +} // namespace + +size_t GetThreadGlobalIdCount() { return GlobalIdStorage::Singleton()->Size(); } + +Maybe CheckWorkerThreadThreadGlobalId(int64_t thread_global_id) { + CHECK_GE_OR_RETURN(thread_global_id, 0) << "thread_global_id should be non negative"; + CHECK_LT_OR_RETURN(thread_global_id, TransportToken::MaxNumberOfThreadGlobalUId()) + << "thread_global_id should be less than " << TransportToken::MaxNumberOfThreadGlobalUId(); + CHECK_NE_OR_RETURN(thread_global_id, kThreadGlobalIdMain) + << "thread_global_id " << thread_global_id << " has been used by main thread."; + return Maybe::Ok(); +} + +Maybe InitThisThreadUniqueGlobalId(int64_t id, const std::string& debug_string) { + JUST(GlobalIdStorage::Singleton()->Emplace(id, debug_string)); + auto* ptr = MutThreadLocalUniqueGlobalId(); + CHECK_OR_RETURN(!ptr->has_value()); + *ptr = id; + return Maybe::Ok(); +} + +const Optional& GetThisThreadGlobalId() { return *MutThreadLocalUniqueGlobalId(); } + +Maybe ResetThisThreadUniqueGlobalId() { return GlobalIdStorage::Singleton()->Reset(); } + +ThreadGlobalIdGuard::ThreadGlobalIdGuard(int64_t thread_global_id) + : old_thread_global_id_(GetThisThreadGlobalId()) { + if (old_thread_global_id_.has_value()) { + int64_t old_thread_global_id = CHECK_JUST(old_thread_global_id_); + CHECK_EQ(old_thread_global_id, thread_global_id) + << "nested ThreadGlobalIdGuard disabled. old thread_global_id: " << old_thread_global_id + << ", new thread_global_id:" << thread_global_id; + } + *MutThreadLocalUniqueGlobalId() = thread_global_id; +} + +ThreadGlobalIdGuard::~ThreadGlobalIdGuard() { + *MutThreadLocalUniqueGlobalId() = old_thread_global_id_; +} + +} // namespace oneflow diff --git a/oneflow/core/thread/thread_global_id.h b/oneflow/core/thread/thread_global_id.h new file mode 100644 index 0000000000000000000000000000000000000000..a7339ab1f333ac3122d0508bce650b31e91f67d6 --- /dev/null +++ b/oneflow/core/thread/thread_global_id.h @@ -0,0 +1,46 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_THREAD_GLOBAL_UNIQUE_ID_H_ +#define ONEFLOW_CORE_THREAD_GLOBAL_UNIQUE_ID_H_ + +#include +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/optional.h" + +namespace oneflow { + +const static int kThreadGlobalIdDefaultWorker = 0; +const static int kThreadGlobalIdMain = 7; + +size_t GetThreadGlobalIdCount(); + +Maybe InitThisThreadUniqueGlobalId(int64_t thread_global_id, const std::string& debug_string); +Maybe CheckWorkerThreadThreadGlobalId(int64_t thread_global_id); +const Optional& GetThisThreadGlobalId(); +Maybe ResetThisThreadUniqueGlobalId(); + +class ThreadGlobalIdGuard final { + public: + explicit ThreadGlobalIdGuard(int64_t thread_global_id); + ~ThreadGlobalIdGuard(); + + private: + Optional old_thread_global_id_; +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_THREAD_GLOBAL_UNIQUE_ID_H_ diff --git a/oneflow/core/thread/thread_manager.h b/oneflow/core/thread/thread_manager.h index 4a6353b294d97623d48a5f94b1529d112c6e0551..41bec0752ef24bf7e1f93d1f4e3c9ffda77313b3 100644 --- a/oneflow/core/thread/thread_manager.h +++ b/oneflow/core/thread/thread_manager.h @@ -69,10 +69,20 @@ void MultiThreadLoop(size_t num, const DoEachT& DoEach) { bc.Decrease(); }); } - // buzy loop wait. + // busy loop wait. bc.WaitForeverUntilCntEqualZero(); } +inline bool* MutIsMainThread() { + thread_local bool is_main_thread = false; + return &is_main_thread; +} + +inline bool IsMainThread() { return *MutIsMainThread(); } +inline void SetIsMainThread(bool is_main_thread) { *MutIsMainThread() = is_main_thread; } + +COMMAND(SetIsMainThread(true)); + } // namespace oneflow #endif // ONEFLOW_CORE_THREAD_THREAD_MANAGER_H_ diff --git a/oneflow/core/thread/thread_pool.cpp b/oneflow/core/thread/thread_pool.cpp index 20b0cf53d556c9610cae5e1a7ca11a2dd70d849c..75f687362aef2c768a74cc4a62fe0ef1e4f00097 100644 --- a/oneflow/core/thread/thread_pool.cpp +++ b/oneflow/core/thread/thread_pool.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/thread/thread_pool.h" #include "oneflow/core/vm/sync_vm_mode_guard.h" + namespace oneflow { ThreadPool::ThreadPool(int32_t thread_num) diff --git a/oneflow/core/vm/access_blob_arg_cb_instruction_policy.h b/oneflow/core/vm/access_blob_arg_cb_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..d4b34db2c131e9817f7ee556244c2bfed9fb07e0 --- /dev/null +++ b/oneflow/core/vm/access_blob_arg_cb_instruction_policy.h @@ -0,0 +1,90 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_INSTRUCTION_POLICY_H_ + +#include +#include +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/instruction_policy_util.h" +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/tensor_storage.h" +#include "oneflow/core/intrusive/list.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/vm/stream_policy.h" + +namespace oneflow { +namespace vm { + +class AccessBlobArgCbInstructionPolicy final : public InstructionPolicy { + public: + AccessBlobArgCbInstructionPolicy( + const std::shared_ptr& eager_blob_object, + const std::function&)>& callback, + const std::string& modifier) + : eager_blob_object_(eager_blob_object), + callback_(callback), + modifier_(modifier), + input_dependences_(), + output_dependences_() { + ForEachConstDependence(InstructionPolicyUtil::SetInserter(&input_dependences_)); + ForEachMutDependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + ForEachMut2Dependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + stream_sequential_dependence_ = nullptr; + } + ~AccessBlobArgCbInstructionPolicy() = default; + + const std::shared_ptr& eager_blob_object() const { return eager_blob_object_; } + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + void ForEachConstDependence(const std::function& DoEach) const { + if (modifier_ == "const") { + DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); + } + } + + void ForEachMutDependence(const std::function& DoEach) const { + if (modifier_ == "mut") { DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); } + } + + void ForEachMut2Dependence(const std::function& DoEach) const { + if (modifier_ == "mut2") { DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); } + } + + std::string DebugName(const Instruction& instruction) const override { + return "AccessBlobByCallback"; + } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override { + StreamPolicy* stream_policy = instruction->mut_stream_policy(); + return callback_(stream_policy->stream(), eager_blob_object()); + } + + private: + std::shared_ptr eager_blob_object_; + std::function&)> callback_; + const std::string modifier_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow +#endif // ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.cpp b/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.cpp deleted file mode 100644 index bc2861b7d1323401b9e16854d4e7d8a09cdb2964..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.cpp +++ /dev/null @@ -1,42 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h" -#include "oneflow/core/eager/local_dep_object.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/framework/tensor_storage.h" -#include "oneflow/core/intrusive/list.h" - -namespace oneflow { - -namespace vm { - -void AccessBlobArgCbPhyInstrOperand::ForEachConstMirroredObject( - const std::function& DoEach) const { - if (modifier_ == "const") { DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); } -} - -void AccessBlobArgCbPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - if (modifier_ == "mut") { DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); } -} - -void AccessBlobArgCbPhyInstrOperand::ForEachMut2MirroredObject( - const std::function& DoEach) const { - if (modifier_ == "mut2") { DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); } -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h b/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h deleted file mode 100644 index 740d296912ad79350d42e922227055ac98840939..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/access_blob_arg_cb_phy_instr_operand.h +++ /dev/null @@ -1,82 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_PHY_INSTR_OPERAND_H_ - -#include -#include -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/eager/local_dep_object.h" - -namespace oneflow { - -namespace one { - -class TensorStorage; -} - -namespace vm { - -class EagerBlobObject; - -// access blob arg callback physical instruction operand -class AccessBlobArgCbPhyInstrOperand : public PhyInstrOperand { - public: - AccessBlobArgCbPhyInstrOperand(const std::shared_ptr& eager_blob_object, - const std::function& callback, - const std::string& modifier) - : eager_blob_object_(eager_blob_object), - callback_(callback), - modifier_(modifier), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - stream_sequential_dependence_ = nullptr; - } - ~AccessBlobArgCbPhyInstrOperand() = default; - - const std::function& callback() const { return callback_; } - const std::shared_ptr& eager_blob_object() const { - return eager_blob_object_; - } - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachConstMirroredObject(const std::function&) const; - - void ForEachMutMirroredObject(const std::function&) const; - - void ForEachMut2MirroredObject(const std::function&) const; - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - DoEach(eager_blob_object_.get()); - } - - private: - std::shared_ptr eager_blob_object_; - std::function callback_; - const std::string modifier_; - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_ACCESS_BLOB_ARG_CB_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/vm/allocate_tensor_instruction_policy.cpp b/oneflow/core/vm/allocate_tensor_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..37ce70871dbe34661532416dbac8f93328d8abd3 --- /dev/null +++ b/oneflow/core/vm/allocate_tensor_instruction_policy.cpp @@ -0,0 +1,42 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/allocate_tensor_instruction_policy.h" + +namespace oneflow { +namespace vm { + +AllocateTensorInstructionPolicy::AllocateTensorInstructionPolicy( + const EagerBlobObjectList& eager_blob_objects, vm::Stream* vm_stream) + : eager_blob_objects_(eager_blob_objects) { + stream_sequential_dependence_ = vm_stream->schedule_local_dep_object().get(); + for (const auto& eager_blob_object : eager_blob_objects) { + output_dependences_.push_back(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + } +} + +std::string AllocateTensorInstructionPolicy::DebugName(const vm::Instruction& instruction) const { + return "AllocateTensor"; +} + +void AllocateTensorInstructionPolicy::Compute(Instruction* instruction) { + Allocator* allocator = instruction->mut_stream()->mut_stream_policy()->mut_allocator(); + for (const auto& eager_blob_object : eager_blob_objects_) { + CHECK_JUST(eager_blob_object->TryAllocateBlobBodyMemory(allocator)); + } +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/allocate_tensor_instruction_policy.h b/oneflow/core/vm/allocate_tensor_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..81b6815d3c4715d76cbe73ab6b434e64bf300663 --- /dev/null +++ b/oneflow/core/vm/allocate_tensor_instruction_policy.h @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_ALLOCATE_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_ALLOCATE_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/stream.h" + +namespace oneflow { + +namespace vm { + +class AllocateTensorInstructionPolicy final : public InstructionPolicy { + public: + AllocateTensorInstructionPolicy(const EagerBlobObjectList& eager_blob_objects, + vm::Stream* vm_stream); + AllocateTensorInstructionPolicy(const AllocateTensorInstructionPolicy&) = delete; + AllocateTensorInstructionPolicy(AllocateTensorInstructionPolicy&&) = delete; + + ~AllocateTensorInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { + static thread_local DependenceVector input_dependences{}; + return input_dependences; + } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAtAnyPosition; } + + std::string DebugName(const vm::Instruction& instruction) const override; + + private: + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override; + + EagerBlobObjectList eager_blob_objects_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_ALLOCATE_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/allocator.h b/oneflow/core/vm/allocator.h index dc4b1bda48c6e2bf4faacd281778489c69a91330..aa4c1bcbc886f12fde098dc34dc9b22731583914 100644 --- a/oneflow/core/vm/allocator.h +++ b/oneflow/core/vm/allocator.h @@ -18,6 +18,7 @@ limitations under the License. #include #include "oneflow/core/common/maybe.h" +#include "glog/logging.h" namespace oneflow { namespace vm { @@ -34,6 +35,22 @@ class Allocator { Allocator() = default; }; +class UnimplementedAllocator final : public Allocator { + public: + explicit UnimplementedAllocator(const std::string& debug_str) : debug_str_(debug_str) {} + virtual ~UnimplementedAllocator() = default; + + Maybe Allocate(char** mem_ptr, std::size_t size) override { + UNIMPLEMENTED_THEN_RETURN() << debug_str_; + } + + void Deallocate(char* mem_ptr, std::size_t size) override { LOG(FATAL) << debug_str_; } + void DeviceReset() override { LOG(FATAL) << debug_str_; } + + private: + std::string debug_str_; +}; + } // namespace vm } // namespace oneflow diff --git a/oneflow/core/vm/barrier_instruction_policy.h b/oneflow/core/vm/barrier_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..a4dae2ad71d17ec52aa4079fbe66b136c8e4745f --- /dev/null +++ b/oneflow/core/vm/barrier_instruction_policy.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_POLICY_H_ + +#include "oneflow/core/vm/instruction_policy.h" +namespace oneflow { +namespace vm { + +class BarrierInstructionPolicy final : public InstructionPolicy { + public: + BarrierInstructionPolicy(const std::function& callback) : callback_(callback) { + stream_sequential_dependence_ = nullptr; + } + ~BarrierInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { + static DependenceVector dependences{}; + return dependences; + } + const DependenceVector& output_dependences() const override { + static DependenceVector dependences{}; + return dependences; + } + + bool IsBarrier() const override { return true; } + + std::string DebugName(const vm::Instruction& instruction) const override { return "Barrier"; } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override { return callback_(); } + + private: + std::function callback_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/barrier_instruction_type.h b/oneflow/core/vm/barrier_instruction_type.h deleted file mode 100644 index bcc7eedea260831db3e6dd225dfad7e165c4521f..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/barrier_instruction_type.h +++ /dev/null @@ -1,66 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/common/util.h" -#include "oneflow/core/rpc/include/base.h" -#include "oneflow/core/vm/control_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/virtual_machine_engine.h" -#include "oneflow/core/vm/barrier_phy_instr_operand.h" -#include "oneflow/core/control/global_process_ctx.h" - -namespace oneflow { -namespace vm { - -class BarrierInstructionType : public InstructionType { - public: - BarrierInstructionType() = default; - virtual ~BarrierInstructionType() override = default; - - bool IsBarrier() const override { return true; } - - std::string DebugName(const vm::Instruction& instruction) const override { return "Barrier"; } - Maybe Prepare(Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(Instruction* instruction) const override { Run(*instruction); } - - protected: - void Run(const Instruction& instruction) const { - const auto& phy_instr_operand = instruction.phy_instr_operand(); - const auto* operand = - CHECK_NOTNULL(dynamic_cast(phy_instr_operand.get())); - operand->callback(); - } -}; - -class GlobalSyncInstructionType : public InstructionType { - public: - GlobalSyncInstructionType() = default; - virtual ~GlobalSyncInstructionType() override = default; - - bool IsBarrier() const override { return true; } - - std::string DebugName(const Instruction& instruction) const override { return "GlobalSync"; } - Maybe Prepare(Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(Instruction* instruction) const override { OF_ENV_BARRIER(); } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_BARRIER_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/vm/barrier_phy_instr_operand.h b/oneflow/core/vm/barrier_phy_instr_operand.h deleted file mode 100644 index 78629b8024b681aaf740689b756a89f5cccdced5..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/barrier_phy_instr_operand.h +++ /dev/null @@ -1,53 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_BARRIER_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_VM_BARRIER_PHY_INSTR_OPERAND_H_ - -#include -#include "oneflow/core/vm/phy_instr_operand.h" - -namespace oneflow { -namespace vm { - -// no arg callback physical instruction operand -class BarrierPhyInstrOperand : public PhyInstrOperand { - public: - BarrierPhyInstrOperand(const std::function& callback) : callback_(callback) { - stream_sequential_dependence_ = nullptr; - } - ~BarrierPhyInstrOperand() {} - - void callback() const { return callback_(); } - - const DependenceVector& input_dependences() const override { - static DependenceVector dependences{}; - return dependences; - } - const DependenceVector& output_dependences() const override { - static DependenceVector dependences{}; - return dependences; - } - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override {} - - private: - std::function callback_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_BARRIER_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/vm/bin_allocator.cpp b/oneflow/core/vm/bin_allocator.cpp deleted file mode 100644 index 3a73d9ccf78c78150dd483df468833b3673ce524..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/bin_allocator.cpp +++ /dev/null @@ -1,316 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/bin_allocator.h" -#include -#include - -namespace oneflow { -namespace vm { - -namespace { - -inline size_t MemAlignedBytes(size_t bytes, size_t alignment) { return RoundUp(bytes, alignment); } - -inline bool IsAlignedSize(size_t size, size_t alignment) { return size % alignment == 0; } - -static const size_t kPieceSplitThreshold = 128 << 20; // 128MiB - -} // namespace - -BinAllocator::BinAllocator(size_t alignment, std::unique_ptr&& backend) - : Allocator(), - alignment_(alignment), - backend_(std::move(backend)), - total_memory_bytes_(0), - recycle_piece_list_(nullptr) { - CHECK_GE(alignment, 1); - CHECK_EQ(1 << static_cast(std::log2(alignment)), alignment); - bins_.resize(kBinNumSize); - for (int i = 0; i < kBinNumSize; ++i) { - size_t bin_size = BinSize4BinNum(i); - bins_.at(i).size = bin_size; - CHECK_EQ(BinNum4BinSize(bin_size), i); - CHECK_EQ(BinNum4BinSize(bin_size + alignment_ - 1), i); - CHECK_EQ(BinNum4BinSize(bin_size * 2 - 1), i); - CHECK_EQ(BinNum4BinSize(bin_size * 2), i == (kBinNumSize - 1) ? i : i + 1); - } -} - -BinAllocator::~BinAllocator() { - if (total_memory_bytes_ == 0) { - CHECK_EQ(mem_ptr2block_.size(), 0); - return; - } - for (auto& pair : mem_ptr2block_) { backend_->Deallocate(pair.first, pair.second.size); } -} - -void BinAllocator::InsertPiece2Bin(Piece* piece) { - CHECK(piece->is_free && piece->bin_num == kInvalidBinNum); - int32_t bin_num = BinNum4BinSize(piece->size); - piece->bin_num = bin_num; - CHECK(bins_.at(bin_num).pieces.insert(piece).second); -} - -void BinAllocator::RemovePieceFromBin(Piece* piece) { - CHECK(piece->is_free); - CHECK_NE(piece->bin_num, kInvalidBinNum); - CHECK_GT(bins_.at(piece->bin_num).pieces.erase(piece), 0); - piece->bin_num = kInvalidBinNum; -} - -BinAllocator::Piece* BinAllocator::AllocatePiece() { - if (recycle_piece_list_) { - Piece* ret = recycle_piece_list_; - recycle_piece_list_ = recycle_piece_list_->next; - return ret; - } else { - pieces_.emplace_back(new Piece()); - return pieces_.at(pieces_.size() - 1).get(); - } -} - -void BinAllocator::DeallocatePiece(Piece* piece) { - piece->ptr = nullptr; - piece->size = 0; - piece->bin_num = kInvalidBinNum; - piece->is_free = true; - piece->prev = nullptr; - piece->next = recycle_piece_list_; - recycle_piece_list_ = piece; -} - -void BinAllocator::MarkPiece(Piece* piece) { - CHECK_NOTNULL(piece->ptr); - CHECK(ptr2piece_.emplace(piece->ptr, piece).second); -} -void BinAllocator::UnMarkPiece(Piece* piece) { - CHECK_NOTNULL(piece->ptr); - auto it = ptr2piece_.find(piece->ptr); - CHECK(it != ptr2piece_.end()); - ptr2piece_.erase(it); -} - -BinAllocator::Piece* BinAllocator::FindPiece(size_t aligned_size) { - CHECK(IsAlignedSize(aligned_size, alignment_)); - for (int32_t bin_num = BinNum4BinSize(aligned_size); bin_num < kBinNumSize; ++bin_num) { - Bin* bin = &bins_.at(bin_num); - for (auto it = bin->pieces.begin(); it != bin->pieces.end(); ++it) { - Piece* piece = *it; - CHECK(piece->is_free); - CHECK_NOTNULL(piece->ptr); - CHECK_EQ(piece->bin_num, bin_num); - CHECK(IsAlignedSize(piece->size, alignment_)); - if (piece->size >= aligned_size) { - bin->pieces.erase(it); - piece->bin_num = kInvalidBinNum; - piece->is_free = false; - if (piece->size >= aligned_size * 2 || piece->size - aligned_size >= kPieceSplitThreshold) { - Piece* new_piece = AllocatePiece(); - new_piece->ptr = piece->ptr + aligned_size; - new_piece->size = piece->size - aligned_size; - piece->size = aligned_size; - - Piece* next_p = piece->next; - piece->next = new_piece; - new_piece->prev = piece; - new_piece->next = next_p; - if (next_p != nullptr) { next_p->prev = new_piece; } - - new_piece->is_free = true; - new_piece->bin_num = kInvalidBinNum; - CHECK(IsAlignedSize(piece->size, alignment_)); - CHECK(IsAlignedSize(new_piece->size, alignment_)); - InsertPiece2Bin(new_piece); - MarkPiece(new_piece); - } - return piece; - } - } - } - return nullptr; -} - -void BinAllocator::MergeNeighbourFreePiece(Piece* lhs, Piece* rhs) { - CHECK(lhs->is_free); - CHECK(rhs->is_free); - CHECK(lhs->next == rhs); - CHECK(lhs == rhs->prev); - CHECK(lhs->ptr + lhs->size == rhs->ptr); - - lhs->size += rhs->size; - lhs->next = rhs->next; - if (rhs->next != nullptr) { rhs->next->prev = lhs; } - UnMarkPiece(rhs); - DeallocatePiece(rhs); -} - -Maybe BinAllocator::AllocateBlockToExtendTotalMem(size_t aligned_size) { - CHECK_OR_RETURN(IsAlignedSize(aligned_size, alignment_)) << "not aligned"; - - size_t allocate_bytes = aligned_size; - if (allocate_bytes < 1048576) { - // Allocate 2MB if `allocate_bytes` is less than 1MB - allocate_bytes = 2097152; - } else if (allocate_bytes < 10485760) { - // Allocate 20MB if `allocate_bytes` is between 1MB and 10MB - allocate_bytes = 20971520; - } else { - // Round up to 2MB if `allocate_bytes` is larger than 10MB - allocate_bytes = RoundUp(allocate_bytes, 2097152); - } - const size_t final_allocate_bytes = MemAlignedBytes(allocate_bytes, alignment_); - - if (final_allocate_bytes < aligned_size) { return false; } - - char* mem_ptr = nullptr; - JUST(backend_->Allocate(&mem_ptr, final_allocate_bytes)); - if (mem_ptr == nullptr) { return false; } - - // extend sucess - total_memory_bytes_ += final_allocate_bytes; - - Piece* piece = AllocatePiece(); - piece->size = final_allocate_bytes; - piece->ptr = mem_ptr; - piece->prev = nullptr; - piece->next = nullptr; - piece->is_free = true; - piece->bin_num = kInvalidBinNum; - InsertPiece2Bin(piece); - MarkPiece(piece); - - CHECK_OR_RETURN(mem_ptr2block_.emplace(mem_ptr, Block(piece)).second) << "existed mem_ptr"; - - return true; -} - -bool BinAllocator::DeallocateFreeBlockForGarbageCollection() { - size_t total_free_bytes = 0; - HashSet free_block_ptrs; - for (const auto& pair : mem_ptr2block_) { - const Block& block = pair.second; - bool all_free = true; - Piece* p = block.start_piece; - while (p != nullptr) { - if (!(p->is_free)) { - all_free = false; - break; - } - p = p->next; - } - - if (all_free) { - total_free_bytes += block.size; - free_block_ptrs.insert(pair.first); - } - } - - total_memory_bytes_ -= total_free_bytes; - - if (total_free_bytes > 0) { - VLOG(3) << "BinAllocator try deallocate free block for garbage collection. " - << " deallocate free bytes : " << total_free_bytes; - for (char* ptr : free_block_ptrs) { - auto it = mem_ptr2block_.find(ptr); - CHECK(it != mem_ptr2block_.end()); - const Block& block = it->second; - - // delete all Piece on Block - size_t piece_size_sum = 0; - Piece* p = block.start_piece; - CHECK_EQ(block.ptr, block.start_piece->ptr); - CHECK_EQ(block.ptr, ptr); - while (p != nullptr) { - Piece* next_p = p->next; - piece_size_sum += p->size; - RemovePieceFromBin(p); - UnMarkPiece(p); - DeallocatePiece(p); - p = next_p; - } - CHECK_EQ(block.size, piece_size_sum); - - mem_ptr2block_.erase(it); - backend_->Deallocate(ptr, block.size); - } - } - return total_free_bytes > 0; -} - -Maybe BinAllocator::Allocate(char** mem_ptr, std::size_t size) { - if (size == 0) { - *mem_ptr = nullptr; - return Maybe::Ok(); - } - size_t aligned_size = MemAlignedBytes(size, alignment_); - - Piece* piece = FindPiece(aligned_size); - - if (piece == nullptr) { - if (JUST(AllocateBlockToExtendTotalMem(aligned_size))) { piece = FindPiece(aligned_size); } - } - - CHECK_NOTNULL_OR_RETURN(piece) - << Error::OutOfMemoryError() << "Error! : Out of memory when allocate size : " << size - << ".\n The total_memory_bytes allocated by this BinAllocator is : " << total_memory_bytes_; - - if (piece == nullptr) { - backend_->DeviceReset(); - LOG(FATAL) << "Error! : Out of memory when allocate size : " << size - << ".\n The total_memory_bytes allocated by this BinAllocator is : " - << total_memory_bytes_; - } - CHECK_NOTNULL_OR_RETURN(piece->ptr) << "invalid piece null ptr"; - CHECK_OR_RETURN(ptr2piece_.find(piece->ptr) != ptr2piece_.end()) << "piece is not found"; - *mem_ptr = piece->ptr; - return Maybe::Ok(); -} - -void BinAllocator::Deallocate(char* mem_ptr, std::size_t size) { - if (mem_ptr == nullptr) { return; } - - auto it = ptr2piece_.find(mem_ptr); - CHECK(it != ptr2piece_.end()) << "Error! : Try deallocate mem_ptr non-existent. mem ptr = " - << mem_ptr << " size = " << size; - Piece* piece = it->second; - CHECK_NOTNULL(piece); - CHECK_EQ(piece->ptr, mem_ptr); - CHECK(!piece->is_free); - - piece->is_free = true; - - Piece* last_piece_insert_to_bin = piece; - Piece* next_p = piece->next; - Piece* prev_p = piece->prev; - - if (next_p != nullptr && next_p->is_free) { - CHECK_EQ(next_p->ptr, piece->ptr + piece->size); - RemovePieceFromBin(next_p); - MergeNeighbourFreePiece(piece, next_p); - } - - if (prev_p != nullptr && prev_p->is_free) { - CHECK_EQ(piece->ptr, prev_p->ptr + prev_p->size); - RemovePieceFromBin(prev_p); - MergeNeighbourFreePiece(prev_p, piece); - last_piece_insert_to_bin = prev_p; - } - InsertPiece2Bin(last_piece_insert_to_bin); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/bin_allocator.h b/oneflow/core/vm/bin_allocator.h index 83b25670eb18e1ef20b4b1373da0e6bb9a18d253..8a384f1d7f97497e48816e904004b7b7da80cd73 100644 --- a/oneflow/core/vm/bin_allocator.h +++ b/oneflow/core/vm/bin_allocator.h @@ -18,21 +18,28 @@ limitations under the License. #include #include "oneflow/core/vm/allocator.h" -#include "oneflow/core/vm/shrinkable_cache.h" +#include "oneflow/core/vm/caching_allocator.h" #include "oneflow/core/common/util.h" namespace oneflow { namespace vm { -class BinAllocator final : public Allocator, public ShrinkableCache { +template +class BinAllocator final : public CachingAllocator { public: explicit BinAllocator(size_t alignment, std::unique_ptr&& backend); - ~BinAllocator() override; + ~BinAllocator(); Maybe Allocate(char** mem_ptr, std::size_t size) override; void Deallocate(char* mem_ptr, std::size_t size) override; - void Shrink() override { DeallocateFreeBlockForGarbageCollection(); } - void DeviceReset() override { backend_->DeviceReset(); } + void DeviceReset() override { + typename ThreadLock::RAIIGuard guard(thread_lock_); + backend_->DeviceReset(); + } + void Shrink() override { + typename ThreadLock::RAIIGuard guard(thread_lock_); + DeallocateFreeBlockForGarbageCollection(); + } private: static constexpr int32_t kInvalidBinNum = -1; @@ -118,6 +125,7 @@ class BinAllocator final : public Allocator, public ShrinkableCache { const size_t alignment_; const std::unique_ptr backend_; + ThreadLock thread_lock_; size_t total_memory_bytes_; HashMap mem_ptr2block_; @@ -127,6 +135,313 @@ class BinAllocator final : public Allocator, public ShrinkableCache { Piece* recycle_piece_list_; }; +namespace { + +inline size_t MemAlignedBytes(size_t bytes, size_t alignment) { return RoundUp(bytes, alignment); } + +inline bool IsAlignedSize(size_t size, size_t alignment) { return size % alignment == 0; } + +static const size_t kPieceSplitThreshold = 128 << 20; // 128MiB + +} // namespace + +template +BinAllocator::BinAllocator(size_t alignment, std::unique_ptr&& backend) + : CachingAllocator(), + alignment_(alignment), + backend_(std::move(backend)), + total_memory_bytes_(0), + recycle_piece_list_(nullptr) { + CHECK_GE(alignment, 1); + CHECK_EQ(1 << static_cast(std::log2(alignment)), alignment); + bins_.resize(kBinNumSize); + for (int i = 0; i < kBinNumSize; ++i) { + size_t bin_size = BinSize4BinNum(i); + bins_.at(i).size = bin_size; + CHECK_EQ(BinNum4BinSize(bin_size), i); + CHECK_EQ(BinNum4BinSize(bin_size + alignment_ - 1), i); + CHECK_EQ(BinNum4BinSize(bin_size * 2 - 1), i); + CHECK_EQ(BinNum4BinSize(bin_size * 2), i == (kBinNumSize - 1) ? i : i + 1); + } +} + +template +BinAllocator::~BinAllocator() { + if (total_memory_bytes_ == 0) { + CHECK_EQ(mem_ptr2block_.size(), 0); + return; + } + for (auto& pair : mem_ptr2block_) { backend_->Deallocate(pair.first, pair.second.size); } +} + +template +void BinAllocator::InsertPiece2Bin(Piece* piece) { + CHECK(piece->is_free && piece->bin_num == kInvalidBinNum); + int32_t bin_num = BinNum4BinSize(piece->size); + piece->bin_num = bin_num; + CHECK(bins_.at(bin_num).pieces.insert(piece).second); +} + +template +void BinAllocator::RemovePieceFromBin(Piece* piece) { + CHECK(piece->is_free); + CHECK_NE(piece->bin_num, kInvalidBinNum); + CHECK_GT(bins_.at(piece->bin_num).pieces.erase(piece), 0); + piece->bin_num = kInvalidBinNum; +} + +template +typename BinAllocator::Piece* BinAllocator::AllocatePiece() { + if (recycle_piece_list_) { + Piece* ret = recycle_piece_list_; + recycle_piece_list_ = recycle_piece_list_->next; + return ret; + } else { + pieces_.emplace_back(new Piece()); + return pieces_.at(pieces_.size() - 1).get(); + } +} + +template +void BinAllocator::DeallocatePiece(Piece* piece) { + piece->ptr = nullptr; + piece->size = 0; + piece->bin_num = kInvalidBinNum; + piece->is_free = true; + piece->prev = nullptr; + piece->next = recycle_piece_list_; + recycle_piece_list_ = piece; +} + +template +void BinAllocator::MarkPiece(Piece* piece) { + CHECK_NOTNULL(piece->ptr); + CHECK(ptr2piece_.emplace(piece->ptr, piece).second); +} +template +void BinAllocator::UnMarkPiece(Piece* piece) { + CHECK_NOTNULL(piece->ptr); + auto it = ptr2piece_.find(piece->ptr); + CHECK(it != ptr2piece_.end()); + ptr2piece_.erase(it); +} + +template +typename BinAllocator::Piece* BinAllocator::FindPiece(size_t aligned_size) { + CHECK(IsAlignedSize(aligned_size, alignment_)); + for (int32_t bin_num = BinNum4BinSize(aligned_size); bin_num < kBinNumSize; ++bin_num) { + Bin* bin = &bins_.at(bin_num); + for (auto it = bin->pieces.begin(); it != bin->pieces.end(); ++it) { + Piece* piece = *it; + CHECK(piece->is_free); + CHECK_NOTNULL(piece->ptr); + CHECK_EQ(piece->bin_num, bin_num); + CHECK(IsAlignedSize(piece->size, alignment_)); + if (piece->size >= aligned_size) { + bin->pieces.erase(it); + piece->bin_num = kInvalidBinNum; + piece->is_free = false; + if (piece->size >= aligned_size * 2 || piece->size - aligned_size >= kPieceSplitThreshold) { + Piece* new_piece = AllocatePiece(); + new_piece->ptr = piece->ptr + aligned_size; + new_piece->size = piece->size - aligned_size; + piece->size = aligned_size; + + Piece* next_p = piece->next; + piece->next = new_piece; + new_piece->prev = piece; + new_piece->next = next_p; + if (next_p != nullptr) { next_p->prev = new_piece; } + + new_piece->is_free = true; + new_piece->bin_num = kInvalidBinNum; + CHECK(IsAlignedSize(piece->size, alignment_)); + CHECK(IsAlignedSize(new_piece->size, alignment_)); + InsertPiece2Bin(new_piece); + MarkPiece(new_piece); + } + return piece; + } + } + } + return nullptr; +} + +template +void BinAllocator::MergeNeighbourFreePiece(Piece* lhs, Piece* rhs) { + CHECK(lhs->is_free); + CHECK(rhs->is_free); + CHECK(lhs->next == rhs); + CHECK(lhs == rhs->prev); + CHECK(lhs->ptr + lhs->size == rhs->ptr); + + lhs->size += rhs->size; + lhs->next = rhs->next; + if (rhs->next != nullptr) { rhs->next->prev = lhs; } + UnMarkPiece(rhs); + DeallocatePiece(rhs); +} + +template +Maybe BinAllocator::AllocateBlockToExtendTotalMem(size_t aligned_size) { + CHECK_OR_RETURN(IsAlignedSize(aligned_size, alignment_)) << "not aligned"; + + size_t allocate_bytes = aligned_size; + if (allocate_bytes < 1048576) { + // Allocate 2MB if `allocate_bytes` is less than 1MB + allocate_bytes = 2097152; + } else if (allocate_bytes < 10485760) { + // Allocate 20MB if `allocate_bytes` is between 1MB and 10MB + allocate_bytes = 20971520; + } else { + // Round up to 2MB if `allocate_bytes` is larger than 10MB + allocate_bytes = RoundUp(allocate_bytes, 2097152); + } + const size_t final_allocate_bytes = MemAlignedBytes(allocate_bytes, alignment_); + + if (final_allocate_bytes < aligned_size) { return false; } + + char* mem_ptr = nullptr; + JUST(backend_->Allocate(&mem_ptr, final_allocate_bytes)); + if (mem_ptr == nullptr) { return false; } + + // extend sucess + total_memory_bytes_ += final_allocate_bytes; + + Piece* piece = AllocatePiece(); + piece->size = final_allocate_bytes; + piece->ptr = mem_ptr; + piece->prev = nullptr; + piece->next = nullptr; + piece->is_free = true; + piece->bin_num = kInvalidBinNum; + InsertPiece2Bin(piece); + MarkPiece(piece); + + CHECK_OR_RETURN(mem_ptr2block_.emplace(mem_ptr, Block(piece)).second) << "existed mem_ptr"; + + return true; +} + +template +bool BinAllocator::DeallocateFreeBlockForGarbageCollection() { + size_t total_free_bytes = 0; + HashSet free_block_ptrs; + for (const auto& pair : mem_ptr2block_) { + const Block& block = pair.second; + bool all_free = true; + Piece* p = block.start_piece; + while (p != nullptr) { + if (!(p->is_free)) { + all_free = false; + break; + } + p = p->next; + } + + if (all_free) { + total_free_bytes += block.size; + free_block_ptrs.insert(pair.first); + } + } + + total_memory_bytes_ -= total_free_bytes; + + if (total_free_bytes > 0) { + VLOG(3) << "BinAllocator try deallocate free block for garbage collection. " + << " deallocate free bytes : " << total_free_bytes; + for (char* ptr : free_block_ptrs) { + auto it = mem_ptr2block_.find(ptr); + CHECK(it != mem_ptr2block_.end()); + const Block& block = it->second; + + // delete all Piece on Block + size_t piece_size_sum = 0; + Piece* p = block.start_piece; + CHECK_EQ(block.ptr, block.start_piece->ptr); + CHECK_EQ(block.ptr, ptr); + while (p != nullptr) { + Piece* next_p = p->next; + piece_size_sum += p->size; + RemovePieceFromBin(p); + UnMarkPiece(p); + DeallocatePiece(p); + p = next_p; + } + CHECK_EQ(block.size, piece_size_sum); + + mem_ptr2block_.erase(it); + backend_->Deallocate(ptr, block.size); + } + } + return total_free_bytes > 0; +} + +template +Maybe BinAllocator::Allocate(char** mem_ptr, std::size_t size) { + typename ThreadLock::RAIIGuard guard(thread_lock_); + if (size == 0) { + *mem_ptr = nullptr; + return Maybe::Ok(); + } + size_t aligned_size = MemAlignedBytes(size, alignment_); + + Piece* piece = FindPiece(aligned_size); + + if (piece == nullptr) { + if (JUST(AllocateBlockToExtendTotalMem(aligned_size))) { piece = FindPiece(aligned_size); } + } + + CHECK_NOTNULL_OR_RETURN(piece) + << Error::OutOfMemoryError() << "Error! : Out of memory when allocate size : " << size + << ".\n The total_memory_bytes allocated by this BinAllocator is : " << total_memory_bytes_; + + if (piece == nullptr) { + backend_->DeviceReset(); + LOG(FATAL) << "Error! : Out of memory when allocate size : " << size + << ".\n The total_memory_bytes allocated by this BinAllocator is : " + << total_memory_bytes_; + } + CHECK_NOTNULL_OR_RETURN(piece->ptr) << "invalid piece null ptr"; + CHECK_OR_RETURN(ptr2piece_.find(piece->ptr) != ptr2piece_.end()) << "piece is not found"; + *mem_ptr = piece->ptr; + return Maybe::Ok(); +} + +template +void BinAllocator::Deallocate(char* mem_ptr, std::size_t size) { + if (mem_ptr == nullptr) { return; } + typename ThreadLock::RAIIGuard guard(thread_lock_); + + auto it = ptr2piece_.find(mem_ptr); + CHECK(it != ptr2piece_.end()) << "Error! : Try deallocate mem_ptr non-existent. mem ptr = " + << mem_ptr << " size = " << size; + Piece* piece = it->second; + CHECK_NOTNULL(piece); + CHECK_EQ(piece->ptr, mem_ptr); + CHECK(!piece->is_free); + + piece->is_free = true; + + Piece* last_piece_insert_to_bin = piece; + Piece* next_p = piece->next; + Piece* prev_p = piece->prev; + + if (next_p != nullptr && next_p->is_free) { + CHECK_EQ(next_p->ptr, piece->ptr + piece->size); + RemovePieceFromBin(next_p); + MergeNeighbourFreePiece(piece, next_p); + } + + if (prev_p != nullptr && prev_p->is_free) { + CHECK_EQ(piece->ptr, prev_p->ptr + prev_p->size); + RemovePieceFromBin(prev_p); + MergeNeighbourFreePiece(prev_p, piece); + last_piece_insert_to_bin = prev_p; + } + InsertPiece2Bin(last_piece_insert_to_bin); +} + } // namespace vm } // namespace oneflow diff --git a/oneflow/core/vm/bin_allocator_test.cpp b/oneflow/core/vm/bin_allocator_test.cpp index 02f74f18f5d42d34cce20a164b832c35d8f84dca..80ea8590be989e5d7c813cc751b2f188fceffd74 100644 --- a/oneflow/core/vm/bin_allocator_test.cpp +++ b/oneflow/core/vm/bin_allocator_test.cpp @@ -13,16 +13,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include #ifdef WITH_CUDA #include "gtest/gtest.h" #include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/thread_safe_allocator.h" +#include "oneflow/core/vm/thread_safe_guard.h" #include "oneflow/core/device/cuda_util.h" namespace oneflow { namespace vm { -class CudaBackendAllocator final : public Allocator { +class CudaBackendAllocator final : public CachingAllocator { public: explicit CudaBackendAllocator(int64_t device_id) : device_id_(device_id) {} ~CudaBackendAllocator() override = default; @@ -30,6 +31,7 @@ class CudaBackendAllocator final : public Allocator { Maybe Allocate(char** mem_ptr, std::size_t size) override; void Deallocate(char* mem_ptr, std::size_t size) override; void DeviceReset() override; + void Shrink() override{}; private: int64_t device_id_; @@ -72,9 +74,8 @@ TEST(CudaBinAllocator, cuda_allocator) { << "CudaBinAllocator Test: Skip because of allocator mem bytes less than 50MiB in GPU 0"; return; } - std::unique_ptr allo( - new BinAllocator(kCudaMemAllocAlignSize, std::make_unique(0))); - allo.reset(new SingleThreadOnlyAllocator(std::move(allo))); + std::unique_ptr allo(new BinAllocator( + kCudaMemAllocAlignSize, std::make_unique(0))); Allocator* a = allo.get(); std::vector ptrs; for (int i = 0; i < 512; ++i) { @@ -134,13 +135,13 @@ TEST(CudaBinAllocator, cuda_allocator) { #include "gtest/gtest.h" #include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/thread_safe_allocator.h" +#include "oneflow/core/vm/thread_safe_guard.h" #include "oneflow/core/device/cuda_util.h" namespace oneflow { namespace vm { -class CudaBackendAllocator final : public Allocator { +class CudaBackendAllocator final : public CachingAllocator { public: explicit CudaBackendAllocator(int64_t device_id) : device_id_(device_id) {} ~CudaBackendAllocator() override = default; @@ -148,6 +149,7 @@ class CudaBackendAllocator final : public Allocator { Maybe Allocate(char** mem_ptr, std::size_t size) override; void Deallocate(char* mem_ptr, std::size_t size) override; void DeviceReset() override; + void Shrink() override{}; private: int64_t device_id_; @@ -190,9 +192,9 @@ TEST(CudaBinAllocator, cuda_allocator) { << "CudaBinAllocator Test: Skip because of allocator mem bytes less than 50MiB in GPU 0"; return; } - std::unique_ptr allo( - new BinAllocator(kCudaMemAllocAlignSize, std::make_unique(0))); - allo.reset(new SingleThreadOnlyAllocator(std::move(allo))); + std::unique_ptr allo(new BinAllocator( + kCudaMemAllocAlignSize, std::make_unique(0))); + Allocator* a = allo.get(); std::vector ptrs; for (int i = 0; i < 512; ++i) { diff --git a/oneflow/core/vm/caching_allocator.h b/oneflow/core/vm/caching_allocator.h new file mode 100644 index 0000000000000000000000000000000000000000..5d6e207b7060d80a59a476a4dd270f7ea19afc44 --- /dev/null +++ b/oneflow/core/vm/caching_allocator.h @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_CACHING_ALLOCATOR_H_ +#define ONEFLOW_CORE_VM_CACHING_ALLOCATOR_H_ + +#include +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/vm/allocator.h" + +namespace oneflow { +namespace vm { + +class CachingAllocator : public Allocator { + public: + virtual ~CachingAllocator() = default; + virtual void Shrink() = 0; + + protected: + CachingAllocator() = default; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_CACHING_ALLOCATOR_H_ diff --git a/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.cpp b/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.cpp deleted file mode 100644 index fb1bdbfa3c8dd24554d24f7105f6d0cde88807f8..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.cpp +++ /dev/null @@ -1,45 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/consume_local_dep_object_phy_instr_operand.h" -#include "oneflow/core/eager/local_dep_object.h" - -namespace oneflow { - -namespace vm { - -void ConsumeLocalDepObjectPhyInstrOperand::ForEachConstMirroredObject( - const std::function& DoEach) const { - if (modifier_ == "const") { - for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } - } -} - -void ConsumeLocalDepObjectPhyInstrOperand::ForEachMutMirroredObject( - const std::function& DoEach) const { - if (modifier_ == "mut") { - for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } - } -} - -void ConsumeLocalDepObjectPhyInstrOperand::ForEachMut2MirroredObject( - const std::function& DoEach) const { - if (modifier_ == "mut2") { - for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } - } -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.h b/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.h deleted file mode 100644 index 7da8748d0d012716c7c093ea3137f64c833db830..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/consume_local_dep_object_phy_instr_operand.h +++ /dev/null @@ -1,65 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_CONSUME_LOCAL_DEP_OBJECT_H -#define ONEFLOW_CORE_VM_CONSUME_LOCAL_DEP_OBJECT_H - -#include -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/eager/local_dep_object.h" - -namespace oneflow { - -namespace vm { - -class ConsumeLocalDepObjectPhyInstrOperand : public PhyInstrOperand { - public: - ConsumeLocalDepObjectPhyInstrOperand( - std::vector>&& compute_local_dep_objects, - const std::string& modifier) - : compute_local_dep_objects_(std::move(compute_local_dep_objects)), - modifier_(modifier), - input_dependences_(), - output_dependences_() { - ForEachConstMirroredObject(SetInserter(&input_dependences_)); - ForEachMutMirroredObject(SetInserter(&output_dependences_)); - ForEachMut2MirroredObject(SetInserter(&output_dependences_)); - stream_sequential_dependence_ = nullptr; - } - - ~ConsumeLocalDepObjectPhyInstrOperand() = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - void ForEachConstMirroredObject(const std::function&) const; - - void ForEachMutMirroredObject(const std::function&) const; - - void ForEachMut2MirroredObject(const std::function&) const; - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override {} - - private: - std::vector> compute_local_dep_objects_; - const std::string modifier_; - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CONSUME_LOCAL_DEP_OBJECT_H diff --git a/oneflow/core/vm/control_stream_policy.h b/oneflow/core/vm/control_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..22e13abbf588cc94e29823c9065afae27e79fbb3 --- /dev/null +++ b/oneflow/core/vm/control_stream_policy.h @@ -0,0 +1,75 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_CONTROL_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_CONTROL_STREAM_POLICY_H_ + +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/naive_instruction_status_querier.h" +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/vm/vm_object.h" + +namespace oneflow { +namespace vm { + +class ControlStreamPolicy final : public StreamPolicy { + public: + ControlStreamPolicy() = default; + ~ControlStreamPolicy() = default; + + vm::Allocator* mut_allocator() override { return (vm::Allocator*)nullptr; } + + DeviceType device_type() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + return DeviceType::kInvalidDevice; + } + + ep::Stream* stream() override { + PRINT_BUG_PROMPT_AND_ABORT(); + return nullptr; + } + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override { + static_assert(sizeof(NaiveInstrStatusQuerier) < kInstructionStatusBufferBytes, ""); + NaiveInstrStatusQuerier::PlacementNew(status_buffer->mut_buffer()); + } + void DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override { + auto* ptr = NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer()); + ptr->~NaiveInstrStatusQuerier(); + } + bool QueryInstructionStatusLaunched(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override { + return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->launched(); + } + bool QueryInstructionStatusDone(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override { + return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->done(); + } + void Run(Instruction* instruction) const override { + instruction->Compute(); + auto* status_buffer = instruction->mut_status_buffer(); + NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer())->set_done(); + } + + bool OnSchedulerThread(StreamType) const override { return true; } + bool SupportingTransportInstructions() const override { return false; } +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_CONTROL_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/control_stream_type.cpp b/oneflow/core/vm/control_stream_type.cpp deleted file mode 100644 index bd21a30964ed98353465ea7fe99c1df4ff6767d9..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/control_stream_type.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/control_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/virtual_machine_engine.h" -#include "oneflow/core/vm/naive_instruction_status_querier.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -void ControlStreamType::Run(Instruction* instruction) const { - instruction->Compute(); - auto* status_buffer = instruction->mut_status_buffer(); - NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer())->set_done(); -} - -void ControlStreamType::InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(NaiveInstrStatusQuerier) < kInstructionStatusBufferBytes, ""); - NaiveInstrStatusQuerier::PlacementNew(status_buffer->mut_buffer()); -} - -void ControlStreamType::DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - auto* ptr = NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~NaiveInstrStatusQuerier(); -} - -bool ControlStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/control_stream_type.h b/oneflow/core/vm/control_stream_type.h deleted file mode 100644 index 6c7cd69d9cfec887919ba65ce5f284c07153244c..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/control_stream_type.h +++ /dev/null @@ -1,49 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_CONTROL_VM_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_CONTROL_VM_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" - -namespace oneflow { -namespace vm { - -class Instruction; - -class ControlStreamType final : public StreamType { - public: - ControlStreamType() = default; - ~ControlStreamType() = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override {} - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - - bool OnSchedulerThread(StreamRole) const override { return true; } - bool SupportingTransportInstructions() const override { return false; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CONTROL_VM_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/cpu_allocator.cpp b/oneflow/core/vm/cpu_allocator.cpp deleted file mode 100644 index 9f306677af8594de40091f0802d1a6db31bb0555..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cpu_allocator.cpp +++ /dev/null @@ -1,33 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include "oneflow/core/vm/cpu_allocator.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -Maybe CpuAllocator::Allocate(char** mem_ptr, std::size_t size) { - *mem_ptr = reinterpret_cast(aligned_alloc(kHostAlignSize, size)); - return Maybe::Ok(); -} - -void CpuAllocator::Deallocate(char* mem_ptr, std::size_t size) { std::free(mem_ptr); } - -COMMAND(Singleton::SetAllocated(new CpuAllocator())); - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/cpu_allocator.h b/oneflow/core/vm/cpu_allocator.h deleted file mode 100644 index 55e6f8787ca1de96f3681448aa0edbac328a1906..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cpu_allocator.h +++ /dev/null @@ -1,38 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_CPU_ALLOCATOR_H_ -#define ONEFLOW_CORE_VM_CPU_ALLOCATOR_H_ - -#include -#include "oneflow/core/vm/allocator.h" - -namespace oneflow { -namespace vm { - -class CpuAllocator final : public Allocator { - public: - explicit CpuAllocator() = default; - ~CpuAllocator() override = default; - - Maybe Allocate(char** mem_ptr, std::size_t size) override; - void Deallocate(char* mem_ptr, std::size_t size) override; - void DeviceReset() override {} -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CPU_ALLOCATOR_H_ diff --git a/oneflow/core/vm/critical_section_instruction_policy.cpp b/oneflow/core/vm/critical_section_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8743c9d0da40517a6b736a44cf63e59ab62c1995 --- /dev/null +++ b/oneflow/core/vm/critical_section_instruction_policy.cpp @@ -0,0 +1,113 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/critical_section_instruction_policy.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/device/ep_based_event_record.h" +#include "oneflow/core/common/container_util.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/vm_object.h" + +namespace oneflow { +namespace vm { + +void CriticalSectionBeginInstructionPolicy::ForEachDependence( + const std::function& DoEach) const { + for (const auto& eager_blob_object : *eager_blob_objects_) { + DoEach(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + } +} + +void CriticalSectionBeginInstructionPolicy::ForEachMutDependence( + const std::function& DoEach) const { + DoEach(vm_stream_->schedule_local_dep_object().get()); +} + +void CriticalSectionBeginInstructionPolicy::FinishInvalidInterfaceEventRecords() { + for (const auto& op_name : interfaces_op_names()) { + size_t index = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); + if (!interfaces_valid().at(index)) { + const auto& iter = op_name2end_event_record_->find(op_name); + CHECK(iter != op_name2end_event_record_->end()); + iter->second->Init(std::make_shared()); + } + } +} + +void CriticalSectionBeginInstructionPolicy::Finish() { + for (const auto& pair : *op_name2end_event_record_) { + pair.second->TryInit(std::make_shared()); + } +} + +void InputCriticalSectionBeginInstructionPolicy::AccessBlobByOpName(ep::Stream* stream, Blob* blob, + const std::string& op_name) { + int64_t i = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); + CHECK(interfaces_valid().at(i)); + const auto& eager_blob_object = eager_blob_objects_->at(i); + { + size_t header_size = blob->blob_desc().ByteSizeOfBlobHeader(); + CHECK_EQ(header_size, eager_blob_object->shape().NumAxes() * sizeof(int64_t)); + CHECK_EQ(blob->static_shape(), eager_blob_object->shape()); + } + const auto& end_event_record = op_name2end_event_record_->at(op_name); + if (eager_blob_object->dptr() == nullptr) { + end_event_record->Init(std::make_shared()); + } else { + { + const size_t body_bytes = blob->ByteSizeOfBlobBody(); + CHECK_EQ(eager_blob_object->ByteSizeOfBlobBody(), body_bytes); + AutoMemcpy(stream, blob->mut_dptr(), eager_blob_object->dptr(), body_bytes, blob->mem_case(), + eager_blob_object->mem_case()); + } + end_event_record->Init(EpBasedEventRecord::MakeEventRecord(stream)); + } +} + +void OutputCriticalSectionBeginInstructionPolicy::AccessBlobByOpName(ep::Stream* stream, Blob* blob, + const std::string& op_name) { + int64_t i = CHECK_JUST(MapAt(op_name2interface_index_, op_name)); + CHECK(interfaces_valid().at(i)); + auto& eager_blob_object = eager_blob_objects_->at(i); + CHECK_EQ(blob->static_shape(), eager_blob_object->shape()); + const auto& end_event_record = op_name2end_event_record_->at(op_name); + if (eager_blob_object->dptr() == nullptr) { + end_event_record->Init(std::make_shared()); + } else { + { + const size_t body_bytes = blob->ByteSizeOfBlobBody(); + CHECK_EQ(eager_blob_object->ByteSizeOfBlobBody(), body_bytes); + AutoMemcpy(stream, eager_blob_object->mut_dptr(), blob->dptr(), body_bytes, + eager_blob_object->mem_case(), blob->mem_case()); + } + end_event_record->Init(EpBasedEventRecord::MakeEventRecord(stream)); + } +} + +void CriticalSectionEndInstructionPolicy::ForEachDependence( + const std::function& DoEach) const { + DoEach(CHECK_JUST(eager_blob_object_->compute_local_dep_object())); +} + +void CriticalSectionEndInstructionPolicy::ForEachMutDependence( + const std::function& DoEach) const { + DoEach(vm_stream_->schedule_local_dep_object().get()); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/critical_section_instruction_policy.h b/oneflow/core/vm/critical_section_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..edbc5fd86569770d3bd4c30f1aafee769fc1344e --- /dev/null +++ b/oneflow/core/vm/critical_section_instruction_policy.h @@ -0,0 +1,352 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_CRITICAL_SECTION_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_CRITICAL_SECTION_INSTRUCTION_POLICY_H_ + +#include "oneflow/core/common/buffer_manager.h" +#include "oneflow/core/device/event_record.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/nn_graph_if.h" +#include "oneflow/core/job/critical_section_instance.h" +#include "oneflow/core/vm/critical_section_status_querier.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/instruction_policy_util.h" +#include "oneflow/core/vm/stream.h" + +namespace oneflow { + +namespace vm { + +class CriticalSectionBeginInstructionPolicy + : public InstructionPolicy, + public std::enable_shared_from_this { + public: + CriticalSectionBeginInstructionPolicy(const CriticalSectionBeginInstructionPolicy&) = delete; + CriticalSectionBeginInstructionPolicy(CriticalSectionBeginInstructionPolicy&&) = delete; + CriticalSectionBeginInstructionPolicy& operator=(const CriticalSectionBeginInstructionPolicy&) = + delete; + CriticalSectionBeginInstructionPolicy& operator=(CriticalSectionBeginInstructionPolicy&&) = + delete; + virtual ~CriticalSectionBeginInstructionPolicy() = default; + explicit CriticalSectionBeginInstructionPolicy( + const std::shared_ptr& nn_graph, const EagerBlobObjectListPtr& eager_blob_objects, + const std::shared_ptr>>& + op_name2end_event_record, + Stream* vm_stream) + : nn_graph_(nn_graph), + eager_blob_objects_(eager_blob_objects), + op_name2end_event_record_(op_name2end_event_record), + vm_stream_(vm_stream) {} + + std::string DebugName(const Instruction& instruction) const override { + return "CriticalSectionBegin"; + } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(vm::Instruction* instruction) override { + OF_PROFILER_RANGE_GUARD("CriticalSectionBegin"); + { + const auto& critical_section_instance = MakeCriticalSectionInstance(); + const auto& job_name = critical_section_instance->job_name(); + auto* buffer_mgr = Singleton>>::Get(); + for (int i = 0; i < interfaces_op_names().size(); ++i) { + if (interfaces_valid().at(i)) { + const std::string& interface_op_name = interfaces_op_names().at(i); + const auto& buffer_name = GetInterfaceBufferName(job_name, interface_op_name); + buffer_mgr->Get(buffer_name)->Push(critical_section_instance); + } + } + const auto& callback_buffer_name = GetInterfaceCriticalSectionCallbackBufferName(job_name); + buffer_mgr->Get(callback_buffer_name)->Push(critical_section_instance); + const auto& wait_buffer_name = GetInterfaceCriticalSectionWaitBufferName(job_name); + buffer_mgr->Get(wait_buffer_name)->Push(critical_section_instance); + } + { + auto* status_buffer_data = instruction->mut_status_buffer()->mut_buffer(); + auto* status_querier = CriticalSectionStatusQuerier::MutCast(status_buffer_data); + status_querier->SetLaunched(std::make_shared()); + } + } + const std::shared_ptr& nn_graph() const { return nn_graph_; } + const EagerBlobObjectListPtr& eager_blob_objects() const { return eager_blob_objects_; } + + void ForEachDependence(const std::function&) const; + + void ForEachMutDependence(const std::function&) const; + + virtual const std::vector& interfaces_op_names() const = 0; + virtual const std::vector& interfaces_valid() const = 0; + virtual std::string GetInterfaceBufferName(const std::string& job_name, + const std::string& op_name) const = 0; + virtual std::string GetInterfaceCriticalSectionCallbackBufferName( + const std::string& job_name) const = 0; + virtual std::string GetInterfaceCriticalSectionWaitBufferName( + const std::string& job_name) const = 0; + virtual void AccessBlobByOpName(ep::Stream* stream, Blob* blob, const std::string& op_name) = 0; + + void FinishInvalidInterfaceEventRecords(); + void Finish(); + + protected: + std::shared_ptr nn_graph_; + EagerBlobObjectListPtr eager_blob_objects_; + std::shared_ptr>> + op_name2end_event_record_; + HashMap op_name2interface_index_; + Stream* vm_stream_; + + private: + class NaiveCriticalSectionInstance final : public CriticalSectionInstance { + public: + NaiveCriticalSectionInstance(const std::shared_ptr& + critical_section_begin_instruction_policy, + const std::string& job_name) + : CriticalSectionInstance(), + critical_section_begin_instruction_policy_(critical_section_begin_instruction_policy), + job_name_(job_name) {} + + ~NaiveCriticalSectionInstance() override = default; + + const std::string& job_name() const override { return job_name_; } + + void AccessBlobByOpName(ep::Stream* stream, Blob* blob, + const std::string& op_name) const override { + critical_section_begin_instruction_policy_->AccessBlobByOpName(stream, blob, op_name); + } + void Finish() const override { critical_section_begin_instruction_policy_->Finish(); } + + private: + std::shared_ptr + critical_section_begin_instruction_policy_; + std::string job_name_; + }; + + std::shared_ptr MakeCriticalSectionInstance() { + return std::make_shared(this->shared_from_this(), + nn_graph_->job_name()); + } +}; + +class InputCriticalSectionBeginInstructionPolicy final + : public CriticalSectionBeginInstructionPolicy { + public: + InputCriticalSectionBeginInstructionPolicy( + const std::shared_ptr& nn_graph, const EagerBlobObjectListPtr& eager_blob_objects, + const std::shared_ptr>>& + op_name2end_event_record, + Stream* vm_stream) + : CriticalSectionBeginInstructionPolicy(nn_graph, eager_blob_objects, + op_name2end_event_record, vm_stream), + input_dependences_(), + output_dependences_() { + ForEachConstDependence(InstructionPolicyUtil::SetInserter(&input_dependences_)); + ForEachMutDependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + ForEachMut2Dependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + CHECK_EQ(nn_graph->inputs_op_names().size(), eager_blob_objects->size()); + CHECK_EQ(nn_graph->inputs_op_names().size(), nn_graph->inputs_valid().size()); + for (int i = 0; i < nn_graph->inputs_op_names().size(); ++i) { + CHECK(op_name2interface_index_.emplace(nn_graph->inputs_op_names().at(i), i).second); + } + } + + ~InputCriticalSectionBeginInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + // for inputs + void ForEachConstDependence(const std::function& DoEach) const { + ForEachDependence(DoEach); + } + + // for outputs + const std::vector& interfaces_op_names() const override { + return nn_graph_->inputs_op_names(); + } + const std::vector& interfaces_valid() const override { return nn_graph_->inputs_valid(); } + std::string GetInterfaceBufferName(const std::string& job_name, + const std::string& op_name) const override { + return GetInputBufferName(job_name, op_name); + } + std::string GetInterfaceCriticalSectionCallbackBufferName( + const std::string& job_name) const override { + return GetInputCriticalSectionCallbackBufferName(job_name); + } + std::string GetInterfaceCriticalSectionWaitBufferName( + const std::string& job_name) const override { + return GetInputCriticalSectionWaitBufferName(job_name); + } + void AccessBlobByOpName(ep::Stream* stream, Blob* blob, const std::string& op_name) override; + void ForEachMut2Dependence(const std::function&) const {} + + private: + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +class OutputCriticalSectionBeginInstructionPolicy final + : public CriticalSectionBeginInstructionPolicy { + public: + OutputCriticalSectionBeginInstructionPolicy( + const std::shared_ptr& nn_graph, const EagerBlobObjectListPtr& eager_blob_objects, + const std::shared_ptr>>& + op_name2end_event_record, + Stream* vm_stream) + : CriticalSectionBeginInstructionPolicy(nn_graph, eager_blob_objects, + op_name2end_event_record, vm_stream), + input_dependences_(), + output_dependences_() { + ForEachConstDependence(InstructionPolicyUtil::SetInserter(&input_dependences_)); + ForEachMutDependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + ForEachMut2Dependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + CHECK_EQ(nn_graph->outputs_op_names().size(), eager_blob_objects->size()); + CHECK_EQ(nn_graph->outputs_op_names().size(), nn_graph->outputs_valid().size()); + for (int i = 0; i < nn_graph->outputs_op_names().size(); ++i) { + CHECK(op_name2interface_index_.emplace(nn_graph->outputs_op_names().at(i), i).second); + } + } + + ~OutputCriticalSectionBeginInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + // for inputs + void ForEachConstDependence(const std::function&) const {} + + // for outputs + void ForEachMut2Dependence(const std::function& DoEach) const { + ForEachDependence(DoEach); + } + + const std::vector& interfaces_op_names() const override { + return nn_graph_->outputs_op_names(); + } + const std::vector& interfaces_valid() const override { return nn_graph_->outputs_valid(); } + std::string GetInterfaceBufferName(const std::string& job_name, + const std::string& op_name) const override { + return GetOutputBufferName(job_name, op_name); + } + std::string GetInterfaceCriticalSectionCallbackBufferName( + const std::string& job_name) const override { + return GetOutputCriticalSectionCallbackBufferName(job_name); + } + std::string GetInterfaceCriticalSectionWaitBufferName( + const std::string& job_name) const override { + return GetOutputCriticalSectionWaitBufferName(job_name); + } + void AccessBlobByOpName(ep::Stream* stream, Blob* blob, const std::string& op_name) override; + + private: + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +class CriticalSectionEndInstructionPolicy : public InstructionPolicy { + public: + CriticalSectionEndInstructionPolicy(const CriticalSectionEndInstructionPolicy&) = delete; + CriticalSectionEndInstructionPolicy(CriticalSectionEndInstructionPolicy&&) = delete; + CriticalSectionEndInstructionPolicy& operator=(const CriticalSectionEndInstructionPolicy&) = + delete; + CriticalSectionEndInstructionPolicy& operator=(CriticalSectionEndInstructionPolicy&&) = delete; + CriticalSectionEndInstructionPolicy(const std::shared_ptr& eager_blob_object, + const std::shared_ptr& event_record, + vm::Stream* vm_stream) + : eager_blob_object_(eager_blob_object), event_record_(event_record), vm_stream_(vm_stream) {} + virtual ~CriticalSectionEndInstructionPolicy() = default; + + std::string DebugName(const Instruction& instruction) const override { + return "CriticalSectionEnd"; + } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override { + auto* status_buffer_data = instruction->mut_status_buffer()->mut_buffer(); + auto* status_querier = CriticalSectionStatusQuerier::MutCast(status_buffer_data); + status_querier->SetLaunched(event_record()); + } + const std::shared_ptr& event_record() const { return event_record_; } + + void ForEachDependence(const std::function&) const; + + void ForEachMutDependence(const std::function&) const; + + private: + std::shared_ptr eager_blob_object_; + std::shared_ptr event_record_; + vm::Stream* vm_stream_; +}; + +class InputCriticalSectionEndInstructionPolicy final : public CriticalSectionEndInstructionPolicy { + public: + InputCriticalSectionEndInstructionPolicy( + const std::shared_ptr& eager_blob_object, + const std::shared_ptr& event_record, vm::Stream* vm_stream) + : CriticalSectionEndInstructionPolicy(eager_blob_object, event_record, vm_stream), + input_dependences_(), + output_dependences_() { + ForEachConstDependence(InstructionPolicyUtil::SetInserter(&input_dependences_)); + ForEachMutDependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + ForEachMut2Dependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + } + ~InputCriticalSectionEndInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + void ForEachConstDependence(const std::function& DoEach) const { + ForEachDependence(DoEach); + } + + void ForEachMut2Dependence(const std::function&) const {} + + private: + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +class OutputCriticalSectionEndInstructionPolicy final : public CriticalSectionEndInstructionPolicy { + public: + OutputCriticalSectionEndInstructionPolicy( + const std::shared_ptr& eager_blob_object, + const std::shared_ptr& event_record, vm::Stream* vm_stream) + : CriticalSectionEndInstructionPolicy(eager_blob_object, event_record, vm_stream), + input_dependences_(), + output_dependences_() { + ForEachConstDependence(InstructionPolicyUtil::SetInserter(&input_dependences_)); + ForEachMutDependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + ForEachMut2Dependence(InstructionPolicyUtil::SetInserter(&output_dependences_)); + } + ~OutputCriticalSectionEndInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + // for inputs + void ForEachConstDependence(const std::function&) const {} + + // for outputs + void ForEachMut2Dependence(const std::function& DoEach) const { + ForEachDependence(DoEach); + } + + private: + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow +#endif // ONEFLOW_CORE_VM_CRITICAL_SECTION_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/critical_section_status_querier.h b/oneflow/core/vm/critical_section_status_querier.h index 8e26fccf4d1d85efb4fe8860a8b8d2a7cb9c36d7..be995ef6829413275e55aabf1a97e7173c0feea4 100644 --- a/oneflow/core/vm/critical_section_status_querier.h +++ b/oneflow/core/vm/critical_section_status_querier.h @@ -27,6 +27,7 @@ class CriticalSectionStatusQuerier final { public: ~CriticalSectionStatusQuerier() = default; + bool QueryLaunched() const { return launched_; } bool QueryDone() const { return launched_ && event_record_->QueryDone(); } void SetLaunched(const std::shared_ptr& event_record) { diff --git a/oneflow/core/vm/critical_section_stream_policy.cpp b/oneflow/core/vm/critical_section_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..69cc11aad7357ef74343524146b98c0328011d5d --- /dev/null +++ b/oneflow/core/vm/critical_section_stream_policy.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/critical_section_stream_policy.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/critical_section_status_querier.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +void CriticalSectionStreamPolicy::InitInstructionStatus( + const Stream& stream, InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(CriticalSectionStatusQuerier) < kInstructionStatusBufferBytes, ""); + CriticalSectionStatusQuerier::PlacementNew(status_buffer->mut_buffer()); +} + +void CriticalSectionStreamPolicy::DeleteInstructionStatus( + const Stream& stream, InstructionStatusBuffer* status_buffer) const { + auto* ptr = CriticalSectionStatusQuerier::MutCast(status_buffer->mut_buffer()); + ptr->~CriticalSectionStatusQuerier(); +} + +bool CriticalSectionStreamPolicy::QueryInstructionStatusLaunched( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return CriticalSectionStatusQuerier::Cast(status_buffer.buffer())->QueryLaunched(); +} + +bool CriticalSectionStreamPolicy::QueryInstructionStatusDone( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return CriticalSectionStatusQuerier::Cast(status_buffer.buffer())->QueryDone(); +} + +void CriticalSectionStreamPolicy::Run(Instruction* instruction) const { instruction->Compute(); } + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/critical_section_stream_policy.h b/oneflow/core/vm/critical_section_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..b250d799fe8dc25e61a023e1f03d64c00dde69f1 --- /dev/null +++ b/oneflow/core/vm/critical_section_stream_policy.h @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_POLICY_H_ + +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/vm/instruction.h" + +namespace oneflow { +namespace vm { + +class CriticalSectionStreamPolicy final : public StreamPolicy { + public: + CriticalSectionStreamPolicy() = default; + virtual ~CriticalSectionStreamPolicy() = default; + + vm::Allocator* mut_allocator() override { return (vm::Allocator*)nullptr; } + + DeviceType device_type() const override { + PRINT_BUG_PROMPT_AND_ABORT(); + return DeviceType::kInvalidDevice; + } + + ep::Stream* stream() override { + PRINT_BUG_PROMPT_AND_ABORT(); + return nullptr; + } + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + void DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + bool QueryInstructionStatusLaunched(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + bool QueryInstructionStatusDone(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + void Run(Instruction* instruction) const override; + bool SupportingTransportInstructions() const override { return false; } +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/critical_section_stream_type.cpp b/oneflow/core/vm/critical_section_stream_type.cpp deleted file mode 100644 index 18bb127f6f22636f5e9bb194b54a3bd13c4d9b42..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/critical_section_stream_type.cpp +++ /dev/null @@ -1,52 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/critical_section_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/critical_section_status_querier.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -void CriticalSectionStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, - Stream* stream) const { - device_ctx->reset(); -} - -void CriticalSectionStreamType::InitInstructionStatus( - const Stream& stream, InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(CriticalSectionStatusQuerier) < kInstructionStatusBufferBytes, ""); - CriticalSectionStatusQuerier::PlacementNew(status_buffer->mut_buffer()); -} - -void CriticalSectionStreamType::DeleteInstructionStatus( - const Stream& stream, InstructionStatusBuffer* status_buffer) const { - auto* ptr = CriticalSectionStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~CriticalSectionStatusQuerier(); -} - -bool CriticalSectionStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return CriticalSectionStatusQuerier::Cast(status_buffer.buffer())->QueryDone(); -} - -void CriticalSectionStreamType::Run(Instruction* instruction) const { instruction->Compute(); } - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/critical_section_stream_type.h b/oneflow/core/vm/critical_section_stream_type.h deleted file mode 100644 index be66b5af436964579d9bf0f3d229b8914bc65689..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/critical_section_stream_type.h +++ /dev/null @@ -1,48 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifndef ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class CriticalSectionStreamType final : public StreamType { - public: - CriticalSectionStreamType() = default; - virtual ~CriticalSectionStreamType() = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return false; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CRITICAL_SECTION_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/cuda_backend_allocator.cpp b/oneflow/core/vm/cuda_backend_allocator.cpp deleted file mode 100644 index 0b67c302f40a5658d8c56d222f69690b5be7a9a4..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cuda_backend_allocator.cpp +++ /dev/null @@ -1,90 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifdef WITH_CUDA - -#include "oneflow/core/vm/cuda_backend_allocator.h" -#include "oneflow/core/device/cuda_util.h" -#include - -namespace oneflow { -namespace vm { - -Maybe CudaBackendAllocator::Allocate(char** mem_ptr, std::size_t size) { - cudaSetDevice(device_id_); - if (cudaMalloc(mem_ptr, size) != cudaSuccess) { - *mem_ptr = nullptr; - return Error::OutOfMemoryError() << "cuda allocator out of memory"; - } - return Maybe::Ok(); -} - -void CudaBackendAllocator::Deallocate(char* mem_ptr, std::size_t size) { - cudaSetDevice(device_id_); - OF_CUDA_CHECK(cudaFree(mem_ptr)); -} - -void CudaBackendAllocator::DeviceReset() { - cudaSetDevice(device_id_); - // NOTE(chengcheng): In some corner case on ubuntu, cuda memory not released even if OOM. - // So there need release all cuda memory allocated by this process before core dump. - LOG(WARNING) << "OOM error is detected, process will exit. And it will start to reset CUDA " - << "device for releasing device memory."; - OF_CUDA_CHECK(cudaDeviceReset()); -} - -} // namespace vm -} // namespace oneflow - -#endif - -#ifdef WITH_ROCM - -#include "oneflow/core/vm/cuda_backend_allocator.h" -#include "oneflow/core/device/cuda_util.h" -#include - -namespace oneflow { -namespace vm { - -Maybe CudaBackendAllocator::Allocate(char** mem_ptr, std::size_t size) { - hipSetDevice(device_id_); - if (hipMalloc(mem_ptr, size) != hipSuccess) { - *mem_ptr = nullptr; - return Error::OutOfMemoryError() << "cuda allocator out of memory"; - } - return Maybe::Ok(); -} - -void CudaBackendAllocator::Deallocate(char* mem_ptr, std::size_t size) { - hipSetDevice(device_id_); - OF_CUDA_CHECK(hipFree(mem_ptr)); -} - -void CudaBackendAllocator::DeviceReset() { - hipSetDevice(device_id_); - // NOTE(chengcheng): In some corner case on ubuntu, cuda memory not released even if OOM. - // So there need release all cuda memory allocated by this process before core dump. - LOG(WARNING) << "OOM error is detected, process will exit. And it will start to reset CUDA " - << "device for releasing device memory."; - OF_CUDA_CHECK(hipDeviceReset()); -} - -} // namespace vm -} // namespace oneflow - -#endif - diff --git a/oneflow/core/vm/cuda_backend_allocator.h b/oneflow/core/vm/cuda_backend_allocator.h deleted file mode 100644 index 2fcb6a22670765b2f3610db55f7dfe91d0ce983f..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cuda_backend_allocator.h +++ /dev/null @@ -1,42 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_CUDA_BACKEND_ALLOCATOR_H_ -#define ONEFLOW_CORE_VM_CUDA_BACKEND_ALLOCATOR_H_ - -#include -#include "oneflow/core/vm/allocator.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -class CudaBackendAllocator final : public Allocator { - public: - explicit CudaBackendAllocator(int64_t device_id) : device_id_(device_id) {} - ~CudaBackendAllocator() override = default; - - Maybe Allocate(char** mem_ptr, std::size_t size) override; - void Deallocate(char* mem_ptr, std::size_t size) override; - void DeviceReset() override; - - private: - int64_t device_id_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CUDA_BACKEND_ALLOCATOR_H_ diff --git a/oneflow/core/vm/cuda_host_allocator.cpp b/oneflow/core/vm/cuda_host_allocator.cpp deleted file mode 100644 index ae8ab54f00c7299e004b4f3590891fec65fab00e..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cuda_host_allocator.cpp +++ /dev/null @@ -1,116 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_CUDA - -#include "oneflow/core/vm/cuda_host_allocator.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { -namespace vm { - -CudaHostAllocator::~CudaHostAllocator() { - CudaCurrentDeviceGuard guard(device_id_); - for (const auto& ptr_vec : granularity2free_ptrs_) { - for (char* ptr : ptr_vec) { OF_CUDA_CHECK(cudaFreeHost(ptr)); } - } - for (const auto& pair : occupied_ptr2granularity_) { OF_CUDA_CHECK(cudaFreeHost(pair.first)); } -} - -Maybe CudaHostAllocator::Allocate(char** mem_ptr, std::size_t size) { - std::size_t granularity = std::ceil(std::log2(size)); - CHECK_GE_OR_RETURN(granularity, 0) << "out of range"; - CHECK_LT_OR_RETURN(granularity, kCudaHostMaxGranularity) << "invalid granularity"; - CHECK_LE_OR_RETURN(size, 1 << granularity) << "out of range"; - CudaCurrentDeviceGuard guard(device_id_); - std::unique_lock lock(mutex_); - auto* vec = &granularity2free_ptrs_[granularity]; - if (vec->empty()) { - char* ptr = nullptr; - OF_CUDA_CHECK(cudaMallocHost(&ptr, 1 << granularity)); - vec->emplace_back(ptr); - } - *mem_ptr = vec->back(); - vec->pop_back(); - occupied_ptr2granularity_[*mem_ptr] = granularity; - return Maybe::Ok(); -} - -void CudaHostAllocator::Deallocate(char* mem_ptr, std::size_t size) { - std::unique_lock lock(mutex_); - auto iter = occupied_ptr2granularity_.find(mem_ptr); - CHECK(iter != occupied_ptr2granularity_.end()); - std::size_t granularity = iter->second; - occupied_ptr2granularity_.erase(iter); - granularity2free_ptrs_[granularity].emplace_back(mem_ptr); -} - -COMMAND(Singleton::SetAllocated(new CudaHostAllocator(0))); - -} // namespace vm -} // namespace oneflow - -#endif - -#ifdef WITH_ROCM - -#include "oneflow/core/vm/cuda_host_allocator.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { -namespace vm { - -CudaHostAllocator::~CudaHostAllocator() { - CudaCurrentDeviceGuard guard(device_id_); - for (const auto& ptr_vec : granularity2free_ptrs_) { - for (char* ptr : ptr_vec) { OF_CUDA_CHECK(hipHostFree(ptr)); } - } - for (const auto& pair : occupied_ptr2granularity_) { OF_CUDA_CHECK(hipHostFree(pair.first)); } -} - -Maybe CudaHostAllocator::Allocate(char** mem_ptr, std::size_t size) { - std::size_t granularity = std::ceil(std::log2(size)); - CHECK_GE_OR_RETURN(granularity, 0) << "out of range"; - CHECK_LT_OR_RETURN(granularity, kCudaHostMaxGranularity) << "invalid granularity"; - CHECK_LE_OR_RETURN(size, 1 << granularity) << "out of range"; - CudaCurrentDeviceGuard guard(device_id_); - std::unique_lock lock(mutex_); - auto* vec = &granularity2free_ptrs_[granularity]; - if (vec->empty()) { - char* ptr = nullptr; - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&ptr), 1 << granularity)); - vec->emplace_back(ptr); - } - *mem_ptr = vec->back(); - vec->pop_back(); - occupied_ptr2granularity_[*mem_ptr] = granularity; - return Maybe::Ok(); -} - -void CudaHostAllocator::Deallocate(char* mem_ptr, std::size_t size) { - std::unique_lock lock(mutex_); - auto iter = occupied_ptr2granularity_.find(mem_ptr); - CHECK(iter != occupied_ptr2granularity_.end()); - std::size_t granularity = iter->second; - occupied_ptr2granularity_.erase(iter); - granularity2free_ptrs_[granularity].emplace_back(mem_ptr); -} - -COMMAND(Singleton::SetAllocated(new CudaHostAllocator(0))); - -} // namespace vm -} // namespace oneflow - -#endif diff --git a/oneflow/core/vm/cuda_host_allocator.h b/oneflow/core/vm/cuda_host_allocator.h deleted file mode 100644 index 941e665faeb385edbdf2374e801a212960a457d4..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/cuda_host_allocator.h +++ /dev/null @@ -1,55 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_CUDA_HOST_ALLOCATOR_H_ -#define ONEFLOW_CORE_VM_CUDA_HOST_ALLOCATOR_H_ - -#include -#include -#include -#include -#include -#include "oneflow/core/vm/allocator.h" - -namespace oneflow { -namespace vm { - -static constexpr int kCudaHostMaxGranularity = 64; - -class CudaHostAllocator final : public Allocator { - public: - CudaHostAllocator(const CudaHostAllocator&) = delete; - CudaHostAllocator(CudaHostAllocator&&) = delete; - CudaHostAllocator& operator=(const CudaHostAllocator&) = delete; - CudaHostAllocator& operator=(CudaHostAllocator&&) = delete; - - explicit CudaHostAllocator(int64_t device_id) : Allocator(), device_id_(device_id) {} - ~CudaHostAllocator() override; - - Maybe Allocate(char** mem_ptr, std::size_t size) override; - void Deallocate(char* mem_ptr, std::size_t size) override; - void DeviceReset() override {} - - private: - int64_t device_id_; - std::mutex mutex_; - std::array, kCudaHostMaxGranularity> granularity2free_ptrs_; - std::unordered_map occupied_ptr2granularity_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_CUDA_HOST_ALLOCATOR_H_ diff --git a/oneflow/core/vm/ep_d2h_stream_policy.cpp b/oneflow/core/vm/ep_d2h_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0d0a0f826493ac7b7c35c1c0d26962651c03c857 --- /dev/null +++ b/oneflow/core/vm/ep_d2h_stream_policy.cpp @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/ep_d2h_stream_policy.h" +#include +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/ep_backend_host_allocator.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +namespace { + +std::unique_ptr> CreateEpBackendHostAllocator(Symbol device) { + DeviceType device_type = device->enum_type(); + size_t device_index = device->device_id(); + auto ep_device = + Singleton::Get()->GetDevice(device_type, device_index); + auto ep_backend_allocator = + std::make_unique(ep_device, ep::AllocationOptions{}); + return std::make_unique>(ep::kMaxAlignmentRequirement, + std::move(ep_backend_allocator)); +} + +} // namespace + +EpD2HStreamPolicy::EpD2HStreamPolicy(Symbol device) + : EpStreamPolicyBase(device, CreateEpBackendHostAllocator(device)) {} + +void EpD2HStreamPolicy::InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); + EpStreamPolicyBase* ep_stream_policy_base = + dynamic_cast(const_cast(stream).mut_stream_policy()); + CHECK_NOTNULL(ep_stream_policy_base); + auto* ep_event_provider = ep_stream_policy_base->ep_event_provider(); + auto* data_ptr = status_buffer->mut_buffer(); + const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); + EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, ep_event); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/ep_d2h_stream_policy.h b/oneflow/core/vm/ep_d2h_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..d6f411af8540daf5cf05db2fc77276b5166c7eb3 --- /dev/null +++ b/oneflow/core/vm/ep_d2h_stream_policy.h @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_EP_D2H_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_EP_D2H_STREAM_POLICY_H_ + +#include "oneflow/core/vm/ep_stream_policy_base.h" + +namespace oneflow { +namespace vm { + +class EpD2HStreamPolicy final : public EpStreamPolicyBase { + public: + EpD2HStreamPolicy(Symbol device); + ~EpD2HStreamPolicy() override = default; + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_EP_D2H_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/ep_d2h_stream_type.cpp b/oneflow/core/vm/ep_d2h_stream_type.cpp deleted file mode 100644 index 1d799889df5661dcedf5fb14f482d33da483ac6b..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/ep_d2h_stream_type.cpp +++ /dev/null @@ -1,76 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/ep_d2h_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/vm/ep_device_context.h" -#include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/ep_backend_host_allocator.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/ep/include/device_manager_registry.h" -#include "oneflow/core/ep/include/allocation_options.h" - -namespace oneflow { -namespace vm { - -void EpD2HStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const { - DeviceType device_type = stream->device()->enum_type(); - size_t device_index = stream->device()->device_id(); - auto ep_device = - Singleton::Get()->GetDevice(device_type, device_index); - auto ep_backend_allocator = - std::make_unique(ep_device, ep::AllocationOptions{}); - device_ctx->reset(new EpDeviceCtx(stream->device(), std::move(ep_backend_allocator))); -} - -void EpD2HStreamType::InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); - auto* ep_device_ctx = static_cast(stream.device_ctx().get()); // NOLINT - auto* ep_event_provider = ep_device_ctx->ep_event_provider(); - auto* data_ptr = status_buffer->mut_buffer(); - const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); - EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, ep_event); -} - -void EpD2HStreamType::DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - auto* ptr = EpOptionalEventRecordStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~EpOptionalEventRecordStatusQuerier(); -} - -bool EpD2HStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -void EpD2HStreamType::Run(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD("S:" + instruction->DebugName()); - auto* stream = instruction->mut_stream(); - auto* ep_device_ctx = static_cast(stream->device_ctx().get()); // NOLINT - auto* ep_device = ep_device_ctx->GetOrCreateEpDevice(); - ep_device->SetAsActiveDevice(); - instruction->Compute(); - char* data_ptr = instruction->mut_status_buffer()->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->SetLaunched(ep_device_ctx); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/ep_d2h_stream_type.h b/oneflow/core/vm/ep_d2h_stream_type.h deleted file mode 100644 index b4256aa066c2a5f459d4db0a1fcef19beab1c673..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/ep_d2h_stream_type.h +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_EP_D2H_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_EP_D2H_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class EpD2HStreamType final : public StreamType { - public: - EpD2HStreamType() = default; - ~EpD2HStreamType() override = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return true; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_EP_D2H_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/ep_device_context.h b/oneflow/core/vm/ep_device_context.h deleted file mode 100644 index 0287974564f75e6c6e843a9b09577be16000e14a..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/ep_device_context.h +++ /dev/null @@ -1,100 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_EP_DEVICE_CONTEXT_H_ -#define ONEFLOW_CORE_VM_EP_DEVICE_CONTEXT_H_ - -#include "oneflow/core/kernel/kernel_context.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/vm/ep_event.h" -#include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/thread_safe_allocator.h" -#include "oneflow/core/common/single_thread_obj_pool.h" -#include "oneflow/core/ep/include/stream.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/common/cpp_attribute.h" -#include "oneflow/core/ep/include/device_manager_registry.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else -#include "oneflow/core/ep/cuda/cuda_stream.h" -#endif -#include "oneflow/core/framework/device.h" - -namespace oneflow { -namespace vm { - -class EpDeviceCtx : public DeviceCtx { - public: - OF_DISALLOW_COPY_AND_MOVE(EpDeviceCtx); - EpDeviceCtx() = delete; - ~EpDeviceCtx() override { - if (ep_stream_ != nullptr) { - CHECK(ep_device_); - ep_device_->DestroyStream(ep_stream_); - } - } - - EpDeviceCtx(Symbol device, std::unique_ptr&& backend_allocator) - : DeviceCtx(), - device_(device), - ep_event_provier_(), - ep_stream_(nullptr), - ep_allocator_(new ThreadSafeAllocator(std::make_unique( - ep::kMaxAlignmentRequirement, std::move(backend_allocator)))) {} - - ep::Stream* stream() override { return GetOrCreateEpStream(); } - - vm::Allocator* mut_allocator() override { return ep_allocator_.get(); } - - DeviceType device_type() const override { return device_->enum_type(); } - - EpEventProvider* ep_event_provider() { - if (unlikely(ep_event_provier_ == nullptr)) { - ep_event_provier_.reset(new SingleThreadEpEventProvider(GetOrCreateEpDevice())); - } - return ep_event_provier_.get(); - } - - ep::Device* GetOrCreateEpDevice() const { - if (unlikely(ep_device_ == nullptr)) { - ep_device_ = Singleton::Get()->GetDevice(device_->enum_type(), - device_->device_id()); - CHECK(ep_device_); - } - return ep_device_.get(); - } - - private: - ep::Stream* GetOrCreateEpStream() const { - if (unlikely(ep_stream_ == nullptr)) { - ep_stream_ = GetOrCreateEpDevice()->CreateStream(); - CHECK(ep_stream_ != nullptr); - } - return ep_stream_; - } - - protected: - Symbol device_; - std::unique_ptr ep_event_provier_; - mutable std::shared_ptr ep_device_; - mutable ep::Stream* ep_stream_; - std::unique_ptr ep_allocator_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_EP_DEVICE_CONTEXT_H_ diff --git a/oneflow/core/vm/ep_optional_event_record_status_querier.cpp b/oneflow/core/vm/ep_optional_event_record_status_querier.cpp index f173a6e4c19a6afc477eae6a6e42b36d0a28ba34..243b1212295868d909a8c3fbd04a809aaa0ebc29 100644 --- a/oneflow/core/vm/ep_optional_event_record_status_querier.cpp +++ b/oneflow/core/vm/ep_optional_event_record_status_querier.cpp @@ -13,17 +13,16 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/core/vm/ep_device_context.h" #include "oneflow/core/vm/ep_optional_event_record_status_querier.h" namespace oneflow { namespace vm { -void EpOptionalEventRecordStatusQuerier::SetLaunched(EpDeviceCtx* device_ctx) { +void EpOptionalEventRecordStatusQuerier::SetLaunched(ep::Stream* stream) { CHECK(!launched_); if (ep_event_) { ep_event_->mut_device()->SetAsActiveDevice(); - device_ctx->stream()->RecordEvent(ep_event_->mut_event()); + stream->RecordEvent(ep_event_->mut_event()); } launched_ = true; } diff --git a/oneflow/core/vm/ep_optional_event_record_status_querier.h b/oneflow/core/vm/ep_optional_event_record_status_querier.h index ad4e158b38a1332a35398b660147cec574c966da..fabebf599a744570dee5a96f5262bd7a4f0b2156 100644 --- a/oneflow/core/vm/ep_optional_event_record_status_querier.h +++ b/oneflow/core/vm/ep_optional_event_record_status_querier.h @@ -25,16 +25,16 @@ class DeviceCtx; namespace vm { -class EpDeviceCtx; - class EpOptionalEventRecordStatusQuerier { public: OF_DISALLOW_COPY_AND_MOVE(EpOptionalEventRecordStatusQuerier); ~EpOptionalEventRecordStatusQuerier(); + bool launched() const { return launched_; } + bool done() const { return launched_ && (ep_event_ == nullptr || ep_event_->Query()); } - void SetLaunched(EpDeviceCtx* device_ctx); + void SetLaunched(ep::Stream* stream); void reset_ep_event(const std::shared_ptr& ep_event) { ep_event_ = ep_event; } diff --git a/oneflow/core/vm/ep_record_event_instruction_policy.h b/oneflow/core/vm/ep_record_event_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..d418b19e23a47c6b2ba62d7021fcf8c89d189eb3 --- /dev/null +++ b/oneflow/core/vm/ep_record_event_instruction_policy.h @@ -0,0 +1,138 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_EP_RECORD_EVENT_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_EP_RECORD_EVENT_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/vm/ep_stream_policy_base.h" +#include "oneflow/core/vm/stream.h" + +namespace oneflow { +namespace vm { +class EpRecordEventInstructionPolicy final : public InstructionPolicy { + public: + EpRecordEventInstructionPolicy(small_vector, + kOpArgsReservedSize>&& compute_local_dep_objects, + const std::string& modifier) + : compute_local_dep_objects_(std::move(compute_local_dep_objects)), + modifier_(modifier), + input_dependences_(), + output_dependences_() { + ForEachConstDependence([&](auto* dep) { input_dependences_.emplace_back(dep); }); + ForEachMutDependence([&](auto* dep) { output_dependences_.emplace_back(dep); }); + ForEachMut2Dependence([&](auto* dep) { output_dependences_.emplace_back(dep); }); + } + + ~EpRecordEventInstructionPolicy() override = default; + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + template + void ForEachConstDependence(const DoEachT& DoEach) const { + if (modifier_ == "const") { + for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } + } + } + + template + void ForEachMutDependence(const DoEachT& DoEach) const { + if (modifier_ == "mut") { + for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } + } + } + + template + void ForEachMut2Dependence(const DoEachT& DoEach) const { + if (modifier_ == "mut2") { + for (const auto& dep : compute_local_dep_objects_) { DoEach(dep.get()); } + } + } + InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAsTailOnly; } + + void InitInstructionStatus(Instruction* instruction) override { + auto* status_buffer = instruction->mut_status_buffer(); + auto* stream = instruction->mut_stream(); + instruction->stream_policy().InitInstructionStatus(*stream, status_buffer); + EpStreamPolicyBase* ep_stream_policy_base = + dynamic_cast(stream->mut_stream_policy()); + CHECK_NOTNULL(ep_stream_policy_base); + auto* ep_event_provider = ep_stream_policy_base->ep_event_provider(); + const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); + auto* data_ptr = status_buffer->mut_buffer(); + EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->reset_ep_event(ep_event); + } + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + std::string DebugName(const vm::Instruction&) const override { return "RecordEvent"; } + void Compute(vm::Instruction* instruction) override {} + + private: + small_vector, kOpArgsReservedSize> + compute_local_dep_objects_; + const std::string modifier_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm + +struct GetRecordEventInstructionPolicy : public StreamTypeVisitor { + template + static Maybe VisitCompute(DeviceType device_type, Args&&... args) { + return std::shared_ptr( + new vm::EpRecordEventInstructionPolicy(std::forward(args)...)); + } + template + static Maybe VisitHost2Device(DeviceType device_type, Args&&... args) { + return std::shared_ptr( + new vm::EpRecordEventInstructionPolicy(std::forward(args)...)); + } + template + static Maybe VisitDevice2Host(DeviceType device_type, Args&&... args) { + return std::shared_ptr( + new vm::EpRecordEventInstructionPolicy(std::forward(args)...)); + } + template + static Maybe VisitCcl(DeviceType device_type, Args&&... args) { + return std::shared_ptr( + new vm::EpRecordEventInstructionPolicy(std::forward(args)...)); + } + template + static Maybe VisitBarrier(DeviceType device_type, Args&&... args) { + UNIMPLEMENTED_THEN_RETURN() << "EpRecordEvent instruction not supported in Barrier stream"; + } + template + static Maybe VisitCriticalSection(DeviceType device_type, Args&&... args) { + UNIMPLEMENTED_THEN_RETURN() + << "EpRecordEvent instruction not supported in CriticalSection stream"; + } + template + static Maybe VisitLazyJobLauncher(DeviceType device_type, Args&&... args) { + UNIMPLEMENTED_THEN_RETURN() + << "EpRecordEvent instruction not supported in LaunchLazyJob stream"; + } + template + static Maybe VisitPinnedCompute(DeviceType device_type, Args&&... args) { + return std::shared_ptr( + new vm::EpRecordEventInstructionPolicy(std::forward(args)...)); + } +}; + +} // namespace oneflow +#endif // ONEFLOW_CORE_EAGER_BLOB_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/vm/ep_stream_policy.cpp b/oneflow/core/vm/ep_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a30adc9a06d3141449a954cddfe276d55d42a22a --- /dev/null +++ b/oneflow/core/vm/ep_stream_policy.cpp @@ -0,0 +1,56 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/ep_stream_policy.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/ep_backend_allocator.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +namespace { + +std::unique_ptr> CreateEpBackendDeviceAllocator( + Symbol device) { + DeviceType device_type = device->enum_type(); + size_t device_index = device->device_id(); + auto ep_device = + Singleton::Get()->GetDevice(device_type, device_index); + auto ep_backend_allocator = + std::make_unique(ep_device, ep::AllocationOptions{}); + return std::make_unique>(ep::kMaxAlignmentRequirement, + std::move(ep_backend_allocator)); +} + +} // namespace + +EpStreamPolicy::EpStreamPolicy(Symbol device) + : EpStreamPolicyBase(device, CreateEpBackendDeviceAllocator(device)) {} + +void EpStreamPolicy::InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); + auto* data_ptr = status_buffer->mut_buffer(); + EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, nullptr); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/ep_stream_policy.h b/oneflow/core/vm/ep_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..9284b4db900240372a22ae77fabc45ce133cf176 --- /dev/null +++ b/oneflow/core/vm/ep_stream_policy.h @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_EP_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_EP_STREAM_POLICY_H_ + +#include "oneflow/core/vm/ep_stream_policy_base.h" + +namespace oneflow { +namespace vm { + +class EpStreamPolicy final : public EpStreamPolicyBase { + public: + EpStreamPolicy(Symbol device); + ~EpStreamPolicy() override = default; + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_EP_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/ep_stream_policy_base.cpp b/oneflow/core/vm/ep_stream_policy_base.cpp new file mode 100644 index 0000000000000000000000000000000000000000..97d61747ade0bed2861ecf1751b7548275150a3a --- /dev/null +++ b/oneflow/core/vm/ep_stream_policy_base.cpp @@ -0,0 +1,61 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/ep_stream_policy_base.h" +#include +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/ep_backend_host_allocator.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/profiler/profiler.h" + +namespace oneflow { +namespace vm { + +void EpStreamPolicyBase::DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + auto* ptr = EpOptionalEventRecordStatusQuerier::MutCast(status_buffer->mut_buffer()); + ptr->~EpOptionalEventRecordStatusQuerier(); +} + +bool EpStreamPolicyBase::QueryInstructionStatusLaunched( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->launched(); +} + +bool EpStreamPolicyBase::QueryInstructionStatusDone( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->done(); +} + +void EpStreamPolicyBase::Run(Instruction* instruction) const { + OF_PROFILER_RANGE_GUARD("S:" + instruction->DebugName()); + auto* stream = instruction->mut_stream(); + EpStreamPolicyBase* ep_stream_policy_base = + dynamic_cast(stream->mut_stream_policy()); + CHECK_NOTNULL(ep_stream_policy_base); + auto* ep_device = ep_stream_policy_base->GetOrCreateEpDevice(); + ep_device->SetAsActiveDevice(); + instruction->Compute(); + char* data_ptr = instruction->mut_status_buffer()->mut_buffer(); + EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->SetLaunched( + stream->mut_stream_policy()->stream()); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/ep_stream_policy_base.h b/oneflow/core/vm/ep_stream_policy_base.h new file mode 100644 index 0000000000000000000000000000000000000000..21dca8dca4673de08ef6237d26809fbc5bae7d5e --- /dev/null +++ b/oneflow/core/vm/ep_stream_policy_base.h @@ -0,0 +1,97 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_EP_STREAM_POLICY_BASE_H_ +#define ONEFLOW_CORE_VM_EP_STREAM_POLICY_BASE_H_ + +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/vm/ep_event.h" +#include "oneflow/core/vm/bin_allocator.h" +#include "oneflow/core/vm/thread_safe_guard.h" +#include "oneflow/core/ep/include/device_manager_registry.h" + +namespace oneflow { +namespace vm { + +class EpStreamPolicyBase : public StreamPolicy { + public: + EpStreamPolicyBase(Symbol device, std::unique_ptr&& backend_allocator) + : device_(device), + ep_event_provier_(), + ep_stream_(nullptr), + ep_allocator_(std::move(backend_allocator)) {} + virtual ~EpStreamPolicyBase() override { + if (ep_stream_ != nullptr) { + CHECK(ep_device_); + ep_device_->DestroyStream(ep_stream_); + } + } + + ep::Stream* stream() override { return GetOrCreateEpStream(); } + + vm::Allocator* mut_allocator() override { return ep_allocator_.get(); } + + DeviceType device_type() const override { return device_->enum_type(); } + + EpEventProvider* ep_event_provider() { + if (unlikely(ep_event_provier_ == nullptr)) { + ep_event_provier_.reset(new SingleThreadEpEventProvider(GetOrCreateEpDevice())); + } + return ep_event_provier_.get(); + } + + ep::Device* GetOrCreateEpDevice() const { + if (unlikely(ep_device_ == nullptr)) { + ep_device_ = Singleton::Get()->GetDevice(device_->enum_type(), + device_->device_id()); + CHECK(ep_device_); + } + return ep_device_.get(); + } + + bool SupportingTransportInstructions() const override { return true; } + + void DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + + bool QueryInstructionStatusLaunched(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + + bool QueryInstructionStatusDone(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + + void Run(Instruction* instruction) const override; + + private: + ep::Stream* GetOrCreateEpStream() const { + if (unlikely(ep_stream_ == nullptr)) { + ep_stream_ = GetOrCreateEpDevice()->CreateStream(); + CHECK(ep_stream_ != nullptr); + } + return ep_stream_; + } + + Symbol device_; + std::unique_ptr ep_event_provier_; + mutable std::shared_ptr ep_device_; + mutable ep::Stream* ep_stream_; + std::unique_ptr ep_allocator_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_EP_STREAM_POLICY_BASE_H_ diff --git a/oneflow/core/vm/ep_stream_type.cpp b/oneflow/core/vm/ep_stream_type.cpp deleted file mode 100644 index e6609394ac7661e69a62eab78b447c9f20cbd7f3..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/ep_stream_type.cpp +++ /dev/null @@ -1,74 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/ep_stream_type.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/vm/ep_device_context.h" -#include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/ep_backend_allocator.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/ep/include/device_manager_registry.h" - -namespace oneflow { -namespace vm { - -void EpStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const { - DeviceType device_type = stream->device()->enum_type(); - size_t device_index = stream->device()->device_id(); - auto ep_device = - Singleton::Get()->GetDevice(device_type, device_index); - auto ep_backend_allocator = - std::make_unique(ep_device, ep::AllocationOptions{}); - device_ctx->reset(new EpDeviceCtx(stream->device(), std::move(ep_backend_allocator))); -} - -void EpStreamType::InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); - auto* data_ptr = status_buffer->mut_buffer(); - EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, nullptr); -} - -void EpStreamType::DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - auto* ptr = EpOptionalEventRecordStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~EpOptionalEventRecordStatusQuerier(); -} - -bool EpStreamType::QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const { - return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -void EpStreamType::Run(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD("S:" + instruction->DebugName()); - auto* stream = instruction->mut_stream(); - auto* ep_device_ctx = static_cast(stream->device_ctx().get()); // NOLINT - auto* ep_device = ep_device_ctx->GetOrCreateEpDevice(); - ep_device->SetAsActiveDevice(); - instruction->Compute(); - char* data_ptr = instruction->mut_status_buffer()->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->SetLaunched(ep_device_ctx); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/ep_stream_type.h b/oneflow/core/vm/ep_stream_type.h deleted file mode 100644 index 90cba6ff91e39ae51ce1645ca792131311f4f114..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/ep_stream_type.h +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_EP_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_EP_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class EpStreamType final : public StreamType { - public: - EpStreamType() = default; - ~EpStreamType() override = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return true; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_EP_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/event_recorded_ep_stream_policy.cpp b/oneflow/core/vm/event_recorded_ep_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3fb624bab3a624ec0c50bfed1658503a351a866a --- /dev/null +++ b/oneflow/core/vm/event_recorded_ep_stream_policy.cpp @@ -0,0 +1,56 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/event_recorded_ep_stream_policy.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/ep_backend_allocator.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +/*static*/ std::unique_ptr> +EventRecordedEpStreamPolicy::CreateEpBackendDeviceAllocator(Symbol device) { + DeviceType device_type = device->enum_type(); + size_t device_index = device->device_id(); + auto ep_device = + Singleton::Get()->GetDevice(device_type, device_index); + auto ep_backend_allocator = + std::make_unique(ep_device, ep::AllocationOptions{}); + return std::make_unique>(ep::kMaxAlignmentRequirement, + std::move(ep_backend_allocator)); +} + +EventRecordedEpStreamPolicy::EventRecordedEpStreamPolicy(Symbol device, + std::unique_ptr&& allocator) + : EpStreamPolicyBase(device, std::move(allocator)) {} + +void EventRecordedEpStreamPolicy::InitInstructionStatus( + const Stream& stream, InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); + EpStreamPolicyBase* ep_stream_policy_base = + dynamic_cast(const_cast(stream).mut_stream_policy()); + CHECK_NOTNULL(ep_stream_policy_base); + auto* ep_event_provider = ep_stream_policy_base->ep_event_provider(); + auto* data_ptr = status_buffer->mut_buffer(); + const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); + EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, ep_event); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/event_recorded_ep_stream_policy.h b/oneflow/core/vm/event_recorded_ep_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..7c93911bb881159684da916b26c5b3dc5a93d33e --- /dev/null +++ b/oneflow/core/vm/event_recorded_ep_stream_policy.h @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_POLICY_H_ + +#include "oneflow/core/vm/ep_stream_policy_base.h" + +namespace oneflow { +namespace vm { + +class EventRecordedEpStreamPolicy final : public EpStreamPolicyBase { + public: + EventRecordedEpStreamPolicy(Symbol device, std::unique_ptr&& allocator); + ~EventRecordedEpStreamPolicy() override = default; + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + + static std::unique_ptr> CreateEpBackendDeviceAllocator( + Symbol device); +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/event_recorded_ep_stream_type.cpp b/oneflow/core/vm/event_recorded_ep_stream_type.cpp deleted file mode 100644 index ddd159423166dbddb822c7a67594e4150a37aede..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/event_recorded_ep_stream_type.cpp +++ /dev/null @@ -1,76 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/event_recorded_ep_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/vm/ep_device_context.h" -#include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/ep_backend_allocator.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/ep/include/device_manager_registry.h" - -namespace oneflow { -namespace vm { - -void EventRecordedEpStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, - Stream* stream) const { - DeviceType device_type = stream->device()->enum_type(); - size_t device_index = stream->device()->device_id(); - auto ep_device = - Singleton::Get()->GetDevice(device_type, device_index); - auto ep_backend_allocator = - std::make_unique(ep_device, ep::AllocationOptions{}); - device_ctx->reset(new EpDeviceCtx(stream->device(), std::move(ep_backend_allocator))); -} - -void EventRecordedEpStreamType::InitInstructionStatus( - const Stream& stream, InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); - auto* ep_device_ctx = static_cast(stream.device_ctx().get()); // NOLINT - auto* ep_event_provider = ep_device_ctx->ep_event_provider(); - auto* data_ptr = status_buffer->mut_buffer(); - const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); - EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, ep_event); -} - -void EventRecordedEpStreamType::DeleteInstructionStatus( - const Stream& stream, InstructionStatusBuffer* status_buffer) const { - auto* ptr = EpOptionalEventRecordStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~EpOptionalEventRecordStatusQuerier(); -} - -bool EventRecordedEpStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -void EventRecordedEpStreamType::Run(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD("S:" + instruction->DebugName()); - auto* stream = instruction->mut_stream(); - auto* ep_device_ctx = static_cast(stream->device_ctx().get()); // NOLINT - auto* ep_device = ep_device_ctx->GetOrCreateEpDevice(); - ep_device->SetAsActiveDevice(); - instruction->Compute(); - char* data_ptr = instruction->mut_status_buffer()->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->SetLaunched(ep_device_ctx); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/event_recorded_ep_stream_type.h b/oneflow/core/vm/event_recorded_ep_stream_type.h deleted file mode 100644 index 1d7e36eb72b769df7455f8519615b98a5eed7105..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/event_recorded_ep_stream_type.h +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class EventRecordedEpStreamType final : public StreamType { - public: - EventRecordedEpStreamType() = default; - ~EventRecordedEpStreamType() override = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return true; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_EVENT_RECORDED_EP_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/fuse_instruction_policy.h b/oneflow/core/vm/fuse_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..209682ddd869a7f0c443167fe1d95cb57f9274ba --- /dev/null +++ b/oneflow/core/vm/fuse_instruction_policy.h @@ -0,0 +1,92 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_FUSE_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_FUSE_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/instruction_policy_util.h" +#include "oneflow/core/vm/vm_object.h" + +namespace oneflow { +namespace vm { + +class FuseInstructionPolicy final : public InstructionPolicy { + public: + explicit FuseInstructionPolicy(InstructionList&& instruction_list) + : instruction_list_(), input_dependences_(), output_dependences_() { + instruction_list.MoveTo(&instruction_list_); + auto ReadOnlyDepsInserter = InstructionPolicyUtil::SetInserter(&input_dependences_); + auto WritableDepsInserter = InstructionPolicyUtil::SetInserter(&output_dependences_); + auto* last_instruction = instruction_list_.Last(); + INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, &instruction_list_) { + if (instruction == last_instruction) { + CHECK(instruction->instruction_policy().fuse_type() == kEnableInstructionFuseAsTailOnly + || instruction->instruction_policy().fuse_type() + == kEnableInstructionFuseAtAnyPosition); + } else { + CHECK(instruction->instruction_policy().fuse_type() == kEnableInstructionFuseAtAnyPosition); + } + if (unlikely(stream_sequential_dependence_ == nullptr)) { + stream_sequential_dependence_ = + instruction->instruction_policy().stream_sequential_dependence(); + } else { + CHECK_EQ(stream_sequential_dependence_, + instruction->instruction_policy().stream_sequential_dependence()); + } + for (auto* dep : instruction->instruction_policy().input_dependences()) { + ReadOnlyDepsInserter(dep); + } + for (auto* dep : instruction->instruction_policy().output_dependences()) { + WritableDepsInserter(dep); + } + } + } + + ~FuseInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + InstructionList* mut_instruction_list() { return &instruction_list_; } + + private: + Maybe Prepare(Instruction* instruction) override { + INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, mut_instruction_list()) { + JUST(instruction->Prepare()); + } + return Maybe::Ok(); + } + void Compute(Instruction* instruction) override { + OF_PROFILER_RANGE_GUARD("F:" + instruction->DebugName()); + INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, mut_instruction_list()) { instruction->Compute(); } + } + void InitInstructionStatus(Instruction* instruction) override { + auto* last_instruction = CHECK_NOTNULL(mut_instruction_list()->Last()); + last_instruction->mut_instruction_policy()->InitInstructionStatusIf(instruction); + } + + std::string DebugName(const Instruction&) const override { return "Fuse"; } + + InstructionList instruction_list_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_FUSE_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/fuse_instruction_type.h b/oneflow/core/vm/fuse_instruction_type.h deleted file mode 100644 index 46ab24d23c3706dfd2cd81f8d650598236b365ac..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/fuse_instruction_type.h +++ /dev/null @@ -1,64 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_FUSE_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_VM_FUSE_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/fuse_phy_instr_operand.h" -#include "oneflow/core/profiler/profiler.h" - -namespace oneflow { - -namespace vm { - -class FuseInstructionType : public vm::InstructionType { - public: - FuseInstructionType() = default; - ~FuseInstructionType() override = default; - - std::string DebugName(const Instruction&) const override { return "Fuse"; } - - void InitInstructionStatus(Instruction* instruction) const override { - const auto& phy_instr_operand = instruction->phy_instr_operand(); - auto* ptr = dynamic_cast(phy_instr_operand.get()); - auto* instruction_list = CHECK_NOTNULL(ptr)->mut_instruction_list(); - auto* last_instruction = CHECK_NOTNULL(instruction_list->Last()); - last_instruction->instruction_type().InitInstructionStatusIf(instruction); - } - - Maybe Prepare(vm::Instruction* instruction) const override { - const auto& phy_instr_operand = instruction->phy_instr_operand(); - auto* ptr = dynamic_cast(phy_instr_operand.get()); - CHECK_NOTNULL_OR_RETURN(ptr); - auto* instruction_list = ptr->mut_instruction_list(); - INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, instruction_list) { - JUST(instruction->instruction_type().PrepareIf(instruction)); - } - return Maybe::Ok(); - } - void Compute(vm::Instruction* instruction) const override { - const auto& phy_instr_operand = instruction->phy_instr_operand(); - auto* ptr = dynamic_cast(phy_instr_operand.get()); - auto* instruction_list = CHECK_NOTNULL(ptr)->mut_instruction_list(); - OF_PROFILER_RANGE_GUARD("F:" + instruction->DebugName()); - INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, instruction_list) { instruction->Compute(); } - } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_FUSE_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/vm/fuse_phy_instr_operand.h b/oneflow/core/vm/fuse_phy_instr_operand.h deleted file mode 100644 index 526a7d9d8f7739fefa9b5999d753220e6fc5a81f..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/fuse_phy_instr_operand.h +++ /dev/null @@ -1,76 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_FUSE_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_VM_FUSE_PHY_INSTR_OPERAND_H_ - -#include -#include "oneflow/core/vm/phy_instr_operand.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/eager/local_dep_object.h" - -namespace oneflow { -namespace vm { - -class FusePhyInstrOperand : public PhyInstrOperand { - public: - explicit FusePhyInstrOperand(InstructionList&& instruction_list) - : instruction_list_(), input_dependences_(), output_dependences_() { - instruction_list.MoveTo(&instruction_list_); - auto ReadOnlyDepsInserter = SetInserter(&input_dependences_); - auto WritableDepsInserter = SetInserter(&output_dependences_); - auto* last_instruction = instruction_list_.Last(); - INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, &instruction_list_) { - if (instruction == last_instruction) { - CHECK(instruction->instruction_type().fuse_type() == kEnableInstructionFuseAsTailOnly - || instruction->instruction_type().fuse_type() - == kEnableInstructionFuseAtAnyPosition); - } else { - CHECK(instruction->instruction_type().fuse_type() == kEnableInstructionFuseAtAnyPosition); - } - if (unlikely(stream_sequential_dependence_ == nullptr)) { - stream_sequential_dependence_ = - instruction->phy_instr_operand()->stream_sequential_dependence(); - } else { - CHECK_EQ(stream_sequential_dependence_, - instruction->phy_instr_operand()->stream_sequential_dependence()); - } - for (auto* dep : instruction->phy_instr_operand()->input_dependences()) { - ReadOnlyDepsInserter(dep); - } - for (auto* dep : instruction->phy_instr_operand()->output_dependences()) { - WritableDepsInserter(dep); - } - } - } - ~FusePhyInstrOperand() override = default; - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { return output_dependences_; } - - InstructionList* mut_instruction_list() { return &instruction_list_; } - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override {} - - private: - InstructionList instruction_list_; - DependenceVector input_dependences_; - DependenceVector output_dependences_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_FUSE_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/vm/global_sync_instruction_policy.h b/oneflow/core/vm/global_sync_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..8e6d7e2ad6d401aadfe65f97a5f3111d31318f91 --- /dev/null +++ b/oneflow/core/vm/global_sync_instruction_policy.h @@ -0,0 +1,48 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_GLOBAL_SYNC_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_GLOBAL_SYNC_INSTRUCTION_POLICY_H_ + +#include "oneflow/core/rpc/include/base.h" +#include "oneflow/core/vm/instruction_policy.h" +namespace oneflow { +namespace vm { + +class GlobalSyncInstructionPolicy final : public InstructionPolicy { + public: + GlobalSyncInstructionPolicy() = default; + ~GlobalSyncInstructionPolicy() override = default; + + const DependenceVector& input_dependences() const override { + static DependenceVector dependences{}; + return dependences; + } + const DependenceVector& output_dependences() const override { + static DependenceVector dependences{}; + return dependences; + } + + bool IsBarrier() const override { return true; } + + std::string DebugName(const vm::Instruction& instruction) const override { return "GlobalSync"; } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override { OF_ENV_BARRIER(); } +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_GLOBAL_SYNC_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/instruction.cpp b/oneflow/core/vm/instruction.cpp index fb4c1c97ffc027fe154d281a45f5f77e938cdf36..1ca7d791607fb5070c66476b77dcb75358f2a934 100644 --- a/oneflow/core/vm/instruction.cpp +++ b/oneflow/core/vm/instruction.cpp @@ -14,48 +14,70 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction_type.h" +#include "oneflow/core/thread/thread_manager.h" #include "oneflow/core/vm/stream.h" #include "oneflow/core/vm/thread_ctx.h" #include "oneflow/core/vm/virtual_machine_engine.h" -#include "oneflow/core/framework/stream_get_stream_role_name.h" +#include "oneflow/core/framework/stream_get_stream_type_name.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/cpp_attribute.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" #include "oneflow/core/profiler/profiler.h" namespace oneflow { namespace vm { std::string Instruction::DebugName() const { - std::string instr_name = instruction_type().DebugName(*this); - return instr_name + ":" + GetStreamRoleName::Visit(stream().stream_role()); + std::string instr_name = instruction_policy().DebugName(*this); + return instr_name + ":s_" + GetStreamTypeName::Visit(stream().stream_type()); } -void Instruction::__Init__(Stream* stream, const InstructionType* instruction_type, - const std::shared_ptr& phy_instr_operand) { +void Instruction::__Init__(Stream* stream, + std::shared_ptr&& instruction_policy) { stream_ = stream; - instruction_type_ = instruction_type; - phy_instr_operand_ = phy_instr_operand; + instruction_policy_ = std::move(instruction_policy); + if (IsMainThread()) { + if (auto* stack_getter = Singleton::Get()) { + foreign_frame_ = stack_getter->GetCurrentFrame(); + } + } } -void Instruction::InitStatus() { instruction_type().InitInstructionStatusIf(this); } +void Instruction::InitStatus() { instruction_policy_->InitInstructionStatusIf(this); } -Maybe Instruction::Prepare() { return instruction_type().PrepareIf(this); } -void Instruction::Compute() { return instruction_type().ComputeIf(this); } +Maybe Instruction::Prepare() { + ForeignFrameThreadLocalGuard guard(foreign_frame_); + return instruction_policy_->PrepareIf(this); +} +void Instruction::Compute() { + ForeignFrameThreadLocalGuard guard(foreign_frame_); + instruction_policy_->ComputeIf(this); +} -void Instruction::DeleteStatusAndClearEdges() { - OF_PROFILER_RANGE_GUARD("Instruction::DeleteStatusAndClearEdges"); - instruction_type().DeleteInstructionStatusIf(this); - mut_in_edges()->Clear(); - mut_out_edges()->Clear(); +void Instruction::DeleteStatusAndCheckEdges() { + OF_PROFILER_RANGE_GUARD("Instruction::DeleteStatusAndCheckEdges"); + instruction_policy_->DeleteInstructionStatusIf(this); + INTRUSIVE_FOR_EACH_PTR(edge, mut_in_edges()) { + Instruction* in_instruction = edge->mut_src_instruction(); + LOG(FATAL) << "unerased edge: " << in_instruction->DebugName() << " -> " << this->DebugName(); + } + INTRUSIVE_FOR_EACH_PTR(edge, mut_out_edges()) { + Instruction* out_instruction = edge->mut_dst_instruction(); + LOG(FATAL) << "unerased edge: " << this->DebugName() << " -> " << out_instruction->DebugName(); + } +} + +bool Instruction::Launched() const { + return stream_policy().QueryInstructionStatusLaunched(stream(), status_buffer()); } bool Instruction::Done() const { - return stream_type().QueryInstructionStatusDone(stream(), status_buffer()); + return stream_policy().QueryInstructionStatusDone(stream(), status_buffer()); } -const StreamType& Instruction::stream_type() const { return stream().stream_type(); } +StreamPolicy* Instruction::mut_stream_policy() { return mut_stream()->mut_stream_policy(); } + +const StreamPolicy& Instruction::stream_policy() const { return stream().stream_policy(); } } // namespace vm } // namespace oneflow diff --git a/oneflow/core/vm/instruction.h b/oneflow/core/vm/instruction.h index ec54271cf6d4687f3008a307251548772e30c7a6..8986014614c86b7d93fe4748dcc0b0e032185bb9 100644 --- a/oneflow/core/vm/instruction.h +++ b/oneflow/core/vm/instruction.h @@ -17,13 +17,15 @@ limitations under the License. #define ONEFLOW_CORE_VM_VPU_INSTRUCTION__H_ #include +#include #include #include "oneflow/core/common/symbol.h" #include "oneflow/core/intrusive/intrusive.h" #include "oneflow/core/intrusive/object_pool.h" #include "oneflow/core/vm/vm_object.h" -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/phy_instr_operand.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" namespace oneflow { @@ -103,15 +105,13 @@ class Instruction final : public intrusive::Base { using DependenceAccessList = intrusive::List; - void __Init__(Stream* stream, const InstructionType* instruction_type, - const std::shared_ptr& phy_instr_operand); + void __Init__(Stream* stream, std::shared_ptr&& instruction_policy); // Getters const Stream& stream() const { return *stream_; } const InstructionStatusBuffer& status_buffer() const { return status_buffer_; } const intrusive::ListHook& main_instruction_hook() const { return main_instruction_hook_; } - const InstructionType& instruction_type() const { return *instruction_type_; } - const std::shared_ptr& phy_instr_operand() const { return phy_instr_operand_; } + const InstructionPolicy& instruction_policy() const { return *instruction_policy_; } std::string DebugName() const; const intrusive::ListHook& dispatched_instruction_hook() const { @@ -132,15 +132,19 @@ class Instruction final : public intrusive::Base { // Setters Stream* mut_stream() { return stream_; } InstructionStatusBuffer* mut_status_buffer() { return &status_buffer_; } + InstructionPolicy* mut_instruction_policy() { return instruction_policy_.get(); } InEdgeList* mut_in_edges() { return &in_edges_; } OutEdgeList* mut_out_edges() { return &out_edges_; } DependenceAccessList* mut_access_list() { return &access_list_; } // methods void InitStatus(); - void DeleteStatusAndClearEdges(); + void DeleteStatusAndCheckEdges(); + bool Launched() const; bool Done() const; - const StreamType& stream_type() const; + StreamPolicy* mut_stream_policy(); + const StreamPolicy& stream_policy() const; + intrusive::shared_ptr foreign_frame() const { return foreign_frame_; } intrusive::Ref::RefCntType ref_cnt() const { return intrusive_ref_.ref_cnt(); } @@ -186,8 +190,7 @@ class Instruction final : public intrusive::Base { out_edges_(), intrusive_ref_(), stream_(), - instruction_type_(), - phy_instr_operand_(), + instruction_policy_(), status_buffer_() {} // lists @@ -198,9 +201,9 @@ class Instruction final : public intrusive::Base { // fields intrusive::Ref intrusive_ref_; Stream* stream_; - const InstructionType* instruction_type_; - std::shared_ptr phy_instr_operand_; + std::shared_ptr instruction_policy_; InstructionStatusBuffer status_buffer_; + intrusive::shared_ptr foreign_frame_; }; using InstructionList = intrusive::List; diff --git a/oneflow/core/vm/instruction_fuse_type.h b/oneflow/core/vm/instruction_fuse_type.h new file mode 100644 index 0000000000000000000000000000000000000000..924a9fd60b50052cadf4c68e545d518811910bc4 --- /dev/null +++ b/oneflow/core/vm/instruction_fuse_type.h @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_INSTRUCTION_FUSE_TYPE_H_ +#define ONEFLOW_CORE_VM_INSTRUCTION_FUSE_TYPE_H_ + +namespace oneflow { +namespace vm { + +enum InstructionFuseType { + kInvalidInstructionFuseType = 0, + kDisableInstructionFuse, + kEnableInstructionFuseAtAnyPosition, + kEnableInstructionFuseAsTailOnly, +}; + +} +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_INSTRUCTION_FUSE_TYPE_H_ diff --git a/oneflow/core/vm/instruction_policy.cpp b/oneflow/core/vm/instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ecc5beccab7fc762127c87332a4be3aa3cceb21d --- /dev/null +++ b/oneflow/core/vm/instruction_policy.cpp @@ -0,0 +1,35 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +void InstructionPolicy::InitInstructionStatus(Instruction* instruction) { + instruction->stream_policy().InitInstructionStatus(instruction->stream(), + instruction->mut_status_buffer()); +} + +void InstructionPolicy::DeleteInstructionStatus(Instruction* instruction) { + instruction->stream_policy().DeleteInstructionStatus(instruction->stream(), + instruction->mut_status_buffer()); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/instruction_policy.h b/oneflow/core/vm/instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..4ea576143ac554a4d9c3b2ad4b6ebbbfc8c368dc --- /dev/null +++ b/oneflow/core/vm/instruction_policy.h @@ -0,0 +1,81 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_INSTRUCTION_POLICY_H_ + +#include +#include +#include +#include "oneflow/core/intrusive/intrusive.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/profiler/profiler.h" +#include "oneflow/core/vm/instruction_fuse_type.h" +#include "oneflow/core/vm/vm_object.h" + +namespace oneflow { +namespace vm { + +class EagerBlobObject; +class Stream; + +class InstructionPolicy { + public: + virtual ~InstructionPolicy() = default; + + // Same stream. + virtual bool Prescheduleable(const vm::Stream* src, const vm::Stream* dst) const { + return src == dst; + } + + virtual const DependenceVector& input_dependences() const = 0; + virtual const DependenceVector& output_dependences() const = 0; + virtual Dependence* stream_sequential_dependence() const { return stream_sequential_dependence_; } + + virtual bool IsBarrier() const { return false; } + virtual InstructionFuseType fuse_type() const { return kDisableInstructionFuse; } + virtual std::string DebugName(const Instruction&) const = 0; + + Maybe PrepareIf(Instruction* instruction) { + OF_PROFILER_RANGE_GUARD(std::string("Prepare:") + DebugName(*instruction)); + return Prepare(instruction); + } + + void ComputeIf(Instruction* instruction) { + OF_PROFILER_RANGE_GUARD(std::string("Compute:") + DebugName(*instruction)); + Compute(instruction); + } + + void InitInstructionStatusIf(Instruction* instruction) { InitInstructionStatus(instruction); } + + void DeleteInstructionStatusIf(Instruction* instruction) { DeleteInstructionStatus(instruction); } + + protected: + InstructionPolicy() : stream_sequential_dependence_(nullptr) {} + + Dependence* stream_sequential_dependence_; + + private: + // Usually for Allocating and deallocating tensors. + virtual Maybe Prepare(Instruction* instruction) = 0; + virtual void Compute(Instruction* instruction) = 0; + virtual void InitInstructionStatus(Instruction* instruction); + virtual void DeleteInstructionStatus(Instruction* instruction); +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/instruction_policy_util.h b/oneflow/core/vm/instruction_policy_util.h new file mode 100644 index 0000000000000000000000000000000000000000..e73c5514e77750a886b20503172ba87a153a7aff --- /dev/null +++ b/oneflow/core/vm/instruction_policy_util.h @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_INSTRUCTION_POLICY_UTIL_H_ +#define ONEFLOW_CORE_VM_INSTRUCTION_POLICY_UTIL_H_ + +#include +#include +#include "oneflow/core/vm/vm_object.h" + +namespace oneflow { +namespace vm { + +struct InstructionPolicyUtil { + static std::function SetInserter(DependenceVector* dependences) { + auto existed = + std::make_shared>(dependences->begin(), dependences->end()); + return [dependences, existed](Dependence* object) { + if (existed->insert(object).second) { dependences->push_back(object); } + }; + } +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_INSTRUCTION_POLICY_UTIL_H_ diff --git a/oneflow/core/vm/instruction_type.cpp b/oneflow/core/vm/instruction_type.cpp deleted file mode 100644 index 37d56a53a8afda8f00710df399a30e8ff2a3c0d0..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/instruction_type.cpp +++ /dev/null @@ -1,49 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -void InstructionType::InitInstructionStatus(Instruction* instruction) const { - instruction->stream_type().InitInstructionStatus(instruction->stream(), - instruction->mut_status_buffer()); -} - -void InstructionType::DeleteInstructionStatus(Instruction* instruction) const { - instruction->stream_type().DeleteInstructionStatus(instruction->stream(), - instruction->mut_status_buffer()); -} - -namespace { - -void InitOrCheckMemPtrForAllocationCompuationPipelining(EagerBlobObject* eager_blob_object) { - eager_blob_object->InitOrCheckMemPtrForAllocationComputationPipelining(); -} - -} // namespace - -void InstructionType::InitOrCheckInputBlobsMemPtrForAllocationCompuationPipelining( - Instruction* instruction) const { - const auto& operand = *instruction->phy_instr_operand(); - operand.ForEachInputEagerBlobObjects(&InitOrCheckMemPtrForAllocationCompuationPipelining); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/instruction_type.h b/oneflow/core/vm/instruction_type.h deleted file mode 100644 index 441bd7b0dade71b5db2a65f86c30660d5ded7472..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/instruction_type.h +++ /dev/null @@ -1,81 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_VM_INSTRUCTION_TYPE_H_ - -#include -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/profiler/profiler.h" - -namespace oneflow { -namespace vm { - -class Instruction; - -enum InstructionFuseType { - kInvalidInstructionFuseType = 0, - kDisableInstructionFuse, - kEnableInstructionFuseAtAnyPosition, - kEnableInstructionFuseAsTailOnly, -}; - -class InstructionType { - public: - virtual ~InstructionType() = default; - - Maybe PrepareIf(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD(std::string("Prepare:") + DebugName(*instruction)); - InitOrCheckInputBlobsMemPtrForAllocationCompuationPipelining(instruction); - return Prepare(instruction); - } - - void ComputeIf(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD(std::string("Compute:") + DebugName(*instruction)); - Compute(instruction); - } - - virtual bool IsBarrier() const { return false; } - virtual InstructionFuseType fuse_type() const { return kDisableInstructionFuse; } - void InitInstructionStatusIf(Instruction* instruction) const { - InitInstructionStatus(instruction); - } - - void DeleteInstructionStatusIf(Instruction* instruction) const { - DeleteInstructionStatus(instruction); - } - - virtual std::string DebugName(const Instruction&) const = 0; - - protected: - InstructionType() = default; - - private: - // Allocating tensors, deallocating tensors, preparing opkernel states and preparing opkernel - // caches. - virtual Maybe Prepare(Instruction* instruction) const = 0; - - virtual void Compute(Instruction* instruction) const = 0; - - virtual void InitInstructionStatus(Instruction* instruction) const; - virtual void DeleteInstructionStatus(Instruction* instruction) const; - void InitOrCheckInputBlobsMemPtrForAllocationCompuationPipelining(Instruction* instruction) const; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/vm/lazy_job_device_context.h b/oneflow/core/vm/lazy_job_device_context.h deleted file mode 100644 index d9ad9f46b40478f1eb624a04da10acd8f444f7ba..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/lazy_job_device_context.h +++ /dev/null @@ -1,78 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_LAZY_JOB_DEVICE_CONTEXT_H_ -#define ONEFLOW_CORE_VM_LAZY_JOB_DEVICE_CONTEXT_H_ - -#include "oneflow/core/framework/nn_graph_if.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/device/device_context.h" - -namespace oneflow { - -namespace vm { - -class LazyJobDeviceCtx final : public DeviceCtx { - public: - OF_DISALLOW_COPY_AND_MOVE(LazyJobDeviceCtx); - LazyJobDeviceCtx() = default; - ~LazyJobDeviceCtx() override = default; - - vm::Allocator* mut_allocator() override { return (vm::Allocator*)nullptr; } - - DeviceType device_type() const override { - UNIMPLEMENTED(); - return DeviceType::kInvalidDevice; - } - - ep::Stream* stream() override { - UNIMPLEMENTED(); - return nullptr; - } - - std::queue>* mut_queue() { return &queue_; } - std::mutex* mut_mutex() { return &mutex_; } - std::condition_variable* mut_cond() { return &cond_; } - - void WaitUntilQueueEmptyIfFrontNNGraphNotEquals(const std::shared_ptr& nn_graph) { - std::unique_lock lock(mutex_); - if (queue_.empty()) { return; } - const auto& last_nn_graph = queue_.front().lock(); - if (!last_nn_graph) { return; } - if (last_nn_graph == nn_graph) { return; } - cond_.wait(lock, [this]() { return queue_.empty(); }); - } - - void EnqueueNNGraph(const std::shared_ptr& nn_graph) { - std::unique_lock lock(mutex_); - queue_.emplace(nn_graph); - } - - void DequeueNNGraph() { - std::unique_lock lock(mutex_); - queue_.pop(); - cond_.notify_all(); - } - - private: - std::queue> queue_; - std::mutex mutex_; - std::condition_variable cond_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_LAZY_JOB_DEVICE_CONTEXT_H_ diff --git a/oneflow/core/vm/lazy_job_instruction_policy.h b/oneflow/core/vm/lazy_job_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..a03b22dc4daf6c90b6308a920d2844dd71a46f2a --- /dev/null +++ b/oneflow/core/vm/lazy_job_instruction_policy.h @@ -0,0 +1,144 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_LAZY_JOB_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_LAZY_JOB_INSTRUCTION_POLICY_H_ + +#include "oneflow/core/common/buffer_manager.h" +#include "oneflow/core/common/of_unused.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/nn_graph_if.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/job/job_instance.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/instruction_policy_util.h" +#include "oneflow/core/vm/naive_instruction_status_querier.h" +#include "oneflow/core/vm/lazy_job_stream_policy.h" +#include "oneflow/core/vm/virtual_machine.h" +#include + +namespace oneflow { + +class LazyJobInstance final : public JobInstance { + public: + LazyJobInstance(const LazyJobInstance&) = delete; + LazyJobInstance(LazyJobInstance&&) = delete; + ~LazyJobInstance() override = default; + LazyJobInstance(const std::string& job_name, const std::function& finish_cb) + : job_name_(job_name), finish_cb_(finish_cb) {} + + std::string job_name() const override { return job_name_; } + void Finish() const override { finish_cb_(); } + + private: + const std::string job_name_; + const std::function finish_cb_; +}; + +namespace vm { + +class LaunchLazyJobInstructionPolicy final : public InstructionPolicy { // NOLINT + public: + LaunchLazyJobInstructionPolicy(const LaunchLazyJobInstructionPolicy&) = delete; + LaunchLazyJobInstructionPolicy(LaunchLazyJobInstructionPolicy&&) = delete; + ~LaunchLazyJobInstructionPolicy() = default; + + LaunchLazyJobInstructionPolicy(const std::shared_ptr& nn_graph, + const EagerBlobObjectListPtr& param_blob_objects) + : nn_graph_(nn_graph), + param_blob_objects_(param_blob_objects), + input_dependences_(), + output_dependences_() { + robin_hood::unordered_flat_map unique_map; + ForEachConstDependence([&](Dependence* compute) { + if (unique_map.emplace(compute, true).second) { input_dependences_.emplace_back(compute); } + }); + unique_map.clear(); + output_dependences_.reserve(param_blob_objects_->size()); + unique_map.reserve(param_blob_objects_->size()); + ForEachMutDependence([&](Dependence* compute) { + if (unique_map.emplace(compute, true).second) { output_dependences_.emplace_back(compute); } + }); + ForEachMut2Dependence([&](Dependence* compute) { + if (unique_map.emplace(compute, true).second) { output_dependences_.emplace_back(compute); } + }); + } + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + void ForEachConstDependence(const std::function&) const {} + + void ForEachMutDependence(const std::function& DoEach) const { + for (const auto& eager_blob_object : *param_blob_objects_) { + DoEach(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + } + DoEach(CHECK_JUST(SingletonMaybe()) + ->FindOrCreateTransportLocalDepObject() + .Mutable()); + } + + void ForEachMut2Dependence(const std::function&) const {} + + std::string DebugName(const Instruction&) const override { return "LaunchLazyJob"; } + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + void Compute(Instruction* instruction) override { + auto* lazy_job_stream_policy = GetLazyJobStreamPolicy(instruction); + + static thread_local int64_t run_id = 0; + { + OF_PROFILER_RANGE_GUARD("WaitUntilQueueEmptyIfFrontNNGraphNotEquals"); + lazy_job_stream_policy->WaitUntilQueueEmptyIfFrontNNGraphNotEquals(nn_graph_); + } + { + OF_PROFILER_RANGE_GUARD("Send all buffers to BufferMgr"); + const auto& job_instance = MakeJobInstance(instruction); + const auto& job_name = job_instance->job_name(); + auto* buffer_mgr = Singleton>>::Get(); + buffer_mgr->Get(GetCallbackNotifierBufferName(job_name))->Push(job_instance); + buffer_mgr->Get(GetSourceTickBufferName(job_name))->Push(job_instance); + } + OF_UNUSED(run_id); // disable compiler warning. + OF_PROFILER_RANGE_GUARD("EnqueueNNGraph"); + lazy_job_stream_policy->EnqueueNNGraph(nn_graph_); + } + + private: + LazyJobStreamPolicy* GetLazyJobStreamPolicy(Instruction* instruction) const { + StreamPolicy* stream_policy = instruction->mut_stream()->mut_stream_policy(); + LazyJobStreamPolicy* lazy_job_stream_policy = dynamic_cast(stream_policy); + CHECK_NOTNULL(lazy_job_stream_policy); + return lazy_job_stream_policy; + } + + std::shared_ptr MakeJobInstance(Instruction* instruction) const { + const auto& FinishCb = [this, instruction]() { + auto* lazy_job_stream_policy = GetLazyJobStreamPolicy(instruction); + lazy_job_stream_policy->DequeueNNGraph(); + auto* status_buffer = instruction->mut_status_buffer(); + NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer())->set_done(); + }; + return std::make_shared(nn_graph_->job_name(), FinishCb); + } + + std::shared_ptr nn_graph_; + EagerBlobObjectListPtr param_blob_objects_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow +#endif // ONEFLOW_CORE_VM_LAZY_JOB_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/lazy_job_stream_policy.cpp b/oneflow/core/vm/lazy_job_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7459bdbdabcbe9c7cafd789a6dec4f31b06e829 --- /dev/null +++ b/oneflow/core/vm/lazy_job_stream_policy.cpp @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/lazy_job_stream_policy.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/naive_instruction_status_querier.h" +#include "oneflow/core/framework/nn_graph_if.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +void LazyJobStreamPolicy::WaitUntilQueueEmptyIfFrontNNGraphNotEquals( + const std::shared_ptr& nn_graph) { + std::unique_lock lock(mutex_); + if (queue_.empty()) { return; } + const auto& last_nn_graph = queue_.front().lock(); + if (!last_nn_graph) { return; } + if (last_nn_graph == nn_graph) { return; } + cond_.wait(lock, [this]() { return queue_.empty(); }); +} + +void LazyJobStreamPolicy::EnqueueNNGraph(const std::shared_ptr& nn_graph) { + std::unique_lock lock(mutex_); + queue_.emplace(nn_graph); +} + +void LazyJobStreamPolicy::DequeueNNGraph() { + std::unique_lock lock(mutex_); + queue_.pop(); + cond_.notify_all(); +} + +void LazyJobStreamPolicy::InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(NaiveInstrStatusQuerier) < kInstructionStatusBufferBytes, ""); + NaiveInstrStatusQuerier::PlacementNew(status_buffer->mut_buffer()); +} + +void LazyJobStreamPolicy::DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + auto* ptr = NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer()); + ptr->~NaiveInstrStatusQuerier(); +} + +bool LazyJobStreamPolicy::QueryInstructionStatusLaunched( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->launched(); +} + +bool LazyJobStreamPolicy::QueryInstructionStatusDone( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const { + return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->done(); +} + +void LazyJobStreamPolicy::Run(Instruction* instruction) const { instruction->Compute(); } + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/lazy_job_stream_policy.h b/oneflow/core/vm/lazy_job_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..42dc1f6534bdfdb45e19c109a8d27bd912a14b86 --- /dev/null +++ b/oneflow/core/vm/lazy_job_stream_policy.h @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_VM_LAZY_JOB_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_LAZY_JOB_STREAM_POLICY_H_ + +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/vm/instruction.h" + +namespace oneflow { + +class NNGraphIf; + +namespace vm { + +class LazyJobStreamPolicy final : public StreamPolicy { + public: + LazyJobStreamPolicy() = default; + virtual ~LazyJobStreamPolicy() = default; + + vm::Allocator* mut_allocator() override { return (vm::Allocator*)nullptr; } + + DeviceType device_type() const override { + UNIMPLEMENTED(); + return DeviceType::kInvalidDevice; + } + + ep::Stream* stream() override { + UNIMPLEMENTED(); + return nullptr; + } + + void WaitUntilQueueEmptyIfFrontNNGraphNotEquals(const std::shared_ptr& nn_graph); + + void EnqueueNNGraph(const std::shared_ptr& nn_graph); + + void DequeueNNGraph(); + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + void DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; + bool QueryInstructionStatusLaunched(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + bool QueryInstructionStatusDone(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const override; + void Run(Instruction* instruction) const override; + bool SupportingTransportInstructions() const override { return false; } + + private: + std::queue> queue_; + std::mutex mutex_; + std::condition_variable cond_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_LAZY_JOB_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/lazy_job_stream_type.cpp b/oneflow/core/vm/lazy_job_stream_type.cpp deleted file mode 100644 index d83803211c05eb6b3627096f6a4768a2fd78983d..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/lazy_job_stream_type.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/lazy_job_stream_type.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/lazy_job_device_context.h" -#include "oneflow/core/vm/naive_instruction_status_querier.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -void LazyJobStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, - Stream* stream) const { - device_ctx->reset(new LazyJobDeviceCtx()); -} - -void LazyJobStreamType::InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(NaiveInstrStatusQuerier) < kInstructionStatusBufferBytes, ""); - NaiveInstrStatusQuerier::PlacementNew(status_buffer->mut_buffer()); -} - -void LazyJobStreamType::DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - auto* ptr = NaiveInstrStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~NaiveInstrStatusQuerier(); -} - -bool LazyJobStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return NaiveInstrStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -void LazyJobStreamType::Run(Instruction* instruction) const { instruction->Compute(); } - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/lazy_job_stream_type.h b/oneflow/core/vm/lazy_job_stream_type.h deleted file mode 100644 index d6d4568ed0d3049d5c878f40b270a46062f47b12..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/lazy_job_stream_type.h +++ /dev/null @@ -1,48 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#ifndef ONEFLOW_CORE_VM_LAZY_JOB_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_LAZY_JOB_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class LazyJobStreamType final : public StreamType { - public: - LazyJobStreamType() = default; - virtual ~LazyJobStreamType() = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return false; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_LAZY_JOB_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/naive_instruction_status_querier.h b/oneflow/core/vm/naive_instruction_status_querier.h index 1626e036a088e6efd46df92d44e0d5d143064638..a2c5c5c50d6db9e1c3dc7374a983fad339225dcc 100644 --- a/oneflow/core/vm/naive_instruction_status_querier.h +++ b/oneflow/core/vm/naive_instruction_status_querier.h @@ -25,6 +25,7 @@ class NaiveInstrStatusQuerier { public: ~NaiveInstrStatusQuerier() = default; + bool launched() const { return done_; } bool done() const { return done_; } void set_done() { done_ = true; } diff --git a/oneflow/core/vm/op_call_instruction_policy.cpp b/oneflow/core/vm/op_call_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e9495bdd4ca79ffcf690e34b8b4b3a55b0507cb7 --- /dev/null +++ b/oneflow/core/vm/op_call_instruction_policy.cpp @@ -0,0 +1,217 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/op_call_instruction_policy.h" +#include "oneflow/core/vm/allocator.h" +#include "oneflow/user/kernels/stateful_opkernel.h" +#include "oneflow/core/eager/dev_vm_dep_object_consume_mode.h" +#include "oneflow/core/framework/stream_is_comm_net_stream.h" +#include "oneflow/core/framework/stream_get_stream_type_name.h" +#include "oneflow/core/vm/stream_get_allocator_stream_type.h" +#include "oneflow/core/profiler/profiler.h" + +namespace oneflow { +namespace vm { + +struct OpCallInstructionUtil final { + static inline Maybe Prepare(OpCallInstructionPolicy* op_call_instruction_policy, + Instruction* instruction) { + if (unlikely(op_call_instruction_policy->need_temp_storage())) { + InferTempStorageSize(op_call_instruction_policy); + } + return Maybe::Ok(); + } + + static inline Maybe Compute(OpCallInstructionPolicy* op_call_instruction_policy, + Instruction* instruction) { + Allocator* allocator = instruction->mut_stream()->mut_stream_policy()->mut_allocator(); + JUST(AllocateOutputBlobsMemory(op_call_instruction_policy, allocator, instruction)); + if (unlikely(op_call_instruction_policy->need_temp_storage())) { + JUST(TryAllocateTempStorage(op_call_instruction_policy, allocator)); + } + ep::Stream* stream = instruction->mut_stream()->mut_stream_policy()->stream(); + user_op::OpKernelState* state = nullptr; + user_op::OpKernelCache* cache = nullptr; + if (op_call_instruction_policy->user_opkernel()->has_state_or_cache()) { + TryInitOpKernelStateAndCache(op_call_instruction_policy, stream, &state, &cache); + } + OpKernelCompute(op_call_instruction_policy, stream, state, cache); + if (unlikely(op_call_instruction_policy->need_temp_storage())) { + DeallocateTempStorage(op_call_instruction_policy, allocator); + } + return Maybe::Ok(); + } + + private: + static inline void InferTempStorageSize(OpCallInstructionPolicy* op_call_instruction_policy) { + auto* tmp_tensor = op_call_instruction_policy->mut_call_ctx()->mut_tmp_tensor(); + size_t temp_size = op_call_instruction_policy->opkernel().InferTmpSize( + op_call_instruction_policy->mut_call_ctx(), op_call_instruction_policy->user_opkernel()); + tmp_tensor->set_tmp_buffer_size(temp_size); + } + + static inline void TryInitOpKernelStateAndCache( + OpCallInstructionPolicy* op_call_instruction_policy, ep::Stream* stream, + user_op::OpKernelState** state, user_op::OpKernelCache** cache) { + OF_PROFILER_RANGE_GUARD("TryInitOpKernelStateAndCache"); + if (likely(op_call_instruction_policy->op_interp_ctx().state)) { + *state = op_call_instruction_policy->op_interp_ctx().state.get(); + // set state to nullptr so that state initialization in TryInitOpKernelStateAndCache will be + // skipped. + state = nullptr; + } + op_call_instruction_policy->mut_opkernel()->TryInitOpKernelStateAndCache( + op_call_instruction_policy->mut_call_ctx(), stream, + op_call_instruction_policy->user_opkernel(), state, cache); + } + + // Returns true if allocation happened. + static inline Maybe AllocateOutputBlobsMemory( + OpCallInstructionPolicy* op_call_instruction_policy, Allocator* allocator, + Instruction* instruction) { + OF_PROFILER_RANGE_GUARD("AllocateOutputBlobsMemory"); + StreamType stream_type = instruction->stream().stream_type(); + StreamType allocator_stream_type = JUST(GetAllocatorStreamType::Visit(stream_type)); + for (const auto& blob_object : op_call_instruction_policy->outputs()) { + if (JUST(blob_object->TryAllocateBlobBodyMemory(allocator))) { + CHECK_OR_RETURN(stream_type == allocator_stream_type) + << "no allocator supported on stream type " << GetStreamTypeName::Visit(stream_type); + } + } + return Maybe::Ok(); + } + + static inline Maybe TryAllocateTempStorage( + OpCallInstructionPolicy* op_call_instruction_policy, Allocator* allocator) { + OF_PROFILER_RANGE_GUARD("TryAllocateTempStorage"); + auto* tmp_tensor = op_call_instruction_policy->mut_call_ctx()->mut_tmp_tensor(); + size_t byte_size = tmp_tensor->tmp_buffer_size(); + if (byte_size > 0) { + char* mem_ptr = nullptr; + JUST(allocator->Allocate(&mem_ptr, byte_size)); + tmp_tensor->set_tmp_buffer_ptr(mem_ptr); + } + return Maybe::Ok(); + } + + static inline void DeallocateTempStorage(OpCallInstructionPolicy* op_call_instruction_policy, + Allocator* allocator) { + auto* tmp_tensor = op_call_instruction_policy->mut_call_ctx()->mut_tmp_tensor(); + allocator->Deallocate(tmp_tensor->mut_tmp_buffer_ptr(), tmp_tensor->tmp_buffer_size()); + } + + static inline void OpKernelCompute(OpCallInstructionPolicy* op_call_instruction_policy, + ep::Stream* stream, user_op::OpKernelState* state, + user_op::OpKernelCache* cache) { + auto* user_kernel = op_call_instruction_policy->user_opkernel(); + op_call_instruction_policy->mut_opkernel()->Compute(op_call_instruction_policy->mut_call_ctx(), + stream, user_kernel, state, cache); + } +}; + +OpCallInstructionPolicy::OpCallInstructionPolicy( + Stream* vm_stream, const std::shared_ptr& opkernel, + EagerBlobObjectList&& inputs, EagerBlobObjectList&& outputs, + const std::shared_ptr& global_tensor_infer_result, + const one::OpExprInterpContext& op_interp_ctx, + const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode) + : vm_stream_(vm_stream), + call_ctx_(ComposedAttrMap(op_interp_ctx.attrs, opkernel->base_attrs()), std::move(inputs), + std::move(outputs), global_tensor_infer_result, op_interp_ctx, + opkernel->mem_case()), + opkernel_(opkernel), + user_opkernel_(nullptr), + infer_tmp_size_fn_(nullptr), + need_temp_storage_(false), + dev_vm_dep_object_consume_mode_(dev_vm_dep_object_consume_mode), + input_dependences_(), + output_dependences_() { + ForEachConstDependence([&](auto* dep) { input_dependences_.emplace_back(dep); }); + ForEachMutDependence([&](auto* dep) { output_dependences_.emplace_back(dep); }); + ForEachMut2Dependence([&](auto* dep) { output_dependences_.emplace_back(dep); }); + InitStreamSequentialDependence(); +} + +Maybe OpCallInstructionPolicy::Init() { + return mut_opkernel()->ChooseOpKernel(&call_ctx_, &user_opkernel_, &need_temp_storage_); +} + +template +void OpCallInstructionPolicy::ForEachConstDependence(const DoEachT& DoEach) const { + const auto& input_list = inputs(); + for (int64_t index : opkernel().input_tuple_indexes4const_ibns()) { + const auto& input = input_list.at(index); + DoEach(CHECK_JUST(input->compute_local_dep_object())); + } +} + +void OpCallInstructionPolicy::InitStreamSequentialDependence() { + auto* device_schedule_dep_object = vm_stream_->schedule_local_dep_object().get(); + if (IsCommNetStream::Visit(vm_stream_->stream_type())) { + // Sequantialize nccl instructions to avoid deadlock + stream_sequential_dependence_ = device_schedule_dep_object; + } else { + // Sequantialize instructions to avoid explosive memory allocation of source ops + if (dev_vm_dep_object_consume_mode() == one::DevVmDepObjectConsumeMode::MUTABLE) { + stream_sequential_dependence_ = device_schedule_dep_object; + } else if (opkernel().input_tuple_indexes4const_ibns().empty() + && opkernel().input_tuple_indexes4mut_ibns().empty()) { + stream_sequential_dependence_ = device_schedule_dep_object; + } + } +} + +template +void OpCallInstructionPolicy::ForEachMutDependence(const DoEachT& DoEach) const { + for (const auto& transport_dependence : vm_stream_->transport_dependences()) { + DoEach(transport_dependence.get()); + } + + const auto& input_list = inputs(); + for (int64_t index : opkernel().input_tuple_indexes4mut_ibns()) { + const auto& input = input_list.at(index); + DoEach(CHECK_JUST(input->compute_local_dep_object())); + } + const auto& output_list = outputs(); + for (int64_t index : opkernel().output_tuple_indexes4mut_obns()) { + const auto& output = output_list.at(index); + DoEach(CHECK_JUST(output->compute_local_dep_object())); + } +} + +template +void OpCallInstructionPolicy::ForEachMut2Dependence(const DoEachT& DoEach) const { + const auto& output_list = outputs(); + for (int64_t index : opkernel().output_tuple_indexes4mut2_obns()) { + const auto& output = output_list.at(index); + DoEach(CHECK_JUST(output->compute_local_dep_object())); + } +} + +Maybe OpCallInstructionPolicy::Prepare(vm::Instruction* instruction) { + return OpCallInstructionUtil::Prepare(this, instruction); +} + +void OpCallInstructionPolicy::Compute(vm::Instruction* instruction) { + CHECK_JUST_MSG(OpCallInstructionUtil::Compute(this, instruction), instruction->DebugName()); +} + +std::string OpCallInstructionPolicy::DebugName(const vm::Instruction& instruction) const { + return opkernel().op_type_name() + ":OpCall"; +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/op_call_instruction_policy.h b/oneflow/core/vm/op_call_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..dc9f2232d9b903033a57c32c6f0dbede39f6dd71 --- /dev/null +++ b/oneflow/core/vm/op_call_instruction_policy.h @@ -0,0 +1,123 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_OP_CALL_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_OP_CALL_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/eager/call_context.h" +#include "oneflow/core/eager/dev_vm_dep_object_consume_mode.h" +#include "oneflow/core/framework/user_op_kernel_registry.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/user/kernels/stateful_opkernel.h" + +namespace oneflow { + +namespace user_op { + +class OpKernel; + +} // namespace user_op + +namespace vm { + +class OpCallInstructionPolicy final : public InstructionPolicy { + public: + OpCallInstructionPolicy(const OpCallInstructionPolicy&) = delete; + OpCallInstructionPolicy(OpCallInstructionPolicy&&) = delete; + + ~OpCallInstructionPolicy() override = default; + + template + static Maybe New(Args&&... args) { + auto* ptr = new OpCallInstructionPolicy(std::forward(args)...); + JUST(ptr->Init()); + return std::shared_ptr(ptr); + } + + const one::StatefulOpKernel& opkernel() const { return *opkernel_; } + const EagerBlobObjectList& inputs() const { return call_ctx_.inputs(); } + const EagerBlobObjectList& outputs() const { return call_ctx_.outputs(); } + const AttrMap& attrs() const { return call_ctx_.op_interp_ctx().attrs; } + const one::OpExprInterpContext& op_interp_ctx() const { return call_ctx_.op_interp_ctx(); } + const one::DevVmDepObjectConsumeMode& dev_vm_dep_object_consume_mode() const { + return dev_vm_dep_object_consume_mode_; + } + + one::StatefulOpKernel* mut_opkernel() { return opkernel_.get(); } + + template + Maybe ForEachOutputTensor(const DoEachT& DoEach) { + for (const auto& output : outputs()) { JUST(DoEach(output.get())); } + return Maybe::Ok(); + } + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + template + void ForEachConstDependence(const DoEachT& DoEach) const; + + template + void ForEachMutDependence(const DoEachT& DoEach) const; + + template + void ForEachMut2Dependence(const DoEachT& DoEach) const; + + bool need_temp_storage() const { return need_temp_storage_; } + const user_op::OpKernel* user_opkernel() const { return user_opkernel_; } + const user_op::InferTmpSizeFn& infer_tmp_size_fn() const { return *infer_tmp_size_fn_; } + + const std::shared_ptr& global_tensor_infer_result() const { + return call_ctx_.global_tensor_infer_result(); + } + + eager::CallContext* mut_call_ctx() { return &call_ctx_; } + + Stream* vm_stream() const { return vm_stream_; } + + InstructionFuseType fuse_type() const override { return kEnableInstructionFuseAtAnyPosition; } + + std::string DebugName(const vm::Instruction& instruction) const override; + + private: + OpCallInstructionPolicy( + Stream* vm_stream, const std::shared_ptr& opkernel, + EagerBlobObjectList&& inputs, EagerBlobObjectList&& outputs, + const std::shared_ptr& global_tensor_infer_result, + const one::OpExprInterpContext& op_interp_ctx, + const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode); + Maybe Init(); + void InitStreamSequentialDependence(); + Maybe Prepare(Instruction* instruction) override; + void Compute(Instruction* instruction) override; + Maybe MaybeCompute(vm::Instruction* instruction) const; + + Stream* vm_stream_; + eager::CallContext call_ctx_; + std::shared_ptr opkernel_; + const user_op::OpKernel* user_opkernel_; + const user_op::InferTmpSizeFn* infer_tmp_size_fn_; + bool need_temp_storage_; + const one::DevVmDepObjectConsumeMode dev_vm_dep_object_consume_mode_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_OP_CALL_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/phy_instr_operand.h b/oneflow/core/vm/phy_instr_operand.h deleted file mode 100644 index 36c3f27a06396e94da0fbe169e63e846660e064a..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/phy_instr_operand.h +++ /dev/null @@ -1,63 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_PHY_INSTR_OPERAND_H_ -#define ONEFLOW_CORE_VM_PHY_INSTR_OPERAND_H_ - -#include -#include -#include -#include -#include "oneflow/core/intrusive/intrusive.h" - -namespace oneflow { -namespace vm { - -class MirroredObject; -class EagerBlobObject; - -using DependenceVector = std::vector; - -// physical instruction operand -class PhyInstrOperand { - public: - virtual ~PhyInstrOperand() = default; - - virtual const DependenceVector& input_dependences() const = 0; - virtual const DependenceVector& output_dependences() const = 0; - virtual MirroredObject* stream_sequential_dependence() const { - return stream_sequential_dependence_; - } - - static std::function SetInserter(DependenceVector* dependences) { - auto existed = - std::make_shared>(dependences->begin(), dependences->end()); - return [dependences, existed](MirroredObject* object) { - if (existed->insert(object).second) { dependences->push_back(object); } - }; - } - - virtual void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const = 0; - - protected: - PhyInstrOperand() : stream_sequential_dependence_(nullptr) {} - - MirroredObject* stream_sequential_dependence_; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_PHY_INSTR_OPERAND_H_ diff --git a/oneflow/core/vm/pinned_ep_stream_policy.cpp b/oneflow/core/vm/pinned_ep_stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ea9250799615f90fca388c564272a9e143b898bb --- /dev/null +++ b/oneflow/core/vm/pinned_ep_stream_policy.cpp @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/vm/pinned_ep_stream_policy.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/thread_ctx.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/vm/ep_backend_host_allocator.h" +#include "oneflow/core/common/util.h" + +namespace oneflow { +namespace vm { + +namespace { + +std::unique_ptr> CreatePinedEpBackendHostAllocator( + Symbol device) { + // TODO:(zhaoluyang) empty/cast/copy op support pin_memory_device + DeviceType device_type = device->enum_type(); + size_t device_index = device->device_id(); + auto ep_device = + Singleton::Get()->GetDevice(device_type, device_index); + ep::AllocationOptions options{}; + options.SetPinnedDevice(device_type, device_index); + auto ep_backend_allocator = std::make_unique(ep_device, options); + return std::make_unique>(ep::kMaxAlignmentRequirement, + std::move(ep_backend_allocator)); +} + +} // namespace + +PinnedEpStreamPolicy::PinnedEpStreamPolicy(Symbol device) + : EpStreamPolicyBase(device, CreatePinedEpBackendHostAllocator(device)) {} + +void PinnedEpStreamPolicy::InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const { + static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); + auto* data_ptr = status_buffer->mut_buffer(); + EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, nullptr); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/pinned_ep_stream_policy.h b/oneflow/core/vm/pinned_ep_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..090f7c133c1d2c6576a78ad6dc5e31c79ca1a473 --- /dev/null +++ b/oneflow/core/vm/pinned_ep_stream_policy.h @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_PINNED_EP_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_PINNED_EP_STREAM_POLICY_H_ + +#include "oneflow/core/vm/ep_stream_policy_base.h" + +namespace oneflow { +namespace vm { + +class PinnedEpStreamPolicy final : public EpStreamPolicyBase { + public: + PinnedEpStreamPolicy(Symbol device); + ~PinnedEpStreamPolicy() override = default; + + void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const override; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_PINNED_EP_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/pinned_ep_stream_type.cpp b/oneflow/core/vm/pinned_ep_stream_type.cpp deleted file mode 100644 index 031a3c548b618d0209b4f8d3bfd7aa0669b3fa47..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/pinned_ep_stream_type.cpp +++ /dev/null @@ -1,79 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/vm/pinned_ep_stream_type.h" -#include "oneflow/core/common/maybe.h" -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/stream.h" -#include "oneflow/core/vm/thread_ctx.h" -#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" -#include "oneflow/core/vm/ep_device_context.h" -#include "oneflow/core/vm/bin_allocator.h" -#include "oneflow/core/vm/ep_backend_host_allocator.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/ep/include/device_manager_registry.h" - -namespace oneflow { -namespace vm { - -void PinnedEpStreamType::InitDeviceCtx(std::unique_ptr* device_ctx, - Stream* stream) const { - // TODO:(zhaoluyang) empty/cast/copy op support pin_memory_device - DeviceType device_type = stream->device()->enum_type(); - size_t device_index = stream->device()->device_id(); - auto ep_device = - Singleton::Get()->GetDevice(device_type, device_index); - ep::AllocationOptions options{}; - CHECK_EQ(stream->stream_role(), StreamRole::kPinnedCompute) - << "stream role must be 'StreamRole::kPinnedCompute'"; - options.SetPinnedDevice(device_type, device_index); - auto ep_backend_allocator = std::make_unique(ep_device, options); - device_ctx->reset(new EpDeviceCtx(stream->device(), std::move(ep_backend_allocator))); -} - -void PinnedEpStreamType::InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - static_assert(sizeof(EpOptionalEventRecordStatusQuerier) < kInstructionStatusBufferBytes, ""); - auto* data_ptr = status_buffer->mut_buffer(); - EpOptionalEventRecordStatusQuerier::PlacementNew(data_ptr, nullptr); -} - -void PinnedEpStreamType::DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const { - auto* ptr = EpOptionalEventRecordStatusQuerier::MutCast(status_buffer->mut_buffer()); - ptr->~EpOptionalEventRecordStatusQuerier(); -} - -bool PinnedEpStreamType::QueryInstructionStatusDone( - const Stream& stream, const InstructionStatusBuffer& status_buffer) const { - return EpOptionalEventRecordStatusQuerier::Cast(status_buffer.buffer())->done(); -} - -void PinnedEpStreamType::Run(Instruction* instruction) const { - OF_PROFILER_RANGE_GUARD("S:" + instruction->DebugName()); - auto* stream = instruction->mut_stream(); - auto* ep_device_ctx = static_cast(stream->device_ctx().get()); // NOLINT - auto* ep_device = ep_device_ctx->GetOrCreateEpDevice(); - ep_device->SetAsActiveDevice(); - instruction->Compute(); - char* data_ptr = instruction->mut_status_buffer()->mut_buffer(); - EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->SetLaunched(ep_device_ctx); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/pinned_ep_stream_type.h b/oneflow/core/vm/pinned_ep_stream_type.h deleted file mode 100644 index 91177aa3b613299cdf12ac32b057502b33e642d2..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/pinned_ep_stream_type.h +++ /dev/null @@ -1,47 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_PINNED_EP_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_PINNED_EP_STREAM_TYPE_H_ - -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction.h" -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" - -namespace oneflow { -namespace vm { - -class PinnedEpStreamType final : public StreamType { - public: - PinnedEpStreamType() = default; - ~PinnedEpStreamType() override = default; - - void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const override; - - void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const override; - bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const override; - void Run(Instruction* instruction) const override; - bool SupportingTransportInstructions() const override { return true; } -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_EP_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/release_tensor_instruction_policy.h b/oneflow/core/vm/release_tensor_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..a9b4e05b973b7a1103d13668c0e399ab49d5eb93 --- /dev/null +++ b/oneflow/core/vm/release_tensor_instruction_policy.h @@ -0,0 +1,197 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_RELEASE_TENSOR_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_RELEASE_TENSOR_INSTRUCTION_POLICY_H_ + +#include +#include +#include "oneflow/core/common/singleton_ptr.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/intrusive/intrusive.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/optional.h" +#include "oneflow/core/framework/device.h" +#include "oneflow/core/framework/stream.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/framework/stream_need_soft_sync.h" + +namespace oneflow { + +namespace vm { + +class EagerBlobObject; + +class ReleaseTensorInstructionPolicy : public InstructionPolicy { + public: + ReleaseTensorInstructionPolicy(const std::shared_ptr& eager_blob_object, + const Optional& stream) + : eager_blob_object_(eager_blob_object), output_dependences_() { + output_dependences_.push_back(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + if (stream.has_value()) { + stream_sequential_dependence_ = CHECK_JUST(stream)->schedule_local_dep_object().get(); + } + } + ~ReleaseTensorInstructionPolicy() override = default; + + const std::shared_ptr& eager_blob_object() const { + return eager_blob_object_; + } + + const DependenceVector& input_dependences() const override { + static thread_local DependenceVector empty{}; + return empty; + } + + const DependenceVector& output_dependences() const override { return output_dependences_; } + + Dependence* stream_sequential_dependence() const override { + return stream_sequential_dependence_; + } + + protected: + void Release(const std::shared_ptr& eager_blob_object) const { + CHECK_JUST(eager_blob_object->DeallocateBlobDataPtr()); + } + + private: + void InitInstructionStatus(Instruction* instruction) override { + auto* status_buffer = instruction->mut_status_buffer(); + auto* stream = instruction->mut_stream(); + instruction->stream_policy().InitInstructionStatus(*stream, status_buffer); + auto* data_ptr = status_buffer->mut_buffer(); + EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->reset_ep_event(nullptr); + } + std::shared_ptr eager_blob_object_; + DependenceVector output_dependences_; +}; + +class FastReleaseTensorInstructionPolicy final : public ReleaseTensorInstructionPolicy { + public: + using ReleaseTensorInstructionPolicy::ReleaseTensorInstructionPolicy; + + bool Prescheduleable(const vm::Stream* src, const vm::Stream* dst) const override { + return false; + } + + private: + std::string DebugName(const vm::Instruction& instruction) const override { + return "FastReleaseTensor"; + } + + Maybe Prepare(vm::Instruction* instruction) override { + DataType data_type = eager_blob_object()->data_type(); + CHECK_OR_RETURN(IsPODDataType(data_type)); + if (eager_blob_object()->tensor_storage()->is_allocated_in_vm()) { + Release(eager_blob_object()); + } + return Maybe::Ok(); + } + + void Compute(vm::Instruction* instruction) override { + if (!eager_blob_object()->tensor_storage()->is_allocated_in_vm()) { + Release(eager_blob_object()); + } + } +}; + +class SlowReleaseTensorInstructionPolicy final : public ReleaseTensorInstructionPolicy { + public: + using ReleaseTensorInstructionPolicy::ReleaseTensorInstructionPolicy; + + private: + std::string DebugName(const vm::Instruction& instruction) const override { + return "SlowReleaseTensor"; + } + + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + + void Compute(vm::Instruction* instruction) override { Release(eager_blob_object()); } +}; + +struct MakeReleaseTensorInstructionPolicy + : public StreamTypeVisitor { + static Maybe VisitCompute( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + return Make(eager_blob_object, stream); + } + static Maybe VisitHost2Device( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + return Make(eager_blob_object, stream); + } + static Maybe VisitDevice2Host( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + return Make(eager_blob_object, stream); + } + static Maybe VisitCcl( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + return Make(eager_blob_object, stream); + } + static Maybe VisitBarrier( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + UNIMPLEMENTED_THEN_RETURN() << "ReleaseTensor instruction not supported in Barrier stream"; + } + static Maybe VisitCriticalSection( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + UNIMPLEMENTED_THEN_RETURN() + << "ReleaseTensor instruction not supported in CriticalSection stream"; + } + static Maybe VisitLazyJobLauncher( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + UNIMPLEMENTED_THEN_RETURN() + << "ReleaseTensor instruction not supported in LaunchLazyJob stream"; + } + static Maybe VisitPinnedCompute( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + return VisitCompute(eager_blob_object, stream); + } + + private: + static Maybe Make( + const std::shared_ptr& eager_blob_object, + const Optional& stream) { + DataType data_type = eager_blob_object->data_type(); + if (!IsPODDataType(data_type)) { + return std::shared_ptr( + new vm::SlowReleaseTensorInstructionPolicy(eager_blob_object, stream)); + } + Symbol last_used_stream = JUST(eager_blob_object->last_used_stream()); + DeviceType device_type = last_used_stream->device()->enum_type(); + if (NeedSoftSync::Visit(last_used_stream->stream_type(), device_type)) { + return std::shared_ptr( + new vm::SlowReleaseTensorInstructionPolicy(eager_blob_object, stream)); + } else { + return std::shared_ptr( + new vm::FastReleaseTensorInstructionPolicy(eager_blob_object, stream)); + } + } +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_RELEASE_TENSOR_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/shrinkable_cache.h b/oneflow/core/vm/shrinkable_cache.h deleted file mode 100644 index d64c1f794e689067065c60525213ca89bb2ead82..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/shrinkable_cache.h +++ /dev/null @@ -1,33 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_SHRINKABLE_CACHE_H_ -#define ONEFLOW_CORE_VM_SHRINKABLE_CACHE_H_ - -namespace oneflow { -namespace vm { - -class ShrinkableCache { - public: - ShrinkableCache() = default; - virtual ~ShrinkableCache() = default; - - virtual void Shrink() = 0; -}; - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_SHRINKABLE_CACHE_H_ diff --git a/oneflow/core/vm/stream.cpp b/oneflow/core/vm/stream.cpp index 056dc096abf314876886e5f80772dc2a0d72c945..9e4497aacbccef497e7b338f044104bfe2656139 100644 --- a/oneflow/core/vm/stream.cpp +++ b/oneflow/core/vm/stream.cpp @@ -18,29 +18,41 @@ limitations under the License. #include "oneflow/core/common/util.h" #include "oneflow/core/common/cpp_attribute.h" #include "oneflow/core/framework/device.h" -#include "oneflow/core/vm/stream_get_stream_type.h" +#include "oneflow/core/vm/stream_create_stream_policy.h" #include "oneflow/core/framework/stream_on_independent_thread.h" namespace oneflow { namespace vm { -void Stream::__Init__( - ThreadCtx* thread_ctx, Symbol device, StreamRole stream_role, - const intrusive::shared_ptr& schedule_local_dep_object, - const Optional>& transport_local_dep_object) { +void Stream::__Init__(ThreadCtx* thread_ctx, Symbol device, StreamType stream_type, + const intrusive::shared_ptr& schedule_local_dep_object, + const std::vector>& transport_dependences) { set_thread_ctx(thread_ctx); device_ = device; - stream_role_ = stream_role; - stream_type_ = CHECK_JUST(GetStreamType::Visit(stream_role, device->enum_type())); - stream_type_->InitDeviceCtx(mut_device_ctx(), this); + stream_type_ = stream_type; + stream_policy_ = CHECK_JUST(CreateStreamPolicy::Visit(stream_type, device)); schedule_local_dep_object_ = schedule_local_dep_object; - transport_local_dep_object_ = transport_local_dep_object; - on_scheduler_thread_ = stream_type_->OnSchedulerThread(stream_role); + transport_dependences_ = transport_dependences; + on_scheduler_thread_ = stream_policy_->OnSchedulerThread(stream_type); } int64_t Stream::device_id() const { return device_->device_id(); } -const StreamType& Stream::stream_type() const { return *stream_type_; } +char* Stream::CheckSizeAndGetTmpSmallPinnedMemPtr(size_t size) { + static constexpr int kSmallSize = 512; + CHECK_LE(size, kSmallSize); + if (!static_cast(small_pinned_mem_ptr_)) { + auto* ep_device = stream_policy_->stream()->device(); + void* mem_ptr = nullptr; + CHECK_JUST(ep_device->AllocPinned(ep::AllocationOptions{}, &mem_ptr, kSmallSize)); + std::function Deleter = [ep_device](char* ptr) { + ep_device->FreePinned(ep::AllocationOptions{}, ptr); + }; + char* ptr = reinterpret_cast(mem_ptr); + small_pinned_mem_ptr_ = decltype(small_pinned_mem_ptr_)(ptr, Deleter); + } + return small_pinned_mem_ptr_.get(); +} } // namespace vm } // namespace oneflow diff --git a/oneflow/core/vm/stream.h b/oneflow/core/vm/stream.h index 40af1644db030b94188a5050d3053793e28edb8f..e2fc7fed5d2f618112128d2349733834aa4f5358 100644 --- a/oneflow/core/vm/stream.h +++ b/oneflow/core/vm/stream.h @@ -20,7 +20,8 @@ limitations under the License. #include "oneflow/core/device/device_context.h" #include "oneflow/core/common/symbol.h" #include "oneflow/core/common/optional.h" -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/vm/stream_policy.h" namespace oneflow { @@ -29,8 +30,8 @@ class Device; namespace vm { class ThreadCtx; -class StreamType; class MirroredObject; +class Dependence; class Stream final : public intrusive::Base { public: @@ -39,39 +40,40 @@ class Stream final : public intrusive::Base { intrusive::List; // Getters + const StreamPolicy& stream_policy() const { return *stream_policy_; } const ThreadCtx& thread_ctx() const { return *thread_ctx_; } bool has_thread_ctx() const { return thread_ctx_ != nullptr; } - const std::unique_ptr& device_ctx() const { return device_ctx_; } const intrusive::ListHook& active_stream_hook() const { return active_stream_hook_; } const DispatchedInstructionList& running_instruction_list() const { return running_instruction_list_; } // Setters + StreamPolicy* mut_stream_policy() { return stream_policy_.get(); } ThreadCtx* mut_thread_ctx() { return thread_ctx_; } void set_thread_ctx(ThreadCtx* val) { thread_ctx_ = val; } void clear_thread_ctx() { thread_ctx_ = nullptr; } - std::unique_ptr* mut_device_ctx() { return &device_ctx_; } DispatchedInstructionList* mut_running_instruction_list() { return &running_instruction_list_; } // methods - void __Init__(ThreadCtx* thread_ctx, Symbol device, StreamRole stream_role, - const intrusive::shared_ptr& schedule_local_dep_object, - const Optional>& transport_local_dep_object); + void __Init__(ThreadCtx* thread_ctx, Symbol device, StreamType stream_type, + const intrusive::shared_ptr& schedule_local_dep_object, + const std::vector>& transport_dependences); int64_t device_id() const; Symbol device() const { return device_; } - StreamRole stream_role() const { return stream_role_; } - const StreamType& stream_type() const; + StreamType stream_type() const { return stream_type_; } bool on_scheduler_thread() const { return on_scheduler_thread_; } - const intrusive::shared_ptr& schedule_local_dep_object() const { + const intrusive::shared_ptr& schedule_local_dep_object() const { return schedule_local_dep_object_; } - const Optional>& transport_local_dep_object() const { - return transport_local_dep_object_; + const std::vector>& transport_dependences() const { + return transport_dependences_; } + char* CheckSizeAndGetTmpSmallPinnedMemPtr(size_t size); + private: void MoveToFreeList(intrusive::shared_ptr&& instruction); void MoveFromZombieListToFreeList(); @@ -83,10 +85,10 @@ class Stream final : public intrusive::Base { : intrusive_ref_(), thread_ctx_(), device_(), - stream_role_(StreamRole::kInvalid), - stream_type_(), + stream_type_(StreamType::kInvalid), + stream_policy_(), on_scheduler_thread_(false), - device_ctx_(), + small_pinned_mem_ptr_(), running_instruction_list_(), active_stream_hook_(), thread_ctx_stream_hook_() {} @@ -94,15 +96,15 @@ class Stream final : public intrusive::Base { // fields ThreadCtx* thread_ctx_; Symbol device_; - StreamRole stream_role_; - const StreamType* stream_type_; + StreamType stream_type_; + std::shared_ptr stream_policy_; bool on_scheduler_thread_; - std::unique_ptr device_ctx_; + std::unique_ptr> small_pinned_mem_ptr_; // lists DispatchedInstructionList running_instruction_list_; - intrusive::shared_ptr schedule_local_dep_object_; - Optional> transport_local_dep_object_; + intrusive::shared_ptr schedule_local_dep_object_; + std::vector> transport_dependences_; public: // list hooks diff --git a/oneflow/core/vm/stream_create_stream_policy.h b/oneflow/core/vm/stream_create_stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..fc7eb91d68dbbf16c92c2cb3061d4f53893d49f6 --- /dev/null +++ b/oneflow/core/vm/stream_create_stream_policy.h @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_STREAM_CREATE_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_STREAM_CREATE_STREAM_POLICY_H_ + +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/vm/control_stream_policy.h" +#include "oneflow/core/vm/event_recorded_ep_stream_policy.h" +#include "oneflow/core/vm/critical_section_stream_policy.h" +#include "oneflow/core/vm/ep_d2h_stream_policy.h" +#include "oneflow/core/vm/ep_stream_policy.h" +#include "oneflow/core/vm/pinned_ep_stream_policy.h" +#include "oneflow/core/vm/lazy_job_stream_policy.h" + +namespace oneflow { + +class Device; + +struct CreateStreamPolicy final : public StreamTypeVisitor { + static Maybe VisitCompute(Symbol device) { + return std::shared_ptr(new vm::EpStreamPolicy(device)); + } + static Maybe VisitHost2Device(Symbol device) { + std::unique_ptr allocator{}; + if (device->enum_type() == DeviceType::kCPU) { + allocator = vm::EventRecordedEpStreamPolicy::CreateEpBackendDeviceAllocator(device); + } else { + allocator = + std::make_unique("allocator is not supported on h2d stream."); + } + return std::shared_ptr( + new vm::EventRecordedEpStreamPolicy(device, std::move(allocator))); + } + static Maybe VisitDevice2Host(Symbol device) { + return std::shared_ptr(new vm::EpD2HStreamPolicy(device)); + } + static Maybe VisitCcl(Symbol device) { + auto allocator = vm::EventRecordedEpStreamPolicy::CreateEpBackendDeviceAllocator(device); + return std::shared_ptr( + new vm::EventRecordedEpStreamPolicy(device, std::move(allocator))); + } + static Maybe VisitBarrier(Symbol device) { + return std::shared_ptr(new vm::ControlStreamPolicy()); + } + static Maybe VisitCriticalSection(Symbol device) { + return std::shared_ptr(new vm::CriticalSectionStreamPolicy()); + } + static Maybe VisitLazyJobLauncher(Symbol device) { + return std::shared_ptr(new vm::LazyJobStreamPolicy()); + } + static Maybe VisitPinnedCompute(Symbol device) { + return std::shared_ptr(new vm::PinnedEpStreamPolicy(device)); + } +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_CREATE_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/stream_get_allocator_stream_type.h b/oneflow/core/vm/stream_get_allocator_stream_type.h new file mode 100644 index 0000000000000000000000000000000000000000..b10a637f53e55778aa7daf6b66a831226acffe96 --- /dev/null +++ b/oneflow/core/vm/stream_get_allocator_stream_type.h @@ -0,0 +1,42 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_STREAM_GET_ALLOCATOR_STREAM_TYPE_H_ +#define ONEFLOW_CORE_VM_STREAM_GET_ALLOCATOR_STREAM_TYPE_H_ + +#include "oneflow/core/common/stream_type.h" + +namespace oneflow { + +struct GetAllocatorStreamType final : public StreamTypeVisitor { + static Maybe VisitCompute() { return StreamType::kCompute; } + static Maybe VisitHost2Device() { return StreamType::kCompute; } + static Maybe VisitCcl() { return StreamType::kCompute; } + static Maybe VisitPinnedCompute() { return StreamType::kPinnedCompute; } + static Maybe VisitDevice2Host() { return StreamType::kDevice2Host; } + static Maybe VisitBarrier() { + UNIMPLEMENTED_THEN_RETURN() << "no allocator supported on 'barrier' stream_type."; + } + static Maybe VisitCriticalSection() { + UNIMPLEMENTED_THEN_RETURN() << "no allocator supported on 'critical_section' stream_type."; + } + static Maybe VisitLazyJobLauncher() { + UNIMPLEMENTED_THEN_RETURN() << "no allocator supported on 'lazy_job_launcher' stream_type."; + } +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_GET_ALLOCATOR_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/stream_get_stream_type.h b/oneflow/core/vm/stream_get_stream_type.h deleted file mode 100644 index 574abd351532e34acc42117b96b7daf192631f5e..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/stream_get_stream_type.h +++ /dev/null @@ -1,64 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_STREAM_GET_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_STREAM_GET_STREAM_TYPE_H_ - -#include "oneflow/core/common/stream_role.h" -#include "oneflow/core/common/singleton_ptr.h" -#include "oneflow/core/vm/event_recorded_ep_stream_type.h" -#include "oneflow/core/vm/control_stream_type.h" -#include "oneflow/core/vm/critical_section_stream_type.h" -#include "oneflow/core/vm/ep_d2h_stream_type.h" -#include "oneflow/core/vm/ep_stream_type.h" -#include "oneflow/core/vm/pinned_ep_stream_type.h" -#include "oneflow/core/vm/lazy_job_stream_type.h" -#include "oneflow/core/vm/stream_get_stream_type.h" - -namespace oneflow { - -struct GetStreamType final : public StreamRoleVisitor { - static Maybe VisitCompute(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitHost2Device(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitDevice2Host(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitSyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitAsyncedLaunchedCommNet(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitBarrier(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitCriticalSection(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitLazyJobLauncher(DeviceType device_type) { - return SingletonPtr(); - } - static Maybe VisitPinnedCompute(DeviceType device_type) { - return SingletonPtr(); - } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_STREAM_GET_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/stream_policy.cpp b/oneflow/core/vm/stream_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8f3a42121fbc5543e2fd2790d86c22a1bd74fcf8 --- /dev/null +++ b/oneflow/core/vm/stream_policy.cpp @@ -0,0 +1,43 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/framework/stream_on_independent_thread.h" +#include "oneflow/core/framework/stream_is_comm_net_stream.h" +#include "oneflow/core/common/env_var/vm.h" +#include "oneflow/core/thread/thread_global_id.h" + +namespace oneflow { +namespace vm { + +bool StreamPolicy::OnSchedulerThread(StreamType stream_type) const { + if (StreamOnIndependentThread::Visit(stream_type)) { return false; } + return !ThreadLocalEnvBool(); +} + +void StreamPolicy::RunIf(Instruction* instruction) const { + if (IsCommNetStream::Visit(instruction->stream().stream_type()) + && ThreadLocalEnvBool()) { + ThreadGlobalIdGuard guard{kThreadGlobalIdDefaultWorker}; + Run(instruction); + } else { + Run(instruction); + } +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/stream_policy.h b/oneflow/core/vm/stream_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..96ffc2a3c4d5f7dd484b831a7fa298c3bead1cbb --- /dev/null +++ b/oneflow/core/vm/stream_policy.h @@ -0,0 +1,77 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_STREAM_POLICY_H_ +#define ONEFLOW_CORE_VM_STREAM_POLICY_H_ + +#include +#include +#include +#include "oneflow/core/framework/nn_graph_if.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/job/resource.pb.h" +#include "oneflow/core/common/stream_type.h" +#include "oneflow/core/common/symbol.h" + +namespace oneflow { + +class EpEventProvider; + +namespace ep { + +class Device; +class Stream; + +} // namespace ep + +namespace vm { + +class Allocator; +class Stream; +class InstructionStatusBuffer; +class Instruction; + +class StreamPolicy { + public: + virtual ~StreamPolicy() = default; + + virtual ep::Stream* stream() = 0; + virtual vm::Allocator* mut_allocator() = 0; + virtual DeviceType device_type() const = 0; + + virtual void InitInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const = 0; + virtual void DeleteInstructionStatus(const Stream& stream, + InstructionStatusBuffer* status_buffer) const = 0; + virtual bool QueryInstructionStatusLaunched( + const Stream& stream, const InstructionStatusBuffer& status_buffer) const = 0; + virtual bool QueryInstructionStatusDone(const Stream& stream, + const InstructionStatusBuffer& status_buffer) const = 0; + virtual bool OnSchedulerThread(StreamType stream_type) const; + virtual bool SupportingTransportInstructions() const = 0; + + void RunIf(Instruction* instruction) const; + + protected: + StreamPolicy() = default; + + private: + virtual void Run(Instruction* instruction) const = 0; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_POLICY_H_ diff --git a/oneflow/core/vm/stream_record_event_instruction_policy.cpp b/oneflow/core/vm/stream_record_event_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c35ee3a6b8d232d3356d34ee16a9be24cc18aede --- /dev/null +++ b/oneflow/core/vm/stream_record_event_instruction_policy.cpp @@ -0,0 +1,62 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/stream_record_event_instruction_policy.h" +#include "oneflow/core/vm/ep_event.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/ep/cuda/cuda_event.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/core/vm/ep_stream_policy_base.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" + +namespace oneflow { +namespace vm { + +StreamRecordEventInstructionPolicy::StreamRecordEventInstructionPolicy( + const small_vector, kOpArgsReservedSize>& dependences) + : dependences_(dependences), input_dependences_(), output_dependences_() { + for (const auto& dep : dependences_) { output_dependences_.push_back(dep.get()); } +} + +void StreamRecordEventInstructionPolicy::InitInstructionStatus(Instruction* instruction) { + auto* stream = instruction->mut_stream(); + { + auto* ep_stream_policy_base = + CHECK_NOTNULL(dynamic_cast(instruction->mut_stream_policy())); + ep_stream_policy_base->InitInstructionStatus(*stream, instruction->mut_status_buffer()); + auto* ep_event_provider = ep_stream_policy_base->ep_event_provider(); + const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); + mut_ep_event() = ep_event; + } + { + auto* status_buffer = instruction->mut_status_buffer(); + instruction->stream_policy().InitInstructionStatus(*stream, status_buffer); + auto* data_ptr = status_buffer->mut_buffer(); + EpOptionalEventRecordStatusQuerier::MutCast(data_ptr)->reset_ep_event(nullptr); + } +} + +void StreamRecordEventInstructionPolicy::Compute(vm::Instruction* instruction) { + const auto& ep_event = mut_ep_event(); + // Record event. + auto* stream_policy = + dynamic_cast(instruction->mut_stream()->mut_stream_policy()); + CHECK_NOTNULL(stream_policy)->stream()->RecordEvent(ep_event->mut_event()); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/stream_record_event_instruction_policy.h b/oneflow/core/vm/stream_record_event_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..b62c1d651e5080eb87cc321d51b39f6a70c6aef4 --- /dev/null +++ b/oneflow/core/vm/stream_record_event_instruction_policy.h @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_VM_STREAM_RECORD_EVENT_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_STREAM_RECORD_EVENT_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/common/op_args_reserved_size.h" +#include "oneflow/core/common/small_vector.h" + +namespace oneflow { +class EpEvent; +namespace vm { + +class Stream; + +class StreamRecordEventInstructionPolicy final : public vm::InstructionPolicy { + public: + StreamRecordEventInstructionPolicy( + const small_vector, kOpArgsReservedSize>& dependences); + ~StreamRecordEventInstructionPolicy() = default; + + std::string DebugName(const vm::Instruction&) const override { return "StreamRecordEvent"; } + + void InitInstructionStatus(Instruction* instruction) override; + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + void Compute(vm::Instruction* instruction) override; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + std::shared_ptr& mut_ep_event() { return ep_event_; } + + private: + small_vector, kOpArgsReservedSize> dependences_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; + std::shared_ptr ep_event_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_RECORD_EVENT_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/stream_type.cpp b/oneflow/core/vm/stream_type.cpp deleted file mode 100644 index de1c7a253c9feaafbcd3dd4c4d08c6263d5b19d8..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/stream_type.cpp +++ /dev/null @@ -1,29 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/framework/stream_on_independent_thread.h" -#include "oneflow/core/common/env_var/vm.h" - -namespace oneflow { -namespace vm { - -bool StreamType::OnSchedulerThread(StreamRole stream_role) const { - if (StreamOnIndependentThread::Visit(stream_role)) { return false; } - return ThreadLocalEnvBool(); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/stream_type.h b/oneflow/core/vm/stream_type.h deleted file mode 100644 index f1214e3c7ea83db5cf568c0a362ba207acb20136..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/stream_type.h +++ /dev/null @@ -1,65 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_STREAM_TYPE_H_ -#define ONEFLOW_CORE_VM_STREAM_TYPE_H_ - -#include -#include -#include -#include "oneflow/core/device/device_context.h" -#include "oneflow/core/job/resource.pb.h" -#include "oneflow/core/common/stream_role.h" - -namespace oneflow { - -namespace vm { - -class Stream; -class InstructionStatusBuffer; -class Instruction; -class InstructionType; - -class StreamType { - public: - virtual ~StreamType() = default; - - virtual void InitDeviceCtx(std::unique_ptr* device_ctx, Stream* stream) const = 0; - - virtual void InitInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const = 0; - virtual void DeleteInstructionStatus(const Stream& stream, - InstructionStatusBuffer* status_buffer) const = 0; - virtual bool QueryInstructionStatusDone(const Stream& stream, - const InstructionStatusBuffer& status_buffer) const = 0; - virtual void Run(Instruction* instruction) const = 0; - - virtual bool OnSchedulerThread(StreamRole stream_role) const; - virtual bool SupportingTransportInstructions() const = 0; - - protected: - StreamType() = default; -}; - -template -const StreamType* StaticGlobalStreamType() { - static const StreamType* stream_type = new T(); - return stream_type; -} - -} // namespace vm -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_STREAM_TYPE_H_ diff --git a/oneflow/core/vm/stream_wait_event_instruction_policy.cpp b/oneflow/core/vm/stream_wait_event_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b204433025e49dcb90910fcb094f400b955eb5c6 --- /dev/null +++ b/oneflow/core/vm/stream_wait_event_instruction_policy.cpp @@ -0,0 +1,74 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/stream_wait_event_instruction_policy.h" +#include "oneflow/core/vm/ep_event.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/ep/cuda/cuda_event.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/core/vm/ep_stream_policy_base.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" + +namespace oneflow { +namespace vm { + +StreamWaitEventInstructionPolicy::StreamWaitEventInstructionPolicy( + const small_vector, kOpArgsReservedSize>& dependences, + const std::shared_ptr& + stream_record_event_instruction_policy) + : dependences_(dependences), + input_dependences_(), + output_dependences_(), + stream_record_event_instruction_policy_(stream_record_event_instruction_policy) { + for (const auto& dep : dependences_) { output_dependences_.push_back(dep.get()); } +} + +void StreamWaitEventInstructionPolicy::DeleteInstructionStatus(Instruction* instruction) { + auto* stream = instruction->mut_stream(); + instruction->stream_policy().DeleteInstructionStatus(*stream, instruction->mut_status_buffer()); + stream_record_event_instruction_policy_->mut_ep_event().reset(); +} + +void StreamWaitEventInstructionPolicy::Compute(vm::Instruction* instruction) { + const auto& ep_event = stream_record_event_instruction_policy_->mut_ep_event(); + // Wait event. + auto* ep_stream_policy_base = + dynamic_cast(instruction->mut_stream()->mut_stream_policy()); + CHECK_NOTNULL(ep_stream_policy_base); + auto* ep_stream = ep_stream_policy_base->stream(); + CHECK_EQ(ep_event->mut_device(), ep_stream->device()) + << "only support waiting events from same device"; + ep_event->mut_device()->SetAsActiveDevice(); +#if defined(WITH_CUDA) + + auto* ep_cuda_event = CHECK_NOTNULL(dynamic_cast(ep_event->mut_event())); + auto* ep_cuda_stream = CHECK_NOTNULL(dynamic_cast(ep_stream)); + + OF_CUDA_CHECK(cudaStreamWaitEvent(ep_cuda_stream->cuda_stream(), ep_cuda_event->cuda_event(), 0)); +#elif defined(WITH_ROCM) + + auto* ep_cuda_event = CHECK_NOTNULL(dynamic_cast(ep_event->mut_event())); + auto* ep_cuda_stream = CHECK_NOTNULL(dynamic_cast(ep_stream)); + + OF_CUDA_CHECK(hipStreamWaitEvent(ep_cuda_stream->cuda_stream(), ep_cuda_event->cuda_event(), 0)); +#else + UNIMPLEMENTED(); +#endif // WITH_CUDA +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/stream_wait_event_instruction_policy.h b/oneflow/core/vm/stream_wait_event_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..851733bb3f05bc6b2ed8706847fc1233e8b2c35a --- /dev/null +++ b/oneflow/core/vm/stream_wait_event_instruction_policy.h @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_VM_STREAM_WAIT_EVENT_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_STREAM_WAIT_EVENT_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/common/op_args_reserved_size.h" +#include "oneflow/core/common/small_vector.h" +#include "oneflow/core/vm/stream_record_event_instruction_policy.h" + +namespace oneflow { +namespace vm { + +class Stream; + +class StreamWaitEventInstructionPolicy final : public vm::InstructionPolicy { + public: + StreamWaitEventInstructionPolicy( + const small_vector, kOpArgsReservedSize>& dependences, + const std::shared_ptr& + stream_record_event_instruction_policy); + ~StreamWaitEventInstructionPolicy() = default; + + std::string DebugName(const vm::Instruction&) const override { return "StreamWaitEvent"; } + + void DeleteInstructionStatus(Instruction* instruction) override; + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + void Compute(vm::Instruction* instruction) override; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + private: + small_vector, kOpArgsReservedSize> dependences_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; + std::shared_ptr stream_record_event_instruction_policy_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_WAIT_EVENT_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/stream_wait_instruction_policy.cpp b/oneflow/core/vm/stream_wait_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..13dff1346ef505451ec17efc8f0d74d7ef547237 --- /dev/null +++ b/oneflow/core/vm/stream_wait_instruction_policy.cpp @@ -0,0 +1,99 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/stream_wait_instruction_policy.h" +#include "oneflow/core/vm/ep_event.h" +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/ep/cuda/cuda_event.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/core/vm/ep_stream_policy_base.h" +#include "oneflow/core/vm/ep_optional_event_record_status_querier.h" + +namespace oneflow { +namespace vm { + +StreamWaitInstructionPolicy::StreamWaitInstructionPolicy( + small_vector, kOpArgsReservedSize>&& dependences, + vm::Stream* from_vm_stream, vm::Stream* to_vm_stream) + : dependences_(std::move(dependences)), + input_dependences_(), + output_dependences_(), + from_vm_stream_(from_vm_stream) { + for (const auto& dep : dependences_) { output_dependences_.push_back(dep.get()); } + stream_sequential_dependence_ = to_vm_stream->schedule_local_dep_object().get(); +} + +bool StreamWaitInstructionPolicy::Prescheduleable(const Stream* src, const Stream* dst) const { + return &src->thread_ctx() == &dst->thread_ctx(); +} + +void StreamWaitInstructionPolicy::InitInstructionStatus(Instruction* instruction) { + auto* stream = instruction->mut_stream(); + auto* ep_stream_policy_base = + CHECK_NOTNULL(dynamic_cast(instruction->mut_stream_policy())); + ep_stream_policy_base->InitInstructionStatus(*stream, instruction->mut_status_buffer()); + auto* ep_event_provider = ep_stream_policy_base->ep_event_provider(); + const auto& ep_event = CHECK_NOTNULL(ep_event_provider)->GetReusedEpEvent(); + mut_ep_event() = ep_event; +} + +void StreamWaitInstructionPolicy::DeleteInstructionStatus(Instruction* instruction) { + auto* stream = instruction->mut_stream(); + instruction->stream_policy().DeleteInstructionStatus(*stream, instruction->mut_status_buffer()); + mut_ep_event().reset(); +} + +void StreamWaitInstructionPolicy::Compute(vm::Instruction* instruction) { + const auto& ep_event = mut_ep_event(); + { + // Record event. + auto* from_naive_stream_policy = + dynamic_cast(mut_from_vm_stream()->mut_stream_policy()); + CHECK_NOTNULL(from_naive_stream_policy); + auto* from_stream = from_naive_stream_policy->stream(); + from_stream->RecordEvent(ep_event->mut_event()); + } + { + // Wait event. + auto* to_ep_stream_policy_base = + dynamic_cast(instruction->mut_stream()->mut_stream_policy()); + CHECK_NOTNULL(to_ep_stream_policy_base); + auto* to_ep_stream = to_ep_stream_policy_base->stream(); + CHECK_EQ(ep_event->mut_device(), to_ep_stream->device()) + << "only support waiting events from same device"; + ep_event->mut_device()->SetAsActiveDevice(); +#if defined(WITH_CUDA) + + auto* ep_cuda_event = CHECK_NOTNULL(dynamic_cast(ep_event->mut_event())); + auto* ep_cuda_stream = CHECK_NOTNULL(dynamic_cast(to_ep_stream)); + + OF_CUDA_CHECK( + cudaStreamWaitEvent(ep_cuda_stream->cuda_stream(), ep_cuda_event->cuda_event(), 0)); +#elif defined(WITH_ROCM) + auto* ep_cuda_event = CHECK_NOTNULL(dynamic_cast(ep_event->mut_event())); + auto* ep_cuda_stream = CHECK_NOTNULL(dynamic_cast(to_ep_stream)); + + OF_CUDA_CHECK( + hipStreamWaitEvent(ep_cuda_stream->cuda_stream(), ep_cuda_event->cuda_event(), 0)); +#else + UNIMPLEMENTED(); +#endif // WITH_CUDA + } +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/stream_wait_instruction_policy.h b/oneflow/core/vm/stream_wait_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..e806ab71f3883965daa5b8fb5e1398f13afdfc4f --- /dev/null +++ b/oneflow/core/vm/stream_wait_instruction_policy.h @@ -0,0 +1,64 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_CORE_VM_STREAM_WAIT_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_STREAM_WAIT_INSTRUCTION_POLICY_H_ + +#include +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/common/op_args_reserved_size.h" +#include "oneflow/core/common/small_vector.h" + +namespace oneflow { +class EpEvent; +namespace vm { + +class Stream; + +class StreamWaitInstructionPolicy final : public vm::InstructionPolicy { + public: + StreamWaitInstructionPolicy( + small_vector, kOpArgsReservedSize>&& dependences, + vm::Stream* from_vm_stream, vm::Stream* to_vm_stream); + ~StreamWaitInstructionPolicy() = default; + + std::string DebugName(const vm::Instruction&) const override { return "StreamWait"; } + + bool Prescheduleable(const Stream* src, const Stream* dst) const override; + void InitInstructionStatus(Instruction* instruction) override; + void DeleteInstructionStatus(Instruction* instruction) override; + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + void Compute(vm::Instruction* instruction) override; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { return output_dependences_; } + + private: + vm::Stream* mut_from_vm_stream() { return from_vm_stream_; } + std::shared_ptr& mut_ep_event() { return ep_event_; } + + small_vector, kOpArgsReservedSize> dependences_; + DependenceVector input_dependences_; + DependenceVector output_dependences_; + vm::Stream* from_vm_stream_; + std::shared_ptr ep_event_; +}; + +} // namespace vm +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_STREAM_WAIT_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/sync_access_instruction_policy.cpp b/oneflow/core/vm/sync_access_instruction_policy.cpp new file mode 100644 index 0000000000000000000000000000000000000000..76899c5a0756df5133962df5d567ae1797074049 --- /dev/null +++ b/oneflow/core/vm/sync_access_instruction_policy.cpp @@ -0,0 +1,82 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/vm/sync_access_instruction_policy.h" +#include "oneflow/core/vm/stream.h" +#include "oneflow/core/kernel/kernel_util.h" + +namespace oneflow { +namespace vm { + +SyncAccessInstructionPolicy::SyncAccessInstructionPolicy() + : host_mem_case_(memory::MakeHostMemCase()), + btb_(), + mem_ptr_(nullptr), + bytes_(0), + eager_blob_object_(nullptr) { + ResetBase(nullptr, 0, nullptr); +} + +void SyncAccessInstructionPolicy::ResetBase(char* mem_ptr, size_t bytes, + EagerBlobObject* eager_blob_object) { + btb_.Reset(); + mem_ptr_ = mem_ptr; + bytes_ = bytes; + eager_blob_object_ = eager_blob_object; +} + +namespace { + +void FastCopy(char* dst, const char* src, size_t bytes) { + switch (bytes) { + case 1: { + *dst = *src; + return; + } + case 2: { + *reinterpret_cast(dst) = *reinterpret_cast(src); + return; + } + case 4: { + *reinterpret_cast(dst) = *reinterpret_cast(src); + return; + } + case 8: { + *reinterpret_cast(dst) = *reinterpret_cast(src); + return; + } + case 16: { + using Bit128 = std::pair; + *reinterpret_cast(dst) = *reinterpret_cast(src); + return; + } + default: UNIMPLEMENTED() << "FastCopy on bytes " << bytes << " not supported."; + } +} + +} // namespace + +void SyncReadInstructionPolicy::Compute(Instruction* instruction) { + StreamPolicy* stream_policy = instruction->mut_stream_policy(); + char* pinned_buffer = instruction->mut_stream()->CheckSizeAndGetTmpSmallPinnedMemPtr(bytes_); + mut_btb()->mut_notifier()->Notify(); + SyncAutoMemcpy(stream_policy->stream(), pinned_buffer, eager_blob_object_->mut_dptr(), bytes_, + host_mem_case_, eager_blob_object_->mem_case()); + FastCopy(mem_ptr_, pinned_buffer, bytes_); + mut_btb()->mut_spin_counter()->Decrease(); +} + +} // namespace vm +} // namespace oneflow diff --git a/oneflow/core/vm/sync_access_instruction_policy.h b/oneflow/core/vm/sync_access_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..d0b3f747d7b0ce610692ab7c2c55cbc16b21315b --- /dev/null +++ b/oneflow/core/vm/sync_access_instruction_policy.h @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_SYNC_ACCESS_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_SYNC_ACCESS_INSTRUCTION_POLICY_H_ + +#include +#include +#include "oneflow/core/vm/instruction.h" +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/vm/instruction_policy_util.h" +#include "oneflow/core/eager/local_dep_object.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/tensor_storage.h" +#include "oneflow/core/common/blocking_then_busy.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/vm/stream_policy.h" +#include "oneflow/core/memory/memory_case_util.h" + +namespace oneflow { +namespace vm { + +class SyncAccessInstructionPolicy : public InstructionPolicy { + public: + SyncAccessInstructionPolicy(); + virtual ~SyncAccessInstructionPolicy() = default; + + Maybe Prepare(Instruction* instruction) override { return Maybe::Ok(); } + + BlockingThenBusy* mut_btb() { return &btb_; } + + protected: + void ResetBase(char* mem_ptr, size_t bytes, EagerBlobObject* eager_blob_object); + + const MemoryCase host_mem_case_; + BlockingThenBusy btb_; + char* mem_ptr_; + size_t bytes_; + EagerBlobObject* eager_blob_object_; +}; + +class SyncReadInstructionPolicy final : public SyncAccessInstructionPolicy { + public: + SyncReadInstructionPolicy() = default; + ~SyncReadInstructionPolicy() = default; + + const DependenceVector& input_dependences() const override { + CHECK_EQ(input_dependences_.size(), 1); + return input_dependences_; + } + + const DependenceVector& output_dependences() const override { + static thread_local DependenceVector empty{}; + return empty; + } + + std::string DebugName(const Instruction& instruction) const override { return "SyncRead"; } + + void Reset(char* mem_ptr, size_t bytes, EagerBlobObject* eager_blob_object) { + ResetBase(mem_ptr, bytes, eager_blob_object); + if (likely(input_dependences_.size())) { input_dependences_.clear(); } + input_dependences_.push_back(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + } + + void Compute(Instruction* instruction) override; + + private: + DependenceVector input_dependences_; +}; + +} // namespace vm +} // namespace oneflow +#endif // ONEFLOW_CORE_VM_SYNC_ACCESS_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/sync_vm_mode_guard.h b/oneflow/core/vm/sync_vm_mode_guard.h index 5e636071ce7c94e82094a46af34579c6125f64aa..3dbae727e8f52ea24cffcd40036b1907083c14e4 100644 --- a/oneflow/core/vm/sync_vm_mode_guard.h +++ b/oneflow/core/vm/sync_vm_mode_guard.h @@ -1,39 +1,42 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ -#define ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ - -#include "oneflow/core/common/thread_local_guard.h" - -namespace oneflow { - -enum class SyncVmMode { - kInvalid = 0, - kEnable = 1, - kDisable = 2, -}; - -class SyncVmModeGuard final : public ThreadLocalGuard { - public: - using ThreadLocalGuard::ThreadLocalGuard; - ~SyncVmModeGuard() = default; - - static bool IsCurrentSyncVmMode() { - const auto& opt_sync_mode = Current(); - return opt_sync_mode.has_value() && CHECK_JUST(opt_sync_mode) == SyncVmMode::kEnable; - } -}; - -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ \ No newline at end of file +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ +#define ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ + +#include "oneflow/core/common/thread_local_guard.h" + +namespace oneflow { + +enum class SyncVmMode { + kInvalid = 0, + kEnable = 1, + kDisable = 2, +}; + +class SyncVmModeGuard final : public ThreadLocalGuard { + public: + using ThreadLocalGuard::ThreadLocalGuard; + ~SyncVmModeGuard() = default; + + static bool IsCurrentSyncVmMode() { + const auto& opt_sync_mode = Current(); + return opt_sync_mode.has_value() && CHECK_JUST(opt_sync_mode) == SyncVmMode::kEnable; + } +}; + +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_SYNC_VM_MODE_GUARD_H_ diff --git a/oneflow/core/vm/thread_ctx.cpp b/oneflow/core/vm/thread_ctx.cpp index 1d29b0b3abfc4b5fad09f9917dfff4b989ab3597..3b11cae609890c241caa780acfc9025de9970c92 100644 --- a/oneflow/core/vm/thread_ctx.cpp +++ b/oneflow/core/vm/thread_ctx.cpp @@ -19,14 +19,23 @@ limitations under the License. namespace oneflow { namespace vm { +ThreadCtx::ThreadCtx() + : intrusive_ref_(), + stream_list_(), + worker_pending_instruction_mutex_(), + worker_pending_instruction_list_(&worker_pending_instruction_mutex_), + notifier_(), + transport_dependence_(intrusive::make_shared()), + thread_ctx_hook_() {} + size_t ThreadCtx::TryReceiveAndRun() { intrusive::List tmp_list; mut_worker_pending_instruction_list()->MoveTo(&tmp_list); size_t size = tmp_list.size(); INTRUSIVE_FOR_EACH(instruction, &tmp_list) { tmp_list.Erase(instruction.Mutable()); - const StreamType& stream_type = instruction->stream().stream_type(); - stream_type.Run(instruction.Mutable()); + const StreamPolicy& stream_policy = instruction->stream().stream_policy(); + stream_policy.RunIf(instruction.Mutable()); } return size; } diff --git a/oneflow/core/vm/thread_ctx.h b/oneflow/core/vm/thread_ctx.h index 189d48b3b7e311a7199afa159454e0453659f466..bc1dd97a4fbbbdad8909624a3089be4519539fde 100644 --- a/oneflow/core/vm/thread_ctx.h +++ b/oneflow/core/vm/thread_ctx.h @@ -21,6 +21,7 @@ limitations under the License. #include "oneflow/core/intrusive/mutexed_list.h" #include "oneflow/core/common/notifier.h" #include "oneflow/core/vm/stream.h" +#include "oneflow/core/vm/vm_object.h" namespace oneflow { namespace vm { @@ -47,23 +48,23 @@ class ThreadCtx final : public intrusive::Base { Notifier* mut_notifier() { return ¬ifier_; } + const intrusive::shared_ptr& transport_dependence() const { + return transport_dependence_; + }; + private: friend class intrusive::Ref; intrusive::Ref* mut_intrusive_ref() { return &intrusive_ref_; } - ThreadCtx() - : intrusive_ref_(), - stream_list_(), - worker_pending_instruction_mutex_(), - worker_pending_instruction_list_(&worker_pending_instruction_mutex_), - notifier_(), - thread_ctx_hook_() {} + ThreadCtx(); + intrusive::Ref intrusive_ref_; // lists StreamList stream_list_; std::mutex worker_pending_instruction_mutex_; WorkerPendingInstructionMutexedList worker_pending_instruction_list_; Notifier notifier_; + intrusive::shared_ptr transport_dependence_; public: // list hooks diff --git a/oneflow/core/vm/thread_safe_allocator.cpp b/oneflow/core/vm/thread_safe_allocator.cpp deleted file mode 100644 index 7a706e441cfe4ccedc598c33b667ee3bcb2298d8..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/thread_safe_allocator.cpp +++ /dev/null @@ -1,70 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/thread_safe_allocator.h" -#include "oneflow/core/common/util.h" - -namespace oneflow { -namespace vm { - -Maybe ThreadSafeAllocator::Allocate(char** mem_ptr, std::size_t size) { - std::unique_lock lock(mutex4backend_allocator_); - return backend_allocator_->Allocate(mem_ptr, size); -} - -void ThreadSafeAllocator::Deallocate(char* mem_ptr, std::size_t size) { - std::unique_lock lock(mutex4backend_allocator_); - backend_allocator_->Deallocate(mem_ptr, size); -} - -void ThreadSafeAllocator::Shrink() { - std::unique_lock lock(mutex4backend_allocator_); - auto* cache = dynamic_cast(backend_allocator_.get()); - if (cache != nullptr) { cache->Shrink(); } -} - -void ThreadSafeAllocator::DeviceReset() { - std::unique_lock lock(mutex4backend_allocator_); - backend_allocator_->DeviceReset(); -} - -Maybe SingleThreadOnlyAllocator::Allocate(char** mem_ptr, std::size_t size) { - CheckUniqueThreadAccess(); - return backend_allocator_->Allocate(mem_ptr, size); -} - -void SingleThreadOnlyAllocator::Deallocate(char* mem_ptr, std::size_t size) { - CheckUniqueThreadAccess(); - backend_allocator_->Deallocate(mem_ptr, size); -} - -void SingleThreadOnlyAllocator::Shrink() { - CheckUniqueThreadAccess(); - auto* cache = dynamic_cast(backend_allocator_.get()); - if (cache != nullptr) { cache->Shrink(); } -} - -void SingleThreadOnlyAllocator::DeviceReset() { - CheckUniqueThreadAccess(); - backend_allocator_->DeviceReset(); -} - -void SingleThreadOnlyAllocator::CheckUniqueThreadAccess() { - std::unique_lock lock(mutex4accessed_thread_id_); - CHECK(accessed_thread_id_ == std::this_thread::get_id()); -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/thread_safe_allocator.h b/oneflow/core/vm/thread_safe_allocator.h deleted file mode 100644 index 3a2148820ea6299c311835de3de0063a730c5f0f..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/thread_safe_allocator.h +++ /dev/null @@ -1,70 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ -#define ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ - -#include -#include -#include -#include "oneflow/core/vm/allocator.h" -#include "oneflow/core/vm/shrinkable_cache.h" - -namespace oneflow { - -namespace vm { - -class ThreadSafeAllocator final : public Allocator, public ShrinkableCache { - public: - explicit ThreadSafeAllocator(std::unique_ptr&& backend_allocator) - : Allocator(), backend_allocator_(std::move(backend_allocator)) {} - ~ThreadSafeAllocator() override = default; - - Maybe Allocate(char** mem_ptr, std::size_t size) override; - void Deallocate(char* mem_ptr, std::size_t size) override; - void Shrink() override; - void DeviceReset() override; - - private: - std::unique_ptr backend_allocator_; - std::mutex mutex4backend_allocator_; -}; - -class SingleThreadOnlyAllocator final : public Allocator, public ShrinkableCache { - public: - explicit SingleThreadOnlyAllocator(std::unique_ptr&& backend_allocator) - : Allocator(), - backend_allocator_(std::move(backend_allocator)), - accessed_thread_id_(std::this_thread::get_id()) {} - ~SingleThreadOnlyAllocator() override = default; - - Maybe Allocate(char** mem_ptr, std::size_t size) override; - void Deallocate(char* mem_ptr, std::size_t size) override; - void Shrink() override; - void DeviceReset() override; - - private: - void CheckUniqueThreadAccess(); - - std::unique_ptr backend_allocator_; - std::thread::id accessed_thread_id_; - std::mutex mutex4accessed_thread_id_; -}; - -} // namespace vm - -} // namespace oneflow - -#endif // ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ diff --git a/oneflow/core/vm/thread_safe_guard.h b/oneflow/core/vm/thread_safe_guard.h new file mode 100644 index 0000000000000000000000000000000000000000..86a9412407e14514c192d97cd934dc3a029b6458 --- /dev/null +++ b/oneflow/core/vm/thread_safe_guard.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ +#define ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ + +#include +#include +#include +#include +#include "oneflow/core/common/util.h" + +namespace oneflow { + +namespace vm { +class ThreadSafeLock final { + public: + ThreadSafeLock() = default; + ~ThreadSafeLock() = default; + OF_DISALLOW_COPY_AND_MOVE(ThreadSafeLock); + + class RAIIGuard final { + public: + explicit RAIIGuard(ThreadSafeLock& lock) : guard_(lock.mutex4guard) {} + ~RAIIGuard() = default; + OF_DISALLOW_COPY_AND_MOVE(RAIIGuard); + + private: + std::unique_lock guard_; + }; + + private: + std::mutex mutex4guard; +}; + +} // namespace vm + +} // namespace oneflow + +#endif // ONEFLOW_CORE_VM_THREAD_SAFE_ALLOCATOR_H_ diff --git a/oneflow/core/vm/touch_tensors_instruction_policy.h b/oneflow/core/vm/touch_tensors_instruction_policy.h new file mode 100644 index 0000000000000000000000000000000000000000..298aa85b6a4b0c3417538e1d497f041304f7b419 --- /dev/null +++ b/oneflow/core/vm/touch_tensors_instruction_policy.h @@ -0,0 +1,56 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_CORE_VM_TOUCH_TENSORS_INSTRUCTION_POLICY_H_ +#define ONEFLOW_CORE_VM_TOUCH_TENSORS_INSTRUCTION_POLICY_H_ + +#include "oneflow/core/vm/instruction_policy.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/vm/instruction_policy_util.h" + +namespace oneflow { +namespace vm { + +class TouchTensorsInstructionPolicy final : public InstructionPolicy { + public: + explicit TouchTensorsInstructionPolicy(const vm::EagerBlobObjectList& eager_blob_objects) + : eager_blob_objects_(eager_blob_objects) { + const auto& Insert = InstructionPolicyUtil::SetInserter(&input_dependences_); + for (const auto& eager_blob_object : eager_blob_objects_) { + Insert(CHECK_JUST(eager_blob_object->compute_local_dep_object())); + } + } + ~TouchTensorsInstructionPolicy() = default; + + const DependenceVector& input_dependences() const override { return input_dependences_; } + const DependenceVector& output_dependences() const override { + static DependenceVector empty{}; + return empty; + } + + std::string DebugName(const vm::Instruction& instruction) const override { + return "TouchTensors"; + } + Maybe Prepare(vm::Instruction* instruction) override { return Maybe::Ok(); } + void Compute(vm::Instruction* instruction) override {} + + private: + vm::EagerBlobObjectList eager_blob_objects_; + DependenceVector input_dependences_; +}; + +} // namespace vm +} // namespace oneflow +#endif // ONEFLOW_CORE_VM_TOUCH_TENSORS_INSTRUCTION_POLICY_H_ diff --git a/oneflow/core/vm/touch_tensors_instruction_type.cpp b/oneflow/core/vm/touch_tensors_instruction_type.cpp deleted file mode 100644 index 5004ddb0ed6e7199b45e48341717569e0c52fb52..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/touch_tensors_instruction_type.cpp +++ /dev/null @@ -1,32 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/vm/touch_tensors_instruction_type.h" -#include "oneflow/core/eager/eager_blob_object.h" - -namespace oneflow { -namespace vm { - -TouchTensorsPhyInstrOperand::TouchTensorsPhyInstrOperand( - const std::vector>& eager_blob_objects) - : eager_blob_objects_(eager_blob_objects) { - const auto& Insert = SetInserter(&input_dependences_); - for (const auto& eager_blob_object : eager_blob_objects_) { - Insert(CHECK_JUST(eager_blob_object->compute_local_dep_object())); - } -} - -} // namespace vm -} // namespace oneflow diff --git a/oneflow/core/vm/touch_tensors_instruction_type.h b/oneflow/core/vm/touch_tensors_instruction_type.h deleted file mode 100644 index 9b259865688f87526fcf80923a55f21e14d75637..0000000000000000000000000000000000000000 --- a/oneflow/core/vm/touch_tensors_instruction_type.h +++ /dev/null @@ -1,62 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_CORE_EAGER_TOUCH_TENSORS_INSTRUCTION_TYPE_H_ -#define ONEFLOW_CORE_EAGER_TOUCH_TENSORS_INSTRUCTION_TYPE_H_ - -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/phy_instr_operand.h" - -namespace oneflow { -namespace vm { - -class EagerBlobObject; -class Instruction; - -class TouchTensorsPhyInstrOperand final : public PhyInstrOperand { - public: - TouchTensorsPhyInstrOperand( - const std::vector>& eager_blob_objects); - - const DependenceVector& input_dependences() const override { return input_dependences_; } - const DependenceVector& output_dependences() const override { - static DependenceVector empty{}; - return empty; - } - - void ForEachInputEagerBlobObjects(void (*DoEach)(EagerBlobObject*)) const override { - for (const auto& eager_blob_object : eager_blob_objects_) { DoEach(eager_blob_object.get()); } - } - - private: - std::vector> eager_blob_objects_; - DependenceVector input_dependences_; -}; - -class TouchTensorsInstructionType final : public InstructionType { - public: - TouchTensorsInstructionType() = default; - ~TouchTensorsInstructionType() override = default; - - std::string DebugName(const vm::Instruction& instruction) const override { - return "TouchTensors"; - } - Maybe Prepare(vm::Instruction* instruction) const override { return Maybe::Ok(); } - void Compute(vm::Instruction* instruction) const override {} -}; - -} // namespace vm -} // namespace oneflow -#endif // ONEFLOW_CORE_EAGER_TOUCH_TENSORS_INSTRUCTION_TYPE_H_ diff --git a/oneflow/core/vm/virtual_machine.cpp b/oneflow/core/vm/virtual_machine.cpp index 10e39bdd11867c5a103fe713125f24ef0f546287..1c87835474b390747934abe2551488a024789a53 100644 --- a/oneflow/core/vm/virtual_machine.cpp +++ b/oneflow/core/vm/virtual_machine.cpp @@ -14,22 +14,23 @@ See the License for the specific language governing permissions and limitations under the License. */ #include +#include +#include #include "oneflow/core/vm/sync_vm_mode_guard.h" +#include "oneflow/core/vm/barrier_instruction_policy.h" +#include "oneflow/core/vm/caching_allocator.h" +#include "oneflow/core/vm/global_sync_instruction_policy.h" #include "oneflow/core/vm/virtual_machine.h" #include "oneflow/core/vm/instruction.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/barrier_instruction_type.h" -#include "oneflow/core/vm/barrier_phy_instr_operand.h" #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/vm/allocator.h" -#include "oneflow/core/vm/shrinkable_cache.h" #include "oneflow/core/common/blocking_counter.h" #include "oneflow/core/common/cpp_attribute.h" #include "oneflow/core/common/singleton_ptr.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/job/global_for.h" #include "oneflow/core/common/foreign_lock_helper.h" -#include "oneflow/core/thread/thread_consistent_id.h" +#include "oneflow/core/thread/thread_global_id.h" #include "oneflow/core/framework/transport_token.h" #include "oneflow/core/framework/to_string.h" #include "oneflow/core/framework/stream_on_independent_thread.h" @@ -37,9 +38,11 @@ limitations under the License. #include "oneflow/core/profiler/profiler.h" #include "oneflow/core/platform/include/pthread_fork.h" #include "oneflow/core/common/env_var/env_var.h" +#include "oneflow/core/common/env_var/vm.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/stream.h" +#include "oneflow/core/framework/stream_get_stream_type_name.h" #include "oneflow/core/framework/stream_mgr.h" namespace oneflow { @@ -62,59 +65,64 @@ Maybe ForEachThreadCtx(vm::VirtualMachineEngine* engine, } void GetSchedulerThreadInitializer(std::function* Initializer) { - *Initializer = [&]() { - CHECK_JUST(InitThisThreadUniqueConsistentId(kThreadConsistentIdScheduler, "scheduler")); - OF_PROFILER_NAME_THIS_HOST_THREAD("_VM::Scheduler"); - }; + *Initializer = [&]() { OF_PROFILER_NAME_THIS_HOST_THREAD("_VM::Scheduler"); }; } void WorkerLoop(vm::ThreadCtx* thread_ctx, const std::function& Initializer) { SyncVmModeGuard guard(SyncVmMode::kEnable); Initializer(thread_ctx); + constexpr static size_t kExpireMicroseconds = 200; while (thread_ctx->mut_notifier()->WaitAndClearNotifiedCnt() == kNotifierStatusSuccess) { - while (thread_ctx->TryReceiveAndRun()) {} + std::chrono::time_point start{}; + do { + while (thread_ctx->TryReceiveAndRun()) { start = std::chrono::steady_clock::now(); } + std::this_thread::yield(); + } while (MicrosecondsFrom(start) < kExpireMicroseconds); } } } // namespace -VirtualMachine::VirtualMachine() : disable_vm_threads_(false), scheduler_stopped_(false) { +VirtualMachine::VirtualMachine() + : multi_thread_(ThreadLocalEnvBool()), + threads_closed_(false), + scheduler_stopped_(false) { // Class VirtualMachineEngine only cares the basic logical of vm, while class VirtualMachine // manages threads and condition variables. // In order to notify threads in VirtualMachineEngine, a notify callback lambda should be take as // an argument for VirtualMachineEngine's constructor. engine_ = intrusive::make_shared(); OF_PROFILER_NAME_THIS_HOST_THREAD("_Main"); - std::function SchedulerInitializer; - GetSchedulerThreadInitializer(&SchedulerInitializer); - schedule_thread_ = std::thread(&VirtualMachine::ScheduleLoop, this, SchedulerInitializer); - transport_local_dep_object_.Reset(); + + if (multi_thread_) { + std::function SchedulerInitializer; + GetSchedulerThreadInitializer(&SchedulerInitializer); + schedule_thread_ = std::thread(&VirtualMachine::ScheduleLoop, this, SchedulerInitializer); + } + transport_dependence_.Reset(); } namespace { Maybe> GetBarrierStream() { auto device = JUST(Device::New("cpu")); - return Stream::New(device, StreamRole::kBarrier); + return Stream::New(device, StreamType::kBarrier); } void MakeBarrierInstructions(vm::InstructionList* list, const std::function& BarrierCallback) { auto* vm = Singleton::Get(); { - const auto& phy_instr_operand = std::make_shared([]() {}); auto stream = CHECK_JUST(GetBarrierStream()); auto instruction = intrusive::make_shared( - CHECK_JUST(vm->GetVmStream(stream)), SingletonPtr(), - phy_instr_operand); + CHECK_JUST(vm->GetVmStream(stream)), std::make_shared()); list->EmplaceBack(std::move(instruction)); } { - const auto& phy_instr_operand = std::make_shared(BarrierCallback); auto stream = CHECK_JUST(GetBarrierStream()); auto instruction = intrusive::make_shared( - CHECK_JUST(vm->GetVmStream(stream)), SingletonPtr(), - phy_instr_operand); + CHECK_JUST(vm->GetVmStream(stream)), + std::make_shared(BarrierCallback)); list->EmplaceBack(std::move(instruction)); } } @@ -130,11 +138,16 @@ void VirtualMachine::ControlSync() { } Maybe VirtualMachine::CloseVMThreads() { - CHECK_OR_RETURN(!disable_vm_threads_) << "vm threads closed"; + CHECK_OR_RETURN(!threads_closed_) << "vm threads closed"; ControlSync(); pending_notifier_.Close(); - schedule_thread_.join(); - disable_vm_threads_ = true; + if (multi_thread_) { + schedule_thread_.join(); + } else { + // For technical reasons, worker threads are always created even in single thread mode + JUST(CloseWorkerThreads()); + } + threads_closed_ = true; return Maybe::Ok(); } @@ -165,7 +178,7 @@ Maybe VirtualMachine::BlockingRunProbeFunc( bc->Decrease(); return true; }); - if (disable_vm_threads_) { + if (threads_closed_ || !multi_thread_) { ScheduleUntilVMEmpty(engine_.Mutable(), SingleThreadScheduleCtx()); } else { pending_notifier_.Notify(); @@ -181,10 +194,9 @@ Maybe VirtualMachine::ShrinkAllMem() { if (engine->mut_active_stream_list()->size()) { return false; } INTRUSIVE_FOR_EACH_PTR(thread_ctx, engine->mut_thread_ctx_list()) { INTRUSIVE_FOR_EACH_PTR(stream, thread_ctx->mut_stream_list()) { - const auto& device_ctx = stream->device_ctx(); - if (device_ctx.get() && device_ctx->mut_allocator()) { - auto* allocator = device_ctx->mut_allocator(); - auto* cache = dynamic_cast(allocator); + vm::Allocator* allocator = stream->mut_stream_policy()->mut_allocator(); + if (allocator) { + auto* cache = dynamic_cast(allocator); if (cache != nullptr) { cache->Shrink(); } } } @@ -195,7 +207,7 @@ Maybe VirtualMachine::ShrinkAllMem() { } VirtualMachine::~VirtualMachine() { - if (!disable_vm_threads_) { CHECK_JUST(CloseVMThreads()); } + if (!threads_closed_) { CHECK_JUST(CloseVMThreads()); } CHECK(engine_->SchedulerEmpty()); engine_.Reset(); } @@ -236,7 +248,8 @@ Maybe VirtualMachine::Receive(vm::InstructionList* instruction_list) { JUST(instruction->Prepare()); instruction->Compute(); } - } else if (unlikely(disable_vm_threads_)) { + instruction_list->Clear(); + } else if (unlikely(threads_closed_ || !multi_thread_)) { JUST(RunInCurrentThread(instruction_list)); } else { const int64_t kHighWaterMark = GetInstructionHighWaterMark(); @@ -263,7 +276,7 @@ Maybe VirtualMachine::Receive(vm::InstructionList* instruction_list) { } Maybe VirtualMachine::NotifyOrRunScheduler() { - if (unlikely(pthread_fork::IsForkedSubProcess() || disable_vm_threads_)) { + if (unlikely(pthread_fork::IsForkedSubProcess() || threads_closed_ || !multi_thread_)) { ScheduleUntilVMEmpty(engine_.Mutable(), SingleThreadScheduleCtx()); } else { pending_notifier_.Notify(); @@ -271,6 +284,18 @@ Maybe VirtualMachine::NotifyOrRunScheduler() { return Maybe::Ok(); } +Maybe VirtualMachine::CloseWorkerThreads() { + JUST(ForEachThreadCtx(engine_.Mutable(), [&](vm::ThreadCtx* thread_ctx) -> Maybe { + thread_ctx->mut_notifier()->Close(); + return Maybe::Ok(); + })); + { + std::unique_lock lock(worker_threads_mutex_); + for (const auto& worker_thread : worker_threads_) { worker_thread->join(); } + } + return Maybe::Ok(); +} + Maybe VirtualMachine::RunInCurrentThread(vm::InstructionList* instr_list) { CHECK_OR_RETURN(engine_->SchedulerEmpty()) << "vm scheduler not empty. May be a fatal error occured"; @@ -305,74 +330,66 @@ void VirtualMachine::ScheduleLoop(const std::function& Initializer) { // The cost of os thread switching is about 5-10 microseconds. Doing more scheduling in // a single waiting up can reach higher performance. do { - static constexpr int kNumSchedulingPerTimoutTest = 10000; - // Every time kWorkingMicroseconds timeout tested, engine_ is scheduled for about - // kNumSchedulingPerTimoutTest. - // The cost of `MicrosecondsFrom(start)` is about 400ns, while the empty scheduling costs - // about 10ns. - int i = 0; + // Use SchedulerThreadUnsafeEmpty to avoid acquiring mutex lock. + // It's safe to use SchedulerThreadUnsafeEmpty here. pending_notifier_.notified_cnt_ will be + // greater than zero when inconsistency between + // engine_->pending_instruction_list.list_head_.list_head_.container_ and + // engine_->pending_instruction_list.list_head_.list_head_.size_ occured. hence the pending + // instructions + // will get handled in the next iteration. + // VirtualMachine::Receive may be less effiencient if the thread safe version + // `engine_->SchedulerEmpty()` + // used + // here, because VirtualMachine::ScheduleLoop is more likely to get the mutex lock. do { - // Use SchedulerThreadUnsafeEmpty to avoid acquiring mutex lock. - // It's safe to use SchedulerThreadUnsafeEmpty here. pending_notifier_.notified_cnt_ will be - // greater than zero when inconsistency between - // engine_->pending_instruction_list.list_head_.list_head_.container_ and - // engine_->pending_instruction_list.list_head_.list_head_.size_ occured. hence the pending - // instructions - // will get handled in the next iteration. - // VirtualMachine::Receive may be less effiencient if the thread safe version - // `engine_->SchedulerEmpty()` - // used - // here, because VirtualMachine::ScheduleLoop is more likely to get the mutex lock. - do { engine_->Schedule(schedule_ctx); } while (!engine_->SchedulerThreadUnsafeEmpty()); - } while (++i < kNumSchedulingPerTimoutTest); + const size_t total_inserted = engine_->total_inserted_instruction_cnt(); + const size_t total_erased = engine_->total_erased_instruction_cnt(); + engine_->Schedule(schedule_ctx); + if (ThreadLocalEnvBool() + && total_inserted == engine_->total_inserted_instruction_cnt() + && total_erased == engine_->total_erased_instruction_cnt()) { // nothing handled. + std::this_thread::yield(); + } + } while (!engine_->SchedulerThreadUnsafeEmpty()); } while (MicrosecondsFrom(start) < kWorkingMicroseconds); } ScheduleUntilVMEmpty(engine_.Mutable(), schedule_ctx); - CHECK_JUST(ForEachThreadCtx(engine_.Mutable(), [&](vm::ThreadCtx* thread_ctx) -> Maybe { - thread_ctx->mut_notifier()->Close(); - return Maybe::Ok(); - })); - { - std::unique_lock lock(worker_threads_mutex_); - for (const auto& worker_thread : worker_threads_) { worker_thread->join(); } - } + CHECK_JUST(CloseWorkerThreads()); scheduler_stopped_ = true; } -intrusive::shared_ptr VirtualMachine::FindOrCreateScheduleLocalDepObject( - Symbol device, StreamRole stream_role) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); - auto key = std::make_pair(device, stream_role); - intrusive::shared_ptr* ptr = &device_stream_role2local_dep_object_[key]; - if (!*ptr) { *ptr = intrusive::make_shared(); } +intrusive::shared_ptr VirtualMachine::FindOrCreateScheduleDependence( + Symbol stream) { + std::unique_lock lock(stream_and_thread_ctx_mutex_); + intrusive::shared_ptr* ptr = &stream2dependence_[stream]; + if (!*ptr) { *ptr = intrusive::make_shared(); } return *ptr; } -intrusive::shared_ptr VirtualMachine::FindOrCreateTransportLocalDepObject() { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); - if (!transport_local_dep_object_) { - transport_local_dep_object_ = intrusive::make_shared(); - } - return transport_local_dep_object_; +intrusive::shared_ptr VirtualMachine::FindOrCreateTransportLocalDepObject() { + std::unique_lock lock(stream_and_thread_ctx_mutex_); + if (!transport_dependence_) { transport_dependence_ = intrusive::make_shared(); } + return transport_dependence_; } -Maybe VirtualMachine::CreateStream(Symbol device, StreamRole stream_role) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); - vm::ThreadCtx* thread_ctx = JUST(FindOrCreateThreadCtx(device, stream_role)); - return JUST(CreateStream(thread_ctx, device, stream_role)); +Maybe VirtualMachine::CreateStream(Symbol stream) { + std::unique_lock lock(stream_and_thread_ctx_mutex_); + vm::ThreadCtx* thread_ctx = + JUST(FindOrCreateThreadCtx(stream->device(), stream->stream_type(), stream->thread_uid())); + return JUST(CreateStream(thread_ctx, stream)); } Maybe VirtualMachine::GetVmStream(Symbol stream) { if (stream->unique_stream_id() >= unique_stream_id2vm_stream_.size()) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); + std::unique_lock lock(stream_and_thread_ctx_mutex_); if (stream->unique_stream_id() >= unique_stream_id2vm_stream_.size()) { auto* stream_mgr = JUST(SingletonMaybe()); for (int i = unique_stream_id2vm_stream_.size(); i <= stream->unique_stream_id(); ++i) { Symbol cur_stream = JUST(stream_mgr->GetStreamSymbol(i)); CHECK_EQ_OR_RETURN(cur_stream->unique_stream_id(), i) << "invalid Stream::unique_stream_id()"; - *unique_stream_id2vm_stream_.MutableOrAdd(cur_stream->unique_stream_id()) = - JUST(CreateStream(cur_stream->device(), cur_stream->stream_role())); + unique_stream_id2vm_stream_.SetOrAdd(cur_stream->unique_stream_id(), + JUST(CreateStream(cur_stream))); } } } @@ -380,22 +397,25 @@ Maybe VirtualMachine::GetVmStream(Symbol stream) { } Maybe VirtualMachine::FindOrCreateThreadCtx(Symbol device, - StreamRole stream_role) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); + StreamType stream_type, + size_t thread_uid) { + std::unique_lock lock(stream_and_thread_ctx_mutex_); vm::ThreadCtx** thread_ctx_ptr = nullptr; - if (StreamOnIndependentThread::Visit(stream_role)) { - auto key = std::make_pair(device->enum_type(), stream_role); - thread_ctx_ptr = &devcie_type_stream_role_2independent_thread_ctx_[key]; + if (StreamOnIndependentThread::Visit(stream_type)) { + auto key = std::make_pair(device->enum_type(), stream_type); + thread_ctx_ptr = &devcie_type_stream_type_2independent_thread_ctx_[key]; } else { - thread_ctx_ptr = &devcie_type2non_independent_thread_ctx_[device->enum_type()]; + thread_ctx_ptr = &thread_uid2shared_thread_ctx_[thread_uid]; + } + if (*thread_ctx_ptr == nullptr) { + *thread_ctx_ptr = JUST(CreateThreadCtx(device, stream_type, thread_uid)); } - if (*thread_ctx_ptr == nullptr) { *thread_ctx_ptr = JUST(CreateThreadCtx(device, stream_role)); } return *thread_ctx_ptr; } -Maybe VirtualMachine::CreateThreadCtx(Symbol device, - StreamRole stream_role) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); +Maybe VirtualMachine::CreateThreadCtx(Symbol device, StreamType stream_type, + size_t thread_uid) { + std::unique_lock lock(stream_and_thread_ctx_mutex_); // thread_ctx_ptr may be used after timout. auto thread_ctx_ptr = std::make_shared(nullptr); { @@ -412,15 +432,16 @@ Maybe VirtualMachine::CreateThreadCtx(Symbol device, } auto* thread_ctx = *thread_ctx_ptr; { - const auto& WorkerInitializer = [device, stream_role](vm::ThreadCtx* thread_ctx) { - int device_type_value = static_cast(device->enum_type()); - CHECK_GT(device_type_value, 0); + const std::string thread_tag = [&] { std::string device_tag = *CHECK_JUST(DeviceTag4DeviceType(device->enum_type())); - if (!StreamOnIndependentThread::Visit(stream_role)) { - CHECK_JUST(InitThisThreadConsistentId(device_type_value + kThreadConsistentIdScheduler, - device_tag)); + if (StreamOnIndependentThread::Visit(stream_type)) { + return device_tag + GetStreamTypeName::Visit(stream_type); + } else { + return std::to_string(thread_uid); } - OF_PROFILER_NAME_THIS_HOST_THREAD("_VM::Worker_" + device_tag); + }(); + const auto& WorkerInitializer = [thread_tag](vm::ThreadCtx* thread_ctx) { + OF_PROFILER_NAME_THIS_HOST_THREAD("_VM::Worker_" + thread_tag); }; auto thread = std::make_unique(&WorkerLoop, thread_ctx, WorkerInitializer); { @@ -431,30 +452,27 @@ Maybe VirtualMachine::CreateThreadCtx(Symbol device, return thread_ctx; } -Maybe VirtualMachine::CreateStream(vm::ThreadCtx* thread_ctx, Symbol device, - StreamRole stream_role) { - std::unique_lock lock(creating_stream_and_thread_ctx_mutex_); - // stream_ptr may be used after timout. - auto stream_ptr = std::make_shared(nullptr); - auto bc = std::make_shared(1); - intrusive::shared_ptr schedule_local_dep_object = - FindOrCreateScheduleLocalDepObject(device, stream_role); - Optional> transport_local_dep_object; - if (IsCommNetStream::Visit(stream_role)) { - transport_local_dep_object = FindOrCreateTransportLocalDepObject(); +Maybe VirtualMachine::CreateStream(vm::ThreadCtx* thread_ctx, Symbol stream) { + std::unique_lock lock(stream_and_thread_ctx_mutex_); + intrusive::shared_ptr schedule_dependence = + FindOrCreateScheduleDependence(stream); + std::vector> transport_dependences{}; + if (IsCommNetStream::Visit(stream->stream_type())) { + transport_dependences.push_back(FindOrCreateTransportLocalDepObject()); } - engine_->InsertProbe([stream_ptr, thread_ctx, device, stream_role, bc, schedule_local_dep_object, - transport_local_dep_object](vm::VirtualMachineEngine* engine) { - auto stream = intrusive::make_shared( - thread_ctx, device, stream_role, schedule_local_dep_object, transport_local_dep_object); - thread_ctx->mut_stream_list()->PushBack(stream.Mutable()); - *stream_ptr = stream.Mutable(); + auto vm_stream = + intrusive::make_shared(thread_ctx, stream->device(), stream->stream_type(), + schedule_dependence, transport_dependences); + + auto bc = std::make_shared(1); + engine_->InsertProbe([&vm_stream, thread_ctx, bc](vm::VirtualMachineEngine* engine) { + thread_ctx->mut_stream_list()->PushBack(vm_stream.Mutable()); bc->Decrease(); return true; }); JUST(NotifyOrRunScheduler()); JUST(bc->WaitUntilCntEqualZero(VirtualMachine::GetPredicatorNoMoreInstructionsFinished())); - return *stream_ptr; + return vm_stream.Mutable(); } } // namespace oneflow diff --git a/oneflow/core/vm/virtual_machine.h b/oneflow/core/vm/virtual_machine.h index 6ffa946b37c1549e8af4e543a9fe74fd60053c99..9b7297bd16fa8e8b343062ca3c37df51606d6b6d 100644 --- a/oneflow/core/vm/virtual_machine.h +++ b/oneflow/core/vm/virtual_machine.h @@ -20,7 +20,7 @@ limitations under the License. #include "oneflow/core/common/notifier.h" #include "oneflow/core/vm/virtual_machine_engine.h" #include "oneflow/core/thread/thread_pool.h" -#include "oneflow/core/common/stream_role.h" +#include "oneflow/core/common/stream_type.h" #include "oneflow/core/common/steady_vector.h" namespace oneflow { @@ -37,7 +37,7 @@ class VirtualMachine final { static std::function()> GetPredicatorNoMoreInstructionsFinished(); - intrusive::shared_ptr FindOrCreateTransportLocalDepObject(); + intrusive::shared_ptr FindOrCreateTransportLocalDepObject(); std::string GetBlockingDebugString(); @@ -50,25 +50,27 @@ class VirtualMachine final { Maybe ShrinkAllMem(); Maybe GetVmStream(Symbol stream); + size_t flying_instruction_cnt() const { return engine().flying_instruction_cnt(); } + private: friend class InstructionsBuilder; void ScheduleLoop(const std::function& Initializer); - intrusive::shared_ptr FindOrCreateScheduleLocalDepObject( - Symbol device, StreamRole stream_role); + intrusive::shared_ptr FindOrCreateScheduleDependence(Symbol stream); bool NoMoreErasedInstructions(size_t* last_total_erased_instruction_cnt) const; const vm::VirtualMachineEngine& engine() const { return *engine_; } vm::VirtualMachineEngine* mut_engine() { return engine_.Mutable(); } void ControlSync(); - Maybe FindOrCreateThreadCtx(Symbol device, StreamRole stream_role); - Maybe CreateThreadCtx(Symbol device, StreamRole stream_role); - Maybe CreateStream(Symbol device, StreamRole stream_role); + Maybe FindOrCreateThreadCtx(Symbol device, StreamType stream_type, + size_t thread_uid); + Maybe CreateThreadCtx(Symbol device, StreamType stream_type, + size_t thread_uid); + Maybe CreateStream(Symbol stream); - Maybe CreateStream(vm::ThreadCtx* thread_ctx, Symbol device, - StreamRole stream_role); + Maybe CreateStream(vm::ThreadCtx* thread_ctx, Symbol stream); Maybe RunInCurrentThread(vm::InstructionList* instr_list); @@ -76,7 +78,10 @@ class VirtualMachine final { Maybe NotifyOrRunScheduler(); - bool disable_vm_threads_; + Maybe CloseWorkerThreads(); + + bool multi_thread_; + bool threads_closed_; bool scheduler_stopped_; intrusive::shared_ptr engine_; @@ -84,14 +89,13 @@ class VirtualMachine final { std::mutex worker_threads_mutex_; std::list> worker_threads_; - // for creating vm::Stream and vm::ThreadCtx - std::recursive_mutex creating_stream_and_thread_ctx_mutex_; - HashMap devcie_type2non_independent_thread_ctx_; - HashMap, vm::ThreadCtx*> - devcie_type_stream_role_2independent_thread_ctx_; - HashMap, StreamRole>, intrusive::shared_ptr> - device_stream_role2local_dep_object_; - intrusive::shared_ptr transport_local_dep_object_; + // for vm::Stream and vm::ThreadCtx + std::recursive_mutex stream_and_thread_ctx_mutex_; + HashMap thread_uid2shared_thread_ctx_; + HashMap, vm::ThreadCtx*> + devcie_type_stream_type_2independent_thread_ctx_; + HashMap, intrusive::shared_ptr> stream2dependence_; + intrusive::shared_ptr transport_dependence_; SteadyVector unique_stream_id2vm_stream_; std::thread schedule_thread_; diff --git a/oneflow/core/vm/virtual_machine_engine.cpp b/oneflow/core/vm/virtual_machine_engine.cpp index 117bb2022acc4e49e058168726a634fd05a5df5e..0faa2961fa6ec3003cbaec8a7be38b8dc8c51efa 100644 --- a/oneflow/core/vm/virtual_machine_engine.cpp +++ b/oneflow/core/vm/virtual_machine_engine.cpp @@ -14,12 +14,11 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/vm/virtual_machine_engine.h" -#include "oneflow/core/vm/instruction_type.h" -#include "oneflow/core/vm/fuse_instruction_type.h" -#include "oneflow/core/vm/fuse_phy_instr_operand.h" -#include "oneflow/core/vm/barrier_phy_instr_operand.h" +#include "oneflow/core/common/env_var/vm.h" +#include "oneflow/core/vm/caching_allocator.h" +#include "oneflow/core/vm/fuse_instruction_policy.h" +#include "oneflow/core/vm/release_tensor_instruction_policy.h" #include "oneflow/core/vm/allocator.h" -#include "oneflow/core/vm/shrinkable_cache.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/balanced_splitter.h" #include "oneflow/core/common/cpp_attribute.h" @@ -30,6 +29,7 @@ limitations under the License. #include "oneflow/core/common/singleton.h" #include "oneflow/core/common/singleton_ptr.h" #include "oneflow/core/common/foreign_lock_helper.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" namespace oneflow { @@ -41,9 +41,9 @@ void VirtualMachineEngine::ReleaseInstruction(Instruction* instruction) { INTRUSIVE_FOR_EACH(access, access_list) { CHECK_GT(access->ref_cnt(), 1); access_list->Erase(access.Mutable()); - auto* mirrored_object = access->mut_mirrored_object(); + auto* dependence = access->mut_dependence(); if (unlikely(!access->rw_mutexed_object_access_hook().empty())) { - mirrored_object->mut_access_list()->Erase(access.Mutable()); + dependence->mut_access_list()->Erase(access.Mutable()); } } auto* out_edges = instruction->mut_out_edges(); @@ -65,13 +65,13 @@ void VirtualMachineEngine::HandleLocalPending() { InstructionList pending_instructions; FetchAndTryFusePendingInstructions(&pending_instructions); INTRUSIVE_FOR_EACH_PTR(instruction, &pending_instructions) { - const auto& instruction_type = instruction->instruction_type(); + const auto& instruction_policy = instruction->instruction_policy(); instruction->InitStatus(); LivelyInstructionListPushBack(instruction); - if (unlikely(instruction_type.IsBarrier())) { + if (unlikely(instruction_policy.IsBarrier())) { mut_barrier_instruction_list()->PushBack(instruction); } else { - ConsumeMirroredObjects(instruction); + ConsumeDependences(instruction); if (likely(Dispatchable(instruction))) { mut_ready_instruction_list()->PushBack(instruction); } @@ -83,16 +83,16 @@ namespace { bool FusableBetween(InstructionFuseType fuse_type, Instruction* instruction, Instruction* prev_instruction) { - if (unlikely(instruction->instruction_type().fuse_type() != fuse_type)) { return false; } + if (unlikely(instruction->instruction_policy().fuse_type() != fuse_type)) { return false; } auto* stream = instruction->mut_stream(); if (unlikely(stream == nullptr)) { return false; } - auto* sequential_dep = instruction->phy_instr_operand()->stream_sequential_dependence(); + auto* sequential_dep = instruction->instruction_policy().stream_sequential_dependence(); if (unlikely(sequential_dep == nullptr)) { return false; } if (unlikely(prev_instruction == nullptr)) { return true; } if (unlikely(stream != prev_instruction->mut_stream())) { return false; } if (unlikely(sequential_dep - != prev_instruction->phy_instr_operand()->stream_sequential_dependence())) { + != prev_instruction->instruction_policy().stream_sequential_dependence())) { return false; } return true; @@ -108,16 +108,15 @@ void VirtualMachineEngine::MakeAndAppendFusedInstruction( return; } auto* begin = fused_instruction_list.Begin(); - auto phy_instr_operand = std::make_shared(std::move(fused_instruction_list)); auto instruction = intrusive::make_shared( - begin->mut_stream(), SingletonPtr(), phy_instr_operand); + begin->mut_stream(), + std::make_shared(std::move(fused_instruction_list))); pending_instructions->EmplaceBack(std::move(instruction)); } -constexpr static int kPendingHandleWindow = 10; void VirtualMachineEngine::FetchAndTryFusePendingInstructions( InstructionList* /*out*/ pending_instructions) { - size_t window_size = kPendingHandleWindow; + size_t window_size = ThreadLocalEnvInteger(); InstructionList fused_instruction_list; INTRUSIVE_FOR_EACH_PTR(instruction, mut_local_pending_instruction_list()) { if (window_size-- <= 0) { break; } @@ -142,7 +141,14 @@ std::string VirtualMachineEngine::GetLivelyInstructionListDebugString(int64_t de std::stringstream ss; INTRUSIVE_UNSAFE_FOR_EACH_PTR(instruction, mut_lively_instruction_list()) { if (--debug_cnt <= 0) { break; } - ss << instruction->DebugName() << "\n"; + ss << instruction->DebugName() << " ptr: " << instruction + << " dispatched:" << (instruction->dispatched_instruction_hook().empty() ? "0" : "1") + << " launched:" << (instruction->Launched() ? "1" : "0") + << " done:" << (instruction->Done() ? "1" : "0"); + INTRUSIVE_UNSAFE_FOR_EACH_PTR(edge, instruction->mut_in_edges()) { + ss << " dep-ptr:" << &edge->src_instruction(); + } + ss << "\n"; } return ss.str(); } @@ -179,25 +185,26 @@ void VirtualMachineEngine::ReleaseFinishedInstructions(const ScheduleCtx& schedu INTRUSIVE_FOR_EACH_PTR(stream, mut_active_stream_list()) { while (true) { auto* instruction_ptr = stream->mut_running_instruction_list()->Begin(); - if (instruction_ptr == nullptr || !instruction_ptr->Done()) { break; } + if (instruction_ptr == nullptr) { break; } + if (!(instruction_ptr->in_edges().empty() && instruction_ptr->Done())) { break; } ReleaseInstruction(instruction_ptr); // Prevent destructing instruction_ptr. intrusive::shared_ptr instruction = stream->mut_running_instruction_list()->Erase(instruction_ptr); LivelyInstructionListErase(instruction_ptr); - instruction_ptr->DeleteStatusAndClearEdges(); + instruction_ptr->DeleteStatusAndCheckEdges(); } if (stream->running_instruction_list().empty()) { mut_active_stream_list()->Erase(stream); } } } -DependenceAccess* VirtualMachineEngine::AccessMirroredObject(OperandAccessType access_type, - MirroredObject* mirrored_object, - Instruction* instruction) { - auto access = access_pool_.make_shared(instruction, mirrored_object, access_type); +DependenceAccess* VirtualMachineEngine::AccessDependence(OperandAccessType access_type, + Dependence* dependence, + Instruction* instruction) { + auto access = access_pool_.make_shared(instruction, dependence, access_type); auto* ptr = access.Mutable(); instruction->mut_access_list()->PushBack(ptr); - mirrored_object->mut_access_list()->EmplaceBack(std::move(access)); + dependence->mut_access_list()->EmplaceBack(std::move(access)); return ptr; } @@ -212,9 +219,9 @@ void VirtualMachineEngine::TryConnectInstruction(Instruction* src_instruction, void VirtualMachineEngine::ConnectInstructionsByWrite(DependenceAccess* dst_access) { CHECK(dst_access->is_mut_operand()); - auto* mirrored_object = dst_access->mut_mirrored_object(); + auto* dependence = dst_access->mut_dependence(); auto* dst_instruction = dst_access->mut_instruction(); - auto* access_list = mirrored_object->mut_access_list(); + auto* access_list = dependence->mut_access_list(); if (likely(access_list->Begin() == dst_access)) { return; } INTRUSIVE_FOR_EACH_PTR(src_access, access_list) { if (unlikely(src_access == dst_access)) { break; } @@ -225,9 +232,9 @@ void VirtualMachineEngine::ConnectInstructionsByWrite(DependenceAccess* dst_acce void VirtualMachineEngine::ConnectInstructionsByRead(DependenceAccess* dst_access) { CHECK(dst_access->is_const_operand()); - auto* mirrored_object = dst_access->mut_mirrored_object(); + auto* dependence = dst_access->mut_dependence(); auto* dst_instruction = dst_access->mut_instruction(); - auto* first = mirrored_object->mut_access_list()->Begin(); + auto* first = dependence->mut_access_list()->Begin(); if (first->is_mut_operand()) { TryConnectInstruction(first->mut_instruction(), dst_instruction); } else if (first->is_const_operand()) { @@ -237,26 +244,24 @@ void VirtualMachineEngine::ConnectInstructionsByRead(DependenceAccess* dst_acces } } -void VirtualMachineEngine::ConsumeMirroredObjects(Instruction* instruction) { - const auto& phy_instr_operand = CHECK_NOTNULL(instruction->phy_instr_operand()); - auto* stream_sequential_dep = phy_instr_operand->stream_sequential_dependence(); +void VirtualMachineEngine::ConsumeDependences(Instruction* instruction) { + const auto& instruction_policy = instruction->instruction_policy(); + auto* stream_sequential_dep = instruction_policy.stream_sequential_dependence(); if (likely(stream_sequential_dep != nullptr)) { ConnectInstructionsByWrite( - AccessMirroredObject(kMutableOperandAccess, stream_sequential_dep, instruction)); + AccessDependence(kMutableOperandAccess, stream_sequential_dep, instruction)); } // Connect instructions by write before connecting by read. - for (auto* mirrored_object : phy_instr_operand->output_dependences()) { - ConnectInstructionsByWrite( - AccessMirroredObject(kMutableOperandAccess, mirrored_object, instruction)); + for (auto* dependence : instruction_policy.output_dependences()) { + ConnectInstructionsByWrite(AccessDependence(kMutableOperandAccess, dependence, instruction)); } - for (auto* mirrored_object : phy_instr_operand->input_dependences()) { - ConnectInstructionsByRead( - AccessMirroredObject(kConstOperandAccess, mirrored_object, instruction)); + for (auto* dependence : instruction_policy.input_dependences()) { + ConnectInstructionsByRead(AccessDependence(kConstOperandAccess, dependence, instruction)); } } bool VirtualMachineEngine::EdgeDispatchable(const Instruction* src, const Instruction* dst) const { - return (&src->stream() == &dst->stream()) /* same stream*/ + return dst->instruction_policy().Prescheduleable(&src->stream(), &dst->stream()) && !src->dispatched_instruction_hook().empty() /* dispatched */; } @@ -293,18 +298,8 @@ void VirtualMachineEngine::DispatchAndPrescheduleInstructions(const ScheduleCtx& namespace { -void StreamWaitPreviousInstructionsDone(vm::Stream* stream, vm::Instruction* instruction) { - auto* running_list = stream->mut_running_instruction_list(); - CHECK_GE(running_list->size(), 1); - CHECK_EQ(running_list->Last(), instruction); - if (running_list->size() == 1) { return; } - auto* prev = running_list->Prev(instruction); - // busy wait the previous instruction done. - while (!prev->Done()) {} -} - std::string DebugDeviceReset(vm::Stream* stream) { - stream->device_ctx()->mut_allocator()->DeviceReset(); + stream->mut_stream_policy()->mut_allocator()->DeviceReset(); return "reset device"; } @@ -312,32 +307,24 @@ std::string DebugDeviceReset(vm::Stream* stream) { void VirtualMachineEngine::DispatchInstruction(Instruction* instruction, const ScheduleCtx& schedule_ctx) { + ForeignFrameThreadLocalGuard guard(instruction->foreign_frame()); auto* stream = instruction->mut_stream(); - stream->mut_running_instruction_list()->PushBack(instruction); - if (stream->active_stream_hook().empty()) { mut_active_stream_list()->PushBack(stream); } // Prepare { const auto& ret = TRY(instruction->Prepare()); if (unlikely(!ret.IsOk())) { if (ret.error()->has_out_of_memory_error()) { - // Waits previous instructions done before shrinking memory.. - StreamWaitPreviousInstructionsDone(stream, instruction); - // Shrinks allocator to reduce fragmentation of memory. - { - auto* allocator = stream->device_ctx()->mut_allocator(); - auto* shrinkable_cache = dynamic_cast(allocator); - if (shrinkable_cache != nullptr) { shrinkable_cache->Shrink(); } - } - // Infers the instruction again. - CHECK_JUST_MSG(instruction->Prepare(), std::stringstream() << DebugDeviceReset(stream)); + CHECK_JUST_MSG(ret, std::stringstream() << DebugDeviceReset(stream)); } else { CHECK_JUST(ret); } } } + stream->mut_running_instruction_list()->PushBack(instruction); + if (stream->active_stream_hook().empty()) { mut_active_stream_list()->PushBack(stream); } // Compute if (OnSchedulerThread(*stream)) { - stream->stream_type().Run(instruction); + stream->stream_policy().RunIf(instruction); } else { stream->mut_thread_ctx()->mut_worker_pending_instruction_list()->PushBack(instruction); schedule_ctx.OnWorkerLoadPending(stream->mut_thread_ctx()); @@ -352,6 +339,7 @@ Maybe VirtualMachineEngine::Receive(InstructionList* compute_instruction_l OF_PROFILER_RANGE_GUARD(compute_instruction->DebugName()); } #endif + bool old_list_empty = mut_pending_instruction_list()->MoveFrom(compute_instruction_list); return old_list_empty; } @@ -418,7 +406,7 @@ bool VirtualMachineEngine::OnSchedulerThread(const Stream& stream) { // instructions are scarcely received by vm, there is no need for vm to run // VirtualMachineEngine::TryRunBarrierInstruction every time VirtualMachineEngine::Schedule run. On // the other hand, `barrier_instruction_hook_.size() == 0` is more lightweight than -// `lively_instruction_list_.Begin()?->instruction_type().IsBarrier()` +// `lively_instruction_list_.Begin()?->instruction_policy().IsBarrier()` // void VirtualMachineEngine::TryRunBarrierInstruction(const ScheduleCtx& schedule_ctx) { auto* sequnential_instruction = mut_barrier_instruction_list()->Begin(); @@ -427,11 +415,11 @@ void VirtualMachineEngine::TryRunBarrierInstruction(const ScheduleCtx& schedule_ // All instructions before `sequnential_instruction` are handled now, it's time to handle // `sequnential_instruction`. OF_PROFILER_RANGE_GUARD("TryRunBarrierInstruction"); - const auto& instruction_type = sequnential_instruction->instruction_type(); - CHECK(instruction_type.IsBarrier()); + const auto& instruction_policy = sequnential_instruction->instruction_policy(); + CHECK(instruction_policy.IsBarrier()); CHECK(OnSchedulerThread(sequnential_instruction->stream())); - const StreamType& stream_type = sequnential_instruction->stream().stream_type(); - stream_type.Run(sequnential_instruction); + const StreamPolicy& stream_policy = sequnential_instruction->stream().stream_policy(); + stream_policy.RunIf(sequnential_instruction); mut_barrier_instruction_list()->Erase(sequnential_instruction); LivelyInstructionListErase(sequnential_instruction); } @@ -445,7 +433,7 @@ void VirtualMachineEngine::Schedule(const ScheduleCtx& schedule_ctx) { // Use thread_unsafe_size to avoid acquiring mutex lock. // The inconsistency between pending_instruction_list.list_head_.list_head_.container_ and // pending_instruction_list.list_head_.list_head_.size_ is not a fatal error because - // VirtualMachineEngine::Schedule is always in a buzy loop. All instructions will get handled + // VirtualMachineEngine::Schedule is always in a busy loop. All instructions will get handled // eventually. // VirtualMachineEngine::Receive may be less effiencient if the thread safe version // `pending_instruction_list().size()` used here, because VirtualMachineEngine::Schedule is more diff --git a/oneflow/core/vm/virtual_machine_engine.h b/oneflow/core/vm/virtual_machine_engine.h index 9e3036c3e4cb4f26889c1d3da1d79fb74fcd42b1..6b2d11af6ead42579ceb50cf1bec99bd2125ece4 100644 --- a/oneflow/core/vm/virtual_machine_engine.h +++ b/oneflow/core/vm/virtual_machine_engine.h @@ -41,6 +41,9 @@ class ScheduleCtx { virtual void OnWorkerLoadPending(vm::ThreadCtx* thread_ctx) const = 0; }; +using ReadyInstructionList = + intrusive::List; + class VirtualMachineEngine final : public intrusive::Base { public: // types @@ -90,9 +93,6 @@ class VirtualMachineEngine final : public intrusive::Base { void MoveToGarbageListAndNotifyGC(const ScheduleCtx& schedule_ctx); private: - using ReadyInstructionList = - intrusive::List; - ReadyInstructionList* mut_ready_instruction_list() { return &ready_instruction_list_; } void ReleaseFinishedInstructions(const ScheduleCtx& schedule_ctx); @@ -109,13 +109,14 @@ class VirtualMachineEngine final : public intrusive::Base { void TryConnectInstruction(Instruction* src_instruction, Instruction* dst_instruction); void ConnectInstructionsByWrite(DependenceAccess* dst_access); void ConnectInstructionsByRead(DependenceAccess* dst_access); - DependenceAccess* AccessMirroredObject(OperandAccessType access_type, - MirroredObject* mirrored_object, Instruction* instrution); - void ConsumeMirroredObjects(Instruction* instruction); + DependenceAccess* AccessDependence(OperandAccessType access_type, Dependence* dependence, + Instruction* instrution); + void ConsumeDependences(Instruction* instruction); void DispatchInstruction(Instruction* instruction, const ScheduleCtx& schedule_ctx); bool EdgeDispatchable(const Instruction* src, const Instruction* dst) const; bool Dispatchable(Instruction* instruction) const; + void TryDispatchReadyInstructions(); void LivelyInstructionListPushBack(Instruction* instruction); diff --git a/oneflow/core/vm/vm_object.cpp b/oneflow/core/vm/vm_object.cpp index 524cb649fa4947a145e5d341880cdcf8400bcfec..327b293edfdaa5a6477a4477dca64c20eb85347a 100644 --- a/oneflow/core/vm/vm_object.cpp +++ b/oneflow/core/vm/vm_object.cpp @@ -22,14 +22,14 @@ namespace vm { void DependenceAccess::__Init__() { clear_instruction(); - clear_mirrored_object(); + clear_dependence(); } -void DependenceAccess::__Init__(Instruction* instruction, MirroredObject* mirrored_object, +void DependenceAccess::__Init__(Instruction* instruction, Dependence* dependence, OperandAccessType access_type) { __Init__(); set_instruction(instruction); - set_mirrored_object(mirrored_object); + set_dependence(dependence); set_access_type(access_type); } diff --git a/oneflow/core/vm/vm_object.h b/oneflow/core/vm/vm_object.h index e717c93280b7838992c406ee0010ffe24323196d..80d10af7c157f518e8f3e5b855d2872b1d5feb19 100644 --- a/oneflow/core/vm/vm_object.h +++ b/oneflow/core/vm/vm_object.h @@ -25,7 +25,9 @@ namespace oneflow { namespace vm { class Instruction; -class MirroredObject; +class Dependence; + +using DependenceVector = std::vector; enum OperandAccessType { kConstOperandAccess = 0, @@ -41,9 +43,9 @@ class DependenceAccess final // Getters OperandAccessType access_type() const { return access_type_; } bool has_instruction() const { return instruction_ != nullptr; } - bool has_mirrored_object() const { return mirrored_object_ != nullptr; } + bool has_dependence() const { return dependence_ != nullptr; } const Instruction& instruction() const { return *instruction_; } - const MirroredObject& mirrored_object() const { return *mirrored_object_; } + const Dependence& dependence() const { return *dependence_; } const intrusive::ListHook& rw_mutexed_object_access_hook() const { return rw_mutexed_object_access_hook_; } @@ -51,15 +53,14 @@ class DependenceAccess final // Setters void set_access_type(OperandAccessType val) { access_type_ = val; } void set_instruction(Instruction* val) { instruction_ = val; } - void set_mirrored_object(MirroredObject* val) { mirrored_object_ = val; } + void set_dependence(Dependence* val) { dependence_ = val; } void clear_instruction() { instruction_ = nullptr; } - void clear_mirrored_object() { mirrored_object_ = nullptr; } + void clear_dependence() { dependence_ = nullptr; } Instruction* mut_instruction() { return instruction_; } - MirroredObject* mut_mirrored_object() { return mirrored_object_; } + Dependence* mut_dependence() { return dependence_; } // methods - void __Init__(Instruction* instruction, MirroredObject* mirrored_object, - OperandAccessType access_type); + void __Init__(Instruction* instruction, Dependence* dependence, OperandAccessType access_type); bool is_const_operand() const { return kConstOperandAccess == access_type(); } bool is_mut_operand() const { return kMutableOperandAccess == access_type(); } @@ -74,14 +75,14 @@ class DependenceAccess final : intrusive_ref_(), access_type_(), instruction_(), - mirrored_object_(), + dependence_(), instruction_access_hook_(), rw_mutexed_object_access_hook_() {} intrusive::Ref intrusive_ref_; // fields OperandAccessType access_type_; Instruction* instruction_; - MirroredObject* mirrored_object_; + Dependence* dependence_; public: // list hooks @@ -89,7 +90,7 @@ class DependenceAccess final intrusive::ListHook rw_mutexed_object_access_hook_; }; // NOLINT -class MirroredObject final : public intrusive::Base { +class Dependence final : public intrusive::Base { public: // types using DependenceAccessList = @@ -107,7 +108,7 @@ class MirroredObject final : public intrusive::Base { friend class intrusive::Ref; intrusive::Ref* mut_intrusive_ref() { return &intrusive_ref_; } - MirroredObject() : intrusive_ref_(), access_list_() {} + Dependence() : intrusive_ref_(), access_list_() {} intrusive::Ref intrusive_ref_; // list hooks diff --git a/oneflow/core/vm/vm_util.cpp b/oneflow/core/vm/vm_util.cpp index d7d3970c841006c5f1dc335521d00bbf131b1e7a..a5a6e44339629b01d83badc516974e5de5bec8fa 100644 --- a/oneflow/core/vm/vm_util.cpp +++ b/oneflow/core/vm/vm_util.cpp @@ -20,8 +20,6 @@ limitations under the License. #include "oneflow/core/job/cluster_instruction.h" #include "oneflow/core/vm/vm_util.h" #include "oneflow/core/vm/virtual_machine.h" -#include "oneflow/core/vm/stream_type.h" -#include "oneflow/core/vm/instruction_type.h" #include "oneflow/core/framework/instructions_builder.h" #include "oneflow/core/job/resource_desc.h" #include "oneflow/core/job/global_for.h" diff --git a/oneflow/extension/python/numpy.cpp b/oneflow/extension/python/numpy.cpp index 6cc9d61c0ee64c8540da93a40e069a9a1cc685ab..333994a036106c64d0dcf6ffe24bcb571e904dc9 100644 --- a/oneflow/extension/python/numpy.cpp +++ b/oneflow/extension/python/numpy.cpp @@ -48,8 +48,8 @@ Maybe OFDataTypeToNumpyType(DataType of_data_type) { case DataType::kUInt8: return NPY_UINT8; case DataType::kFloat16: return NPY_FLOAT16; default: - return Error::InvalidValueError("OneFlow data type " + DataType_Name(of_data_type) - + " is not valid to Numpy data type."); + return Error::InvalidValueError() << "OneFlow data type " << DataType_Name(of_data_type) + << " is not valid to Numpy data type."; } } @@ -65,8 +65,8 @@ Maybe NumpyTypeToOFDataType(int np_type) { case NPY_UINT8: return DataType::kUInt8; case NPY_FLOAT16: return DataType::kFloat16; default: - return Error::InvalidValueError("Numpy data type " + std::to_string(np_type) - + " is not valid to OneFlow data type."); + return Error::InvalidValueError() << "Numpy data type " << std::to_string(np_type) + << " is not valid to OneFlow data type."; } } diff --git a/oneflow/extension/stack/foreign_stack_getter.h b/oneflow/extension/stack/foreign_stack_getter.h new file mode 100644 index 0000000000000000000000000000000000000000..c700dd514cea509bf80508d20664c18986e9b12b --- /dev/null +++ b/oneflow/extension/stack/foreign_stack_getter.h @@ -0,0 +1,46 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_EXTENSION_STACK_STACK_GETTER_H_ +#define ONEFLOW_EXTENSION_STACK_STACK_GETTER_H_ + +#include +#include +#include "oneflow/core/common/thread_local_guard.h" +#include "oneflow/core/intrusive/base.h" +#include "oneflow/core/intrusive/shared_ptr.h" + +namespace oneflow { + +class Frame : public intrusive::Base { + public: + virtual ~Frame() = default; + intrusive::Ref* mut_intrusive_ref() { return &intrusive_ref_; } + + private: + intrusive::Ref intrusive_ref_; +}; + +using ForeignFrameThreadLocalGuard = ThreadLocalGuard>; + +class ForeignStackGetter { + public: + virtual ~ForeignStackGetter() = default; + virtual intrusive::shared_ptr GetCurrentFrame() const = 0; + virtual std::string GetFormattedStack(intrusive::shared_ptr frame) const = 0; +}; +} // namespace oneflow + +#endif // ONEFLOW_EXTENSION_STACK_STACK_GETTER_H_ diff --git a/oneflow/extension/stack/python/custom_eval_frame.c b/oneflow/extension/stack/python/custom_eval_frame.c new file mode 100644 index 0000000000000000000000000000000000000000..ffbfee32d50800e5d5820bae879c4721801ca400 --- /dev/null +++ b/oneflow/extension/stack/python/custom_eval_frame.c @@ -0,0 +1,47 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +// see https://bugs.python.org/issue23644 for why this file is written +// as .c instead of .cpp + +#include "oneflow/extension/stack/python/custom_eval_frame.h" + +#define PY_SSIZE_T_CLEAN +#include +#include +#include +// see https://bugs.python.org/issue35886 +#if PY_VERSION_HEX >= 0x03080000 +#define Py_BUILD_CORE +#include "internal/pycore_pystate.h" +#undef Py_BUILD_CORE +#endif + +inline static void EnableCustomEvalFrame(PyThreadState* tstate, _PyFrameEvalFunction eval_func) { +#if PY_VERSION_HEX >= 0x03090000 + if (_PyInterpreterState_GetEvalFrameFunc(tstate->interp) != eval_func) { + _PyInterpreterState_SetEvalFrameFunc(tstate->interp, eval_func); + } +#else + if (tstate->interp->eval_frame != eval_func) { + tstate->interp->eval_frame = eval_func; + } +#endif +} + +void EnableCustomEvalFrameForCurrentThread(_PyFrameEvalFunction eval_func) { + return EnableCustomEvalFrame(PyThreadState_GET(), eval_func); +} diff --git a/oneflow/extension/stack/python/custom_eval_frame.h b/oneflow/extension/stack/python/custom_eval_frame.h new file mode 100644 index 0000000000000000000000000000000000000000..44d2b81054cc09453e6b90dc20a7f3d331444442 --- /dev/null +++ b/oneflow/extension/stack/python/custom_eval_frame.h @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_API_PYTHON_CUSTOM_EVAL_FRAME_H_ +#define ONEFLOW_API_PYTHON_CUSTOM_EVAL_FRAME_H_ + +#ifdef __cplusplus +extern "C" { +#endif + +#include + +#if PY_VERSION_HEX >= 0x03090000 +typedef PyObject* (*PyFrameEvalFunc)(struct _ts*, struct _frame*, int); +#else +typedef PyObject* (*PyFrameEvalFunc)(struct _frame*, int); +#endif +void EnableCustomEvalFrameForCurrentThread(PyFrameEvalFunc eval_func); + +#ifdef __cplusplus +} +#endif + +#endif // ONEFLOW_API_PYTHON_CUSTOM_EVAL_FRAME_H_ diff --git a/oneflow/extension/stack/python/stack_getter.cpp b/oneflow/extension/stack/python/stack_getter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a15358df4c6987cb9076630f9ecf25cfb19c9854 --- /dev/null +++ b/oneflow/extension/stack/python/stack_getter.cpp @@ -0,0 +1,175 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include + +#include "fmt/core.h" +#include "fmt/color.h" +#include "fmt/ostream.h" +#include "pybind11/pybind11.h" + +#include "oneflow/api/python/of_api_registry.h" +#include "oneflow/core/common/env_var/debug_mode.h" +#include "oneflow/core/common/singleton.h" +#include "oneflow/core/framework/shut_down_util.h" +#include "oneflow/core/common/foreign_lock_helper.h" +#include "oneflow/extension/stack/foreign_stack_getter.h" +#include "oneflow/extension/stack/python/custom_eval_frame.h" + +namespace py = pybind11; + +namespace oneflow { + +namespace { +std::string PyUnicodeToStdString(const PyObject* py_str) { + return PyBytes_AsString(PyUnicode_AsEncodedString(const_cast(py_str), "utf-8", "~E~")); +} +} // namespace + +class PyFrame final + : public Frame, + public intrusive::EnableObjectPool { + public: + PyFrame() + : EnableObjectPool(), + filename(nullptr), + funcname(nullptr), + cpython_frame(nullptr), + lineno(0) {} + void __Init__(PyFrameObject* frame, intrusive::shared_ptr back) { + // There is no need to increase the reference count of these cpython objects + // because they must be alive during the lifetime of `PyFrame`. + this->filename = frame->f_code->co_filename; + this->funcname = frame->f_code->co_name; + this->cpython_frame = frame; + this->back = std::move(back); + } + OF_DISALLOW_COPY_AND_MOVE(PyFrame); + ~PyFrame() = default; + + PyObject* filename; + PyObject* funcname; + PyFrameObject* cpython_frame; + int lineno; + intrusive::shared_ptr back; +}; + +class PyStackGetter final : public ForeignStackGetter { + public: + PyStackGetter() { + auto* frame = PyEval_GetFrame(); + // Get the first frame. It assumes `import oneflow` is called in global scope, + while (frame->f_back != nullptr) { frame = frame->f_back; } + current_frame_ = object_pool_.make_shared(frame, nullptr); + } + // indended to be called in main thread. + intrusive::shared_ptr GetCurrentFrame() const override { + if (IsShuttingDown() || !current_frame_) { return nullptr; } + // See `RecordAndEvalFrame` for documentation. + current_frame_->lineno = PyFrame_GetLineNumber(current_frame_->cpython_frame); + return intrusive::shared_ptr(current_frame_.get()); + } + + // bad path, performance is not a concern. + std::string GetFormattedStack(intrusive::shared_ptr frame) const override { + if (frame == nullptr) { return " \n"; } + std::string buffer; + const auto* py_frame = dynamic_cast(frame.get()); + py::gil_scoped_acquire acquire; + while (py_frame != nullptr) { + const auto& lineno = py_frame->lineno; + const std::string line_text = [&]() -> std::string { + std::string line_text; + std::ifstream ifs(PyUnicodeToStdString(py_frame->filename)); + if (!ifs.is_open()) { return ""; } + for (int j = 0; j < lineno; ++j) { std::getline(ifs, line_text); } + line_text.erase(line_text.find_last_not_of(' ') + 1); // suffixing spaces + line_text.erase(0, line_text.find_first_not_of(' ')); // prefixing spaces + return line_text; + }(); + // immitate python's stack trace format + fmt::format_to(std::back_inserter(buffer), " File \"{}\", line {}, in {}\n {}\n", + PyUnicodeToStdString(py_frame->filename), lineno, + PyUnicodeToStdString(py_frame->funcname), line_text); + py_frame = py_frame->back.get(); + } + return buffer; + }; + +#if PY_VERSION_HEX >= 0x03090000 + PyObject* RecordAndEvalFrame(PyThreadState* tstate, PyFrameObject* frame, +#else + PyObject* RecordAndEvalFrame(PyFrameObject* frame, +#endif + int throw_flag) { + // Example: + // >> def f(): # Line 1 + // >> pass # Line 2 + // >> f() # Line 3 + // + // When we call f(), `RecordAndEvalFrame` is triggered and the `frame` + // argument is the frame of function `f`, which is Line 1 at that time. It is not + // useful to us, but we can adjust it in `GetCurrentFrame` method. + // + PushFrame(frame); +#if PY_VERSION_HEX >= 0x03090000 + if (tstate == NULL) { tstate = PyThreadState_GET(); } + PyObject* ret = _PyEval_EvalFrameDefault(tstate, frame, throw_flag); +#else + PyObject* ret = _PyEval_EvalFrameDefault(frame, throw_flag); +#endif + PopFrame(); + return ret; + } + + private: + intrusive::shared_ptr current_frame_; + + intrusive::ObjectPool object_pool_; + + void PushFrame(PyFrameObject* frame) { + if (auto* f = frame->f_back) { current_frame_->lineno = PyFrame_GetLineNumber(f); } + current_frame_ = object_pool_.make_shared(frame, current_frame_); + } + void PopFrame() { + CHECK_NOTNULL(current_frame_); + current_frame_ = current_frame_->back; + } +}; + +#if PY_VERSION_HEX >= 0x03090000 +PyObject* RecordAndEvalFrame(PyThreadState* tstate, PyFrameObject* frame, +#else +PyObject* RecordAndEvalFrame(PyFrameObject* frame, +#endif + int throw_flag) { + using namespace oneflow; + return dynamic_cast(Singleton::Get()) +#if PY_VERSION_HEX >= 0x03090000 + ->RecordAndEvalFrame(tstate, frame, throw_flag); +#else + ->RecordAndEvalFrame(frame, throw_flag); +#endif +} + +void RegisterPyStackGetter() { + if (!IsPythonStackGetterEnabled()) { return; } + Singleton::Delete(); + Singleton::SetAllocated(new PyStackGetter()); + EnableCustomEvalFrameForCurrentThread(&RecordAndEvalFrame); +} + +} // namespace oneflow diff --git a/oneflow/extension/stack/python/stack_getter.h b/oneflow/extension/stack/python/stack_getter.h new file mode 100644 index 0000000000000000000000000000000000000000..53cbe3f93f01d31252e51efbb8384958fd2686ee --- /dev/null +++ b/oneflow/extension/stack/python/stack_getter.h @@ -0,0 +1,18 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +namespace oneflow { +void RegisterPyStackGetter(); +} diff --git a/oneflow/ir/CMakeLists.txt b/oneflow/ir/CMakeLists.txt index d006778f909ac88f707b1ff47ccd91bd2b640f6c..226eebd118b80659c64562951838c65ec6f19079 100644 --- a/oneflow/ir/CMakeLists.txt +++ b/oneflow/ir/CMakeLists.txt @@ -35,6 +35,7 @@ endif() set_property(GLOBAL PROPERTY LLVM_INSTALL_DIR ${LLVM_INSTALL_DIR}) set(MLIR_TABLEGEN_EXE mlir-tblgen) +set(MLIR_PDLL_TABLEGEN_EXE mlir-pdll) include_directories(${LLVM_INCLUDE_DIRS}) include_directories(${MLIR_INCLUDE_DIRS}) @@ -87,9 +88,46 @@ if(WITH_MLIR) add_subdirectory(include) add_subdirectory(lib) add_subdirectory(test) - add_subdirectory(oneflow-opt) add_subdirectory(oneflow-translate) add_subdirectory(oneflow-runtime) add_subdirectory(oneflow-extension) + add_subdirectory(oneflow-opt) add_subdirectory(oneflow-runner) endif(WITH_MLIR) + +if(BUILD_PYTHON) + foreach(llvm_include_dir ${LLVM_INCLUDE_DIRS}) + if(llvm_include_dir MATCHES "/include$") + list(APPEND LLVM_INSTALL_INCLUDE_DIRS "${llvm_include_dir}//") + else() + list(APPEND LLVM_INSTALL_INCLUDE_DIRS "${llvm_include_dir}") + endif() + endforeach() + install( + DIRECTORY ${LLVM_INSTALL_INCLUDE_DIRS} + DESTINATION ${ONEFLOW_INCLUDE_DIR} + COMPONENT oneflow_py_include + EXCLUDE_FROM_ALL FILES_MATCHING + PATTERN llvm/ADT/ArrayRef.h + PATTERN llvm/ADT/Hashing.h + PATTERN llvm/ADT/iterator.h + PATTERN llvm/ADT/None.h + PATTERN llvm/ADT/SmallVector.h + PATTERN llvm/ADT/STLExtras.h + PATTERN llvm/ADT/STLFunctionalExtras.h + PATTERN llvm/ADT/DenseMapInfo.h + PATTERN llvm/ADT/identity.h + PATTERN llvm/ADT/iterator_range.h + PATTERN llvm/ADT/Optional.h + PATTERN llvm/ADT/STLArrayExtras.h + PATTERN llvm/ADT/STLForwardCompat.h + PATTERN llvm/ADT/StringRef.h + PATTERN llvm/Config/abi-breaking.h + PATTERN llvm/Config/llvm-config.h + PATTERN llvm/Support/Compiler.h + PATTERN llvm/Support/DataTypes.h + PATTERN llvm/Support/ErrorHandling.h + PATTERN llvm/Support/SwapByteOrder.h + PATTERN llvm/Support/type_traits.h + PATTERN llvm-c/DataTypes.h) +endif() diff --git a/oneflow/ir/README.md b/oneflow/ir/README.md index f4eb7b6ebd3d862c0d86065d5581b6591eff3717..5f75bf12ac6fddf9d64c22da297269fe9863ecea 100644 --- a/oneflow/ir/README.md +++ b/oneflow/ir/README.md @@ -26,3 +26,31 @@ Optimizations on OneFlow MLIR dialect. A CLI to optimize .mlir file. [read more] - ### OneFlow dialect In the `include` and `lib` directories, there are definitions of MLIR OneFlow dialect and its operators. + +## Parallel Signature + +- There is parallel signature as 0 for OneFlow Ops in MLIR. It is implemented as MLIR dialect attribute. Some examples: + - 1D SBP + ```mlir + %100 = "oneflow.relu"(%99) {parallel = #sbp.parallel<[#sbp.S<0>] -> [#sbp.S<0>]>, ... + ``` + - multiple inputs and outputs 1D SBP + ```mlir + %102 = "oneflow.add_n2"(%101, %97) {parallel = #sbp.parallel<[#sbp.S<0>, #sbp.S<0>] -> [#sbp.S<0>]>, ... + ``` + - 2D SBP `matmul` + ``` + %120 = "oneflow.matmul"(%119, %output_105) {parallel = #sbp.parallel<[[#sbp.S<0>, #sbp.P], #sbp.S<0>] -> [#sbp.S<0>]>, ... + ``` + +- To avoid confusion and potential parsing error, use the term "parallel" instead of using "sbp" but conceptually and documentally there are the same. + +### Principle +- In IR, The signature should be orthogonal to device placement information althogh in some passes they might be related to each other. + +## Development + +- To run all the regression tests. The `-j3` option for [`LIT`](https://llvm.org/docs/CommandGuide/lit.html) is to prevent OOM on GPU. + ```bash + LIT_OPTS="-j3" cmake --build build -t c1 -j24 + ``` diff --git a/oneflow/ir/include/OneFlow/CMakeLists.txt b/oneflow/ir/include/OneFlow/CMakeLists.txt index d62e58df562a38a7ce1f98a377e0c92b1fdf0f2d..56873311436c5bd316d084127d863a12b465250f 100644 --- a/oneflow/ir/include/OneFlow/CMakeLists.txt +++ b/oneflow/ir/include/OneFlow/CMakeLists.txt @@ -16,7 +16,7 @@ mlir_tablegen(OneFlowPatterns.cpp.inc -gen-rewriters) add_public_tablegen_target(MLIROneFlowPatternsIncGen) # NOTE: seperate conversion and opt with --name -set(LLVM_TARGET_DEFINITIONS OneFlowOps.td) +set(LLVM_TARGET_DEFINITIONS OneFlowPasses.td) mlir_tablegen(OneFlowPasses.h.inc -gen-pass-decls) add_public_tablegen_target(MLIROneFlowPassIncGen) @@ -26,7 +26,7 @@ add_mlir_interface(OneFlowInterfaces) set(LLVM_TARGET_DEFINITIONS OneFlowOpGetGen.td) set(ONEFLOW_OP_GROUPS - "ASSIGN;BINARY;BROADCAST;CONV;CROSS_ENTROPY;CUDA;DATASET;DETECTION;EAGER;FUSED;IDEMPOTENT;IDENTITY;IMAGE;INDICES;INVOLUTION;LOSS;MATH;MATMUL;MISC;NCCL;NORMALIZATION;OPTIMIZER;PADDING;PARALLEL_CAST;POOL;QUANTIZATION;REDUCE;RESHAPE;SCALAR;SOFTMAX;SUMMARY;TENSOR_BUFFER;TEST;TRIGONOMETRIC;UNARY;UPSAMPLE;ONE_EMBEDDING" + "ASSIGN;BINARY;BROADCAST;CONV;CROSS_ENTROPY;CUDA;DATASET;DETECTION;EAGER;FUSED;IDEMPOTENT;IDENTITY;IMAGE;INDICES;INVOLUTION;LOSS;MATH;MATMUL;MISC;NCCL;NORMALIZATION;OPTIMIZER;PADDING;PARALLEL_CAST;POOL;QUANTIZATION;REDUCE;RESHAPE;SCALAR;SOFTMAX;SUMMARY;TENSOR_BUFFER;TEST;TRIGONOMETRIC;UNARY;UPSAMPLE;ONE_EMBEDDING;LINEAR_ALGEBRA;SYSTEM;MLIR_JIT" ) foreach(OP_GROUP_NAME IN LISTS ONEFLOW_OP_GROUPS) message(STATUS "Enable OneFlow MLIR op group: ${OP_GROUP_NAME}") @@ -45,11 +45,33 @@ set(LLVM_TABLEGEN_FLAGS "${FULL_LLVM_TABLEGEN_FLAGS}") mlir_tablegen(OneFlow.gen_ops.h.inc -gen-op-decls) add_public_tablegen_target(MLIROneFlowOpGroupDeclsIncGen) +set(LLVM_TARGET_DEFINITIONS SBP/SBPOps.td) +mlir_tablegen(SBPDialect.h.inc -gen-dialect-decls) +mlir_tablegen(SBPDialect.cpp.inc -gen-dialect-defs) +mlir_tablegen(SBPAttributes.h.inc -gen-attrdef-decls) +mlir_tablegen(SBPAttributes.cpp.inc -gen-attrdef-defs) +add_public_tablegen_target(MLIRSBPIncGen) + +set(LLVM_TARGET_DEFINITIONS OKL/OKLOps.td) +mlir_tablegen(OKLDialect.h.inc -gen-dialect-decls -dialect=okl) +mlir_tablegen(OKLDialect.cpp.inc -gen-dialect-defs -dialect=okl) +mlir_tablegen(OKLOps.h.inc -gen-op-decls) +mlir_tablegen(OKLOps.cpp.inc -gen-op-defs) +mlir_tablegen(OKLTypes.h.inc -gen-typedef-decls) +mlir_tablegen(OKLTypes.cpp.inc -gen-typedef-defs) +mlir_tablegen(OKLPasses.h.inc -gen-pass-decls) +mlir_tablegen(OKLEnums.h.inc -gen-enum-decls) +mlir_tablegen(OKLEnums.cpp.inc -gen-enum-defs) +mlir_tablegen(OKLAttributes.h.inc -gen-attrdef-decls) +mlir_tablegen(OKLAttributes.cpp.inc -gen-attrdef-defs) +add_public_tablegen_target(MLIROKLIncGen) + set(LLVM_TABLEGEN_FLAGS "") add_mlir_dialect( OneFlowOps oneflow DEPENDS + MLIRSBPIncGen MLIROneFlowEnumsIncGen MLIROneFlowPatternsIncGen MLIROneFlowPassIncGen diff --git a/oneflow/ir/include/OneFlow/Conversion/OneFlowToTosa.h b/oneflow/ir/include/OneFlow/Conversion/OneFlowToTosa.h index bdc375abcf6836701c42afc376ccacacbdc1f01e..f929d3589a8154c8f5c07f8095cf1a6c7bba7dbb 100644 --- a/oneflow/ir/include/OneFlow/Conversion/OneFlowToTosa.h +++ b/oneflow/ir/include/OneFlow/Conversion/OneFlowToTosa.h @@ -24,6 +24,7 @@ namespace mlir { namespace oneflow { std::unique_ptr createLowerOneFlowToTosaPass(); +std::unique_ptr createConvertToSignlessForTosaPass(); } // namespace oneflow diff --git a/oneflow/ir/include/OneFlow/OKL/Conversion/Conversion.h b/oneflow/ir/include/OneFlow/OKL/Conversion/Conversion.h new file mode 100644 index 0000000000000000000000000000000000000000..c6857272744efed5358c0c7fe14b590992608b55 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/Conversion/Conversion.h @@ -0,0 +1,37 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_CONVERSION_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_CONVERSION_H_ + +#include "OneFlow/OKL/Conversion/FetchFromLauncher.h" +#include "OneFlow/OKL/Conversion/OKLToLLVM.h" +#include "OneFlow/OKL/Conversion/OnlyKeepComputeOps.h" +#include "OneFlow/OKL/Conversion/SplitIntoFuncs.h" +#include "mlir/IR/BuiltinOps.h" + +namespace mlir { +namespace okl { + +// convert oneflow.kernel_launch wrap function to okl dialect +LogicalResult LowerWrapOpsToOKL(ModuleOp module); + +// convert okl dialect to llvm dialect +LogicalResult LowerOKLComputeToLLVM(ModuleOp module); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_CONVERSION_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/Conversion/FetchFromLauncher.h b/oneflow/ir/include/OneFlow/OKL/Conversion/FetchFromLauncher.h new file mode 100644 index 0000000000000000000000000000000000000000..73a4710438d245275565ddbdba518a5cd93e0cae --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/Conversion/FetchFromLauncher.h @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_FETCHFROMLAUNCHER_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_FETCHFROMLAUNCHER_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { +namespace okl { + +std::unique_ptr createFetchFromLauncherPass(); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_FETCHFROMLAUNCHER_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/Conversion/OKLToLLVM.h b/oneflow/ir/include/OneFlow/OKL/Conversion/OKLToLLVM.h new file mode 100644 index 0000000000000000000000000000000000000000..c3f8238ec3913ad288f6724f09ac5992053a43ce --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/Conversion/OKLToLLVM.h @@ -0,0 +1,33 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_OKLTOLLVM_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_OKLTOLLVM_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { +namespace okl { + +// lower !okl.launcher_ctx to !llvm.ptr +std::unique_ptr createLowerLauncherToLLVMPtrPass(); + +// lower okl ops to llvm.call @{callee in liboneflow.so} +std::unique_ptr createLowerOKLToLLVMCallPass(); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_OKLTOLLVM_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/Conversion/OnlyKeepComputeOps.h b/oneflow/ir/include/OneFlow/OKL/Conversion/OnlyKeepComputeOps.h new file mode 100644 index 0000000000000000000000000000000000000000..06f71ba341627d4e43bd9663bde7631676d22ada --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/Conversion/OnlyKeepComputeOps.h @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_ONLYKEEPCOMPUTEOPS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_ONLYKEEPCOMPUTEOPS_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { +namespace okl { + +std::unique_ptr createOnlyKeepComputeOpsPass(); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_ONLYKEEPCOMPUTEOPS_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/Conversion/SplitIntoFuncs.h b/oneflow/ir/include/OneFlow/OKL/Conversion/SplitIntoFuncs.h new file mode 100644 index 0000000000000000000000000000000000000000..5744b69f33b31d4883ee9bd1b764f5a91c5138f1 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/Conversion/SplitIntoFuncs.h @@ -0,0 +1,30 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_SPLITINTOFUNCS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_SPLITINTOFUNCS_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { +namespace okl { + +// split okl dialect with single function into different functions according lifetime. +std::unique_ptr createSplitIntoFuncsPass(); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_CONVERSION_SPLITINTOFUNCS_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/OKLAttributes.h b/oneflow/ir/include/OneFlow/OKL/OKLAttributes.h new file mode 100644 index 0000000000000000000000000000000000000000..daebc82c9bb7997bd58bb093e14c9dcc2c4301a6 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLAttributes.h @@ -0,0 +1,25 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES_H_ +#define ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES_H_ + +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/Support/LLVM.h" +#include "OneFlow/OKLEnums.h.inc" +#define GET_ATTRDEF_CLASSES +#include "OneFlow/OKLAttributes.h.inc" + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/OKLAttributes.td b/oneflow/ir/include/OneFlow/OKL/OKLAttributes.td new file mode 100644 index 0000000000000000000000000000000000000000..2b1a01e24d36d8909279e0b963cc92e20db4ff5d --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLAttributes.td @@ -0,0 +1,27 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES +#define ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES + +include "OneFlow/OKL/OKLDialect.td" +include "mlir/IR/AttrTypeBase.td" +include "mlir/IR/SymbolInterfaces.td" +include "mlir/Interfaces/SideEffectInterfaces.td" +include "mlir/Interfaces/InferTypeOpInterface.td" + + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLATTRIBUTES diff --git a/oneflow/ir/include/OneFlow/OKL/OKLBase.td b/oneflow/ir/include/OneFlow/OKL/OKLBase.td new file mode 100644 index 0000000000000000000000000000000000000000..bbacce7d8c19b78fe5c8fe140fbe3514b01037cf --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLBase.td @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLBASE +#define ONEFLOW_IR_INCLUDE_OKL_OKLBASE + +include "OneFlow/OKL/OKLDialect.td" +include "mlir/IR/AttrTypeBase.td" +include "mlir/IR/SymbolInterfaces.td" +include "mlir/Interfaces/SideEffectInterfaces.td" +include "mlir/Interfaces/InferTypeOpInterface.td" + +class OKL_Op traits = []> : + Op; + +class OKL_Type traits = []> + : TypeDef { + let mnemonic = typeMnemonic; +} + +class OKL_Attr traits = []> + : AttrDef { + let mnemonic = attrMnemonic; +} + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLBASE diff --git a/oneflow/ir/include/OneFlow/OKL/OKLDialect.h b/oneflow/ir/include/OneFlow/OKL/OKLDialect.h new file mode 100644 index 0000000000000000000000000000000000000000..69d81b2e4e9a419ca9b7bfacbe5a98d049ee8352 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLDialect.h @@ -0,0 +1,34 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT_H_ +#define ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT_H_ + +#include "OneFlow/Passes.h" +#include "mlir/IR/Dialect.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" + +#include "OneFlow/OKLDialect.h.inc" +#include "OneFlow/OKL/OKLOps.h" + +namespace mlir { +namespace okl { + +void registerOneFlowPasses(); + +} // namespace okl +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/OKLDialect.td b/oneflow/ir/include/OneFlow/OKL/OKLDialect.td new file mode 100644 index 0000000000000000000000000000000000000000..c4650f083ac708f67335cdd481a57473a321e26b --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLDialect.td @@ -0,0 +1,34 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT +#define ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT + +include "mlir/IR/OpBase.td" + +def OKL_Dialect : Dialect { + let name = "okl"; + let summary = "OneFlow Kernel Launch Dialect."; + let description = [{ + This dialect is the IR of abstract represent of OneFlow Kernel Launch Op. + }]; + let cppNamespace = "::mlir::okl"; + let dependentDialects = [ + "func::FuncDialect" + ]; + let useDefaultTypePrinterParser = 1; +} + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLDIALECT diff --git a/oneflow/ir/include/OneFlow/OKL/OKLOps.h b/oneflow/ir/include/OneFlow/OKL/OKLOps.h new file mode 100644 index 0000000000000000000000000000000000000000..2acd6ac51608c4f119f7bc0c437c3f778bc80050 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLOps.h @@ -0,0 +1,41 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLOPS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLOPS_H_ + +#include "mlir/IR/Dialect.h" +#include "mlir/IR/OpDefinition.h" +#include "mlir/IR/OpImplementation.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/FunctionInterfaces.h" +#include "mlir/Interfaces/CallInterfaces.h" +#include "mlir/Interfaces/SideEffectInterfaces.h" +#include "mlir/Interfaces/ControlFlowInterfaces.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/OKLAttributes.h" + +namespace mlir { +namespace func { +class FuncOp; +} // namespace func +} // namespace mlir + +#define GET_OP_CLASSES +#include "OneFlow/OKLOps.h.inc" + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLOPS_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/OKLOps.td b/oneflow/ir/include/OneFlow/OKL/OKLOps.td new file mode 100644 index 0000000000000000000000000000000000000000..7bf59493d273716acda7ced0be290df1e42f6f7a --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLOps.td @@ -0,0 +1,239 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_OKL_OKLOPS +#define ONEFLOW_IR_INCLUDE_OKL_OKLOPS + +include "OneFlow/OKL/OKLDialect.td" +include "OneFlow/OKL/OKLBase.td" +include "OneFlow/OKL/OKLTypes.td" +include "mlir/Pass/PassBase.td" +include "mlir/Dialect/LLVMIR/LLVMOpBase.td" +include "mlir/IR/OpBase.td" +include "mlir/IR/EnumAttr.td" + +// define type of tensor in okl to handle different situation. +def OKL_ArgumentTensor : I32EnumAttrCase<"TT_Argument", 0>; +def OKL_BufferTensor: I32EnumAttrCase<"TT_Buffer", 1>; +def OKL_ReturnTensor: I32EnumAttrCase<"TT_Return", 2>; + +def OKL_TensorAttr: I32EnumAttr<"TensorType", "OKL Tensor Type enum", + [ + OKL_ArgumentTensor, + OKL_BufferTensor, + OKL_ReturnTensor + ] +> { + let cppNamespace = "::mlir::okl"; + let stringToSymbolFnName = "ConvertToEnum"; + let symbolToStringFnName = "ConvertToString"; +} + +def GetTensorFromArgOp : OKL_Op<"get_tensor_from_arg"> { + let summary = "get tensor as arguments from operands of context"; + let description = [{ + get tensor value from launcher context as arguments. + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + OKL_TensorAttr:$tensor_type, + I32Attr:$index + ); + let results = (outs AnyTensor); +} + +def GetTensorFromRetOp : OKL_Op<"get_tensor_from_ret"> { + let summary = "get tensor as arguments from results of context"; + let description = [{ + get tensor value from launcher context as arguments. + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + OKL_TensorAttr:$tensor_type, + I32Attr:$index + ); + let results = (outs AnyTensor); +} + +def GetTensorAsRetOp : OKL_Op<"get_tensor_as_ret"> { + let summary = "get tensor as outcomes from results of context"; + let description = [{ + get tensor value from launcher context as outcomes. + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + AnyTensor:$tensor, + OKL_TensorAttr:$tensor_type, + I32Attr:$index + ); + let results = (outs AnyTensor); +} + + +def BuildRegContextOp : OKL_Op<"build_reg_ctx"> { + let summary = "build reg context operation"; + let description = [{ + this context is generated from module op and used on kernel/run_ctx build phase. + each wrapped op has their own reg_ctx with their own attrs. + }]; + + let regions = (region AnyRegion:$body); + + let results = (outs RegContextType:$out); +} + +def ReturnOp : OKL_Op<"return", [HasParent<"BuildRegContextOp">, Terminator]> { + let summary = "return operation"; + let description = [{ + return oneflow ops in reg context + ``` + }]; + + let arguments = (ins Variadic:$operands); + + let builders = [ + OpBuilder<(ins), + [{ build($_builder, $_state, llvm::None); }]>]; + + let assemblyFormat = "attr-dict ($operands^ `:` type($operands))?"; +} + +def DestroyRegContextOp : OKL_Op<"destroy_reg_ctx"> { + let summary = "destroy reg context operation"; + let description = [{ + delete the pointer source. + }]; + + let arguments = (ins RegContextType:$ptr); +} + +def BuildRunContextOp : OKL_Op<"build_run_ctx"> { + let summary = "build run context operation"; + let description = [{ + this context is generated from reg_ctx and compute_ctx. + compute_ctx is passed by jit engine from llvm function operand. + each wrapped op has their own run_ctx with their own reg_ctx. + }]; + + let arguments = (ins + RegContextType:$reg_ctx + ); + let results = (outs RunContextType:$out); +} + +def DestroyRunContextOp : OKL_Op<"destroy_run_ctx"> { + let summary = "destroy run context operation"; + let description = [{ + delete the pointer source. + }]; + + let arguments = (ins RunContextType:$ptr); +} + +def BuildKernelOp : OKL_Op<"build_op_kernel"> { + let summary = "build kernel operation"; + let description = [{ + kernel is generated from reg_ctx and op_name. + }]; + + let arguments = (ins + RegContextType:$reg_ctx + ); + let results = (outs KernelType:$out); +} + +def LaunchOp : OKL_Op<"launch"> { + let summary = "launch operation"; + let description = [{ + launch use kernel and run_ctx to run the real kernel compute func. + }]; + + let arguments = (ins + RunContextType:$run_ctx, + KernelType:$kernel + ); +} + +def FetchRegContextOp: OKL_Op<"fetch_reg_ctx", [NoSideEffect]> { + let summary = "fetch reg_ctx from launcher_ctx with index"; + let description = [{ + fetch reg_ctx from launcher_ctx with index + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + SI64Attr: $index + ); + + let results = (outs RegContextType:$out); +} + +def FetchRunContextOp: OKL_Op<"fetch_run_ctx", [NoSideEffect]> { + let summary = "fetch run_ctx from launcher_ctx with index"; + let description = [{ + fetch run_ctx from launcher_ctx with index + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + SI64Attr: $index + ); + + let results = (outs RunContextType:$out); +} + +def FetchKernelOp: OKL_Op<"fetch_kernel", [NoSideEffect]> { + let summary = "fetch kernel from launcher_ctx with index"; + let description = [{ + fetch kernel from launcher_ctx with index + }]; + + let arguments = (ins + LauncherContextType:$launcher_ctx, + SI64Attr: $index + ); + + let results = (outs KernelType:$out); +} + +def SplitIntoFuncsPass : Pass<"split-into-funcs", "ModuleOp"> { + let summary = "split okl dialect op into different func with respective lifetime"; + let constructor = "mlir::okl::createSplitIntoFuncsPass()"; +} + +def FetchFromLauncherPass : Pass<"fetch-from-launcher", "ModuleOp"> { + let summary = "fetch okl resources from okl.launcher_ctx"; + let constructor = "mlir::okl::createFetchFromLauncherPass()"; +} + +def OnlyKeepComputeOpsPass : Pass<"only-keep-compute-ops", "ModuleOp"> { + let summary = "only keep compute ops perpared for llvm lowering"; + let constructor = "mlir::okl::createOnlyKeepComputeOpsPass()"; +} + +def LowerLauncherToLLVMPtrPass : Pass<"lower-launcher-to-llvm-ptr", "ModuleOp"> { + let summary = "convert okl dialect func to llvm dialect"; + let constructor = "mlir::okl::createLowerLauncherToLLVMPtrPass()"; +} + +def LowerOKLToLLVMCallPass : Pass<"lower-okl-to-llvm-call", "ModuleOp"> { + let summary = "convert okl dialect ops to llvm dialect llvm.call"; + let constructor = "mlir::okl::createLowerOKLToLLVMCallPass()"; +} + +#endif // ONEFLOW_IR_INCLUDE_OKL_OKLOPS diff --git a/oneflow/ir/include/OneFlow/OKL/OKLTypes.h b/oneflow/ir/include/OneFlow/OKL/OKLTypes.h new file mode 100644 index 0000000000000000000000000000000000000000..4fabf8b01c6cef64f2a6157ebfb6a9a241439336 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLTypes.h @@ -0,0 +1,24 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES_H_ + +#include "mlir/IR/Types.h" + +#define GET_TYPEDEF_CLASSES +#include "OneFlow/OKLTypes.h.inc" + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES_H_ diff --git a/oneflow/ir/include/OneFlow/OKL/OKLTypes.td b/oneflow/ir/include/OneFlow/OKL/OKLTypes.td new file mode 100644 index 0000000000000000000000000000000000000000..07ccb0447fbad76716aeae5ef90ea69f033b673c --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/OKLTypes.td @@ -0,0 +1,27 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES + +include "OneFlow/OKL/OKLBase.td" +include "mlir/IR/AttrTypeBase.td" + +def LauncherContextType : OKL_Type<"LauncherContext", "launcher_ctx">; +def RegContextType : OKL_Type<"RegContext", "reg_ctx">; +def RunContextType : OKL_Type<"RunContext", "run_ctx">; +def KernelType : OKL_Type<"Kernel", "kernel">; + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_OKLTYPES diff --git a/oneflow/ir/include/OneFlow/OKL/passes.h b/oneflow/ir/include/OneFlow/OKL/passes.h new file mode 100644 index 0000000000000000000000000000000000000000..11e59e75ce12ddcc2446daacfbbca0f5cacb7e47 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OKL/passes.h @@ -0,0 +1,38 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_PASSES_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_PASSES_H_ + +#include "mlir/IR/BuiltinOps.h" +#include "mlir/Pass/Pass.h" +#include "OneFlow/OKL/Conversion/SplitIntoFuncs.h" +#include "OneFlow/OKL/Conversion/FetchFromLauncher.h" +#include "OneFlow/OKL/Conversion/OnlyKeepComputeOps.h" +#include "OneFlow/OKL/Conversion/OKLToLLVM.h" + +namespace mlir { + +namespace okl { + +#define GEN_PASS_CLASSES +#define GEN_PASS_REGISTRATION +#include "OneFlow/OKLPasses.h.inc" + +} // namespace okl + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_OKL_PASSES_H_ diff --git a/oneflow/ir/include/OneFlow/OneFlowBase.td b/oneflow/ir/include/OneFlow/OneFlowBase.td index 22d99134aeb623dbd895b86b7070a8dfc761f6f1..a0b029d1b093690ede249bb085021d819ad24854 100644 --- a/oneflow/ir/include/OneFlow/OneFlowBase.td +++ b/oneflow/ir/include/OneFlow/OneFlowBase.td @@ -87,6 +87,9 @@ class OneFlow_BaseOp traits = []> : bit has_output_arg_modify_fn = 0; bit has_output_blob_time_shape_infer_fn = 0; bit has_nd_sbp_infer_fn = 0; + bit has_compute_complexity_fn = 0; + bit has_get_nd_sbp_fn = 0; + bit has_dump_nd_sbp_signature_for_op_conf_fn = 0; } class OneFlow_Op traits = []> : @@ -136,7 +139,7 @@ class OneFlow_ConvolutionBaseOp traits = []> : OneFlow_Tensor:$in, OneFlow_Tensor:$weight, Optional:$bias, - Optional:$bias_multiplier + Optional:$_add_to_output ); let output = (outs OneFlow_Tensor:$out); let attrs = (ins @@ -156,6 +159,7 @@ class OneFlow_ConvolutionBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } class OneFlow_TFPoolBaseOp traits = []> : @@ -176,6 +180,7 @@ class OneFlow_TFPoolBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } class OneFlow_TFPoolGradBaseOp traits = []> : @@ -200,6 +205,7 @@ class OneFlow_TFPoolGradBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } @@ -226,6 +232,30 @@ class OneFlow_MaxPoolBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; +} + +class OneFlow_MaxUnpoolBaseOp traits = []> : + OneFlow_BaseOp])> { + let summary = "OneFlow Max Unpooling operation"; + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$indices + ); + let output = (outs + OneFlow_Tensor:$y + ); + let attrs = (ins + SI64ArrayAttr:$kernel_size, + SI64ArrayAttr:$stride, + SI64ArrayAttr:$padding, + DefaultValuedAttr:$has_output_size, + ShapeAttr:$output_size + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } class OneFlow_AvgPoolBaseOp traits = []> : @@ -250,6 +280,7 @@ class OneFlow_AvgPoolBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } class OneFlow_MaxPoolGradBaseOp traits = []> : @@ -276,6 +307,7 @@ class OneFlow_MaxPoolGradBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } class OneFlow_AvgPoolGradBaseOp traits = []> : @@ -301,6 +333,24 @@ class OneFlow_AvgPoolGradBaseOp traits = []> : let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; +} + +class OneFlow_MaxUnpoolGradBaseOp traits = []> : + OneFlow_BaseOp])> { + let summary = "OneFlow Max Unpooling Grad operation"; + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$indices, + OneFlow_Tensor:$dy + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } class OneFlow_AdaptivePoolBaseOp traits = []> : @@ -347,6 +397,40 @@ class OneFlow_UnaryBaseOp traits = []> : let has_data_type_infer_fn = 1; } +class OneFlow_AdaptiveMaxPoolBaseOp traits = []> : + OneFlow_BaseOp])> { + let summary = "OneFlow adaptive max pool operation"; + let input = (ins + AnyType:$x + ); + let output = (outs + AnyType:$y, + AnyType:$index + ); + let attrs = (ins + SI64ArrayAttr:$output_size + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +class OneFlow_AdaptiveMaxPoolGradBaseOp traits = []> : + OneFlow_BaseOp])> { + let summary = "OneFlow adaptive max pool grad operation"; + let input = (ins + AnyType:$dy, + AnyType:$x, + AnyType:$index + ); + let output = (outs AnyType:$dx); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_Idempotent : NativeOpTrait<"IsIdempotentOfIdenticalPlacement">; class OneFlow_IdempotentBaseOp traits = []> : diff --git a/oneflow/ir/include/OneFlow/OneFlowDataTypeConversion.h b/oneflow/ir/include/OneFlow/OneFlowDataTypeConversion.h new file mode 100644 index 0000000000000000000000000000000000000000..a56b956d3d9b75694e7572994a32b4827845e695 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OneFlowDataTypeConversion.h @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWDATATYPECONVERSION_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWDATATYPECONVERSION_H_ + +#include "mlir/IR/Builders.h" +#include "OneFlow/OneFlowSupport.h" + +namespace mlir { + +namespace oneflow { + +Type getTypeFromOneFlowDataType(MLIRContext* context, ::oneflow::DataType dt); + +} // namespace oneflow + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWDATATYPECONVERSION_H_ diff --git a/oneflow/ir/include/OneFlow/OneFlowDialect.h b/oneflow/ir/include/OneFlow/OneFlowDialect.h index 029428db13b96e23b807428fbfea76d3ae506408..6c09d8f69e1ebf9deed23ba995fa2c0868c1ddba 100644 --- a/oneflow/ir/include/OneFlow/OneFlowDialect.h +++ b/oneflow/ir/include/OneFlow/OneFlowDialect.h @@ -18,6 +18,7 @@ limitations under the License. #include "mlir/IR/Dialect.h" #include "mlir/Dialect/Func/IR/FuncOps.h" +#include "OneFlow/SBP/SBPDialect.h" #include "OneFlow/OneFlowOpsDialect.h.inc" diff --git a/oneflow/ir/include/OneFlow/OneFlowDialect.td b/oneflow/ir/include/OneFlow/OneFlowDialect.td index 94e4d31ac5be7e77a8a518c54fee4ced1718fe83..80795a07c5555408dee17dd5430a0c585c26bab3 100644 --- a/oneflow/ir/include/OneFlow/OneFlowDialect.td +++ b/oneflow/ir/include/OneFlow/OneFlowDialect.td @@ -11,6 +11,7 @@ def OneFlow_Dialect : Dialect { }]; let cppNamespace = "::mlir::oneflow"; let dependentDialects = [ + "sbp::SBPDialect", "func::FuncDialect" ]; let hasConstantMaterializer = 1; diff --git a/oneflow/ir/include/OneFlow/OneFlowInterfaces.td b/oneflow/ir/include/OneFlow/OneFlowInterfaces.td index c203559f88abd5751db1283bec4fc3eb780bc703..72ed76d643074e2a8df8393f7eb070848c1efbf1 100644 --- a/oneflow/ir/include/OneFlow/OneFlowInterfaces.td +++ b/oneflow/ir/include/OneFlow/OneFlowInterfaces.td @@ -116,5 +116,46 @@ def NCHWCompatibleInterface : OpInterface<"NCHWCompatible"> { let cppNamespace = "::mlir::oneflow"; } +def BiasAddCompatibleInterface : OpInterface<"BiasAddCompatible"> { + let description = [{ + Interface of ops used as bias add + }]; + + let methods = [ + InterfaceMethod<"", + "bool", "isLastDim", (ins) + >, + InterfaceMethod<"", + "mlir::Value", "biasAddGetBias", (ins) + >, + InterfaceMethod<"", + "mlir::Value", "biasAddGetOut", (ins) + >, + ]; + let cppNamespace = "::mlir::oneflow"; +} + +def MatMulCompatibleInterface : OpInterface<"MatMulCompatible"> { + let description = [{ + Interface of ops used as matmul + }]; + + let methods = [ + InterfaceMethod<"is this a transpose_a=false, transpose_b=true matmul", + "bool", "isLinear", (ins) + >, + InterfaceMethod<"", + "mlir::Value", "matMulGetX", (ins) + >, + InterfaceMethod<"", + "mlir::Value", "matMulGetW", (ins) + >, + InterfaceMethod<"", + "mlir::Value", "matMulGetY", (ins) + >, + ]; + let cppNamespace = "::mlir::oneflow"; +} + #endif // ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWINTERFACES_H_ diff --git a/oneflow/ir/include/OneFlow/OneFlowOpTraits.h b/oneflow/ir/include/OneFlow/OneFlowOpTraits.h index c930371d6fb3c2cfcfbe16383e76a4f75794e29e..213b2c4135045e4e90e59b381bb2fbc35e39b442 100644 --- a/oneflow/ir/include/OneFlow/OneFlowOpTraits.h +++ b/oneflow/ir/include/OneFlow/OneFlowOpTraits.h @@ -133,6 +133,7 @@ class TensorSource : public TraitBase { static StringRef getDataTypeAttrName() { return "data_type"; } static StringRef getIsDynamicAttrName() { return "is_dynamic"; } static StringRef getNdSbpAttrName() { return "nd_sbp"; } + static StringRef getSbpAttrName() { return "parallel"; } static LogicalResult verifyTrait(Operation* op) { if (!op->hasAttrOfType(getShapeAttrName())) { diff --git a/oneflow/ir/include/OneFlow/OneFlowOps.h b/oneflow/ir/include/OneFlow/OneFlowOps.h index 3ef74ae5cab13d286c58afa1e7d5f71cfbfcda2a..514ee8a702334f02b0b5f36dd51408519dd40dd4 100644 --- a/oneflow/ir/include/OneFlow/OneFlowOps.h +++ b/oneflow/ir/include/OneFlow/OneFlowOps.h @@ -24,13 +24,12 @@ limitations under the License. #include "mlir/Interfaces/CallInterfaces.h" #include "mlir/Interfaces/SideEffectInterfaces.h" #include "mlir/Interfaces/ControlFlowInterfaces.h" - -#include "OneFlow/Passes.h" +#include "mlir/Interfaces/InferTypeOpInterface.h" +#include "mlir/IR/PatternMatch.h" #include "OneFlow/OneFlowSupport.h" #include "OneFlow/OneFlowInterfaces.h.inc" #include "OneFlow/OneFlowOpTraits.h" -#include "OneFlow/OneFlowEnums.h.inc" -#include "OneFlow/OneFlowOpTraits.h" +#include "OneFlow/SBP/SBPAttributes.h" namespace mlir { diff --git a/oneflow/ir/include/OneFlow/OneFlowOps.td b/oneflow/ir/include/OneFlow/OneFlowOps.td index c22a87143b32257f0f2a6ffd8cfadfdff129322d..e7da927cf1e330ca59fa35f891b0647c966ddb43 100644 --- a/oneflow/ir/include/OneFlow/OneFlowOps.td +++ b/oneflow/ir/include/OneFlow/OneFlowOps.td @@ -14,29 +14,9 @@ include "mlir/Pass/PassBase.td" include "mlir/IR/AttrTypeBase.td" include "mlir/IR/OpBase.td" -def OneFlow_NormalizationAddReluOp : OneFlow_NormalizationAddReluBaseOp { - let builders = [ - OpBuilder<(ins - "Value":$x, - "Value":$addend, - "Value":$moving_mean, - "Value":$moving_variance, - "Value":$gamma, - "Value":$beta, - "StringRef":$op_name, - "StringRef":$device_tag, - "ArrayAttr":$device_name, - "IntegerAttr":$scope_symbol_id, - "ArrayAttr":$hierarchy, - "DenseElementsAttr":$operand_segment_sizes, - "DenseElementsAttr":$result_segment_sizes, - "IntegerAttr":$axis, - "FloatAttr":$epsilon, - "BoolAttr":$training, - "FloatAttr":$momentum - )> - ]; -} + +include "OneFlow/SBP/SBPOps.td" + #ifndef REMOVE_ONEFLOW_MLIR_ONLY_OP_DEFINITIONS @@ -73,6 +53,7 @@ def OneFlow_FrozenVariableOp : OneFlow_IROp<"variable_ir", [ConstantLike, NoSide let arguments = (ins F32ElementsAttr:$value, StrAttr:$op_name, + OptionalAttr:$data_type, StrAttr:$device_tag, StrArrayAttr:$device_name, // TODO: change device_name to dict and parse the literal fmt like "0:0-0" OptionalAttr:$scope_symbol_id, @@ -85,11 +66,6 @@ def OneFlow_FrozenVariableOp : OneFlow_IROp<"variable_ir", [ConstantLike, NoSide let hasFolder = 1; } -def OneFlow_LossMarkerOp : OneFlow_IROp<"loss_marker", []> { - let summary = "mark a result as loss to prevent its defining op from optimized away"; - let arguments = (ins Variadic:$loss); -} - def OneFlow_Add2Op : OneFlow_BaseOp<"add_n2", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let summary = ""; let input = (ins @@ -99,40 +75,6 @@ def OneFlow_Add2Op : OneFlow_BaseOp<"add_n2", [NoSideEffect, DeclareOpInterfaceM let output = (outs AnyType:$out); } -// JIT ops - -def OneFlow_MlirJitOp : OneFlow_BaseOp<"mlir_jit", [ CallOpInterface, DeclareOpInterfaceMethods ] > { - let input = (ins Variadic:$data_input); - let output = (outs Variadic:$data_output); - let attrs = (ins - FlatSymbolRefAttr:$callee, - StrAttr:$mlir_assembly - ); - let builders = [ - OpBuilder<(ins "func::FuncOp":$callee, - "NamedAttrList":$attributes, - CArg<"ValueRange", "{}">:$data_input), [{ - $_state.addOperands(data_input); - $_state.addAttributes(attributes); - $_state.addAttribute("callee", SymbolRefAttr::get(callee)); - $_state.addTypes(callee.getFunctionType().getResults()); - }]> - ]; - let extraClassDeclaration = [{ - operand_range getArgOperands() { - return {arg_operand_begin(), arg_operand_end()}; - } - - operand_iterator arg_operand_begin() { return operand_begin(); } - operand_iterator arg_operand_end() { return operand_end(); } - CallInterfaceCallable getCallableForCallee() { - return (*this)->getAttrOfType("callee"); - } - }]; - let assemblyFormat = [{ - $callee `(` $data_input `)` attr-dict `:` functional-type($data_input, results) - }]; -} class OneFlow_ConcreteSystemOp traits = []> : OneFlow_BaseOp:$trainable, OptionalAttr:$float_initializer, OptionalAttr:$integer_initializer, - StrArrayAttr:$nd_sbp + OptionalAttr:$parallel ); } @@ -275,59 +217,3 @@ def OneFlow_NormalizationInferenceOp : OneFlow_NormalizationBaseOp<"normalizatio #endif // ONEFLOW_OPS -#ifndef ONEFLOW_PASSES -#define ONEFLOW_PASSES - -def LowerOneFlowToTosaPass : Pass<"lower-oneflow-to-tosa", "ModuleOp"> { - let summary = ""; - let constructor = "mlir::oneflow::createLowerOneFlowToTosaPass()"; - let dependentDialects = ["tosa::TosaDialect", "memref::MemRefDialect", "mlir::func::FuncDialect"]; - let options = [ - Option<"variableAsConstant", "variable-as-constant", "int", "0", - "convert variable op as const op of tosa">, - ]; -} - -def BufferHostRegisterPass : Pass<"buffer-host-register", "func::FuncOp"> { - let summary = ""; - let constructor = "mlir::oneflow::createBufferHostRegisterPass()"; - let dependentDialects = ["gpu::GPUDialect"]; -} - -def GpuCopyArgPass : Pass<"gpu-copy-arg", "func::FuncOp"> { - let summary = ""; - let constructor = "mlir::oneflow::createGpuCopyArgPass()"; - let dependentDialects = ["memref::MemRefDialect", "gpu::GPUDialect"]; -} - -def OutlineJitFunctionPass : Pass<"outline-jit-function", "ModuleOp"> { - let summary = "move ops could be jitted to jit function"; - let constructor = "mlir::oneflow::createOutlineJitFunctionPass()"; -} - -def FuseIntoExistingOpPass : Pass<"fuse-into-existing-op", "ModuleOp"> { - let summary = ""; - let constructor = "mlir::oneflow::createFuseIntoExistingOpPass()"; -} - -def AutoNhwcPass : Pass<"auto-nhwc", "ModuleOp"> { - let summary = ""; - let constructor = "mlir::oneflow::createAutoNhwcPass()"; -} - -def PreConvertInferenceOpPass : Pass<"pre-convert-inference-op", "ModuleOp"> { - let summary = "Convert variable op to variable ir op for constant folding."; - let constructor = "mlir::oneflow::createPreConvertInferenceOpPass()"; -} - -def ConvertInferenceOpPass : Pass<"convert-inference-op", "ModuleOp"> { - let summary = "Convert ops to their inference version and rewrite them with a more performant equivalent in inference workflow."; - let constructor = "mlir::oneflow::createConvertInferenceOpPass()"; -} - -def PostConvertInferenceOpPass : Pass<"post-convert-inference-op", "ModuleOp"> { - let summary = "Convert variable ir op to variable op after contant folding."; - let constructor = "mlir::oneflow::createPostConvertInferenceOpPass()"; -} - -#endif // ONEFLOW_PASSES diff --git a/oneflow/ir/include/OneFlow/OneFlowPDLLPatterns.h b/oneflow/ir/include/OneFlow/OneFlowPDLLPatterns.h new file mode 100644 index 0000000000000000000000000000000000000000..631f5b958103e1c81e08169e4a2b783e0003d32c --- /dev/null +++ b/oneflow/ir/include/OneFlow/OneFlowPDLLPatterns.h @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWPDLLPATTERNS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWPDLLPATTERNS_H_ +#include "mlir/IR/PatternMatch.h" + +namespace mlir { + +namespace oneflow { + +void populateForwardOpPatterns(RewritePatternSet& patterns); +void populateNormalizationOpPatterns(RewritePatternSet& patterns); +void populateFuseConv2DBatchNormPattern(RewritePatternSet& patterns); + +} // namespace oneflow + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWPDLLPATTERNS_H_ diff --git a/oneflow/ir/include/OneFlow/OneFlowPasses.td b/oneflow/ir/include/OneFlow/OneFlowPasses.td new file mode 100644 index 0000000000000000000000000000000000000000..88276bc0c0ec075d59bed9bcda5715303f003828 --- /dev/null +++ b/oneflow/ir/include/OneFlow/OneFlowPasses.td @@ -0,0 +1,140 @@ +#ifndef ONEFLOW_PASSES +#define ONEFLOW_PASSES + +include "OneFlow/OneFlowOps.td" + +def LowerOneFlowToTosaPass : Pass<"lower-oneflow-to-tosa", "ModuleOp"> { + let summary = "lower oneflow dialect to tosa dialect"; + let constructor = "mlir::oneflow::createLowerOneFlowToTosaPass()"; + let dependentDialects = ["tosa::TosaDialect", "memref::MemRefDialect", "mlir::func::FuncDialect"]; + let options = [ + Option<"variableAsConstant", "variable-as-constant", "int", "0", + "convert variable op as const op of tosa">, + ]; +} + +def BufferHostRegisterPass : Pass<"buffer-host-register", "func::FuncOp"> { + let summary = ""; + let constructor = "mlir::oneflow::createBufferHostRegisterPass()"; + let dependentDialects = ["gpu::GPUDialect"]; +} + +def GpuCopyArgPass : Pass<"gpu-copy-arg", "func::FuncOp"> { + let summary = ""; + let constructor = "mlir::oneflow::createGpuCopyArgPass()"; + let dependentDialects = ["memref::MemRefDialect", "gpu::GPUDialect"]; +} + +def OutlineJitFunctionPass : Pass<"outline-jit-function", "ModuleOp"> { + let summary = "move ops could be jitted to jit function"; + let constructor = "mlir::oneflow::createOutlineJitFunctionPass()"; +} + +def AggregateComputeOpsPass : Pass<"aggregate-compute-ops", "ModuleOp"> { + let summary = "aggregate compute ops together"; + let constructor = "mlir::oneflow::createAggregateComputeOpsPass()"; +} + +def WrapOpsToKernelLaunchPass : Pass<"wrap-ops-to-kernel-launch", "ModuleOp"> { + let summary = "wrap user ops with a single kernel launch op in OneFlow Job"; + let constructor = "mlir::oneflow::createWrapOpsToKernelLaunchPass()"; +} + +def ExtractKernelLaunchTensorPass : Pass<"extract-kernel-launch-tensor", "ModuleOp"> { + let summary = "extract kernel launch tensor from !okl.launcher_ctx"; + let constructor = "mlir::oneflow::createExtractKernelLaunchTensorPass()"; +} + +def TrimReturnAsVoidPass : Pass<"trim-return-to-void", "ModuleOp"> { + let summary = "trim return types to void type"; + let constructor = "mlir::oneflow::createTrimReturnAsVoidPass()"; +} + +def LowerToOKLPass : Pass<"lower-to-okl", "ModuleOp"> { + let summary = "lower oneflow dialect ops to okl dialect"; + let constructor = "mlir::oneflow::createLowerToOKLPass()"; +} + +def FuseIntoExistingOpPass : Pass<"fuse-into-existing-op", "ModuleOp"> { + let summary = ""; + let constructor = "mlir::oneflow::createFuseIntoExistingOpPass()"; + let dependentDialects = ["pdl_interp::PDLInterpDialect", "pdl::PDLDialect"]; +} + +def AutoNhwcPass : Pass<"auto-nhwc", "ModuleOp"> { + let summary = ""; + let constructor = "mlir::oneflow::createAutoNhwcPass()"; +} + +def PreConvertInferenceOpPass : Pass<"pre-convert-inference-op", "ModuleOp"> { + let summary = "Convert variable op to variable ir op for constant folding."; + let constructor = "mlir::oneflow::createPreConvertInferenceOpPass()"; +} + +def ConvertInferenceOpPass : Pass<"convert-inference-op", "ModuleOp"> { + let summary = "Convert ops to their inference version and rewrite them with a more performant equivalent in inference workflow."; + let constructor = "mlir::oneflow::createConvertInferenceOpPass()"; + let dependentDialects = ["pdl_interp::PDLInterpDialect", "pdl::PDLDialect"]; +} + +def PostConvertInferenceOpPass : Pass<"post-convert-inference-op", "ModuleOp"> { + let summary = "Convert variable ir op to variable op after contant folding."; + let constructor = "mlir::oneflow::createPostConvertInferenceOpPass()"; +} + + +def ConvertToSignlessForTosaPass : Pass<"convert-to-signless-for-tosa", "ModuleOp"> { + let summary = "convert func type to unsigned before lowering to tosa"; + let description = [{ + In oneflow, int typed tensor is explicit signed. Convert them before lowering to TOSA. + }]; + let constructor = "mlir::oneflow::createConvertToSignlessForTosaPass()"; + let dependentDialects = ["func::FuncDialect"]; +} + +def CSEWithAttributesIgnored : Pass<"cse-with-attributes-ignored", "ModuleOp"> { + let summary = "ignore oneflow attributes to have cse work"; + let description = [{ + cse and ignore oneflow attributes like op name, symbol id, etc. + }]; + let constructor = "mlir::oneflow::createCSEWithAttributesIgnored()"; + let dependentDialects = []; +} + +def CSEPutAttributes : Pass<"cse-put-attributes", "ModuleOp"> { + let summary = "cse and ignore oneflow attributes"; + let description = [{ + put back oneflow attributes like op name, symbol id, etc. + }]; + let constructor = "mlir::oneflow::createCSEPutAttributes()"; + let dependentDialects = []; +} + +def GroupMatMul : Pass<"group-matmul", "ModuleOp"> { + let summary = "group matmul together"; + let description = [{ + group matmul ops together and use cudnn batched matmul + }]; + let constructor = "mlir::oneflow::createGroupMatMul()"; + let dependentDialects = []; +} + +def FuseForwardOps : Pass<"fuse-forward-only-ops", "ModuleOp"> { + let summary = "fuse forward ops"; + let description = [{ + fuse forward ops. Usually they are actions after an op. + }]; + let constructor = "mlir::oneflow::createFuseForwardOps()"; + let dependentDialects = []; +} + +def FuseNormalizationOps : Pass<"fuse-normalization-ops", "ModuleOp"> { + let summary = "fuse forward ops"; + let description = [{ + fuse forward ops. Usually they are actions after an op. + }]; + let constructor = "mlir::oneflow::createFuseNormalizationOps()"; + let dependentDialects = ["pdl_interp::PDLInterpDialect", "pdl::PDLDialect"]; +} + +#endif // ONEFLOW_PASSES diff --git a/oneflow/ir/include/OneFlow/OneFlowPatternUtils.h b/oneflow/ir/include/OneFlow/OneFlowPatternUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..24bef35ec3218b247991e97f17c0a9f41e0ab99a --- /dev/null +++ b/oneflow/ir/include/OneFlow/OneFlowPatternUtils.h @@ -0,0 +1,37 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/PatternMatch.h" + +namespace mlir { + +namespace oneflow { + +namespace rewrites { + +mlir::IntegerAttr GetDefaultSeed(::mlir::PatternRewriter& rewriter); +void populateRewrites(RewritePatternSet& patterns); + +} // namespace rewrites + +namespace constraints { + +void populateConstraints(RewritePatternSet& patterns); + +} // namespace constraints +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/include/OneFlow/OneFlowPatterns.td b/oneflow/ir/include/OneFlow/OneFlowPatterns.td index d286e88d2db721c7183e1a73e802a5b0abd64095..f9c4b083188b6d2e3d432602919f080427ec8a7b 100644 --- a/oneflow/ir/include/OneFlow/OneFlowPatterns.td +++ b/oneflow/ir/include/OneFlow/OneFlowPatterns.td @@ -67,291 +67,10 @@ def BroadcastMulToScalarMulPattern : Pat< def GetFirstValue : NativeCodeCall<"*$0.begin()">; -def IsGPU: Constraint, "is GPU device">; - -def FusedScaleTrilPattern : Pat< - ( - OneFlow_TrilOp - ( - OneFlow_ScalarMulOp - $x, - $scale_op_name, - $scale_device_tag, - $scale_device_name, - $scale_scope_symbol_id, - $scale_hierarchy, - $has_int_operand, - $has_float_operand, - $int_operand, - $float_operand - ), - $tril_op_name, - $tril_device_tag, - $tril_device_name, - $tril_scope_symbol_id, - $tril_hierarchy, - $diagonal, - $floating_fill_value, - $integer_fill_value, - $is_floating_fill_value - ), - (OneFlow_FusedScaleTrilOp $x, - $tril_op_name, - $tril_device_tag, - $tril_device_name, - $tril_scope_symbol_id, - $tril_hierarchy, - $diagonal, - $floating_fill_value, - $integer_fill_value, - $is_floating_fill_value, - $float_operand, - $int_operand, - $has_float_operand - ), - [ - (IsGPU $tril_device_tag), - (IsGPU $scale_device_tag) - ] ->; - -def FusePadAndConv2d : - NativeCodeCall<"mlir::oneflow::CreateConv2dAndErasePad($_builder, $0, $1)">; - -def HasIdenticalPadding: Constraint, "">; - -def FusedPadConv2DPattern : Pat< - ( - OneFlow_Conv2DOp : $conv_2d_res - ( - OneFlow_PadOp : $pad_res - $x, - $pad_op_name, - $pad_device_tag, - $pad_device_name, - $pad_scope_symbol_id, - $pad_hierarchy, - $pad_op_padding_before, - $pad_op_padding_after, - $pad_op_padding, - $floating_constant_value, - $integral_constant_value - ), - $weight, - $bias, - $bias_multiplier, - $conv2d_op_name, - $conv2d_device_tag, - $conv2d_device_name, - $conv2d_scope_symbol_id, - $conv2d_hierarchy, - $operand_segment_sizes, - $filters, - $padding_before, - $data_format, - $kernel_size, - $strides, - $dilation_rate, - $groups - ), - (FusePadAndConv2d $conv_2d_res, $pad_res), - [ - (HasIdenticalPadding $pad_op_padding_before, $pad_op_padding_after, $data_format) - ] ->; - -def GetDefaultSeed : - NativeCodeCall<"mlir::oneflow::GetDefaultSeed($_builder)">; - -def FusedBiasAddMaskScale : - NativeCodeCall<"mlir::oneflow::CreateFusedBiasAddMaskScale($_builder, $0, $1, $2)">; def IsAddToOutputNone: Constraint, "">; -def FusedBiasAddDropoutPattern : Pattern< - ( - OneFlow_DropoutOp: $dropout_res - ( - OneFlow_BiasAddOp: $bias_add_res - $a, - $b, - $bias_add_op_name, - $bias_add_device_tag, - $bias_add_device_name, - $bias_add_scope_symbol_id, - $bias_add_hierarchy, - $bias_add_op_axis - ), - $_add_to_output, - $dropout_op_name, - $dropout_device_tag, - $dropout_device_name, - $dropout_scope_symbol_id, - $dropout_hierarchy, - $dropout_op_rate - ), - [ - ( - FusedBiasAddMaskScale - $dropout_res__0, - $bias_add_res, - ( - OneFlow_RandomMaskLikeOp : $mask - $a, - $bias_add_op_name, - $dropout_device_tag, - $dropout_device_name, - $dropout_scope_symbol_id, - $dropout_hierarchy, - $dropout_op_rate, - (GetDefaultSeed) - ) - ), - (replaceWithValue $mask) - ], - [(IsAddToOutputNone $_add_to_output)] ->; - -// TODO: use either to merge two patterns -def FusedScaleTrilPattern2 : Pat< - ( - OneFlow_ScalarMulOp - ( - OneFlow_TrilOp - $x, - $tril_op_name, - $tril_device_tag, - $tril_device_name, - $tril_scope_symbol_id, - $tril_hierarchy, - $diagonal, - $floating_fill_value, - $integer_fill_value, - $is_floating_fill_value - ), - $scale_op_name, - $scale_device_tag, - $scale_device_name, - $scale_scope_symbol_id, - $scale_hierarchy, - $has_int_operand, - $has_float_operand, - $int_operand, - $float_operand - ), - (OneFlow_FusedScaleTrilOp $x, - $scale_op_name, - $scale_device_tag, - $scale_device_name, - $scale_scope_symbol_id, - $scale_hierarchy, - $diagonal, - $floating_fill_value, - $integer_fill_value, - $is_floating_fill_value, - $float_operand, - $int_operand, - $has_float_operand - ), - [ - (IsGPU $tril_device_tag), - (IsGPU $scale_device_tag) - ] ->; - -def FusedBiasAddGeluPattern : Pat< - ( - OneFlow_GeluOp : $gelu_op - ( - OneFlow_BiasAddOp - $a, - $b, - $bias_add_op_name, - $bias_add_device_tag, - $bias_add_device_name, - $bias_add_scope_symbol_id, - $bias_add_hierarchy, - $axis - ), - $gelu_op_name, - $gelu_device_tag, - $gelu_device_name, - $gelu_scope_symbol_id, - $gelu_hierarchy - ), - (OneFlow_FusedBiasAddGeluOp $a, $b, - $gelu_op_name, - $gelu_device_tag, - $gelu_device_name, - $gelu_scope_symbol_id, - $gelu_hierarchy, - $axis - ), - [] ->; - def IsTraingTrue: Constraint, "">; -// TODO: check mean and inv_variance are not used -def NormalizationAddReluPattern : Pattern< - /* match */ ( - OneFlow_ReluOp ( - OneFlow_Add2Op ( - OneFlow_NormalizationOp:$results - $bn_x, - $bn_moving_mean, - $bn_moving_variance, - $bn_gamma, - $bn_beta, - $bn__add_to_output, // TODO: check this is none - $bn_op_name, - $bn_device_tag, - $bn_device_name, - $bn_scope_symbol_id, - $bn_hierarchy, - $operand_segment_sizes, - $result_segment_sizes, - $bn_axis, - $bn_epsilon, - $bn_training, - $bn_momentum - ), - $addend, - $add_op_name, - $add_device_tag, - $add_device_name, - $add_scope_symbol_id, - $add_hierarchy - ), - $relu_op_name, - $relu_device_tag, - $relu_device_name, - $relu_scope_symbol_id, - $relu_hierarchy - ), - /* replace */ [( - OneFlow_NormalizationAddReluOp: $fuse_results - $bn_x, - $addend, - (GetFirstValue $bn_moving_mean), - (GetFirstValue $bn_moving_variance), - $bn_gamma, - $bn_beta, - $bn_op_name, - $bn_device_tag, - $bn_device_name, - $bn_scope_symbol_id, - $bn_hierarchy, - /* not used */ $operand_segment_sizes, - /* not used */ $result_segment_sizes, - $bn_axis, - $bn_epsilon, - $bn_training, - $bn_momentum - ), - (replaceWithValue $fuse_results__0), - ], - [(IsTraingTrue $bn_training)] ->; def IsArg: Constraint()">, "">; def getResultTypes : NativeCodeCall<"$0.getResultTypes()">; @@ -368,80 +87,4 @@ def ReplaceCopyWithGPUPattern : Pat< [(IsArg $dst)] >; -def FuseConv2DBatchNorm : NativeCodeCall<"mlir::oneflow::FuseConv2DBatchNorm($_builder, $0, $1)">; -def FuseConv2DBatchNormPattern : Pat< - ( - OneFlow_NormalizationInferenceOp: $batch_norm_op - ( - OneFlow_Conv2DOp : $conv_2d_op - $x, - ( - OneFlow_FrozenVariableOp : $variable_conv_2d_weight - $value, - $v_op_name, - $v_device_tag, - $v_device_name, - $v_scope_symbol_id, - $v_hierarchy, - $v_nd_sbp - ), - $bias, - $bias_multiplier, - $conv2d_op_name, - $conv2d_device_tag, - $conv2d_device_name, - $conv2d_scope_symbol_id, - $conv2d_hierarchy, - $conv2d_operand_segment_sizes, - $filters, - $padding_before, - $data_format, - $kernel_size, - $strides, - $dilation_rate, - $groups - ), - $bn_moving_mean, - $bn_moving_variance, - $bn_gamma, - $bn_beta, - $bn__add_to_output, - $bn_op_name, - $bn_device_tag, - $bn_device_name, - $bn_scope_symbol_id, - $bn_hierarchy, - $bn_operand_segment_sizes, - $bn_result_segment_sizes, - $bn_axis, - $bn_epsilon, - $bn_training, - $bn_momentum - ), - ( - FuseConv2DBatchNorm $conv_2d_op, $batch_norm_op__0 - ), - [] ->; - -def IsSameDtype : Constraint>; - -def DeleteSameDtypeCastOpPattern : Pat< - ( - OneFlow_CastOp : $res - $x, - $cast_op_name, - $cast_device_tag, - $cast_device_name, - $cast_scope_symbol_id, - $cast_hierarchy, - $dtype, - $pin_memory - ), - ( - replaceWithValue $x - ), - [(IsSameDtype $res, $x)] ->; - #endif // ONEFLOW_PATTERNS diff --git a/oneflow/ir/include/OneFlow/OneFlowSupport.h b/oneflow/ir/include/OneFlow/OneFlowSupport.h index 44233d2f45d09e79aee2e527845a60ab8dfe4c4a..6a05a6ec8c2a8d3a9317d3fc6d6b6584ac76a6dc 100644 --- a/oneflow/ir/include/OneFlow/OneFlowSupport.h +++ b/oneflow/ir/include/OneFlow/OneFlowSupport.h @@ -22,6 +22,7 @@ limitations under the License. #include "mlir/IR/BuiltinAttributes.h" #include "mlir/IR/BuiltinTypes.h" #include "mlir/IR/MLIRContext.h" +#include "OneFlow/OneFlowEnums.h.inc" #include "oneflow/core/common/shape.h" #include "oneflow/core/framework/tensor.h" // This include is not necessary now, but it is here for testing the namespace collision @@ -33,6 +34,7 @@ namespace oneflow { namespace support { +const ::oneflow::UserOpDef& getUserOpDef(const std::string& op_type_name); static const std::vector* inputKeys() { static std::vector val({"in"}); return &val; @@ -48,6 +50,13 @@ mlir::DenseElementsAttr TensorToDenseElementsAttr( std::shared_ptr<::oneflow::one::Tensor> DenseElementsAttrToTensor( const mlir::Attribute& attr, const mlir::Attribute& device_tag, const mlir::Attribute& device_name); +void DenseElementsAttrToTensor(const mlir::Attribute& attr, const mlir::Attribute& device_tag, + const mlir::Attribute& device_name, + std::shared_ptr<::oneflow::one::Tensor>& tensor); + +FailureOr<::oneflow::DataType> FromMLIRTypeToOFDataType(Type mlir_type); +FailureOr<::oneflow::DataType> FromMLIRDataTypeToOFDataType(::mlir::oneflow::DataType data_type); +FailureOr<::oneflow::DataType> FromMLIRAttrToOFDataType(Attribute attr); } // namespace support diff --git a/oneflow/ir/include/OneFlow/OneFlowUserOps.td b/oneflow/ir/include/OneFlow/OneFlowUserOps.td index e7c20595bffc74869df98c0501592293d99e1b1a..64b3c8cc34c9a4c1df39c30885fc18d2e8cf9616 100644 --- a/oneflow/ir/include/OneFlow/OneFlowUserOps.td +++ b/oneflow/ir/include/OneFlow/OneFlowUserOps.td @@ -1,115 +1,6 @@ -// ASSIGN;BASE;BINARY;BROADCAST;CONV;CROSS_ENTROPY;CUDA;DATASET;DETECTION;EAGER;FUSED;IDEMPOTENT;IDENTITY;IMAGE;INDICES;INVOLUTION;LOSS;MATH;MATMUL;MISC;NCCL;NORMALIZATION;OPTIMIZER;PADDING;PARALLEL_CAST;POOL;QUANTIZATION;REDUCE;RESHAPE;SCALAR;SOFTMAX;SUMMARY;TENSOR_BUFFER;TEST;TRIGONOMETRIC;UNARY;UPSAMPLE - -/* -#define GET_OP_LIST -#include "OneFlow/OneFlow.assign_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.binary_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.broadcast_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.conv_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.cross_entropy_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.cuda_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.dataset_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.detection_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.eager_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.fused_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.idempotent_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.identity_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.image_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.indices_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.involution_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.loss_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.math_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.matmul_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.misc_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.nccl_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.normalization_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.optimizer_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.padding_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.parallel_cast_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.pool_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.quantization_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.reduce_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.reshape_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.scalar_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.softmax_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.summary_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.tensor_buffer_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.trigonometric_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.unary_ops.cpp.inc" -, -#define GET_OP_LIST -#include "OneFlow/OneFlow.upsample_ops.cpp.inc" -*/ - // Group: ASSIGN // assign, assign_if, assign_if_not -// Total: 4 +// Total: 3 #ifdef GET_ONEFLOW_ASSIGN_OP_DEFINITIONS @@ -159,37 +50,7 @@ def OneFlow_AssignIfNotOp : OneFlow_BaseOp<"assign_if_not", [NoGrad, DeclareOpIn #ifdef GET_ONEFLOW_BASE_OP_DEFINITIONS -class OneFlow_NormalizationAddReluBaseOp : OneFlow_BaseOp<"normalization_add_relu", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$x, - Optional:$addend, - Optional:$moving_mean, - Optional:$moving_variance, - OneFlow_Tensor:$gamma, - OneFlow_Tensor:$beta - ); - let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$reserve_space, - Optional:$mean, - Optional:$inv_variance - ); - let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$epsilon, - DefaultValuedAttr:$training, - DefaultValuedAttr:$momentum - ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes, - I32ElementsAttr:$result_segment_sizes - ); - let has_logical_tensor_desc_infer_fn = 1; - let has_physical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; -} + class OneFlow_NormalizationBaseOp traits = []> : OneFlow_BaseOp])> { let input = (ins @@ -225,12 +86,12 @@ class OneFlow_NormalizationBaseOp traits = []> : On #endif // GET_ONEFLOW_BASE_OP_DEFINITIONS // Group: BINARY -// bias_add, cast_like, celu_grad, diag_grad, diagonal_grad, dot, dropout_grad, elementwise_maximum, elementwise_minimum, elu_grad, floordiv, gelu_grad, grid_sample, hardsigmoid_grad, hardshrink_grad, hardswish_grad, l1_l2_regularize_gradient, leaky_relu_grad, masked_fill, mish_grad, narrow_grad, pow, prelu, relu_grad, selu_grad, silu_grad, softshrink_grad, threshold_grad, tf_prelu, unfold_tensor_grad, xdivy, xlogy -// Total: 32 +// bias_add, cast_like, celu_grad, diag_grad, diagonal_grad, dot, dropout_grad, elementwise_maximum, elementwise_minimum, elu_grad, floordiv, truncdiv, gelu_grad, fast_gelu_grad, grid_sample, hardsigmoid_grad, hardshrink_grad, hardswish_grad, l1_l2_regularize_gradient, leaky_relu_grad, masked_fill, mish_grad, narrow_grad, pow, prelu, relu_grad, selu_grad, silu_grad, softshrink_grad, threshold_grad, tf_prelu, unfold_tensor_grad, xdivy, xlogy +// Total: 34 #ifdef GET_ONEFLOW_BINARY_OP_DEFINITIONS -def OneFlow_BiasAddOp : OneFlow_BaseOp<"bias_add", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { +def OneFlow_BiasAddOp : OneFlow_BaseOp<"bias_add", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$a, OneFlow_Tensor:$b @@ -404,6 +265,20 @@ def OneFlow_FloordivOp : OneFlow_BaseOp<"floordiv", [NoSideEffect, DeclareOpInte let has_data_type_infer_fn = 1; } +def OneFlow_TruncdivOp : OneFlow_BaseOp<"truncdiv", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$y + ); + let output = (outs + OneFlow_Tensor:$z + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_GeluGradOp : OneFlow_BaseOp<"gelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, @@ -418,6 +293,34 @@ def OneFlow_GeluGradOp : OneFlow_BaseOp<"gelu_grad", [NoSideEffect, DeclareOpInt let has_data_type_infer_fn = 1; } +def OneFlow_FastGeluGradOp : OneFlow_BaseOp<"fast_gelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_QuickGeluGradOp : OneFlow_BaseOp<"quick_gelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_GridSampleOp : OneFlow_BaseOp<"grid_sample", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$input, @@ -743,12 +646,12 @@ def OneFlow_XlogyOp : OneFlow_BaseOp<"xlogy", [NoSideEffect, DeclareOpInterfaceM #endif // GET_ONEFLOW_BINARY_OP_DEFINITIONS // Group: BROADCAST -// broadcast_add, broadcast_div, broadcast_div_grad, broadcast_equal, broadcast_floor_mod, broadcast_fmod, broadcast_greater, broadcast_greater_equal, broadcast_less, broadcast_less_equal, broadcast_like, broadcast_logical_and, broadcast_logical_or, broadcast_logical_xor, broadcast_maximum, broadcast_minimum, broadcast_mul, broadcast_not_equal, broadcast_pow, broadcast_pow_x_grad, broadcast_pow_y_grad, broadcast_sub +// broadcast_add, broadcast_div, broadcast_div_grad, broadcast_equal, broadcast_floor_mod, broadcast_fmod, broadcast_greater, broadcast_greater_equal, broadcast_less, broadcast_less_equal, broadcast_like, broadcast_logical_and, broadcast_logical_or, broadcast_logical_xor, broadcast_maximum, broadcast_minimum, broadcast_mul, broadcast_not_equal, broadcast_pow, broadcast_sub, broadcast_isclose_eq_nan, broadcast_isclose_neq_nan // Total: 22 #ifdef GET_ONEFLOW_BROADCAST_OP_DEFINITIONS -def OneFlow_BroadcastAddOp : OneFlow_BaseOp<"broadcast_add", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastAddOp : OneFlow_BaseOp<"broadcast_add", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -890,25 +793,25 @@ def OneFlow_BroadcastLessEqualOp : OneFlow_BaseOp<"broadcast_less_equal", [NoSid let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastLikeOp : OneFlow_BaseOp<"broadcast_like", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastIsCloseEqualNanOp : OneFlow_BaseOp<"broadcast_isclose_eq_nan", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$like + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$z ); let attrs = (ins - SI32ArrayAttr:$broadcast_axes + DefaultValuedAttr:$atol, + DefaultValuedAttr:$rtol ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_BroadcastLogicalAndOp : OneFlow_BaseOp<"broadcast_logical_and", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastIsCloseNotEqualNanOp : OneFlow_BaseOp<"broadcast_isclose_neq_nan", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -916,27 +819,35 @@ def OneFlow_BroadcastLogicalAndOp : OneFlow_BaseOp<"broadcast_logical_and", [NoS let output = (outs OneFlow_Tensor:$z ); + let attrs = (ins + DefaultValuedAttr:$atol, + DefaultValuedAttr:$rtol + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastLogicalOrOp : OneFlow_BaseOp<"broadcast_logical_or", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastLikeOp : OneFlow_BaseOp<"broadcast_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y + OneFlow_Tensor:$like ); let output = (outs - OneFlow_Tensor:$z + OneFlow_Tensor:$y + ); + let attrs = (ins + SI32ArrayAttr:$broadcast_axes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_BroadcastLogicalXorOp : OneFlow_BaseOp<"broadcast_logical_xor", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastLogicalAndOp : OneFlow_BaseOp<"broadcast_logical_and", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -950,7 +861,7 @@ def OneFlow_BroadcastLogicalXorOp : OneFlow_BaseOp<"broadcast_logical_xor", [NoS let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastMaximumOp : OneFlow_BaseOp<"broadcast_maximum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastLogicalOrOp : OneFlow_BaseOp<"broadcast_logical_or", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -964,7 +875,7 @@ def OneFlow_BroadcastMaximumOp : OneFlow_BaseOp<"broadcast_maximum", [NoSideEffe let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastMinimumOp : OneFlow_BaseOp<"broadcast_minimum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastLogicalXorOp : OneFlow_BaseOp<"broadcast_logical_xor", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -978,7 +889,7 @@ def OneFlow_BroadcastMinimumOp : OneFlow_BaseOp<"broadcast_minimum", [NoSideEffe let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastMulOp : OneFlow_BaseOp<"broadcast_mul", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastMaximumOp : OneFlow_BaseOp<"broadcast_maximum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -990,11 +901,9 @@ def OneFlow_BroadcastMulOp : OneFlow_BaseOp<"broadcast_mul", [NoSideEffect, Decl let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let hasCanonicalizer = 1; - let hasFolder = 1; } -def OneFlow_BroadcastNotEqualOp : OneFlow_BaseOp<"broadcast_not_equal", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastMinimumOp : OneFlow_BaseOp<"broadcast_minimum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -1008,7 +917,7 @@ def OneFlow_BroadcastNotEqualOp : OneFlow_BaseOp<"broadcast_not_equal", [NoSideE let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastPowOp : OneFlow_BaseOp<"broadcast_pow", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastMulOp : OneFlow_BaseOp<"broadcast_mul", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$y @@ -1020,17 +929,17 @@ def OneFlow_BroadcastPowOp : OneFlow_BaseOp<"broadcast_pow", [NoSideEffect, Decl let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let hasCanonicalizer = 1; + let hasFolder = 1; } -def OneFlow_BroadcastPowXGradOp : OneFlow_BaseOp<"broadcast_pow_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastNotEqualOp : OneFlow_BaseOp<"broadcast_not_equal", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$z, - OneFlow_Tensor:$dz + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$z ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -1038,15 +947,13 @@ def OneFlow_BroadcastPowXGradOp : OneFlow_BaseOp<"broadcast_pow_x_grad", [NoSide let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastPowYGradOp : OneFlow_BaseOp<"broadcast_pow_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastPowOp : OneFlow_BaseOp<"broadcast_pow", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$z, - OneFlow_Tensor:$dz + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$dy + OneFlow_Tensor:$z ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -1072,8 +979,8 @@ def OneFlow_BroadcastSubOp : OneFlow_BaseOp<"broadcast_sub", [NoSideEffect, Decl #endif // GET_ONEFLOW_BROADCAST_OP_DEFINITIONS // Group: CONV -// conv1d, conv2d, conv3d, conv_bias_grad, conv_data_grad, conv_filter_grad, deconv1d, deconv2d, deconv3d -// Total: 9 +// conv1d, conv2d, conv3d, conv_bias_grad, conv_data_grad, conv_filter_grad, deconv1d, deconv2d, deconv3d,deform_conv2d, deform_conv2d_input_grad,deform_conv2d_param_grad +// Total: 12 #ifdef GET_ONEFLOW_CONV_OP_DEFINITIONS @@ -1099,6 +1006,7 @@ def OneFlow_ConvBiasGradOp : OneFlow_BaseOp<"conv_bias_grad", [NoSideEffect, Dec let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } def OneFlow_ConvDataGradOp : OneFlow_BaseOp<"conv_data_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -1125,6 +1033,7 @@ def OneFlow_ConvDataGradOp : OneFlow_BaseOp<"conv_data_grad", [NoSideEffect, Dec let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } def OneFlow_ConvFilterGradOp : OneFlow_BaseOp<"conv_filter_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -1149,6 +1058,7 @@ def OneFlow_ConvFilterGradOp : OneFlow_BaseOp<"conv_filter_grad", [NoSideEffect, let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } def OneFlow_Deconv1DOp : OneFlow_BaseOp<"deconv1d", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -1225,6 +1135,100 @@ def OneFlow_Deconv3DOp : OneFlow_BaseOp<"deconv3d", [NoSideEffect, DeclareOpInte let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } +def OneFlow_DeformConv2dOp : OneFlow_BaseOp<"deform_conv2d", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + OneFlow_Tensor:$offset, + OneFlow_Tensor:$weight, + Optional:$bias, + OneFlow_Tensor:$mask + ); + let output = (outs + OneFlow_Tensor:$out + ); + + let attrs = (ins + DefaultValuedAttr:$stride_h, + DefaultValuedAttr:$stride_w, + DefaultValuedAttr:$pad_h, + DefaultValuedAttr:$pad_w, + DefaultValuedAttr:$dilation_h, + DefaultValuedAttr:$dilation_w, + DefaultValuedAttr:$groups, + DefaultValuedAttr:$offset_groups, + DefaultValuedAttr:$use_mask + ); + + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_DeformConv2dInputGradOp : OneFlow_BaseOp<"deform_conv2d_input_grad", [NoSideEffect, +DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$output_grad, + OneFlow_Tensor:$input, + OneFlow_Tensor:$offset, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$mask + ); + let output = (outs + OneFlow_Tensor:$input_grad, + //OneFlow_Tensor:$weight_grad, + OneFlow_Tensor:$offset_grad, + OneFlow_Tensor:$mask_grad + ); + + let attrs = (ins + DefaultValuedAttr:$stride_h, + DefaultValuedAttr:$stride_w, + DefaultValuedAttr:$pad_h, + DefaultValuedAttr:$pad_w, + DefaultValuedAttr:$dilation_h, + DefaultValuedAttr:$dilation_w, + DefaultValuedAttr:$groups, + DefaultValuedAttr:$offset_groups, + DefaultValuedAttr:$use_mask + ); + + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_DeformConv2dParamGradOp : OneFlow_BaseOp<"deform_conv2d_param_grad", [NoSideEffect, +DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$output_grad, + OneFlow_Tensor:$input, + OneFlow_Tensor:$offset, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$mask + ); + let output = (outs + OneFlow_Tensor:$weight_grad + ); + + let attrs = (ins + DefaultValuedAttr:$stride_h, + DefaultValuedAttr:$stride_w, + DefaultValuedAttr:$pad_h, + DefaultValuedAttr:$pad_w, + DefaultValuedAttr:$dilation_h, + DefaultValuedAttr:$dilation_w, + DefaultValuedAttr:$groups, + DefaultValuedAttr:$offset_groups, + DefaultValuedAttr:$use_mask + ); + + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} #endif // GET_ONEFLOW_CONV_OP_DEFINITIONS @@ -1342,6 +1346,25 @@ def OneFlow_BinaryCrossEntropyWithLogitsReduceMeanGradOp : OneFlow_BaseOp<"binar let has_data_type_infer_fn = 1; } +def OneFlow_FusedBCEReduceMeanFwBwOp : OneFlow_BaseOp<"fused_bce_reduce_mean_fw_bw", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + OneFlow_Tensor:$target + ); + let output = (outs + OneFlow_Tensor:$out, + OneFlow_Tensor:$dx + ); + let attrs = (ins + OneFlow_DataType:$out_dtype, + DefaultValuedAttr:$constant_value + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_SigmoidCrossEntropyOp : OneFlow_BaseOp<"sigmoid_cross_entropy", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$prediction, @@ -1488,7 +1511,7 @@ def OneFlow_NvtxStartOp : OneFlow_BaseOp<"nvtx_start", [NoSideEffect, DeclareOpI #endif // GET_ONEFLOW_CUDA_OP_DEFINITIONS // Group: DATASET -// COCOReader, OFRecordReader, OneRecReader, ctc_greedy_decoder, megatron_gpt_mmap_data_loader, ofrecord_bytes_decoder, ofrecord_image_classification_reader, ofrecord_image_decoder, ofrecord_image_decoder_random_crop, ofrecord_raw_decoder, onerec_decoder +// COCOReader, OFRecordReader, OneRecReader, ctc_greedy_decoder, megatron_gpt_mmap_data_loader, ofrecord_bytes_decoder, ofrecord_image_classification_reader, ofrecord_image_decoder, ofrecord_image_decoder_random_crop, ofrecord_raw_decoder, onerec_decoder, raw_reader // Total: 11 #ifdef GET_ONEFLOW_DATASET_OP_DEFINITIONS @@ -1545,6 +1568,8 @@ def OneFlow_OFRecordReaderOp : OneFlow_BaseOp<"OFRecordReader", [NoSideEffect, N let has_data_type_infer_fn = 1; let has_output_arg_modify_fn = 1; let has_nd_sbp_infer_fn = 1; + let has_get_nd_sbp_fn = 1; + let has_compute_complexity_fn = 1; } def OneFlow_OneRecReaderOp : OneFlow_BaseOp<"OneRecReader", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { @@ -1745,6 +1770,27 @@ def OneFlow_OnerecDecoderOp : OneFlow_BaseOp<"onerec_decoder", [NoSideEffect, No let has_output_arg_modify_fn = 1; } +def OneFlow_RawReaderOp : OneFlow_BaseOp<"raw_reader", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$files, + OneFlow_DataType:$data_type, + ShapeAttr:$shape, + SI64Attr:$batch_size, + SI64Attr:$shuffle_block_size, + DefaultValuedAttr:$random_shuffle, + DefaultValuedAttr:$seed, + StrArrayAttr:$nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_data_type_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + #endif // GET_ONEFLOW_DATASET_OP_DEFINITIONS // Group: DETECTION @@ -1924,7 +1970,7 @@ def OneFlow_TopKOp : OneFlow_BaseOp<"top_k", [NoSideEffect, NoGrad, DeclareOpInt #endif // GET_ONEFLOW_DETECTION_OP_DEFINITIONS // Group: EAGER -// eager_b_to_s, eager_naive_s_to_s, eager_nccl_all_gather, eager_nccl_all_reduce, eager_nccl_broadcast, eager_nccl_reduce, eager_nccl_reduce_scatter, eager_nccl_s2s, eager_p_to_b, eager_p_to_s, eager_s_to_b, eager_symmetric_s_to_p +// eager_b_to_s, eager_naive_s_to_s, eager_ccl_all_gather, eager_ccl_all_reduce, eager_ccl_broadcast, eager_ccl_reduce, eager_ccl_reduce_scatter, eager_nccl_s2s, eager_p_to_b, eager_p_to_s, eager_s_to_b, eager_symmetric_s_to_p // Total: 12 #ifdef GET_ONEFLOW_EAGER_OP_DEFINITIONS @@ -1972,7 +2018,7 @@ def OneFlow_EagerNaiveSToSOp : OneFlow_BaseOp<"eager_naive_s_to_s", [NoSideEffec let has_nd_sbp_infer_fn = 1; } -def OneFlow_EagerNcclAllGatherOp : OneFlow_BaseOp<"eager_nccl_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_EagerCclAllGatherOp : OneFlow_BaseOp<"eager_ccl_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -1980,7 +2026,9 @@ def OneFlow_EagerNcclAllGatherOp : OneFlow_BaseOp<"eager_nccl_all_gather", [NoSi OneFlow_Tensor:$out ); let attrs = (ins - StrAttr:$parallel_conf + StrAttr:$parallel_conf, + ShapeAttr:$output_shape, + OneFlow_DataType:$output_dtype ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -1990,7 +2038,7 @@ def OneFlow_EagerNcclAllGatherOp : OneFlow_BaseOp<"eager_nccl_all_gather", [NoSi let has_nd_sbp_infer_fn = 1; } -def OneFlow_EagerNcclAllReduceOp : OneFlow_BaseOp<"eager_nccl_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_EagerCclAllReduceOp : OneFlow_BaseOp<"eager_ccl_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -1998,8 +2046,7 @@ def OneFlow_EagerNcclAllReduceOp : OneFlow_BaseOp<"eager_nccl_all_reduce", [NoSi OneFlow_Tensor:$out ); let attrs = (ins - StrAttr:$parallel_conf, - DefaultValuedAttr:$async_launch + StrAttr:$parallel_conf ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2008,7 +2055,7 @@ def OneFlow_EagerNcclAllReduceOp : OneFlow_BaseOp<"eager_nccl_all_reduce", [NoSi let has_device_and_stream_infer_fn = 1; } -def OneFlow_EagerNcclBroadcastOp : OneFlow_BaseOp<"eager_nccl_broadcast", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_EagerCclBroadcastOp : OneFlow_BaseOp<"eager_ccl_broadcast", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -2017,6 +2064,7 @@ def OneFlow_EagerNcclBroadcastOp : OneFlow_BaseOp<"eager_nccl_broadcast", [NoSid ); let attrs = (ins StrAttr:$parallel_conf, + ShapeArrayAttr:$shape_list, DefaultValuedAttr:$root, DefaultValuedAttr:$async_launch ); @@ -2041,7 +2089,7 @@ def OneFlow_EagerNcclTouchOp : OneFlow_BaseOp<"eager_nccl_touch", [NoSideEffect, let has_device_and_stream_infer_fn = 1; } -def OneFlow_EagerNcclReduceOp : OneFlow_BaseOp<"eager_nccl_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_EagerCclReduceOp : OneFlow_BaseOp<"eager_ccl_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -2059,7 +2107,7 @@ def OneFlow_EagerNcclReduceOp : OneFlow_BaseOp<"eager_nccl_reduce", [NoSideEffec let has_device_and_stream_infer_fn = 1; } -def OneFlow_EagerNcclReduceScatterOp : OneFlow_BaseOp<"eager_nccl_reduce_scatter", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_EagerCclReduceScatterOp : OneFlow_BaseOp<"eager_ccl_reduce_scatter", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -2068,6 +2116,8 @@ def OneFlow_EagerNcclReduceScatterOp : OneFlow_BaseOp<"eager_nccl_reduce_scatter ); let attrs = (ins StrAttr:$parallel_conf, + ShapeAttr:$output_shape, + OneFlow_DataType:$output_dtype, DefaultValuedAttr:$op_type ); let has_logical_tensor_desc_infer_fn = 1; @@ -2202,50 +2252,136 @@ def OneFlow_EagerSymmetricSToPOp : OneFlow_BaseOp<"eager_symmetric_s_to_p", [NoS #endif // GET_ONEFLOW_EAGER_OP_DEFINITIONS // Group: FUSED -// cudnn_fused_normalization_add_relu, cudnn_fused_normalization_add_relu_grad, fused_bias_add_gelu, fused_bias_add_gelu_grad, fused_bias_add_mask_scale, fused_cast_scale, fused_scale_mask_softmax, fused_scale_mask_softmax_dropout, fused_scale_mask_softmax_dropout_grad, fused_scale_mask_softmax_grad, fused_scale_tril, fused_self_attention_query_mul_key_and_value, fused_self_attention_query_mul_key_and_value_grad, fused_tril_scale_softmax_mask_scale, fused_tril_scale_softmax_mask_scale_grad, normalization_add_relu_grad, fused_dot_feature_interaction, fused_dot_feature_interaction_grad, fused_cross_feature_interaction, fused_cross_feature_interaction_grad_v1, fused_cross_feature_interaction_grad_v2 -// Total: 21 +// cudnn_fused_normalization_add_relu, cudnn_fused_normalization_add_relu_grad, fused_bias_add_gelu, fused_bias_add_gelu_grad, fused_bias_add_mask_scale, fused_cast_scale, fused_scale_mask_softmax, fused_scale_mask_softmax_dropout, fused_scale_mask_softmax_dropout_grad, fused_scale_mask_softmax_grad, fused_scale_tril, fused_self_attention_query_mul_key_and_value, fused_self_attention_query_mul_key_and_value_grad, fused_tril_scale_softmax_mask_scale, fused_tril_scale_softmax_mask_scale_grad, normalization_add_relu_grad, fused_dot_feature_interaction, fused_dot_feature_interaction_grad, fused_cross_feature_interaction, fused_cross_feature_interaction_grad_v1, fused_cross_feature_interaction_grad_v2, fused_lstm_cell, fused_lstm_cell_grad, fused_gru_cell, fused_gru_cell_grad, fused_multi_head_attention_inference, fused_fast_gelu_mul, fused_fast_gelu_mul_grad, fused_get_boundding_boxes_coord, fused_get_boundding_boxes_coord_grad, fused_get_center_dist, fused_get_center_dist_grad, fused_get_ciou_result, fused_get_ciou_result_grad, fused_get_ciou_diagonal_angle, fused_get_ciou_diagonal_angle_grad, fused_get_intersection_area, fused_get_intersection_area_grad +// Total: 42 #ifdef GET_ONEFLOW_FUSED_OP_DEFINITIONS -def OneFlow_CudnnFusedNormalizationAddReluOp : OneFlow_BaseOp<"cudnn_fused_normalization_add_relu", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_FusedLstmCellOp : OneFlow_BaseOp<"fused_lstm_cell", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - Optional:$addend, - Optional:$moving_mean, - Optional:$moving_variance, - OneFlow_Tensor:$gamma, - OneFlow_Tensor:$beta + OneFlow_Tensor:$input_gates, + OneFlow_Tensor:$hidden_gates, + OneFlow_Tensor:$cx, + Optional:$input_bias, + Optional:$hidden_bias ); let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$reserve_space, - Optional:$mean, - Optional:$inv_variance - ); - let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$epsilon, - DefaultValuedAttr:$momentum + OneFlow_Tensor:$hy, + OneFlow_Tensor:$cy, + OneFlow_Tensor:$workspace ); let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes, - I32ElementsAttr:$result_segment_sizes + I32ElementsAttr:$operand_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_CudnnFusedNormalizationAddReluGradOp : OneFlow_BaseOp<"cudnn_fused_normalization_add_relu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedLstmCellGradOp : OneFlow_BaseOp<"fused_lstm_cell_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy, - OneFlow_Tensor:$mean, - OneFlow_Tensor:$inv_variance, - OneFlow_Tensor:$gamma, - OneFlow_Tensor:$beta, + OneFlow_Tensor:$grad_hy, + OneFlow_Tensor:$grad_cy, + OneFlow_Tensor:$cx, + OneFlow_Tensor:$cy, + OneFlow_Tensor:$workspace + ); + let output = (outs + OneFlow_Tensor:$grad_gates, + Optional:$grad_cx, + Optional:$grad_bias + ); + let trait_attrs = (ins + I32ElementsAttr:$result_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_FusedGruCellOp : OneFlow_BaseOp<"fused_gru_cell", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input_gates, + OneFlow_Tensor:$hidden_gates, + OneFlow_Tensor:$hx, + Optional:$input_bias, + Optional:$hidden_bias + ); + let output = (outs + OneFlow_Tensor:$hy, + OneFlow_Tensor:$workspace + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_FusedGruCellGradOp : OneFlow_BaseOp<"fused_gru_cell_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$grad_hy, + OneFlow_Tensor:$workspace + ); + let output = (outs + OneFlow_Tensor:$grad_input_gates, + OneFlow_Tensor:$grad_hidden_gates, + Optional:$grad_hx, + Optional:$grad_input_bias, + Optional:$grad_hidden_bias + ); + let trait_attrs = (ins + I32ElementsAttr:$result_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_CudnnFusedNormalizationAddReluOp : OneFlow_BaseOp<"cudnn_fused_normalization_add_relu", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$addend, + Optional:$moving_mean, + Optional:$moving_variance, + OneFlow_Tensor:$gamma, + OneFlow_Tensor:$beta + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$reserve_space, + Optional:$mean, + Optional:$inv_variance + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$momentum + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes, + I32ElementsAttr:$result_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_CudnnFusedNormalizationAddReluGradOp : OneFlow_BaseOp<"cudnn_fused_normalization_add_relu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance, + OneFlow_Tensor:$gamma, + OneFlow_Tensor:$beta, OneFlow_Tensor:$reserve_space, OneFlow_Tensor:$y ); @@ -2312,7 +2448,7 @@ def OneFlow_FusedBiasAddMaskScaleOp : OneFlow_BaseOp<"fused_bias_add_mask_scale" ); let attrs = (ins DefaultValuedAttr:$axis, - DefaultValuedAttr:$scale + DefaultValuedAttr:$scale ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2399,6 +2535,29 @@ def OneFlow_FusedScaleMaskSoftmaxDropoutGradOp : OneFlow_BaseOp<"fused_scale_mas let has_data_type_infer_fn = 1; } +def OneFlow_FusedBiasAddScaleMaskSoftmaxDropoutOp : OneFlow_BaseOp<"fused_bias_add_scale_mask_softmax_dropout", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$bias, + OneFlow_Tensor:$mask, + OneFlow_Tensor:$dropout_mask + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$softmax_y + ); + let attrs = (ins + DefaultValuedAttr:$scale_value, + DefaultValuedAttr:$mask_fill_value, + DefaultValuedAttr:$dropout_scale_value + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + def OneFlow_FusedScaleMaskSoftmaxGradOp : OneFlow_BaseOp<"fused_scale_mask_softmax_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$y, @@ -2544,10 +2703,14 @@ def OneFlow_NormalizationAddReluGradOp : OneFlow_BaseOp<"normalization_add_relu_ let has_data_type_infer_fn = 1; } + def OneFlow_FusedDotFeatureInteractionOp : OneFlow_BaseOp<"fused_dot_feature_interaction", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins Variadic:$features, - Optional:$output_concat + Optional:$output_concat, + Optional:$num_valid_sparse_feature, + Optional:$sparse_feature, + Optional:$sparse_indices ); let output = (outs OneFlow_Tensor:$out @@ -2564,14 +2727,18 @@ def OneFlow_FusedDotFeatureInteractionOp : OneFlow_BaseOp<"fused_dot_feature_int let has_data_type_infer_fn = 1; } -def OneFlow_FusedDotFeatureInteractionGradOp : OneFlow_BaseOp<"fused_dot_feature_interaction_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_FusedDotFeatureInteractionGradOp : OneFlow_BaseOp<"fused_dot_feature_interaction_grad", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$dy, - Variadic:$features + Variadic:$features, + Optional:$num_valid_sparse_feature, + Optional:$sparse_feature, + Optional:$sparse_indices ); let output = (outs Variadic:$features_grad, - Optional:$output_concat_grad + Optional:$output_concat_grad, + Optional:$sparse_feature_grad ); let attrs = (ins DefaultValuedAttr:$self_interaction, @@ -2592,7 +2759,7 @@ def OneFlow_FusedCrossFeatureInteractionOp : OneFlow_BaseOp<"fused_cross_feature OneFlow_Tensor:$x0 ); let output = (outs - OneFlow_Tensor:$out, + OneFlow_Tensor:$out, OneFlow_Tensor:$matmul_result ); let attrs = (ins @@ -2609,15 +2776,15 @@ def OneFlow_FusedCrossFeatureInteractionV1GradOp : OneFlow_BaseOp<"fused_cross_f let input = (ins OneFlow_Tensor:$dy, OneFlow_Tensor:$weight, - OneFlow_Tensor:$x0, - OneFlow_Tensor:$x, + OneFlow_Tensor:$x0, + OneFlow_Tensor:$x, OneFlow_Tensor:$matmul_result ); let output = (outs - OneFlow_Tensor:$dx0, - OneFlow_Tensor:$dw, - OneFlow_Tensor:$dx, - OneFlow_Tensor:$dbias + OneFlow_Tensor:$dx0, + OneFlow_Tensor:$dw, + OneFlow_Tensor:$dx, + OneFlow_Tensor:$dbias ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2630,15 +2797,15 @@ def OneFlow_FusedCrossFeatureInteractionV2GradOp : OneFlow_BaseOp<"fused_cross_f OneFlow_Tensor:$dy, OneFlow_Tensor:$weight, OneFlow_Tensor:$bias, - OneFlow_Tensor:$x0, - OneFlow_Tensor:$x, + OneFlow_Tensor:$x0, + OneFlow_Tensor:$x, OneFlow_Tensor:$matmul_result ); let output = (outs - OneFlow_Tensor:$dx0, - OneFlow_Tensor:$dw, - OneFlow_Tensor:$dx, - OneFlow_Tensor:$dbias + OneFlow_Tensor:$dx0, + OneFlow_Tensor:$dw, + OneFlow_Tensor:$dx, + OneFlow_Tensor:$dbias ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2646,59 +2813,35 @@ def OneFlow_FusedCrossFeatureInteractionV2GradOp : OneFlow_BaseOp<"fused_cross_f let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_FUSED_OP_DEFINITIONS - -// Group: IDEMPOTENT -// abs, ceil, floor, ones_like, relu, rint, round, sign -// Total: 8 - -#ifdef GET_ONEFLOW_IDEMPOTENT_OP_DEFINITIONS - -def OneFlow_AbsOp : OneFlow_IdempotentBaseOp<"abs", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_CeilOp : OneFlow_IdempotentBaseOp<"ceil", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_FloorOp : OneFlow_IdempotentBaseOp<"floor", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_OnesLikeOp : OneFlow_IdempotentBaseOp<"ones_like", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let same_output_regst_num = 1; - let has_nd_sbp_infer_fn = 1; - let input = (ins AnyType:$like); - let output = (outs AnyType:$out); -} - -def OneFlow_ReluOp : OneFlow_IdempotentBaseOp<"relu", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> {} - -def OneFlow_RintOp : OneFlow_IdempotentBaseOp<"rint", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_RoundOp : OneFlow_IdempotentBaseOp<"round", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_SignOp : OneFlow_IdempotentBaseOp<"sign", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -#endif // GET_ONEFLOW_IDEMPOTENT_OP_DEFINITIONS - -// Group: IDENTITY -// amp_white_identity, identity, identity_buffer, tuple_identity -// Total: 4 - -#ifdef GET_ONEFLOW_IDENTITY_OP_DEFINITIONS - -def OneFlow_AmpWhiteIdentityOp : OneFlow_BaseOp<"amp_white_identity", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedMultiHeadAttentionInferenceOp : OneFlow_BaseOp<"fused_multi_head_attention_inference", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$query, + OneFlow_Tensor:$key, + OneFlow_Tensor:$value ); let output = (outs OneFlow_Tensor:$out ); + let attrs = (ins + DefaultValuedAttr:$num_heads, + DefaultValuedAttr:$causal, + DefaultValuedAttr:$query_hidden_slice_start, + DefaultValuedAttr:$query_hidden_slice_end, + DefaultValuedAttr:$key_hidden_slice_start, + DefaultValuedAttr:$key_hidden_slice_end, + DefaultValuedAttr:$value_hidden_slice_start, + DefaultValuedAttr:$value_hidden_slice_end + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_IdentityOp : OneFlow_BaseOp<"identity", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedFastGeluMulOp : OneFlow_BaseOp<"fused_fast_gelu_mul", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$in, + OneFlow_Tensor:$multiplier ); let output = (outs OneFlow_Tensor:$out @@ -2709,15 +2852,15 @@ def OneFlow_IdentityOp : OneFlow_BaseOp<"identity", [NoSideEffect, DeclareOpInte let has_data_type_infer_fn = 1; } -def OneFlow_IdentityBufferOp : OneFlow_BaseOp<"identity_buffer", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_FusedFastGeluMulGradOp : OneFlow_BaseOp<"fused_fast_gelu_mul_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$out_diff, + OneFlow_Tensor:$in, + OneFlow_Tensor:$multiplier ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - DefaultValuedAttr:$buffer_size + OneFlow_Tensor:$in_diff, + OneFlow_Tensor:$multiplier_diff ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2725,75 +2868,92 @@ def OneFlow_IdentityBufferOp : OneFlow_BaseOp<"identity_buffer", [NoSideEffect, let has_data_type_infer_fn = 1; } -def OneFlow_TupleIdentityOp : OneFlow_BaseOp<"tuple_identity", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetBounddingBoxesCoordOp : OneFlow_BaseOp<"fused_get_boundding_boxes_coord", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$in + OneFlow_Tensor:$x1, + OneFlow_Tensor:$y1, + OneFlow_Tensor:$w1, + OneFlow_Tensor:$h1, + OneFlow_Tensor:$x2, + OneFlow_Tensor:$y2, + OneFlow_Tensor:$w2, + OneFlow_Tensor:$h2 ); let output = (outs - Variadic:$out + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2 ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_sbp_signature_infer_fn = 1; } -#endif // GET_ONEFLOW_IDENTITY_OP_DEFINITIONS - -// Group: IMAGE -// image_batch_align, image_decode, image_flip, image_random_crop, image_resize_keep_aspect_ratio, image_resize_to_fixed -// Total: 6 - -#ifdef GET_ONEFLOW_IMAGE_OP_DEFINITIONS - -def OneFlow_ImageBatchAlignOp : OneFlow_BaseOp<"image_batch_align", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetBounddingBoxesCoordGradOp : OneFlow_BaseOp<"fused_get_boundding_boxes_coord_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$b1_x1_diff, + OneFlow_Tensor:$b1_x2_diff, + OneFlow_Tensor:$b1_y1_diff, + OneFlow_Tensor:$b1_y2_diff, + OneFlow_Tensor:$b2_x1_diff, + OneFlow_Tensor:$b2_x2_diff, + OneFlow_Tensor:$b2_y1_diff, + OneFlow_Tensor:$b2_y2_diff ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - ShapeAttr:$shape, - OneFlow_DataType:$data_type, - DefaultValuedAttr:$alignment, - DefaultValuedAttr:$dynamic_out + OneFlow_Tensor:$x1_diff, + OneFlow_Tensor:$y1_diff, + OneFlow_Tensor:$w1_diff, + OneFlow_Tensor:$h1_diff, + OneFlow_Tensor:$x2_diff, + OneFlow_Tensor:$y2_diff, + OneFlow_Tensor:$w2_diff, + OneFlow_Tensor:$h2_diff ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_output_arg_modify_fn = 1; } -def OneFlow_ImageDecodeOp : OneFlow_BaseOp<"image_decode", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetCiouResultOp : OneFlow_BaseOp<"fused_get_ciou_result", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$v, + OneFlow_Tensor:$iou, + OneFlow_Tensor:$rho2, + OneFlow_Tensor:$c2 ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$y, + OneFlow_Tensor:$alpha ); let attrs = (ins - DefaultValuedAttr:$color_space, - OneFlow_DataType:$data_type + F32Attr: $eps ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ImageFlipOp : OneFlow_BaseOp<"image_flip", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetCiouResultGradOp : OneFlow_BaseOp<"fused_get_ciou_result_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in, - OneFlow_Tensor:$flip_code + OneFlow_Tensor:$dy, + OneFlow_Tensor:$alpha, + OneFlow_Tensor:$rho2, + OneFlow_Tensor:$c2 ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$dv, + OneFlow_Tensor:$diou, + OneFlow_Tensor:$drho2, + OneFlow_Tensor:$dc2 ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2801,103 +2961,112 @@ def OneFlow_ImageFlipOp : OneFlow_BaseOp<"image_flip", [NoSideEffect, NoGrad, Cp let has_data_type_infer_fn = 1; } -def OneFlow_ImageRandomCropOp : OneFlow_BaseOp<"image_random_crop", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetIouOp : OneFlow_BaseOp<"fused_get_iou", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$w1, + OneFlow_Tensor:$h1, + OneFlow_Tensor:$w2, + OneFlow_Tensor:$h2, + OneFlow_Tensor:$inter ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$iou ); let attrs = (ins - DefaultValuedAttr:$num_attempts, - DefaultValuedAttr:$seed, - DefaultValuedAttr:$has_seed, - F32ArrayAttr:$random_area, - F32ArrayAttr:$random_aspect_ratio + F32Attr: $eps ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_ImageResizeKeepAspectRatioOp : OneFlow_BaseOp<"image_resize_keep_aspect_ratio", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetIouGradOp : OneFlow_BaseOp<"fused_get_iou_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$diou, + OneFlow_Tensor:$w1, + OneFlow_Tensor:$h1, + OneFlow_Tensor:$w2, + OneFlow_Tensor:$h2, + OneFlow_Tensor:$inter + ); + let attrs = (ins + F32Attr: $eps ); let output = (outs - OneFlow_Tensor:$out, - OneFlow_Tensor:$size, - OneFlow_Tensor:$scale + OneFlow_Tensor:$dw1, + OneFlow_Tensor:$dh1, + OneFlow_Tensor:$dinter ); let attrs = (ins - DefaultValuedAttr:$target_size, - DefaultValuedAttr:$min_size, - DefaultValuedAttr:$max_size, - DefaultValuedAttr:$resize_longer, - DefaultValuedAttr:$interpolation_type + F32Attr: $eps ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ImageResizeToFixedOp : OneFlow_BaseOp<"image_resize_to_fixed", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_FusedCenterOp : OneFlow_BaseOp<"fused_get_center_dist", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2 ); let output = (outs - OneFlow_Tensor:$out, - OneFlow_Tensor:$scale - ); - let attrs = (ins - DefaultValuedAttr:$target_width, - DefaultValuedAttr:$target_height, - DefaultValuedAttr:$channels, - OneFlow_DataType:$data_type, - DefaultValuedAttr:$interpolation_type + OneFlow_Tensor:$rho2 ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_IMAGE_OP_DEFINITIONS - -// Group: INDICES -// arg_sort, argmax, argwhere, batch_gather, dim_gather, dim_scatter_add, dim_scatter_add_like, dim_scatter_add_scalar, dim_scatter_mul, dim_scatter_mul_scalar, dim_scatter_update, dim_scatter_update_scalar, embedding_renorm, embedding, embedding_grad, gather, gather_nd, generate_random_batch_permutation_indices, image_target_resize, scatter_nd, scatter_nd_like, slice, slice_update, slice_grad, tensor_scatter_nd_add, tensor_scatter_nd_update, unsorted_batch_segment_sum, unsorted_segment_sum, unsorted_segment_sum_like, where, where_scalar_x, where_scalar_xy, where_scalar_y, median, searchsorted, searchsorted_scalar -// Total: 36 - -#ifdef GET_ONEFLOW_INDICES_OP_DEFINITIONS - -def OneFlow_ArgSortOp : OneFlow_BaseOp<"arg_sort", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_FusedCenterGradOp : OneFlow_BaseOp<"fused_get_center_dist_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2, + OneFlow_Tensor:$rho2_diff ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrAttr:$direction + OneFlow_Tensor:$b1_x1_diff, + OneFlow_Tensor:$b1_x2_diff, + OneFlow_Tensor:$b2_x1_diff, + OneFlow_Tensor:$b2_x2_diff, + OneFlow_Tensor:$b1_y1_diff, + OneFlow_Tensor:$b1_y2_diff, + OneFlow_Tensor:$b2_y1_diff, + OneFlow_Tensor:$b2_y2_diff ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ArgmaxOp : OneFlow_BaseOp<"argmax", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetCiouDiagonalAngleOp : OneFlow_BaseOp<"fused_get_ciou_diagonal_angle", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$w1, + OneFlow_Tensor:$h1, + OneFlow_Tensor:$w2, + OneFlow_Tensor:$h2 ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$v + ); + let attrs = (ins + DefaultValuedAttr:$eps ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2905,16 +3074,22 @@ def OneFlow_ArgmaxOp : OneFlow_BaseOp<"argmax", [NoSideEffect, NoGrad, DeclareOp let has_data_type_infer_fn = 1; } -def OneFlow_ArgwhereOp : OneFlow_BaseOp<"argwhere", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetCiouDiagonalAngleGradOp : OneFlow_BaseOp<"fused_get_ciou_diagonal_angle_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input + OneFlow_Tensor:$w1, + OneFlow_Tensor:$h1, + OneFlow_Tensor:$w2, + OneFlow_Tensor:$h2, + OneFlow_Tensor:$v_diff ); let output = (outs - OneFlow_Tensor:$output, - OneFlow_Tensor:$output_size + OneFlow_Tensor:$w1_diff, + OneFlow_Tensor:$h1_diff, + OneFlow_Tensor:$w2_diff, + OneFlow_Tensor:$h2_diff ); let attrs = (ins - OneFlow_DataType:$dtype + DefaultValuedAttr:$eps ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -2922,255 +3097,303 @@ def OneFlow_ArgwhereOp : OneFlow_BaseOp<"argwhere", [NoSideEffect, NoGrad, Decla let has_data_type_infer_fn = 1; } -def OneFlow_BatchGatherOp : OneFlow_BaseOp<"batch_gather", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetIntersectionAreaOp : OneFlow_BaseOp<"fused_get_intersection_area", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in, - OneFlow_Tensor:$indices + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2 ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$inter ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimGatherOp : OneFlow_BaseOp<"dim_gather", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetIntersectionAreaGradOp : OneFlow_BaseOp<"fused_get_intersection_area_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2, + OneFlow_Tensor:$inter_diff ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - DefaultValuedAttr:$dim + OneFlow_Tensor:$b1_x1_diff, + OneFlow_Tensor:$b1_x2_diff, + OneFlow_Tensor:$b2_x1_diff, + OneFlow_Tensor:$b2_x2_diff, + OneFlow_Tensor:$b1_y1_diff, + OneFlow_Tensor:$b1_y2_diff, + OneFlow_Tensor:$b2_y1_diff, + OneFlow_Tensor:$b2_y2_diff ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterAddOp : OneFlow_BaseOp<"dim_scatter_add", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetConvexDiagonalSquaredOp : OneFlow_BaseOp<"fused_get_convex_diagonal_squared", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index, - OneFlow_Tensor:$src + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2 ); let output = (outs - OneFlow_Tensor:$output + OneFlow_Tensor:$c2 ); let attrs = (ins - DefaultValuedAttr:$dim + DefaultValuedAttr:$eps ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterAddLikeOp : OneFlow_BaseOp<"dim_scatter_add_like", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedGetConvexDiagonalSquaredGradOp : OneFlow_BaseOp<"fused_get_convex_diagonal_squared_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$like, - OneFlow_Tensor:$index, - OneFlow_Tensor:$src + OneFlow_Tensor:$c2_diff, + OneFlow_Tensor:$b1_x1, + OneFlow_Tensor:$b1_x2, + OneFlow_Tensor:$b2_x1, + OneFlow_Tensor:$b2_x2, + OneFlow_Tensor:$b1_y1, + OneFlow_Tensor:$b1_y2, + OneFlow_Tensor:$b2_y1, + OneFlow_Tensor:$b2_y2 ); let output = (outs - OneFlow_Tensor:$output + OneFlow_Tensor:$b1_x1_diff, + OneFlow_Tensor:$b1_x2_diff, + OneFlow_Tensor:$b2_x1_diff, + OneFlow_Tensor:$b2_x2_diff, + OneFlow_Tensor:$b1_y1_diff, + OneFlow_Tensor:$b1_y2_diff, + OneFlow_Tensor:$b2_y1_diff, + OneFlow_Tensor:$b2_y2_diff ); let attrs = (ins - DefaultValuedAttr:$dim + DefaultValuedAttr:$eps ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterAddScalarOp : OneFlow_BaseOp<"dim_scatter_add_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_FUSED_OP_DEFINITIONS + +// Group: IDEMPOTENT +// abs, ceil, floor, ones_like, relu, rint, round, sign +// Total: 8 + +#ifdef GET_ONEFLOW_IDEMPOTENT_OP_DEFINITIONS + +def OneFlow_AbsOp : OneFlow_IdempotentBaseOp<"abs", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_CeilOp : OneFlow_IdempotentBaseOp<"ceil", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_FloorOp : OneFlow_IdempotentBaseOp<"floor", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_OnesLikeOp : OneFlow_IdempotentBaseOp<"ones_like", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let same_output_regst_num = 1; + let has_nd_sbp_infer_fn = 1; + let input = (ins AnyType:$like); + let output = (outs AnyType:$out); +} + +def OneFlow_ReluOp : OneFlow_IdempotentBaseOp<"relu", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> {} + +def OneFlow_RintOp : OneFlow_IdempotentBaseOp<"rint", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_RoundOp : OneFlow_IdempotentBaseOp<"round", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_SignOp : OneFlow_IdempotentBaseOp<"sign", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +#endif // GET_ONEFLOW_IDEMPOTENT_OP_DEFINITIONS + +// Group: IDENTITY +// amp_white_identity, amp_black_identity, identity, identity_buffer, tuple_identity, pinned_identity +// Total: 5 + +#ifdef GET_ONEFLOW_IDENTITY_OP_DEFINITIONS + +def OneFlow_AmpWhiteIdentityOp : OneFlow_BaseOp<"amp_white_identity", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - DefaultValuedAttr:$src_scalar, - DefaultValuedAttr:$dim + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterMulOp : OneFlow_BaseOp<"dim_scatter_mul", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_AmpBlackIdentityOp : OneFlow_BaseOp<"amp_black_identity", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index, - OneFlow_Tensor:$src + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - DefaultValuedAttr:$dim + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; - let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterMulScalarOp : OneFlow_BaseOp<"dim_scatter_mul_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_IdentityOp : OneFlow_BaseOp<"identity", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - DefaultValuedAttr:$src_scalar, - DefaultValuedAttr:$dim + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterUpdateOp : OneFlow_BaseOp<"dim_scatter_update", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_IdentityBufferOp : OneFlow_BaseOp<"identity_buffer", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index, - OneFlow_Tensor:$src + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$output + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$dim + DefaultValuedAttr:$buffer_size ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DimScatterUpdateScalarOp : OneFlow_BaseOp<"dim_scatter_update_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_TupleIdentityOp : OneFlow_BaseOp<"tuple_identity", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$index + Variadic:$in ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - DefaultValuedAttr:$src_scalar, - DefaultValuedAttr:$dim + Variadic:$out ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; + let has_sbp_signature_infer_fn = 1; } -def OneFlow_EmbeddingRenormOp : OneFlow_BaseOp<"embedding_renorm", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_PinnedIdentityOp : OneFlow_BaseOp<"pinned_identity", [DeclareOpInterfaceMethods]> { + let summary = "mark defining op of operand can't be erased"; let input = (ins - OneFlow_Tensor:$in, - OneFlow_Tensor:$indices + OneFlow_Tensor:$in ); let output = (outs OneFlow_Tensor:$out ); - let attrs = (ins - DefaultValuedAttr:$max_norm, - DefaultValuedAttr:$norm_type - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_EmbeddingOp : OneFlow_BaseOp<"embedding", [NoSideEffect, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_IDENTITY_OP_DEFINITIONS + +// Group: IMAGE +// image_batch_align, image_decode, image_flip, image_random_crop, image_resize_keep_aspect_ratio, image_resize_to_fixed +// Total: 6 + +#ifdef GET_ONEFLOW_IMAGE_OP_DEFINITIONS + +def OneFlow_ImageBatchAlignOp : OneFlow_BaseOp<"image_batch_align", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$weight, - OneFlow_Tensor:$indices + OneFlow_Tensor:$in ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$padding_idx, - DefaultValuedAttr:$scale_grad_by_freq + ShapeAttr:$shape, + OneFlow_DataType:$data_type, + DefaultValuedAttr:$alignment, + DefaultValuedAttr:$dynamic_out ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; + let has_output_arg_modify_fn = 1; } -def OneFlow_EmbeddingGradOp : OneFlow_BaseOp<"embedding_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ImageDecodeOp : OneFlow_BaseOp<"image_decode", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$weight, - OneFlow_Tensor:$indices + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$padding_idx, - DefaultValuedAttr:$scale_grad_by_freq + DefaultValuedAttr:$color_space, + OneFlow_DataType:$data_type ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_GatherOp : OneFlow_BaseOp<"gather", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ImageFlipOp : OneFlow_BaseOp<"image_flip", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in, - OneFlow_Tensor:$indices + OneFlow_Tensor:$flip_code ); let output = (outs OneFlow_Tensor:$out ); - let attrs = (ins - DefaultValuedAttr:$axis - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_GatherNdOp : OneFlow_BaseOp<"gather_nd", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ImageRandomCropOp : OneFlow_BaseOp<"image_random_crop", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$params, - OneFlow_Tensor:$indices + OneFlow_Tensor:$in ); let output = (outs OneFlow_Tensor:$out ); + let attrs = (ins + DefaultValuedAttr:$num_attempts, + DefaultValuedAttr:$seed, + DefaultValuedAttr:$has_seed, + F32ArrayAttr:$random_area, + F32ArrayAttr:$random_aspect_ratio + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -3178,34 +3401,43 @@ def OneFlow_GatherNdOp : OneFlow_BaseOp<"gather_nd", [NoSideEffect, DeclareOpInt let has_input_arg_modify_fn = 1; } -def OneFlow_GenerateRandomBatchPermutationIndicesOp : OneFlow_BaseOp<"generate_random_batch_permutation_indices", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_ImageResizeKeepAspectRatioOp : OneFlow_BaseOp<"image_resize_keep_aspect_ratio", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$out, + OneFlow_Tensor:$size, + OneFlow_Tensor:$scale ); let attrs = (ins - DefaultValuedAttr:$seed + DefaultValuedAttr:$target_size, + DefaultValuedAttr:$min_size, + DefaultValuedAttr:$max_size, + DefaultValuedAttr:$resize_longer, + DefaultValuedAttr:$interpolation_type ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ImageTargetResizeOp : OneFlow_BaseOp<"image_target_resize", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_ImageResizeToFixedOp : OneFlow_BaseOp<"image_resize_to_fixed", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); let output = (outs OneFlow_Tensor:$out, - OneFlow_Tensor:$size, OneFlow_Tensor:$scale ); let attrs = (ins - DefaultValuedAttr:$target_size, - DefaultValuedAttr:$max_size + DefaultValuedAttr:$target_width, + DefaultValuedAttr:$target_height, + DefaultValuedAttr:$channels, + OneFlow_DataType:$data_type, + DefaultValuedAttr:$interpolation_type ); let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -3214,36 +3446,37 @@ def OneFlow_ImageTargetResizeOp : OneFlow_BaseOp<"image_target_resize", [NoSideE let has_data_type_infer_fn = 1; } -def OneFlow_SliceOp : OneFlow_BaseOp<"slice", [NoSideEffect, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_IMAGE_OP_DEFINITIONS + +// Group: INDICES +// arg_sort, argmax, argwhere, batch_gather, dim_gather, dim_scatter_add, dim_scatter_add_like, dim_scatter_add_scalar, dim_scatter_mul, dim_scatter_mul_scalar, dim_scatter_update, dim_scatter_update_scalar, embedding_renorm, embedding, embedding_grad, gather, gather_nd, generate_random_batch_permutation_indices, image_target_resize, scatter_nd, scatter_nd_like, slice, slice_update, slice_grad, tensor_scatter_nd_add, tensor_scatter_nd_update, unsorted_batch_segment_sum, unsorted_segment_sum, unsorted_segment_sum_like, where, where_scalar_x, where_scalar_xy, where_scalar_y, median, searchsorted, searchsorted_scalar +// Total: 36 + +#ifdef GET_ONEFLOW_INDICES_OP_DEFINITIONS + +def OneFlow_ArgSortOp : OneFlow_BaseOp<"arg_sort", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$out ); let attrs = (ins - SI64ArrayAttr:$start, - SI64ArrayAttr:$stop, - SI64ArrayAttr:$step + StrAttr:$direction ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_SliceUpdateOp : OneFlow_BaseOp<"slice_update", [DeclareOpInterfaceMethods]> { +def OneFlow_ArgmaxOp : OneFlow_BaseOp<"argmax", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$ref, - OneFlow_Tensor:$value + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$y - ); - let attrs = (ins - SI64ArrayAttr:$start, - SI64ArrayAttr:$stop, - SI64ArrayAttr:$step + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3251,37 +3484,31 @@ def OneFlow_SliceUpdateOp : OneFlow_BaseOp<"slice_update", [DeclareOpInterfaceMe let has_data_type_infer_fn = 1; } -def OneFlow_SliceGradOp : OneFlow_BaseOp<"slice_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ArgwhereOp : OneFlow_BaseOp<"argwhere", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy + OneFlow_Tensor:$input ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$output, + OneFlow_Tensor:$output_size ); let attrs = (ins - ShapeAttr:$like_shape, - SI64ArrayAttr:$start, - SI64ArrayAttr:$stop, - SI64ArrayAttr:$step + OneFlow_DataType:$dtype ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_ScatterNdOp : OneFlow_BaseOp<"scatter_nd", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BatchGatherOp : OneFlow_BaseOp<"batch_gather", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$indices, - OneFlow_Tensor:$updates + OneFlow_Tensor:$in, + OneFlow_Tensor:$indices ); let output = (outs OneFlow_Tensor:$out ); - let attrs = (ins - ShapeAttr:$shape - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -3289,29 +3516,35 @@ def OneFlow_ScatterNdOp : OneFlow_BaseOp<"scatter_nd", [NoSideEffect, DeclareOpI let has_input_arg_modify_fn = 1; } -def OneFlow_ScatterNdLikeOp : OneFlow_BaseOp<"scatter_nd_like", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimGatherOp : OneFlow_BaseOp<"dim_gather", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$like, - OneFlow_Tensor:$indices, - OneFlow_Tensor:$updates + OneFlow_Tensor:$input, + OneFlow_Tensor:$index ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output + ); + let attrs = (ins + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_TensorScatterNdAddOp : OneFlow_BaseOp<"tensor_scatter_nd_add", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterAddOp : OneFlow_BaseOp<"dim_scatter_add", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$params, - OneFlow_Tensor:$updates, - OneFlow_Tensor:$indices + OneFlow_Tensor:$input, + OneFlow_Tensor:$index, + OneFlow_Tensor:$src ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output + ); + let attrs = (ins + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3320,14 +3553,17 @@ def OneFlow_TensorScatterNdAddOp : OneFlow_BaseOp<"tensor_scatter_nd_add", [NoSi let has_input_arg_modify_fn = 1; } -def OneFlow_TensorScatterNdUpdateOp : OneFlow_BaseOp<"tensor_scatter_nd_update", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterAddLikeOp : OneFlow_BaseOp<"dim_scatter_add_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$params, - OneFlow_Tensor:$updates, - OneFlow_Tensor:$indices + OneFlow_Tensor:$like, + OneFlow_Tensor:$index, + OneFlow_Tensor:$src ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output + ); + let attrs = (ins + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3336,16 +3572,17 @@ def OneFlow_TensorScatterNdUpdateOp : OneFlow_BaseOp<"tensor_scatter_nd_update", let has_input_arg_modify_fn = 1; } -def OneFlow_UnsortedBatchSegmentSumOp : OneFlow_BaseOp<"unsorted_batch_segment_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterAddScalarOp : OneFlow_BaseOp<"dim_scatter_add_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$data, - OneFlow_Tensor:$segment_ids + OneFlow_Tensor:$input, + OneFlow_Tensor:$index ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output ); let attrs = (ins - DefaultValuedAttr:$num_segments + DefaultValuedAttr:$src_scalar, + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3354,17 +3591,17 @@ def OneFlow_UnsortedBatchSegmentSumOp : OneFlow_BaseOp<"unsorted_batch_segment_s let has_input_arg_modify_fn = 1; } -def OneFlow_UnsortedSegmentSumOp : OneFlow_BaseOp<"unsorted_segment_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterMulOp : OneFlow_BaseOp<"dim_scatter_mul", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$data, - OneFlow_Tensor:$segment_ids + OneFlow_Tensor:$input, + OneFlow_Tensor:$index, + OneFlow_Tensor:$src ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$num_segments + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3373,17 +3610,17 @@ def OneFlow_UnsortedSegmentSumOp : OneFlow_BaseOp<"unsorted_segment_sum", [NoSid let has_input_arg_modify_fn = 1; } -def OneFlow_UnsortedSegmentSumLikeOp : OneFlow_BaseOp<"unsorted_segment_sum_like", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterMulScalarOp : OneFlow_BaseOp<"dim_scatter_mul_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$data, - OneFlow_Tensor:$segment_ids, - OneFlow_Tensor:$like + OneFlow_Tensor:$input, + OneFlow_Tensor:$index ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output ); let attrs = (ins - DefaultValuedAttr:$axis + DefaultValuedAttr:$src_scalar, + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3392,14 +3629,17 @@ def OneFlow_UnsortedSegmentSumLikeOp : OneFlow_BaseOp<"unsorted_segment_sum_like let has_input_arg_modify_fn = 1; } -def OneFlow_WhereOp : OneFlow_BaseOp<"where", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterUpdateOp : OneFlow_BaseOp<"dim_scatter_update", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$condition, - OneFlow_Tensor:$x, - OneFlow_Tensor:$y + OneFlow_Tensor:$input, + OneFlow_Tensor:$index, + OneFlow_Tensor:$src ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output + ); + let attrs = (ins + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3408,21 +3648,17 @@ def OneFlow_WhereOp : OneFlow_BaseOp<"where", [NoSideEffect, DeclareOpInterfaceM let has_input_arg_modify_fn = 1; } -def OneFlow_WhereScalarXOp : OneFlow_BaseOp<"where_scalar_x", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DimScatterUpdateScalarOp : OneFlow_BaseOp<"dim_scatter_update_scalar", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$condition, - OneFlow_Tensor:$y + OneFlow_Tensor:$input, + OneFlow_Tensor:$index ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output ); let attrs = (ins - DefaultValuedAttr:$has_int_operand, - DefaultValuedAttr:$has_float_operand, - DefaultValuedAttr:$has_bool_operand, - DefaultValuedAttr:$int_operand, - DefaultValuedAttr:$float_operand, - DefaultValuedAttr:$bool_operand + DefaultValuedAttr:$src_scalar, + DefaultValuedAttr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3431,49 +3667,35 @@ def OneFlow_WhereScalarXOp : OneFlow_BaseOp<"where_scalar_x", [NoSideEffect, Dec let has_input_arg_modify_fn = 1; } -def OneFlow_WhereScalarXyOp : OneFlow_BaseOp<"where_scalar_xy", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_EmbeddingRenormOp : OneFlow_BaseOp<"embedding_renorm", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$condition + OneFlow_Tensor:$in, + OneFlow_Tensor:$indices ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$has_x_int_operand, - DefaultValuedAttr:$has_x_float_operand, - DefaultValuedAttr:$has_y_int_operand, - DefaultValuedAttr:$has_y_float_operand, - DefaultValuedAttr:$has_x_bool_operand, - DefaultValuedAttr:$has_y_bool_operand, - DefaultValuedAttr:$x_int_operand, - DefaultValuedAttr:$x_float_operand, - DefaultValuedAttr:$x_bool_operand, - DefaultValuedAttr:$y_int_operand, - DefaultValuedAttr:$y_float_operand, - DefaultValuedAttr:$y_bool_operand + DefaultValuedAttr:$max_norm, + DefaultValuedAttr:$norm_type ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_WhereScalarYOp : OneFlow_BaseOp<"where_scalar_y", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_EmbeddingOp : OneFlow_BaseOp<"embedding", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$condition, - OneFlow_Tensor:$x + OneFlow_Tensor:$weight, + OneFlow_Tensor:$indices ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$has_int_operand, - DefaultValuedAttr:$has_float_operand, - DefaultValuedAttr:$has_bool_operand, - DefaultValuedAttr:$int_operand, - DefaultValuedAttr:$float_operand, - DefaultValuedAttr:$bool_operand + DefaultValuedAttr:$padding_idx, + DefaultValuedAttr:$scale_grad_by_freq ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3482,127 +3704,106 @@ def OneFlow_WhereScalarYOp : OneFlow_BaseOp<"where_scalar_y", [NoSideEffect, Dec let has_input_arg_modify_fn = 1; } -def OneFlow_MedianOp : OneFlow_BaseOp<"median", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_EmbeddingGradOp : OneFlow_BaseOp<"embedding_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input + OneFlow_Tensor:$dy, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$indices ); let output = (outs - OneFlow_Tensor:$output + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$padding_idx, + DefaultValuedAttr:$scale_grad_by_freq ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_MedianWithIndicesOp : OneFlow_BaseOp<"median_with_indices", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_GatherOp : OneFlow_BaseOp<"gather", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input + OneFlow_Tensor:$in, + OneFlow_Tensor:$indices ); let output = (outs - OneFlow_Tensor:$values, - OneFlow_Tensor:$indices + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_SearchSortedOp : OneFlow_BaseOp<"searchsorted", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_GatherNdOp : OneFlow_BaseOp<"gather_nd", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$sorted_sequence, - OneFlow_Tensor:$values + OneFlow_Tensor:$params, + OneFlow_Tensor:$indices ); let output = (outs OneFlow_Tensor:$out ); - let attrs = (ins - DefaultValuedAttr:$out_int32, - DefaultValuedAttr:$right - ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_SearchSortedScalarOp : OneFlow_BaseOp<"searchsorted_scalar", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_GenerateRandomBatchPermutationIndicesOp : OneFlow_BaseOp<"generate_random_batch_permutation_indices", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$sorted_sequence + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$out_int32, - DefaultValuedAttr:$right, - DefaultValuedAttr:$values + DefaultValuedAttr:$seed ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_INDICES_OP_DEFINITIONS - -// Group: INVOLUTION -// negative, reciprocal -// Total: 2 - -#ifdef GET_ONEFLOW_INVOLUTION_OP_DEFINITIONS - -def OneFlow_NegativeOp : OneFlow_InvolutionBaseOp<"negative", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -def OneFlow_ReciprocalOp : OneFlow_InvolutionBaseOp<"reciprocal", [NoSideEffect, DeclareOpInterfaceMethods]> {} - -#endif // GET_ONEFLOW_INVOLUTION_OP_DEFINITIONS - -// Group: LOSS -// combined_margin_loss, combined_margin_loss_grad, ctc_loss, ctc_loss_grad, dynamic_loss_scale_schedule, kl_div_loss, kl_div_loss_grad, smooth_l1_loss, smooth_l1_loss_grad -// Total: 9 - -#ifdef GET_ONEFLOW_LOSS_OP_DEFINITIONS - -def OneFlow_CombinedMarginLossOp : OneFlow_BaseOp<"combined_margin_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ImageTargetResizeOp : OneFlow_BaseOp<"image_target_resize", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$label + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$theta + OneFlow_Tensor:$out, + OneFlow_Tensor:$size, + OneFlow_Tensor:$scale ); let attrs = (ins - DefaultValuedAttr:$m1, - DefaultValuedAttr:$m2, - DefaultValuedAttr:$m3, - DefaultValuedAttr:$depth + DefaultValuedAttr:$target_size, + DefaultValuedAttr:$max_size ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_CombinedMarginLossGradOp : OneFlow_BaseOp<"combined_margin_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SliceOp : OneFlow_BaseOp<"slice", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$label, - OneFlow_Tensor:$theta + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$m1, - DefaultValuedAttr:$m2, - DefaultValuedAttr:$m3, - DefaultValuedAttr:$depth + SI64ArrayAttr:$start, + SI64ArrayAttr:$stop, + SI64ArrayAttr:$step ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3610,21 +3811,18 @@ def OneFlow_CombinedMarginLossGradOp : OneFlow_BaseOp<"combined_margin_loss_grad let has_data_type_infer_fn = 1; } -def OneFlow_CtcLossOp : OneFlow_BaseOp<"ctc_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SliceUpdateOp : OneFlow_BaseOp<"slice_update", [SupportNonContiguous, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$log_probs, - OneFlow_Tensor:$targets, - OneFlow_Tensor:$input_lengths, - OneFlow_Tensor:$target_lengths + OneFlow_Tensor:$ref, + OneFlow_Tensor:$value ); let output = (outs - OneFlow_Tensor:$loss, - OneFlow_Tensor:$alpha + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$max_target_length, - DefaultValuedAttr:$blank, - DefaultValuedAttr:$zero_infinity + SI64ArrayAttr:$start, + SI64ArrayAttr:$stop, + SI64ArrayAttr:$step ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3632,39 +3830,18 @@ def OneFlow_CtcLossOp : OneFlow_BaseOp<"ctc_loss", [NoSideEffect, DeclareOpInter let has_data_type_infer_fn = 1; } -def OneFlow_CtcLossGradOp : OneFlow_BaseOp<"ctc_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SliceGradOp : OneFlow_BaseOp<"slice_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$grad_out, - OneFlow_Tensor:$log_probs, - OneFlow_Tensor:$targets, - OneFlow_Tensor:$input_lengths, - OneFlow_Tensor:$target_lengths, - OneFlow_Tensor:$loss, - OneFlow_Tensor:$alpha + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$grad - ); - let attrs = (ins - DefaultValuedAttr:$max_target_length, - DefaultValuedAttr:$blank, - DefaultValuedAttr:$zero_infinity - ); - let has_logical_tensor_desc_infer_fn = 1; - let has_physical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; -} - -def OneFlow_DynamicLossScaleScheduleOp : OneFlow_BaseOp<"dynamic_loss_scale_schedule", [NoSideEffect, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$count_not_finite, - OneFlow_Tensor:$loss_scale, - OneFlow_Tensor:$good_step_counter + OneFlow_Tensor:$dx ); let attrs = (ins - DefaultValuedAttr:$increment_period, - DefaultValuedAttr:$multiplier + ShapeAttr:$like_shape, + SI64ArrayAttr:$start, + SI64ArrayAttr:$stop, + SI64ArrayAttr:$step ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3673,16 +3850,16 @@ def OneFlow_DynamicLossScaleScheduleOp : OneFlow_BaseOp<"dynamic_loss_scale_sche let has_input_arg_modify_fn = 1; } -def OneFlow_KlDivLossOp : OneFlow_BaseOp<"kl_div_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ScatterNdOp : OneFlow_BaseOp<"scatter_nd", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$target + OneFlow_Tensor:$indices, + OneFlow_Tensor:$updates ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$log_target + ShapeAttr:$shape ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3691,17 +3868,14 @@ def OneFlow_KlDivLossOp : OneFlow_BaseOp<"kl_div_loss", [NoSideEffect, DeclareOp let has_input_arg_modify_fn = 1; } -def OneFlow_KlDivLossGradOp : OneFlow_BaseOp<"kl_div_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ScatterNdLikeOp : OneFlow_BaseOp<"scatter_nd_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$target, - OneFlow_Tensor:$dy + OneFlow_Tensor:$like, + OneFlow_Tensor:$indices, + OneFlow_Tensor:$updates ); let output = (outs - OneFlow_Tensor:$dx - ); - let attrs = (ins - DefaultValuedAttr:$log_target + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3709,17 +3883,15 @@ def OneFlow_KlDivLossGradOp : OneFlow_BaseOp<"kl_div_loss_grad", [NoSideEffect, let has_data_type_infer_fn = 1; } -def OneFlow_SmoothL1LossOp : OneFlow_BaseOp<"smooth_l1_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_TensorScatterNdAddOp : OneFlow_BaseOp<"tensor_scatter_nd_add", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$target + OneFlow_Tensor:$params, + OneFlow_Tensor:$updates, + OneFlow_Tensor:$indices ); let output = (outs OneFlow_Tensor:$out ); - let attrs = (ins - DefaultValuedAttr:$beta - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -3727,137 +3899,174 @@ def OneFlow_SmoothL1LossOp : OneFlow_BaseOp<"smooth_l1_loss", [NoSideEffect, Dec let has_input_arg_modify_fn = 1; } -def OneFlow_SmoothL1LossGradOp : OneFlow_BaseOp<"smooth_l1_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_TensorScatterNdUpdateOp : OneFlow_BaseOp<"tensor_scatter_nd_update", [NoSideEffect, SupportNonContiguous, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$target, - OneFlow_Tensor:$dy + OneFlow_Tensor:$params, + OneFlow_Tensor:$updates, + OneFlow_Tensor:$indices ); let output = (outs - OneFlow_Tensor:$dx - ); - let attrs = (ins - DefaultValuedAttr:$beta + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -#endif // GET_ONEFLOW_LOSS_OP_DEFINITIONS - -// Group: MATH -// abs_grad, ceil_grad, erf, erf_grad, exp, exp_grad, expand_grad, expm1, expm1_grad, floor_grad, floordiv_x_grad, floordiv_y_grad, lgamma, lgamma_grad, log, log1p, log1p_grad, log2_grad, log_grad, log_sigmoid, log_sigmoid_grad, negative_grad, reciprocal_grad, reciprocal_no_nan, reciprocal_no_nan_grad, rint_grad, round_grad, rsqrt, rsqrt_grad, sigmoid_v2, sigmoid_v2_grad, sign_grad, softplus, softplus_grad, softsign_grad, var, sqrt, sqrt_grad, square, square_grad, xlogy_x_grad, xlogy_y_grad, cumsum, erfinv -// Total: 44 - -#ifdef GET_ONEFLOW_MATH_OP_DEFINITIONS - -def OneFlow_AbsGradOp : OneFlow_BaseOp<"abs_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_UnsortedBatchSegmentSumOp : OneFlow_BaseOp<"unsorted_batch_segment_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$data, + OneFlow_Tensor:$segment_ids ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$num_segments ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_CeilGradOp : OneFlow_BaseOp<"ceil_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_UnsortedSegmentSumOp : OneFlow_BaseOp<"unsorted_segment_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$data, + OneFlow_Tensor:$segment_ids ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$num_segments ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ErfOp : OneFlow_BaseOp<"erf", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_UnsortedSegmentSumLikeOp : OneFlow_BaseOp<"unsorted_segment_sum_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$data, + OneFlow_Tensor:$segment_ids, + OneFlow_Tensor:$like ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ErfGradOp : OneFlow_BaseOp<"erf_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_WhereOp : OneFlow_BaseOp<"where", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins + OneFlow_Tensor:$condition, OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ExpOp : OneFlow_BaseOp<"exp", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_WhereScalarXOp : OneFlow_BaseOp<"where_scalar_x", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$condition, + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$has_int_operand, + DefaultValuedAttr:$has_float_operand, + DefaultValuedAttr:$has_bool_operand, + DefaultValuedAttr:$int_operand, + DefaultValuedAttr:$float_operand, + DefaultValuedAttr:$bool_operand ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ExpGradOp : OneFlow_BaseOp<"exp_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_WhereScalarXyOp : OneFlow_BaseOp<"where_scalar_xy", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$condition ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$has_x_int_operand, + DefaultValuedAttr:$has_x_float_operand, + DefaultValuedAttr:$has_y_int_operand, + DefaultValuedAttr:$has_y_float_operand, + DefaultValuedAttr:$has_x_bool_operand, + DefaultValuedAttr:$has_y_bool_operand, + DefaultValuedAttr:$x_int_operand, + DefaultValuedAttr:$x_float_operand, + DefaultValuedAttr:$x_bool_operand, + DefaultValuedAttr:$y_int_operand, + DefaultValuedAttr:$y_float_operand, + DefaultValuedAttr:$y_bool_operand ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ExpandGradOp : OneFlow_BaseOp<"expand_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_WhereScalarYOp : OneFlow_BaseOp<"where_scalar_y", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$condition, + OneFlow_Tensor:$x ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - SI32ArrayAttr:$logical_out_shape, - SI32ArrayAttr:$logical_expand_shape + DefaultValuedAttr:$has_int_operand, + DefaultValuedAttr:$has_float_operand, + DefaultValuedAttr:$has_bool_operand, + DefaultValuedAttr:$int_operand, + DefaultValuedAttr:$float_operand, + DefaultValuedAttr:$bool_operand ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_Expm1Op : OneFlow_BaseOp<"expm1", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_MedianOp : OneFlow_BaseOp<"median", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$input ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$output ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3865,13 +4074,13 @@ def OneFlow_Expm1Op : OneFlow_BaseOp<"expm1", [NoSideEffect, DeclareOpInterfaceM let has_data_type_infer_fn = 1; } -def OneFlow_Expm1GradOp : OneFlow_BaseOp<"expm1_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_MedianWithIndicesOp : OneFlow_BaseOp<"median_with_indices", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$input ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$values, + OneFlow_Tensor:$indices ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3879,56 +4088,100 @@ def OneFlow_Expm1GradOp : OneFlow_BaseOp<"expm1_grad", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } -def OneFlow_FloorGradOp : OneFlow_BaseOp<"floor_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SearchSortedOp : OneFlow_BaseOp<"searchsorted", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$sorted_sequence, + OneFlow_Tensor:$values ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$out_int32, + DefaultValuedAttr:$right ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_FloordivXGradOp : OneFlow_BaseOp<"floordiv_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SearchSortedScalarOp : OneFlow_BaseOp<"searchsorted_scalar", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$sorted_sequence ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$out_int32, + DefaultValuedAttr:$right, + DefaultValuedAttr:$values ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_FloordivYGradOp : OneFlow_BaseOp<"floordiv_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz - ); - let output = (outs - OneFlow_Tensor:$dy +#endif // GET_ONEFLOW_INDICES_OP_DEFINITIONS + +// Group: INVOLUTION +// negative, reciprocal +// Total: 2 + +#ifdef GET_ONEFLOW_INVOLUTION_OP_DEFINITIONS + +def OneFlow_NegativeOp : OneFlow_InvolutionBaseOp<"negative", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_ReciprocalOp : OneFlow_InvolutionBaseOp<"reciprocal", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +#endif // GET_ONEFLOW_INVOLUTION_OP_DEFINITIONS + +// Group: LOSS +// combined_margin_loss, combined_margin_loss_grad, ctc_loss, ctc_loss_grad, dynamic_loss_scale_schedule, kl_div_loss, kl_div_loss_grad, smooth_l1_loss, smooth_l1_loss_grad +// Total: 9 + +#ifdef GET_ONEFLOW_LOSS_OP_DEFINITIONS + +def OneFlow_CombinedMarginLossOp : OneFlow_BaseOp<"combined_margin_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$label + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$theta + ); + let attrs = (ins + DefaultValuedAttr:$m1, + DefaultValuedAttr:$m2, + DefaultValuedAttr:$m3, + DefaultValuedAttr:$depth ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_LgammaOp : OneFlow_BaseOp<"lgamma", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CombinedMarginLossGradOp : OneFlow_BaseOp<"combined_margin_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$label, + OneFlow_Tensor:$theta ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$m1, + DefaultValuedAttr:$m2, + DefaultValuedAttr:$m3, + DefaultValuedAttr:$depth ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3936,13 +4189,21 @@ def OneFlow_LgammaOp : OneFlow_BaseOp<"lgamma", [NoSideEffect, DeclareOpInterfac let has_data_type_infer_fn = 1; } -def OneFlow_LgammaGradOp : OneFlow_BaseOp<"lgamma_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CtcLossOp : OneFlow_BaseOp<"ctc_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$log_probs, + OneFlow_Tensor:$targets, + OneFlow_Tensor:$input_lengths, + OneFlow_Tensor:$target_lengths ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$loss, + OneFlow_Tensor:$alpha + ); + let attrs = (ins + DefaultValuedAttr:$max_target_length, + DefaultValuedAttr:$blank, + DefaultValuedAttr:$zero_infinity ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3950,12 +4211,23 @@ def OneFlow_LgammaGradOp : OneFlow_BaseOp<"lgamma_grad", [NoSideEffect, DeclareO let has_data_type_infer_fn = 1; } -def OneFlow_LogOp : OneFlow_BaseOp<"log", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CtcLossGradOp : OneFlow_BaseOp<"ctc_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$grad_out, + OneFlow_Tensor:$log_probs, + OneFlow_Tensor:$targets, + OneFlow_Tensor:$input_lengths, + OneFlow_Tensor:$target_lengths, + OneFlow_Tensor:$loss, + OneFlow_Tensor:$alpha ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$grad + ); + let attrs = (ins + DefaultValuedAttr:$max_target_length, + DefaultValuedAttr:$blank, + DefaultValuedAttr:$zero_infinity ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -3963,67 +4235,88 @@ def OneFlow_LogOp : OneFlow_BaseOp<"log", [NoSideEffect, DeclareOpInterfaceMetho let has_data_type_infer_fn = 1; } -def OneFlow_Log1pOp : OneFlow_BaseOp<"log1p", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DynamicLossScaleScheduleOp : OneFlow_BaseOp<"dynamic_loss_scale_schedule", [DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$count_not_finite, + OneFlow_Tensor:$loss_scale, + OneFlow_Tensor:$good_step_counter ); - let output = (outs - OneFlow_Tensor:$y + let attrs = (ins + DefaultValuedAttr:$increment_period, + DefaultValuedAttr:$multiplier ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_Log1pGradOp : OneFlow_BaseOp<"log1p_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_KlDivLossOp : OneFlow_BaseOp<"kl_div_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$input, + OneFlow_Tensor:$target ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$log_target ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_Log2GradOp : OneFlow_BaseOp<"log2_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_KlDivLossGradOp : OneFlow_BaseOp<"kl_div_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, + OneFlow_Tensor:$input, + OneFlow_Tensor:$target, OneFlow_Tensor:$dy ); let output = (outs OneFlow_Tensor:$dx ); + let attrs = (ins + DefaultValuedAttr:$log_target + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_LogGradOp : OneFlow_BaseOp<"log_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SmoothL1LossOp : OneFlow_BaseOp<"smooth_l1_loss", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$input, + OneFlow_Tensor:$target ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$beta ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_LogSigmoidOp : OneFlow_BaseOp<"log_sigmoid", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SmoothL1LossGradOp : OneFlow_BaseOp<"smooth_l1_loss_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$input, + OneFlow_Tensor:$target, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$beta ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4031,7 +4324,15 @@ def OneFlow_LogSigmoidOp : OneFlow_BaseOp<"log_sigmoid", [NoSideEffect, DeclareO let has_data_type_infer_fn = 1; } -def OneFlow_LogSigmoidGradOp : OneFlow_BaseOp<"log_sigmoid_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_LOSS_OP_DEFINITIONS + +// Group: MATH +// abs_grad, ceil_grad, erf, erf_grad, exp, exp_grad, expm1, expand_grad, expm1, expm1_grad, floor_grad, floordiv_x_grad, floordiv_y_grad, truncdiv_x_grad, truncdiv_y_grad, lgamma, lgamma_grad, log, log1p, log1p_grad, log2_grad, log10_grad, log_grad, log_sigmoid, log_sigmoid_grad, negative_grad, reciprocal_grad, reciprocal_no_nan, reciprocal_no_nan_grad, rint_grad, round_grad, rsqrt, rsqrt_grad, sigmoid_v2, sigmoid_v2_grad, sign_grad, softplus, softplus_grad, softsign_grad, var, sqrt, sqrt_grad, square, square_grad, xlogy_x_grad, xlogy_y_grad, cumsum, erfinv +// Total: 48 + +#ifdef GET_ONEFLOW_MATH_OP_DEFINITIONS + +def OneFlow_AbsGradOp : OneFlow_BaseOp<"abs_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4045,13 +4346,12 @@ def OneFlow_LogSigmoidGradOp : OneFlow_BaseOp<"log_sigmoid_grad", [NoSideEffect, let has_data_type_infer_fn = 1; } -def OneFlow_NegativeGradOp : OneFlow_BaseOp<"negative_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ErfOp : OneFlow_BaseOp<"erf", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4059,7 +4359,7 @@ def OneFlow_NegativeGradOp : OneFlow_BaseOp<"negative_grad", [NoSideEffect, Decl let has_data_type_infer_fn = 1; } -def OneFlow_ReciprocalGradOp : OneFlow_BaseOp<"reciprocal_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ErfGradOp : OneFlow_BaseOp<"erf_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4073,7 +4373,7 @@ def OneFlow_ReciprocalGradOp : OneFlow_BaseOp<"reciprocal_grad", [NoSideEffect, let has_data_type_infer_fn = 1; } -def OneFlow_ReciprocalNoNanOp : OneFlow_BaseOp<"reciprocal_no_nan", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ExpOp : OneFlow_BaseOp<"exp", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); @@ -4086,7 +4386,7 @@ def OneFlow_ReciprocalNoNanOp : OneFlow_BaseOp<"reciprocal_no_nan", [NoSideEffec let has_data_type_infer_fn = 1; } -def OneFlow_ReciprocalNoNanGradOp : OneFlow_BaseOp<"reciprocal_no_nan_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ExpGradOp : OneFlow_BaseOp<"exp_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4100,7 +4400,20 @@ def OneFlow_ReciprocalNoNanGradOp : OneFlow_BaseOp<"reciprocal_no_nan_grad", [No let has_data_type_infer_fn = 1; } -def OneFlow_RintGradOp : OneFlow_BaseOp<"rint_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_Expm1Op : OneFlow_BaseOp<"expm1", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_Expm1GradOp : OneFlow_BaseOp<"expm1_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4114,10 +4427,12 @@ def OneFlow_RintGradOp : OneFlow_BaseOp<"rint_grad", [NoSideEffect, DeclareOpInt let has_data_type_infer_fn = 1; } -def OneFlow_RoundGradOp : OneFlow_BaseOp<"round_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + +def OneFlow_FloordivXGradOp : OneFlow_BaseOp<"floordiv_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs OneFlow_Tensor:$dx @@ -4128,12 +4443,14 @@ def OneFlow_RoundGradOp : OneFlow_BaseOp<"round_grad", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } -def OneFlow_RsqrtOp : OneFlow_BaseOp<"rsqrt", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FloordivYGradOp : OneFlow_BaseOp<"floordiv_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dy ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4141,10 +4458,11 @@ def OneFlow_RsqrtOp : OneFlow_BaseOp<"rsqrt", [NoSideEffect, DeclareOpInterfaceM let has_data_type_infer_fn = 1; } -def OneFlow_RsqrtGradOp : OneFlow_BaseOp<"rsqrt_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_TruncdivXGradOp : OneFlow_BaseOp<"truncdiv_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs OneFlow_Tensor:$dx @@ -4155,14 +4473,14 @@ def OneFlow_RsqrtGradOp : OneFlow_BaseOp<"rsqrt_grad", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } - - -def OneFlow_SigmoidV2Op : OneFlow_BaseOp<"sigmoid_v2", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_TruncdivYGradOp : OneFlow_BaseOp<"truncdiv_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dy ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4170,13 +4488,12 @@ def OneFlow_SigmoidV2Op : OneFlow_BaseOp<"sigmoid_v2", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } -def OneFlow_SigmoidV2GradOp : OneFlow_BaseOp<"sigmoid_v2_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LgammaOp : OneFlow_BaseOp<"lgamma", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4184,7 +4501,7 @@ def OneFlow_SigmoidV2GradOp : OneFlow_BaseOp<"sigmoid_v2_grad", [NoSideEffect, D let has_data_type_infer_fn = 1; } -def OneFlow_SignGradOp : OneFlow_BaseOp<"sign_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LgammaGradOp : OneFlow_BaseOp<"lgamma_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4198,34 +4515,25 @@ def OneFlow_SignGradOp : OneFlow_BaseOp<"sign_grad", [NoSideEffect, DeclareOpInt let has_data_type_infer_fn = 1; } -def OneFlow_SoftplusOp : OneFlow_BaseOp<"softplus", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LogOp : OneFlow_BaseOp<"log", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); let output = (outs OneFlow_Tensor:$y ); - let attrs = (ins - DefaultValuedAttr:$beta, - DefaultValuedAttr:$threshold - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_SoftplusGradOp : OneFlow_BaseOp<"softplus_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_Log1pOp : OneFlow_BaseOp<"log1p", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dx - ); - let attrs = (ins - DefaultValuedAttr:$beta, - DefaultValuedAttr:$threshold + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4233,7 +4541,7 @@ def OneFlow_SoftplusGradOp : OneFlow_BaseOp<"softplus_grad", [NoSideEffect, Decl let has_data_type_infer_fn = 1; } -def OneFlow_SoftsignGradOp : OneFlow_BaseOp<"softsign_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_Log1pGradOp : OneFlow_BaseOp<"log1p_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4247,18 +4555,13 @@ def OneFlow_SoftsignGradOp : OneFlow_BaseOp<"softsign_grad", [NoSideEffect, Decl let has_data_type_infer_fn = 1; } -def OneFlow_VarOp : OneFlow_BaseOp<"var", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_Log2GradOp : OneFlow_BaseOp<"log2_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$output - ); - let attrs = (ins - SI32ArrayAttr:$dim, - DefaultValuedAttr:$unbiased, - DefaultValuedAttr:$keepdim, - OneFlow_DataType:$dtype + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4266,22 +4569,21 @@ def OneFlow_VarOp : OneFlow_BaseOp<"var", [NoSideEffect, DeclareOpInterfaceMetho let has_data_type_infer_fn = 1; } - -def OneFlow_SqrtOp : OneFlow_BaseOp<"sqrt", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_Log10GradOp : OneFlow_BaseOp<"log10_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let hasFolder = 1; } -def OneFlow_SqrtGradOp : OneFlow_BaseOp<"sqrt_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LogGradOp : OneFlow_BaseOp<"log_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4295,7 +4597,7 @@ def OneFlow_SqrtGradOp : OneFlow_BaseOp<"sqrt_grad", [NoSideEffect, DeclareOpInt let has_data_type_infer_fn = 1; } -def OneFlow_SquareOp : OneFlow_BaseOp<"square", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LogSigmoidOp : OneFlow_BaseOp<"log_sigmoid", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); @@ -4308,7 +4610,7 @@ def OneFlow_SquareOp : OneFlow_BaseOp<"square", [NoSideEffect, DeclareOpInterfac let has_data_type_infer_fn = 1; } -def OneFlow_SquareGradOp : OneFlow_BaseOp<"square_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_LogSigmoidGradOp : OneFlow_BaseOp<"log_sigmoid_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4322,11 +4624,10 @@ def OneFlow_SquareGradOp : OneFlow_BaseOp<"square_grad", [NoSideEffect, DeclareO let has_data_type_infer_fn = 1; } -def OneFlow_XlogyXGradOp : OneFlow_BaseOp<"xlogy_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ReciprocalGradOp : OneFlow_BaseOp<"reciprocal_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$dy ); let output = (outs OneFlow_Tensor:$dx @@ -4337,14 +4638,12 @@ def OneFlow_XlogyXGradOp : OneFlow_BaseOp<"xlogy_x_grad", [NoSideEffect, Declare let has_data_type_infer_fn = 1; } -def OneFlow_XlogyYGradOp : OneFlow_BaseOp<"xlogy_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ReciprocalNoNanOp : OneFlow_BaseOp<"reciprocal_no_nan", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dy + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4352,15 +4651,13 @@ def OneFlow_XlogyYGradOp : OneFlow_BaseOp<"xlogy_y_grad", [NoSideEffect, Declare let has_data_type_infer_fn = 1; } -def OneFlow_CumsumOp : OneFlow_BaseOp<"cumsum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ReciprocalNoNanGradOp : OneFlow_BaseOp<"reciprocal_no_nan_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$y - ); - let attrs = (ins - SI64Attr:$dim + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4368,41 +4665,36 @@ def OneFlow_CumsumOp : OneFlow_BaseOp<"cumsum", [NoSideEffect, DeclareOpInterfac let has_data_type_infer_fn = 1; } -def OneFlow_CumProdOp : OneFlow_BaseOp<"cumprod", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_RsqrtOp : OneFlow_BaseOp<"rsqrt", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); let output = (outs OneFlow_Tensor:$y ); - let attrs = (ins - SI64Attr:$dim - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_CumProdGradOp : OneFlow_BaseOp<"cumprod_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_RsqrtGradOp : OneFlow_BaseOp<"rsqrt_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$output, - OneFlow_Tensor:$input + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs OneFlow_Tensor:$dx ); - let attrs = (ins - SI64Attr:$dim - ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ErfInvOp : OneFlow_BaseOp<"erfinv", [NoSideEffect, DeclareOpInterfaceMethods]> { + + +def OneFlow_SigmoidOp : OneFlow_BaseOp<"sigmoid", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); @@ -4415,27 +4707,13 @@ def OneFlow_ErfInvOp : OneFlow_BaseOp<"erfinv", [NoSideEffect, DeclareOpInterfac let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_MATH_OP_DEFINITIONS - -// Group: MATMUL -// batch_matmul, broadcast_matmul, broadcast_matmul_grad_b, distributed_partial_fc_sample, distributed_partial_fc_sample_disable_boxing, erfc, erfc_grad, matmul, cublas_fused_mlp, cublas_bias_add_relu_matmul_grad, cublas_matmul_bias_add_grad, fused_matmul_bias_add_relu_dropout, fused_relu_dropout_grad -// Total: 13 - -#ifdef GET_ONEFLOW_MATMUL_OP_DEFINITIONS - -def OneFlow_BatchMatmulOp : OneFlow_BaseOp<"batch_matmul", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SigmoidGradOp : OneFlow_BaseOp<"sigmoid_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$a, - OneFlow_Tensor:$b, - Optional:$_add_to_output + OneFlow_Tensor:$y, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - DefaultValuedAttr:$transpose_a, - DefaultValuedAttr:$transpose_b, - DefaultValuedAttr:$alpha + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4443,19 +4721,16 @@ def OneFlow_BatchMatmulOp : OneFlow_BaseOp<"batch_matmul", [NoSideEffect, Declar let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastMatmulOp : OneFlow_BaseOp<"broadcast_matmul", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SoftplusOp : OneFlow_BaseOp<"softplus", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$a, - OneFlow_Tensor:$b, - Optional:$_add_to_output + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$transpose_a, - DefaultValuedAttr:$transpose_b, - DefaultValuedAttr:$alpha + DefaultValuedAttr:$beta, + DefaultValuedAttr:$threshold ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4463,17 +4738,17 @@ def OneFlow_BroadcastMatmulOp : OneFlow_BaseOp<"broadcast_matmul", [NoSideEffect let has_data_type_infer_fn = 1; } -def OneFlow_BroadcastMatmulGradBOp : OneFlow_BaseOp<"broadcast_matmul_grad_b", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SoftplusGradOp : OneFlow_BaseOp<"softplus_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$a, - OneFlow_Tensor:$b, - Optional:$_add_to_output + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$dx ); let attrs = (ins - DefaultValuedAttr:$alpha + DefaultValuedAttr:$beta, + DefaultValuedAttr:$threshold ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4481,35 +4756,32 @@ def OneFlow_BroadcastMatmulGradBOp : OneFlow_BaseOp<"broadcast_matmul_grad_b", [ let has_data_type_infer_fn = 1; } -def OneFlow_DistributedPartialFcSampleOp : OneFlow_BaseOp<"distributed_partial_fc_sample", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SoftsignGradOp : OneFlow_BaseOp<"softsign_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$weight, - OneFlow_Tensor:$label + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$mapped_label, - OneFlow_Tensor:$sampled_label, - OneFlow_Tensor:$sampled_weight - ); - let attrs = (ins - DefaultValuedAttr:$num_sample, - DefaultValuedAttr:$seed + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_DistributedPartialFcSampleDisableBoxingOp : OneFlow_BaseOp<"distributed_partial_fc_sample_disable_boxing", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_VarOp : OneFlow_BaseOp<"var", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$sampled_weight_diff, - OneFlow_Tensor:$sampled_label + OneFlow_Tensor:$input ); let output = (outs - OneFlow_Tensor:$boxing_disabled_sampled_weight_diff, - OneFlow_Tensor:$boxing_disabled_sampled_label + OneFlow_Tensor:$output + ); + let attrs = (ins + SI32ArrayAttr:$dim, + DefaultValuedAttr:$unbiased, + DefaultValuedAttr:$keepdim, + OneFlow_DataType:$dtype ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4517,7 +4789,8 @@ def OneFlow_DistributedPartialFcSampleDisableBoxingOp : OneFlow_BaseOp<"distribu let has_data_type_infer_fn = 1; } -def OneFlow_ErfcOp : OneFlow_BaseOp<"erfc", [NoSideEffect, DeclareOpInterfaceMethods]> { + +def OneFlow_SqrtOp : OneFlow_BaseOp<"sqrt", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x ); @@ -4528,9 +4801,10 @@ def OneFlow_ErfcOp : OneFlow_BaseOp<"erfc", [NoSideEffect, DeclareOpInterfaceMet let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let hasFolder = 1; } -def OneFlow_ErfcGradOp : OneFlow_BaseOp<"erfc_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SqrtGradOp : OneFlow_BaseOp<"sqrt_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$dy @@ -4544,19 +4818,12 @@ def OneFlow_ErfcGradOp : OneFlow_BaseOp<"erfc_grad", [NoSideEffect, DeclareOpInt let has_data_type_infer_fn = 1; } -def OneFlow_MatmulOp : OneFlow_BaseOp<"matmul", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SquareOp : OneFlow_BaseOp<"square", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$a, - OneFlow_Tensor:$b, - Optional:$_add_to_output + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - DefaultValuedAttr:$transpose_a, - DefaultValuedAttr:$transpose_b, - DefaultValuedAttr:$alpha + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4564,19 +4831,13 @@ def OneFlow_MatmulOp : OneFlow_BaseOp<"matmul", [NoSideEffect, DeclareOpInterfac let has_data_type_infer_fn = 1; } -def OneFlow_CublasFusedMLPOp : OneFlow_BaseOp<"cublas_fused_mlp", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_SquareGradOp : OneFlow_BaseOp<"square_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - Variadic:$weights, - Variadic:$biases + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$out, - Variadic:$cublas_aux, - Variadic:$hidden - ); - let attrs = (ins - DefaultValuedAttr:$skip_final_activation + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4584,18 +4845,14 @@ def OneFlow_CublasFusedMLPOp : OneFlow_BaseOp<"cublas_fused_mlp", [NoSideEffect, let has_data_type_infer_fn = 1; } -def OneFlow_CublasBiasAddReluMatmulGradOp : OneFlow_BaseOp<"cublas_bias_add_relu_matmul_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_XlogyXGradOp : OneFlow_BaseOp<"xlogy_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$weight, - OneFlow_Tensor:$aux + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$d_grad, - OneFlow_Tensor:$d_bias - ); - let attrs = (ins - DefaultValuedAttr:$alpha + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4603,14 +4860,14 @@ def OneFlow_CublasBiasAddReluMatmulGradOp : OneFlow_BaseOp<"cublas_bias_add_relu let has_data_type_infer_fn = 1; } -def OneFlow_CublasMatmulBiasAddGradOp : OneFlow_BaseOp<"cublas_matmul_bias_add_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_XlogyYGradOp : OneFlow_BaseOp<"xlogy_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$x + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$w_grad, - OneFlow_Tensor:$b_grad + OneFlow_Tensor:$dy ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4618,20 +4875,15 @@ def OneFlow_CublasMatmulBiasAddGradOp : OneFlow_BaseOp<"cublas_matmul_bias_add_g let has_data_type_infer_fn = 1; } -def OneFlow_FusedMatmulBiasAddReluDropoutOp : OneFlow_BaseOp<"fused_matmul_bias_add_relu_dropout", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_CumsumOp : OneFlow_BaseOp<"cumsum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - Variadic:$weights, - Variadic:$biases + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$out, - Variadic:$cublas_aux, - Variadic:$hidden + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$skip_final_activation, - F32ArrayAttr:$dropout_rate_list + SI64Attr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4639,16 +4891,15 @@ def OneFlow_FusedMatmulBiasAddReluDropoutOp : OneFlow_BaseOp<"fused_matmul_bias_ let has_data_type_infer_fn = 1; } -def OneFlow_FusedReluDropoutGradOp : OneFlow_BaseOp<"fused_relu_dropout_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_CumProdOp : OneFlow_BaseOp<"cumprod", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$mask + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$y ); let attrs = (ins - DefaultValuedAttr:$scale + SI64Attr:$dim ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4656,157 +4907,161 @@ def OneFlow_FusedReluDropoutGradOp : OneFlow_BaseOp<"fused_relu_dropout_grad", [ let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_MATMUL_OP_DEFINITIONS - -// Group: MISC -// CategoricalOrdinalEncode, add_n, arange, coin_flip, concat, constant, dropout, elementwise_maximum_backward, elementwise_minimum_backward, empty, eye, grid_sample_grad, multi_count_not_finite, multi_square_sum, nll, nll_grad, pow_x_grad, pow_y_grad, prelu_grad, randperm, recv, send, split_like, ssp_variable_proxy, tf_prelu_grad, uniform, uniform_int, unique_with_counts, xdivy_x_grad, xdivy_y_grad, stack, stack_grad, fill_, fill_tensor_ -// Total: 34 - -#ifdef GET_ONEFLOW_MISC_OP_DEFINITIONS - -def OneFlow_CategoricalOrdinalEncodeOp : OneFlow_BaseOp<"CategoricalOrdinalEncode", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_CumProdGradOp : OneFlow_BaseOp<"cumprod_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$table, - OneFlow_Tensor:$size, - OneFlow_Tensor:$in + OneFlow_Tensor:$dy, + OneFlow_Tensor:$output, + OneFlow_Tensor:$input ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$dx ); let attrs = (ins - DefaultValuedAttr:$hash_precomputed + SI64Attr:$dim ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_AddNOp : OneFlow_BaseOp<"add_n", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ErfInvOp : OneFlow_BaseOp<"erfinv", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$in + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$y ); - let hasCanonicalizer = 1; - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_ArangeOp : OneFlow_BaseOp<"arange", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_StftOp : OneFlow_BaseOp<"stft", [SupportNonContiguous,NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + Optional:$window + ); let output = (outs OneFlow_Tensor:$out ); + let attrs = (ins - DefaultValuedAttr:$integer_start, - DefaultValuedAttr:$integer_delta, - DefaultValuedAttr:$integer_limit, - DefaultValuedAttr:$float_start, - DefaultValuedAttr:$float_delta, - DefaultValuedAttr:$float_limit, - OneFlow_DataType:$dtype, - StrArrayAttr:$nd_sbp + DefaultValuedAttr:$n_fft, + DefaultValuedAttr:$hop_length, + DefaultValuedAttr:$win_length, + DefaultValuedAttr:$center, + DefaultValuedAttr:$pad_mode, + DefaultValuedAttr:$normalized, + DefaultValuedAttr:$onesided, + DefaultValuedAttr:$return_complex ); + let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_compute_complexity_fn = 1; } +#endif // GET_ONEFLOW_MATH_OP_DEFINITIONS -def OneFlow_CoinFlipOp : OneFlow_BaseOp<"coin_flip", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +// Group: MATMUL +// batch_matmul, broadcast_matmul, broadcast_matmul_grad_b, distributed_partial_fc_sample, distributed_partial_fc_sample_disable_boxing, erfc, erfc_grad, matmul, cublas_fused_mlp, cublas_bias_add_relu_matmul_grad, cublas_matmul_bias_add_grad, fused_matmul_bias, fused_matmul_bias_add_relu_dropout, fused_relu_dropout_grad, matrix_vector_product, matrix_vector_product_grad_a, matrix_vector_product_grad_b, vector_matrix_product, vector_matrix_product_grad_a, vector_matrix_product_grad_b, grouped_matmul_bias, fused_glu +// Total: 22 + +#ifdef GET_ONEFLOW_MATMUL_OP_DEFINITIONS + +def OneFlow_BatchMatmulOp : OneFlow_BaseOp<"batch_matmul", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$a, + OneFlow_Tensor:$b, + Optional:$_add_to_output + ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$probability, - DefaultValuedAttr:$batch_size, - DefaultValuedAttr:$seed, - DefaultValuedAttr:$has_seed, - StrArrayAttr:$nd_sbp + DefaultValuedAttr:$transpose_a, + DefaultValuedAttr:$transpose_b, + DefaultValuedAttr:$alpha ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_compute_complexity_fn = 1; } -def OneFlow_ConcatOp : OneFlow_BaseOp<"concat", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastMatmulOp : OneFlow_BaseOp<"broadcast_matmul", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$in + OneFlow_Tensor:$a, + OneFlow_Tensor:$b, + Optional:$_add_to_output ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$max_dim_size + DefaultValuedAttr:$transpose_a, + DefaultValuedAttr:$transpose_b, + DefaultValuedAttr:$alpha ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } -def OneFlow_ConstantOp : OneFlow_BaseOp<"constant", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BroadcastMatmulGradBOp : OneFlow_BaseOp<"broadcast_matmul_grad_b", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$a, + OneFlow_Tensor:$b, + Optional:$_add_to_output + ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$floating_value, - DefaultValuedAttr:$integer_value, - DefaultValuedAttr:$is_floating_value, - OneFlow_DataType:$dtype, - ShapeAttr:$shape, - StrArrayAttr:$nd_sbp + DefaultValuedAttr:$alpha ); - let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_compute_complexity_fn = 1; } -def OneFlow_DropoutOp : OneFlow_BaseOp<"dropout", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_DistributedPartialFcSampleOp : OneFlow_BaseOp<"distributed_partial_fc_sample", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in, - Optional:$_add_to_output + OneFlow_Tensor:$weight, + OneFlow_Tensor:$label ); let output = (outs - OneFlow_Tensor:$out, - OneFlow_Tensor:$mask + OneFlow_Tensor:$mapped_label, + OneFlow_Tensor:$sampled_label, + OneFlow_Tensor:$sampled_weight ); let attrs = (ins - DefaultValuedAttr:$rate + DefaultValuedAttr:$num_sample, + DefaultValuedAttr:$seed ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_ElementwiseMaximumBackwardOp : OneFlow_BaseOp<"elementwise_maximum_backward", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_DistributedPartialFcSampleDisableBoxingOp : OneFlow_BaseOp<"distributed_partial_fc_sample_disable_boxing", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dz, - OneFlow_Tensor:$x, - OneFlow_Tensor:$y + OneFlow_Tensor:$sampled_weight_diff, + OneFlow_Tensor:$sampled_label ); let output = (outs - Optional:$dx, - Optional:$dy - ); - let trait_attrs = (ins - I32ElementsAttr:$result_segment_sizes + OneFlow_Tensor:$boxing_disabled_sampled_weight_diff, + OneFlow_Tensor:$boxing_disabled_sampled_label ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4814,18 +5069,12 @@ def OneFlow_ElementwiseMaximumBackwardOp : OneFlow_BaseOp<"elementwise_maximum_b let has_data_type_infer_fn = 1; } -def OneFlow_ElementwiseMinimumBackwardOp : OneFlow_BaseOp<"elementwise_minimum_backward", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_ErfcOp : OneFlow_BaseOp<"erfc", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dz, - OneFlow_Tensor:$x, - OneFlow_Tensor:$y + OneFlow_Tensor:$x ); let output = (outs - Optional:$dx, - Optional:$dy - ); - let trait_attrs = (ins - I32ElementsAttr:$result_segment_sizes + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -4833,218 +5082,232 @@ def OneFlow_ElementwiseMinimumBackwardOp : OneFlow_BaseOp<"elementwise_minimum_b let has_data_type_infer_fn = 1; } -def OneFlow_EmptyOp : OneFlow_BaseOp<"empty", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let output = (outs - OneFlow_Tensor:$out +def OneFlow_ErfcGradOp : OneFlow_BaseOp<"erfc_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy ); - let attrs = (ins - OneFlow_DataType:$dtype, - ShapeAttr:$shape, - StrArrayAttr:$nd_sbp, - DefaultValuedAttr:$pin_memory, - StrAttr:$device_type, - DefaultValuedAttr:$device_id + let output = (outs + OneFlow_Tensor:$dx ); - let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; } -def OneFlow_EyeOp : OneFlow_BaseOp<"eye", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MatmulOp : OneFlow_BaseOp<"matmul", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$a, + OneFlow_Tensor:$b, + Optional:$_add_to_output + ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$rows, - DefaultValuedAttr:$cols, - OneFlow_DataType:$dtype, - StrArrayAttr:$nd_sbp + DefaultValuedAttr:$transpose_a, + DefaultValuedAttr:$transpose_b, + DefaultValuedAttr:$alpha ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } -def OneFlow_GridSampleGradOp : OneFlow_BaseOp<"grid_sample_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_MatrixVectorProductOp : OneFlow_BaseOp<"matrix_vector_product", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$doutput, - OneFlow_Tensor:$input, - OneFlow_Tensor:$grid + OneFlow_Tensor:$a, + OneFlow_Tensor:$b ); let output = (outs - OneFlow_Tensor:$dinput, - OneFlow_Tensor:$dgrid - ); - let attrs = (ins - StrAttr:$interpolation_mode, - StrAttr:$padding_mode, - DefaultValuedAttr:$align_corners + OneFlow_Tensor:$out ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_MultiCountNotFiniteOp : OneFlow_BaseOp<"multi_count_not_finite", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MatrixVectorProductGradAOp : OneFlow_BaseOp<"matrix_vector_product_grad_a", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$b ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_MultiSquareSumOp : OneFlow_BaseOp<"multi_square_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_MatrixVectorProductGradBOp : OneFlow_BaseOp<"matrix_vector_product_grad_b", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$a ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_MultiReduceSumPowAbsOp : OneFlow_BaseOp<"multi_reduce_sum_pow_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_VectorMatrixProductOp : OneFlow_BaseOp<"vector_matrix_product", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$a, + OneFlow_Tensor:$b ); let output = (outs - OneFlow_Tensor:$y - ); - let attrs = (ins - DefaultValuedAttr:$p + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; - let has_data_type_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_MultiReduceMaxAbsOp : OneFlow_BaseOp<"multi_reduce_max_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_VectorMatrixProductGradAOp : OneFlow_BaseOp<"vector_matrix_product_grad_a", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$b ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; - let has_data_type_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_MultiReduceMinAbsOp : OneFlow_BaseOp<"multi_reduce_min_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_VectorMatrixProductGradBOp : OneFlow_BaseOp<"vector_matrix_product_grad_b", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$a ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; - let has_data_type_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_LocalMultiReduceMaxAbsOp : OneFlow_BaseOp<"local_multi_reduce_max_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { + +def OneFlow_CublasFusedMLPOp : OneFlow_BaseOp<"cublas_fused_mlp", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$x, + Variadic:$weights, + Variadic:$biases ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$out, + Variadic:$cublas_aux, + Variadic:$hidden + ); + let attrs = (ins + DefaultValuedAttr:$skip_final_activation ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; - let has_data_type_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_LocalMultiReduceMinAbsOp : OneFlow_BaseOp<"local_multi_reduce_min_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CublasFusedMLPGradOp : OneFlow_BaseOp<"cublas_fused_mlp_grad", [NoSideEffect, NoGrad, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins - Variadic:$x + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + Variadic:$weights, + Variadic:$cublas_aux, + Variadic:$hidden ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$d_x, + Variadic:$d_biases, + Variadic:$d_weights + ); + let attrs = (ins + F32ArrayAttr:$alpha_list ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; - let has_data_type_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_NLLOp : OneFlow_BaseOp<"nll", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CublasBiasAddReluMatmulGradOp : OneFlow_BaseOp<"cublas_bias_add_relu_matmul_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input, - OneFlow_Tensor:$target, - Optional:$weight + OneFlow_Tensor:$dy, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$aux ); let output = (outs - OneFlow_Tensor:$output, - OneFlow_Tensor:$out_weight + OneFlow_Tensor:$d_grad, + OneFlow_Tensor:$d_bias ); let attrs = (ins - DefaultValuedAttr:$ignore_index + DefaultValuedAttr:$alpha ); - let has_data_type_infer_fn = 1; let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; - let has_input_arg_modify_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_NLLGradOp : OneFlow_BaseOp<"nll_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_CublasMatmulBiasAddGradOp : OneFlow_BaseOp<"cublas_matmul_bias_add_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$out_grad, - OneFlow_Tensor:$input, - OneFlow_Tensor:$target, - Optional:$weight + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$in_grad - ); - let attrs = (ins - DefaultValuedAttr:$ignore_index + OneFlow_Tensor:$w_grad, + OneFlow_Tensor:$b_grad ); - let has_data_type_infer_fn = 1; let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; } -def OneFlow_PowXGradOp : OneFlow_BaseOp<"pow_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedMatmulBiasOp : OneFlow_BaseOp<"fused_matmul_bias", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$weight, + OneFlow_Tensor:$bias, + Optional:$_add_to_output ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow_PowYGradOp : OneFlow_BaseOp<"pow_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedMatmulBiasAddReluDropoutOp : OneFlow_BaseOp<"fused_matmul_bias_add_relu_dropout", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + Variadic:$weights, + Variadic:$biases ); let output = (outs - OneFlow_Tensor:$dy + OneFlow_Tensor:$out, + Variadic:$cublas_aux, + Variadic:$hidden + ); + let attrs = (ins + DefaultValuedAttr:$skip_final_activation, + F32ArrayAttr:$dropout_rate_list ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5052,18 +5315,16 @@ def OneFlow_PowYGradOp : OneFlow_BaseOp<"pow_y_grad", [NoSideEffect, DeclareOpIn let has_data_type_infer_fn = 1; } -def OneFlow_PreluGradOp : OneFlow_BaseOp<"prelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_FusedReluDropoutGradOp : OneFlow_BaseOp<"fused_relu_dropout_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$dy, - OneFlow_Tensor:$x, - OneFlow_Tensor:$alpha + OneFlow_Tensor:$mask ); let output = (outs - OneFlow_Tensor:$dx, - OneFlow_Tensor:$alpha_diff + OneFlow_Tensor:$dx ); let attrs = (ins - DefaultValuedAttr:$alpha_requires_grad + DefaultValuedAttr:$scale ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5071,115 +5332,171 @@ def OneFlow_PreluGradOp : OneFlow_BaseOp<"prelu_grad", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } -def OneFlow_RandpermOp : OneFlow_BaseOp<"randperm", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let output = (outs - OneFlow_Tensor:$out +def OneFlow_FusedGluOp : OneFlow_BaseOp<"fused_glu", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$w, + OneFlow_Tensor:$b, + Optional:$v, + Optional:$c ); let attrs = (ins - DefaultValuedAttr:$n, - DefaultValuedAttr:$seed, - StrArrayAttr:$nd_sbp + DefaultValuedAttr:$activation + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$matmul_wx, + Optional:$matmul_vx ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow_RecvOp : OneFlow_BaseOp<"recv", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let output = (outs - OneFlow_Tensor:$out +def OneFlow_GroupedMatmulBiasOp : OneFlow_BaseOp<"grouped_matmul_bias", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + Variadic:$xs, + Variadic:$weights, + Variadic:$biases ); - let attrs = (ins - DefaultValuedAttr:$src_process_id, - OneFlow_DataType:$dtype, - ShapeAttr:$shape, - StrAttr:$device_type, - DefaultValuedAttr:$device_id + let output = (outs + Variadic:$ys ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; } -def OneFlow_SendOp : OneFlow_BaseOp<"send", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_MATMUL_OP_DEFINITIONS + +// Group: MISC +// CategoricalOrdinalEncode, add_n, arange, bincount, coin_flip, concat, tensor_constant, constant, dropout, elementwise_maximum_backward, elementwise_minimum_backward, empty, eye, grid_sample_grad, multi_count_not_finite, multi_square_sum, nll, nll_grad, pow_x_grad, pow_y_grad, prelu_grad, randperm, recv, send, split_like, ssp_variable_proxy, tf_prelu_grad, uniform, uniform_int, unique, unique_with_counts, xdivy_x_grad, xdivy_y_grad, stack, stack_grad, fill_, fill_tensor_, exponential, multinomial_with_replacement, fused_weighted_sum +// Total: 40 + +#ifdef GET_ONEFLOW_MISC_OP_DEFINITIONS + +def OneFlow_CategoricalOrdinalEncodeOp : OneFlow_BaseOp<"CategoricalOrdinalEncode", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins + OneFlow_Tensor:$table, + OneFlow_Tensor:$size, OneFlow_Tensor:$in ); + let output = (outs + OneFlow_Tensor:$out + ); let attrs = (ins - DefaultValuedAttr:$dst_process_id + DefaultValuedAttr:$hash_precomputed ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_SplitLikeOp : OneFlow_BaseOp<"split_like", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_AddNOp : OneFlow_BaseOp<"add_n", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in, - Variadic:$like + Variadic:$in ); let output = (outs - Variadic:$out + OneFlow_Tensor:$out + ); + let hasCanonicalizer = 1; + let has_check_fn = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_ArangeOp : OneFlow_BaseOp<"arange", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let output = (outs + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$axis + DefaultValuedAttr:$integer_start, + DefaultValuedAttr:$integer_delta, + DefaultValuedAttr:$integer_limit, + DefaultValuedAttr:$float_start, + DefaultValuedAttr:$float_delta, + DefaultValuedAttr:$float_limit, + OneFlow_DataType:$dtype, + StrArrayAttr:$nd_sbp ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; + let has_nd_sbp_infer_fn = 1; } -def OneFlow_SspVariableProxyOp : OneFlow_BaseOp<"ssp_variable_proxy", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_BinCountOp : OneFlow_BaseOp<"bincount", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$var + OneFlow_Tensor:$in, + Optional:$weight ); let output = (outs - OneFlow_Tensor:$ref, - OneFlow_Tensor:$value + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$buffer_size + DefaultValuedAttr:$size + ); + let has_data_type_infer_fn = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; +} + +def OneFlow_CoinFlipOp : OneFlow_BaseOp<"coin_flip", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$probability, + DefaultValuedAttr:$batch_size, + DefaultValuedAttr:$seed, + DefaultValuedAttr:$has_seed, + StrArrayAttr:$nd_sbp ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_output_arg_modify_fn = 1; + let has_nd_sbp_infer_fn = 1; } -def OneFlow_TfPreluGradOp : OneFlow_BaseOp<"tf_prelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ConcatOp : OneFlow_BaseOp<"concat", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$x, - OneFlow_Tensor:$alpha + Variadic:$in ); let output = (outs - OneFlow_Tensor:$dx, - OneFlow_Tensor:$alpha_diff + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$max_dim_size ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_UniformOp : OneFlow_BaseOp<"uniform", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_TensorConstantOp : OneFlow_BaseOp<"tensor_constant", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$from, - DefaultValuedAttr:$to, - DefaultValuedAttr:$seed, OneFlow_DataType:$dtype, ShapeAttr:$shape, StrArrayAttr:$nd_sbp @@ -5192,14 +5509,14 @@ def OneFlow_UniformOp : OneFlow_BaseOp<"uniform", [NoSideEffect, NoGrad, Declare let has_nd_sbp_infer_fn = 1; } -def OneFlow_UniformIntOp : OneFlow_BaseOp<"uniform_int", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_ConstantOp : OneFlow_BaseOp<"constant", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$from, - DefaultValuedAttr:$to, - DefaultValuedAttr:$seed, + DefaultValuedAttr:$floating_value, + DefaultValuedAttr:$integer_value, + DefaultValuedAttr:$is_floating_value, OneFlow_DataType:$dtype, ShapeAttr:$shape, StrArrayAttr:$nd_sbp @@ -5212,33 +5529,37 @@ def OneFlow_UniformIntOp : OneFlow_BaseOp<"uniform_int", [NoSideEffect, NoGrad, let has_nd_sbp_infer_fn = 1; } -def OneFlow_UniqueWithCountsOp : OneFlow_BaseOp<"unique_with_counts", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_DropoutOp : OneFlow_BaseOp<"dropout", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$in, + Optional:$_add_to_output ); let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$idx, - OneFlow_Tensor:$count, - OneFlow_Tensor:$num_unique + OneFlow_Tensor:$out, + OneFlow_Tensor:$mask ); let attrs = (ins - OneFlow_DataType:$out_idx + DefaultValuedAttr:$rate ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_XdivyXGradOp : OneFlow_BaseOp<"xdivy_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ElementwiseMaximumBackwardOp : OneFlow_BaseOp<"elementwise_maximum_backward", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins + OneFlow_Tensor:$dz, OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$dx + Optional:$dx, + Optional:$dy + ); + let trait_attrs = (ins + I32ElementsAttr:$result_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5246,14 +5567,18 @@ def OneFlow_XdivyXGradOp : OneFlow_BaseOp<"xdivy_x_grad", [NoSideEffect, Declare let has_data_type_infer_fn = 1; } -def OneFlow_XdivyYGradOp : OneFlow_BaseOp<"xdivy_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ElementwiseMinimumBackwardOp : OneFlow_BaseOp<"elementwise_minimum_backward", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let input = (ins + OneFlow_Tensor:$dz, OneFlow_Tensor:$x, - OneFlow_Tensor:$y, - OneFlow_Tensor:$dz + OneFlow_Tensor:$y ); let output = (outs - OneFlow_Tensor:$dy + Optional:$dx, + Optional:$dy + ); + let trait_attrs = (ins + I32ElementsAttr:$result_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5261,293 +5586,237 @@ def OneFlow_XdivyYGradOp : OneFlow_BaseOp<"xdivy_y_grad", [NoSideEffect, Declare let has_data_type_infer_fn = 1; } -def OneFlow_StackOp : OneFlow_BaseOp<"stack", [NoSideEffect, DeclareOpInterfaceMethods]> { - let input = (ins - Variadic:$in - ); +def OneFlow_EmptyOp : OneFlow_BaseOp<"empty", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$max_dim_size + OneFlow_DataType:$dtype, + ShapeAttr:$shape, + StrArrayAttr:$nd_sbp, + DefaultValuedAttr:$pin_memory, + StrAttr:$device_type, + DefaultValuedAttr:$device_id ); - let has_check_fn = 1; + let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; } -def OneFlow_StackGradOp : OneFlow_BaseOp<"stack_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$in, - Variadic:$like - ); +def OneFlow_EyeOp : OneFlow_BaseOp<"eye", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs - Variadic:$out + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$axis + DefaultValuedAttr:$rows, + DefaultValuedAttr:$cols, + OneFlow_DataType:$dtype, + StrArrayAttr:$nd_sbp ); - let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -#endif // GET_ONEFLOW_MISC_OP_DEFINITIONS - -// Group: NCCL -// _nccl_logical_2D_same_dim0_all2all, _nccl_logical_2D_same_dim0_all_gather, _nccl_logical_2D_same_dim0_all_gather_noncontinuous, _nccl_logical_2D_same_dim0_all_reduce, _nccl_logical_2D_same_dim1_all_reduce, _nccl_logical_all_gather, _nccl_logical_all_gather_noncontinuous, _nccl_logical_all_reduce, _nccl_logical_reduce_scatter, _nccl_logical_s2s, _nccl_logical_send_recv -// Total: 11 - -#ifdef GET_ONEFLOW_NCCL_OP_DEFINITIONS - -def OneFlow__ncclLogical_2DSameDim0All2allOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all2all", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_GridSampleGradOp : OneFlow_BaseOp<"grid_sample_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$doutput, + OneFlow_Tensor:$input, + OneFlow_Tensor:$grid ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$dinput, + OneFlow_Tensor:$dgrid ); let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + StrAttr:$interpolation_mode, + StrAttr:$padding_mode, + DefaultValuedAttr:$align_corners ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow__ncclLogical_2DSameDim0AllGatherOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiCountNotFiniteOp : OneFlow_BaseOp<"multi_count_not_finite", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow__ncclLogical_2DSameDim0AllGatherNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_gather_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiSquareSumOp : OneFlow_BaseOp<"multi_square_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow__ncclLogical_2DSameDim0AllReduceOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiReduceSumPowAbsOp : OneFlow_BaseOp<"multi_reduce_sum_pow_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$y ); let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + DefaultValuedAttr:$p ); let has_logical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow__ncclLogical_2DSameDim1AllReduceOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim1_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiReduceMaxAbsOp : OneFlow_BaseOp<"multi_reduce_max_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow__ncclLogicalAllGatherOp : OneFlow_BaseOp<"_nccl_logical_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiReduceMinAbsOp : OneFlow_BaseOp<"multi_reduce_min_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow__ncclLogicalAllGatherNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_all_gather_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_LocalMultiReduceMaxAbsOp : OneFlow_BaseOp<"local_multi_reduce_max_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow__ncclLogicalAllReduceOp : OneFlow_BaseOp<"_nccl_logical_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_LocalMultiReduceMinAbsOp : OneFlow_BaseOp<"local_multi_reduce_min_abs", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + Variadic:$x ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_get_sbp_fn = 1; } -def OneFlow__ncclLogicalReduceScatterOp : OneFlow_BaseOp<"_nccl_logical_reduce_scatter", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_NLLOp : OneFlow_BaseOp<"nll", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$input, + OneFlow_Tensor:$target, + Optional:$weight ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$output, + OneFlow_Tensor:$out_weight ); let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + DefaultValuedAttr:$ignore_index ); + let has_data_type_infer_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow__ncclLogicalReduceScatterNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_reduce_scatter_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_NLLGradOp : OneFlow_BaseOp<"nll_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$out_grad, + OneFlow_Tensor:$input, + OneFlow_Tensor:$target, + Optional:$weight ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$in_grad ); let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + DefaultValuedAttr:$ignore_index ); + let has_data_type_infer_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow__ncclLogicalS2sOp : OneFlow_BaseOp<"_nccl_logical_s2s", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_PowXGradOp : OneFlow_BaseOp<"pow_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_reduced_nd_sbp, - StrArrayAttr:$dst_reduced_nd_sbp + OneFlow_Tensor:$dx ); let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -def OneFlow__ncclLogicalSendRecvOp : OneFlow_BaseOp<"_nccl_logical_send_recv", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_PowYGradOp : OneFlow_BaseOp<"pow_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz ); let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - StrArrayAttr:$src_nd_sbp, - StrArrayAttr:$dst_nd_sbp + OneFlow_Tensor:$dy ); let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_device_and_stream_infer_fn = 1; - let has_nd_sbp_infer_fn = 1; } -#endif // GET_ONEFLOW_NCCL_OP_DEFINITIONS - -// Group: NORMALIZATION -// crop_mirror_normalize_from_tensorbuffer, crop_mirror_normalize_from_uint8, image_normalize, l2_normalize, l2_normalize_grad, layer_norm, layer_norm_grad, layer_norm_param_grad, normal, normalization, normalization_grad -// Total: 11 - -#ifdef GET_ONEFLOW_NORMALIZATION_OP_DEFINITIONS - -def OneFlow_CropMirrorNormalizeFromTensorbufferOp : OneFlow_BaseOp<"crop_mirror_normalize_from_tensorbuffer", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { +def OneFlow_PreluGradOp : OneFlow_BaseOp<"prelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$in, - Optional:$mirror + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$alpha ); let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$dx, + OneFlow_Tensor:$alpha_diff ); let attrs = (ins - DefaultValuedAttr:$color_space, - DefaultValuedAttr:$output_layout, - F32ArrayAttr:$mean, - F32ArrayAttr:$std, - DefaultValuedAttr:$crop_h, - DefaultValuedAttr:$crop_w, - DefaultValuedAttr:$crop_pos_x, - DefaultValuedAttr:$crop_pos_y, - OneFlow_DataType:$output_dtype + DefaultValuedAttr:$alpha_requires_grad ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5555,130 +5824,100 @@ def OneFlow_CropMirrorNormalizeFromTensorbufferOp : OneFlow_BaseOp<"crop_mirror_ let has_data_type_infer_fn = 1; } -def OneFlow_CropMirrorNormalizeFromUint8Op : OneFlow_BaseOp<"crop_mirror_normalize_from_uint8", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$in, - Optional:$mirror - ); +def OneFlow_RandpermOp : OneFlow_BaseOp<"randperm", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$color_space, - DefaultValuedAttr:$output_layout, - F32ArrayAttr:$mean, - F32ArrayAttr:$std, - DefaultValuedAttr:$crop_h, - DefaultValuedAttr:$crop_w, - DefaultValuedAttr:$crop_pos_x, - DefaultValuedAttr:$crop_pos_y, - OneFlow_DataType:$output_dtype + DefaultValuedAttr:$n, + DefaultValuedAttr:$seed, + StrArrayAttr:$nd_sbp ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; } -def OneFlow_ImageNormalizeOp : OneFlow_BaseOp<"image_normalize", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$in - ); +def OneFlow_RecvOp : OneFlow_BaseOp<"recv", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - F32ArrayAttr:$std, - F32ArrayAttr:$mean + DefaultValuedAttr:$src_process_id, + OneFlow_DataType:$dtype, + ShapeAttr:$shape, + StrAttr:$device_type, + DefaultValuedAttr:$device_id ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; } -def OneFlow_L2NormalizeOp : OneFlow_BaseOp<"l2_normalize", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SendOp : OneFlow_BaseOp<"send", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x - ); - let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$square_x_sum + OneFlow_Tensor:$in ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$epsilon + DefaultValuedAttr:$dst_process_id ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; } -def OneFlow_L2NormalizeGradOp : OneFlow_BaseOp<"l2_normalize_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SplitLikeOp : OneFlow_BaseOp<"split_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$y, - OneFlow_Tensor:$square_x_sum + OneFlow_Tensor:$in, + Variadic:$like ); let output = (outs - OneFlow_Tensor:$dx + Variadic:$out ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$epsilon + DefaultValuedAttr:$axis ); + let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_LayerNormOp : OneFlow_BaseOp<"layer_norm", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_SspVariableProxyOp : OneFlow_BaseOp<"ssp_variable_proxy", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - Optional:$beta, - Optional:$gamma + OneFlow_Tensor:$var ); let output = (outs - OneFlow_Tensor:$y, - OneFlow_Tensor:$mean, - OneFlow_Tensor:$inv_variance + OneFlow_Tensor:$ref, + OneFlow_Tensor:$value ); let attrs = (ins - DefaultValuedAttr:$center, - DefaultValuedAttr:$scale, - DefaultValuedAttr:$begin_norm_axis, - DefaultValuedAttr:$begin_params_axis, - DefaultValuedAttr:$epsilon - ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes + DefaultValuedAttr:$buffer_size ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_output_arg_modify_fn = 1; } -def OneFlow_LayerNormGradOp : OneFlow_BaseOp<"layer_norm_grad", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_TfPreluGradOp : OneFlow_BaseOp<"tf_prelu_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$dy, OneFlow_Tensor:$x, - OneFlow_Tensor:$mean, - OneFlow_Tensor:$inv_variance, - Optional:$gamma, - Optional:$_add_to_output + OneFlow_Tensor:$alpha ); let output = (outs - OneFlow_Tensor:$dx - ); - let attrs = (ins - DefaultValuedAttr:$begin_norm_axis, - DefaultValuedAttr:$epsilon - ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes + OneFlow_Tensor:$dx, + OneFlow_Tensor:$alpha_diff ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5686,36 +5925,34 @@ def OneFlow_LayerNormGradOp : OneFlow_BaseOp<"layer_norm_grad", [NoSideEffect, A let has_data_type_infer_fn = 1; } -def OneFlow_LayerNormParamGradOp : OneFlow_BaseOp<"layer_norm_param_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$dy, - OneFlow_Tensor:$x, - OneFlow_Tensor:$mean, - OneFlow_Tensor:$inv_variance - ); +def OneFlow_UniformOp : OneFlow_BaseOp<"uniform", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs - Optional:$beta_diff, - Optional:$gamma_diff + OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$begin_params_axis - ); - let trait_attrs = (ins - I32ElementsAttr:$result_segment_sizes + DefaultValuedAttr:$from, + DefaultValuedAttr:$to, + DefaultValuedAttr:$seed, + OneFlow_DataType:$dtype, + ShapeAttr:$shape, + StrArrayAttr:$nd_sbp ); + let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; + let has_dump_nd_sbp_signature_for_op_conf_fn = 1; } -def OneFlow_NormalOp : OneFlow_BaseOp<"normal", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_UniformIntOp : OneFlow_BaseOp<"uniform_int", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let output = (outs OneFlow_Tensor:$out ); let attrs = (ins - DefaultValuedAttr:$mean, - DefaultValuedAttr:$std, + DefaultValuedAttr:$from, + DefaultValuedAttr:$to, DefaultValuedAttr:$seed, OneFlow_DataType:$dtype, ShapeAttr:$shape, @@ -5729,60 +5966,1169 @@ def OneFlow_NormalOp : OneFlow_BaseOp<"normal", [NoSideEffect, NoGrad, DeclareOp let has_nd_sbp_infer_fn = 1; } -def OneFlow_NormalizationOp : OneFlow_NormalizationBaseOp<"normalization", [AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_ExponentialOp : OneFlow_BaseOp<"exponential", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$seed, + DefaultValuedAttr:$lambd, + OneFlow_DataType:$dtype, + ShapeAttr:$out_shape, + StrArrayAttr:$nd_sbp + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow_MultinomialWithReplacementOp : OneFlow_BaseOp<"multinomial_with_replacement", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$prefix_sum + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$seed, + DefaultValuedAttr:$num_samples + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_UniqueOp : OneFlow_BaseOp<"unique", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$idx, + OneFlow_Tensor:$num_unique + ); + let attrs = (ins + OneFlow_DataType:$out_idx, + DefaultValuedAttr:$sorted + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_UniqueWithCountsOp : OneFlow_BaseOp<"unique_with_counts", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$idx, + OneFlow_Tensor:$num_unique, + OneFlow_Tensor:$count + ); + let attrs = (ins + OneFlow_DataType:$out_idx, + DefaultValuedAttr:$sorted + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_XdivyXGradOp : OneFlow_BaseOp<"xdivy_x_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_XdivyYGradOp : OneFlow_BaseOp<"xdivy_y_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$y, + OneFlow_Tensor:$dz + ); + let output = (outs + OneFlow_Tensor:$dy + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_StackOp : OneFlow_BaseOp<"stack", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + Variadic:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$max_dim_size + ); + let has_check_fn = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_StackGradOp : OneFlow_BaseOp<"stack_grad", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in, + Variadic:$like + ); + let output = (outs + Variadic:$out + ); + let attrs = (ins + DefaultValuedAttr:$axis + ); + let has_check_fn = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_FusedWeightedSumOp : OneFlow_BaseOp<"fused_weighted_sum", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + Variadic:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + F32ArrayAttr:$weights, + DefaultValuedAttr:$alpha + ); + let has_check_fn = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +#endif // GET_ONEFLOW_MISC_OP_DEFINITIONS + +// Group: NCCL +// _nccl_logical_2D_same_dim0_all2all, _nccl_logical_2D_same_dim0_all_gather, _nccl_logical_2D_same_dim0_all_gather_noncontinuous, _nccl_logical_2D_same_dim0_all_reduce, _nccl_logical_2D_same_dim1_all_reduce, _nccl_logical_all_gather, _nccl_logical_all_gather_noncontinuous, _nccl_logical_all_reduce, _nccl_logical_reduce_scatter, _nccl_logical_s2s, _nccl_logical_send_recv +// Total: 11 + +#ifdef GET_ONEFLOW_NCCL_OP_DEFINITIONS + +def OneFlow__ncclLogical_2DSameDim0All2allOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all2all", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogical_2DSameDim0AllGatherOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogical_2DSameDim0AllGatherNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_gather_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogical_2DSameDim0AllReduceOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim0_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogical_2DSameDim1AllReduceOp : OneFlow_BaseOp<"_nccl_logical_2D_same_dim1_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalAllGatherOp : OneFlow_BaseOp<"_nccl_logical_all_gather", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalAllGatherNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_all_gather_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalAllReduceOp : OneFlow_BaseOp<"_nccl_logical_all_reduce", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalReduceScatterOp : OneFlow_BaseOp<"_nccl_logical_reduce_scatter", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalReduceScatterNoncontinuousOp : OneFlow_BaseOp<"_nccl_logical_reduce_scatter_noncontinuous", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalS2sOp : OneFlow_BaseOp<"_nccl_logical_s2s", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_reduced_nd_sbp, + StrArrayAttr:$dst_reduced_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow__ncclLogicalSendRecvOp : OneFlow_BaseOp<"_nccl_logical_send_recv", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + StrArrayAttr:$src_nd_sbp, + StrArrayAttr:$dst_nd_sbp + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_device_and_stream_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +#endif // GET_ONEFLOW_NCCL_OP_DEFINITIONS + +// Group: NORMALIZATION +// crop_mirror_normalize_from_tensorbuffer, crop_mirror_normalize_from_uint8, image_normalize, l2_normalize, l2_normalize_grad, layer_norm, layer_norm_grad, layer_norm_param_grad, normal, normalization, normalization_grad, group_norm, group_norm_grad, group_norm_param_grad, batch_norm_stats, batch_norm_gather_stats_with_counts, batch_norm_elemt, batch_norm_backward_reduce, batch_norm_backward_elemt, rms_norm, rms_norm_param_grad, rms_norm_grad +// Total: 22 + +#ifdef GET_ONEFLOW_NORMALIZATION_OP_DEFINITIONS + +def OneFlow_NormalizationAddReluOp : OneFlow_BaseOp<"normalization_add_relu", [NoSideEffect, AttrSizedOperandSegments, AttrSizedResultSegments, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$addend, + Optional:$moving_mean, + Optional:$moving_variance, + OneFlow_Tensor:$gamma, + OneFlow_Tensor:$beta + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$reserve_space, + Optional:$mean, + Optional:$inv_variance + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$training, + DefaultValuedAttr:$momentum + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes, + I32ElementsAttr:$result_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_BatchNormStatsOp : OneFlow_BaseOp<"batch_norm_stats", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input + ); + let output = (outs + OneFlow_Tensor:$mean, + OneFlow_Tensor:$invstd + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$eps + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_BatchNormGatherStatsWithCountsOp : OneFlow_BaseOp<"batch_norm_gather_stats_with_counts", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$invstd, + OneFlow_Tensor:$counts, + Optional:$running_mean, + Optional:$running_var + ); + let output = (outs + OneFlow_Tensor:$global_mean, + OneFlow_Tensor:$global_invstd + ); + let attrs = (ins + DefaultValuedAttr:$eps, + DefaultValuedAttr:$momentum + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_BatchNormElemtOp : OneFlow_BaseOp<"batch_norm_elemt", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$bias, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$invstd + ); + let output = (outs + OneFlow_Tensor:$output + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$eps + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_BatchNormBackwardReduceOp : OneFlow_BaseOp<"batch_norm_backward_reduce", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$grad_out, + OneFlow_Tensor:$input, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$invstd + ); + let output = (outs + OneFlow_Tensor:$sum_dy, + OneFlow_Tensor:$sum_dy_xmu, + OneFlow_Tensor:$grad_weight, + OneFlow_Tensor:$grad_bias + ); + let attrs = (ins + DefaultValuedAttr:$axis + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_BatchNormBackwardElemtOp : OneFlow_BaseOp<"batch_norm_backward_elemt", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$grad_out, + OneFlow_Tensor:$input, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$invstd, + OneFlow_Tensor:$weight, + OneFlow_Tensor:$sum_dy, + OneFlow_Tensor:$sum_dy_xmu, + OneFlow_Tensor:$count + ); + let output = (outs + OneFlow_Tensor:$grad_in + ); + let attrs = (ins + DefaultValuedAttr:$axis + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_CropMirrorNormalizeFromTensorbufferOp : OneFlow_BaseOp<"crop_mirror_normalize_from_tensorbuffer", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in, + Optional:$mirror + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$color_space, + DefaultValuedAttr:$output_layout, + F32ArrayAttr:$mean, + F32ArrayAttr:$std, + DefaultValuedAttr:$crop_h, + DefaultValuedAttr:$crop_w, + DefaultValuedAttr:$crop_pos_x, + DefaultValuedAttr:$crop_pos_y, + OneFlow_DataType:$output_dtype + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_CropMirrorNormalizeFromUint8Op : OneFlow_BaseOp<"crop_mirror_normalize_from_uint8", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in, + Optional:$mirror + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$color_space, + DefaultValuedAttr:$output_layout, + F32ArrayAttr:$mean, + F32ArrayAttr:$std, + DefaultValuedAttr:$crop_h, + DefaultValuedAttr:$crop_w, + DefaultValuedAttr:$crop_pos_x, + DefaultValuedAttr:$crop_pos_y, + OneFlow_DataType:$output_dtype + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_ImageNormalizeOp : OneFlow_BaseOp<"image_normalize", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + F32ArrayAttr:$std, + F32ArrayAttr:$mean + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_L2NormalizeOp : OneFlow_BaseOp<"l2_normalize", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$square_x_sum + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$epsilon + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_L2NormalizeGradOp : OneFlow_BaseOp<"l2_normalize_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$y, + OneFlow_Tensor:$square_x_sum + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$epsilon + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_LayerNormOp : OneFlow_BaseOp<"layer_norm", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$beta, + Optional:$gamma + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance + ); + let attrs = (ins + DefaultValuedAttr:$center, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$begin_norm_axis, + DefaultValuedAttr:$begin_params_axis, + DefaultValuedAttr:$epsilon + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_LayerNormGradOp : OneFlow_BaseOp<"layer_norm_grad", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance, + Optional:$gamma, + Optional:$_add_to_output + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$begin_norm_axis, + DefaultValuedAttr:$epsilon + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_LayerNormParamGradOp : OneFlow_BaseOp<"layer_norm_param_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance + ); + let output = (outs + Optional:$beta_diff, + Optional:$gamma_diff + ); + let attrs = (ins + DefaultValuedAttr:$begin_params_axis + ); + let trait_attrs = (ins + I32ElementsAttr:$result_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_NormalOp : OneFlow_BaseOp<"normal", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$mean, + DefaultValuedAttr:$std, + DefaultValuedAttr:$seed, + OneFlow_DataType:$dtype, + ShapeAttr:$shape, + StrArrayAttr:$nd_sbp + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; +} + +def OneFlow_NormalizationOp : OneFlow_NormalizationBaseOp<"normalization", [AttrSizedResultSegments, DeclareOpInterfaceMethods]> { let hasCanonicalizer = 1; } -def OneFlow_NormalizationGradOp : OneFlow_BaseOp<"normalization_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_NormalizationGradOp : OneFlow_BaseOp<"normalization_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance, + OneFlow_Tensor:$gamma + ); + let output = (outs + OneFlow_Tensor:$gamma_diff, + OneFlow_Tensor:$beta_diff, + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$axis, + DefaultValuedAttr:$epsilon + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_GroupNormOp : OneFlow_BaseOp<"group_norm", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$beta, + Optional:$gamma + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance + ); + let attrs = (ins + DefaultValuedAttr:$affine, + DefaultValuedAttr:$num_groups, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$data_format, + DefaultValuedAttr:$activation + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_GroupNormGradOp : OneFlow_BaseOp<"group_norm_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance, + Optional:$gamma + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let attrs = (ins + DefaultValuedAttr:$num_groups, + DefaultValuedAttr:$epsilon + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_GroupNormParamGradOp : OneFlow_BaseOp<"group_norm_param_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$mean, + OneFlow_Tensor:$inv_variance + ); + let output = (outs + OneFlow_Tensor:$dgamma, + OneFlow_Tensor:$dbeta + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_RmsNormOp : OneFlow_BaseOp<"rms_norm", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + Optional:$weight + ); + let output = (outs + OneFlow_Tensor:$y, + OneFlow_Tensor:$inv_rms + ); + let attrs = (ins + ShapeAttr:$normalized_shape, + DefaultValuedAttr:$epsilon + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_RmsNormParamGradOp : OneFlow_BaseOp<"rms_norm_param_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$inv_rms + ); + let output = (outs + OneFlow_Tensor:$weight_grad + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_RmsNormGradOp : OneFlow_BaseOp<"rms_norm_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy, + OneFlow_Tensor:$x, + OneFlow_Tensor:$inv_rms, + Optional:$weight + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +#endif // GET_ONEFLOW_NORMALIZATION_OP_DEFINITIONS + +// Group: OPTIMIZER +// adagrad_update, adam_bias_correction_factor, adam_update, indexed_slices_adam_update, indexed_slices_momentum_update, indexed_slices_sgd_update, lamb_update, lars_update, momentum_update, rmsprop_update, sgd_update, ftrl_update, adadelta_update, multi_tensor_sgd_update, multi_tensor_momentum_update, multi_tensor_adam_update, multi_tensor_sgd_update_with_cast, multi_tensor_momentum_update_with_cast, multi_tensor_adam_update_with_cast +// Total: 19 + +#ifdef GET_ONEFLOW_OPTIMIZER_OP_DEFINITIONS + +def OneFlow_AdagradUpdateOp : OneFlow_BaseOp<"adagrad_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if, + Optional:$train_step, + OneFlow_Tensor:$sum + ); + let attrs = (ins + DefaultValuedAttr:$train_step_val, + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$lr_decay, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$epsilon + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_AdamBiasCorrectionFactorOp : OneFlow_BaseOp<"adam_bias_correction_factor", [NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$train_step + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$beta + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_AdamUpdateOp : OneFlow_BaseOp<"adam_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + Optional:$model_copy, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if, + Optional:$bias_correction1, + Optional:$bias_correction2, + OneFlow_Tensor:$m, + OneFlow_Tensor:$v, + Optional:$max_v + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$bias_correction1_val, + DefaultValuedAttr:$bias_correction2_val, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$beta1, + DefaultValuedAttr:$beta2, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$amsgrad, + DefaultValuedAttr:$do_bias_correction + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_IndexedSlicesAdamUpdateOp : OneFlow_BaseOp<"indexed_slices_adam_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff_indices, + OneFlow_Tensor:$model_diff_values, + OneFlow_Tensor:$learning_rate, + Optional:$bias_correction1, + Optional:$bias_correction2, + OneFlow_Tensor:$m, + OneFlow_Tensor:$v, + Optional:$max_v + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$beta1, + DefaultValuedAttr:$beta2, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$amsgrad, + DefaultValuedAttr:$do_bias_correction + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_IndexedSlicesMomentumUpdateOp : OneFlow_BaseOp<"indexed_slices_momentum_update", [NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff_indices, + OneFlow_Tensor:$model_diff_values, + OneFlow_Tensor:$learning_rate, + OneFlow_Tensor:$momentum + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$beta, + DefaultValuedAttr:$dampening, + DefaultValuedAttr:$nesterov, + DefaultValuedAttr:$maximize, + DefaultValuedAttr:$weight_decay + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_IndexedSlicesSgdUpdateOp : OneFlow_BaseOp<"indexed_slices_sgd_update", [NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff_indices, + OneFlow_Tensor:$model_diff_values, + OneFlow_Tensor:$learning_rate + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$weight_decay + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_LambUpdateOp : OneFlow_BaseOp<"lamb_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if, + Optional:$bias_correction1, + Optional:$bias_correction2, + OneFlow_Tensor:$m, + OneFlow_Tensor:$v + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$bias_correction1_val, + DefaultValuedAttr:$bias_correction2_val, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$beta1, + DefaultValuedAttr:$beta2, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$do_bias_correction + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_LarsUpdateOp : OneFlow_BaseOp<"lars_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + OneFlow_Tensor:$learning_rate, + OneFlow_Tensor:$momentum, + Optional:$scale_by_tensor, + Optional:$skip_if + ); + let attrs = (ins + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$momentum_beta, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$lars_coefficient, + DefaultValuedAttr:$weight_decay + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_MomentumUpdateOp : OneFlow_BaseOp<"momentum_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy, - OneFlow_Tensor:$mean, - OneFlow_Tensor:$inv_variance, - OneFlow_Tensor:$gamma - ); - let output = (outs - OneFlow_Tensor:$gamma_diff, - OneFlow_Tensor:$beta_diff, - OneFlow_Tensor:$dx + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + OneFlow_Tensor:$momentum, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if ); let attrs = (ins - DefaultValuedAttr:$axis, - DefaultValuedAttr:$epsilon + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$beta, + DefaultValuedAttr:$dampening, + DefaultValuedAttr:$nesterov, + DefaultValuedAttr:$maximize, + DefaultValuedAttr:$weight_decay + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -#endif // GET_ONEFLOW_NORMALIZATION_OP_DEFINITIONS - -// Group: OPTIMIZER -// adagrad_update, adam_bias_correction_factor, adam_update, indexed_slices_adam_update, indexed_slices_momentum_update, indexed_slices_sgd_update, lamb_update, lars_update, momentum_update, rmsprop_update, sgd_update, ftrl_update -// Total: 13 - -#ifdef GET_ONEFLOW_OPTIMIZER_OP_DEFINITIONS - -def OneFlow_AdagradUpdateOp : OneFlow_BaseOp<"adagrad_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_RmspropUpdateOp : OneFlow_BaseOp<"rmsprop_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$model, OneFlow_Tensor:$model_diff, Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$skip_if, - Optional:$train_step, - OneFlow_Tensor:$sum + OneFlow_Tensor:$mean_square, + Optional:$mean_gradient ); let attrs = (ins - DefaultValuedAttr:$train_step_val, DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$lr_decay, - DefaultValuedAttr:$weight_decay, - DefaultValuedAttr:$epsilon + DefaultValuedAttr:$centered, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$decay_rate, + DefaultValuedAttr:$weight_decay ); let trait_attrs = (ins I32ElementsAttr:$operand_segment_sizes @@ -5794,48 +7140,53 @@ def OneFlow_AdagradUpdateOp : OneFlow_BaseOp<"adagrad_update", [NoGrad, AttrSize let has_input_arg_modify_fn = 1; } -def OneFlow_AdamBiasCorrectionFactorOp : OneFlow_BaseOp<"adam_bias_correction_factor", [NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_SgdUpdateOp : OneFlow_BaseOp<"sgd_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$train_step - ); - let output = (outs - OneFlow_Tensor:$out + OneFlow_Tensor:$model, + OneFlow_Tensor:$model_diff, + Optional:$model_copy, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if ); let attrs = (ins - DefaultValuedAttr:$beta + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$weight_decay + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; } -def OneFlow_AdamUpdateOp : OneFlow_BaseOp<"adam_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_FtrlUpdateOp : OneFlow_BaseOp<"ftrl_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$model, OneFlow_Tensor:$model_diff, Optional:$learning_rate, - Optional:$scale_by_tensor, Optional:$skip_if, - Optional:$bias_correction1, - Optional:$bias_correction2, - OneFlow_Tensor:$m, - OneFlow_Tensor:$v, - Optional:$max_v + OneFlow_Tensor:$accumulate, + OneFlow_Tensor:$z ); let attrs = (ins DefaultValuedAttr:$learning_rate_val, - DefaultValuedAttr:$bias_correction1_val, - DefaultValuedAttr:$bias_correction2_val, + DefaultValuedAttr:$learning_rate_scale, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$beta1, - DefaultValuedAttr:$beta2, - DefaultValuedAttr:$epsilon, DefaultValuedAttr:$weight_decay, - DefaultValuedAttr:$amsgrad, - DefaultValuedAttr:$do_bias_correction + DefaultValuedAttr:$lr_power, + DefaultValuedAttr:$lambda1, + DefaultValuedAttr:$lambda2, + DefaultValuedAttr:$beta ); let trait_attrs = (ins I32ElementsAttr:$operand_segment_sizes @@ -5847,26 +7198,25 @@ def OneFlow_AdamUpdateOp : OneFlow_BaseOp<"adam_update", [NoGrad, AttrSizedOpera let has_input_arg_modify_fn = 1; } -def OneFlow_IndexedSlicesAdamUpdateOp : OneFlow_BaseOp<"indexed_slices_adam_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_AdadeltaUpdateOp : OneFlow_BaseOp<"adadelta_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff_indices, - OneFlow_Tensor:$model_diff_values, - OneFlow_Tensor:$learning_rate, - Optional:$bias_correction1, - Optional:$bias_correction2, - OneFlow_Tensor:$m, - OneFlow_Tensor:$v, - Optional:$max_v + OneFlow_Tensor:$model_diff, + Optional:$learning_rate, + Optional:$skip_if, + OneFlow_Tensor:$square_avgs, + OneFlow_Tensor:$acc_deltas ); let attrs = (ins DefaultValuedAttr:$learning_rate_val, - DefaultValuedAttr:$beta1, - DefaultValuedAttr:$beta2, - DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, DefaultValuedAttr:$weight_decay, - DefaultValuedAttr:$amsgrad, - DefaultValuedAttr:$do_bias_correction + DefaultValuedAttr:$rho, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$maximize ); let trait_attrs = (ins I32ElementsAttr:$operand_segment_sizes @@ -5878,18 +7228,25 @@ def OneFlow_IndexedSlicesAdamUpdateOp : OneFlow_BaseOp<"indexed_slices_adam_upda let has_input_arg_modify_fn = 1; } -def OneFlow_IndexedSlicesMomentumUpdateOp : OneFlow_BaseOp<"indexed_slices_momentum_update", [NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorSgdUpdateOp : OneFlow_BaseOp<"multi_tensor_sgd_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff_indices, - OneFlow_Tensor:$model_diff_values, - OneFlow_Tensor:$learning_rate, - OneFlow_Tensor:$momentum + Variadic:$model, + Variadic:$model_diff, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if ); let attrs = (ins - DefaultValuedAttr:$beta, + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, DefaultValuedAttr:$weight_decay ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -5897,15 +7254,29 @@ def OneFlow_IndexedSlicesMomentumUpdateOp : OneFlow_BaseOp<"indexed_slices_momen let has_input_arg_modify_fn = 1; } -def OneFlow_IndexedSlicesSgdUpdateOp : OneFlow_BaseOp<"indexed_slices_sgd_update", [NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorMomentumUpdateOp : OneFlow_BaseOp<"multi_tensor_momentum_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff_indices, - OneFlow_Tensor:$model_diff_values, - OneFlow_Tensor:$learning_rate + Variadic:$model, + Variadic:$model_diff, + Variadic:$momentum_buf, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$skip_if ); let attrs = (ins - DefaultValuedAttr:$weight_decay + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$momentum, + DefaultValuedAttr:$dampening, + DefaultValuedAttr:$nesterov, + DefaultValuedAttr:$maximize + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -5914,20 +7285,21 @@ def OneFlow_IndexedSlicesSgdUpdateOp : OneFlow_BaseOp<"indexed_slices_sgd_update let has_input_arg_modify_fn = 1; } -def OneFlow_LambUpdateOp : OneFlow_BaseOp<"lamb_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorAdamUpdateOp : OneFlow_BaseOp<"multi_tensor_adam_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, + Variadic:$model, + Variadic:$model_diff, Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$skip_if, Optional:$bias_correction1, Optional:$bias_correction2, - OneFlow_Tensor:$m, - OneFlow_Tensor:$v + Variadic:$m, + Variadic:$v ); let attrs = (ins DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, DefaultValuedAttr:$bias_correction1_val, DefaultValuedAttr:$bias_correction2_val, DefaultValuedAttr:$scale, @@ -5935,8 +7307,9 @@ def OneFlow_LambUpdateOp : OneFlow_BaseOp<"lamb_update", [NoGrad, AttrSizedOpera DefaultValuedAttr:$l2, DefaultValuedAttr:$beta1, DefaultValuedAttr:$beta2, - DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$epsilon, DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$amsgrad, DefaultValuedAttr:$do_bias_correction ); let trait_attrs = (ins @@ -5949,22 +7322,21 @@ def OneFlow_LambUpdateOp : OneFlow_BaseOp<"lamb_update", [NoGrad, AttrSizedOpera let has_input_arg_modify_fn = 1; } -def OneFlow_LarsUpdateOp : OneFlow_BaseOp<"lars_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorSgdUpdateWithCastOp : OneFlow_BaseOp<"multi_tensor_sgd_update_with_cast", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, - OneFlow_Tensor:$learning_rate, - OneFlow_Tensor:$momentum, + Variadic:$model, + Variadic:$model_diff, + Variadic:$model_copy, + Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$skip_if ); let attrs = (ins + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$momentum_beta, - DefaultValuedAttr:$epsilon, - DefaultValuedAttr:$lars_coefficient, DefaultValuedAttr:$weight_decay ); let trait_attrs = (ins @@ -5977,22 +7349,27 @@ def OneFlow_LarsUpdateOp : OneFlow_BaseOp<"lars_update", [NoGrad, AttrSizedOpera let has_input_arg_modify_fn = 1; } -def OneFlow_MomentumUpdateOp : OneFlow_BaseOp<"momentum_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorMomentumUpdateWithCastOp : OneFlow_BaseOp<"multi_tensor_momentum_update_with_cast", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, - OneFlow_Tensor:$momentum, + Variadic:$model, + Variadic:$model_diff, + Variadic:$model_copy, + Variadic:$momentum_buf, Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$skip_if ); let attrs = (ins DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$beta, - DefaultValuedAttr:$weight_decay + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$momentum, + DefaultValuedAttr:$dampening, + DefaultValuedAttr:$nesterov, + DefaultValuedAttr:$maximize ); let trait_attrs = (ins I32ElementsAttr:$operand_segment_sizes @@ -6004,25 +7381,33 @@ def OneFlow_MomentumUpdateOp : OneFlow_BaseOp<"momentum_update", [NoGrad, AttrSi let has_input_arg_modify_fn = 1; } -def OneFlow_RmspropUpdateOp : OneFlow_BaseOp<"rmsprop_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorAdamUpdateWithCastOp : OneFlow_BaseOp<"multi_tensor_adam_update_with_cast", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, + Variadic:$model, + Variadic:$model_diff, + Variadic:$model_copy, Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$skip_if, - OneFlow_Tensor:$mean_square, - Optional:$mean_gradient + Optional:$bias_correction1, + Optional:$bias_correction2, + Variadic:$m, + Variadic:$v ); let attrs = (ins DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$learning_rate_scale, + DefaultValuedAttr:$bias_correction1_val, + DefaultValuedAttr:$bias_correction2_val, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$centered, - DefaultValuedAttr:$epsilon, - DefaultValuedAttr:$decay_rate, - DefaultValuedAttr:$weight_decay + DefaultValuedAttr:$beta1, + DefaultValuedAttr:$beta2, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$amsgrad, + DefaultValuedAttr:$do_bias_correction ); let trait_attrs = (ins I32ElementsAttr:$operand_segment_sizes @@ -6034,53 +7419,95 @@ def OneFlow_RmspropUpdateOp : OneFlow_BaseOp<"rmsprop_update", [NoGrad, AttrSize let has_input_arg_modify_fn = 1; } -def OneFlow_SgdUpdateOp : OneFlow_BaseOp<"sgd_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_MultiTensorYoloV5WeightUpdateOp : OneFlow_BaseOp<"multi_tensor_yolov5_weight_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, - Optional:$learning_rate, - Optional:$scale_by_tensor, - Optional:$skip_if + Variadic:$model, + Variadic:$model_update ); let attrs = (ins - DefaultValuedAttr:$learning_rate_val, - DefaultValuedAttr:$scale, - DefaultValuedAttr:$l1, - DefaultValuedAttr:$l2, - DefaultValuedAttr:$weight_decay + DefaultValuedAttr:$d + ); + let trait_attrs = (ins + I32ElementsAttr:$operand_segment_sizes + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +#endif // GET_ONEFLOW_OPTIMIZER_OP_DEFINITIONS + +// Group: PADDING +// pad, reflection_pad1d, reflection_pad1d_grad, reflection_pad2d, reflection_pad2d_grad, replication_pad1d, replication_pad1d_grad, replication_pad2d, replication_pad2d_grad, same_padding, same_padding_grad +// Total: 11 + +#ifdef GET_ONEFLOW_PADDING_OP_DEFINITIONS + + +def OneFlow_PadOp : OneFlow_BaseOp<"pad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let attrs = (ins + SI64ArrayAttr:$padding_before, + SI64ArrayAttr:$padding_after, + SI64ArrayAttr:$padding, + DefaultValuedAttr:$floating_constant_value, + DefaultValuedAttr:$integral_constant_value + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_ReplicationPad1DOp : OneFlow_BaseOp<"replication_pad1d", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let attrs = (ins + SI64ArrayAttr:$padding + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let has_input_arg_modify_fn = 1; +} + +def OneFlow_ReplicationPad1DGradOp : OneFlow_BaseOp<"replication_pad1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$dy ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes + let output = (outs + OneFlow_Tensor:$dx + ); + let attrs = (ins + SI64ArrayAttr:$padding ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; - let has_input_arg_modify_fn = 1; } -def OneFlow_FtrlUpdateOp : OneFlow_BaseOp<"ftrl_update", [NoGrad, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_ReflectionPad1DOp : OneFlow_BaseOp<"reflection_pad1d", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$model, - OneFlow_Tensor:$model_diff, - Optional:$learning_rate, - Optional:$skip_if, - OneFlow_Tensor:$accumulate, - OneFlow_Tensor:$z + OneFlow_Tensor:$x ); - let attrs = (ins - DefaultValuedAttr:$learning_rate_val, - DefaultValuedAttr:$scale, - DefaultValuedAttr:$l1, - DefaultValuedAttr:$l2, - DefaultValuedAttr:$weight_decay, - DefaultValuedAttr:$lr_power, - DefaultValuedAttr:$lambda1, - DefaultValuedAttr:$lambda2, - DefaultValuedAttr:$beta + let output = (outs + OneFlow_Tensor:$y ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes + let attrs = (ins + SI64ArrayAttr:$padding ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -6089,28 +7516,15 @@ def OneFlow_FtrlUpdateOp : OneFlow_BaseOp<"ftrl_update", [NoGrad, AttrSizedOpera let has_input_arg_modify_fn = 1; } -#endif // GET_ONEFLOW_OPTIMIZER_OP_DEFINITIONS - -// Group: PADDING -// pad, reflection_pad2d, reflection_pad2d_grad, replication_pad2d, replication_pad2d_grad, same_padding, same_padding_grad -// Total: 7 - -#ifdef GET_ONEFLOW_PADDING_OP_DEFINITIONS - - -def OneFlow_PadOp : OneFlow_BaseOp<"pad", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ReflectionPad1DGradOp : OneFlow_BaseOp<"reflection_pad1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x + OneFlow_Tensor:$dy ); let output = (outs - OneFlow_Tensor:$y + OneFlow_Tensor:$dx ); let attrs = (ins - SI64ArrayAttr:$padding_before, - SI64ArrayAttr:$padding_after, - SI64ArrayAttr:$padding, - DefaultValuedAttr:$floating_constant_value, - DefaultValuedAttr:$integral_constant_value + SI64ArrayAttr:$padding ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -6118,7 +7532,6 @@ def OneFlow_PadOp : OneFlow_BaseOp<"pad", [NoSideEffect, DeclareOpInterfaceMetho let has_data_type_infer_fn = 1; } - def OneFlow_ReflectionPad2DOp : OneFlow_BaseOp<"reflection_pad2d", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x @@ -6251,6 +7664,7 @@ def OneFlow_HierarchicalParallelCastOp : OneFlow_BaseOp<"hierarchical_parallel_c let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; let has_nd_sbp_infer_fn = 1; + let has_get_nd_sbp_fn = 1; } def OneFlow_HierarchicalParallelCastLikeOp : OneFlow_BaseOp<"hierarchical_parallel_cast_like", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -6289,8 +7703,8 @@ def OneFlow_ParallelCastOp : OneFlow_BaseOp<"parallel_cast", [NoSideEffect, Decl #endif // GET_ONEFLOW_PARALLEL_CAST_OP_DEFINITIONS // Group: POOL -// adaptive_avg_pool1d, adaptive_avg_pool1d_grad, adaptive_avg_pool2d, adaptive_avg_pool2d_grad, adaptive_avg_pool3d, adaptive_avg_pool3d_grad, avg_pool_1d, avg_pool_1d_grad, avg_pool_2d, avg_pool_2d_grad, avg_pool_3d, avg_pool_3d_grad, max_pool_1d, max_pool_1d_grad, max_pool_2d, max_pool_2d_grad, max_pool_3d, max_pool_3d_grad, tf_avg_pool_1d, tf_avg_pool_1d_grad, tf_avg_pool_2d, tf_avg_pool_2d_grad, tf_avg_pool_3d, tf_avg_pool_3d_grad, tf_max_pool_1d, tf_max_pool_1d_grad, tf_max_pool_2d, tf_max_pool_2d_grad, tf_max_pool_3d, tf_max_pool_3d_grad -// Total: 30 +// adaptive_avg_pool1d, adaptive_avg_pool1d_grad, adaptive_avg_pool2d, adaptive_avg_pool2d_grad, adaptive_avg_pool3d, adaptive_avg_pool3d_grad, avg_pool_1d, avg_pool_1d_grad, avg_pool_2d, avg_pool_2d_grad, avg_pool_3d, avg_pool_3d_grad, max_pool_1d, max_pool_1d_grad, max_pool_2d, max_pool_2d_grad, max_pool_3d, max_pool_3d_grad, max_unpool_1d, max_unpool_2d, max_unpool_3d, max_unpool_1d_grad, max_unpool_2d_grad, max_unpool_3d_grad, tf_avg_pool_1d, tf_avg_pool_1d_grad, tf_avg_pool_2d, tf_avg_pool_2d_grad, tf_avg_pool_3d, tf_avg_pool_3d_grad, tf_max_pool_1d, tf_max_pool_1d_grad, tf_max_pool_2d, tf_max_pool_2d_grad, tf_max_pool_3d, tf_max_pool_3d_grad +// Total: 36 #ifdef GET_ONEFLOW_POOL_OP_DEFINITIONS @@ -6306,6 +7720,15 @@ def OneFlow_AdaptiveAvgPool3DOp : OneFlow_AdaptivePoolBaseOp<"adaptive_avg_pool3 def OneFlow_AdaptiveAvgPool3DGradOp : OneFlow_AdaptivePoolGradBaseOp<"adaptive_avg_pool3d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} +def OneFlow_AdaptiveMaxPool1DOp : OneFlow_AdaptiveMaxPoolBaseOp<"adaptive_max_pool1d", [NoSideEffect, DeclareOpInterfaceMethods]> {} +def OneFlow_AdaptiveMaxPool1DGradOp : OneFlow_AdaptiveMaxPoolGradBaseOp<"adaptive_max_pool1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_AdaptiveMaxPool2DOp : OneFlow_AdaptiveMaxPoolBaseOp<"adaptive_max_pool2d", [NoSideEffect, DeclareOpInterfaceMethods]> {} +def OneFlow_AdaptiveMaxPool2DGradOp : OneFlow_AdaptiveMaxPoolGradBaseOp<"adaptive_max_pool2d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_AdaptiveMaxPool3DOp : OneFlow_AdaptiveMaxPoolBaseOp<"adaptive_max_pool3d", [NoSideEffect, DeclareOpInterfaceMethods]> {} +def OneFlow_AdaptiveMaxPool3DGradOp : OneFlow_AdaptiveMaxPoolGradBaseOp<"adaptive_max_pool3d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + def OneFlow_AvgPool1DOp : OneFlow_AvgPoolBaseOp<"avg_pool_1d", [NoSideEffect, DeclareOpInterfaceMethods]> {} def OneFlow_AvgPool1DGradOp : OneFlow_AvgPoolGradBaseOp<"avg_pool_1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} @@ -6330,6 +7753,18 @@ def OneFlow_MaxPool3DOp : OneFlow_MaxPoolBaseOp<"max_pool_3d", [NoSideEffect, De def OneFlow_MaxPool3DGradOp : OneFlow_MaxPoolGradBaseOp<"max_pool_3d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} +def OneFlow_MaxUnpool1DOp : OneFlow_MaxUnpoolBaseOp<"max_unpool_1d", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_MaxUnpool2DOp : OneFlow_MaxUnpoolBaseOp<"max_unpool_2d", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_MaxUnpool3DOp : OneFlow_MaxUnpoolBaseOp<"max_unpool_3d", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_MaxUnpool1DGradOp : OneFlow_MaxUnpoolGradBaseOp<"max_unpool_1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_MaxUnpool2DGradOp : OneFlow_MaxUnpoolGradBaseOp<"max_unpool_2d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + +def OneFlow_MaxUnpool3DGradOp : OneFlow_MaxUnpoolGradBaseOp<"max_unpool_3d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} + def OneFlow_TfAvgPool1DOp : OneFlow_TFPoolBaseOp<"tf_avg_pool_1d", [NoSideEffect, DeclareOpInterfaceMethods]> {} def OneFlow_TfAvgPool1DGradOp : OneFlow_TFPoolGradBaseOp<"tf_avg_pool_1d_grad", [NoSideEffect, DeclareOpInterfaceMethods]> {} @@ -6458,8 +7893,8 @@ def OneFlow_QuantizationOp : OneFlow_BaseOp<"quantization", [NoSideEffect, Decla #endif // GET_ONEFLOW_QUANTIZATION_OP_DEFINITIONS // Group: REDUCE -// indexed_slices_reduce_sum, reduce_all, reduce_any, reduce_max, reduce_max_device_stage, reduce_max_device_stage_grad, reduce_max_global_stage, reduce_max_global_stage_grad, reduce_min, reduce_min_device_stage, reduce_min_device_stage_grad, reduce_min_global_stage, reduce_min_global_stage_grad, reduce_prod, reduce_sum, reduce_sum_like -// Total: 16 +// indexed_slices_reduce_sum, reduce_all, reduce_any, reduce_max, reduce_max_device_stage, reduce_max_device_stage_grad, reduce_max_global_stage, reduce_max_global_stage_grad, reduce_min, reduce_min_device_stage, reduce_min_device_stage_grad, reduce_min_global_stage, reduce_min_global_stage_grad, reduce_prod, reduce_sum, reduce_sum_like, reduce_nansum +// Total: 17 #ifdef GET_ONEFLOW_REDUCE_OP_DEFINITIONS @@ -6731,7 +8166,24 @@ def OneFlow_ReduceSumOp : OneFlow_BaseOp<"reduce_sum", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } -def OneFlow_ReduceSumLikeOp : OneFlow_BaseOp<"reduce_sum_like", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { +def OneFlow_ReduceNanSumOp : OneFlow_BaseOp<"reduce_nansum", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input_tensor + ); + let output = (outs + OneFlow_Tensor:$output_tensor + ); + let attrs = (ins + SI32ArrayAttr:$axis, + DefaultValuedAttr:$keepdims + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_ReduceSumLikeOp : OneFlow_BaseOp<"reduce_sum_like", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x, OneFlow_Tensor:$like @@ -6792,8 +8244,8 @@ def OneFlow_ReshapeLikeOp : OneFlow_BaseOp<"reshape_like", [NoSideEffect, Declar #endif // GET_ONEFLOW_RESHAPE_OP_DEFINITIONS // Group: SCALAR -// clip_by_scalar, clip_by_scalar_grad, clip_by_scalar_max, clip_by_scalar_max_grad, clip_by_scalar_min, clip_by_scalar_min_grad, scalar_add, scalar_add_by_tensor, scalar_div_by_tensor, scalar_floordiv, scalar_fmod, scalar_logical_and, scalar_logical_equal, scalar_logical_greater, scalar_logical_greater_equal, scalar_logical_less, scalar_logical_less_equal, scalar_logical_not_equal, scalar_logical_or, scalar_logical_xor, scalar_mul, scalar_mul_by_tensor, scalar_pow, scalar_pow_grad, scalar_reverse_pow, scalar_reverse_pow_grad, scalar_sub_by_tensor -// Total: 27 +// clip_by_scalar, clip_by_scalar_grad, clip_by_scalar_max, clip_by_scalar_max_grad, clip_by_scalar_min, clip_by_scalar_min_grad, scalar_add, scalar_add_by_tensor, scalar_div_by_tensor, scalar_floordiv, scalar_truncdiv, scalar_fmod, scalar_logical_and, scalar_logical_equal, scalar_logical_greater, scalar_logical_greater_equal, scalar_logical_less, scalar_logical_less_equal, scalar_logical_not_equal, scalar_logical_or, scalar_logical_xor, scalar_mul, scalar_mul_by_tensor, scalar_pow, scalar_pow_grad, scalar_reverse_pow, scalar_reverse_pow_grad, scalar_sub_by_tensor +// Total: 28 #ifdef GET_ONEFLOW_SCALAR_OP_DEFINITIONS @@ -6973,6 +8425,25 @@ def OneFlow_ScalarFloordivOp : OneFlow_BaseOp<"scalar_floordiv", [NoSideEffect, let has_data_type_infer_fn = 1; } +def OneFlow_ScalarTruncdivOp : OneFlow_BaseOp<"scalar_truncdiv", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$has_int_operand, + DefaultValuedAttr:$has_float_operand, + DefaultValuedAttr:$int_operand, + DefaultValuedAttr:$float_operand + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_ScalarFmodOp : OneFlow_BaseOp<"scalar_fmod", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in @@ -7196,7 +8667,7 @@ def OneFlow_ScalarMulByTensorOp : OneFlow_BaseOp<"scalar_mul_by_tensor", [NoSide let has_data_type_infer_fn = 1; } -def OneFlow_ScalarDivOp : OneFlow_BaseOp<"scalar_div", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ScalarDivOp : OneFlow_BaseOp<"scalar_div", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -7353,6 +8824,7 @@ def OneFlow_SoftmaxOp : OneFlow_BaseOp<"softmax", [NoSideEffect, DeclareOpInterf let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_compute_complexity_fn = 1; } def OneFlow_SoftmaxCrossEntropyOp : OneFlow_BaseOp<"softmax_cross_entropy", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -7646,6 +9118,25 @@ def OneFlow_TensorToTensorBufferOp : OneFlow_BaseOp<"tensor_to_tensor_buffer", [ #endif // GET_ONEFLOW_TENSOR_BUFFER_OP_DEFINITIONS +// Group: TEST +// throw_error +// Total: 1 + +#ifdef GET_ONEFLOW_TEST_OP_DEFINITIONS + +def OneFlow_ThrowErrorOp : OneFlow_BaseOp<"throw_error", [NoSideEffect, NoGrad, CpuOnly, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +#endif // GET_ONEFLOW_TEST_OP_DEFINITIONS // Group: TRIGONOMETRIC // acos, acos_grad, acosh, acosh_grad, asin, asin_grad, asinh, asinh_grad, atan, atan2, atan2_x_grad, atan2_y_grad, atan_grad, atanh, atanh_grad, cos, cos_grad, cosh, cosh_grad, hardtanh, hardtanh_grad, sin, sin_grad, sinh, sinh_grad, tan, tan_grad, tanh, tanh_grad, not_equal_zero, not_equal_zero_grad // Total: 31 @@ -8068,29 +9559,32 @@ def OneFlow_NotEqualZeroOp : OneFlow_BaseOp<"not_equal_zero", [NoSideEffect, Dec let has_data_type_infer_fn = 1; } -def OneFlow_NotEqualZeroGradOp : OneFlow_BaseOp<"not_equal_zero_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_TRIGONOMETRIC_OP_DEFINITIONS + +// Group: UNARY +// acc, acc_ctrl_tick, affine_grid, affine_grid_grad, bernoulli, cast, cast_to_static_shape, cast_to_tick, celu, copy, count_not_finite, diag, diagonal, elu, expand, expand_dims, flip, fold, gelu, fast_gelu, quick_gelu, hardsigmoid, hardshrink, hardswish, leaky_relu, log2, log10, logical_not, mish, narrow, one_hot, pack, random_mask_like, repeat, roll, selu, silu, softshrink, softsign, sort, square_sum, squeeze, threshold, transpose, tril, triu, unfold, unfold_tensor, unpack, zero_like, to_contiguous, isnan, isinf, isfinite, repeat_interleave, mutable_cast_once, trunc, index_add +// Total: 58 + +#ifdef GET_ONEFLOW_UNARY_OP_DEFINITIONS + +def OneFlow_AccOp : OneFlow_BaseOp<"acc", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$x, - OneFlow_Tensor:$dy + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$dx + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$max_acc_num ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_output_blob_time_shape_infer_fn = 1; } -#endif // GET_ONEFLOW_TRIGONOMETRIC_OP_DEFINITIONS - -// Group: UNARY -// acc, affine_grid, affine_grid_grad, bernoulli, cast, cast_to_static_shape, cast_to_tick, celu, copy, count_not_finite, diag, diagonal, elu, expand, expand_dims, flatten, flip, fold, gelu, hardsigmoid, hardshrink, hardswish, leaky_relu, log2, logical_not, mish, narrow, one_hot, pack, random_mask_like, repeat, roll, selu, silu, softshrink, softsign, sort, square_sum, squeeze, threshold, transpose, tril, triu, unfold, unfold_tensor, unpack, zero_like, to_contiguous, isnan, isinf, repeat_interleave -// Total: 50 - -#ifdef GET_ONEFLOW_UNARY_OP_DEFINITIONS - -def OneFlow_AccOp : OneFlow_BaseOp<"acc", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_AccCtrlTickOp : OneFlow_BaseOp<"acc_ctrl_tick", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -8104,6 +9598,7 @@ def OneFlow_AccOp : OneFlow_BaseOp<"acc", [NoSideEffect, DeclareOpInterfaceMetho let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let has_nd_sbp_infer_fn = 1; let has_output_blob_time_shape_infer_fn = 1; } @@ -8161,7 +9656,7 @@ def OneFlow_BernoulliOp : OneFlow_BaseOp<"bernoulli", [NoSideEffect, NoGrad, Cpu let has_data_type_infer_fn = 1; } -def OneFlow_CastOp : OneFlow_BaseOp<"cast", [NoSideEffect, SupportNonContiguous, DeclareOpInterfaceMethods]> { +def OneFlow_CastOp : OneFlow_BaseOp<"cast", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -8179,6 +9674,23 @@ def OneFlow_CastOp : OneFlow_BaseOp<"cast", [NoSideEffect, SupportNonContiguous, let has_data_type_infer_fn = 1; } +def OneFlow_MutableCastOnceOp : OneFlow_BaseOp<"mutable_cast_once", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + OneFlow_DataType:$dtype + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; + let same_output_regst_num = 1; +} + def OneFlow_CastToStaticShapeOp : OneFlow_BaseOp<"cast_to_static_shape", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$input @@ -8302,7 +9814,7 @@ def OneFlow_EluOp : OneFlow_BaseOp<"elu", [NoSideEffect, DeclareOpInterfaceMetho let has_data_type_infer_fn = 1; } -def OneFlow_ExpandOp : OneFlow_BaseOp<"expand", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_ExpandOp : OneFlow_BaseOp<"expand", [NoSideEffect, SupportNonContiguous, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -8310,8 +9822,7 @@ def OneFlow_ExpandOp : OneFlow_BaseOp<"expand", [NoSideEffect, DeclareOpInterfac OneFlow_Tensor:$out ); let attrs = (ins - SI32ArrayAttr:$logical_in_shape, - SI32ArrayAttr:$logical_expand_shape + ShapeAttr:$expand_shape ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -8335,23 +9846,6 @@ def OneFlow_ExpandDimsOp : OneFlow_BaseOp<"expand_dims", [NoSideEffect, DeclareO let has_data_type_infer_fn = 1; } -def OneFlow_FlattenOp : OneFlow_BaseOp<"flatten", [NoSideEffect, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$in - ); - let output = (outs - OneFlow_Tensor:$out - ); - let attrs = (ins - DefaultValuedAttr:$start_dim, - DefaultValuedAttr:$end_dim - ); - let has_logical_tensor_desc_infer_fn = 1; - let has_physical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; -} - def OneFlow_FlipOp : OneFlow_BaseOp<"flip", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x @@ -8402,6 +9896,32 @@ def OneFlow_GeluOp : OneFlow_BaseOp<"gelu", [NoSideEffect, DeclareOpInterfaceMet let has_data_type_infer_fn = 1; } +def OneFlow_FastGeluOp : OneFlow_BaseOp<"fast_gelu", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_QuickGeluOp : OneFlow_BaseOp<"quick_gelu", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_HardsigmoidOp : OneFlow_BaseOp<"hardsigmoid", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in @@ -8473,6 +9993,19 @@ def OneFlow_Log2Op : OneFlow_BaseOp<"log2", [NoSideEffect, DeclareOpInterfaceMet let has_data_type_infer_fn = 1; } +def OneFlow_Log10Op : OneFlow_BaseOp<"log10", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x + ); + let output = (outs + OneFlow_Tensor:$y + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_LogicalNotOp : OneFlow_BaseOp<"logical_not", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x @@ -8567,23 +10100,12 @@ def OneFlow_RandomMaskLikeOp : OneFlow_BaseOp<"random_mask_like", [NoSideEffect, DefaultValuedAttr:$rate, DefaultValuedAttr:$seed ); - let builders = [ - OpBuilder<(ins - "Value":$like, - "StringRef":$op_name, - "StringRef":$device_tag, - "ArrayAttr":$device_name, - "IntegerAttr":$scope_symbol_id, - "ArrayAttr":$hierarchy, - "FloatAttr":$rate, - "IntegerAttr":$seed - )> - ]; let has_check_fn = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; + let hasCanonicalizer = 1; } def OneFlow_RepeatOp : OneFlow_BaseOp<"repeat", [NoSideEffect, DeclareOpInterfaceMethods]> { @@ -8650,7 +10172,7 @@ def OneFlow_SeluOp : OneFlow_BaseOp<"selu", [NoSideEffect, DeclareOpInterfaceMet let has_data_type_infer_fn = 1; } -def OneFlow_SiluOp : OneFlow_BaseOp<"silu", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_SiluOp : OneFlow_BaseOp<"silu", [NoSideEffect, DeclareOpInterfaceMethods, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in ); @@ -8793,9 +10315,28 @@ def OneFlow_AsStridedOp : OneFlow_BaseOp<"as_strided", [NoSideEffect, DeclareOpI OneFlow_Tensor:$output ); let attrs = (ins - SI32ArrayAttr:$size, - SI32ArrayAttr:$stride, - DefaultValuedAttr:$storage_offset + SI64ArrayAttr:$size, + SI64ArrayAttr:$stride, + DefaultValuedAttr:$storage_offset + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_IndexAddOp : OneFlow_BaseOp<"index_add", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$input, + OneFlow_Tensor:$index, + OneFlow_Tensor:$source + ); + let output = (outs + OneFlow_Tensor:$output + ); + let attrs = (ins + SI64Attr: $dim, + F32Attr: $alpha ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -8812,9 +10353,9 @@ def OneFlow_AsStridedGradOp : OneFlow_BaseOp<"as_strided_grad", [NoSideEffect, D OneFlow_Tensor:$dx ); let attrs = (ins - SI32ArrayAttr:$size, - SI32ArrayAttr:$stride, - DefaultValuedAttr:$storage_offset + SI64ArrayAttr:$size, + SI64ArrayAttr:$stride, + DefaultValuedAttr:$storage_offset ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -8857,6 +10398,33 @@ def OneFlow_TriuOp : OneFlow_BaseOp<"triu", [NoSideEffect, DeclareOpInterfaceMet let has_data_type_infer_fn = 1; } +def OneFlow_TruncOp : OneFlow_BaseOp<"trunc", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_TruncGradOp : OneFlow_BaseOp<"trunc_grad", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$x, + OneFlow_Tensor:$dy + ); + let output = (outs + OneFlow_Tensor:$dx + ); + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_UnfoldOp : OneFlow_BaseOp<"unfold", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$x @@ -8938,7 +10506,7 @@ def OneFlow_ToContiguousOp : OneFlow_BaseOp<"to_contiguous", [NoSideEffect, Supp let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; -} +} def OneFlow_IsNanOp : OneFlow_BaseOp<"isnan", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins @@ -8947,7 +10515,6 @@ def OneFlow_IsNanOp : OneFlow_BaseOp<"isnan", [NoSideEffect, NoGrad, DeclareOpIn let output = (outs OneFlow_Tensor:$out ); - let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -8961,7 +10528,19 @@ def OneFlow_IsInfOp : OneFlow_BaseOp<"isinf", [NoSideEffect, NoGrad, DeclareOpIn let output = (outs OneFlow_Tensor:$out ); - let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_IsFiniteOp : OneFlow_BaseOp<"isfinite", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in + ); + let output = (outs + OneFlow_Tensor:$out + ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; @@ -9184,7 +10763,7 @@ def OneFlow_UpsampleNearest3DOp : OneFlow_BaseOp<"upsample_nearest_3d", [NoSideE DefaultValuedAttr:$depth_scale, DefaultValuedAttr:$height_scale, DefaultValuedAttr:$width_scale, - SI64ArrayAttr:$output_size, + SI64ArrayAttr:$output_size, StrAttr:$data_format ); let has_logical_tensor_desc_infer_fn = 1; @@ -9259,12 +10838,12 @@ def OneFlow_UpsampleTrilinear3DGradOp : OneFlow_BaseOp<"upsample_trilinear_3d_gr #endif // GET_ONEFLOW_UPSAMPLE_OP_DEFINITIONS // Group: OneEmbedding -// embedding_lookup_placeholder, embedding_update_placeholder, id_shuffle, embedding_shuffle, embedding_gradient_shuffle, embedding_prefetch, embedding_lookup, sgd_embedding_update, embedding_put, adagrad_embedding_update, ftrl_embedding_update +// one_embedding_fused_lookup, one_embedding_fused_lookup_grad, id_shuffle, embedding_shuffle, embedding_gradient_shuffle, embedding_prefetch, embedding_lookup, one_embedding_sgd_update, embedding_put, one_embedding_adagrad_update, one_embedding_ftrl_update // Total: 11 #ifdef GET_ONEFLOW_ONE_EMBEDDING_OP_DEFINITIONS -def OneFlow_EmbeddingLookupPlaceholderOp : OneFlow_BaseOp<"embedding_lookup_placeholder", [NoSideEffect, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingFusedLookupOp : OneFlow_BaseOp<"one_embedding_fused_lookup", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$shadow, OneFlow_Tensor:$ids, @@ -9275,10 +10854,15 @@ def OneFlow_EmbeddingLookupPlaceholderOp : OneFlow_BaseOp<"embedding_lookup_plac ); let attrs = (ins OneFlow_DataType:$dtype, + StrAttr:$embedding_name, + DefaultValuedAttr:$line_size, DefaultValuedAttr:$embedding_size, + DefaultValuedAttr:$is_full_cache, DefaultValuedAttr:$num_tables, + DefaultValuedAttr:$padding_idx, + DefaultValuedAttr:$has_padding_idx, StrAttr:$embedding_tables, - StrAttr:$key_value_store_options + DefaultValuedAttr:$seed ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -9287,13 +10871,15 @@ def OneFlow_EmbeddingLookupPlaceholderOp : OneFlow_BaseOp<"embedding_lookup_plac let has_input_arg_modify_fn = 1; } -def OneFlow_EmbeddingUpdatePlaceholderOp : OneFlow_BaseOp<"embedding_update_placeholder", [DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingFusedLookupGradOp : OneFlow_BaseOp<"one_embedding_fused_lookup_grad", [DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$ids, OneFlow_Tensor:$embedding_grad ); let attrs = (ins - StrAttr:$key_value_store_options + StrAttr:$embedding_name, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -9313,7 +10899,10 @@ def OneFlow_UniqueKeyValuePairOp : OneFlow_BaseOp<"unique_key_value_pair", [NoSi OneFlow_Tensor:$inverse_indices ); let attrs = (ins - DefaultValuedAttr:$num_tables + DefaultValuedAttr:$num_tables, + DefaultValuedAttr:$padding_idx, + DefaultValuedAttr:$has_padding_idx, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9336,7 +10925,10 @@ def OneFlow_IdShuffleOp : OneFlow_BaseOp<"id_shuffle", [NoSideEffect, DeclareOpI OneFlow_Tensor:$cur_rank_inverse_indices ); let attrs = (ins - DefaultValuedAttr:$num_tables + DefaultValuedAttr:$num_tables, + DefaultValuedAttr:$padding_idx, + DefaultValuedAttr:$has_padding_idx, + StrAttr:$embedding_name ); let same_output_regst_num = 2; let has_logical_tensor_desc_infer_fn = 1; @@ -9345,6 +10937,52 @@ def OneFlow_IdShuffleOp : OneFlow_BaseOp<"id_shuffle", [NoSideEffect, DeclareOpI let has_data_type_infer_fn = 1; } +def OneFlow_IdShuffleCopyOutOp : OneFlow_BaseOp<"id_shuffle_copy_out", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$num_unique_matrix, + OneFlow_Tensor:$inverse_unique_partition_indices, + OneFlow_Tensor:$cur_rank_num_unique, + OneFlow_Tensor:$cur_rank_unique_ids, + OneFlow_Tensor:$cur_rank_unique_table_ids, + OneFlow_Tensor:$cur_rank_inverse_indices + ); + let output = (outs + OneFlow_Tensor:$out_num_unique_matrix, + OneFlow_Tensor:$out_inverse_unique_partition_indices, + OneFlow_Tensor:$out_cur_rank_num_unique, + OneFlow_Tensor:$out_cur_rank_unique_ids, + OneFlow_Tensor:$out_cur_rank_unique_table_ids, + OneFlow_Tensor:$out_cur_rank_inverse_indices + ); + let attrs = (ins + StrAttr:$embedding_name + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_OneEmbeddingGatherOp : OneFlow_BaseOp<"one_embedding_gather", [NoSideEffect, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in, + OneFlow_Tensor:$indices + ); + let output = (outs + OneFlow_Tensor:$out + ); + let attrs = (ins + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + def OneFlow_EmbeddingShuffleOp : OneFlow_BaseOp<"embedding_shuffle", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$cur_rank_embeddings, @@ -9355,6 +10993,12 @@ def OneFlow_EmbeddingShuffleOp : OneFlow_BaseOp<"embedding_shuffle", [NoSideEffe let output = (outs OneFlow_Tensor:$embeddings ); + let attrs = (ins + DefaultValuedAttr:$embedding_size, + DefaultValuedAttr:$skip_last_gather, + DefaultValuedAttr:$is_train, + StrAttr:$embedding_name + ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -9372,6 +11016,12 @@ def OneFlow_EmbeddingGradientShuffleOp : OneFlow_BaseOp<"embedding_gradient_shuf let output = (outs OneFlow_Tensor:$cur_rank_unique_embedding_grad ); + let attrs = (ins + DefaultValuedAttr:$embedding_size, + DefaultValuedAttr:$only_zero_valid_grad, + DefaultValuedAttr:$skip_first_scatter, + StrAttr:$embedding_name + ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -9393,7 +11043,8 @@ def OneFlow_EmbeddingPrefetchOp : OneFlow_BaseOp<"embedding_prefetch", [NoSideEf DefaultValuedAttr:$embedding_size, StrAttr:$embedding_name, StrAttr:$embedding_tables, - StrAttr:$state_initializer + StrAttr:$state_initializer, + DefaultValuedAttr:$seed ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9420,7 +11071,8 @@ def OneFlow_EmbeddingLookupOp : OneFlow_BaseOp<"embedding_lookup", [NoSideEffect DefaultValuedAttr:$embedding_size, StrAttr:$embedding_name, StrAttr:$embedding_tables, - StrAttr:$state_initializer + StrAttr:$state_initializer, + DefaultValuedAttr:$seed ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9429,12 +11081,33 @@ def OneFlow_EmbeddingLookupOp : OneFlow_BaseOp<"embedding_lookup", [NoSideEffect let has_data_type_infer_fn = 1; } -def OneFlow_SgdEmbeddingUpdateOp : OneFlow_BaseOp<"sgd_embedding_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingFusedSgdUpdatePutOp : OneFlow_BaseOp<"one_embedding_fused_sgd_update_put", [DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$num_unique_ids, + OneFlow_Tensor:$unique_ids, OneFlow_Tensor:$unique_embeddings, OneFlow_Tensor:$embedding_grad, - OneFlow_Tensor:$learning_rate, + OneFlow_Tensor:$learning_rate + ); + let attrs = (ins + DefaultValuedAttr:$scale, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_OneEmbeddingSgdUpdateOp : OneFlow_BaseOp<"one_embedding_sgd_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$num_unique_ids, + OneFlow_Tensor:$unique_embeddings, + OneFlow_Tensor:$embedding_grad, + Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$down_scale_by_tensor, Optional:$skip_if @@ -9443,10 +11116,14 @@ def OneFlow_SgdEmbeddingUpdateOp : OneFlow_BaseOp<"sgd_embedding_update", [AttrS OneFlow_Tensor:$updated_unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$learning_rate_val, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, - DefaultValuedAttr:$weight_decay + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9455,12 +11132,12 @@ def OneFlow_SgdEmbeddingUpdateOp : OneFlow_BaseOp<"sgd_embedding_update", [AttrS let has_data_type_infer_fn = 1; } -def OneFlow_MomentumEmbeddingUpdateOp : OneFlow_BaseOp<"momentum_embedding_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingMomentumUpdateOp : OneFlow_BaseOp<"one_embedding_momentum_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$num_unique_ids, OneFlow_Tensor:$unique_embeddings, OneFlow_Tensor:$embedding_grad, - OneFlow_Tensor:$learning_rate, + Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$down_scale_by_tensor, Optional:$skip_if @@ -9469,11 +11146,15 @@ def OneFlow_MomentumEmbeddingUpdateOp : OneFlow_BaseOp<"momentum_embedding_updat OneFlow_Tensor:$updated_unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$learning_rate_val, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, DefaultValuedAttr:$weight_decay, - DefaultValuedAttr:$beta + DefaultValuedAttr:$beta, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9482,12 +11163,12 @@ def OneFlow_MomentumEmbeddingUpdateOp : OneFlow_BaseOp<"momentum_embedding_updat let has_data_type_infer_fn = 1; } -def OneFlow_AdamEmbeddingUpdateOp : OneFlow_BaseOp<"adam_embedding_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingAdamUpdateOp : OneFlow_BaseOp<"one_embedding_adam_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$num_unique_ids, OneFlow_Tensor:$unique_embeddings, OneFlow_Tensor:$embedding_grad, - OneFlow_Tensor:$learning_rate, + Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$down_scale_by_tensor, Optional:$skip_if, @@ -9498,6 +11179,9 @@ def OneFlow_AdamEmbeddingUpdateOp : OneFlow_BaseOp<"adam_embedding_update", [Att OneFlow_Tensor:$updated_unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$bias_correction1_val, + DefaultValuedAttr:$bias_correction2_val, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, @@ -9505,7 +11189,10 @@ def OneFlow_AdamEmbeddingUpdateOp : OneFlow_BaseOp<"adam_embedding_update", [Att DefaultValuedAttr:$beta1, DefaultValuedAttr:$beta2, DefaultValuedAttr:$epsilon, - DefaultValuedAttr:$do_bias_correction + DefaultValuedAttr:$do_bias_correction, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9514,13 +11201,49 @@ def OneFlow_AdamEmbeddingUpdateOp : OneFlow_BaseOp<"adam_embedding_update", [Att let has_data_type_infer_fn = 1; } -def OneFlow_AdagradEmbeddingUpdateOp : OneFlow_BaseOp<"adagrad_embedding_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingSmartDecaySparseAdamUpdateOp : OneFlow_BaseOp<"one_embedding_smart_decay_sparse_adam_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$num_unique_ids, OneFlow_Tensor:$unique_embeddings, OneFlow_Tensor:$embedding_grad, - OneFlow_Tensor:$learning_rate, - OneFlow_Tensor:$train_step, + Optional:$train_step, + Optional:$learning_rate, + Optional:$scale_by_tensor, + Optional:$down_scale_by_tensor, + Optional:$skip_if + ); + let output = (outs + OneFlow_Tensor:$updated_unique_embeddings + ); + let attrs = (ins + DefaultValuedAttr:$train_step_val, + DefaultValuedAttr:$learning_rate_val, + DefaultValuedAttr:$scale, + DefaultValuedAttr:$l1, + DefaultValuedAttr:$l2, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$beta1, + DefaultValuedAttr:$beta2, + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$do_bias_correction, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name + ); + let same_output_regst_num = 1; + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +def OneFlow_OneEmbeddingAdagradUpdateOp : OneFlow_BaseOp<"one_embedding_adagrad_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$num_unique_ids, + OneFlow_Tensor:$unique_embeddings, + OneFlow_Tensor:$embedding_grad, + Optional:$train_step, + Optional:$learning_rate, Optional:$scale_by_tensor, Optional:$down_scale_by_tensor, Optional:$skip_if @@ -9529,12 +11252,17 @@ def OneFlow_AdagradEmbeddingUpdateOp : OneFlow_BaseOp<"adagrad_embedding_update" OneFlow_Tensor:$updated_unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$train_step_val, + DefaultValuedAttr:$learning_rate_val, DefaultValuedAttr:$scale, DefaultValuedAttr:$l1, DefaultValuedAttr:$l2, DefaultValuedAttr:$weight_decay, DefaultValuedAttr:$lr_decay, - DefaultValuedAttr:$epsilon + DefaultValuedAttr:$epsilon, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9550,6 +11278,7 @@ def OneFlow_EmbeddingPutOp : OneFlow_BaseOp<"embedding_put", [DeclareOpInterface OneFlow_Tensor:$unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$line_size, StrAttr:$embedding_name ); let same_output_regst_num = 1; @@ -9559,12 +11288,13 @@ def OneFlow_EmbeddingPutOp : OneFlow_BaseOp<"embedding_put", [DeclareOpInterface let has_data_type_infer_fn = 1; } -def OneFlow_FtrlEmbeddingUpdateOp : OneFlow_BaseOp<"ftrl_embedding_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_OneEmbeddingFtrlUpdateOp : OneFlow_BaseOp<"one_embedding_ftrl_update", [AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$num_unique_ids, OneFlow_Tensor:$unique_embeddings, OneFlow_Tensor:$embedding_grad, - OneFlow_Tensor:$learning_rate, + Optional:$learning_rate, + Optional:$scale_by_tensor, Optional:$down_scale_by_tensor, Optional:$skip_if ); @@ -9572,13 +11302,18 @@ def OneFlow_FtrlEmbeddingUpdateOp : OneFlow_BaseOp<"ftrl_embedding_update", [Att OneFlow_Tensor:$updated_unique_embeddings ); let attrs = (ins + DefaultValuedAttr:$learning_rate_val, DefaultValuedAttr:$scale, - DefaultValuedAttr:$weight_decay, DefaultValuedAttr:$l1, - DefaultValuedAttr:$l2, - DefaultValuedAttr:$lambda1, - DefaultValuedAttr:$lambda2, - DefaultValuedAttr:$beta + DefaultValuedAttr:$l2, + DefaultValuedAttr:$weight_decay, + DefaultValuedAttr:$lr_power, + DefaultValuedAttr:$lambda1, + DefaultValuedAttr:$lambda2, + DefaultValuedAttr:$beta, + DefaultValuedAttr:$line_size, + DefaultValuedAttr:$embedding_size, + StrAttr:$embedding_name ); let same_output_regst_num = 1; let has_logical_tensor_desc_infer_fn = 1; @@ -9601,28 +11336,6 @@ def OneFlow_RocAucScoreOp : OneFlow_BaseOp<"roc_auc_score", [NoSideEffect, NoGra let has_data_type_infer_fn = 1; } -def OneFlow_FusedLstmCellOp : OneFlow_BaseOp<"fused_lstm_cell", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { - let input = (ins - OneFlow_Tensor:$input_gates, - OneFlow_Tensor:$hidden_gates, - OneFlow_Tensor:$cx, - Optional:$input_bias, - Optional:$hidden_bias - ); - let output = (outs - OneFlow_Tensor:$hy, - OneFlow_Tensor:$cy, - OneFlow_Tensor:$workspace - ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes - ); - let has_logical_tensor_desc_infer_fn = 1; - let has_physical_tensor_desc_infer_fn = 1; - let has_get_sbp_fn = 1; - let has_data_type_infer_fn = 1; -} - def OneFlow_FillOp : OneFlow_BaseOp<"fill_", [NoSideEffect, SupportNonContiguous, DeclareOpInterfaceMethods]> { let input = (ins OneFlow_Tensor:$in @@ -9655,21 +11368,20 @@ def OneFlow_FillTensorOp : OneFlow_BaseOp<"fill_tensor_", [NoSideEffect, Support let has_data_type_infer_fn = 1; } -def OneFlow_FusedLstmCellGradOp : OneFlow_BaseOp<"fused_lstm_cell_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +#endif // GET_ONEFLOW_ONE_EMBEDDING_OP_DEFINITIONS + +// Group: linear algebra. +// inv, linalg_cross +// Total: 2 + +#ifdef GET_ONEFLOW_LINEAR_ALGEBRA_OP_DEFINITIONS + +def OneFlow_InvOp : OneFlow_BaseOp<"inv", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$grad_hy, - OneFlow_Tensor:$grad_cy, - OneFlow_Tensor:$cx, - OneFlow_Tensor:$cy, - OneFlow_Tensor:$workspace + OneFlow_Tensor:$x ); let output = (outs - OneFlow_Tensor:$grad_gates, - Optional:$grad_cx, - Optional:$grad_bias - ); - let trait_attrs = (ins - I32ElementsAttr:$result_segment_sizes + OneFlow_Tensor:$y ); let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; @@ -9677,46 +11389,108 @@ def OneFlow_FusedLstmCellGradOp : OneFlow_BaseOp<"fused_lstm_cell_grad", [NoSide let has_data_type_infer_fn = 1; } -def OneFlow_FusedGruCellOp : OneFlow_BaseOp<"fused_gru_cell", [NoSideEffect, AttrSizedOperandSegments, DeclareOpInterfaceMethods]> { +def OneFlow_LinalgCrossOp : OneFlow_BaseOp<"linalg_cross", [NoSideEffect, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$input_gates, - OneFlow_Tensor:$hidden_gates, - OneFlow_Tensor:$hx, - Optional:$input_bias, - Optional:$hidden_bias + OneFlow_Tensor:$input, + OneFlow_Tensor:$other + ); + let attrs = (ins + SI64Attr:$dim ); let output = (outs - OneFlow_Tensor:$hy, - OneFlow_Tensor:$workspace + OneFlow_Tensor:$out ); - let trait_attrs = (ins - I32ElementsAttr:$operand_segment_sizes + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +#endif // GET_ONEFLOW_LINEAR_ALGEBRA_OP_DEFINITIONS + +// Group: System +// copy_h2d, copy_d2h +// Total: 2 + +#ifdef GET_ONEFLOW_SYSTEM_OP_DEFINITIONS + +def OneFlow_CopyH2DOp : OneFlow_BaseOp<"copy_h2d", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { + let input = (ins + OneFlow_Tensor:$in ); + let output = (outs + OneFlow_Tensor:$out + ); + let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -def OneFlow_FusedGruCellGradOp : OneFlow_BaseOp<"fused_gru_cell_grad", [NoSideEffect, AttrSizedResultSegments, DeclareOpInterfaceMethods]> { +def OneFlow_CopyD2HOp : OneFlow_BaseOp<"copy_d2h", [NoSideEffect, NoGrad, DeclareOpInterfaceMethods]> { let input = (ins - OneFlow_Tensor:$grad_hy, - OneFlow_Tensor:$workspace + OneFlow_Tensor:$in ); let output = (outs - OneFlow_Tensor:$grad_input_gates, - OneFlow_Tensor:$grad_hidden_gates, - Optional:$grad_hx, - Optional:$grad_input_bias, - Optional:$grad_hidden_bias + OneFlow_Tensor:$out ); - let trait_attrs = (ins - I32ElementsAttr:$result_segment_sizes + + let has_logical_tensor_desc_infer_fn = 1; + let has_physical_tensor_desc_infer_fn = 1; + let has_get_sbp_fn = 1; + let has_data_type_infer_fn = 1; +} + +#endif // GET_ONEFLOW_SYSTEM_OP_DEFINITIONS + +include "mlir/Interfaces/CallInterfaces.td" + +class OneFlow_JITLikeOp : OneFlow_BaseOp]> { + let input = (ins Variadic:$in); + let output = (outs Variadic:$out); + let attrs = (ins + FlatSymbolRefAttr:$callee, + StrAttr:$mlir_assembly ); + let builders = [ + OpBuilder<(ins "func::FuncOp":$callee, + "NamedAttrList":$attributes, + CArg<"ValueRange", "{}">:$in), [{ + $_state.addOperands(in); + $_state.addAttributes(attributes); + $_state.addAttribute("callee", SymbolRefAttr::get(callee)); + $_state.addTypes(callee.getFunctionType().getResults()); + }]> + ]; + let extraClassDeclaration = [{ + operand_range getArgOperands() { + return {arg_operand_begin(), arg_operand_end()}; + } + + operand_iterator arg_operand_begin() { return operand_begin(); } + operand_iterator arg_operand_end() { return operand_end(); } + CallInterfaceCallable getCallableForCallee() { + return (*this)->getAttrOfType("callee"); + } + }]; + let assemblyFormat = [{ + $callee `(` $in `)` attr-dict `:` functional-type($in, results) + }]; let has_logical_tensor_desc_infer_fn = 1; let has_physical_tensor_desc_infer_fn = 1; let has_get_sbp_fn = 1; let has_data_type_infer_fn = 1; } -#endif // GET_ONEFLOW_ONE_EMBEDDING_OP_DEFINITIONS +// Group: MLIR_JIT +// mlir_jit, kernel_launch +// Total: 2 + +#ifdef GET_ONEFLOW_MLIR_JIT_OP_DEFINITIONS + +def OneFlow_MlirJitOp : OneFlow_JITLikeOp<"mlir_jit"> {} + +def OneFlow_KernelLaunchOp : OneFlow_JITLikeOp<"kernel_launch"> {} + +#endif // GET_ONEFLOW_MLIR_JIT_OP_DEFINITIONS diff --git a/oneflow/ir/include/OneFlow/OneFlowUtils.h b/oneflow/ir/include/OneFlow/OneFlowUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..2c8ecb35d3757e4bfadb130b7c0c6e6760b4f93b --- /dev/null +++ b/oneflow/ir/include/OneFlow/OneFlowUtils.h @@ -0,0 +1,31 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWUTILS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWUTILS_H_ +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LLVM.h" +#include "llvm/ADT/StringExtras.h" +namespace mlir { +namespace oneflow { +void CheckEnableIRPrinting(mlir::PassManager& pm); +// sanitize identifier to make the special name allowed as a legal token +StringRef SanitizeIdentifier(StringRef name, SmallString<16>& buffer, + StringRef allowedPunctChars = "$._", bool allowTrailingDigit = true); +} // namespace oneflow +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_ONEFLOWUTILS_H_ diff --git a/oneflow/ir/include/OneFlow/Passes.h b/oneflow/ir/include/OneFlow/Passes.h index cd07771f9078de4a826180c287ed74374ea7959c..f1a6b26ca48e98ad4a88c2a8f463edbee501842d 100644 --- a/oneflow/ir/include/OneFlow/Passes.h +++ b/oneflow/ir/include/OneFlow/Passes.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_PASSES_H_ #define ONEFLOW_IR_INCLUDE_ONEFLOW_PASSES_H_ +#include "mlir/Dialect/PDL/IR/PDL.h" +#include "mlir/Dialect/PDLInterp/IR/PDLInterp.h" #include "mlir/Dialect/MemRef/IR/MemRef.h" #include "mlir/Dialect/Tosa/IR/TosaOps.h" #include "mlir/Dialect/SCF/IR/SCF.h" @@ -29,6 +31,8 @@ limitations under the License. #include "OneFlow/Transform/ConvertInferenceOp.h" #include "OneFlow/Transform/OutlineAndFuse.h" #include "OneFlow/Transform/AutoNhwc.h" +#include "OneFlow/Transform/AggregateComputeOps.h" +#include "OneFlow/Transform/CSEWithAttributesIgnored.h" #ifdef WITH_MLIR_CUDA_CODEGEN #include "OneFlow/Conversion/PTXToCubin.h" @@ -47,6 +51,10 @@ LogicalResult LowerModuleToLLVM(mlir::MLIRContext* context, ModuleOp module); LogicalResult LowerModuleToCUDALLVM(mlir::MLIRContext* context, ModuleOp module); #endif // WITH_MLIR_CUDA_CODEGEN void populateFuserPasses(::mlir::RewritePatternSet& patterns); +void populateLowerToOKLPasses(::mlir::RewritePatternSet& patterns); +void populateWrapOpsToKernelLaunchPasses(::mlir::RewritePatternSet& patterns); +void populateExtractKernelLaunchTensorPasses(::mlir::RewritePatternSet& patterns); +void populateTrimReturnAsVoidPasses(::mlir::RewritePatternSet& patterns); void populateFuserForExistingOp(::mlir::RewritePatternSet& patterns); void populateGpuHelperPatterns(::mlir::RewritePatternSet& patterns); void populateAutoNhwcPatterns(::mlir::RewritePatternSet& patterns); diff --git a/oneflow/ir/include/OneFlow/SBP/SBPAttributes.h b/oneflow/ir/include/OneFlow/SBP/SBPAttributes.h new file mode 100644 index 0000000000000000000000000000000000000000..691e7566c89c66ef08170419620cb79285c01faf --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPAttributes.h @@ -0,0 +1,24 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPATTRIBUTES_H_ +#define ONEFLOW_IR_INCLUDE_SBP_SBPATTRIBUTES_H_ + +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/Support/LLVM.h" +#define GET_ATTRDEF_CLASSES +#include "OneFlow/SBPAttributes.h.inc" + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPATTRIBUTES_H_ diff --git a/oneflow/ir/include/OneFlow/SBP/SBPBase.td b/oneflow/ir/include/OneFlow/SBP/SBPBase.td new file mode 100644 index 0000000000000000000000000000000000000000..3b409b8fce6495976d8fbb4416f55efe9bdccac7 --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPBase.td @@ -0,0 +1,77 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPBASE +#define ONEFLOW_IR_INCLUDE_SBP_SBPBASE + +include "OneFlow/SBP/SBPDialect.td" +include "mlir/IR/AttrTypeBase.td" +include "mlir/IR/SymbolInterfaces.td" +include "mlir/Interfaces/SideEffectInterfaces.td" +include "mlir/Interfaces/InferTypeOpInterface.td" + + +class SBP_Attr traits = []> + : AttrDef { + let mnemonic = attrMnemonic; +} + +def SBP_SplitAttr : SBP_Attr<"Split", "S"> { + let summary = "Signature S"; + let description = [{ + signature split, representing a sharded tensor at the `axis` + }]; + let parameters = (ins "int":$axis); + let assemblyFormat = "`<` $axis `>`"; +} + +def SBP_BroadcastAttr : SBP_Attr<"Broadcast", "B"> { + let summary = "Signature B"; + let description = [{ + signature broadcast, representing a tensor to be duplicated + }]; +} + +def SBP_PartialSumAttr : SBP_Attr<"PartialSum", "P"> { + let summary = "Signature P"; + let description = [{ + signature partial sum, representing a shareded tensor will be reduced lazily + }]; +} + +def SBP_AnyAttr : SBP_Attr<"Any", "Any"> { + let summary = "Signature Any"; + let description = [{ + signature any, representing one of sbp tensor; + }]; +} + +def SBP_ParallelSignatureAttr : SBP_Attr<"ParallelSignature", "parallel"> { + let summary = "Parallel signature of OneFlow Op, aka. SBP"; + let description = [{ + To represent a signature, with a arrow in beween, pass two listes corepondent to the data input and data output tensors. For example: + ``` + #sbp.parallel<[#sbp.S<0>] -> [#sbp.S<0>]> + ``` + One level nested list is used to represent a 2D parallelism signature. For example: + ``` + #sbp.parallel<[[#sbp.S<0>, #sbp.P]] -> [#sbp.S<0>]> + ``` + }]; + let parameters = (ins "ArrayAttr":$inputs, "ArrayAttr":$outputs); + let assemblyFormat = "`<` custom($inputs)` ` `->` ` ` custom($outputs) `>`"; +} + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPBASE diff --git a/oneflow/ir/include/OneFlow/SBP/SBPDialect.h b/oneflow/ir/include/OneFlow/SBP/SBPDialect.h new file mode 100644 index 0000000000000000000000000000000000000000..93ec10cc73f7f9eefe59c21b02d958ba6a64fcc7 --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPDialect.h @@ -0,0 +1,24 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT_H_ +#define ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT_H_ + +#include "mlir/IR/Dialect.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" + +#include "OneFlow/SBPDialect.h.inc" + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT_H_ diff --git a/oneflow/ir/include/OneFlow/SBP/SBPDialect.td b/oneflow/ir/include/OneFlow/SBP/SBPDialect.td new file mode 100644 index 0000000000000000000000000000000000000000..1448a4e24299d949fd8bbad83be9ccb9e2a9a792 --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPDialect.td @@ -0,0 +1,37 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT +#define ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT + +include "mlir/IR/OpBase.td" + +def SBP_Dialect : Dialect { + let name = "sbp"; + let summary = "S(split)B(broadcast)P(partial sum) MLIR dialect."; + let description = [{ + This dialect is the IR of S(split)B(broadcast)P(partial sum). + }]; + let cppNamespace = "::mlir::sbp"; + let dependentDialects = [ + "func::FuncDialect" + ]; + let extraClassDeclaration = [{ + void registerAttributes(); + }]; + let useDefaultAttributePrinterParser = 1; +} + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPDIALECT diff --git a/oneflow/ir/include/OneFlow/SBP/SBPImporter.h b/oneflow/ir/include/OneFlow/SBP/SBPImporter.h new file mode 100644 index 0000000000000000000000000000000000000000..3822b1b770b4186ccaf47222cce9a1c4800e8387 --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPImporter.h @@ -0,0 +1,46 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPIMPORTER_H_ +#define ONEFLOW_IR_INCLUDE_SBP_SBPIMPORTER_H_ +#include "oneflow/core/job/job.pb.h" +#include "oneflow/core/job/sbp_parallel.pb.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "OneFlow/OneFlowOps.h" + +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/MLIRContext.h" + +#include +#include + +namespace mlir { +namespace oneflow { + +class SBPTranslation { + public: + static mlir::LogicalResult PrintSbpAttrToString(mlir::Attribute sbp_attr, std::string& sbp); + static mlir::Attribute ConvertSBPToString(mlir::Builder& builder, + mlir::sbp::ParallelSignatureAttr& parallel); + static mlir::Attribute ConvertNdSbpToPsig(mlir::Builder& builder, + const std::vector& nd_sbp, + const int nd_size); +}; + +} // namespace oneflow +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPIMPORTER_H_ diff --git a/oneflow/ir/include/OneFlow/SBP/SBPOps.td b/oneflow/ir/include/OneFlow/SBP/SBPOps.td new file mode 100644 index 0000000000000000000000000000000000000000..3d6dcdc909478fc95176221e13a202c365039ceb --- /dev/null +++ b/oneflow/ir/include/OneFlow/SBP/SBPOps.td @@ -0,0 +1,30 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_SBP_SBPOPS +#define ONEFLOW_IR_INCLUDE_SBP_SBPOPS + +include "OneFlow/SBP/SBPDialect.td" +include "OneFlow/SBP/SBPBase.td" + +include "mlir/Interfaces/SideEffectInterfaces.td" +include "mlir/IR/FunctionInterfaces.td" +include "mlir/Interfaces/CallInterfaces.td" +include "mlir/Interfaces/ControlFlowInterfaces.td" +include "mlir/Pass/PassBase.td" + +include "mlir/IR/OpBase.td" + +#endif // ONEFLOW_IR_INCLUDE_SBP_SBPOPS diff --git a/oneflow/ir/include/OneFlow/Transform/AggregateComputeOps.h b/oneflow/ir/include/OneFlow/Transform/AggregateComputeOps.h new file mode 100644 index 0000000000000000000000000000000000000000..2686cac703573c183c6eea96ccdc6774be73d4cd --- /dev/null +++ b/oneflow/ir/include/OneFlow/Transform/AggregateComputeOps.h @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_AGGREGATE_COMPUTE_OPS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_AGGREGATE_COMPUTE_OPS_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { +namespace oneflow { + +std::unique_ptr createAggregateComputeOpsPass(); + +} // namespace oneflow +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_AGGREGATE_COMPUTE_OPS_H_ diff --git a/oneflow/ir/include/OneFlow/Transform/CSEWithAttributesIgnored.h b/oneflow/ir/include/OneFlow/Transform/CSEWithAttributesIgnored.h new file mode 100644 index 0000000000000000000000000000000000000000..d6a71cde2883e70da9998fd838f69b3418e938d4 --- /dev/null +++ b/oneflow/ir/include/OneFlow/Transform/CSEWithAttributesIgnored.h @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_CSEWITHATTRIBUTESIGNORED_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_CSEWITHATTRIBUTESIGNORED_H_ + +#include "mlir/Pass/Pass.h" + +namespace mlir { + +namespace oneflow { + +struct CSEState { + llvm::DenseMap scopeSymbolIDs; + llvm::DenseMap opNames; +}; +std::unique_ptr createCSEWithAttributesIgnored(); +std::unique_ptr createCSEPutAttributes(); +std::pair, std::unique_ptr> createCSEPasses( + std::shared_ptr state); +void registerCSEPasses(std::shared_ptr state); + +} // namespace oneflow + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_CSEWITHATTRIBUTESIGNORED_H_ diff --git a/oneflow/ir/include/OneFlow/Transform/OutlineAndFuse.h b/oneflow/ir/include/OneFlow/Transform/OutlineAndFuse.h index 728716376b259310854e849990f0318807f411e9..7e901584143d09946fe78cef331f90cc5af1a0d4 100644 --- a/oneflow/ir/include/OneFlow/Transform/OutlineAndFuse.h +++ b/oneflow/ir/include/OneFlow/Transform/OutlineAndFuse.h @@ -21,9 +21,15 @@ limitations under the License. namespace mlir { namespace oneflow { - +std::unique_ptr createLowerToOKLPass(); +std::unique_ptr createWrapOpsToKernelLaunchPass(); +std::unique_ptr createExtractKernelLaunchTensorPass(); +std::unique_ptr createTrimReturnAsVoidPass(); std::unique_ptr createOutlineJitFunctionPass(); std::unique_ptr createFuseIntoExistingOpPass(); +std::unique_ptr createGroupMatMul(); +std::unique_ptr createFuseForwardOps(); +std::unique_ptr createFuseNormalizationOps(); } // namespace oneflow diff --git a/oneflow/ir/include/OneFlow/Transform/TransposeHelpers.h b/oneflow/ir/include/OneFlow/Transform/TransposeHelpers.h new file mode 100644 index 0000000000000000000000000000000000000000..3c2e2a36e5de882047eaa854f8d09a75a89c9a6a --- /dev/null +++ b/oneflow/ir/include/OneFlow/Transform/TransposeHelpers.h @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_TRANSPOSEHELPERS_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_TRANSPOSEHELPERS_H_ + +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/Value.h" +#include "OneFlow/OneFlowOps.h" + +namespace mlir { + +namespace oneflow { + +RankedTensorType getNHWCType(RankedTensorType t); +RankedTensorType getNHWCType(Type t); +RankedTensorType getNHWCType(Value v); +RankedTensorType getNCHWType(RankedTensorType t); +RankedTensorType getNCHWType(Type t); +RankedTensorType getNCHWType(Value v); +llvm::SmallVector getNHWCResultTypes(NCHWCompatible op); + +} // namespace oneflow + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_TRANSFORM_TRANSPOSEHELPERS_H_ diff --git a/oneflow/ir/include/OneFlow/UserOpConversion.h b/oneflow/ir/include/OneFlow/UserOpConversion.h new file mode 100644 index 0000000000000000000000000000000000000000..38832744076522a19248da205a58359133164e1b --- /dev/null +++ b/oneflow/ir/include/OneFlow/UserOpConversion.h @@ -0,0 +1,47 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPCONVERSION_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPCONVERSION_H_ +#include "OneFlow/OneFlowOps.h" + +namespace mlir { + +namespace oneflow { + +namespace user_op { + +::oneflow::ShapeProto getAttrAsShape(mlir::Attribute& attr); +::oneflow::Int64ListProto getAttrAsStride(mlir::Attribute& attr); +::oneflow::AttrType queryAttrType(const std::string& op_type_name, const std::string& attr_name); +LogicalResult saveAttrDictionaryToOpConf(DictionaryAttr attributes, + ::oneflow::OperatorConf* op_conf); +LogicalResult ConvertUserOpAttributes(llvm::StringRef op_type_name, ValueRange operands, + DictionaryAttr attributes, ::oneflow::OperatorConf& op_conf); +LogicalResult ConvertUserOpAttributes(Operation* op, ::oneflow::OperatorConf& op_conf); +LogicalResult ConvertUserOpInputs(llvm::StringRef op_type_name, ValueRange operands, + DictionaryAttr attributes, ::oneflow::UserOpConf* user_conf); +::oneflow::ParallelConf getParallelConfFromAttrDictionary(DictionaryAttr attributes); +::oneflow::ParallelConf getParallelConfFromAttrs(Attribute device_name_attr, + Attribute device_tag_attr); +::oneflow::DeviceType getDeviceTypeFromAttrDictionary(DictionaryAttr attributes); + +} // namespace user_op + +} // namespace oneflow + +} // namespace mlir + +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPCONVERSION_H_ diff --git a/oneflow/ir/include/OneFlow/UserOpReflection.h b/oneflow/ir/include/OneFlow/UserOpReflection.h new file mode 100644 index 0000000000000000000000000000000000000000..45a97d9cc21333058b73566ee661953f0e231920 --- /dev/null +++ b/oneflow/ir/include/OneFlow/UserOpReflection.h @@ -0,0 +1,67 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPRELFECTION_H_ +#define ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPRELFECTION_H_ +#include "OneFlow/OneFlowOps.h" + +namespace mlir { + +namespace oneflow { + +namespace user_op { + +template class Trait> +LogicalResult GetFilteredSegmentKeyAndSizes(Operation* op, std::vector& keys, + std::vector& sizes); +template class Trait> +LogicalResult GetFilteredSegmentKeyAndSizes(llvm::StringRef op_type_name, size_t valueSize, + DictionaryAttr attributes, + std::vector& keys, + std::vector& sizes); + +struct Source { + enum { + INPUT, + OUTPUT, + BUFFER, + INVALID, + } type; + int offset; +}; +Source GetOpSourceByName(Operation* op, const std::string& to_find); + +using ArgID = std::pair; + +template class Trait> +class ArgIds { + public: + explicit ArgIds(Operation* op); + ArgIds(llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes); + std::vector::const_iterator begin() const { return ids_.begin(); } + std::vector::const_iterator end() const { return ids_.end(); } + + private: + std::vector ids_; +}; + +llvm::Optional GetOutputLbn(OpResult result); + +} // namespace user_op + +} // namespace oneflow + +} // namespace mlir +#endif // ONEFLOW_IR_INCLUDE_ONEFLOW_USEROPRELFECTION_H_ diff --git a/oneflow/ir/lib/OneFlow/CMakeLists.txt b/oneflow/ir/lib/OneFlow/CMakeLists.txt index b8d0ce21d1f342fc6e89edcaae7f93a541fc8bbe..0a908b85c645f5e031a1d7bcf26d3143ec02be90 100644 --- a/oneflow/ir/lib/OneFlow/CMakeLists.txt +++ b/oneflow/ir/lib/OneFlow/CMakeLists.txt @@ -5,7 +5,7 @@ if(WITH_MLIR_CUDA_CODEGEN) endif(WITH_MLIR_CUDA_CODEGEN) set(ONEFLOW_OP_GROUPS - "ASSIGN;BINARY;BROADCAST;CONV;CROSS_ENTROPY;CUDA;DATASET;DETECTION;EAGER;FUSED;IDEMPOTENT;IDENTITY;IMAGE;INDICES;INVOLUTION;LOSS;MATH;MATMUL;MISC;NCCL;NORMALIZATION;OPTIMIZER;PADDING;PARALLEL_CAST;POOL;QUANTIZATION;REDUCE;RESHAPE;SCALAR;SOFTMAX;SUMMARY;TENSOR_BUFFER;TEST;TRIGONOMETRIC;UNARY;UPSAMPLE;ONE_EMBEDDING" + "ASSIGN;BINARY;BROADCAST;CONV;CROSS_ENTROPY;CUDA;DATASET;DETECTION;EAGER;FUSED;IDEMPOTENT;IDENTITY;IMAGE;INDICES;INVOLUTION;LOSS;MATH;MATMUL;MISC;NCCL;NORMALIZATION;OPTIMIZER;PADDING;PARALLEL_CAST;POOL;QUANTIZATION;REDUCE;RESHAPE;SCALAR;SOFTMAX;SUMMARY;TENSOR_BUFFER;TEST;TRIGONOMETRIC;UNARY;UPSAMPLE;ONE_EMBEDDING;LINEAR_ALGEBRA;SYSTEM;MLIR_JIT" ) foreach(OP_GROUP_NAME IN LISTS ONEFLOW_OP_GROUPS) @@ -15,13 +15,31 @@ foreach(OP_GROUP_NAME IN LISTS ONEFLOW_OP_GROUPS) configure_file(OneFlowOpGetGen.cpp.in ${CPP_FILE} @ONLY) endforeach() +add_subdirectory(PDLL) + oneflow_add_mlir_dialect_library( MLIROneFlow + OKL/OKLDialect.cpp + OKL/OKLOps.cpp + OKL/OKLTypes.cpp + OKL/Conversion/OKLToLLVM.cpp + OKL/Conversion/SplitIntoFuncs.cpp + OKL/Conversion/FetchFromLauncher.cpp + OKL/Conversion/OnlyKeepComputeOps.cpp + OKL/Conversion/Conversion.cpp + SBP/SBPDialect.cpp + SBP/SBPAttributes.cpp + SBP/SBPImporter.cpp OneFlowDialect.cpp OneFlowTypes.cpp + OneFlowInferReturnTypes.cpp OneFlowOps.cpp OneFlowOpTraits.cpp OneFlowSupport.cpp + OneFlowUtils.cpp + OneFlowDataTypeConversion.cpp + UserOpReflection.cpp + UserOpConversion.cpp OneFlowOpFolders.cpp Conversion/OneFlowToTosa.cpp Conversion/PTXToCubin.cpp @@ -29,7 +47,14 @@ oneflow_add_mlir_dialect_library( Transform/OutlineAndFuse.cpp Transform/AutoNhwc.cpp Transform/ConvertInferenceOp.cpp + Transform/AggregateComputeOps.cpp + Transform/CSEWithAttributesIgnored.cpp + Transform/GroupMatMulOps.cpp + Transform/AutoNHWCOps.cpp + TransposeHelpers.cpp Passes.cpp + OneFlowCanonicalizers.cpp + OneFlowRewrites.cpp ${GROUPED_OP_CPP_FILES} ADDITIONAL_HEADER_DIRS ${PROJECT_SOURCE_DIR}/include/OneFlow @@ -46,6 +71,7 @@ oneflow_add_mlir_dialect_library( MLIRReconcileUnrealizedCasts ${MLIR_GPU_LIBS} MLIRIR + MLIROneFlowPDLLPatterns oneflow) if(WITH_MLIR_CUDA_CODEGEN) diff --git a/oneflow/ir/lib/OneFlow/Conversion/OneFlowToTosa.cpp b/oneflow/ir/lib/OneFlow/Conversion/OneFlowToTosa.cpp index 8516ca183e597b774b454b9a212faf53e5e4e695..9b88c1e885cdd2838f3ded3608d9201ac27424a3 100644 --- a/oneflow/ir/lib/OneFlow/Conversion/OneFlowToTosa.cpp +++ b/oneflow/ir/lib/OneFlow/Conversion/OneFlowToTosa.cpp @@ -40,6 +40,7 @@ limitations under the License. #include "mlir/Pass/PassManager.h" #include "mlir/Support/LogicalResult.h" #include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" #include "mlir/Transforms/Passes.h" #include "oneflow/core/framework/op_expr_grad_function.h" #include "oneflow/core/framework/variable_tensor_mgr.h" @@ -50,8 +51,56 @@ namespace mlir { namespace oneflow { -Value CreateTranspose(Location& loc, ConversionPatternRewriter& rewriter, Value input, - ArrayRef perms) { +Type convertToSignless(MLIRContext* context, Type type) { + if (auto ranked_tensor = type.dyn_cast()) { + if (auto intTy = ranked_tensor.getElementType().dyn_cast()) { + if (!intTy.isSignless()) { + return RankedTensorType::get( + ranked_tensor.getShape(), + IntegerType::get(context, intTy.getWidth(), + mlir::IntegerType::SignednessSemantics::Signless)); + } + } + } + return type; +} + +FunctionType convertToSignlessFuncType(MLIRContext* context, FunctionType funcType) { + llvm::SmallVector inputs; + llvm::SmallVector results; + for (auto arg : funcType.getInputs()) { inputs.push_back(convertToSignless(context, arg)); } + for (auto res : funcType.getResults()) { results.push_back(convertToSignless(context, res)); } + return FunctionType::get(context, inputs, results); +} + +bool isSignLessTensorOrOther(Type type) { + if (auto ranked_tensor = type.dyn_cast()) { + if (auto intTy = ranked_tensor.getElementType().dyn_cast()) { + if (intTy.isUnsigned()) { return false; } + if (intTy.isSigned()) { return false; } + } + } + return true; +} +bool allSignless(mlir::TypeRange types) { + for (auto type : types) { + if (!isSignLessTensorOrOther(type)) { return false; } + } + return true; +} + +bool allSignless(FunctionType funcType) { + for (auto arg : funcType.getInputs()) { + if (!isSignLessTensorOrOther(arg)) { return false; } + } + for (auto res : funcType.getResults()) { + if (!isSignLessTensorOrOther(res)) { return false; } + } + return true; +} + +Value CreateTransposeValue(Location& loc, ConversionPatternRewriter& rewriter, Value input, + ArrayRef perms) { int perms_size = perms.size(); auto transpose_perms = rewriter.create( loc, RankedTensorType::get({perms_size}, rewriter.getI32Type()), @@ -63,6 +112,12 @@ Value CreateTranspose(Location& loc, ConversionPatternRewriter& rewriter, Value loc, RankedTensorType::get(ranked_type, shape_type.getElementType()), input, transpose_perms); }; +RankedTensorType CreateTransposeType(ShapedType output, ArrayRef perms) { + std::vector ranked_type; + for (auto index : perms) ranked_type.push_back(output.getDimSize(index)); + return RankedTensorType::get(ranked_type, output.getElementType()); +}; + Value CreateBNOp(Location loc, ConversionPatternRewriter& rewriter, Value output, Value x, Value mean, Value variance, Value epsilon, Value gamma, Value beta) { const auto output_type = output.getType(); @@ -88,20 +143,6 @@ struct ScalarMulByTensorOpLowering final : public OpConversionPattern()) { - auto rank = op.x().getType().dyn_cast().getRank(); - if (scalar_type.getRank() != rank) { - std::vector perm(rank); - std::fill(perm.begin(), perm.end(), 1); - scalar = rewriter - .create( - op->getLoc(), - RankedTensorType::get( - perm, scalar.getType().cast().getElementType()), - scalar, rewriter.getI64ArrayAttr(perm)) - .output(); - } - } rewriter.replaceOpWithNewOp( op, /* output */ op->getResultTypes().front().cast(), @@ -117,8 +158,8 @@ struct JobLowering final : public OpConversionPattern { using OpConversionPattern::OpConversionPattern; LogicalResult matchAndRewrite(Job op, OpAdaptor adaptor, ConversionPatternRewriter& rewriter) const override { - auto func = - rewriter.create(op.getLoc(), op.getName(), op.getFunctionType()); + auto func_type = convertToSignlessFuncType(op->getContext(), op.function_type()); + auto func = rewriter.create(op.getLoc(), op.getName(), func_type); rewriter.inlineRegionBefore(op.getRegion(), func.getBody(), func.end()); rewriter.eraseOp(op); return success(); @@ -170,7 +211,7 @@ struct VariableOpLowering final : public OpConversionPattern { const auto mgr = ::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get(); if (!mgr) { return op->emitError("global variable tensor manager miss"); } - const auto tensor = mgr->Get(op.op_name().str()); + const auto tensor = CHECK_JUST(mgr->Get(op.op_name().str())); if (!tensor) { return op->emitError("tensor is null"); } const auto value = support::TensorToDenseElementsAttr(tensor, rewriter.getContext()); const auto output = op.output().getType(); @@ -283,12 +324,6 @@ struct AvgPool2DOpLowering final : public OpConversionPattern { arr.getValue()[1].cast().getSInt()}; }; - auto reshape_type = [](ShapedType shape_type, ArrayRef perms) -> RankedTensorType { - std::vector ranked_type; - for (auto index : perms) ranked_type.push_back(shape_type.getDimSize(index)); - return RankedTensorType::get(ranked_type, shape_type.getElementType()); - }; - auto stride_pairs = get_pair_int64_from_array(op.stride()); auto pad_pairs = get_pair_int64_from_array(op.padding()); auto kernel_pairs = get_pair_int64_from_array(op.kernel_size()); @@ -301,12 +336,12 @@ struct AvgPool2DOpLowering final : public OpConversionPattern { const auto pad = rewriter.getI64ArrayAttr( {pad_pairs.first, pad_pairs.second, pad_pairs.first, pad_pairs.second}); - auto input = CreateTranspose(loc, rewriter, op.x(), perms); - auto output = reshape_type(op.y().getType().cast(), perms); + auto input = CreateTransposeValue(loc, rewriter, op.x(), perms); + auto output = CreateTransposeType(op.y().getType().cast(), perms); auto avg_pool2d = rewriter.create(loc, output, input, kernel, stride, pad); - auto out = CreateTranspose(loc, rewriter, avg_pool2d, {0, 3, 1, 2}); + auto out = CreateTransposeValue(loc, rewriter, avg_pool2d, {0, 3, 1, 2}); rewriter.replaceOp(op, {out}); return success(); } @@ -321,11 +356,6 @@ struct MaxPool2DOpLowering final : public OpConversionPattern { return {arr.getValue()[0].cast().getSInt(), arr.getValue()[1].cast().getSInt()}; }; - auto reshape_type = [](ShapedType shape_type, ArrayRef perms) -> RankedTensorType { - std::vector ranked_type; - for (auto index : perms) ranked_type.push_back(shape_type.getDimSize(index)); - return RankedTensorType::get(ranked_type, shape_type.getElementType()); - }; // TODO: support return indice if (op.return_indices()) { return op->emitError("not support return indices now"); } auto stride_pairs = get_pair_int64_from_array(op.stride()); @@ -340,52 +370,33 @@ struct MaxPool2DOpLowering final : public OpConversionPattern { const auto pad = rewriter.getI64ArrayAttr( {pad_pairs.first, pad_pairs.second, pad_pairs.first, pad_pairs.second}); - auto input = CreateTranspose(loc, rewriter, op.x(), perms); - auto output = reshape_type(op.y().getType().cast(), perms); + auto input = CreateTransposeValue(loc, rewriter, op.x(), perms); + auto output = CreateTransposeType(op.y().getType().cast(), perms); auto max_pool2d = rewriter.create(loc, output, input, kernel, stride, pad); + auto y = CreateTransposeValue(loc, rewriter, max_pool2d, {0, 3, 1, 2}); - auto y = CreateTranspose(loc, rewriter, max_pool2d, {0, 3, 1, 2}); - - auto indice_output = op.indice().getType(); + auto indice_output = convertToSignless(op->getContext(), op.indice().getType()); auto value = DenseElementsAttr::get(indice_output, rewriter.getZeroAttr(rewriter.getI64Type())); - - auto indice = rewriter.create(loc, indice_output, value); + tosa::ConstOp indice = rewriter.create(loc, indice_output, value); rewriter.replaceOp(op, {y, indice}); return success(); } }; -struct FlattenOpLowering final : public OpConversionPattern { +struct ReshapeOpLowering final : public OpConversionPattern { public: - using OpConversionPattern::OpConversionPattern; - LogicalResult matchAndRewrite(FlattenOp op, OpAdaptor adaptor, + using OpConversionPattern::OpConversionPattern; + LogicalResult matchAndRewrite(ReshapeOp op, OpAdaptor adaptor, ConversionPatternRewriter& rewriter) const override { - const auto start_dim = op.start_dim(); - const auto end_dim = op.end_dim(); - const auto in_type = op.in().getType(); - - const auto in_shape = in_type.cast(); - const auto rank = in_type.dyn_cast().getRank(); - - // calculate reshape_vec - std::vector reshape_vec; - for (auto dim = 0; dim < start_dim; ++dim) { reshape_vec.push_back(in_shape.getDimSize(dim)); } - auto last_dim = end_dim < 0 ? rank : end_dim + 1; - int flatten_size = 1; - for (auto dim = start_dim; dim < last_dim; ++dim) { flatten_size *= in_shape.getDimSize(dim); } - reshape_vec.push_back(flatten_size); - if (end_dim > 0) { - for (auto dim = end_dim + 1; dim < rank; ++dim) { - reshape_vec.push_back(in_shape.getDimSize(dim)); - } + auto output = op.out().getType(); + auto input = op.in(); + llvm::SmallVector new_shape; + for (const auto& dim_attr : op.shape()) { + new_shape.push_back(dim_attr.cast().getSInt()); } - // generate reshape op - const auto output = RankedTensorType::get(reshape_vec, in_shape.getElementType()); - auto input1 = op.in(); - auto new_shape = rewriter.getI64ArrayAttr(reshape_vec); - - rewriter.replaceOpWithNewOp(op, output, input1, new_shape); + rewriter.replaceOpWithNewOp(op, output, input, + rewriter.getI64ArrayAttr(new_shape)); return success(); } }; @@ -400,7 +411,7 @@ struct MatmulOpLowering final : public OpConversionPattern { auto preprocess = [&](Value matrix, bool transpose) -> Value { auto shape_type = matrix.getType().cast(); - if (transpose) { matrix = CreateTranspose(loc, rewriter, matrix, {1, 0}); } + if (transpose) { matrix = CreateTransposeValue(loc, rewriter, matrix, {1, 0}); } shape_type = matrix.getType().cast(); auto reshape_type = RankedTensorType::get( @@ -448,16 +459,11 @@ struct NormalizationInferenceOpLowering final const auto out_type = op.y().getType(); const auto epsilon_type = RankedTensorType::get({}, rewriter.getF32Type()); - // epsilon = reshape(epsilon, shape_1) auto epsilon = rewriter.create( loc, epsilon_type, DenseElementsAttr::get(epsilon_type, op.epsilon())); - // mean = reshape(mean, shape_0) auto mean = reshape_dim(out_type, adaptor.moving_mean()); - // variance= reshape(variance, shape_0) auto variance = reshape_dim(out_type, adaptor.moving_variance()); - // scale = reshape(scale, shape_0) auto gamma = reshape_dim(out_type, adaptor.gamma()); - // beta = reshape(beta, shape_0) auto beta = reshape_dim(out_type, adaptor.beta()); auto output = op.y(); auto x = op.x(); @@ -521,11 +527,6 @@ struct Conv2DOpLowering final : public OpConversionPattern { return {arr.getValue()[0].cast().getSInt(), arr.getValue()[1].cast().getSInt()}; }; - auto reshape_type = [](ShapedType shape_type, ArrayRef perms) -> RankedTensorType { - std::vector ranked_type; - for (auto index : perms) ranked_type.push_back(shape_type.getDimSize(index)); - return RankedTensorType::get(ranked_type, shape_type.getElementType()); - }; auto stride_pairs = get_pair_int64_from_array(op.strides()); auto pad_pairs = get_pair_int64_from_array(op.padding_beforeAttr()); @@ -548,30 +549,40 @@ struct Conv2DOpLowering final : public OpConversionPattern { } auto perms = {0, 2, 3, 1}; - auto in = CreateTranspose(loc, rewriter, op.in(), perms); - auto weight = CreateTranspose(loc, rewriter, op.weight(), perms); - const auto output = reshape_type(op.out().getType().cast(), perms); + auto in = CreateTransposeValue(loc, rewriter, op.in(), perms); + auto weight = CreateTransposeValue(loc, rewriter, op.weight(), perms); + const auto output = CreateTransposeType(op.out().getType().cast(), perms); auto conv2d = rewriter.create(loc, output, in, weight, bias, pad, stride, dilation); - auto res = CreateTranspose(loc, rewriter, conv2d, {0, 3, 1, 2}); + auto res = CreateTransposeValue(loc, rewriter, conv2d, {0, 3, 1, 2}); rewriter.replaceOp(op, {res}); return success(); - getTypeConverter(); } }; namespace { + struct OneFlowLoweringToTosaPass : public LowerOneFlowToTosaPassBase { void runOnOperation() override; }; + +struct ConvertToSignlessForTosaPass + : public ConvertToSignlessForTosaPassBase { + void runOnOperation() override; +}; + } // namespace std::unique_ptr createLowerOneFlowToTosaPass() { return std::make_unique(); } +std::unique_ptr createConvertToSignlessForTosaPass() { + return std::make_unique(); +} + void OneFlowLoweringToTosaPass::runOnOperation() { MLIRContext* context = &getContext(); ConversionTarget target(*context); @@ -580,11 +591,21 @@ void OneFlowLoweringToTosaPass::runOnOperation() { target.addIllegalDialect(); TypeConverter typeConverter; - typeConverter.addConversion([](Type type) { return type; }); + typeConverter.addConversion([context](Type type) { return convertToSignless(context, type); }); + typeConverter.addSourceMaterialization( + [&](OpBuilder& builder, Type resultType, ValueRange inputs, Location loc) -> Optional { + CHECK_EQ(inputs.size(), 1) << "expect to materialize a single value"; + return builder.create(loc, resultType, inputs).getResult(0); + }); + typeConverter.addTargetMaterialization( + [&](OpBuilder& builder, Type resultType, ValueRange inputs, Location loc) -> Optional { + CHECK_EQ(inputs.size(), 1) << "expect to materialize a single value"; + return builder.create(loc, resultType, inputs).getResult(0); + }); RewritePatternSet patterns(context); const auto mgr = ::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get(); - // judge whether the pass is trigger by python through the existence of variable tensor manger + // check if the pass is triggered by python based on the presence of variable tensor manger if (mgr) { patterns.add(typeConverter, context); } else { @@ -592,14 +613,63 @@ void OneFlowLoweringToTosaPass::runOnOperation() { } patterns .add( typeConverter, context); if (failed(applyPartialConversion(getOperation(), target, std::move(patterns)))) { - getOperation()->dump(); signalPassFailure(); + LOG(ERROR) << "Failed to lower OneFlow to Tosa"; + getOperation()->dump(); + } +} + +struct ConvertReturnToSignlessPattern : public OpRewritePattern { + explicit ConvertReturnToSignlessPattern(::mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/1) {} + ::mlir::LogicalResult matchAndRewrite(func::ReturnOp op, + ::mlir::PatternRewriter& rewriter) const override { + // make sure result not converted + if (allSignless(op.getOperandTypes())) { return failure(); } + llvm::SmallVector results; + for (auto res : op->getOperandTypes()) { + results.push_back(convertToSignless(op->getContext(), res)); + } + auto uc = rewriter.create(op->getLoc(), results, op.operands()); + rewriter.replaceOpWithNewOp(op, op->getResultTypes(), uc->getResults(), + op->getAttrs()); + return success(); } +}; + +struct ConvertFuncToSignlessPattern : public OpRewritePattern { + explicit ConvertFuncToSignlessPattern(::mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/1) {} + ::mlir::LogicalResult matchAndRewrite(func::FuncOp op, + ::mlir::PatternRewriter& rewriter) const override { + if (allSignless(op.getFunctionType())) { return failure(); } + auto ft = convertToSignlessFuncType(op->getContext(), op.getFunctionType()); + auto func = rewriter.create(op.getLoc(), op.getName(), ft); + BlockAndValueMapping bvm; + op.getRegion().cloneInto(&func.getRegion(), bvm); + for (auto& block : func.getBody().getBlocks()) { + for (auto arg : block.getArguments()) { + arg.setType(convertToSignless(op.getContext(), arg.getType())); + } + } + rewriter.eraseOp(op); + RewritePatternSet patterns(func->getContext()); + patterns.add(func->getContext()); + (void)applyPatternsAndFoldGreedily(func, std::move(patterns)); + return success(); + } +}; + +void ConvertToSignlessForTosaPass::runOnOperation() { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(op->getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); } } // namespace oneflow diff --git a/oneflow/ir/lib/OneFlow/OKL/Conversion/Conversion.cpp b/oneflow/ir/lib/OneFlow/OKL/Conversion/Conversion.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8f853e3e10a7c955ad65b7313a08aa31e5b34e75 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/Conversion/Conversion.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/Conversion/Conversion.h" +#include "OneFlow/OKL/Conversion/FetchFromLauncher.h" +#include "OneFlow/OKL/Conversion/OKLToLLVM.h" +#include "OneFlow/OKL/Conversion/OnlyKeepComputeOps.h" +#include "OneFlow/OKL/Conversion/SplitIntoFuncs.h" +#include "OneFlow/Passes.h" +#include "OneFlow/Transform/OutlineAndFuse.h" +#include "mlir/Conversion/FuncToLLVM/ConvertFuncToLLVMPass.h" +#include "mlir/Conversion/ReconcileUnrealizedCasts/ReconcileUnrealizedCasts.h" +#include "mlir/Pass/PassManager.h" +#include "oneflow/ir/include/OneFlow/OneFlowUtils.h" +namespace mlir { +namespace okl { + +LogicalResult LowerWrapOpsToOKL(ModuleOp module) { + PassManager pm(module->getContext()); + pm.addPass(oneflow::createExtractKernelLaunchTensorPass()); // extract-kernel-launch-tensor + pm.addPass(oneflow::createTrimReturnAsVoidPass()); // trim-return-to-void + pm.addPass(oneflow::createLowerToOKLPass()); // lower-to-okl + pm.addPass(createSplitIntoFuncsPass()); // split-into-funcs + pm.addPass(createFetchFromLauncherPass()); // fetch-from-launcher + oneflow::CheckEnableIRPrinting(pm); + return pm.run(module); +} + +LogicalResult LowerOKLComputeToLLVM(ModuleOp module) { + PassManager pm(module->getContext()); + pm.addPass(createOnlyKeepComputeOpsPass()); // only-keep-compute-ops + pm.addPass(createLowerLauncherToLLVMPtrPass()); // lower-launcher-to-llvm-ptr + pm.addPass(createLowerOKLToLLVMCallPass()); // lower-okl-to-llvm-call + pm.addPass(createConvertFuncToLLVMPass()); // convert-func-to-llvm + pm.addPass(createReconcileUnrealizedCastsPass()); // reconcile-unrealized-casts + oneflow::CheckEnableIRPrinting(pm); + return pm.run(module); +} + +} // namespace okl +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/Conversion/FetchFromLauncher.cpp b/oneflow/ir/lib/OneFlow/OKL/Conversion/FetchFromLauncher.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1b3c6e718e26fff76d4bc289a299a1d9d939daea --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/Conversion/FetchFromLauncher.cpp @@ -0,0 +1,128 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/passes.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/Passes.h" +#include "llvm/ADT/SmallVector.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/Dialect/Tensor/IR/Tensor.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/IR/Operation.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/IR/PatternMatch.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "mlir/Transforms/Passes.h" +#include "llvm/ADT/TypeSwitch.h" + +#include +#include +#include +#include + +namespace mlir { + +namespace okl { + +struct FetchFromLauncherPattern : public mlir::OpRewritePattern { + explicit FetchFromLauncherPattern(mlir::MLIRContext* context) + : mlir::OpRewritePattern(context, 0) {} + mlir::LogicalResult matchAndRewrite(func::CallOp op, + mlir::PatternRewriter& rewriter) const override { + // this pattern only replace func.call @get_resources_type_{X} + auto prefix = "get_resources_type_"; + if (op.getCallee().find(prefix) == std::string::npos) { return success(); } + + // if the result number equals to zero, it means this kind of resources is not needed in this + // function scope. + if (op->getNumResults() == 0) { + rewriter.eraseOp(op); + return success(); + } + + auto elem_type = op->getResult(0).getType(); + auto launcher_ctx = op->getParentOfType().getBody().getArgument(0); + llvm::SmallVector new_ops; + // convert single function: get_resources_type_{X} to multiple functions: + // fetch_{resources}_from_launcher_ctx + for (int index = 0; index < op->getNumResults(); ++index) { + Value val; + llvm::TypeSwitch(elem_type) + .Case([&](auto elem) { + val = rewriter.create(op->getLoc(), launcher_ctx, index)->getResult(0); + }) + .Case([&](auto elem) { + val = + rewriter.create(op->getLoc(), launcher_ctx, index)->getResult(0); + }) + .Case([&](auto elem) { + val = + rewriter.create(op->getLoc(), launcher_ctx, index)->getResult(0); + }) + .Default([&](auto elem) { + op->emitError("Failed to fetch from launcher with illegal tensor.extract op"); + exit(1); + }); + new_ops.push_back(val); + } + op->replaceAllUsesWith(new_ops); + rewriter.eraseOp(op); + return success(); + } +}; + +namespace { +struct FetchFromLauncherPass : public FetchFromLauncherPassBase { + void runOnOperation() override; + + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + registry.insert(); + registry.insert(); + } +}; +} // namespace + +std::unique_ptr createFetchFromLauncherPass() { + return std::make_unique(); +} + +void FetchFromLauncherPass::runOnOperation() { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(patterns.getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); +} + +} // namespace okl + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/Conversion/OKLToLLVM.cpp b/oneflow/ir/lib/OneFlow/OKL/Conversion/OKLToLLVM.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3b4a5d7c9570ef202f070e7dd5d0a67ea0de51ad --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/Conversion/OKLToLLVM.cpp @@ -0,0 +1,247 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/passes.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/Passes.h" +#include "llvm/Support/raw_ostream.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "mlir/Transforms/Passes.h" + +namespace mlir { +namespace okl { + +template +ModuleOp GetModuleOpFromJobBodyOp(T op) { + auto parent_func_op = op->template getParentOfType(); + if (!parent_func_op) { return nullptr; } + return parent_func_op->template getParentOfType(); +} + +// use this func to union the ptr type in this conversion phase. +LLVM::LLVMPointerType GetPtrType(::mlir::PatternRewriter& rewriter) { + return LLVM::LLVMPointerType::get(IntegerType::get(rewriter.getContext(), 8)); +} +template +std::string FetchName() { + if (std::is_same::value) { return "oneflow_okl_fetch_kernel"; } + if (std::is_same::value) { return "oneflow_okl_fetch_reg_ctx"; } + if (std::is_same::value) { return "oneflow_okl_fetch_run_ctx"; } + llvm::errs() << "Faile to get type name"; + exit(1); +} + +template +LLVM::LLVMFuncOp DeclareFetchPtr(::mlir::PatternRewriter& rewriter, ModuleOp* module) { + LLVM::LLVMFuncOp func; + const auto func_name = FetchName(); + if (!(func = module->lookupSymbol(func_name))) { + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(module->getBody()); + + auto func_type = LLVM::LLVMFunctionType::get( + {GetPtrType(rewriter)}, {GetPtrType(rewriter), rewriter.getI64Type()}, false); + func = rewriter.create(rewriter.getUnknownLoc(), func_name, func_type, + LLVM::Linkage::External); + func->setAttr("llvm.emit_c_interface", mlir::UnitAttr::get(rewriter.getContext())); + } + return func; +} + +// lower !okl.launch to !llvm.call +struct LaunchOpLowering final : public OpConversionPattern { + // raw: create okl.launch(*run_ctx, *kernel) -> llvm_ptr + // dst: llvm.call launch(run_ctx: llvm_ptr, kernel: llvm_ptr) + static LLVM::LLVMFuncOp DeclareBuildLaunch(::mlir::PatternRewriter& rewriter, ModuleOp* module) { + const auto func_name = "oneflow_okl_launch"; + LLVM::LLVMFuncOp func; + if (!(func = module->lookupSymbol(func_name))) { + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(module->getBody()); + + auto void_type = LLVM::LLVMVoidType::get(rewriter.getContext()); + auto func_type = LLVM::LLVMFunctionType::get( + void_type, {GetPtrType(rewriter), GetPtrType(rewriter)}, false); + func = rewriter.create(rewriter.getUnknownLoc(), func_name, func_type, + LLVM::Linkage::External); + func->setAttr("llvm.emit_c_interface", mlir::UnitAttr::get(rewriter.getContext())); + } + return func; + } + + public: + static BlockAndValueMapping mapping; + using OpConversionPattern::OpConversionPattern; + LogicalResult matchAndRewrite(LaunchOp op, OpAdaptor adaptor, + ConversionPatternRewriter& rewriter) const override { + auto module = GetModuleOpFromJobBodyOp(op); + if (!module) { + op->emitError("Failed to lowering llvm call because of op is not in a module"); + exit(1); + }; + + auto build_launch = DeclareBuildLaunch(rewriter, &module); + auto run_ctx = mapping.lookup(op.run_ctx()); + auto kernel = mapping.lookup(op.kernel()); + + rewriter.create(op->getLoc(), build_launch, ValueRange{run_ctx, kernel}); + rewriter.eraseOp(op); + return success(); + } +}; +BlockAndValueMapping LaunchOpLowering::mapping; + +// lower !okl.fetch_from_{T} to !llvm.call +template +struct FetchOpLowering final : public OpConversionPattern { + using OpConversionPattern::OpConversionPattern; + using OpAdaptor = typename T::Adaptor; + LogicalResult matchAndRewrite(T op, OpAdaptor adaptor, + ConversionPatternRewriter& rewriter) const override { + auto module = GetModuleOpFromJobBodyOp(op); + if (!module) { + op->emitError("Failed to lowering llvm call because of op is not in a module"); + exit(1); + }; + + auto fetch_ctx = DeclareFetchPtr(rewriter, &module); + auto launcher_ctx = op->template getParentOfType().getBody().getArgument(0); + auto index = rewriter.create(op->getLoc(), rewriter.getI64Type(), + rewriter.getIndexAttr(op.index())); + auto new_op = + rewriter.create(op->getLoc(), fetch_ctx, ValueRange{launcher_ctx, index}); + rewriter.replaceOp(op, new_op.getResults()); + LaunchOpLowering::mapping.map(op->getResult(0), new_op.getResult(0)); + return success(); + } +}; + +// change func.func(!okl.launcher_ctx) -> func.func(!llvm.ptr) { unrealized_conversion_cast(): +// !llvm.ptr -> !okl.launcher_ctx } +struct RewriteFunctionArgsPattern final : public mlir::OpRewritePattern { + static LogicalResult ConvertLauncherToLLVMPtr(func::FuncOp op, mlir::PatternRewriter& rewriter) { + auto func_type = rewriter.getFunctionType({GetPtrType(rewriter)}, {}); + auto func = rewriter.create(op.getLoc(), op.getSymName(), func_type); + func->setAttr("llvm.emit_c_interface", mlir::UnitAttr::get(rewriter.getContext())); + BlockAndValueMapping bvm; + op.getRegion().cloneInto(&func.getRegion(), bvm); + auto& block = func.getBody().getBlocks().front(); + auto launcher_ctx = block.getArgument(0); + + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(&block); + auto cast_op = rewriter.create(op->getLoc(), launcher_ctx.getType(), + launcher_ctx); + launcher_ctx.setType(GetPtrType(rewriter)); + launcher_ctx.replaceAllUsesExcept(cast_op->getResult(0), {cast_op}); + rewriter.setInsertionPointToEnd(&block); + rewriter.replaceOpWithNewOp(&block.back(), ValueRange()); + rewriter.eraseOp(op); + return success(); + } + + public: + explicit RewriteFunctionArgsPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + if (op.getNumArguments() == 1 + && op.getArgumentTypes().begin()->isa()) { + return ConvertLauncherToLLVMPtr(op, rewriter); + } + return success(); + } +}; + +namespace { +struct LowerLauncherToLLVMPtrPass + : public LowerLauncherToLLVMPtrPassBase { + void runOnOperation() override; + + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + } +}; + +struct LowerOKLToLLVMCallPass : public LowerOKLToLLVMCallPassBase { + void runOnOperation() override; + + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + } +}; +} // namespace + +std::unique_ptr createLowerOKLToLLVMCallPass() { + return std::make_unique(); +} +std::unique_ptr createLowerLauncherToLLVMPtrPass() { + return std::make_unique(); +} + +void LowerLauncherToLLVMPtrPass::runOnOperation() { + MLIRContext* context = &getContext(); + RewritePatternSet patterns(context); + + patterns.add(context); + + (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)); +} + +void LowerOKLToLLVMCallPass::runOnOperation() { + MLIRContext* context = &getContext(); + ConversionTarget target(*context); + target.addLegalDialect(); + target.addIllegalDialect(); + + auto llvm_ptr_type = LLVM::LLVMPointerType::get(IntegerType::get(context, 8)); + TypeConverter typeConverter; + typeConverter.addConversion([&](mlir::okl::LauncherContextType type) { return llvm_ptr_type; }); + typeConverter.addConversion([&](mlir::okl::RegContextType type) { return llvm_ptr_type; }); + typeConverter.addConversion([&](mlir::okl::RunContextType type) { return llvm_ptr_type; }); + typeConverter.addConversion([&](mlir::okl::KernelType type) { return llvm_ptr_type; }); + + RewritePatternSet patterns(context); + + patterns.add, FetchOpLowering, + FetchOpLowering, LaunchOpLowering>(typeConverter, context); + + if (failed(applyPartialConversion(getOperation(), target, std::move(patterns)))) { + signalPassFailure(); + getOperation()->emitError("Failed to lower OKL to LLVM Call"); + } +} + +} // namespace okl +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/Conversion/OnlyKeepComputeOps.cpp b/oneflow/ir/lib/OneFlow/OKL/Conversion/OnlyKeepComputeOps.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9a3c6deb2cc375316aaf66278f1b46476c93e1be --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/Conversion/OnlyKeepComputeOps.cpp @@ -0,0 +1,88 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/passes.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/Passes.h" +#include "llvm/ADT/SmallVector.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/Dialect/Tensor/IR/Tensor.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/IR/Operation.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/IR/PatternMatch.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "mlir/Transforms/Passes.h" + +namespace mlir { + +namespace okl { + +struct OnlyKeepComputeOpPattern : public mlir::OpRewritePattern { + explicit OnlyKeepComputeOpPattern(mlir::MLIRContext* context) + : mlir::OpRewritePattern(context, 0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + if (op.getSymName() != "okl_compute") { + rewriter.eraseOp(op); + return success(); + } + return success(); + } +}; + +namespace { +struct OnlyKeepComputeOpsPass : public OnlyKeepComputeOpsPassBase { + void runOnOperation() override; + + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + registry.insert(); + registry.insert(); + } +}; +} // namespace + +std::unique_ptr createOnlyKeepComputeOpsPass() { + return std::make_unique(); +} + +void OnlyKeepComputeOpsPass::runOnOperation() { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(patterns.getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); +} + +} // namespace okl + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/Conversion/SplitIntoFuncs.cpp b/oneflow/ir/lib/OneFlow/OKL/Conversion/SplitIntoFuncs.cpp new file mode 100644 index 0000000000000000000000000000000000000000..966c142bf7fdc42268902b8e324617c820321ecb --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/Conversion/SplitIntoFuncs.cpp @@ -0,0 +1,236 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/passes.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/Passes.h" +#include "llvm/ADT/SmallVector.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/Dialect/Tensor/IR/Tensor.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/IR/Operation.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/IR/PatternMatch.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "mlir/Transforms/Passes.h" + +namespace mlir { +namespace okl { + +struct SplitIntoFuncsPattern : public mlir::OpRewritePattern { + static mlir::LogicalResult SplitIntoFuncs(func::FuncOp func, mlir::PatternRewriter& rewriter) { + SmallVector new_op_list{BuildRegContextOp::getOperationName(), + BuildRunContextOp::getOperationName(), + BuildKernelOp::getOperationName()}; + SmallVector del_op_list{DestroyRegContextOp::getOperationName(), + DestroyRunContextOp::getOperationName()}; + SmallVector> new_ops(new_op_list.size()), del_ops(del_op_list.size()); + SmallVector compute_ops; + auto launcher_to_void_func = rewriter.getFunctionType( + TypeRange{LauncherContextType::get(rewriter.getContext())}, TypeRange{}); + mlir::OpBuilder::InsertionGuard guard(rewriter); + + auto split_into_op_groups = [&]() { + auto is_in_list = [](const SmallVector& list, Operation& op, + SmallVector>& ops) { + for (int i = 0; i < list.size(); ++i) { + if (op.getName().getStringRef() == list[i]) { + ops[i].push_back(&op); + return true; + } + } + return false; + }; + auto& ops = func.getBody().front(); + for (auto& op : ops) { + if (is_in_list(new_op_list, op, new_ops) || is_in_list(del_op_list, op, del_ops)) { + continue; + } + compute_ops.push_back(&op); + } + }; + + auto declare_resource_funcs = [&]() { + SmallVector res; + auto index = 0; + for (auto resources : new_ops) { + auto func_name = + SplitIntoFuncsPattern::prefix_get_resources_.str() + std::to_string(index++); + auto func_type = rewriter.getFunctionType( + TypeRange{LauncherContextType::get(rewriter.getContext())}, + TypeRange{std::vector(resources.size(), resources[0]->getResult(0).getType())}); + // this function is not an external function, it only plays a role of placeholder to + // abstraction. + res.emplace_back(rewriter.create(func->getLoc(), func_name, func_type, + rewriter.getStringAttr("private"))); + } + return res; + }; + + auto map_resource_funcs = [&](SmallVector& resource_funcs, Value& launcher_ctx) { + BlockAndValueMapping res; + for (auto resource_index = 0; resource_index < new_ops.size(); ++resource_index) { + auto call_op = rewriter.create(func->getLoc(), resource_funcs[resource_index], + ValueRange{launcher_ctx}); + auto from_op = new_ops[resource_index]; + for (auto op_index = 0; op_index < from_op.size(); ++op_index) { + res.map(from_op[op_index]->getResult(0), call_op->getResult(op_index)); + } + } + return res; + }; + + split_into_op_groups(); + rewriter.setInsertionPointAfter(func); + auto resource_funcs = declare_resource_funcs(); + + rewriter.setInsertionPointAfter(func); + auto new_func = + rewriter.create(func.getLoc(), new_ops_func_, launcher_to_void_func); + new_func.getBody().emplaceBlock(); + new_func.getBody().addArgument(LauncherContextType::get(rewriter.getContext()), func->getLoc()); + rewriter.setInsertionPointToStart(&new_func.getBody().front()); + + BlockAndValueMapping new_mapping; + new_mapping.map(func.getBody().getArgument(0), new_func.getBody().getArgument(0)); + ImplicitLocOpBuilder new_block(func->getLoc(), rewriter); + + for (const auto& op_vec : new_ops) { + for (auto op : op_vec) { new_block.clone(*op, new_mapping); } + } + rewriter.create(func.getLoc()); + + rewriter.setInsertionPointAfter(func); + auto compute_func = + rewriter.create(func.getLoc(), compute_ops_func_, launcher_to_void_func); + compute_func.getBody().emplaceBlock(); + compute_func.getBody().addArgument(LauncherContextType::get(rewriter.getContext()), + func->getLoc()); + auto compute_launcher_ctx = compute_func.getBody().getArgument(0); + rewriter.setInsertionPointToStart(&compute_func.getBody().front()); + + BlockAndValueMapping compute_mapping = map_resource_funcs(resource_funcs, compute_launcher_ctx); + + ImplicitLocOpBuilder compute_block(func->getLoc(), rewriter); + for (const auto& op : compute_ops) { compute_block.clone(*op, compute_mapping); } + + rewriter.setInsertionPointAfter(func); + auto del_func = + rewriter.create(func.getLoc(), del_ops_func_, launcher_to_void_func); + del_func.getBody().emplaceBlock(); + del_func.getBody().addArgument(LauncherContextType::get(rewriter.getContext()), func->getLoc()); + auto del_launcher_ctx = del_func.getBody().getArgument(0); + rewriter.setInsertionPointToStart(&del_func.getBody().front()); + + BlockAndValueMapping del_mapping = map_resource_funcs(resource_funcs, del_launcher_ctx); + ImplicitLocOpBuilder del_block(func->getLoc(), rewriter); + for (const auto& op_vec : del_ops) { + for (auto op : op_vec) { del_block.clone(*op, del_mapping); } + } + rewriter.create(func->getLoc()); + + rewriter.eraseOp(func); + return success(); + } + + explicit SplitIntoFuncsPattern(mlir::MLIRContext* context) + : mlir::OpRewritePattern(context, 0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + SmallVector legal_func_list{new_ops_func_, del_ops_func_, compute_ops_func_}; + if (std::find(legal_func_list.begin(), legal_func_list.end(), op.getSymName()) + != legal_func_list.end()) { + return success(); + } + if (op.getSymName().find(SplitIntoFuncsPattern::prefix_get_resources_) != std::string::npos) { + return success(); + } + + mlir::ModuleOp module_op; + if (!(module_op = op->getParentOfType())) { + op->emitError("Failed on -split-into-funcs pass because there are some funcs is not a child " + "of the module"); + exit(1); + } + + if (!(op.getFunctionType().getNumInputs() == 1 && op.getFunctionType().getNumResults() == 0 + && op.getFunctionType().getInput(0).dyn_cast())) { + op->emitError("Failed on -split-into-funcs pass because the illegal func type is not " + "(!okl.launcher_ctx) -> ()"); + exit(1); + } + + // the legal functions and illegal func can not exist at the same time, because the legalization + // of illegal func will generate new legal functions. + for (auto name : legal_func_list) { + if (module_op.lookupSymbol(name)) { + op->emitError("Failed on -split-into-funcs pass because illegal funcs and legal funcs " + "exist at the same time"); + exit(1); + } + } + + return SplitIntoFuncs(op, rewriter); + } + + static const StringLiteral new_ops_func_, del_ops_func_, compute_ops_func_, prefix_get_resources_; +}; + +// define the name of split functions +const StringLiteral SplitIntoFuncsPattern::new_ops_func_ = "okl_init_context", + SplitIntoFuncsPattern::del_ops_func_ = "okl_recycle", + SplitIntoFuncsPattern::compute_ops_func_ = "okl_compute", + SplitIntoFuncsPattern::prefix_get_resources_ = "get_resources_type_"; + +namespace { +struct SplitIntoFuncsPass : public SplitIntoFuncsPassBase { + void runOnOperation() override; + + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + registry.insert(); + registry.insert(); + } +}; +} // namespace + +std::unique_ptr createSplitIntoFuncsPass() { return std::make_unique(); } + +void SplitIntoFuncsPass::runOnOperation() { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(patterns.getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); +} + +} // namespace okl +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/OKLDialect.cpp b/oneflow/ir/lib/OneFlow/OKL/OKLDialect.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a3c8dcc947955426b96ef41d5e11cf7190e3d758 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/OKLDialect.cpp @@ -0,0 +1,64 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/OKLAttributes.h" +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/Passes.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "OneFlow/OKLDialect.cpp.inc" +#include "mlir/IR/Dialect.h" +#include "mlir/IR/TypeRange.h" +#include "mlir/IR/Dialect.h" +#include "llvm/ADT/SmallVector.h" +#include "llvm/ADT/TypeSwitch.h" +#include "mlir/IR/Attributes.h" +#include "mlir/IR/DialectImplementation.h" +#include "mlir/Support/LogicalResult.h" + +#define GET_ATTRDEF_CLASSES +#include "OneFlow/OKLAttributes.cpp.inc" +namespace mlir { + +namespace okl { + +void OKLDialect::initialize() { + addOperations< +#define GET_OP_LIST +#include "OneFlow/OKLOps.cpp.inc" + >(); + addTypes< +#define GET_TYPEDEF_LIST +#include "OneFlow/OKLTypes.cpp.inc" + >(); + addAttributes< +#define GET_ATTRDEF_LIST +#include "OneFlow/OKLAttributes.cpp.inc" + >(); +} + +void registerOneFlowPasses() { + mlir::registerAggregateComputeOpsPassPass(); + mlir::registerWrapOpsToKernelLaunchPassPass(); + mlir::registerExtractKernelLaunchTensorPassPass(); + mlir::registerTrimReturnAsVoidPassPass(); + mlir::registerLowerToOKLPassPass(); +} + +} // namespace okl + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OKL/OKLOps.cpp b/oneflow/ir/lib/OneFlow/OKL/OKLOps.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f5b1d2c1e4595298e47b0b23dc2eba1c71ebe650 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/OKLOps.cpp @@ -0,0 +1,29 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLAttributes.h" +#include "OneFlow/OneFlowOps.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/Dialect.h" +#include "mlir/IR/TypeRange.h" +#include "mlir/IR/Dialect.h" + +#include "OneFlow/OKLEnums.cpp.inc" + +#define GET_OP_CLASSES +#include "OneFlow/OKLOps.cpp.inc" diff --git a/oneflow/ir/lib/OneFlow/OKL/OKLTypes.cpp b/oneflow/ir/lib/OneFlow/OKL/OKLTypes.cpp new file mode 100644 index 0000000000000000000000000000000000000000..53d109b3a7b77b939d4a9222f4c471fea22e7245 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/OKLTypes.cpp @@ -0,0 +1,22 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "mlir/IR/DialectImplementation.h" +#include "llvm/ADT/TypeSwitch.h" + +#define GET_TYPEDEF_CLASSES +#include "OneFlow/OKLTypes.cpp.inc" diff --git a/oneflow/ir/lib/OneFlow/OKL/readme.md b/oneflow/ir/lib/OneFlow/OKL/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..e9315fe647f42ad2e3a4bb8103524e0137e93bd1 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OKL/readme.md @@ -0,0 +1,149 @@ +## okl dialect +设置 ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH = 1，启动oneflow kernel launch功能打包计算型op。 + +在roundtrip的结尾，用于计算的连续的op被合并成单个kernel launch op +oneflow ops -> oneflow.kernel_launch{mlir_assembly="wrap"} +例如 +```mlir +module { + oneflow.job @GraphToRun_1(%arg0: tensor<2xf32>) -> tensor<2xf32> { + %output = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_input.0.0_2", output_lbns = ["_GraphToRun_1_input.0.0_2/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + %0 = "oneflow.relu"(%output) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %output_0 = "oneflow.output"(%1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_output.0.0_2", output_lbns = ["_GraphToRun_1_output.0.0_2/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + oneflow.return %output_0 : tensor<2xf32> + } +} +``` +中的连续的relu和tanh两个op被合并成如下单个的kernel launch op，其资源在kernel launch op的wrap 函数中以一定的规则得以映射 +```mlir +module { + func.func @wrap0(%arg0: tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) attributes {llvm.emit_c_interface} { + %0 = "oneflow.relu"(%arg0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + return %0, %1 : tensor<2xf32>, tensor<2xf32> + } + oneflow.job @GraphToRun_1(%arg0: tensor<2xf32>) -> tensor<2xf32> { + %output = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_input.0.0_2", output_lbns = ["_GraphToRun_1_input.0.0_2/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + %0:2 = oneflow.kernel_launch @wrap0(%output) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], mlir_assembly = "\22func.func\22() ({\0A^bb0(%arg0: tensor<2xf32>):\0A %0 = \22oneflow.relu\22(%arg0) {device_name = [\22@0:0\22], device_tag = \22cuda\22, hierarchy = [1], op_name = \22relu-0\22, scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32>\0A %1 = \22oneflow.tanh\22(%0) {device_name = [\22@0:0\22], device_tag = \22cuda\22, hierarchy = [1], op_name = \22tanh-1\22, scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32>\0A \22func.return\22(%0, %1) : (tensor<2xf32>, tensor<2xf32>) -> ()\0A}) {function_type = (tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>), llvm.emit_c_interface, sym_name = \22wrap0\22} : () -> ()", op_name = "wrap0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) + %output_0 = "oneflow.output"(%0#1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_output.0.0_2", output_lbns = ["_GraphToRun_1_output.0.0_2/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + oneflow.return %output_0 : tensor<2xf32> + } +} +``` + +在kernel launch的类中，通过wrap函数的func type来推导input和output形状 + - set input shape(infer from function_type operands) + - set output shape(infer from function_type results) + +在kernel launch的初始化过程中 + - lower mlir_assembly="wrap" to okl module + - globally create launcher context(init_context, okl_module) once + - vec : datatype/device/input/output(information from wrap op) + - vec : input/output for kernel compute function(information from reg_ctx, tensor from init_context) + - vec : datatype/device(information from reg_ctx) + - lower okl compute to llvm module + +其中 lower oneflow to okl 将： +```mlir +module { + func.func @wrap0(%arg0: tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) attributes {llvm.emit_c_interface} { + %0 = "oneflow.relu"(%arg0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + return %0, %1 : tensor<2xf32>, tensor<2xf32> + } +``` +通过如下几个pass转换成okl格式： + - -extract-kernel-launch-tensor + - -trim-return-to-void + - -lower-to-okl + - -split-into-funcs + - -fetch-from-launcher +结果为： +```mlir +module { + func.func @okl_recycle(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_reg_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %1 = "okl.fetch_reg_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %2 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %3 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + "okl.destroy_reg_ctx"(%0) : (!okl.reg_ctx) -> () + "okl.destroy_reg_ctx"(%1) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%2) : (!okl.run_ctx) -> () + "okl.destroy_run_ctx"(%3) : (!okl.run_ctx) -> () + return + } + func.func @okl_compute(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %1 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %2 = "okl.fetch_kernel"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel + %3 = "okl.fetch_kernel"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel + "okl.launch"(%0, %2) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%1, %3) : (!okl.run_ctx, !okl.kernel) -> () + return + } + func.func @okl_init_context(%arg0: !okl.launcher_ctx) { + %0 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_arg"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.relu"(%6) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %1 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_ret"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.tanh"(%6) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %2 = "okl.build_run_ctx"(%0) : (!okl.reg_ctx) -> !okl.run_ctx + %3 = "okl.build_run_ctx"(%1) : (!okl.reg_ctx) -> !okl.run_ctx + %4 = "okl.build_op_kernel"(%0) : (!okl.reg_ctx) -> !okl.kernel + %5 = "okl.build_op_kernel"(%1) : (!okl.reg_ctx) -> !okl.kernel + return + } + func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +} +``` +其中okl_init_context用来实现第二部的资源初始化，okl_compute用来后面调用各自的kernel运行compute。 + +通过如下几个pass，将okl转换成llvm格式： + - -only-keep-compute-ops + - -lower-launcher-to-llvm-ptr + - -lower-okl-to-llvm-call + - -reconcile-unrealized-casts + - -convert-func-to-llvm +```mlir +module attributes {llvm.data_layout = ""} { + llvm.func @launch(!llvm.ptr, !llvm.ptr) attributes {llvm.emit_c_interface} + llvm.func @fetch_kernel(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} + llvm.func @fetch_run_ctx(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} + llvm.func @okl_compute(%arg0: !llvm.ptr) attributes {llvm.emit_c_interface} { + %0 = builtin.unrealized_conversion_cast %arg0 : !llvm.ptr to !okl.launcher_ctx + %1 = llvm.mlir.constant(0 : index) : i64 + %2 = llvm.call @fetch_run_ctx(%arg0, %1) : (!llvm.ptr, i64) -> !llvm.ptr + %3 = llvm.mlir.constant(1 : index) : i64 + %4 = llvm.call @fetch_run_ctx(%arg0, %3) : (!llvm.ptr, i64) -> !llvm.ptr + %5 = llvm.mlir.constant(0 : index) : i64 + %6 = llvm.call @fetch_kernel(%arg0, %5) : (!llvm.ptr, i64) -> !llvm.ptr + %7 = llvm.mlir.constant(1 : index) : i64 + %8 = llvm.call @fetch_kernel(%arg0, %7) : (!llvm.ptr, i64) -> !llvm.ptr + llvm.call @launch(%2, %6) : (!llvm.ptr, !llvm.ptr) -> () + llvm.call @launch(%4, %8) : (!llvm.ptr, !llvm.ptr) -> () + llvm.return + } + llvm.func @_mlir_ciface_okl_compute(%arg0: !llvm.ptr) attributes {llvm.emit_c_interface} { + llvm.call @okl_compute(%arg0) : (!llvm.ptr) -> () + llvm.return + } +} +``` +最后compute函数中通过llvm engine运行该llvm +Compute[may execute many times] + - llvm.engine(llvm module, launcher context) + - fetch run_ctx(from launcher context with index) + - fetch kernel(from launcher context with index) + - launch kernel(run_ctx) + +llvm.call的callee逻辑在liboneflow.so，通过extern C实现 diff --git a/oneflow/ir/lib/OneFlow/OneFlowCanonicalizers.cpp b/oneflow/ir/lib/OneFlow/OneFlowCanonicalizers.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8f8df308318b02a49e7a2b1a5eacae1858725758 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OneFlowCanonicalizers.cpp @@ -0,0 +1,49 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/random_generator.h" +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/OneFlowPatternUtils.h" + +namespace mlir { + +namespace oneflow { + +namespace { + +struct PutSeed : public OpRewritePattern { + explicit PutSeed(MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/1) {} + LogicalResult matchAndRewrite(oneflow::RandomMaskLikeOp op, + PatternRewriter& rewriter) const override { + if (op->hasAttr(op.seedAttrName())) { + return failure(); + } else { + op->setAttr(op.seedAttrName(), rewrites::GetDefaultSeed(rewriter)); + return success(); + } + } +}; + +} // namespace + +void RandomMaskLikeOp::getCanonicalizationPatterns(RewritePatternSet& results, + MLIRContext* context) { + results.insert(context); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OneFlowDataTypeConversion.cpp b/oneflow/ir/lib/OneFlow/OneFlowDataTypeConversion.cpp new file mode 100644 index 0000000000000000000000000000000000000000..45018c550818f2a0c541542d43f1848c2dac1c3b --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OneFlowDataTypeConversion.cpp @@ -0,0 +1,64 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OneFlowDataTypeConversion.h" +#include "OneFlow/OneFlowTypes.h" + +namespace mlir { + +namespace oneflow { + +Type getTypeFromOneFlowDataType(MLIRContext* context, ::oneflow::DataType dt) { + if (dt == ::oneflow::DataType::kInvalidDataType) { return InvalidElementType::get(context); } + if (dt == ::oneflow::DataType::kChar) { return CharElementType::get(context); } + if (dt == ::oneflow::DataType::kFloat16) { return FloatType::getF16(context); } + if (dt == ::oneflow::DataType::kFloat) { return FloatType::getF32(context); } + if (dt == ::oneflow::DataType::kDouble) { return FloatType::getF64(context); } + if (dt == ::oneflow::DataType::kInt8) { + return IntegerType::get(context, 8, IntegerType::Signed); + } + if (dt == ::oneflow::DataType::kInt32) { + return IntegerType::get(context, 32, IntegerType::Signed); + } + if (dt == ::oneflow::DataType::kInt64) { + return IntegerType::get(context, 64, IntegerType::Signed); + } + if (dt == ::oneflow::DataType::kOFRecord) { return OFRecordElementType::get(context); } + if (dt == ::oneflow::DataType::kTensorBuffer) { return TensorBufferElementType::get(context); } + if (dt == ::oneflow::DataType::kBool) { + return IntegerType::get(context, 8, IntegerType::Signed); + } + if (dt == ::oneflow::DataType::kUInt8) { + return IntegerType::get(context, 8, IntegerType::Unsigned); + } + if (dt == ::oneflow::DataType::kUInt16) { + return IntegerType::get(context, 16, IntegerType::Unsigned); + } + if (dt == ::oneflow::DataType::kUInt32) { + return IntegerType::get(context, 32, IntegerType::Unsigned); + } + if (dt == ::oneflow::DataType::kUInt64) { + return IntegerType::get(context, 64, IntegerType::Unsigned); + } + if (dt == ::oneflow::DataType::kUInt128) { + return IntegerType::get(context, 128, IntegerType::Unsigned); + } + llvm::errs() << "unsupported oneflow data type: " << dt << "\n"; + return Type(); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OneFlowDialect.cpp b/oneflow/ir/lib/OneFlow/OneFlowDialect.cpp index f5b740d596327bce374f9918df0adb5cbefd0a62..24624422b7a0175e45d6f658778fa1c5f5410635 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowDialect.cpp +++ b/oneflow/ir/lib/OneFlow/OneFlowDialect.cpp @@ -137,6 +137,15 @@ void OneFlowDialect::initialize() { , #define GET_OP_LIST #include "OneFlow/OneFlow.one_embedding_ops.cpp.inc" + , +#define GET_OP_LIST +#include "OneFlow/OneFlow.linear_algebra_ops.cpp.inc" + , +#define GET_OP_LIST +#include "OneFlow/OneFlow.system_ops.cpp.inc" + , +#define GET_OP_LIST +#include "OneFlow/OneFlow.mlir_jit_ops.cpp.inc" >(); addTypes< #define GET_TYPEDEF_LIST diff --git a/oneflow/ir/lib/OneFlow/OneFlowInferReturnTypes.cpp b/oneflow/ir/lib/OneFlow/OneFlowInferReturnTypes.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4e13e18cee0e8c3254d7d3329bc31f60516542bd --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OneFlowInferReturnTypes.cpp @@ -0,0 +1,145 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/UserOpConversion.h" +#include "OneFlow/UserOpReflection.h" +#include "OneFlow/OneFlowDataTypeConversion.h" +#include "mlir/Support/LogicalResult.h" + +namespace mlir { + +namespace oneflow { + +namespace { + +std::unique_ptr<::oneflow::BlobDesc> getBlobDescFromTensorType(TensorType tensor_type) { + auto data_type = mlir::oneflow::support::FromMLIRTypeToOFDataType(tensor_type.getElementType()); + if (mlir::succeeded(data_type)) { + auto shape_from_mlir = new ::oneflow::Shape(llvm::SmallVector( + {tensor_type.getShape().begin(), tensor_type.getShape().end()})); + return std::make_unique<::oneflow::BlobDesc>(*shape_from_mlir, data_type.getValue()); + } + tensor_type.dump(); + LOG(FATAL) << "fail to get BlobDesc from TensorType"; +} + +Type getTensorTypeFromBlobDesc(MLIRContext* context, const ::oneflow::BlobDesc* blob_desc) { + if (auto type = getTypeFromOneFlowDataType(context, blob_desc->data_type())) { + return RankedTensorType::get( + llvm::SmallVector( + {blob_desc->shape().dim_vec().begin(), blob_desc->shape().dim_vec().end()}), + type); + } else { + return Type{}; + } +} + +static auto MagicalOpName = "INFER_MAGICAL"; +LogicalResult ConvertUserOp(llvm::StringRef op_type_name, ::oneflow::OperatorConf& op_conf, + ValueRange operands, DictionaryAttr attributes) { + oneflow::ConfOpAdaptor conf_op_adaptor(operands, attributes); + op_conf.set_name(MagicalOpName); + CHECK( + user_op::ConvertUserOpInputs(op_type_name, operands, attributes, op_conf.mutable_user_conf()) + .succeeded()); + if (!succeeded(user_op::ConvertUserOpAttributes(op_type_name, operands, attributes, op_conf))) { + return failure(); + } + return success(); +} + +size_t getResultSize(DictionaryAttr attributes) { + const StringRef attr_name = OpTrait::AttrSizedResultSegments::getResultSegmentSizeAttr(); + const DenseIntElementsAttr& size_attr = + attributes.get(attr_name).dyn_cast_or_null(); + CHECK(size_attr) << "Attr " << attr_name.str() << " is not found or not DenseIntElementsAttr"; + auto size = 0; + for (auto s : size_attr.getValues()) { size += s; } + return size; +} + +::mlir::LogicalResult inferReturnTypesWithOpTypeName( + llvm::StringRef op_type_name, ::mlir::MLIRContext* context, ::mlir::ValueRange operands, + ::mlir::DictionaryAttr attributes, ::mlir::RegionRange regions, + ::llvm::SmallVectorImpl<::mlir::Type>& inferredReturnTypes) { + ::oneflow::OperatorConf op_conf{}; + CHECK(ConvertUserOp(op_type_name, op_conf, operands, attributes).succeeded()); + std::unordered_map> lbi2logical_blob_desc_; + auto operand_ids = + user_op::ArgIds(op_type_name, operands.size(), attributes); + auto operand_index = 0; + for (const auto& idOperand : llvm::zip(operand_ids, operands)) { + const auto& arg_name = std::get<0>(idOperand).first; + const auto& arg_id = std::get<0>(idOperand).second; + const auto operand = std::get<1>(idOperand); + auto blob_desc = getBlobDescFromTensorType(operand.getType().cast()); + auto bn = ::oneflow::GenRepeatedBn(arg_name, arg_id); + lbi2logical_blob_desc_.emplace(bn, std::move(blob_desc)); + operand_index += 1; + } + auto result_ids = user_op::ArgIds( + op_type_name, getResultSize(attributes), attributes); + for (const auto& result_id : result_ids) { + const auto& arg_name = result_id.first; + const auto& arg_id = result_id.second; + const auto bn = ::oneflow::GenRepeatedBn(arg_name, arg_id); + auto blob_desc = std::make_unique<::oneflow::BlobDesc>(::oneflow::kInvalidDataType); + lbi2logical_blob_desc_.emplace(bn, std::move(blob_desc)); + (*op_conf.mutable_user_conf()->mutable_output())[arg_name].add_s( + ::oneflow::GenLogicalBlobName(op_conf.name(), bn)); + } + auto op = CHECK_JUST(ConstructOp(op_conf, user_op::getDeviceTypeFromAttrDictionary(attributes))); + auto GetLogicalBlobDesc4BnInOp = [&](const std::string& bn) -> ::oneflow::BlobDesc* { + auto it = lbi2logical_blob_desc_.find(bn); + if (it == lbi2logical_blob_desc_.end()) { + LOG(FATAL) << "fail to find blob name in op: " << bn; + } + return it->second.get(); + }; + ::oneflow::ParallelConf parallel_conf = user_op::getParallelConfFromAttrDictionary(attributes); + ::oneflow::ParallelDesc parallel_desc{parallel_conf}; + op->FillOpParallelDesc(parallel_desc); + CHECK_JUST(op->InferLogicalOutBlobDescs(GetLogicalBlobDesc4BnInOp, parallel_desc)); + for (const auto& result_id : result_ids) { + const auto& arg_name = result_id.first; + const auto& arg_id = result_id.second; + const auto bn = ::oneflow::GenRepeatedBn(arg_name, arg_id); + const auto* desc = lbi2logical_blob_desc_.at(bn).get(); + if (auto t = getTensorTypeFromBlobDesc(context, desc)) { inferredReturnTypes.push_back(t); } + } + return success(); +} + +} // namespace + +::mlir::LogicalResult NormalizationAddReluOp::refineReturnTypes( + ::mlir::MLIRContext* context, ::llvm::Optional<::mlir::Location> location, + ::mlir::ValueRange operands, ::mlir::DictionaryAttr attributes, ::mlir::RegionRange regions, + ::llvm::SmallVectorImpl<::mlir::Type>& inferredReturnTypes) { + return success(); +} + +::mlir::LogicalResult NormalizationAddReluOp::inferReturnTypes( + ::mlir::MLIRContext* context, ::llvm::Optional<::mlir::Location> location, + ::mlir::ValueRange operands, ::mlir::DictionaryAttr attributes, ::mlir::RegionRange regions, + ::llvm::SmallVectorImpl<::mlir::Type>& inferredReturnTypes) { + return inferReturnTypesWithOpTypeName("normalization_add_relu", context, operands, attributes, + regions, inferredReturnTypes); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OneFlowOpFolders.cpp b/oneflow/ir/lib/OneFlow/OneFlowOpFolders.cpp index c3d491cf5977f44618ddcb67b9690bf6cebc7ecb..16a4c58737440e9ab03f0fc92f7768e558b8c3ec 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowOpFolders.cpp +++ b/oneflow/ir/lib/OneFlow/OneFlowOpFolders.cpp @@ -16,6 +16,7 @@ limitations under the License. #include #include #include +#include "OneFlow/OneFlowOpTraits.h" #include "OneFlow/OneFlowOps.h" #include "mlir/IR/Attributes.h" #include "mlir/IR/BuiltinAttributes.h" @@ -43,6 +44,24 @@ StringAttr GenNewVariableOpName(MLIRContext* ctx, const std::string& key = "") { return StringAttr::get(ctx, "variable_" + key + "_" + ::oneflow::NewUniqueId()); } +bool MLIRDataTypesAreSame(const std::vector& data_types) { + if (data_types.empty() || data_types.size() == 1) { return true; } + bool result = true; + const auto first_data_type = data_types[0]; + for (size_t i = 1; i < data_types.size(); ++i) { result &= (first_data_type == data_types[i]); } + return result; +} + +bool DictionaryAttrsHaveSameDataType(const std::vector& attrs) { + std::vector data_types; + for (const auto& attr : attrs) { + data_types.push_back(attr.get(OpTrait::TensorSource::getDataTypeAttrName()) + .cast() + .getValue()); + } + return MLIRDataTypesAreSame(data_types); +} + OpFoldResult UnaryFold(MLIRContext* ctx, ArrayRef operands, const std::function& f) { ::oneflow::LazyMode::Guard guard{false}; @@ -56,6 +75,8 @@ OpFoldResult UnaryFold(MLIRContext* ctx, ArrayRef operands, const auto result = f(tensor).GetPtrOrThrow(); attrs.set("value", support::TensorToDenseElementsAttr(result, ctx)); attrs.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), GenNewVariableOpName(ctx)); + attrs.set(OpTrait::TensorSource::getDataTypeAttrName(), + attr_dict.get(OpTrait::TensorSource::getDataTypeAttrName())); return attrs.getDictionary(ctx); } @@ -66,6 +87,12 @@ OpFoldResult BinaryFold(MLIRContext* ctx, ArrayRef operands, if (!(operands.front() && operands.back())) { return {}; } // Important! auto lhs_attr_dict = operands.front().cast(); auto rhs_attr_dict = operands.back().cast(); + if (!DictionaryAttrsHaveSameDataType({lhs_attr_dict, rhs_attr_dict})) { + llvm::errs() + << "Input tensors should have same data type in binary operation of constant folding." + << "\n"; + return nullptr; + } auto attrs = NamedAttrList(lhs_attr_dict); const auto lhs_tensor = support::DenseElementsAttrToTensor( @@ -81,6 +108,8 @@ OpFoldResult BinaryFold(MLIRContext* ctx, ArrayRef operands, attrs.set("value", support::TensorToDenseElementsAttr(result, ctx)); attrs.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), GenNewVariableOpName(ctx)); + attrs.set(OpTrait::TensorSource::getDataTypeAttrName(), + lhs_attr_dict.get(OpTrait::TensorSource::getDataTypeAttrName())); return attrs.getDictionary(ctx); } @@ -91,6 +120,7 @@ OpFoldResult FrozenVariableOp::fold(ArrayRef operands) { NamedAttrList attrs; attrs.set(valueAttrName(), valueAttr()); attrs.set(op_nameAttrName(), op_nameAttr()); + attrs.set(data_typeAttrName(), data_typeAttr()); attrs.set(device_tagAttrName(), device_tagAttr()); attrs.set(device_nameAttrName(), device_nameAttr()); attrs.set(scope_symbol_idAttrName(), scope_symbol_idAttr()); @@ -122,7 +152,7 @@ OpFoldResult ScalarAddOp::fold(ArrayRef operands) { return UnaryFold(getContext(), operands, [this](const auto& tensor) -> MaybeTensor { if (has_int_operand()) { return functional::ScalarAdd(tensor, int_operand(), 1, false); } if (has_float_operand()) { - return functional::ScalarAdd(tensor, float_operand().convertToFloat(), 1, false); + return functional::ScalarAdd(tensor, float_operand().convertToDouble(), 1, false); } emitError("Scalar op must has a int operand or a float operand."); return TensorPtr(); diff --git a/oneflow/ir/lib/OneFlow/OneFlowOpGetGen.cpp.in b/oneflow/ir/lib/OneFlow/OneFlowOpGetGen.cpp.in index 40cfd932593be8969cbbee6056809f4b070071b5..9879c1667acb9d5a9b400445ef29b67529665aa4 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowOpGetGen.cpp.in +++ b/oneflow/ir/lib/OneFlow/OneFlowOpGetGen.cpp.in @@ -8,7 +8,6 @@ #include "mlir/Support/LogicalResult.h" #include "OneFlow/OneFlowDialect.h" #include "OneFlow/OneFlowOpTraits.h" -#include "OneFlow/OneFlowEnums.h.inc" #include "OneFlow/OneFlowSupport.h" #include "OneFlow/OneFlowInterfaces.h.inc" #include "OneFlow/OneFlowTypes.h" diff --git a/oneflow/ir/lib/OneFlow/OneFlowOpTraits.cpp b/oneflow/ir/lib/OneFlow/OneFlowOpTraits.cpp index 4fee88c3744a4c362e8419f08383814eabf687ac..2cfeec1502de2afdd52e0a4acee2fdf740b80c3e 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowOpTraits.cpp +++ b/oneflow/ir/lib/OneFlow/OneFlowOpTraits.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "OneFlow/OneFlowOps.h" +#include "OneFlow/UserOpConversion.h" namespace mlir { @@ -88,17 +89,7 @@ LogicalResult VerifyIsImportCompatible(Operation* op) { } LogicalResult saveAttrToOpConf(Operation* op, ::oneflow::OperatorConf* op_conf) { - if (auto scope_symbol_id = op->getAttrOfType( - OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr())) { - op_conf->set_scope_symbol_id(scope_symbol_id.getInt()); - } - if (auto op_name = - op->getAttrOfType(OpTrait::IsOpConfCompatible::getOpNameAttr())) { - op_conf->set_name(op_name.str()); - } - op_conf->set_device_tag( - op->getAttrOfType(IsOpConfCompatible::getDeviceTagAttr()).str()); - return success(); + return oneflow::user_op::saveAttrDictionaryToOpConf(op->getAttrDictionary(), op_conf); } StringAttr getOpName(Operation* op) { diff --git a/oneflow/ir/lib/OneFlow/OneFlowOps.cpp b/oneflow/ir/lib/OneFlow/OneFlowOps.cpp index db388dcd150fe164db48da0ad53649e3c338164f..15b0afcb63bcdeaab0aee158d62f09b81bdd4390 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowOps.cpp +++ b/oneflow/ir/lib/OneFlow/OneFlowOps.cpp @@ -16,7 +16,8 @@ limitations under the License. #include "OneFlow/OneFlowOps.h" #include "OneFlow/OneFlowDialect.h" #include "OneFlow/OneFlowSupport.h" -#include "OneFlow/Passes.h" +#include "OneFlow/SBP/SBPAttributes.h" +#include "OneFlow/Transform/TransposeHelpers.h" #include "llvm/ADT/StringRef.h" #include "mlir/IR/Attributes.h" #include "mlir/IR/BuiltinTypes.h" @@ -29,7 +30,6 @@ limitations under the License. #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/functional/functional_api.yaml.h" -#include "oneflow/api/common/ofblob.h" #include "oneflow/core/common/data_type.h" #include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/job/lazy_mode.h" @@ -221,8 +221,8 @@ struct ConvertAddOpWithArity : public OpRewritePattern { const auto arity = op.in().size(); if (arity == 2) { NamedAttrList attributes = op->getAttrs(); - attributes.push_back(rewriter.getNamedAttr(OpTrait::IsAlternative::getOpTypeNameAttr(), - rewriter.getStringAttr("add_n"))); + attributes.set(OpTrait::IsAlternative::getOpTypeNameAttr(), + rewriter.getStringAttr("add_n")); if (auto created_op = rewriter.replaceOpWithNewOp(op, op->getResultTypes(), op.getOperands(), attributes)) { return success(); @@ -271,62 +271,6 @@ void OutputOp::getCanonicalizationPatterns(RewritePatternSet& results, MLIRConte results.insert>(context); } -void NormalizationAddReluOp::build(::mlir::OpBuilder& odsBuilder, ::mlir::OperationState& odsState, - Value x, Value addend, Value moving_mean, Value moving_variance, - Value gamma, Value beta, StringRef op_name, StringRef device_tag, - ArrayAttr device_name, IntegerAttr scope_symbol_id, - ArrayAttr hierarchy, DenseElementsAttr operand_segment_sizes, - DenseElementsAttr result_segment_sizes, IntegerAttr axis, - FloatAttr epsilon, BoolAttr training, FloatAttr momentum) { - odsState.addOperands(x); - if (addend) odsState.addOperands(addend); - if (moving_mean) odsState.addOperands(moving_mean); - if (moving_variance) odsState.addOperands(moving_variance); - odsState.addOperands(gamma); - odsState.addOperands(beta); - odsState.addAttribute(operand_segment_sizesAttrName(odsState.name), - odsBuilder.getI32VectorAttr({1, (addend ? 1 : 0), (moving_mean ? 1 : 0), - (moving_variance ? 1 : 0), 1, 1})); - - odsState.addAttribute(op_nameAttrName(odsState.name), odsBuilder.getStringAttr(op_name)); - odsState.addAttribute(device_tagAttrName(odsState.name), odsBuilder.getStringAttr(device_tag)); - odsState.addAttribute(device_nameAttrName(odsState.name), device_name); - if (scope_symbol_id) { - odsState.addAttribute(scope_symbol_idAttrName(odsState.name), scope_symbol_id); - } - if (hierarchy) { odsState.addAttribute(hierarchyAttrName(odsState.name), hierarchy); } - // TODO: remove the workaround if normalization_add_relu supports infererence mode - odsState.addAttribute(result_segment_sizesAttrName(odsState.name), - odsBuilder.getI32VectorAttr({1, 1, 1, 1})); - odsState.addAttribute(axisAttrName(odsState.name), axis); - odsState.addAttribute(epsilonAttrName(odsState.name), epsilon); - odsState.addAttribute(trainingAttrName(odsState.name), training); - odsState.addAttribute(momentumAttrName(odsState.name), momentum); - auto y = x.getType(); - odsState.addTypes(y); - // TODO: add real type infer, or get types from user of x and moving_mean, if it is a bn - /*reserve_space */ odsState.addTypes(x.getType()); - /*mean */ odsState.addTypes(x.getType()); - /*inv_variance */ odsState.addTypes(x.getType()); -} - -void RandomMaskLikeOp::build(mlir::OpBuilder& odsBuilder, mlir::OperationState& odsState, - mlir::Value like, StringRef op_name, StringRef device_tag, - ArrayAttr device_name, IntegerAttr scope_symbol_id, - ArrayAttr hierarchy, mlir::FloatAttr rate, mlir::IntegerAttr seed) { - odsState.addOperands(like); - odsState.addAttribute(op_nameAttrName(odsState.name), odsBuilder.getStringAttr(op_name)); - odsState.addAttribute(device_tagAttrName(odsState.name), odsBuilder.getStringAttr(device_tag)); - odsState.addAttribute(device_nameAttrName(odsState.name), device_name); - if (scope_symbol_id) { - odsState.addAttribute(scope_symbol_idAttrName(odsState.name), scope_symbol_id); - } - if (hierarchy) { odsState.addAttribute(hierarchyAttrName(odsState.name), hierarchy); } - odsState.addAttribute(rateAttrName(odsState.name), rate); - odsState.addAttribute(seedAttrName(odsState.name), seed); - odsState.addTypes(like.getType()); -} - std::string Add2Op::getOriginalOpTypeName() { return "add_n"; } std::string NormalizationInferenceOp::getOriginalOpTypeName() { return "normalization"; } @@ -437,140 +381,6 @@ llvm::Optional GetCtrlOutputResult(Operation* op) { return llvm::None; } -bool Conv2DOp::IsNCHW() { return this->data_format().str() == "channels_first"; } - -llvm::DenseSet Conv2DOp::OperandsToTranspose() { return {this->in(), this->weight()}; } - -llvm::DenseSet Conv2DOp::ResultsToTranspose() { return {this->out()}; } - -llvm::SmallVector Conv2DOp::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto conv_op = *this; - SmallVector operands; - operands.push_back(value[0]); - operands.push_back(value[1]); - if (conv_op.bias()) operands.push_back(conv_op.bias()); - if (conv_op.bias_multiplier()) operands.push_back(conv_op.bias_multiplier()); - NamedAttrList attributes = conv_op->getAttrs(); - attributes.set(conv_op.data_formatAttrName(), rewriter.getStringAttr("channels_last")); - auto res = rewriter - .create(conv_op.getLoc(), conv_op->getResultTypes(), operands, - attributes) - ->getResults(); - llvm::SmallVector results; - results.push_back(res[0]); - return results; -} - -bool BiasAddOp::IsNCHW() { return this->axisAttr().getValue().getSExtValue() == 1; } - -llvm::DenseSet BiasAddOp::OperandsToTranspose() { return {this->a()}; } - -llvm::DenseSet BiasAddOp::ResultsToTranspose() { return {this->out()}; } - -llvm::SmallVector BiasAddOp::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto bias_add_op = *this; - SmallVector operands; - operands.push_back(value[0]); - operands.push_back(bias_add_op.b()); - NamedAttrList attributes = bias_add_op->getAttrs(); - attributes.set(bias_add_op.axisAttrName(), rewriter.getSI32IntegerAttr(3)); - auto res = rewriter - .create(bias_add_op.getLoc(), bias_add_op->getResultTypes(), - operands, attributes) - ->getResults(); - llvm::SmallVector results; - results.push_back(res[0]); - return results; -} - -bool NormalizationOp::IsNCHW() { return this->axisAttr().getValue().getSExtValue() == 1; } - -llvm::DenseSet NormalizationOp::OperandsToTranspose() { return {this->x()}; } - -llvm::DenseSet NormalizationOp::ResultsToTranspose() { return {this->y()}; } - -llvm::SmallVector NormalizationOp::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto normalization_op = *this; - SmallVector operands; - operands.push_back(value[0]); - if (normalization_op.moving_mean()) operands.push_back(normalization_op.moving_mean()); - if (normalization_op.moving_variance()) operands.push_back(normalization_op.moving_variance()); - operands.push_back(normalization_op.gamma()); - operands.push_back(normalization_op.beta()); - if (normalization_op._add_to_output()) operands.push_back(normalization_op._add_to_output()); - NamedAttrList attributes = normalization_op->getAttrs(); - attributes.set(normalization_op.axisAttrName(), rewriter.getSI32IntegerAttr(3)); - auto res = - rewriter - .create( - normalization_op.getLoc(), normalization_op->getResultTypes(), operands, attributes) - ->getResults(); - llvm::SmallVector results; - results.push_back(res[0]); - return results; -} - -bool MaxPool2DOp::IsNCHW() { return this->data_format().str() == "channels_first"; } - -llvm::DenseSet MaxPool2DOp::OperandsToTranspose() { return {this->x()}; } - -llvm::DenseSet MaxPool2DOp::ResultsToTranspose() { return {this->y(), this->indice()}; } - -llvm::SmallVector MaxPool2DOp::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto max_pool_2d_op = *this; - SmallVector operands; - operands.push_back(value[0]); - NamedAttrList attributes = max_pool_2d_op->getAttrs(); - attributes.set(max_pool_2d_op.data_formatAttrName(), rewriter.getStringAttr("channels_last")); - auto res = - rewriter - .create(max_pool_2d_op.getLoc(), max_pool_2d_op->getResultTypes(), - operands, attributes) - ->getResults(); - llvm::SmallVector results; - results.push_back(res[0]); - results.push_back(res[1]); - return results; -} - -bool ReluOp::IsNCHW() { return false; } - -llvm::DenseSet ReluOp::OperandsToTranspose() { return {this->x()}; } - -llvm::DenseSet ReluOp::ResultsToTranspose() { return {this->y()}; } - -llvm::SmallVector ReluOp::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto relu_op = *this; - SmallVector operands{value[0]}; - auto res = rewriter - .create(relu_op.getLoc(), relu_op->getResultTypes(), operands, - relu_op->getAttrs()) - ->getResults(); - return {res[0]}; -} - -bool Add2Op::IsNCHW() { return false; } - -llvm::DenseSet Add2Op::OperandsToTranspose() { return {this->in0(), this->in1()}; } - -llvm::DenseSet Add2Op::ResultsToTranspose() { return {this->out()}; } - -llvm::SmallVector Add2Op::NchwToNhwc(llvm::SmallVector value, - PatternRewriter& rewriter) { - auto add2_op = *this; - SmallVector operands{value[0], value[1]}; - auto res = rewriter - .create(add2_op.getLoc(), add2_op->getResultTypes(), operands, - add2_op->getAttrs()) - ->getResults(); - return {res[0]}; -} - } // namespace oneflow } // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OneFlowRewrites.cpp b/oneflow/ir/lib/OneFlow/OneFlowRewrites.cpp new file mode 100644 index 0000000000000000000000000000000000000000..dbcad7fb70dfd00022f82d538912882f1901adcb --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OneFlowRewrites.cpp @@ -0,0 +1,308 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +//===- TestPDLByteCode.cpp - Test PDLL functionality ----------------------===// +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// +//===----------------------------------------------------------------------===// + +#include "OneFlow/UserOpConversion.h" +#include "mlir/Dialect/PDL/IR/PDL.h" +#include "mlir/Dialect/PDLInterp/IR/PDLInterp.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/OneFlowPDLLPatterns.h" +#include "OneFlow/OneFlowOps.h" +#include "oneflow/core/framework/random_generator.h" + +using namespace mlir; + +#include "oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.h.inc" + +namespace mlir { + +namespace oneflow { + +namespace { + +static std::atomic uniqID{0}; + +std::string getUniqName(llvm::StringRef name) { + uniqID += 1; + return name.str() + "-mlir-gen-" + std::to_string(uniqID); +} + +static Operation* CopyUserOpAttrs(PatternRewriter& rewriter, Operation* src, Operation* dst) { + dst->setAttr(OpTrait::IsOpConfCompatible::getDeviceTagAttr(), + OpTrait::IsOpConfCompatible::getDeviceTag(src)); + dst->setAttr(OpTrait::IsOpConfCompatible::getDeviceNameAttr(), + OpTrait::IsOpConfCompatible::getDeviceName(src)); + if (auto hierarchy = OpTrait::IsOpConfCompatible::getHierarchy(src)) { + dst->setAttr(OpTrait::IsOpConfCompatible::getHierarchyAttr(), hierarchy); + } + if (auto scope_symbol_id = OpTrait::IsOpConfCompatible::getScopeSymbolID(src)) { + dst->setAttr(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr(), scope_symbol_id); + } + dst->setAttr( + OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr(getUniqName(OpTrait::IsOpConfCompatible::getOpName(src).str()))); + return dst; +} + +static Operation* BuildFusedBiasAddMaskScaleOpWithRate(PatternRewriter& rewriter, Value a, Value b, + Value mask, Attribute axis, Attribute rate, + Operation* dropout) { + auto dropout_op = llvm::dyn_cast(dropout); + assert(dropout_op); + SmallVector operands; + operands.push_back(a); + operands.push_back(b); + operands.push_back(mask); + NamedAttrList attributes = dropout_op->getAttrs(); + attributes.set("axis", axis); + attributes.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr(OpTrait::IsOpConfCompatible::getOpName(dropout).str() + + "-fuse-bias-add")); + float scale = 1.0f; + float rate_float = rate.cast().getValueAsDouble(); + if (rate_float < 1.0f) { scale = 1.0f / (1.0f - rate_float); } + attributes.set("scale", rewriter.getF32FloatAttr(scale)); + attributes.erase(dropout_op.rateAttrName()); + return rewriter.create(dropout_op->getLoc(), dropout_op.out().getType(), + operands, attributes); +} + +static Operation* CreateConv2dAndErasePad(PatternRewriter& rewriter, Value x, Value weight, + Attribute padding_before, Attribute data_format, + Operation* conv) { + auto conv_op = llvm::dyn_cast(conv); + assert(conv_op); + SmallVector operands; + operands.push_back(x); + operands.push_back(weight); + NamedAttrList attributes = conv_op->getAttrs(); + llvm::SmallVector padding_before_array; + + attributes.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr(OpTrait::IsOpConfCompatible::getOpName(conv).str() + + "-fuse-conv")); + + if (data_format.cast().str() == "channels_first") { + for (auto val : padding_before.cast().getValue().take_back(2)) { + padding_before_array.push_back(val.cast().getValue().getSExtValue()); + } + } else { + padding_before_array.push_back(padding_before.cast() + .getValue()[1] + .cast() + .getValue() + .getSExtValue()); + padding_before_array.push_back(padding_before.cast() + .getValue()[2] + .cast() + .getValue() + .getSExtValue()); + } + + attributes.set(conv_op.padding_beforeAttrName(), + getSI32ArrayAttr(rewriter, padding_before_array)); + return rewriter.create(conv_op->getLoc(), conv_op.out().getType(), operands, + attributes); +} + +IntegerAttr getSI64IntegerAttr(::mlir::PatternRewriter& rewriter, int64_t value) { + return IntegerAttr::get(rewriter.getIntegerType(64, /*isSigned=*/true), + APInt(64, value, /*isSigned=*/true)); +} + +NamedAttrList GetUserOpCommonAttrs(MLIRContext* ctx, const std::string& op_name) { + NamedAttrList attrs; + attrs.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), StringAttr::get(ctx, op_name)); + attrs.set(OpTrait::IsOpConfCompatible::getDeviceTagAttr(), StringAttr::get(ctx, "cpu")); + attrs.set(OpTrait::IsOpConfCompatible::getDeviceNameAttr(), + ArrayAttr::get(ctx, llvm::to_vector<8>(llvm::map_range(ArrayRef({"@0:0"}), + [&](StringRef v) -> Attribute { + return StringAttr::get(ctx, v); + })))); + return attrs; +} +static Operation* CreateConv2DBatchNorm(PatternRewriter& rewriter, Attribute epsilon, + Operation* conv, Operation* bn) { + auto conv_op = llvm::dyn_cast(conv); + auto bn_op = llvm::dyn_cast(bn); + auto ctx = rewriter.getContext(); + NamedAttrList attributes = conv_op->getAttrs(); + + attributes.set("operand_segment_sizes", rewriter.getI32VectorAttr({1, 1, 1, 0})); + + SmallVector operands; + operands.push_back(conv_op.in()); + + // deal with weight + auto add_op_attrs = GetUserOpCommonAttrs(ctx, "scalar_add"); + add_op_attrs.set("has_float_operand", BoolAttr::get(ctx, true)); + + double epsilon_attr = epsilon.cast().getValueAsDouble(); + add_op_attrs.set("float_operand", rewriter.getF64FloatAttr(epsilon_attr)); + + auto add_op = rewriter.create( + conv_op->getLoc(), conv_op.out().getType(), SmallVector({bn_op.moving_variance()}), + add_op_attrs); + + auto sqrt_op = rewriter.create(conv_op->getLoc(), conv_op.out().getType(), + SmallVector({add_op.out()}), + GetUserOpCommonAttrs(ctx, "sqrt")); + + auto div_op = rewriter.create( + conv_op->getLoc(), conv_op.out().getType(), + SmallVector({bn_op.gamma(), sqrt_op.y()}), GetUserOpCommonAttrs(ctx, "div")); + + auto bn_gamma_variable_op = + llvm::dyn_cast(bn_op.gamma().getDefiningOp()); + + CHECK(bn_gamma_variable_op) << "Gamma of batchnorm should be a FrozenVariableOp."; + + auto bn_gamma_shape = + bn_gamma_variable_op.value().getType().cast().getShape(); + + auto conv_weight_variable_op = + llvm::dyn_cast(conv_op.weight().getDefiningOp()); + + CHECK(conv_weight_variable_op) << "Weight of conv2d should be a FrozenVariableOp."; + + auto conv_weight_shape = + conv_weight_variable_op.value().getType().cast().getShape(); + + std::vector bn_gamma_new_shape({bn_gamma_shape.front()}); + for (int i = 1; i < conv_weight_shape.size(); ++i) { bn_gamma_new_shape.emplace_back(1); } + auto reshape_op_attrs = GetUserOpCommonAttrs(ctx, "reshape"); + reshape_op_attrs.set( + "shape", + ArrayAttr::get(ctx, llvm::to_vector<8>(llvm::map_range( + ArrayRef(bn_gamma_new_shape), [&](int64_t v) -> Attribute { + return getSI64IntegerAttr(rewriter, v); + })))); + auto reshape_op = + rewriter.create(conv_op->getLoc(), conv_op.out().getType(), + SmallVector({div_op.z()}), reshape_op_attrs); + + auto mul_op = rewriter.create( + conv_op->getLoc(), conv_op.out().getType(), + SmallVector({conv_op.weight(), reshape_op.out()}), + GetUserOpCommonAttrs(ctx, "multiply")); + operands.push_back(mul_op.z()); + + // deal with bias + CHECK(!conv_op.bias()) << "Fusing conv2d and batch_norm only supports conv2d without bias now."; + + auto mul_op_bias = rewriter.create( + conv_op->getLoc(), conv_op.out().getType(), + SmallVector({bn_op.moving_mean(), div_op.z()}), + GetUserOpCommonAttrs(ctx, "multiply_bias")); + auto sub_op_bias = rewriter.create( + conv_op->getLoc(), conv_op.out().getType(), + SmallVector({bn_op.beta(), mul_op_bias.z()}), + GetUserOpCommonAttrs(ctx, "sub_bias")); + operands.push_back(sub_op_bias.z()); + + auto new_conv_op = rewriter.create(conv_op->getLoc(), conv_op.out().getType(), + operands, attributes); + + return new_conv_op; +} + +static LogicalResult IsPaddingCouldBeAssimilatedIntoConv(PatternRewriter& rewriter, + Attribute padding_before, + Attribute padding_after, + Attribute data_format) { + if (padding_before.cast().size() == 4 && padding_after.cast().size() == 4) { + if (padding_before.cast().getValue().equals( + padding_after.cast().getValue())) { + if (data_format.cast().str() == "channels_first") { + return success(padding_before.cast() + .getValue()[0] + .cast() + .getValue() + .getSExtValue() + == 0 + && padding_before.cast() + .getValue()[1] + .cast() + .getValue() + .getSExtValue() + == 0); + } + if (data_format.cast().str() == "channels_last") { + return success(padding_before.cast() + .getValue()[0] + .cast() + .getValue() + .getSExtValue() + == 0 + && padding_before.cast() + .getValue()[3] + .cast() + .getValue() + .getSExtValue() + == 0); + } + } + } + return failure(); +} + +} // namespace + +namespace rewrites { + +void populateRewrites(RewritePatternSet& patterns) { + patterns.getPDLPatterns().registerRewriteFunction("BuildFusedBiasAddMaskScaleOpWithRate", + BuildFusedBiasAddMaskScaleOpWithRate); + patterns.getPDLPatterns().registerRewriteFunction("CopyUserOpAttrs", CopyUserOpAttrs); + patterns.getPDLPatterns().registerRewriteFunction("CreateConv2dAndErasePad", + CreateConv2dAndErasePad); + patterns.getPDLPatterns().registerRewriteFunction("CreateConv2DBatchNorm", CreateConv2DBatchNorm); +} + +mlir::IntegerAttr GetDefaultSeed(::mlir::PatternRewriter& rewriter) { + const auto gen = CHECK_JUST(::oneflow::one::DefaultAutoGenerator()); + return getSI64IntegerAttr(rewriter, (int64_t)gen->current_seed()); +} + +} // namespace rewrites + +namespace constraints { + +void populateConstraints(RewritePatternSet& patterns) { + auto& pdll_patterns = patterns.getPDLPatterns(); + +#define PDLL_REGISTER(NAME) pdll_patterns.registerConstraintFunction(#NAME, NAME); + + PDLL_REGISTER(IsPaddingCouldBeAssimilatedIntoConv); + +#undef PDLL_REGISTER +} + +} // namespace constraints +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/OneFlowSupport.cpp b/oneflow/ir/lib/OneFlow/OneFlowSupport.cpp index 6a4e3bb380bad774835204909253c171c8283350..b56c42361e8cbc28a800d638bba29b47b888681d 100644 --- a/oneflow/ir/lib/OneFlow/OneFlowSupport.cpp +++ b/oneflow/ir/lib/OneFlow/OneFlowSupport.cpp @@ -13,21 +13,26 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include -#include +#include "OneFlow/OneFlowTypes.h" #include "mlir/IR/Attributes.h" #include "mlir/IR/BuiltinAttributes.h" #include "mlir/IR/BuiltinTypes.h" - #include "mlir/IR/MLIRContext.h" -#include "oneflow/api/common/ofblob.h" +#include "oneflow/ir/include/OneFlow/OneFlowSupport.h" #include "oneflow/core/common/data_type.pb.h" #include "oneflow/core/common/just.h" +#include "oneflow/core/eager/eager_blob_object.h" #include "oneflow/core/job/lazy_mode.h" #include "oneflow/core/functional/functional_api.yaml.h" #include "oneflow/core/framework/tensor.h" #include "oneflow/core/framework/tensor_util.h" #include "oneflow/core/framework/user_op_registry_manager.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/memory/memory_case_util.h" +#include "oneflow/core/common/data_type.h" + +#include +#include namespace mlir { @@ -35,25 +40,15 @@ namespace oneflow { namespace support { -using ::oneflow::UserOpDef; -using ::oneflow::user_op::OpRegistryResult; -using ::oneflow::user_op::UserOpRegistryMgr; - -const UserOpDef& GetUserOpDef(const std::string& op_type_name) { - const OpRegistryResult* val = UserOpRegistryMgr::Get().GetOpRegistryResult(op_type_name); - CHECK(val) << " Cannot find op_type_name: " << op_type_name; - return val->op_def; -} - std::vector GetInputKeys(const std::string& op_type_name) { std::vector ret{}; - for (auto& arg : GetUserOpDef(op_type_name).input()) { ret.push_back(arg.name()); } + for (auto& arg : getUserOpDef(op_type_name).input()) { ret.push_back(arg.name()); } return ret; } std::vector GetOutputKeys(const std::string& op_type_name) { std::vector ret{}; - for (auto& arg : GetUserOpDef(op_type_name).output()) { ret.push_back(arg.name()); } + for (auto& arg : getUserOpDef(op_type_name).output()) { ret.push_back(arg.name()); } return ret; } @@ -77,9 +72,13 @@ mlir::DenseElementsAttr __TensorToDenseElementsAttr( auto shape = tensor_->shape(); std::vector shape_vec(shape->dim_vec().begin(), shape->dim_vec().end()); std::vector data(shape->elem_cnt()); - const auto& callback = [&](uint64_t ofblob_ptr) { - CHECK_JUST(::oneflow::BlobBufferCopyUtil::To(ofblob_ptr, data.data(), data.size())); - }; + const auto& callback = + [&](::oneflow::ep::Stream* stream, + const std::shared_ptr<::oneflow::vm::EagerBlobObject>& eager_blob_object) { + ::oneflow::AutoMemcpy(stream, data.data(), eager_blob_object->dptr(), + data.size() * sizeof(T), ::oneflow::memory::MakeHostMemCase(), + eager_blob_object->mem_case()); + }; ::oneflow::one::SyncAccessTensorWithTimeOut(tensor_, callback, "const").GetOrThrow(); return mlir::DenseElementsAttr::get(mlir::RankedTensorType::get(shape_vec, mlir_type), llvm::makeArrayRef(data)); @@ -101,14 +100,62 @@ std::shared_ptr<::oneflow::one::Tensor> __DenseElementsAttrToTensor( .GetPtrOrThrow(); std::vector data(dense_attr.getValues().begin(), dense_attr.getValues().end()); - const auto& callback = [&](uint64_t of_blob_ptr) { - ::oneflow::BlobBufferCopyUtil::From(of_blob_ptr, data.data(), tensor->shape()->elem_cnt()) - .GetOrThrow(); - }; + const auto& callback = + [&](::oneflow::ep::Stream* stream, + const std::shared_ptr<::oneflow::vm::EagerBlobObject>& eager_blob_object) { + ::oneflow::AutoMemcpy(stream, eager_blob_object->mut_dptr(), data.data(), + tensor->shape()->elem_cnt() * sizeof(T), + eager_blob_object->mem_case(), ::oneflow::memory::MakeHostMemCase()); + }; ::oneflow::one::SyncAccessTensorWithTimeOut(tensor, callback, "mut").GetOrThrow(); return tensor; } +template +void __DenseElementsAttrToTensor(const mlir::DenseElementsAttr dense_attr, + const mlir::Attribute& device_tag_attr, + const mlir::Attribute& device_name_attr, + const ::oneflow::DataType& dtype, + std::shared_ptr<::oneflow::one::Tensor>& tensor) { + const auto dense_type = dense_attr.getType().cast(); + std::vector shape = dense_type.getShape().vec(); + int ndim = shape.size(); + CHECK_EQ(tensor->shape()->size(), ndim); + for (int i = 0; i < ndim; ++i) { CHECK_EQ(tensor->shape()->at(i), shape[i]); } + + const auto device = MakeDevice(device_tag_attr, device_name_attr); + CHECK(CHECK_JUST(tensor->device()) == device); + + std::vector data; + std::vector<::oneflow::float16> fp16_data; + void* dptr = nullptr; + const size_t tensor_size = + tensor->shape()->elem_cnt() * ::oneflow::GetSizeOfDataType(tensor->dtype()->data_type()); + + CHECK_EQ(::oneflow::GetDataType::value, dtype); + if (tensor->dtype()->data_type() == ::oneflow::DataType::kFloat16) { + for (const T elem : dense_attr.getValues()) { + fp16_data.push_back(static_cast<::oneflow::float16>(elem)); + } + CHECK_EQ(fp16_data.size() * sizeof(::oneflow::float16), tensor_size); + dptr = fp16_data.data(); + } else if (tensor->dtype()->data_type() == dtype) { + for (const T elem : dense_attr.getValues()) { data.push_back(elem); } + CHECK_EQ(data.size() * sizeof(T), tensor_size); + dptr = data.data(); + } else { + UNIMPLEMENTED(); + } + + const auto& callback = + [=](::oneflow::ep::Stream* stream, + const std::shared_ptr<::oneflow::vm::EagerBlobObject>& eager_blob_object) { + ::oneflow::AutoMemcpy(stream, eager_blob_object->mut_dptr(), dptr, tensor_size, + eager_blob_object->mem_case(), ::oneflow::memory::MakeHostMemCase()); + }; + ::oneflow::one::SyncAccessTensorWithTimeOut(tensor, callback, "mut").GetOrThrow(); +} + } // namespace mlir::DenseElementsAttr TensorToDenseElementsAttr( @@ -143,6 +190,85 @@ std::shared_ptr<::oneflow::one::Tensor> DenseElementsAttrToTensor( exit(EXIT_FAILURE); } +void DenseElementsAttrToTensor(const mlir::Attribute& dense_attr, + const mlir::Attribute& device_tag_attr, + const mlir::Attribute& device_name_attr, + std::shared_ptr<::oneflow::one::Tensor>& tensor) { + ::oneflow::LazyMode::Guard guard{false}; + const auto dense_attr_ = dense_attr.cast(); + const auto dense_element_type = dense_attr_.getElementType(); + if (dense_element_type.isF32()) { + __DenseElementsAttrToTensor(dense_attr_, device_tag_attr, device_name_attr, + ::oneflow::DataType::kFloat, tensor); + } else { + llvm::errs() << "Converting mlir::DenseElementsAttr to oneflow::Tensor only support float32 " + "and int64 now." + << "\n"; + exit(EXIT_FAILURE); + } +} + +FailureOr<::oneflow::DataType> FromMLIRTypeToOFDataType(Type mlir_type) { + if (mlir_type.dyn_cast()) { return ::oneflow::DataType::kInvalidDataType; } + if (mlir_type.dyn_cast()) { return ::oneflow::DataType::kChar; } + if (mlir_type.dyn_cast()) { return ::oneflow::DataType::kOFRecord; } + if (mlir_type.dyn_cast()) { return ::oneflow::DataType::kTensorBuffer; } + if (mlir_type.isF16()) { return ::oneflow::DataType::kFloat16; } + if (mlir_type.isF32()) { return ::oneflow::DataType::kFloat; } + if (mlir_type.isF64()) { return ::oneflow::DataType::kDouble; } + + if (mlir_type.isSignlessInteger(8)) { return ::oneflow::DataType::kBool; } + if (mlir_type.isSignlessInteger(16)) { return ::oneflow::DataType::kUInt16; } + if (mlir_type.isSignlessInteger(32)) { return ::oneflow::DataType::kUInt32; } + if (mlir_type.isSignlessInteger(64)) { return ::oneflow::DataType::kUInt64; } + if (mlir_type.isSignlessInteger(128)) { return ::oneflow::DataType::kUInt128; } + + if (mlir_type.isSignedInteger(8)) { return ::oneflow::DataType::kInt8; } + if (mlir_type.isSignedInteger(16)) { return ::oneflow::DataType::kInt16; } + if (mlir_type.isSignedInteger(32)) { return ::oneflow::DataType::kInt32; } + if (mlir_type.isSignedInteger(64)) { return ::oneflow::DataType::kInt64; } + if (mlir_type.isSignedInteger(128)) { return ::oneflow::DataType::kInt128; } + llvm::errs() << "Unsupported data type: " << mlir_type << "\n"; + return failure(); +} + +FailureOr<::oneflow::DataType> FromMLIRDataTypeToOFDataType(::mlir::oneflow::DataType data_type) { + switch (data_type) { + case ::mlir::oneflow::DataType::DT_InvalidDataType: + return ::oneflow::DataType::kInvalidDataType; +#define DEFINE_ONE_CASE(datatype) \ + case ::mlir::oneflow::DataType::DT_##datatype: return ::oneflow::DataType::k##datatype; + DEFINE_ONE_CASE(Char) + DEFINE_ONE_CASE(Float) + DEFINE_ONE_CASE(Double) + DEFINE_ONE_CASE(Int8) + DEFINE_ONE_CASE(Int32) + DEFINE_ONE_CASE(Int64) + DEFINE_ONE_CASE(UInt8) + DEFINE_ONE_CASE(OFRecord) + DEFINE_ONE_CASE(Float16) + DEFINE_ONE_CASE(TensorBuffer) + DEFINE_ONE_CASE(Bool) +#undef DEFINE_ONE_CASE + default: { + return failure(); + } + } + return failure(); +} + +FailureOr<::oneflow::DataType> FromMLIRAttrToOFDataType(Attribute attr) { + const auto data_type_attr = attr.dyn_cast(); + return FromMLIRDataTypeToOFDataType(data_type_attr.getValue()); +} + +const ::oneflow::UserOpDef& getUserOpDef(const std::string& op_type_name) { + const ::oneflow::user_op::OpRegistryResult* val = + ::oneflow::user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_type_name); + CHECK(val) << " Cannot find op_type_name: " << op_type_name; + return val->op_def; +} + } // namespace support } // namespace oneflow diff --git a/oneflow/ir/lib/OneFlow/OneFlowUtils.cpp b/oneflow/ir/lib/OneFlow/OneFlowUtils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ccb9c7e16697a85493e9b4abb51c4f02fb941a20 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/OneFlowUtils.cpp @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OneFlowUtils.h" +#include "oneflow/core/common/util.h" +namespace mlir { +namespace oneflow { + +void CheckEnableIRPrinting(mlir::PassManager& pm) { + bool enable_ir_printing = + ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_IR_PRINTING", false); + pm.getContext()->disableMultithreading(enable_ir_printing); + if (enable_ir_printing) { pm.enableIRPrinting(); } +} + +StringRef SanitizeIdentifier(StringRef name, SmallString<16>& buffer, StringRef allowedPunctChars, + bool allowTrailingDigit) { + assert(!name.empty() && "Shouldn't have an empty name here"); + + auto copyNameToBuffer = [&] { + for (char ch : name) { + if (llvm::isAlnum(ch) || allowedPunctChars.contains(ch)) + buffer.push_back(ch); + else if (ch == ' ') + buffer.push_back('_'); + else + buffer.append(llvm::utohexstr((unsigned char)ch)); + } + }; + + // Check to see if this name is valid. If it starts with a digit, then it + // could conflict with the autogenerated numeric ID's, so add an underscore + // prefix to avoid problems. + if (isdigit(name[0])) { + buffer.push_back('_'); + copyNameToBuffer(); + return buffer; + } + + // If the name ends with a trailing digit, add a '_' to avoid potential + // conflicts with autogenerated ID's. + if (!allowTrailingDigit && isdigit(name.back())) { + copyNameToBuffer(); + buffer.push_back('_'); + return buffer; + } + + // Check to see that the name consists of only valid identifier characters. + for (char ch : name) { + if (!llvm::isAlnum(ch) && !allowedPunctChars.contains(ch)) { + copyNameToBuffer(); + return buffer; + } + } + + // If there are no invalid characters, return the original name. + return name; +} +} // namespace oneflow +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/PDLL/CMakeLists.txt b/oneflow/ir/lib/OneFlow/PDLL/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a4ef49b63b6bf60f45af36b32e6e7374e6000b0 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/CMakeLists.txt @@ -0,0 +1,15 @@ +add_mlir_pdll_library(MLIROneFlowPDLLForwardOpPatternsIncGen ForwardOpPatterns.pdll + ForwardOpPatterns.h.inc) +add_mlir_pdll_library(MLIROneFlowPDLLNormalizationPatternsIncGen NormalizationPatterns.pdll + NormalizationPatterns.h.inc) +add_mlir_pdll_library(MLIROneFlowPDLLFuseConv2DBatchNormPatternIncGen + FuseConv2DBatchNormPattern.pdll FuseConv2DBatchNormPattern.h.inc) +oneflow_add_mlir_dialect_library( + MLIROneFlowPDLLPatterns + ForwardOpPatterns.cpp + NormalizationPatterns.cpp + FuseConv2DBatchNormPattern.cpp + DEPENDS + MLIROneFlowPDLLForwardOpPatternsIncGen + MLIROneFlowPDLLNormalizationPatternsIncGen + MLIROneFlowPDLLFuseConv2DBatchNormPatternIncGen) diff --git a/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.cpp b/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.cpp new file mode 100644 index 0000000000000000000000000000000000000000..75bc0786815b573c46c4184cec5f9cedfa374746 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.cpp @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "mlir/Dialect/PDL/IR/PDL.h" +#include "mlir/Dialect/PDLInterp/IR/PDLInterp.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/OneFlowPDLLPatterns.h" + +using namespace mlir; + +#include "oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.h.inc" + +namespace mlir { + +namespace oneflow { + +void populateForwardOpPatterns(RewritePatternSet& patterns) { + populateGeneratedPDLLPatterns(patterns); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.pdll b/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.pdll new file mode 100644 index 0000000000000000000000000000000000000000..40ece67ec5b19ba9d60b3b1930227b14093c01d8 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/ForwardOpPatterns.pdll @@ -0,0 +1,104 @@ +#include "OneFlow/OneFlowOps.td" +#include "OneFlowPDLLUtils.pdll" + +Pattern { + let rate: Attr; + let device_name: Attr; + let device_tag: Attr; + let axis: Attr; + let dropout = + op( + op(a: Value, b: Value) {axis = axis, device_name = device_name, device_tag = device_tag}) + {rate = rate, device_name = device_name, device_tag = device_tag} -> (out: Type, mask: Type); + + rewrite dropout with { + let random_mask_like = CopyUserOpAttrs(dropout, op(a){rate = rate} -> (mask)); + let fused_bias_add_mask_scale = BuildFusedBiasAddMaskScaleOpWithRate(a, b, random_mask_like.0, axis, rate, dropout); + replace dropout with (fused_bias_add_mask_scale.0, random_mask_like.0); + }; +} + +Pattern { + let device_name: Attr; + let device_tag: Attr; + let axis: Attr; + let gelu = + op( + op(a: Value, b: Value) {axis = axis, device_name = device_name, device_tag = device_tag}) + {device_name = device_name, device_tag = device_tag} -> (out: Type); + + rewrite gelu with{ + replace gelu with CopyUserOpAttrs(gelu, op(a, b){axis = axis} -> (out)); + }; +} + +Pattern { + let device_name: Attr; + let device_tag: Attr; + let padding_before: Attr; + let padding_after: Attr; + let data_format: Attr; + + let conv = + op( + op(x: Value){device_name = device_name, device_tag = device_tag, padding_before = padding_before, padding_after = padding_after}, weight: Value) + {device_name = device_name, device_tag = device_tag, data_format = data_format}; + IsPaddingCouldBeAssimilatedIntoConv(padding_before, padding_after, data_format); + + rewrite conv with{ + let conv2d_and_erase_pad = CreateConv2dAndErasePad(x, weight, padding_before, data_format, conv); + replace conv with CopyUserOpAttrs(conv, conv2d_and_erase_pad); + }; +} + +Pattern { + let valueType: Type; + let x: Value; + let cast = op(x) -> (valueType); + + replace cast with x; +} + +Pattern { + let device_name: Attr; + let has_float_operand: Attr; + let int_operand: Attr; + let float_operand: Attr; + let diagonal: Attr; + let floating_fill_value: Attr; + let integer_fill_value: Attr; + let is_floating_fill_value: Attr; + + let tril = + op( + op(x: Value) + {device_name = device_name, device_tag = attr<"\"cuda\"">, has_float_operand = has_float_operand, + int_operand = int_operand, float_operand = float_operand}) + {device_name = device_name, device_tag = attr<"\"cuda\"">, diagonal = diagonal, floating_fill_value = floating_fill_value, + integer_fill_value =integer_fill_value, is_floating_fill_value = is_floating_fill_value} -> (out: Type); + + replace tril with CopyUserOpAttrs(tril, CreatScaleTrilOp(x, diagonal, floating_fill_value, integer_fill_value, + is_floating_fill_value, float_operand ,int_operand, has_float_operand, out)); +} + +Pattern { + let device_name: Attr; + let has_float_operand: Attr; + let int_operand: Attr; + let float_operand: Attr; + let diagonal: Attr; + let floating_fill_value: Attr; + let integer_fill_value: Attr; + let is_floating_fill_value: Attr; + + let scalar = + op( + op(x: Value) + {device_name = device_name, device_tag = attr<"\"cuda\"">, diagonal = diagonal, floating_fill_value = floating_fill_value, + integer_fill_value =integer_fill_value, is_floating_fill_value = is_floating_fill_value }) + {device_name = device_name, device_tag = attr<"\"cuda\"">, has_float_operand = has_float_operand, + int_operand = int_operand, float_operand = float_operand} -> (out: Type); + + replace scalar with CopyUserOpAttrs(scalar, CreatScaleTrilOp(x, diagonal, floating_fill_value, integer_fill_value, + is_floating_fill_value, float_operand ,int_operand, has_float_operand, out)); +} \ No newline at end of file diff --git a/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.cpp b/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cff7e2469747a8cb29e94f61f774c21c85dfb177 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.cpp @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "mlir/Dialect/PDL/IR/PDL.h" +#include "mlir/Dialect/PDLInterp/IR/PDLInterp.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/OneFlowPDLLPatterns.h" + +using namespace mlir; + +#include "oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.h.inc" + +namespace mlir { + +namespace oneflow { + +void populateFuseConv2DBatchNormPattern(RewritePatternSet& patterns) { + populateGeneratedPDLLPatterns(patterns); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.pdll b/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.pdll new file mode 100644 index 0000000000000000000000000000000000000000..b2d6a05e1599be06318bcf2cd7a584d56bd6f1a2 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/FuseConv2DBatchNormPattern.pdll @@ -0,0 +1,26 @@ +#include "OneFlowPDLLUtils.pdll" + +Pattern { + let device_name: Attr; + let device_tag: Attr; + let epsilon: Attr; + let moving_mean: Value; + let moving_variance: Value; + let beta: Value; + + let weight = op; + let gamma = op; + + let conv = op(x: Value, weight.0){device_name = device_name, device_tag = device_tag}; + + let normalization = op(conv, moving_mean, moving_variance, gamma.0, beta) {device_name = device_name, device_tag = device_tag, epsilon = epsilon} -> (y: Type); + + rewrite normalization with{ + let conv2d_bn = CreateConv2DBatchNorm(epsilon, conv, normalization); + replace normalization with CopyUserOpAttrs(normalization, conv2d_bn); + }; + +} + + + diff --git a/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.cpp b/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.cpp new file mode 100644 index 0000000000000000000000000000000000000000..74bac80b7e717fc28ffd45637a76e300626e6007 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.cpp @@ -0,0 +1,39 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "mlir/Dialect/PDL/IR/PDL.h" +#include "mlir/Dialect/PDLInterp/IR/PDLInterp.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/OneFlowPDLLPatterns.h" + +using namespace mlir; + +#include "oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.h.inc" + +namespace mlir { + +namespace oneflow { + +void populateNormalizationOpPatterns(RewritePatternSet& patterns) { + populateGeneratedPDLLPatterns(patterns); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.pdll b/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.pdll new file mode 100644 index 0000000000000000000000000000000000000000..8e170bd95aae8676150854d5116e3fca2e43c3b2 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/NormalizationPatterns.pdll @@ -0,0 +1,51 @@ +#include "OneFlowPDLLUtils.pdll" + +Pattern { + let device_name: Attr; + let device_tag: Attr; + let axis: Attr; + let epsilon: Attr; + let training = attr<"true : bool">; + let momentum: Attr; + let x: Value; + let moving_mean: Value; + let moving_variance: Value; + let gamma: Value; + let beta: Value; + let addend: Value; + let normalization = op(x, moving_mean, moving_variance, gamma, beta) {operand_segment_sizes = attr<"dense<[1, 1, 1, 1, 1, 0]> : vector<6xi32>">, axis = axis, epsilon = epsilon, training = training, momentum = momentum, device_name = device_name, device_tag = device_tag} -> (y: Type, mean: Type, inv_variance: Type); + let relu = + op( + op(normalization.0, addend) {device_name = device_name, device_tag = device_tag}) + {device_name = device_name, device_tag = device_tag} -> (out: Type); + + rewrite relu with{ + let fused_bn = CopyUserOpAttrs(normalization, op(x, addend, moving_mean, moving_variance, gamma, beta) {operand_segment_sizes = attr<"dense<[1, 1, 1, 1, 1, 1]> : vector<6xi32>">, result_segment_sizes = attr<"dense<[1, 1, 1, 1]> : vector<4xi32>">, axis = axis, epsilon = epsilon, training = training, momentum = momentum, device_name = device_name, device_tag = device_tag}); + replace relu with fused_bn.0; + }; +} + +Pattern { + let device_name: Attr; + let device_tag: Attr; + let axis: Attr; + let epsilon: Attr; + let training = attr<"true : bool">; + let momentum: Attr; + let x: Value; + let moving_mean: Value; + let moving_variance: Value; + let gamma: Value; + let beta: Value; + let addend: Value; + let normalization = op(x, moving_mean, moving_variance, gamma, beta) {operand_segment_sizes = attr<"dense<[1, 1, 1, 1, 1, 0]> : vector<6xi32>">, axis = axis, epsilon = epsilon, training = training, momentum = momentum, device_name = device_name, device_tag = device_tag} -> (y: Type); + let relu = + op( + op(normalization.0, addend) {device_name = device_name, device_tag = device_tag}) + {device_name = device_name, device_tag = device_tag} -> (out: Type); + + rewrite relu with{ + let fused_bn = CopyUserOpAttrs(normalization, op(x, addend, moving_mean, moving_variance, gamma, beta) {operand_segment_sizes = attr<"dense<[1, 1, 1, 1, 1, 1]> : vector<6xi32>">, result_segment_sizes = attr<"dense<[1, 1, 1, 1]> : vector<4xi32>">, axis = axis, epsilon = epsilon, training = training, momentum = momentum, device_name = device_name, device_tag = device_tag}); + replace relu with fused_bn.0; + }; +} diff --git a/oneflow/ir/lib/OneFlow/PDLL/OneFlowPDLLUtils.pdll b/oneflow/ir/lib/OneFlow/PDLL/OneFlowPDLLUtils.pdll new file mode 100644 index 0000000000000000000000000000000000000000..ca4f00f148c65dd55fe489447a638822b2fe78b6 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/PDLL/OneFlowPDLLUtils.pdll @@ -0,0 +1,18 @@ +Rewrite BuildFusedBiasAddMaskScaleOpWithRate(a: Value, b: Value, mask: Value, axis: Attr, rate: Attr, dropout: Op) -> Op; +Rewrite CopyUserOpAttrs(src: Op, dst: Op) -> Op; +Rewrite CreateConv2dAndErasePad(x: Value, weight: Value, padding_before: Attr, data_format: Attr, conv: Op) -> Op; +Rewrite CreatScaleTrilOp(x: Value, diagonal: Attr, floating_fill_value: Attr, integer_fill_value: Attr, + is_floating_fill_value: Attr, float_operand: Attr, int_operand: Attr, has_float_operand: Attr, out: Type) -> Op { + let floating_scale_value = float_operand; + let integer_scale_value = int_operand; + let is_floating_scale_value = has_float_operand; + let scale_tril_op = op(x){diagonal = diagonal, floating_fill_value = floating_fill_value, integer_fill_value = integer_fill_value, + is_floating_fill_value = is_floating_fill_value, floating_scale_value = floating_scale_value, + integer_scale_value = integer_scale_value, is_floating_scale_value = is_floating_scale_value} -> (out); + return scale_tril_op; +} + +Rewrite CreateConv2DBatchNorm(epsilon: Attr, conv: Op, bn: Op) -> Op; + +Constraint IsPaddingCouldBeAssimilatedIntoConv(padding_before: Attr, padding_after: Attr, data_format:Attr); + diff --git a/oneflow/ir/lib/OneFlow/Passes.cpp b/oneflow/ir/lib/OneFlow/Passes.cpp index cad4fffe51fabd956fe1efc37a8c65c505cf8819..b62095f08926d29777462afc20bf135c9fac2f1a 100644 --- a/oneflow/ir/lib/OneFlow/Passes.cpp +++ b/oneflow/ir/lib/OneFlow/Passes.cpp @@ -13,22 +13,59 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include +#include "OneFlow/OKL/passes.h" +#include "OneFlow/OKL/OKLAttributes.h" +#include "OneFlow/Transform/OutlineAndFuse.h" +#include "mlir/Dialect/Tosa/Transforms/Passes.h" +#include "OneFlow/OneFlowPDLLPatterns.h" +#include "mlir/Dialect/LLVMIR/FunctionCallUtils.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/Dialect/LLVMIR/Transforms/RequestCWrappers.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/Diagnostics.h" +#include "mlir/IR/SymbolTable.h" +#include "oneflow/core/common/data_type.pb.h" +#include "oneflow/core/framework/dtype.h" +#include "oneflow/core/framework/variable_tensor_mgr.h" +#include "oneflow/core/operator/variable_op.h" +#include "oneflow/core/framework/sbp_context.h" +#include "oneflow/core/job/sbp_signature_builder.h" +#include "OneFlow/SBP/SBPImporter.h" #include "OneFlow/OneFlowOps.h" #include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowUtils.h" #include "OneFlow/Passes.h" +#include "OneFlow/OneFlowUtils.h" +#include "OneFlow/OneFlowPatternUtils.h" #include "OneFlow/OneFlowSupport.h" -#include "llvm/ADT/DenseSet.h" -#include "llvm/ADT/SmallVector.h" +#include "OneFlow/SBP/SBPAttributes.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/OKLTypes.h" +#include "OneFlow/Transform/TransposeHelpers.h" +#include "oneflow/core/framework/random_generator.h" +#include "oneflow/core/framework/variable_tensor_mgr.h" +#include "oneflow/core/operator/variable_op.h" +#include "oneflow/core/framework/sbp_context.h" +#include "oneflow/core/job/sbp_signature_builder.h" #include "mlir-c/BuiltinAttributes.h" #include "mlir/IR/Attributes.h" #include "mlir/IR/OperationSupport.h" #include "mlir/IR/MLIRContext.h" -#include "oneflow/core/framework/random_generator.h" +#include "mlir/Dialect/Tosa/Transforms/Passes.h" +#include "mlir/Dialect/LLVMIR/FunctionCallUtils.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/Dialect/LLVMIR/LLVMTypes.h" +#include "mlir/Dialect/LLVMIR/Transforms/RequestCWrappers.h" +#include "mlir/IR/Builders.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/Diagnostics.h" +#include "mlir/IR/Location.h" +#include "mlir/IR/Operation.h" +#include "mlir/IR/SymbolTable.h" +#include "mlir/IR/TypeRange.h" +#include "mlir/Support/LLVM.h" -#include "llvm/ADT/ArrayRef.h" -#include "llvm/ADT/None.h" -#include "llvm/Support/Casting.h" #include "mlir/Conversion/LinalgToLLVM/LinalgToLLVM.h" #include "mlir/Conversion/MemRefToLLVM/MemRefToLLVM.h" #include "mlir/Conversion/ReconcileUnrealizedCasts/ReconcileUnrealizedCasts.h" @@ -56,7 +93,19 @@ limitations under the License. #include "mlir/Transforms/Passes.h" #include "mlir/Dialect/Bufferization/Transforms/Passes.h" #include "mlir/Conversion/SCFToControlFlow/SCFToControlFlow.h" -#include "oneflow/core/framework/variable_tensor_mgr.h" + +#include "llvm/ADT/ArrayRef.h" +#include "llvm/ADT/None.h" +#include "llvm/Support/Casting.h" +#include "llvm/Support/FormatVariadic.h" +#include "llvm/ADT/DenseSet.h" +#include "llvm/ADT/SmallVector.h" +#include "llvm/ADT/SetOperations.h" +#include "oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h" + +#include +#include +#include #ifdef WITH_MLIR_CUDA_CODEGEN #include "mlir/Conversion/AffineToStandard/AffineToStandard.h" @@ -72,19 +121,23 @@ limitations under the License. #include namespace mlir { - namespace oneflow { -LogicalResult DumpAssembly(::mlir::PatternRewriter& rewriter, MlirJitOp op) { +LLVM::LLVMPointerType GetPtr(::mlir::PatternRewriter& rewriter) { + return LLVM::LLVMPointerType::get(IntegerType::get(rewriter.getContext(), 8)); +} + +template +LogicalResult DumpAssembly(::mlir::PatternRewriter& rewriter, T op, StringRef func_name) { // TODO: now we only need one JIT engine - auto parent_func_op = op->getParentOfType(); + auto parent_func_op = op->template getParentOfType(); if (!parent_func_op) { return failure(); } - auto parent_module_op = parent_func_op->getParentOfType(); + auto parent_module_op = parent_func_op->template getParentOfType(); if (!parent_module_op) { return failure(); } SymbolTable symbol_table(parent_module_op); std::string mlir; llvm::raw_string_ostream os_mlir(mlir); - if (auto found = symbol_table.lookup(op.op_name())) { + if (auto found = symbol_table.lookup(func_name)) { found->print(os_mlir); } else { parent_module_op->dump(); @@ -94,6 +147,80 @@ LogicalResult DumpAssembly(::mlir::PatternRewriter& rewriter, MlirJitOp op) { return success(); } +LLVM::LLVMFuncOp DeclareKernelLaunchCInterface(::mlir::PatternRewriter& rewriter, + mlir::Location loc, ModuleOp* module, + StringRef c_api_callee, Type llvm_ptr_type) { + LLVM::LLVMFuncOp func; + if (!(func = module->lookupSymbol(c_api_callee))) { + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(module->getBody()); + auto void_type = LLVM::LLVMVoidType::get(rewriter.getContext()); + auto func_type = LLVM::LLVMFunctionType::get(void_type, {llvm_ptr_type, llvm_ptr_type}, false); + func = rewriter.create(loc, c_api_callee, func_type, LLVM::Linkage::External); + + func->setAttr("llvm.emit_c_interface", mlir::UnitAttr::get(rewriter.getContext())); + } + return func; +} + +LLVM::GlobalOp DeclareOrGetGlobalString(::mlir::PatternRewriter& rewriter, mlir::Location loc, + ModuleOp* module, StringRef func_name) { + LLVM::GlobalOp global; + StringRef variable = rewriter.getStringAttr(func_name + "_var"); + if (!(global = module->lookupSymbol(variable))) { + OpBuilder::InsertionGuard insertGuard(rewriter); + rewriter.setInsertionPointToStart(module->getBody()); + auto type = + LLVM::LLVMArrayType::get(IntegerType::get(rewriter.getContext(), 8), func_name.size()); + global = + rewriter.create(loc, type, /*isConstant=*/true, LLVM::Linkage::Internal, + variable, rewriter.getStringAttr(func_name), + /*alignment=*/0); + } + return global; +} + +template +ModuleOp GetModuleOpFromJobBodyOp(Operation* op) { + auto parent_func_op = op->getParentOfType(); + if (!parent_func_op) { return nullptr; } + return parent_func_op->template getParentOfType(); +} + +func::FuncOp InsertKernelOFFuncOp(::mlir::PatternRewriter& rewriter, Operation* op, + const std::string& func_name) { + auto loc = op->getLoc(); + auto module = GetModuleOpFromJobBodyOp(op); + if (!module) { + emitError(loc) << "null ModuleOp " << *op; + return nullptr; + } + + BlockAndValueMapping mapping; + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(module.getBody()); + + auto func_type = + rewriter.getFunctionType(TypeRange(op->getOperandTypes()), TypeRange(op->getResultTypes())); + func::FuncOp func = rewriter.create(loc, func_name, func_type); + func->setAttr("compiled", rewriter.getStringAttr("true")); + func.getBody().emplaceBlock(); + for (auto& arg : func_type.getInputs()) { func.getBody().addArguments(arg, loc); } + for (auto argument_pair : + llvm::zip(ValueRange(op->getOperands()), func.getBody().getArguments())) { + mapping.map(std::get<0>(argument_pair), std::get<1>(argument_pair)); + } + rewriter.setInsertionPointToStart(&func.getBody().front()); + ImplicitLocOpBuilder new_block(loc, rewriter); + new_block.clone(*op, mapping); + SmallVector<::mlir::Value, 4> mapped_results; + for (auto result : ValueRange(op->getResults())) { + mapped_results.push_back(mapping.lookup(result)); + } + rewriter.create(loc, mapped_results); + return func; +} + // TODO: cfg/multi block support func::FuncOp GetOrInsertFuncOp(::mlir::PatternRewriter& rewriter, mlir::Location loc, StringRef func_name, ValueRange operands, ValueRange results, @@ -163,51 +290,6 @@ NamedAttrList GetJitOpAttributes(::mlir::PatternRewriter& rewriter, StringRef op return attributes; } -static StringRef sanitizeIdentifier(StringRef name, SmallString<16>& buffer, - StringRef allowedPunctChars = "$._", - bool allowTrailingDigit = true) { - assert(!name.empty() && "Shouldn't have an empty name here"); - - auto copyNameToBuffer = [&] { - for (char ch : name) { - if (llvm::isAlnum(ch) || allowedPunctChars.contains(ch)) - buffer.push_back(ch); - else if (ch == ' ') - buffer.push_back('_'); - else - buffer.append(llvm::utohexstr((unsigned char)ch)); - } - }; - - // Check to see if this name is valid. If it starts with a digit, then it - // could conflict with the autogenerated numeric ID's, so add an underscore - // prefix to avoid problems. - if (isdigit(name[0])) { - buffer.push_back('_'); - copyNameToBuffer(); - return buffer; - } - - // If the name ends with a trailing digit, add a '_' to avoid potential - // conflicts with autogenerated ID's. - if (!allowTrailingDigit && isdigit(name.back())) { - copyNameToBuffer(); - buffer.push_back('_'); - return buffer; - } - - // Check to see that the name consists of only valid identifier characters. - for (char ch : name) { - if (!llvm::isAlnum(ch) && !allowedPunctChars.contains(ch)) { - copyNameToBuffer(); - return buffer; - } - } - - // If there are no invalid characters, return the original name. - return name; -} - ::llvm::SmallVector<::mlir::Value, 4> OutlineMulCast(::mlir::PatternRewriter& rewriter, mlir::OpResult mul_res, mlir::OpResult cast_res) { @@ -238,7 +320,7 @@ static StringRef sanitizeIdentifier(StringRef name, SmallString<16>& buffer, .str()) .toStringRef(op_name_storage); SmallString<16> tempBuffer; - op_name = sanitizeIdentifier(op_name, tempBuffer); + op_name = SanitizeIdentifier(op_name, tempBuffer); SmallVector<::mlir::Value, 2> operands; operands.push_back(cast_op.in()); operands.push_back(scale); @@ -249,7 +331,7 @@ static StringRef sanitizeIdentifier(StringRef name, SmallString<16>& buffer, SmallVector ops = {cast_op, mul_op}; auto function = GetOrInsertFuncOp(rewriter, mul_op->getLoc(), op_name, operands, results, ops); auto created = rewriter.create(mul_op->getLoc(), function, attributes, operands); - if (failed(DumpAssembly(rewriter, created))) { exit(1); } + if (failed(DumpAssembly(rewriter, created, created.op_name()))) { exit(1); } cast_op->dropAllUses(); cast_op.erase(); return created->getResults(); @@ -289,64 +371,6 @@ bool HasZeroPadding(mlir::ArrayAttr padding) { return true; } -bool IsPaddingCouldBeAssimilatedIntoConv(::mlir::ArrayAttr padding_before, - ::mlir::ArrayAttr padding_after, - ::mlir::StringAttr data_format) { - if (padding_before.size() == 4 && padding_after.size() == 4) { - if (padding_before.getValue().equals(padding_after.getValue())) { - if (data_format.str() == "channels_first") { - return padding_before.getValue()[0].cast().getValue().getSExtValue() == 0 - && padding_before.getValue()[1].cast().getValue().getSExtValue() == 0; - } - if (data_format.str() == "channels_last") { - return padding_before.getValue()[0].cast().getValue().getSExtValue() == 0 - && padding_before.getValue()[3].cast().getValue().getSExtValue() == 0; - } - } - } - return false; -} - -IntegerAttr getSI64IntegerAttr(::mlir::PatternRewriter& rewriter, int64_t value) { - return IntegerAttr::get(rewriter.getIntegerType(64, /*isSigned=*/true), - APInt(64, value, /*isSigned=*/true)); -} - -::llvm::SmallVector<::mlir::Value, 4> CreateConv2dAndErasePad(::mlir::PatternRewriter& rewriter, - OpResult conv_result, - OpResult pad_result) { - if (auto conv_op = llvm::dyn_cast(conv_result.getDefiningOp())) { - if (auto pad_op = llvm::dyn_cast(pad_result.getDefiningOp())) { - NamedAttrList attributes = conv_op->getAttrs(); - SmallVector operands; - operands.push_back(pad_op.x()); - operands.push_back(conv_op.weight()); - if (conv_op.bias()) operands.push_back(conv_op.bias()); - if (conv_op.bias_multiplier()) operands.push_back(conv_op.bias_multiplier()); - llvm::SmallVector padding_before_array; - if (conv_op.data_formatAttr().getValue().str() == "channels_first") { - for (auto val : pad_op.padding_before().getValue().take_back(2)) { - padding_before_array.push_back(val.cast().getValue().getSExtValue()); - } - } else { - padding_before_array.push_back( - pad_op.padding_before().getValue()[1].cast().getValue().getSExtValue()); - padding_before_array.push_back( - pad_op.padding_before().getValue()[2].cast().getValue().getSExtValue()); - } - attributes.set(conv_op.padding_beforeAttrName(), - getSI32ArrayAttr(rewriter, padding_before_array)); - auto res = rewriter - .create(conv_op->getLoc(), conv_op->getResultTypes(), - operands, attributes) - ->getResults(); - // pad op is expected to be erased if it is not used - return res; - } - } - return {}; -} - NamedAttrList GetUserOpCommonAttrs(MLIRContext* ctx, const std::string& op_name) { NamedAttrList attrs; attrs.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), StringAttr::get(ctx, op_name)); @@ -359,123 +383,6 @@ NamedAttrList GetUserOpCommonAttrs(MLIRContext* ctx, const std::string& op_name) return attrs; } -::llvm::SmallVector<::mlir::Value, 4> FuseConv2DBatchNorm(::mlir::PatternRewriter& rewriter, - OpResult conv_result, - OpResult bn_result) { - if (auto conv_op = llvm::dyn_cast(conv_result.getDefiningOp())) { - if (auto bn_op = llvm::dyn_cast(bn_result.getDefiningOp())) { - auto ctx = rewriter.getContext(); - SmallVector final_results; - NamedAttrList attributes = conv_op->getAttrs(); - - attributes.set("operand_segment_sizes", rewriter.getI32VectorAttr({1, 1, 1, 0})); - - SmallVector operands; - operands.push_back(conv_op.in()); - - // deal with weight - auto add_op_attrs = GetUserOpCommonAttrs(ctx, "scalar_add"); - add_op_attrs.set("has_float_operand", BoolAttr::get(ctx, true)); - add_op_attrs.set("float_operand", bn_op.epsilonAttr()); - auto add_op = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({bn_op.moving_variance()}), add_op_attrs); - - auto sqrt_op = rewriter.create(conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({add_op.out()}), - GetUserOpCommonAttrs(ctx, "sqrt")); - - auto div_op = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({bn_op.gamma(), sqrt_op.y()}), GetUserOpCommonAttrs(ctx, "div")); - - auto bn_gamma_variable_op = - llvm::dyn_cast(bn_op.gamma().getDefiningOp()); - if (!bn_gamma_variable_op) { - emitError(conv_op.getLoc()) << "Gamma of batchnorm should be a FrozenVariableOp."; - } - auto bn_gamma_shape = - bn_gamma_variable_op.value().getType().cast().getShape(); - - auto conv_weight_variable_op = - llvm::dyn_cast(conv_op.weight().getDefiningOp()); - if (!conv_weight_variable_op) { - emitError(conv_op.getLoc()) << "Weight of conv2d should be a FrozenVariableOp."; - } - auto conv_weight_shape = - conv_weight_variable_op.value().getType().cast().getShape(); - - std::vector bn_gamma_new_shape({bn_gamma_shape.front()}); - for (int i = 1; i < conv_weight_shape.size(); ++i) { bn_gamma_new_shape.emplace_back(1); } - auto reshape_op_attrs = GetUserOpCommonAttrs(ctx, "reshape"); - reshape_op_attrs.set("shape", ArrayAttr::get(ctx, llvm::to_vector<8>(llvm::map_range( - ArrayRef(bn_gamma_new_shape), - [&](int64_t v) -> Attribute { - return rewriter.getI64IntegerAttr(v); - })))); - auto reshape_op = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), SmallVector({div_op.z()}), - reshape_op_attrs); - - auto mul_op = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({conv_op.weight(), reshape_op.out()}), - GetUserOpCommonAttrs(ctx, "multiply")); - operands.push_back(mul_op.z()); - - // deal with bias - if (!conv_op.bias()) { - auto mul_op_bias = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({bn_op.moving_mean(), div_op.z()}), - GetUserOpCommonAttrs(ctx, "multiply_bias")); - auto sub_op_bias = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), - SmallVector({bn_op.beta(), mul_op_bias.z()}), - GetUserOpCommonAttrs(ctx, "sub_bias")); - operands.push_back(sub_op_bias.z()); - } else { - emitError(conv_op.getLoc()) - << "Fusing conv2d and batch_norm only supports conv2d without bias now."; - } - if (conv_op.bias_multiplier()) operands.push_back(conv_op.bias_multiplier()); - - auto new_conv_op = rewriter.create( - conv_op->getLoc(), conv_op->getResultTypes(), operands, attributes); - - final_results.push_back(new_conv_op.out()); - return final_results; - } - } - return {}; -} - -::llvm::SmallVector<::mlir::Value, 4> CreateFusedBiasAddMaskScale(::mlir::PatternRewriter& rewriter, - OpResult dropout_result, - OpResult bias_add_result, - Operation* mask) { - if (auto dropout_op = llvm::dyn_cast(dropout_result.getDefiningOp())) { - if (auto bias_add_op = llvm::dyn_cast(bias_add_result.getDefiningOp())) { - SmallVector operands; - operands.push_back(bias_add_op.a()); - operands.push_back(bias_add_op.b()); - operands.push_back(mask->getResults()[0]); - NamedAttrList fused_bias_add_dropout_attributes = dropout_op->getAttrs(); - fused_bias_add_dropout_attributes.append(llvm::StringRef("axis"), bias_add_op.axisAttr()); - fused_bias_add_dropout_attributes.append(llvm::StringRef("scale"), dropout_op.rateAttr()); - fused_bias_add_dropout_attributes.erase(dropout_op.rateAttrName()); - auto res = rewriter - .create( - dropout_op->getLoc(), dropout_op->getResultTypes().front(), operands, - fused_bias_add_dropout_attributes) - ->getResults(); - // bias_add and dropout op is expected to be erased if it is not used - return res; - } - } - return {}; -} - struct ReplaceVariablePattern : public ::mlir::RewritePattern { explicit ReplaceVariablePattern(::mlir::MLIRContext* context) : ::mlir::RewritePattern("oneflow.variable", 1, context, {"oneflow.variable_ir"}) {} @@ -485,17 +392,22 @@ struct ReplaceVariablePattern : public ::mlir::RewritePattern { if (!op) return failure(); NamedAttrList attrs; if (op.op_name().str().find("FreeEagerTensor") != std::string::npos) { return failure(); } - attrs.set( - StringAttr::get(getContext(), "value"), - support::TensorToDenseElementsAttr( - ::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get()->Get(op.op_name().str()), - rewriter.getContext())); + attrs.set(StringAttr::get(getContext(), "value"), + support::TensorToDenseElementsAttr( + CHECK_JUST(::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get()->Get( + op.op_name().str(), ::oneflow::DType::Float())), + rewriter.getContext())); attrs.set(op.op_nameAttrName(), op.op_nameAttr()); + attrs.set(op.data_typeAttrName(), op.data_typeAttr()); attrs.set(op.device_tagAttrName(), op.device_tagAttr()); attrs.set(op.device_nameAttrName(), op.device_nameAttr()); attrs.set(op.scope_symbol_idAttrName(), op.scope_symbol_idAttr()); attrs.set(op.hierarchyAttrName(), op.hierarchyAttr()); - attrs.set(op.nd_sbpAttrName(), op.nd_sbpAttr()); + auto name = FrozenVariableOp::nd_sbpAttrName( + OperationName(FrozenVariableOp::getOperationName(), rewriter.getContext())); + + auto parallel_attr = op.parallelAttr(); + attrs.set(name, SBPTranslation::ConvertSBPToString(rewriter, parallel_attr)); auto op_new = rewriter.create(op->getLoc(), op.output().getType(), ValueRange(), attrs); rewriter.replaceOp(op0, op_new->getResults()); @@ -524,30 +436,53 @@ struct ReplaceVariableIrPattern : public ::mlir::RewritePattern { auto output_lbns_attr = rewriter.getStrArrayAttr({op.op_name().str() + "/out"}); attrs.set(OpTrait::IsImportCompatible::getOutputLBNsAttr(), output_lbns_attr); attrs.set(op.op_nameAttrName(), op.op_nameAttr()); + attrs.set(op.data_typeAttrName(), op.data_typeAttr()); attrs.set(op.device_tagAttrName(), op.device_tagAttr()); attrs.set(op.device_nameAttrName(), op.device_nameAttr()); attrs.set(op.scope_symbol_idAttrName(), op.scope_symbol_idAttr()); attrs.set(op.hierarchyAttrName(), op.hierarchyAttr()); - attrs.set(op.nd_sbpAttrName(), op.nd_sbpAttr()); + auto name = VariableOp::parallelAttrName( + OperationName(VariableOp::getOperationName(), rewriter.getContext())); + + auto nd_size = op.hierarchy()->size(); + ArrayAttr nd_sbp = op.nd_sbp(); + std::vector nd_sbp_str; + std::for_each(nd_sbp.begin(), nd_sbp.end(), [&](Attribute elem) { + if (auto sbp_str_attr = elem.dyn_cast()) { + nd_sbp_str.push_back(sbp_str_attr.str()); + } + }); + attrs.set(name, SBPTranslation::ConvertNdSbpToPsig(rewriter, nd_sbp_str, nd_size)); auto op_new = rewriter.create(op->getLoc(), op.output().getType(), ValueRange(), attrs); - rewriter.replaceOp(op0, op_new->getResults()); const std::string tensor_name = op.op_nameAttr().str(); - ::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get()->Set( - tensor_name, // tensor_name can't be replaced by op.op_nameAttr().str() directly when - // compiling with gcc and I has no idea why. - // But it works when compiling with clang. - // Maybe temporary objects would be released earlier when using gcc. - support::DenseElementsAttrToTensor(tensor_attr, op.device_tagAttr(), op.device_nameAttr())); + const auto data_type = support::FromMLIRAttrToOFDataType(op.data_typeAttr()); + if (failed(data_type)) { + op0->emitError(::llvm::formatv("unsupported data type: {0}", + ConvertToString(op.data_typeAttr().getValue()))); + return ::mlir::failure(); + } + auto var_tensor = CHECK_JUST( + ::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get()->Get(op.op_name().str())); + if (var_tensor) { + support::DenseElementsAttrToTensor(tensor_attr, op.device_tagAttr(), op.device_nameAttr(), + var_tensor); + } else { + CHECK_JUST(::oneflow::Singleton<::oneflow::VariableTensorMgr>::Get()->Set( + tensor_name, // tensor_name can't be replaced by op.op_nameAttr().str() directly when + // compiling with gcc and I has no idea why. + // But it works when compiling with clang. + // Maybe temporary objects would be released earlier when using gcc. + support::DenseElementsAttrToTensor(tensor_attr, op.device_tagAttr(), + op.device_nameAttr()), + CHECK_JUST(::oneflow::DType::Get(data_type.getValue())))); + } + // replaceOp may deallocate `op0` (and also `op`), so we should not use `op` after this call. + rewriter.replaceOp(op0, op_new->getResults()); return ::mlir::success(); } }; -mlir::IntegerAttr GetDefaultSeed(::mlir::PatternRewriter& rewriter) { - const auto gen = CHECK_JUST(::oneflow::one::DefaultAutoGenerator()); - return getSI64IntegerAttr(rewriter, (int64_t)gen->current_seed()); -} - LogicalResult InitTransposeAttributes(Operation* op, NamedAttrList& transpose_attributes, PatternRewriter& rewriter) { if (op->hasTrait()) { @@ -575,8 +510,8 @@ llvm::SmallVector getInputOperandTransposeOp(NCHWCompatible op, SmallVector input_operands; input_operands.push_back(val); auto res = rewriter - .create(op.getLoc(), val.getType(), input_operands, - transpose_attributes) + .create(op.getLoc(), getNHWCType(val.getType()), + input_operands, transpose_attributes) ->getResults(); return res; } @@ -589,8 +524,8 @@ TransposeOp getResultTransposeOp(NCHWCompatible op, Value val, NamedAttrList tra rewriter.getStringAttr(transpose_name)); SmallVector operands; operands.push_back(val); - TransposeOp transpose_op = rewriter.create(op.getLoc(), val.getType(), - operands, transpose_attributes); + TransposeOp transpose_op = rewriter.create( + op.getLoc(), getNCHWType(val.getType()), operands, transpose_attributes); return transpose_op; } @@ -610,10 +545,6 @@ bool IsInsertTransposeOpBefore(NCHWCompatible op, PatternRewriter& rewriter) { return insert_transpose_op_flag; } -bool IsSameDtype(mlir::OpResult cast_result, mlir::Value input) { - return cast_result.getType() == input.getType(); -} - } // namespace oneflow } // namespace mlir @@ -623,6 +554,67 @@ bool IsSameDtype(mlir::OpResult cast_result, mlir::Value input) { namespace mlir { namespace oneflow { + +template +struct FusedConsecutiveAddPattern : public OpRewritePattern { + explicit FusedConsecutiveAddPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/1) {} + + public: + LogicalResult matchAndRewrite(Op op, PatternRewriter& rewriter) const override; +}; + +template +LogicalResult TryFusedConsecutiveAdd(Op op, const SmallVector& opOperands, + PatternRewriter& rewriter) { + for (mlir::Value operand : opOperands) { + if (!operand.getDefiningOp() && !operand.getDefiningOp()) { continue; } + // check if the operand has only one user + LogicalResult checkResult = [&]() { + for (const auto& use : operand.getUses()) { + if (use.getOwner() != op) { return failure(); } + } + return success(); + }(); + if (failed(checkResult)) { continue; } + + SmallVector operands; + SmallVector inputOpOperands; + mlir::Value inputOpResult; + if (AddNOp addInputOp = operand.getDefiningOp()) { + inputOpOperands = addInputOp.in(); + inputOpResult = addInputOp.out(); + } else if (Add2Op addInputOp = operand.getDefiningOp()) { + inputOpOperands = {addInputOp.in0(), addInputOp.in1()}; + inputOpResult = addInputOp.out(); + } + for (mlir::Value operand : opOperands) { + if (operand != inputOpResult) { + operands.push_back(operand); + } else { + operands.insert(operands.end(), inputOpOperands.begin(), inputOpOperands.end()); + } + } + auto new_op = + rewriter.create(op->getLoc(), op->getResultTypes(), operands, op->getAttrs()); + rewriter.replaceOp(op, new_op.out()); + return success(); + } + return failure(); +} + +template<> +LogicalResult FusedConsecutiveAddPattern::matchAndRewrite(AddNOp op, + PatternRewriter& rewriter) const { + return TryFusedConsecutiveAdd(op, op.in(), rewriter); +} + +template<> +LogicalResult FusedConsecutiveAddPattern::matchAndRewrite(Add2Op op, + PatternRewriter& rewriter) const { + return TryFusedConsecutiveAdd(op, {op.in0(), op.in1()}, rewriter); +} + struct AutoNhwcPattern : public OpInterfaceRewritePattern { explicit AutoNhwcPattern(mlir::MLIRContext* context) : OpInterfaceRewritePattern(context, /*benefit=*/1) {} @@ -630,8 +622,10 @@ struct AutoNhwcPattern : public OpInterfaceRewritePattern { public: LogicalResult matchAndRewrite(NCHWCompatible op, PatternRewriter& rewriter) const override { if (op->hasTrait()) { - if (op->getOperands()[0].getType().cast().getShape().size() != 4) { - return failure(); + for (mlir::Value operand : op.OperandsToTranspose()) { + if (operand.getType().cast().getShape().size() != 4) { + return failure(); + } } const auto device_name = OpTrait::IsOpConfCompatible::getDeviceTag(op) .cast() @@ -732,8 +726,316 @@ void BroadcastMulOp::getCanonicalizationPatterns(RewritePatternSet& results, MLI results.insert(context); } +struct LowerToOKLPattern : public mlir::OpRewritePattern { + static LogicalResult LowerToOKLOp(::mlir::PatternRewriter& rewriter, Operation* op, + func::FuncOp okl_func) { + auto op_type_name = op->getAttr("op_name").dyn_cast(); + auto raw_func = op->getParentOfType(); + if (!op_type_name) { return failure(); } + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToEnd(&okl_func.getBody().back()); + + auto loc = op->getLoc(); + + auto reg_ctx = rewriter.create( + loc, okl::RegContextType::get(rewriter.getContext())); + reg_ctx.body().emplaceBlock(); + rewriter.setInsertionPointToEnd(®_ctx.body().back()); + + BlockAndValueMapping mapping; + + // map launcher_ctx from wrap func to block + mapping.map(raw_func.getArgument(0), okl_func.getArgument(0)); + + ImplicitLocOpBuilder new_block(loc, rewriter); + for (auto arg : op->getOperands()) { + auto define_op = arg.getDefiningOp(); + if (define_op->getName().getStringRef() == okl::GetTensorFromArgOp::getOperationName()) { + new_block.clone(*define_op, mapping); + } else { + auto find = false; + for (auto use : arg.getUsers()) { + if (use->getName().getStringRef() == okl::GetTensorAsRetOp::getOperationName()) { + find = true; + auto index = use->getAttr("index").cast().getInt(); + auto source = rewriter.create( + op->getLoc(), arg.getType(), okl_func.getArgument(0), okl::TensorType::TT_Argument, + index); + mapping.map(arg, source->getResult(0)); + break; + } + } + if (!find) { op->emitError("Fail to find operand source"); } + } + } + new_block.clone(*op, mapping); + for (auto ret : op->getResults()) { + auto find = false; + for (auto use : ret.getUsers()) { + if (use->getName().getStringRef() == okl::GetTensorAsRetOp::getOperationName()) { + find = true; + new_block.clone(*use, mapping); + break; + } + } + if (!find) { op->emitError("Fail to find result source"); } + } + rewriter.create(loc); + + rewriter.setInsertionPointToEnd(&okl_func.getBody().back()); + auto run_ctx = rewriter.create( + loc, okl::RunContextType::get(rewriter.getContext()), reg_ctx); + auto kernel = rewriter.create( + loc, okl::KernelType::get(rewriter.getContext()), reg_ctx); + rewriter.create(loc, run_ctx, kernel); + rewriter.create(loc, reg_ctx); + rewriter.create(loc, run_ctx); + return success(); + } + + explicit LowerToOKLPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + if (op->hasAttr("compiled")) { return success(); } + op->setAttr("compiled", rewriter.getStringAttr("true")); + + auto func_name = "okl_func"; + + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointAfter(op); + auto& block = op.getBody().front(); + auto loc = op->getLoc(); + + auto func_type = rewriter.getFunctionType( + {mlir::okl::LauncherContextType::get(rewriter.getContext())}, TypeRange{}); + auto okl_func = rewriter.create(loc, func_name, func_type); + okl_func->setAttr("compiled", rewriter.getStringAttr("true")); + okl_func.getBody().emplaceBlock(); + okl_func.getBody().addArguments(mlir::okl::LauncherContextType::get(rewriter.getContext()), + loc); + + for (auto& op : block) { + if (!op.hasAttr("op_name")) { + if (op.getDialect()->getNamespace() == "okl") { continue; } + if (isa(op)) { break; } + op.emitError("Failed to parse this op in kernel launch wrap func."); + } + if (failed(LowerToOKLOp(rewriter, &op, okl_func))) { + op.emitError("Failed to lowering OneFlow op to okl dialect."); + return failure(); + } + } + + rewriter.setInsertionPointToEnd(&okl_func.getBody().back()); + rewriter.create(loc); + rewriter.eraseOp(op); + return success(); + } +}; + +// {func, ins} +std::pair> CreateWrapFuncAndReturnWithIns( + mlir::Location loc, std::vector& wrap_ops, mlir::PatternRewriter& rewriter, + int& name_index) { + auto getProto = [&]() -> std::pair, std::vector> { + std::vector ins, outs, diff_ins; + for (auto op : wrap_ops) { + auto operands = op->getOperands(); + auto results = op->getResults(); + for (auto it = operands.begin(); it != operands.end(); ++it) { ins.push_back(*it); } + for (auto it = results.begin(); it != results.end(); ++it) { outs.push_back(*it); } + } + for (auto in : ins) { + if (std::find(outs.begin(), outs.end(), in) == outs.end()) { diff_ins.push_back(in); } + } + return {diff_ins, outs}; + }; + + std::pair, std::vector> proto = getProto(); + auto func_type = rewriter.getFunctionType(TypeRange(ValueRange(ArrayRef(proto.first))), + TypeRange(ValueRange(ArrayRef(proto.second)))); + auto func_name = "wrap" + std::to_string(name_index++); + auto module = GetModuleOpFromJobBodyOp(wrap_ops[0]); + if (!module) { + emitError(loc) << "Fail to find parent ModuleOp"; + return {nullptr, {}}; + } + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(module.getBody()); + auto function = rewriter.create(loc, func_name, func_type); + function->setAttr("llvm.emit_c_interface", mlir::UnitAttr::get(rewriter.getContext())); + function.getBody().emplaceBlock(); + for (auto arg : proto.first) { function.getBody().addArgument(arg.getType(), loc); } + + BlockAndValueMapping mapping; + for (auto args_pair : llvm::zip(proto.first, function.getBody().getArguments())) { + mapping.map(std::get<0>(args_pair), std::get<1>(args_pair)); + } + rewriter.setInsertionPointToStart(&function.getBody().front()); + ImplicitLocOpBuilder new_block(loc, rewriter); + for (auto op : wrap_ops) { new_block.clone(*op, mapping); } + + SmallVector<::mlir::Value, 4> mapped_results; + for (auto result : proto.second) { mapped_results.push_back(mapping.lookup(result)); } + rewriter.create(loc, mapped_results); + return {function, proto.first}; +}; + +KernelLaunchOp CreateKernelLaunchFunc(mlir::Location loc, std::vector& wrap_ops, + mlir::PatternRewriter& rewriter, int& name_index) { + if (!wrap_ops.size()) return nullptr; + OpBuilder::InsertionGuard guard(rewriter); + + auto wrap_res = CreateWrapFuncAndReturnWithIns(loc, wrap_ops, rewriter, name_index); + auto wrap_func = wrap_res.first; + auto wrap_ins = wrap_res.second; + + auto func_name = wrap_func.getSymNameAttr(); + std::vector attrs; + for (auto attr : wrap_ops[0]->getAttrs()) { + auto attr_list = {"scope_symbol_id", "device_tag", "device_name"}; + if (std::find(attr_list.begin(), attr_list.end(), attr.getName()) != attr_list.end()) { + attrs.push_back(attr); + } + } + attrs.emplace_back(rewriter.getStringAttr("op_name"), func_name); + + rewriter.setInsertionPointAfter(wrap_ops.back()); + auto func = rewriter.create(wrap_ops[0]->getLoc(), wrap_func, + ArrayRef(attrs), wrap_ins); + + if (failed(DumpAssembly(rewriter, func, func_name))) { exit(1); } + int res_idx = 0; + for (auto op : wrap_ops) { + std::vector vals; + for (int idx = 0; idx < op->getNumResults(); ++idx) { + vals.push_back(func->getResult(res_idx++)); + } + rewriter.replaceOp(op, vals); + } + wrap_ops.clear(); + return func; +} +struct ExtractKernelLaunchTensorPattern : public mlir::OpRewritePattern { + static func::FuncOp ExtractArgTensors(func::FuncOp op, mlir::PatternRewriter& rewriter) { + auto launcher_ctx_type = okl::LauncherContextType::get(rewriter.getContext()); + auto return_types = op.getBody().front().back().getOperandTypes(); + auto func_type = rewriter.getFunctionType({launcher_ctx_type}, return_types); + + auto func = rewriter.create(op.getLoc(), op.getName(), func_type); + auto& body = func.getBody(); + + body.emplaceBlock(); + body.addArgument(launcher_ctx_type, op->getLoc()); + auto launcher_ctx = body.getArgument(0); + + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPointToStart(&body.front()); + + BlockAndValueMapping mapping; + for (const auto& arg : llvm::enumerate(op.getBody().getArguments())) { + auto tensor = rewriter.create( + func->getLoc(), arg.value().getType(), launcher_ctx, okl::TensorType::TT_Argument, + arg.index()); + mapping.map(arg.value(), tensor); + } + + ImplicitLocOpBuilder new_block(func->getLoc(), rewriter); + for (auto& op : op.getBody().front().getOperations()) { new_block.clone(op, mapping); } + rewriter.eraseOp(op); + return func; + } + + static func::FuncOp ExtractRetTensors(func::FuncOp op, mlir::PatternRewriter& rewriter) { + auto& block = op.getBody().front(); + auto launcher_ctx = op.getArgument(0); + auto& return_op = block.back(); + + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPoint(&return_op); + + std::vector returns; + for (const auto& ret_val : llvm::enumerate(return_op.getOperands())) { + auto new_ret = rewriter.create( + op->getLoc(), ret_val.value().getType(), launcher_ctx, ret_val.value(), + okl::TensorType::TT_Return, ret_val.index()); + returns.push_back(new_ret); + } + + rewriter.replaceOpWithNewOp(&return_op, ValueRange{returns}); + return op; + } + + explicit ExtractKernelLaunchTensorPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + if (op.getBody().getArgument(0).getType().isa()) { return success(); } + op = ExtractArgTensors(op, rewriter); + op = ExtractRetTensors(op, rewriter); + return success(); + } +}; + +struct TrimReturnAsVoidPattern : public mlir::OpRewritePattern { + explicit TrimReturnAsVoidPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + mlir::LogicalResult matchAndRewrite(func::FuncOp op, + mlir::PatternRewriter& rewriter) const override { + if (op.getBody().front().back().getNumOperands() == 0) { return success(); } + OpBuilder::InsertionGuard guard(rewriter); + rewriter.setInsertionPoint(op); + + auto func_type = rewriter.getFunctionType(op.getFunctionType().getInputs(), TypeRange{}); + auto func = rewriter.create(op.getLoc(), op.getName(), func_type); + + BlockAndValueMapping bvm; + op.getRegion().cloneInto(&func.getRegion(), bvm); + + auto& old_ret = func.getBody().front().back(); + rewriter.setInsertionPoint(&old_ret); + rewriter.replaceOpWithNewOp(&old_ret); + rewriter.eraseOp(op); + return success(); + } +}; + +struct KernelLaunchPattern : public mlir::OpRewritePattern { + explicit KernelLaunchPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + mlir::LogicalResult matchAndRewrite(oneflow::Job op, + mlir::PatternRewriter& rewriter) const override { + auto& ops = op->getRegion(0).front(); + if (ops.empty()) { return success(); } + std::vector white_list{ + KernelLaunchOp::getOperationName(), + OutputOp::getOperationName(), + InputOp::getOperationName(), + VariableOp::getOperationName(), + }; + int name_index = 0; + std::vector wrap_ops; + for (auto op_it = ops.begin(); op_it != ops.end(); ++op_it) { + if (std::count(white_list.begin(), white_list.end(), op_it->getName().getStringRef()) + || !op_it->getAttr("op_name") || !GetModuleOpFromJobBodyOp(&(*op_it))) { + CreateKernelLaunchFunc(op_it->getLoc(), wrap_ops, rewriter, name_index); + continue; + } + wrap_ops.push_back(&(*op_it)); + } + CreateKernelLaunchFunc(ops.back().getLoc(), wrap_ops, rewriter, name_index); + return success(); + } +}; + void AddLowerToLinalgMemRefPasses(PassManager& pm) { - pm.addPass(createLowerOneFlowToTosaPass()); // lower-oneflow-to-tosa + pm.addPass(createConvertToSignlessForTosaPass()); // convert-to-signless-for-tosa + pm.addNestedPass(LLVM::createRequestCWrappersPass()); // llvm-request-c-wrappers + pm.addPass(createConvertToSignlessForTosaPass()); // convert-to-signless-for-tosa + pm.addPass(createLowerOneFlowToTosaPass()); // lower-oneflow-to-tosa + pm.addNestedPass( + tosa::createTosaMakeBroadcastablePass()); // tosa-make-broadcastable pm.addPass(createCSEPass()); // cse pm.addNestedPass(tosa::createTosaToLinalg()); // tosa-to-linalg-on-tensors pm.addNestedPass( @@ -764,9 +1066,7 @@ LogicalResult LowerModuleToLLVM(mlir::MLIRContext* context, ModuleOp module) { LogicalResult LowerModuleToCUDALLVM(mlir::MLIRContext* context, ModuleOp module) { InitializeLLVMNVPTXBackend(); mlir::PassManager pm(context); - bool enable_ir_printing = - ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_IR_PRINTING", false); - context->disableMultithreading(enable_ir_printing); + mlir::oneflow::CheckEnableIRPrinting(pm); AddLowerToLinalgMemRefPasses(pm); pm.addNestedPass( createConvertLinalgToParallelLoopsPass()); // convert-linalg-to-parallel-loops @@ -784,7 +1084,6 @@ LogicalResult LowerModuleToCUDALLVM(mlir::MLIRContext* context, ModuleOp module) pm.addNestedPass(createGpuCopyArgPass()); // buffer-host-register pm.addPass(createGpuToLLVMConversionPass()); pm.addPass(createReconcileUnrealizedCastsPass()); // reconcile-unrealized-casts - if (enable_ir_printing) pm.enableIRPrinting(); return pm.run(module); } @@ -794,14 +1093,28 @@ void populateFuserPasses(::mlir::RewritePatternSet& patterns) { patterns.add(patterns.getContext()); } +void populateLowerToOKLPasses(::mlir::RewritePatternSet& patterns) { + patterns.add(patterns.getContext()); +} + +void populateExtractKernelLaunchTensorPasses(::mlir::RewritePatternSet& patterns) { + patterns.add(patterns.getContext()); +} + +void populateTrimReturnAsVoidPasses(::mlir::RewritePatternSet& patterns) { + patterns.add(patterns.getContext()); +} + +void populateWrapOpsToKernelLaunchPasses(::mlir::RewritePatternSet& patterns) { + patterns.add(patterns.getContext()); +} void populateFuserForExistingOp(::mlir::RewritePatternSet& patterns) { - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); - patterns.add(patterns.getContext()); + populateForwardOpPatterns(patterns); + rewrites::populateRewrites(patterns); + constraints::populateConstraints(patterns); + populateNormalizationOpPatterns(patterns); + patterns.add>(patterns.getContext()); + patterns.add>(patterns.getContext()); } void populateAutoNhwcPatterns(::mlir::RewritePatternSet& patterns) { @@ -821,7 +1134,7 @@ void populatePreConvertInferenceOp(::mlir::RewritePatternSet& patterns) { } void populateConvertInferenceOp(::mlir::RewritePatternSet& patterns) { - patterns.add(patterns.getContext()); + populateFuseConv2DBatchNormPattern(patterns); } void populatePostConvertInferenceOp(::mlir::RewritePatternSet& patterns) { diff --git a/oneflow/ir/lib/OneFlow/SBP/SBPAttributes.cpp b/oneflow/ir/lib/OneFlow/SBP/SBPAttributes.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a6ad9b21706f5a916d6e80fcbabbebc50c3c17fc --- /dev/null +++ b/oneflow/ir/lib/OneFlow/SBP/SBPAttributes.cpp @@ -0,0 +1,86 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/SBP/SBPDialect.h" +#include "OneFlow/SBP/SBPAttributes.h" +#include "llvm/ADT/SmallVector.h" +#include "llvm/ADT/TypeSwitch.h" +#include "mlir/IR/Attributes.h" +#include "mlir/IR/DialectImplementation.h" +#include "mlir/Support/LogicalResult.h" + +using namespace mlir; + +LogicalResult parseSBP(AsmParser& parser, FailureOr& args) { + if (failed(parser.parseLSquare())) { return failure(); } + if (succeeded(parser.parseOptionalRSquare())) { + args = parser.getBuilder().getArrayAttr({}); + return success(); + } + llvm::SmallVector res; + llvm::SmallVector nd_list; + + auto parserListElem = [&](llvm::SmallVector& list) { + auto loc = parser.getCurrentLocation(); + if (failed(parser.parseAttribute(list.emplace_back()))) { + parser.emitError(loc, "failed to parse an attribute here"); + return failure(); + } + if (list.back().dyn_cast() || list.back().dyn_cast() + || list.back().dyn_cast() || list.back().dyn_cast()) { + return success(); + } + parser.emitError(loc, "failed to parse a sbp attribute here"); + return failure(); + }; + + auto parserList = [&]() { + nd_list.clear(); + if (parser.parseCommaSeparatedList([&]() { return parserListElem(nd_list); }) + || parser.parseRSquare()) { + return failure(); + } + res.emplace_back(parser.getBuilder().getArrayAttr(nd_list)); + return success(); + }; + + if (parser.parseCommaSeparatedList([&]() { + if (succeeded(parser.parseOptionalLSquare())) { return parserList(); } + return parserListElem(res); + }) + || parser.parseRSquare()) { + return failure(); + } + args = parser.getBuilder().getArrayAttr(res); + return success(); +} +void printSBP(AsmPrinter& printer, ArrayAttr args) { printer << args; } + +#define GET_ATTRDEF_CLASSES +#include "OneFlow/SBPAttributes.cpp.inc" +namespace mlir { + +namespace sbp { + +void SBPDialect::registerAttributes() { + addAttributes< +#define GET_ATTRDEF_LIST +#include "OneFlow/SBPAttributes.cpp.inc" + >(); +} + +} // namespace sbp + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/SBP/SBPDialect.cpp b/oneflow/ir/lib/OneFlow/SBP/SBPDialect.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8db9ad30ab7c9ba1a61e6dc737d5c1d69ea00058 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/SBP/SBPDialect.cpp @@ -0,0 +1,30 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/SBP/SBPDialect.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "OneFlow/SBPDialect.cpp.inc" +#include "mlir/IR/Dialect.h" +#include "mlir/IR/TypeRange.h" + +namespace mlir { + +namespace sbp { + +void SBPDialect::initialize() { registerAttributes(); } + +} // namespace sbp + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/SBP/SBPImporter.cpp b/oneflow/ir/lib/OneFlow/SBP/SBPImporter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..41f9f2d34c477c784be301abcd340d7a48816294 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/SBP/SBPImporter.cpp @@ -0,0 +1,99 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/SBP/SBPImporter.h" + +#include +#include + +namespace mlir { +namespace oneflow { + +mlir::LogicalResult SBPTranslation::PrintSbpAttrToString(mlir::Attribute sbp_attr, + std::string& sbp) { + if (auto sbp_s_attr = sbp_attr.dyn_cast()) { + sbp = "S(" + std::to_string(sbp_s_attr.getAxis()) + ")"; + } else if (auto sbp_b_attr = sbp_attr.dyn_cast()) { + sbp = "B"; + } else if (auto sbp_p_attr = sbp_attr.dyn_cast()) { + sbp = "P"; + } else if (auto sbp_p_attr = sbp_attr.dyn_cast()) { + sbp = ""; + } else { + return mlir::failure(); + } + return mlir::success(); +} +mlir::Attribute SBPTranslation::ConvertSBPToString(mlir::Builder& builder, + mlir::sbp::ParallelSignatureAttr& parallel) { + std::vector list; + for (auto output : parallel.getOutputs()) { + if (auto nd_outputs = output.dyn_cast()) { + for (auto nd_output : nd_outputs) { + std::string sbp; + if (failed(SBPTranslation::PrintSbpAttrToString(nd_output, sbp))) return {}; + list.push_back(sbp); + } + } else { + std::string sbp; + if (failed(SBPTranslation::PrintSbpAttrToString(output, sbp))) return {}; + list.push_back(sbp); + } + } + return builder.getStrArrayAttr( + makeArrayRef(llvm::SmallVector(list.begin(), list.end()))); +} + +mlir::Attribute SBPTranslation::ConvertNdSbpToPsig(mlir::Builder& builder, + const std::vector& nd_sbp, + const int nd_size) { + auto ctx = builder.getContext(); + std::vector outputs_vec; + for (const auto& sbp_data : nd_sbp) { + mlir::Attribute attr; + if (sbp_data == "") { + attr = mlir::sbp::AnyAttr::get(ctx); + } else { + ::oneflow::SbpParallel sbp; + ParseSbpParallelFromString(sbp_data, &sbp); + if (sbp.has_split_parallel()) { + attr = mlir::sbp::SplitAttr::get(ctx, sbp.split_parallel().axis()); + } else if (sbp.has_broadcast_parallel()) { + attr = mlir::sbp::BroadcastAttr::get(ctx); + } else if (sbp.has_partial_sum_parallel()) { + attr = mlir::sbp::PartialSumAttr::get(ctx); + } else { + llvm::errs() << "Unsupported sbp type from nd_sbp: "; + for (const auto& sbp_data : nd_sbp) { llvm::errs() << sbp_data << " "; } + llvm::errs() << "\n"; + exit(EXIT_FAILURE); + } + } + outputs_vec.push_back(attr); + } + + auto inputs = builder.getArrayAttr({}); + mlir::ArrayAttr outputs; + + std::vector outputs_vec_nd; + for (auto iter = outputs_vec.begin(); iter < outputs_vec.end(); iter += nd_size) { + outputs_vec_nd.emplace_back( + builder.getArrayAttr(std::vector(iter, iter + nd_size))); + } + outputs = builder.getArrayAttr(outputs_vec_nd); + return mlir::sbp::ParallelSignatureAttr::get(ctx, inputs, outputs); +} +} // namespace oneflow +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/Transform/AggregateComputeOps.cpp b/oneflow/ir/lib/OneFlow/Transform/AggregateComputeOps.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e62673dfa83ff6b605929525a74c7e31651de245 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/Transform/AggregateComputeOps.cpp @@ -0,0 +1,79 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/Passes.h" +#include "llvm/Support/Casting.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/IR/BlockAndValueMapping.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Transforms/DialectConversion.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" + +#include +#include + +using namespace mlir; + +namespace mlir { +namespace oneflow { + +struct AggregateComputeOpsPattern : public mlir::OpRewritePattern { + explicit AggregateComputeOpsPattern(mlir::MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/0) {} + + mlir::LogicalResult matchAndRewrite(OutputOp op, mlir::PatternRewriter& rewriter) const override { + if (op->getNumResults() != 1) { return failure(); } + if (llvm::isa(op->getNextNode())) { return failure(); } + // oneflow.output only have a single result + for (auto user : op->getResult(0).getUsers()) { + if (!llvm::isa(user)) { return failure(); } + rewriter.setInsertionPoint(user); + } + + auto new_val = rewriter.clone(*op)->getResults(); + rewriter.replaceOp(op, new_val); + return success(); + }; +}; + +namespace { + +class AggregateComputeOpsPass : public AggregateComputeOpsPassBase { + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(patterns.getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +} // namespace + +std::unique_ptr createAggregateComputeOpsPass() { + return std::make_unique(); +} + +} // namespace oneflow +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/Transform/AutoNHWCOps.cpp b/oneflow/ir/lib/OneFlow/Transform/AutoNHWCOps.cpp new file mode 100644 index 0000000000000000000000000000000000000000..71cdfecdc71c6a26565cbeb4b81e13e40506943b --- /dev/null +++ b/oneflow/ir/lib/OneFlow/Transform/AutoNHWCOps.cpp @@ -0,0 +1,291 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/Transform/TransposeHelpers.h" + +namespace mlir { + +namespace oneflow { + +bool Conv2DOp::IsNCHW() { return this->data_format().str() == "channels_first"; } + +llvm::DenseSet Conv2DOp::OperandsToTranspose() { + if (this->_add_to_output()) { + return {this->in(), this->weight(), this->_add_to_output()}; + } else { + return {this->in(), this->weight()}; + } +} + +llvm::DenseSet Conv2DOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector Conv2DOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto conv_op = *this; + SmallVector operands; + operands.push_back(value[0]); + operands.push_back(value[1]); + if (conv_op.bias()) operands.push_back(conv_op.bias()); + if (this->_add_to_output()) { operands.push_back(value[2]); } + NamedAttrList attributes = conv_op->getAttrs(); + attributes.set(conv_op.data_formatAttrName(), rewriter.getStringAttr("channels_last")); + auto res = rewriter + .create(conv_op.getLoc(), getNHWCResultTypes(conv_op), operands, + attributes) + ->getResults(); + llvm::SmallVector results; + results.push_back(res[0]); + return results; +} + +bool BiasAddOp::IsNCHW() { return this->axisAttr().getValue().getSExtValue() == 1; } + +llvm::DenseSet BiasAddOp::OperandsToTranspose() { return {this->a()}; } + +llvm::DenseSet BiasAddOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector BiasAddOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto bias_add_op = *this; + SmallVector operands; + operands.push_back(value[0]); + operands.push_back(bias_add_op.b()); + NamedAttrList attributes = bias_add_op->getAttrs(); + attributes.set(bias_add_op.axisAttrName(), rewriter.getSI32IntegerAttr(3)); + auto res = rewriter + .create(bias_add_op.getLoc(), getNHWCResultTypes(bias_add_op), + operands, attributes) + ->getResults(); + llvm::SmallVector results; + results.push_back(res[0]); + return results; +} + +bool BroadcastAddOp::IsNCHW() { return false; } + +llvm::DenseSet BroadcastAddOp::OperandsToTranspose() { return {this->x(), this->y()}; } + +llvm::DenseSet BroadcastAddOp::ResultsToTranspose() { return {this->z()}; } + +llvm::SmallVector BroadcastAddOp::NchwToNhwc(llvm::SmallVector values, + PatternRewriter& rewriter) { + auto broadcast_op = *this; + NamedAttrList attributes = broadcast_op->getAttrs(); + auto res = rewriter + .create( + broadcast_op.getLoc(), getNHWCResultTypes(broadcast_op), values, attributes) + .z(); + llvm::SmallVector results; + results.push_back(res); + return results; +} + +bool NormalizationOp::IsNCHW() { return this->axisAttr().getValue().getSExtValue() == 1; } + +llvm::DenseSet NormalizationOp::OperandsToTranspose() { return {this->x()}; } + +llvm::DenseSet NormalizationOp::ResultsToTranspose() { return {this->y()}; } + +llvm::SmallVector NormalizationOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto normalization_op = *this; + SmallVector operands; + operands.push_back(value[0]); + if (normalization_op.moving_mean()) operands.push_back(normalization_op.moving_mean()); + if (normalization_op.moving_variance()) operands.push_back(normalization_op.moving_variance()); + operands.push_back(normalization_op.gamma()); + operands.push_back(normalization_op.beta()); + if (normalization_op._add_to_output()) operands.push_back(normalization_op._add_to_output()); + NamedAttrList attributes = normalization_op->getAttrs(); + attributes.set(normalization_op.axisAttrName(), rewriter.getSI32IntegerAttr(3)); + auto res = + rewriter + .create( + normalization_op.getLoc(), getNHWCResultTypes(normalization_op), operands, attributes) + ->getResults(); + llvm::SmallVector results; + results.push_back(res[0]); + return results; +} + +bool MaxPool2DOp::IsNCHW() { return this->data_format().str() == "channels_first"; } + +llvm::DenseSet MaxPool2DOp::OperandsToTranspose() { return {this->x()}; } + +llvm::DenseSet MaxPool2DOp::ResultsToTranspose() { return {this->y(), this->indice()}; } + +llvm::SmallVector MaxPool2DOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto max_pool_2d_op = *this; + SmallVector operands; + operands.push_back(value[0]); + NamedAttrList attributes = max_pool_2d_op->getAttrs(); + attributes.set(max_pool_2d_op.data_formatAttrName(), rewriter.getStringAttr("channels_last")); + auto res = + rewriter + .create(max_pool_2d_op.getLoc(), getNHWCResultTypes(max_pool_2d_op), + operands, attributes) + ->getResults(); + llvm::SmallVector results; + results.push_back(res[0]); + results.push_back(res[1]); + return results; +} + +bool ReluOp::IsNCHW() { return false; } + +llvm::DenseSet ReluOp::OperandsToTranspose() { return {this->x()}; } + +llvm::DenseSet ReluOp::ResultsToTranspose() { return {this->y()}; } + +llvm::SmallVector ReluOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto relu_op = *this; + SmallVector operands{value[0]}; + auto res = rewriter + .create(relu_op.getLoc(), getNHWCResultTypes(relu_op), operands, + relu_op->getAttrs()) + ->getResults(); + return {res[0]}; +} + +bool ScalarDivOp::IsNCHW() { return false; } + +llvm::DenseSet ScalarDivOp::OperandsToTranspose() { return {this->in()}; } + +llvm::DenseSet ScalarDivOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector ScalarDivOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto elementwise_op = *this; + SmallVector operands{value[0]}; + auto res = + rewriter + .create(elementwise_op.getLoc(), getNHWCResultTypes(elementwise_op), + operands, elementwise_op->getAttrs()) + ->getResults(); + return {res[0]}; +} + +bool SiluOp::IsNCHW() { return false; } + +llvm::DenseSet SiluOp::OperandsToTranspose() { return {this->in()}; } + +llvm::DenseSet SiluOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector SiluOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto elementwise_op = *this; + SmallVector operands{value[0]}; + auto res = + rewriter + .create(elementwise_op.getLoc(), getNHWCResultTypes(elementwise_op), + operands, elementwise_op->getAttrs()) + ->getResults(); + return {res[0]}; +} + +bool CastOp::IsNCHW() { return false; } + +llvm::DenseSet CastOp::OperandsToTranspose() { return {this->in()}; } + +llvm::DenseSet CastOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector CastOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto elementwise_op = *this; + SmallVector operands{value[0]}; + auto res = + rewriter + .create(elementwise_op.getLoc(), getNHWCResultTypes(elementwise_op), + operands, elementwise_op->getAttrs()) + ->getResults(); + return {res[0]}; +} + +bool Add2Op::IsNCHW() { return false; } + +llvm::DenseSet Add2Op::OperandsToTranspose() { return {this->in0(), this->in1()}; } + +llvm::DenseSet Add2Op::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector Add2Op::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto add2_op = *this; + SmallVector operands{value[0], value[1]}; + auto res = rewriter + .create(add2_op.getLoc(), getNHWCResultTypes(add2_op), operands, + add2_op->getAttrs()) + ->getResults(); + return {res[0]}; +} + +bool ConcatOp::IsNCHW() { return this->axisAttr().getValue().getSExtValue() == 1; } + +llvm::DenseSet ConcatOp::OperandsToTranspose() { + llvm::DenseSet operands; + for (auto operand : this->in()) { operands.insert(operand); } + return operands; +} + +llvm::DenseSet ConcatOp::ResultsToTranspose() { return {this->out()}; } + +llvm::SmallVector ConcatOp::NchwToNhwc(llvm::SmallVector values, + PatternRewriter& rewriter) { + auto elementwise_op = *this; + NamedAttrList attributes = elementwise_op->getAttrs(); + attributes.set(elementwise_op.axisAttrName(), + IntegerAttr::get(rewriter.getIntegerType(64, /*isSigned=*/true), + APInt(64, 3, /*isSigned=*/true))); + auto out = rewriter + .create(elementwise_op.getLoc(), + getNHWCResultTypes(elementwise_op), values, attributes) + .out(); + return {out}; +} + +bool GroupNormOp::IsNCHW() { return this->data_format().str() == "channels_first"; } + +llvm::DenseSet GroupNormOp::OperandsToTranspose() { return {this->x()}; } + +llvm::DenseSet GroupNormOp::ResultsToTranspose() { return {this->y()}; } + +llvm::SmallVector GroupNormOp::NchwToNhwc(llvm::SmallVector value, + PatternRewriter& rewriter) { + auto group_norm_op = *this; + SmallVector operands; + operands.push_back(value[0]); + if (this->affine()) { + operands.push_back(this->beta()); + operands.push_back(this->gamma()); + } + NamedAttrList attributes = group_norm_op->getAttrs(); + attributes.set(group_norm_op.data_formatAttrName(), rewriter.getStringAttr("channels_last")); + auto res = + rewriter + .create(group_norm_op.getLoc(), getNHWCResultTypes(group_norm_op), + operands, attributes) + ->getResults(); + llvm::SmallVector results; + results.push_back(res[0]); + results.push_back(res[1]); + results.push_back(res[2]); + return results; +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/Transform/CSEWithAttributesIgnored.cpp b/oneflow/ir/lib/OneFlow/Transform/CSEWithAttributesIgnored.cpp new file mode 100644 index 0000000000000000000000000000000000000000..008f8d9094bdfd55e27a8dd27ccdb9c0d2ccff6d --- /dev/null +++ b/oneflow/ir/lib/OneFlow/Transform/CSEWithAttributesIgnored.cpp @@ -0,0 +1,143 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/Passes.h" +#include "mlir/Pass/Pass.h" +#include "mlir/Transforms/GreedyPatternRewriteDriver.h" + +using namespace mlir; + +namespace mlir { + +namespace oneflow { + +namespace { + +static const auto MAGIC_OP_NAME = "ONEFLOW_ERASE_MAGIC"; +static const auto MAGIC_SCOPE_SYMBOL_ID = 77777; + +struct EraseAttributes : public mlir::OpInterfaceRewritePattern { + explicit EraseAttributes(mlir::MLIRContext* context, std::shared_ptr state) + : OpInterfaceRewritePattern(context, /*benefit=*/1), state_{state} {} + mlir::LogicalResult matchAndRewrite(UserOpCompatible op, + mlir::PatternRewriter& rewriter) const override { + if (op->getAttrOfType(OpTrait::IsOpConfCompatible::getOpNameAttr()) + .getValue() + .str() + != MAGIC_OP_NAME) { + if (state_) { + state_->opNames[op] = + op->getAttrOfType(OpTrait::IsOpConfCompatible::getOpNameAttr()); + state_->scopeSymbolIDs[op] = op->getAttrOfType( + OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr()); + } + op->setAttr(OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr(MAGIC_OP_NAME)); + op->setAttr(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr(), + rewriter.getI64IntegerAttr(MAGIC_SCOPE_SYMBOL_ID)); + return success(); + } else { + return failure(); + } + } + + private: + std::shared_ptr state_; +}; + +struct PutAttributes : public mlir::OpInterfaceRewritePattern { + explicit PutAttributes(mlir::MLIRContext* context, std::shared_ptr state) + : OpInterfaceRewritePattern(context, /*benefit=*/1), state_{state} {} + mlir::LogicalResult matchAndRewrite(UserOpCompatible op, + mlir::PatternRewriter& rewriter) const override { + if (op->getAttrOfType(OpTrait::IsOpConfCompatible::getOpNameAttr()) + .getValue() + .str() + == MAGIC_OP_NAME) { + if (state_) { + op->setAttr(OpTrait::IsOpConfCompatible::getOpNameAttr(), state_->opNames[op]); + op->setAttr(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr(), + state_->scopeSymbolIDs[op]); + } + return success(); + } else { + return failure(); + } + } + + private: + std::shared_ptr state_; +}; + +class CSEWithAttributesIgnored : public CSEWithAttributesIgnoredBase { + public: + explicit CSEWithAttributesIgnored() {} + explicit CSEWithAttributesIgnored(std::shared_ptr state) : state_(state) {} + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(op->getContext(), state_); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } + + private: + std::shared_ptr state_; +}; + +class CSEPutAttributes : public CSEPutAttributesBase { + public: + explicit CSEPutAttributes() {} + explicit CSEPutAttributes(std::shared_ptr state) { state_ = state; } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(op->getContext(), state_); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } + + private: + std::shared_ptr state_; +}; + +} // namespace + +std::unique_ptr createCSEWithAttributesIgnored() { + return std::make_unique(); +} + +std::unique_ptr createCSEPutAttributes() { return std::make_unique(); } + +std::pair, std::unique_ptr> createCSEPasses( + std::shared_ptr state) { + return std::make_pair(std::make_unique(state), + std::make_unique(state)); +} + +void registerCSEPasses(std::shared_ptr state) { + ::mlir::registerPass([state]() -> std::unique_ptr<::mlir::Pass> { + return std::make_unique(state); + }); + ::mlir::registerPass([state]() -> std::unique_ptr<::mlir::Pass> { + return std::make_unique(state); + }); +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/Transform/ConvertInferenceOp.cpp b/oneflow/ir/lib/OneFlow/Transform/ConvertInferenceOp.cpp index 8a8b2145b2f6a64ac4f683132ee770afcfad3c3b..aee1ef612a1b75dbe6c046dc6e89a7de04976fdd 100644 --- a/oneflow/ir/lib/OneFlow/Transform/ConvertInferenceOp.cpp +++ b/oneflow/ir/lib/OneFlow/Transform/ConvertInferenceOp.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "OneFlow/Passes.h" #include "mlir/Pass/Pass.h" #include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/OneFlowPatternUtils.h" using namespace mlir; @@ -37,6 +38,7 @@ class ConvertInferenceOpPass : public ConvertInferenceOpPassBasegetContext()); oneflow::populateConvertInferenceOp(patterns); + oneflow::rewrites::populateRewrites(patterns); (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); } }; diff --git a/oneflow/ir/lib/OneFlow/Transform/GroupMatMulOps.cpp b/oneflow/ir/lib/OneFlow/Transform/GroupMatMulOps.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1a10d2b3020f6b6941ef5b772ed3d8d591088fdd --- /dev/null +++ b/oneflow/ir/lib/OneFlow/Transform/GroupMatMulOps.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OneFlowOps.h" + +namespace mlir { + +namespace oneflow { + +template +bool isLinearMatmulOp(OpTy op) { + const bool isAlphaOne = op.alpha().convertToDouble() == 1.0; + const bool isLinear = op.transpose_a() == false && op.transpose_b() == true; + const bool hasNoAddToOutput = !op._add_to_output(); + const bool isCUDA = op.device_tag() == "cuda"; + return isAlphaOne && isLinear && hasNoAddToOutput && isCUDA; +} + +bool MatmulOp::isLinear() { return isLinearMatmulOp(*this); } + +Value MatmulOp::matMulGetX() { return a(); } + +Value MatmulOp::matMulGetW() { return b(); } + +Value MatmulOp::matMulGetY() { return out(); } + +bool BroadcastMatmulOp::isLinear() { return isLinearMatmulOp(*this); } + +Value BroadcastMatmulOp::matMulGetX() { return a(); } + +Value BroadcastMatmulOp::matMulGetW() { return b(); } + +Value BroadcastMatmulOp::matMulGetY() { return out(); } + +bool BiasAddOp::isLastDim() { + return axis() == -1 || axis() == out().getType().cast().getRank() - 1; +} + +Value BiasAddOp::biasAddGetBias() { return b(); } + +Value BiasAddOp::biasAddGetOut() { return out(); } + +Value BroadcastAddOp::biasAddGetBias() { return y(); } + +Value BroadcastAddOp::biasAddGetOut() { return z(); } + +bool BroadcastAddOp::isLastDim() { return true; } + +Value FusedMatmulBiasOp::matMulGetX() { return x(); } + +Value FusedMatmulBiasOp::matMulGetW() { return weight(); } + +Value FusedMatmulBiasOp::matMulGetY() { return out(); } + +namespace { + +bool shouldGroupFusedMatmulBiasOp(FusedMatmulBiasOp& op) { + return !op._add_to_output() && op.device_tag() == "cuda"; +} + +} // namespace + +bool FusedMatmulBiasOp::isLinear() { return shouldGroupFusedMatmulBiasOp(*this); } + +bool FusedMatmulBiasOp::isLastDim() { return shouldGroupFusedMatmulBiasOp(*this); } + +Value FusedMatmulBiasOp::biasAddGetBias() { return bias(); } + +Value FusedMatmulBiasOp::biasAddGetOut() { return out(); } + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/Transform/OutlineAndFuse.cpp b/oneflow/ir/lib/OneFlow/Transform/OutlineAndFuse.cpp index 216c9fb9594677e4155672b03fb97dfcd1661d34..99d58dca0ee3e1a9bedef15744654ea0a5f85a5b 100644 --- a/oneflow/ir/lib/OneFlow/Transform/OutlineAndFuse.cpp +++ b/oneflow/ir/lib/OneFlow/Transform/OutlineAndFuse.cpp @@ -13,21 +13,90 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include -#include +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowOps.h" #include "OneFlow/Passes.h" +#include "OneFlow/OneFlowPDLLPatterns.h" +#include "OneFlow/OneFlowPatternUtils.h" +#include "llvm/Support/Casting.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/IR/BuiltinOps.h" #include "mlir/Pass/Pass.h" +#include "mlir/Transforms/DialectConversion.h" #include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include +#include + using namespace mlir; +namespace mlir { +namespace oneflow { + namespace { class OutlineJitFunctionPass : public OutlineJitFunctionPassBase { void runOnOperation() override { Operation* op = getOperation(); RewritePatternSet patterns(op->getContext()); - oneflow::populateFuserPasses(patterns); + populateFuserPasses(patterns); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +class LowerToOKLPass : public LowerToOKLPassBase { + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + populateLowerToOKLPasses(patterns); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +class WrapOpsToKernelLaunchPass : public WrapOpsToKernelLaunchPassBase { + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + populateWrapOpsToKernelLaunchPasses(patterns); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +class ExtractKernelLaunchTensorPass + : public ExtractKernelLaunchTensorPassBase { + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + populateExtractKernelLaunchTensorPasses(patterns); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +class TrimReturnAsVoidPass : public TrimReturnAsVoidPassBase { + void getDependentDialects(DialectRegistry& registry) const override { + registry.insert(); + registry.insert(); + } + + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + populateTrimReturnAsVoidPasses(patterns); (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); } }; @@ -36,25 +105,178 @@ class FuseIntoExistingOpPass : public FuseIntoExistingOpPassBasegetContext()); - oneflow::populateFuserForExistingOp(patterns); + populateFuserForExistingOp(patterns); (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); } }; +namespace { + +BiasAddCompatible getBiasAddCompatibleOp(MatMulCompatible op) { + BiasAddCompatible bias_add; + auto self_bias_op = dyn_cast(op.getOperation()); + if (self_bias_op) /* matmul itself is also bias add op */ { + bias_add = self_bias_op; + } else /* there is bias add op */ { + for (auto u : op.matMulGetY().getUsers()) { + if (auto b = dyn_cast(u)) { + bias_add = b; + break; + } + } + } + if (bias_add && bias_add.isLastDim()) { + return bias_add; + } else { + return BiasAddCompatible{}; + } +} + } // namespace +struct GroupMatMulPattern : public mlir::OpInterfaceRewritePattern { + explicit GroupMatMulPattern(mlir::MLIRContext* context) + : OpInterfaceRewritePattern(context, /*benefit=*/1) {} + mlir::LogicalResult matchAndRewrite(MatMulCompatible op, + mlir::PatternRewriter& rewriter) const override { + if (!op.isLinear()) { return failure(); } + auto bias_add = getBiasAddCompatibleOp(op); + llvm::SmallVector all_matmuls{}; + llvm::SmallVector all_bias_adds{}; + for (auto xUser : op.matMulGetX().getUsers()) { + if (auto matmul = dyn_cast(xUser)) { + if (!matmul.isLinear()) { continue; } + auto each_bias_add = getBiasAddCompatibleOp(matmul); + if (each_bias_add) { all_bias_adds.push_back(each_bias_add); } + if (!!bias_add == !!each_bias_add) { all_matmuls.push_back(matmul); } + } + } + // all_matmuls has only self, means no other matmul can be grouped + if (all_matmuls.size() == 1) { return failure(); } + llvm::SmallVector operands{}; + for (auto matmul : all_matmuls) { operands.push_back(matmul.matMulGetX()); } + for (auto matmul : all_matmuls) { operands.push_back(matmul.matMulGetW()); } + for (auto bias_adds : all_bias_adds) { operands.push_back(bias_adds.biasAddGetBias()); } + llvm::SmallVector results{}; + for (auto matmul : all_matmuls) { results.push_back(matmul.matMulGetY().getType()); } + NamedAttrList attributes{}; + attributes.set(OpTrait::IsOpConfCompatible::getDeviceTagAttr(), + OpTrait::IsOpConfCompatible::getDeviceTag(op)); + attributes.set(OpTrait::IsOpConfCompatible::getDeviceNameAttr(), + OpTrait::IsOpConfCompatible::getDeviceName(op)); + if (auto hierarchy = OpTrait::IsOpConfCompatible::getHierarchy(op)) { + attributes.set(OpTrait::IsOpConfCompatible::getHierarchyAttr(), hierarchy); + } + if (auto scope_symbol_id = OpTrait::IsOpConfCompatible::getScopeSymbolID(op)) { + attributes.set(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr(), scope_symbol_id); + } + attributes.set("operand_segment_sizes", + rewriter.getI32VectorAttr({static_cast(all_matmuls.size()), + static_cast(all_matmuls.size()), + static_cast(all_bias_adds.size())})); + attributes.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr( + "grouped_matmul_" + OpTrait::IsOpConfCompatible::getOpName(op).str())); + auto grouped_matmul = + rewriter.create(op->getLoc(), results, operands, attributes); + if (all_bias_adds.empty()) { + for (const auto& matmul : llvm::enumerate(all_matmuls)) { + matmul.value().matMulGetY().replaceAllUsesWith(grouped_matmul.ys()[matmul.index()]); + } + } else { + CHECK(all_bias_adds.size() == all_matmuls.size()); + for (const auto& bias_add : llvm::enumerate(all_bias_adds)) { + bias_add.value().biasAddGetOut().replaceAllUsesWith(grouped_matmul.ys()[bias_add.index()]); + } + } + return success(); + } +}; -namespace mlir { +class GroupMatMulPass : public GroupMatMulBase { + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(op->getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; -namespace oneflow { +struct GroupNormActivationPattern : public OpRewritePattern { + explicit GroupNormActivationPattern(MLIRContext* context) + : OpRewritePattern(context, /*benefit=*/1) {} + LogicalResult matchAndRewrite(oneflow::GroupNormOp op, PatternRewriter& rewriter) const override { + if (op.activation() == "none") { + llvm::SmallVector act_ops{}; + for (auto& u : op.y().getUses()) { + if (auto act_op = dyn_cast(u.getOwner())) { act_ops.push_back(act_op); } + } + NamedAttrList attributes(op->getAttrs()); + attributes.set(OpTrait::IsOpConfCompatible::getOpNameAttr(), + rewriter.getStringAttr(OpTrait::IsOpConfCompatible::getOpName(op).str() + + "_with_activation")); + attributes.set("activation", rewriter.getStringAttr("silu")); + auto gn_with_act = rewriter.create(op->getLoc(), op->getResultTypes(), + op.getOperands(), attributes); + for (auto act : act_ops) { + if (auto op = dyn_cast(act)) { + op.out().replaceAllUsesWith(gn_with_act.y()); + } + } + return success(); + } + return failure(); + } +}; + +class FuseForwardOpsPass : public FuseForwardOpsBase { + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + patterns.add(op->getContext()); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +class FuseNormalizationOpsPass : public FuseNormalizationOpsBase { + void runOnOperation() override { + Operation* op = getOperation(); + RewritePatternSet patterns(op->getContext()); + populateNormalizationOpPatterns(patterns); + rewrites::populateRewrites(patterns); + (void)applyPatternsAndFoldGreedily(op, std::move(patterns)); + } +}; + +} // namespace std::unique_ptr createOutlineJitFunctionPass() { return std::make_unique(); } +std::unique_ptr createWrapOpsToKernelLaunchPass() { + return std::make_unique(); +} + +std::unique_ptr createExtractKernelLaunchTensorPass() { + return std::make_unique(); +} + +std::unique_ptr createTrimReturnAsVoidPass() { + return std::make_unique(); +} + +std::unique_ptr createLowerToOKLPass() { return std::make_unique(); } + std::unique_ptr createFuseIntoExistingOpPass() { return std::make_unique(); } -} // namespace oneflow +std::unique_ptr createGroupMatMul() { return std::make_unique(); } +std::unique_ptr createFuseForwardOps() { return std::make_unique(); } +std::unique_ptr createFuseNormalizationOps() { + return std::make_unique(); +} + +} // namespace oneflow } // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/TransposeHelpers.cpp b/oneflow/ir/lib/OneFlow/TransposeHelpers.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f3abc701f92c0268916f9d6fffc54caf5c319fe3 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/TransposeHelpers.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include + +namespace mlir { + +namespace oneflow { + +RankedTensorType getNHWCType(RankedTensorType t) { + return RankedTensorType::get({t.getShape()[0], t.getShape()[2], t.getShape()[3], t.getShape()[1]}, + t.getElementType()); +} + +RankedTensorType getNHWCType(Type t) { return getNHWCType(t.cast()); } +RankedTensorType getNHWCType(Value v) { return getNHWCType(v.getType()); } + +RankedTensorType getNCHWType(RankedTensorType t) { + return RankedTensorType::get({t.getShape()[0], t.getShape()[3], t.getShape()[1], t.getShape()[2]}, + t.getElementType()); +} +RankedTensorType getNCHWType(Type t) { return getNCHWType(t.cast()); } +RankedTensorType getNCHWType(Value v) { return getNCHWType(v.getType()); } + +llvm::SmallVector getNHWCResultTypes(NCHWCompatible op) { + llvm::SmallVector result_types; + llvm::DenseSet transpose_result = op.ResultsToTranspose(); + for (Value result : op->getOpResults()) { + Type t = result.getType(); + if (transpose_result.find(result) != transpose_result.end()) { + result_types.push_back(getNHWCType(t)); + } else { + result_types.push_back(t); + } + } + return result_types; +} + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/UserOpConversion.cpp b/oneflow/ir/lib/OneFlow/UserOpConversion.cpp new file mode 100644 index 0000000000000000000000000000000000000000..628ab9bb4cf59d14e1b197a64a67701b94d5535d --- /dev/null +++ b/oneflow/ir/lib/OneFlow/UserOpConversion.cpp @@ -0,0 +1,310 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +// this file should contains functions to get operands and results with user op name and index + +#include "OneFlow/UserOpConversion.h" +#include "OneFlow/UserOpReflection.h" +#include "oneflow/core/framework/user_op_def.h" + +namespace mlir { + +namespace oneflow { + +namespace user_op { + +LogicalResult saveAttrDictionaryToOpConf(DictionaryAttr attributes, + ::oneflow::OperatorConf* op_conf) { + if (auto scope_symbol_id = + attributes.get(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr()) + .dyn_cast_or_null()) { + op_conf->set_scope_symbol_id(scope_symbol_id.getInt()); + } + if (auto op_name = attributes.get(OpTrait::IsOpConfCompatible::getOpNameAttr()) + .dyn_cast_or_null()) { + op_conf->set_name(op_name.str()); + } + auto device_tag = attributes.get(OpTrait::IsOpConfCompatible::getDeviceTagAttr()) + .dyn_cast_or_null(); + CHECK(device_tag) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceTagAttr().str(); + op_conf->set_device_tag(device_tag.str()); + return success(); +} + +LogicalResult doConvertUserOpAttributes(llvm::StringRef op_type_name, DictionaryAttr attributes, + ::oneflow::OperatorConf& op_conf) { + auto user_conf = op_conf.mutable_user_conf(); + op_conf.mutable_user_conf()->set_op_type_name(op_type_name.str()); + CHECK(saveAttrDictionaryToOpConf(attributes, &op_conf).succeeded()); + for (auto id_attr : attributes) { + auto id = id_attr.getName(); + // mlir only attrs + // TODO: prefix special attributes with "oneflow.". For example: `oneflow.op_type_name = "add"` + if (id.strref().equals("callee") + || id.strref().equals(OpTrait::IsOpConfCompatible::getDeviceNameAttr()) + || id.strref().equals(OpTrait::IsOpConfCompatible::getHierarchyAttr()) + || id.strref().equals(OpTrait::IsImportCompatible::getOutputLBNsAttr()) + || id.strref().equals(OpTrait::IsAlternative::getOpTypeNameAttr()) + || id.strref().equals( + mlir::OpTrait::AttrSizedOperandSegments::getOperandSegmentSizeAttr()) + || id.strref().equals( + mlir::OpTrait::AttrSizedResultSegments::getResultSegmentSizeAttr())) { + continue; + } else if (id.strref().equals("input_sizes") || id.strref().equals("output_sizes")) { + continue; + } + // convert op conf attributes + else if (id.strref().equals(OpTrait::IsOpConfCompatible::getOpNameAttr())) { + continue; + } else if (id.strref().equals(OpTrait::IsOpConfCompatible::getDeviceTagAttr())) { + continue; + } else if (id.strref().equals(OpTrait::IsOpConfCompatible::getScopeSymbolIDAttr())) { + continue; + } + // convert user conf attributes + else { + auto attr_name = id.str(); + Attribute attr = id_attr.getValue(); + auto user_attr = ::oneflow::AttrValue(); + const ::oneflow::AttrType attr_type = queryAttrType(op_type_name.str(), attr_name); + if (attr_type == ::oneflow::kAtInt32) { + user_attr.set_at_int32(attr.dyn_cast().getSInt()); + } else if (attr_type == ::oneflow::kAtInt64) { + user_attr.set_at_int64(attr.dyn_cast().getSInt()); + } else if (attr_type == ::oneflow::kAtBool) { + user_attr.set_at_bool(attr.dyn_cast().getValue()); + } else if (attr_type == ::oneflow::kAtFloat) { + user_attr.set_at_float(attr.dyn_cast().getValue().convertToFloat()); + } else if (attr_type == ::oneflow::kAtDouble) { + user_attr.set_at_double(attr.dyn_cast().getValue().convertToDouble()); + } else if (attr_type == ::oneflow::kAtString) { + user_attr.set_at_string(attr.dyn_cast().getValue().str()); + } else if (attr_type == ::oneflow::kAtShape) { + *user_attr.mutable_at_shape() = getAttrAsShape(attr); + } else if (attr_type == ::oneflow::kAtStride) { + *user_attr.mutable_at_stride() = getAttrAsStride(attr); + } else if (attr_type == ::oneflow::kAtDataType) { + const auto dt = support::FromMLIRAttrToOFDataType(attr); + if (succeeded(dt)) { + user_attr.set_at_data_type(dt.getValue()); + } else { + LOG(FATAL) << "fail to convert op attr to data type, key: " + id.str(); + return failure(); + } + } else if (attr_type == ::oneflow::kAtListInt32) { + user_attr.mutable_at_list_int32(); + auto ref = attr.dyn_cast(); + for (auto v : ref.getValue()) { + user_attr.mutable_at_list_int32()->add_val(v.dyn_cast().getSInt()); + } + } else if (attr_type == ::oneflow::kAtListInt64) { + user_attr.mutable_at_list_int64(); + auto ref = attr.dyn_cast(); + for (auto v : ref.getValue()) { + user_attr.mutable_at_list_int64()->add_val(v.dyn_cast().getSInt()); + } + } else if (attr_type == ::oneflow::kAtListFloat) { + user_attr.mutable_at_list_float(); + auto ref = attr.dyn_cast(); + for (auto v : ref.getValue()) { + user_attr.mutable_at_list_float()->add_val( + v.dyn_cast().getValue().convertToFloat()); + } + } else if (attr_type == ::oneflow::kAtListDataType) { + for (auto v : attr.dyn_cast().getValue()) { + const auto dt = support::FromMLIRAttrToOFDataType(attr); + if (succeeded(dt)) { + user_attr.mutable_at_list_data_type()->add_val(dt.getValue()); + } else { + LOG(FATAL) << "fail to convert op attr to data type, key: " + id.str(); + return failure(); + } + } + } else if (attr_type == ::oneflow::kAtListShape) { + for (auto shape_attr : attr.dyn_cast().getValue()) { + ::oneflow::ShapeProto* shape_ptr = user_attr.mutable_at_list_shape()->add_val(); + *shape_ptr = getAttrAsShape(shape_attr); + } + } else if (attr_type == ::oneflow::kAtListStride) { + for (auto stride_attr : attr.dyn_cast().getValue()) { + ::oneflow::Int64ListProto* stride_ptr = user_attr.mutable_at_list_stride()->add_val(); + *stride_ptr = getAttrAsStride(stride_attr); + } + } else if (attr_type == ::oneflow::kAtListString) { + // attr like nd_sbp requires the existence of list even it is empty + user_attr.mutable_at_list_string(); + for (auto s : attr.dyn_cast().getValue()) { + user_attr.mutable_at_list_string()->add_val(s.dyn_cast().getValue().str()); + } + } else { + return failure(); + } + (*user_conf->mutable_attr())[id.str()] = user_attr; + } + } + return success(); +} + +LogicalResult ConvertUserOpAttributes(llvm::StringRef op_type_name, ValueRange operands, + DictionaryAttr attributes, ::oneflow::OperatorConf& op_conf) { + { + std::vector keys{}; + std::vector sizes{}; + if (failed(user_op::GetFilteredSegmentKeyAndSizes( + op_type_name, operands.size(), attributes, keys, sizes))) { + LOG(FATAL) << "fail to get filtered segment key and sizes"; + return failure(); + } + for (const auto& s : keys) { op_conf.mutable_user_conf()->add_input_order(s); } + } + return doConvertUserOpAttributes(op_type_name, attributes, op_conf); +} + +LogicalResult ConvertUserOpAttributes(Operation* op, ::oneflow::OperatorConf& op_conf) { + std::string op_type_name = GetOpTypeName(op); + { + std::vector keys{}; + std::vector sizes{}; + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { + op->emitError("fail to convert user op input order"); + return failure(); + } + for (const auto& s : keys) { op_conf.mutable_user_conf()->add_input_order(s); } + } + { + std::vector keys{}; + std::vector sizes{}; + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { + op->emitError("fail to convert user op output order"); + return failure(); + } + for (const auto& s : keys) { op_conf.mutable_user_conf()->add_output_order(s); } + } + return doConvertUserOpAttributes(op_type_name, op->getAttrDictionary(), op_conf); +} + +LogicalResult ConvertUserOpInputs(llvm::StringRef op_type_name, ValueRange operands, + DictionaryAttr attributes, ::oneflow::UserOpConf* user_conf) { + std::vector keys{}; + std::vector sizes{}; + CHECK(user_op::GetFilteredSegmentKeyAndSizes( + op_type_name, operands.size(), attributes, keys, sizes) + .succeeded()); + int32_t input_idx = 0; + for (auto tuple : llvm::zip(keys, sizes)) { + auto input_key = std::get<0>(tuple); + auto input_size = std::get<1>(tuple); + for (int32_t i = 0; i < input_size; i++) { + auto input_s_ptr = (*user_conf->mutable_input())[input_key].mutable_s()->Add(); + if (auto result = operands[input_idx].dyn_cast()) { + *(input_s_ptr) = GetOutputLbn(result).getValue(); + } else if (auto argument = operands[input_idx].dyn_cast()) { + *(input_s_ptr) = "BlockArgument/" + std::to_string(argument.getArgNumber()); + } else { + LOG(FATAL) << "fail to convert MLIR result to protobuf, op_type_name: " + + op_type_name.str(); + return failure(); + } + input_idx += 1; + } + } + return success(); +} + +::oneflow::ShapeProto getAttrAsShape(mlir::Attribute& attr) { + ::oneflow::ShapeProto shape{}; + for (auto v : attr.dyn_cast().getValue()) { + shape.add_dim(v.dyn_cast().getSInt()); + } + return shape; +} + +::oneflow::Int64ListProto getAttrAsStride(mlir::Attribute& attr) { + ::oneflow::Int64ListProto stride{}; + for (auto v : attr.dyn_cast().getValue()) { + stride.add_dim(v.dyn_cast().getSInt()); + } + return stride; +} + +::oneflow::ParallelConf getParallelConfFromAttrDictionary(DictionaryAttr attributes) { + ::oneflow::ParallelConf parallel_conf{}; + auto device_tag = attributes.get(OpTrait::IsOpConfCompatible::getDeviceTagAttr()) + .dyn_cast_or_null(); + CHECK(device_tag) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceTagAttr().str(); + parallel_conf.set_device_tag(device_tag.str()); + auto device_name = attributes.get(OpTrait::IsOpConfCompatible::getDeviceNameAttr()) + .dyn_cast_or_null(); + CHECK(device_name) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceNameAttr().str(); + for (auto s : device_name.getValue()) { + parallel_conf.add_device_name(s.cast().str()); + } + if (auto hierarchy = attributes.get(OpTrait::IsOpConfCompatible::getHierarchyAttr()) + .dyn_cast_or_null()) { + for (auto dim : hierarchy.getValue()) { + parallel_conf.mutable_hierarchy()->add_dim(dim.template dyn_cast().getInt()); + } + } + return parallel_conf; +} + +::oneflow::ParallelConf getParallelConfFromAttrs(Attribute device_name_attr, + Attribute device_tag_attr) { + ::oneflow::ParallelConf parallel_conf{}; + auto device_tag = device_tag_attr.dyn_cast_or_null(); + CHECK(device_tag) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceTagAttr().str(); + parallel_conf.set_device_tag(device_tag.str()); + auto device_name = device_name_attr.dyn_cast_or_null(); + CHECK(device_name) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceNameAttr().str(); + for (auto s : device_name.getValue()) { + parallel_conf.add_device_name(s.cast().str()); + } + return parallel_conf; +} + +::oneflow::DeviceType getDeviceTypeFromAttrDictionary(DictionaryAttr attributes) { + ::oneflow::ParallelConf parallel_conf{}; + auto device_tag = attributes.get(OpTrait::IsOpConfCompatible::getDeviceTagAttr()) + .dyn_cast_or_null(); + CHECK(device_tag) << "attr absent: " + << OpTrait::IsOpConfCompatible::getDeviceTagAttr().str(); + if (device_tag.str() == "cpu") { + return ::oneflow::DeviceType::kCPU; + } else if (device_tag.str() == "cuda") { + return ::oneflow::DeviceType::kCUDA; + } else { + LOG(FATAL) << "unsupported device tag: " << device_tag.str(); + return ::oneflow::DeviceType::kInvalidDevice; + } +} + +::oneflow::AttrType queryAttrType(const std::string& op_type_name, const std::string& attr_name) { + ::oneflow::user_op::UserOpDefWrapper op_def(support::getUserOpDef(op_type_name)); + CHECK(op_def.IsAttrName(attr_name)) << attr_name << " not a attr name for op: " << op_type_name; + return op_def.GetAttrType(attr_name); +} + +} // namespace user_op + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/lib/OneFlow/UserOpReflection.cpp b/oneflow/ir/lib/OneFlow/UserOpReflection.cpp new file mode 100644 index 0000000000000000000000000000000000000000..54fafeb5b6d5b0d9b7c5b7b6fc4162488ddd7d42 --- /dev/null +++ b/oneflow/ir/lib/OneFlow/UserOpReflection.cpp @@ -0,0 +1,352 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +// this file should contains functions to get operands and results with user op name and index + +#include "OneFlow/UserOpReflection.h" +#include "llvm/ADT/STLExtras.h" +#include "llvm/Support/Casting.h" + +namespace mlir { + +namespace oneflow { + +namespace user_op { + +template class Trait> +const std::vector* GetFullKeys(UserOpCompatible& uc, Operation* op); +template class Trait> +std::vector GetFullKeys(UserOp op); +template class Trait> +std::vector GetFullKeys(::llvm::StringRef op_type_name); + +template<> +const std::vector* GetFullKeys(UserOpCompatible& uc, + Operation* op) { + if (auto alternative_name = dyn_cast(op)) { + return alternative_name.inputKeys(); + } + return uc.inputKeys(); +} + +template<> +const std::vector* GetFullKeys(UserOpCompatible& uc, + Operation* op) { + if (auto alternative_name = dyn_cast(op)) { + return alternative_name.outputKeys(); + } + return uc.outputKeys(); +} + +template<> +std::vector GetFullKeys(UserOp op) { + return mlir::oneflow::support::GetInputKeys(op.op_type_name().str()); +} + +template<> +std::vector GetFullKeys(UserOp op) { + return mlir::oneflow::support::GetOutputKeys(op.op_type_name().str()); +} + +template<> +std::vector GetFullKeys( + ::llvm::StringRef op_type_name) { + return mlir::oneflow::support::GetInputKeys(op_type_name.str()); +} + +template<> +std::vector GetFullKeys( + ::llvm::StringRef op_type_name) { + return mlir::oneflow::support::GetOutputKeys(op_type_name.str()); +} + +template class Trait> +std::pair getODSIndexAndLength(UserOpCompatible& op, unsigned index); + +template<> +std::pair getODSIndexAndLength( + UserOpCompatible& op, unsigned index) { + return op.getODSOperandIndexAndLength(index); +} + +template<> +std::pair getODSIndexAndLength( + UserOpCompatible& op, unsigned index) { + return op.getODSResultIndexAndLength(index); +} + +template class Trait> +StringRef GetSegmentSizeAttr(); + +template<> +StringRef GetSegmentSizeAttr() { + return OpTrait::AttrSizedOperandSegments::getOperandSegmentSizeAttr(); +} + +template<> +StringRef GetSegmentSizeAttr() { + return OpTrait::AttrSizedResultSegments::getResultSegmentSizeAttr(); +} + +template class Trait> +int32_t GetSingleSegmentSize(Operation*); + +template<> +int32_t GetSingleSegmentSize(Operation* op) { + return op->getNumOperands(); +} + +template<> +int32_t GetSingleSegmentSize(Operation* op) { + return op->getNumResults(); +} + +template class Trait> +ArrayAttr GetUserOpArgSizes(UserOp); + +template<> +ArrayAttr GetUserOpArgSizes(UserOp op) { + return op.input_sizes(); +} + +template<> +ArrayAttr GetUserOpArgSizes(UserOp op) { + return op.output_sizes(); +} + +template class Trait> +LogicalResult GetUserOpFilteredSegmentKeyAndSizes(UserOp op, std::vector& keys, + std::vector& sizes) { + auto full_keys = GetFullKeys(op); + for (const auto& key_size_tuple : llvm::zip(full_keys, GetUserOpArgSizes(op).getValue())) { + const std::string& key = std::get<0>(key_size_tuple); + const int32_t size = + std::get<1>(key_size_tuple).template cast().getValue().getSExtValue(); + if (size > 0) { + keys.push_back(key); + sizes.push_back(size); + } + } + return success(); +} + +Source GetOpSourceByName(Operation* op, const std::string& to_find) { + if (auto user_op = dyn_cast(op)) { + auto found = [&](std::vector keys, + bool find_in_results /*or in operands*/ = false) -> int { + auto offset = 0; + for (const auto& key : llvm::enumerate(keys)) { + if (key.value() == to_find) { return offset; } + offset += find_in_results ? user_op.getODSResultIndexAndLength(key.index()).second + : user_op.getODSOperandIndexAndLength(key.index()).second; + } + return -1; + }; + + if (auto alternative_name = dyn_cast(op)) { + if (auto offset = found(*alternative_name.inputKeys()); offset != -1) { + return {Source::INPUT, offset}; + } + if (auto offset = found(*alternative_name.outputKeys(), true); offset != -1) { + return {Source::OUTPUT, offset}; + } + } + + if (to_find == "tmp_buffer") { return {Source::BUFFER, 0}; } + + if (auto offset = found(*user_op.inputKeys()); offset != -1) { return {Source::INPUT, offset}; } + if (auto offset = found(*user_op.outputKeys(), true); offset != -1) { + return {Source::OUTPUT, offset}; + } + + op->emitError(to_find + " not found in this op"); + return {Source::INVALID, -1}; + } + op->emitError("Not support op which is not user op"); + return {Source::INVALID, -1}; +} + +template class Trait> +LogicalResult GetFilteredSegmentKeyAndSizes(Operation* op, std::vector& keys, + std::vector& sizes) { + if (auto user_op = dyn_cast(op)) { + return GetUserOpFilteredSegmentKeyAndSizes(user_op, keys, sizes); + } + const std::vector* full_keys = nullptr; + std::vector full_sizes{}; + auto uc = dyn_cast(op); + if (!uc) { + op->emitError("interface UserOpCompatible not supported"); + return failure(); + } + full_keys = GetFullKeys(uc, op); + if (op->hasTrait()) { + const StringRef attr_name = GetSegmentSizeAttr(); + const DenseIntElementsAttr& size_attr = op->getAttrOfType(attr_name); + if (!size_attr) return failure(); + auto segment_sizes = size_attr.getValues(); + if (full_keys->size() != segment_sizes.size()) { + op->emitError() << "fail to convert op inputs, attr_name: " << attr_name + << ", full_keys: " << full_keys->size() + << ", segment_sizes: " << segment_sizes.size() << ", name: " << op->getName(); + op->dump(); + return failure(); + }; + full_sizes = {segment_sizes.begin(), segment_sizes.end()}; + } else { + if (full_keys->size() == 1) { + full_sizes.push_back(GetSingleSegmentSize(op)); + } else { + for (const auto& key : llvm::enumerate(*full_keys)) { + full_sizes.push_back(getODSIndexAndLength(uc, key.index()).second); + } + } + } + for (const auto& key_size_tuple : llvm::zip(*full_keys, full_sizes)) { + const std::string& key = std::get<0>(key_size_tuple); + const int32_t size = std::get<1>(key_size_tuple); + if (size > 0) { + keys.push_back(key); + sizes.push_back(size); + } + } + return success(); +} + +template class Trait> +LogicalResult GetFilteredSegmentKeyAndSizes(llvm::StringRef op_type_name, size_t valueSize, + DictionaryAttr attributes, + std::vector& keys, + std::vector& sizes) { + const std::vector full_keys = GetFullKeys(op_type_name); + std::vector full_sizes{}; + const StringRef attr_name = GetSegmentSizeAttr(); + if (auto size_attr = attributes.get(attr_name).dyn_cast_or_null()) { + if (!size_attr) return failure(); + auto segment_sizes = size_attr.getValues(); + if (full_keys.size() != segment_sizes.size()) { + LOG(FATAL) << "fail to convert op inputs, attr_name: " << attr_name.str() + << ", full_keys: " << full_keys.size() + << ", segment_sizes: " << segment_sizes.size(); + return failure(); + }; + full_sizes = {segment_sizes.begin(), segment_sizes.end()}; + } else { + if (full_keys.size() == 1) { + full_sizes.push_back(valueSize); + } else { + LOG(FATAL) << "set attr: " << attr_name.str(); + } + } + for (const auto& key_size_tuple : llvm::zip(full_keys, full_sizes)) { + const std::string& key = std::get<0>(key_size_tuple); + const int32_t size = std::get<1>(key_size_tuple); + if (size > 0) { + keys.push_back(key); + sizes.push_back(size); + } + } + return success(); +} + +template LogicalResult GetFilteredSegmentKeyAndSizes( + Operation* op, std::vector& keys, std::vector& sizes); +template LogicalResult GetFilteredSegmentKeyAndSizes( + Operation* op, std::vector& keys, std::vector& sizes); +template LogicalResult GetFilteredSegmentKeyAndSizes( + llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes, + std::vector& keys, std::vector& sizes); +template LogicalResult GetFilteredSegmentKeyAndSizes( + llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes, + std::vector& keys, std::vector& sizes); + +template class Trait> +ArgIds::ArgIds(Operation* op) { + std::vector keys; + std::vector sizes; + if (failed(GetFilteredSegmentKeyAndSizes(op, keys, sizes))) { + op->emitError("fail to get filtered segment key and sizes"); + exit(1); + } + for (int i = 0; i < keys.size(); i += 1) { + auto& key = keys[i]; + for (size_t j = 0; j < sizes[i]; j += 1) { + ArgID id{key, j}; + ids_.push_back(id); + } + } +} + +template class Trait> +ArgIds::ArgIds(llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes) { + std::vector keys{}; + std::vector sizes{}; + CHECK(user_op::GetFilteredSegmentKeyAndSizes(op_type_name, valueSize, attributes, keys, + sizes) + .succeeded()); + for (int i = 0; i < keys.size(); i += 1) { + auto& key = keys[i]; + for (size_t j = 0; j < sizes[i]; j += 1) { + ArgID id{key, j}; + ids_.push_back(id); + } + } +} + +template oneflow::user_op::ArgIds::ArgIds(Operation*); +template oneflow::user_op::ArgIds::ArgIds(Operation*); +template oneflow::user_op::ArgIds::ArgIds( + llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes); +template oneflow::user_op::ArgIds::ArgIds( + llvm::StringRef op_type_name, size_t valueSize, DictionaryAttr attributes); + +llvm::Optional GetOutputLbn(OpResult result) { + const auto def_op = result.getDefiningOp(); + if (def_op->hasTrait()) { + return def_op + ->getAttrOfType( + OpTrait::IsImportCompatible::getOutputLBNsAttr())[result.getResultNumber()] + .dyn_cast() + .getValue() + .str(); + } else { + std::vector def_op_keys{}; + std::vector def_op_sizes{}; + if (failed(user_op::GetFilteredSegmentKeyAndSizes( + def_op, def_op_keys, def_op_sizes))) { + def_op->emitError("fail to get output lbn"); + return llvm::None; + } + const auto result_number = result.getResultNumber(); + uint32_t size_sum = 0; + for (const auto& name_size_tuple : llvm::zip(def_op_keys, def_op_sizes)) { + auto name = std::get<0>(name_size_tuple); + auto size = std::get<1>(name_size_tuple); + if ((size_sum + size) > result_number) { + const uint32_t bn_i = result_number - size_sum; + return OpTrait::IsOpConfCompatible::getOpName(def_op).str() + "/" + name + "_" + + std::to_string(bn_i); + } + size_sum += size; + } + } + return llvm::None; +} + +} // namespace user_op + +} // namespace oneflow + +} // namespace mlir diff --git a/oneflow/ir/oneflow-extension/CMakeLists.txt b/oneflow/ir/oneflow-extension/CMakeLists.txt index e7e2f1fbd18ac7e487054cd1acf855abab1ae4dd..727c0fcf3265d573fe1a085df4eeca92311ed08f 100644 --- a/oneflow/ir/oneflow-extension/CMakeLists.txt +++ b/oneflow/ir/oneflow-extension/CMakeLists.txt @@ -1,7 +1,21 @@ +include_directories(${PROJECT_SOURCE_DIR}/oneflow-extension/include) + oneflow_add_mlir_library( MLIROneFlowExtension - extension.cpp + mlir_jit_op.cpp + mlir_jit_op_infer.cpp + mlir_jit_op_kernel.cpp + kernel_launch/kernel_launch_op.cpp + kernel_launch/kernel_launch_state.cpp + kernel_launch/launcher_context.cpp + kernel_launch/reg_context.cpp + kernel_launch/run_context.cpp + kernel_launch/infer_context.cpp + kernel_launch/tmp_buffer_manager.cpp ir_pass.cpp + lr_jit.cpp + mlir_gen.cpp + jit_engine.cpp DEPENDS LINK_LIBS PUBLIC diff --git a/oneflow/ir/oneflow-extension/README.md b/oneflow/ir/oneflow-extension/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d6cae49a9a40a613c18294fb2e10cf5aff4bbf03 --- /dev/null +++ b/oneflow/ir/oneflow-extension/README.md @@ -0,0 +1,21 @@ +# OneFlow extension of MLIR features] + +## KernelLaunchOp + +### Stage 1 + +- 1:1 conversion from user op to kernel launch op + +### Stage 2 + +- multi user op merged into one single kernel launch op + +### Stage 3 + +- oneflow-opt and similar non-python execution environment +- multi-gpu/multi-node compilation support (in the beginning it is all single-node with broadcast SBP signature) + +### relationship with MlirJitOp + +- the graph of a MlirJitOp might contain one or multiple kernel launch op +- an op inside the graph of MlirJitOp could be optionally lowered to a kernel launch op diff --git a/oneflow/ir/oneflow-extension/extension.cpp b/oneflow/ir/oneflow-extension/extension.cpp deleted file mode 100644 index 9954ed6dd8ddf3e2fbf5892a5d0578ddb89d6b91..0000000000000000000000000000000000000000 --- a/oneflow/ir/oneflow-extension/extension.cpp +++ /dev/null @@ -1,264 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "mlir/Parser/Parser.h" -#include "mlir/Dialect/Func/IR/FuncOps.h" -#include "mlir/Dialect/Linalg/IR/Linalg.h" -#include "mlir/ExecutionEngine/ExecutionEngine.h" -#include "mlir/ExecutionEngine/MemRefUtils.h" -#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" -#include "llvm/Support/TargetSelect.h" -#include "OneFlow/OneFlowDialect.h" -#include "oneflow/core/common/str_util.h" -#include "oneflow/core/common/switch_func.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/persistence/tee_persistent_log_stream.h" -#include "oneflow/ir/include/OneFlow/Passes.h" -#include "oneflow/ir/include/OneFlow/Extension.h" - -namespace oneflow { - -SharedLibs* MutSharedLibPaths() { - static SharedLibs libs = {}; - return &libs; -} - -const SharedLibs* SharedLibPaths() { return MutSharedLibPaths(); } - -namespace { - -REGISTER_USER_OP("mlir_jit") - .Attr("mlir_assembly") - .Input("in") - .Output("out") - .SetTensorDescInferFn([](user_op::InferContext* ctx) -> Maybe { - // TODO: infer shape by extracting Ops from mlir_assembly - CHECK_EQ(ctx->inputs().size(), 2); - CHECK_EQ(ctx->outputs().size(), 1); - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 1); - return Maybe::Ok(); - }) - .SetGetSbpFn([](user_op::SbpContext* ctx) -> Maybe { - const user_op::TensorDesc& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); - FOR_RANGE(int64_t, i, 0, in_tensor.shape().NumAxes()) { - ctx->NewBuilder() - .Split(user_op::OpArg("in", 0), i) - .Split(user_op::OpArg("out", 0), i) - .Build(); - } - return Maybe::Ok(); - }) - .SetDataTypeInferFn([](user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - return Maybe::Ok(); - }); - -using OpaqueMemRefDescriptor = std::shared_ptr; - -template -OpaqueMemRefDescriptor CreateMemRefDescriptor(user_op::Tensor* tensor) { - using MemRefType = StridedMemRefType; - auto desc = new MemRefType(); - *desc = mlir::detail::makeStridedMemRefDescriptor( - tensor->dptr(), tensor->dptr(), - {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}, - {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}); - auto deleter = [](void const* data) { - auto p = static_cast(data); - delete p; - }; - return OpaqueMemRefDescriptor(desc, deleter); -} - -template -OpaqueMemRefDescriptor CreateMutMemRefDescriptor(user_op::Tensor* tensor) { - using MemRefType = StridedMemRefType; - auto desc = new MemRefType(); - *desc = mlir::detail::makeStridedMemRefDescriptor( - tensor->mut_dptr(), tensor->mut_dptr(), - {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}, - {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}); - auto deleter = [](void const* data) { - auto p = static_cast(data); - delete p; - }; - return OpaqueMemRefDescriptor(desc, deleter); -} - -#define MAKE_STRIDED_MEM_REF_SWITCH_ENTRY(func_name, N, T) func_name -DEFINE_STATIC_SWITCH_FUNC(OpaqueMemRefDescriptor, CreateMemRefDescriptor, - MAKE_STRIDED_MEM_REF_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ), - MAKE_DATA_TYPE_CTRV_SEQ(ARITHMETIC_DATA_TYPE_SEQ)); -DEFINE_STATIC_SWITCH_FUNC(OpaqueMemRefDescriptor, CreateMutMemRefDescriptor, - MAKE_STRIDED_MEM_REF_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ), - MAKE_DATA_TYPE_CTRV_SEQ(ARITHMETIC_DATA_TYPE_SEQ)); -#undef MAKE_STRIDED_MEM_REF_SWITCH_ENTRY - -std::string GetMLIRCInterface(const std::string& func_name) { - return std::string("_mlir_ciface_") + func_name; -} - -llvm::SmallVector GetMLIRCInterfaceArgs( - user_op::KernelComputeContext* ctx) { - llvm::SmallVector args{}; - for (auto& pair : ctx->inputs()) { - auto tensor = ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); - auto ref = SwitchCreateMemRefDescriptor( - SwitchCase(tensor->shape_view().NumAxes(), tensor->data_type()), tensor); - args.push_back(ref); - } - for (auto& pair : ctx->outputs()) { - auto tensor = ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); - auto ref = SwitchCreateMutMemRefDescriptor( - SwitchCase(tensor->shape_view().NumAxes(), tensor->data_type()), tensor); - args.push_back(ref); - } - return args; -} - -void WithMlirContext( - user_op::KernelComputeContext* ctx, const llvm::SmallVector& ext_libs, - const std::function(mlir::MLIRContext* mlir_ctx)>& parse, - const std::function& lower) { - mlir::DialectRegistry registry; - registry - .insert(); - mlir::registerLLVMDialectTranslation(registry); - mlir::MLIRContext mlir_ctx(registry); - mlir::OwningOpRef module = parse(&mlir_ctx); - CHECK(!!module) << "fail to parse MLIR, op: " << ctx->op_name(); - if (ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false)) { module->print(llvm::outs()); } - llvm::InitializeNativeTarget(); - llvm::InitializeNativeTargetAsmPrinter(); - lower(&mlir_ctx, *module); - if (ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false)) { module->print(llvm::outs()); } - if (ParseBooleanFromEnv("ONEFLOW_MLIR_DUMP_IR", false)) { - std::string mlir; - llvm::raw_string_ostream os_mlir(mlir); - module->print(os_mlir); - TeePersistentLogStream::Create(JoinPath("jit", ctx->op_name() + ".mlir"))->Write(mlir); - } - - mlir::ExecutionEngineOptions jitOptions; - jitOptions.transformer = {}; - jitOptions.jitCodeGenOptLevel = llvm::None; - jitOptions.sharedLibPaths = ext_libs; - - auto jit_or_error = mlir::ExecutionEngine::create(*module, jitOptions); - CHECK(!!jit_or_error) << "failed to create JIT exe engine, " - << llvm::toString(jit_or_error.takeError()); - auto jit = std::move(jit_or_error.get()); - llvm::SmallVector args /* args must outlive JIT invocation */ = - GetMLIRCInterfaceArgs(ctx); - llvm::SmallVector packed_args{}; - for (auto& arg /* arg must be a reference*/ : args) { packed_args.push_back(&arg); } - auto error = jit->invokePacked(GetMLIRCInterface(ctx->op_name()), packed_args); - CHECK(!error) << "fail to invoke jit engine, error: " << llvm::toString(std::move(error)); -} - -template -class MlirJitCpuKernel final : public user_op::OpKernel { - public: - MlirJitCpuKernel() = default; - ~MlirJitCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - llvm::SmallVector ext_libs( - {SharedLibPaths()->begin(), SharedLibPaths()->end()}); - WithMlirContext( - ctx, ext_libs, - [&ctx](mlir::MLIRContext* mlir_ctx) { - return mlir::parseSourceString(ctx->Attr("mlir_assembly"), - mlir_ctx); - }, - [](mlir::MLIRContext* mlir_ctx, mlir::ModuleOp module) { - CHECK(mlir::succeeded(mlir::oneflow::LowerModuleToLLVM(mlir_ctx, module))) - << "fail to lower OneFlow to LLVM"; - }); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MLIR_JIT_CPU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("mlir_jit") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - return Maybe::Ok(); \ - }); - -REGISTER_MLIR_JIT_CPU_KERNEL(float) -REGISTER_MLIR_JIT_CPU_KERNEL(double) -REGISTER_MLIR_JIT_CPU_KERNEL(int32_t) -REGISTER_MLIR_JIT_CPU_KERNEL(int64_t) - -#undef REGISTER_MLIR_JIT_CPU_KERNEL - -#ifdef WITH_MLIR_CUDA_CODEGEN - -template -class MlirJitGpuKernel final : public user_op::OpKernel { - public: - MlirJitGpuKernel() = default; - ~MlirJitGpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - llvm::SmallVector ext_libs( - {SharedLibPaths()->begin(), SharedLibPaths()->end()}); - WithMlirContext( - ctx, ext_libs, - [&ctx](mlir::MLIRContext* mlir_ctx) { - return mlir::parseSourceString(ctx->Attr("mlir_assembly"), - mlir_ctx); - }, - [](mlir::MLIRContext* mlir_ctx, mlir::ModuleOp module) { - CHECK(mlir::succeeded(mlir::oneflow::LowerModuleToCUDALLVM(mlir_ctx, module))) - << "fail to lower OneFlow to CUDA LLVM"; - }); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MLIR_JIT_GPU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("mlir_jit") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - return Maybe::Ok(); \ - }); - -REGISTER_MLIR_JIT_GPU_KERNEL(float) -REGISTER_MLIR_JIT_GPU_KERNEL(double) -REGISTER_MLIR_JIT_GPU_KERNEL(int32_t) -REGISTER_MLIR_JIT_GPU_KERNEL(int64_t) - -#undef REGISTER_MLIR_JIT_GPU_KERNEL - -#endif // WITH_MLIR_CUDA_CODEGEN - -} // namespace - -} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/JITOpInfer.h b/oneflow/ir/oneflow-extension/include/OneFlow/JITOpInfer.h new file mode 100644 index 0000000000000000000000000000000000000000..36390954b7bff6c02d833fdee7a4dc77d7d35438 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/JITOpInfer.h @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITOPINFER_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITOPINFER_H_ + +#include "oneflow/core/framework/infer_util.h" + +namespace oneflow { + +namespace ir { + +namespace jit { + +Maybe InferTensorDesc(user_op::InferContext* ctx); +Maybe SetTensorDataType(user_op::InferContext* ctx); + +} // namespace jit + +} // namespace ir + +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITOPINFER_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowLRJITRegistry.h b/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowLRJITRegistry.h new file mode 100644 index 0000000000000000000000000000000000000000..98504e67ed4595e090d3474f28a011e80af50412 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowLRJITRegistry.h @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ONEFLOW_LRJIT_REGISTRY_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ONEFLOW_LRJIT_REGISTRY_H_ + +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/singleton.h" +#include "oneflow/core/common/util.h" +#include "oneflow/ir/oneflow-extension/include/PyAst/Ast.h" + +#include +#include +#include +#include +#include +#include + +namespace mlir { +class ExecutionEngine; +} + +typedef std::pair, std::function> + LRJITRegistry_Store_; + +class LRJITRegistry final { + public: + OF_DISALLOW_COPY_AND_MOVE(LRJITRegistry); + ~LRJITRegistry() = default; + + void Register(const std::string& function_id, pyast::FunctionDef& ast, bool is_dump); + std::function LookUp(const std::string& function_id); + + private: + friend class oneflow::Singleton; + LRJITRegistry() = default; + + std::unordered_map functionId2engine_; +}; + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ONEFLOW_LRJIT_REGISTRY_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h b/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h index 5bde75e233351fa0ae60ce348c0e70609e60e045..afa99f1070fca9e0a2f7a89b7d42931802b5bbd0 100644 --- a/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h +++ b/oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h @@ -13,6 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ROUNDTRIP_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ROUNDTRIP_H_ + #include "oneflow/core/job_rewriter/job_pass.h" namespace oneflow { @@ -29,3 +32,5 @@ class IRRoundTrip final : public JobPass { }; } // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_ROUNDTRIP_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InferContext.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InferContext.h new file mode 100644 index 0000000000000000000000000000000000000000..cc5f2ce64e046bc0d4f93e1712af4b03bbc889da --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InferContext.h @@ -0,0 +1,126 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_INFERCONTEXT_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_INFERCONTEXT_H_ + +#include "oneflow/core/kernel/kernel_context.h" +#include "oneflow/core/kernel/user_kernel.h" +#include "OneFlow/kernel_launch/KernelLaunchState.h" +#include "OneFlow/kernel_launch/RegContext.h" + +#include +#include + +namespace oneflow { +namespace okl { + +class InferContext final : public user_op::InferContext { + public: + static size_t InferTmpSize(user_op::InferContext* ctx); + + explicit InferContext(RegContext* reg_ctx); + + const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return *LogicalTensorDesc4ArgNameAndIndex(arg_name, index); + } + const user_op::TensorDesc& OutputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return *LogicalTensorDesc4ArgNameAndIndex(arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(const std::string&, int32_t) override { TODO(); } + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override; + + const Shape& InputShape(const std::string& arg_name, int32_t index) const override; + + const Shape& OutputShape(const std::string&, int32_t) const override { TODO(); } + void SetOutputShape(const std::string&, int32_t, const Shape&) override { TODO(); } + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override; + void SetShape4ArgNameAndIndex(const std::string&, int32_t, const Shape&) override { TODO(); } + const Stride& InputStride(const std::string&, int32_t) const override { TODO(); } + const Stride& OutputStride(const std::string&, int32_t) const override { TODO(); } + void SetOutputStride(const std::string&, int32_t, const Stride&) override { TODO(); } + const Stride& Stride4ArgNameAndIndex(const std::string&, int32_t) const override { TODO(); } + void SetStride4ArgNameAndIndex(const std::string&, int32_t, const Stride&) override { TODO(); } + DataType InputDType(const std::string&, int32_t) const override { TODO(); } + DataType OutputDType(const std::string&, int32_t) const override { TODO(); } + void SetOutputDType(const std::string&, int32_t, DataType) override { TODO(); } + DataType Dtype4ArgNameAndIndex(const std::string&, int32_t) const override { TODO(); } + void SetDtype4ArgNameAndIndex(const std::string&, int32_t, DataType) override { TODO(); } + + const std::vector>& inputs() const override { + return reg_ctx_->inputs(); + } + const std::vector>& outputs() const override { + return reg_ctx_->outputs(); + } + + const std::string& input(const std::string& arg_name, int32_t index) const override { + return reg_ctx_->user_op_conf().input(arg_name, index); + } + const std::string& output(const std::string& arg_name, int32_t index) const override { + return reg_ctx_->user_op_conf().output(arg_name, index); + } + + bool has_input(const std::string& arg_name, int32_t index) const override { + return reg_ctx_->user_op_conf().has_input(arg_name, index); + } + bool has_output(const std::string& arg_name, int32_t index) const override { + return reg_ctx_->user_op_conf().has_input(arg_name, index); + } + + int32_t input_size(const std::string& arg_name) const override { + return reg_ctx_->user_op_conf().input_size(arg_name); + } + int32_t output_size(const std::string& arg_name) const override { + return reg_ctx_->user_op_conf().output_size(arg_name); + } + const std::string& op_name() const override { return reg_ctx_->user_op_conf().op_name(); } + const std::string& op_type_name() const override { + return reg_ctx_->user_op_conf().op_type_name(); + } + const std::string& op_loc() const override { TODO(); } + + const ParallelContext& parallel_ctx() const override { TODO(); } + const ParallelDesc& parallel_desc() const override { TODO(); } + + const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string&, int32_t) const override { + TODO(); + } + + const NdSbp& NdSbp4ArgNameAndIndex(const std::string&, int32_t) const override { TODO(); } + + bool InputIsDynamic(const std::string&, int32_t) const override { TODO(); } + bool OutputIsDynamic(const std::string&, int32_t) const override { TODO(); } + void SetOutputIsDynamic(const std::string&, int32_t, bool) override { TODO(); } + bool IsDynamic4ArgNameAndIndex(const std::string&, int32_t) const override { TODO(); } + void SetIsDynamic4ArgNameAndIndex(const std::string&, int32_t, bool) override { TODO(); } + + int64_t parallel_num() const override { TODO(); } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override; + + RegContext* reg_ctx_; +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_INFERCONTEXT_H_ \ No newline at end of file diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InitContext.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InitContext.h new file mode 100644 index 0000000000000000000000000000000000000000..4d9eeb1d4d87e687889af116b504294f0e6838bd --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/InferMisc/InitContext.h @@ -0,0 +1,75 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_CACHECONTEXT_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_CACHECONTEXT_H_ + +#include "oneflow/core/common/tensor_desc.h" +#include "oneflow/core/common/util.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/kernel_launch/RunContext.h" + +namespace oneflow { +namespace okl { + +class InitContext final : public user_op::KernelCacheContext, public user_op::KernelInitContext { + public: + explicit InitContext(RunContext* run_ctx) + : run_ctx_(run_ctx), reg_ctx_(run_ctx->GetRegContext()) {} + + ep::Stream* stream() override { return run_ctx_->stream(); } + + DeviceType device_type() const override { return reg_ctx_->device_type(); } + const ParallelContext& parallel_ctx() const override { return run_ctx_->parallel_ctx(); } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return reg_ctx_->TensorDesc4ArgNameAndIndex(arg_name, index); + } + + const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string&, int32_t) const override { + TODO(); + } + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return reg_ctx_->TensorDesc4ArgNameAndIndex(arg_name, index); + } + const ParallelDesc& parallel_desc() const override { TODO(); } + const NdSbp& NdSbp4ArgNameAndIndex(const std::string&, int32_t) const override { TODO(); } + + const std::vector>& inputs() const override { + return reg_ctx_->inputs(); + } + const std::vector>& outputs() const override { + return reg_ctx_->outputs(); + } + + private: + RunContext* run_ctx_; + RegContext* reg_ctx_; + + const user_op::UserOpConfWrapper& user_op_conf() const override { + return reg_ctx_->user_op_conf(); + } + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return reg_ctx_->Attr4Name(attr_name); + } +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_INFERMISC_CACHECONTEXT_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/JITEngine.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/JITEngine.h new file mode 100644 index 0000000000000000000000000000000000000000..c1b6e5518a5faa30e3b8c6dc1b83103f48e525c2 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/JITEngine.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITENGINE_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITENGINE_H_ + +#include "mlir/ExecutionEngine/ExecutionEngine.h" +#include "mlir/IR/BuiltinOps.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/kernel_launch/LauncherContext.h" + +namespace oneflow { +namespace okl { + +using FetchArgs = std::tuple; +using LaunchArgs = std::tuple; + +class JITEngine { + public: + explicit JITEngine(mlir::ModuleOp module); + void Run(const std::string& name, LauncherContext* launcher) const { + auto error = engine_->invoke(name, launcher); + CHECK(!error) << "fail to invoke jit engine, error: " << llvm::toString(std::move(error)); + } + + private: + std::unique_ptr engine_; +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_JITENGINE_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/KernelLaunchState.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/KernelLaunchState.h new file mode 100644 index 0000000000000000000000000000000000000000..bed6ec992f7ab7634cd443faa535d540fc88f066 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/KernelLaunchState.h @@ -0,0 +1,60 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_OP_KERNEL_STATE_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_OP_KERNEL_STATE_H_ + +#include "OneFlow/OKL/Conversion/Conversion.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/IR/DialectRegistry.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OKL/OKLDialect.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "OneFlow/kernel_launch/JITEngine.h" +#include "OneFlow/kernel_launch/LauncherContext.h" + +namespace oneflow { +namespace okl { + +class KernelLaunchState final : public user_op::OpKernelState { + public: + static mlir::DialectRegistry GetRegistry() { + mlir::DialectRegistry registry; + registry.insert(); + mlir::registerLLVMDialectTranslation(registry); + return registry; + } + + explicit KernelLaunchState(user_op::KernelInitContext* ctx); + ~KernelLaunchState() = default; + + void DoCompute(user_op::KernelComputeContext* ctx); + + private: + mlir::MLIRContext mlir_ctx_; + mlir::OwningOpRef module_; + std::shared_ptr launcher_context_{}; + std::shared_ptr engine_{}; + void LazyInitLauncher(user_op::KernelComputeContext* ctx); +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_OP_KERNEL_STATE_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/LauncherContext.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/LauncherContext.h new file mode 100644 index 0000000000000000000000000000000000000000..56d4618ddac54a31e270a130b7ab94e793a45136 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/LauncherContext.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_LAUNCHER_CONTEXT_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_LAUNCHER_CONTEXT_H_ + +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/Operation.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/kernel_launch/RegContext.h" +#include "OneFlow/kernel_launch/RunContext.h" + +namespace oneflow { +namespace okl { + +class LauncherContext final { + using RegContextResource = std::shared_ptr; + using RunContextResource = std::shared_ptr; + + public: + explicit LauncherContext(user_op::KernelComputeContext* compute_context, mlir::ModuleOp module); + + void* FetchKernel(int index); + void* FetchRunCtx(int index); + + private: + std::vector kernel_vec_; + std::vector reg_ctx_vec_; + std::vector run_ctx_vec_; + ::mlir::ModuleOp module_; +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_LAUNCHER_CONTEXT_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RegContext.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RegContext.h new file mode 100644 index 0000000000000000000000000000000000000000..44dec1e34613c99d73c1f07ffcd79cd65ebb318b --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RegContext.h @@ -0,0 +1,70 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_REGCONTEXT_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_REGCONTEXT_H_ + +#include "OneFlow/UserOpReflection.h" +#include "oneflow/core/framework/user_op_kernel_registry.h" +#include "oneflow/core/register/blob.h" +#include "mlir/IR/OpDefinition.h" +#include "mlir/IR/Operation.h" + +#include +#include +#include +namespace oneflow { +namespace okl { +// this context should support querying information about the kernel from representation in MLIR +using ArgVec = std::vector>; +class RegContext final : public user_op::KernelRegContext { + public: + explicit RegContext(mlir::Operation* op); + ~RegContext() = default; + + DeviceType device_type() const override; + const ParallelContext& parallel_ctx() const override; + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override; + const ArgVec& inputs() const override; + const ArgVec& outputs() const override; + + const user_op::UserOpConfWrapper& user_op_conf() const override; + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override; + + ::mlir::Operation* GetOp() const { return op_; }; + const user_op::OpKernel* GetKernel() const { return kernel_; }; + + size_t GetTmpBufferSize(); + + private: + friend class RunContext; + + ::mlir::Operation* op_; + DeviceType device_type_ = DeviceType::kInvalidDevice; + std::unordered_map arg2tensor_desc_{}; + ArgVec inputs_; + ArgVec outputs_; + user_op::UserOpConfWrapper conf_wrapper_; + + const user_op::OpKernelRegistryResult* reg_res_ = nullptr; + const user_op::OpKernel* kernel_ = nullptr; +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_REGCONTEXT_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RunContext.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RunContext.h new file mode 100644 index 0000000000000000000000000000000000000000..774df31f1385fa5b2b7dbea9601a5c447a36961e --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/RunContext.h @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_RUNCONTEXT_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_RUNCONTEXT_H_ + +#include "mlir/IR/BuiltinAttributes.h" +#include "OneFlow/kernel_launch/RegContext.h" +#include "OneFlow/kernel_launch/TmpBufferManager.h" +#include "OneFlow/OKL/OKLOps.h" + +namespace oneflow { +namespace okl { +class RunContext final : public user_op::KernelComputeContext { + public: + explicit RunContext(std::shared_ptr reg, user_op::KernelComputeContext* comp); + ~RunContext() = default; + + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override; + user_op::Tensor* Tensor4ArgNameAndIndex(const std::string& arg_name, int32_t index) override; + + ep::Stream* stream() override; + + DeviceType device_type() const override; + const ParallelContext& parallel_ctx() const override; + + const ArgVec& inputs() const override; + const ArgVec& outputs() const override; + + const user_op::UserOpConfWrapper& user_op_conf() const override; + + RegContext* GetRegContext() { return reg_ctx_.get(); } + user_op::OpKernelState* FetchState() const { return kernel_state_.get(); } + user_op::OpKernelCache* FetchCache() const { return kernel_cache_.get(); } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override; + std::shared_ptr reg_ctx_; + KernelComputeContext* comp_ctx_ = nullptr; + std::unordered_map tensor_desc_{}; + + std::shared_ptr kernel_state_; + std::shared_ptr kernel_cache_; + std::shared_ptr tmp_buffer_manager_; +}; +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_RUNCONTEXT_H_ diff --git a/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/TmpBufferManager.h b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/TmpBufferManager.h new file mode 100644 index 0000000000000000000000000000000000000000..e2e642b475b31838c0883e445847fe949515cbaa --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/OneFlow/kernel_launch/TmpBufferManager.h @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_TMP_BUFFER_MANAGER_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_TMP_BUFFER_MANAGER_H_ +#include "oneflow/core/framework/infer_util.h" +#include "oneflow/core/framework/user_op_tensor.h" + +namespace oneflow { +namespace okl { + +class TmpBufferTensor final : public oneflow::user_op::Tensor { + public: + explicit TmpBufferTensor(user_op::Tensor* tensor) : tensor_(tensor) {} + ShapeView shape_view() const override { return tensor_->shape_view(); } + MutShapeView mut_shape_view() override { return tensor_->mut_shape_view(); } + const Stride& stride() const override { return tensor_->stride(); } + DataType data_type() const override { return tensor_->data_type(); } + const MemoryCase& mem_case() const override { return tensor_->mem_case(); } + + const void* raw_dptr() const override { + return (reinterpret_cast(tensor_->raw_dptr())); + } + void* mut_raw_dptr() override { return (reinterpret_cast(tensor_->mut_raw_dptr())); } + + private: + user_op::Tensor* tensor_; +}; + +class TmpBufferManager { + public: + explicit TmpBufferManager(user_op::Tensor* tensor) : tensor_(tensor) {} + + static std::shared_ptr InitTmpBufferManager(user_op::Tensor* tensor); + static size_t InferTmpSize(user_op::InferContext* ctx); + + user_op::Tensor* GetBufferTensor() { return &tensor_; } + + private: + TmpBufferTensor tensor_; +}; + +} // namespace okl +} // namespace oneflow + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_ONEFLOW_KERNEL_LAUNCH_TMP_BUFFER_MANAGER_H_ \ No newline at end of file diff --git a/oneflow/ir/oneflow-extension/include/PyAst/Ast.h b/oneflow/ir/oneflow-extension/include/PyAst/Ast.h new file mode 100644 index 0000000000000000000000000000000000000000..57c27ade588f31376d9b77d0d2c27814409ac118 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/PyAst/Ast.h @@ -0,0 +1,413 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_H_ + +#include +#include +#include +#include + +namespace pyast { + +using namespace std; +typedef string identifier; + +class arg { + identifier id; + + public: + explicit arg(const identifier& arg) : id(arg) {} + + identifier get_arg() { return id; } + + static shared_ptr arg_(const identifier& arg_) { return make_shared(arg_); } +}; + +class arguments { + vector> args; + + public: + explicit arguments(vector> args) : args(std::move(args)) {} + + vector> get_args() { return args; } + + static shared_ptr arguments_(vector> args) { + return make_shared(args); + } +}; + +class stmt { + public: + enum StmtKind { + kFunctionDef, + kReturn, + kAssign, + kIf, + kRaise, + kAssert, + kExpr, + }; + + explicit stmt(StmtKind kind) : kind(kind) {} + virtual ~stmt() = default; + + StmtKind get_kind() const { return kind; } + + private: + StmtKind kind; +}; + +class expr { + public: + enum ExprKind { + kBoolOp, + kBinOp, + kLambda, + kCompare, + kCall, + kNum, + kConstant, + kAttribute, + kName, + }; + + explicit expr(ExprKind kind) : kind(kind) {} + virtual ~expr() = default; + + ExprKind get_kind() const { return kind; } + + private: + ExprKind kind; +}; + +class FunctionDef : public stmt { + identifier name; + shared_ptr args; + vector> body; + + public: + FunctionDef(identifier name, shared_ptr args, vector> body) + : stmt(kFunctionDef), name(std::move(name)), args(std::move(args)), body(std::move(body)) {} + + static shared_ptr FunctionDef_(identifier name, shared_ptr args, + vector> body) { + return make_shared(name, args, body); + } + + identifier get_name() { return name; } + shared_ptr get_args() { return args; } + vector> get_body() { return body; } + + static bool classof(const stmt* c) { return c->get_kind() == kFunctionDef; } +}; + +class Return : public stmt { + shared_ptr value; + + public: + explicit Return(shared_ptr value) : stmt(kReturn), value(std::move(value)) {} + + static shared_ptr Return_(shared_ptr value) { return make_shared(value); } + + shared_ptr get_value() { return value; } + + static bool classof(const stmt* c) { return c->get_kind() == kReturn; } +}; + +class Assign : public stmt { + vector> targets; + shared_ptr value; + + public: + Assign(vector> targets, shared_ptr value) + : stmt(kAssign), targets(std::move(targets)), value(std::move(value)) {} + + static shared_ptr Assign_(vector> targets, shared_ptr value) { + return make_shared(targets, value); + } + + shared_ptr get_value() { return value; } + vector> get_targets() { return targets; } + + static bool classof(const stmt* c) { return c->get_kind() == kAssign; } +}; + +class If : public stmt { + shared_ptr test; + vector> body; + vector> orelse; + + public: + If(shared_ptr test, vector> body, vector> orelse) + : stmt(kIf), test(std::move(test)), body(std::move(body)), orelse(orelse) {} + + static shared_ptr If_(shared_ptr test, vector> body, + vector> orelse) { + return make_shared(test, body, orelse); + } + + shared_ptr get_test() { return test; } + vector> get_body() { return body; } + vector> get_orelse() { return orelse; } + + static bool classof(const stmt* c) { return c->get_kind() == kIf; } +}; + +class Raise : public stmt { + shared_ptr exc; + shared_ptr cause; + + public: + Raise(shared_ptr exc, shared_ptr cause) + : stmt(kRaise), exc(std::move(exc)), cause(std::move(cause)) {} + + static shared_ptr Raise_(shared_ptr exc, shared_ptr cause) { + return make_shared(exc, cause); + } + + shared_ptr get_exc() { return exc; } + shared_ptr get_cause() { return cause; } + + static bool classof(const stmt* c) { return c->get_kind() == kRaise; } +}; + +class Assert : public stmt { + shared_ptr test; + shared_ptr msg; + + public: + Assert(shared_ptr test, shared_ptr msg) + : stmt(kAssert), test(std::move(test)), msg(std::move(msg)) {} + + static shared_ptr Assert_(shared_ptr test, shared_ptr msg) { + return make_shared(test, msg); + } + shared_ptr get_test() { return test; } + shared_ptr get_msg() { return msg; } + + static bool classof(const stmt* c) { return c->get_kind() == kAssert; } +}; + +class Expr : public stmt { + shared_ptr value; + + public: + explicit Expr(shared_ptr value) : stmt(kExpr), value(std::move(value)) {} + + static shared_ptr Expr_(shared_ptr value) { return make_shared(value); } + + shared_ptr get_value() { return value; } + + static bool classof(const stmt* c) { return c->get_kind() == kExpr; } +}; + +class BoolOp : public expr { + public: + enum boolop_t { + kAnd = 1, + kOr, + }; + BoolOp(boolop_t op, vector> values) + : expr(kBoolOp), op(op), values(std::move(values)) {} + + static shared_ptr BoolOp_(boolop_t op, vector> values) { + return make_shared(op, values); + } + + boolop_t get_op() { return op; } + vector> get_values() { return values; } + + static bool classof(const expr* c) { return c->get_kind() == kBoolOp; } + + private: + boolop_t op; + vector> values; +}; + +class BinOp : public expr { + public: + enum operator_t { + kAdd = 1, + kSub, + kMult, + kDiv, + kPow, + }; + + BinOp(shared_ptr left, operator_t op, shared_ptr right) + : expr(kBinOp), left(std::move(left)), right(std::move(right)), op(std::move(op)) {} + + BinOp(shared_ptr left, int op, shared_ptr right) + : expr(kBinOp), left(std::move(left)), right(std::move(right)), op(int2op(op)) {} + + static shared_ptr BinOp_(shared_ptr left, int op, shared_ptr right) { + return make_shared(left, op, right); + } + + static operator_t int2op(int op) { return operator_t(op); } + + operator_t get_op() { return op; } + shared_ptr get_left() { return left; } + shared_ptr get_right() { return right; } + + static bool classof(const expr* c) { return c->get_kind() == kBinOp; } + + private: + shared_ptr left; + shared_ptr right; + operator_t op; +}; + +class Lambda : public expr { + shared_ptr args; + shared_ptr body; + + public: + Lambda(shared_ptr args, shared_ptr body) + : expr(kLambda), args(std::move(args)), body(std::move(body)) {} + + static shared_ptr Lambda_(shared_ptr args, shared_ptr body) { + return make_shared(args, body); + } + + shared_ptr get_args() { return args; } + shared_ptr get_body() { return body; } + + static bool classof(const expr* c) { return c->get_kind() == kLambda; } +}; + +class Compare : public expr { + public: + enum cmpop_t { + kEq = 1, + kNotEq, + kLt, + kLtE, + kGt, + kGtE, + }; + + Compare(shared_ptr left, vector ops, vector> comparators) + : expr(kCompare), + left(std::move(left)), + ops(std::move(ops)), + comparators(std::move(comparators)) {} + + Compare(shared_ptr left, const vector& ops, vector> comparators) + : expr(kCompare), + left(std::move(left)), + ops(int2op(ops)), + comparators(std::move(comparators)) {} + + static shared_ptr Compare_(shared_ptr left, vector ops, + vector> comparators) { + return make_shared(left, ops, comparators); + } + + static vector int2op(const vector& op) { + vector res; + for (auto i : op) res.emplace_back(cmpop_t(i)); + return res; + } + + vector get_ops() { return ops; } + shared_ptr get_left() { return left; } + vector> get_comparators() { return comparators; } + + static bool classof(const expr* c) { return c->get_kind() == kCompare; } + + private: + shared_ptr left; + vector ops; + vector> comparators; +}; + +class Call : public expr { + shared_ptr func; + vector> args; + + public: + Call(shared_ptr func, vector> args) + : expr(kCall), func(std::move(func)), args(std::move(args)) {} + + static shared_ptr Call_(shared_ptr func, vector> args) { + return make_shared(func, args); + } + + shared_ptr get_func() { return func; } + vector> get_args() { return args; } + + static bool classof(const expr* c) { return c->get_kind() == kCall; } +}; + +class Num : public expr { + double value; + + public: + explicit Num(double value) : expr(kNum), value(value) {} + + static shared_ptr Num_(double value) { return make_shared(value); } + + double get_value() { return value; } + static bool classof(const expr* c) { return c->get_kind() == kNum; } +}; + +class Constant : public expr { + double value; + + public: + explicit Constant(double value) : expr(kConstant), value(value) {} + + static shared_ptr Constant_(double value) { return make_shared(value); } + + double get_value() { return value; } + static bool classof(const expr* c) { return c->get_kind() == kConstant; } +}; + +class Attribute : public expr { + shared_ptr value; + identifier attr; + + public: + Attribute(shared_ptr value, const identifier& attr) + : expr(kAttribute), value(std::move(value)), attr(attr) {} + + static shared_ptr Attribute_(shared_ptr value, const identifier& attr) { + return make_shared(value, attr); + } + + shared_ptr get_value() { return value; } + identifier get_attr() { return attr; } + + static bool classof(const expr* c) { return c->get_kind() == kAttribute; } +}; + +class Name : public expr { + identifier id; + + public: + explicit Name(const identifier& id) : expr(kName), id(id) {} + + static shared_ptr Name_(const identifier& id) { return make_shared(id); } + + identifier get_id() { return id; } + static bool classof(const expr* c) { return c->get_kind() == kName; } +}; + +} // namespace pyast + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_H_ diff --git a/oneflow/ir/oneflow-extension/include/PyAst/AstMlirGen.h b/oneflow/ir/oneflow-extension/include/PyAst/AstMlirGen.h new file mode 100644 index 0000000000000000000000000000000000000000..be0a08465806fd3de83947ea3a37c1011a5e63f1 --- /dev/null +++ b/oneflow/ir/oneflow-extension/include/PyAst/AstMlirGen.h @@ -0,0 +1,110 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_MLIR_GEN_H_ +#define ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_MLIR_GEN_H_ + +#include "OneFlow/OneFlowLRJITRegistry.h" +#include "PyAst/Ast.h" + +#include "mlir/Conversion/MemRefToLLVM/MemRefToLLVM.h" +#include "mlir/Conversion/ReconcileUnrealizedCasts/ReconcileUnrealizedCasts.h" +#include "mlir/Conversion/AffineToStandard/AffineToStandard.h" +#include "mlir/Conversion/ArithmeticToLLVM/ArithmeticToLLVM.h" +#include "mlir/Conversion/MathToLLVM/MathToLLVM.h" +#include "mlir/Conversion/FuncToLLVM/ConvertFuncToLLVMPass.h" +#include "mlir/Conversion/SCFToControlFlow/SCFToControlFlow.h" +#include "mlir/Conversion/ControlFlowToLLVM/ControlFlowToLLVM.h" +#include "mlir/Dialect/Func/Transforms/Passes.h" +#include "mlir/Dialect/Affine/IR/AffineOps.h" +#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/Math/IR/Math.h" +#include "mlir/Dialect/MemRef/IR/MemRef.h" +#include "mlir/Dialect/SCF/IR/SCF.h" +#include "mlir/IR/Attributes.h" +#include "mlir/IR/Block.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/IR/TypeRange.h" +#include "mlir/IR/Value.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/OwningOpRef.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/IR/Verifier.h" +#include "mlir/IR/Builders.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" +#include "mlir/ExecutionEngine/ExecutionEngine.h" +#include "mlir/ExecutionEngine/MemRefUtils.h" +#include "mlir/Dialect/Linalg/Passes.h" +#include "mlir/InitAllDialects.h" +#include "mlir/Transforms/Passes.h" + +#include "llvm/ADT/ScopedHashTable.h" +#include "llvm/Support/raw_ostream.h" +#include "llvm/Support/TargetSelect.h" +#include "llvm/ADT/StringRef.h" +#include "llvm/ADT/STLExtras.h" +#include "llvm/ADT/TypeSwitch.h" + +#include +#include +#include +#include +#include + +class BuilderWithSymbolTable { + protected: + mlir::OpBuilder builder_; + mlir::ModuleOp theModule_; + std::map symbolTable_; + mlir::Block* symbolTableForDeclareBlock_{}; + + explicit BuilderWithSymbolTable(mlir::MLIRContext& context) : builder_(&context) {} + virtual ~BuilderWithSymbolTable() = default; + + mlir::LogicalResult Declare(const std::string& var, mlir::Value value); + mlir::Value LoopUp(const std::string& var); + mlir::Location Loc(const std::string& file_name = "unknown", int line = 0, int col = 0); + void Dump(); +}; + +class MLIRGenImpl : public BuilderWithSymbolTable { + public: + explicit MLIRGenImpl(mlir::MLIRContext& context) : BuilderWithSymbolTable(context) {} + + mlir::ModuleOp GenModule(pyast::FunctionDef* func); + + mlir::Value MlirGen(pyast::Compare* expr); + mlir::Value MlirGen(pyast::BinOp* expr); + mlir::Value MlirGen(pyast::Call* expr); + mlir::Value MlirGen(pyast::Constant* expr); + mlir::Value MlirGen(pyast::Name* expr); + + mlir::Value MlirGen(pyast::expr* expr); + + void MlirGen(pyast::If* stmt); + void MlirGen(pyast::Assign* stmt); + void MlirGen(pyast::Return* stmt); + + void MlirGen(pyast::stmt* stmt); +}; + +#endif // ONEFLOW_IR_ONEFLOW_EXTENSION_INCLUDE_PYAST_AST_MLIR_GEN_H_ diff --git a/oneflow/ir/oneflow-extension/ir_pass.cpp b/oneflow/ir/oneflow-extension/ir_pass.cpp index 038c9578f55450e2b8bf8a50f282c8e981047948..56f2d0322eeecba1e492aa7a9e805e86a54e70f1 100644 --- a/oneflow/ir/oneflow-extension/ir_pass.cpp +++ b/oneflow/ir/oneflow-extension/ir_pass.cpp @@ -16,7 +16,7 @@ limitations under the License. #include #include #include "oneflow/core/graph/op_graph.h" -#include "oneflow/ir/oneflow-extension/include/OneFlow/OneFlowRoundTrip.h" +#include "OneFlow/OneFlowRoundTrip.h" #include "oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h" #include "oneflow/core/framework/user_op_def.h" #include "oneflow/core/framework/user_op_registry.h" @@ -70,7 +70,9 @@ class RoundTripOneFlowJobWrapper : public mlir::oneflow::RoundTripOneFlowJobWrap is_updated_ = true; } void DumpLog(const std::string& filename, const std::string& content) override { - TeePersistentLogStream::Create(JoinPath(LogDir(), filename))->Write(content); + if (IsInDebugMode()) { + TeePersistentLogStream::Create(JoinPath(LogDir(), filename))->Write(content); + } } const ::oneflow::ParallelConf& ParallelConf4OpName(const std::string& op_name) const override { @@ -110,19 +112,6 @@ class RoundTripOneFlowJobWrapper : public mlir::oneflow::RoundTripOneFlowJobWrap return ::oneflow::ReplaceInputLbnInOpCustomizedConf(op_conf, ibn, new_val); } - AttrType QueryAttrType(const std::string& op_type_name, const std::string& attr_name) const { - user_op::UserOpDefWrapper op_def(GetUserOpDef(op_type_name)); - CHECK(op_def.IsAttrName(attr_name)) << attr_name << " not a attr name for op: " << op_type_name; - return op_def.GetAttrType(attr_name); - } - - UserOpDef GetUserOpDef(const std::string& op_type_name) const { - const user_op::OpRegistryResult* val = - user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_type_name); - CHECK(val) << " Cannot find op_type_name: " << op_type_name; - return val->op_def; - } - void QueryLogicalBlob( const std::string& lbn, std::function cb) @@ -157,20 +146,38 @@ bool IRRoundTrip::IsEnabled(const JobPassCtx& ctx) const { return ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_ROUND_TRIP", false); } +void SortJob(Job& job) { + auto* ops = job.mutable_net()->mutable_op(); + std::sort(ops->begin(), ops->end(), + [](const oneflow::OperatorConf& l, const oneflow::OperatorConf& r) { + return l.name() < r.name(); + }); +} + template Maybe IRRoundTrip::Apply(Job* job, JobPassCtx* ctx) const { if (!IsEnabled(*ctx)) { return Maybe::Ok(); } const OpGraph op_graph(*job); + Job job_before{}; + job_before.CopyFrom(*job); RoundTripOneFlowJobWrapper w(job); - TeePersistentLogStream::Create(JoinPath(w.LogDir(), "job_before_ir_round_trip.prototxt")) - ->Write(*job); + SortJob(job_before); + if (IsInDebugMode()) { + TeePersistentLogStream::Create(JoinPath(w.LogDir(), "job_before_ir_round_trip.prototxt")) + ->Write(job_before); + } mlir::oneflow::RoundTripOneFlowJob(w, [](::oneflow::Job* job, std::string& reason) { // TODO: It is not clear how to define if extra boxing is introduced TODO(); return true; }); - TeePersistentLogStream::Create(JoinPath(w.LogDir(), "job_after_ir_round_trip.prototxt")) - ->Write(*job); + if (IsInDebugMode()) { + Job job_after{}; + job_after.CopyFrom(*job); + SortJob(job_after); + TeePersistentLogStream::Create(JoinPath(w.LogDir(), "job_after_ir_round_trip.prototxt")) + ->Write(job_after); + } return Maybe::Ok(); } @@ -190,6 +197,12 @@ Maybe SaveJobToIR(Job* job, const std::string& path) { return Maybe::Ok(); } +Maybe ConvertJobToIR(Job* job) { + if (IsInDebugMode()) { TeePersistentLogStream::Create("saved_job")->Write(*job); } + RoundTripOneFlowJobWrapper job_wrapper(job); + return ::mlir::oneflow::ConvertJobToIR(job_wrapper); +} + Maybe LoadJobFromIR(Job* job, const std::string& path) { job->Clear(); RoundTripOneFlowJobWrapper job_wrapper(job); diff --git a/oneflow/ir/oneflow-extension/jit_engine.cpp b/oneflow/ir/oneflow-extension/jit_engine.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3f7e20bebe8cfc3c3c527ad194eb35923aabde25 --- /dev/null +++ b/oneflow/ir/oneflow-extension/jit_engine.cpp @@ -0,0 +1,66 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/Extension.h" +#include "llvm/ExecutionEngine/ExecutionEngine.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/Operation.h" +#include "OneFlow/kernel_launch/JITEngine.h" +#include "OneFlow/kernel_launch/RunContext.h" + +extern "C" { +void* oneflow_okl_fetch_run_ctx(void* launcher, int64_t index) { + return static_cast>(launcher) + ->FetchRunCtx(static_cast>(index)); +} + +void* oneflow_okl_fetch_kernel(void* launcher, int64_t index) { + return static_cast>(launcher) + ->FetchKernel(static_cast>(index)); +} + +void oneflow_okl_launch(void* run_ctx, void* kernel) { + const oneflow::user_op::OpKernel* engine = + static_cast>(kernel); + + oneflow::okl::RunContext* compute_ctx_ = + static_cast>(run_ctx); + engine->Compute(compute_ctx_, compute_ctx_->FetchState(), compute_ctx_->FetchCache()); +} +} // extern "C" + +namespace oneflow { +SharedLibs* MutSharedLibPaths() { + static SharedLibs libs = {}; + return &libs; +} +const SharedLibs* SharedLibPaths() { return MutSharedLibPaths(); } +} // namespace oneflow + +oneflow::okl::JITEngine::JITEngine(mlir::ModuleOp module) { + llvm::SmallVector ext_libs( + {oneflow::SharedLibPaths()->begin(), oneflow::SharedLibPaths()->end()}); + mlir::ExecutionEngineOptions jitOptions; + jitOptions.transformer = {}; + jitOptions.jitCodeGenOptLevel = llvm::None; + jitOptions.sharedLibPaths = ext_libs; + + auto jit_or_error = mlir::ExecutionEngine::create(module, jitOptions); + CHECK(!!jit_or_error) << "failed to create JIT exe engine, " + << llvm::toString((jit_or_error).takeError()); + jit_or_error->swap(engine_); +} diff --git a/oneflow/ir/oneflow-extension/kernel_launch/CMakeLists.txt b/oneflow/ir/oneflow-extension/kernel_launch/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/CMakeLists.txt @@ -0,0 +1 @@ + diff --git a/oneflow/ir/oneflow-extension/kernel_launch/infer_context.cpp b/oneflow/ir/oneflow-extension/kernel_launch/infer_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..35cc58533ce6f03400b1eabcca2745c9b24ed6d8 --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/infer_context.cpp @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/kernel_launch/InferMisc/InferContext.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/Parser/Parser.h" +#include "llvm/Support/Casting.h" + +namespace oneflow { +namespace okl { +using namespace user_op; + +InferContext::InferContext(RegContext* reg_ctx) : reg_ctx_(reg_ctx) {} + +const TensorDesc* InferContext::LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + return reg_ctx_->TensorDesc4ArgNameAndIndex(arg_name, index); +} + +const Shape& InferContext::InputShape(const std::string& arg_name, int32_t index) const { + return Shape4ArgNameAndIndex(arg_name, index); +} + +const Shape& InferContext::Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const { + return LogicalTensorDesc4ArgNameAndIndex(arg_name, index)->shape(); +} + +const std::shared_ptr& InferContext::Attr4Name(const std::string& attr_name) const { + return reg_ctx_->user_op_conf().Attr4Name(attr_name); +} + +} // namespace okl +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_op.cpp b/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d542e58f60429be4561c61ee879c8873fc36adf0 --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_op.cpp @@ -0,0 +1,139 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OKL/Conversion/Conversion.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowOps.h" +#include "OneFlow/UserOpReflection.h" +#include "OneFlow/Passes.h" +#include "OneFlow/Extension.h" +#include "oneflow/core/common/singleton.h" +#include "oneflow/core/common/str_util.h" +#include "oneflow/core/common/switch_func.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_kernel.h" +#include "oneflow/core/kernel/blob_tensor_view.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/persistence/tee_persistent_log_stream.h" +#include "oneflow/core/framework/op_generated.h" +#include "OneFlow/JITOpInfer.h" +#include "OneFlow/kernel_launch/JITEngine.h" +#include "OneFlow/kernel_launch/KernelLaunchState.h" +#include "OneFlow/kernel_launch/TmpBufferManager.h" + +#include "mlir/IR/DialectRegistry.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/Linalg/IR/Linalg.h" +#include "mlir/ExecutionEngine/ExecutionEngine.h" +#include "mlir/ExecutionEngine/MemRefUtils.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" + +#include "llvm/Support/Error.h" +#include "llvm/Support/TargetSelect.h" + +#include +#include +#include +#include + +namespace oneflow { + +Maybe KernelLaunchOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return ir::jit::InferTensorDesc(ctx); +} + +Maybe KernelLaunchOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ir::jit::InferTensorDesc(ctx); +} + +Maybe KernelLaunchOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +Maybe KernelLaunchOp::InferDataType(user_op::InferContext* ctx) { + return ir::jit::SetTensorDataType(ctx); +} + +namespace { + +using namespace oneflow::okl; + +template +class KernelLaunchKernel final : public user_op::OpKernel { + public: + KernelLaunchKernel() = default; + ~KernelLaunchKernel() = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + // use ctx to create module, reg_ctx and fn; + std::shared_ptr res(new KernelLaunchState(ctx)); + return res; + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* okl_state = dynamic_cast(state); + okl_state->DoCompute(ctx); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_KERNEL_LAUNCH_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("kernel_launch") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + return oneflow::okl::TmpBufferManager::InferTmpSize(ctx); \ + }) \ + .SetInplaceProposalFn([](const user_op::InferContext&, \ + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ + return Maybe::Ok(); \ + }); + +REGISTER_KERNEL_LAUNCH_CPU_KERNEL(float) +REGISTER_KERNEL_LAUNCH_CPU_KERNEL(double) +REGISTER_KERNEL_LAUNCH_CPU_KERNEL(int32_t) +REGISTER_KERNEL_LAUNCH_CPU_KERNEL(int64_t) +#undef REGISTER_KERNEL_LAUNCH_CPU_KERNEL + +#define REGISTER_KERNEL_LAUNCH_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("kernel_launch") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + return oneflow::okl::TmpBufferManager::InferTmpSize(ctx); \ + }) \ + .SetInplaceProposalFn([](const user_op::InferContext&, \ + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ + return Maybe::Ok(); \ + }); + +REGISTER_KERNEL_LAUNCH_GPU_KERNEL(float) +REGISTER_KERNEL_LAUNCH_GPU_KERNEL(double) +REGISTER_KERNEL_LAUNCH_GPU_KERNEL(int32_t) +REGISTER_KERNEL_LAUNCH_GPU_KERNEL(int64_t) + +#undef REGISTER_KERNEL_LAUNCH_GPU_KERNEL + +} // namespace + +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_state.cpp b/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_state.cpp new file mode 100644 index 0000000000000000000000000000000000000000..de8d65447e0858fdd4024ecdf35271ca382c459f --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/kernel_launch_state.cpp @@ -0,0 +1,61 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/OKL/Conversion/Conversion.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/IR/DialectRegistry.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OKL/OKLDialect.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Dialect/LLVMIR/LLVMDialect.h" +#include "OneFlow/kernel_launch/JITEngine.h" +#include "OneFlow/kernel_launch/LauncherContext.h" +#include "OneFlow/kernel_launch/KernelLaunchState.h" + +namespace oneflow { +namespace okl { + +KernelLaunchState::KernelLaunchState(user_op::KernelInitContext* ctx) : mlir_ctx_(GetRegistry()) { + // get raw module from ctx attr + module_ = + mlir::parseSourceString(ctx->Attr("mlir_assembly"), &mlir_ctx_); + if (!module_) { LOG(FATAL) << "Fail to load mlir assembly"; } + // lower oneflow wrap ops into okl dialect + if (failed(mlir::okl::LowerWrapOpsToOKL(*module_))) { + LOG(ERROR) << "Fail lowering kernel launch Module to okl ir"; + exit(1); + } +}; + +void KernelLaunchState::DoCompute(user_op::KernelComputeContext* ctx) { + if (!launcher_context_) { LazyInitLauncher(ctx); } + engine_->Run("okl_compute", launcher_context_.get()); +} + +void KernelLaunchState::LazyInitLauncher(user_op::KernelComputeContext* ctx) { + launcher_context_ = std::make_shared(ctx, module_->clone()); + + if (failed(mlir::okl::LowerOKLComputeToLLVM(*module_))) { + LOG(ERROR) << "Fail lowering okl compute Module to llvm ir"; + exit(1); + } + + engine_ = std::make_shared(*module_); +} + +} // namespace okl +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/kernel_launch/launcher_context.cpp b/oneflow/ir/oneflow-extension/kernel_launch/launcher_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ee9c0881e148f7cdfb789e35f383ea4adafd182f --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/launcher_context.cpp @@ -0,0 +1,93 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/Operation.h" +#include "oneflow/core/framework/op_kernel.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/kernel_launch/RegContext.h" +#include "OneFlow/kernel_launch/RunContext.h" +#include "OneFlow/kernel_launch/LauncherContext.h" +#include "llvm/ADT/TypeSwitch.h" + +namespace oneflow { +namespace okl { + +static int GetOpIndex(mlir::Operation* op, int index) { + return op->getOperand(index) + .getDefiningOp() + ->getAttr("index") + .dyn_cast() + .getInt(); +}; + +LauncherContext::LauncherContext(user_op::KernelComputeContext* compute_context, + mlir::ModuleOp module) + : module_(module) { + auto func = module.lookupSymbol("okl_init_context"); + auto context = func->getContext(); + + auto& ops = func->getRegion(0).front(); + + for (auto& op : ops) { + auto index = 0; + llvm::TypeSwitch(&op) + .Case([&](mlir::okl::BuildKernelOp elem) { + index = kernel_vec_.size(); + + auto reg_ctx = reg_ctx_vec_[GetOpIndex(&op, 0)]; + kernel_vec_.push_back(reg_ctx->GetKernel()); + op.setAttr("index", mlir::IntegerAttr::get(mlir::IntegerType::get(context, 32), index)); + }) + .Case([&](mlir::okl::BuildRegContextOp elem) { + index = reg_ctx_vec_.size(); + + mlir::Operation* reg_op = nullptr; + for (auto& op_it : op.getRegion(0).front().getOperations()) { + if (op_it.getDialect()->getNamespace() == "oneflow") { + reg_op = &op_it; + break; + } + } + + if (!reg_op) { LOG(FATAL) << "Failed to find reg_op in okl.build_reg_context_op"; } + + reg_ctx_vec_.emplace_back(std::make_shared(reg_op)); + op.setAttr("index", mlir::IntegerAttr::get(mlir::IntegerType::get(context, 32), index)); + }) + .Case([&](mlir::okl::BuildRunContextOp elem) { + index = run_ctx_vec_.size(); + + auto reg_ctx = reg_ctx_vec_[GetOpIndex(&op, 0)]; + run_ctx_vec_.emplace_back( + std::make_shared(std::move(reg_ctx), compute_context)); + op.setAttr("index", mlir::IntegerAttr::get(mlir::IntegerType::get(context, 32), index)); + }) + .Case([&](mlir::func::ReturnOp elem) {}) + .Default([&](mlir::Operation* elem) { + op.emitError("Fail to parse this op in okl init context"); + exit(1); + }); + } +} +void* LauncherContext::FetchKernel(int index) { return (void*)kernel_vec_[index]; } + +void* LauncherContext::FetchRunCtx(int index) { return run_ctx_vec_[index].get(); } + +} // namespace okl +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/kernel_launch/reg_context.cpp b/oneflow/ir/oneflow-extension/kernel_launch/reg_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..af06a796f5c7a0a4c626603eac7e30ea1a909b07 --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/reg_context.cpp @@ -0,0 +1,128 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/UserOpReflection.h" +#include "oneflow/core/framework/infer_util.h" +#include "oneflow/core/framework/user_op_attr.pb.h" +#include "oneflow/core/common/protobuf.h" +#include "oneflow/core/kernel/blob_tensor_view.h" +#include "oneflow/core/memory/memory_case.pb.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "OneFlow/kernel_launch/InferMisc/InferContext.h" +#include "OneFlow/kernel_launch/RegContext.h" +#include "oneflow/core/framework/user_op_kernel_registry.h" +#include "oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/IR/OpDefinition.h" +#include "mlir/IR/Operation.h" + +namespace oneflow { +namespace okl { + +static user_op::UserOpConfWrapper GetConfWrapper(mlir::Operation* op, + bool is_mapping_size = false) { + OperatorConf op_conf; + if (mlir::failed(mlir::oneflow::ConvertUserOpAttributes(op, op_conf, is_mapping_size))) { + op->emitError("fail to convert user op attributes"); + exit(1); + } + auto conf_wrapper_ = user_op::UserOpConfWrapper(std::make_shared(op_conf)); + return conf_wrapper_; +} + +RegContext::RegContext(mlir::Operation* op) : op_(op), conf_wrapper_(GetConfWrapper(op)) { + const auto handle_operands_or_results = + [&op, this](const auto& arg_ids, const auto& get_operand_or_result, ArgVec& arg_vec) { + for (const auto& obj_id : ::llvm::enumerate(arg_ids)) { + user_op::NaiveTensorDesc tensor_desc{}; + auto obj = get_operand_or_result(op, obj_id.index()); + if (auto rankedTensorType = obj.getType().template dyn_cast()) { + tensor_desc.set_shape( + Shape{rankedTensorType.getShape().begin(), rankedTensorType.getShape().end()}); + const auto data_type = + mlir::oneflow::support::FromMLIRTypeToOFDataType(rankedTensorType.getElementType()); + if (mlir::failed(data_type)) { exit(1); } + tensor_desc.set_data_type(data_type.getValue()); + // TODO: set stride + // TODO: set is_dynamic + } else { + LOG(FATAL) << "Unranked tensor type not supported"; + } + CHECK(arg2tensor_desc_.emplace(obj_id.value(), tensor_desc).second) << "duplicate key"; + arg_vec.push_back(obj_id.value()); + } + }; + handle_operands_or_results( + ::mlir::oneflow::user_op::ArgIds(op), + [](auto& x, size_t index) { return x->getOperand(index); }, inputs_); + handle_operands_or_results( + ::mlir::oneflow::user_op::ArgIds(op), + [](auto& x, size_t index) { return x->getResult(index); }, outputs_); + + auto dev_tag = mlir::OpTrait::IsOpConfCompatible::getDeviceTag(op); + if (dev_tag == "cpu") { + device_type_ = DeviceType::kCPU; + } else if (dev_tag == "cuda") { + device_type_ = DeviceType::kCUDA; + } else { + LOG(FATAL) << "Unsupported device tag: " << dev_tag.str(); + } + auto op_name = GetOp()->getName().stripDialect().str(); + if (const auto op_type_name = + GetOp()->getAttr("op_type_name").dyn_cast_or_null()) { + op_name = op_type_name.str(); + } + + reg_res_ = + CHECK_JUST(user_op::UserOpRegistryMgr::Get().GetOpKernelRegistryResult(op_name, *this)); + kernel_ = reg_res_->create_fn(); + + conf_wrapper_ = GetConfWrapper(op_, true); +} + +DeviceType RegContext::device_type() const { return device_type_; } +const ParallelContext& RegContext::parallel_ctx() const { + TODO() << "create parallel_ctx from op in mlir"; + ParallelContext* parallel_ctx = nullptr; + return *parallel_ctx; +} +const user_op::TensorDesc* RegContext::TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + auto it = arg2tensor_desc_.find(std::make_pair(arg_name, index)); + if (it == arg2tensor_desc_.end()) { return nullptr; } + return &(it->second); +} +const ArgVec& RegContext::inputs() const { return inputs_; } +const ArgVec& RegContext::outputs() const { return outputs_; } + +// TODO: more information is needed +const user_op::UserOpConfWrapper& RegContext::user_op_conf() const { return conf_wrapper_; } + +const std::shared_ptr& RegContext::Attr4Name( + const std::string& attr_name) const { + return user_op_conf().Attr4Name(attr_name); +} + +size_t RegContext::GetTmpBufferSize() { + if (reg_res_->need_temp_storage) { + InferContext infer_ctx(this); + return reg_res_->infer_tmp_size_fn(&infer_ctx); + } + return 0; +} + +} // namespace okl +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/kernel_launch/run_context.cpp b/oneflow/ir/oneflow-extension/kernel_launch/run_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..84a1c7e2e24ccfb3737f1c27cadbb6899508a0a6 --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/run_context.cpp @@ -0,0 +1,110 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/UserOpReflection.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/kernel_launch/RegContext.h" +#include "OneFlow/kernel_launch/RunContext.h" +#include "OneFlow/kernel_launch/InferMisc/InitContext.h" +#include "OneFlow/kernel_launch/InferMisc/InferContext.h" +#include "mlir/IR/BuiltinAttributes.h" +#include "llvm/ADT/TypeSwitch.h" +#include "llvm/Support/Casting.h" +#include +#include + +namespace oneflow { +namespace okl { + +RunContext::RunContext(std::shared_ptr reg, user_op::KernelComputeContext* comp) + : reg_ctx_(std::move(reg)), comp_ctx_(comp) { + InitContext init_ctx(this); + kernel_state_ = reg_ctx_->kernel_->CreateOpKernelState(&init_ctx); + kernel_cache_ = reg_ctx_->kernel_->InitOpKernelCache(&init_ctx); + + tmp_buffer_manager_ = + TmpBufferManager::InitTmpBufferManager(comp_ctx_->Tensor4ArgNameAndIndex("tmp_buffer", 0)); +} + +const user_op::TensorDesc* RunContext::TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + return reg_ctx_->TensorDesc4ArgNameAndIndex(arg_name, index); +} + +user_op::Tensor* RunContext::Tensor4ArgNameAndIndex(const std::string& arg_name, int32_t index) { + auto op = reg_ctx_->GetOp(); + using namespace mlir::oneflow::user_op; + auto source = GetOpSourceByName(op, arg_name); + + if (source.type == Source::OUTPUT) { + if (op->getNumResults() <= index + source.offset) { return nullptr; } + mlir::Value val = op->getResult(index + source.offset); + for (auto use : val.getUsers()) { + if (llvm::isa(use)) { + auto index = use->getAttr("index").cast().getInt(); + return comp_ctx_->Tensor4ArgNameAndIndex("out", index); + } + } + op->emitError("Failed to find " + std::to_string(index) + "in outputs"); + exit(1); + } + + if (source.type == Source::INPUT) { + if (op->getNumOperands() <= index + source.offset) { return nullptr; } + mlir::Value val = op->getOperand(index + source.offset); + auto define_op = val.getDefiningOp(); + return llvm::TypeSwitch<::mlir::Operation*, user_op::Tensor*>(define_op) + .Case([&](mlir::okl::GetTensorFromArgOp elem) { + auto index = elem.index(); + return comp_ctx_->Tensor4ArgNameAndIndex("in", index); + }) + .Case([&](mlir::okl::GetTensorFromRetOp elem) { + auto index = elem.index(); + return comp_ctx_->Tensor4ArgNameAndIndex("out", index); + }) + .Default([&](::mlir::Operation* op) { + LOG(FATAL) << "Signature: " << arg_name << " Not supported"; + return nullptr; + }); + } + + if (source.type == Source::BUFFER) { + auto op_name = op->getAttr("op_name").dyn_cast().str(); + return tmp_buffer_manager_->GetBufferTensor(); + } + + op->emitError("Failed to check source type"); + exit(1); +} + +ep::Stream* RunContext::stream() { return comp_ctx_->stream(); } + +DeviceType RunContext::device_type() const { return reg_ctx_->device_type(); } +const ParallelContext& RunContext::parallel_ctx() const { return comp_ctx_->parallel_ctx(); } + +const ArgVec& RunContext::inputs() const { return reg_ctx_->inputs(); } +const ArgVec& RunContext::outputs() const { return reg_ctx_->outputs(); } + +const user_op::UserOpConfWrapper& RunContext::user_op_conf() const { + return reg_ctx_->user_op_conf(); +} + +const std::shared_ptr& RunContext::Attr4Name( + const std::string& attr_name) const { + return user_op_conf().Attr4Name(attr_name); +} + +} // namespace okl +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/kernel_launch/tmp_buffer_manager.cpp b/oneflow/ir/oneflow-extension/kernel_launch/tmp_buffer_manager.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c3157cd005167a6b6559b776a9ed22deb63d8dc8 --- /dev/null +++ b/oneflow/ir/oneflow-extension/kernel_launch/tmp_buffer_manager.cpp @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "OneFlow/kernel_launch/InferMisc/InferContext.h" +#include "OneFlow/kernel_launch/TmpBufferManager.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/Parser/Parser.h" +#include "llvm/Support/Casting.h" + +namespace oneflow { +namespace okl { + +using namespace user_op; +std::shared_ptr TmpBufferManager::InitTmpBufferManager(user_op::Tensor* tensor) { + return std::make_shared(tensor); +} + +size_t TmpBufferManager::InferTmpSize(user_op::InferContext* ctx) { + mlir::MLIRContext mlir_ctx(KernelLaunchState::GetRegistry()); + + auto module = + mlir::parseSourceString(ctx->Attr("mlir_assembly"), &mlir_ctx); + if (!module) { LOG(FATAL) << "Fail to load mlir assembly"; } + if (failed(mlir::okl::LowerWrapOpsToOKL(*module))) { + LOG(ERROR) << "Fail lowering kernel launch Module to okl ir"; + exit(1); + } + + auto& ops = module->lookupSymbol("okl_init_context")->getRegion(0).front(); + + size_t max_size = 0; + for (auto& op : ops) { + if (!llvm::dyn_cast_or_null(op)) { break; } + mlir::Operation* reg_op = nullptr; + for (auto& op_it : op.getRegion(0).front().getOperations()) { + if (op_it.getDialect()->getNamespace() == "oneflow") { + reg_op = &op_it; + break; + } + } + if (!reg_op) { LOG(FATAL) << "Failed to find reg_op in okl.build_reg_context_op"; } + + auto op_name = reg_op->getAttr("op_name").dyn_cast().str(); + auto size = RegContext(reg_op).GetTmpBufferSize(); + max_size = std::max(max_size, size); + } + return max_size; +} + +} // namespace okl +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/lr_jit.cpp b/oneflow/ir/oneflow-extension/lr_jit.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a56e4395eaef5f2f81ec1c341ca0d534dd4dbb44 --- /dev/null +++ b/oneflow/ir/oneflow-extension/lr_jit.cpp @@ -0,0 +1,177 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "PyAst/Ast.h" +#include "PyAst/AstMlirGen.h" + +#include "mlir/Conversion/AffineToStandard/AffineToStandard.h" +#include "mlir/Conversion/ArithmeticToLLVM/ArithmeticToLLVM.h" +#include "mlir/Conversion/MathToLLVM/MathToLLVM.h" +#include "mlir/Conversion/FuncToLLVM/ConvertFuncToLLVMPass.h" +#include "mlir/Conversion/SCFToControlFlow/SCFToControlFlow.h" +#include "mlir/Conversion/ControlFlowToLLVM/ControlFlowToLLVM.h" +#include "mlir/Dialect/Arithmetic/Transforms/Passes.h" +#include "mlir/Dialect/Func/Transforms/Passes.h" +#include "mlir/Conversion/MemRefToLLVM/MemRefToLLVM.h" +#include "mlir/Conversion/ReconcileUnrealizedCasts/ReconcileUnrealizedCasts.h" +#include "mlir/Dialect/Affine/IR/AffineOps.h" +#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h" +#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h" +#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/LLVMIR/Transforms/RequestCWrappers.h" +#include "mlir/Dialect/Math/IR/Math.h" +#include "mlir/Dialect/MemRef/IR/MemRef.h" +#include "mlir/Dialect/SCF/IR/SCF.h" +#include "mlir/IR/Attributes.h" +#include "mlir/IR/OperationSupport.h" +#include "mlir/IR/TypeRange.h" +#include "mlir/IR/Value.h" +#include "mlir/InitAllDialects.h" +#include "mlir/IR/Builders.h" +#include "mlir/Parser/Parser.h" +#include "mlir/Pass/PassManager.h" +#include "mlir/Support/LogicalResult.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" +#include "mlir/ExecutionEngine/ExecutionEngine.h" +#include "mlir/ExecutionEngine/MemRefUtils.h" +#include "mlir/IR/BuiltinOps.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/OwningOpRef.h" +#include "mlir/Dialect/Linalg/Passes.h" +#include "mlir/IR/Verifier.h" +#include "mlir/IR/MLIRContext.h" +#include "mlir/Transforms/Passes.h" + +#include "llvm/Support/TargetSelect.h" +#include "llvm/Support/raw_ostream.h" +#include "llvm/ADT/STLExtras.h" +#include "llvm/ADT/ScopedHashTable.h" +#include "llvm/Support/raw_ostream.h" +#include "llvm/ADT/TypeSwitch.h" +#include "llvm/ADT/StringRef.h" + +#include +#include +#include +#include +#include + +using llvm::ArrayRef; +using llvm::ScopedHashTableScope; +using llvm::SmallVector; +using llvm::StringRef; +using llvm::Twine; + +static struct LLVMInitializer { + LLVMInitializer() { + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + } +} initializer; + +static mlir::LogicalResult lowerToLLVMDialect(mlir::ModuleOp module) { + mlir::PassManager pm(module.getContext()); + + pm.addNestedPass(mlir::LLVM::createRequestCWrappersPass()); + pm.addPass(mlir::createCSEPass()); + pm.addPass(mlir::createCanonicalizerPass()); + pm.addPass(mlir::createMemRefToLLVMPass()); + pm.addPass(mlir::createConvertFuncToLLVMPass()); + pm.addPass(mlir::createConvertSCFToCFPass()); + pm.addPass(mlir::cf::createConvertControlFlowToLLVMPass()); + pm.addPass(mlir::createConvertMathToLLVMPass()); + pm.addPass(mlir::arith::createArithmeticExpandOpsPass()); + pm.addPass(mlir::arith::createConvertArithmeticToLLVMPass()); + pm.addPass(mlir::createReconcileUnrealizedCastsPass()); + return pm.run(module); +} + +// generate a simple mlir module for test +static mlir::OwningOpRef GenModuleForTest(mlir::MLIRContext& context) { + std::string moduleStr = R"mlir( + func.func @get_lr(%arg0 : f32, %arg1 : i32) -> f32 attributes { llvm.emit_c_interface } { + return %arg0 : f32 + } + )mlir"; + mlir::OwningOpRef module = + mlir::parseSourceString(moduleStr, &context); + return module; +} + +// generate a module op from a function def python ast +static mlir::OwningOpRef GenModule(mlir::MLIRContext& context, + pyast::FunctionDef& ast) { + using namespace pyast; + + MLIRGenImpl mlir_gen(context); + mlir::OwningOpRef module = mlir_gen.GenModule(&ast); + // module->dump(); + return module; +} + +// generate store of lr jit registry from a function def python ast +static LRJITRegistry_Store_ GenFunc(pyast::FunctionDef& ast, bool is_dump) { + mlir::DialectRegistry registry; + mlir::registerAllDialects(registry); + mlir::registerLLVMDialectTranslation(registry); + mlir::MLIRContext context(registry); + context.loadDialect(); + context.loadDialect(); + context.loadDialect(); + context.loadDialect(); + context.loadDialect(); + context.loadDialect(); + context.loadDialect(); + + auto module = GenModule(context, ast); + if (is_dump) { module->dump(); } + // auto module = genModuleForTest(context); + CHECK(!!module) << "failed to parse module"; + CHECK(succeeded(lowerToLLVMDialect(*module))) << "failed to lower to llvm dialect"; + auto jit_or_err = mlir::ExecutionEngine::create(*module); + CHECK(jit_or_err) << "failed to create JIT exe engine, " + << llvm::toString(jit_or_err.takeError()); + + std::shared_ptr engine = cantFail(std::move(jit_or_err)); + + std::weak_ptr engine_ = engine; + + auto func = [engine_](double base_lr, double step) { + float res = 0; + if (!engine_.expired()) { + auto engine = engine_.lock(); + auto&& out = mlir::ExecutionEngine::result(res); + auto base_lr_jit = static_cast(base_lr); + auto step_jit = static_cast(step); + auto err = engine->invoke("get_lr", base_lr_jit, step_jit, out); + } + return res; + }; + return {engine, func}; +} + +void LRJITRegistry::Register(const std::string& function_id, pyast::FunctionDef& ast, + bool is_dump) { + auto jit = GenFunc(ast, is_dump); + functionId2engine_[function_id] = jit; +} + +std::function LRJITRegistry::LookUp(const std::string& function_id) { + auto iter = functionId2engine_.find(function_id); + if (iter != functionId2engine_.end()) { return iter->second.second; } + llvm::errs() << "function '" << function_id << "' not be registered before lookup."; + return nullptr; +}; diff --git a/oneflow/ir/oneflow-extension/mlir_gen.cpp b/oneflow/ir/oneflow-extension/mlir_gen.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a4dab127fd9afa723c994e54f7f1c819ada4cc98 --- /dev/null +++ b/oneflow/ir/oneflow-extension/mlir_gen.cpp @@ -0,0 +1,265 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "PyAst/AstMlirGen.h" + +// declare any scope variables in the front of function block to ensure the enough lifetime. +mlir::LogicalResult BuilderWithSymbolTable::Declare(const std::string& var, mlir::Value value) { + auto iter = symbolTable_.find(var); + if (iter != symbolTable_.end()) { + builder_.create(Loc(), value, iter->second); + return mlir::failure(); + } + + auto history_block = builder_.getInsertionBlock(); + auto history_point = builder_.getInsertionPoint(); + + builder_.setInsertionPointToStart(symbolTableForDeclareBlock_); + + auto single_type = mlir::Float32Type::getF32(builder_.getContext()); + auto type = mlir::MemRefType::get({}, single_type); + auto key = builder_.create(Loc(), type); + + builder_.setInsertionPoint(history_block, history_point); + builder_.create(Loc(), value, key); + symbolTable_[var] = key; + return mlir::success(); +} + +// look up memref of the special symbol with variable name +mlir::Value BuilderWithSymbolTable::LoopUp(const std::string& var) { + if (symbolTable_.count(var) == 1) { return symbolTable_[var]; } + theModule_->emitError("error: unknown variable '" + var + "'"); + return nullptr; +} + +// generate a location of mlir for ops +mlir::Location BuilderWithSymbolTable::Loc(const std::string& file_name, int line, int col) { + return mlir::FileLineColLoc::get(builder_.getStringAttr(file_name), line, col); +} + +// dump the current whole module up +void BuilderWithSymbolTable::Dump() { theModule_.dump(); } + +// generate a module op for lr jit registry from a ast +mlir::ModuleOp MLIRGenImpl::GenModule(pyast::FunctionDef* func) { + theModule_ = mlir::ModuleOp::create(Loc()); + + if (failed(verify(theModule_))) { + theModule_.emitError("module verification error"); + return nullptr; + } + + builder_.setInsertionPointToEnd(theModule_.getBody()); + + auto args = func->get_args()->get_args(); + auto type = mlir::Float32Type::getF32(builder_.getContext()); + llvm::SmallVector arg_types(args.size(), type); + llvm::SmallVector res_types(1, type); + + auto func_type = builder_.getFunctionType(arg_types, res_types); + auto function = mlir::func::FuncOp::create(Loc(), func->get_name(), func_type); + + auto* entry_block = function.addEntryBlock(); + symbolTableForDeclareBlock_ = entry_block; + theModule_.push_back(function); + builder_.setInsertionPointToStart(entry_block); + + for (const auto nameValue : llvm::zip(args, entry_block->getArguments())) { + if (failed(Declare(std::get<0>(nameValue)->get_arg(), std::get<1>(nameValue)))) { + return nullptr; + } + } + + builder_.setInsertionPointToStart(entry_block); + for (auto& stmt : func->get_body()) { MlirGen(stmt.get()); } + + return theModule_; +} + +// use llvm rtti to dispatch respective code gen tasks of stmt +void MLIRGenImpl::MlirGen(pyast::stmt* stmt) { + llvm::TypeSwitch(stmt) + .Case([&](auto* node) { MlirGen(node); }) + .Default([&](auto* node) { theModule_->emitError("StmtKind not support yet"); }); +} + +// use llvm rtti to dispatch respective code gen tasks of expr +mlir::Value MLIRGenImpl::MlirGen(pyast::expr* expr) { + mlir::Value res; + llvm::TypeSwitch(expr) + .Case( + [&](auto* node) { res = MlirGen(node); }) + .Default([&](auto* node) { theModule_->emitError("ExprKind not support yet"); }); + return res; +} + +void MLIRGenImpl::MlirGen(pyast::If* expr) { + auto test = MlirGen(expr->get_test().get()); + + if (test.getType().isF32()) { + auto eq = mlir::arith::CmpFPredicate::ONE; + auto zero_attr = builder_.getF32FloatAttr(0); + auto zero = builder_.create(Loc(), zero_attr); + test = builder_.create(Loc(), eq, test, zero); + } + + mlir::Block* then_block = builder_.createBlock(builder_.getBlock()->getParent()); + mlir::Block* else_block = builder_.createBlock(builder_.getBlock()->getParent()); + mlir::Block* after_block = builder_.createBlock(builder_.getBlock()->getParent()); + builder_.setInsertionPointAfterValue(test); + builder_.create(Loc(), test, then_block, llvm::None, else_block, + llvm::None); + + builder_.setInsertionPointToStart(then_block); + for (auto& expr : expr->get_body()) { MlirGen(expr.get()); } + if (then_block->empty() || !llvm::dyn_cast(then_block->back())) { + builder_.create(Loc(), after_block); + } + + builder_.setInsertionPointToStart(else_block); + for (auto& expr : expr->get_orelse()) { MlirGen(expr.get()); } + if (else_block->empty() || !llvm::dyn_cast(else_block->back())) { + builder_.create(Loc(), after_block); + } + + builder_.setInsertionPointToStart(after_block); +} + +mlir::Value MLIRGenImpl::MlirGen(pyast::Compare* expr) { + if (expr->get_comparators().size() != 1 || expr->get_ops().size() != 1) { + theModule_->emitError("compare only support once compare now"); + } + + mlir::arith::CmpFPredicate op = mlir::arith::CmpFPredicate::OEQ; + switch (expr->get_ops()[0]) { + case pyast::Compare::kEq: op = mlir::arith::CmpFPredicate::OEQ; break; + case pyast::Compare::kNotEq: op = mlir::arith::CmpFPredicate::ONE; break; + case pyast::Compare::kLt: op = mlir::arith::CmpFPredicate::OLT; break; + case pyast::Compare::kLtE: op = mlir::arith::CmpFPredicate::OLE; break; + case pyast::Compare::kGt: op = mlir::arith::CmpFPredicate::OGT; break; + case pyast::Compare::kGtE: op = mlir::arith::CmpFPredicate::OGE; break; + default: theModule_->emitError("compare_ not support op now"); + } + + auto lhs = MlirGen(expr->get_left().get()); + auto rhs = MlirGen(expr->get_comparators()[0].get()); + auto res = builder_.create(Loc(), op, lhs, rhs); + return res; +} + +mlir::Value MLIRGenImpl::MlirGen(pyast::BinOp* expr) { + auto lhs = MlirGen(expr->get_left().get()); + auto rhs = MlirGen(expr->get_right().get()); + mlir::Value res; + + switch (expr->get_op()) { + case pyast::BinOp::kAdd: res = builder_.create(Loc(), lhs, rhs); break; + case pyast::BinOp::kSub: res = builder_.create(Loc(), lhs, rhs); break; + case pyast::BinOp::kDiv: res = builder_.create(Loc(), lhs, rhs); break; + case pyast::BinOp::kMult: res = builder_.create(Loc(), lhs, rhs); break; + case pyast::BinOp::kPow: res = builder_.create(Loc(), lhs, rhs); break; + default: break; + } + + return res; +} + +mlir::Value MLIRGenImpl::MlirGen(pyast::Call* expr) { + mlir::Value res; + if (expr->get_func()->get_kind() == pyast::expr::kAttribute) { + auto func_ = expr->get_func().get(); + auto func = *dynamic_cast(func_); + auto func_value = func.get_value(); + + if (func_value->get_kind() != pyast::expr::kName + || dynamic_cast(func_value.get())->get_id() != "math") { + theModule_->emitError("only support call func is python math lib"); + } + if (expr->get_args().size() != 1) { + theModule_->emitError("attribute node only support call func with one param"); + } + + auto value = MlirGen(expr->get_args()[0].get()); + auto attr = func.get_attr(); + + if (attr == "floor") { + res = builder_.create(Loc(), value); + } else if (attr == "cos") { + res = builder_.create(Loc(), value); + } else if (attr == "ceil") { + res = builder_.create(Loc(), value); + } else { + theModule_->emitError(attr + " not support yet"); + } + } else if (expr->get_func()->get_kind() == pyast::expr::kName) { + auto func_ = expr->get_func().get(); + auto func = *dynamic_cast(func_); + + if (expr->get_args().size() != 2) { + theModule_->emitError("name node only support call func with two param"); + } + + auto left = MlirGen(expr->get_args()[0].get()); + auto right = MlirGen(expr->get_args()[1].get()); + + auto attr = func.get_id(); + + if (attr == "max") { + res = builder_.create(Loc(), left, right); + } else if (attr == "min") { + res = builder_.create(Loc(), left, right); + } else { + theModule_->emitError(attr + " not support yet"); + } + + } else { + theModule_->emitError("only support call func is attribute and name node"); + } + + return res; +} + +mlir::Value MLIRGenImpl::MlirGen(pyast::Constant* expr) { + float value = expr->get_value(); + auto constant = builder_.create(Loc(), builder_.getF32FloatAttr(value)); + return constant; +} + +mlir::Value MLIRGenImpl::MlirGen(pyast::Name* expr) { + auto key = LoopUp(expr->get_id()); + builder_.setInsertionPointToEnd(builder_.getInsertionBlock()); + auto value = builder_.create(Loc(), key); + return value; +} + +void MLIRGenImpl::MlirGen(pyast::Assign* stmt) { + auto value = MlirGen(stmt->get_value().get()); + + for (auto& target : stmt->get_targets()) { + if (target->get_kind() != pyast::expr::kName) { + theModule_->emitError("only support assign to name node"); + } + auto name = dynamic_cast(target.get())->get_id(); + + Declare(name, value); + } +} + +void MLIRGenImpl::MlirGen(pyast::Return* stmt) { + auto value = MlirGen(stmt->get_value().get()); + + builder_.create(Loc(), mlir::ValueRange({value})); +} diff --git a/oneflow/ir/oneflow-extension/mlir_jit_op.cpp b/oneflow/ir/oneflow-extension/mlir_jit_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..91990004210052a50af67c6f541a897a7af8b4a5 --- /dev/null +++ b/oneflow/ir/oneflow-extension/mlir_jit_op.cpp @@ -0,0 +1,64 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowSupport.h" +#include "llvm/Support/raw_ostream.h" +#include "oneflow/core/common/data_type.pb.h" +#include "oneflow/core/common/device_type.pb.h" +#include "oneflow/core/common/shape.h" +#include "oneflow/core/framework/dtype.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/user/ops/nn_util.h" +#include "OneFlow/JITOpInfer.h" + +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/IR/Block.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/OwningOpRef.h" +#include "mlir/IR/Types.h" +#include "mlir/InitAllDialects.h" +#include "mlir/Parser/Parser.h" + +namespace oneflow { + +namespace { + +Maybe GetSbpFn(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +} // namespace + +Maybe MlirJitOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return ir::jit::InferTensorDesc(ctx); +} + +Maybe MlirJitOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ir::jit::InferTensorDesc(ctx); +} + +Maybe MlirJitOp::GetSbp(user_op::SbpContext* ctx) { return GetSbpFn(ctx); } + +Maybe MlirJitOp::InferDataType(user_op::InferContext* ctx) { + return ir::jit::SetTensorDataType(ctx); + ; +} + +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/mlir_jit_op_infer.cpp b/oneflow/ir/oneflow-extension/mlir_jit_op_infer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..171ab3e076c0b0b03b6654d6fe807b0aa6a30ca2 --- /dev/null +++ b/oneflow/ir/oneflow-extension/mlir_jit_op_infer.cpp @@ -0,0 +1,152 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "OneFlow/OneFlowDialect.h" +#include "OneFlow/OneFlowSupport.h" +#include "llvm/Support/raw_ostream.h" +#include "oneflow/core/common/data_type.pb.h" +#include "oneflow/core/common/device_type.pb.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/common/shape.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/core/framework/dtype.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/user/ops/nn_util.h" + +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/IR/Block.h" +#include "mlir/IR/BuiltinTypes.h" +#include "mlir/IR/OwningOpRef.h" +#include "mlir/IR/Types.h" +#include "mlir/InitAllDialects.h" +#include "mlir/Parser/Parser.h" + +namespace oneflow { + +namespace ir { + +namespace jit { + +static Maybe GetFunctionType(user_op::InferContext* ctx, + mlir::OwningOpRef& module) { + mlir::func::FuncOp funcOp = mlir::SymbolTable::lookupNearestSymbolFrom( + module.get(), mlir::SymbolRefAttr::get(module->getContext(), ctx->op_name())); + CHECK_OR_RETURN(funcOp) << "Fail to find funcOp of symbol " << ctx->op_name(); + const auto funcType = funcOp.getFunctionType(); + CHECK_EQ_OR_RETURN(funcType.getNumInputs(), ctx->input_size("in")) + << "input size mismatch with mlir assembly"; + CHECK_EQ_OR_RETURN(funcType.getNumResults(), ctx->output_size("out")) + << "output size mismatch with mlir assembly"; + int32_t arg_i = 0; + for (mlir::Type arg_type : funcType.getInputs()) { + if (auto rankedTensorType = arg_type.dyn_cast()) { + CHECK_EQ_OR_RETURN( + (Shape{rankedTensorType.getShape().begin(), rankedTensorType.getShape().end()}), + ctx->InputShape("in", arg_i)) + << "arg #" << arg_i; + const auto data_type = + mlir::oneflow::support::FromMLIRTypeToOFDataType(rankedTensorType.getElementType()); + if (mlir::failed(data_type)) { exit(1); } + CHECK_EQ_OR_RETURN(data_type.getValue(), ctx->InputDType("in", arg_i)) << "arg #" << arg_i; + arg_i += 1; + } else { + std::string arg_type_str = ""; + llvm::raw_string_ostream os(arg_type_str); + arg_type.print(os); + THROW(RuntimeError) << "Unsupported arg type " << arg_type_str; + } + } + return funcType; +} + +Maybe SetTensorDataType(user_op::InferContext* ctx) { + auto mlir_assembly_str = ctx->Attr("mlir_assembly"); + mlir::DialectRegistry registry; + mlir::registerAllDialects(registry); + mlir::MLIRContext context(registry); + context.loadDialect(); + context.loadDialect(); + + mlir::OwningOpRef module = + mlir::parseSourceString(mlir_assembly_str, &context); + if (!module) { + LOG(ERROR) << "Fail to load mlir assembly"; + exit(1); + } + + auto funcType = *JUST(GetFunctionType(ctx, module)); + int32_t res_i = 0; + for (mlir::Type res_type : funcType.getResults()) { + if (auto rankedTensorType = res_type.dyn_cast()) { + const auto data_type = + mlir::oneflow::support::FromMLIRTypeToOFDataType(rankedTensorType.getElementType()); + if (mlir::failed(data_type)) { exit(1); } + ctx->SetDtype4ArgNameAndIndex("out", res_i, data_type.getValue()); + res_i += 1; + } else { + std::string res_type_str = ""; + llvm::raw_string_ostream os(res_type_str); + res_type.print(os); + THROW(RuntimeError) << "Unsupported arg type " << res_type_str; + } + } + return Maybe::Ok(); +} + +Maybe InferTensorDesc(user_op::InferContext* ctx) { + auto mlir_assembly_str = ctx->Attr("mlir_assembly"); + mlir::DialectRegistry registry; + mlir::registerAllDialects(registry); + mlir::MLIRContext context(registry); + context.loadDialect(); + context.loadDialect(); + + mlir::OwningOpRef module = + mlir::parseSourceString(mlir_assembly_str, &context); + if (!module) { + LOG(ERROR) << "Fail to load mlir assembly"; + exit(1); + } + auto funcType = *JUST(GetFunctionType(ctx, module)); + int32_t res_i = 0; + for (mlir::Type res_type : funcType.getResults()) { + if (auto rankedTensorType = res_type.dyn_cast()) { + ctx->SetOutputShape( + "out", res_i, + Shape{rankedTensorType.getShape().begin(), rankedTensorType.getShape().end()}); + + const auto data_type = + mlir::oneflow::support::FromMLIRTypeToOFDataType(rankedTensorType.getElementType()); + if (mlir::failed(data_type)) { exit(1); } + ctx->SetOutputDType("out", res_i, data_type.getValue()); + res_i += 1; + } else { + std::string res_type_str = ""; + llvm::raw_string_ostream os(res_type_str); + res_type.print(os); + THROW(RuntimeError) << "Unsupported arg type " << res_type_str; + } + } + return Maybe::Ok(); +} + +} // namespace jit + +} // namespace ir + +} // namespace oneflow diff --git a/oneflow/ir/oneflow-extension/mlir_jit_op_kernel.cpp b/oneflow/ir/oneflow-extension/mlir_jit_op_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..603b69096e8990c44c50c6af61cfeace0f051924 --- /dev/null +++ b/oneflow/ir/oneflow-extension/mlir_jit_op_kernel.cpp @@ -0,0 +1,228 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "mlir/Parser/Parser.h" +#include "mlir/Dialect/Func/IR/FuncOps.h" +#include "mlir/Dialect/Linalg/IR/Linalg.h" +#include "mlir/ExecutionEngine/ExecutionEngine.h" +#include "mlir/ExecutionEngine/MemRefUtils.h" +#include "mlir/Target/LLVMIR/Dialect/LLVMIR/LLVMToLLVMIRTranslation.h" +#include "llvm/Support/TargetSelect.h" +#include "OneFlow/OneFlowDialect.h" +#include "oneflow/core/common/str_util.h" +#include "oneflow/core/common/switch_func.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/persistence/tee_persistent_log_stream.h" +#include "oneflow/ir/include/OneFlow/Passes.h" +#include "oneflow/ir/include/OneFlow/Extension.h" + +namespace oneflow { + +namespace { + +using OpaqueMemRefDescriptor = std::shared_ptr; + +template +OpaqueMemRefDescriptor CreateMemRefDescriptor(user_op::Tensor* tensor) { + using MemRefType = StridedMemRefType; + auto desc = new MemRefType(); + *desc = mlir::detail::makeStridedMemRefDescriptor( + tensor->dptr(), tensor->dptr(), + {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}, + {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}); + auto deleter = [](void const* data) { + auto p = static_cast(data); + delete p; + }; + return OpaqueMemRefDescriptor(desc, deleter); +} + +template +OpaqueMemRefDescriptor CreateMutMemRefDescriptor(user_op::Tensor* tensor) { + using MemRefType = StridedMemRefType; + auto desc = new MemRefType(); + *desc = mlir::detail::makeStridedMemRefDescriptor( + tensor->mut_dptr(), tensor->mut_dptr(), + {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}, + {tensor->shape_view().ptr(), tensor->shape_view().ptr() + tensor->shape_view().NumAxes()}); + auto deleter = [](void const* data) { + auto p = static_cast(data); + delete p; + }; + return OpaqueMemRefDescriptor(desc, deleter); +} + +#define MAKE_STRIDED_MEM_REF_SWITCH_ENTRY(func_name, N, T) func_name +DEFINE_STATIC_SWITCH_FUNC(OpaqueMemRefDescriptor, CreateMemRefDescriptor, + MAKE_STRIDED_MEM_REF_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ), + MAKE_DATA_TYPE_CTRV_SEQ(ARITHMETIC_DATA_TYPE_SEQ)); +DEFINE_STATIC_SWITCH_FUNC(OpaqueMemRefDescriptor, CreateMutMemRefDescriptor, + MAKE_STRIDED_MEM_REF_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ), + MAKE_DATA_TYPE_CTRV_SEQ(ARITHMETIC_DATA_TYPE_SEQ)); +#undef MAKE_STRIDED_MEM_REF_SWITCH_ENTRY + +std::string GetMLIRCInterface(const std::string& func_name) { + return std::string("_mlir_ciface_") + func_name; +} + +llvm::SmallVector GetMLIRCInterfaceArgs( + user_op::KernelComputeContext* ctx) { + llvm::SmallVector args{}; + for (auto& pair : ctx->inputs()) { + auto tensor = ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); + auto ref = SwitchCreateMemRefDescriptor( + SwitchCase(tensor->shape_view().NumAxes(), tensor->data_type()), tensor); + args.push_back(ref); + } + for (auto& pair : ctx->outputs()) { + auto tensor = ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); + auto ref = SwitchCreateMutMemRefDescriptor( + SwitchCase(tensor->shape_view().NumAxes(), tensor->data_type()), tensor); + args.push_back(ref); + } + return args; +} + +void WithMlirContext( + user_op::KernelComputeContext* ctx, const llvm::SmallVector& ext_libs, + const std::function(mlir::MLIRContext* mlir_ctx)>& parse, + const std::function& lower) { + mlir::DialectRegistry registry; + registry + .insert(); + mlir::registerLLVMDialectTranslation(registry); + mlir::MLIRContext mlir_ctx(registry); + mlir::OwningOpRef module = parse(&mlir_ctx); + CHECK(!!module) << "fail to parse MLIR, op: " << ctx->op_name(); + if (ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false)) { module->print(llvm::outs()); } + llvm::InitializeNativeTarget(); + llvm::InitializeNativeTargetAsmPrinter(); + lower(&mlir_ctx, *module); + if (ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false)) { module->print(llvm::outs()); } + if (ParseBooleanFromEnv("ONEFLOW_MLIR_DUMP_IR", false)) { + std::string mlir; + llvm::raw_string_ostream os_mlir(mlir); + module->print(os_mlir); + TeePersistentLogStream::Create(JoinPath("jit", ctx->op_name() + ".mlir"))->Write(mlir); + } + + mlir::ExecutionEngineOptions jitOptions; + jitOptions.transformer = {}; + jitOptions.jitCodeGenOptLevel = llvm::None; + jitOptions.sharedLibPaths = ext_libs; + + auto jit_or_error = mlir::ExecutionEngine::create(*module, jitOptions); + CHECK(!!jit_or_error) << "failed to create JIT exe engine, " + << llvm::toString(jit_or_error.takeError()); + auto jit = std::move(jit_or_error.get()); + llvm::SmallVector args /* args must outlive JIT invocation */ = + GetMLIRCInterfaceArgs(ctx); + llvm::SmallVector packed_args{}; + for (auto& arg /* arg must be a reference*/ : args) { packed_args.push_back(&arg); } + auto error = jit->invokePacked(GetMLIRCInterface(ctx->op_name()), packed_args); + CHECK(!error) << "fail to invoke jit engine, error: " << llvm::toString(std::move(error)); +} + +template +class MlirJitCpuKernel final : public user_op::OpKernel { + public: + MlirJitCpuKernel() = default; + ~MlirJitCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + llvm::SmallVector ext_libs( + {SharedLibPaths()->begin(), SharedLibPaths()->end()}); + WithMlirContext( + ctx, ext_libs, + [&ctx](mlir::MLIRContext* mlir_ctx) { + return mlir::parseSourceString(ctx->Attr("mlir_assembly"), + mlir_ctx); + }, + [](mlir::MLIRContext* mlir_ctx, mlir::ModuleOp module) { + CHECK(mlir::succeeded(mlir::oneflow::LowerModuleToLLVM(mlir_ctx, module))) + << "fail to lower OneFlow to LLVM"; + }); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_MLIR_JIT_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("mlir_jit") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInplaceProposalFn([](const user_op::InferContext&, \ + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ + return Maybe::Ok(); \ + }); + +REGISTER_MLIR_JIT_CPU_KERNEL(float) +REGISTER_MLIR_JIT_CPU_KERNEL(double) +REGISTER_MLIR_JIT_CPU_KERNEL(int32_t) +REGISTER_MLIR_JIT_CPU_KERNEL(int64_t) + +#undef REGISTER_MLIR_JIT_CPU_KERNEL + +#ifdef WITH_MLIR_CUDA_CODEGEN + +template +class MlirJitGpuKernel final : public user_op::OpKernel { + public: + MlirJitGpuKernel() = default; + ~MlirJitGpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + llvm::SmallVector ext_libs( + {SharedLibPaths()->begin(), SharedLibPaths()->end()}); + WithMlirContext( + ctx, ext_libs, + [&ctx](mlir::MLIRContext* mlir_ctx) { + return mlir::parseSourceString(ctx->Attr("mlir_assembly"), + mlir_ctx); + }, + [](mlir::MLIRContext* mlir_ctx, mlir::ModuleOp module) { + CHECK(mlir::succeeded(mlir::oneflow::LowerModuleToCUDALLVM(mlir_ctx, module))) + << "fail to lower OneFlow to CUDA LLVM"; + }); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_MLIR_JIT_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("mlir_jit") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInplaceProposalFn([](const user_op::InferContext&, \ + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ + return Maybe::Ok(); \ + }); + +REGISTER_MLIR_JIT_GPU_KERNEL(float) +REGISTER_MLIR_JIT_GPU_KERNEL(double) +REGISTER_MLIR_JIT_GPU_KERNEL(int32_t) +REGISTER_MLIR_JIT_GPU_KERNEL(int64_t) + +#undef REGISTER_MLIR_JIT_GPU_KERNEL + +#endif // WITH_MLIR_CUDA_CODEGEN + +} // namespace + +} // namespace oneflow diff --git a/oneflow/ir/oneflow-opt/CMakeLists.txt b/oneflow/ir/oneflow-opt/CMakeLists.txt index 65136158a34938323b8c12df9b96a61fb3e96a07..0a5a58a49b36a90e8a411b3cbf196238b8c6cd06 100644 --- a/oneflow/ir/oneflow-opt/CMakeLists.txt +++ b/oneflow/ir/oneflow-opt/CMakeLists.txt @@ -10,6 +10,7 @@ set_target_properties( oneflow-opt PROPERTIES BUILD_WITH_INSTALL_RPATH OFF BUILD_RPATH "${_origin_prefix}" INSTALL_RPATH "${_origin_prefix}") llvm_update_compile_flags(oneflow-opt) -target_link_libraries(oneflow-opt PRIVATE MLIROneFlow ${dialect_libs} ${conversion_libs} MLIROptLib) +target_link_libraries(oneflow-opt PRIVATE MLIROneFlow ${dialect_libs} ${conversion_libs} MLIROptLib + $ MLIROneFlowExtension) mlir_check_all_link_libraries(oneflow-opt) diff --git a/oneflow/ir/oneflow-opt/oneflow-opt.cpp b/oneflow/ir/oneflow-opt/oneflow-opt.cpp index f8b35f58d59f0e92c87ec3f026d3c7939fe6b69c..ab426c4fe1b8d3bde756ec37e9ea1b7503f4824e 100644 --- a/oneflow/ir/oneflow-opt/oneflow-opt.cpp +++ b/oneflow/ir/oneflow-opt/oneflow-opt.cpp @@ -25,9 +25,15 @@ limitations under the License. #include "mlir/Pass/Pass.h" #include "mlir/Transforms/GreedyPatternRewriteDriver.h" +#include "OneFlow/SBP/SBPDialect.h" #include "OneFlow/OneFlowDialect.h" #include "OneFlow/OneFlowOps.h" #include "OneFlow/Passes.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/control/ctrl_bootstrap.pb.h" +#include "OneFlow/OKL/OKLDialect.h" +#include "OneFlow/OKL/OKLOps.h" +#include "OneFlow/OKL/passes.h" namespace mlir { struct TestOneFlowTraitFolder @@ -43,9 +49,14 @@ void registerTestOneFlowTraitsPass() { PassRegistration( } // namespace mlir +const auto global_cse_state = std::make_shared(); + int32_t main(int32_t argc, char** argv) { + ::oneflow::Singleton<::oneflow::ProcessCtx>::New(); + mlir::okl::registerPasses(); mlir::registerAllPasses(); mlir::registerTestOneFlowTraitsPass(); + mlir::registerConvertToSignlessForTosaPassPass(); mlir::registerLowerOneFlowToTosaPassPass(); mlir::registerGpuMapParallelLoopsPassPass(); mlir::registerBufferHostRegisterPassPass(); @@ -53,8 +64,17 @@ int32_t main(int32_t argc, char** argv) { #ifdef WITH_MLIR_CUDA_CODEGEN mlir::oneflow::registerGpuSerializeToCubinPass(); #endif // WITH_MLIR_CUDA_CODEGEN + mlir::okl::registerOneFlowPasses(); mlir::registerOutlineJitFunctionPassPass(); + mlir::oneflow::registerCSEPasses(global_cse_state); + mlir::registerFuseForwardOpsPass(); + mlir::registerFuseIntoExistingOpPassPass(); + mlir::registerFuseNormalizationOpsPass(); + mlir::registerConvertInferenceOpPassPass(); + mlir::registerGroupMatMulPass(); mlir::DialectRegistry registry; + registry.insert(); + registry.insert(); registry.insert(); registry.insert(); registry.insert(); diff --git a/oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h b/oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h index b2afa4fbae1622c0c8ef285a439a198c73ba796a..4e20e5727e35950facf372ea754f27b94947c5bc 100644 --- a/oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h +++ b/oneflow/ir/oneflow-translate/include/OneFlow/MLIROneFlowTranslation.h @@ -20,6 +20,7 @@ limitations under the License. #include "oneflow/core/job/job.pb.h" #include "oneflow/core/job/sbp_parallel.pb.h" #include "oneflow/core/operator/op_conf.pb.h" +#include "OneFlow/SBP/SBPImporter.h" #include "OneFlow/OneFlowOps.h" @@ -39,12 +40,18 @@ namespace oneflow { // TODO: wrap in a helper namespace +LogicalResult IsAttrBelong2Op(const std::string& op_type_name, const std::string& attr_name); + LogicalResult ConvertUserOpInputs(Operation* op, StringRef op_name, ::oneflow::UserOpConf* user_conf); LogicalResult ConvertUserOpOutputs(Operation* op, StringRef op_name, ::oneflow::UserOpConf* user_conf); +LogicalResult ConvertUserOpAttributes( + Operation* op, ::oneflow::OperatorConf& op_conf, + bool is_mapping_size /* the input and output size should be mapped after building kernel and + offer information for the next query*/ + = false); LogicalResult ConvertCtrlInputs(Operation* op, ::oneflow::OperatorConf& op_conf); -llvm::Optional GetOutputLbn(OpResult result); llvm::Optional GetDataTypeAttr(MLIRContext* context, ::oneflow::DataType oneflow_value); LogicalResult ConvertVariableOpConf(VariableOp op, ::oneflow::OperatorConf* op_conf); @@ -106,13 +113,12 @@ class Importer { ArrayAttr GetAttrFromShape(const ::oneflow::ShapeProto& shape); ArrayAttr GetAttrFromStride(const ::oneflow::Int64ListProto& stride); - llvm::Optional GetTypeFromOneFlowDataType(::oneflow::DataType dt); OpBuilder& GetBuilder() { return builder_; } MLIRContext* GetMLIRContext() { return context_; } ModuleOp& GetModule() { return module_; } Location& GetRootLocation() { return unknown_loc_; } virtual Type GetTensorTypeOfLbn(const std::string& lbn) = 0; - LogicalResult ConvertUserOpAttributes(Operation* op, ::oneflow::OperatorConf& op_conf); + void SetOpStateLoc(const ::oneflow::OperatorConf&, OperationState&); private: OpBuilder builder_; @@ -152,6 +158,7 @@ void registerFromOneFlowJobTranslation(); std::string ConvertJobToTosaIR(RoundTripOneFlowJobWrapperInterface& job_wrapper); void SaveJobToIR(RoundTripOneFlowJobWrapperInterface& job_wrapper, const std::string& path); +std::string ConvertJobToIR(RoundTripOneFlowJobWrapperInterface& job_wrapper); void LoadJobFromIR(RoundTripOneFlowJobWrapperInterface& job_wrapper, const std::string& path); } // namespace oneflow diff --git a/oneflow/ir/oneflow-translate/lib/OneFlow/Importer.cpp b/oneflow/ir/oneflow-translate/lib/OneFlow/Importer.cpp index 97814d09633ffcb699dd62d6b79352531a2b77d1..4eac7cc6e2a9f11c6562e7816b7850a878f51851 100644 --- a/oneflow/ir/oneflow-translate/lib/OneFlow/Importer.cpp +++ b/oneflow/ir/oneflow-translate/lib/OneFlow/Importer.cpp @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "OneFlow/UserOpConversion.h" #include "oneflow/core/common/data_type.pb.h" #include "oneflow/core/framework/user_op_conf.pb.h" #include "oneflow/core/job/job.pb.h" @@ -21,11 +22,16 @@ limitations under the License. #include "oneflow/core/framework/user_op_registry_manager.h" #include "OneFlow/OneFlowDialect.h" +#include "OneFlow/SBP/SBPDialect.h" +#include "OneFlow/SBP/SBPAttributes.h" #include "OneFlow/OneFlowOps.h" +#include "OneFlow/UserOpReflection.h" #include "OneFlow/OneFlowTypes.h" #include "OneFlow/OneFlowSupport.h" #include "OneFlow/Passes.h" #include "OneFlow/MLIROneFlowTranslation.h" +#include "OneFlow/OneFlowSupport.h" +#include "OneFlow/OneFlowDataTypeConversion.h" #include "mlir/Dialect/Func/IR/FuncOps.h" #include "mlir/IR/Attributes.h" @@ -58,6 +64,8 @@ limitations under the License. #include +#include "oneflow/core/framework/sbp_context.h" +#include "oneflow/core/job/sbp_signature_builder.h" namespace mlir { namespace oneflow { @@ -66,19 +74,6 @@ using PbMessage = google::protobuf::Message; namespace { -const ::oneflow::UserOpDef& GetUserOpDef(const std::string& op_type_name) { - const ::oneflow::user_op::OpRegistryResult* val = - ::oneflow::user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_type_name); - CHECK(val) << " Cannot find op_type_name: " << op_type_name; - return val->op_def; -} - -::oneflow::AttrType QueryAttrType(const std::string& op_type_name, const std::string& attr_name) { - ::oneflow::user_op::UserOpDefWrapper op_def(GetUserOpDef(op_type_name)); - CHECK(op_def.IsAttrName(attr_name)) << attr_name << " not a attr name for op: " << op_type_name; - return op_def.GetAttrType(attr_name); -} - using SizeVec = SmallVector; SizeVec GetSizesFromArgs(UserOpArgs args, UserOpArgDefs arg_defs) { @@ -115,11 +110,16 @@ std::vector GetOutputLbns(const ::oneflow::OperatorConf& op, UserOp } // namespace +LogicalResult IsAttrBelong2Op(const std::string& op_type_name, const std::string& attr_name) { + ::oneflow::user_op::UserOpDefWrapper op_def(support::getUserOpDef(op_type_name)); + return success(op_def.IsAttrName(attr_name)); +} + LogicalResult Importer::AddUserOpInputOutputSegments(const ::oneflow::OperatorConf& op, std::vector& attr_vec) { if (op.has_user_conf() == false) return failure(); const auto& user_conf = op.user_conf(); - const ::oneflow::UserOpDef& op_def = GetUserOpDef(op.user_conf().op_type_name()); + const ::oneflow::UserOpDef& op_def = support::getUserOpDef(op.user_conf().op_type_name()); const auto UserOpOperationName = OperationName(UserOp::getOperationName(), GetMLIRContext()); attr_vec.push_back(GetBuilder().getNamedAttr( oneflow::UserOp::input_sizesAttrName(UserOpOperationName), @@ -172,18 +172,6 @@ ArrayAttr Importer::GetAttrFromStride(const ::oneflow::Int64ListProto& stride) { stride.dim(), [this](int64_t v) -> Attribute { return getSI64IntegerAttr(v); }))); } -void WriteAttrToShape(mlir::Attribute& attr, ::oneflow::ShapeProto* shape) { - for (auto v : attr.dyn_cast().getValue()) { - shape->add_dim(v.dyn_cast().getSInt()); - } -} - -void WriteAttrToStride(mlir::Attribute& attr, ::oneflow::Int64ListProto* stride) { - for (auto v : attr.dyn_cast().getValue()) { - stride->add_dim(v.dyn_cast().getSInt()); - } -} - LogicalResult Importer::namedAttributesFromUserOp(const ::oneflow::OperatorConf& op, std::vector& attr_vec) { if (op.has_user_conf() == false) { @@ -325,33 +313,6 @@ LogicalResult Importer::AddOpConf(const ::oneflow::OperatorConf& op, return success(); } -llvm::Optional Importer::GetTypeFromOneFlowDataType(::oneflow::DataType dt) { - { - if (dt == ::oneflow::DataType::kInvalidDataType) { - return InvalidElementType::get(GetMLIRContext()); - } - if (dt == ::oneflow::DataType::kChar) { return CharElementType::get(GetMLIRContext()); } - if (dt == ::oneflow::DataType::kFloat) { return GetBuilder().getF32Type(); } - if (dt == ::oneflow::DataType::kDouble) { return GetBuilder().getF64Type(); } - if (dt == ::oneflow::DataType::kInt8) { return GetBuilder().getIntegerType(8, true); } - if (dt == ::oneflow::DataType::kInt32) { return GetBuilder().getI32Type(); } - if (dt == ::oneflow::DataType::kInt64) { return GetBuilder().getI64Type(); } - if (dt == ::oneflow::DataType::kUInt8) { return GetBuilder().getIntegerType(8, false); } - if (dt == ::oneflow::DataType::kOFRecord) { return OFRecordElementType::get(GetMLIRContext()); } - if (dt == ::oneflow::DataType::kFloat16) { return GetBuilder().getF16Type(); } - if (dt == ::oneflow::DataType::kTensorBuffer) { - return TensorBufferElementType::get(GetMLIRContext()); - } - if (dt == ::oneflow::DataType::kBool) { return GetBuilder().getI8Type(); } - if (dt == ::oneflow::DataType::kUInt16) { return GetBuilder().getIntegerType(16, false); } - if (dt == ::oneflow::DataType::kUInt32) { return GetBuilder().getI32Type(); } - if (dt == ::oneflow::DataType::kUInt64) { return GetBuilder().getI64Type(); } - if (dt == ::oneflow::DataType::kUInt128) { return GetBuilder().getIntegerType(128, false); } - llvm::errs() << "unsupported data type: " << dt << "\n"; - return llvm::None; - } -} - LogicalResult ParseNdSbpFromAttr(::llvm::ArrayRef nd_sbp_attr, ::oneflow::NdSbp* nd_sbp) { for (const auto& sbp_attr : nd_sbp_attr) { @@ -390,7 +351,8 @@ Attribute ConvertNdSbpToAttr(Builder& builder, const ::oneflow::NdSbp& nd_sbp) { } else if (sbp.has_partial_sum_parallel()) { sbp_strs.emplace_back("P"); } else { - llvm::errs() << "unsupported sbp"; + llvm::errs() << "unsupported sbp: " << nd_sbp.DebugString(); + exit(EXIT_FAILURE); } } return builder.getStrArrayAttr( @@ -428,7 +390,7 @@ LogicalResult Importer::ProcessUserOp(const ::oneflow::OperatorConf& op) { GetBuilder().getStringAttr(op.user_conf().op_type_name()))); std::vector<::mlir::Value> operand_vec; if (failed(namedAttributesFromUserOp(op, attr_vec))) { return failure(); } - const auto& op_def = GetUserOpDef(op.user_conf().op_type_name()); + const auto& op_def = support::getUserOpDef(op.user_conf().op_type_name()); if (failed(ValidateUserOpConf(op, op.user_conf().input(), op_def.input()))) { return failure(); } if (failed(ValidateUserOpConf(op, op.user_conf().output(), op_def.output()))) { return failure(); @@ -475,6 +437,7 @@ LogicalResult Importer::ProcessUserOp(const ::oneflow::OperatorConf& op) { state.addAttributes(named_attributes); state.addOperands(operands); state.addTypes(out_types); + SetOpStateLoc(op, state); created_op = GetBuilder().create(state); if (created_op == nullptr) { @@ -498,194 +461,12 @@ LogicalResult ConvertCtrlInputs(Operation* op, ::oneflow::OperatorConf& op_conf) return success(); } -template class Trait> -const std::vector* GetFullKeys(UserOpCompatible& uc, Operation* op); -template class Trait> -std::vector GetFullKeys(UserOp op); - -template<> -const std::vector* GetFullKeys(UserOpCompatible& uc, - Operation* op) { - if (auto alternative_name = dyn_cast(op)) { - return alternative_name.inputKeys(); - } - return uc.inputKeys(); -} - -template<> -const std::vector* GetFullKeys(UserOpCompatible& uc, - Operation* op) { - if (auto alternative_name = dyn_cast(op)) { - return alternative_name.outputKeys(); - } - return uc.outputKeys(); -} - -template<> -std::vector GetFullKeys(UserOp op) { - return mlir::oneflow::support::GetInputKeys(op.op_type_name().str()); -} - -template<> -std::vector GetFullKeys(UserOp op) { - return mlir::oneflow::support::GetOutputKeys(op.op_type_name().str()); -} - -template class Trait> -std::pair getODSIndexAndLength(UserOpCompatible& op, unsigned index); - -template<> -std::pair getODSIndexAndLength( - UserOpCompatible& op, unsigned index) { - return op.getODSOperandIndexAndLength(index); -} - -template<> -std::pair getODSIndexAndLength( - UserOpCompatible& op, unsigned index) { - return op.getODSResultIndexAndLength(index); -} - -template class Trait> -StringRef GetSegmentSizeAttr(); - -template<> -StringRef GetSegmentSizeAttr() { - return OpTrait::AttrSizedOperandSegments::getOperandSegmentSizeAttr(); -} - -template<> -StringRef GetSegmentSizeAttr() { - return OpTrait::AttrSizedResultSegments::getResultSegmentSizeAttr(); -} - -template class Trait> -int32_t GetSingleSegmentSize(Operation*); - -template<> -int32_t GetSingleSegmentSize(Operation* op) { - return op->getNumOperands(); -} - -template<> -int32_t GetSingleSegmentSize(Operation* op) { - return op->getNumResults(); -} - -template class Trait> -ArrayAttr GetUserOpArgSizes(UserOp); - -template<> -ArrayAttr GetUserOpArgSizes(UserOp op) { - return op.input_sizes(); -} - -template<> -ArrayAttr GetUserOpArgSizes(UserOp op) { - return op.output_sizes(); -} - -template class Trait> -LogicalResult GetUserOpFilteredSegmentKeyAndSizes(UserOp op, std::vector& keys, - std::vector& sizes) { - auto full_keys = GetFullKeys(op); - for (const auto& key_size_tuple : llvm::zip(full_keys, GetUserOpArgSizes(op).getValue())) { - const std::string& key = std::get<0>(key_size_tuple); - const int32_t size = - std::get<1>(key_size_tuple).template cast().getValue().getSExtValue(); - if (size > 0) { - keys.push_back(key); - sizes.push_back(size); - } - } - return success(); -} - -template class Trait> -LogicalResult GetFilteredSegmentKeyAndSizes(Operation* op, std::vector& keys, - std::vector& sizes) { - if (auto user_op = dyn_cast(op)) { - return GetUserOpFilteredSegmentKeyAndSizes(user_op, keys, sizes); - } - const std::vector* full_keys = nullptr; - std::vector full_sizes{}; - auto uc = dyn_cast(op); - if (!uc) { - op->emitError("interface UserOpCompatible not supported"); - return failure(); - } - full_keys = GetFullKeys(uc, op); - if (op->hasTrait()) { - const StringRef attr_name = GetSegmentSizeAttr(); - const DenseIntElementsAttr& size_attr = op->getAttrOfType(attr_name); - if (!size_attr) return failure(); - auto segment_sizes = size_attr.getValues(); - if (full_keys->size() != segment_sizes.size()) { - op->emitError() << "fail to convert op inputs, attr_name: " << attr_name - << ", full_keys: " << full_keys->size() - << ", segment_sizes: " << segment_sizes.size() << ", name: " << op->getName(); - op->dump(); - return failure(); - }; - full_sizes = {segment_sizes.begin(), segment_sizes.end()}; - } else { - if (full_keys->size() == 1) { - full_sizes.push_back(GetSingleSegmentSize(op)); - } else { - for (const auto& key : llvm::enumerate(*full_keys)) { - full_sizes.push_back(getODSIndexAndLength(uc, key.index()).second); - } - } - } - for (const auto& key_size_tuple : llvm::zip(*full_keys, full_sizes)) { - const std::string& key = std::get<0>(key_size_tuple); - const int32_t size = std::get<1>(key_size_tuple); - if (size > 0) { - keys.push_back(key); - sizes.push_back(size); - } - } - return success(); -} - -llvm::Optional GetOutputLbn(OpResult result) { - const auto def_op = result.getDefiningOp(); - if (def_op->hasTrait()) { - return def_op - ->getAttrOfType( - OpTrait::IsImportCompatible::getOutputLBNsAttr())[result.getResultNumber()] - .dyn_cast() - .getValue() - .str(); - } else { - std::vector def_op_keys{}; - std::vector def_op_sizes{}; - if (failed(GetFilteredSegmentKeyAndSizes(def_op, def_op_keys, - def_op_sizes))) { - def_op->emitError("fail to get output lbn"); - return llvm::None; - } - const auto result_number = result.getResultNumber(); - uint32_t size_sum = 0; - for (const auto& name_size_tuple : llvm::zip(def_op_keys, def_op_sizes)) { - auto name = std::get<0>(name_size_tuple); - auto size = std::get<1>(name_size_tuple); - if ((size_sum + size) > result_number) { - const uint32_t bn_i = result_number - size_sum; - return OpTrait::IsOpConfCompatible::getOpName(def_op).str() + "/" + name + "_" - + std::to_string(bn_i); - } - size_sum += size; - } - } - return llvm::None; -} - LogicalResult ConvertUserOpInputs(Operation* op, StringRef op_name, ::oneflow::UserOpConf* user_conf) { std::vector keys{}; std::vector sizes{}; - if (failed(GetFilteredSegmentKeyAndSizes(op, keys, sizes))) { + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { op->emitError("fail to convert user op inputs"); return failure(); } @@ -698,7 +479,7 @@ LogicalResult ConvertUserOpInputs(Operation* op, StringRef op_name, for (int32_t i = 0; i < input_size; i++) { if (auto result = GetDataInputOperands(op)[input_idx].dyn_cast()) { auto input_s_ptr = (*user_conf->mutable_input())[input_key].mutable_s()->Add(); - *(input_s_ptr) = GetOutputLbn(result).getValue(); + *(input_s_ptr) = user_op::GetOutputLbn(result).getValue(); input_idx += 1; } else { op->emitError() << "fail to convert MLIR result to protobuf, name: " + op_name; @@ -714,7 +495,8 @@ LogicalResult ConvertUserOpOutputs(Operation* op, StringRef op_name, ::oneflow::UserOpConf* user_conf) { std::vector keys{}; std::vector sizes{}; - if (failed(GetFilteredSegmentKeyAndSizes(op, keys, sizes))) { + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { op->emitError("fail to convert user op outputs"); return failure(); } @@ -759,7 +541,8 @@ LogicalResult ConvertDTFromAttr(Attribute attr, ::oneflow::DataType& data_type) return ConvertDT(dt_attr.getValue(), data_type); } -LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::OperatorConf& op_conf) { +LogicalResult ConvertUserOpAttributes(Operation* op, ::oneflow::OperatorConf& op_conf, + bool is_mapping_size) { auto user_conf = op_conf.mutable_user_conf(); std::string op_type_name = GetOpTypeName(op); op_conf.mutable_user_conf()->set_op_type_name(op_type_name); @@ -769,10 +552,22 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat } } + auto writeAttrToShape = [](mlir::Attribute& attr, ::oneflow::ShapeProto* shape) { + for (auto v : attr.dyn_cast().getValue()) { + shape->add_dim(v.dyn_cast().getSInt()); + } + }; + + auto writeAttrToStride = [](mlir::Attribute& attr, ::oneflow::Int64ListProto* stride) { + for (auto v : attr.dyn_cast().getValue()) { + stride->add_dim(v.dyn_cast().getSInt()); + } + }; + for (auto id_attr : op->getAttrDictionary()) { auto id = id_attr.getName(); // mlir only attrs - // TODO: find a way to skip attrs like callee in a declarative way + // TODO: prefix special attributes with "oneflow.". For example: `oneflow.op_type_name = "add"` if (id.strref().equals("callee") || id.strref().equals(OpTrait::IsOpConfCompatible::getDeviceNameAttr()) || id.strref().equals(OpTrait::IsOpConfCompatible::getHierarchyAttr()) @@ -799,7 +594,7 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat auto attr_name = id.str(); Attribute attr = id_attr.getValue(); auto user_attr = ::oneflow::AttrValue(); - const ::oneflow::AttrType attr_type = QueryAttrType(op_type_name, attr_name); + const ::oneflow::AttrType attr_type = user_op::queryAttrType(op_type_name, attr_name); if (attr_type == ::oneflow::kAtInt32) { user_attr.set_at_int32(attr.dyn_cast().getSInt()); } else if (attr_type == ::oneflow::kAtInt64) { @@ -813,13 +608,13 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat } else if (attr_type == ::oneflow::kAtString) { user_attr.set_at_string(attr.dyn_cast().getValue().str()); } else if (attr_type == ::oneflow::kAtShape) { - WriteAttrToShape(attr, user_attr.mutable_at_shape()); + writeAttrToShape(attr, user_attr.mutable_at_shape()); } else if (attr_type == ::oneflow::kAtStride) { - WriteAttrToStride(attr, user_attr.mutable_at_stride()); + writeAttrToStride(attr, user_attr.mutable_at_stride()); } else if (attr_type == ::oneflow::kAtDataType) { - ::oneflow::DataType dt = ::oneflow::kInvalidDataType; - if (succeeded(ConvertDTFromAttr(attr, dt))) { - user_attr.set_at_data_type(dt); + const auto dt = support::FromMLIRAttrToOFDataType(attr); + if (succeeded(dt)) { + user_attr.set_at_data_type(dt.getValue()); } else { op->emitError() << "fail to convert op attr to data type, key: " + id.str(); return failure(); @@ -845,9 +640,9 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat } } else if (attr_type == ::oneflow::kAtListDataType) { for (auto v : attr.dyn_cast().getValue()) { - ::oneflow::DataType dt = ::oneflow::kInvalidDataType; - if (succeeded(ConvertDTFromAttr(v, dt))) { - user_attr.mutable_at_list_data_type()->add_val(dt); + const auto dt = support::FromMLIRAttrToOFDataType(attr); + if (succeeded(dt)) { + user_attr.mutable_at_list_data_type()->add_val(dt.getValue()); } else { op->emitError() << "fail to convert op attr to data type, key: " + id.str(); return failure(); @@ -856,12 +651,12 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat } else if (attr_type == ::oneflow::kAtListShape) { for (auto shape_attr : attr.dyn_cast().getValue()) { ::oneflow::ShapeProto* shape_ptr = user_attr.mutable_at_list_shape()->add_val(); - WriteAttrToShape(shape_attr, shape_ptr); + writeAttrToShape(shape_attr, shape_ptr); } } else if (attr_type == ::oneflow::kAtListStride) { for (auto stride_attr : attr.dyn_cast().getValue()) { ::oneflow::Int64ListProto* stride_ptr = user_attr.mutable_at_list_stride()->add_val(); - WriteAttrToStride(stride_attr, stride_ptr); + writeAttrToStride(stride_attr, stride_ptr); } } else if (attr_type == ::oneflow::kAtListString) { // attr like nd_sbp requires the existence of list even it is empty @@ -879,24 +674,53 @@ LogicalResult Importer::ConvertUserOpAttributes(Operation* op, ::oneflow::Operat { std::vector keys{}; std::vector sizes{}; - if (failed(GetFilteredSegmentKeyAndSizes(op, keys, sizes))) { + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { op->emitError("fail to convert user op input order"); return failure(); } for (const auto& s : keys) { op_conf.mutable_user_conf()->add_input_order(s); } + + if (is_mapping_size) { + for (const auto it : llvm::zip(keys, sizes)) { + auto key = std::get<0>(it).c_str(); + auto size = std::get<1>(it); + auto tar = op_conf.mutable_user_conf()->mutable_input(); + auto val = ::oneflow::UserOpConf_ListString::default_instance(); + tar->insert({key, val}); + for (int i = 0; i < size; ++i) { tar->at(key).add_s(); } + } + } } { std::vector keys{}; std::vector sizes{}; - if (failed(GetFilteredSegmentKeyAndSizes(op, keys, sizes))) { + if (failed(user_op::GetFilteredSegmentKeyAndSizes(op, keys, + sizes))) { op->emitError("fail to convert user op output order"); return failure(); } for (const auto& s : keys) { op_conf.mutable_user_conf()->add_output_order(s); } + if (is_mapping_size) { + for (const auto it : llvm::zip(keys, sizes)) { + auto key = std::get<0>(it).c_str(); + auto size = std::get<1>(it); + auto tar = op_conf.mutable_user_conf()->mutable_output(); + auto val = ::oneflow::UserOpConf_ListString::default_instance(); + tar->insert({key, val}); + for (int i = 0; i < size; ++i) { tar->at(key).add_s(); } + } + } } return success(); } +void Importer::SetOpStateLoc(const ::oneflow::OperatorConf& op_conf, OperationState& state) { + if (op_conf.has_loc()) { + state.location = (FileLineColLoc::get(GetMLIRContext(), op_conf.loc(), 0, 0)); + } +} + LogicalResult ConvertVariableOpConf(VariableOp op, ::oneflow::OperatorConf* op_conf) { op_conf->set_name(op.op_name().str()); op_conf->set_device_tag(op.device_tag().str()); @@ -910,15 +734,15 @@ LogicalResult ConvertVariableOpConf(VariableOp op, ::oneflow::OperatorConf* op_c if (auto shape_attr = op->getAttrOfType(OpTrait::TensorSource::getShapeAttrName())) { - WriteAttrToShape(shape_attr, var_op_conf->mutable_shape()); + *var_op_conf->mutable_shape() = user_op::getAttrAsShape(shape_attr); } if (op->hasAttr(OpTrait::TensorSource::getDataTypeAttrName())) { - ::oneflow::DataType dt = ::oneflow::DataType::kInvalidDataType; if (auto dt_mlir = op.data_type()) { - if (failed(ConvertDT(dt_mlir.getValue(), dt))) { return failure(); } + const auto dt = support::FromMLIRDataTypeToOFDataType(dt_mlir.getValue()); + if (failed(dt)) { return failure(); } + var_op_conf->set_data_type(dt.getValue()); } - var_op_conf->set_data_type(dt); } if (op->hasAttr("model_name")) { var_op_conf->set_model_name(op.model_name().str()); } @@ -935,10 +759,19 @@ LogicalResult ConvertVariableOpConf(VariableOp op, ::oneflow::OperatorConf* op_c if (op->hasAttr("trainable")) { var_op_conf->set_trainable(op.trainable()); } - for (const auto& sbp : op.nd_sbp()) { - var_op_conf->add_nd_sbp(sbp.cast().getValue().str()); + for (auto output : op.parallel()->getOutputs()) { + if (auto nd_outputs = output.dyn_cast()) { + for (auto nd_output : nd_outputs) { + std::string sbp{}; + if (failed(SBPTranslation::PrintSbpAttrToString(nd_output, sbp))) return failure(); + var_op_conf->add_nd_sbp(sbp); + } + } else { + std::string sbp{}; + if (failed(SBPTranslation::PrintSbpAttrToString(output, sbp))) return failure(); + var_op_conf->add_nd_sbp(sbp); + } } - // all operands are ctrl_inputs for (const auto& operand : op->getOperands()) { op_conf->add_ctrl_in_op_name( @@ -971,15 +804,15 @@ LogicalResult ConvertInputOpConf(InputOp op, ::oneflow::OperatorConf* op_conf) { if (auto shape_attr = op->getAttrOfType(OpTrait::TensorSource::getShapeAttrName())) { - WriteAttrToShape(shape_attr, input_op_conf->mutable_blob_conf()->mutable_shape()); + *input_op_conf->mutable_blob_conf()->mutable_shape() = user_op::getAttrAsShape(shape_attr); } if (op->hasAttr(OpTrait::TensorSource::getDataTypeAttrName())) { - ::oneflow::DataType dt = ::oneflow::DataType::kInvalidDataType; if (auto dt_mlir = op.data_type()) { - if (failed(ConvertDT(dt_mlir.getValue(), dt))) { return failure(); } + const auto dt = support::FromMLIRDataTypeToOFDataType(dt_mlir.getValue()); + if (failed(dt)) { return failure(); } + input_op_conf->mutable_blob_conf()->set_data_type(dt.getValue()); } - input_op_conf->mutable_blob_conf()->set_data_type(dt); } if (op->hasAttr(OpTrait::TensorSource::getIsDynamicAttrName())) { @@ -1017,15 +850,15 @@ LogicalResult ConvertOutputOpConf(OutputOp op, ::oneflow::OperatorConf* op_conf) if (auto shape_attr = op->getAttrOfType(OpTrait::TensorSource::getShapeAttrName())) { - WriteAttrToShape(shape_attr, output_op_conf->mutable_blob_conf()->mutable_shape()); + *output_op_conf->mutable_blob_conf()->mutable_shape() = user_op::getAttrAsShape(shape_attr); } if (op->hasAttr(OpTrait::TensorSource::getDataTypeAttrName())) { - ::oneflow::DataType dt = ::oneflow::DataType::kInvalidDataType; if (auto dt_mlir = op.data_type()) { - if (failed(ConvertDT(dt_mlir.getValue(), dt))) { return failure(); } + const auto dt = support::FromMLIRDataTypeToOFDataType(dt_mlir.getValue()); + if (failed(dt)) { return failure(); } + output_op_conf->mutable_blob_conf()->set_data_type(dt.getValue()); } - output_op_conf->mutable_blob_conf()->set_data_type(dt); } if (op->hasAttr(OpTrait::TensorSource::getIsDynamicAttrName())) { @@ -1046,7 +879,7 @@ LogicalResult ConvertOutputOpConf(OutputOp op, ::oneflow::OperatorConf* op_conf) return failure(); } auto result = op->getOperand(0).dyn_cast(); - auto output_lbn = GetOutputLbn(result).getValue(); + auto output_lbn = user_op::GetOutputLbn(result).getValue(); output_op_conf->set_in(output_lbn); for (size_t i = 1; i < op->getNumOperands(); ++i) { op_conf->add_ctrl_in_op_name( diff --git a/oneflow/ir/oneflow-translate/lib/OneFlow/MLIROneFlowTranslation.cpp b/oneflow/ir/oneflow-translate/lib/OneFlow/MLIROneFlowTranslation.cpp index 9491b71559361e4027c641c637bf98b39f762dae..8a264be05825fa5123b9c1f0d574fac4d6cf3d4c 100644 --- a/oneflow/ir/oneflow-translate/lib/OneFlow/MLIROneFlowTranslation.cpp +++ b/oneflow/ir/oneflow-translate/lib/OneFlow/MLIROneFlowTranslation.cpp @@ -14,6 +14,9 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include "OneFlow/OneFlowDataTypeConversion.h" +#include "OneFlow/UserOpReflection.h" +#include "OneFlow/Transform/AggregateComputeOps.h" #include "oneflow/core/common/util.h" #include "oneflow/core/common/data_type.pb.h" #include "oneflow/core/framework/user_op_conf.pb.h" @@ -26,7 +29,10 @@ limitations under the License. #include "OneFlow/OneFlowOpTraits.h" #include "OneFlow/Passes.h" #include "OneFlow/MLIROneFlowTranslation.h" +#include "OneFlow/OneFlowUtils.h" +#include "OneFlow/UserOpConversion.h" +#include "mlir/Dialect/Tosa/Transforms/Passes.h" #include "mlir/Dialect/Func/IR/FuncOps.h" #include "mlir/IR/Attributes.h" #include "mlir/IR/Builders.h" @@ -175,12 +181,12 @@ LogicalResult JobImporter::AddDeviceName(const ::oneflow::OperatorConf& op, } Type JobImporter::GetTensorTypeOfLbn(const std::string& lbn) { - Type ret = this->GetBuilder().getNoneType(); + Type ret{}; job_wrapper_.QueryLogicalBlob( lbn, [this, &ret, &lbn](const int64_t* shape_begin, const int64_t* shape_end, ::oneflow::DataType dt) { - if (auto t = this->GetTypeFromOneFlowDataType(dt)) { - ret = RankedTensorType::get(ArrayRef(shape_begin, shape_end), t.getValue()); + if (auto t = getTypeFromOneFlowDataType(GetMLIRContext(), dt)) { + ret = RankedTensorType::get(ArrayRef(shape_begin, shape_end), t); } else { llvm::errs() << "fail to get data tensor type for: " << lbn << "\n"; } @@ -311,12 +317,15 @@ LogicalResult JobImporter::ProcessVariableOp(const ::oneflow::OperatorConf& op_c GetBuilder().getNamedAttr("integer_initializer", const_initialize_attr)); } } - // attr nd_sbp - const std::vector nd_sbp_str_vec{op_conf.variable_conf().nd_sbp().begin(), - op_conf.variable_conf().nd_sbp().end()}; - auto nd_sbp_attr = GetBuilder().getStrArrayAttr(makeArrayRef(nd_sbp_str_vec)); + // attr parallel + auto conf = this->job_wrapper_.ParallelConf4OpName(op_conf.name()); + + auto nd_size = conf.hierarchy().dim().size(); + auto nd_sbp = op_conf.variable_conf().nd_sbp(); + auto parallel = mlir::oneflow::SBPTranslation::ConvertNdSbpToPsig( + GetBuilder(), std::vector(nd_sbp.begin(), nd_sbp.end()), nd_size); attr_vec.emplace_back( - GetBuilder().getNamedAttr(OpTrait::TensorSource::getNdSbpAttrName(), nd_sbp_attr)); + GetBuilder().getNamedAttr(OpTrait::TensorSource::getSbpAttrName(), parallel)); // add attrs state.addAttributes(attr_vec); // operands @@ -329,6 +338,7 @@ LogicalResult JobImporter::ProcessVariableOp(const ::oneflow::OperatorConf& op_c out_types.push_back(GetTensorTypeOfLbn(output_lbn)); if (failed(AppendCtrlOutType(out_types))) { return failure(); } state.addTypes(out_types); + SetOpStateLoc(op_conf, state); // create op auto op = GetBuilder().create(state); if (!op) { @@ -575,13 +585,6 @@ LogicalResult JobImporter::ProcessJob() { } }); if (is_succeeded == false) { return failure(); } - SmallVector loss_tensors; - for (auto& loss_lbn : job_wrapper_.job()->job_conf().train_conf().loss_lbn()) { - loss_tensors.push_back(lbn2result_.at(loss_lbn)); - } - if (job_wrapper_.job()->job_conf().train_conf().loss_lbn_size() > 0) { - GetBuilder().create(GetRootLocation(), loss_tensors); - } mlir::oneflow::ReturnOp return_op; if (!entryBlock->empty()) { return_op = dyn_cast(entryBlock->back()); } if (!return_op) { GetBuilder().create(GetRootLocation(), results); } @@ -714,7 +717,7 @@ LogicalResult JobImporter::ConvertUserOp(Operation* op, ::oneflow::Job& job) { op->emitError("fail to convert user op outputs"); return failure(); } - if (!succeeded(ConvertUserOpAttributes(op, *op_conf))) { + if (!succeeded(user_op::ConvertUserOpAttributes(op, *op_conf))) { op->emitError("fail to convert user op attributes"); return failure(); } @@ -732,7 +735,7 @@ LogicalResult JobImporter::ConvertSystemOp(Operation* op, ::oneflow::Job& job) { ::oneflow::OperatorConf op_conf = job_wrapper_.OpConf4OpName(op_name); for (const auto& ibn : llvm::enumerate(op->getAttrOfType("input_bns"))) { auto result = GetDataInputOperands(op)[ibn.index()].dyn_cast(); - std::string new_val = GetOutputLbn(result).getValue(); + std::string new_val = user_op::GetOutputLbn(result).getValue(); job_wrapper_.ReplaceInputLbnInOpCustomizedConf( &op_conf, ibn.value().dyn_cast().getValue().str(), new_val); } @@ -764,11 +767,13 @@ LogicalResult JobImporter::ConvertOutputOp(OutputOp op, ::oneflow::Job& job) { Type JobImporter::GetInterfaceBlobConfType(const ::oneflow::InterfaceBlobConf& blob_conf) { if (!blob_conf.has_data_type()) { return Type{}; } - if (!blob_conf.has_shape()) { return Type{}; } - auto data_type = GetTypeFromOneFlowDataType(blob_conf.data_type()); - if (!data_type.hasValue()) { return Type{}; } - return RankedTensorType::get({blob_conf.shape().dim().begin(), blob_conf.shape().dim().end()}, - *data_type); + if (!blob_conf.has_shape()) { return Type{}; }; + if (auto data_type = getTypeFromOneFlowDataType(GetMLIRContext(), blob_conf.data_type())) { + return RankedTensorType::get({blob_conf.shape().dim().begin(), blob_conf.shape().dim().end()}, + data_type); + } else { + return Type{}; + } } void DumpMLIR(RoundTripOneFlowJobWrapperInterface& job_wrapper, ModuleOp module, @@ -782,29 +787,67 @@ void DumpMLIR(RoundTripOneFlowJobWrapperInterface& job_wrapper, ModuleOp module, LogicalResult ApplyRoundTripPatterns(RoundTripOneFlowJobWrapperInterface& job_wrapper, MLIRContext* context, OwningOpRef& module) { mlir::PassManager pm(context); + if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_TIMING", false)) { pm.enableTiming(); } + mlir::oneflow::CheckEnableIRPrinting(pm); // this canonicalizer should create concrete ops and create fuse opportunities pm.addPass(createCanonicalizerPass()); std::string graphviz; - if (job_wrapper.IsLastIRPass() && std::getenv("ONEFLOW_MLIR_ENABLE_CODEGEN_FUSERS") != nullptr) { + if (job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_CODEGEN_FUSERS", false)) { pm.addPass(oneflow::createOutlineJitFunctionPass()); } // we must do auto nhwc and eliminate redundant transpose op first, avoid insert redundant // transpose op due to fuse pattern like normlazation_add_relu. pm.addPass(oneflow::createAutoNhwcPass()); - pm.addPass(oneflow::createFuseIntoExistingOpPass()); - if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION", false)) { + if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_CSE", false)) { + auto cse_state = std::make_shared(); + auto passes = createCSEPasses(cse_state); + pm.addPass(std::move(passes.first)); + pm.addPass(createCSEPass()); + pm.addPass(std::move(passes.second)); + } + if (job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_FUSE_FORWARD_OPS", false)) { + pm.addPass(oneflow::createFuseForwardOps()); + pm.addPass(oneflow::createFuseIntoExistingOpPass()); + } + // TODO: support backward or put it in a env flag + if (job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_GROUP_MATMUL", false)) { + pm.addPass(oneflow::createGroupMatMul()); + } + if (!job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION", false)) { pm.addPass(oneflow::createPreConvertInferenceOpPass()); pm.addPass(oneflow::createConvertInferenceOpPass()); pm.addPass(oneflow::createPostConvertInferenceOpPass()); } + if (!job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_FUSE_NORMALIZATION_OPS", false)) { + pm.addPass(oneflow::createFuseNormalizationOps()); + } + if (job_wrapper.IsLastIRPass() + && ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH", false)) { + pm.addPass(createAggregateComputeOpsPass()); + pm.addPass(createWrapOpsToKernelLaunchPass()); + } pm.addPass(createCanonicalizerPass()); - llvm::raw_string_ostream os_graphviz(graphviz); - pm.addPass(createPrintOpGraphPass(os_graphviz)); + if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_PRINT_STATS", false)) { + pm.addPass(createPrintOpStatsPass()); + } + const bool shouldPrintGraphviz = + ::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_PRINT_OP_GRAPH", false); + if (shouldPrintGraphviz) { + llvm::raw_string_ostream os_graphviz(graphviz); + pm.addPass(createPrintOpGraphPass(os_graphviz)); + } if (mlir::failed(pm.run(*module))) { module->emitError("Failed to run round-trip passes"); return failure(); } - job_wrapper.DumpLog("RoundTripOneFlowJob.optimized.mlir.dot", graphviz); + if (shouldPrintGraphviz) { + job_wrapper.DumpLog("RoundTripOneFlowJob.optimized.mlir.dot", graphviz); + } DumpMLIR(job_wrapper, module.get(), "RoundTripOneFlowJob.optimized"); return success(); } @@ -835,7 +878,9 @@ void RoundTripOneFlowJob( if (succeeded(imp.ProcessJob())) { DumpMLIR(job_wrapper, module.get(), "RoundTripOneFlowJob.imported"); if (failed(ApplyRoundTripPatterns(job_wrapper, &context, module))) { exit(EXIT_FAILURE); } - if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false)) { + if (::oneflow::ParseBooleanFromEnv("ONEFLOW_MLIR_STDOUT", false) + && job_wrapper.IsLastIRPass()) { + // for FileCheck module->print(llvm::outs()); } // TODO: Add flag in oneflow to define if failure in MLIR is allowed @@ -862,7 +907,9 @@ std::string ConvertJobToTosaIR(RoundTripOneFlowJobWrapperInterface& job_wrapper) if (succeeded(imp.ProcessJob())) { mlir::PassManager pm(&context); pm.addPass(createCanonicalizerPass()); + pm.addPass(createConvertToSignlessForTosaPass()); pm.addPass(createLowerOneFlowToTosaPass()); + pm.addNestedPass(tosa::createTosaMakeBroadcastablePass()); if (mlir::failed(pm.run(*module))) { module->emitError("Failed to run oneflow-to-tosa pass"); exit(EXIT_FAILURE); @@ -879,6 +926,34 @@ std::string ConvertJobToTosaIR(RoundTripOneFlowJobWrapperInterface& job_wrapper) } } +std::string ConvertJobToIR(RoundTripOneFlowJobWrapperInterface& job_wrapper) { + const ::oneflow::Job* job = job_wrapper.job(); + mlir::MLIRContext context; + context.getOrLoadDialect(); + context.loadDialect(); + + OwningOpRef module( + ModuleOp::create(FileLineColLoc::get(&context, "", /*line=*/0, /*column=*/0))); + JobImporter imp(job_wrapper, &context, module.get()); + if (succeeded(imp.ProcessJob())) { + mlir::PassManager pm(&context); + pm.addPass(createCanonicalizerPass()); + if (mlir::failed(pm.run(*module))) { + module->emitError("Failed to run canonicalizer pass"); + exit(EXIT_FAILURE); + } + + std::string mlir; + llvm::raw_string_ostream os_mlir(mlir); + module->print(os_mlir); + return mlir; + } else { + const auto& job_name = job->job_conf().job_name(); + llvm::errs() << "Failed to convert Job to IR, job_name: " << job_name << "\n"; + exit(EXIT_FAILURE); + } +} + void SaveJobToIR(RoundTripOneFlowJobWrapperInterface& job_wrapper, const std::string& path) { const ::oneflow::Job* job = job_wrapper.job(); mlir::MLIRContext context; diff --git a/oneflow/ir/test/Frontend/OneFlowToIree.mlir b/oneflow/ir/test/Frontend/OneFlowToIree.mlir index daa56a14d3403de3746748c369cf47f900755be5..e35cfb7069ce4c1f035eaec8351698c3d2bd12de 100644 --- a/oneflow/ir/test/Frontend/OneFlowToIree.mlir +++ b/oneflow/ir/test/Frontend/OneFlowToIree.mlir @@ -1,6 +1,7 @@ // RUN: oneflow-opt %s \ // RUN: -split-input-file \ // RUN: -lower-oneflow-to-tosa \ +// RUN: -tosa-make-broadcastable \ // RUN: -verify-diagnostics -o - \ // RUN: | python3 -m iree.compiler.tools.scripts.ireec \ // RUN: --iree-input-type=tosa \ @@ -60,7 +61,7 @@ oneflow.job @test_variable() -> tensor<64x3x7x7xf32> device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], - nd_sbp = ["B"], + parallel = #sbp.parallel<[] -> [#sbp.B]>, op_name = "fw.model.conv1.weight", output_lbns = ["fw.model.conv1.weight/out"], scope_symbol_id = 4611686018427432959 : i64, @@ -167,23 +168,6 @@ oneflow.job @test_conv2d(%arg0: tensor<1x3x224x224xf32>, %arg1: tensor<5x3x1x1xf } -oneflow.job @test_flatten(%arg0: tensor<4x3x2x1xf32>) -> tensor<4x6x1xf32> -{ - %res = "oneflow.flatten"(%arg0) - { - device_name = ["@0:0"], - device_tag = "cpu", - end_dim = 2 : si32, - hierarchy = [1], - op_name = "", - output_lbns = [""], - scope_symbol_id = 4611686018431217663 : i64, - start_dim = 1 : si32 - } : (tensor<4x3x2x1xf32>) -> tensor<4x6x1xf32> - oneflow.return %res : tensor<4x6x1xf32> -} - - oneflow.job @test_matmul(%arg0: tensor<1x2048xf32>, %arg1: tensor<1000x2048xf32>) ->tensor<1x1000xf32> { %res = "oneflow.matmul"(%arg0, %arg1) diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_batchnorm_relu.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_batchnorm_relu.py index 7c103238f4a1b7be8545c754cb4221b23ae250c5..588553abf6572cd56b71bcf171bea730f90de2f4 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_batchnorm_relu.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_batchnorm_relu.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_bias_add.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_bias_add.py index 21201e5fddaf8e099b2e43a04973299b2ee2dd95..991c0d20b3289e5e11abb4e88fdb4c88092e1a27 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_bias_add.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_bias_add.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv.py index 81118c9489370504a43eee525f25d902f6fb562d..833d08add2b051c7bc9df20e794a7b5510c6f1df 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv2d_maxpool2d.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv2d_maxpool2d.py index 9a9333414caf27cc22712cf7c031af4f8d6a0b72..98d9aed9f56716d86d166257a9633542213f0a38 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv2d_maxpool2d.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv2d_maxpool2d.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv_relu_add.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv_relu_add.py index 36796d301f01f3545dfc5e50918de50568507fd9..42ba1c1886053072785dcf677d9eea3025fe885f 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv_relu_add.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_conv_relu_add.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_lenet.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_lenet.py index c4ca285d85f9179f45d7ad23fb6fe52bb31e07e2..4c1eb2ae7629a697057d68c29f0972da3658cee8 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_lenet.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_lenet.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_maxpool_2d.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_maxpool_2d.py index c7d74cec82861080de234692e943df8aefceea61..e8b87995dd1feae516c9bf0c061e55eb97b3b505 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_maxpool_2d.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_maxpool_2d.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_resnet.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_resnet.py index 4f4770a86d1118380d0786daa3cf1535c7eb3d02..a52e13bf4bd967694a0e8e5d86947c69d08e253b 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_resnet.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_resnet.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest import numpy as np diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_transpose_eliminate.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_transpose_eliminate.py index 859f4f8ea0d292580b8309f0ccec574d0e5afafd..1dc875e62cab8cef1663a06d385ca90f07109472 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_transpose_eliminate.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_nhwc_transpose_eliminate.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK-NOT: oneflow.transpose import unittest diff --git a/oneflow/ir/test/OneFlow/auto_nhwc/test_resnet101_benchmark.py b/oneflow/ir/test/OneFlow/auto_nhwc/test_resnet101_benchmark.py index 82598b1421db1e38ad5adcd11500a093acf43c2a..c81a6fbbc4b1170e3c061b337c88627aabe9c7ea 100644 --- a/oneflow/ir/test/OneFlow/auto_nhwc/test_resnet101_benchmark.py +++ b/oneflow/ir/test/OneFlow/auto_nhwc/test_resnet101_benchmark.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import unittest import numpy as np diff --git a/oneflow/ir/test/OneFlow/conversion/OneFlowToTosa.mlir b/oneflow/ir/test/OneFlow/conversion/OneFlowToTosa.mlir index 3028b3c04bfb981990039baa23d49e6ced37b080..07baecf5a2ac5bcdf8baa31ca7987da5aabb380d 100644 --- a/oneflow/ir/test/OneFlow/conversion/OneFlowToTosa.mlir +++ b/oneflow/ir/test/OneFlow/conversion/OneFlowToTosa.mlir @@ -65,7 +65,7 @@ oneflow.job @test_variable() -> tensor<64x3x7x7xf32> device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], - nd_sbp = ["B"], + parallel = #sbp.parallel<[] -> [#sbp.B]>, op_name = "fw.model.conv1.weight", output_lbns = ["fw.model.conv1.weight/out"], scope_symbol_id = 4611686018427432959 : i64, @@ -203,26 +203,6 @@ oneflow.job @test_conv2d(%arg0: tensor<1x3x224x224xf32>, %arg1: tensor<5x3x1x1xf } -//CHECK-LABEL: test_flatten -//CHECK: [[V0:%.+]] = "tosa.reshape"(%arg0) {new_shape = [4, 6, 1]} : (tensor<4x3x2x1xf32>) -> tensor<4x6x1xf32> -//CHECK: return [[V0]] : tensor<4x6x1xf32> -oneflow.job @test_flatten(%arg0: tensor<4x3x2x1xf32>) -> tensor<4x6x1xf32> -{ - %res = "oneflow.flatten"(%arg0) - { - device_name = ["@0:0"], - device_tag = "cpu", - end_dim = 2 : si32, - hierarchy = [1], - op_name = "", - output_lbns = [""], - scope_symbol_id = 4611686018431217663 : i64, - start_dim = 1 : si32 - } : (tensor<4x3x2x1xf32>) -> tensor<4x6x1xf32> - oneflow.return %res : tensor<4x6x1xf32> -} - - //CHECK-LABEL: test_matmul //CHECK: [[V0:%.+]] = "tosa.reshape"(%arg0) {new_shape = [1, 1, 2048]} : (tensor<1x2048xf32>) -> tensor<1x1x2048xf32> //CHECK: [[V1:%.+]] = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32> diff --git a/oneflow/ir/test/OneFlow/cse.mlir b/oneflow/ir/test/OneFlow/cse.mlir new file mode 100644 index 0000000000000000000000000000000000000000..5a76657a1311ded0e9f3e07ed9123eb910d73680 --- /dev/null +++ b/oneflow/ir/test/OneFlow/cse.mlir @@ -0,0 +1,24 @@ +// RUN: oneflow-opt %s \ +// RUN: -cse-with-attributes-ignored -cse -cse-put-attributes -canonicalize | FileCheck %s + +module { + func.func @Cast_1__FUSE__ScalarMulByTensor_2(%arg0: tensor<96x96xi64>) -> tensor<96x96xf32> { + %0 = "oneflow.cast"(%arg0) {device_name = ["0:0"], device_tag = "cpu", dtype = 2 : i32, hierarchy = [1], op_name = "Cast_1", op_type_name = "cast", scope_symbol_id = 4611686018427416574 : i64} : (tensor<96x96xi64>) -> tensor<96x96xf32> + %1 = "oneflow.cast"(%arg0) {device_name = ["0:0"], device_tag = "cpu", dtype = 2 : i32, hierarchy = [1], op_name = "Cast_2", op_type_name = "cast", scope_symbol_id = 4611686018427416574 : i64} : (tensor<96x96xi64>) -> tensor<96x96xf32> + %2 = "oneflow.add_n"(%0, %1) {device_name = ["0:0"], device_tag = "cpu", hierarchy = [1], op_name = "ScalarMulByTensor_2", op_type_name = "add_n", scope_symbol_id = 4611686018427416574 : i64} : (tensor<96x96xf32>, tensor<96x96xf32>) -> tensor<96x96xf32> + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.cast" + // CHECK: "oneflow.add_n2"(%[[OUT]], %[[OUT]]) + // CHECK: op_name = "ScalarMulByTensor_2" + return %2 : tensor<96x96xf32> + } + func.func @f2(%input: tensor<2x64x64x320xf16>, %w: tensor<320x320x3x3xf16>, %bias: tensor<320xf16>) -> (tensor<2x64x64x320xf16>, tensor<2x64x64x320xf16>) { + %transpose_w = "oneflow.transpose"(%w) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.down_blocks.0.resnets.0.conv1-conv2d-31_transpose_input_1", perm = [0 : si32, 2 : si32, 3 : si32, 1 : si32], scope_symbol_id = 163 : i64} : (tensor<320x320x3x3xf16>) -> tensor<320x3x3x320xf16> + %conv2d = "oneflow.conv2d"(%input, %transpose_w, %bias) {data_format = "channels_last", device_name = ["@0:0"], device_tag = "cuda", dilation_rate = [1 : si32, 1 : si32], filters = 320 : si32, groups = 1 : si32, hierarchy = [1], kernel_size = [3 : si32, 3 : si32], op_name = "unet.down_blocks.0.resnets.0.conv1-conv2d-31", operand_segment_sizes = dense<[1, 1, 1, 0]> : vector<4xi32>, padding_before = [1 : si32, 1 : si32], scope_symbol_id = 163 : i64, strides = [1 : si32, 1 : si32]} : (tensor<2x64x64x320xf16>, tensor<320x3x3x320xf16>, tensor<320xf16>) -> tensor<2x64x64x320xf16> + %transpose_w1 = "oneflow.transpose"(%w) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.down_blocks.0.resnets.0.conv1-conv2d-31_transpose_input_2", perm = [0 : si32, 2 : si32, 3 : si32, 1 : si32], scope_symbol_id = 163 : i64} : (tensor<320x320x3x3xf16>) -> tensor<320x3x3x320xf16> + %conv2d_2 = "oneflow.conv2d"(%input, %transpose_w1, %bias) {data_format = "channels_last", device_name = ["@0:0"], device_tag = "cuda", dilation_rate = [1 : si32, 1 : si32], filters = 320 : si32, groups = 1 : si32, hierarchy = [1], kernel_size = [3 : si32, 3 : si32], op_name = "unet.down_blocks.0.resnets.0.conv1-conv2d-31", operand_segment_sizes = dense<[1, 1, 1, 0]> : vector<4xi32>, padding_before = [1 : si32, 1 : si32], scope_symbol_id = 163 : i64, strides = [1 : si32, 1 : si32]} : (tensor<2x64x64x320xf16>, tensor<320x3x3x320xf16>, tensor<320xf16>) -> tensor<2x64x64x320xf16> + return %conv2d, %conv2d_2 : tensor<2x64x64x320xf16>, tensor<2x64x64x320xf16> + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.conv2d" + // CHECK: scope_symbol_id = 163 : i64 + // CHECK: return %[[OUT]], %[[OUT]] + } +} diff --git a/oneflow/ir/test/OneFlow/cuda_code_gen/fuse_cast_scale.mlir b/oneflow/ir/test/OneFlow/cuda_code_gen/fuse_cast_scale.mlir index 9eaf154ac6fd661db6b001a68ae7cf459892df68..cef23581fb092b0799f679ec8dd08ac74c805b05 100644 --- a/oneflow/ir/test/OneFlow/cuda_code_gen/fuse_cast_scale.mlir +++ b/oneflow/ir/test/OneFlow/cuda_code_gen/fuse_cast_scale.mlir @@ -1,4 +1,4 @@ -// RUN: oneflow-opt %s -lower-oneflow-to-tosa -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ +// RUN: oneflow-opt %s -lower-oneflow-to-tosa -tosa-make-broadcastable -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ // RUN: -convert-parallel-loops-to-gpu -gpu-kernel-outlining -buffer-host-register -canonicalize \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,lower-affine,convert-gpu-to-nvvm,out-of-tree-gpu-to-cubin)' \ // RUN: --func-bufferize -buffer-results-to-out-params -gpu-copy-arg --tensor-bufferize \ @@ -12,7 +12,7 @@ // RUN: --shared-libs=%linalg_test_lib_dir/libmlir_c_runner_utils%shlibext \ // RUN: --entry-point-result=void -// RUN: oneflow-opt %s -lower-oneflow-to-tosa -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ +// RUN: oneflow-opt %s -lower-oneflow-to-tosa -tosa-make-broadcastable -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ // RUN: -convert-parallel-loops-to-gpu -gpu-kernel-outlining -buffer-host-register -canonicalize \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,lower-affine,convert-gpu-to-nvvm,out-of-tree-gpu-to-cubin)' \ // RUN: --func-bufferize --tensor-bufferize \ diff --git a/oneflow/ir/test/OneFlow/cuda_code_gen/gpu_copy_arg.mlir b/oneflow/ir/test/OneFlow/cuda_code_gen/gpu_copy_arg.mlir index f63e65b7431f1968c7adaa26c9223f2b8617714d..4513e91f5c0b02648e6cacdee954859afdba07dc 100644 --- a/oneflow/ir/test/OneFlow/cuda_code_gen/gpu_copy_arg.mlir +++ b/oneflow/ir/test/OneFlow/cuda_code_gen/gpu_copy_arg.mlir @@ -1,4 +1,4 @@ -// RUN: oneflow-opt %s -lower-oneflow-to-tosa -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ +// RUN: oneflow-opt %s -lower-oneflow-to-tosa -tosa-make-broadcastable -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -convert-linalg-to-parallel-loops -gpu-map-parallel-loops \ // RUN: -convert-parallel-loops-to-gpu -gpu-kernel-outlining -buffer-host-register -canonicalize \ // RUN: -pass-pipeline='gpu.module(strip-debuginfo,lower-affine,convert-gpu-to-nvvm,out-of-tree-gpu-to-cubin)' \ // RUN: --func-bufferize -buffer-results-to-out-params -gpu-copy-arg diff --git a/oneflow/ir/test/OneFlow/cuda_code_gen/test_fuser_cast_scale.py b/oneflow/ir/test/OneFlow/cuda_code_gen/test_fuser_cast_scale.py index 8d961630363b157d35035072c1965055f88fa6a2..191dc810e9c701910da4652c0a8b1c04a3133bb7 100644 --- a/oneflow/ir/test/OneFlow/cuda_code_gen/test_fuser_cast_scale.py +++ b/oneflow/ir/test/OneFlow/cuda_code_gen/test_fuser_cast_scale.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: jit import unittest diff --git a/oneflow/ir/test/OneFlow/folding/test_simple_multiply.py b/oneflow/ir/test/OneFlow/folding/test_simple_multiply.py index c07e307f82292b41adf03992acdee64a0057a1ad..5f0eb4364a475ef214214a419d649ee9764ced1c 100644 --- a/oneflow/ir/test/OneFlow/folding/test_simple_multiply.py +++ b/oneflow/ir/test/OneFlow/folding/test_simple_multiply.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK-NOT: oneflow.broadcast_mul import os @@ -28,39 +28,36 @@ os.environ["ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION"] = "1" class MultiplyModel(nn.Module): - def __init__(self): + def __init__(self, dtype=flow.float32): super().__init__() - self.x = nn.Parameter(flow.tensor([2, 2], dtype=flow.float32), False) - self.y = nn.Parameter(flow.tensor([3, 3], dtype=flow.float32), False) + self.dtype = dtype + self.x = nn.Parameter(flow.tensor([2, 2], dtype=self.dtype), False) + self.y = nn.Parameter(flow.tensor([3, 3], dtype=self.dtype), False) def forward(self): return self.x * self.y -class MultiplyModelComplex(nn.Module): - def __init__(self): - super().__init__() - self.x = nn.Parameter(flow.tensor([2, 2], dtype=flow.float32), False) - self.y = nn.Parameter(flow.tensor([3, 3], dtype=flow.float32), False) - self.z = nn.Parameter(flow.tensor([4, 5], dtype=flow.float32), False) +class MultiplyModelComplex(MultiplyModel): + def __init__(self, dtype=flow.float32): + super().__init__(dtype) + self.z = nn.Parameter(flow.tensor([4, 5], dtype=self.dtype), False) def forward(self): return self.x * self.y * self.z -class MultiplyModelWithInput(nn.Module): - def __init__(self): - super().__init__() - self.x = nn.Parameter(flow.tensor([2, 2], dtype=flow.float32), False) - self.y = nn.Parameter(flow.tensor([3, 3], dtype=flow.float32), False) +class MultiplyModelWithInput(MultiplyModel): + def __init__(self, dtype=flow.float32): + super().__init__(dtype) def forward(self, a: flow.Tensor, b: flow.Tensor): z = self.x * self.y return a + b + z -def _test_fold_multiply(test_case, module, with_cuda, *args): - model = module() +def _test_fold_multiply(test_case, module, with_cuda, *args, dtype=oneflow.float32): + model = module(dtype) if with_cuda: model.to("cuda") @@ -81,34 +78,57 @@ def _test_fold_multiply(test_case, module, with_cuda, *args): test_case.assertTrue( np.allclose(eager_res.numpy(), lazy_res.numpy(), rtol=1e-5, atol=1e-5) ) + test_case.assertTrue(eager_res.dtype == dtype and lazy_res.dtype == dtype) @flow.unittest.skip_unless_1n1d() class TestFoldMultiply(oneflow.unittest.TestCase): def test_fold_multiply(test_case): _test_fold_multiply(test_case, MultiplyModel, with_cuda=False) + _test_fold_multiply( + test_case, MultiplyModel, with_cuda=False, dtype=flow.float16 + ) @unittest.skipUnless(oneflow.sysconfig.with_cuda(), "only test cpu cases") def test_fold_multiply_cuda(test_case): _test_fold_multiply(test_case, MultiplyModel, with_cuda=True) + _test_fold_multiply( + test_case, MultiplyModel, with_cuda=True, dtype=flow.float16 + ) def test_fold_multiply_complex(test_case): _test_fold_multiply(test_case, MultiplyModelComplex, with_cuda=False) + _test_fold_multiply( + test_case, MultiplyModelComplex, with_cuda=False, dtype=flow.float16 + ) @unittest.skipUnless(oneflow.sysconfig.with_cuda(), "only test cpu cases") def test_fold_multiply_complex_cuda(test_case): _test_fold_multiply(test_case, MultiplyModelComplex, with_cuda=True) + _test_fold_multiply( + test_case, MultiplyModelComplex, with_cuda=True, dtype=flow.float16 + ) def test_fold_multiply_with_input(test_case): a = flow.tensor([3, 7], dtype=flow.float32) b = flow.tensor([9, -1], dtype=flow.float32) + a_fp16 = flow.tensor([3, 7], dtype=flow.float16) + b_fp16 = flow.tensor([9, -1], dtype=flow.float16) _test_fold_multiply(test_case, MultiplyModelWithInput, False, a, b) + _test_fold_multiply( + test_case, MultiplyModelWithInput, False, a_fp16, b_fp16, dtype=flow.float16 + ) @unittest.skipUnless(oneflow.sysconfig.with_cuda(), "only test cpu cases") def test_fold_multiply_with_input_cuda(test_case): a = flow.tensor([3, 7], dtype=flow.float32, device="cuda") b = flow.tensor([9, -1], dtype=flow.float32, device="cuda") + a_fp16 = flow.tensor([3, 7], dtype=flow.float16, device="cuda") + b_fp16 = flow.tensor([9, -1], dtype=flow.float16, device="cuda") _test_fold_multiply(test_case, MultiplyModelWithInput, True, a, b) + _test_fold_multiply( + test_case, MultiplyModelWithInput, True, a_fp16, b_fp16, dtype=flow.float16 + ) if __name__ == "__main__": diff --git a/oneflow/ir/test/OneFlow/fuse_forward_ops.mlir b/oneflow/ir/test/OneFlow/fuse_forward_ops.mlir new file mode 100644 index 0000000000000000000000000000000000000000..030209b3ac5bca289f4ecf0b09f5f6696743fe3e --- /dev/null +++ b/oneflow/ir/test/OneFlow/fuse_forward_ops.mlir @@ -0,0 +1,139 @@ +// RUN: oneflow-opt %s \ +// RUN: -fuse-forward-only-ops -fuse-into-existing-op -fuse-normalization-ops -convert-inference-op -canonicalize | FileCheck %s + +module { + func.func @Cast_1__FUSE__ScalarMulByTensor_2(%685: tensor<2x64x64x320xf16>, %output_574: tensor<320xf16>, %output_573: tensor<320xf16>) -> tensor<2x64x64x320xf16> { + %y_958, %mean_959, %inv_variance_960 = "oneflow.group_norm"(%685, %output_574, %output_573) {activation = "none", affine = true, data_format = "channels_last", device_name = ["@0:0"], device_tag = "cuda", epsilon = 1.000000e-05 : f64, hierarchy = [1], num_groups = 32 : si32, op_name = "unet.up_blocks.3.resnets.0.norm2-group_norm-877", operand_segment_sizes = dense<1> : vector<3xi32>, scope_symbol_id = 5517 : i64} : (tensor<2x64x64x320xf16>, tensor<320xf16>, tensor<320xf16>) -> (tensor<2x64x64x320xf16>, tensor<2x32xf32>, tensor<2x32xf32>) + %686 = "oneflow.silu"(%y_958) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.up_blocks.3.resnets.0.nonlinearity-silu-878", scope_symbol_id = 5466 : i64} : (tensor<2x64x64x320xf16>) -> tensor<2x64x64x320xf16> + // CHECK: activation = "silu" + // CHECK-NOT: oneflow.silu + return %686 : tensor<2x64x64x320xf16> + } + + func.func @GraphToRun_bias_add_and_dropout_0(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<5xf32>) -> (tensor<2x3x4x5xf32>, tensor<2x3x4x5xi8>) { + %0 = "oneflow.bias_add"(%arg0, %arg1) {axis = 3 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "bias_add-0", scope_symbol_id = 12 : i64} : (tensor<2x3x4x5xf32>, tensor<5xf32>) -> tensor<2x3x4x5xf32> + %out, %mask = "oneflow.dropout"(%0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "dropout-dropout-1", rate = 0.750000e+00 : f32, scope_symbol_id = 22 : i64} : (tensor<2x3x4x5xf32>) -> (tensor<2x3x4x5xf32>, tensor<2x3x4x5xi8>) + // CHECK: func.func @GraphToRun_bias_add_and_dropout_0(%[[A:[a-zA-Z0-9_]+]]: tensor<2x3x4x5xf32>, %[[B:[a-zA-Z0-9_]+]]: tensor<5xf32>) -> (tensor<2x3x4x5xf32>, tensor<2x3x4x5xi8>) + // CHECK: %[[MASK:[a-zA-Z0-9_]+]] = "oneflow.random_mask_like"(%[[A]]) + // CHECK: "oneflow.fused_bias_add_mask_scale"(%[[A]], %[[B]], %[[MASK]]) + // CHECK: scale = 4.000000e+00 + return %out, %mask : tensor<2x3x4x5xf32>, tensor<2x3x4x5xi8> + } + + func.func @GraphToRun_bias_add_and_gelu_0(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<5xf32>) -> tensor<2x3x4x5xf32> { + %0 = "oneflow.bias_add"(%arg0, %arg1) {axis = 3 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "bias_add-0", scope_symbol_id = 12 : i64} : (tensor<2x3x4x5xf32>, tensor<5xf32>) -> tensor<2x3x4x5xf32> + %out = "oneflow.gelu"(%0) {axis = 3 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "gelu-gelu-1", scope_symbol_id = 22 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + // CHECK: func.func @GraphToRun_bias_add_and_gelu_0(%[[A:[a-zA-Z0-9_]+]]: tensor<2x3x4x5xf32>, %[[B:[a-zA-Z0-9_]+]]: tensor<5xf32>) -> tensor<2x3x4x5xf32> + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.fused_bias_add_gelu"(%[[A]], %[[B]]) {axis = 3 : si32 + // CHECK： return %[[OUT0]] + return %out : tensor<2x3x4x5xf32> + } + + func.func @GraphToRun_pad_and_conv2d_0(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x5x6xf32> { + %output = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "conv.weight", output_lbns = ["conv.weight/out"], parallel = #sbp.parallel<[] -> [[#sbp.B]]>, scope_symbol_id = 73 : i64, shape = [3 : si64, 3 : si64, 2 : si64, 2 : si64]} : () -> tensor<3x3x2x2xf32> + %output_0 = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_2_input.0.0_2", output_lbns = ["_GraphToRun_2_input.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 4 : si64, 5 : si64]} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + %0 = "oneflow.pad"(%output_0) {device_name = ["@0:0"], device_tag = "cpu", floating_constant_value = 0.000000e+00 : f64, hierarchy = [1], integral_constant_value = 0 : si64, op_name = "pad-0", padding = [1 : si64, 1 : si64, 1 : si64, 1 : si64], padding_after = [0 : si64, 0 : si64, 1 : si64, 1 : si64], padding_before = [0 : si64, 0 : si64, 1 : si64, 1 : si64], scope_symbol_id = 65 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x6x7xf32> + %1 = "oneflow.conv2d"(%0, %output) {data_format = "channels_first", device_name = ["@0:0"], device_tag = "cpu", dilation_rate = [1 : si32, 1 : si32], filters = 3 : si32, groups = 1 : si32, hierarchy = [1], kernel_size = [2 : si32, 2 : si32], op_name = "conv-conv2d-1", operand_segment_sizes = dense<[1, 1, 0, 0]> : vector<4xi32>, padding_before = [0 : si32, 0 : si32], scope_symbol_id = 76 : i64, strides = [1 : si32, 1 : si32]} : (tensor<2x3x6x7xf32>, tensor<3x3x2x2xf32>) -> tensor<2x3x5x6xf32> + %output_1 = "oneflow.output"(%1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_2_output.0.0_2", output_lbns = ["_GraphToRun_2_output.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 5 : si64, 6 : si64]} : (tensor<2x3x5x6xf32>) -> tensor<2x3x5x6xf32> + // CHECK: func.func @GraphToRun_pad_and_conv2d_0(%[[A:[a-zA-Z0-9_]+]]: tensor<2x3x4x5xf32>) -> tensor<2x3x5x6xf32> { + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.variable"() + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.input"(%[[A]]) + // CHECK-NOT: oneflow.pad + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]] = "oneflow.conv2d"(%[[OUT0]], %[[OUT]]) + // CHECK: %[[OUT2:[a-zA-Z0-9_]+]] = "oneflow.output" + return %output_1 : tensor<2x3x5x6xf32> + } + + func.func @GraphToRun_same_dtype_cast_0(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> { + %output_0 = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_3_input.0.0_2", output_lbns = ["_GraphToRun_3_input.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 4 : si64, 5 : si64]} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + %0 = "oneflow.cast"(%output_0) {device_name = ["0:0"], device_tag = "cpu", dtype = 2 : i32, hierarchy = [1], op_name = "Cast_1", op_type_name = "cast", scope_symbol_id = 65 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + %output_1 = "oneflow.output"(%0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_3_output.0.0_2", output_lbns = ["_GraphToRun_3_output.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 4 : si64, 5 : si64]} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + // CHECK: func.func @GraphToRun_same_dtype_cast_0(%[[A:[a-zA-Z0-9_]+]]: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> { + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.input"(%[[A]]) + // CHECK-NOT: oneflow.cast + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.output"(%[[OUT0]]) + // CHECK：return %[[OUT]] : tensor<2x3x4x5xf32> + return %output_1 : tensor<2x3x4x5xf32> + } + + func.func @GraphToRun_same_dtype_cast_1(%arg0: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xi32> { + %output_0 = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_4_input.0.0_2", output_lbns = ["_GraphToRun_4_input.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 4 : si64, 5 : si64]} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32> + %0 = "oneflow.cast"(%output_0) {device_name = ["0:0"], device_tag = "cpu", dtype = 5 : i32, hierarchy = [1], op_name = "Cast_1", op_type_name = "cast", scope_symbol_id = 65 : i64} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xi32> + %output_1 = "oneflow.output"(%0) {data_type = 5 : i32, device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_4_output.0.0_2", output_lbns = ["_GraphToRun_4_output.0.0_2/out"], scope_symbol_id = 65 : i64, shape = [2 : si64, 3 : si64, 4 : si64, 5 : si64]} : (tensor<2x3x4x5xi32>) -> tensor<2x3x4x5xi32> + // CHECK: func.func @GraphToRun_same_dtype_cast_1(%[[A:[a-zA-Z0-9_]+]]: tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xi32> { + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.input"(%[[A]]) + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]] = "oneflow.cast"(%[[OUT0]]) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.output"(%[[OUT1]]) + // CHECK：return %[[OUT]] : tensor<2x3x4x5xi32> + return %output_1 : tensor<2x3x4x5xi32> + } + + func.func @GraphToRun_scale_tril_0() -> tensor<5x5xf32> { + %output = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "FreeEagerTensor-1", output_lbns = ["FreeEagerTensor-1/out"], parallel = #sbp.parallel<[] -> [[#sbp.B]]>, scope_symbol_id = 12 : i64, shape = [5 : si64, 5 : si64], trainable = false} : () -> tensor<5x5xf32> + %0 = "oneflow.scalar_mul"(%output) {device_name = ["@0:0"], device_tag = "cuda", float_operand = -2.300000e+00 : f64, has_float_operand = true, has_int_operand = false, hierarchy = [1], int_operand = 0 : si64, op_name = "scalar_mul-0", scope_symbol_id = 12 : i64} : (tensor<5x5xf32>) -> tensor<5x5xf32> + %1 = "oneflow.tril"(%0) {device_name = ["@0:0"], device_tag = "cuda", diagonal = -1 : si64, floating_fill_value = 0.000000e+00 : f64, hierarchy = [1], integer_fill_value = 0 : si64, is_floating_fill_value = false, op_name = "tril-2", scope_symbol_id = 12 : i64} : (tensor<5x5xf32>) -> tensor<5x5xf32> + %output_0 = "oneflow.output"(%1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_TestFuseScaleTril_0_output.0.0_2", output_lbns = ["_TestFuseScaleTril_0_output.0.0_2/out"], scope_symbol_id = 12 : i64, shape = [5 : si64, 5 : si64]} : (tensor<5x5xf32>) -> tensor<5x5xf32> + // CHECK: func.func @GraphToRun_scale_tril_0() -> tensor<5x5xf32> { + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.variable"() + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]] = "oneflow.fused_scale_tril"(%[[OUT0]]) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.output"(%[[OUT1]]) + // CHECK：return %[[OUT]] + return %output_0 : tensor<5x5xf32> + } + + func.func @GraphToRun_scale_tril_1() -> tensor<5x5xf32> { + %output = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "FreeEagerTensor-1", output_lbns = ["FreeEagerTensor-1/out"], parallel = #sbp.parallel<[] -> [[#sbp.B]]>, scope_symbol_id = 66 : i64, shape = [5 : si64, 5 : si64], trainable = false} : () -> tensor<5x5xf32> + %0 = "oneflow.tril"(%output) {device_name = ["@0:0"], device_tag = "cuda", diagonal = -1 : si64, floating_fill_value = 0.000000e+00 : f64, hierarchy = [1], integer_fill_value = 0 : si64, is_floating_fill_value = false, op_name = "tril-0", scope_symbol_id = 66 : i64} : (tensor<5x5xf32>) -> tensor<5x5xf32> + %1 = "oneflow.scalar_mul"(%0) {device_name = ["@0:0"], device_tag = "cuda", float_operand = 2.000000e+00 : f64, has_float_operand = true, has_int_operand = false, hierarchy = [1], int_operand = 0 : si64, op_name = "scalar_mul-2", scope_symbol_id = 66 : i64} : (tensor<5x5xf32>) -> tensor<5x5xf32> + %output_0 = "oneflow.output"(%1) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_TestFuseTrilScale_1_output.0.0_2", output_lbns = ["_TestFuseTrilScale_1_output.0.0_2/out"], scope_symbol_id = 66 : i64, shape = [5 : si64, 5 : si64]} : (tensor<5x5xf32>) -> tensor<5x5xf32> + // CHECK: func.func @GraphToRun_scale_tril_1() -> tensor<5x5xf32> { + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]] = "oneflow.variable"() + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]] = "oneflow.fused_scale_tril"(%[[OUT0]]) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.output"(%[[OUT1]]) + // CHECK：return %[[OUT]] + return %output_0 : tensor<5x5xf32> + } + + func.func @GraphToRun_normalization_1(%x: tensor<2x3x224x224xf32>, %moving_mean: tensor<3xf32>, %moving_variance: tensor<3xf32>, %gamma: tensor<3xf32>, %beta: tensor<3xf32>, %addend: tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> { + %y, %mean, %inv_variance = "oneflow.normalization"(%x, %moving_mean, %moving_variance, %gamma, %beta) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cpu", epsilon = 9.99999974E-6 : f32, hierarchy = [1], momentum = 0.899999976 : f32, op_name = "normalization-2", operand_segment_sizes = dense<[1, 1, 1, 1, 1, 0]> : vector<6xi32>, result_segment_sizes = dense<1> : vector<3xi32>, scope_symbol_id = 12 : i64, training = true} : (tensor<2x3x224x224xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<2x3x224x224xf32>, tensor<3xf32>, tensor<3xf32>) + %0 = "oneflow.add_n2"(%y, %addend) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "add_n-7", op_type_name = "add_n", scope_symbol_id = 12 : i64} : (tensor<2x3x224x224xf32>, tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> + %1 = "oneflow.relu"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-8", scope_symbol_id = 12 : i64} : (tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> + // CHECK: func.func @GraphToRun_normalization_1(%[[X:[a-zA-Z0-9_]+]]: tensor<2x3x224x224xf32>, %[[MOVING_MEAN:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[MOVING_VARIANCE:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[GAMMA:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[BETA:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[ADDEND:[a-zA-Z0-9_]+]]: tensor<2x3x224x224xf32>) + // CHECK: %[[Y:[a-zA-Z0-9_]+]], %[[reserve_space:[a-zA-Z0-9_]+]], %[[mean:[a-zA-Z0-9_]+]], %[[inv_variance:[a-zA-Z0-9_]+]] = "oneflow.normalization_add_relu"(%[[X]], %[[ADDEND]], %[[MOVING_MEAN]], %[[MOVING_VARIANCE]], %[[GAMMA]], %[[BETA]]) + // CHECK： return %[[Y]] + return %1 : tensor<2x3x224x224xf32> + } + + func.func @GraphToRun_normalization_2(%x: tensor<2x3x224x224xf32>, %moving_mean: tensor<3xf32>, %moving_variance: tensor<3xf32>, %gamma: tensor<3xf32>, %beta: tensor<3xf32>, %addend: tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> { + %y = "oneflow.normalization_infer"(%x, %moving_mean, %moving_variance, %gamma, %beta) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cpu", epsilon = 9.99999974E-6 : f32, hierarchy = [1], momentum = 0.899999976 : f32, op_name = "normalization-2", operand_segment_sizes = dense<[1, 1, 1, 1, 1, 0]> : vector<6xi32>, result_segment_sizes = dense<1> : vector<3xi32>, scope_symbol_id = 12 : i64, training = true} : (tensor<2x3x224x224xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<2x3x224x224xf32>) + %0 = "oneflow.add_n2"(%y, %addend) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "add_n-7", op_type_name = "add_n", scope_symbol_id = 12 : i64} : (tensor<2x3x224x224xf32>, tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> + %1 = "oneflow.relu"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-8", scope_symbol_id = 12 : i64} : (tensor<2x3x224x224xf32>) -> tensor<2x3x224x224xf32> + // CHECK: func.func @GraphToRun_normalization_2(%[[X:[a-zA-Z0-9_]+]]: tensor<2x3x224x224xf32>, %[[MOVING_MEAN:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[MOVING_VARIANCE:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[GAMMA:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[BETA:[a-zA-Z0-9_]+]]: tensor<3xf32>, %[[ADDEND:[a-zA-Z0-9_]+]]: tensor<2x3x224x224xf32>) + // CHECK: %[[Y:[a-zA-Z0-9_]+]], %[[reserve_space:[a-zA-Z0-9_]+]], %[[mean:[a-zA-Z0-9_]+]], %[[inv_variance:[a-zA-Z0-9_]+]] = "oneflow.normalization_add_relu"(%[[X]], %[[ADDEND]], %[[MOVING_MEAN]], %[[MOVING_VARIANCE]], %[[GAMMA]], %[[BETA]]) + // CHECK： return %[[Y]] + return %1 : tensor<2x3x224x224xf32> + } + + func.func @GraphToRun_conv_bn_1(%arg0: tensor<1x3x224x224xf32>, %moving_mean: tensor<64xf32>, %moving_variance: tensor<64xf32>, %beta: tensor<64xf32>) -> tensor<1x64x112x112xf32> { + %output = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_conv_bn_1_input.0.0_2", output_lbns = ["_conv_bn_1_input.0.0_2/out"], scope_symbol_id = 12 : i64, shape = [1 : si64, 3 : si64, 224 : si64, 224 : si64]} : (tensor<1x3x224x224xf32>) -> tensor<1x3x224x224xf32> + %0 = "oneflow.variable_ir"() {value = dense<1.0> : tensor<64x3x7x7xf32> ,data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "model.conv1.weight", output_lbns = ["model.conv1.weight/out"], parallel = #sbp.parallel<[] -> [[#sbp.B]]>, scope_symbol_id = 18 : i64, shape = [64 : si64, 3 : si64, 7 : si64, 7 : si64], nd_sbp = ["B"]} : () -> tensor<64x3x7x7xf32> + %gamma = "oneflow.variable_ir"() {value = dense<1.0> : tensor<64xf32> ,data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "model.bn.gamma", output_lbns = ["model.bn.gamma/out"], parallel = #sbp.parallel<[] -> [[#sbp.B]]>, scope_symbol_id = 18 : i64, shape = [64 : si64], nd_sbp = ["B"]} : () -> tensor<64xf32> + %1 = "oneflow.conv2d"(%output, %0) {data_format = "channels_first", device_name = ["@0:0"], device_tag = "cuda", dilation_rate = [1 : si32, 1 : si32], filters = 64 : si32, groups = 1 : si32, hierarchy = [1], kernel_size = [7 : si32, 7 : si32], op_name = "model.conv1-conv2d-0", operand_segment_sizes = dense<[1, 1, 0, 0]> : vector<4xi32>, padding_before = [3 : si32, 3 : si32], scope_symbol_id = 21 : i64, strides = [2 : si32, 2 : si32]} : (tensor<1x3x224x224xf32>, tensor<64x3x7x7xf32>) -> tensor<1x64x112x112xf32> + %2 = "oneflow.normalization_infer"(%1, %moving_mean, %moving_variance, %gamma, %beta) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", epsilon = 9.99999974E-6 : f32, hierarchy = [1], momentum = 0.899999976 : f32, op_name = "model.bn1-normalization-1", operand_segment_sizes = dense<[1, 1, 1, 1, 1, 0]> : vector<6xi32>, result_segment_sizes = dense<[1, 0, 0]> : vector<3xi32>, scope_symbol_id = 41 : i64, training = false} : (tensor<1x64x112x112xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x64x112x112xf32> + // CHECK: func.func @GraphToRun_conv_bn_1(%[[ARG_0:[a-zA-Z0-9_]+]]: tensor<1x3x224x224xf32>, %[[MOVING_MEAN:[a-zA-Z0-9_]+]]: tensor<64xf32>, %[[MOVING_VARIANCE:[a-zA-Z0-9_]+]]: tensor<64xf32>, %[[BETA:[a-zA-Z0-9_]+]]: tensor<64xf32>) + // CHECK: %[[GAMMA:[a-zA-Z0-9_]+]] = "oneflow.variable_ir"() + // CHECK: %[[WEIGHT:[a-zA-Z0-9_]+]] = "oneflow.variable_ir"() + // CHECK: %[[OUT:[a-zA-Z0-9_]+]] = "oneflow.input"(%[[ARG_0]]) + // CHECK: %[[OUT2:[a-zA-Z0-9_]+]] = "oneflow.scalar_add"(%[[MOVING_VARIANCE]]) + // CHECK: %[[OUT3:[a-zA-Z0-9_]+]] = "oneflow.sqrt"(%[[OUT2]]) + // CHECK: %[[OUT4:[a-zA-Z0-9_]+]] = "oneflow.broadcast_div"(%[[GAMMA]], %[[OUT3]]) + // CHECK: %[[OUT5:[a-zA-Z0-9_]+]] = "oneflow.reshape"(%[[OUT4]]) + // CHECK: %[[OUT6:[a-zA-Z0-9_]+]] = "oneflow.broadcast_mul"(%[[WEIGHT]], %[[OUT5]]) + // CHECK: %[[OUT7:[a-zA-Z0-9_]+]] = "oneflow.broadcast_mul"(%[[MOVING_MEAN]], %[[OUT4]]) + // CHECK: %[[OUT8:[a-zA-Z0-9_]+]] = "oneflow.broadcast_sub"(%[[BETA]], %[[OUT7]]) + // CHECK: %[[OUT9:[a-zA-Z0-9_]+]] = "oneflow.conv2d"(%[[OUT]], %[[OUT6]], %[[OUT8]]) + // CHECK： return %[[OUT9]] + return %2 : tensor<1x64x112x112xf32> + } + +} diff --git a/oneflow/ir/test/OneFlow/group_matmul.mlir b/oneflow/ir/test/OneFlow/group_matmul.mlir new file mode 100644 index 0000000000000000000000000000000000000000..52ea0f04486d0fe270e324f2e7fe54d4d998d254 --- /dev/null +++ b/oneflow/ir/test/OneFlow/group_matmul.mlir @@ -0,0 +1,95 @@ +// RUN: oneflow-opt %s \ +// RUN: -group-matmul | FileCheck %s +module { + // CHECK-LABEL: func.func + func.func @no_bias(%x: tensor<2x320xf16>, %weight1: tensor<1280x320xf16>, %weight2: tensor<1280x320xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>) { + %1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + return %1, %2 : tensor<2x1280xf16>, tensor<2x1280xf16> + // CHECK: @no_bias(%[[X:[a-zA-Z0-9_]+]]: tensor<2x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1]]) + // CHECK: return %[[OUT]]#1, %[[OUT]]#0 + } + + // CHECK-LABEL: func.func + func.func @with_bias(%x: tensor<2x320xf16>, %weight1: tensor<1280x320xf16>, %weight2: tensor<1280x320xf16>, %bias1: tensor<1280xf16>, %bias2: tensor<1280xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>) { + %1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r1 = "oneflow.bias_add"(%1, %bias1) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r2 = "oneflow.bias_add"(%2, %bias2) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + return %r1, %r2 : tensor<2x1280xf16>, tensor<2x1280xf16> + // CHECK: @with_bias(%[[X:[a-zA-Z0-9_]+]]: tensor<2x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[BIAS1:[a-zA-Z0-9_]+]]: tensor<1280xf16>, %[[BIAS2:[a-zA-Z0-9_]+]]: tensor<1280xf16>) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1:[a-zA-Z0-9_]+]], %[[BIAS2:[a-zA-Z0-9_]+]], %[[BIAS1:[a-zA-Z0-9_]+]]) + // CHECK: return %[[OUT]]#1, %[[OUT]]#0 + } + + // CHECK-LABEL: func.func + func.func @with_broadcast_add(%x: tensor<2x320xf16>, %weight1: tensor<1280x320xf16>, %weight2: tensor<1280x320xf16>, %bias1: tensor<1280xf16>, %bias2: tensor<1280xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>) { + %1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r1 = "oneflow.broadcast_add"(%1, %bias1) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r2 = "oneflow.broadcast_add"(%2, %bias2) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + return %r1, %r2 : tensor<2x1280xf16>, tensor<2x1280xf16> + // CHECK: @with_broadcast_add(%[[X:[a-zA-Z0-9_]+]]: tensor<2x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[BIAS1:[a-zA-Z0-9_]+]]: tensor<1280xf16>, %[[BIAS2:[a-zA-Z0-9_]+]]: tensor<1280xf16>) + // CHECK: %[[OUT:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1:[a-zA-Z0-9_]+]], %[[BIAS2:[a-zA-Z0-9_]+]], %[[BIAS1:[a-zA-Z0-9_]+]]) + // CHECK: return %[[OUT]]#1, %[[OUT]]#0 + } + + // CHECK-LABEL: func.func + func.func @mixed(%x: tensor<2x320xf16>, %weight1: tensor<1280x320xf16>, %weight2: tensor<1280x320xf16>, %bias1: tensor<1280xf16>, %bias2: tensor<1280xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16>) { + %1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r1 = "oneflow.bias_add"(%1, %bias1) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r2 = "oneflow.bias_add"(%2, %bias2) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %m1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %m2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + return %r1, %r2, %m1, %m2: tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16> + // CHECK: @mixed(%[[X:[a-zA-Z0-9_]+]]: tensor<2x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[BIAS1:[a-zA-Z0-9_]+]]: tensor<1280xf16>, %[[BIAS2:[a-zA-Z0-9_]+]]: tensor<1280xf16>) + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1:[a-zA-Z0-9_]+]], %[[BIAS2:[a-zA-Z0-9_]+]], %[[BIAS1:[a-zA-Z0-9_]+]]) + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1]]) + // CHECK: return %[[OUT0]]#1, %[[OUT0]]#0, %[[OUT1]]#1, %[[OUT1]]#0 + } + + // CHECK-LABEL: func.func + func.func @left_alone(%x: tensor<2x320xf16>, %weight1: tensor<1280x320xf16>, %weight2: tensor<1280x320xf16>, %bias1: tensor<1280xf16>, %bias2: tensor<1280xf16>) -> (tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16>) { + %1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r1 = "oneflow.bias_add"(%1, %bias1) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %2 = "oneflow.matmul"(%x, %weight2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + %r2 = "oneflow.bias_add"(%2, %bias2) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<2x1280xf16>, tensor<1280xf16>) -> tensor<2x1280xf16> + %m1 = "oneflow.matmul"(%x, %weight1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<2x320xf16>, tensor<1280x320xf16>) -> tensor<2x1280xf16> + return %r1, %r2, %m1: tensor<2x1280xf16>, tensor<2x1280xf16>, tensor<2x1280xf16> + // CHECK: @left_alone(%[[X:[a-zA-Z0-9_]+]]: tensor<2x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<1280x320xf16>, %[[BIAS1:[a-zA-Z0-9_]+]]: tensor<1280xf16>, %[[BIAS2:[a-zA-Z0-9_]+]]: tensor<1280xf16>) + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[WEIGHT2]], %[[WEIGHT1:[a-zA-Z0-9_]+]], %[[BIAS2:[a-zA-Z0-9_]+]], %[[BIAS1:[a-zA-Z0-9_]+]]) + // CHECK: %[[OUT1:[a-zA-Z0-9_]+]] = "oneflow.matmul"(%arg0, %arg1) + // CHECK: return %[[OUT0]]#1, %[[OUT0]]#0, %[[OUT1]] + } + func.func @f_broadcast_matmul(%x: tensor<2x4096x320xf16>, %w1: tensor<320x320xf16>, %w2: tensor<320x320xf16>, %w3: tensor<320x320xf16>) -> (tensor<2x4096x320xf16>, tensor<2x4096x320xf16>, tensor<2x4096x320xf16>) { + %matmul1 = "oneflow.broadcast_matmul"(%x, %w1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_q-broadcast_matmul-16315", scope_symbol_id = 5497 : i64, transpose_a = false, transpose_b = true} : (tensor<2x4096x320xf16>, tensor<320x320xf16>) -> tensor<2x4096x320xf16> + %matmul2 = "oneflow.broadcast_matmul"(%x, %w2) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_k-broadcast_matmul-16316", scope_symbol_id = 5505 : i64, transpose_a = false, transpose_b = true} : (tensor<2x4096x320xf16>, tensor<320x320xf16>) -> tensor<2x4096x320xf16> + %matmul3 = "oneflow.broadcast_matmul"(%x, %w3) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_v-broadcast_matmul-16317", scope_symbol_id = 5513 : i64, transpose_a = false, transpose_b = true} : (tensor<2x4096x320xf16>, tensor<320x320xf16>) -> tensor<2x4096x320xf16> + return %matmul1, %matmul2, %matmul3 : tensor<2x4096x320xf16>, tensor<2x4096x320xf16>, tensor<2x4096x320xf16> + // CHECK: @f_broadcast_matmul(%[[X:[a-zA-Z0-9_]+]]: tensor<2x4096x320xf16>, %[[WEIGHT1:[a-zA-Z0-9_]+]]: tensor<320x320xf16>, %[[WEIGHT2:[a-zA-Z0-9_]+]]: tensor<320x320xf16>, %[[WEIGHT3:[a-zA-Z0-9_]+]]: tensor<320x320xf16>) + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:3 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[X]], %[[WEIGHT3]], %[[WEIGHT2]], %[[WEIGHT1]]) + // CHECK: return %[[OUT0]]#2, %[[OUT0]]#1, %[[OUT0]]#0 + } + + func.func @test_fused_matmul_bias_graph(%x: tensor<8x9xf64>, %w: tensor<10x9xf64>, %bias: tensor<10xf64>) -> (tensor<8x10xf64>, tensor<8x10xf64>) { + %y0 = "oneflow.fused_matmul_bias"(%x, %w, %bias) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "fused_matmul_bias-0", scope_symbol_id = 12 : i64} : (tensor<8x9xf64>, tensor<10x9xf64>, tensor<10xf64>) -> tensor<8x10xf64> + %y1 = "oneflow.fused_matmul_bias"(%x, %w, %bias) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "fused_matmul_bias-0", scope_symbol_id = 12 : i64} : (tensor<8x9xf64>, tensor<10x9xf64>, tensor<10xf64>) -> tensor<8x10xf64> + return %y0, %y1 : tensor<8x10xf64>, tensor<8x10xf64> + // CHECK: @test_fused_matmul_bias_graph(%[[X:[a-zA-Z0-9_]+]]: tensor<8x9xf64>, %[[W:[a-zA-Z0-9_]+]]: tensor<10x9xf64>, %[[BIAS:[a-zA-Z0-9_]+]]: tensor<10xf64>) + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:2 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[W]], %[[W]], %[[BIAS]], %[[BIAS]]) + // CHECK: return %[[OUT0]]#1, %[[OUT0]]#0 + } + + func.func @test_fused_matmul_bias_graph_mixed(%x: tensor<8x9xf64>, %w: tensor<10x9xf64>, %bias: tensor<10xf64>, %w1: tensor<10x9xf64>, %bias1: tensor<10xf64>) -> (tensor<8x10xf64>, tensor<8x10xf64>, tensor<8x10xf64>) { + %y0 = "oneflow.fused_matmul_bias"(%x, %w, %bias) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "fused_matmul_bias-0", scope_symbol_id = 12 : i64} : (tensor<8x9xf64>, tensor<10x9xf64>, tensor<10xf64>) -> tensor<8x10xf64> + %y1 = "oneflow.fused_matmul_bias"(%x, %w, %bias) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "fused_matmul_bias-0", scope_symbol_id = 12 : i64} : (tensor<8x9xf64>, tensor<10x9xf64>, tensor<10xf64>) -> tensor<8x10xf64> + %matmul = "oneflow.matmul"(%x, %w1) {alpha = 1.000000e+00 : f64, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-matmul-20", scope_symbol_id = 90 : i64, transpose_a = false, transpose_b = true} : (tensor<8x9xf64>, tensor<10x9xf64>) -> tensor<8x10xf64> + %bias_add = "oneflow.bias_add"(%matmul, %bias1) {axis = 1 : si32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "unet.time_embedding.linear_1-bias_add-21", scope_symbol_id = 90 : i64} : (tensor<8x10xf64>, tensor<10xf64>) -> tensor<8x10xf64> + return %y0, %y1, %bias_add : tensor<8x10xf64>, tensor<8x10xf64>, tensor<8x10xf64> + // CHECK: @test_fused_matmul_bias_graph_mixed(%[[X:[a-zA-Z0-9_]+]]: tensor<8x9xf64>, %[[W:[a-zA-Z0-9_]+]]: tensor<10x9xf64>, %[[BIAS:[a-zA-Z0-9_]+]]: tensor<10xf64>, %[[W1:[a-zA-Z0-9_]+]]: tensor<10x9xf64>, %[[BIAS1:[a-zA-Z0-9_]+]]: tensor<10xf64>) + // CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:3 = "oneflow.grouped_matmul_bias"(%[[X]], %[[X]], %[[X]], %[[W1]], %[[W]], %[[W]], %[[BIAS1]], %[[BIAS]], %[[BIAS]]) + // CHECK: return %[[OUT0]]#2, %[[OUT0]]#1, %[[OUT0]]#0 + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/convert-okl-to-llvm.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/convert-okl-to-llvm.mlir new file mode 100644 index 0000000000000000000000000000000000000000..3717b3e361fc992d65c2a21f1942c5eb3b253b3a --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/convert-okl-to-llvm.mlir @@ -0,0 +1,65 @@ +// RUN: oneflow-opt %s \ +// RUN: -only-keep-compute-ops \ +// RUN: -lower-launcher-to-llvm-ptr \ +// RUN: -lower-okl-to-llvm-call \ +// RUN: -reconcile-unrealized-casts \ +// RUN: -convert-func-to-llvm \ +// RUN: -mlir-print-ir-after-all \ +// RUN: | FileCheck %s + +// CHECK: module attributes {llvm.data_layout = ""} { +// CHECK: llvm.func @oneflow_okl_launch(!llvm.ptr, !llvm.ptr) attributes {llvm.emit_c_interface} +// CHECK: llvm.func @oneflow_okl_fetch_kernel(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} +// CHECK: llvm.func @oneflow_okl_fetch_run_ctx(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} +// CHECK: llvm.func @okl_compute(%[[ARG0:[a-zA-Z0-9_]+]]: !llvm.ptr) attributes {llvm.emit_c_interface} { +// CHECK: } +// CHECK: llvm.func @_mlir_ciface_okl_compute(%[[ARG0:[a-zA-Z0-9_]+]]: !llvm.ptr) attributes {llvm.emit_c_interface} { +// CHECK: llvm.call @okl_compute(%[[ARG0]]) : (!llvm.ptr) -> () +// CHECK: llvm.return +// CHECK: } +// CHECK: } + +module { + func.func @okl_recycle(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_reg_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %1 = "okl.fetch_reg_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %2 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %3 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + "okl.destroy_reg_ctx"(%0) : (!okl.reg_ctx) -> () + "okl.destroy_reg_ctx"(%1) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%2) : (!okl.run_ctx) -> () + "okl.destroy_run_ctx"(%3) : (!okl.run_ctx) -> () + return + } + func.func @okl_compute(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %1 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %2 = "okl.fetch_kernel"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel + %3 = "okl.fetch_kernel"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel + "okl.launch"(%0, %2) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%1, %3) : (!okl.run_ctx, !okl.kernel) -> () + return + } + func.func @okl_init_context(%arg0: !okl.launcher_ctx) { + %0 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_arg"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.relu"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %1 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_ret"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.tanh"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %2 = "okl.build_run_ctx"(%0) : (!okl.reg_ctx) -> !okl.run_ctx + %3 = "okl.build_run_ctx"(%1) : (!okl.reg_ctx) -> !okl.run_ctx + %4 = "okl.build_op_kernel"(%0) : (!okl.reg_ctx) -> !okl.kernel + %5 = "okl.build_op_kernel"(%1) : (!okl.reg_ctx) -> !okl.kernel + return + } + func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-launcher-to-llvm-ptr.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-launcher-to-llvm-ptr.mlir new file mode 100644 index 0000000000000000000000000000000000000000..3b5699f0678ee6f2af6cbbbc9f8ad2df60c37f16 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-launcher-to-llvm-ptr.mlir @@ -0,0 +1,28 @@ +// RUN: oneflow-opt %s \ +// RUN: -lower-launcher-to-llvm-ptr \ +// RUN: | FileCheck %s + +// CHECK: module { +// CHECK: func.func @okl_compute(%[[ARG:[a-zA-Z0-9_]+]]: !llvm.ptr) attributes {llvm.emit_c_interface} { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = builtin.unrealized_conversion_cast %[[ARG]] : !llvm.ptr to !okl.launcher_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG0]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG0]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG0]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: %[[ARG4:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG0]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: "okl.launch"(%[[ARG1]], %[[ARG3]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: "okl.launch"(%[[ARG2]], %[[ARG4]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: return +// CHECK: } +// CHECK: } + +module { + func.func @okl_compute(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %1 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %2 = "okl.fetch_kernel"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel + %3 = "okl.fetch_kernel"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel + "okl.launch"(%0, %2) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%1, %3) : (!okl.run_ctx, !okl.kernel) -> () + return + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-okl-to-llvm-call.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-okl-to-llvm-call.mlir new file mode 100644 index 0000000000000000000000000000000000000000..f03b9f30cc332e0671e149f6516c8232a58d8a10 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-lower-okl-to-llvm-call.mlir @@ -0,0 +1,35 @@ +// RUN: oneflow-opt %s \ +// RUN: -lower-okl-to-llvm-call \ +// RUN: | FileCheck %s + +// CHECK: module { +// CHECK: llvm.func @oneflow_okl_launch(!llvm.ptr, !llvm.ptr) attributes {llvm.emit_c_interface} +// CHECK: llvm.func @oneflow_okl_fetch_kernel(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} +// CHECK: llvm.func @oneflow_okl_fetch_run_ctx(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} +// CHECK: func.func @okl_compute(%[[ARG:[a-zA-Z0-9_]+]]: !llvm.ptr) attributes {llvm.emit_c_interface} { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = builtin.unrealized_conversion_cast %[[ARG]] : !llvm.ptr to !okl.launcher_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = llvm.mlir.constant(0 : index) : i64 +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = llvm.call @oneflow_okl_fetch_run_ctx(%[[ARG]], %[[ARG1]]) : (!llvm.ptr, i64) -> !llvm.ptr +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = llvm.mlir.constant(1 : index) : i64 +// CHECK: %[[ARG4:[a-zA-Z0-9_]+]] = llvm.call @oneflow_okl_fetch_run_ctx(%[[ARG]], %[[ARG3]]) : (!llvm.ptr, i64) -> !llvm.ptr +// CHECK: %[[ARG5:[a-zA-Z0-9_]+]] = llvm.mlir.constant(0 : index) : i64 +// CHECK: %[[ARG6:[a-zA-Z0-9_]+]] = llvm.call @oneflow_okl_fetch_kernel(%[[ARG]], %[[ARG5]]) : (!llvm.ptr, i64) -> !llvm.ptr +// CHECK: %[[ARG7:[a-zA-Z0-9_]+]] = llvm.mlir.constant(1 : index) : i64 +// CHECK: %[[ARG8:[a-zA-Z0-9_]+]] = llvm.call @oneflow_okl_fetch_kernel(%[[ARG]], %[[ARG7]]) : (!llvm.ptr, i64) -> !llvm.ptr +// CHECK: llvm.call @oneflow_okl_launch(%[[ARG2]], %[[ARG6]]) : (!llvm.ptr, !llvm.ptr) -> () +// CHECK: llvm.call @oneflow_okl_launch(%[[ARG4]], %[[ARG8]]) : (!llvm.ptr, !llvm.ptr) -> () +// CHECK: return +// CHECK: } +// CHECK: } +module { + func.func @okl_compute(%arg0: !llvm.ptr) attributes {llvm.emit_c_interface} { + %0 = builtin.unrealized_conversion_cast %arg0 : !llvm.ptr to !okl.launcher_ctx + %1 = "okl.fetch_run_ctx"(%0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %2 = "okl.fetch_run_ctx"(%0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %3 = "okl.fetch_kernel"(%0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel + %4 = "okl.fetch_kernel"(%0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel + "okl.launch"(%1, %3) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%2, %4) : (!okl.run_ctx, !okl.kernel) -> () + return + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-only-keep-compute-ops.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-only-keep-compute-ops.mlir new file mode 100644 index 0000000000000000000000000000000000000000..30f2044c6f5efe5121e530f92b68bef7eb0c9518 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-only-keep-compute-ops.mlir @@ -0,0 +1,60 @@ +// RUN: oneflow-opt %s \ +// RUN: -only-keep-compute-ops \ +// RUN: | FileCheck %s + +// CHECK: module { +// CHECK: func.func @okl_compute(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: "okl.launch"(%[[ARG0]], %[[ARG2]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: "okl.launch"(%[[ARG1]], %[[ARG3]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: return +// CHECK: } +// CHECK: } + +module { + func.func @okl_recycle(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_reg_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %1 = "okl.fetch_reg_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx + %2 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %3 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + "okl.destroy_reg_ctx"(%0) : (!okl.reg_ctx) -> () + "okl.destroy_reg_ctx"(%1) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%2) : (!okl.run_ctx) -> () + "okl.destroy_run_ctx"(%3) : (!okl.run_ctx) -> () + return + } + func.func @okl_compute(%arg0: !okl.launcher_ctx) { + %0 = "okl.fetch_run_ctx"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %1 = "okl.fetch_run_ctx"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx + %2 = "okl.fetch_kernel"(%arg0) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel + %3 = "okl.fetch_kernel"(%arg0) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel + "okl.launch"(%0, %2) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%1, %3) : (!okl.run_ctx, !okl.kernel) -> () + return + } + func.func @okl_init_context(%arg0: !okl.launcher_ctx) { + %0 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_arg"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.relu"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %1 = "okl.build_reg_ctx"() ({ + %6 = "okl.get_tensor_from_ret"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.tanh"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg0, %7) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = () -> ()} : () -> !okl.reg_ctx + %2 = "okl.build_run_ctx"(%0) : (!okl.reg_ctx) -> !okl.run_ctx + %3 = "okl.build_run_ctx"(%1) : (!okl.reg_ctx) -> !okl.run_ctx + %4 = "okl.build_op_kernel"(%0) : (!okl.reg_ctx) -> !okl.kernel + %5 = "okl.build_op_kernel"(%1) : (!okl.reg_ctx) -> !okl.kernel + return + } + func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-reconcile-unrealized-casts.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-reconcile-unrealized-casts.mlir new file mode 100644 index 0000000000000000000000000000000000000000..79bcc97a9058df94007cae7cf4034d97f8542606 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OKLToLLVM/test-reconcile-unrealized-casts.mlir @@ -0,0 +1,25 @@ +// RUN: oneflow-opt %s \ +// RUN: -reconcile-unrealized-casts \ +// RUN: | FileCheck %s + +// CHECK-NOT: builtin.unrealized_conversion_cast + +module { + llvm.func @launch(!llvm.ptr, !llvm.ptr) attributes {llvm.emit_c_interface} + llvm.func @fetch_kernel(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} + llvm.func @fetch_run_ctx(!llvm.ptr, i64) -> !llvm.ptr attributes {llvm.emit_c_interface} + llvm.func @_mlir__mlir_ciface_okl_compute(%arg0: !llvm.ptr) attributes {llvm.emit_c_interface} { + %0 = builtin.unrealized_conversion_cast %arg0 : !llvm.ptr to !okl.launcher_ctx + %1 = llvm.mlir.constant(0 : index) : i64 + %2 = llvm.call @fetch_run_ctx(%arg0, %1) : (!llvm.ptr, i64) -> !llvm.ptr + %3 = llvm.mlir.constant(1 : index) : i64 + %4 = llvm.call @fetch_run_ctx(%arg0, %3) : (!llvm.ptr, i64) -> !llvm.ptr + %5 = llvm.mlir.constant(0 : index) : i64 + %6 = llvm.call @fetch_kernel(%arg0, %5) : (!llvm.ptr, i64) -> !llvm.ptr + %7 = llvm.mlir.constant(1 : index) : i64 + %8 = llvm.call @fetch_kernel(%arg0, %7) : (!llvm.ptr, i64) -> !llvm.ptr + llvm.call @launch(%2, %6) : (!llvm.ptr, !llvm.ptr) -> () + llvm.call @launch(%4, %8) : (!llvm.ptr, !llvm.ptr) -> () + llvm.return + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/convert-oneflow-to-okl.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/convert-oneflow-to-okl.mlir new file mode 100644 index 0000000000000000000000000000000000000000..7ce12dbec97e4f8e1612b0309e1baa9089484cf2 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/convert-oneflow-to-okl.mlir @@ -0,0 +1,62 @@ +// RUN: oneflow-opt %s \ +// RUN: -extract-kernel-launch-tensor \ +// RUN: -trim-return-to-void \ +// RUN: -lower-to-okl \ +// RUN: -split-into-funcs \ +// RUN: -fetch-from-launcher \ +// RUN: -mlir-print-ir-after-all \ +// RUN: | FileCheck %s + + +// CHECK: module { +// CHECK: func.func @okl_recycle(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.fetch_reg_ctx"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.fetch_reg_ctx"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.reg_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: "okl.destroy_reg_ctx"(%[[ARG0]]) : (!okl.reg_ctx) -> () +// CHECK: "okl.destroy_reg_ctx"(%[[ARG1]]) : (!okl.reg_ctx) -> () +// CHECK: "okl.destroy_run_ctx"(%[[ARG2]]) : (!okl.run_ctx) -> () +// CHECK: "okl.destroy_run_ctx"(%[[ARG3]]) : (!okl.run_ctx) -> () +// CHECK: return +// CHECK: } +// CHECK: func.func @okl_compute(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.fetch_run_ctx"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.run_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG]]) {index = 0 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.fetch_kernel"(%[[ARG]]) {index = 1 : si64} : (!okl.launcher_ctx) -> !okl.kernel +// CHECK: "okl.launch"(%[[ARG0]], %[[ARG2]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: "okl.launch"(%[[ARG1]], %[[ARG3]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: return +// CHECK: } +// CHECK: func.func @okl_init_context(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.build_reg_ctx"() ({ +// CHECK: %[[ARG6:[a-zA-Z0-9_]+]] = "okl.get_tensor_from_arg"(%[[ARG]]) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> +// CHECK: %[[ARG7:[a-zA-Z0-9_]+]] = "oneflow.relu"(%[[ARG6]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG8:[a-zA-Z0-9_]+]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG7]]) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: okl.return +// CHECK: }) : () -> !okl.reg_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.build_reg_ctx"() ({ +// CHECK: %[[ARG6:[a-zA-Z0-9_]+]] = "okl.get_tensor_from_ret"(%[[ARG]]) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> +// CHECK: %[[ARG7:[a-zA-Z0-9_]+]] = "oneflow.tanh"(%[[ARG6]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG8:[a-zA-Z0-9_]+]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG7]]) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: okl.return +// CHECK: }) : () -> !okl.reg_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.build_run_ctx"(%[[ARG0]]) : (!okl.reg_ctx) -> !okl.run_ctx +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.build_run_ctx"(%[[ARG1]]) : (!okl.reg_ctx) -> !okl.run_ctx +// CHECK: %[[ARG4:[a-zA-Z0-9_]+]] = "okl.build_op_kernel"(%[[ARG0]]) : (!okl.reg_ctx) -> !okl.kernel +// CHECK: %[[ARG5:[a-zA-Z0-9_]+]] = "okl.build_op_kernel"(%[[ARG1]]) : (!okl.reg_ctx) -> !okl.kernel +// CHECK: return +// CHECK: } +// CHECK: func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) +// CHECK: func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) +// CHECK: func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +// CHECK: } + + module { + func.func @wrap0(%arg0: tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) attributes {llvm.emit_c_interface} { + %0 = "oneflow.relu"(%arg0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + return %0, %1 : tensor<2xf32>, tensor<2xf32> + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-aggregate-compute-ops.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-aggregate-compute-ops.mlir new file mode 100644 index 0000000000000000000000000000000000000000..e2f751f8944af6296c159973a80be430fb75e3cb --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-aggregate-compute-ops.mlir @@ -0,0 +1,20 @@ +// RUN: oneflow-opt %s \ +// RUN: -aggregate-compute-ops \ +// RUN: | FileCheck %s + +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "oneflow.arg_sort" +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "oneflow.dim_gather" +// CHECK: "oneflow.output"(%[[ARG0]]) + +module { + oneflow.job @GraphToRun_1(%arg0: tensor<2xf32>) -> (tensor<2xsi32>, tensor<2xf32>) { + %output = "oneflow.input"(%arg0) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_input.0.0_2", output_lbns = ["_GraphToRun_1_input.0.0_2/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + %0 = "oneflow.relu"(%output) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %2 = "oneflow.arg_sort"(%1) {device_name = ["@0:0"], device_tag = "cuda", direction = "ASCENDING", hierarchy = [1], op_name = "arg_sort-2", scope_symbol_id = 30 : i64} : (tensor<2xf32>) -> tensor<2xsi32> + %output_0 = "oneflow.output"(%2) {data_type = 5 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_output.0.0.1_4", output_lbns = ["_GraphToRun_1_output.0.0.1_4/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xsi32>) -> tensor<2xsi32> + %3 = "oneflow.dim_gather"(%1, %2) {device_name = ["@0:0"], device_tag = "cuda", dim = 0 : si32, hierarchy = [1], op_name = "dim_gather-3", scope_symbol_id = 30 : i64} : (tensor<2xf32>, tensor<2xsi32>) -> tensor<2xf32> + %output_1 = "oneflow.output"(%3) {data_type = 2 : i32, device_name = ["@0:0"], device_tag = "cuda", hierarchy = [1], is_dynamic = false, nd_sbp = ["B"], op_name = "_GraphToRun_1_output.0.0.0_3", output_lbns = ["_GraphToRun_1_output.0.0.0_3/out"], scope_symbol_id = 30 : i64, shape = [2 : si64]} : (tensor<2xf32>) -> tensor<2xf32> + oneflow.return %output_0, %output_1 : tensor<2xsi32>, tensor<2xf32> + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-extract-kernel-launch-tensor.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-extract-kernel-launch-tensor.mlir new file mode 100644 index 0000000000000000000000000000000000000000..d1936e0e8ab58a77ab5aaa2658d22d18ba3df3d4 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-extract-kernel-launch-tensor.mlir @@ -0,0 +1,22 @@ +// RUN: oneflow-opt %s \ +// RUN: -extract-kernel-launch-tensor \ +// RUN: | FileCheck %s + +// CHECK: module { +// CHECK: func.func @wrap0(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) -> (tensor<2xf32>, tensor<2xf32>) { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.get_tensor_from_arg"(%[[ARG]]) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "oneflow.relu"(%[[ARG0]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "oneflow.tanh"(%[[ARG1]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG1]]) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG4:[a-zA-Z0-9_]+]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG2]]) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: return %[[ARG3]], %[[ARG4]] : tensor<2xf32>, tensor<2xf32> +// CHECK: } +// CHECK: } + +module { + func.func @wrap0(%arg0: tensor<2xf32>) -> (tensor<2xf32>, tensor<2xf32>) attributes {llvm.emit_c_interface} { + %0 = "oneflow.relu"(%arg0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %1 = "oneflow.tanh"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + return %0, %1 : tensor<2xf32>, tensor<2xf32> + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-fetch-from-launcher.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-fetch-from-launcher.mlir new file mode 100644 index 0000000000000000000000000000000000000000..817da5ad15b478eb36acb2a5bf23bf6d73ece055 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-fetch-from-launcher.mlir @@ -0,0 +1,50 @@ +// RUN: oneflow-opt %s \ +// RUN: -fetch-from-launcher \ +// RUN: | FileCheck %s + +// CHECK-NOT: call @get_resources_type + +module { + func.func @okl_recycle(%arg0: !okl.launcher_ctx) { + %0:2 = call @get_resources_type_0(%arg0) : (!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + %1:2 = call @get_resources_type_1(%arg0) : (!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + %2:2 = call @get_resources_type_2(%arg0) : (!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) + "okl.destroy_reg_ctx"(%0#0) : (!okl.reg_ctx) -> () + "okl.destroy_reg_ctx"(%0#1) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%1#0) : (!okl.run_ctx) -> () + "okl.destroy_run_ctx"(%1#1) : (!okl.run_ctx) -> () + return + } + func.func @okl_compute(%arg0: !okl.launcher_ctx) { + %0:2 = call @get_resources_type_0(%arg0) : (!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + %1:2 = call @get_resources_type_1(%arg0) : (!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + %2:2 = call @get_resources_type_2(%arg0) : (!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) + "okl.launch"(%1#0, %2#0) : (!okl.run_ctx, !okl.kernel) -> () + "okl.launch"(%1#1, %2#1) : (!okl.run_ctx, !okl.kernel) -> () + return + } + func.func @okl_init_context(%arg0: !okl.launcher_ctx) { + %0 = "okl.build_reg_ctx"() ({ + ^bb0(%arg1: !okl.launcher_ctx): + %6 = "okl.get_tensor_from_arg"(%arg1) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.relu"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg1, %7) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = (!okl.launcher_ctx) -> ()} : () -> !okl.reg_ctx + %1 = "okl.build_reg_ctx"() ({ + ^bb0(%arg1: !okl.launcher_ctx): + %6 = "okl.get_tensor_from_ret"(%arg1) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.tanh"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg1, %7) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = (!okl.launcher_ctx) -> ()} : () -> !okl.reg_ctx + %2 = "okl.build_run_ctx"(%0) : (!okl.reg_ctx) -> !okl.run_ctx + %3 = "okl.build_run_ctx"(%1) : (!okl.reg_ctx) -> !okl.run_ctx + %4 = "okl.build_op_kernel"(%0) : (!okl.reg_ctx) -> !okl.kernel + %5 = "okl.build_op_kernel"(%1) : (!okl.reg_ctx) -> !okl.kernel + return + } + func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) + func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) + func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-lower-to-okl.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-lower-to-okl.mlir new file mode 100644 index 0000000000000000000000000000000000000000..8fb51d7e059acc8ebd167a15705f221c8ad9657e --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-lower-to-okl.mlir @@ -0,0 +1,43 @@ +// RUN: oneflow-opt %s \ +// RUN: -lower-to-okl \ +// RUN: | FileCheck %s + + +// CHECK: module { +// CHECK: func.func @okl_func(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) attributes {compiled = "true"} { +// CHECK: %[[ARG0:[a-zA-Z0-9_]+]] = "okl.build_reg_ctx"() ({ +// CHECK: %[[ARG6:[a-zA-Z0-9_]+]] = "okl.get_tensor_from_arg"(%[[ARG]]) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> +// CHECK: %[[ARG7:[a-zA-Z0-9_]+]] = "oneflow.relu"(%[[ARG6]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG8:[a-zA-Z0-9_]+]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG7]]) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: okl.return +// CHECK: }) : () -> !okl.reg_ctx +// CHECK: %[[ARG1:[a-zA-Z0-9_]+]] = "okl.build_run_ctx"(%[[ARG0]]) : (!okl.reg_ctx) -> !okl.run_ctx +// CHECK: %[[ARG2:[a-zA-Z0-9_]+]] = "okl.build_op_kernel"(%[[ARG0]]) : (!okl.reg_ctx) -> !okl.kernel +// CHECK: "okl.launch"(%[[ARG1]], %[[ARG2]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: "okl.destroy_reg_ctx"(%[[ARG0]]) : (!okl.reg_ctx) -> () +// CHECK: "okl.destroy_run_ctx"(%[[ARG1]]) : (!okl.run_ctx) -> () +// CHECK: %[[ARG3:[a-zA-Z0-9_]+]] = "okl.build_reg_ctx"() ({ +// CHECK: %[[ARG6]] = "okl.get_tensor_from_ret"(%[[ARG]]) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> +// CHECK: %[[ARG7]] = "oneflow.tanh"(%[[ARG6]]) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> +// CHECK: %[[ARG8]] = "okl.get_tensor_as_ret"(%[[ARG]], %[[ARG7]]) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> +// CHECK: okl.return +// CHECK: }) : () -> !okl.reg_ctx +// CHECK: %[[ARG4:[a-zA-Z0-9_]+]] = "okl.build_run_ctx"(%[[ARG3]]) : (!okl.reg_ctx) -> !okl.run_ctx +// CHECK: %[[ARG5:[a-zA-Z0-9_]+]] = "okl.build_op_kernel"(%[[ARG3]]) : (!okl.reg_ctx) -> !okl.kernel +// CHECK: "okl.launch"(%[[ARG4]], %[[ARG5]]) : (!okl.run_ctx, !okl.kernel) -> () +// CHECK: "okl.destroy_reg_ctx"(%[[ARG3]]) : (!okl.reg_ctx) -> () +// CHECK: "okl.destroy_run_ctx"(%[[ARG4]]) : (!okl.run_ctx) -> () +// CHECK: return +// CHECK: } +// CHECK: } + +module { + func.func @wrap0(%arg0: !okl.launcher_ctx) { + %0 = "okl.get_tensor_from_arg"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %1 = "oneflow.relu"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %2 = "oneflow.tanh"(%1) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %3 = "okl.get_tensor_as_ret"(%arg0, %1) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + %4 = "okl.get_tensor_as_ret"(%arg0, %2) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + return + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-split-into-funcs.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-split-into-funcs.mlir new file mode 100644 index 0000000000000000000000000000000000000000..f90f2d002cb299e351db1f4cad746c5b211b25df --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-split-into-funcs.mlir @@ -0,0 +1,43 @@ +// RUN: oneflow-opt %s \ +// RUN: -split-into-funcs \ +// RUN: | FileCheck %s + + +// CHECK: module { +// CHECK: func.func @okl_recycle(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) +// CHECK: func.func @okl_compute(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) +// CHECK: func.func @okl_init_context(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) +// CHECK: func.func private @get_resources_type_0(!okl.launcher_ctx) -> (!okl.reg_ctx, !okl.reg_ctx) +// CHECK: func.func private @get_resources_type_1(!okl.launcher_ctx) -> (!okl.run_ctx, !okl.run_ctx) +// CHECK: func.func private @get_resources_type_2(!okl.launcher_ctx) -> (!okl.kernel, !okl.kernel) +// CHECK: } + +module { + func.func @okl_func(%arg0: !okl.launcher_ctx) attributes {compiled = "true"} { + %0 = "okl.build_reg_ctx"() ({ + ^bb0(%arg1: !okl.launcher_ctx): + %6 = "okl.get_tensor_from_arg"(%arg1) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.relu"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg1, %7) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = (!okl.launcher_ctx) -> ()} : () -> !okl.reg_ctx + %1 = "okl.build_run_ctx"(%0) : (!okl.reg_ctx) -> !okl.run_ctx + %2 = "okl.build_op_kernel"(%0) : (!okl.reg_ctx) -> !okl.kernel + "okl.launch"(%1, %2) : (!okl.run_ctx, !okl.kernel) -> () + "okl.destroy_reg_ctx"(%0) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%1) : (!okl.run_ctx) -> () + %3 = "okl.build_reg_ctx"() ({ + ^bb0(%arg1: !okl.launcher_ctx): + %6 = "okl.get_tensor_from_ret"(%arg1) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %7 = "oneflow.tanh"(%6) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %8 = "okl.get_tensor_as_ret"(%arg1, %7) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + okl.return + }) {function_type = (!okl.launcher_ctx) -> ()} : () -> !okl.reg_ctx + %4 = "okl.build_run_ctx"(%3) : (!okl.reg_ctx) -> !okl.run_ctx + %5 = "okl.build_op_kernel"(%3) : (!okl.reg_ctx) -> !okl.kernel + "okl.launch"(%4, %5) : (!okl.run_ctx, !okl.kernel) -> () + "okl.destroy_reg_ctx"(%3) : (!okl.reg_ctx) -> () + "okl.destroy_run_ctx"(%4) : (!okl.run_ctx) -> () + return + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-trim-return-to-void.mlir b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-trim-return-to-void.mlir new file mode 100644 index 0000000000000000000000000000000000000000..bd1c63b080be1155ba8b63c6b751a2bf54ae8198 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/OneFlowToOKL/test-trim-return-to-void.mlir @@ -0,0 +1,16 @@ +// RUN: oneflow-opt %s \ +// RUN: -trim-return-to-void \ +// RUN: | FileCheck %s + +// CHECK: func.func @wrap0(%[[ARG:[a-zA-Z0-9_]+]]: !okl.launcher_ctx) + +module { + func.func @wrap0(%arg0: !okl.launcher_ctx) -> (tensor<2xf32>, tensor<2xf32>) { + %0 = "okl.get_tensor_from_arg"(%arg0) {index = 0 : i32, tensor_type = 0 : i32} : (!okl.launcher_ctx) -> tensor<2xf32> + %1 = "oneflow.relu"(%0) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "relu-0", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %2 = "oneflow.tanh"(%1) {device_name = ["@0:0"], device_tag = "cpu", hierarchy = [1], op_name = "tanh-1", scope_symbol_id = 12 : i64} : (tensor<2xf32>) -> tensor<2xf32> + %3 = "okl.get_tensor_as_ret"(%arg0, %1) {index = 0 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + %4 = "okl.get_tensor_as_ret"(%arg0, %2) {index = 1 : i32, tensor_type = 2 : i32} : (!okl.launcher_ctx, tensor<2xf32>) -> tensor<2xf32> + return %3, %4 : tensor<2xf32>, tensor<2xf32> + } +} diff --git a/oneflow/ir/test/OneFlow/kernel_launch/test_conv2d.py b/oneflow/ir/test/OneFlow/kernel_launch/test_conv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..007334d1f06b4b86d3aa72d546f02a301a07e518 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/test_conv2d.py @@ -0,0 +1,67 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s + +import os +import sys + +sys.path.append(os.path.abspath(os.path.dirname(__file__))) +sys.path.append(os.path.abspath(os.path.dirname(__file__)) + "/..") + +import unittest +import numpy as np + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH"] = "1" +os.environ["ONEFLOW_MLIR_ENABLE_IR_PRINTING"] = "1" + +import oneflow as flow +import oneflow.unittest +import oneflow.nn as nn + + +def _test_okl_conv2d(test_case): + x = flow.randn(2, 3, 224, 224) + x = x.cuda() + conv = nn.Conv2d(3, 2, kernel_size=7, stride=2, padding=3, bias=False).to("cuda") + + eager_res = conv(x) + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + + self.net = conv + + def build(self, x): + return self.net(x) + + graph_to_run = GraphToRun() + lazy_res = graph_to_run(x) + test_case.assertTrue( + np.allclose(eager_res.numpy(), lazy_res.numpy(), rtol=1e-4, atol=1e-4) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestOKLResNet(flow.unittest.TestCase): + @unittest.skipUnless(flow.sysconfig.with_cuda(), "only test cpu cases") + def test_okl_conv2d(test_case): + _test_okl_conv2d(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/kernel_launch/test_fuse.py b/oneflow/ir/test/OneFlow/kernel_launch/test_fuse.py new file mode 100644 index 0000000000000000000000000000000000000000..255af9c4c34fa9041c64fa4824747e90ba795774 --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/test_fuse.py @@ -0,0 +1,66 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s + +import os + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "0" + +import sys + +sys.path.append(os.path.abspath(os.path.dirname(__file__))) +sys.path.append(os.path.abspath(os.path.dirname(__file__)) + "/..") + +import unittest +import numpy as np +import oneflow as flow +import oneflow.nn as nn +import oneflow.unittest + + +def _test_okl_resnet(test_case): + x = flow.randn(2, 3, 224, 224) + bn = nn.BatchNorm2d(3) + eager_res = flow.relu(bn(x) + x) + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + identity = x + out = bn(x) + out += identity + return flow.relu(out) + + graph_to_run = GraphToRun() + lazy_res = graph_to_run(x) + test_case.assertTrue( + np.allclose(eager_res.numpy(), lazy_res.numpy(), rtol=1e-4, atol=1e-4) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestOKLResNet(flow.unittest.TestCase): + @unittest.skipUnless(flow.sysconfig.with_cuda(), "only test cpu cases") + def test_okl_resnet(test_case): + _test_okl_resnet(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/kernel_launch/test_okl_ops.py b/oneflow/ir/test/OneFlow/kernel_launch/test_okl_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..8c17c4bd4d9c1214a2b305c06fc3ea787c4d4dec --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/test_okl_ops.py @@ -0,0 +1,74 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 %s +import unittest +import os + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH"] = "1" + +import oneflow as flow +import oneflow.unittest + + +def _test_okl_ops(test_case: flow.unittest.TestCase): + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = flow.relu(x) + z = flow.tanh(y) + return flow.sort(z) + + x = flow.Tensor([1, -1]) + graph_to_run = GraphToRun() + lazy_relu = graph_to_run(x) + + cmp = flow.sort(flow.tanh(flow.relu(x))) + test_case.assertTrue(flow.equal(cmp[0], lazy_relu[0])) + + +def _test_okl_ops_with_cuda(test_case: flow.unittest.TestCase): + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = flow.relu(x) + z = flow.tanh(y) + return flow.sort(z) + + x = flow.Tensor([1, -1]).cuda() + graph_to_run = GraphToRun() + lazy_relu = graph_to_run(x) + + cmp = flow.sort(flow.tanh(flow.relu(x))) + test_case.assertTrue(flow.equal(cmp[0], lazy_relu[0])) + + +@flow.unittest.skip_unless_1n1d() +class TestOKLOps(flow.unittest.TestCase): + def test_okl_ops(test_case): + _test_okl_ops(test_case) + + @unittest.skipUnless(flow.sysconfig.with_cuda(), "only test cpu cases") + def test_okl_ops_with_cuda(test_case): + _test_okl_ops_with_cuda(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/kernel_launch/test_resnet.py b/oneflow/ir/test/OneFlow/kernel_launch/test_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..a836247bc933949f2e5ac98356a0fae6e80c137d --- /dev/null +++ b/oneflow/ir/test/OneFlow/kernel_launch/test_resnet.py @@ -0,0 +1,67 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s + +import os +import sys + +sys.path.append(os.path.abspath(os.path.dirname(__file__))) +sys.path.append(os.path.abspath(os.path.dirname(__file__)) + "/..") + +# TODO(peihong): extract MLIR ir env variables into a single module to control. +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "0" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from networks.resnet50 import resnet50 + + +def _test_okl_resnet(test_case): + x = flow.randn(2, 3, 224, 224) + resnet = resnet50() + x = x.cuda() + resnet.to("cuda") + + eager_res = resnet(x) + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + self.resnet = resnet + + def build(self, x): + return self.resnet(x) + + graph_to_run = GraphToRun() + lazy_res = graph_to_run(x) + test_case.assertTrue( + np.allclose(eager_res.numpy(), lazy_res.numpy(), rtol=1e-4, atol=1e-4) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestOKLResNet(flow.unittest.TestCase): + @unittest.skipUnless(flow.sysconfig.with_cuda(), "only test cpu cases") + def test_okl_resnet(test_case): + _test_okl_resnet(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/lower_to_tosa.mlir b/oneflow/ir/test/OneFlow/lower_to_tosa.mlir index f65ed33275ce5ef725993c4d6e0c00e17e9b2436..66962777f6f4046abf3d7ea863e3773900eaa8f6 100644 --- a/oneflow/ir/test/OneFlow/lower_to_tosa.mlir +++ b/oneflow/ir/test/OneFlow/lower_to_tosa.mlir @@ -1,4 +1,7 @@ -// RUN: oneflow-opt -lower-oneflow-to-tosa -pass-pipeline="func.func(tosa-to-linalg)" -cse --linalg-fuse-elementwise-ops -linalg-bufferize -tensor-bufferize -func-bufferize -buffer-results-to-out-params -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-func-to-llvm -convert-memref-to-llvm -reconcile-unrealized-casts --print-after-all %s +// RUN: oneflow-opt \ +// RUN: -lower-oneflow-to-tosa \ +// RUN: -tosa-make-broadcastable \ +// RUN: --print-after-all %s module { func.func @Cast_1__FUSE__ScalarMulByTensor_2(%arg0: tensor<96x96xi64>, %arg1: tensor<1xf32>) -> tensor<96x96xf32> { diff --git a/oneflow/ir/test/OneFlow/lower_to_tosa_signed.mlir b/oneflow/ir/test/OneFlow/lower_to_tosa_signed.mlir new file mode 100644 index 0000000000000000000000000000000000000000..c454e0875e32dbcccfd79f482fcaaa10fca6553c --- /dev/null +++ b/oneflow/ir/test/OneFlow/lower_to_tosa_signed.mlir @@ -0,0 +1,24 @@ +// RUN: oneflow-opt -convert-to-signless-for-tosa --mlir-print-ir-before-all --mlir-print-ir-after-all \ +// RUN: -lower-oneflow-to-tosa \ +// RUN: -tosa-make-broadcastable \ +// RUN: -reconcile-unrealized-casts --print-after-all %s + +module { + func.func @test(%arg0: tensor<1x64x112x112xf32>) -> tensor<1x64x56x56xsi64> { + %1, %indice = "oneflow.max_pool_2d"(%arg0) { + ceil_mode = false, + data_format = "channels_first", + device_name = ["@0:0"], + device_tag = "cpu", + dilation = [1 : si32, 1 : si32], + hierarchy = [1], + kernel_size = [3 : si32, 3 : si32], + op_name = "model.maxpool-max_pool_2d-3", + padding = [1 : si32, 1 : si32], + return_indices = false, + scope_symbol_id = 49 : i64, + stride = [2 : si32, 2 : si32] + } : (tensor<1x64x112x112xf32>) -> (tensor<1x64x56x56xf32>, tensor<1x64x56x56xsi64>) + return %indice : tensor<1x64x56x56xsi64> + } +} diff --git a/oneflow/ir/test/OneFlow/psig/error_parse.mlir b/oneflow/ir/test/OneFlow/psig/error_parse.mlir new file mode 100644 index 0000000000000000000000000000000000000000..63a53523f9eb705cd5e06aaa0982bb259b7823cf --- /dev/null +++ b/oneflow/ir/test/OneFlow/psig/error_parse.mlir @@ -0,0 +1,11 @@ +// RUN: not oneflow-opt %s \ +// RUN: -split-input-file \ +// RUN: -verify-diagnostics -o - 2>&1 | FileCheck --check-prefix=CHECK_ERROR_1 %s + +// CHECK_ERROR_1: unexpected error: failed to parse a sbp attribute here +module { + oneflow.job @test_err(){ + %output_0 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0", "@1:1"], device_tag = "cuda", hierarchy = [2, 1], parallel = #sbp.parallel<[] -> [[[]], "S(0)", #sbp.P]>, op_name = "net-FreeEagerTensor-2", output_lbns = ["net-FreeEagerTensor-2/out"], scope_symbol_id = 14 : i64, shape = [5 : si64, 8 : si64], trainable = false} : () -> tensor<5x8xf32> + oneflow.return + } +} diff --git a/oneflow/ir/test/OneFlow/psig/sbp_parse.mlir b/oneflow/ir/test/OneFlow/psig/sbp_parse.mlir new file mode 100644 index 0000000000000000000000000000000000000000..ddf76c3e3ab15687e0fa2df5d9e8a32572b2c1b1 --- /dev/null +++ b/oneflow/ir/test/OneFlow/psig/sbp_parse.mlir @@ -0,0 +1,25 @@ +// RUN: oneflow-opt %s \ +// RUN: -split-input-file \ +// RUN: -verify-diagnostics -o - | FileCheck %s + +// CHECK-LABEL: test_single +module { + oneflow.job @test_single(){ +// CHECK: parallel = #sbp.parallel<[] -> [#sbp.B, #sbp.S<0>]> + %output = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0", "@1:1"], device_tag = "cuda", hierarchy = [2, 1], parallel = #sbp.parallel<[] -> [#sbp.B, #sbp.S<0>]>, op_name = "net-FreeEagerTensor-1", output_lbns = ["net-FreeEagerTensor-1/out"], scope_symbol_id = 14 : i64, shape = [4 : si64, 5 : si64], trainable = false} : () -> tensor<4x5xf32> +// CHECK: parallel = #sbp.parallel<[] -> [#sbp.B, #sbp.P]> + %output_0 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0", "@1:1"], device_tag = "cuda", hierarchy = [2, 1], parallel = #sbp.parallel<[] -> [#sbp.B, #sbp.P]>, op_name = "net-FreeEagerTensor-2", output_lbns = ["net-FreeEagerTensor-2/out"], scope_symbol_id = 14 : i64, shape = [5 : si64, 8 : si64], trainable = false} : () -> tensor<5x8xf32> + oneflow.return + } +} + +// CHECK-LABEL: test_nd +module { + oneflow.job @test_nd(){ + // CHECK: #sbp.B, #sbp.S<0> + %output = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0", "@1:1"], device_tag = "cuda", hierarchy = [2, 1], parallel = #sbp.parallel<[] -> [[#sbp.B, #sbp.S<0>]]>, op_name = "net-FreeEagerTensor-1", output_lbns = ["net-FreeEagerTensor-1/out"], scope_symbol_id = 14 : i64, shape = [4 : si64, 5 : si64], trainable = false} : () -> tensor<4x5xf32> + // CHECK: [#sbp.B, #sbp.P] + %output_0 = "oneflow.variable"() {data_type = 2 : i32, device_name = ["@0:0", "@1:1"], device_tag = "cuda", hierarchy = [2, 1], parallel = #sbp.parallel<[] -> [[#sbp.B, #sbp.P]]>, op_name = "net-FreeEagerTensor-2", output_lbns = ["net-FreeEagerTensor-2/out"], scope_symbol_id = 14 : i64, shape = [5 : si64, 8 : si64], trainable = false} : () -> tensor<5x8xf32> + oneflow.return + } +} diff --git a/oneflow/ir/test/OneFlow/psig/test_2nd_basic_parse.py b/oneflow/ir/test/OneFlow/psig/test_2nd_basic_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..fd67fb2c07ce59f2f3d35a36b679bac399a4c3cc --- /dev/null +++ b/oneflow/ir/test/OneFlow/psig/test_2nd_basic_parse.py @@ -0,0 +1,68 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.distributed.launch --nproc_per_node 2 %s | FileCheck %s +# CHECK: [#sbp.B, #sbp.S<0>] +# CHECK: [#sbp.B, #sbp.S<0>] + +import oneflow as flow +import unittest +import oneflow.unittest +import os +from google.protobuf import text_format + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" + + +def _test_nd_basic_parse(test_case): + class ModuleToRun(flow.nn.Module): + def __init__(self): + super().__init__() + P0 = flow.placement("cpu", ranks=[[0], [1]]) + a0_sbp = (flow.sbp.broadcast, flow.sbp.split(0)) + b0_sbp = (flow.sbp.broadcast, flow.sbp.split(0)) + + self.A0 = flow.randn(4, 5, placement=P0, sbp=a0_sbp) + self.B0 = flow.randn(5, 8, placement=P0, sbp=b0_sbp) + + def forward(self): + return flow.matmul(self.A0, self.B0) + + net = ModuleToRun() + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + self.net = net + + def build(self): + return self.net() + + graph_to_run = GraphToRun() + lazy_output = graph_to_run() + + serialized_job = graph_to_run._forward_job_proto.SerializeToString() + mlir = flow._oneflow_internal.nn.graph.ConvertJobToIR(serialized_job) + print(mlir) + + +@flow.unittest.skip_unless_1n1d() +class TestBasicParse(flow.unittest.TestCase): + def test_nd_basic_parse(test_case): + _test_nd_basic_parse(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/psig/test_basic_parse.py b/oneflow/ir/test/OneFlow/psig/test_basic_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..9616fc9d2880cbf7a7e9415fb82901f81b8d59e5 --- /dev/null +++ b/oneflow/ir/test/OneFlow/psig/test_basic_parse.py @@ -0,0 +1,67 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 %s | FileCheck %s +# CHECK: [#sbp.B] +# CHECK: [#sbp.S<0>] + +import oneflow as flow +import unittest +import oneflow.unittest +import os +from google.protobuf import text_format + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" + + +def _test_1nd_basic_parse(test_case): + class ModuleToRun(flow.nn.Module): + def __init__(self): + super().__init__() + P0 = flow.placement("cpu", ranks=[0]) + a0_sbp = flow.sbp.broadcast + b0_sbp = flow.sbp.split(0) + self.A0 = flow.randn(4, 5, placement=P0, sbp=a0_sbp) + self.B0 = flow.randn(5, 8, placement=P0, sbp=b0_sbp) + + def forward(self): + return flow.matmul(self.A0, self.B0) + + net = ModuleToRun() + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + self.net = net + + def build(self): + return self.net() + + graph_to_run = GraphToRun() + lazy_output = graph_to_run() + + serialized_job = graph_to_run._forward_job_proto.SerializeToString() + mlir = flow._oneflow_internal.nn.graph.ConvertJobToIR(serialized_job) + print(mlir) + + +@flow.unittest.skip_unless_1n1d() +class TestBasicParse(flow.unittest.TestCase): + def test_1nd_basic_parse(test_case): + _test_1nd_basic_parse(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/test_cast_optimal_pass.py b/oneflow/ir/test/OneFlow/test_cast_optimal_pass.py index 70eff6fc86ff6b1066af2df373430b5ff32f4de7..35bd42bb751dd6086cb2eb93ab570ff68f09a7d2 100644 --- a/oneflow/ir/test/OneFlow/test_cast_optimal_pass.py +++ b/oneflow/ir/test/OneFlow/test_cast_optimal_pass.py @@ -21,6 +21,11 @@ import unittest import numpy as np os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" +os.environ["ONEFLOW_MLIR_ENABLE_TIMING"] = "1" +os.environ["ONEFLOW_MLIR_PRINT_STATS"] = "1" +os.environ["ONEFLOW_MLIR_ENABLE_IR_PRINTING"] = "1" import oneflow as flow import oneflow.unittest diff --git a/oneflow/ir/test/OneFlow/test_fuse_pad_conv.py b/oneflow/ir/test/OneFlow/test_fuse_pad_conv.py index 8914dd628573aef22dd6c6e97f0223f886b16203..69d8b03ca5a3f1e3b03ff836b99ca2891ce95ac9 100644 --- a/oneflow/ir/test/OneFlow/test_fuse_pad_conv.py +++ b/oneflow/ir/test/OneFlow/test_fuse_pad_conv.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK-NOT: oneflow.pad import unittest @@ -22,7 +22,11 @@ import numpy as np import os os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" - +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" +os.environ["ONEFLOW_MLIR_ENABLE_TIMING"] = "1" +os.environ["ONEFLOW_MLIR_PRINT_STATS"] = "1" +os.environ["ONEFLOW_MLIR_ENABLE_IR_PRINTING"] = "1" import oneflow as flow import oneflow.unittest import oneflow.sysconfig diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_conv_bn_auto_nhwc.py b/oneflow/ir/test/OneFlow/with_cuda/test_conv_bn_auto_nhwc.py index 8202c49ae893f22a39af165855e8e39169986e65..dea32c66d0b433a89749ccec1021f728f9eaef5f 100644 --- a/oneflow/ir/test/OneFlow/with_cuda/test_conv_bn_auto_nhwc.py +++ b/oneflow/ir/test/OneFlow/with_cuda/test_conv_bn_auto_nhwc.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s | FileCheck %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s # CHECK: oneflow.transpose import os @@ -27,6 +27,9 @@ from flowvision.models.resnet import resnet50 os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" os.environ["ONEFLOW_MLIR_PREFER_NHWC"] = "1" os.environ["ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_NORMALIZATION_OPS"] = "1" +os.environ["ONEFLOW_MLIR_PRINT_STATS"] = "1" def _test_fuse_conv_bn(test_case, with_cuda): diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_dropout.py b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_dropout.py index 07d19c795986a334121843e1e41e00bbbd307861..6d6e89a772166fdbf692620db30cece93020e209 100644 --- a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_dropout.py +++ b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_dropout.py @@ -22,7 +22,8 @@ import numpy as np import os os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" - +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" import oneflow as flow import oneflow.unittest import oneflow.sysconfig @@ -49,7 +50,10 @@ def do_bias_add_dropout_graph(test_case, with_cuda, prob): graph_to_run = GraphToRun() lazy_res = graph_to_run(x, bias) - test_case.assertTrue(np.array_equal(eager_res.numpy(), lazy_res.numpy())) + if prob == 1.0: + test_case.assertTrue(np.array_equal(eager_res.numpy(), lazy_res.numpy())) + else: + test_case.assertTrue(lazy_res.sum().item() != 0.0) @flow.unittest.skip_unless_1n1d() @@ -57,6 +61,7 @@ def do_bias_add_dropout_graph(test_case, with_cuda, prob): class TestBiasAddDropout(oneflow.unittest.TestCase): def test_bias_add_dropout_graph(test_case): do_bias_add_dropout_graph(test_case, True, 1.0) + do_bias_add_dropout_graph(test_case, True, 0.5) if __name__ == "__main__": diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_gelu.py b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_gelu.py new file mode 100644 index 0000000000000000000000000000000000000000..8a95c3d6203d89e9a3de0c6902200ba89616f58e --- /dev/null +++ b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bias_add_gelu.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 %s | FileCheck %s +# CHECK-NOT: oneflow.bias_add + +import unittest +import numpy as np + +import os + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" +import oneflow as flow +import oneflow.unittest +import oneflow.sysconfig + + +def do_bias_add_gelu_graph(test_case, with_cuda): + x = flow.randn(2, 3, 4, 5) + bias = flow.randn(5) + gelu = flow.nn.GELU() + if with_cuda: + x = x.cuda() + bias = bias.to("cuda") + gelu.to("cuda") + + eager_res = gelu(flow._C.bias_add(x, bias, axis=3)) + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + self.gelu = gelu + + def build(self, x, bias): + return self.gelu(flow._C.bias_add(x, bias, axis=3)) + + graph_to_run = GraphToRun() + lazy_res = graph_to_run(x, bias) + test_case.assertTrue(np.array_equal(eager_res.numpy(), lazy_res.numpy())) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipUnless(oneflow.sysconfig.with_cuda(), "needs -DBUILD_CUDA=ON") +class TestBiasAddGelu(oneflow.unittest.TestCase): + def test_bias_add_gelu_graph(test_case): + do_bias_add_gelu_graph(test_case, True) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bn_add_relu.py b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bn_add_relu.py new file mode 100644 index 0000000000000000000000000000000000000000..16c1d1e3d4e49e68d38b9649b7059919d45c78b2 --- /dev/null +++ b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_bn_add_relu.py @@ -0,0 +1,82 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s +# CHECK-NOT: "oneflow.normalization" + +import unittest +import numpy as np + +import os + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_NORMALIZATION_OPS"] = "1" +os.environ["ONEFLOW_MLIR_PRINT_STATS"] = "1" + +import oneflow as flow +import oneflow.unittest +import oneflow.sysconfig + + +def do_normalization_add_relu_graph(test_case, with_cuda): + def get_bn(fused=True): + if fused: + return flow.nn.FusedBatchNorm2d(num_features=2, eps=1e-5, momentum=0.1).to( + "cuda" + ) + else: + return flow.nn.BatchNorm2d(num_features=2, eps=1e-5, momentum=0.1).to( + "cuda" + ) + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = get_bn() + + def build(self, x, addend): + return self.m(x, addend=addend) + + class GraphToRunWithOpt(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = get_bn(fused=False) + + def build(self, x, addend): + return flow.relu(self.m(x) + addend) + + graph_to_run = GraphToRun() + graph_to_run_opt = GraphToRunWithOpt() + x = flow.Tensor(np.random.randn(4, 2, 8, 3)).to("cuda") + addend = flow.Tensor(np.random.randn(4, 2, 8, 3)).to("cuda") + + eager_res = flow.relu(get_bn(fused=False)(x) + addend) + eager_res_fuse = get_bn()(x, addend=addend) + lazy_res = graph_to_run(x, addend) + lazy_res_opt = graph_to_run_opt(x, addend) + test_case.assertTrue(np.array_equal(eager_res.numpy(), eager_res_fuse.numpy())) + test_case.assertTrue(np.array_equal(eager_res.numpy(), lazy_res.numpy())) + test_case.assertTrue(np.array_equal(eager_res.numpy(), lazy_res_opt.numpy())) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipUnless(oneflow.sysconfig.with_cuda(), "needs -DBUILD_CUDA=ON") +class TestNormalizationAddRelu(oneflow.unittest.TestCase): + def test_normalization_add_relu_graph(test_case): + do_normalization_add_relu_graph(test_case, True) + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_scale_tril.py b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_scale_tril.py index a858ac2894793e3db215fd5dbe892d2c5acffa91..209b945a56e6d4ebd0fe80ed0df57c50b063e052 100644 --- a/oneflow/ir/test/OneFlow/with_cuda/test_fuse_scale_tril.py +++ b/oneflow/ir/test/OneFlow/with_cuda/test_fuse_scale_tril.py @@ -21,6 +21,8 @@ import unittest import numpy as np os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_FUSE_FORWARD_OPS"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" import oneflow as flow from collections import OrderedDict from oneflow.test_utils.test_util import GenArgDict diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_fused_matmul_bias.py b/oneflow/ir/test/OneFlow/with_cuda/test_fused_matmul_bias.py new file mode 100644 index 0000000000000000000000000000000000000000..10b52c96006370d8f52f7a3d76d86697d6098a28 --- /dev/null +++ b/oneflow/ir/test/OneFlow/with_cuda/test_fused_matmul_bias.py @@ -0,0 +1,80 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s | FileCheck %s +# CHECK-NOT: oneflow.bias_add +# CHECK: %[[OUT0:[a-zA-Z0-9_]+]]:5 = "oneflow.grouped_matmul_bias" + +import unittest +import numpy as np + +import os + +os.environ["ONEFLOW_MLIR_ENABLE_ROUND_TRIP"] = "1" +os.environ["ONEFLOW_MLIR_GROUP_MATMUL"] = "1" +os.environ["ONEFLOW_MLIR_STDOUT"] = "1" +os.environ["ONEFLOW_MLIR_CSE"] = "0" + +import oneflow as flow +import oneflow.unittest +import oneflow.sysconfig + + +def _matmul_bias0(x, weight, bias): + return flow._C.bias_add( + flow._C.matmul(x, weight, transpose_b=True), bias, axis=len(x.shape) - 1 + ) + + +def _matmul_bias1(x, w, bias): + return flow._C.fused_matmul_bias(x, w, bias) + + +def do_fused_matmul_bias_graph(test_case, dev): + x = np.random.uniform(low=-1, high=1, size=(8, 9)) + w = np.random.uniform(low=-1, high=1, size=(10, 9)) + bias = np.random.uniform(low=-1, high=1, size=(10)) + x = flow.from_numpy(x).to(dev).to(flow.float32) + w = flow.from_numpy(w).to(dev).to(flow.float32) + bias = flow.from_numpy(bias).to(dev).to(flow.float32) + eager_res = _matmul_bias0(x, w, bias) * 5 + + class GraphToRun(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x, w, bias): + return ( + _matmul_bias0(x, w, bias) + + _matmul_bias1(x, w, bias) + + _matmul_bias0(x, w, bias) + + _matmul_bias1(x, w, bias) + + _matmul_bias0(x, w, bias) + ) + + graph_to_run = GraphToRun() + lazy_res = graph_to_run(x, w, bias) + test_case.assertTrue(np.allclose(eager_res.numpy(), lazy_res.numpy())) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipUnless(oneflow.sysconfig.with_cuda(), "needs -DBUILD_CUDA=ON") +class TestGroupMatMulBias(oneflow.unittest.TestCase): + def test_fused_matmul_bias_graph(test_case): + do_fused_matmul_bias_graph(test_case, "cuda") + + +if __name__ == "__main__": + unittest.main() diff --git a/oneflow/ir/test/OneFlow/with_cuda/test_graph_save_and_load.py b/oneflow/ir/test/OneFlow/with_cuda/test_graph_save_and_load.py index 48cb43e6af1cebf7a65e1746a6d9c71a80dcf91e..6c9903df018e44d5ccd8b1970b13149dc674ed60 100644 --- a/oneflow/ir/test/OneFlow/with_cuda/test_graph_save_and_load.py +++ b/oneflow/ir/test/OneFlow/with_cuda/test_graph_save_and_load.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -# RUN: python3 %s +# RUN: python3 -m oneflow.test_utils.throttle --with-cuda=%with_cuda python3 %s import os import sys diff --git a/oneflow/ir/test/lit.cfg.py b/oneflow/ir/test/lit.cfg.py index 55ba1bdf6b5fb4386bc6f9b4f2eb125e2f7399e2..93b290ff37ad8352c76d4fca90741dee27ae0855 100644 --- a/oneflow/ir/test/lit.cfg.py +++ b/oneflow/ir/test/lit.cfg.py @@ -92,6 +92,8 @@ llvm_config.with_environment( llvm_config.with_environment("ONEFLOW_MLIR_STDOUT", "1") llvm_config.with_environment("ONEFLOW_MLIR_ENABLE_CODEGEN_FUSERS", "1") llvm_config.with_environment("ONEFLOW_MLIR_ENABLE_ROUND_TRIP", "1") +llvm_config.with_environment("ONEFLOW_MLIR_CSE", "1") +llvm_config.with_environment("ONEFLOW_MLIR_FUSE_FORWARD_OPS", "1") llvm_config.with_environment( "PYTHONPATH", os.path.join(config.oneflow_src_root, "python"), append_path=True, ) @@ -100,6 +102,7 @@ tool_dirs = [config.oneflow_tools_dir, config.llvm_tools_dir] tools = ["oneflow-opt", "oneflow-translate", "oneflow-runner"] tools.extend( [ + ToolSubst("%with_cuda", config.BUILD_CUDA, unresolved="ignore"), ToolSubst("%linalg_test_lib_dir", config.llvm_lib_dir, unresolved="ignore"), ToolSubst("%test_exec_root", config.test_exec_root, unresolved="ignore"), ] diff --git a/oneflow/user/data/ofrecord_dataset.h b/oneflow/user/data/ofrecord_dataset.h index 4bb0ff09ee38a6eee2b941c93dda5850cae9cb3a..e4d6cbf56e6f4e5b12b3fde33a17670a4f31fda2 100644 --- a/oneflow/user/data/ofrecord_dataset.h +++ b/oneflow/user/data/ofrecord_dataset.h @@ -62,7 +62,7 @@ class OFRecordDataset final : public Dataset { // or been deprecated. if (ctx->op_type_name() == "OFRecordReader") { auto nd_sbp_str_vec = ctx->Attr>("nd_sbp"); - // NOTE(zwx): OFRecordDataset is not consistent since attr nd_sbp is empty, + // NOTE(zwx): OFRecordDataset is not global since attr nd_sbp is empty, // we assume that it works in DDP if (nd_sbp_str_vec.empty()) { is_local = true; } } diff --git a/oneflow/user/kernels/acc_kernel.cpp b/oneflow/user/kernels/acc_kernel.cpp index e3261a2a89a9bf4251be315ff84453010739567d..d31be4c8c66be37afc5396b1388963a702412f70 100644 --- a/oneflow/user/kernels/acc_kernel.cpp +++ b/oneflow/user/kernels/acc_kernel.cpp @@ -21,7 +21,6 @@ namespace oneflow { namespace { -template class AccKernel final : public user_op::OpKernel { public: AccKernel() = default; @@ -42,23 +41,7 @@ class AccKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_ACC_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("acc") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ACC_KERNEL, DEVICE_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ACC_KERNEL, OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ACC_KERNEL, OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif -#undef REGISTER_ACC_KERNEL +REGISTER_USER_KERNEL("acc").SetCreateFn().SetIsMatchedHob(user_op::HobTrue()); } // namespace diff --git a/oneflow/user/kernels/activation_kernels.cpp b/oneflow/user/kernels/activation_kernels.cpp index d816fc4d7bc36521380e862744c8a3a8c74b080e..59374549c311ab15d4f6f61ad85d64cfc431f359 100644 --- a/oneflow/user/kernels/activation_kernels.cpp +++ b/oneflow/user/kernels/activation_kernels.cpp @@ -229,6 +229,60 @@ REGISTER_USER_KERNEL("gelu_grad") .SetIsMatchedHob(BinaryPrimitiveExists(ep::primitive::BinaryOp::kGeluBackwardWithDyX, "dx", "dy")); +REGISTER_USER_KERNEL("fast_gelu") + .SetCreateFn([]() { + return user_op::NewOpKernel( + "out", "in", [](user_op::KernelComputeContext* ctx) { + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("in", 0); + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("out", 0); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::UnaryOp::kFastGelu, src->data_type(), + dst->data_type()); + }); + }) + .SetIsMatchedHob(UnaryPrimitiveExists(ep::primitive::UnaryOp::kFastGelu, "out", "in")); + +REGISTER_USER_KERNEL("fast_gelu_grad") + .SetCreateFn([]() { + return user_op::NewOpKernel( + "dx", "dy", "x", [](user_op::KernelComputeContext* ctx) { + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("dx", 0); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kFastGeluBackwardWithDyX, + src->data_type(), dst->data_type(), 1 /*max_num_dims*/); + }); + }) + .SetIsMatchedHob(BinaryPrimitiveExists(ep::primitive::BinaryOp::kFastGeluBackwardWithDyX, "dx", + "dy")); + +REGISTER_USER_KERNEL("quick_gelu") + .SetCreateFn([]() { + return user_op::NewOpKernel( + "y", "x", [](user_op::KernelComputeContext* ctx) { + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("x", 0); + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("y", 0); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::UnaryOp::kQuickGelu, src->data_type(), + dst->data_type()); + }); + }) + .SetIsMatchedHob(UnaryPrimitiveExists(ep::primitive::UnaryOp::kQuickGelu, "y", "x")); + +REGISTER_USER_KERNEL("quick_gelu_grad") + .SetCreateFn([]() { + return user_op::NewOpKernel( + "dx", "dy", "x", [](user_op::KernelComputeContext* ctx) { + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("dx", 0); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kQuickGeluBackwardWithDyX, + src->data_type(), dst->data_type(), 1 /*max_num_dims*/); + }); + }) + .SetIsMatchedHob(BinaryPrimitiveExists(ep::primitive::BinaryOp::kQuickGeluBackwardWithDyX, "dx", + "dy")); + REGISTER_USER_KERNEL("leaky_relu") .SetCreateFn([]() { return user_op::NewOpKernel( @@ -346,6 +400,18 @@ REGISTER_USER_KERNEL("silu_grad") }) .SetIsMatchedHob(BinaryPrimitiveExists(ep::primitive::BinaryOp::kSiluBackwardWithDyX, "dx", "dy")); +REGISTER_USER_KERNEL("trunc") + .SetCreateFn([]() { + return user_op::NewOpKernel( + "out", "in", [](user_op::KernelComputeContext* ctx) { + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("in", 0); + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("out", 0); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::UnaryOp::kTrunc, src->data_type(), + dst->data_type()); + }); + }) + .SetIsMatchedHob(UnaryPrimitiveExists(ep::primitive::UnaryOp::kTrunc, "out", "in")); REGISTER_USER_KERNEL("selu") .SetCreateFn([]() { diff --git a/oneflow/user/kernels/adaptive_avg_pool_cpu_kernel.cpp b/oneflow/user/kernels/adaptive_avg_pool_cpu_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f52d69fc96250cf154a6fa5eb5d7ca59c059174d --- /dev/null +++ b/oneflow/user/kernels/adaptive_avg_pool_cpu_kernel.cpp @@ -0,0 +1,243 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/adaptive_pool_kernel_util.h" + +namespace oneflow { + +namespace { + +template +void AvgForwardCompute(user_op::KernelComputeContext* ctx, const int32_t& dim) { + user_op::Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); + const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); + const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); + + // TODO (Tianyu): Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(x_shape, data_format, dim); + const Shape& out = GetShape5D(y_shape, data_format, dim); + + const T* in_ptr = in_tensor->dptr(); + T* out_ptr = out_tensor->mut_dptr(); + + const int64_t input_width = in.Count(4); + const int64_t output_width = out.Count(4); + const int64_t input_image_size = in.Count(3); + const int64_t output_image_size = out.Count(3); + const int64_t input_size = in.Count(2); + const int64_t output_size = out.Count(2); + + FOR_RANGE(int64_t, n, 0, in.At(0)) { + FOR_RANGE(int64_t, c, 0, in.At(1)) { + FOR_RANGE(int64_t, od, 0, out.At(2)) { + int64_t id0 = start_index(od, out.At(2), in.At(2)); + int64_t id1 = end_index(od, out.At(2), in.At(2)); + int64_t kd = id1 - id0; + FOR_RANGE(int64_t, oh, 0, out.At(3)) { + int64_t ih0 = start_index(oh, out.At(3), in.At(3)); + int64_t ih1 = end_index(oh, out.At(3), in.At(3)); + int64_t kh = ih1 - ih0; + FOR_RANGE(int64_t, ow, 0, out.At(4)) { + int64_t iw0 = start_index(ow, out.At(4), in.At(4)); + int64_t iw1 = end_index(ow, out.At(4), in.At(4)); + int64_t kw = iw1 - iw0; + + // Compute local average + T sum = static_cast(0); + FOR_RANGE(int64_t, id, id0, id1) { + FOR_RANGE(int64_t, ih, ih0, ih1) { + FOR_RANGE(int64_t, iw, iw0, iw1) { + sum += in_ptr[id * input_image_size + ih * input_width + iw]; + } + } + } + out_ptr[od * output_image_size + oh * output_width + ow] = sum / kd / kh / kw; + } + } + } + in_ptr += input_size; + out_ptr += output_size; + } + } +} + +template +void AvgBackwardCompute(user_op::KernelComputeContext* ctx, const int32_t& dim) { + user_op::Tensor* grad_input = ctx->Tensor4ArgNameAndIndex("dx", 0); + const user_op::Tensor* grad_output = ctx->Tensor4ArgNameAndIndex("dy", 0); + const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); + const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); + + // TODO (Tianyu): Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(dx_shape, data_format, dim); + const Shape& out = GetShape5D(dy_shape, data_format, dim); + + const T* out_ptr = grad_output->dptr(); + T* in_ptr = grad_input->mut_dptr(); + + std::fill(in_ptr, in_ptr + grad_input->shape_view().elem_cnt(), static_cast(0)); + + const int64_t input_width = in.Count(4); + const int64_t output_width = out.Count(4); + const int64_t input_image_size = in.Count(3); + const int64_t output_image_size = out.Count(3); + const int64_t input_size = in.Count(2); + const int64_t output_size = out.Count(2); + + FOR_RANGE(int64_t, n, 0, in.At(0)) { + FOR_RANGE(int64_t, c, 0, in.At(1)) { + FOR_RANGE(int64_t, od, 0, out.At(2)) { + int64_t id0 = start_index(od, out.At(2), in.At(2)); + int64_t id1 = end_index(od, out.At(2), in.At(2)); + int64_t kd = id1 - id0; + FOR_RANGE(int64_t, oh, 0, out.At(3)) { + int64_t ih0 = start_index(oh, out.At(3), in.At(3)); + int64_t ih1 = end_index(oh, out.At(3), in.At(3)); + int64_t kh = ih1 - ih0; + FOR_RANGE(int64_t, ow, 0, out.At(4)) { + int64_t iw0 = start_index(ow, out.At(4), in.At(4)); + int64_t iw1 = end_index(ow, out.At(4), in.At(4)); + int64_t kw = iw1 - iw0; + + T grad_delta = out_ptr[od * output_image_size + oh * output_width + ow] / kd / kh / kw; + FOR_RANGE(int64_t, id, id0, id1) { + FOR_RANGE(int64_t, ih, ih0, ih1) { + FOR_RANGE(int64_t, iw, iw0, iw1) { + in_ptr[id * input_image_size + ih * input_width + iw] += grad_delta; + } + } + } + } + } + } + in_ptr += input_size; + out_ptr += output_size; + } + } +} +} // namespace + +template +class AdaptivePool1DCpuKernel final : public user_op::OpKernel { + public: + AdaptivePool1DCpuKernel() = default; + ~AdaptivePool1DCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 1); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class AdaptivePool2DCpuKernel final : public user_op::OpKernel { + public: + AdaptivePool2DCpuKernel() = default; + ~AdaptivePool2DCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 2); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class AdaptivePool3DCpuKernel final : public user_op::OpKernel { + public: + AdaptivePool3DCpuKernel() = default; + ~AdaptivePool3DCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 3); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; +template +class AdaptivePool1DCpuGradKernel final : public user_op::OpKernel { + public: + AdaptivePool1DCpuGradKernel() = default; + ~AdaptivePool1DCpuGradKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 1); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class AdaptivePool2DCpuGradKernel final : public user_op::OpKernel { + public: + AdaptivePool2DCpuGradKernel() = default; + ~AdaptivePool2DCpuGradKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 2); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; +template +class AdaptivePool3DCpuGradKernel final : public user_op::OpKernel { + public: + AdaptivePool3DCpuGradKernel() = default; + ~AdaptivePool3DCpuGradKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 3); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_ADAPTIVE_POOL_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_avg_pool1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool3d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); + +#define REGISTER_ADAPTIVE_POOL_KERNEL_WITH_DEVICE(device) \ + REGISTER_ADAPTIVE_POOL_KERNEL(device, float) \ + REGISTER_ADAPTIVE_POOL_KERNEL(device, double) \ + REGISTER_ADAPTIVE_POOL_KERNEL(device, int) + +REGISTER_ADAPTIVE_POOL_KERNEL_WITH_DEVICE(DeviceType::kCPU) + +#define REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_avg_pool1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool3d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +#define REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL_WITH_DEVICE(device) \ + REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, float) \ + REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, double) \ + REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, int) + +REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL_WITH_DEVICE(DeviceType::kCPU) +} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_avg_pool_gpu_kernel.cu b/oneflow/user/kernels/adaptive_avg_pool_gpu_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..6fb031e80d5506a8728981598ded254d2eb94cad --- /dev/null +++ b/oneflow/user/kernels/adaptive_avg_pool_gpu_kernel.cu @@ -0,0 +1,286 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/kernel_util.cuh" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/kernel/util/cuda_half_util.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/operator/operator_util.h" +#include "oneflow/user/utils/pool_util.h" +#include "oneflow/user/kernels/adaptive_pool_kernel_util.h" + +namespace oneflow { + +namespace user_op { + +template +__global__ void InitPtr(int elements, T* ptr) { + int gid = (blockDim.x * blockIdx.x) + threadIdx.x; + int step = gridDim.x * blockDim.x; + while (gid < elements) { + ptr[gid] = static_cast(0); + gid += step; + } +} + +inline Shape GetShape5D(const Shape& shape, const std::string& data_format, int32_t dim) { + FixedDimVector shape_3d = {GetInDim(shape, data_format, 0, dim), + GetInDim(shape, data_format, 1, dim), + GetInDim(shape, data_format, 2, dim)}; + return Shape({shape.At(0), shape.At(1), shape_3d.at(0), shape_3d.at(1), shape_3d.at(2)}); +} + +template +__global__ void AdaptiveAvgPoolCudaKernel(const T* input, T* output, int num_elems, int in_d, + int in_h, int in_w, int out_d, int out_h, int out_w) { + const int out_panel_size = out_d * out_h * out_w; + const int in_panel_size = in_d * in_h * in_w; + + CUDA_1D_KERNEL_LOOP(idx, num_elems) { + // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' + int bc_idx = idx / out_panel_size; + int out_d_idx = (idx % out_panel_size) / out_w / out_h; + int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; + int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; + + int in_start_d = START_IND(out_d_idx, out_d, in_d); + int in_end_d = END_IND(out_d_idx, out_d, in_d); + int k_d = in_end_d - in_start_d; + + int in_start_h = START_IND(out_h_idx, out_h, in_h); + int in_end_h = END_IND(out_h_idx, out_h, in_h); + int k_h = in_end_h - in_start_h; + + int in_start_w = START_IND(out_w_idx, out_w, in_w); + int in_end_w = END_IND(out_w_idx, out_w, in_w); + int k_w = in_end_w - in_start_w; + + const T* in_ptr = + input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; + T sum = static_cast(0); + for (int id = 0; id < k_d; ++id) { + for (int ih = 0; ih < k_h; ++ih) { + for (int iw = 0; iw < k_w; ++iw) { + T val = *(in_ptr + ih * in_w + iw); + sum += val; + } + } + in_ptr += in_h * in_w; // next input depth + } + // Update output + output[idx] = sum / k_d / k_h / k_w; + } +} + +template +__global__ void AdaptiveAvgPoolGradCudaKernel(T* input, const T* output, int num_elems, int in_d, + int in_h, int in_w, int out_d, int out_h, int out_w) { + const int out_panel_size = out_d * out_h * out_w; + const int in_panel_size = in_d * in_h * in_w; + + CUDA_1D_KERNEL_LOOP(idx, num_elems) { + // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' + int bc_idx = idx / out_panel_size; + int out_d_idx = (idx % out_panel_size) / out_w / out_h; + int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; + int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; + + int in_start_d = START_IND(out_d_idx, out_d, in_d); + int in_end_d = END_IND(out_d_idx, out_d, in_d); + int k_d = in_end_d - in_start_d; + + int in_start_h = START_IND(out_h_idx, out_h, in_h); + int in_end_h = END_IND(out_h_idx, out_h, in_h); + int k_h = in_end_h - in_start_h; + + int in_start_w = START_IND(out_w_idx, out_w, in_w); + int in_end_w = END_IND(out_w_idx, out_w, in_w); + int k_w = in_end_w - in_start_w; + + const T grad_delta = output[idx] / k_d / k_h / k_w; + T* input_ptr = + input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; + for (int id = 0; id < k_d; ++id) { + for (int ih = 0; ih < k_h; ++ih) { + for (int iw = 0; iw < k_w; ++iw) { + // TODO (Tianyu): Use 'atmoic::Add' when necessary + cuda::atomic::Add(input_ptr + ih * in_w + iw, grad_delta); + } + } + input_ptr += in_h * in_w; // next input depth + } + } +} + +template +void AvgForwardCompute(KernelComputeContext* ctx, const int32_t& dim) { + const Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); + const T* in_ptr = in_tensor->dptr(); + T* out_ptr = out_tensor->mut_dptr(); + + const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); + const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); + + // TODO (Tianyu): Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(x_shape, data_format, dim); + const Shape& out = GetShape5D(y_shape, data_format, dim); + + const int out_elems = out_tensor->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((AdaptiveAvgPoolCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, + out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); +} + +template +void AvgBackwardCompute(KernelComputeContext* ctx, const int32_t& dim) { + const Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); + const T* out_ptr = out_tensor->dptr(); + T* in_ptr = in_tensor->mut_dptr(); + + const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); + const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); + + // TODO (Tianyu): Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(dx_shape, data_format, dim); + const Shape& out = GetShape5D(dy_shape, data_format, dim); + + const int in_elems = in_tensor->shape_view().elem_cnt(); + const int out_elems = out_tensor->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((InitPtr), ctx->stream(), in_elems, in_elems, in_ptr); + RUN_CUDA_KERNEL((AdaptiveAvgPoolGradCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, + out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); +} + +template +class GpuAdaptiveAvgPool1dKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool1dKernel() = default; + ~GpuAdaptiveAvgPool1dKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 1); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveAvgPool2dKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool2dKernel() = default; + ~GpuAdaptiveAvgPool2dKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 2); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveAvgPool3dKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool3dKernel() = default; + ~GpuAdaptiveAvgPool3dKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 3); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveAvgPool1dGradKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool1dGradKernel() = default; + ~GpuAdaptiveAvgPool1dGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 1); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveAvgPool2dGradKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool2dGradKernel() = default; + ~GpuAdaptiveAvgPool2dGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 2); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveAvgPool3dGradKernel final : public OpKernel { + public: + GpuAdaptiveAvgPool3dGradKernel() = default; + ~GpuAdaptiveAvgPool3dGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 3); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_avg_pool1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool3d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); + +REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, float); +REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, double); +REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, int); + +#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_avg_pool1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_avg_pool3d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); + +REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, float); +REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, double); +REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, int); + +} // namespace user_op + +} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_max_pool_cpu_kernel.cpp b/oneflow/user/kernels/adaptive_max_pool_cpu_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e21c8726f6d4fa48d77b97f5fc3b22d1d684d49d --- /dev/null +++ b/oneflow/user/kernels/adaptive_max_pool_cpu_kernel.cpp @@ -0,0 +1,175 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/adaptive_pool_kernel_util.h" + +namespace oneflow { + +namespace { +template +void AdapativeMaxPoolForward(user_op::KernelComputeContext* ctx) { + user_op::Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* index_tensor = ctx->Tensor4ArgNameAndIndex("index", 0); + const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); + const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); + + // TODO : Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(x_shape, data_format, dim); + const Shape& out = GetShape5D(y_shape, data_format, dim); + + const T* in_ptr = in_tensor->dptr(); + T* out_ptr = out_tensor->mut_dptr(); + int64_t* index_ptr = index_tensor->mut_dptr(); + + const int64_t input_width = in.Count(4); + const int64_t output_width = out.Count(4); + const int64_t input_image_size = in.Count(3); + const int64_t output_image_size = out.Count(3); + const int64_t input_size = in.Count(2); + const int64_t output_size = out.Count(2); + + FOR_RANGE(int64_t, n, 0, in.At(0)) { + FOR_RANGE(int64_t, c, 0, in.At(1)) { + FOR_RANGE(int64_t, od, 0, out.At(2)) { + int64_t id0 = start_index(od, out.At(2), in.At(2)); + int64_t id1 = end_index(od, out.At(2), in.At(2)); + FOR_RANGE(int64_t, oh, 0, out.At(3)) { + int64_t ih0 = start_index(oh, out.At(3), in.At(3)); + int64_t ih1 = end_index(oh, out.At(3), in.At(3)); + FOR_RANGE(int64_t, ow, 0, out.At(4)) { + int64_t iw0 = start_index(ow, out.At(4), in.At(4)); + int64_t iw1 = end_index(ow, out.At(4), in.At(4)); + + // Find out local max + auto start_offset = id0 * input_image_size + ih0 * input_width + iw0; + T local_max = in_ptr[start_offset]; + int64_t local_max_index = start_offset; + FOR_RANGE(int64_t, id, id0, id1) { + FOR_RANGE(int64_t, ih, ih0, ih1) { + FOR_RANGE(int64_t, iw, iw0, iw1) { + auto cur_index = id * input_image_size + ih * input_width + iw; + if (in_ptr[cur_index] > local_max) { + local_max_index = cur_index; + local_max = in_ptr[cur_index]; + } + } + } + } + auto i = od * output_image_size + oh * output_width + ow; + out_ptr[i] = local_max; + index_ptr[i] = local_max_index; + } + } + } + in_ptr += input_size; + index_ptr += output_size; + out_ptr += output_size; + } + } +} + +template +void AdaptiveMaxPoolBackward(user_op::KernelComputeContext* ctx) { + user_op::Tensor* grad_input = ctx->Tensor4ArgNameAndIndex("dx", 0); + const user_op::Tensor* grad_output = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* return_indices = ctx->Tensor4ArgNameAndIndex("index", 0); + const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); + const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); + + // TODO : Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(dx_shape, data_format, dim); + const Shape& out = GetShape5D(dy_shape, data_format, dim); + + const T* dy_ptr = grad_output->dptr(); + const int64_t* indices_ptr = return_indices->dptr(); + T* dx_ptr = grad_input->mut_dptr(); + + std::fill(dx_ptr, dx_ptr + grad_input->shape_view().elem_cnt(), static_cast(0)); + + const int64_t output_width = out.Count(4); + const int64_t output_image_size = out.Count(3); + const int64_t input_size = in.Count(2); + const int64_t output_size = out.Count(2); + + FOR_RANGE(int64_t, n, 0, in.At(0)) { + FOR_RANGE(int64_t, c, 0, in.At(1)) { + FOR_RANGE(int64_t, od, 0, out.At(2)) { + FOR_RANGE(int64_t, oh, 0, out.At(3)) { + FOR_RANGE(int64_t, ow, 0, out.At(4)) { + auto i = od * output_image_size + oh * output_width + ow; + dx_ptr[indices_ptr[i]] += dy_ptr[i]; + } + } + } + dx_ptr += input_size; + dy_ptr += output_size; + indices_ptr += output_size; + } + } +} +} // namespace + +template +class AdaptiveMaxPoolNDCpuKernel final : public user_op::OpKernel { + public: + AdaptiveMaxPoolNDCpuKernel() = default; + ~AdaptiveMaxPoolNDCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + AdapativeMaxPoolForward(ctx); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class AdaptiveMaxPoolNDGradCpuKernel final : public user_op::OpKernel { + public: + AdaptiveMaxPoolNDGradCpuKernel() = default; + ~AdaptiveMaxPoolNDGradCpuKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + AdaptiveMaxPoolBackward(ctx); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_ADAPTIVE_MAX_POOLND_CPU(op_type_name, dtype, dim) \ + REGISTER_USER_KERNEL(op_type_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + \ + REGISTER_USER_KERNEL(op_type_name "_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +#define REGISTER_ADAPTIVE_MAX_POOL_CPU(op_type_name, dim) \ + REGISTER_ADAPTIVE_MAX_POOLND_CPU(op_type_name, double, dim); \ + REGISTER_ADAPTIVE_MAX_POOLND_CPU(op_type_name, float, dim); \ + REGISTER_ADAPTIVE_MAX_POOLND_CPU(op_type_name, int, dim); + +REGISTER_ADAPTIVE_MAX_POOL_CPU("adaptive_max_pool1d", 1); +REGISTER_ADAPTIVE_MAX_POOL_CPU("adaptive_max_pool2d", 2); +REGISTER_ADAPTIVE_MAX_POOL_CPU("adaptive_max_pool3d", 3); + +} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_max_pool_gpu_kernel.cu b/oneflow/user/kernels/adaptive_max_pool_gpu_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..2849bd16295cce8795859a1214b571af10555e28 --- /dev/null +++ b/oneflow/user/kernels/adaptive_max_pool_gpu_kernel.cu @@ -0,0 +1,207 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/kernel_util.cuh" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/kernel/util/cuda_half_util.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/operator/operator_util.h" +#include "oneflow/user/utils/pool_util.h" +#include "oneflow/user/kernels/adaptive_pool_kernel_util.h" + +namespace oneflow { + +namespace user_op { + +template +__global__ void AdaptiveMaxPoolCudaKernel(const T* input, T* output, int64_t* return_index, + int num_elems, int in_d, int in_h, int in_w, int out_d, + int out_h, int out_w) { + const int out_panel_size = out_d * out_h * out_w; + const int in_panel_size = in_d * in_h * in_w; + const int out_hw = out_w * out_h; + + CUDA_1D_KERNEL_LOOP(idx, num_elems) { + int bc_idx = idx / out_panel_size; + int out_d_idx = (idx % out_panel_size) / out_hw; + int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; + int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; + + int in_start_d = START_IND(out_d_idx, out_d, in_d); + int in_end_d = END_IND(out_d_idx, out_d, in_d); + int k_d = in_end_d - in_start_d; + + int in_start_h = START_IND(out_h_idx, out_h, in_h); + int in_end_h = END_IND(out_h_idx, out_h, in_h); + int k_h = in_end_h - in_start_h; + + int in_start_w = START_IND(out_w_idx, out_w, in_w); + int in_end_w = END_IND(out_w_idx, out_w, in_w); + int k_w = in_end_w - in_start_w; + + int64_t batch_idx_base = bc_idx * in_panel_size; + const T* in_ptr = + input + batch_idx_base + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; + T local_max = in_ptr[0]; + int64_t local_max_index = static_cast(in_ptr - input) - batch_idx_base; + for (int id = 0; id < k_d; ++id) { + for (int ih = 0; ih < k_h; ++ih) { + for (int iw = 0; iw < k_w; ++iw) { + T val = *(in_ptr + ih * in_w + iw); + if (val > local_max) { + local_max = val; + local_max_index = in_ptr - input - batch_idx_base + ih * in_w + iw; + } + } + } + in_ptr += in_h * in_w; // next input depth + } + + output[idx] = local_max; + return_index[idx] = local_max_index; + } +} + +template +__global__ void AdaptiveMaxPoolGradCudaKernel(T* input, const T* output, const int64_t* index, + int dy_elems, int in_panel_size, int out_panel_size) { + CUDA_1D_KERNEL_LOOP(idx, dy_elems) { + int bc_idx = idx / out_panel_size; + T* input_ptr = input + bc_idx * in_panel_size; + cuda::atomic::Add(input_ptr + index[idx], output[idx]); + } +} + +template +void MaxForwardCompute(KernelComputeContext* ctx) { + const Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); + Tensor* return_indices = ctx->Tensor4ArgNameAndIndex("index", 0); + + const T* in_ptr = in_tensor->dptr(); + T* out_ptr = out_tensor->mut_dptr(); + int64_t* index_ptr = return_indices->mut_dptr(); + + const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); + const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); + + // TODO: Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(x_shape, data_format, dim); + const Shape& out = GetShape5D(y_shape, data_format, dim); + + const int out_elems = out_tensor->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((AdaptiveMaxPoolCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, + index_ptr, out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), + out.At(4)); +} + +template +void MaxBackwardCompute(KernelComputeContext* ctx) { + const Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); + const user_op::Tensor* return_indices = ctx->Tensor4ArgNameAndIndex("index", 0); + + const T* out_ptr = out_tensor->dptr(); + T* in_ptr = in_tensor->mut_dptr(); + const int64_t* index_ptr = return_indices->dptr(); + + const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); + const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); + + // TODO (Tianyu): Support 'channels_last' + std::string data_format = "channels_first"; + const Shape& in = GetShape5D(dx_shape, data_format, dim); + const Shape& out = GetShape5D(dy_shape, data_format, dim); + + const int in_elems = in_tensor->shape_view().elem_cnt(); + const int out_elems = out_tensor->shape_view().elem_cnt(); + + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(ctx->device_type()); + CHECK(memset_primitive); + memset_primitive->Launch(ctx->stream(), in_ptr, 0, in_elems * sizeof(T)); + RUN_CUDA_KERNEL((AdaptiveMaxPoolGradCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, + index_ptr, out_elems, in.At(2) * in.At(3) * in.At(4), + out.At(2) * out.At(3) * out.At(4)); +} + +template +class GpuAdaptiveMaxPoolNdKernel final : public OpKernel { + public: + GpuAdaptiveMaxPoolNdKernel() = default; + ~GpuAdaptiveMaxPoolNdKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { MaxForwardCompute(ctx); } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class GpuAdaptiveMaxPoolNdGradKernel final : public OpKernel { + public: + GpuAdaptiveMaxPoolNdGradKernel() = default; + ~GpuAdaptiveMaxPoolNdGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(KernelComputeContext* ctx) const override { MaxBackwardCompute(ctx); } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUDA_ADAPTIVE_MAXPOOL_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_max_pool1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_max_pool2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_max_pool3d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("y", 0) == GetDataType::value)); + +REGISTER_CUDA_ADAPTIVE_MAXPOOL_KERNEL(DeviceType::kCUDA, float); +REGISTER_CUDA_ADAPTIVE_MAXPOOL_KERNEL(DeviceType::kCUDA, double); +REGISTER_CUDA_ADAPTIVE_MAXPOOL_KERNEL(DeviceType::kCUDA, int); + +#define REGISTER_CUDA_ADAPTIVE_MAXPOOL_BACKWARD_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("adaptive_max_pool1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_max_pool2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("adaptive_max_pool3d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((HobDeviceType() == device) \ + && (HobDataType("dx", 0) == GetDataType::value)); + +REGISTER_CUDA_ADAPTIVE_MAXPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, float); +REGISTER_CUDA_ADAPTIVE_MAXPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, double); +REGISTER_CUDA_ADAPTIVE_MAXPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, int); + +} // namespace user_op + +} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_pool_cpu_kernel.cpp b/oneflow/user/kernels/adaptive_pool_cpu_kernel.cpp deleted file mode 100644 index ba4ab9544ebcd557fa3571452a7cfca21eca86af..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/adaptive_pool_cpu_kernel.cpp +++ /dev/null @@ -1,261 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/operator/operator_util.h" -#include "oneflow/user/utils/pool_util.h" - -namespace oneflow { - -namespace { - -inline int64_t start_index(int64_t a, int64_t b, int64_t c) { - return (int64_t)std::floor((float)(a * c) / b); -} - -inline int64_t end_index(int64_t a, int64_t b, int64_t c) { - return (int64_t)std::ceil((float)((a + 1) * c) / b); -} - -inline Shape GetShape5D(const Shape& shape, const std::string& data_format, int32_t dim) { - FixedDimVector shape_3d = {GetInDim(shape, data_format, 0, dim), - GetInDim(shape, data_format, 1, dim), - GetInDim(shape, data_format, 2, dim)}; - return Shape({shape.At(0), shape.At(1), shape_3d.at(0), shape_3d.at(1), shape_3d.at(2)}); -} - -template -void AvgForwardCompute(user_op::KernelComputeContext* ctx, const int32_t& dim) { - user_op::Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); - const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(x_shape, data_format, dim); - const Shape& out = GetShape5D(y_shape, data_format, dim); - - const T* in_ptr = in_tensor->dptr(); - T* out_ptr = out_tensor->mut_dptr(); - - const int64_t input_width = in.Count(4); - const int64_t output_width = out.Count(4); - const int64_t input_image_size = in.Count(3); - const int64_t output_image_size = out.Count(3); - const int64_t input_size = in.Count(2); - const int64_t output_size = out.Count(2); - - FOR_RANGE(int64_t, n, 0, in.At(0)) { - FOR_RANGE(int64_t, c, 0, in.At(1)) { - FOR_RANGE(int64_t, od, 0, out.At(2)) { - int64_t id0 = start_index(od, out.At(2), in.At(2)); - int64_t id1 = end_index(od, out.At(2), in.At(2)); - int64_t kd = id1 - id0; - FOR_RANGE(int64_t, oh, 0, out.At(3)) { - int64_t ih0 = start_index(oh, out.At(3), in.At(3)); - int64_t ih1 = end_index(oh, out.At(3), in.At(3)); - int64_t kh = ih1 - ih0; - FOR_RANGE(int64_t, ow, 0, out.At(4)) { - int64_t iw0 = start_index(ow, out.At(4), in.At(4)); - int64_t iw1 = end_index(ow, out.At(4), in.At(4)); - int64_t kw = iw1 - iw0; - - // Compute local average - T sum = static_cast(0); - FOR_RANGE(int64_t, id, id0, id1) { - FOR_RANGE(int64_t, ih, ih0, ih1) { - FOR_RANGE(int64_t, iw, iw0, iw1) { - sum += in_ptr[id * input_image_size + ih * input_width + iw]; - } - } - } - out_ptr[od * output_image_size + oh * output_width + ow] = sum / kd / kh / kw; - } - } - } - in_ptr += input_size; - out_ptr += output_size; - } - } -} - -template -void AvgBackwardCompute(user_op::KernelComputeContext* ctx, const int32_t& dim) { - user_op::Tensor* grad_input = ctx->Tensor4ArgNameAndIndex("dx", 0); - const user_op::Tensor* grad_output = ctx->Tensor4ArgNameAndIndex("dy", 0); - const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); - const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(dx_shape, data_format, dim); - const Shape& out = GetShape5D(dy_shape, data_format, dim); - - const T* out_ptr = grad_output->dptr(); - T* in_ptr = grad_input->mut_dptr(); - - std::fill(in_ptr, in_ptr + grad_input->shape_view().elem_cnt(), static_cast(0)); - - const int64_t input_width = in.Count(4); - const int64_t output_width = out.Count(4); - const int64_t input_image_size = in.Count(3); - const int64_t output_image_size = out.Count(3); - const int64_t input_size = in.Count(2); - const int64_t output_size = out.Count(2); - - FOR_RANGE(int64_t, n, 0, in.At(0)) { - FOR_RANGE(int64_t, c, 0, in.At(1)) { - FOR_RANGE(int64_t, od, 0, out.At(2)) { - int64_t id0 = start_index(od, out.At(2), in.At(2)); - int64_t id1 = end_index(od, out.At(2), in.At(2)); - int64_t kd = id1 - id0; - FOR_RANGE(int64_t, oh, 0, out.At(3)) { - int64_t ih0 = start_index(oh, out.At(3), in.At(3)); - int64_t ih1 = end_index(oh, out.At(3), in.At(3)); - int64_t kh = ih1 - ih0; - FOR_RANGE(int64_t, ow, 0, out.At(4)) { - int64_t iw0 = start_index(ow, out.At(4), in.At(4)); - int64_t iw1 = end_index(ow, out.At(4), in.At(4)); - int64_t kw = iw1 - iw0; - - T grad_delta = out_ptr[od * output_image_size + oh * output_width + ow] / kd / kh / kw; - FOR_RANGE(int64_t, id, id0, id1) { - FOR_RANGE(int64_t, ih, ih0, ih1) { - FOR_RANGE(int64_t, iw, iw0, iw1) { - in_ptr[id * input_image_size + ih * input_width + iw] += grad_delta; - } - } - } - } - } - } - in_ptr += input_size; - out_ptr += output_size; - } - } -} -} // namespace - -template -class AdaptivePool1DCpuKernel final : public user_op::OpKernel { - public: - AdaptivePool1DCpuKernel() = default; - ~AdaptivePool1DCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 1); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class AdaptivePool2DCpuKernel final : public user_op::OpKernel { - public: - AdaptivePool2DCpuKernel() = default; - ~AdaptivePool2DCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 2); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class AdaptivePool3DCpuKernel final : public user_op::OpKernel { - public: - AdaptivePool3DCpuKernel() = default; - ~AdaptivePool3DCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 3); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; -template -class AdaptivePool1DCpuGradKernel final : public user_op::OpKernel { - public: - AdaptivePool1DCpuGradKernel() = default; - ~AdaptivePool1DCpuGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 1); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class AdaptivePool2DCpuGradKernel final : public user_op::OpKernel { - public: - AdaptivePool2DCpuGradKernel() = default; - ~AdaptivePool2DCpuGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 2); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; -template -class AdaptivePool3DCpuGradKernel final : public user_op::OpKernel { - public: - AdaptivePool3DCpuGradKernel() = default; - ~AdaptivePool3DCpuGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 3); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_ADAPTIVE_POOL_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -#define REGISTER_ADAPTIVE_POOL_KERNEL_WITH_DEVICE(device) \ - REGISTER_ADAPTIVE_POOL_KERNEL(device, float) \ - REGISTER_ADAPTIVE_POOL_KERNEL(device, double) \ - REGISTER_ADAPTIVE_POOL_KERNEL(device, int) - -REGISTER_ADAPTIVE_POOL_KERNEL_WITH_DEVICE(DeviceType::kCPU) - -#define REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -#define REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL_WITH_DEVICE(device) \ - REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, float) \ - REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, double) \ - REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL(device, int) - -REGISTER_ADAPTIVE_POOL_BACKWARD_KERNEL_WITH_DEVICE(DeviceType::kCPU) -} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_pool_gpu_kernel.cu b/oneflow/user/kernels/adaptive_pool_gpu_kernel.cu deleted file mode 100644 index 8648576c513b43e737c23edd3080d17d77bed9bb..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/adaptive_pool_gpu_kernel.cu +++ /dev/null @@ -1,295 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.cuh" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/cuda/atomic.cuh" -#include "oneflow/core/operator/operator_util.h" -#include "oneflow/user/utils/pool_util.h" - -#include -#include -#include - -namespace oneflow { - -namespace user_op { - -#define START_IND(a, b, c) (int)std::floor((float)(a * c) / b) -#define END_IND(a, b, c) (int)std::ceil((float)((a + 1) * c) / b) - -#define START_IND_INT(a, b, c) ((a * c) / b) -#define END_IND_INT(a, b, c) (((a + 1) * c + b - 1) / b) - -template -__global__ void InitPtr(int elements, T* ptr) { - int gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int step = gridDim.x * blockDim.x; - while (gid < elements) { - ptr[gid] = static_cast(0); - gid += step; - } -} - -inline Shape GetShape5D(const Shape& shape, const std::string& data_format, int32_t dim) { - FixedDimVector shape_3d = {GetInDim(shape, data_format, 0, dim), - GetInDim(shape, data_format, 1, dim), - GetInDim(shape, data_format, 2, dim)}; - return Shape({shape.At(0), shape.At(1), shape_3d.at(0), shape_3d.at(1), shape_3d.at(2)}); -} - -template -__global__ void AdaptiveAvgPoolCudaKernel(const T* input, T* output, int num_elems, int in_d, - int in_h, int in_w, int out_d, int out_h, int out_w) { - const int out_panel_size = out_d * out_h * out_w; - const int in_panel_size = in_d * in_h * in_w; - - CUDA_1D_KERNEL_LOOP(idx, num_elems) { - // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' - int bc_idx = idx / out_panel_size; - int out_d_idx = (idx % out_panel_size) / out_w / out_h; - int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; - int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; - - int in_start_d = START_IND(out_d_idx, out_d, in_d); - int in_end_d = END_IND(out_d_idx, out_d, in_d); - int k_d = in_end_d - in_start_d; - - int in_start_h = START_IND(out_h_idx, out_h, in_h); - int in_end_h = END_IND(out_h_idx, out_h, in_h); - int k_h = in_end_h - in_start_h; - - int in_start_w = START_IND(out_w_idx, out_w, in_w); - int in_end_w = END_IND(out_w_idx, out_w, in_w); - int k_w = in_end_w - in_start_w; - - const T* in_ptr = - input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; - T sum = static_cast(0); - for (int id = 0; id < k_d; ++id) { - for (int ih = 0; ih < k_h; ++ih) { - for (int iw = 0; iw < k_w; ++iw) { - T val = *(in_ptr + ih * in_w + iw); - sum += val; - } - } - in_ptr += in_h * in_w; // next input depth - } - // Update output - output[idx] = sum / k_d / k_h / k_w; - } -} - -template -__global__ void AdaptiveAvgPoolGradCudaKernel(T* input, const T* output, int num_elems, int in_d, - int in_h, int in_w, int out_d, int out_h, int out_w) { - const int out_panel_size = out_d * out_h * out_w; - const int in_panel_size = in_d * in_h * in_w; - - CUDA_1D_KERNEL_LOOP(idx, num_elems) { - // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' - int bc_idx = idx / out_panel_size; - int out_d_idx = (idx % out_panel_size) / out_w / out_h; - int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; - int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; - - int in_start_d = START_IND(out_d_idx, out_d, in_d); - int in_end_d = END_IND(out_d_idx, out_d, in_d); - int k_d = in_end_d - in_start_d; - - int in_start_h = START_IND(out_h_idx, out_h, in_h); - int in_end_h = END_IND(out_h_idx, out_h, in_h); - int k_h = in_end_h - in_start_h; - - int in_start_w = START_IND(out_w_idx, out_w, in_w); - int in_end_w = END_IND(out_w_idx, out_w, in_w); - int k_w = in_end_w - in_start_w; - - const T grad_delta = output[idx] / k_d / k_h / k_w; - T* input_ptr = - input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; - for (int id = 0; id < k_d; ++id) { - for (int ih = 0; ih < k_h; ++ih) { - for (int iw = 0; iw < k_w; ++iw) { - // TODO (Tianyu): Use 'atmoic::Add' when necessary - cuda::atomic::Add(input_ptr + ih * in_w + iw, grad_delta); - } - } - input_ptr += in_h * in_w; // next input depth - } - } -} - -template -void AvgForwardCompute(KernelComputeContext* ctx, const int32_t& dim) { - const Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* in_ptr = in_tensor->dptr(); - T* out_ptr = out_tensor->mut_dptr(); - - const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); - const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(x_shape, data_format, dim); - const Shape& out = GetShape5D(y_shape, data_format, dim); - - const int out_elems = out_tensor->shape_view().elem_cnt(); - - RUN_CUDA_KERNEL((AdaptiveAvgPoolCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, - out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); -} - -template -void AvgBackwardCompute(KernelComputeContext* ctx, const int32_t& dim) { - const Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* out_ptr = out_tensor->dptr(); - T* in_ptr = in_tensor->mut_dptr(); - - const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); - const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(dx_shape, data_format, dim); - const Shape& out = GetShape5D(dy_shape, data_format, dim); - - const int in_elems = in_tensor->shape_view().elem_cnt(); - const int out_elems = out_tensor->shape_view().elem_cnt(); - - RUN_CUDA_KERNEL((InitPtr), ctx->stream(), in_elems, in_elems, in_ptr); - RUN_CUDA_KERNEL((AdaptiveAvgPoolGradCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, - out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); -} - -template -class GpuAdaptiveAvgPool1dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool1dKernel() = default; - ~GpuAdaptiveAvgPool1dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 1); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool2dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool2dKernel() = default; - ~GpuAdaptiveAvgPool2dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 2); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool3dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool3dKernel() = default; - ~GpuAdaptiveAvgPool3dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 3); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool1dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool1dGradKernel() = default; - ~GpuAdaptiveAvgPool1dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 1); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool2dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool2dGradKernel() = default; - ~GpuAdaptiveAvgPool2dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 2); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool3dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool3dGradKernel() = default; - ~GpuAdaptiveAvgPool3dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 3); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, double); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, int); - -#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, double); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, int); - -} // namespace user_op - -} // namespace oneflow diff --git a/oneflow/user/kernels/adaptive_pool_gpu_kernel.hip.cpp b/oneflow/user/kernels/adaptive_pool_gpu_kernel.hip.cpp deleted file mode 100644 index 326c408c9caca9d0746d2f21d935a1dcd598f784..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/adaptive_pool_gpu_kernel.hip.cpp +++ /dev/null @@ -1,296 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/operator/operator_util.h" -#include "oneflow/user/utils/pool_util.h" - -#include -#include -#include - -namespace oneflow { - -namespace user_op { - -#define START_IND(a, b, c) (int)std::floor((float)(a * c) / b) -#define END_IND(a, b, c) (int)std::ceil((float)((a + 1) * c) / b) - -#define START_IND_INT(a, b, c) ((a * c) / b) -#define END_IND_INT(a, b, c) (((a + 1) * c + b - 1) / b) - -template -__global__ void InitPtr(int elements, T* ptr) { - int gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int step = gridDim.x * blockDim.x; - while (gid < elements) { - ptr[gid] = static_cast(0); - gid += step; - } -} - -inline Shape GetShape5D(const Shape& shape, const std::string& data_format, int32_t dim) { - FixedDimVector shape_3d = {GetInDim(shape, data_format, 0, dim), - GetInDim(shape, data_format, 1, dim), - GetInDim(shape, data_format, 2, dim)}; - return Shape({shape.At(0), shape.At(1), shape_3d.at(0), shape_3d.at(1), shape_3d.at(2)}); -} - -template -__global__ void AdaptiveAvgPoolCudaKernel(const T* input, T* output, int num_elems, int in_d, - int in_h, int in_w, int out_d, int out_h, int out_w) { - const int out_panel_size = out_d * out_h * out_w; - const int in_panel_size = in_d * in_h * in_w; - - CUDA_1D_KERNEL_LOOP(idx, num_elems) { - // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' - int bc_idx = idx / out_panel_size; - int out_d_idx = (idx % out_panel_size) / out_w / out_h; - int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; - int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; - - int in_start_d = START_IND(out_d_idx, out_d, in_d); - int in_end_d = END_IND(out_d_idx, out_d, in_d); - int k_d = in_end_d - in_start_d; - - int in_start_h = START_IND(out_h_idx, out_h, in_h); - int in_end_h = END_IND(out_h_idx, out_h, in_h); - int k_h = in_end_h - in_start_h; - - int in_start_w = START_IND(out_w_idx, out_w, in_w); - int in_end_w = END_IND(out_w_idx, out_w, in_w); - int k_w = in_end_w - in_start_w; - - const T* in_ptr = - input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; - T sum = static_cast(0); - for (int id = 0; id < k_d; ++id) { - for (int ih = 0; ih < k_h; ++ih) { - for (int iw = 0; iw < k_w; ++iw) { - T val = *(in_ptr + ih * in_w + iw); - sum += val; - } - } - in_ptr += in_h * in_w; // next input depth - } - // Update output - output[idx] = sum / k_d / k_h / k_w; - } -} - -template -__global__ void AdaptiveAvgPoolGradCudaKernel(T* input, const T* output, int num_elems, int in_d, - int in_h, int in_w, int out_d, int out_h, int out_w) { - const int out_panel_size = out_d * out_h * out_w; - const int in_panel_size = in_d * in_h * in_w; - - CUDA_1D_KERNEL_LOOP(idx, num_elems) { - // TODO (Tianyu): Replace following codes with 'NdIndexOffsetHelper' - int bc_idx = idx / out_panel_size; - int out_d_idx = (idx % out_panel_size) / out_w / out_h; - int out_h_idx = (idx % out_panel_size) % (out_h * out_w) / out_w; - int out_w_idx = (idx % out_panel_size) % (out_h * out_w) % out_w; - - int in_start_d = START_IND(out_d_idx, out_d, in_d); - int in_end_d = END_IND(out_d_idx, out_d, in_d); - int k_d = in_end_d - in_start_d; - - int in_start_h = START_IND(out_h_idx, out_h, in_h); - int in_end_h = END_IND(out_h_idx, out_h, in_h); - int k_h = in_end_h - in_start_h; - - int in_start_w = START_IND(out_w_idx, out_w, in_w); - int in_end_w = END_IND(out_w_idx, out_w, in_w); - int k_w = in_end_w - in_start_w; - - const T grad_delta = output[idx] / k_d / k_h / k_w; - T* input_ptr = - input + bc_idx * in_panel_size + in_start_d * in_h * in_w + in_start_h * in_w + in_start_w; - for (int id = 0; id < k_d; ++id) { - for (int ih = 0; ih < k_h; ++ih) { - for (int iw = 0; iw < k_w; ++iw) { - // TODO (Tianyu): Use 'atmoic::Add' when necessary - cuda::atomic::Add(input_ptr + ih * in_w + iw, grad_delta); - } - } - input_ptr += in_h * in_w; // next input depth - } - } -} - -template -void AvgForwardCompute(KernelComputeContext* ctx, const int32_t& dim) { - const Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* in_ptr = in_tensor->dptr(); - T* out_ptr = out_tensor->mut_dptr(); - - const Shape& x_shape = ctx->TensorDesc4ArgNameAndIndex("x", 0)->shape(); - const Shape& y_shape = ctx->TensorDesc4ArgNameAndIndex("y", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(x_shape, data_format, dim); - const Shape& out = GetShape5D(y_shape, data_format, dim); - - const int out_elems = out_tensor->shape_view().elem_cnt(); - - RUN_CUDA_KERNEL((AdaptiveAvgPoolCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, - out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); -} - -template -void AvgBackwardCompute(KernelComputeContext* ctx, const int32_t& dim) { - const Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - Tensor* in_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* out_ptr = out_tensor->dptr(); - T* in_ptr = in_tensor->mut_dptr(); - - const Shape& dx_shape = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->shape(); - const Shape& dy_shape = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->shape(); - - // TODO (Tianyu): Support 'channels_last' - std::string data_format = "channels_first"; - const Shape& in = GetShape5D(dx_shape, data_format, dim); - const Shape& out = GetShape5D(dy_shape, data_format, dim); - - const int in_elems = in_tensor->shape_view().elem_cnt(); - const int out_elems = out_tensor->shape_view().elem_cnt(); - - RUN_CUDA_KERNEL((InitPtr), ctx->stream(), in_elems, in_elems, in_ptr); - RUN_CUDA_KERNEL((AdaptiveAvgPoolGradCudaKernel), ctx->stream(), out_elems, in_ptr, out_ptr, - out_elems, in.At(2), in.At(3), in.At(4), out.At(2), out.At(3), out.At(4)); -} - -template -class GpuAdaptiveAvgPool1dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool1dKernel() = default; - ~GpuAdaptiveAvgPool1dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 1); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool2dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool2dKernel() = default; - ~GpuAdaptiveAvgPool2dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 2); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool3dKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool3dKernel() = default; - ~GpuAdaptiveAvgPool3dKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgForwardCompute(ctx, 3); } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool1dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool1dGradKernel() = default; - ~GpuAdaptiveAvgPool1dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 1); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool2dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool2dGradKernel() = default; - ~GpuAdaptiveAvgPool2dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 2); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAdaptiveAvgPool3dGradKernel final : public OpKernel { - public: - GpuAdaptiveAvgPool3dGradKernel() = default; - ~GpuAdaptiveAvgPool3dGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { AvgBackwardCompute(ctx, 3); } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, double); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_KERNEL(DeviceType::kCUDA, int); - -#define REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("adaptive_avg_pool1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("adaptive_avg_pool3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == device) \ - && (HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, double); -REGISTER_CUDA_ADAPTIVE_AVGPOOL_BACKWARD_KERNEL(DeviceType::kCUDA, int); - -} // namespace user_op - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/adaptive_pool_kernel_util.h b/oneflow/user/kernels/adaptive_pool_kernel_util.h new file mode 100644 index 0000000000000000000000000000000000000000..816799fefdcaf241f8dd7bb484c2a28f4e13b7ee --- /dev/null +++ b/oneflow/user/kernels/adaptive_pool_kernel_util.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef _ONEFLOW_USER_KERNELS_ADAPTIVE_POOL_UTIL_H_ +#define _ONEFLOW_USER_KERNELS_ADAPTIVE_POOL_UTIL_H_ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/operator/operator_util.h" +#include "oneflow/user/utils/pool_util.h" + +namespace oneflow { + +namespace { + +inline int64_t start_index(int64_t a, int64_t b, int64_t c) { + return (int64_t)std::floor((float)(a * c) / b); +} + +inline int64_t end_index(int64_t a, int64_t b, int64_t c) { + return (int64_t)std::ceil((float)((a + 1) * c) / b); +} + +#define START_IND(a, b, c) (int)std::floor((float)(a * c) / b) +#define END_IND(a, b, c) (int)std::ceil((float)((a + 1) * c) / b) + +#define START_IND_INT(a, b, c) ((a * c) / b) +#define END_IND_INT(a, b, c) (((a + 1) * c + b - 1) / b) + +inline Shape GetShape5D(const Shape& shape, const std::string& data_format, int32_t dim) { + FixedDimVector shape_3d = {GetInDim(shape, data_format, 0, dim), + GetInDim(shape, data_format, 1, dim), + GetInDim(shape, data_format, 2, dim)}; + return Shape({shape.At(0), shape.At(1), shape_3d.at(0), shape_3d.at(1), shape_3d.at(2)}); +} + +} // namespace +} // namespace oneflow + +#endif // _ONEFLOW_USER_KERNELS_ADAPTIVE_POOL_UTIL_H_ diff --git a/oneflow/user/kernels/affine_grid_kernel.cpp b/oneflow/user/kernels/affine_grid_kernel.cpp index b8fd7f9a093098ece0f301090008736683561f3d..bbf8138a4ee7867a01a743a0c928ce53a5ed9ed3 100644 --- a/oneflow/user/kernels/affine_grid_kernel.cpp +++ b/oneflow/user/kernels/affine_grid_kernel.cpp @@ -137,11 +137,7 @@ class AffineGridKernel final : public user_op::OpKernel { REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCPU, float); REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCPU, double); -#ifdef WITH_CUDA -REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCUDA, float); -REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCUDA, double); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCUDA, float); REGISTER_AFFINE_GRID_KERNEL(DeviceType::kCUDA, double); #endif @@ -214,11 +210,7 @@ class AffineGridGradKernel final : public user_op::OpKernel { REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCPU, float); REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCPU, double); -#ifdef WITH_CUDA -REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCUDA, float); -REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCUDA, double); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCUDA, float); REGISTER_AFFINE_GRID_GRAD_KERNEL(DeviceType::kCUDA, double); #endif diff --git a/oneflow/user/kernels/affine_grid_kernel.hip.cpp b/oneflow/user/kernels/affine_grid_kernel.hip.cpp deleted file mode 100644 index 9fe19bd74510a00e4c101f51e5f1aad3c9b0d693..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/affine_grid_kernel.hip.cpp +++ /dev/null @@ -1,133 +0,0 @@ - -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/device/cuda_util.h" -#include "affine_grid_kernel.h" - -namespace oneflow { - -namespace { - -template -OF_DEVICE_FUNC data_type LinspaceGPU(int32_t index, int32_t num_steps) { - if (num_steps <= 1) { return static_cast(0.0); } - - if (align_corners) { - return static_cast(-1.0 + 2.0 / (num_steps - 1) * index); - } else { - return static_cast((-1.0 + 2.0 / (num_steps - 1) * index) * (num_steps - 1) - / num_steps); - } -} - -template -__global__ void Generate2DBaseGridGPUKernel(const int32_t nthreads, data_type* grid_ptr, int32_t H, - int32_t W) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int32_t h = index / W; - const int32_t w = index % W; - const int32_t pixel_length = 3; - data_type* row_ptr = grid_ptr + h * W * pixel_length; - data_type* pixel_ptr = row_ptr + w * pixel_length; - data_type h_value = LinspaceGPU(h, H); - data_type w_value = LinspaceGPU(w, W); - - pixel_ptr[0] = w_value; - pixel_ptr[1] = h_value; - pixel_ptr[2] = static_cast(1.0); - } -} - -template -__global__ void Generate3DBaseGridGPUKernel(const int32_t nthreads, data_type* grid_ptr, int32_t D, - int32_t H, int32_t W) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int32_t d = index / H; - const int32_t h = index % H; - const int32_t pixel_length = 4; - data_type* image_ptr = grid_ptr + d * H * W * pixel_length; - data_type* row_ptr = image_ptr + h * W * pixel_length; - data_type d_value = LinspaceGPU(d, D); - data_type h_value = LinspaceGPU(h, H); - - for (int32_t w = 0; w < W; ++w) { - data_type* pixel_ptr = row_ptr + w * pixel_length; - data_type w_value = LinspaceGPU(w, W); - pixel_ptr[0] = w_value; - pixel_ptr[1] = h_value; - pixel_ptr[2] = d_value; - pixel_ptr[3] = static_cast(1.0); - } - } -} - -} // namespace - -void GenerateBaseGridImp::Generate2D(user_op::KernelComputeContext* ctx, - float* grid_ptr, int64_t H, int64_t W, - bool align_corners) { - int count = H * W; - if (align_corners) { - RUN_CUDA_KERNEL((Generate2DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, H, W); - } else { - RUN_CUDA_KERNEL((Generate2DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, H, W); - } -} -void GenerateBaseGridImp::Generate2D(user_op::KernelComputeContext* ctx, - double* grid_ptr, int64_t H, int64_t W, - bool align_corners) { - int count = H * W; - if (align_corners) { - RUN_CUDA_KERNEL((Generate2DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, H, W); - } else { - RUN_CUDA_KERNEL((Generate2DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, H, W); - } -} - -void GenerateBaseGridImp::Generate3D(user_op::KernelComputeContext* ctx, - float* grid_ptr, int64_t D, int64_t H, - int64_t W, bool align_corners) { - int count = D * H; - if (align_corners) { - RUN_CUDA_KERNEL((Generate3DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, D, H, W); - } else { - RUN_CUDA_KERNEL((Generate3DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, D, H, W); - } -} - -void GenerateBaseGridImp::Generate3D(user_op::KernelComputeContext* ctx, - double* grid_ptr, int64_t D, int64_t H, - int64_t W, bool align_corners) { - int count = D * H; - if (align_corners) { - RUN_CUDA_KERNEL((Generate3DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, D, H, W); - } else { - RUN_CUDA_KERNEL((Generate3DBaseGridGPUKernel), ctx->stream(), count, count, - grid_ptr, D, H, W); - } -} - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/arange_kernel.cpp b/oneflow/user/kernels/arange_kernel.cpp index 4fed9dd173603c2fe509f67c19a24e5e82aa442a..a5a37be0faaea2ff300cc29f71a8272556a98880 100644 --- a/oneflow/user/kernels/arange_kernel.cpp +++ b/oneflow/user/kernels/arange_kernel.cpp @@ -17,7 +17,9 @@ limitations under the License. #include "oneflow/user/kernels/arange_kernel_util.h" #include "oneflow/core/common/data_type.h" #include "oneflow/core/job/nd_sbp_util.h" - +#ifdef WITH_ROCM +#include +#endif namespace oneflow { namespace user_op { class ArangeOpKernelCache final : public user_op::OpKernelCache { @@ -76,13 +78,13 @@ class ArangeKernel final : public OpKernel { T* output = out->mut_dptr(); const DataType dtype = ctx->Attr("dtype"); int64_t arange_elem_cnt = 0; - T start = 0; - T delta = 0; - T limit = 0; + T start = static_cast(0.0); + T delta = static_cast(0.0); + T limit = static_cast(0.0); if (IsIntegralDataType(dtype)) { - start = ctx->Attr("integer_start"); - delta = ctx->Attr("integer_delta"); - limit = ctx->Attr("integer_limit"); + start = static_cast(static_cast(ctx->Attr("integer_start"))); + delta = static_cast(static_cast(ctx->Attr("integer_delta"))); + limit = static_cast(static_cast(ctx->Attr("integer_limit"))); arange_elem_cnt = std::ceil(static_cast(limit - start) / delta); } else { // If we use static_cast(start, delta, limit) and std::ceil to calculate arange_elem_cnt, @@ -121,14 +123,17 @@ class ArangeKernel final : public OpKernel { REGISTER_ARANGE_KERNEL(device, float) \ REGISTER_ARANGE_KERNEL(device, double) +#define REGISTER_ARANGE_KERNELS_WITH_CUDA_HALF(device) REGISTER_ARANGE_KERNEL(device, half) + // Register CPU version REGISTER_ARANGE_KERNELS_WITH_DEVICE(DeviceType::kCPU); // Register GPU version -#ifdef WITH_CUDA +#if defined(WITH_CUDA) REGISTER_ARANGE_KERNELS_WITH_DEVICE(DeviceType::kCUDA); +REGISTER_ARANGE_KERNELS_WITH_CUDA_HALF(DeviceType::kCUDA); #endif -#ifdef WITH_ROCM +#if defined(WITH_ROCM) REGISTER_ARANGE_KERNELS_WITH_DEVICE(DeviceType::kCUDA); #endif } // namespace user_op diff --git a/oneflow/user/kernels/arange_kernel_util.cpp b/oneflow/user/kernels/arange_kernel_util.cpp index adf79ce5cf6b974ad77af9c4b14097370656cbb1..699eacee59027d7a66f630841db2497af8b1bb1d 100644 --- a/oneflow/user/kernels/arange_kernel_util.cpp +++ b/oneflow/user/kernels/arange_kernel_util.cpp @@ -28,6 +28,8 @@ struct ArangeFunctor final { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ARANGE_FUNCTOR, (DeviceType::kCPU), ARANGE_DATA_TYPE_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ARANGE_FUNCTOR, (DeviceType::kCPU), + FLOAT16_DATA_TYPE_SEQ); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/arange_kernel_util.cu b/oneflow/user/kernels/arange_kernel_util.cu index 570a61988f9df9c50affb93a1945e25d54678213..8d6305d4f95a18d8f497cfd40df108ee731a743b 100644 --- a/oneflow/user/kernels/arange_kernel_util.cu +++ b/oneflow/user/kernels/arange_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/user/kernels/arange_kernel_util.h" @@ -28,6 +28,15 @@ __global__ void ArangeForwardGpuKernel(const T start, const T delta, const int64 DoArange(start, delta, arange_elem_cnt, out); } +template<> +__global__ void ArangeForwardGpuKernel(const half start, const half delta, + const int64_t arange_elem_cnt, half* out) { + // Use Loop to set the value + XPU_1D_KERNEL_LOOP(i, arange_elem_cnt) { + out[i] = start + static_cast(static_cast(i)) * delta; + } +} + template struct ArangeFunctor final { void operator()(ep::Stream* stream, const T start, const T delta, const int64_t arange_elem_cnt, @@ -40,6 +49,8 @@ struct ArangeFunctor final { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ARANGE_FUNCTOR, (DeviceType::kCUDA), ARANGE_DATA_TYPE_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ARANGE_FUNCTOR, (DeviceType::kCUDA), + HALF_DATA_TYPE_SEQ); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/arange_kernel_util.hip.cpp b/oneflow/user/kernels/arange_kernel_util.hip.cpp deleted file mode 100644 index 2df4427ac46fbc194971a673315baa203b28c49c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/arange_kernel_util.hip.cpp +++ /dev/null @@ -1,48 +0,0 @@ - -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/arange_kernel_util.h" - -namespace oneflow { - -namespace user_op { - -template -__global__ void ArangeForwardGpuKernel(const T start, const T delta, const int64_t arange_elem_cnt, - T* out) { - // Use Loop to set the value - DoArange(start, delta, arange_elem_cnt, out); -} - -template -struct ArangeFunctor final { - void operator()(ep::Stream* stream, const T start, const T delta, const int64_t arange_elem_cnt, - T* out) { - // The thread num is set as arange_elem_cnt - RUN_CUDA_KERNEL((ArangeForwardGpuKernel), stream, arange_elem_cnt, start, delta, - arange_elem_cnt, out); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ARANGE_FUNCTOR, (DeviceType::kCUDA), - ARANGE_DATA_TYPE_SEQ); -} // namespace user_op -} // namespace oneflow - -#endif // End WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/arg_sort_kernel.hip.cpp b/oneflow/user/kernels/arg_sort_kernel.hip.cpp deleted file mode 100644 index 46372db9522d0e04832d8b3965352290ccdb490c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/arg_sort_kernel.hip.cpp +++ /dev/null @@ -1,148 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/user/kernels/radix_sort.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(int32_t capacity, void* ptr, const ShapeView& in_shape) - : capacity_{capacity}, - sorted_in_elem_cnt_{in_shape.elem_cnt()}, - indices_elem_cnt_{sorted_in_elem_cnt_} { - const int32_t sorted_in_aligned_bytes = GetCudaAlignedSize(sorted_in_elem_cnt_ * sizeof(T)); - const int32_t indices_aligned_bytes = GetCudaAlignedSize(indices_elem_cnt_ * sizeof(int32_t)); - sorted_in_ptr_ = reinterpret_cast(ptr); - indices_ptr_ = reinterpret_cast(reinterpret_cast(sorted_in_ptr_) - + sorted_in_aligned_bytes); - temp_storage_ptr_ = - reinterpret_cast(reinterpret_cast(indices_ptr_) + indices_aligned_bytes); - temp_storage_bytes_ = capacity_ - sorted_in_aligned_bytes - indices_aligned_bytes; - CHECK_GE(temp_storage_bytes_, 0); - } - ~TmpBufferManager() = default; - - T* SortedInPtr() const { return sorted_in_ptr_; } - int32_t* IndicesPtr() const { return indices_ptr_; } - void* TempStoragePtr() const { return temp_storage_ptr_; } - - int32_t TempStorageBytes() const { return temp_storage_bytes_; } - - private: - int32_t capacity_; - - T* sorted_in_ptr_; - int32_t* indices_ptr_; - void* temp_storage_ptr_; - - int64_t sorted_in_elem_cnt_; - int64_t indices_elem_cnt_; - int32_t temp_storage_bytes_; -}; - -__global__ void InitializeIndices(int32_t elem_cnt, int32_t* indices_ptr, int32_t instance_size) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { indices_ptr[i] = i % instance_size; }; -} - -} // namespace - -template -class GpuArgSortKernel final : public user_op::OpKernel { - public: - GpuArgSortKernel() = default; - ~GpuArgSortKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - TmpBufferManager buf_manager(static_cast(tmp_buffer->shape_view().elem_cnt()), - tmp_buffer->mut_dptr(), in->shape_view()); - - const int32_t elem_cnt = in->shape_view().elem_cnt(); - const int32_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int32_t instance_num = elem_cnt / instance_size; - const std::string& direction = ctx->Attr("direction"); - InitializeIndices<<stream()->As()->cuda_stream()>>>( - elem_cnt, buf_manager.IndicesPtr(), instance_size); - if (direction == "ASCENDING") { - SortPairsAscending(in->dptr(), buf_manager.IndicesPtr(), instance_num, instance_size, - buf_manager.TempStoragePtr(), buf_manager.TempStorageBytes(), - buf_manager.SortedInPtr(), out->mut_dptr(), - ctx->stream()->As()->cuda_stream()); - } else if (direction == "DESCENDING") { - SortPairsDescending(in->dptr(), buf_manager.IndicesPtr(), instance_num, instance_size, - buf_manager.TempStoragePtr(), buf_manager.TempStorageBytes(), - buf_manager.SortedInPtr(), out->mut_dptr(), - ctx->stream()->As()->cuda_stream()); - } else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ARG_SORT_KERNEL(dtype) \ - REGISTER_USER_KERNEL("arg_sort") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - const int32_t elem_cnt = in_shape.elem_cnt(); \ - const int32_t instance_size = in_shape.dim_vec().back(); \ - const int32_t instance_num = elem_cnt / instance_size; \ - \ - /* Sorted In */ \ - const int32_t sorted_in_aligned_bytes = GetCudaAlignedSize(elem_cnt * sizeof(dtype)); \ - /* Indices */ \ - const int32_t indices_aligned_bytes = GetCudaAlignedSize(elem_cnt * sizeof(int32_t)); \ - /* CUB Temp Storage */ \ - int32_t temp_storage_bytes = -1; \ - const std::string& direction = ctx->Attr("direction"); \ - if (direction == "ASCENDING") { \ - temp_storage_bytes = \ - InferTempStorageForSortPairsAscending(instance_num, instance_size); \ - } else if (direction == "DESCENDING") { \ - temp_storage_bytes = \ - InferTempStorageForSortPairsDescending(instance_num, instance_size); \ - } else { \ - UNIMPLEMENTED(); \ - } \ - \ - return sorted_in_aligned_bytes + indices_aligned_bytes + temp_storage_bytes; \ - }); - -REGISTER_CUDA_ARG_SORT_KERNEL(float) -REGISTER_CUDA_ARG_SORT_KERNEL(double) -REGISTER_CUDA_ARG_SORT_KERNEL(bool) -REGISTER_CUDA_ARG_SORT_KERNEL(int8_t) -REGISTER_CUDA_ARG_SORT_KERNEL(uint8_t) -REGISTER_CUDA_ARG_SORT_KERNEL(int32_t) -REGISTER_CUDA_ARG_SORT_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/arg_where_kernel.cpp b/oneflow/user/kernels/arg_where_kernel.cpp index 97ffeaa015edc9711f5b1c9b950bfc95cf08d3d8..9b8112fec30b47abe705137a6a651d1cc9a434c5 100644 --- a/oneflow/user/kernels/arg_where_kernel.cpp +++ b/oneflow/user/kernels/arg_where_kernel.cpp @@ -31,7 +31,14 @@ class ArgWhereKernel final : public user_op::OpKernel { private: void Compute(user_op::KernelComputeContext* ctx) const override { int64_t ndims = ctx->Tensor4ArgNameAndIndex("input", 0)->shape_view().NumAxes(); - if (ndims == 0) { return; } + if (ndims == 0) { + // 0-dim tensor, elem_cnt of input is 1 + CHECK_EQ(ctx->Tensor4ArgNameAndIndex("input", 0)->shape_view().elem_cnt(), 1); + SetOutputSize( + ctx->stream(), ctx->Tensor4ArgNameAndIndex("input", 0)->dptr(), + ctx->Tensor4ArgNameAndIndex("output_size", 0)->mut_dptr()); + return; + } SwitchNdimCompute(SwitchCase(ndims), ctx); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } @@ -75,8 +82,8 @@ template size_t InferTempStorageBytesSize(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("input", 0); if (input_shape.NumAxes() == 0) { return 0; } - const DataType& input_dtype = ctx->InputDType("input", 0); - DataType output_dtype = *ctx->OutputDType("output", 0); + DataType input_dtype = ctx->InputDType("input", 0); + DataType output_dtype = ctx->OutputDType("output", 0); return SwitchUtil::SwitchGetWorkspaceBytesSize( SwitchCase(device_type, input_dtype, output_dtype, input_shape.NumAxes()), input_shape.elem_cnt()); diff --git a/oneflow/user/kernels/arg_where_kernel_util.cpp b/oneflow/user/kernels/arg_where_kernel_util.cpp index cc85cad8ad02ede320f3515ec91cf5e3501500c2..bb3962229e71da9f24a686ffc6d808e7733a4812 100644 --- a/oneflow/user/kernels/arg_where_kernel_util.cpp +++ b/oneflow/user/kernels/arg_where_kernel_util.cpp @@ -52,4 +52,15 @@ struct ArgWhereKernelUtil { INSTANTIATE_ARG_WHERE_KERNEL_UTIL_FOR_DEVICE(DeviceType::kCPU) +template +void SetOutputSize(ep::Stream* stream, const IN_T* input_ptr, OUT_T* output_size_ptr) { + if (*input_ptr == GetZeroVal()) { + *output_size_ptr = GetZeroVal(); + } else { + *output_size_ptr = GetOneVal(); + } +} + +INSTANTIATE_SET_OUTPUT_SIZE_FOR_DEVICE(DeviceType::kCPU) + } // namespace oneflow diff --git a/oneflow/user/kernels/arg_where_kernel_util.cu b/oneflow/user/kernels/arg_where_kernel_util.cu index 522078e42ab435ca9da112ae034241927a5002d6..24df2f39c7e0148e404c42f9e51fc355aada9dba 100644 --- a/oneflow/user/kernels/arg_where_kernel_util.cu +++ b/oneflow/user/kernels/arg_where_kernel_util.cu @@ -19,7 +19,12 @@ limitations under the License. #include "oneflow/core/cuda/elementwise.cuh" #include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif namespace oneflow { @@ -70,14 +75,30 @@ struct IsTrue { }; template -cudaError_t SelectTrue(cudaStream_t stream, int num_items, void* temp_storage, +GPU(Error_t) SelectTrue(GPU(Stream_t) stream, int num_items, void* temp_storage, size_t& temp_storage_bytes, const IN_T* input, OUT_ITER output_iter, OUT_T* num_selected) { IsTrue is_true; +#ifdef WITH_ROCM + hipcub::TransformInputIterator, const IN_T*> flag_iter(input, is_true); + hipcub::CountingInputIterator offset_counter(0); + return hipcub::DeviceSelect::Flagged(temp_storage, temp_storage_bytes, offset_counter, flag_iter, + output_iter, num_selected, num_items, stream, false); +#else cub::TransformInputIterator, const IN_T*> flag_iter(input, is_true); cub::CountingInputIterator offset_counter(0); return cub::DeviceSelect::Flagged(temp_storage, temp_storage_bytes, offset_counter, flag_iter, output_iter, num_selected, num_items, stream, false); +#endif +} + +template +__global__ void SetOutputSizeKernel(const IN_T* input_ptr, OUT_T* output_size_ptr) { + if (*input_ptr == GetZeroVal()) { + *output_size_ptr = GetZeroVal(); + } else { + *output_size_ptr = GetOneVal(); + } } } // namespace @@ -121,7 +142,7 @@ struct ArgWhereKernelUtil { } static size_t GetWorkspaceBytesSize(ep::Stream* stream, int64_t elem_cnt) { - cudaStream_t cuda_stream = stream ? stream->As()->cuda_stream() : 0; + GPU(Stream_t) cuda_stream = stream ? stream->As()->cuda_stream() : 0; size_t workspace = 0; if (NDIM == 1) { OF_CUDA_CHECK((SelectTrue(cuda_stream, elem_cnt, nullptr, workspace, @@ -138,4 +159,12 @@ struct ArgWhereKernelUtil { INSTANTIATE_ARG_WHERE_KERNEL_UTIL_FOR_DEVICE(DeviceType::kCUDA) +template +void SetOutputSize(ep::Stream* stream, const IN_T* input_ptr, OUT_T* output_size_ptr) { + SetOutputSizeKernel + <<<1, 1, 0, stream->As()->cuda_stream()>>>(input_ptr, output_size_ptr); +} + +INSTANTIATE_SET_OUTPUT_SIZE_FOR_DEVICE(DeviceType::kCUDA) + } // namespace oneflow diff --git a/oneflow/user/kernels/arg_where_kernel_util.h b/oneflow/user/kernels/arg_where_kernel_util.h index 35f10edd7ab42c790b13aeb021cf71495317b4c9..26ea6cbfee981aa11e7e01c2ebe9c8e968d646a3 100644 --- a/oneflow/user/kernels/arg_where_kernel_util.h +++ b/oneflow/user/kernels/arg_where_kernel_util.h @@ -42,6 +42,21 @@ struct ArgWhereKernelUtil { ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, \ DIM_SEQ) +template +void SetOutputSize(ep::Stream* stream, const IN_T* input_ptr, OUT_T* output_size_ptr); + +#define INSTANTIATE_SET_OUTPUT_SIZE(device, itype, otype) \ + template void SetOutputSize(ep::Stream * stream, const itype* input_ptr, \ + otype* output_size_ptr); + +#define INSTANTIATE_SET_OUTPUT_SIZE_WITH_DTYPE_PAIR(device, itype_pair, otype_pair) \ + INSTANTIATE_SET_OUTPUT_SIZE(device, OF_PP_PAIR_FIRST(itype_pair), OF_PP_PAIR_FIRST(otype_pair)) + +#define INSTANTIATE_SET_OUTPUT_SIZE_FOR_DEVICE(device) \ + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( \ + INSTANTIATE_SET_OUTPUT_SIZE_WITH_DTYPE_PAIR, (device), \ + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + } // namespace oneflow #endif // ONEFLOW_USER_KERNELS_ARG_WHERE_KERNEL_UTIL_H_ diff --git a/oneflow/user/kernels/arg_where_kernel_util.hip.cpp b/oneflow/user/kernels/arg_where_kernel_util.hip.cpp deleted file mode 100644 index 4157777350b8785eaf5a3c6ed89c9685ed354543..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/arg_where_kernel_util.hip.cpp +++ /dev/null @@ -1,142 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/arg_where_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/common/small_vector.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace { - -constexpr int kBlockSize = cuda::elementwise::kBlockSize; - -int GetNumBlocks(int64_t elem_cnt) { - int num_blocks = 0; - OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); - return num_blocks; -} - -template -struct StrideIterator { - typedef StrideIterator self_type; - typedef std::ptrdiff_t difference_type; - typedef T value_type; - typedef T* pointer; - typedef T& reference; - typedef std::random_access_iterator_tag iterator_category; - - explicit StrideIterator(T* ptr, size_t max_iters) : ptr_(ptr), max_iters_(max_iters) {} - - OF_DEVICE_FUNC reference operator[](int i) { - assert(0 <= i && i < max_iters_); - return *(ptr_ + (i * NDIM)); - } - - private: - T* ptr_; - size_t max_iters_; -}; - -template -__global__ void __launch_bounds__(kBlockSize) - CudaOffsetToNdIndexInplace(NdIndexOffsetHelper index_converter, - const T* output_size_ptr, T* output_ptr) { - CUDA_1D_KERNEL_LOOP_T(T, i, *output_size_ptr) { - T* index_ptr = output_ptr + i * NDIM; - index_converter.OffsetToNdIndex(*index_ptr, index_ptr); - } -} - -template -struct IsTrue { - __device__ __forceinline__ bool operator()(const T& val) const { return static_cast(val); } -}; - -template -hipError_t SelectTrue(hipStream_t stream, int num_items, void* temp_storage, - size_t& temp_storage_bytes, const IN_T* input, OUT_ITER output_iter, - OUT_T* num_selected) { - IsTrue is_true; - hipcub::TransformInputIterator, const IN_T*> flag_iter(input, is_true); - hipcub::CountingInputIterator offset_counter(0); - return hipcub::DeviceSelect::Flagged(temp_storage, temp_storage_bytes, offset_counter, flag_iter, - output_iter, num_selected, num_items, stream, false); -} - -} // namespace - -template -struct ArgWhereKernelUtil { - static void ArgWhere(ep::Stream* stream, const ShapeView& input_shape, const IN_T* input_ptr, - void* temp_storage, size_t temp_storage_bytes, OUT_T* output_ptr, - OUT_T* output_size_ptr) { - const int64_t elem_cnt = input_shape.elem_cnt(); - // deal with empty blob - if (elem_cnt == 0) { - Memset(stream, output_size_ptr, 0, sizeof(OUT_T)); - return; - } - - CHECK_NOTNULL(stream); - CHECK_LE(elem_cnt, std::numeric_limits::max()); - size_t workspace = GetWorkspaceBytesSize(stream, elem_cnt); - CHECK_LE(workspace, temp_storage_bytes); - - if (NDIM == 1) { - OF_CUDA_CHECK((SelectTrue( - stream->As()->cuda_stream(), input_shape.elem_cnt(), temp_storage, - workspace, input_ptr, output_ptr, output_size_ptr))); - } else { - using OutputIterator = StrideIterator; - OutputIterator output_iter(output_ptr, elem_cnt); - OF_CUDA_CHECK((SelectTrue( - stream->As()->cuda_stream(), elem_cnt, temp_storage, workspace, input_ptr, - output_iter, output_size_ptr))); - - OUT_T dims[NDIM] = {0}; - std::transform(input_shape.ptr(), input_shape.ptr() + input_shape.NumAxes(), dims, - [](int64_t dim) { return static_cast(dim); }); - NdIndexOffsetHelper index_converter(dims); - CudaOffsetToNdIndexInplace - <<As()->cuda_stream()>>>( - index_converter, output_size_ptr, output_ptr); - } - } - - static size_t GetWorkspaceBytesSize(ep::Stream* stream, int64_t elem_cnt) { - hipStream_t cuda_stream = stream ? stream->As()->cuda_stream() : 0; - size_t workspace = 0; - if (NDIM == 1) { - OF_CUDA_CHECK((SelectTrue(cuda_stream, elem_cnt, nullptr, workspace, - nullptr, nullptr, nullptr))); - } else { - using OutputIterator = StrideIterator; - OutputIterator output_iter(nullptr, elem_cnt); - OF_CUDA_CHECK((SelectTrue( - cuda_stream, elem_cnt, nullptr, workspace, nullptr, output_iter, nullptr))); - } - return workspace; - } -}; - -INSTANTIATE_ARG_WHERE_KERNEL_UTIL_FOR_DEVICE(DeviceType::kCUDA) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/argmax_kernel.cpp b/oneflow/user/kernels/argmax_kernel.cpp index d0e0ab14f8dcdcc55b86792e86612c194fb1d1cf..6c6f8d04eb6d03bfb000d84777f36d33068ffbf0 100644 --- a/oneflow/user/kernels/argmax_kernel.cpp +++ b/oneflow/user/kernels/argmax_kernel.cpp @@ -29,11 +29,16 @@ class CpuArgMaxKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + + const int32_t elem_cnt = in->shape_view().elem_cnt(); + CHECK_GE(elem_cnt, 0); + if (elem_cnt == 0) { return; } + const T* in_ptr = in->dptr(); int64_t* out_ptr = out->mut_dptr(); const int64_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int64_t instance_num = in->shape_view().elem_cnt() / instance_size; + const int64_t instance_num = elem_cnt / instance_size; const int64_t num_thread = std::min(instance_num, (int64_t)Singleton::Get()->thread_num()); const BalancedSplitter bs(instance_num, num_thread); @@ -59,7 +64,9 @@ class CpuArgMaxKernel final : public user_op::OpKernel { (user_op::HobDeviceType() == DeviceType::kCPU) \ && (user_op::HobDataType("in", 0) == GetDataType::value)); +REGISTER_CPU_ARGMAX_KERNEL(bool) REGISTER_CPU_ARGMAX_KERNEL(float) +REGISTER_CPU_ARGMAX_KERNEL(float16) REGISTER_CPU_ARGMAX_KERNEL(double) REGISTER_CPU_ARGMAX_KERNEL(uint8_t) REGISTER_CPU_ARGMAX_KERNEL(int8_t) diff --git a/oneflow/user/kernels/argmax_kernel.cu b/oneflow/user/kernels/argmax_kernel.cu index eacd32531cb7eb51345794ede38cbf5ec0e03c11..4ed2f24ccd77138a8be8fe46214e8fd26455745d 100644 --- a/oneflow/user/kernels/argmax_kernel.cu +++ b/oneflow/user/kernels/argmax_kernel.cu @@ -14,9 +14,16 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_CUDA + namespace oneflow { namespace { @@ -131,6 +138,9 @@ class GpuArgMaxKernel final : public user_op::OpKernel { user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int32_t elem_cnt = in->shape_view().elem_cnt(); + CHECK_GE(elem_cnt, 0); + if (elem_cnt == 0) { return; } + const int32_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); const int32_t instance_num = elem_cnt / instance_size; TmpBufferManager buffer_manager(tmp_buffer->shape_view().elem_cnt(), @@ -166,11 +176,196 @@ class GpuArgMaxKernel final : public user_op::OpKernel { return key_value_out_bytes + temp_storage_bytes; \ }); +REGISTER_CUDA_ARGMAX_KERNEL(bool) REGISTER_CUDA_ARGMAX_KERNEL(float) REGISTER_CUDA_ARGMAX_KERNEL(double) REGISTER_CUDA_ARGMAX_KERNEL(uint8_t) REGISTER_CUDA_ARGMAX_KERNEL(int8_t) REGISTER_CUDA_ARGMAX_KERNEL(int32_t) REGISTER_CUDA_ARGMAX_KERNEL(int64_t) +REGISTER_CUDA_ARGMAX_KERNEL(half) } // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace { + +template +class TmpBufferManager final { + public: + OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); + TmpBufferManager(int32_t capacity, void* ptr, int32_t instance_num) + : capacity_{capacity}, key_value_out_elem_cnt_{instance_num} { + const int32_t key_value_out_aligned_bytes = + GetCudaAlignedSize(key_value_out_elem_cnt_ * sizeof(hipcub::KeyValuePair)); + key_value_out_ptr_ = reinterpret_cast*>(ptr); + temp_storage_ptr_ = reinterpret_cast(reinterpret_cast(key_value_out_ptr_) + + key_value_out_aligned_bytes); + temp_storage_bytes_ = capacity_ - key_value_out_aligned_bytes; + CHECK_GE(temp_storage_bytes_, 0); + } + ~TmpBufferManager() = default; + + hipcub::KeyValuePair* KeyValueOutPtr() const { return key_value_out_ptr_; } + void* TempStoragePtr() const { return temp_storage_ptr_; } + + int32_t TempStorageBytes() const { return temp_storage_bytes_; } + + private: + int32_t capacity_; + + hipcub::KeyValuePair* key_value_out_ptr_; + void* temp_storage_ptr_; + + int32_t key_value_out_elem_cnt_; + int32_t temp_storage_bytes_; +}; + +class MultiplyFunctor final { + public: + MultiplyFunctor(int32_t num_col) : num_col_(num_col) {} + __host__ __device__ __forceinline__ int32_t operator()(int32_t idx) const { + return idx * num_col_; + } + + private: + int32_t num_col_; +}; + +template +size_t InferTempStorageForArgMax(int32_t num_row, int32_t num_col) { + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + size_t temp_storage_bytes = 0; + auto err = + hipcub::DeviceSegmentedReduce::ArgMax*, SegmentOffsetIter>( + /* d_temp_storage */ nullptr, /* temp_storage_bytes */ temp_storage_bytes, + /* d_in */ nullptr, /* d_out */ nullptr, /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, /* d_end_offsets */ segment_offset_iter + 1, + /* stream */ 0); + + // auto err = + // hipcub::DeviceReduce::ArgMax*>( + // nullptr, temp_storage_bytes, + // nullptr, nullptr, num_row, + // 0); + + OF_CUDA_CHECK(err); + + return temp_storage_bytes; +} + +template +void ArgMax(const T* in_ptr, int32_t num_row, int32_t num_col, void* temp_storage_ptr, + int32_t temp_storage_bytes, hipcub::KeyValuePair* out_ptr, + hipStream_t stream) { + size_t rt_inferred_temp_storage_bytes = InferTempStorageForArgMax(num_row, num_col); + CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); + + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + // void * d_temp_storage = nullptr; + // hipMalloc((void **)&d_temp_storage, rt_inferred_temp_storage_bytes); + + auto err = hipcub::DeviceSegmentedReduce::ArgMax( + /* d_temp_storage */ temp_storage_ptr, + /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, + /* d_in */ in_ptr, + /* d_out */ out_ptr, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* stream */ stream); + + // auto err = + // hipcub::DeviceReduce::ArgMax( + // d_temp_storage, rt_inferred_temp_storage_bytes, + // in_ptr, out_ptr, num_row, + // stream); + + OF_CUDA_CHECK(err); +} + +template +__global__ void WriteKeysToOutput(const int32_t instance_num, const int32_t instance_size, + const hipcub::KeyValuePair* key_value_out_ptr, + int64_t* out_ptr) { + CUDA_1D_KERNEL_LOOP(i, instance_num) { out_ptr[i] = key_value_out_ptr[i].key % instance_size; } +} + +} // namespace + +template +class GpuArgMaxKernel final : public user_op::OpKernel { + public: + GpuArgMaxKernel() = default; + ~GpuArgMaxKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + + const int32_t elem_cnt = in->shape_view().elem_cnt(); + const int32_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); + const int32_t instance_num = elem_cnt / instance_size; + TmpBufferManager buffer_manager(tmp_buffer->shape_view().elem_cnt(), + tmp_buffer->mut_dptr(), instance_num); + + ArgMax(in->dptr(), instance_num, instance_size, buffer_manager.TempStoragePtr(), + buffer_manager.TempStorageBytes(), buffer_manager.KeyValueOutPtr(), + ctx->stream()->As()->cuda_stream()); + WriteKeysToOutput<<stream()->As()->cuda_stream()>>>( + instance_num, instance_size, buffer_manager.KeyValueOutPtr(), out->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUDA_ARGMAX_KERNEL(dtype) \ + REGISTER_USER_KERNEL("argmax") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("in", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& in_shape = ctx->InputShape("in", 0); \ + const int32_t instance_size = in_shape.dim_vec().back(); \ + const int32_t instance_num = in_shape.elem_cnt() / instance_size; \ + \ + /* Key-Value Out */ \ + int32_t key_value_out_bytes = \ + GetCudaAlignedSize(instance_num * sizeof(hipcub::KeyValuePair)); \ + \ + /* CUB Temp Storage */ \ + size_t temp_storage_bytes = InferTempStorageForArgMax(instance_num, instance_size); \ + \ + return key_value_out_bytes + temp_storage_bytes; \ + }); + +REGISTER_CUDA_ARGMAX_KERNEL(bool) +REGISTER_CUDA_ARGMAX_KERNEL(float) +REGISTER_CUDA_ARGMAX_KERNEL(double) +REGISTER_CUDA_ARGMAX_KERNEL(uint8_t) +REGISTER_CUDA_ARGMAX_KERNEL(int8_t) +REGISTER_CUDA_ARGMAX_KERNEL(int32_t) +REGISTER_CUDA_ARGMAX_KERNEL(int64_t) +REGISTER_CUDA_ARGMAX_KERNEL(half) + +} // namespace oneflow + +#endif // WITH_ROCM diff --git a/oneflow/user/kernels/argmax_kernel.hip.cpp b/oneflow/user/kernels/argmax_kernel.hip.cpp deleted file mode 100644 index 80b75e424dd69ab53b75e1f20b9d24f91660455b..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/argmax_kernel.hip.cpp +++ /dev/null @@ -1,194 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(int32_t capacity, void* ptr, int32_t instance_num) - : capacity_{capacity}, key_value_out_elem_cnt_{instance_num} { - const int32_t key_value_out_aligned_bytes = - GetCudaAlignedSize(key_value_out_elem_cnt_ * sizeof(hipcub::KeyValuePair)); - key_value_out_ptr_ = reinterpret_cast*>(ptr); - temp_storage_ptr_ = reinterpret_cast(reinterpret_cast(key_value_out_ptr_) - + key_value_out_aligned_bytes); - temp_storage_bytes_ = capacity_ - key_value_out_aligned_bytes; - CHECK_GE(temp_storage_bytes_, 0); - } - ~TmpBufferManager() = default; - - hipcub::KeyValuePair* KeyValueOutPtr() const { return key_value_out_ptr_; } - void* TempStoragePtr() const { return temp_storage_ptr_; } - - int32_t TempStorageBytes() const { return temp_storage_bytes_; } - - private: - int32_t capacity_; - - hipcub::KeyValuePair* key_value_out_ptr_; - void* temp_storage_ptr_; - - int32_t key_value_out_elem_cnt_; - int32_t temp_storage_bytes_; -}; - -class MultiplyFunctor final { - public: - MultiplyFunctor(int32_t num_col) : num_col_(num_col) {} - __host__ __device__ __forceinline__ int32_t operator()(int32_t idx) const { - return idx * num_col_; - } - - private: - int32_t num_col_; -}; - -template -size_t InferTempStorageForArgMax(int32_t num_row, int32_t num_col) { - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - size_t temp_storage_bytes = 0; - auto err = - hipcub::DeviceSegmentedReduce::ArgMax*, SegmentOffsetIter>( - /* d_temp_storage */ nullptr, /* temp_storage_bytes */ temp_storage_bytes, - /* d_in */ nullptr, /* d_out */ nullptr, /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, /* d_end_offsets */ segment_offset_iter + 1, - /* stream */ 0); - - // auto err = - // hipcub::DeviceReduce::ArgMax*>( - // nullptr, temp_storage_bytes, - // nullptr, nullptr, num_row, - // 0); - - OF_CUDA_CHECK(err); - - return temp_storage_bytes; -} - -template -void ArgMax(const T* in_ptr, int32_t num_row, int32_t num_col, void* temp_storage_ptr, - int32_t temp_storage_bytes, hipcub::KeyValuePair* out_ptr, - hipStream_t stream) { - size_t rt_inferred_temp_storage_bytes = InferTempStorageForArgMax(num_row, num_col); - CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); - - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - // void * d_temp_storage = nullptr; - // hipMalloc((void **)&d_temp_storage, rt_inferred_temp_storage_bytes); - - auto err = hipcub::DeviceSegmentedReduce::ArgMax( - /* d_temp_storage */ temp_storage_ptr, - /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, - /* d_in */ in_ptr, - /* d_out */ out_ptr, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* stream */ stream); - - // auto err = - // hipcub::DeviceReduce::ArgMax( - // d_temp_storage, rt_inferred_temp_storage_bytes, - // in_ptr, out_ptr, num_row, - // stream); - - OF_CUDA_CHECK(err); -} - -template -__global__ void WriteKeysToOutput(const int32_t instance_num, const int32_t instance_size, - const hipcub::KeyValuePair* key_value_out_ptr, - int64_t* out_ptr) { - CUDA_1D_KERNEL_LOOP(i, instance_num) { out_ptr[i] = key_value_out_ptr[i].key % instance_size; } -} - -} // namespace - -template -class GpuArgMaxKernel final : public user_op::OpKernel { - public: - GpuArgMaxKernel() = default; - ~GpuArgMaxKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const int32_t elem_cnt = in->shape_view().elem_cnt(); - const int32_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int32_t instance_num = elem_cnt / instance_size; - TmpBufferManager buffer_manager(tmp_buffer->shape_view().elem_cnt(), - tmp_buffer->mut_dptr(), instance_num); - - ArgMax(in->dptr(), instance_num, instance_size, buffer_manager.TempStoragePtr(), - buffer_manager.TempStorageBytes(), buffer_manager.KeyValueOutPtr(), - ctx->stream()->As()->cuda_stream()); - WriteKeysToOutput<<stream()->As()->cuda_stream()>>>( - instance_num, instance_size, buffer_manager.KeyValueOutPtr(), out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ARGMAX_KERNEL(dtype) \ - REGISTER_USER_KERNEL("argmax") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - const int32_t instance_size = in_shape.dim_vec().back(); \ - const int32_t instance_num = in_shape.elem_cnt() / instance_size; \ - \ - /* Key-Value Out */ \ - int32_t key_value_out_bytes = \ - GetCudaAlignedSize(instance_num * sizeof(hipcub::KeyValuePair)); \ - \ - /* CUB Temp Storage */ \ - size_t temp_storage_bytes = InferTempStorageForArgMax(instance_num, instance_size); \ - \ - return key_value_out_bytes + temp_storage_bytes; \ - }); - -REGISTER_CUDA_ARGMAX_KERNEL(float) -REGISTER_CUDA_ARGMAX_KERNEL(double) -REGISTER_CUDA_ARGMAX_KERNEL(uint8_t) -REGISTER_CUDA_ARGMAX_KERNEL(int8_t) -REGISTER_CUDA_ARGMAX_KERNEL(int32_t) -REGISTER_CUDA_ARGMAX_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/as_strided_kernel.cpp b/oneflow/user/kernels/as_strided_kernel.cpp index 4ecedcfeb649875d4ffb4009dff7ebecd2d79ef2..96f85c602c28962822c449d5ee560195b0a0d7a3 100644 --- a/oneflow/user/kernels/as_strided_kernel.cpp +++ b/oneflow/user/kernels/as_strided_kernel.cpp @@ -14,19 +14,11 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include -#include "oneflow/core/common/just.h" #include "oneflow/core/common/util.h" -#include "oneflow/core/framework/consistency_check.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/kernel_util.h" -#ifdef WITH_CUDA #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#endif #include "oneflow/core/common/nd_index_offset_helper.h" namespace oneflow { @@ -38,13 +30,13 @@ constexpr size_t NUM_DIM = 20; template struct AsStridedFunctor final { void operator()(ep::Stream* stream, const T* input_buf, T* output_buf, const int64_t* dest_dims, - const int32_t* stride, const int32_t dest_num_dims, const int32_t storage_offset, - const int32_t input_num, const int32_t output_num) { + const int64_t* stride, const int64_t dest_num_dims, const int64_t storage_offset, + const int64_t input_num, const int64_t output_num) { NdIndexOffsetHelper destIndexOffsetHelper(dest_dims, dest_num_dims); FOR_RANGE(int64_t, i, 0, output_num) { int64_t dst_index[NUM_DIM]; destIndexOffsetHelper.OffsetToNdIndex(i, dst_index, dest_num_dims); - int32_t index_in_input = storage_offset; + int64_t index_in_input = storage_offset; FOR_RANGE(int64_t, j, 0, dest_num_dims) { index_in_input += dst_index[j] * stride[j]; } output_buf[i] = input_buf[index_in_input]; } @@ -54,13 +46,13 @@ struct AsStridedFunctor final { template struct AsStridedGradFunctor final { void operator()(ep::Stream* stream, const T* dy_buf, T* dx_buf, const int64_t* dy_dims, - const int32_t* stride, const int32_t dy_num_dims, const int32_t storage_offset, - const int32_t dx_num, const int32_t dy_num) { + const int64_t* stride, const int64_t dy_num_dims, const int64_t storage_offset, + const int64_t dx_num, const int64_t dy_num) { NdIndexOffsetHelper destIndexOffsetHelper(dy_dims, dy_num_dims); FOR_RANGE(int64_t, i, 0, dy_num) { int64_t dy_index[NUM_DIM]; destIndexOffsetHelper.OffsetToNdIndex(i, dy_index, dy_num_dims); - int32_t index_in_dx = storage_offset; + int64_t index_in_dx = storage_offset; FOR_RANGE(int64_t, j, 0, dy_num_dims) { index_in_dx += dy_index[j] * stride[j]; } dx_buf[index_in_dx] += dy_buf[i]; } @@ -80,9 +72,9 @@ class CpuAsStridedKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); + const auto size = ctx->Attr>("size"); + const auto stride = ctx->Attr>("stride"); + const int64_t storage_offset = ctx->Attr("storage_offset"); size_t dest_num_dims = output->shape_view().NumAxes(); const int64_t* dest_dims = output->shape_view().ptr(); @@ -106,9 +98,9 @@ class CpuAsStridedGradKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); + const auto size = ctx->Attr>("size"); + const auto stride = ctx->Attr>("stride"); + const int64_t storage_offset = ctx->Attr("storage_offset"); size_t dy_num_dims = dy->shape_view().NumAxes(); const int64_t* dy_dims = dy->shape_view().ptr(); @@ -124,23 +116,35 @@ class CpuAsStridedGradKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_CPUASSTRIDED_KERNEL(in_type) \ - REGISTER_USER_KERNEL("as_strided") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("as_strided_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ +#define REGISTER_CPU_ASSTRIDED_KERNEL(in_type) \ + REGISTER_USER_KERNEL("as_strided") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ && (user_op::HobDataType("input", 0) == GetDataType::value)); -REGISTER_CPUASSTRIDED_KERNEL(float); -REGISTER_CPUASSTRIDED_KERNEL(double); -REGISTER_CPUASSTRIDED_KERNEL(bool); -REGISTER_CPUASSTRIDED_KERNEL(int8_t); -REGISTER_CPUASSTRIDED_KERNEL(int32_t); -REGISTER_CPUASSTRIDED_KERNEL(int64_t); +REGISTER_CPU_ASSTRIDED_KERNEL(float); +REGISTER_CPU_ASSTRIDED_KERNEL(double); +REGISTER_CPU_ASSTRIDED_KERNEL(int8_t); +REGISTER_CPU_ASSTRIDED_KERNEL(uint8_t); +REGISTER_CPU_ASSTRIDED_KERNEL(int32_t); +REGISTER_CPU_ASSTRIDED_KERNEL(int64_t); -#undef REGISTER_CPUASSTRIDED_KERNEL +#undef REGISTER_CPU_ASSTRIDED_KERNEL -} // namespace oneflow \ No newline at end of file +#define REGISTER_CPU_ASSTRIDED_GRAD_KERNEL(in_type) \ + REGISTER_USER_KERNEL("as_strided_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +REGISTER_CPU_ASSTRIDED_GRAD_KERNEL(float); +REGISTER_CPU_ASSTRIDED_GRAD_KERNEL(double); + +#undef REGISTER_CPU_ASSTRIDED_GRAD_KERNEL + +REGISTER_USER_KERNEL("as_strided") + .SetCreateFn>() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +} // namespace oneflow diff --git a/oneflow/user/kernels/as_strided_kernel.cu b/oneflow/user/kernels/as_strided_kernel.cu index 2f528e00a0b3b75472fe09f20818ad4b836dc807..3d7e675c7e442064e94fb547ce5f5c3d91e7b042 100644 --- a/oneflow/user/kernels/as_strided_kernel.cu +++ b/oneflow/user/kernels/as_strided_kernel.cu @@ -14,11 +14,8 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include #include "oneflow/core/cuda/atomic.cuh" -#include "oneflow/core/common/just.h" #include "oneflow/core/common/util.h" -#include "oneflow/core/framework/consistency_check.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/kernel_util.h" @@ -74,8 +71,8 @@ __global__ void AsStridedGrad_kernel(const T* dy_buf, T* dx_buf, template struct AsStridedFunctor final { void operator()(ep::Stream* stream, const T* input_buf, T* output_buf, const int64_t* dest_dims, - const int32_t* stride, const int32_t dest_num_dims, const int32_t storage_offset, - const int32_t input_num, const int32_t output_num) { + const int64_t* stride, const int64_t dest_num_dims, const int64_t storage_offset, + const int64_t input_num, const int64_t output_num) { NdIndexOffsetHelper destIndexOffsetHelper(dest_dims, dest_num_dims); AsStridedParams params; params.destIndexOffsetHelper = destIndexOffsetHelper; @@ -97,8 +94,8 @@ struct AsStridedFunctor final { template struct AsStridedGradFunctor final { void operator()(ep::Stream* stream, const T* dy_buf, T* dx_buf, const int64_t* dy_dims, - const int32_t* stride, const int32_t dy_num_dims, const int32_t storage_offset, - const int32_t dx_num, const int32_t dy_num) { + const int64_t* stride, const int64_t dy_num_dims, const int64_t storage_offset, + const int64_t dx_num, const int64_t dy_num) { NdIndexOffsetHelper dyIndexOffsetHelper(dy_dims, dy_num_dims); AsStridedParams params; params.destIndexOffsetHelper = dyIndexOffsetHelper; @@ -130,9 +127,9 @@ class GpuAsStridedKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); + const auto size = ctx->Attr>("size"); + const auto stride = ctx->Attr>("stride"); + const int64_t storage_offset = ctx->Attr("storage_offset"); size_t dest_num_dims = output->shape_view().NumAxes(); const int64_t* dest_dims = output->shape_view().ptr(); @@ -160,9 +157,9 @@ class GpuAsStridedGradKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); + const auto size = ctx->Attr>("size"); + const auto stride = ctx->Attr>("stride"); + const int64_t storage_offset = ctx->Attr("storage_offset"); size_t dy_num_dims = dy->shape_view().NumAxes(); const int64_t* dy_dims = dy->shape_view().ptr(); @@ -178,21 +175,37 @@ class GpuAsStridedGradKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_GPUASSTRIDED_KERNEL(in_type) \ - REGISTER_USER_KERNEL("as_strided") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("as_strided_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ +#define REGISTER_GPU_ASSTRIDED_KERNEL(in_type) \ + REGISTER_USER_KERNEL("as_strided") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("input", 0) == GetDataType::value)); -REGISTER_GPUASSTRIDED_KERNEL(half); -REGISTER_GPUASSTRIDED_KERNEL(float); -REGISTER_GPUASSTRIDED_KERNEL(double); -REGISTER_GPUASSTRIDED_KERNEL(int64_t); +REGISTER_GPU_ASSTRIDED_KERNEL(half); +REGISTER_GPU_ASSTRIDED_KERNEL(float); +REGISTER_GPU_ASSTRIDED_KERNEL(double); +REGISTER_GPU_ASSTRIDED_KERNEL(int8_t); +REGISTER_GPU_ASSTRIDED_KERNEL(uint8_t); +REGISTER_GPU_ASSTRIDED_KERNEL(int32_t); +REGISTER_GPU_ASSTRIDED_KERNEL(int64_t); -#undef REGISTER_GPUASSTRIDED_KERNEL +#undef REGISTER_GPU_ASSTRIDED_KERNEL -} // namespace oneflow \ No newline at end of file +#define REGISTER_GPU_ASSTRIDED_GRAD_KERNEL(in_type) \ + REGISTER_USER_KERNEL("as_strided_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +REGISTER_GPU_ASSTRIDED_GRAD_KERNEL(half); +REGISTER_GPU_ASSTRIDED_GRAD_KERNEL(float); +REGISTER_GPU_ASSTRIDED_GRAD_KERNEL(double); + +#undef REGISTER_GPU_ASSTRIDED_GRAD_KERNEL + +REGISTER_USER_KERNEL("as_strided") + .SetCreateFn>() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +} // namespace oneflow diff --git a/oneflow/user/kernels/as_strided_kernel.hip.cpp b/oneflow/user/kernels/as_strided_kernel.hip.cpp deleted file mode 100644 index ef8972ecf875f502d43ba668ba3058c95890b68c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/as_strided_kernel.hip.cpp +++ /dev/null @@ -1,199 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/common/just.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/framework/consistency_check.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/common/nd_index_offset_helper.h" - -namespace oneflow { - -namespace { - -constexpr size_t NUM_DIM = 8; - -template -struct AsStridedParams { - NdIndexOffsetHelper destIndexOffsetHelper; - int64_t dest_dims[num_dims]; - int32_t stride[num_dims]; - int32_t dest_num_dims; - int32_t storage_offset; - int32_t input_num; - int32_t output_num; -}; - -template -__global__ void AsStrided_kernel(const T* input_buf, T* output_buf, - AsStridedParams params) { - const int64_t* dest_dims = reinterpret_cast(params.dest_dims); - const int32_t* stride = reinterpret_cast(params.stride); - - CUDA_1D_KERNEL_LOOP_T(int64_t, i, params.output_num) { - int64_t dst_index[NUM_DIM]; - params.destIndexOffsetHelper.OffsetToNdIndex(i, dst_index, params.dest_num_dims); - int32_t index_in_input = params.storage_offset; - FOR_RANGE(int64_t, j, 0, params.dest_num_dims) { index_in_input += dst_index[j] * stride[j]; } - output_buf[i] = input_buf[index_in_input]; - } -} - -template -__global__ void AsStridedGrad_kernel(const T* dy_buf, T* dx_buf, - AsStridedParams params) { - const int64_t* dest_dims = reinterpret_cast(params.dest_dims); - const int32_t* stride = reinterpret_cast(params.stride); - CUDA_1D_KERNEL_LOOP_T(int64_t, i, params.output_num) { - int64_t dy_index[NUM_DIM]; - params.destIndexOffsetHelper.OffsetToNdIndex(i, dy_index, params.dest_num_dims); - int32_t index_in_dx = params.storage_offset; - FOR_RANGE(int64_t, j, 0, params.dest_num_dims) { index_in_dx += dy_index[j] * stride[j]; } - cuda::atomic::Add(dx_buf + index_in_dx, dy_buf[i]); - } -} - -template -struct AsStridedFunctor final { - void operator()(ep::Stream* stream, const T* input_buf, T* output_buf, const int64_t* dest_dims, - const int32_t* stride, const int32_t dest_num_dims, const int32_t storage_offset, - const int32_t input_num, const int32_t output_num) { - NdIndexOffsetHelper destIndexOffsetHelper(dest_dims, dest_num_dims); - AsStridedParams params; - params.destIndexOffsetHelper = destIndexOffsetHelper; - FOR_RANGE(size_t, i, 0, dest_num_dims) { - params.dest_dims[i] = dest_dims[i]; - params.stride[i] = stride[i]; - } - params.dest_num_dims = dest_num_dims; - params.storage_offset = storage_offset; - params.input_num = input_num; - params.output_num = output_num; - - AsStrided_kernel - <<As()->cuda_stream()>>>(input_buf, output_buf, params); - } -}; - -template -struct AsStridedGradFunctor final { - void operator()(ep::Stream* stream, const T* dy_buf, T* dx_buf, const int64_t* dy_dims, - const int32_t* stride, const int32_t dy_num_dims, const int32_t storage_offset, - const int32_t dx_num, const int32_t dy_num) { - NdIndexOffsetHelper dyIndexOffsetHelper(dy_dims, dy_num_dims); - AsStridedParams params; - params.destIndexOffsetHelper = dyIndexOffsetHelper; - FOR_RANGE(size_t, i, 0, dy_num_dims) { - params.dest_dims[i] = dy_dims[i]; - params.stride[i] = stride[i]; - } - params.dest_num_dims = dy_num_dims; - params.storage_offset = storage_offset; - params.input_num = dx_num; - params.output_num = dy_num; - - AsStridedGrad_kernel - <<As()->cuda_stream()>>>(dy_buf, dx_buf, params); - } -}; - -} // namespace - -template -class GpuAsStridedKernel final : public user_op::OpKernel { - public: - GpuAsStridedKernel() = default; - ~GpuAsStridedKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); - user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); - - size_t dest_num_dims = output->shape_view().NumAxes(); - const int64_t* dest_dims = output->shape_view().ptr(); - const size_t input_num = input->shape_view().Count(0); - const size_t output_num = output->shape_view().Count(0); - if (input_num == 0) { - // 0-size tensor - return; - } - - AsStridedFunctor()(ctx->stream(), input->dptr(), output->mut_dptr(), dest_dims, - stride.data(), dest_num_dims, storage_offset, input_num, output_num); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuAsStridedGradKernel final : public user_op::OpKernel { - public: - GpuAsStridedGradKernel() = default; - ~GpuAsStridedGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto size = ctx->Attr>("size"); - const auto stride = ctx->Attr>("stride"); - const int32_t storage_offset = ctx->Attr("storage_offset"); - - size_t dy_num_dims = dy->shape_view().NumAxes(); - const int64_t* dy_dims = dy->shape_view().ptr(); - const size_t dx_num = dx->shape_view().Count(0); - const size_t dy_num = dy->shape_view().Count(0); - - Memset(ctx->stream(), dx->mut_dptr(), 0, - dx->shape_view().Count(0) * sizeof(T)); - - AsStridedGradFunctor()(ctx->stream(), dy->dptr(), dx->mut_dptr(), dy_dims, - stride.data(), dy_num_dims, storage_offset, dx_num, dy_num); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_GPUASSTRIDED_KERNEL(in_type) \ - REGISTER_USER_KERNEL("as_strided") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("as_strided_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)); - -REGISTER_GPUASSTRIDED_KERNEL(half); -REGISTER_GPUASSTRIDED_KERNEL(float); -REGISTER_GPUASSTRIDED_KERNEL(double); -REGISTER_GPUASSTRIDED_KERNEL(int64_t); - -#undef REGISTER_GPUASSTRIDED_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/assign_if_kernel.hip.cpp b/oneflow/user/kernels/assign_if_kernel.hip.cpp deleted file mode 100644 index 3752a71cd8988301bdddcdb2a9ae3f92fbbf6869..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/assign_if_kernel.hip.cpp +++ /dev/null @@ -1,76 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void AssignGpu(int64_t elem_cnt, const C* condition, const T* value, T* ref) { - if (assign_if == (*condition == 0)) { return; } - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { ref[i] = value[i]; } -} - -template -class AssignIfGPUKernel final : public user_op::OpKernel { - public: - AssignIfGPUKernel() = default; - ~AssignIfGPUKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* condition = ctx->Tensor4ArgNameAndIndex("condition", 0); - CHECK_EQ(condition->shape_view().NumAxes(), 1); - CHECK_EQ(condition->shape_view().At(0), 1); - const user_op::Tensor* value = ctx->Tensor4ArgNameAndIndex("value", 0); - user_op::Tensor* ref = ctx->Tensor4ArgNameAndIndex("ref", 0); - if (value->dptr() == ref->dptr()) { return; } - CHECK_EQ(value->shape_view(), ref->shape_view()); - CHECK_EQ(value->data_type(), ref->data_type()); - const size_t elem_cnt = ref->shape_view().elem_cnt(); - AssignGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, condition->dptr(), value->dptr(), ref->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } -}; - -} // namespace - -#define REGISTER_ASSIGN_WITH_CONDITION_VALUE_CUDA_KERNEL(op_type_name, assign_if, condition_type, \ - value_type) \ - REGISTER_USER_KERNEL(op_type_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("condition", 0) == GetDataType::value) \ - && (user_op::HobDataType("value", 0) == GetDataType::value)); - -#define REGISTER_ASSIGN_IF_CUDA_KERNEL(condition_type, value_type) \ - REGISTER_ASSIGN_WITH_CONDITION_VALUE_CUDA_KERNEL( \ - "assign_if", true, OF_PP_PAIR_FIRST(condition_type), OF_PP_PAIR_FIRST(value_type)); \ - REGISTER_ASSIGN_WITH_CONDITION_VALUE_CUDA_KERNEL( \ - "assign_if_not", false, OF_PP_PAIR_FIRST(condition_type), OF_PP_PAIR_FIRST(value_type)) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ASSIGN_IF_CUDA_KERNEL, INT_DATA_TYPE_SEQ, - POD_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/avg_pool_kernel.cpp b/oneflow/user/kernels/avg_pool_kernel.cpp index 95a815b8e29e0aef44b7468b12f2c1d68dc5cf5c..8402e71b59c67f9799eb5c0e81c856c50bb830ed 100644 --- a/oneflow/user/kernels/avg_pool_kernel.cpp +++ b/oneflow/user/kernels/avg_pool_kernel.cpp @@ -394,13 +394,9 @@ class AvgPool3dGradKernel final : public user_op::OpKernel { REGISTER_AVG_POOL_WITH_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_AVG_POOL_WITH_DEVICE(DeviceType::kCUDA) -// TODO: REGISTER_POOL_KERNELS(DeviceType::kCUDA, float16) -#endif -#ifdef WITH_ROCM -REGISTER_AVG_POOL_WITH_DEVICE(DeviceType::kCUDA) -// TODO: REGISTER_POOL_KERNELS(DeviceType::kCUDA, float16) +REGISTER_AVG_POOL_KERNELS(DeviceType::kCUDA, half) #endif OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_AVG_POOL_KERNEL_UTIL, (DeviceType::kCPU), diff --git a/oneflow/user/kernels/avg_pool_kernel.cu b/oneflow/user/kernels/avg_pool_kernel.cu index 20f3d1843bba95d9dd0a18130f7386064248aed3..29b17a708319ee34d1efdbce4a91718336c12d83 100644 --- a/oneflow/user/kernels/avg_pool_kernel.cu +++ b/oneflow/user/kernels/avg_pool_kernel.cu @@ -120,6 +120,94 @@ __launch_bounds__(kBlockSize) __global__ void DoCUDAAvgPool3dBackward( count_include_pad, divisor_override); }; +template +__launch_bounds__(kBlockSize) __global__ + void DoHalfAvgPool1dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const half* src, half* dest, int32_t padding_l, + const int32_t n_batch, const int32_t n_channel, + const int32_t x_length, const int32_t kernel_size_l, + const int32_t stride_l, const bool count_include_pad, + const int32_t divisor_override) { + HalfAvgpool1dForwardCompute(index_helper, elem_num, src, dest, padding_l, n_batch, n_channel, + x_length, kernel_size_l, stride_l, count_include_pad, + divisor_override); +}; + +template +__launch_bounds__(kBlockSize) __global__ + void DoHalfAvgPool2dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const half* src, half* dest, const int32_t padding_h, + const int32_t padding_w, const int32_t n_batch, + const int32_t n_channel, const int32_t x_height, + const int32_t x_width, const int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, + const int32_t divisor_override) { + HalfAvgpool2dForwardCompute(index_helper, elem_num, src, dest, padding_h, padding_w, n_batch, + n_channel, x_height, x_width, kernel_size_h, kernel_size_w, + stride_h, stride_w, count_include_pad, divisor_override); +}; + +template +__launch_bounds__(kBlockSize) __global__ + void DoHalfAvgPool3dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const half* src, half* dest, int32_t padding_t, + const int32_t padding_h, const int32_t padding_w, + const int32_t n_batch, const int32_t n_channel, + const int32_t x_time, const int32_t x_height, const int32_t x_width, + const int32_t kernel_size_t, int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_t, + const int32_t stride_h, const int32_t stride_w, + const bool count_include_pad, const int32_t divisor_override) { + HalfAvgpool3dForwardCompute(index_helper, elem_num, src, dest, padding_t, padding_h, + padding_w, n_batch, n_channel, x_time, x_height, x_width, + kernel_size_t, kernel_size_h, kernel_size_w, stride_t, stride_h, + stride_w, count_include_pad, divisor_override); +}; + +template +__launch_bounds__(kBlockSize) __global__ + void DoHalfAvgPool1dBackward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const half* src, half* dest, const int32_t padding_l, + const int32_t n_batch, const int32_t n_channel, + const int32_t input_length, const int32_t kernel_size_l, + const int32_t stride_l, const bool count_include_pad, + const int32_t divisor_override) { + HalfAvgpool1dBackwardCompute(index_helper, elem_num, src, dest, padding_l, n_batch, + n_channel, input_length, kernel_size_l, stride_l, + count_include_pad, divisor_override); +}; + +template +__launch_bounds__(kBlockSize) __global__ + void DoHalfAvgPool2dBackward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const half* src, half* dest, const int32_t padding_h, + const int32_t padding_w, const int32_t n_batch, + const int32_t n_channel, const int32_t input_height, + const int32_t input_width, const int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, + int32_t divisor_override) { + HalfAvgpool2dBackwardCompute(index_helper, elem_num, src, dest, padding_h, padding_w, + n_batch, n_channel, input_height, input_width, kernel_size_h, + kernel_size_w, stride_h, stride_w, count_include_pad, + divisor_override); +}; + +template +__launch_bounds__(kBlockSize) __global__ void DoHalfAvgPool3dBackward( + const NdIndexOffsetHelper index_helper, IDX elem_num, const half* src, half* dest, + const int32_t padding_t, const int32_t padding_h, const int32_t padding_w, + const int32_t n_batch, const int32_t n_channel, const int32_t x_time, const int32_t x_height, + const int32_t x_width, const int32_t kernel_size_t, const int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_t, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, const int32_t divisor_override) { + HalfAvgpool3dBackwardCompute(index_helper, elem_num, src, dest, padding_t, padding_h, + padding_w, n_batch, n_channel, x_time, x_height, x_width, + kernel_size_t, kernel_size_h, kernel_size_w, stride_t, stride_h, + stride_w, count_include_pad, divisor_override); +}; + template struct AvgPoolKernelUtil { static void Avgpool1dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, @@ -193,7 +281,82 @@ struct AvgPoolKernelUtil { } }; +template +struct AvgPoolKernelUtil { + static void Avgpool1dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool1dForward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[2], params_3d.num_batch(), + params_3d.num_channel(), params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[2], + params_3d.stride_3d()[2], params_3d.count_include_pad(), params_3d.divisor_override()); + } + + static void Avgpool1dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool1dBackward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[2], params_3d.num_batch(), + params_3d.num_channel(), params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[2], + params_3d.stride_3d()[2], params_3d.count_include_pad(), params_3d.divisor_override()); + } + + static void Avgpool2dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool2dForward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[1], params_3d.padding()[2], + params_3d.num_batch(), params_3d.num_channel(), params_3d.GetXShape5D().At(3), + params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], + params_3d.stride_3d()[1], params_3d.stride_3d()[2], params_3d.count_include_pad(), + params_3d.divisor_override()); + } + + static void Avgpool2dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool2dBackward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[1], params_3d.padding()[2], + params_3d.num_batch(), params_3d.num_channel(), params_3d.GetXShape5D().At(3), + params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], + params_3d.stride_3d()[1], params_3d.stride_3d()[2], params_3d.count_include_pad(), + params_3d.divisor_override()); + } + + static void Avgpool3dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool3dForward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[0], params_3d.padding()[1], + params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), + params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), + params_3d.pool_size_3d()[0], params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], + params_3d.stride_3d()[0], params_3d.stride_3d()[1], params_3d.stride_3d()[2], + params_3d.count_include_pad(), params_3d.divisor_override()); + } + + static void Avgpool3dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d) { + DoHalfAvgPool3dBackward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, params_3d.padding()[0], params_3d.padding()[1], + params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), + params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), + params_3d.pool_size_3d()[0], params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], + params_3d.stride_3d()[0], params_3d.stride_3d()[1], params_3d.stride_3d()[2], + params_3d.count_include_pad(), params_3d.divisor_override()); + } +}; + OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_AVG_POOL_KERNEL_UTIL, (DeviceType::kCUDA), AVG_POOL_DATA_TYPE_CUDA_SEQ, AVG_POOL_IDX_DATA_TYPE_SEQ); +template struct AvgPoolKernelUtil; +template struct AvgPoolKernelUtil; } // namespace oneflow diff --git a/oneflow/user/kernels/avg_pool_kernel.hip.cpp b/oneflow/user/kernels/avg_pool_kernel.hip.cpp deleted file mode 100644 index e2abbff0626aecc00969619afc71ba299c97a4d9..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/avg_pool_kernel.hip.cpp +++ /dev/null @@ -1,200 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/avg_pool_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -constexpr int kBlockSize = cuda::elementwise::kBlockSize; - -int GetMinThreadNum(const int64_t elem_num) { return std::min(elem_num, kBlockSize); } - -int GetNumBlocks(int32_t elem_cnt) { - int num_blocks = 0; - OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); - return num_blocks; -} - -} // namespace - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAAvgPool1dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int32_t padding_l, const int32_t n_batch, - const int32_t n_channel, const int32_t x_length, - const int32_t kernel_size_l, const int32_t stride_l, - const bool count_include_pad, const int32_t divisor_override) { - Avgpool1dForwardCompute(index_helper, elem_num, src, dest, padding_l, n_batch, n_channel, - x_length, kernel_size_l, stride_l, count_include_pad, - divisor_override); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAAvgPool2dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, const int32_t padding_h, - const int32_t padding_w, const int32_t n_batch, - const int32_t n_channel, const int32_t x_height, - const int32_t x_width, const int32_t kernel_size_h, - const int32_t kernel_size_w, const int32_t stride_h, - const int32_t stride_w, const bool count_include_pad, - const int32_t divisor_override) { - Avgpool2dForwardCompute(index_helper, elem_num, src, dest, padding_h, padding_w, n_batch, - n_channel, x_height, x_width, kernel_size_h, kernel_size_w, stride_h, - stride_w, count_include_pad, divisor_override); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAAvgPool3dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int32_t padding_t, const int32_t padding_h, - const int32_t padding_w, const int32_t n_batch, - const int32_t n_channel, const int32_t x_time, - const int32_t x_height, const int32_t x_width, - const int32_t kernel_size_t, int32_t kernel_size_h, - const int32_t kernel_size_w, const int32_t stride_t, - const int32_t stride_h, const int32_t stride_w, - const bool count_include_pad, const int32_t divisor_override) { - Avgpool3dForwardCompute(index_helper, elem_num, src, dest, padding_t, padding_h, padding_w, - n_batch, n_channel, x_time, x_height, x_width, kernel_size_t, - kernel_size_h, kernel_size_w, stride_t, stride_h, stride_w, - count_include_pad, divisor_override); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAAvgPool1dBackward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, const int32_t padding_l, - const int32_t n_batch, const int32_t n_channel, - const int32_t input_length, const int32_t kernel_size_l, - const int32_t stride_l, const bool count_include_pad, - const int32_t divisor_override) { - Avgpool1dBackwardCompute(index_helper, elem_num, src, dest, padding_l, n_batch, n_channel, - input_length, kernel_size_l, stride_l, count_include_pad, - divisor_override); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAAvgPool2dBackward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, const int32_t padding_h, - const int32_t padding_w, const int32_t n_batch, - const int32_t n_channel, const int32_t input_height, - const int32_t input_width, const int32_t kernel_size_h, - const int32_t kernel_size_w, const int32_t stride_h, - const int32_t stride_w, const bool count_include_pad, - int32_t divisor_override) { - Avgpool2dBackwardCompute(index_helper, elem_num, src, dest, padding_h, padding_w, n_batch, - n_channel, input_height, input_width, kernel_size_h, kernel_size_w, - stride_h, stride_w, count_include_pad, divisor_override); -}; - -template -__launch_bounds__(kBlockSize) __global__ void DoCUDAAvgPool3dBackward( - const NdIndexOffsetHelper index_helper, IDX elem_num, const T* src, T* dest, - const int32_t padding_t, const int32_t padding_h, const int32_t padding_w, - const int32_t n_batch, const int32_t n_channel, const int32_t x_time, const int32_t x_height, - const int32_t x_width, const int32_t kernel_size_t, const int32_t kernel_size_h, - const int32_t kernel_size_w, const int32_t stride_t, const int32_t stride_h, - const int32_t stride_w, const bool count_include_pad, const int32_t divisor_override) { - Avgpool3dBackwardCompute(index_helper, elem_num, src, dest, padding_t, padding_h, padding_w, - n_batch, n_channel, x_time, x_height, x_width, kernel_size_t, - kernel_size_h, kernel_size_w, stride_t, stride_h, stride_w, - count_include_pad, divisor_override); -}; - -template -struct AvgPoolKernelUtil { - static void Avgpool1dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool1dForward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[2], params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[2], - params_3d.stride_3d()[2], params_3d.count_include_pad(), params_3d.divisor_override()); - } - - static void Avgpool1dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool1dBackward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[2], params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[2], - params_3d.stride_3d()[2], params_3d.count_include_pad(), params_3d.divisor_override()); - } - - static void Avgpool2dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool2dForward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[1], params_3d.padding()[2], - params_3d.num_batch(), params_3d.num_channel(), params_3d.GetXShape5D().At(3), - params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], - params_3d.stride_3d()[1], params_3d.stride_3d()[2], params_3d.count_include_pad(), - params_3d.divisor_override()); - } - - static void Avgpool2dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool2dBackward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[1], params_3d.padding()[2], - params_3d.num_batch(), params_3d.num_channel(), params_3d.GetXShape5D().At(3), - params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], - params_3d.stride_3d()[1], params_3d.stride_3d()[2], params_3d.count_include_pad(), - params_3d.divisor_override()); - } - - static void Avgpool3dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool3dForward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[0], params_3d.padding()[1], - params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), - params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), - params_3d.pool_size_3d()[0], params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], - params_3d.stride_3d()[0], params_3d.stride_3d()[1], params_3d.stride_3d()[2], - params_3d.count_include_pad(), params_3d.divisor_override()); - } - - static void Avgpool3dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const AvgPoolParams3D& params_3d) { - DoCUDAAvgPool3dBackward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, params_3d.padding()[0], params_3d.padding()[1], - params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), - params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), - params_3d.pool_size_3d()[0], params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], - params_3d.stride_3d()[0], params_3d.stride_3d()[1], params_3d.stride_3d()[2], - params_3d.count_include_pad(), params_3d.divisor_override()); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_AVG_POOL_KERNEL_UTIL, (DeviceType::kCUDA), - AVG_POOL_DATA_TYPE_CUDA_SEQ, AVG_POOL_IDX_DATA_TYPE_SEQ); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/avg_pool_kernel_util.h b/oneflow/user/kernels/avg_pool_kernel_util.h index 90ab092ff070e04808d01fce85bffc3677afa2e6..b6cce61367b33c2b44a1a5a34862fb7dff0ba67d 100644 --- a/oneflow/user/kernels/avg_pool_kernel_util.h +++ b/oneflow/user/kernels/avg_pool_kernel_util.h @@ -20,17 +20,11 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/common/nd_index_offset_helper.h" #include "oneflow/core/operator/operator_util.h" - -#ifdef WITH_CUDA #include "oneflow/core/kernel/util/numerics.cuh" #include "oneflow/core/kernel/util/numeric_limits.cuh" +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/cuda/atomic.cuh" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/kernel/util/numerics.hip.h" -#include "oneflow/core/kernel/util/numeric_limits.hip.h" -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_ROCM namespace oneflow { @@ -408,6 +402,301 @@ OF_DEVICE_FUNC void Avgpool3dBackwardCompute( } } +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template +struct AvgPoolKernelUtil { + static void Avgpool1dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); + + static void Avgpool1dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); + + static void Avgpool2dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); + + static void Avgpool2dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); + + static void Avgpool3dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); + + static void Avgpool3dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const half* src, half* dest, + const AvgPoolParams3D& params_3d); +}; + +template +OF_DEVICE_FUNC void HalfAvgpool1dForwardCompute(const NdIndexOffsetHelper index_helper, + IDX elem_num, const half* src, half* dest, + const int32_t padding_l, const int32_t n_batch, + const int32_t n_channel, const int32_t x_length, + const int32_t kernel_size_l, const int32_t stride_l, + const bool count_include_pad, + const int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, l; + index_helper.OffsetToNdIndex(num, n_c, l); + + const IDX start_idx = n_c * x_length; + IDX lstart = l * stride_l - padding_l; + IDX lend = XPU_INT_MIN(lstart + kernel_size_l, x_length + padding_l); + const IDX pool_size = (lend - lstart); + + lstart = XPU_INT_MAX(0, lstart); + lend = XPU_INT_MIN(lend, x_length); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (lend - lstart); + } + } + float sum = 0; + + const half* data = src + start_idx; + for (IDX idx = lstart; idx < lend; idx += 1) { sum += __half2float(data[idx]); } + dest[num] = __float2half(sum / divide_factor); + } +} + +template +OF_DEVICE_FUNC void HalfAvgpool1dBackwardCompute( + const NdIndexOffsetHelper index_helper, IDX elem_num, const half* src, half* dest, + const int32_t padding_l, const int32_t n_batch, const int32_t n_channel, + const int32_t input_length, const int32_t kernel_size_l, const int32_t stride_l, + const bool count_include_pad, const int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, l; + index_helper.OffsetToNdIndex(num, n_c, l); + + const IDX start_idx = n_c * input_length; + IDX lstart = l * stride_l - padding_l; + IDX lend = XPU_INT_MIN(lstart + kernel_size_l, input_length + padding_l); + const IDX pool_size = (lend - lstart); + + lstart = XPU_INT_MAX(IDX(0), lstart); + lend = XPU_INT_MIN(lend, input_length); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (lend - lstart); + } + } + half grad_delta = static_cast(__half2float(src[num]) / divide_factor); + half* data = dest + start_idx; + for (IDX idx = lstart; idx < lend; idx += 1) { XPUAdd::Invoke(&grad_delta, &data[idx]); } + } +} + +template +OF_DEVICE_FUNC void HalfAvgpool2dForwardCompute( + const NdIndexOffsetHelper index_helper, int64_t elem_num, const half* src, half* dest, + const int32_t padding_h, const int32_t padding_w, const int32_t n_batch, + const int32_t n_channel, const int32_t x_height, const int32_t x_width, + const int32_t kernel_size_h, const int32_t kernel_size_w, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, h, w; + index_helper.OffsetToNdIndex(num, n_c, h, w); + + const IDX start_idx = n_c * x_width * x_height; + IDX hstart = h * stride_h - padding_h; + IDX wstart = w * stride_w - padding_w; + + IDX hend = XPU_INT_MIN(hstart + kernel_size_h, x_height + padding_h); + IDX wend = XPU_INT_MIN(wstart + kernel_size_w, x_width + padding_w); + const IDX pool_size = (hend - hstart) * (wend - wstart); + + hstart = XPU_INT_MAX(0, hstart); + wstart = XPU_INT_MAX(0, wstart); + hend = XPU_INT_MIN(hend, x_height); + wend = XPU_INT_MIN(wend, x_width); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (hend - hstart) * (wend - wstart); + } + } + float sum = 0; + const half* data = src + start_idx; + for (int64_t i = hstart; i < hend; i += 1) { + for (int64_t j = wstart; j < wend; j += 1) { + const IDX window_idx = i * x_width + j; + sum += __half2float(data[window_idx]); + } + } + dest[num] = __float2half(sum / divide_factor); + } +} + +template +OF_DEVICE_FUNC void HalfAvgpool2dBackwardCompute( + const NdIndexOffsetHelper index_helper, IDX elem_num, const half* src, half* dest, + const int32_t padding_h, const int32_t padding_w, const int32_t n_batch, + const int32_t n_channel, const int32_t input_height, const int32_t input_width, + const int32_t kernel_size_h, const int32_t kernel_size_w, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, h, w; + index_helper.OffsetToNdIndex(num, n_c, h, w); + + const IDX start_idx = n_c * input_width * input_height; + IDX hstart = h * stride_h - padding_h; + IDX wstart = w * stride_w - padding_w; + IDX hend = XPU_INT_MIN(hstart + kernel_size_h, input_height + padding_h); + IDX wend = XPU_INT_MIN(wstart + kernel_size_w, input_width + padding_w); + const IDX pool_size = (hend - hstart) * (wend - wstart); + + hstart = XPU_INT_MAX(IDX(0), hstart); + wstart = XPU_INT_MAX(IDX(0), wstart); + hend = XPU_INT_MIN(hend, input_height); + wend = XPU_INT_MIN(wend, input_width); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (hend - hstart) * (wend - wstart); + } + } + half grad_delta = static_cast(__half2float(src[num]) / divide_factor); + half* data = dest + start_idx; + for (IDX i = hstart; i < hend; i += 1) { + for (IDX j = wstart; j < wend; j += 1) { + const IDX window_idx = i * input_width + j; + XPUAdd::Invoke(&grad_delta, &data[window_idx]); + } + } + } +} + +template +OF_DEVICE_FUNC void HalfAvgpool3dForwardCompute( + const NdIndexOffsetHelper index_helper, IDX elem_num, const half* src, half* dest, + const int32_t padding_t, const int32_t padding_h, const int32_t padding_w, + const int32_t n_batch, const int32_t n_channel, const int32_t x_time, const int32_t x_height, + const int32_t x_width, const int32_t kernel_size_t, const int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_t, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, t, h, w; + index_helper.OffsetToNdIndex(num, n_c, t, h, w); + + const IDX start_idx = n_c * x_time * x_height * x_width; + IDX tstart = t * stride_t - padding_t; + IDX hstart = h * stride_h - padding_h; + IDX wstart = w * stride_w - padding_w; + IDX tend = XPU_INT_MIN(tstart + kernel_size_t, x_time + padding_t); + IDX hend = XPU_INT_MIN(hstart + kernel_size_h, x_height + padding_h); + IDX wend = XPU_INT_MIN(wstart + kernel_size_w, x_width + padding_w); + const IDX pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart); + + tstart = XPU_INT_MAX(IDX(0), tstart); + hstart = XPU_INT_MAX(IDX(0), hstart); + wstart = XPU_INT_MAX(IDX(0), wstart); + tend = XPU_INT_MIN(tend, x_time); + hend = XPU_INT_MIN(hend, x_height); + wend = XPU_INT_MIN(wend, x_width); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (tend - tstart) * (hend - hstart) * (wend - wstart); + } + } + float sum = 0; + const half* data = src + start_idx; + for (IDX i = tstart; i < tend; i += 1) { + for (IDX j = hstart; j < hend; j += 1) { + for (IDX k = wstart; k < wend; k += 1) { + const IDX window_idx = i * x_height * x_width + j * x_width + k; + sum += __half2float(data[window_idx]); + } + } + } + dest[num] = __float2half(sum / divide_factor); + } +} + +template +OF_DEVICE_FUNC void HalfAvgpool3dBackwardCompute( + const NdIndexOffsetHelper index_helper, IDX elem_num, const half* src, half* dest, + const int32_t padding_t, const int32_t padding_h, const int32_t padding_w, + const int32_t n_batch, const int32_t n_channel, const int32_t x_time, const int32_t x_height, + const int32_t x_width, const int32_t kernel_size_t, const int32_t kernel_size_h, + const int32_t kernel_size_w, const int32_t stride_t, const int32_t stride_h, + const int32_t stride_w, const bool count_include_pad, const int32_t divisor_override) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX n_c, t, h, w; + index_helper.OffsetToNdIndex(num, n_c, t, h, w); + + const IDX start_idx = n_c * x_time * x_width * x_height; + IDX tstart = t * stride_t - padding_t; + IDX hstart = h * stride_h - padding_h; + IDX wstart = w * stride_w - padding_w; + IDX tend = XPU_INT_MIN(tstart + kernel_size_t, x_time + padding_t); + IDX hend = XPU_INT_MIN(hstart + kernel_size_h, x_height + padding_h); + IDX wend = XPU_INT_MIN(wstart + kernel_size_w, x_width + padding_w); + const IDX pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart); + + tstart = XPU_INT_MAX(IDX(0), tstart); + hstart = XPU_INT_MAX(IDX(0), hstart); + wstart = XPU_INT_MAX(IDX(0), wstart); + tend = XPU_INT_MIN(tend, x_time); + hend = XPU_INT_MIN(hend, x_height); + wend = XPU_INT_MIN(wend, x_width); + + IDX divide_factor; + if (divisor_override != static_cast(0)) { + divide_factor = divisor_override; + } else { + if (count_include_pad) { + divide_factor = pool_size; + } else { + divide_factor = (tend - tstart) * (hend - hstart) * (wend - wstart); + } + } + half grad_delta = static_cast(__half2float(src[num]) / divide_factor); + half* data = dest + start_idx; + for (IDX i = tstart; i < tend; i += 1) { + for (IDX j = hstart; j < hend; j += 1) { + for (IDX k = wstart; k < wend; k += 1) { + const IDX window_idx = i * x_height * x_width + j * x_width + k; + XPUAdd::Invoke(&grad_delta, &data[window_idx]); + } + } + } + } +} + +#endif // WITH_CUDA + #define INSTANTIATE_AVG_POOL_KERNEL_UTIL(device_type_v, dtype_pair, index_dtype_pair) \ template struct AvgPoolKernelUtil; diff --git a/oneflow/user/kernels/batch_gather_kernel_util.hip.cpp b/oneflow/user/kernels/batch_gather_kernel_util.hip.cpp deleted file mode 100644 index 547fea4a368b2043104b4ceaa24ba764be3e6366..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/batch_gather_kernel_util.hip.cpp +++ /dev/null @@ -1,103 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/batch_gather_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace { - -template -__device__ int64_t GetInOffset(const int64_t out_offset, const K* indices, - const int64_t indices_num, const int64_t instance_size, - const int64_t gather_dim_size) { - const int64_t batch_idx = out_offset / (indices_num * instance_size); - const int64_t indices_idx = out_offset % (indices_num * instance_size) / instance_size; - const int64_t inner_idx = out_offset % instance_size; - const int64_t idx = indices[batch_idx * indices_num + indices_idx]; - assert(idx >= 0 && idx < gather_dim_size); - return batch_idx * gather_dim_size * instance_size + idx * instance_size + inner_idx; -} - -template -__global__ void BatchGatherForwardGpu(const int64_t elem_cnt, const T* in, const K* indices, - const int64_t indices_num, const int64_t instance_size, - const int64_t gather_dim_size, T* out) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - out[i] = in[GetInOffset(i, indices, indices_num, instance_size, gather_dim_size)]; - } -} - -template -__global__ void BatchGatherBackwardGpu(const int64_t elem_cnt, const T* out_diff, const K* indices, - const int64_t indices_num, const int64_t instance_size, - const int64_t gather_dim_size, T* in_diff) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - cuda::atomic::Add( - in_diff + GetInOffset(i, indices, indices_num, instance_size, gather_dim_size), - out_diff[i]); - } -} - -} // namespace - -template -struct BatchGatherKernelUtilImpl final { - static void Forward(ep::Stream* stream, const T* in, const K* indices, - const Shape& flat_out_shape, const int64_t gather_dim_size, T* out); - static void Backward(ep::Stream* stream, const T* out_diff, const K* indices, - const Shape& flat_out_diff_shape, const int64_t gather_dim_size, T* in_diff); -}; - -template -void BatchGatherKernelUtilImpl::Forward(ep::Stream* stream, const T* in, - const K* indices, - const Shape& flat_out_shape, - const int64_t gather_dim_size, - T* out) { - const int64_t batch_num = flat_out_shape.At(0); - const int64_t indices_num = flat_out_shape.At(1); - const int64_t instance_size = flat_out_shape.At(2); - const int64_t elem_cnt = batch_num * indices_num * instance_size; - BatchGatherForwardGpu<<As()->cuda_stream()>>>( - elem_cnt, in, indices, indices_num, instance_size, gather_dim_size, out); -} - -template -void BatchGatherKernelUtilImpl::Backward( - ep::Stream* stream, const T* out_diff, const K* indices, const Shape& flat_out_diff_shape, - const int64_t gather_dim_size, T* in_diff) { - const int64_t batch_num = flat_out_diff_shape.At(0); - const int64_t indices_num = flat_out_diff_shape.At(1); - const int64_t instance_size = flat_out_diff_shape.At(2); - const int64_t elem_cnt = batch_num * indices_num * instance_size; - BatchGatherBackwardGpu<<As()->cuda_stream()>>>( - elem_cnt, out_diff, indices, indices_num, instance_size, gather_dim_size, in_diff); -} - -#define INSTANTIATE_BATCH_GATHER_KERNEL_UTIL_IMPL_CUDA(in_type_pair, index_type_pair) \ - template struct BatchGatherKernelUtilImpl; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_BATCH_GATHER_KERNEL_UTIL_IMPL_CUDA, - FLOATING_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ); -#undef INSTANTIATE_BATCH_GATHER_KERNEL_UTIL_IMPL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/batch_norm_backward_elemt_kernel.cu b/oneflow/user/kernels/batch_norm_backward_elemt_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..80d6fa5ebea973ebca213d8ab932e491f98ad4b4 --- /dev/null +++ b/oneflow/user/kernels/batch_norm_backward_elemt_kernel.cu @@ -0,0 +1,255 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/user/kernels/batch_norm_kernel_utils.h" + +// NOTE(Liang Depeng): +// The implementation of batch_norm_backward_elemt kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +namespace oneflow { + +namespace { + +template +__global__ void batch_norm_backward_elemt_kernel( + const IDX_TYPE batch_size, const IDX_TYPE channel_size, const IDX_TYPE spatial_size, + const T* grad_out_ptr, const T* input_ptr, const T* mean_ptr, const T* invstd_ptr, + const T* weight_ptr, const T* sum_dy_ptr, const T* sum_dy_xmu_ptr, T* grad_in_ptr, + const int32_t* count_ptr, const int64_t world_size) { + int64_t total_numel = 0; + for (int i = 0; i < world_size; i++) { total_numel += count_ptr[i]; } + + const ACC_T norm_fct = static_cast(1) / static_cast(total_numel); + + IDX_TYPE channel = blockIdx.x; + + if (channel >= channel_size) { return; } + + ACC_T m_c = mean_ptr[channel]; + ACC_T m_dy_c = sum_dy_ptr[channel] * norm_fct; + ACC_T factor_1_c = invstd_ptr[channel]; + ACC_T factor_2_c = static_cast(weight_ptr[channel]); + factor_2_c *= factor_1_c; + factor_1_c = factor_1_c * factor_1_c * sum_dy_xmu_ptr[channel] * norm_fct; + + IDX_TYPE batch_offset = spatial_size * channel_size; + IDX_TYPE channel_offset = channel * spatial_size; + + IDX_TYPE bstep = blockDim.y * gridDim.y; + for (IDX_TYPE batch = threadIdx.y + blockIdx.y * blockDim.y; batch < batch_size; batch += bstep) { + IDX_TYPE offset = batch * batch_offset; + for (IDX_TYPE feature = threadIdx.x; feature < spatial_size; feature += blockDim.x) { + grad_in_ptr[offset + channel_offset + feature] = + static_cast((grad_out_ptr[offset + channel_offset + feature] - m_dy_c + - (input_ptr[offset + channel_offset + feature] - m_c) * factor_1_c) + * factor_2_c); + } + } +} + +template +__global__ void batch_norm_backward_elemt_channels_last_kernel( + const T* grad_out_ptr, const T* input_ptr, const ACC_T* mean_ptr, const ACC_T* invstd_ptr, + const T* weight_ptr, const ACC_T* sum_dy_ptr, const ACC_T* sum_dy_xmu_ptr, + const int32_t* count_ptr, T* grad_in_ptr, const IDX_TYPE world_size, const IDX_TYPE stride, + const IDX_TYPE reduction_size) { + IDX_TYPE total_numel = 0; + for (IDX_TYPE i = 0; i < world_size; i++) { total_numel += count_ptr[i]; } + + auto norm_fct = static_cast(1) / static_cast(total_numel); + + // tensor dimension (m,c) + // loop along m dimension + IDX_TYPE inner_loop_stride = blockDim.y * gridDim.y; + + // offset along m dimension + IDX_TYPE m_offset = blockIdx.y * blockDim.y + threadIdx.y; + IDX_TYPE c_offset = blockIdx.x * blockDim.x + threadIdx.x; + + if (c_offset >= stride || m_offset >= reduction_size) { return; } + + auto m_c = mean_ptr[c_offset]; + auto m_dy_c = sum_dy_ptr[c_offset] * norm_fct; + auto factor_1_c = invstd_ptr[c_offset]; + auto factor_2_c = + (weight_ptr == nullptr ? ACC_T(1.0) : static_cast(weight_ptr[c_offset])) * factor_1_c; + factor_1_c = factor_1_c * factor_1_c * sum_dy_xmu_ptr[c_offset] * norm_fct; + + int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS); + int address_base = m_offset * stride + c_offset; + int address_increment = inner_loop_stride * stride; + + for (int i = 0; i < loop_count; i++) { +#pragma unroll + for (int j = 0; j < PARALLEL_LOADS; j++) { + if (c_offset < stride && m_offset < reduction_size) { + grad_in_ptr[address_base] = + static_cast((static_cast(grad_out_ptr[address_base]) - m_dy_c + - (static_cast(input_ptr[address_base]) - m_c) * factor_1_c) + * factor_2_c); + } + m_offset += inner_loop_stride; + address_base += address_increment; + } + } +} + +template +struct BatchNormBackwardElemtFunctor final { + void operator()(ep::Stream* stream, const int64_t batch_size, const int64_t channel_size, + const int64_t spatial_size, const T* grad_out_ptr, const T* input_ptr, + const T* mean_ptr, const T* invstd_ptr, const T* weight_ptr, const T* sum_dy_ptr, + const T* sum_dy_xmu_ptr, T* grad_in_ptr, const int32_t* count_ptr, + const int64_t world_size) { + using ACC_T = acc_type; + + // The kernel is pointwise, but we need to balance reading parameters (save_var/mean, + // weight/bias) - which we only do once and have a for loop afterwards - with having many + // threads and blocks and good occupancy. Quiet likely, we could go with even more blocks than + // 1024. The various planes are independent, so we use blocks for them. + int tf = std::max(getNumThreads(spatial_size / 4), + std::min(getNumThreads(spatial_size), 64)); + int tb = std::max(64 / tf, 1); + dim3 blocks_trans(channel_size, std::max(1, std::min((256 * 1024) / channel_size, + (batch_size + tb - 1) / tb))); + blocks_trans.y = std::min(blocks_trans.y, MAX_GRID_SIZE); + dim3 threads_trans(tf, tb); + + if (batch_size * channel_size * spatial_size < std::numeric_limits::max()) { + batch_norm_backward_elemt_kernel + <<As()->cuda_stream()>>>( + static_cast(batch_size), static_cast(channel_size), + static_cast(spatial_size), grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, grad_in_ptr, count_ptr, world_size); + } else { + batch_norm_backward_elemt_kernel + <<As()->cuda_stream()>>>( + batch_size, channel_size, spatial_size, grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, grad_in_ptr, count_ptr, world_size); + } + } +}; + +template +struct BatchNormBackwardElemtChannelLastFunctor final { + void operator()(ep::Stream* stream, const int64_t stride, const int64_t reduction_size, + const T* grad_out_ptr, const T* input_ptr, const T* mean_ptr, const T* invstd_ptr, + const T* weight_ptr, const T* sum_dy_ptr, const T* sum_dy_xmu_ptr, T* grad_in_ptr, + const int32_t* count_ptr, const int64_t world_size) { + using ACC_T = acc_type; + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid); + + if (stride * reduction_size < std::numeric_limits::max()) { + batch_norm_backward_elemt_channels_last_kernel + <<As()->cuda_stream()>>>( + grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, + count_ptr, grad_in_ptr, world_size, static_cast(stride), + static_cast(reduction_size)); + } else { + batch_norm_backward_elemt_channels_last_kernel + <<As()->cuda_stream()>>>( + grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, + count_ptr, grad_in_ptr, world_size, stride, reduction_size); + } + } +}; + +} // namespace + +template +class GpuBatchNormBackwardElemtKernel final : public user_op::OpKernel { + public: + GpuBatchNormBackwardElemtKernel() = default; + ~GpuBatchNormBackwardElemtKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* grad_out = ctx->Tensor4ArgNameAndIndex("grad_out", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* invstd = ctx->Tensor4ArgNameAndIndex("invstd", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* sum_dy = ctx->Tensor4ArgNameAndIndex("sum_dy", 0); + const user_op::Tensor* sum_dy_xmu = ctx->Tensor4ArgNameAndIndex("sum_dy_xmu", 0); + const user_op::Tensor* count = ctx->Tensor4ArgNameAndIndex("count", 0); + + user_op::Tensor* grad_in = ctx->Tensor4ArgNameAndIndex("grad_in", 0); + + const T* grad_out_ptr = grad_out->dptr(); + const T* input_ptr = input->dptr(); + const T* mean_ptr = mean->dptr(); + const T* invstd_ptr = invstd->dptr(); + const T* weight_ptr = weight->dptr(); + const T* sum_dy_ptr = sum_dy->dptr(); + const T* sum_dy_xmu_ptr = sum_dy_xmu->dptr(); + const int32_t* count_ptr = count->dptr(); + + T* grad_in_ptr = grad_in->mut_dptr(); + const int32_t axis = ctx->Attr("axis"); + + bool use_channels_last_kernel = axis == 1 ? false : true; + const int64_t world_size = count->shape_view().elem_cnt(); + if (use_channels_last_kernel) { // NHWC format + const int64_t stride = input->shape_view().At(axis); + const int64_t reduction_size = input->shape_view().elem_cnt() / stride; + BatchNormBackwardElemtChannelLastFunctor()( + ctx->stream(), stride, reduction_size, grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, grad_in_ptr, count_ptr, world_size); + } else { // NCHW format + const int64_t batch_size = input->shape_view().At(0); + const int64_t channel_size = input->shape_view().At(1); + const int64_t spatial_size = input->shape_view().Count(2); + + BatchNormBackwardElemtFunctor()( + ctx->stream(), batch_size, channel_size, spatial_size, grad_out_ptr, input_ptr, mean_ptr, + invstd_ptr, weight_ptr, sum_dy_ptr, sum_dy_xmu_ptr, grad_in_ptr, count_ptr, world_size); + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_BATCH_NORM_BACKWARD_ELEMT_KERNEL(dtype) \ + REGISTER_USER_KERNEL("batch_norm_backward_elemt") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("grad_out", 0) == GetDataType::value) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("mean", 0) == GetDataType::value) \ + && (user_op::HobDataType("invstd", 0) == GetDataType::value) \ + && (user_op::HobDataType("weight", 0) == GetDataType::value) \ + && (user_op::HobDataType("sum_dy", 0) == GetDataType::value) \ + && (user_op::HobDataType("sum_dy_xmu", 0) == GetDataType::value) \ + && (user_op::HobDataType("count", 0) == GetDataType::value)) + +REGISTER_BATCH_NORM_BACKWARD_ELEMT_KERNEL(float); +REGISTER_BATCH_NORM_BACKWARD_ELEMT_KERNEL(double); + +} // namespace oneflow diff --git a/oneflow/user/kernels/batch_norm_backward_reduce_kernel.cu b/oneflow/user/kernels/batch_norm_backward_reduce_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..2e026f2dd49383e5c29709c6786d662d27323e72 --- /dev/null +++ b/oneflow/user/kernels/batch_norm_backward_reduce_kernel.cu @@ -0,0 +1,439 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/user/kernels/batch_norm_kernel_utils.h" + +// NOTE(Liang Depeng): +// The implementation of batch_norm_backward_reduce kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +namespace oneflow { + +namespace { + +template +static size_t InferTmpSizeForChannelLastKernel(user_op::InferContext* ctx) { + const int32_t axis = ctx->Attr("axis"); + const Shape& in_shape = ctx->InputTensorDesc("input", 0).shape(); + const int64_t stride = in_shape.At(axis); + const int64_t reduction_size = in_shape.elem_cnt() / stride; + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid, true); + size_t tmp_size = 0; + if (grid.y > 1) { + tmp_size += 2 * stride * grid.y * sizeof(T); + tmp_size += grid.x * sizeof(int32_t); + } + return tmp_size; +} + +template +struct Float2 { + ACC_T v1, v2; + __device__ Float2() {} + __device__ Float2(T v1, T v2) : v1(static_cast(v1)), v2(static_cast(v2)) {} + __device__ Float2(int v) : v1(static_cast(v)), v2(static_cast(v)) {} + __device__ Float2& operator+=(const Float2& a) { + v1 += a.v1; + v2 += a.v2; + return *this; + } +}; + +// Sum across all threads within a warp +template +static __device__ __forceinline__ T warpSum_(T val) { + for (int i = 0; i < getMSB(WARP_SIZE); ++i) { val += WARP_SHFL_XOR(val, 1 << i, WARP_SIZE); } + return val; +} + +template +static __device__ __forceinline__ RES_T warpSum(RES_T value) { + value.v1 = warpSum_(value.v1); + value.v2 = warpSum_(value.v2); + return value; +} + +template +__device__ RES_T reduce(const T* input_ptr, const T* grad_out_ptr, ACC_T r_mean, IDX_TYPE channel, + IDX_TYPE batch_size, IDX_TYPE channel_size, IDX_TYPE spatial_size) { + IDX_TYPE batch_offset = spatial_size * channel_size; + IDX_TYPE channel_offset = channel * spatial_size; + // first the reductions each thread does separately + RES_T sum = static_cast(0); + for (int batch = threadIdx.y; batch < batch_size; batch += blockDim.y) { + IDX_TYPE offset = batch * batch_offset; + for (int x = threadIdx.x; x < spatial_size; x += blockDim.x) { + // sum += op(batch, plane, x); + ACC_T g = grad_out_ptr[offset + channel_offset + x]; + ACC_T c = static_cast(input_ptr[offset + channel_offset + x]) - r_mean; + sum.v1 += g; + sum.v2 += g * c; + } + } + + // first warpSum to get one value per thread to + // one value per warp + sum = warpSum(sum); + + // this writes each warps item into shared memory + // there are at most WARP_SIZE items left because + // there are at most WARP_SIZE**2 threads at the beginning + __shared__ RES_T shared[WARP_SIZE]; + __syncthreads(); + int tid = threadIdx.x + threadIdx.y * blockDim.x; + if (tid % WARP_SIZE == 0) { shared[tid / WARP_SIZE] = sum; } + if (tid >= blockDim.x * blockDim.y / WARP_SIZE && tid < WARP_SIZE) { + // zero out the other entries in shared + shared[tid] = (RES_T)0; + } + __syncthreads(); + // now have a second warpSum to reduce the intermediate values + // from shared memory to a single number. The very first + // thread writes it to shared memory. + + if (tid / WARP_SIZE == 0) { + sum = warpSum(shared[tid]); + if (tid == 0) { shared[0] = sum; } + } + __syncthreads(); + + // Everyone picks it up, should be broadcast into the whole grad_input + return shared[0]; +} + +template +__global__ void batch_norm_backward_reduce_kernel( + const IDX_TYPE batch_size, const IDX_TYPE channel_size, const IDX_TYPE spatial_size, + const T* grad_out_ptr, const T* input_ptr, const T* mean_ptr, const T* invstd_ptr, + T* sum_dy_ptr, T* sum_dy_xmu_ptr, T* grad_weight_ptr, T* grad_bias_ptr) { + IDX_TYPE channel = blockIdx.x; + ACC_T r_mean = mean_ptr[channel]; + ACC_T factor = invstd_ptr[channel]; + + auto res = reduce, T, ACC_T, IDX_TYPE>(input_ptr, grad_out_ptr, r_mean, channel, + batch_size, channel_size, spatial_size); + + if (threadIdx.x == 0) { + if (grad_weight_ptr != nullptr) { grad_weight_ptr[channel] = static_cast(res.v2 * factor); } + if (grad_bias_ptr != nullptr) { grad_bias_ptr[channel] = static_cast(res.v1); } + if (sum_dy_ptr != nullptr) { sum_dy_ptr[channel] = static_cast(res.v1); } + if (sum_dy_xmu_ptr != nullptr) { sum_dy_xmu_ptr[channel] = static_cast(res.v2); } + } +} + +template +__device__ __forceinline__ void merge_block_vertical_backward(T& sum_dy, T& sum_dy_xmu, + T* shmem_sum_dy, + T* shmem_sum_dy_xmu) { + // write to shared memory + auto address_base = threadIdx.x + threadIdx.y * blockDim.x; + +#pragma unroll + for (int offset = blockDim.y / 2; offset > 0; offset >>= 1) { + if (threadIdx.y < offset * 2) { + shmem_sum_dy[address_base] = sum_dy; + shmem_sum_dy_xmu[address_base] = sum_dy_xmu; + } + __syncthreads(); + if (threadIdx.y < offset && threadIdx.y + offset < blockDim.y) { + auto address = address_base + offset * blockDim.x; + + sum_dy += shmem_sum_dy[address]; + sum_dy_xmu += shmem_sum_dy_xmu[address]; + } + } +} + +template +__global__ void batch_norm_backward_reduce_channels_last_kernel( + const T* __restrict__ grad_output_ptr, const T* __restrict__ input_ptr, + const ACC_T* __restrict__ mean_ptr, const ACC_T* __restrict__ inv_std_ptr, + ACC_T* __restrict__ sum_dy_o_ptr, ACC_T* __restrict__ sum_dy_xmu_o_ptr, + T* __restrict__ grad_weight_ptr, T* __restrict__ grad_bias_ptr, + volatile ACC_T* staging_data_ptr, int32_t* semaphores_ptr, const IDX_TYPE reduction_size, + const IDX_TYPE stride) { + // hide latency with concurrency + ACC_T sum_dy[PARALLEL_LOADS]; + ACC_T sum_dy_xmu[PARALLEL_LOADS]; + +#pragma unroll + for (int i = 0; i < PARALLEL_LOADS; i++) { + sum_dy[i] = ACC_T(0); + sum_dy_xmu[i] = ACC_T(0); + } + // tensor dimension (m,c) + + // loop along m dimension + int inner_loop_stride = blockDim.y * gridDim.y; + + // offset along m dimension + int m_offset = blockIdx.y * blockDim.y + threadIdx.y; + int c_offset = blockIdx.x * blockDim.x + threadIdx.x; + + if (c_offset >= stride || m_offset >= reduction_size) { return; } + + int loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS); + int address_base = m_offset * stride + c_offset; + int address_increment = inner_loop_stride * stride; + + auto r_mean = mean_ptr[c_offset]; + auto factor = inv_std_ptr[c_offset]; + + for (int i = 0; i < loop_count; i++) { + ACC_T x_input[PARALLEL_LOADS]; + ACC_T x_grad_output[PARALLEL_LOADS]; + + // load multiple data in +#pragma unroll + for (int j = 0; j < PARALLEL_LOADS; j++) { + if (c_offset < stride && m_offset < reduction_size) { + x_input[j] = input_ptr[address_base]; + x_grad_output[j] = grad_output_ptr[address_base]; + } else { + x_input[j] = ACC_T(0); + x_grad_output[j] = ACC_T(0); + } + m_offset += inner_loop_stride; + address_base += address_increment; + } + + // calculate sum_dy / sum_dy_xmu +#pragma unroll + for (int j = 0; j < PARALLEL_LOADS; j++) { + sum_dy[j] += x_grad_output[j]; + sum_dy_xmu[j] += x_grad_output[j] * (x_input[j] - r_mean); + } + } + + // thread reduction to accumulate sum_dy / sum_dy_xmu between PARALLEL_LOADS +#pragma unroll + for (int j = 1; j < PARALLEL_LOADS; j++) { + sum_dy[0] += sum_dy[j]; + sum_dy_xmu[0] += sum_dy_xmu[j]; + } + + // release array of registers + auto sum_dy_th = sum_dy[0]; + auto sum_dy_xmu_th = sum_dy_xmu[0]; + + // block-wise reduction with shared memory (since reduction cannot be done within a warp) + static __shared__ ACC_T shmem_sum_dy[MAX_BLOCK_SIZE]; + static __shared__ ACC_T shmem_sum_dy_xmu[MAX_BLOCK_SIZE]; + + merge_block_vertical_backward(sum_dy_th, sum_dy_xmu_th, shmem_sum_dy, shmem_sum_dy_xmu); + + if (gridDim.y > 1) { + volatile ACC_T* staging_sum_dy = staging_data_ptr; + volatile ACC_T* staging_sum_dy_xmu = &staging_data_ptr[stride * gridDim.y]; + + address_base = c_offset + blockIdx.y * stride; + // write data to staging_data; + if (threadIdx.y == 0 && c_offset < stride) { + staging_sum_dy[address_base] = sum_dy_th; + staging_sum_dy_xmu[address_base] = sum_dy_xmu_th; + } + + __threadfence(); + __syncthreads(); // ensuring writes to staging_ is visible to all blocks + + __shared__ bool is_last_block_done; + // mark block done + if (threadIdx.x == 0 && threadIdx.y == 0) { + int old = atomicAdd(&semaphores_ptr[blockIdx.x], 1); + is_last_block_done = (old == (gridDim.y - 1)); + } + + __syncthreads(); + + // check that all data is now available in global memory + if (is_last_block_done) { + sum_dy_th = ACC_T(0.0); + sum_dy_xmu_th = ACC_T(0.0); + + for (int y = threadIdx.y; y < gridDim.y; y += blockDim.y) { + address_base = c_offset + y * stride; + sum_dy_th += (c_offset < stride ? staging_sum_dy[address_base] : ACC_T(0.0)); + sum_dy_xmu_th += (c_offset < stride ? staging_sum_dy_xmu[address_base] : ACC_T(0.0)); + } + + merge_block_vertical_backward(sum_dy_th, sum_dy_xmu_th, shmem_sum_dy, shmem_sum_dy_xmu); + if (threadIdx.y == 0 && c_offset < stride) { + if (grad_bias_ptr != nullptr) { grad_bias_ptr[c_offset] = static_cast(sum_dy_th); } + if (grad_weight_ptr != nullptr) { + grad_weight_ptr[c_offset] = static_cast(sum_dy_xmu_th * factor); + } + sum_dy_o_ptr[c_offset] = sum_dy_th; + sum_dy_xmu_o_ptr[c_offset] = sum_dy_xmu_th; + } + } + } else { + if (blockIdx.y == 0 && threadIdx.y == 0 && c_offset < stride) { + if (grad_bias_ptr != nullptr) { grad_bias_ptr[c_offset] = static_cast(sum_dy_th); } + if (grad_weight_ptr != nullptr) { + grad_weight_ptr[c_offset] = static_cast(sum_dy_xmu_th * factor); + } + sum_dy_o_ptr[c_offset] = sum_dy_th; + sum_dy_xmu_o_ptr[c_offset] = sum_dy_xmu_th; + } + } +} + +template +struct BatchNormBackwardReduceFunctor final { + void operator()(ep::Stream* stream, const int64_t batch_size, const int64_t channel_size, + const int64_t spatial_size, const T* grad_out_ptr, const T* input_ptr, + const T* mean_ptr, const T* invstd_ptr, T* sum_dy_ptr, T* sum_dy_xmu_ptr, + T* grad_weight_ptr, T* grad_bias_ptr) { + using ACC_T = acc_type; + int block_y = std::min(lastPow2(batch_size), MAX_BLOCK_SIZE / WARP_SIZE); + // We want block_x to be at least a warp width + int block_x = std::min(std::max(getNumThreads(spatial_size), WARP_SIZE), + MAX_BLOCK_SIZE / block_y); + const dim3 block(block_x, block_y); + const dim3 grid(channel_size); + + if (batch_size * channel_size * spatial_size < std::numeric_limits::max()) { + batch_norm_backward_reduce_kernel + <<As()->cuda_stream()>>>( + static_cast(batch_size), static_cast(channel_size), + static_cast(spatial_size), grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + sum_dy_ptr, sum_dy_xmu_ptr, grad_weight_ptr, grad_bias_ptr); + } else { + batch_norm_backward_reduce_kernel + <<As()->cuda_stream()>>>( + batch_size, channel_size, spatial_size, grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + sum_dy_ptr, sum_dy_xmu_ptr, grad_weight_ptr, grad_bias_ptr); + } + } +}; + +template +struct BatchNormBackwardReduceChannelLastFunctor final { + void operator()(ep::Stream* stream, const int64_t stride, const int64_t reduction_size, + const T* grad_out_ptr, const T* input_ptr, const T* mean_ptr, const T* invstd_ptr, + T* sum_dy_ptr, T* sum_dy_xmu_ptr, T* grad_weight_ptr, T* grad_bias_ptr, + user_op::Tensor* tmp_buffer) { + using ACC_T = acc_type; + + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid, true); + + T* staging_data_ptr = nullptr; + int32_t* semaphores_ptr = nullptr; + if (grid.y > 1) { + staging_data_ptr = tmp_buffer->mut_dptr(); + semaphores_ptr = reinterpret_cast(tmp_buffer->mut_dptr() + + 2 * stride * grid.y * sizeof(T)); + } + + if (stride * reduction_size < std::numeric_limits::max()) { + batch_norm_backward_reduce_channels_last_kernel + <<As()->cuda_stream()>>>( + grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, sum_dy_ptr, sum_dy_xmu_ptr, + grad_weight_ptr, grad_bias_ptr, staging_data_ptr, semaphores_ptr, + static_cast(reduction_size), static_cast(stride)); + } else { + batch_norm_backward_reduce_channels_last_kernel + <<As()->cuda_stream()>>>( + grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, sum_dy_ptr, sum_dy_xmu_ptr, + grad_weight_ptr, grad_bias_ptr, staging_data_ptr, semaphores_ptr, reduction_size, + stride); + } + } +}; + +} // namespace + +template +class GpuBatchNormBackwardReduceKernel final : public user_op::OpKernel { + public: + GpuBatchNormBackwardReduceKernel() = default; + ~GpuBatchNormBackwardReduceKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* grad_out = ctx->Tensor4ArgNameAndIndex("grad_out", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* invstd = ctx->Tensor4ArgNameAndIndex("invstd", 0); + + user_op::Tensor* sum_dy = ctx->Tensor4ArgNameAndIndex("sum_dy", 0); + user_op::Tensor* sum_dy_xmu = ctx->Tensor4ArgNameAndIndex("sum_dy_xmu", 0); + user_op::Tensor* grad_weight = ctx->Tensor4ArgNameAndIndex("grad_weight", 0); + user_op::Tensor* grad_bias = ctx->Tensor4ArgNameAndIndex("grad_bias", 0); + + const T* grad_out_ptr = grad_out->dptr(); + const T* input_ptr = input->dptr(); + const T* mean_ptr = mean->dptr(); + const T* invstd_ptr = invstd->dptr(); + + T* sum_dy_ptr = sum_dy->mut_dptr(); + T* sum_dy_xmu_ptr = sum_dy_xmu->mut_dptr(); + T* grad_weight_ptr = grad_weight->mut_dptr(); + T* grad_bias_ptr = grad_bias->mut_dptr(); + + const int32_t axis = ctx->Attr("axis"); + + bool use_channels_last_kernel = axis == 1 ? false : true; + if (use_channels_last_kernel) { // NHWC format + const int64_t stride = input->shape_view().At(axis); + const int64_t reduction_size = input->shape_view().elem_cnt() / stride; + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + BatchNormBackwardReduceChannelLastFunctor()( + ctx->stream(), stride, reduction_size, grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, + sum_dy_ptr, sum_dy_xmu_ptr, grad_weight_ptr, grad_bias_ptr, tmp_buffer); + } else { // NCHW format + const int64_t batch_size = input->shape_view().At(0); + const int64_t channel_size = input->shape_view().At(1); + const int64_t spatial_size = input->shape_view().Count(2); + + BatchNormBackwardReduceFunctor()(ctx->stream(), batch_size, channel_size, spatial_size, + grad_out_ptr, input_ptr, mean_ptr, invstd_ptr, sum_dy_ptr, + sum_dy_xmu_ptr, grad_weight_ptr, grad_bias_ptr); + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_BATCH_NORM_BACKWARD_REDUCE_KERNEL(dtype) \ + REGISTER_USER_KERNEL("batch_norm_backward_reduce") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("grad_out", 0) == GetDataType::value) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("mean", 0) == GetDataType::value) \ + && (user_op::HobDataType("invstd", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferTmpSizeForChannelLastKernel) + +REGISTER_BATCH_NORM_BACKWARD_REDUCE_KERNEL(float); +REGISTER_BATCH_NORM_BACKWARD_REDUCE_KERNEL(double); + +} // namespace oneflow diff --git a/oneflow/user/kernels/batch_norm_elemt_kernel.cu b/oneflow/user/kernels/batch_norm_elemt_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..24c96a9f0fcf863b1dd63fb5dc27c10f947f991e --- /dev/null +++ b/oneflow/user/kernels/batch_norm_elemt_kernel.cu @@ -0,0 +1,215 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/user/kernels/batch_norm_kernel_utils.h" + +// NOTE(Liang Depeng): +// The implementation of batch_norm_elemt kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +namespace oneflow { + +namespace { + +template +__global__ void batch_norm_transform_input_channels_last_kernel( + const T* __restrict__ input_ptr, const T* __restrict__ mean_ptr, + const T* __restrict__ inv_std_ptr, const T* __restrict__ weight_ptr, + const T* __restrict__ bias_ptr, T* __restrict__ out_ptr, const IDX_TYPE reduction_size, + const IDX_TYPE stride) { + // tensor dimension (m,c) + // loop along m dimension + IDX_TYPE inner_loop_stride = blockDim.y * gridDim.y; + + // offset along m dimension + IDX_TYPE m_offset = blockIdx.y * blockDim.y + threadIdx.y; + IDX_TYPE c_offset = blockIdx.x * blockDim.x + threadIdx.x; + + if (c_offset >= stride || m_offset >= reduction_size) { return; } + + auto m_c = mean_ptr[c_offset]; + auto inv_std_c = static_cast(inv_std_ptr[c_offset]); + auto w_c = weight_ptr == nullptr ? T(1.0) : static_cast(weight_ptr[c_offset]); + auto b_c = bias_ptr == nullptr ? T(0.0) : static_cast(bias_ptr[c_offset]); + + IDX_TYPE loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS); + IDX_TYPE address_base = m_offset * stride + c_offset; + IDX_TYPE address_increment = inner_loop_stride * stride; + + for (IDX_TYPE i = 0; i < loop_count; i++) { +#pragma unroll + for (int j = 0; j < PARALLEL_LOADS; j++) { + if (c_offset < stride && m_offset < reduction_size) { + out_ptr[address_base] = + static_cast(w_c * (static_cast(input_ptr[address_base]) - m_c) * inv_std_c + b_c); + } + m_offset += inner_loop_stride; + address_base += address_increment; + } + } +} + +template +__global__ void batch_norm_transform_input_kernel(const IDX_TYPE batch_size, + const IDX_TYPE channel_size, + const IDX_TYPE spatial_size, const T* input_ptr, + const T* mean_ptr, const T* invstd_ptr, + const T* weight_ptr, const T* bias_ptr, + T* output_ptr) { + IDX_TYPE channel = blockIdx.x; + IDX_TYPE channel_offset = channel * spatial_size; + IDX_TYPE batch_step = channel_size * spatial_size; + + if (channel >= channel_size) { return; } + + T gamma = static_cast(weight_ptr[channel]); + T beta = static_cast(bias_ptr[channel]); + T mean = static_cast(mean_ptr[channel]); + T invstd = invstd_ptr[channel]; + + IDX_TYPE bstep = blockDim.y * gridDim.y; + for (IDX_TYPE batch = threadIdx.y + blockIdx.y * blockDim.y; batch < batch_size; batch += bstep) { + IDX_TYPE offset = batch * batch_step + channel_offset; + for (IDX_TYPE feature = threadIdx.x; feature < spatial_size; feature += blockDim.x) { + output_ptr[offset + feature] = + static_cast(gamma * (input_ptr[offset + feature] - mean) * invstd + beta); + } + } +} + +template +struct BatchNormElemtFunctor final { + void operator()(ep::Stream* stream, const int64_t batch_size, const int64_t channel_size, + const int64_t spatial_size, const T* input_ptr, const T* mean_ptr, + const T* invstd_ptr, const T* weight_ptr, const T* bias_ptr, T* output_ptr) { + // The input_transform kernel is pointwise, but we need to balance reading parameters + // (save_var/mean, weight/bias) - which we only do once and have a for loop afterwards - with + // having many threads and blocks and good occupancy. Quiet likely, we could go with even more + // blocks than 1024. The various planes are independent, so we use blocks for them. + int tf = std::max(getNumThreads(spatial_size / 4), + std::min(getNumThreads(spatial_size), 64)); + int tb = std::max(64 / tf, 1); + dim3 blocks_trans(channel_size, std::max(1, std::min((256 * 1024) / channel_size, + (batch_size + tb - 1) / tb))); + blocks_trans.y = std::min(blocks_trans.y, MAX_GRID_SIZE); + dim3 threads_trans(tf, tb); + + if (batch_size * channel_size * spatial_size < std::numeric_limits::max()) { + batch_norm_transform_input_kernel + <<As()->cuda_stream()>>>( + static_cast(batch_size), static_cast(channel_size), + static_cast(spatial_size), input_ptr, mean_ptr, invstd_ptr, weight_ptr, + bias_ptr, output_ptr); + } else { + batch_norm_transform_input_kernel + <<As()->cuda_stream()>>>( + batch_size, channel_size, spatial_size, input_ptr, mean_ptr, invstd_ptr, weight_ptr, + bias_ptr, output_ptr); + } + } +}; + +template +struct BatchNormElemtChannelLastFunctor final { + void operator()(ep::Stream* stream, const int64_t stride, const int64_t reduction_size, + const T* input_ptr, const T* mean_ptr, const T* invstd_ptr, const T* weight_ptr, + const T* bias_ptr, T* output_ptr) { + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid); + + if (reduction_size * stride < std::numeric_limits::max()) { + batch_norm_transform_input_channels_last_kernel + <<As()->cuda_stream()>>>( + input_ptr, mean_ptr, invstd_ptr, weight_ptr, bias_ptr, output_ptr, + static_cast(reduction_size), static_cast(stride)); + } else { + batch_norm_transform_input_channels_last_kernel + <<As()->cuda_stream()>>>( + input_ptr, mean_ptr, invstd_ptr, weight_ptr, bias_ptr, output_ptr, reduction_size, + stride); + } + } +}; + +} // namespace + +template +class GpuBatchNormElemtKernel final : public user_op::OpKernel { + public: + GpuBatchNormElemtKernel() = default; + ~GpuBatchNormElemtKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* invstd = ctx->Tensor4ArgNameAndIndex("invstd", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + + const T* input_ptr = input->dptr(); + const T* mean_ptr = mean->dptr(); + const T* invstd_ptr = invstd->dptr(); + const T* weight_ptr = weight->dptr(); + const T* bias_ptr = bias->dptr(); + T* output_ptr = output->mut_dptr(); + const int32_t axis = ctx->Attr("axis"); + + bool use_channels_last_kernel = axis == 1 ? false : true; + if (use_channels_last_kernel) { // NHWC format + const int64_t stride = input->shape_view().At(axis); + const int64_t reduction_size = input->shape_view().elem_cnt() / stride; + BatchNormElemtChannelLastFunctor()(ctx->stream(), stride, reduction_size, input_ptr, + mean_ptr, invstd_ptr, weight_ptr, bias_ptr, output_ptr); + } else { // NCHW format + const int64_t batch_size = input->shape_view().At(0); + const int64_t channel_size = input->shape_view().At(1); + const int64_t spatial_size = input->shape_view().Count(2); + + BatchNormElemtFunctor()(ctx->stream(), batch_size, channel_size, spatial_size, input_ptr, + mean_ptr, invstd_ptr, weight_ptr, bias_ptr, output_ptr); + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_BATCH_NORM_ELEMT_KERNEL(dtype) \ + REGISTER_USER_KERNEL("batch_norm_elemt") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("mean", 0) == GetDataType::value) \ + && (user_op::HobDataType("invstd", 0) == GetDataType::value) \ + && (user_op::HobDataType("weight", 0) == GetDataType::value) \ + && (user_op::HobDataType("bias", 0) == GetDataType::value)) + +REGISTER_BATCH_NORM_ELEMT_KERNEL(float); +REGISTER_BATCH_NORM_ELEMT_KERNEL(double); + +} // namespace oneflow diff --git a/oneflow/user/kernels/batch_norm_gather_stats_with_counts_kernel.cu b/oneflow/user/kernels/batch_norm_gather_stats_with_counts_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..d4cd382bc97cead548789615c6b9da5e3a9a5093 --- /dev/null +++ b/oneflow/user/kernels/batch_norm_gather_stats_with_counts_kernel.cu @@ -0,0 +1,156 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/user/kernels/batch_norm_kernel_utils.h" + +// NOTE(Liang Depeng): +// The implementation of batch_norm_gather_stats_with_counts kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +namespace oneflow { + +namespace { + +template +__global__ void batch_norm_reduce_statistics_kernel(const int64_t world_size, + const int64_t channel_size, const T* mean_ptr, + const T* invstd_ptr, const T* counts_ptr, + T* global_mean_ptr, T* global_invstd_ptr, + T* running_mean_ptr, T* running_var_ptr, + const float eps, const float momentum) { + IDX_TYPE bid = blockIdx.x; + IDX_TYPE tid = threadIdx.x; + + // first the reductions each thread does separately + for (IDX_TYPE i = bid * blockDim.x + tid; i < channel_size; i += gridDim.x * blockDim.x) { + ACC_T avg = 0; + ACC_T var_n = 0; + IDX_TYPE n = 0; + for (IDX_TYPE j = 0; j < world_size; j++) { + T count = counts_ptr[j]; + ACC_T m = mean_ptr[j * channel_size + i]; + ACC_T v = ACC_T(1.0) / (invstd_ptr[j * channel_size + i]); + v = (v * v - eps) * count; + ACC_T factor = 1.0 / (n + count); + var_n += v + (avg - m) * (avg - m) * n * count * factor; + avg = n * factor * avg + count * factor * m; + n += count; + } + global_mean_ptr[i] = avg; + global_invstd_ptr[i] = static_cast(1) / device_sqrt(var_n / n + eps); + if (running_mean_ptr != nullptr) { + running_mean_ptr[i] = static_cast((1 - momentum) * running_mean_ptr[i] + momentum * avg); + } + ACC_T unbiasedVar = var_n / (n - 1); + if (running_var_ptr != nullptr) { + running_var_ptr[i] = + static_cast((1 - momentum) * running_var_ptr[i] + momentum * unbiasedVar); + } + } +} + +template +struct BatchNormGatherStatsWithCountsFunctor final { + void operator()(ep::Stream* stream, const int64_t world_size, const int64_t channel_size, + const T* mean_ptr, const T* invstd_ptr, const T* counts_ptr, T* global_mean_ptr, + T* global_invstd_ptr, T* running_mean_ptr, T* running_var_ptr, const float eps, + const float momentum) { + using ACC_T = acc_type; + int32_t block = getNumThreads(channel_size); + int32_t grid = std::max(1, channel_size / block); + + if (world_size * channel_size < std::numeric_limits::max()) { + batch_norm_reduce_statistics_kernel + <<As()->cuda_stream()>>>( + static_cast(world_size), static_cast(channel_size), mean_ptr, + invstd_ptr, counts_ptr, global_mean_ptr, global_invstd_ptr, running_mean_ptr, + running_var_ptr, eps, momentum); + } else { + batch_norm_reduce_statistics_kernel + <<As()->cuda_stream()>>>( + world_size, channel_size, mean_ptr, invstd_ptr, counts_ptr, global_mean_ptr, + global_invstd_ptr, running_mean_ptr, running_var_ptr, eps, momentum); + } + } +}; + +} // namespace + +template +class GpuBatchNormGatherStatsWithCountsKernel final : public user_op::OpKernel { + public: + GpuBatchNormGatherStatsWithCountsKernel() = default; + ~GpuBatchNormGatherStatsWithCountsKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* invstd = ctx->Tensor4ArgNameAndIndex("invstd", 0); + const user_op::Tensor* counts = ctx->Tensor4ArgNameAndIndex("counts", 0); + user_op::Tensor* global_mean = ctx->Tensor4ArgNameAndIndex("global_mean", 0); + user_op::Tensor* global_invstd = ctx->Tensor4ArgNameAndIndex("global_invstd", 0); + + const T* mean_ptr = mean->dptr(); + const T* invstd_ptr = invstd->dptr(); + const T* counts_ptr = counts->dptr(); + T* global_mean_ptr = global_mean->mut_dptr(); + T* global_invstd_ptr = global_invstd->mut_dptr(); + T* running_mean_ptr = nullptr; + T* running_var_ptr = nullptr; + if (ctx->has_input("running_mean", 0)) { + CHECK(ctx->has_input("running_var", 0)); + running_mean_ptr = ctx->Tensor4ArgNameAndIndex("running_mean", 0)->mut_dptr(); + running_var_ptr = ctx->Tensor4ArgNameAndIndex("running_var", 0)->mut_dptr(); + } + + const float eps = ctx->Attr("eps"); + const float momentum = ctx->Attr("momentum"); + + const int64_t world_size = mean->shape_view().At(0); + const int64_t channel_size = mean->shape_view().At(1); + + BatchNormGatherStatsWithCountsFunctor()( + ctx->stream(), world_size, channel_size, mean_ptr, invstd_ptr, counts_ptr, global_mean_ptr, + global_invstd_ptr, running_mean_ptr, running_var_ptr, eps, momentum); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_BATCH_NORM_GATHER_STATS_WITH_COUNTS_KERNEL(dtype) \ + REGISTER_USER_KERNEL("batch_norm_gather_stats_with_counts") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("mean", 0) == GetDataType::value) \ + && (user_op::HobDataType("invstd", 0) == GetDataType::value) \ + && (user_op::HobDataType("counts", 0) == GetDataType::value)) + +REGISTER_BATCH_NORM_GATHER_STATS_WITH_COUNTS_KERNEL(float); +REGISTER_BATCH_NORM_GATHER_STATS_WITH_COUNTS_KERNEL(double); + +} // namespace oneflow diff --git a/oneflow/user/kernels/batch_norm_kernel_utils.h b/oneflow/user/kernels/batch_norm_kernel_utils.h new file mode 100644 index 0000000000000000000000000000000000000000..2f26e75a3ec7b549eacc0cb75326b1a02c59c20f --- /dev/null +++ b/oneflow/user/kernels/batch_norm_kernel_utils.h @@ -0,0 +1,134 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_BATCH_NORM_UTILS_H_ +#define ONEFLOW_USER_KERNELS_BATCH_NORM_UTILS_H_ +// NOTE(Liang Depeng): +// Modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +#if defined(__CUDACC__) || defined(__HIPCC__) + +constexpr int ELEMENTS_PER_ITER = 4; // enables concurrency within each thread to hide latency +constexpr int ELEMENTS_PER_THREAD = 16; +constexpr int OPTIMAL_TILE_W = 32; +constexpr int MAX_H_BLOCK = 128; +constexpr int32_t MAX_BLOCK_SIZE = 512; +constexpr unsigned MAX_GRID_SIZE = 65535u; +#define WARP_SIZE 32 + +// returns 2**floor(log2(n)) +static int lastPow2(unsigned int n) { + n |= (n >> 1); + n |= (n >> 2); + n |= (n >> 4); + n |= (n >> 8); + n |= (n >> 16); + return std::max(1, n - (n >> 1)); +} + +/** + Computes ceil(a / b) +*/ +template::value>> +static T ceil_div(T a, T b) { + return (a + b - 1) / b; +} + +static void flexible_launch_configs(const int reduction, const int stride, dim3& block, dim3& grid, + const bool coop_flag = false) { + int block_x = std::min(lastPow2(stride), OPTIMAL_TILE_W); + int block_y = + std::min(lastPow2(ceil_div(reduction, ELEMENTS_PER_THREAD)), MAX_BLOCK_SIZE / block_x); + if (block_x * block_y != MAX_BLOCK_SIZE) { + block_x = std::min(lastPow2(stride), MAX_BLOCK_SIZE / block_y); + } + + int grid_x = ceil_div(stride, block_x); + int grid_y = std::min(ceil_div(reduction, block_y * ELEMENTS_PER_THREAD), MAX_H_BLOCK); + if (coop_flag) { + // it's not worth having a grid reduction if the reduction dimension is not big enough + grid_y = grid_y < 8 ? 1 : grid_y; + } + + block.x = block_x; + block.y = block_y; + block.z = 1; + grid.x = grid_x; + grid.y = grid_y; + grid.z = 1; +} + +template +struct AccumulateType {}; +template<> +struct AccumulateType { + using type = float; +}; +template<> +struct AccumulateType { + using type = double; +}; + +template +using acc_type = typename AccumulateType::type; + +// Number of threads in a block given an input size up to MAX_BLOCK_SIZE +static int32_t getNumThreads(int64_t nElem) { + int32_t threadSizes[5] = {32, 64, 128, 256, MAX_BLOCK_SIZE}; + for (int32_t i = 0; i != 5; ++i) { + if (nElem <= threadSizes[i]) { return threadSizes[i]; } + } + return MAX_BLOCK_SIZE; +} + +template +static __forceinline__ __device__ T device_sqrt(T val); + +template<> +__forceinline__ __device__ float device_sqrt(float val) { + return ::sqrtf(val); +} + +template<> +__forceinline__ __device__ double device_sqrt(double val) { + return ::sqrt(val); +} + +template +__device__ __forceinline__ T inv_std(T var, double eps) { + T invstd = 0; + if (var != static_cast(0) || eps != static_cast(0)) { + invstd = static_cast(1) / device_sqrt(var + eps); + } + return invstd; +} + +// Returns the index of the most significant 1 bit in `val`. +__device__ __forceinline__ int32_t getMSB(int32_t val) { return 31 - __clz(val); } + +template +__device__ __forceinline__ T WARP_SHFL_XOR(T value, int laneMask, int width = warpSize, + unsigned int mask = 0xffffffff) { +#ifdef WITH_ROCM + return __shfl_xor(value, laneMask, width); +#else + return __shfl_xor_sync(mask, value, laneMask, width); +#endif +} + +#endif + +#endif // ONEFLOW_USER_KERNELS_BATCH_NORM_UTILS_H_ diff --git a/oneflow/user/kernels/batch_norm_stats_kernel.cu b/oneflow/user/kernels/batch_norm_stats_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..7467266caf44f680f52c56335b2bfb17f6a0b6c6 --- /dev/null +++ b/oneflow/user/kernels/batch_norm_stats_kernel.cu @@ -0,0 +1,424 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/cuda/cuda_device.h" +#include "oneflow/user/kernels/batch_norm_kernel_utils.h" + +// NOTE(Liang Depeng): +// The implementation of batch_norm_stats kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/Normalization.cuh + +namespace oneflow { + +namespace { + +template +static size_t InferTmpSizeForChannelLastKernel(user_op::InferContext* ctx) { + const int32_t axis = ctx->Attr("axis"); + const Shape& in_shape = ctx->InputTensorDesc("input", 0).shape(); + const int64_t stride = in_shape.At(axis); + const int64_t reduction_size = in_shape.elem_cnt() / stride; + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid, true); + size_t tmp_size = 0; + if (grid.y > 1) { + tmp_size += 4 * stride * grid.y * sizeof(T); + tmp_size += grid.x * sizeof(int32_t); + } + return tmp_size; +} + +template +__device__ __forceinline__ void welford_merge_element(C& count, T& mean, T& m2n, const C& count_new, + const T& mean_new, const T& m2n_new) { + T factor = T(1.0) / ::max(C(1), (count + count_new)); + T delta0 = mean - mean_new; + mean = (mean_new * count_new + mean * count) * factor; + m2n += m2n_new + delta0 * delta0 * count_new * count * factor; + count += count_new; +} + +// merge mean/m2n among threadIdx.y within block +template +__device__ __forceinline__ void welford_merge_block_vertical(C& count, T& mean, T& m2n, + C* shmem_count, T* shmem_mean, + T* shmem_m2n) { + // write to shared memory + auto address_base = threadIdx.x + threadIdx.y * blockDim.x; + +#pragma unroll + for (int offset = blockDim.y / 2; offset > 0; offset >>= 1) { + if (threadIdx.y < offset * 2) { + shmem_mean[address_base] = mean; + shmem_m2n[address_base] = m2n; + shmem_count[address_base] = count; + } + __syncthreads(); + if (threadIdx.y < offset && threadIdx.y + offset < blockDim.y) { + auto address = address_base + offset * blockDim.x; + // read shared memory back to register for reduction + auto count_new = shmem_count[address]; + auto mean_new = shmem_mean[address]; + auto m2n_new = shmem_m2n[address]; + + welford_merge_element(count, mean, m2n, count_new, mean_new, m2n_new); + } + } +} + +template +__global__ void batch_norm_collect_statistics_channels_last_kernel( + const T* __restrict__ input_ptr, ACC_T* __restrict__ out_mean_ptr, + ACC_T* __restrict__ out_invstd_ptr, volatile ACC_T* staging_data_ptr, int32_t* semaphores_ptr, + const IDX_TYPE reduction_size, const IDX_TYPE stride, ACC_T epsilon) { + // hide latency with concurrency + ACC_T x_mean[PARALLEL_LOADS]; + ACC_T m_2_n[PARALLEL_LOADS]; + IDX_TYPE count[PARALLEL_LOADS]; + +#pragma unroll + for (IDX_TYPE i = 0; i < PARALLEL_LOADS; i++) { + x_mean[i] = ACC_T(0); + m_2_n[i] = ACC_T(0); + count[i] = ACC_T(0); + } + // tensor dimension (m,c) + + // loop along m dimension + IDX_TYPE inner_loop_stride = blockDim.y * gridDim.y; + + // offset along m dimension + IDX_TYPE m_offset = blockIdx.y * blockDim.y + threadIdx.y; + IDX_TYPE c_offset = blockIdx.x * blockDim.x + threadIdx.x; + + IDX_TYPE loop_count = 1 + (reduction_size - 1) / (inner_loop_stride * PARALLEL_LOADS); + IDX_TYPE address_base = m_offset * stride + c_offset; + IDX_TYPE address_increment = inner_loop_stride * stride; + + for (IDX_TYPE i = 0; i < loop_count; i++) { + ACC_T x_math[PARALLEL_LOADS]; + ACC_T x_count_inv[PARALLEL_LOADS]; + ACC_T is_valid[PARALLEL_LOADS]; + + // load multiple data in +#pragma unroll + for (IDX_TYPE j = 0; j < PARALLEL_LOADS; j++) { + if (c_offset < stride && m_offset < reduction_size) { + x_math[j] = input_ptr[address_base]; + count[j]++; + x_count_inv[j] = ACC_T(1) / count[j]; + is_valid[j] = ACC_T(1); + } else { + x_math[j] = ACC_T(0); + x_count_inv[j] = ACC_T(0); + is_valid[j] = ACC_T(0); + } + m_offset += inner_loop_stride; + address_base += address_increment; + } + + // calculate mean/m2n with welford +#pragma unroll + for (IDX_TYPE j = 0; j < PARALLEL_LOADS; j++) { + ACC_T delta0 = x_math[j] - x_mean[j]; + x_mean[j] += delta0 * x_count_inv[j]; + ACC_T delta1 = x_math[j] - x_mean[j]; + m_2_n[j] += delta0 * delta1 * is_valid[j]; + } + } + + // thread reduction to accumulate mean/m_2_n/count between PARALLEL_LOADS +#pragma unroll + for (IDX_TYPE j = 1; j < PARALLEL_LOADS; j++) { + welford_merge_element(count[0], x_mean[0], m_2_n[0], count[j], x_mean[j], m_2_n[j]); + } + + // release x_mean / m_2_n + auto mean_th = x_mean[0]; + auto m2_th = m_2_n[0]; + auto count_th = count[0]; + + // block-wise reduction with shared memory (since reduction cannot be done within a warp) + static __shared__ ACC_T shmem_mean[MAX_BLOCK_SIZE]; + static __shared__ ACC_T shmem_m2n[MAX_BLOCK_SIZE]; + static __shared__ IDX_TYPE shmem_count[MAX_BLOCK_SIZE]; + + welford_merge_block_vertical(count_th, mean_th, m2_th, shmem_count, shmem_mean, shmem_m2n); + + if (gridDim.y > 1) { + volatile ACC_T* staging_mean = staging_data_ptr; + volatile ACC_T* staging_m2n = &staging_data_ptr[stride * gridDim.y]; + volatile IDX_TYPE* staging_count = + reinterpret_cast(&staging_m2n[stride * gridDim.y]); + + address_base = c_offset + blockIdx.y * stride; + // write data to staging_data_ptr; + if (threadIdx.y == 0 && c_offset < stride) { + staging_mean[address_base] = mean_th; + staging_m2n[address_base] = m2_th; + staging_count[address_base] = count_th; + } + + __threadfence(); + __syncthreads(); // ensuring writes to staging_ is visible to all blocks + + __shared__ bool is_last_block_done; + // mark block done + if (threadIdx.x == 0 && threadIdx.y == 0) { + IDX_TYPE old = atomicAdd(&semaphores_ptr[blockIdx.x], 1); + is_last_block_done = (old == (gridDim.y - 1)); + } + + __syncthreads(); + + // check that all data is now available in global memory + if (is_last_block_done) { + count_th = 0; + mean_th = ACC_T(0.0); + m2_th = ACC_T(0.0); + + for (IDX_TYPE y = threadIdx.y; y < gridDim.y; y += blockDim.y) { + address_base = c_offset + y * stride; + IDX_TYPE count_new = c_offset < stride ? staging_count[address_base] : 0; + ACC_T mean_new = c_offset < stride ? staging_mean[address_base] : ACC_T(0.0); + ACC_T m2n_new = c_offset < stride ? staging_m2n[address_base] : ACC_T(0.0); + + welford_merge_element(count_th, mean_th, m2_th, count_new, mean_new, m2n_new); + } + + welford_merge_block_vertical(count_th, mean_th, m2_th, shmem_count, shmem_mean, shmem_m2n); + if (threadIdx.y == 0 && c_offset < stride) { + out_mean_ptr[c_offset] = static_cast(mean_th); + out_invstd_ptr[c_offset] = inv_std(m2_th / count_th, epsilon); + } + } + } else { + if (blockIdx.y == 0 && threadIdx.y == 0 && c_offset < stride) { + out_mean_ptr[c_offset] = static_cast(mean_th); + out_invstd_ptr[c_offset] = inv_std(m2_th / count_th, epsilon); + } + } +} + +template +__global__ void batch_norm_collect_statistics_kernel(const T* input_ptr, const IDX_TYPE batch_size, + const IDX_TYPE channel_size, + const IDX_TYPE spatial_size, const ACC_T eps, + T* mean_ptr, T* invstd_ptr) { + __shared__ IDX_TYPE shared_n[2 * 2 * WARP_SIZE + WARP_SIZE]; + + IDX_TYPE channel_idx = blockIdx.x; + IDX_TYPE N = batch_size * spatial_size; + IDX_TYPE tid = threadIdx.x + threadIdx.y * blockDim.x; + + // Compute the mean and variance across (batch, x/y/z) + // this uses the Welford (in the for loop)/parallel algorithm (to sum across the block) + // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm + // and the parallel algorithm on the same page. + // We use two shuffles to reduce across the entire block. + // https://devblogs.nvidia.com/faster-parallel-reductions-kepler/ has a description. + ACC_T* shared_avg_var = (ACC_T*)&shared_n[WARP_SIZE]; + + // first the reductions each thread does separately + ACC_T avg = 0; + ACC_T var_n = 0; + IDX_TYPE n = 0; + const IDX_TYPE channel_offset = channel_idx * spatial_size; + const IDX_TYPE batch_offset = channel_size * spatial_size; + for (IDX_TYPE batch = threadIdx.y; batch < batch_size; batch += blockDim.y) { + IDX_TYPE offset = batch * batch_offset + channel_offset; + for (IDX_TYPE x = threadIdx.x; x < spatial_size; x += blockDim.x) { + ACC_T v = input_ptr[offset + x]; + ACC_T d1 = v - avg; + n++; + avg += d1 / n; + var_n += d1 * (v - avg); + } + } + + // summing the result of all the threads within a warp + // refer to: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm + // first warpSum to get one value per thread to one value per warp + for (IDX_TYPE i = 0; i < getMSB(WARP_SIZE); ++i) { + ACC_T o_avg = WARP_SHFL_XOR(avg, 1 << i, WARP_SIZE); + IDX_TYPE o_n = WARP_SHFL_XOR(n, 1 << i, WARP_SIZE); + ACC_T factor = 1.0 / fmaxf(1.0, n + o_n); + var_n += + WARP_SHFL_XOR(var_n, 1 << i, WARP_SIZE) + (avg - o_avg) * (avg - o_avg) * n * o_n * factor; + avg = (n * avg + o_n * o_avg) * factor; + n += o_n; + } + + // this writes each warp's final sum result into shared memory + // there are at most (thread_number_of_a_block / WARP_SIZE) results + __syncthreads(); + if (tid % WARP_SIZE == 0) { + shared_n[tid / WARP_SIZE] = n; + shared_avg_var[tid / WARP_SIZE * 2] = avg; + shared_avg_var[tid / WARP_SIZE * 2 + 1] = var_n; + } + __syncthreads(); + + // now have a second warpSum to reduce the intermediate values + // from shared memory to a single number. The very first + // thread writes it to shared memory. + if (tid < WARP_SIZE) { + // initialize n, avg and var_n of each thread within the first warp + n = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_n[tid] : 0); + avg = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_avg_var[2 * tid] : ACC_T(0)); + var_n = (tid < blockDim.x * blockDim.y / WARP_SIZE ? shared_avg_var[2 * tid + 1] : ACC_T(0)); + + for (IDX_TYPE i = 0; i < getMSB(WARP_SIZE); ++i) { + ACC_T o_avg = WARP_SHFL_XOR(avg, 1 << i, WARP_SIZE); + IDX_TYPE o_n = WARP_SHFL_XOR(n, 1 << i, WARP_SIZE); + ACC_T factor = 1.0 / fmaxf(1.0, n + o_n); + var_n += WARP_SHFL_XOR(var_n, 1 << i, WARP_SIZE) + + (avg - o_avg) * (avg - o_avg) * n * o_n * factor; + avg = (n * avg + o_n * o_avg) * factor; + n += o_n; + } + } + + // save the mean and inverse standard deviation + if (tid == 0) { + mean_ptr[channel_idx] = avg; + invstd_ptr[channel_idx] = inv_std(var_n / N, eps); + } +} + +template +struct BatchNormStatsFunctor final { + void operator()(ep::Stream* stream, const user_op::Tensor* input, user_op::Tensor* mean, + user_op::Tensor* invstd, const float eps) { + using ACC_T = acc_type; + const ShapeView& input_shape = input->shape_view(); + const int64_t input_numel = input_shape.elem_cnt(); + const int64_t spatial_size = input_shape.Count(2); + + dim3 blocks(input_shape.At(1)); + int32_t tf = getNumThreads(spatial_size); + dim3 threads(tf, std::max(1, MAX_BLOCK_SIZE / tf)); + + const T* input_ptr = input->dptr(); + T* mean_ptr = mean->mut_dptr(); + T* invstd_ptr = invstd->mut_dptr(); + + if (input_numel < std::numeric_limits::max()) { + batch_norm_collect_statistics_kernel + <<As()->cuda_stream()>>>( + input_ptr, static_cast(input_shape.At(0)), + static_cast(input_shape.At(1)), static_cast(spatial_size), eps, + mean_ptr, invstd_ptr); + } else { + batch_norm_collect_statistics_kernel + <<As()->cuda_stream()>>>( + input_ptr, input_shape.At(0), input_shape.At(1), spatial_size, eps, mean_ptr, + invstd_ptr); + } + } +}; + +template +struct BatchNormStatsChannelLastFunctor final { + void operator()(ep::Stream* stream, const user_op::Tensor* input, user_op::Tensor* mean, + user_op::Tensor* invstd, user_op::Tensor* tmp_buffer, const float eps, + const int32_t axis) { + using ACC_T = acc_type; + const ShapeView& input_shape = input->shape_view(); + const int64_t stride = input_shape.At(axis); + const int64_t reduction_size = input_shape.elem_cnt() / stride; + + dim3 block; + dim3 grid; + flexible_launch_configs(reduction_size, stride, block, grid, true); + + T* staging_data_ptr = nullptr; + int32_t* semaphores_ptr = nullptr; + if (grid.y > 1) { + staging_data_ptr = tmp_buffer->mut_dptr(); + semaphores_ptr = reinterpret_cast(tmp_buffer->mut_dptr() + + 4 * stride * grid.y * sizeof(T)); + } + + const T* input_ptr = input->dptr(); + T* mean_ptr = mean->mut_dptr(); + T* invstd_ptr = invstd->mut_dptr(); + + if (input_shape.elem_cnt() < std::numeric_limits::max()) { + batch_norm_collect_statistics_channels_last_kernel + <<As()->cuda_stream()>>>( + input_ptr, mean_ptr, invstd_ptr, staging_data_ptr, semaphores_ptr, + static_cast(reduction_size), static_cast(stride), eps); + } else { + batch_norm_collect_statistics_channels_last_kernel + <<As()->cuda_stream()>>>( + input_ptr, mean_ptr, invstd_ptr, staging_data_ptr, semaphores_ptr, reduction_size, + stride, eps); + } + } +}; + +} // namespace + +template +class GpuBatchNormStatsKernel final : public user_op::OpKernel { + public: + GpuBatchNormStatsKernel() = default; + ~GpuBatchNormStatsKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + user_op::Tensor* invstd = ctx->Tensor4ArgNameAndIndex("invstd", 0); + + const int32_t axis = ctx->Attr("axis"); + const float eps = ctx->Attr("eps"); + + bool use_channels_last_kernel = axis == 1 ? false : true; + if (use_channels_last_kernel) { // NHWC format + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + BatchNormStatsChannelLastFunctor()(ctx->stream(), input, mean, invstd, tmp_buffer, eps, + axis); + } else { // NCHW format + BatchNormStatsFunctor()(ctx->stream(), input, mean, invstd, eps); + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_BATCH_NORM_STATS_KERNEL(dtype) \ + REGISTER_USER_KERNEL("batch_norm_stats") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferTmpSizeForChannelLastKernel) + +REGISTER_BATCH_NORM_STATS_KERNEL(float); +REGISTER_BATCH_NORM_STATS_KERNEL(double); + +} // namespace oneflow diff --git a/oneflow/user/kernels/bernoulli_kernel.cpp b/oneflow/user/kernels/bernoulli_kernel.cpp index 1a72325921c9f79d3c30a523ba7dfc5f93649fce..b786516eac25aaaf4edab75beefea1df05a5da04 100644 --- a/oneflow/user/kernels/bernoulli_kernel.cpp +++ b/oneflow/user/kernels/bernoulli_kernel.cpp @@ -51,11 +51,20 @@ class BernoulliKerenl final : public user_op::OpKernel { CHECK_NOTNULL(generator); const auto& cpu_generator = CHECK_JUST(generator->Get()); - for (int32_t i = 0; i < out_blob->shape_view().elem_cnt(); ++i) { - double prob = static_cast(*(in_dptr + i)); - CHECK(prob >= 0.0 && prob <= 1.0); - std::bernoulli_distribution dis(prob); - *(out_dptr + i) = dis(cpu_generator->engine()) ? GetOneVal() : GetZeroVal(); + double p = ctx->Attr("p"); + // prob != -1 means use prob instead of tensor to generate random number + if (p != static_cast(-1.0)) { + for (int32_t i = 0; i < out_blob->shape_view().elem_cnt(); ++i) { + std::bernoulli_distribution dis(p); + *(out_dptr + i) = dis(cpu_generator->engine()) ? GetOneVal() : GetZeroVal(); + } + } else { + for (int32_t i = 0; i < out_blob->shape_view().elem_cnt(); ++i) { + double prob = static_cast(*(in_dptr + i)); + CHECK(prob >= 0.0 && prob <= 1.0); + std::bernoulli_distribution dis(prob); + *(out_dptr + i) = dis(cpu_generator->engine()) ? GetOneVal() : GetZeroVal(); + } } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } diff --git a/oneflow/user/kernels/binary_concat_kernel.cu b/oneflow/user/kernels/binary_concat_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..a99f434c3d8dfdf683bab2ec472bee4c642525f0 --- /dev/null +++ b/oneflow/user/kernels/binary_concat_kernel.cu @@ -0,0 +1,153 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +namespace oneflow { + +namespace { + +template +__global__ void BinaryConcatKernel(const IDX out_elems, const IDX out_cols, const IDX in0_cols, + const IDX in1_cols, const T* src0, const T* src1, T* dst) { + CUDA_1D_KERNEL_LOOP_T(IDX, i, out_elems) { + const IDX row = i / out_cols; + const IDX col = i - row * out_cols; + const T* src_ptr = nullptr; + if (col < in0_cols) { + src_ptr = src0 + row * in0_cols + col; + } else { + src_ptr = src1 + row * in1_cols + (col - in0_cols); + } + dst[i] = *src_ptr; + } +} + +template +void LaunchBinaryConcatKernel(ep::Stream* stream, const IDX rows, const IDX in0_cols, + const IDX in1_cols, const void* src0, const void* src1, void* dst) { + const IDX out_cols = in0_cols + in1_cols; + const IDX out_elems = rows * out_cols; + RUN_CUDA_KERNEL((BinaryConcatKernel), stream, out_elems, out_elems, out_cols, in0_cols, + in1_cols, reinterpret_cast(src0), reinterpret_cast(src1), + reinterpret_cast(dst)); +} + +template +void DispatchIndexType(ep::Stream* stream, const int64_t rows, const int64_t in0_cols, + const int64_t in1_cols, const void* src0, const void* src1, void* dst) { + if (rows * (in0_cols + in1_cols) >= (1 >> 30)) { + LaunchBinaryConcatKernel(stream, rows, in0_cols, in1_cols, src0, src1, dst); + } else { + LaunchBinaryConcatKernel(stream, rows, in0_cols, in1_cols, src0, src1, dst); + } +} + +void DispatchDataType(ep::Stream* stream, const int64_t rows, const int64_t in0_cols, + const int64_t in1_cols, const void* src0, const void* src1, void* dst) { + const uintptr_t src0_ptr = reinterpret_cast(src0); + const uintptr_t src1_ptr = reinterpret_cast(src1); + const uintptr_t dst_ptr = reinterpret_cast(dst); + const auto IsAligned = [&](const size_t alignment) { + return src0_ptr % alignment == 0 && src1_ptr % alignment == 0 && dst_ptr % alignment == 0 + && in0_cols % alignment == 0 && in1_cols % alignment == 0; + }; + if (IsAligned(16)) { + DispatchIndexType(stream, rows, in0_cols / 16, in1_cols / 16, src0, src1, dst); + } else if (IsAligned(8)) { + DispatchIndexType(stream, rows, in0_cols / 8, in1_cols / 8, src0, src1, dst); + } else if (IsAligned(4)) { + DispatchIndexType(stream, rows, in0_cols / 4, in1_cols / 4, src0, src1, dst); + } else if (IsAligned(2)) { + DispatchIndexType(stream, rows, in0_cols / 2, in1_cols / 2, src0, src1, dst); + } else { + DispatchIndexType(stream, rows, in0_cols, in1_cols, src0, src1, dst); + } +} + +void DispatchBinaryConcat(ep::Stream* stream, const int64_t elem_size, const int64_t rows, + const int64_t in0_cols, const int64_t in1_cols, const void* src0, + const void* src1, void* dst) { + DispatchDataType(stream, rows, in0_cols * elem_size, in1_cols * elem_size, src0, src1, dst); +} + +class ConcatKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + ConcatKernel() = default; + ~ConcatKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + user_op::Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + const DataType data_type = out_tensor->data_type(); + if (out_tensor->shape_view().elem_cnt() == 0) { return; } + const int64_t axis = ctx->Attr("axis"); + CHECK_GE(axis, 0); + const int64_t num_axes = out_tensor->shape_view().NumAxes(); + CHECK_LT(axis, num_axes); + const int64_t out_cols = out_tensor->shape_view().Count(axis); + const int64_t rows = out_tensor->shape_view().elem_cnt() / out_cols; + CHECK_GT(rows, 0); + + CHECK_EQ(ctx->input_size("in"), 2); + const user_op::Tensor* in0_tensor = ctx->Tensor4ArgNameAndIndex("in", 0); + const user_op::Tensor* in1_tensor = ctx->Tensor4ArgNameAndIndex("in", 1); + CHECK_EQ(in0_tensor->data_type(), data_type); + CHECK_EQ(in1_tensor->data_type(), data_type); + if (in0_tensor->shape_view().elem_cnt() == 0) { + CHECK_EQ(in1_tensor->shape_view(), out_tensor->shape_view()); + Memcpy(ctx->stream(), out_tensor->mut_dptr(), in1_tensor->dptr(), + out_tensor->shape_view().elem_cnt() * GetSizeOfDataType(data_type)); + return; + } + if (in1_tensor->shape_view().elem_cnt() == 0) { + CHECK_EQ(in0_tensor->shape_view(), out_tensor->shape_view()); + Memcpy(ctx->stream(), out_tensor->mut_dptr(), in0_tensor->dptr(), + out_tensor->shape_view().elem_cnt() * GetSizeOfDataType(data_type)); + return; + } + CHECK_EQ(in0_tensor->shape_view().NumAxes(), num_axes); + CHECK_EQ(in1_tensor->shape_view().NumAxes(), num_axes); + for (int64_t i = 0; i < num_axes; ++i) { + if (i != axis) { + CHECK_EQ(in0_tensor->shape_view().At(i), out_tensor->shape_view().At(i)); + CHECK_EQ(in1_tensor->shape_view().At(i), out_tensor->shape_view().At(i)); + } + } + CHECK_EQ(in0_tensor->shape_view().At(axis) + in1_tensor->shape_view().At(axis), + out_tensor->shape_view().At(axis)); + const int64_t in0_cols = in0_tensor->shape_view().Count(axis); + const int64_t in1_cols = in1_tensor->shape_view().Count(axis); + + DispatchBinaryConcat(ctx->stream(), GetSizeOfDataType(data_type), rows, in0_cols, in1_cols, + in0_tensor->dptr(), in1_tensor->dptr(), out_tensor->mut_dptr()); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace + +REGISTER_USER_KERNEL("concat") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobInputSize("in") == 2)) + .SetPriority(user_op::kKernelPriorityOptimized); + +} // namespace oneflow diff --git a/oneflow/user/kernels/binary_cross_entropy_kernel.hip.cpp b/oneflow/user/kernels/binary_cross_entropy_kernel.hip.cpp deleted file mode 100644 index c1fe0cda051f19645a0975f4d9661505dfec3264..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/binary_cross_entropy_kernel.hip.cpp +++ /dev/null @@ -1,204 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/loss_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { -namespace { - -using namespace loss; - -template -struct BinaryCrossEntropyFunctor { - T zero_; - T one_; - T negative_hundred_; - BinaryCrossEntropyFunctor() - : zero_(GetZeroVal()), one_(GetOneVal()), negative_hundred_(static_cast(-100)) {} - __device__ __forceinline__ T operator()(T input_val, T target_val) const { - assert(input_val >= zero_); - assert(input_val <= one_); - return (target_val - one_) * max(static_cast(log(one_ - input_val)), negative_hundred_) - - target_val * max(static_cast(log(input_val)), negative_hundred_); - } - - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val) const { - return (*this)(input_val, target_val) * weight_val; - } -}; - -template<> -struct BinaryCrossEntropyFunctor { - float zero_; - float one_; - float negative_hundred_; - BinaryCrossEntropyFunctor() : zero_(0.f), one_(1.f), negative_hundred_(-100.f) {} - __device__ __forceinline__ float operator()(float input_val, float target_val) const { - assert(input_val >= zero_); - assert(input_val <= one_); - return (target_val - one_) * max(logf(one_ - input_val), negative_hundred_) - - target_val * max(logf(input_val), negative_hundred_); - } - - __device__ __forceinline__ float operator()(float input_val, float target_val, - float weight_val) const { - return (*this)(input_val, target_val) * weight_val; - } -}; - -template<> -struct BinaryCrossEntropyFunctor { - BinaryCrossEntropyFunctor float_functor; - __device__ __forceinline__ half operator()(half input_val, half target_val) const { - return __float2half(float_functor(__half2float(input_val), __half2float(target_val))); - } - - __device__ __forceinline__ half operator()(half input_val, half target_val, - half weight_val) const { - return (*this)(input_val, target_val) * weight_val; - } -}; - -template -struct BinaryCrossEntropyGradFunctor { - T eps_; - T one_; - BinaryCrossEntropyGradFunctor() : eps_(static_cast(1e-12)), one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val) const { - return dy_val * (input_val - target_val) / max((one_ - input_val) * input_val, eps_); - } - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val) const { - return (*this)(input_val, target_val, dy_val) * weight_val; - } -}; - -template<> -struct BinaryCrossEntropyGradFunctor { - BinaryCrossEntropyGradFunctor float_functor; - BinaryCrossEntropyGradFunctor() {} - __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val) const { - return __float2half( - float_functor(__half2float(input_val), __half2float(target_val), __half2float(dy_val))); - } - __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val, - half weight_val) const { - return __float2half(float_functor(__half2float(input_val), __half2float(target_val), - __half2float(dy_val), __half2float(weight_val))); - } -}; - -template -class BinaryCrossEntropyKernel final : public user_op::OpKernel { - public: - BinaryCrossEntropyKernel() = default; - ~BinaryCrossEntropyKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - auto* out_blob = ctx->Tensor4ArgNameAndIndex("out", 0); - - const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); - - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - OF_CUDA_CHECK( - (cuda::elementwise::Ternary(BinaryCrossEntropyFunctor(), elem_cnt, out, input, target, - weight, ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK( - (cuda::elementwise::Binary(BinaryCrossEntropyFunctor(), elem_cnt, out, input, target, - ctx->stream()->As()->cuda_stream()))); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class BinaryCrossEntropyGradKernel final : public user_op::OpKernel { - public: - BinaryCrossEntropyGradKernel() = default; - ~BinaryCrossEntropyGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - const auto* dy_blob = ctx->Tensor4ArgNameAndIndex("dy", 0); - auto* dx_blob = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); - - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyGradFunctor; - using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, - ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyGradFunctor(), elem_cnt, dx, input, target, dy, - ctx->stream()->As()->cuda_stream()))); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace - -#define REGISTER_BINARY_CROSS_ENTROPY_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -#define REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double) - -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cpp b/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cpp index 00e176d8b72db55747f4de27019e171f2c1e9631..e8ab7b5a46add694ce5fdd1a189628c5106e5344 100644 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cpp +++ b/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cpp @@ -17,6 +17,7 @@ limitations under the License. #include "oneflow/core/ndarray/ndarray_util.h" #include "oneflow/core/ndarray/xpu_var_ndarray.h" #include "oneflow/user/kernels/loss_kernel_util.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" namespace oneflow { namespace user_op { @@ -36,20 +37,21 @@ inline T CalSigmoid(const T x) { return half_of_one * std::tanh(half_of_one * x) + half_of_one; } -template -void ComputeBinaryCrossEntropyWithLogitsOut(int64_t elem_cnt, const T* input, const T* target, - T* out, const T* weight, - const T* pos_weight_processed) { +template +void ComputeBinaryCrossEntropyWithLogitsOut(int64_t elem_cnt, const INPUT_T* input, + const TARGET_T* target, TARGET_T* out, + const TARGET_T* weight, + const TARGET_T* pos_weight_processed) { FOR_RANGE(int64_t, i, 0, elem_cnt) { - T input_val = input[i]; - T target_val = target[i]; - T max_val = ComputeMaxVal(input_val); + TARGET_T input_val = static_cast(input[i]); + TARGET_T target_val = target[i]; + TARGET_T max_val = ComputeMaxVal(input_val); if (out != nullptr) { if (pos_weight_processed == nullptr) { out[i] = (1 - target_val) * input_val + max_val + (std::log(std::exp(-max_val) + std::exp(-input_val - max_val))); } else { - T pos_weight_processed_val = pos_weight_processed[i] - target_val + 1; + TARGET_T pos_weight_processed_val = pos_weight_processed[i] - target_val + 1; out[i] = (1 - target_val) * input_val + (pos_weight_processed_val * (std::log(std::exp(-max_val) + std::exp(-input_val - max_val)) + max_val)); @@ -58,28 +60,32 @@ void ComputeBinaryCrossEntropyWithLogitsOut(int64_t elem_cnt, const T* input, co if (weight != nullptr && out != nullptr) { out[i] *= weight[i]; } } } -template -void ComputeBinaryCrossEntropyWithLogitsGradOut(int64_t elem_cnt, const T* input, const T* target, - const T* dy, T* dx, const T* weight, - const T* pos_weight_processed) { + +template +void ComputeBinaryCrossEntropyWithLogitsGradOut(int64_t elem_cnt, const INPUT_T* input, + const TARGET_T* target, const TARGET_T* dy, + INPUT_T* dx, const TARGET_T* weight, + const TARGET_T* pos_weight_processed) { FOR_RANGE(int64_t, i, 0, elem_cnt) { - T input_val = input[i]; - T target_val = target[i]; - T dy_val = dy[i]; - T input_sigmoid = CalSigmoid(input_val); + INPUT_T input_val = input[i]; + TARGET_T target_val = target[i]; + TARGET_T dy_val = dy[i]; + TARGET_T input_sigmoid = static_cast(CalSigmoid(input_val)); + TARGET_T dx_i_buffer = 0.0; if (pos_weight_processed == nullptr) { - dx[i] = (input_sigmoid - target_val) * dy_val; + dx_i_buffer = (input_sigmoid - target_val) * dy_val; } else { - dx[i] = + dx_i_buffer = dy_val * ((pos_weight_processed[i] + 1 - target_val) * input_sigmoid - pos_weight_processed[i]); } - if (weight != nullptr) { dx[i] *= weight[i]; } + if (weight != nullptr) { dx_i_buffer *= weight[i]; } + dx[i] = static_cast(dx_i_buffer); } } -template +template class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsKernel() = default; @@ -95,26 +101,31 @@ class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + TARGET_T* out = out_blob->mut_dptr(); - const T* weight = - ctx->has_input("weight", 0) ? ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr() : nullptr; + const TARGET_T* weight = ctx->has_input("weight", 0) + ? ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr() + : nullptr; - T* pos_weight_processed = nullptr; + TARGET_T* pos_weight_processed = nullptr; if (ctx->Attr("has_pos_weight")) { - pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); + pos_weight_processed = tmp_buffer_blob->mut_dptr(); + const TARGET_T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); + auto bcast_mul = + ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kMul, target_blob->data_type(), + target_blob->data_type(), target_blob->shape_view().NumAxes()); + CHECK(bcast_mul); + bcast_mul->Launch(ctx->stream(), target_blob->shape_view().NumAxes(), + target_blob->shape_view().ptr(), target, pos_weight_shape.NumAxes(), + pos_weight_shape.dim_vec().data(), pos_weight, pos_weight_processed); } ComputeBinaryCrossEntropyWithLogitsOut(elem_cnt, input, target, out, weight, pos_weight_processed); @@ -122,7 +133,7 @@ class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template +template class BinaryCrossEntropyWithLogitsGradKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsGradKernel() = default; @@ -139,26 +150,31 @@ class BinaryCrossEntropyWithLogitsGradKernel final : public user_op::OpKernel { const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); - const T* weight = - ctx->has_input("weight", 0) ? ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr() : nullptr; + const TARGET_T* dy = dy_blob->dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + INPUT_T* dx = dx_blob->mut_dptr(); + const TARGET_T* weight = ctx->has_input("weight", 0) + ? ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr() + : nullptr; - T* pos_weight_processed = nullptr; + TARGET_T* pos_weight_processed = nullptr; if (ctx->Attr("has_pos_weight")) { - pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); + pos_weight_processed = tmp_buffer_blob->mut_dptr(); + const TARGET_T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); + auto bcast_mul = + ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kMul, target_blob->data_type(), + target_blob->data_type(), target_blob->shape_view().NumAxes()); + CHECK(bcast_mul); + bcast_mul->Launch(ctx->stream(), target_blob->shape_view().NumAxes(), + target_blob->shape_view().ptr(), target, pos_weight_shape.NumAxes(), + pos_weight_shape.dim_vec().data(), pos_weight, pos_weight_processed); } ComputeBinaryCrossEntropyWithLogitsGradOut(elem_cnt, input, target, dy, dx, weight, pos_weight_processed); @@ -188,29 +204,34 @@ user_op::InferTmpSizeFn GenBwInferTmpSizeFn() { } // namespace -#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenFwInferTmpSizeFn()); - -#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenBwInferTmpSizeFn()); - -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(double) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(double) +#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(GenFwInferTmpSizeFn()); + +#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(GenBwInferTmpSizeFn()); + +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_KERNEL(double, double) + +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_GRAD_KERNEL(double, double) } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cu b/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cu index 27ebbff8cd5f313c46f43d18fe511f170969e18b..a43421c1894626c4c12a45dbd6744546646d6220 100644 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cu +++ b/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.cu @@ -19,6 +19,7 @@ limitations under the License. #include "oneflow/core/ndarray/xpu_var_ndarray.h" #include "oneflow/user/kernels/loss_kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" namespace oneflow { namespace user_op { @@ -33,89 +34,131 @@ enum class WeightType { kBoth, }; -template +template struct BinaryCrossEntropyWithLogitsFunctor; -template -struct BinaryCrossEntropyWithLogitsFunctor { - T zero_; - T one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(GetZeroVal()), one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val) const { - const T max_val = -input_val < zero_ ? zero_ : -input_val; - return (one_ - target_val) * input_val + max_val - + (log(exp(-max_val) + exp(-input_val - max_val))); +template +struct BinaryCrossEntropyWithLogitsFunctor { + TARGET_T zero_; + TARGET_T one_; + BinaryCrossEntropyWithLogitsFunctor() + : zero_(GetZeroVal()), one_(GetOneVal()) {} + __device__ __forceinline__ TARGET_T operator()(INPUT_T input_val, TARGET_T target_val) const { + const TARGET_T input_val_ = static_cast(input_val); + const TARGET_T max_val = -input_val_ < zero_ ? zero_ : -input_val_; + return (one_ - target_val) * input_val_ + max_val + + (log(exp(-max_val) + exp(-input_val_ - max_val))); } }; -template -struct BinaryCrossEntropyWithLogitsFunctor { - T zero_; - T one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(GetZeroVal()), one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val) const { - const T max_val = -input_val < zero_ ? zero_ : -input_val; - const T pos_weight_processed_val = weight_val - target_val + one_; - return (one_ - target_val) * input_val +template +struct BinaryCrossEntropyWithLogitsFunctor { + TARGET_T zero_; + TARGET_T one_; + BinaryCrossEntropyWithLogitsFunctor() + : zero_(GetZeroVal()), one_(GetOneVal()) {} + __device__ __forceinline__ TARGET_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T weight_val) const { + const TARGET_T input_val_ = static_cast(input_val); + const TARGET_T max_val = -input_val_ < zero_ ? zero_ : -input_val_; + const TARGET_T pos_weight_processed_val = weight_val - target_val + one_; + return (one_ - target_val) * input_val_ + (pos_weight_processed_val - * (log(exp(-max_val) + exp(-input_val - max_val)) + max_val)); + * (log(exp(-max_val) + exp(-input_val_ - max_val)) + max_val)); } }; -template<> -struct BinaryCrossEntropyWithLogitsFunctor { +template +struct BinaryCrossEntropyWithLogitsFunctor { float zero_; float one_; BinaryCrossEntropyWithLogitsFunctor() : zero_(0.f), one_(1.f) {} - __device__ __forceinline__ float operator()(float input_val, float target_val) const { - const float max_val = -input_val < zero_ ? zero_ : -input_val; - return (one_ - target_val) * input_val + max_val - + (logf(expf(-max_val) + expf(-input_val - max_val))); + __device__ __forceinline__ float operator()(INPUT_T input_val, float target_val) const { + const float input_val_ = static_cast(input_val); + const float max_val = -input_val_ < zero_ ? zero_ : -input_val_; + return (one_ - target_val) * input_val_ + max_val + + (logf(expf(-max_val) + expf(-input_val_ - max_val))); } }; -template<> -struct BinaryCrossEntropyWithLogitsFunctor { +template +struct BinaryCrossEntropyWithLogitsFunctor { float zero_; float one_; BinaryCrossEntropyWithLogitsFunctor() : zero_(0.f), one_(1.f) {} - __device__ __forceinline__ float operator()(float input_val, float target_val, + __device__ __forceinline__ float operator()(INPUT_T input_val, float target_val, float weight_val) const { - const float max_val = -input_val < zero_ ? zero_ : -input_val; + const float input_val_ = static_cast(input_val); + const float max_val = -input_val_ < zero_ ? zero_ : -input_val_; const float pos_weight_processed_val = weight_val - target_val + one_; - return (one_ - target_val) * input_val + return (one_ - target_val) * input_val_ + (pos_weight_processed_val - * (logf(expf(-max_val) + expf(-input_val - max_val)) + max_val)); + * (logf(expf(-max_val) + expf(-input_val_ - max_val)) + max_val)); } }; -template -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val) const { +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ TARGET_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T weight_val) const { return f(input_val, target_val) * weight_val; } }; -template -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val, - T pos_weight_val) const { +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ TARGET_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T weight_val, + TARGET_T pos_weight_val) const { return f(input_val, target_val, pos_weight_val) * weight_val; } }; +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ half operator()(INPUT_T input_val, half target_val) const { + return __float2half(f(input_val, __half2float(target_val))); + } +}; +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ half operator()(INPUT_T input_val, half target_val, + half weight_val) const { + return __float2half(f(input_val, __half2float(target_val), __half2float(weight_val))); + } +}; +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ half operator()(INPUT_T input_val, half target_val, + half weight_val) const { + return __float2half(f(input_val, __half2float(target_val), __half2float(weight_val))); + } +}; +template +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; + __device__ __forceinline__ half operator()(INPUT_T input_val, half target_val, half weight_val, + half pos_weight_val) const { + return __float2half(f(input_val, __half2float(target_val), __half2float(weight_val), + __half2float(pos_weight_val))); + } +}; + template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; __device__ __forceinline__ half operator()(half input_val, half target_val) const { return __float2half(f(__half2float(input_val), __half2float(target_val))); } }; template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; __device__ __forceinline__ half operator()(half input_val, half target_val, half weight_val) const { return __float2half( @@ -123,8 +166,8 @@ struct BinaryCrossEntropyWithLogitsFunctor { } }; template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; __device__ __forceinline__ half operator()(half input_val, half target_val, half weight_val) const { return __float2half( @@ -132,8 +175,8 @@ struct BinaryCrossEntropyWithLogitsFunctor { } }; template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; +struct BinaryCrossEntropyWithLogitsFunctor { + BinaryCrossEntropyWithLogitsFunctor f; __device__ __forceinline__ half operator()(half input_val, half target_val, half weight_val, half pos_weight_val) const { return __float2half(f(__half2float(input_val), __half2float(target_val), @@ -158,43 +201,158 @@ __device__ __forceinline__ half CalSigmoid(const half x) { return __float2half(CalSigmoid(__half2float(x))); } -template +template struct BinaryCrossEntropyWithLogitsGradFunctor; -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val) const { - return (CalSigmoid(input_val) - target_val) * dy_val; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T dy_val) const { + return (CalSigmoid(input_val) - static_cast(target_val)) + * static_cast(dy_val); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + INPUT_T one_; + BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T dy_val, TARGET_T weight_val) const { + TARGET_T dx_tmp = + dy_val + * ((weight_val + one_ - target_val) * static_cast(CalSigmoid(input_val)) + - weight_val); + return static_cast(dx_tmp); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T dy_val, TARGET_T weight_val) const { + return f(input_val, target_val, dy_val) * static_cast(weight_val); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, TARGET_T target_val, + TARGET_T dy_val, TARGET_T weight_val, + TARGET_T pos_weight_val) const { + return f(input_val, target_val, dy_val, pos_weight_val) * static_cast(weight_val); } }; -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - T one_; - BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val) const { +template<> +struct BinaryCrossEntropyWithLogitsGradFunctor { + __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val) const { + return (CalSigmoid(input_val) - target_val) * dy_val; + } +}; +template<> +struct BinaryCrossEntropyWithLogitsGradFunctor { + half one_; + BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} + __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val, + half weight_val) const { return dy_val * ((weight_val + one_ - target_val) * CalSigmoid(input_val) - weight_val); } }; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - BinaryCrossEntropyWithLogitsGradFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val) const { +template<> +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val, + half weight_val) const { return f(input_val, target_val, dy_val) * weight_val; } }; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - BinaryCrossEntropyWithLogitsGradFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val, - T pos_weight_val) const { +template<> +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val, + half weight_val, half pos_weight_val) const { return f(input_val, target_val, dy_val, pos_weight_val) * weight_val; } }; -template +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, half target_val, + half dy_val) const { + return (CalSigmoid(input_val) - static_cast(__half2float(target_val))) + * static_cast(__half2float(dy_val)); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + INPUT_T one_; + BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, half target_val, half dy_val, + half weight_val) const { + const INPUT_T dy_val_f = static_cast(__half2float(dy_val)); + const INPUT_T target_val_f = static_cast(__half2float(target_val)); + const INPUT_T weight_val_f = static_cast(__half2float(weight_val)); + return dy_val_f * ((weight_val_f + one_ - target_val_f) * CalSigmoid(input_val)) - weight_val_f; + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, half target_val, half dy_val, + half weight_val) const { + return f(input_val, target_val, dy_val) * static_cast(__half2float(weight_val)); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ INPUT_T operator()(INPUT_T input_val, half target_val, half dy_val, + half weight_val, half pos_weight_val) const { + return f(input_val, target_val, dy_val, pos_weight_val) + * static_cast(__half2float(weight_val)); + } +}; + +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + __device__ __forceinline__ half operator()(half input_val, TARGET_T target_val, + TARGET_T dy_val) const { + const half dy_val_h = __float2half(static_cast(dy_val)); + const half target_val_h = __float2half(static_cast(target_val)); + return (CalSigmoid(input_val) - target_val_h) * dy_val_h; + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + half one_; + BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} + __device__ __forceinline__ half operator()(half input_val, TARGET_T target_val, TARGET_T dy_val, + TARGET_T weight_val) const { + const half dy_val_h = __float2half(static_cast(dy_val)); + const half target_val_h = __float2half(static_cast(target_val)); + const half weight_val_h = __float2half(static_cast(weight_val)); + return dy_val_h * ((weight_val_h + one_ - target_val_h) * CalSigmoid(input_val) - weight_val_h); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ half operator()(half input_val, TARGET_T target_val, TARGET_T dy_val, + TARGET_T weight_val) const { + return f(input_val, target_val, dy_val) * __float2half(static_cast(weight_val)); + } +}; +template +struct BinaryCrossEntropyWithLogitsGradFunctor { + BinaryCrossEntropyWithLogitsGradFunctor f; + __device__ __forceinline__ half operator()(half input_val, TARGET_T target_val, TARGET_T dy_val, + TARGET_T weight_val, TARGET_T pos_weight_val) const { + return f(input_val, target_val, dy_val, pos_weight_val) + * __float2half(static_cast(weight_val)); + } +}; + +template class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsKernel() = default; @@ -210,51 +368,57 @@ class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + TARGET_T* out = out_blob->mut_dptr(); if (ctx->Attr("has_pos_weight")) { - T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); + TARGET_T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); + const TARGET_T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); + auto bcast_mul = + ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kMul, target_blob->data_type(), + target_blob->data_type(), target_blob->shape_view().NumAxes()); + CHECK(bcast_mul); + bcast_mul->Launch(ctx->stream(), target_blob->shape_view().NumAxes(), + target_blob->shape_view().ptr(), target, pos_weight_shape.NumAxes(), + pos_weight_shape.dim_vec().data(), pos_weight, pos_weight_processed); if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsFunctor; + const TARGET_T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + using FunctorT = BinaryCrossEntropyWithLogitsFunctor; using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, out, input, target, weight, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); - + OF_CUDA_CHECK( + (cuda::elementwise:: + GenericLauncher::Launch( + FactoryT(FunctorT()), elem_cnt, out, input, target, weight, + pos_weight_processed, ctx->stream()->As()->cuda_stream()))); } else { OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, pos_weight_processed, ctx->stream()->As()->cuda_stream()))); + BinaryCrossEntropyWithLogitsFunctor(), + elem_cnt, out, input, target, pos_weight_processed, + ctx->stream()->As()->cuda_stream()))); } } else { if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + const TARGET_T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, weight, ctx->stream()->As()->cuda_stream()))); + BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, + out, input, target, weight, ctx->stream()->As()->cuda_stream()))); } else { OF_CUDA_CHECK((cuda::elementwise::Binary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, ctx->stream()->As()->cuda_stream()))); + BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, + out, input, target, ctx->stream()->As()->cuda_stream()))); } } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template +template class BinaryCrossEntropyWithLogitsGradKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsGradKernel() = default; @@ -271,50 +435,62 @@ class BinaryCrossEntropyWithLogitsGradKernel final : public user_op::OpKernel { const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); + const TARGET_T* dy = dy_blob->dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + INPUT_T* dx = dx_blob->mut_dptr(); if (ctx->Attr("has_pos_weight")) { - T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); + TARGET_T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); + const TARGET_T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); - + auto bcast_mul = + ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kMul, target_blob->data_type(), + target_blob->data_type(), target_blob->shape_view().NumAxes()); + CHECK(bcast_mul); + bcast_mul->Launch(ctx->stream(), target_blob->shape_view().NumAxes(), + target_blob->shape_view().ptr(), target, pos_weight_shape.NumAxes(), + pos_weight_shape.dim_vec().data(), pos_weight, pos_weight_processed); if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; + const TARGET_T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + using FunctorT = + BinaryCrossEntropyWithLogitsGradFunctor; using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); + OF_CUDA_CHECK((cuda::elementwise::GenericLauncher< + FactoryT, INPUT_T, INPUT_T, TARGET_T, TARGET_T, TARGET_T, + TARGET_T>::Launch(FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, + weight, pos_weight_processed, + ctx->stream()->As()->cuda_stream()))); } else { - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; + using FunctorT = + BinaryCrossEntropyWithLogitsGradFunctor; using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); + OF_CUDA_CHECK( + (cuda::elementwise:: + GenericLauncher::Launch( + FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, pos_weight_processed, + ctx->stream()->As()->cuda_stream()))); } } else { if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; + const TARGET_T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + using FunctorT = + BinaryCrossEntropyWithLogitsGradFunctor; using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, - ctx->stream()->As()->cuda_stream()))); + OF_CUDA_CHECK( + (cuda::elementwise:: + GenericLauncher::Launch( + FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, + ctx->stream()->As()->cuda_stream()))); } else { OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsGradFunctor(), elem_cnt, dx, input, - target, dy, ctx->stream()->As()->cuda_stream()))); + BinaryCrossEntropyWithLogitsGradFunctor(), + elem_cnt, dx, input, target, dy, ctx->stream()->As()->cuda_stream()))); } } } @@ -330,6 +506,7 @@ user_op::InferTmpSizeFn GenFwInferTmpSizeFn() { return tmp_buffer_size; }; } + template user_op::InferTmpSizeFn GenBwInferTmpSizeFn() { return [](user_op::InferContext* ctx) { @@ -342,32 +519,44 @@ user_op::InferTmpSizeFn GenBwInferTmpSizeFn() { } // namespace -#define REGISTER_BINARY_CROSS_ENTROPY_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenFwInferTmpSizeFn()); - -#define REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenBwInferTmpSizeFn()); - -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double) - -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double) +#define REGISTER_BINARY_CROSS_ENTROPY_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(GenFwInferTmpSizeFn()); + +#define REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(GenBwInferTmpSizeFn()); + +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half, half) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half, float) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float, half) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half, double) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double, half) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double, double) + +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half, half) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half, float) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float, half) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half, double) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double, half) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double, double) } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.hip.cpp b/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.hip.cpp deleted file mode 100644 index fc19e37493d56289ffd1f7f03102f699e42dd81c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_kernel.hip.cpp +++ /dev/null @@ -1,373 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#include "oneflow/user/kernels/loss_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { -namespace { - -using namespace loss; - -enum class WeightType { - kNone, - kWeight, - kPosWeight, - kBoth, -}; - -template -struct BinaryCrossEntropyWithLogitsFunctor; - -template -struct BinaryCrossEntropyWithLogitsFunctor { - T zero_; - T one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(GetZeroVal()), one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val) const { - const T max_val = -input_val < zero_ ? zero_ : -input_val; - return (one_ - target_val) * input_val + max_val - + (log(exp(-max_val) + exp(-input_val - max_val))); - } -}; - -template -struct BinaryCrossEntropyWithLogitsFunctor { - T zero_; - T one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(GetZeroVal()), one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val) const { - const T max_val = -input_val < zero_ ? zero_ : -input_val; - const T pos_weight_processed_val = weight_val - target_val + one_; - return (one_ - target_val) * input_val - + (pos_weight_processed_val - * (log(exp(-max_val) + exp(-input_val - max_val)) + max_val)); - } -}; - -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - float zero_; - float one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(0.f), one_(1.f) {} - __device__ __forceinline__ float operator()(float input_val, float target_val) const { - const float max_val = -input_val < zero_ ? zero_ : -input_val; - return (one_ - target_val) * input_val + max_val - + (logf(expf(-max_val) + expf(-input_val - max_val))); - } -}; - -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - float zero_; - float one_; - BinaryCrossEntropyWithLogitsFunctor() : zero_(0.f), one_(1.f) {} - __device__ __forceinline__ float operator()(float input_val, float target_val, - float weight_val) const { - const float max_val = -input_val < zero_ ? zero_ : -input_val; - const float pos_weight_processed_val = weight_val - target_val + one_; - return (one_ - target_val) * input_val - + (pos_weight_processed_val - * (logf(expf(-max_val) + expf(-input_val - max_val)) + max_val)); - } -}; - -template -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val) const { - return f(input_val, target_val) * weight_val; - } -}; - -template -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T weight_val, - T pos_weight_val) const { - return f(input_val, target_val, pos_weight_val) * weight_val; - } -}; - -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ half operator()(half input_val, half target_val) const { - return __float2half(f(__half2float(input_val), __half2float(target_val))); - } -}; -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ half operator()(half input_val, half target_val, - half weight_val) const { - return __float2half( - f(__half2float(input_val), __half2float(target_val), __half2float(weight_val))); - } -}; -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ half operator()(half input_val, half target_val, - half weight_val) const { - return __float2half( - f(__half2float(input_val), __half2float(target_val), __half2float(weight_val))); - } -}; -template<> -struct BinaryCrossEntropyWithLogitsFunctor { - BinaryCrossEntropyWithLogitsFunctor f; - __device__ __forceinline__ half operator()(half input_val, half target_val, half weight_val, - half pos_weight_val) const { - return __float2half(f(__half2float(input_val), __half2float(target_val), - __half2float(weight_val), __half2float(pos_weight_val))); - } -}; - -template -__device__ __forceinline__ T CalSigmoid(const T x) { - const T half_of_one = static_cast(0.5); - return half_of_one * tanh(half_of_one * x) + half_of_one; -} - -template<> -__device__ __forceinline__ float CalSigmoid(const float x) { - const float half_of_one = static_cast(0.5); - return half_of_one * tanhf(half_of_one * x) + half_of_one; -} - -template<> -__device__ __forceinline__ half CalSigmoid(const half x) { - return __float2half(CalSigmoid(__half2float(x))); -} - -template -struct BinaryCrossEntropyWithLogitsGradFunctor; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val) const { - return (CalSigmoid(input_val) - target_val) * dy_val; - } -}; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - T one_; - BinaryCrossEntropyWithLogitsGradFunctor() : one_(GetOneVal()) {} - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val) const { - return dy_val * ((weight_val + one_ - target_val) * CalSigmoid(input_val) - weight_val); - } -}; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - BinaryCrossEntropyWithLogitsGradFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val) const { - return f(input_val, target_val, dy_val) * weight_val; - } -}; - -template -struct BinaryCrossEntropyWithLogitsGradFunctor { - BinaryCrossEntropyWithLogitsGradFunctor f; - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val, T weight_val, - T pos_weight_val) const { - return f(input_val, target_val, dy_val, pos_weight_val) * weight_val; - } -}; - -template -class BinaryCrossEntropyWithLogitsKernel final : public user_op::OpKernel { - public: - BinaryCrossEntropyWithLogitsKernel() = default; - ~BinaryCrossEntropyWithLogitsKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - auto* out_blob = ctx->Tensor4ArgNameAndIndex("out", 0); - auto* tmp_buffer_blob = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); - - if (ctx->Attr("has_pos_weight")) { - T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); - - Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); - pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, - ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsFunctor; - using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, out, input, target, weight, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); - - } else { - OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, pos_weight_processed, ctx->stream()->As()->cuda_stream()))); - } - } else { - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, weight, ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK((cuda::elementwise::Binary( - BinaryCrossEntropyWithLogitsFunctor(), elem_cnt, out, input, - target, ctx->stream()->As()->cuda_stream()))); - } - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class BinaryCrossEntropyWithLogitsGradKernel final : public user_op::OpKernel { - public: - BinaryCrossEntropyWithLogitsGradKernel() = default; - ~BinaryCrossEntropyWithLogitsGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - const auto* dy_blob = ctx->Tensor4ArgNameAndIndex("dy", 0); - auto* dx_blob = ctx->Tensor4ArgNameAndIndex("dx", 0); - auto* tmp_buffer_blob = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const int64_t elem_cnt = input_blob->shape_view().elem_cnt(); - - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); - - if (ctx->Attr("has_pos_weight")) { - T* pos_weight_processed = tmp_buffer_blob->mut_dptr(); - const T* pos_weight = ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->dptr(); - - Shape pos_weight_shape = Shape::Ones(target_blob->shape_view().NumAxes()); - pos_weight_shape.Set(pos_weight_shape.NumAxes() - 1, - ctx->Tensor4ArgNameAndIndex("pos_weight", 0)->shape_view().elem_cnt()); - NdarrayUtil::BroadcastMul( - ctx->stream(), XpuVarNdarray(target_blob->shape_view(), pos_weight_processed), - XpuVarNdarray(pos_weight_shape, pos_weight), - XpuVarNdarray(target_blob->shape_view(), target)); - - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; - using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); - - } else { - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; - using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, pos_weight_processed, - ctx->stream()->As()->cuda_stream()))); - } - } else { - if (ctx->has_input("weight", 0)) { - const T* weight = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); - using FunctorT = BinaryCrossEntropyWithLogitsGradFunctor; - using FactoryT = cuda::elementwise::SimpleFactory; - OF_CUDA_CHECK((cuda::elementwise::GenericLauncher::Launch( - FactoryT(FunctorT()), elem_cnt, dx, input, target, dy, weight, - ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK((cuda::elementwise::Ternary( - BinaryCrossEntropyWithLogitsGradFunctor(), elem_cnt, dx, input, - target, dy, ctx->stream()->As()->cuda_stream()))); - } - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -user_op::InferTmpSizeFn GenFwInferTmpSizeFn() { - return [](user_op::InferContext* ctx) { - const int64_t n = ctx->InputShape("input", 0).elem_cnt(); - size_t tmp_buffer_size = 0; - if (ctx->Attr("has_pos_weight")) { tmp_buffer_size += GetCudaAlignedSize(n * sizeof(T)); } - return tmp_buffer_size; - }; -} -template -user_op::InferTmpSizeFn GenBwInferTmpSizeFn() { - return [](user_op::InferContext* ctx) { - const int64_t n = ctx->InputShape("target", 0).elem_cnt(); - size_t tmp_buffer_size = 0; - if (ctx->Attr("has_pos_weight")) { tmp_buffer_size += GetCudaAlignedSize(n * sizeof(T)); } - return tmp_buffer_size; - }; -} - -} // namespace - -#define REGISTER_BINARY_CROSS_ENTROPY_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenFwInferTmpSizeFn()); - -#define REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(GenBwInferTmpSizeFn()); - -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_KERNEL(double) - -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_GRAD_KERNEL(double) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.cu b/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.cu index 566c03f94ad70bbbed0bad7648e9cd7642b365ab..b6c290ec969ec6d0bf27125d307f7b0ed2319396 100644 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.cu +++ b/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.cu @@ -16,7 +16,12 @@ limitations under the License. #include "oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" #include "oneflow/core/cuda/elementwise.cuh" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/kernel/cuda_graph_support.h" namespace oneflow { @@ -40,38 +45,54 @@ struct DefaultComputeType { }; template -inline cudaError_t GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, +inline GPU(Error_t) GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, int64_t max_blocks, int64_t waves, int* num_blocks) { int dev; { - cudaError_t err = cudaGetDevice(&dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } } int sm_count; { - cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } } int max_active_blocks; { - cudaError_t err = cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_active_blocks, func, + GPU(Error_t) err = GPU(OccupancyMaxActiveBlocksPerMultiprocessor)(&max_active_blocks, func, block_size, dynamic_smem_size); } *num_blocks = std::max(1, std::min(max_blocks, sm_count * max_active_blocks * waves)); - return cudaSuccess; + return GPU(Success); } -template -__global__ void FusedBinaryCrossEntropyWithLogitsReduceMeanKernel(const In* input, const In* target, - Out* out, - const int32_t local_elem_cnt, - const int32_t reduce_elem_cnt) { +template +__device__ __forceinline__ T Sigmoid(const T x) { + const T half_of_one = static_cast(0.5); + return half_of_one * tanh(half_of_one * x) + half_of_one; +} + +template<> +__device__ __forceinline__ half Sigmoid(const half x) { + return __float2half(Sigmoid(__half2float(x))); +} + +template +__global__ void FusedBinaryCrossEntropyWithLogitsReduceMeanKernel(const INPUT_T* input, + const TARGET_T* target, + OUTPUT_T* out, + const int64_t local_elem_cnt, + const int64_t reduce_elem_cnt) { ComputeType zero = static_cast(0.0); ComputeType one = static_cast(1.0); +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else using BlockReduce = cub::BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; - ComputeType reduce_sum = 0.0; + ComputeType reduce_sum = static_cast(0.0); CUDA_1D_KERNEL_LOOP(i, local_elem_cnt) { const ComputeType input_val = static_cast(input[i]); const ComputeType target_val = static_cast(target[i]); @@ -82,56 +103,155 @@ __global__ void FusedBinaryCrossEntropyWithLogitsReduceMeanKernel(const In* inpu } const ComputeType block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); - if (threadIdx.x == 0) { out[blockIdx.x] = static_cast(block_reduce_sum / reduce_elem_cnt); } + if (threadIdx.x == 0) { + out[blockIdx.x] = static_cast(block_reduce_sum / reduce_elem_cnt); + } } -template -__global__ void ReduceLocalSumKernel(ComputeType* block_local_sum_buf, Out* out, int64_t elem_cnt) { - using BlockReduce = cub::BlockReduce; +template +__global__ void ReduceLocalSumKernel(INPUT_T* block_local_sum_buf, TARGET_T* out, + int64_t elem_cnt) { +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else + using BlockReduce = cub::BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; - ComputeType reduce_sum = 0.0; + INPUT_T reduce_sum = 0.0; CUDA_1D_KERNEL_LOOP(i, elem_cnt) { reduce_sum += block_local_sum_buf[i]; } - const ComputeType block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); - if (threadIdx.x == 0) { out[0] = static_cast(block_reduce_sum); } -} - -template -__device__ __forceinline__ T Sigmoid(const T x) { - const T half_of_one = static_cast(0.5); - return half_of_one * tanh(half_of_one * x) + half_of_one; + const INPUT_T block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); + if (threadIdx.x == 0) { out[0] = block_reduce_sum; } } -template<> -__device__ __forceinline__ half Sigmoid(const half x) { - return __float2half(Sigmoid(__half2float(x))); -} - -template +template struct BinaryCrossEntropyWithLogitsReduceMeanGradFunctor { OF_DEVICE_FUNC explicit BinaryCrossEntropyWithLogitsReduceMeanGradFunctor( - const T elem_cnt_reciprocal, const T dy) + const INPUT_T elem_cnt_reciprocal, const TARGET_T dy) : elem_cnt_reciprocal(elem_cnt_reciprocal), dy(dy) {} - __device__ T operator()(const T input_val, const T target_val) const { - return (Sigmoid(input_val) - target_val) * dy * elem_cnt_reciprocal; + __device__ ComputeType operator()(const INPUT_T input_val, const TARGET_T target_val) const { + const ComputeType input_val_ = static_cast(input_val); + const ComputeType target_val_ = static_cast(target_val); + const ComputeType dy_ = static_cast(dy); + const ComputeType elem_cnt_reciprocal_ = static_cast(elem_cnt_reciprocal); + return (Sigmoid(input_val_) - target_val_) * dy_ * elem_cnt_reciprocal_; } - const T dy; - const T elem_cnt_reciprocal; + const TARGET_T dy; + const INPUT_T elem_cnt_reciprocal; }; -template +template struct BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor { OF_DEVICE_FUNC explicit BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor( - const int32_t elem_cnt, const T* dy_ptr) + const int32_t elem_cnt, const TARGET_T* dy_ptr) : elem_cnt_reciprocal(1.0f / elem_cnt), dy_ptr(dy_ptr) {} - __device__ BinaryCrossEntropyWithLogitsReduceMeanGradFunctor operator()() const { - return BinaryCrossEntropyWithLogitsReduceMeanGradFunctor(elem_cnt_reciprocal, - *dy_ptr); + __device__ BinaryCrossEntropyWithLogitsReduceMeanGradFunctor + operator()() const { + return BinaryCrossEntropyWithLogitsReduceMeanGradFunctor( + elem_cnt_reciprocal, *dy_ptr); } - const T* dy_ptr; - const T elem_cnt_reciprocal; + const TARGET_T* dy_ptr; + const INPUT_T elem_cnt_reciprocal; }; -template +template +__global__ void FusedBCEReduceMeanFwBwKernel(const INPUT_T* input, const TARGET_T* target, + TARGET_T* out, INPUT_T* input_grad, + const ComputeType constant_output_grad, + const ComputeType elem_cnt_reciprocal, + const int32_t local_elem_cnt, + const int32_t reduce_elem_cnt) { + ComputeType zero = static_cast(0.0); + ComputeType one = static_cast(1.0); + BinaryCrossEntropyWithLogitsReduceMeanGradFunctor grad_functor( + elem_cnt_reciprocal, constant_output_grad); +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else + using BlockReduce = cub::BlockReduce; +#endif + __shared__ typename BlockReduce::TempStorage temp_storage; + TARGET_T reduce_sum = 0.0; + CUDA_1D_KERNEL_LOOP(i, local_elem_cnt) { + const INPUT_T input_val = input[i]; + const TARGET_T target_val = target[i]; + input_grad[i] = grad_functor(input_val, target_val); + const ComputeType input_val_ = static_cast(input_val); + const ComputeType target_val_ = static_cast(target_val); + const ComputeType max_val = -input_val_ < zero ? zero : -input_val_; + const ComputeType result = (one - target_val_) * input_val_ + max_val + + (log(exp(-max_val) + exp(-input_val_ - max_val))); + reduce_sum += result; + } + const ComputeType block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); + if (threadIdx.x == 0) { + out[blockIdx.x] = static_cast(block_reduce_sum / reduce_elem_cnt); + } +} + +template +class FusedBCEMeanFwBwKernel final : public user_op::OpKernel, public CudaGraphSupport { + public: + FusedBCEMeanFwBwKernel() = default; + ~FusedBCEMeanFwBwKernel() override = default; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + std::shared_ptr InitOpKernelCache( + user_op::KernelCacheContext* ctx) const override { + return CreateBCEWithLogitsReduceMeanKernelCache(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); + const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); + auto* out_blob = ctx->Tensor4ArgNameAndIndex("out", 0); + auto* dx_blob = ctx->Tensor4ArgNameAndIndex("dx", 0); + + int64_t local_elem_cnt = input_blob->shape_view().elem_cnt(); + int64_t reduce_elem_cnt = local_elem_cnt; + + if (cache != nullptr) { + // Because `out`'s SBP maybe P or B, we need to use reduce_elem_cnt as reduce_mean factor. + const auto* bce_cache = dynamic_cast(cache); + CHECK_NOTNULL(bce_cache); + reduce_elem_cnt = bce_cache->reduce_elem_cnt(); + } + + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + using ComputeType = typename DefaultComputeType::type; + ComputeType constant_output_grad = ctx->Attr("constant_value"); + ComputeType elem_cnt_reciprocal = static_cast(1) / reduce_elem_cnt; + + if (local_elem_cnt <= kSingleBlockProcessNumThreshold) { + FusedBCEReduceMeanFwBwKernel + <<<1, kBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( + input_blob->dptr(), target_blob->dptr(), + out_blob->mut_dptr(), dx_blob->mut_dptr(), constant_output_grad, + elem_cnt_reciprocal, local_elem_cnt, reduce_elem_cnt); + } else { + auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const int64_t tmp_buffer_elem_cnt = tmp_buffer->shape_view().elem_cnt() / sizeof(TARGET_T); + const int64_t block_num = (local_elem_cnt + kBlockSize - 1) / kBlockSize; + int launch_block = block_num; + OF_CUDA_CHECK(GetNumBlocks(FusedBCEReduceMeanFwBwKernel, + kBlockSize, 0, block_num, 32, &launch_block)); + launch_block = std::min(tmp_buffer_elem_cnt, launch_block); + FusedBCEReduceMeanFwBwKernel + <<stream()->As()->cuda_stream()>>>( + input_blob->dptr(), target_blob->dptr(), + tmp_buffer->mut_dptr(), dx_blob->mut_dptr(), constant_output_grad, + elem_cnt_reciprocal, local_elem_cnt, reduce_elem_cnt); + ReduceLocalSumKernel + <<<1, kReduceLocalSumBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( + tmp_buffer->mut_dptr(), out_blob->mut_dptr(), block_num); + } + } +}; + +template class BinaryCrossEntropyWithLogitsMeanKernel final : public user_op::OpKernel, public CudaGraphSupport { public: @@ -162,37 +282,38 @@ class BinaryCrossEntropyWithLogitsMeanKernel final : public user_op::OpKernel, reduce_elem_cnt = bce_cache->reduce_elem_cnt(); } - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); - using ComputeType = typename DefaultComputeType::type; + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + TARGET_T* out = out_blob->mut_dptr(); + using ComputeType = typename DefaultComputeType::type; if (local_elem_cnt <= kSingleBlockProcessNumThreshold) { - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel + FusedBinaryCrossEntropyWithLogitsReduceMeanKernel <<<1, kBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( - input_blob->dptr(), target_blob->dptr(), out_blob->mut_dptr(), - local_elem_cnt, reduce_elem_cnt); + input_blob->dptr(), target_blob->dptr(), + out_blob->mut_dptr(), local_elem_cnt, reduce_elem_cnt); } else { auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int64_t tmp_buffer_elem_cnt = tmp_buffer->shape_view().elem_cnt() / sizeof(T); + const int64_t tmp_buffer_elem_cnt = tmp_buffer->shape_view().elem_cnt() / sizeof(TARGET_T); const int64_t block_num = (local_elem_cnt + kBlockSize - 1) / kBlockSize; int launch_block = block_num; - OF_CUDA_CHECK(GetNumBlocks( - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, - kBlockSize, 0, block_num, 32, &launch_block)); - launch_block = std::min(tmp_buffer_elem_cnt, launch_block); - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel + OF_CUDA_CHECK( + GetNumBlocks(FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, + kBlockSize, 0, block_num, 32, &launch_block)); + launch_block = std::min(tmp_buffer_elem_cnt, launch_block); + FusedBinaryCrossEntropyWithLogitsReduceMeanKernel <<stream()->As()->cuda_stream()>>>( - input_blob->dptr(), target_blob->dptr(), tmp_buffer->mut_dptr(), - local_elem_cnt, reduce_elem_cnt); - ReduceLocalSumKernel + input_blob->dptr(), target_blob->dptr(), + tmp_buffer->mut_dptr(), local_elem_cnt, reduce_elem_cnt); + ReduceLocalSumKernel <<<1, kReduceLocalSumBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( - tmp_buffer->mut_dptr(), out_blob->mut_dptr(), block_num); + tmp_buffer->mut_dptr(), out_blob->mut_dptr(), block_num); } } }; -template +template class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsReduceMeanGradKernel() = default; @@ -222,55 +343,90 @@ class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::O reduce_elem_cnt = bce_cache->reduce_elem_cnt(); } - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); - using ComputeType = typename DefaultComputeType::type; + const TARGET_T* dy = dy_blob->dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + INPUT_T* dx = dx_blob->mut_dptr(); + using ComputeType = typename DefaultComputeType::type; OF_CUDA_CHECK((cuda::elementwise::BinaryWithFactory( - BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor(reduce_elem_cnt, dy), + BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor( + reduce_elem_cnt, dy), local_elem_cnt, dx, input, target, ctx->stream()->As()->cuda_stream()))); } }; } // namespace -#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const int64_t elem_cnt = ctx->InputShape("input", 0).elem_cnt(); \ - const int64_t block_num = (elem_cnt + kBlockSize - 1) / kBlockSize; \ - int launch_block = block_num; \ - using ComputeType = typename DefaultComputeType::type; \ - OF_CUDA_CHECK(GetNumBlocks( \ - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, \ - kBlockSize, 0, block_num, 32, &launch_block)); \ - const int64_t tmp_buffer_size = GetCudaAlignedSize(launch_block * sizeof(dtype)); \ - return tmp_buffer_size; \ +#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const int64_t elem_cnt = ctx->InputShape("input", 0).elem_cnt(); \ + const int64_t block_num = (elem_cnt + kBlockSize - 1) / kBlockSize; \ + int launch_block = block_num; \ + using compute_dtype = typename DefaultComputeType::type; \ + OF_CUDA_CHECK(GetNumBlocks( \ + FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, \ + kBlockSize, 0, block_num, 32, &launch_block)); \ + const int64_t tmp_buffer_size = GetCudaAlignedSize(launch_block * sizeof(compute_dtype)); \ + return tmp_buffer_size; \ + }); + +#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(half, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(half, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(float, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(half, double) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(double, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(double, double) + +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(half, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(half, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(float, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(half, double) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(double, half) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(double, double) + +#define REGISTER_FUSED_BCE_REDUCE_MEAN_FW_BW_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("fused_bce_reduce_mean_fw_bw") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const int64_t elem_cnt = ctx->InputShape("input", 0).elem_cnt(); \ + const int64_t block_num = (elem_cnt + kBlockSize - 1) / kBlockSize; \ + int launch_block = block_num; \ + using compute_dtype = typename DefaultComputeType::type; \ + OF_CUDA_CHECK(GetNumBlocks( \ + FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, \ + kBlockSize, 0, block_num, 32, &launch_block)); \ + const int64_t tmp_buffer_size = GetCudaAlignedSize(launch_block * sizeof(target_dtype)); \ + return tmp_buffer_size; \ }); -#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(double) - -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(double) +REGISTER_FUSED_BCE_REDUCE_MEAN_FW_BW_KERNEL(half, half) +REGISTER_FUSED_BCE_REDUCE_MEAN_FW_BW_KERNEL(float, float) } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.hip.cpp b/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.hip.cpp deleted file mode 100644 index 2de08216583ba48bbf033f25df5d9c4ab4cc56d2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel.hip.cpp +++ /dev/null @@ -1,277 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include -#include "oneflow/core/kernel/cuda_graph_support.h" - -namespace oneflow { - -namespace user_op { - -namespace { - -constexpr int32_t kBlockSize = 1024; -constexpr int32_t kReduceLocalSumBlockSize = 1024; -constexpr int32_t kSingleBlockProcessNumThreshold = 1024; - -template -struct DefaultComputeType { - using type = T; -}; - -template<> -struct DefaultComputeType { - using type = float; -}; - -template -inline hipError_t GetNumBlocks(Func func, int64_t block_size, size_t dynamic_smem_size, - int64_t max_blocks, int64_t waves, int* num_blocks) { - int dev; - { - hipError_t err = hipGetDevice(&dev); - if (err != hipSuccess) { return err; } - } - int sm_count; - { - hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev); - if (err != hipSuccess) { return err; } - } - int max_active_blocks; - { - hipError_t err = hipOccupancyMaxActiveBlocksPerMultiprocessor(&max_active_blocks, func, - block_size, dynamic_smem_size); - } - *num_blocks = - std::max(1, std::min(max_blocks, sm_count * max_active_blocks * waves)); - return hipSuccess; -} - -template -__global__ void FusedBinaryCrossEntropyWithLogitsReduceMeanKernel(const In* input, const In* target, - Out* out, - const int32_t local_elem_cnt, - const int32_t reduce_elem_cnt) { - ComputeType zero = static_cast(0.0); - ComputeType one = static_cast(1.0); - using BlockReduce = hipcub::BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - ComputeType reduce_sum = 0.0; - CUDA_1D_KERNEL_LOOP(i, local_elem_cnt) { - const ComputeType input_val = static_cast(input[i]); - const ComputeType target_val = static_cast(target[i]); - const ComputeType max_val = -input_val < zero ? zero : -input_val; - const ComputeType result = - (one - target_val) * input_val + max_val + (log(exp(-max_val) + exp(-input_val - max_val))); - reduce_sum += result; - } - - const ComputeType block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); - if (threadIdx.x == 0) { out[blockIdx.x] = static_cast(block_reduce_sum / reduce_elem_cnt); } -} - -template -__global__ void ReduceLocalSumKernel(ComputeType* block_local_sum_buf, Out* out, int64_t elem_cnt) { - using BlockReduce = hipcub::BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - ComputeType reduce_sum = 0.0; - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { reduce_sum += block_local_sum_buf[i]; } - const ComputeType block_reduce_sum = BlockReduce(temp_storage).Sum(reduce_sum); - if (threadIdx.x == 0) { out[0] = static_cast(block_reduce_sum); } -} - -template -__device__ __forceinline__ T Sigmoid(const T x) { - const T half_of_one = static_cast(0.5); - return half_of_one * tanh(half_of_one * x) + half_of_one; -} - -template<> -__device__ __forceinline__ half Sigmoid(const half x) { - return __float2half(Sigmoid(__half2float(x))); -} - -template -struct BinaryCrossEntropyWithLogitsReduceMeanGradFunctor { - OF_DEVICE_FUNC explicit BinaryCrossEntropyWithLogitsReduceMeanGradFunctor( - const T elem_cnt_reciprocal, const T dy) - : elem_cnt_reciprocal(elem_cnt_reciprocal), dy(dy) {} - __device__ T operator()(const T input_val, const T target_val) const { - return (Sigmoid(input_val) - target_val) * dy * elem_cnt_reciprocal; - } - const T dy; - const T elem_cnt_reciprocal; -}; - -template -struct BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor { - OF_DEVICE_FUNC explicit BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor( - const int32_t elem_cnt, const T* dy_ptr) - : elem_cnt_reciprocal(1.0f / elem_cnt), dy_ptr(dy_ptr) {} - __device__ BinaryCrossEntropyWithLogitsReduceMeanGradFunctor operator()() const { - return BinaryCrossEntropyWithLogitsReduceMeanGradFunctor(elem_cnt_reciprocal, - *dy_ptr); - } - const T* dy_ptr; - const T elem_cnt_reciprocal; -}; - -template -class BinaryCrossEntropyWithLogitsMeanKernel final : public user_op::OpKernel, - public CudaGraphSupport { - public: - BinaryCrossEntropyWithLogitsMeanKernel() = default; - ~BinaryCrossEntropyWithLogitsMeanKernel() override = default; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - return CreateBCEWithLogitsReduceMeanKernelCache(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache* cache) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - auto* out_blob = ctx->Tensor4ArgNameAndIndex("out", 0); - - int64_t local_elem_cnt = input_blob->shape_view().elem_cnt(); - int64_t reduce_elem_cnt = local_elem_cnt; - - if (cache != nullptr) { - // Because `out`'s SBP maybe P or B, we need to use reduce_elem_cnt as reduce_mean factor. - const auto* bce_cache = dynamic_cast(cache); - CHECK_NOTNULL(bce_cache); - reduce_elem_cnt = bce_cache->reduce_elem_cnt(); - } - - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); - using ComputeType = typename DefaultComputeType::type; - - if (local_elem_cnt <= kSingleBlockProcessNumThreshold) { - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel - <<<1, kBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( - input_blob->dptr(), target_blob->dptr(), out_blob->mut_dptr(), - local_elem_cnt, reduce_elem_cnt); - } else { - auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int64_t tmp_buffer_elem_cnt = tmp_buffer->shape_view().elem_cnt() / sizeof(T); - const int64_t block_num = (local_elem_cnt + kBlockSize - 1) / kBlockSize; - int launch_block = block_num; - OF_CUDA_CHECK(GetNumBlocks( - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, - kBlockSize, 0, block_num, 32, &launch_block)); - launch_block = std::min(tmp_buffer_elem_cnt, launch_block); - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel - <<stream()->As()->cuda_stream()>>>( - input_blob->dptr(), target_blob->dptr(), tmp_buffer->mut_dptr(), - local_elem_cnt, reduce_elem_cnt); - ReduceLocalSumKernel - <<<1, kReduceLocalSumBlockSize, 0, ctx->stream()->As()->cuda_stream()>>>( - tmp_buffer->mut_dptr(), out_blob->mut_dptr(), block_num); - } - } -}; - -template -class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::OpKernel { - public: - BinaryCrossEntropyWithLogitsReduceMeanGradKernel() = default; - ~BinaryCrossEntropyWithLogitsReduceMeanGradKernel() = default; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - return CreateBCEWithLogitsReduceMeanKernelCache(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache* cache) const override { - const auto* input_blob = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* target_blob = ctx->Tensor4ArgNameAndIndex("target", 0); - const auto* dy_blob = ctx->Tensor4ArgNameAndIndex("dy", 0); - auto* dx_blob = ctx->Tensor4ArgNameAndIndex("dx", 0); - - int64_t local_elem_cnt = input_blob->shape_view().elem_cnt(); - int64_t reduce_elem_cnt = local_elem_cnt; - if (cache != nullptr) { - // Because `out`'s SBP maybe P or B, we need to use reduce_elem_cnt as reduce_mean factor. - const auto* bce_cache = dynamic_cast(cache); - CHECK_NOTNULL(bce_cache); - reduce_elem_cnt = bce_cache->reduce_elem_cnt(); - } - - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); - using ComputeType = typename DefaultComputeType::type; - - OF_CUDA_CHECK((cuda::elementwise::BinaryWithFactory( - BinaryCrossEntropyWithLogitsReduceMeanGradDyptrFunctor(reduce_elem_cnt, dy), - local_elem_cnt, dx, input, target, ctx->stream()->As()->cuda_stream()))); - } -}; - -} // namespace - -#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const int64_t elem_cnt = ctx->InputShape("input", 0).elem_cnt(); \ - const int64_t block_num = (elem_cnt + kBlockSize - 1) / kBlockSize; \ - int launch_block = block_num; \ - using ComputeType = typename DefaultComputeType::type; \ - OF_CUDA_CHECK(GetNumBlocks( \ - FusedBinaryCrossEntropyWithLogitsReduceMeanKernel, \ - kBlockSize, 0, block_num, 32, &launch_block)); \ - const int64_t tmp_buffer_size = GetCudaAlignedSize(launch_block * sizeof(dtype)); \ - return tmp_buffer_size; \ - }); - -#define REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_KERNEL(double) - -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(half) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_REDUCE_MEAN_GRAD_KERNEL(double) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/binary_cross_entropy_with_logits_reduce_mean.cpp b/oneflow/user/kernels/binary_cross_entropy_with_logits_reduce_mean.cpp index 896dad8fdeef5f2d700156c7f698091c206aef4b..8100c290e044fa1fea80ea83a0a28d921649efd7 100644 --- a/oneflow/user/kernels/binary_cross_entropy_with_logits_reduce_mean.cpp +++ b/oneflow/user/kernels/binary_cross_entropy_with_logits_reduce_mean.cpp @@ -13,8 +13,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/common/data_type.pb.h" #include "oneflow/user/kernels/binary_cross_entropy_with_logits_mean_kernel_util.h" #include "oneflow/user/kernels/loss_kernel_util.h" + namespace oneflow { namespace user_op { namespace { @@ -33,35 +35,50 @@ inline T CalSigmoid(const T x) { return half_of_one * std::tanh(half_of_one * x) + half_of_one; } -template -void ComputeBinaryCrossEntropyWithLogitsReduceMeanOut(int64_t elem_cnt, const T* input, - const T* target, T* out, +template +struct ComputeBinaryCrossEntropyWithLogitsReduceMeanOutFunctor { + inline ComputeType Compute(int64_t elem_cnt, const INPUT_T* input, const TARGET_T* target, + int64_t reduce_elem_cnt) { + ComputeType result = 0.0; + FOR_RANGE(int64_t, i, 0, elem_cnt) { + ComputeType input_val = static_cast(input[i]); + ComputeType target_val = static_cast(target[i]); + ComputeType max_val = ComputeMaxVal(input_val); + result += (1 - target_val) * input_val + max_val + + (std::log(std::exp(-max_val) + std::exp(-input_val - max_val))); + } + return static_cast(result) / reduce_elem_cnt; + } +}; + +template +void ComputeBinaryCrossEntropyWithLogitsReduceMeanOut(int64_t elem_cnt, const INPUT_T* input, + const TARGET_T* target, TARGET_T* out, int64_t reduce_elem_cnt) { - T result = 0.0; - FOR_RANGE(int64_t, i, 0, elem_cnt) { - T input_val = input[i]; - T target_val = target[i]; - T max_val = ComputeMaxVal(input_val); - result += (1 - target_val) * input_val + max_val - + (std::log(std::exp(-max_val) + std::exp(-input_val - max_val))); + if (sizeof(INPUT_T) > sizeof(TARGET_T)) { + ComputeBinaryCrossEntropyWithLogitsReduceMeanOutFunctor f; + out[0] = f.Compute(elem_cnt, input, target, reduce_elem_cnt); + } else { + ComputeBinaryCrossEntropyWithLogitsReduceMeanOutFunctor f; + out[0] = f.Compute(elem_cnt, input, target, reduce_elem_cnt); } - out[0] = result / reduce_elem_cnt; } -template -void ComputeBinaryCrossEntropyWithLogitsReduceMeanGradOut(int64_t elem_cnt, const T* input, - const T* target, const T* dy, T* dx, +template +void ComputeBinaryCrossEntropyWithLogitsReduceMeanGradOut(int64_t elem_cnt, const INPUT_T* input, + const TARGET_T* target, + const TARGET_T* dy, INPUT_T* dx, int64_t reduce_elem_cnt) { - T dy_val = dy[0] / reduce_elem_cnt; + INPUT_T dy_val = static_cast(dy[0]) / reduce_elem_cnt; FOR_RANGE(int64_t, i, 0, elem_cnt) { - T input_val = input[i]; - T target_val = target[i]; - T input_sigmoid = CalSigmoid(input_val); + INPUT_T input_val = input[i]; + INPUT_T target_val = static_cast(target[i]); + INPUT_T input_sigmoid = CalSigmoid(input_val); dx[i] = (input_sigmoid - target_val) * dy_val; } } -template +template class BinaryCrossEntropyWithLogitsReduceMeanKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsReduceMeanKernel() = default; @@ -90,16 +107,16 @@ class BinaryCrossEntropyWithLogitsReduceMeanKernel final : public user_op::OpKer reduce_elem_cnt = bce_cache->reduce_elem_cnt(); } - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* out = out_blob->mut_dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + TARGET_T* out = out_blob->mut_dptr(); ComputeBinaryCrossEntropyWithLogitsReduceMeanOut(local_elem_cnt, input, target, out, reduce_elem_cnt); } }; -template +template class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::OpKernel { public: BinaryCrossEntropyWithLogitsReduceMeanGradKernel() = default; @@ -129,10 +146,10 @@ class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::O reduce_elem_cnt = bce_cache->reduce_elem_cnt(); } - const T* dy = dy_blob->dptr(); - const T* input = input_blob->dptr(); - const T* target = target_blob->dptr(); - T* dx = dx_blob->mut_dptr(); + const TARGET_T* dy = dy_blob->dptr(); + const INPUT_T* input = input_blob->dptr(); + const TARGET_T* target = target_blob->dptr(); + INPUT_T* dx = dx_blob->mut_dptr(); ComputeBinaryCrossEntropyWithLogitsReduceMeanGradOut(local_elem_cnt, input, target, dy, dx, reduce_elem_cnt); } @@ -140,27 +157,32 @@ class BinaryCrossEntropyWithLogitsReduceMeanGradKernel final : public user_op::O } // namespace -#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("input", 0) == GetDataType::value) \ - && (user_op::HobDataType("target", 0) == GetDataType::value) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(double) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(float) -REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(double) +#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(input_dtype, target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)); + +#define REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(input_dtype, \ + target_dtype) \ + REGISTER_USER_KERNEL("binary_cross_entropy_with_logits_reduce_mean_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("target", 0) == GetDataType::value) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_KERNEL(double, double) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(float, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(float, double) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(double, float) +REGISTER_BINARY_CROSS_ENTROPY_WITH_LOGITS_REDUCE_MEAN_GRAD_KERNEL(double, double) } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/bincount_kernel.cpp b/oneflow/user/kernels/bincount_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d63658ad4a81066115b3455aafc794a763bbd2c4 --- /dev/null +++ b/oneflow/user/kernels/bincount_kernel.cpp @@ -0,0 +1,78 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/include/primitive/memset.h" +#include "oneflow/core/framework/user_op_hob.h" +#include "oneflow/core/thread/thread_manager.h" + +namespace oneflow { + +template +void BinCountComputeWeight(const IDX* in_ptr, const T* weight, T* out_ptr, int64_t size) { + FOR_RANGE(int64_t, i, 0, size) { + IDX idx = *(in_ptr + i); + out_ptr[idx] += weight[i]; + } +} + +template +void BinCountCompute(const IDX* in_ptr, T* out_ptr, int64_t size) { + FOR_RANGE(int64_t, i, 0, size) { + IDX idx = *(in_ptr + i); + out_ptr[idx] += 1L; + } +} + +template +class CpuBinCountKernel final : public user_op::OpKernel { + public: + CpuBinCountKernel() = default; + ~CpuBinCountKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + size_t out_size = ctx->Attr("size") * sizeof(T); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const IDX* in_ptr = in->dptr(); + T* out_ptr = out->mut_dptr(); + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(ctx->device_type()); + CHECK(memset_primitive); + memset_primitive->Launch(ctx->stream(), out_ptr, 0, out_size); + int64_t in_size = in->shape_view().elem_cnt(); + if (ctx->has_input("weight", 0)) { + const T* weight_ptr = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + BinCountComputeWeight(in_ptr, weight_ptr, out_ptr, in_size); + } else { + BinCountCompute(in_ptr, out_ptr, in_size); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CPU_BINCOUNT_KERNEL(idx_type, dtype) \ + REGISTER_USER_KERNEL("bincount") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("in", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)); + +REGISTER_CPU_BINCOUNT_KERNEL(int64_t, int64_t) +REGISTER_CPU_BINCOUNT_KERNEL(int64_t, float16) +REGISTER_CPU_BINCOUNT_KERNEL(int64_t, float) +REGISTER_CPU_BINCOUNT_KERNEL(int64_t, double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/bincount_kernel.cu b/oneflow/user/kernels/bincount_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..5e6c3910237aad0b54f692fa83120b045defac82 --- /dev/null +++ b/oneflow/user/kernels/bincount_kernel.cu @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/framework/op_kernel.h" +#include "oneflow/core/framework/user_op_hob.h" +#include "oneflow/core/ep/include/primitive/memset.h" + +namespace oneflow { +namespace user_op { +namespace { + +// clang-format off +template +__global__ static void BinCountCompute(const IDX* in_ptr, const T* weight, T* out_ptr, int64_t size) { + CUDA_1D_KERNEL_LOOP(i, size) { + IDX idx = *(in_ptr + i); + cuda::atomic::Add(out_ptr + idx, weight[i]); + } +}; +// clang-format on + +template +__global__ static void BinCountCompute(const IDX* in_ptr, T* out_ptr, int64_t size) { + T one = GetOneVal(); + CUDA_1D_KERNEL_LOOP(i, size) { + IDX idx = *(in_ptr + i); + cuda::atomic::Add(out_ptr + idx, one); + } +}; + +template +class CUDABinCountKernel final : public user_op::OpKernel { + public: + CUDABinCountKernel() = default; + ~CUDABinCountKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + size_t out_size = ctx->Attr("size") * sizeof(T); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const IDX* in_ptr = in->dptr(); + T* out_ptr = out->mut_dptr(); + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(ctx->device_type()); + CHECK(memset_primitive); + memset_primitive->Launch(ctx->stream(), out_ptr, 0, out_size); + int64_t in_size = in->shape_view().elem_cnt(); + if (in_size == 0) { return; } + if (ctx->has_input("weight", 0)) { + const T* weight_ptr = ctx->Tensor4ArgNameAndIndex("weight", 0)->dptr(); + BinCountCompute<<stream()->As()->cuda_stream()>>>( + in_ptr, weight_ptr, out_ptr, in_size); + } else { + BinCountCompute + <<stream()->As()->cuda_stream()>>>(in_ptr, out_ptr, in_size); + } + }; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace oneflow + +#define REGISTER_CUDA_BINCOUNT_KERNEL(idx_type, dtype) \ + REGISTER_USER_KERNEL("bincount") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("in", 0) == GetDataType::value) \ + && (user_op::HobDataType("out", 0) == GetDataType::value)); + +REGISTER_CUDA_BINCOUNT_KERNEL(int64_t, int64_t) +REGISTER_CUDA_BINCOUNT_KERNEL(int64_t, half) +REGISTER_CUDA_BINCOUNT_KERNEL(int64_t, float) +REGISTER_CUDA_BINCOUNT_KERNEL(int64_t, double) + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/broadcast_div_grad_kernel.cpp b/oneflow/user/kernels/broadcast_div_grad_kernel.cpp index d172398fecc0c6407f2fc8e9d51b0330d250b1a9..2489be8b28273013cadae640b9c25a9af42415ba 100644 --- a/oneflow/user/kernels/broadcast_div_grad_kernel.cpp +++ b/oneflow/user/kernels/broadcast_div_grad_kernel.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/ndarray/ndarray_util.h" #include "oneflow/core/ndarray/xpu_var_ndarray.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" namespace oneflow { @@ -40,18 +41,34 @@ class BroadcastDivGradKernel final : public user_op::OpKernel { XpuVarNdarray const_tmp(dz.shape(), tmp_buffer->dptr()); XpuVarNdarray tmp(dz.shape(), tmp_buffer->mut_dptr()); - NdarrayUtil::BroadcastDiv( - ctx->stream(), tmp, - XpuVarNdarray(z_tensor->shape_view(), z_tensor->dptr(), num_axes), - XpuVarNdarray(y_tensor->shape_view(), y_tensor->dptr(), num_axes)); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, dz, const_tmp); + auto bcast_div = ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kDiv, z_tensor->data_type(), + z_tensor->data_type(), z_tensor->shape_view().NumAxes()); + CHECK(bcast_div); + bcast_div->Launch(ctx->stream(), z_tensor->shape_view().NumAxes(), z_tensor->shape_view().ptr(), + z_tensor->dptr(), y_tensor->shape_view().NumAxes(), + y_tensor->shape_view().ptr(), y_tensor->dptr(), tmp_buffer->mut_dptr()); + + auto bcast_mul = ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kMul, dz_tensor->data_type(), + dz_tensor->data_type(), dz_tensor->shape_view().NumAxes()); + CHECK(bcast_mul); + bcast_mul->Launch(ctx->stream(), dz_tensor->shape_view().NumAxes(), + dz_tensor->shape_view().ptr(), tmp_buffer->dptr(), + dz_tensor->shape_view().NumAxes(), dz_tensor->shape_view().ptr(), + dz_tensor->dptr(), tmp_buffer->mut_dptr()); + NdarrayUtil::ReduceSum( ctx->stream(), XpuVarNdarray(dy_tensor->shape_view(), dy_tensor->mut_dptr(), num_axes), const_tmp, tmp); - NdarrayUtil::InplaceNegative( - ctx->stream(), - XpuVarNdarray(dy_tensor->shape_view(), dy_tensor->mut_dptr(), num_axes)); + + auto negative = ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::UnaryOp::kNegative, dy_tensor->data_type(), + dy_tensor->data_type()); + CHECK(negative); + negative->Launch(ctx->stream(), dy_tensor->dptr(), dy_tensor->mut_dptr(), + dy_tensor->shape_view().elem_cnt()); }; bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; @@ -65,18 +82,14 @@ class BroadcastDivGradKernel final : public user_op::OpKernel { && (user_op::HobDataType("y", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ const user_op::TensorDesc& z = ctx->InputTensorDesc("z", 0); \ - const DataType& data_type = z.data_type(); \ + DataType data_type = z.data_type(); \ const int64_t elem_cnt = z.shape().elem_cnt(); \ return GetCudaAlignedSize(elem_cnt * GetSizeOfDataType(data_type)); \ }); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_DIV_GRAD_KERNEL, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_DIV_GRAD_KERNEL, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_DIV_GRAD_KERNEL, (DeviceType::kCUDA), FLOAT16_DATA_TYPE_SEQ) #endif diff --git a/oneflow/user/kernels/broadcast_like_kernel.cpp b/oneflow/user/kernels/broadcast_like_kernel.cpp index abeecdcfea9ddacceae262275293de202c7bc8c3..78a2b5aabe5b58206d869722c4d76de74757dad2 100644 --- a/oneflow/user/kernels/broadcast_like_kernel.cpp +++ b/oneflow/user/kernels/broadcast_like_kernel.cpp @@ -51,11 +51,7 @@ class BroadcastLikeKernel final : public user_op::OpKernel, public user_op::Cuda .SetIsMatchedHob((user_op::HobDeviceType() == device) \ && (user_op::HobDataType("y", 0) == GetDataType::value)); -#if defined(WITH_CUDA) -#define REGISTER_BROADCAST_LIKE_KERNEL(dtype) \ - REGISTER_BROADCAST_LIKE_XPU_KERNEL(DeviceType::kCPU, dtype) \ - REGISTER_BROADCAST_LIKE_XPU_KERNEL(DeviceType::kCUDA, dtype) -#elif defined(WITH_ROCM) +#if defined(WITH_CUDA) || defined(WITH_ROCM) #define REGISTER_BROADCAST_LIKE_KERNEL(dtype) \ REGISTER_BROADCAST_LIKE_XPU_KERNEL(DeviceType::kCPU, dtype) \ REGISTER_BROADCAST_LIKE_XPU_KERNEL(DeviceType::kCUDA, dtype) diff --git a/oneflow/user/kernels/broadcast_pow_grad_kernel.cpp b/oneflow/user/kernels/broadcast_pow_grad_kernel.cpp deleted file mode 100644 index 29e5175a488df19fd18ee4d40f0863d9ae967b85..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/broadcast_pow_grad_kernel.cpp +++ /dev/null @@ -1,137 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#ifdef WITH_CUDA -#include "oneflow/core/kernel/kernel_util.cuh" -#endif -#ifdef WITH_ROCM -#include "oneflow/core/kernel/kernel_util.hip.h" -#endif - -namespace oneflow { - -namespace { - -template -class BroadcastPowXGradKernel final : public user_op::OpKernel { - public: - BroadcastPowXGradKernel() = default; - ~BroadcastPowXGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* z_tensor = ctx->Tensor4ArgNameAndIndex("z", 0); - const user_op::Tensor* dz_tensor = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const int64_t num_axes = dz_tensor->shape_view().NumAxes(); - XpuVarNdarray dz(dz_tensor->shape_view(), dz_tensor->dptr(), num_axes); - XpuVarNdarray y(y_tensor->shape_view(), y_tensor->dptr(), num_axes); - XpuVarNdarray const_tmp(dz.shape(), tmp_buffer->dptr()); - XpuVarNdarray tmp(dz.shape(), tmp_buffer->mut_dptr()); - - NdarrayUtil::BroadcastDiv( - ctx->stream(), tmp, - XpuVarNdarray(z_tensor->shape_view(), z_tensor->dptr(), num_axes), - XpuVarNdarray(x_tensor->shape_view(), x_tensor->dptr(), num_axes)); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, y, const_tmp); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, dz, const_tmp); - NdarrayUtil::ReduceSum( - ctx->stream(), - XpuVarNdarray(dx_tensor->shape_view(), dx_tensor->mut_dptr(), num_axes), const_tmp, - tmp); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class BroadcastPowYGradKernel final : public user_op::OpKernel { - public: - BroadcastPowYGradKernel() = default; - ~BroadcastPowYGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* z_tensor = ctx->Tensor4ArgNameAndIndex("z", 0); - const user_op::Tensor* dz_tensor = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - - const int64_t num_axes = dz_tensor->shape_view().NumAxes(); - const int64_t elem_cnt = z_tensor->shape_view().elem_cnt(); - Memset(ctx->stream(), tmp_buffer->mut_dptr(), 0, - GetCudaAlignedSize(elem_cnt * sizeof(T))); - T* tmp_ptr = tmp_buffer->mut_dptr(); - XpuVarNdarray z(z_tensor->shape_view(), z_tensor->dptr(), num_axes); - XpuVarNdarray dz(dz_tensor->shape_view(), dz_tensor->dptr(), num_axes); - XpuVarNdarray const_tmp(dz.shape(), tmp_buffer->dptr()); - XpuVarNdarray tmp(dz.shape(), tmp_buffer->mut_dptr()); - XpuVarNdarray x(x_tensor->shape_view(), x_tensor->dptr(), num_axes); - XpuVarNdarray dy(dy_tensor->shape_view(), dy_tensor->mut_dptr(), num_axes); - NdarrayUtil::BroadcastAdd(ctx->stream(), tmp, x, const_tmp); - FOR_RANGE(int64_t, i, 0, elem_cnt) { tmp_ptr[i] = SafeLog(tmp_ptr[i]); } - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, dz, const_tmp); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, z, const_tmp); - NdarrayUtil::ReduceSum(ctx->stream(), dy, const_tmp, tmp); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace - -#define REGISTER_BROADCAST_POW_X_GRAD_KERNEL(device, dtype_pair) \ - REGISTER_USER_KERNEL("broadcast_pow_x_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ - const user_op::TensorDesc& z = ctx->InputTensorDesc("z", 0); \ - const DataType& data_type = z.data_type(); \ - const int64_t elem_cnt = z.shape().elem_cnt(); \ - return GetCudaAlignedSize(elem_cnt * GetSizeOfDataType(data_type)); \ - }); - -#define REGISTER_BROADCAST_POW_Y_GRAD_KERNEL(device, dtype_pair) \ - REGISTER_USER_KERNEL("broadcast_pow_y_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ - const user_op::TensorDesc& z = ctx->InputTensorDesc("z", 0); \ - const DataType& data_type = z.data_type(); \ - const int64_t elem_cnt = z.shape().elem_cnt(); \ - return GetCudaAlignedSize(elem_cnt * GetSizeOfDataType(data_type)); \ - }); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_X_GRAD_KERNEL, DEVICE_TYPE_SEQ, - ARITHMETIC_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_Y_GRAD_KERNEL, (DeviceType::kCPU), - ARITHMETIC_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_X_GRAD_KERNEL, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_X_GRAD_KERNEL, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif - -} // namespace oneflow diff --git a/oneflow/user/kernels/broadcast_pow_grad_kernel.cu b/oneflow/user/kernels/broadcast_pow_grad_kernel.cu deleted file mode 100644 index 1471f2383c462631951278cb42b1508540bc7e67..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/broadcast_pow_grad_kernel.cu +++ /dev/null @@ -1,87 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.cuh" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#include "oneflow/core/ep/cuda/cuda_stream.h" - -namespace oneflow { -namespace { -template -__global__ void ComputeLogGpu(const int64_t len, T* out, const T* in) { - CUDA_1D_KERNEL_LOOP(i, len) { out[i] = SafeLog(in[i]); } -} -template<> -__global__ void ComputeLogGpu(const int64_t len, float16* out, const float16* in) { - const half* _in = reinterpret_cast(in); - half* _out = reinterpret_cast(out); - CUDA_1D_KERNEL_LOOP(i, len) { _out[i] = SafeLog(_in[i]); } -} - -template -class BroadcastPowYGradKernel final : public user_op::OpKernel { - public: - BroadcastPowYGradKernel() = default; - ~BroadcastPowYGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* z_tensor = ctx->Tensor4ArgNameAndIndex("z", 0); - const user_op::Tensor* dz_tensor = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - - const int64_t num_axes = dz_tensor->shape_view().NumAxes(); - const int64_t elem_cnt = z_tensor->shape_view().elem_cnt(); - Memset(ctx->stream(), tmp_buffer->mut_dptr(), 0, - GetCudaAlignedSize(elem_cnt * sizeof(T))); - XpuVarNdarray z(z_tensor->shape_view(), z_tensor->dptr(), num_axes); - XpuVarNdarray dz(dz_tensor->shape_view(), dz_tensor->dptr(), num_axes); - XpuVarNdarray const_tmp(dz.shape(), tmp_buffer->dptr()); - XpuVarNdarray tmp(dz.shape(), tmp_buffer->mut_dptr()); - XpuVarNdarray x(x_tensor->shape_view(), x_tensor->dptr(), num_axes); - XpuVarNdarray dy(dy_tensor->shape_view(), dy_tensor->mut_dptr(), num_axes); - NdarrayUtil::BroadcastAdd(ctx->stream(), tmp, x, const_tmp); - ComputeLogGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, tmp_buffer->mut_dptr(), tmp_buffer->dptr()); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, dz, const_tmp); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, z, const_tmp); - NdarrayUtil::ReduceSum(ctx->stream(), dy, const_tmp, tmp); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace -#define REGISTER_BROADCAST_POW_Y_GRAD_KERNEL(device, dtype_pair) \ - REGISTER_USER_KERNEL("broadcast_pow_y_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ - const user_op::TensorDesc& z = ctx->InputTensorDesc("z", 0); \ - const DataType& data_type = z.data_type(); \ - const int64_t elem_cnt = z.shape().elem_cnt(); \ - return GetCudaAlignedSize(elem_cnt * GetSizeOfDataType(data_type)); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_Y_GRAD_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) -} // namespace oneflow diff --git a/oneflow/user/kernels/broadcast_pow_grad_kernel.hip.cpp b/oneflow/user/kernels/broadcast_pow_grad_kernel.hip.cpp deleted file mode 100644 index 562f69c67c87f311a888d651c307a918fc875fbf..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/broadcast_pow_grad_kernel.hip.cpp +++ /dev/null @@ -1,88 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace { -template -__global__ void ComputeLogGpu(const int64_t len, T* out, const T* in) { - CUDA_1D_KERNEL_LOOP(i, len) { out[i] = SafeLog(in[i]); } -} -template<> -__global__ void ComputeLogGpu(const int64_t len, float16* out, const float16* in) { - const half* _in = reinterpret_cast(in); - half* _out = reinterpret_cast(out); - CUDA_1D_KERNEL_LOOP(i, len) { _out[i] = SafeLog(_in[i]); } -} - -template -class BroadcastPowYGradKernel final : public user_op::OpKernel { - public: - BroadcastPowYGradKernel() = default; - ~BroadcastPowYGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* z_tensor = ctx->Tensor4ArgNameAndIndex("z", 0); - const user_op::Tensor* dz_tensor = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - - const int64_t num_axes = dz_tensor->shape_view().NumAxes(); - const int64_t elem_cnt = z_tensor->shape_view().elem_cnt(); - Memset(ctx->stream(), tmp_buffer->mut_dptr(), 0, - GetCudaAlignedSize(elem_cnt * sizeof(T))); - XpuVarNdarray z(z_tensor->shape_view(), z_tensor->dptr(), num_axes); - XpuVarNdarray dz(dz_tensor->shape_view(), dz_tensor->dptr(), num_axes); - XpuVarNdarray const_tmp(dz.shape(), tmp_buffer->dptr()); - XpuVarNdarray tmp(dz.shape(), tmp_buffer->mut_dptr()); - XpuVarNdarray x(x_tensor->shape_view(), x_tensor->dptr(), num_axes); - XpuVarNdarray dy(dy_tensor->shape_view(), dy_tensor->mut_dptr(), num_axes); - NdarrayUtil::BroadcastAdd(ctx->stream(), tmp, x, const_tmp); - ComputeLogGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, tmp_buffer->mut_dptr(), tmp_buffer->dptr()); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, dz, const_tmp); - NdarrayUtil::BroadcastMul(ctx->stream(), tmp, z, const_tmp); - NdarrayUtil::ReduceSum(ctx->stream(), dy, const_tmp, tmp); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace -#define REGISTER_BROADCAST_POW_Y_GRAD_KERNEL(device, dtype_pair) \ - REGISTER_USER_KERNEL("broadcast_pow_y_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ - const user_op::TensorDesc& z = ctx->InputTensorDesc("z", 0); \ - const DataType& data_type = z.data_type(); \ - const int64_t elem_cnt = z.shape().elem_cnt(); \ - return GetCudaAlignedSize(elem_cnt * GetSizeOfDataType(data_type)); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_BROADCAST_POW_Y_GRAD_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/categorical_ordinal_encode_kernel.cpp b/oneflow/user/kernels/categorical_ordinal_encode_kernel.cpp index 8f4002795f1cb7053381a7b259f3a0b0bbedf3e9..475e149f42d486d1d10192ee58153ab8808d202f 100644 --- a/oneflow/user/kernels/categorical_ordinal_encode_kernel.cpp +++ b/oneflow/user/kernels/categorical_ordinal_encode_kernel.cpp @@ -50,11 +50,7 @@ class CategoricalOrdinalEncodeKernel final : public user_op::OpKernel { REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCPU, DataType::kInt32, int32_t); REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCPU, DataType::kInt64, int64_t); -#ifdef WITH_CUDA -REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCUDA, DataType::kInt32, int32_t); -REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCUDA, DataType::kInt64, int64_t); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCUDA, DataType::kInt32, int32_t); REGISTER_CATEGORICAL_ORDINAL_ENCODE_KERNEL(DeviceType::kCUDA, DataType::kInt64, int64_t); #endif diff --git a/oneflow/user/kernels/categorical_ordinal_encode_kernel_util.hip.cpp b/oneflow/user/kernels/categorical_ordinal_encode_kernel_util.hip.cpp deleted file mode 100644 index 927b0c83dcbe35b768befab2473c2454f76c2442..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/categorical_ordinal_encode_kernel_util.hip.cpp +++ /dev/null @@ -1,125 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef NDEBUG -#undef NDEBUG -#endif -#include -#include "oneflow/user/kernels/categorical_ordinal_encode_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -using CuInt64T = unsigned long long int; - -__device__ __inline__ int32_t AtomicCAS(int32_t* address, int32_t compare, int32_t val) { - return atomicCAS(address, compare, val); -} - -__device__ __inline__ int64_t AtomicCAS(int64_t* address, int64_t compare, int64_t val) { - static_assert(sizeof(int64_t) == sizeof(CuInt64T), "size error"); - return static_cast(atomicCAS(reinterpret_cast(address), - static_cast(compare), - static_cast(val))); -} - -__device__ __inline__ int32_t AtomicAdd(int32_t* address, int32_t val) { - return atomicAdd(address, val); -} - -__device__ __inline__ int64_t AtomicAdd(int64_t* address, int64_t val) { - static_assert(sizeof(int64_t) == sizeof(CuInt64T), "size error"); - return static_cast( - atomicAdd(reinterpret_cast(address), static_cast(val))); -} - -template -__device__ bool TryGetOrInsert(K* key, volatile V* value, V* size, const K hash, V* out) { - K old_key = AtomicCAS(key, static_cast(0), hash); - if (old_key == 0) { - V v = AtomicAdd(size, 1) + 1; - *value = v; - *out = v; - return true; - } else if (old_key == hash) { - while (true) { - V v = *value; - if (v != 0) { - *out = v; - break; - } - } - return true; - } else { - return false; - } -} - -template -__device__ bool GetOrInsertOne(const size_t capacity, T* table, T* size, const T hash, T* out) { - if (hash == 0) { - *out = 0; - return true; - } - const size_t start_idx = static_cast(hash) % capacity; - // fast path - { - T* key = table + start_idx * 2; - T* value = key + 1; - if (*key == hash && *value != 0) { - *out = *value; - return true; - } - } - for (size_t count = 0; count < capacity; ++count) { - const size_t idx = (start_idx + count) % capacity; - T* key = table + idx * 2; - T* value = key + 1; - if (TryGetOrInsert(key, value, size, hash, out)) { return true; } - } - return false; -} - -template -__global__ void EncodeGpu(const size_t capacity, T* table, T* size, const int64_t n, const T* hash, - T* out) { - CUDA_1D_KERNEL_LOOP(i, n) { - bool success = GetOrInsertOne(capacity, table, size, hash[i], out + i); - assert(success); - } -} - -} // namespace - -template -struct CategoricalOrdinalEncodeKernelUtil { - static void Encode(ep::Stream* stream, int64_t capacity, T* table, T* size, int64_t n, - const T* hash, T* out) { - EncodeGpu - <<As()->cuda_stream()>>>(capacity, table, size, n, hash, out); - } -}; - -#define INSTANTIATE_CATEGORICAL_ORDINAL_ENCODE_KERNEL_UTIL_CUDA(type_cpp, type_proto) \ - template struct CategoricalOrdinalEncodeKernelUtil; -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_CATEGORICAL_ORDINAL_ENCODE_KERNEL_UTIL_CUDA, INDEX_DATA_TYPE_SEQ); -#undef INSTANTIATE_CATEGORICAL_ORDINAL_ENCODE_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/clip_by_value_kernel.cpp b/oneflow/user/kernels/clip_by_value_kernel.cpp index eb4e016e94d1b5be36375409156bde2478ae8e79..5a247f7d4edc766db9b05304f132453851a75c15 100644 --- a/oneflow/user/kernels/clip_by_value_kernel.cpp +++ b/oneflow/user/kernels/clip_by_value_kernel.cpp @@ -15,6 +15,12 @@ limitations under the License. */ #include "oneflow/user/kernels/clip_by_value_kernel.h" #include "oneflow/core/framework/framework.h" +#ifdef WITH_CUDA +#include +#endif +#ifdef WITH_ROCM +#include +#endif namespace oneflow { @@ -48,6 +54,22 @@ int64_t GetDtypeMatchedValue(double floating, int64_t integral) { return integral; } +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template<> +half GetDtypeMatchedValue(double floating, int64_t integral) { +#if CUDA_VERSION >= 11000 + return __double2half(floating); +#else + return __float2half(static_cast(floating)); +#endif +} +#endif + +template<> +float16 GetDtypeMatchedValue(double floating, int64_t integral) { + return static_cast(floating); +} + } // namespace template @@ -187,27 +209,29 @@ class ClipByScalarMaxGradKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_CLIP_KERNEL(op_type_name, kernel_name, device_type_v, dtype) \ - REGISTER_USER_KERNEL(#op_type_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device_type_v) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "x", 0, true)); \ - return Maybe::Ok(); \ - }); - -#define REGISTER_CLIP_GRAD_KERNEL(op_type_name, kernel_name, device_type_v, dtype) \ - REGISTER_USER_KERNEL(#op_type_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device_type_v) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "dy", 0, true)); \ - return Maybe::Ok(); \ - }); +#define REGISTER_CLIP_KERNEL(op_type_name, kernel_name, device_type_v, dtype) \ + REGISTER_USER_KERNEL(#op_type_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device_type_v) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext&, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "x", 0, true)); \ + return Maybe::Ok(); \ + }); + +#define REGISTER_CLIP_GRAD_KERNEL(op_type_name, kernel_name, device_type_v, dtype) \ + REGISTER_USER_KERNEL(#op_type_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device_type_v) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext&, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "dy", 0, true)); \ + return Maybe::Ok(); \ + }); #define REGISTER_CLIP_KERNELS(device_type_v, dtype_pair) \ REGISTER_CLIP_KERNEL(clip_by_scalar, ClipByScalar, device_type_v, OF_PP_PAIR_FIRST(dtype_pair)) \ @@ -223,5 +247,9 @@ class ClipByScalarMaxGradKernel final : public user_op::OpKernel { OF_PP_PAIR_FIRST(dtype_pair)) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CLIP_KERNELS, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ) +REGISTER_CLIP_KERNELS(DeviceType::kCPU, (float16, DataType::kFloat16)) +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_CLIP_KERNELS(DeviceType::kCUDA, (half, DataType::kFloat16)) +#endif // WITH_CUDA } // namespace oneflow diff --git a/oneflow/user/kernels/clip_by_value_kernel.cu b/oneflow/user/kernels/clip_by_value_kernel.cu index 3c8b594f761b9344c490ddb6a85d2ff01c133889..c786a0ad8a9c4710540f8e6d7e6a89592ea8b18c 100644 --- a/oneflow/user/kernels/clip_by_value_kernel.cu +++ b/oneflow/user/kernels/clip_by_value_kernel.cu @@ -67,5 +67,6 @@ struct ClipKernelUtil { dtype*); OF_PP_FOR_EACH_TUPLE(INITIATE_CLIP_KERNEL_UTIL_CUDA, ARITHMETIC_DATA_TYPE_SEQ) +INITIATE_CLIP_KERNEL_UTIL_CUDA(half, DataType::kFloat16) } // namespace oneflow diff --git a/oneflow/user/kernels/clip_by_value_kernel.h b/oneflow/user/kernels/clip_by_value_kernel.h index 23607704203657109f50f11d6e07988cc6eb7aa7..6f214c71ab95645239225e70dd4bba05deaa582b 100644 --- a/oneflow/user/kernels/clip_by_value_kernel.h +++ b/oneflow/user/kernels/clip_by_value_kernel.h @@ -24,7 +24,7 @@ namespace oneflow { template OF_DEVICE_FUNC T DeviceMin(T a, T b) { #if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - return min(a, b); + return a < b ? a : b; #else return std::min(a, b); #endif @@ -33,7 +33,7 @@ OF_DEVICE_FUNC T DeviceMin(T a, T b) { template OF_DEVICE_FUNC T DeviceMax(T a, T b) { #if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - return max(a, b); + return a > b ? a : b; #else return std::max(a, b); #endif diff --git a/oneflow/user/kernels/clip_by_value_kernel.hip.cpp b/oneflow/user/kernels/clip_by_value_kernel.hip.cpp deleted file mode 100644 index eb2e5500adaf98b844eb737f797848c1dfbbdf4f..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/clip_by_value_kernel.hip.cpp +++ /dev/null @@ -1,72 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/clip_by_value_kernel.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void CudaClipForward(F clip_func, int64_t n, const T* x, T* y) { - CUDA_1D_KERNEL_LOOP(i, n) { y[i] = clip_func(x[i]); } -} - -template -__global__ void CudaClipBackward(F clip_func, int64_t n, const T* x, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP(i, n) { dx[i] = clip_func(x[i], dy[i]); } -} - -} // namespace - -template -struct ClipKernelUtil { - template - static void Forward(ep::Stream* stream, F clip_func, const int64_t n, const T* x, T* y) { - if (n == 0) { return; } - RUN_CUDA_KERNEL((CudaClipForward), stream, n, clip_func, n, x, y); - } - - template - static void Backward(ep::Stream* stream, F clip_func, const int64_t n, const T* x, const T* dy, - T* dx) { - if (n == 0) { return; } - RUN_CUDA_KERNEL((CudaClipBackward), stream, n, clip_func, n, x, dy, dx); - } -}; - -#define INITIATE_CLIP_KERNEL_UTIL_CUDA(dtype, dtype_v) \ - template struct ClipKernelUtil; \ - template void ClipKernelUtil::Forward( \ - ep::Stream*, ClipByMinFunctor, const int64_t n, const dtype*, dtype*); \ - template void ClipKernelUtil::Forward( \ - ep::Stream*, ClipByMaxFunctor, const int64_t n, const dtype*, dtype*); \ - template void ClipKernelUtil::Forward( \ - ep::Stream*, ClipByMinMaxFunctor, const int64_t n, const dtype*, dtype*); \ - template void ClipKernelUtil::Backward( \ - ep::Stream*, ClipByMinGradFunctor, const int64_t n, const dtype*, const dtype*, \ - dtype*); \ - template void ClipKernelUtil::Backward( \ - ep::Stream*, ClipByMaxGradFunctor, const int64_t n, const dtype*, const dtype*, \ - dtype*); \ - template void ClipKernelUtil::Backward( \ - ep::Stream*, ClipByMinMaxGradFunctor, const int64_t n, const dtype*, const dtype*, \ - dtype*); - -OF_PP_FOR_EACH_TUPLE(INITIATE_CLIP_KERNEL_UTIL_CUDA, ARITHMETIC_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_all_gather.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_all_gather.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f6b6f2005fed8f8edb2afcf5200945b2b71ee614 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_all_gather.cpp @@ -0,0 +1,106 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/all_gather.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +Maybe AllGatherImpl(const void* in, void* out, size_t elem_cnt, DataType dtype, + Symbol parallel_desc) { + int64_t parallel_num = parallel_desc->parallel_num(); + if (parallel_num == 1) { + if (in != out) { std::memcpy(out, in, elem_cnt * GetSizeOfDataType(dtype)); } + return Maybe::Ok(); + } + char* char_out = reinterpret_cast(out); + size_t chunk_size = elem_cnt * GetSizeOfDataType(dtype); + BalancedSplitter bs(chunk_size * parallel_num, parallel_num); + const auto& opt_parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); + CHECK_OR_RETURN(opt_parallel_id->has_value()) << kOfBugIssueUploadPrompt; + const auto& rank_group = JUST(RankGroup::New(parallel_desc)); + TransportToken transport_token = JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); + int64_t parallel_id = JUST(*opt_parallel_id); + // In-place operation will happen if in == out + parallel_id * chunk_size + if (in != &char_out[parallel_id * chunk_size]) { + memcpy(&char_out[parallel_id * chunk_size], in, chunk_size); + } + for (int64_t i = 0, part_id = parallel_id; i < parallel_num - 1; + ++i, part_id = RingDecrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + const void* send_ptr = &char_out[bs.At(send_part_id).begin()]; + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = RingDecrease(part_id, parallel_num); + void* recv_ptr = &char_out[bs.At(recv_part_id).begin()]; + size_t recv_size = bs.At(recv_part_id).size(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size; + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size; + *Cb = [] {}; + return Maybe::Ok(); + }); + if (send_size > 0) { + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + } + if (recv_size > 0) { + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + } + JUST(ctx.WaitDone()); + } + return Maybe::Ok(); +} +} // namespace + +class CpuAllGather final : public AllGather { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuAllGather); + CpuAllGather() : datatype_(kInvalidDataType) {} + ~CpuAllGather() = default; + + void Init(DataType datatype) override { this->datatype_ = datatype; } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cpu_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cpu_communication_ctx); + CHECK_JUST(AllGatherImpl(in, out, elem_cnt, datatype_, cpu_communication_ctx->parallel_desc())); + } + + private: + DataType datatype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, AllGather, CpuAllGather); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_all_reduce.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_all_reduce.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6550f9b81fb2b84a2752045aefaaf09dc8d7d28e --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_all_reduce.cpp @@ -0,0 +1,160 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/all_reduce.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +template +struct AllReduceImpl final { + static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, + Symbol parallel_desc) { + int64_t parallel_num = parallel_desc->parallel_num(); + if (parallel_num == 1) { + if (void_in != void_out) { std::memcpy(void_out, void_in, elem_cnt * sizeof(T)); } + return Maybe::Ok(); + } + const T* in = reinterpret_cast(void_in); + T* out = reinterpret_cast(void_out); + BalancedSplitter bs(elem_cnt, parallel_num); + auto recv_buffer = std::make_unique(bs.At(0).size()); + Optional parallel_id; + JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); + const auto& rank_group = JUST(RankGroup::New(parallel_desc)); + TransportToken transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); + for (int64_t i = 0, part_id = JUST(parallel_id); i < parallel_num - 1; + ++i, part_id = RingDecrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + const T* send_ptr = nullptr; + if (i == 0) { + send_ptr = &in[bs.At(send_part_id).begin()]; + } else { + send_ptr = &out[bs.At(send_part_id).begin()]; + } + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = RingDecrease(part_id, parallel_num); + T* recv_ptr = recv_buffer.get(); + size_t recv_size = bs.At(recv_part_id).size(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }); + if (send_size > 0) { + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + } + if (recv_size > 0) { + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + } + JUST(ctx.WaitDone()); + const T* cur_in = &in[bs.At(recv_part_id).begin()]; + T* cur_out = &out[bs.At(recv_part_id).begin()]; + if (recv_size > 0) { + ReduceFunctor::Call(recv_size, cur_out, cur_in, recv_ptr); + } + } + for (int64_t i = 0, part_id = RingIncrease(JUST(parallel_id), parallel_num); + i < parallel_num - 1; ++i, part_id = RingDecrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + const T* send_ptr = &out[bs.At(send_part_id).begin()]; + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = RingDecrease(part_id, parallel_num); + T* recv_ptr = &out[bs.At(recv_part_id).begin()]; + size_t recv_size = bs.At(recv_part_id).size(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }); + if (send_size > 0) { + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + } + if (recv_size > 0) { + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + } + JUST(ctx.WaitDone()); + } + return Maybe::Ok(); + } +}; + +#define MAKE_ALL_REDUCE_ENTRY(func_name, T, reduce_type) func_name::Call + +DEFINE_STATIC_SWITCH_FUNC(Maybe, AllReduceImpl, MAKE_ALL_REDUCE_ENTRY, // NOLINT + MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), // NOLINT + REDUCE_TYPE_CTRV_SEQ); // NOLINT + +#undef MAKE_ALL_REDUCE_ENTRY + +} // namespace + +class CpuAllReduce final : public AllReduce { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuAllReduce); + CpuAllReduce() : datatype_(kInvalidDataType), reduce_type_(kInvalidReduceFunctorType) {} + ~CpuAllReduce() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->datatype_ = datatype; + this->reduce_type_ = reduce_type; + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cpu_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cpu_communication_ctx) << kOfBugIssueUploadPrompt; + CHECK_JUST(SwitchAllReduceImpl(SwitchCase(datatype_, reduce_type_), in, out, elem_cnt, + cpu_communication_ctx->parallel_desc())); + } + + private: + DataType datatype_; + ReduceType reduce_type_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, AllReduce, CpuAllReduce); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_broadcast.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_broadcast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..873ea779cbb6518444abffaa2b4ef919eca45dd2 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_broadcast.cpp @@ -0,0 +1,59 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ccl/ccl.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/broadcast.h" + +namespace oneflow { + +namespace ccl { + +// Use CpuBroadcastImpl to avoid name conflict +class CpuBroadcastImpl final : public Broadcast { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuBroadcastImpl); + CpuBroadcastImpl() : size_of_dtype_(0) {} + ~CpuBroadcastImpl() = default; + + void Init(DataType datatype) override { + CHECK(IsPODDataType(datatype)); + this->size_of_dtype_ = GetSizeOfDataType(datatype); + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communication_ctx) const override { + const auto& cpu_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cpu_communication_ctx); + size_t buffer_size = elem_cnt * size_of_dtype_; + const auto& transport_token = + CHECK_JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); + CHECK_JUST(CpuBroadcast(in, out, buffer_size, root, cpu_communication_ctx->parallel_desc(), + transport_token)); + } + + private: + size_t size_of_dtype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, Broadcast, CpuBroadcastImpl); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h b/oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h new file mode 100644 index 0000000000000000000000000000000000000000..263c41c7f001ed0b798b764fafc77f068174938c --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COLLECTIVE_COMMUNICATION_UTIL_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COLLECTIVE_COMMUNICATION_UTIL_H_ + +#include "oneflow/core/thread/thread_manager.h" +#include "oneflow/core/common/balanced_splitter.h" + +namespace oneflow { + +namespace ccl { + +inline int64_t RingDecrease(int64_t n, int64_t size) { return (n - 1 + size) % size; } + +inline int64_t RingIncrease(int64_t n, int64_t size) { return (n + 1 + size) % size; } + +template +struct ReduceFunctor; + +template +struct ReduceFunctor { + static void Call(size_t size, T* out, const T* in0, const T* in1) { + size_t thread_num = Singleton::Get()->thread_num(); + BalancedSplitter bs(size, thread_num); + MultiThreadLoop(thread_num, [&](size_t thread_idx) { + size_t end = bs.At(thread_idx).end(); + for (size_t i = bs.At(thread_idx).begin(); i < end; ++i) { out[i] = in0[i] + in1[i]; } + }); + } +}; + +template +struct ReduceFunctor { + static void Call(size_t size, T* out, const T* in0, const T* in1) { + size_t thread_num = Singleton::Get()->thread_num(); + BalancedSplitter bs(size, thread_num); + MultiThreadLoop(thread_num, [&](size_t thread_idx) { + size_t end = bs.At(thread_idx).end(); + for (size_t i = bs.At(thread_idx).begin(); i < end; ++i) { + out[i] = std::max(in0[i], in1[i]); + } + }); + } +}; + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COLLECTIVE_COMMUNICATION_UTIL_H_ diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..158998c1426c765e7e4cb8b1674b50bf3654dca2 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.cpp @@ -0,0 +1,31 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/core/job/parallel_desc.h" + +namespace oneflow { + +namespace ccl { + +void CpuCommunicationContext::Init(Symbol parallel_desc) { + parallel_desc_ = parallel_desc; +} + +REGISTER_COLLECTIVE_COMMUNICATION_COMMUNICATOR(DeviceType::kCPU, CpuCommunicationContext); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h b/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h new file mode 100644 index 0000000000000000000000000000000000000000..b0c64c87ae015075beff96937214292f4968f3fc --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COMMUNICATION_CONTEXT_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COMMUNICATION_CONTEXT_H_ + +#include "oneflow/user/kernels/collective_communication/include/communication_context.h" +#include "oneflow/core/common/symbol.h" + +namespace oneflow { + +class ParallelDesc; + +namespace ccl { + +class CpuCommunicationContext : public CommunicationContext { + public: + explicit CpuCommunicationContext() = default; + ~CpuCommunicationContext() override = default; + + void Init(Symbol) override; + + Symbol parallel_desc() const { return parallel_desc_; } + + private: + Symbol parallel_desc_; +}; + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CPU_CPU_COMMUNICATION_CONTEXT_H_ diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_recv.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_recv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c7dc335b40403158c9c6d68383abe0c4dc55798a --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_recv.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ccl/ccl.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/include/recv.h" + +namespace oneflow { + +namespace ccl { + +// Use CpuRecvImpl to avoid name conflict +class CpuRecvImpl final : public Recv { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuRecvImpl); + CpuRecvImpl() : size_of_dtype_(0) {} + ~CpuRecvImpl() = default; + + void Init(DataType datatype) override { + CHECK(IsPODDataType(datatype)); + this->size_of_dtype_ = GetSizeOfDataType(datatype); + } + + void Launch(ep::Stream* stream, void* out, size_t elem_cnt, int64_t src) const override { + size_t buffer_size = elem_cnt * size_of_dtype_; + CHECK_JUST(CpuRecv(out, buffer_size, src)); + } + + private: + size_t size_of_dtype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, Recv, CpuRecvImpl); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_reduce.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_reduce.cpp new file mode 100644 index 0000000000000000000000000000000000000000..607bce6631eb7ef98ebe2d9a78aa35da26d07f2a --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_reduce.cpp @@ -0,0 +1,187 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/control/global_process_ctx.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/reduce.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +template +struct ReduceImpl final { + static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, int64_t root, + Symbol parallel_desc) { + const T* in = reinterpret_cast(void_in); + T* out = reinterpret_cast(void_out); + + int64_t parallel_num = parallel_desc->parallel_num(); + BalancedSplitter bs(elem_cnt, parallel_num); + + size_t size = root == GlobalProcessCtx::Rank() && void_in != void_out ? 0 : bs.At(0).size(); + T* tmp_out = nullptr; + // void_out is only used on rank root and ignored for other ranks. + auto tmp_out_buffer = std::make_unique(size); + int64_t parallel_id_of_root = + JUST(parallel_desc->ParallelId4MachineDeviceId(root, GlobalProcessCtx::LocalRank(root))); + if (root == GlobalProcessCtx::Rank() && void_in != void_out) { + tmp_out = &reinterpret_cast(void_out)[bs.At(parallel_id_of_root).begin()]; + } else { + tmp_out = tmp_out_buffer.get(); + } + + auto recv_buffer = std::make_unique(bs.At(0).size()); + Optional parallel_id; + JUST(GetTensorDevice4CurrentProcessCtx(parallel_desc, ¶llel_id)); + const auto& rank_group = JUST(RankGroup::New(parallel_desc)); + TransportToken transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); + for (int64_t i = 0, part_id = RingDecrease(JUST(parallel_id), parallel_num); + i < parallel_num - 1; ++i, part_id = RingDecrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + const T* send_ptr = nullptr; + if (i == 0) { + send_ptr = &in[bs.At(send_part_id).begin()]; + } else { + send_ptr = tmp_out; + } + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = RingDecrease(part_id, parallel_num); + T* recv_ptr = recv_buffer.get(); + size_t recv_size = bs.At(recv_part_id).size(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }); + if (send_size > 0) { + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + } + if (recv_size > 0) { + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + } + JUST(ctx.WaitDone()); + const T* cur_in = &in[bs.At(recv_part_id).begin()]; + if (recv_size > 0) { + ReduceFunctor::Call(recv_size, tmp_out, cur_in, recv_ptr); + } + } + + if (root == GlobalProcessCtx::Rank() && void_in == void_out) { + memcpy(&out[bs.At(parallel_id_of_root).begin()], tmp_out, + bs.At(parallel_id_of_root).size() * sizeof(T)); + } + + for (int64_t i = 0, part_id = RingIncrease(parallel_id_of_root, parallel_num); + i < parallel_num - 1; ++i, part_id = RingIncrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + int64_t src_rank = JUST(parallel_desc->MachineId4ParallelId(send_part_id)); + const T* send_ptr = tmp_out; + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = part_id; + T* recv_ptr = &out[bs.At(recv_part_id).begin()]; + size_t recv_size = bs.At(recv_part_id).size(); + + if (send_size > 0 && src_rank == GlobalProcessCtx::Rank()) { + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + UNIMPLEMENTED_THEN_RETURN(); + }); + JUST(TransportUtil::SendDataToRank(root, transport_token, &ctx)); + JUST(ctx.WaitDone()); + } + if (recv_size > 0 && root == GlobalProcessCtx::Rank()) { + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + UNIMPLEMENTED_THEN_RETURN(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }); + JUST(TransportUtil::ReceiveDataFromRank(src_rank, transport_token, &ctx)); + JUST(ctx.WaitDone()); + } + } + return Maybe::Ok(); + } +}; + +#define MAKE_ALL_REDUCE_ENTRY(func_name, T, reduce_type) func_name::Call + +DEFINE_STATIC_SWITCH_FUNC(Maybe, ReduceImpl, MAKE_ALL_REDUCE_ENTRY, // NOLINT + MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), // NOLINT + REDUCE_TYPE_CTRV_SEQ); // NOLINT + +#undef MAKE_ALL_REDUCE_ENTRY + +} // namespace + +class CpuReduce final : public Reduce { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuReduce); + CpuReduce() : datatype_(kInvalidDataType), reduce_type_(kInvalidReduceFunctorType) {} + ~CpuReduce() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->datatype_ = datatype; + this->reduce_type_ = reduce_type; + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communication_ctx) const override { + const auto& cpu_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cpu_communication_ctx) << kOfBugIssueUploadPrompt; + CHECK_JUST(SwitchReduceImpl(SwitchCase(datatype_, reduce_type_), in, out, elem_cnt, root, + cpu_communication_ctx->parallel_desc())); + } + + private: + DataType datatype_; + ReduceType reduce_type_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, Reduce, CpuReduce); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_reduce_scatter.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_reduce_scatter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8ff362c2eaa377fccdb5751511bc05eb3a652123 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_reduce_scatter.cpp @@ -0,0 +1,132 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/reduce_scatter.h" +#include "oneflow/user/kernels/collective_communication/cpu/cpu_collective_communication_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +template +struct ReduceScatterImpl final { + static Maybe Call(const void* void_in, void* void_out, size_t elem_cnt, + Symbol parallel_desc) { + int64_t parallel_num = parallel_desc->parallel_num(); + if (parallel_num == 1) { + if (void_in != void_out) { std::memcpy(void_out, void_in, elem_cnt * sizeof(T)); } + return Maybe::Ok(); + } + + const T* in = reinterpret_cast(void_in); + T* out = reinterpret_cast(void_out); + + BalancedSplitter bs(elem_cnt * parallel_num, parallel_num); + const auto& opt_parallel_id = JUST(GetParallelId4CurrentProcessCtx(parallel_desc)); + CHECK_OR_RETURN(opt_parallel_id->has_value()) << kOfBugIssueUploadPrompt; + int64_t parallel_id = JUST(*opt_parallel_id); + + auto recv_buffer = std::make_unique(bs.At(0).size()); + const auto& rank_group = JUST(RankGroup::New(parallel_desc)); + + TransportToken transport_token = + JUST(TransportToken::NewTransportToken(kTransportTokenTypeData)); + for (int64_t i = 0, part_id = RingDecrease(parallel_id, parallel_num); i < parallel_num - 1; + ++i, part_id = RingDecrease(part_id, parallel_num)) { + int64_t send_part_id = part_id; + const T* send_ptr = nullptr; + if (i == 0) { + send_ptr = &in[bs.At(send_part_id).begin()]; + } else { + send_ptr = out; + } + size_t send_size = bs.At(send_part_id).size(); + int64_t recv_part_id = RingDecrease(part_id, parallel_num); + T* recv_ptr = recv_buffer.get(); + size_t recv_size = bs.At(recv_part_id).size(); + NaiveAsyncTransportCtx ctx( + transport_token, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = const_cast(send_ptr); + *size = send_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }, + [&](void** buffer, std::size_t* size, std::function* Cb) -> Maybe { + *buffer = recv_ptr; + *size = recv_size * sizeof(T); + *Cb = [] {}; + return Maybe::Ok(); + }); + if (send_size > 0) { + JUST(TransportUtil::SendToNextRankInRing(rank_group, transport_token, &ctx)); + } + if (recv_size > 0) { + JUST(TransportUtil::ReceiveFromPrevRankInRing(rank_group, transport_token, &ctx)); + } + JUST(ctx.WaitDone()); + const T* cur_in = &in[bs.At(recv_part_id).begin()]; + if (recv_size > 0) { ReduceFunctor::Call(recv_size, out, cur_in, recv_ptr); } + } + return Maybe::Ok(); + } +}; + +#define MAKE_ALL_REDUCE_ENTRY(func_name, T, reduce_type) func_name::Call + +DEFINE_STATIC_SWITCH_FUNC(Maybe, ReduceScatterImpl, MAKE_ALL_REDUCE_ENTRY, // NOLINT + MAKE_DATA_TYPE_CTRV_SEQ(POD_DATA_TYPE_SEQ), // NOLINT + REDUCE_TYPE_CTRV_SEQ); // NOLINT + +#undef MAKE_ALL_REDUCE_ENTRY + +} // namespace + +class CpuReduceScatter final : public ReduceScatter { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuReduceScatter); + CpuReduceScatter() : datatype_(kInvalidDataType), reduce_type_(kInvalidReduceFunctorType) {} + ~CpuReduceScatter() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->datatype_ = datatype; + this->reduce_type_ = reduce_type; + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cpu_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cpu_communication_ctx) << kOfBugIssueUploadPrompt; + CHECK_JUST(SwitchReduceScatterImpl(SwitchCase(datatype_, reduce_type_), in, out, elem_cnt, + cpu_communication_ctx->parallel_desc())); + } + + private: + DataType datatype_; + ReduceType reduce_type_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, ReduceScatter, CpuReduceScatter); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cpu/cpu_send.cpp b/oneflow/user/kernels/collective_communication/cpu/cpu_send.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5a93b9255c5432de3d609fbe0b4c973e8bb6af5a --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cpu/cpu_send.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ccl/ccl.h" +#include "oneflow/core/job/rank_group.h" +#include "oneflow/core/framework/transport_util.h" +#include "oneflow/user/kernels/collective_communication/include/send.h" + +namespace oneflow { + +namespace ccl { + +// Use CpuSendImpl to avoid name conflict +class CpuSendImpl final : public Send { + public: + OF_DISALLOW_COPY_AND_MOVE(CpuSendImpl); + CpuSendImpl() : size_of_dtype_(0) {} + ~CpuSendImpl() = default; + + void Init(DataType datatype) override { + CHECK(IsPODDataType(datatype)); + this->size_of_dtype_ = GetSizeOfDataType(datatype); + } + + void Launch(ep::Stream* stream, const void* in, size_t elem_cnt, int64_t dst) const override { + size_t buffer_size = elem_cnt * size_of_dtype_; + CHECK_JUST(CpuSend(in, buffer_size, dst)); + } + + private: + size_t size_of_dtype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCPU, Send, CpuSendImpl); + +} // namespace ccl + +} // namespace oneflow diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_all_gather.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_all_gather.cpp new file mode 100644 index 0000000000000000000000000000000000000000..813664b99a9e695b7afc8ac5e272e49382d85772 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_all_gather.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/all_gather.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +class CudaAllGather final : public AllGather { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaAllGather); + CudaAllGather() : nccl_datatype_() {} + ~CudaAllGather() = default; + + void Init(DataType datatype) override { this->nccl_datatype_ = GetNcclDataType(datatype); } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cuda_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cuda_communication_ctx) << kOfBugIssueUploadPrompt; + OF_NCCL_CHECK(ncclAllGather(in, out, elem_cnt, nccl_datatype_, + cuda_communication_ctx->nccl_comm(), + stream->As()->cuda_stream())); + } + + private: + ncclDataType_t nccl_datatype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, AllGather, CudaAllGather); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_all_reduce.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_all_reduce.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7a26cc2fae264dab1fe7d326dbbcab21f7d28553 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_all_reduce.cpp @@ -0,0 +1,71 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/all_reduce.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +inline ncclRedOp_t GetNcclReduceType(ReduceType reduce_type) { + switch (reduce_type) { +#define NCCL_REDUCE_TYPE_CASE(dtype) \ + case ReduceType::k##dtype: return ncclRedOp_t::nccl##dtype + NCCL_REDUCE_TYPE_CASE(Sum); + NCCL_REDUCE_TYPE_CASE(Max); + default: PRINT_BUG_PROMPT_AND_ABORT(); + } +} + +} // namespace + +class CudaAllReduce final : public AllReduce { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaAllReduce); + CudaAllReduce() : nccl_datatype_(), nccl_reduce_op_() {} + ~CudaAllReduce() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->nccl_datatype_ = GetNcclDataType(datatype); + this->nccl_reduce_op_ = GetNcclReduceType(reduce_type); + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cuda_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cuda_communication_ctx); + OF_NCCL_CHECK(ncclAllReduce(in, out, elem_cnt, nccl_datatype_, nccl_reduce_op_, + cuda_communication_ctx->nccl_comm(), + stream->As()->cuda_stream())); + } + + private: + ncclDataType_t nccl_datatype_; + ncclRedOp_t nccl_reduce_op_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, AllReduce, CudaAllReduce); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_broadcast.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_broadcast.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a8d43408b8d13e9e8e0c3eea04d4a559c4d8d562 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_broadcast.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/broadcast.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +class CudaBroadcast final : public Broadcast { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaBroadcast); + CudaBroadcast() : nccl_datatype_() {} + ~CudaBroadcast() = default; + + void Init(DataType datatype) override { this->nccl_datatype_ = GetNcclDataType(datatype); } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communication_ctx) const override { + const auto& cuda_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cuda_communication_ctx); + OF_NCCL_CHECK(ncclBroadcast( + in, out, elem_cnt, nccl_datatype_, cuda_communication_ctx->nccl_index4rank(root), + cuda_communication_ctx->nccl_comm(), stream->As()->cuda_stream())); + } + + private: + ncclDataType_t nccl_datatype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, Broadcast, CudaBroadcast); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.cpp new file mode 100644 index 0000000000000000000000000000000000000000..56358f6614e8b762aeb7b0bc40fea18c3d377fbc --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.cpp @@ -0,0 +1,42 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/job/eager_nccl_comm_manager.h" + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + +namespace oneflow { + +namespace ccl { + +void CudaCommunicationContext::Init(Symbol parallel_desc) { + std::set> device_set; + FOR_RANGE(int64_t, parallel_id, 0, parallel_desc->parallel_num()) { + int64_t machine_id = CHECK_JUST(parallel_desc->MachineId4ParallelId(parallel_id)); + int64_t device_id = CHECK_JUST(parallel_desc->DeviceId4ParallelId(parallel_id)); + device_set.emplace(std::make_pair(machine_id, device_id)); + rank2nccl_index_.emplace(machine_id, parallel_id); + } + nccl_comm_ = CHECK_NOTNULL(Singleton::Get())->GetCommForDevice(device_set); +} + +REGISTER_COLLECTIVE_COMMUNICATION_COMMUNICATOR(DeviceType::kCUDA, CudaCommunicationContext); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h b/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h new file mode 100644 index 0000000000000000000000000000000000000000..04e11c4ef1607ffe2c646abc12a844bd7e96c16a --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h @@ -0,0 +1,52 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_COMMUNICATION_CONTEXT_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_COMMUNICATION_CONTEXT_H_ + +#include "oneflow/user/kernels/collective_communication/include/communication_context.h" +#include "oneflow/core/common/symbol.h" +#include "oneflow/core/job/parallel_desc.h" + +#if defined(WITH_CUDA) || defined(WITH_ROCM) + +#include "oneflow/core/device/cuda_util.h" + +namespace oneflow { + +namespace ccl { + +class CudaCommunicationContext : public CommunicationContext { + public: + explicit CudaCommunicationContext() = default; + ~CudaCommunicationContext() override = default; + + void Init(Symbol) override; + + ncclComm_t nccl_comm() const { return nccl_comm_; } + int64_t nccl_index4rank(int rank) const { return rank2nccl_index_.at(rank); } + + private: + ncclComm_t nccl_comm_; + HashMap rank2nccl_index_; +}; + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_COMMUNICATION_CONTEXT_H_ diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_recv.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_recv.cpp new file mode 100644 index 0000000000000000000000000000000000000000..96b54d23470a69053c65a67cfe68ea0615292b3c --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_recv.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/recv.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +class CudaRecv final : public Recv { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaRecv); + CudaRecv() : nccl_datatype_() {} + ~CudaRecv() = default; + + void Init(DataType datatype) override { this->nccl_datatype_ = GetNcclDataType(datatype); } + + void Launch(ep::Stream* stream, void* out, size_t elem_cnt, int64_t src) const override { +#if HAS_NCCL_SEND_RECV + const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(src); + OF_NCCL_CHECK(ncclRecv(out, elem_cnt, nccl_datatype_, comm_and_peer_rank.second, + comm_and_peer_rank.first, stream->As()->cuda_stream())); +#else + UNIMPLEMENTED() << "GPU recv is only supported when nccl version >= 2.7" +#endif // HAS_NCCL_SEND_RECV + } + + private: + ncclDataType_t nccl_datatype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, Recv, CudaRecv); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_reduce.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_reduce.cpp new file mode 100644 index 0000000000000000000000000000000000000000..62ea53452650199e8cc229b399c13b44bb78f1ab --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_reduce.cpp @@ -0,0 +1,72 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/reduce.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +inline ncclRedOp_t GetNcclReduceType(ReduceType reduce_type) { + switch (reduce_type) { +#define NCCL_REDUCE_TYPE_CASE(dtype) \ + case ReduceType::k##dtype: return ncclRedOp_t::nccl##dtype + NCCL_REDUCE_TYPE_CASE(Sum); + NCCL_REDUCE_TYPE_CASE(Max); + default: PRINT_BUG_PROMPT_AND_ABORT(); + } +} + +} // namespace + +class CudaReduce final : public Reduce { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaReduce); + CudaReduce() : nccl_datatype_(), nccl_reduce_op_() {} + ~CudaReduce() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->nccl_datatype_ = GetNcclDataType(datatype); + this->nccl_reduce_op_ = GetNcclReduceType(reduce_type); + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communication_ctx) const override { + const auto& cuda_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cuda_communication_ctx) << kOfBugIssueUploadPrompt; + OF_NCCL_CHECK(ncclReduce(in, out, elem_cnt, nccl_datatype_, nccl_reduce_op_, + cuda_communication_ctx->nccl_index4rank(root), + cuda_communication_ctx->nccl_comm(), + stream->As()->cuda_stream())); + } + + private: + ncclDataType_t nccl_datatype_; + ncclRedOp_t nccl_reduce_op_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, Reduce, CudaReduce); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_reduce_scatter.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_reduce_scatter.cpp new file mode 100644 index 0000000000000000000000000000000000000000..da64ac066bf8f75804bd9a0a3accee3d27f0237f --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_reduce_scatter.cpp @@ -0,0 +1,71 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/reduce_scatter.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_communication_context.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +namespace { + +inline ncclRedOp_t GetNcclReduceType(ReduceType reduce_type) { + switch (reduce_type) { +#define NCCL_REDUCE_TYPE_CASE(dtype) \ + case ReduceType::k##dtype: return ncclRedOp_t::nccl##dtype + NCCL_REDUCE_TYPE_CASE(Sum); + NCCL_REDUCE_TYPE_CASE(Max); + default: PRINT_BUG_PROMPT_AND_ABORT(); + } +} + +} // namespace + +class CudaReduceScatter final : public ReduceScatter { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaReduceScatter); + CudaReduceScatter() : nccl_datatype_(), nccl_reduce_op_() {} + ~CudaReduceScatter() = default; + + void Init(DataType datatype, ReduceType reduce_type) override { + this->nccl_datatype_ = GetNcclDataType(datatype); + this->nccl_reduce_op_ = GetNcclReduceType(reduce_type); + } + + void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communication_ctx) const override { + const auto& cuda_communication_ctx = + std::dynamic_pointer_cast(communication_ctx); + CHECK(cuda_communication_ctx) << kOfBugIssueUploadPrompt; + OF_NCCL_CHECK(ncclReduceScatter(in, out, elem_cnt, nccl_datatype_, nccl_reduce_op_, + cuda_communication_ctx->nccl_comm(), + stream->As()->cuda_stream())); + } + + private: + ncclDataType_t nccl_datatype_; + ncclRedOp_t nccl_reduce_op_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, ReduceScatter, CudaReduceScatter); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_send.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_send.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e236ac8e0eb89a2fce2d3bd5bd7eb7552c54f9ca --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_send.cpp @@ -0,0 +1,53 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/user/kernels/collective_communication/include/send.h" +#include "oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h" +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +class CudaSend final : public Send { + public: + OF_DISALLOW_COPY_AND_MOVE(CudaSend); + CudaSend() : nccl_datatype_() {} + ~CudaSend() = default; + + void Init(DataType datatype) override { this->nccl_datatype_ = GetNcclDataType(datatype); } + + void Launch(ep::Stream* stream, const void* in, size_t elem_cnt, int64_t dst) const override { +#if HAS_NCCL_SEND_RECV + const auto& comm_and_peer_rank = GetNcclCommAndPeerNcclRank(dst); + OF_NCCL_CHECK(ncclSend(in, elem_cnt, nccl_datatype_, comm_and_peer_rank.second, + comm_and_peer_rank.first, stream->As()->cuda_stream())); +#else + UNIMPLEMENTED() << "GPU send is only supported when nccl version >= 2.7" +#endif // HAS_NCCL_SEND_RECV + } + + private: + ncclDataType_t nccl_datatype_; +}; + +REGISTER_COLLECTIVE_COMMUNICATION(DeviceType::kCUDA, Send, CudaSend); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.cpp b/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..76db14a03436efda69ffd99725f1dccd201fafad --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.cpp @@ -0,0 +1,43 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" +#include "oneflow/core/common/decorator.h" +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/job/eager_nccl_comm_manager.h" + +namespace oneflow { + +namespace ccl { + +std::pair RawGetNcclCommAndPeerNcclRank(int64_t peer_process_id) { + std::set> device_set; + const int64_t& rank = GlobalProcessCtx::Rank(); + const int64_t peer_nccl_rank = (peer_process_id > rank) ? 1 : 0; + device_set.emplace(rank, GlobalProcessCtx::LocalRank()); + device_set.emplace(peer_process_id, GlobalProcessCtx::LocalRank(peer_process_id)); + return {CHECK_NOTNULL(Singleton::Get())->GetCommForDevice(device_set), + peer_nccl_rank}; +} + +decltype(GetNcclCommAndPeerNcclRank) GetNcclCommAndPeerNcclRank = + DECORATE(&RawGetNcclCommAndPeerNcclRank, ThreadLocal); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h b/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h new file mode 100644 index 0000000000000000000000000000000000000000..a5c26d637aefd47b0c8bb1e5a857ef1de04fe150 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/cuda/cuda_send_recv_util.h @@ -0,0 +1,34 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_SEND_RECV_UTIL_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_SEND_RECV_UTIL_H_ + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/device/nccl_util.h" + +namespace oneflow { + +namespace ccl { + +extern std::pair (*GetNcclCommAndPeerNcclRank)(int64_t peer_process_i); + +} // namespace ccl + +} // namespace oneflow + +#endif // WITH_CUDA + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_CUDA_CUDA_SEND_RECV_UTIL_H_ diff --git a/oneflow/user/kernels/collective_communication/include/all_gather.h b/oneflow/user/kernels/collective_communication/include/all_gather.h new file mode 100644 index 0000000000000000000000000000000000000000..66b520be6a5778ed1f4e8945cf889b23d69c196c --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/all_gather.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_GATHER_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_GATHER_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class AllGather : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(AllGather); + AllGather() = default; + ~AllGather() override = default; + + virtual void Init(DataType dtype) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communicator) const = 0; +}; + +inline bool IsAllGatherRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_GATHER_H_ diff --git a/oneflow/user/kernels/collective_communication/include/all_reduce.h b/oneflow/user/kernels/collective_communication/include/all_reduce.h new file mode 100644 index 0000000000000000000000000000000000000000..0dcc685b9663252cf3e96c3239cdc6a6d1b69b04 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/all_reduce.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_REDUCE_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_REDUCE_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class AllReduce : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(AllReduce); + AllReduce() = default; + ~AllReduce() override = default; + + virtual void Init(DataType dtype, ReduceType reduce_type) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communicator) const = 0; +}; + +inline bool IsAllReduceRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_ALL_REDUCE_H_ diff --git a/oneflow/user/kernels/collective_communication/include/broadcast.h b/oneflow/user/kernels/collective_communication/include/broadcast.h new file mode 100644 index 0000000000000000000000000000000000000000..ed163749a9a2e0fce8ec2c1429614a0881c4ff24 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/broadcast.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_BROADCAST_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_BROADCAST_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class Broadcast : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(Broadcast); + Broadcast() = default; + ~Broadcast() override = default; + + virtual void Init(DataType dtype) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communicator) const = 0; +}; + +inline bool IsBroadcastRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_BROADCAST_H_ diff --git a/oneflow/user/kernels/collective_communication/include/collective_communication.h b/oneflow/user/kernels/collective_communication/include/collective_communication.h new file mode 100644 index 0000000000000000000000000000000000000000..c197820d974c3d3ce661a877ffe645bfe3261ba6 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/collective_communication.h @@ -0,0 +1,68 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COLLECTIVE_COMMUNICATION_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COLLECTIVE_COMMUNICATION_H_ + +#include "oneflow/core/common/auto_registration_factory.h" +#include "oneflow/core/common/switch_func.h" +#include "oneflow/user/kernels/collective_communication/include/communication_context.h" +#include "oneflow/core/ep/include/stream.h" + +namespace oneflow { + +namespace ccl { + +#define REDUCE_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(kSum) \ + OF_PP_MAKE_TUPLE_SEQ(kMax) + +enum ReduceType { + kInvalidReduceFunctorType = 0, +#define DEFINE_REDUCE_TYPE_ENUM_VALUE(enum_value) enum_value, + OF_PP_FOR_EACH_TUPLE(DEFINE_REDUCE_TYPE_ENUM_VALUE, REDUCE_TYPE_SEQ) +#undef DEFINE_REDUCE_TYPE_ENUM_VALUE + kReduceTypeSize +}; + +#define REDUCE_TYPE_CTRV_SEQ \ + MAKE_TYPED_CTRV_SEQ(ReduceType, \ + OF_PP_FOR_EACH_TUPLE(OF_PP_I_MAKE_REPLICATE_TUPLE_SEQ, REDUCE_TYPE_SEQ)) + +class CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(CollectiveCommunication); + CollectiveCommunication() = default; + virtual ~CollectiveCommunication() = default; +}; + +template +static std::unique_ptr NewCollectiveCommunication( + DeviceType device_type, Args&&... args) { + std::unique_ptr collective_communication_entry = + NewObjUniquePtr(device_type); + if (!collective_communication_entry) { return nullptr; } + collective_communication_entry->Init(std::forward(args)...); + return collective_communication_entry; +} + +#define REGISTER_COLLECTIVE_COMMUNICATION(device, Base, Derived) \ + REGISTER_CLASS(DeviceType, device, Base, Derived) + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COLLECTIVE_COMMUNICATION_H_ diff --git a/oneflow/user/kernels/collective_communication/include/communication_context.h b/oneflow/user/kernels/collective_communication/include/communication_context.h new file mode 100644 index 0000000000000000000000000000000000000000..68e520a094758050d627bb78e12c1ee774dc2002 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/communication_context.h @@ -0,0 +1,57 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COMMUNICATION_CONTEXT_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COMMUNICATION_CONTEXT_H_ + +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/common/auto_registration_factory.h" + +namespace oneflow { + +namespace ccl { + +class CommunicationContext { + public: + CommunicationContext() = default; + virtual ~CommunicationContext() = default; + + virtual void Init(Symbol) = 0; +}; + +inline std::shared_ptr NewCommunicationContext( + DeviceType device_type, Symbol parallel_desc) { + CHECK_EQ(device_type, parallel_desc->device_type()) + << "device_type not match placement (" << DeviceType_Name(device_type) << " vs. " + << DeviceType_Name(parallel_desc->device_type()) << ". " << kOfBugIssueUploadPrompt; + ; + std::shared_ptr communication_ctx = + std::shared_ptr(NewObj(device_type)); + communication_ctx->Init(parallel_desc); + return communication_ctx; +} + +inline bool IsCommunicationContextRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +#define REGISTER_COLLECTIVE_COMMUNICATION_COMMUNICATOR(device, Derived) \ + REGISTER_CLASS(DeviceType, device, CommunicationContext, Derived) + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_COMMUNICATION_CONTEXT_H_ diff --git a/oneflow/user/kernels/collective_communication/include/recv.h b/oneflow/user/kernels/collective_communication/include/recv.h new file mode 100644 index 0000000000000000000000000000000000000000..59c1aef849f0183ed8f977fc6e0b06f7179f9965 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/recv.h @@ -0,0 +1,44 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_RECVH_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_RECVH_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class Recv : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(Recv); + Recv() = default; + ~Recv() override = default; + + virtual void Init(DataType dtype) = 0; + + virtual void Launch(ep::Stream* stream, void* out, size_t elem_cnt, int64_t src) const = 0; +}; + +inline bool IsRecvRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_RECVH_ diff --git a/oneflow/user/kernels/collective_communication/include/reduce.h b/oneflow/user/kernels/collective_communication/include/reduce.h new file mode 100644 index 0000000000000000000000000000000000000000..9c126d8b1f1178bff65a68a2372d4a2d2cae960f --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/reduce.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class Reduce : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(Reduce); + Reduce() = default; + ~Reduce() override = default; + + virtual void Init(DataType dtype, ReduceType reduce_type) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, int64_t root, + const std::shared_ptr& communicator) const = 0; +}; + +inline bool IsReduceRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_H_ diff --git a/oneflow/user/kernels/collective_communication/include/reduce_scatter.h b/oneflow/user/kernels/collective_communication/include/reduce_scatter.h new file mode 100644 index 0000000000000000000000000000000000000000..a3b179b48fb7eee3c8521ace7252b16ab23d96c6 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/reduce_scatter.h @@ -0,0 +1,45 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_SCATTER_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_SCATTER_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class ReduceScatter : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(ReduceScatter); + ReduceScatter() = default; + ~ReduceScatter() override = default; + + virtual void Init(DataType dtype, ReduceType reduce_type) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, void* out, size_t elem_cnt, + const std::shared_ptr& communicator) const = 0; +}; + +inline bool IsReduceScatterRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_REDUCE_SCATTER_H_ diff --git a/oneflow/user/kernels/collective_communication/include/send.h b/oneflow/user/kernels/collective_communication/include/send.h new file mode 100644 index 0000000000000000000000000000000000000000..6658c7de292d4384683604471686efc1eda233f2 --- /dev/null +++ b/oneflow/user/kernels/collective_communication/include/send.h @@ -0,0 +1,44 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_SEND_H_ +#define ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_SEND_H_ + +#include "oneflow/user/kernels/collective_communication/include/collective_communication.h" + +namespace oneflow { + +namespace ccl { + +class Send : public CollectiveCommunication { + public: + OF_DISALLOW_COPY_AND_MOVE(Send); + Send() = default; + ~Send() override = default; + + virtual void Init(DataType dtype) = 0; + + virtual void Launch(ep::Stream* stream, const void* in, size_t elem_cnt, int64_t dst) const = 0; +}; + +inline bool IsSendRegistered(DeviceType device_type) { + return IsClassRegistered(device_type); +} + +} // namespace ccl + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_COLLECTIVE_COMMUNICATION_INCLUDE_SEND_H_ diff --git a/oneflow/user/kernels/combined_margin_loss_kernel.hip.cpp b/oneflow/user/kernels/combined_margin_loss_kernel.hip.cpp deleted file mode 100644 index 871ae34f45d1646b478c7280056d19189fe908ae..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/combined_margin_loss_kernel.hip.cpp +++ /dev/null @@ -1,225 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/user/kernels/math_unary_elementwise_func.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void GpuForward(const int64_t n, const int64_t num_classes, const int64_t lower_bound, - const T m1, const T m2, const T m3, const T* in, const K* labels, T* out, - T* theta) { - CUDA_1D_KERNEL_LOOP(i, n) { - const int32_t row_id = i / num_classes; - const int32_t col_id = i - row_id * num_classes; - const T in_data = in[i]; - T out_data = in_data; - K label = labels[row_id] - lower_bound; - if (is_cosine_loss) { - if (label == col_id) { out_data = in_data - m3; } - } else { - if (label == col_id) { - const T theta_data = AcosFunctor::Forward(in_data); - out_data = CosFunctor::Forward(theta_data * m1 + m2) - m3; - theta[row_id] = theta_data; - } else if ((label < 0 || label >= num_classes) && col_id == 0) { - theta[row_id] = 0; - } - } - out[i] = out_data; - } -} - -template -__global__ void GpuBackward(const int64_t n, const int64_t num_classes, const int64_t lower_bound, - const T m1, const T m2, const T m3, const T* dy, const K* labels, - const T* theta, T* dx) { - CUDA_1D_KERNEL_LOOP(i, n) { - const int32_t row_id = i / num_classes; - const int32_t col_id = i - row_id * num_classes; - K label = labels[row_id] - lower_bound; - const T dy_data = dy[i]; - const T theta_data = theta[row_id]; - T dx_data = dy_data; - if (label == col_id && !is_cosine_loss) { - dx_data = dy_data * SinFunctor::Forward(theta_data * m1 + m2) * m1 - / SinFunctor::Forward(theta_data); - } - dx[i] = dx_data; - } -} - -class CombinedMarginLossOpKernelCache final : public user_op::OpKernelCache { - public: - CombinedMarginLossOpKernelCache(int64_t lower, int64_t upper) : lower_(lower), upper_(upper) {} - ~CombinedMarginLossOpKernelCache() override = default; - - int64_t lower() const { return lower_; } - int64_t upper() const { return upper_; } - - private: - const int64_t lower_; - const int64_t upper_; -}; - -std::shared_ptr CreateCombinedMarginLossOpKernelCache( - user_op::KernelCacheContext* ctx, const std::string& in_arg_name) { - if (ctx->parallel_ctx().parallel_num() == 1) { return nullptr; } - - const SbpParallel& in_sbp = ctx->SbpParallel4ArgNameAndIndex(in_arg_name, 0); - if (in_sbp.has_split_parallel() && in_sbp.split_parallel().axis() == 1 - && ctx->parallel_ctx().parallel_num() > 1) { - CHECK(ctx->SbpParallel4ArgNameAndIndex("label", 0).has_broadcast_parallel()); - const user_op::TensorDesc* in_logical_desc = - ctx->LogicalTensorDesc4ArgNameAndIndex(in_arg_name, 0); - const auto depth = ctx->Attr("depth"); - CHECK_EQ(depth, in_logical_desc->shape().At(1)); - BalancedSplitter bs(depth, ctx->parallel_ctx().parallel_num()); - return std::make_shared( - bs.At(ctx->parallel_ctx().parallel_id()).begin(), - bs.At(ctx->parallel_ctx().parallel_id()).end()); - } else { - return nullptr; - } -} - -} // namespace - -template -class CombinedMarginLossGpuKernel final : public user_op::OpKernel { - public: - CombinedMarginLossGpuKernel() = default; - ~CombinedMarginLossGpuKernel() override = default; - - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - return CreateCombinedMarginLossOpKernelCache(ctx, "x"); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* label = ctx->Tensor4ArgNameAndIndex("label", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* theta = ctx->Tensor4ArgNameAndIndex("theta", 0); - const float m1 = ctx->Attr("m1"); - const float m2 = ctx->Attr("m2"); - const float m3 = ctx->Attr("m3"); - int64_t lower_bound = 0; - if (cache != nullptr) { - auto* kernel_cache = dynamic_cast(cache); - CHECK_NOTNULL(kernel_cache); - CHECK_EQ(x->shape_view().Count(1), kernel_cache->upper() - kernel_cache->lower()); - lower_bound = kernel_cache->lower(); - } - if (m1 == 1.0 && m2 == 0.0) { - GpuForward - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, 0, - ctx->stream()->As()->cuda_stream()>>>( - x->shape_view().elem_cnt(), x->shape_view().Count(1), lower_bound, static_cast(m1), - static_cast(m2), static_cast(m3), x->dptr(), label->dptr(), - y->mut_dptr(), theta->mut_dptr()); - } else { - GpuForward - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, 0, - ctx->stream()->As()->cuda_stream()>>>( - x->shape_view().elem_cnt(), x->shape_view().Count(1), lower_bound, static_cast(m1), - static_cast(m2), static_cast(m3), x->dptr(), label->dptr(), - y->mut_dptr(), theta->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_COMBINED_MARGIN_LOSS_CUDA_KERNEL(in_type, indices_type) \ - REGISTER_USER_KERNEL("combined_margin_loss") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(in_type)) \ - && (user_op::HobDataType("label", 0) == OF_PP_PAIR_SECOND(indices_type))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_COMBINED_MARGIN_LOSS_CUDA_KERNEL, FLOATING_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -template -class CombinedMarginLossGradGpuKernel final : public user_op::OpKernel { - public: - CombinedMarginLossGradGpuKernel() = default; - ~CombinedMarginLossGradGpuKernel() override = default; - - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - return CreateCombinedMarginLossOpKernelCache(ctx, "dy"); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* label = ctx->Tensor4ArgNameAndIndex("label", 0); - const user_op::Tensor* theta = ctx->Tensor4ArgNameAndIndex("theta", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float m1 = ctx->Attr("m1"); - const float m2 = ctx->Attr("m2"); - const float m3 = ctx->Attr("m3"); - int64_t lower_bound = 0; - if (cache != nullptr) { - auto* kernel_cache = dynamic_cast(cache); - CHECK_NOTNULL(kernel_cache); - CHECK_EQ(dy->shape_view().Count(1), kernel_cache->upper() - kernel_cache->lower()); - lower_bound = kernel_cache->lower(); - } - if (m1 == 1.0 && m2 == 0.0) { - GpuBackward - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, 0, - ctx->stream()->As()->cuda_stream()>>>( - dy->shape_view().elem_cnt(), dy->shape_view().Count(1), lower_bound, - static_cast(m1), static_cast(m2), static_cast(m3), dy->dptr(), - label->dptr(), theta->dptr(), dx->mut_dptr()); - } else { - GpuBackward - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, 0, - ctx->stream()->As()->cuda_stream()>>>( - dy->shape_view().elem_cnt(), dy->shape_view().Count(1), lower_bound, - static_cast(m1), static_cast(m2), static_cast(m3), dy->dptr(), - label->dptr(), theta->dptr(), dx->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_COMBINED_MARGIN_LOSS_GRAD_CUDA_KERNEL(dy_type, indices_type) \ - REGISTER_USER_KERNEL("combined_margin_loss_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == OF_PP_PAIR_SECOND(dy_type)) \ - && (user_op::HobDataType("label", 0) == OF_PP_PAIR_SECOND(indices_type))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_COMBINED_MARGIN_LOSS_GRAD_CUDA_KERNEL, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/communicate_util.cpp b/oneflow/user/kernels/communicate_util.cpp index d753f79b8a8c14ec94356b53f22e6aa5875426b2..d9795cd8587d9366b8188d79349ed9748fc99454 100644 --- a/oneflow/user/kernels/communicate_util.cpp +++ b/oneflow/user/kernels/communicate_util.cpp @@ -14,13 +14,11 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/user/kernels/communicate_util.h" -#include "oneflow/core/device/nccl_util.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ep/include/primitive/memcpy.h" #include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ccl/ccl.h" -#include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/control/global_process_ctx.h" +#include "oneflow/user/kernels/collective_communication/include/send.h" +#include "oneflow/user/kernels/collective_communication/include/recv.h" namespace oneflow { @@ -33,52 +31,43 @@ const void** ThreadLocalSrcDataPtr() { } // namespace -template -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, ep::Stream* stream) { +bool IsSendAndRecvRegistered(DeviceType device_type) { + return ccl::IsSendRegistered(device_type) && ccl::IsRecvRegistered(device_type); +} + +Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, + DeviceType device_type, ep::Stream* stream) { if (GlobalProcessCtx::Rank() == dst) { auto** src_data_ptr = ThreadLocalSrcDataPtr(); CHECK_OR_RETURN(*src_data_ptr == nullptr); *src_data_ptr = in; } else { - JUST(ccl::Send(in, elem_cnt, dtype, dst, stream)); + std::unique_ptr send = + ccl::NewCollectiveCommunication(device_type, dtype); + send->Launch(stream, in, elem_cnt, dst); } return Maybe::Ok(); } -template -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, ep::Stream* stream) { +Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, DeviceType device_type, + ep::Stream* stream) { if (GlobalProcessCtx::Rank() == src) { size_t buffer_size = elem_cnt * GetSizeOfDataType(dtype); auto** src_data_ptr = ThreadLocalSrcDataPtr(); const void* in = *src_data_ptr; CHECK_OR_RETURN(*src_data_ptr != nullptr); - Memcpy(stream, out, in, buffer_size); + std::unique_ptr memcpy_primitive = + ep::primitive::NewPrimitive(device_type, + ep::primitive::MemcpyKind::kDtoD); + CHECK(memcpy_primitive) << "Can not create Memcpy primitive for device type " << device_type; + memcpy_primitive->Launch(stream, out, in, buffer_size); *src_data_ptr = nullptr; } else { - JUST(ccl::Recv(out, elem_cnt, dtype, src, stream)); + std::unique_ptr recv = + ccl::NewCollectiveCommunication(device_type, dtype); + recv->Launch(stream, out, elem_cnt, src); } return Maybe::Ok(); } -template Maybe Send(const void* in, size_t elem_cnt, DataType dtype, - int64_t dst, ep::Stream* stream); - -template Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, - ep::Stream* stream); - -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -template Maybe Send(const void* in, size_t elem_cnt, DataType dtype, - int64_t dst, ep::Stream* stream); - -template Maybe Recv(void* out, size_t elem_cnt, DataType dtype, - int64_t src, ep::Stream* stream); -#endif - -#if defined(WITH_ROCM) -template Maybe Send(const void* in, size_t elem_cnt, DataType dtype, - int64_t dst, ep::Stream* stream); - -template Maybe Recv(void* out, size_t elem_cnt, DataType dtype, - int64_t src, ep::Stream* stream); -#endif } // namespace oneflow diff --git a/oneflow/user/kernels/communicate_util.h b/oneflow/user/kernels/communicate_util.h index 75f8c33f731844ca883d167c28c83691ecdd9170..3db423f484c0fccd87c654ba8f760836b577e5c2 100644 --- a/oneflow/user/kernels/communicate_util.h +++ b/oneflow/user/kernels/communicate_util.h @@ -18,18 +18,27 @@ limitations under the License. #include "oneflow/core/common/data_type.h" #include "oneflow/core/ep/include/stream.h" +#include "oneflow/core/framework/user_op_kernel_registry.h" namespace oneflow { +bool IsSendAndRecvRegistered(DeviceType device_type); + +ALWAYS_INLINE inline auto HobIsSendAndRecvRegistered() { + return hob::make_custom("HobIsSendAndRecvRegistered", [](const user_op::KernelRegContext& ctx) { + return IsSendAndRecvRegistered(ctx.device_type()); + }); +} + // Send data from in to rank dst, if cur rank equal dst, memcopy will happen. // Rank dst needs to call Recv with the same datatype and the same count from this rank. -template -Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, ep::Stream* stream); +Maybe Send(const void* in, size_t elem_cnt, DataType dtype, int64_t dst, + DeviceType device_type, ep::Stream* stream); // Receive data from rank src into out, if cur rank equal src, memcopy will happen. // Rank src needs to call Send with the same datatype and the same count to this rank. -template -Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, ep::Stream* stream); +Maybe Recv(void* out, size_t elem_cnt, DataType dtype, int64_t src, DeviceType device_type, + ep::Stream* stream); } // namespace oneflow diff --git a/oneflow/user/kernels/conv_cudnn_kernels.cpp b/oneflow/user/kernels/conv_cudnn_kernels.cpp index 6e15e6dacc28685746a32254a7d646723205a719..d99ff2fc79168b89f83dfac59b51ce3df6a1d2db 100644 --- a/oneflow/user/kernels/conv_cudnn_kernels.cpp +++ b/oneflow/user/kernels/conv_cudnn_kernels.cpp @@ -16,13 +16,13 @@ limitations under the License. #ifdef WITH_CUDA #include "oneflow/core/framework/framework.h" -#include "oneflow/user/ops/nn_util.h" #include "oneflow/core/device/cudnn_conv_util.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/job/resource_desc.h" #include "oneflow/core/job/global_for.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { @@ -41,9 +41,10 @@ struct CudnnConvArgsAndAlgo final { : args(*ctx, x->data_type(), x->shape_view(), w->data_type(), w->shape_view(), y->data_type(), y->shape_view(), ctx->Attr("data_format"), buf->shape_view().elem_cnt(), Singleton::Get() - ->resource() - .cudnn_conf() - .cudnn_conv_heuristic_search_algo(), + ->resource() + .cudnn_conf() + .cudnn_conv_heuristic_search_algo() + || (!LazyMode::is_enabled()), Singleton::Get() ->resource() .cudnn_conf() @@ -88,12 +89,12 @@ size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::Tensor CudnnConvArgs args(ctx, x->data_type(), ShapeView(x->shape()), w->data_type(), ShapeView(w->shape()), y->data_type(), ShapeView(y->shape()), ctx.Attr("data_format"), workspace_size, - cudnn_conf.cudnn_conv_heuristic_search_algo(), + cudnn_conf.cudnn_conv_heuristic_search_algo() || (!LazyMode::is_enabled()), cudnn_conf.cudnn_conv_use_deterministic_algo_only(), cudnn_conf.cudnn_conv_enable_pseudo_half() || (ctx.Attr("data_format") == "channels_last" && std::is_same::value)); - PerfT algo_perf; + PerfT algo_perf{}; if (has_forced_algo) { algo_perf = GetCudnnConvAlgorithmPerference(&args, static_cast(forced_algo)); } else { @@ -119,8 +120,6 @@ CudnnTensorDesc* GetBiasCudnnTensorDesc(const std::string& data_format, int32_t return new CudnnTensorDesc(CUDNN_TENSOR_NCHW, data_type, 1, filters, 1, 1); } else { CHECK_EQ("channels_last", data_format); - CHECK_EQ(DataType::kFloat, data_type) - << "CUDNN 1d & 2d support channels last only if data type is float"; return new CudnnTensorDesc(CUDNN_TENSOR_NHWC, data_type, 1, filters, 1, 1); } } @@ -142,7 +141,7 @@ struct ConvCudnnOpKernelCache final : public user_op::OpKernelCache { std::unique_ptr bias_desc; }; -template +template class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: ConvGpuKernel() = default; @@ -160,7 +159,7 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS const user_op::TensorDesc* bias = ctx->TensorDesc4ArgNameAndIndex("bias", 0); if (bias != nullptr) { state->bias_desc.reset( - GetBiasCudnnTensorDesc(data_format, filters, GetDataType::value)); + GetBiasCudnnTensorDesc(data_format, filters, bias->data_type())); } return state; @@ -185,21 +184,34 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS cudnn_conf.cudnn_conv_force_fwd_algo()); const CudnnConvArgs& args = args_and_algo.args; const cudnnConvolutionFwdAlgoPerf_t& algo_perf = args_and_algo.algo_perf; + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); - OF_CUDNN_CHECK(cudnnConvolutionForward(ctx->stream()->As()->cudnn_handle(), - CudnnSPOnePtr(), args.xdesc.Get(), in->dptr(), - args.wdesc.Get(), weight->dptr(), args.cdesc.Get(), - algo_perf.algo, buf->mut_dptr(), args.params.max_ws_size, - CudnnSPZeroPtr(), args.ydesc.Get(), out->mut_dptr())); + const void* beta = nullptr; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), out->data_type()); + CHECK_EQ(add_to_output->shape_view(), out->shape_view()); + Memcpy( + ctx->stream(), out->mut_dptr(), add_to_output->dptr(), + add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); + beta = CudnnSPOnePtr(in->data_type()); + } else { + beta = CudnnSPZeroPtr(in->data_type()); + } + + OF_CUDNN_CHECK(cudnnConvolutionForward( + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(in->data_type()), + args.xdesc.Get(), in->dptr(), args.wdesc.Get(), weight->dptr(), args.cdesc.Get(), + algo_perf.algo, buf->mut_dptr(), args.params.max_ws_size, beta, args.ydesc.Get(), + out->mut_dptr())); - const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); if (bias != nullptr) { const auto* conv_cache = dynamic_cast(cache); CHECK_NOTNULL(conv_cache); OF_CUDNN_CHECK(cudnnAddTensor(ctx->stream()->As()->cudnn_handle(), - CudnnSPOnePtr(), conv_cache->bias_desc->Get(), - bias->dptr(), CudnnSPOnePtr(), args.ydesc.Get(), - out->mut_dptr())); + CudnnSPOnePtr(in->data_type()), conv_cache->bias_desc->Get(), + bias->dptr(), CudnnSPOnePtr(in->data_type()), args.ydesc.Get(), + out->mut_dptr())); } } @@ -212,34 +224,34 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS } }; -#define REGISTER_CONV_KERNEL(op_name, dtype, ndims) \ - REGISTER_USER_KERNEL(#op_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const auto& in = ctx->InputTensorDesc("in", 0); \ - if (in.shape().elem_cnt() == 0) return 0; \ - const auto& weight = ctx->InputTensorDesc("weight", 0); \ - const auto* out = ctx->OutputTensorDesc("out", 0); \ - const auto& cudnn_conf = \ - Singleton::Get()->resource().cudnn_conf(); \ - return InferTmpSizeWithCudnn( \ - &in, &weight, out, *ctx, cudnn_conf.has_cudnn_conv_force_fwd_algo(), \ - cudnn_conf.cudnn_conv_force_fwd_algo()); \ - }) - -REGISTER_CONV_KERNEL(conv1d, float, 1); -REGISTER_CONV_KERNEL(conv2d, float, 2); -REGISTER_CONV_KERNEL(conv3d, float, 3); -REGISTER_CONV_KERNEL(conv1d, double, 1); -REGISTER_CONV_KERNEL(conv2d, double, 2); -REGISTER_CONV_KERNEL(conv3d, double, 3); -REGISTER_CONV_KERNEL(conv1d, float16, 1); -REGISTER_CONV_KERNEL(conv2d, float16, 2); -REGISTER_CONV_KERNEL(conv3d, float16, 3); - -template +#define REGISTER_CONV_KERNEL(op_name, ndims) \ + REGISTER_USER_KERNEL(#op_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ + const auto& in = ctx->InputTensorDesc("in", 0); \ + if (in.shape().elem_cnt() == 0) return 0; \ + const auto& weight = ctx->InputTensorDesc("weight", 0); \ + const auto& out = ctx->OutputTensorDesc("out", 0); \ + const auto& cudnn_conf = \ + Singleton::Get()->resource().cudnn_conf(); \ + return InferTmpSizeWithCudnn( \ + &in, &weight, &out, *ctx, cudnn_conf.has_cudnn_conv_force_fwd_algo(), \ + cudnn_conf.cudnn_conv_force_fwd_algo()); \ + }) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext& ctx, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + if (ctx.has_input("_add_to_output", 0)) { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "_add_to_output", 0, true)); \ + } \ + return Maybe::Ok(); \ + }); + +REGISTER_CONV_KERNEL(conv1d, 1); +REGISTER_CONV_KERNEL(conv2d, 2); +REGISTER_CONV_KERNEL(conv3d, 3); + class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: OF_DISALLOW_COPY_AND_MOVE(ConvDataGradGpuKernel); @@ -263,8 +275,8 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu const CudnnConvArgs& args = args_and_algo.args; const cudnnConvolutionBwdDataAlgoPerf_t& algo_perf = args_and_algo.algo_perf; - const void* alpha = CudnnSPOnePtr(); - const void* beta; + const void* alpha = CudnnSPOnePtr(dy->data_type()); + const void* beta = nullptr; if (ctx->has_input("_add_to_output", 0)) { const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); CHECK_EQ(add_to_output->data_type(), dx->data_type()); @@ -272,9 +284,9 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu Memcpy( ctx->stream(), dx->mut_dptr(), add_to_output->dptr(), add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - beta = CudnnSPOnePtr(); + beta = CudnnSPOnePtr(dy->data_type()); } else { - beta = CudnnSPZeroPtr(); + beta = CudnnSPZeroPtr(dy->data_type()); } OF_CUDNN_CHECK(cudnnConvolutionBackwardData( @@ -292,35 +304,27 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu } }; -#define REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_data_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const auto& dy = ctx->InputTensorDesc("dy", 0); \ - const auto& filter = ctx->InputTensorDesc("filter", 0); \ - const auto* dx = ctx->OutputTensorDesc("dx", 0); \ - if (dx->shape().elem_cnt() == 0) return 0; \ - const auto& cudnn_conf = \ - Singleton::Get()->resource().cudnn_conf(); \ - return InferTmpSizeWithCudnn( \ - dx, &filter, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), \ - cudnn_conf.cudnn_conv_force_bwd_data_algo()); \ - }) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }) - -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(float16); - -template +REGISTER_USER_KERNEL("conv_data_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { + const auto& dy = ctx->InputTensorDesc("dy", 0); + const auto& filter = ctx->InputTensorDesc("filter", 0); + const auto& dx = ctx->OutputTensorDesc("dx", 0); + if (dx.shape().elem_cnt() == 0) return 0; + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + return InferTmpSizeWithCudnn( + &dx, &filter, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), + cudnn_conf.cudnn_conv_force_bwd_data_algo()); + }) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); + class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: OF_DISALLOW_COPY_AND_MOVE(ConvFilterGradGpuKernel); @@ -335,8 +339,9 @@ class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op:: const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); user_op::Tensor* filter_diff = ctx->Tensor4ArgNameAndIndex("filter_diff", 0); if (x->shape_view().elem_cnt() == 0) { - Memset(ctx->stream(), filter_diff->mut_dptr(), 0, - filter_diff->shape_view().elem_cnt() * sizeof(T)); + Memset( + ctx->stream(), filter_diff->mut_dptr(), 0, + filter_diff->shape_view().elem_cnt() * GetSizeOfDataType(filter_diff->data_type())); return; } user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); @@ -350,9 +355,10 @@ class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op:: const cudnnConvolutionBwdFilterAlgoPerf_t& algo_perf = args_and_algo.algo_perf; OF_CUDNN_CHECK(cudnnConvolutionBackwardFilter( - ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(), args.xdesc.Get(), - x->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, buf->mut_dptr(), - args.params.max_ws_size, CudnnSPZeroPtr(), args.wdesc.Get(), filter_diff->mut_dptr())); + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(dy->data_type()), + args.xdesc.Get(), x->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, + buf->mut_dptr(), args.params.max_ws_size, CudnnSPZeroPtr(dy->data_type()), args.wdesc.Get(), + filter_diff->mut_dptr())); } bool IsCudaGraphSupported(user_op::KernelInitContext* ctx, @@ -364,32 +370,24 @@ class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op:: } }; -#define REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_filter_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const auto& dy = ctx->InputTensorDesc("dy", 0); \ - const auto& x = ctx->InputTensorDesc("x", 0); \ - if (x.shape().elem_cnt() == 0) return 0; \ - const auto* filter_diff = ctx->OutputTensorDesc("filter_diff", 0); \ - const auto& cudnn_conf = \ - Singleton::Get()->resource().cudnn_conf(); \ - return InferTmpSizeWithCudnn( \ - &x, filter_diff, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_filter_algo(), \ - cudnn_conf.cudnn_conv_force_bwd_filter_algo()); \ - }) - -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(float16); +REGISTER_USER_KERNEL("conv_filter_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { + const auto& dy = ctx->InputTensorDesc("dy", 0); + const auto& x = ctx->InputTensorDesc("x", 0); + if (x.shape().elem_cnt() == 0) return 0; + const auto& filter_diff = ctx->OutputTensorDesc("filter_diff", 0); + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + return InferTmpSizeWithCudnn( + &x, &filter_diff, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_filter_algo(), + cudnn_conf.cudnn_conv_force_bwd_filter_algo()); + }); struct ConvBiasGradState final : public user_op::OpKernelState { std::unique_ptr bias_diff_desc; }; -template class ConvBiasGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: ConvBiasGradGpuKernel() = default; @@ -434,21 +432,15 @@ class ConvBiasGradGpuKernel final : public user_op::OpKernel, public user_op::Cu const auto& bias_grad_state = CreateConvBiasGradState(ctx); CHECK_NOTNULL(bias_grad_state.get()); OF_CUDNN_CHECK(cudnnConvolutionBackwardBias( - ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(), dy_desc->Get(), - dy->dptr(), CudnnSPZeroPtr(), bias_grad_state->bias_diff_desc->Get(), - bias_diff->mut_dptr())); + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(dy->data_type()), + dy_desc->Get(), dy->dptr(), CudnnSPZeroPtr(dy->data_type()), + bias_grad_state->bias_diff_desc->Get(), bias_diff->mut_dptr())); } }; -#define REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_bias_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)); - -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(float16); +REGISTER_USER_KERNEL("conv_bias_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); } // namespace @@ -456,20 +448,16 @@ REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(float16); #endif - - - - #ifdef WITH_ROCM #include "oneflow/core/framework/framework.h" -#include "oneflow/user/ops/nn_util.h" #include "oneflow/core/device/cudnn_conv_util.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/job/resource_desc.h" #include "oneflow/core/job/global_for.h" #include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { @@ -483,16 +471,15 @@ struct CudnnConvArgsAndAlgo final { PerfT algo_perf; CudnnConvArgsAndAlgo(const user_op::Tensor* x, const user_op::Tensor* w, const user_op::Tensor* y, - // user_op::Tensor* buf, const user_op::KernelComputeContext* ctx, - AllocatedCudnnConvResource& res, const user_op::KernelComputeContext* ctx, + user_op::Tensor* buf, const user_op::KernelComputeContext* ctx, ep::Stream* stream, bool has_forced_algo, int32_t forced_algo) : args(*ctx, x->data_type(), x->shape_view(), w->data_type(), w->shape_view(), y->data_type(), - // y->shape_view(), ctx->Attr("data_format"), buf->shape_view().elem_cnt(), - y->shape_view(), ctx->Attr("data_format"), 0, + y->shape_view(), ctx->Attr("data_format"), buf->shape_view().elem_cnt(), Singleton::Get() - ->resource() - .cudnn_conf() - .cudnn_conv_heuristic_search_algo(), + ->resource() + .cudnn_conf() + .cudnn_conv_heuristic_search_algo() + || (!LazyMode::is_enabled()), Singleton::Get() ->resource() .cudnn_conf() @@ -503,29 +490,22 @@ struct CudnnConvArgsAndAlgo final { .cudnn_conv_enable_pseudo_half() || (ctx->Attr("data_format") == "channels_last" && std::is_same::value)) { - - // size_t workspace_size; - // AllocatedCudnnConvResource res(stream->As()->cudnn_handle(), - // const_cast(x->dptr()), const_cast(w->dptr()), - // const_cast(y->dptr()), nullptr, 0); - - // OF_CUDNN_CHECK(GetCudnnConvWorkspaceSize(args, &res, algo_perf.algo, &workspace_size)); - // size_t byte_size_of_buf = workspace_size; - // args.params.max_ws_size = workspace_size; - + size_t byte_size_of_buf = buf->shape_view().elem_cnt(); + AllocatedCudnnConvResource res(stream->As()->cudnn_handle(), + const_cast(x->dptr()), const_cast(w->dptr()), + const_cast(y->dptr()), buf->mut_dptr()); if (has_forced_algo) { algo_perf = GetCudnnConvAlgorithmPerferenceWithResource( &args, &res, static_cast(forced_algo)); } else { algo_perf = FindCudnnConvAlgorithmWithResource(&args, &res); } - CHECK_EQ(algo_perf.status, HIPDNN_STATUS_SUCCESS) << "op (" << ctx->op_name() << ") find algorithm perference failed. algo: " << algo_perf.algo; - // CHECK_LE(algo_perf.memory, byte_size_of_buf) - // << "op (" << ctx->op_name() << ") find algorithm " << algo_perf.algo << ", need memory " - // << algo_perf.memory << ", but cudnn_buf_limit_byte is " << byte_size_of_buf; + CHECK_LE(algo_perf.memory, byte_size_of_buf) + << "op (" << ctx->op_name() << ") find algorithm " << algo_perf.algo << ", need memory " + << algo_perf.memory << ", but cudnn_buf_limit_byte is " << byte_size_of_buf; OF_CUDNN_CHECK(hipdnnSetConvolutionMathType(args.cdesc.Get(), algo_perf.mathType)); } CudnnConvArgsAndAlgo() = delete; @@ -536,36 +516,88 @@ template size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::TensorDesc* w, const user_op::TensorDesc* y, const user_op::InferContext& ctx, bool has_forced_algo, int32_t forced_algo) { - using AlgoT = decltype(std::declval().algo); +} + +template<> +size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::TensorDesc* w, + const user_op::TensorDesc* y, const user_op::InferContext& ctx, + bool has_forced_algo, int32_t forced_algo) { const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); size_t workspace_size = cudnn_conf.cudnn_buf_limit_mbyte() * 1024 * 1024; - if (!x->is_dynamic()) { CudnnConvArgs args(ctx, x->data_type(), ShapeView(x->shape()), w->data_type(), ShapeView(w->shape()), y->data_type(), ShapeView(y->shape()), ctx.Attr("data_format"), workspace_size, - cudnn_conf.cudnn_conv_heuristic_search_algo(), + cudnn_conf.cudnn_conv_heuristic_search_algo() || (!LazyMode::is_enabled()), cudnn_conf.cudnn_conv_use_deterministic_algo_only(), cudnn_conf.cudnn_conv_enable_pseudo_half() || (ctx.Attr("data_format") == "channels_last" - && std::is_same::value)); - PerfT algo_perf; - if (has_forced_algo) { - algo_perf = GetCudnnConvAlgorithmPerference(&args, static_cast(forced_algo)); - } else { - ManagedCudnnConvResource res(args); - OF_CUDNN_CHECK(GetCudnnConvWorkspaceSize(args, &res, algo_perf.algo, &workspace_size)); - args.params.max_ws_size = workspace_size; - algo_perf = FindCudnnConvAlgorithm(&args); - } - CHECK_EQ(algo_perf.status, HIPDNN_STATUS_SUCCESS) - << "op (" << ctx.op_name() - << ") find algorithm perference failed. algo: " << algo_perf.algo; - CHECK_LE(algo_perf.memory, workspace_size) - << "op (" << ctx.op_name() << ") find algorithm " << algo_perf.algo << ", need memory " - << algo_perf.memory << ", but cudnn_buf_limit_byte is " << workspace_size; - workspace_size = algo_perf.memory; + && std::is_same::value)); + size_t ws = 0; + hipdnnConvolutionFwdAlgo_t algo; + ManagedCudnnConvResource res(args); + hipdnnGetConvolutionForwardWorkspaceSize(res.cudnn_handle(), args.xdesc.Get(), + args.wdesc.Get(), args.cdesc.Get(), + args.ydesc.Get(), algo, &ws); + + workspace_size = ws; + } + workspace_size = std::max(size_t(1), workspace_size); + return workspace_size; +} + +template<> +size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::TensorDesc* w, + const user_op::TensorDesc* y, const user_op::InferContext& ctx, + bool has_forced_algo, int32_t forced_algo) { + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + size_t workspace_size = cudnn_conf.cudnn_buf_limit_mbyte() * 1024 * 1024; + if (!x->is_dynamic()) { + CudnnConvArgs args(ctx, x->data_type(), ShapeView(x->shape()), w->data_type(), + ShapeView(w->shape()), y->data_type(), ShapeView(y->shape()), + ctx.Attr("data_format"), workspace_size, + cudnn_conf.cudnn_conv_heuristic_search_algo() || (!LazyMode::is_enabled()), + cudnn_conf.cudnn_conv_use_deterministic_algo_only(), + cudnn_conf.cudnn_conv_enable_pseudo_half() + || (ctx.Attr("data_format") == "channels_last" + && std::is_same::value)); + size_t ws = 0; + hipdnnConvolutionBwdDataAlgo_t algo; + ManagedCudnnConvResource res(args); + hipdnnGetConvolutionBackwardDataWorkspaceSize(res.cudnn_handle(), args.wdesc.Get(), + args.ydesc.Get(), args.cdesc.Get(), + args.xdesc.Get(), algo, &ws); + + workspace_size = ws; + } + workspace_size = std::max(size_t(1), workspace_size); + return workspace_size; +} + +template<> +size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::TensorDesc* w, + const user_op::TensorDesc* y, const user_op::InferContext& ctx, + bool has_forced_algo, int32_t forced_algo) { + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + size_t workspace_size = cudnn_conf.cudnn_buf_limit_mbyte() * 1024 * 1024; + if (!x->is_dynamic()) { + CudnnConvArgs args(ctx, x->data_type(), ShapeView(x->shape()), w->data_type(), + ShapeView(w->shape()), y->data_type(), ShapeView(y->shape()), + ctx.Attr("data_format"), workspace_size, + cudnn_conf.cudnn_conv_heuristic_search_algo() || (!LazyMode::is_enabled()), + cudnn_conf.cudnn_conv_use_deterministic_algo_only(), + cudnn_conf.cudnn_conv_enable_pseudo_half() + || (ctx.Attr("data_format") == "channels_last" + && std::is_same::value)); + size_t ws = 0; + hipdnnConvolutionBwdFilterAlgo_t algo; + ManagedCudnnConvResource res(args); + hipdnnGetConvolutionBackwardFilterWorkspaceSize(res.cudnn_handle(), args.xdesc.Get(), + args.ydesc.Get(), args.cdesc.Get(), + args.wdesc.Get(), algo, &ws); + + workspace_size = ws; } workspace_size = std::max(size_t(1), workspace_size); return workspace_size; @@ -579,8 +611,6 @@ CudnnTensorDesc* GetBiasCudnnTensorDesc(const std::string& data_format, int32_t return new CudnnTensorDesc(HIPDNN_TENSOR_NCHW, data_type, 1, filters, 1, 1); } else { CHECK_EQ("channels_last", data_format); - CHECK_EQ(DataType::kFloat, data_type) - << "CUDNN 1d & 2d support channels last only if data type is float"; return new CudnnTensorDesc(HIPDNN_TENSOR_NHWC, data_type, 1, filters, 1, 1); } } @@ -602,7 +632,7 @@ struct ConvCudnnOpKernelCache final : public user_op::OpKernelCache { std::unique_ptr bias_desc; }; -template +template class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: ConvGpuKernel() = default; @@ -620,7 +650,7 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS const user_op::TensorDesc* bias = ctx->TensorDesc4ArgNameAndIndex("bias", 0); if (bias != nullptr) { state->bias_desc.reset( - GetBiasCudnnTensorDesc(data_format, filters, GetDataType::value)); + GetBiasCudnnTensorDesc(data_format, filters, bias->data_type())); } return state; @@ -637,36 +667,42 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); if (in->shape_view().elem_cnt() == 0) return; const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - // user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); - - AllocatedCudnnConvResource res(ctx->stream()->As()->cudnn_handle(), - const_cast(in->dptr()), const_cast(weight->dptr()), - const_cast(out->dptr()), nullptr, 0); - CudnnConvArgsAndAlgo args_and_algo( - // in, weight, out, buf, ctx, ctx->stream(), 0, - in, weight, out, res, ctx, ctx->stream(), 0, + in, weight, out, buf, ctx, ctx->stream(), 0, cudnn_conf.cudnn_conv_force_fwd_algo()); const CudnnConvArgs& args = args_and_algo.args; const hipdnnConvolutionFwdAlgoPerf_t& algo_perf = args_and_algo.algo_perf; + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); - OF_CUDNN_CHECK(hipdnnConvolutionForward(ctx->stream()->As()->cudnn_handle(), - CudnnSPOnePtr(), args.xdesc.Get(), in->dptr(), - args.wdesc.Get(), weight->dptr(), args.cdesc.Get(), - // algo_perf.algo, buf->mut_dptr(), args.params.max_ws_size, - algo_perf.algo, res.ws_dptr(), args.params.max_ws_size, - CudnnSPZeroPtr(), args.ydesc.Get(), out->mut_dptr())); + const void* beta = nullptr; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), out->data_type()); + CHECK_EQ(add_to_output->shape_view(), out->shape_view()); + Memcpy( + ctx->stream(), out->mut_dptr(), add_to_output->dptr(), + add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); + beta = CudnnSPOnePtr(in->data_type()); + } else { + beta = CudnnSPZeroPtr(in->data_type()); + } + + OF_CUDNN_CHECK(hipdnnConvolutionForward( + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(in->data_type()), + args.xdesc.Get(), in->dptr(), args.wdesc.Get(), weight->dptr(), args.cdesc.Get(), + algo_perf.algo, buf->mut_dptr(), args.max_ws_size, beta, args.ydesc.Get(), + out->mut_dptr())); - const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); if (bias != nullptr) { const auto* conv_cache = dynamic_cast(cache); CHECK_NOTNULL(conv_cache); OF_CUDNN_CHECK(hipdnnAddTensor(ctx->stream()->As()->cudnn_handle(), - CudnnSPOnePtr(), conv_cache->bias_desc->Get(), - bias->dptr(), CudnnSPOnePtr(), args.ydesc.Get(), - out->mut_dptr())); + CudnnSPOnePtr(in->data_type()), conv_cache->bias_desc->Get(), + bias->dptr(), CudnnSPOnePtr(in->data_type()), args.ydesc.Get(), + out->mut_dptr())); } } @@ -679,34 +715,34 @@ class ConvGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphS } }; -#define REGISTER_CONV_KERNEL(op_name, dtype, ndims) \ - REGISTER_USER_KERNEL(#op_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - // .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - // const auto& in = ctx->InputTensorDesc("in", 0); \ - // if (in.shape().elem_cnt() == 0) return 0; \ - // const auto& weight = ctx->InputTensorDesc("weight", 0); \ - // const auto* out = ctx->OutputTensorDesc("out", 0); \ - // const auto& cudnn_conf = \ - // Singleton::Get()->resource().cudnn_conf(); \ - // return InferTmpSizeWithCudnn( \ - // &in, &weight, out, *ctx, 0, \ - // cudnn_conf.cudnn_conv_force_fwd_algo()); \ - // }) - -REGISTER_CONV_KERNEL(conv1d, float, 1); -REGISTER_CONV_KERNEL(conv2d, float, 2); -REGISTER_CONV_KERNEL(conv3d, float, 3); -REGISTER_CONV_KERNEL(conv1d, double, 1); -REGISTER_CONV_KERNEL(conv2d, double, 2); -REGISTER_CONV_KERNEL(conv3d, double, 3); -REGISTER_CONV_KERNEL(conv1d, float16, 1); -REGISTER_CONV_KERNEL(conv2d, float16, 2); -REGISTER_CONV_KERNEL(conv3d, float16, 3); - -template +#define REGISTER_CONV_KERNEL(op_name, ndims) \ + REGISTER_USER_KERNEL(#op_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ + const auto& in = ctx->InputTensorDesc("in", 0); \ + if (in.shape().elem_cnt() == 0) return 0; \ + const auto& weight = ctx->InputTensorDesc("weight", 0); \ + const auto& out = ctx->OutputTensorDesc("out", 0); \ + const auto& cudnn_conf = \ + Singleton::Get()->resource().cudnn_conf(); \ + return InferTmpSizeWithCudnn( \ + &in, &weight, &out, *ctx, cudnn_conf.has_cudnn_conv_force_fwd_algo(), \ + cudnn_conf.cudnn_conv_force_fwd_algo()); \ + }) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext& ctx, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + if (ctx.has_input("_add_to_output", 0)) { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "_add_to_output", 0, true)); \ + } \ + return Maybe::Ok(); \ + }); + +REGISTER_CONV_KERNEL(conv1d, 1); +REGISTER_CONV_KERNEL(conv2d, 2); +REGISTER_CONV_KERNEL(conv3d, 3); + class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: OF_DISALLOW_COPY_AND_MOVE(ConvDataGradGpuKernel); @@ -721,22 +757,17 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu const user_op::Tensor* filter = ctx->Tensor4ArgNameAndIndex("filter", 0); user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); if (dx->shape_view().elem_cnt() == 0) return; - // user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); - - AllocatedCudnnConvResource res(ctx->stream()->As()->cudnn_handle(), - const_cast(dx->dptr()), const_cast(filter->dptr()), - const_cast(dy->dptr()), nullptr, 0); CudnnConvArgsAndAlgo args_and_algo( - // dx, filter, dy, buf, ctx, ctx->stream(), 0, - dx, filter, dy, res, ctx, ctx->stream(), 0, + dx, filter, dy, buf, ctx, ctx->stream(), 0, cudnn_conf.cudnn_conv_force_bwd_data_algo()); const CudnnConvArgs& args = args_and_algo.args; const hipdnnConvolutionBwdDataAlgoPerf_t& algo_perf = args_and_algo.algo_perf; - const void* alpha = CudnnSPOnePtr(); - const void* beta; + const void* alpha = CudnnSPOnePtr(dy->data_type()); + const void* beta = nullptr; if (ctx->has_input("_add_to_output", 0)) { const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); CHECK_EQ(add_to_output->data_type(), dx->data_type()); @@ -744,16 +775,15 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu Memcpy( ctx->stream(), dx->mut_dptr(), add_to_output->dptr(), add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - beta = CudnnSPOnePtr(); + beta = CudnnSPOnePtr(dy->data_type()); } else { - beta = CudnnSPZeroPtr(); + beta = CudnnSPZeroPtr(dy->data_type()); } OF_CUDNN_CHECK(hipdnnConvolutionBackwardData( ctx->stream()->As()->cudnn_handle(), alpha, args.wdesc.Get(), filter->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, - // buf->mut_dptr(), args.params.max_ws_size, beta, args.xdesc.Get(), dx->mut_dptr())); - res.ws_dptr(), args.params.max_ws_size, beta, args.xdesc.Get(), dx->mut_dptr())); + buf->mut_dptr(), args.max_ws_size, beta, args.xdesc.Get(), dx->mut_dptr())); } bool IsCudaGraphSupported(user_op::KernelInitContext* ctx, @@ -765,42 +795,27 @@ class ConvDataGradGpuKernel final : public user_op::OpKernel, public user_op::Cu } }; -#define REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_data_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }) - // .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - // const auto& dy = ctx->InputTensorDesc("dy", 0); \ - // const auto& filter = ctx->InputTensorDesc("filter", 0); \ - // const auto* dx = ctx->OutputTensorDesc("dx", 0); \ - // if (dx->shape().elem_cnt() == 0) return 0; \ - // const auto& cudnn_conf = \ - // Singleton::Get()->resource().cudnn_conf(); \ - // return InferTmpSizeWithCudnn( \ - dx, &filter, &dy, *ctx, 0, \ - cudnn_conf.cudnn_conv_force_bwd_data_algo()); \ - }) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }) - -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_DATA_GRAD_FLOATING_KERNEL(float16); - -template +REGISTER_USER_KERNEL("conv_data_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { + const auto& dy = ctx->InputTensorDesc("dy", 0); + const auto& filter = ctx->InputTensorDesc("filter", 0); + const auto& dx = ctx->OutputTensorDesc("dx", 0); + if (dx.shape().elem_cnt() == 0) return 0; + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + return InferTmpSizeWithCudnn( + &dx, &filter, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), + cudnn_conf.cudnn_conv_force_bwd_data_algo()); + }) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); + class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: OF_DISALLOW_COPY_AND_MOVE(ConvFilterGradGpuKernel); @@ -815,30 +830,26 @@ class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op:: const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); user_op::Tensor* filter_diff = ctx->Tensor4ArgNameAndIndex("filter_diff", 0); if (x->shape_view().elem_cnt() == 0) { - Memset(ctx->stream(), filter_diff->mut_dptr(), 0, - filter_diff->shape_view().elem_cnt() * sizeof(T)); + Memset( + ctx->stream(), filter_diff->mut_dptr(), 0, + filter_diff->shape_view().elem_cnt() * GetSizeOfDataType(filter_diff->data_type())); return; } - // user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); - AllocatedCudnnConvResource res(ctx->stream()->As()->cudnn_handle(), - const_cast(x->dptr()), const_cast(filter_diff->dptr()), - const_cast(dy->dptr()), nullptr, 0); - CudnnConvArgsAndAlgo args_and_algo( - // x, filter_diff, dy, buf, ctx, ctx->stream(), - x, filter_diff, dy, res, ctx, ctx->stream(), + x, filter_diff, dy, buf, ctx, ctx->stream(), 0, cudnn_conf.cudnn_conv_force_bwd_filter_algo()); const CudnnConvArgs& args = args_and_algo.args; const hipdnnConvolutionBwdFilterAlgoPerf_t& algo_perf = args_and_algo.algo_perf; OF_CUDNN_CHECK(hipdnnConvolutionBackwardFilter( - ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(), args.xdesc.Get(), - // x->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, buf->mut_dptr(), - x->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, res.ws_dptr(), - args.params.max_ws_size, CudnnSPZeroPtr(), args.wdesc.Get(), filter_diff->mut_dptr())); + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(dy->data_type()), + args.xdesc.Get(), x->dptr(), args.ydesc.Get(), dy->dptr(), args.cdesc.Get(), algo_perf.algo, + buf->mut_dptr(), args.max_ws_size, CudnnSPZeroPtr(dy->data_type()), args.wdesc.Get(), + filter_diff->mut_dptr())); } bool IsCudaGraphSupported(user_op::KernelInitContext* ctx, @@ -850,32 +861,24 @@ class ConvFilterGradGpuKernel final : public user_op::OpKernel, public user_op:: } }; -#define REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_filter_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - // .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - // const auto& dy = ctx->InputTensorDesc("dy", 0); \ - // const auto& x = ctx->InputTensorDesc("x", 0); \ - if (x.shape().elem_cnt() == 0) return 0; \ - const auto* filter_diff = ctx->OutputTensorDesc("filter_diff", 0); \ - const auto& cudnn_conf = \ - Singleton::Get()->resource().cudnn_conf(); \ - return InferTmpSizeWithCudnn( \ - &x, filter_diff, &dy, *ctx, 0, \ - cudnn_conf.cudnn_conv_force_bwd_filter_algo()); \ - }) - -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_FILTER_GRAD_FLOATING_KERNEL(float16); +REGISTER_USER_KERNEL("conv_filter_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { + const auto& dy = ctx->InputTensorDesc("dy", 0); + const auto& x = ctx->InputTensorDesc("x", 0); + if (x.shape().elem_cnt() == 0) return 0; + const auto& filter_diff = ctx->OutputTensorDesc("filter_diff", 0); + const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); + return InferTmpSizeWithCudnn( + &x, &filter_diff, &dy, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_filter_algo(), + cudnn_conf.cudnn_conv_force_bwd_filter_algo()); + }); struct ConvBiasGradState final : public user_op::OpKernelState { std::unique_ptr bias_diff_desc; }; -template class ConvBiasGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: ConvBiasGradGpuKernel() = default; @@ -920,21 +923,15 @@ class ConvBiasGradGpuKernel final : public user_op::OpKernel, public user_op::Cu const auto& bias_grad_state = CreateConvBiasGradState(ctx); CHECK_NOTNULL(bias_grad_state.get()); OF_CUDNN_CHECK(hipdnnConvolutionBackwardBias( - ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(), dy_desc->Get(), - dy->dptr(), CudnnSPZeroPtr(), bias_grad_state->bias_diff_desc->Get(), - bias_diff->mut_dptr())); + ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(dy->data_type()), + dy_desc->Get(), dy->dptr(), CudnnSPZeroPtr(dy->data_type()), + bias_grad_state->bias_diff_desc->Get(), bias_diff->mut_dptr())); } }; -#define REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(dtype) \ - REGISTER_USER_KERNEL("conv_bias_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)); - -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(float); -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(double); -REGISTER_CONV_BIAS_GRAD_FLOATING_KERNEL(float16); +REGISTER_USER_KERNEL("conv_bias_grad") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); } // namespace diff --git a/oneflow/user/kernels/conv_cutlass_kernels.cu b/oneflow/user/kernels/conv_cutlass_kernels.cu new file mode 100644 index 0000000000000000000000000000000000000000..c67f1cd7298dea6e72b66b8c812bd5125d4845e6 --- /dev/null +++ b/oneflow/user/kernels/conv_cutlass_kernels.cu @@ -0,0 +1,168 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifdef WITH_CUTLASS + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" +#include "oneflow/user/kernels/cutlass_conv_tuner.h" +#include +#include +#include + +namespace oneflow { + +namespace { + +class Conv2dCutlassKernel final : public user_op::OpKernel { + public: + Conv2dCutlassKernel() = default; + ~Conv2dCutlassKernel() override = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK(add_to_output == nullptr); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + + const auto& padding_before = ctx->Attr>("padding_before"); + const auto& dilation_rate = ctx->Attr>("dilation_rate"); + const auto& strides = ctx->Attr>("strides"); + + const int n = in->shape_view().At(0); + const int h = in->shape_view().At(1); + const int w = in->shape_view().At(2); + const int c = in->shape_view().At(3); + + const int k = weight->shape_view().At(0); + const int r = weight->shape_view().At(1); + const int s = weight->shape_view().At(2); + CHECK_EQ(weight->shape_view().At(3), c); + + const int p = out->shape_view().At(1); + const int q = out->shape_view().At(2); + + auto* stream = ctx->stream()->As(); + + cutlass::library::ConvFunctionalKey key( + cutlass::library::Provider::kCUTLASS, cutlass::library::ConvKind::kFprop, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF16, cutlass::library::LayoutTypeID::kTensorNHWC, + cutlass::library::NumericTypeID::kF32, cutlass::library::NumericTypeID::kF32); + + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_CONV_ALLOW_HALF_PRECISION_ACCUMULATION", false); + + if (allow_half_accumulation) { + key.element_accumulator = cutlass::library::NumericTypeID::kF16; + key.element_compute = cutlass::library::NumericTypeID::kF16; + } + + cutlass::conv::Conv2dProblemSize problem_size( + n, h, w, c, k, r, s, p, q, padding_before.at(0), padding_before.at(1), strides.at(0), + strides.at(1), dilation_rate.at(0), dilation_rate.at(1), + cutlass::conv::Mode::kCrossCorrelation); + cutlass::library::Conv2dConfiguration configuraion; + configuraion.split_k_mode = cutlass::conv::SplitKMode::kSerial; + configuraion.problem_size = problem_size; + configuraion.stride_a = {c, w * c, h * w * c}; + configuraion.stride_b = {c, s * c, r * s * c}; + configuraion.stride_c = {0, 0, 0}; + + cutlass::library::ConvArguments arguments; + arguments.A = in->dptr(); + arguments.B = weight->dptr(); + arguments.reordered_B = nullptr; + if (bias == nullptr) { + arguments.C = nullptr; + } else { + arguments.C = bias->dptr(); + } + arguments.D = out->mut_dptr(); + + union SP { + float f; + half h; + }; + + SP alpha; + SP beta; + + if (allow_half_accumulation) { + alpha.h = static_cast(1.0F); + if (bias == nullptr) { + beta.h = static_cast(0.0F); + } else { + beta.h = static_cast(1.0F); + } + } else { + alpha.f = 1.0F; + if (bias == nullptr) { + beta.f = 0.0F; + } else { + beta.f = 1.0F; + } + } + arguments.alpha = α + arguments.beta = β + arguments.pointer_mode = cutlass::library::ScalarPointerMode::kHost; + + const cutlass::library::Operation* operation = CutlassConvTuner::Get().FindConv2dOperation( + stream, key, configuraion, arguments, tmp_buffer->mut_dptr(), + tmp_buffer->shape_view().elem_cnt()); + CHECK(operation != nullptr); + const size_t host_workspace_size = operation->get_host_workspace_size(&configuraion); + std::vector host_workspace(host_workspace_size, 0); + auto init_status = operation->initialize(&configuraion, host_workspace.data(), + tmp_buffer->mut_dptr(), stream->cuda_stream()); + CHECK(init_status == cutlass::Status::kSuccess); + auto run_status = operation->run(&arguments, host_workspace.data(), tmp_buffer->mut_dptr(), + stream->cuda_stream()); + CHECK(run_status == cutlass::Status::kSuccess); + } +}; + +REGISTER_USER_KERNEL("conv2d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobAttr("data_format") == "channels_last") + && (user_op::HobAttr("groups") == 1) + && (user_op::HobDataType("in", 0) == DataType::kFloat16) + && (user_op::HobTrue() + == ParseBooleanFromEnv("ONEFLOW_KERENL_CONV_ENABLE_CUTLASS_IMPL", false))) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { + // use static workspace size + return 128 * 1024 * 1024; + }) + .SetPriority(user_op::kKernelPriorityOptimized); + +} // namespace + +} // namespace oneflow + +#endif // WITH_CUTLASS diff --git a/oneflow/user/kernels/conv_kernels.cpp b/oneflow/user/kernels/conv_kernels.cpp index e483340d44a8113d8ce3aa618533fe060bc70930..8b61fd706a607c21da660f0c8234a5e82cf18dea 100644 --- a/oneflow/user/kernels/conv_kernels.cpp +++ b/oneflow/user/kernels/conv_kernels.cpp @@ -517,6 +517,17 @@ class ConvCpuKernel final : public user_op::OpKernel { } CHECK(matmul); + float beta = 0; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), out->data_type()); + CHECK_EQ(add_to_output->shape_view(), out->shape_view()); + Memcpy( + ctx->stream(), out->mut_dptr(), add_to_output->dptr(), + add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); + beta = 1; + } + for (int64_t i = 0; i < in->shape_view().At(0); ++i) { conv_cache->im2col_func_(GetImgDptr(in, i), ShapeView(conv_cache->in_5d_shape_), ShapeView(conv_cache->weight_5d_shape_), @@ -531,7 +542,7 @@ class ConvCpuKernel final : public user_op::OpKernel { conv_cache->weight_5d_shape_.At(0), // filter conv_cache->out_5d_shape_.Count(idx_offset, idx_offset + 3), // od * oh * ow conv_cache->weight_5d_shape_.Count(1), // ci * kd * kh * kw - static_cast(1), weight->dptr(), col_buf_dptr, static_cast(0), + static_cast(1), weight->dptr(), col_buf_dptr, beta, GetImgMutDptr(out, i)); const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); @@ -570,7 +581,7 @@ class ConvCpuKernel final : public user_op::OpKernel { && ChannelsLastMatmulPrimitiveExists()) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ size_t tmp_buffer_size = 0; \ - const auto& out_shape = ctx->OutputTensorDesc("out", 0)->shape(); \ + const auto& out_shape = ctx->OutputTensorDesc("out", 0).shape(); \ const auto& weight_shape = ctx->InputTensorDesc("weight", 0).shape(); \ \ int64_t idx_offset = IdxOffset(ctx->Attr("data_format")); \ @@ -583,7 +594,15 @@ class ConvCpuKernel final : public user_op::OpKernel { tmp_buffer_size += bias_mul_cnt * sizeof(dtype); \ } \ return tmp_buffer_size; \ - }) + }) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext& ctx, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + if (ctx.has_input("_add_to_output", 0)) { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "_add_to_output", 0, true)); \ + } \ + return Maybe::Ok(); \ + }); REGISTER_CONV_KERNEL(conv1d, float, 1); REGISTER_CONV_KERNEL(conv2d, float, 2); @@ -748,7 +767,7 @@ class ConvFilterGradCpuKernel final : public user_op::OpKernel { .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ size_t tmp_buffer_size = 0; \ const auto& out_diff_shape = ctx->InputTensorDesc("dy", 0).shape(); \ - const auto& weight_diff_shape = ctx->OutputTensorDesc("filter_diff", 0)->shape(); \ + const auto& weight_diff_shape = ctx->OutputTensorDesc("filter_diff", 0).shape(); \ \ int64_t idx_offset = IdxOffset(ctx->Attr("data_format")); \ tmp_buffer_size += \ diff --git a/oneflow/user/kernels/copy_data_content_kernel.cpp b/oneflow/user/kernels/copy_data_content_kernel.cpp index be1a5dfb5f5e3787f1623b2cd17caf5f1e0f1f29..50e1ad8860a2408670f13491661c299a01c52c32 100644 --- a/oneflow/user/kernels/copy_data_content_kernel.cpp +++ b/oneflow/user/kernels/copy_data_content_kernel.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/include/primitive/memcpy.h" +#include "oneflow/core/ep/include/primitive/fill.h" namespace oneflow { @@ -31,6 +32,20 @@ class CopyDataContentKernel final : public user_op::OpKernel, public user_op::Cu const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); const int64_t elem_cnt = in->shape_view().elem_cnt(); + // For 0-size tensor, we don't need to copy data, but we must + // fill output tensor with Scalar(0) because during the backward propogation, this kernel will + // also be used. + if (elem_cnt == 0) { + const int64_t out_elem_cnt = out->shape_view().elem_cnt(); + CHECK_GE(out_elem_cnt, 0); + if (out_elem_cnt == 0) { return; } + std::unique_ptr fill = + ep::primitive::NewPrimitive(ctx->device_type(), + out->data_type()); + CHECK(fill); + fill->Launch(ctx->stream(), out->mut_dptr(), Scalar(0), out_elem_cnt); + return; + } CHECK_EQ(out->shape_view().elem_cnt(), elem_cnt); CHECK_EQ(in->data_type(), out->data_type()); if (elem_cnt > 0) { @@ -59,15 +74,16 @@ class CopyDataContentKernel final : public user_op::OpKernel, public user_op::Cu REGISTER_COPY_DATA_CONTENT_KERNEL("squeeze"); REGISTER_COPY_DATA_CONTENT_KERNEL("reshape_like"); -REGISTER_COPY_DATA_CONTENT_KERNEL("flatten"); REGISTER_COPY_DATA_CONTENT_KERNEL("expand_dims"); REGISTER_COPY_DATA_CONTENT_KERNEL("reshape"); REGISTER_COPY_DATA_CONTENT_KERNEL("amp_white_identity"); +REGISTER_COPY_DATA_CONTENT_KERNEL("amp_black_identity"); REGISTER_COPY_DATA_CONTENT_KERNEL("identity"); REGISTER_COPY_DATA_CONTENT_KERNEL("identity_buffer"); REGISTER_COPY_DATA_CONTENT_KERNEL("parallel_cast"); REGISTER_COPY_DATA_CONTENT_KERNEL("hierarchical_parallel_cast"); REGISTER_COPY_DATA_CONTENT_KERNEL("hierarchical_parallel_cast_like"); +REGISTER_COPY_DATA_CONTENT_KERNEL("pinned_identity"); } // namespace diff --git a/oneflow/user/kernels/copy_hd_kernel.cpp b/oneflow/user/kernels/copy_hd_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f9e70ee78adccc18ff2d78e1417afc5954adbd45 --- /dev/null +++ b/oneflow/user/kernels/copy_hd_kernel.cpp @@ -0,0 +1,65 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/kernel_util.h" + +namespace oneflow { + +namespace { + +class CopyHdKernel final : public user_op::OpKernel { + public: + CopyHdKernel() = default; + ~CopyHdKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + CHECK(in) << "input of copy not found"; + const ShapeView& in_shape = in->shape_view(); + if (in_shape.elem_cnt() == 0) { + // 0 shape tensor do not need copy + } else { + const DataType in_data_type = in->data_type(); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK(out) << "output of copy not found, op: " << ctx->op_name(); + CHECK_EQ(out->shape_view(), in_shape); + CHECK_EQ(out->data_type(), in_data_type); + + ep::primitive::MemcpyKind kind{}; + if (ctx->op_type_name() == "copy_h2d") { + kind = ep::primitive::MemcpyKind::kHtoD; + } else if (ctx->op_type_name() == "copy_d2h") { + kind = ep::primitive::MemcpyKind::kDtoH; + } else { + UNIMPLEMENTED(); + } + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type(), + kind); + primitive->Launch(ctx->stream(), out->mut_raw_dptr(), in->raw_dptr(), + in_shape.elem_cnt() * GetSizeOfDataType(in_data_type)); + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("copy_h2d").SetCreateFn().SetIsMatchedHob(user_op::HobTrue()); +REGISTER_USER_KERNEL("copy_d2h").SetCreateFn().SetIsMatchedHob(user_op::HobTrue()); + +} // namespace +} // namespace oneflow diff --git a/oneflow/user/kernels/count_not_finite_kernel.cu b/oneflow/user/kernels/count_not_finite_kernel.cu index b3425fa24d00426569e31e6570e255679554b7fb..7798c17cefc58999acc6cb1c658994854599ef3b 100644 --- a/oneflow/user/kernels/count_not_finite_kernel.cu +++ b/oneflow/user/kernels/count_not_finite_kernel.cu @@ -14,7 +14,12 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/cuda/cuda_stream.h" @@ -51,20 +56,32 @@ __inline__ __device__ bool IsFinite(half x) { template __global__ void CountNotFiniteGpu(const int64_t n, const T* x, int64_t* y) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage cub_reduce_tmp_storage; int64_t thread_count = 0; CUDA_1D_KERNEL_LOOP(i, n) { if (!IsFinite(x[i])) { thread_count += 1; } } __syncthreads(); +#ifdef WITH_ROCM + int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, hipcub::Sum()); +#else int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, cub::Sum()); +#endif if (threadIdx.x == 0) { AtomicAdd(y, block_count_sum); } } template __global__ void MultiCountNotFiniteGpu(Param param) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage cub_reduce_tmp_storage; int64_t thread_count = 0; for (int32_t k = 0; k < param.num_x; ++k) { @@ -73,7 +90,11 @@ __global__ void MultiCountNotFiniteGpu(Param param) { } } __syncthreads(); +#ifdef WITH_ROCM + int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, hipcub::Sum()); +#else int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, cub::Sum()); +#endif if (threadIdx.x == 0) { AtomicAdd(param.y, block_count_sum); } } diff --git a/oneflow/user/kernels/count_not_finite_kernel.hip.cpp b/oneflow/user/kernels/count_not_finite_kernel.hip.cpp deleted file mode 100644 index 72752831dd186bd0a908a51ff97efb4f340205d7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/count_not_finite_kernel.hip.cpp +++ /dev/null @@ -1,173 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -struct Param { - const T* x[N]; - int64_t x_elem_cnt[N]; - int64_t* y; - int64_t num_x; -}; - -using CuInt64T = unsigned long long int; - -__device__ __inline__ int64_t AtomicAdd(int64_t* address, int64_t val) { - static_assert(sizeof(int64_t) == sizeof(CuInt64T), "size error"); - return static_cast( - atomicAdd(reinterpret_cast(address), static_cast(val))); -} - -template -__inline__ __device__ bool IsFinite(T x) { - return isfinite(x); -} - -template<> -__inline__ __device__ bool IsFinite(half x) { - return IsFinite(static_cast(x)); -} - -template -__global__ void CountNotFiniteGpu(const int64_t n, const T* x, int64_t* y) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage cub_reduce_tmp_storage; - int64_t thread_count = 0; - CUDA_1D_KERNEL_LOOP(i, n) { - if (!IsFinite(x[i])) { thread_count += 1; } - } - __syncthreads(); - int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, hipcub::Sum()); - if (threadIdx.x == 0) { AtomicAdd(y, block_count_sum); } -} - -template -__global__ void MultiCountNotFiniteGpu(Param param) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage cub_reduce_tmp_storage; - int64_t thread_count = 0; - for (int32_t k = 0; k < param.num_x; ++k) { - CUDA_1D_KERNEL_LOOP(i, param.x_elem_cnt[k]) { - if (!IsFinite(param.x[k][i])) { thread_count += 1; } - } - } - __syncthreads(); - int64_t block_count_sum = BlockReduce(cub_reduce_tmp_storage).Reduce(thread_count, hipcub::Sum()); - if (threadIdx.x == 0) { AtomicAdd(param.y, block_count_sum); } -} - -constexpr int64_t kCountNotFiniteNumBlocks = 512; - -int GetCountNotFiniteNumBlocks(const int64_t elem_cnt) { - return std::min((elem_cnt + kCudaThreadsNumPerBlock - 1) / kCudaThreadsNumPerBlock, - kCountNotFiniteNumBlocks); -} - -} // namespace - -template -class CountNotFiniteGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - CountNotFiniteGpuKernel() = default; - ~CountNotFiniteGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int64_t elem_cnt = x->shape_view().elem_cnt(); - Memset(ctx->stream(), y->mut_dptr(), 0, - y->shape_view().elem_cnt() * sizeof(int64_t)); - CountNotFiniteGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, x->dptr(), y->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_COUNT_NOT_FINITE_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("count_not_finite") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_COUNT_NOT_FINITE_CUDA_KERNEL(half) -REGISTER_COUNT_NOT_FINITE_CUDA_KERNEL(float) -REGISTER_COUNT_NOT_FINITE_CUDA_KERNEL(double) - -template -class MultiCountNotFiniteGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MultiCountNotFiniteGpuKernel() = default; - ~MultiCountNotFiniteGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - Param para; - Memset(ctx->stream(), y->mut_dptr(), 0, - y->shape_view().elem_cnt() * sizeof(int64_t)); - para.y = y->mut_dptr(); - - int64_t remain_size = ctx->inputs().size(); - int64_t input_id = 0; - while (remain_size > 0) { - if (remain_size > 128) { - remain_size -= 128; - para.num_x = 128; - } else { - para.num_x = remain_size; - remain_size = 0; - } - int64_t max_elem_cnt = 0; - for (int32_t i = 0; i < para.num_x; ++i) { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", input_id); - input_id++; - para.x[i] = x->dptr(); - para.x_elem_cnt[i] = x->shape_view().elem_cnt(); - max_elem_cnt = std::max(max_elem_cnt, x->shape_view().elem_cnt()); - } - MultiCountNotFiniteGpu - <<stream()->As()->cuda_stream()>>>(para); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MULTI_COUNT_NOT_FINITE_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("multi_count_not_finite") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_MULTI_COUNT_NOT_FINITE_CUDA_KERNEL(half) -REGISTER_MULTI_COUNT_NOT_FINITE_CUDA_KERNEL(float) -REGISTER_MULTI_COUNT_NOT_FINITE_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/ctc_greedy_decoder.cu b/oneflow/user/kernels/ctc_greedy_decoder.cu index 2433844b9d6e04e8ed9bbdcc57f467285e34d17b..b192bbf893a64f7af1b3f3b56b509b6e412974a2 100644 --- a/oneflow/user/kernels/ctc_greedy_decoder.cu +++ b/oneflow/user/kernels/ctc_greedy_decoder.cu @@ -35,7 +35,7 @@ __global__ void CtcGreedyDecodeGpuMultiThread(int64_t* decoded_ptr, T* neg_sum_l for (int64_t b = bid; b < batch_size; b += gridDim.x) { if (tid == 0) { - if (input_lengths_ptr[b] > max_input_length) __trap(); + if (input_lengths_ptr[b] > max_input_length) TRAP(); } } @@ -82,7 +82,7 @@ __global__ void CtcGreedyDecodeGpu(int64_t* decoded_ptr, T* neg_sum_logits_ptr, const bool merge_repeated, const int64_t max_input_length, const int64_t batch_size, const int64_t num_labels) { for (int64_t b = 0; b < batch_size; b++) { - if (input_lengths_ptr[b] > max_input_length) __trap(); + if (input_lengths_ptr[b] > max_input_length) TRAP(); } NdIndexOffsetHelper input_helper(max_input_length, batch_size, num_labels); @@ -120,7 +120,7 @@ struct CTCGreedyDecoderFunctor final { int64_t shared_mem_size = max_input_length * sizeof(int64_t); int max_active_blocks; - OF_CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor( + OF_CUDA_CHECK(GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks, CtcGreedyDecodeGpu, kCudaThreadsNumPerBlock, shared_mem_size)); if (max_active_blocks > 0) { CtcGreedyDecodeGpuMultiThread<< -__global__ void CtcGreedyDecodeGpuMultiThread(int64_t* decoded_ptr, T* neg_sum_logits_ptr, - const T* log_probs_ptr, - const int64_t* input_lengths_ptr, - const bool merge_repeated, - const int64_t max_input_length, - const int64_t batch_size, const int64_t num_labels) { - const int64_t bid = blockIdx.x; - const int64_t tid = threadIdx.x; - - for (int64_t b = bid; b < batch_size; b += gridDim.x) { - if (tid == 0) { - if (input_lengths_ptr[b] > max_input_length) {asm volatile("s_trap 0;");} - } - } - - for (int64_t b = bid; b < batch_size; b += gridDim.x) { - extern __shared__ int64_t shared_max_indices_memory[]; - int64_t* shared_max_indices = (int64_t*)shared_max_indices_memory; - NdIndexOffsetHelper input_helper(max_input_length, batch_size, num_labels); - for (int64_t t = tid; t < max_input_length; t += blockDim.x) { - const T* prob_data_t = &log_probs_ptr[input_helper.NdIndexToOffset(t, b, 0)]; - int64_t max_indice = 0; - T max_value = -FLT_MAX; - FOR_RANGE(int64_t, c, 0, num_labels) { - const T prob = prob_data_t[c]; - if (prob > max_value) { - max_indice = c; - max_value = prob; - } - } - shared_max_indices[t] = max_indice; - } - - __syncthreads(); - - if (tid == 0) { - int64_t prev_indices = -1, t_dec = 0; - FOR_RANGE(int64_t, t, 0, input_lengths_ptr[b]) { - const T* prob_data_t = &log_probs_ptr[input_helper.NdIndexToOffset(t, b, 0)]; - const int64_t indice_t = shared_max_indices[t]; - neg_sum_logits_ptr[b] -= prob_data_t[indice_t]; - if (indice_t != num_labels - 1 && !(merge_repeated && (prev_indices == indice_t))) { - decoded_ptr[b * max_input_length + t_dec] = indice_t; - t_dec++; - } - prev_indices = indice_t; - } - FOR_RANGE(int64_t, t, t_dec, max_input_length) { decoded_ptr[b * max_input_length + t] = 0; } - } - } -} - -template -__global__ void CtcGreedyDecodeGpu(int64_t* decoded_ptr, T* neg_sum_logits_ptr, - const T* log_probs_ptr, const int64_t* input_lengths_ptr, - const bool merge_repeated, const int64_t max_input_length, - const int64_t batch_size, const int64_t num_labels) { - for (int64_t b = 0; b < batch_size; b++) { - if (input_lengths_ptr[b] > max_input_length) {asm volatile("s_trap 0;");} - } - NdIndexOffsetHelper input_helper(max_input_length, batch_size, num_labels); - - CUDA_1D_KERNEL_LOOP(b, batch_size) { - int prev_indices = -1, t_dec = 0; - neg_sum_logits_ptr[b] = 0; - FOR_RANGE(int64_t, t, 0, input_lengths_ptr[b]) { - const T* prob_data_t = &log_probs_ptr[input_helper.NdIndexToOffset(t, b, 0)]; - int64_t max_indice = -1; - T max_value = -FLT_MAX; - FOR_RANGE(int64_t, c, 0, num_labels) { - if (prob_data_t[c] > max_value) { - max_indice = c; - max_value = prob_data_t[c]; - } - } - neg_sum_logits_ptr[b] -= max_value; - if (max_indice != num_labels - 1 && !(merge_repeated && (prev_indices == max_indice))) { - decoded_ptr[b * max_input_length + t_dec] = max_indice; - t_dec++; - } - prev_indices = max_indice; - } - FOR_RANGE(int64_t, t, t_dec, max_input_length) { decoded_ptr[b * max_input_length + t] = 0; } - } -} - -template -struct CTCGreedyDecoderFunctor final { - void operator()(ep::Stream* stream, int64_t* decoded_ptr, T* neg_sum_logits_ptr, - const T* log_probs_ptr, const int64_t* input_lengths_ptr, - const bool merge_repeated, const int64_t max_input_length, - const int64_t batch_size, const int64_t num_labels) { - int32_t thread_num = batch_size * kCudaThreadsNumPerBlock; - int64_t shared_mem_size = max_input_length * sizeof(int64_t); - - int max_active_blocks; - OF_CUDA_CHECK(hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks, CtcGreedyDecodeGpu, kCudaThreadsNumPerBlock, shared_mem_size)); - if (max_active_blocks > 0) { - CtcGreedyDecodeGpuMultiThread<<As()->cuda_stream()>>>( - decoded_ptr, neg_sum_logits_ptr, log_probs_ptr, input_lengths_ptr, merge_repeated, - max_input_length, batch_size, num_labels); - - } else { - CtcGreedyDecodeGpu<<As()->cuda_stream()>>>( - decoded_ptr, neg_sum_logits_ptr, log_probs_ptr, input_lengths_ptr, merge_repeated, - max_input_length, batch_size, num_labels); - } - } -}; - -} // namespace - -REGISTER_CTC_GREEDY_DECODER_KERNELS(DeviceType::kCUDA, float); -REGISTER_CTC_GREEDY_DECODER_KERNELS(DeviceType::kCUDA, double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/ctc_loss_kernel.cpp b/oneflow/user/kernels/ctc_loss_kernel.cpp index 92c630c5d45da304517301fe9516279037e09746..c97059dacf66eb2ededd3394fe39f066a5ce2c45 100644 --- a/oneflow/user/kernels/ctc_loss_kernel.cpp +++ b/oneflow/user/kernels/ctc_loss_kernel.cpp @@ -18,7 +18,7 @@ limitations under the License. namespace oneflow { -template +template class CtcLossKernel final : public user_op::OpKernel { public: CtcLossKernel() = default; @@ -34,10 +34,10 @@ class CtcLossKernel final : public user_op::OpKernel { user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); const T* log_probs_ptr = log_probs->dptr(); - const int* targets_ptr = targets->dptr(); + const TARGET* targets_ptr = targets->dptr(); const IDX* input_lengths_ptr = input_lengths->dptr(); const IDX* target_lengths_ptr = target_lengths->dptr(); - const int32_t blank = ctx->Attr("blank"); + const int64_t blank = ctx->Attr("blank"); const int64_t max_input_length = log_probs->shape_view().At(0); const int64_t batch_size = log_probs->shape_view().At(1); const int64_t num_labels = log_probs->shape_view().At(2); @@ -49,7 +49,7 @@ class CtcLossKernel final : public user_op::OpKernel { 2 * max_target_length + 1); T* loss_ptr = loss->mut_dptr(); T* alpha_ptr = alpha->mut_dptr(); - CtcLossKernelUtil::CtcLossForward( + CtcLossKernelUtil::CtcLossForward( ctx->stream(), log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, alpha_ptr, loss_ptr, input_helper, alpha_helper, batch_size, max_input_length, max_target_length, blank, targets_ndim); @@ -57,18 +57,20 @@ class CtcLossKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_CTC_LOSS_KERNEL(device, dtype, idx_dtype) \ - REGISTER_USER_KERNEL("ctc_loss") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("log_probs", 0) == OF_PP_PAIR_SECOND(dtype)) \ +#define REGISTER_CTC_LOSS_KERNEL(device, dtype, target_type, idx_dtype) \ + REGISTER_USER_KERNEL("ctc_loss") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("log_probs", 0) == OF_PP_PAIR_SECOND(dtype)) \ + && (user_op::HobDataType("targets", 0) == OF_PP_PAIR_SECOND(target_type)) \ && (user_op::HobDataType("input_lengths", 0) == OF_PP_PAIR_SECOND(idx_dtype))); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CTC_LOSS_KERNEL, DEVICE_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) + INDEX_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -template +template class CtcLossGradKernel final : public user_op::OpKernel { public: CtcLossGradKernel() = default; @@ -90,10 +92,10 @@ class CtcLossGradKernel final : public user_op::OpKernel { const T* loss_ptr = loss->dptr(); const T* alpha_ptr = alpha->dptr(); const T* log_probs_ptr = log_probs->dptr(); - const int* targets_ptr = targets->dptr(); + const TARGET* targets_ptr = targets->dptr(); const IDX* input_lengths_ptr = input_lengths->dptr(); const IDX* target_lengths_ptr = target_lengths->dptr(); - const int32_t blank = ctx->Attr("blank"); + const int64_t blank = ctx->Attr("blank"); const bool zero_infinity = ctx->Attr("zero_infinity"); const int64_t batch_size = log_probs->shape_view().At(1); const int64_t num_labels = log_probs->shape_view().At(2); @@ -106,7 +108,7 @@ class CtcLossGradKernel final : public user_op::OpKernel { 2 * max_target_length + 1); T* grad_ptr = grad->mut_dptr(); T* beta_ptr = tmp_buffer->mut_dptr(); - CtcLossKernelUtil::CtcLossBackward( + CtcLossKernelUtil::CtcLossBackward( ctx->stream(), grad_out_ptr, loss_ptr, alpha_ptr, log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, beta_ptr, grad_ptr, input_helper, beta_helper, batch_size, max_input_length, max_target_length, num_labels, blank, zero_infinity, @@ -115,23 +117,25 @@ class CtcLossGradKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_CTC_LOSS_BACKWARD_KERNEL(device, dtype, idx_dtype) \ - REGISTER_USER_KERNEL("ctc_loss_grad") \ - .SetCreateFn< \ - CtcLossGradKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("log_probs", 0) == OF_PP_PAIR_SECOND(dtype)) \ - && (user_op::HobDataType("input_lengths", 0) == OF_PP_PAIR_SECOND(idx_dtype))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& log_probs_shape = ctx->InputShape("log_probs", 0); \ - const int64_t max_target_length = ctx->Attr("max_target_length"); \ - int64_t elem_cnt = \ - log_probs_shape.At(1) * log_probs_shape.At(0) * (2 * max_target_length + 1); \ - return elem_cnt * sizeof(OF_PP_PAIR_FIRST(dtype)); \ +#define REGISTER_CTC_LOSS_BACKWARD_KERNEL(device, dtype, target_type, idx_dtype) \ + REGISTER_USER_KERNEL("ctc_loss_grad") \ + .SetCreateFn< \ + CtcLossGradKernel>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("log_probs", 0) == OF_PP_PAIR_SECOND(dtype)) \ + && (user_op::HobDataType("targets", 0) == OF_PP_PAIR_SECOND(target_type)) \ + && (user_op::HobDataType("input_lengths", 0) == OF_PP_PAIR_SECOND(idx_dtype))) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& log_probs_shape = ctx->InputShape("log_probs", 0); \ + const int64_t max_target_length = ctx->Attr("max_target_length"); \ + int64_t elem_cnt = \ + log_probs_shape.At(1) * log_probs_shape.At(0) * (2 * max_target_length + 1); \ + return elem_cnt * sizeof(OF_PP_PAIR_FIRST(dtype)); \ }); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CTC_LOSS_BACKWARD_KERNEL, DEVICE_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) } // namespace oneflow diff --git a/oneflow/user/kernels/ctc_loss_kernel_util.cpp b/oneflow/user/kernels/ctc_loss_kernel_util.cpp index d46f56c11dfe1cbc72916bf28d80eb21f25f0ac7..cc1d1feb9d0c1bd5378301a2b6e190c13d12f20d 100644 --- a/oneflow/user/kernels/ctc_loss_kernel_util.cpp +++ b/oneflow/user/kernels/ctc_loss_kernel_util.cpp @@ -17,10 +17,10 @@ limitations under the License. namespace oneflow { -template -int get_target_prime(const int* targets_ptr, const IDX* target_lengths_ptr, - int64_t max_target_length, int64_t b, int64_t s, int blank, - const int32_t targets_ndim) { +template +int64_t get_target_prime(const TARGET* targets_ptr, const IDX* target_lengths_ptr, + int64_t max_target_length, int64_t b, int64_t s, int64_t blank, + const int32_t targets_ndim) { if (s % 2 == 0) { return blank; } else { @@ -31,40 +31,40 @@ int get_target_prime(const int* targets_ptr, const IDX* target_lengths_ptr, idx = b * max_target_length; } idx += s / 2; - return targets_ptr[idx]; + return static_cast(targets_ptr[idx]); } } -template -struct CtcLossKernelUtil final { - static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const int* targets_ptr, +template +struct CtcLossKernelUtil final { + static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& alpha_helper, const int64_t batch_size, const int64_t max_input_length, - const int64_t max_target_length, const int blank, + const int64_t max_target_length, const int64_t blank, const int32_t targets_ndim); static void CtcLossBackward(ep::Stream* stream, const T* grad_out_ptr, const T* loss_ptr, - const T* alpha_ptr, const T* log_probs_ptr, const int* targets_ptr, + const T* alpha_ptr, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& beta_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, + const int64_t blank, const bool zero_infinity, const int32_t targets_ndim); }; -template -void CtcLossKernelUtil::CtcLossForward( - ep::Stream* stream, const T* log_probs_ptr, const int* targets_ptr, +template +void CtcLossKernelUtil::CtcLossForward( + ep::Stream* stream, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& alpha_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, - const int blank, const int32_t targets_ndim) { + const int64_t blank, const int32_t targets_ndim) { constexpr T neginf = -std::numeric_limits::infinity(); FOR_RANGE(int64_t, b, 0, batch_size) { CHECK_GE(max_input_length, input_lengths_ptr[b]); @@ -78,15 +78,15 @@ void CtcLossKernelUtil::CtcLossForward( for (IDX s = 0; s < 2 * target_length + 1; s++) { alpha_ptr[alpha_idx + s] = neginf; } alpha_ptr[alpha_idx] = log_probs_ptr[input_helper.NdIndexToOffset(0, b, blank)]; if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, 1, blank, - targets_ndim); + TARGET target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, 1, + blank, targets_ndim); alpha_ptr[alpha_idx + 1] = log_probs_ptr[input_helper.NdIndexToOffset(0, b, target)]; } for (IDX t = 1; t < input_length; t++) { for (IDX s = 0; s < 2 * target_length + 1; s++) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); + TARGET current_target_prime = get_target_prime( + targets_ptr, target_lengths_ptr, max_target_length, b, s, blank, targets_ndim); T la1 = alpha_ptr[alpha_helper.NdIndexToOffset(b, t - 1, s)]; T la2, la3, lamax = la1; if (s > 0) { @@ -129,14 +129,14 @@ void CtcLossKernelUtil::CtcLossForward( } } -template -void CtcLossKernelUtil::CtcLossBackward( +template +void CtcLossKernelUtil::CtcLossBackward( ep::Stream* stream, const T* grad_out_ptr, const T* loss_ptr, const T* alpha_ptr, - const T* log_probs_ptr, const int* targets_ptr, const IDX* input_lengths_ptr, + const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& beta_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, - const int64_t num_labels, const int blank, const bool zero_infinity, + const int64_t num_labels, const int64_t blank, const bool zero_infinity, const int32_t targets_ndim) { constexpr T neginf = -std::numeric_limits::infinity(); int64_t elem_cnt = max_input_length * batch_size * num_labels; @@ -165,8 +165,8 @@ void CtcLossKernelUtil::CtcLossBackward( + beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)]; if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, - 2 * target_length - 1, blank, targets_ndim); + TARGET target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, + 2 * target_length - 1, blank, targets_ndim); beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)] = log_probs_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)]; grad_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)] = @@ -177,8 +177,8 @@ void CtcLossKernelUtil::CtcLossBackward( for (IDX t = input_length - 2; t >= 0; t--) { for (IDX s = 2 * target_length; s >= 0; s--) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); + TARGET current_target_prime = get_target_prime( + targets_ptr, target_lengths_ptr, max_target_length, b, s, blank, targets_ndim); T lb1 = beta_ptr[beta_helper.NdIndexToOffset(b, t + 1, s)]; T lb2, lb3, lbmax = lb1; @@ -237,12 +237,13 @@ void CtcLossKernelUtil::CtcLossBackward( } #define INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CPU(device_type_v, log_probs_dtype_pair, \ - input_lengths_dtype_pair) \ + targets_dtype_pair, input_lengths_dtype_pair) \ template struct CtcLossKernelUtil; OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CPU, (DeviceType::kCPU), - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) #undef INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CPU } // namespace oneflow diff --git a/oneflow/user/kernels/ctc_loss_kernel_util.cu b/oneflow/user/kernels/ctc_loss_kernel_util.cu index c160781e4d80fb803f1e359c0b76b9cbbbd5e48f..e80aa90db1a05e65529940f12a69d24d1e7cd599 100644 --- a/oneflow/user/kernels/ctc_loss_kernel_util.cu +++ b/oneflow/user/kernels/ctc_loss_kernel_util.cu @@ -20,11 +20,12 @@ namespace oneflow { namespace { -template -__device__ __inline__ static int get_target_prime(const int* targets_ptr, - const IDX* target_lengths_ptr, - int64_t max_target_length, int64_t b, int64_t s, - int blank, const int32_t targets_ndim) { +template +__device__ __inline__ static int64_t get_target_prime(const TARGET* targets_ptr, + const IDX* target_lengths_ptr, + int64_t max_target_length, int64_t b, + int64_t s, int64_t blank, + const int32_t targets_ndim) { if (s % 2 == 0) { return blank; } else { @@ -35,24 +36,24 @@ __device__ __inline__ static int get_target_prime(const int* targets_ptr, idx = b * max_target_length; } idx += s / 2; - return targets_ptr[idx]; + return static_cast(targets_ptr[idx]); } } -template -__global__ void CtcLossGpu(const T* log_probs_ptr, const int* targets_ptr, +template +__global__ void CtcLossGpu(const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper input_helper, NdIndexOffsetHelper alpha_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, - const int blank, const int32_t targets_ndim) { + const int64_t blank, const int32_t targets_ndim) { constexpr T neginf = -INFINITY; const int32_t bid = blockIdx.x; const int32_t tid = threadIdx.x; for (int64_t b = bid; b < batch_size; b += gridDim.x) { if (tid == 0) { - if (input_lengths_ptr[b] > max_input_length) __trap(); - if (target_lengths_ptr[b] > max_target_length) __trap(); + if (input_lengths_ptr[b] > max_input_length) TRAP(); + if (target_lengths_ptr[b] > max_target_length) TRAP(); } } for (int64_t b = bid; b < batch_size; b += gridDim.x) { @@ -66,8 +67,8 @@ __global__ void CtcLossGpu(const T* log_probs_ptr, const int* targets_ptr, alpha_ptr[alpha_helper.NdIndexToOffset(b, 0, 0)] = log_probs_ptr[input_helper.NdIndexToOffset(0, b, blank)]; if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, 1, - blank, targets_ndim); + TARGET target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, 1, + blank, targets_ndim); alpha_ptr[alpha_helper.NdIndexToOffset(b, 0, 1)] = log_probs_ptr[input_helper.NdIndexToOffset(0, b, target)]; } @@ -75,8 +76,8 @@ __global__ void CtcLossGpu(const T* log_probs_ptr, const int* targets_ptr, __syncthreads(); for (IDX t = 1; t < input_length; t++) { for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); + TARGET current_target_prime = get_target_prime( + targets_ptr, target_lengths_ptr, max_target_length, b, s, blank, targets_ndim); T la1 = alpha_ptr[alpha_helper.NdIndexToOffset(b, t - 1, s)]; T la2, la3, lamax = la1; if (s > 0) { @@ -121,14 +122,14 @@ __global__ void CtcLossGpu(const T* log_probs_ptr, const int* targets_ptr, } } -template +template __global__ void CtcLossGradGpu( const T* grad_out_ptr, const T* loss_ptr, const T* alpha_ptr, const T* log_probs_ptr, - const int* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, + const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper input_helper, NdIndexOffsetHelper beta_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, const int32_t targets_ndim) { + const int64_t blank, const bool zero_infinity, const int32_t targets_ndim) { constexpr T neginf = -INFINITY; const int32_t bid = blockIdx.x; const int32_t tid = threadIdx.x; @@ -155,8 +156,8 @@ __global__ void CtcLossGradGpu( beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)] = log_probs_ptr[input_helper.NdIndexToOffset(input_length - 1, b, blank)]; if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, - 2 * target_length - 1, blank, targets_ndim); + TARGET target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, + 2 * target_length - 1, blank, targets_ndim); beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)] = log_probs_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)]; } @@ -165,8 +166,8 @@ __global__ void CtcLossGradGpu( } for (IDX t = input_length - 2; t >= 0; t--) { for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); + TARGET current_target_prime = get_target_prime( + targets_ptr, target_lengths_ptr, max_target_length, b, s, blank, targets_ndim); T lb1 = beta_ptr[beta_helper.NdIndexToOffset(b, t + 1, s)]; T lb2, lb3, lbmax = lb1; if (s < 2 * target_length) { @@ -204,8 +205,8 @@ __global__ void CtcLossGradGpu( alpha_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)] + beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)]; if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, - 2 * target_length - 1, blank, targets_ndim); + TARGET target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, + 2 * target_length - 1, blank, targets_ndim); grad_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)] = alpha_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)] + beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)]; @@ -214,8 +215,8 @@ __global__ void CtcLossGradGpu( __syncthreads(); for (IDX t = tid; t < input_length; t += blockDim.x) { for (IDX s = 0; (t < input_length - 1) && (s < 2 * target_length + 1); s += 1) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); + TARGET current_target_prime = get_target_prime( + targets_ptr, target_lengths_ptr, max_target_length, b, s, blank, targets_ndim); int64_t idx_t_s = beta_helper.NdIndexToOffset(b, t, s); T log_alpha_beta = alpha_ptr[idx_t_s] + beta_ptr[idx_t_s]; T& lcab = grad_ptr[input_helper.NdIndexToOffset(t, b, current_target_prime)]; @@ -237,48 +238,49 @@ __global__ void CtcLossGradGpu( } // namespace -template -struct CtcLossKernelUtil { - static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const int* targets_ptr, +template +struct CtcLossKernelUtil { + static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& alpha_helper, const int64_t batch_size, const int64_t max_input_length, - const int64_t max_target_length, const int blank, + const int64_t max_target_length, const int64_t blank, const int32_t targets_ndim) { int32_t thread_num = batch_size * kCudaThreadsNumPerBlock; - RUN_CUDA_KERNEL((CtcLossGpu), stream, thread_num, log_probs_ptr, targets_ptr, + RUN_CUDA_KERNEL((CtcLossGpu), stream, thread_num, log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, alpha_ptr, loss_ptr, input_helper, alpha_helper, batch_size, max_input_length, max_target_length, blank, targets_ndim); } static void CtcLossBackward(ep::Stream* stream, const T* grad_out_ptr, const T* loss_ptr, - const T* alpha_ptr, const T* log_probs_ptr, const int* targets_ptr, + const T* alpha_ptr, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& beta_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, + const int64_t blank, const bool zero_infinity, const int32_t targets_ndim) { int32_t thread_num = batch_size * kCudaThreadsNumPerBlock; - RUN_CUDA_KERNEL((CtcLossGradGpu), stream, thread_num, grad_out_ptr, loss_ptr, alpha_ptr, - log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, beta_ptr, - grad_ptr, input_helper, beta_helper, batch_size, max_input_length, + RUN_CUDA_KERNEL((CtcLossGradGpu), stream, thread_num, grad_out_ptr, loss_ptr, + alpha_ptr, log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, + beta_ptr, grad_ptr, input_helper, beta_helper, batch_size, max_input_length, max_target_length, num_labels, blank, zero_infinity, targets_ndim); } }; -#define INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA(device_type_v, log_probs_dtype_pair, \ - input_lengths_dtype_pair) \ - template struct CtcLossKernelUtil; OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA, (DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) #undef INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA } // namespace oneflow diff --git a/oneflow/user/kernels/ctc_loss_kernel_util.h b/oneflow/user/kernels/ctc_loss_kernel_util.h index bfe443ce191b602c6e709f06529ccefc969aa221..45090c0ff73801613247b92e15e9114eabde886d 100644 --- a/oneflow/user/kernels/ctc_loss_kernel_util.h +++ b/oneflow/user/kernels/ctc_loss_kernel_util.h @@ -21,26 +21,26 @@ limitations under the License. namespace oneflow { -template +template struct CtcLossKernelUtil final { - static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const int* targets_ptr, + static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& alpha_helper, const int64_t batch_size, const int64_t max_input_length, - const int64_t max_target_length, const int blank, + const int64_t max_target_length, const int64_t blank, const int32_t targets_ndim); static void CtcLossBackward(ep::Stream* stream, const T* grad_out_ptr, const T* loss_ptr, - const T* alpha_ptr, const T* log_probs_ptr, const int* targets_ptr, + const T* alpha_ptr, const T* log_probs_ptr, const TARGET* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper& input_helper, NdIndexOffsetHelper& beta_helper, const int64_t batch_size, const int64_t max_input_length, const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, + const int64_t blank, const bool zero_infinity, const int32_t targets_ndim); }; diff --git a/oneflow/user/kernels/ctc_loss_kernel_util.hip.cpp b/oneflow/user/kernels/ctc_loss_kernel_util.hip.cpp deleted file mode 100644 index abbaccfd451dfbf6f394e40c84c3c4bd5bd58ffb..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/ctc_loss_kernel_util.hip.cpp +++ /dev/null @@ -1,285 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/ctc_loss_kernel_util.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template -__device__ __inline__ static int get_target_prime(const int* targets_ptr, - const IDX* target_lengths_ptr, - int64_t max_target_length, int64_t b, int64_t s, - int blank, const int32_t targets_ndim) { - if (s % 2 == 0) { - return blank; - } else { - int64_t idx = 0; - if (targets_ndim == 1) { - FOR_RANGE(int64_t, i, 0, b) { idx += target_lengths_ptr[i]; } - } else { // targets_ndim == 2 - idx = b * max_target_length; - } - idx += s / 2; - return targets_ptr[idx]; - } -} - -template -__global__ void CtcLossGpu(const T* log_probs_ptr, const int* targets_ptr, - const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, - T* alpha_ptr, T* loss_ptr, NdIndexOffsetHelper input_helper, - NdIndexOffsetHelper alpha_helper, const int64_t batch_size, - const int64_t max_input_length, const int64_t max_target_length, - const int blank, const int32_t targets_ndim) { - constexpr T neginf = -INFINITY; - const int32_t bid = blockIdx.x; - const int32_t tid = threadIdx.x; - for (int64_t b = bid; b < batch_size; b += gridDim.x) { - if (tid == 0) { - if (input_lengths_ptr[b] > max_input_length) {asm("s_trap 0;");} - if (target_lengths_ptr[b] > max_target_length) {asm("s_trap 0;");} - } - } - for (int64_t b = bid; b < batch_size; b += gridDim.x) { - IDX input_length = input_lengths_ptr[b]; - IDX target_length = target_lengths_ptr[b]; - - for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - alpha_ptr[alpha_helper.NdIndexToOffset(b, 0, s)] = neginf; - } - if (tid == 0) { - alpha_ptr[alpha_helper.NdIndexToOffset(b, 0, 0)] = - log_probs_ptr[input_helper.NdIndexToOffset(0, b, blank)]; - if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, 1, - blank, targets_ndim); - alpha_ptr[alpha_helper.NdIndexToOffset(b, 0, 1)] = - log_probs_ptr[input_helper.NdIndexToOffset(0, b, target)]; - } - } - __syncthreads(); - for (IDX t = 1; t < input_length; t++) { - for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); - T la1 = alpha_ptr[alpha_helper.NdIndexToOffset(b, t - 1, s)]; - T la2, la3, lamax = la1; - if (s > 0) { - la2 = alpha_ptr[alpha_helper.NdIndexToOffset(b, t - 1, s - 1)]; - if (la2 > lamax) lamax = la2; - } else { - la2 = neginf; - } - if ((s > 1) - && (get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, s - 2, - blank, targets_ndim) - != current_target_prime)) { - la3 = alpha_ptr[alpha_helper.NdIndexToOffset(b, t - 1, s - 2)]; - if (la3 > lamax) lamax = la3; - } else { - la3 = neginf; - } - if (lamax == neginf) lamax = 0; - - int64_t idx_t_s = alpha_helper.NdIndexToOffset(b, t, s); - alpha_ptr[idx_t_s] = - log(exp(la1 - lamax) + exp(la2 - lamax) + exp(la3 - lamax)) + lamax - + log_probs_ptr[input_helper.NdIndexToOffset(t, b, current_target_prime)]; - } - __syncthreads(); - } - if (tid == 0) { - if (target_length == 0) { - int64_t idx = alpha_helper.NdIndexToOffset(b, input_length - 1, 0); - loss_ptr[b] = -alpha_ptr[idx]; - } else { - int64_t idx1 = alpha_helper.NdIndexToOffset(b, input_length - 1, target_length * 2); - int64_t idx2 = alpha_helper.NdIndexToOffset(b, input_length - 1, target_length * 2 - 1); - T l1 = alpha_ptr[idx1]; - T l2 = alpha_ptr[idx2]; - T m = max(l1, l2); - m = ((m == neginf) ? 0 : m); - T log_likelihood = log(exp(l1 - m) + exp(l2 - m)) + m; - loss_ptr[b] = -log_likelihood; - } - } - } -} - -template -__global__ void CtcLossGradGpu( - const T* grad_out_ptr, const T* loss_ptr, const T* alpha_ptr, const T* log_probs_ptr, - const int* targets_ptr, const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, - T* beta_ptr, T* grad_ptr, NdIndexOffsetHelper input_helper, - NdIndexOffsetHelper beta_helper, const int64_t batch_size, - const int64_t max_input_length, const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, const int32_t targets_ndim) { - constexpr T neginf = -INFINITY; - const int32_t bid = blockIdx.x; - const int32_t tid = threadIdx.x; - - for (int64_t b = bid; b < batch_size; b += gridDim.x) { - IDX input_length = input_lengths_ptr[b]; - IDX target_length = target_lengths_ptr[b]; - T nll = loss_ptr[b]; - if (zero_infinity && nll == INFINITY) { - for (IDX t = tid; t < max_input_length; t += blockDim.x) { - for (IDX c = 0; c < num_labels; c++) { - grad_ptr[input_helper.NdIndexToOffset(t, b, c)] = 0; - } - } - __syncthreads(); - continue; - } - - if (input_length > 0) { - for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, s)] = neginf; - } - if (tid == 0) { - beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)] = - log_probs_ptr[input_helper.NdIndexToOffset(input_length - 1, b, blank)]; - if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, - 2 * target_length - 1, blank, targets_ndim); - beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)] = - log_probs_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)]; - } - } - __syncthreads(); - } - for (IDX t = input_length - 2; t >= 0; t--) { - for (IDX s = tid; s < 2 * target_length + 1; s += blockDim.x) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); - T lb1 = beta_ptr[beta_helper.NdIndexToOffset(b, t + 1, s)]; - T lb2, lb3, lbmax = lb1; - if (s < 2 * target_length) { - lb2 = beta_ptr[beta_helper.NdIndexToOffset(b, t + 1, s + 1)]; - if (lb2 > lbmax) lbmax = lb2; - } else { - lb2 = neginf; - } - if ((s < 2 * target_length - 1) - && (get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, s + 2, - blank, targets_ndim) - != current_target_prime)) { - lb3 = beta_ptr[beta_helper.NdIndexToOffset(b, t + 1, s + 2)]; - if (lb3 > lbmax) lbmax = lb3; - } else { - lb3 = neginf; - } - if (lbmax == neginf) lbmax = 0; - - int64_t idx_t_s = beta_helper.NdIndexToOffset(b, t, s); - beta_ptr[idx_t_s] = - log(exp(lb1 - lbmax) + exp(lb2 - lbmax) + exp(lb3 - lbmax)) + lbmax - + log_probs_ptr[input_helper.NdIndexToOffset(t, b, current_target_prime)]; - } - __syncthreads(); - } - for (IDX t = tid; t < max_input_length; t += blockDim.x) { - for (IDX c = 0; c < num_labels; c++) { - grad_ptr[input_helper.NdIndexToOffset(t, b, c)] = t < input_length ? neginf : 0; - } - } - __syncthreads(); - if (tid == 0) { - grad_ptr[input_helper.NdIndexToOffset(input_length - 1, b, blank)] = - alpha_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)] - + beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length)]; - if (target_length > 0) { - int target = get_target_prime(targets_ptr, target_lengths_ptr, max_target_length, b, - 2 * target_length - 1, blank, targets_ndim); - grad_ptr[input_helper.NdIndexToOffset(input_length - 1, b, target)] = - alpha_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)] - + beta_ptr[beta_helper.NdIndexToOffset(b, input_length - 1, 2 * target_length - 1)]; - } - } - __syncthreads(); - for (IDX t = tid; t < input_length; t += blockDim.x) { - for (IDX s = 0; (t < input_length - 1) && (s < 2 * target_length + 1); s += 1) { - int current_target_prime = get_target_prime(targets_ptr, target_lengths_ptr, - max_target_length, b, s, blank, targets_ndim); - int64_t idx_t_s = beta_helper.NdIndexToOffset(b, t, s); - T log_alpha_beta = alpha_ptr[idx_t_s] + beta_ptr[idx_t_s]; - T& lcab = grad_ptr[input_helper.NdIndexToOffset(t, b, current_target_prime)]; - if (lcab == neginf) { - lcab = log_alpha_beta; - } else { - T m = max(lcab, log_alpha_beta); - lcab = log(exp(lcab - m) + exp(log_alpha_beta - m)) + m; - } - } - for (int32_t c = 0; c < num_labels; c++) { - T& res = grad_ptr[input_helper.NdIndexToOffset(t, b, c)]; - T lp = log_probs_ptr[input_helper.NdIndexToOffset(t, b, c)]; - res = (exp(lp) - exp(res + nll - lp)) * grad_out_ptr[b]; - } - } - } -} - -} // namespace - -template -struct CtcLossKernelUtil { - static void CtcLossForward(ep::Stream* stream, const T* log_probs_ptr, const int* targets_ptr, - const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, - T* alpha_ptr, T* loss_ptr, - NdIndexOffsetHelper& input_helper, - NdIndexOffsetHelper& alpha_helper, - const int64_t batch_size, const int64_t max_input_length, - const int64_t max_target_length, const int blank, - const int32_t targets_ndim) { - int32_t thread_num = batch_size * kCudaThreadsNumPerBlock; - RUN_CUDA_KERNEL((CtcLossGpu), stream, thread_num, log_probs_ptr, targets_ptr, - input_lengths_ptr, target_lengths_ptr, alpha_ptr, loss_ptr, input_helper, - alpha_helper, batch_size, max_input_length, max_target_length, blank, - targets_ndim); - } - - static void CtcLossBackward(ep::Stream* stream, const T* grad_out_ptr, const T* loss_ptr, - const T* alpha_ptr, const T* log_probs_ptr, const int* targets_ptr, - const IDX* input_lengths_ptr, const IDX* target_lengths_ptr, - T* beta_ptr, T* grad_ptr, - NdIndexOffsetHelper& input_helper, - NdIndexOffsetHelper& beta_helper, - const int64_t batch_size, const int64_t max_input_length, - const int64_t max_target_length, const int64_t num_labels, - const int blank, const bool zero_infinity, - const int32_t targets_ndim) { - int32_t thread_num = batch_size * kCudaThreadsNumPerBlock; - RUN_CUDA_KERNEL((CtcLossGradGpu), stream, thread_num, grad_out_ptr, loss_ptr, alpha_ptr, - log_probs_ptr, targets_ptr, input_lengths_ptr, target_lengths_ptr, beta_ptr, - grad_ptr, input_helper, beta_helper, batch_size, max_input_length, - max_target_length, num_labels, blank, zero_infinity, targets_ndim); - } -}; - -#define INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA(device_type_v, log_probs_dtype_pair, \ - input_lengths_dtype_pair) \ - template struct CtcLossKernelUtil; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA, (DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#undef INSTANTIATE_CTC_LOSS_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/cublas_fused_mlp_grad_kernel.cu b/oneflow/user/kernels/cublas_fused_mlp_grad_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..6f53f27aa4f81e83f6729e659d4315b76e6371fa --- /dev/null +++ b/oneflow/user/kernels/cublas_fused_mlp_grad_kernel.cu @@ -0,0 +1,420 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/user/kernels/cublas_fused_mlp_util.cuh" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/device/nccl_util.h" +#include "oneflow/core/job/eager_nccl_comm_manager.h" +// CUBLAS_AUX_EPILOGUE only support in cuda11.4 or higher version, in cuda11.4 it need static link. +#if CUDA_VERSION >= 11060 + +namespace oneflow { + +namespace { + +struct Comm { + Comm(ncclComm_t comm) : comm(comm) {} + ncclComm_t comm; +}; + +class MatmulGradKernelState final : public user_op::OpKernelState { + public: + MatmulGradKernelState(user_op::KernelInitContext* ctx) + : if_need_comm_(false), stream_name_(EagerNcclCommMgr::kDefaultStreamName) { + OF_CUDA_CHECK(cudaStreamCreate(&cuda_stream_)); + OF_CUDA_CHECK(cudaStreamCreate(&allreduce_stream_)); + OF_CUBLAS_CHECK(cublasLtCreate(&cublas_lt_handle_)); + workspace_size_ = + ParseIntegerFromEnv("ONEFLOW_EP_CUDA_CUBLAS_WORKSPACE_SIZE_MB", kDefaultWorkspaceSizeMb) + * 1024 * 1024; + OF_CUDA_CHECK(cudaMalloc(&workspace_, workspace_size_)); + if (ctx->parallel_ctx().parallel_num() > 1) { + parallel_conf_ = ctx->parallel_desc().parallel_conf(); + } + } + ~MatmulGradKernelState() { + OF_CUDA_CHECK(cudaStreamSynchronize(cuda_stream_)); + OF_CUBLAS_CHECK(cublasLtDestroy(cublas_lt_handle_)); + OF_CUDA_CHECK(cudaStreamDestroy(cuda_stream_)); + OF_CUDA_CHECK(cudaStreamSynchronize(allreduce_stream_)); + OF_CUDA_CHECK(cudaStreamDestroy(allreduce_stream_)); + OF_CUDA_CHECK(cudaFree(workspace_)); + } + cudaStream_t grad_cuda_stream() const { return cuda_stream_; } + cudaStream_t allreduce_stream() const { return allreduce_stream_; } + cublasLtHandle_t cublas_lt_handle() const { return cublas_lt_handle_; } + size_t cublas_workspace_size() const { return workspace_size_; } + void* cublas_workspace() const { return workspace_; } + + bool IfCommCreate() const { + if (!comm_) { return false; } + return true; + } + + bool IfNeedComm() const { return if_need_comm_; } + + ncclComm_t comm() { return GetOrCreate().comm; } + + const Comm& GetOrCreate() { + if (!comm_) { InitCommMgr(); } + return *comm_; + } + + void InitNeedComm(user_op::KernelInitContext* ctx) { + if_need_comm_ = false; + if (ctx->parallel_ctx().parallel_num() > 1) { + const int64_t d_weights_size = ctx->output_size("d_weights"); + if (ctx->SbpParallel4ArgNameAndIndex("d_weights", 0).has_broadcast_parallel()) { + for (int i = 0; i < d_weights_size; i++) { + CHECK(ctx->SbpParallel4ArgNameAndIndex("d_weights", i).has_broadcast_parallel()) + << "All d_weight's SBP should be Broadcast. "; + CHECK(ctx->SbpParallel4ArgNameAndIndex("d_biases", i).has_broadcast_parallel()) + << "All d_bias's SBP should be Broadcast. "; + } + if (ctx->SbpParallel4ArgNameAndIndex("dy", 0).has_split_parallel()) { + if_need_comm_ = true; + } + } + } + } + + void InitCommMgr() { + std::set> device_set; + const ParallelDesc parallel_desc(parallel_conf_); + for (int64_t parallel_id = 0; parallel_id < parallel_desc.parallel_num(); ++parallel_id) { + int64_t machine_id = CHECK_JUST(parallel_desc.MachineId4ParallelId(parallel_id)); + int64_t device_id = CHECK_JUST(parallel_desc.DeviceId4ParallelId(parallel_id)); + device_set.emplace(std::make_pair(machine_id, device_id)); + } + EagerNcclCommMgr* comm_mgr = CHECK_NOTNULL(Singleton::Get()); + ncclComm_t comm; + comm = comm_mgr->GetCommForDeviceAndStreamName(device_set, stream_name_); + comm_.reset(new Comm(comm)); + } + + private: + cudaStream_t cuda_stream_{}; + cudaStream_t allreduce_stream_{}; + cublasLtHandle_t cublas_lt_handle_{}; + void* workspace_{}; + size_t workspace_size_; + std::string stream_name_; + std::unique_ptr comm_; + bool if_need_comm_; + ParallelConf parallel_conf_; +}; + +template +class CublasFusedMLPGradKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + CublasFusedMLPGradKernel() { + OF_CUDA_CHECK(cudaEventCreate(&main_stream_event_)); + OF_CUDA_CHECK(cudaEventCreate(&async_weight_grad_event_)); + OF_CUDA_CHECK(cudaEventCreate(&dweight_event_)); + OF_CUDA_CHECK(cudaEventCreate(&allreduce_event_)); + }; + ~CublasFusedMLPGradKernel() override { + OF_CUDA_CHECK(cudaEventDestroy(main_stream_event_)); + OF_CUDA_CHECK(cudaEventDestroy(async_weight_grad_event_)); + OF_CUDA_CHECK(cudaEventDestroy(dweight_event_)); + OF_CUDA_CHECK(cudaEventDestroy(allreduce_event_)); + }; + + std::shared_ptr InitOpKernelCache( + user_op::KernelCacheContext* ctx) const override { + return CreateCublasFusedMLPKernelCache(); + } + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + std::shared_ptr kernel_state = + std::make_shared(ctx); + kernel_state->InitNeedComm(ctx); + return kernel_state; + } + + private: + cudaEvent_t main_stream_event_; + cudaEvent_t async_weight_grad_event_; + cudaEvent_t dweight_event_; + cudaEvent_t allreduce_event_; + + bool IsReadyForCapture(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + auto* kernel_state = dynamic_cast(state); + if (kernel_state->IfNeedComm()) { + return kernel_state->IfCommCreate(); + } else { + return true; + } + } + + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + int64_t tmp_buf_elem_cnt = tmp_buffer->shape_view().elem_cnt(); + const int64_t weight_num = ctx->input_size("weights"); + user_op::Tensor* d_x = ctx->Tensor4ArgNameAndIndex("d_x", 0); + const std::vector alpha_list = ctx->Attr>("alpha_list"); + + auto* kernel_state = dynamic_cast(state); + const auto* matmul_grad_cache = + CHECK_NOTNULL(dynamic_cast(cache)); + + ncclComm_t comm{}; + bool if_need_comm = kernel_state->IfNeedComm(); + + if (if_need_comm) { comm = kernel_state->comm(); } + + void* dy_tmp_buf = tmp_buffer->mut_dptr(); + size_t tmp_buf_offset = 0; + auto* cuda_stream = ctx->stream()->As(); + + const DataType data_type = dy->data_type(); + const cublasComputeType_t cublas_compute_dtype = GetComputeType(data_type); + const cudaDataType_t cuda_data_type = GetCudaDataType(data_type); + size_t cublas_m = 0, cublas_n = 0, cublas_k = 0; + int64_t cublas_lda = 0, cublas_ldb = 0, cublas_ldc = 0; + + const double alpha_one = 1.0; + auto sp_alpha_one = GetCublasScalarParameter(alpha_one, cublas_compute_dtype); + double alpha = 1.0; + auto sp_alpha = GetCublasScalarParameter(alpha, cublas_compute_dtype); + double beta = 0.0; + auto sp_beta = GetCublasScalarParameter(beta, cublas_compute_dtype); + + cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_DEFAULT; + + // currently only support 2D matmul. + DimVector weight_shape(2); + DimVector hidden_shape(2); + DimVector dy_shape(2); + dy->shape_view().ToDimVector(&dy_shape); + const void* dgrad_buf = dy->dptr(); + + const int64_t batch_size = dy->shape_view().At(0); + const void* ones = nullptr; + ep::CudaDevice* cuda_device = dynamic_cast(ctx->stream()->device()); + CHECK_NOTNULL(cuda_device); + ones = cuda_device->GetConstOnes(dy->data_type(), batch_size); + if (ones == nullptr) { + std::unique_ptr fill = + ep::primitive::NewPrimitive(ctx->stream()->device_type(), + data_type); + CHECK(fill); + fill->Launch(ctx->stream(), tmp_buffer->mut_dptr(), 1.0, batch_size); + ones = tmp_buffer->mut_dptr(); + tmp_buf_offset += GetCudaAlignedSize(batch_size * sizeof(T)); + dy_tmp_buf = reinterpret_cast(tmp_buffer->mut_dptr() + tmp_buf_offset); + } + + for (int idx = weight_num - 1; idx >= 0; idx--) { + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weights", idx); + weight->shape_view().ToDimVector(&weight_shape); + InferMatmulCublasMNK(dy_shape, weight_shape, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, &cublas_m, + &cublas_n, &cublas_k, &cublas_lda, &cublas_ldb, &cublas_ldc); + if (idx != 0) { + alpha = alpha_list.at(idx - 1); + sp_alpha = GetCublasScalarParameter(alpha, cublas_compute_dtype); + const user_op::Tensor* aux = ctx->Tensor4ArgNameAndIndex("cublas_aux", idx - 1); + user_op::Tensor* d_bias = ctx->Tensor4ArgNameAndIndex("d_biases", idx - 1); + epilogue = CUBLASLT_EPILOGUE_DRELU_BGRAD; + SetCublasAttr(matmul_grad_cache, cublas_compute_dtype, cuda_data_type, /*need_aux=*/true, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, epilogue, + d_bias->mut_dptr(), aux->dptr(), cublas_m, cublas_n, cublas_k, cublas_lda, + cublas_ldb, cublas_ldc); + /* + a = dy, b = weight + cublas_a=weight, cublas_b=dy + */ + OF_CUDA_CHECK(cudaEventRecord(main_stream_event_, cuda_stream->cuda_stream())); + OF_CUBLAS_CHECK(cublasLtMatmul( + cuda_stream->cublas_lt_handle(), matmul_grad_cache->operation_desc, &sp_alpha, + weight->dptr(), matmul_grad_cache->cublas_a_desc, dgrad_buf, + matmul_grad_cache->cublas_b_desc, &sp_beta, dy_tmp_buf, + matmul_grad_cache->cublas_c_desc, dy_tmp_buf, matmul_grad_cache->cublas_c_desc, nullptr, + cuda_stream->cublas_workspace(), cuda_stream->cublas_workspace_size(), + cuda_stream->cuda_stream())); + } else { + epilogue = CUBLASLT_EPILOGUE_DEFAULT; + SetCublasAttr(matmul_grad_cache, cublas_compute_dtype, cuda_data_type, /*need_aux=*/false, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, epilogue, nullptr, + nullptr, cublas_m, cublas_n, cublas_k, cublas_lda, cublas_ldb, cublas_ldc); + /* + a = dy, b = weight + cublas_a=weight, cublas_b=dy + */ + OF_CUDA_CHECK(cudaEventRecord(main_stream_event_, cuda_stream->cuda_stream())); + OF_CUBLAS_CHECK(cublasLtMatmul( + cuda_stream->cublas_lt_handle(), matmul_grad_cache->operation_desc, &sp_alpha_one, + weight->dptr(), matmul_grad_cache->cublas_a_desc, dgrad_buf, + matmul_grad_cache->cublas_b_desc, &sp_beta, d_x->mut_dptr(), + matmul_grad_cache->cublas_c_desc, d_x->mut_dptr(), matmul_grad_cache->cublas_c_desc, + nullptr, cuda_stream->cublas_workspace(), cuda_stream->cublas_workspace_size(), + cuda_stream->cuda_stream())); + } + + // step1: Get last layer's dbias. + if (idx == weight_num - 1) { + user_op::Tensor* d_last_bias = ctx->Tensor4ArgNameAndIndex("d_biases", weight_num - 1); + DimVector ones_buf_shape(2); + ones_buf_shape.at(0) = 1; + ones_buf_shape.at(1) = batch_size; + epilogue = CUBLASLT_EPILOGUE_DEFAULT; + InferMatmulCublasMNK(ones_buf_shape, dy_shape, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, &cublas_m, + &cublas_n, &cublas_k, &cublas_lda, &cublas_ldb, &cublas_ldc); + SetCublasAttr(matmul_grad_cache, cublas_compute_dtype, cuda_data_type, /*need_aux=*/false, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, epilogue, nullptr, + nullptr, cublas_m, cublas_n, cublas_k, cublas_lda, cublas_ldb, cublas_ldc); + OF_CUDA_CHECK(cudaStreamWaitEvent(kernel_state->grad_cuda_stream(), main_stream_event_)); + OF_CUBLAS_CHECK(cublasLtMatmul( + kernel_state->cublas_lt_handle(), matmul_grad_cache->operation_desc, &sp_alpha_one, + dgrad_buf, matmul_grad_cache->cublas_a_desc, ones, matmul_grad_cache->cublas_b_desc, + &sp_beta, d_last_bias->mut_dptr(), matmul_grad_cache->cublas_c_desc, + d_last_bias->mut_dptr(), matmul_grad_cache->cublas_c_desc, nullptr, + kernel_state->cublas_workspace(), kernel_state->cublas_workspace_size(), + kernel_state->grad_cuda_stream())); + } + + user_op::Tensor* d_weight = ctx->Tensor4ArgNameAndIndex("d_weights", idx); + epilogue = CUBLASLT_EPILOGUE_DEFAULT; + if (idx != 0) { + const user_op::Tensor* hidden = ctx->Tensor4ArgNameAndIndex("hidden", idx - 1); // here + hidden->shape_view().ToDimVector(&hidden_shape); + InferMatmulCublasMNK(dy_shape, hidden_shape, + /*transpose_a=*/ep::primitive::BlasTransposeType::T, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, &cublas_m, + &cublas_n, &cublas_k, &cublas_lda, &cublas_ldb, &cublas_ldc); + + SetCublasAttr(matmul_grad_cache, cublas_compute_dtype, cuda_data_type, /*need_aux=*/false, + /*transpose_a=*/ep::primitive::BlasTransposeType::T, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, epilogue, nullptr, + nullptr, cublas_m, cublas_n, cublas_k, cublas_lda, cublas_ldb, cublas_ldc); + if (idx != weight_num - 1) { + // if idx == weight_num - 1, async_stream has wait main_stream_event_ in d_bias. + OF_CUDA_CHECK(cudaStreamWaitEvent(kernel_state->grad_cuda_stream(), main_stream_event_)); + } + OF_CUBLAS_CHECK(cublasLtMatmul( + kernel_state->cublas_lt_handle(), matmul_grad_cache->operation_desc, &sp_alpha_one, + hidden->dptr(), matmul_grad_cache->cublas_a_desc, dgrad_buf, + matmul_grad_cache->cublas_b_desc, &sp_beta, d_weight->mut_dptr(), + matmul_grad_cache->cublas_c_desc, d_weight->mut_dptr(), + matmul_grad_cache->cublas_c_desc, nullptr, kernel_state->cublas_workspace(), + kernel_state->cublas_workspace_size(), kernel_state->grad_cuda_stream())); + OF_CUDA_CHECK(cudaEventRecord(dweight_event_, kernel_state->grad_cuda_stream())); + // compute dy shape + dy_shape.at(1) = weight_shape.at(1); + // compute dybuf + dgrad_buf = dy_tmp_buf; + tmp_buf_offset += GetCudaAlignedSize(dy_shape.at(0) * dy_shape.at(1) * sizeof(T)); + CHECK_LE(tmp_buf_offset, tmp_buf_elem_cnt) + << "Tmp buffer offset should <= Tmp buffer elem_cnt. "; + dy_tmp_buf = reinterpret_cast(tmp_buffer->mut_dptr() + tmp_buf_offset); + } else { + x->shape_view().ToDimVector(&hidden_shape); + InferMatmulCublasMNK(dy_shape, hidden_shape, + /*transpose_a=*/ep::primitive::BlasTransposeType::T, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, &cublas_m, + &cublas_n, &cublas_k, &cublas_lda, &cublas_ldb, &cublas_ldc); + SetCublasAttr(matmul_grad_cache, cublas_compute_dtype, cuda_data_type, /*need_aux=*/false, + /*transpose_a=*/ep::primitive::BlasTransposeType::T, + /*transpose_b=*/ep::primitive::BlasTransposeType::N, epilogue, nullptr, + nullptr, cublas_m, cublas_n, cublas_k, cublas_lda, cublas_ldb, cublas_ldc); + OF_CUDA_CHECK(cudaStreamWaitEvent(kernel_state->grad_cuda_stream(), main_stream_event_)); + OF_CUBLAS_CHECK(cublasLtMatmul( + kernel_state->cublas_lt_handle(), matmul_grad_cache->operation_desc, &sp_alpha_one, + x->dptr(), matmul_grad_cache->cublas_a_desc, dgrad_buf, + matmul_grad_cache->cublas_b_desc, &sp_beta, d_weight->mut_dptr(), + matmul_grad_cache->cublas_c_desc, d_weight->mut_dptr(), + matmul_grad_cache->cublas_c_desc, nullptr, kernel_state->cublas_workspace(), + kernel_state->cublas_workspace_size(), kernel_state->grad_cuda_stream())); + OF_CUDA_CHECK(cudaEventRecord(dweight_event_, kernel_state->grad_cuda_stream())); + } + + if (if_need_comm) { + // Do Allreduce for d_bias and d_weight. + // Here we wait wgrad event, and set a ncclGroup to Allreduce d_bias and d_weight. + OF_CUDA_CHECK(cudaStreamWaitEvent(kernel_state->allreduce_stream(), dweight_event_)); + OF_NCCL_CHECK(ncclGroupStart()); + user_op::Tensor* allreduce_d_bias = ctx->Tensor4ArgNameAndIndex("d_biases", idx); + OF_NCCL_CHECK(ncclAllReduce(allreduce_d_bias->mut_dptr(), allreduce_d_bias->mut_dptr(), + allreduce_d_bias->shape_view().elem_cnt(), + GetNcclDataType(allreduce_d_bias->data_type()), + ncclRedOp_t::ncclSum, comm, kernel_state->allreduce_stream())); + OF_NCCL_CHECK(ncclAllReduce(d_weight->mut_dptr(), d_weight->mut_dptr(), + d_weight->shape_view().elem_cnt(), + GetNcclDataType(d_weight->data_type()), ncclRedOp_t::ncclSum, + comm, kernel_state->allreduce_stream())); + OF_NCCL_CHECK(ncclGroupEnd()); + if (idx == 0) { + // We should sync allreduce before the kernel finish. + OF_CUDA_CHECK(cudaEventRecord(allreduce_event_, kernel_state->allreduce_stream())); + } + } + } + + if (if_need_comm) { + OF_CUDA_CHECK(cudaStreamWaitEvent(cuda_stream->cuda_stream(), allreduce_event_)); + } else { + OF_CUDA_CHECK(cudaStreamWaitEvent(cuda_stream->cuda_stream(), dweight_event_)); + } + }; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUBLAS_FUSED_MLP_GRAD_KERNEL(dtype) \ + REGISTER_USER_KERNEL("cublas_fused_mlp_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const int64_t weight_num = ctx->input_size("weights"); \ + const Shape& dy_shape = ctx->InputShape("dy", 0); \ + int64_t m = dy_shape.At(0); \ + int64_t k = dy_shape.At(1); \ + int64_t tmp_buffer_size = 0; \ + tmp_buffer_size += GetCudaAlignedSize(m * sizeof(dtype)); /*For last layer's bias grad*/ \ + for (int idx = weight_num - 1; idx > 0; idx--) { \ + const Shape& weight_shape = ctx->InputShape("weights", idx); \ + k = weight_shape.At(1); \ + tmp_buffer_size += GetCudaAlignedSize(m * k * sizeof(dtype)); \ + } \ + return tmp_buffer_size; \ + }); + +REGISTER_CUBLAS_FUSED_MLP_GRAD_KERNEL(float) +REGISTER_CUBLAS_FUSED_MLP_GRAD_KERNEL(double) +REGISTER_CUBLAS_FUSED_MLP_GRAD_KERNEL(half) + +REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("cublas_fused_mlp_grad"); + +} // namespace + +} // namespace oneflow + +#endif // CUDA_VERSION >= 11060 diff --git a/oneflow/user/kernels/cublas_fused_mlp_util.cuh b/oneflow/user/kernels/cublas_fused_mlp_util.cuh index 3d4a57ad936b133831ec39d195e09051072158dd..5ec99e6002f7a70da45da433b99389c2302b5d45 100644 --- a/oneflow/user/kernels/cublas_fused_mlp_util.cuh +++ b/oneflow/user/kernels/cublas_fused_mlp_util.cuh @@ -21,13 +21,14 @@ limitations under the License. #include "oneflow/core/ep/cuda/cuda_stream.h" #include // CUBLAS_AUX_EPILOGUE only support in cuda11.4 or higher version, in cuda11.4 it need static link. -#if CUDA_VERSION >= 11060 +#if CUDA_VERSION >= 11020 namespace oneflow { namespace { constexpr int32_t kAuxReluLdAlignRequirement = 128; +constexpr size_t kDefaultWorkspaceSizeMb = 4; // 4M long AlignReluAuxLd(long aux_ld) { /* @@ -47,17 +48,20 @@ class CublasFusedMLPKernelCache final : public user_op::OpKernelCache { OF_CUBLAS_CHECK(cublasLtMatrixLayoutCreate(&cublas_a_desc, CUDA_R_32F, 1, 1, 1)); OF_CUBLAS_CHECK(cublasLtMatrixLayoutCreate(&cublas_b_desc, CUDA_R_32F, 1, 1, 1)); OF_CUBLAS_CHECK(cublasLtMatrixLayoutCreate(&cublas_c_desc, CUDA_R_32F, 1, 1, 1)); + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceCreate(&cublas_preference)); } ~CublasFusedMLPKernelCache() override { OF_CUBLAS_CHECK(cublasLtMatmulDescDestroy(operation_desc)); OF_CUBLAS_CHECK(cublasLtMatrixLayoutDestroy(cublas_a_desc)); OF_CUBLAS_CHECK(cublasLtMatrixLayoutDestroy(cublas_b_desc)); OF_CUBLAS_CHECK(cublasLtMatrixLayoutDestroy(cublas_c_desc)); + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceDestroy(cublas_preference)); } cublasLtMatmulDesc_t operation_desc; cublasLtMatrixLayout_t cublas_a_desc; cublasLtMatrixLayout_t cublas_b_desc; cublasLtMatrixLayout_t cublas_c_desc; + cublasLtMatmulPreference_t cublas_preference; }; std::shared_ptr CreateCublasFusedMLPKernelCache() { @@ -90,7 +94,15 @@ cublasComputeType_t GetComputeType(DataType data_type) { return CUBLAS_COMPUTE_32F; } case kDouble: return CUBLAS_COMPUTE_64F; - case kFloat16: return CUBLAS_COMPUTE_32F; + case kFloat16: { + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + if (allow_half_accumulation) { + return CUBLAS_COMPUTE_16F; + } else { + return CUBLAS_COMPUTE_32F; + } + } case kBFloat16: return CUBLAS_COMPUTE_32F; default: UNIMPLEMENTED(); return CUBLAS_COMPUTE_32F; } @@ -99,6 +111,7 @@ cublasComputeType_t GetComputeType(DataType data_type) { union CublasScalarParameter { double d; float s; + half h; }; CublasScalarParameter GetCublasScalarParameter(Scalar scalar, cublasComputeType_t compute_type) { @@ -107,6 +120,8 @@ CublasScalarParameter GetCublasScalarParameter(Scalar scalar, cublasComputeType_ sp.d = scalar.Value(); } else if (compute_type == CUBLAS_COMPUTE_32F || compute_type == CUBLAS_COMPUTE_32F_FAST_TF32) { sp.s = scalar.Value(); + } else if (compute_type == CUBLAS_COMPUTE_16F) { + sp.h = static_cast(scalar.Value()); } else { UNIMPLEMENTED(); } @@ -168,21 +183,32 @@ void SetCublasMatrixLayout(cublasLtMatrixLayout_t layout_desc, cudaDataType_t cu void SetCublasEpilogue(const CublasFusedMLPKernelCache* matmul_cache, cublasLtEpilogue_t epilogue, const void* bias_ptr, const void* aux_ptr) { - if (epilogue == CUBLASLT_EPILOGUE_RELU_BIAS || epilogue == CUBLASLT_EPILOGUE_BIAS - || epilogue == CUBLASLT_EPILOGUE_RELU_AUX_BIAS || epilogue == CUBLASLT_EPILOGUE_DRELU_BGRAD - || epilogue == CUBLASLT_EPILOGUE_BGRADB) { - // Set epilogue - OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute( - matmul_cache->operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue))); + // Set epilogue + OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute( + matmul_cache->operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue))); +#if CUDA_VERSION >= 11060 + const bool has_bias = + (epilogue == CUBLASLT_EPILOGUE_RELU_BIAS || epilogue == CUBLASLT_EPILOGUE_BIAS + || epilogue == CUBLASLT_EPILOGUE_RELU_AUX_BIAS || epilogue == CUBLASLT_EPILOGUE_DRELU_BGRAD + || epilogue == CUBLASLT_EPILOGUE_BGRADB); +#else + const bool has_bias = + (epilogue == CUBLASLT_EPILOGUE_RELU_BIAS || epilogue == CUBLASLT_EPILOGUE_BIAS); +#endif // CUDA_VERSION >= 11060 + if (has_bias) { // Set bias ptr OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute(matmul_cache->operation_desc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr))); } else { - Error::UnimplementedError() << "Unsupported Epilogue. "; + // unset + bias_ptr = nullptr; + OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute(matmul_cache->operation_desc, + CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, + sizeof(bias_ptr))); } - // TODO: Support GELU_AUX_BIAS +#if CUDA_VERSION >= 11060 if (epilogue == CUBLASLT_EPILOGUE_RELU_AUX_BIAS || epilogue == CUBLASLT_EPILOGUE_DRELU_BGRAD) { // Set aux ptr for backward. OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute(matmul_cache->operation_desc, @@ -195,6 +221,7 @@ void SetCublasEpilogue(const CublasFusedMLPKernelCache* matmul_cache, cublasLtEp CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER, &aux_ptr, sizeof(aux_ptr))); } +#endif // CUDA_VERSION >= 11060 } void SetCublasAttr(const CublasFusedMLPKernelCache* matmul_grad_cache, @@ -208,12 +235,17 @@ void SetCublasAttr(const CublasFusedMLPKernelCache* matmul_grad_cache, matmul_grad_cache->operation_desc, CUBLASLT_MATMUL_DESC_COMPUTE_TYPE, &cublas_compute_dtype, sizeof(cublas_compute_dtype))); - // For best performance when using the bias vector, specify beta == 0 and - // CUBLASLT_POINTER_MODE_HOST.(from - // https://docs.nvidia.com/cuda/cublas/index.html#cublasLtPointerMode_t) - cublasLtPointerMode_t mode = CUBLASLT_POINTER_MODE_HOST; - OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute( - matmul_grad_cache->operation_desc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &mode, sizeof(mode))); + size_t workspace_size = + ParseIntegerFromEnv("ONEFLOW_EP_CUDA_CUBLAS_WORKSPACE_SIZE_MB", kDefaultWorkspaceSizeMb) + * 1024 * 1024; + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceSetAttribute(matmul_grad_cache->cublas_preference, + CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, + &workspace_size, sizeof(workspace_size))); + + uint32_t pointer_mode = CUBLASLT_POINTER_MODE_MASK_HOST; + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceSetAttribute(matmul_grad_cache->cublas_preference, + CUBLASLT_MATMUL_PREF_POINTER_MODE_MASK, + &pointer_mode, sizeof(pointer_mode))); // transpose_a = False, transpose_b = True. But in cublas is reversed. const cublasOperation_t cublas_trans_a = @@ -229,14 +261,15 @@ void SetCublasAttr(const CublasFusedMLPKernelCache* matmul_grad_cache, // Set epilogue SetCublasEpilogue(matmul_grad_cache, epilogue, d_bias_ptr, aux_ptr); - /* - Set AUX pointer LD - If is used for CUBLASLT_EPILOGUE_DRELU_BGRAD, the AUX_LD need to align 128bit. - If is used for CUBLASLT_EPILOGUE_DGELU_BGRAD, the AUX_LD need to align 8. - For more details you can refer to CUBLAS docs: - https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmulDescAttributes_t - `CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD`. - */ +/* +Set AUX pointer LD +If is used for CUBLASLT_EPILOGUE_DRELU_BGRAD, the AUX_LD need to align 128bit. +If is used for CUBLASLT_EPILOGUE_DGELU_BGRAD, the AUX_LD need to align 8. +For more details you can refer to CUBLAS docs: +https://docs.nvidia.com/cuda/cublas/index.html#cublasLtMatmulDescAttributes_t +`CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD`. +*/ +#if CUDA_VERSION >= 11060 if (need_aux) { long aligned_aux_ld = AlignReluAuxLd(cublas_ldc); OF_CUBLAS_CHECK(cublasLtMatmulDescSetAttribute(matmul_grad_cache->operation_desc, @@ -248,6 +281,7 @@ void SetCublasAttr(const CublasFusedMLPKernelCache* matmul_grad_cache, matmul_grad_cache->operation_desc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &no_need_aligned_aux_ld, sizeof(no_need_aligned_aux_ld))); } +#endif // CUDA_VERSION >= 11060 // Set matrix layout SetCublasMatrixLayout(matmul_grad_cache->cublas_a_desc, cuda_data_type, cublas_trans_a, cublas_m, cublas_k, cublas_lda); @@ -261,6 +295,6 @@ void SetCublasAttr(const CublasFusedMLPKernelCache* matmul_grad_cache, } // namespace oneflow -#endif // CUDA_VERSION >= 11060 +#endif // CUDA_VERSION >= 11020 #endif // defined(__CUDACC__) diff --git a/oneflow/user/kernels/cufftplancache.h b/oneflow/user/kernels/cufftplancache.h new file mode 100644 index 0000000000000000000000000000000000000000..3095fa1d8610cb843aec195c2b148d459e4f657c --- /dev/null +++ b/oneflow/user/kernels/cufftplancache.h @@ -0,0 +1,164 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/kernel/kernel.h" + +namespace oneflow { +namespace { + +constexpr int max_rank = 3; + +struct CuFFtParams { + int32_t ndim; + int32_t output_shape[max_rank + 1]; + int32_t input_shape[max_rank + 1]; + int32_t input_strides[max_rank + 1]; + int32_t output_strides[max_rank + 1]; + int32_t* rank; + int32_t batch; + CuFFtParams(int32_t dims, int32_t* r, const Stride& in_strides, const Stride& out_strides, + const Shape& in_shape, const Shape& out_shape, int32_t b) + : ndim(dims), rank(r), batch(b) { + std::copy(in_strides.begin(), in_strides.end(), input_strides); + std::copy(out_strides.begin(), out_strides.end(), output_strides); + std::copy(in_shape.begin(), in_shape.end(), input_shape); + std::copy(out_shape.begin(), out_shape.end(), output_shape); + } +}; + +template +class CuFFtConfig { + public: + CuFFtConfig(const CuFFtConfig&) = delete; + CuFFtConfig& operator=(CuFFtConfig const&) = delete; + + explicit CuFFtConfig(CuFFtParams& params) { + infer_cufft_type_(); + hipfftPlanMany(&plan_handle_, params.ndim, params.rank, params.input_shape, + params.input_strides[0], params.input_strides[1], params.output_shape, + params.output_strides[0], params.output_strides[1], exectype_, params.batch); + } + + void excute_plan(const T* in, C* out) { + switch (exectype_) { + case HIPFFT_R2C: hipfftExecR2C(plan_handle_, (hipfftReal*)in, (hipfftComplex*)out); break; + + case HIPFFT_D2Z: + hipfftExecD2Z(plan_handle_, (hipfftDoubleReal*)in, (hipfftDoubleComplex*)out); + break; + default: break; + } + } + + private: + // infer representing the FFT type(暂时只支持R2C,D2Z) + void infer_cufft_type_() { + bool isDouble = std::is_same::value; + if (isDouble) { + exectype_ = HIPFFT_D2Z; + } else { + exectype_ = HIPFFT_R2C; + } + } + + hipfftHandle plan_handle_; + hipfftType exectype_; +}; + +} // namespace + +} // namespace oneflow + +#else + +#include +#include +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/kernel/kernel.h" + +namespace oneflow { +namespace { + +constexpr int max_rank = 3; + +struct CuFFtParams { + int32_t ndim; + int32_t output_shape[max_rank + 1]; + int32_t input_shape[max_rank + 1]; + int32_t input_strides[max_rank + 1]; + int32_t output_strides[max_rank + 1]; + int32_t* rank; + int32_t batch; + CuFFtParams(int32_t dims, int32_t* r, const Stride& in_strides, const Stride& out_strides, + const Shape& in_shape, const Shape& out_shape, int32_t b) + : ndim(dims), rank(r), batch(b) { + std::copy(in_strides.begin(), in_strides.end(), input_strides); + std::copy(out_strides.begin(), out_strides.end(), output_strides); + std::copy(in_shape.begin(), in_shape.end(), input_shape); + std::copy(out_shape.begin(), out_shape.end(), output_shape); + } +}; + +template +class CuFFtConfig { + public: + CuFFtConfig(const CuFFtConfig&) = delete; + CuFFtConfig& operator=(CuFFtConfig const&) = delete; + + explicit CuFFtConfig(CuFFtParams& params) { + infer_cufft_type_(); + cufftPlanMany(&plan_handle_, params.ndim, params.rank, params.input_shape, + params.input_strides[0], params.input_strides[1], params.output_shape, + params.output_strides[0], params.output_strides[1], exectype_, params.batch); + } + + void excute_plan(const T* in, C* out) { + switch (exectype_) { + case CUFFT_R2C: cufftExecR2C(plan_handle_, (cufftReal*)in, (cufftComplex*)out); break; + + case CUFFT_D2Z: + cufftExecD2Z(plan_handle_, (cufftDoubleReal*)in, (cufftDoubleComplex*)out); + break; + default: break; + } + } + + private: + // infer representing the FFT type(暂时只支持R2C,D2Z) + void infer_cufft_type_() { + bool isDouble = std::is_same::value; + if (isDouble) { + exectype_ = CUFFT_D2Z; + } else { + exectype_ = CUFFT_R2C; + } + } + + cufftHandle plan_handle_; + cufftType exectype_; +}; + +} // namespace + +} // namespace oneflow +#endif \ No newline at end of file diff --git a/oneflow/user/kernels/cum_backward_kernel.cu b/oneflow/user/kernels/cum_backward_kernel.cu index c3d4bc717bbf29745bfb8bd828958ddfd2acc02c..80f1696485bf3fd1bf18ed41cd0034d63cf6b618 100644 --- a/oneflow/user/kernels/cum_backward_kernel.cu +++ b/oneflow/user/kernels/cum_backward_kernel.cu @@ -19,7 +19,7 @@ limitations under the License. #include "oneflow/core/kernel/new_kernel_util.h" namespace oneflow { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) namespace { template __global__ void CumProdBackward(const T* dy_ptr, T* dx_ptr, const T* output_ptr, const T* input_ptr, diff --git a/oneflow/user/kernels/cum_backward_kernel.hip.cpp b/oneflow/user/kernels/cum_backward_kernel.hip.cpp deleted file mode 100644 index b609c6e839c96507c4374011880124121df26ed2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/cum_backward_kernel.hip.cpp +++ /dev/null @@ -1,139 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/kernel/new_kernel_util.h" - -namespace oneflow { -#ifdef WITH_ROCM -namespace { -template -__global__ void CumProdBackward(const T* dy_ptr, T* dx_ptr, const T* output_ptr, const T* input_ptr, - const int64_t up_space, const int64_t space, - const int64_t down_space, const int64_t thread_num) { - // A thread is responsible for a row along specific dimension. - const size_t up_space_step = space * down_space; - CUDA_1D_KERNEL_LOOP_T(size_t, i, thread_num) { - const size_t up_space_id = i / down_space; - const size_t down_space_id = i % down_space; - const size_t ptr_offset = up_space_id * up_space_step + down_space_id; - auto* dy_ptr_base = dy_ptr + ptr_offset; - auto* dx_ptr_base = dx_ptr + ptr_offset; - auto* input_ptr_base = input_ptr + ptr_offset; - auto* output_ptr_base = output_ptr + ptr_offset; - - // Buffer storing number of zero element along specific dimension. - // Use dx as tmp buffer. - for (size_t j = 0; j < space; j++) { - const size_t data_offset = j * down_space; - int is_zero = input_ptr_base[data_offset] == 0 ? 1 : 0; - dx_ptr_base[data_offset] = is_zero + (j == 0 ? 0 : dx_ptr_base[data_offset - down_space]); - } - - // Find index of first zero in input. - size_t first_zero_index = space; - for (size_t j = 0; j < space; j++) { - const size_t data_offset = j * down_space; - if (dx_ptr_base[data_offset] == 1) { - first_zero_index = j; - break; - } - } - - // Suppose z is index of first zero element in input, - // for element which index is less than z grad is computed as below: - T reverse_cumsum = 0; - for (size_t j = 0; j < first_zero_index; j++) { - const size_t cur_index = first_zero_index - j - 1; - const size_t data_offset = cur_index * down_space; - reverse_cumsum += output_ptr_base[data_offset] * dy_ptr_base[data_offset]; - dx_ptr_base[data_offset] = reverse_cumsum / input_ptr_base[data_offset]; - } - - // Where index is z, its grad is computed as below: - if (first_zero_index == space) { return; } - T cumprod = 1; - T cumsum = 0; - T cumprod_before_first_zero = - first_zero_index == 0 ? 1 : output_ptr_base[(first_zero_index - 1) * down_space]; - for (size_t j = first_zero_index; j < space; j++) { - const size_t down_space_offset = j * down_space; - // Recover dx_ptr default value - if (dx_ptr_base[down_space_offset] >= 1) { dx_ptr_base[down_space_offset] = 0; } - if (j != first_zero_index) { cumprod *= input_ptr_base[down_space_offset]; } - cumsum += cumprod_before_first_zero * dy_ptr_base[down_space_offset] * cumprod; - } - dx_ptr_base[first_zero_index * down_space] = cumsum; - } -} -} // namespace - -template -class GpuCumProdGradKernel final : public user_op::OpKernel { - public: - GpuCumProdGradKernel() = default; - ~GpuCumProdGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* output = ctx->Tensor4ArgNameAndIndex("output", 0); - const auto* input = ctx->Tensor4ArgNameAndIndex("input", 0); - const auto* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - auto* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto elem_cnt = dy->shape_view().elem_cnt(); - if (!elem_cnt) { return; } - - const auto* output_ptr = output->dptr(); - const auto* input_ptr = input->dptr(); - const auto* dy_ptr = dy->dptr(); - auto* dx_ptr = dx->mut_dptr(); - - // Data partition: up_space|space|down_space - auto dim = ctx->Attr("dim"); - const auto up_space = elem_cnt / dx->shape_view().Count(dim); - const auto space = dx->shape_view().At(dim); - const auto down_space = dx->shape_view().Count(dim + 1); - const size_t thread_num = up_space * down_space; - - if (space == 1) { - Memcpy(ctx->stream(), dx_ptr, dy_ptr, elem_cnt * sizeof(T)); - return; - } - ep::CudaLaunchConfig config{}; - ctx->stream()->As()->InitLaunchConfigWithWaves( - &config, thread_num, /*DefaultBlockSize*/ 256, /*max_wave*/ 1); - CumProdBackward<<stream()->As()->cuda_stream()>>>( - dy_ptr, dx_ptr, output_ptr, input_ptr, up_space, space, down_space, thread_num); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_CUMPROD_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumprod_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_CUMPROD_GRAD_KERNEL(float) -REGISTER_CUDA_CUMPROD_GRAD_KERNEL(double) -#undef REGISTER_CUDA_CUMPROD_GRAD_KERNEL -#endif -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/cum_forward_kernel.cpp b/oneflow/user/kernels/cum_forward_kernel.cpp index add96f69d4d77eff9d2742fabd8bb9494fd6ed0f..46d412bf55d5f7576a0118e8f6d1b7d5664f6e20 100644 --- a/oneflow/user/kernels/cum_forward_kernel.cpp +++ b/oneflow/user/kernels/cum_forward_kernel.cpp @@ -67,39 +67,24 @@ class CpuCumKernel : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template -class CpuCumSumKernel final : public CpuCumKernel { - public: - CpuCumSumKernel() = default; - ~CpuCumSumKernel() = default; -}; +#define CUMOP_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("cumprod", BinaryFuncMul) \ + OF_PP_MAKE_TUPLE_SEQ("cumsum", BinaryFuncAdd) -#define REGISTER_CUMSUM_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumsum").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCPU) \ +#define REGISTER_CUMOP_KERNEL(dtype, op_name, op_functor) \ + REGISTER_USER_KERNEL(op_name).SetCreateFn>().SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCPU) \ && (user_op::HobDataType("y", 0) == GetDataType::value)); -REGISTER_CUMSUM_KERNEL(int32_t) -REGISTER_CUMSUM_KERNEL(int64_t) -REGISTER_CUMSUM_KERNEL(float) -REGISTER_CUMSUM_KERNEL(double) -#undef REGISTER_CUMSUM_KERNEL - -template -class CpuCumProdKernel final : public CpuCumKernel { - public: - CpuCumProdKernel() = default; - ~CpuCumProdKernel() = default; -}; +#define REGISTER_CUMOP_KERNEL_WITH_DTYPE(op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(int32_t, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(int64_t, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(float, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(double, op_name, op_functor) -#define REGISTER_CUMPROD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumprod").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); +OF_PP_FOR_EACH_TUPLE(REGISTER_CUMOP_KERNEL_WITH_DTYPE, CUMOP_SEQ); -REGISTER_CUMPROD_KERNEL(int32_t) -REGISTER_CUMPROD_KERNEL(int64_t) -REGISTER_CUMPROD_KERNEL(float) -REGISTER_CUMPROD_KERNEL(double) -#undef REGISTER_CUMPROD_KERNEL +#undef REGISTER_CUMOP_KERNEL +#undef REGISTER_CUMOP_KERNEL_WITH_DTYPE +#undef CUMOP_SEQ } // namespace oneflow diff --git a/oneflow/user/kernels/cum_forward_kernel.cu b/oneflow/user/kernels/cum_forward_kernel.cu index 32d725868e1c6a4237009e7cf18241902f5c12c7..a2ae8cf46e59c0f2be93a1d02ad7cceca18d028e 100644 --- a/oneflow/user/kernels/cum_forward_kernel.cu +++ b/oneflow/user/kernels/cum_forward_kernel.cu @@ -13,6 +13,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else +#include +#endif +#include #include "oneflow/core/framework/framework.h" #include "oneflow/core/device/cuda_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" @@ -20,24 +27,50 @@ limitations under the License. #include "oneflow/core/ndarray/binary_func.h" namespace oneflow { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) namespace { +template +inline T CeilDiv(T n, T m) { + return (n + m - 1) / m; +} + +template +struct SumFunctor { + __device__ __forceinline__ T operator()(const T a, const T b) const { return a + b; } +}; +template +struct ProdFunctor { + __device__ __forceinline__ T operator()(const T a, const T b) const { return a * b; } +}; + +template class BinaryFunc> +size_t InferTmpBufferSize(user_op::InferContext* ctx) { + const Shape& in_shape = ctx->InputShape("x", 0); + const int64_t dim = ctx->Attr("dim"); + const size_t dim_size = in_shape.At(dim); + if (in_shape.elem_cnt() == dim_size) { + size_t temp_storage_bytes = 0; +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipcub::DeviceScan::InclusiveScan(nullptr, temp_storage_bytes, + static_cast(nullptr), static_cast(nullptr), + BinaryFunc(), dim_size)); +#else + OF_CUDA_CHECK(cub::DeviceScan::InclusiveScan(nullptr, temp_storage_bytes, + static_cast(nullptr), static_cast(nullptr), + BinaryFunc(), dim_size)); +#endif + return GetCudaAlignedSize(temp_storage_bytes); + } + return 0; +} + // total thread number: cs_up_space * cs_down_space // in cs_down_space part, use cs_down_space threads // to calculate as follows(m=cs_down_space-1, n=cs_space-1, '|' stands for dependency): -// dm0, ..., d10, d00 -// | | | -// dm1, ..., d11, d01 -// | | | -// dm2, ..., d12, d02 -// | | | -// ... ... ... -// | | | -// dmn, ..., d1n, d0n template class BinaryFunc> -__global__ void CumsumForwardGpu(const T* in_ptr, T* out_ptr, int64_t cs_up_space, int64_t cs_space, - int64_t cs_down_space) { +__global__ void CumForwardGpu(const T* in_ptr, T* out_ptr, int64_t cs_up_space, int64_t cs_space, + int64_t cs_down_space) { CUDA_1D_KERNEL_LOOP(i, cs_up_space * cs_down_space) { auto cs_up_space_id = i / cs_down_space; auto cs_down_space_id = i - (i / cs_down_space) * cs_down_space; @@ -50,44 +83,130 @@ __global__ void CumsumForwardGpu(const T* in_ptr, T* out_ptr, int64_t cs_up_spac auto idx = j * cs_down_space; out_ptr_base[idx] = in_ptr_base[idx]; if (j != 0) { - out_ptr_base[idx] = - BinaryFunc::Invoke(out_ptr_base[idx], out_ptr_base[idx - cs_down_space]); + out_ptr_base[idx] = BinaryFunc()(out_ptr_base[idx], out_ptr_base[idx - cs_down_space]); } } } } + template class BinaryFunc> -__global__ void CumsumForwardGpuUpSpaceIs1(const T* in_ptr, T* out_ptr, int64_t cs_space, - int64_t cs_down_space) { - CUDA_1D_KERNEL_LOOP(i, cs_down_space) { - auto* in_ptr_base = in_ptr + i; - auto* out_ptr_base = out_ptr + i; +void ScanOuterDim(ep::Stream* ep_stream, const ShapeView& in_shape, int64_t dim, const T* in_ptr, + T* out_ptr) { + // data partition: up_space|space|down_space + auto up_space = in_shape.elem_cnt() / in_shape.Count(dim); + auto space = in_shape.At(dim); + auto down_space = in_shape.Count(dim + 1); + auto thread_num = up_space * down_space; + RUN_CUDA_KERNEL((CumForwardGpu), ep_stream, thread_num, in_ptr, out_ptr, up_space, + space, down_space); +} - // calculate cs_space data in one thread - for (auto j = 0; j < cs_space; j++) { - auto idx = j * cs_down_space; - out_ptr_base[idx] = in_ptr_base[idx]; - if (j != 0) { - out_ptr_base[idx] = - BinaryFunc::Invoke(out_ptr_base[idx], out_ptr_base[idx - cs_down_space]); +// Refer from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/ScanKernels.cu +template class BinaryFunc> +__device__ void ScanInnerMostDimKernelImpl(T* row_buf, T* src_, T* tgt_, const uint32_t num_rows, + const uint32_t row_size, T init) { + for (uint32_t block_row = blockIdx.x * blockDim.y; block_row < num_rows; + block_row += blockDim.y * gridDim.x) { + uint32_t row = block_row + threadIdx.y; + T block_total = init; + + T* row_src = src_ + row * row_size; + T* row_tgt = tgt_ + row * row_size; + + // Perform scan on one block at a time, keeping track of the total value of + // all blocks processed so far. + for (uint32_t block_col = 0; block_col < row_size; block_col += 2 * num_threads_x) { + // Load data into shared memory (two values per thread). + uint32_t col1 = block_col + threadIdx.x; + uint32_t col2 = block_col + num_threads_x + threadIdx.x; + if (row < num_rows) { + if (col1 < row_size) { + row_buf[threadIdx.x] = row_src[col1]; + } else { + row_buf[threadIdx.x] = init; + } + + if (col2 < row_size) { + row_buf[num_threads_x + threadIdx.x] = row_src[col2]; + } else { + row_buf[num_threads_x + threadIdx.x] = init; + } + + // Add the total value of all previous blocks to the first value of this block. + if (threadIdx.x == 0) { row_buf[0] = BinaryFunc()(row_buf[0], block_total); } } + __syncthreads(); + + for (uint32_t s = num_threads_x, d = 1; s >= 1; s >>= 1, d <<= 1) { + if (row < num_rows && threadIdx.x < s) { + uint32_t offset = (2 * threadIdx.x + 1) * d - 1; + row_buf[offset + d] = BinaryFunc()(row_buf[offset], row_buf[offset + d]); + } + __syncthreads(); + } + + for (uint32_t s = 2, d = num_threads_x / 2; d >= 1; s <<= 1, d >>= 1) { + if (row < num_rows && threadIdx.x < s - 1) { + uint32_t offset = 2 * (threadIdx.x + 1) * d - 1; + row_buf[offset + d] = BinaryFunc()(row_buf[offset], row_buf[offset + d]); + } + __syncthreads(); + } + // Write back to output. + if (row < num_rows) { + if (col1 < row_size) row_tgt[col1] = row_buf[threadIdx.x]; + if (col2 < row_size) row_tgt[col2] = row_buf[num_threads_x + threadIdx.x]; + } + block_total = row_buf[2 * num_threads_x - 1]; + __syncthreads(); } } } -template class BinaryFunc> -__global__ void CumsumForwardGpuDownSpaceIs1(const T* in_ptr, T* out_ptr, int64_t cs_up_space, - int64_t cs_space) { - CUDA_1D_KERNEL_LOOP(i, cs_up_space) { - auto* in_ptr_base = in_ptr + i * cs_space; - auto* out_ptr_base = out_ptr + i * cs_space; - // calculate cs_space data in one thread - for (auto j = 0; j < cs_space; j++) { - out_ptr_base[j] = in_ptr_base[j]; - if (j != 0) { out_ptr_base[j] = BinaryFunc::Invoke(out_ptr_base[j], out_ptr_base[j - 1]); } - } +template class BinaryFunc> +__global__ void ScanInnerMostDimKernel(const T* in_ptr, T* out_ptr, const int64_t num_rows, + const int64_t row_size, T init) { + __shared__ T sbuf[num_threads_y][2 * num_threads_x]; + T* row_buf = sbuf[threadIdx.y]; + ScanInnerMostDimKernelImpl( + row_buf, const_cast(in_ptr), out_ptr, num_rows, row_size, init); +} + +template class BinaryFunctor> +void ScanInnerMostDim(const T* in_ptr, T* out_ptr, const int64_t num_rows, const int64_t row_size, + const ep::CudaStream* cuda_stream) { + dim3 block(16, 32); + const int64_t max_grid_dim = cuda_stream->device()->properties().maxGridSize[0]; + dim3 grid(std::min(max_grid_dim, CeilDiv(num_rows, (int64_t)block.y))); + if (std::is_same, SumFunctor>::value) { + ScanInnerMostDimKernel + <<cuda_stream()>>>(in_ptr, out_ptr, num_rows, row_size, + /*init*/ 0); + } else if (std::is_same, ProdFunctor>::value) { + ScanInnerMostDimKernel + <<cuda_stream()>>>(in_ptr, out_ptr, num_rows, row_size, + /*init*/ 1); + } else { + UNIMPLEMENTED() << "Only Support cumsum and cumprod for now."; } } + +template class BinaryFunc> +void CubInclusiveScan(user_op::Tensor* temp_buffer, const T* in_ptr, T* out_ptr, int64_t elem_cnt, + const ep::CudaStream* cuda_stream) { + auto* temp_storage = temp_buffer->mut_dptr(); + size_t temp_storage_bytes = temp_buffer->shape_view().elem_cnt(); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipcub::DeviceScan::InclusiveScan(temp_storage, temp_storage_bytes, in_ptr, out_ptr, + BinaryFunc(), elem_cnt, + cuda_stream->cuda_stream())); +#else + OF_CUDA_CHECK(cub::DeviceScan::InclusiveScan(temp_storage, temp_storage_bytes, in_ptr, out_ptr, + BinaryFunc(), elem_cnt, + cuda_stream->cuda_stream())); +#endif +} } // namespace template class BinaryFunc> @@ -99,70 +218,57 @@ class GpuCumKernel : public user_op::OpKernel { private: using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx) const override { - // judge whether tensor has 0 size dimension first const auto* in = ctx->Tensor4ArgNameAndIndex("x", 0); - auto elem_cnt = in->shape_view().elem_cnt(); + auto* out = ctx->Tensor4ArgNameAndIndex("y", 0); + const ShapeView& in_shape = in->shape_view(); + const int64_t dim = ctx->Attr("dim"); + const int64_t dim_size = in_shape.At(dim); + + // Judge whether tensor has 0 size dimension first. + auto elem_cnt = in_shape.elem_cnt(); if (!elem_cnt) { return; } - auto* out = ctx->Tensor4ArgNameAndIndex("y", 0); - auto dim = ctx->Attr("dim"); const auto* in_ptr = in->dptr(); auto* out_ptr = out->mut_dptr(); - // data partition: up_space|space|down_space - auto up_space = elem_cnt / in->shape_view().Count(dim); - auto space = in->shape_view().At(dim); - auto down_space = in->shape_view().Count(dim + 1); - auto thread_num = up_space * down_space; - - if (up_space == 1) { - RUN_CUDA_KERNEL((CumsumForwardGpuUpSpaceIs1), ctx->stream(), thread_num, - in_ptr, out_ptr, space, down_space); - } else if (down_space == 1) { - RUN_CUDA_KERNEL((CumsumForwardGpuDownSpaceIs1), ctx->stream(), thread_num, - in_ptr, out_ptr, up_space, space); + const auto* cuda_stream = ctx->stream()->As(); + + if (elem_cnt == dim_size) { + auto* temp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + CubInclusiveScan(temp_buffer, in_ptr, out_ptr, elem_cnt, cuda_stream); + } else if (dim == in_shape.NumAxes() - 1) { + // Treat all outer dimension as a single dimension. + const int64_t num_rows = elem_cnt / dim_size; + ScanInnerMostDim(in_ptr, out_ptr, num_rows, dim_size, cuda_stream); } else { - RUN_CUDA_KERNEL((CumsumForwardGpu), ctx->stream(), thread_num, in_ptr, out_ptr, - up_space, space, down_space); + ScanOuterDim(ctx->stream(), in_shape, dim, in_ptr, out_ptr); } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template -class GpuCumSumKernel final : public GpuCumKernel { - public: - GpuCumSumKernel() = default; - ~GpuCumSumKernel() = default; -}; +#define CUMOP_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("cumprod", ProdFunctor) \ + OF_PP_MAKE_TUPLE_SEQ("cumsum", SumFunctor) -#define REGISTER_CUDA_CUMSUM_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumsum").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); +#define REGISTER_CUMOP_KERNEL(dtype, op_name, op_functor) \ + REGISTER_USER_KERNEL(op_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferTmpBufferSize); -REGISTER_CUDA_CUMSUM_KERNEL(int32_t) -REGISTER_CUDA_CUMSUM_KERNEL(int64_t) -REGISTER_CUDA_CUMSUM_KERNEL(float) -REGISTER_CUDA_CUMSUM_KERNEL(double) -#undef REGISTER_CUDA_CUMSUM_KERNEL +#define REGISTER_CUMOP_KERNEL_WITH_DTYPE(op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(int32_t, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(int64_t, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(float, op_name, op_functor) \ + REGISTER_CUMOP_KERNEL(double, op_name, op_functor) -template -class GpuCumProdKernel final : public GpuCumKernel { - public: - GpuCumProdKernel() = default; - ~GpuCumProdKernel() = default; -}; +OF_PP_FOR_EACH_TUPLE(REGISTER_CUMOP_KERNEL_WITH_DTYPE, CUMOP_SEQ); -#define REGISTER_CUDA_CUMPROD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumprod").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); +#undef REGISTER_CUMOP_KERNEL +#undef REGISTER_CUMOP_KERNEL_WITH_DTYPE +#undef CUMOP_SEQ -REGISTER_CUDA_CUMPROD_KERNEL(int32_t) -REGISTER_CUDA_CUMPROD_KERNEL(int64_t) -REGISTER_CUDA_CUMPROD_KERNEL(float) -REGISTER_CUDA_CUMPROD_KERNEL(double) -#undef REGISTER_CUDA_CUMPROD_KERNEL #endif } // namespace oneflow diff --git a/oneflow/user/kernels/cum_forward_kernel.hip.cpp b/oneflow/user/kernels/cum_forward_kernel.hip.cpp deleted file mode 100644 index 1a96affbc2833e08be62ce3ede6f1adcb03db943..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/cum_forward_kernel.hip.cpp +++ /dev/null @@ -1,169 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ndarray/binary_func.h" - -namespace oneflow { -#ifdef WITH_ROCM -namespace { - -// total thread number: cs_up_space * cs_down_space -// in cs_down_space part, use cs_down_space threads -// to calculate as follows(m=cs_down_space-1, n=cs_space-1, '|' stands for dependency): -// dm0, ..., d10, d00 -// | | | -// dm1, ..., d11, d01 -// | | | -// dm2, ..., d12, d02 -// | | | -// ... ... ... -// | | | -// dmn, ..., d1n, d0n -template class BinaryFunc> -__global__ void CumsumForwardGpu(const T* in_ptr, T* out_ptr, int64_t cs_up_space, int64_t cs_space, - int64_t cs_down_space) { - CUDA_1D_KERNEL_LOOP(i, cs_up_space * cs_down_space) { - auto cs_up_space_id = i / cs_down_space; - auto cs_down_space_id = i - (i / cs_down_space) * cs_down_space; - - auto* in_ptr_base = in_ptr + cs_up_space_id * cs_space * cs_down_space + cs_down_space_id; - auto* out_ptr_base = out_ptr + cs_up_space_id * cs_space * cs_down_space + cs_down_space_id; - - // calculate cs_space data in one thread - for (auto j = 0; j < cs_space; j++) { - auto idx = j * cs_down_space; - out_ptr_base[idx] = in_ptr_base[idx]; - if (j != 0) { - out_ptr_base[idx] = - BinaryFunc::Invoke(out_ptr_base[idx], out_ptr_base[idx - cs_down_space]); - } - } - } -} -template class BinaryFunc> -__global__ void CumsumForwardGpuUpSpaceIs1(const T* in_ptr, T* out_ptr, int64_t cs_space, - int64_t cs_down_space) { - CUDA_1D_KERNEL_LOOP(i, cs_down_space) { - auto* in_ptr_base = in_ptr + i; - auto* out_ptr_base = out_ptr + i; - - // calculate cs_space data in one thread - for (auto j = 0; j < cs_space; j++) { - auto idx = j * cs_down_space; - out_ptr_base[idx] = in_ptr_base[idx]; - if (j != 0) { - out_ptr_base[idx] = - BinaryFunc::Invoke(out_ptr_base[idx], out_ptr_base[idx - cs_down_space]); - } - } - } -} -template class BinaryFunc> -__global__ void CumsumForwardGpuDownSpaceIs1(const T* in_ptr, T* out_ptr, int64_t cs_up_space, - int64_t cs_space) { - CUDA_1D_KERNEL_LOOP(i, cs_up_space) { - auto* in_ptr_base = in_ptr + i * cs_space; - auto* out_ptr_base = out_ptr + i * cs_space; - - // calculate cs_space data in one thread - for (auto j = 0; j < cs_space; j++) { - out_ptr_base[j] = in_ptr_base[j]; - if (j != 0) { out_ptr_base[j] = BinaryFunc::Invoke(out_ptr_base[j], out_ptr_base[j - 1]); } - } - } -} -} // namespace - -template class BinaryFunc> -class GpuCumKernel : public user_op::OpKernel { - public: - GpuCumKernel() = default; - ~GpuCumKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - // judge whether tensor has 0 size dimension first - const auto* in = ctx->Tensor4ArgNameAndIndex("x", 0); - auto elem_cnt = in->shape_view().elem_cnt(); - if (!elem_cnt) { return; } - - auto* out = ctx->Tensor4ArgNameAndIndex("y", 0); - auto dim = ctx->Attr("dim"); - const auto* in_ptr = in->dptr(); - auto* out_ptr = out->mut_dptr(); - - // data partition: up_space|space|down_space - auto up_space = elem_cnt / in->shape_view().Count(dim); - auto space = in->shape_view().At(dim); - auto down_space = in->shape_view().Count(dim + 1); - auto thread_num = up_space * down_space; - - if (up_space == 1) { - RUN_CUDA_KERNEL((CumsumForwardGpuUpSpaceIs1), ctx->stream(), thread_num, - in_ptr, out_ptr, space, down_space); - } else if (down_space == 1) { - RUN_CUDA_KERNEL((CumsumForwardGpuDownSpaceIs1), ctx->stream(), thread_num, - in_ptr, out_ptr, up_space, space); - } else { - RUN_CUDA_KERNEL((CumsumForwardGpu), ctx->stream(), thread_num, in_ptr, out_ptr, - up_space, space, down_space); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuCumSumKernel final : public GpuCumKernel { - public: - GpuCumSumKernel() = default; - ~GpuCumSumKernel() = default; -}; - -#define REGISTER_CUDA_CUMSUM_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumsum").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_CUMSUM_KERNEL(int32_t) -REGISTER_CUDA_CUMSUM_KERNEL(int64_t) -REGISTER_CUDA_CUMSUM_KERNEL(float) -REGISTER_CUDA_CUMSUM_KERNEL(double) -#undef REGISTER_CUDA_CUMSUM_KERNEL - -template -class GpuCumProdKernel final : public GpuCumKernel { - public: - GpuCumProdKernel() = default; - ~GpuCumProdKernel() = default; -}; - -#define REGISTER_CUDA_CUMPROD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cumprod").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_CUMPROD_KERNEL(int32_t) -REGISTER_CUDA_CUMPROD_KERNEL(int64_t) -REGISTER_CUDA_CUMPROD_KERNEL(float) -REGISTER_CUDA_CUMPROD_KERNEL(double) -#undef REGISTER_CUDA_CUMPROD_KERNEL -#endif -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/cutlass_conv_tuner.cpp b/oneflow/user/kernels/cutlass_conv_tuner.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4f5b7530581d607f7a58117c86ceba1147475e54 --- /dev/null +++ b/oneflow/user/kernels/cutlass_conv_tuner.cpp @@ -0,0 +1,328 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifdef WITH_CUTLASS + +#include "oneflow/user/kernels/cutlass_conv_tuner.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" +#include +#include +#include + +namespace oneflow { + +namespace { + +bool IsWeakerAlginOperation(const cutlass::library::Operation* lhs, + const cutlass::library::Operation* rhs) { + const char* lhs_name = lhs->description().name; + const char* rhs_name = rhs->description().name; + const size_t len = std::strlen(lhs_name); + const size_t suffix_len = std::strlen("align8"); + if (std::strlen(rhs_name) != len) { return false; } + if (len < suffix_len) { return false; } + const size_t prefix_len = len - suffix_len; + if (std::strncmp(lhs_name, rhs_name, prefix_len) != 0) { return false; } + const auto& HasLegalSuffix = [&](const char* str) { + if (std::strncmp(str + prefix_len, "align", std::strlen("align")) != 0) { return false; } + const char align = str[len - 1]; + return align == '8' || align == '4' || align == '2' || align == '1'; + }; + if ((!HasLegalSuffix(lhs_name)) || (!HasLegalSuffix(rhs_name))) { return false; } + return lhs_name[len - 1] < rhs_name[len - 1]; +} + +struct Conv2dOperationCacheKey { + cutlass::library::ConvFunctionalKey functional_key; + cutlass::library::Conv2dConfiguration configuraion; + size_t alignment; + Conv2dOperationCacheKey(cutlass::library::ConvFunctionalKey functional_key, + cutlass::library::Conv2dConfiguration configuraion, + cutlass::library::ConvArguments arguments) + : functional_key(functional_key), configuraion(configuraion) { + const auto IsStrideAligned = [&](const std::vector& stride, size_t n) { + return std::all_of(stride.cbegin(), stride.cend(), + [&](const int64_t& s) { return s % n == 0; }); + }; + const auto IsAligned = [&](size_t n) { + return reinterpret_cast(arguments.A) % n == 0 + && reinterpret_cast(arguments.B) % n == 0 + && reinterpret_cast(arguments.C) % n == 0 + && reinterpret_cast(arguments.D) % n == 0 + && IsStrideAligned(configuraion.stride_a, n) + && IsStrideAligned(configuraion.stride_b, n) + && IsStrideAligned(configuraion.stride_c, n); + }; + if (IsAligned(8)) { + alignment = 8; + } else if (IsAligned(4)) { + alignment = 4; + } else if (IsAligned(2)) { + alignment = 2; + } else { + alignment = 1; + } + } +}; + +struct Conv2dProblemSizeHasher { + size_t operator()(const cutlass::conv::Conv2dProblemSize& problem_size) const { + size_t hash = 0; + hash = HashCombine(hash, std::hash()(problem_size.N)); + hash = HashCombine(hash, std::hash()(problem_size.H)); + hash = HashCombine(hash, std::hash()(problem_size.W)); + hash = HashCombine(hash, std::hash()(problem_size.C)); + hash = HashCombine(hash, std::hash()(problem_size.P)); + hash = HashCombine(hash, std::hash()(problem_size.Q)); + hash = HashCombine(hash, std::hash()(problem_size.K)); + hash = HashCombine(hash, std::hash()(problem_size.R)); + hash = HashCombine(hash, std::hash()(problem_size.S)); + hash = HashCombine(hash, std::hash()(problem_size.pad_h)); + hash = HashCombine(hash, std::hash()(problem_size.pad_w)); + hash = HashCombine(hash, std::hash()(problem_size.stride_h)); + hash = HashCombine(hash, std::hash()(problem_size.stride_w)); + hash = HashCombine(hash, std::hash()(problem_size.dilation_h)); + hash = HashCombine(hash, std::hash()(problem_size.dilation_w)); + hash = HashCombine(hash, std::hash()(static_cast(problem_size.mode))); + hash = HashCombine(hash, std::hash()(problem_size.split_k_slices)); + hash = HashCombine(hash, std::hash()(problem_size.groups)); + return hash; + } +}; + +struct Conv2dConfigurationHasher { + size_t operator()(const cutlass::library::Conv2dConfiguration& configuraion) const { + size_t hash = std::hash()(static_cast(configuraion.split_k_mode)); + hash = HashCombine(hash, Conv2dProblemSizeHasher()(configuraion.problem_size)); + for (const int64_t v : configuraion.stride_a) { + hash = HashCombine(hash, std::hash()(v)); + } + for (const int64_t v : configuraion.stride_b) { + hash = HashCombine(hash, std::hash()(v)); + } + for (const int64_t v : configuraion.stride_c) { + hash = HashCombine(hash, std::hash()(v)); + } + return hash; + } +}; + +struct Conv2dOperationCacheKeyHasher { + size_t operator()(const Conv2dOperationCacheKey& key) const { + size_t hash = cutlass::library::ConvFunctionalKeyHasher()(key.functional_key); + hash = HashCombine(hash, Conv2dConfigurationHasher()(key.configuraion)); + hash = HashCombine(hash, std::hash()(key.alignment)); + return hash; + } +}; + +inline bool operator==(const cutlass::library::Conv2dConfiguration& lhs, + const cutlass::library::Conv2dConfiguration& rhs) { + return lhs.split_k_mode == rhs.split_k_mode && lhs.problem_size == rhs.problem_size + && lhs.stride_a == rhs.stride_a && lhs.stride_b == rhs.stride_b + && lhs.stride_c == rhs.stride_c; +} + +inline bool operator==(const Conv2dOperationCacheKey& lhs, const Conv2dOperationCacheKey& rhs) { + return lhs.functional_key == rhs.functional_key && lhs.configuraion == rhs.configuraion + && lhs.alignment == rhs.alignment; +} + +}; // namespace + +using CacheMap = std::unordered_map; +struct CutlassConvTuner::Impl { + std::mutex mutex; + std::unordered_map cache; + + const cutlass::library::Operation* FindConv2dOperation( + ep::CudaStream* stream, cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, size_t workspace_size); + + const cutlass::library::Operation* GetConv2dOperation( + const std::string& name, ep::CudaStream* stream, + cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, size_t workspace_size); +}; + +const cutlass::library::Operation* CutlassConvTuner::Impl::FindConv2dOperation( + ep::CudaStream* stream, cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, size_t workspace_size) { + int dev = 0; + OF_CUDA_CHECK(cudaGetDevice(&dev)); + Conv2dOperationCacheKey cache_key(functional_key, configuraion, arguments); + { + std::lock_guard lock(mutex); + const auto& device_cache = cache[dev]; + const auto& it = device_cache.find(cache_key); + if (it != device_cache.end()) { return it->second; } + } + + constexpr int turing_warmup_iters = 2; + constexpr int turing_iters = 5; + cudaEvent_t start{}; + cudaEvent_t end{}; + OF_CUDA_CHECK(cudaEventCreate(&start)); + OF_CUDA_CHECK(cudaEventCreate(&end)); + const cutlass::library::Operation* fastest_operation = nullptr; + float fastest_time = 0; + const auto& operations_map_it = + cutlass::library::Singleton::get().operation_table.conv2d_operations.find(functional_key); + CHECK(operations_map_it + != cutlass::library::Singleton::get().operation_table.conv2d_operations.cend()); + const cutlass::library::ConvOperationVectorMap& operations_map = operations_map_it->second; + + for (const auto& pair : operations_map) { + std::map> operations; + for (auto operation : pair.second) { + operations.emplace(operation->description().name, operation); + } + const cutlass::library::Operation* prev_operation = nullptr; + for (const auto& name_operation : operations) { + const cutlass::library::Operation* operation = name_operation.second; + if (prev_operation != nullptr && IsWeakerAlginOperation(operation, prev_operation)) { + continue; + } + if (operation->description().tile_description.minimum_compute_capability * 10 + > stream->cuda_arch() + || operation->description().tile_description.maximum_compute_capability * 10 + < stream->cuda_arch()) { + continue; + } + auto status = operation->can_implement(&configuraion, &arguments); + if (status != cutlass::Status::kSuccess) { continue; } + const size_t host_workspace_size = operation->get_host_workspace_size(&configuraion); + const size_t device_workspace_size = operation->get_device_workspace_size(&configuraion); + if (device_workspace_size > workspace_size) { continue; } + std::vector host_workspace(host_workspace_size, 0); + if (operation->initialize(&configuraion, host_workspace.data(), workspace, + stream->cuda_stream()) + != cutlass::Status::kSuccess) { + continue; + } + + const auto Run = [&]() { + auto init_status = operation->initialize(&configuraion, host_workspace.data(), workspace, + stream->cuda_stream()); + CHECK(init_status == cutlass::Status::kSuccess); + auto run_status = + operation->run(&arguments, host_workspace.data(), workspace, stream->cuda_stream()); + CHECK(run_status == cutlass::Status::kSuccess); + }; + OF_CUDA_CHECK(cudaStreamSynchronize(stream->cuda_stream())); + for (int i = 0; i < turing_warmup_iters; ++i) { Run(); } + OF_CUDA_CHECK(cudaEventRecord(start, stream->cuda_stream())); + for (int i = 0; i < turing_iters; ++i) { Run(); } + OF_CUDA_CHECK(cudaEventRecord(end, stream->cuda_stream())); + OF_CUDA_CHECK(cudaEventSynchronize(end)); + float time = 0; + OF_CUDA_CHECK(cudaEventElapsedTime(&time, start, end)); + VLOG(3) << operation->description().name << " " << time; + prev_operation = operation; + if (fastest_operation == nullptr || time < fastest_time) { + fastest_operation = operation; + fastest_time = time; + } + } + } + OF_CUDA_CHECK(cudaEventDestroy(start)); + OF_CUDA_CHECK(cudaEventDestroy(end)); + if (fastest_operation != nullptr) { + VLOG(3) << "Fastest: " << fastest_operation->description().name << " " << fastest_time; + { + std::lock_guard lock(mutex); + cache[dev][cache_key] = fastest_operation; + } + } + return fastest_operation; +} + +const cutlass::library::Operation* CutlassConvTuner::Impl::GetConv2dOperation( + const std::string& name, ep::CudaStream* stream, + cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, size_t workspace_size) { + int dev = 0; + OF_CUDA_CHECK(cudaGetDevice(&dev)); + const auto& operations_map_it = + cutlass::library::Singleton::get().operation_table.conv2d_operations.find(functional_key); + if (operations_map_it + == cutlass::library::Singleton::get().operation_table.conv2d_operations.cend()) { + return nullptr; + } + const cutlass::library::ConvOperationVectorMap& operations_map = operations_map_it->second; + for (const auto& pair : operations_map) { + for (auto operation : pair.second) { + if (name != operation->description().name) { continue; } + if (operation->description().tile_description.minimum_compute_capability * 10 + > stream->cuda_arch() + || operation->description().tile_description.maximum_compute_capability * 10 + < stream->cuda_arch()) { + continue; + } + auto status = operation->can_implement(&configuraion, &arguments); + if (status != cutlass::Status::kSuccess) { continue; } + const size_t host_workspace_size = operation->get_host_workspace_size(&configuraion); + const size_t device_workspace_size = operation->get_device_workspace_size(&configuraion); + if (device_workspace_size > workspace_size) { continue; } + std::vector host_workspace(host_workspace_size, 0); + if (operation->initialize(&configuraion, host_workspace.data(), workspace, + stream->cuda_stream()) + != cutlass::Status::kSuccess) { + continue; + } + return operation; + } + } + return nullptr; +} + +CutlassConvTuner::CutlassConvTuner() { impl_.reset(new Impl()); } + +const CutlassConvTuner& CutlassConvTuner::Get() { + static CutlassConvTuner instance; + return instance; +} + +const cutlass::library::Operation* CutlassConvTuner::FindConv2dOperation( + ep::CudaStream* stream, cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, + size_t workspace_size) const { + return impl_->FindConv2dOperation(stream, functional_key, configuraion, arguments, workspace, + workspace_size); +} + +const cutlass::library::Operation* CutlassConvTuner::GetConv2dOperation( + const std::string& name, ep::CudaStream* stream, + cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, + size_t workspace_size) const { + return impl_->GetConv2dOperation(name, stream, functional_key, configuraion, arguments, workspace, + workspace_size); +} + +} // namespace oneflow + +#endif // WITH_CUTLASS diff --git a/oneflow/user/kernels/cutlass_conv_tuner.h b/oneflow/user/kernels/cutlass_conv_tuner.h new file mode 100644 index 0000000000000000000000000000000000000000..7ee665ad23e421a4361b181ec128f30bc08d943d --- /dev/null +++ b/oneflow/user/kernels/cutlass_conv_tuner.h @@ -0,0 +1,61 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_USER_KERNELS_CUTLASS_CONV_TUNER_H_ +#define ONEFLOW_USER_KERNELS_CUTLASS_CONV_TUNER_H_ + +#ifdef WITH_CUTLASS + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" +#include +#include +#include + +namespace oneflow { + +class CutlassConvTuner { + public: + OF_DISALLOW_COPY_AND_MOVE(CutlassConvTuner); + ~CutlassConvTuner() = default; + + const cutlass::library::Operation* FindConv2dOperation( + ep::CudaStream* stream, cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, + size_t workspace_size) const; + + const cutlass::library::Operation* GetConv2dOperation( + const std::string& name, ep::CudaStream* stream, + cutlass::library::ConvFunctionalKey functional_key, + const cutlass::library::Conv2dConfiguration& configuraion, + const cutlass::library::ConvArguments& arguments, void* workspace, + size_t workspace_size) const; + + static const CutlassConvTuner& Get(); + + private: + CutlassConvTuner(); + struct Impl; + std::unique_ptr impl_; +}; + +} // namespace oneflow + +#endif // WITH_CUTLASS +#endif // ONEFLOW_USER_KERNELS_CUTLASS_CONV_TUNER_H_ diff --git a/oneflow/user/kernels/data_shuffle_kernel.cu b/oneflow/user/kernels/data_shuffle_kernel.cu index 16210d9a4382dd2ace51b4369c55fa9aebe6f025..8043b3eaf4da838393ab803dd5e421ff606fe36c 100644 --- a/oneflow/user/kernels/data_shuffle_kernel.cu +++ b/oneflow/user/kernels/data_shuffle_kernel.cu @@ -25,160 +25,13 @@ limitations under the License. #include "oneflow/core/cuda/elementwise.cuh" #include "oneflow/core/ep/include/primitive/copy_nd.h" #include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/embedding/embedding_manager.h" +#include "oneflow/user/kernels/one_embedding_data_shuffle.cuh" namespace oneflow { namespace { -template -struct TableEntry { - K key; - uint32_t value; -}; - -template -__global__ void HashTableUniqueAndPartitionPairs(const uint32_t table_capacity, - const uint32_t num_keys, int32_t num_partition, - IDX* unique_counts, TableEntry* table, - const K* keys, const V* values, - K* partitioned_unique_keys, - V* partitioned_unique_values, IDX* reverse_index, - bool need_process_values) { - CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_keys) { - IDX r_index_plus_one = 0; - const K key = keys[i]; - size_t key_hash = HASH()(key); - uint32_t partition_id = key_hash % num_partition; - IDX* unique_count = unique_counts + partition_id; - K* unique_keys = partitioned_unique_keys + partition_id * num_keys; - uint32_t pos = key_hash % table_capacity; - const K key_hi = (key | 0x1); - const K key_lo = (key & 0x1); - uint32_t counter = 0; - while (r_index_plus_one == 0) { - bool prob_next = false; - K* key_ptr = &table[pos].key; - volatile uint32_t* table_value_ptr = &table[pos].value; - const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi); - if (old_key == 0) { - IDX unique_pos = cuda::atomic::Add(unique_count, 1); - r_index_plus_one = unique_pos + 1; - unique_keys[unique_pos] = key; - if (need_process_values) { - partitioned_unique_values[partition_id * num_keys + unique_pos] = values[i]; - } - *table_value_ptr = ((r_index_plus_one << 1U) | key_lo); - } else if (old_key == key_hi) { - const uint32_t value = *table_value_ptr; - if (value == 0) { - // do nothing - } else if ((value & 0x1) == key_lo) { - r_index_plus_one = (value >> 1U); - } else { - prob_next = true; - } - } else { - prob_next = true; - } - if (prob_next) { - pos += 1; - counter += 1; - if (pos >= table_capacity) { pos -= table_capacity; } - if (counter >= table_capacity) { __trap(); } - } - } - reverse_index[i] = partition_id * num_keys + r_index_plus_one - 1; - } -} - -template -__global__ void GenerateTableIds(int32_t elem_cnt, int32_t num_tables, U* table_ids) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { table_ids[i] = i % num_tables; } -} - -template -void UniqueAndPartition(cudaStream_t cuda_stream, int64_t num_ids, size_t capacity, - int64_t num_partition, const K* ids, const V* table_ids, - IDX* num_partitioned_unique_ids_ptr, K* partitioned_unique_ids, - V* partitioned_unique_table_ids, IDX* inverse_unique_partition_indices, - void* workspace_ptr, size_t workspace_bytes, bool need_process_table_ids) { - size_t table_capacity_bytes = capacity * sizeof(TableEntry); - CHECK_GE(workspace_bytes, table_capacity_bytes); - OF_CUDA_CHECK(cudaMemsetAsync(workspace_ptr, 0, table_capacity_bytes, cuda_stream)); - OF_CUDA_CHECK( - cudaMemsetAsync(num_partitioned_unique_ids_ptr, 0, num_partition * sizeof(IDX), cuda_stream)); - HashTableUniqueAndPartitionPairs - <<>>( - capacity, num_ids, num_partition, num_partitioned_unique_ids_ptr, - reinterpret_cast*>(workspace_ptr), ids, table_ids, partitioned_unique_ids, - partitioned_unique_table_ids, inverse_unique_partition_indices, need_process_table_ids); -} - -template -void ShuffleData(cudaStream_t cuda_stream, ncclComm_t comm, DataType data_type, - const std::vector& send_offsets, - const std::vector& send_elem_cnt, const T* send_data, - const std::vector& recv_offsets, - const std::vector& recv_elem_cnt, T* recv_data) { - ncclDataType_t nccl_data_type = GetNcclDataType(data_type); - const int64_t parallel_num = send_offsets.size(); - OF_NCCL_CHECK(ncclGroupStart()); - for (int64_t i = 0; i < parallel_num; ++i) { - OF_NCCL_CHECK(ncclSend(send_data + send_offsets.at(i), send_elem_cnt.at(i), nccl_data_type, i, - comm, cuda_stream)); - OF_NCCL_CHECK(ncclRecv(recv_data + recv_offsets.at(i), recv_elem_cnt.at(i), nccl_data_type, i, - comm, cuda_stream)); - } - OF_NCCL_CHECK(ncclGroupEnd()); -} - -template -void MakeShuffleParams(const IDX* host_num_unique_matrix, const int64_t num_ids, - const int64_t row_size, int64_t parallel_id, int64_t parallel_num, - std::vector* scatter_offset_vec, - std::vector* scatter_elem_cnt_vec, - std::vector* gather_offset_vec, - std::vector* gather_elem_cnt_vec) { - scatter_offset_vec->resize(parallel_num); - scatter_elem_cnt_vec->resize(parallel_num); - gather_offset_vec->resize(parallel_num); - gather_elem_cnt_vec->resize(parallel_num); - int64_t gather_offset = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t scatter_elem_cnt = - host_num_unique_matrix[parallel_id * parallel_num + i] * row_size; - const int64_t gather_elem_cnt = - host_num_unique_matrix[i * parallel_num + parallel_id] * row_size; - scatter_offset_vec->at(i) = i * num_ids * row_size; - scatter_elem_cnt_vec->at(i) = scatter_elem_cnt; - gather_offset_vec->at(i) = gather_offset; - gather_elem_cnt_vec->at(i) = gather_elem_cnt; - gather_offset += gather_elem_cnt; - } -} - -template -void ShuffleIdsAndTableIds(cudaStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, DataType ids_data_type, - DataType table_ids_data_type, IDX* host_num_unique_matrix, - K* partitioned_unique_ids, U* partitioned_unique_table_ids, - K* received_ids, U* received_table_ids, int64_t* received_elem_cnt, - bool need_process_table_ids) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, 1, parallel_id, parallel_num, &send_offsets, - &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, ids_data_type, send_offsets, send_elem_cnt, partitioned_unique_ids, - recv_offsets, recv_elem_cnt, received_ids); - *received_elem_cnt = recv_offsets.at(parallel_num - 1) + recv_elem_cnt.at(parallel_num - 1); - if (need_process_table_ids) { - ShuffleData(cuda_stream, comm, table_ids_data_type, send_offsets, send_elem_cnt, - partitioned_unique_table_ids, recv_offsets, recv_elem_cnt, received_table_ids); - } -} - enum class IdShuffleBufferType { kNumPartitionedUnique = 0, kPartitionedUniqueIds, @@ -211,7 +64,8 @@ class IdShuffleTmpBufferManager final { AllocBuffer(IdShuffleBufferType::kPartitionedUniqueTableIds, partitioned_table_ids_bytes); AllocBuffer(IdShuffleBufferType::kReceivedTableIds, partitioned_table_ids_bytes); const size_t hash_table_capacity = parallel_num * num_ids; - AllocBuffer(IdShuffleBufferType::kWorkspace, hash_table_capacity * sizeof(TableEntry)); + AllocBuffer(IdShuffleBufferType::kWorkspace, + hash_table_capacity * sizeof(data_shuffle::TableEntry)); } template @@ -247,20 +101,35 @@ class DataShuffleKernelState final : public user_op::OpKernelState { : device_index_(-1), stream_name_(EagerNcclCommMgr::kDefaultStreamName), parallel_desc_(ctx->parallel_desc()) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); if (ctx->op_conf().has_stream_name_hint()) { stream_name_ = ctx->op_conf().stream_name_hint(); } +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_keys_), sizeof(IDX))); + OF_CUDA_CHECK(hipMallocHost( + reinterpret_cast(&host_num_unique_matrix_), + parallel_desc_.parallel_num() * parallel_desc_.parallel_num() * sizeof(IDX))); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); OF_CUDA_CHECK(cudaMallocHost( &host_num_unique_matrix_, parallel_desc_.parallel_num() * parallel_desc_.parallel_num() * sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); } ~DataShuffleKernelState() { CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(cudaFreeHost(host_num_unique_matrix_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_unique_matrix_)); } ncclComm_t comm() { return GetOrCreate().comm; } IDX* HostNumUniqueMatrix() { return host_num_unique_matrix_; } + IDX* HostNumKeys() { return host_num_keys_; } + + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } private: struct Comm { @@ -292,6 +161,8 @@ class DataShuffleKernelState final : public user_op::OpKernelState { ParallelDesc parallel_desc_; std::unique_ptr comm_; IDX* host_num_unique_matrix_; + IDX* host_num_keys_; + embedding::EmbeddingState* embedding_state_; }; } // namespace @@ -299,7 +170,7 @@ class DataShuffleKernelState final : public user_op::OpKernelState { template class IdShuffleKernel final : public user_op::OpKernel { public: - IdShuffleKernel() = default; + IdShuffleKernel() : current_iter_(0){}; ~IdShuffleKernel() override = default; std::shared_ptr CreateOpKernelState( @@ -325,74 +196,74 @@ class IdShuffleKernel final : public user_op::OpKernel { ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int32_t num_tables = ctx->Attr("num_tables"); + const int64_t padding_idx = ctx->Attr("padding_idx"); + const bool has_padding_idx = ctx->Attr("has_padding_idx"); const bool has_table_ids = ctx->has_input("table_ids", 0); const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); const bool need_process_table_ids = (has_table_ids || num_tables > 1); const int64_t num_ids = ids->shape_view().elem_cnt(); const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); IdShuffleTmpBufferManager buffer_manager( tmp_buffer->mut_dptr(), num_ids, parallel_num, need_gen_table_ids, need_process_table_ids); CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize()); - const U* table_ids_ptr; + ncclComm_t comm = kernel_state->comm(); + IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); + IDX* host_num_keys = kernel_state->HostNumKeys(); + data_shuffle::IdShuffleDataPtrs data_ptrs; + data_ptrs.ids_ptr = reinterpret_cast(ids->dptr()); if (has_table_ids) { const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); - table_ids_ptr = reinterpret_cast(table_ids->dptr()); + data_ptrs.table_ids_ptr = reinterpret_cast(table_ids->dptr()); } else if (need_gen_table_ids) { - GenerateTableIds<<>>( + data_shuffle::GenerateTableIds<<>>( num_ids, num_tables, buffer_manager.template Ptr(IdShuffleBufferType::kTableIds)); - table_ids_ptr = buffer_manager.template Ptr(IdShuffleBufferType::kTableIds); + data_ptrs.table_ids_ptr = buffer_manager.template Ptr(IdShuffleBufferType::kTableIds); } else { - table_ids_ptr = nullptr; + data_ptrs.table_ids_ptr = nullptr; } - IDX* num_partitioned_unique = + data_ptrs.num_partitioned_unique = buffer_manager.template Ptr(IdShuffleBufferType::kNumPartitionedUnique); - K* partitioned_unique_ids = + data_ptrs.partitioned_unique_ids = buffer_manager.template Ptr(IdShuffleBufferType::kPartitionedUniqueIds); - U* partitioned_unique_table_ids = + data_ptrs.partitioned_unique_table_ids = buffer_manager.template Ptr(IdShuffleBufferType::kPartitionedUniqueTableIds); - IDX* num_unique_matrix_ptr = reinterpret_cast(num_unique_matrix->mut_dptr()); - size_t hash_table_capacity = parallel_num * num_ids; - void* workspace_ptr = buffer_manager.Ptr(IdShuffleBufferType::kWorkspace); - size_t workspace_size = buffer_manager.Size(IdShuffleBufferType::kWorkspace); - UniqueAndPartition( - cuda_stream, num_ids, hash_table_capacity, parallel_num, - reinterpret_cast(ids->dptr()), table_ids_ptr, num_partitioned_unique, - partitioned_unique_ids, partitioned_unique_table_ids, - reinterpret_cast(inverse_unique_partition_indices->mut_dptr()), workspace_ptr, - workspace_size, need_process_table_ids); - ncclComm_t comm = kernel_state->comm(); - OF_NCCL_CHECK(ncclAllGather(num_partitioned_unique, num_unique_matrix_ptr, parallel_num, - GetNcclDataType(num_unique_matrix->data_type()), comm, - cuda_stream)); - IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_unique_matrix, num_unique_matrix_ptr, - parallel_num * parallel_num * sizeof(IDX), cudaMemcpyDefault, - cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); - - K* received_ids = buffer_manager.template Ptr(IdShuffleBufferType::kReceivedIds); - U* received_table_ids = buffer_manager.template Ptr(IdShuffleBufferType::kReceivedTableIds); - int64_t received_elem_cnt = 0; - ShuffleIdsAndTableIds(cuda_stream, comm, parallel_id, parallel_num, num_ids, ids->data_type(), - cur_rank_unique_table_ids->data_type(), host_num_unique_matrix, - partitioned_unique_ids, partitioned_unique_table_ids, received_ids, - received_table_ids, &received_elem_cnt, need_process_table_ids); - UniqueAndPartition( - cuda_stream, received_elem_cnt, hash_table_capacity, 1, received_ids, received_table_ids, - reinterpret_cast(cur_rank_num_unique->mut_dptr()), - reinterpret_cast(cur_rank_unique_ids->mut_dptr()), - reinterpret_cast(cur_rank_unique_table_ids->mut_dptr()), - reinterpret_cast(cur_rank_inverse_indices->mut_dptr()), workspace_ptr, workspace_size, - need_process_table_ids); - if (!need_process_table_ids) { - OF_CUDA_CHECK(cudaMemsetAsync(cur_rank_unique_table_ids->mut_dptr(), 0, - received_elem_cnt * sizeof(U), cuda_stream)); - } + data_ptrs.workspace_ptr = buffer_manager.Ptr(IdShuffleBufferType::kWorkspace); + data_ptrs.workspace_size = buffer_manager.Size(IdShuffleBufferType::kWorkspace); + data_ptrs.received_ids = buffer_manager.template Ptr(IdShuffleBufferType::kReceivedIds); + data_ptrs.received_table_ids = + buffer_manager.template Ptr(IdShuffleBufferType::kReceivedTableIds); + data_ptrs.num_unique_matrix_ptr = reinterpret_cast(num_unique_matrix->mut_dptr()); + data_ptrs.inverse_unique_partition_indices_ptr = + reinterpret_cast(inverse_unique_partition_indices->mut_dptr()); + data_ptrs.cur_rank_num_unique_ptr = reinterpret_cast(cur_rank_num_unique->mut_dptr()); + data_ptrs.cur_rank_unique_ids_ptr = reinterpret_cast(cur_rank_unique_ids->mut_dptr()); + data_ptrs.cur_rank_unique_table_ids_ptr = + reinterpret_cast(cur_rank_unique_table_ids->mut_dptr()); + data_ptrs.cur_rank_inverse_indices_ptr = + reinterpret_cast(cur_rank_inverse_indices->mut_dptr()); + + data_shuffle::IdShuffle(ctx->stream(), comm, data_ptrs, num_ids, parallel_id, parallel_num, + num_unique_matrix->data_type(), ids->data_type(), + cur_rank_unique_table_ids->data_type(), need_process_table_ids, + has_padding_idx, padding_idx, host_num_unique_matrix, host_num_keys); + + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::vector num_unique_matrix_vec(parallel_num * parallel_num); + std::memcpy(num_unique_matrix_vec.data(), host_num_unique_matrix, + parallel_num * parallel_num * sizeof(IDX)); + CHECK_EQ(sizeof(IDX), sizeof(uint32_t)) << "assume sizeof(IDX) equals to sizeof(uint32_t)"; + embedding_state->SetIdNumUniqueMatrix(num_unique_matrix_vec, current_iter_); + + uint32_t final_num_unique = *host_num_keys; + embedding_state->SetIdFinalNumUnique(final_num_unique, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define ID_DATA_TYPE_SEQ \ @@ -413,72 +284,35 @@ class IdShuffleKernel final : public user_op::OpKernel { OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) -#define REGISTER_CUDA_ID_SHUFFLE_KERNEL(k_dtype_pair, table_id_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("id_shuffle") \ - .SetCreateFn< \ - IdShuffleKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ - && (user_op::HobDataType("cur_rank_unique_table_ids", 0) \ - == OF_PP_PAIR_SECOND(table_id_dtype_pair)) \ - && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ - const bool has_table_ids = ctx->has_input("table_ids", 0); \ - const int32_t num_tables = ctx->Attr("num_tables"); \ - const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); \ - const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ - IdShuffleTmpBufferManager \ - buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_desc().parallel_num(), \ - need_gen_table_ids, need_process_table_ids); \ - return buffer_manager.TotalBufferSize(); \ +#define REGISTER_CUDA_ID_SHUFFLE_KERNEL(k_dtype_pair, table_id_dtype_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("id_shuffle") \ + .SetCreateFn< \ + IdShuffleKernel>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("cur_rank_unique_table_ids", 0) \ + == OF_PP_PAIR_SECOND(table_id_dtype_pair)) \ + && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ + && (!ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ID_SHUFFLE_USE_P2P", false))) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ + const bool has_table_ids = ctx->has_input("table_ids", 0); \ + const int32_t num_tables = ctx->Attr("num_tables"); \ + const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); \ + const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ + IdShuffleTmpBufferManager \ + buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_desc().parallel_num(), \ + need_gen_table_ids, need_process_table_ids); \ + return buffer_manager.TotalBufferSize(); \ }); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ID_SHUFFLE_KERNEL, ID_DATA_TYPE_SEQ, TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) -template -void ShuffleEmbeddings(cudaStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - T* reverse_unique_cur_rank_embeddings, T* received_embeddings) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); -} - -// Quantized Version. -template -void ShuffleEmbeddings(cudaStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - int8_t* reverse_unique_cur_rank_embeddings, int8_t* received_embeddings, - T* reverse_cur_rank_quantize_factor, T* recv_quantize_factor) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - // shuffle quantized_embedding - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, - reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); - // shuffle quantize_factor - MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, - parallel_num, &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - reverse_cur_rank_quantize_factor, recv_offsets, recv_elem_cnt, recv_quantize_factor); -} - __device__ float RoundHalfAwayFromZero(const float x) { float abs_val = abs(x); float floor_val = floor(abs_val + static_cast(0.5)); @@ -486,37 +320,45 @@ __device__ float RoundHalfAwayFromZero(const float x) { } // warp reduce version. +#ifdef WITH_ROCM +constexpr int32_t kWarpSize = 64; +#else constexpr int32_t kWarpSize = 32; +#endif constexpr int32_t kMaxColSize = 1024; template __inline__ __device__ T WarpMaxAllReduce(T val) { for (int32_t lane_mask = thread_group_width / 2; lane_mask > 0; lane_mask /= 2) { +#ifdef WITH_ROCM + val = max(val, __shfl_xor(val, lane_mask, thread_group_width)); +#else val = max(val, __shfl_xor_sync(0xffffffff, val, lane_mask, thread_group_width)); +#endif } return val; } -inline cudaError_t GetWarpImplNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, +inline GPU(Error_t) GetWarpImplNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, int* num_blocks) { int dev; { - cudaError_t err = cudaGetDevice(&dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } } int sm_count; { - cudaError_t err = cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } } int tpm; { - cudaError_t err = cudaDeviceGetAttribute(&tpm, cudaDevAttrMaxThreadsPerMultiProcessor, dev); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GPU(DeviceGetAttribute)(&tpm, GPUMaxThreadsPerMultiProcessor, dev); + if (err != GPU(Success)) { return err; } } *num_blocks = std::max(1, std::min(max_blocks, sm_count * tpm / block_size * waves)); - return cudaSuccess; + return GPU(Success); } template -inline cudaError_t LaunchQuantizeWarpImpl(cudaStream_t stream, const T* src, int8_t* dst, +inline GPU(Error_t) LaunchQuantizeWarpImpl(GPU(Stream_t) stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, const int64_t cols) { constexpr int block_size = 128; @@ -605,18 +447,18 @@ inline cudaError_t LaunchQuantizeWarpImpl(cudaStream_t stream, const T* src, int (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; int grid_dim_x = 0; - cudaError_t err = GetWarpImplNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = GetWarpImplNumBlocks(block_size, num_blocks, waves, &grid_dim_x); + if (err != GPU(Success)) { return err; } QuantizeWarpImplKernel <<>>(src, dst, quantize_factor, rows, cols); - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template -inline cudaError_t DispatchQuantizeWarpImplPadding(cudaStream_t stream, const T* src, int8_t* dst, +inline GPU(Error_t) DispatchQuantizeWarpImplPadding(GPU(Stream_t) stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, const int64_t cols) { if (cols == cols_per_thread * thread_group_width) { @@ -631,10 +473,10 @@ inline cudaError_t DispatchQuantizeWarpImplPadding(cudaStream_t stream, const T* } template -typename std::enable_if::type DispatchQuantizeWarpImplCols( - cudaStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, +typename std::enable_if::type DispatchQuantizeWarpImplCols( + GPU(Stream_t) stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -692,15 +534,15 @@ typename std::enable_if::type DispatchQuantizeWarpI DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template -typename std::enable_if::type DispatchQuantizeWarpImplCols( - cudaStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, +typename std::enable_if::type DispatchQuantizeWarpImplCols( + GPU(Stream_t) stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, const int64_t cols) { - if (cols <= 0) { return cudaErrorInvalidValue; } + if (cols <= 0) { return GPU(ErrorInvalidValue); } #define DEFINE_ONE_ELIF(thread_group_width) \ else if (cols <= (thread_group_width)*pack_size) { \ if (rows % 2 == 0) { \ @@ -742,13 +584,13 @@ typename std::enable_if::type DispatchQuantizeWarpI DEFINE_ONE_ELIF(32) #undef DEFINE_ONE_ELIF else { - return cudaErrorInvalidValue; + return GPU(ErrorInvalidValue); } } template struct DispatchQuantizeWarpImplPackSize { - cudaError_t operator()(cudaStream_t stream, const T* src, int8_t* dst, T* quantize_factor, + GPU(Error_t) operator()(GPU(Stream_t) stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, const int64_t cols) { if (cols % 2 == 0) { return DispatchQuantizeWarpImplCols(stream, src, dst, quantize_factor, @@ -791,44 +633,44 @@ __global__ void DequantizeKernel(const int8_t* x, T* quantize_factor, T* out, ID } template -cudaError_t DispatchDequantizeKernelPackSize(cudaStream_t stream, const int8_t* src, +GPU(Error_t) DispatchDequantizeKernelPackSize(GPU(Stream_t) stream, const int8_t* src, T* quantize_factor, T* dst, const int64_t col_size, const int64_t elem_cnt) { const int64_t pack_num = elem_cnt / pack_size; int grid_size = 0; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (err != cudaSuccess) { return err; } + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + if (err != GPU(Success)) { return err; } DequantizeKernel <<>>(src, quantize_factor, dst, col_size, elem_cnt); - return cudaSuccess; + return GPU(Success); } template -inline cudaError_t LaunchDequantizeKernel(cudaStream_t stream, const int8_t* src, +inline GPU(Error_t) LaunchDequantizeKernel(GPU(Stream_t) stream, const int8_t* src, T* quantize_factor, T* dst, const int64_t col_size, const int64_t elem_cnt) { constexpr int quantized_src_pack_size = cuda::elementwise::PackSize(); constexpr int dst_pack_size = cuda::elementwise::PackSize(); int launch_pack_size = std::min(quantized_src_pack_size, dst_pack_size); if (launch_pack_size == 8 && col_size % 8 == 0) { - cudaError_t err = DispatchDequantizeKernelPackSize( + GPU(Error_t) err = DispatchDequantizeKernelPackSize( stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } else if (launch_pack_size == 4 && col_size % 4 == 0) { - cudaError_t err = DispatchDequantizeKernelPackSize( + GPU(Error_t) err = DispatchDequantizeKernelPackSize( stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } else if (launch_pack_size == 2 && col_size % 2 == 0) { - cudaError_t err = DispatchDequantizeKernelPackSize( + GPU(Error_t) err = DispatchDequantizeKernelPackSize( stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } else { - cudaError_t err = DispatchDequantizeKernelPackSize( + GPU(Error_t) err = DispatchDequantizeKernelPackSize( stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != cudaSuccess) { return err; } + if (err != GPU(Success)) { return err; } } - return cudaPeekAtLastError(); + return GPU(PeekAtLastError)(); } template @@ -844,7 +686,7 @@ struct DefaultComputeType { template class EmbeddingShuffleKernel final : public user_op::OpKernel { public: - EmbeddingShuffleKernel() = default; + EmbeddingShuffleKernel() : current_iter_(0) {} ~EmbeddingShuffleKernel() override = default; std::shared_ptr CreateOpKernelState( @@ -858,8 +700,10 @@ class EmbeddingShuffleKernel final : public user_op::OpKernel { const user_op::OpKernelCache*) const override { auto* kernel_state = dynamic_cast*>(state); CHECK(kernel_state != nullptr); - const user_op::Tensor* cur_rank_embeddings = - ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::unique_ptr allocator = + embedding_state->NewTmpBufferAllocator(ctx); + embedding_state->OnEmbeddingShuffleStart(ctx, current_iter_); const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); const user_op::Tensor* cur_rank_inverse_indices = ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); @@ -869,12 +713,13 @@ class EmbeddingShuffleKernel final : public user_op::OpKernel { user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); ncclComm_t comm = kernel_state->comm(); using ComputeType = typename DefaultComputeType::type; - const int64_t embedding_size = cur_rank_embeddings->shape_view().At(1); + const int64_t embedding_size = ctx->Attr("embedding_size"); IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); - DataType data_type = cur_rank_embeddings->data_type(); + DataType data_type = embeddings->data_type(); const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt(); const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const bool skip_last_gather = ctx->Attr("skip_last_gather"); bool enable_quantized_comm_env_var = ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); bool enable_quantized_comm = enable_quantized_comm_env_var && (embedding_size < kMaxColSize); @@ -882,132 +727,155 @@ class EmbeddingShuffleKernel final : public user_op::OpKernel { LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and " "embedding_size less equal than 1024 can use quantized communication. "; } - cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - OF_CUDA_CHECK(cudaMemcpyAsync( - host_num_unique_matrix, reinterpret_cast(num_unique_matrix->dptr()), - parallel_num * parallel_num * sizeof(IDX), cudaMemcpyDefault, cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + const std::vector& num_unique_matrix_vec = + embedding_state->GetIdNumUniqueMatrix(current_iter_); + CHECK_EQ(sizeof(IDX), sizeof(uint32_t)) << "assume sizeof(IDX) equals to sizeof(uint32_t)"; + ; + std::memcpy(host_num_unique_matrix, num_unique_matrix_vec.data(), + parallel_num * parallel_num * sizeof(IDX)); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + int64_t cur_rank_num_ids = 0; for (int64_t i = 0; i < parallel_num; ++i) { cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id]; } - size_t full_elem_cnt = parallel_num * num_ids * embedding_size; - CHECK_EQ(full_elem_cnt, cur_rank_embeddings->shape_view().elem_cnt()); + int64_t unique_partitioned_num_ids = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + unique_partitioned_num_ids += host_num_unique_matrix[parallel_id * parallel_num + i]; + } + const T* cur_rank_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingShuffleCurRankEmbeddings(current_iter_)); if (!enable_quantized_comm) { - size_t reverse_unique_cur_rank_embeddings_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size; - - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - reverse_unique_cur_rank_embeddings_size + received_embeddings_size); - - T* reverse_unique_cur_rank_embeddings = reinterpret_cast(tmp_buffer->mut_dptr()); - T* received_embeddings = reinterpret_cast(tmp_buffer->mut_dptr() - + reverse_unique_cur_rank_embeddings_size); - // reverse cur_rank unique + // 1. reverse cur_rank unique, from (num_unique, embedding_size) to (cur_rank_num_ids, + // embedding_size) + void* reverse_unique_cur_rank_embeddings; + allocator->Allocate(&reverse_unique_cur_rank_embeddings, + cur_rank_num_ids * embedding_size * sizeof(T)); GatherKernelUtilImpl::Forward( ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, cur_rank_embeddings->dptr(), - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}), - reverse_unique_cur_rank_embeddings, 0); - - ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size, - data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings, - received_embeddings); - - // reverse unique_partition - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings, - Shape({1, parallel_num * num_ids, embedding_size}), embeddings->mut_dptr(), 0); + cur_rank_num_ids, cur_rank_embeddings_ptr, Shape({1, num_unique, embedding_size}), + reinterpret_cast(reverse_unique_cur_rank_embeddings), 0); + + // 2. send recv embedding, from (cur_rank_num_ids, embedding_size) to + // (unique_partitioned_num_ids, embedding_size) + if (skip_last_gather) { + data_shuffle::ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, + embedding_size, data_type, host_num_unique_matrix, + reinterpret_cast(reverse_unique_cur_rank_embeddings), + embeddings->mut_dptr()); + allocator->Free(reverse_unique_cur_rank_embeddings); + } else { + void* received_embeddings; // T + allocator->Allocate(&received_embeddings, GetCudaAlignedSize(unique_partitioned_num_ids + * embedding_size * sizeof(T))); + + data_shuffle::ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, + embedding_size, data_type, host_num_unique_matrix, + reinterpret_cast(reverse_unique_cur_rank_embeddings), + reinterpret_cast(received_embeddings)); + allocator->Free(reverse_unique_cur_rank_embeddings); + + // 3. reverse unique_partition, from (unique_partitioned_num_ids, embedding_size) to + // (num_ids, embedding_size) + GatherKernelUtilImpl::Forward( + ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), + num_ids, reinterpret_cast(received_embeddings), + Shape({1, unique_partitioned_num_ids, embedding_size}), embeddings->mut_dptr(), 0); + allocator->Free(received_embeddings); + } } else { - size_t reverse_unique_cur_rank_embeddings_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t)); - size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size; - size_t quantize_cur_rank_embeddings_size = reverse_unique_cur_rank_embeddings_size; - size_t reverse_recv_quantize_cur_rank_embeddings_size = - reverse_unique_cur_rank_embeddings_size; - size_t cur_rank_quantize_factor_size = - GetCudaAlignedSize(cur_rank_embeddings->shape_view().At(0) * sizeof(T)); - size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; - size_t recv_quantize_factor_size = cur_rank_quantize_factor_size; - size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size; - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - reverse_unique_cur_rank_embeddings_size + received_embeddings_size - + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size - + reverse_recv_quantize_factor_size); - int8_t* reverse_unique_cur_rank_embeddings = - reinterpret_cast(tmp_buffer->mut_dptr()); - int8_t* received_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size); - int8_t* quantize_cur_rank_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size); - int8_t* reverse_recv_quantize_cur_rank_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size); - T* cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size); - T* reverse_cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size); - T* recv_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size); - T* reverse_recv_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size); + CHECK(!skip_last_gather) << "when enable_quantized_comm, should not use fuse kernel."; + // 1. quantize cur_rank_embeddings, from (num_unique, embedding_size) T to (num_unique, + // embedding_size) int8_t, and get (num_unique,) T factor + void* quantize_cur_rank_embeddings; // int8_t + allocator->Allocate(&quantize_cur_rank_embeddings, + num_unique * embedding_size * sizeof(int8_t)); + void* cur_rank_quantize_factor; // T + allocator->Allocate(&cur_rank_quantize_factor, num_unique * sizeof(T)); DispatchQuantizeWarpImplPackSize()( - cuda_stream, cur_rank_embeddings->dptr(), quantize_cur_rank_embeddings, - cur_rank_quantize_factor, cur_rank_num_ids, embedding_size); - // reverse cur_rank embedding unique + cuda_stream, cur_rank_embeddings_ptr, + reinterpret_cast(quantize_cur_rank_embeddings), + reinterpret_cast(cur_rank_quantize_factor), num_unique, embedding_size); + // 2. reverse cur_rank unique, from (num_unique, embedding_size) to (cur_rank_num_ids, + // embedding_size) + void* reverse_unique_cur_rank_embeddings; // int8_t + + allocator->Allocate(&reverse_unique_cur_rank_embeddings, + cur_rank_num_ids * embedding_size * sizeof(int8_t)); + GatherKernelUtilImpl::Forward( ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, quantize_cur_rank_embeddings, - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}), - reverse_unique_cur_rank_embeddings, 0); + cur_rank_num_ids, reinterpret_cast(quantize_cur_rank_embeddings), + Shape({1, num_unique, embedding_size}), + reinterpret_cast(reverse_unique_cur_rank_embeddings), 0); + allocator->Free(quantize_cur_rank_embeddings); + + // 3. reverse cur_rank quantize factor unique, from (num_unique) to (cur_rank_num_ids) + void* reverse_cur_rank_quantize_factor; // T + allocator->Allocate(&reverse_cur_rank_quantize_factor, cur_rank_num_ids * sizeof(T)); - // reverse cur_rank quantize factor unique GatherKernelUtilImpl::Forward( ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, cur_rank_quantize_factor, - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, 1}), - reverse_cur_rank_quantize_factor, 0); + cur_rank_num_ids, reinterpret_cast(cur_rank_quantize_factor), + Shape({1, num_unique, 1}), reinterpret_cast(reverse_cur_rank_quantize_factor), 0); + allocator->Free(cur_rank_quantize_factor); + // 4. send recv embedding and factor, from (cur_rank_num_ids, embedding_size) to + // (unique_partitioned_num_ids, embedding_size) + void* received_embeddings; // int8_t + void* recv_quantize_factor; // T + allocator->Allocate(&received_embeddings, + unique_partitioned_num_ids * embedding_size * sizeof(int8_t)); + allocator->Allocate(&recv_quantize_factor, unique_partitioned_num_ids * sizeof(T)); + + data_shuffle::ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, + embedding_size, data_type, host_num_unique_matrix, + reinterpret_cast(reverse_unique_cur_rank_embeddings), + reinterpret_cast(received_embeddings), + reinterpret_cast(reverse_cur_rank_quantize_factor), + reinterpret_cast(recv_quantize_factor)); + allocator->Free(reverse_unique_cur_rank_embeddings); + allocator->Free(reverse_cur_rank_quantize_factor); + + // 5. reverse unique_partition, from (unique_partitioned_num_ids, embedding_size) to (num_ids, + // embedding_size) + void* reverse_recv_quantize_cur_rank_embeddings; // int8_t + allocator->Allocate(&reverse_recv_quantize_cur_rank_embeddings, + num_ids * embedding_size * sizeof(int8_t)); - ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size, - data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings, - received_embeddings, reverse_cur_rank_quantize_factor, - recv_quantize_factor); - - // reverse unique_partition GatherKernelUtilImpl::Forward( ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings, - Shape({1, parallel_num * num_ids, embedding_size}), - reverse_recv_quantize_cur_rank_embeddings, 0); + num_ids, reinterpret_cast(received_embeddings), + Shape({1, unique_partitioned_num_ids, embedding_size}), + reinterpret_cast(reverse_recv_quantize_cur_rank_embeddings), 0); + allocator->Free(received_embeddings); + // 6. reverse unique_partition_factor, from (unique_partitioned_num_ids) to (num_ids) + void* reverse_recv_quantize_factor; // T + allocator->Allocate(&reverse_recv_quantize_factor, num_ids * sizeof(T)); GatherKernelUtilImpl::Forward( ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), recv_quantize_factor, - Shape({1, parallel_num * num_ids, 1}), reverse_recv_quantize_factor, 0); - - int32_t dequantize_row_size = inverse_unique_partition_indices->shape_view().elem_cnt(); + num_ids, reinterpret_cast(recv_quantize_factor), + Shape({1, unique_partitioned_num_ids, 1}), + reinterpret_cast(reverse_recv_quantize_factor), 0); + allocator->Free(recv_quantize_factor); + + // 7. dequantize embeddings, from (num_ids, embedding_size) int8_t to (num_ids, + // embedding_size) T + int32_t dequantize_row_size = num_ids; IDX dequantize_elem_cnt = dequantize_row_size * embedding_size; OF_CUDA_CHECK((LaunchDequantizeKernel( - cuda_stream, reverse_recv_quantize_cur_rank_embeddings, reverse_recv_quantize_factor, - embeddings->mut_dptr(), embedding_size, dequantize_elem_cnt))); + cuda_stream, reinterpret_cast(reverse_recv_quantize_cur_rank_embeddings), + reinterpret_cast(reverse_recv_quantize_factor), embeddings->mut_dptr(), + embedding_size, dequantize_elem_cnt))); + allocator->Free(reverse_recv_quantize_cur_rank_embeddings); + allocator->Free(reverse_recv_quantize_factor); } + embedding_state->OnEmbeddingShuffleEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair) \ @@ -1017,29 +885,37 @@ class EmbeddingShuffleKernel final : public user_op::OpKernel { .SetIsMatchedHob( \ (user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("cur_rank_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ + && ((user_op::HobAttr("skip_last_gather") == false) \ + || (!embedding::UseEmbeddingShuffleP2PKernel(OF_PP_PAIR_SECOND(t_dtype_pair), \ + OF_PP_PAIR_SECOND(idx_dtype_pair)))) \ && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& cur_rank_embeddings = \ - ctx->InputTensorDesc("cur_rank_embeddings", 0); \ + const user_op::TensorDesc& inverse_unique_partition_indices = \ + ctx->InputTensorDesc("inverse_unique_partition_indices", 0); \ + const int64_t num_ids = inverse_unique_partition_indices.shape().elem_cnt(); \ + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); \ + const int64_t cur_rank_max_num_ids = parallel_num * num_ids; \ + const int64_t embedding_size = ctx->Attr("embedding_size"); \ bool enable_quantized_comm = \ ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false) \ - && (cur_rank_embeddings.shape().At(1) < kMaxColSize); \ + && (embedding_size < kMaxColSize); \ size_t tmp_size = 0; \ + if (embedding::UseDynamicMemoryAllocation()) { return tmp_size; } \ if (!enable_quantized_comm) { \ size_t reverse_cur_rank_embeddings_size = GetCudaAlignedSize( \ - cur_rank_embeddings.shape().elem_cnt() * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ + cur_rank_max_num_ids * embedding_size * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ size_t recv_unique_embeddings_size = reverse_cur_rank_embeddings_size; \ tmp_size = reverse_cur_rank_embeddings_size + recv_unique_embeddings_size; \ } else { \ - size_t total_elem_cnt = cur_rank_embeddings.shape().elem_cnt(); \ + size_t total_elem_cnt = cur_rank_max_num_ids * embedding_size; \ size_t reverse_cur_rank_embeddings_size = \ GetCudaAlignedSize(total_elem_cnt * sizeof(int8_t)); \ size_t recv_unique_embeddings = reverse_cur_rank_embeddings_size; \ size_t quantize_cur_rank_embeddings_size = reverse_cur_rank_embeddings_size; \ size_t reverse_recv_quantize_cur_rank_embeddings_size = \ reverse_cur_rank_embeddings_size; \ - size_t cur_rank_quantize_factor_size = GetCudaAlignedSize( \ - cur_rank_embeddings.shape().At(0) * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ + size_t cur_rank_quantize_factor_size = \ + GetCudaAlignedSize(cur_rank_max_num_ids * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; \ size_t recv_quantize_factor_size = cur_rank_quantize_factor_size; \ size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size; \ @@ -1055,176 +931,10 @@ class EmbeddingShuffleKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) -template -void ShuffleEmbeddingsGrad(cudaStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - T* unique_partition_embedding_grad, T* received_embeddings_grad) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, - received_embeddings_grad); -} - -// Quantize Version. -template -void ShuffleEmbeddingsGrad(cudaStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - int8_t* unique_partition_embedding_grad, - int8_t* received_embeddings_grad, T* cur_rank_quantize_factor, - T* received_cur_rank_quantize_factor) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - // Shuffle Embedding Grad. - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, - unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, - received_embeddings_grad); - // Shuffle Quantize factor. - MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, - parallel_num, &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, cur_rank_quantize_factor, - recv_offsets, recv_elem_cnt, received_cur_rank_quantize_factor); -} - -template -__global__ void UnsortedSegmentHalfGpu(const IDX in_h2_elem_cnt, const IDX h2_inner_dim_size, - const IDX inner_dim_size, const half* data, - const K* segment_ids, const IDX num_segments, - half2* out_h2) { - CUDA_1D_KERNEL_LOOP_T(IDX, i, in_h2_elem_cnt) { - const IDX segment_id_idx = i / h2_inner_dim_size; - const IDX h2_inner_idx = i - segment_id_idx * h2_inner_dim_size; - const IDX inner_idx_0 = 2 * h2_inner_idx; - const IDX inner_idx_1 = inner_idx_0 + 1; - const half* data_row = data + segment_id_idx * inner_dim_size; - half2 val; - val.x = data_row[inner_idx_0]; - val.y = (inner_idx_1 >= inner_dim_size) ? static_cast(0) : data_row[inner_idx_1]; - const IDX idx = segment_ids[segment_id_idx]; - const IDX out_h2_offset = idx * h2_inner_dim_size + h2_inner_idx; - cuda::atomic::Add(out_h2 + out_h2_offset, val); - } -} - -template -struct UnsortedSegmentSumPad { - void operator()(ep::Stream* stream, const K* segment_ids, const T* data, int64_t num_segment_ids, - int64_t num_segments, int64_t inner_dim_size, int64_t padded_inner_dim_size, - T* out) const { - UNIMPLEMENTED(); - } -}; - -template -struct UnsortedSegmentSumPad { - void operator()(ep::Stream* stream, const K* segment_ids, const half* data, - int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, - int64_t padded_inner_dim_size, half* out) const { - const int64_t data_elem_cnt = num_segment_ids * inner_dim_size; - const int64_t out_elem_cnt = num_segments * padded_inner_dim_size; - CHECK_EQ(padded_inner_dim_size % 2, 0); - CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); - const int64_t h2_inner_dim_size = padded_inner_dim_size / 2; - const int64_t in_h2_elem_cnt = num_segment_ids * h2_inner_dim_size; - if (std::max(data_elem_cnt, out_elem_cnt) < GetMaxVal() / 2) { - UnsortedSegmentHalfGpu - <<As()->cuda_stream()>>>( - in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, - reinterpret_cast(out)); - } else { - UnsortedSegmentHalfGpu - <<As()->cuda_stream()>>>( - in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, - reinterpret_cast(out)); - } - } -}; - -template -void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const T* data, - int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, - int64_t padded_inner_dim_size, T* out) { - if (inner_dim_size == padded_inner_dim_size) { - UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( - stream, segment_ids, data, num_segment_ids, num_segments, 1, inner_dim_size, 0, out); - } else { - CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); - UnsortedSegmentSumPad()(stream, segment_ids, data, num_segment_ids, num_segments, - inner_dim_size, padded_inner_dim_size, out); - } -} - -template -void UniquePartitionEmbeddingGrad(ep::Stream* stream, int64_t parallel_id, int64_t parallel_num, - int64_t num_ids, int64_t embedding_size, - int64_t padded_embedding_size, const IDX* host_num_unique_matrix, - const T* embedding_grad, - const IDX* inverse_unique_partition_indices, - T* unique_partition_embedding_grad) { - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t offset = i * num_ids * padded_embedding_size; - const int64_t valid_value_size = - host_num_unique_matrix[parallel_id * parallel_num + i] * padded_embedding_size * sizeof(T); - OF_CUDA_CHECK(cudaMemsetAsync(unique_partition_embedding_grad + offset, 0, valid_value_size, - stream->As()->cuda_stream())); - } - UnsortedSegmentSum(stream, inverse_unique_partition_indices, embedding_grad, num_ids, - parallel_num * num_ids, embedding_size, padded_embedding_size, - unique_partition_embedding_grad); -} - -template -void UniqueCurRankEmbeddingGrad(ep::Stream* stream, DataType data_type, int64_t cur_rank_num_ids, - int64_t embedding_size, int64_t padded_embedding_size, - const T* cur_rank_embedding_grad, - const IDX* cur_rank_inverse_indices, - T* cur_rank_unique_embedding_grad, T* tmp_buffer) { - T* unsorted_segment_sum_out = - (embedding_size == padded_embedding_size) ? cur_rank_unique_embedding_grad : tmp_buffer; - OF_CUDA_CHECK(cudaMemsetAsync(unsorted_segment_sum_out, 0, - cur_rank_num_ids * padded_embedding_size * sizeof(T), - stream->As()->cuda_stream())); - UnsortedSegmentSum(stream, cur_rank_inverse_indices, cur_rank_embedding_grad, - cur_rank_num_ids, cur_rank_num_ids, padded_embedding_size, - padded_embedding_size, unsorted_segment_sum_out); - if (embedding_size != padded_embedding_size) { - std::unique_ptr primitive = - ep::primitive::NewPrimitive(DeviceType::kCUDA, 2); - DimVector dst_shape = {cur_rank_num_ids, embedding_size}; - DimVector dst_pos_vec = {0, 0}; - DimVector src_shape = {cur_rank_num_ids, padded_embedding_size}; - DimVector src_pos_vec = {0, 0}; - DimVector extent_vec = {cur_rank_num_ids, embedding_size}; - primitive->Launch(stream, data_type, 2, cur_rank_unique_embedding_grad, dst_shape.data(), - dst_pos_vec.data(), unsorted_segment_sum_out, src_shape.data(), - src_pos_vec.data(), extent_vec.data()); - } -} - -int64_t GetPaddedEmbeddingSize(DataType data_type, int64_t embedding_size) { - if (data_type == DataType::kFloat16 && embedding_size % 2 != 0) { - return embedding_size + 1; - } else { - return embedding_size; - } -} - template class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { public: - EmbeddingGradientShuffleKernel() = default; + EmbeddingGradientShuffleKernel() : current_iter_(0){}; ~EmbeddingGradientShuffleKernel() override = default; std::shared_ptr CreateOpKernelState( @@ -1238,6 +948,9 @@ class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { const user_op::OpKernelCache*) const override { auto* kernel_state = dynamic_cast*>(state); CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::unique_ptr allocator = + embedding_state->NewTmpBufferAllocator(ctx); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); @@ -1247,13 +960,15 @@ class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); user_op::Tensor* cur_rank_unique_embedding_grad = ctx->Tensor4ArgNameAndIndex("cur_rank_unique_embedding_grad", 0); - const int64_t embedding_size = cur_rank_unique_embedding_grad->shape_view().At(1); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const bool only_zero_valid_grad = ctx->Attr("only_zero_valid_grad"); IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); DataType data_type = embedding_grad->data_type(); const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt(); const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - const int64_t padded_embedding_size = GetPaddedEmbeddingSize(data_type, embedding_size); + const int64_t padded_embedding_size = + data_shuffle::GetPaddedEmbeddingSize(data_type, embedding_size); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); ncclComm_t comm = kernel_state->comm(); using ComputeType = typename DefaultComputeType::type; @@ -1265,125 +980,158 @@ class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and " "embedding_size less equal than 1024 can use quantized communication. "; } - cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_unique_matrix, num_unique_matrix->dptr(), - parallel_num * parallel_num * sizeof(IDX), cudaMemcpyDefault, - cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); + const bool skip_first_scatter = ctx->Attr("skip_first_scatter"); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + const std::vector& num_unique_matrix_vec = + embedding_state->GetIdNumUniqueMatrix(current_iter_); + CHECK_EQ(sizeof(IDX), sizeof(uint32_t)) << "assume sizeof(IDX) equals to sizeof(uint32_t)"; + std::memcpy(host_num_unique_matrix, num_unique_matrix_vec.data(), + parallel_num * parallel_num * sizeof(IDX)); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); int64_t cur_rank_num_ids = 0; for (int64_t i = 0; i < parallel_num; ++i) { cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id]; } - size_t full_num_ids = parallel_num * num_ids; - size_t full_elem_cnt = full_num_ids * padded_embedding_size; - size_t unique_partition_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - + int64_t unique_partitioned_num_ids = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + unique_partitioned_num_ids += host_num_unique_matrix[parallel_id * parallel_num + i]; + } if (!enable_quantized_comm) { - size_t received_embedding_grad_size = unique_partition_embedding_grad_size; - T* unique_partition_embedding_grad = reinterpret_cast(tmp_buffer->mut_dptr()); - T* received_embedding_grad = - reinterpret_cast(tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - unique_partition_embedding_grad_size + received_embedding_grad_size); - - UniquePartitionEmbeddingGrad( - ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size, - host_num_unique_matrix, embedding_grad->dptr(), - reinterpret_cast(inverse_unique_partition_indices->dptr()), - unique_partition_embedding_grad); - - ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids, - padded_embedding_size, data_type, host_num_unique_matrix, - unique_partition_embedding_grad, received_embedding_grad); - + // 1. sum to unique grad, from (num_ids, embedding_size) to (unique_partitioned_num_ids, + // padded_embedding_size) + void* unique_partition_embedding_grad; // T + allocator->Allocate(&unique_partition_embedding_grad, + unique_partitioned_num_ids * padded_embedding_size * sizeof(T)); + + const T* unique_embedding_grad_ptr; + if (skip_first_scatter) { + unique_embedding_grad_ptr = embedding_grad->dptr(); + } else { + data_shuffle::UniquePartitionEmbeddingGrad( + ctx->stream(), unique_partitioned_num_ids, num_ids, embedding_size, + padded_embedding_size, host_num_unique_matrix, embedding_grad->dptr(), + reinterpret_cast(inverse_unique_partition_indices->dptr()), + reinterpret_cast(unique_partition_embedding_grad)); + unique_embedding_grad_ptr = reinterpret_cast(unique_partition_embedding_grad); + } + // 2. send recv grad, from (unique_partitioned_num_ids, padded_embedding_size) to + // (cur_rank_num_ids, padded_embedding_size) + void* received_embedding_grad; // T + allocator->Allocate(&received_embedding_grad, + cur_rank_num_ids * padded_embedding_size * sizeof(T)); + + data_shuffle::ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids, + padded_embedding_size, data_type, host_num_unique_matrix, + unique_embedding_grad_ptr, + reinterpret_cast(received_embedding_grad)); + + // 3. sum to unique grad, from (cur_rank_num_ids, padded_embedding_size) to (num_unique, + // padded_embedding_size) then slice to out from (num_unique, padded_embedding_size) to + // (num_unique, embedding_size) should memset cur_rank_unique_embedding_grad all tensor for + // amp count_not_finite // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer. - T* buffer_ptr = unique_partition_embedding_grad; - UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size, - padded_embedding_size, received_embedding_grad, - reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); + T* buffer_ptr = reinterpret_cast(unique_partition_embedding_grad); + data_shuffle::UniqueCurRankEmbeddingGrad( + ctx->stream(), data_type, cur_rank_num_ids, num_unique, embedding_size, + padded_embedding_size, only_zero_valid_grad, + cur_rank_unique_embedding_grad->shape_view().elem_cnt(), + reinterpret_cast(received_embedding_grad), + reinterpret_cast(cur_rank_inverse_indices->dptr()), + cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); + allocator->Free(unique_partition_embedding_grad); + allocator->Free(received_embedding_grad); } else { - size_t received_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t)); - size_t quantize_cur_rank_embedding_grad_size = received_embedding_grad_size; - size_t cur_rank_quantize_factor_size = GetCudaAlignedSize(full_num_ids * sizeof(T)); - size_t received_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; - size_t dequantize_cur_rank_embedding_grad_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - unique_partition_embedding_grad_size + received_embedding_grad_size - + quantize_cur_rank_embedding_grad_size + cur_rank_quantize_factor_size - + received_cur_rank_quantize_factor_size - + dequantize_cur_rank_embedding_grad_size); - T* unique_partition_embedding_grad = reinterpret_cast(tmp_buffer->mut_dptr()); - int8_t* received_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size); - - int8_t* quantize_cur_rank_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size); - T* cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size); - T* received_cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size - + cur_rank_quantize_factor_size); - T* dequantize_cur_rank_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size - + cur_rank_quantize_factor_size + received_cur_rank_quantize_factor_size); - - UniquePartitionEmbeddingGrad( - ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size, + CHECK(!skip_first_scatter) << "when enable_quantized_comm, should not use fuse kernel."; + // 1. sum to unique grad, from (num_ids, embedding_size) to (unique_partitioned_num_ids, + // padded_embedding_size) + void* unique_partition_embedding_grad; // T + allocator->Allocate(&unique_partition_embedding_grad, + unique_partitioned_num_ids * padded_embedding_size * sizeof(T)); + + data_shuffle::UniquePartitionEmbeddingGrad( + ctx->stream(), unique_partitioned_num_ids, num_ids, embedding_size, padded_embedding_size, host_num_unique_matrix, embedding_grad->dptr(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - unique_partition_embedding_grad); - - // Quantize. - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t embedding_grad_offset = i * num_ids * padded_embedding_size; - const int64_t quantize_factor_offset = i * num_ids; - const int64_t valid_row_size = host_num_unique_matrix[parallel_id * parallel_num + i]; - DispatchQuantizeWarpImplPackSize()( - cuda_stream, unique_partition_embedding_grad + embedding_grad_offset, - quantize_cur_rank_embedding_grad + embedding_grad_offset, - cur_rank_quantize_factor + quantize_factor_offset, valid_row_size, - padded_embedding_size); - } + reinterpret_cast(unique_partition_embedding_grad)); + + // 2. Quantize unique_partition_embedding_grad, get + // quantize_cur_rank_embedding_grad(unique_partitioned_num_ids, padded_embedding_size) int8_t + // and cur_rank_quantize_factor(unique_partitioned_num_ids) T + void* quantize_cur_rank_embedding_grad; // int8_t + allocator->Allocate(&quantize_cur_rank_embedding_grad, + unique_partitioned_num_ids * padded_embedding_size * sizeof(int8_t)); + void* cur_rank_quantize_factor; // T + allocator->Allocate(&cur_rank_quantize_factor, unique_partitioned_num_ids * sizeof(T)); + + DispatchQuantizeWarpImplPackSize()( + cuda_stream, reinterpret_cast(unique_partition_embedding_grad), + reinterpret_cast(quantize_cur_rank_embedding_grad), + reinterpret_cast(cur_rank_quantize_factor), unique_partitioned_num_ids, + padded_embedding_size); + + // 3. send recv grad, from (unique_partitioned_num_ids, padded_embedding_size) int8_t to + // (cur_rank_num_ids, padded_embedding_size) int8_t send recv quantize_factor, from + // (unique_partitioned_num_ids) T to (cur_rank_num_ids) T + void* received_embedding_grad; // int8_t + allocator->Allocate(&received_embedding_grad, + cur_rank_num_ids * padded_embedding_size * sizeof(int8_t)); + void* received_cur_rank_quantize_factor; // T + allocator->Allocate(&received_cur_rank_quantize_factor, cur_rank_num_ids * sizeof(T)); + + data_shuffle::ShuffleEmbeddingsGrad( + cuda_stream, comm, parallel_id, parallel_num, num_ids, padded_embedding_size, data_type, + host_num_unique_matrix, reinterpret_cast(quantize_cur_rank_embedding_grad), + reinterpret_cast(received_embedding_grad), + reinterpret_cast(cur_rank_quantize_factor), + reinterpret_cast(received_cur_rank_quantize_factor)); + allocator->Free(quantize_cur_rank_embedding_grad); + allocator->Free(cur_rank_quantize_factor); + + /* + Host num unique matrix: + | Partition0 | Partition1 | + | Rank0 | 2 | 4 | + | Rank1 | 3 | 3 | + After ShuffleEmbeddingGrads, each rank will exchange partition. + For example: + Rank0 will have (matrix[rank0][part0] + matrix[rank1][part0]) grad tensor. + Rank1 will have (matrix[rank0][part1] + matrix[rank1][part1]) grad tensor. + */ + // 4. dequantize grad, from (cur_rank_num_ids, padded_embedding_size) int8_t to + // (cur_rank_num_ids, padded_embedding_size) T + void* dequantize_cur_rank_embedding_grad; // T + allocator->Allocate(&dequantize_cur_rank_embedding_grad, + cur_rank_num_ids * padded_embedding_size * sizeof(T)); - ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids, - padded_embedding_size, data_type, host_num_unique_matrix, - quantize_cur_rank_embedding_grad, received_embedding_grad, - cur_rank_quantize_factor, received_cur_rank_quantize_factor); - - int64_t dequantize_cur_rank_num = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - /* - Host num unique matrix: - | Partition0 | Partition1 | - | Rank0 | 2 | 4 | - | Rank1 | 3 | 3 | - After ShuffleEmbeddingGrads, each rank will exchange partition. - For example: - Rank0 will have (matrix[rank0][part0] + matrix[rank1][part0]) grad tensor. - Rank1 will have (matrix[rank0][part1] + matrix[rank1][part1]) grad tensor. - */ - dequantize_cur_rank_num += host_num_unique_matrix[i * parallel_num + parallel_id]; - } - IDX dequantize_elem_cnt = dequantize_cur_rank_num * padded_embedding_size; OF_CUDA_CHECK((LaunchDequantizeKernel( - cuda_stream, received_embedding_grad, received_cur_rank_quantize_factor, - dequantize_cur_rank_embedding_grad, padded_embedding_size, dequantize_elem_cnt))); + cuda_stream, reinterpret_cast(received_embedding_grad), + reinterpret_cast(received_cur_rank_quantize_factor), + reinterpret_cast(dequantize_cur_rank_embedding_grad), padded_embedding_size, + cur_rank_num_ids * padded_embedding_size))); + allocator->Free(received_embedding_grad); + allocator->Free(received_cur_rank_quantize_factor); + // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer. - T* buffer_ptr = unique_partition_embedding_grad; - UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size, - padded_embedding_size, dequantize_cur_rank_embedding_grad, - reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); + T* buffer_ptr = reinterpret_cast(unique_partition_embedding_grad); + // 5. sum to unique grad, from (cur_rank_num_ids, padded_embedding_size) to (num_unique, + // padded_embedding_size) then slice to out from (num_unique, padded_embedding_size) to + // (num_unique, embedding_size) should memset cur_rank_unique_embedding_grad all tensor for + // amp count_not_finite + data_shuffle::UniqueCurRankEmbeddingGrad( + ctx->stream(), data_type, cur_rank_num_ids, num_unique, embedding_size, + padded_embedding_size, only_zero_valid_grad, + cur_rank_unique_embedding_grad->shape_view().elem_cnt(), + reinterpret_cast(dequantize_cur_rank_embedding_grad), + reinterpret_cast(cur_rank_inverse_indices->dptr()), + cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); + allocator->Free(unique_partition_embedding_grad); + allocator->Free(dequantize_cur_rank_embedding_grad); } + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair) \ @@ -1393,20 +1141,24 @@ class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { .SetIsMatchedHob( \ (user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ + && ((user_op::HobAttr("skip_first_scatter") == false) \ + || (!embedding::UseEmbeddingGradientShuffleP2PKernel( \ + OF_PP_PAIR_SECOND(t_dtype_pair), OF_PP_PAIR_SECOND(idx_dtype_pair)))) \ && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ const user_op::TensorDesc& cur_rank_unique_embedding_grad = \ ctx->InputTensorDesc("cur_rank_unique_embedding_grad", 0); \ size_t cur_rank_embedding_grad_num = cur_rank_unique_embedding_grad.shape().At(0); \ size_t embedding_size = cur_rank_unique_embedding_grad.shape().At(1); \ - size_t padded_embedding_size = \ - GetPaddedEmbeddingSize(cur_rank_unique_embedding_grad.data_type(), embedding_size); \ + size_t padded_embedding_size = data_shuffle::GetPaddedEmbeddingSize( \ + cur_rank_unique_embedding_grad.data_type(), embedding_size); \ size_t cur_rank_embedding_grad_elem_cnt = \ cur_rank_embedding_grad_num * padded_embedding_size; \ bool enable_quantized_comm = \ ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false) \ && (padded_embedding_size < kMaxColSize); \ size_t tmp_size = 0; \ + if (embedding::UseDynamicMemoryAllocation()) { return tmp_size; } \ if (!enable_quantized_comm) { \ size_t cur_rank_embedding_grad_size = GetCudaAlignedSize( \ cur_rank_embedding_grad_elem_cnt * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ @@ -1432,16 +1184,54 @@ class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) +template +class EmbeddingUniqueKeyValuePairKernelState final : public user_op::OpKernelState { + public: + explicit EmbeddingUniqueKeyValuePairKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_keys_), sizeof(IDX))); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); + } + ~EmbeddingUniqueKeyValuePairKernelState() { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_keys_)); + } + + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } + + IDX* HostNumKeys() { return host_num_keys_; } + + private: + int device_index_; + embedding::EmbeddingState* embedding_state_; + IDX* host_num_keys_; +}; + template class UniqueKeyValuePairKernel final : public user_op::OpKernel { public: - UniqueKeyValuePairKernel() = default; + UniqueKeyValuePairKernel() : current_iter_(0){}; ~UniqueKeyValuePairKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + private: using user_op::OpKernel::Compute; - - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); const user_op::Tensor* keys = ctx->Tensor4ArgNameAndIndex("keys", 0); user_op::Tensor* num_unique = ctx->Tensor4ArgNameAndIndex("num_unique", 0); user_op::Tensor* unique_keys = ctx->Tensor4ArgNameAndIndex("unique_keys", 0); @@ -1449,39 +1239,54 @@ class UniqueKeyValuePairKernel final : public user_op::OpKernel { user_op::Tensor* inverse_indices = ctx->Tensor4ArgNameAndIndex("inverse_indices", 0); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int32_t num_tables = ctx->Attr("num_tables"); + const int64_t padding_idx = ctx->Attr("padding_idx"); + const bool has_padding_idx = ctx->Attr("has_padding_idx"); const bool has_values = ctx->has_input("values", 0); const bool need_values_buffer = (!has_values && num_tables > 1); size_t values_buffer_bytes = need_values_buffer ? GetCudaAlignedSize(keys->shape_view().elem_cnt() * sizeof(V)) : 0; const int64_t num_keys = keys->shape_view().elem_cnt(); const int64_t hash_capacity = num_keys; - const size_t workspace_bytes = GetCudaAlignedSize(hash_capacity * sizeof(TableEntry)); + const size_t workspace_bytes = + GetCudaAlignedSize(hash_capacity * sizeof(data_shuffle::TableEntry)); CHECK_LE(values_buffer_bytes + workspace_bytes, tmp_buffer->shape_view().elem_cnt()); - cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); const V* values_ptr; if (has_values) { const user_op::Tensor* values = ctx->Tensor4ArgNameAndIndex("values", 0); values_ptr = reinterpret_cast(values->dptr()); } else if (need_values_buffer) { V* values_buffer_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); - GenerateTableIds<<>>( - num_keys, num_tables, values_buffer_ptr); + data_shuffle::GenerateTableIds<<>>(num_keys, num_tables, values_buffer_ptr); values_ptr = values_buffer_ptr; } else { values_ptr = nullptr; } const bool need_process_table_ids = (has_values || num_tables > 1); - TableEntry* workspace_ptr = - reinterpret_cast*>(tmp_buffer->mut_dptr() + values_buffer_bytes); - UniqueAndPartition( + data_shuffle::TableEntry* workspace_ptr = reinterpret_cast*>( + tmp_buffer->mut_dptr() + values_buffer_bytes); + data_shuffle::UniqueAndPartition( cuda_stream, num_keys, hash_capacity, 1, reinterpret_cast(keys->dptr()), values_ptr, reinterpret_cast(num_unique->mut_dptr()), reinterpret_cast(unique_keys->mut_dptr()), reinterpret_cast(unique_values->mut_dptr()), reinterpret_cast(inverse_indices->mut_dptr()), workspace_ptr, workspace_bytes, - need_process_table_ids); + need_process_table_ids, has_padding_idx, padding_idx); + + IDX* host_num_keys = kernel_state->HostNumKeys(); + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_keys, num_unique->mut_dptr(), sizeof(IDX), + GPU(MemcpyDefault), cuda_stream)); + CHECK_JUST(ctx->stream()->Sync()); + uint32_t num_unique_ids = *host_num_keys; + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::vector num_unique_matrix_vec({num_unique_ids}); + embedding_state->SetIdNumUniqueMatrix(num_unique_matrix_vec, current_iter_); + embedding_state->SetIdFinalNumUnique(num_unique_ids, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL(k_dtype_pair, value_dtype_pair, idx_dtype_pair) \ @@ -1499,7 +1304,7 @@ class UniqueKeyValuePairKernel final : public user_op::OpKernel { const int64_t num_keys = keys.shape().elem_cnt(); \ const int64_t hash_capacity = num_keys; \ const size_t workspace_bytes = GetCudaAlignedSize( \ - hash_capacity * sizeof(TableEntry)); \ + hash_capacity * sizeof(data_shuffle::TableEntry)); \ const int32_t num_tables = ctx->Attr("num_tables"); \ const bool has_values = ctx->has_input("values", 0); \ const bool need_values_buffer = (!has_values && num_tables > 1); \ @@ -1511,7 +1316,55 @@ class UniqueKeyValuePairKernel final : public user_op::OpKernel { }); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL, ID_DATA_TYPE_SEQ, - ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + +template +class OneEmbeddingGatherKernel final : public user_op::OpKernel { + public: + OneEmbeddingGatherKernel() : current_iter_(0) {} + ~OneEmbeddingGatherKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingGatherStart(ctx, current_iter_); + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); + const int64_t num_indices = indices->shape_view().elem_cnt(); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const T* in_ptr = reinterpret_cast(embedding_state->EmbeddingGatherIn(current_iter_)); + GatherKernelUtilImpl::Forward( + ctx->stream(), reinterpret_cast(indices->dptr()), num_indices, in_ptr, + Shape({1, num_unique, embedding_size}), out->mut_dptr(), 0); + embedding_state->OnEmbeddingGatherEnd(ctx, current_iter_); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +#define REGISTER_ONE_EMBEDDING_GATHER_KERNEL(in_type, indices_type) \ + REGISTER_USER_KERNEL("one_embedding_gather") \ + .SetCreateFn< \ + OneEmbeddingGatherKernel>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("in", 0) == OF_PP_PAIR_SECOND(in_type)) \ + && (user_op::HobDataType("indices", 0) == OF_PP_PAIR_SECOND(indices_type))); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ONE_EMBEDDING_GATHER_KERNEL, + FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("id_shuffle"); REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("embedding_shuffle"); diff --git a/oneflow/user/kernels/data_shuffle_kernel.hip.cpp b/oneflow/user/kernels/data_shuffle_kernel.hip.cpp deleted file mode 100644 index 703b0445ba22d8d13f030403d268a8b26797fbcf..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/data_shuffle_kernel.hip.cpp +++ /dev/null @@ -1,1523 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/nccl_util.h" -#include "oneflow/core/job/eager_nccl_comm_manager.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/user/kernels/gather_kernel_util.h" -#include "oneflow/user/kernels/unsorted_segment_sum_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/embedding/hash_functions.hip.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/include/primitive/copy_nd.h" -#include "oneflow/core/hip/atomic.hip.h" - -namespace oneflow { - -namespace { - -template -struct TableEntry { - K key; - uint32_t value; -}; - -template -__global__ void HashTableUniqueAndPartitionPairs(const uint32_t table_capacity, - const uint32_t num_keys, int32_t num_partition, - IDX* unique_counts, TableEntry* table, - const K* keys, const V* values, - K* partitioned_unique_keys, - V* partitioned_unique_values, IDX* reverse_index, - bool need_process_values) { - CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_keys) { - IDX r_index_plus_one = 0; - const K key = keys[i]; - size_t key_hash = HASH()(key); - uint32_t partition_id = key_hash % num_partition; - IDX* unique_count = unique_counts + partition_id; - K* unique_keys = partitioned_unique_keys + partition_id * num_keys; - uint32_t pos = key_hash % table_capacity; - const K key_hi = (key | 0x1); - const K key_lo = (key & 0x1); - uint32_t counter = 0; - while (r_index_plus_one == 0) { - bool prob_next = false; - K* key_ptr = &table[pos].key; - volatile uint32_t* table_value_ptr = &table[pos].value; - const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi); - if (old_key == 0) { - IDX unique_pos = cuda::atomic::Add(unique_count, 1); - r_index_plus_one = unique_pos + 1; - unique_keys[unique_pos] = key; - if (need_process_values) { - partitioned_unique_values[partition_id * num_keys + unique_pos] = values[i]; - } - *table_value_ptr = ((r_index_plus_one << 1U) | key_lo); - } else if (old_key == key_hi) { - const uint32_t value = *table_value_ptr; - if (value == 0) { - // do nothing - } else if ((value & 0x1) == key_lo) { - r_index_plus_one = (value >> 1U); - } else { - prob_next = true; - } - } else { - prob_next = true; - } - if (prob_next) { - pos += 1; - counter += 1; - if (pos >= table_capacity) { pos -= table_capacity; } - if (counter >= table_capacity) { asm volatile("s_trap 0;"); } - } - } - reverse_index[i] = partition_id * num_keys + r_index_plus_one - 1; - } -} - -template -__global__ void GenerateTableIds(int32_t elem_cnt, int32_t num_tables, U* table_ids) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { table_ids[i] = i % num_tables; } -} - -template -void UniqueAndPartition(hipStream_t cuda_stream, int64_t num_ids, size_t capacity, - int64_t num_partition, const K* ids, const V* table_ids, - IDX* num_partitioned_unique_ids_ptr, K* partitioned_unique_ids, - V* partitioned_unique_table_ids, IDX* inverse_unique_partition_indices, - void* workspace_ptr, size_t workspace_bytes, bool need_process_table_ids) { - size_t table_capacity_bytes = capacity * sizeof(TableEntry); - CHECK_GE(workspace_bytes, table_capacity_bytes); - OF_CUDA_CHECK(hipMemsetAsync(workspace_ptr, 0, table_capacity_bytes, cuda_stream)); - OF_CUDA_CHECK( - hipMemsetAsync(num_partitioned_unique_ids_ptr, 0, num_partition * sizeof(IDX), cuda_stream)); - hipLaunchKernelGGL(HIP_KERNEL_NAME(HashTableUniqueAndPartitionPairs), BlocksNum4ThreadsNum(num_ids), kCudaThreadsNumPerBlock, 0, cuda_stream, - capacity, num_ids, num_partition, num_partitioned_unique_ids_ptr, - reinterpret_cast*>(workspace_ptr), ids, table_ids, partitioned_unique_ids, - partitioned_unique_table_ids, inverse_unique_partition_indices, need_process_table_ids); -} - -template -void ShuffleData(hipStream_t cuda_stream, ncclComm_t comm, DataType data_type, - const std::vector& send_offsets, - const std::vector& send_elem_cnt, const T* send_data, - const std::vector& recv_offsets, - const std::vector& recv_elem_cnt, T* recv_data) { - ncclDataType_t nccl_data_type = GetNcclDataType(data_type); - const int64_t parallel_num = send_offsets.size(); - OF_NCCL_CHECK(ncclGroupStart()); - for (int64_t i = 0; i < parallel_num; ++i) { - OF_NCCL_CHECK(ncclSend(send_data + send_offsets.at(i), send_elem_cnt.at(i), nccl_data_type, i, - comm, cuda_stream)); - OF_NCCL_CHECK(ncclRecv(recv_data + recv_offsets.at(i), recv_elem_cnt.at(i), nccl_data_type, i, - comm, cuda_stream)); - } - OF_NCCL_CHECK(ncclGroupEnd()); -} - -template -void MakeShuffleParams(const IDX* host_num_unique_matrix, const int64_t num_ids, - const int64_t row_size, int64_t parallel_id, int64_t parallel_num, - std::vector* scatter_offset_vec, - std::vector* scatter_elem_cnt_vec, - std::vector* gather_offset_vec, - std::vector* gather_elem_cnt_vec) { - scatter_offset_vec->resize(parallel_num); - scatter_elem_cnt_vec->resize(parallel_num); - gather_offset_vec->resize(parallel_num); - gather_elem_cnt_vec->resize(parallel_num); - int64_t gather_offset = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t scatter_elem_cnt = - host_num_unique_matrix[parallel_id * parallel_num + i] * row_size; - const int64_t gather_elem_cnt = - host_num_unique_matrix[i * parallel_num + parallel_id] * row_size; - scatter_offset_vec->at(i) = i * num_ids * row_size; - scatter_elem_cnt_vec->at(i) = scatter_elem_cnt; - gather_offset_vec->at(i) = gather_offset; - gather_elem_cnt_vec->at(i) = gather_elem_cnt; - gather_offset += gather_elem_cnt; - } -} - -template -void ShuffleIdsAndTableIds(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, DataType ids_data_type, - DataType table_ids_data_type, IDX* host_num_unique_matrix, - K* partitioned_unique_ids, U* partitioned_unique_table_ids, - K* received_ids, U* received_table_ids, int64_t* received_elem_cnt, - bool need_process_table_ids) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, 1, parallel_id, parallel_num, &send_offsets, - &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, ids_data_type, send_offsets, send_elem_cnt, partitioned_unique_ids, - recv_offsets, recv_elem_cnt, received_ids); - *received_elem_cnt = recv_offsets.at(parallel_num - 1) + recv_elem_cnt.at(parallel_num - 1); - if (need_process_table_ids) { - ShuffleData(cuda_stream, comm, table_ids_data_type, send_offsets, send_elem_cnt, - partitioned_unique_table_ids, recv_offsets, recv_elem_cnt, received_table_ids); - } -} - -enum class IdShuffleBufferType { - kNumPartitionedUnique = 0, - kPartitionedUniqueIds, - kReceivedIds, - kTableIds, - kPartitionedUniqueTableIds, - kReceivedTableIds, - kWorkspace, - kMaxType -}; - -template -class IdShuffleTmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(IdShuffleTmpBufferManager); - IdShuffleTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t parallel_num, - bool need_table_ids, bool need_process_table_ids) - : offset_(0), - offsets_(static_cast(IdShuffleBufferType::kMaxType), -1), - sizes_(static_cast(IdShuffleBufferType::kMaxType)), - ptr_(ptr) { - const int64_t num_table_ids = need_process_table_ids ? num_ids : 0; - const size_t table_ids_bytes = need_table_ids ? num_ids * sizeof(U) : 0; - AllocBuffer(IdShuffleBufferType::kNumPartitionedUnique, parallel_num * sizeof(IDX)); - size_t partitioned_ids_bytes = parallel_num * num_ids * sizeof(K); - AllocBuffer(IdShuffleBufferType::kPartitionedUniqueIds, partitioned_ids_bytes); - AllocBuffer(IdShuffleBufferType::kReceivedIds, partitioned_ids_bytes); - AllocBuffer(IdShuffleBufferType::kTableIds, table_ids_bytes); - size_t partitioned_table_ids_bytes = parallel_num * num_table_ids * sizeof(U); - AllocBuffer(IdShuffleBufferType::kPartitionedUniqueTableIds, partitioned_table_ids_bytes); - AllocBuffer(IdShuffleBufferType::kReceivedTableIds, partitioned_table_ids_bytes); - const size_t hash_table_capacity = parallel_num * num_ids; - AllocBuffer(IdShuffleBufferType::kWorkspace, hash_table_capacity * sizeof(TableEntry)); - } - - template - T* Ptr(IdShuffleBufferType type) { - CHECK(ptr_ != nullptr); - int64_t offset = offsets_.at(static_cast(type)); - CHECK_NE(offset, -1); - return reinterpret_cast(reinterpret_cast(ptr_) + offset); - } - - int64_t Size(IdShuffleBufferType type) { return sizes_.at(static_cast(type)); } - - size_t TotalBufferSize() const { return offset_; } - - private: - void AllocBuffer(IdShuffleBufferType type, size_t size) { - const size_t type_id = static_cast(type); - CHECK_EQ(offsets_.at(type_id), -1); - offsets_.at(type_id) = offset_; - sizes_.at(type_id) = size; - offset_ += GetCudaAlignedSize(size); - } - size_t offset_; - std::vector offsets_; - std::vector sizes_; - void* ptr_; -}; - -template -class DataShuffleKernelState final : public user_op::OpKernelState { - public: - explicit DataShuffleKernelState(user_op::KernelInitContext* ctx) - : device_index_(-1), - stream_name_(EagerNcclCommMgr::kDefaultStreamName), - parallel_desc_(ctx->parallel_desc()) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - if (ctx->op_conf().has_stream_name_hint()) { stream_name_ = ctx->op_conf().stream_name_hint(); } - OF_CUDA_CHECK(hipMallocHost( - reinterpret_cast(&host_num_unique_matrix_), - parallel_desc_.parallel_num() * parallel_desc_.parallel_num() * sizeof(IDX))); - } - ~DataShuffleKernelState() { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipHostFree(host_num_unique_matrix_)); - } - - ncclComm_t comm() { return GetOrCreate().comm; } - - IDX* HostNumUniqueMatrix() { return host_num_unique_matrix_; } - - private: - struct Comm { - Comm(ncclComm_t comm) : comm(comm) {} - ncclComm_t comm; - }; - - const Comm& GetOrCreate() { - if (!comm_) { Init(); } - return *comm_; - } - - void Init() { - std::set> device_set; - for (int64_t parallel_id = 0; parallel_id < parallel_desc_.parallel_num(); ++parallel_id) { - int64_t machine_id = CHECK_JUST(parallel_desc_.MachineId4ParallelId(parallel_id)); - int64_t device_id = CHECK_JUST(parallel_desc_.DeviceId4ParallelId(parallel_id)); - device_set.emplace(std::make_pair(machine_id, device_id)); - } - EagerNcclCommMgr* comm_mgr = CHECK_NOTNULL(Singleton::Get()); - ncclComm_t comm; - comm = comm_mgr->GetCommForDeviceAndStreamName(device_set, stream_name_); - comm_.reset(new Comm(comm)); - } - - int device_index_; - bool has_independent_stream_; - std::string stream_name_; - ParallelDesc parallel_desc_; - std::unique_ptr comm_; - IDX* host_num_unique_matrix_; -}; - -} // namespace - -template -class IdShuffleKernel final : public user_op::OpKernel { - public: - IdShuffleKernel() = default; - ~IdShuffleKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* kernel_state = dynamic_cast*>(state); - CHECK(kernel_state != nullptr); - const user_op::Tensor* ids = ctx->Tensor4ArgNameAndIndex("ids", 0); - user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); - user_op::Tensor* inverse_unique_partition_indices = - ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); - user_op::Tensor* cur_rank_num_unique = ctx->Tensor4ArgNameAndIndex("cur_rank_num_unique", 0); - user_op::Tensor* cur_rank_unique_ids = ctx->Tensor4ArgNameAndIndex("cur_rank_unique_ids", 0); - user_op::Tensor* cur_rank_unique_table_ids = - ctx->Tensor4ArgNameAndIndex("cur_rank_unique_table_ids", 0); - user_op::Tensor* cur_rank_inverse_indices = - ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int32_t num_tables = ctx->Attr("num_tables"); - const bool has_table_ids = ctx->has_input("table_ids", 0); - const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); - const bool need_process_table_ids = (has_table_ids || num_tables > 1); - const int64_t num_ids = ids->shape_view().elem_cnt(); - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - hipStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - IdShuffleTmpBufferManager buffer_manager( - tmp_buffer->mut_dptr(), num_ids, parallel_num, need_gen_table_ids, need_process_table_ids); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize()); - - const U* table_ids_ptr; - if (has_table_ids) { - const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); - table_ids_ptr = reinterpret_cast(table_ids->dptr()); - } else if (need_gen_table_ids) { - hipLaunchKernelGGL(GenerateTableIds, BlocksNum4ThreadsNum(num_ids), kCudaThreadsNumPerBlock, 0, cuda_stream, - num_ids, num_tables, buffer_manager.template Ptr(IdShuffleBufferType::kTableIds)); - table_ids_ptr = buffer_manager.template Ptr(IdShuffleBufferType::kTableIds); - } else { - table_ids_ptr = nullptr; - } - IDX* num_partitioned_unique = - buffer_manager.template Ptr(IdShuffleBufferType::kNumPartitionedUnique); - K* partitioned_unique_ids = - buffer_manager.template Ptr(IdShuffleBufferType::kPartitionedUniqueIds); - U* partitioned_unique_table_ids = - buffer_manager.template Ptr(IdShuffleBufferType::kPartitionedUniqueTableIds); - IDX* num_unique_matrix_ptr = reinterpret_cast(num_unique_matrix->mut_dptr()); - size_t hash_table_capacity = parallel_num * num_ids; - void* workspace_ptr = buffer_manager.Ptr(IdShuffleBufferType::kWorkspace); - size_t workspace_size = buffer_manager.Size(IdShuffleBufferType::kWorkspace); - UniqueAndPartition( - cuda_stream, num_ids, hash_table_capacity, parallel_num, - reinterpret_cast(ids->dptr()), table_ids_ptr, num_partitioned_unique, - partitioned_unique_ids, partitioned_unique_table_ids, - reinterpret_cast(inverse_unique_partition_indices->mut_dptr()), workspace_ptr, - workspace_size, need_process_table_ids); - ncclComm_t comm = kernel_state->comm(); - OF_NCCL_CHECK(ncclAllGather(num_partitioned_unique, num_unique_matrix_ptr, parallel_num, - GetNcclDataType(num_unique_matrix->data_type()), comm, - cuda_stream)); - IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_unique_matrix, num_unique_matrix_ptr, - parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault, - cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); - - K* received_ids = buffer_manager.template Ptr(IdShuffleBufferType::kReceivedIds); - U* received_table_ids = buffer_manager.template Ptr(IdShuffleBufferType::kReceivedTableIds); - int64_t received_elem_cnt = 0; - ShuffleIdsAndTableIds(cuda_stream, comm, parallel_id, parallel_num, num_ids, ids->data_type(), - cur_rank_unique_table_ids->data_type(), host_num_unique_matrix, - partitioned_unique_ids, partitioned_unique_table_ids, received_ids, - received_table_ids, &received_elem_cnt, need_process_table_ids); - UniqueAndPartition( - cuda_stream, received_elem_cnt, hash_table_capacity, 1, received_ids, received_table_ids, - reinterpret_cast(cur_rank_num_unique->mut_dptr()), - reinterpret_cast(cur_rank_unique_ids->mut_dptr()), - reinterpret_cast(cur_rank_unique_table_ids->mut_dptr()), - reinterpret_cast(cur_rank_inverse_indices->mut_dptr()), workspace_ptr, workspace_size, - need_process_table_ids); - if (!need_process_table_ids) { - OF_CUDA_CHECK(hipMemsetAsync(cur_rank_unique_table_ids->mut_dptr(), 0, - received_elem_cnt * sizeof(U), cuda_stream)); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define ID_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) - -#define TABLE_ID_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ - OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) - -#define IDX_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) - -#define REGISTER_CUDA_ID_SHUFFLE_KERNEL(k_dtype_pair, table_id_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("id_shuffle") \ - .SetCreateFn< \ - IdShuffleKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ - && (user_op::HobDataType("cur_rank_unique_table_ids", 0) \ - == OF_PP_PAIR_SECOND(table_id_dtype_pair)) \ - && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ - const bool has_table_ids = ctx->has_input("table_ids", 0); \ - const int32_t num_tables = ctx->Attr("num_tables"); \ - const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); \ - const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ - IdShuffleTmpBufferManager \ - buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_desc().parallel_num(), \ - need_gen_table_ids, need_process_table_ids); \ - return buffer_manager.TotalBufferSize(); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ID_SHUFFLE_KERNEL, ID_DATA_TYPE_SEQ, - TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -template -void ShuffleEmbeddings(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - T* reverse_unique_cur_rank_embeddings, T* received_embeddings) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); -} - -// Quantized Version. -template -void ShuffleEmbeddings(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - int8_t* reverse_unique_cur_rank_embeddings, int8_t* received_embeddings, - T* reverse_cur_rank_quantize_factor, T* recv_quantize_factor) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - // shuffle quantized_embedding - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, - reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); - // shuffle quantize_factor - MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, - parallel_num, &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - reverse_cur_rank_quantize_factor, recv_offsets, recv_elem_cnt, recv_quantize_factor); -} - -__device__ float RoundHalfAwayFromZero(const float x) { - float abs_val = abs(x); - float floor_val = floor(abs_val + static_cast(0.5)); - return copysignf(floor_val, x); -} - -// warp reduce version. -constexpr int32_t kWarpSize = 32; -constexpr int32_t kMaxColSize = 1024; - -template -__inline__ __device__ T WarpMaxAllReduce(T val) { - for (int32_t lane_mask = thread_group_width / 2; lane_mask > 0; lane_mask /= 2) { - // val = max(val, __shfl_xor_sync(0xffffffff, val, lane_mask, thread_group_width)); - val = max(val, __shfl_xor(val, lane_mask, thread_group_width)); - } - return val; -} - -inline hipError_t GetWarpImplNumBlocks(int64_t block_size, int64_t max_blocks, int64_t waves, - int* num_blocks) { - int dev; - { - hipError_t err = hipGetDevice(&dev); - if (err != hipSuccess) { return err; } - } - int sm_count; - { - hipError_t err = hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev); - if (err != hipSuccess) { return err; } - } - int tpm; - { - hipError_t err = hipDeviceGetAttribute(&tpm, hipDeviceAttributeMaxThreadsPerMultiProcessor, dev); - if (err != hipSuccess) { return err; } - } - *num_blocks = - std::max(1, std::min(max_blocks, sm_count * tpm / block_size * waves)); - return hipSuccess; -} - -template -__global__ void QuantizeWarpImplKernel(const T* src, int8_t* dst, T* quantize_factor, - const int64_t rows, const int64_t cols) { - static_assert(cols_per_thread % pack_size == 0, ""); - static_assert(thread_group_width <= kWarpSize, ""); - static_assert(kWarpSize % thread_group_width == 0, ""); - constexpr int num_packs = cols_per_thread / pack_size; - assert(cols <= cols_per_thread * thread_group_width); - ComputeType buf[rows_per_access][cols_per_thread]; - const int global_thread_group_id = blockIdx.x * blockDim.y + threadIdx.y; - const int num_global_thread_group = gridDim.x * blockDim.y; - const int lane_id = threadIdx.x; - const int64_t step = num_global_thread_group * rows_per_access; - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - using StoreType = cuda::elementwise::PackType; - using StorePack = cuda::elementwise::Pack; - - for (int64_t row = global_thread_group_id * rows_per_access; row < rows; row += step) { - ComputeType thread_abs_max[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; row_id++) { - ComputeType* row_buf = buf[row_id]; - thread_abs_max[row_id] = 0.0; -#pragma unroll - for (int pack_id = 0; pack_id < num_packs; pack_id++) { - const int pack_offset = pack_id * pack_size; - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - LoadPack load_pack; - if (!padding || col < cols) { - const int64_t load_offset = ((row + row_id) * cols + col) / pack_size; - load_pack.storage = *(reinterpret_cast(src) + load_offset); -#pragma unroll - for (int i = 0; i < pack_size; i++) { - row_buf[pack_offset + i] = static_cast(load_pack.elem[i]); - thread_abs_max[row_id] = max(thread_abs_max[row_id], abs(row_buf[pack_offset + i])); - } - } else { -#pragma unroll - for (int i = 0; i < pack_size; i++) { row_buf[pack_offset + i] = 0.0; } - } - } - } - ComputeType warp_max[rows_per_access]; -#pragma unroll - for (int row_id = 0; row_id < rows_per_access; row_id++) { - warp_max[row_id] = WarpMaxAllReduce(thread_abs_max[row_id]); - if (threadIdx.x == 0) { quantize_factor[row + row_id] = static_cast(warp_max[row_id]); } - ComputeType* row_buf = buf[row_id]; - ComputeType quantize_factor_val = static_cast(127.0) / warp_max[row_id]; -#pragma unroll - for (int col = 0; col < cols_per_thread; col++) { - row_buf[col] = RoundHalfAwayFromZero(row_buf[col] * quantize_factor_val); - } -#pragma unroll - for (int pack_id = 0; pack_id < num_packs; pack_id++) { - const int pack_offset = pack_id * pack_size; - const int col = (pack_id * thread_group_width + lane_id) * pack_size; - StorePack store_pack; - if (!padding || col < cols) { - const int64_t store_offset = ((row + row_id) * cols + col) / pack_size; - for (int i = 0; i < pack_size; i++) { - store_pack.elem[i] = static_cast(row_buf[pack_id * pack_size + i]); - } - *(reinterpret_cast(dst) + store_offset) = store_pack.storage; - } - } - } - } -} - -template -inline hipError_t LaunchQuantizeWarpImpl(hipStream_t stream, const T* src, int8_t* dst, - T* quantize_factor, const int64_t rows, - const int64_t cols) { - constexpr int block_size = 128; - constexpr int waves = 32; - static_assert(block_size % thread_group_width == 0, ""); - constexpr int thread_groups_per_block = block_size / thread_group_width; - dim3 block_dim(thread_group_width, thread_groups_per_block); - const int64_t num_blocks = - (rows / rows_per_access + thread_groups_per_block - 1) / thread_groups_per_block; - int grid_dim_x = 0; - - hipError_t err = GetWarpImplNumBlocks(block_size, num_blocks, waves, &grid_dim_x); - if (err != hipSuccess) { return err; } - - QuantizeWarpImplKernel - <<>>(src, dst, quantize_factor, rows, cols); - return hipPeekAtLastError(); -} - -template -inline hipError_t DispatchQuantizeWarpImplPadding(hipStream_t stream, const T* src, int8_t* dst, - T* quantize_factor, const int64_t rows, - const int64_t cols) { - if (cols == cols_per_thread * thread_group_width) { - return LaunchQuantizeWarpImpl(stream, src, dst, quantize_factor, rows, - cols); - } else { - return LaunchQuantizeWarpImpl(stream, src, dst, quantize_factor, rows, - cols); - } -} - -template -typename std::enable_if::type DispatchQuantizeWarpImplCols( - hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchQuantizeWarpImplPadding(stream, src, dst, \ - quantize_factor, rows, cols); \ - } else { \ - return DispatchQuantizeWarpImplPadding(stream, src, dst, \ - quantize_factor, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchQuantizeWarpImplPadding( \ - stream, src, dst, quantize_factor, rows, cols); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(5) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(7) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(9) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(11) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(13) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(15) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(17) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(19) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(21) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(23) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(25) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(27) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(29) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(31) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -typename std::enable_if::type DispatchQuantizeWarpImplCols( - hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor, const int64_t rows, - const int64_t cols) { - if (cols <= 0) { return hipErrorInvalidValue; } -#define DEFINE_ONE_ELIF(thread_group_width) \ - else if (cols <= (thread_group_width)*pack_size) { \ - if (rows % 2 == 0) { \ - return DispatchQuantizeWarpImplPadding(stream, src, dst, \ - quantize_factor, rows, cols); \ - } else { \ - return DispatchQuantizeWarpImplPadding(stream, src, dst, \ - quantize_factor, rows, cols); \ - } \ - } - DEFINE_ONE_ELIF(1) - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF -#define DEFINE_ONE_ELIF(col) \ - else if (cols <= (col)*kWarpSize) { \ - return DispatchQuantizeWarpImplPadding( \ - stream, src, dst, quantize_factor, rows, cols); \ - } - DEFINE_ONE_ELIF(4) - DEFINE_ONE_ELIF(6) - DEFINE_ONE_ELIF(8) - DEFINE_ONE_ELIF(10) - DEFINE_ONE_ELIF(12) - DEFINE_ONE_ELIF(14) - DEFINE_ONE_ELIF(16) - DEFINE_ONE_ELIF(18) - DEFINE_ONE_ELIF(20) - DEFINE_ONE_ELIF(22) - DEFINE_ONE_ELIF(24) - DEFINE_ONE_ELIF(26) - DEFINE_ONE_ELIF(28) - DEFINE_ONE_ELIF(30) - DEFINE_ONE_ELIF(32) -#undef DEFINE_ONE_ELIF - else { - return hipErrorInvalidValue; - } -} - -template -struct DispatchQuantizeWarpImplPackSize { - hipError_t operator()(hipStream_t stream, const T* src, int8_t* dst, T* quantize_factor, - const int64_t rows, const int64_t cols) { - if (cols % 2 == 0) { - return DispatchQuantizeWarpImplCols(stream, src, dst, quantize_factor, - rows, cols); - } else { - return DispatchQuantizeWarpImplCols(stream, src, dst, quantize_factor, - rows, cols); - } - } -}; - -template -__global__ void DequantizeKernel(const int8_t* x, T* quantize_factor, T* out, IDX col_size, - IDX elem_cnt); - -template -__global__ void DequantizeKernel(const int8_t* x, T* quantize_factor, T* out, IDX col_size, - IDX elem_cnt) { - IDX global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - - for (int index = global_thread_id * pack_size; index < elem_cnt; - index += gridDim.x * blockDim.x * pack_size) { - IDX quantize_factor_idx = index / col_size; - ComputeType quantize_factor_val = static_cast(quantize_factor[quantize_factor_idx]) - / static_cast(127.0); - using LoadPackType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - using StorePackType = cuda::elementwise::PackType; - using StorePack = cuda::elementwise::Pack; - LoadPack load_pack{}; - StorePack store_pack{}; - load_pack.storage = *(reinterpret_cast(x) + index / pack_size); -#pragma unroll - for (int i = 0; i < pack_size; i++) { - store_pack.elem[i] = - static_cast(static_cast(load_pack.elem[i]) * quantize_factor_val); - } - *(reinterpret_cast(out) + index / pack_size) = store_pack.storage; - } -} - -template -hipError_t DispatchDequantizeKernelPackSize(hipStream_t stream, const int8_t* src, - T* quantize_factor, T* dst, const int64_t col_size, - const int64_t elem_cnt) { - const int64_t pack_num = elem_cnt / pack_size; - int grid_size = 0; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (err != hipSuccess) { return err; } - hipLaunchKernelGGL(HIP_KERNEL_NAME(DequantizeKernel), grid_size, cuda::elementwise::kBlockSize, 0, stream, src, quantize_factor, dst, col_size, - elem_cnt); - return hipSuccess; -} - -template -inline hipError_t LaunchDequantizeKernel(hipStream_t stream, const int8_t* src, - T* quantize_factor, T* dst, const int64_t col_size, - const int64_t elem_cnt) { - constexpr int quantized_src_pack_size = cuda::elementwise::PackSize(); - constexpr int dst_pack_size = cuda::elementwise::PackSize(); - int launch_pack_size = std::min(quantized_src_pack_size, dst_pack_size); - if (launch_pack_size == 8 && col_size % 8 == 0) { - hipError_t err = DispatchDequantizeKernelPackSize( - stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != hipSuccess) { return err; } - } else if (launch_pack_size == 4 && col_size % 4 == 0) { - hipError_t err = DispatchDequantizeKernelPackSize( - stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != hipSuccess) { return err; } - } else if (launch_pack_size == 2 && col_size % 2 == 0) { - hipError_t err = DispatchDequantizeKernelPackSize( - stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != hipSuccess) { return err; } - } else { - hipError_t err = DispatchDequantizeKernelPackSize( - stream, src, quantize_factor, dst, col_size, elem_cnt); - if (err != hipSuccess) { return err; } - } - return hipPeekAtLastError(); -} - -template -struct DefaultComputeType { - using type = T; -}; - -template<> -struct DefaultComputeType { - using type = float; -}; - -template -class EmbeddingShuffleKernel final : public user_op::OpKernel { - public: - EmbeddingShuffleKernel() = default; - ~EmbeddingShuffleKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* kernel_state = dynamic_cast*>(state); - CHECK(kernel_state != nullptr); - const user_op::Tensor* cur_rank_embeddings = - ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0); - const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); - const user_op::Tensor* cur_rank_inverse_indices = - ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); - const user_op::Tensor* inverse_unique_partition_indices = - ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); - user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - ncclComm_t comm = kernel_state->comm(); - using ComputeType = typename DefaultComputeType::type; - const int64_t embedding_size = cur_rank_embeddings->shape_view().At(1); - IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); - DataType data_type = cur_rank_embeddings->data_type(); - const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt(); - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - bool enable_quantized_comm_env_var = - ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); - bool enable_quantized_comm = enable_quantized_comm_env_var && (embedding_size < kMaxColSize); - if (enable_quantized_comm_env_var && !enable_quantized_comm) { - LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and " - "embedding_size less equal than 1024 can use quantized communication. "; - } - hipStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - OF_CUDA_CHECK(hipMemcpyAsync( - host_num_unique_matrix, reinterpret_cast(num_unique_matrix->dptr()), - parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault, cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); - int64_t cur_rank_num_ids = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id]; - } - size_t full_elem_cnt = parallel_num * num_ids * embedding_size; - CHECK_EQ(full_elem_cnt, cur_rank_embeddings->shape_view().elem_cnt()); - if (!enable_quantized_comm) { - size_t reverse_unique_cur_rank_embeddings_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size; - - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - reverse_unique_cur_rank_embeddings_size + received_embeddings_size); - - T* reverse_unique_cur_rank_embeddings = reinterpret_cast(tmp_buffer->mut_dptr()); - T* received_embeddings = reinterpret_cast(tmp_buffer->mut_dptr() - + reverse_unique_cur_rank_embeddings_size); - // reverse cur_rank unique - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, cur_rank_embeddings->dptr(), - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}), - reverse_unique_cur_rank_embeddings, 0); - - ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size, - data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings, - received_embeddings); - - // reverse unique_partition - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings, - Shape({1, parallel_num * num_ids, embedding_size}), embeddings->mut_dptr(), 0); - } else { - size_t reverse_unique_cur_rank_embeddings_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t)); - size_t received_embeddings_size = reverse_unique_cur_rank_embeddings_size; - size_t quantize_cur_rank_embeddings_size = reverse_unique_cur_rank_embeddings_size; - size_t reverse_recv_quantize_cur_rank_embeddings_size = - reverse_unique_cur_rank_embeddings_size; - size_t cur_rank_quantize_factor_size = - GetCudaAlignedSize(cur_rank_embeddings->shape_view().At(0) * sizeof(T)); - size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; - size_t recv_quantize_factor_size = cur_rank_quantize_factor_size; - size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size; - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - reverse_unique_cur_rank_embeddings_size + received_embeddings_size - + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size - + reverse_recv_quantize_factor_size); - int8_t* reverse_unique_cur_rank_embeddings = - reinterpret_cast(tmp_buffer->mut_dptr()); - int8_t* received_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size); - int8_t* quantize_cur_rank_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size); - int8_t* reverse_recv_quantize_cur_rank_embeddings = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size); - T* cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size); - T* reverse_cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size); - T* recv_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size); - T* reverse_recv_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + reverse_unique_cur_rank_embeddings_size - + received_embeddings_size + quantize_cur_rank_embeddings_size - + reverse_recv_quantize_cur_rank_embeddings_size + cur_rank_quantize_factor_size - + reverse_cur_rank_quantize_factor_size + recv_quantize_factor_size); - DispatchQuantizeWarpImplPackSize()( - cuda_stream, cur_rank_embeddings->dptr(), quantize_cur_rank_embeddings, - cur_rank_quantize_factor, cur_rank_num_ids, embedding_size); - // reverse cur_rank embedding unique - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, quantize_cur_rank_embeddings, - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, embedding_size}), - reverse_unique_cur_rank_embeddings, 0); - - // reverse cur_rank quantize factor unique - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_num_ids, cur_rank_quantize_factor, - Shape({1, cur_rank_embeddings->shape_view().elem_cnt() / embedding_size, 1}), - reverse_cur_rank_quantize_factor, 0); - - ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, embedding_size, - data_type, host_num_unique_matrix, reverse_unique_cur_rank_embeddings, - received_embeddings, reverse_cur_rank_quantize_factor, - recv_quantize_factor); - - // reverse unique_partition - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), received_embeddings, - Shape({1, parallel_num * num_ids, embedding_size}), - reverse_recv_quantize_cur_rank_embeddings, 0); - - GatherKernelUtilImpl::Forward( - ctx->stream(), reinterpret_cast(inverse_unique_partition_indices->dptr()), - inverse_unique_partition_indices->shape_view().elem_cnt(), recv_quantize_factor, - Shape({1, parallel_num * num_ids, 1}), reverse_recv_quantize_factor, 0); - - int32_t dequantize_row_size = inverse_unique_partition_indices->shape_view().elem_cnt(); - IDX dequantize_elem_cnt = dequantize_row_size * embedding_size; - OF_CUDA_CHECK((LaunchDequantizeKernel( - cuda_stream, reverse_recv_quantize_cur_rank_embeddings, reverse_recv_quantize_factor, - embeddings->mut_dptr(), embedding_size, dequantize_elem_cnt))); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("embedding_shuffle") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("cur_rank_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ - && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& cur_rank_embeddings = \ - ctx->InputTensorDesc("cur_rank_embeddings", 0); \ - bool enable_quantized_comm = \ - ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false) \ - && (cur_rank_embeddings.shape().At(1) < kMaxColSize); \ - size_t tmp_size = 0; \ - if (!enable_quantized_comm) { \ - size_t reverse_cur_rank_embeddings_size = GetCudaAlignedSize( \ - cur_rank_embeddings.shape().elem_cnt() * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ - size_t recv_unique_embeddings_size = reverse_cur_rank_embeddings_size; \ - tmp_size = reverse_cur_rank_embeddings_size + recv_unique_embeddings_size; \ - } else { \ - size_t total_elem_cnt = cur_rank_embeddings.shape().elem_cnt(); \ - size_t reverse_cur_rank_embeddings_size = \ - GetCudaAlignedSize(total_elem_cnt * sizeof(int8_t)); \ - size_t recv_unique_embeddings = reverse_cur_rank_embeddings_size; \ - size_t quantize_cur_rank_embeddings_size = reverse_cur_rank_embeddings_size; \ - size_t reverse_recv_quantize_cur_rank_embeddings_size = \ - reverse_cur_rank_embeddings_size; \ - size_t cur_rank_quantize_factor_size = GetCudaAlignedSize( \ - cur_rank_embeddings.shape().At(0) * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ - size_t reverse_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; \ - size_t recv_quantize_factor_size = cur_rank_quantize_factor_size; \ - size_t reverse_recv_quantize_factor_size = cur_rank_quantize_factor_size; \ - tmp_size = reverse_cur_rank_embeddings_size + recv_unique_embeddings \ - + quantize_cur_rank_embeddings_size \ - + reverse_recv_quantize_cur_rank_embeddings_size \ - + cur_rank_quantize_factor_size + reverse_cur_rank_quantize_factor_size \ - + recv_quantize_factor_size + reverse_recv_quantize_factor_size; \ - } \ - return tmp_size; \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_SHUFFLE_KERNEL, - FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - // FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - - -template -void ShuffleEmbeddingsGrad(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - T* unique_partition_embedding_grad, T* received_embeddings_grad) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, - unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, - received_embeddings_grad); -} - -// Quantize Version. -template -void ShuffleEmbeddingsGrad(hipStream_t cuda_stream, ncclComm_t comm, int64_t parallel_id, - int64_t parallel_num, int64_t num_ids, int64_t embedding_size, - DataType data_type, IDX* host_num_unique_matrix, - int8_t* unique_partition_embedding_grad, - int8_t* received_embeddings_grad, T* cur_rank_quantize_factor, - T* received_cur_rank_quantize_factor) { - std::vector send_offsets; - std::vector send_elem_cnt; - std::vector recv_offsets; - std::vector recv_elem_cnt; - // Shuffle Embedding Grad. - MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, - &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, - unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, - received_embeddings_grad); - // Shuffle Quantize factor. - MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, - parallel_num, &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); - ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, cur_rank_quantize_factor, - recv_offsets, recv_elem_cnt, received_cur_rank_quantize_factor); -} - -template -__global__ void UnsortedSegmentHalfGpu(const IDX in_h2_elem_cnt, const IDX h2_inner_dim_size, - const IDX inner_dim_size, const half* data, - const K* segment_ids, const IDX num_segments, - half2* out_h2) { - CUDA_1D_KERNEL_LOOP_T(IDX, i, in_h2_elem_cnt) { - const IDX segment_id_idx = i / h2_inner_dim_size; - const IDX h2_inner_idx = i - segment_id_idx * h2_inner_dim_size; - const IDX inner_idx_0 = 2 * h2_inner_idx; - const IDX inner_idx_1 = inner_idx_0 + 1; - const half* data_row = data + segment_id_idx * inner_dim_size; - half2 val; - val.data.x = data_row[inner_idx_0]; - val.data.y = (inner_idx_1 >= inner_dim_size) ? static_cast(0) : data_row[inner_idx_1]; - const IDX idx = segment_ids[segment_id_idx]; - const IDX out_h2_offset = idx * h2_inner_dim_size + h2_inner_idx; - cuda::atomic::Add(out_h2 + out_h2_offset, val); - } -} - -template -struct UnsortedSegmentSumPad { - void operator()(ep::Stream* stream, const K* segment_ids, const T* data, int64_t num_segment_ids, - int64_t num_segments, int64_t inner_dim_size, int64_t padded_inner_dim_size, - T* out) const { - UNIMPLEMENTED(); - } -}; - -template -struct UnsortedSegmentSumPad { - void operator()(ep::Stream* stream, const K* segment_ids, const half* data, - int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, - int64_t padded_inner_dim_size, half* out) const { - const int64_t data_elem_cnt = num_segment_ids * inner_dim_size; - const int64_t out_elem_cnt = num_segments * padded_inner_dim_size; - CHECK_EQ(padded_inner_dim_size % 2, 0); - CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); - const int64_t h2_inner_dim_size = padded_inner_dim_size / 2; - const int64_t in_h2_elem_cnt = num_segment_ids * h2_inner_dim_size; - if (std::max(data_elem_cnt, out_elem_cnt) < GetMaxVal() / 2) { - UnsortedSegmentHalfGpu - <<As()->cuda_stream()>>>( - in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, - reinterpret_cast(out)); - } else { - UnsortedSegmentHalfGpu - <<As()->cuda_stream()>>>( - in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, - reinterpret_cast(out)); - } - } -}; - -template -void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const T* data, - int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, - int64_t padded_inner_dim_size, T* out) { - if (inner_dim_size == padded_inner_dim_size) { - UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( - stream, segment_ids, data, num_segment_ids, num_segments, 1, inner_dim_size, 0, out); - } else { - CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); - UnsortedSegmentSumPad()(stream, segment_ids, data, num_segment_ids, num_segments, - inner_dim_size, padded_inner_dim_size, out); - } -} - -template -void UniquePartitionEmbeddingGrad(ep::Stream* stream, int64_t parallel_id, int64_t parallel_num, - int64_t num_ids, int64_t embedding_size, - int64_t padded_embedding_size, const IDX* host_num_unique_matrix, - const T* embedding_grad, - const IDX* inverse_unique_partition_indices, - T* unique_partition_embedding_grad) { - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t offset = i * num_ids * padded_embedding_size; - const int64_t valid_value_size = - host_num_unique_matrix[parallel_id * parallel_num + i] * padded_embedding_size * sizeof(T); - OF_CUDA_CHECK(hipMemsetAsync(unique_partition_embedding_grad + offset, 0, valid_value_size, - stream->As()->cuda_stream())); - } - UnsortedSegmentSum(stream, inverse_unique_partition_indices, embedding_grad, num_ids, - parallel_num * num_ids, embedding_size, padded_embedding_size, - unique_partition_embedding_grad); -} - -template -void UniqueCurRankEmbeddingGrad(ep::Stream* stream, DataType data_type, int64_t cur_rank_num_ids, - int64_t embedding_size, int64_t padded_embedding_size, - const T* cur_rank_embedding_grad, - const IDX* cur_rank_inverse_indices, - T* cur_rank_unique_embedding_grad, T* tmp_buffer) { - T* unsorted_segment_sum_out = - (embedding_size == padded_embedding_size) ? cur_rank_unique_embedding_grad : tmp_buffer; - OF_CUDA_CHECK(hipMemsetAsync(unsorted_segment_sum_out, 0, - cur_rank_num_ids * padded_embedding_size * sizeof(T), - stream->As()->cuda_stream())); - UnsortedSegmentSum(stream, cur_rank_inverse_indices, cur_rank_embedding_grad, - cur_rank_num_ids, cur_rank_num_ids, padded_embedding_size, - padded_embedding_size, unsorted_segment_sum_out); - if (embedding_size != padded_embedding_size) { - std::unique_ptr primitive = - ep::primitive::NewPrimitive(DeviceType::kCUDA, 2); - DimVector dst_shape = {cur_rank_num_ids, embedding_size}; - DimVector dst_pos_vec = {0, 0}; - DimVector src_shape = {cur_rank_num_ids, padded_embedding_size}; - DimVector src_pos_vec = {0, 0}; - DimVector extent_vec = {cur_rank_num_ids, embedding_size}; - primitive->Launch(stream, data_type, 2, cur_rank_unique_embedding_grad, dst_shape.data(), - dst_pos_vec.data(), unsorted_segment_sum_out, src_shape.data(), - src_pos_vec.data(), extent_vec.data()); - } -} - -int64_t GetPaddedEmbeddingSize(DataType data_type, int64_t embedding_size) { - if (data_type == DataType::kFloat16 && embedding_size % 2 != 0) { - return embedding_size + 1; - } else { - return embedding_size; - } -} - -template -class EmbeddingGradientShuffleKernel final : public user_op::OpKernel { - public: - EmbeddingGradientShuffleKernel() = default; - ~EmbeddingGradientShuffleKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* kernel_state = dynamic_cast*>(state); - CHECK(kernel_state != nullptr); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - - const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); - const user_op::Tensor* cur_rank_inverse_indices = - ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); - const user_op::Tensor* inverse_unique_partition_indices = - ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); - user_op::Tensor* cur_rank_unique_embedding_grad = - ctx->Tensor4ArgNameAndIndex("cur_rank_unique_embedding_grad", 0); - const int64_t embedding_size = cur_rank_unique_embedding_grad->shape_view().At(1); - IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); - DataType data_type = embedding_grad->data_type(); - const int64_t num_ids = inverse_unique_partition_indices->shape_view().elem_cnt(); - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - const int64_t padded_embedding_size = GetPaddedEmbeddingSize(data_type, embedding_size); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - ncclComm_t comm = kernel_state->comm(); - using ComputeType = typename DefaultComputeType::type; - bool enable_quantized_comm_env_var = - ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false); - bool enable_quantized_comm = - enable_quantized_comm_env_var && (padded_embedding_size < kMaxColSize); - if (enable_quantized_comm_env_var && !enable_quantized_comm) { - LOG(WARNING) << "Only envrionment variable ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM=1 and " - "embedding_size less equal than 1024 can use quantized communication. "; - } - hipStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_unique_matrix, num_unique_matrix->dptr(), - parallel_num * parallel_num * sizeof(IDX), hipMemcpyDefault, - cuda_stream)); - CHECK_JUST(ctx->stream()->Sync()); - - int64_t cur_rank_num_ids = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id]; - } - size_t full_num_ids = parallel_num * num_ids; - size_t full_elem_cnt = full_num_ids * padded_embedding_size; - size_t unique_partition_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - - if (!enable_quantized_comm) { - size_t received_embedding_grad_size = unique_partition_embedding_grad_size; - T* unique_partition_embedding_grad = reinterpret_cast(tmp_buffer->mut_dptr()); - T* received_embedding_grad = - reinterpret_cast(tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - unique_partition_embedding_grad_size + received_embedding_grad_size); - - UniquePartitionEmbeddingGrad( - ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size, - host_num_unique_matrix, embedding_grad->dptr(), - reinterpret_cast(inverse_unique_partition_indices->dptr()), - unique_partition_embedding_grad); - - ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids, - padded_embedding_size, data_type, host_num_unique_matrix, - unique_partition_embedding_grad, received_embedding_grad); - - // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer. - T* buffer_ptr = unique_partition_embedding_grad; - UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size, - padded_embedding_size, received_embedding_grad, - reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); - } else { - size_t received_embedding_grad_size = GetCudaAlignedSize(full_elem_cnt * sizeof(int8_t)); - size_t quantize_cur_rank_embedding_grad_size = received_embedding_grad_size; - size_t cur_rank_quantize_factor_size = GetCudaAlignedSize(full_num_ids * sizeof(T)); - size_t received_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; - size_t dequantize_cur_rank_embedding_grad_size = - GetCudaAlignedSize(full_elem_cnt * sizeof(T)); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - unique_partition_embedding_grad_size + received_embedding_grad_size - + quantize_cur_rank_embedding_grad_size + cur_rank_quantize_factor_size - + received_cur_rank_quantize_factor_size - + dequantize_cur_rank_embedding_grad_size); - T* unique_partition_embedding_grad = reinterpret_cast(tmp_buffer->mut_dptr()); - int8_t* received_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size); - - int8_t* quantize_cur_rank_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size); - T* cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size); - T* received_cur_rank_quantize_factor = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size - + cur_rank_quantize_factor_size); - T* dequantize_cur_rank_embedding_grad = reinterpret_cast( - tmp_buffer->mut_dptr() + unique_partition_embedding_grad_size - + received_embedding_grad_size + quantize_cur_rank_embedding_grad_size - + cur_rank_quantize_factor_size + received_cur_rank_quantize_factor_size); - - UniquePartitionEmbeddingGrad( - ctx->stream(), parallel_id, parallel_num, num_ids, embedding_size, padded_embedding_size, - host_num_unique_matrix, embedding_grad->dptr(), - reinterpret_cast(inverse_unique_partition_indices->dptr()), - unique_partition_embedding_grad); - - // Quantize. - for (int64_t i = 0; i < parallel_num; ++i) { - const int64_t embedding_grad_offset = i * num_ids * padded_embedding_size; - const int64_t quantize_factor_offset = i * num_ids; - const int64_t valid_row_size = host_num_unique_matrix[parallel_id * parallel_num + i]; - DispatchQuantizeWarpImplPackSize()( - cuda_stream, unique_partition_embedding_grad + embedding_grad_offset, - quantize_cur_rank_embedding_grad + embedding_grad_offset, - cur_rank_quantize_factor + quantize_factor_offset, valid_row_size, - padded_embedding_size); - } - - ShuffleEmbeddingsGrad(cuda_stream, comm, parallel_id, parallel_num, num_ids, - padded_embedding_size, data_type, host_num_unique_matrix, - quantize_cur_rank_embedding_grad, received_embedding_grad, - cur_rank_quantize_factor, received_cur_rank_quantize_factor); - - int64_t dequantize_cur_rank_num = 0; - for (int64_t i = 0; i < parallel_num; ++i) { - /* - Host num unique matrix: - | Partition0 | Partition1 | - | Rank0 | 2 | 4 | - | Rank1 | 3 | 3 | - After ShuffleEmbeddingGrads, each rank will exchange partition. - For example: - Rank0 will have (matrix[rank0][part0] + matrix[rank1][part0]) grad tensor. - Rank1 will have (matrix[rank0][part1] + matrix[rank1][part1]) grad tensor. - */ - dequantize_cur_rank_num += host_num_unique_matrix[i * parallel_num + parallel_id]; - } - IDX dequantize_elem_cnt = dequantize_cur_rank_num * padded_embedding_size; - OF_CUDA_CHECK((LaunchDequantizeKernel( - cuda_stream, received_embedding_grad, received_cur_rank_quantize_factor, - dequantize_cur_rank_embedding_grad, padded_embedding_size, dequantize_elem_cnt))); - // use unique_partition_embedding_grad as UniqueCurRankEmbeddingGrad buffer. - T* buffer_ptr = unique_partition_embedding_grad; - UniqueCurRankEmbeddingGrad(ctx->stream(), data_type, cur_rank_num_ids, embedding_size, - padded_embedding_size, dequantize_cur_rank_embedding_grad, - reinterpret_cast(cur_rank_inverse_indices->dptr()), - cur_rank_unique_embedding_grad->mut_dptr(), buffer_ptr); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL(t_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("embedding_gradient_shuffle") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ - && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& cur_rank_unique_embedding_grad = \ - ctx->InputTensorDesc("cur_rank_unique_embedding_grad", 0); \ - size_t cur_rank_embedding_grad_num = cur_rank_unique_embedding_grad.shape().At(0); \ - size_t embedding_size = cur_rank_unique_embedding_grad.shape().At(1); \ - size_t padded_embedding_size = \ - GetPaddedEmbeddingSize(cur_rank_unique_embedding_grad.data_type(), embedding_size); \ - size_t cur_rank_embedding_grad_elem_cnt = \ - cur_rank_embedding_grad_num * padded_embedding_size; \ - bool enable_quantized_comm = \ - ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM", false) \ - && (padded_embedding_size < kMaxColSize); \ - size_t tmp_size = 0; \ - if (!enable_quantized_comm) { \ - size_t cur_rank_embedding_grad_size = GetCudaAlignedSize( \ - cur_rank_embedding_grad_elem_cnt * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ - tmp_size = 2 * cur_rank_embedding_grad_size; \ - } else { \ - size_t unique_partition_embedding_grad_size = GetCudaAlignedSize( \ - cur_rank_embedding_grad_elem_cnt * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ - size_t received_embedding_grad_size = \ - GetCudaAlignedSize(cur_rank_embedding_grad_elem_cnt * sizeof(int8_t)); \ - size_t quantize_cur_rank_embedding_grad_size = received_embedding_grad_size; \ - size_t cur_rank_quantize_factor_size = GetCudaAlignedSize( \ - cur_rank_embedding_grad_num * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair))); \ - size_t received_cur_rank_quantize_factor_size = cur_rank_quantize_factor_size; \ - size_t dequantize_cur_rank_embedding_grad_size = unique_partition_embedding_grad_size; \ - tmp_size = unique_partition_embedding_grad_size + received_embedding_grad_size \ - + quantize_cur_rank_embedding_grad_size + cur_rank_quantize_factor_size \ - + received_cur_rank_quantize_factor_size \ - + dequantize_cur_rank_embedding_grad_size; \ - } \ - return tmp_size; \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_GRADIENT_SHUFFLE_KERNEL, - FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - // FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -template -class UniqueKeyValuePairKernel final : public user_op::OpKernel { - public: - UniqueKeyValuePairKernel() = default; - ~UniqueKeyValuePairKernel() override = default; - - private: - using user_op::OpKernel::Compute; - - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* keys = ctx->Tensor4ArgNameAndIndex("keys", 0); - user_op::Tensor* num_unique = ctx->Tensor4ArgNameAndIndex("num_unique", 0); - user_op::Tensor* unique_keys = ctx->Tensor4ArgNameAndIndex("unique_keys", 0); - user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); - user_op::Tensor* inverse_indices = ctx->Tensor4ArgNameAndIndex("inverse_indices", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int32_t num_tables = ctx->Attr("num_tables"); - const bool has_values = ctx->has_input("values", 0); - const bool need_values_buffer = (!has_values && num_tables > 1); - size_t values_buffer_bytes = - need_values_buffer ? GetCudaAlignedSize(keys->shape_view().elem_cnt() * sizeof(V)) : 0; - const int64_t num_keys = keys->shape_view().elem_cnt(); - const int64_t hash_capacity = num_keys; - const size_t workspace_bytes = GetCudaAlignedSize(hash_capacity * sizeof(TableEntry)); - CHECK_LE(values_buffer_bytes + workspace_bytes, tmp_buffer->shape_view().elem_cnt()); - hipStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); - const V* values_ptr; - if (has_values) { - const user_op::Tensor* values = ctx->Tensor4ArgNameAndIndex("values", 0); - values_ptr = reinterpret_cast(values->dptr()); - } else if (need_values_buffer) { - V* values_buffer_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); - hipLaunchKernelGGL(GenerateTableIds, BlocksNum4ThreadsNum(num_keys), kCudaThreadsNumPerBlock, 0, cuda_stream, - num_keys, num_tables, values_buffer_ptr); - values_ptr = values_buffer_ptr; - } else { - values_ptr = nullptr; - } - const bool need_process_table_ids = (has_values || num_tables > 1); - TableEntry* workspace_ptr = - reinterpret_cast*>(tmp_buffer->mut_dptr() + values_buffer_bytes); - UniqueAndPartition( - cuda_stream, num_keys, hash_capacity, 1, reinterpret_cast(keys->dptr()), - values_ptr, reinterpret_cast(num_unique->mut_dptr()), - reinterpret_cast(unique_keys->mut_dptr()), - reinterpret_cast(unique_values->mut_dptr()), - reinterpret_cast(inverse_indices->mut_dptr()), workspace_ptr, workspace_bytes, - need_process_table_ids); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL(k_dtype_pair, value_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("unique_key_value_pair") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("keys", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ - && (user_op::HobDataType("inverse_indices", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("unique_values", 0) == OF_PP_PAIR_SECOND(value_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& keys = ctx->InputTensorDesc("keys", 0); \ - const int64_t num_keys = keys.shape().elem_cnt(); \ - const int64_t hash_capacity = num_keys; \ - const size_t workspace_bytes = GetCudaAlignedSize( \ - hash_capacity * sizeof(TableEntry)); \ - const int32_t num_tables = ctx->Attr("num_tables"); \ - const bool has_values = ctx->has_input("values", 0); \ - const bool need_values_buffer = (!has_values && num_tables > 1); \ - size_t values_buffer_bytes = \ - need_values_buffer \ - ? GetCudaAlignedSize(num_keys * sizeof(OF_PP_PAIR_FIRST(value_dtype_pair))) \ - : 0; \ - return workspace_bytes + values_buffer_bytes; \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_UNIQUE_KEY_VALUE_PAIR_KERNEL, ID_DATA_TYPE_SEQ, - ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("id_shuffle"); -REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("embedding_shuffle"); -REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("embedding_gradient_shuffle"); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/deconv_cudnn_kernel.cpp b/oneflow/user/kernels/deconv_cudnn_kernel.cpp index 92405e169dfb0ccc2b22c52e6482fba864bdf3dd..bbfa8a983254a6dfc62e92dfa8bf015af6dc46dd 100644 --- a/oneflow/user/kernels/deconv_cudnn_kernel.cpp +++ b/oneflow/user/kernels/deconv_cudnn_kernel.cpp @@ -146,11 +146,11 @@ class DeConvGpuKernel final : public user_op::OpKernel { const auto& in = ctx->InputTensorDesc("in", 0); \ if (in.shape().elem_cnt() == 0) return 0; \ const auto& weight = ctx->InputTensorDesc("weight", 0); \ - const auto* out = ctx->OutputTensorDesc("out", 0); \ + const auto& out = ctx->OutputTensorDesc("out", 0); \ const auto& cudnn_conf = \ Singleton::Get()->resource().cudnn_conf(); \ return InferTmpSizeWithCudnn( \ - out, &weight, &in, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), \ + &out, &weight, &in, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), \ cudnn_conf.cudnn_conv_force_bwd_data_algo()); \ }) @@ -172,7 +172,8 @@ REGISTER_DECONV_KERNEL(deconv3d, double, 3); #include "oneflow/core/device/cudnn_conv_util.h" #include "oneflow/core/job/resource_desc.h" #include "oneflow/core/job/global_for.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/job/lazy_mode.h" namespace oneflow { namespace { @@ -185,13 +186,12 @@ struct CudnnDeConvArgsAndAlgo final { PerfT algo_perf; // CudnnDeConvArgsAndAlgo - CudnnDeConvArgsAndAlgo(const user_op::Tensor* x, const user_op::Tensor* w, const user_op::Tensor* y, - // user_op::Tensor* buf, const user_op::KernelComputeContext* ctx, - AllocatedCudnnConvResource& res, const user_op::KernelComputeContext* ctx, - ep::Stream* stream, bool has_forced_algo, int32_t forced_algo) + CudnnDeConvArgsAndAlgo(const user_op::Tensor* x, const user_op::Tensor* w, + const user_op::Tensor* y, user_op::Tensor* buf, + const user_op::KernelComputeContext* ctx, ep::Stream* stream, + bool has_forced_algo, int32_t forced_algo) : args(*ctx, x->data_type(), x->shape_view(), w->data_type(), w->shape_view(), y->data_type(), - // y->shape_view(), ctx->Attr("data_format"), buf->shape_view().elem_cnt(), - y->shape_view(), ctx->Attr("data_format"), 0, + y->shape_view(), ctx->Attr("data_format"), buf->shape_view().elem_cnt(), Singleton::Get() ->resource() .cudnn_conf() @@ -204,19 +204,10 @@ struct CudnnDeConvArgsAndAlgo final { ->resource() .cudnn_conf() .cudnn_conv_enable_pseudo_half()) { - - // size_t byte_size_of_buf = buf->shape_view().elem_cnt(); - - // size_t workspace_size; - - // AllocatedCudnnConvResource res(stream->As()->cudnn_handle(), - // const_cast(x->dptr()), const_cast(w->dptr()), - // const_cast(y->dptr()), nullptr, 0); - - // OF_CUDNN_CHECK(GetCudnnConvWorkspaceSize(args, &res, algo_perf.algo, &workspace_size)); - // byte_size_of_buf = workspace_size; - // args.params.max_ws_size = workspace_size; - + size_t byte_size_of_buf = buf->shape_view().elem_cnt(); + AllocatedCudnnConvResource res(stream->As()->cudnn_handle(), + const_cast(x->dptr()), const_cast(w->dptr()), + const_cast(y->dptr()), buf->mut_dptr()); if (has_forced_algo) { algo_perf = GetCudnnConvAlgorithmPerferenceWithResource( &args, &res, static_cast(forced_algo)); @@ -226,44 +217,36 @@ struct CudnnDeConvArgsAndAlgo final { CHECK_EQ(algo_perf.status, HIPDNN_STATUS_SUCCESS) << "op (" << ctx->op_name() << ") find algorithm perference failed. algo: " << algo_perf.algo; - // CHECK_LE(algo_perf.memory, byte_size_of_buf) - // << "op (" << ctx->op_name() << ") find algorithm " << algo_perf.algo << ", need memory " - // << algo_perf.memory << ", but cudnn_buf_limit_byte is " << byte_size_of_buf; + CHECK_LE(algo_perf.memory, byte_size_of_buf) + << "op (" << ctx->op_name() << ") find algorithm " << algo_perf.algo << ", need memory " + << algo_perf.memory << ", but cudnn_buf_limit_byte is " << byte_size_of_buf; } CudnnDeConvArgsAndAlgo() = delete; OF_DISALLOW_COPY_AND_MOVE(CudnnDeConvArgsAndAlgo); }; -template size_t InferTmpSizeWithCudnn(const user_op::TensorDesc* x, const user_op::TensorDesc* w, const user_op::TensorDesc* y, const user_op::InferContext& ctx, bool has_forced_algo, int32_t forced_algo) { - using AlgoT = decltype(std::declval().algo); const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); size_t workspace_size = cudnn_conf.cudnn_buf_limit_mbyte() * 1024 * 1024; if (!x->is_dynamic()) { CudnnConvArgs args(ctx, x->data_type(), ShapeView(x->shape()), w->data_type(), ShapeView(w->shape()), y->data_type(), ShapeView(y->shape()), ctx.Attr("data_format"), workspace_size, - cudnn_conf.cudnn_conv_heuristic_search_algo(), + cudnn_conf.cudnn_conv_heuristic_search_algo() || (!LazyMode::is_enabled()), cudnn_conf.cudnn_conv_use_deterministic_algo_only(), - cudnn_conf.cudnn_conv_enable_pseudo_half()); - PerfT algo_perf; - if (has_forced_algo) { - algo_perf = GetCudnnConvAlgorithmPerference(&args, static_cast(forced_algo)); - } else { - ManagedCudnnConvResource res(args); - OF_CUDNN_CHECK(GetCudnnConvWorkspaceSize(args, &res, algo_perf.algo, &workspace_size)); - args.params.max_ws_size = workspace_size; - algo_perf = FindCudnnConvAlgorithm(&args); - } - CHECK_EQ(algo_perf.status, HIPDNN_STATUS_SUCCESS) - << "op (" << ctx.op_name() - << ") find algorithm perference failed. algo: " << algo_perf.algo; - CHECK_LE(algo_perf.memory, workspace_size) - << "op (" << ctx.op_name() << ") find algorithm " << algo_perf.algo << ", need memory " - << algo_perf.memory << ", but cudnn_buf_limit_byte is " << workspace_size; - workspace_size = algo_perf.memory; + cudnn_conf.cudnn_conv_enable_pseudo_half() + || (ctx.Attr("data_format") == "channels_last" + && std::is_same::value)); + size_t ws = 0; + hipdnnConvolutionBwdDataAlgo_t algo; + ManagedCudnnConvResource res(args); + hipdnnGetConvolutionBackwardDataWorkspaceSize(res.cudnn_handle(), args.wdesc.Get(), + args.ydesc.Get(), args.cdesc.Get(), + args.xdesc.Get(), algo, &ws); + + workspace_size = ws; } workspace_size = std::max(size_t(1), workspace_size); return workspace_size; @@ -283,18 +266,13 @@ class DeConvGpuKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - // user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); if (in->shape_view().elem_cnt() == 0) return; const auto& cudnn_conf = Singleton::Get()->resource().cudnn_conf(); - AllocatedCudnnConvResource res(ctx->stream()->As()->cudnn_handle(), - const_cast(out->dptr()), const_cast(weight->dptr()), - const_cast(in->dptr()), nullptr, 0); - CudnnDeConvArgsAndAlgo args_and_algo( - // out, weight, in, buf, ctx, ctx->stream(), 0, - out, weight, in, res, ctx, ctx->stream(), 0, + out, weight, in, buf, ctx, ctx->stream(), 0, cudnn_conf.cudnn_conv_force_bwd_data_algo()); const CudnnConvArgs& args = args_and_algo.args; const hipdnnConvolutionBwdDataAlgoPerf_t& algo_perf = args_and_algo.algo_perf; @@ -302,8 +280,7 @@ class DeConvGpuKernel final : public user_op::OpKernel { OF_CUDNN_CHECK(hipdnnConvolutionBackwardData( ctx->stream()->As()->cudnn_handle(), CudnnSPOnePtr(), args.wdesc.Get(), weight->dptr(), args.ydesc.Get(), in->dptr(), args.cdesc.Get(), algo_perf.algo, - // buf->mut_dptr(), args.params.max_ws_size, CudnnSPZeroPtr(), args.xdesc.Get(), - res.ws_dptr(), args.params.max_ws_size, CudnnSPZeroPtr(), args.xdesc.Get(), + buf->mut_dptr(), args.max_ws_size, CudnnSPZeroPtr(), args.xdesc.Get(), out->mut_dptr())); } }; @@ -313,17 +290,17 @@ class DeConvGpuKernel final : public user_op::OpKernel { .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - // .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - // const auto& in = ctx->InputTensorDesc("in", 0); \ - // if (in.shape().elem_cnt() == 0) return 0; \ - // const auto& weight = ctx->InputTensorDesc("weight", 0); \ - // const auto* out = ctx->OutputTensorDesc("out", 0); \ - // const auto& cudnn_conf = \ - // Singleton::Get()->resource().cudnn_conf(); \ - // return InferTmpSizeWithCudnn( \ - // out, &weight, &in, *ctx, 0, \ - // cudnn_conf.cudnn_conv_force_bwd_data_algo()); \ - // }) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ + const auto& in = ctx->InputTensorDesc("in", 0); \ + if (in.shape().elem_cnt() == 0) return 0; \ + const auto& weight = ctx->InputTensorDesc("weight", 0); \ + const auto& out = ctx->OutputTensorDesc("out", 0); \ + const auto& cudnn_conf = \ + Singleton::Get()->resource().cudnn_conf(); \ + return InferTmpSizeWithCudnn( \ + &out, &weight, &in, *ctx, cudnn_conf.has_cudnn_conv_force_bwd_data_algo(), \ + cudnn_conf.cudnn_conv_force_bwd_data_algo()); \ + }) REGISTER_DECONV_KERNEL(deconv1d, float, 1); REGISTER_DECONV_KERNEL(deconv2d, float, 2); @@ -336,3 +313,4 @@ REGISTER_DECONV_KERNEL(deconv3d, double, 3); #endif + diff --git a/oneflow/user/kernels/deform_conv_kernel.cpp b/oneflow/user/kernels/deform_conv_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6f51a9a10ebba813223706dcce13913ce49946cf --- /dev/null +++ b/oneflow/user/kernels/deform_conv_kernel.cpp @@ -0,0 +1,701 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/user_op_hob.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/include/primitive/permute.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/matmul.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +namespace oneflow { + +namespace { + +template +T get_coordinate_weight(const T* im_data, int height, int width, T y, T x, bool is_y_direction) { + int y_l = floor(y); + int x_l = floor(x); + int y_h = y_l + 1; + int x_h = x_l + 1; + + bool valid_y_l = 0 <= y_l && y_l < height; + bool valid_y_h = 0 <= y_h && y_h < height; + bool valid_x_l = 0 <= x_l && x_l < width; + bool valid_x_h = 0 <= x_h && x_h < width; + + T zero = 0; + T v_yx = (valid_y_l && valid_x_l) ? im_data[y_l * width + x_l] : zero; + T v_yX = (valid_y_l && valid_x_h) ? im_data[y_l * width + x_h] : zero; + T v_Yx = (valid_y_h && valid_x_l) ? im_data[y_h * width + x_l] : zero; + T v_YX = (valid_y_h && valid_x_h) ? im_data[y_h * width + x_h] : zero; + + if (is_y_direction) { + T dx = x - x_l; + return dx * (v_YX - v_yX) + (1 - dx) * (v_Yx - v_yx); + } else { + T dy = y - y_l; + return dy * (v_YX - v_Yx) + (1 - dy) * (v_yX - v_yx); + } +} + +template +T DeformableIm2ColBilinear(const T* bottom_data, const int data_width, const int height, + const int width, T h, T w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + T lh = h - h_low; + T lw = w - w_low; + T hh = 1 - lh, hw = 1 - lw; + + T v1 = 0; + if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; + T v2 = 0; + if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; + T v3 = 0; + if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; + T v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; + + T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +T GetGradientWeight(T argmax_h, T argmax_w, const int h, const int w, const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + T weight = 0; + if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +T GetCoordinateWeight(T argmax_h, T argmax_w, const int height, const int width, const T* im_data, + const int data_width, const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { + // empty + return static_cast(0); + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + T weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += + -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += + -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +T bilinear_interpolate(const T* in, int height, int width, T h, T w) { + if (h <= -1 || height <= h || w <= -1 || width <= w) { return 0; } + + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + T lh = h - h_low; + T lw = w - w_low; + T hh = 1 - lh, hw = 1 - lw; + + T v1 = 0; + if (h_low >= 0 && w_low >= 0) v1 = in[h_low * width + w_low]; + T v2 = 0; + if (h_low >= 0 && w_high <= width - 1) v2 = in[h_low * width + w_high]; + T v3 = 0; + if (h_high <= height - 1 && w_low >= 0) v3 = in[h_high * width + w_low]; + T v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) v4 = in[h_high * width + w_high]; + + T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +void DeformableIm2Col(int n, const T* input, const T* offset, const T* mask, int height, int width, + int weight_h, int weight_w, int pad_h, int pad_w, int stride_h, int stride_w, + int dilation_h, int dilation_w, int batch_sz, int n_in_channels, + int n_offset_grps, int out_h, int out_w, bool use_mask, T* columns) { + for (int index = 0; index != n; ++index) { + const int out_x = index % out_w; + const int out_y = (index / out_w) % out_h; + const int out_b = (index / (out_w * out_h)) % batch_sz; + const int in_c = index / (out_w * out_h * batch_sz); + const int out_c = in_c * weight_h * weight_w; + + int c_per_offset_grp = n_in_channels / n_offset_grps; + const int grp_idx = in_c / c_per_offset_grp; + + auto columns_ptr = + columns + + (out_c * (batch_sz * out_h * out_w) + out_b * (out_h * out_w) + out_y * out_w + out_x); + auto input_ptr = input + (out_b * (n_in_channels * height * width) + in_c * (height * width)); + + auto offset_ptr = + offset + (out_b * n_offset_grps + grp_idx) * 2 * weight_h * weight_w * out_h * out_w; + + auto mask_ptr = mask; + if (use_mask) { + mask_ptr += (out_b * n_offset_grps + grp_idx) * weight_h * weight_w * out_h * out_w; + } + + for (int i = 0; i < weight_h; ++i) { + for (int j = 0; j < weight_w; ++j) { + const int mask_idx = i * weight_w + j; + const int offset_idx = 2 * mask_idx; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[mask_idx * (out_h * out_w) + out_y * out_w + out_x]; } + + const T offset_h = offset_ptr[offset_idx * (out_h * out_w) + out_y * out_w + out_x]; + const T offset_w = offset_ptr[(offset_idx + 1) * (out_h * out_w) + out_y * out_w + out_x]; + const T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + const T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + *columns_ptr = mask_value * bilinear_interpolate(input_ptr, height, width, y, x); + columns_ptr += batch_sz * out_h * out_w; + } + } + } +} + +template +void DeformableCol2Im(int n, const T* col, const T* offset_data, const T* mask_data, int channels, + int height, int width, int kernel_h, int kernel_w, int pad_h, int pad_w, + int stride_h, int stride_w, int dilation_h, int dilation_w, int batch_sz, + int n_offset_grps, int out_h, int out_w, bool use_mask, T* grad_im) { + for (int index = 0; index != n; ++index) { + const int out_x = index % out_w; + const int out_y = (index / out_w) % out_h; + const int b = (index / (out_w * out_h)) % batch_sz; + const int j = (index / (out_w * out_h * batch_sz)) % kernel_w; + const int i = (index / (out_w * out_h * batch_sz * kernel_w)) % kernel_h; + const int c = index / (out_w * out_h * batch_sz * kernel_w * kernel_h); + + int c_per_offset_grp = channels / n_offset_grps; + const int offset_grp = c / c_per_offset_grp; + auto offset_ptr = offset_data; + offset_ptr += (b * n_offset_grps + offset_grp) * 2 * kernel_h * kernel_w * out_h * out_w; + auto mask_ptr = mask_data; + if (use_mask) { + mask_ptr += (b * n_offset_grps + offset_grp) * kernel_h * kernel_w * out_h * out_w; + } + + const int mask_idx = i * kernel_w + j; + const int offset_idx = 2 * mask_idx; + + const int offset_h_ptr = ((offset_idx)*out_h + out_y) * out_w + out_x; + const int offset_w_ptr = ((offset_idx + 1) * out_h + out_y) * out_w + out_x; + + const T offset_h = offset_ptr[offset_h_ptr]; + const T offset_w = offset_ptr[offset_w_ptr]; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[(mask_idx * out_h + out_y) * out_w + out_x]; } + + const T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + const T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + + for (int dy = -1; dy <= 1; dy++) { + for (int dx = -1; dx <= 1; dx++) { + int yp = (int)y + dy; + int xp = (int)x + dx; + if (0 <= yp && yp < height && 0 <= xp && xp < width && std::abs(y - yp) < 1 + && std::abs(x - xp) < 1) { + int grad_pos = ((b * channels + c) * height + yp) * width + xp; + T weight = (1 - std::abs(y - yp)) * (1 - std::abs(x - xp)); + grad_im[grad_pos] += mask_value * weight * col[index]; + } + } + } + } +} + +template +void DeformableCol2ImCoord(int n, const T* col_data, const T* im_data, const T* offset_data, + const T* mask_data, int channels, int height, int width, int weight_h, + int weight_w, int pad_h, int pad_w, int stride_h, int stride_w, + int dilation_h, int dilation_w, int batch_sz, int offset_channels, + int n_offset_grps, int out_h, int out_w, const bool use_mask, + T* grad_offset, T* grad_mask) { + for (int index = 0; index != n; ++index) { + T grad_offset_val = 0; + T grad_mask_val = 0; + int w = index % out_w; + int h = (index / out_w) % out_h; + int w_w = (index / (out_w * out_h * 2)) % weight_w; + int w_h = (index / (out_w * out_h * 2 * weight_w)) % weight_h; + int c = (index / (out_w * out_h)) % offset_channels; + int b = index / (out_w * out_h * offset_channels); + + const int offset_grp = c / (2 * weight_h * weight_w); + const int col_step = weight_h * weight_w; + + int c_per_offset_grp = channels / n_offset_grps; + auto col_ptr = col_data; + col_ptr += offset_grp * c_per_offset_grp * weight_h * weight_w * batch_sz * out_w * out_h; + auto im_ptr = im_data; + im_ptr += (b * n_offset_grps + offset_grp) * c_per_offset_grp * height * width; + auto offset_ptr = offset_data; + offset_ptr += (b * n_offset_grps + offset_grp) * 2 * weight_h * weight_w * out_h * out_w; + + auto mask_ptr = mask_data; + if (use_mask) { + mask_ptr += (b * n_offset_grps + offset_grp) * weight_h * weight_w * out_h * out_w; + } + + const int offset_c = c - offset_grp * 2 * weight_h * weight_w; + const bool is_y_direction = offset_c % 2 == 0; + + const int c_bound = c_per_offset_grp * weight_h * weight_w; + for (int col_c = (offset_c / 2); col_c < c_bound; col_c += col_step) { + const int col_pos = (((col_c * batch_sz + b) * out_h) + h) * out_w + w; + + int out_x = col_pos % out_w; + int out_y = (col_pos / out_w) % out_h; + int j = (col_pos / (out_w * out_h * batch_sz)) % weight_w; + int i = (col_pos / (out_w * out_h * batch_sz * weight_w)) % weight_h; + + const int mask_idx = i * weight_w + j; + + const int offset_h_ptr = (((2 * mask_idx) * out_h + out_y) * out_w + out_x); + const int offset_w_ptr = (((2 * mask_idx + 1) * out_h + out_y) * out_w + out_x); + const T offset_h = offset_ptr[offset_h_ptr]; + const T offset_w = offset_ptr[offset_w_ptr]; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[(mask_idx * out_h + out_y) * out_w + out_x]; } + + T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + + const T weight = get_coordinate_weight(im_ptr, height, width, y, x, is_y_direction); + grad_offset_val += mask_value * weight * col_ptr[col_pos]; + + if (use_mask && is_y_direction) { + grad_mask_val += col_ptr[col_pos] * bilinear_interpolate(im_ptr, height, width, y, x); + } + + im_ptr += height * width; + } + + grad_offset[index] = grad_offset_val; + + if (use_mask && is_y_direction) { + const int idx = + ((((b * n_offset_grps + offset_grp) * weight_h + w_h) * weight_w + w_w) * out_h + h) + * out_w + + w; + grad_mask[idx] = grad_mask_val; + } + } +} + +ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { + return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; +} + +std::unique_ptr NewMatmulPrimitive(DeviceType device_type, + DataType data_type, bool transpose_a, + bool transpose_b) { + const auto trans_a = GetBlasTransposeType(transpose_a); + const auto trans_b = GetBlasTransposeType(transpose_b); + return ep::primitive::NewPrimitive(device_type, data_type, trans_a, + trans_b); +} +template +std::unique_ptr NewPermutePrimitive(Context* ctx, const int& num_dims) { + return ep::primitive::NewPrimitive(ctx->device_type(), num_dims); +} + +template +class DeformableConv2dCpuKernel final : public user_op::OpKernel { + public: + DeformableConv2dCpuKernel() = default; + ~DeformableConv2dCpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& output_shape = output->shape_view(); + const ShapeView& weight_shape = weight->shape_view(); + const int64_t out_elem_cnt = output_shape.elem_cnt(); + const int64_t output_bytes = (out_elem_cnt * sizeof(T)); + + T* column_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr() + output_bytes); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + + const int64_t outputWidth = + ((input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW) + 1; + const int64_t outputHeight = + ((input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH) + 1; + const int64_t column_nums = input_shape.At(1) * input_shape.At(0) * outputHeight * outputWidth; + + if (column_nums > 0) { + DeformableIm2Col(column_nums, input->dptr(), offset->dptr(), mask->dptr(), + input_shape.At(2), input_shape.At(3), kH, kW, padH, padW, dH, dW, + dilationH, dilationW, input_shape.At(0), input_shape.At(1), + deformable_group, output_shape.At(2), output_shape.At(3), use_mask, + column_tmp_buffer); + + const int64_t weight_group_offset = weight->shape_view().elem_cnt() / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight * outputWidth / group; + const int64_t output_group_offset = out_elem_cnt / group; + + auto matmul = NewMatmulPrimitive(ctx->device_type(), output->data_type(), false, false); + CHECK(matmul); + FOR_RANGE(int, g, 0, group) { + matmul->Launch(ctx->stream(), weight_shape.At(0) / group, + input_shape.At(0) * outputHeight * outputWidth, + input_shape.At(1) * kW * kH / group, static_cast(1), + weight->dptr() + g * weight_group_offset, + column_tmp_buffer + g * column_group_offset, static_cast(0), + tmp_buffer->mut_dptr() + g * output_group_offset); + } + + std::vector out_shapevec( + {output_shape.At(1), output_shape.At(0), output_shape.At(2), output_shape.At(3)}); + auto transpose = NewPermutePrimitive(ctx, output_shape.NumAxes()); + CHECK(transpose); + transpose->Launch(ctx->stream(), output->data_type(), output_shape.NumAxes(), + out_shapevec.data(), tmp_buffer->dptr(), + std::vector({1, 0, 2, 3}).data(), output->mut_dptr()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class DeformableConv2dInputGradCpuKernel final : public user_op::OpKernel { + public: + DeformableConv2dInputGradCpuKernel() = default; + ~DeformableConv2dInputGradCpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* output_grad = ctx->Tensor4ArgNameAndIndex("output_grad", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + user_op::Tensor* input_grad = ctx->Tensor4ArgNameAndIndex("input_grad", 0); + user_op::Tensor* offset_grad = ctx->Tensor4ArgNameAndIndex("offset_grad", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& output_grad_shape = output_grad->shape_view(); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& weight_shape = weight->shape_view(); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + const T* data_mask = nullptr; + T* data_mask_grad = nullptr; + if (use_mask) { + data_mask = ctx->Tensor4ArgNameAndIndex("mask", 0)->dptr(); + data_mask_grad = ctx->Tensor4ArgNameAndIndex("mask_grad", 0)->mut_dptr(); + } + + const int64_t outputWidth = + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + const int64_t outputHeight = + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + + primitive->Launch(ctx->stream(), input_grad->mut_dptr(), 0, + input_grad->shape_view().elem_cnt() * sizeof(T)); + if (use_mask) { + primitive->Launch( + ctx->stream(), data_mask_grad, 0, + ctx->Tensor4ArgNameAndIndex("mask_grad", 0)->shape_view().elem_cnt() * sizeof(T)); + } + const int64_t nthreads_coord = + outputHeight * outputWidth * 2 * kH * kW * deformable_group * input_shape.At(0); + const int64_t nthreads_feat = + outputHeight * outputWidth * input_shape.At(0) * kH * kW * input_shape.At(1); + if (nthreads_coord > 0 && nthreads_feat > 0) { + const int64_t weight_group_offset = weight_shape.elem_cnt() / group; + const int64_t output_grad_group_offset = output_grad_shape.Count(1) / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight * outputWidth / group; + + auto matmul = NewMatmulPrimitive(ctx->device_type(), input_grad->data_type(), true, true); + CHECK(matmul); + FOR_RANGE(int, g, 0, group) { + matmul->Launch(ctx->stream(), weight_shape.Count(1), + input_shape.At(0) * outputHeight * outputWidth, weight_shape.At(0) / group, + static_cast(1), weight->dptr() + g * weight_group_offset, + output_grad->dptr() + g * output_grad_group_offset, static_cast(0), + tmp_buffer->mut_dptr() + g * column_group_offset); + } + DeformableCol2ImCoord( + nthreads_coord, tmp_buffer->dptr(), input->dptr(), offset->dptr(), data_mask, + input_shape.At(1), input_shape.At(2), input_shape.At(3), kH, kW, padH, padW, dH, dW, + dilationH, dilationW, input_shape.At(0), 2 * kH * kW * deformable_group, deformable_group, + outputHeight, outputWidth, use_mask, offset_grad->mut_dptr(), data_mask_grad); + + DeformableCol2Im(nthreads_feat, tmp_buffer->dptr(), offset->dptr(), data_mask, + input_shape.At(1), input_shape.At(2), input_shape.At(3), kH, kW, padH, + padW, dH, dW, dilationH, dilationW, input_shape.At(0), deformable_group, + outputHeight, outputWidth, use_mask, input_grad->mut_dptr()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class DeformableConv2dParamGradCpuKernel final : public user_op::OpKernel { + public: + DeformableConv2dParamGradCpuKernel() = default; + ~DeformableConv2dParamGradCpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* output_grad = ctx->Tensor4ArgNameAndIndex("output_grad", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + user_op::Tensor* weight_grad = ctx->Tensor4ArgNameAndIndex("weight_grad", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& output_grad_shape = output_grad->shape_view(); + const ShapeView& weight_grad_shape = weight_grad->shape_view(); + const ShapeView& input_shape = input->shape_view(); + const int64_t out_elem_cnt = output_grad_shape.elem_cnt(); + const int64_t output_bytes = (out_elem_cnt * sizeof(T)); + + T* column_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr() + output_bytes); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + const int64_t outputWidth = + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + const int64_t outputHeight = + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + const int64_t column_nums = input_shape.At(1) * input_shape.At(0) * outputHeight * outputWidth; + if (column_nums > 0) { + DeformableIm2Col(column_nums, input->dptr(), offset->dptr(), mask->dptr(), + input_shape.At(2), input_shape.At(3), kH, kW, padH, padW, dH, dW, + dilationH, dilationW, input_shape.At(0), input_shape.At(1), + deformable_group, output_grad_shape.At(2), output_grad_shape.At(3), + use_mask, column_tmp_buffer); + + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + primitive->Launch(ctx->stream(), weight_grad->mut_dptr(), 0, + weight_grad->shape_view().elem_cnt()); + + std::vector output_grad_buffer_vec({output_grad_shape.At(1), output_grad_shape.At(0), + output_grad_shape.At(2), + output_grad_shape.At(3)}); + + auto transpose = NewPermutePrimitive(ctx, output_grad_shape.NumAxes()); + CHECK(transpose); + transpose->Launch(ctx->stream(), output_grad->data_type(), output_grad_shape.NumAxes(), + output_grad_buffer_vec.data(), output_grad->dptr(), + std::vector({1, 0, 2, 3}).data(), tmp_buffer->mut_dptr()); + + const int64_t output_grad_group_offset = output_grad_shape.elem_cnt() / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kW * input_shape.At(0) * outputHeight * outputWidth / group; + const int64_t weight_grad_group_offset = weight_grad->shape_view().elem_cnt() / group; + FOR_RANGE(int, g, 0, group) { + auto matmul = NewMatmulPrimitive(ctx->device_type(), weight_grad->data_type(), false, true); + CHECK(matmul); + + matmul->Launch(ctx->stream(), weight_grad_shape.At(0) / group, + input_shape.At(1) * kW * kH / group, + input_shape.At(0) * outputHeight * outputWidth, static_cast(1), + tmp_buffer->dptr() + g * output_grad_group_offset, + column_tmp_buffer + g * column_group_offset, static_cast(0), + weight_grad->mut_dptr() + g * weight_grad_group_offset); + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_DEFORM_CONV2D_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& output_shape = ctx->OutputShape("output", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + ((input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW) + 1; \ + const int64_t outputHeight = \ + ((input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH) + 1; \ + const int64_t column_bytes = (input_shape.At(1) * kW * kH * input_shape.At(0) \ + * outputHeight * outputWidth * sizeof(dtype)); \ + const int64_t output_bytes = (output_shape.elem_cnt() * sizeof(dtype)); \ + return column_bytes + output_bytes; \ + }); +REGISTER_DEFORM_CONV2D_CPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_CPU_KERNEL(double) + +#define REGISTER_DEFORM_CONV2D_INPUT_GRAD_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d_input_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("weight", 0) == GetDataType::value) \ + && (user_op::HobDataType("offset", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; \ + const int64_t outputHeight = \ + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; \ + const int64_t column_bytes = input_shape.At(1) * kW * kH * input_shape.At(0) \ + * outputHeight * outputWidth * sizeof(dtype); \ + return column_bytes; \ + }); +REGISTER_DEFORM_CONV2D_INPUT_GRAD_CPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_INPUT_GRAD_CPU_KERNEL(double) + +#define REGISTER_DEFORM_CONV2D_PARAM_GRAD_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d_param_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("offset", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& output_grad_shape = ctx->InputShape("output_grad", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; \ + const int64_t outputHeight = \ + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; \ + const int64_t column_bytes = (input_shape.At(1) * kW * kH * input_shape.At(0) \ + * outputHeight * outputWidth * sizeof(dtype)); \ + const int64_t output_bytes = (output_grad_shape.elem_cnt() * sizeof(dtype)); \ + return column_bytes + output_bytes; \ + }); +REGISTER_DEFORM_CONV2D_PARAM_GRAD_CPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_PARAM_GRAD_CPU_KERNEL(double) + +} // namespace +} // namespace oneflow diff --git a/oneflow/user/kernels/deform_conv_kernel.cu b/oneflow/user/kernels/deform_conv_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..fcde65d51a658b3f611aa5cc558577c1c9f2abe3 --- /dev/null +++ b/oneflow/user/kernels/deform_conv_kernel.cu @@ -0,0 +1,727 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/user_op_hob.h" +#include "oneflow/core/ep/include/primitive/permute.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/matmul.h" +#include "oneflow/core/ep/include/primitive/memset.h" + +namespace oneflow { + +namespace { + +__device__ __forceinline__ float Add(float* address, float val) { return atomicAdd(address, val); } + +__device__ __forceinline__ double Add(double* address, double val) { +#if (__CUDA_ARCH__ >= 600) || defined(WITH_ROCM) + return atomicAdd(address, val); +#else + auto address_as_ull = reinterpret_cast(address); + unsigned long long int old = *address_as_ull; + unsigned long long int assumed = 0; + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + } while (assumed != old); + return __longlong_as_double(old); +#endif +} + +template +__device__ T bilinear_interpolate(const T* in, int height, int width, T h, T w) { + if (h <= -1 || height <= h || w <= -1 || width <= w) { return 0; } + + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + T lh = h - h_low; + T lw = w - w_low; + T hh = 1 - lh, hw = 1 - lw; + + T v1 = 0; + if (h_low >= 0 && w_low >= 0) v1 = in[h_low * width + w_low]; + T v2 = 0; + if (h_low >= 0 && w_high <= width - 1) v2 = in[h_low * width + w_high]; + T v3 = 0; + if (h_high <= height - 1 && w_low >= 0) v3 = in[h_high * width + w_low]; + T v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) v4 = in[h_high * width + w_high]; + + T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ T DeformableIm2ColBilinear(const T* bottom_data, const int data_width, const int height, + const int width, T h, T w) { + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + T lh = h - h_low; + T lw = w - w_low; + T hh = 1 - lh, hw = 1 - lw; + + T v1 = 0; + if (h_low >= 0 && w_low >= 0) v1 = bottom_data[h_low * data_width + w_low]; + T v2 = 0; + if (h_low >= 0 && w_high <= width - 1) v2 = bottom_data[h_low * data_width + w_high]; + T v3 = 0; + if (h_high <= height - 1 && w_low >= 0) v3 = bottom_data[h_high * data_width + w_low]; + T v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) v4 = bottom_data[h_high * data_width + w_high]; + + T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ T get_coordinate_weight(const T* im_data, int height, int width, T y, T x, + bool is_y_direction) { + int y_l = floor(y); + int x_l = floor(x); + int y_h = y_l + 1; + int x_h = x_l + 1; + + bool valid_y_l = 0 <= y_l && y_l < height; + bool valid_y_h = 0 <= y_h && y_h < height; + bool valid_x_l = 0 <= x_l && x_l < width; + bool valid_x_h = 0 <= x_h && x_h < width; + + T zero = 0; + T v_yx = (valid_y_l && valid_x_l) ? im_data[y_l * width + x_l] : zero; + T v_yX = (valid_y_l && valid_x_h) ? im_data[y_l * width + x_h] : zero; + T v_Yx = (valid_y_h && valid_x_l) ? im_data[y_h * width + x_l] : zero; + T v_YX = (valid_y_h && valid_x_h) ? im_data[y_h * width + x_h] : zero; + + if (is_y_direction) { + T dx = x - x_l; + return dx * (v_YX - v_yX) + (1 - dx) * (v_Yx - v_yx); + } else { + T dy = y - y_l; + return dy * (v_YX - v_Yx) + (1 - dy) * (v_yX - v_yx); + } +} + +template +__device__ T GetGradientWeight(T argmax_h, T argmax_w, const int h, const int w, const int height, + const int width) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { + // empty + return static_cast(0); + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + T weight = 0; + if (h == argmax_h_low && w == argmax_w_low) weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ T GetCoordinateWeight(T argmax_h, T argmax_w, const int height, const int width, + const T* im_data, const int data_width, const int bp_dir) { + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) { + // empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + T weight = 0; + + if (bp_dir == 0) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += + -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } else if (bp_dir == 1) { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += + -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void DeformableCol2Im(int n, const T* col, const T* offset_data, const T* mask_data, + int channels, int height, int width, int kernel_h, int kernel_w, + int pad_h, int pad_w, int stride_h, int stride_w, int dilation_h, + int dilation_w, int batch_sz, int n_offset_grps, int out_h, + int out_w, bool use_mask, T* grad_im) { + CUDA_1D_KERNEL_LOOP(index, n) { + const int out_x = index % out_w; + const int out_y = (index / out_w) % out_h; + const int b = (index / (out_w * out_h)) % batch_sz; + const int j = (index / (out_w * out_h * batch_sz)) % kernel_w; + const int i = (index / (out_w * out_h * batch_sz * kernel_w)) % kernel_h; + const int c = index / (out_w * out_h * batch_sz * kernel_w * kernel_h); + + int c_per_offset_grp = channels / n_offset_grps; + const int offset_grp = c / c_per_offset_grp; + auto offset_ptr = offset_data; + + offset_ptr += (b * n_offset_grps + offset_grp) * 2 * kernel_h * kernel_w * out_h * out_w; + auto mask_ptr = mask_data; + if (use_mask) { + mask_ptr += (b * n_offset_grps + offset_grp) * kernel_h * kernel_w * out_h * out_w; + } + + const int mask_idx = i * kernel_w + j; + const int offset_idx = 2 * mask_idx; + + const int offset_h_ptr = ((offset_idx)*out_h + out_y) * out_w + out_x; + const int offset_w_ptr = ((offset_idx + 1) * out_h + out_y) * out_w + out_x; + + const T offset_h = offset_ptr[offset_h_ptr]; + const T offset_w = offset_ptr[offset_w_ptr]; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[(mask_idx * out_h + out_y) * out_w + out_x]; } + + const T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + const T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + + for (int dy = -1; dy <= 1; dy++) { + for (int dx = -1; dx <= 1; dx++) { + int yp = (int)y + dy; + int xp = (int)x + dx; + if (0 <= yp && yp < height && 0 <= xp && xp < width && abs(y - yp) < 1 && abs(x - xp) < 1) { + int grad_pos = ((b * channels + c) * height + yp) * width + xp; + T weight = (1 - abs(y - yp)) * (1 - abs(x - xp)); + Add(grad_im + grad_pos, mask_value * weight * col[index]); + } + } + } + } +} + +template +__global__ void DeformableIm2Col(int n, const T* input, const T* offset, const T* mask, int height, + int width, int weight_h, int weight_w, int pad_h, int pad_w, + int stride_h, int stride_w, int dilation_h, int dilation_w, + int batch_sz, int n_in_channels, int n_offset_grps, int out_h, + int out_w, bool use_mask, T* columns) { + CUDA_1D_KERNEL_LOOP(index, n) { + const int out_x = index % out_w; + const int out_y = (index / out_w) % out_h; + const int out_b = (index / (out_w * out_h)) % batch_sz; + const int in_c = index / (out_w * out_h * batch_sz); + const int out_c = in_c * weight_h * weight_w; + + int c_per_offset_grp = n_in_channels / n_offset_grps; + const int grp_idx = in_c / c_per_offset_grp; + auto columns_ptr = columns; + columns_ptr += + (out_c * (batch_sz * out_h * out_w) + out_b * (out_h * out_w) + out_y * out_w + out_x); + auto input_ptr = input; + input_ptr += (out_b * (n_in_channels * height * width) + in_c * (height * width)); + auto offset_ptr = offset; + offset_ptr += (out_b * n_offset_grps + grp_idx) * 2 * weight_h * weight_w * out_h * out_w; + auto mask_ptr = mask; + if (use_mask) { + mask_ptr += (out_b * n_offset_grps + grp_idx) * weight_h * weight_w * out_h * out_w; + } + + for (int i = 0; i < weight_h; ++i) { + for (int j = 0; j < weight_w; ++j) { + const int mask_idx = i * weight_w + j; + const int offset_idx = 2 * mask_idx; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[mask_idx * (out_h * out_w) + out_y * out_w + out_x]; } + + const T offset_h = offset_ptr[offset_idx * (out_h * out_w) + out_y * out_w + out_x]; + const T offset_w = offset_ptr[(offset_idx + 1) * (out_h * out_w) + out_y * out_w + out_x]; + const T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + const T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + *columns_ptr = mask_value * bilinear_interpolate(input_ptr, height, width, y, x); + columns_ptr += batch_sz * out_h * out_w; + } + } + } +} + +template +__global__ void DeformableCol2imCoord(int n, const T* col_data, const T* im_data, + const T* offset_data, const T* mask_data, int channels, + int height, int width, int weight_h, int weight_w, int pad_h, + int pad_w, int stride_h, int stride_w, int dilation_h, + int dilation_w, int batch_sz, int offset_channels, + int n_offset_grps, int out_h, int out_w, const bool use_mask, + T* grad_offset, T* grad_mask) { + CUDA_1D_KERNEL_LOOP(index, n) { + T grad_offset_val = 0; + T grad_mask_val = 0; + + int w = index % out_w; + int h = (index / out_w) % out_h; + int w_w = (index / (out_w * out_h * 2)) % weight_w; + int w_h = (index / (out_w * out_h * 2 * weight_w)) % weight_h; + int c = (index / (out_w * out_h)) % offset_channels; + int b = index / (out_w * out_h * offset_channels); + + const int offset_grp = c / (2 * weight_h * weight_w); + const int col_step = weight_h * weight_w; + + int c_per_offset_grp = channels / n_offset_grps; + auto col_ptr = col_data; + col_ptr += offset_grp * c_per_offset_grp * weight_h * weight_w * batch_sz * out_w * out_h; + auto im_ptr = im_data; + im_ptr += (b * n_offset_grps + offset_grp) * c_per_offset_grp * height * width; + auto offset_ptr = offset_data; + offset_ptr += (b * n_offset_grps + offset_grp) * 2 * weight_h * weight_w * out_h * out_w; + auto mask_ptr = mask_data; + if (use_mask) { + mask_ptr += (b * n_offset_grps + offset_grp) * weight_h * weight_w * out_h * out_w; + } + + const int offset_c = c - offset_grp * 2 * weight_h * weight_w; + const bool is_y_direction = offset_c % 2 == 0; + + const int c_bound = c_per_offset_grp * weight_h * weight_w; + for (int col_c = (offset_c / 2); col_c < c_bound; col_c += col_step) { + const int col_pos = (((col_c * batch_sz + b) * out_h) + h) * out_w + w; + + int out_x = col_pos % out_w; + int out_y = (col_pos / out_w) % out_h; + int j = (col_pos / (out_w * out_h * batch_sz)) % weight_w; + int i = (col_pos / (out_w * out_h * batch_sz * weight_w)) % weight_h; + + const int mask_idx = i * weight_w + j; + + const int offset_h_ptr = (((2 * mask_idx) * out_h + out_y) * out_w + out_x); + const int offset_w_ptr = (((2 * mask_idx + 1) * out_h + out_y) * out_w + out_x); + const T offset_h = offset_ptr[offset_h_ptr]; + const T offset_w = offset_ptr[offset_w_ptr]; + + T mask_value = 1; + if (use_mask) { mask_value = mask_ptr[(mask_idx * out_h + out_y) * out_w + out_x]; } + + T y = (out_y * stride_h - pad_h) + i * dilation_h + offset_h; + T x = (out_x * stride_w - pad_w) + j * dilation_w + offset_w; + + const T weight = get_coordinate_weight(im_ptr, height, width, y, x, is_y_direction); + grad_offset_val += mask_value * weight * col_ptr[col_pos]; + + if (use_mask && is_y_direction) { + grad_mask_val += col_ptr[col_pos] * bilinear_interpolate(im_ptr, height, width, y, x); + } + + im_ptr += height * width; + } + + grad_offset[index] = grad_offset_val; + + if (use_mask && is_y_direction) { + const int idx = + ((((b * n_offset_grps + offset_grp) * weight_h + w_h) * weight_w + w_w) * out_h + h) + * out_w + + w; + grad_mask[idx] = grad_mask_val; + } + } +} + +} // namespace + +ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { + return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; +} + +std::unique_ptr NewMatmulPrimitive(DeviceType device_type, + DataType data_type, bool transpose_a, + bool transpose_b) { + const auto trans_a = GetBlasTransposeType(transpose_a); + const auto trans_b = GetBlasTransposeType(transpose_b); + return ep::primitive::NewPrimitive(device_type, data_type, trans_a, + trans_b); +} + +template +std::unique_ptr NewPermutePrimitive(Context* ctx, const int& num_dims) { + return ep::primitive::NewPrimitive(ctx->device_type(), num_dims); +} + +template +class DeformableConv2dCudaKernel final : public user_op::OpKernel { + public: + DeformableConv2dCudaKernel() = default; + ~DeformableConv2dCudaKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& output_shape = output->shape_view(); + const int64_t out_elem_cnt = output_shape.elem_cnt(); + const int64_t output_bytes = GetCudaAlignedSize(out_elem_cnt * sizeof(T)); + + T* column_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr() + output_bytes); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + + const int32_t channel_per_deformable_group = input_shape.At(1) / deformable_group; + const int64_t outputWidth = + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + const int64_t outputHeight = + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + const int64_t column_nums = input_shape.At(1) * input_shape.At(0) * outputWidth * outputHeight; + if (column_nums > 0) { + DeformableIm2Col<<stream()->As()->cuda_stream()>>>( + column_nums, input->dptr(), offset->dptr(), mask->dptr(), input_shape.At(2), + input_shape.At(3), kH, kW, padH, padW, dH, dW, dilationH, dilationW, input_shape.At(0), + input_shape.At(1), deformable_group, output_shape.At(2), output_shape.At(3), use_mask, + column_tmp_buffer); + + const int64_t weight_group_offset = weight->shape_view().elem_cnt() / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight * outputWidth / group; + const int64_t output_group_offset = out_elem_cnt / group; + + auto matmul = NewMatmulPrimitive(ctx->device_type(), output->data_type(), false, false); + CHECK(matmul); + + FOR_RANGE(int, g, 0, group) { + matmul->Launch(ctx->stream(), weight->shape_view().At(0) / group, + input_shape.At(0) * outputHeight * outputWidth, + input_shape.At(1) * kW * kH / group, static_cast(1), + weight->dptr() + g * weight_group_offset, + column_tmp_buffer + g * column_group_offset, static_cast(0), + tmp_buffer->mut_dptr() + g * output_group_offset); + } + + std::vector out_shapevec( + {output_shape.At(1), output_shape.At(0), output_shape.At(2), output_shape.At(3)}); + + auto transpose = NewPermutePrimitive(ctx, output_shape.NumAxes()); + CHECK(transpose); + transpose->Launch(ctx->stream(), output->data_type(), output_shape.NumAxes(), + out_shapevec.data(), tmp_buffer->dptr(), + std::vector({1, 0, 2, 3}).data(), output->mut_dptr()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class DeformableConv2dInputGradKernel final : public user_op::OpKernel { + public: + DeformableConv2dInputGradKernel() = default; + ~DeformableConv2dInputGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* output_grad = ctx->Tensor4ArgNameAndIndex("output_grad", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + user_op::Tensor* input_grad = ctx->Tensor4ArgNameAndIndex("input_grad", 0); + user_op::Tensor* offset_grad = ctx->Tensor4ArgNameAndIndex("offset_grad", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& output_grad_shape = output_grad->shape_view(); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& weight_shape = weight->shape_view(); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + const T* data_mask = nullptr; + T* data_mask_grad = nullptr; + if (use_mask) { + data_mask = ctx->Tensor4ArgNameAndIndex("mask", 0)->dptr(); + data_mask_grad = ctx->Tensor4ArgNameAndIndex("mask_grad", 0)->mut_dptr(); + } + + const int64_t outputWidth = + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + const int64_t outputHeight = + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + + primitive->Launch(ctx->stream(), input_grad->mut_dptr(), 0, + input_grad->shape_view().elem_cnt() * sizeof(T)); + + const int64_t nthreads_coord = + outputHeight * outputWidth * 2 * deformable_group * input_shape.At(0) * kW * kH; + const int64_t nthreads_feat = + outputHeight * outputWidth * input_shape.At(0) * input_shape.At(1) * kW * kH; + if (nthreads_coord > 0 && nthreads_feat > 0) { + const int64_t weight_group_offset = weight_shape.elem_cnt() / group; + const int64_t output_grad_group_offset = output_grad_shape.Count(1) / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight * outputWidth / group; + + auto matmul = NewMatmulPrimitive(ctx->device_type(), input_grad->data_type(), true, true); + CHECK(matmul); + FOR_RANGE(int, g, 0, group) { + matmul->Launch(ctx->stream(), weight_shape.Count(1), + input_shape.At(0) * outputHeight * outputWidth, weight_shape.At(0) / group, + static_cast(1), weight->dptr() + g * weight_group_offset, + output_grad->dptr() + g * output_grad_group_offset, static_cast(0), + tmp_buffer->mut_dptr() + g * column_group_offset); + } + DeformableCol2imCoord<<stream()->As()->cuda_stream()>>>( + nthreads_coord, tmp_buffer->dptr(), input->dptr(), offset->dptr(), data_mask, + input_shape.At(1), input_shape.At(2), input_shape.At(3), kH, kW, padH, padW, dH, dW, + dilationH, dilationW, input_shape.At(0), 2 * kH * kW * deformable_group, deformable_group, + outputHeight, outputWidth, use_mask, offset_grad->mut_dptr(), data_mask_grad); + DeformableCol2Im<<stream()->As()->cuda_stream()>>>( + nthreads_feat, tmp_buffer->dptr(), offset->dptr(), data_mask, input_shape.At(1), + input_shape.At(2), input_shape.At(3), kH, kW, padH, padW, dH, dW, dilationH, dilationW, + input_shape.At(0), deformable_group, outputHeight, outputWidth, use_mask, + input_grad->mut_dptr()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; +template +class DeformableConv2dParamGradKernel final : public user_op::OpKernel { + public: + DeformableConv2dParamGradKernel() = default; + ~DeformableConv2dParamGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* output_grad = ctx->Tensor4ArgNameAndIndex("output_grad", 0); + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* offset = ctx->Tensor4ArgNameAndIndex("offset", 0); + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + user_op::Tensor* weight_grad = ctx->Tensor4ArgNameAndIndex("weight_grad", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const ShapeView& output_grad_shape = output_grad->shape_view(); + const ShapeView& weight_grad_shape = weight_grad->shape_view(); + const ShapeView& input_shape = input->shape_view(); + const int64_t out_elem_cnt = output_grad_shape.elem_cnt(); + const int64_t output_bytes = GetCudaAlignedSize(out_elem_cnt * sizeof(T)); + T* column_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr() + output_bytes); + const int32_t kW = weight->shape_view().At(2); + const int32_t kH = weight->shape_view().At(3); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t group = ctx->Attr("groups"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + const int32_t channel_per_deformable_group = input_shape.At(1) / deformable_group; + const int64_t outputWidth = + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + const int64_t outputHeight = + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + const T* data_mask = nullptr; + if (use_mask) { data_mask = ctx->Tensor4ArgNameAndIndex("mask", 0)->dptr(); } + const int64_t column_nums = input_shape.At(1) * input_shape.At(0) * outputHeight * outputWidth; + if (column_nums > 0) { + DeformableIm2Col<<stream()->As()->cuda_stream()>>>( + column_nums, input->dptr(), offset->dptr(), data_mask, input_shape.At(2), + input_shape.At(3), kH, kW, padH, padW, dH, dW, dilationH, dilationW, input_shape.At(0), + input_shape.At(1), deformable_group, output_grad_shape.At(2), output_grad_shape.At(3), + use_mask, column_tmp_buffer); + + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + primitive->Launch(ctx->stream(), weight_grad->mut_dptr(), 0, + weight_grad->shape_view().elem_cnt() * sizeof(T)); + + std::vector output_grad_buffer_vec({output_grad_shape.At(1), output_grad_shape.At(0), + output_grad_shape.At(2), + output_grad_shape.At(3)}); + + auto transpose = NewPermutePrimitive(ctx, output_grad_shape.NumAxes()); + CHECK(transpose); + transpose->Launch(ctx->stream(), output_grad->data_type(), output_grad_shape.NumAxes(), + output_grad_buffer_vec.data(), output_grad->dptr(), + std::vector({1, 0, 2, 3}).data(), tmp_buffer->mut_dptr()); + + const int64_t output_grad_group_offset = output_grad_shape.elem_cnt() / group; + const int64_t column_group_offset = + input_shape.At(1) * kW * kW * input_shape.At(0) * outputHeight * outputWidth / group; + const int64_t weight_grad_group_offset = weight_grad->shape_view().elem_cnt() / group; + FOR_RANGE(int, g, 0, group) { + auto matmul = NewMatmulPrimitive(ctx->device_type(), weight_grad->data_type(), false, true); + CHECK(matmul); + + matmul->Launch(ctx->stream(), weight_grad_shape.At(0) / group, + input_shape.At(1) * kW * kH / group, + input_shape.At(0) * outputHeight * outputWidth, static_cast(1), + tmp_buffer->dptr() + g * output_grad_group_offset, + column_tmp_buffer + g * column_group_offset, static_cast(0), + weight_grad->mut_dptr() + g * weight_grad_group_offset); + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_DEFORM_CONV2D_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& output_shape = ctx->OutputShape("output", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; \ + const int64_t outputHeight = \ + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; \ + const int64_t column_bytes = \ + GetCudaAlignedSize(input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight \ + * outputWidth * sizeof(dtype)); \ + const int64_t output_bytes = GetCudaAlignedSize(output_shape.elem_cnt() * sizeof(dtype)); \ + return column_bytes + output_bytes; \ + }); +REGISTER_DEFORM_CONV2D_GPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_GPU_KERNEL(double) + +#define REGISTER_DEFORM_CONV2D_INPUT_GRAD_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d_input_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("weight", 0) == GetDataType::value) \ + && (user_op::HobDataType("offset", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; \ + const int64_t outputHeight = \ + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; \ + const int64_t column_bytes = \ + GetCudaAlignedSize(input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight \ + * outputWidth * sizeof(dtype)); \ + return column_bytes; \ + }); +REGISTER_DEFORM_CONV2D_INPUT_GRAD_GPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_INPUT_GRAD_GPU_KERNEL(double) + +#define REGISTER_DEFORM_CONV2D_PARAM_GRAD_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("deform_conv2d_param_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value) \ + && (user_op::HobDataType("offset", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& input_shape = ctx->InputShape("input", 0); \ + const Shape& output_grad_shape = ctx->InputShape("output_grad", 0); \ + const Shape& weight_shape = ctx->InputShape("weight", 0); \ + const int32_t kW = weight_shape.At(2); \ + const int32_t kH = weight_shape.At(3); \ + const int32_t dW = ctx->Attr("stride_w"); \ + const int32_t dH = ctx->Attr("stride_h"); \ + const int32_t padW = ctx->Attr("pad_w"); \ + const int32_t padH = ctx->Attr("pad_h"); \ + const int32_t dilationW = ctx->Attr("dilation_w"); \ + const int32_t dilationH = ctx->Attr("dilation_h"); \ + const int64_t outputWidth = \ + (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; \ + const int64_t outputHeight = \ + (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; \ + const int64_t column_bytes = \ + GetCudaAlignedSize(input_shape.At(1) * kW * kH * input_shape.At(0) * outputHeight \ + * outputWidth * sizeof(dtype)); \ + const int64_t output_bytes = \ + GetCudaAlignedSize(output_grad_shape.elem_cnt() * sizeof(dtype)); \ + return column_bytes + output_bytes; \ + }); +REGISTER_DEFORM_CONV2D_PARAM_GRAD_GPU_KERNEL(float) +REGISTER_DEFORM_CONV2D_PARAM_GRAD_GPU_KERNEL(double) + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/diag_kernel.hip.cpp b/oneflow/user/kernels/diag_kernel.hip.cpp deleted file mode 100644 index fa8a382a0eec085b41ea58d0236faebb5b3a17ec..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/diag_kernel.hip.cpp +++ /dev/null @@ -1,80 +0,0 @@ -#include "hip/hip_runtime.h" -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/user/kernels/diag_kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace { - -template -__global__ void vector_diagonal_kernel(T* out_buf, const T* in_buf, int32_t size, int32_t stride) { - CUDA_1D_KERNEL_LOOP(i, size) { out_buf[i * stride] = in_buf[i]; } -} - -template -__global__ void matrix_diagonal_kernel(T* out_buf, const T* in_buf, int32_t size, int32_t stride) { - CUDA_1D_KERNEL_LOOP(i, size) { out_buf[i] = in_buf[i * stride]; } -} - -template -struct DiagFunctor final { - void operator()(ep::Stream* stream, T* out_buf, const T* in_buf, int32_t size, int32_t stride, - int32_t in_dim) { - if (in_dim == 1) { - vector_diagonal_kernel<<As()->cuda_stream()>>>(out_buf, in_buf, size, - stride); - } else { - matrix_diagonal_kernel<<As()->cuda_stream()>>>(out_buf, in_buf, size, - stride); - } - } -}; - -template -struct DiagGradFunctor final { - void operator()(ep::Stream* stream, T* dx_buf, const T* dy_buf, int32_t dx_cnt, int32_t dy_cnt, - int32_t stride, int32_t in_dim) { - if (in_dim == 1) { - matrix_diagonal_kernel<<As()->cuda_stream()>>>(dx_buf, dy_buf, - dx_cnt, stride); - } else { - vector_diagonal_kernel<<As()->cuda_stream()>>>(dx_buf, dy_buf, - dy_cnt, stride); - } - } -}; - -} // namespace - -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, half); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, float); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, double); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, bool); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, uint8_t); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, int8_t); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, int32_t); -REGISTER_DIAG_KERNELS(DeviceType::kCUDA, int64_t); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/diagonal_kernel.cpp b/oneflow/user/kernels/diagonal_kernel.cpp index 3468aac03a45dd7d43f4e93adf51c960322c0cd0..77e888bbc8d15f56ba00bdaeee95753a5152bbd8 100644 --- a/oneflow/user/kernels/diagonal_kernel.cpp +++ b/oneflow/user/kernels/diagonal_kernel.cpp @@ -18,11 +18,7 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/kernel_util.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#else #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif namespace oneflow { namespace { diff --git a/oneflow/user/kernels/diagonal_kernel.cu b/oneflow/user/kernels/diagonal_kernel.cu index dbe94ba15268b41cc319e1ebcdfe1339e1749e0d..f1ddf0ec9d7a2c81b2ddaf9e106aa9989aeef504 100644 --- a/oneflow/user/kernels/diagonal_kernel.cu +++ b/oneflow/user/kernels/diagonal_kernel.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "hip/hip_runtime.h" + #include "oneflow/core/common/util.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" diff --git a/oneflow/user/kernels/diagonal_kernel.hip.cpp b/oneflow/user/kernels/diagonal_kernel.hip.cpp deleted file mode 100644 index cd0815efc928dba18744fcc3b34df047eb3ec332..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/diagonal_kernel.hip.cpp +++ /dev/null @@ -1,163 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace { - -template -__global__ void forward_diagonal_kernel(T* out_buf, const T* in_buf, int32_t size, int32_t dim1, - int32_t dim2) { - int32_t offset_index = (dim1 + 1) * dim2; - CUDA_1D_KERNEL_LOOP(index, size * dim2) { - int32_t i = index / dim2; - int32_t j = index - i * dim2; - out_buf[j * size + i] = in_buf[i * offset_index + j]; - } -} - -template -__global__ void backward_diagonal_kernel(T* dx_buf, const T* dy_buf, int32_t size, int32_t dim1, - int32_t dim2) { - int32_t offset_index = (dim1 + 1) * dim2; - CUDA_1D_KERNEL_LOOP(index, size * dim2) { - int32_t i = index / dim2; - int32_t j = index - i * dim2; - dx_buf[i * offset_index + j] = dy_buf[j * size + i]; - } -} - -template -struct DiagonalFunctor final { - void operator()(ep::Stream* stream, T* out_buf, const T* in_buf, int32_t size, int32_t dim1, - int32_t dim2) { - if (size * dim2 > 0) { - forward_diagonal_kernel - <<As()->cuda_stream()>>>(out_buf, in_buf, size, dim1, dim2); - } - } -}; - -template -struct DiagonalGradFunctor final { - void operator()(ep::Stream* stream, T* dx_buf, const T* dy_buf, int32_t size, int32_t dim1, - int32_t dim2) { - if (size * dim2 > 0) { - backward_diagonal_kernel - <<As()->cuda_stream()>>>(dx_buf, dy_buf, size, dim1, dim2); - } - } -}; - -} // namespace - -template -class GpuDiagonalKernel final : public user_op::OpKernel { - public: - GpuDiagonalKernel() = default; - ~GpuDiagonalKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const int32_t offset = ctx->Attr("offset"); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const ShapeView& out_shape = out->shape_view(); - const ShapeView& in_shape = in->shape_view(); - const T* in_buf = in->dptr(); - T* out_buf = out->mut_dptr(); - - int32_t size = out_shape.At(out_shape.NumAxes() - 1); - int32_t dim1 = in_shape.At(1); - int32_t dim2 = 0; - if (in_shape.NumAxes() <= 2) { - dim2 = 1; - } else { - dim2 = in_shape.Count(2, in_shape.NumAxes()); - } - - int32_t offset_in_bufer = (offset >= 0 ? offset * dim2 : -offset * dim1 * dim2); - in_buf += offset_in_bufer; - - DiagonalFunctor()(ctx->stream(), out_buf, in_buf, size, dim1, dim2); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuDiagonalBackwardKernel final : public user_op::OpKernel { - public: - GpuDiagonalBackwardKernel() = default; - ~GpuDiagonalBackwardKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - int32_t offset = ctx->Attr("offset"); - const ShapeView& dx_shape = dx->shape_view(); - const ShapeView& dy_shape = dy->shape_view(); - T* dx_buf = dx->mut_dptr(); - const T* dy_buf = dy->dptr(); - - Memset(ctx->stream(), dx->mut_dptr(), 0, dx_shape.elem_cnt() * sizeof(T)); - - int32_t dim1 = dx_shape.At(1); - int32_t dim2 = 0; - if (dx_shape.NumAxes() <= 2) { - dim2 = 1; - } else { - dim2 = dx_shape.Count(2, dx_shape.NumAxes()); - } - int32_t size = dy_shape.At(dy_shape.NumAxes() - 1); - int32_t offset_in_bufer = (offset >= 0 ? offset * dim2 : -offset * dim1 * dim2); - dx_buf += offset_in_bufer; - - DiagonalGradFunctor()(ctx->stream(), dx_buf, dy_buf, size, dim1, dim2); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_DIAGONAL_KERNELS(dtype) \ - REGISTER_USER_KERNEL("diagonal") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("diagonal_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)); - -REGISTER_DIAGONAL_KERNELS(bool); -REGISTER_DIAGONAL_KERNELS(half); -REGISTER_DIAGONAL_KERNELS(float); -REGISTER_DIAGONAL_KERNELS(double); -REGISTER_DIAGONAL_KERNELS(int8_t); -REGISTER_DIAGONAL_KERNELS(int32_t); -REGISTER_DIAGONAL_KERNELS(int64_t); - -#undef REGISTER_DIAGONAL_KERNELS - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/dim_gather_kernel_util.cu b/oneflow/user/kernels/dim_gather_kernel_util.cu index c8963c54e0410048702fb762b4c8f8e2a6836c78..558ac1985109084c7b6ebd466f0a94ad22e95df7 100644 --- a/oneflow/user/kernels/dim_gather_kernel_util.cu +++ b/oneflow/user/kernels/dim_gather_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/user/kernels/dim_gather_kernel_util.h" diff --git a/oneflow/user/kernels/dim_gather_kernel_util.h b/oneflow/user/kernels/dim_gather_kernel_util.h index ba7a0eeeb4df660f16c8cef8bf980b31d65ef28e..385b6c4018019e2235ac964bd6bc8b6d7d2b0eac 100644 --- a/oneflow/user/kernels/dim_gather_kernel_util.h +++ b/oneflow/user/kernels/dim_gather_kernel_util.h @@ -15,12 +15,9 @@ limitations under the License. */ #ifndef ONEFLOW_USER_KERNELS_DIM_GATHER_KERNEL_UTIL_H_ #define ONEFLOW_USER_KERNELS_DIM_GATHER_KERNEL_UTIL_H_ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/cuda/atomic.cuh" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_ROCM #include "oneflow/core/ndarray/xpu_util.h" #include "oneflow/core/common/nd_index_offset_helper.h" diff --git a/oneflow/user/kernels/dim_gather_kernel_util.hip.cpp b/oneflow/user/kernels/dim_gather_kernel_util.hip.cpp deleted file mode 100644 index 69ce03c6f8d34e652769e633b1d384f2f8d5f566..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/dim_gather_kernel_util.hip.cpp +++ /dev/null @@ -1,65 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/dim_gather_kernel_util.h" - -namespace oneflow { - -namespace user_op { - -template -__global__ void DoCUDADimGather(const DimOpIndexNdHelper input_nd_helper, - const DimOpIndexNdHelper index_nd_helper, int ndim, - int64_t elem_cnt, int32_t dim_length, int32_t dim, - const IDX_T* index, const IN_T* input, IN_T* output) { - DoDimGather(input_nd_helper, index_nd_helper, ndim, elem_cnt, dim_length, dim, index, - input, output); -} - -template -struct DimGatherFunctor final { - void operator()(ep::Stream* stream, const DimOpIndexNdHelper& input_nd_helper, - const DimOpIndexNdHelper& index_nd_helper, int ndim, int64_t elem_cnt, - int32_t dim_length, int32_t dim, const IDX_T* index, const IN_T* input, - IN_T* output) { - RUN_CUDA_KERNEL((DoCUDADimGather), stream, BlocksNum4ThreadsNum(elem_cnt), - input_nd_helper, index_nd_helper, ndim, elem_cnt, dim_length, dim, index, input, - output); - } -}; - -// float16 special case of DimGatherFunctor template -template -struct DimGatherFunctor final { - void operator()(ep::Stream* stream, const DimOpIndexNdHelper& input_nd_helper, - const DimOpIndexNdHelper& index_nd_helper, int ndim, int64_t elem_cnt, - int32_t dim_length, int32_t dim, const IDX_T* index, const float16* input, - float16* output) { - RUN_CUDA_KERNEL((DoCUDADimGather), stream, BlocksNum4ThreadsNum(elem_cnt), - input_nd_helper, index_nd_helper, ndim, elem_cnt, dim_length, dim, index, - reinterpret_cast(input), reinterpret_cast(output)); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_DIM_GATHER_FUNCTOR, (DeviceType::kCUDA), - DIM_GATHER_SCATTER_DATA_TYPE_CUDA_SEQ, INDEX_DATA_TYPE_SEQ); - -} // namespace user_op -} // namespace oneflow - -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/dim_gather_kernels.cpp b/oneflow/user/kernels/dim_gather_kernels.cpp index b5d5b667bde0d7b29ff0ff18b99c2571c5a054a1..43f00c635ee333bd252863d5786f260c4a629698 100644 --- a/oneflow/user/kernels/dim_gather_kernels.cpp +++ b/oneflow/user/kernels/dim_gather_kernels.cpp @@ -74,18 +74,12 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( REGISTER_DIM_GATHER_KERNEL, (DeviceType::kCPU), ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DIM_GATHER_KERNEL, (DeviceType::kCUDA), ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) #endif // WITH_CUDA -#ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DIM_GATHER_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) -#endif // WITH_ROCM } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_kernel_util.cpp b/oneflow/user/kernels/dim_scatter_kernel_util.cpp index 0295ba99ec10b4eaee853b0f07a175a5dfd34bab..0fc67d49db3e11d0127eef23a459a5c383b87385 100644 --- a/oneflow/user/kernels/dim_scatter_kernel_util.cpp +++ b/oneflow/user/kernels/dim_scatter_kernel_util.cpp @@ -32,8 +32,9 @@ struct DimScatterFunctor final { } }; -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCPU, BinOpAddFunctor); -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCPU, BinOpUpdateFunctor); +INSTANTIATE_DIM_SCATTER_CPU_FUNCTORS(DeviceType::kCPU, BinOpAddFunctor); +INSTANTIATE_DIM_SCATTER_CPU_FUNCTORS(DeviceType::kCPU, BinOpMulFunctor); +INSTANTIATE_DIM_SCATTER_CPU_FUNCTORS(DeviceType::kCPU, BinOpUpdateFunctor); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_kernel_util.cu b/oneflow/user/kernels/dim_scatter_kernel_util.cu index d5aaadf7a034f13a8ac263bac270622f6d2d2b9f..fbc8df00f3e8469440ee1bc73693f2012e6a301b 100644 --- a/oneflow/user/kernels/dim_scatter_kernel_util.cu +++ b/oneflow/user/kernels/dim_scatter_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/user/kernels/dim_scatter_kernel_util.h" namespace oneflow { @@ -57,8 +57,9 @@ struct DimScatterFunctor final { } }; -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCUDA, BinOpAddFunctor); -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCUDA, BinOpUpdateFunctor); +INSTANTIATE_DIM_SCATTER_CUDA_FUNCTORS(DeviceType::kCUDA, BinOpAddFunctor); +INSTANTIATE_DIM_SCATTER_CUDA_FUNCTORS(DeviceType::kCUDA, BinOpMulFunctor); +INSTANTIATE_DIM_SCATTER_CUDA_FUNCTORS(DeviceType::kCUDA, BinOpUpdateFunctor); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_kernel_util.h b/oneflow/user/kernels/dim_scatter_kernel_util.h index d55b1287c64cc2ef23270f26bae1fffc949f953a..73227be5a15a11acdd996b24ec9eb73a4316a6db 100644 --- a/oneflow/user/kernels/dim_scatter_kernel_util.h +++ b/oneflow/user/kernels/dim_scatter_kernel_util.h @@ -17,10 +17,13 @@ limitations under the License. #define ONEFLOW_USER_KERNELS_DIM_SCATTER_KERNEL_UTIL_H_ #ifdef WITH_CUDA #include "oneflow/core/cuda/atomic.cuh" +#include #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_CUDA +#if defined(WITH_ROCM) +#include +#include "oneflow/core/cuda/atomic.cuh" +#include +#endif #include "oneflow/core/ndarray/xpu_util.h" #include "oneflow/core/common/nd_index_offset_helper.h" @@ -31,6 +34,10 @@ limitations under the License. namespace oneflow { +#define NO_HALF_UTIL_FOUND \ + printf("cuda arch must >= 530"); \ + assert(false) + namespace user_op { constexpr int kDimGatherMaxDimCount = 8; @@ -38,7 +45,8 @@ constexpr int kDimGatherMaxDimCount = 8; template using DimOpIndexNdHelper = NdIndexOffsetHelper; -#define INSTANTIATE_DIM_SCATTER_FUNCTORS(device_type, opt) \ +#ifdef WITH_ROCM +#define INSTANTIATE_DIM_SCATTER_CPU_FUNCTORS(device_type, opt) \ template struct DimScatterFunctor; \ template struct DimScatterFunctor; \ template struct DimScatterFunctor; \ @@ -52,7 +60,44 @@ using DimOpIndexNdHelper = NdIndexOffsetHelper; template struct DimScatterFunctor; \ template struct DimScatterFunctor; \ template struct DimScatterFunctor; \ - template struct DimScatterFunctor; + template struct DimScatterFunctor; +#else +#define INSTANTIATE_DIM_SCATTER_CPU_FUNCTORS(device_type, opt) \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; +#endif + +#define INSTANTIATE_DIM_SCATTER_CUDA_FUNCTORS(device_type, opt) \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; \ + template struct DimScatterFunctor; template struct BinOpAddFunctor { @@ -65,24 +110,67 @@ struct BinOpAddFunctor { } }; +#if defined(WITH_CUDA) || defined(WITH_ROCM) template<> -struct BinOpAddFunctor { - OF_DEVICE_FUNC static void apply(const bool* x, bool* y) { *y += *x; } +struct BinOpAddFunctor { + OF_DEVICE_FUNC static void apply(const half* x, half* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + *y = __float2half(__half2float(*x) + __half2float(*y)); +#else + NO_HALF_UTIL_FOUND; +#endif + } }; +#endif -template<> -struct BinOpAddFunctor { - OF_DEVICE_FUNC static void apply(const int8_t* x, int8_t* y) { *y += *x; } +#define SPECIALIZE_BIN_OP_ADD_FUNCTOR(name, dtype) \ + template<> \ + struct name { \ + OF_DEVICE_FUNC static void apply(const dtype* x, dtype* y) { *y += *x; } \ + }; + +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpAddFunctor, bool) +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpAddFunctor, int8_t) +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpAddFunctor, uint8_t) +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpAddFunctor, int64_t) + +template +struct BinOpMulFunctor { + OF_DEVICE_FUNC static void apply(const T* x, T* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + cuda::atomic::Mul(y, *x); +#else + *y *= *x; +#endif + } }; +#if defined(WITH_CUDA) || defined(WITH_ROCM) template<> -struct BinOpAddFunctor { - OF_DEVICE_FUNC static void apply(const uint8_t* x, uint8_t* y) { *y += *x; } +struct BinOpMulFunctor { + OF_DEVICE_FUNC static void apply(const half* x, half* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + *y = __float2half(__half2float(*x) * __half2float(*y)); +#else + NO_HALF_UTIL_FOUND; +#endif + } }; +#endif + +#define SPECIALIZE_BIN_OP_MUL_FUNCTOR(name, dtype) \ + template<> \ + struct name { \ + OF_DEVICE_FUNC static void apply(const dtype* x, dtype* y) { *y *= *x; } \ + }; + +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpMulFunctor, int8_t) +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpMulFunctor, uint8_t) +SPECIALIZE_BIN_OP_ADD_FUNCTOR(BinOpMulFunctor, int64_t) template<> -struct BinOpAddFunctor { - OF_DEVICE_FUNC static void apply(const int64_t* x, int64_t* y) { *y += *x; } +struct BinOpMulFunctor { + OF_DEVICE_FUNC static void apply(const bool* x, bool* y) { *y &= *x; } }; template @@ -110,10 +198,8 @@ OF_DEVICE_FUNC void DoDimScatter(const DimOpIndexNdHelper& src_nd_helper, idx_nd_helper.OffsetToNdIndex(idx_offset, coordinate, ndim); // idx_offset -> ijk IDX_T idx_elem = index[idx_offset]; if (upper_bound != 0 && idx_elem >= upper_bound) { -#if defined(__CUDA_ARCH__) - __trap(); -#elif defined(__HIP_DEVICE_COMPILE__) - asm volatile("s_trap 0;"); +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + TRAP(); #else UNIMPLEMENTED() << "The index element " << idx_elem << " is out of bounds for dimension " << dim << " with size " << upper_bound << "."; diff --git a/oneflow/user/kernels/dim_scatter_kernel_util.hip.cpp b/oneflow/user/kernels/dim_scatter_kernel_util.hip.cpp deleted file mode 100644 index cc996eafe2bf4516fa77f0e6024292c427cd5050..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/dim_scatter_kernel_util.hip.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/dim_scatter_kernel_util.h" - -namespace oneflow { -namespace user_op { - -template class Opt> -__global__ void DoCUDADimScatter(const DimOpIndexNdHelper src_nd_helper, - const DimOpIndexNdHelper idx_nd_helper, - const DimOpIndexNdHelper output_nd_helper, const int ndim, - const int64_t elem_cnt, const int32_t dim, - const int64_t upper_bound, const IDX_T* index, const IN_T* src, - IN_T* output) { - DoDimScatter(src_nd_helper, idx_nd_helper, output_nd_helper, ndim, elem_cnt, - dim, upper_bound, index, src, output); -} - -template class Opt> -struct DimScatterFunctor final { - void operator()(ep::Stream* stream, const DimOpIndexNdHelper& src_nd_helper, - const DimOpIndexNdHelper& idx_nd_helper, - const DimOpIndexNdHelper& output_nd_helper, const int ndim, - const int64_t elem_cnt, const int32_t dim, const int64_t upper_bound, - const IDX_T* index, const IN_T* src, IN_T* output) { - RUN_CUDA_KERNEL((DoCUDADimScatter), stream, BlocksNum4ThreadsNum(elem_cnt), - src_nd_helper, idx_nd_helper, output_nd_helper, ndim, elem_cnt, dim, - upper_bound, index, src, output); - } -}; - -template class Opt> -struct DimScatterFunctor final { - void operator()(ep::Stream* stream, const DimOpIndexNdHelper& src_nd_helper, - const DimOpIndexNdHelper& idx_nd_helper, - const DimOpIndexNdHelper& output_nd_helper, const int ndim, - const int64_t elem_cnt, const int32_t dim, const int64_t upper_bound, - const IDX_T* index, const float16* src, float16* output) { - RUN_CUDA_KERNEL((DoCUDADimScatter), stream, BlocksNum4ThreadsNum(elem_cnt), - src_nd_helper, idx_nd_helper, output_nd_helper, ndim, elem_cnt, dim, - upper_bound, index, reinterpret_cast(src), - reinterpret_cast(output)); - } -}; - -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCUDA, BinOpAddFunctor); -INSTANTIATE_DIM_SCATTER_FUNCTORS(DeviceType::kCUDA, BinOpUpdateFunctor); - -} // namespace user_op -} // namespace oneflow - -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/dim_scatter_kernels.cpp b/oneflow/user/kernels/dim_scatter_kernels.cpp index 23023b76b2f0c74b3f355d5d3d0a4b6d4ba58526..d74eee69c85f4947add673d27c61c2b7f83ad3cf 100644 --- a/oneflow/user/kernels/dim_scatter_kernels.cpp +++ b/oneflow/user/kernels/dim_scatter_kernels.cpp @@ -81,24 +81,40 @@ class DimScatterKernel final : public user_op::OpKernel { && (user_op::HobDataType("like", 0) == GetDataType::value) \ && (user_op::HobDataType("index", 0) == GetDataType::value)); +#ifdef WITH_ROCM +#define REGISTER_DIM_SCATTER_LIKE_CPU_KERNELS(op_type, opt) \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, bool, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, double, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, int32_t, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, bool, int64_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float, int64_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, double, int64_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, int32_t, int64_t, opt); +#else #define REGISTER_DIM_SCATTER_LIKE_CPU_KERNELS(op_type, opt) \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, bool, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, double, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float16, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, int32_t, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, bool, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, double, int64_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, float16, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCPU, int32_t, int64_t, opt); +#endif #define REGISTER_DIM_SCATTER_LIKE_CUDA_KERNELS(op_type, opt) \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, bool, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, float, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, double, int32_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, half, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, int32_t, int32_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, bool, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, float, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, double, int64_t, opt); \ + REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, half, int64_t, opt); \ REGISTER_DIM_SCATTER_LIKE_KERNEL(op_type, DeviceType::kCUDA, int32_t, int64_t, opt); #define REGISTER_DIM_SCATTER_KERNEL(op_type, device, dtype_pair, itype_pair, opt) \ @@ -112,32 +128,40 @@ class DimScatterKernel final : public user_op::OpKernel { #define REGISTER_DIM_SCATTER_CPU_KERNELS(dtype_pair, itype_pair) \ REGISTER_DIM_SCATTER_KERNEL(dim_scatter_add, DeviceType::kCPU, dtype_pair, itype_pair, \ BinOpAddFunctor); \ + REGISTER_DIM_SCATTER_KERNEL(dim_scatter_mul, DeviceType::kCPU, dtype_pair, itype_pair, \ + BinOpMulFunctor); \ REGISTER_DIM_SCATTER_KERNEL(dim_scatter_update, DeviceType::kCPU, dtype_pair, itype_pair, \ BinOpUpdateFunctor); #define REGISTER_DIM_SCATTER_CUDA_KERNELS(dtype_pair, itype_pair) \ REGISTER_DIM_SCATTER_KERNEL(dim_scatter_add, DeviceType::kCUDA, dtype_pair, itype_pair, \ BinOpAddFunctor); \ + REGISTER_DIM_SCATTER_KERNEL(dim_scatter_mul, DeviceType::kCUDA, dtype_pair, itype_pair, \ + BinOpMulFunctor); \ REGISTER_DIM_SCATTER_KERNEL(dim_scatter_update, DeviceType::kCUDA, dtype_pair, itype_pair, \ BinOpUpdateFunctor); +#ifdef WITH_ROCM REGISTER_DIM_SCATTER_LIKE_CPU_KERNELS("dim_scatter_add_like", BinOpAddFunctor); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_DIM_SCATTER_CPU_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DIM_SCATTER_CPU_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) +#else +REGISTER_DIM_SCATTER_LIKE_CPU_KERNELS("dim_scatter_add_like", BinOpAddFunctor); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DIM_SCATTER_CPU_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) +#endif -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_DIM_SCATTER_LIKE_CUDA_KERNELS("dim_scatter_add_like", BinOpAddFunctor); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_DIM_SCATTER_CUDA_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DIM_SCATTER_CUDA_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_DIM_SCATTER_LIKE_CUDA_KERNELS("dim_scatter_add_like", BinOpAddFunctor); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_DIM_SCATTER_CUDA_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif // WITH_ROCM } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cpp b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cpp index 95deaa7366b8f2e2603091ef4f363f95f5e4b623..675491cccc23e6c2b76612b6b26224d96c65e909 100644 --- a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cpp +++ b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cpp @@ -30,8 +30,8 @@ struct DimScatterScalarFunctor final { } }; -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCPU, UpdateScalarFunctor); -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCPU, AddScalarFunctor); +INSTANTIATE_DIM_SCATTER_SCARLAR_CPU_FUNCTORS(DeviceType::kCPU, UpdateScalarFunctor); +INSTANTIATE_DIM_SCATTER_SCARLAR_CPU_FUNCTORS(DeviceType::kCPU, AddScalarFunctor); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cu b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cu index 765fee7badea90c4ea437b385fd6f7720cb5747c..1d8a7d49a9a64fe8d420eef3de020a9a13abc821 100644 --- a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cu +++ b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/user/kernels/dim_scatter_scalar_kernel_util.h" namespace oneflow { @@ -42,8 +42,20 @@ struct DimScatterScalarFunctor final { } }; -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCUDA, UpdateScalarFunctor); -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCUDA, AddScalarFunctor); +template class Opt> +struct DimScatterScalarFunctor final { + void operator()(ep::Stream* stream, const DimOpIndexNdHelper& idx_nd_helper, + const DimOpIndexNdHelper& output_nd_helper, const int ndim, + const int64_t elem_cnt, const int32_t dim, int64_t upper_bound, + const IDX_T* index, const float16 src, float16* output) { + RUN_CUDA_KERNEL((DoCUDADimScatterScalar), stream, + BlocksNum4ThreadsNum(elem_cnt), idx_nd_helper, output_nd_helper, ndim, elem_cnt, + dim, upper_bound, index, src, reinterpret_cast(output)); + } +}; + +INSTANTIATE_DIM_SCATTER_SCARLAR_CUDA_FUNCTORS(DeviceType::kCUDA, UpdateScalarFunctor); +INSTANTIATE_DIM_SCATTER_SCARLAR_CUDA_FUNCTORS(DeviceType::kCUDA, AddScalarFunctor); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.h b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.h index bef309a0150557c194646ac32c3decab1bb84402..9b8c567acf5743468692d0f167e7c59679fe31ed 100644 --- a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.h +++ b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.h @@ -17,10 +17,13 @@ limitations under the License. #define ONEFLOW_USER_KERNELS_DIM_SCATTER_SCALAR_KERNEL_UTIL_H_ #ifdef WITH_CUDA #include "oneflow/core/cuda/atomic.cuh" +#include #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_ROCM +#if defined(WITH_ROCM) +#include +#include "oneflow/core/cuda/atomic.cuh" +#include +#endif #include "oneflow/core/ep/include/stream.h" #include "oneflow/core/ndarray/xpu_util.h" #include "oneflow/core/common/nd_index_offset_helper.h" @@ -29,6 +32,10 @@ limitations under the License. namespace oneflow { +#define NO_HALF_UTIL_FOUND \ + printf("cuda arch must >= 530"); \ + assert(false) + namespace user_op { constexpr int kDimGatherMaxDimCount = 8; @@ -44,6 +51,19 @@ struct AddScalarFunctor { } }; +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template<> +struct AddScalarFunctor { + OF_DEVICE_FUNC static void apply(const half x, half* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + *y = __float2half(__half2float(*y) + __half2float(x)); +#else + NO_HALF_UTIL_FOUND; +#endif + } +}; +#endif + template<> struct AddScalarFunctor { OF_DEVICE_FUNC static void apply(const int8_t x, int8_t* y) { *y += x; } @@ -64,7 +84,8 @@ struct UpdateScalarFunctor { OF_DEVICE_FUNC static void apply(const T x, T* y) { *y = x; } }; -#define INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(device_type, opt) \ +#ifdef WITH_ROCM +#define INSTANTIATE_DIM_SCATTER_SCARLAR_CPU_FUNCTORS(device_type, opt) \ template struct DimScatterScalarFunctor; \ template struct DimScatterScalarFunctor; \ template struct DimScatterScalarFunctor; \ @@ -76,7 +97,40 @@ struct UpdateScalarFunctor { template struct DimScatterScalarFunctor; \ template struct DimScatterScalarFunctor; \ template struct DimScatterScalarFunctor; \ - template struct DimScatterScalarFunctor; + template struct DimScatterScalarFunctor; +#else +#define INSTANTIATE_DIM_SCATTER_SCARLAR_CPU_FUNCTORS(device_type, opt) \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; +#endif + +#define INSTANTIATE_DIM_SCATTER_SCARLAR_CUDA_FUNCTORS(device_type, opt) \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; \ + template struct DimScatterScalarFunctor; template using DimOpIndexNdHelper = NdIndexOffsetHelper; @@ -101,10 +155,8 @@ OF_DEVICE_FUNC void DoScatterScalarFunctor(const DimOpIndexNdHelper& idx_ idx_nd_helper.OffsetToNdIndex(idx_offset, coordinate, ndim); // idx_offset -> ijk IDX_T idx_elem = index[idx_offset]; if (idx_elem >= upper_bound) { -#if defined(__CUDA_ARCH__) - __trap(); -#elif defined(__HIP_DEVICE_COMPILE__) - asm volatile("s_trap 0;"); +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + TRAP(); #else UNIMPLEMENTED() << "The index element " << idx_elem << " is out of bounds for dimension " << dim << " with size " << upper_bound << "."; diff --git a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.hip.cpp b/oneflow/user/kernels/dim_scatter_scalar_kernel_util.hip.cpp deleted file mode 100644 index 297271b348c880ece12e2077e5d039e9cb897b08..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/dim_scatter_scalar_kernel_util.hip.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/dim_scatter_scalar_kernel_util.h" - -namespace oneflow { - -namespace user_op { - -template class Opt> -__global__ void DoCUDADimScatterScalar(const DimOpIndexNdHelper idx_nd_helper, - const DimOpIndexNdHelper output_nd_helper, - const int ndim, const int64_t elem_cnt, const int32_t dim, - const int64_t upper_bound, const IDX_T* index, - const IN_T src_scalar, IN_T* output) { - DoScatterScalarFunctor(idx_nd_helper, output_nd_helper, ndim, elem_cnt, dim, - upper_bound, index, src_scalar, output); -} - -template class Opt> -struct DimScatterScalarFunctor final { - void operator()(ep::Stream* stream, const DimOpIndexNdHelper& idx_nd_helper, - const DimOpIndexNdHelper& output_nd_helper, const int ndim, - const int64_t elem_cnt, const int32_t dim, int64_t upper_bound, - const IDX_T* index, const IN_T src, IN_T* output) { - RUN_CUDA_KERNEL((DoCUDADimScatterScalar), stream, - BlocksNum4ThreadsNum(elem_cnt), idx_nd_helper, output_nd_helper, ndim, elem_cnt, - dim, upper_bound, index, src, output); - } -}; - -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCUDA, UpdateScalarFunctor); -INSTANTIATE_DIM_SCATTER_SCARLAR_FUNCTORS(DeviceType::kCUDA, AddScalarFunctor); - -} // namespace user_op -} // namespace oneflow -#endif \ No newline at end of file diff --git a/oneflow/user/kernels/dim_scatter_scalar_kernels.cpp b/oneflow/user/kernels/dim_scatter_scalar_kernels.cpp index b78ea2db23ccff9fb8332f1731e875fcdcf4ae8f..2113417860df0add4a61bca8e708dd064ebaccd8 100644 --- a/oneflow/user/kernels/dim_scatter_scalar_kernels.cpp +++ b/oneflow/user/kernels/dim_scatter_scalar_kernels.cpp @@ -95,20 +95,21 @@ class DimScatterScalarKernel final : public user_op::OpKernel { REGISTER_SCATTERSCALAR_KERNEL(dim_scatter_add_scalar, DeviceType::kCUDA, dtype_pair, itype_pair, \ AddScalarFunctor); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCATTER_SCALAR_CPU_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCATTER_SCALAR_CUDA_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) -#endif // WITH_CUDA #ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCATTER_SCALAR_CUDA_KERNELS, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) -#endif // WITH_ROCM +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + REGISTER_SCATTER_SCALAR_CPU_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +#else +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + REGISTER_SCATTER_SCALAR_CPU_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +#endif + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( + REGISTER_SCATTER_SCALAR_CUDA_KERNELS, + ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +#endif // WITH_CUDA } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/common.h b/oneflow/user/kernels/distributions/common.h index 91e972c79c6f0779afaa7530e0d3d9520594aad4..c3b5ba7e1be20350b04f2ac305cdb107960e4510 100644 --- a/oneflow/user/kernels/distributions/common.h +++ b/oneflow/user/kernels/distributions/common.h @@ -56,6 +56,15 @@ void check_from_to_in_range(int64_t from, int64_t to_inc) { constexpr auto digits = std::numeric_limits::digits; WARN_OUT_OF_BOUNDS(from, "from", digits, GetDataType::value); WARN_OUT_OF_BOUNDS(to_inc, "to - 1", digits, GetDataType::value); + } else if (IsFloat16::value) { + const auto min = static_cast(std::numeric_limits::lowest()); + const auto max = static_cast(std::numeric_limits::max()); + CHECK_OUT_OF_BOUNDS(from, "from", min, max, GetDataType::value); + CHECK_OUT_OF_BOUNDS(to_inc, "to - 1", min, max, GetDataType::value); + + constexpr auto digits = std::numeric_limits::digits; + WARN_OUT_OF_BOUNDS(from, "from", digits, GetDataType::value); + WARN_OUT_OF_BOUNDS(to_inc, "to - 1", digits, GetDataType::value); } else if (IsIntegral::value || IsUnsignedIntegral::value) { const auto min = static_cast(std::numeric_limits::lowest()); const auto max = static_cast(std::numeric_limits::max()); diff --git a/oneflow/user/kernels/distributions/exponential_distribution.cpp b/oneflow/user/kernels/distributions/exponential_distribution.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b9b554ebd52fdb17019bd357dd5fc0aebe852a0d --- /dev/null +++ b/oneflow/user/kernels/distributions/exponential_distribution.cpp @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include +#include +#include + +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/distributions/exponential_distribution.h" + +namespace oneflow { + +static uint64_t make64BitsFrom32Bits(uint32_t hi, uint32_t lo) { + return (static_cast(hi) << 32) | lo; +} + +template +static T uniform_real(V val, T from, T to) { + constexpr auto MASK = + static_cast((static_cast(1) << std::numeric_limits::digits) - 1); + constexpr auto DIVISOR = + static_cast(1) / (static_cast(1) << std::numeric_limits::digits); + T x = (val & MASK) * DIVISOR; + return (x * (to - from) + from); +} + +template +void ExponentialDistribution::operator()( + ep::Stream* stream, const int64_t elem_cnt, T* dptr, + const std::shared_ptr& generator) const { + CHECK_GE(elem_cnt, 0); + auto gen = CHECK_JUST(generator->Get()); + one::pytorch_mt19937_engine& engine = gen->torch_engine(); + for (int64_t i = 0; i < elem_cnt; ++i) { + uint32_t random1 = engine(); + uint32_t random2 = engine(); + uint64_t rand_unit = make64BitsFrom32Bits(random1, random2); + T random_val = uniform_real(rand_unit, 0.0, 1.0); + dptr[i] = static_cast(-1.0) / lambd_ * std::log(static_cast(1.0) - random_val); + } +} + +#define INITIATE_CPU_UNIFORM_DISTRIBUTION(T, typeproto) \ + template void ExponentialDistribution::operator()( \ + ep::Stream* stream, const int64_t elem_cnt, T* dptr, \ + const std::shared_ptr& generator) const; + +OF_PP_FOR_EACH_TUPLE(INITIATE_CPU_UNIFORM_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) + +} // namespace oneflow diff --git a/oneflow/user/kernels/distributions/exponential_distribution.cu b/oneflow/user/kernels/distributions/exponential_distribution.cu new file mode 100644 index 0000000000000000000000000000000000000000..b8fbcda912b2cd7b686ebb5e0203fda0804f81cd --- /dev/null +++ b/oneflow/user/kernels/distributions/exponential_distribution.cu @@ -0,0 +1,176 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ep/include/device.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/user/kernels/distributions/exponential_distribution.h" +#include "oneflow/user/kernels/fused_rnn_cell_kernel_util.h" + +namespace oneflow { + +namespace { + +// NOTE(Liang Depeng): the implementation of exponential cuda kernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/DistributionTemplates.h + +// launch bounds used for kernels +const uint32_t block_size_bound = 256; +const uint32_t grid_size_bound = 4; +// number of randoms given by distributions like curand_uniform4, curand_uniform2_double +// used in calculating philox offset. +const uint32_t curand4_engine_calls = 4; + +std::tuple calc_execution_policy(int64_t total_elements, + ep::CudaStream* stream) { + const uint64_t numel = static_cast(total_elements); + const uint32_t block_size = block_size_bound; + const uint32_t unroll = curand4_engine_calls; + dim3 dim_block(block_size); + dim3 grid((numel + block_size - 1) / block_size); + uint32_t blocks_per_sm = stream->device_properties().maxThreadsPerMultiProcessor / block_size; + grid.x = std::min( + static_cast(stream->device_properties().multiProcessorCount) * blocks_per_sm, + grid.x); + // number of times random will be generated per thread, to offset philox counter in thc random + // state + uint64_t counter_offset = + ((numel - 1) / (block_size * grid.x * unroll) + 1) * curand4_engine_calls; + return std::make_tuple(counter_offset, grid, dim_block); +} + +OF_LAUNCH_BOUNDS_2(block_size_bound, grid_size_bound) +__global__ void distribution_elementwise_grid_stride_kernel_double(int32_t numel, uint64_t seed, + uint64_t offset, double lambd, + double epsilon, + double* out_ptr) { + int32_t unroll_factor = 2; + int idx = blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, idx, offset, &state); + + int rounded_size = ((numel - 1) / (blockDim.x * gridDim.x * unroll_factor) + 1) * blockDim.x + * gridDim.x * unroll_factor; + for (int32_t linear_index = idx; linear_index < rounded_size; + linear_index += blockDim.x * gridDim.x * unroll_factor) { + double2 rand = GPURAND(_uniform2_double)(&state); +#pragma unroll + for (int ii = 0; ii < unroll_factor; ii++) { + int li = linear_index + blockDim.x * gridDim.x * ii; + if (li < numel) { + double log_rand = ::log(static_cast((&rand.x)[ii])); + // curand_uniform has (0,1] bounds. log(1) is 0 and exponential excludes 0. + // we need log to be not 0, and not underflow when converted to half + // fast __logf approximation can underflow, so set log to -epsilon/2 for 1 or close to 1 + // args + double log = static_cast((&rand.x)[ii]) >= static_cast(1.) - epsilon / 2 + ? -epsilon / 2 + : log_rand; + out_ptr[li] = static_cast(-1.0) / lambd * log; + } + } + __syncthreads(); + } +} + +OF_LAUNCH_BOUNDS_2(block_size_bound, grid_size_bound) +__global__ void distribution_elementwise_grid_stride_kernel_float(int32_t numel, uint64_t seed, + uint64_t offset, float lambd, + float epsilon, float* out_ptr) { + int32_t unroll_factor = 4; + int idx = blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, idx, offset, &state); + + int rounded_size = ((numel - 1) / (blockDim.x * gridDim.x * unroll_factor) + 1) * blockDim.x + * gridDim.x * unroll_factor; + for (int32_t linear_index = idx; linear_index < rounded_size; + linear_index += blockDim.x * gridDim.x * unroll_factor) { + float4 rand = GPURAND(_uniform4)(&state); +#pragma unroll + for (int ii = 0; ii < unroll_factor; ii++) { + int li = linear_index + blockDim.x * gridDim.x * ii; + if (li < numel) { + float log_rand = __logf(static_cast((&rand.x)[ii])); + // curand_uniform has (0,1] bounds. log(1) is 0 and exponential excludes 0. + // we need log to be not 0, and not underflow when converted to half + // fast __logf approximation can underflow, so set log to -epsilon/2 for 1 or close to 1 + // args + float log = static_cast((&rand.x)[ii]) >= static_cast(1.) - epsilon / 2 + ? -epsilon / 2 + : log_rand; + out_ptr[li] = static_cast(-1.0) / lambd * log; + } + } + __syncthreads(); + } +} + +} // namespace + +template<> +void ExponentialDistribution::operator()( + ep::Stream* stream, const int64_t elem_cnt, double* dptr, + const std::shared_ptr& generator) const { + CHECK_GT(elem_cnt, 0); + const auto device_index = stream->device()->device_index(); + auto gen = CHECK_JUST(generator->Get(device_index)); + ep::CudaStream* cuda_stream = stream->As(); + auto execution_policy = calc_execution_policy(elem_cnt, cuda_stream); + + auto counter_offset = std::get<0>(execution_policy); + auto grid = std::get<1>(execution_policy); + auto block = std::get<2>(execution_policy); + + uint64_t offset = 0; + uint64_t seed = gen->current_seed(); + { + std::lock_guard lock(gen->mutex_); + offset = gen->get_philox_offset(counter_offset); + } + + distribution_elementwise_grid_stride_kernel_double<<< + grid, block, 0, stream->As()->cuda_stream()>>>( + elem_cnt, seed, offset, lambd_, std::numeric_limits::epsilon(), dptr); +} + +template<> +void ExponentialDistribution::operator()( + ep::Stream* stream, const int64_t elem_cnt, float* dptr, + const std::shared_ptr& generator) const { + CHECK_GT(elem_cnt, 0); + const auto device_index = stream->device()->device_index(); + auto gen = CHECK_JUST(generator->Get(device_index)); + ep::CudaStream* cuda_stream = stream->As(); + auto execution_policy = calc_execution_policy(elem_cnt, cuda_stream); + + auto counter_offset = std::get<0>(execution_policy); + auto grid = std::get<1>(execution_policy); + auto block = std::get<2>(execution_policy); + + uint64_t offset = 0; + uint64_t seed = gen->current_seed(); + { + std::lock_guard lock(gen->mutex_); + offset = gen->get_philox_offset(counter_offset); + } + + distribution_elementwise_grid_stride_kernel_float<<< + grid, block, 0, stream->As()->cuda_stream()>>>( + elem_cnt, seed, offset, lambd_, std::numeric_limits::epsilon(), dptr); +} + +} // namespace oneflow diff --git a/oneflow/user/kernels/distributions/exponential_distribution.h b/oneflow/user/kernels/distributions/exponential_distribution.h new file mode 100644 index 0000000000000000000000000000000000000000..3cd2e28f3fad02befa204ee7eb30468054bac398 --- /dev/null +++ b/oneflow/user/kernels/distributions/exponential_distribution.h @@ -0,0 +1,67 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_DISTRIBUTION_H_ +#define ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_DISTRIBUTION_H_ + +#include "oneflow/core/ep/include/stream.h" +#include "oneflow/core/framework/random_generator.h" +#ifdef WITH_CUDA +#include +#include +#endif +#ifdef WITH_ROCM +#include +#include +#endif // WITH_ROCM + +namespace oneflow { + +template +class ExponentialDistribution; + +template +class ExponentialDistribution final { + public: + OF_DISALLOW_COPY_AND_MOVE(ExponentialDistribution); + ExponentialDistribution(T lambd) : lambd_(lambd) {} + ~ExponentialDistribution() = default; + + void operator()(ep::Stream* stream, const int64_t elem_cnt, T* dptr, + const std::shared_ptr& generator) const; + + private: + const T lambd_; +}; + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template +class ExponentialDistribution final { + public: + OF_DISALLOW_COPY_AND_MOVE(ExponentialDistribution); + ExponentialDistribution(T lambd) : lambd_(lambd) {} + ~ExponentialDistribution() = default; + + void operator()(ep::Stream* stream, const int64_t elem_cnt, T* dptr, + const std::shared_ptr& generator) const; + + private: + const T lambd_; +}; +#endif // WITH_CUDA + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_DISTRIBUTION_H_ diff --git a/oneflow/user/kernels/distributions/exponential_kernel.cpp b/oneflow/user/kernels/distributions/exponential_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fcd9dab33432003b97b67855ade15de449e7c43f --- /dev/null +++ b/oneflow/user/kernels/distributions/exponential_kernel.cpp @@ -0,0 +1,36 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/distributions/exponential_kernel.h" + +namespace oneflow { + +namespace { + +#define REGISTER_EXPONENTIAL_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("exponential") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobAttr("dtype") == GetDataType::value)); + +REGISTER_EXPONENTIAL_KERNEL(DeviceType::kCPU, float) +REGISTER_EXPONENTIAL_KERNEL(DeviceType::kCPU, double) +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_EXPONENTIAL_KERNEL(DeviceType::kCUDA, float) +REGISTER_EXPONENTIAL_KERNEL(DeviceType::kCUDA, double) +#endif // WITH_CUDA + +} // namespace +} // namespace oneflow diff --git a/oneflow/user/kernels/distributions/exponential_kernel.h b/oneflow/user/kernels/distributions/exponential_kernel.h new file mode 100644 index 0000000000000000000000000000000000000000..4262bba6d8d20539961e1f99416a96d4096a3385 --- /dev/null +++ b/oneflow/user/kernels/distributions/exponential_kernel.h @@ -0,0 +1,63 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_KERNEL_H_ +#define ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_KERNEL_H_ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/distributions/common.h" +#include "oneflow/user/kernels/distributions/exponential_distribution.h" +#include "oneflow/user/kernels/random_seed_util.h" + +namespace oneflow { + +namespace { + +template +class ExponentialKernel final : public user_op::OpKernel { + public: + ExponentialKernel() = default; + ~ExponentialKernel() = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + const auto& generator = CHECK_JUST(one::MakeGenerator(device_type)); + // When SBP is Split, each rank uses a different seeds, otherwise, ranks use the same seed + generator->set_current_seed( + CHECK_JUST(GetOpKernelRandomSeedInCurrentRank(ctx, ctx->Attr("seed")))); + return std::make_shared(generator); + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const float lambd = ctx->Attr("lambd"); + int64_t elem_cnt = out->shape_view().elem_cnt(); + T* out_dptr = out->mut_dptr(); + auto* distribution_state = dynamic_cast(state); + CHECK_NOTNULL(distribution_state); + const auto& generator = distribution_state->generator(); + CHECK_NOTNULL(generator); + ExponentialDistribution distribution(static_cast(lambd)); + distribution(ctx->stream(), elem_cnt, out_dptr, generator); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_DISTRIBUTIONS_EXPONENTIAL_KERNEL_H_ diff --git a/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cpp b/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cd24e9025747937387e06089e22eb4f0226d9b95 --- /dev/null +++ b/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cpp @@ -0,0 +1,162 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/distributions/common.h" +#include "oneflow/user/kernels/random_seed_util.h" + +// NOTE(Liang Depeng): The implementation of MultinomialWithReplacementCpuKernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cpu/MultinomialKernel.cpp#L23 +namespace oneflow { + +namespace { + +static size_t InferTmpSizeForCpuKernel(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("x", 0); + int64_t n_categories = x.shape().At(x.shape().NumAxes() - 1); + return n_categories * GetSizeOfDataType(x.data_type()); +} + +template +static T uniform_real(V val, T from, T to) { + constexpr auto MASK = + static_cast((static_cast(1) << std::numeric_limits::digits) - 1); + constexpr auto DIVISOR = + static_cast(1) / (static_cast(1) << std::numeric_limits::digits); + T x = (val & MASK) * DIVISOR; + return (x * (to - from) + from); +} + +static uint64_t make64BitsFrom32Bits(uint32_t hi, uint32_t lo) { + return (static_cast(hi) << 32) | lo; +} + +} // namespace + +template +class MultinomialWithReplacementCpuKernel final : public user_op::OpKernel { + public: + MultinomialWithReplacementCpuKernel() = default; + ~MultinomialWithReplacementCpuKernel() = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + const auto& generator = CHECK_JUST(one::MakeGenerator(DeviceType::kCPU)); + // When SBP is Split, each rank uses a different seeds, otherwise, ranks use the same seed + generator->set_current_seed( + CHECK_JUST(GetOpKernelRandomSeedInCurrentRank(ctx, ctx->Attr("seed")))); + return std::make_shared(generator); + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* distribution_state = dynamic_cast(state); + CHECK_NOTNULL(distribution_state); + const auto& generator = distribution_state->generator(); + CHECK_NOTNULL(generator); + auto cpu_gen = CHECK_JUST(generator->Get()); + std::lock_guard lock(cpu_gen->mutex_); + + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + + const T* self_ptr = x->dptr(); + int64_t* result_ptr = out->mut_dptr(); + /* cumulative probability distribution vector */ + T* cum_dist_ptr = tmp_buffer->mut_dptr(); + + int64_t n_categories = x->shape_view().At(x->shape_view().NumAxes() - 1); + int64_t n_dist = x->shape_view().NumAxes() > 1 ? x->shape_view().At(0) : 1; + const int32_t num_samples = ctx->Attr("num_samples"); + + int64_t self_stride_0 = x->shape_view().NumAxes() > 1 ? x->stride().at(0) : 0; + int64_t self_stride_1 = x->stride().at(x->shape_view().NumAxes() - 1); + int64_t result_dist_stride_0 = out->shape_view().NumAxes() > 1 ? out->stride().at(0) : 0; + int64_t result_dist_stride_1 = out->stride().at(out->shape_view().NumAxes() - 1); + + one::pytorch_mt19937_engine& engine = cpu_gen->torch_engine(); + + for (int i = 0; i < n_dist; ++i) { + /* Get normalized cumulative distribution from prob distribution */ + T sum = 0; + T val; + for (int j = 0; j < n_categories; ++j) { + val = self_ptr[i * self_stride_0 + j * self_stride_1]; + CHECK(val >= 0) << "invalid multinomial distribution (encountering probability entry < 0)"; + CHECK(std::isfinite(val)) << "invalid multinomial distribution (encountering probability " + "entry = infinity or NaN)"; + sum += val; + cum_dist_ptr[j] = sum; + } + + CHECK(sum > 0) << "invalid multinomial distribution (sum of probabilities <= 0)"; + + /* normalize cumulative probability distribution so that last val is 1 + i.e. doesn't assume original self row sums to one */ + if ((sum > 0) || ((sum < 1.00001) && (sum > 0.99999))) { + for (int j = 0; j < n_categories; ++j) { cum_dist_ptr[j] /= sum; } + } + + for (int j = 0; j < num_samples; ++j) { + /* sample a probability mass from a uniform distribution */ + // at::uniform_real_distribution uniform(0, 1); + // double uniform_sample = uniform(gen); + uint32_t random1 = engine(); + uint32_t random2 = engine(); + uint64_t rand_unit = make64BitsFrom32Bits(random1, random2); + double uniform_sample = uniform_real(rand_unit, 0.0, 1.0); + + // Do a binary search for the slot in which the prob falls + // ie cum_dist[row][slot-1] < uniform_prob < cum_distr[row][slot] + int left_pointer = 0; + int right_pointer = n_categories; + int mid_pointer = 0; + T cum_prob; + int sample_idx = 0; + // Make sure the last cumulative distribution bucket sums to 1 + cum_dist_ptr[(n_categories - 1)] = 1; + + while (right_pointer - left_pointer > 0) { + mid_pointer = left_pointer + (right_pointer - left_pointer) / 2; + cum_prob = cum_dist_ptr[mid_pointer]; + if (cum_prob < uniform_sample) { + left_pointer = mid_pointer + 1; + } else { + right_pointer = mid_pointer; + } + } + sample_idx = left_pointer; + + // store in result tensor (will be incremented for lua compat by wrapper) + result_ptr[i * result_dist_stride_0 + j * result_dist_stride_1] = sample_idx; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_MULTINOMIAL_WITH_REPLACEMENT_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("multinomial_with_replacement") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferTmpSizeForCpuKernel); + +REGISTER_MULTINOMIAL_WITH_REPLACEMENT_CPU_KERNEL(float) +REGISTER_MULTINOMIAL_WITH_REPLACEMENT_CPU_KERNEL(double) + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cu b/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..c9e39914e81ea135ac8b88487790920ec584b4f5 --- /dev/null +++ b/oneflow/user/kernels/distributions/multinomial_with_replacement_kernel.cu @@ -0,0 +1,166 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/ep/include/device.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/distributions/common.h" +#include "oneflow/user/kernels/random_seed_util.h" + +// NOTE(Liang Depeng): The implementation of MultinomialWithReplacementGpuKernel is modified from +// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/MultinomialKernel.cu#L324 +namespace oneflow { + +namespace { + +template +__device__ int binarySearchForMultinomial(const T* cumdist, const T* dist, int32_t size, T val) { + int start = 0; + int end = size; + + while (end - start > 0) { + int mid = start + (end - start) / 2; + T midVal = cumdist[mid]; + if (midVal < val) { + start = mid + 1; + } else { + end = mid; + } + } + + if (start == size) { + // No probability mass or precision problems; just return the + // first non-zero element by setting start to size-1 here, + // the code below will move it to the last non-zero probability + // this actually can happen when the random number is 1 + // (github pytorch issue #4858). + start = size - 1; + } + + while (start >= 1 && dist[start] == 0) start--; + + return start; +} + +template +__global__ void sampleMultinomialWithReplacement(uint64_t seed, uint64_t offset, + int32_t totalSamples, int64_t* dest, + int64_t distributions, int64_t categories, + const T* normDistPrefixSum, const T* normDist) { + // At the moment, each warp computes one sample value in the binary + // search due to divergence. It seems possible to compute multiple + // values and limit divergence though later on. + + // global index formula for 2D grid of 1D blocks + int idx = blockIdx.y * gridDim.x * blockDim.x + blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, idx, offset, &state); + + // The block determines the distribution for which we generate a point + for (int64_t curDist = blockIdx.y; curDist < distributions; curDist += gridDim.y) { + for (int sample = blockIdx.x * blockDim.x + threadIdx.x; sample < totalSamples; + sample += blockDim.x * gridDim.x) { + // we are losing 3 out of 4 generated numbers but it's ok + // this kernel is not very efficient anyway + auto rand = GPURAND(_uniform4)(&state); + T r = static_cast(rand.x); + + // Find the bucket that a uniform sample lies in + int choice = binarySearchForMultinomial(normDistPrefixSum + curDist * categories, + normDist + curDist * categories, categories, r); + + dest[curDist * totalSamples + sample] = choice; + } + } +} + +} // namespace + +template +class MultinomialWithReplacementGpuKernel final : public user_op::OpKernel { + public: + MultinomialWithReplacementGpuKernel() = default; + ~MultinomialWithReplacementGpuKernel() = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + const auto& generator = CHECK_JUST(one::MakeGenerator(DeviceType::kCPU)); + // When SBP is Split, each rank uses a different seeds, otherwise, ranks use the same seed + generator->set_current_seed( + CHECK_JUST(GetOpKernelRandomSeedInCurrentRank(ctx, ctx->Attr("seed")))); + return std::make_shared(generator); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* distribution_state = dynamic_cast(state); + CHECK_NOTNULL(distribution_state); + const auto& generator = distribution_state->generator(); + CHECK_NOTNULL(generator); + auto gpu_gen = CHECK_JUST(generator->Get()); + + const user_op::Tensor* norm_dist = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* prefix_sum = ctx->Tensor4ArgNameAndIndex("prefix_sum", 0); + CHECK_NOTNULL(prefix_sum); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + + const T* norm_dist_ptr = norm_dist->dptr(); + const T* prefix_sum_ptr = prefix_sum->dptr(); + int64_t* result_ptr = out->mut_dptr(); + + int64_t numCategories = norm_dist->shape_view().At(norm_dist->shape_view().NumAxes() - 1); + int64_t numDist = norm_dist->shape_view().NumAxes() > 1 ? norm_dist->shape_view().At(0) : 1; + const int32_t n_sample = ctx->Attr("num_samples"); + + // Binary search is warp divergent (so effectively we're running + // with just a single thread), but for better utilization, + // we need each block to have at least 4 warps. + dim3 block(128); + + ep::CudaStream* stream = ctx->stream()->As(); + // Each block will generate a sample from one + // distribution concurrently. + int grid_y = std::min(numDist, stream->device_properties().maxGridSize[1]); + dim3 grid((n_sample - 1) / block.x + 1, grid_y); + uint64_t offset = 0; + uint64_t seed = gpu_gen->current_seed(); + { + std::lock_guard lock(gpu_gen->mutex_); + // each thread generates a single sample for (numdist/numblocks.y) distributions, however, + // since we have to use curand_uniform4 offset is 4 times that. + offset = gpu_gen->get_philox_offset(((numDist - 1) / grid.y + 1) * 4); + } + + // Sample with replacement + sampleMultinomialWithReplacement<<cuda_stream()>>>( + seed, offset, n_sample, result_ptr, numDist, numCategories, prefix_sum_ptr, norm_dist_ptr); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_MULTINOMIAL_WITH_REPLACEMENT_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("multinomial_with_replacement") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value) \ + && (user_op::HobDataType("prefix_sum", 0) == GetDataType::value)); + +REGISTER_MULTINOMIAL_WITH_REPLACEMENT_GPU_KERNEL(float) +REGISTER_MULTINOMIAL_WITH_REPLACEMENT_GPU_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/distributions/normal_distribution.cpp b/oneflow/user/kernels/distributions/normal_distribution.cpp index c59b32f3a1b432addc4ffeab6a964524dc7e8c25..260ee677afeda17d83602bccedc55387dba3fb6f 100644 --- a/oneflow/user/kernels/distributions/normal_distribution.cpp +++ b/oneflow/user/kernels/distributions/normal_distribution.cpp @@ -23,7 +23,7 @@ template void NormalDistribution::operator()( ep::Stream* stream, const int64_t elem_cnt, T* dptr, const std::shared_ptr& generator) const { - CHECK_GE(elem_cnt, 0); + CHECK_GE(elem_cnt, 0) << "elem_cnt must be non-negative, but got " << elem_cnt; auto gen = CHECK_JUST(generator->Get()); std::normal_distribution random_distribution(mean_, std_); for (int64_t i = 0; i < elem_cnt; ++i) { dptr[i] = random_distribution(gen->engine()); } @@ -36,4 +36,17 @@ void NormalDistribution::operator()( OF_PP_FOR_EACH_TUPLE(INITIATE_CPU_NORMAL_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) +// specialization for half +template<> +void NormalDistribution::operator()( + ep::Stream* stream, const int64_t elem_cnt, float16* dptr, + const std::shared_ptr& generator) const { + CHECK_GE(elem_cnt, 0) << "elem_cnt must be non-negative, but got " << elem_cnt; + auto gen = CHECK_JUST(generator->Get()); + std::normal_distribution random_distribution(mean_, std_); + for (int64_t i = 0; i < elem_cnt; ++i) { + dptr[i] = static_cast(random_distribution(gen->engine())); + } +} + } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/normal_distribution.cu b/oneflow/user/kernels/distributions/normal_distribution.cu index b9909d32849117e0e478316f1ef54ed82c7f6037..3e7952e41d310e8a640b700ea04be775b1e91419 100644 --- a/oneflow/user/kernels/distributions/normal_distribution.cu +++ b/oneflow/user/kernels/distributions/normal_distribution.cu @@ -18,31 +18,86 @@ limitations under the License. #include "oneflow/core/common/data_type.h" #include "oneflow/core/ep/include/device.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/user/kernels/fused_rnn_cell_kernel_util.h" +#include "oneflow/core/cuda/layer_norm.cuh" namespace oneflow { namespace { -template -__device__ T GenNormal(curandState* state, const T mean, const T std); +// launch bounds used for kernels +const uint32_t block_size_bound = 256; +const uint32_t grid_size_bound = 4; -template<> -__device__ float GenNormal(curandState* state, const float mean, const float std) { - return (curand_normal(state) + mean) / std; +std::tuple CalcExecutionPolicy(int64_t total_elements, + ep::CudaStream* stream) { + const uint64_t numel = static_cast(total_elements); + const uint32_t block_size = block_size_bound; + // number of randoms given by distributions like curand_uniform4, curand_uniform2_double + // used in calculating philox offset. + const uint32_t curand4_engine_calls = 4; + const uint32_t unroll = curand4_engine_calls; + dim3 dim_block(block_size); + dim3 grid((numel + block_size - 1) / block_size); + uint32_t blocks_per_sm = stream->device_properties().maxThreadsPerMultiProcessor / block_size; + grid.x = std::min( + static_cast(stream->device_properties().multiProcessorCount) * blocks_per_sm, + grid.x); + // number of times random will be generated per thread, to offset philox counter in thc random + // state + uint64_t counter_offset = + ((numel - 1) / (block_size * grid.x * unroll) + 1) * curand4_engine_calls; + return std::make_tuple(counter_offset, grid, dim_block); } -template<> -__device__ double GenNormal(curandState* state, const double mean, const double std) { - return (curand_normal_double(state) + mean) / std; +template +OF_LAUNCH_BOUNDS_2(block_size_bound, grid_size_bound) +__global__ void DistributionElementwiseGridStrideKernelDouble(int32_t numel, uint64_t seed, + uint64_t offset, ComputeType mean, + ComputeType std, T* out_ptr) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, idx, offset, &state); + + int rounded_size = ((numel - 1) / (blockDim.x * gridDim.x * unroll_factor) + 1) * blockDim.x + * gridDim.x * unroll_factor; + for (int32_t linear_index = idx; linear_index < rounded_size; + linear_index += blockDim.x * gridDim.x * unroll_factor) { + double2 rand = GPURAND(_normal2_double)(&state); +#pragma unroll + for (int ii = 0; ii < unroll_factor; ii++) { + int li = linear_index + blockDim.x * gridDim.x * ii; + if (li < numel) { + out_ptr[li] = static_cast(static_cast((&rand.x)[ii]) * std + mean); + } + } + __syncthreads(); + } } -template -__global__ void GenerateGpu(curandState* state, const int64_t elem_cnt, T* dptr, const T mean, - const T std) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { dptr[i] = GenNormal(&localState, mean, std); } - state[id] = localState; +template +OF_LAUNCH_BOUNDS_2(block_size_bound, grid_size_bound) +__global__ void DistributionElementwiseGridStrideKernelFloat(int32_t numel, uint64_t seed, + uint64_t offset, ComputeType mean, + ComputeType std, T* out_ptr) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, idx, offset, &state); + + int rounded_size = ((numel - 1) / (blockDim.x * gridDim.x * unroll_factor) + 1) * blockDim.x + * gridDim.x * unroll_factor; + for (int32_t linear_index = idx; linear_index < rounded_size; + linear_index += blockDim.x * gridDim.x * unroll_factor) { + float4 rand = GPURAND(_normal4)(&state); +#pragma unroll + for (int ii = 0; ii < unroll_factor; ii++) { + int li = linear_index + blockDim.x * gridDim.x * ii; + if (li < numel) { + out_ptr[li] = static_cast(static_cast((&rand.x)[ii]) * std + mean); + } + } + __syncthreads(); + } } } // namespace @@ -52,13 +107,36 @@ void NormalDistribution::operator()( ep::Stream* stream, const int64_t elem_cnt, T* dptr, const std::shared_ptr& generator) const { CHECK_GE(elem_cnt, 0); + if (elem_cnt == 0) return; const auto device_index = stream->device()->device_index(); auto gen = CHECK_JUST(generator->Get(device_index)); - int32_t block_num = gen->max_block_num(); - int32_t thread_num = gen->max_thread_num(); - auto* curand_states = gen->curand_states(); - GenerateGpu<<As()->cuda_stream()>>>( - curand_states, elem_cnt, dptr, mean_, std_); + + ep::CudaStream* cuda_stream = stream->As(); + auto execution_policy = CalcExecutionPolicy(elem_cnt, cuda_stream); + + auto counter_offset = std::get<0>(execution_policy); + auto grid = std::get<1>(execution_policy); + auto block = std::get<2>(execution_policy); + + uint64_t offset = 0; + uint64_t seed = gen->current_seed(); + { + std::lock_guard lock(gen->mutex_); + offset = gen->get_philox_offset(counter_offset); + } + + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + if (std::is_same::value) { + DistributionElementwiseGridStrideKernelDouble + <<As()->cuda_stream()>>>( + elem_cnt, seed, offset, static_cast(mean_), static_cast(std_), + dptr); + } else { + DistributionElementwiseGridStrideKernelFloat + <<As()->cuda_stream()>>>( + elem_cnt, seed, offset, static_cast(mean_), static_cast(std_), + dptr); + } } #define INITIATE_CUDA_NORMAL_DISTRIBUTION(T, typeproto) \ @@ -67,5 +145,6 @@ void NormalDistribution::operator()( const std::shared_ptr& generator) const; OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_NORMAL_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) +INITIATE_CUDA_NORMAL_DISTRIBUTION(half, DataType::kFloat16) } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/normal_distribution.h b/oneflow/user/kernels/distributions/normal_distribution.h index acc01bb1f087e05bf1f36f5be58c8401fd35ab75..d2c5d30ec1af8302eceefba92ffda1e196202cd5 100644 --- a/oneflow/user/kernels/distributions/normal_distribution.h +++ b/oneflow/user/kernels/distributions/normal_distribution.h @@ -23,11 +23,10 @@ limitations under the License. #include #include #endif - #ifdef WITH_ROCM #include #include -#endif +#endif // WITH_ROCM namespace oneflow { @@ -49,7 +48,7 @@ class NormalDistribution final { const T std_; }; -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) template class NormalDistribution final { public: @@ -66,23 +65,6 @@ class NormalDistribution final { }; #endif // WITH_CUDA -#ifdef WITH_ROCM -template -class NormalDistribution final { - public: - OF_DISALLOW_COPY_AND_MOVE(NormalDistribution); - NormalDistribution(T mean, T std) : mean_(mean), std_(std) {} - ~NormalDistribution() = default; - - void operator()(ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const; - - private: - const T mean_; - const T std_; -}; -#endif // WITH_ROCM - } // namespace oneflow #endif // ONEFLOW_USER_KERNELS_DISTRIBUTIONS_NORMAL_DISTRIBUTION_H_ \ No newline at end of file diff --git a/oneflow/user/kernels/distributions/normal_distribution.hip.cpp b/oneflow/user/kernels/distributions/normal_distribution.hip.cpp deleted file mode 100644 index 6056f473e71806b20af45d53c54471926246f0a7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/distributions/normal_distribution.hip.cpp +++ /dev/null @@ -1,71 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/distributions/normal_distribution.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__device__ T GenNormal(hiprandState* state, const T mean, const T std); - -template<> -__device__ float GenNormal(hiprandState* state, const float mean, const float std) { - return (hiprand_normal(state) + mean) / std; -} - -template<> -__device__ double GenNormal(hiprandState* state, const double mean, const double std) { - return (hiprand_normal_double(state) + mean) / std; -} - -template -__global__ void GenerateGpu(hiprandState* state, const int64_t elem_cnt, T* dptr, const T mean, - const T std) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { dptr[i] = GenNormal(&localState, mean, std); } - state[id] = localState; -} - -} // namespace - -template -void NormalDistribution::operator()( - ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const { - CHECK_GE(elem_cnt, 0); - const auto device_index = stream->device()->device_index(); - auto gen = CHECK_JUST(generator->Get(device_index)); - int32_t block_num = gen->max_block_num(); - int32_t thread_num = gen->max_thread_num(); - auto* curand_states = gen->curand_states(); - GenerateGpu<<As()->cuda_stream()>>>( - curand_states, elem_cnt, dptr, mean_, std_); -} - -#define INITIATE_CUDA_NORMAL_DISTRIBUTION(T, typeproto) \ - template void NormalDistribution::operator()( \ - ep::Stream* stream, const int64_t elem_cnt, T* dptr, \ - const std::shared_ptr& generator) const; - -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_NORMAL_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/distributions/normal_kernel.cpp b/oneflow/user/kernels/distributions/normal_kernel.cpp index 91daa33cbd3364dc2d0fb898b7f2b2ceffe5dc90..48701444c698835c2bf7509047ae56939d921017 100644 --- a/oneflow/user/kernels/distributions/normal_kernel.cpp +++ b/oneflow/user/kernels/distributions/normal_kernel.cpp @@ -24,16 +24,14 @@ namespace { (user_op::HobDeviceType() == device) \ && (user_op::HobAttr("dtype") == GetDataType::value)); +REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, float16) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, float) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, double) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, half) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) -#endif // WITH_ROCM } // namespace -} // namespace oneflow \ No newline at end of file +} // namespace oneflow diff --git a/oneflow/user/kernels/distributions/uniform_distribution.cpp b/oneflow/user/kernels/distributions/uniform_distribution.cpp index 11f6a0fde6aa34b8731a672bed5a28d19c94d2fe..f742074e67cf112880bdada4be6a0ab64c17985c 100644 --- a/oneflow/user/kernels/distributions/uniform_distribution.cpp +++ b/oneflow/user/kernels/distributions/uniform_distribution.cpp @@ -37,7 +37,7 @@ template void UniformDistribution::operator()( ep::Stream* stream, const int64_t elem_cnt, T* dptr, const std::shared_ptr& generator) const { - CHECK_GE(elem_cnt, 0); + CHECK_GE(elem_cnt, 0) << "elem_cnt must be non-negative, but got " << elem_cnt; auto gen = CHECK_JUST(generator->Get()); CPUUniformDistributionImpl impl(low_, high_); for (int64_t i = 0; i < elem_cnt; ++i) { dptr[i] = impl(gen->engine()); } @@ -50,4 +50,15 @@ void UniformDistribution::operator()( OF_PP_FOR_EACH_TUPLE(INITIATE_CPU_UNIFORM_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) +// specialization for half +template<> +void UniformDistribution::operator()( + ep::Stream* stream, const int64_t elem_cnt, float16* dptr, + const std::shared_ptr& generator) const { + CHECK_GE(elem_cnt, 0) << "elem_cnt must be non-negative, but got " << elem_cnt; + auto gen = CHECK_JUST(generator->Get()); + CPUUniformDistributionImpl impl(low_, high_); + for (int64_t i = 0; i < elem_cnt; ++i) { dptr[i] = static_cast(impl(gen->engine())); } +} + } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/uniform_distribution.cu b/oneflow/user/kernels/distributions/uniform_distribution.cu index e2ed2059de8487dc6b7c7ace19217a19a0be5b20..abc78b389bc365c544faf938f0a6a3ee2473e48e 100644 --- a/oneflow/user/kernels/distributions/uniform_distribution.cu +++ b/oneflow/user/kernels/distributions/uniform_distribution.cu @@ -23,33 +23,45 @@ namespace oneflow { namespace { template -__device__ T GenUniform(curandState* state, const T low, const T high); +__device__ T GenUniform(GPURAND(State)* state, const T low, const T high); template<> -__device__ float GenUniform(curandState* state, const float low, const float high) { - auto rand_num = curand_uniform(state); +__device__ float GenUniform(GPURAND(State)* state, const float low, const float high) { + auto rand_num = GPURAND(_uniform)(state); // curand_uniform generates (0.0, 1.0], but we want [0.0, 1.0) here if (rand_num == 1.0) { rand_num = 0.0; } return rand_num * (high - low) + low; } template<> -__device__ double GenUniform(curandState* state, const double low, const double high) { - auto rand_num = curand_uniform_double(state); +__device__ double GenUniform(GPURAND(State)* state, const double low, const double high) { + auto rand_num = GPURAND(_uniform_double)(state); // curand_uniform_double generates (0.0, 1.0], but we want [0.0, 1.0) here if (rand_num == 1.0) { rand_num = 0.0; } return rand_num * (high - low) + low; } template -__global__ void GenerateGpu(curandState* state, const int64_t elem_cnt, T* dptr, const T low, +__global__ void GenerateGpu(GPURAND(State)* state, const int64_t elem_cnt, T* dptr, const T low, const T high) { const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState localState = state[id]; + GPURAND(State) localState = state[id]; CUDA_1D_KERNEL_LOOP(i, elem_cnt) { dptr[i] = GenUniform(&localState, low, high); } state[id] = localState; } +// specialization for half +template<> +__global__ void GenerateGpu(GPURAND(State)* state, const int64_t elem_cnt, half* dptr, const half low, + const half high) { + const int id = blockIdx.x * blockDim.x + threadIdx.x; + GPURAND(State) localState = state[id]; + CUDA_1D_KERNEL_LOOP(i, elem_cnt) { + dptr[i] = static_cast(GenUniform(&localState, low, high)); + } + state[id] = localState; +} + } // namespace template @@ -72,5 +84,6 @@ void UniformDistribution::operator()( const std::shared_ptr& generator) const; OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_UNIFORM_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) +INITIATE_CUDA_UNIFORM_DISTRIBUTION(half, DataType::kFloat16) } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/uniform_distribution.h b/oneflow/user/kernels/distributions/uniform_distribution.h index 97ea52085cd2edaa25e1ff4de861c027a03dab81..f900bb29530603953dcccc9c01d65fc769fc2322 100644 --- a/oneflow/user/kernels/distributions/uniform_distribution.h +++ b/oneflow/user/kernels/distributions/uniform_distribution.h @@ -25,7 +25,7 @@ limitations under the License. #ifdef WITH_ROCM #include #include -#endif +#endif // WITH_ROCM namespace oneflow { @@ -47,7 +47,7 @@ class UniformDistribution final { const T high_; }; -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) template class UniformDistribution final { public: @@ -64,23 +64,6 @@ class UniformDistribution final { }; #endif // WITH_CUDA -#ifdef WITH_ROCM -template -class UniformDistribution final { - public: - OF_DISALLOW_COPY_AND_MOVE(UniformDistribution); - UniformDistribution(T low, T high) : low_(low), high_(high) {} - ~UniformDistribution() = default; - - void operator()(ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const; - - private: - const T low_; - const T high_; -}; -#endif // WITH_ROCM - } // namespace oneflow #endif // ONEFLOW_USER_KERNELS_DISTRIBUTIONS_UNIFORM_DISTRIBUTION_H_ diff --git a/oneflow/user/kernels/distributions/uniform_distribution.hip.cpp b/oneflow/user/kernels/distributions/uniform_distribution.hip.cpp deleted file mode 100644 index b6fbc7cd263deb39c9015c9020a7b949ace3faf6..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/distributions/uniform_distribution.hip.cpp +++ /dev/null @@ -1,77 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/user/kernels/distributions/uniform_distribution.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__device__ T GenUniform(hiprandState* state, const T low, const T high); - -template<> -__device__ float GenUniform(hiprandState* state, const float low, const float high) { - auto rand_num = hiprand_uniform(state); - // hiprand_uniform generates (0.0, 1.0], but we want [0.0, 1.0) here - if (rand_num == 1.0) { rand_num = 0.0; } - return rand_num * (high - low) + low; -} - -template<> -__device__ double GenUniform(hiprandState* state, const double low, const double high) { - auto rand_num = hiprand_uniform_double(state); - // hiprand_uniform_double generates (0.0, 1.0], but we want [0.0, 1.0) here - if (rand_num == 1.0) { rand_num = 0.0; } - return rand_num * (high - low) + low; -} - -template -__global__ void GenerateGpu(hiprandState* state, const int64_t elem_cnt, T* dptr, const T low, - const T high) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { dptr[i] = GenUniform(&localState, low, high); } - state[id] = localState; -} - -} // namespace - -template -void UniformDistribution::operator()( - ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const { - CHECK_GE(elem_cnt, 0); - const auto device_index = stream->device()->device_index(); - auto gen = CHECK_JUST(generator->Get(device_index)); - int32_t block_num = gen->max_block_num(); - int32_t thread_num = gen->max_thread_num(); - auto* curand_states = gen->curand_states(); - GenerateGpu<<As()->cuda_stream()>>>( - curand_states, elem_cnt, dptr, low_, high_); -} - -#define INITIATE_CUDA_UNIFORM_DISTRIBUTION(T, typeproto) \ - template void UniformDistribution::operator()( \ - ep::Stream* stream, const int64_t elem_cnt, T* dptr, \ - const std::shared_ptr& generator) const; - -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_UNIFORM_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/distributions/uniform_int_distribution.cu b/oneflow/user/kernels/distributions/uniform_int_distribution.cu index d19940d4bc85a5c8a71a768be150006858dbe3ca..bbf6b9101fd5127c4d0c87c9568af17edba1f640 100644 --- a/oneflow/user/kernels/distributions/uniform_int_distribution.cu +++ b/oneflow/user/kernels/distributions/uniform_int_distribution.cu @@ -25,18 +25,18 @@ namespace oneflow { namespace { -__device__ int64_t GenUniformInt(curandState* state, const int64_t low, const int64_t high) { - auto rand_num = curand_uniform(state); +__device__ int64_t GenUniformInt(GPURAND(State)* state, const int64_t low, const int64_t high) { + auto rand_num = GPURAND(_uniform)(state); // curand_uniform generates (0.0, 1.0], but we want [0.0, 1.0) here if (rand_num == 1.0) { rand_num = 0.0; } return static_cast(rand_num * (high - low) + low); } template -__global__ void GenerateGpu(curandState* state, const int64_t elem_cnt, T* dptr, const int64_t low, +__global__ void GenerateGpu(GPURAND(State)* state, const int64_t elem_cnt, T* dptr, const int64_t low, const int64_t high) { const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState localState = state[id]; + GPURAND(State) localState = state[id]; CUDA_1D_KERNEL_LOOP(i, elem_cnt) { dptr[i] = static_cast(GenUniformInt(&localState, low, high)); } diff --git a/oneflow/user/kernels/distributions/uniform_int_distribution.h b/oneflow/user/kernels/distributions/uniform_int_distribution.h index 77a2b8905bda4d19ff94c69a26e3cb56de7e5e54..e853da3809a6079ba3096137ce25d4fda7fd6f7d 100644 --- a/oneflow/user/kernels/distributions/uniform_int_distribution.h +++ b/oneflow/user/kernels/distributions/uniform_int_distribution.h @@ -25,7 +25,7 @@ limitations under the License. #ifdef WITH_ROCM #include #include -#endif +#endif // WITH_ROCM namespace oneflow { @@ -47,7 +47,7 @@ class UniformIntDistribution final { const int64_t high_; }; -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) template class UniformIntDistribution final { public: @@ -64,23 +64,6 @@ class UniformIntDistribution final { }; #endif // WITH_CUDA -#ifdef WITH_ROCM -template -class UniformIntDistribution final { - public: - OF_DISALLOW_COPY_AND_MOVE(UniformIntDistribution); - UniformIntDistribution(int64_t low, int64_t high) : low_(low), high_(high) {} - ~UniformIntDistribution() = default; - - void operator()(ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const; - - private: - const int64_t low_; - const int64_t high_; -}; -#endif // WITH_ROCM - } // namespace oneflow #endif // ONEFLOW_USER_KERNELS_DISTRIBUTIONS_UNIFORM_INT_DISTRIBUTION_H_ diff --git a/oneflow/user/kernels/distributions/uniform_int_distribution.hip.cpp b/oneflow/user/kernels/distributions/uniform_int_distribution.hip.cpp deleted file mode 100644 index 0ff5160ff46d382bf0888c3fa9ec97254296923c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/distributions/uniform_int_distribution.hip.cpp +++ /dev/null @@ -1,72 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/common/preprocessor.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/framework/dtype.h" -#include "oneflow/user/kernels/distributions/uniform_int_distribution.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -__device__ int64_t GenUniformInt(hiprandState* state, const int64_t low, const int64_t high) { - auto rand_num = hiprand_uniform(state); - // hiprand_uniform generates (0.0, 1.0], but we want [0.0, 1.0) here - if (rand_num == 1.0) { rand_num = 0.0; } - return static_cast(rand_num * (high - low) + low); -} - -template -__global__ void GenerateGpu(hiprandState* state, const int64_t elem_cnt, T* dptr, const int64_t low, - const int64_t high) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - dptr[i] = static_cast(GenUniformInt(&localState, low, high)); - } - state[id] = localState; -} - -} // namespace - -template -void UniformIntDistribution::operator()( - ep::Stream* stream, const int64_t elem_cnt, T* dptr, - const std::shared_ptr& generator) const { - CHECK_GE(elem_cnt, 0); - const auto device_index = stream->device()->device_index(); - auto gen = CHECK_JUST(generator->Get(device_index)); - int32_t block_num = gen->max_block_num(); - int32_t thread_num = gen->max_thread_num(); - auto* curand_states = gen->curand_states(); - GenerateGpu<<As()->cuda_stream()>>>( - curand_states, elem_cnt, dptr, low_, high_); -} - -#define INITIATE_CUDA_UNIFORM_INT_DISTRIBUTION(T, typeproto) \ - template void UniformIntDistribution::operator()( \ - ep::Stream* stream, const int64_t elem_cnt, T* dptr, \ - const std::shared_ptr& generator) const; - -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_UNIFORM_INT_DISTRIBUTION, FLOATING_DATA_TYPE_SEQ) -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_UNIFORM_INT_DISTRIBUTION, INT_DATA_TYPE_SEQ) -OF_PP_FOR_EACH_TUPLE(INITIATE_CUDA_UNIFORM_INT_DISTRIBUTION, UNSIGNED_INT_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/distributions/uniform_int_kernel.cpp b/oneflow/user/kernels/distributions/uniform_int_kernel.cpp index d8b3df7386d3ca2739b08228f3d47abe1134d9ae..4ed736f4c29821d9a6825368cd74b437d81e81d9 100644 --- a/oneflow/user/kernels/distributions/uniform_int_kernel.cpp +++ b/oneflow/user/kernels/distributions/uniform_int_kernel.cpp @@ -30,7 +30,7 @@ REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, uint8_t) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, int8_t) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, int32_t) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, int64_t) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, uint8_t) @@ -39,15 +39,6 @@ REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, int32_t) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, int64_t) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, uint8_t) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, int8_t) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, int32_t) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, int64_t) -#endif // WITH_ROCM - } // namespace } // namespace oneflow diff --git a/oneflow/user/kernels/distributions/uniform_kernel.cpp b/oneflow/user/kernels/distributions/uniform_kernel.cpp index 176f692d43707dd483c4c3d9408aef4edd6e0f45..050fe8c356617f816d4d624b56d7b6d8fec170c4 100644 --- a/oneflow/user/kernels/distributions/uniform_kernel.cpp +++ b/oneflow/user/kernels/distributions/uniform_kernel.cpp @@ -23,17 +23,14 @@ namespace { (user_op::HobDeviceType() == device) \ && (user_op::HobAttr("dtype") == GetDataType::value)); +REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, float16) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, float) REGISTER_UNIFORM_KERNEL(DeviceType::kCPU, double) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, half) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) #endif // WITH_CUDA - -#ifdef WITH_ROCM -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, float) -REGISTER_UNIFORM_KERNEL(DeviceType::kCUDA, double) -#endif // WITH_ROCM } // namespace } // namespace oneflow diff --git a/oneflow/user/kernels/dropout_kernel.cu b/oneflow/user/kernels/dropout_kernel.cu index 23c6cfc5c1a4007de1ca8197074bc0d894fdb88a..db3f2120cd03b0bf0251cd10a0315631039e81fb 100644 --- a/oneflow/user/kernels/dropout_kernel.cu +++ b/oneflow/user/kernels/dropout_kernel.cu @@ -132,8 +132,8 @@ __global__ RETURN_VOID_IF_FLOAT FusedDropoutAddGpu( const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - curandStatePhilox4_32_10_t state; - curand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, global_thread_id, cuda_gen_state->dev_offset, &state); using LoadType = cuda::elementwise::PackType; using LoadPack = cuda::elementwise::Pack; using MaskType = cuda::elementwise::PackType; @@ -143,7 +143,7 @@ __global__ RETURN_VOID_IF_FLOAT FusedDropoutAddGpu( RandPack4 rand_uniform_pack4; for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; linear_index += gridDim.x * blockDim.x * pack_size) { - rand_uniform_pack4.storage = curand_uniform4(&state); + rand_uniform_pack4.storage = GPURAND(_uniform4)(&state); const LoadType* x_load = reinterpret_cast(x + linear_index); LoadPack x_vec; @@ -170,7 +170,7 @@ __global__ RETURN_VOID_IF_FLOAT FusedDropoutAddGpu( } if (tail && global_thread_id < n_tail) { - const float rand_uniform = curand_uniform(&state); + const float rand_uniform = GPURAND(_uniform)(&state); const bool mask_val = rand_uniform > rate; tail_mask[global_thread_id] = mask_val; T tmp_float_mask = static_cast(mask_val); @@ -196,8 +196,8 @@ __global__ RETURN_VOID_IF_HALF FusedDropoutAddGpu( const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - curandStatePhilox4_32_10_t state; - curand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, global_thread_id, cuda_gen_state->dev_offset, &state); using LoadType = cuda::elementwise::PackType; using LoadPack = cuda::elementwise::Pack; using StoreType = cuda::elementwise::PackType, pack_size / 2>; @@ -210,7 +210,7 @@ __global__ RETURN_VOID_IF_HALF FusedDropoutAddGpu( for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; linear_index += gridDim.x * blockDim.x * pack_size) { - rand_uniform_pack4.storage = curand_uniform4(&state); + rand_uniform_pack4.storage = GPURAND(_uniform4)(&state); const LoadType* x_load = reinterpret_cast(x + linear_index); H2Pack x_vec{}; x_vec.pack_storage.storage = *x_load; @@ -251,11 +251,15 @@ __global__ RETURN_VOID_IF_HALF FusedDropoutAddGpu( } if (tail && global_thread_id < n_tail) { - const float rand_uniform = curand_uniform(&state); + const float rand_uniform = GPURAND(_uniform)(&state); const bool mask_val = rand_uniform > rate; tail_mask[global_thread_id] = mask_val; float tmp_half_mask = static_cast(mask_val); +#ifdef WITH_ROCM + T tmp_tail_out = tail_x[global_thread_id] * static_cast(tmp_half_mask) * static_cast(h2_scale.data.x); +#else T tmp_tail_out = tail_x[global_thread_id] * static_cast(tmp_half_mask) * h2_scale.x; +#endif if (has_addend) { tmp_tail_out += tail_addend[global_thread_id]; } tail_y[global_thread_id] = tmp_tail_out; } @@ -276,8 +280,8 @@ __global__ RETURN_VOID_IF_DOUBLE FusedDropoutAddGpu( const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - curandStatePhilox4_32_10_t state; - curand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, global_thread_id, cuda_gen_state->dev_offset, &state); using LoadType = cuda::elementwise::PackType; using LoadPack = cuda::elementwise::Pack; using MaskType = cuda::elementwise::PackType; @@ -289,7 +293,7 @@ __global__ RETURN_VOID_IF_DOUBLE FusedDropoutAddGpu( for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; linear_index += gridDim.x * blockDim.x * pack_size) { if (grid_loop_rand_state == 0) { - rand_uniform_pack4.storage = curand_uniform4(&state); + rand_uniform_pack4.storage = GPURAND(_uniform4)(&state); grid_loop_rand_state ^= 1; } else { // Use the last two random numbers we generated in previous iteration. @@ -320,7 +324,7 @@ __global__ RETURN_VOID_IF_DOUBLE FusedDropoutAddGpu( } if (tail && global_thread_id < n_tail) { - const float rand_uniform = curand_uniform(&state); + const float rand_uniform = GPURAND(_uniform)(&state); const bool mask_val = rand_uniform > rate; tail_mask[global_thread_id] = mask_val; double tmp_tail_out = tail_x[global_thread_id] * mask_val * scale; diff --git a/oneflow/user/kernels/dropout_kernel.hip.cpp b/oneflow/user/kernels/dropout_kernel.hip.cpp deleted file mode 100644 index 4fe35523b3d49b3ee698b98b09b0ad82f179c29c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/dropout_kernel.hip.cpp +++ /dev/null @@ -1,463 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/op_kernel_wrapper.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/dropout_kernel.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -// #include "oneflow/core/device/cuda_pseudo_bfloat16.h" -namespace oneflow { - -namespace { - -constexpr int32_t kVecSize = 4; -constexpr int32_t kBlockSize = 256; - -template -constexpr int32_t GetDropoutPackSize() { - // For float, bfloat16, half. - return 4; -}; - -template<> -constexpr int32_t GetDropoutPackSize() { - return 2; -}; - -template<> -constexpr int32_t GetDropoutPackSize() { - return 2; -}; - -union RandPack4 { - float4 storage; - float elem[4]; -}; - -template -struct GetPack2Type { - using T2 = typename std::aligned_storage<2 * sizeof(T), 2 * sizeof(T)>::type; -}; - -template<> -struct GetPack2Type { - using T2 = half2; -}; - - -template -using Pack2Type = typename GetPack2Type::T2; - -using H2PackType = typename std::aligned_storage<4 * sizeof(half), 4 * sizeof(half)>::type; - -template -union H2Pack { - cuda::elementwise::Pack pack_storage; - Pack2Type h2[2]; - __device__ H2Pack() { - // do nothing - } -}; - -template<> -union H2Pack { - cuda::elementwise::Pack pack_storage; - half2 h2[2]; - __device__ H2Pack() { - // do nothing - } -}; - -template -__device__ Pack2Type Make2(float v); - -template<> -__device__ Pack2Type Make2(float v) { - return __float2half2_rn(v); -} - -#define RETURN_VOID_IF_HALF typename std::enable_if_t::value, void> - -#define RETURN_VOID_IF_FLOAT typename std::enable_if_t::value, void> -#define RETURN_VOID_IF_DOUBLE typename std::enable_if_t::value, void> - -template -__global__ RETURN_VOID_IF_FLOAT FusedDropoutAddGpu( - uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, uint64_t inc_offset, - const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, - const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandStatePhilox4_32_10_t state; - hiprand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - using MaskType = cuda::elementwise::PackType; - using MaskPack = cuda::elementwise::Pack; - - T t_scale = static_cast(scale); - RandPack4 rand_uniform_pack4; - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - rand_uniform_pack4.storage = hiprand_uniform4(&state); - - const LoadType* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec; - x_vec.storage = *x_load; - - LoadPack addend_vec; - if (has_addend) { - const LoadType* addend_load = reinterpret_cast(addend + linear_index); - addend_vec.storage = *addend_load; - } - - MaskPack mask_vec; - LoadPack y_vec; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - mask_vec.elem[i] = rand_uniform_pack4.elem[i] > rate; - T tmp_float_mask = static_cast(mask_vec.elem[i]); - y_vec.elem[i] = x_vec.elem[i] * tmp_float_mask * t_scale; - if (has_addend) { y_vec.elem[i] += addend_vec.elem[i]; } - } - - *(reinterpret_cast(y + linear_index)) = y_vec.storage; - *(reinterpret_cast(mask + linear_index)) = mask_vec.storage; - } - - if (tail && global_thread_id < n_tail) { - const float rand_uniform = hiprand_uniform(&state); - const bool mask_val = rand_uniform > rate; - tail_mask[global_thread_id] = mask_val; - T tmp_float_mask = static_cast(mask_val); - T tmp_tail_out = tail_x[global_thread_id] * tmp_float_mask * t_scale; - if (has_addend) { tmp_tail_out += tail_addend[global_thread_id]; } - tail_y[global_thread_id] = tmp_tail_out; - } - - __syncthreads(); - - if (threadIdx.x == 0) { - int32_t new_counter = cuda::atomic::Add(&cuda_gen_state->dev_counter, 1) + 1; - if (new_counter == gridDim.x) { - cuda_gen_state->dev_counter = 0; // reset counter to zero - cuda_gen_state->dev_offset += inc_offset; // maintain the state of generator's dev_offset - } - } -} - -template -__global__ RETURN_VOID_IF_HALF FusedDropoutAddGpu( - uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, uint64_t inc_offset, - const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, - const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandStatePhilox4_32_10_t state; - hiprand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - using StoreType = cuda::elementwise::PackType, pack_size / 2>; - using StorePack = cuda::elementwise::Pack, pack_size / 2>; - using MaskType = cuda::elementwise::PackType; - using MaskPack = cuda::elementwise::Pack; - - RandPack4 rand_uniform_pack4; - Pack2Type h2_scale = Make2(scale); - - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - rand_uniform_pack4.storage = hiprand_uniform4(&state); - const LoadType* x_load = reinterpret_cast(x + linear_index); - H2Pack x_vec{}; - x_vec.pack_storage.storage = *x_load; - - H2Pack addend_vec{}; - if (has_addend) { - const LoadType* addend_load = reinterpret_cast(addend + linear_index); - addend_vec.pack_storage.storage = *addend_load; - } - - MaskPack mask_vec; - StorePack y_vec; - StorePack one_or_zero_h2; - - mask_vec.elem[0] = rand_uniform_pack4.elem[0] > rate; - float tmp_float_mask = static_cast(mask_vec.elem[0]); - one_or_zero_h2.elem[0].x = tmp_float_mask; - mask_vec.elem[1] = rand_uniform_pack4.elem[1] > rate; - tmp_float_mask = static_cast(mask_vec.elem[1]); - one_or_zero_h2.elem[0].y = tmp_float_mask; - y_vec.elem[0] = __hmul2(__hmul2(x_vec.h2[0], one_or_zero_h2.elem[0]), h2_scale); - - mask_vec.elem[2] = rand_uniform_pack4.elem[2] > rate; - tmp_float_mask = static_cast(mask_vec.elem[2]); - one_or_zero_h2.elem[1].x = tmp_float_mask; - mask_vec.elem[3] = rand_uniform_pack4.elem[3] > rate; - tmp_float_mask = static_cast(mask_vec.elem[3]); - one_or_zero_h2.elem[1].y = tmp_float_mask; - y_vec.elem[1] = __hmul2(__hmul2(x_vec.h2[1], one_or_zero_h2.elem[1]), h2_scale); - - if (has_addend) { - y_vec.elem[0] = __hadd2(y_vec.elem[0], addend_vec.h2[0]); - y_vec.elem[1] = __hadd2(y_vec.elem[1], addend_vec.h2[1]); - } - - *(reinterpret_cast(y + linear_index)) = y_vec.storage; - *(reinterpret_cast(mask + linear_index)) = mask_vec.storage; - } - - if (tail && global_thread_id < n_tail) { - const float rand_uniform = hiprand_uniform(&state); - const bool mask_val = rand_uniform > rate; - tail_mask[global_thread_id] = mask_val; - float tmp_half_mask = static_cast(mask_val); - T tmp_tail_out = tail_x[global_thread_id] * static_cast(tmp_half_mask) * static_cast(h2_scale.data.x); - if (has_addend) { tmp_tail_out += tail_addend[global_thread_id]; } - tail_y[global_thread_id] = tmp_tail_out; - } - - __syncthreads(); - if (threadIdx.x == 0) { - int32_t new_counter = cuda::atomic::Add(&cuda_gen_state->dev_counter, 1) + 1; - if (new_counter == gridDim.x) { - cuda_gen_state->dev_counter = 0; // reset counter to zero - cuda_gen_state->dev_offset += inc_offset; // maintain the state of generator's dev_offset - } - } -} - -template -__global__ RETURN_VOID_IF_DOUBLE FusedDropoutAddGpu( - uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, uint64_t inc_offset, - const int64_t elem_cnt, float rate, float scale, int64_t n_tail, const T* x, bool* mask, - const T* addend, T* y, const T* tail_x, bool* tail_mask, const T* tail_addend, T* tail_y) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandStatePhilox4_32_10_t state; - hiprand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - using MaskType = cuda::elementwise::PackType; - using MaskPack = cuda::elementwise::Pack; - - RandPack4 rand_uniform_pack4; - bool grid_loop_rand_state = 0; - - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - if (grid_loop_rand_state == 0) { - rand_uniform_pack4.storage = hiprand_uniform4(&state); - grid_loop_rand_state ^= 1; - } else { - // Use the last two random numbers we generated in previous iteration. - rand_uniform_pack4.elem[0] = rand_uniform_pack4.elem[2]; - rand_uniform_pack4.elem[1] = rand_uniform_pack4.elem[3]; - grid_loop_rand_state ^= 1; - } - const LoadType* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec; - x_vec.storage = *x_load; - - LoadPack addend_vec; - if (has_addend) { - const LoadType* addend_load = reinterpret_cast(addend + linear_index); - addend_vec.storage = *addend_load; - } - - MaskPack mask_vec; - LoadPack y_vec; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - mask_vec.elem[i] = rand_uniform_pack4.elem[i] > rate; - y_vec.elem[i] = x_vec.elem[i] * mask_vec.elem[i] * scale; - if (has_addend) { y_vec.elem[i] += addend_vec.elem[i]; } - } - *(reinterpret_cast(y + linear_index)) = y_vec.storage; - *(reinterpret_cast(mask + linear_index)) = mask_vec.storage; - } - - if (tail && global_thread_id < n_tail) { - const float rand_uniform = hiprand_uniform(&state); - const bool mask_val = rand_uniform > rate; - tail_mask[global_thread_id] = mask_val; - double tmp_tail_out = tail_x[global_thread_id] * mask_val * scale; - if (has_addend) { tmp_tail_out += tail_addend[global_thread_id]; } - tail_y[global_thread_id] = tmp_tail_out; - } - - __syncthreads(); - if (threadIdx.x == 0) { - int32_t new_counter = cuda::atomic::Add(&cuda_gen_state->dev_counter, 1) + 1; - if (new_counter == gridDim.x) { - cuda_gen_state->dev_counter = 0; // reset counter to zero - cuda_gen_state->dev_offset += inc_offset; // maintain the state of generator's dev_offset - } - } -} - -unsigned int ComputeGridSize(ep::Stream* stream, const int32_t block_size, const int64_t elem_cnt) { - auto* cuda_stream = stream->As(); - const int32_t max_threads_multi_process = - cuda_stream->device_properties().maxThreadsPerMultiProcessor; - const int32_t multi_processor_count = cuda_stream->device_properties().multiProcessorCount; - unsigned int blocks_per_sm = max_threads_multi_process / block_size; - unsigned int grid_size = std::max((int64_t)1, ((elem_cnt + block_size - 1) / block_size)); - grid_size = std::min((unsigned int)multi_processor_count * blocks_per_sm, grid_size); - return grid_size; -} - -template -void DispatchTail(ep::Stream* stream, uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, - const int64_t elem_cnt, float rate, float scale, const T* x, bool* mask, - const T* addend, T* y) { - constexpr int pack_size = GetDropoutPackSize(); - const int64_t pack_num = elem_cnt / pack_size; - unsigned int grid_size = ComputeGridSize(stream, kBlockSize, pack_num); - const int64_t tail_offset = pack_num * pack_size; - const int64_t n_tail = elem_cnt - tail_offset; - const bool tail = n_tail > 0 ? true : false; - uint64_t inc_offset = 0; - - if (tail) { - // If tail, we need generate randnum one more time, so here we add another `1`. - inc_offset = ((elem_cnt - 1) / (kBlockSize * grid_size * kVecSize) + 1) * kVecSize + 1; - FusedDropoutAddGpu - <<As()->cuda_stream()>>>( - seed, cuda_gen_state, inc_offset, elem_cnt, rate, scale, n_tail, x, mask, addend, y, - (x + tail_offset), (mask + tail_offset), (addend + tail_offset), (y + tail_offset)); - } else { - inc_offset = ((elem_cnt - 1) / (kBlockSize * grid_size * kVecSize) + 1) * kVecSize; - FusedDropoutAddGpu - <<As()->cuda_stream()>>>( - seed, cuda_gen_state, inc_offset, elem_cnt, rate, scale, n_tail, x, mask, addend, y, - nullptr, nullptr, nullptr, nullptr); - } -} - -template -struct MaskAndScaleFunctor { - OF_DEVICE_FUNC explicit MaskAndScaleFunctor(float scale) : scale(scale) {} - __device__ T operator()(T x, bool mask) const { - return x * static_cast(mask) * static_cast(scale); - } - float scale; -}; - - - -template -class DropoutKernelGPU final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - DropoutKernelGPU() = default; - ~DropoutKernelGPU() = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - const auto& generator = CHECK_JUST(one::MakeGenerator(DeviceType::kCUDA)); - return std::make_shared(generator); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - auto* fused_dropout_kernel_state = dynamic_cast(state); - CHECK_NOTNULL(fused_dropout_kernel_state); - const auto& generator = fused_dropout_kernel_state->generator(); - CHECK_NOTNULL(generator); - auto* stream = ctx->stream(); - const auto device_index = stream->device()->device_index(); - std::shared_ptr cuda_generator = - CHECK_JUST(generator->Get(device_index)); - uint64_t seed = cuda_generator->current_seed(); - - const float rate = ctx->Attr("rate"); - float scale = 0.0; - if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } - one::CUDAGeneratorState* cuda_gen_state = cuda_generator->cuda_gen_state(); - - if (ctx->has_input("_add_to_output", 0)) { - const user_op::Tensor* addend = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); - DispatchTail( - stream, seed, cuda_gen_state, in->shape_view().elem_cnt(), rate, scale, - reinterpret_cast(in->dptr()), reinterpret_cast(mask->mut_dptr()), - reinterpret_cast(addend->dptr()), reinterpret_cast(out->mut_dptr())); - } else { - DispatchTail(stream, seed, cuda_gen_state, in->shape_view().elem_cnt(), rate, scale, - reinterpret_cast(in->dptr()), - reinterpret_cast(mask->mut_dptr()), nullptr, - reinterpret_cast(out->mut_dptr())); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_DROPOUT_KERNEL_GPU(cpp_type, data_type) \ - REGISTER_USER_KERNEL("dropout").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == data_type) \ - && (user_op::HobDataType("mask", 0) == GetDataType::value)) - -REGISTER_DROPOUT_KERNEL_GPU(half, DataType::kFloat16); -REGISTER_DROPOUT_KERNEL_GPU(float, DataType::kFloat); -REGISTER_DROPOUT_KERNEL_GPU(double, DataType::kDouble); - - -template -class DropoutGradKernelGPU final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - DropoutGradKernelGPU() = default; - ~DropoutGradKernelGPU() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float scale = ctx->Attr("scale"); - const int64_t elem_cnt = dy->shape_view().elem_cnt(); - OF_CUDA_CHECK((cuda::elementwise::Binary( - MaskAndScaleFunctor(scale), elem_cnt, reinterpret_cast(dx->mut_dptr()), - reinterpret_cast(dy->dptr()), reinterpret_cast(mask->dptr()), - ctx->stream()->As()->cuda_stream()))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_DROPOUT_GRAD_KERNEL_GPU(cpp_type, data_type) \ - REGISTER_USER_KERNEL("dropout_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == data_type)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "dy", 0, true)); \ - return Maybe::Ok(); \ - }) - -REGISTER_DROPOUT_GRAD_KERNEL_GPU(half, DataType::kFloat16); -REGISTER_DROPOUT_GRAD_KERNEL_GPU(float, DataType::kFloat); -REGISTER_DROPOUT_GRAD_KERNEL_GPU(double, DataType::kDouble); - -} // namespace - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/dynamic_loss_scale_schedule_kernel.hip.cpp b/oneflow/user/kernels/dynamic_loss_scale_schedule_kernel.hip.cpp deleted file mode 100644 index d614a8eb87bb830c6a23f822b0932e0511ddba33..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/dynamic_loss_scale_schedule_kernel.hip.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -__global__ void DynamicLossScaleScheduleGpu(const int64_t increment_period, const float multiplier, - const int64_t* count_not_finite, float* loss_scale, - int64_t* good_step_counter) { - if (*count_not_finite == 0) { - int64_t cur_good_step_counter = *good_step_counter + 1; - if (cur_good_step_counter >= increment_period) { - *loss_scale = static_cast( - min(static_cast(*loss_scale) * multiplier, static_cast(FLT_MAX))); - cur_good_step_counter = 0; - } - *good_step_counter = cur_good_step_counter; - } else { - *good_step_counter = 0; - *loss_scale = static_cast(max(static_cast(*loss_scale) / multiplier, 1.0)); - } -} - -} // namespace - -class DynamicLossScaleScheduleGpuKernel final : public user_op::OpKernel { - public: - DynamicLossScaleScheduleGpuKernel() = default; - ~DynamicLossScaleScheduleGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* count_not_finite = ctx->Tensor4ArgNameAndIndex("count_not_finite", 0); - user_op::Tensor* loss_scale = ctx->Tensor4ArgNameAndIndex("loss_scale", 0); - user_op::Tensor* good_step_counter = ctx->Tensor4ArgNameAndIndex("good_step_counter", 0); - const auto increment_period = ctx->Attr("increment_period"); - const auto multiplier = ctx->Attr("multiplier"); - DynamicLossScaleScheduleGpu<<<1, 1, 0, ctx->stream()->As()->cuda_stream()>>>( - increment_period, multiplier, count_not_finite->dptr(), - loss_scale->mut_dptr(), good_step_counter->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } -}; - -REGISTER_USER_KERNEL("dynamic_loss_scale_schedule") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/eager_b_to_s_kernel.cpp b/oneflow/user/kernels/eager_b_to_s_kernel.cpp index 197cfdf7a3b3cbdc9199da4afc75c41157bbeaee..907dc50a31301b64559592e232b6dd3c25875173 100644 --- a/oneflow/user/kernels/eager_b_to_s_kernel.cpp +++ b/oneflow/user/kernels/eager_b_to_s_kernel.cpp @@ -153,7 +153,6 @@ size_t InferEagerBToSKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerBToSKernel final : public user_op::OpKernel { public: EagerBToSKernel() = default; @@ -185,6 +184,8 @@ class EagerBToSKernel final : public user_op::OpKernel { CHECK_EQ(sorted_elem_cnt2in_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); CHECK_EQ(sorted_elem_cnt2out_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); + DeviceType device_type = ctx->device_type(); + for (int64_t i = 0; i < sorted_p2p_pair.size(); ++i) { const auto& p2p_pair = sorted_p2p_pair.at(i); int64_t src = p2p_pair.first; @@ -202,8 +203,8 @@ class EagerBToSKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; tensor_slice_copier->Copy(ctx->stream(), tmp_buffer_ptr, in_ptr); - CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, - in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, in->data_type(), + dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { const auto& elem_cnt2tensor_slice_copier_pair = @@ -211,7 +212,7 @@ class EagerBToSKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; CHECK_JUST( - Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, ctx->stream())); + Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, device_type, ctx->stream())); tensor_slice_copier->Copy(ctx->stream(), out_ptr, reinterpret_cast(tmp_buffer_ptr)); } @@ -220,18 +221,9 @@ class EagerBToSKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_B_TO_S_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_b_to_s") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device) \ - .SetInferTmpSizeFn(InferEagerBToSKernelTmpBufferSize); - -REGISTER_EAGER_B_TO_S_KERNEL(DeviceType::kCPU) -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_B_TO_S_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_B_TO_S_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_b_to_s") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferEagerBToSKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_ccl_kernel.cpp b/oneflow/user/kernels/eager_ccl_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..13c408ef16115629d3122b5ecb5b18a72ab24be7 --- /dev/null +++ b/oneflow/user/kernels/eager_ccl_kernel.cpp @@ -0,0 +1,294 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/control/global_process_ctx.h" +#include "oneflow/user/kernels/collective_communication/include/communication_context.h" +#include "oneflow/user/kernels/collective_communication/include/all_reduce.h" +#include "oneflow/user/kernels/collective_communication/include/reduce_scatter.h" +#include "oneflow/user/kernels/collective_communication/include/all_gather.h" +#include "oneflow/user/kernels/collective_communication/include/reduce.h" +#include "oneflow/user/kernels/collective_communication/include/broadcast.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +auto AllReduceCollectiveCommunicationExists() { + return hob::make_custom("AllReduceCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsCommunicationContextRegistered(device_type) + && ccl::IsAllReduceRegistered(device_type); + }); +} + +auto ReduceScatterCollectiveCommunicationExists() { + return hob::make_custom("ReduceScatterCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsCommunicationContextRegistered(device_type) + && ccl::IsReduceScatterRegistered(device_type); + }); +} + +auto AllGatherCollectiveCommunicationExists() { + return hob::make_custom("AllGatherCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsCommunicationContextRegistered(device_type) + && ccl::IsAllGatherRegistered(device_type); + }); +} + +auto ReduceCollectiveCommunicationExists() { + return hob::make_custom("ReduceCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsCommunicationContextRegistered(device_type) + && ccl::IsReduceRegistered(device_type); + }); +} + +auto BroadcastCollectiveCommunicationExists() { + return hob::make_custom("BroadcastCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsCommunicationContextRegistered(device_type) + && ccl::IsBroadcastRegistered(device_type); + }); +} + +class EagerCclOpKernelCache final : public user_op::OpKernelCache { + public: + explicit EagerCclOpKernelCache(user_op::KernelCacheContext* ctx) { Init(ctx); } + ~EagerCclOpKernelCache() override = default; + + const std::shared_ptr& communication_ctx() const { + return communication_ctx_; + } + + private: + void Init(user_op::KernelCacheContext* ctx) { + const std::string& parallel_conf_txt = ctx->Attr("parallel_conf"); + ParallelConf parallel_conf; + CHECK(TxtString2PbMessage(parallel_conf_txt, ¶llel_conf)); + Symbol parallel_desc = SymbolOf(ParallelDesc(parallel_conf)); + communication_ctx_ = ccl::NewCommunicationContext(parallel_desc->device_type(), parallel_desc); + } + + std::shared_ptr communication_ctx_; +}; + +void InitEagerCclOpKernelCache(user_op::KernelCacheContext* ctx, + std::shared_ptr* cache_ptr) { + // NOTE(jianhao): the cache only depends on parallel_conf, and the kernel is singleton + // once parallel_conf is determined, so only init the cache at the first time. + if (*cache_ptr == nullptr) { *cache_ptr = std::make_shared(ctx); } +} + +} // namespace + +class EagerCclAllReduceKernel final : public user_op::OpKernel { + public: + EagerCclAllReduceKernel() = default; + ~EagerCclAllReduceKernel() override = default; + + void InitOpKernelCacheWithFlags( + user_op::KernelCacheContext* ctx, int8_t flag, + std::shared_ptr* cache_ptr) const override { + InitEagerCclOpKernelCache(ctx, cache_ptr); + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + auto* kernel_cache = dynamic_cast(cache); + CHECK(kernel_cache != nullptr); + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(in->shape_view(), out->shape_view()) << kOfBugIssueUploadPrompt; + CHECK_EQ(in->data_type(), out->data_type()) << kOfBugIssueUploadPrompt; + + ccl::ReduceType reduce_type = ccl::kSum; + if (in->data_type() == kBool) { reduce_type = ccl::kMax; } + + std::unique_ptr all_reduce = ccl::NewCollectiveCommunication( + ctx->device_type(), in->data_type(), reduce_type); + all_reduce->Launch(ctx->stream(), in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), + kernel_cache->communication_ctx()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("eager_ccl_all_reduce") + .SetCreateFn() + .SetIsMatchedHob(AllReduceCollectiveCommunicationExists()); + +class EagerCclReduceScatterKernel final : public user_op::OpKernel { + public: + EagerCclReduceScatterKernel() = default; + ~EagerCclReduceScatterKernel() override = default; + + void InitOpKernelCacheWithFlags( + user_op::KernelCacheContext* ctx, int8_t flag, + std::shared_ptr* cache_ptr) const override { + InitEagerCclOpKernelCache(ctx, cache_ptr); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + auto* kernel_cache = dynamic_cast(cache); + CHECK(kernel_cache != nullptr) << kOfBugIssueUploadPrompt; + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(in->data_type(), out->data_type()) << kOfBugIssueUploadPrompt; + const auto& op_type = ctx->Attr("op_type"); + CHECK_EQ(op_type, "sum") << kOfBugIssueUploadPrompt; + ccl::ReduceType reduce_type = ccl::kSum; + if (in->data_type() == kBool) { reduce_type = ccl::kMax; } + std::unique_ptr reduce_scatter = + ccl::NewCollectiveCommunication(ctx->device_type(), in->data_type(), + reduce_type); + reduce_scatter->Launch(ctx->stream(), in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), + kernel_cache->communication_ctx()); + }; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("eager_ccl_reduce_scatter") + .SetCreateFn() + .SetIsMatchedHob(ReduceScatterCollectiveCommunicationExists()); + +class EagerCclAllGatherKernel final : public user_op::OpKernel { + public: + EagerCclAllGatherKernel() = default; + ~EagerCclAllGatherKernel() override = default; + + void InitOpKernelCacheWithFlags( + user_op::KernelCacheContext* ctx, int8_t flag, + std::shared_ptr* cache_ptr) const override { + InitEagerCclOpKernelCache(ctx, cache_ptr); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + auto* kernel_cache = dynamic_cast(cache); + CHECK(kernel_cache != nullptr) << kOfBugIssueUploadPrompt; + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(in->data_type(), out->data_type()) << kOfBugIssueUploadPrompt; + std::unique_ptr all_gather = + ccl::NewCollectiveCommunication(ctx->device_type(), in->data_type()); + all_gather->Launch(ctx->stream(), in->dptr(), out->mut_dptr(), in->shape_view().elem_cnt(), + kernel_cache->communication_ctx()); + }; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("eager_ccl_all_gather") + .SetCreateFn() + .SetIsMatchedHob(AllGatherCollectiveCommunicationExists()); + +class EagerCclReduceKernel final : public user_op::OpKernel { + public: + EagerCclReduceKernel() = default; + ~EagerCclReduceKernel() override = default; + + void InitOpKernelCacheWithFlags( + user_op::KernelCacheContext* ctx, int8_t flag, + std::shared_ptr* cache_ptr) const override { + InitEagerCclOpKernelCache(ctx, cache_ptr); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + auto* kernel_cache = dynamic_cast(cache); + CHECK(kernel_cache != nullptr); + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + int64_t root = ctx->Attr("root"); + void* out_ptr = nullptr; + if (GlobalProcessCtx::Rank() == root) { + CHECK_EQ(in->shape_view(), out->shape_view()); + CHECK_EQ(in->data_type(), out->data_type()); + out_ptr = out->mut_dptr(); + } + + ccl::ReduceType reduce_type = ccl::kSum; + if (in->data_type() == kBool) { reduce_type = ccl::kMax; } + + std::unique_ptr reduce = ccl::NewCollectiveCommunication( + ctx->device_type(), in->data_type(), reduce_type); + reduce->Launch(ctx->stream(), in->dptr(), out_ptr, in->shape_view().elem_cnt(), root, + kernel_cache->communication_ctx()); + }; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("eager_ccl_reduce") + .SetCreateFn() + .SetIsMatchedHob(ReduceCollectiveCommunicationExists()); + +class EagerCclBroadcastKernel final : public user_op::OpKernel { + public: + EagerCclBroadcastKernel() = default; + ~EagerCclBroadcastKernel() override = default; + + void InitOpKernelCacheWithFlags( + user_op::KernelCacheContext* ctx, int8_t flag, + std::shared_ptr* cache_ptr) const override { + InitEagerCclOpKernelCache(ctx, cache_ptr); + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + size_t size = ctx->input_size("in"); + CHECK_EQ(size, ctx->output_size("out")); + for (int i = 0; i < size; ++i) { ComputeForOneInput(ctx, cache, i); } + } + void ComputeForOneInput(user_op::KernelComputeContext* ctx, const user_op::OpKernelCache* cache, + int index) const { + auto* kernel_cache = dynamic_cast(cache); + CHECK(kernel_cache != nullptr); + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", index); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", index); + int64_t root = ctx->Attr("root"); + const void* in_ptr = nullptr; + if (GlobalProcessCtx::Rank() == root) { + CHECK_EQ(in->shape_view(), out->shape_view()); + CHECK_EQ(in->data_type(), out->data_type()); + in_ptr = in->dptr(); + } + std::unique_ptr broadcast = + ccl::NewCollectiveCommunication(ctx->device_type(), out->data_type()); + broadcast->Launch(ctx->stream(), in_ptr, out->mut_dptr(), out->shape_view().elem_cnt(), root, + kernel_cache->communication_ctx()); + }; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("eager_ccl_broadcast") + .SetCreateFn() + .SetIsMatchedHob(BroadcastCollectiveCommunicationExists()); + +} // namespace oneflow diff --git a/oneflow/user/kernels/eager_nccl_kernels.cpp b/oneflow/user/kernels/eager_nccl_kernels.cpp index 01a934bacc3ad733c899ebbe5784a6676d7b8005..4272099f8e84724f63e0d53a32a3821d87d44ef2 100644 --- a/oneflow/user/kernels/eager_nccl_kernels.cpp +++ b/oneflow/user/kernels/eager_nccl_kernels.cpp @@ -17,7 +17,6 @@ limitations under the License. #include "oneflow/core/common/decorator.h" #include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/framework.h" -#include "oneflow/core/ccl/ccl.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/kernel/new_kernel_util.h" @@ -75,42 +74,6 @@ void InitEagerCclOpKernelCache(user_op::KernelCacheContext* ctx, } } // namespace -class EagerCclBroadcastKernel final : public user_op::OpKernel { - public: - EagerCclBroadcastKernel() = default; - ~EagerCclBroadcastKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerCclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - const void* in_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - in_ptr = in->dptr(); - } - CHECK_JUST(ccl::Broadcast( - in_ptr, out->mut_dptr(), out->shape_view().elem_cnt(), out->data_type(), root, - kernel_cache->parallel_desc(), ctx->stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_broadcast") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); - class EagerCclTouchKernel final : public user_op::OpKernel { public: EagerCclTouchKernel() = default; @@ -128,139 +91,6 @@ REGISTER_USER_KERNEL("eager_nccl_touch") .SetCreateFn() .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); -class EagerCclReduceKernel final : public user_op::OpKernel { - public: - EagerCclReduceKernel() = default; - ~EagerCclReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerCclOpKernelCache(ctx, cache_ptr); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - void* out_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - out_ptr = out->mut_dptr(); - } - CHECK_JUST(ccl::Reduce(in->dptr(), out_ptr, in->shape_view().elem_cnt(), - in->data_type(), ccl::kSum, root, - kernel_cache->parallel_desc(), ctx->stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); - -class EagerCclAllReduceKernel final : public user_op::OpKernel { - public: - EagerCclAllReduceKernel() = default; - ~EagerCclAllReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerCclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - - CHECK_JUST(ccl::AllReduce( - in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), out->data_type(), ccl::kSum, - kernel_cache->parallel_desc(), ctx->stream())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); - -class EagerCclReduceScatterKernel final : public user_op::OpKernel { - public: - EagerCclReduceScatterKernel() = default; - ~EagerCclReduceScatterKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerCclOpKernelCache(ctx, cache_ptr); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - const auto& op_type = ctx->Attr("op_type"); - CHECK_EQ(op_type, "sum"); - CHECK_JUST(ccl::ReduceScatter( - in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), out->data_type(), ccl::kSum, - kernel_cache->parallel_desc(), ctx->stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_reduce_scatter") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); - -class EagerCclAllGatherKernel final : public user_op::OpKernel { - public: - EagerCclAllGatherKernel() = default; - ~EagerCclAllGatherKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerCclOpKernelCache(ctx, cache_ptr); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - CHECK_JUST(ccl::AllGather(in->dptr(), out->mut_dptr(), - in->shape_view().elem_cnt(), out->data_type(), - kernel_cache->parallel_desc(), ctx->stream())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_gather") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCPU); - template class EagerCclS2SKernel final : public user_op::OpKernel { public: @@ -347,10 +177,9 @@ class EagerCclS2SKernel final : public user_op::OpKernel { int64_t parallel_id = CHECK_JUST(parallel_desc->ParallelId4MachineDeviceId(dst, device_id)); - CHECK_JUST(Send( - reinterpret_cast(reinterpret_cast(pack_to_ptr) - + parallel_id * chunk_size), - elem_per_chunk, in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(reinterpret_cast(pack_to_ptr) + + parallel_id * chunk_size), + elem_per_chunk, in->data_type(), dst, DeviceType::kCPU, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { Symbol parallel_desc = kernel_cache->parallel_desc(); @@ -358,10 +187,9 @@ class EagerCclS2SKernel final : public user_op::OpKernel { int64_t parallel_id = CHECK_JUST(parallel_desc->ParallelId4MachineDeviceId(src, device_id)); - CHECK_JUST(Recv( - reinterpret_cast(reinterpret_cast(unpack_from_ptr) - + parallel_id * chunk_size), - elem_per_chunk, out->data_type(), src, ctx->stream())); + CHECK_JUST(Recv(reinterpret_cast(reinterpret_cast(unpack_from_ptr) + + parallel_id * chunk_size), + elem_per_chunk, out->data_type(), src, DeviceType::kCPU, ctx->stream())); } } } diff --git a/oneflow/user/kernels/eager_nccl_kernels.cu b/oneflow/user/kernels/eager_nccl_kernels.cu index 29e211daec71d863f9d10ae845ce79217b887279..011a8d5e6485bc46ea626fad2f90f238707a8c36 100644 --- a/oneflow/user/kernels/eager_nccl_kernels.cu +++ b/oneflow/user/kernels/eager_nccl_kernels.cu @@ -22,7 +22,7 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/permute.h" #include "oneflow/core/ep/cuda/cuda_stream.h" -#if defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700 +#if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) namespace oneflow { @@ -72,84 +72,13 @@ void InitEagerNcclOpKernelCache(user_op::KernelCacheContext* ctx, } } // namespace -class EagerNcclAllReduceKernel final : public user_op::OpKernel { - public: - EagerNcclAllReduceKernel() = default; - ~EagerNcclAllReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { reduce_type = ncclMax; } - OF_NCCL_CHECK(ncclAllReduce(in->dptr(), out->mut_dptr(), in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), reduce_type, kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclBroadcastKernel final : public user_op::OpKernel { - public: - EagerNcclBroadcastKernel() = default; - ~EagerNcclBroadcastKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - int64_t dev_id = GlobalProcessCtx::LocalRank(root); - int64_t nccl_root = - CHECK_JUST(kernel_cache->parallel_desc()->ParallelId4MachineDeviceId(root, dev_id)); - const void* in_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - in_ptr = in->dptr(); - } - OF_NCCL_CHECK(ncclBroadcast(in_ptr, out->mut_dptr(), out->shape_view().elem_cnt(), - GetNcclDataType(out->data_type()), nccl_root, kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_broadcast") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - class EagerNcclTouchKernel final : public user_op::OpKernel { public: EagerNcclTouchKernel() = default; ~EagerNcclTouchKernel() override = default; private: + using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override{ // Do nothing. @@ -161,117 +90,6 @@ REGISTER_USER_KERNEL("eager_nccl_touch") .SetCreateFn() .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); -class EagerNcclReduceKernel final : public user_op::OpKernel { - public: - EagerNcclReduceKernel() = default; - ~EagerNcclReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - void* out_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - out_ptr = out->mut_dptr(); - } - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { reduce_type = ncclMax; } - OF_NCCL_CHECK(ncclReduce(in->dptr(), out_ptr, in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), reduce_type, root, - kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclReduceScatterKernel final : public user_op::OpKernel { - public: - EagerNcclReduceScatterKernel() = default; - ~EagerNcclReduceScatterKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { - reduce_type = ncclMax; - } else { - const auto& op_type = ctx->Attr("op_type"); - reduce_type = CHECK_JUST(MapAt(op_type2ncclRedOp_t, op_type)); - } - OF_NCCL_CHECK(ncclReduceScatter( - in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), GetNcclDataType(in->data_type()), - reduce_type, kernel_cache->comm(), ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - static HashMap op_type2ncclRedOp_t; -}; - -HashMap EagerNcclReduceScatterKernel::op_type2ncclRedOp_t = { - {"sum", ncclSum}, {"max", ncclMax}}; - -REGISTER_USER_KERNEL("eager_nccl_reduce_scatter") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclAllGatherKernel final : public user_op::OpKernel { - public: - EagerNcclAllGatherKernel() = default; - ~EagerNcclAllGatherKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - OF_NCCL_CHECK(ncclAllGather(in->dptr(), out->mut_dptr(), in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_gather") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - template class EagerNcclS2SKernel final : public user_op::OpKernel { public: @@ -285,6 +103,7 @@ class EagerNcclS2SKernel final : public user_op::OpKernel { } private: + using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { auto* kernel_cache = dynamic_cast(cache); diff --git a/oneflow/user/kernels/eager_nccl_kernels.hip.cpp b/oneflow/user/kernels/eager_nccl_kernels.hip.cpp deleted file mode 100644 index 55ebfcfe0f60cbedd503dcb07834e6692c7de8c4..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/eager_nccl_kernels.hip.cpp +++ /dev/null @@ -1,404 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/control/global_process_ctx.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/nccl_util.h" -#include "oneflow/core/job/eager_nccl_comm_manager.h" -#include "oneflow/core/job/parallel_desc.h" -#include "oneflow/core/ep/include/primitive/permute.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#if defined(WITH_ROCM) - -namespace oneflow { - -namespace { - -class EagerNcclOpKernelCache final : public user_op::OpKernelCache { - public: - explicit EagerNcclOpKernelCache(user_op::KernelCacheContext* ctx) { Init(ctx); } - ~EagerNcclOpKernelCache() override = default; - - Symbol parallel_desc() const { return parallel_desc_; } - ncclComm_t comm() const { return comm_; } - - private: - void Init(user_op::KernelCacheContext* ctx) { - const std::string& parallel_conf_txt = ctx->Attr("parallel_conf"); - ParallelConf parallel_conf; - std::set> device_set; - CHECK(TxtString2PbMessage(parallel_conf_txt, ¶llel_conf)); - parallel_desc_ = SymbolOf(ParallelDesc(parallel_conf)); - FOR_RANGE(int64_t, parallel_id, 0, parallel_desc_->parallel_num()) { - int64_t machine_id = CHECK_JUST(parallel_desc_->MachineId4ParallelId(parallel_id)); - int64_t device_id = CHECK_JUST(parallel_desc_->DeviceId4ParallelId(parallel_id)); - device_set.emplace(std::make_pair(machine_id, device_id)); - } - comm_ = CHECK_NOTNULL(Singleton::Get())->GetCommForDevice(device_set); - } - - Symbol parallel_desc_; - ncclComm_t comm_{}; -}; - -size_t InferEagerNcclS2SKernelTmpBufferSize(user_op::InferContext* ctx) { - const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - size_t tensor_byte_size = - GetCudaAlignedSize(in_tensor.shape().elem_cnt() * GetSizeOfDataType(in_tensor.data_type())); - // NOTE(hanbinbin): Set tmp_buffer_size to twice tensor_byte_size because the - // SbpParallel4ArgNameAndIndex function of LocalUserOpInferContext is unimplemented - return tensor_byte_size * 2; -} - -void InitEagerNcclOpKernelCache(user_op::KernelCacheContext* ctx, - std::shared_ptr* cache_ptr) { - // NOTE(jianhao): the cache only depends on parallel_conf, and the kernel is singleton - // once parallel_conf is determined, so only init the cache at the first time. - if (*cache_ptr == nullptr) { *cache_ptr = std::make_shared(ctx); } -} -} // namespace - -class EagerNcclAllReduceKernel final : public user_op::OpKernel { - public: - EagerNcclAllReduceKernel() = default; - ~EagerNcclAllReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { reduce_type = ncclMax; } - OF_NCCL_CHECK(ncclAllReduce(in->dptr(), out->mut_dptr(), in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), reduce_type, kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclBroadcastKernel final : public user_op::OpKernel { - public: - EagerNcclBroadcastKernel() = default; - ~EagerNcclBroadcastKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - int64_t dev_id = GlobalProcessCtx::LocalRank(root); - int64_t nccl_root = - CHECK_JUST(kernel_cache->parallel_desc()->ParallelId4MachineDeviceId(root, dev_id)); - const void* in_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - in_ptr = in->dptr(); - } - OF_NCCL_CHECK(ncclBroadcast(in_ptr, out->mut_dptr(), out->shape_view().elem_cnt(), - GetNcclDataType(out->data_type()), nccl_root, kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_broadcast") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclTouchKernel final : public user_op::OpKernel { - public: - EagerNcclTouchKernel() = default; - ~EagerNcclTouchKernel() override = default; - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override{ - // Do nothing. - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } -}; - -REGISTER_USER_KERNEL("eager_nccl_touch") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclReduceKernel final : public user_op::OpKernel { - public: - EagerNcclReduceKernel() = default; - ~EagerNcclReduceKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t root = ctx->Attr("root"); - void* out_ptr = nullptr; - if (GlobalProcessCtx::Rank() == root) { - CHECK_EQ(in->shape_view(), out->shape_view()); - CHECK_EQ(in->data_type(), out->data_type()); - out_ptr = out->mut_dptr(); - } - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { reduce_type = ncclMax; } - OF_NCCL_CHECK(ncclReduce(in->dptr(), out_ptr, in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), reduce_type, root, - kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_reduce") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclReduceScatterKernel final : public user_op::OpKernel { - public: - EagerNcclReduceScatterKernel() = default; - ~EagerNcclReduceScatterKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - ncclRedOp_t reduce_type = ncclSum; - if (in->data_type() == kBool) { - reduce_type = ncclMax; - } else { - const auto& op_type = ctx->Attr("op_type"); - reduce_type = CHECK_JUST(MapAt(op_type2ncclRedOp_t, op_type)); - } - OF_NCCL_CHECK(ncclReduceScatter( - in->dptr(), out->mut_dptr(), out->shape_view().elem_cnt(), GetNcclDataType(in->data_type()), - reduce_type, kernel_cache->comm(), ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - static HashMap op_type2ncclRedOp_t; -}; - -HashMap EagerNcclReduceScatterKernel::op_type2ncclRedOp_t = { - {"sum", ncclSum}, {"max", ncclMax}}; - -REGISTER_USER_KERNEL("eager_nccl_reduce_scatter") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -class EagerNcclAllGatherKernel final : public user_op::OpKernel { - public: - EagerNcclAllGatherKernel() = default; - ~EagerNcclAllGatherKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->data_type(), out->data_type()); - OF_NCCL_CHECK(ncclAllGather(in->dptr(), out->mut_dptr(), in->shape_view().elem_cnt(), - GetNcclDataType(in->data_type()), kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("eager_nccl_all_gather") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -template -class EagerNcclS2SKernel final : public user_op::OpKernel { - public: - EagerNcclS2SKernel() = default; - ~EagerNcclS2SKernel() override = default; - - void InitOpKernelCacheWithFlags( - user_op::KernelCacheContext* ctx, int8_t flag, - std::shared_ptr* cache_ptr) const override { - InitEagerNcclOpKernelCache(ctx, cache_ptr); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - auto* kernel_cache = dynamic_cast(cache); - CHECK(kernel_cache != nullptr); - // NOTE(hanbinbin): Compute logic copy from _nccl_logical_s2s - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - int64_t tmp_size = 0; - const int64_t dtype_size = GetSizeOfDataType(in->data_type()); - int64_t data_size = GetCudaAlignedSize(in->shape_view().elem_cnt() * dtype_size); - // NOTE(chengcheng): in (transpose)-> pack_to_ptr (all2all)-> unpack_from_ptr (transpose)-> out - const char* pack_to_ptr = in->dptr(); - char* unpack_from_ptr = out->mut_dptr(); - if (tmp_buffer) { tmp_size = tmp_buffer->shape_view().elem_cnt(); } - CHECK(tmp_size == 0 || tmp_size == data_size || tmp_size == data_size * 2); - - CHECK_EQ(in->data_type(), out->data_type()); - const int64_t num_ranks = kernel_cache->parallel_desc()->parallel_num(); - CHECK_EQ(in->shape_view().elem_cnt(), out->shape_view().elem_cnt()) - << in->shape_view().ToString() << " vs " << out->shape_view().ToString(); - const int64_t elem_cnt = in->shape_view().elem_cnt(); - const int64_t in_split_axis = ctx->Attr("in_split_axis"); - const int64_t out_split_axis = ctx->Attr("out_split_axis"); - - DimVector logical_shape_dim_vec; - in->shape_view().ToDimVector(&logical_shape_dim_vec); - logical_shape_dim_vec[in_split_axis] = logical_shape_dim_vec.at(in_split_axis) * num_ranks; - - if (out_split_axis != 0) { - // NOTE(chengcheng): Do pack. Need transpose in -> pack_to - // pack use temp buffer offset: [0, data_size] - pack_to_ptr = tmp_buffer->dptr(); - DimVector transpose_in_dim_vec = logical_shape_dim_vec; - CHECK_EQ(transpose_in_dim_vec.at(in_split_axis) % num_ranks, 0); - transpose_in_dim_vec[in_split_axis] = transpose_in_dim_vec.at(in_split_axis) / num_ranks; - CHECK_EQ(transpose_in_dim_vec.at(out_split_axis) % num_ranks, 0); - transpose_in_dim_vec[out_split_axis] = transpose_in_dim_vec.at(out_split_axis) / num_ranks; - transpose_in_dim_vec.insert(transpose_in_dim_vec.begin() + out_split_axis, num_ranks); - std::vector perm; - perm.emplace_back(out_split_axis); - FOR_RANGE(int64_t, i, 0, transpose_in_dim_vec.size()) { - if (i != out_split_axis) { perm.emplace_back(i); } - } - auto transpose = ep::primitive::NewPrimitive( - ctx->stream()->device_type(), transpose_in_dim_vec.size()); - CHECK(transpose); - transpose->Launch(ctx->stream(), in->data_type(), transpose_in_dim_vec.size(), - transpose_in_dim_vec.data(), in->dptr(), perm.data(), - tmp_buffer->mut_dptr()); - } - - if (in_split_axis != 0) { - // NOTE(chengcheng): Do unpack. Need transpose unpack_from -> out - // unpack use temp buffer offset: [tmp_size - data_size, tmp_size] - unpack_from_ptr = tmp_buffer->mut_dptr() + (tmp_size - data_size); - } - - { - // NOTE: Do S2S - OF_NCCL_CHECK(ncclGroupStart()); - const int64_t elem_per_chunk = elem_cnt / num_ranks; - const int64_t chunk_size = elem_per_chunk * dtype_size; - for (int64_t j = 0; j < num_ranks; ++j) { - OF_NCCL_CHECK(ncclSend(reinterpret_cast( - reinterpret_cast(pack_to_ptr) + j * chunk_size), - elem_per_chunk, GetNcclDataType(in->data_type()), j, - kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - OF_NCCL_CHECK(ncclRecv( - reinterpret_cast(reinterpret_cast(unpack_from_ptr) + j * chunk_size), - elem_per_chunk, GetNcclDataType(in->data_type()), j, kernel_cache->comm(), - ctx->stream()->As()->cuda_stream())); - } - OF_NCCL_CHECK(ncclGroupEnd()); - } - - if (in_split_axis != 0) { - // Do unpack. - CHECK(unpack_from_ptr != out->mut_dptr()); - DimVector unpack_from_dim_vec = logical_shape_dim_vec; - CHECK_EQ(unpack_from_dim_vec.at(in_split_axis) % num_ranks, 0); - unpack_from_dim_vec[in_split_axis] = unpack_from_dim_vec.at(in_split_axis) / num_ranks; - CHECK_EQ(unpack_from_dim_vec.at(out_split_axis) % num_ranks, 0); - unpack_from_dim_vec[out_split_axis] = unpack_from_dim_vec.at(out_split_axis) / num_ranks; - unpack_from_dim_vec.insert(unpack_from_dim_vec.begin(), num_ranks); - std::vector perm; - FOR_RANGE(int64_t, i, 1, unpack_from_dim_vec.size()) { perm.emplace_back(i); } - perm.insert(perm.begin() + in_split_axis, 0); - auto transpose = ep::primitive::NewPrimitive( - ctx->stream()->device_type(), unpack_from_dim_vec.size()); - CHECK(transpose); - transpose->Launch(ctx->stream(), in->data_type(), unpack_from_dim_vec.size(), - unpack_from_dim_vec.data(), unpack_from_ptr, perm.data(), out->mut_dptr()); - } - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_EAGER_NCCL_S2S_KERNEL(dtype) \ - REGISTER_USER_KERNEL("eager_nccl_s2s") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferEagerNcclS2SKernelTmpBufferSize); - -REGISTER_EAGER_NCCL_S2S_KERNEL(int8_t) -REGISTER_EAGER_NCCL_S2S_KERNEL(int32_t) -REGISTER_EAGER_NCCL_S2S_KERNEL(int64_t) -REGISTER_EAGER_NCCL_S2S_KERNEL(bool) -REGISTER_EAGER_NCCL_S2S_KERNEL(float) -REGISTER_EAGER_NCCL_S2S_KERNEL(double) -REGISTER_EAGER_NCCL_S2S_KERNEL(float16) -} // namespace oneflow - -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/eager_p_to_b_kernel.cpp b/oneflow/user/kernels/eager_p_to_b_kernel.cpp index e94dae5a6806216dfc481b36289f82a1c4323ff7..da6ab32b6fd5081475d7b4092af74a634095f7d2 100644 --- a/oneflow/user/kernels/eager_p_to_b_kernel.cpp +++ b/oneflow/user/kernels/eager_p_to_b_kernel.cpp @@ -22,6 +22,7 @@ limitations under the License. #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/framework/placement_sbp_util.h" #include "oneflow/core/ep/include/primitive/add.h" +#include "oneflow/core/ep/include/primitive/memset.h" namespace oneflow { @@ -65,7 +66,6 @@ size_t InferEagerPToBKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerPToBKernel final : public user_op::OpKernel { public: EagerPToBKernel() = default; @@ -91,8 +91,14 @@ class EagerPToBKernel final : public user_op::OpKernel { const int64_t total_elem_cnt = ctx->Attr("shape").elem_cnt(); const auto& p2p_pair = kernel_cache->p2p_pair(); - Memset(ctx->stream(), out->mut_dptr(), 0, - total_elem_cnt * GetSizeOfDataType(out->data_type())); + DeviceType device_type = ctx->device_type(); + + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(device_type); + CHECK(memset_primitive) << "Can not create Memset primitive for device type " << device_type; + memset_primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + total_elem_cnt * GetSizeOfDataType(out->data_type())); + std::unique_ptr add_primitive = ep::primitive::NewPrimitive(ctx->device_type(), in->data_type()); CHECK(add_primitive); @@ -101,11 +107,11 @@ class EagerPToBKernel final : public user_op::OpKernel { int64_t dst = pair.second; if (GlobalProcessCtx::Rank() == src) { - CHECK_JUST(Send(in_ptr, total_elem_cnt, in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(in_ptr, total_elem_cnt, in->data_type(), dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { - CHECK_JUST(Recv(tmp_buffer_ptr, total_elem_cnt, out->data_type(), src, - ctx->stream())); + CHECK_JUST(Recv(tmp_buffer_ptr, total_elem_cnt, out->data_type(), src, device_type, + ctx->stream())); add_primitive->Launch(ctx->stream(), out->dptr(), tmp_buffer_ptr, out->mut_dptr(), total_elem_cnt); } @@ -114,18 +120,9 @@ class EagerPToBKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_P_TO_B_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_p_to_b") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device)) \ - .SetInferTmpSizeFn(InferEagerPToBKernelTmpBufferSize); - -REGISTER_EAGER_P_TO_B_KERNEL(DeviceType::kCPU) -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_P_TO_B_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_P_TO_B_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_p_to_b") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferEagerPToBKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_p_to_s_kernel.cpp b/oneflow/user/kernels/eager_p_to_s_kernel.cpp index 108e016fb7af5c96b99aa2f4d9ff59ac4ff545fa..b6c1fcf00859580a1745af7092da234156efd331 100644 --- a/oneflow/user/kernels/eager_p_to_s_kernel.cpp +++ b/oneflow/user/kernels/eager_p_to_s_kernel.cpp @@ -25,6 +25,7 @@ limitations under the License. #include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/register/tensor_slice_copier.h" #include "oneflow/core/ep/include/primitive/add.h" +#include "oneflow/core/ep/include/primitive/memset.h" namespace oneflow { @@ -134,7 +135,6 @@ size_t InferEagerPToSKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerPToSKernel final : public user_op::OpKernel { public: EagerPToSKernel() = default; @@ -163,8 +163,14 @@ class EagerPToSKernel final : public user_op::OpKernel { const auto& sorted_p2p_pair = kernel_cache->sorted_p2p_pair(); CHECK_EQ(sorted_elem_cnt2_in_tensor_slice_copier.size(), sorted_p2p_pair.size()); - Memset(ctx->stream(), out->mut_dptr(), 0, - elem_cnt_of_this_chunk * GetSizeOfDataType(out->data_type())); + DeviceType device_type = ctx->device_type(); + + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(device_type); + CHECK(memset_primitive) << "Can not create Memset primitive for device type " << device_type; + memset_primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + elem_cnt_of_this_chunk * GetSizeOfDataType(out->data_type())); + std::unique_ptr add_primitive = ep::primitive::NewPrimitive(ctx->device_type(), in->data_type()); CHECK(add_primitive); @@ -176,12 +182,12 @@ class EagerPToSKernel final : public user_op::OpKernel { const auto& tensor_slice_copier = sorted_elem_cnt2_in_tensor_slice_copier.at(i).second; int64_t send_elem_cnt = sorted_elem_cnt2_in_tensor_slice_copier.at(i).first; tensor_slice_copier->Copy(ctx->stream(), tmp_buffer_ptr, in_ptr); - CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), send_elem_cnt, - in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), send_elem_cnt, + in->data_type(), dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { - CHECK_JUST(Recv(tmp_buffer_ptr, elem_cnt_of_this_chunk, out->data_type(), src, - ctx->stream())); + CHECK_JUST(Recv(tmp_buffer_ptr, elem_cnt_of_this_chunk, out->data_type(), src, device_type, + ctx->stream())); add_primitive->Launch(ctx->stream(), out->dptr(), tmp_buffer_ptr, out->mut_dptr(), elem_cnt_of_this_chunk); } @@ -190,18 +196,9 @@ class EagerPToSKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_P_TO_S_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_p_to_s") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device)) \ - .SetInferTmpSizeFn(InferEagerPToSKernelTmpBufferSize); - -REGISTER_EAGER_P_TO_S_KERNEL(DeviceType::kCPU) -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_P_TO_S_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_P_TO_S_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_p_to_s") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferEagerPToSKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_s_to_b_kernel.cpp b/oneflow/user/kernels/eager_s_to_b_kernel.cpp index 5579582019eda6483ee24f3bd1f106ae68c0d5e7..280e77b944c4f6b04f8a86e5b40f712f491cbe1d 100644 --- a/oneflow/user/kernels/eager_s_to_b_kernel.cpp +++ b/oneflow/user/kernels/eager_s_to_b_kernel.cpp @@ -135,7 +135,6 @@ size_t InferEagerSToBKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerSToBKernel final : public user_op::OpKernel { public: EagerSToBKernel() = default; @@ -167,6 +166,8 @@ class EagerSToBKernel final : public user_op::OpKernel { CHECK_EQ(sorted_elem_cnt2in_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); CHECK_EQ(sorted_elem_cnt2out_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); + DeviceType device_type = ctx->device_type(); + for (int64_t i = 0; i < sorted_p2p_pair.size(); ++i) { const auto& p2p_pair = sorted_p2p_pair.at(i); int64_t src = p2p_pair.first; @@ -177,8 +178,8 @@ class EagerSToBKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; tensor_slice_copier->Copy(ctx->stream(), tmp_buffer_ptr, in_ptr); - CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, - in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, in->data_type(), + dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { const auto& elem_cnt2tensor_slice_copier_pair = @@ -186,7 +187,7 @@ class EagerSToBKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; CHECK_JUST( - Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, ctx->stream())); + Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, device_type, ctx->stream())); tensor_slice_copier->Copy(ctx->stream(), out_ptr, reinterpret_cast(tmp_buffer_ptr)); } @@ -195,18 +196,9 @@ class EagerSToBKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_S_TO_B_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_s_to_b") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device) \ - .SetInferTmpSizeFn(InferEagerSToBKernelTmpBufferSize); - -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCPU) -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_s_to_b") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferEagerSToBKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_s_to_p_kernel.cpp b/oneflow/user/kernels/eager_s_to_p_kernel.cpp index 87db5e154b4c342eb56238254b884e018c3894a4..a65e94e909322151fc201b228c39d21b1563dd04 100644 --- a/oneflow/user/kernels/eager_s_to_p_kernel.cpp +++ b/oneflow/user/kernels/eager_s_to_p_kernel.cpp @@ -152,7 +152,6 @@ size_t InferEagerSToPKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerSToPKernel final : public user_op::OpKernel { public: EagerSToPKernel() = default; @@ -177,8 +176,14 @@ class EagerSToPKernel final : public user_op::OpKernel { void* tmp_buffer_ptr = tmp_buffer->mut_dptr(); const int64_t total_elem_cnt = ctx->Attr("shape").elem_cnt(); - Memset(ctx->stream(), out->mut_dptr(), 0, - total_elem_cnt * GetSizeOfDataType(out->data_type())); + + DeviceType device_type = ctx->device_type(); + + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(device_type); + CHECK(memset_primitive) << "Can not create Memset primitive for device type " << device_type; + memset_primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + total_elem_cnt * GetSizeOfDataType(out->data_type())); const auto& sorted_elem_cnt2in_tensor_slice_copier_pair = kernel_cache->sorted_elem_cnt2in_tensor_slice_copier_pair(); @@ -205,8 +210,8 @@ class EagerSToPKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; tensor_slice_copier->Copy(ctx->stream(), tmp_buffer_ptr, in_ptr); - CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, - in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, in->data_type(), + dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { const auto& elem_cnt2tensor_slice_copier_pair = @@ -214,7 +219,7 @@ class EagerSToPKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; CHECK_JUST( - Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, ctx->stream())); + Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, device_type, ctx->stream())); tensor_slice_copier->Copy(ctx->stream(), out_ptr, reinterpret_cast(tmp_buffer_ptr)); } @@ -223,18 +228,9 @@ class EagerSToPKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_S_TO_B_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_s_to_p") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device) \ - .SetInferTmpSizeFn(InferEagerSToPKernelTmpBufferSize); - -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCPU) -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_S_TO_B_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_s_to_p") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferEagerSToPKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_s_to_s_kernel.cpp b/oneflow/user/kernels/eager_s_to_s_kernel.cpp index 7b9576651e5299fd9f8d256158cced33bf1f158e..b9a14d03e77e413dd93d409415a52a49d0576e22 100644 --- a/oneflow/user/kernels/eager_s_to_s_kernel.cpp +++ b/oneflow/user/kernels/eager_s_to_s_kernel.cpp @@ -19,7 +19,6 @@ limitations under the License. #include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ccl/ccl.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/register/tensor_slice_copier.h" @@ -136,7 +135,6 @@ size_t InferNaiveSToSKernelTmpBufferSize(user_op::InferContext* ctx) { } // namespace -template class EagerNaiveSToSKernel final : public user_op::OpKernel { public: EagerNaiveSToSKernel() = default; @@ -168,6 +166,8 @@ class EagerNaiveSToSKernel final : public user_op::OpKernel { CHECK_EQ(sorted_elem_cnt2in_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); CHECK_EQ(sorted_elem_cnt2out_tensor_slice_copier_pair.size(), sorted_p2p_pair.size()); + DeviceType device_type = ctx->device_type(); + for (int64_t i = 0; i < sorted_p2p_pair.size(); ++i) { const auto& p2p_pair = sorted_p2p_pair.at(i); int64_t src = p2p_pair.first; @@ -178,8 +178,8 @@ class EagerNaiveSToSKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; tensor_slice_copier->Copy(ctx->stream(), tmp_buffer_ptr, in_ptr); - CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, - in->data_type(), dst, ctx->stream())); + CHECK_JUST(Send(reinterpret_cast(tmp_buffer_ptr), elem_cnt, in->data_type(), + dst, device_type, ctx->stream())); } if (GlobalProcessCtx::Rank() == dst) { const auto& elem_cnt2tensor_slice_copier_pair = @@ -187,7 +187,7 @@ class EagerNaiveSToSKernel final : public user_op::OpKernel { const auto& elem_cnt = elem_cnt2tensor_slice_copier_pair.first; const auto& tensor_slice_copier = elem_cnt2tensor_slice_copier_pair.second; CHECK_JUST( - Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, ctx->stream())); + Recv(tmp_buffer_ptr, elem_cnt, out->data_type(), src, device_type, ctx->stream())); tensor_slice_copier->Copy(ctx->stream(), out_ptr, reinterpret_cast(tmp_buffer_ptr)); } @@ -196,19 +196,9 @@ class EagerNaiveSToSKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_NAIVE_S_TO_S_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_naive_s_to_s") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device) \ - .SetInferTmpSizeFn(InferNaiveSToSKernelTmpBufferSize); - -REGISTER_EAGER_NAIVE_S_TO_S_KERNEL(DeviceType::kCPU) - -#if defined(WITH_CUDA) && HAS_NCCL_SEND_RECV -REGISTER_EAGER_NAIVE_S_TO_S_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_NAIVE_S_TO_S_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_naive_s_to_s") + .SetCreateFn() + .SetIsMatchedHob(HobIsSendAndRecvRegistered()) + .SetInferTmpSizeFn(InferNaiveSToSKernelTmpBufferSize); } // namespace oneflow diff --git a/oneflow/user/kernels/eager_symmetric_s_to_p_kernel.cpp b/oneflow/user/kernels/eager_symmetric_s_to_p_kernel.cpp index 6f8da802e8c2c87368b8b52994fa09316eb9cff6..05daf0fd81c30e1112a62a0defada9b6936e7eb6 100644 --- a/oneflow/user/kernels/eager_symmetric_s_to_p_kernel.cpp +++ b/oneflow/user/kernels/eager_symmetric_s_to_p_kernel.cpp @@ -25,6 +25,17 @@ namespace oneflow { namespace { +template +std::unique_ptr NewMemsetPrimitive(Context* ctx) { + return ep::primitive::NewPrimitive(ctx->device_type()); +} + +auto MemsetPrimitiveExists() { + return hob::make_custom("MemsetPrimitiveExists", [](const user_op::KernelRegContext& ctx) { + return NewMemsetPrimitive(&ctx).operator bool(); + }); +} + Maybe> GetAllSplitNdSbp(int64_t axis, int64_t ndim) { NdSbp split_nd_sbp; for (int64_t i = 0; i < ndim; ++i) { @@ -87,7 +98,6 @@ class EagerSymmetricSToPOpKernelCache final : public user_op::OpKernelCache { } // namespace -template class EagerSymmetricSToPKernel final : public user_op::OpKernel { public: EagerSymmetricSToPKernel() = default; @@ -106,6 +116,8 @@ class EagerSymmetricSToPKernel final : public user_op::OpKernel { const user_op::OpKernelCache* cache) const override { auto* kernel_cache = dynamic_cast(cache); CHECK(kernel_cache != nullptr); + auto primitive = NewMemsetPrimitive(ctx); + CHECK(primitive); // NOLINT const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); const auto& out_shape_view = out->shape_view(); @@ -113,27 +125,16 @@ class EagerSymmetricSToPKernel final : public user_op::OpKernel { const void* in_ptr = in->dptr(); void* out_ptr = out->mut_dptr(); - Memset(ctx->stream(), out->mut_dptr(), 0, - out_shape_view.elem_cnt() * GetSizeOfDataType(out->data_type())); - + primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + out_shape_view.elem_cnt() * GetSizeOfDataType(out->data_type())); const auto& tensor_slice_copier = kernel_cache->tensor_slice_copier(); tensor_slice_copier->Copy(ctx->stream(), out_ptr, in_ptr); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EAGER_SYMMETRIC_S_TO_P_KERNEL(device) \ - REGISTER_USER_KERNEL("eager_symmetric_s_to_p") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device); - -REGISTER_EAGER_SYMMETRIC_S_TO_P_KERNEL(DeviceType::kCPU) - -#if defined(WITH_CUDA) -REGISTER_EAGER_SYMMETRIC_S_TO_P_KERNEL(DeviceType::kCUDA) -#endif -#if defined(WITH_ROCM) -REGISTER_EAGER_SYMMETRIC_S_TO_P_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("eager_symmetric_s_to_p") + .SetCreateFn() + .SetIsMatchedHob(MemsetPrimitiveExists() == true); } // namespace oneflow diff --git a/oneflow/user/kernels/elementwise_maximum_minimum_kernel.cu b/oneflow/user/kernels/elementwise_maximum_minimum_kernel.cu index 195984f7f786d591dd176915ba1e2e1b8d6d09c7..35fae2ab146f48c88735c5a5a8e0df11a21132bd 100644 --- a/oneflow/user/kernels/elementwise_maximum_minimum_kernel.cu +++ b/oneflow/user/kernels/elementwise_maximum_minimum_kernel.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/cuda/elementwise.cuh" #include "oneflow/user/kernels/elementwise_maximum_minimum_kernel.h" #include "oneflow/core/ep/cuda/cuda_stream.h" diff --git a/oneflow/user/kernels/elementwise_maximum_minimum_kernel.hip.cpp b/oneflow/user/kernels/elementwise_maximum_minimum_kernel.hip.cpp deleted file mode 100644 index 9e9e330cb682ead8938aadb10f5fbb3aa37bf498..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/elementwise_maximum_minimum_kernel.hip.cpp +++ /dev/null @@ -1,57 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/elementwise_maximum_minimum_kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { -template class Opt, typename T> -__global__ void ElementwiseXimumGradGpuKernel(int64_t elem_cnt, const T* dz, const T* x, const T* y, - T* dx, T* dy) { - XPU_1D_KERNEL_LOOP(idx, elem_cnt) { - Opt()(dz[idx], x[idx], y[idx], dx ? &dx[idx] : nullptr, dy ? &dy[idx] : nullptr); - } -} - -template class Opt, typename T> -struct ElemwiseXimumGradFunctor final { - void operator()(ep::Stream* stream, int64_t elem_cnt, const T* dz, const T* x, const T* y, T* dx, - T* dy) { - ElementwiseXimumGradGpuKernel - <<As()->cuda_stream()>>>(elem_cnt, dz, x, y, dx, dy); - } -}; - -template class Opt, typename T> -struct ElemwiseXimumFunctor final { - void operator()(ep::Stream* stream, int64_t elem_cnt, T* z, const T* x, const T* y) { - OF_CUDA_CHECK(cuda::elementwise::Binary(Opt(), elem_cnt, z, x, y, - stream->As()->cuda_stream())); - } -}; -} // namespace - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MAXIMUM_KERNELS, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MINIMUM_KERNELS, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) -} // namespace oneflow -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/embedding_kernel.hip.cpp b/oneflow/user/kernels/embedding_kernel.hip.cpp deleted file mode 100644 index 5c9c6f77328b9c28fbbfda3a2527cb2a75318998..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/embedding_kernel.hip.cpp +++ /dev/null @@ -1,160 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/ep/include/primitive/memset.h" -#include "oneflow/user/kernels/embedding_kernel_util.h" - -namespace oneflow { - -template -class GpuEmbeddingRenormKernel final : public user_op::OpKernel { - public: - GpuEmbeddingRenormKernel() = default; - ~GpuEmbeddingRenormKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const double max_norm = ctx->Attr("max_norm"); - const double norm_type = ctx->Attr("norm_type"); - - const ShapeView& in_shape = in->shape_view(); - const int64_t emb_size = in_shape.At(0); - const int64_t emb_dim = in_shape.At(1); - const T* in_buf = in->dptr(); - const IndexType* indices_buf = indices->dptr(); - T* out_buf = out->mut_dptr(); - const int64_t num_indices = indices->shape_view().elem_cnt(); - int32_t* tmp_buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr(); - std::unique_ptr memset_primitive = - ep::primitive::NewPrimitive(ctx->device_type()); - CHECK(memset_primitive); - memset_primitive->Launch(ctx->stream(), tmp_buf, 0, - GetCudaAlignedSize(sizeof(int32_t) * emb_size)); - EmbeddingReNormFunctor()( - ctx->stream(), in_buf, indices_buf, out_buf, max_norm, norm_type, num_indices, emb_size, - emb_dim, tmp_buf); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuEmbeddingKernel final : public user_op::OpKernel { - public: - GpuEmbeddingKernel() = default; - ~GpuEmbeddingKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const int64_t padding_idx = ctx->Attr("padding_idx"); - const bool scale_grad_by_freq = ctx->Attr("scale_grad_by_freq"); - - const int64_t num_indices = indices->shape_view().elem_cnt(); - const int64_t emb_size = weight->shape_view().At(0); - const int64_t emb_dim = weight->shape_view().At(1); - const T* weight_buf = weight->dptr(); - const IndexType* indices_buf = indices->dptr(); - T* out_buf = out->mut_dptr(); - - EmbeddingFunctor()(ctx->stream(), weight_buf, indices_buf, - out_buf, padding_idx, scale_grad_by_freq, - num_indices, emb_size, emb_dim); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class GpuEmbeddingGradKernel final : public user_op::OpKernel { - public: - GpuEmbeddingGradKernel() = default; - ~GpuEmbeddingGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const int64_t padding_idx = ctx->Attr("padding_idx"); - const bool scale_grad_by_freq = ctx->Attr("scale_grad_by_freq"); - - const int64_t num_indices = indices->shape_view().elem_cnt(); - const int64_t emb_size = weight->shape_view().At(0); - const int64_t emb_dim = weight->shape_view().At(1); - - const T* dy_buf = dy->dptr(); - const IndexType* indices_buf = indices->dptr(); - T* dx_buf = dx->mut_dptr(); - int32_t* tmp_buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr(); - std::unique_ptr memset_primitive = - ep::primitive::NewPrimitive(ctx->device_type()); - CHECK(memset_primitive); - memset_primitive->Launch(ctx->stream(), dx_buf, 0, dx->shape_view().elem_cnt() * sizeof(T)); - memset_primitive->Launch(ctx->stream(), tmp_buf, 0, - GetCudaAlignedSize(sizeof(int32_t) * emb_size)); - EmbeddingGradFunctor()( - ctx->stream(), dy_buf, indices_buf, dx_buf, padding_idx, scale_grad_by_freq, num_indices, - emb_size, emb_dim, tmp_buf); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_EMBEDDING_KERNEL(in_type, indices_type) \ - REGISTER_USER_KERNEL("embedding_renorm") \ - .SetCreateFn< \ - GpuEmbeddingRenormKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == OF_PP_PAIR_SECOND(in_type)) \ - && (user_op::HobDataType("indices", 0) == OF_PP_PAIR_SECOND(indices_type))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - const int64_t emb_size = in_shape.At(0); \ - return GetCudaAlignedSize(sizeof(int32_t) * emb_size); \ - }); \ - REGISTER_USER_KERNEL("embedding") \ - .SetCreateFn< \ - GpuEmbeddingKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("weight", 0) == OF_PP_PAIR_SECOND(in_type)) \ - && (user_op::HobDataType("indices", 0) == OF_PP_PAIR_SECOND(indices_type))); \ - REGISTER_USER_KERNEL("embedding_grad") \ - .SetCreateFn< \ - GpuEmbeddingGradKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("weight", 0) == OF_PP_PAIR_SECOND(in_type)) \ - && (user_op::HobDataType("indices", 0) == OF_PP_PAIR_SECOND(indices_type))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const Shape& in_shape = ctx->InputShape("weight", 0); \ - const int64_t emb_size = in_shape.At(0); \ - return GetCudaAlignedSize(sizeof(int32_t) * emb_size); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_KERNEL, EMBEDDING_DATA_TYPE_SEQ_CUDA, - INDEX_DATA_TYPE_SEQ) -#undef REGISTER_CUDA_EMBEDDING_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/embedding_kernel_util.cu b/oneflow/user/kernels/embedding_kernel_util.cu index 82c51ed0fa5b174b6646c9413214795256bc4b88..0961aa832175e63d9095fb70531f62383d655c36 100644 --- a/oneflow/user/kernels/embedding_kernel_util.cu +++ b/oneflow/user/kernels/embedding_kernel_util.cu @@ -14,7 +14,12 @@ See the License for the specific language governing permissions and limitations under the License. */ +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/user/kernels/embedding_kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" @@ -96,8 +101,11 @@ __global__ void embedding_renorm_kernel(const T* in_buf, T* out_buf, int32_t* in for (int64_t i = tid; i < emb_dim; i += blockDim.x) { v += pow(abs(static_cast(in_buf[base_index + i])), norm_type); } - +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else using BlockReduce = cub::BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; __shared__ AccumType norm; v = BlockReduce(temp_storage).Sum(v); diff --git a/oneflow/user/kernels/embedding_kernel_util.hip.cpp b/oneflow/user/kernels/embedding_kernel_util.hip.cpp deleted file mode 100644 index 7c0df300caac8309ad0a63929c02e39dd60bbb23..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/embedding_kernel_util.hip.cpp +++ /dev/null @@ -1,182 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/embedding_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -struct AccumulateType { - using type = T; -}; - -template<> -struct AccumulateType { - using type = float; -}; - -template -__global__ void embedding_kernel(const T* weight_buf, const IndexType* indices_buf, T* out_buf, - const int64_t num_indices, const int64_t emb_size, - const int64_t emb_dim) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_indices * emb_dim) { - IndexType indices_index = i / emb_dim; - IndexType emb_dim_index = i - indices_index * emb_dim; - IndexType emb_size_index = indices_buf[indices_index]; - assert(emb_size_index >= 0 && emb_size_index < emb_size); - IndexType from_index = emb_size_index * emb_dim + emb_dim_index; - out_buf[i] = weight_buf[from_index]; - } -} - -template -__global__ void embedding_grad_kernel(const T* dy_buf, const IndexType* indices_buf, T* dx_buf, - const int64_t padding_idx, const int64_t num_indices, - const int64_t emb_dim) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_indices * emb_dim) { - IndexType indices_index = i / emb_dim; - IndexType emb_dim_index = i - indices_index * emb_dim; - IndexType emb_size_index = indices_buf[indices_index]; - if (emb_size_index != padding_idx) { - IndexType from_index = emb_size_index * emb_dim + emb_dim_index; - cuda::atomic::Add(dx_buf + from_index, dy_buf[i]); - } - } -} - -template -__global__ void indices_freq_kernel(const IndexType* indices_buf, const int64_t num_indices, - int32_t* indices_freq, const int64_t emb_size) { - CUDA_1D_KERNEL_LOOP_T(IndexType, i, num_indices) { - IndexType index = indices_buf[i]; - assert(index >= 0 && index < emb_size); - cuda::atomic::Add(indices_freq + index, 1); - } -} - -template -__global__ void emb_scale_kernel(T* dx_buf, const int64_t emb_size, const int64_t emb_dim, - int32_t* indices_freq) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, emb_size * emb_dim) { - IndexType emb_size_index = i / emb_dim; - if (indices_freq[emb_size_index] > 1) { - dx_buf[i] /= static_cast(indices_freq[emb_size_index]); - } - } -} - -template -__global__ void embedding_renorm_kernel(const T* in_buf, T* out_buf, int32_t* indices_freq, - const AccumType max_norm, const AccumType norm_type, - const int64_t emb_size, const int64_t emb_dim) { - int64_t tid = threadIdx.x; - for (int64_t emb_idx = blockIdx.x; emb_idx < emb_size; emb_idx += gridDim.x) { - if (indices_freq[emb_idx] == 0) { continue; } - int64_t base_index = emb_idx * emb_dim; - - AccumType v = 0; - for (int64_t i = tid; i < emb_dim; i += blockDim.x) { - v += pow(abs(static_cast(in_buf[base_index + i])), norm_type); - } - - using BlockReduce = hipcub::BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - __shared__ AccumType norm; - v = BlockReduce(temp_storage).Sum(v); - - if (tid == 0) { norm = pow(v, static_cast(1.0 / norm_type)); } - __syncthreads(); - - if (norm > max_norm) { - auto scale = static_cast(max_norm / (norm + 1e-7)); - for (int64_t i = tid; i < emb_dim; i += blockDim.x) { - out_buf[base_index + i] = in_buf[base_index + i] * scale; - } - } - } -} - -} // namespace - -template -struct EmbeddingReNormFunctor final { - void operator()(ep::Stream* stream, const T* in_buf, const IndexType* indices_buf, T* out_buf, - const double max_norm, const double norm_type, const int64_t num_indices, - const int64_t emb_size, const int64_t emb_dim, int32_t* tmp_buf) { - indices_freq_kernel<<As()->cuda_stream()>>>( - indices_buf, num_indices, tmp_buf, emb_size); - - using AccumType = typename AccumulateType::type; - embedding_renorm_kernel - <<As()->cuda_stream()>>>( - in_buf, out_buf, tmp_buf, static_cast(max_norm), - static_cast(norm_type), emb_size, emb_dim); - } -}; - -template -struct EmbeddingFunctor final { - void operator()(ep::Stream* stream, const T* weight_buf, const IndexType* indices_buf, T* out_buf, - const int64_t padding_idx, const bool scale_grad_by_freq, - const int64_t num_indices, const int64_t emb_size, const int64_t emb_dim) { - embedding_kernel - <<As()->cuda_stream()>>>(weight_buf, indices_buf, out_buf, - num_indices, emb_size, emb_dim); - } -}; - -template -struct EmbeddingGradFunctor final { - void operator()(ep::Stream* stream, const T* dy_buf, const IndexType* indices_buf, T* dx_buf, - const int64_t padding_idx, const bool scale_grad_by_freq, - const int64_t num_indices, const int64_t emb_size, const int64_t emb_dim, - int32_t* tmp_buf) { - embedding_grad_kernel - <<As()->cuda_stream()>>>(dy_buf, indices_buf, dx_buf, padding_idx, - num_indices, emb_dim); - if (scale_grad_by_freq) { - indices_freq_kernel<<As()->cuda_stream()>>>( - indices_buf, num_indices, tmp_buf, emb_size); - emb_scale_kernel - <<As()->cuda_stream()>>>(dx_buf, emb_size, emb_dim, tmp_buf); - } - } -}; - -#define INITIATE_EMBEDDING_KERNEL_UTIL_CUDA_IMPL(in_type_pair, index_type_pair) \ - template struct EmbeddingReNormFunctor; \ - template struct EmbeddingFunctor; \ - template struct EmbeddingGradFunctor; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_EMBEDDING_KERNEL_UTIL_CUDA_IMPL, - EMBEDDING_DATA_TYPE_SEQ_CUDA, INDEX_DATA_TYPE_SEQ); - -#undef INITIATE_EMBEDDING_KERNEL_UTIL_CUDA_IMPL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/erfinv_kernel.hip.cpp b/oneflow/user/kernels/erfinv_kernel.hip.cpp deleted file mode 100644 index 7b057e632615471b926c1cf24afc4d2fc5cf2bb0..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/erfinv_kernel.hip.cpp +++ /dev/null @@ -1,61 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" - -namespace oneflow { - -template -struct ErfInvFunctor { - OF_DEVICE_FUNC ErfInvFunctor() {} - OF_DEVICE_FUNC T operator()(T x) const { return erfinv(x); } -}; - -template -class GpuErfinvKernel final : public user_op::OpKernel { - public: - GpuErfinvKernel() = default; - ~GpuErfinvKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t elem_cnt = x->shape_view().elem_cnt(); - OF_CUDA_CHECK(cuda::elementwise::Unary(ErfInvFunctor(), elem_cnt, y->mut_dptr(), - x->dptr(), - ctx->stream()->As()->cuda_stream())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ERFINV_KERNEL(dtype) \ - REGISTER_USER_KERNEL("erfinv") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "x", 0, true)); \ - return Maybe::Ok(); \ - }); - -REGISTER_CUDA_ERFINV_KERNEL(float) -REGISTER_CUDA_ERFINV_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/expand_kernel.cpp b/oneflow/user/kernels/expand_kernel.cpp index 742f105019a66415d6ed1abc9c4f4d22b950235a..ba568aa81b6de52bc0c5b77132a81b6a928f71d8 100644 --- a/oneflow/user/kernels/expand_kernel.cpp +++ b/oneflow/user/kernels/expand_kernel.cpp @@ -14,114 +14,71 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/expand_kernel_utils.h" - -#include +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_unary.h" +#include "oneflow/core/kernel/cuda_graph_support.h" namespace oneflow { -template -class CpuExpandKernel final : public user_op::OpKernel { - public: - CpuExpandKernel() = default; - ~CpuExpandKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - if (std::any_of(logical_expand_shape.begin(), logical_expand_shape.end(), - [](int32_t dim_size) { return dim_size == 0; })) { - return; - } - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - - std::vector out_shape; - std::vector expand_stride; - CHECK_JUST(getOutShapeAndStrideForFp(in_shape, logical_expand_shape, out_shape, expand_stride)); +namespace { - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - const int32_t out_dims = out->shape_view().NumAxes(); - const int32_t out_size = out->shape_view().elem_cnt(); - int32_t out_stride[out_dims]; - InitStride(out_stride, out_shape.data(), out_dims); - for (int32_t i = 0; i < out_size; ++i) { - int offset = OffsetToNdIndexToOffset(i, out_stride, expand_stride.data(), out_dims); - out_ptr[i] = in_ptr[offset]; - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; +template +std::unique_ptr NewPrimitive(Context* ctx) { + const auto* in_desc = ctx->TensorDesc4ArgNameAndIndex("in", 0); + const auto* out_desc = ctx->TensorDesc4ArgNameAndIndex("out", 0); + size_t max_ndim = std::max(in_desc->shape().size(), out_desc->shape().size()); + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::UnaryOp::kIdentity, in_desc->data_type(), + out_desc->data_type(), max_ndim); +} -#define REGISTER_EXPAND_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) +auto PrimitiveExists() { + return hob::make_custom("BroadcastElementwiseUnaryPrimitiveExists", + [](const user_op::KernelRegContext& ctx) -> bool { + return NewPrimitive(&ctx).operator bool(); + }); +} -REGISTER_EXPAND_KERNEL(float); -REGISTER_EXPAND_KERNEL(double); -REGISTER_EXPAND_KERNEL(bool); -REGISTER_EXPAND_KERNEL(uint8_t); -REGISTER_EXPAND_KERNEL(int8_t); -REGISTER_EXPAND_KERNEL(int32_t); -REGISTER_EXPAND_KERNEL(int64_t); +} // namespace -template -class CpuExpandGradKernel final : public user_op::OpKernel { +class ExpandKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: - CpuExpandGradKernel() = default; - ~CpuExpandGradKernel() = default; + ExpandKernel() = default; + ~ExpandKernel() = default; private: void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_out_shape = - ctx->Attr>("logical_out_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); + auto in_shape = in->shape_view(); + auto out_shape = out->shape_view(); - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - std::vector out_shape; - std::vector expand_stride; - CHECK_JUST(getOutShapeAndStrideForBp(logical_out_shape, logical_expand_shape, in_shape, - out_shape, expand_stride)); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - - const int32_t in_dims = in->shape_view().NumAxes(); - const int32_t in_size = in->shape_view().elem_cnt(); - int32_t in_stride[in_dims]; - InitStride(in_stride, in_shape.data(), in_dims); + // handle 0-size tensor + if (std::any_of(out_shape.begin(), out_shape.end(), [](int64_t dim) { return dim <= 0; })) { + return; + } - std::fill(out_ptr, out_ptr + out->shape_view().elem_cnt(), static_cast(0)); - for (int i = 0; i < in_size; ++i) { - int offset = OffsetToNdIndexToOffset(i, in_stride, expand_stride.data(), in_dims); - out_ptr[offset] += in_ptr[i]; + auto prim = NewPrimitive(ctx); + CHECK(prim); + if (in_shape.size() == 0 && in_shape.elem_cnt() == 1) { + // handle 0-dim tensor + // NOTE: this handle will be remove when BroadcastElementwiseUnary primitive support 0-dim + // tensor + int64_t scalar_ndim = 1; + Shape scalar_shape(DimVector{scalar_ndim}); + Shape scalar_stride(DimVector{scalar_ndim}); + prim->Launch(ctx->stream(), scalar_ndim, scalar_shape.data(), scalar_stride.data(), + in->dptr(), out_shape.size(), out_shape.data(), out->stride().data(), + out->mut_dptr()); + } else { + prim->Launch(ctx->stream(), in_shape.size(), in_shape.data(), in->stride().data(), in->dptr(), + out_shape.size(), out_shape.data(), out->stride().data(), out->mut_dptr()); } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_EXPAND_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_EXPAND_GRAD_KERNEL(float); -REGISTER_EXPAND_GRAD_KERNEL(double); -REGISTER_EXPAND_GRAD_KERNEL(int32_t); -REGISTER_EXPAND_GRAD_KERNEL(int64_t); +REGISTER_USER_KERNEL("expand").SetCreateFn().SetIsMatchedHob(PrimitiveExists() + == true); } // namespace oneflow diff --git a/oneflow/user/kernels/expand_kernel.cu b/oneflow/user/kernels/expand_kernel.cu deleted file mode 100644 index fcfbb5b7dab6af937f137d5fabbcbee270cb3faa..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/expand_kernel.cu +++ /dev/null @@ -1,219 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/cuda/atomic.cuh" -#include "oneflow/user/kernels/expand_kernel_utils.h" - -namespace oneflow { - -namespace { - -const int32_t NDIMS = 16; -struct STRIDES { - int32_t val[NDIMS]; -}; - -template -__global__ void ExpandCudaKernel(const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, - const int32_t elements, T* out_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = OffsetToNdIndexToOffset(gid, in_stride.val, expand_stride.val, dims); - out_ptr[gid] = in_ptr[offset]; - gid += step; - } -} - -template -__global__ void ExpandGradCudaKernel(const T* out_diff_ptr, const STRIDES out_stride, - const STRIDES expand_stride, const int32_t dims, - const int32_t elements, T* in_diff_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = OffsetToNdIndexToOffset(gid, out_stride.val, expand_stride.val, dims); - cuda::atomic::Add(&in_diff_ptr[offset], out_diff_ptr[gid]); - gid += step; - } -} - -template -__global__ void InitPtr(const int32_t elements, T* ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - ptr[gid] = static_cast(0); - gid += step; - } -} - -template -struct GpuExpandFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, const int32_t elements, - T* out_ptr) { - RUN_CUDA_KERNEL((ExpandCudaKernel), stream, elements, in_ptr, in_stride, expand_stride, dims, - elements, out_ptr); - } -}; - -template<> -void GpuExpandFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const STRIDES in_stride, const STRIDES expand_stride, - const int32_t dims, const int32_t elements, - float16* out_ptr) { - RUN_CUDA_KERNEL((ExpandCudaKernel), stream, elements, reinterpret_cast(in_ptr), - in_stride, expand_stride, dims, elements, reinterpret_cast(out_ptr)); -} - -template -struct GpuExpandGradFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, const int32_t elements, - const int32_t out_elements, T* out_ptr) { - RUN_CUDA_KERNEL((InitPtr), stream, out_elements, out_elements, out_ptr); - RUN_CUDA_KERNEL((ExpandGradCudaKernel), stream, elements, in_ptr, in_stride, expand_stride, - dims, elements, out_ptr); - } -}; - -template<> -void GpuExpandGradFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const STRIDES in_stride, const STRIDES expand_stride, - const int32_t dims, const int32_t elements, - const int32_t out_elements, float16* out_ptr) { - RUN_CUDA_KERNEL((InitPtr), stream, out_elements, out_elements, - reinterpret_cast(out_ptr)); - RUN_CUDA_KERNEL((ExpandGradCudaKernel), stream, elements, - reinterpret_cast(in_ptr), in_stride, expand_stride, dims, elements, - reinterpret_cast(out_ptr)); -} - -} // namespace - -template -class GpuExpandKernel final : public user_op::OpKernel { - public: - GpuExpandKernel() = default; - ~GpuExpandKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - if (std::any_of(logical_expand_shape.begin(), logical_expand_shape.end(), - [](int32_t dim_size) { return dim_size == 0; })) { - return; - } - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForFp(in_shape, logical_expand_shape, out_shape, stride)); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - const int32_t out_dims = out->shape_view().NumAxes(); - const int32_t out_size = out->shape_view().elem_cnt(); - - STRIDES expand_stride; - for (int i = 0; i < out_dims; ++i) { expand_stride.val[i] = stride[i]; } - STRIDES out_stride; - InitStride(out_stride.val, out_shape.data(), out_dims); - GpuExpandFunctor()(ctx->stream(), in_ptr, out_stride, expand_stride, out_dims, out_size, - out_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_EXPAND_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_EXPAND_KERNEL(float); -REGISTER_EXPAND_KERNEL(double); -REGISTER_EXPAND_KERNEL(float16); -REGISTER_EXPAND_KERNEL(bool); -REGISTER_EXPAND_KERNEL(uint8_t); -REGISTER_EXPAND_KERNEL(int8_t); -REGISTER_EXPAND_KERNEL(int32_t); -REGISTER_EXPAND_KERNEL(int64_t); - -template -class GpuExpandGradKernel final : public user_op::OpKernel { - public: - GpuExpandGradKernel() = default; - ~GpuExpandGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_out_shape = - ctx->Attr>("logical_out_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForBp(logical_out_shape, logical_expand_shape, in_shape, - out_shape, stride)); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - - const int32_t in_dims = in->shape_view().NumAxes(); - const int32_t in_size = in->shape_view().elem_cnt(); - const int32_t out_size = out->shape_view().elem_cnt(); - - STRIDES expand_stride; - for (int i = 0; i < in_dims; ++i) { expand_stride.val[i] = stride[i]; } - STRIDES in_stride; - InitStride(in_stride.val, in_shape.data(), in_dims); - - GpuExpandGradFunctor()(ctx->stream(), in_ptr, in_stride, expand_stride, in_dims, in_size, - out_size, out_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_EXPAND_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_EXPAND_GRAD_KERNEL(float); -REGISTER_EXPAND_GRAD_KERNEL(double); -REGISTER_EXPAND_GRAD_KERNEL(float16); -REGISTER_EXPAND_GRAD_KERNEL(int32_t); -REGISTER_EXPAND_GRAD_KERNEL(int64_t); - -} // namespace oneflow diff --git a/oneflow/user/kernels/expand_kernel.hip.cpp b/oneflow/user/kernels/expand_kernel.hip.cpp deleted file mode 100644 index 07699da1059389ad872ed34c46cee25d710c4325..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/expand_kernel.hip.cpp +++ /dev/null @@ -1,220 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/expand_kernel_utils.h" - -namespace oneflow { - -namespace { - -const int32_t NDIMS = 16; -struct STRIDES { - int32_t val[NDIMS]; -}; - -template -__global__ void ExpandCudaKernel(const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, - const int32_t elements, T* out_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = OffsetToNdIndexToOffset(gid, in_stride.val, expand_stride.val, dims); - out_ptr[gid] = in_ptr[offset]; - gid += step; - } -} - -template -__global__ void ExpandGradCudaKernel(const T* out_diff_ptr, const STRIDES out_stride, - const STRIDES expand_stride, const int32_t dims, - const int32_t elements, T* in_diff_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = OffsetToNdIndexToOffset(gid, out_stride.val, expand_stride.val, dims); - cuda::atomic::Add(&in_diff_ptr[offset], out_diff_ptr[gid]); - gid += step; - } -} - -template -__global__ void InitPtr(const int32_t elements, T* ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - ptr[gid] = static_cast(0); - gid += step; - } -} - -template -struct GpuExpandFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, const int32_t elements, - T* out_ptr) { - RUN_CUDA_KERNEL((ExpandCudaKernel), stream, elements, in_ptr, in_stride, expand_stride, dims, - elements, out_ptr); - } -}; - -template<> -void GpuExpandFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const STRIDES in_stride, const STRIDES expand_stride, - const int32_t dims, const int32_t elements, - float16* out_ptr) { - RUN_CUDA_KERNEL((ExpandCudaKernel), stream, elements, reinterpret_cast(in_ptr), - in_stride, expand_stride, dims, elements, reinterpret_cast(out_ptr)); -} - -template -struct GpuExpandGradFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const STRIDES in_stride, - const STRIDES expand_stride, const int32_t dims, const int32_t elements, - const int32_t out_elements, T* out_ptr) { - RUN_CUDA_KERNEL((InitPtr), stream, out_elements, out_elements, out_ptr); - RUN_CUDA_KERNEL((ExpandGradCudaKernel), stream, elements, in_ptr, in_stride, expand_stride, - dims, elements, out_ptr); - } -}; - -template<> -void GpuExpandGradFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const STRIDES in_stride, const STRIDES expand_stride, - const int32_t dims, const int32_t elements, - const int32_t out_elements, float16* out_ptr) { - RUN_CUDA_KERNEL((InitPtr), stream, out_elements, out_elements, - reinterpret_cast(out_ptr)); - RUN_CUDA_KERNEL((ExpandGradCudaKernel), stream, elements, - reinterpret_cast(in_ptr), in_stride, expand_stride, dims, elements, - reinterpret_cast(out_ptr)); -} - -} // namespace - -template -class GpuExpandKernel final : public user_op::OpKernel { - public: - GpuExpandKernel() = default; - ~GpuExpandKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - if (std::any_of(logical_expand_shape.begin(), logical_expand_shape.end(), - [](int32_t dim_size) { return dim_size == 0; })) { - return; - } - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForFp(in_shape, logical_expand_shape, out_shape, stride)); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - const int32_t out_dims = out->shape_view().NumAxes(); - const int32_t out_size = out->shape_view().elem_cnt(); - - STRIDES expand_stride; - for (int i = 0; i < out_dims; ++i) { expand_stride.val[i] = stride[i]; } - STRIDES out_stride; - InitStride(out_stride.val, out_shape.data(), out_dims); - GpuExpandFunctor()(ctx->stream(), in_ptr, out_stride, expand_stride, out_dims, out_size, - out_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_EXPAND_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_EXPAND_KERNEL(float); -REGISTER_EXPAND_KERNEL(double); -REGISTER_EXPAND_KERNEL(float16); -REGISTER_EXPAND_KERNEL(bool); -REGISTER_EXPAND_KERNEL(uint8_t); -REGISTER_EXPAND_KERNEL(int8_t); -REGISTER_EXPAND_KERNEL(int32_t); -REGISTER_EXPAND_KERNEL(int64_t); - -template -class GpuExpandGradKernel final : public user_op::OpKernel { - public: - GpuExpandGradKernel() = default; - ~GpuExpandGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& logical_out_shape = - ctx->Attr>("logical_out_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - - std::vector in_shape; - in_shape.resize(in->shape_view().NumAxes()); - for (int i = 0; i < in->shape_view().NumAxes(); ++i) { in_shape[i] = in->shape_view().At(i); } - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForBp(logical_out_shape, logical_expand_shape, in_shape, - out_shape, stride)); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - - const int32_t in_dims = in->shape_view().NumAxes(); - const int32_t in_size = in->shape_view().elem_cnt(); - const int32_t out_size = out->shape_view().elem_cnt(); - - STRIDES expand_stride; - for (int i = 0; i < in_dims; ++i) { expand_stride.val[i] = stride[i]; } - STRIDES in_stride; - InitStride(in_stride.val, in_shape.data(), in_dims); - - GpuExpandGradFunctor()(ctx->stream(), in_ptr, in_stride, expand_stride, in_dims, in_size, - out_size, out_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_EXPAND_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("expand_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_EXPAND_GRAD_KERNEL(float); -REGISTER_EXPAND_GRAD_KERNEL(double); -REGISTER_EXPAND_GRAD_KERNEL(float16); -REGISTER_EXPAND_GRAD_KERNEL(int32_t); -REGISTER_EXPAND_GRAD_KERNEL(int64_t); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/expand_kernel_utils.cpp b/oneflow/user/kernels/expand_kernel_utils.cpp deleted file mode 100644 index e151555f733709a5a412d30cc1ae6bcfc32ff1b6..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/expand_kernel_utils.cpp +++ /dev/null @@ -1,103 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ - -#include "oneflow/user/kernels/expand_kernel_utils.h" - -namespace oneflow { - -Maybe getOutShapeAndStride(const std::vector& in_shape, - const std::vector& expand_shape, - std::vector& out_shape, std::vector& stride) { - // NOTE(Liang Depeng): compute the input stride. - std::vector original_stride(in_shape.size(), 1); - for (int i = in_shape.size() - 2; i >= 0; --i) { - original_stride[i] = in_shape[i + 1] * original_stride[i + 1]; - } - - // NOTE(Liang Depeng): compute the output stride and shape. - out_shape.resize(expand_shape.size()); - stride.resize(expand_shape.size()); - int shift = out_shape.size() - in_shape.size(); - for (int i = out_shape.size() - 1; i >= 0; --i) { - int index = i - shift; - if (index >= 0) { - if (expand_shape[i] == -1 || expand_shape[i] == in_shape[index]) { - out_shape[i] = in_shape[index]; - stride[i] = original_stride[index]; - } else { - CHECK_OR_RETURN(expand_shape[i] >= 0 && in_shape[index] == 1) << "Invalid expand shape "; - out_shape[i] = expand_shape[i]; - stride[i] = 0; - } - } else { - CHECK_GE_OR_RETURN(expand_shape[i], 0) << "Invalid expand shape "; - out_shape[i] = expand_shape[i]; - if (expand_shape[i] == 1 && i < out_shape.size() - 1) { - stride[i] = stride[i + 1]; - } else { - stride[i] = 0; - } - } - } - return Maybe::Ok(); -} - -Maybe getOutShapeAndStrideForFp(const std::vector& in_shape, - const std::vector& logical_expand_shape, - std::vector& out_shape, - std::vector& stride) { - std::vector expand_shape; - expand_shape.resize(logical_expand_shape.size()); - const int offset = logical_expand_shape.size() - in_shape.size(); - // NOTE(Liang Depeng): compute the correct expand shape according to the actual input shape. - for (int i = 0; i < logical_expand_shape.size(); ++i) { - if (i < offset) { - expand_shape[i] = logical_expand_shape[i]; - } else { - expand_shape[i] = in_shape[i - offset] == 1 - ? logical_expand_shape[i] - : std::min(logical_expand_shape[i], in_shape[i - offset]); - } - } - JUST(getOutShapeAndStride(in_shape, expand_shape, out_shape, stride)); - return Maybe::Ok(); -} - -Maybe getOutShapeAndStrideForBp(const std::vector& logical_out_shape, - const std::vector& logical_expand_shape, - const std::vector& in_shape, - std::vector& out_shape, - std::vector& stride) { - std::vector expand_shape; - expand_shape.resize(logical_expand_shape.size()); - // NOTE(Liang Depeng): compute the correct expand shape according to the actual input shape. - for (int i = 0; i < logical_expand_shape.size(); ++i) { - expand_shape[i] = logical_expand_shape[i] == -1 - ? in_shape[i] - : std::min(logical_expand_shape[i], in_shape[i]); - } - // NOTE(Liang Depeng): compute the correct output shape. - const int offset = logical_expand_shape.size() - logical_out_shape.size(); - out_shape.resize(logical_out_shape.size()); - for (int i = 0; i < logical_out_shape.size(); ++i) { - out_shape[i] = std::min(expand_shape[i + offset], logical_out_shape[i]); - } - std::vector duplicated_in_shape; - JUST(getOutShapeAndStride(out_shape, expand_shape, duplicated_in_shape, stride)); - return Maybe::Ok(); -} - -} // namespace oneflow diff --git a/oneflow/user/kernels/expand_kernel_utils.h b/oneflow/user/kernels/expand_kernel_utils.h deleted file mode 100644 index b21a7c1d4cafba06e43a124337b03d5e7c1d52c9..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/expand_kernel_utils.h +++ /dev/null @@ -1,60 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_EXPAND_KERNEL_UTILS_H_ -#define ONEFLOW_EXPAND_KERNEL_UTILS_H_ - -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -OF_DEVICE_FUNC int32_t OffsetToNdIndexToOffset(const int32_t offset, const int32_t* in_stride, - const int32_t* out_stride, const int32_t n) { - int32_t remaining = offset; - int32_t out_offset = 0; -#ifdef __CUDA_ARCH__ -#pragma unroll -#endif - for (int32_t i = 0; i < n; ++i) { - const int32_t idx = remaining / in_stride[i]; - out_offset += idx * out_stride[i]; - remaining = remaining - idx * in_stride[i]; - } - return out_offset; -} - -static void InitStride(int32_t* stride, const int32_t* dim_vec, const int32_t dims) { - stride[dims - 1] = 1; - for (int i = dims - 2; i >= 0; --i) { stride[i] = dim_vec[i + 1] * stride[i + 1]; } -} - -Maybe getOutShapeAndStride(const std::vector& in_shape, - const std::vector& expand_shape, - std::vector& out_shape, std::vector& stride); - -Maybe getOutShapeAndStrideForFp(const std::vector& in_shape, - const std::vector& logical_expand_shape, - std::vector& out_shape, - std::vector& stride); - -Maybe getOutShapeAndStrideForBp(const std::vector& logical_out_shape, - const std::vector& logical_expand_shape, - const std::vector& in_shape, - std::vector& out_shape, - std::vector& stride); - -} // namespace oneflow - -#endif // ONEFLOW_EXPAND_KERNEL_UTILS_H_ diff --git a/oneflow/user/kernels/eye_kernel.cpp b/oneflow/user/kernels/eye_kernel.cpp index bffb9133573c59ca61375907a855626738c36639..09137461bdb0d69eb985a54e16f5686536307a6a 100644 --- a/oneflow/user/kernels/eye_kernel.cpp +++ b/oneflow/user/kernels/eye_kernel.cpp @@ -58,10 +58,7 @@ class EyeKernel final : public OpKernel { REGISTER_EYE_KERNELS_WITH_DEVICE(DeviceType::kCPU); // Register CUDA version -#ifdef WITH_CUDA -REGISTER_EYE_KERNELS_WITH_DEVICE(DeviceType::kCUDA); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_EYE_KERNELS_WITH_DEVICE(DeviceType::kCUDA); #endif #undef REGISTER_EYE_KERNELS_WITH_DEVICE diff --git a/oneflow/user/kernels/eye_kernel_util.cu b/oneflow/user/kernels/eye_kernel_util.cu index c71ee7e6090d68c0ffc0344423d80cae527051f3..5f13b71c32d04c84cae594b21eea2e518f488007 100644 --- a/oneflow/user/kernels/eye_kernel_util.cu +++ b/oneflow/user/kernels/eye_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/user/kernels/eye_kernel_util.h" diff --git a/oneflow/user/kernels/eye_kernel_util.hip.cpp b/oneflow/user/kernels/eye_kernel_util.hip.cpp deleted file mode 100644 index 84fd109b6465da3a27f85b817cd4ff10a4d3e8c2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/eye_kernel_util.hip.cpp +++ /dev/null @@ -1,40 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/eye_kernel_util.h" - -namespace oneflow { - -namespace user_op { - -template -__global__ void EyeForwardGpuKernel(const int64_t cols, const int64_t rows, T* out) { - SetOneInDiag(cols, rows, out); -} - -template -struct EyeFunctor final { - void operator()(ep::Stream* stream, const int64_t& cols, const int64_t& rows, T* out) { - RUN_CUDA_KERNEL((EyeForwardGpuKernel), stream, rows, cols, rows, out); - } -}; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_EYE_FUNCTOR, (DeviceType::kCUDA), EYE_DATA_TYPE_SEQ); -} // namespace user_op -} // namespace oneflow - -#endif // End WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/fake_quantization_kernel.hip.cpp b/oneflow/user/kernels/fake_quantization_kernel.hip.cpp deleted file mode 100644 index 126595ebfc7a6e45f7c77da13d222624aa3b4e06..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fake_quantization_kernel.hip.cpp +++ /dev/null @@ -1,160 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.hip.h" - -namespace oneflow { - -namespace { - -template -__global__ void FakeQuantizationSymmetric(const T* in_ptr, const T* scale_ptr, - const int64_t scale_size, const int64_t elements, - const int64_t panel_size, const double quantization_bit, - T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit - 1)) - 1; - T lower_bound = -upper_bound - 1; - - while (gid < elements) { - int64_t channel_index = gid / panel_size; - int64_t scale_idx = min(scale_size - 1, channel_index); - - T scale = scale_ptr[scale_idx]; - - T out = nearbyint(in_ptr[gid] / scale); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = out * scale; - - gid += step; - } -} - -template -__global__ void FakeQuantizationAffine(const T* in_ptr, const T* scale_ptr, const T* zero_point_ptr, - const int64_t scale_size, const int64_t elements, - const int64_t panel_size, const double quantization_bit, - T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit)) - 1; - T lower_bound = 0; - - while (gid < elements) { - int64_t channel_index = gid / panel_size; - int64_t scale_idx = min(scale_size - 1, channel_index); - - T scale = scale_ptr[scale_idx]; - T zero_point = zero_point_ptr[scale_idx]; - - T out = nearbyint(in_ptr[gid] / scale + zero_point); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = (out - zero_point) * scale; - - gid += step; - } -} - -template -__global__ void FakeQuantizationCambricon(const T* in_ptr, const T* shift, const int64_t scale_size, - const int64_t elements, const int64_t panel_size, - const double quantization_bit, T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit - 1)) - 1; - T lower_bound = -upper_bound - 1; - - T scale = static_cast(pow(2.0, static_cast(shift[0]))); - - while (gid < elements) { - T out = nearbyint(in_ptr[gid] / scale); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = out * scale; - gid += step; - } -} - -} // namespace - -template -class GpuFakeQuantizationKernel final : public user_op::OpKernel { - public: - GpuFakeQuantizationKernel() = default; - ~GpuFakeQuantizationKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - const user_op::Tensor* scale = ctx->Tensor4ArgNameAndIndex("scale", 0); - const user_op::Tensor* zero_point = ctx->Tensor4ArgNameAndIndex("zero_point", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - - const std::string quantization_scheme = ctx->Attr("quantization_scheme"); - const int32_t quantization_bit = ctx->Attr("quantization_bit"); - const std::string quantization_formula = ctx->Attr("quantization_formula"); - - const int64_t elements = in->shape_view().elem_cnt(); - const int64_t panel_size = in->shape_view().Count(1); - const int64_t scale_size = scale->shape_view().elem_cnt(); - - // round to even - auto origin_round_mode = std::fegetround(); - std::fesetround(FE_TONEAREST); - - if (quantization_formula == "google") { - if (quantization_scheme == "symmetric") { - RUN_CUDA_KERNEL((FakeQuantizationSymmetric), ctx->stream(), elements, in->dptr(), - scale->dptr(), scale_size, elements, panel_size, quantization_bit, - out->mut_dptr()); - } else { // quantization_scheme == "affine" - RUN_CUDA_KERNEL((FakeQuantizationAffine), ctx->stream(), elements, in->dptr(), - scale->dptr(), zero_point->dptr(), scale_size, elements, panel_size, - quantization_bit, out->mut_dptr()); - } - } else if (quantization_formula == "cambricon") { - RUN_CUDA_KERNEL((FakeQuantizationCambricon), ctx->stream(), elements, in->dptr(), - scale->dptr(), scale_size, elements, panel_size, quantization_bit, - out->mut_dptr()); - } else { - UNIMPLEMENTED(); - } - - std::fesetround(origin_round_mode); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FAKE_QUANTIZATION_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fake_quantization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_FAKE_QUANTIZATION_KERNEL(float); -REGISTER_FAKE_QUANTIZATION_KERNEL(double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fill_kernel.cpp b/oneflow/user/kernels/fill_kernel.cpp index ffeabbbb80a587a647f770be4f29c2245982677f..f5a2f42669b3cfcfa11d6a08811e2f3409f64efe 100644 --- a/oneflow/user/kernels/fill_kernel.cpp +++ b/oneflow/user/kernels/fill_kernel.cpp @@ -82,6 +82,7 @@ class FillTensorCpuKernel final : public user_op::OpKernel { && (user_op::HobDataType("out", 0) == GetDataType::value)); REGISTER_FILL_CPU_KERNEL(float) +REGISTER_FILL_CPU_KERNEL(float16) REGISTER_FILL_CPU_KERNEL(double) REGISTER_FILL_CPU_KERNEL(int8_t) REGISTER_FILL_CPU_KERNEL(int32_t) diff --git a/oneflow/user/kernels/fill_kernel.cu b/oneflow/user/kernels/fill_kernel.cu index b0b0b71a8382341979ced691624d21899fd12529..4427452ffdde30a46665e820124804450ab3523c 100644 --- a/oneflow/user/kernels/fill_kernel.cu +++ b/oneflow/user/kernels/fill_kernel.cu @@ -52,6 +52,7 @@ class FillTensorGpuKernel final : public user_op::OpKernel { && (user_op::HobDataType("out", 0) == GetDataType::value)); REGISTER_FILL_CUDA_KERNEL(float) +REGISTER_FILL_CUDA_KERNEL(half) REGISTER_FILL_CUDA_KERNEL(double) REGISTER_FILL_CUDA_KERNEL(int8_t) REGISTER_FILL_CUDA_KERNEL(int32_t) diff --git a/oneflow/user/kernels/fill_kernel.hip.cpp b/oneflow/user/kernels/fill_kernel.hip.cpp deleted file mode 100644 index b1233253461809569eff227a97a172e80d34664e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fill_kernel.hip.cpp +++ /dev/null @@ -1,61 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/kernel/new_kernel_util.h" - -namespace oneflow { - -namespace { -template -__global__ void FillTensorGpuForward(const int n, const T* value, T* y) { - CUDA_1D_KERNEL_LOOP(i, n) { y[i] = value[0]; } -} -}; // namespace - -template -class FillTensorGpuKernel final : public user_op::OpKernel { - public: - FillTensorGpuKernel() = default; - ~FillTensorGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const user_op::Tensor* value = ctx->Tensor4ArgNameAndIndex("value", 0); - const int32_t elem_cnt = in->shape_view().elem_cnt(); - RUN_CUDA_KERNEL((FillTensorGpuForward), ctx->stream(), elem_cnt, elem_cnt, value->dptr(), - out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FILL_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fill_tensor_") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -REGISTER_FILL_CUDA_KERNEL(float) -REGISTER_FILL_CUDA_KERNEL(double) -REGISTER_FILL_CUDA_KERNEL(int8_t) -REGISTER_FILL_CUDA_KERNEL(int32_t) -REGISTER_FILL_CUDA_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/flip_kernel.cu b/oneflow/user/kernels/flip_kernel.cu index 0e3654b43515ffbea62a987e2083b5ae2aa9abec..316abdbf154be7c031198f0b3b861daa491b5394 100644 --- a/oneflow/user/kernels/flip_kernel.cu +++ b/oneflow/user/kernels/flip_kernel.cu @@ -94,6 +94,7 @@ class FlipGpuKernel final : public user_op::OpKernel { REGISTER_FLIP_CUDA_KERNEL(bool) REGISTER_FLIP_CUDA_KERNEL(float) +REGISTER_FLIP_CUDA_KERNEL(half) REGISTER_FLIP_CUDA_KERNEL(double) REGISTER_FLIP_CUDA_KERNEL(uint8_t) REGISTER_FLIP_CUDA_KERNEL(int8_t) diff --git a/oneflow/user/kernels/flip_kernel.hip.cpp b/oneflow/user/kernels/flip_kernel.hip.cpp deleted file mode 100644 index 2c191a188cdc33d2ac17b37f5d9bd3c961b1e2f0..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/flip_kernel.hip.cpp +++ /dev/null @@ -1,104 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/ep/include/stream.h" - -namespace oneflow { - -namespace { - -const int32_t NDIMS = 16; -struct SIZE_V { - int32_t val[NDIMS]; -}; - -struct VIS { - bool val[NDIMS] = {false}; -}; - -template -__global__ void FlipGpuForward(const int32_t element, const int64_t total_dims, - const SIZE_V sizes_v, const VIS vis, SIZE_V strides_v, - const T* in_dptr, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(i, element) { - int32_t cur_indices = i; - int32_t rem = 0; - int32_t dst_offset = 0; - for (int32_t d = 0; d < total_dims; d++) { - int32_t temp = cur_indices; - cur_indices = cur_indices / strides_v.val[d]; - rem = temp - cur_indices * strides_v.val[d]; - dst_offset += vis.val[d] ? (sizes_v.val[d] - 1 - cur_indices) * strides_v.val[d] - : cur_indices * strides_v.val[d]; - cur_indices = rem; - } - out_dptr[i] = in_dptr[dst_offset]; - } -} - -} // namespace - -template -class FlipGpuKernel final : public user_op::OpKernel { - public: - FlipGpuKernel() = default; - ~FlipGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t elem_cnt = y_tensor->shape_view().elem_cnt(); - if (elem_cnt == 0) { return; } - const int32_t total_dims = y_tensor->shape_view().NumAxes(); - - std::vector dims = ctx->Attr>("dims"); - VIS vis; - for (auto x : dims) { vis.val[x] = true; } - - SIZE_V sizes_v; - for (int32_t i = 0; i < total_dims; i++) { sizes_v.val[i] = y_tensor->shape_view().At(i); } - - // TODO(bbuf) delete strides caluculate, after tensor strides supported - SIZE_V strides_v; - strides_v.val[total_dims - 1] = 1; - for (int32_t i = total_dims - 2; i >= 0; i--) { - strides_v.val[i] = strides_v.val[i + 1] * y_tensor->shape_view().At(i + 1); - } - RUN_CUDA_KERNEL((FlipGpuForward), ctx->stream(), elem_cnt, elem_cnt, total_dims, sizes_v, - vis, strides_v, x_tensor->dptr(), y_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FLIP_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("flip").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_FLIP_CUDA_KERNEL(bool) -REGISTER_FLIP_CUDA_KERNEL(float) -REGISTER_FLIP_CUDA_KERNEL(double) -REGISTER_FLIP_CUDA_KERNEL(uint8_t) -REGISTER_FLIP_CUDA_KERNEL(int8_t) -REGISTER_FLIP_CUDA_KERNEL(int32_t) -REGISTER_FLIP_CUDA_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.cpp b/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.cpp new file mode 100644 index 0000000000000000000000000000000000000000..885684a3a10d7b312d65fa074a09427d3adec349 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.cpp @@ -0,0 +1,142 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifdef WITH_CUDA +#define CUDA_LIB_NAME "cuda" + +#if defined(_WIN32) +#if !defined(WIN32_LEAN_AND_MEAN) +#define WIN32_LEAN_AND_MEAN +#endif // defined(WIN32_LEAN_AND_MEAN) +#include +#define dllOpen(name) (void*) LoadLibraryA("nv" name ".dll") +#define dllClose(handle) FreeLibrary(static_cast(handle)) +#define dllGetSym(handle, name) GetProcAddress(static_cast(handle), name) +#else +#include +#define dllOpen(name) dlopen("lib" name ".so.1", RTLD_LAZY) +#define dllClose(handle) dlclose(handle) +#define dllGetSym(handle, name) dlsym(handle, name) +#endif + +#include "cudaDriverWrapper.h" +#include +#include +#include + +using namespace nvinfer1; + + +CUDADriverWrapper::CUDADriverWrapper() +{ + handle = dllOpen(CUDA_LIB_NAME); + PLUGIN_ASSERT(handle != nullptr); + + auto load_sym = [](void* handle, const char* name) { + void* ret = dllGetSym(handle, name); + PLUGIN_ASSERT(ret != nullptr); + return ret; + }; + + *(void**)(&_cuGetErrorName) = load_sym(handle, "cuGetErrorName"); + *(void**)(&_cuFuncSetAttribute) = load_sym(handle, "cuFuncSetAttribute"); + *(void**)(&_cuLinkComplete) = load_sym(handle, "cuLinkComplete"); + *(void**)(&_cuModuleUnload) = load_sym(handle, "cuModuleUnload"); + *(void**)(&_cuLinkDestroy) = load_sym(handle, "cuLinkDestroy"); + *(void**)(&_cuModuleLoadData) = load_sym(handle, "cuModuleLoadData"); + *(void**)(&_cuLinkCreate) = load_sym(handle, "cuLinkCreate_v2"); + *(void**)(&_cuModuleGetFunction) = load_sym(handle, "cuModuleGetFunction"); + *(void**)(&_cuLinkAddFile) = load_sym(handle, "cuLinkAddFile_v2"); + *(void**)(&_cuLinkAddData) = load_sym(handle, "cuLinkAddData_v2"); + *(void**)(&_cuLaunchCooperativeKernel) = load_sym(handle, "cuLaunchCooperativeKernel"); + *(void**)(&_cuLaunchKernel) = load_sym(handle, "cuLaunchKernel"); +} + +CUDADriverWrapper::~CUDADriverWrapper() +{ + dllClose(handle); +} + +CUresult CUDADriverWrapper::cuGetErrorName(CUresult error, const char** pStr) const +{ + return (*_cuGetErrorName)(error, pStr); +} + +CUresult CUDADriverWrapper::cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value) const +{ + return (*_cuFuncSetAttribute)(hfunc, attrib, value); +} + +CUresult CUDADriverWrapper::cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) const +{ + return (*_cuLinkComplete)(state, cubinOut, sizeOut); +} + +CUresult CUDADriverWrapper::cuModuleUnload(CUmodule hmod) const +{ + return (*_cuModuleUnload)(hmod); +} + +CUresult CUDADriverWrapper::cuLinkDestroy(CUlinkState state) const +{ + return (*_cuLinkDestroy)(state); +} + +CUresult CUDADriverWrapper::cuModuleLoadData(CUmodule* module, const void* image) const +{ + return (*_cuModuleLoadData)(module, image); +} + +CUresult CUDADriverWrapper::cuLinkCreate( + uint32_t numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) const +{ + return (*_cuLinkCreate)(numOptions, options, optionValues, stateOut); +} + +CUresult CUDADriverWrapper::cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) const +{ + return (*_cuModuleGetFunction)(hfunc, hmod, name); +} + +CUresult CUDADriverWrapper::cuLinkAddFile(CUlinkState state, CUjitInputType type, const char* path, uint32_t numOptions, + CUjit_option* options, void** optionValues) const +{ + return (*_cuLinkAddFile)(state, type, path, numOptions, options, optionValues); +} + +CUresult CUDADriverWrapper::cuLinkAddData(CUlinkState state, CUjitInputType type, void* data, size_t size, + const char* name, uint32_t numOptions, CUjit_option* options, void** optionValues) const +{ + return (*_cuLinkAddData)(state, type, data, size, name, numOptions, options, optionValues); +} + +CUresult CUDADriverWrapper::cuLaunchCooperativeKernel(CUfunction f, uint32_t gridDimX, uint32_t gridDimY, + uint32_t gridDimZ, uint32_t blockDimX, uint32_t blockDimY, uint32_t blockDimZ, uint32_t sharedMemBytes, + CUstream hStream, void** kernelParams) const +{ + return (*_cuLaunchCooperativeKernel)( + f, gridDimX, gridDimY, gridDimZ, blockDimX, blockDimY, blockDimZ, sharedMemBytes, hStream, kernelParams); +} + +CUresult CUDADriverWrapper::cuLaunchKernel(CUfunction f, uint32_t gridDimX, uint32_t gridDimY, uint32_t gridDimZ, + uint32_t blockDimX, uint32_t blockDimY, uint32_t blockDimZ, uint32_t sharedMemBytes, CUstream hStream, + void** kernelParams, void** extra) const +{ + return (*_cuLaunchKernel)( + f, gridDimX, gridDimY, gridDimZ, blockDimX, blockDimY, blockDimZ, sharedMemBytes, hStream, kernelParams, extra); +} + +#endif diff --git a/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.h b/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.h new file mode 100644 index 0000000000000000000000000000000000000000..e3a2aadcc0725a6c9094e73674d062cf65d031ce --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/cudaDriverWrapper.h @@ -0,0 +1,118 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef CUDA_DRIVER_WRAPPER_H +#define CUDA_DRIVER_WRAPPER_H + +#ifdef WITH_CUDA + +#include +#include +#include + +#ifndef PLUGIN_ASSERT +#define PLUGIN_ASSERT(cond) \ +{ \ + if((cond) == false) { \ + abort(); \ + } \ +} +#endif + +#define cuErrCheck(stat, wrap) \ + { \ + nvinfer1::cuErrCheck_((stat), wrap, __FILE__, __LINE__); \ + } + +namespace nvinfer1 +{ +class CUDADriverWrapper +{ +public: + CUDADriverWrapper(); + + ~CUDADriverWrapper(); + + // Delete default copy constructor and copy assignment constructor + CUDADriverWrapper(const CUDADriverWrapper&) = delete; + CUDADriverWrapper& operator=(const CUDADriverWrapper&) = delete; + + CUresult cuGetErrorName(CUresult error, const char** pStr) const; + + CUresult cuFuncSetAttribute(CUfunction hfunc, CUfunction_attribute attrib, int value) const; + + CUresult cuLinkComplete(CUlinkState state, void** cubinOut, size_t* sizeOut) const; + + CUresult cuModuleUnload(CUmodule hmod) const; + + CUresult cuLinkDestroy(CUlinkState state) const; + + CUresult cuModuleLoadData(CUmodule* module, const void* image) const; + + CUresult cuLinkCreate(uint32_t numOptions, CUjit_option* options, void** optionValues, CUlinkState* stateOut) const; + + CUresult cuModuleGetFunction(CUfunction* hfunc, CUmodule hmod, const char* name) const; + + CUresult cuLinkAddFile(CUlinkState state, CUjitInputType type, const char* path, uint32_t numOptions, + CUjit_option* options, void** optionValues) const; + + CUresult cuLinkAddData(CUlinkState state, CUjitInputType type, void* data, size_t size, const char* name, + uint32_t numOptions, CUjit_option* options, void** optionValues) const; + + CUresult cuLaunchCooperativeKernel(CUfunction f, uint32_t gridDimX, uint32_t gridDimY, uint32_t gridDimZ, + uint32_t blockDimX, uint32_t blockDimY, uint32_t blockDimZ, uint32_t sharedMemBytes, CUstream hStream, + void** kernelParams) const; + + CUresult cuLaunchKernel(CUfunction f, uint32_t gridDimX, uint32_t gridDimY, uint32_t gridDimZ, uint32_t blockDimX, + uint32_t blockDimY, uint32_t blockDimZ, uint32_t sharedMemBytes, CUstream hStream, void** kernelParams, + void** extra) const; + +private: + void* handle; + CUresult (*_cuGetErrorName)(CUresult, const char**); + CUresult (*_cuFuncSetAttribute)(CUfunction, CUfunction_attribute, int); + CUresult (*_cuLinkComplete)(CUlinkState, void**, size_t*); + CUresult (*_cuModuleUnload)(CUmodule); + CUresult (*_cuLinkDestroy)(CUlinkState); + CUresult (*_cuLinkCreate)(unsigned int, CUjit_option*, void**, CUlinkState*); + CUresult (*_cuModuleLoadData)(CUmodule*, const void*); + CUresult (*_cuModuleGetFunction)(CUfunction*, CUmodule, const char*); + CUresult (*_cuLinkAddFile)(CUlinkState, CUjitInputType, const char*, unsigned int, CUjit_option*, void**); + CUresult (*_cuLinkAddData)( + CUlinkState, CUjitInputType, void*, size_t, const char*, unsigned int, CUjit_option*, void**); + CUresult (*_cuLaunchCooperativeKernel)(CUfunction, unsigned int, unsigned int, unsigned int, unsigned int, + unsigned int, unsigned int, unsigned int, CUstream, void**); + CUresult (*_cuLaunchKernel)(CUfunction f, uint32_t gridDimX, uint32_t gridDimY, uint32_t gridDimZ, + uint32_t blockDimX, uint32_t blockDimY, uint32_t blockDimZ, uint32_t sharedMemBytes, CUstream hStream, + void** kernelParams, void** extra); +}; + +inline void cuErrCheck_(CUresult stat, const CUDADriverWrapper& wrap, const char* file, int line) +{ + if (stat != CUDA_SUCCESS) + { + const char* msg = nullptr; + wrap.cuGetErrorName(stat, &msg); + fprintf(stderr, "CUDA Error: %s %s %d\n", msg, file, line); + } +} + +} // namespace nvinfer1 + +#endif + +#endif // CUDA_DRIVER_WRAPPER_H diff --git a/oneflow/user/kernels/fmha_flash_attention/fmha.cpp b/oneflow/user/kernels/fmha_flash_attention/fmha.cpp new file mode 100644 index 0000000000000000000000000000000000000000..398d79fa641a89371e756c92af19e8b505530b80 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/fmha.cpp @@ -0,0 +1,165 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#ifdef WITH_CUDA + +#include "fmha.h" +#include +#include +#include +#include + +namespace nvinfer1 +{ +namespace plugin +{ + +void set_alpha(uint32_t& alpha, float norm, Data_type dtype) +{ + if (dtype == DATA_TYPE_FP16) + { + half x = __float2half_rn(norm); + uint16_t h = reinterpret_cast(x); + ushort2 h2 = {h, h}; + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wstrict-aliasing" + alpha = reinterpret_cast(h2); +#pragma GCC diagnostic pop + } + else if (dtype == DATA_TYPE_FP32) + { +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wstrict-aliasing" + alpha = reinterpret_cast(norm); +#pragma GCC diagnostic pop + } + else if (dtype == DATA_TYPE_INT32) + { + int32_t inorm = static_cast(norm); + alpha = reinterpret_cast(inorm); + } + else + { + assert(false); + } +} + +void set_params(Fused_multihead_attention_params_v2& params, + // types + Data_type data_type, Data_type acc_type, + // sizes + size_t b, size_t s, size_t h, size_t d, size_t total, + // device pointers + void* qkv_packed_d, void* cu_seqlens_d, void* o_packed_d, void* p_d, void* s_d, + // scale factors + float scale_bmm1, float scale_softmax, float scale_bmm2, + // flags + bool interleaved, bool ignore_b1opt, bool force_unroll, bool use_int8_scale_max) +{ + + params.clear(); + // Set the pointers. + params.qkv_ptr = qkv_packed_d; + params.qkv_stride_in_bytes = get_size_in_bytes(h * 3 * d, data_type); + params.o_ptr = o_packed_d; + params.o_stride_in_bytes = get_size_in_bytes(h * d, data_type); + + if (interleaved) + { + params.qkv_stride_in_bytes = total; + params.o_stride_in_bytes = total; + } + + params.cu_seqlens = static_cast(cu_seqlens_d); + +#if defined(STORE_P) + params.p_ptr = p_d; + params.p_stride_in_bytes = get_size_in_bytes(b * h * s, acc_type); +#endif // defined(STORE_P) + +#if defined(STORE_S) + params.s_ptr = s_d; + params.s_stride_in_bytes = get_size_in_bytes(b * h * s, data_type); +#endif // defined(STORE_S) + + // Set the dimensions. + params.b = b; + params.h = h; + params.s = s; + params.d = d; + + // Set the different scale values. + Data_type scale_type1 = data_type == DATA_TYPE_FP16 ? acc_type : DATA_TYPE_FP32; + Data_type scale_type2 = data_type == DATA_TYPE_FP16 ? DATA_TYPE_FP16 : DATA_TYPE_FP32; + + set_alpha(params.scale_bmm1, scale_bmm1, scale_type1); + set_alpha(params.scale_softmax, scale_softmax, scale_type1); + set_alpha(params.scale_bmm2, scale_bmm2, scale_type2); + + // Set flags + params.interleaved = interleaved; + params.ignore_b1opt = ignore_b1opt; + params.force_unroll = force_unroll; + params.use_int8_scale_max = use_int8_scale_max; + + // Do we enable the trick to replace I2F with FP math in the 2nd GEMM? + if (data_type == DATA_TYPE_INT8) + { + params.enable_i2f_trick + = -double(1 << 22) * double(scale_bmm2) <= -128.f && double(1 << 22) * double(scale_bmm2) >= 127.f; + } +} + +int run_fmha_v2_api(void* qkv_packed_d, void* cu_seqlens_d, void* o_packed_d, size_t total, int32_t sm, + FusedMultiHeadFlashAttentionKernel const* kernels, size_t b, size_t h, size_t d, size_t s, + cudaStream_t stream) +{ + + // The data type of the kernel. + Data_type data_type = DATA_TYPE_FP16; + // The type of the intermediate P matrix. + Data_type acc_type = DATA_TYPE_FP16; + bool const force_unroll = true; + bool const interleaved = false; + bool const ignore_b1opt = false; + bool const use_int8_scale_max = false; + void* p_d = nullptr; + void* s_d = nullptr; + + float scale_bmm1 = 1.f / sqrtf(d); + // float scale_bmm1 = 1.f / 8; + float scale_softmax = 1.f; + float scale_bmm2 = 1.f; + + // Set the params. + Fused_multihead_attention_params_v2 params_v2; + set_params(params_v2, data_type, acc_type, b, s, h, d, total, qkv_packed_d, cu_seqlens_d, o_packed_d, p_d, s_d, + scale_bmm1, scale_softmax, scale_bmm2, + interleaved, // interleaved + ignore_b1opt, // ignore_b1opt, + force_unroll, // force_unroll, + use_int8_scale_max // use_int8_scale_max, + ); + + // Run a few warm-up kernels. + kernels->run(params_v2, stream); + return 0; +} +} // namespace plugin +} // namespace nvinfer1 + +#endif diff --git a/oneflow/user/kernels/fmha_flash_attention/fmha.h b/oneflow/user/kernels/fmha_flash_attention/fmha.h new file mode 100644 index 0000000000000000000000000000000000000000..20f47192d0aa12be22c9f2ab80244bd1946245fc --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/fmha.h @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once +#ifndef _FMHA_ +#define _FMHA_ + +#ifdef WITH_CUDA + +#include "include/fmha_flash_attention.h" +#include +#include + +namespace nvinfer1 +{ +namespace plugin +{ + +int run_fmha_v2_api(void* qkv_packed_d, void* cu_seqlens_d, void* o_packed_d, size_t total, int32_t sm, + FusedMultiHeadFlashAttentionKernel const* kernels, size_t b = 2, size_t h = 8, size_t d = 64, size_t s = 4096, + cudaStream_t stream = 0); + +} +} // namespace nvinfer1 + +#endif + +#endif diff --git a/oneflow/user/kernels/fmha_flash_attention/include/commonDatatype.h b/oneflow/user/kernels/fmha_flash_attention/include/commonDatatype.h new file mode 100644 index 0000000000000000000000000000000000000000..f66ba0b1a1b588b9ab74593356290410e8d9498a --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/include/commonDatatype.h @@ -0,0 +1,46 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once +#ifndef _COMMON_DATATYPE_FLASH +#define _COMMON_DATATYPE_FLASH + +#ifdef WITH_CUDA + +namespace nvinfer1 +{ +namespace plugin +{ + +enum Data_type +{ + DATA_TYPE_BOOL, + DATA_TYPE_E8M10, + DATA_TYPE_E8M7, + DATA_TYPE_FP16, + DATA_TYPE_FP32, + DATA_TYPE_INT4, + DATA_TYPE_INT8, + DATA_TYPE_INT32 +}; + +} // namespace plugin +} // namespace nvinfer1 + +#endif + +#endif diff --git a/oneflow/user/kernels/fmha_flash_attention/include/fmha_flash_attention.h b/oneflow/user/kernels/fmha_flash_attention/include/fmha_flash_attention.h new file mode 100644 index 0000000000000000000000000000000000000000..8c302dc5277a5b6e0f6f303ed9a241693c8200c9 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/include/fmha_flash_attention.h @@ -0,0 +1,317 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once +#ifndef _FMHA_FLASH_ATTENTION +#define _FMHA_FLASH_ATTENTION + +#ifdef WITH_CUDA + +#include "commonDatatype.h" +#include "sharedCubinLoader.h" + +#define ENABLE_SM80 +#define ENABLE_SM86 +#define ENABLE_SM89 + +constexpr uint32_t kSM_80 = 80; +constexpr uint32_t kSM_86 = 86; +constexpr uint32_t kSM_89 = 89; + +namespace +{ + static inline size_t get_size_in_bytes(size_t n, nvinfer1::plugin::Data_type dtype) + { + switch (dtype) + { + case nvinfer1::plugin::DATA_TYPE_E8M10: return n * 4; + case nvinfer1::plugin::DATA_TYPE_FP32: return n * 4; + case nvinfer1::plugin::DATA_TYPE_FP16: return n * 2; + case nvinfer1::plugin::DATA_TYPE_INT32: return n * 4; + case nvinfer1::plugin::DATA_TYPE_INT8: return n; + case nvinfer1::plugin::DATA_TYPE_INT4: return n / 2U; + case nvinfer1::plugin::DATA_TYPE_BOOL: return n / 8U; + case nvinfer1::plugin::DATA_TYPE_E8M7: return n * 2; + default: PLUGIN_ASSERT(false); return 0; + } + } +} + +namespace nvinfer1 +{ +namespace plugin +{ + +//////////////////////////////////////////////////////////////////////////////////////////////////// + +struct Fused_multihead_attention_params_v2 +{ + // The QKV matrices. + void* qkv_ptr; + // The mask to implement drop-out. + void* packed_mask_ptr; + // The O matrix (output). + void* o_ptr; + + // The stride between rows of the Q, K and V matrices. + int64_t qkv_stride_in_bytes; + // The stride between matrices of packed mask. + int64_t packed_mask_stride_in_bytes; + // The stride between rows of O. + int64_t o_stride_in_bytes; + +#if defined(STORE_P) + // The pointer to the P matrix (for debugging). + void* p_ptr; + // The stride between rows of the P matrix (for debugging). + int64_t p_stride_in_bytes; +#endif // defined(STORE_P) + +#if defined(STORE_S) + // The pointer to the S matrix (for debugging). + void* s_ptr; + // The stride between rows of the S matrix (for debugging). + int64_t s_stride_in_bytes; +#endif // defined(STORE_S) + + // The dimensions. + int32_t b, h, s, d; + // The scaling factors for the kernel. + uint32_t scale_bmm1, scale_softmax, scale_bmm2; + + // Do we use Niall's trick to avoid I2F/F2I in the INT8 kernel. + // See https://confluence.nvidia.com/pages/viewpage.action?pageId=302779721 for details. + bool enable_i2f_trick; + + // array of length b+1 holding prefix sum of actual sequence lenghts + int32_t* cu_seqlens; + + // use C/32 Format. + bool interleaved = false; + bool ignore_b1opt = false; + bool force_unroll = false; + bool use_int8_scale_max = false; + + void clear() + { + qkv_ptr = nullptr; + packed_mask_ptr = nullptr; + o_ptr = nullptr; + + qkv_stride_in_bytes = 0; + packed_mask_stride_in_bytes = 0; + o_stride_in_bytes = 0; +#if defined(STORE_P) + p_ptr = nullptr; + p_stride_in_bytes = 0 +#endif // defined(STORE_P) + +#if defined(STORE_S) + s_ptr + = nullptr; + s_stride_in_bytes = 0; +#endif // defined(STORE_S) + + b = 0; + h = 0; + s = 0; + d = 0; + // The scaling factors for the kernel. + scale_bmm1 = 0; + scale_softmax = 0; + scale_bmm2 = 0; + + enable_i2f_trick = false; + + cu_seqlens = nullptr; + interleaved = false; + ignore_b1opt = false; + force_unroll = false; + use_int8_scale_max = false; + } +}; + +//////////////////////////////////////////////////////////////////////////////////////////////////// +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin[]; +extern unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin[]; + +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin_len; +extern uint32_t cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin_len; + +constexpr uint32_t S{0}; + +#if !(defined(ENABLE_SM80) || defined(ENABLE_SM86) || defined(ENABLE_SM89)) +#error This file can only be included one of sm 80, 86 or 89 are defined. +#endif +static const struct FusedMultiHeadFlashAttentionKernelMetaInfoV2 +{ + Data_type mDataType; + uint32_t mS; + uint32_t mD; + uint32_t mSM; + unsigned char const* mCubin; + uint32_t mCubinSize; + char const* mFuncName; + uint32_t mSharedMemBytes; + uint32_t mThreadsPerCTA; + uint32_t mUnrollStep; + bool mInterleaved; +} sMhaKernelMetaInfos[] = { +#if defined(ENABLE_SM80) + { DATA_TYPE_FP16, S, 16, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm80_kernel", 8192, 128, 0, false }, + { DATA_TYPE_FP16, S, 16, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm80_kernel_nl", 8192, 128, 64, false }, + { DATA_TYPE_FP16, S, 32, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm80_kernel", 16384, 128, 0, false }, + { DATA_TYPE_FP16, S, 32, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm80_kernel_nl", 16384, 128, 64, false }, + { DATA_TYPE_FP16, S, 40, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm80_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 40, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm80_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 64, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm80_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 64, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm80_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 80, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm80_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 80, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm80_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 128, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm80_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 128, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm80_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 160, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm80_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 160, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm80_kernel_nl", 98304, 128, 64, false }, + { DATA_TYPE_FP16, S, 256, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm80_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 256, kSM_80, cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm80_kernel_nl", 98304, 128, 64, false }, +#endif // defined(ENABLE_SM80) +#if defined(ENABLE_SM86) + { DATA_TYPE_FP16, S, 16, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm86_kernel", 8192, 128, 0, false }, + { DATA_TYPE_FP16, S, 16, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm86_kernel_nl", 8192, 128, 64, false }, + { DATA_TYPE_FP16, S, 32, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm86_kernel", 16384, 128, 0, false }, + { DATA_TYPE_FP16, S, 32, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm86_kernel_nl", 16384, 128, 64, false }, + { DATA_TYPE_FP16, S, 40, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm86_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 40, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm86_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 64, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm86_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 64, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm86_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 80, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm86_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 80, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm86_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 128, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm86_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 128, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm86_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 160, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm86_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 160, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm86_kernel_nl", 98304, 128, 64, false }, + { DATA_TYPE_FP16, S, 256, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm86_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 256, kSM_86, cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm86_kernel_nl", 98304, 128, 64, false }, +#endif +#if defined(ENABLE_SM89) + { DATA_TYPE_FP16, S, 16, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm89_kernel", 8192, 128, 0, false }, + { DATA_TYPE_FP16, S, 16, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_16_sm89_kernel_nl", 8192, 128, 64, false }, + { DATA_TYPE_FP16, S, 32, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm89_kernel", 16384, 128, 0, false }, + { DATA_TYPE_FP16, S, 32, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_32_sm89_kernel_nl", 16384, 128, 64, false }, + { DATA_TYPE_FP16, S, 40, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm89_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 40, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_40_sm89_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 64, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm89_kernel", 32768, 128, 0, false }, + { DATA_TYPE_FP16, S, 64, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_64_sm89_kernel_nl", 32768, 128, 64, false }, + { DATA_TYPE_FP16, S, 80, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm89_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 80, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_80_sm89_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 128, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm89_kernel", 49152, 128, 0, false }, + { DATA_TYPE_FP16, S, 128, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_128_sm89_kernel_nl", 49152, 128, 64, false }, + { DATA_TYPE_FP16, S, 160, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm89_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 160, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_160_sm89_kernel_nl", 98304, 128, 64, false }, + { DATA_TYPE_FP16, S, 256, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm89_kernel", 98304, 128, 0, false }, + { DATA_TYPE_FP16, S, 256, kSM_89, cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin, cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin_len, "fmha_v2_flash_attention_fp16_S_256_sm89_kernel_nl", 98304, 128, 64, false } + +#endif // defined(ENABLE_SM89) +}; + +class FusedMultiHeadFlashAttentionKernel + : public TSharedCubinKernel +{ +public: + FusedMultiHeadFlashAttentionKernel(FusedMultiHeadFlashAttentionKernelMetaInfoV2 const* pMetaStart, + uint32_t nMetaCount, Data_type type, uint32_t sm) + : TSharedCubinKernel( + pMetaStart, nMetaCount, type, sm) + { + } + + uint64_t hashID(uint32_t headsize, bool interleaved, bool unroll) const + { + // we only have 30 bits room for head size + PLUGIN_ASSERT(headsize <= 0x3FFFFFFF); + return (headsize << 2) | (interleaved ? 2U : 0U) | (unroll ? 1U : 0U); + } + + uint64_t hashID(Fused_multihead_attention_params_v2 const& param) const + { + return hashID(param.d, param.interleaved, param.force_unroll); + } + + uint64_t hashID(KernelMeta const& kernelMeta) const + { + return hashID(kernelMeta.mD, kernelMeta.mInterleaved, kernelMeta.mUnrollStep > 0); + } +}; + +using FusedMHAFlashKernelFactory = TSharedCubinKernelFactory; + +inline FusedMultiHeadFlashAttentionKernel const* getFMHACubinKernels(Data_type type, uint32_t sm) +{ + return FusedMHAFlashKernelFactory::Get().getCubinKernels( + sMhaKernelMetaInfos, sizeof(sMhaKernelMetaInfos) / sizeof(sMhaKernelMetaInfos[0]), type, sm); +} + +} // namespace plugin +} // namespace nvinfer1 + +#endif + +#endif diff --git a/oneflow/user/kernels/fmha_flash_attention/include/sharedCubinLoader.h b/oneflow/user/kernels/fmha_flash_attention/include/sharedCubinLoader.h new file mode 100644 index 0000000000000000000000000000000000000000..e4f81f54a8bc9866e0d1b02a314602d9a57d72a0 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/include/sharedCubinLoader.h @@ -0,0 +1,221 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once +#ifndef _SHARED_CUBIN_LOADER_ +#define _SHARED_CUBIN_LOADER_ +#ifdef WITH_CUDA +#include "../cudaDriverWrapper.h" +#include "commonDatatype.h" +#include "cuda_runtime_api.h" +#include +#include +#include +#include +#include +#include + +namespace nvinfer1 +{ +namespace plugin +{ +template +class TSharedCubinKernel +{ +public: + using KernelMeta = TKernelMeta; + using KernelParam = TKernelParam; + + virtual uint64_t hashID(KernelMeta const& kernelMeta) const = 0; + virtual uint64_t hashID(TKernelParam const& param) const = 0; + + TSharedCubinKernel(TKernelMeta const* pMetaStart, uint32_t nMetaCount, Data_type type, uint32_t sm) + : mDataType(type) + , mKernelMeta(pMetaStart) + , mKernelMetaCount(nMetaCount) + , mSM(sm) + { + PLUGIN_ASSERT(mKernelMetaCount && "No kernels were loaded correctly."); + } + + void loadCubinKernels(uint32_t smVersion) + { + for (uint32_t i = 0; i < mKernelMetaCount; ++i) + { + auto const& kernelMeta = mKernelMeta[i]; + auto const kernelKey = hashID(kernelMeta); + if (kernelMeta.mSM == smVersion && kernelMeta.mDataType == mDataType + && mFunctions.find(kernelKey) == mFunctions.end()) + { + uint32_t const DEFAULT_SMEM_SIZE{48 * 1024}; + if (kernelMeta.mSharedMemBytes >= DEFAULT_SMEM_SIZE) + { + int32_t deviceID{0}; + cudaGetDevice(&deviceID); + int32_t sharedMemPerMultiprocessor{0}; + if (cudaDeviceGetAttribute( + &sharedMemPerMultiprocessor, cudaDevAttrMaxSharedMemoryPerBlockOptin, deviceID) + != cudaSuccess + || sharedMemPerMultiprocessor < static_cast(kernelMeta.mSharedMemBytes)) + { + // skip load function because not enough shared memory to launch the kernel + continue; + } + } + + CUmodule hmod{0}; + auto findModuleIter = mModules.find(kernelMeta.mCubin); + if (findModuleIter != mModules.end()) + { + hmod = findModuleIter->second; + } + else + { + cuErrCheck(mDriver.cuModuleLoadData(&hmod, kernelMeta.mCubin), mDriver); + mModules.insert(std::make_pair(kernelMeta.mCubin, hmod)); + } + + FusedMultiHeadAttentionKernelInfo funcInfo; + funcInfo.mMetaInfoIndex = i; + cuErrCheck(mDriver.cuModuleGetFunction(&funcInfo.mDeviceFunction, hmod, kernelMeta.mFuncName), mDriver); + if (kernelMeta.mSharedMemBytes >= DEFAULT_SMEM_SIZE) + { + if (mDriver.cuFuncSetAttribute(funcInfo.mDeviceFunction, + CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, kernelMeta.mSharedMemBytes) + != CUDA_SUCCESS) + { + // some chip may not have enough shared memory to launch the kernel + continue; + } + } + mFunctions.insert({kernelKey, funcInfo}); + } + } + } + + void loadCubinKernels() + { + if (!mFunctions.empty()) + { + return; + } + + loadCubinKernels(mSM); + } + + bool isValid(int32_t s) const + { + return !mFunctions.empty(); + } + + virtual void run(TKernelParam& params, cudaStream_t ss) const + { + if (params.interleaved) + { + PLUGIN_ASSERT(mDataType == DATA_TYPE_INT8); + } + + auto const findIter = mFunctions.find(hashID(params)); + PLUGIN_ASSERT(findIter != mFunctions.end()); + + auto const& kernelMeta = mKernelMeta[findIter->second.mMetaInfoIndex]; + CUfunction const func = findIter->second.mDeviceFunction; + + void* kernelParams[] = {¶ms, nullptr}; + if (!params.force_unroll) + { + cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, 1, kernelMeta.mThreadsPerCTA, 1, 1, + kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr), + mDriver); + } + else + { + int32_t unroll = (params.s + kernelMeta.mUnrollStep - 1) / kernelMeta.mUnrollStep; + cuErrCheck(mDriver.cuLaunchKernel(func, params.h, params.b, unroll, kernelMeta.mThreadsPerCTA, 1, 1, + kernelMeta.mSharedMemBytes, ss, kernelParams, nullptr), + mDriver); + } + } + + virtual ~TSharedCubinKernel() = default; + +protected: + nvinfer1::CUDADriverWrapper mDriver; + + Data_type mDataType; + TKernelMeta const* mKernelMeta; + uint32_t mKernelMetaCount; + uint32_t mSM; + std::unordered_map mModules; + struct FusedMultiHeadAttentionKernelInfo + { + uint32_t mMetaInfoIndex; + CUfunction mDeviceFunction; + }; + std::unordered_map mFunctions; +}; + +template +class TSharedCubinKernelFactory +{ +public: + TKernelList const* getCubinKernels( + typename TKernelList::KernelMeta const* pKernelList, uint32_t nbKernels, Data_type type, uint32_t sm) + { + static std::mutex s_mutex; + std::lock_guard lg(s_mutex); + + auto const id = hashID(type, sm); + auto const findIter = mKernels.find(id); + if (findIter == mKernels.end()) + { + TKernelList* newKernel = new TKernelList{pKernelList, nbKernels, type, sm}; + newKernel->loadCubinKernels(); + mKernels.insert(std::make_pair(id, std::unique_ptr(newKernel))); + return newKernel; + } + return findIter->second.get(); + } + + static TSharedCubinKernelFactory& Get() + { + static TSharedCubinKernelFactory s_factory; + return s_factory; + } + +private: + TSharedCubinKernelFactory() = default; + + inline uint64_t hashID(Data_type type, uint32_t sm) const + { + // use deviceID in hasID for multi GPU support before driver support context-less loading of cubin + int32_t deviceID{0}; + cudaGetDevice(&deviceID); + + PLUGIN_ASSERT((deviceID & 0xFFFF) == deviceID); + PLUGIN_ASSERT((type & 0xFFFF) == type); + PLUGIN_ASSERT((sm & 0xFFFFFFFF) == sm); + return (uint64_t) type << 48 | (uint64_t) deviceID << 32 | sm; + } + + std::unordered_map const> mKernels; +}; + +} // namespace plugin +} // namespace nvinfer1 +#endif +#endif // _SHARED_CUBIN_LOADER_ diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm80.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm80.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7600b2c9bf89581cf0c58c93c34ae3ebe67a7d8a --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm80.cubin.cpp @@ -0,0 +1,15512 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xd5, 0x02, 0x00, + 0x00, 0x00, 0x00, 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a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm86.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f325aa58ede3bdd45673b718f1f25457241cd4d9 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm86.cubin.cpp @@ -0,0 +1,15501 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xd4, 0x02, 0x00, + 0x00, 0x00, 0x00, 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0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_128_sm86_cu_cubin_len = 185696; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm89.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm89.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0a73d303262f28b6bc7b71643b40dc63fae0fcce --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_128_sm89.cubin.cpp @@ -0,0 +1,15501 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xd4, 0x02, 0x00, + 0x00, 0x00, 0x00, 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0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_128_sm89_cu_cubin_len = 185696; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm80.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm80.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..dbc59c7e49797b77ca8d0e01c36b3aaa7d9373e9 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm80.cubin.cpp @@ -0,0 +1,18669 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x69, 0x03, 0x00, + 0x00, 0x00, 0x00, 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b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..cc70ba6242f4229891e975b0fc39cd9641d4e618 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm86.cubin.cpp @@ -0,0 +1,18701 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x6a, 0x03, 0x00, + 0x00, 0x00, 0x00, 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0x00, 0x00, 0x00, + 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_160_sm86_cu_cubin_len = 224096; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm89.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm89.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e4260c40fe7569a1a6b608d1a363840bf0a11e01 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_160_sm89.cubin.cpp @@ -0,0 +1,18701 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x6a, 0x03, 0x00, + 0x00, 0x00, 0x00, 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0x00, 0x00, 0x00, + 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 +}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_160_sm89_cu_cubin_len = 224096; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm80.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm80.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..973492fc959f8d5a11b7f5dca58cbda47275ffcb --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm80.cubin.cpp @@ -0,0 +1,9912 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xce, 0x01, 0x00, + 0x00, 0x00, 0x00, 0x00, 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+}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_16_sm80_cu_cubin_len = 118624; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm86.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..84b0661b8d63677a02b399b92fb6e3b4e2e4b3f9 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm86.cubin.cpp @@ -0,0 +1,9880 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xcd, 0x01, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0000000000000000000000000000000000000000..e72861023323f6f39bb5dd1d2d9db281d7d4559f --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_16_sm89.cubin.cpp @@ -0,0 +1,9880 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_16_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xcd, 0x01, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0000000000000000000000000000000000000000..7495d94b341beedd691731c08aa491b623066e9f --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_256_sm80.cubin.cpp @@ -0,0 +1,19832 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x9f, 0x03, 0x00, + 0x00, 0x00, 0x00, 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a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_256_sm86.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_256_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..80a30389e3e7ff114c445a9c44967a779934fb21 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_256_sm86.cubin.cpp @@ -0,0 +1,19917 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xa3, 0x03, 0x00, + 0x00, 0x00, 0x00, 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_256_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xa3, 0x03, 0x00, + 0x00, 0x00, 0x00, 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x1c, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_32_sm86.cubin.cpp @@ -0,0 +1,11554 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x1b, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_32_sm89.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..95b516db9e9af40b220b47d0b356a9cc3f748593 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_32_sm89.cubin.cpp @@ -0,0 +1,11554 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_32_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x1b, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_40_sm80.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..49cfffdd08b350c7ad1b627b54ad30b009148314 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_40_sm80.cubin.cpp @@ -0,0 +1,14701 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xaf, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_40_sm86.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_40_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f8d204389e74fd28c73c34ada9b389e1ddb67c3c --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_40_sm86.cubin.cpp @@ -0,0 +1,14680 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xae, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_40_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xae, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xaf, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0000000000000000000000000000000000000000..39df1975ebf96026e79a121ae4556a463e573734 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_64_sm86.cubin.cpp @@ -0,0 +1,14712 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xaf, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0000000000000000000000000000000000000000..f066f8b679d0c9e5a60e4a1bb05001b45adae35a --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_64_sm89.cubin.cpp @@ -0,0 +1,14712 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_64_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0xaf, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0000000000000000000000000000000000000000..d04a3f70738ded470e1adb280d162eab1191648f --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_80_sm80.cubin.cpp @@ -0,0 +1,14082 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x92, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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cubin_fmha_v2_flash_attention_fp16_S_80_sm80_cu_cubin_len = 168672; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_80_sm86.cubin.cpp b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_80_sm86.cubin.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a58b170aeb9fc5436a64490e507cfd30cc2da099 --- /dev/null +++ b/oneflow/user/kernels/fmha_flash_attention/src/fmha_v2_flash_attention_fp16_S_80_sm86.cubin.cpp @@ -0,0 +1,14072 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm86_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x91, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +namespace nvinfer1 +{ +namespace plugin +{ +unsigned char cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin[] = { + 0x7f, 0x45, 0x4c, 0x46, 0x02, 0x01, 0x01, 0x33, 0x07, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0xbe, 0x00, 0x76, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x91, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 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0x00, 0x00, + 0xe0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, + 0x28, 0x1d, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xd8, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xd8, 0x6e, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x8c, 0x02, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x05, 0x00, 0x00, 0x00, 0x80, 0x91, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0xe0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xe0, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00 +}; +unsigned int cubin_fmha_v2_flash_attention_fp16_S_80_sm89_cu_cubin_len = 168544; +} // namespace plugin +} // namespace nvinfer1 diff --git a/oneflow/user/kernels/fold_kernel.cpp b/oneflow/user/kernels/fold_kernel.cpp index 75207d652d50e94532aec9eef582c64947bb79cf..816aa366827b097c1c72598282e315029507cc3e 100644 --- a/oneflow/user/kernels/fold_kernel.cpp +++ b/oneflow/user/kernels/fold_kernel.cpp @@ -95,14 +95,10 @@ class FoldKernel final : public OpKernel { REGISTER_FOLD_KERNEL(DeviceType::kCPU, float) REGISTER_FOLD_KERNEL(DeviceType::kCPU, double) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_FOLD_KERNEL(DeviceType::kCUDA, float) REGISTER_FOLD_KERNEL(DeviceType::kCUDA, double) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_FOLD_KERNEL(DeviceType::kCUDA, float) -REGISTER_FOLD_KERNEL(DeviceType::kCUDA, double) -#endif // WITH_ROCM } // namespace user_op diff --git a/oneflow/user/kernels/fold_kernel_util.cu b/oneflow/user/kernels/fold_kernel_util.cu index d2e3ca9d90b7104cdda4dd2732d53c8c0aa0f94e..32db052d031c85859bcfc9b43caf3d083c38ee7b 100644 --- a/oneflow/user/kernels/fold_kernel_util.cu +++ b/oneflow/user/kernels/fold_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/core/cuda/elementwise.cuh" diff --git a/oneflow/user/kernels/fold_kernel_util.h b/oneflow/user/kernels/fold_kernel_util.h index ec1013711c7d18209fd653c03e92af98553fa7a9..2feb082f1aaf5feca0e965c4c07813a0b2d39fed 100644 --- a/oneflow/user/kernels/fold_kernel_util.h +++ b/oneflow/user/kernels/fold_kernel_util.h @@ -21,12 +21,9 @@ limitations under the License. #include "oneflow/core/common/nd_index_offset_helper.h" #include "oneflow/core/common/switch_func.h" #include "oneflow/core/ndarray/xpu_util.h" -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/cuda/atomic.cuh" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_ROCM namespace oneflow { diff --git a/oneflow/user/kernels/fold_kernel_util.hip.cpp b/oneflow/user/kernels/fold_kernel_util.hip.cpp deleted file mode 100644 index 7085abc56005b3c22bf28a9940886789ed41842e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fold_kernel_util.hip.cpp +++ /dev/null @@ -1,75 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM - -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/fold_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace user_op { - -namespace { - -constexpr int kBlockSize = cuda::elementwise::kBlockSize; - -int GetNumBlocks(int64_t elem_cnt) { - int num_blocks = 0; - OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); - return num_blocks; -} - -// NDIM range: (1, 2, 3) -// SDIM range: (1, 2), 1 indicates channels_last, 2 indicates channels_first -template -__global__ void CudaFoldForward(FoldParams params, const T* input_ptr, - T* output_ptr) { - CUDA_1D_KERNEL_LOOP_T(INDEX_T, in_offset, params.in_elem_cnt) { - using ParamType = FoldParams; - INDEX_T in_index[ParamType::kInputNDim] = {0}; - INDEX_T out_index[ParamType::kOutputNDim] = {0}; - params.in_index_helper.OffsetToNdIndex(in_offset, in_index); - if (!FoldIndexTransform(params, in_index, out_index)) { - INDEX_T out_offset = params.out_index_helper.NdIndexToOffset(out_index); - XPUAdd::Invoke(&input_ptr[in_offset], &output_ptr[out_offset]); - } else { - continue; - } - } -} - -} // namespace - -template -struct FoldKernelUtil { - using ParamType = FoldParams; - static void Forward(ep::Stream* stream, const void* raw_params, const T* input_ptr, - T* output_ptr) { - const auto* fold_params = static_cast(raw_params); - CudaFoldForward - <<in_elem_cnt), kBlockSize, 0, - stream->As()->cuda_stream()>>>(*fold_params, input_ptr, output_ptr); - } -}; - -INSTANTIATE_FOLD_KERNEL_UTIL_FOR_DEVICE(DeviceType::kCUDA) - -} // namespace user_op -} // namespace oneflow -#endif \ No newline at end of file diff --git a/oneflow/user/kernels/fused_bias_add_kernel.cu b/oneflow/user/kernels/fused_bias_add_kernel.cu index 8acf3601c507bb55eaa45259ba71fddcc8afba0b..38e950dd15ce5c7400eaf0b798f5b94ba6d55328 100644 --- a/oneflow/user/kernels/fused_bias_add_kernel.cu +++ b/oneflow/user/kernels/fused_bias_add_kernel.cu @@ -16,6 +16,10 @@ limitations under the License. #include "oneflow/core/device/cuda_util.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 +#include "oneflow/core/device/cuda_pseudo_bfloat16.h" namespace oneflow { @@ -36,16 +40,31 @@ struct GeluFunctor { } __device__ half2 ComputeHalf2(half2 x, int64_t i) const { half2 y; +#ifdef WITH_ROCM + y.data.x = __float2half(float_functor.Compute(__half2float(x.data.x), 2 * i)); + y.data.y = __float2half(float_functor.Compute(__half2float(x.data.y), 2 * i + 1)); +#else y.x = __float2half(float_functor.Compute(__half2float(x.x), 2 * i)); y.y = __float2half(float_functor.Compute(__half2float(x.y), 2 * i + 1)); +#endif return y; } }; +#if CUDA_VERSION >= 11000 +template<> +struct GeluFunctor { + GeluFunctor float_functor; + __device__ nv_bfloat16 Compute(nv_bfloat16 x, int64_t i) const { + return static_cast(float_functor.Compute(static_cast(x), i)); + } +}; +#endif + template struct MaskAndScaleFunctor { MaskAndScaleFunctor(const bool* mask, float scale) : mask(mask), scale(scale) {} - __device__ T Compute(T x, int64_t i) const { return x * static_cast(mask[i]) * scale; } + __device__ T Compute(T x, int64_t i) const { return x * static_cast(mask[i] * scale); } const bool* mask; float scale; }; @@ -61,8 +80,13 @@ struct MaskAndScaleFunctor { char2 mask_val = mask_c2[i]; half2 one_or_zero_h2; half2 h2_scale = __float2half2_rn(scale); +#ifdef WITH_ROCM + one_or_zero_h2.data.x = mask_val.x; + one_or_zero_h2.data.y = mask_val.y; +#else one_or_zero_h2.x = mask_val.x; one_or_zero_h2.y = mask_val.y; +#endif return __hmul2(__hmul2(x, one_or_zero_h2), h2_scale); } const bool* mask; @@ -74,7 +98,7 @@ struct MaskAndScaleAddFunctor { MaskAndScaleAddFunctor(const bool* mask, const T* addend, float scale) : mask(mask), addend(addend), scale(scale) {} __device__ T Compute(T x, int64_t i) const { - return x * static_cast(mask[i]) * scale + addend[i]; + return x * static_cast(mask[i] * scale) + addend[i]; } const bool* mask; const T* addend; @@ -94,8 +118,13 @@ struct MaskAndScaleAddFunctor { char2 mask_val = mask_c2[i]; half2 one_or_zero_h2; half2 h2_scale = __float2half2_rn(scale); +#ifdef WITH_ROCM + one_or_zero_h2.data.x = mask_val.x; + one_or_zero_h2.data.y = mask_val.y; +#else one_or_zero_h2.x = mask_val.x; one_or_zero_h2.y = mask_val.y; +#endif return __hadd2(__hmul2(__hmul2(x, one_or_zero_h2), h2_scale), addend_h2[i]); } const bool* mask; @@ -122,6 +151,17 @@ struct GeluGradFunctor { } }; +#if CUDA_VERSION >= 11000 +template<> +struct GeluGradFunctor { + GeluGradFunctor float_functor; + __device__ nv_bfloat16 Compute(nv_bfloat16 x, nv_bfloat16 dy, int64_t i) const { + return static_cast( + float_functor.Compute(static_cast(x), static_cast(dy), i)); + } +}; +#endif + template __global__ void FusedBiasAddGpu(FUNCTOR functor, const Index elem_cnt, const Index bias_size, const Index inner_size, const T* x, const T* bias, T* y) { @@ -191,8 +231,13 @@ __global__ void FusedBiasAddGradRowGpuHalf2(FUNCTOR grad_functor, const Index el half2 x_i = __hadd2(x_h2[i], bias_h2[i % h2_bias_size]); half2 dy_i = dy_h2[i]; half2 dx_i; +#ifdef WITH_ROCM + dx_i.data.x = grad_functor.Compute(x_i.data.x, dy_i.data.x, 2 * i); + dx_i.data.y = grad_functor.Compute(x_i.data.y, dy_i.data.y, 2 * i + 1); +#else dx_i.x = grad_functor.Compute(x_i.x, dy_i.x, 2 * i); dx_i.y = grad_functor.Compute(x_i.y, dy_i.y, 2 * i + 1); +#endif dx_h2[i] = dx_i; } } @@ -361,6 +406,9 @@ class FusedFusedBiasAddKernel final : public user_op::OpKernel { REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(float) REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(double) REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(nv_bfloat16) +#endif template class FusedBiasAddMaskScaleKernel final : public user_op::OpKernel { @@ -408,6 +456,9 @@ class FusedBiasAddMaskScaleKernel final : public user_op::OpKernel { REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(float) REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(double) REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(nv_bfloat16) +#endif template class FusedFusedBiasAddGradKernel final : public user_op::OpKernel { @@ -451,5 +502,8 @@ class FusedFusedBiasAddGradKernel final : public user_op::OpKernel { REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(float) REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(double) REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(nv_bfloat16) +#endif } // namespace oneflow diff --git a/oneflow/user/kernels/fused_bias_add_kernel.hip.cpp b/oneflow/user/kernels/fused_bias_add_kernel.hip.cpp deleted file mode 100644 index 079f3946b279c171e5763b8942afdda20a87e46b..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_bias_add_kernel.hip.cpp +++ /dev/null @@ -1,456 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -struct GeluFunctor { - __device__ T Compute(T x, int64_t i) const { - return static_cast(0.5) * x * (static_cast(1.0) + erf(static_cast(M_SQRT1_2) * x)); - } -}; - -template<> -struct GeluFunctor { - GeluFunctor float_functor; - __device__ half Compute(half x, int64_t i) const { - return __float2half(float_functor.Compute(__half2float(x), i)); - } - __device__ half2 ComputeHalf2(half2 x, int64_t i) const { - half2 y; - y.data.x = __float2half(float_functor.Compute(__half2float(x.data.x), 2 * i)); - y.data.y = __float2half(float_functor.Compute(__half2float(x.data.y), 2 * i + 1)); - return y; - } -}; - -template -struct MaskAndScaleFunctor { - MaskAndScaleFunctor(const bool* mask, float scale) : mask(mask), scale(scale) {} - __device__ T Compute(T x, int64_t i) const { return x * static_cast(mask[i]) * scale; } - const bool* mask; - float scale; -}; - -template<> -struct MaskAndScaleFunctor { - MaskAndScaleFunctor(const bool* mask, float scale) : mask(mask), scale(scale) {} - __device__ half Compute(half x, int64_t i) const { - return x * static_cast(mask[i] * scale); - } - __device__ half2 ComputeHalf2(half2 x, int64_t i) const { - const char2* mask_c2 = reinterpret_cast(mask); - char2 mask_val = mask_c2[i]; - half2 one_or_zero_h2; - half2 h2_scale = __float2half2_rn(scale); - one_or_zero_h2.data.x = mask_val.x; - one_or_zero_h2.data.y = mask_val.y; - return __hmul2(__hmul2(x, one_or_zero_h2), h2_scale); - } - const bool* mask; - float scale; -}; - -template -struct MaskAndScaleAddFunctor { - MaskAndScaleAddFunctor(const bool* mask, const T* addend, float scale) - : mask(mask), addend(addend), scale(scale) {} - __device__ T Compute(T x, int64_t i) const { - return x * static_cast(mask[i]) * scale + addend[i]; - } - const bool* mask; - const T* addend; - float scale; -}; - -template<> -struct MaskAndScaleAddFunctor { - MaskAndScaleAddFunctor(const bool* mask, const half* addend, float scale) - : mask(mask), addend(addend), scale(scale) {} - __device__ half Compute(half x, int64_t i) const { - return x * static_cast(mask[i] * scale) + addend[i]; - } - __device__ half2 ComputeHalf2(half2 x, int64_t i) const { - const char2* mask_c2 = reinterpret_cast(mask); - const half2* addend_h2 = reinterpret_cast(addend); - char2 mask_val = mask_c2[i]; - half2 one_or_zero_h2; - half2 h2_scale = __float2half2_rn(scale); - one_or_zero_h2.data.x = mask_val.x; - one_or_zero_h2.data.y = mask_val.y; - return __hadd2(__hmul2(__hmul2(x, one_or_zero_h2), h2_scale), addend_h2[i]); - } - const bool* mask; - const half* addend; - float scale; -}; - -template -struct GeluGradFunctor { - const T coef = std::sqrt(static_cast(2.0) / std::acos(static_cast(-1.0))); - __device__ T Compute(T x, T dy, int64_t i) const { - return static_cast(0.5) - * (static_cast(1.0) + erf(static_cast(M_SQRT1_2) * x) - + x * coef * exp(static_cast(-0.5) * x * x)) - * dy; - } -}; - -template<> -struct GeluGradFunctor { - GeluGradFunctor float_functor; - __device__ half Compute(half x, half dy, int64_t i) const { - return __float2half(float_functor.Compute(__half2float(x), __half2float(dy), i)); - } -}; - -template -__global__ void FusedBiasAddGpu(FUNCTOR functor, const Index elem_cnt, const Index bias_size, - const Index inner_size, const T* x, const T* bias, T* y) { - const Index block_size = bias_size * inner_size; - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[(i % block_size) / inner_size]; - y[i] = functor.Compute(x_i, i); - } -} - -template -__global__ void FusedBiasAddGradGpu(FUNCTOR grad_functor, const Index elem_cnt, - const Index bias_size, const Index inner_size, const T* x, - const T* bias, const T* dy, T* dx) { - const Index block_size = bias_size * inner_size; - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[(i % block_size) / inner_size]; - dx[i] = grad_functor.Compute(x_i, dy[i], i); - } -} - -template -__global__ void FusedBiasAddRowGpu(FUNCTOR functor, const Index elem_cnt, const Index bias_size, - const T* x, const T* bias, T* y) { - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[i % bias_size]; - y[i] = functor.Compute(x_i, i); - } -} - -template -__global__ void FusedBiasAddGradRowGpu(FUNCTOR grad_functor, const Index elem_cnt, - const Index bias_size, const T* x, const T* bias, - const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[i % bias_size]; - dx[i] = grad_functor.Compute(x_i, dy[i], i); - } -} - -template -__global__ void FusedBiasAddRowGpuHalf2(FUNCTOR functor, const Index elem_cnt, - const Index bias_size, const half* x, const half* bias, - half* y) { - const Index h2_elem_cnt = elem_cnt / 2; - const Index h2_bias_size = bias_size / 2; - const auto* x_h2 = reinterpret_cast(x); - const auto* bias_h2 = reinterpret_cast(bias); - auto* y_h2 = reinterpret_cast(y); - CUDA_1D_KERNEL_LOOP_T(Index, i, h2_elem_cnt) { - half2 x_i = __hadd2(x_h2[i], bias_h2[i % h2_bias_size]); - y_h2[i] = functor.ComputeHalf2(x_i, i); - } -} - -template -__global__ void FusedBiasAddGradRowGpuHalf2(FUNCTOR grad_functor, const Index elem_cnt, - const Index bias_size, const half* x, const half* bias, - const half* dy, half* dx) { - const Index h2_elem_cnt = elem_cnt / 2; - const Index h2_bias_size = bias_size / 2; - const auto* x_h2 = reinterpret_cast(x); - const auto* bias_h2 = reinterpret_cast(bias); - const auto* dy_h2 = reinterpret_cast(dy); - auto* dx_h2 = reinterpret_cast(dx); - CUDA_1D_KERNEL_LOOP_T(Index, i, h2_elem_cnt) { - half2 x_i = __hadd2(x_h2[i], bias_h2[i % h2_bias_size]); - half2 dy_i = dy_h2[i]; - half2 dx_i; - dx_i.data.x = grad_functor.Compute(x_i.data.x, dy_i.data.x, 2 * i); - dx_i.data.y = grad_functor.Compute(x_i.data.y, dy_i.data.y, 2 * i + 1); - dx_h2[i] = dx_i; - } -} - -template -__global__ void FusedBiasAddColGpu(FUNCTOR functor, const Index elem_cnt, const Index inner_size, - const T* x, const T* bias, T* y) { - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[i / inner_size]; - y[i] = functor.Compute(x_i, i); - } -} - -template -__global__ void FusedBiasAddGradColGpu(FUNCTOR grad_functor, const Index elem_cnt, - const Index inner_size, const T* x, const T* bias, - const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(Index, i, elem_cnt) { - T x_i = x[i] + bias[i / inner_size]; - dx[i] = grad_functor.Compute(x_i, dy[i], i); - } -} - -template -struct FusedBiasAddRow { - static void Invoke(ep::Stream* stream, FUNCTOR functor, Index elem_cnt, Index bias_size, - const T* x, const T* bias, T* y) { - FusedBiasAddRowGpu - <<As()->cuda_stream()>>>(functor, elem_cnt, bias_size, x, bias, y); - } -}; - -template -struct FusedBiasAddRow { - static void Invoke(ep::Stream* stream, FUNCTOR functor, Index elem_cnt, Index bias_size, - const half* x, const half* bias, half* y) { - if (bias_size % 2 == 0) { - FusedBiasAddRowGpuHalf2 - <<As()->cuda_stream()>>>(functor, elem_cnt, bias_size, x, bias, - y); - } else { - FusedBiasAddRowGpu - <<As()->cuda_stream()>>>(functor, elem_cnt, bias_size, x, bias, - y); - } - } -}; - -template -void FusedBiasAddForwardImpl(ep::Stream* stream, FUNCTOR functor, Index outer_size, Index bias_size, - Index inner_size, const T* x, const T* bias, T* y) { - const Index elem_cnt = outer_size * bias_size * inner_size; - if (inner_size == 1) { - FusedBiasAddRow::Invoke(stream, functor, elem_cnt, bias_size, x, bias, y); - } else if (outer_size == 1) { - FusedBiasAddColGpu<<As()->cuda_stream()>>>( - functor, elem_cnt, inner_size, x, bias, y); - } else { - FusedBiasAddGpu<<As()->cuda_stream()>>>( - functor, elem_cnt, bias_size, inner_size, x, bias, y); - } -} - -template -struct FusedBiasAddGradRow { - static void Invoke(ep::Stream* stream, FUNCTOR grad_functor, Index elem_cnt, Index bias_size, - const T* x, const T* bias, const T* dy, T* dx) { - FusedBiasAddGradRowGpu - <<As()->cuda_stream()>>>(grad_functor, elem_cnt, bias_size, x, - bias, dy, dx); - } -}; - -template -struct FusedBiasAddGradRow { - static void Invoke(ep::Stream* stream, FUNCTOR grad_functor, Index elem_cnt, Index bias_size, - const half* x, const half* bias, const half* dy, half* dx) { - if (bias_size % 2 == 0) { - FusedBiasAddGradRowGpuHalf2 - <<As()->cuda_stream()>>>(grad_functor, elem_cnt, bias_size, x, - bias, dy, dx); - } else { - FusedBiasAddGradRowGpu - <<As()->cuda_stream()>>>(grad_functor, elem_cnt, bias_size, x, - bias, dy, dx); - } - } -}; - -template -void FusedBiasAddGradImpl(ep::Stream* stream, FUNCTOR grad_functor, Index outer_size, - Index bias_size, Index inner_size, const T* x, const T* bias, const T* dy, - T* dx) { - const Index elem_cnt = outer_size * bias_size * inner_size; - if (inner_size == 1) { - FusedBiasAddGradRow::Invoke(stream, grad_functor, elem_cnt, bias_size, x, - bias, dy, dx); - } else if (outer_size == 1) { - FusedBiasAddGradColGpu - <<As()->cuda_stream()>>>(grad_functor, elem_cnt, inner_size, x, - bias, dy, dx); - } else { - FusedBiasAddGradGpu - <<As()->cuda_stream()>>>(grad_functor, elem_cnt, bias_size, - inner_size, x, bias, dy, dx); - } -} - -template -void DispatchFusedBiasAddForwardImpl(ep::Stream* stream, FUNCTOR functor, int64_t n, - int64_t outer_size, int64_t bias_size, int64_t inner_size, - const T* x, const T* bias, T* y) { - if (IsKernelSafeInt32(n)) { - FusedBiasAddForwardImpl(stream, functor, outer_size, bias_size, inner_size, - x, bias, y); - } else { - FusedBiasAddForwardImpl(stream, functor, outer_size, bias_size, inner_size, - x, bias, y); - } -} - -} // namespace - -template -class FusedFusedBiasAddKernel final : public user_op::OpKernel { - public: - FusedFusedBiasAddKernel() = default; - ~FusedFusedBiasAddKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* a_tensor = ctx->Tensor4ArgNameAndIndex("a", 0); - const auto* b_tensor = ctx->Tensor4ArgNameAndIndex("b", 0); - auto* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); - const int32_t bias_add_axis = ctx->Attr("axis"); - const int64_t outer_size = a_tensor->shape_view().Count(0, bias_add_axis); - const int64_t bias_size = a_tensor->shape_view().At(bias_add_axis); - const int64_t inner_size = a_tensor->shape_view().Count(bias_add_axis + 1); - const auto n = a_tensor->shape_view().elem_cnt(); - GeluFunctor gelu_functor{}; - DispatchFusedBiasAddForwardImpl( - ctx->stream(), gelu_functor, n, outer_size, bias_size, inner_size, a_tensor->dptr(), - b_tensor->dptr(), out_tensor->mut_dptr()); - }; - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_bias_add_gelu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(float) -REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(double) -REGISTER_FUSED_BIAS_ADD_GELU_KERNEL(half) - -template -class FusedBiasAddMaskScaleKernel final : public user_op::OpKernel { - public: - FusedBiasAddMaskScaleKernel() = default; - ~FusedBiasAddMaskScaleKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* a_tensor = ctx->Tensor4ArgNameAndIndex("a", 0); - const auto* b_tensor = ctx->Tensor4ArgNameAndIndex("b", 0); - const auto* mask_tensor = ctx->Tensor4ArgNameAndIndex("mask", 0); - auto* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); - const int32_t bias_add_axis = ctx->Attr("axis"); - const float scale = ctx->Attr("scale"); - const int64_t outer_size = a_tensor->shape_view().Count(0, bias_add_axis); - const int64_t bias_size = a_tensor->shape_view().At(bias_add_axis); - const int64_t inner_size = a_tensor->shape_view().Count(bias_add_axis + 1); - const auto n = a_tensor->shape_view().elem_cnt(); - if (ctx->has_input("_add_to_output", 0)) { - const user_op::Tensor* addend = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); - MaskAndScaleAddFunctor mask_and_scale_add_functor(mask_tensor->dptr(), - addend->dptr(), scale); - DispatchFusedBiasAddForwardImpl( - ctx->stream(), mask_and_scale_add_functor, n, outer_size, bias_size, inner_size, - a_tensor->dptr(), b_tensor->dptr(), out_tensor->mut_dptr()); - } else { - MaskAndScaleFunctor mask_and_scale_functor(mask_tensor->dptr(), scale); - DispatchFusedBiasAddForwardImpl( - ctx->stream(), mask_and_scale_functor, n, outer_size, bias_size, inner_size, - a_tensor->dptr(), b_tensor->dptr(), out_tensor->mut_dptr()); - } - }; - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_bias_add_mask_scale") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(float) -REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(double) -REGISTER_FUSED_BIAS_ADD_MASK_SCALE_KERNEL(half) - -template -class FusedFusedBiasAddGradKernel final : public user_op::OpKernel { - public: - FusedFusedBiasAddGradKernel() = default; - ~FusedFusedBiasAddGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* a_tensor = ctx->Tensor4ArgNameAndIndex("a", 0); - const auto* b_tensor = ctx->Tensor4ArgNameAndIndex("b", 0); - const auto* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - auto* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const int32_t bias_add_axis = ctx->Attr("axis"); - const int64_t outer_size = a_tensor->shape_view().Count(0, bias_add_axis); - const int64_t bias_size = a_tensor->shape_view().At(bias_add_axis); - const int64_t inner_size = a_tensor->shape_view().Count(bias_add_axis + 1); - const auto n = a_tensor->shape_view().elem_cnt(); - GeluGradFunctor gelu_grad_functor; - if (IsKernelSafeInt32(n)) { - FusedBiasAddGradImpl( - ctx->stream(), gelu_grad_functor, outer_size, bias_size, inner_size, a_tensor->dptr(), - b_tensor->dptr(), dy_tensor->dptr(), dx_tensor->mut_dptr()); - } else { - FusedBiasAddGradImpl( - ctx->stream(), gelu_grad_functor, outer_size, bias_size, inner_size, a_tensor->dptr(), - b_tensor->dptr(), dy_tensor->dptr(), dx_tensor->mut_dptr()); - } - }; - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_bias_add_gelu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(float) -REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(double) -REGISTER_FUSED_BIAS_ADD_GELU_GRAD_KERNEL(half) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_bias_add_scale_mask_softmax_dropout.cu b/oneflow/user/kernels/fused_bias_add_scale_mask_softmax_dropout.cu new file mode 100644 index 0000000000000000000000000000000000000000..3c5559282571e79c486bfbc209059b49f7cb72a8 --- /dev/null +++ b/oneflow/user/kernels/fused_bias_add_scale_mask_softmax_dropout.cu @@ -0,0 +1,243 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/cuda/softmax.cuh" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/user/kernels/fused_softmax.cuh" + +namespace oneflow { +namespace cuda { + +namespace { + +template +struct BroadcastMapper { + using index_type = IndexType; + IndexType src_dims[NDIM] = {0}; + IndexType dst_dims[NDIM] = {0}; + + template + BroadcastMapper(const DimType* arg_src_dims, const DimType* arg_dst_dims) { + for (size_t i = 0; i < NDIM; ++i) { src_dims[i] = arg_src_dims[i]; } + for (size_t i = 0; i < NDIM; ++i) { dst_dims[i] = arg_dst_dims[i]; } + } + + __device__ IndexType map(IndexType src) const { + NdIndexOffsetHelper src_index_helper(src_dims); + NdIndexOffsetHelper dst_index_helper(dst_dims); + IndexType src_index[NDIM]; + IndexType dst_index[NDIM]; + src_index_helper.OffsetToNdIndex(src, src_index); +#pragma unroll + for (int dim = 0; dim < NDIM; ++dim) { + if (dst_dims[dim] == 1) { + dst_index[dim] = 0; + } else { + dst_index[dim] = src_index[dim]; + } + } + return dst_index_helper.NdIndexToOffset(dst_index); + } +}; + +template +struct ElementwiseMapper { + using index_type = IndexType; + ElementwiseMapper() {} + __device__ IndexType map(IndexType index) const { return index; } +}; + +template +struct BiasAddScaleMaskLoad { + static_assert( + std::is_same::value, ""); + using IndexType = typename BiasMapper::index_type; + const SRC* src; + const SRC* bias; + const MASK* mask; + const DST fill; + const DST scale; + const IndexType row_size; + const BiasMapper bias_mapper; + const MaskMapper mask_mapper; + + BiasAddScaleMaskLoad(const SRC* src, const SRC* bias, const MASK* mask, const DST fill, + const DST scale, const IndexType row_size, const BiasMapper bias_mapper, + const MaskMapper mask_mapper) + : src(src), + bias(bias), + mask(mask), + fill(fill), + scale(scale), + row_size(row_size), + bias_mapper(bias_mapper), + mask_mapper(mask_mapper) {} + + template + __device__ void load(DST* dst, IndexType row, IndexType col) { + softmax::Pack src_pack; + softmax::Pack bias_pack; + softmax::Pack mask_pack; + const IndexType offset = row * row_size + col; + const IndexType bias_offset = bias_mapper.map(offset); + const IndexType mask_offset = mask_mapper.map(offset); + src_pack.storage = *(reinterpret_cast*>(src) + offset / N); + bias_pack.storage = + *(reinterpret_cast*>(bias) + bias_offset / N); + mask_pack.storage = + *(reinterpret_cast*>(mask) + mask_offset / N); +#pragma unroll + for (int i = 0; i < N; ++i) { + if (mask_pack.elem[i] == 0) { + dst[i] = fill; + } else { + dst[i] = static_cast(src_pack.elem[i] + bias_pack.elem[i]) * scale; + } + } + } +}; + +template +void DispatchForward(GPU(Stream_t) stream, const user_op::Tensor* x, const user_op::Tensor* bias, + const user_op::Tensor* mask, const user_op::Tensor* dropout_mask, + const float mask_fill, const float scale, const float dropout_scale, + user_op::Tensor* y, user_op::Tensor* softmax_y) { + using ComputeType = typename softmax::DefaultComputeType::type; + using IndexType = int32_t; + constexpr int kMaxNDim = 5; + + const auto& x_shape = x->shape_view(); + CHECK_GE(x_shape.size(), 2); + // the last dim is softmax dim which is considered as col + int64_t ncol = x_shape[x_shape.size() - 1]; + int64_t nrow = x_shape.elem_cnt() / ncol; + fused_softmax::DropoutStore store( + y->mut_dptr(), softmax_y->mut_dptr(), dropout_mask->dptr(), ncol, dropout_scale); + + size_t bias_sndim = 0; + int64_t bias_x_sdims[kMaxNDim]; + int64_t bias_sdims[kMaxNDim]; + const auto& bias_shape = bias->shape_view(); + fused_softmax::SimplifyBroadcastDims(x_shape.size(), x_shape.ptr(), bias_shape.size(), + bias_shape.ptr(), &bias_sndim, bias_x_sdims, bias_sdims); + size_t mask_sndim = 0; + int64_t mask_x_sdims[kMaxNDim]; + int64_t mask_sdims[kMaxNDim]; + const auto& mask_shape = mask->shape_view(); + fused_softmax::SimplifyBroadcastDims(x_shape.size(), x_shape.ptr(), mask_shape.size(), + mask_shape.ptr(), &mask_sndim, mask_x_sdims, mask_sdims); + +#define DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper) \ + BiasAddScaleMaskLoad load( \ + x->dptr(), bias->dptr(), mask->dptr(), mask_fill, scale, ncol, bias_mapper, \ + mask_mapper); \ + OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( \ + stream, load, store, nrow, ncol))) + + if (bias_sndim == 1 && mask_sndim == 1) { + // bias elementwise + // mask elementwise + ElementwiseMapper bias_mapper; + ElementwiseMapper mask_mapper; + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + } else if (bias_sndim == 1 && mask_sndim == 2) { + // bias elementwise + // mask broadcast: (M, 1) -> (M, N) or (1, N) -> (M, N) + ElementwiseMapper bias_mapper; + BroadcastMapper mask_mapper(mask_x_sdims, mask_sdims); + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + } else if (bias_sndim == 1 && mask_sndim == 3) { + // bias elementwise + // mask broadcast: (M, 1, N) -> (M, K, N) + ElementwiseMapper bias_mapper; + BroadcastMapper mask_mapper(mask_x_sdims, mask_sdims); + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + } else if (bias_sndim == 2 && mask_sndim == 1) { + // bias broadcast: (M, 1) -> (M, N) or (1, N) -> (M, N) + // mask elementwise + BroadcastMapper bias_mapper(bias_x_sdims, bias_sdims); + ElementwiseMapper mask_mapper; + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + } else if (bias_sndim == 2 && mask_sndim == 2) { + // bias broadcast: (M, 1) -> (M, N) or (1, N) -> (M, N) + // mask broadcast: (M, 1) -> (M, N) or (1, N) -> (M, N) + BroadcastMapper bias_mapper(bias_x_sdims, bias_sdims); + BroadcastMapper mask_mapper(mask_x_sdims, mask_sdims); + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + } else if (bias_sndim == 2 && mask_sndim == 3) { + // bias broadcast: (M, 1) -> (M, N) or (1, N) -> (M, N) + // mask broadcast: (M, 1, N) -> (M, K, N) + BroadcastMapper bias_mapper(bias_x_sdims, bias_sdims); + BroadcastMapper mask_mapper(mask_x_sdims, mask_sdims); + DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX(bias_mapper, mask_mapper); + // not support for now + // } else if (bias_sndim == 3 && mask_sndim == 1) { + // } else if (bias_sndim == 3 && mask_sndim == 2) { + // } else if (bias_sndim == 3 && mask_sndim == 3) { + } else { + UNIMPLEMENTED() << ", bias_sndim=" << bias_sndim << ", mask_sndim=" << mask_sndim; + } + +#undef DISPATCH_BIAS_ADD_SCALE_MASK_SOFTMAX +} + +template +class FusedBiasAddScaleMaskSoftmaxDropoutKernel final : public user_op::OpKernel { + public: + FusedBiasAddScaleMaskSoftmaxDropoutKernel() = default; + ~FusedBiasAddScaleMaskSoftmaxDropoutKernel() override = default; + + private: + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); + const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); + const user_op::Tensor* dropout_mask = ctx->Tensor4ArgNameAndIndex("dropout_mask", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* softmax_y = ctx->Tensor4ArgNameAndIndex("softmax_y", 0); + + const float mask_fill = ctx->Attr("mask_fill_value"); + const float scale = ctx->Attr("scale_value"); + const float dropout_scale = ctx->Attr("dropout_scale_value"); + + const ShapeView& x_shape = x->shape_view(); + // int32 index computing is much faster than int64 + // TODO: consider using multiple int32 computing to substitute int64 computing + CHECK_LT(x_shape.elem_cnt(), INT_MAX) << "only support int32 max limits size of elements"; + DispatchForward(ctx->stream()->As()->cuda_stream(), x, bias, mask, + dropout_mask, mask_fill, scale, dropout_scale, y, softmax_y); + } +}; + +} // namespace + +#define REGISTER_FUSED_BIAS_ADD_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(dtype, mask_dtype) \ + REGISTER_USER_KERNEL("fused_bias_add_scale_mask_softmax_dropout") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value) \ + && (user_op::HobDataType("mask", 0) == GetDataType::value)); + +REGISTER_FUSED_BIAS_ADD_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(float, bool) +REGISTER_FUSED_BIAS_ADD_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(half, bool) + +#undef REGISTER_FUSED_BIAS_ADD_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL + +} // namespace cuda +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_cast_scale_kernel.cu b/oneflow/user/kernels/fused_cast_scale_kernel.cu index 77502a78af8b0b148296f2e452976aa5ec42e39e..9648d56d7d6f029054c5086f54548414610e0ac0 100644 --- a/oneflow/user/kernels/fused_cast_scale_kernel.cu +++ b/oneflow/user/kernels/fused_cast_scale_kernel.cu @@ -17,6 +17,16 @@ limitations under the License. #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include +#include +#else +#include +#endif +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 +#include "oneflow/core/device/cuda_pseudo_bfloat16.h" namespace oneflow { @@ -66,6 +76,46 @@ __global__ void FusedCastScaleGpu(const int64_t n, const half scale } } +#if CUDA_VERSION >= 11000 && __CUDA_ARCH__ >= 800 +template<> +__global__ void FusedCastScaleGpu(const int64_t n, const float scale_val, + const nv_bfloat16* in, + const float* scale_by_ptr, float* out) { + const float scale = *scale_by_ptr * scale_val; + const int64_t n_2 = n / 2; + const auto* in_2 = reinterpret_cast(in); + auto* out_2 = reinterpret_cast(out); + CUDA_1D_KERNEL_LOOP(i, n_2) { + float2 f2 = __bfloat1622float2(in_2[i]); + f2.x *= scale; + f2.y *= scale; + out_2[i] = f2; + } + if (n % 2 == 1 && blockIdx.x == 0 && threadIdx.x == 0) { + out[n - 1] = __bfloat162float(in[n - 1]) * scale; + } +} + +template<> +__global__ void FusedCastScaleGpu(const int64_t n, const nv_bfloat16 scale_val, + const float* in, + const nv_bfloat16* scale_by_ptr, + nv_bfloat16* out) { + const nv_bfloat16 scale = *scale_by_ptr * scale_val; + const nv_bfloat162 scale_h2 = __bfloat162bfloat162(scale); + const int64_t n_2 = n / 2; + const auto* in_2 = reinterpret_cast(in); + auto* out_h2 = reinterpret_cast(out); + CUDA_1D_KERNEL_LOOP(i, n_2) { + nv_bfloat162 in_h2 = __float22bfloat162_rn(in_2[i]); + out_h2[i] = __hmul2(in_h2, scale_h2); + } + if (n % 2 == 1 && blockIdx.x == 0 && threadIdx.x == 0) { + out[n - 1] = __float2bfloat16(in[n - 1]) * scale; + } +} +#endif + template class FusedCastScaleGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -80,10 +130,12 @@ class FusedCastScaleGpuKernel final : public user_op::OpKernel, public user_op:: user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); const int64_t n = x->shape_view().elem_cnt(); const double scale = ctx->Attr("scale"); - const int64_t launch_n = ((std::is_same::value && std::is_same::value) - || (std::is_same::value && std::is_same::value)) - ? RoundUp(n, 2) / 2 - : n; + const bool use_pack = + (x->data_type() == DataType::kFloat + && (y->data_type() == DataType::kFloat16 || y->data_type() == DataType::kBFloat16)) + || (y->data_type() == DataType::kFloat + && (x->data_type() == DataType::kFloat16 || x->data_type() == DataType::kBFloat16)); + const int64_t launch_n = use_pack ? RoundUp(n, 2) / 2 : n; FusedCastScaleGpu<<stream()->As()->cuda_stream()>>>( n, static_cast(scale), x->dptr(), scale_by_tensor->dptr(), y->mut_dptr()); @@ -106,6 +158,10 @@ REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(float, half); REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(float, double); REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(double, half); REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(double, float); +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(nv_bfloat16, float); +REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(float, nv_bfloat16); +#endif #undef REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL } // namespace oneflow diff --git a/oneflow/user/kernels/fused_cast_scale_kernel.hip.cpp b/oneflow/user/kernels/fused_cast_scale_kernel.hip.cpp deleted file mode 100644 index 5e898fdfd11fd501ad959b4eebd936876bd73cb2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_cast_scale_kernel.hip.cpp +++ /dev/null @@ -1,112 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void FusedCastScaleGpu(const int64_t n, const T scale_val, const U* in, - const T* scale_by_ptr, T* out) { - const T scale = *scale_by_ptr * scale_val; - CUDA_1D_KERNEL_LOOP(i, n) { out[i] = static_cast(in[i]) * scale; } -} - -template<> -__global__ void FusedCastScaleGpu(const int64_t n, const float scale_val, - const half* in, const float* scale_by_ptr, - float* out) { - const float scale = *scale_by_ptr * scale_val; - const int64_t n_2 = n / 2; - const auto* in_2 = reinterpret_cast(in); - auto* out_2 = reinterpret_cast(out); - CUDA_1D_KERNEL_LOOP(i, n_2) { - float2 f2 = __half22float2(in_2[i]); - f2.x *= scale; - f2.y *= scale; - out_2[i] = f2; - } - if (n % 2 == 1 && blockIdx.x == 0 && threadIdx.x == 0) { - out[n - 1] = __half2float(in[n - 1]) * scale; - } -} - -template<> -__global__ void FusedCastScaleGpu(const int64_t n, const half scale_val, - const float* in, const half* scale_by_ptr, - half* out) { - const half scale = *scale_by_ptr * scale_val; - const half2 scale_h2 = __half2half2(scale); - const int64_t n_2 = n / 2; - const auto* in_2 = reinterpret_cast(in); - auto* out_h2 = reinterpret_cast(out); - CUDA_1D_KERNEL_LOOP(i, n_2) { - half2 in_h2 = __float22half2_rn(in_2[i]); - out_h2[i] = __hmul2(in_h2, scale_h2); - } - if (n % 2 == 1 && blockIdx.x == 0 && threadIdx.x == 0) { - out[n - 1] = __float2half(in[n - 1]) * scale; - } -} - -template -class FusedCastScaleGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - FusedCastScaleGpuKernel() = default; - ~FusedCastScaleGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int64_t n = x->shape_view().elem_cnt(); - const double scale = ctx->Attr("scale"); - const int64_t launch_n = ((std::is_same::value && std::is_same::value) - || (std::is_same::value && std::is_same::value)) - ? RoundUp(n, 2) / 2 - : n; - FusedCastScaleGpu<<stream()->As()->cuda_stream()>>>( - n, static_cast(scale), x->dptr(), scale_by_tensor->dptr(), y->mut_dptr()); - }; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace - -#define REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(x_type, y_type) \ - REGISTER_USER_KERNEL("fused_cast_scale") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(half, float); -// REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(half, double); -REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(float, half); -REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(float, double); -// REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(double, half); -REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL(double, float); -#undef REGISTER_FUSED_CAST_SCALE_CUDA_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_center_kernel.cu b/oneflow/user/kernels/fused_center_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..7a93bbaf8b8437e6e11bbd86fdcb44a757f5941e --- /dev/null +++ b/oneflow/user/kernels/fused_center_kernel.cu @@ -0,0 +1,168 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { + +template +struct FusedCenterForwardFunctor { + __device__ T Compute(T b_x_delta, T b_y_delta) const { + return (b_x_delta * b_x_delta + b_y_delta * b_y_delta) / static_cast(4.0); + } +}; + +template<> +struct FusedCenterForwardFunctor { + FusedCenterForwardFunctor float_functor; + __device__ half Compute(half b_x_delta, half b_y_delta) const { + return __float2half(float_functor.Compute(__half2float(b_x_delta), __half2float(b_y_delta))); + } +}; + +template +__global__ void FusedCenterForward(FUNCTOR functor, const int n, const T* b1_x1, const T* b1_x2, + const T* b2_x1, const T* b2_x2, const T* b1_y1, const T* b1_y2, + const T* b2_y1, const T* b2_y2, T* rho) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T b_x_delta = (b2_x1[i] + b2_x2[i] - b1_x1[i] - b1_x2[i]); + const T b_y_delta = (b2_y1[i] + b2_y2[i] - b1_y1[i] - b1_y2[i]); + rho[i] = functor.Compute(b_x_delta, b_y_delta); + } +} + +template +__global__ void FusedCenterBackward(const int n, const T* b1_x1, const T* b1_x2, const T* b2_x1, + const T* b2_x2, const T* b1_y1, const T* b1_y2, const T* b2_y1, + const T* b2_y2, const T* rho2_diff, T* b1_x1_diff, + T* b1_x2_diff, T* b2_x1_diff, T* b2_x2_diff, T* b1_y1_diff, + T* b1_y2_diff, T* b2_y1_diff, T* b2_y2_diff) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T rho2_diff_i_2 = rho2_diff[i] / static_cast(2.0); + + const T b_x_diff = rho2_diff_i_2 * (b1_x1[i] + b1_x2[i] - b2_x1[i] - b2_x2[i]); + const T b_y_diff = rho2_diff_i_2 * (b1_y1[i] + b1_y2[i] - b2_y1[i] - b2_y2[i]); + + b1_x1_diff[i] = b_x_diff; + b1_x2_diff[i] = b_x_diff; + b2_x1_diff[i] = b_x_diff * static_cast(-1.0); + b2_x2_diff[i] = b_x_diff * static_cast(-1.0); + + b1_y1_diff[i] = b_y_diff; + b1_y2_diff[i] = b_y_diff; + b2_y1_diff[i] = b_y_diff * static_cast(-1.0); + b2_y2_diff[i] = b_y_diff * static_cast(-1.0); + } +} + +} // namespace + +template +class FusedCenterKernel final : public user_op::OpKernel { + public: + FusedCenterKernel() = default; + ~FusedCenterKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + user_op::Tensor* rho = ctx->Tensor4ArgNameAndIndex("rho2", 0); + + const int64_t elem_cnt = b1_x1->shape_view().elem_cnt(); + + FusedCenterForwardFunctor fused_center_forward_functor{}; + + RUN_CUDA_KERNEL((FusedCenterForward), ctx->stream(), + elem_cnt, fused_center_forward_functor, elem_cnt, b1_x1->dptr(), + b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), b1_y1->dptr(), + b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), rho->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CENTER_DIST_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_center_dist") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("rho2", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CENTER_DIST_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CENTER_DIST_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CENTER_DIST_CUDA_KERNEL(half) + +template +class FusedCenterGradKernel final : public user_op::OpKernel { + public: + FusedCenterGradKernel() = default; + ~FusedCenterGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + const user_op::Tensor* rho2_diff = ctx->Tensor4ArgNameAndIndex("rho2_diff", 0); + + user_op::Tensor* b1_x1_diff = ctx->Tensor4ArgNameAndIndex("b1_x1_diff", 0); + user_op::Tensor* b1_x2_diff = ctx->Tensor4ArgNameAndIndex("b1_x2_diff", 0); + user_op::Tensor* b2_x1_diff = ctx->Tensor4ArgNameAndIndex("b2_x1_diff", 0); + user_op::Tensor* b2_x2_diff = ctx->Tensor4ArgNameAndIndex("b2_x2_diff", 0); + user_op::Tensor* b1_y1_diff = ctx->Tensor4ArgNameAndIndex("b1_y1_diff", 0); + user_op::Tensor* b1_y2_diff = ctx->Tensor4ArgNameAndIndex("b1_y2_diff", 0); + user_op::Tensor* b2_y1_diff = ctx->Tensor4ArgNameAndIndex("b2_y1_diff", 0); + user_op::Tensor* b2_y2_diff = ctx->Tensor4ArgNameAndIndex("b2_y2_diff", 0); + + const int64_t elem_cnt = b1_x1_diff->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((FusedCenterBackward), ctx->stream(), elem_cnt, elem_cnt, b1_x1->dptr(), + b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), b1_y1->dptr(), + b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), rho2_diff->dptr(), + b1_x1_diff->mut_dptr(), b1_x2_diff->mut_dptr(), b2_x1_diff->mut_dptr(), + b2_x2_diff->mut_dptr(), b1_y1_diff->mut_dptr(), b1_y2_diff->mut_dptr(), + b2_y1_diff->mut_dptr(), b2_y2_diff->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CENTER_DIST_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_center_dist_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CENTER_DIST_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CENTER_DIST_GRAD_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CENTER_DIST_GRAD_CUDA_KERNEL(half) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_cross_feature_interaction.cu b/oneflow/user/kernels/fused_cross_feature_interaction.cu index ac54a09a86380577923652f688e424f59e07b898..7a42f0754941436c329452985ff5958732b5e72a 100644 --- a/oneflow/user/kernels/fused_cross_feature_interaction.cu +++ b/oneflow/user/kernels/fused_cross_feature_interaction.cu @@ -110,7 +110,7 @@ __global__ void FusedBiasAddMulAddResidualKernel(const T* in, const T* x, const out_store.elem[i] = (in_vec.elem[i] + bias_vec.elem[i]) * x0_vec.elem[i] + x_vec.elem[i]; } } else { - __trap(); + TRAP(); } *(reinterpret_cast(out + linear_index)) = out_store; } @@ -134,7 +134,7 @@ void DispatchFusedBiasAddMulAddResidualPackSize(ep::Stream* stream, const T* in, int grid_size; const int pack_size = GetLaunchPackSize(cols); const int64_t pack_num = elem_cnt / pack_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); if (pack_size == 8) { FusedBiasAddMulAddResidualKernel <<As()->cuda_stream()>>>( diff --git a/oneflow/user/kernels/fused_cross_feature_interaction.hip.cpp b/oneflow/user/kernels/fused_cross_feature_interaction.hip.cpp deleted file mode 100644 index a2dd5bbe97eaef6e33e5c2e9e79cb363c0dd1dde..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_cross_feature_interaction.hip.cpp +++ /dev/null @@ -1,259 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/primitive/matmul.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -enum InteractionMode { kVector = 0, kMatrix }; - -constexpr int kBlockSize = 256; - -void InferMatmulMNK(const ShapeView& a_shape, const ShapeView& b_shape, bool transpose_a, - bool transpose_b, size_t* m, size_t* n, size_t* k) { - const int64_t num_a_axes = a_shape.NumAxes(); - CHECK_GE(num_a_axes, 2); - const int64_t num_b_axes = b_shape.NumAxes(); - CHECK_GE(num_b_axes, 2); - if (!transpose_a) { - *m = a_shape.At(num_a_axes - 2); - *k = a_shape.At(num_a_axes - 1); - } else { - *m = a_shape.At(num_a_axes - 1); - *k = a_shape.At(num_a_axes - 2); - } - if (!transpose_b) { - CHECK_EQ(b_shape.At(num_b_axes - 2), *k); - *n = b_shape.At(num_b_axes - 1); - } else { - CHECK_EQ(b_shape.At(num_b_axes - 1), *k); - *n = b_shape.At(num_b_axes - 2); - } -} - -ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { - return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; -} - -std::unique_ptr NewMatmulPrimitive(DeviceType device_type, - DataType data_type, bool transpose_a, - bool transpose_b) { - const auto trans_a = GetBlasTransposeType(transpose_a); - const auto trans_b = GetBlasTransposeType(transpose_b); - return ep::primitive::NewPrimitive(device_type, data_type, trans_a, - trans_b); -} - -template -std::unique_ptr NewMatmulPrimitive(Context* ctx) { - const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("x", 0)->data_type(); - return NewMatmulPrimitive(ctx->device_type(), data_type, /*transpose_a=*/false, - /*transpose_b=*/true); -} - -auto MatmulPrimitiveExists() { - return hob::make_custom("MatmulPrimitiveExists", [](const user_op::KernelRegContext& ctx) { - return NewMatmulPrimitive(&ctx).operator bool(); - }); -} - -template -__global__ void FusedBiasAddMulAddResidualKernel(const T* in, const T* x, const T* x0, - const T* bias, T* out, const IndexType cols, - const IndexType elem_cnt) { - const IndexType global_thread_id = blockDim.x * blockIdx.x + threadIdx.x; - using LoadPack = cuda::elementwise::Packed; - for (IndexType linear_index = global_thread_id * pack_size, - step = gridDim.x * blockDim.x * pack_size; - linear_index < elem_cnt; linear_index += step) { - const IndexType row_idx = linear_index / cols; - const IndexType col_idx = linear_index - row_idx * cols; - - const LoadPack* x0_load = reinterpret_cast(x0 + linear_index); - const LoadPack* x_load = reinterpret_cast(x + linear_index); - const LoadPack* bias_load = reinterpret_cast(bias + col_idx); - - LoadPack x0_vec = *x0_load; - LoadPack x_vec = *x_load; - LoadPack bias_vec = *bias_load; - - LoadPack out_store; - if (mode == InteractionMode::kVector) { - T in_val = in[row_idx]; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - out_store.elem[i] = x0_vec.elem[i] * in_val + bias_vec.elem[i] + x_vec.elem[i]; - } - } else if (mode == InteractionMode::kMatrix) { - const LoadPack* in_load = reinterpret_cast(in + linear_index); - LoadPack in_vec = *in_load; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - out_store.elem[i] = (in_vec.elem[i] + bias_vec.elem[i]) * x0_vec.elem[i] + x_vec.elem[i]; - } - } else { - asm volatile("s_trap 0;"); - } - *(reinterpret_cast(out + linear_index)) = out_store; - } -} - -template -int GetLaunchPackSize(const int64_t cols) { - constexpr int type_pack_size = cuda::elementwise::PackSize(); - for (int launch_pack_size = 8; launch_pack_size > 0; launch_pack_size /= 2) { - if (type_pack_size >= launch_pack_size && cols % launch_pack_size == 0) { - return launch_pack_size; - } - } - return 1; -} - -template -void DispatchFusedBiasAddMulAddResidualPackSize(ep::Stream* stream, const T* in, const T* x, - const T* x0, const T* bias, T* out, - const IndexType cols, const IndexType elem_cnt) { - int grid_size; - const int pack_size = GetLaunchPackSize(cols); - const int64_t pack_num = elem_cnt / pack_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (pack_size == 8) { - FusedBiasAddMulAddResidualKernel - <<As()->cuda_stream()>>>( - in, x, x0, bias, out, cols, elem_cnt); - } else if (pack_size == 4) { - FusedBiasAddMulAddResidualKernel - <<As()->cuda_stream()>>>( - in, x, x0, bias, out, cols, elem_cnt); - } else if (pack_size == 2) { - FusedBiasAddMulAddResidualKernel - <<As()->cuda_stream()>>>( - in, x, x0, bias, out, cols, elem_cnt); - } else { - FusedBiasAddMulAddResidualKernel - <<As()->cuda_stream()>>>( - in, x, x0, bias, out, cols, elem_cnt); - } -} - -template -void DispatchFusedBiasAddMulAddResidualIndexType(ep::Stream* stream, const T* in, const T* x, - const T* x0, const T* bias, T* out, - const int64_t cols, const int64_t elem_cnt) { - if (elem_cnt < GetMaxVal()) { - DispatchFusedBiasAddMulAddResidualPackSize(stream, in, x, x0, bias, out, cols, - elem_cnt); - } else { - DispatchFusedBiasAddMulAddResidualPackSize(stream, in, x, x0, bias, out, cols, - elem_cnt); - } -} - -template -class FusedCrossFeatureInteractionKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedCrossFeatureInteractionKernel() = default; - ~FusedCrossFeatureInteractionKernel() override = default; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - /* - Cross Interaction v1: - 1. x matmul weight. matmul_result0 -> (B, E) matmul (1, E) -> (B, 1) - dx = dmatmul_result0 matmul weight - dw = x matmul dmatmul_result0 - - 2. matmul_result0 broadcast_mul x0. matmul_result1 -> (B, 1) broadcast_mul (B, E) -> (B, E) - dmatmul_result0 = reduce_sum(dmatmul_result1 * x0, axis=1) - dx0 = dmatmul_result1 broadcast_mul matmul_result0 - - 3. matmul_result1 broadcast_add bias. matmul_result2 -> (B, E) broadcast_add (1, E) -> (B, E) - dmatmul_result1 = dout - dbias = reduce_sum(dmatmul_result2, axis=0) - - 4. matmul_result2 add x. out -> (B, E) elementwise_add (B, E) -> (B, E) - dmatmul_result2 = dout, dx = dout. - - Cross Interaction Grad: - dw = x matmul dmatmul_result0 - dx0 = dmatmul_result1 broadcast_mul matmul_result0 - dbias = reduce_sum(dmatmul_result2, axis=0) - dx = (dmatmul_result0 matmul weight) + dout. - - Cross Interaction v2: - 1. x matmul weight. matmul_result0 -> (B, E) matmul (E, E) -> (B, E) - - 2. matmul_result0 add bias. matmul_result1 -> (B, E) bias_add (1, E) -> (B, E) - - 3. matmul_result1 multiply x0. matmul_result2 -> (B, E) elementwise_mul (B, E) -> (B, E) - - 4. matmul_result2 add x. out -> (B, E) elementwise_add (B, E) -> (B, E) - - */ - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const user_op::Tensor* x0 = ctx->Tensor4ArgNameAndIndex("x0", 0); - const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* matmul_result = ctx->Tensor4ArgNameAndIndex("matmul_result", 0); - const std::string interaction_mode = ctx->Attr("interaction_mode"); - - CHECK_EQ(out->shape_view().NumAxes(), 2); - size_t m = 0, n = 0, k = 0; - InferMatmulMNK(x->shape_view(), weight->shape_view(), /*trans_a=*/false, /*trans_b=*/true, &m, - &n, &k); - const double alpha = 1.0; - double beta = 0.0; - auto matmul = NewMatmulPrimitive(ctx); - CHECK(matmul); - matmul->Launch(ctx->stream(), m, n, k, alpha, x->dptr(), weight->dptr(), beta, - matmul_result->mut_dptr()); - const int64_t elem_cnt = out->shape_view().elem_cnt(); - const int64_t cols = out->shape_view().At(1); - if (interaction_mode == "vector") { - DispatchFusedBiasAddMulAddResidualIndexType( - ctx->stream(), matmul_result->mut_dptr(), x->dptr(), x0->dptr(), bias->dptr(), - out->mut_dptr(), cols, elem_cnt); - } else { - DispatchFusedBiasAddMulAddResidualIndexType( - ctx->stream(), matmul_result->mut_dptr(), x->dptr(), x0->dptr(), bias->dptr(), - out->mut_dptr(), cols, elem_cnt); - } - } -}; - -#define REGISTER_FUSED_CROSS_FEATURE_INTERACTION_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_cross_feature_interaction") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value) \ - && MatmulPrimitiveExists()); - -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_KERNEL(float) -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_KERNEL(half) - -} // namespace - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_cross_feature_interaction_grad.cu b/oneflow/user/kernels/fused_cross_feature_interaction_grad.cu index 05fd4e1348827396d12f6e377a802214b2adca5a..25f07e926a4cba8d7f438e0812006bc8bbe89017 100644 --- a/oneflow/user/kernels/fused_cross_feature_interaction_grad.cu +++ b/oneflow/user/kernels/fused_cross_feature_interaction_grad.cu @@ -97,7 +97,7 @@ void DispatchBroadcastMulPackSize(ep::Stream* stream, const T* x, const T* y, T* int grid_size; const int pack_size = GetLaunchPackSize(cols); const int64_t pack_num = elem_cnt / pack_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); if (pack_size == 8) { BroadcastMulKernel <<As()->cuda_stream()>>>(x, y, out, cols, @@ -161,7 +161,7 @@ void DispatchBroadcastAddElementwiseMulPackSize(ep::Stream* stream, const T* x, int grid_size; const int pack_size = GetLaunchPackSize(cols); const int64_t pack_num = elem_cnt / pack_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); if (pack_size == 8) { BroadcastAddElementwiseMulKernel <<As()->cuda_stream()>>>(x, y, z, out, diff --git a/oneflow/user/kernels/fused_cross_feature_interaction_grad.hip.cpp b/oneflow/user/kernels/fused_cross_feature_interaction_grad.hip.cpp deleted file mode 100644 index 6e5483a377310d5d8ee802cb13bc7b12aba7dbf5..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_cross_feature_interaction_grad.hip.cpp +++ /dev/null @@ -1,455 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/primitive/matmul.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -constexpr int kBlockSize = 256; - -void InferMatmulMNK(const DimVector& a_shape, const DimVector& b_shape, bool transpose_a, - bool transpose_b, size_t* m, size_t* n, size_t* k) { - const int64_t num_a_axes = a_shape.size(); - CHECK_GE(num_a_axes, 2); - const int64_t num_b_axes = b_shape.size(); - CHECK_GE(num_b_axes, 2); - if (!transpose_a) { - *m = a_shape.at(num_a_axes - 2); - *k = a_shape.at(num_a_axes - 1); - } else { - *m = a_shape.at(num_a_axes - 1); - *k = a_shape.at(num_a_axes - 2); - } - if (!transpose_b) { - CHECK_EQ(b_shape.at(num_b_axes - 2), *k); - *n = b_shape.at(num_b_axes - 1); - } else { - CHECK_EQ(b_shape.at(num_b_axes - 1), *k); - *n = b_shape.at(num_b_axes - 2); - } -} - -ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { - return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; -} - -template -struct MulOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return a * b; } -}; - -template -struct AddOp { - __device__ __forceinline__ T operator()(const T& a, const T& b) const { return a + b; } -}; - -template -int GetLaunchPackSize(const int64_t cols) { - constexpr int type_pack_size = cuda::elementwise::PackSize(); - for (int launch_pack_size = 8; launch_pack_size > 0; launch_pack_size /= 2) { - if (type_pack_size >= launch_pack_size && cols % launch_pack_size == 0) { - return launch_pack_size; - } - } - return 1; -} - -template -__global__ void BroadcastMulKernel(const T* x, const T* y, T* out, const IndexType cols, - const IndexType elem_cnt) { - const IndexType global_thread_id = blockDim.x * blockIdx.x + threadIdx.x; - using LoadPack = cuda::elementwise::Packed; - for (IndexType linear_index = global_thread_id * pack_size, - step = gridDim.x * blockDim.x * pack_size; - linear_index < elem_cnt; linear_index += step) { - const IndexType row_idx = linear_index / cols; - const LoadPack* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec = *x_load; - LoadPack out_store; - const T y_val = y[row_idx]; -#pragma unroll - for (int i = 0; i < pack_size; i++) { out_store.elem[i] = x_vec.elem[i] * y_val; } - *(reinterpret_cast(out + linear_index)) = out_store; - } -} - -template -void DispatchBroadcastMulPackSize(ep::Stream* stream, const T* x, const T* y, T* out, - const IndexType cols, const IndexType elem_cnt) { - int grid_size; - const int pack_size = GetLaunchPackSize(cols); - const int64_t pack_num = elem_cnt / pack_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (pack_size == 8) { - BroadcastMulKernel - <<As()->cuda_stream()>>>(x, y, out, cols, - elem_cnt); - } else if (pack_size == 4) { - BroadcastMulKernel - <<As()->cuda_stream()>>>(x, y, out, cols, - elem_cnt); - } else if (pack_size == 2) { - BroadcastMulKernel - <<As()->cuda_stream()>>>(x, y, out, cols, - elem_cnt); - } else { - BroadcastMulKernel - <<As()->cuda_stream()>>>(x, y, out, cols, - elem_cnt); - } -} - -template -void DispatchBroadcastMulIndexType(ep::Stream* stream, const T* x, const T* y, T* out, - const int64_t cols, const int64_t elem_cnt) { - if (elem_cnt < GetMaxVal()) { - DispatchBroadcastMulPackSize(stream, x, y, out, cols, elem_cnt); - } else { - DispatchBroadcastMulPackSize(stream, x, y, out, cols, elem_cnt); - } -} - -template -__global__ void BroadcastAddElementwiseMulKernel(const T* x, const T* y, const T* z, T* out, - const IndexType cols, const IndexType elem_cnt) { - const IndexType global_thread_id = blockDim.x * blockIdx.x + threadIdx.x; - using LoadPack = cuda::elementwise::Packed; - for (IndexType linear_index = global_thread_id * pack_size, - step = gridDim.x * blockDim.x * pack_size; - linear_index < elem_cnt; linear_index += step) { - const IndexType row_idx = linear_index / cols; - const IndexType col_idx = linear_index - row_idx * cols; - const LoadPack* x_load = reinterpret_cast(x + linear_index); - const LoadPack* y_load = reinterpret_cast(y + col_idx); - const LoadPack* z_load = reinterpret_cast(z + linear_index); - - LoadPack x_vec = *x_load; - LoadPack y_vec = *y_load; - LoadPack z_vec = *z_load; - LoadPack out_store; - -#pragma unroll - for (int i = 0; i < pack_size; i++) { - out_store.elem[i] = (x_vec.elem[i] + y_vec.elem[i]) * z_vec.elem[i]; - } - *(reinterpret_cast(out + linear_index)) = out_store; - } -} - -template -void DispatchBroadcastAddElementwiseMulPackSize(ep::Stream* stream, const T* x, const T* y, - const T* z, T* out, const IndexType cols, - const IndexType elem_cnt) { - int grid_size; - const int pack_size = GetLaunchPackSize(cols); - const int64_t pack_num = elem_cnt / pack_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (pack_size == 8) { - BroadcastAddElementwiseMulKernel - <<As()->cuda_stream()>>>(x, y, z, out, - cols, elem_cnt); - } else if (pack_size == 4) { - BroadcastAddElementwiseMulKernel - <<As()->cuda_stream()>>>(x, y, z, out, - cols, elem_cnt); - } else if (pack_size == 2) { - BroadcastAddElementwiseMulKernel - <<As()->cuda_stream()>>>(x, y, z, out, - cols, elem_cnt); - } else { - BroadcastAddElementwiseMulKernel - <<As()->cuda_stream()>>>(x, y, z, out, - cols, elem_cnt); - } -} - -template -void DispatchBroadcastAddElementwiseMulIndexType(ep::Stream* stream, const T* x, const T* y, - const T* z, T* out, const int64_t cols, - const int64_t elem_cnt) { - if (elem_cnt < GetMaxVal()) { - DispatchBroadcastAddElementwiseMulPackSize(stream, x, y, z, out, cols, elem_cnt); - } else { - DispatchBroadcastAddElementwiseMulPackSize(stream, x, y, z, out, cols, elem_cnt); - } -} - -} // namespace - -namespace user_op { - -std::unique_ptr NewMatmulPrimitive(DeviceType device_type, - DataType data_type, bool transpose_a, - bool transpose_b) { - const auto trans_a = GetBlasTransposeType(transpose_a); - const auto trans_b = GetBlasTransposeType(transpose_b); - return ep::primitive::NewPrimitive(device_type, data_type, trans_a, - trans_b); -} - -template -std::unique_ptr NewReduceMatmulPrimitive(Context* ctx) { - const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("dy", 0)->data_type(); - return NewMatmulPrimitive(ctx->device_type(), data_type, /*transpose_a=*/false, - /*transpose_b=*/false); -} - -auto ReduceMatmulPrimitiveExists() { - return hob::make_custom("MatmulPrimitiveExists", [](const KernelRegContext& ctx) { - return NewReduceMatmulPrimitive(&ctx).operator bool(); - }); -} - -template -std::unique_ptr NewWeightGradMatmulPrimitive(Context* ctx) { - const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("x", 0)->data_type(); - return NewMatmulPrimitive(ctx->device_type(), data_type, /*transpose_a=*/true, - /*transpose_b=*/false); -} - -auto WeightGradMatmulPrimitiveExists() { - return hob::make_custom("MatmulPrimitiveExists", [](const KernelRegContext& ctx) { - return NewWeightGradMatmulPrimitive(&ctx).operator bool(); - }); -} - -template -class FusedCrossFeatureInteractionGradKernel final : public OpKernel, public CudaGraphSupport { - public: - FusedCrossFeatureInteractionGradKernel() = default; - ~FusedCrossFeatureInteractionGradKernel() override = default; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { - const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const Tensor* x0 = ctx->Tensor4ArgNameAndIndex("x0", 0); - const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const Tensor* matmul_result = ctx->Tensor4ArgNameAndIndex("matmul_result", 0); - - const int64_t batch_size = dy->shape_view().At(0); - const int64_t hidden_size = dy->shape_view().At(1); - const int64_t out_size = weight->shape_view().At(0); - const int64_t dy_elem_cnt = dy->shape_view().elem_cnt(); - - Tensor* dx0 = ctx->Tensor4ArgNameAndIndex("dx0", 0); - Tensor* dw = ctx->Tensor4ArgNameAndIndex("dw", 0); - Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - Tensor* dbias = ctx->Tensor4ArgNameAndIndex("dbias", 0); - Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - // step1: Get dbias. - const T* ones = nullptr; - auto* cuda_device = dynamic_cast(ctx->stream()->device()); - if (cuda_device != nullptr) { - ones = static_cast(cuda_device->GetConstOnes(dy->data_type(), batch_size)); - } - size_t m = 0, n = 0, k = 0; - DimVector dy_shape(2); - dy->shape_view().ToDimVector(&dy_shape); - DimVector ones_buf_shape(2); - ones_buf_shape.at(0) = 1; - ones_buf_shape.at(1) = batch_size; - InferMatmulMNK(ones_buf_shape, dy_shape, /*trans_a=*/false, /*trans_b=*/false, &m, &n, &k); - auto reduce_matmul = NewReduceMatmulPrimitive(ctx); - CHECK(reduce_matmul); - reduce_matmul->Launch(ctx->stream(), m, n, k, 1.0, ones, dy->dptr(), 0.0, dbias->mut_dptr()); - - // step2: Get dmatmul_result0. - T* dy_mul_x0 = reinterpret_cast(tmp_buffer->mut_dptr()); - T* dmatmul_result0 = reinterpret_cast(tmp_buffer->mut_dptr() - + GetCudaAlignedSize(dy_elem_cnt * sizeof(T))); - OF_CUDA_CHECK(cuda::elementwise::Binary(MulOp(), dy_elem_cnt, dy_mul_x0, dy->dptr(), - x0->dptr(), - ctx->stream()->As()->cuda_stream())); - - ones = static_cast(cuda_device->GetConstOnes(dy->data_type(), hidden_size)); - DimVector dy_mul_x0_shape(2); - dy->shape_view().ToDimVector(&dy_mul_x0_shape); - ones_buf_shape.at(0) = hidden_size; - ones_buf_shape.at(1) = 1; - InferMatmulMNK(dy_mul_x0_shape, ones_buf_shape, /*trans_a=*/false, /*trans_b=*/false, &m, &n, - &k); - reduce_matmul->Launch(ctx->stream(), m, n, k, 1.0, dy_mul_x0, ones, 0.0, dmatmul_result0); - - // step3: Get dx - T* dx_buf = reinterpret_cast(tmp_buffer->mut_dptr() - + GetCudaAlignedSize(dy_elem_cnt * sizeof(T)) - + GetCudaAlignedSize(batch_size * sizeof(T))); - DimVector dmatmul_result_shape(2); - dmatmul_result_shape.at(0) = batch_size; - dmatmul_result_shape.at(1) = 1; // todo change to hidden size - DimVector weight_shape(2); - weight->shape_view().ToDimVector(&weight_shape); - InferMatmulMNK(dmatmul_result_shape, weight_shape, /*trans_a=*/false, /*trans_b=*/false, &m, &n, - &k); - reduce_matmul->Launch(ctx->stream(), m, n, k, 1.0, dmatmul_result0, weight->dptr(), 0.0, - reinterpret_cast(dx_buf)); - OF_CUDA_CHECK(cuda::elementwise::Binary(AddOp(), dy_elem_cnt, dx->mut_dptr(), dx_buf, - dy->dptr(), - ctx->stream()->As()->cuda_stream())); - - // step4: Get dw. - DimVector x_shape(2); - x->shape_view().ToDimVector(&x_shape); - - InferMatmulMNK(dmatmul_result_shape, x_shape, /*trans_a=*/true, /*trans_b=*/false, &m, &n, &k); - auto weight_grad_matmul = NewWeightGradMatmulPrimitive(ctx); - CHECK(weight_grad_matmul); - weight_grad_matmul->Launch(ctx->stream(), m, n, k, 1.0, dmatmul_result0, x->dptr(), 0.0, - dw->mut_dptr()); - - // step5: Get dx0. - DispatchBroadcastMulIndexType(ctx->stream(), dy->dptr(), matmul_result->dptr(), - dx0->mut_dptr(), hidden_size, dy_elem_cnt); - } -}; - -#define REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V1_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_cross_feature_interaction_v1_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == DeviceType::kCUDA) \ - && (HobDataType("dy", 0) == GetDataType::value) \ - && ReduceMatmulPrimitiveExists() && WeightGradMatmulPrimitiveExists()) \ - .SetInferTmpSizeFn([](InferContext* ctx) { \ - size_t tmp_size = 0; \ - const TensorDesc& dy = ctx->InputTensorDesc("dy", 0); \ - const int64_t dy_elem_cnt = dy.shape().elem_cnt(); \ - const int64_t batch_size = dy.shape().At(0); \ - size_t dy_mul_x0_size = GetCudaAlignedSize(dy_elem_cnt * sizeof(dtype)); \ - size_t dmatmul_result_size = GetCudaAlignedSize(batch_size * sizeof(dtype)); \ - size_t dx_buf_size = dy_mul_x0_size; \ - tmp_size = dy_mul_x0_size + dmatmul_result_size + dx_buf_size; \ - return tmp_size; \ - }); - -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V1_GRAD_KERNEL(float) -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V1_GRAD_KERNEL(half) - -template -class FusedCrossFeatureInteractionV2GradKernel final : public OpKernel, public CudaGraphSupport { - public: - FusedCrossFeatureInteractionV2GradKernel() = default; - ~FusedCrossFeatureInteractionV2GradKernel() = default; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - - private: - using user_op::OpKernel::Compute; - void Compute(KernelComputeContext* ctx) const override { - const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); - const Tensor* x0 = ctx->Tensor4ArgNameAndIndex("x0", 0); - const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const Tensor* matmul_result = ctx->Tensor4ArgNameAndIndex("matmul_result", 0); - - const int64_t batch_size = dy->shape_view().At(0); - const int64_t in_size = weight->shape_view().At(1); - const int64_t hidden_size = weight->shape_view().At(0); - const int64_t dy_elem_cnt = dy->shape_view().elem_cnt(); - - Tensor* dx0 = ctx->Tensor4ArgNameAndIndex("dx0", 0); - Tensor* dw = ctx->Tensor4ArgNameAndIndex("dw", 0); - Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - Tensor* dbias = ctx->Tensor4ArgNameAndIndex("dbias", 0); - Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - // step1: Get dx0. - DispatchBroadcastAddElementwiseMulIndexType(ctx->stream(), matmul_result->dptr(), - bias->dptr(), dy->dptr(), - dx0->mut_dptr(), hidden_size, dy_elem_cnt); - - // step2: Get dmatmul_result0. - T* dmatmul_result0 = reinterpret_cast(tmp_buffer->mut_dptr()); - OF_CUDA_CHECK(cuda::elementwise::Binary(MulOp(), dy_elem_cnt, dmatmul_result0, dy->dptr(), - x0->dptr(), - ctx->stream()->As()->cuda_stream())); - // step3: Get dx - T* dx_buf = reinterpret_cast(tmp_buffer->mut_dptr() - + GetCudaAlignedSize(dy_elem_cnt * sizeof(T))); - DimVector dmatmul_result_shape(2); - dmatmul_result_shape.at(0) = batch_size; - dmatmul_result_shape.at(1) = hidden_size; - DimVector weight_shape(2); - weight->shape_view().ToDimVector(&weight_shape); - size_t m = 0, n = 0, k = 0; - InferMatmulMNK(dmatmul_result_shape, weight_shape, /*trans_a=*/false, /*trans_b=*/false, &m, &n, - &k); - auto reduce_matmul = NewReduceMatmulPrimitive(ctx); - CHECK(reduce_matmul); - reduce_matmul->Launch(ctx->stream(), m, n, k, 1.0, dmatmul_result0, weight->dptr(), 0.0, - reinterpret_cast(dx_buf)); - OF_CUDA_CHECK(cuda::elementwise::Binary(AddOp(), dy_elem_cnt, dx->mut_dptr(), dx_buf, - dy->dptr(), - ctx->stream()->As()->cuda_stream())); - - // step4: Get dw. - DimVector x_shape(2); - x->shape_view().ToDimVector(&x_shape); - - InferMatmulMNK(dmatmul_result_shape, x_shape, /*trans_a=*/true, /*trans_b=*/false, &m, &n, &k); - auto weight_grad_matmul = NewWeightGradMatmulPrimitive(ctx); - CHECK(weight_grad_matmul); - weight_grad_matmul->Launch(ctx->stream(), m, n, k, 1.0, dmatmul_result0, x->dptr(), 0.0, - dw->mut_dptr()); - - // step5: Get dbias. - const T* ones = nullptr; - auto* cuda_device = dynamic_cast(ctx->stream()->device()); - if (cuda_device != nullptr) { - ones = static_cast(cuda_device->GetConstOnes(dy->data_type(), batch_size)); - } - DimVector dy_shape(2); - dy->shape_view().ToDimVector(&dy_shape); - DimVector ones_buf_shape(2); - ones_buf_shape.at(0) = 1; - ones_buf_shape.at(1) = batch_size; - InferMatmulMNK(ones_buf_shape, dy_shape, /*trans_a=*/false, /*trans_b=*/false, &m, &n, &k); - reduce_matmul->Launch(ctx->stream(), m, n, k, 1.0, ones, - reinterpret_cast(dmatmul_result0), 0.0, dbias->mut_dptr()); - } -}; - -#define REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V2_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_cross_feature_interaction_v2_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((HobDeviceType() == DeviceType::kCUDA) \ - && (HobDataType("dy", 0) == GetDataType::value) \ - && ReduceMatmulPrimitiveExists() && WeightGradMatmulPrimitiveExists()) \ - .SetInferTmpSizeFn([](InferContext* ctx) { \ - size_t tmp_size = 0; \ - const TensorDesc& dy = ctx->InputTensorDesc("dy", 0); \ - const int64_t dy_elem_cnt = dy.shape().elem_cnt(); \ - size_t dmatmul_result_size = GetCudaAlignedSize(dy_elem_cnt * sizeof(dtype)); \ - size_t dx_buf_size = dmatmul_result_size; \ - tmp_size = dmatmul_result_size + dx_buf_size; \ - return tmp_size; \ - }); - -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V2_GRAD_KERNEL(float) -REGISTER_FUSED_CROSS_FEATURE_INTERACTION_V2_GRAD_KERNEL(half) - -} // namespace user_op - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_dot_feature_interaction_kernel.cu b/oneflow/user/kernels/fused_dot_feature_interaction_kernel.cu index 250e7588780626afbce86cc9eb4cd16c795a3cf3..b70a183755dc02211c575130670e2b2d0f837ac5 100644 --- a/oneflow/user/kernels/fused_dot_feature_interaction_kernel.cu +++ b/oneflow/user/kernels/fused_dot_feature_interaction_kernel.cu @@ -18,7 +18,12 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/copy_nd.h" #include "oneflow/core/ep/include/primitive/batch_matmul.h" #include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/cuda/atomic.cuh" +#ifdef WITH_ROCM +#include +#else #include +#endif namespace oneflow { @@ -123,7 +128,7 @@ void ConcatFeatures(user_op::KernelComputeContext* ctx, int64_t dst_rows, int64_ int64_t pad_dim = dst_cols - out_col_offset; if (pad_dim > 0) { char* out_ptr = reinterpret_cast(dst_ptr) + out_col_offset * sizeof(T); - OF_CUDA_CHECK(cudaMemset2DAsync(out_ptr, dst_cols * sizeof(T), 0, pad_dim * sizeof(T), dst_rows, + OF_CUDA_CHECK(GPU(Memset2DAsync)(out_ptr, dst_cols * sizeof(T), 0, pad_dim * sizeof(T), dst_rows, ctx->stream()->As()->cuda_stream())); } } @@ -134,7 +139,7 @@ void GatherConcatKernel(ep::Stream* stream, int32_t elem_cnt, int32_t out_dim, int32_t concated_padded_dim, int32_t output_concat_end_dim, bool self_interaction, const T* matmul_out, const T* output_concat_ptr, int32_t* gather_indices_ptr, T* out_ptr) { - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); const int32_t gen_indices_elem_cnt = features_concated_dim * features_concated_dim; int32_t offset = self_interaction ? 1 : 0; GenerateGatherIndicesGpu<< { } __device__ void InitAcc() { nvcuda::wmma::fill_fragment(acc_, 0.0f); } #else - __device__ void LoadA(const float* ptr, int ldm) { __trap(); } - __device__ void LoadB(const float* ptr, int ldm) { __trap(); } - __device__ void Mma() { __trap(); } + __device__ void LoadA(const float* ptr, int ldm) { TRAP(); } + __device__ void LoadB(const float* ptr, int ldm) { TRAP(); } + __device__ void Mma() { TRAP(); } __device__ __forceinline__ float Convert(float src) { return src; } - __device__ void Store(AccType* ptr, int ldm) { __trap(); } - __device__ void InitAcc() { __trap(); } + __device__ void Store(AccType* ptr, int ldm) { TRAP(); } + __device__ void InitAcc() { TRAP(); } #endif private: @@ -293,7 +302,13 @@ __global__ void DotFeatureInteractionWmmaImpl( const int output_concat_size = param.output_concat_size; const T* batch_output_concat = (param.output_concat) ? (param.output_concat + batch_idx * output_concat_size) : nullptr; + const uint32_t* batch_sparse_indices = + (param.sparse_indices) ? (param.sparse_indices + batch_idx * param.sparse_dim) : nullptr; + const Pack* sparse_feature_pack = + (param.sparse_feature) ? reinterpret_cast*>(param.sparse_feature) + : nullptr; for (int col = threadIdx.x; col < vector_num_pack; col += blockDim.x) { +// load dense feature to shared_mem #pragma unroll for (int i = 0; i < max_in; ++i) { if (i >= param.num_in) { break; } @@ -315,6 +330,22 @@ __global__ void DotFeatureInteractionWmmaImpl( } } } + // load sparse feature to shared_mem + for (int j = threadIdx.y * kUnrollDim; j < param.sparse_dim; j += blockDim.y * kUnrollDim) { +#pragma unroll + for (int k = 0; k < kUnrollDim; ++k) { + int in_row = j + k; + if (in_row >= param.sparse_dim) { break; } + int buf_row = param.sparse_dim_start + in_row; + int sparse_in_row = batch_sparse_indices[in_row]; + Pack pack_in_val = sparse_feature_pack[sparse_in_row * vector_num_pack + col]; +#pragma unroll + for (int t = 0; t < pack_size; ++t) { + pack_in_val.elem[t] = wmma.Convert(pack_in_val.elem[t]); + } + buf_pack[buf_row * in_shared_mem_cols_num_pack + col] = pack_in_val; + } + } } Pack zero; #pragma unroll @@ -366,7 +397,7 @@ __global__ void DotFeatureInteractionWmmaImpl( batch_out[out_num_cols - 1 - i] = 0; } #else - __trap(); + TRAP(); #endif // __CUDA_ARCH__ >= 700 } @@ -410,12 +441,12 @@ struct DotFeatureInteractionKernel { const int in_shared_mem_cols_num_pack = in_shared_mem_num_cols / pack_size; const int acc_shared_mem_cols_num_pack = acc_shared_mem_num_cols / pack_size; int max_active_blocks; - OF_CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor( + OF_CUDA_CHECK(GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks, DotFeatureInteractionWmmaImpl, block_size, total_shared_mem_bytes)); if (max_active_blocks <= 0) { return false; } - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); DotFeatureInteractionWmmaImpl <<>>( m_num_tiles, k_num_tiles, batch_size, concated_padded_dim, vector_num_pack, @@ -432,6 +463,11 @@ struct DotBwdParam { const T* in[max_in]; T* in_grad[max_in]; T* output_concat_grad; + const T* sparse_feature; + const uint32_t* sparse_indices; + int32_t sparse_dim; + int32_t sparse_dim_start; + T* sparse_feature_grad; int32_t output_concat_size; int32_t in_feature_dim[max_in]; int32_t dim_start_offset[max_in]; @@ -439,12 +475,35 @@ struct DotBwdParam { int32_t num_in; }; -template +template +__device__ __inline__ void AtomicAdd(Pack* address, + Pack val) { +#pragma unroll + for (int i = 0; i < pack_size; ++i) { + cuda::atomic::Add(reinterpret_cast(address) + i, static_cast(val.elem[i])); + } +} + +template<> +__device__ __inline__ void AtomicAdd(Pack* address, Pack val) { + half2 h2_val; +#ifdef WITH_ROCM + h2_val.data.x = static_cast(val.elem[0]); + h2_val.data.y = static_cast(val.elem[1]); +#else + h2_val.x = static_cast(val.elem[0]); + h2_val.y = static_cast(val.elem[1]); +#endif + cuda::atomic::Add(reinterpret_cast(address), h2_val); +} + +template __global__ void DotFeatureInteractionBackwardWmmaImpl( int m_num_tiles, int n_num_tiles, int k_num_tiles, int64_t batch_size, int padded_num_rows, - int vector_num_pack, int padded_vector_num_pack, int out_num_cols, int in_shared_mem_cols, - int in_shared_mem_cols_num_pack, int matrix_out_grad_shared_mem_cols, int offset, + int vector_num_pack, int vector_num_sparse_grad_pack, int padded_vector_num_pack, + int out_num_cols, int in_shared_mem_cols, int in_shared_mem_cols_num_pack, + int in_shared_mem_cols_num_sparse_grad_pack, int matrix_out_grad_shared_mem_cols, int offset, DotBwdParam param) { #if __CUDA_ARCH__ >= 700 Wmma* sparse_feature_pack = + (param.sparse_feature) ? reinterpret_cast*>(param.sparse_feature) + : nullptr; + int features_dim = param.features_dim; // 1.split out_grad to concat_out_grad and matrix_out_grad buf int thread_id = threadIdx.x + threadIdx.y * blockDim.x; @@ -520,6 +585,22 @@ __global__ void DotFeatureInteractionBackwardWmmaImpl( } } } + // load sparse feature to shared_mem + for (int j = threadIdx.y * kUnrollDim; j < param.sparse_dim; j += blockDim.y * kUnrollDim) { +#pragma unroll + for (int k = 0; k < kUnrollDim; ++k) { + int in_row = j + k; + if (in_row >= param.sparse_dim) { break; } + int buf_row = param.sparse_dim_start + in_row; + int sparse_in_row = batch_sparse_indices[in_row]; + Pack pack_in_val = sparse_feature_pack[sparse_in_row * vector_num_pack + col]; +#pragma unroll + for (int t = 0; t < pack_size; ++t) { + pack_in_val.elem[t] = wmma.Convert(pack_in_val.elem[t]); + } + in_buf_pack[buf_row * in_shared_mem_cols_num_pack + col] = pack_in_val; + } + } } Pack zero; #pragma unroll @@ -559,6 +640,7 @@ __global__ void DotFeatureInteractionBackwardWmmaImpl( __syncthreads(); // 4.split in_grad buf to dx + // shared_mem to dense dx for (int col = threadIdx.x; col < vector_num_pack; col += blockDim.x) { #pragma unroll for (int i = 0; i < max_in; ++i) { @@ -584,12 +666,34 @@ __global__ void DotFeatureInteractionBackwardWmmaImpl( } } } + // shared_mem to sparse dx, sparse in grad use sparse_grad_pack_size + Pack* in_grad_buf_sparse_grad_pack = + reinterpret_cast*>(in_grad_buf); + Pack* sparse_feature_grad_pack = + reinterpret_cast*>(param.sparse_feature_grad); + for (int col = threadIdx.x; col < vector_num_sparse_grad_pack; col += blockDim.x) { + for (int j = threadIdx.y * kUnrollDim; j < param.sparse_dim; j += blockDim.y * kUnrollDim) { +#pragma unroll + for (int k = 0; k < kUnrollDim; ++k) { + int in_row = j + k; + if (in_row >= param.sparse_dim) { break; } + int buf_row = param.sparse_dim_start + in_row; + int sparse_in_row = batch_sparse_indices[in_row]; + Pack buf_grad_val = + in_grad_buf_sparse_grad_pack[buf_row * in_shared_mem_cols_num_sparse_grad_pack + col]; + AtomicAdd( + sparse_feature_grad_pack + sparse_in_row * vector_num_sparse_grad_pack + col, + buf_grad_val); + } + } + } + #else - __trap(); + TRAP(); #endif // __CUDA_ARCH__ >= 700 } -template +template struct DotFeatureInteractionBackwardKernel { static bool Launch(ep::Stream* stream, int64_t batch_size, int concated_padded_dim, int vector_size, int out_num_cols, bool self_interaction, @@ -619,25 +723,86 @@ struct DotFeatureInteractionBackwardKernel { const int vector_num_pack = vector_size / pack_size; const int padded_vector_num_pack = padded_vector_size / pack_size; const int in_shared_mem_cols_num_pack = in_shared_mem_num_cols / pack_size; + const int vector_num_sparse_grad_pack = vector_size / sparse_grad_pack_size; + const int in_shared_mem_cols_num_sparse_grad_pack = + in_shared_mem_num_cols / sparse_grad_pack_size; + int max_active_blocks; - OF_CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor( + OF_CUDA_CHECK(GPU(OccupancyMaxActiveBlocksPerMultiprocessor)( &max_active_blocks, - DotFeatureInteractionBackwardWmmaImpl, + DotFeatureInteractionBackwardWmmaImpl, block_size, total_shared_mem_bytes)); if (max_active_blocks <= 0) { return false; } - cudaStream_t cuda_stream = stream->As()->cuda_stream(); - DotFeatureInteractionBackwardWmmaImpl + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); + DotFeatureInteractionBackwardWmmaImpl <<>>( m_num_tiles, n_num_tiles, k_num_tiles, batch_size, concated_padded_dim, vector_num_pack, - padded_vector_num_pack, out_num_cols, in_shared_mem_num_cols, - in_shared_mem_cols_num_pack, matrix_out_grad_shared_mem_cols, offset, param); + vector_num_sparse_grad_pack, padded_vector_num_pack, out_num_cols, + in_shared_mem_num_cols, in_shared_mem_cols_num_pack, + in_shared_mem_cols_num_sparse_grad_pack, matrix_out_grad_shared_mem_cols, offset, + param); return true; } }; +template +__global__ void MemsetGpu(int64_t parallel_num, int64_t vector_size, const uint32_t* num_valid, + T* dst) { + size_t count = 0; + for (int i = 0; i < parallel_num; ++i) { count += num_valid[i] * vector_size; } + const size_t pack_count = count / pack; + Pack pack_value; + for (int i = 0; i < pack; ++i) { pack_value.elem[i] = static_cast(0); } + auto* pack_dst = reinterpret_cast*>(dst); + CUDA_1D_KERNEL_LOOP_T(size_t, i, pack_count) { pack_dst[i] = pack_value; } + T* tail_dst = dst + pack_count * pack; + const size_t tail_count = count - pack_count * pack; + CUDA_1D_KERNEL_LOOP_T(size_t, i, tail_count) { tail_dst[i] = static_cast(0); } +} + +template +typename std::enable_if<(pack != 0), void>::type LaunchPackMemsetGpu(GPU(Stream_t) stream, + const uint32_t* num_valid, + T* ptr, size_t sm_count, + int64_t vector_size, + int64_t parallel_num) { + MemsetGpu<<<2 * sm_count, 1024, 0, stream>>>(parallel_num, vector_size, num_valid, ptr); +} + +template +typename std::enable_if<(pack == 0), void>::type LaunchPackMemsetGpu(GPU(Stream_t) stream, + const uint32_t* num_valid, + T* ptr, size_t sm_count, + int64_t vector_size, + int64_t parallel_num) { + LOG(FATAL) << "wrong alignment"; +} + +template +void LaunchMemset(GPU(Stream_t) stream, size_t sm_count, int64_t vector_size, int64_t parallel_num, + const uint32_t* num_valid, T* ptr) { + auto uintptr = reinterpret_cast(ptr); + if (uintptr % 16 == 0) { + LaunchPackMemsetGpu(stream, num_valid, ptr, sm_count, vector_size, + parallel_num); + } else if (uintptr % 8 == 0) { + LaunchPackMemsetGpu(stream, num_valid, ptr, sm_count, vector_size, + parallel_num); + } else if (uintptr % 4 == 0) { + LaunchPackMemsetGpu(stream, num_valid, ptr, sm_count, vector_size, + parallel_num); + } else if (uintptr % 2 == 0) { + LaunchPackMemsetGpu(stream, num_valid, ptr, sm_count, vector_size, + parallel_num); + } else { + LaunchPackMemsetGpu(stream, num_valid, ptr, sm_count, vector_size, + parallel_num); + } +} + template bool DispatchFeatureInteractionDotPackSize(user_op::KernelComputeContext* ctx, const int32_t input_size) { @@ -656,6 +821,22 @@ bool DispatchFeatureInteractionDotPackSize(user_op::KernelComputeContext* ctx, param.dim_start_offset[i] = features_concated_dim; features_concated_dim += param.in_feature_dim[i]; } + if (ctx->has_input("sparse_feature", 0)) { + CHECK(ctx->has_input("sparse_indices", 0)); + const user_op::Tensor* sparse_feature = ctx->Tensor4ArgNameAndIndex("sparse_feature", 0); + const user_op::Tensor* sparse_indices = ctx->Tensor4ArgNameAndIndex("sparse_indices", 0); + param.sparse_feature = sparse_feature->dptr(); + CHECK_EQ(sparse_indices->data_type(), DataType::kUInt32); + param.sparse_indices = reinterpret_cast(sparse_indices->dptr()); + param.sparse_dim = ctx->TensorDesc4ArgNameAndIndex("sparse_indices", 0)->shape().At(1); + param.sparse_dim_start = features_concated_dim; + features_concated_dim += param.sparse_dim; + } else { + param.sparse_feature = nullptr; + param.sparse_indices = nullptr; + param.sparse_dim = 0; + param.sparse_dim_start = 0; + } const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); param.features_dim = features_concated_dim; if (ctx->has_input("output_concat", 0)) { @@ -702,6 +883,38 @@ bool DispatchFeatureInteractionDotBackwardPackSize(user_op::KernelComputeContext param.dim_start_offset[i] = features_concated_dim; features_concated_dim += param.in_feature_dim[i]; } + if (ctx->has_input("sparse_feature", 0)) { + CHECK(ctx->has_input("sparse_indices", 0)); + CHECK(ctx->has_input("num_valid_sparse_feature", 0)); + CHECK(ctx->has_output("sparse_feature_grad", 0)); + const user_op::Tensor* sparse_feature = ctx->Tensor4ArgNameAndIndex("sparse_feature", 0); + const user_op::Tensor* sparse_indices = ctx->Tensor4ArgNameAndIndex("sparse_indices", 0); + const user_op::Tensor* num_valid_sparse_feature = + ctx->Tensor4ArgNameAndIndex("num_valid_sparse_feature", 0); + param.sparse_feature = sparse_feature->dptr(); + CHECK_EQ(sparse_indices->data_type(), DataType::kUInt32); + param.sparse_indices = reinterpret_cast(sparse_indices->dptr()); + param.sparse_dim = ctx->TensorDesc4ArgNameAndIndex("sparse_indices", 0)->shape().At(1); + param.sparse_dim_start = features_concated_dim; + features_concated_dim += param.sparse_dim; + param.sparse_feature_grad = + ctx->Tensor4ArgNameAndIndex("sparse_feature_grad", 0)->mut_dptr(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + CHECK_EQ(num_valid_sparse_feature->data_type(), DataType::kUInt32); + LaunchMemset(ctx->stream()->As()->cuda_stream(), + ctx->stream()->As()->device_properties().multiProcessorCount, + vector_size, parallel_num, + reinterpret_cast(num_valid_sparse_feature->dptr()) + + parallel_id * parallel_num, + param.sparse_feature_grad); + } else { + param.sparse_feature = nullptr; + param.sparse_indices = nullptr; + param.sparse_feature_grad = nullptr; + param.sparse_dim = 0; + param.sparse_dim_start = 0; + } const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); param.features_dim = features_concated_dim; if (ctx->has_output("output_concat_grad", 0)) { @@ -714,15 +927,21 @@ bool DispatchFeatureInteractionDotBackwardPackSize(user_op::KernelComputeContext } const bool self_interaction = ctx->Attr("self_interaction"); if (vector_size % 4 == 0) { - return DotFeatureInteractionBackwardKernel::Launch( + return DotFeatureInteractionBackwardKernel::Launch( ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, param); } else if (vector_size % 2 == 0) { - return DotFeatureInteractionBackwardKernel::Launch( + return DotFeatureInteractionBackwardKernel::Launch( ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, param); } else { - return DotFeatureInteractionBackwardKernel::Launch( + if (ctx->has_input("sparse_feature", 0) && dy->data_type() == DataType::kFloat16) { + UNIMPLEMENTED() + << "fused dot interaction backward kernel not support sparse_feature with pack_size 1, " + "because atomicAdd(half) is too slow"; + return false; + } + return DotFeatureInteractionBackwardKernel::Launch( ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, param); } @@ -847,7 +1066,7 @@ void DispatchFeatureInteractionSumPackSize(ep::Stream* stream, const int64_t bat GetBlockDims(vector_num_pack, &block_dim_x, &block_dim_y); const int num_blocks = GetNumBlocks(batch_size, block_dim_y); dim3 block_dims = dim3(block_dim_x, block_dim_y); - cudaStream_t cuda_stream = stream->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); if (pack_size == 2) { FeatureInteractionSum <<>>(batch_size, vector_num_pack, param); @@ -911,6 +1130,7 @@ class FusedDotFeatureInteractionPoolingSumKernel final : public user_op::OpKerne private: using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx) const override { + CHECK(!ctx->has_input("sparse_feature", 0)) << "pooling sum, sparse_feature is not supported. "; const int input_size = ctx->input_size("features"); if (input_size == 1) { DispatchFeatureInteractionSumInputSize(ctx, input_size); @@ -984,6 +1204,7 @@ class FusedDotFeatureInteractionKernel final : public user_op::OpKernel, bool success = TryLaunchTensorCoreDotKernel(ctx); if (success == true) { return; } } + CHECK(!ctx->has_input("sparse_feature", 0)) << "sparse_feature is not supported. "; user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int64_t batch_size = out->shape_view().At(0); int64_t features_concated_dim = 0; @@ -1091,6 +1312,7 @@ class FusedDotFeatureInteractionGradKernel final : public user_op::OpKernel, bool success = TryLaunchTensorCoreDotBackwardKernel(ctx); if (success == true) { return; } } + CHECK(!ctx->has_input("sparse_feature", 0)) << "sparse_feature is not supported. "; const int64_t batch_size = dy->shape_view().At(0); int64_t features_concated_dim = 0; for (int32_t i = 0; i < ctx->output_size("features_grad"); ++i) { diff --git a/oneflow/user/kernels/fused_dot_feature_interaction_kernel.hip.cpp b/oneflow/user/kernels/fused_dot_feature_interaction_kernel.hip.cpp deleted file mode 100644 index 07993ac029b0eab181503dc3cd34868797f23e21..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_dot_feature_interaction_kernel.hip.cpp +++ /dev/null @@ -1,923 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/ep/include/primitive/copy_nd.h" -#include "oneflow/core/ep/include/primitive/batch_matmul.h" -#include "oneflow/core/kernel/cuda_graph_support.h" - -namespace oneflow { - -namespace { - -__global__ void GenerateGatherIndicesGpu(const int32_t elem_cnt, const int32_t stride, - const int32_t in_cols, const int32_t offset, - int32_t* gather_indices) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int32_t row = i / stride; - const int32_t col = i - row * stride; - if (col < row + offset) { - int32_t in_index = row * in_cols + col; - int32_t idx = row * (offset + row - 1 + offset) / 2 + col; - gather_indices[idx] = in_index; - } - } -} - -template -__global__ void GatherConcatGpu(int32_t elem_cnt, int32_t out_cols, int32_t valid_out_cols, - int32_t in_cols, int32_t output_concat_end_dim, - const int32_t* gather_indices, const T* in, - const T* output_concat_ptr, T* out_ptr) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int32_t row = i / out_cols; - const int32_t col = i - row * out_cols; - T out_val; - if (col < output_concat_end_dim) { - const int32_t output_concat_idx = row * output_concat_end_dim + col; - out_val = output_concat_ptr[output_concat_idx]; - } else if (col < valid_out_cols) { - const int32_t gather_col_idx = gather_indices[col - output_concat_end_dim]; - const int32_t in_offset = row * in_cols + gather_col_idx; - out_val = in[in_offset]; - } else { - out_val = 0; - } - out_ptr[i] = out_val; - } -} - -template -__global__ void ScatterSplitAddTransposeGpu(int32_t elem_cnt, int32_t stride_dim, int32_t out_dim, - int32_t in_grad_stride, int32_t in_grad_matrix_dim, - int32_t in_grad_matrix_valid_dim, - int32_t output_concat_end_dim, const int32_t offset, - const T* dy, T* output_concat_grad, T* in_grad) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int32_t row = i / stride_dim; - const int32_t col = i - row * stride_dim; - if (col < output_concat_end_dim) { - output_concat_grad[row * output_concat_end_dim + col] = dy[row * out_dim + col]; - } else { - int32_t in_col_id = col - output_concat_end_dim; - const int32_t matrix_row = in_col_id / in_grad_matrix_dim; - const int32_t matrix_col = in_col_id - matrix_row * in_grad_matrix_dim; - T grad_val = 0; - const T* row_dy = dy + row * out_dim + output_concat_end_dim; - if (matrix_row < in_grad_matrix_valid_dim && matrix_col < in_grad_matrix_valid_dim) { - if (matrix_col < matrix_row) { - int32_t dy_col_idx = matrix_row * (offset + matrix_row - 1 + offset) / 2 + matrix_col; - grad_val = row_dy[dy_col_idx]; - } else if (matrix_row < matrix_col) { - // transpose add - int32_t trans_row_id = matrix_col; - int32_t trans_col_id = matrix_row; - int32_t dy_col_idx = - trans_row_id * (offset + trans_row_id - 1 + offset) / 2 + trans_col_id; - grad_val = row_dy[dy_col_idx]; - } else if ((matrix_row == matrix_col) && (offset == 1)) { - int32_t dy_col_idx = matrix_row * (offset + matrix_row - 1 + offset) / 2 + matrix_col; - grad_val = row_dy[dy_col_idx] * static_cast(2); - } - } - int32_t in_grad_offset = row * in_grad_stride + in_col_id; - in_grad[in_grad_offset] = grad_val; - } - } -} - -template -void ConcatFeatures(user_op::KernelComputeContext* ctx, int64_t dst_rows, int64_t dst_cols, - void* dst_ptr) { - const int64_t feature_input_size = ctx->input_size("features"); - auto primitive = ep::primitive::NewPrimitive(DeviceType::kCUDA, 2); - DimVector dst_shape = {dst_rows, dst_cols}; - int64_t out_col_offset = 0; - for (int64_t i = 0; i < feature_input_size; ++i) { - const user_op::Tensor* feature = ctx->Tensor4ArgNameAndIndex("features", i); - const int64_t feature_rows = feature->shape_view().At(0); - const int64_t feature_cols = feature->shape_view().Count(1); - DimVector dst_pos_vec = {0, out_col_offset}; - DimVector src_shape = {feature_rows, feature_cols}; - DimVector src_pos_vec = {0, 0}; - DimVector extent_vec = {feature_rows, feature_cols}; - primitive->Launch(ctx->stream(), feature->data_type(), 2, dst_ptr, dst_shape.data(), - dst_pos_vec.data(), feature->dptr(), src_shape.data(), src_pos_vec.data(), - extent_vec.data()); - out_col_offset += feature_cols; - } - int64_t pad_dim = dst_cols - out_col_offset; - if (pad_dim > 0) { - char* out_ptr = reinterpret_cast(dst_ptr) + out_col_offset * sizeof(T); - OF_CUDA_CHECK(hipMemset2DAsync(out_ptr, dst_cols * sizeof(T), 0, pad_dim * sizeof(T), dst_rows, - ctx->stream()->As()->cuda_stream())); - } -} - -template -void GatherConcatKernel(ep::Stream* stream, int32_t elem_cnt, int32_t out_dim, - int32_t valid_out_dim, int32_t features_concated_dim, - int32_t concated_padded_dim, int32_t output_concat_end_dim, - bool self_interaction, const T* matmul_out, const T* output_concat_ptr, - int32_t* gather_indices_ptr, T* out_ptr) { - hipStream_t cuda_stream = stream->As()->cuda_stream(); - const int32_t gen_indices_elem_cnt = features_concated_dim * features_concated_dim; - int32_t offset = self_interaction ? 1 : 0; - hipLaunchKernelGGL(GenerateGatherIndicesGpu, BlocksNum4ThreadsNum(gen_indices_elem_cnt), kCudaThreadsNumPerBlock, 0, cuda_stream, gen_indices_elem_cnt, features_concated_dim, - concated_padded_dim, offset, gather_indices_ptr); - - int32_t matmul_stride = concated_padded_dim * concated_padded_dim; - hipLaunchKernelGGL(GatherConcatGpu, BlocksNum4ThreadsNum(elem_cnt), kCudaThreadsNumPerBlock, 0, cuda_stream, - elem_cnt, out_dim, valid_out_dim, matmul_stride, output_concat_end_dim, gather_indices_ptr, - matmul_out, output_concat_ptr, out_ptr); -} - -template -void ScatterSplitAddTranspose(ep::Stream* stream, int32_t batch_size, int32_t out_dim, - int32_t concated_padded_dim, int32_t features_concated_dim, - int32_t output_concat_end_dim, const bool self_interaction, - const T* dy, T* output_concat_grad, T* matmul_out_grad_ptr) { - int32_t stride_dim = output_concat_end_dim + concated_padded_dim * concated_padded_dim; - int32_t matmul_stride = concated_padded_dim * concated_padded_dim; - const int32_t elem_cnt = batch_size * stride_dim; - int32_t offset = self_interaction ? 1 : 0; - ScatterSplitAddTransposeGpu<<As()->cuda_stream()>>>( - elem_cnt, stride_dim, out_dim, matmul_stride, concated_padded_dim, features_concated_dim, - output_concat_end_dim, offset, dy, output_concat_grad, matmul_out_grad_ptr); -} - -template -void ConcatFeaturesGrad(user_op::KernelComputeContext* ctx, const int64_t batch_size, - const int64_t concated_padded_dim, const int64_t vector_size, - const T* concated_features_grad) { - auto primitive = ep::primitive::NewPrimitive(DeviceType::kCUDA, 2); - DimVector src_shape = {batch_size, concated_padded_dim * vector_size}; - int64_t in_col_offset = 0; - for (int64_t i = 0; i < ctx->output_size("features_grad"); ++i) { - user_op::Tensor* feature_grad = ctx->Tensor4ArgNameAndIndex("features_grad", i); - const int64_t feature_grad_rows = feature_grad->shape_view().At(0); - const int64_t feature_grad_cols = feature_grad->shape_view().Count(1); - DimVector dst_shape = {feature_grad_rows, feature_grad_cols}; - DimVector dst_pos_vec = {0, 0}; - DimVector src_pos_vec = {0, in_col_offset}; - DimVector extent_vec = {feature_grad_rows, feature_grad_cols}; - in_col_offset += feature_grad_cols; - primitive->Launch(ctx->stream(), feature_grad->data_type(), 2, feature_grad->mut_dptr(), - dst_shape.data(), dst_pos_vec.data(), concated_features_grad, - src_shape.data(), src_pos_vec.data(), extent_vec.data()); - } -} - -template -struct DefaultComputeType { - using type = T; -}; - -template<> -struct DefaultComputeType { - using type = float; -}; - -template -struct alignas(sizeof(T) * pack_size) Pack { - T elem[pack_size]; -}; - -int64_t GetPaddedDim(int64_t dim) { - const int64_t align_dim = 16; - const int64_t padded_dim = (dim + align_dim - 1) / align_dim * align_dim; - return padded_dim; -} - -template -struct DotFwdParam { - const T* in[max_in]; - int32_t in_feature_dim[max_in]; - int32_t dim_start_offset[max_in]; - int32_t features_dim; - const T* output_concat; - int32_t output_concat_size; - T* out; - int32_t num_in; -}; - -constexpr int kUnrollDim = 2; -template -__global__ void DotFeatureInteractionWmmaImpl( - int m_num_tiles, int k_num_tiles, int64_t batch_size, int padded_num_rows, int vector_num_pack, - int padded_vector_num_pack, int out_num_cols, int out_num_cols_num_pack, int in_shared_mem_cols, - int in_shared_mem_cols_num_pack, int acc_shared_mem_cols, int acc_shared_mem_cols_num_pack, - int offset, int output_padding, DotFwdParam param) { - asm volatile("s_trap 0;"); -} - -template -struct KTileDim { - static const int val = 16; -}; - -template<> -struct KTileDim { - static const int val = 8; -}; - -template -struct DotFeatureInteractionKernel { - static bool Launch(ep::Stream* stream, int64_t batch_size, int concated_padded_dim, - int vector_size, int out_num_cols, bool self_interaction, int output_padding, - const DotFwdParam& param) { - const int block_size = 128; - const int block_dim_x = 32; - const int block_dim_y = block_size / block_dim_x; - const int num_blocks = batch_size; - const int mn_tile_dim = 16; - const int k_tile_dim = KTileDim::val; - const int64_t padded_vector_size = GetPaddedDim(vector_size); - const int m_num_tiles = concated_padded_dim / mn_tile_dim; - const int k_num_tiles = padded_vector_size / k_tile_dim; - const int skew_in = 8; - const int skew_acc = 8; - const int in_shared_mem_num_cols = padded_vector_size + skew_in; - const int acc_shared_mem_num_cols = concated_padded_dim + skew_acc; - const size_t in_shared_mem_bytes = concated_padded_dim * in_shared_mem_num_cols * sizeof(T); - using ComputeType = typename DefaultComputeType::type; - const size_t acc_shared_mem_bytes = - concated_padded_dim * acc_shared_mem_num_cols * sizeof(ComputeType); - const size_t total_shared_mem_bytes = in_shared_mem_bytes + acc_shared_mem_bytes; - const int32_t offset = self_interaction ? 1 : 0; - const int out_num_cols_num_pack = out_num_cols / pack_size; - const int vector_num_pack = vector_size / pack_size; - const int padded_vector_num_pack = padded_vector_size / pack_size; - const int in_shared_mem_cols_num_pack = in_shared_mem_num_cols / pack_size; - const int acc_shared_mem_cols_num_pack = acc_shared_mem_num_cols / pack_size; - int max_active_blocks; - OF_CUDA_CHECK(hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks, - DotFeatureInteractionWmmaImpl, - block_size, total_shared_mem_bytes)); - if (max_active_blocks <= 0) { return false; } - hipStream_t cuda_stream = stream->As()->cuda_stream(); - hipLaunchKernelGGL(HIP_KERNEL_NAME(DotFeatureInteractionWmmaImpl), num_blocks, dim3(block_dim_x, block_dim_y), total_shared_mem_bytes, cuda_stream, - m_num_tiles, k_num_tiles, batch_size, concated_padded_dim, vector_num_pack, - padded_vector_num_pack, out_num_cols, out_num_cols_num_pack, in_shared_mem_num_cols, - in_shared_mem_cols_num_pack, acc_shared_mem_num_cols, acc_shared_mem_cols_num_pack, - offset, output_padding, param); - return true; - } -}; - -template -struct DotBwdParam { - const T* out_grad; - const T* in[max_in]; - T* in_grad[max_in]; - T* output_concat_grad; - int32_t output_concat_size; - int32_t in_feature_dim[max_in]; - int32_t dim_start_offset[max_in]; - int32_t features_dim; - int32_t num_in; -}; - -template -__global__ void DotFeatureInteractionBackwardWmmaImpl( - int m_num_tiles, int n_num_tiles, int k_num_tiles, int64_t batch_size, int padded_num_rows, - int vector_num_pack, int padded_vector_num_pack, int out_num_cols, int in_shared_mem_cols, - int in_shared_mem_cols_num_pack, int matrix_out_grad_shared_mem_cols, int offset, - DotBwdParam param) { - asm volatile("s_trap 0;"); -} - -template -struct DotFeatureInteractionBackwardKernel { - static bool Launch(ep::Stream* stream, int64_t batch_size, int concated_padded_dim, - int vector_size, int out_num_cols, bool self_interaction, - const DotBwdParam& param) { - const int block_size = 256; - const int block_dim_x = 32; - const int block_dim_y = block_size / block_dim_x; - const int num_blocks = batch_size; - const int mn_tile_dim = 16; - const int k_tile_dim = KTileDim::val; - const int64_t padded_vector_size = GetPaddedDim(vector_size); - const int m_num_tiles = concated_padded_dim / mn_tile_dim; - const int k_num_tiles = concated_padded_dim / k_tile_dim; - const int n_num_tiles = padded_vector_size / mn_tile_dim; - const int skew_in = 8; - const int in_shared_mem_num_cols = padded_vector_size + skew_in; - const int matrix_out_grad_shared_mem_cols = concated_padded_dim + skew_in; - const size_t in_shared_mem_bytes = concated_padded_dim * in_shared_mem_num_cols * sizeof(T); - const size_t matrix_out_grad_shared_mem_bytes = - concated_padded_dim * matrix_out_grad_shared_mem_cols * sizeof(T); - using ComputeType = typename DefaultComputeType::type; - const size_t in_grad_shared_mem_bytes = - concated_padded_dim * in_shared_mem_num_cols * sizeof(ComputeType); - const size_t total_shared_mem_bytes = - in_shared_mem_bytes + matrix_out_grad_shared_mem_bytes + in_grad_shared_mem_bytes; - const int32_t offset = self_interaction ? 1 : 0; - const int vector_num_pack = vector_size / pack_size; - const int padded_vector_num_pack = padded_vector_size / pack_size; - const int in_shared_mem_cols_num_pack = in_shared_mem_num_cols / pack_size; - int max_active_blocks; - OF_CUDA_CHECK(hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks, - DotFeatureInteractionBackwardWmmaImpl, - block_size, total_shared_mem_bytes)); - if (max_active_blocks <= 0) { return false; } - hipStream_t cuda_stream = stream->As()->cuda_stream(); - DotFeatureInteractionBackwardWmmaImpl - <<>>( - m_num_tiles, n_num_tiles, k_num_tiles, batch_size, concated_padded_dim, vector_num_pack, - padded_vector_num_pack, out_num_cols, in_shared_mem_num_cols, - in_shared_mem_cols_num_pack, matrix_out_grad_shared_mem_cols, offset, param); - - return true; - } -}; - -template -bool DispatchFeatureInteractionDotPackSize(user_op::KernelComputeContext* ctx, - const int32_t input_size) { - CHECK_LE(input_size, max_in) << input_size; - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const int64_t batch_size = out->shape_view().At(0); - const int64_t out_num_cols = out->shape_view().At(1); - const int64_t vector_size = ctx->TensorDesc4ArgNameAndIndex("features", 0)->shape().At(2); - DotFwdParam param; - param.num_in = input_size; - param.out = out->mut_dptr(); - int64_t features_concated_dim = 0; - for (int i = 0; i < input_size; ++i) { - param.in[i] = ctx->Tensor4ArgNameAndIndex("features", i)->dptr(); - param.in_feature_dim[i] = ctx->TensorDesc4ArgNameAndIndex("features", i)->shape().At(1); - param.dim_start_offset[i] = features_concated_dim; - features_concated_dim += param.in_feature_dim[i]; - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - param.features_dim = features_concated_dim; - if (ctx->has_input("output_concat", 0)) { - const user_op::Tensor* output_concat = ctx->Tensor4ArgNameAndIndex("output_concat", 0); - param.output_concat = output_concat->dptr(); - param.output_concat_size = output_concat->shape_view().At(1); - } else { - param.output_concat = nullptr; - param.output_concat_size = 0; - } - const bool self_interaction = ctx->Attr("self_interaction"); - const int32_t output_padding = ctx->Attr("output_padding"); - if (vector_size % 4 == 0 && out_num_cols % 4 == 0) { - return DotFeatureInteractionKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - output_padding, param); - } else if (vector_size % 2 == 0 && out_num_cols % 2 == 0) { - return DotFeatureInteractionKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - output_padding, param); - } else { - return DotFeatureInteractionKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - output_padding, param); - } -} - -template -bool DispatchFeatureInteractionDotBackwardPackSize(user_op::KernelComputeContext* ctx, - const int32_t input_size) { - CHECK_LE(input_size, max_in) << input_size; - user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const int64_t batch_size = dy->shape_view().At(0); - const int64_t out_num_cols = dy->shape_view().At(1); - const int64_t vector_size = ctx->TensorDesc4ArgNameAndIndex("features", 0)->shape().At(2); - DotBwdParam param; - param.num_in = input_size; - param.out_grad = dy->dptr(); - int64_t features_concated_dim = 0; - for (int i = 0; i < input_size; ++i) { - param.in[i] = ctx->Tensor4ArgNameAndIndex("features", i)->dptr(); - param.in_grad[i] = ctx->Tensor4ArgNameAndIndex("features_grad", i)->mut_dptr(); - param.in_feature_dim[i] = ctx->TensorDesc4ArgNameAndIndex("features", i)->shape().At(1); - param.dim_start_offset[i] = features_concated_dim; - features_concated_dim += param.in_feature_dim[i]; - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - param.features_dim = features_concated_dim; - if (ctx->has_output("output_concat_grad", 0)) { - user_op::Tensor* output_concat_grad = ctx->Tensor4ArgNameAndIndex("output_concat_grad", 0); - param.output_concat_grad = output_concat_grad->mut_dptr(); - param.output_concat_size = output_concat_grad->shape_view().At(1); - } else { - param.output_concat_grad = nullptr; - param.output_concat_size = 0; - } - const bool self_interaction = ctx->Attr("self_interaction"); - if (vector_size % 4 == 0) { - return DotFeatureInteractionBackwardKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - param); - } else if (vector_size % 2 == 0) { - return DotFeatureInteractionBackwardKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - param); - } else { - return DotFeatureInteractionBackwardKernel::Launch( - ctx->stream(), batch_size, concated_padded_dim, vector_size, out_num_cols, self_interaction, - param); - } -} - -template -struct Param { - const T* in[max_in]; - int32_t in_feature_dim[max_in]; - T* out; - int32_t num_in; -}; - -template -__global__ void FeatureInteractionSum(int64_t batch_size, int64_t vector_num_pack, - Param param) { - using ComputeType = typename DefaultComputeType::type; - Pack* dst_pack = reinterpret_cast*>(param.out); - for (int batch_idx = blockIdx.x * blockDim.y + threadIdx.y; batch_idx < batch_size; - batch_idx += gridDim.x * blockDim.y) { - Pack* batch_out = dst_pack + batch_idx * vector_num_pack; - for (int col_id = threadIdx.x; col_id < vector_num_pack; col_id += blockDim.x) { - Pack sum; - Pack square_sum; -#pragma unroll - for (int k = 0; k < pack_size; ++k) { - sum.elem[k] = static_cast(0); - square_sum.elem[k] = static_cast(0); - } - for (int i = 0; i < max_in; ++i) { - if (i >= param.num_in) { break; } - const Pack* batch_in = - reinterpret_cast*>(param.in[i]) - + batch_idx * param.in_feature_dim[i] * vector_num_pack; -#pragma unroll - for (int j = 0; j < param.in_feature_dim[i]; ++j) { - Pack val = batch_in[j * vector_num_pack + col_id]; -#pragma unroll - for (int k = 0; k < pack_size; ++k) { - const ComputeType compute_val = static_cast(val.elem[k]); - sum.elem[k] += compute_val; - square_sum.elem[k] += compute_val * compute_val; - } - } - } - Pack out; -#pragma unroll - for (int k = 0; k < pack_size; ++k) { - out.elem[k] = static_cast((sum.elem[k] * sum.elem[k] - square_sum.elem[k]) - * static_cast(0.5)); - } - batch_out[col_id] = out; - } - } -} - -template -struct GradParam { - const T* out_grad; - const T* in[max_in]; - int32_t in_feature_dim[max_in]; - T* in_grad[max_in]; - int32_t num_in; -}; - -template -__global__ void FeatureInteractionSumGrad(int64_t batch_size, int64_t vector_size, - GradParam param) { - using ComputeType = typename DefaultComputeType::type; - for (int batch_idx = blockIdx.x * blockDim.y + threadIdx.y; batch_idx < batch_size; - batch_idx += gridDim.x * blockDim.y) { - const T* batch_out_grad = param.out_grad + batch_idx * vector_size; - for (int col_id = threadIdx.x; col_id < vector_size; col_id += blockDim.x) { - ComputeType sum = 0; - for (int i = 0; i < max_in; ++i) { - if (i >= param.num_in) { break; } - const T* batch_in = param.in[i] + batch_idx * param.in_feature_dim[i] * vector_size; - for (int j = 0; j < param.in_feature_dim[i]; ++j) { - sum += static_cast(batch_in[j * vector_size + col_id]); - } - } - for (int i = 0; i < max_in; ++i) { - if (i >= param.num_in) { break; } - const int64_t in_batch_offset = batch_idx * param.in_feature_dim[i] * vector_size; - const T* batch_in = param.in[i] + in_batch_offset; - T* batch_in_grad = param.in_grad[i] + in_batch_offset; - for (int j = 0; j < param.in_feature_dim[i]; ++j) { - const int64_t offset = j * vector_size + col_id; - batch_in_grad[offset] = - static_cast(static_cast(batch_out_grad[col_id]) - * (sum - static_cast(batch_in[offset]))); - } - } - } - } -} - -void GetBlockDims(const int64_t vector_size, int* block_dim_x, int* block_dim_y) { - const int block_size = 256; - if (vector_size < block_size) { - *block_dim_x = std::ceil(static_cast(vector_size) / 8) * 8; - *block_dim_y = (block_size + *block_dim_x - 1) / *block_dim_x; - } else { - *block_dim_x = block_size; - *block_dim_y = 1; - } -} - -int GetNumBlocks(const int64_t num_instances, const int64_t instance_per_block) { - int max_blocks = (num_instances + instance_per_block - 1) / instance_per_block; - return std::min(max_blocks, kCudaMaxBlocksNum); -} - -template -void DispatchFeatureInteractionSumPackSize(ep::Stream* stream, const int64_t batch_size, - const int64_t vector_size, - const Param& param) { - int block_dim_x; - int block_dim_y; - const int pack_size = (vector_size % 2 == 0) ? 2 : 1; - const int64_t vector_num_pack = vector_size / pack_size; - GetBlockDims(vector_num_pack, &block_dim_x, &block_dim_y); - const int num_blocks = GetNumBlocks(batch_size, block_dim_y); - dim3 block_dims = dim3(block_dim_x, block_dim_y); - hipStream_t cuda_stream = stream->As()->cuda_stream(); - if (pack_size == 2) { - hipLaunchKernelGGL(HIP_KERNEL_NAME(FeatureInteractionSum), num_blocks, block_dims, 0, cuda_stream, batch_size, vector_num_pack, param); - } else { - hipLaunchKernelGGL(HIP_KERNEL_NAME(FeatureInteractionSum), num_blocks, block_dims, 0, cuda_stream, batch_size, vector_num_pack, param); - } -} - -template -void DispatchFeatureInteractionSumInputSize(user_op::KernelComputeContext* ctx, - const int32_t input_size) { - CHECK_LE(input_size, max_in) << input_size; - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const int64_t batch_size = out->shape_view().At(0); - const int64_t vector_size = out->shape_view().At(1); - Param param; - param.num_in = input_size; - param.out = out->mut_dptr(); - for (int i = 0; i < input_size; ++i) { - param.in[i] = ctx->Tensor4ArgNameAndIndex("features", i)->dptr(); - param.in_feature_dim[i] = ctx->TensorDesc4ArgNameAndIndex("features", i)->shape().At(1); - } - DispatchFeatureInteractionSumPackSize(ctx->stream(), batch_size, vector_size, param); -} - -template -void DispatchFeatureInteractionSumGradInputSize(user_op::KernelComputeContext* ctx, - const int32_t input_size) { - CHECK_LE(input_size, max_in) << input_size; - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const int64_t batch_size = dy->shape_view().At(0); - const int64_t vector_size = dy->shape_view().At(1); - int block_dim_x; - int block_dim_y; - GetBlockDims(vector_size, &block_dim_x, &block_dim_y); - const int num_blocks = GetNumBlocks(batch_size, block_dim_y); - dim3 block_dims = dim3(block_dim_x, block_dim_y); - GradParam param; - param.num_in = input_size; - param.out_grad = dy->dptr(); - for (int i = 0; i < input_size; ++i) { - param.in[i] = ctx->Tensor4ArgNameAndIndex("features", i)->dptr(); - param.in_grad[i] = ctx->Tensor4ArgNameAndIndex("features_grad", i)->mut_dptr(); - param.in_feature_dim[i] = ctx->TensorDesc4ArgNameAndIndex("features_grad", i)->shape().At(1); - } - FeatureInteractionSumGrad - <<stream()->As()->cuda_stream()>>>( - batch_size, vector_size, param); -} - -} // namespace - -template -class FusedDotFeatureInteractionPoolingSumKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedDotFeatureInteractionPoolingSumKernel() = default; - ~FusedDotFeatureInteractionPoolingSumKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const int input_size = ctx->input_size("features"); - if (input_size == 1) { - DispatchFeatureInteractionSumInputSize(ctx, input_size); - } else if (input_size == 2) { - DispatchFeatureInteractionSumInputSize(ctx, input_size); - } else if (input_size <= 8) { - DispatchFeatureInteractionSumInputSize(ctx, input_size); - } else { - CHECK_LE(input_size, 128) << "input_size must not greater than 128. "; - DispatchFeatureInteractionSumInputSize(ctx, input_size); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_dot_feature_interaction") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value) \ - && (user_op::HobAttr("pooling") == "sum")); - -REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_KERNEL(float) -REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_KERNEL(half) - -template -bool TryLaunchTensorCoreDotKernel(user_op::KernelComputeContext* ctx) { - const int input_size = ctx->input_size("features"); - if (input_size == 1) { - return DispatchFeatureInteractionDotPackSize(ctx, input_size); - } else if (input_size == 2) { - return DispatchFeatureInteractionDotPackSize(ctx, input_size); - } else if (input_size <= 8) { - return DispatchFeatureInteractionDotPackSize(ctx, input_size); - } else { - CHECK_LE(input_size, 128) << "input_size must not greater than 128. "; - return DispatchFeatureInteractionDotPackSize(ctx, input_size); - } -} - -template -bool TryLaunchTensorCoreDotBackwardKernel(user_op::KernelComputeContext* ctx) { - const int input_size = ctx->input_size("features"); - if (input_size == 1) { - return DispatchFeatureInteractionDotBackwardPackSize(ctx, input_size); - } else if (input_size == 2) { - return DispatchFeatureInteractionDotBackwardPackSize(ctx, input_size); - } else if (input_size <= 8) { - return DispatchFeatureInteractionDotBackwardPackSize(ctx, input_size); - } else { - CHECK_LE(input_size, 128) << "input_size must not greater than 128. "; - return DispatchFeatureInteractionDotBackwardPackSize(ctx, input_size); - } -} -template -class FusedDotFeatureInteractionKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedDotFeatureInteractionKernel() = default; - ~FusedDotFeatureInteractionKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const DataType data_type = out->data_type(); - CHECK_LT(out->shape_view().elem_cnt(), GetMaxVal()); - auto* cuda_stream = ctx->stream()->As(); - // if ((cuda_stream->device_properties().major >= 7 && data_type == DataType::kFloat16) - // || (cuda_stream->device_properties().major >= 8 && data_type == DataType::kFloat)) { - // bool success = TryLaunchTensorCoreDotKernel(ctx); - // if (success == true) { return; } - // } - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int64_t batch_size = out->shape_view().At(0); - int64_t features_concated_dim = 0; - for (int64_t i = 0; i < ctx->input_size("features"); ++i) { - features_concated_dim += ctx->TensorDesc4ArgNameAndIndex("features", i)->shape().At(1); - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - const int64_t vector_size = ctx->TensorDesc4ArgNameAndIndex("features", 0)->shape().At(2); - const int64_t out_dim = out->shape_view().At(1); - const int32_t output_padding = ctx->Attr("output_padding"); - const int64_t valid_out_dim = out_dim - output_padding; - const bool self_interaction = ctx->Attr("self_interaction"); - - T* matmul_out = reinterpret_cast(tmp_buffer->mut_dptr()); - size_t matmul_out_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * concated_padded_dim * sizeof(T)); - const int64_t interaction_dim = self_interaction - ? features_concated_dim * (features_concated_dim + 1) / 2 - : features_concated_dim * (features_concated_dim - 1) / 2; - int32_t* gather_indices_ptr = - reinterpret_cast(tmp_buffer->mut_dptr() + matmul_out_size); - size_t gather_indices_size = GetCudaAlignedSize(interaction_dim * sizeof(int32_t)); - T* padded_concated_features_ptr = - reinterpret_cast(tmp_buffer->mut_dptr() + matmul_out_size + gather_indices_size); - size_t padded_concated_features_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * vector_size * sizeof(T)); - CHECK_GE(tmp_buffer->shape_view().elem_cnt(), - matmul_out_size + gather_indices_size + padded_concated_features_size); - ConcatFeatures(ctx, batch_size, concated_padded_dim * vector_size, - padded_concated_features_ptr); - auto batch_matmul = ep::primitive::NewPrimitive( - ctx->device_type(), data_type, ep::primitive::BlasTransposeType::N, - ep::primitive::BlasTransposeType::T); - batch_matmul->Launch(ctx->stream(), batch_size, concated_padded_dim, concated_padded_dim, - vector_size, 1.0, padded_concated_features_ptr, - padded_concated_features_ptr, 0.0, matmul_out); - - int64_t output_concat_end_dim = 0; - const T* output_concat_ptr = nullptr; - if (ctx->has_input("output_concat", 0)) { - user_op::Tensor* output_concat = ctx->Tensor4ArgNameAndIndex("output_concat", 0); - output_concat_end_dim = output_concat->shape_view().At(1); - output_concat_ptr = output_concat->dptr(); - } - CHECK_EQ(valid_out_dim, output_concat_end_dim + interaction_dim); - GatherConcatKernel(ctx->stream(), out->shape_view().elem_cnt(), out_dim, valid_out_dim, - features_concated_dim, concated_padded_dim, output_concat_end_dim, - self_interaction, matmul_out, output_concat_ptr, gather_indices_ptr, - out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -user_op::InferTmpSizeFn GenFusedDotFeatureInteractionInferTmpSizeFn() { - return [](user_op::InferContext* ctx) { - const Shape& first_feature_shape = ctx->InputShape("features", 0); - const int64_t batch_size = first_feature_shape.At(0); - const int64_t vector_size = first_feature_shape.At(2); - int64_t features_concated_dim = 0; - for (int32_t i = 0; i < ctx->input_size("features"); ++i) { - features_concated_dim += ctx->InputShape("features", i).At(1); - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - size_t matmul_out_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * concated_padded_dim * sizeof(T)); - const bool self_interaction = ctx->Attr("self_interaction"); - const int64_t interaction_dim = self_interaction - ? features_concated_dim * (features_concated_dim + 1) / 2 - : features_concated_dim * (features_concated_dim - 1) / 2; - size_t gather_indices_size = GetCudaAlignedSize(interaction_dim * sizeof(int32_t)); - size_t padded_concated_features_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * vector_size * sizeof(T)); - return matmul_out_size + gather_indices_size + padded_concated_features_size; - }; -} - -#define REGISTER_FUSED_DOT_FEATURE_INTERACTION_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_dot_feature_interaction") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value) \ - && (user_op::HobAttr("pooling") == "none")) \ - .SetInferTmpSizeFn(GenFusedDotFeatureInteractionInferTmpSizeFn()); - -REGISTER_FUSED_DOT_FEATURE_INTERACTION_KERNEL(float) -REGISTER_FUSED_DOT_FEATURE_INTERACTION_KERNEL(half) - -template -class FusedDotFeatureInteractionGradKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedDotFeatureInteractionGradKernel() = default; - ~FusedDotFeatureInteractionGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const DataType data_type = dy->data_type(); - auto* cuda_stream = ctx->stream()->As(); - // if ((cuda_stream->device_properties().major >= 7 && data_type == DataType::kFloat16) - // || (cuda_stream->device_properties().major >= 8 && data_type == DataType::kFloat)) { - // bool success = TryLaunchTensorCoreDotBackwardKernel(ctx); - // if (success == true) { return; } - // } - const int64_t batch_size = dy->shape_view().At(0); - int64_t features_concated_dim = 0; - for (int32_t i = 0; i < ctx->output_size("features_grad"); ++i) { - features_concated_dim += ctx->TensorDesc4ArgNameAndIndex("features_grad", i)->shape().At(1); - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - const int64_t vector_size = ctx->TensorDesc4ArgNameAndIndex("features_grad", 0)->shape().At(2); - const int64_t out_dim = dy->shape_view().At(1); - const bool self_interaction = ctx->Attr("self_interaction"); - T* matmul_out_grad_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); - size_t matmul_out_grad_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * concated_padded_dim * sizeof(T)); - T* padded_concated_features_grad_ptr = - reinterpret_cast(tmp_buffer->mut_dptr() + matmul_out_grad_size); - size_t padded_concated_features_grad_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * vector_size * sizeof(T)); - T* padded_concated_features_ptr = reinterpret_cast( - tmp_buffer->mut_dptr() + matmul_out_grad_size + padded_concated_features_grad_size); - size_t padded_concated_features_size = padded_concated_features_grad_size; - CHECK_LE( - matmul_out_grad_size + padded_concated_features_grad_size + padded_concated_features_size, - tmp_buffer->shape_view().elem_cnt()); - ConcatFeatures(ctx, batch_size, concated_padded_dim * vector_size, - padded_concated_features_ptr); - - T* output_concat_grad_ptr = nullptr; - int64_t output_concat_end_dim = 0; - if (ctx->has_output("output_concat_grad", 0)) { - user_op::Tensor* output_concat_grad = ctx->Tensor4ArgNameAndIndex("output_concat_grad", 0); - output_concat_grad_ptr = output_concat_grad->mut_dptr(); - output_concat_end_dim = output_concat_grad->shape_view().At(1); - } - ScatterSplitAddTranspose(ctx->stream(), batch_size, out_dim, concated_padded_dim, - features_concated_dim, output_concat_end_dim, self_interaction, - dy->dptr(), output_concat_grad_ptr, matmul_out_grad_ptr); - - auto batch_matmul = ep::primitive::NewPrimitive( - ctx->device_type(), data_type, ep::primitive::BlasTransposeType::N, - ep::primitive::BlasTransposeType::N); - batch_matmul->Launch(ctx->stream(), batch_size, concated_padded_dim, vector_size, - concated_padded_dim, 1.0, matmul_out_grad_ptr, - padded_concated_features_ptr, 0.0, padded_concated_features_grad_ptr); - - ConcatFeaturesGrad(ctx, batch_size, concated_padded_dim, vector_size, - padded_concated_features_grad_ptr); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -user_op::InferTmpSizeFn GenFusedDotFeatureInteractionGradInferTmpSizeFn() { - return [](user_op::InferContext* ctx) { - int64_t features_concated_dim = 0; - for (int32_t i = 0; i < ctx->output_size("features_grad"); ++i) { - features_concated_dim += ctx->InputShape("features_grad", i).At(1); - } - const int64_t concated_padded_dim = GetPaddedDim(features_concated_dim); - const int64_t batch_size = ctx->InputShape("features_grad", 0).At(0); - const int64_t vector_size = ctx->InputShape("features_grad", 0).At(2); - size_t matmul_out_grad_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * concated_padded_dim * sizeof(T)); - size_t padded_concated_features_grad_size = - GetCudaAlignedSize(batch_size * concated_padded_dim * vector_size * sizeof(T)); - size_t padded_concated_features_size = padded_concated_features_grad_size; - return matmul_out_grad_size + padded_concated_features_grad_size - + padded_concated_features_size; - }; -} - -#define REGISTER_FUSED_DOT_FEATURE_INTERACTION_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_dot_feature_interaction_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobAttr("pooling") == "none")) \ - .SetInferTmpSizeFn(GenFusedDotFeatureInteractionGradInferTmpSizeFn()); - -REGISTER_FUSED_DOT_FEATURE_INTERACTION_GRAD_KERNEL(float) -REGISTER_FUSED_DOT_FEATURE_INTERACTION_GRAD_KERNEL(half) - -template -class FusedDotFeatureInteractionPoolingSumGradKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedDotFeatureInteractionPoolingSumGradKernel() = default; - ~FusedDotFeatureInteractionPoolingSumGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const int input_size = ctx->input_size("features"); - if (input_size == 1) { - DispatchFeatureInteractionSumGradInputSize(ctx, input_size); - } else if (input_size == 2) { - DispatchFeatureInteractionSumGradInputSize(ctx, input_size); - } else if (input_size <= 8) { - DispatchFeatureInteractionSumGradInputSize(ctx, input_size); - } else { - CHECK_LE(input_size, 128) << "input_size must not greater than 128. "; - DispatchFeatureInteractionSumGradInputSize(ctx, input_size); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_dot_feature_interaction_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobAttr("pooling") == "sum")); - -REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_GRAD_KERNEL(float) -REGISTER_FUSED_DOT_FEATURE_INTERACTION_POOLING_SUM_GRAD_KERNEL(half) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_gelu_mul_kernel.cu b/oneflow/user/kernels/fused_gelu_mul_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..a59398a0bb6d2c4edb5b29eda7578b69cd39c561 --- /dev/null +++ b/oneflow/user/kernels/fused_gelu_mul_kernel.cu @@ -0,0 +1,381 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/cuda/elementwise.cuh" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { +namespace cuda { + +namespace fused_gelu { + +OF_DEVICE_FUNC float TanhApprox(float x) { +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + float r; + asm("tanh.approx.f32 %0,%1; \n\t" : "=f"(r) : "f"(x)); + return r; +#else + return tanhf(x); +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) +} + +template +struct FusedFastGeluMulFunctor { + static constexpr T alpha = static_cast(0.7978845608028654); + static constexpr T beta = static_cast(0.044714998453855515); + + OF_DEVICE_FUNC FusedFastGeluMulFunctor() {} + + OF_DEVICE_FUNC T operator()(T x, T m) const { + // ref to UnaryFunctor of kFastGelu + const T half = static_cast(0.5); + const T one = static_cast(1); + const T tanh_in = alpha * (x + beta * x * x * x); + return half * x * (one + tanh(tanh_in)) * m; + } +}; + +template<> +struct FusedFastGeluMulFunctor { + static constexpr float alpha = FusedFastGeluMulFunctor::alpha; + static constexpr float beta = FusedFastGeluMulFunctor::beta; + FusedFastGeluMulFunctor float_functor; + + OF_DEVICE_FUNC FusedFastGeluMulFunctor() {} + + OF_DEVICE_FUNC half operator()(const half x, const half m) const { +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + const float tanh_in = + __half2float(__float2half_rn(alpha) * (x + __float2half_rn(beta) * x * x * x)); + const float tanh_out = TanhApprox(tanh_in); + return __float2half_rn(0.5F) * x * (__float2half_rn(1.0F) + __float2half_rn(tanh_out)) * m; +#else + return static_cast(float_functor(static_cast(x), static_cast(m))); +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + } + +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + __device__ void Apply2(half* y, const half* x, const half* m) const { + const half2 x2 = *(reinterpret_cast(x)); + const float2 tanh_in = __half22float2( + __hmul2(__float2half2_rn(alpha), + __hadd2(x2, __hmul2(__hmul2(__hmul2(__float2half2_rn(beta), x2), x2), x2)))); + float2 tanh_out; + tanh_out.x = TanhApprox(tanh_in.x); + tanh_out.y = TanhApprox(tanh_in.y); + const half2 m2 = *(reinterpret_cast(m)); + const half2 y2 = __hmul2(__hmul2(__hmul2(__float2half2_rn(0.5F), x2), + __hadd2(__float2half2_rn(1.0F), __float22half2_rn(tanh_out))), + m2); + *reinterpret_cast(y) = y2; + } +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) +}; + +#if CUDA_VERSION >= 11000 + +template<> +struct FusedFastGeluMulFunctor { + FusedFastGeluMulFunctor float_functor; + + OF_DEVICE_FUNC FusedFastGeluMulFunctor() {} + + OF_DEVICE_FUNC nv_bfloat16 operator()(const nv_bfloat16 x, const nv_bfloat16 m) const { + return __float2bfloat16(float_functor(__bfloat162float(x), __bfloat162float(m))); + } +}; + +#endif // CUDA_VERSION >= 11000 + +} // namespace fused_gelu + +template +class FusedFastGeluMulKernel final : public user_op::OpKernel { + public: + FusedFastGeluMulKernel() = default; + ~FusedFastGeluMulKernel() override = default; + + private: + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* in = ctx->Tensor4ArgNameAndIndex("in", 0); + const auto* multiplier = ctx->Tensor4ArgNameAndIndex("multiplier", 0); + auto* out = ctx->Tensor4ArgNameAndIndex("out", 0); + + int64_t elem_cnt = in->shape_view().elem_cnt(); + OF_CUDA_CHECK((elementwise::Binary(fused_gelu::FusedFastGeluMulFunctor(), elem_cnt, + out->mut_dptr(), in->dptr(), multiplier->dptr(), + ctx->stream()->As()->cuda_stream()))); + }; +}; + +#define REGISTER_FUSED_FAST_GELU_MUL_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_fast_gelu_mul") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("in", 0) == GetDataType::value)); + +REGISTER_FUSED_FAST_GELU_MUL_CUDA_KERNEL(float) +REGISTER_FUSED_FAST_GELU_MUL_CUDA_KERNEL(double) +REGISTER_FUSED_FAST_GELU_MUL_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_FAST_GELU_MUL_CUDA_KERNEL(nv_bfloat16) +#endif + +namespace fused_gelu { + +template +struct FusedFastGeluMulGradFunctor { + static constexpr T alpha = static_cast(0.7978845608028654); + static constexpr T beta = static_cast(0.044714998453855515); + + __device__ FusedFastGeluMulGradFunctor() {} + + __device__ void operator()(T& x_diff, T& m_diff, const T& dy, const T& x, const T& m) const { + const T one = static_cast(1); + const T half = static_cast(0.5); + const T pow3 = x * x * x; + const T tanh_in = alpha * (x + beta * pow3); + const T tanh_out = tanh(alpha * (x + beta * pow3)); + // calc m_diff ref to UnaryFunctor of kFastGelu + m_diff = half * x * (one + tanh(tanh_in)) * dy; + // calc x_diff ref to BinaryOp::kFastGeluBackwardWithDyX + const T dtanh = alpha * (half * x + beta * static_cast(1.5) * pow3); + x_diff = (half + half * tanh_out + dtanh * (one - tanh_out * tanh_out)) * m * dy; + } +}; + +template<> +struct FusedFastGeluMulGradFunctor { + static constexpr float alpha = FusedFastGeluMulGradFunctor::alpha; + static constexpr float beta = FusedFastGeluMulGradFunctor::beta; + FusedFastGeluMulGradFunctor float_functor; + + __device__ FusedFastGeluMulGradFunctor() {} + + __device__ void operator()(half& x_diff, half& m_diff, const half& dy, const half& x, + const half& m) const { +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + const half halpha = __float2half_rn(alpha); + const half hbeta = __float2half_rn(beta); + const half hone = __float2half_rn(1.0F); + const half hhalf = __float2half_rn(0.5F); + const half pow3 = x * x * x; + const float tanh_in = __half2float(halpha * (x + hbeta * pow3)); + const half tanh_out = __float2half_rn(TanhApprox(tanh_in)); + // m_diff + m_diff = hhalf * x * (hone + tanh_out) * dy; + // x_diff + const half dtanh = halpha * (hhalf * x + hbeta * __float2half_rn(1.5F) * pow3); + x_diff = (hhalf + hhalf * tanh_out + dtanh * (hone - tanh_out * tanh_out)) * m * dy; +#else + float x_diff_float; + float m_diff_float; + float_functor(x_diff_float, m_diff_float, static_cast(dy), static_cast(x), + static_cast(m)); + x_diff = static_cast(x_diff_float); + m_diff = static_cast(m_diff_float); +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + } + +#if (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) + __device__ void Apply2(half* x_diff, half* m_diff, const half* dy, const half* x, + const half* m) const { + const half2 dy2 = *(reinterpret_cast(dy)); + const half2 x2 = *(reinterpret_cast(x)); + const half2 m2 = *(reinterpret_cast(m)); + const half2 alpha2 = __float2half2_rn(alpha); + const half2 beta2 = __float2half2_rn(beta); + const half2 one2 = __float2half2_rn(1.0F); + const half2 hhalf2 = __float2half2_rn(0.5F); + const half2 pow3 = __hmul2(__hmul2(x2, x2), x2); + const float2 tanh_in = __half22float2(__hmul2(alpha2, __hadd2(x2, __hmul2(beta2, pow3)))); + float2 tanh_out; + tanh_out.x = TanhApprox(tanh_in.x); + tanh_out.y = TanhApprox(tanh_in.y); + const half2 tanh_out2 = __float22half2_rn(tanh_out); + // m_diff + const half2 m_diff2 = __hmul2(__hmul2(hhalf2, __hmul2(x2, __hadd2(one2, tanh_out2))), dy2); + // x_diff + const half2 dtanh = __hmul2( + alpha2, + __hadd2(__hmul2(hhalf2, x2), __hmul2(beta2, __hmul2(pow3, __float2half2_rn(1.5F))))); + const half2 x_diff2 = + __hmul2(__hmul2(__hadd2(__hadd2(hhalf2, __hmul2(hhalf2, tanh_out2)), + __hmul2(dtanh, __hsub2(one2, __hmul2(tanh_out2, tanh_out2)))), + m2), + dy2); + *reinterpret_cast(x_diff) = x_diff2; + *reinterpret_cast(m_diff) = m_diff2; + } +#endif // (__CUDA_ARCH__ >= 750 && CUDA_VERSION >= 11000) +}; + +#if CUDA_VERSION >= 11000 + +template<> +struct FusedFastGeluMulGradFunctor { + FusedFastGeluMulGradFunctor float_functor; + + __device__ FusedFastGeluMulGradFunctor() {} + + __device__ void operator()(nv_bfloat16& x_diff, nv_bfloat16& m_diff, const nv_bfloat16& dy, + const nv_bfloat16& x, const nv_bfloat16& m) const { + float x_diff_float; + float m_diff_float; + float_functor(x_diff_float, m_diff_float, __bfloat162float(dy), __bfloat162float(x), + __bfloat162float(m)); + x_diff = __float2bfloat16(x_diff_float); + m_diff = __float2bfloat16(m_diff_float); + } +}; + +#endif // CUDA_VERSION >= 11000 + +template +__device__ __forceinline__ + typename std::enable_if::value == true && pack_size % 2 == 0, + void>::type + FusedFastGeluMulGradFunctorApplyPack(const FunctorT& functor, + elementwise::Packed& x_diff_pack, + elementwise::Packed& m_diff_pack, + const elementwise::Packed& dy_pack, + const elementwise::Packed& x_pack, + const elementwise::Packed& m_pack) { +#pragma unroll + for (int j = 0; j < pack_size; j += 2) { + functor.Apply2(x_diff_pack.elem + j, m_diff_pack.elem + j, dy_pack.elem + j, x_pack.elem + j, + m_pack.elem + j); + } +} + +template +__device__ __forceinline__ + typename std::enable_if::value == false || pack_size % 2 != 0, + void>::type + FusedFastGeluMulGradFunctorApplyPack(const FunctorT& functor, + elementwise::Packed& x_diff_pack, + elementwise::Packed& m_diff_pack, + const elementwise::Packed& dy_pack, + const elementwise::Packed& x_pack, + const elementwise::Packed& m_pack) { +#pragma unroll + for (int j = 0; j < pack_size; ++j) { + functor(x_diff_pack.elem[j], m_diff_pack.elem[j], dy_pack.elem[j], x_pack.elem[j], + m_pack.elem[j]); + } +} + +template +__global__ void __launch_bounds__(elementwise::kBlockSize) + FusedFastGeluMulGradCudaKernel(int64_t n_pack, elementwise::Packed* x_diff_pack, + elementwise::Packed* m_diff_pack, + const elementwise::Packed* dy_pack, + const elementwise::Packed* x_pack, + const elementwise::Packed* m_pack, int64_t n_tail, + T* x_diff_tail, T* m_diff_tail, const T* dy_tail, + const T* x_tail, const T* m_tail) { + FusedFastGeluMulGradFunctor functor; + const int global_tid = blockIdx.x * elementwise::kBlockSize + threadIdx.x; + for (int64_t i = global_tid; i < n_pack; i += blockDim.x * gridDim.x) { + FusedFastGeluMulGradFunctorApplyPack(functor, x_diff_pack[i], m_diff_pack[i], + dy_pack[i], x_pack[i], m_pack[i]); + } + if (global_tid < n_tail) { + functor(x_diff_tail[global_tid], m_diff_tail[global_tid], dy_tail[global_tid], + x_tail[global_tid], m_tail[global_tid]); + } +} + +template +GPU(Error_t) LaunchFusedFastGeluMulGradCudaKernelByPack(GPU(Stream_t) stream, int64_t n, T* x_diff, + T* m_diff, const T* dy, const T* x, + const T* m) { + const int64_t n_pack = n / pack_size; + const int64_t tail_offset = n_pack * pack_size; + const int64_t n_tail = n - tail_offset; + int num_blocks; + { + GPU(Error_t) err = elementwise::GetNumBlocks(n_pack, &num_blocks); + if (err != GPU(Success)) { return err; } + } + FusedFastGeluMulGradCudaKernel<<>>( + n_pack, reinterpret_cast*>(x_diff), + reinterpret_cast*>(m_diff), + reinterpret_cast*>(dy), + reinterpret_cast*>(x), + reinterpret_cast*>(m), n_tail, x_diff + tail_offset, + m_diff + tail_offset, dy + tail_offset, x + tail_offset, m + tail_offset); + return GPU(PeekAtLastError)(); +} + +template +static GPU(Error_t) LaunchFusedFastGeluMulGradCudaKernel(GPU(Stream_t) stream, int64_t n, T* x_diff, + T* m_diff, const T* dy, const T* x, + const T* m) { + constexpr int max_pack_size = elementwise::PackSize(); + if (elementwise::IsAlignedForPack(x_diff, m_diff, dy, x, m)) { + return LaunchFusedFastGeluMulGradCudaKernelByPack(stream, n, x_diff, m_diff, dy, + x, m); + } else { + return LaunchFusedFastGeluMulGradCudaKernelByPack<1>(stream, n, x_diff, m_diff, dy, x, m); + } +} + +} // namespace fused_gelu + +template +class FusedFastGeluMulGradKernel final : public user_op::OpKernel { + public: + FusedFastGeluMulGradKernel() = default; + ~FusedFastGeluMulGradKernel() override = default; + + private: + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* out_diff = ctx->Tensor4ArgNameAndIndex("out_diff", 0); + const auto* in = ctx->Tensor4ArgNameAndIndex("in", 0); + const auto* multiplier = ctx->Tensor4ArgNameAndIndex("multiplier", 0); + auto* in_diff = ctx->Tensor4ArgNameAndIndex("in_diff", 0); + auto* multiplier_diff = ctx->Tensor4ArgNameAndIndex("multiplier_diff", 0); + + int64_t elem_cnt = in->shape_view().elem_cnt(); + OF_CUDA_CHECK((fused_gelu::LaunchFusedFastGeluMulGradCudaKernel( + ctx->stream()->As()->cuda_stream(), elem_cnt, in_diff->mut_dptr(), + multiplier_diff->mut_dptr(), out_diff->dptr(), in->dptr(), + multiplier->dptr()))); + }; +}; + +#define REGISTER_FUSED_FAST_GELU_MUL_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_fast_gelu_mul_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out_diff", 0) == GetDataType::value)); + +REGISTER_FUSED_FAST_GELU_MUL_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_FAST_GELU_MUL_GRAD_CUDA_KERNEL(double) +REGISTER_FUSED_FAST_GELU_MUL_GRAD_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_FAST_GELU_MUL_GRAD_CUDA_KERNEL(nv_bfloat16) +#endif + +} // namespace cuda +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_bounding_boxes_coord_kernel.cu b/oneflow/user/kernels/fused_get_bounding_boxes_coord_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..538c8d60c033621e0a19be8e769bb7e77180c47e --- /dev/null +++ b/oneflow/user/kernels/fused_get_bounding_boxes_coord_kernel.cu @@ -0,0 +1,168 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { +template +__global__ void FusedGetBounddingBoxesCoordForward(const int n, const T* x1, const T* y1, + const T* w1, const T* h1, const T* x2, + const T* y2, const T* w2, const T* h2, T* b1_x1, + T* b1_x2, T* b1_y1, T* b1_y2, T* b2_x1, T* b2_x2, + T* b2_y1, T* b2_y2) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T w1_ = w1[i] / static_cast(2.0); + const T h1_ = h1[i] / static_cast(2.0); + const T w2_ = w2[i] / static_cast(2.0); + const T h2_ = h2[i] / static_cast(2.0); + const T x1_i = x1[i], y1_i = y1[i], x2_i = x2[i], y2_i = y2[i]; + b1_x1[i] = x1_i - w1_; + b1_x2[i] = x1_i + w1_; + b1_y1[i] = y1_i - h1_; + b1_y2[i] = y1_i + h1_; + b2_x1[i] = x2_i - w2_; + b2_x2[i] = x2_i + w2_; + b2_y1[i] = y2_i - h2_; + b2_y2[i] = y2_i + h2_; + } +} + +template +__global__ void FusedGetBounddingBoxesCoordBackward( + const int n, const T* b1_x1_diff, const T* b1_x2_diff, const T* b1_y1_diff, const T* b1_y2_diff, + const T* b2_x1_diff, const T* b2_x2_diff, const T* b2_y1_diff, const T* b2_y2_diff, T* x1_diff, + T* y1_diff, T* w1_diff, T* h1_diff, T* x2_diff, T* y2_diff, T* w2_diff, T* h2_diff) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T b1_x1_diff_i = b1_x1_diff[i]; + const T b1_x2_diff_i = b1_x2_diff[i]; + const T b1_y1_diff_i = b1_y1_diff[i]; + const T b1_y2_diff_i = b1_y2_diff[i]; + const T b2_x1_diff_i = b2_x1_diff[i]; + const T b2_x2_diff_i = b2_x2_diff[i]; + const T b2_y2_diff_i = b2_y2_diff[i]; + const T b2_y1_diff_i = b2_y1_diff[i]; + x1_diff[i] = b1_x1_diff_i + b1_x2_diff_i; + y1_diff[i] = b1_y1_diff_i + b1_y2_diff_i; + w1_diff[i] = (b1_x2_diff_i - b1_x1_diff_i) / static_cast(2.0); + h1_diff[i] = (b1_y2_diff_i - b1_y1_diff_i) / static_cast(2.0); + x2_diff[i] = b2_x1_diff_i + b2_x2_diff_i; + y2_diff[i] = b2_y1_diff_i + b2_y2_diff_i; + w2_diff[i] = (b2_x2_diff_i - b2_x1_diff_i) / static_cast(2.0); + h2_diff[i] = (b2_y2_diff_i - b2_y1_diff_i) / static_cast(2.0); + } +} +}; // namespace + +template +class FusedGetBounddingBoxesCoordGpuKernel final : public user_op::OpKernel { + public: + FusedGetBounddingBoxesCoordGpuKernel() = default; + ~FusedGetBounddingBoxesCoordGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x1 = ctx->Tensor4ArgNameAndIndex("x1", 0); + const user_op::Tensor* y1 = ctx->Tensor4ArgNameAndIndex("y1", 0); + const user_op::Tensor* w1 = ctx->Tensor4ArgNameAndIndex("w1", 0); + const user_op::Tensor* h1 = ctx->Tensor4ArgNameAndIndex("h1", 0); + const user_op::Tensor* x2 = ctx->Tensor4ArgNameAndIndex("x2", 0); + const user_op::Tensor* y2 = ctx->Tensor4ArgNameAndIndex("y2", 0); + const user_op::Tensor* w2 = ctx->Tensor4ArgNameAndIndex("w2", 0); + const user_op::Tensor* h2 = ctx->Tensor4ArgNameAndIndex("h2", 0); + + user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + const int32_t elem_cnt = x1->shape_view().elem_cnt(); + RUN_CUDA_KERNEL((FusedGetBounddingBoxesCoordForward), ctx->stream(), elem_cnt, elem_cnt, + x1->dptr(), y1->dptr(), w1->dptr(), h1->dptr(), x2->dptr(), + y2->dptr(), w2->dptr(), h2->dptr(), b1_x1->mut_dptr(), + b1_x2->mut_dptr(), b1_y1->mut_dptr(), b1_y2->mut_dptr(), + b2_x1->mut_dptr(), b2_x2->mut_dptr(), b2_y1->mut_dptr(), + b2_y2->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_boundding_boxes_coord") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_CUDA_KERNEL(half) +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_CUDA_KERNEL(double) + +template +class FusedGetBounddingBoxesCoordGradGpuKernel final : public user_op::OpKernel { + public: + FusedGetBounddingBoxesCoordGradGpuKernel() = default; + ~FusedGetBounddingBoxesCoordGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1_diff = ctx->Tensor4ArgNameAndIndex("b1_x1_diff", 0); + const user_op::Tensor* b1_x2_diff = ctx->Tensor4ArgNameAndIndex("b1_x2_diff", 0); + const user_op::Tensor* b1_y1_diff = ctx->Tensor4ArgNameAndIndex("b1_y1_diff", 0); + const user_op::Tensor* b1_y2_diff = ctx->Tensor4ArgNameAndIndex("b1_y2_diff", 0); + const user_op::Tensor* b2_x1_diff = ctx->Tensor4ArgNameAndIndex("b2_x1_diff", 0); + const user_op::Tensor* b2_x2_diff = ctx->Tensor4ArgNameAndIndex("b2_x2_diff", 0); + const user_op::Tensor* b2_y1_diff = ctx->Tensor4ArgNameAndIndex("b2_y1_diff", 0); + const user_op::Tensor* b2_y2_diff = ctx->Tensor4ArgNameAndIndex("b2_y2_diff", 0); + + user_op::Tensor* x1_diff = ctx->Tensor4ArgNameAndIndex("x1_diff", 0); + user_op::Tensor* y1_diff = ctx->Tensor4ArgNameAndIndex("y1_diff", 0); + user_op::Tensor* w1_diff = ctx->Tensor4ArgNameAndIndex("w1_diff", 0); + user_op::Tensor* h1_diff = ctx->Tensor4ArgNameAndIndex("h1_diff", 0); + user_op::Tensor* x2_diff = ctx->Tensor4ArgNameAndIndex("x2_diff", 0); + user_op::Tensor* y2_diff = ctx->Tensor4ArgNameAndIndex("y2_diff", 0); + user_op::Tensor* w2_diff = ctx->Tensor4ArgNameAndIndex("w2_diff", 0); + user_op::Tensor* h2_diff = ctx->Tensor4ArgNameAndIndex("h2_diff", 0); + + const int32_t elem_cnt = b1_x1_diff->shape_view().elem_cnt(); + RUN_CUDA_KERNEL((FusedGetBounddingBoxesCoordBackward), ctx->stream(), elem_cnt, elem_cnt, + b1_x1_diff->dptr(), b1_x2_diff->dptr(), b1_y1_diff->dptr(), + b1_y2_diff->dptr(), b2_x1_diff->dptr(), b2_x2_diff->dptr(), + b2_y1_diff->dptr(), b2_y2_diff->dptr(), x1_diff->mut_dptr(), + y1_diff->mut_dptr(), w1_diff->mut_dptr(), h1_diff->mut_dptr(), + x2_diff->mut_dptr(), y2_diff->mut_dptr(), w2_diff->mut_dptr(), + h2_diff->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_boundding_boxes_coord_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1_diff", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_GRAD_CUDA_KERNEL(half) +REGISTER_FUSED_GET_BOUNDDING_BOXES_COORD_GRAD_CUDA_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_ciou_diagonal_angle_kernel.cu b/oneflow/user/kernels/fused_get_ciou_diagonal_angle_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..64d843e5facf89da85dee9141ec7286b3e8f89f9 --- /dev/null +++ b/oneflow/user/kernels/fused_get_ciou_diagonal_angle_kernel.cu @@ -0,0 +1,255 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { + +template +struct FusedCiouAngleForwardFunctor { + __device__ T Compute(T w1, T h1, T w2, T h2, float eps) const { + T angle = (atan(w2 / (h2 + eps)) - atan(w1 / (h1 + eps))) + * (atan(w2 / (h2 + eps)) - atan(w1 / (h1 + eps))); + return static_cast(4.0 / (M_PI * M_PI)) * angle; + } +}; + +template<> +struct FusedCiouAngleForwardFunctor { + __device__ half Compute(half w1, half h1, half w2, half h2, float eps) const { + float w1f = __half2float(w1); + float h1f = __half2float(h1); + float w2f = __half2float(w2); + float h2f = __half2float(h2); + float angle = (atan(w2f / (h2f + eps)) - atan(w1f / (h1f + eps))) + * (atan(w2f / (h2f + eps)) - atan(w1f / (h1f + eps))); + return __float2half(static_cast(4.0 / (M_PI * M_PI)) * angle); + } +}; + +template +__global__ void FusedCiouAngleForward(FUNCTOR functor, const int n, const T* w1, const T* h1, + const T* w2, const T* h2, const float eps, T* v) { + CUDA_1D_KERNEL_LOOP(i, n) { v[i] = functor.Compute(w1[i], h1[i], w2[i], h2[i], eps); } +} + +template +struct FusedCiouAngleBackwardFunctor { + __device__ T ComputeW1(T h1, T angle_delta, T angle1, float eps) const { + return static_cast(-1.0) * angle_delta / ((h1 + eps) * angle1); + } + + __device__ T ComputeW2(T h2, T angle_delta, T angle2, float eps) const { + return angle_delta / ((h2 + eps) * angle2); + } + + __device__ T ComputeH1(T w1, T h1, T angle_delta, T angle1, float eps) const { + return w1 * angle_delta / ((h1 + eps) * (h1 + eps) * angle1); + } + + __device__ T ComputeH2(T w2, T h2, T angle_delta, T angle2, float eps) const { + return static_cast(-1.0) * w2 * angle_delta / ((h2 + eps) * (h2 + eps) * angle2); + } +}; + +template<> +struct FusedCiouAngleBackwardFunctor { + __device__ half ComputeW1(half h1, half angle_delta, half angle1, float eps) const { + float h1f = __half2float(h1); + float angle_delta_f = __half2float(angle_delta); + float angle1f = __half2float(angle1); + return __float2half(-1.0 * angle_delta_f / ((h1f + eps) * angle1f)); + } + + __device__ half ComputeW2(half h2, half angle_delta, half angle2, float eps) const { + float h2f = __half2float(h2); + float angle_delta_f = __half2float(angle_delta); + float angle2f = __half2float(angle2); + return __float2half(angle_delta_f / ((h2f + eps) * angle2f)); + } + + __device__ half ComputeH1(half w1, half h1, half angle_delta, half angle1, float eps) const { + float w1f = __half2float(w1); + float h1f = __half2float(h1); + float angle_delta_f = __half2float(angle_delta); + float angle1f = __half2float(angle1); + return __float2half(w1f * angle_delta_f / ((h1f + eps) * (h1f + eps) * angle1f)); + } + + __device__ half ComputeH2(half w2, half h2, half angle_delta, half angle2, float eps) const { + float w2f = __half2float(w2); + float h2f = __half2float(h2); + float angle_delta_f = __half2float(angle_delta); + float angle2f = __half2float(angle2); + return __float2half(-1.0 * w2f * angle_delta_f / ((h2f + eps) * (h2f + eps) * angle2f)); + } +}; + +template +struct CalcAngleFunctor { + __device__ T ComputeDelta(T w1, T h1, T w2, T h2, float eps) const { + return static_cast(8.0) * (atan(w2 / (h2 + eps)) - atan(w1 / (h1 + eps))) + / static_cast((M_PI * M_PI)); + } + + __device__ T Compute1(T w1, T h1, float eps) const { + return static_cast(1.0) + (w1 * w1 / ((h1 + eps) * (h1 + eps))); + } + + __device__ T Compute2(T w2, T h2, float eps) const { + return static_cast(1.0) + (w2 * w2 / ((h2 + eps) * (h2 + eps))); + } +}; + +template<> +struct CalcAngleFunctor { + __device__ half ComputeDelta(half w1, half h1, half w2, half h2, float eps) const { + float w1f = __half2float(w1); + float h1f = __half2float(h1); + float w2f = __half2float(w2); + float h2f = __half2float(h2); + return __float2half(8.0 * (atan(w2f / (h2f + eps)) - atan(w1f / (h1f + eps))) + / static_cast((M_PI * M_PI))); + } + + __device__ half Compute1(half w1, half h1, float eps) const { + float w1f = __half2float(w1); + float h1f = __half2float(h1); + return __float2half(1.0 + (w1f * w1f / ((h1f + eps) * (h1f + eps)))); + } + + __device__ half Compute2(half w2, half h2, float eps) const { + float w2f = __half2float(w2); + float h2f = __half2float(h2); + return __float2half(1.0 + (w2f * w2f / ((h2f + eps) * (h2f + eps)))); + } +}; + +template +__global__ void FusedCiouAngleBackward(FUNCTOR_BACKWARD functor_backward, + FUNCTOR_ANGLE functor_angle, const int n, const T* w1, + const T* h1, const T* w2, const T* h2, const T* v_diff, + const float eps, T* w1_diff, T* h1_diff, T* w2_diff, + T* h2_diff) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T w1_i = w1[i]; + const T h1_i = h1[i]; + const T w2_i = w2[i]; + const T h2_i = h2[i]; + const T v_diff_i = v_diff[i]; + const T angle_delta_i = functor_angle.ComputeDelta(w1_i, h1_i, w2_i, h2_i, eps); + const T angle1_i = functor_angle.Compute1(w1_i, h1_i, eps); + const T angle2_i = functor_angle.Compute2(w2_i, h2_i, eps); + w1_diff[i] = functor_backward.ComputeW1(h1_i, angle_delta_i, angle1_i, eps) * v_diff_i; + w2_diff[i] = functor_backward.ComputeW2(h2_i, angle_delta_i, angle2_i, eps) * v_diff_i; + h1_diff[i] = functor_backward.ComputeH1(w1_i, h1_i, angle_delta_i, angle1_i, eps) * v_diff_i; + h2_diff[i] = functor_backward.ComputeH2(w2_i, h2_i, angle_delta_i, angle2_i, eps) * v_diff_i; + } +} + +} // namespace + +template +class FusedGetCiouDiagonalAngleKernel final : public user_op::OpKernel { + public: + FusedGetCiouDiagonalAngleKernel() = default; + ~FusedGetCiouDiagonalAngleKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* w1 = ctx->Tensor4ArgNameAndIndex("w1", 0); + const user_op::Tensor* h1 = ctx->Tensor4ArgNameAndIndex("h1", 0); + const user_op::Tensor* w2 = ctx->Tensor4ArgNameAndIndex("w2", 0); + const user_op::Tensor* h2 = ctx->Tensor4ArgNameAndIndex("h2", 0); + const auto eps = ctx->Attr("eps"); + + user_op::Tensor* v = ctx->Tensor4ArgNameAndIndex("v", 0); + + const int64_t elem_cnt = w1->shape_view().elem_cnt(); + + FusedCiouAngleForwardFunctor fused_get_ciou_diagonal_angle_functor{}; + + RUN_CUDA_KERNEL((FusedCiouAngleForward), + ctx->stream(), elem_cnt, fused_get_ciou_diagonal_angle_functor, elem_cnt, + w1->dptr(), h1->dptr(), w2->dptr(), h2->dptr(), eps, + v->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_ciou_diagonal_angle") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("v", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_CUDA_KERNEL(half) + +template +class FusedGetCiouDiagonalAngleGradKernel final : public user_op::OpKernel { + public: + FusedGetCiouDiagonalAngleGradKernel() = default; + ~FusedGetCiouDiagonalAngleGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* w1 = ctx->Tensor4ArgNameAndIndex("w1", 0); + const user_op::Tensor* h1 = ctx->Tensor4ArgNameAndIndex("h1", 0); + const user_op::Tensor* w2 = ctx->Tensor4ArgNameAndIndex("w2", 0); + const user_op::Tensor* h2 = ctx->Tensor4ArgNameAndIndex("h2", 0); + const user_op::Tensor* v_diff = ctx->Tensor4ArgNameAndIndex("v_diff", 0); + const auto eps = ctx->Attr("eps"); + + user_op::Tensor* w1_diff = ctx->Tensor4ArgNameAndIndex("w1_diff", 0); + user_op::Tensor* h1_diff = ctx->Tensor4ArgNameAndIndex("h1_diff", 0); + user_op::Tensor* w2_diff = ctx->Tensor4ArgNameAndIndex("w2_diff", 0); + user_op::Tensor* h2_diff = ctx->Tensor4ArgNameAndIndex("h2_diff", 0); + + const int64_t elem_cnt = w1->shape_view().elem_cnt(); + + FusedCiouAngleBackwardFunctor fused_get_ciou_diagonal_angle_grad_functor{}; + CalcAngleFunctor calc_angle_functor{}; + + RUN_CUDA_KERNEL((FusedCiouAngleBackward), + ctx->stream(), elem_cnt, fused_get_ciou_diagonal_angle_grad_functor, + calc_angle_functor, elem_cnt, w1->dptr(), h1->dptr(), w2->dptr(), + h2->dptr(), v_diff->dptr(), eps, w1_diff->mut_dptr(), + h1_diff->mut_dptr(), w2_diff->mut_dptr(), h2_diff->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_ciou_diagonal_angle_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("w1_diff", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_GRAD_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CIOU_DIAGONAL_ANGLE_GRAD_CUDA_KERNEL(half) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_ciou_result_kernel.cu b/oneflow/user/kernels/fused_get_ciou_result_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..7511ce48290a8d000b839a855af3e4f353c6c3d9 --- /dev/null +++ b/oneflow/user/kernels/fused_get_ciou_result_kernel.cu @@ -0,0 +1,123 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { +template +__global__ void FusedGetCiouResultForward(const int n, const T* v, const T* iou, const T* rho2, + const T* c2, T* y, T* alpha, float eps) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T v_i = v[i]; + const T iou_i = iou[i]; + const T alpha_i = v_i / (v_i - iou_i + static_cast(1.0 + eps)); + y[i] = iou_i - (rho2[i] / c2[i] + v_i * alpha_i); + alpha[i] = alpha_i; + } +} + +template +__global__ void FusedGetCiouResultBackward(const int n, const T* dy, const T* alpha, const T* rho2, + const T* c2, T* dv, T* diou, T* drho2, T* dc2) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T c2_i = c2[i]; + const T dy_i = dy[i]; + dv[i] = -alpha[i] * dy_i; + diou[i] = dy_i; + drho2[i] = -dy_i / c2[i]; + dc2[i] = rho2[i] / (c2_i * c2_i) * dy_i; + } +} +}; // namespace + +template +class FusedGetCiouResultGpuKernel final : public user_op::OpKernel { + public: + FusedGetCiouResultGpuKernel() = default; + ~FusedGetCiouResultGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* v = ctx->Tensor4ArgNameAndIndex("v", 0); + const user_op::Tensor* iou = ctx->Tensor4ArgNameAndIndex("iou", 0); + const user_op::Tensor* rho2 = ctx->Tensor4ArgNameAndIndex("rho2", 0); + const user_op::Tensor* c2 = ctx->Tensor4ArgNameAndIndex("c2", 0); + + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); + + float eps = ctx->Attr("eps"); + + const int32_t elem_cnt = v->shape_view().elem_cnt(); + RUN_CUDA_KERNEL((FusedGetCiouResultForward), ctx->stream(), elem_cnt, elem_cnt, v->dptr(), + iou->dptr(), rho2->dptr(), c2->dptr(), y->mut_dptr(), + alpha->mut_dptr(), eps); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CIOU_RESULT_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_ciou_result") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("v", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CIOU_RESULT_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CIOU_RESULT_CUDA_KERNEL(half) +REGISTER_FUSED_GET_CIOU_RESULT_CUDA_KERNEL(double) + +template +class FusedGetCiouResultGradGpuKernel final : public user_op::OpKernel { + public: + FusedGetCiouResultGradGpuKernel() = default; + ~FusedGetCiouResultGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); + const user_op::Tensor* rho2 = ctx->Tensor4ArgNameAndIndex("rho2", 0); + const user_op::Tensor* c2 = ctx->Tensor4ArgNameAndIndex("c2", 0); + + user_op::Tensor* dv = ctx->Tensor4ArgNameAndIndex("dv", 0); + user_op::Tensor* diou = ctx->Tensor4ArgNameAndIndex("diou", 0); + user_op::Tensor* drho2 = ctx->Tensor4ArgNameAndIndex("drho2", 0); + user_op::Tensor* dc2 = ctx->Tensor4ArgNameAndIndex("dc2", 0); + + const int32_t elem_cnt = dy->shape_view().elem_cnt(); + RUN_CUDA_KERNEL((FusedGetCiouResultBackward), ctx->stream(), elem_cnt, elem_cnt, + dy->dptr(), alpha->dptr(), rho2->dptr(), c2->dptr(), + dv->mut_dptr(), diou->mut_dptr(), drho2->mut_dptr(), + dc2->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CIOU_RESULT_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_ciou_result_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CIOU_RESULT_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CIOU_RESULT_GRAD_CUDA_KERNEL(half) +REGISTER_FUSED_GET_CIOU_RESULT_GRAD_CUDA_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_convex_diagonal_squared_kernel.cu b/oneflow/user/kernels/fused_get_convex_diagonal_squared_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..c7e6778db111b8aa63340c587c094b6083af0239 --- /dev/null +++ b/oneflow/user/kernels/fused_get_convex_diagonal_squared_kernel.cu @@ -0,0 +1,155 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/common/math_util.h" + +namespace oneflow { + +namespace { + +template +__global__ void FusedGetConvexDiagonalSquaredForward(const int n, const T* b1_x1, const T* b1_x2, + const T* b2_x1, const T* b2_x2, const T* b1_y1, + const T* b1_y2, const T* b2_y1, const T* b2_y2, + T* c2, const float eps) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T cw = DeviceMax(b1_x2[i], b2_x2[i]) - DeviceMin(b1_x1[i], b2_x1[i]); + const T ch = DeviceMax(b1_y2[i], b2_y2[i]) - DeviceMin(b1_y1[i], b2_y1[i]); + c2[i] = cw * cw + ch * ch + static_cast(eps); + } +} + +template +__global__ void FusedGetConvexDiagonalSquaredBackward( + const int n, const T* b1_x1, const T* b1_x2, const T* b2_x1, const T* b2_x2, const T* b1_y1, + const T* b1_y2, const T* b2_y1, const T* b2_y2, const T* c2_diff, T* b1_x1_diff, T* b1_x2_diff, + T* b2_x1_diff, T* b2_x2_diff, T* b1_y1_diff, T* b1_y2_diff, T* b2_y1_diff, T* b2_y2_diff, + const float eps) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T zero = static_cast(0), one = static_cast(1); + const T cw = DeviceMax(b1_x2[i], b2_x2[i]) - DeviceMin(b1_x1[i], b2_x1[i]); + const T ch = DeviceMax(b1_y2[i], b2_y2[i]) - DeviceMin(b1_y1[i], b2_y1[i]); + const T c2_diff_cw = static_cast(2) * cw * c2_diff[i]; + const T c2_diff_ch = static_cast(2) * ch * c2_diff[i]; + b1_x2_diff[i] = c2_diff_cw * (b1_x2[i] > b2_x2[i] ? one : zero); + b2_x2_diff[i] = c2_diff_cw * (b1_x2[i] > b2_x2[i] ? zero : one); + b1_x1_diff[i] = -c2_diff_cw * (b1_x1[i] < b2_x1[i] ? one : zero); + b2_x1_diff[i] = -c2_diff_cw * (b1_x1[i] < b2_x1[i] ? zero : one); + b1_y2_diff[i] = c2_diff_ch * (b1_y2[i] > b2_y2[i] ? one : zero); + b2_y2_diff[i] = c2_diff_ch * (b1_y2[i] > b2_y2[i] ? zero : one); + b1_y1_diff[i] = -c2_diff_ch * (b1_y1[i] < b2_y1[i] ? one : zero); + b2_y1_diff[i] = -c2_diff_ch * (b1_y1[i] < b2_y1[i] ? zero : one); + } +} + +} // namespace + +template +class FusedGetConvexDiagonalSquaredKernel final : public user_op::OpKernel { + public: + FusedGetConvexDiagonalSquaredKernel() = default; + ~FusedGetConvexDiagonalSquaredKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + user_op::Tensor* c2 = ctx->Tensor4ArgNameAndIndex("c2", 0); + const float eps = ctx->Attr("eps"); + + const int64_t elem_cnt = b1_x1->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((FusedGetConvexDiagonalSquaredForward), ctx->stream(), elem_cnt, elem_cnt, + b1_x1->dptr(), b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), + b1_y1->dptr(), b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), + c2->mut_dptr(), eps); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_convex_diagonal_squared") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_CUDA_KERNEL(half) + +template +class FusedGetConvexDiagonalSquaredGradKernel final : public user_op::OpKernel { + public: + FusedGetConvexDiagonalSquaredGradKernel() = default; + ~FusedGetConvexDiagonalSquaredGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* c2_diff = ctx->Tensor4ArgNameAndIndex("c2_diff", 0); + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + user_op::Tensor* b1_x1_diff = ctx->Tensor4ArgNameAndIndex("b1_x1_diff", 0); + user_op::Tensor* b1_x2_diff = ctx->Tensor4ArgNameAndIndex("b1_x2_diff", 0); + user_op::Tensor* b2_x1_diff = ctx->Tensor4ArgNameAndIndex("b2_x1_diff", 0); + user_op::Tensor* b2_x2_diff = ctx->Tensor4ArgNameAndIndex("b2_x2_diff", 0); + user_op::Tensor* b1_y1_diff = ctx->Tensor4ArgNameAndIndex("b1_y1_diff", 0); + user_op::Tensor* b1_y2_diff = ctx->Tensor4ArgNameAndIndex("b1_y2_diff", 0); + user_op::Tensor* b2_y1_diff = ctx->Tensor4ArgNameAndIndex("b2_y1_diff", 0); + user_op::Tensor* b2_y2_diff = ctx->Tensor4ArgNameAndIndex("b2_y2_diff", 0); + + const float eps = ctx->Attr("eps"); + const int64_t elem_cnt = b1_x1_diff->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((FusedGetConvexDiagonalSquaredBackward), ctx->stream(), elem_cnt, elem_cnt, + b1_x1->dptr(), b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), + b1_y1->dptr(), b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), + c2_diff->dptr(), b1_x1_diff->mut_dptr(), b1_x2_diff->mut_dptr(), + b2_x1_diff->mut_dptr(), b2_x2_diff->mut_dptr(), b1_y1_diff->mut_dptr(), + b1_y2_diff->mut_dptr(), b2_y1_diff->mut_dptr(), b2_y2_diff->mut_dptr(), + eps); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_convex_diagonal_squared_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_GRAD_CUDA_KERNEL(double) +REGISTER_FUSED_GET_CONVEX_DIAGOAL_SQUARED_GRAD_CUDA_KERNEL(half) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_intersection_area_kernel.cu b/oneflow/user/kernels/fused_get_intersection_area_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..3a242dd932785a88dd961f4bd0875f923121cc16 --- /dev/null +++ b/oneflow/user/kernels/fused_get_intersection_area_kernel.cu @@ -0,0 +1,191 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { + +template +struct MinMaxDeltaFunctor { + __device__ T Compute(T b1_x2_i, T b2_x2_i, T b1_x1_i, T b2_x1_i) const { + return min(b1_x2_i, b2_x2_i) - max(b1_x1_i, b2_x1_i); + } +}; + +template<> +struct MinMaxDeltaFunctor { + __device__ half Compute(half b1_x2_i, half b2_x2_i, half b1_x1_i, half b2_x1_i) const { + const half b_x2_min = b1_x2_i < b2_x2_i ? b1_x2_i : b2_x2_i; + const half b_x1_max = b1_x1_i > b2_x1_i ? b1_x1_i : b2_x1_i; + return b_x2_min - b_x1_max; + } +}; + +template +__global__ void FusedGetIntersectionAreaBackward(FUNCTOR functor, const int n, const T* b1_x1, + const T* b1_x2, const T* b2_x1, const T* b2_x2, + const T* b1_y1, const T* b1_y2, const T* b2_y1, + const T* b2_y2, const T* inter_diff, T* b1_x1_diff, + T* b1_x2_diff, T* b2_x1_diff, T* b2_x2_diff, + T* b1_y1_diff, T* b1_y2_diff, T* b2_y1_diff, + T* b2_y2_diff) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T inter_diff_i = inter_diff[i]; + const T b_x_min_max = functor.Compute(b1_x2[i], b2_x2[i], b1_x1[i], b2_x1[i]); + const T b_y_min_max = functor.Compute(b1_y2[i], b2_y2[i], b1_y1[i], b2_y1[i]); + const T b_x_min_max_inter = b_x_min_max * inter_diff_i; + const T b_y_min_max_inter = b_y_min_max * inter_diff_i; + + b1_x1_diff[i] = static_cast(0.0); + b1_x2_diff[i] = static_cast(0.0); + b2_x1_diff[i] = static_cast(0.0); + b2_x2_diff[i] = static_cast(0.0); + b1_y1_diff[i] = static_cast(0.0); + b1_y2_diff[i] = static_cast(0.0); + b2_y1_diff[i] = static_cast(0.0); + b2_y2_diff[i] = static_cast(0.0); + + if (b_x_min_max > static_cast(0.0) && b_y_min_max > static_cast(0.0)) { + if (b1_x1[i] >= b2_x1[i]) { b1_x1_diff[i] = static_cast(-1.0) * b_y_min_max_inter; } + if (b1_x1[i] <= b2_x1[i]) { b2_x1_diff[i] = static_cast(-1.0) * b_y_min_max_inter; } + if (b1_x2[i] <= b2_x2[i]) { b1_x2_diff[i] = b_y_min_max_inter; } + if (b1_x2[i] >= b2_x2[i]) { b2_x2_diff[i] = b_y_min_max_inter; } + + if (b1_y1[i] >= b2_y1[i]) { b1_y1_diff[i] = static_cast(-1.0) * b_x_min_max_inter; } + if (b1_y1[i] <= b2_y1[i]) { b2_y1_diff[i] = static_cast(-1.0) * b_x_min_max_inter; } + if (b1_y2[i] <= b2_y2[i]) { b1_y2_diff[i] = b_x_min_max_inter; } + if (b1_y2[i] >= b2_y2[i]) { b2_y2_diff[i] = b_x_min_max_inter; } + } + } +} + +template +__global__ void FusedGetIntersectionAreaForward(FUNCTOR functor, const int n, const T* b1_x1, + const T* b1_x2, const T* b2_x1, const T* b2_x2, + const T* b1_y1, const T* b1_y2, const T* b2_y1, + const T* b2_y2, T* inter) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T b_x_min_max = functor.Compute(b1_x2[i], b2_x2[i], b1_x1[i], b2_x1[i]); + const T b_y_min_max = functor.Compute(b1_y2[i], b2_y2[i], b1_y1[i], b2_y1[i]); + inter[i] = static_cast(0.0); + if (b_x_min_max > static_cast(0.0) && b_y_min_max > static_cast(0.0)) { + inter[i] = b_x_min_max * b_y_min_max; + } + } +} + +} // namespace + +template +class FusedGetIntersectionAreaKernel final : public user_op::OpKernel { + public: + FusedGetIntersectionAreaKernel() = default; + ~FusedGetIntersectionAreaKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + user_op::Tensor* inter = ctx->Tensor4ArgNameAndIndex("inter", 0); + + const int64_t elem_cnt = b1_x2->shape_view().elem_cnt(); + + MinMaxDeltaFunctor min_max_delta_functor{}; + + RUN_CUDA_KERNEL((FusedGetIntersectionAreaForward), + ctx->stream(), elem_cnt, min_max_delta_functor, elem_cnt, b1_x1->dptr(), + b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), b1_y1->dptr(), + b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), inter->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_INTERSECTION_AREA_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_intersection_area") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("inter", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_INTERSECTION_AREA_CUDA_KERNEL(float) +REGISTER_FUSED_GET_INTERSECTION_AREA_CUDA_KERNEL(double) +REGISTER_FUSED_GET_INTERSECTION_AREA_CUDA_KERNEL(half) + +template +class FusedGetIntersectionAreaGradKernel final : public user_op::OpKernel { + public: + FusedGetIntersectionAreaGradKernel() = default; + ~FusedGetIntersectionAreaGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* b1_x1 = ctx->Tensor4ArgNameAndIndex("b1_x1", 0); + const user_op::Tensor* b1_x2 = ctx->Tensor4ArgNameAndIndex("b1_x2", 0); + const user_op::Tensor* b2_x1 = ctx->Tensor4ArgNameAndIndex("b2_x1", 0); + const user_op::Tensor* b2_x2 = ctx->Tensor4ArgNameAndIndex("b2_x2", 0); + const user_op::Tensor* b1_y1 = ctx->Tensor4ArgNameAndIndex("b1_y1", 0); + const user_op::Tensor* b1_y2 = ctx->Tensor4ArgNameAndIndex("b1_y2", 0); + const user_op::Tensor* b2_y1 = ctx->Tensor4ArgNameAndIndex("b2_y1", 0); + const user_op::Tensor* b2_y2 = ctx->Tensor4ArgNameAndIndex("b2_y2", 0); + + user_op::Tensor* inter_diff = ctx->Tensor4ArgNameAndIndex("inter_diff", 0); + + user_op::Tensor* b1_x1_diff = ctx->Tensor4ArgNameAndIndex("b1_x1_diff", 0); + user_op::Tensor* b1_x2_diff = ctx->Tensor4ArgNameAndIndex("b1_x2_diff", 0); + user_op::Tensor* b2_x1_diff = ctx->Tensor4ArgNameAndIndex("b2_x1_diff", 0); + user_op::Tensor* b2_x2_diff = ctx->Tensor4ArgNameAndIndex("b2_x2_diff", 0); + user_op::Tensor* b1_y1_diff = ctx->Tensor4ArgNameAndIndex("b1_y1_diff", 0); + user_op::Tensor* b1_y2_diff = ctx->Tensor4ArgNameAndIndex("b1_y2_diff", 0); + user_op::Tensor* b2_y1_diff = ctx->Tensor4ArgNameAndIndex("b2_y1_diff", 0); + user_op::Tensor* b2_y2_diff = ctx->Tensor4ArgNameAndIndex("b2_y2_diff", 0); + + const int64_t elem_cnt = b1_x1->shape_view().elem_cnt(); + + MinMaxDeltaFunctor min_max_delta_functor{}; + + RUN_CUDA_KERNEL((FusedGetIntersectionAreaBackward), + ctx->stream(), elem_cnt, min_max_delta_functor, elem_cnt, b1_x1->dptr(), + b1_x2->dptr(), b2_x1->dptr(), b2_x2->dptr(), b1_y1->dptr(), + b1_y2->dptr(), b2_y1->dptr(), b2_y2->dptr(), inter_diff->dptr(), + b1_x1_diff->mut_dptr(), b1_x2_diff->mut_dptr(), b2_x1_diff->mut_dptr(), + b2_x2_diff->mut_dptr(), b1_y1_diff->mut_dptr(), b1_y2_diff->mut_dptr(), + b2_y1_diff->mut_dptr(), b2_y2_diff->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_INTERSECTION_AREA_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_intersection_area_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("b1_x1_diff", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_INTERSECTION_AREA_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_INTERSECTION_AREA_GRAD_CUDA_KERNEL(double) +REGISTER_FUSED_GET_INTERSECTION_AREA_GRAD_CUDA_KERNEL(half) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_get_iou_kernel.cu b/oneflow/user/kernels/fused_get_iou_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..83d16c40f0730fe6c14893267e84ca624c1c72a5 --- /dev/null +++ b/oneflow/user/kernels/fused_get_iou_kernel.cu @@ -0,0 +1,127 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { +template +__global__ void FusedGetIouForward(const int n, const T* w1, const T* h1, const T* w2, const T* h2, + const T* inter, T* iou, const float eps) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T inter_i = inter[i]; + iou[i] = inter_i / (w1[i] * h1[i] + w2[i] * h2[i] - inter_i + static_cast(eps)); + } +} + +template +__global__ void FusedGetIouBackward(const int n, const T* diou, const T* w1, const T* h1, + const T* w2, const T* h2, const T* inter, T* dw1, T* dh1, + T* dinter, const float eps) { + CUDA_1D_KERNEL_LOOP(i, n) { + const T w1_i = w1[i], h1_i = h1[i], w2_i = w2[i], h2_i = h2[i], inter_i = inter[i], + diou_i = diou[i]; + const T w_h_eps = w1_i * h1_i + w2_i * h2_i + static_cast(eps); + const T w_h_eps_inter_diff = w_h_eps - inter_i; + const T w_h_eps_inter_diff_square = w_h_eps_inter_diff * w_h_eps_inter_diff; + const T common_for_dwh = -inter_i * diou_i / w_h_eps_inter_diff_square; + dinter[i] = w_h_eps * diou_i / w_h_eps_inter_diff_square; + dw1[i] = h1_i * common_for_dwh; + dh1[i] = w1_i * common_for_dwh; + } +} +}; // namespace + +template +class FusedGetIouGpuKernel final : public user_op::OpKernel { + public: + FusedGetIouGpuKernel() = default; + ~FusedGetIouGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* w1 = ctx->Tensor4ArgNameAndIndex("w1", 0); + const user_op::Tensor* h1 = ctx->Tensor4ArgNameAndIndex("h1", 0); + const user_op::Tensor* w2 = ctx->Tensor4ArgNameAndIndex("w2", 0); + const user_op::Tensor* h2 = ctx->Tensor4ArgNameAndIndex("h2", 0); + const user_op::Tensor* inter = ctx->Tensor4ArgNameAndIndex("inter", 0); + + user_op::Tensor* iou = ctx->Tensor4ArgNameAndIndex("iou", 0); + + float eps = ctx->Attr("eps"); + + const int32_t elem_cnt = w1->shape_view().elem_cnt(); + RUN_CUDA_KERNEL((FusedGetIouForward), ctx->stream(), elem_cnt, elem_cnt, w1->dptr(), + h1->dptr(), w2->dptr(), h2->dptr(), inter->dptr(), + iou->mut_dptr(), eps); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_IOU_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_iou") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("iou", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_IOU_CUDA_KERNEL(float) +REGISTER_FUSED_GET_IOU_CUDA_KERNEL(half) +REGISTER_FUSED_GET_IOU_CUDA_KERNEL(double) + +template +class FusedGetIouGradGpuKernel final : public user_op::OpKernel { + public: + FusedGetIouGradGpuKernel() = default; + ~FusedGetIouGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* diou = ctx->Tensor4ArgNameAndIndex("diou", 0); + const user_op::Tensor* w1 = ctx->Tensor4ArgNameAndIndex("w1", 0); + const user_op::Tensor* h1 = ctx->Tensor4ArgNameAndIndex("h1", 0); + const user_op::Tensor* w2 = ctx->Tensor4ArgNameAndIndex("w2", 0); + const user_op::Tensor* h2 = ctx->Tensor4ArgNameAndIndex("h2", 0); + const user_op::Tensor* inter = ctx->Tensor4ArgNameAndIndex("inter", 0); + + user_op::Tensor* dw1 = ctx->Tensor4ArgNameAndIndex("dw1", 0); + user_op::Tensor* dh1 = ctx->Tensor4ArgNameAndIndex("dh1", 0); + user_op::Tensor* dinter = ctx->Tensor4ArgNameAndIndex("dinter", 0); + + float eps = ctx->Attr("eps"); + + const int32_t elem_cnt = diou->shape_view().elem_cnt(); + + RUN_CUDA_KERNEL((FusedGetIouBackward), ctx->stream(), elem_cnt, elem_cnt, diou->dptr(), + w1->dptr(), h1->dptr(), w2->dptr(), h2->dptr(), inter->dptr(), + dw1->mut_dptr(), dh1->mut_dptr(), dinter->mut_dptr(), eps); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_GET_IOU_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_get_iou_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("diou", 0) == GetDataType::value)); + +REGISTER_FUSED_GET_IOU_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_GET_IOU_GRAD_CUDA_KERNEL(half) +REGISTER_FUSED_GET_IOU_GRAD_CUDA_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_glu_kernel.cu b/oneflow/user/kernels/fused_glu_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..47919917a52c011d6c609ccfb18e3cac28a6466e --- /dev/null +++ b/oneflow/user/kernels/fused_glu_kernel.cu @@ -0,0 +1,636 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/cuda/elementwise.cuh" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/matmul.h" +#include "oneflow/core/ep/include/primitive/unary_op.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/common/device_type.h" +#include "oneflow/core/ep/common/primitive/unary_functor.h" +#include "oneflow/core/ep/cuda/primitive/unary_functor.cuh" +#include "oneflow/core/kernel/util/cuda_half_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 +#include "oneflow/core/device/cuda_pseudo_bfloat16.h" + +#ifdef WITH_CUTLASS + +#include "device/dual_gemm.h" +#include "thread/left_silu_and_mul.h" + +namespace cutlass { +namespace epilogue { +namespace thread { + +template typename Activation, + typename ElementAccumulator_ = ElementOutput_, typename ElementCompute_ = ElementOutput_, + FloatRoundStyle Round = FloatRoundStyle::round_to_nearest> +class RightActivationAndMul { + public: + using ElementOutput = ElementOutput_; + using ElementAccumulator = ElementAccumulator_; + using ElementCompute = ElementCompute_; + + static int const kCount = Count; + using FragmentOutput = Array; + using FragmentAccumulator = Array; + using ComputeFragment = Array; + + static FloatRoundStyle const kRound = Round; + + struct Params {}; + + private: + ElementCompute alpha_; + ElementCompute beta_; + + public: + CUTLASS_HOST_DEVICE + RightActivationAndMul(Params const& /*params*/) {} + + CUTLASS_HOST_DEVICE + bool is_source_needed() const { return true; } + + CUTLASS_HOST_DEVICE + void set_k_partition(int k_partition, int k_partition_count) { assert(false); } + + CUTLASS_HOST_DEVICE + FragmentOutput operator()(FragmentAccumulator const& lhs, FragmentAccumulator const& rhs) const { + NumericArrayConverter accumulator_to_output; + + FragmentOutput converted_lhs = accumulator_to_output(lhs); + FragmentOutput converted_rhs = accumulator_to_output(rhs); + + Activation act; + cutlass::multiplies mul; + auto act_rhs = act(converted_rhs); + return mul(act_rhs, converted_lhs); + } + + CUTLASS_HOST_DEVICE + ElementOutput operator()(ElementAccumulator const& lhs, ElementAccumulator const& rhs) const { + ElementOutput convert_lhs(lhs); + ElementOutput convert_rhs(rhs); + Activation act; + cutlass::multiplies mul; + auto act_lhs = fast_gelu(convert_lhs); + return mul(act_lhs, convert_rhs); + } +}; +} // namespace thread +} // namespace epilogue +} // namespace cutlass + +#endif // WITH_CUTLASS + +namespace oneflow { + +namespace { + +#ifdef WITH_CUTLASS + +template +struct GetCutlassType { + using type = T; +}; + +template<> +struct GetCutlassType { + using type = cutlass::half_t; +}; + +#if CUDA_VERSION >= 11000 + +template<> +struct GetCutlassType { + using type = cutlass::bfloat16_t; +}; + +#endif + +template typename Activation> +void DualGemmGegluHalf(ep::CudaStream* stream, int32_t m, int32_t n, int32_t k, const void* x, + const void* w, const void* v, const void* b, const void* c, void* wx, + int32_t wx_stride, void* vx, int32_t vx_stride, void* y) { + constexpr int kStages = 5; + constexpr bool kSplitKSerial = false; + constexpr bool kUseBias = true; + using ElementOperandA = cutlass::half_t; + using ElementOperandB = cutlass::half_t; + using ElementOutput = cutlass::half_t; + using ElementAccumulator = Acc; + using ElementCompute = Acc; + using ThreadblockShape = cutlass::gemm::GemmShape<128, 64, 32>; + using WarpShape = cutlass::gemm::GemmShape<64, 32, 32>; + using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>; + + constexpr auto kScaleType = + kUseBias ? cutlass::epilogue::thread::ScaleType::NoBetaScaling + : ( + // No bias + kSplitKSerial ? cutlass::epilogue::thread::ScaleType::Default + : cutlass::epilogue::thread::ScaleType::Nothing); + using EpilogueOutputOp0 = + cutlass::epilogue::thread::LinearCombination::value, + ElementAccumulator, ElementCompute, kScaleType>; + using EpilogueOutputOp1 = + cutlass::epilogue::thread::LinearCombination::value, + ElementAccumulator, ElementCompute, kScaleType>; + using EpilogueOutputOp2 = cutlass::epilogue::thread::RightActivationAndMul< + ElementOutput, 128 / cutlass::sizeof_bits::value, Activation, ElementOutput, + ElementCompute>; + + const ElementCompute alpha0 = ElementCompute(1); + const ElementCompute beta0 = ElementCompute(kUseBias ? 1 : 0); + const ElementCompute alpha1 = ElementCompute(1); + const ElementCompute beta1 = ElementCompute(kUseBias ? 1 : 0); + + // Optionally, we might not need intermediate GEMM outputs + constexpr bool kStoreD0 = true; + constexpr bool kStoreD1 = true; + using DualGemm = cutlass::gemm::device::DualGemm< + ElementOperandA, cutlass::layout::RowMajor, ElementOperandB, cutlass::layout::ColumnMajor, + ElementOutput, cutlass::layout::RowMajor, ElementAccumulator, cutlass::arch::OpClassTensorOp, + Arch, ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp0, EpilogueOutputOp1, + EpilogueOutputOp2, cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>, kStages, + kStoreD0, kStoreD1, kSplitKSerial>; + + int split_k_slices = DualGemm::kSplitKSerial ? 2 : 1; + + typename cutlass::TensorRef tensor_a0( + reinterpret_cast(x), k); + typename cutlass::TensorRef tensor_b0( + reinterpret_cast(w), k); + typename cutlass::TensorRef tensor_b1( + reinterpret_cast(v), k); + typename cutlass::TensorRef tensor_bias0( + reinterpret_cast(b), {0}); + typename cutlass::TensorRef tensor_bias1( + reinterpret_cast(c), {0}); + typename cutlass::TensorRef tensor_d0( + reinterpret_cast(wx), wx_stride); + typename cutlass::TensorRef tensor_d1( + reinterpret_cast(vx), vx_stride); + typename cutlass::TensorRef tensor_out( + reinterpret_cast(y), n); + + cutlass::gemm::GemmCoord problem_size(m, n, k); + typename DualGemm::Arguments arguments{ + problem_size, tensor_a0, tensor_b0, tensor_bias0, tensor_d0, + tensor_b1, tensor_bias1, tensor_d1, tensor_out, {alpha0, beta0}, + {alpha1, beta1}, {}, split_k_slices}; + + DualGemm dual_gemm_op; + dual_gemm_op.initialize(arguments, stream->cublas_workspace(), stream->cuda_stream()); + dual_gemm_op(stream->cuda_stream()); +} + +template +bool TryDispatchDualGemmImplActivation(ep::CudaStream* stream, const std::string& activation, + int32_t m, int32_t n, int32_t k, const void* x, + const void* w, const void* v, const void* b, const void* c, + void* wx, int32_t wx_stride, void* vx, int32_t vx_stride, + void* y) { + if (activation == "fast_gelu") { + DualGemmGegluHalf( + stream, m, n, k, x, w, v, b, c, wx, wx_stride, vx, vx_stride, y); + return true; + } else if (activation == "gelu") { + DualGemmGegluHalf(stream, m, n, k, x, w, v, b, c, + wx, wx_stride, vx, vx_stride, y); + return true; + } else { + return false; + } +} + +template +bool TryDispatchDualGemmImplAccType(ep::CudaStream* stream, const std::string& activation, + int32_t m, int32_t n, int32_t k, const T* x, const T* w, + const T* v, const T* b, const T* c, T* wx, int32_t wx_stride, + T* vx, int32_t vx_stride, T* y) { + const bool allow_half_precision = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + if (std::is_same::value) { + if (allow_half_precision) { + return TryDispatchDualGemmImplActivation( + stream, activation, m, n, k, x, w, v, b, c, wx, wx_stride, vx, vx_stride, y); + } else { + return TryDispatchDualGemmImplActivation(stream, activation, m, n, k, x, w, v, b, + c, wx, wx_stride, vx, vx_stride, y); + } + } else { + return false; + } +} + +template +bool TryDispatchDualGemmImplAlignment(ep::CudaStream* stream, const std::string& activation, + int32_t m, int32_t n, int32_t k, const T* x, const T* w, + const T* v, const T* b, const T* c, T* wx, int32_t wx_stride, + T* vx, int32_t vx_stride, T* y) { + if (m % 8 == 0 && n % 8 == 0 && k % 8 == 0 + && reinterpret_cast(x) % (8 * sizeof(T)) == 0 + && reinterpret_cast(w) % (8 * sizeof(T)) == 0 + && reinterpret_cast(v) % (8 * sizeof(T)) == 0 + && reinterpret_cast(b) % (8 * sizeof(T)) == 0 + && reinterpret_cast(c) % (8 * sizeof(T)) == 0 + && reinterpret_cast(wx) % (8 * sizeof(T)) == 0 && wx_stride % 8 == 0 + && reinterpret_cast(vx) % (8 * sizeof(T)) == 0 + && reinterpret_cast(y) % (8 * sizeof(T)) == 0 && vx_stride % 8 == 0) { + return TryDispatchDualGemmImplAccType(stream, activation, m, n, k, x, w, v, b, c, wx, + wx_stride, vx, vx_stride, y); + } else { + return false; + } +} + +template +bool TryDispatchDualGemmImplArchTag(ep::CudaStream* stream, const std::string& activation, + int32_t m, int32_t n, int32_t k, const T* x, const T* w, + const T* v, const T* b, const T* c, T* wx, int32_t wx_stride, + T* vx, int32_t vx_stride, T* y) { + const int arch = stream->cuda_arch(); + if (arch == 800) { + return TryDispatchDualGemmImplAlignment( + stream, activation, m, n, k, x, w, v, b, c, wx, wx_stride, vx, vx_stride, y); + } else { + return false; + } +} + +#endif // WITH_CUTLASS +template +bool TryDispatchDualGemmImpl(ep::CudaStream* stream, const std::string& activation, int32_t m, + int32_t n, int32_t k, const T* x, const T* w, const T* v, const T* b, + const T* c, T* wx, int32_t wx_stride, T* vx, int32_t vx_stride, T* y) { +#ifdef WITH_CUTLASS + const bool enabled = ParseBooleanFromEnv("ONEFLOW_KERNEL_GLU_ENABLE_DUAL_GEMM_IMPL", false); + if (enabled) { + return TryDispatchDualGemmImplArchTag(stream, activation, m, n, k, x, w, v, b, c, wx, + wx_stride, vx, vx_stride, y); + } else { + return false; + } +#else + return false; +#endif // WITH_CUTLASS +} + +template +__global__ void FusedGluForwardGpu( + const IndexType m, const IndexType packed_n, const IndexType k, const IndexType packed_stride, + const IndexType packed_num, ep::primitive::UnaryFunctor act, + const T* matmul_wx, const T* b, const T* matmul_vx, const T* c, T* y) { + // obtain global thread index + IndexType global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; + + // define type of Pack + using LoadPack = cuda::elementwise::Packed; + + // workload of current thread + for (IndexType packed_index = global_thread_id, step = gridDim.x * blockDim.x; + packed_index < packed_num; packed_index += step) { + // obtain the row and col index in output tensor "y" + const IndexType y_packed_row = packed_index / packed_n; + const IndexType y_packed_col = packed_index - y_packed_row * packed_n; + + // cast type to load type + const LoadPack* matmul_wx_load = reinterpret_cast(matmul_wx) + + (y_packed_row * packed_stride + y_packed_col); + const LoadPack* matmul_vx_load = reinterpret_cast(matmul_vx) + + (y_packed_row * packed_stride + y_packed_col); + const LoadPack* b_load = reinterpret_cast(b) + y_packed_col; + const LoadPack* c_load = reinterpret_cast(c) + y_packed_col; + + // init vectors + LoadPack matmul_wx_vec = *matmul_wx_load; + LoadPack matmul_vx_vec = *matmul_vx_load; + LoadPack b_vec = *b_load; + LoadPack c_vec = *c_load; + LoadPack y_vec; + +#pragma unroll + for (int i = 0; i < pack_size; i++) { + // calculate the hidden_state and gate + T hidden_state = matmul_wx_vec.elem[i] + b_vec.elem[i]; + T gate = matmul_vx_vec.elem[i] + c_vec.elem[i]; + + // calculate activation + T act_gate = act(gate); + + // calculate element-wise product + y_vec.elem[i] = hidden_state * act_gate; + } + *(reinterpret_cast(y + packed_index * pack_size)) = y_vec; + } +} + +template +void LaunchFusedGluForwardGpu(ep::Stream* stream, const int64_t m, const int64_t packed_n, + const int64_t k, int64_t packed_stride, const T* matmul_wx, + const T* b, const T* matmul_vx, const T* c, T* y) { + ep::primitive::UnaryFunctor act(0, 0); + int64_t pack_num = m * packed_n; + constexpr int32_t block_size = 128; + unsigned int grid_size = (pack_num + block_size - 1) / block_size; + FusedGluForwardGpu + <<As()->cuda_stream()>>>( + m, packed_n, k, packed_stride, pack_num, act, matmul_wx, b, matmul_vx, c, y); +} + +template +void DispatchIndexType(ep::Stream* stream, const int64_t m, const int64_t n, const int64_t k, + int64_t packed_stride, const T* matmul_wx, const T* b, const T* matmul_vx, + const T* c, T* y) { + // convert n based on pack size + const int64_t packed_n = n / pack_size; + + // dispatch index type + if (m * packed_n < (1 << 30)) { + LaunchFusedGluForwardGpu(stream, m, packed_n, k, packed_stride, + matmul_wx, b, matmul_vx, c, y); + } else { + LaunchFusedGluForwardGpu(stream, m, packed_n, k, packed_stride, + matmul_wx, b, matmul_vx, c, y); + } +} + +template +void DispatchAlignment(ep::Stream* stream, const int64_t m, const int64_t n, const int64_t k, + int64_t stride, const T* matmul_wx, const T* b, const T* matmul_vx, + const T* c, T* y) { + const auto IsAligned = [&](const size_t alignment) { + const uintptr_t matmul_wx_ptr = reinterpret_cast(matmul_wx); + const uintptr_t matmul_vx_ptr = reinterpret_cast(matmul_vx); + const uintptr_t b_ptr = reinterpret_cast(b); + const uintptr_t c_ptr = reinterpret_cast(c); + const uintptr_t y_ptr = reinterpret_cast(y); + + return (/* memory address alignment */ + matmul_wx_ptr % alignment == 0 && matmul_vx_ptr % alignment == 0 + && b_ptr % alignment == 0 && c_ptr % alignment == 0 + && y_ptr % alignment == 0 + /* #element per row alignment */ + && n % (alignment / sizeof(T)) == 0); + }; + + if (IsAligned(16)) { + switch (sizeof(T)) { + case 8: + DispatchIndexType(stream, m, n, k, stride / 2, matmul_wx, b, matmul_vx, c, + y); + break; + case 4: + DispatchIndexType(stream, m, n, k, stride / 4, matmul_wx, b, matmul_vx, c, + y); + break; + case 2: + DispatchIndexType(stream, m, n, k, stride / 8, matmul_wx, b, matmul_vx, c, + y); + break; + case 1: + DispatchIndexType(stream, m, n, k, stride / 16, matmul_wx, b, matmul_vx, c, + y); + break; + default: + DispatchIndexType(stream, m, n, k, stride / 1, matmul_wx, b, matmul_vx, c, + y); + break; + } + } else if (IsAligned(8)) { + switch (sizeof(T)) { + case 4: + DispatchIndexType(stream, m, n, k, stride / 2, matmul_wx, b, matmul_vx, c, + y); + break; + case 2: + DispatchIndexType(stream, m, n, k, stride / 4, matmul_wx, b, matmul_vx, c, + y); + break; + case 1: + DispatchIndexType(stream, m, n, k, stride / 8, matmul_wx, b, matmul_vx, c, + y); + break; + default: + DispatchIndexType(stream, m, n, k, stride / 1, matmul_wx, b, matmul_vx, c, + y); + break; + } + } else if (IsAligned(4)) { + switch (sizeof(T)) { + case 2: + DispatchIndexType(stream, m, n, k, stride / 2, matmul_wx, b, matmul_vx, c, + y); + break; + case 1: + DispatchIndexType(stream, m, n, k, stride / 4, matmul_wx, b, matmul_vx, c, + y); + break; + default: + DispatchIndexType(stream, m, n, k, stride / 1, matmul_wx, b, matmul_vx, c, + y); + break; + } + } else if (IsAligned(2)) { + switch (sizeof(T)) { + case 1: + DispatchIndexType(stream, m, n, k, stride / 2, matmul_wx, b, matmul_vx, c, + y); + break; + default: + DispatchIndexType(stream, m, n, k, stride / 1, matmul_wx, b, matmul_vx, c, + y); + break; + } + } else { + DispatchIndexType(stream, m, n, k, stride / 1, matmul_wx, b, matmul_vx, c, y); + } +} + +template +void DispatchActivationType(ep::Stream* stream, const int64_t m, const int64_t n, const int64_t k, + int64_t stride, const T* matmul_wx, const T* b, const T* matmul_vx, + const T* c, T* y, const std::string& activation) { + if (activation == "none") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else if (activation == "sigmoid") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else if (activation == "relu") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else if (activation == "gelu") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else if (activation == "fast_gelu") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else if (activation == "silu") { + DispatchAlignment(stream, m, n, k, stride, matmul_wx, b, + matmul_vx, c, y); + } else { + UNIMPLEMENTED(); + } +} + +template +class GpuFusedGluKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + GpuFusedGluKernel() = default; + ~GpuFusedGluKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + // obtain tensors from context + const user_op::Tensor* input_tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* input_tensor_w = ctx->Tensor4ArgNameAndIndex("w", 0); + const user_op::Tensor* input_tensor_b = ctx->Tensor4ArgNameAndIndex("b", 0); + user_op::Tensor* out_tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* out_tensor_matmul_wx = ctx->Tensor4ArgNameAndIndex("matmul_wx", 0); + + // obtain optional tensors from context + bool is_split_mode = false; + user_op::Tensor* input_tensor_v = nullptr; + user_op::Tensor* input_tensor_c = nullptr; + user_op::Tensor* out_tensor_matmul_vx = nullptr; + + if (ctx->has_input("v", 0) && ctx->has_input("c", 0)) { + input_tensor_v = ctx->Tensor4ArgNameAndIndex("v", 0); + input_tensor_c = ctx->Tensor4ArgNameAndIndex("c", 0); + out_tensor_matmul_vx = ctx->Tensor4ArgNameAndIndex("matmul_vx", 0); + is_split_mode = true; + } else { + CHECK((!ctx->has_input("v", 0)) && (!(ctx->has_input("c", 0)))); + } + + // obtain tensor shapes + const ShapeView& x_shape = input_tensor_x->shape_view(); + const ShapeView& w_shape = input_tensor_w->shape_view(); + const ShapeView& b_shape = input_tensor_b->shape_view(); + const ShapeView& y_shape = out_tensor_y->shape_view(); + + // validate dimension and number of axes + CHECK_GE(x_shape.NumAxes(), 2) + << "number of axes of \'x\' should have be greater than 1, yet get " << x_shape.NumAxes(); + CHECK_EQ(w_shape.NumAxes(), 2) + << "number of axes of \'w\' should have be equal to 2, yet get " << w_shape.NumAxes(); + CHECK_EQ(b_shape.NumAxes(), 1) + << "number of axes of \'b\' should have be equal to 1, yet get " << b_shape.NumAxes(); + + // check input tensor shapes + size_t x_num_axes = x_shape.NumAxes(); + CHECK_EQ(w_shape.At(1), x_shape.At(x_num_axes - 1)) + << "dimension 1 of \'w\'(" << w_shape.At(1) + << ") is not consistant with the last dimension of \'x\'(" << x_shape.At(x_num_axes - 1) + << ")"; + CHECK_EQ(b_shape.At(0), w_shape.At(0)) + << "dimension 0 of \'b\'(" << b_shape.At(0) + << ") is not consistant with dimension 0 of \'w\'(" << w_shape.At(0) << ")"; + if (!is_split_mode) { + CHECK_EQ(w_shape.At(1) % 2, 0) << "dimension 1 of \'w\' is not divisible by 2"; + } + + // check optional input tensor shapes + if (is_split_mode) { + const ShapeView& v_shape = input_tensor_v->shape_view(); + const ShapeView& c_shape = input_tensor_b->shape_view(); + + CHECK_EQ(v_shape.NumAxes(), 2) + << "number of axes of \'v\' should have be equal to 2, yet get " << v_shape.NumAxes(); + CHECK_EQ(c_shape.NumAxes(), 1) + << "number of axes of \'c\' should have be equal to 1, yet get " << c_shape.NumAxes(); + CHECK_EQ(v_shape, w_shape) << "the shape of \'v\' is not consistant with \'w\'"; + CHECK_EQ(c_shape, b_shape) << "the shape of \'c\' is not consistant with \'b\'"; + } + + // infer m, n, k + const int64_t m = x_shape.Count(0, x_num_axes - 1); + const int64_t n = y_shape.At(x_num_axes - 1); + const int64_t k = x_shape.At(x_num_axes - 1); + + if (TryDispatchDualGemmImpl( + ctx->stream()->As(), ctx->Attr("activation"), m, n, k, + input_tensor_x->dptr(), input_tensor_w->dptr(), + is_split_mode ? input_tensor_v->dptr() : input_tensor_w->dptr() + n * k, + input_tensor_b->dptr(), + is_split_mode ? input_tensor_c->dptr() : input_tensor_b->dptr() + n, + out_tensor_matmul_wx->mut_dptr(), is_split_mode ? n : 2 * n, + is_split_mode ? out_tensor_matmul_vx->mut_dptr() + : out_tensor_matmul_wx->mut_dptr() + n, + is_split_mode ? n : 2 * n, out_tensor_y->mut_dptr())) { + return; + } + + // calculate matmul_wx (and matmul_vx) through primitive + auto matmul = ep::primitive::NewPrimitive( + DeviceType::kCUDA, input_tensor_x->data_type(), ep::primitive::BlasTransposeType::N, + ep::primitive::BlasTransposeType::T); + CHECK(matmul); + /* Launch(Stream* stream, size_t m, size_t n, size_t k, Scalar alpha, const void* a, + const void* b, Scalar beta, void* c) = 0; */ + if (is_split_mode) { + matmul->Launch(ctx->stream(), m, n, k, 1.0, input_tensor_x->dptr(), input_tensor_w->dptr(), + 0.0, out_tensor_matmul_wx->mut_dptr()); + matmul->Launch(ctx->stream(), m, n, k, 1.0, input_tensor_x->dptr(), input_tensor_v->dptr(), + 0.0, out_tensor_matmul_vx->mut_dptr()); + } else { + matmul->Launch(ctx->stream(), m, n * 2, k, 1.0, input_tensor_x->dptr(), + input_tensor_w->dptr(), 0.0, out_tensor_matmul_wx->mut_dptr()); + } + + // dispatch according to activation type + DispatchActivationType( + ctx->stream(), + /*m, n, k=*/m, n, k, + /*stride=*/is_split_mode ? n : 2 * n, + /*matmul_wx=*/out_tensor_matmul_wx->dptr(), + /*b=*/input_tensor_b->dptr(), + /*matmul_vx=*/ + is_split_mode ? out_tensor_matmul_vx->dptr() : out_tensor_matmul_wx->dptr() + n, + /*c=*/is_split_mode ? input_tensor_c->dptr() : input_tensor_b->dptr() + n, + /*y=*/out_tensor_y->mut_dptr(), + /*activation=*/ctx->Attr("activation")); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace + +#define REGISTER_GPU_FUSED_GLU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_glu") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); + +REGISTER_GPU_FUSED_GLU_KERNEL(double) +REGISTER_GPU_FUSED_GLU_KERNEL(float) +REGISTER_GPU_FUSED_GLU_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_GPU_FUSED_GLU_KERNEL(nv_bfloat16) +#endif + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_gru_cell_kernel.cu b/oneflow/user/kernels/fused_gru_cell_kernel.cu index 3e91268e9398bd1dbf80aec4b3df87c86f47fb60..a584282cf7d3ef7ef2c10dd4b161fe06b43e0b59 100644 --- a/oneflow/user/kernels/fused_gru_cell_kernel.cu +++ b/oneflow/user/kernels/fused_gru_cell_kernel.cu @@ -459,7 +459,7 @@ REGISTER_USER_KERNEL("fused_gru_cell_grad") size_t tmp_bytes = 0; if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { const Shape& in_shape = ctx->InputTensorDesc("grad_hy", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("grad_input_bias", 0)->shape(); + const Shape& out_shape = ctx->OutputTensorDesc("grad_input_bias", 0).shape(); tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * 3 * sizeof(float)) + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); } else { diff --git a/oneflow/user/kernels/fused_gru_cell_kernel.hip.cpp b/oneflow/user/kernels/fused_gru_cell_kernel.hip.cpp deleted file mode 100644 index d910b5d3a8f14b68b018ef7b2cde00f4685429da..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_gru_cell_kernel.hip.cpp +++ /dev/null @@ -1,472 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/primitive/cast.h" -#include "oneflow/core/ep/include/primitive/fill.h" -#include "oneflow/core/ep/rocm/cuda_device.h" -#include "oneflow/core/ep/include/primitive/matmul.h" -#include "oneflow/user/kernels/fused_rnn_cell_kernel_util.h" - -// NOTE(Liang Depeng): The implementation of fused_gru_cell is modified from -// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/RNN.cu - -namespace oneflow { - -namespace { - -template -struct AccumulateType {}; -template<> -struct AccumulateType { - using type = float; -}; -template<> -struct AccumulateType { - using type = double; -}; - -template -using acc_type = typename AccumulateType::type; - -#define H2F(input) static_cast(input) -#define F2H(input) static_cast(input) - -template -__device__ __forceinline__ T sigmoid(T in) { - T one = static_cast(1.0); - return one / (one + ::exp(-in)); -} - -template -#if __CUDA_ARCH__ >= 350 -OF_LAUNCH_BOUNDS_2(512, 4) -#endif -__global__ void gru_cell_forward(const IDX_TYPE numel, const IDX_TYPE hidden_size, - const T* input_gates_ptr, const T* hidden_gates_ptr, - const T* hx_ptr, const T* input_bias_ptr, const T* hidden_bias_ptr, - T* hy_ptr, T* workspace_ptr) { - bool has_bias = input_bias_ptr != nullptr; - for (IDX_TYPE linearIndex = blockIdx.x * blockDim.x + threadIdx.x; linearIndex < numel; - linearIndex += gridDim.x * blockDim.x) { - IDX_TYPE offset = (linearIndex / hidden_size) * 3 * hidden_size + linearIndex % hidden_size; - - T ir = input_gates_ptr[offset + 0 * hidden_size]; - T ii = input_gates_ptr[offset + 1 * hidden_size]; - T in = input_gates_ptr[offset + 2 * hidden_size]; - T hr = hidden_gates_ptr[offset + 0 * hidden_size]; - T hi = hidden_gates_ptr[offset + 1 * hidden_size]; - T hn = hidden_gates_ptr[offset + 2 * hidden_size]; - - T hx = hx_ptr[linearIndex]; - T* hy = &(hy_ptr[linearIndex]); - - T b1r, b1i, b1n, b2r, b2i, b2n; - - if (has_bias) { - b1r = input_bias_ptr[linearIndex % hidden_size + 0 * hidden_size]; - b1i = input_bias_ptr[linearIndex % hidden_size + 1 * hidden_size]; - b1n = input_bias_ptr[linearIndex % hidden_size + 2 * hidden_size]; - - b2r = hidden_bias_ptr[linearIndex % hidden_size + 0 * hidden_size]; - b2i = hidden_bias_ptr[linearIndex % hidden_size + 1 * hidden_size]; - b2n = hidden_bias_ptr[linearIndex % hidden_size + 2 * hidden_size]; - } else { - b1r = F2H(0.0); - b1i = F2H(0.0); - b1n = F2H(0.0); - b2r = F2H(0.0); - b2i = F2H(0.0); - b2n = F2H(0.0); - } - - offset = (linearIndex / hidden_size) * 5 * hidden_size + linearIndex % hidden_size; - ACC_T rg, ig, ng; - rg = sigmoid(H2F(ir) + H2F(hr) + H2F(b1r) + H2F(b2r)); - ig = sigmoid(H2F(ii) + H2F(hi) + H2F(b1i) + H2F(b2i)); - - ng = H2F(in) + H2F(b1n) + rg * (H2F(hn) + H2F(b2n)); - ng = ::tanh(ng); - *hy = F2H(ng + ig * (H2F(hx) - ng)); - - // SAVE FOR BACKWARDS - workspace_ptr[offset + 0 * hidden_size] = F2H(rg); - workspace_ptr[offset + 1 * hidden_size] = F2H(ig); - workspace_ptr[offset + 2 * hidden_size] = F2H(ng); - workspace_ptr[offset + 3 * hidden_size] = hx; - workspace_ptr[offset + 4 * hidden_size] = F2H(H2F(hn) + H2F(b2n)); - } -} - -template -#if __CUDA_ARCH__ >= 350 -OF_LAUNCH_BOUNDS_2(512, 4) -#endif -__global__ - void gru_cell_backward(const IDX_TYPE numel, const IDX_TYPE hidden_size, const T* grad_hy_ptr, - const T* workspace_ptr, T* grad_input_gates_ptr, - T* grad_hidden_gates_ptr, T* grad_hx_ptr) { - for (IDX_TYPE linearIndex = blockIdx.x * blockDim.x + threadIdx.x; linearIndex < numel; - linearIndex += gridDim.x * blockDim.x) { - IDX_TYPE offset = (linearIndex / hidden_size) * 5 * hidden_size + linearIndex % hidden_size; - - T rg = workspace_ptr[offset + 0 * hidden_size]; - T ig = workspace_ptr[offset + 1 * hidden_size]; - T ng = workspace_ptr[offset + 2 * hidden_size]; - T hx = workspace_ptr[offset + 3 * hidden_size]; - T hn = workspace_ptr[offset + 4 * hidden_size]; - - T go = grad_hy_ptr[linearIndex]; - - offset = (linearIndex / hidden_size) * 3 * hidden_size + linearIndex % hidden_size; - - ACC_T gig = H2F(go) * (H2F(hx) - H2F(ng)) * (1 - H2F(ig)) * H2F(ig); - ACC_T ghx = H2F(go) * H2F(ig); - ACC_T gin = H2F(go) * (1 - H2F(ig)) * (1 - H2F(ng) * H2F(ng)); - ACC_T ghn = gin * H2F(rg); - ACC_T grg = gin * H2F(hn) * (1 - H2F(rg)) * H2F(rg); - - grad_input_gates_ptr[offset + 0 * hidden_size] = F2H(grg); - grad_input_gates_ptr[offset + 1 * hidden_size] = F2H(gig); - grad_input_gates_ptr[offset + 2 * hidden_size] = F2H(gin); - - grad_hidden_gates_ptr[offset + 0 * hidden_size] = F2H(grg); - grad_hidden_gates_ptr[offset + 1 * hidden_size] = F2H(gig); - grad_hidden_gates_ptr[offset + 2 * hidden_size] = F2H(ghn); - if (grad_hx_ptr != nullptr) { grad_hx_ptr[linearIndex] = F2H(ghx); } - } -} - -template -struct FusedGruCellGradFunctor final { - void operator()(ep::Stream* stream, const int64_t hx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const T* grad_hy_ptr, const T* workspace_ptr, - T* grad_input_gates_ptr, T* grad_hidden_gates_ptr, T* grad_hx_ptr) { - using ACC_T = acc_type; - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL((gru_cell_backward), stream, hx_numel, - static_cast(hx_numel), static_cast(hidden_size), - grad_hy_ptr, workspace_ptr, grad_input_gates_ptr, grad_hidden_gates_ptr, - grad_hx_ptr); - } else { - RUN_CUDA_KERNEL((gru_cell_backward), stream, hx_numel, hx_numel, - hidden_size, grad_hy_ptr, workspace_ptr, grad_input_gates_ptr, - grad_hidden_gates_ptr, grad_hx_ptr); - } - } -}; - -template<> -void FusedGruCellGradFunctor::operator()( - ep::Stream* stream, const int64_t hx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const float16* grad_hy_ptr, const float16* workspace_ptr, - float16* grad_input_gates_ptr, float16* grad_hidden_gates_ptr, float16* grad_hx_ptr) { - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL( - (gru_cell_backward), stream, hx_numel, static_cast(hx_numel), - static_cast(hidden_size), reinterpret_cast(grad_hy_ptr), - reinterpret_cast(workspace_ptr), reinterpret_cast(grad_input_gates_ptr), - reinterpret_cast(grad_hidden_gates_ptr), reinterpret_cast(grad_hx_ptr)); - } else { - RUN_CUDA_KERNEL( - (gru_cell_backward), stream, hx_numel, hx_numel, hidden_size, - reinterpret_cast(grad_hy_ptr), reinterpret_cast(workspace_ptr), - reinterpret_cast(grad_input_gates_ptr), - reinterpret_cast(grad_hidden_gates_ptr), reinterpret_cast(grad_hx_ptr)); - } -} - -template -struct FusedGruCellFunctor final { - void operator()(ep::Stream* stream, const int64_t hx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const T* input_gates_ptr, const T* hidden_gates_ptr, - const T* hx_ptr, const T* input_bias_ptr, const T* hidden_bias_ptr, T* hy_ptr, - T* workspace_ptr) { - using ACC_T = acc_type; - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL((gru_cell_forward), stream, hx_numel, - static_cast(hx_numel), static_cast(hidden_size), - input_gates_ptr, hidden_gates_ptr, hx_ptr, input_bias_ptr, hidden_bias_ptr, - hy_ptr, workspace_ptr); - } else { - RUN_CUDA_KERNEL((gru_cell_forward), stream, hx_numel, hx_numel, - hidden_size, input_gates_ptr, hidden_gates_ptr, hx_ptr, input_bias_ptr, - hidden_bias_ptr, hy_ptr, workspace_ptr); - } - } -}; - -template<> -void FusedGruCellFunctor::operator()( - ep::Stream* stream, const int64_t hx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const float16* input_gates_ptr, const float16* hidden_gates_ptr, - const float16* hx_ptr, const float16* input_bias_ptr, const float16* hidden_bias_ptr, - float16* hy_ptr, float16* workspace_ptr) { - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL( - (gru_cell_forward), stream, hx_numel, static_cast(hx_numel), - static_cast(hidden_size), reinterpret_cast(input_gates_ptr), - reinterpret_cast(hidden_gates_ptr), reinterpret_cast(hx_ptr), - reinterpret_cast(input_bias_ptr), - reinterpret_cast(hidden_bias_ptr), reinterpret_cast(hy_ptr), - reinterpret_cast(workspace_ptr)); - } else { - RUN_CUDA_KERNEL((gru_cell_forward), stream, hx_numel, hx_numel, - hidden_size, reinterpret_cast(input_gates_ptr), - reinterpret_cast(hidden_gates_ptr), - reinterpret_cast(hx_ptr), - reinterpret_cast(input_bias_ptr), - reinterpret_cast(hidden_bias_ptr), reinterpret_cast(hy_ptr), - reinterpret_cast(workspace_ptr)); - } -} - -} // namespace - -template -class GpuFusedGruCellKernel final : public user_op::OpKernel { - public: - GpuFusedGruCellKernel() = default; - ~GpuFusedGruCellKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* input_gates = ctx->Tensor4ArgNameAndIndex("input_gates", 0); - const user_op::Tensor* hidden_gates = ctx->Tensor4ArgNameAndIndex("hidden_gates", 0); - const user_op::Tensor* hx = ctx->Tensor4ArgNameAndIndex("hx", 0); - user_op::Tensor* hy = ctx->Tensor4ArgNameAndIndex("hy", 0); - user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - - const T* input_bias_ptr = nullptr; - const T* hidden_bias_ptr = nullptr; - if (ctx->has_input("input_bias", 0)) { - CHECK(ctx->has_input("hidden_bias", 0)); - input_bias_ptr = ctx->Tensor4ArgNameAndIndex("input_bias", 0)->dptr(); - hidden_bias_ptr = ctx->Tensor4ArgNameAndIndex("hidden_bias", 0)->dptr(); - } - const T* input_gates_ptr = input_gates->dptr(); - const T* hidden_gates_ptr = hidden_gates->dptr(); - const T* hx_ptr = hx->dptr(); - - T* hy_ptr = hy->mut_dptr(); - T* workspace_ptr = workspace->mut_dptr(); - const int64_t hx_numel = hx->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = hx->shape_view().At(hx->shape_view().NumAxes() - 1); - FusedGruCellFunctor()(ctx->stream(), hx_numel, workspace_numel, hidden_size, input_gates_ptr, - hidden_gates_ptr, hx_ptr, input_bias_ptr, hidden_bias_ptr, hy_ptr, - workspace_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_GRU_CELL_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_gru_cell") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("hx", 0) == GetDataType::value) \ - && (user_op::HobDataType("input_gates", 0) == GetDataType::value) \ - && (user_op::HobDataType("hidden_gates", 0) == GetDataType::value)) - -REGISTER_FUSED_GRU_CELL_KERNEL(float); -REGISTER_FUSED_GRU_CELL_KERNEL(float16); - -class GpuFusedGruCellGradFloatKernel final : public user_op::OpKernel { - public: - GpuFusedGruCellGradFloatKernel() = default; - ~GpuFusedGruCellGradFloatKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* grad_hy = ctx->Tensor4ArgNameAndIndex("grad_hy", 0); - const user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - user_op::Tensor* grad_input_gates = ctx->Tensor4ArgNameAndIndex("grad_input_gates", 0); - user_op::Tensor* grad_hidden_gates = ctx->Tensor4ArgNameAndIndex("grad_hidden_gates", 0); - - const float* grad_hy_ptr = grad_hy->dptr(); - const float* workspace_ptr = workspace->dptr(); - - float* grad_input_gates_ptr = grad_input_gates->mut_dptr(); - float* grad_hidden_gates_ptr = grad_hidden_gates->mut_dptr(); - - float* grad_hx_ptr = nullptr; - if (ctx->has_output("grad_hx", 0)) { - user_op::Tensor* grad_hx = ctx->Tensor4ArgNameAndIndex("grad_hx", 0); - grad_hx_ptr = grad_hx->mut_dptr(); - } - - const int64_t hx_numel = grad_hy->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = grad_hy->shape_view().At(grad_hy->shape_view().NumAxes() - 1); - FusedGruCellGradFunctor()(ctx->stream(), hx_numel, workspace_numel, hidden_size, - grad_hy_ptr, workspace_ptr, grad_input_gates_ptr, - grad_hidden_gates_ptr, grad_hx_ptr); - - if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { - float* grad_input_bias_ptr = - ctx->Tensor4ArgNameAndIndex("grad_input_bias", 0)->mut_dptr(); - std::vector axis; - axis.push_back(0); - const Shape& reduced_shape = - CreateReducedShape(grad_input_gates->shape_view(), {axis.begin(), axis.end()}); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, grad_input_bias_ptr), - XpuVarNdarray(grad_input_gates->shape_view(), - grad_input_gates->dptr()), - XpuVarNdarray(tmp_buffer->shape_view(), tmp_buffer->mut_dptr())); - - float* grad_hidden_bias_ptr = - ctx->Tensor4ArgNameAndIndex("grad_hidden_bias", 0)->mut_dptr(); - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, grad_hidden_bias_ptr), - XpuVarNdarray(grad_hidden_gates->shape_view(), - grad_hidden_gates->dptr()), - XpuVarNdarray(tmp_buffer->shape_view(), tmp_buffer->mut_dptr())); - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("fused_gru_cell_grad") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("grad_hy", 0) == GetDataType::value) - && (user_op::HobDataType("workspace", 0) == GetDataType::value)) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - size_t tmp_bytes = 0; - if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { - const Shape& in_shape = ctx->InputTensorDesc("grad_hy", 0).shape(); - tmp_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * 3 * sizeof(float)); - } else { - tmp_bytes = 0; - } - return tmp_bytes; - }); - -class GpuFusedGruCellGradHalfKernel final : public user_op::OpKernel { - public: - GpuFusedGruCellGradHalfKernel() = default; - ~GpuFusedGruCellGradHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* grad_hy = ctx->Tensor4ArgNameAndIndex("grad_hy", 0); - const user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - user_op::Tensor* grad_input_gates = ctx->Tensor4ArgNameAndIndex("grad_input_gates", 0); - user_op::Tensor* grad_hidden_gates = ctx->Tensor4ArgNameAndIndex("grad_hidden_gates", 0); - - const float16* grad_hy_ptr = grad_hy->dptr(); - const float16* workspace_ptr = workspace->dptr(); - - float16* grad_input_gates_ptr = grad_input_gates->mut_dptr(); - float16* grad_hidden_gates_ptr = grad_hidden_gates->mut_dptr(); - - float16* grad_hx_ptr = nullptr; - if (ctx->has_output("grad_hx", 0)) { - user_op::Tensor* grad_hx = ctx->Tensor4ArgNameAndIndex("grad_hx", 0); - grad_hx_ptr = grad_hx->mut_dptr(); - } - - const int64_t hx_numel = grad_hy->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = grad_hy->shape_view().At(grad_hy->shape_view().NumAxes() - 1); - FusedGruCellGradFunctor()(ctx->stream(), hx_numel, workspace_numel, hidden_size, - grad_hy_ptr, workspace_ptr, grad_input_gates_ptr, - grad_hidden_gates_ptr, grad_hx_ptr); - - if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { - std::vector axis; - axis.push_back(0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const ShapeView& in_shape = grad_input_gates->shape_view(); - const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); - float* in_tmp_buffer = tmp_buffer->mut_dptr(); - const size_t in_tmp_buffer_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - float* out_tmp_buffer = - reinterpret_cast(tmp_buffer->mut_dptr() + in_tmp_buffer_bytes); - const size_t out_tmp_buffer_bytes = - GetCudaAlignedSize(reduced_shape.elem_cnt() * sizeof(float)); - float* reduce_tmp_buffer = reinterpret_cast( - tmp_buffer->mut_dptr() + in_tmp_buffer_bytes + out_tmp_buffer_bytes); - const size_t reduce_tmp_buffer_bytes = - GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, - tmp_buffer->shape_view().elem_cnt()); - auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); - CHECK(h2f); - auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); - CHECK(f2h); - h2f->Launch(ctx->stream(), grad_input_gates->dptr(), in_tmp_buffer, - in_shape.elem_cnt()); - - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), - XpuVarNdarray(in_shape, in_tmp_buffer), - XpuVarNdarray(in_shape, reduce_tmp_buffer)); - - user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("grad_input_bias", 0); - f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), - output_tensor->shape_view().elem_cnt()); - - h2f->Launch(ctx->stream(), grad_hidden_gates->dptr(), in_tmp_buffer, - in_shape.elem_cnt()); - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), - XpuVarNdarray(in_shape, in_tmp_buffer), - XpuVarNdarray(in_shape, reduce_tmp_buffer)); - - output_tensor = ctx->Tensor4ArgNameAndIndex("grad_hidden_bias", 0); - f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), - output_tensor->shape_view().elem_cnt()); - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("fused_gru_cell_grad") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("grad_hy", 0) == GetDataType::value) - && (user_op::HobDataType("workspace", 0) == GetDataType::value)) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - size_t tmp_bytes = 0; - if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { - const Shape& in_shape = ctx->InputTensorDesc("grad_hy", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("grad_input_bias", 0)->shape(); - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * 3 * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } else { - tmp_bytes = 0; - } - return tmp_bytes; - }); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_lstm_cell_kernel.cu b/oneflow/user/kernels/fused_lstm_cell_kernel.cu index 568ab44d48248a39fd46b6f58d38b102fb33a175..e532becb2a412af405a47d23d411986a4ce48bfb 100644 --- a/oneflow/user/kernels/fused_lstm_cell_kernel.cu +++ b/oneflow/user/kernels/fused_lstm_cell_kernel.cu @@ -492,7 +492,7 @@ REGISTER_USER_KERNEL("fused_lstm_cell_grad") size_t tmp_bytes = 0; if (ctx->has_output("grad_bias", 0)) { const Shape& in_shape = ctx->InputTensorDesc("workspace", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("grad_bias", 0)->shape(); + const Shape& out_shape = ctx->OutputTensorDesc("grad_bias", 0).shape(); tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); } else { diff --git a/oneflow/user/kernels/fused_lstm_cell_kernel.hip.cpp b/oneflow/user/kernels/fused_lstm_cell_kernel.hip.cpp deleted file mode 100644 index 60cbc6c977a6fdf97daf265cd58633ecf444d54d..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_lstm_cell_kernel.hip.cpp +++ /dev/null @@ -1,505 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ndarray/xpu_var_ndarray.h" -#include "oneflow/core/kernel/kernel_util.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/primitive/cast.h" -#include "oneflow/core/ep/include/primitive/fill.h" -#include "oneflow/core/ep/rocm/cuda_device.h" -#include "oneflow/core/ep/include/primitive/matmul.h" -#include "oneflow/user/kernels/fused_rnn_cell_kernel_util.h" - -// NOTE(Liang Depeng): The implementation of fused_lstm_cell is modified from -// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cuda/RNN.cu - -namespace oneflow { - -namespace { - -template -struct AccumulateType {}; -template<> -struct AccumulateType { - using type = float; -}; -template<> -struct AccumulateType { - using type = double; -}; - -template -using acc_type = typename AccumulateType::type; - -#define H2F(input) static_cast(input) -#define F2H(input) static_cast(input) - -template -__device__ __forceinline__ T sigmoid(T in) { - T one = static_cast(1.0); - return one / (one + ::exp(-in)); -} - -template -#if __CUDA_ARCH__ >= 350 -OF_LAUNCH_BOUNDS_2(512, 4) -#endif -__global__ - void lstm_cell_forward(const IDX_TYPE numel, const IDX_TYPE hidden_size, - const T* input_gates_ptr, const T* hidden_gates_ptr, const T* cx_ptr, - const T* input_bias_ptr, const T* hidden_bias_ptr, T* hy_ptr, T* cy_ptr, - T* workspace_ptr) { - bool has_bias = input_bias_ptr != nullptr; - for (IDX_TYPE linearIndex = blockIdx.x * blockDim.x + threadIdx.x; linearIndex < numel; - linearIndex += gridDim.x * blockDim.x) { - IDX_TYPE offset = (linearIndex / hidden_size) * 4 * hidden_size + linearIndex % hidden_size; - - T iig = input_gates_ptr[offset + 0 * hidden_size]; - T ifg = input_gates_ptr[offset + 1 * hidden_size]; - T icg = input_gates_ptr[offset + 2 * hidden_size]; - T iog = input_gates_ptr[offset + 3 * hidden_size]; - - T hig = hidden_gates_ptr[offset + 0 * hidden_size]; - T hfg = hidden_gates_ptr[offset + 1 * hidden_size]; - T hcg = hidden_gates_ptr[offset + 2 * hidden_size]; - T hog = hidden_gates_ptr[offset + 3 * hidden_size]; - - T* wig = &(workspace_ptr[offset + 0 * hidden_size]); - T* wfg = &(workspace_ptr[offset + 1 * hidden_size]); - T* wcg = &(workspace_ptr[offset + 2 * hidden_size]); - T* wog = &(workspace_ptr[offset + 3 * hidden_size]); - - T cx = cx_ptr[linearIndex]; - - T* hy = &(hy_ptr[linearIndex]); - T* cy = &(cy_ptr[linearIndex]); - - T b1i, b1f, b1c, b1o; - T b2i, b2f, b2c, b2o; - - if (has_bias) { - b1i = input_bias_ptr[linearIndex % hidden_size + 0 * hidden_size]; - b1f = input_bias_ptr[linearIndex % hidden_size + 1 * hidden_size]; - b1c = input_bias_ptr[linearIndex % hidden_size + 2 * hidden_size]; - b1o = input_bias_ptr[linearIndex % hidden_size + 3 * hidden_size]; - - b2i = hidden_bias_ptr[linearIndex % hidden_size + 0 * hidden_size]; - b2f = hidden_bias_ptr[linearIndex % hidden_size + 1 * hidden_size]; - b2c = hidden_bias_ptr[linearIndex % hidden_size + 2 * hidden_size]; - b2o = hidden_bias_ptr[linearIndex % hidden_size + 3 * hidden_size]; - } else { - b1i = F2H(0.0); - b1f = F2H(0.0); - b1c = F2H(0.0); - b1o = F2H(0.0); - b2i = F2H(0.0); - b2f = F2H(0.0); - b2c = F2H(0.0); - b2o = F2H(0.0); - } - - ACC_T ig, fg, cg, og; - ACC_T f_hy, f_cy; - - ig = sigmoid(H2F(iig) + H2F(hig) + H2F(b1i) + H2F(b2i)); - fg = sigmoid(H2F(ifg) + H2F(hfg) + H2F(b1f) + H2F(b2f)); - cg = ::tanh(H2F(icg) + H2F(hcg) + H2F(b1c) + H2F(b2c)); - og = sigmoid(H2F(iog) + H2F(hog) + H2F(b1o) + H2F(b2o)); - - f_cy = (fg * H2F(cx)) + (ig * cg); - f_hy = og * ::tanh(f_cy); - - *hy = F2H(f_hy); - *cy = F2H(f_cy); - - // SAVE FOR BACKWARDS - // Also need cy and cx but can be saved easily in python - *wig = F2H(ig); - *wfg = F2H(fg); - *wcg = F2H(cg); - *wog = F2H(og); - } -} - -template -#if __CUDA_ARCH__ >= 350 -OF_LAUNCH_BOUNDS_2(512, 4) -#endif -__global__ - void lstm_cell_backward(const IDX_TYPE numel, const IDX_TYPE hidden_size, const T* grad_hy_ptr, - const T* grad_cy_ptr, const T* cx_ptr, const T* cy_ptr, - const T* workspace_ptr, T* grad_gates_ptr, T* grad_cx_ptr) { - for (IDX_TYPE linearIndex = blockIdx.x * blockDim.x + threadIdx.x; linearIndex < numel; - linearIndex += gridDim.x * blockDim.x) { - IDX_TYPE offset = (linearIndex / hidden_size) * 4 * hidden_size + linearIndex % hidden_size; - - T ig = workspace_ptr[offset + 0 * hidden_size]; - T fg = workspace_ptr[offset + 1 * hidden_size]; - T cg = workspace_ptr[offset + 2 * hidden_size]; - T og = workspace_ptr[offset + 3 * hidden_size]; - - T* ih = &(grad_gates_ptr[offset + 0 * hidden_size]); - T* fh = &(grad_gates_ptr[offset + 1 * hidden_size]); - T* ch = &(grad_gates_ptr[offset + 2 * hidden_size]); - T* oh = &(grad_gates_ptr[offset + 3 * hidden_size]); - - // will return hidden grads here - T cx = cx_ptr[linearIndex]; - T cy = cy_ptr[linearIndex]; - - ACC_T go = H2F(grad_hy_ptr[linearIndex]); - ACC_T goc = H2F(grad_cy_ptr[linearIndex]); - - ACC_T gcx = ::tanh(H2F(cy)); - - ACC_T gog = go * gcx; - gcx = go * H2F(og) * (1 - gcx * gcx) + goc; - - ACC_T gig = gcx * H2F(cg); - ACC_T gfg = gcx * H2F(cx); - ACC_T gcg = gcx * H2F(ig); - - gig = gig * (1 - H2F(ig)) * H2F(ig); - gfg = gfg * (1 - H2F(fg)) * H2F(fg); - gcg = gcg * (1 - H2F(cg) * H2F(cg)); - gog = gog * (1 - H2F(og)) * H2F(og); - - *ih = F2H(gig); - *fh = F2H(gfg); - *ch = F2H(gcg); - *oh = F2H(gog); - - if (grad_cx_ptr != nullptr) { - gcx = gcx * H2F(fg); - T* gi = &(grad_cx_ptr[linearIndex]); - *gi = F2H(gcx); - } - } -} - -template -struct FusedLstmCellFunctor final { - void operator()(ep::Stream* stream, const int64_t cx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const T* input_gates_ptr, const T* hidden_gates_ptr, - const T* cx_ptr, const T* input_bias_ptr, const T* hidden_bias_ptr, T* hy_ptr, - T* cy_ptr, T* workspace_ptr) { - using ACC_T = acc_type; - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL((lstm_cell_forward), stream, cx_numel, - static_cast(cx_numel), static_cast(hidden_size), - input_gates_ptr, hidden_gates_ptr, cx_ptr, input_bias_ptr, hidden_bias_ptr, - hy_ptr, cy_ptr, workspace_ptr); - } else { - RUN_CUDA_KERNEL((lstm_cell_forward), stream, cx_numel, cx_numel, - hidden_size, input_gates_ptr, hidden_gates_ptr, cx_ptr, input_bias_ptr, - hidden_bias_ptr, hy_ptr, cy_ptr, workspace_ptr); - } - } -}; - -template<> -void FusedLstmCellFunctor::operator()( - ep::Stream* stream, const int64_t cx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const float16* input_gates_ptr, const float16* hidden_gates_ptr, - const float16* cx_ptr, const float16* input_bias_ptr, const float16* hidden_bias_ptr, - float16* hy_ptr, float16* cy_ptr, float16* workspace_ptr) { - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL( - (lstm_cell_forward), stream, cx_numel, static_cast(cx_numel), - static_cast(hidden_size), reinterpret_cast(input_gates_ptr), - reinterpret_cast(hidden_gates_ptr), reinterpret_cast(cx_ptr), - reinterpret_cast(input_bias_ptr), - reinterpret_cast(hidden_bias_ptr), reinterpret_cast(hy_ptr), - reinterpret_cast(cy_ptr), reinterpret_cast(workspace_ptr)); - } else { - RUN_CUDA_KERNEL((lstm_cell_forward), stream, cx_numel, cx_numel, - hidden_size, reinterpret_cast(input_gates_ptr), - reinterpret_cast(hidden_gates_ptr), - reinterpret_cast(cx_ptr), - reinterpret_cast(input_bias_ptr), - reinterpret_cast(hidden_bias_ptr), reinterpret_cast(hy_ptr), - reinterpret_cast(cy_ptr), reinterpret_cast(workspace_ptr)); - } -} - -template -struct FusedLstmCellGradFunctor final { - void operator()(ep::Stream* stream, const int64_t cx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const T* grad_hy_ptr, const T* grad_cy_ptr, - const T* cx_ptr, const T* cy_ptr, const T* workspace_ptr, T* grad_gates_ptr, - T* grad_cx_ptr) { - using ACC_T = acc_type; - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL((lstm_cell_backward), stream, cx_numel, - static_cast(cx_numel), static_cast(hidden_size), - grad_hy_ptr, grad_cy_ptr, cx_ptr, cy_ptr, workspace_ptr, grad_gates_ptr, - grad_cx_ptr); - } else { - RUN_CUDA_KERNEL((lstm_cell_backward), stream, cx_numel, cx_numel, - hidden_size, grad_hy_ptr, grad_cy_ptr, cx_ptr, cy_ptr, workspace_ptr, - grad_gates_ptr, grad_cx_ptr); - } - } -}; - -template<> -void FusedLstmCellGradFunctor::operator()( - ep::Stream* stream, const int64_t cx_numel, const int64_t workspace_numel, - const int64_t hidden_size, const float16* grad_hy_ptr, const float16* grad_cy_ptr, - const float16* cx_ptr, const float16* cy_ptr, const float16* workspace_ptr, - float16* grad_gates_ptr, float16* grad_cx_ptr) { - if (workspace_numel < std::numeric_limits::max()) { - RUN_CUDA_KERNEL((lstm_cell_backward), stream, cx_numel, - static_cast(cx_numel), static_cast(hidden_size), - reinterpret_cast(grad_hy_ptr), - reinterpret_cast(grad_cy_ptr), - reinterpret_cast(cx_ptr), reinterpret_cast(cy_ptr), - reinterpret_cast(workspace_ptr), - reinterpret_cast(grad_gates_ptr), reinterpret_cast(grad_cx_ptr)); - } else { - RUN_CUDA_KERNEL((lstm_cell_backward), stream, cx_numel, cx_numel, - hidden_size, reinterpret_cast(grad_hy_ptr), - reinterpret_cast(grad_cy_ptr), - reinterpret_cast(cx_ptr), reinterpret_cast(cy_ptr), - reinterpret_cast(workspace_ptr), - reinterpret_cast(grad_gates_ptr), reinterpret_cast(grad_cx_ptr)); - } -} - -} // namespace - -template -class GpuFusedLstmCellKernel final : public user_op::OpKernel { - public: - GpuFusedLstmCellKernel() = default; - ~GpuFusedLstmCellKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* input_gates = ctx->Tensor4ArgNameAndIndex("input_gates", 0); - const user_op::Tensor* hidden_gates = ctx->Tensor4ArgNameAndIndex("hidden_gates", 0); - const user_op::Tensor* cx = ctx->Tensor4ArgNameAndIndex("cx", 0); - user_op::Tensor* hy = ctx->Tensor4ArgNameAndIndex("hy", 0); - user_op::Tensor* cy = ctx->Tensor4ArgNameAndIndex("cy", 0); - user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - - const T* input_bias_ptr = nullptr; - const T* hidden_bias_ptr = nullptr; - if (ctx->has_input("input_bias", 0)) { - CHECK(ctx->has_input("hidden_bias", 0)); - input_bias_ptr = ctx->Tensor4ArgNameAndIndex("input_bias", 0)->dptr(); - hidden_bias_ptr = ctx->Tensor4ArgNameAndIndex("hidden_bias", 0)->dptr(); - } - const T* input_gates_ptr = input_gates->dptr(); - const T* hidden_gates_ptr = hidden_gates->dptr(); - const T* cx_ptr = cx->dptr(); - - T* hy_ptr = hy->mut_dptr(); - T* cy_ptr = cy->mut_dptr(); - T* workspace_ptr = workspace->mut_dptr(); - const int64_t cx_numel = cx->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = cx->shape_view().At(cx->shape_view().NumAxes() - 1); - FusedLstmCellFunctor()(ctx->stream(), cx_numel, workspace_numel, hidden_size, - input_gates_ptr, hidden_gates_ptr, cx_ptr, input_bias_ptr, - hidden_bias_ptr, hy_ptr, cy_ptr, workspace_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_LSTM_CELL_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_lstm_cell") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("cx", 0) == GetDataType::value) \ - && (user_op::HobDataType("input_gates", 0) == GetDataType::value) \ - && (user_op::HobDataType("hidden_gates", 0) == GetDataType::value)) - -REGISTER_FUSED_LSTM_CELL_KERNEL(float); -REGISTER_FUSED_LSTM_CELL_KERNEL(float16); - -class GpuFusedLstmCellGradFloatKernel final : public user_op::OpKernel { - public: - GpuFusedLstmCellGradFloatKernel() = default; - ~GpuFusedLstmCellGradFloatKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* grad_hy = ctx->Tensor4ArgNameAndIndex("grad_hy", 0); - const user_op::Tensor* grad_cy = ctx->Tensor4ArgNameAndIndex("grad_cy", 0); - const user_op::Tensor* cx = ctx->Tensor4ArgNameAndIndex("cx", 0); - const user_op::Tensor* cy = ctx->Tensor4ArgNameAndIndex("cy", 0); - const user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - user_op::Tensor* grad_gates = ctx->Tensor4ArgNameAndIndex("grad_gates", 0); - user_op::Tensor* grad_cx = ctx->Tensor4ArgNameAndIndex("grad_cx", 0); - - const float* grad_hy_ptr = grad_hy->dptr(); - const float* grad_cy_ptr = grad_cy->dptr(); - const float* cx_ptr = cx->dptr(); - const float* cy_ptr = cy->dptr(); - const float* workspace_ptr = workspace->dptr(); - - float* grad_gates_ptr = grad_gates->mut_dptr(); - float* grad_cx_ptr = nullptr; - - if (ctx->has_output("grad_cx", 0)) { grad_cx_ptr = grad_cx->mut_dptr(); } - - const int64_t cx_numel = cx->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = cx->shape_view().At(cx->shape_view().NumAxes() - 1); - FusedLstmCellGradFunctor()(ctx->stream(), cx_numel, workspace_numel, hidden_size, - grad_hy_ptr, grad_cy_ptr, cx_ptr, cy_ptr, workspace_ptr, - grad_gates_ptr, grad_cx_ptr); - - if (ctx->has_output("grad_bias", 0)) { - float* grad_bias_ptr = ctx->Tensor4ArgNameAndIndex("grad_bias", 0)->mut_dptr(); - std::vector axis; - axis.push_back(0); - const Shape& reduced_shape = - CreateReducedShape(workspace->shape_view(), {axis.begin(), axis.end()}); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, grad_bias_ptr), - XpuVarNdarray(grad_gates->shape_view(), grad_gates->dptr()), - XpuVarNdarray(tmp_buffer->shape_view(), tmp_buffer->mut_dptr())); - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("fused_lstm_cell_grad") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("grad_hy", 0) == GetDataType::value) - && (user_op::HobDataType("grad_cy", 0) == GetDataType::value) - && (user_op::HobDataType("cx", 0) == GetDataType::value) - && (user_op::HobDataType("cy", 0) == GetDataType::value) - && (user_op::HobDataType("workspace", 0) == GetDataType::value)) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - size_t tmp_bytes = 0; - if (ctx->has_output("grad_bias", 0)) { - const Shape& in_shape = ctx->InputTensorDesc("workspace", 0).shape(); - tmp_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - } else { - tmp_bytes = 0; - } - return tmp_bytes; - }); - -class GpuFusedLstmCellGradHalfKernel final : public user_op::OpKernel { - public: - GpuFusedLstmCellGradHalfKernel() = default; - ~GpuFusedLstmCellGradHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* grad_hy = ctx->Tensor4ArgNameAndIndex("grad_hy", 0); - const user_op::Tensor* grad_cy = ctx->Tensor4ArgNameAndIndex("grad_cy", 0); - const user_op::Tensor* cx = ctx->Tensor4ArgNameAndIndex("cx", 0); - const user_op::Tensor* cy = ctx->Tensor4ArgNameAndIndex("cy", 0); - const user_op::Tensor* workspace = ctx->Tensor4ArgNameAndIndex("workspace", 0); - user_op::Tensor* grad_gates = ctx->Tensor4ArgNameAndIndex("grad_gates", 0); - user_op::Tensor* grad_cx = ctx->Tensor4ArgNameAndIndex("grad_cx", 0); - - const float16* grad_hy_ptr = grad_hy->dptr(); - const float16* grad_cy_ptr = grad_cy->dptr(); - const float16* cx_ptr = cx->dptr(); - const float16* cy_ptr = cy->dptr(); - const float16* workspace_ptr = workspace->dptr(); - - float16* grad_gates_ptr = grad_gates->mut_dptr(); - float16* grad_cx_ptr = nullptr; - - if (ctx->has_output("grad_cx", 0)) { grad_cx_ptr = grad_cx->mut_dptr(); } - - const int64_t cx_numel = cx->shape_view().elem_cnt(); - const int64_t workspace_numel = workspace->shape_view().elem_cnt(); - const int64_t hidden_size = cx->shape_view().At(cx->shape_view().NumAxes() - 1); - FusedLstmCellGradFunctor()(ctx->stream(), cx_numel, workspace_numel, hidden_size, - grad_hy_ptr, grad_cy_ptr, cx_ptr, cy_ptr, workspace_ptr, - grad_gates_ptr, grad_cx_ptr); - - if (ctx->has_output("grad_bias", 0)) { - std::vector axis; - axis.push_back(0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const ShapeView& in_shape = grad_gates->shape_view(); - const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); - float* in_tmp_buffer = tmp_buffer->mut_dptr(); - const size_t in_tmp_buffer_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - float* out_tmp_buffer = - reinterpret_cast(tmp_buffer->mut_dptr() + in_tmp_buffer_bytes); - const size_t out_tmp_buffer_bytes = - GetCudaAlignedSize(reduced_shape.elem_cnt() * sizeof(float)); - float* reduce_tmp_buffer = reinterpret_cast( - tmp_buffer->mut_dptr() + in_tmp_buffer_bytes + out_tmp_buffer_bytes); - const size_t reduce_tmp_buffer_bytes = - GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, - tmp_buffer->shape_view().elem_cnt()); - auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); - CHECK(h2f); - auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); - CHECK(f2h); - h2f->Launch(ctx->stream(), grad_gates->dptr(), in_tmp_buffer, in_shape.elem_cnt()); - - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), - XpuVarNdarray(in_shape, in_tmp_buffer), - XpuVarNdarray(in_shape, reduce_tmp_buffer)); - - user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("grad_bias", 0); - f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), - output_tensor->shape_view().elem_cnt()); - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("fused_lstm_cell_grad") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("grad_hy", 0) == GetDataType::value) - && (user_op::HobDataType("grad_cy", 0) == GetDataType::value) - && (user_op::HobDataType("cx", 0) == GetDataType::value) - && (user_op::HobDataType("cy", 0) == GetDataType::value) - && (user_op::HobDataType("workspace", 0) == GetDataType::value)) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - size_t tmp_bytes = 0; - if (ctx->has_output("grad_bias", 0)) { - const Shape& in_shape = ctx->InputTensorDesc("workspace", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("grad_bias", 0)->shape(); - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } else { - tmp_bytes = 0; - } - return tmp_bytes; - }); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_matmul_bias_kernel.cu b/oneflow/user/kernels/fused_matmul_bias_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..ef9d0ab746cf343dd4bb381008fb0e5f839b44c4 --- /dev/null +++ b/oneflow/user/kernels/fused_matmul_bias_kernel.cu @@ -0,0 +1,125 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/user/kernels/cublas_fused_mlp_util.cuh" + +#if CUDA_VERSION >= 10200 + +namespace oneflow { + +namespace { + +class FusedMatmulBiasKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + FusedMatmulBiasKernel() = default; + ~FusedMatmulBiasKernel() override = default; + + std::shared_ptr InitOpKernelCache( + user_op::KernelCacheContext* ctx) const override { + return CreateCublasFusedMLPKernelCache(); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, + const user_op::OpKernelCache* cache) const override { + auto* cuda_stream = ctx->stream()->As(); + const auto* matmul_cache = CHECK_NOTNULL(dynamic_cast(cache)); + + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const user_op::Tensor* _add_to_output = (ctx->has_input("_add_to_output", 0)) + ? ctx->Tensor4ArgNameAndIndex("_add_to_output", 0) + : nullptr; + + const DataType data_type = out->data_type(); + const cublasComputeType_t cublas_compute_dtype = GetComputeType(data_type); + const cudaDataType_t cuda_data_type = GetCudaDataType(data_type); + size_t cublas_m = 0, cublas_n = 0, cublas_k = 0; + int64_t cublas_lda = 0, cublas_ldb = 0, cublas_ldc = 0; + + const double alpha = 1.0; + const double beta = (ctx->has_input("_add_to_output", 0)) ? 1.0 : 0.0; + + const auto sp_alpha = GetCublasScalarParameter(alpha, cublas_compute_dtype); + const auto sp_beta = GetCublasScalarParameter(beta, cublas_compute_dtype); + + DimVector in_shape({x->shape_view().Count(0, x->shape_view().NumAxes() - 1), + x->shape_view().At(x->shape_view().NumAxes() - 1)}); + + DimVector weight_shape(2); + + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + const user_op::Tensor* bias = ctx->Tensor4ArgNameAndIndex("bias", 0); + + weight->shape_view().ToDimVector(&weight_shape); + + InferMatmulCublasMNK(in_shape, weight_shape, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::T, &cublas_m, &cublas_n, + &cublas_k, &cublas_lda, &cublas_ldb, &cublas_ldc); + + cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_BIAS; + void* y_ptr = ctx->Tensor4ArgNameAndIndex("out", 0)->mut_dptr(); + + SetCublasAttr(matmul_cache, cublas_compute_dtype, cuda_data_type, false, + /*transpose_a=*/ep::primitive::BlasTransposeType::N, + /*transpose_b=*/ep::primitive::BlasTransposeType::T, epilogue, bias->dptr(), + nullptr, cublas_m, cublas_n, cublas_k, cublas_lda, cublas_ldb, cublas_ldc); + + cublasLtMatmulPreference_t preference = nullptr; + size_t workspace_size = cuda_stream->cublas_workspace_size(); + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceCreate(&preference)); + OF_CUBLAS_CHECK(cublasLtMatmulPreferenceSetAttribute(preference, + CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, + &workspace_size, sizeof(workspace_size))); + int returned_results = 0; + cublasLtMatmulHeuristicResult_t heuristic_result; + OF_CUBLAS_CHECK(cublasLtMatmulAlgoGetHeuristic( + cuda_stream->cublas_lt_handle(), matmul_cache->operation_desc, matmul_cache->cublas_a_desc, + matmul_cache->cublas_b_desc, matmul_cache->cublas_c_desc, matmul_cache->cublas_c_desc, + preference, 1, &heuristic_result, &returned_results)); + CHECK_EQ(returned_results, 1); + cublasLtMatmulPreferenceDestroy(preference); + OF_CUBLAS_CHECK(cublasLtMatmul( + cuda_stream->cublas_lt_handle(), matmul_cache->operation_desc, &sp_alpha, weight->dptr(), + matmul_cache->cublas_a_desc, x->dptr(), matmul_cache->cublas_b_desc, &sp_beta, + (_add_to_output == nullptr) ? y_ptr : _add_to_output->dptr(), matmul_cache->cublas_c_desc, + y_ptr, matmul_cache->cublas_c_desc, &heuristic_result.algo, cuda_stream->cublas_workspace(), + cuda_stream->cublas_workspace_size(), cuda_stream->cuda_stream())); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_FUSED_MATMUL_BIAS_KERNEL_GPU(data_type) \ + REGISTER_USER_KERNEL("fused_matmul_bias") \ + .SetCreateFn() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == data_type)); + +REGISTER_FUSED_MATMUL_BIAS_KERNEL_GPU(DataType::kDouble); +REGISTER_FUSED_MATMUL_BIAS_KERNEL_GPU(DataType::kFloat); +REGISTER_FUSED_MATMUL_BIAS_KERNEL_GPU(DataType::kFloat16); +#if CUDA_VERSION >= 11000 +REGISTER_FUSED_MATMUL_BIAS_KERNEL_GPU(DataType::kBFloat16); +#endif // CUDA_VERSION >= 11000 + +} // namespace + +} // namespace oneflow + +#endif // CUDA_VERSION >= 10200 diff --git a/oneflow/user/kernels/fused_multi_head_attention_inference_kernel.cu b/oneflow/user/kernels/fused_multi_head_attention_inference_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..c81069c8a983d51a0e137076cc42f00c768bcc96 --- /dev/null +++ b/oneflow/user/kernels/fused_multi_head_attention_inference_kernel.cu @@ -0,0 +1,351 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifdef WITH_CUTLASS + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/permute.h" +#include "cutlass/gemm/warp/mma.h" +#include "kernel_forward.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/user/kernels/fmha_flash_attention/fmha.h" +#include "oneflow/user/kernels/fmha_flash_attention/include/fmha_flash_attention.h" + +namespace oneflow { + +namespace user_op { + +namespace { + +template +struct alignas(pack_size * sizeof(T)) Pack { + T elem[pack_size]; +}; + +template +__global__ void PackQkv(int b, int s, int nh, int d, const T* q, const T* k, const T* v, T* o, + int32_t* seq_len) { + int count = b * s * nh * d * 3; + for (int i = threadIdx.x + blockIdx.x * blockDim.x; i < count; i += blockDim.x * gridDim.x) { + int row = i / (d * 3); + int out_col = i - row * (d * 3); + T out; + if (out_col < d) { + out = q[row * d + out_col]; + } else if (out_col < 2 * d) { + out = k[row * d + out_col - d]; + } else { + out = v[row * d + out_col - d * 2]; + } + o[i] = out; + } + for (int i = threadIdx.x + blockIdx.x * blockDim.x; i < b + 1; i += blockDim.x * gridDim.x) { + seq_len[i] = i * s; + } +} + +struct Params { + DataType data_type; + int64_t num_batches; + int64_t num_heads; + int64_t query_seq_len; + int64_t kv_seq_len; + int64_t head_size; + int64_t value_head_size; + int64_t query_hidden_stride; + int64_t key_hidden_stride; + int64_t value_hidden_stride; + bool causal; + const void* query_ptr; + const void* key_ptr; + const void* value_ptr; + void* out_ptr; + void* workspace; + int64_t workspace_size; +}; + +template +void LaunchCutlassFmha(const Params& params, ep::CudaStream* stream) { + using Attention = AttentionKernel; + typename Attention::Params p; + p.query_ptr = const_cast(reinterpret_cast(params.query_ptr)); + p.key_ptr = const_cast(reinterpret_cast(params.key_ptr)); + p.value_ptr = const_cast(reinterpret_cast(params.value_ptr)); + p.logsumexp_ptr = nullptr; + p.output_ptr = reinterpret_cast(params.out_ptr); + if (Attention::kNeedsOutputAccumulatorBuffer) { + using Acc = typename Attention::accum_t; + CHECK_GE(params.workspace_size, params.num_batches * params.query_seq_len * params.num_heads + * params.value_head_size * sizeof(Acc)); + p.output_accum_ptr = reinterpret_cast(params.workspace); + } else { + p.output_accum_ptr = nullptr; + } + p.num_heads = params.num_heads; + p.num_batches = params.num_batches; + p.head_dim = params.head_size; + p.head_dim_value = params.value_head_size; + p.num_queries = params.query_seq_len; + p.num_keys = params.kv_seq_len; + p.q_strideM = params.query_hidden_stride; + p.k_strideM = params.key_hidden_stride; + p.v_strideM = params.value_hidden_stride; + p.o_strideM = p.num_heads * params.value_head_size; + + p.q_strideH = params.head_size; + p.k_strideH = params.head_size; + p.v_strideH = params.value_head_size; + p.o_strideH = params.value_head_size; + + p.q_strideB = params.query_seq_len * p.q_strideM; + p.k_strideB = params.kv_seq_len * p.k_strideM; + p.v_strideB = params.kv_seq_len * p.v_strideM; + p.o_strideB = params.query_seq_len * p.o_strideM; + + p.causal = params.causal; + + constexpr auto kernel_fn = attention_kernel_batched_impl; + int smem_bytes = sizeof(typename Attention::SharedStorage); + if (smem_bytes > 0xc000) { + static bool once = [&]() { + cudaFuncSetAttribute(kernel_fn, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_bytes); + return true; + }(); + } + CHECK(Attention::check_supported(p)); + kernel_fn<<cuda_stream()>>>(p); +} + +template +void DispatchSingleValueIteration(const Params& params, ep::CudaStream* stream) { + if (params.value_head_size <= keys_per_block) { + LaunchCutlassFmha(params, + stream); + } else { + LaunchCutlassFmha(params, + stream); + } +} + +template +void DispatchKeysPerBlock(const Params& params, ep::CudaStream* stream) { + if (params.value_head_size <= 64) { + DispatchSingleValueIteration(params, stream); + } else { + DispatchSingleValueIteration(params, stream); + } +} + +template +void DispatchIsAligned(const Params& params, ep::CudaStream* stream) { + if (reinterpret_cast(params.query_ptr) % 16 == 0 + && reinterpret_cast(params.key_ptr) % 16 == 0 + && params.query_hidden_stride % (16 / sizeof(T)) == 0 + && params.key_hidden_stride % (16 / sizeof(T)) == 0) { + DispatchKeysPerBlock(params, stream); + } else { + DispatchKeysPerBlock(params, stream); + } +} + +template +void DispatchArchTag(const Params& params, ep::CudaStream* stream) { + const int major = stream->device_properties().major; + const int minor = stream->device_properties().minor; + + if (major == 8) { + DispatchIsAligned(params, stream); + } else if (major == 7) { + if (minor == 5) { + DispatchIsAligned(params, stream); + } else { + DispatchIsAligned(params, stream); + } + } else { + UNIMPLEMENTED(); + } +} + +void DispatchCutlassFmha(const Params& params, ep::CudaStream* stream) { + if (params.data_type == DataType::kFloat16) { + DispatchArchTag(params, stream); + } else if (params.data_type == DataType::kFloat) { + DispatchArchTag(params, stream); + } else { + UNIMPLEMENTED(); + } +} + +class FusedMultiHeadAttentionInferenceKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + FusedMultiHeadAttentionInferenceKernel() = default; + ~FusedMultiHeadAttentionInferenceKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* query = ctx->Tensor4ArgNameAndIndex("query", 0); + const Tensor* key = ctx->Tensor4ArgNameAndIndex("key", 0); + const Tensor* value = ctx->Tensor4ArgNameAndIndex("value", 0); + Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + Tensor* tmp = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const DataType data_type = query->data_type(); + CHECK_EQ(key->data_type(), data_type); + CHECK_EQ(value->data_type(), data_type); + CHECK_EQ(out->data_type(), data_type); + CHECK_EQ(query->shape_view().NumAxes(), 3); + CHECK_EQ(key->shape_view().NumAxes(), 3); + CHECK_EQ(value->shape_view().NumAxes(), 3); + CHECK_EQ(out->shape_view().NumAxes(), 3); + const int64_t batch_size = query->shape_view().At(0); + CHECK_EQ(key->shape_view().At(0), batch_size); + CHECK_EQ(value->shape_view().At(0), batch_size); + CHECK_EQ(out->shape_view().At(0), batch_size); + const int64_t query_seq_len = query->shape_view().At(1); + CHECK_EQ(out->shape_view().At(1), query_seq_len); + const int64_t kv_seq_len = key->shape_view().At(1); + CHECK_EQ(value->shape_view().At(1), kv_seq_len); + const int64_t num_heads = ctx->Attr("num_heads"); + const bool causal = ctx->Attr("causal"); + + const auto ParseHiddenDim = [&](const std::string& tag, const ShapeView& shape, + int64_t* hidden_slice_start, int64_t* hidden_size) { + *hidden_slice_start = ctx->Attr(tag + "_hidden_slice_start"); + CHECK_GE(*hidden_slice_start, 0); + int64_t hidden_slice_end = ctx->Attr(tag + "_hidden_slice_end"); + if (hidden_slice_end < 0) { hidden_slice_end = hidden_slice_end + shape.At(2) + 1; } + CHECK_GT(hidden_slice_end, 0); + CHECK_LE(hidden_slice_end, shape.At(2)); + CHECK_GT(hidden_slice_end, *hidden_slice_start); + *hidden_size = hidden_slice_end - *hidden_slice_start; + CHECK_EQ(*hidden_size % num_heads, 0); + }; + + int64_t query_hidden_offset = 0; + int64_t query_hidden_size = 0; + ParseHiddenDim("query", query->shape_view(), &query_hidden_offset, &query_hidden_size); + + int64_t key_hidden_offset = 0; + int64_t key_hidden_size = 0; + ParseHiddenDim("key", key->shape_view(), &key_hidden_offset, &key_hidden_size); + CHECK_EQ(key_hidden_size, query_hidden_size); + + int64_t value_hidden_offset = 0; + int64_t value_hidden_size = 0; + ParseHiddenDim("value", value->shape_view(), &value_hidden_offset, &value_hidden_size); + + CHECK_EQ(out->shape_view().At(2), value_hidden_size); + + auto* cuda_stream = ctx->stream()->As(); + + const bool enable_trt_flash_attn = + ParseBooleanFromEnv("ONEFLOW_KERENL_FMHA_ENABLE_TRT_FLASH_ATTN_IMPL", false) + && ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + const int arch = cuda_stream->cuda_arch() / 10; + const bool inputs_contiguous = + query_hidden_offset == 0 && query_hidden_size == query->shape_view().At(2) + && key_hidden_offset == 0 && key_hidden_size == key->shape_view().At(2) + && value_hidden_offset == 0 && value_hidden_size == value->shape_view().At(2); + const bool is_trt_supported_arch = (arch == 80 || arch == 86 || arch == 89); + const int64_t query_head_size = query_hidden_size / num_heads; + const bool is_trt_supported_head_size = ((query_head_size == 40) || (query_head_size == 64)); + // Avoid PackQKV overhead when seq_len is small. + const bool is_long_seq_len = query_seq_len >= 512; + if (enable_trt_flash_attn && inputs_contiguous && data_type == DataType::kFloat16 + && query_seq_len == kv_seq_len && query_hidden_size == value_hidden_size + && is_trt_supported_head_size && is_long_seq_len && is_trt_supported_arch && (!causal)) { + // The fmha implementation below is based on TensorRT's multiHeadFlashAttentionPlugin + // implementation at: + // https://github.com/NVIDIA/TensorRT/tree/main/plugin/multiHeadFlashAttentionPlugin + int32_t cu_seqlens_d_size = (batch_size + 1) * sizeof(int32_t); + int32_t* cu_seqlens_d = reinterpret_cast(tmp->mut_dptr()); + half* packed_qkv = + reinterpret_cast(tmp->mut_dptr() + GetCudaAlignedSize(cu_seqlens_d_size)); + constexpr int pack_size = 4; + using PackType = Pack; + int count = batch_size * query_seq_len * query_hidden_size * 3 / pack_size; + PackQkv<<<(count - 1 + 256) / 256, 256, 0, cuda_stream->cuda_stream()>>>( + batch_size, query_seq_len, num_heads, query_head_size / pack_size, + reinterpret_cast(query->dptr()), + reinterpret_cast(key->dptr()), + reinterpret_cast(value->dptr()), reinterpret_cast(packed_qkv), + cu_seqlens_d); + + nvinfer1::plugin::FusedMultiHeadFlashAttentionKernel const* kernels = + nvinfer1::plugin::getFMHACubinKernels(nvinfer1::plugin::DATA_TYPE_FP16, arch); + run_fmha_v2_api(packed_qkv, cu_seqlens_d, out->mut_dptr(), batch_size * query_seq_len, arch, + kernels, batch_size, num_heads, query_head_size, query_seq_len, + cuda_stream->cuda_stream()); + return; + } + + Params params{}; + params.data_type = data_type; + params.num_batches = batch_size; + params.num_heads = num_heads; + params.query_seq_len = query_seq_len; + params.kv_seq_len = kv_seq_len; + params.head_size = query_hidden_size / num_heads; + params.value_head_size = value_hidden_size / num_heads; + params.query_hidden_stride = query->shape_view().At(2); + params.key_hidden_stride = key->shape_view().At(2); + params.value_hidden_stride = value->shape_view().At(2); + params.query_ptr = query->dptr() + query_hidden_offset; + params.key_ptr = key->dptr() + key_hidden_offset; + params.value_ptr = value->dptr() + value_hidden_offset; + params.out_ptr = out->mut_dptr(); + const int64_t tmp_buffer_size = tmp->shape_view().elem_cnt(); + params.workspace = tmp->mut_dptr(); + params.workspace_size = tmp_buffer_size; + params.causal = causal; + DispatchCutlassFmha(params, cuda_stream); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +size_t InferTmpBufferSize(InferContext* ctx) { + const auto& out_desc = ctx->OutputTensorDesc("out", 0); + size_t buffer_size = 0; + buffer_size += + GetCudaAlignedSize(out_desc.shape().elem_cnt() * GetSizeOfDataType(DataType::kFloat)); + buffer_size += + GetCudaAlignedSize(out_desc.shape().elem_cnt() * GetSizeOfDataType(out_desc.data_type())) * 3; + buffer_size += + GetCudaAlignedSize((out_desc.shape().At(0) + 1) * GetSizeOfDataType(DataType::kInt32)); + return buffer_size; +} + +} // namespace + +#define REGISTER_FUSED_MULTI_HEAD_ATTENTION_INFERENCE_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_multi_head_attention_inference") \ + .SetCreateFn() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == dtype)) \ + .SetInferTmpSizeFn(InferTmpBufferSize); + +REGISTER_FUSED_MULTI_HEAD_ATTENTION_INFERENCE_KERNEL(DataType::kFloat16) +REGISTER_FUSED_MULTI_HEAD_ATTENTION_INFERENCE_KERNEL(DataType::kFloat) + +} // namespace user_op + +} // namespace oneflow + +#endif // WITH_CUTLASS diff --git a/oneflow/user/kernels/fused_relu_dropout_grad_kernel.cu b/oneflow/user/kernels/fused_relu_dropout_grad_kernel.cu index 3d91a5240e00c70b3d635c6d25fb6a44e202d83b..a77b25d9d9ef6923dab1960c18dbe77a03fec0b5 100644 --- a/oneflow/user/kernels/fused_relu_dropout_grad_kernel.cu +++ b/oneflow/user/kernels/fused_relu_dropout_grad_kernel.cu @@ -16,7 +16,11 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/device/cuda_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include +#else #include +#endif #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/cuda/elementwise.cuh" @@ -24,7 +28,11 @@ namespace oneflow { namespace { +#ifdef WITH_ROCM +constexpr int32_t kWarpSize = 64; +#else constexpr int32_t kWarpSize = 32; +#endif template __global__ void VectorizedReluDropoutBitmaskBackwardKernel( diff --git a/oneflow/user/kernels/fused_relu_dropout_grad_kernel.hip.cpp b/oneflow/user/kernels/fused_relu_dropout_grad_kernel.hip.cpp deleted file mode 100644 index e1f6dc6354f6f7653e9549a9f88206f4cede0b45..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_relu_dropout_grad_kernel.hip.cpp +++ /dev/null @@ -1,146 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/hip/elementwise.hip.h" - -namespace oneflow { - -namespace { - -constexpr int32_t kWarpSize = 64; - -template -__global__ void VectorizedReluDropoutBitmaskBackwardKernel( - const IndexType elem_cnt, const IndexType cols, const IndexType aux_ld, const float scale, - const IndexType n_tail, const IndexType tail_offset, const T* dy, const int32_t* mask, T* dx) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - using LoadStoreType = cuda::elementwise::PackType; - using LoadStorePack = cuda::elementwise::Pack; - - T t_scale = static_cast(scale); - for (IndexType linear_pack_index = global_thread_id * pack_size; linear_pack_index < elem_cnt; - linear_pack_index += gridDim.x * blockDim.x * pack_size) { - const LoadStoreType* dy_load = reinterpret_cast(dy + linear_pack_index); - LoadStorePack dy_vec; - dy_vec.storage = *dy_load; - - LoadStorePack dx_vec; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - const IndexType linear_index = (linear_pack_index + i); - const IndexType row = linear_index / cols; - const IndexType col = linear_index - row * cols; - const int32_t col_mod_warpsize = col % kWarpSize; - const IndexType aux_idx = ((row * aux_ld) + col) / kWarpSize; - bool is_positive = mask[aux_idx] & (1 << col_mod_warpsize); - dx_vec.elem[i] = - dy_vec.elem[i] * static_cast(static_cast(is_positive)) * static_cast(scale); - } - *(reinterpret_cast(dx + linear_pack_index)) = dx_vec.storage; - } - - if (tail && global_thread_id < n_tail) { - const IndexType tail_index = tail_offset + global_thread_id; - const IndexType tail_row = tail_index / cols; - const IndexType tail_col = tail_index - tail_row * cols; - const IndexType tail_col_mod_warpsize = tail_col % kWarpSize; - const IndexType tail_aux_idx = ((tail_row * aux_ld) + tail_col) / kWarpSize; - bool is_positive = mask[tail_aux_idx] & (1 << tail_col_mod_warpsize); - dx[tail_index] = - dy[tail_index] * static_cast(static_cast(is_positive)) * static_cast(scale); - } -} - -template -void LaunchVectorizedReluDropoutBackwardKernel(ep::Stream* stream, const int64_t elem_cnt, - const int64_t cols, const int64_t aux_ld, - float scale, const T* dy, const int32_t* mask, - T* dx) { - constexpr int pack_size = cuda::elementwise::PackSize(); - const int64_t pack_num = elem_cnt / pack_size; - const int64_t tail_offset = pack_num * pack_size; - const int64_t n_tail = elem_cnt - tail_offset; - const bool tail = n_tail > 0 ? true : false; - if (tail) { - if (elem_cnt < GetMaxVal()) { - stream->As()->LaunchKernelDefaultWaves( - (VectorizedReluDropoutBitmaskBackwardKernel), - std::max(1, pack_num), elem_cnt, cols, aux_ld, scale, n_tail, tail_offset, dy, - mask, dx); - } else { - stream->As()->LaunchKernelDefaultWaves( - (VectorizedReluDropoutBitmaskBackwardKernel), - std::max(1, pack_num), elem_cnt, cols, aux_ld, scale, n_tail, tail_offset, dy, - mask, dx); - } - } else { - if (elem_cnt < GetMaxVal()) { - stream->As()->LaunchKernelDefaultWaves( - (VectorizedReluDropoutBitmaskBackwardKernel), - std::max(1, pack_num), elem_cnt, cols, aux_ld, scale, /*n_tail=*/0, tail_offset, - dy, mask, dx); - } else { - stream->As()->LaunchKernelDefaultWaves( - (VectorizedReluDropoutBitmaskBackwardKernel), - std::max(1, pack_num), elem_cnt, cols, aux_ld, scale, /*n_tail=*/0, tail_offset, - dy, mask, dx); - } - } -} - -template -class FusedReluDropoutGradKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - FusedReluDropoutGradKernel() = default; - ~FusedReluDropoutGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float scale = ctx->Attr("scale"); - - const int64_t cols = dy->shape_view().At(1); - const int64_t aux_ld = mask->shape_view().At(1) * 32; - const int64_t elem_cnt = dy->shape_view().elem_cnt(); - LaunchVectorizedReluDropoutBackwardKernel( - ctx->stream(), elem_cnt, cols, aux_ld, scale, reinterpret_cast(dy->dptr()), - mask->dptr(), reinterpret_cast(dx->mut_dptr())); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_RELU_DROPOUT_GRAD_KERNEL_GPU(cpp_type, data_type) \ - REGISTER_USER_KERNEL("fused_relu_dropout_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == data_type)); - -REGISTER_FUSED_RELU_DROPOUT_GRAD_KERNEL_GPU(float, DataType::kFloat) -REGISTER_FUSED_RELU_DROPOUT_GRAD_KERNEL_GPU(half, DataType::kFloat16) - - -} // namespace - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_rnn_cell_kernel_util.h b/oneflow/user/kernels/fused_rnn_cell_kernel_util.h index 99ee77376c8f5391889f59713db11ef22eb01772..0e7ca9d6a82aafb81c6404a7df956d0fe6495005 100644 --- a/oneflow/user/kernels/fused_rnn_cell_kernel_util.h +++ b/oneflow/user/kernels/fused_rnn_cell_kernel_util.h @@ -15,7 +15,7 @@ limitations under the License. */ // NOTE(Liang Depeng): Modified from // https://github.com/pytorch/pytorch/blob/master/c10/macros/Macros.h#L256 -#if defined(__CUDACC__) +#if defined(__CUDACC__) || defined(__HIPCC__) // constants from // (https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications) // The maximum number of threads per multiprocessor is 1024 for Turing @@ -23,7 +23,7 @@ limitations under the License. // architectures. You'll get warnings if you exceed these constants. Hence, the // following macros adjust the input values from the user to resolve potential // warnings. -#if __CUDA_ARCH__ == 750 +#if (__CUDA_ARCH__ == 750) || defined(WITH_ROCM) constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1024; #elif __CUDA_ARCH__ == 860 constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1536; @@ -62,47 +62,3 @@ constexpr uint32_t CUDA_THREADS_PER_BLOCK_FALLBACK = 256; __launch_bounds__((OF_MAX_THREADS_PER_BLOCK((max_threads_per_block))), \ (OF_MIN_BLOCKS_PER_SM((max_threads_per_block), (min_blocks_per_sm)))) #endif - -#if defined(__HIPCC__) -// constants from -// (https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications) -// The maximum number of threads per multiprocessor is 1024 for Turing -// architecture (7.5), 1536 for Geforce Ampere (8.6), and 2048 for all other -// architectures. You'll get warnings if you exceed these constants. Hence, the -// following macros adjust the input values from the user to resolve potential -// warnings. - -constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1024; - -// CUDA_MAX_THREADS_PER_BLOCK is same for all architectures currently -constexpr uint32_t CUDA_MAX_THREADS_PER_BLOCK = 1024; -// CUDA_THREADS_PER_BLOCK_FALLBACK is the "canonical fallback" choice of block -// size. 256 is a good number for this fallback and should give good occupancy -// and versatility across all architectures. -constexpr uint32_t CUDA_THREADS_PER_BLOCK_FALLBACK = 256; -// NOTE: if you are thinking of constexpr-ify the inputs to launch bounds, it -// turns out that although __launch_bounds__ can take constexpr, it -// can't take a constexpr that has anything to do with templates. -// Currently we use launch_bounds that depend on template arguments in -// Loops.cuh, Reduce.cuh and LossCTC.cuh. Hence, OF_MAX_THREADS_PER_BLOCK -// and OF_MIN_BLOCKS_PER_SM are kept as macros. -// Suppose you were planning to write __launch_bounds__(a, b), based on your -// performance tuning on a modern GPU. Instead, you should write -// __launch_bounds__(OF_MAX_THREADS_PER_BLOCK(a), OF_MIN_BLOCKS_PER_SM(a, b)), -// which will also properly respect limits on old architectures. -#define OF_MAX_THREADS_PER_BLOCK(val) \ - (((val) <= CUDA_MAX_THREADS_PER_BLOCK) ? (val) : CUDA_THREADS_PER_BLOCK_FALLBACK) -#define OF_MIN_BLOCKS_PER_SM(threads_per_block, blocks_per_sm) \ - ((((threads_per_block) * (blocks_per_sm) <= CUDA_MAX_THREADS_PER_SM) \ - ? (blocks_per_sm) \ - : ((CUDA_MAX_THREADS_PER_SM + (threads_per_block)-1) / (threads_per_block)))) -// OF_LAUNCH_BOUNDS is analogous to __launch_bounds__ -#define OF_LAUNCH_BOUNDS_0 \ - __launch_bounds__(256, 4) // default launch bounds that should give good occupancy and - // versatility across all architectures. -#define OF_LAUNCH_BOUNDS_1(max_threads_per_block) \ - __launch_bounds__((OF_MAX_THREADS_PER_BLOCK((max_threads_per_block)))) -#define OF_LAUNCH_BOUNDS_2(max_threads_per_block, min_blocks_per_sm) \ - __launch_bounds__((OF_MAX_THREADS_PER_BLOCK((max_threads_per_block))), \ - (OF_MIN_BLOCKS_PER_SM((max_threads_per_block), (min_blocks_per_sm)))) -#endif diff --git a/oneflow/user/kernels/fused_scale_mask_softmax.cu b/oneflow/user/kernels/fused_scale_mask_softmax.cu index f977e6cf20f85d2cd941ab7a35249b39840abeb4..f372044cb81d73ed2673defc070a2038211cc8b3 100644 --- a/oneflow/user/kernels/fused_scale_mask_softmax.cu +++ b/oneflow/user/kernels/fused_scale_mask_softmax.cu @@ -16,54 +16,55 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/cuda/softmax.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" -#include "oneflow/user/kernels/fused_scale_mask_softmax.cuh" +#include "oneflow/user/kernels/fused_softmax.cuh" + namespace oneflow { namespace { template -void LaunchBroadcastForwardKernel(cudaStream_t stream, const T* x, T* y, const MASK* mask, +void LaunchBroadcastForwardKernel(GPU(Stream_t) stream, const T* x, T* y, const MASK* mask, const int64_t elem_cnt, const int64_t rows, const int64_t cols, const float fill, const float scale, const int64_t* input_dims, const int64_t* mask_dims) { NdIndexOffsetHelper input_index_helper(input_dims); NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; + cuda::fused_softmax::BroadcastMaskSoftmaxParams params; params.src_index_helper = input_index_helper; params.mask_index_helper = mask_index_helper; params.mask_dims = mask_dims; params.row_size = cols; params.fill = fill; params.scale = scale; - fused_scale_mask_softmax::BroadcastScaleMaskLoad load( - x, mask, params); + cuda::fused_softmax::BroadcastScaleMaskLoad load(x, mask, + params); cuda::softmax::DirectStore store(y, cols); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( stream, load, store, rows, cols))); } template -void LaunchElementwiseForwardKernel(cudaStream_t stream, const T* x, T* y, const MASK* mask, +void LaunchElementwiseForwardKernel(GPU(Stream_t) stream, const T* x, T* y, const MASK* mask, const int64_t rows, const int64_t cols, const float fill, const float scale) { - oneflow::fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; + cuda::fused_softmax::ElementwiseMaskSoftmaxParams params; params.row_size = cols; params.fill = fill; params.scale = scale; - fused_scale_mask_softmax::ElementwiseScaleMaskLoad load(x, mask, params); + cuda::fused_softmax::ElementwiseScaleMaskLoad load(x, mask, params); cuda::softmax::DirectStore store(y, cols); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( stream, load, store, rows, cols))); } template -void LaunchBroadcastBackwardKernel(cudaStream_t stream, const T* y, const T* dy, T* dx, +void LaunchBroadcastBackwardKernel(GPU(Stream_t) stream, const T* y, const T* dy, T* dx, const MASK* mask, const int64_t elem_cnt, const int64_t rows, const int64_t cols, const float fill, const float scale, const int64_t* input_dims, const int64_t* mask_dims) { NdIndexOffsetHelper input_index_helper(input_dims); NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; + cuda::fused_softmax::BroadcastMaskSoftmaxParams params; params.src_index_helper = input_index_helper; params.mask_index_helper = mask_index_helper; params.mask_dims = mask_dims; @@ -72,7 +73,7 @@ void LaunchBroadcastBackwardKernel(cudaStream_t stream, const T* y, const T* dy, params.scale = scale; cuda::softmax::DirectLoad load_y(y, cols); cuda::softmax::DirectLoad load_dy(dy, cols); - fused_scale_mask_softmax::BroadcastScaleMaskStore store( + cuda::fused_softmax::BroadcastScaleMaskStore store( dx, mask, params); OF_CUDA_CHECK(( cuda::softmax::DispatchSoftmaxGrad -void LaunchElementwiseBackwardKernel(cudaStream_t stream, const T* y, const T* dy, T* dx, +void LaunchElementwiseBackwardKernel(GPU(Stream_t) stream, const T* y, const T* dy, T* dx, const MASK* mask, const int64_t rows, const int64_t cols, const float fill, const float scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; + cuda::fused_softmax::ElementwiseMaskSoftmaxParams params; params.row_size = cols; params.fill = fill; params.scale = scale; cuda::softmax::DirectLoad load_y(y, cols); cuda::softmax::DirectLoad load_dy(dy, cols); - fused_scale_mask_softmax::ElementwiseScaleMaskStore store(dx, mask, params); + cuda::fused_softmax::ElementwiseScaleMaskStore store(dx, mask, params); OF_CUDA_CHECK(( cuda::softmax::DispatchSoftmaxGrad(stream, load_y, load_dy, store, rows, cols))); @@ -126,9 +127,9 @@ class FusedScaleMaskSoftmaxKernel final : public user_op::OpKernel { size_t simplified_num_dims = 0; int64_t simplified_input_dims[kMaxNumDims]; int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); + cuda::fused_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, mask_dims, + &simplified_num_dims, simplified_input_dims, + simplified_mask_dims); if (simplified_num_dims == 1) { LaunchElementwiseForwardKernel( ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), @@ -183,9 +184,9 @@ class FusedScaleMaskSoftmaxGradKernel final : public user_op::OpKernel { size_t simplified_num_dims = 0; int64_t simplified_input_dims[kMaxNumDims]; int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); + cuda::fused_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, mask_dims, + &simplified_num_dims, simplified_input_dims, + simplified_mask_dims); if (simplified_num_dims == 1) { LaunchElementwiseBackwardKernel( ctx->stream()->As()->cuda_stream(), y->dptr(), dy->dptr(), diff --git a/oneflow/user/kernels/fused_scale_mask_softmax.cuh b/oneflow/user/kernels/fused_scale_mask_softmax.cuh deleted file mode 100644 index 1d36daadca189213394cd26b59ee197c95cd92fc..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_scale_mask_softmax.cuh +++ /dev/null @@ -1,216 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/nd_index_offset_helper.h" - -namespace oneflow { - -namespace fused_scale_mask_softmax { - -namespace { - -void SimplifyBroadcastDims(size_t num_a_dims, const int64_t* a_dims, size_t num_b_dims, - const int64_t* b_dims, size_t* simplified_num_dims, - int64_t* simplified_a_dims, int64_t* simplified_b_dims) { - const size_t num_max_dims = std::max(num_a_dims, num_b_dims); - auto MakeGetDim = [num_max_dims](size_t num_dims, const int64_t* dims) { - const int64_t num_padding_dims = num_max_dims - num_dims; - return [num_padding_dims, dims](size_t index) { - return index < num_padding_dims ? 1 : dims[index - num_padding_dims]; - }; - }; - auto GetADim = MakeGetDim(num_a_dims, a_dims); - auto GetBDim = MakeGetDim(num_b_dims, b_dims); - *simplified_num_dims = 0; - bool prev_broadcast_a = false; - bool prev_broadcast_b = false; - for (int64_t i = 0; i < num_max_dims; ++i) { - const int64_t a_dim = GetADim(i); - const int64_t b_dim = GetBDim(i); - const int64_t broadcast_dim = std::max(a_dim, b_dim); - CHECK_GT(broadcast_dim, 0); - const bool broadcast_a = (a_dim == 1); - const bool broadcast_b = (b_dim == 1); - CHECK((a_dim == broadcast_dim) || broadcast_a); - CHECK((b_dim == broadcast_dim) || broadcast_b); - if (broadcast_dim == 1) { - continue; - } else if (*simplified_num_dims != 0 - && (prev_broadcast_a == broadcast_a && prev_broadcast_b == broadcast_b)) { - simplified_a_dims[*simplified_num_dims - 1] *= a_dim; - simplified_b_dims[*simplified_num_dims - 1] *= b_dim; - } else { - simplified_a_dims[*simplified_num_dims] = a_dim; - simplified_b_dims[*simplified_num_dims] = b_dim; - *simplified_num_dims += 1; - prev_broadcast_a = broadcast_a; - prev_broadcast_b = broadcast_b; - } - } -} - -template -struct BroadcastMaskSoftmaxParams { - NdIndexOffsetHelper src_index_helper; - NdIndexOffsetHelper mask_index_helper; - const int64_t* mask_dims{}; - int64_t row_size; - float fill; - float scale; -}; - -struct ElementwiseMaskSoftmaxParams { - int64_t row_size; - float fill; - float scale; -}; - -template -struct BroadcastScaleMaskLoad { - BroadcastScaleMaskLoad(const SRC* src, const MASK* mask, - BroadcastMaskSoftmaxParams params) - : src(src), mask(mask), params(params) { - for (int i = 0; i < num_dims; i++) { mask_dims[i] = params.mask_dims[i]; } - } - template - __device__ void load(DST* dst, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - cuda::softmax::Pack mask_pack; - const IndexType offset = row * params.row_size + col; - IndexType input_index[num_dims]; - IndexType mask_index[num_dims]; - params.src_index_helper.OffsetToNdIndex(offset, input_index); - for (int dim = 0; dim < num_dims; ++dim) { - if (mask_dims[dim] == 1) { - mask_index[dim] = 0; - } else { - mask_index[dim] = input_index[dim]; - } - } - const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); - pack.storage = *(reinterpret_cast*>(src) + offset / N); - mask_pack.storage = - *(reinterpret_cast*>(mask) + mask_offset / N); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - dst[i] = static_cast(params.fill); - } else { - dst[i] = static_cast(pack.elem[i]) * static_cast(params.scale); - } - } - } - const SRC* src; - const MASK* mask; - int64_t mask_dims[num_dims]; - BroadcastMaskSoftmaxParams params; -}; - -template -struct ElementwiseScaleMaskLoad { - ElementwiseScaleMaskLoad(const SRC* src, const MASK* mask, ElementwiseMaskSoftmaxParams param) - : src(src), mask(mask), param(param) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - const int64_t offset = (row * param.row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - dst[i] = static_cast(param.fill); - } else { - dst[i] = static_cast(pack.elem[i]) * static_cast(param.scale); - } - } - } - const SRC* src; - const MASK* mask; - ElementwiseMaskSoftmaxParams param; -}; - -template -struct BroadcastScaleMaskStore { - BroadcastScaleMaskStore(DST* dst, const MASK* mask, - BroadcastMaskSoftmaxParams params) - : dst(dst), mask(mask), params(params) { - for (int i = 0; i < num_dims; ++i) { mask_dims[i] = params.mask_dims[i]; } - } - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - cuda::softmax::Pack mask_pack; - const IndexType offset = row * params.row_size + col; - IndexType input_index[num_dims]; - IndexType mask_index[num_dims]; - params.src_index_helper.OffsetToNdIndex(offset, input_index); - for (int dim = 0; dim < num_dims; ++dim) { - if (mask_dims[dim] == 1) { - mask_index[dim] = 0; - } else { - mask_index[dim] = input_index[dim]; - } - } - const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); - mask_pack.storage = - *(reinterpret_cast*>(mask) + mask_offset / N); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - pack.elem[i] = static_cast(params.fill); - } else { - pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); - } - } - *(reinterpret_cast*>(dst) + offset / N) = pack.storage; - } - DST* dst; - const MASK* mask; - int64_t mask_dims[num_dims]; - BroadcastMaskSoftmaxParams params; -}; - -template -struct ElementwiseScaleMaskStore { - ElementwiseScaleMaskStore(DST* dst, const MASK* mask, ElementwiseMaskSoftmaxParams params) - : dst(dst), mask(mask), params(params) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - const int64_t offset = (row * params.row_size + col) / N; - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - pack.elem[i] = params.fill; - } else { - pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); - } - } - *(reinterpret_cast*>(dst) + offset) = pack.storage; - } - DST* dst; - const MASK* mask; - ElementwiseMaskSoftmaxParams params; -}; - -} // namespace - -} // namespace fused_scale_mask_softmax - -} // namespace oneflow diff --git a/oneflow/user/kernels/fused_scale_mask_softmax.hip.cpp b/oneflow/user/kernels/fused_scale_mask_softmax.hip.cpp deleted file mode 100644 index 8813fb77e02e089c4e7707021d48da21d5d8a673..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_scale_mask_softmax.hip.cpp +++ /dev/null @@ -1,236 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/user/kernels/fused_scale_mask_softmax.hip.h" -namespace oneflow { - -namespace { - -template -void LaunchBroadcastForwardKernel(hipStream_t stream, const T* x, T* y, const MASK* mask, - const int64_t elem_cnt, const int64_t rows, const int64_t cols, - const float fill, const float scale, const int64_t* input_dims, - const int64_t* mask_dims) { - NdIndexOffsetHelper input_index_helper(input_dims); - NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; - params.src_index_helper = input_index_helper; - params.mask_index_helper = mask_index_helper; - params.mask_dims = mask_dims; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - fused_scale_mask_softmax::BroadcastScaleMaskLoad load( - x, mask, params); - cuda::softmax::DirectStore store(y, cols); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - stream, load, store, rows, cols))); -} - -template -void LaunchElementwiseForwardKernel(hipStream_t stream, const T* x, T* y, const MASK* mask, - const int64_t rows, const int64_t cols, const float fill, - const float scale) { - oneflow::fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - fused_scale_mask_softmax::ElementwiseScaleMaskLoad load(x, mask, params); - cuda::softmax::DirectStore store(y, cols); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - stream, load, store, rows, cols))); -} - -template -void LaunchBroadcastBackwardKernel(hipStream_t stream, const T* y, const T* dy, T* dx, - const MASK* mask, const int64_t elem_cnt, const int64_t rows, - const int64_t cols, const float fill, const float scale, - const int64_t* input_dims, const int64_t* mask_dims) { - NdIndexOffsetHelper input_index_helper(input_dims); - NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; - params.src_index_helper = input_index_helper; - params.mask_index_helper = mask_index_helper; - params.mask_dims = mask_dims; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - cuda::softmax::DirectLoad load_y(y, cols); - cuda::softmax::DirectLoad load_dy(dy, cols); - fused_scale_mask_softmax::BroadcastScaleMaskStore store( - dx, mask, params); - OF_CUDA_CHECK(( - cuda::softmax::DispatchSoftmaxGrad(stream, load_y, load_dy, store, rows, cols))); -} - -template -void LaunchElementwiseBackwardKernel(hipStream_t stream, const T* y, const T* dy, T* dx, - const MASK* mask, const int64_t rows, const int64_t cols, - const float fill, const float scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - cuda::softmax::DirectLoad load_y(y, cols); - cuda::softmax::DirectLoad load_dy(dy, cols); - fused_scale_mask_softmax::ElementwiseScaleMaskStore store(dx, mask, params); - OF_CUDA_CHECK(( - cuda::softmax::DispatchSoftmaxGrad(stream, load_y, load_dy, store, rows, cols))); -} - -constexpr int32_t kMaxNumDims = 5; - -template -class FusedScaleMaskSoftmaxKernel final : public user_op::OpKernel { - public: - FusedScaleMaskSoftmaxKernel() = default; - ~FusedScaleMaskSoftmaxKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const float mask_fill_value = ctx->Attr("mask_fill_value"); - const float scale_value = ctx->Attr("scale_value"); - const ShapeView& x_shape = x->shape_view(); - const ShapeView& mask_shape = mask->shape_view(); - CHECK_GE(x_shape.NumAxes(), 2); - const int64_t elem_cnt = x_shape.elem_cnt(); - const int64_t cols = x_shape.At(x_shape.NumAxes() - 1); - const int64_t rows = x_shape.Count(0, x_shape.NumAxes() - 1); - const size_t num_input_dims = x_shape.NumAxes(); - const int64_t* input_dims = x_shape.ptr(); - const size_t num_mask_dims = mask_shape.NumAxes(); - const int64_t* mask_dims = mask_shape.ptr(); - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - - size_t simplified_num_dims = 0; - int64_t simplified_input_dims[kMaxNumDims]; - int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); - if (simplified_num_dims == 1) { - LaunchElementwiseForwardKernel( - ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), - mask->dptr(), rows, cols, mask_fill_value, scale_value); - } -#define DEFINE_ONE_ELIF(dims) \ - else if (simplified_num_dims == dims) { \ - LaunchBroadcastForwardKernel( \ - ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), \ - mask->dptr(), elem_cnt, rows, cols, mask_fill_value, scale_value, \ - simplified_input_dims, simplified_mask_dims); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) -#undef DEFINE_ONE_ELIF - else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class FusedScaleMaskSoftmaxGradKernel final : public user_op::OpKernel { - public: - FusedScaleMaskSoftmaxGradKernel() = default; - ~FusedScaleMaskSoftmaxGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float scale_value = ctx->Attr("scale_value"); - const float mask_fill_value = static_cast(0.0); - const ShapeView& dy_shape = dy->shape_view(); - const ShapeView& mask_shape = mask->shape_view(); - CHECK_GE(dy_shape.NumAxes(), 2); - const int64_t elem_cnt = dy_shape.elem_cnt(); - const int64_t cols = dy_shape.At(dy_shape.NumAxes() - 1); - const int64_t rows = dy_shape.Count(0, dy_shape.NumAxes() - 1); - const int64_t* input_dims = dy_shape.ptr(); - const size_t num_input_dims = dy_shape.NumAxes(); - const int64_t* mask_dims = mask_shape.ptr(); - const size_t num_mask_dims = mask_shape.NumAxes(); - - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - - size_t simplified_num_dims = 0; - int64_t simplified_input_dims[kMaxNumDims]; - int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); - if (simplified_num_dims == 1) { - LaunchElementwiseBackwardKernel( - ctx->stream()->As()->cuda_stream(), y->dptr(), dy->dptr(), - dx->mut_dptr(), mask->dptr(), rows, cols, mask_fill_value, scale_value); - } -#define DEFINE_ONE_ELIF(dims) \ - else if (simplified_num_dims == dims) { \ - LaunchBroadcastBackwardKernel( \ - ctx->stream()->As()->cuda_stream(), y->dptr(), dy->dptr(), \ - dx->mut_dptr(), mask->dptr(), elem_cnt, rows, cols, mask_fill_value, scale_value, \ - simplified_input_dims, simplified_mask_dims); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) -#undef DEFINE_ONE_ELIF - else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace - -#define REGISTER_FUSED_SCALE_MASK_SOFTMAX_CUDA_KERNEL(dtype, mask_dtype) \ - REGISTER_USER_KERNEL("fused_scale_mask_softmax") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value) \ - && (user_op::HobDataType("mask", 0) == GetDataType::value)); - -REGISTER_FUSED_SCALE_MASK_SOFTMAX_CUDA_KERNEL(half, bool) -REGISTER_FUSED_SCALE_MASK_SOFTMAX_CUDA_KERNEL(float, bool) -#undef REGISTER_FUSED_SCALE_MASK_SOFTMAX_CUDA_KERNEL - -#define REGISTER_FUSED_SCALE_MASK_SOFTMAX_GRAD_KERNEL(dtype, mask_dtype) \ - REGISTER_USER_KERNEL("fused_scale_mask_softmax_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value) \ - && (user_op::HobDataType("mask", 0) == GetDataType::value)); - -REGISTER_FUSED_SCALE_MASK_SOFTMAX_GRAD_KERNEL(half, bool) -REGISTER_FUSED_SCALE_MASK_SOFTMAX_GRAD_KERNEL(float, bool) -#undef REGISTER_FUSED_SCALE_MASK_SOFTMAX_GRAD_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_scale_mask_softmax.hip.h b/oneflow/user/kernels/fused_scale_mask_softmax.hip.h deleted file mode 100644 index 84adfb62a5b517f8e7dfae67471836c4c5058c0f..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_scale_mask_softmax.hip.h +++ /dev/null @@ -1,216 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/nd_index_offset_helper.h" - -namespace oneflow { - -namespace fused_scale_mask_softmax { - -namespace { - -void SimplifyBroadcastDims(size_t num_a_dims, const int64_t* a_dims, size_t num_b_dims, - const int64_t* b_dims, size_t* simplified_num_dims, - int64_t* simplified_a_dims, int64_t* simplified_b_dims) { - const size_t num_max_dims = std::max(num_a_dims, num_b_dims); - auto MakeGetDim = [num_max_dims](size_t num_dims, const int64_t* dims) { - const int64_t num_padding_dims = num_max_dims - num_dims; - return [num_padding_dims, dims](size_t index) { - return index < num_padding_dims ? 1 : dims[index - num_padding_dims]; - }; - }; - auto GetADim = MakeGetDim(num_a_dims, a_dims); - auto GetBDim = MakeGetDim(num_b_dims, b_dims); - *simplified_num_dims = 0; - bool prev_broadcast_a = false; - bool prev_broadcast_b = false; - for (int64_t i = 0; i < num_max_dims; ++i) { - const int64_t a_dim = GetADim(i); - const int64_t b_dim = GetBDim(i); - const int64_t broadcast_dim = std::max(a_dim, b_dim); - CHECK_GT(broadcast_dim, 0); - const bool broadcast_a = (a_dim == 1); - const bool broadcast_b = (b_dim == 1); - CHECK((a_dim == broadcast_dim) || broadcast_a); - CHECK((b_dim == broadcast_dim) || broadcast_b); - if (broadcast_dim == 1) { - continue; - } else if (*simplified_num_dims != 0 - && (prev_broadcast_a == broadcast_a && prev_broadcast_b == broadcast_b)) { - simplified_a_dims[*simplified_num_dims - 1] *= a_dim; - simplified_b_dims[*simplified_num_dims - 1] *= b_dim; - } else { - simplified_a_dims[*simplified_num_dims] = a_dim; - simplified_b_dims[*simplified_num_dims] = b_dim; - *simplified_num_dims += 1; - prev_broadcast_a = broadcast_a; - prev_broadcast_b = broadcast_b; - } - } -} - -template -struct BroadcastMaskSoftmaxParams { - NdIndexOffsetHelper src_index_helper; - NdIndexOffsetHelper mask_index_helper; - const int64_t* mask_dims{}; - int64_t row_size; - float fill; - float scale; -}; - -struct ElementwiseMaskSoftmaxParams { - int64_t row_size; - float fill; - float scale; -}; - -template -struct BroadcastScaleMaskLoad { - BroadcastScaleMaskLoad(const SRC* src, const MASK* mask, - BroadcastMaskSoftmaxParams params) - : src(src), mask(mask), params(params) { - for (int i = 0; i < num_dims; i++) { mask_dims[i] = params.mask_dims[i]; } - } - template - __device__ void load(DST* dst, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - cuda::softmax::Pack mask_pack; - const IndexType offset = row * params.row_size + col; - IndexType input_index[num_dims]; - IndexType mask_index[num_dims]; - params.src_index_helper.OffsetToNdIndex(offset, input_index); - for (int dim = 0; dim < num_dims; ++dim) { - if (mask_dims[dim] == 1) { - mask_index[dim] = 0; - } else { - mask_index[dim] = input_index[dim]; - } - } - const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); - pack.storage = *(reinterpret_cast*>(src) + offset / N); - mask_pack.storage = - *(reinterpret_cast*>(mask) + mask_offset / N); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - dst[i] = static_cast(params.fill); - } else { - dst[i] = static_cast(pack.elem[i]) * static_cast(params.scale); - } - } - } - const SRC* src; - const MASK* mask; - int64_t mask_dims[num_dims]; - BroadcastMaskSoftmaxParams params; -}; - -template -struct ElementwiseScaleMaskLoad { - ElementwiseScaleMaskLoad(const SRC* src, const MASK* mask, ElementwiseMaskSoftmaxParams param) - : src(src), mask(mask), param(param) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - const int64_t offset = (row * param.row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - dst[i] = static_cast(param.fill); - } else { - dst[i] = static_cast(pack.elem[i]) * static_cast(param.scale); - } - } - } - const SRC* src; - const MASK* mask; - ElementwiseMaskSoftmaxParams param; -}; - -template -struct BroadcastScaleMaskStore { - BroadcastScaleMaskStore(DST* dst, const MASK* mask, - BroadcastMaskSoftmaxParams params) - : dst(dst), mask(mask), params(params) { - for (int i = 0; i < num_dims; ++i) { mask_dims[i] = params.mask_dims[i]; } - } - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - cuda::softmax::Pack mask_pack; - const IndexType offset = row * params.row_size + col; - IndexType input_index[num_dims]; - IndexType mask_index[num_dims]; - params.src_index_helper.OffsetToNdIndex(offset, input_index); - for (int dim = 0; dim < num_dims; ++dim) { - if (mask_dims[dim] == 1) { - mask_index[dim] = 0; - } else { - mask_index[dim] = input_index[dim]; - } - } - const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); - mask_pack.storage = - *(reinterpret_cast*>(mask) + mask_offset / N); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - pack.elem[i] = static_cast(params.fill); - } else { - pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); - } - } - *(reinterpret_cast*>(dst) + offset / N) = pack.storage; - } - DST* dst; - const MASK* mask; - int64_t mask_dims[num_dims]; - BroadcastMaskSoftmaxParams params; -}; - -template -struct ElementwiseScaleMaskStore { - ElementwiseScaleMaskStore(DST* dst, const MASK* mask, ElementwiseMaskSoftmaxParams params) - : dst(dst), mask(mask), params(params) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - const int64_t offset = (row * params.row_size + col) / N; - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - if (mask_pack.elem[i] == 0) { - pack.elem[i] = params.fill; - } else { - pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); - } - } - *(reinterpret_cast*>(dst) + offset) = pack.storage; - } - DST* dst; - const MASK* mask; - ElementwiseMaskSoftmaxParams params; -}; - -} // namespace - -} // namespace fused_scale_mask_softmax - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_scale_mask_softmax_dropout.cu b/oneflow/user/kernels/fused_scale_mask_softmax_dropout.cu index a0bec673a4abfa3d8bd9c145d8b992c02ad4fd5b..bfc93288aa1ff1e38a5d71ceab8df4cea5bc13dc 100644 --- a/oneflow/user/kernels/fused_scale_mask_softmax_dropout.cu +++ b/oneflow/user/kernels/fused_scale_mask_softmax_dropout.cu @@ -16,110 +16,61 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/cuda/softmax.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" -#include "oneflow/user/kernels/fused_scale_mask_softmax.cuh" +#include "oneflow/user/kernels/fused_softmax.cuh" namespace oneflow { namespace { -template -struct DropoutLoad { - DropoutLoad(const SRC* src, const bool* mask, int64_t row_size, SRC scale) - : src(src), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - cuda::softmax::Pack pack; - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - dst[i] = static_cast(pack.elem[i]) * static_cast(mask_pack.elem[i]) - * static_cast(scale); - } - } - const SRC* src; - const bool* mask; - int64_t row_size; - SRC scale; -}; - -template -struct DropoutStore { - DropoutStore(DST* dst, DST* softmax_y, const bool* mask, int64_t row_size, DST scale) - : dst(dst), softmax_y(softmax_y), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack softmax_y_pack; - cuda::softmax::Pack dst_pack; - const int64_t offset = (row * row_size + col) / N; - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - softmax_y_pack.elem[i] = static_cast(src[i]); - dst_pack.elem[i] = - static_cast(src[i]) * static_cast(mask_pack.elem[i]) * static_cast(scale); - } - *(reinterpret_cast*>(softmax_y) + offset) = - softmax_y_pack.storage; - *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; - } - DST* dst; - DST* softmax_y; - const bool* mask; - int64_t row_size; - DST scale; -}; - template -void LaunchBroadcastForwardKernel(cudaStream_t stream, const T* x, T* y, T* softmax_y, +void LaunchBroadcastForwardKernel(GPU(Stream_t) stream, const T* x, T* y, T* softmax_y, const MASK* mask, const bool* dropout_mask, const int64_t elem_cnt, const int64_t rows, const int64_t cols, const float fill, const float scale, const float dropout_scale, const int64_t* input_dims, const int64_t* mask_dims) { - DropoutStore store(y, softmax_y, dropout_mask, cols, dropout_scale); + cuda::fused_softmax::DropoutStore store(y, softmax_y, dropout_mask, cols, + dropout_scale); NdIndexOffsetHelper input_index_helper(input_dims); NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; + cuda::fused_softmax::BroadcastMaskSoftmaxParams params; params.src_index_helper = input_index_helper; params.mask_index_helper = mask_index_helper; params.mask_dims = mask_dims; params.row_size = cols; params.fill = fill; params.scale = scale; - fused_scale_mask_softmax::BroadcastScaleMaskLoad load( - x, mask, params); + cuda::fused_softmax::BroadcastScaleMaskLoad load(x, mask, + params); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( stream, load, store, rows, cols))); } template -void LaunchElementwiseForwardKernel(cudaStream_t stream, const T* x, T* y, T* softmax_y, +void LaunchElementwiseForwardKernel(GPU(Stream_t) stream, const T* x, T* y, T* softmax_y, const MASK* mask, const bool* dropout_mask, const int64_t rows, const int64_t cols, const float fill, const float scale, const float dropout_scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; + cuda::fused_softmax::ElementwiseMaskSoftmaxParams params; params.row_size = cols; params.fill = fill; params.scale = scale; - fused_scale_mask_softmax::ElementwiseScaleMaskLoad load(x, mask, params); - DropoutStore store(y, softmax_y, dropout_mask, cols, dropout_scale); + cuda::fused_softmax::ElementwiseScaleMaskLoad load(x, mask, params); + cuda::fused_softmax::DropoutStore store(y, softmax_y, dropout_mask, cols, + dropout_scale); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( stream, load, store, rows, cols))); } template -void LaunchBroadcastBackwardKernel(cudaStream_t stream, const T* softmax_y, const T* dy, T* dx, +void LaunchBroadcastBackwardKernel(GPU(Stream_t) stream, const T* softmax_y, const T* dy, T* dx, const MASK* mask, const bool* dropout_mask, const int64_t elem_cnt, const int64_t rows, const int64_t cols, const float fill, const float scale, const float dropout_scale, const int64_t* input_dims, const int64_t* mask_dims) { - DropoutLoad load_dy(dy, dropout_mask, cols, dropout_scale); + cuda::fused_softmax::MaskScaleLoad load_dy(dy, dropout_mask, cols, dropout_scale); NdIndexOffsetHelper input_index_helper(input_dims, num_dims); NdIndexOffsetHelper mask_index_helper(mask_dims, num_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; + cuda::fused_softmax::BroadcastMaskSoftmaxParams params; params.src_index_helper = input_index_helper; params.mask_index_helper = mask_index_helper; params.mask_dims = mask_dims; @@ -127,7 +78,7 @@ void LaunchBroadcastBackwardKernel(cudaStream_t stream, const T* softmax_y, cons params.fill = fill; params.scale = scale; cuda::softmax::DirectLoad load_softmax_y(softmax_y, cols); - fused_scale_mask_softmax::BroadcastScaleMaskStore store( + cuda::fused_softmax::BroadcastScaleMaskStore store( dx, mask, params); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( @@ -135,17 +86,17 @@ void LaunchBroadcastBackwardKernel(cudaStream_t stream, const T* softmax_y, cons } template -void LaunchElementwiseBackwardKernel(cudaStream_t stream, const T* softmax_y, const T* dy, T* dx, +void LaunchElementwiseBackwardKernel(GPU(Stream_t) stream, const T* softmax_y, const T* dy, T* dx, const MASK* mask, const bool* dropout_mask, const int64_t rows, const int64_t cols, const float fill, const float scale, const float dropout_scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; + cuda::fused_softmax::ElementwiseMaskSoftmaxParams params; params.row_size = cols; params.fill = fill; params.scale = scale; cuda::softmax::DirectLoad load_softmax_y(softmax_y, cols); - DropoutLoad load_dy(dy, dropout_mask, cols, dropout_scale); - fused_scale_mask_softmax::ElementwiseScaleMaskStore store(dx, mask, params); + cuda::fused_softmax::MaskScaleLoad load_dy(dy, dropout_mask, cols, dropout_scale); + cuda::fused_softmax::ElementwiseScaleMaskStore store(dx, mask, params); OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( stream, load_softmax_y, load_dy, store, rows, cols))); @@ -185,9 +136,9 @@ class FusedScaleMaskSoftmaxDropoutKernel final : public user_op::OpKernel { size_t simplified_num_dims = 0; int64_t simplified_input_dims[kMaxNumDims]; int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); + cuda::fused_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, mask_dims, + &simplified_num_dims, simplified_input_dims, + simplified_mask_dims); if (simplified_num_dims == 1) { LaunchElementwiseForwardKernel( ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), @@ -248,9 +199,9 @@ class FusedScaleMaskSoftmaxDropoutGradKernel final : public user_op::OpKernel { size_t simplified_num_dims = 0; int64_t simplified_input_dims[kMaxNumDims]; int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); + cuda::fused_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, mask_dims, + &simplified_num_dims, simplified_input_dims, + simplified_mask_dims); if (simplified_num_dims == 1) { LaunchElementwiseBackwardKernel( ctx->stream()->As()->cuda_stream(), softmax_y->dptr(), dy->dptr(), diff --git a/oneflow/user/kernels/fused_scale_mask_softmax_dropout.hip.cpp b/oneflow/user/kernels/fused_scale_mask_softmax_dropout.hip.cpp deleted file mode 100644 index a0e6482af18ecc9d2b2cbfad29e5c867c117704e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_scale_mask_softmax_dropout.hip.cpp +++ /dev/null @@ -1,303 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/user/kernels/fused_scale_mask_softmax.hip.h" - -namespace oneflow { - -namespace { - -template -struct DropoutLoad { - DropoutLoad(const SRC* src, const bool* mask, int64_t row_size, SRC scale) - : src(src), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - cuda::softmax::Pack pack; - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - dst[i] = static_cast(pack.elem[i]) * static_cast(mask_pack.elem[i]) - * static_cast(scale); - } - } - const SRC* src; - const bool* mask; - int64_t row_size; - SRC scale; -}; - -template -struct DropoutStore { - DropoutStore(DST* dst, DST* softmax_y, const bool* mask, int64_t row_size, DST scale) - : dst(dst), softmax_y(softmax_y), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack softmax_y_pack; - cuda::softmax::Pack dst_pack; - const int64_t offset = (row * row_size + col) / N; - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - softmax_y_pack.elem[i] = static_cast(src[i]); - dst_pack.elem[i] = - static_cast(src[i]) * static_cast(mask_pack.elem[i]) * static_cast(scale); - } - *(reinterpret_cast*>(softmax_y) + offset) = - softmax_y_pack.storage; - *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; - } - DST* dst; - DST* softmax_y; - const bool* mask; - int64_t row_size; - DST scale; -}; - -template -void LaunchBroadcastForwardKernel(hipStream_t stream, const T* x, T* y, T* softmax_y, - const MASK* mask, const bool* dropout_mask, - const int64_t elem_cnt, const int64_t rows, const int64_t cols, - const float fill, const float scale, const float dropout_scale, - const int64_t* input_dims, const int64_t* mask_dims) { - DropoutStore store(y, softmax_y, dropout_mask, cols, dropout_scale); - NdIndexOffsetHelper input_index_helper(input_dims); - NdIndexOffsetHelper mask_index_helper(mask_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; - params.src_index_helper = input_index_helper; - params.mask_index_helper = mask_index_helper; - params.mask_dims = mask_dims; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - fused_scale_mask_softmax::BroadcastScaleMaskLoad load( - x, mask, params); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - stream, load, store, rows, cols))); -} - -template -void LaunchElementwiseForwardKernel(hipStream_t stream, const T* x, T* y, T* softmax_y, - const MASK* mask, const bool* dropout_mask, const int64_t rows, - const int64_t cols, const float fill, const float scale, - const float dropout_scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - fused_scale_mask_softmax::ElementwiseScaleMaskLoad load(x, mask, params); - DropoutStore store(y, softmax_y, dropout_mask, cols, dropout_scale); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - stream, load, store, rows, cols))); -} - -template -void LaunchBroadcastBackwardKernel(hipStream_t stream, const T* softmax_y, const T* dy, T* dx, - const MASK* mask, const bool* dropout_mask, - const int64_t elem_cnt, const int64_t rows, const int64_t cols, - const float fill, const float scale, const float dropout_scale, - const int64_t* input_dims, const int64_t* mask_dims) { - DropoutLoad load_dy(dy, dropout_mask, cols, dropout_scale); - NdIndexOffsetHelper input_index_helper(input_dims, num_dims); - NdIndexOffsetHelper mask_index_helper(mask_dims, num_dims); - fused_scale_mask_softmax::BroadcastMaskSoftmaxParams params; - params.src_index_helper = input_index_helper; - params.mask_index_helper = mask_index_helper; - params.mask_dims = mask_dims; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - cuda::softmax::DirectLoad load_softmax_y(softmax_y, cols); - fused_scale_mask_softmax::BroadcastScaleMaskStore store( - dx, mask, params); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( - stream, load_softmax_y, load_dy, store, rows, cols))); -} - -template -void LaunchElementwiseBackwardKernel(hipStream_t stream, const T* softmax_y, const T* dy, T* dx, - const MASK* mask, const bool* dropout_mask, const int64_t rows, - const int64_t cols, const float fill, const float scale, - const float dropout_scale) { - fused_scale_mask_softmax::ElementwiseMaskSoftmaxParams params; - params.row_size = cols; - params.fill = fill; - params.scale = scale; - cuda::softmax::DirectLoad load_softmax_y(softmax_y, cols); - DropoutLoad load_dy(dy, dropout_mask, cols, dropout_scale); - fused_scale_mask_softmax::ElementwiseScaleMaskStore store(dx, mask, params); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( - stream, load_softmax_y, load_dy, store, rows, cols))); -} - -constexpr int32_t kMaxNumDims = 5; - -template -class FusedScaleMaskSoftmaxDropoutKernel final : public user_op::OpKernel { - public: - FusedScaleMaskSoftmaxDropoutKernel() = default; - ~FusedScaleMaskSoftmaxDropoutKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - const user_op::Tensor* dropout_mask = ctx->Tensor4ArgNameAndIndex("dropout_mask", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const float mask_fill_value = ctx->Attr("mask_fill_value"); - const float scale_value = ctx->Attr("scale_value"); - const float dropout_scale_value = ctx->Attr("dropout_scale_value"); - user_op::Tensor* softmax_y = ctx->Tensor4ArgNameAndIndex("softmax_y", 0); - const ShapeView& x_shape = x->shape_view(); - const ShapeView& mask_shape = mask->shape_view(); - CHECK_GE(x_shape.NumAxes(), 2); - const int64_t elem_cnt = x_shape.elem_cnt(); - const int64_t cols = x_shape.At(x_shape.NumAxes() - 1); - const int64_t rows = x_shape.Count(0, x_shape.NumAxes() - 1); - const size_t num_input_dims = x_shape.NumAxes(); - const int64_t* input_dims = x_shape.ptr(); - const size_t num_mask_dims = mask_shape.NumAxes(); - const int64_t* mask_dims = mask_shape.ptr(); - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - - size_t simplified_num_dims = 0; - int64_t simplified_input_dims[kMaxNumDims]; - int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); - if (simplified_num_dims == 1) { - LaunchElementwiseForwardKernel( - ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), - softmax_y->mut_dptr(), mask->dptr(), dropout_mask->dptr(), rows, cols, - mask_fill_value, scale_value, dropout_scale_value); - } - -#define DEFINE_ONE_ELIF(dims) \ - else if (simplified_num_dims == dims) { \ - LaunchBroadcastForwardKernel( \ - ctx->stream()->As()->cuda_stream(), x->dptr(), y->mut_dptr(), \ - softmax_y->mut_dptr(), mask->dptr(), dropout_mask->dptr(), elem_cnt, rows, \ - cols, mask_fill_value, scale_value, dropout_scale_value, simplified_input_dims, \ - simplified_mask_dims); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) -#undef DEFINE_ONE_ELIF - else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class FusedScaleMaskSoftmaxDropoutGradKernel final : public user_op::OpKernel { - public: - FusedScaleMaskSoftmaxDropoutGradKernel() = default; - ~FusedScaleMaskSoftmaxDropoutGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* softmax_y = ctx->Tensor4ArgNameAndIndex("softmax_y", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - const user_op::Tensor* dropout_mask = ctx->Tensor4ArgNameAndIndex("dropout_mask", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float mask_fill_value = static_cast(0.0); - const float scale_value = ctx->Attr("scale_value"); - const float dropout_scale_value = ctx->Attr("dropout_scale_value"); - const ShapeView& dy_shape = dy->shape_view(); - const int64_t elem_cnt = dy_shape.elem_cnt(); - const ShapeView& mask_shape = mask->shape_view(); - CHECK_GE(dy_shape.NumAxes(), 2); - const int64_t cols = dy_shape.At(dy_shape.NumAxes() - 1); - const int64_t rows = dy_shape.Count(0, dy_shape.NumAxes() - 1); - const int64_t* input_dims = dy_shape.ptr(); - const size_t num_input_dims = dy_shape.NumAxes(); - const int64_t* mask_dims = mask_shape.ptr(); - const size_t num_mask_dims = mask_shape.NumAxes(); - - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - cuda::softmax::DirectLoad load_softmax_y(softmax_y->dptr(), cols); - - size_t simplified_num_dims = 0; - int64_t simplified_input_dims[kMaxNumDims]; - int64_t simplified_mask_dims[kMaxNumDims]; - fused_scale_mask_softmax::SimplifyBroadcastDims(num_input_dims, input_dims, num_mask_dims, - mask_dims, &simplified_num_dims, - simplified_input_dims, simplified_mask_dims); - if (simplified_num_dims == 1) { - LaunchElementwiseBackwardKernel( - ctx->stream()->As()->cuda_stream(), softmax_y->dptr(), dy->dptr(), - dx->mut_dptr(), mask->dptr(), dropout_mask->dptr(), rows, cols, - mask_fill_value, scale_value, dropout_scale_value); - } -#define DEFINE_ONE_ELIF(dims) \ - else if (simplified_num_dims == dims) { \ - LaunchBroadcastBackwardKernel( \ - ctx->stream()->As()->cuda_stream(), softmax_y->dptr(), dy->dptr(), \ - dx->mut_dptr(), mask->dptr(), dropout_mask->dptr(), elem_cnt, rows, cols, \ - static_cast(0.0), ctx->Attr("scale_value"), \ - ctx->Attr("dropout_scale_value"), simplified_input_dims, simplified_mask_dims); \ - } - DEFINE_ONE_ELIF(2) - DEFINE_ONE_ELIF(3) - DEFINE_ONE_ELIF(4) -#undef DEFINE_ONE_ELIF - else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -} // namespace - -#define REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(dtype, mask_dtype) \ - REGISTER_USER_KERNEL("fused_scale_mask_softmax_dropout") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value) \ - && (user_op::HobDataType("mask", 0) == GetDataType::value)); - -REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(half, bool) -REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL(float, bool) -#undef REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_CUDA_KERNEL - -#define REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_GRAD_KERNEL(dtype, mask_dtype) \ - REGISTER_USER_KERNEL("fused_scale_mask_softmax_dropout_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value) \ - && (user_op::HobDataType("mask", 0) == GetDataType::value)); - -REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_GRAD_KERNEL(half, bool) -REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_GRAD_KERNEL(float, bool) -#undef REGISTER_FUSED_SCALE_MASK_SOFTMAX_DROPOUT_GRAD_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.cu b/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.cu index c7b36e033b2253c77475696d823653e5ee359cd4..7d34c947875ca5c545e7c20098f8512bab20140a 100644 --- a/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.cu +++ b/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.cu @@ -19,6 +19,8 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/permute.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_CUDA + namespace oneflow { namespace { @@ -58,17 +60,17 @@ struct CudaDataTypeTrait { }; template -void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int64_t m, int64_t n, +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, int64_t n, int64_t k, T alpha, const T* a, int64_t lda, int64_t stridea, const T* b, int64_t ldb, int64_t strideb, T beta, T* c, int64_t ldc, int64_t stridec, int64_t batch_size) { cublasOperation_t opa = GetCublasOp(transa); cublasOperation_t opb = GetCublasOp(transb); - if (CUDA_VERSION >= 9010 && GetCudaSmVersion() >= 500) { + if (CUDA_VERSION >= 9010 && stream->cuda_arch() >= 500) { #if CUDA_VERSION >= 9010 cudaDataType_t data_type = CudaDataTypeTrait::value; OF_CUBLAS_CHECK(cublasGemmStridedBatchedEx( - handle, opa, opb, m, n, k, reinterpret_cast(&alpha), + stream->cublas_handle(), opa, opb, m, n, k, reinterpret_cast(&alpha), reinterpret_cast(a), data_type, lda, stridea, reinterpret_cast(b), data_type, ldb, strideb, reinterpret_cast(&beta), reinterpret_cast(c), data_type, ldc, stridec, batch_size, data_type, CUBLAS_GEMM_DEFAULT)); @@ -81,7 +83,7 @@ void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int64_t m, #if CUDA_VERSION >= 9010 template<> -void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int64_t m, int64_t n, +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, int64_t n, int64_t k, half alpha, const half* a, int64_t lda, int64_t stridea, const half* b, int64_t ldb, int64_t strideb, half beta, half* c, int64_t ldc, int64_t stridec, int64_t batch_size) { @@ -89,7 +91,7 @@ void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int6 cublasOperation_t opa = GetCublasOp(transa); cublasOperation_t opb = GetCublasOp(transb); - if (GetCudaSmVersion() >= 500) { + if (stream->cuda_arch() >= 500) { float alpha_f = static_cast(alpha); float beta_f = static_cast(beta); #if CUDA_VERSION >= 11000 @@ -100,19 +102,19 @@ void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int6 cudaDataType_t data_type = CudaDataTypeTrait::value; cudaDataType_t comp_type = CudaDataTypeTrait::value; OF_CUBLAS_CHECK(cublasGemmStridedBatchedEx( - handle, opa, opb, m, n, k, &alpha_f, reinterpret_cast(a), data_type, lda, - stridea, reinterpret_cast(b), data_type, ldb, strideb, &beta_f, + stream->cublas_handle(), opa, opb, m, n, k, &alpha_f, reinterpret_cast(a), + data_type, lda, stridea, reinterpret_cast(b), data_type, ldb, strideb, &beta_f, reinterpret_cast(c), data_type, ldc, stridec, batch_size, comp_type, algo)); } } template<> -void CublasBatchGemm(cublasHandle_t handle, char transa, char transb, int64_t m, int64_t n, - int64_t k, float16 alpha, const float16* a, int64_t lda, +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, + int64_t n, int64_t k, float16 alpha, const float16* a, int64_t lda, int64_t stridea, const float16* b, int64_t ldb, int64_t strideb, float16 beta, float16* c, int64_t ldc, int64_t stridec, int64_t batch_size) { - CublasBatchGemm(handle, transa, transb, m, n, k, static_cast(alpha), + CublasBatchGemm(stream, transa, transb, m, n, k, static_cast(alpha), reinterpret_cast(a), lda, stridea, reinterpret_cast(b), ldb, strideb, static_cast(beta), reinterpret_cast(c), ldc, stridec, batch_size); @@ -126,7 +128,281 @@ void BatchedGemm(ep::Stream* stream, char opa, char opb, int64_t m, int64_t n, i int64_t strideb, float beta, T* c, int64_t ldc, int64_t stridec, int64_t batch_size) { // swap m and n, a and b to convert from row-major to col-major - CublasBatchGemm(stream->As()->cublas_handle(), opb, opa, n, m, k, + CublasBatchGemm(stream->As(), opb, opa, n, m, k, static_cast(alpha), b, ldb, + strideb, a, lda, stridea, static_cast(beta), c, ldc, stridec, batch_size); +} + +SliceParams ConstructSliceParams4Value(int64_t seq_len, int64_t batch_size, int64_t num_heads, + int64_t head_size) { + // slice (s, b, n, 3, h) to (s, b, n, 1, h) + SliceParams params; + params.ndim = 4; + params.dims[0] = seq_len; + params.dims[1] = batch_size; + params.dims[2] = num_heads; + params.dims[3] = 3 * head_size; + params.start[0] = 0; + params.start[1] = 0; + params.start[2] = 0; + params.start[3] = 2 * head_size; + params.step[0] = 1; + params.step[1] = 1; + params.step[2] = 1; + params.step[3] = 1; + params.size[0] = seq_len; + params.size[1] = batch_size; + params.size[2] = num_heads; + params.size[3] = head_size; + return params; +} + +template +void TransposeGpu(ep::Stream* stream, DataType data_type, const ShapeView& in_shape, + const ShapeView& out_shape, const std::vector& perm, const T* in, + T* out) { + CHECK_EQ(in_shape.NumAxes(), out_shape.NumAxes()); + int32_t num_axes = in_shape.NumAxes(); + CHECK_EQ(num_axes, perm.size()); + for (int i = 0; i < perm.size(); ++i) { CHECK_EQ(in_shape.At(perm[i]), out_shape.At(i)); } + auto transpose = ep::primitive::NewPrimitive(stream->device_type(), + in_shape.NumAxes()); + CHECK(transpose); + transpose->Launch(stream, data_type, in_shape.NumAxes(), in_shape.ptr(), in, perm.data(), out); +} + +template +class FusedSelfAttentionQueryMulKeyAndValueGpuKernel final : public user_op::OpKernel { + public: + FusedSelfAttentionQueryMulKeyAndValueGpuKernel() = default; + ~FusedSelfAttentionQueryMulKeyAndValueGpuKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* h_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states", 0); + int64_t seq_len = h_tensor->shape_view().At(0); + int64_t batch_size = h_tensor->shape_view().At(1); + int64_t hidden_size = h_tensor->shape_view().At(2); + int64_t head_size = ctx->Attr("head_size"); + int64_t num_heads = hidden_size / (3 * head_size); + int64_t ld = batch_size * hidden_size; + int64_t stride = 3 * head_size; + int64_t k_offset = head_size; + + // q * k: (sq, b, n, h) x (sk, b, n, h) => (b, n, sq, h) x (b, n, sk, h) + // => (b, n, sq, h) x (b, n, h, sk) -> (b, n, sq, sk) + float alpha = ctx->Attr("alpha"); + user_op::Tensor* qmk_tensor = ctx->Tensor4ArgNameAndIndex("query_mul_key", 0); + const T* q_dptr = h_tensor->dptr(); + const T* k_dptr = h_tensor->dptr() + k_offset; + BatchedGemm(ctx->stream(), 'N', 'T', seq_len, seq_len, head_size, alpha, q_dptr, ld, stride, + k_dptr, ld, stride, 0.0f, qmk_tensor->mut_dptr(), seq_len, seq_len * seq_len, + batch_size * num_heads); + + // slice v + user_op::Tensor* tmp_v_tensor = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* v_tensor = ctx->Tensor4ArgNameAndIndex("value", 0); + SliceParams params = ConstructSliceParams4Value(seq_len, batch_size, num_heads, head_size); + SliceKernelUtil::Forward(ctx->stream(), params, h_tensor->dptr(), + tmp_v_tensor->mut_dptr()); + // v from (s, b, n, h) transpose to (b, n, s, h) + Shape value_shape({seq_len, batch_size, num_heads, head_size}); + TransposeGpu(ctx->stream(), h_tensor->data_type(), value_shape, v_tensor->shape_view(), + {1, 2, 0, 3}, tmp_v_tensor->dptr(), v_tensor->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel final : public user_op::OpKernel { + public: + FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel() = default; + ~FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* v_grad_tensor = ctx->Tensor4ArgNameAndIndex("value_grad", 0); + const user_op::Tensor* qmk_grad_tensor = ctx->Tensor4ArgNameAndIndex("query_mul_key_grad", 0); + const user_op::Tensor* h_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states", 0); + user_op::Tensor* tmp_v_tensor = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* h_grad_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states_grad", 0); + + float alpha = ctx->Attr("alpha"); + int64_t seq_len = h_grad_tensor->shape_view().At(0); + int64_t batch_size = h_grad_tensor->shape_view().At(1); + int64_t hidden_size = h_grad_tensor->shape_view().At(2); + int64_t num_heads = v_grad_tensor->shape_view().At(1); + int64_t head_size = v_grad_tensor->shape_view().At(3); + int64_t ld = batch_size * hidden_size; + int64_t stride = 3 * head_size; + CHECK_EQ(hidden_size, num_heads * stride); + + // transpose from (b, n, s, h) to (s, b, n, h) + Shape value_shape({seq_len, batch_size, num_heads, head_size}); + TransposeGpu(ctx->stream(), v_grad_tensor->data_type(), v_grad_tensor->shape_view(), + value_shape, {2, 0, 1, 3}, v_grad_tensor->dptr(), + tmp_v_tensor->mut_dptr()); + // slice v grad + SliceParams params = ConstructSliceParams4Value(seq_len, batch_size, num_heads, head_size); + SliceKernelUtil::Backward(ctx->stream(), params, tmp_v_tensor->dptr(), + h_grad_tensor->mut_dptr()); + + // grad_q = grad_qmk * k + // (b, n, sq, sk) x (b, n, sk, h) -> (b, n, s, h) <= (s, b, n, h) <= (s, b, n, 3, h) + const T* qmk_grad_dptr = qmk_grad_tensor->dptr(); + const T* k_dptr = h_tensor->dptr() + head_size; + T* grad_q_dptr = h_grad_tensor->mut_dptr(); + BatchedGemm(ctx->stream(), 'N', 'N', seq_len, head_size, seq_len, alpha, qmk_grad_dptr, + seq_len, seq_len * seq_len, k_dptr, ld, stride, 0.0f, grad_q_dptr, ld, stride, + batch_size * num_heads); + // grad_k = grad_qmk * q + // (b, n, sk, sq) x (b, n, sq, h) -> (b, n, sk, h) <= (s, b, n, h) <= (s, b, n, 3, h) + const T* q_dptr = h_tensor->dptr(); + T* grad_k_dptr = h_grad_tensor->mut_dptr() + head_size; + BatchedGemm(ctx->stream(), 'T', 'N', seq_len, head_size, seq_len, alpha, qmk_grad_dptr, + seq_len, seq_len * seq_len, q_dptr, ld, stride, 0.0f, grad_k_dptr, ld, stride, + batch_size * num_heads); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +size_t InferTmpBufferSize(user_op::InferContext* ctx) { + const Shape& value_shape = ctx->OutputShape("value", 0); + DataType value_dtype = ctx->OutputDType("value", 0); + return value_shape.elem_cnt() * GetSizeOfDataType(value_dtype); +} + +size_t InferGradTmpBufferSize(user_op::InferContext* ctx) { + const Shape& value_shape = ctx->InputShape("value_grad", 0); + DataType value_dtype = ctx->InputDType("value_grad", 0); + return value_shape.elem_cnt() * GetSizeOfDataType(value_dtype); +} + +} // namespace + +#define REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_self_attention_query_mul_key_and_value") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("hidden_states", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferTmpBufferSize); + +#define REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("fused_self_attention_query_mul_key_and_value_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("hidden_states", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferGradTmpBufferSize); + +REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(float) +REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(float16) +REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float) +REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float16) + +} // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +namespace oneflow { + +namespace { + +inline hipblasOperation_t GetCublasOp(char op) { + switch (op) { + case 'n': + case 'N': { + return HIPBLAS_OP_N; + } + case 't': + case 'T': { + return HIPBLAS_OP_T; + } + case 'c': + case 'C': { + return HIPBLAS_OP_C; + } + default: { + UNIMPLEMENTED(); + } + } + return HIPBLAS_OP_N; +} + +template +struct CudaDataTypeTrait; + +template<> +struct CudaDataTypeTrait { + const static hipblasDatatype_t value = HIPBLAS_R_32F; +}; + +template<> +struct CudaDataTypeTrait { + const static hipblasDatatype_t value = HIPBLAS_R_16F; +}; + +template +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, int64_t n, + int64_t k, T alpha, const T* a, int64_t lda, int64_t stridea, const T* b, + int64_t ldb, int64_t strideb, T beta, T* c, int64_t ldc, int64_t stridec, + int64_t batch_size) { + hipblasOperation_t opa = GetCublasOp(transa); + hipblasOperation_t opb = GetCublasOp(transb); + + hipblasDatatype_t data_type = CudaDataTypeTrait::value; + OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( + stream->cublas_handle(), opa, opb, m, n, k, reinterpret_cast(&alpha), + reinterpret_cast(a), data_type, lda, stridea, reinterpret_cast(b), + data_type, ldb, strideb, reinterpret_cast(&beta), reinterpret_cast(c), + data_type, ldc, stridec, batch_size, data_type, HIPBLAS_GEMM_DEFAULT)); +} + + +template<> +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, int64_t n, + int64_t k, half alpha, const half* a, int64_t lda, int64_t stridea, + const half* b, int64_t ldb, int64_t strideb, half beta, half* c, + int64_t ldc, int64_t stridec, int64_t batch_size) { + using comp_t = float; + hipblasOperation_t opa = GetCublasOp(transa); + hipblasOperation_t opb = GetCublasOp(transb); + + + float alpha_f = static_cast(alpha); + float beta_f = static_cast(beta); + hipblasGemmAlgo_t algo = HIPBLAS_GEMM_DEFAULT; + hipblasDatatype_t data_type = CudaDataTypeTrait::value; + hipblasDatatype_t comp_type = CudaDataTypeTrait::value; + OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( + stream->cublas_handle(), opa, opb, m, n, k, &alpha_f, reinterpret_cast(a), data_type, lda, + stridea, reinterpret_cast(b), data_type, ldb, strideb, &beta_f, + reinterpret_cast(c), data_type, ldc, stridec, batch_size, comp_type, algo)); + +} + +template<> +void CublasBatchGemm(ep::CudaStream* stream, char transa, char transb, int64_t m, int64_t n, + int64_t k, float16 alpha, const float16* a, int64_t lda, + int64_t stridea, const float16* b, int64_t ldb, int64_t strideb, + float16 beta, float16* c, int64_t ldc, int64_t stridec, + int64_t batch_size) { + CublasBatchGemm(stream, transa, transb, m, n, k, static_cast(alpha), + reinterpret_cast(a), lda, stridea, + reinterpret_cast(b), ldb, strideb, static_cast(beta), + reinterpret_cast(c), ldc, stridec, batch_size); +} + + +template +void BatchedGemm(ep::Stream* stream, char opa, char opb, int64_t m, int64_t n, int64_t k, + float alpha, const T* a, int64_t lda, int64_t stridea, const T* b, int64_t ldb, + int64_t strideb, float beta, T* c, int64_t ldc, int64_t stridec, + int64_t batch_size) { + // swap m and n, a and b to convert from row-major to col-major + CublasBatchGemm(stream->As(), opb, opa, n, m, k, static_cast(alpha), b, ldb, strideb, a, lda, stridea, static_cast(beta), c, ldc, stridec, batch_size); } @@ -267,14 +543,14 @@ class FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel final : public user_op: }; size_t InferTmpBufferSize(user_op::InferContext* ctx) { - const Shape* value_shape = ctx->OutputShape("value", 0); - DataType value_dtype = *ctx->OutputDType("value", 0); - return value_shape->elem_cnt() * GetSizeOfDataType(value_dtype); + const Shape& value_shape = ctx->OutputShape("value", 0); + DataType value_dtype = ctx->OutputDType("value", 0); + return value_shape.elem_cnt() * GetSizeOfDataType(value_dtype); } size_t InferGradTmpBufferSize(user_op::InferContext* ctx) { const Shape& value_shape = ctx->InputShape("value_grad", 0); - const DataType& value_dtype = ctx->InputDType("value_grad", 0); + DataType value_dtype = ctx->InputDType("value_grad", 0); return value_shape.elem_cnt() * GetSizeOfDataType(value_dtype); } @@ -300,3 +576,5 @@ REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float) REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float16) } // namespace oneflow + +#endif diff --git a/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.hip.cpp b/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.hip.cpp deleted file mode 100644 index c72cc2aecae1b2dc10e637f04e884a34e575a10f..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_self_attention_query_mul_key_and_value_kernel.hip.cpp +++ /dev/null @@ -1,293 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/slice_util.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ep/include/primitive/permute.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -inline hipblasOperation_t GetCublasOp(char op) { - switch (op) { - case 'n': - case 'N': { - return HIPBLAS_OP_N; - } - case 't': - case 'T': { - return HIPBLAS_OP_T; - } - case 'c': - case 'C': { - return HIPBLAS_OP_C; - } - default: { - UNIMPLEMENTED(); - } - } - return HIPBLAS_OP_N; -} - -template -struct CudaDataTypeTrait; - -template<> -struct CudaDataTypeTrait { - const static hipblasDatatype_t value = HIPBLAS_R_32F; -}; - -template<> -struct CudaDataTypeTrait { - const static hipblasDatatype_t value = HIPBLAS_R_16F; -}; - -template -void CublasBatchGemm(hipblasHandle_t handle, char transa, char transb, int64_t m, int64_t n, - int64_t k, T alpha, const T* a, int64_t lda, int64_t stridea, const T* b, - int64_t ldb, int64_t strideb, T beta, T* c, int64_t ldc, int64_t stridec, - int64_t batch_size) { - hipblasOperation_t opa = GetCublasOp(transa); - hipblasOperation_t opb = GetCublasOp(transb); - - - hipblasDatatype_t data_type = CudaDataTypeTrait::value; - OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( - handle, opa, opb, m, n, k, reinterpret_cast(&alpha), - reinterpret_cast(a), data_type, lda, stridea, reinterpret_cast(b), - data_type, ldb, strideb, reinterpret_cast(&beta), reinterpret_cast(c), - data_type, ldc, stridec, batch_size, data_type, HIPBLAS_GEMM_DEFAULT)); - -} - - -template<> -void CublasBatchGemm(hipblasHandle_t handle, char transa, char transb, int64_t m, int64_t n, - int64_t k, half alpha, const half* a, int64_t lda, int64_t stridea, - const half* b, int64_t ldb, int64_t strideb, half beta, half* c, - int64_t ldc, int64_t stridec, int64_t batch_size) { - using comp_t = float; - hipblasOperation_t opa = GetCublasOp(transa); - hipblasOperation_t opb = GetCublasOp(transb); - - - float alpha_f = static_cast(alpha); - float beta_f = static_cast(beta); - hipblasGemmAlgo_t algo = HIPBLAS_GEMM_DEFAULT; - hipblasDatatype_t data_type = CudaDataTypeTrait::value; - hipblasDatatype_t comp_type = CudaDataTypeTrait::value; - OF_CUBLAS_CHECK(hipblasGemmStridedBatchedEx( - handle, opa, opb, m, n, k, &alpha_f, reinterpret_cast(a), data_type, lda, - stridea, reinterpret_cast(b), data_type, ldb, strideb, &beta_f, - reinterpret_cast(c), data_type, ldc, stridec, batch_size, comp_type, algo)); - -} - -template<> -void CublasBatchGemm(hipblasHandle_t handle, char transa, char transb, int64_t m, int64_t n, - int64_t k, float16 alpha, const float16* a, int64_t lda, - int64_t stridea, const float16* b, int64_t ldb, int64_t strideb, - float16 beta, float16* c, int64_t ldc, int64_t stridec, - int64_t batch_size) { - CublasBatchGemm(handle, transa, transb, m, n, k, static_cast(alpha), - reinterpret_cast(a), lda, stridea, - reinterpret_cast(b), ldb, strideb, static_cast(beta), - reinterpret_cast(c), ldc, stridec, batch_size); -} - - -template -void BatchedGemm(ep::Stream* stream, char opa, char opb, int64_t m, int64_t n, int64_t k, - float alpha, const T* a, int64_t lda, int64_t stridea, const T* b, int64_t ldb, - int64_t strideb, float beta, T* c, int64_t ldc, int64_t stridec, - int64_t batch_size) { - // swap m and n, a and b to convert from row-major to col-major - CublasBatchGemm(stream->As()->cublas_handle(), opb, opa, n, m, k, - static_cast(alpha), b, ldb, strideb, a, lda, stridea, static_cast(beta), - c, ldc, stridec, batch_size); -} - -SliceParams ConstructSliceParams4Value(int64_t seq_len, int64_t batch_size, int64_t num_heads, - int64_t head_size) { - // slice (s, b, n, 3, h) to (s, b, n, 1, h) - SliceParams params; - params.ndim = 4; - params.dims[0] = seq_len; - params.dims[1] = batch_size; - params.dims[2] = num_heads; - params.dims[3] = 3 * head_size; - params.start[0] = 0; - params.start[1] = 0; - params.start[2] = 0; - params.start[3] = 2 * head_size; - params.step[0] = 1; - params.step[1] = 1; - params.step[2] = 1; - params.step[3] = 1; - params.size[0] = seq_len; - params.size[1] = batch_size; - params.size[2] = num_heads; - params.size[3] = head_size; - return params; -} - -template -void TransposeGpu(ep::Stream* stream, DataType data_type, const ShapeView& in_shape, - const ShapeView& out_shape, const std::vector& perm, const T* in, - T* out) { - CHECK_EQ(in_shape.NumAxes(), out_shape.NumAxes()); - int32_t num_axes = in_shape.NumAxes(); - CHECK_EQ(num_axes, perm.size()); - for (int i = 0; i < perm.size(); ++i) { CHECK_EQ(in_shape.At(perm[i]), out_shape.At(i)); } - auto transpose = ep::primitive::NewPrimitive(stream->device_type(), - in_shape.NumAxes()); - CHECK(transpose); - transpose->Launch(stream, data_type, in_shape.NumAxes(), in_shape.ptr(), in, perm.data(), out); -} - -template -class FusedSelfAttentionQueryMulKeyAndValueGpuKernel final : public user_op::OpKernel { - public: - FusedSelfAttentionQueryMulKeyAndValueGpuKernel() = default; - ~FusedSelfAttentionQueryMulKeyAndValueGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* h_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states", 0); - int64_t seq_len = h_tensor->shape_view().At(0); - int64_t batch_size = h_tensor->shape_view().At(1); - int64_t hidden_size = h_tensor->shape_view().At(2); - int64_t head_size = ctx->Attr("head_size"); - int64_t num_heads = hidden_size / (3 * head_size); - int64_t ld = batch_size * hidden_size; - int64_t stride = 3 * head_size; - int64_t k_offset = head_size; - - // q * k: (sq, b, n, h) x (sk, b, n, h) => (b, n, sq, h) x (b, n, sk, h) - // => (b, n, sq, h) x (b, n, h, sk) -> (b, n, sq, sk) - float alpha = ctx->Attr("alpha"); - user_op::Tensor* qmk_tensor = ctx->Tensor4ArgNameAndIndex("query_mul_key", 0); - const T* q_dptr = h_tensor->dptr(); - const T* k_dptr = h_tensor->dptr() + k_offset; - BatchedGemm(ctx->stream(), 'N', 'T', seq_len, seq_len, head_size, alpha, q_dptr, ld, stride, - k_dptr, ld, stride, 0.0f, qmk_tensor->mut_dptr(), seq_len, seq_len * seq_len, - batch_size * num_heads); - - // slice v - user_op::Tensor* tmp_v_tensor = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* v_tensor = ctx->Tensor4ArgNameAndIndex("value", 0); - SliceParams params = ConstructSliceParams4Value(seq_len, batch_size, num_heads, head_size); - SliceKernelUtil::Forward(ctx->stream(), params, h_tensor->dptr(), - tmp_v_tensor->mut_dptr()); - // v from (s, b, n, h) transpose to (b, n, s, h) - Shape value_shape({seq_len, batch_size, num_heads, head_size}); - TransposeGpu(ctx->stream(), h_tensor->data_type(), value_shape, v_tensor->shape_view(), - {1, 2, 0, 3}, tmp_v_tensor->dptr(), v_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel final : public user_op::OpKernel { - public: - FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel() = default; - ~FusedSelfAttentionQueryMulKeyAndValueGradGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* v_grad_tensor = ctx->Tensor4ArgNameAndIndex("value_grad", 0); - const user_op::Tensor* qmk_grad_tensor = ctx->Tensor4ArgNameAndIndex("query_mul_key_grad", 0); - const user_op::Tensor* h_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states", 0); - user_op::Tensor* tmp_v_tensor = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* h_grad_tensor = ctx->Tensor4ArgNameAndIndex("hidden_states_grad", 0); - - float alpha = ctx->Attr("alpha"); - int64_t seq_len = h_grad_tensor->shape_view().At(0); - int64_t batch_size = h_grad_tensor->shape_view().At(1); - int64_t hidden_size = h_grad_tensor->shape_view().At(2); - int64_t num_heads = v_grad_tensor->shape_view().At(1); - int64_t head_size = v_grad_tensor->shape_view().At(3); - int64_t ld = batch_size * hidden_size; - int64_t stride = 3 * head_size; - CHECK_EQ(hidden_size, num_heads * stride); - - // transpose from (b, n, s, h) to (s, b, n, h) - Shape value_shape({seq_len, batch_size, num_heads, head_size}); - TransposeGpu(ctx->stream(), v_grad_tensor->data_type(), v_grad_tensor->shape_view(), - value_shape, {2, 0, 1, 3}, v_grad_tensor->dptr(), - tmp_v_tensor->mut_dptr()); - // slice v grad - SliceParams params = ConstructSliceParams4Value(seq_len, batch_size, num_heads, head_size); - SliceKernelUtil::Backward(ctx->stream(), params, tmp_v_tensor->dptr(), - h_grad_tensor->mut_dptr()); - - // grad_q = grad_qmk * k - // (b, n, sq, sk) x (b, n, sk, h) -> (b, n, s, h) <= (s, b, n, h) <= (s, b, n, 3, h) - const T* qmk_grad_dptr = qmk_grad_tensor->dptr(); - const T* k_dptr = h_tensor->dptr() + head_size; - T* grad_q_dptr = h_grad_tensor->mut_dptr(); - BatchedGemm(ctx->stream(), 'N', 'N', seq_len, head_size, seq_len, alpha, qmk_grad_dptr, - seq_len, seq_len * seq_len, k_dptr, ld, stride, 0.0f, grad_q_dptr, ld, stride, - batch_size * num_heads); - // grad_k = grad_qmk * q - // (b, n, sk, sq) x (b, n, sq, h) -> (b, n, sk, h) <= (s, b, n, h) <= (s, b, n, 3, h) - const T* q_dptr = h_tensor->dptr(); - T* grad_k_dptr = h_grad_tensor->mut_dptr() + head_size; - BatchedGemm(ctx->stream(), 'T', 'N', seq_len, head_size, seq_len, alpha, qmk_grad_dptr, - seq_len, seq_len * seq_len, q_dptr, ld, stride, 0.0f, grad_k_dptr, ld, stride, - batch_size * num_heads); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -size_t InferTmpBufferSize(user_op::InferContext* ctx) { - const Shape* value_shape = ctx->OutputShape("value", 0); - DataType value_dtype = *ctx->OutputDType("value", 0); - return value_shape->elem_cnt() * GetSizeOfDataType(value_dtype); -} - -size_t InferGradTmpBufferSize(user_op::InferContext* ctx) { - const Shape& value_shape = ctx->InputShape("value_grad", 0); - const DataType& value_dtype = ctx->InputDType("value_grad", 0); - return value_shape.elem_cnt() * GetSizeOfDataType(value_dtype); -} - -} // namespace - -#define REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_self_attention_query_mul_key_and_value") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("hidden_states", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTmpBufferSize); - -#define REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_self_attention_query_mul_key_and_value_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("hidden_states", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferGradTmpBufferSize); - -REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(float) -REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_CUDA_KERNEL(float16) -REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float) -REGISTER_FUSED_SELF_ATTENTION_QUERY_MUL_KEY_AND_VALUE_GRAD_CUDA_KERNEL(float16) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_softmax.cuh b/oneflow/user/kernels/fused_softmax.cuh new file mode 100644 index 0000000000000000000000000000000000000000..bf3d128a30411d0b8d16c02fc7cefa11c654b74a --- /dev/null +++ b/oneflow/user/kernels/fused_softmax.cuh @@ -0,0 +1,269 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#ifndef ONEFLOW_USER_KERNELS_FUSED_SOFTMAX_H_ +#define ONEFLOW_USER_KERNELS_FUSED_SOFTMAX_H_ + +#include "oneflow/core/common/nd_index_offset_helper.h" + +namespace oneflow { +namespace cuda { +namespace fused_softmax { + +inline void SimplifyBroadcastDims(size_t num_a_dims, const int64_t* a_dims, size_t num_b_dims, + const int64_t* b_dims, size_t* simplified_num_dims, + int64_t* simplified_a_dims, int64_t* simplified_b_dims) { + const size_t num_max_dims = std::max(num_a_dims, num_b_dims); + auto MakeGetDim = [num_max_dims](size_t num_dims, const int64_t* dims) { + const int64_t num_padding_dims = num_max_dims - num_dims; + return [num_padding_dims, dims](size_t index) { + return index < num_padding_dims ? 1 : dims[index - num_padding_dims]; + }; + }; + auto GetADim = MakeGetDim(num_a_dims, a_dims); + auto GetBDim = MakeGetDim(num_b_dims, b_dims); + *simplified_num_dims = 0; + bool prev_broadcast_a = false; + bool prev_broadcast_b = false; + for (int64_t i = 0; i < num_max_dims; ++i) { + const int64_t a_dim = GetADim(i); + const int64_t b_dim = GetBDim(i); + const int64_t broadcast_dim = std::max(a_dim, b_dim); + CHECK_GT(broadcast_dim, 0); + const bool broadcast_a = (a_dim == 1); + const bool broadcast_b = (b_dim == 1); + CHECK((a_dim == broadcast_dim) || broadcast_a); + CHECK((b_dim == broadcast_dim) || broadcast_b); + if (broadcast_dim == 1) { + continue; + } else if (*simplified_num_dims != 0 + && (prev_broadcast_a == broadcast_a && prev_broadcast_b == broadcast_b)) { + simplified_a_dims[*simplified_num_dims - 1] *= a_dim; + simplified_b_dims[*simplified_num_dims - 1] *= b_dim; + } else { + simplified_a_dims[*simplified_num_dims] = a_dim; + simplified_b_dims[*simplified_num_dims] = b_dim; + *simplified_num_dims += 1; + prev_broadcast_a = broadcast_a; + prev_broadcast_b = broadcast_b; + } + } +} + +template +struct BroadcastMaskSoftmaxParams { + NdIndexOffsetHelper src_index_helper; + NdIndexOffsetHelper mask_index_helper; + const int64_t* mask_dims{}; + int64_t row_size; + float fill; + float scale; +}; + +struct ElementwiseMaskSoftmaxParams { + int64_t row_size; + float fill; + float scale; +}; + +template +struct BroadcastScaleMaskLoad { + BroadcastScaleMaskLoad(const SRC* src, const MASK* mask, + BroadcastMaskSoftmaxParams params) + : src(src), mask(mask), params(params) { + for (int i = 0; i < num_dims; i++) { mask_dims[i] = params.mask_dims[i]; } + } + template + __device__ void load(DST* dst, int64_t row, int64_t col) { + cuda::softmax::Pack pack; + cuda::softmax::Pack mask_pack; + const IndexType offset = row * params.row_size + col; + IndexType input_index[num_dims]; + IndexType mask_index[num_dims]; + params.src_index_helper.OffsetToNdIndex(offset, input_index); + for (int dim = 0; dim < num_dims; ++dim) { + if (mask_dims[dim] == 1) { + mask_index[dim] = 0; + } else { + mask_index[dim] = input_index[dim]; + } + } + const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); + pack.storage = *(reinterpret_cast*>(src) + offset / N); + mask_pack.storage = + *(reinterpret_cast*>(mask) + mask_offset / N); +#pragma unroll + for (int i = 0; i < N; ++i) { + if (mask_pack.elem[i] == 0) { + dst[i] = static_cast(params.fill); + } else { + dst[i] = static_cast(pack.elem[i]) * static_cast(params.scale); + } + } + } + const SRC* src; + const MASK* mask; + int64_t mask_dims[num_dims]; + BroadcastMaskSoftmaxParams params; +}; + +template +struct ElementwiseScaleMaskLoad { + ElementwiseScaleMaskLoad(const SRC* src, const MASK* mask, ElementwiseMaskSoftmaxParams param) + : src(src), mask(mask), param(param) {} + template + __device__ void load(DST* dst, int64_t row, int64_t col) { + cuda::softmax::Pack pack; + const int64_t offset = (row * param.row_size + col) / N; + pack.storage = *(reinterpret_cast*>(src) + offset); + cuda::softmax::Pack mask_pack; + mask_pack.storage = *(reinterpret_cast*>(mask) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { + if (mask_pack.elem[i] == 0) { + dst[i] = static_cast(param.fill); + } else { + dst[i] = static_cast(pack.elem[i]) * static_cast(param.scale); + } + } + } + const SRC* src; + const MASK* mask; + ElementwiseMaskSoftmaxParams param; +}; + +template +struct BroadcastScaleMaskStore { + BroadcastScaleMaskStore(DST* dst, const MASK* mask, + BroadcastMaskSoftmaxParams params) + : dst(dst), mask(mask), params(params) { + for (int i = 0; i < num_dims; ++i) { mask_dims[i] = params.mask_dims[i]; } + } + template + __device__ void store(const SRC* src, int64_t row, int64_t col) { + cuda::softmax::Pack pack; + cuda::softmax::Pack mask_pack; + const IndexType offset = row * params.row_size + col; + IndexType input_index[num_dims]; + IndexType mask_index[num_dims]; + params.src_index_helper.OffsetToNdIndex(offset, input_index); + for (int dim = 0; dim < num_dims; ++dim) { + if (mask_dims[dim] == 1) { + mask_index[dim] = 0; + } else { + mask_index[dim] = input_index[dim]; + } + } + const IndexType mask_offset = params.mask_index_helper.NdIndexToOffset(mask_index); + mask_pack.storage = + *(reinterpret_cast*>(mask) + mask_offset / N); +#pragma unroll + for (int i = 0; i < N; ++i) { + if (mask_pack.elem[i] == 0) { + pack.elem[i] = static_cast(params.fill); + } else { + pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); + } + } + *(reinterpret_cast*>(dst) + offset / N) = pack.storage; + } + DST* dst; + const MASK* mask; + int64_t mask_dims[num_dims]; + BroadcastMaskSoftmaxParams params; +}; + +template +struct ElementwiseScaleMaskStore { + ElementwiseScaleMaskStore(DST* dst, const MASK* mask, ElementwiseMaskSoftmaxParams params) + : dst(dst), mask(mask), params(params) {} + template + __device__ void store(const SRC* src, int64_t row, int64_t col) { + cuda::softmax::Pack pack; + const int64_t offset = (row * params.row_size + col) / N; + cuda::softmax::Pack mask_pack; + mask_pack.storage = *(reinterpret_cast*>(mask) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { + if (mask_pack.elem[i] == 0) { + pack.elem[i] = params.fill; + } else { + pack.elem[i] = static_cast(src[i]) * static_cast(params.scale); + } + } + *(reinterpret_cast*>(dst) + offset) = pack.storage; + } + DST* dst; + const MASK* mask; + ElementwiseMaskSoftmaxParams params; +}; + +template +struct MaskScaleLoad { + MaskScaleLoad(const SRC* src, const bool* mask, int64_t row_size, SRC scale) + : src(src), mask(mask), row_size(row_size), scale(scale) {} + template + __device__ void load(DST* dst, int64_t row, int64_t col) const { + cuda::softmax::Pack pack; + const int64_t offset = (row * row_size + col) / N; + pack.storage = *(reinterpret_cast*>(src) + offset); + cuda::softmax::Pack mask_pack; + mask_pack.storage = *(reinterpret_cast*>(mask) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { + dst[i] = static_cast(pack.elem[i]) * static_cast(mask_pack.elem[i]) + * static_cast(scale); + } + } + const SRC* src; + const bool* mask; + int64_t row_size; + SRC scale; +}; + +template +struct DropoutStore { + DropoutStore(DST* dst, DST* softmax_y, const bool* mask, int64_t row_size, DST scale) + : dst(dst), softmax_y(softmax_y), mask(mask), row_size(row_size), scale(scale) {} + template + __device__ void store(const SRC* src, int64_t row, int64_t col) { + cuda::softmax::Pack softmax_y_pack; + cuda::softmax::Pack dst_pack; + const int64_t offset = (row * row_size + col) / N; + cuda::softmax::Pack mask_pack; + mask_pack.storage = *(reinterpret_cast*>(mask) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { + softmax_y_pack.elem[i] = static_cast(src[i]); + dst_pack.elem[i] = + static_cast(src[i]) * static_cast(mask_pack.elem[i]) * static_cast(scale); + } + *(reinterpret_cast*>(softmax_y) + offset) = + softmax_y_pack.storage; + *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; + } + DST* dst; + DST* softmax_y; + const bool* mask; + int64_t row_size; + DST scale; +}; + +} // namespace fused_softmax +} // namespace cuda +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_FUSED_SOFTMAX_H_ diff --git a/oneflow/user/kernels/fused_tril_scale_softmax_mask_scale_kernel.hip.cpp b/oneflow/user/kernels/fused_tril_scale_softmax_mask_scale_kernel.hip.cpp deleted file mode 100644 index 0f8f340afb1e6470042eb27f03fd2f270c7bc3e7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/fused_tril_scale_softmax_mask_scale_kernel.hip.cpp +++ /dev/null @@ -1,229 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -template -struct TrilScaleLoad { - TrilScaleLoad(const SRC* src, int64_t tril_num_rows, int64_t row_size, int64_t diagonal, SRC fill, - SRC scale) - : src(src), - tril_num_rows(tril_num_rows), - row_size(row_size), - diagonal(diagonal), - fill(fill), - scale(scale) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) { - int64_t tril_row = row % tril_num_rows; - int64_t diagonal_col_id = tril_row + diagonal; - bool need_load = (col <= diagonal_col_id); - cuda::softmax::Pack pack; - if (need_load) { - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - } -#pragma unroll - for (int i = 0; i < N; ++i) { - if (col + i > diagonal_col_id) { - dst[i] = static_cast(fill); - } else { - dst[i] = static_cast(pack.elem[i]) * static_cast(scale); - } - } - } - const SRC* src; - int64_t tril_num_rows; - int64_t row_size; - int64_t diagonal; - SRC fill; - SRC scale; -}; - -template -struct MaskAndScaleStore { - MaskAndScaleStore(DST* dst, DST* softmax_y, const bool* mask, int64_t row_size, DST scale) - : dst(dst), softmax_y(softmax_y), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack softmax_y_pack; - cuda::softmax::Pack dst_pack; - const int64_t offset = (row * row_size + col) / N; - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - softmax_y_pack.elem[i] = static_cast(src[i]); - dst_pack.elem[i] = - static_cast(src[i]) * static_cast(mask_pack.elem[i]) * static_cast(scale); - } - *(reinterpret_cast*>(softmax_y) + offset) = - softmax_y_pack.storage; - *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; - } - DST* dst; - DST* softmax_y; - const bool* mask; - int64_t row_size; - DST scale; -}; - -template -struct MaskAndScaleLoad { - MaskAndScaleLoad(const SRC* src, const bool* mask, int64_t row_size, SRC scale) - : src(src), mask(mask), row_size(row_size), scale(scale) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - cuda::softmax::Pack pack; - const int64_t offset = (row * row_size + col) / N; - pack.storage = *(reinterpret_cast*>(src) + offset); - cuda::softmax::Pack mask_pack; - mask_pack.storage = *(reinterpret_cast*>(mask) + offset); -#pragma unroll - for (int i = 0; i < N; ++i) { - dst[i] = static_cast(pack.elem[i]) * static_cast(mask_pack.elem[i]) - * static_cast(scale); - } - } - const SRC* src; - const bool* mask; - int64_t row_size; - SRC scale; -}; - -template -struct TrilScaleStore { - TrilScaleStore(DST* dst, int64_t tril_num_rows, int64_t row_size, int64_t diagonal, DST fill, - DST scale) - : dst(dst), - tril_num_rows(tril_num_rows), - row_size(row_size), - diagonal(diagonal), - fill(fill), - scale(scale) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::softmax::Pack pack; - const int64_t offset = (row * row_size + col) / N; - int64_t tril_row = row % tril_num_rows; -#pragma unroll - for (int i = 0; i < N; ++i) { - if (col + i > tril_row + diagonal) { - pack.elem[i] = fill; - } else { - pack.elem[i] = static_cast(src[i]) * static_cast(scale); - } - } - *(reinterpret_cast*>(dst) + offset) = pack.storage; - } - DST* dst; - int64_t tril_num_rows; - int64_t row_size; - int64_t diagonal; - DST fill; - DST scale; -}; - -template -class FusedTrilScaleSoftmaxMaskScaleKernel final : public user_op::OpKernel { - public: - FusedTrilScaleSoftmaxMaskScaleKernel() = default; - ~FusedTrilScaleSoftmaxMaskScaleKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* softmax_y = ctx->Tensor4ArgNameAndIndex("softmax_y", 0); - const ShapeView& x_shape = x->shape_view(); - CHECK_GE(x_shape.NumAxes(), 2); - const int64_t cols = x_shape.At(x_shape.NumAxes() - 1); - const int64_t rows = x_shape.Count(0, x_shape.NumAxes() - 1); - const int64_t tril_num_rows = x_shape.At(x_shape.NumAxes() - 2); - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - TrilScaleLoad load( - x->dptr(), tril_num_rows, cols, ctx->Attr("diagonal"), - ctx->Attr("tril_fill_value"), ctx->Attr("tril_scale_value")); - MaskAndScaleStore store(y->mut_dptr(), softmax_y->mut_dptr(), - mask->dptr(), cols, - ctx->Attr("mask_scale_value")); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmax( - ctx->stream()->As()->cuda_stream(), load, store, rows, cols))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_tril_scale_softmax_mask_scale") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_CUDA_KERNEL(half) -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_CUDA_KERNEL(float) -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_CUDA_KERNEL(double) -#undef REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_CUDA_KERNEL - -template -class FusedTrilScaleSoftmaxMaskScaleGradKernel final : public user_op::OpKernel { - public: - FusedTrilScaleSoftmaxMaskScaleGradKernel() = default; - ~FusedTrilScaleSoftmaxMaskScaleGradKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* softmax_y = ctx->Tensor4ArgNameAndIndex("softmax_y", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* mask = ctx->Tensor4ArgNameAndIndex("mask", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const ShapeView& dy_shape = dy->shape_view(); - CHECK_GE(dy_shape.NumAxes(), 2); - const int64_t cols = dy_shape.At(dy_shape.NumAxes() - 1); - const int64_t rows = dy_shape.Count(0, dy_shape.NumAxes() - 1); - const int64_t tril_num_rows = dy_shape.At(dy_shape.NumAxes() - 2); - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - cuda::softmax::DirectLoad load_softmax_y(softmax_y->dptr(), cols); - MaskAndScaleLoad load_dy(dy->dptr(), mask->dptr(), cols, - ctx->Attr("mask_scale_value")); - TrilScaleStore store(dx->mut_dptr(), tril_num_rows, cols, - ctx->Attr("diagonal"), static_cast(0.0), - ctx->Attr("tril_scale_value")); - OF_CUDA_CHECK((cuda::softmax::DispatchSoftmaxGrad( - ctx->stream()->As()->cuda_stream(), load_softmax_y, load_dy, store, rows, - cols))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_tril_scale_softmax_mask_scale_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_GRAD_KERNEL(half) -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_GRAD_KERNEL(float) -REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_GRAD_KERNEL(double) -#undef REGISTER_FUSED_TRIL_SCALE_SOFTMAX_MASK_SCALE_GRAD_KERNEL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/fused_weighted_sum_kernel.cpp b/oneflow/user/kernels/fused_weighted_sum_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3d7a353767cecffe281567b68e7487b56efb552f --- /dev/null +++ b/oneflow/user/kernels/fused_weighted_sum_kernel.cpp @@ -0,0 +1,74 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cpu/cpu_stream.h" + +namespace oneflow { + +namespace { + +template +class FusedWeightedSumKernel final : public user_op::OpKernel { + public: + FusedWeightedSumKernel() = default; + ~FusedWeightedSumKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const int64_t arity = ctx->input_size("in"); + CHECK_GE(arity, 1); + const std::vector& weights = ctx->Attr>("weights"); + CHECK_EQ(weights.size(), arity); + const float alpha = ctx->Attr("alpha"); + const DataType data_type = out->data_type(); + const ShapeView& shape = out->shape_view(); + std::vector inputs(arity); + for (int i = 0; i < arity; ++i) { + const user_op::Tensor* in_i = ctx->Tensor4ArgNameAndIndex("in", i); + CHECK(in_i->shape_view() == shape); + CHECK_EQ(in_i->data_type(), data_type); + inputs[i] = in_i->dptr(); + } + T* out_ptr = out->mut_dptr(); + auto* cpu_stream = ctx->stream()->As(); + cpu_stream->ParallelFor(0, shape.elem_cnt(), [&](int64_t s, int64_t e) { + for (int64_t i = s; i < e; ++i) { + T out = static_cast(0.0); + for (int j = 0; j < arity; ++j) { out += inputs[j][i] * static_cast(weights[j]); } + out_ptr[i] = out * static_cast(alpha); + } + }); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace + +#define REGISTER_FUSED_WEIGHT_SUM_KERNEL(data_type, cpp_type) \ + REGISTER_USER_KERNEL("fused_weighted_sum") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("out", 0) == data_type)) + +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kDouble, double); +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kFloat, float); +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kFloat16, float16); + +} // namespace oneflow diff --git a/oneflow/user/kernels/fused_weighted_sum_kernel.cu b/oneflow/user/kernels/fused_weighted_sum_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..dc5d4a8a67ae2dd8e6bf486157b9bcbed0c8b55e --- /dev/null +++ b/oneflow/user/kernels/fused_weighted_sum_kernel.cu @@ -0,0 +1,130 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { + +template +struct Params { + const T* inputs[arity]; + float weights[arity]; + float alpha{}; + T* output; + int64_t n; +}; + +template +__global__ void WeightedSumKernel(Params params) { + CUDA_1D_KERNEL_LOOP_T(int64_t, i, params.n) { + T out = 0; + if (acc) { out = params.output[i]; } +#pragma unroll + for (int j = 0; j < arity; ++j) { + out += params.inputs[j][i] * static_cast(params.weights[j]); + } + params.output[i] = out * static_cast(params.alpha); + } +} + +template +void LaunchWeightedSum(ep::Stream* stream, int n, const T** inputs, const float* weights, + float alpha, T* output) { + Params params{}; + for (int i = 0; i < arity; ++i) { + params.inputs[i] = *(inputs + i); + params.weights[i] = *(weights + i); + } + params.alpha = alpha; + params.output = output; + params.n = n; + RUN_CUDA_KERNEL((WeightedSumKernel), stream, n, params); +} + +template +void DispatchWeightedSum(ep::Stream* stream, int arity, int64_t n, const T** inputs, + const float* weights, float alpha, T* output) { + if (arity == 1) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 2) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 3) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 4) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 5) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 6) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 7) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity == 8) { + LaunchWeightedSum(stream, n, inputs, weights, alpha, output); + } else if (arity > 8) { + LaunchWeightedSum(stream, n, inputs, weights, 1.0F, output); + DispatchWeightedSum(stream, arity - 8, n, inputs + 8, weights + 8, alpha, output); + } else { + UNIMPLEMENTED(); + } +} + +template +class FusedWeightedSumKernel final : public user_op::OpKernel { + public: + FusedWeightedSumKernel() = default; + ~FusedWeightedSumKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const int64_t arity = ctx->input_size("in"); + CHECK_GE(arity, 1) << "input_size should be greater than 0."; + const std::vector& weights = ctx->Attr>("weights"); + CHECK_EQ(weights.size(), arity); + const float alpha = ctx->Attr("alpha"); + const DataType data_type = out->data_type(); + const ShapeView& shape = out->shape_view(); + std::vector inputs(arity); + for (int i = 0; i < arity; ++i) { + const user_op::Tensor* in_i = ctx->Tensor4ArgNameAndIndex("in", i); + CHECK(in_i->shape_view() == shape); + CHECK_EQ(in_i->data_type(), data_type); + inputs[i] = in_i->dptr(); + } + DispatchWeightedSum(ctx->stream(), arity, shape.elem_cnt(), inputs.data(), + weights.data(), alpha, out->mut_dptr()); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +} // namespace + +#define REGISTER_FUSED_WEIGHT_SUM_KERNEL(data_type, cpp_type) \ + REGISTER_USER_KERNEL("fused_weighted_sum") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("out", 0) == data_type)) + +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kDouble, double); +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kFloat, float); +REGISTER_FUSED_WEIGHT_SUM_KERNEL(DataType::kFloat16, half); + +} // namespace oneflow diff --git a/oneflow/user/kernels/gather_kernel.cpp b/oneflow/user/kernels/gather_kernel.cpp index 1b625f704d7d337ea7905658f7b39c71c851262b..7f0d4be3ef4dcbfa1510c2888992e01b5c433483 100644 --- a/oneflow/user/kernels/gather_kernel.cpp +++ b/oneflow/user/kernels/gather_kernel.cpp @@ -120,16 +120,20 @@ class GatherKernel final : public user_op::OpKernel, public user_op::CudaGraphSu OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GATHER_KERNEL, DEVICE_TYPE_SEQ, GATHER_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +// For cpu float16/bfloat16 +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GATHER_KERNEL, OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), + FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -// For Half +#if defined(WITH_CUDA) || defined(WITH_ROCM) +// For cuda half OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GATHER_KERNEL, OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), HALF_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM -// For Half +#if CUDA_VERSION >= 11000 OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GATHER_KERNEL, OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - HALF_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16), + INDEX_DATA_TYPE_SEQ) +#endif + #endif } // namespace user_op diff --git a/oneflow/user/kernels/gather_kernel_util.cpp b/oneflow/user/kernels/gather_kernel_util.cpp index 9482e6d5547647e7e3055254426d0eee5cf4b815..eaa0901863f6b202aa7f67e3d5c075223aef19c2 100644 --- a/oneflow/user/kernels/gather_kernel_util.cpp +++ b/oneflow/user/kernels/gather_kernel_util.cpp @@ -94,7 +94,8 @@ void GatherKernelUtilImpl::Forward(ep::Stream* stream, c #define INITIATE_GATHER_KERNEL_UTIL_CPU_IMPL(in_type_pair, index_type_pair) \ template struct GatherKernelUtilImpl; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CPU_IMPL, GATHER_DATA_TYPE_SEQ, +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CPU_IMPL, + GATHER_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ, GATHER_INDEX_TYPE_SEQ); #undef INITIATE_GATHER_KERNEL_UTIL_CPU_IMPL @@ -102,6 +103,10 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CPU_IMPL, GATHER_DA template struct GatherKernelUtil; OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL, DEVICE_TYPE_SEQ, GATHER_DATA_TYPE_SEQ); +// For cpu float16/bfloat16 +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL, + OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), + FLOAT16_DATA_TYPE_SEQ BFLOAT16_DATA_TYPE_SEQ); #undef INITIATE_GATHER_KERNEL_UTIL } // namespace oneflow diff --git a/oneflow/user/kernels/gather_kernel_util.cu b/oneflow/user/kernels/gather_kernel_util.cu index 492eca7b82593e72acd475ce6877b88a6c2a4845..f4ca0a5ea2bcfba767e4aa4a755c4787923c71b4 100644 --- a/oneflow/user/kernels/gather_kernel_util.cu +++ b/oneflow/user/kernels/gather_kernel_util.cu @@ -18,7 +18,9 @@ limitations under the License. #include "oneflow/core/ep/cuda/cuda_stream.h" #include "oneflow/core/common/nd_index_offset_helper.h" #include - +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 namespace oneflow { namespace { @@ -117,6 +119,11 @@ struct GatherKernelUtilImpl final { OF_PP_PAIR_FIRST(index_type_pair)>; OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL, GATHER_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, GATHER_INDEX_TYPE_SEQ); +#if CUDA_VERSION >= 11000 +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL, + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16), + GATHER_INDEX_TYPE_SEQ); +#endif #undef INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL } // namespace oneflow diff --git a/oneflow/user/kernels/gather_kernel_util.hip.cpp b/oneflow/user/kernels/gather_kernel_util.hip.cpp deleted file mode 100644 index 675a617e5b299bbb7c833029762ebf278c811b3a..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/gather_kernel_util.hip.cpp +++ /dev/null @@ -1,123 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/gather_kernel_util.h" -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include - -namespace oneflow { - -namespace { - -template -__global__ void GatherForwardGpu(const IDX elem_cnt, NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, const K* indices, - const T* in, const IDX gather_dim_size, T* out, const IDX offset) { - IDX index[3]; - CUDA_1D_KERNEL_LOOP_T(IDX, i, elem_cnt) { - out_helper.OffsetToNdIndex(i, index); - index[1] = indices[index[1]] - offset; - T v{}; - if (index[1] >= 0 && index[1] < gather_dim_size) { v = in[in_helper.NdIndexToOffset(index)]; } - out[i] = v; - } -} - -bool IsSafeUseIndex32(int64_t outer_dim_size, int64_t gather_dim_size, int64_t inner_dim_size, - int64_t num_indices) { - const int64_t in_elem_cnt = outer_dim_size * gather_dim_size * inner_dim_size; - const int64_t out_elem_cnt = outer_dim_size * num_indices * inner_dim_size; - return std::max(out_elem_cnt, in_elem_cnt) < GetMaxVal() / 2; -} - -template -void DispatchIndexSize(ep::Stream* stream, int64_t outer_dim_size, int64_t gather_dim_size, - int64_t inner_dim_size, int64_t num_indices, int64_t offset, - const K* indices, const T* in, T* out) { - const int64_t out_elem_cnt = outer_dim_size * num_indices * inner_dim_size; - if (IsSafeUseIndex32(outer_dim_size, gather_dim_size, inner_dim_size, num_indices)) { - NdIndexOffsetHelper in_helper(outer_dim_size, gather_dim_size, inner_dim_size); - NdIndexOffsetHelper out_helper(outer_dim_size, num_indices, inner_dim_size); - GatherForwardGpu<<As()->cuda_stream()>>>( - out_elem_cnt, in_helper, out_helper, indices, in, gather_dim_size, out, offset); - } else { - NdIndexOffsetHelper in_helper(outer_dim_size, gather_dim_size, inner_dim_size); - NdIndexOffsetHelper out_helper(outer_dim_size, num_indices, inner_dim_size); - GatherForwardGpu<<As()->cuda_stream()>>>( - out_elem_cnt, in_helper, out_helper, indices, in, gather_dim_size, out, offset); - } -} - -template -bool TryDispatchMovementType(ep::Stream* stream, int64_t outer_dim_size, int64_t gather_dim_size, - int64_t inner_dim_size, int64_t num_indices, int64_t offset, - const K* indices, const void* in, void* out) { - if (reinterpret_cast(in) % sizeof(T) == 0 - && reinterpret_cast(out) % sizeof(T) == 0 && inner_dim_size % sizeof(T) == 0) { - DispatchIndexSize(stream, outer_dim_size, gather_dim_size, inner_dim_size / sizeof(T), - num_indices, offset, indices, static_cast(in), - static_cast(out)); - return true; - } else { - return false; - } -} - -template -void DispatchMovementSize(ep::Stream* stream, int64_t outer_dim_size, int64_t gather_dim_size, - int64_t inner_dim_size, int64_t num_indices, int64_t offset, - const K* indices, const void* in, void* out) { - using Func = bool (*)(ep::Stream * stream, int64_t outer_dim_size, int64_t gather_dim_size, - int64_t inner_dim_size, int64_t num_indices, int64_t offset, - const K* indices, const void* in, void* out); - Func funcs[] = { - TryDispatchMovementType, // 16B - TryDispatchMovementType, // 8B - TryDispatchMovementType, // 4B - TryDispatchMovementType, // 2B - TryDispatchMovementType, // 1B - }; - for (size_t i = 0; i < sizeof(funcs) / sizeof(funcs[0]); ++i) { - if (funcs[i](stream, outer_dim_size, gather_dim_size, inner_dim_size, num_indices, offset, - indices, in, out)) { - break; - } - } -} - -} // namespace - -template -struct GatherKernelUtilImpl final { - static void Forward(ep::Stream* stream, const K* indices, int64_t num_indices, const T* in, - const Shape& flat_in_shape, T* out, const int64_t offset) { - DispatchMovementSize(stream, flat_in_shape.At(0), flat_in_shape.At(1), - flat_in_shape.At(2) * sizeof(T), num_indices, offset, indices, in, out); - } -}; - -#define INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL(in_type_pair, index_type_pair) \ - template struct GatherKernelUtilImpl; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL, - GATHER_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, GATHER_INDEX_TYPE_SEQ); -#undef INITIATE_GATHER_KERNEL_UTIL_CUDA_IMPL - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.cu b/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.cu index 97ec84abf6d557d671c97d3d7063662ffa8587ec..8928fc5bd9ed4d2c50b79dee820135e239c57ac2 100644 --- a/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.cu +++ b/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.cu @@ -119,8 +119,8 @@ REGISTER_USER_KERNEL("generate_random_batch_permutation_indices") .SetCreateFn() .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { - const Shape* y_shape = ctx->OutputShape("y", 0); - const int32_t batch_size = y_shape->At(0); + const Shape& y_shape = ctx->OutputShape("y", 0); + const int32_t batch_size = y_shape.At(0); const int32_t random_value_aligned_bytes = GetCudaAlignedSize(batch_size * sizeof(float)); const int32_t sorted_value_aligned_bytes = GetCudaAlignedSize(batch_size * sizeof(float)); diff --git a/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.hip.cpp b/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.hip.cpp deleted file mode 100644 index 3b75fe2be78d5f9ba2219a6fd9dbcec1b7ef41ee..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/generate_random_batch_permutation_indices_kernel.hip.cpp +++ /dev/null @@ -1,138 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/random_generator.h" -#include "oneflow/user/kernels/radix_sort.hip.h" -#include "oneflow/user/kernels/op_kernel_wrapper.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(const int32_t& batch_size, const int32_t& capacity, void* ptr) - : capacity_{capacity}, - random_value_elem_cnt_{batch_size}, - sorted_value_elem_cnt_{batch_size}, - indices_elem_cnt_{batch_size} { - const int32_t random_value_aligned_bytes = - GetCudaAlignedSize(random_value_elem_cnt_ * sizeof(float)); - const int32_t sorted_value_aligned_bytes = - GetCudaAlignedSize(sorted_value_elem_cnt_ * sizeof(float)); - const int32_t indices_aligned_bytes = GetCudaAlignedSize(indices_elem_cnt_ * sizeof(int32_t)); - random_value_ptr_ = reinterpret_cast(ptr); - sorted_value_ptr_ = reinterpret_cast(reinterpret_cast(random_value_ptr_) - + random_value_aligned_bytes); - indices_ptr_ = reinterpret_cast(reinterpret_cast(sorted_value_ptr_) - + sorted_value_aligned_bytes); - temp_storage_ptr_ = - reinterpret_cast(reinterpret_cast(indices_ptr_) + indices_aligned_bytes); - temp_storage_bytes_ = - capacity_ - random_value_aligned_bytes - sorted_value_aligned_bytes - indices_aligned_bytes; - CHECK_GE(temp_storage_bytes_, 0); - } - ~TmpBufferManager() = default; - - float* RandomValuePtr() const { return random_value_ptr_; } - float* SortedValuePtr() const { return sorted_value_ptr_; } - int32_t* IndicesPtr() const { return indices_ptr_; } - void* TempStoragePtr() const { return temp_storage_ptr_; } - - int32_t TempStorageBytes() const { return temp_storage_bytes_; } - - private: - int32_t capacity_; - - float* random_value_ptr_; - float* sorted_value_ptr_; - int32_t* indices_ptr_; - void* temp_storage_ptr_; - - int32_t random_value_elem_cnt_; - int32_t sorted_value_elem_cnt_; - int32_t indices_elem_cnt_; - int32_t temp_storage_bytes_; -}; - -__global__ void InitializeIndices(int32_t elem_cnt, int32_t* indices_ptr) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { indices_ptr[i] = i; }; -} - -} // namespace - -class GenerateRandomBatchPermutationIndicesGPUKernel final : public user_op::OpKernel { - public: - GenerateRandomBatchPermutationIndicesGPUKernel() = default; - ~GenerateRandomBatchPermutationIndicesGPUKernel() = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - int64_t seed = ctx->Attr("seed"); - return std::make_shared>>( - seed, ctx->stream()); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* random_generator = - dynamic_cast>*>(state); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t batch_size = y->shape_view().At(0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - TmpBufferManager buf_manager(batch_size, - static_cast(tmp_buffer->shape_view().elem_cnt()), - tmp_buffer->mut_dptr()); - random_generator->Mutable()->Uniform(batch_size, buf_manager.RandomValuePtr()); - InitializeIndices<<stream()->As()->cuda_stream()>>>( - batch_size, buf_manager.IndicesPtr()); - const int32_t argsort_instance_num = 1; - const int32_t argsort_instance_size = batch_size; - SortPairsAscending(buf_manager.RandomValuePtr(), buf_manager.IndicesPtr(), argsort_instance_num, - argsort_instance_size, buf_manager.TempStoragePtr(), - buf_manager.TempStorageBytes(), buf_manager.SortedValuePtr(), - y->mut_dptr(), ctx->stream()->As()->cuda_stream()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("generate_random_batch_permutation_indices") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { - const Shape* y_shape = ctx->OutputShape("y", 0); - const int32_t batch_size = y_shape->At(0); - - const int32_t random_value_aligned_bytes = GetCudaAlignedSize(batch_size * sizeof(float)); - const int32_t sorted_value_aligned_bytes = GetCudaAlignedSize(batch_size * sizeof(float)); - const int32_t indices_aligned_bytes = GetCudaAlignedSize(batch_size * sizeof(int32_t)); - const int32_t argsort_instance_num = 1; - const int32_t argsort_instance_size = batch_size; - const int32_t temp_storage_bytes = InferTempStorageForSortPairsAscending( - argsort_instance_num, argsort_instance_size); - - return random_value_aligned_bytes + sorted_value_aligned_bytes + indices_aligned_bytes - + temp_storage_bytes; - }); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/grid_sample_kernel_util.cu b/oneflow/user/kernels/grid_sample_kernel_util.cu index d6df7b1a6c1d4d8208ac9b6c256cd8d3d470b35d..af92ae4e58af0d5c21cf36713378380833d17530 100644 --- a/oneflow/user/kernels/grid_sample_kernel_util.cu +++ b/oneflow/user/kernels/grid_sample_kernel_util.cu @@ -17,6 +17,8 @@ limitations under the License. #include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#if !defined(WITH_ROCM) + #include "grid_sample_kernel_util.h" namespace oneflow { @@ -220,3 +222,5 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_GRID_SAMPLE_KERNEL_UTIL, (DeviceTyp FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ); } // namespace oneflow + +#endif diff --git a/oneflow/user/kernels/group_conv_kernel.cpp b/oneflow/user/kernels/group_conv_kernel.cpp index f85f221bb87adc47c4eb6ce8668cb54d79104758..c3aa8dfab4612b7dd599f4b0f68e50ad2699291f 100644 --- a/oneflow/user/kernels/group_conv_kernel.cpp +++ b/oneflow/user/kernels/group_conv_kernel.cpp @@ -566,7 +566,7 @@ class ConvCpuKernel final : public user_op::OpKernel { && ChannelsLastMatmulPrimitiveExists()) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ size_t tmp_buffer_size = 0; \ - const auto& out_shape = ctx->OutputTensorDesc("out", 0)->shape(); \ + const auto& out_shape = ctx->OutputTensorDesc("out", 0).shape(); \ const auto& weight_shape = ctx->InputTensorDesc("weight", 0).shape(); \ \ int64_t idx_offset = IdxOffset(ctx->Attr("data_format")); \ @@ -781,7 +781,7 @@ class ConvFilterGradCpuKernel final : public user_op::OpKernel { .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ size_t tmp_buffer_size = 0; \ const auto& out_diff_shape = ctx->InputTensorDesc("dy", 0).shape(); \ - const auto& weight_diff_shape = ctx->OutputTensorDesc("filter_diff", 0)->shape(); \ + const auto& weight_diff_shape = ctx->OutputTensorDesc("filter_diff", 0).shape(); \ \ int64_t idx_offset = IdxOffset(ctx->Attr("data_format")); \ tmp_buffer_size += \ diff --git a/oneflow/user/kernels/group_norm_kernel.cu b/oneflow/user/kernels/group_norm_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..2e322dfaa00489ed7d1b8f95f7ff4dfa6bf1ed09 --- /dev/null +++ b/oneflow/user/kernels/group_norm_kernel.cu @@ -0,0 +1,769 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/device/cudnn_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ndarray/ndarray_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/cuda/primitive/unary_functor.cuh" +#include "oneflow/core/cuda/layer_norm.cuh" + +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else +#include +#endif +#include "oneflow/core/kernel/cuda_graph_support.h" + +#ifdef WITH_CUTLASS +#include +#endif // WITH_CUTLASS + +namespace oneflow { + +namespace { + +template +struct AffineStore { + AffineStore(DST* y, int64_t row_size, int64_t channel_size, int64_t spatial_size, + const DST* gamma, const DST* beta) + : y(y), + row_size(row_size), + channel_size(channel_size), + spatial_size(spatial_size), + gamma(gamma), + beta(beta), + act(0, 0) {} + + template + __device__ void store(const SRC* src, int64_t row, int64_t col) { + cuda::layer_norm::Pack y_pack; + const int64_t offset = row * row_size + col; + const int64_t packed_offset = offset / PackSize; + const int64_t gamma_beta_offset = (offset / spatial_size) % channel_size; + DST gamma_val = 1.0; + DST beta_val = 0.0; + if (affine) { + gamma_val = gamma[gamma_beta_offset]; + beta_val = beta[gamma_beta_offset]; + } + +#pragma unroll + for (int i = 0; i < PackSize; ++i) { + DST normalized_i = static_cast(src[i]); + if (affine) { + y_pack.elem[i] = act(normalized_i * gamma_val + beta_val); + } else { + // Direct Store. + y_pack.elem[i] = act(normalized_i); + } + } + *(reinterpret_cast*>(y) + packed_offset) = + y_pack.storage; + } + bool CanPackAs(size_t pack_size) { return (spatial_size % pack_size) == 0; } + DST* y; + int64_t row_size; + int64_t channel_size; + int64_t spatial_size; + const DST* gamma; + const DST* beta; + ep::primitive::UnaryFunctor act; +}; + +template +struct ScaleLoad { + using LoadType = DST; + ScaleLoad(const SRC* src, const SRC* gamma, int64_t row_size, int64_t channel_size, + int64_t spatial_size) + : src(src), + gamma(gamma), + row_size(row_size), + channel_size(channel_size), + spatial_size(spatial_size) {} + template + __device__ void load(DST* dst, int64_t row, int64_t col) const { + cuda::layer_norm::Pack src_pack; + cuda::layer_norm::Pack gamma_pack; + + const int64_t offset = row * row_size + col; + const int64_t packed_offset = offset / PackSize; + const int64_t gamma_offset = (offset / spatial_size) % channel_size; + + src_pack.storage = + *(reinterpret_cast*>(src) + packed_offset); + SRC gamma_val = static_cast(1.0); + if (affine) { gamma_val = gamma[gamma_offset]; } +#pragma unroll + for (int i = 0; i < PackSize; ++i) { dst[i] = static_cast(src_pack.elem[i] * gamma_val); } + } + bool CanPackAs(size_t pack_size) { return (spatial_size % pack_size) == 0; } + const SRC* src; + const SRC* gamma; + int64_t row_size; + int64_t channel_size; + int64_t spatial_size; +}; + +#ifdef WITH_CUTLASS + +template +struct ChannelsLastStore { + ChannelsLastStore(DST* y, const DST* gamma, const DST* beta, int64_t spatial_size, + int64_t channel_size, int64_t num_groups) + : y(y), + gamma(gamma), + beta(beta), + spatial_size(spatial_size), + c0(num_groups), + c1(channel_size / num_groups), + act(0, 0) {} + + template + __device__ void store(const SRC* src, int32_t row, int32_t col) { + cuda::layer_norm::Pack y_pack; + cuda::layer_norm::Pack gamma_pack; + cuda::layer_norm::Pack beta_pack; + int32_t spatial_idx; + int32_t c1_idx; + c1(spatial_idx, c1_idx, col); + int32_t batch_idx; + int32_t c0_idx; + c0(batch_idx, c0_idx, row); + const int32_t y_offset = + (batch_idx * c0.divisor * c1.divisor * spatial_size + spatial_idx * c0.divisor * c1.divisor + + c0_idx * c1.divisor + c1_idx) + / PackSize; + const int32_t gamma_beta_offset = (c0_idx * c1.divisor + c1_idx) / PackSize; + if (affine) { + gamma_pack.storage = + *(reinterpret_cast*>(gamma) + + gamma_beta_offset); + beta_pack.storage = *(reinterpret_cast*>(beta) + + gamma_beta_offset); + } + +#pragma unroll + for (int i = 0; i < PackSize; ++i) { + DST normalized_i = static_cast(src[i]); + if (affine) { + y_pack.elem[i] = act(normalized_i * gamma_pack.elem[i] + beta_pack.elem[i]); + } else { + // Direct Store. + y_pack.elem[i] = act(normalized_i); + } + } + *(reinterpret_cast*>(y) + y_offset) = y_pack.storage; + } + bool CanPackAs(size_t pack_size) { return (c1.divisor % pack_size) == 0; } + DST* y; + const DST* gamma; + const DST* beta; + int32_t spatial_size; + cutlass::FastDivmod c0; + cutlass::FastDivmod c1; + ep::primitive::UnaryFunctor act; +}; + +template +struct ChannelsLastLoad { + using LoadType = DST; + ChannelsLastLoad(const SRC* src, int64_t spatial_size, int64_t channel_size, int64_t num_groups) + : src(src), spatial_size(spatial_size), c0(num_groups), c1(channel_size / num_groups) {} + template + __device__ void load(DST* dst, int32_t row, int32_t col) const { + int32_t spatial_idx; + int32_t c1_idx; + c1(spatial_idx, c1_idx, col); + int32_t batch_idx; + int32_t c0_idx; + c0(batch_idx, c0_idx, row); + cuda::layer_norm::Pack pack; + const int32_t offset = (batch_idx * c0.divisor * c1.divisor * spatial_size + + spatial_idx * c0.divisor * c1.divisor + c0_idx * c1.divisor + c1_idx) + / N; + + pack.storage = *(reinterpret_cast*>(src) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { dst[i] = static_cast(pack.elem[i]); } + } + bool CanPackAs(size_t pack_size) { return (c1.divisor % pack_size) == 0; } + const SRC* src; + int32_t spatial_size; + cutlass::FastDivmod c0; + cutlass::FastDivmod c1; +}; + +#else + +template +struct ChannelsLastStore { + ChannelsLastStore(DST* y, const DST* gamma, const DST* beta, int64_t spatial_size, + int64_t channel_size, int64_t num_groups) + : y(y), + gamma(gamma), + beta(beta), + spatial_size(spatial_size), + c0(num_groups), + c1(channel_size / num_groups), + act(0, 0) {} + + template + __device__ void store(const SRC* src, int32_t row, int32_t col) { + cuda::layer_norm::Pack y_pack; + cuda::layer_norm::Pack gamma_pack; + cuda::layer_norm::Pack beta_pack; + int32_t spatial_idx = col / c1; + int32_t c1_idx = col - spatial_idx * c1; + int32_t batch_idx = row / c0; + int32_t c0_idx = row - batch_idx * c0; + const int32_t y_offset = + (batch_idx * c0 * c1 * spatial_size + spatial_idx * c0 * c1 + c0_idx * c1 + c1_idx) + / PackSize; + const int32_t gamma_beta_offset = (c0_idx * c1 + c1_idx) / PackSize; + if (affine) { + gamma_pack.storage = + *(reinterpret_cast*>(gamma) + + gamma_beta_offset); + beta_pack.storage = *(reinterpret_cast*>(beta) + + gamma_beta_offset); + } + +#pragma unroll + for (int i = 0; i < PackSize; ++i) { + DST normalized_i = static_cast(src[i]); + if (affine) { + y_pack.elem[i] = act(normalized_i * gamma_pack.elem[i] + beta_pack.elem[i]); + } else { + // Direct Store. + y_pack.elem[i] = act(normalized_i); + } + } + *(reinterpret_cast*>(y) + y_offset) = y_pack.storage; + } + bool CanPackAs(size_t pack_size) { return (c1 % pack_size) == 0; } + DST* y; + const DST* gamma; + const DST* beta; + int32_t spatial_size; + int32_t c0; + int32_t c1; + ep::primitive::UnaryFunctor act; +}; + +template +struct ChannelsLastLoad { + using LoadType = DST; + ChannelsLastLoad(const SRC* src, int64_t spatial_size, int64_t channel_size, int64_t num_groups) + : src(src), spatial_size(spatial_size), c0(num_groups), c1(channel_size / num_groups) {} + template + __device__ void load(DST* dst, int32_t row, int32_t col) const { + int32_t spatial_idx = col / c1; + int32_t c1_idx = col - spatial_idx * c1; + int32_t batch_idx = row / c0; + int32_t c0_idx = row - batch_idx * c0; + cuda::layer_norm::Pack pack; + const int32_t offset = + (batch_idx * c0 * c1 * spatial_size + spatial_idx * c0 * c1 + c0_idx * c1 + c1_idx) / N; + + pack.storage = *(reinterpret_cast*>(src) + offset); +#pragma unroll + for (int i = 0; i < N; ++i) { dst[i] = static_cast(pack.elem[i]); } + } + bool CanPackAs(size_t pack_size) { return (c1 % pack_size) == 0; } + const SRC* src; + int32_t spatial_size; + int32_t c0; + int32_t c1; +}; + +#endif // WITH_CUTLASS + +template +void GroupNormForwardGpu(ep::Stream* stream, const int64_t num_instances, const int64_t norm_size, + const int64_t channel_size, const int64_t spatial_size, + const double epsilon, const T* x_ptr, const T* gamma_ptr, + const T* beta_ptr, T* y_ptr, user_op::Tensor* mean, + user_op::Tensor* inv_variance, bool channels_first) { + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + if (channels_first) { + cuda::layer_norm::DirectLoad load(x_ptr, norm_size); + AffineStore store(y_ptr, norm_size, channel_size, + spatial_size, gamma_ptr, beta_ptr); + + cuda::layer_norm::DispatchLayerNorm( + stream->As()->cuda_stream(), load, store, num_instances, norm_size, epsilon, + mean->mut_dptr(), inv_variance->mut_dptr()); + } else { + ChannelsLastLoad load(x_ptr, spatial_size, channel_size, + channel_size / (norm_size / spatial_size)); + ChannelsLastStore store( + y_ptr, gamma_ptr, beta_ptr, spatial_size, channel_size, + channel_size / (norm_size / spatial_size)); + + cuda::layer_norm::DispatchLayerNorm( + stream->As()->cuda_stream(), load, store, num_instances, norm_size, epsilon, + mean->mut_dptr(), inv_variance->mut_dptr()); + } +} + +template +void DispatchGroupNormAffine(ep::Stream* stream, const int64_t num_instances, + const int64_t norm_size, const int64_t channel_size, + const int64_t spatial_size, const double epsilon, const T* x_ptr, + const T* gamma_ptr, const T* beta_ptr, T* y_ptr, user_op::Tensor* mean, + user_op::Tensor* inv_variance, bool channels_first) { + if (gamma_ptr != nullptr && beta_ptr != nullptr) { + GroupNormForwardGpu(stream, num_instances, norm_size, channel_size, + spatial_size, epsilon, x_ptr, gamma_ptr, beta_ptr, + y_ptr, mean, inv_variance, channels_first); + } else { + GroupNormForwardGpu(stream, num_instances, norm_size, channel_size, + spatial_size, epsilon, x_ptr, gamma_ptr, beta_ptr, + y_ptr, mean, inv_variance, channels_first); + } +} + +template +void DispatchGroupNormForwardGpu(ep::Stream* stream, const int64_t num_instances, + const int64_t norm_size, const int64_t channel_size, + const int64_t spatial_size, const double epsilon, const T* x_ptr, + const T* gamma_ptr, const T* beta_ptr, T* y_ptr, + user_op::Tensor* mean, user_op::Tensor* inv_variance, + bool channels_first, const std::string& activation) { + if (activation == "none") { + DispatchGroupNormAffine( + stream, num_instances, norm_size, channel_size, spatial_size, epsilon, x_ptr, gamma_ptr, + beta_ptr, y_ptr, mean, inv_variance, channels_first); + } else if (activation == "silu") { + DispatchGroupNormAffine( + stream, num_instances, norm_size, channel_size, spatial_size, epsilon, x_ptr, gamma_ptr, + beta_ptr, y_ptr, mean, inv_variance, channels_first); + } else { + UNIMPLEMENTED(); + } +} + +template +void GroupNormBackwardGpu(ep::Stream* stream, const int64_t num_instances, const int64_t norm_size, + const int64_t channel_size, const int64_t spatial_size, const T* dy_ptr, + const T* x_ptr, const user_op::Tensor* mean, + const user_op::Tensor* inv_variance, const T* gamma_ptr, T* dx_ptr) { + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + cuda::layer_norm::DirectLoad load_x(x_ptr, norm_size); + ScaleLoad load_scaled_dy(dy_ptr, gamma_ptr, norm_size, channel_size, spatial_size); + cuda::layer_norm::DirectStore store(dx_ptr, norm_size); + OF_CUDA_CHECK((cuda::layer_norm::DispatchLayerNormGrad( + stream->As()->cuda_stream(), load_x, load_scaled_dy, store, + mean->dptr(), inv_variance->dptr(), num_instances, norm_size))); +} + +template +void LaunchGroupNormBackward(ep::Stream* stream, const int64_t num_instances, + const int64_t norm_size, const int64_t channel_size, + const int64_t spatial_size, const T* dy_ptr, const T* x_ptr, + const user_op::Tensor* mean, const user_op::Tensor* inv_variance, + const T* gamma_ptr, T* dx_ptr) { + if (gamma_ptr != nullptr) { + GroupNormBackwardGpu(stream, num_instances, norm_size, channel_size, spatial_size, + dy_ptr, x_ptr, mean, inv_variance, gamma_ptr, dx_ptr); + } else { + GroupNormBackwardGpu(stream, num_instances, norm_size, channel_size, spatial_size, + dy_ptr, x_ptr, mean, inv_variance, gamma_ptr, dx_ptr); + } +} + +} // namespace + +template +class GroupNormGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + GroupNormGpuKernel() = default; + ~GroupNormGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + const double epsilon = ctx->Attr("epsilon"); + const int32_t num_groups = ctx->Attr("num_groups"); + const std::string& data_format = ctx->Attr("data_format"); +#ifdef WITH_CUDA + CHECK_GE(epsilon, CUDNN_BN_MIN_EPSILON); +#endif + const int64_t num_instances = mean->shape_view().elem_cnt(); // N*num_groups + const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + const int64_t batch_size = x->shape_view().At(0); + int64_t channel_size = 0; + bool channels_first = false; + if (data_format == "channels_first") { + channel_size = x->shape_view().At(1); + channels_first = true; + } else if (data_format == "channels_last") { + channel_size = x->shape_view().At(x->shape_view().NumAxes() - 1); + channels_first = false; + } else { + UNIMPLEMENTED(); + } + const int64_t spatial_size = x->shape_view().elem_cnt() / batch_size / channel_size; + const T* gamma_ptr = nullptr; + const T* beta_ptr = nullptr; + if (ctx->has_input("gamma", 0) && ctx->has_input("beta", 0)) { + const user_op::Tensor* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + gamma_ptr = gamma->dptr(); + CHECK_EQ(gamma->shape_view().elem_cnt(), channel_size); + const user_op::Tensor* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); + beta_ptr = ctx->Tensor4ArgNameAndIndex("beta", 0)->dptr(); + CHECK_EQ(beta->shape_view().elem_cnt(), channel_size); + } + DispatchGroupNormForwardGpu(ctx->stream(), num_instances, norm_size, channel_size, + spatial_size, epsilon, x->dptr(), gamma_ptr, beta_ptr, + y->mut_dptr(), mean, inv_variance, channels_first, + ctx->Attr("activation")); + } +}; + +#define REGISTER_GROUP_NORM_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("group_norm") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); + +REGISTER_GROUP_NORM_CUDA_KERNEL(half) +REGISTER_GROUP_NORM_CUDA_KERNEL(float) +REGISTER_GROUP_NORM_CUDA_KERNEL(double) +#if CUDA_VRSION >= 11000 +REGISTER_GROUP_NORM_CUDA_KERNEL(nv_bfloat16) +#endif + +template +class GroupNormGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + GroupNormGradGpuKernel() = default; + ~GroupNormGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const int64_t num_instances = mean->shape_view().elem_cnt(); + const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + const int64_t batch_size = x->shape_view().At(0); + const int64_t channel_size = x->shape_view().At(1); + const int64_t spatial_size = x->shape_view().elem_cnt() / batch_size / channel_size; + const T* gamma_ptr = nullptr; + if (ctx->has_input("gamma", 0)) { + gamma_ptr = ctx->Tensor4ArgNameAndIndex("gamma", 0)->dptr(); + } + LaunchGroupNormBackward(ctx->stream(), num_instances, norm_size, channel_size, spatial_size, + dy->dptr(), x->dptr(), mean, inv_variance, gamma_ptr, + dx->mut_dptr()); + }; +}; + +#define REGISTER_GROUP_NORM_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("group_norm_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)); + +REGISTER_GROUP_NORM_GRAD_CUDA_KERNEL(half) +REGISTER_GROUP_NORM_GRAD_CUDA_KERNEL(float) +REGISTER_GROUP_NORM_GRAD_CUDA_KERNEL(double) +#if CUDA_VRSION >= 11000 +REGISTER_GROUP_NORM_GRAD_CUDA_KERNEL(nv_bfloat16) +#endif + +constexpr int kReduceBlockSize = 512; +constexpr int kBlockSize = 128; +constexpr int kNumWaves = 32; + +inline GPU(Error_t) GetReduceNumBlocks(int64_t n, int* num_blocks) { + int dev; + { + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } + } + int sm_count; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } + } + int tpm; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&tpm, GPUMaxThreadsPerMultiProcessor, dev); + if (err != GPU(Success)) { return err; } + } + *num_blocks = + std::max(1, std::min(n, sm_count * tpm / kReduceBlockSize * kNumWaves)); + return GPU(Success); +} + +inline GPU(Error_t) GetNumBlocks(int64_t n, int* num_blocks) { + int dev; + { + GPU(Error_t) err = GPU(GetDevice)(&dev); + if (err != GPU(Success)) { return err; } + } + int sm_count; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&sm_count, GPUMultiProcessorCount, dev); + if (err != GPU(Success)) { return err; } + } + int tpm; + { + GPU(Error_t) err = GPU(DeviceGetAttribute)(&tpm, GPUMaxThreadsPerMultiProcessor, dev); + if (err != GPU(Success)) { return err; } + } + *num_blocks = std::max(1, std::min((n + kBlockSize - 1) / kBlockSize, + sm_count * tpm / kBlockSize * kNumWaves)); + return GPU(Success); +} + +template +struct SumOp { + __device__ __forceinline__ T operator()(const T& a, const T& b) const { return a + b; } +}; + +template +struct GetPackType { + using type = typename std::aligned_storage::type; +}; + +template +using PackType = typename GetPackType::type; + +template +union Pack { + static_assert(sizeof(PackType) == sizeof(T) * PackSize, ""); + __device__ Pack(T val) { + for (int i = 0; i < PackSize; i++) { elem[i] = val; } + } + + T elem[PackSize]; + PackType storage; +}; + +constexpr int kMaxPackBytes = 128 / 8; +constexpr int kMaxPackSize = 8; + +constexpr int Min(int a, int b) { return a < b ? a : b; } + +template +constexpr int GetPackSize() { + return Min(kMaxPackBytes / sizeof(T), kMaxPackSize); +} + +template +__global__ void GroupNormParamGradKernel(const T* dy, const T* x, const ComputeType* mean, + const ComputeType* inv_var, + ComputeType* dgamma_partial_sum, + ComputeType* dbeta_partial_sum, const int32_t batch_size, + const int32_t group_size, const int32_t channel_size, + const int32_t spatial_size) { + using LoadType = PackType; + const int32_t batch_channel_size = batch_size * channel_size; + for (int32_t batch_channel_id = blockIdx.x; batch_channel_id < batch_channel_size; + batch_channel_id += gridDim.x) { + const int32_t batch_id = batch_channel_id / channel_size; + const int32_t channel_id = batch_channel_id % channel_size; + const int32_t group_num = channel_size / group_size; + const int32_t batch_group_id = batch_id * group_size + channel_id / group_num; + + ComputeType mean_val = mean[batch_group_id]; + ComputeType inv_var_val = inv_var[batch_group_id]; + + Pack ds_sum_pack(0); + Pack db_sum_pack(0); + + for (int32_t spatial = threadIdx.x * PackSize; spatial < spatial_size; + spatial += blockDim.x * PackSize) { + Pack dy_pack(0); + Pack x_pack(0); + const int32_t load_idx = batch_channel_id * spatial_size + spatial; + const LoadType* dy_load = reinterpret_cast(dy + load_idx); + dy_pack.storage = *dy_load; + const LoadType* x_load = reinterpret_cast(x + load_idx); + x_pack.storage = *x_load; +#pragma unroll + for (int i = 0; i < PackSize; i++) { + ds_sum_pack.elem[i] += static_cast(dy_pack.elem[i]) + * (static_cast(x_pack.elem[i]) - mean_val) + * inv_var_val; + db_sum_pack.elem[i] += static_cast(dy_pack.elem[i]); + } + } + + ComputeType ds_sum = 0.0; + ComputeType db_sum = 0.0; + +#pragma unroll + for (int i = 0; i < PackSize; i++) { + ds_sum += ds_sum_pack.elem[i]; + db_sum += db_sum_pack.elem[i]; + } + + __syncthreads(); +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else + typedef cub::BlockReduce BlockReduce; +#endif + __shared__ typename BlockReduce::TempStorage temp_storage1; + __shared__ typename BlockReduce::TempStorage temp_storage2; + ComputeType ds_sum_result = BlockReduce(temp_storage1).Reduce(ds_sum, SumOp()); + ComputeType db_sum_result = BlockReduce(temp_storage2).Reduce(db_sum, SumOp()); + if (threadIdx.x == 0) { + dgamma_partial_sum[batch_channel_id] = ds_sum_result; + dbeta_partial_sum[batch_channel_id] = db_sum_result; + } + } +} + +template +__global__ void BatchReduceGammaBetaGradKernel(ComputeType* ds_sum, ComputeType* db_sum, T* dgamma, + T* dbeta, const int32_t batch_size, + const int32_t group_size, const int32_t channel_size, + const int32_t spatial_size) { + const int32_t group_num = channel_size / group_size; + CUDA_1D_KERNEL_LOOP(channel_idx, channel_size) { + ComputeType dgamma_sum = 0.0; + ComputeType dbeta_sum = 0.0; + for (int batch_id = 0; batch_id < batch_size; batch_id++) { + const int32_t batch_group_id = batch_id * group_size + channel_idx / group_num; + const int32_t batch_channel_id = batch_id * channel_size + channel_idx; + dgamma_sum += ds_sum[batch_channel_id]; + dbeta_sum += db_sum[batch_channel_id]; + } + dgamma[channel_idx] = dgamma_sum; + dbeta[channel_idx] = dbeta_sum; + } +} + +template +int32_t GetLaunchPackSize(const int32_t spatial_size) { + for (int pack_size = GetPackSize(); pack_size > 0; pack_size /= 2) { + if (spatial_size % pack_size == 0) { return pack_size; } + } + return 1; +} + +template +void DispatchGroupNormParamGradKernel(ep::Stream* stream, const T* dy, const T* x, + const ComputeType* mean, const ComputeType* inv_var, + ComputeType* reduce_ds_buf, ComputeType* reduce_db_buf, + const int32_t batch_size, const int32_t group_size, + const int32_t channel_size, const int32_t spatial_size) { + const int launch_pack_size = GetLaunchPackSize(spatial_size); + int num_blocks; + OF_CUDA_CHECK(GetReduceNumBlocks(batch_size * channel_size, &num_blocks)); + if (launch_pack_size == 8) { + GroupNormParamGradKernel + <<As()->cuda_stream()>>>( + dy, x, mean, inv_var, reduce_ds_buf, reduce_db_buf, batch_size, group_size, + channel_size, spatial_size); + } else if (launch_pack_size == 4) { + GroupNormParamGradKernel + <<As()->cuda_stream()>>>( + dy, x, mean, inv_var, reduce_ds_buf, reduce_db_buf, batch_size, group_size, + channel_size, spatial_size); + } else if (launch_pack_size == 2) { + GroupNormParamGradKernel + <<As()->cuda_stream()>>>( + dy, x, mean, inv_var, reduce_ds_buf, reduce_db_buf, batch_size, group_size, + channel_size, spatial_size); + } else { + GroupNormParamGradKernel + <<As()->cuda_stream()>>>( + dy, x, mean, inv_var, reduce_ds_buf, reduce_db_buf, batch_size, group_size, + channel_size, spatial_size); + } +} + +template +class GroupNormParamGradGpuKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + GroupNormParamGradGpuKernel() = default; + ~GroupNormParamGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + user_op::Tensor* dgamma = ctx->Tensor4ArgNameAndIndex("dgamma", 0); + user_op::Tensor* dbeta = ctx->Tensor4ArgNameAndIndex("dbeta", 0); + const int64_t num_instances = mean->shape_view().elem_cnt(); + const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + const int64_t batch_size = x->shape_view().At(0); + const int64_t channel_size = x->shape_view().At(1); + const int64_t spatial_size = x->shape_view().elem_cnt() / batch_size / channel_size; + const int64_t group_size = num_instances / batch_size; + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + ComputeType* reduce_ds_buf_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); + ComputeType* reduce_db_buf_ptr = reinterpret_cast( + tmp_buffer->mut_dptr() + batch_size * channel_size * sizeof(T)); + DispatchGroupNormParamGradKernel( + ctx->stream(), dy->dptr(), x->dptr(), mean->dptr(), + inv_variance->dptr(), reduce_ds_buf_ptr, reduce_db_buf_ptr, batch_size, + group_size, channel_size, spatial_size); + int num_blocks; + OF_CUDA_CHECK(GetNumBlocks(channel_size, &num_blocks)); + // Note(zhengzekang): In large batchsize, it is recommend to use gemm to reduce. (1, N) matmul + // (N, C) + BatchReduceGammaBetaGradKernel + <<stream()->As()->cuda_stream()>>>( + reduce_ds_buf_ptr, reduce_db_buf_ptr, dgamma->mut_dptr(), dbeta->mut_dptr(), + batch_size, group_size, channel_size, spatial_size); + }; +}; + +#define REGISTER_GROUP_NORM_PARAM_GRAD_CUDA_KERNEL(dtype, compute_dtype) \ + REGISTER_USER_KERNEL("group_norm_param_grad") \ + .SetCreateFn>() \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const auto& x = ctx->InputTensorDesc("x", 0); \ + const int64_t batch_size = x.shape().At(0); \ + const int64_t channel_size = x.shape().At(1); \ + size_t tmp_buffer_size = (2 * batch_size * channel_size) * sizeof(compute_dtype); \ + return tmp_buffer_size; \ + }) \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)); + +REGISTER_GROUP_NORM_PARAM_GRAD_CUDA_KERNEL(half, float) +REGISTER_GROUP_NORM_PARAM_GRAD_CUDA_KERNEL(float, float) +REGISTER_GROUP_NORM_PARAM_GRAD_CUDA_KERNEL(double, double) +#if CUDA_VRSION >= 11000 +REGISTER_GROUP_NORM_PARAM_GRAD_CUDA_KERNEL(nv_bfloat16, float) +#endif + +} // namespace oneflow diff --git a/oneflow/user/kernels/grouped_matmul_bias.cu b/oneflow/user/kernels/grouped_matmul_bias.cu new file mode 100644 index 0000000000000000000000000000000000000000..0f1d998a5d35c52ebfd9dddeb46f71d036571cf9 --- /dev/null +++ b/oneflow/user/kernels/grouped_matmul_bias.cu @@ -0,0 +1,425 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/cuda/elementwise.cuh" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/common/scalar.h" + +#ifdef WITH_CUDA + +namespace oneflow { + +struct Problem { + Problem(int64_t m, int64_t n, int64_t k) : m(m), n(n), k(k) {} + int64_t m; + int64_t n; + int64_t k; +}; + +inline bool operator==(const Problem& lhs, const Problem& rhs) { + return lhs.m == rhs.m && lhs.n == rhs.n && lhs.k == rhs.k; +} + +} // namespace oneflow + +namespace std { + +template<> +struct hash { + std::size_t operator()(const oneflow::Problem& p) const { + return oneflow::Hash(p.m, p.n, p.k); + } +}; + +} // namespace std + +namespace oneflow { + +namespace { + +constexpr int64_t kMaxProblemBatch = 64; + +template +struct Buffer { + const T* x; + const T* w; + const T* b; + T* y; +}; + +template +struct Param { + Param(const Problem& problem, std::vector> buffers) + : problem(problem), n(buffers.size()) { + std::copy(buffers.cbegin(), buffers.cend(), buffer); + elem_cnt = n * problem.m * problem.n; + } + Problem problem; + Buffer buffer[kMaxProblemBatch]; + int n; + int elem_cnt; +}; + +template +__global__ void InitPtrAndApplyBias(Param p, void** ptr_arr) { + if (has_biases) { + CUDA_1D_KERNEL_LOOP(i, p.elem_cnt) { + const int32_t p_idx = i / (p.problem.m * p.problem.n); + const int32_t y_idx = i % (p.problem.m * p.problem.n); + const int32_t m_idx = y_idx / p.problem.n; + const int32_t n_idx = y_idx % p.problem.n; + p.buffer[p_idx].y[y_idx] = p.buffer[p_idx].b[n_idx]; + } + } + CUDA_1D_KERNEL_LOOP(i, p.n) { + ptr_arr[i] = const_cast(p.buffer[i].x); + ptr_arr[i + kMaxProblemBatch] = const_cast(p.buffer[i].w); + ptr_arr[i + 2 * kMaxProblemBatch] = p.buffer[i].y; + } +} + +union CublasScalarParameter { + double d; + float s; + half h; +}; + +CublasScalarParameter GetCublasScalarParameter(Scalar scalar, cudaDataType_t compute_type) { + CublasScalarParameter sp{}; + if (compute_type == CUDA_R_64F) { + sp.d = scalar.Value(); + } else if (compute_type == CUDA_R_32F) { + sp.s = scalar.Value(); + } else if (compute_type == CUDA_R_16F) { + sp.h = static_cast(scalar.Value()); + } else { + UNIMPLEMENTED(); + } + return sp; +} + +template +void ApplyGroup(const Problem& problem, std::vector> ptrs, bool has_biases, + void* workspace, ep::Stream* stream) { + Param params(problem, ptrs); + void** ptr_arr = reinterpret_cast(workspace); + if (has_biases) { + RUN_CUDA_KERNEL((InitPtrAndApplyBias), stream, params.elem_cnt, params, ptr_arr); + } else { + RUN_CUDA_KERNEL((InitPtrAndApplyBias), stream, params.n, params, ptr_arr); + } + float alpha = 1.0; + float beta = has_biases ? 1.0 : 0.0; + cudaDataType_t data_type{}; + cudaDataType_t compute_type{}; + if (std::is_same::value) { + data_type = CUDA_R_16F; + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + if (allow_half_accumulation) { + compute_type = CUDA_R_16F; + } else { + compute_type = CUDA_R_32F; + } + } else if (std::is_same::value) { + data_type = CUDA_R_32F; + compute_type = CUDA_R_32F; + } else { + UNIMPLEMENTED(); + } + auto sp_alpha = GetCublasScalarParameter(alpha, compute_type); + auto sp_beta = GetCublasScalarParameter(beta, compute_type); + OF_CUBLAS_CHECK(cublasGemmBatchedEx( + stream->As()->cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, problem.n, problem.m, + problem.k, &sp_alpha, ptr_arr + kMaxProblemBatch, data_type, problem.k, ptr_arr, data_type, + problem.k, &sp_beta, ptr_arr + 2 * kMaxProblemBatch, data_type, problem.n, params.n, + compute_type, CUBLAS_GEMM_DEFAULT)); +} + +template +class GroupedMatmulBiasKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + GroupedMatmulBiasKernel() = default; + ~GroupedMatmulBiasKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + HashMap>> groups; + const int32_t input_size = ctx->input_size("xs"); + CHECK_EQ(ctx->input_size("weights"), input_size); + const bool has_biases = ctx->has_input("biases", 0); + if (has_biases) { CHECK_EQ(ctx->input_size("biases"), input_size); } + CHECK_EQ(ctx->output_size("ys"), input_size); + for (int32_t i = 0; i < input_size; ++i) { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("xs", i); + const user_op::Tensor* w = ctx->Tensor4ArgNameAndIndex("weights", i); + const user_op::Tensor* b = has_biases ? ctx->Tensor4ArgNameAndIndex("biases", i) : nullptr; + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("ys", i); + CHECK_GE(x->shape_view().NumAxes(), 2); + const int64_t k = x->shape_view().At(x->shape_view().NumAxes() - 1); + const int64_t m = x->shape_view().elem_cnt() / k; + CHECK_EQ(w->shape_view().NumAxes(), 2); + CHECK_EQ(w->shape_view().At(1), k); + const int64_t n = w->shape_view().At(0); + if (has_biases) { + CHECK_EQ(b->shape_view().NumAxes(), 1); + CHECK_EQ(b->shape_view().At(0), n); + } + CHECK_EQ(y->shape_view().NumAxes(), x->shape_view().NumAxes()); + CHECK_EQ(y->shape_view().At(y->shape_view().NumAxes() - 1), n); + for (int32_t j = 0; j < y->shape_view().NumAxes() - 1; ++j) { + CHECK_EQ(y->shape_view().At(j), x->shape_view().At(j)); + } + groups[Problem(m, n, k)].push_back(Buffer{ + x->dptr(), w->dptr(), has_biases ? b->dptr() : nullptr, y->mut_dptr()}); + } + void* workspace = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr(); + for (const auto& group : groups) { + ApplyGroup(group.first, group.second, has_biases, workspace, ctx->stream()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(cpp_type, data_type) \ + REGISTER_USER_KERNEL("grouped_matmul_bias") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ys", 0) == data_type)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ + return kMaxProblemBatch * 3 * sizeof(void*); \ + }); \ + ; + +REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(float, DataType::kFloat) +REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(half, DataType::kFloat16) + +} // namespace + +} // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +#include "hip/hip_runtime.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/cuda/elementwise.cuh" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/common/scalar.h" + +namespace oneflow { + +struct Problem { + Problem(int64_t m, int64_t n, int64_t k) : m(m), n(n), k(k) {} + int64_t m; + int64_t n; + int64_t k; +}; + +inline bool operator==(const Problem& lhs, const Problem& rhs) { + return lhs.m == rhs.m && lhs.n == rhs.n && lhs.k == rhs.k; +} + +} // namespace oneflow + +namespace std { + +template<> +struct hash { + std::size_t operator()(const oneflow::Problem& p) const { + return oneflow::Hash(p.m, p.n, p.k); + } +}; + +} // namespace std + +namespace oneflow { + +namespace { + +constexpr int64_t kMaxProblemBatch = 64; + +template +struct Buffer { + const T* x; + const T* w; + const T* b; + T* y; +}; + +template +struct Param { + Param(const Problem& problem, std::vector> buffers) + : problem(problem), n(buffers.size()) { + std::copy(buffers.cbegin(), buffers.cend(), buffer); + elem_cnt = n * problem.m * problem.n; + } + Problem problem; + Buffer buffer[kMaxProblemBatch]; + int n; + int elem_cnt; +}; + +template +__global__ void InitPtrAndApplyBias(Param p, void** ptr_arr) { + if (has_biases) { + CUDA_1D_KERNEL_LOOP(i, p.elem_cnt) { + const int32_t p_idx = i / (p.problem.m * p.problem.n); + const int32_t y_idx = i % (p.problem.m * p.problem.n); + const int32_t m_idx = y_idx / p.problem.n; + const int32_t n_idx = y_idx % p.problem.n; + p.buffer[p_idx].y[y_idx] = p.buffer[p_idx].b[n_idx]; + } + } + CUDA_1D_KERNEL_LOOP(i, p.n) { + ptr_arr[i] = const_cast(p.buffer[i].x); + ptr_arr[i + kMaxProblemBatch] = const_cast(p.buffer[i].w); + ptr_arr[i + 2 * kMaxProblemBatch] = p.buffer[i].y; + } +} + +union CublasScalarParameter { + double d; + float s; + half h; +}; + +CublasScalarParameter GetCublasScalarParameter(Scalar scalar, hipblasDatatype_t compute_type) { + CublasScalarParameter sp{}; + if (compute_type == HIPBLAS_R_64F) { + sp.d = scalar.Value(); + } else if (compute_type == HIPBLAS_R_32F) { + sp.s = scalar.Value(); + } else if (compute_type == HIPBLAS_R_16F) { + sp.h = static_cast(scalar.Value()); + } else { + UNIMPLEMENTED(); + } + return sp; +} + +template +void ApplyGroup(const Problem& problem, std::vector> ptrs, bool has_biases, + void* workspace, ep::Stream* stream) { + Param params(problem, ptrs); + void** ptr_arr = reinterpret_cast(workspace); + if (has_biases) { + RUN_CUDA_KERNEL((InitPtrAndApplyBias), stream, params.elem_cnt, params, ptr_arr); + } else { + RUN_CUDA_KERNEL((InitPtrAndApplyBias), stream, params.n, params, ptr_arr); + } + float alpha = 1.0; + float beta = has_biases ? 1.0 : 0.0; + hipblasDatatype_t data_type{}; + hipblasDatatype_t compute_type{}; + if (std::is_same::value) { + data_type = HIPBLAS_R_16F; + const bool allow_half_accumulation = + ParseBooleanFromEnv("ONEFLOW_MATMUL_ALLOW_HALF_PRECISION_ACCUMULATION", false); + if (allow_half_accumulation) { + compute_type = HIPBLAS_R_16F; + } else { + compute_type = HIPBLAS_R_32F; + } + } else if (std::is_same::value) { + data_type = HIPBLAS_R_32F; + compute_type = HIPBLAS_R_32F; + } else { + UNIMPLEMENTED(); + } + auto sp_alpha = GetCublasScalarParameter(alpha, compute_type); + auto sp_beta = GetCublasScalarParameter(beta, compute_type); + OF_CUBLAS_CHECK(hipblasGemmBatchedEx( + stream->As()->cublas_handle(), HIPBLAS_OP_T, HIPBLAS_OP_N, problem.n, problem.m, + problem.k, &sp_alpha, (const void**)(ptr_arr + kMaxProblemBatch), data_type, problem.k, (const void**)ptr_arr, data_type, + problem.k, &sp_beta, ptr_arr + 2 * kMaxProblemBatch, data_type, problem.n, params.n, + compute_type, HIPBLAS_GEMM_DEFAULT)); +} + +template +class GroupedMatmulBiasKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + GroupedMatmulBiasKernel() = default; + ~GroupedMatmulBiasKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + HashMap>> groups; + const int32_t input_size = ctx->input_size("xs"); + CHECK_EQ(ctx->input_size("weights"), input_size); + const bool has_biases = ctx->has_input("biases", 0); + if (has_biases) { CHECK_EQ(ctx->input_size("biases"), input_size); } + CHECK_EQ(ctx->output_size("ys"), input_size); + for (int32_t i = 0; i < input_size; ++i) { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("xs", i); + const user_op::Tensor* w = ctx->Tensor4ArgNameAndIndex("weights", i); + const user_op::Tensor* b = has_biases ? ctx->Tensor4ArgNameAndIndex("biases", i) : nullptr; + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("ys", i); + CHECK_GE(x->shape_view().NumAxes(), 2); + const int64_t k = x->shape_view().At(x->shape_view().NumAxes() - 1); + const int64_t m = x->shape_view().elem_cnt() / k; + CHECK_EQ(w->shape_view().NumAxes(), 2); + CHECK_EQ(w->shape_view().At(1), k); + const int64_t n = w->shape_view().At(0); + if (has_biases) { + CHECK_EQ(b->shape_view().NumAxes(), 1); + CHECK_EQ(b->shape_view().At(0), n); + } + CHECK_EQ(y->shape_view().NumAxes(), x->shape_view().NumAxes()); + CHECK_EQ(y->shape_view().At(y->shape_view().NumAxes() - 1), n); + for (int32_t j = 0; j < y->shape_view().NumAxes() - 1; ++j) { + CHECK_EQ(y->shape_view().At(j), x->shape_view().At(j)); + } + groups[Problem(m, n, k)].push_back(Buffer{ + x->dptr(), w->dptr(), has_biases ? b->dptr() : nullptr, y->mut_dptr()}); + } + void* workspace = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr(); + for (const auto& group : groups) { + ApplyGroup(group.first, group.second, has_biases, workspace, ctx->stream()); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(cpp_type, data_type) \ + REGISTER_USER_KERNEL("grouped_matmul_bias") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ys", 0) == data_type)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ + return kMaxProblemBatch * 3 * sizeof(void*); \ + }); \ + ; + +REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(float, DataType::kFloat) +REGISTER_GROUPED_MATMUL_BIAS_KERNEL_GPU(half, DataType::kFloat16) + +} // namespace + +} // namespace oneflow + +#endif + + diff --git a/oneflow/user/kernels/heap_selection_top_k_kernel.hip.cpp b/oneflow/user/kernels/heap_selection_top_k_kernel.hip.cpp deleted file mode 100644 index 0b593863d6b1993666cd41ecc905ea0197f9c9d0..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/heap_selection_top_k_kernel.hip.cpp +++ /dev/null @@ -1,233 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -T PowOf2Floor(T val, int64_t max_power) { - CHECK_GT(val, GetZeroVal()); - T max_floor = static_cast(std::pow(2, max_power)); - val = std::min(val, max_floor); - T ret = GetOneVal(); - while (true) { - ret *= 2; - if (ret >= val) { return ret == val ? ret : ret / 2; } - } -} - -template -T PowOf2Ceil(T val, int64_t max_power) { - CHECK_GT(val, GetZeroVal()); - T max_ceil = static_cast(std::pow(2, max_power)); - val = std::min(val, max_ceil); - T ret = GetOneVal(); - while (true) { - ret *= 2; - if (ret >= val) { return ret; } - } -} - -template -__device__ void BitonicSwap(T* data, const int64_t i, const int64_t j, const bool dir, - const Compare& comp) { - if (comp(data[i], data[j]) == dir) { - T tmp = data[i]; - data[i] = data[j]; - data[j] = tmp; - } -} - -// https://en.wikipedia.org/wiki/Bitonic_sorter -template -__device__ void BitonicSort(T* data, const int64_t elem_cnt, const Compare& comp) { - // The element count of instance should be pow-of-2 - assert(elem_cnt > 0 && !(elem_cnt & (elem_cnt - 1))); - - // Generate a bitonic sequence from input - for (int64_t size = 2; size <= elem_cnt / 2; size *= 2) { - // Merge 2 bitonic sequences of length 'size' into a bitonic sequence of length '2 * size' - for (int64_t stride = size / 2; stride > 0; stride /= 2) { - for (int64_t swap_id = threadIdx.x; swap_id < elem_cnt / 2; swap_id += blockDim.x) { - // Change dir at intervals of 'size / 2' swaps - const bool dir = swap_id & (size / 2); - // Locate the pair {pos, pos + stride} which is going te be swaped if needed - const int pos = 2 * swap_id - (swap_id & (stride - 1)); - - BitonicSwap(data, pos, pos + stride, dir, comp); - - __syncthreads(); - } - } - } - - // Sort the bitonic sequence - for (int64_t stride = elem_cnt / 2; stride > 0; stride /= 2) { - for (int64_t swap_id = threadIdx.x; swap_id < elem_cnt / 2; swap_id += blockDim.x) { - // Locate the pair {pos, pos + stride} which is going te be swaped if needed - const int pos = 2 * swap_id - (swap_id & (stride - 1)); - - BitonicSwap(data, pos, pos + stride, false, comp); - - __syncthreads(); - } - } -} - -template -class Entry final { - public: - __device__ __forceinline__ Entry(int64_t index, T value) : index_(index), value_(value) {} - - __device__ __forceinline__ int64_t GetIndex() const { return index_; } - __device__ __forceinline__ T GetValue() const { return value_; } - __device__ __forceinline__ void SetIndex(int64_t index) { index_ = index; } - __device__ __forceinline__ void SetValue(T value) { value_ = value; } - - __device__ __forceinline__ bool operator<(const Entry& entry) const { - return (value_ < entry.GetValue()) || (value_ == entry.GetValue() && index_ > entry.GetIndex()); - } - __device__ __forceinline__ bool operator>(const Entry& entry) const { - return (value_ > entry.GetValue()) || (value_ == entry.GetValue() && index_ < entry.GetIndex()); - } - - private: - int64_t index_; - T value_; -}; - -template -class MinHeap final { - public: - __device__ __forceinline__ MinHeap(Entry* data, const int64_t heap_size, - const int64_t init_index, const T init_value) - : data_(data), heap_size_(heap_size) { - for (int64_t i = 0; i < heap_size; ++i) { - data_[i].SetIndex(init_index); - data_[i].SetValue(init_value); - } - } - __device__ __forceinline__ Entry& Top() { return data_[0]; } - __device__ __forceinline__ void Swap(const int64_t i, const int64_t j) { - auto tmp = data_[j]; - data_[j] = data_[i]; - data_[i] = tmp; - } - __device__ __forceinline__ void MinHeapify(int64_t index) { - while (true) { - const int64_t left = 2 * index + 1; - const int64_t right = 2 * index + 2; - int64_t min = index; - if (left < heap_size_ && data_[left] < data_[min]) { min = left; } - if (right < heap_size_ && data_[right] < data_[min]) { min = right; } - if (min == index) { return; } - Swap(min, index); - index = min; - } - } - - private: - Entry* data_; - int64_t heap_size_; -}; - -template -__global__ void HeapTopKKernel(const T* in_ptr, const int64_t instance_num, - const int64_t instance_size, const int64_t k, - const int64_t heap_size, const int64_t init_index, - const T init_value, int64_t* out_ptr) { - extern __shared__ char smem[]; - auto* shared_entries = reinterpret_cast*>(smem); - - // Divide elements to be sorted into disjoint sets (# of sets == # of heaps). - // Each thread in the thread block manipulates one heap to select top heap_size entries from - // corresponding set - const T* input = in_ptr + blockIdx.x * instance_size; - auto heap = - MinHeap(shared_entries + threadIdx.x * heap_size, heap_size, init_index, init_value); - for (int64_t i = threadIdx.x; i < instance_size; i += blockDim.x) { - auto entry = Entry(i, input[i]); - if (entry > heap.Top()) { - heap.Top() = entry; - heap.MinHeapify(0); - } - } - - __syncthreads(); - - // Merge all heaps into a unified, sorted array - BitonicSort(shared_entries, blockDim.x * heap_size, - [](const Entry& x, const Entry& y) { return x > y; }); - - // Write top_k elements in sorted array to output - for (int64_t i = threadIdx.x; i < k; i += blockDim.x) { - (out_ptr + blockIdx.x * k)[i] = shared_entries[i].GetIndex(); - } -} - -} // namespace - -template -class GpuHeapSelectionTopKKernel final : public user_op::OpKernel { - public: - GpuHeapSelectionTopKKernel() = default; - ~GpuHeapSelectionTopKKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - if (in->shape_view().elem_cnt() == 0) { return; } - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - - const int64_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int64_t instance_num = in->shape_view().elem_cnt() / instance_size; - const int64_t k = std::min(static_cast(ctx->Attr("k")), instance_size); - - // Use as many heaps as possible (# of heaps == # of threads used in thread block). - // Limitation 1: size of shared memory - // We also need heap_size * num_heap to be pow-of-2 which is necessary for bitonic sort - const int64_t heap_size = PowOf2Ceil(k, 16); - int32_t num_heap = - PowOf2Floor(kCudaMaxSharedMemoryByteSize / (heap_size * sizeof(Entry)), 16); - // Limitation 2: # of threads in thread block - num_heap = std::min(num_heap, kCudaThreadsNumPerBlock); - - HeapTopKKernel<<), - ctx->stream()->As()->cuda_stream()>>>( - in->dptr(), instance_num, instance_size, k, heap_size, GetMaxVal(), - GetMinVal(), out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(dtype) \ - REGISTER_USER_KERNEL("top_k").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) && (user_op::HobAttr("k") <= 128) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)); - -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(float) -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(double) -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(uint8_t) -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(int8_t) -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(int32_t) -REGISTER_CUDA_HEAP_SELECTION_TOP_K_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/image_preprocess_kernels.hip.cpp b/oneflow/user/kernels/image_preprocess_kernels.hip.cpp deleted file mode 100644 index 26a961e0ded15a509dc0795e57c21dc979f0501c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/image_preprocess_kernels.hip.cpp +++ /dev/null @@ -1,216 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/common/small_vector.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -struct NormalizeVal { - float val[3]; -}; - -enum TensorLayout { - kNCHW = 0, - kNHWC = 1, -}; - -class NormalizeAttr final : public user_op::OpKernelState { - public: - NormalizeAttr(user_op::KernelInitContext* ctx) { - const std::vector& mean_vec = ctx->Attr>("mean"); - if (mean_vec.size() == 1) { - for (int i = 0; i < 3; ++i) { mean_.val[i] = mean_vec.at(0); } - } else if (mean_vec.size() == 3) { - for (int i = 0; i < 3; ++i) { mean_.val[i] = mean_vec.at(i); } - } else { - UNIMPLEMENTED(); - } - - const std::vector& std_vec = ctx->Attr>("std"); - if (std_vec.size() == 1) { - for (int i = 0; i < 3; ++i) { inv_std_.val[i] = 1.0f / std_vec.at(0); } - } else if (std_vec.size() == 3) { - for (int i = 0; i < 3; ++i) { inv_std_.val[i] = 1.0f / std_vec.at(i); } - } else { - UNIMPLEMENTED(); - } - } - ~NormalizeAttr() = default; - - const NormalizeVal& mean() const { return mean_; } - const NormalizeVal& inv_std() const { return inv_std_; } - - private: - NormalizeVal mean_; - NormalizeVal inv_std_; -}; - -template -__device__ __forceinline__ void OutIdx2InIdx(int32_t* out_idx, int32_t* in_idx, - const int8_t* mirror_dptr, int32_t out_W, - int32_t H_offset, int32_t W_offset); -template<> -__device__ __forceinline__ void OutIdx2InIdx(int32_t* out_idx, int32_t* in_idx, - const int8_t* mirror_dptr, - int32_t out_W, int32_t H_offset, - int32_t W_offset) { - if (mirror_dptr && mirror_dptr[out_idx[0]]) { out_idx[3] = out_W - 1 - out_idx[3]; } - in_idx[0] = out_idx[0]; // N - in_idx[1] = out_idx[2] + H_offset; // H - in_idx[2] = out_idx[3] + W_offset; // W - in_idx[3] = out_idx[1]; // C -} - -template<> -__device__ __forceinline__ void OutIdx2InIdx(int32_t* out_idx, int32_t* in_idx, - const int8_t* mirror_dptr, - int32_t out_W, int32_t H_offset, - int32_t W_offset) { - if (mirror_dptr && mirror_dptr[out_idx[0]]) { out_idx[2] = out_W - 1 - out_idx[2]; } - in_idx[0] = out_idx[0]; // N - in_idx[1] = out_idx[1] + H_offset; // H - in_idx[2] = out_idx[2] + W_offset; // W - in_idx[3] = out_idx[3]; // C -} - -template -__global__ void CropMirrorNormalizeGpuImpl(int32_t elem_cnt, const uint8_t* in_dptr, - float* out_dptr, const int8_t* mirror_dptr, - int32_t out_W, - const NdIndexOffsetHelper in_helper, - const NdIndexOffsetHelper out_helper, - int32_t H_offset, int32_t W_offset, - const NormalizeVal mean, const NormalizeVal inv_std) { - CUDA_1D_KERNEL_LOOP(out_offset, elem_cnt) { - int32_t in_idx[4]; - int32_t out_idx[4]; - out_helper.OffsetToNdIndex(out_offset, out_idx); - OutIdx2InIdx(out_idx, in_idx, mirror_dptr, out_W, H_offset, W_offset); - float mean_val; - float inv_std_val; - const int32_t c = in_idx[3]; - // When the compiler can't resolve array indices to constants it will put private arrays into - // GPU local memory. Using local memory is slower than keeping array elements directly in - // registers. - if (c == 0) { - mean_val = mean.val[0]; - inv_std_val = inv_std.val[0]; - } else if (c == 1) { - mean_val = mean.val[1]; - inv_std_val = inv_std.val[1]; - } else if (c == 2) { - mean_val = mean.val[2]; - inv_std_val = inv_std.val[2]; - } else { - // undefined behavior - assert(false); - } - int32_t in_offset = in_helper.NdIndexToOffset(in_idx); - out_dptr[out_offset] = (static_cast(in_dptr[in_offset]) - mean_val) * inv_std_val; - } -} - -} // namespace - -class CropMirrorNormalizeGpuKernel final : public user_op::OpKernel { - public: - CropMirrorNormalizeGpuKernel() = default; - ~CropMirrorNormalizeGpuKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* normalize_attr = dynamic_cast(state); - const NormalizeVal& mean = normalize_attr->mean(); - const NormalizeVal& inv_std = normalize_attr->inv_std(); - user_op::Tensor* in_blob = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out_blob = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::string& output_layout = ctx->Attr("output_layout"); - float* out_dptr = out_blob->mut_dptr(); - const uint8_t* in_dptr = in_blob->dptr(); - const ShapeView& in_shape = in_blob->shape_view(); - const ShapeView& out_shape = out_blob->shape_view(); - CHECK_EQ(in_shape.NumAxes(), 4); - CHECK_EQ(out_shape.NumAxes(), 4); - int32_t elem_cnt = out_shape.elem_cnt(); - CHECK_LE(elem_cnt, GetMaxVal()); - float crop_pos_y = ctx->Attr("crop_pos_y"); - float crop_pos_x = ctx->Attr("crop_pos_x"); - - int32_t N = in_shape.At(0); - int32_t in_H = in_shape.At(1); - int32_t in_W = in_shape.At(2); - int32_t C = in_shape.At(3); - const NdIndexOffsetHelper in_helper(N, in_H, in_W, C); - const int8_t* mirror_dptr = nullptr; - user_op::Tensor* mirror_blob = ctx->Tensor4ArgNameAndIndex("mirror", 0); - if (mirror_blob) { mirror_dptr = mirror_blob->dptr(); } - - if (output_layout == "NCHW") { - CHECK_EQ(N, out_shape.At(0)); - CHECK_EQ(C, out_shape.At(1)); - int32_t out_H = out_shape.At(2); - int32_t out_W = out_shape.At(3); - CHECK_LE(out_H, in_H); - CHECK_LE(out_W, in_W); - int32_t H_offset = (in_H - out_H) * crop_pos_y; - int32_t W_offset = (in_W - out_W) * crop_pos_x; - const NdIndexOffsetHelper out_helper(N, C, out_H, out_W); - CropMirrorNormalizeGpuImpl - <<stream()->As()->cuda_stream()>>>( - elem_cnt, in_dptr, out_dptr, mirror_dptr, out_W, in_helper, out_helper, H_offset, - W_offset, mean, inv_std); - } else if (output_layout == "NHWC") { - CHECK_EQ(N, out_shape.At(0)); - int32_t out_H = out_shape.At(1); - int32_t out_W = out_shape.At(2); - CHECK_EQ(C, out_shape.At(3)); - CHECK_LE(out_H, in_H); - CHECK_LE(out_W, in_W); - int32_t H_offset = (in_H - out_H) * crop_pos_y; - int32_t W_offset = (in_W - out_W) * crop_pos_x; - const NdIndexOffsetHelper out_helper(N, out_H, out_W, C); - CropMirrorNormalizeGpuImpl - <<stream()->As()->cuda_stream()>>>( - elem_cnt, in_dptr, out_dptr, mirror_dptr, out_W, in_helper, out_helper, H_offset, - W_offset, mean, inv_std); - } else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("crop_mirror_normalize_from_uint8") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("in", 0) == DataType::kUInt8) - && (user_op::HobDataType("out", 0) == DataType::kFloat)); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/in_top_k_kernel_util.hip.cpp b/oneflow/user/kernels/in_top_k_kernel_util.hip.cpp deleted file mode 100644 index b971dc1054a35d8bf0026adedbb1a6aa9262919e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/in_top_k_kernel_util.hip.cpp +++ /dev/null @@ -1,68 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/in_top_k_kernel_util.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void InTopkGpu(const int instance_num, const int classes_num, const T* targets, - const float* predictions, const int k, bool* out) { - CUDA_1D_KERNEL_LOOP(idx, instance_num) { - T target = targets[idx]; - bool cannot_say = (target >= classes_num) || !isfinite(predictions[idx * classes_num + target]); - - int32_t more_probable_classes = 0; - if (!cannot_say) { - const float target_prediction = predictions[idx * classes_num + target]; - FOR_RANGE(int32_t, class_idx, 0, classes_num) { - float pred = predictions[idx * classes_num + class_idx]; - - if (!isfinite(pred)) { - cannot_say = true; - break; - } else if (pred > target_prediction) { - ++more_probable_classes; - if (more_probable_classes > k) break; - } - } - } - out[idx] = cannot_say ? false : (more_probable_classes < k); - } -} - -} // namespace - -template -struct InTopkKernelUtil { - static void InTopk(ep::Stream* stream, const int instance_num, const int classes_num, - const T* targets, const float* predictions, const int k, bool* out) { - RUN_CUDA_KERNEL((InTopkGpu), stream, instance_num, instance_num, classes_num, targets, - predictions, k, out); - } -}; - -#define INSTANTIATE_IN_TOP_K_KERNEL_UTIL_CUDA(cpp_data_type, data_type) \ - template struct InTopkKernelUtil; - -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_IN_TOP_K_KERNEL_UTIL_CUDA, INDEX_DATA_TYPE_SEQ) - -#undef INSTANTIATE_IN_TOP_K_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/index_add_kernel.cpp b/oneflow/user/kernels/index_add_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..92cc1a7c1fc5ad9cff0b3af54e05f210001f292e --- /dev/null +++ b/oneflow/user/kernels/index_add_kernel.cpp @@ -0,0 +1,85 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" + +namespace oneflow { + +namespace { +template +void index_add_cpu_kernel(const int64_t n, const T* input, const IndexT* index, const T* source, + T* output, const int64_t stride, const int64_t source_dim, + const int64_t delta, const float alpha) { + const int64_t stride_source_dim = stride * source_dim; + for (int i = 0; i < n; i++) { + int64_t pre_index = i / stride_source_dim; + int64_t dim_index = (i - pre_index * stride_source_dim) / stride; + IndexT source_dim_idx = index[dim_index]; + int64_t output_index = i + (delta * pre_index + source_dim_idx - dim_index) * stride; + output[output_index] += static_cast(alpha) * source[i]; + } +} +}; // namespace + +template +class IndexAddCpuKernel final : public user_op::OpKernel { + public: + IndexAddCpuKernel() = default; + ~IndexAddCpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* index = ctx->Tensor4ArgNameAndIndex("index", 0); + const user_op::Tensor* source = ctx->Tensor4ArgNameAndIndex("source", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + const int64_t dim = ctx->Attr("dim"); + const float alpha = ctx->Attr("alpha"); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& source_shape = source->shape_view(); + std::vector input_stride(input->stride().begin(), input->stride().end()); + const int64_t stride = input_stride[dim]; + const int64_t source_dim = source_shape.At(dim); + const int64_t delta = input_shape.At(dim) - source_dim; + DataType index_dtype = index->data_type(); + const int32_t n = source->shape_view().elem_cnt(); + Memcpy( + ctx->stream(), output->mut_dptr(), input->dptr(), + input->shape_view().elem_cnt() * GetSizeOfDataType(input->data_type())); + if (GetSizeOfDataType(index_dtype) == 4) { + index_add_cpu_kernel(n, input->dptr(), index->dptr(), source->dptr(), + output->mut_dptr(), stride, source_dim, delta, alpha); + } else { + index_add_cpu_kernel(n, input->dptr(), index->dptr(), source->dptr(), + output->mut_dptr(), stride, source_dim, delta, alpha); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_INDEX_ADD_CPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("index_add") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("output", 0) == GetDataType::value)); + +REGISTER_INDEX_ADD_CPU_KERNEL(int8_t) +REGISTER_INDEX_ADD_CPU_KERNEL(int32_t) +REGISTER_INDEX_ADD_CPU_KERNEL(float) +REGISTER_INDEX_ADD_CPU_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/index_add_kernel.cu b/oneflow/user/kernels/index_add_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..717efa9b46ebe2d75c9c4353c592b851fe5db08c --- /dev/null +++ b/oneflow/user/kernels/index_add_kernel.cu @@ -0,0 +1,109 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/common/util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/kernel/kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { + +namespace { +template +__global__ void index_add_cuda_kernel(const int64_t n, const T* input, const IndexT* index, + const T* source, T* output, const int64_t stride, + const int64_t source_dim, const int64_t delta, + const float alpha) { + // For x = flow.ones(5, 3) + // source = flow.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=flow.float) + // index = flow.tensor([0, 4, 2]) + // dim = 0 + // We have: + // stride = 3 + // source_dim = 3 + // stride * source_dim = 9 + // alpha = 1.0 + // delta = 5 - 3 = 2 + + // For i = 8 + // pre_index = i / stride_source_dim = 8 / 9 = 0 + // dim_index = i % stride_source_dim / stride = 8 % 9 / 3 = 0 + // source_dim_idx = index[dim_index] = index[0] = 0 + // output_index = i + (delta * pre_index + source_dim_idx - dim_index) * stride = 9 + (2 * 0 + 0 - + // 0) * 3 = 9 cuda::atomic::Add(output + output_index, static_cast(alpha) * source[i])=> + // output[9] += 1.0 * 9 = 10.0 + const int64_t stride_source_dim = stride * source_dim; + CUDA_1D_KERNEL_LOOP(i, n) { + int64_t pre_index = i / stride_source_dim; + int64_t dim_index = (i - pre_index * stride_source_dim) / stride; + IndexT source_dim_idx = index[dim_index]; + int64_t output_index = i + (delta * pre_index + source_dim_idx - dim_index) * stride; + cuda::atomic::Add(output + output_index, static_cast(alpha) * source[i]); + } +} +}; // namespace + +template +class IndexAddGpuKernel final : public user_op::OpKernel { + public: + IndexAddGpuKernel() = default; + ~IndexAddGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + const user_op::Tensor* index = ctx->Tensor4ArgNameAndIndex("index", 0); + const user_op::Tensor* source = ctx->Tensor4ArgNameAndIndex("source", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + const int64_t dim = ctx->Attr("dim"); + const float alpha = ctx->Attr("alpha"); + const ShapeView& input_shape = input->shape_view(); + const ShapeView& source_shape = source->shape_view(); + std::vector input_stride(input->stride().begin(), input->stride().end()); + const int64_t stride = input_stride[dim]; + const int64_t source_dim = source_shape.At(dim); + const int64_t delta = input_shape.At(dim) - source_dim; + DataType index_dtype = index->data_type(); + const int32_t n = source->shape_view().elem_cnt(); + Memcpy( + ctx->stream(), output->mut_dptr(), input->dptr(), + input->shape_view().elem_cnt() * GetSizeOfDataType(input->data_type())); + if (GetSizeOfDataType(index_dtype) == 4) { + RUN_CUDA_KERNEL((index_add_cuda_kernel), ctx->stream(), n, n, input->dptr(), + index->dptr(), source->dptr(), output->mut_dptr(), stride, + source_dim, delta, alpha); + } else { + RUN_CUDA_KERNEL((index_add_cuda_kernel), ctx->stream(), n, n, input->dptr(), + index->dptr(), source->dptr(), output->mut_dptr(), stride, + source_dim, delta, alpha); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_INDEX_ADD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("index_add") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("output", 0) == GetDataType::value)); + +REGISTER_INDEX_ADD_CUDA_KERNEL(float) +REGISTER_INDEX_ADD_CUDA_KERNEL(half) +REGISTER_INDEX_ADD_CUDA_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/indexed_slices_reduce_sum_kernel_util.cpp b/oneflow/user/kernels/indexed_slices_reduce_sum_kernel_util.cpp index 1aa9ed3c2c147627e0137d6b429f15f665ae8c20..29f775bbc28dce89c0b28a00aa30577b6db18c29 100644 --- a/oneflow/user/kernels/indexed_slices_reduce_sum_kernel_util.cpp +++ b/oneflow/user/kernels/indexed_slices_reduce_sum_kernel_util.cpp @@ -36,7 +36,7 @@ void IndexedSlicesReduceSumKernelUtil::ReduceSum( const int64_t unique_workspace_size = workspace_size_in_bytes - unique_idx_size; UniqueKernelUtil::Unique(stream, n, indices, num_unique_indices, indices_out, unique_idx_ptr, unique_workspace_ptr, - unique_workspace_size); + unique_workspace_size, /*sorted*/ false); const Shape flat_in_shape({1, n, m}); Memset(stream, values_out, 0, n * m * sizeof(T)); diff --git a/oneflow/user/kernels/inv_kernels.cpp b/oneflow/user/kernels/inv_kernels.cpp new file mode 100644 index 0000000000000000000000000000000000000000..db6c5f4781aa18bd16fcc8135d5fe289d048217d --- /dev/null +++ b/oneflow/user/kernels/inv_kernels.cpp @@ -0,0 +1,76 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/common/eigen_util.h" + +namespace oneflow { + +namespace { + +static inline size_t BatchCount(const user_op::Tensor* batched_matrices) { + size_t result = 1; + for (size_t i = 0; i < batched_matrices->shape_view().NumAxes() - 2; i++) { + result *= batched_matrices->shape_view().At(i); + } + return result; +} + +static inline size_t MatrixStride(const user_op::Tensor* batched_matrices) { + const int64_t num_axes = batched_matrices->shape_view().NumAxes(); + return batched_matrices->shape_view().At(num_axes - 2) + * batched_matrices->shape_view().At(num_axes - 1); +} + +} // namespace + +template +class CpuInvKernel final : public user_op::OpKernel { + public: + CpuInvKernel() = default; + ~CpuInvKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + auto batch_count = BatchCount(x); + auto matrix_stride = MatrixStride(x); + auto matrix_size = x->shape_view().At(x->shape_view().NumAxes() - 2); + const T* x_ptr = x->dptr(); + T* y_ptr = y->mut_dptr(); + FOR_RANGE(int64_t, i, 0, batch_count) { + ConstEigenMatrixMap x_mat(x_ptr + i * matrix_stride, matrix_size, matrix_size); + EigenMatrixMap y_mat(y_ptr + i * matrix_stride, matrix_size, matrix_size); + if (x_mat.determinant() == 0) { + LOG(FATAL) + << "(Batch element " << i + << "): the inversion could not be completed because the input matrix is singular."; + } + y_mat = x_mat.inverse(); + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CPU_INV_KERNEL(dtype) \ + REGISTER_USER_KERNEL("inv").SetCreateFn>().SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); + +REGISTER_CPU_INV_KERNEL(float) +REGISTER_CPU_INV_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/inv_kernels.cu b/oneflow/user/kernels/inv_kernels.cu new file mode 100644 index 0000000000000000000000000000000000000000..ee54bdca665702184be9cc25af4bc6b83c144e15 --- /dev/null +++ b/oneflow/user/kernels/inv_kernels.cu @@ -0,0 +1,182 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/user/kernels/arange_kernel_util.h" + +namespace oneflow { + +namespace { + +static inline size_t BatchCount(const user_op::Tensor* batched_matrices) { + size_t result = 1; + for (size_t i = 0; i < batched_matrices->shape_view().NumAxes() - 2; i++) { + result *= batched_matrices->shape_view().At(i); + } + return result; +} + +static inline size_t MatrixStride(const user_op::Tensor* batched_matrices) { + const int64_t num_axes = batched_matrices->shape_view().NumAxes(); + return batched_matrices->shape_view().At(num_axes - 2) + * batched_matrices->shape_view().At(num_axes - 1); +} + +#ifdef WITH_ROCM +void OFgetrfBatched(ep::Stream* stream, int n, float** dA_array, int ldda, int* ipiv_array, + int* info_array, int batchsize) { + OF_CUBLAS_CHECK(hipblasSgetrfBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, info_array, batchsize)); +} +void OFgetrfBatched(ep::Stream* stream, int n, double** dA_array, int ldda, int* ipiv_array, + int* info_array, int batchsize) { + OF_CUBLAS_CHECK(hipblasDgetrfBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, info_array, batchsize)); +} +void OFgetriBatched(ep::Stream* stream, int n, float** dA_array, int ldda, int* ipiv_array, + float** dC_array, int lddc, int* info_array, int batchsize) { + OF_CUBLAS_CHECK(hipblasSgetriBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, dC_array, lddc, info_array, batchsize)); +} +void OFgetriBatched(ep::Stream* stream, int n, double** dA_array, int ldda, int* ipiv_array, + double** dC_array, int lddc, int* info_array, int batchsize) { + OF_CUBLAS_CHECK(hipblasDgetriBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, dC_array, lddc, info_array, batchsize)); +} +#else +void OFgetrfBatched(ep::Stream* stream, int n, float** dA_array, int ldda, int* ipiv_array, + int* info_array, int batchsize) { + OF_CUBLAS_CHECK(cublasSgetrfBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, info_array, batchsize)); +} +void OFgetrfBatched(ep::Stream* stream, int n, double** dA_array, int ldda, int* ipiv_array, + int* info_array, int batchsize) { + OF_CUBLAS_CHECK(cublasDgetrfBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, info_array, batchsize)); +} +void OFgetriBatched(ep::Stream* stream, int n, float** dA_array, int ldda, int* ipiv_array, + float** dC_array, int lddc, int* info_array, int batchsize) { + OF_CUBLAS_CHECK(cublasSgetriBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, dC_array, lddc, info_array, batchsize)); +} +void OFgetriBatched(ep::Stream* stream, int n, double** dA_array, int ldda, int* ipiv_array, + double** dC_array, int lddc, int* info_array, int batchsize) { + OF_CUBLAS_CHECK(cublasDgetriBatched(stream->As()->cublas_handle(), n, dA_array, + ldda, ipiv_array, dC_array, lddc, info_array, batchsize)); +} +#endif + +} // namespace + +namespace user_op { + +template +class CudaInvKernel final : public user_op::OpKernel { + public: + CudaInvKernel() = default; + ~CudaInvKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + auto batch_count = BatchCount(x); + auto matrix_stride = MatrixStride(x); + auto matrix_size = x->shape_view().At(x->shape_view().NumAxes() - 2); + + const ShapeView& x_shape = x->shape_view(); + const int64_t instance_num = x_shape.Count(0, x_shape.NumAxes() - 2); + const int64_t infos_bytes = GetCudaAlignedSize(instance_num * sizeof(int)); + const int64_t ipiv_bytes = + GetCudaAlignedSize(batch_count * x_shape.At(x_shape.NumAxes() - 2) * sizeof(int)); + const int64_t pptr_bytes = GetCudaAlignedSize(batch_count * sizeof(T*)); + int* infos_getrf_ptr = tmp_buffer->mut_dptr(); + int* infos_getrs_ptr = + reinterpret_cast(reinterpret_cast(infos_getrf_ptr) + infos_bytes); + int* ipiv_ptr = reinterpret_cast(reinterpret_cast(infos_getrs_ptr) + infos_bytes); + T** x_pptr = reinterpret_cast(reinterpret_cast(ipiv_ptr) + ipiv_bytes); + T** y_pptr = reinterpret_cast(reinterpret_cast(x_pptr) + pptr_bytes); + T* x_copy_ptr = reinterpret_cast(reinterpret_cast(y_pptr) + pptr_bytes); + Memcpy(ctx->stream(), x_copy_ptr, x->dptr(), + x_shape.elem_cnt() * sizeof(T)); + ArangeFunctor()(ctx->stream(), + reinterpret_cast(x_copy_ptr), + static_cast(matrix_stride * sizeof(T)), + batch_count, reinterpret_cast(x_pptr)); + ArangeFunctor()(ctx->stream(), + reinterpret_cast(y->mut_dptr()), + static_cast(matrix_stride * sizeof(T)), + batch_count, reinterpret_cast(y_pptr)); + Memset(ctx->stream(), infos_getrf_ptr, 0, infos_bytes); + Memset(ctx->stream(), infos_getrs_ptr, 0, infos_bytes); + Memset(ctx->stream(), ipiv_ptr, 0, ipiv_bytes); + OFgetrfBatched(ctx->stream(), matrix_size, x_pptr, matrix_size, ipiv_ptr, infos_getrf_ptr, + batch_count); + OFgetriBatched(ctx->stream(), matrix_size, x_pptr, matrix_size, ipiv_ptr, y_pptr, matrix_size, + infos_getrs_ptr, batch_count); + std::vector infos_getrf_vec_host(batch_count, 0); + std::vector infos_getrs_vec_host(batch_count, 0); + OF_CUDA_CHECK(GPU(MemcpyAsync)(infos_getrf_vec_host.data(), infos_getrf_ptr, + batch_count * sizeof(int), GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + OF_CUDA_CHECK(GPU(MemcpyAsync)(infos_getrs_vec_host.data(), infos_getrs_ptr, + batch_count * sizeof(int), GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + CHECK_JUST(ctx->stream()->Sync()); + FOR_RANGE(int64_t, i, 0, batch_count) { + if (infos_getrf_vec_host[i] > 0) { + LOG(FATAL) << "(Batch element " << i << "): The diagonal element " + << infos_getrf_vec_host[i] + << " is zero, the inversion could not be completed because the input matrix is " + "singular."; + } + } + FOR_RANGE(int64_t, i, 0, batch_count) { + if (infos_getrs_vec_host[i] > 0) { + LOG(FATAL) << "(Batch element " << i << "): The diagonal element " + << infos_getrs_vec_host[i] + << " is zero, the inversion could not be completed because the input matrix is " + "singular."; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUDA_INV_KERNEL(dtype) \ + REGISTER_USER_KERNEL("inv") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& x_shape = ctx->InputShape("x", 0); \ + auto batch_size = x_shape.Count(0, x_shape.NumAxes() - 2); \ + const int64_t instance_num = x_shape.Count(0, x_shape.NumAxes() - 2); \ + const int64_t infos_bytes = GetCudaAlignedSize(instance_num * sizeof(int)); \ + const int64_t ipiv_bytes = \ + GetCudaAlignedSize(batch_size * x_shape.At(x_shape.NumAxes() - 2) * sizeof(int)); \ + const int64_t pptr_bytes = GetCudaAlignedSize(batch_size * sizeof(dtype*)); \ + const int64_t x_copy_bytes = GetCudaAlignedSize(x_shape.elem_cnt() * sizeof(dtype)); \ + return infos_bytes * 2 + ipiv_bytes + pptr_bytes * 2 + x_copy_bytes; \ + }); + +REGISTER_CUDA_INV_KERNEL(float) +REGISTER_CUDA_INV_KERNEL(double) + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/kl_div_kernel.cpp b/oneflow/user/kernels/kl_div_kernel.cpp index c852a0776cccd8082cbadb40240d2290e605429c..ef79deaa23fff2a0b637031df051e308bd760ef3 100644 --- a/oneflow/user/kernels/kl_div_kernel.cpp +++ b/oneflow/user/kernels/kl_div_kernel.cpp @@ -14,11 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#ifdef WITH_ROCM -#include "oneflow/core/kernel/kernel_util.hip.h" -#else #include "oneflow/core/kernel/kernel_util.cuh" -#endif #include "oneflow/user/kernels/loss_kernel_util.h" namespace oneflow { diff --git a/oneflow/user/kernels/kl_div_kernel.hip.cpp b/oneflow/user/kernels/kl_div_kernel.hip.cpp deleted file mode 100644 index cc78fa56a21faee992cca08f24335878a6269a14..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/kl_div_kernel.hip.cpp +++ /dev/null @@ -1,121 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/user/kernels/loss_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { -namespace { - -using namespace loss; - -template -struct KLDivFunctor { - __device__ __forceinline__ T operator()(T input_val, T target_val) const { - if (LOG_TARGET) { - return exp(target_val) * (target_val - input_val); - } else { - const T zero_val = static_cast(0); - const T out_val = target_val * (SafeLog(target_val) - input_val); - return target_val > zero_val ? out_val : zero_val; - } - } -}; - -template -struct KLDivFunctor { - __device__ __forceinline__ half operator()(half input_val, half target_val) const { - if (LOG_TARGET) { - return hexp(target_val) * (target_val - input_val); - } else { - const half zero_val = __float2half(0.f); - const half out_val = target_val * (SafeLog(target_val) - input_val); - return target_val > zero_val ? out_val : zero_val; - } - } -}; - -template -struct KLDivGradFunctor { - __device__ __forceinline__ T operator()(T target_val, T dy_val) const { - if (LOG_TARGET) { - return -exp(target_val) * dy_val; - } else { - const T zero_val = static_cast(0); - return target_val > zero_val ? -target_val * dy_val : zero_val; - } - } -}; - -template -struct KLDivGradFunctor { - __device__ __forceinline__ half operator()(half target_val, half dy_val) const { - if (LOG_TARGET) { - return __hneg(hexp(target_val) * dy_val); - } else { - const half zero_val = __float2half(0.f); - return target_val > zero_val ? __hneg(target_val * dy_val) : zero_val; - } - } -}; - -template -class KLDivKernel : public SimpleLossKernel> { - public: - void ComputeOut(user_op::KernelComputeContext* ctx, int64_t elem_cnt, const T* input, - const T* target, T* out) const { - const bool log_target = ctx->Attr("log_target"); - if (log_target) { - OF_CUDA_CHECK( - (cuda::elementwise::Binary(KLDivFunctor(), elem_cnt, out, input, target, - ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK( - (cuda::elementwise::Binary(KLDivFunctor(), elem_cnt, out, input, target, - ctx->stream()->As()->cuda_stream()))); - } - } -}; - -template -class KLDivGradKernel : public SimpleLossGradKernel> { - public: - void ComputeOut(user_op::KernelComputeContext* ctx, int64_t elem_cnt, const T* input, - const T* target, const T* dy, T* dx) const { - const bool log_target = ctx->Attr("log_target"); - if (log_target) { - OF_CUDA_CHECK((cuda::elementwise::Binary( - KLDivGradFunctor(), elem_cnt, dx, target, dy, - ctx->stream()->As()->cuda_stream()))); - } else { - OF_CUDA_CHECK((cuda::elementwise::Binary( - KLDivGradFunctor(), elem_cnt, dx, target, dy, - ctx->stream()->As()->cuda_stream()))); - } - } -}; - -} // namespace - -REGISTER_SIMPLE_LOSS_KERNEL_CUDA("kl_div_loss", KLDivKernel) -REGISTER_SIMPLE_LOSS_GRAD_KERNEL_CUDA("kl_div_loss_grad", KLDivGradKernel) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/l1_l2_regularize_gradient_kernel.cpp b/oneflow/user/kernels/l1_l2_regularize_gradient_kernel.cpp index edfff76bed61c09ec6cb938a2c47afb055043d90..7acc0d3544613a2f6897c622e10b1de6e2cd7425 100644 --- a/oneflow/user/kernels/l1_l2_regularize_gradient_kernel.cpp +++ b/oneflow/user/kernels/l1_l2_regularize_gradient_kernel.cpp @@ -53,11 +53,7 @@ class L1L2RegularizeGradientKernel final : public user_op::OpKernel { REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCPU, float) REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCPU, double) -#ifdef WITH_CUDA -REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCUDA, float) -REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCUDA, double) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCUDA, float) REGISTER_L1_L2_REGULARIZE_GRADIENT_KERNEL(DeviceType::kCUDA, double) #endif diff --git a/oneflow/user/kernels/l1_l2_regularize_gradient_kernel_util.hip.cpp b/oneflow/user/kernels/l1_l2_regularize_gradient_kernel_util.hip.cpp deleted file mode 100644 index 9672d978ce7ca0cb61128f4c9956417ff1f931f7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/l1_l2_regularize_gradient_kernel_util.hip.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/l1_l2_regularize_gradient_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void L1L2RegularizeGradientGpu(int64_t n, const T* model, const T* model_diff, T* out, - const T l1, const T l2) { - CUDA_1D_KERNEL_LOOP(i, n) { - const T model_val = model[i]; - out[i] = model_diff[i] + l1 * ((model_val >= 0) - (model_val <= 0)) + l2 * model_val; - } -} - -} // namespace - -template -struct L1L2RegularizeGradientKernelUtil { - static void RegularizeGradient(ep::Stream* stream, int64_t n, const T* model, const T* model_diff, - T* out, const T l1, const T l2) { - L1L2RegularizeGradientGpu<<As()->cuda_stream()>>>(n, model, model_diff, - out, l1, l2); - } -}; - -#define INSTANTIATE_L1_L2_REGULARIZE_GRADIENT_KERNEL_UTIL_CUDA(type_cpp, type_proto) \ - template struct L1L2RegularizeGradientKernelUtil; -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_L1_L2_REGULARIZE_GRADIENT_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ); -#undef INSTANTIATE_L1_L2_REGULARIZE_GRADIENT_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/l2_normalize_kernel.cpp b/oneflow/user/kernels/l2_normalize_kernel.cpp index 07779768d0cd54dfe7b623a9e0422dff9cbf530d..3784c65590f5a26f4268742293d6dab05490587e 100644 --- a/oneflow/user/kernels/l2_normalize_kernel.cpp +++ b/oneflow/user/kernels/l2_normalize_kernel.cpp @@ -97,6 +97,7 @@ class CpuL2NormalizeKernel final : public user_op::OpKernel { && (user_op::HobDataType("y", 0) == GetDataType::value)); REGISTER_CPU_L2_NORMALIZE_KERNEL(float) +REGISTER_CPU_L2_NORMALIZE_KERNEL(double) template class CpuL2NormalizeGradKernel final : public user_op::OpKernel { @@ -128,5 +129,6 @@ class CpuL2NormalizeGradKernel final : public user_op::OpKernel { && (user_op::HobDataType("dx", 0) == GetDataType::value)); REGISTER_CPU_L2_NORMALIZE_GRAD_KERNEL(float) +REGISTER_CPU_L2_NORMALIZE_GRAD_KERNEL(double) } // namespace oneflow diff --git a/oneflow/user/kernels/l2_normalize_kernel.cu b/oneflow/user/kernels/l2_normalize_kernel.cu index 33c0786faa8afdc089cbff673dba509d768094c1..1f382fe504a117777ea668d3174725346ae4a475 100644 --- a/oneflow/user/kernels/l2_normalize_kernel.cu +++ b/oneflow/user/kernels/l2_normalize_kernel.cu @@ -14,34 +14,45 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/cuda/layer_norm.cuh" namespace oneflow { namespace { -template +template __global__ void L2NormalizeForward(const int32_t n, const int32_t c, const int32_t d, - const T epsilon, const T* in, T* square_x_sum, T* out) { - using BlockReduce = cub::BlockReduce; + const ComputeType epsilon, const T* in, + ComputeType* square_x_sum, T* out) { +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else + using BlockReduce = cub::BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; for (int32_t i = blockIdx.x; i < n; i += gridDim.x) { - T sum = GetZeroVal(); + ComputeType sum = GetZeroVal(); const int32_t offset = (i / d) * d * c + (i % d); for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const T x = in[offset + j * d]; + const ComputeType x = static_cast(in[offset + j * d]); sum += x * x; } - const T reduce_sum = BlockReduce(temp_storage).Sum(sum); + const ComputeType reduce_sum = BlockReduce(temp_storage).Sum(sum); if (threadIdx.x == 0) { square_x_sum[i] = reduce_sum; } __syncthreads(); - const T inv_norm = rsqrtf(fmaxf(square_x_sum[i], epsilon)); + const ComputeType inv_norm = rsqrtf(fmaxf(square_x_sum[i], epsilon)); for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { const int32_t index = offset + j * d; - out[index] = inv_norm * in[index]; + out[index] = static_cast(inv_norm * static_cast(in[index])); } } } @@ -54,7 +65,11 @@ __global__ void L2NormalizeBackward(const int32_t n, const int32_t c, const int3 const T inv_norm = rsqrt(fmaxf(square_x_sum[i], epsilon)); const int32_t offset = (i / d) * d * c + (i % d); if (square_x_sum[i] >= epsilon) { +#ifdef WITH_ROCM + using BlockReduce = hipcub::BlockReduce; +#else using BlockReduce = cub::BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage_prod_sum; T y_dy_prod_sum = GetZeroVal(); @@ -100,8 +115,10 @@ class GpuL2NormalizeKernel final : public user_op::OpKernel { int32_t c = x->shape_view().At(axis); int32_t n = x->shape_view().elem_cnt() / c; int32_t d = x->shape_view().Count(axis + 1); - RUN_CUDA_KERNEL((L2NormalizeForward), ctx->stream(), n, n, c, d, static_cast(epsilon), - x->dptr(), square_x_sum->mut_dptr(), y->mut_dptr()); + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + RUN_CUDA_KERNEL((L2NormalizeForward), ctx->stream(), n, n, c, d, + static_cast(epsilon), x->dptr(), + square_x_sum->mut_dptr(), y->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; @@ -112,7 +129,9 @@ class GpuL2NormalizeKernel final : public user_op::OpKernel { .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("y", 0) == GetDataType::value)); +REGISTER_CUDA_L2_NORMALIZE_KERNEL(half) REGISTER_CUDA_L2_NORMALIZE_KERNEL(float) +REGISTER_CUDA_L2_NORMALIZE_KERNEL(double) template class GpuL2NormalizeGradKernel final : public user_op::OpKernel { @@ -145,5 +164,6 @@ class GpuL2NormalizeGradKernel final : public user_op::OpKernel { && (user_op::HobDataType("dx", 0) == GetDataType::value)); REGISTER_CUDA_L2_NORMALIZE_GRAD_KERNEL(float) +REGISTER_CUDA_L2_NORMALIZE_GRAD_KERNEL(double) } // namespace oneflow diff --git a/oneflow/user/kernels/l2_normalize_kernel.hip.cpp b/oneflow/user/kernels/l2_normalize_kernel.hip.cpp deleted file mode 100644 index 5228003275453f60da1514057bfcea0e39c234f2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/l2_normalize_kernel.hip.cpp +++ /dev/null @@ -1,150 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void L2NormalizeForward(const int32_t n, const int32_t c, const int32_t d, - const T epsilon, const T* in, T* square_x_sum, T* out) { - using BlockReduce = hipcub::BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - - for (int32_t i = blockIdx.x; i < n; i += gridDim.x) { - T sum = GetZeroVal(); - const int32_t offset = (i / d) * d * c + (i % d); - for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const T x = in[offset + j * d]; - sum += x * x; - } - const T reduce_sum = BlockReduce(temp_storage).Sum(sum); - if (threadIdx.x == 0) { square_x_sum[i] = reduce_sum; } - __syncthreads(); - - const T inv_norm = rsqrtf(fmaxf(square_x_sum[i], epsilon)); - for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const int32_t index = offset + j * d; - out[index] = inv_norm * in[index]; - } - } -} - -template -__global__ void L2NormalizeBackward(const int32_t n, const int32_t c, const int32_t d, - const float epsilon, const T* out, const T* out_diff, - const T* square_x_sum, T* in_diff) { - for (int32_t i = blockIdx.x; i < n; i += gridDim.x) { - const T inv_norm = rsqrt(fmaxf(square_x_sum[i], epsilon)); - const int32_t offset = (i / d) * d * c + (i % d); - if (square_x_sum[i] >= epsilon) { - using BlockReduce = hipcub::BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage_prod_sum; - - T y_dy_prod_sum = GetZeroVal(); - for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const int32_t index = offset + j * d; - y_dy_prod_sum += out[index] * out_diff[index]; - } - - const T reduce_y_dy_prod_sum = BlockReduce(temp_storage_prod_sum).Sum(y_dy_prod_sum); - __shared__ T y_dy_inner_prod; - if (threadIdx.x == 0) { y_dy_inner_prod = reduce_y_dy_prod_sum; } - __syncthreads(); - - for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const int32_t index = offset + j * d; - in_diff[index] = inv_norm * (out_diff[index] - y_dy_inner_prod * out[index]); - } - } else { - for (int32_t j = threadIdx.x; j < c; j += blockDim.x) { - const int32_t index = offset + j * d; - in_diff[index] = inv_norm * out_diff[index]; - } - } - } -} - -} // namespace - -template -class GpuL2NormalizeKernel final : public user_op::OpKernel { - public: - GpuL2NormalizeKernel() = default; - ~GpuL2NormalizeKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* square_x_sum = ctx->Tensor4ArgNameAndIndex("square_x_sum", 0); - const float epsilon = ctx->Attr("epsilon"); - int32_t axis = ctx->Attr("axis"); - int32_t c = x->shape_view().At(axis); - int32_t n = x->shape_view().elem_cnt() / c; - int32_t d = x->shape_view().Count(axis + 1); - RUN_CUDA_KERNEL((L2NormalizeForward), ctx->stream(), n, n, c, d, static_cast(epsilon), - x->dptr(), square_x_sum->mut_dptr(), y->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_L2_NORMALIZE_KERNEL(dtype) \ - REGISTER_USER_KERNEL("l2_normalize") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_L2_NORMALIZE_KERNEL(float) - -template -class GpuL2NormalizeGradKernel final : public user_op::OpKernel { - public: - GpuL2NormalizeGradKernel() = default; - ~GpuL2NormalizeGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* square_x_sum = ctx->Tensor4ArgNameAndIndex("square_x_sum", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const float epsilon = ctx->Attr("epsilon"); - int32_t axis = ctx->Attr("axis"); - int32_t c = dy->shape_view().At(axis); - int32_t n = dy->shape_view().elem_cnt() / c; - int32_t d = dy->shape_view().Count(axis + 1); - RUN_CUDA_KERNEL((L2NormalizeBackward), ctx->stream(), n, n, c, d, static_cast(epsilon), - y->dptr(), dy->dptr(), square_x_sum->dptr(), dx->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_L2_NORMALIZE_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("l2_normalize_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_L2_NORMALIZE_GRAD_KERNEL(float) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/layer_norm_gpu_kernel.cu b/oneflow/user/kernels/layer_norm_gpu_kernel.cu index c2736f448a68c6084570665b07dbb716d5ef650d..17c034b22f205e9533eef605b96878b71948d30d 100644 --- a/oneflow/user/kernels/layer_norm_gpu_kernel.cu +++ b/oneflow/user/kernels/layer_norm_gpu_kernel.cu @@ -18,13 +18,18 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/ndarray/ndarray_util.h" #include "oneflow/core/cuda/atomic.cuh" -#include #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/include/primitive/fill.h" #include "oneflow/core/ep/include/primitive/matmul.h" #include "oneflow/core/ep/cuda/cuda_stream.h" #include "oneflow/core/cuda/layer_norm.cuh" +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 +#ifdef WITH_CUDA +#include +#include namespace oneflow { namespace { @@ -45,14 +50,14 @@ struct AffineStore { *(reinterpret_cast*>(gamma) + gamma_offset); } else { #pragma unroll - for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = 1; } + for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = static_cast(1.f); } } if (do_center) { beta_pack.storage = *(reinterpret_cast*>(beta) + gamma_offset); } else { #pragma unroll - for (int i = 0; i < N; ++i) { beta_pack.elem[i] = 0; } + for (int i = 0; i < N; ++i) { beta_pack.elem[i] = static_cast(0.f); } } #pragma unroll for (int i = 0; i < N; ++i) { @@ -73,6 +78,7 @@ struct AffineStore { template struct ScaleLoad { + using LoadType = DST; ScaleLoad(const SRC* src, const SRC* gamma, int64_t row_size) : src(src), gamma(gamma), row_size(row_size) {} template @@ -87,7 +93,7 @@ struct ScaleLoad { *(reinterpret_cast*>(gamma) + gamma_offset); } else { #pragma unroll - for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = static_cast(1); } + for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = static_cast(1.f); } } #pragma unroll for (int i = 0; i < N; ++i) { @@ -129,7 +135,11 @@ struct AddStore { template __inline__ __device__ T WarpReduce(T val) { +#ifdef WITH_ROCM + for (int mask = 32; mask > 0; mask /= 2) { val += __shfl_down(val, mask, 64); } +#else for (int mask = 16; mask > 0; mask /= 2) { val += __shfl_down_sync(0xffffffff, val, mask); } +#endif return val; } @@ -218,7 +228,7 @@ void LayerNormForwardGpu(ep::Stream* stream, const int64_t num_instances, const const T* beta_ptr, T* y_ptr, user_op::Tensor* mean, user_op::Tensor* inv_variance) { using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - cuda::layer_norm::DirectLoad load(x_ptr, norm_size); + cuda::layer_norm::DirectLoad load(x_ptr, norm_size); AffineStore store(y_ptr, norm_size, gamma_ptr, beta_ptr); cuda::layer_norm::DispatchLayerNorm( stream->As()->cuda_stream(), load, store, num_instances, norm_size, epsilon, @@ -251,8 +261,8 @@ void LayerNormBackwardGpu(ep::Stream* stream, const int64_t num_instances, const const user_op::Tensor* inv_variance, const T* gamma_ptr, const T* add_to_output_ptr, T* dx_ptr) { using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - cuda::layer_norm::DirectLoad load_x(x_ptr, norm_size); - ScaleLoad load_scaled_dy(dy_ptr, gamma_ptr, norm_size); + cuda::layer_norm::DirectLoad load_x(x_ptr, norm_size); + ScaleLoad load_scaled_dy(dy_ptr, gamma_ptr, norm_size); AddStore store(add_to_output_ptr, dx_ptr, norm_size); OF_CUDA_CHECK((cuda::layer_norm::DispatchLayerNormGrad( @@ -306,7 +316,9 @@ class LayerNormGpuKernel final : public user_op::OpKernel, public user_op::CudaG user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); const double epsilon = ctx->Attr("epsilon"); +#ifdef WITH_CUDA CHECK_GE(epsilon, CUDNN_BN_MIN_EPSILON); +#endif const int64_t num_instances = mean->shape_view().elem_cnt(); const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; const T* gamma_ptr = nullptr; @@ -331,6 +343,9 @@ class LayerNormGpuKernel final : public user_op::OpKernel, public user_op::CudaG REGISTER_LAYER_NORM_CUDA_KERNEL(float) REGISTER_LAYER_NORM_CUDA_KERNEL(double) REGISTER_LAYER_NORM_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_CUDA_KERNEL(nv_bfloat16) +#endif template class LayerNormGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { @@ -382,6 +397,9 @@ class LayerNormGradGpuKernel final : public user_op::OpKernel, public user_op::C REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(float) REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(double) REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(nv_bfloat16) +#endif template class LayerNormParamGradGpuKernel final : public user_op::OpKernel, @@ -460,5 +478,649 @@ class LayerNormParamGradGpuKernel final : public user_op::OpKernel, REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(float) REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(double) REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(nv_bfloat16) +#endif } // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +#include +#include + +template +struct AccumulateType { }; + +#if defined(__HIPCC__) +template <> struct AccumulateType { using type = float; }; +#endif +template <> struct AccumulateType { using type = float; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType {using type = bool; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = double; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType { using type = int64_t; }; +template <> struct AccumulateType {using type = bool; }; + +template +using acc_type = typename AccumulateType::type; + +#define C10_HOST_DEVICE __host__ __device__ +#define C10_DEVICE __device__ +#define C10_HOST __host__ +#define C10_WARP_SIZE 64 + +#define VEC 4 +typedef int64_t IndexType ; + +constexpr int BlockReduceNumThreads=512; +constexpr int NumThreads = 256; +constexpr int ColwiseReduceTileSize = 32; + +template +struct WelfordData { + scalar_t mean; + scalar_t m2; + index_t n; + combine_t nf; + + C10_HOST_DEVICE WelfordData() : mean(0), m2(0), n(0), nf(0) {} + + C10_HOST_DEVICE WelfordData( + scalar_t mean, + scalar_t m2, + index_t n, + combine_t nf) + : mean(mean), m2(m2), n(n), nf(nf) {} +}; + + +template +struct WelfordOps { + public: + using acc_t = WelfordData; + inline C10_DEVICE acc_t reduce(acc_t acc, scalar_t data) const { + acc_scalar_t delta = data - acc.mean; + // using acc.nf(combine_t) here, as acc.n(index_t) would still be converted + // accumulation in reduce is done through index_T + acc_scalar_t new_mean = acc.mean + delta / (acc.nf + 1); + acc_scalar_t new_delta = data - new_mean; + return { + new_mean, + acc.m2 + delta * new_delta, + acc.n + 1, + combine_t(acc.n + 1), // accumulate for combine_t uses index_t + }; + } + inline C10_DEVICE acc_t combine(acc_t a, acc_t b) const { + if (a.nf == 0) { + return b; + } + if (b.nf == 0) { + return a; + } + acc_scalar_t delta = b.mean - a.mean; + combine_t new_count = a.nf + b.nf; + acc_scalar_t nb_over_n = b.nf / new_count; + return { + a.mean + delta * nb_over_n, + a.m2 + b.m2 + delta * delta * a.nf * nb_over_n, + // setting acc.n as -1 since acc.n might not be able to represent the count + // correctly within its range, setting it to -1 to avoid confusion + -1, + new_count + }; + } + inline C10_DEVICE res_t project(acc_t acc) const { + return res_t(acc.m2 / acc.nf, static_cast(acc.mean)); + } + + inline __device__ acc_t warp_shfl_down(acc_t acc, int offset) const { + return { + __shfl_down(acc.mean, offset) + , __shfl_down(acc.m2, offset) + , __shfl_down(acc.n, offset) + , __shfl_down(acc.nf, offset) + }; + } +}; + +template +__inline__ __device__ T WarpReduce(T val,const ReduceOp& op) { +#pragma unroll + for (int offset = max; offset > 0; offset >>= 1) { + val = op.combine(val, op.warp_shfl_down(val, offset)); + } + return val; +} + +template +__inline__ __device__ T +BlockReduce(T val, const ReduceOp& op, T* shared) { + const int lid = threadIdx.x % C10_WARP_SIZE; + const int wid = threadIdx.x / C10_WARP_SIZE; + val = WarpReduce(val, op); + __syncthreads(); + if (lid == 0) { + shared[wid] = val; + } + __syncthreads(); + if (wid == 0) { + val= shared[lid]; + val = WarpReduce<4>(val,op); + } + return val; +} + +template +__inline__ __device__ T WarpReduceSum(T val) { +#pragma unroll + for (int offset = max; offset > 0; offset >>= 1) { + val += __shfl_down(val, offset); + } + return val; +} + + +template +__inline__ __device__ T BlockReduceSum(T val, T* shared) { + const int lid = threadIdx.x % C10_WARP_SIZE; + const int wid = threadIdx.x / C10_WARP_SIZE; + val = WarpReduceSum(val); + __syncthreads(); + if (lid == 0) { + shared[wid] = val; + } + __syncthreads(); + if (wid == 0) { + val= shared[lid]; + val = WarpReduceSum<4>(val); + } + return val; +} + +template +__global__ void layernorm_forward_kernel(const scalar_t* input,scalar_t* ret,acc_type* mean,acc_type* rstd, + const scalar_t* gamma,const scalar_t* beta,IndexType cols,double eps) +{ + //dropout do nothing in val mode + IndexType i=blockIdx.x; + // add + layernorm get mean and rstd + using T_ACC = acc_type; + using WelfordType = WelfordData; + using WelfordOp = WelfordOps>; + __shared__ typename std::aligned_storage::type val_shared[BlockReduceNumThreads/C10_WARP_SIZE]; + WelfordType* val_shared_ptr = reinterpret_cast(val_shared); + WelfordOp welford_op; + WelfordType val; + + #pragma unroll + for (IndexType j = threadIdx.x; j < cols; j += blockDim.x) { + IndexType index = i * cols + j; + val = welford_op.reduce(val, static_cast(input[index])); + } + val = BlockReduce(val,welford_op,val_shared_ptr); + + __shared__ T_ACC s_mean; + __shared__ T_ACC s_rstd; + if (threadIdx.x == 0) { + thrust::tie(s_rstd, s_mean) = welford_op.project(val); + mean[i] = s_mean; + s_rstd=rsqrt(s_rstd + static_cast(eps)); + rstd[i] = s_rstd; + } + __syncthreads(); + //layernorm (x-mean)*rstd*gamma+beta + #pragma unroll + for (IndexType j = threadIdx.x; j < cols; j += blockDim.x) { + IndexType index = i * cols + j; + ret[index] = static_cast((static_cast(input[index]) - s_mean)*s_rstd * (gamma == nullptr ? T_ACC(1) : static_cast(gamma[j])) + + (beta == nullptr ? T_ACC(0) : static_cast(beta[j]))); + } +} + +template +void LayerNormKernelImplInternal( + oneflow::ep::Stream* stream, + const T* X, + const T* gamma, + const T* beta, + int64_t M, + int64_t N, + double eps, + T* Y, + acc_type* mean, + acc_type* rstd) { + using T_ACC = acc_type; + const T* X_data = X; + const T* gamma_data = gamma; + const T* beta_data = beta; + T* Y_data = Y; + T_ACC* mean_data = mean; + T_ACC* rstd_data = rstd; + hipStream_t cuda_stream = stream->As()->cuda_stream(); + layernorm_forward_kernel<<>>( + X_data,Y_data,mean_data,rstd_data,gamma_data,beta_data,N,eps); +} + +template +__global__ void GammaBetaBackwardSimple(IndexType M,IndexType N,const scalar_t* dY,const scalar_t* X,const acc_type* mean, + const acc_type* rstd,scalar_t* dg,scalar_t* db) +{ + using T_ACC = acc_type; + const int64_t j = blockIdx.x * blockDim.x + threadIdx.x; + if (j < N) { + T_ACC sum1 = 0; + T_ACC sum2 = 0; + for (int64_t i = 0; i < M; ++i) { + const int64_t index = i * N + j; + sum1 += dg == nullptr ? T_ACC(0) + : static_cast(dY[index]) * + (static_cast(X[index]) - static_cast(mean[i])) * + static_cast(rstd[i]); + sum2 += db == nullptr ? T_ACC(0) : static_cast(dY[index]); + } + if (dg != nullptr) { + dg[j] = static_cast(sum1); + } + if (db != nullptr) { + db[j] = static_cast(sum2); + } + } +} + +template +__global__ void GammaBetaBackward(IndexType M,IndexType N,const scalar_t* dY,const scalar_t* X,const acc_type* mean, + const acc_type* rstd,scalar_t* dg,scalar_t* db) +{ + using T_ACC = acc_type; + __shared__ T_ACC g_shared[ColwiseReduceTileSize][ColwiseReduceTileSize + 1]; + __shared__ T_ACC b_shared[ColwiseReduceTileSize][ColwiseReduceTileSize + 1]; + const int64_t j = blockIdx.x * blockDim.x + threadIdx.x; + T_ACC dg_sum1 = 0; + T_ACC dg_sum2 = 0; + T_ACC db_sum1 = 0; + T_ACC db_sum2 = 0; + if (j < N) { + for (int64_t i = threadIdx.y; i < M; i += blockDim.y * 2) { + const int64_t i1 = i; + const int64_t i2 = i + blockDim.y; + const int64_t index1 = i1 * N + j; + const int64_t index2 = i2 * N + j; + dg_sum1 += dg == nullptr ? T_ACC(0) + : static_cast(dY[index1]) * + (static_cast(X[index1]) - static_cast(mean[i1])) * + static_cast(rstd[i1]); + db_sum1 += db == nullptr ? T_ACC(0) : static_cast(dY[index1]); + if (i2 < M) { + dg_sum2 += dg == nullptr ? T_ACC(0) + : static_cast(dY[index2]) * + (static_cast(X[index2]) - static_cast(mean[i2])) * + static_cast(rstd[i2]); + db_sum2 += db == nullptr ? T_ACC(0) : static_cast(dY[index2]); + } + } + } + g_shared[threadIdx.y][threadIdx.x] = dg_sum1; + g_shared[threadIdx.y + blockDim.y][threadIdx.x] = dg_sum2; + b_shared[threadIdx.y][threadIdx.x] = db_sum1; + b_shared[threadIdx.y + blockDim.y][threadIdx.x] = db_sum2; + __syncthreads(); + T_ACC sum1 = g_shared[threadIdx.x][threadIdx.y]; + T_ACC sum2 = b_shared[threadIdx.x][threadIdx.y]; + sum1 = WarpReduceSum<16>(sum1); + sum2 = WarpReduceSum<16>(sum2); + if (threadIdx.x == 0) { + const int64_t j = blockIdx.x * blockDim.x + threadIdx.y; + if (j < N) { + if (dg != nullptr) { + dg[j] = static_cast(sum1); + } + if (db != nullptr) { + db[j] = static_cast(sum2); + } + } + } + sum1 = g_shared[threadIdx.x][threadIdx.y + blockDim.y]; + sum2 = b_shared[threadIdx.x][threadIdx.y + blockDim.y]; + sum1 = WarpReduceSum<16>(sum1); + sum2 = WarpReduceSum<16>(sum2); + if (threadIdx.x == 0) { + const int64_t j = blockIdx.x * blockDim.x + threadIdx.y + blockDim.y; + if (j < N) { + if (dg != nullptr) { + dg[j] = static_cast(sum1); + } + if (db != nullptr) { + db[j] = static_cast(sum2); + } + } + } +} + +template +__global__ void LayerNormBackward_kernel(IndexType N,const scalar_t* dY,const scalar_t* X,const scalar_t* gamma,const acc_type* mean, + const acc_type* rstd, scalar_t* dX, const scalar_t* add_to_output) +{ + using T_ACC = acc_type; + __shared__ T_ACC ds_shared[C10_WARP_SIZE]; + __shared__ T_ACC db_shared[C10_WARP_SIZE]; + const IndexType i = blockIdx.x; + T_ACC sum1 = 0; + T_ACC sum2 = 0; + #pragma unroll + for (IndexType j = threadIdx.x; j < N; j += blockDim.x) { + const IndexType index = i * N + j; + const T_ACC gamma_v = gamma == nullptr ? T_ACC(1) : static_cast(gamma[j]); + sum1 += static_cast(dY[index]) * static_cast(X[index]) * gamma_v; + sum2 += static_cast(dY[index]) * gamma_v; + } + sum1 = BlockReduceSum(sum1, ds_shared); + sum2 = BlockReduceSum(sum2, db_shared); + const T_ACC s = T_ACC(1) / static_cast(N); + __shared__ T_ACC b; + __shared__ T_ACC c; + if (threadIdx.x == 0) { + b = (sum2 * static_cast(mean[i]) - sum1) * static_cast(rstd[i]) * static_cast(rstd[i]) *static_cast(rstd[i]) * s; + c = -(b * static_cast(mean[i]) + sum2 * static_cast(rstd[i]) * s); + } + __syncthreads(); + #pragma unroll + for (IndexType j = threadIdx.x; j < N; j += blockDim.x) { + const IndexType index = i * N + j; + const T_ACC gamma_v = gamma == nullptr ? T_ACC(1) : static_cast(gamma[j]); + dX[index] = static_cast(static_cast(rstd[i]) * static_cast(dY[index]) * gamma_v + b * static_cast(X[index]) + c + + (add_to_output == nullptr ? T_ACC(0) : static_cast(add_to_output[index]))); + } +} + +template +void LayerNormBackwardKernelImplInternal( + oneflow::ep::Stream* stream, + const T* dY, + const T* X, + const acc_type* mean, + const acc_type* rstd, + const T* gamma, + int64_t M, + int64_t N, + T* dX, + const T* add_to_output) { + using T_ACC = acc_type; + const T* dY_data = dY; + const T* X_data = X; + const T_ACC* mean_data = mean; + const T_ACC* rstd_data = rstd; + const T* gamma_data = gamma; + T* dX_data = dX; + const T* add_to_output_data = add_to_output; + hipStream_t cuda_stream = stream->As()->cuda_stream(); + if (dX_data != nullptr) { + LayerNormBackward_kernel<<>>( + N, dY_data, X_data,gamma_data,mean_data,rstd_data,dX_data,add_to_output_data); + } +} + +template +void LayerNormBackwardKernelImplInternalParam( + oneflow::ep::Stream* stream, + const T* dY, + const T* X, + const acc_type* mean, + const acc_type* rstd, + int64_t M, + int64_t N, + T* dgamma, + T* dbeta) { + using T_ACC = acc_type; + const T* dY_data = dY; + const T* X_data = X; + const T_ACC* mean_data = mean; + const T_ACC* rstd_data = rstd; + hipStream_t cuda_stream = stream->As()->cuda_stream(); + T* dgamma_data = dgamma; + T* dbeta_data = dbeta; + if (M < 512) { + // For small batch size, do colwise reduce directly. + const int64_t B = (N + NumThreads - 1) / NumThreads; + GammaBetaBackwardSimple + <<>>( + M, + N, + dY_data, + X_data, + mean_data, + rstd_data, + dgamma_data, + dbeta_data); + } else { + const int64_t B = + (N + ColwiseReduceTileSize - 1) / ColwiseReduceTileSize; + constexpr int kThreadX = ColwiseReduceTileSize; + constexpr int kThreadY = ColwiseReduceTileSize / 2; + GammaBetaBackward + <<>>( + M, + N, + dY_data, + X_data, + mean_data, + rstd_data, + dgamma_data, + dbeta_data); + } +} + +namespace oneflow { + +template +class LayerNormGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + LayerNormGpuKernel() = default; + ~LayerNormGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + double epsilon = ctx->Attr("epsilon"); + int64_t num_instances = mean->shape_view().elem_cnt(); + int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + const T* gamma_ptr = nullptr; + const T* beta_ptr = nullptr; + if (ctx->has_input("gamma", 0)) { + const user_op::Tensor* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + gamma_ptr = gamma->dptr(); + CHECK_EQ(gamma->shape_view().elem_cnt(), norm_size); + } + if (ctx->has_input("beta", 0)) { beta_ptr = ctx->Tensor4ArgNameAndIndex("beta", 0)->dptr(); } + // DispatchLayerNormForwardGpu(ctx->stream(), num_instances, norm_size, epsilon, x->dptr(), + // gamma_ptr, beta_ptr, y->mut_dptr(), mean, inv_variance); + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + LayerNormKernelImplInternal(ctx->stream(), x->dptr(), gamma_ptr, beta_ptr, num_instances, norm_size, epsilon, + y->mut_dptr(), mean->mut_dptr(), inv_variance->mut_dptr()); + }; +}; + +#define REGISTER_LAYER_NORM_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("layer_norm") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); + +REGISTER_LAYER_NORM_CUDA_KERNEL(float) +REGISTER_LAYER_NORM_CUDA_KERNEL(double) +REGISTER_LAYER_NORM_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_CUDA_KERNEL(nv_bfloat16) +#endif + +template +class LayerNormGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + LayerNormGradGpuKernel() = default; + ~LayerNormGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + int64_t num_instances = mean->shape_view().elem_cnt(); + int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + const T* gamma_ptr = nullptr; + if (ctx->has_input("gamma", 0)) { + gamma_ptr = ctx->Tensor4ArgNameAndIndex("gamma", 0)->dptr(); + } + const T* add_to_output_ptr = nullptr; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), dx->data_type()); + CHECK_EQ(add_to_output->shape_view(), dx->shape_view()); + add_to_output_ptr = add_to_output->dptr(); + } + // LaunchLayerNormBackward(ctx->stream(), num_instances, norm_size, dy->dptr(), x->dptr(), + // mean, inv_variance, gamma_ptr, add_to_output_ptr, dx->mut_dptr()); + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + LayerNormBackwardKernelImplInternal(ctx->stream(), dy->dptr(), x->dptr(), mean->dptr(), inv_variance->dptr(), + gamma_ptr, num_instances, norm_size, dx->mut_dptr(), add_to_output_ptr); + }; +}; + +#define REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("layer_norm_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext& ctx, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + if (ctx.has_input("_add_to_output", 0)) { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); \ + } \ + return Maybe::Ok(); \ + }); + +REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(float) +REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(double) +REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(nv_bfloat16) +#endif + +template +class LayerNormParamGradGpuKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + LayerNormParamGradGpuKernel() = default; + ~LayerNormParamGradGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + int64_t num_instances = mean->shape_view().elem_cnt(); + int64_t norm_size = x->shape_view().elem_cnt() / num_instances; + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + // const DataType data_type = dy->data_type(); + // const int grid_dim_x = (norm_size + tile_size - 1) / tile_size; + // const int grid_dim_y = GetGirdDimY(num_instances, norm_size); + // const size_t tmp_gamma_diff_size = grid_dim_y * norm_size * sizeof(T); + // T* tmp_gamma_diff_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); + // T* tmp_beta_diff_ptr = reinterpret_cast(tmp_buffer->mut_dptr() + tmp_gamma_diff_size); + // T* reduce_buf_ptr = + // reinterpret_cast(tmp_buffer->mut_dptr() + 2 * tmp_gamma_diff_size); + using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; + // LayerNormParamGrad<<stream()->As()->cuda_stream()>>>( + // num_instances, norm_size, dy->dptr(), x->dptr(), mean->dptr(), + // inv_variance->dptr(), tmp_gamma_diff_ptr, tmp_beta_diff_ptr); + // const int32_t m = norm_size; + // const int32_t n = 1; + // const int32_t k = grid_dim_y; + // std::unique_ptr fill = + // ep::primitive::NewPrimitive(ctx->stream()->device_type(), + // data_type); + // CHECK(fill); + // fill->Launch(ctx->stream(), reduce_buf_ptr, 1.0, grid_dim_y); + // std::unique_ptr matmul = + // ep::primitive::NewPrimitive( + // ctx->stream()->device_type(), data_type, ep::primitive::BlasTransposeType::T, + // ep::primitive::BlasTransposeType::N); + // CHECK(matmul); + // if (ctx->has_output("gamma_diff", 0)) { + // user_op::Tensor* gamma_diff = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0); + // matmul->Launch(ctx->stream(), m, n, k, 1.0, tmp_gamma_diff_ptr, reduce_buf_ptr, 0.0, + // gamma_diff->mut_dptr()); + // } + // if (ctx->has_output("beta_diff", 0)) { + // user_op::Tensor* beta_diff = ctx->Tensor4ArgNameAndIndex("beta_diff", 0); + // matmul->Launch(ctx->stream(), m, n, k, 1.0, tmp_beta_diff_ptr, reduce_buf_ptr, 0.0, + // beta_diff->mut_dptr()); + // } + T* gamma_diff_ptr = nullptr; + T* beta_diff_ptr = nullptr; + if (ctx->has_output("gamma_diff", 0)) { + gamma_diff_ptr = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0)->mut_dptr(); + } + if (ctx->has_output("beta_diff", 0)) { + beta_diff_ptr = ctx->Tensor4ArgNameAndIndex("beta_diff", 0)->mut_dptr(); + } + LayerNormBackwardKernelImplInternalParam(ctx->stream(), dy->dptr(), x->dptr(), mean->dptr(), inv_variance->dptr(), + num_instances, norm_size, gamma_diff_ptr, beta_diff_ptr); + }; +}; + +#define REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("layer_norm_param_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const int64_t begin_params_axis = ctx->Attr("begin_params_axis"); \ + const bool has_gamma_diff = ctx->has_output("gamma_diff", 0); \ + const bool has_beta_diff = ctx->has_output("beta_diff", 0); \ + const auto& dy = ctx->InputTensorDesc("dy", 0); \ + const int64_t num_instances = dy.shape().Count(0, begin_params_axis); \ + const int64_t norm_size = dy.shape().Count(begin_params_axis); \ + const int grid_dim_y = num_instances; \ + size_t tmp_buffer_size = (2 * grid_dim_y * norm_size + grid_dim_y) * sizeof(dtype); \ + return tmp_buffer_size; \ + }); + +REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(float) +REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(double) +REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(nv_bfloat16) +#endif + +} + +#endif diff --git a/oneflow/user/kernels/layer_norm_gpu_kernel.hip.cpp b/oneflow/user/kernels/layer_norm_gpu_kernel.hip.cpp deleted file mode 100644 index 70da59aaa8659e6385e4c5dfe0cbe50a90733009..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/layer_norm_gpu_kernel.hip.cpp +++ /dev/null @@ -1,465 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cudnn_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/include/primitive/fill.h" -#include "oneflow/core/ep/include/primitive/matmul.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/hip/layer_norm.hip.h" - -namespace oneflow { - -namespace { - -template -struct AffineStore { - AffineStore(DST* y, int64_t row_size, const DST* gamma, const DST* beta) - : y(y), row_size(row_size), gamma(gamma), beta(beta) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::layer_norm::Pack y_pack; - cuda::layer_norm::Pack gamma_pack; - cuda::layer_norm::Pack beta_pack; - const int64_t offset = (row * row_size + col) / N; - const int64_t gamma_offset = col / N; - if (do_scale) { - gamma_pack.storage = - *(reinterpret_cast*>(gamma) + gamma_offset); - } else { -#pragma unroll - for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = 1; } - } - if (do_center) { - beta_pack.storage = - *(reinterpret_cast*>(beta) + gamma_offset); - } else { -#pragma unroll - for (int i = 0; i < N; ++i) { beta_pack.elem[i] = 0; } - } -#pragma unroll - for (int i = 0; i < N; ++i) { - DST normalized_i = static_cast(src[i]); - if (do_scale || do_center) { - y_pack.elem[i] = normalized_i * gamma_pack.elem[i] + beta_pack.elem[i]; - } else { - y_pack.elem[i] = normalized_i; - } - } - *(reinterpret_cast*>(y) + offset) = y_pack.storage; - } - DST* y; - int64_t row_size; - const DST* gamma; - const DST* beta; -}; - -template -struct ScaleLoad { - ScaleLoad(const SRC* src, const SRC* gamma, int64_t row_size) - : src(src), gamma(gamma), row_size(row_size) {} - template - __device__ void load(DST* dst, int64_t row, int64_t col) const { - cuda::layer_norm::Pack src_pack; - cuda::layer_norm::Pack gamma_pack; - const int64_t offset = (row * row_size + col) / N; - const int64_t gamma_offset = col / N; - src_pack.storage = *(reinterpret_cast*>(src) + offset); - if (do_scale) { - gamma_pack.storage = - *(reinterpret_cast*>(gamma) + gamma_offset); - } else { -#pragma unroll - for (int i = 0; i < N; ++i) { gamma_pack.elem[i] = static_cast(1); } - } -#pragma unroll - for (int i = 0; i < N; ++i) { - dst[i] = static_cast(src_pack.elem[i] * gamma_pack.elem[i]); - } - } - const SRC* src; - const SRC* gamma; - int64_t row_size; -}; - -template -struct AddStore { - AddStore(const DST* add_to_output, DST* dst, int64_t row_size) - : add_to_output(add_to_output), dst(dst), row_size(row_size) {} - template - __device__ void store(const SRC* src, int64_t row, int64_t col) { - cuda::layer_norm::Pack add_to_output_pack; - cuda::layer_norm::Pack dst_pack; - const int64_t offset = (row * row_size + col) / N; - if (do_add) { - add_to_output_pack.storage = - *(reinterpret_cast*>(add_to_output) + offset); - } -#pragma unroll - for (int i = 0; i < N; ++i) { - if (do_add) { - dst_pack.elem[i] = static_cast(src[i]) + add_to_output_pack.elem[i]; - } else { - dst_pack.elem[i] = static_cast(src[i]); - } - } - *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; - } - const DST* add_to_output; - DST* dst; - int64_t row_size; -}; - -template -__inline__ __device__ T WarpReduce(T val) { -// for (int mask = 16; mask > 0; mask /= 2) { val += __shfl_down_sync(0xffffffff, val, mask); } - for (int mask = 32; mask > 0; mask /= 2) { val += __shfl_down(val, mask, 64); } - return val; -} - -constexpr int tile_size = 32; -constexpr int num_per_block = 4; -constexpr int block_dim_x = 32; -constexpr int block_dim_y = 32 / num_per_block; - -template -__global__ void LayerNormParamGrad(int rows, int cols, const T* __restrict__ dy, - const T* __restrict__ x, const ComputeType* __restrict__ mean, - const ComputeType* __restrict__ inv_var, - T* __restrict__ tmp_gamma_diff, T* __restrict__ tmp_beta_diff) { - __shared__ ComputeType dgamma[32][33]; - __shared__ ComputeType dbeta[32][33]; - ComputeType dgamma_sum[num_per_block]; - ComputeType dbeta_sum[num_per_block]; -#pragma unroll - for (int index = 0; index < num_per_block; ++index) { - dgamma_sum[index] = 0; - dbeta_sum[index] = 0; - } - const int col_id = blockIdx.x * blockDim.x + threadIdx.x; - if (col_id < cols) { - for (int i = blockIdx.y * tile_size + threadIdx.y; i < rows; i += tile_size * gridDim.y) { -#pragma unroll - for (int index = 0; index < num_per_block; ++index) { - int row_id = i + index * blockDim.y; - if (row_id < rows) { - int offset = row_id * cols + col_id; - const ComputeType dy_val = static_cast(dy[offset]); - const ComputeType x_val = static_cast(x[offset]); - const ComputeType mean_val = mean[row_id]; - const ComputeType inv_var_val = inv_var[row_id]; - dgamma_sum[index] += dy_val * (x_val - mean_val) * inv_var_val; - dbeta_sum[index] += dy_val; - } - } - } - } -#pragma unroll - for (int index = 0; index < num_per_block; ++index) { - dgamma[index * blockDim.y + threadIdx.y][threadIdx.x] = dgamma_sum[index]; - dbeta[index * blockDim.y + threadIdx.y][threadIdx.x] = dbeta_sum[index]; - } - __syncthreads(); -#pragma unroll - for (int index = 0; index < num_per_block; ++index) { - const int col_id = blockIdx.x * blockDim.x + threadIdx.y + index * blockDim.y; - if (col_id < cols) { - ComputeType gamma_sum = dgamma[threadIdx.x][threadIdx.y + index * blockDim.y]; - ComputeType beta_sum = dbeta[threadIdx.x][threadIdx.y + index * blockDim.y]; - ComputeType global_dgamma = WarpReduce(gamma_sum); - ComputeType global_dbeta = WarpReduce(beta_sum); - if (threadIdx.x == 0) { - const int offset = blockIdx.y * cols + col_id; - tmp_gamma_diff[offset] = global_dgamma; - tmp_beta_diff[offset] = global_dbeta; - } - } - } -} - -template -int GetGirdDimY(const int64_t num_instances, const int64_t norm_size) { - using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - const int grid_dim_x = (norm_size + tile_size - 1) / tile_size; - const int max_grid_dim_y = (num_instances + tile_size - 1) / tile_size; - const int block_size = block_dim_x * block_dim_y; - int max_active_blocks = 0; - OF_CUDA_CHECK(hipOccupancyMaxActiveBlocksPerMultiprocessor( - &max_active_blocks, LayerNormParamGrad, block_size, 0)); - int waves = 1; - int dev; - OF_CUDA_CHECK(hipGetDevice(&dev)); - int sm_count; - OF_CUDA_CHECK(hipDeviceGetAttribute(&sm_count, hipDeviceAttributeMultiprocessorCount, dev)); - int num_blocks = max_active_blocks * sm_count * waves; - int grid_dim_y = std::min(max_grid_dim_y, static_cast(num_blocks / grid_dim_x)); - return std::max(grid_dim_y, 1); -} - -template -void LayerNormForwardGpu(ep::Stream* stream, const int64_t num_instances, const int64_t norm_size, - const double epsilon, const T* x_ptr, const T* gamma_ptr, - const T* beta_ptr, T* y_ptr, user_op::Tensor* mean, - user_op::Tensor* inv_variance) { - using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - cuda::layer_norm::DirectLoad load(x_ptr, norm_size); - AffineStore store(y_ptr, norm_size, gamma_ptr, beta_ptr); - cuda::layer_norm::DispatchLayerNorm( - stream->As()->cuda_stream(), load, store, num_instances, norm_size, epsilon, - mean->mut_dptr(), inv_variance->mut_dptr()); -} - -template -void DispatchLayerNormForwardGpu(ep::Stream* stream, const int64_t num_instances, - const int64_t norm_size, const double epsilon, const T* x_ptr, - const T* gamma_ptr, const T* beta_ptr, T* y_ptr, - user_op::Tensor* mean, user_op::Tensor* inv_variance) { - if (gamma_ptr != nullptr && beta_ptr != nullptr) { - LayerNormForwardGpu(stream, num_instances, norm_size, epsilon, x_ptr, gamma_ptr, - beta_ptr, y_ptr, mean, inv_variance); - } else if (gamma_ptr != nullptr && beta_ptr == nullptr) { - LayerNormForwardGpu(stream, num_instances, norm_size, epsilon, x_ptr, gamma_ptr, - beta_ptr, y_ptr, mean, inv_variance); - } else if (gamma_ptr == nullptr && beta_ptr != nullptr) { - LayerNormForwardGpu(stream, num_instances, norm_size, epsilon, x_ptr, gamma_ptr, - beta_ptr, y_ptr, mean, inv_variance); - } else { - LayerNormForwardGpu(stream, num_instances, norm_size, epsilon, x_ptr, - gamma_ptr, beta_ptr, y_ptr, mean, inv_variance); - } -} - -template -void LayerNormBackwardGpu(ep::Stream* stream, const int64_t num_instances, const int64_t norm_size, - const T* dy_ptr, const T* x_ptr, const user_op::Tensor* mean, - const user_op::Tensor* inv_variance, const T* gamma_ptr, - const T* add_to_output_ptr, T* dx_ptr) { - using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - cuda::layer_norm::DirectLoad load_x(x_ptr, norm_size); - ScaleLoad load_scaled_dy(dy_ptr, gamma_ptr, norm_size); - AddStore store(add_to_output_ptr, dx_ptr, norm_size); - OF_CUDA_CHECK((cuda::layer_norm::DispatchLayerNormGrad( - stream->As()->cuda_stream(), load_x, load_scaled_dy, store, - mean->dptr(), inv_variance->dptr(), num_instances, norm_size))); -} - -template -void DispatchLayerNormBackwardDoAdd(ep::Stream* stream, const int64_t num_instances, - const int64_t norm_size, const T* dy_ptr, const T* x_ptr, - const user_op::Tensor* mean, - const user_op::Tensor* inv_variance, const T* gamma_ptr, - const T* add_to_output_ptr, T* dx_ptr) { - if (add_to_output_ptr != nullptr) { - LayerNormBackwardGpu(stream, num_instances, norm_size, dy_ptr, x_ptr, mean, - inv_variance, gamma_ptr, add_to_output_ptr, dx_ptr); - } else { - LayerNormBackwardGpu(stream, num_instances, norm_size, dy_ptr, x_ptr, mean, - inv_variance, gamma_ptr, add_to_output_ptr, dx_ptr); - } -} - -template -void LaunchLayerNormBackward(ep::Stream* stream, const int64_t num_instances, - const int64_t norm_size, const T* dy_ptr, const T* x_ptr, - const user_op::Tensor* mean, const user_op::Tensor* inv_variance, - const T* gamma_ptr, const T* add_to_output_ptr, T* dx_ptr) { - if (gamma_ptr != nullptr) { - DispatchLayerNormBackwardDoAdd(stream, num_instances, norm_size, dy_ptr, x_ptr, mean, - inv_variance, gamma_ptr, add_to_output_ptr, dx_ptr); - } else { - DispatchLayerNormBackwardDoAdd(stream, num_instances, norm_size, dy_ptr, x_ptr, mean, - inv_variance, gamma_ptr, add_to_output_ptr, dx_ptr); - } -} - -} // namespace - -template -class LayerNormGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - LayerNormGpuKernel() = default; - ~LayerNormGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); - user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); - const double epsilon = ctx->Attr("epsilon"); - CHECK_GE(epsilon, HIPDNN_BN_MIN_EPSILON); - const int64_t num_instances = mean->shape_view().elem_cnt(); - const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; - const T* gamma_ptr = nullptr; - const T* beta_ptr = nullptr; - if (ctx->has_input("gamma", 0)) { - const user_op::Tensor* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); - gamma_ptr = gamma->dptr(); - CHECK_EQ(gamma->shape_view().elem_cnt(), norm_size); - } - if (ctx->has_input("beta", 0)) { beta_ptr = ctx->Tensor4ArgNameAndIndex("beta", 0)->dptr(); } - DispatchLayerNormForwardGpu(ctx->stream(), num_instances, norm_size, epsilon, x->dptr(), - gamma_ptr, beta_ptr, y->mut_dptr(), mean, inv_variance); - }; -}; - -#define REGISTER_LAYER_NORM_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("layer_norm") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_LAYER_NORM_CUDA_KERNEL(float) -REGISTER_LAYER_NORM_CUDA_KERNEL(double) -REGISTER_LAYER_NORM_CUDA_KERNEL(half) - -template -class LayerNormGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - LayerNormGradGpuKernel() = default; - ~LayerNormGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); - const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const int64_t num_instances = mean->shape_view().elem_cnt(); - const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; - const T* gamma_ptr = nullptr; - if (ctx->has_input("gamma", 0)) { - gamma_ptr = ctx->Tensor4ArgNameAndIndex("gamma", 0)->dptr(); - } - const T* add_to_output_ptr = nullptr; - if (ctx->has_input("_add_to_output", 0)) { - const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); - CHECK_EQ(add_to_output->data_type(), dx->data_type()); - CHECK_EQ(add_to_output->shape_view(), dx->shape_view()); - add_to_output_ptr = add_to_output->dptr(); - } - LaunchLayerNormBackward(ctx->stream(), num_instances, norm_size, dy->dptr(), x->dptr(), - mean, inv_variance, gamma_ptr, add_to_output_ptr, dx->mut_dptr()); - }; -}; - -#define REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("layer_norm_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - .SetInplaceProposalFn( \ - [](const user_op::InferContext& ctx, \ - const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("dx", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }); - -REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(float) -REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(double) -REGISTER_LAYER_NORM_GRAD_CUDA_KERNEL(half) - -template -class LayerNormParamGradGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - LayerNormParamGradGpuKernel() = default; - ~LayerNormParamGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); - const user_op::Tensor* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); - const int64_t num_instances = mean->shape_view().elem_cnt(); - const int64_t norm_size = x->shape_view().elem_cnt() / num_instances; - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const DataType data_type = dy->data_type(); - const int grid_dim_x = (norm_size + tile_size - 1) / tile_size; - const int grid_dim_y = GetGirdDimY(num_instances, norm_size); - const size_t tmp_gamma_diff_size = grid_dim_y * norm_size * sizeof(T); - T* tmp_gamma_diff_ptr = reinterpret_cast(tmp_buffer->mut_dptr()); - T* tmp_beta_diff_ptr = reinterpret_cast(tmp_buffer->mut_dptr() + tmp_gamma_diff_size); - T* reduce_buf_ptr = - reinterpret_cast(tmp_buffer->mut_dptr() + 2 * tmp_gamma_diff_size); - using ComputeType = typename cuda::layer_norm::DefaultComputeType::type; - LayerNormParamGrad<<stream()->As()->cuda_stream()>>>( - num_instances, norm_size, dy->dptr(), x->dptr(), mean->dptr(), - inv_variance->dptr(), tmp_gamma_diff_ptr, tmp_beta_diff_ptr); - const int32_t m = norm_size; - const int32_t n = 1; - const int32_t k = grid_dim_y; - std::unique_ptr fill = - ep::primitive::NewPrimitive(ctx->stream()->device_type(), - data_type); - CHECK(fill); - fill->Launch(ctx->stream(), reduce_buf_ptr, 1.0, grid_dim_y); - std::unique_ptr matmul = - ep::primitive::NewPrimitive( - ctx->stream()->device_type(), data_type, ep::primitive::BlasTransposeType::T, - ep::primitive::BlasTransposeType::N); - CHECK(matmul); - if (ctx->has_output("gamma_diff", 0)) { - user_op::Tensor* gamma_diff = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0); - matmul->Launch(ctx->stream(), m, n, k, 1.0, tmp_gamma_diff_ptr, reduce_buf_ptr, 0.0, - gamma_diff->mut_dptr()); - } - if (ctx->has_output("beta_diff", 0)) { - user_op::Tensor* beta_diff = ctx->Tensor4ArgNameAndIndex("beta_diff", 0); - matmul->Launch(ctx->stream(), m, n, k, 1.0, tmp_beta_diff_ptr, reduce_buf_ptr, 0.0, - beta_diff->mut_dptr()); - } - }; -}; - -#define REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("layer_norm_param_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const int64_t begin_params_axis = ctx->Attr("begin_params_axis"); \ - const bool has_gamma_diff = ctx->has_output("gamma_diff", 0); \ - const bool has_beta_diff = ctx->has_output("beta_diff", 0); \ - const auto& dy = ctx->InputTensorDesc("dy", 0); \ - const int64_t num_instances = dy.shape().Count(0, begin_params_axis); \ - const int64_t norm_size = dy.shape().Count(begin_params_axis); \ - const int grid_dim_y = GetGirdDimY(num_instances, norm_size); \ - size_t tmp_buffer_size = (2 * grid_dim_y * norm_size + grid_dim_y) * sizeof(dtype); \ - return tmp_buffer_size; \ - }); - -REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(float) -REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(double) -REGISTER_LAYER_NORM_PARAM_GRAD_GPU_KERNEL(half) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/linalg_cross_kernel.cpp b/oneflow/user/kernels/linalg_cross_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..fded09c8c59f0c8c4ad63633c3370c83d1af3e8b --- /dev/null +++ b/oneflow/user/kernels/linalg_cross_kernel.cpp @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_kernel.h" + +namespace oneflow { + +template +class CpuLinalgCrossKernel final : public user_op::OpKernel { + public: + CpuLinalgCrossKernel() = default; + ~CpuLinalgCrossKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* input_tensor = ctx->Tensor4ArgNameAndIndex("input", 0); + const auto* other_tensor = ctx->Tensor4ArgNameAndIndex("other", 0); + auto* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + + const auto shape = input_tensor->shape_view(); + const auto num_axes = shape.NumAxes(); + + int64_t dim = ctx->Attr("dim"); + + const auto strides = [&shape]() -> std::vector { + std::vector result(shape.NumAxes(), 1); + for (size_t i(0); i < result.size() - 1; ++i) { result[i] = shape.Count(i + 1); } + return result; + }(); + + const int64_t total = shape.elem_cnt() / 3; + int64_t stride = strides[dim]; + + const T* input_ptr = input_tensor->dptr(); + const T* other_ptr = other_tensor->dptr(); + T* out_dtr = out_tensor->mut_dptr(); + + std::vector positions_in_dims(num_axes); + + int64_t start = 0; + + int64_t s = 0; + while (s < total) { + out_dtr[start + 0 * stride] = input_ptr[start + 1 * stride] * other_ptr[start + 2 * stride] + - input_ptr[start + 2 * stride] * other_ptr[start + 1 * stride]; + out_dtr[start + 1 * stride] = input_ptr[start + 2 * stride] * other_ptr[start + 0 * stride] + - input_ptr[start + 0 * stride] * other_ptr[start + 2 * stride]; + out_dtr[start + 2 * stride] = input_ptr[start + 0 * stride] * other_ptr[start + 1 * stride] + - input_ptr[start + 1 * stride] * other_ptr[start + 0 * stride]; + + ++s; + + FOR_RANGE(int64_t, i, 0, num_axes) { + if (i == dim) continue; + + ++positions_in_dims[i]; + start += strides[i]; + + if (positions_in_dims[i] == shape.At(i) && i != num_axes - 1) { + start -= positions_in_dims[i] * strides[i]; + positions_in_dims[i] = 0; + } else { + break; + } + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CPU_LINALG_CROSS_KERNEL(dtype) \ + REGISTER_USER_KERNEL("linalg_cross") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +REGISTER_CPU_LINALG_CROSS_KERNEL(float) +REGISTER_CPU_LINALG_CROSS_KERNEL(double) + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/linalg_cross_kernel.cu b/oneflow/user/kernels/linalg_cross_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..f012b3d36cf4f616f338bfac0994e7c1dd93e857 --- /dev/null +++ b/oneflow/user/kernels/linalg_cross_kernel.cu @@ -0,0 +1,67 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_kernel.h" +#include "oneflow/core/device/cuda_util.h" + +namespace { + +template +__global__ void LinalgCrossForward(const int64_t n, const T* input, const T* other, T* out) { + CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { + const int64_t index = i * 3; + out[index] = input[index + 1] * other[index + 2] - input[index + 2] * other[index + 1]; + out[index + 1] = input[index + 2] * other[index] - input[index] * other[index + 2]; + out[index + 2] = input[index] * other[index + 1] - input[index + 1] * other[index]; + } +} + +} // namespace + +namespace oneflow { + +template +class CudaLinalgCrossKernel final : public user_op::OpKernel { + public: + CudaLinalgCrossKernel() = default; + ~CudaLinalgCrossKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* input_tensor = ctx->Tensor4ArgNameAndIndex("input", 0); + const auto* other_tensor = ctx->Tensor4ArgNameAndIndex("other", 0); + auto* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + + const int64_t n = input_tensor->shape_view().elem_cnt() / 3; + + if (n == 0) { return; } + RUN_CUDA_KERNEL((LinalgCrossForward), ctx->stream(), n, n, input_tensor->dptr(), + other_tensor->dptr(), out_tensor->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_CUDA_LINALG_CROSS_KERNEL(dtype) \ + REGISTER_USER_KERNEL("linalg_cross") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)); + +REGISTER_CUDA_LINALG_CROSS_KERNEL(float) +REGISTER_CUDA_LINALG_CROSS_KERNEL(double) + +} // namespace oneflow diff --git a/oneflow/user/kernels/log_softmax_kernel.cpp b/oneflow/user/kernels/log_softmax_kernel.cpp index 5df0bc9443caceced18193da069852c9c6c052d1..37e7e9123ff93dfc7dff62de4013e1d789cba33c 100644 --- a/oneflow/user/kernels/log_softmax_kernel.cpp +++ b/oneflow/user/kernels/log_softmax_kernel.cpp @@ -60,8 +60,9 @@ class LogSoftmaxKernel final : public user_op::OpKernel, public user_op::CudaGra void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* prob = ctx->Tensor4ArgNameAndIndex("prob", 0); - const int64_t num_classes = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int64_t num_instances = in->shape_view().Count(0, in->shape_view().NumAxes() - 1); + const ShapeView& in_shape = in->shape_view(); + const int64_t num_classes = in_shape.At(in_shape.NumAxes() - 1); + const int64_t num_instances = in_shape.Count(0, in_shape.NumAxes() - 1); std::unique_ptr primitive = NewLogSoftmaxPrimitive(ctx); CHECK(primitive); primitive->Launch(ctx->stream(), num_instances, num_classes, in->dptr(), prob->mut_dptr()); diff --git a/oneflow/user/kernels/masked_fill_kernel.cpp b/oneflow/user/kernels/masked_fill_kernel.cpp index f2e5970e9ef0a725c0aed2792261b5a30ebff704..e698e5daad63f5f68a6cf99930a2224ce3811790 100644 --- a/oneflow/user/kernels/masked_fill_kernel.cpp +++ b/oneflow/user/kernels/masked_fill_kernel.cpp @@ -58,11 +58,7 @@ class MaskedFillKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MASKED_FILL_KERNEL, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MASKED_FILL_KERNEL, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MASKED_FILL_KERNEL, (DeviceType::kCUDA), FLOAT16_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) #endif diff --git a/oneflow/user/kernels/math_binary_broadcast_kernels.cpp b/oneflow/user/kernels/math_binary_broadcast_kernels.cpp index c8d11795867ee15ebc74f2421c475d14444c5fc6..e2f6d95734b91526977c58301d1c4a6498f795e6 100644 --- a/oneflow/user/kernels/math_binary_broadcast_kernels.cpp +++ b/oneflow/user/kernels/math_binary_broadcast_kernels.cpp @@ -24,8 +24,23 @@ limitations under the License. namespace oneflow { template -std::unique_ptr NewBroadcastElementwiseBinaryPrimitive( - Context* ctx) { +std::enable_if_t> +NewBroadcastElementwiseBinaryPrimitive(Context* ctx) { + const user_op::TensorDesc* x = ctx->TensorDesc4ArgNameAndIndex("x", 0); + const user_op::TensorDesc* z = ctx->TensorDesc4ArgNameAndIndex("z", 0); + size_t num_axes = z->shape().NumAxes(); + return ep::primitive::NewPrimitive( + ctx->device_type(), binary_op, x->data_type(), z->data_type(), num_axes, + ctx->template Attr("atol"), ctx->template Attr("rtol")); +} + +template +std::enable_if_t> +NewBroadcastElementwiseBinaryPrimitive(Context* ctx) { const user_op::TensorDesc* x = ctx->TensorDesc4ArgNameAndIndex("x", 0); const user_op::TensorDesc* z = ctx->TensorDesc4ArgNameAndIndex("z", 0); size_t num_axes = z->shape().NumAxes(); @@ -72,7 +87,7 @@ class MathBinaryBroadcastEpKernel final : public user_op::OpKernel, primitive->Launch(ctx->stream(), num_src0_dims, src0_dims, x->dptr(), num_src1_dims, src1_dims, y->dptr(), z->mut_dptr()); } else { - // For 0-d Tensor + // For 0-size Tensor return; } } @@ -107,60 +122,13 @@ REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_greater_equal", ep::primitive::BinaryOp::kGreaterEqual) REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_less", ep::primitive::BinaryOp::kLessThan) REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_less_equal", ep::primitive::BinaryOp::kLessEqual) +REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_isclose_eq_nan", + ep::primitive::BinaryOp::kIsCloseEqualNan) +REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_isclose_neq_nan", ep::primitive::BinaryOp::kIsClose) REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_logical_and", ep::primitive::BinaryOp::kLogicalAnd) REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_logical_or", ep::primitive::BinaryOp::kLogicalOr) REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_logical_xor", ep::primitive::BinaryOp::kLogicalXor) - -template& z, - const XpuVarNdarray& x, const XpuVarNdarray& y)> -class MathBinaryBroadcastKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - MathBinaryBroadcastKernel() = default; - ~MathBinaryBroadcastKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* tensor_z = ctx->Tensor4ArgNameAndIndex("z", 0); - const T* dptr_x = tensor_x->dptr(); - const T* dptr_y = tensor_y->dptr(); - K* dptr_z = tensor_z->mut_dptr(); - size_t num_axes = tensor_z->shape_view().NumAxes(); - binary_func(ctx->stream(), XpuVarNdarray(tensor_z->shape_view(), dptr_z, num_axes), - XpuVarNdarray(tensor_x->shape_view(), dptr_x, num_axes), - XpuVarNdarray(tensor_y->shape_view(), dptr_y, num_axes)); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define MATH_BINARY_BROADCAST_DEFAULT_FUNC_SEQ \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_floor_mod", FloorMod) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_fmod", FMod) - -#define REGISTER_MATH_BINARY_BROADCAST_KERNEL(math_type_pair, device, data_type_pair) \ - REGISTER_USER_KERNEL(OF_PP_PAIR_FIRST(math_type_pair)) \ - .SetCreateFn::OF_PP_CAT( \ - Broadcast, OF_PP_PAIR_SECOND(math_type_pair))>>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("z", 0) == OF_PP_PAIR_SECOND(data_type_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_MATH_BINARY_BROADCAST_KERNEL, MATH_BINARY_BROADCAST_DEFAULT_FUNC_SEQ, DEVICE_TYPE_SEQ, - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -// gpu half -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_BROADCAST_KERNEL, - MATH_BINARY_BROADCAST_DEFAULT_FUNC_SEQ, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_BROADCAST_KERNEL, - MATH_BINARY_BROADCAST_DEFAULT_FUNC_SEQ, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ) -#endif +REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_floor_mod", ep::primitive::BinaryOp::kFloorMod) +REGISTER_BINARY_BROADCAST_EP_KERNEL("broadcast_fmod", ep::primitive::BinaryOp::kFmod) } // namespace oneflow diff --git a/oneflow/user/kernels/math_binary_elementwise_func.h b/oneflow/user/kernels/math_binary_elementwise_func.h index fd9dfbbf767885334273e74a5bb8343987612d4f..df3e7c3cfb4c408d7a5075924ea7bbb6939eea17 100644 --- a/oneflow/user/kernels/math_binary_elementwise_func.h +++ b/oneflow/user/kernels/math_binary_elementwise_func.h @@ -37,7 +37,6 @@ limitations under the License. #define MATH_FUNC(name) std::name #endif - #else #include @@ -98,6 +97,21 @@ struct FloorDivFunctor { static OF_DEVICE_FUNC const T BackwardYGrad(const T x, const T y, const T dz) { return T(0); } }; +template +struct TruncDivFunctor { + static OF_DEVICE_FUNC const T Forward(const T x, const T y) { +#if defined(__CUDACC__) || defined(__HIP_DEVICE_COMPILE__) + return trunc(fdividef(x, y)); +#else + return std::trunc(x / y); +#endif + } + + static OF_DEVICE_FUNC const T BackwardXGrad(const T x, const T y, const T dz) { return T(0); } + + static OF_DEVICE_FUNC const T BackwardYGrad(const T x, const T y, const T dz) { return T(0); } +}; + template struct XdivyFunctor { static OF_DEVICE_FUNC const T Forward(const T x, const T y) { @@ -197,6 +211,21 @@ struct FloorDivFunctor { } }; +template<> +struct TruncDivFunctor { + static OF_HALF_FUNC const half Forward(const half x, const half y) { + return htrunc(__hdiv(x, y)); + } + + static OF_HALF_FUNC const half BackwardXGrad(const half x, const half y, const half dz) { + return GetZeroVal(); + } + + static OF_HALF_FUNC const half BackwardYGrad(const half x, const half y, const half dz) { + return GetZeroVal(); + } +}; + template<> struct XdivyFunctor { static OF_HALF_FUNC const half Forward(const half x, const half y) { diff --git a/oneflow/user/kernels/math_binary_elementwise_kernel.cpp b/oneflow/user/kernels/math_binary_elementwise_kernel.cpp index c5927b73fccfa52a17c3a5c1fc056584baceb4ab..c6c8191abc58a767ee8ffb2852105ae5bb3a5220 100644 --- a/oneflow/user/kernels/math_binary_elementwise_kernel.cpp +++ b/oneflow/user/kernels/math_binary_elementwise_kernel.cpp @@ -117,7 +117,8 @@ class MathBinaryElementwiseYGradCpuKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD, MATH_BINARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv), + OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv) + OF_PP_MAKE_TUPLE_SEQ("truncdiv", TruncDiv), INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) } // namespace oneflow diff --git a/oneflow/user/kernels/math_binary_elementwise_kernel.cu b/oneflow/user/kernels/math_binary_elementwise_kernel.cu index 844d82636e50d91f937fa90287f91bb997d40bcb..5af9f0a694790a3b1a206cfc02a34dd30e826eb9 100644 --- a/oneflow/user/kernels/math_binary_elementwise_kernel.cu +++ b/oneflow/user/kernels/math_binary_elementwise_kernel.cu @@ -138,7 +138,8 @@ class MathBinaryElementwiseYGradGpuKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, MATH_BINARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv), + OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv) + OF_PP_MAKE_TUPLE_SEQ("truncdiv", TruncDiv), INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) template class BinaryFunctor> diff --git a/oneflow/user/kernels/math_binary_elementwise_kernel.hip.cpp b/oneflow/user/kernels/math_binary_elementwise_kernel.hip.cpp deleted file mode 100644 index db2ebfddb25fe2b417cb39d2431c7319b24b7777..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/math_binary_elementwise_kernel.hip.cpp +++ /dev/null @@ -1,244 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/math_binary_elementwise_func.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template class BinaryFunctor, typename T> -__global__ void MathBinaryElementwiseForwardGpu(const int64_t n, const T* x, const T* y, T* z) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { z[i] = BinaryFunctor::Forward(x[i], y[i]); } -} - -template class BinaryFunctor, typename T> -__global__ void MathBinaryElementwiseBackwardXGradGpu(const int64_t n, const T* x, const T* y, - const T* dz, T* dx) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { - dx[i] = BinaryFunctor::BackwardXGrad(x[i], y[i], dz[i]); - } -} - -template class BinaryFunctor, typename T> -__global__ void MathBinaryElementwiseBackwardYGradGpu(const int64_t n, const T* x, const T* y, - const T* dz, T* dy) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { - dy[i] = BinaryFunctor::BackwardYGrad(x[i], y[i], dz[i]); - } -} - -} // namespace - -template class BinaryFunctor, typename T> -class MathBinaryElementwiseGpuKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseGpuKernel() = default; - ~MathBinaryElementwiseGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* tensor_z = ctx->Tensor4ArgNameAndIndex("z", 0); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>( - n, tensor_x->dptr(), tensor_y->dptr(), tensor_z->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class BinaryFunctor, typename T> -class MathBinaryElementwiseXGradGpuKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseXGradGpuKernel() = default; - ~MathBinaryElementwiseXGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* tensor_dz = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseBackwardXGradGpu - <<stream()->As()->cuda_stream()>>>( - n, tensor_x->dptr(), tensor_y->dptr(), tensor_dz->dptr(), - tensor_dx->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class BinaryFunctor, typename T> -class MathBinaryElementwiseYGradGpuKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseYGradGpuKernel() = default; - ~MathBinaryElementwiseYGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* tensor_dz = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseBackwardYGradGpu - <<stream()->As()->cuda_stream()>>>( - n, tensor_x->dptr(), tensor_y->dptr(), tensor_dz->dptr(), - tensor_dy->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD(math_type_pair, data_type_pair) \ - REGISTER_USER_KERNEL(OF_PP_PAIR_FIRST(math_type_pair)) \ - .SetCreateFn< \ - MathBinaryElementwiseGpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); \ - \ - REGISTER_USER_KERNEL((std::string("") + OF_PP_PAIR_FIRST(math_type_pair) + "_x_grad")) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); \ - REGISTER_USER_KERNEL((std::string("") + OF_PP_PAIR_FIRST(math_type_pair) + "_y_grad")) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - MATH_BINARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv), - INT_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ) - -template class BinaryFunctor> -class MathBinaryElementwiseGpuHalfKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseGpuHalfKernel() = default; - ~MathBinaryElementwiseGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - user_op::Tensor* tensor_z = ctx->Tensor4ArgNameAndIndex("z", 0); - const half* x = reinterpret_cast(tensor_x->dptr()); - const half* y = reinterpret_cast(tensor_y->dptr()); - half* z = reinterpret_cast(tensor_z->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, y, z); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class BinaryFunctor> -class MathBinaryElementwiseXGradGpuHalfKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseXGradGpuHalfKernel() = default; - ~MathBinaryElementwiseXGradGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* tensor_dz = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const half* x = reinterpret_cast(tensor_x->dptr()); - const half* y = reinterpret_cast(tensor_y->dptr()); - const half* dz = reinterpret_cast(tensor_dz->dptr()); - half* dx = reinterpret_cast(tensor_dx->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseBackwardXGradGpu - <<stream()->As()->cuda_stream()>>>(n, x, y, dz, dx); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class BinaryFunctor> -class MathBinaryElementwiseYGradGpuHalfKernel final : public user_op::OpKernel { - public: - MathBinaryElementwiseYGradGpuHalfKernel() = default; - ~MathBinaryElementwiseYGradGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const user_op::Tensor* tensor_dz = ctx->Tensor4ArgNameAndIndex("dz", 0); - user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - - const half* x = reinterpret_cast(tensor_x->dptr()); - const half* y = reinterpret_cast(tensor_y->dptr()); - const half* dz = reinterpret_cast(tensor_dz->dptr()); - half* dy = reinterpret_cast(tensor_dy->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathBinaryElementwiseBackwardYGradGpu - <<stream()->As()->cuda_stream()>>>(n, x, y, dz, dy); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_HALF_KERNEL_AND_GRAD(math_type_str, \ - math_func_prefix) \ - REGISTER_USER_KERNEL(math_type_str) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16)); \ - \ - REGISTER_USER_KERNEL((std::string("") + math_type_str + "_x_grad")) \ - .SetCreateFn< \ - MathBinaryElementwiseXGradGpuHalfKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16)); \ - REGISTER_USER_KERNEL((std::string("") + math_type_str + "_y_grad")) \ - .SetCreateFn< \ - MathBinaryElementwiseYGradGpuHalfKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16)); - -OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_CUDA_HALF_KERNEL_AND_GRAD, - MATH_BINARY_ELEMENTWISE_FUNC_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/math_unary_elementwise_func.h b/oneflow/user/kernels/math_unary_elementwise_func.h index c55ecf49df1a613a779251e52044bb654d1a8218..aff50c3163d1e9f85a0a15bafebc7e11c7eff7e7 100644 --- a/oneflow/user/kernels/math_unary_elementwise_func.h +++ b/oneflow/user/kernels/math_unary_elementwise_func.h @@ -1,983 +1,983 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ -#define ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ - -#include "oneflow/core/common/util.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/user/ops/math_unary_elementwise_seq.h" -#include "oneflow/core/device/cuda_pseudo_half.h" - -#if defined(__CUDACC__) - -#include -#define MATH_FUNC_F(name, x) name##f(x) -#define MATH_FUNC_D(name, x) name(x) - -#elif defined(__HIPCC__) -#include -#include - -#if defined(__HIP_DEVICE_COMPILE__) -#define MATH_FUNC_F(name, x) name##f(x) -#define MATH_FUNC_D(name, x) name(x) -#else -#define MATH_FUNC_F(name, x) std::name(x) -#define MATH_FUNC_D(name, x) std::name(x) -#endif - -#else - -#include -#define MATH_FUNC_F(name, x) std::name(x) -#define MATH_FUNC_D(name, x) std::name(x) - -#endif - -namespace oneflow { - -#define DECLARE_UNARY_FUNCTOR(math_unary_elementwise_type, func_prefix) \ - template \ - struct func_prefix##Functor; - -OF_PP_FOR_EACH_TUPLE(DECLARE_UNARY_FUNCTOR, MATH_UNARY_ELEMENTWISE_FUNC_SEQ) - -template -struct AbsFunctor { - static OF_DEVICE_FUNC T Forward(const T x) { - if (x == T(0)) - return T(0); - else - return x < T(0) ? -x : x; - } - - static OF_DEVICE_FUNC T Backward(const T x, const T dy) { - if (x == T(0)) - return T(0); - else - return x < T(0) ? -dy : dy; - } -}; - -template -struct SignFunctor { - static OF_DEVICE_FUNC T Forward(const T x) { return (T(0) < x) - (x < T(0)); } - - static OF_DEVICE_FUNC T Backward(const T x, const T dy) { return T(0); } -}; - -template<> -struct RsqrtFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { -#if defined(__CUDACC__) - return rsqrtf(x); -#elif defined(__HIP_DEVICE_COMPILE__) - return rsqrtf(x); -#else - return 1.0f / std::sqrt(x); -#endif - } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (-1.0f / (2.0f * MATH_FUNC_F(sqrt, x * x * x))); - } -}; - -template<> -struct RsqrtFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { -#if defined(__CUDACC__) - return rsqrt(x); -#elif defined(__HIP_DEVICE_COMPILE__) - return rsqrt(x); -#else - return 1.0 / std::sqrt(x); -#endif - } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (-1.0 / (2.0 * MATH_FUNC_D(sqrt, x * x * x))); - } -}; - -// float version - -template<> -struct AcosFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(acos, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * -RsqrtFunctor::Forward(1.0f - x * x); - } -}; - -template<> -struct AcoshFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(acosh, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * RsqrtFunctor::Forward(x * x - 1.0f); - } -}; - -template<> -struct AsinFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(asin, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * RsqrtFunctor::Forward(1.0f - x * x); - } -}; - -template<> -struct AsinhFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(asinh, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * RsqrtFunctor::Forward(1.0f + x * x); - } -}; - -template<> -struct AtanFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(atan, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (1.0f + x * x)); - } -}; - -template<> -struct AtanhFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(atanh, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (1.0f - x * x)); - } -}; - -template<> -struct NotEqualZeroFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return x != 0; } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } -}; - -template<> -struct CeilFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(ceil, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } -}; - -template<> -struct CosFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(cos, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (-MATH_FUNC_F(sin, x)); - } -}; - -template<> -struct CoshFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(cosh, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * MATH_FUNC_F(sinh, x); - } -}; - -template<> -struct ErfFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(erf, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * 2.0f * RsqrtFunctor::Forward(M_PI) * expf(-x * x); - } -}; - -template<> -struct ErfcFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(erfc, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * -2.0f * RsqrtFunctor::Forward(M_PI) * expf(-x * x); - } -}; - -template<> -struct ExpFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(exp, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * MATH_FUNC_F(exp, x); - } -}; - -template<> -struct Expm1Functor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(expm1, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * MATH_FUNC_F(exp, x); - } -}; - -template<> -struct FloorFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(floor, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } -}; - -template<> -struct LgammaFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(lgamma, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - // TODO(chengcheng): return: dy * digamma(x) - // assert(false); - return 0.0f; - } -}; - -template<> -struct LogFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return dy * (1.0f / x); } -}; - -template<> -struct Log2Functor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log2, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (x * MATH_FUNC_F(log, 2.0f))); - } -}; - -template<> -struct Log1pFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log1p, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (x + 1.0f)); - } -}; - -template<> -struct LogSigmoidFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { - return -MATH_FUNC_F(log, (1.0f + MATH_FUNC_F(exp, -x))); - } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (MATH_FUNC_F(exp, x) + 1.0f)); - } -}; - -template<> -struct NegativeFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return -x; } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return -dy; } -}; - -template<> -struct ReciprocalFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return 1.0f / x; } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (-1.0f / (x * x)); - } -}; - -template<> -struct ReciprocalNoNanFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { - if (fabsf(x) <= 0.0f) { return 0.0f; } - return 1.0f / x; - } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - if (fabsf(x) <= 0.0f) { return 0.0f; } - return dy * (-1.0f / (x * x)); - } -}; - -template<> -struct RintFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(rint, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } -}; - -template<> -struct RoundFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(nearbyint, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } -}; - -template<> -struct SigmoidFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { - return 1.0f / (1.0f + MATH_FUNC_F(exp, -x)); - } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - float y = 1.0f / (1.0f + MATH_FUNC_F(exp, -x)); - return dy * (y * (1.0f - y)); - } -}; - -template<> -struct SinFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sin, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * MATH_FUNC_F(cos, x); - } -}; - -template<> -struct SinhFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sinh, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * MATH_FUNC_F(cosh, x); - } -}; - -template<> -struct SqrtFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sqrt, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * 0.5f / MATH_FUNC_F(sqrt, x); - } -}; - -template<> -struct SquareFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return x * x; } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return dy * 2.0f * x; } -}; - -template<> -struct TanFunctor { - static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(tan, x); } - - static OF_DEVICE_FUNC float Backward(const float x, const float dy) { - return dy * (1.0f / (MATH_FUNC_F(cos, x) * MATH_FUNC_F(cos, x))); - } -}; - -// double version - -template<> -struct AcosFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(acos, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * -RsqrtFunctor::Forward(1.0 - x * x); - } -}; - -template<> -struct AcoshFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(acosh, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * -RsqrtFunctor::Forward(x * x - 1.0); - } -}; - -template<> -struct AsinFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(asin, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * RsqrtFunctor::Forward(1.0 - x * x); - } -}; - -template<> -struct AsinhFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(asinh, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * RsqrtFunctor::Forward(1.0 + x * x); - } -}; - -template<> -struct AtanFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(atan, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (1.0 + x * x)); - } -}; - -template<> -struct AtanhFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(atanh, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (1.0 - x * x)); - } -}; - -template<> -struct NotEqualZeroFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return x != 0; } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0f; } -}; - -template<> -struct CeilFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(ceil, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } -}; - -template<> -struct CosFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(cos, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (-MATH_FUNC_D(sin, x)); - } -}; - -template<> -struct CoshFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(cosh, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * MATH_FUNC_D(sinh, x); - } -}; - -template<> -struct ErfFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(erf, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * 2.0 * RsqrtFunctor::Forward(M_PI) * expf(-x * x); - } -}; - -template<> -struct ErfcFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(erfc, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * -2.0 * RsqrtFunctor::Forward(M_PI) * expf(-x * x); - } -}; - -template<> -struct ExpFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(exp, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * MATH_FUNC_D(exp, x); - } -}; - -template<> -struct Expm1Functor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(expm1, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * MATH_FUNC_D(exp, x); - } -}; - -template<> -struct FloorFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(floor, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } -}; - -template<> -struct LgammaFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(lgamma, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - // TODO(chengcheng): return: dy * digamma(x) - // assert(false); - return 0.0; - } -}; - -template<> -struct LogFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return dy * (1.0 / x); } -}; - -template<> -struct Log2Functor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log2, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (x * MATH_FUNC_D(log, 2.0))); - } -}; - -template<> -struct Log1pFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log1p, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (x + 1.0)); - } -}; - -template<> -struct LogSigmoidFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { - return -MATH_FUNC_D(log, (1.0 + MATH_FUNC_D(exp, -x))); - } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (MATH_FUNC_D(exp, x) + 1.0)); - } -}; - -template<> -struct NegativeFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return -x; } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return -dy; } -}; - -template<> -struct ReciprocalFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return 1.0 / x; } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (-1.0 / (x * x)); - } -}; - -template<> -struct ReciprocalNoNanFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { - if (fabs(x) <= 0.0) { return 0.0; } - return 1.0 / x; - } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - if (fabs(x) <= 0.0) { return 0.0; } - return dy * (-1.0 / (x * x)); - } -}; - -template<> -struct RintFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(rint, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } -}; - -template<> -struct RoundFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(nearbyint, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } -}; - -template<> -struct SigmoidFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { - return 1.0 / (1.0 + MATH_FUNC_D(exp, -x)); - } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - double y = 1.0 / (1.0 + MATH_FUNC_D(exp, -x)); - return dy * (y * (1.0 - y)); - } -}; - -template<> -struct SinFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sin, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * MATH_FUNC_D(cos, x); - } -}; - -template<> -struct SinhFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sinh, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * MATH_FUNC_D(cosh, x); - } -}; - -template<> -struct SqrtFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sqrt, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (double)0.5 / MATH_FUNC_D(sqrt, x); - } -}; - -template<> -struct SquareFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return x * x; } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return dy * 2.0 * x; } -}; - -template<> -struct TanFunctor { - static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(tan, x); } - - static OF_DEVICE_FUNC double Backward(const double x, const double dy) { - return dy * (1.0 / (MATH_FUNC_D(cos, x) * MATH_FUNC_D(cos, x))); - } -}; - -#if defined(__CUDACC__) || defined(__HIPCC__) -// half version - -#define OF_HALF_FUNC __device__ __forceinline__ - -#define MATH_FUNC_H(name, x) __float2half(name##f(__half2float(x))) -#define HALF_VAL_HALF __float2half(0.5f) -#define HALF_VAL_TWO __float2half(2.0f) -#define HALF_VAL_2RSQRT_PI __float2half(1.1283791671f) - -template<> -struct AbsFunctor { - static OF_HALF_FUNC half Forward(const half x) { - return __hlt(x, GetZeroVal()) ? __hneg(x) : x; - } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hlt(x, GetZeroVal()) ? __hneg(dy) : dy; - } -}; - -template<> -struct AcosFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(acos, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hneg(hrsqrt(__hsub(GetOneVal(), __hmul(x, x))))); - } -}; - -template<> -struct AcoshFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(acosh, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrsqrt(__hsub(__hmul(x, x), GetOneVal()))); - } -}; - -template<> -struct AsinFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(asin, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrsqrt(__hsub(GetOneVal(), __hmul(x, x)))); - } -}; - -template<> -struct AsinhFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(asinh, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrsqrt(__hadd(GetOneVal(), __hmul(x, x)))); - } -}; - -template<> -struct AtanFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(atan, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hdiv(GetOneVal(), __hadd(GetOneVal(), __hmul(x, x)))); - } -}; - -template<> -struct AtanhFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(atanh, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hdiv(GetOneVal(), __hsub(GetOneVal(), __hmul(x, x)))); - } -}; - -template<> -struct CeilFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hceil(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct NotEqualZeroFunctor { - static OF_HALF_FUNC half Forward(const half x) { return x != static_cast(0.0); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct CosFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hcos(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hneg(hsin(x))); - } -}; - -template<> -struct CoshFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(cosh, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, MATH_FUNC_H(sinh, x)); - } -}; - -template<> -struct ErfFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(erf, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hmul(HALF_VAL_2RSQRT_PI, hexp(__hmul(__hneg(x), x)))); - } -}; - -template<> -struct ErfcFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(erfc, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hneg(__hmul(HALF_VAL_2RSQRT_PI, hexp(__hmul(__hneg(x), x))))); - } -}; - -template<> -struct ExpFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hexp(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hexp(x)); } -}; - -template<> -struct Expm1Functor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(expm1, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hexp(x)); } -}; - -template<> -struct FloorFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hfloor(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct LgammaFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(lgamma, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - // TODO(chengcheng): return: dy * digamma(x) - // assert(false); - return GetZeroVal(); - } -}; - -template<> -struct LogFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hlog(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hrcp(x)); } -}; - -template<> -struct Log2Functor { - static OF_HALF_FUNC half Forward(const half x) { return hlog2(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrcp(__hmul(x, hlog(HALF_VAL_TWO)))); - } -}; - -template<> -struct Log1pFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(log1p, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrcp(__hadd(x, GetOneVal()))); - } -}; - -template<> -struct LogSigmoidFunctor { - static OF_HALF_FUNC half Forward(const half x) { - return __hneg(hlog(__hadd(GetOneVal(), hexp(__hneg(x))))); - } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrcp(__hadd(hexp(x), GetOneVal()))); - } -}; - -template<> -struct NegativeFunctor { - static OF_HALF_FUNC half Forward(const half x) { return __hneg(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hneg(dy); } -}; - -template<> -struct ReciprocalFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hrcp(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hneg(hrcp(__hmul(x, x)))); - } -}; - -template<> -struct ReciprocalNoNanFunctor { - static OF_HALF_FUNC half Forward(const half x) { - if (__heq(GetZeroVal(), x)) { return GetZeroVal(); } - return hrcp(x); - } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - if (__heq(GetZeroVal(), x)) { return GetZeroVal(); } - return __hmul(dy, __hneg(hrcp(__hmul(x, x)))); - } -}; - -template<> -struct RintFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hrint(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct RoundFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(nearbyint, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct RsqrtFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hrsqrt(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hneg(hrcp(__hmul(HALF_VAL_TWO, hsqrt(__hmul(x, __hmul(x, x))))))); - } -}; - -template<> -struct SigmoidFunctor { - static OF_HALF_FUNC half Forward(const half x) { - return hrcp(__hadd(GetOneVal(), hexp(__hneg(x)))); - } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - half y = hrcp(__hadd(GetOneVal(), hexp(__hneg(x)))); - return __hmul(dy, __hmul(y, __hsub(GetOneVal(), y))); - } -}; - -template<> -struct SignFunctor { - static OF_HALF_FUNC half Forward(const half x) { - if (__hgt(x, GetZeroVal())) { return GetOneVal(); } - if (__hlt(x, GetZeroVal())) { return __hneg(GetOneVal()); } - return GetZeroVal(); - } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } -}; - -template<> -struct SinFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hsin(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hcos(x)); } -}; - -template<> -struct SinhFunctor { - static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(sinh, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, MATH_FUNC_H(cosh, x)); - } -}; - -template<> -struct SqrtFunctor { - static OF_HALF_FUNC half Forward(const half x) { return hsqrt(x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hdiv(HALF_VAL_HALF, hsqrt(x))); - } -}; - -template<> -struct SquareFunctor { - static OF_HALF_FUNC half Forward(const half x) { return __hmul(x, x); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, __hmul(HALF_VAL_TWO, x)); - } -}; - -template<> -struct TanFunctor { - static OF_HALF_FUNC half Forward(const half x) { return __hdiv(hsin(x), hcos(x)); } - - static OF_HALF_FUNC half Backward(const half x, const half dy) { - return __hmul(dy, hrcp(__hmul(hcos(x), hcos(x)))); - } -}; - -#endif - -} // namespace oneflow - -#endif // ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ +#define ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ + +#include "oneflow/core/common/util.h" +#include "oneflow/core/common/data_type.h" +#include "oneflow/user/ops/math_unary_elementwise_seq.h" +#include "oneflow/core/device/cuda_pseudo_half.h" + +#if defined(__CUDACC__) + +#include +#define MATH_FUNC_F(name, x) name##f(x) +#define MATH_FUNC_D(name, x) name(x) + +#elif defined(__HIPCC__) +#include +#include + +#if defined(__HIP_DEVICE_COMPILE__) +#define MATH_FUNC_F(name, x) name##f(x) +#define MATH_FUNC_D(name, x) name(x) +#else +#define MATH_FUNC_F(name, x) std::name(x) +#define MATH_FUNC_D(name, x) std::name(x) +#endif + +#else + +#include +#define MATH_FUNC_F(name, x) std::name(x) +#define MATH_FUNC_D(name, x) std::name(x) + +#endif + +namespace oneflow { + +#define DECLARE_UNARY_FUNCTOR(math_unary_elementwise_type, func_prefix) \ + template \ + struct func_prefix##Functor; + +OF_PP_FOR_EACH_TUPLE(DECLARE_UNARY_FUNCTOR, MATH_UNARY_ELEMENTWISE_FUNC_SEQ) + +template +struct AbsFunctor { + static OF_DEVICE_FUNC T Forward(const T x) { + if (x == T(0)) + return T(0); + else + return x < T(0) ? -x : x; + } + + static OF_DEVICE_FUNC T Backward(const T x, const T dy) { + if (x == T(0)) + return T(0); + else + return x < T(0) ? -dy : dy; + } +}; + +template +struct SignFunctor { + static OF_DEVICE_FUNC T Forward(const T x) { return (T(0) < x) - (x < T(0)); } + + static OF_DEVICE_FUNC T Backward(const T x, const T dy) { return T(0); } +}; + +template<> +struct RsqrtFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { +#if defined(__CUDACC__) + return rsqrtf(x); +#elif defined(__HIP_DEVICE_COMPILE__) + return rsqrtf(x); +#else + return 1.0f / std::sqrt(x); +#endif + } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (-1.0f / (2.0f * MATH_FUNC_F(sqrt, x * x * x))); + } +}; + +template<> +struct RsqrtFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { +#if defined(__CUDACC__) + return rsqrt(x); +#elif defined(__HIP_DEVICE_COMPILE__) + return rsqrt(x); +#else + return 1.0 / std::sqrt(x); +#endif + } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (-1.0 / (2.0 * MATH_FUNC_D(sqrt, x * x * x))); + } +}; + +// float version + +template<> +struct AcosFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(acos, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * -RsqrtFunctor::Forward(1.0f - x * x); + } +}; + +template<> +struct AcoshFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(acosh, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * RsqrtFunctor::Forward(x * x - 1.0f); + } +}; + +template<> +struct AsinFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(asin, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * RsqrtFunctor::Forward(1.0f - x * x); + } +}; + +template<> +struct AsinhFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(asinh, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * RsqrtFunctor::Forward(1.0f + x * x); + } +}; + +template<> +struct AtanFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(atan, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (1.0f + x * x)); + } +}; + +template<> +struct AtanhFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(atanh, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (1.0f - x * x)); + } +}; + +template<> +struct NotEqualZeroFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return x != 0; } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } +}; + +template<> +struct CeilFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(ceil, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } +}; + +template<> +struct CosFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(cos, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (-MATH_FUNC_F(sin, x)); + } +}; + +template<> +struct CoshFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(cosh, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * MATH_FUNC_F(sinh, x); + } +}; + +template<> +struct ErfFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(erf, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * 2.0f * RsqrtFunctor::Forward(M_PI) * expf(-x * x); + } +}; + +template<> +struct ErfcFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(erfc, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * -2.0f * RsqrtFunctor::Forward(M_PI) * expf(-x * x); + } +}; + +template<> +struct ExpFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(exp, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * MATH_FUNC_F(exp, x); + } +}; + +template<> +struct Expm1Functor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(expm1, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * MATH_FUNC_F(exp, x); + } +}; + +template<> +struct FloorFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(floor, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } +}; + +template<> +struct LgammaFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(lgamma, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + // TODO(chengcheng): return: dy * digamma(x) + // assert(false); + return 0.0f; + } +}; + +template<> +struct LogFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return dy * (1.0f / x); } +}; + +template<> +struct Log2Functor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log2, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (x * MATH_FUNC_F(log, 2.0f))); + } +}; + +template<> +struct Log1pFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(log1p, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (x + 1.0f)); + } +}; + +template<> +struct LogSigmoidFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { + return -MATH_FUNC_F(log, (1.0f + MATH_FUNC_F(exp, -x))); + } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (MATH_FUNC_F(exp, x) + 1.0f)); + } +}; + +template<> +struct NegativeFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return -x; } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return -dy; } +}; + +template<> +struct ReciprocalFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return 1.0f / x; } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (-1.0f / (x * x)); + } +}; + +template<> +struct ReciprocalNoNanFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { + if (fabsf(x) <= 0.0f) { return 0.0f; } + return 1.0f / x; + } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + if (fabsf(x) <= 0.0f) { return 0.0f; } + return dy * (-1.0f / (x * x)); + } +}; + +template<> +struct RintFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(rint, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } +}; + +template<> +struct RoundFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(nearbyint, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return 0.0f; } +}; + +template<> +struct SigmoidFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { + return 1.0f / (1.0f + MATH_FUNC_F(exp, -x)); + } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + float y = 1.0f / (1.0f + MATH_FUNC_F(exp, -x)); + return dy * (y * (1.0f - y)); + } +}; + +template<> +struct SinFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sin, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * MATH_FUNC_F(cos, x); + } +}; + +template<> +struct SinhFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sinh, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * MATH_FUNC_F(cosh, x); + } +}; + +template<> +struct SqrtFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(sqrt, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * 0.5f / MATH_FUNC_F(sqrt, x); + } +}; + +template<> +struct SquareFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return x * x; } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { return dy * 2.0f * x; } +}; + +template<> +struct TanFunctor { + static OF_DEVICE_FUNC float Forward(const float x) { return MATH_FUNC_F(tan, x); } + + static OF_DEVICE_FUNC float Backward(const float x, const float dy) { + return dy * (1.0f / (MATH_FUNC_F(cos, x) * MATH_FUNC_F(cos, x))); + } +}; + +// double version + +template<> +struct AcosFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(acos, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * -RsqrtFunctor::Forward(1.0 - x * x); + } +}; + +template<> +struct AcoshFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(acosh, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * -RsqrtFunctor::Forward(x * x - 1.0); + } +}; + +template<> +struct AsinFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(asin, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * RsqrtFunctor::Forward(1.0 - x * x); + } +}; + +template<> +struct AsinhFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(asinh, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * RsqrtFunctor::Forward(1.0 + x * x); + } +}; + +template<> +struct AtanFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(atan, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (1.0 + x * x)); + } +}; + +template<> +struct AtanhFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(atanh, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (1.0 - x * x)); + } +}; + +template<> +struct NotEqualZeroFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return x != 0; } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0f; } +}; + +template<> +struct CeilFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(ceil, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } +}; + +template<> +struct CosFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(cos, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (-MATH_FUNC_D(sin, x)); + } +}; + +template<> +struct CoshFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(cosh, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * MATH_FUNC_D(sinh, x); + } +}; + +template<> +struct ErfFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(erf, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * 2.0 * RsqrtFunctor::Forward(M_PI) * expf(-x * x); + } +}; + +template<> +struct ErfcFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(erfc, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * -2.0 * RsqrtFunctor::Forward(M_PI) * expf(-x * x); + } +}; + +template<> +struct ExpFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(exp, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * MATH_FUNC_D(exp, x); + } +}; + +template<> +struct Expm1Functor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(expm1, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * MATH_FUNC_D(exp, x); + } +}; + +template<> +struct FloorFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(floor, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } +}; + +template<> +struct LgammaFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(lgamma, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + // TODO(chengcheng): return: dy * digamma(x) + // assert(false); + return 0.0; + } +}; + +template<> +struct LogFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return dy * (1.0 / x); } +}; + +template<> +struct Log2Functor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log2, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (x * MATH_FUNC_D(log, 2.0))); + } +}; + +template<> +struct Log1pFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(log1p, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (x + 1.0)); + } +}; + +template<> +struct LogSigmoidFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { + return -MATH_FUNC_D(log, (1.0 + MATH_FUNC_D(exp, -x))); + } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (MATH_FUNC_D(exp, x) + 1.0)); + } +}; + +template<> +struct NegativeFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return -x; } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return -dy; } +}; + +template<> +struct ReciprocalFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return 1.0 / x; } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (-1.0 / (x * x)); + } +}; + +template<> +struct ReciprocalNoNanFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { + if (fabs(x) <= 0.0) { return 0.0; } + return 1.0 / x; + } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + if (fabs(x) <= 0.0) { return 0.0; } + return dy * (-1.0 / (x * x)); + } +}; + +template<> +struct RintFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(rint, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } +}; + +template<> +struct RoundFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(nearbyint, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return 0.0; } +}; + +template<> +struct SigmoidFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { + return 1.0 / (1.0 + MATH_FUNC_D(exp, -x)); + } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + double y = 1.0 / (1.0 + MATH_FUNC_D(exp, -x)); + return dy * (y * (1.0 - y)); + } +}; + +template<> +struct SinFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sin, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * MATH_FUNC_D(cos, x); + } +}; + +template<> +struct SinhFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sinh, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * MATH_FUNC_D(cosh, x); + } +}; + +template<> +struct SqrtFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(sqrt, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (double)0.5 / MATH_FUNC_D(sqrt, x); + } +}; + +template<> +struct SquareFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return x * x; } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { return dy * 2.0 * x; } +}; + +template<> +struct TanFunctor { + static OF_DEVICE_FUNC double Forward(const double x) { return MATH_FUNC_D(tan, x); } + + static OF_DEVICE_FUNC double Backward(const double x, const double dy) { + return dy * (1.0 / (MATH_FUNC_D(cos, x) * MATH_FUNC_D(cos, x))); + } +}; + +#if defined(__CUDACC__) || defined(__HIPCC__) +// half version + +#define OF_HALF_FUNC __device__ __forceinline__ + +#define MATH_FUNC_H(name, x) __float2half(name##f(__half2float(x))) +#define HALF_VAL_HALF __float2half(0.5f) +#define HALF_VAL_TWO __float2half(2.0f) +#define HALF_VAL_2RSQRT_PI __float2half(1.1283791671f) + +template<> +struct AbsFunctor { + static OF_HALF_FUNC half Forward(const half x) { + return __hlt(x, GetZeroVal()) ? __hneg(x) : x; + } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hlt(x, GetZeroVal()) ? __hneg(dy) : dy; + } +}; + +template<> +struct AcosFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(acos, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hneg(hrsqrt(__hsub(GetOneVal(), __hmul(x, x))))); + } +}; + +template<> +struct AcoshFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(acosh, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrsqrt(__hsub(__hmul(x, x), GetOneVal()))); + } +}; + +template<> +struct AsinFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(asin, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrsqrt(__hsub(GetOneVal(), __hmul(x, x)))); + } +}; + +template<> +struct AsinhFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(asinh, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrsqrt(__hadd(GetOneVal(), __hmul(x, x)))); + } +}; + +template<> +struct AtanFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(atan, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hdiv(GetOneVal(), __hadd(GetOneVal(), __hmul(x, x)))); + } +}; + +template<> +struct AtanhFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(atanh, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hdiv(GetOneVal(), __hsub(GetOneVal(), __hmul(x, x)))); + } +}; + +template<> +struct CeilFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hceil(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct NotEqualZeroFunctor { + static OF_HALF_FUNC half Forward(const half x) { return x != static_cast(0.0); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct CosFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hcos(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hneg(hsin(x))); + } +}; + +template<> +struct CoshFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(cosh, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, MATH_FUNC_H(sinh, x)); + } +}; + +template<> +struct ErfFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(erf, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hmul(HALF_VAL_2RSQRT_PI, hexp(__hmul(__hneg(x), x)))); + } +}; + +template<> +struct ErfcFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(erfc, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hneg(__hmul(HALF_VAL_2RSQRT_PI, hexp(__hmul(__hneg(x), x))))); + } +}; + +template<> +struct ExpFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hexp(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hexp(x)); } +}; + +template<> +struct Expm1Functor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(expm1, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hexp(x)); } +}; + +template<> +struct FloorFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hfloor(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct LgammaFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(lgamma, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + // TODO(chengcheng): return: dy * digamma(x) + // assert(false); + return GetZeroVal(); + } +}; + +template<> +struct LogFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hlog(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hrcp(x)); } +}; + +template<> +struct Log2Functor { + static OF_HALF_FUNC half Forward(const half x) { return hlog2(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrcp(__hmul(x, hlog(HALF_VAL_TWO)))); + } +}; + +template<> +struct Log1pFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(log1p, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrcp(__hadd(x, GetOneVal()))); + } +}; + +template<> +struct LogSigmoidFunctor { + static OF_HALF_FUNC half Forward(const half x) { + return __hneg(hlog(__hadd(GetOneVal(), hexp(__hneg(x))))); + } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrcp(__hadd(hexp(x), GetOneVal()))); + } +}; + +template<> +struct NegativeFunctor { + static OF_HALF_FUNC half Forward(const half x) { return __hneg(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hneg(dy); } +}; + +template<> +struct ReciprocalFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hrcp(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hneg(hrcp(__hmul(x, x)))); + } +}; + +template<> +struct ReciprocalNoNanFunctor { + static OF_HALF_FUNC half Forward(const half x) { + if (__heq(GetZeroVal(), x)) { return GetZeroVal(); } + return hrcp(x); + } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + if (__heq(GetZeroVal(), x)) { return GetZeroVal(); } + return __hmul(dy, __hneg(hrcp(__hmul(x, x)))); + } +}; + +template<> +struct RintFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hrint(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct RoundFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(nearbyint, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct RsqrtFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hrsqrt(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hneg(hrcp(__hmul(HALF_VAL_TWO, hsqrt(__hmul(x, __hmul(x, x))))))); + } +}; + +template<> +struct SigmoidFunctor { + static OF_HALF_FUNC half Forward(const half x) { + return hrcp(__hadd(GetOneVal(), hexp(__hneg(x)))); + } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + half y = hrcp(__hadd(GetOneVal(), hexp(__hneg(x)))); + return __hmul(dy, __hmul(y, __hsub(GetOneVal(), y))); + } +}; + +template<> +struct SignFunctor { + static OF_HALF_FUNC half Forward(const half x) { + if (__hgt(x, GetZeroVal())) { return GetOneVal(); } + if (__hlt(x, GetZeroVal())) { return __hneg(GetOneVal()); } + return GetZeroVal(); + } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return GetZeroVal(); } +}; + +template<> +struct SinFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hsin(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { return __hmul(dy, hcos(x)); } +}; + +template<> +struct SinhFunctor { + static OF_HALF_FUNC half Forward(const half x) { return MATH_FUNC_H(sinh, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, MATH_FUNC_H(cosh, x)); + } +}; + +template<> +struct SqrtFunctor { + static OF_HALF_FUNC half Forward(const half x) { return hsqrt(x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hdiv(HALF_VAL_HALF, hsqrt(x))); + } +}; + +template<> +struct SquareFunctor { + static OF_HALF_FUNC half Forward(const half x) { return __hmul(x, x); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, __hmul(HALF_VAL_TWO, x)); + } +}; + +template<> +struct TanFunctor { + static OF_HALF_FUNC half Forward(const half x) { return __hdiv(hsin(x), hcos(x)); } + + static OF_HALF_FUNC half Backward(const half x, const half dy) { + return __hmul(dy, hrcp(__hmul(hcos(x), hcos(x)))); + } +}; + +#endif + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_MATH_UNARY_ELEMENTWISE_FUNC_H_ diff --git a/oneflow/user/kernels/math_unary_elementwise_kernel.cpp b/oneflow/user/kernels/math_unary_elementwise_kernel.cpp deleted file mode 100644 index 40e5f6a004a63ede723b10e24d71bf1b149ac6bc..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/math_unary_elementwise_kernel.cpp +++ /dev/null @@ -1,87 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/math_unary_elementwise_func.h" - -namespace oneflow { - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseCpuKernel final : public user_op::OpKernel { - public: - MathUnaryElementwiseCpuKernel() = default; - ~MathUnaryElementwiseCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* x = tensor_x->dptr(); - T* y = tensor_y->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - CHECK_LE(n, GetMaxVal() / 2); - for (int32_t i = 0; i < n; ++i) { y[i] = UnaryFunctor::Forward(x[i]); } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseGradCpuKernel final : public user_op::OpKernel { - public: - MathUnaryElementwiseGradCpuKernel() = default; - ~MathUnaryElementwiseGradCpuKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const T* x = tensor_x->dptr(); - const T* dy = tensor_dy->dptr(); - T* dx = tensor_dx->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - CHECK_LE(n, GetMaxVal() / 2); - for (int32_t i = 0; i < n; ++i) { dx[i] = UnaryFunctor::Backward(x[i], dy[i]); } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_UNARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD(math_type_pair, data_type_pair) \ - REGISTER_USER_KERNEL(OF_PP_PAIR_FIRST(math_type_pair)) \ - .SetCreateFn< \ - MathUnaryElementwiseCpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair)) \ - && (user_op::HobDataType("y", 0) == OF_PP_PAIR_SECOND(data_type_pair))); \ - \ - REGISTER_USER_KERNEL((std::string("") + OF_PP_PAIR_FIRST(math_type_pair) + "_grad")) \ - .SetCreateFn< \ - MathUnaryElementwiseGradCpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD, - MATH_UNARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) - -// For some special dtype kernel register. -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), UNSIGNED_INT_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CPU_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), INT_DATA_TYPE_SEQ) - -} // namespace oneflow diff --git a/oneflow/user/kernels/math_unary_elementwise_kernel.cu b/oneflow/user/kernels/math_unary_elementwise_kernel.cu deleted file mode 100644 index 74ee49aa004144f002f8341f671f9b926241b3ad..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/math_unary_elementwise_kernel.cu +++ /dev/null @@ -1,176 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/math_unary_elementwise_func.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/cuda/cuda_stream.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template class UnaryFunctor, typename T> -__global__ void MathUnaryElementwiseForwardGpu(const int64_t n, const T* x, T* y) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { y[i] = UnaryFunctor::Forward(x[i]); } -} - -template class UnaryFunctor, typename T> -__global__ void MathUnaryElementwiseBackwardGpu(const int64_t n, const T* x, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { dx[i] = UnaryFunctor::Backward(x[i], dy[i]); } -} - -} // namespace - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGpuKernel() = default; - ~MathUnaryElementwiseGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* x = tensor_x->dptr(); - T* y = tensor_y->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, y); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseGradGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGradGpuKernel() = default; - ~MathUnaryElementwiseGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const T* x = tensor_x->dptr(); - const T* dy = tensor_dy->dptr(); - T* dx = tensor_dx->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseBackwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, dy, dx); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD(math_type_pair, data_type_pair) \ - REGISTER_USER_KERNEL(OF_PP_PAIR_FIRST(math_type_pair)) \ - .SetCreateFn< \ - MathUnaryElementwiseGpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair)) \ - && (user_op::HobDataType("y", 0) == OF_PP_PAIR_SECOND(data_type_pair))); \ - \ - REGISTER_USER_KERNEL((std::string("") + OF_PP_PAIR_FIRST(math_type_pair) + "_grad")) \ - .SetCreateFn< \ - MathUnaryElementwiseGradGpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - MATH_UNARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) - -// For some special dtype kernel register. -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), UNSIGNED_INT_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), INT_DATA_TYPE_SEQ) - -template class UnaryFunctor> -class MathUnaryElementwiseGpuHalfKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGpuHalfKernel() = default; - ~MathUnaryElementwiseGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const half* x = reinterpret_cast(tensor_x->dptr()); - half* y = reinterpret_cast(tensor_y->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, y); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class UnaryFunctor> -class MathUnaryElementwiseGradGpuHalfKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGradGpuHalfKernel() = default; - ~MathUnaryElementwiseGradGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const half* x = reinterpret_cast(tensor_x->dptr()); - const half* dy = reinterpret_cast(tensor_dy->dptr()); - half* dx = reinterpret_cast(tensor_dx->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseBackwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, dy, dx); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_UNARY_ELEMENTWISE_GUDA_HALF_KERNEL_AND_GRAD(math_type_str, math_func_prefix) \ - REGISTER_USER_KERNEL(math_type_str) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16) \ - && (user_op::HobDataType("y", 0) == DataType::kFloat16)); \ - \ - REGISTER_USER_KERNEL((std::string("") + math_type_str + "_grad")) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16)); - -OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_GUDA_HALF_KERNEL_AND_GRAD, - MATH_UNARY_ELEMENTWISE_FUNC_SEQ) - -} // namespace oneflow diff --git a/oneflow/user/kernels/math_unary_elementwise_kernel.hip.cpp b/oneflow/user/kernels/math_unary_elementwise_kernel.hip.cpp deleted file mode 100644 index 160dd50126ea690eef168062e165ee7154633cb6..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/math_unary_elementwise_kernel.hip.cpp +++ /dev/null @@ -1,177 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/math_unary_elementwise_func.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template class UnaryFunctor, typename T> -__global__ void MathUnaryElementwiseForwardGpu(const int64_t n, const T* x, T* y) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { y[i] = UnaryFunctor::Forward(x[i]); } -} - -template class UnaryFunctor, typename T> -__global__ void MathUnaryElementwiseBackwardGpu(const int64_t n, const T* x, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, n) { dx[i] = UnaryFunctor::Backward(x[i], dy[i]); } -} - -} // namespace - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGpuKernel() = default; - ~MathUnaryElementwiseGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* x = tensor_x->dptr(); - T* y = tensor_y->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, y); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class UnaryFunctor, typename T> -class MathUnaryElementwiseGradGpuKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGradGpuKernel() = default; - ~MathUnaryElementwiseGradGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const T* x = tensor_x->dptr(); - const T* dy = tensor_dy->dptr(); - T* dx = tensor_dx->mut_dptr(); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseBackwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, dy, dx); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD(math_type_pair, data_type_pair) \ - REGISTER_USER_KERNEL(OF_PP_PAIR_FIRST(math_type_pair)) \ - .SetCreateFn< \ - MathUnaryElementwiseGpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair)) \ - && (user_op::HobDataType("y", 0) == OF_PP_PAIR_SECOND(data_type_pair))); \ - \ - REGISTER_USER_KERNEL((std::string("") + OF_PP_PAIR_FIRST(math_type_pair) + "_grad")) \ - .SetCreateFn< \ - MathUnaryElementwiseGradGpuKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - MATH_UNARY_ELEMENTWISE_FUNC_SEQ, FLOATING_DATA_TYPE_SEQ) - -// For some special dtype kernel register. -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), UNSIGNED_INT_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_CUDA_KERNEL_AND_GRAD, - OF_PP_MAKE_TUPLE_SEQ("abs", Abs), INT_DATA_TYPE_SEQ) - -template class UnaryFunctor> -class MathUnaryElementwiseGpuHalfKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGpuHalfKernel() = default; - ~MathUnaryElementwiseGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - const half* x = reinterpret_cast(tensor_x->dptr()); - half* y = reinterpret_cast(tensor_y->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseForwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, y); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template class UnaryFunctor> -class MathUnaryElementwiseGradGpuHalfKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - MathUnaryElementwiseGradGpuHalfKernel() = default; - ~MathUnaryElementwiseGradGpuHalfKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* tensor_dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* tensor_dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const half* x = reinterpret_cast(tensor_x->dptr()); - const half* dy = reinterpret_cast(tensor_dy->dptr()); - half* dx = reinterpret_cast(tensor_dx->mut_dptr()); - int64_t n = tensor_x->shape_view().elem_cnt(); - if (n == 0) { return; } - MathUnaryElementwiseBackwardGpu - <<stream()->As()->cuda_stream()>>>(n, x, dy, dx); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MATH_UNARY_ELEMENTWISE_GUDA_HALF_KERNEL_AND_GRAD(math_type_str, math_func_prefix) \ - REGISTER_USER_KERNEL(math_type_str) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16) \ - && (user_op::HobDataType("y", 0) == DataType::kFloat16)); \ - \ - REGISTER_USER_KERNEL((std::string("") + math_type_str + "_grad")) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == DataType::kFloat16)); - -// OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_GUDA_HALF_KERNEL_AND_GRAD, -// MATH_UNARY_ELEMENTWISE_FUNC_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/math_unary_elementwise_primitive_kernel.cpp b/oneflow/user/kernels/math_unary_elementwise_primitive_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..84747243138eeddae774b545af5cc1c128486380 --- /dev/null +++ b/oneflow/user/kernels/math_unary_elementwise_primitive_kernel.cpp @@ -0,0 +1,141 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/ep/include/primitive/binary_op.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" +#include "oneflow/user/kernels/elementwise_primitive_kernel.h" + +namespace oneflow { + +#define MATH_UNARY_ELEMENTWISE_PRIMITIVE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("abs", ep::primitive::UnaryOp::kAbs) \ + OF_PP_MAKE_TUPLE_SEQ("acos", ep::primitive::UnaryOp::kAcos) \ + OF_PP_MAKE_TUPLE_SEQ("acosh", ep::primitive::UnaryOp::kAcosh) \ + OF_PP_MAKE_TUPLE_SEQ("asin", ep::primitive::UnaryOp::kAsin) \ + OF_PP_MAKE_TUPLE_SEQ("asinh", ep::primitive::UnaryOp::kAsinh) \ + OF_PP_MAKE_TUPLE_SEQ("atan", ep::primitive::UnaryOp::kAtan) \ + OF_PP_MAKE_TUPLE_SEQ("atanh", ep::primitive::UnaryOp::kAtanh) \ + OF_PP_MAKE_TUPLE_SEQ("ceil", ep::primitive::UnaryOp::kCeil) \ + OF_PP_MAKE_TUPLE_SEQ("cos", ep::primitive::UnaryOp::kCos) \ + OF_PP_MAKE_TUPLE_SEQ("cosh", ep::primitive::UnaryOp::kCosh) \ + OF_PP_MAKE_TUPLE_SEQ("erf", ep::primitive::UnaryOp::kErf) \ + OF_PP_MAKE_TUPLE_SEQ("erfc", ep::primitive::UnaryOp::kErfc) \ + OF_PP_MAKE_TUPLE_SEQ("exp", ep::primitive::UnaryOp::kExp) \ + OF_PP_MAKE_TUPLE_SEQ("expm1", ep::primitive::UnaryOp::kExpm1) \ + OF_PP_MAKE_TUPLE_SEQ("floor", ep::primitive::UnaryOp::kFloor) \ + OF_PP_MAKE_TUPLE_SEQ("lgamma", ep::primitive::UnaryOp::kLgamma) \ + OF_PP_MAKE_TUPLE_SEQ("log", ep::primitive::UnaryOp::kLog) \ + OF_PP_MAKE_TUPLE_SEQ("log2", ep::primitive::UnaryOp::kLog2) \ + OF_PP_MAKE_TUPLE_SEQ("log10", ep::primitive::UnaryOp::kLog10) \ + OF_PP_MAKE_TUPLE_SEQ("log1p", ep::primitive::UnaryOp::kLog1p) \ + OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", ep::primitive::UnaryOp::kLogSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ("negative", ep::primitive::UnaryOp::kNegative) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal", ep::primitive::UnaryOp::kReciprocal) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan", ep::primitive::UnaryOp::kReciprocalNoNan) \ + OF_PP_MAKE_TUPLE_SEQ("rint", ep::primitive::UnaryOp::kRint) \ + OF_PP_MAKE_TUPLE_SEQ("round", ep::primitive::UnaryOp::kRound) \ + OF_PP_MAKE_TUPLE_SEQ("rsqrt", ep::primitive::UnaryOp::kRsqrt) \ + OF_PP_MAKE_TUPLE_SEQ("sigmoid", ep::primitive::UnaryOp::kSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ("sign", ep::primitive::UnaryOp::kSign) \ + OF_PP_MAKE_TUPLE_SEQ("sin", ep::primitive::UnaryOp::kSin) \ + OF_PP_MAKE_TUPLE_SEQ("sinh", ep::primitive::UnaryOp::kSinh) \ + OF_PP_MAKE_TUPLE_SEQ("sqrt", ep::primitive::UnaryOp::kSqrt) \ + OF_PP_MAKE_TUPLE_SEQ("square", ep::primitive::UnaryOp::kSquare) \ + OF_PP_MAKE_TUPLE_SEQ("tan", ep::primitive::UnaryOp::kTan) \ + OF_PP_MAKE_TUPLE_SEQ("not_equal_zero", ep::primitive::UnaryOp::kNotEqualZero) + +#define MATH_UNARY_ELEMENTWISE_GRAD_WITH_DY_X_PRIMITIVE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("abs_grad", ep::primitive::BinaryOp::kAbsBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("acos_grad", ep::primitive::BinaryOp::kAcosBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("acosh_grad", ep::primitive::BinaryOp::kAcoshBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("asin_grad", ep::primitive::BinaryOp::kAsinBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("asinh_grad", ep::primitive::BinaryOp::kAsinhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("atan_grad", ep::primitive::BinaryOp::kAtanBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("atanh_grad", ep::primitive::BinaryOp::kAtanhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("cos_grad", ep::primitive::BinaryOp::kCosBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("cosh_grad", ep::primitive::BinaryOp::kCoshBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("erf_grad", ep::primitive::BinaryOp::kErfBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("erfc_grad", ep::primitive::BinaryOp::kErfcBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("exp_grad", ep::primitive::BinaryOp::kExpBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("expm1_grad", ep::primitive::BinaryOp::kExpm1BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("log_grad", ep::primitive::BinaryOp::kLogBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("lgamma_grad", ep::primitive::BinaryOp::kLgammaBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("log2_grad", ep::primitive::BinaryOp::kLog2BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("log10_grad", ep::primitive::BinaryOp::kLog10BackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("log1p_grad", ep::primitive::BinaryOp::kLog1pBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("log_sigmoid_grad", ep::primitive::BinaryOp::kLogSigmoidBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_grad", ep::primitive::BinaryOp::kReciprocalBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan_grad", \ + ep::primitive::BinaryOp::kReciprocalNoNanBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("rsqrt_grad", ep::primitive::BinaryOp::kRsqrtBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("sin_grad", ep::primitive::BinaryOp::kSinBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("sinh_grad", ep::primitive::BinaryOp::kSinhBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("sqrt_grad", ep::primitive::BinaryOp::kSqrtBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("square_grad", ep::primitive::BinaryOp::kSquareBackwardWithDyX) \ + OF_PP_MAKE_TUPLE_SEQ("tan_grad", ep::primitive::BinaryOp::kTanBackwardWithDyX) + +#define MATH_UNARY_ELEMENTWISE_GRAD_WITH_DY_Y_PRIMITIVE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("sigmoid_grad", ep::primitive::BinaryOp::kSigmoidBackwardWithDyY) + +#define REGISTER_MATH_UNARY_PRIMITIVE_KERNEL(name, UnaryOp) \ + REGISTER_USER_KERNEL(name) \ + .SetCreateFn([]() { \ + return user_op::NewOpKernel( \ + "y", "x", [](user_op::KernelComputeContext* ctx) { \ + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("y", 0); \ + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("x", 0); \ + return ep::primitive::NewPrimitive( \ + ctx->device_type(), UnaryOp, src->data_type(), dst->data_type()); \ + }); \ + }) \ + .SetIsMatchedHob(UnaryPrimitiveExists(UnaryOp, "y", "x")); + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_PRIMITIVE_KERNEL, MATH_UNARY_ELEMENTWISE_PRIMITIVE_SEQ) + +#define REGISTER_MATH_UNARY_GRAD_PRIMITIVE_WITH_DY_X_KERNEL(name, BinaryOp) \ + REGISTER_USER_KERNEL(name) \ + .SetCreateFn([]() { \ + return user_op::NewOpKernel< \ + BinaryPrimitiveKernel>("dx", "dy", "x", [](user_op::KernelComputeContext* ctx) { \ + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("dy", 0); \ + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("dx", 0); \ + return ep::primitive::NewPrimitive( \ + ctx->device_type(), BinaryOp, src->data_type(), dst->data_type(), \ + 1 /*max_num_dims*/); \ + }); \ + }) \ + .SetIsMatchedHob(BinaryPrimitiveExists(BinaryOp, "dx", "dy")); + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_GRAD_PRIMITIVE_WITH_DY_X_KERNEL, + MATH_UNARY_ELEMENTWISE_GRAD_WITH_DY_X_PRIMITIVE_SEQ) + +#define REGISTER_MATH_UNARY_GRAD_PRIMITIVE_WITH_DY_Y_KERNEL(name, BinaryOp) \ + REGISTER_USER_KERNEL(name) \ + .SetCreateFn([]() { \ + return user_op::NewOpKernel< \ + BinaryPrimitiveKernel>("dx", "dy", "y", [](user_op::KernelComputeContext* ctx) { \ + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("dy", 0); \ + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("dx", 0); \ + return ep::primitive::NewPrimitive( \ + ctx->device_type(), BinaryOp, src->data_type(), dst->data_type(), \ + 1 /*max_num_dims*/); \ + }); \ + }) \ + .SetIsMatchedHob(BinaryPrimitiveExists(BinaryOp, "dx", "dy")); + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_GRAD_PRIMITIVE_WITH_DY_Y_KERNEL, + MATH_UNARY_ELEMENTWISE_GRAD_WITH_DY_Y_PRIMITIVE_SEQ) + +} // namespace oneflow diff --git a/oneflow/user/kernels/matmul_kernels.cpp b/oneflow/user/kernels/matmul_kernels.cpp index e8584f58e62c55ad83aa893e2991d54a5bef02e2..29acfec424d51be280bfe744edfd99091ab97002 100644 --- a/oneflow/user/kernels/matmul_kernels.cpp +++ b/oneflow/user/kernels/matmul_kernels.cpp @@ -20,6 +20,7 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/memcpy.h" #include "oneflow/core/ep/include/primitive/matmul.h" #include "oneflow/core/ep/include/primitive/batch_matmul.h" +#include "oneflow/core/ep/include/primitive/broadcast_matmul.h" namespace oneflow { @@ -96,6 +97,18 @@ std::unique_ptr NewBatchMatmulPrimitive(Context* ctx ctx->device_type(), data_type, trans_a, trans_b); } +template +std::unique_ptr NewBroadcastMatmulPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("out", 0)->data_type(); + const auto trans_a = GetBlasTransposeType(ctx, "transpose_a"); + const auto trans_b = GetBlasTransposeType(ctx, "transpose_b"); + const int64_t a_num_axes = ctx->TensorDesc4ArgNameAndIndex("a", 0)->shape().NumAxes(); + const int64_t b_num_axes = ctx->TensorDesc4ArgNameAndIndex("b", 0)->shape().NumAxes(); + const int64_t max_num_axes = std::max(a_num_axes, b_num_axes); + return ep::primitive::NewPrimitive( + ctx->device_type(), data_type, trans_a, trans_b, max_num_axes); +} + auto MemcpyPrimitiveExists() { return hob::make_custom("MemcpyPrimitiveExists", [](const user_op::KernelRegContext& ctx) { return NewMemcpyPrimitive(&ctx).operator bool(); @@ -114,6 +127,13 @@ auto BatchMatmulPrimitiveExists() { }); } +auto BroadcastMatmulPrimitiveExists() { + return hob::make_custom("BroadcastMatmulPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewBroadcastMatmulPrimitive(&ctx).operator bool(); + }); +} + class MatmulKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MatmulKernel() = default; @@ -131,6 +151,13 @@ class MatmulKernel final : public user_op::OpKernel, public user_op::CudaGraphSu const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); CHECK_EQ(b->shape_view().NumAxes(), 2); CHECK_EQ(b->data_type(), data_type); + + const int32_t elem_cnt_a = a->shape_view().elem_cnt(); + const int32_t elem_cnt_b = b->shape_view().elem_cnt(); + CHECK_GE(elem_cnt_a, 0); + CHECK_GE(elem_cnt_b, 0); + if (elem_cnt_a == 0 || elem_cnt_b == 0) { return; } + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); CHECK_EQ(out->shape_view().NumAxes(), 2); CHECK_EQ(out->data_type(), data_type); @@ -230,7 +257,6 @@ REGISTER_USER_KERNEL("batch_matmul") return Maybe::Ok(); }); -// TODO(liujuncheng): fully support class BroadcastMatmulKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: BroadcastMatmulKernel() = default; @@ -241,9 +267,6 @@ class BroadcastMatmulKernel final : public user_op::OpKernel, public user_op::Cu private: void Compute(user_op::KernelComputeContext* ctx) const override { double alpha = ctx->Attr("alpha"); - bool transpose_a = ctx->Attr("transpose_a"); - bool transpose_b = ctx->Attr("transpose_b"); - CHECK(!transpose_a); const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); @@ -261,27 +284,20 @@ class BroadcastMatmulKernel final : public user_op::OpKernel, public user_op::Cu beta = 1.0; } - CHECK_EQ(b->shape_view().NumAxes(), 2); - CHECK_GT(a->shape_view().NumAxes(), b->shape_view().NumAxes()); - int64_t m = a->shape_view().Count(0, a->shape_view().NumAxes() - 1); - int64_t k = a->shape_view().At(a->shape_view().NumAxes() - 1); - int64_t n = -1; - if (!transpose_b) { - n = b->shape_view().At(1); - CHECK_EQ(k, b->shape_view().At(0)); - } else { - n = b->shape_view().At(0); - CHECK_EQ(k, b->shape_view().At(1)); - } - auto matmul = NewMatmulPrimitive(ctx); - CHECK(matmul); - matmul->Launch(ctx->stream(), m, n, k, alpha, a->dptr(), b->dptr(), beta, out->mut_dptr()); + const int64_t a_num_axes = a->shape_view().NumAxes(); + const int64_t b_num_axes = b->shape_view().NumAxes(); + const int64_t out_num_axes = out->shape_view().NumAxes(); + auto broadcast_matmul = NewBroadcastMatmulPrimitive(ctx); + CHECK(broadcast_matmul); + broadcast_matmul->Launch(ctx->stream(), alpha, a_num_axes, a->shape_view().ptr(), a->dptr(), + b_num_axes, b->shape_view().ptr(), b->dptr(), beta, out_num_axes, + out->shape_view().ptr(), out->mut_dptr()); } }; REGISTER_USER_KERNEL("broadcast_matmul") .SetCreateFn() - .SetIsMatchedHob(MemcpyPrimitiveExists() && MatmulPrimitiveExists()) + .SetIsMatchedHob(MemcpyPrimitiveExists() && BroadcastMatmulPrimitiveExists()) .SetInplaceProposalFn([](const user_op::InferContext& ctx, const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { if (ctx.has_input("_add_to_output", 0)) { @@ -310,7 +326,6 @@ class BroadcastMatmulGradBKernel final : public user_op::OpKernel, const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - double beta = 0.0; if (ctx->has_input("_add_to_output", 0)) { const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); @@ -328,7 +343,6 @@ class BroadcastMatmulGradBKernel final : public user_op::OpKernel, CHECK_EQ(b->shape_view().Count(0, b->shape_view().NumAxes() - 1), k); int64_t m = a->shape_view().At(a->shape_view().NumAxes() - 1); int64_t n = b->shape_view().At(b->shape_view().NumAxes() - 1); - auto matmul = NewMatmulPrimitiveForBroadcastMatmulGradB(ctx); CHECK(matmul); matmul->Launch(ctx->stream(), m, n, k, alpha, a->dptr(), b->dptr(), beta, out->mut_dptr()); @@ -351,7 +365,6 @@ REGISTER_USER_KERNEL("broadcast_matmul_grad_b") } return Maybe::Ok(); }); - } // namespace } // namespace oneflow diff --git a/oneflow/user/kernels/matrix_vector_product_kernel.cpp b/oneflow/user/kernels/matrix_vector_product_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5e0c4bb794a50bb5b6d37de6a4c8ff983b6df13a --- /dev/null +++ b/oneflow/user/kernels/matrix_vector_product_kernel.cpp @@ -0,0 +1,187 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/framework/config_def.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/memcpy.h" +#include "oneflow/core/ep/include/primitive/matmul.h" + +namespace oneflow { + +namespace { + +ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { + return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; +} + +template +std::unique_ptr NewMemcpyPrimitive(Context* ctx) { + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::MemcpyKind::kDtoD); +} + +std::unique_ptr NewMatmulPrimitive(DeviceType device_type, + DataType data_type, bool transpose_a, + bool transpose_b) { + const auto trans_a = GetBlasTransposeType(transpose_a); + const auto trans_b = GetBlasTransposeType(transpose_b); + return ep::primitive::NewPrimitive(device_type, data_type, trans_a, + trans_b); +} + +template +std::unique_ptr NewMatrixVectorProductPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("out", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, false, false); +} + +template +std::unique_ptr NewMatrixVectorProductGradAPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, false, true); +} + +template +std::unique_ptr NewMatrixVectorProductGradBPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, true, false); +} + +auto MatrixVectorProductPrimitiveExists() { + return hob::make_custom("NewMatrixVectorProductPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewMatrixVectorProductPrimitive(&ctx).operator bool(); + }); +} + +auto MatrixVectorProductGradAPrimitiveExists() { + return hob::make_custom("NewMatrixVectorProductGradAPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewMatrixVectorProductGradAPrimitive(&ctx).operator bool(); + }); +} + +auto MatrixVectorProductGradBPrimitiveExists() { + return hob::make_custom("NewMatrixVectorProductGradBPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewMatrixVectorProductGradBPrimitive(&ctx).operator bool(); + }); +} + +class MatrixVectorProductKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + MatrixVectorProductKernel() = default; + ~MatrixVectorProductKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + */ + const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); + CHECK_EQ(a->shape_view().NumAxes(), 2) << "A Numdims should be equal to 2. "; + const DataType data_type = a->data_type(); + const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); + CHECK_EQ(b->shape_view().NumAxes(), 1) << "B Numdims should be equal to 1. "; + CHECK_EQ(b->data_type(), data_type) << "Matrix A Datatype should be equal to Vector B"; + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(out->shape_view().NumAxes(), 1) << "Out Numdims should be equal to 1. "; + CHECK_EQ(out->data_type(), data_type) << "Out Datatype should be equal to input's. "; + size_t m = a->shape_view().At(0); + size_t k = a->shape_view().At(1); + size_t n = 1; + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewMatrixVectorProductPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, a->dptr(), b->dptr(), beta, out->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("matrix_vector_product") + .SetCreateFn() + .SetIsMatchedHob(MatrixVectorProductPrimitiveExists()); + +class MatrixVectorProductGradAKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MatrixVectorProductGradAKernel() = default; + ~MatrixVectorProductGradAKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + GradA = dy (m) matmul B(k) -> (m, 1) (k, 1)_transpose + */ + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + size_t m = dy->shape_view().At(0); + size_t k = 1; + size_t n = b->shape_view().At(0); + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewMatrixVectorProductGradAPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, dy->dptr(), b->dptr(), beta, dx->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("matrix_vector_product_grad_a") + .SetCreateFn() + .SetIsMatchedHob(MatrixVectorProductGradAPrimitiveExists()); + +class MatrixVectorProductGradBKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MatrixVectorProductGradBKernel() = default; + ~MatrixVectorProductGradBKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + GradB = dy_transpose (1, m) matmul A(m, k) + */ + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + size_t m = 1; + size_t k = dy->shape_view().At(0); + size_t n = a->shape_view().At(1); + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewMatrixVectorProductGradBPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, dy->dptr(), a->dptr(), beta, dx->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("matrix_vector_product_grad_b") + .SetCreateFn() + .SetIsMatchedHob(MatrixVectorProductGradBPrimitiveExists()); + +} // namespace + +} // namespace oneflow diff --git a/oneflow/user/kernels/max_pool_kernel.cpp b/oneflow/user/kernels/max_pool_kernel.cpp index 96b49745972f0363f7fcb80833e3e8a5ec9ec87d..9212508a8653febffd0b03335f7ec4929f490239 100644 --- a/oneflow/user/kernels/max_pool_kernel.cpp +++ b/oneflow/user/kernels/max_pool_kernel.cpp @@ -520,13 +520,9 @@ class MaxPool3dGradKernel final : public user_op::OpKernel { REGISTER_POOL_WITH_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_POOL_WITH_DEVICE(DeviceType::kCUDA) -// TODO: REGISTER_POOL_KERNELS(DeviceType::kCUDA, float16) -#endif -#ifdef WITH_ROCM -REGISTER_POOL_WITH_DEVICE(DeviceType::kCUDA) -// TODO: REGISTER_POOL_KERNELS(DeviceType::kCUDA, float16) +REGISTER_POOL_KERNELS(DeviceType::kCUDA, half) #endif OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_POOL_KERNEL_UTIL, (DeviceType::kCPU), diff --git a/oneflow/user/kernels/max_pool_kernel.cu b/oneflow/user/kernels/max_pool_kernel.cu index b50c08c69b6951d51e6276952f007a3ded7d9d9c..c4ce3ac7e4ab0abcd390f7eaff7eb57a27dc6bd2 100644 --- a/oneflow/user/kernels/max_pool_kernel.cu +++ b/oneflow/user/kernels/max_pool_kernel.cu @@ -14,11 +14,17 @@ See the License for the specific language governing permissions and limitations under the License. */ #include -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/cuda/elementwise.cuh" #include "oneflow/user/kernels/max_pool_kernel_util.h" #include "oneflow/core/ep/cuda/cuda_stream.h" + +#if defined(WITH_ROCM) +#include +#include +#else #include +#endif namespace oneflow { namespace { @@ -64,8 +70,8 @@ __device__ __inline__ void Maxpool2dForwardComputeCLast( /* equal to -std::numeric_limits::infinity(); */ T max_value = detail::numeric_limits::lower_bound(); - for (IDX i = hstart; i < hend; i++) { - for (IDX j = wstart; j < wend; j++) { + for (IDX i = hstart; i < hend; i += dilation_h) { + for (IDX j = wstart; j < wend; j += dilation_w) { const IDX window_idx = i * x_width * n_channel + j * n_channel + c; const IDX search_idx = x_start_idx + window_idx; T val = src[search_idx]; diff --git a/oneflow/user/kernels/max_pool_kernel.hip.cpp b/oneflow/user/kernels/max_pool_kernel.hip.cpp deleted file mode 100644 index cd3e86f70c85f25ef9c924132b7e78e198b1d323..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/max_pool_kernel.hip.cpp +++ /dev/null @@ -1,289 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/max_pool_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { -namespace { - -constexpr int kBlockSize = cuda::elementwise::kBlockSize; - -int GetMinThreadNum(int64_t elem_num) { return std::min(elem_num, kBlockSize); } - -int GetNumBlocks(int64_t elem_cnt) { - int num_blocks = 0; - OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); - return num_blocks; -} - -template -__device__ __inline__ void Maxpool2dForwardComputeCLast( - const NdIndexOffsetHelper& index_helper, IDX elem_num, const T* src, T* dest, - int64_t* indice_ptr, const int32_t padding_h, const int32_t padding_w, const int64_t n_batch, - const int64_t n_channel, const int64_t x_height, const int64_t x_width, const int64_t y_height, - const int64_t y_width, const int32_t kernel_size_h, const int32_t kernel_size_w, - const int32_t stride_h, const int32_t stride_w, const int32_t dilation_h, - const int32_t dilation_w) { - IDX n, h, w, c; - CUDA_1D_KERNEL_LOOP(num, elem_num) { - index_helper.OffsetToNdIndex(num, n, h, w, c); - - const IDX x_start_idx = n * n_channel * x_width * x_height; - const IDX y_start_idx = n * n_channel * y_height * y_width; - IDX hstart = h * stride_h - padding_h; - IDX wstart = w * stride_w - padding_w; - const IDX hend = (hstart + (kernel_size_h - 1) * dilation_h + 1) <= x_height - ? (hstart + (kernel_size_h - 1) * dilation_h + 1) - : x_height; - const IDX wend = (wstart + (kernel_size_w - 1) * dilation_w + 1) <= x_width - ? (wstart + (kernel_size_w - 1) * dilation_w + 1) - : x_width; - - while (hstart < 0) { hstart += dilation_h; } - while (wstart < 0) { wstart += dilation_w; } - /* compute max value(src[src_idx]) in kernel box region, and save the value to dest[num] */ - IDX max_index = hstart * x_width + wstart; - IDX src_idx = 0; - /* equal to -std::numeric_limits::infinity(); */ - T max_value = detail::numeric_limits::lower_bound(); - - for (IDX i = hstart; i < hend; i++) { - for (IDX j = wstart; j < wend; j++) { - const IDX window_idx = i * x_width * n_channel + j * n_channel + c; - const IDX search_idx = x_start_idx + window_idx; - T val = src[search_idx]; - if (val > max_value || detail::numerics::isnan(val)) { - max_value = val; - max_index = window_idx; - src_idx = search_idx; - } - } - } - const IDX out_idx = y_start_idx + h * y_width * n_channel + w * n_channel + c; - dest[out_idx] = src[src_idx]; - indice_ptr[out_idx] = max_index; - } -} - -} // namespace - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool1dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int64_t* indice_ptr, int32_t padding_l, - int32_t n_batch, int32_t n_channel, int32_t x_length, - int32_t kernel_size_l, int32_t stride_l, int32_t dilation_l) { - Maxpool1dForwardCompute(index_helper, elem_num, src, dest, indice_ptr, padding_l, n_batch, - n_channel, x_length, kernel_size_l, stride_l, dilation_l); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool2dForwardCFirst(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int64_t* indice_ptr, int32_t padding_h, - int32_t padding_w, int32_t n_batch, int32_t n_channel, - int32_t x_height, int32_t x_width, int32_t kernel_size_h, - int32_t kernel_size_w, int32_t stride_h, int32_t stride_w, - int32_t dilation_h, int32_t dilation_w) { - Maxpool2dForwardComputeCFirst( - index_helper, elem_num, src, dest, indice_ptr, padding_h, padding_w, n_batch, n_channel, - x_height, x_width, kernel_size_h, kernel_size_w, stride_h, stride_w, dilation_h, dilation_w); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool2dForwardCLast(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int64_t* indice_ptr, int32_t padding_h, - int32_t padding_w, int32_t n_batch, int32_t n_channel, - int32_t x_height, int32_t x_width, int32_t y_height, - int32_t y_width, int32_t kernel_size_h, int32_t kernel_size_w, - int32_t stride_h, int32_t stride_w, int32_t dilation_h, - int32_t dilation_w) { - Maxpool2dForwardComputeCLast(index_helper, elem_num, src, dest, indice_ptr, padding_h, - padding_w, n_batch, n_channel, x_height, x_width, y_height, - y_width, kernel_size_h, kernel_size_w, stride_h, stride_w, - dilation_h, dilation_w); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool3dForward(const NdIndexOffsetHelper index_helper, IDX elem_num, - const T* src, T* dest, int64_t* indice_ptr, int32_t padding_t, - int32_t padding_h, int32_t padding_w, int32_t n_batch, - int32_t n_channel, int32_t x_time, int32_t x_height, - int32_t x_width, int32_t kernel_size_t, int32_t kernel_size_h, - int32_t kernel_size_w, int32_t stride_t, int32_t stride_h, - int32_t stride_w, int32_t dilation_t, int32_t dilation_h, - int32_t dilation_w) { - Maxpool3dForwardCompute(index_helper, elem_num, src, dest, indice_ptr, padding_t, - padding_h, padding_w, n_batch, n_channel, x_time, x_height, - x_width, kernel_size_t, kernel_size_h, kernel_size_w, stride_t, - stride_h, stride_w, dilation_t, dilation_h, dilation_w); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool1dBackward(const NdIndexOffsetHelper index_helper, const IDX elem_num, - const T* src, T* dest, const int64_t* indice_ptr, - const int32_t n_batch, const int32_t n_channel, - const int32_t src_length, const int32_t dst_length) { - Maxpool1dBackwardCompute(index_helper, elem_num, src, dest, indice_ptr, n_batch, - n_channel, src_length, dst_length); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool2dBackwardCFirst(const NdIndexOffsetHelper index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const int32_t n_batch, - const int32_t n_channel, const int32_t src_height, - const int32_t src_width, const int32_t dst_height, - const int32_t dst_width) { - Maxpool2dBackwardComputeCFirst(index_helper, elem_num, src, dest, indice_ptr, n_batch, - n_channel, src_height, src_width, dst_height, dst_width); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool2dBackwardCLast(const NdIndexOffsetHelper index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const int32_t n_batch, - const int32_t n_channel, const int32_t src_height, - const int32_t src_width, const int32_t dst_height, - const int32_t dst_width) { - Maxpool2dBackwardComputeCLast(index_helper, elem_num, src, dest, indice_ptr, n_batch, - n_channel, src_height, src_width, dst_height, dst_width); -}; - -template -__launch_bounds__(kBlockSize) __global__ - void DoCUDAMaxPool3dBackward(const NdIndexOffsetHelper index_helper, const IDX elem_num, - const T* src, T* dest, const int64_t* indice_ptr, - const int32_t n_batch, const int32_t n_channel, - const int32_t src_time, const int32_t src_height, - const int32_t src_width, const int32_t dst_time, - const int32_t dst_height, const int32_t dst_width) { - Maxpool3dBackwardCompute(index_helper, elem_num, src, dest, indice_ptr, n_batch, - n_channel, src_time, src_height, src_width, dst_time, dst_height, - dst_width); -}; - -template -struct PoolKernelUtil { - static void Maxpool1dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, int64_t* indice_ptr, - const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool1dForward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.padding()[2], - params_3d.num_batch(), params_3d.num_channel(), params_3d.GetXShape5D().At(4), - params_3d.pool_size_3d()[2], params_3d.stride_3d()[2], params_3d.dilation_3d()[2]); - } - - static void Maxpool1dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool1dBackward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetYShape5D().At(4), params_3d.GetXShape5D().At(4)); - } - - static void Maxpool2dForwardCFirst(ep::Stream* stream, - const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, int64_t* indice_ptr, - const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool2dForwardCFirst<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.padding()[1], - params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), - params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[1], - params_3d.pool_size_3d()[2], params_3d.stride_3d()[1], params_3d.stride_3d()[2], - params_3d.dilation_3d()[1], params_3d.dilation_3d()[2]); - } - - static void Maxpool2dBackwardCFirst(ep::Stream* stream, - const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool2dBackwardCFirst<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetYShape5D().At(3), params_3d.GetYShape5D().At(4), - params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4)); - } - - static void Maxpool2dForwardCLast(ep::Stream* stream, - const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, int64_t* indice_ptr, - const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool2dForwardCLast<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.padding()[1], - params_3d.padding()[2], params_3d.num_batch(), params_3d.num_channel(), - params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4), params_3d.GetYShape5D().At(3), - params_3d.GetYShape5D().At(4), params_3d.pool_size_3d()[1], params_3d.pool_size_3d()[2], - params_3d.stride_3d()[1], params_3d.stride_3d()[2], params_3d.dilation_3d()[1], - params_3d.dilation_3d()[2]); - } - - static void Maxpool2dBackwardCLast(ep::Stream* stream, - const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool2dBackwardCLast<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetYShape5D().At(3), params_3d.GetYShape5D().At(4), - params_3d.GetXShape5D().At(3), params_3d.GetXShape5D().At(4)); - } - - static void Maxpool3dForward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, int64_t* indice_ptr, - const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool3dForward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.padding()[0], - params_3d.padding()[1], params_3d.padding()[2], params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), - params_3d.GetXShape5D().At(4), params_3d.pool_size_3d()[0], params_3d.pool_size_3d()[1], - params_3d.pool_size_3d()[2], params_3d.stride_3d()[0], params_3d.stride_3d()[1], - params_3d.stride_3d()[2], params_3d.dilation_3d()[0], params_3d.dilation_3d()[1], - params_3d.dilation_3d()[2]); - } - - static void Maxpool3dBackward(ep::Stream* stream, const NdIndexOffsetHelper& index_helper, - const IDX elem_num, const T* src, T* dest, - const int64_t* indice_ptr, const MaxPoolParams3D& params_3d) { - DoCUDAMaxPool3dBackward<<As()->cuda_stream()>>>( - index_helper, elem_num, src, dest, indice_ptr, params_3d.num_batch(), - params_3d.num_channel(), params_3d.GetYShape5D().At(2), params_3d.GetYShape5D().At(3), - params_3d.GetYShape5D().At(4), params_3d.GetXShape5D().At(2), params_3d.GetXShape5D().At(3), - params_3d.GetXShape5D().At(4)); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_POOL_KERNEL_UTIL, (DeviceType::kCUDA), - POOL_DATA_TYPE_CUDA_SEQ, POOL_IDX_DATA_TYPE_SEQ); - -} // namespace oneflow -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/max_pool_kernel_util.cpp b/oneflow/user/kernels/max_pool_kernel_util.cpp index e6e9b25371aa8602ba7bf0187122cc56af5658de..d880c40d4fa5692274a46937d9dda01dbb19b0db 100644 --- a/oneflow/user/kernels/max_pool_kernel_util.cpp +++ b/oneflow/user/kernels/max_pool_kernel_util.cpp @@ -13,36 +13,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/user/utils/pool_util.h" #include "oneflow/user/kernels/max_pool_kernel_util.h" namespace oneflow { -std::vector Get3DVec(const std::vector& original_vec, int32_t NDims) { - std::vector vec; - FOR_RANGE(uint8_t, dim, 0, 3) { - int64_t index = static_cast(dim) - (3 - NDims); - if (index < 0) { - vec.emplace_back(1); - } else { - vec.emplace_back(original_vec.at(index)); - } - } - return vec; -} - -std::vector Get3DPadVec(const std::vector& original_vec, int32_t NDims) { - std::vector vec; - FOR_RANGE(uint8_t, dim, 0, 3) { - int64_t index = static_cast(dim) - (3 - NDims); - if (index < 0) { - vec.emplace_back(0); - } else { - vec.emplace_back(original_vec.at(index)); - } - } - return vec; -} - void GetWindowedOutputShape(int64_t input_size, int32_t filter_size, int32_t stride, int32_t padding, bool ceil_mode, int32_t dilation_rate, int64_t* output_ptr) { @@ -79,7 +54,7 @@ MaxPoolParams3D::MaxPoolParams3D(const int32_t dim, const ShapeView& x_shape, const bool ceil_mode) : dim_(dim), data_format_(data_format), - padding_(Get3DPadVec(padding, dim)), + padding_(Get3DVec(padding, dim)), pool_size_3d_(Get3DVec(kernel_size, dim)), stride_3d_(Get3DVec(stride, dim)), dilation_3d_(Get3DVec(dilation, dim)), diff --git a/oneflow/user/kernels/max_pool_kernel_util.h b/oneflow/user/kernels/max_pool_kernel_util.h index d1db228e15c86925b1db334d414b7d4259f31f97..9a0b33066688a63a42c27f96a1783e035b6f23d0 100644 --- a/oneflow/user/kernels/max_pool_kernel_util.h +++ b/oneflow/user/kernels/max_pool_kernel_util.h @@ -20,17 +20,11 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/common/nd_index_offset_helper.h" #include "oneflow/core/operator/operator_util.h" - -#ifdef WITH_CUDA -#include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/kernel/util/numerics.cuh" #include "oneflow/core/kernel/util/numeric_limits.cuh" +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/cuda/atomic.cuh" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/kernel/util/numerics.hip.h" -#include "oneflow/core/kernel/util/numeric_limits.hip.h" -#endif // WITH_ROCM namespace oneflow { @@ -44,8 +38,7 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) #define POOL_DATA_TYPE_CPU_SEQ POOL_DATA_TYPE_SEQ - -#define POOL_DATA_TYPE_CUDA_SEQ POOL_DATA_TYPE_SEQ +#define POOL_DATA_TYPE_CUDA_SEQ POOL_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) typedef small_vector FixedDimVector; diff --git a/oneflow/user/kernels/max_unpool_kernel.cpp b/oneflow/user/kernels/max_unpool_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..58b6eb42998120458b8b36e6878bf7ace210fdd1 --- /dev/null +++ b/oneflow/user/kernels/max_unpool_kernel.cpp @@ -0,0 +1,199 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "fmt/core.h" +#include "oneflow/core/common/bfloat16.h" +#include "oneflow/core/common/throw.h" +#include "oneflow/user/kernels/max_unpool_kernel_util.h" + +namespace oneflow { +namespace { + +template +void MaxUnpoolNdForwardOrBackward(const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const int64_t* indice_ptr, + const int64_t hwd_size, const int64_t out_elem_num, const F& f) { + XPU_1D_KERNEL_LOOP(num, elem_num) { + IDX bc_idx, hwd_idx; + index_helper.OffsetToNdIndex(num, bc_idx, hwd_idx); + IDX idx = bc_idx * hwd_size + indice_ptr[num]; + CHECK_OR_THROW(idx >= 0 && idx < out_elem_num) << fmt::format( + "Found an invalid max index: {}, output volumes are of size {}", idx, out_elem_num); + f(num, idx); + } +} + +} // namespace + +template +struct UnpoolKernelUtil { + static void MaxUnpoolNdForward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const T* src, T* dest, + const int64_t* indice_ptr, const int64_t y_hwd_size, + const int64_t y_elem_num) { + MaxUnpoolNdForwardOrBackward(index_helper, elem_num, indice_ptr, y_hwd_size, y_elem_num, + [&](int64_t num, IDX idx) { dest[idx] = src[num]; }); + } + + static void MaxUnpoolNdBackward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const T* src, T* dest, + const int64_t* indice_ptr, const int64_t dy_hwd_size, + const int64_t dy_elem_num) { + MaxUnpoolNdForwardOrBackward(index_helper, elem_num, indice_ptr, dy_hwd_size, dy_elem_num, + [&](int64_t num, IDX idx) { dest[num] = src[idx]; }); + } +}; + +template +class MaxUnpoolNdKernel final : public user_op::OpKernel { + public: + MaxUnpoolNdKernel() = default; + ~MaxUnpoolNdKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* indice = ctx->Tensor4ArgNameAndIndex("indices", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + + const int64_t elem_num = x->shape_view().elem_cnt(); + const T* src = x->dptr(); + const int64_t* indice_ptr = indice->dptr(); + T* dest = y->mut_dptr(); + + DimVector x_vector(2); + x_vector.at(0) = x->shape_view().At(0) * x->shape_view().At(1); + + int64_t y_hwd_size = 1; + + x_vector.at(1) = std::accumulate(x->shape_view().begin() + 2, x->shape_view().end(), 1, + std::multiplies()); + y_hwd_size = std::accumulate(y->shape_view().begin() + 2, y->shape_view().end(), 1, + std::multiplies()); + + std::unique_ptr memset_primitive = + ep::primitive::NewPrimitive(ctx->device_type()); + CHECK(memset_primitive); + memset_primitive->Launch(ctx->stream(), dest, 0, + y->shape_view().elem_cnt() * GetSizeOfDataType(y->data_type())); + + const int64_t y_elem_num = y->shape_view().elem_cnt(); + + if (elem_num < GetMaxVal()) { + NdIndexOffsetHelper index_helper(x_vector.data()); + UnpoolKernelUtil::MaxUnpoolNdForward( + ctx->stream(), index_helper, elem_num, src, dest, indice_ptr, y_hwd_size, y_elem_num); + } else { + NdIndexOffsetHelper index_helper(x_vector.data()); + UnpoolKernelUtil::MaxUnpoolNdForward( + ctx->stream(), index_helper, elem_num, src, dest, indice_ptr, y_hwd_size, y_elem_num); + } + } +}; + +template +class MaxUnpoolNdGradKernel final : public user_op::OpKernel { + public: + MaxUnpoolNdGradKernel() = default; + ~MaxUnpoolNdGradKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* indice = ctx->Tensor4ArgNameAndIndex("indices", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + + const int64_t elem_num = dx->shape_view().elem_cnt(); + const T* src = dy->dptr(); + const int64_t* indice_ptr = indice->dptr(); + T* dest = dx->mut_dptr(); + + DimVector dx_vector(2); + dx_vector.at(0) = dx->shape_view().At(0) * dx->shape_view().At(1); + int64_t dy_hwd_size = 1; + + dx_vector.at(1) = std::accumulate(dx->shape_view().begin() + 2, dx->shape_view().end(), 1, + std::multiplies()); + dy_hwd_size = std::accumulate(dy->shape_view().begin() + 2, dy->shape_view().end(), 1, + std::multiplies()); + + const int64_t dy_elem_num = dy->shape_view().elem_cnt(); + + if (elem_num < GetMaxVal()) { + NdIndexOffsetHelper index_helper(dx_vector.data()); + UnpoolKernelUtil::MaxUnpoolNdBackward( + ctx->stream(), index_helper, elem_num, src, dest, indice_ptr, dy_hwd_size, dy_elem_num); + } else { + NdIndexOffsetHelper index_helper(dx_vector.data()); + UnpoolKernelUtil::MaxUnpoolNdBackward( + ctx->stream(), index_helper, elem_num, src, dest, indice_ptr, dy_hwd_size, dy_elem_num); + } + }; +}; + +#define REGISTER_UNPOOL_KERNELS(device, dtype) \ + REGISTER_USER_KERNEL("max_unpool_1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("max_unpool_2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("max_unpool_3d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("max_unpool_1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("max_unpool_2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("max_unpool_3d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_UNPOOL_KERNEL_UTIL, (DeviceType::kCPU), + UNPOOL_DATA_TYPE_CPU_SEQ, UNPOOL_IDX_DATA_TYPE_SEQ); + +#define REGISTER_UNPOOL_WITH_DEVICE(device) \ + REGISTER_UNPOOL_KERNELS(device, int32_t) \ + REGISTER_UNPOOL_KERNELS(device, int64_t) \ + REGISTER_UNPOOL_KERNELS(device, float) \ + REGISTER_UNPOOL_KERNELS(device, double) + +REGISTER_UNPOOL_WITH_DEVICE(DeviceType::kCPU) +REGISTER_UNPOOL_KERNELS(DeviceType::kCPU, float16) +REGISTER_UNPOOL_KERNELS(DeviceType::kCPU, bfloat16) + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_UNPOOL_WITH_DEVICE(DeviceType::kCUDA) +REGISTER_UNPOOL_KERNELS(DeviceType::kCUDA, half) +#if CUDA_VERSION >= 11000 +REGISTER_UNPOOL_KERNELS(DeviceType::kCUDA, nv_bfloat16) +#endif // CUDA_VERSION >= 11000 +#endif // WITH_CUDA + +} // namespace oneflow diff --git a/oneflow/user/kernels/max_unpool_kernel.cu b/oneflow/user/kernels/max_unpool_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..3526b3090dedd2cb062cbf2852370048f138dfdd --- /dev/null +++ b/oneflow/user/kernels/max_unpool_kernel.cu @@ -0,0 +1,102 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/cuda/elementwise.cuh" +#include "oneflow/user/kernels/max_unpool_kernel_util.h" + +#if defined(WITH_ROCM) +#include +#include +#else +#include +#endif + +namespace oneflow { +namespace { + +constexpr int kBlockSize = cuda::elementwise::kBlockSize; + +int GetMinThreadNum(int64_t elem_num) { return std::min(elem_num, kBlockSize); } + +int GetNumBlocks(int64_t elem_cnt) { + int num_blocks = 0; + OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); + return num_blocks; +} + +} // namespace + +template +__launch_bounds__(kBlockSize) __global__ + void DoCUDAMaxUnpoolNdForward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const T* src, T* dest, const int64_t* indice_ptr, + const int64_t y_hwd_size, const int64_t y_elem_num) { + CUDA_1D_KERNEL_LOOP_T(IDX, num, elem_num) { + IDX bc_idx, hwd_idx; + index_helper.OffsetToNdIndex(num, bc_idx, hwd_idx); + IDX dest_idx = bc_idx * y_hwd_size + indice_ptr[num]; + if (dest_idx >= 0 && dest_idx < y_elem_num) { dest[dest_idx] = src[num]; } + } +} + +template +__launch_bounds__(kBlockSize) __global__ + void DoCUDAMaxUnpoolNdBackward(const NdIndexOffsetHelper index_helper, IDX elem_num, + const T* src, T* dest, const int64_t* indice_ptr, + const int64_t dy_hwd_size, const int64_t dy_elem_num) { + CUDA_1D_KERNEL_LOOP_T(IDX, num, elem_num) { + IDX bc_idx, hwd_idx; + index_helper.OffsetToNdIndex(num, bc_idx, hwd_idx); + IDX src_idx = bc_idx * dy_hwd_size + indice_ptr[num]; + if (src_idx >= 0 && src_idx < dy_elem_num) { + dest[num] = src[src_idx]; + } else { + dest[num] = 0.0f; + } + } +} + +template +struct UnpoolKernelUtil { + static void MaxUnpoolNdForward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, IDX elem_num, + const T* src, T* dest, const int64_t* indice_ptr, + const int64_t y_hwd_size, const int64_t y_elem_num) { + DoCUDAMaxUnpoolNdForward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, indice_ptr, y_hwd_size, y_elem_num); + } + + static void MaxUnpoolNdBackward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, IDX elem_num, + const T* src, T* dest, const int64_t* indice_ptr, + const int64_t dy_hwd_size, const int64_t dy_elem_num) { + DoCUDAMaxUnpoolNdBackward<<As()->cuda_stream()>>>( + index_helper, elem_num, src, dest, indice_ptr, dy_hwd_size, dy_elem_num); + } +}; + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_UNPOOL_KERNEL_UTIL, (DeviceType::kCUDA), + UNPOOL_DATA_TYPE_CUDA_SEQ, UNPOOL_IDX_DATA_TYPE_SEQ); +#if CUDA_VERSION >= 11000 +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_UNPOOL_KERNEL_UTIL, (DeviceType::kCUDA), + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16), + UNPOOL_IDX_DATA_TYPE_SEQ); +#endif // CUDA_VERSION >= 11000 + +} // namespace oneflow +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/max_unpool_kernel_util.cpp b/oneflow/user/kernels/max_unpool_kernel_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0bda12ad02fe7225bb8ca79024d6a29cd82a20f3 --- /dev/null +++ b/oneflow/user/kernels/max_unpool_kernel_util.cpp @@ -0,0 +1,91 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/operator/operator_util.h" +#include "oneflow/user/utils/pool_util.h" +#include "oneflow/user/kernels/max_unpool_kernel_util.h" + +namespace oneflow { +namespace { + +void GetWindowedOutputShape(int64_t input_size, int32_t filter_size, int32_t stride, + int32_t padding, int64_t* output_ptr) { + int64_t output_size = (input_size - 1) * stride - 2 * padding + filter_size; + *output_ptr = output_size; +} + +void Get3DOutputShape(const DimVector& in, const std::vector& pool_size, + const std::vector& strides, const std::vector& padding, + DimVector* out) { + out->clear(); + out->resize(3); + FOR_RANGE(size_t, i, 0, 3) { + int64_t* out_ptr = &(*out).at(i); + GetWindowedOutputShape(in.at(i), pool_size.at(i), strides.at(i), padding.at(i), out_ptr); + } +} + +} // namespace + +MaxUnpoolParams3D::MaxUnpoolParams3D(const int32_t dim, const ShapeView& x_shape, + const std::vector& padding, + const std::vector& kernel_size, + const std::vector& stride) + : dim_(dim), + padding_(Get3DVec(padding, dim)), + pool_size_3d_(Get3DVec(kernel_size, dim)), + stride_3d_(Get3DVec(stride, dim)), + batch_num_(x_shape.At(0)), + channel_num_(x_shape.At(1)) { + std::string data_format = "channels_first"; + x_3d_ = {GetInDim(x_shape, data_format, 0, dim), GetInDim(x_shape, data_format, 1, dim), + GetInDim(x_shape, data_format, 2, dim)}; + Get3DOutputShape(x_3d_, pool_size_3d_, stride_3d_, padding_, &y_3d_); +} + +void MaxUnpoolParams3D::Reset(const ShapeView& x_shape) { + std::string data_format = "channels_first"; + x_3d_ = {GetInDim(x_shape, data_format, 0, dim_), GetInDim(x_shape, data_format, 1, dim_), + GetInDim(x_shape, data_format, 2, dim_)}; + Get3DOutputShape(x_3d_, pool_size_3d_, stride_3d_, padding_, &y_3d_); +} + +int64_t MaxUnpoolParams3D::GetYStride() const { return y_3d_.at(0) * y_3d_.at(1) * y_3d_.at(2); } + +Shape MaxUnpoolParams3D::GetYShape() const { + DimVector y_dim_vec; + if (dim_ == 1) { + y_dim_vec = {y_3d_.at(2)}; + } else if (dim_ == 2) { + y_dim_vec = {y_3d_.at(1), y_3d_.at(2)}; + } else if (dim_ == 3) { + y_dim_vec = {y_3d_.at(0), y_3d_.at(1), y_3d_.at(2)}; + } else { + UNIMPLEMENTED(); + } + y_dim_vec.insert(y_dim_vec.begin(), channel_num_); + y_dim_vec.insert(y_dim_vec.begin(), batch_num_); + return Shape(y_dim_vec); +} + +Shape MaxUnpoolParams3D::GetXShape5D() const { + return Shape({batch_num_, channel_num_, x_3d_.at(0), x_3d_.at(1), x_3d_.at(2)}); +} + +Shape MaxUnpoolParams3D::GetYShape5D() const { + return Shape({batch_num_, channel_num_, y_3d_.at(0), y_3d_.at(1), y_3d_.at(2)}); +} + +} // namespace oneflow diff --git a/oneflow/user/kernels/max_unpool_kernel_util.h b/oneflow/user/kernels/max_unpool_kernel_util.h new file mode 100644 index 0000000000000000000000000000000000000000..46547a2e43743cb5182060178a0a27b1e8a60137 --- /dev/null +++ b/oneflow/user/kernels/max_unpool_kernel_util.h @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_UNPOOL_KERNEL_UTIL_H_ +#define ONEFLOW_USER_KERNELS_UNPOOL_KERNEL_UTIL_H_ +#include "oneflow/core/ndarray/xpu_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/common/nd_index_offset_helper.h" + +namespace oneflow { + +#define UNPOOL_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) \ + OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) \ + OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) + +#define UNPOOL_IDX_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) + +#define UNPOOL_DATA_TYPE_CPU_SEQ \ + UNPOOL_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float16, DataType::kFloat16) \ + OF_PP_MAKE_TUPLE_SEQ(bfloat16, DataType::kBFloat16) +#define UNPOOL_DATA_TYPE_CUDA_SEQ \ + UNPOOL_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) +// OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) + +typedef small_vector FixedDimVector; + +class MaxUnpoolParams3D { + public: + MaxUnpoolParams3D(const int32_t dim, const ShapeView& x_shape, + const std::vector& padding, const std::vector& kernel_size, + const std::vector& stride); + ~MaxUnpoolParams3D() = default; + + const std::vector& padding() const { return padding_; } + const std::vector& pool_size_3d() const { return pool_size_3d_; } + const std::vector& stride_3d() const { return stride_3d_; } + const int32_t& num_batch() const { return batch_num_; } + const int32_t& num_channel() const { return channel_num_; } + + void Reset(const ShapeView& x_shape); + + Shape GetYShape() const; + Shape GetXShape5D() const; + Shape GetYShape5D() const; + int64_t GetYStride() const; + + private: + int32_t dim_; + FixedDimVector x_3d_; + FixedDimVector y_3d_; + std::vector padding_; + std::vector pool_size_3d_; + std::vector stride_3d_; + int32_t batch_num_; + int32_t channel_num_; +}; + +template +struct UnpoolKernelUtil { + static void MaxUnpoolNdForward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const T* src, T* dest, + const int64_t* indice_ptr, const int64_t y_hwd_size, + const int64_t y_elem_num); + static void MaxUnpoolNdBackward(ep::Stream* stream, + const NdIndexOffsetHelper& index_helper, + const IDX elem_num, const T* src, T* dest, + const int64_t* indice_ptr, const int64_t dy_hwd_size, + const int64_t dy_elem_num); +}; + +#define INSTANTIATE_UNPOOL_KERNEL_UTIL(device_type_v, dtype_pair, index_dtype_pair) \ + template struct UnpoolKernelUtil; + +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_UNPOOL_KERNEL_UTIL_H_ diff --git a/oneflow/user/kernels/median_kernel.hip.cpp b/oneflow/user/kernels/median_kernel.hip.cpp deleted file mode 100644 index 34bdae2412de2b3e9c8818e6396ca978c15dad50..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/median_kernel.hip.cpp +++ /dev/null @@ -1,69 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/user/kernels/radix_sort.hip.h" - -namespace oneflow { - -template -class CudaMedianKernel final : public user_op::OpKernel { - public: - CudaMedianKernel() = default; - ~CudaMedianKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("input", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("output", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const int32_t instance_size = in->shape_view().elem_cnt(); - const size_t sort_tensor_buffer_bytes = GetCudaAlignedSize(instance_size * sizeof(T)); - SortKeysAscending( - in->dptr(), 1, instance_size, - reinterpret_cast(tmp_buffer->mut_dptr() + sort_tensor_buffer_bytes), - tmp_buffer->shape_view().elem_cnt() - sort_tensor_buffer_bytes, tmp_buffer->mut_dptr(), - ctx->stream()->As()->cuda_stream()); - Memcpy(ctx->stream(), out->mut_dptr(), - tmp_buffer->mut_dptr() + (instance_size - 1) / 2, sizeof(T)); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_MEDIAN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("median") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const Shape& in_shape = ctx->InputShape("input", 0); \ - const int32_t instance_size = in_shape.elem_cnt(); \ - size_t sort_tmp_buffer_bytes = \ - InferTempStorageForSortKeysAscending(1, instance_size); \ - size_t sort_tensor_buffer_bytes = GetCudaAlignedSize(instance_size * sizeof(dtype)); \ - return sort_tmp_buffer_bytes + sort_tensor_buffer_bytes; \ - }); - -REGISTER_CUDA_MEDIAN_KERNEL(float) -REGISTER_CUDA_MEDIAN_KERNEL(double) -REGISTER_CUDA_MEDIAN_KERNEL(int8_t) -REGISTER_CUDA_MEDIAN_KERNEL(uint8_t) -REGISTER_CUDA_MEDIAN_KERNEL(int32_t) -REGISTER_CUDA_MEDIAN_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/median_with_indices_kernel.hip.cpp b/oneflow/user/kernels/median_with_indices_kernel.hip.cpp deleted file mode 100644 index e5a8187a016a037d789e187d89ae8e3610ff43a8..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/median_with_indices_kernel.hip.cpp +++ /dev/null @@ -1,156 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/user/kernels/radix_sort.hip.h" - -namespace oneflow { - -namespace { - -template -__global__ void MedianSelectCuda(const IDX reduce_elem_cnt, const IDX stride, const T* in, - const int64_t* sort_indices, T* values, int64_t* indices) { - IDX nth = (stride - 1) / 2; - CUDA_1D_KERNEL_LOOP_T(IDX, i, reduce_elem_cnt) { - values[i] = in[i * stride + nth]; - indices[i] = sort_indices[i * stride + nth]; - } -} - -bool IsSafeUseIndex32(int64_t elem_cnt) { return elem_cnt < GetMaxVal() / 2; } - -template -void DispatchIndexSize(ep::Stream* stream, const int64_t elem_cnt, const int64_t stride, - const T* in, const int64_t* sort_indices, T* out, int64_t* out_indices) { - const int64_t reduce_elem_cnt = elem_cnt / stride; - if (IsSafeUseIndex32(elem_cnt)) { - RUN_CUDA_KERNEL((MedianSelectCuda), stream, reduce_elem_cnt, reduce_elem_cnt, - stride, in, sort_indices, out, out_indices); - } else { - RUN_CUDA_KERNEL((MedianSelectCuda), stream, reduce_elem_cnt, reduce_elem_cnt, - stride, in, sort_indices, out, out_indices); - } -} - -template -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(size_t capacity, void* ptr, const ShapeView& in_shape) - : capacity_{capacity}, - sorted_in_elem_cnt_{in_shape.elem_cnt()}, - indices_elem_cnt_{sorted_in_elem_cnt_} { - const size_t sort_tensor_buffer_bytes = GetCudaAlignedSize(sorted_in_elem_cnt_ * sizeof(T)); - const size_t sort_indices_buffer_bytes = - GetCudaAlignedSize(indices_elem_cnt_ * sizeof(int64_t)); - sorted_in_ptr_ = reinterpret_cast(ptr); - in_indices_ptr_ = reinterpret_cast(reinterpret_cast(sorted_in_ptr_) - + sort_tensor_buffer_bytes); - out_indices_ptr_ = reinterpret_cast(reinterpret_cast(in_indices_ptr_) - + sort_indices_buffer_bytes); - temp_storage_ptr_ = reinterpret_cast(reinterpret_cast(out_indices_ptr_) - + sort_indices_buffer_bytes); - temp_storage_bytes_ = capacity_ - sort_tensor_buffer_bytes - sort_indices_buffer_bytes * 2; - CHECK_GE(temp_storage_bytes_, 0); - } - ~TmpBufferManager() = default; - - T* SortedInPtr() const { return sorted_in_ptr_; } - int64_t* InIndicesPtr() const { return in_indices_ptr_; } - int64_t* OutIndicesPtr() const { return out_indices_ptr_; } - void* TempStoragePtr() const { return temp_storage_ptr_; } - - size_t TempStorageBytes() const { return temp_storage_bytes_; } - - private: - size_t capacity_; - - T* sorted_in_ptr_; - int64_t* in_indices_ptr_; - int64_t* out_indices_ptr_; - void* temp_storage_ptr_; - - int64_t sorted_in_elem_cnt_; - int64_t indices_elem_cnt_; - size_t temp_storage_bytes_; -}; - -__global__ void InitializeIndices(int64_t elem_cnt, int64_t* indices_ptr, int64_t instance_size) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { indices_ptr[i] = i % instance_size; }; -} - -} // namespace - -template -class CudaMedianWithIndicesKernel final : public user_op::OpKernel { - public: - CudaMedianWithIndicesKernel() = default; - ~CudaMedianWithIndicesKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("input", 0); - if (in->shape_view().elem_cnt() == 0) return; - user_op::Tensor* values = ctx->Tensor4ArgNameAndIndex("values", 0); - user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - TmpBufferManager buf_manager(tmp_buffer->shape_view().elem_cnt(), - tmp_buffer->mut_dptr(), in->shape_view()); - - const int64_t elem_cnt = in->shape_view().elem_cnt(); - const int64_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int64_t instance_num = elem_cnt / instance_size; - RUN_CUDA_KERNEL(InitializeIndices, ctx->stream(), elem_cnt, elem_cnt, - buf_manager.InIndicesPtr(), instance_size); - SortPairsAscending(in->dptr(), buf_manager.InIndicesPtr(), instance_num, instance_size, - buf_manager.TempStoragePtr(), buf_manager.TempStorageBytes(), - buf_manager.SortedInPtr(), buf_manager.OutIndicesPtr(), - ctx->stream()->As()->cuda_stream()); - DispatchIndexSize(ctx->stream(), elem_cnt, instance_size, buf_manager.SortedInPtr(), - buf_manager.OutIndicesPtr(), values->mut_dptr(), - indices->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(dtype) \ - REGISTER_USER_KERNEL("median_with_indices") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("input", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const Shape& in_shape = ctx->InputShape("input", 0); \ - const int64_t instance_size = in_shape.dim_vec().back(); \ - const int64_t instance_num = in_shape.elem_cnt() / instance_size; \ - size_t sort_tmp_buffer_bytes = \ - InferTempStorageForSortPairsAscending(instance_num, instance_size); \ - size_t sort_tensor_buffer_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(dtype)); \ - size_t sort_indices_buffer_bytes = \ - GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(int64_t)); \ - return sort_tmp_buffer_bytes + sort_tensor_buffer_bytes + sort_indices_buffer_bytes * 2; \ - }); - -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(float) -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(double) -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(int8_t) -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(uint8_t) -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(int32_t) -REGISTER_CUDA_MEDIAN_WITH_INDICES_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/min_max_observer_kernel.hip.cpp b/oneflow/user/kernels/min_max_observer_kernel.hip.cpp deleted file mode 100644 index 82c4cb4b6b65215286e0e7062f1841c809c1c168..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/min_max_observer_kernel.hip.cpp +++ /dev/null @@ -1,260 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#include - -namespace oneflow { - -namespace { - -// NOTE(Liang Depeng): refer to -// https://stackoverflow.com/questions/17371275/implementing-max-reduce-in-cuda -template -__global__ void ReduceMaxMinPerLayer(const T* input_ptr, const int64_t elements, T* max_ptr, - T* min_ptr) { - extern __shared__ unsigned char shared_max_min_memory[]; - T* shared_max = reinterpret_cast(shared_max_min_memory); - T* shared_min = shared_max + blockDim.x; - - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - shared_max[tid] = -FLT_MAX; - shared_min[tid] = -FLT_MAX; - - while (gid < elements) { - shared_max[tid] = max(shared_max[tid], input_ptr[gid]); - shared_min[tid] = max(shared_min[tid], -input_ptr[gid]); - gid += gridDim.x * blockDim.x; - } - __syncthreads(); - gid = (blockDim.x * blockIdx.x) + tid; - for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s && gid < elements) { - shared_max[tid] = max(shared_max[tid], shared_max[tid + s]); - shared_min[tid] = max(shared_min[tid], shared_min[tid + s]); - } - __syncthreads(); - } - - if (tid == 0) { - cuda::atomic::Max(max_ptr, shared_max[0]); - cuda::atomic::Max(min_ptr, shared_min[0]); - } -} - -template -__global__ void ReduceMaxMinPerChannel(const T* input_ptr, const int64_t elements, - const int64_t num_channels, const int64_t panel_size, - T* max_ptr, T* min_ptr) { - extern __shared__ unsigned char shared_max_min_memory[]; - T* shared_max = reinterpret_cast(shared_max_min_memory); - T* shared_min = shared_max + blockDim.x; - - int64_t cur_channel = blockIdx.x; - int64_t tid = threadIdx.x; - - while (cur_channel < num_channels) { - shared_max[tid] = -FLT_MAX; - shared_min[tid] = -FLT_MAX; - - int64_t index = (panel_size * cur_channel) + tid; - int64_t end = panel_size * (cur_channel + 1); - - while (index < end && index < elements) { - shared_max[tid] = max(shared_max[tid], input_ptr[index]); - shared_min[tid] = max(shared_min[tid], -input_ptr[index]); - index += blockDim.x; - } - __syncthreads(); - - for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - shared_max[tid] = max(shared_max[tid], shared_max[tid + s]); - shared_min[tid] = max(shared_min[tid], shared_min[tid + s]); - } - __syncthreads(); - } - - if (tid == 0) { - cuda::atomic::Max(&max_ptr[cur_channel], shared_max[0]); - cuda::atomic::Max(&min_ptr[cur_channel], shared_min[0]); - } - - // __syncthreads(); - cur_channel += gridDim.x; - } -} - -template -__global__ void InitMaxMin(const int64_t elements, T* max_ptr, T* min_ptr) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - max_ptr[gid] = -FLT_MAX; - min_ptr[gid] = -FLT_MAX; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointSymmetric(const T* max_ptr, const T* min_ptr, - const int64_t elements, const double quantization_bit, - T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T weight_max = max(fabs(max_ptr[gid]), fabs(min_ptr[gid])); - T denominator = static_cast(pow(2.0, quantization_bit - 1)) - 1; - scale[gid] = weight_max / denominator; - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointAffine(const T* max_ptr, const T* min_ptr, const int64_t elements, - const double quantization_bit, T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T denominator = static_cast(pow(2.0, quantization_bit)) - 1; - T min = -min_ptr[gid]; - T s = (max_ptr[gid] - min) / denominator; - scale[gid] = s; - zero_point[gid] = -nearbyint(min / s); - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointCambricon(const T* max_ptr, const T* min_ptr, - const int64_t elements, const double quantization_bit, - T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T weight_max = max(fabs(max_ptr[gid]), fabs(min_ptr[gid])); - // T denominator = static_cast(pow(2.0, quantization_bit - 1)) - 1; - scale[gid] = floor(log2(weight_max)) - (quantization_bit - 2); - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -ep::CudaLaunchConfig GetLaunchConfig(ep::CudaStream* stream, size_t thread_num, - size_t shared_mem_size) { - ep::CudaLaunchConfig config; - stream->InitLaunchConfigWithWaves(&config, thread_num, kCudaThreadsNumPerBlock, 1); - config.shared_mem_size = shared_mem_size; - return config; -} - -} // namespace - -#define LAUNCH_CUDA_KERNEL(func, stream, thread_num, shared_mem_size, ...) \ - (stream)->LaunchKernel(func, GetLaunchConfig((stream), thread_num, shared_mem_size), __VA_ARGS__); - -template -class GpuMinMaxObserverKernel final : public user_op::OpKernel { - public: - GpuMinMaxObserverKernel() = default; - ~GpuMinMaxObserverKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* scale = ctx->Tensor4ArgNameAndIndex("scale", 0); - user_op::Tensor* zero_point = ctx->Tensor4ArgNameAndIndex("zero_point", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const std::string quantization_scheme = ctx->Attr("quantization_scheme"); - const int32_t quantization_bit = ctx->Attr("quantization_bit"); - const bool per_layer_quantization = ctx->Attr("per_layer_quantization"); - const std::string quantization_formula = ctx->Attr("quantization_formula"); - - const int64_t elements = in->shape_view().elem_cnt(); - const int64_t channel = scale->shape_view().At(0); - const int64_t panel_size = elements / channel; - T* max_ptr = tmp_buffer->mut_dptr(); - T* min_ptr = max_ptr + channel; - auto* cuda_stream = ctx->stream()->As(); - LAUNCH_CUDA_KERNEL((InitMaxMin), cuda_stream, channel, 0, channel, max_ptr, min_ptr); - - if (per_layer_quantization) { - LAUNCH_CUDA_KERNEL((ReduceMaxMinPerLayer), cuda_stream, elements, - kCudaThreadsNumPerBlock * 2 * sizeof(T), in->dptr(), elements, max_ptr, - min_ptr); - } else { // per-channel quantization - // NOTE(Liang Depeng): each block of threads will be responsible for - // computing the max and min values of the whole channel. - LAUNCH_CUDA_KERNEL((ReduceMaxMinPerChannel), cuda_stream, - channel * kCudaThreadsNumPerBlock, kCudaThreadsNumPerBlock * 2 * sizeof(T), - in->dptr(), elements, channel, panel_size, max_ptr, min_ptr); - } - - if (quantization_formula == "google") { - if (quantization_scheme == "symmetric") { - LAUNCH_CUDA_KERNEL((CalScaleZeroPointSymmetric), cuda_stream, channel, 0, max_ptr, - min_ptr, channel, static_cast(quantization_bit), - scale->mut_dptr(), zero_point->mut_dptr()); - } else { // quantization_scheme == "affine" - LAUNCH_CUDA_KERNEL((CalScaleZeroPointAffine), cuda_stream, channel, 0, max_ptr, min_ptr, - channel, static_cast(quantization_bit), scale->mut_dptr(), - zero_point->mut_dptr()); - } - } else if (quantization_formula == "cambricon") { - if (!per_layer_quantization) { - UNIMPLEMENTED() << " per-channel mode is not supported in cambricon scheme"; - } - LAUNCH_CUDA_KERNEL((CalScaleZeroPointCambricon), cuda_stream, channel, 0, max_ptr, min_ptr, - channel, static_cast(quantization_bit), scale->mut_dptr(), - zero_point->mut_dptr()); - } else { - UNIMPLEMENTED(); - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MIN_MAX_OBSERVER_KERNEL(dtype) \ - REGISTER_USER_KERNEL("min_max_observer") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - size_t tmp_buffer_size = 1; \ - if (ctx->Attr("per_layer_quantization") == false) { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - tmp_buffer_size = in_shape.At(0); \ - } \ - return 2 * tmp_buffer_size * sizeof(dtype); \ - }) - -REGISTER_MIN_MAX_OBSERVER_KERNEL(float); -REGISTER_MIN_MAX_OBSERVER_KERNEL(double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/model_update_kernel_util.cpp b/oneflow/user/kernels/model_update_kernel_util.cpp index 8ba953370cd3e8d0fa52d16ec0f448b074e365b1..fd78f13d673a564919f536ff72e0797d9dd9ddc4 100644 --- a/oneflow/user/kernels/model_update_kernel_util.cpp +++ b/oneflow/user/kernels/model_update_kernel_util.cpp @@ -40,33 +40,40 @@ void SumSquares2(int64_t n, const T* src0, T* dst0, const T* src1, T* dst1) { } // namespace -template -struct SGDUpdateKernelUtil { +template +struct SGDUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + C* model_copy); }; -template -void SGDUpdateKernelUtil::Update( +template +void SGDUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model) { + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, C* model_copy) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; for (int64_t i = 0; i != n; ++i) { - SGDUpdateFunctor()(model_diff + i, model + i, scale, l1, l2, weight_decay, - learning_rate_val); + if (model_copy != nullptr) { + FusedSGDUpdateFunctor()(model_diff + i, model + i, model_copy + i, scale, l1, l2, + weight_decay, learning_rate_val); + } else { + SGDUpdateFunctor()(model_diff + i, model + i, scale, l1, l2, weight_decay, + learning_rate_val); + } } } -template struct SGDUpdateKernelUtil; -template struct SGDUpdateKernelUtil; +template struct SGDUpdateKernelUtil; +template struct SGDUpdateKernelUtil; template struct IndexedSlicesSGDUpdateKernelUtil { - static void Update(ep::Stream* stream, float weight_decay, int64_t num_indices, + static void Update(ep::Stream* stream, float weight_decay, float lr_scale, int64_t num_indices, int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model); @@ -74,11 +81,12 @@ struct IndexedSlicesSGDUpdateKernelUtil { template void IndexedSlicesSGDUpdateKernelUtil::Update( - ep::Stream* stream, float weight_decay, int64_t num_indices, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, + ep::Stream* stream, float weight_decay, float lr_scale, int64_t num_indices, + int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model) { const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; + T lr = *learning_rate; + lr *= lr_scale; FOR_RANGE(int64_t, i, 0, n) { const IDX indices_idx = i / feature_size; const IDX inner_idx = i - indices_idx * feature_size; @@ -103,22 +111,25 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_INDEXED_SLICES_SGD_UPDATE_KERNEL_UTIL_ template struct MomentumUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, + float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum); }; template void MomentumUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum) { + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float dampening, + bool nesterov, bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* momentum) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; for (int64_t i = 0; i != n; ++i) { MomentumUpdateFunctor()(model_diff + i, model + i, momentum + i, scale, l1, l2, beta, - weight_decay, learning_rate_val); + dampening, nesterov, maximize, weight_decay, learning_rate_val); } } @@ -127,19 +138,22 @@ template struct MomentumUpdateKernelUtil; template struct IndexedSlicesMomentumMdUpdateKernelUtil { - static void Update(ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, - int64_t feature_size, int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, const float* learning_rate, const K* indices, - const T* values, T* model, T* momentum); + static void Update(ep::Stream* stream, T beta, float dampening, bool nesterov, bool maximize, + float weight_decay, float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, + const float* learning_rate, const K* indices, const T* values, T* model, + T* momentum); }; template void IndexedSlicesMomentumMdUpdateKernelUtil::Update( - ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const K* indices, const T* values, T* model, T* momentum) { + ep::Stream* stream, T beta, float dampening, bool nesterov, bool maximize, float weight_decay, + float lr_scale, int64_t num_instance, int64_t feature_size, int64_t lower_bound, + int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, + const K* indices, const T* values, T* model, T* momentum) { const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; + T lr = *learning_rate; + lr *= lr_scale; for (int64_t i = 0; i != n; ++i) { const IDX indices_idx = i / feature_size; const IDX inner_idx = i - indices_idx * feature_size; @@ -147,7 +161,7 @@ void IndexedSlicesMomentumMdUpdateKernelUtil::Updat if (instance_id >= lower_bound && instance_id < upper_bound) { const IDX model_idx = (instance_id - lower_bound) * feature_size + inner_idx; MomentumUpdateFunctor()(values + i, model + model_idx, momentum + model_idx, 1.0, 0.0, - 0.0, beta, weight_decay, lr); + 0.0, beta, dampening, nesterov, maximize, weight_decay, lr); } } } @@ -161,51 +175,60 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDAT FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ); #undef INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDATE_KERNEL_UTIL_CPU -template -struct AdamUpdateKernelUtil { +template +struct AdamUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1, - const float* bias_correction2, const G* model_diff, T* model, T* m, T* v, - T* max_v); + bool do_bias_correction, float learning_rate_val, float lr_scale, + float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1, const float* bias_correction2, + const G* model_diff, T* model, C* model_copy, T* m, T* v, T* max_v); }; -template -void AdamUpdateKernelUtil::Update( +template +void AdamUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, - float learning_rate_val, float bias_correction1_val, float bias_correction2_val, + float learning_rate_val, float lr_scale, float bias_correction1_val, float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const G* model_diff, - T* model, T* m, T* v, T* max_v) { + T* model, C* model_copy, T* m, T* v, T* max_v) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } + learning_rate_val *= lr_scale; FOR_RANGE(int64_t, i, 0, n) { - AdamUpdateFunctor()(model_diff + i, model + i, m + i, v + i, max_v + i, scale, l1, l2, - beta1, beta2, epsilon, weight_decay, amsgrad, bias_correction1_val, - bias_correction2_val, learning_rate_val); + if (model_copy != nullptr) { + FusedAdamUpdateFunctor()(model_diff + i, model + i, model_copy + i, m + i, v + i, + max_v + i, scale, l1, l2, beta1, beta2, epsilon, + weight_decay, amsgrad, bias_correction1_val, + bias_correction2_val, learning_rate_val); + } else { + AdamUpdateFunctor()(model_diff + i, model + i, m + i, v + i, max_v + i, scale, l1, l2, + beta1, beta2, epsilon, weight_decay, amsgrad, bias_correction1_val, + bias_correction2_val, learning_rate_val); + } } } -template struct AdamUpdateKernelUtil; -template struct AdamUpdateKernelUtil; +template struct AdamUpdateKernelUtil; +template struct AdamUpdateKernelUtil; template struct IndexedSlicesAdamMdUpdateKernelUtil { static void Update(ep::Stream* stream, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, float lr, - int64_t num_instance, int64_t feature_size, int64_t lower_bound, - int64_t upper_bound, const IDX* num_unique_instance, + float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, T* max_v) { if (learning_rate != nullptr) { lr = *learning_rate; } + lr *= lr_scale; float bias_correction1 = 1.0; float bias_correction2 = 1.0; if (bias_correction1_ptr != nullptr) { bias_correction1 = *bias_correction1_ptr; } @@ -240,8 +263,8 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_INDEXED_SLICES_ADAM_MODEL_UPDATE_KE template struct AdagradUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, - float epsilon, float weight_decay, float learning_rate_val, int64_t train_step, - const float* learning_rate, const int64_t* train_step_ptr, + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + int64_t train_step, const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* sum); }; @@ -249,16 +272,16 @@ struct AdagradUpdateKernelUtil { template void AdagradUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, float epsilon, - float weight_decay, float learning_rate_val, int64_t train_step, const float* learning_rate, - const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* sum) { + float weight_decay, float learning_rate_val, float lr_scale, int64_t train_step, + const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* sum) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (train_step_ptr != nullptr) { train_step = *train_step_ptr + 1; } // train_step_ptr start from zero. if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - learning_rate_val = learning_rate_val / (1 + (train_step - 1) * lr_decay); + learning_rate_val = learning_rate_val * lr_scale / (1 + (train_step - 1) * lr_decay); FOR_RANGE(int64_t, i, 0, n) { AdagradUpdateFunctor()(model_diff + i, model + i, sum + i, scale, l1, l2, epsilon, @@ -273,7 +296,7 @@ template struct LambUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, + float lr_scale, bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, @@ -283,11 +306,11 @@ struct LambUpdateKernelUtil { template void LambUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, float learning_rate_val, bool do_bias_correction, - float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* adam_diff, T* model, T* m, T* v, - T* norm_buffer) { + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, + const float* learning_rate_ptr, const float* bias_correction1_ptr, + const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, + const G* model_diff, T* adam_diff, T* model, T* m, T* v, T* norm_buffer) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate_ptr != nullptr) { learning_rate_val = *learning_rate_ptr; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } @@ -303,6 +326,7 @@ void LambUpdateKernelUtil::Update( T* g_norm_2 = norm_buffer + 1; Memset(stream, norm_buffer, 0, 2 * sizeof(T)); SumSquares2(n, model, w_norm_2, adam_diff, g_norm_2); + learning_rate_val *= lr_scale; const float lr = LambLRFunctor()(learning_rate_val, w_norm_2, g_norm_2); FOR_RANGE(int64_t, i, 0, n) { LambUpdateFunctor()(lr, weight_decay, adam_diff + i, model + i); @@ -328,19 +352,21 @@ template struct RmsPropUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* mean_square, T* mean_gradient); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, + T* mean_gradient); }; template void RmsPropUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, - float weight_decay, float decay_rate, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, - T* mean_gradient) { + float weight_decay, float decay_rate, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* mean_square, T* mean_gradient) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; if (centered) { FOR_RANGE(int64_t, i, 0, n) { RmsPropUpdateFunctor()(model_diff + i, model + i, n, scale, l1, l2, @@ -363,16 +389,17 @@ template struct LarsUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* momentum, T* data_tmp, T* model_diff_tmp); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp); }; template void LarsUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, - float lars_coefficient, float weight_decay, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp) { + float lars_coefficient, float weight_decay, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp) { if (skip_if != nullptr && *skip_if != 0) { return; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } T model_norm = data_tmp[0]; @@ -389,7 +416,9 @@ void LarsUpdateKernelUtil::Update( if (model_norm > 0 && model_diff_norm > 0) { lars = lars_coefficient * model_norm / (epsilon + model_diff_norm + weight_decay * model_norm); } - T local_learning_rate = *learning_rate * lars; + T lr = *learning_rate; + lr *= lr_scale; + T local_learning_rate = lr * lars; FOR_RANGE(int64_t, i, 0, n) { LarsUpdateFunctor()(model_diff_tmp + i, model + i, momentum_beta, momentum + i, weight_decay, local_learning_rate); @@ -403,19 +432,21 @@ template struct FtrlUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + T* accumulate, T* z); }; template void FtrlUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, float learning_rate_val, + float lambda2, float beta, float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; for (int64_t i = 0; i != n; ++i) { FtrlUpdateFunctor()(model_diff + i, model + i, accumulate + i, z + i, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val); @@ -425,4 +456,32 @@ void FtrlUpdateKernelUtil::Update( template struct FtrlUpdateKernelUtil; template struct FtrlUpdateKernelUtil; +template +struct AdadeltaUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, + float epsilon, bool maximize, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* square_avgs, + T* acc_deltas); +}; + +template +void AdadeltaUpdateKernelUtil::Update( + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, float epsilon, + bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* square_avgs, T* acc_deltas) { + if (skip_if != nullptr && *skip_if != 0) { return; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; + for (int64_t i = 0; i != n; ++i) { + AdadeltaUpdateFunctor()(model_diff + i, model + i, square_avgs + i, acc_deltas + i, scale, + l1, l2, rho, epsilon, maximize, weight_decay, learning_rate_val); + } +} + +template struct AdadeltaUpdateKernelUtil; +template struct AdadeltaUpdateKernelUtil; + } // namespace oneflow diff --git a/oneflow/user/kernels/model_update_kernel_util.cu b/oneflow/user/kernels/model_update_kernel_util.cu index b8dc7c832a1bd96dfb120c21679beabc2f40ed99..76a4f507f115e1d7e9eb35a37d13485f3c56183b 100644 --- a/oneflow/user/kernels/model_update_kernel_util.cu +++ b/oneflow/user/kernels/model_update_kernel_util.cu @@ -16,35 +16,47 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/user/kernels/model_update_kernel_util.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include "oneflow/core/ep/cuda/cuda_stream.h" namespace oneflow { namespace { -template +template __global__ void SGDUpdateGpu(int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, - T* model) { + T* model, C* model_copy) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { - SGDUpdateFunctor()(model_diff + i, model + i, scale, l1, l2, weight_decay, - learning_rate_val); + if (model_copy != nullptr) { + FusedSGDUpdateFunctor()(model_diff + i, model + i, model_copy + i, scale, l1, l2, + weight_decay, learning_rate_val); + } else { + SGDUpdateFunctor()(model_diff + i, model + i, scale, l1, l2, weight_decay, + learning_rate_val); + } } } template -__global__ void IndexedSlicesSGDUpdateGpu(float weight_decay, const IDX feature_size, - const int64_t lower_bound, const int64_t upper_bound, - const IDX* num_unique_instance, +__global__ void IndexedSlicesSGDUpdateGpu(float weight_decay, float lr_scale, + const IDX feature_size, const int64_t lower_bound, + const int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model) { const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; + T lr = *learning_rate; + lr *= lr_scale; CUDA_1D_KERNEL_LOOP_T(IDX, i, n) { const IDX indices_idx = i / feature_size; const IDX inner_idx = i - indices_idx * feature_size; @@ -65,7 +77,11 @@ __global__ void SumSquares2(int64_t n, const T* src0, T* dst0, const T* src1, T* t_sum0 += src0[i] * src0[i]; t_sum1 += src1[i] * src1[i]; } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage0; __shared__ typename BlockReduce::TempStorage temp_storage1; T b_sum0 = BlockReduce(temp_storage0).Sum(t_sum0); @@ -78,48 +94,69 @@ __global__ void SumSquares2(int64_t n, const T* src0, T* dst0, const T* src1, T* } // namespace +template +struct SGDUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + C* model_copy); +}; + +template +void SGDUpdateKernelUtil::Update( + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, C* model_copy) { + SGDUpdateGpu<<As()->cuda_stream()>>>( + n, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, learning_rate, scale_by_ptr, + skip_if, model_diff, model, model_copy); +} + template -struct SGDUpdateKernelUtil { +struct SGDUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + float16* model_copy); }; template -void SGDUpdateKernelUtil::Update( +void SGDUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model) { - SGDUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, skip_if, - model_diff, model); + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, float16* model_copy) { + SGDUpdateKernelUtil::Update( + stream, n, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, learning_rate, + scale_by_ptr, skip_if, model_diff, model, reinterpret_cast(model_copy)); } template -struct SGDUpdateKernelUtil { +struct SGDUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, + T* model, float16* model_copy); }; template -void SGDUpdateKernelUtil::Update( +void SGDUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model) { - SGDUpdateKernelUtil::Update( - stream, n, scale, l1, l2, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, - skip_if, reinterpret_cast(model_diff), model); + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float16* model_diff, T* model, float16* model_copy) { + SGDUpdateKernelUtil::Update( + stream, n, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, learning_rate, + scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, + reinterpret_cast(model_copy)); } -template struct SGDUpdateKernelUtil; -template struct SGDUpdateKernelUtil; -template struct SGDUpdateKernelUtil; +template struct SGDUpdateKernelUtil; +template struct SGDUpdateKernelUtil; +template struct SGDUpdateKernelUtil; template struct IndexedSlicesSGDUpdateKernelUtil { - static void Update(ep::Stream* stream, float weight_decay, int64_t num_indices, + static void Update(ep::Stream* stream, float weight_decay, float lr_scale, int64_t num_indices, int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model); @@ -127,14 +164,14 @@ struct IndexedSlicesSGDUpdateKernelUtil { template void IndexedSlicesSGDUpdateKernelUtil::Update( - ep::Stream* stream, float weight_decay, int64_t num_indices, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, + ep::Stream* stream, float weight_decay, float lr_scale, int64_t num_indices, + int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model) { IndexedSlicesSGDUpdateGpu <<As()->cuda_stream()>>>(weight_decay, feature_size, lower_bound, - upper_bound, num_unique_instance, - learning_rate, indices, values, model); + stream->As()->cuda_stream()>>>( + weight_decay, lr_scale, feature_size, lower_bound, upper_bound, num_unique_instance, + learning_rate, indices, values, model); } #define INITIATE_INDEXED_SLICES_SGD_UPDATE_KERNEL_UTIL_CUDA(val_type_pair, key_type_pair, \ @@ -150,27 +187,31 @@ namespace { template __global__ void MomentumUpdateGpu(int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, + float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { MomentumUpdateFunctor()(model_diff + i, model + i, momentum + i, scale, l1, l2, beta, - weight_decay, learning_rate_val); + dampening, nesterov, maximize, weight_decay, learning_rate_val); } } template -__global__ void IndexedSlicesMomentumUpdateGpu(T beta, float weight_decay, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, +__global__ void IndexedSlicesMomentumUpdateGpu(T beta, float dampening, bool nesterov, + bool maximize, float weight_decay, float lr_scale, + int64_t feature_size, int64_t lower_bound, + int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model, T* momentum) { const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; + T lr = *learning_rate; + lr *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { const IDX indices_idx = i / feature_size; const IDX inner_idx = i - indices_idx * feature_size; @@ -178,48 +219,53 @@ __global__ void IndexedSlicesMomentumUpdateGpu(T beta, float weight_decay, int64 if (instance_id >= lower_bound && instance_id < upper_bound) { const IDX model_idx = (instance_id - lower_bound) * feature_size + inner_idx; MomentumUpdateFunctor()(values + i, model + model_idx, momentum + model_idx, - static_cast(1), 0.0, 0.0, beta, weight_decay, lr); + static_cast(1), 0.0, 0.0, beta, dampening, nesterov, + maximize, weight_decay, lr); } } } - } // namespace template struct MomentumUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, + float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum); }; template void MomentumUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum) { + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float dampening, + bool nesterov, bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* momentum) { MomentumUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, beta, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, skip_if, - model_diff, model, momentum); + n, scale, l1, l2, beta, dampening, nesterov, maximize, weight_decay, learning_rate_val, + lr_scale, learning_rate, scale_by_ptr, skip_if, model_diff, model, momentum); } template struct MomentumUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, + float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, T* model, T* momentum); }; template void MomentumUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* momentum) { + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float dampening, + bool nesterov, bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float16* model_diff, T* model, T* momentum) { MomentumUpdateKernelUtil::Update( - stream, n, scale, l1, l2, beta, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, - skip_if, reinterpret_cast(model_diff), model, momentum); + stream, n, scale, l1, l2, beta, dampening, nesterov, maximize, weight_decay, + learning_rate_val, lr_scale, learning_rate, scale_by_ptr, skip_if, + reinterpret_cast(model_diff), model, momentum); } template struct MomentumUpdateKernelUtil; @@ -228,22 +274,24 @@ template struct MomentumUpdateKernelUtil; template struct IndexedSlicesMomentumMdUpdateKernelUtil { - static void Update(ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, - int64_t feature_size, int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, const float* learning_rate, const K* indices, - const T* values, T* model, T* momentum); + static void Update(ep::Stream* stream, T beta, float dampening, bool nesterov, bool maximize, + float weight_decay, float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, + const float* learning_rate, const K* indices, const T* values, T* model, + T* momentum); }; template void IndexedSlicesMomentumMdUpdateKernelUtil::Update( - ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const K* indices, const T* values, T* model, T* momentum) { + ep::Stream* stream, T beta, float dampening, bool nesterov, bool maximize, float weight_decay, + float lr_scale, int64_t num_instance, int64_t feature_size, int64_t lower_bound, + int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, + const K* indices, const T* values, T* model, T* momentum) { IndexedSlicesMomentumUpdateGpu <<As()->cuda_stream()>>>( - beta, weight_decay, feature_size, lower_bound, upper_bound, num_unique_instance, - learning_rate, indices, values, model, momentum); + beta, dampening, nesterov, maximize, weight_decay, lr_scale, feature_size, lower_bound, + upper_bound, num_unique_instance, learning_rate, indices, values, model, momentum); } #define INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDATE_KERNEL_UTIL_CUDA( \ @@ -263,25 +311,33 @@ __global__ void BiasCorrectionFactorKernelGpu(float beta, const int64_t* train_s *out = bias_correction_factor; } -template +template __global__ void AdamUpdateGpu(int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, + bool do_bias_correction, float learning_rate_val, float lr_scale, float bias_correction1_val, float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const G* model_diff, T* model, - T* m, T* v, T* max_v) { + C* model_copy, T* m, T* v, T* max_v) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } + learning_rate_val *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { - AdamUpdateFunctor()(model_diff + i, model + i, m + i, v + i, max_v + i, scale, l1, l2, - beta1, beta2, epsilon, weight_decay, amsgrad, bias_correction1_val, - bias_correction2_val, learning_rate_val); + if (model_copy != nullptr) { + FusedAdamUpdateFunctor()(model_diff + i, model + i, model_copy + i, m + i, v + i, + max_v + i, scale, l1, l2, beta1, beta2, epsilon, + weight_decay, amsgrad, bias_correction1_val, + bias_correction2_val, learning_rate_val); + } else { + AdamUpdateFunctor()(model_diff + i, model + i, m + i, v + i, max_v + i, scale, l1, l2, + beta1, beta2, epsilon, weight_decay, amsgrad, bias_correction1_val, + bias_correction2_val, learning_rate_val); + } } } @@ -296,16 +352,16 @@ __global__ void AdamUpdateBetaTGpu(const T beta1, const T beta2, const int64_t* template __global__ void IndexedSlicesAdamUpdateGpu( float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float lr, int64_t feature_size, int64_t lower_bound, + bool do_bias_correction, float lr, float lr_scale, int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, T* max_v) { if (learning_rate != nullptr) { lr = *learning_rate; } + lr *= lr_scale; float bias_correction1 = 1.0; float bias_correction2 = 1.0; if (bias_correction1_ptr != nullptr) { bias_correction1 = *bias_correction1_ptr; } if (bias_correction2_ptr != nullptr) { bias_correction2 = *bias_correction2_ptr; } - const int64_t n = *num_unique_instance * feature_size; CUDA_1D_KERNEL_LOOP(i, n) { const IDX indices_idx = i / feature_size; @@ -340,76 +396,107 @@ __global__ void LambGradGpu(int64_t n, T scale, float l1, float l2, float beta1, template __global__ void LambUpdateGpu(int64_t n, float weight_decay, float learning_rate_val, - const float* learning_rate_ptr, const int64_t* skip_if, - const T* w_norm_2, const T* g_norm_2, const T* adam_diff, T* model) { + float lr_scale, const float* learning_rate_ptr, + const int64_t* skip_if, const T* w_norm_2, const T* g_norm_2, + const T* adam_diff, T* model) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate_ptr != nullptr) { learning_rate_val = *learning_rate_ptr; } + learning_rate_val *= lr_scale; const float lr = LambLRFunctor()(learning_rate_val, w_norm_2, g_norm_2); CUDA_1D_KERNEL_LOOP(i, n) { LambUpdateFunctor()(lr, weight_decay, adam_diff + i, model + i); } } } // namespace +template +struct AdamUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, + float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction, float learning_rate_val, float lr_scale, + float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1_ptr, const float* bias_correction2_ptr, + const G* model_diff, T* model, C* model_copy, T* m, T* v, T* max_v); +}; + +template +void AdamUpdateKernelUtil::Update( + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, + float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float lr_scale, float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1_ptr, const float* bias_correction2_ptr, const G* model_diff, + T* model, C* model_copy, T* m, T* v, T* max_v) { + AdamUpdateGpu<<As()->cuda_stream()>>>( + n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, + learning_rate_val, lr_scale, bias_correction1_val, bias_correction2_val, learning_rate, + scale_by_ptr, skip_if, bias_correction1_ptr, bias_correction2_ptr, model_diff, model, + model_copy, m, v, max_v); +} + template -struct AdamUpdateKernelUtil { +struct AdamUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const G* model_diff, T* model, T* m, T* v, - T* max_v); + bool do_bias_correction, float learning_rate_val, float lr_scale, + float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1_ptr, const float* bias_correction2_ptr, + const G* model_diff, T* model, float16* model_copy, T* m, T* v, T* max_v); }; template -void AdamUpdateKernelUtil::Update( +void AdamUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, - float learning_rate_val, float bias_correction1_val, float bias_correction2_val, + float learning_rate_val, float lr_scale, float bias_correction1_val, float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const G* model_diff, - T* model, T* m, T* v, T* max_v) { - AdamUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, - learning_rate_val, bias_correction1_val, bias_correction2_val, learning_rate, scale_by_ptr, - skip_if, bias_correction1_ptr, bias_correction2_ptr, model_diff, model, m, v, max_v); + T* model, float16* model_copy, T* m, T* v, T* max_v) { + AdamUpdateKernelUtil::Update( + stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, + learning_rate_val, lr_scale, bias_correction1_val, bias_correction2_val, learning_rate, + scale_by_ptr, skip_if, bias_correction1_ptr, bias_correction2_ptr, model_diff, model, + reinterpret_cast(model_copy), m, v, max_v); } template -struct AdamUpdateKernelUtil { +struct AdamUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const float16* model_diff, T* model, T* m, - T* v, T* max_v); + bool do_bias_correction, float learning_rate_val, float lr_scale, + float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1_ptr, const float* bias_correction2_ptr, + const float16* model_diff, T* model, float16* model_copy, T* m, T* v, + T* max_v); }; template -void AdamUpdateKernelUtil::Update( +void AdamUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, - float learning_rate_val, float bias_correction1_val, float bias_correction2_val, + float learning_rate_val, float lr_scale, float bias_correction1_val, float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const float16* model_diff, - T* model, T* m, T* v, T* max_v) { - AdamUpdateKernelUtil::Update( + T* model, float16* model_copy, T* m, T* v, T* max_v) { + AdamUpdateKernelUtil::Update( stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, - learning_rate_val, bias_correction1_val, bias_correction2_val, learning_rate, scale_by_ptr, - skip_if, bias_correction1_ptr, bias_correction2_ptr, - reinterpret_cast(model_diff), model, m, v, max_v); + learning_rate_val, lr_scale, bias_correction1_val, bias_correction2_val, learning_rate, + scale_by_ptr, skip_if, bias_correction1_ptr, bias_correction2_ptr, + reinterpret_cast(model_diff), model, reinterpret_cast(model_copy), m, v, + max_v); } -template struct AdamUpdateKernelUtil; -template struct AdamUpdateKernelUtil; -template struct AdamUpdateKernelUtil; +template struct AdamUpdateKernelUtil; +template struct AdamUpdateKernelUtil; +template struct AdamUpdateKernelUtil; template __global__ void AdagradUpdateGpu(int64_t n, T scale, float l1, float l2, float lr_decay, float epsilon, float weight_decay, float learning_rate_val, - int64_t train_step, const float* learning_rate, + float lr_scale, int64_t train_step, const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* sum) { if (skip_if != nullptr && *skip_if != 0) { return; } @@ -418,8 +505,7 @@ __global__ void AdagradUpdateGpu(int64_t n, T scale, float l1, float l2, float l train_step = *train_step_ptr + 1; } // train_step_ptr start from zero. if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - learning_rate_val = learning_rate_val / (1 + (train_step - 1) * lr_decay); - + learning_rate_val = learning_rate_val * lr_scale / (1 + (train_step - 1) * lr_decay); CUDA_1D_KERNEL_LOOP(i, n) { AdagradUpdateFunctor()(model_diff + i, model + i, sum + i, scale, l1, l2, epsilon, weight_decay, learning_rate_val); @@ -429,8 +515,8 @@ __global__ void AdagradUpdateGpu(int64_t n, T scale, float l1, float l2, float l template struct AdagradUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, - float epsilon, float weight_decay, float learning_rate_val, int64_t train_step, - const float* learning_rate, const int64_t* train_step_ptr, + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + int64_t train_step, const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* sum); }; @@ -438,12 +524,12 @@ struct AdagradUpdateKernelUtil { template void AdagradUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, float epsilon, - float weight_decay, float learning_rate_val, int64_t train_step, const float* learning_rate, - const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* sum) { + float weight_decay, float learning_rate_val, float lr_scale, int64_t train_step, + const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* sum) { AdagradUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, lr_decay, epsilon, weight_decay, learning_rate_val, train_step, + n, scale, l1, l2, lr_decay, epsilon, weight_decay, learning_rate_val, lr_scale, train_step, learning_rate, train_step_ptr, scale_by_ptr, skip_if, model_diff, model, sum); } @@ -454,7 +540,7 @@ template struct LambUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, + float lr_scale, bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, @@ -464,11 +550,11 @@ struct LambUpdateKernelUtil { template void LambUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, float learning_rate_val, bool do_bias_correction, - float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* adam_diff, T* model, T* m, T* v, - T* norm_buffer) { + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, + const float* learning_rate_ptr, const float* bias_correction1_ptr, + const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, + const G* model_diff, T* adam_diff, T* model, T* m, T* v, T* norm_buffer) { LambGradGpu<<As()->cuda_stream()>>>( n, scale, l1, l2, beta1, beta2, epsilon, scale_by_ptr, skip_if, model_diff, adam_diff, model, @@ -482,15 +568,15 @@ void LambUpdateKernelUtil::Update( stream->As()->cuda_stream()>>>(n, model, w_norm_2, adam_diff, g_norm_2); LambUpdateGpu<<As()->cuda_stream()>>>( - n, weight_decay, learning_rate_val, learning_rate_ptr, skip_if, w_norm_2, g_norm_2, adam_diff, - model); + n, weight_decay, learning_rate_val, lr_scale, learning_rate_ptr, skip_if, w_norm_2, g_norm_2, + adam_diff, model); } template struct LambUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, + float lr_scale, bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, @@ -500,13 +586,13 @@ struct LambUpdateKernelUtil { template void LambUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, float learning_rate_val, bool do_bias_correction, - float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* adam_diff, T* model, T* m, T* v, - T* norm_buffer) { + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, + const float* learning_rate_ptr, const float* bias_correction1_ptr, + const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, + const float16* model_diff, T* adam_diff, T* model, T* m, T* v, T* norm_buffer) { LambUpdateKernelUtil::Update( - stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, learning_rate_val, + stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, learning_rate_val, lr_scale, do_bias_correction, bias_correction1_val, bias_correction2_val, learning_rate_ptr, bias_correction1_ptr, bias_correction2_ptr, scale_by_ptr, skip_if, reinterpret_cast(model_diff), adam_diff, model, m, v, norm_buffer); @@ -520,8 +606,8 @@ template struct IndexedSlicesAdamMdUpdateKernelUtil { static void Update(ep::Stream* stream, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, float lr, - int64_t num_instance, int64_t feature_size, int64_t lower_bound, - int64_t upper_bound, const IDX* num_unique_instance, + float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, T* max_v); @@ -530,7 +616,7 @@ struct IndexedSlicesAdamMdUpdateKernelUtil { template void IndexedSlicesAdamMdUpdateKernelUtil::Update( ep::Stream* stream, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float lr, int64_t num_instance, int64_t feature_size, + bool do_bias_correction, float lr, float lr_scale, int64_t num_instance, int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, @@ -538,11 +624,10 @@ void IndexedSlicesAdamMdUpdateKernelUtil::Update( IndexedSlicesAdamUpdateGpu <<As()->cuda_stream()>>>( - beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, lr, feature_size, - lower_bound, upper_bound, num_unique_instance, learning_rate, bias_correction1_ptr, - bias_correction2_ptr, indices, values, model, m, v, max_v); + beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, lr, lr_scale, + feature_size, lower_bound, upper_bound, num_unique_instance, learning_rate, + bias_correction1_ptr, bias_correction2_ptr, indices, values, model, m, v, max_v); } - #define INSTANTIATE_INDEXED_SLICES_ADAM_MODEL_UPDATE_KERNEL_UTIL_CUDA( \ val_type_pair, key_type_pair, idx_type_pair) \ template struct IndexedSlicesAdamMdUpdateKernelUtil< \ @@ -569,12 +654,13 @@ namespace { template __global__ void RmsPropUpdateGpu(int64_t n, T scale, float l1, float l2, T* mean_square, T* mean_gradient, float epsilon, float weight_decay, - float decay_rate, float learning_rate_val, + float decay_rate, float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { RmsPropUpdateFunctor()(model_diff + i, model + i, n, scale, l1, l2, mean_square + i, @@ -589,26 +675,27 @@ template struct RmsPropUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* mean_square, T* mean_gradient); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, + T* mean_gradient); }; template void RmsPropUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, - float weight_decay, float decay_rate, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, - T* mean_gradient) { + float weight_decay, float decay_rate, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* mean_square, T* mean_gradient) { if (centered) { RmsPropUpdateGpu<<As()->cuda_stream()>>>( n, scale, l1, l2, mean_square, mean_gradient, epsilon, weight_decay, decay_rate, - learning_rate_val, learning_rate, scale_by_ptr, skip_if, model_diff, model); + learning_rate_val, lr_scale, learning_rate, scale_by_ptr, skip_if, model_diff, model); } else { RmsPropUpdateGpu<<As()->cuda_stream()>>>( n, scale, l1, l2, mean_square, mean_gradient, epsilon, weight_decay, decay_rate, - learning_rate_val, learning_rate, scale_by_ptr, skip_if, model_diff, model); + learning_rate_val, lr_scale, learning_rate, scale_by_ptr, skip_if, model_diff, model); } } @@ -616,20 +703,21 @@ template struct RmsPropUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float16* model_diff, T* model, T* mean_square, T* mean_gradient); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float16* model_diff, T* model, T* mean_square, + T* mean_gradient); }; template void RmsPropUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, - float weight_decay, float decay_rate, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, T* model, - T* mean_square, T* mean_gradient) { + float weight_decay, float decay_rate, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float16* model_diff, T* model, T* mean_square, T* mean_gradient) { RmsPropUpdateKernelUtil::Update( stream, n, scale, l1, l2, centered, epsilon, weight_decay, decay_rate, learning_rate_val, - learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, - mean_square, mean_gradient); + lr_scale, learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), + model, mean_square, mean_gradient); } template struct RmsPropUpdateKernelUtil; @@ -651,9 +739,9 @@ __global__ void LarsScaleModelDiffGpu(int64_t n, T scale, float l1, float l2, co } template -__global__ void LarsGetLocalLearningRateGpu(const float* learning_rate, T weight_decay, T epsilon, - T lars_coefficient, const int64_t* skip_if, - T* data_tmp) { +__global__ void LarsGetLocalLearningRateGpu(const float* learning_rate, float lr_scale, + T weight_decay, T epsilon, T lars_coefficient, + const int64_t* skip_if, T* data_tmp) { if (skip_if != nullptr && *skip_if != 0) { return; } T* model_norm = &data_tmp[0]; T* model_diff_norm = &data_tmp[1]; @@ -665,7 +753,9 @@ __global__ void LarsGetLocalLearningRateGpu(const float* learning_rate, T weight lars = lars_coefficient * (*model_norm) / (epsilon + (*model_diff_norm) + weight_decay * (*model_norm)); } - *local_learning_rate = *learning_rate * lars; + T lr = *learning_rate; + lr *= lr_scale; + *local_learning_rate = lr * lars; } template @@ -678,22 +768,24 @@ __global__ void LarsUpdateGpu(int64_t n, float momentum_beta, T* momentum, float *local_learning_rate); } } + } // namespace template struct LarsUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* momentum, T* data_tmp, T* model_diff_tmp); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp); }; template void LarsUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, - float lars_coefficient, float weight_decay, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp) { + float lars_coefficient, float weight_decay, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp) { LarsScaleModelDiffGpu<<As()->cuda_stream()>>>( n, scale, l1, l2, scale_by_ptr, skip_if, model_diff, model, model_diff_tmp); @@ -705,7 +797,7 @@ void LarsUpdateKernelUtil::Update( stream->As()->cuda_stream()>>>(n, model, model_norm, model_diff_tmp, model_diff_norm); LarsGetLocalLearningRateGpu<<<1, 1, 0, stream->As()->cuda_stream()>>>( - learning_rate, weight_decay, epsilon, lars_coefficient, skip_if, data_tmp); + learning_rate, lr_scale, weight_decay, epsilon, lars_coefficient, skip_if, data_tmp); LarsUpdateGpu<<As()->cuda_stream()>>>( n, momentum_beta, momentum, weight_decay, skip_if, local_learning_rate, model_diff_tmp, @@ -716,19 +808,19 @@ template struct LarsUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float16* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float16* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp); }; template void LarsUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, - float lars_coefficient, float weight_decay, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp) { + float lars_coefficient, float weight_decay, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp) { LarsUpdateKernelUtil::Update( - stream, n, scale, l1, l2, momentum_beta, epsilon, lars_coefficient, weight_decay, + stream, n, scale, l1, l2, momentum_beta, epsilon, lars_coefficient, weight_decay, lr_scale, learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, momentum, data_tmp, model_diff_tmp); } @@ -740,12 +832,13 @@ template struct LarsUpdateKernelUtil; template __global__ void FtrlUpdateGpu(int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z) { if (skip_if != nullptr && *skip_if != 0) { return; } if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; CUDA_1D_KERNEL_LOOP(i, n) { FtrlUpdateFunctor()(model_diff + i, model + i, accumulate + i, z + i, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val); @@ -756,19 +849,20 @@ template struct FtrlUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + T* accumulate, T* z); }; template void FtrlUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, float learning_rate_val, + float lambda2, float beta, float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z) { FtrlUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val, + n, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val, lr_scale, learning_rate, scale_by_ptr, skip_if, model_diff, model, accumulate, z); } @@ -776,24 +870,87 @@ template struct FtrlUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* accumulate, - T* z); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, + T* model, T* accumulate, T* z); }; template void FtrlUpdateKernelUtil::Update( ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, float learning_rate_val, + float lambda2, float beta, float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, T* model, T* accumulate, T* z) { FtrlUpdateKernelUtil::Update( stream, n, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val, - learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, - accumulate, z); + lr_scale, learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), + model, accumulate, z); } template struct FtrlUpdateKernelUtil; template struct FtrlUpdateKernelUtil; template struct FtrlUpdateKernelUtil; + +template +__global__ void AdadeltaUpdateGpu(int64_t n, T scale, float l1, float l2, float rho, float epsilon, + bool maximize, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, + T* square_avgs, T* acc_deltas) { + if (skip_if != nullptr && *skip_if != 0) { return; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; + CUDA_1D_KERNEL_LOOP(i, n) { + AdadeltaUpdateFunctor()(model_diff + i, model + i, square_avgs + i, acc_deltas + i, scale, + l1, l2, rho, epsilon, maximize, weight_decay, learning_rate_val); + } +} + +template +struct AdadeltaUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, + float epsilon, bool maximize, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* square_avgs, + T* acc_deltas); +}; + +template +void AdadeltaUpdateKernelUtil::Update( + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, float epsilon, + bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, + T* model, T* square_avgs, T* acc_deltas) { + AdadeltaUpdateGpu<<As()->cuda_stream()>>>( + n, scale, l1, l2, rho, epsilon, maximize, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, model_diff, model, square_avgs, acc_deltas); +} + +template +struct AdadeltaUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, + float epsilon, bool maximize, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float16* model_diff, T* model, T* square_avgs, + T* acc_deltas); +}; + +template +void AdadeltaUpdateKernelUtil::Update( + ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, float epsilon, + bool maximize, float weight_decay, float learning_rate_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float16* model_diff, T* model, T* square_avgs, T* acc_deltas) { + AdadeltaUpdateKernelUtil::Update( + stream, n, scale, l1, l2, rho, epsilon, maximize, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, + square_avgs, acc_deltas); +} + +template struct AdadeltaUpdateKernelUtil; +template struct AdadeltaUpdateKernelUtil; +template struct AdadeltaUpdateKernelUtil; + } // namespace oneflow diff --git a/oneflow/user/kernels/model_update_kernel_util.h b/oneflow/user/kernels/model_update_kernel_util.h index 8e89c5715153b26c39717458203d7650abf6ba62..99d44bffd114412942efb55c60ce77dde0d1df74 100644 --- a/oneflow/user/kernels/model_update_kernel_util.h +++ b/oneflow/user/kernels/model_update_kernel_util.h @@ -43,16 +43,31 @@ struct SGDUpdateFunctor { } }; -template +template +struct FusedSGDUpdateFunctor { + OF_DEVICE_FUNC + void operator()(const G* model_diff, T* model, C* model_copy, T scale, float l1, float l2, + float weight_decay, float learning_rate) const { + const T model_val = *model; + const T model_diff_t = + CastScaleRegularizeGradientFunctor()(*model_diff, model_val, scale, l1, l2); + const T next_model = model_val - learning_rate * (model_diff_t + weight_decay * model_val); + *model = next_model; + *model_copy = static_cast(next_model); + } +}; + +template struct SGDUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + C* model_copy); }; template struct IndexedSlicesSGDUpdateKernelUtil final { - static void Update(ep::Stream* stream, float weight_decay, int64_t num_indices, + static void Update(ep::Stream* stream, float weight_decay, float lr_scale, int64_t num_indices, int64_t feature_size, int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const K* indices, const T* values, T* model); @@ -62,13 +77,25 @@ template struct MomentumUpdateFunctor { OF_DEVICE_FUNC void operator()(const G* model_diff, T* model, T* momentum, T scale, float l1, float l2, - float beta, float weight_decay, float learning_rate) const { + float beta, float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate) const { const T model_val = *model; T model_diff_t = CastScaleRegularizeGradientFunctor()(*model_diff, model_val, scale, l1, l2); - const T next_momentum = beta * *momentum - learning_rate * model_diff_t; + + T next_momentum = beta * *momentum + (1.0f - dampening) * model_diff_t; *momentum = next_momentum; - const T next_model = model_val + next_momentum - learning_rate * weight_decay * model_val; + + if (!nesterov) { + model_diff_t = next_momentum; + } else { + model_diff_t += beta * next_momentum; + } + + T alpha = -learning_rate; + if (maximize) { alpha = learning_rate; } + const T next_model = + model_val + alpha * model_diff_t - learning_rate * weight_decay * model_val; *model = next_model; } }; @@ -103,6 +130,40 @@ struct AdamUpdateFunctor { } }; +template +struct FusedAdamUpdateFunctor { + OF_DEVICE_FUNC + void operator()(const G* model_diff, T* model, C* model_copy, T* m, T* v, T* max_v, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, + bool amsgrad, float bias_correction1, float bias_correction2, + float learning_rate) const { + const T model_val = *model; + T model_diff_t = + CastScaleRegularizeGradientFunctor()(*model_diff, model_val, scale, l1, l2); + + const T next_m = beta1 * *m + (1 - beta1) * model_diff_t; + *m = next_m; + + const T next_v = beta2 * *v + (1 - beta2) * model_diff_t * model_diff_t; + *v = next_v; + + T denom = 0; + if (amsgrad) { + const T next_max_v = + *max_v > next_v ? *max_v : next_v; // use std::max has bug in GPU kernel. + *max_v = next_max_v; + denom = (sqrt(next_max_v) / sqrt(bias_correction2)) + epsilon; + } else { + denom = (sqrt(next_v) / sqrt(bias_correction2)) + epsilon; + } + const T step_size = learning_rate / bias_correction1; + const T next_model = + model_val - step_size * (next_m / denom) - learning_rate * weight_decay * model_val; + *model = next_model; + *model_copy = static_cast(next_model); + } +}; + template struct AdagradUpdateFunctor { OF_DEVICE_FUNC @@ -196,6 +257,29 @@ struct FtrlUpdateFunctor { } }; +template +struct AdadeltaUpdateFunctor { + OF_DEVICE_FUNC void operator()(const G* model_diff, T* model, T* square_avgs, T* acc_deltas, + T scale, float l1, float l2, float rho, float epsilon, + bool maximize, float weight_decay, float learning_rate) { + const T model_val = *model; + T model_diff_val = *model_diff; + if (maximize) { model_diff_val = -model_diff_val; } + T model_diff_t = + CastScaleRegularizeGradientFunctor()(model_diff_val, model_val, scale, l1, l2); + T square_avgs_val = *square_avgs; + T new_square_avgs_val = square_avgs_val * rho + model_diff_t * model_diff_t * (1.0f - rho); + T square_avgs_std = sqrt(new_square_avgs_val + epsilon); + T acc_delta_val = *acc_deltas; + T delta = sqrt(acc_delta_val + epsilon) / square_avgs_std * model_diff_t; + T new_acc_deltas = acc_delta_val * rho + delta * delta * (1.0f - rho); + T new_model = model_val - learning_rate * delta; + *model = new_model; + *square_avgs = new_square_avgs_val; + *acc_deltas = new_acc_deltas; + } +}; + template struct BiasCorrectionFactorKernelUtil { public: @@ -206,35 +290,37 @@ struct BiasCorrectionFactorKernelUtil { template struct MomentumUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, + float dampening, bool nesterov, bool maximize, float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* momentum); }; template struct IndexedSlicesMomentumMdUpdateKernelUtil { - static void Update(ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, - int64_t feature_size, int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, const float* learning_rate, const K* indices, - const T* values, T* model, T* momentum); + static void Update(ep::Stream* stream, T beta, float dampening, bool nesterov, bool maximize, + float weight_decay, float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, + const float* learning_rate, const K* indices, const T* values, T* model, + T* momentum); }; -template +template struct AdamUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1, - const float* bias_correction2, const G* model_diff, T* model, T* m, T* v, - T* max_v); + bool do_bias_correction, float learning_rate_val, float lr_scale, + float bias_correction1_val, float bias_correction2_val, + const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, + const float* bias_correction1, const float* bias_correction2, + const G* model_diff, T* model, C* model_copy, T* m, T* v, T* max_v); }; template struct AdagradUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, - float epsilon, float weight_decay, float learning_rate_val, int64_t train_step, - const float* learning_rate, const int64_t* train_step_ptr, + float epsilon, float weight_decay, float learning_rate_val, float lr_scale, + int64_t train_step, const float* learning_rate, const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* sum); }; @@ -243,8 +329,8 @@ template struct IndexedSlicesAdamMdUpdateKernelUtil { static void Update(ep::Stream* stream, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, float lr, - int64_t num_instance, int64_t feature_size, int64_t lower_bound, - int64_t upper_bound, const IDX* num_unique_instance, + float lr_scale, int64_t num_instance, int64_t feature_size, + int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, T* max_v); @@ -255,7 +341,7 @@ struct LambUpdateKernelUtil { public: static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, + float lr_scale, bool do_bias_correction, float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, @@ -266,8 +352,18 @@ template struct FtrlUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z); + float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, + T* accumulate, T* z); +}; + +template +struct AdadeltaUpdateKernelUtil { + static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float rho, + float epsilon, bool maximize, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* square_avgs, + T* acc_deltas); }; template @@ -298,8 +394,9 @@ template struct RmsPropUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* mean_square, T* mean_gradient); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, + T* mean_gradient); }; template @@ -319,8 +416,9 @@ template struct LarsUpdateKernelUtil { static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* momentum, T* data_tmp, T* model_diff_tmp); + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const G* model_diff, T* model, T* momentum, + T* data_tmp, T* model_diff_tmp); }; #endif diff --git a/oneflow/user/kernels/model_update_kernel_util.hip.cpp b/oneflow/user/kernels/model_update_kernel_util.hip.cpp deleted file mode 100644 index 22e15100553e5f2d0f65851213563a497ec5dbef..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/model_update_kernel_util.hip.cpp +++ /dev/null @@ -1,799 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/model_update_kernel_util.h" -#include -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void SGDUpdateGpu(int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, - T* model) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - SGDUpdateFunctor()(model_diff + i, model + i, scale, l1, l2, weight_decay, - learning_rate_val); - } -} - -template -__global__ void IndexedSlicesSGDUpdateGpu(float weight_decay, const IDX feature_size, - const int64_t lower_bound, const int64_t upper_bound, - const IDX* num_unique_instance, - const float* learning_rate, const K* indices, - const T* values, T* model) { - const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; - CUDA_1D_KERNEL_LOOP_T(IDX, i, n) { - const IDX indices_idx = i / feature_size; - const IDX inner_idx = i - indices_idx * feature_size; - const IDX instance_id = indices[indices_idx]; - if (instance_id >= lower_bound && instance_id < upper_bound) { - const IDX model_idx = (instance_id - lower_bound) * feature_size + inner_idx; - SGDUpdateFunctor()(values + i, model + model_idx, static_cast(1), 0.0, 0.0, - weight_decay, lr); - } - } -} - -template -__global__ void SumSquares2(int64_t n, const T* src0, T* dst0, const T* src1, T* dst1) { - T t_sum0 = 0; - T t_sum1 = 0; - CUDA_1D_KERNEL_LOOP(i, n) { - t_sum0 += src0[i] * src0[i]; - t_sum1 += src1[i] * src1[i]; - } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage0; - __shared__ typename BlockReduce::TempStorage temp_storage1; - T b_sum0 = BlockReduce(temp_storage0).Sum(t_sum0); - T b_sum1 = BlockReduce(temp_storage1).Sum(t_sum1); - if (threadIdx.x == 0) { - cuda::atomic::Add(dst0, b_sum0); - cuda::atomic::Add(dst1, b_sum1); - } -} - -} // namespace - -template -struct SGDUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model); -}; - -template -void SGDUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model) { - SGDUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, skip_if, - model_diff, model); -} - -template -struct SGDUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model); -}; - -template -void SGDUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model) { - SGDUpdateKernelUtil::Update( - stream, n, scale, l1, l2, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, - skip_if, reinterpret_cast(model_diff), model); -} - -template struct SGDUpdateKernelUtil; -template struct SGDUpdateKernelUtil; -template struct SGDUpdateKernelUtil; - -template -struct IndexedSlicesSGDUpdateKernelUtil { - static void Update(ep::Stream* stream, float weight_decay, int64_t num_indices, - int64_t feature_size, int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, const float* learning_rate, const K* indices, - const T* values, T* model); -}; - -template -void IndexedSlicesSGDUpdateKernelUtil::Update( - ep::Stream* stream, float weight_decay, int64_t num_indices, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const K* indices, const T* values, T* model) { - IndexedSlicesSGDUpdateGpu - <<As()->cuda_stream()>>>(weight_decay, feature_size, lower_bound, - upper_bound, num_unique_instance, - learning_rate, indices, values, model); -} - -#define INITIATE_INDEXED_SLICES_SGD_UPDATE_KERNEL_UTIL_CUDA(val_type_pair, key_type_pair, \ - idx_type_pair) \ - template struct IndexedSlicesSGDUpdateKernelUtil< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(val_type_pair), OF_PP_PAIR_FIRST(key_type_pair), \ - OF_PP_PAIR_FIRST(idx_type_pair)>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_INDEXED_SLICES_SGD_UPDATE_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ); -#undef INITIATE_INDEXED_SLICES_SGD_UPDATE_KERNEL_UTIL_CUDA - -namespace { - -template -__global__ void MomentumUpdateGpu(int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, - T* momentum) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - MomentumUpdateFunctor()(model_diff + i, model + i, momentum + i, scale, l1, l2, beta, - weight_decay, learning_rate_val); - } -} - -template -__global__ void IndexedSlicesMomentumUpdateGpu(T beta, float weight_decay, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, - const float* learning_rate, const K* indices, - const T* values, T* model, T* momentum) { - const int64_t n = *num_unique_instance * feature_size; - const T lr = *learning_rate; - CUDA_1D_KERNEL_LOOP(i, n) { - const IDX indices_idx = i / feature_size; - const IDX inner_idx = i - indices_idx * feature_size; - const IDX instance_id = indices[indices_idx]; - if (instance_id >= lower_bound && instance_id < upper_bound) { - const IDX model_idx = (instance_id - lower_bound) * feature_size + inner_idx; - MomentumUpdateFunctor()(values + i, model + model_idx, momentum + model_idx, - static_cast(1), 0.0, 0.0, beta, weight_decay, lr); - } - } -} - -} // namespace - -template -struct MomentumUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, - T* momentum); -}; - -template -void MomentumUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum) { - MomentumUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, beta, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, skip_if, - model_diff, model, momentum); -} - -template -struct MomentumUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, - float weight_decay, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, - T* model, T* momentum); -}; - -template -void MomentumUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* momentum) { - MomentumUpdateKernelUtil::Update( - stream, n, scale, l1, l2, beta, weight_decay, learning_rate_val, learning_rate, scale_by_ptr, - skip_if, reinterpret_cast(model_diff), model, momentum); -} - -template struct MomentumUpdateKernelUtil; -template struct MomentumUpdateKernelUtil; -template struct MomentumUpdateKernelUtil; - -template -struct IndexedSlicesMomentumMdUpdateKernelUtil { - static void Update(ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, - int64_t feature_size, int64_t lower_bound, int64_t upper_bound, - const IDX* num_unique_instance, const float* learning_rate, const K* indices, - const T* values, T* model, T* momentum); -}; - -template -void IndexedSlicesMomentumMdUpdateKernelUtil::Update( - ep::Stream* stream, T beta, float weight_decay, int64_t num_instance, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const K* indices, const T* values, T* model, T* momentum) { - IndexedSlicesMomentumUpdateGpu - <<As()->cuda_stream()>>>( - beta, weight_decay, feature_size, lower_bound, upper_bound, num_unique_instance, - learning_rate, indices, values, model, momentum); -} - -#define INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDATE_KERNEL_UTIL_CUDA( \ - val_type_pair, key_type_pair, idx_type_pair) \ - template struct IndexedSlicesMomentumMdUpdateKernelUtil< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(val_type_pair), OF_PP_PAIR_FIRST(key_type_pair), \ - OF_PP_PAIR_FIRST(idx_type_pair)>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDATE_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ); -#undef INSTANTIATE_INDEXED_SLICES_MOMENTUM_MODEL_UPDATE_KERNEL_UTIL_CUDA - -namespace { - -__global__ void BiasCorrectionFactorKernelGpu(float beta, const int64_t* train_step, float* out) { - const auto exponent = static_cast(*train_step + 1); - const float bias_correction_factor = 1.0 - static_cast(pow(beta, exponent)); - *out = bias_correction_factor; -} - -template -__global__ void AdamUpdateGpu(int64_t n, T scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, - float bias_correction1_val, float bias_correction2_val, - const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const G* model_diff, T* model, - T* m, T* v, T* max_v) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } - if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } - - CUDA_1D_KERNEL_LOOP(i, n) { - AdamUpdateFunctor()(model_diff + i, model + i, m + i, v + i, max_v + i, scale, l1, l2, - beta1, beta2, epsilon, weight_decay, amsgrad, bias_correction1_val, - bias_correction2_val, learning_rate_val); - } -} - -template -__global__ void AdamUpdateBetaTGpu(const T beta1, const T beta2, const int64_t* skip_if, T* beta1_t, - T* beta2_t) { - if (skip_if != nullptr && *skip_if != 0) { return; } - *beta1_t *= beta1; - *beta2_t *= beta2; -} - -template -__global__ void IndexedSlicesAdamUpdateGpu( - float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float lr, int64_t feature_size, int64_t lower_bound, - int64_t upper_bound, const IDX* num_unique_instance, const float* learning_rate, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const K* indices, - const T* values, T* model, T* m, T* v, T* max_v) { - if (learning_rate != nullptr) { lr = *learning_rate; } - float bias_correction1 = 1.0; - float bias_correction2 = 1.0; - if (bias_correction1_ptr != nullptr) { bias_correction1 = *bias_correction1_ptr; } - if (bias_correction2_ptr != nullptr) { bias_correction2 = *bias_correction2_ptr; } - - const int64_t n = *num_unique_instance * feature_size; - CUDA_1D_KERNEL_LOOP(i, n) { - const IDX indices_idx = i / feature_size; - const IDX inner_idx = i - indices_idx * feature_size; - const IDX instance_id = indices[indices_idx]; - if (instance_id >= lower_bound && instance_id < upper_bound) { - const IDX model_idx = (instance_id - lower_bound) * feature_size + inner_idx; - AdamUpdateFunctor()(values + i, model + model_idx, m + model_idx, v + model_idx, - max_v + i, static_cast(1), 0, 0, beta1, beta2, epsilon, - weight_decay, amsgrad, bias_correction1, bias_correction2, lr); - } - } -} - -template -__global__ void LambGradGpu(int64_t n, T scale, float l1, float l2, float beta1, float beta2, - float epsilon, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* adam_diff, T* model, T* m, T* v, - bool do_bias_correction, float bias_correction1_val, - float bias_correction2_val, const float* bias_correction1_ptr, - const float* bias_correction2_ptr) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } - if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - LambGradFunctor()(model_diff + i, adam_diff + i, model + i, m + i, v + i, scale, l1, l2, - beta1, beta2, epsilon, do_bias_correction, bias_correction1_val, - bias_correction2_val); - } -} - -template -__global__ void LambUpdateGpu(int64_t n, float weight_decay, float learning_rate_val, - const float* learning_rate_ptr, const int64_t* skip_if, - const T* w_norm_2, const T* g_norm_2, const T* adam_diff, T* model) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate_ptr != nullptr) { learning_rate_val = *learning_rate_ptr; } - const float lr = LambLRFunctor()(learning_rate_val, w_norm_2, g_norm_2); - CUDA_1D_KERNEL_LOOP(i, n) { LambUpdateFunctor()(lr, weight_decay, adam_diff + i, model + i); } -} - -} // namespace - -template -struct AdamUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, - float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const G* model_diff, T* model, T* m, T* v, - T* max_v); -}; - -template -void AdamUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, - float learning_rate_val, float bias_correction1_val, float bias_correction2_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const G* model_diff, - T* model, T* m, T* v, T* max_v) { - AdamUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, - learning_rate_val, bias_correction1_val, bias_correction2_val, learning_rate, scale_by_ptr, - skip_if, bias_correction1_ptr, bias_correction2_ptr, model_diff, model, m, v, max_v); -} - -template -struct AdamUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, - float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float learning_rate_val, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const float16* model_diff, T* model, T* m, - T* v, T* max_v); -}; - -template -void AdamUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, bool amsgrad, bool do_bias_correction, - float learning_rate_val, float bias_correction1_val, float bias_correction2_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const float16* model_diff, - T* model, T* m, T* v, T* max_v) { - AdamUpdateKernelUtil::Update( - stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, - learning_rate_val, bias_correction1_val, bias_correction2_val, learning_rate, scale_by_ptr, - skip_if, bias_correction1_ptr, bias_correction2_ptr, - reinterpret_cast(model_diff), model, m, v, max_v); -} - -template struct AdamUpdateKernelUtil; -template struct AdamUpdateKernelUtil; -template struct AdamUpdateKernelUtil; - -template -__global__ void AdagradUpdateGpu(int64_t n, T scale, float l1, float l2, float lr_decay, - float epsilon, float weight_decay, float learning_rate_val, - int64_t train_step, const float* learning_rate, - const int64_t* train_step_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* sum) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (train_step_ptr != nullptr) { - train_step = *train_step_ptr + 1; - } // train_step_ptr start from zero. - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - learning_rate_val = learning_rate_val / (1 + (train_step - 1) * lr_decay); - - CUDA_1D_KERNEL_LOOP(i, n) { - AdagradUpdateFunctor()(model_diff + i, model + i, sum + i, scale, l1, l2, epsilon, - weight_decay, learning_rate_val); - } -} - -template -struct AdagradUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, - float epsilon, float weight_decay, float learning_rate_val, int64_t train_step, - const float* learning_rate, const int64_t* train_step_ptr, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, - T* sum); -}; - -template -void AdagradUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_decay, float epsilon, - float weight_decay, float learning_rate_val, int64_t train_step, const float* learning_rate, - const int64_t* train_step_ptr, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* sum) { - AdagradUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, lr_decay, epsilon, weight_decay, learning_rate_val, train_step, - learning_rate, train_step_ptr, scale_by_ptr, skip_if, model_diff, model, sum); -} - -template struct AdagradUpdateKernelUtil; -template struct AdagradUpdateKernelUtil; - -template -struct LambUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, - float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, - T* adam_diff, T* model, T* m, T* v, T* norm_buffer); -}; - -template -void LambUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, float learning_rate_val, bool do_bias_correction, - float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* adam_diff, T* model, T* m, T* v, - T* norm_buffer) { - LambGradGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, beta1, beta2, epsilon, scale_by_ptr, skip_if, model_diff, adam_diff, model, - m, v, do_bias_correction, bias_correction1_val, bias_correction2_val, bias_correction1_ptr, - bias_correction2_ptr); - T* w_norm_2 = norm_buffer; - T* g_norm_2 = norm_buffer + 1; - Memset(stream, norm_buffer, 0, 2 * sizeof(T)); - SumSquares2 - <<As()->cuda_stream()>>>(n, model, w_norm_2, adam_diff, g_norm_2); - LambUpdateGpu<<As()->cuda_stream()>>>( - n, weight_decay, learning_rate_val, learning_rate_ptr, skip_if, w_norm_2, g_norm_2, adam_diff, - model); -} - -template -struct LambUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, - float beta2, float epsilon, float weight_decay, float learning_rate_val, - bool do_bias_correction, float bias_correction1_val, - float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, - const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, - T* adam_diff, T* model, T* m, T* v, T* norm_buffer); -}; - -template -void LambUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, float scale, float l1, float l2, float beta1, float beta2, - float epsilon, float weight_decay, float learning_rate_val, bool do_bias_correction, - float bias_correction1_val, float bias_correction2_val, const float* learning_rate_ptr, - const float* bias_correction1_ptr, const float* bias_correction2_ptr, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* adam_diff, T* model, T* m, T* v, - T* norm_buffer) { - LambUpdateKernelUtil::Update( - stream, n, scale, l1, l2, beta1, beta2, epsilon, weight_decay, learning_rate_val, - do_bias_correction, bias_correction1_val, bias_correction2_val, learning_rate_ptr, - bias_correction1_ptr, bias_correction2_ptr, scale_by_ptr, skip_if, - reinterpret_cast(model_diff), adam_diff, model, m, v, norm_buffer); -} - -template struct LambUpdateKernelUtil; -template struct LambUpdateKernelUtil; -template struct LambUpdateKernelUtil; - -template -struct IndexedSlicesAdamMdUpdateKernelUtil { - static void Update(ep::Stream* stream, float beta1, float beta2, float epsilon, - float weight_decay, bool amsgrad, bool do_bias_correction, float lr, - int64_t num_instance, int64_t feature_size, int64_t lower_bound, - int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const K* indices, const T* values, T* model, - T* m, T* v, T* max_v); -}; - -template -void IndexedSlicesAdamMdUpdateKernelUtil::Update( - ep::Stream* stream, float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, - bool do_bias_correction, float lr, int64_t num_instance, int64_t feature_size, - int64_t lower_bound, int64_t upper_bound, const IDX* num_unique_instance, - const float* learning_rate, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const K* indices, const T* values, T* model, T* m, T* v, - T* max_v) { - IndexedSlicesAdamUpdateGpu - <<As()->cuda_stream()>>>( - beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, lr, feature_size, - lower_bound, upper_bound, num_unique_instance, learning_rate, bias_correction1_ptr, - bias_correction2_ptr, indices, values, model, m, v, max_v); -} - -#define INSTANTIATE_INDEXED_SLICES_ADAM_MODEL_UPDATE_KERNEL_UTIL_CUDA( \ - val_type_pair, key_type_pair, idx_type_pair) \ - template struct IndexedSlicesAdamMdUpdateKernelUtil< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(val_type_pair), OF_PP_PAIR_FIRST(key_type_pair), \ - OF_PP_PAIR_FIRST(idx_type_pair)>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_INDEXED_SLICES_ADAM_MODEL_UPDATE_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ); -#undef INSTANTIATE_INDEXED_SLICES_ADAM_MODEL_UPDATE_KERNEL_UTIL_CUDA - -template<> -struct BiasCorrectionFactorKernelUtil { - static void BiasCorrectionFactorCompute(ep::Stream* stream, float beta, const int64_t* train_step, - float* out); -}; - -void BiasCorrectionFactorKernelUtil::BiasCorrectionFactorCompute( - ep::Stream* stream, float beta, const int64_t* train_step, float* out) { - BiasCorrectionFactorKernelGpu<<<1, 1, 0, stream->As()->cuda_stream()>>>( - beta, train_step, out); -} - -namespace { - -template -__global__ void RmsPropUpdateGpu(int64_t n, T scale, float l1, float l2, T* mean_square, - T* mean_gradient, float epsilon, float weight_decay, - float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - RmsPropUpdateFunctor()(model_diff + i, model + i, n, scale, l1, l2, - mean_square + i, - (centered ? mean_gradient + i : nullptr), epsilon, - weight_decay, decay_rate, learning_rate_val); - } -} - -} // namespace - -template -struct RmsPropUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, - float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* mean_square, T* mean_gradient); -}; - -template -void RmsPropUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, - float weight_decay, float decay_rate, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, T* model, T* mean_square, - T* mean_gradient) { - if (centered) { - RmsPropUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, mean_square, mean_gradient, epsilon, weight_decay, decay_rate, - learning_rate_val, learning_rate, scale_by_ptr, skip_if, model_diff, model); - } else { - RmsPropUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, mean_square, mean_gradient, epsilon, weight_decay, decay_rate, - learning_rate_val, learning_rate, scale_by_ptr, skip_if, model_diff, model); - } -} - -template -struct RmsPropUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, - float epsilon, float weight_decay, float decay_rate, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float16* model_diff, T* model, T* mean_square, T* mean_gradient); -}; - -template -void RmsPropUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, bool centered, float epsilon, - float weight_decay, float decay_rate, float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const float16* model_diff, T* model, - T* mean_square, T* mean_gradient) { - RmsPropUpdateKernelUtil::Update( - stream, n, scale, l1, l2, centered, epsilon, weight_decay, decay_rate, learning_rate_val, - learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, - mean_square, mean_gradient); -} - -template struct RmsPropUpdateKernelUtil; -template struct RmsPropUpdateKernelUtil; -template struct RmsPropUpdateKernelUtil; - -namespace { - -template -__global__ void LarsScaleModelDiffGpu(int64_t n, T scale, float l1, float l2, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, - T* model_diff_tmp) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - model_diff_tmp[i] = - CastScaleRegularizeGradientFunctor()(model_diff[i], model[i], scale, l1, l2); - } -} - -template -__global__ void LarsGetLocalLearningRateGpu(const float* learning_rate, T weight_decay, T epsilon, - T lars_coefficient, const int64_t* skip_if, - T* data_tmp) { - if (skip_if != nullptr && *skip_if != 0) { return; } - T* model_norm = &data_tmp[0]; - T* model_diff_norm = &data_tmp[1]; - T* local_learning_rate = &data_tmp[2]; - *model_norm = std::sqrt(*model_norm); - *model_diff_norm = std::sqrt(*model_diff_norm); - T lars = static_cast(1); - if (*model_norm > 0 && *model_diff_norm > 0) { - lars = lars_coefficient * (*model_norm) - / (epsilon + (*model_diff_norm) + weight_decay * (*model_norm)); - } - *local_learning_rate = *learning_rate * lars; -} - -template -__global__ void LarsUpdateGpu(int64_t n, float momentum_beta, T* momentum, float weight_decay, - const int64_t* skip_if, T* local_learning_rate, T* model_diff_tmp, - T* model) { - if (skip_if != nullptr && *skip_if != 0) { return; } - CUDA_1D_KERNEL_LOOP(i, n) { - LarsUpdateFunctor()(model_diff_tmp + i, model + i, momentum_beta, momentum + i, weight_decay, - *local_learning_rate); - } -} -} // namespace - -template -struct LarsUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, - float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const G* model_diff, T* model, T* momentum, T* data_tmp, T* model_diff_tmp); -}; - -template -void LarsUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, - float lars_coefficient, float weight_decay, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp) { - LarsScaleModelDiffGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, scale_by_ptr, skip_if, model_diff, model, model_diff_tmp); - T* model_norm = data_tmp; - T* model_diff_norm = data_tmp + 1; - T* local_learning_rate = data_tmp + 2; - Memset(stream, data_tmp, 0, 2 * sizeof(T)); - SumSquares2<<As()->cuda_stream()>>>(n, model, model_norm, - model_diff_tmp, model_diff_norm); - LarsGetLocalLearningRateGpu<<<1, 1, 0, stream->As()->cuda_stream()>>>( - learning_rate, weight_decay, epsilon, lars_coefficient, skip_if, data_tmp); - LarsUpdateGpu<<As()->cuda_stream()>>>( - n, momentum_beta, momentum, weight_decay, skip_if, local_learning_rate, model_diff_tmp, - model); -} - -template -struct LarsUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, - float momentum_beta, float epsilon, float lars_coefficient, float weight_decay, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float16* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp); -}; - -template -void LarsUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float momentum_beta, float epsilon, - float lars_coefficient, float weight_decay, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* momentum, T* data_tmp, - T* model_diff_tmp) { - LarsUpdateKernelUtil::Update( - stream, n, scale, l1, l2, momentum_beta, epsilon, lars_coefficient, weight_decay, - learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, - momentum, data_tmp, model_diff_tmp); -} - -template struct LarsUpdateKernelUtil; -template struct LarsUpdateKernelUtil; -template struct LarsUpdateKernelUtil; - -template -__global__ void FtrlUpdateGpu(int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, - const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, - T* model, T* accumulate, T* z) { - if (skip_if != nullptr && *skip_if != 0) { return; } - if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - CUDA_1D_KERNEL_LOOP(i, n) { - FtrlUpdateFunctor()(model_diff + i, model + i, accumulate + i, z + i, scale, l1, l2, - lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val); - } -} - -template -struct FtrlUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, - float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const G* model_diff, T* model, T* accumulate, T* z); -}; - -template -void FtrlUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, const G* model_diff, - T* model, T* accumulate, T* z) { - FtrlUpdateGpu<<As()->cuda_stream()>>>( - n, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val, - learning_rate, scale_by_ptr, skip_if, model_diff, model, accumulate, z); -} - -template -struct FtrlUpdateKernelUtil { - static void Update(ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, - float lambda1, float lambda2, float beta, float weight_decay, - float learning_rate_val, const float* learning_rate, const T* scale_by_ptr, - const int64_t* skip_if, const float16* model_diff, T* model, T* accumulate, - T* z); -}; - -template -void FtrlUpdateKernelUtil::Update( - ep::Stream* stream, int64_t n, T scale, float l1, float l2, float lr_power, float lambda1, - float lambda2, float beta, float weight_decay, float learning_rate_val, - const float* learning_rate, const T* scale_by_ptr, const int64_t* skip_if, - const float16* model_diff, T* model, T* accumulate, T* z) { - FtrlUpdateKernelUtil::Update( - stream, n, scale, l1, l2, lr_power, lambda1, lambda2, beta, weight_decay, learning_rate_val, - learning_rate, scale_by_ptr, skip_if, reinterpret_cast(model_diff), model, - accumulate, z); -} - -template struct FtrlUpdateKernelUtil; -template struct FtrlUpdateKernelUtil; -template struct FtrlUpdateKernelUtil; -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/model_update_kernels.cpp b/oneflow/user/kernels/model_update_kernels.cpp index 3bfc649a9f8e23c651bc4d89539e48f0c8b2fd47..95b1bb00afa72934f536220d1af3cead647557b7 100644 --- a/oneflow/user/kernels/model_update_kernels.cpp +++ b/oneflow/user/kernels/model_update_kernels.cpp @@ -110,7 +110,7 @@ std::shared_ptr CreateIndexedSlicesUpdateOpKernelCache( } } -template +template class SGDUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: SGDUpdateKernel() = default; @@ -125,7 +125,13 @@ class SGDUpdateKernel final : public user_op::OpKernel, public user_op::CudaGrap const auto l2 = ctx->Attr("l2"); const auto weight_decay = ctx->Attr("weight_decay"); const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); const float* learning_rate_ptr = nullptr; + C* model_copy_ptr = nullptr; + if (ctx->has_input("model_copy", 0)) { + user_op::Tensor* model_copy = ctx->Tensor4ArgNameAndIndex("model_copy", 0); + model_copy_ptr = model_copy->mut_dptr(); + } if (ctx->has_input("learning_rate", 0)) { const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); learning_rate_ptr = learning_rate->dptr(); @@ -143,33 +149,28 @@ class SGDUpdateKernel final : public user_op::OpKernel, public user_op::CudaGrap CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); skip_if_ptr = skip_if->dptr(); } - SGDUpdateKernelUtil::Update( + SGDUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, weight_decay, - learning_rate_val, learning_rate_ptr, scale_by_ptr, skip_if_ptr, model_diff->dptr(), - model->mut_dptr()); + learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, skip_if_ptr, + model_diff->dptr(), model->mut_dptr(), model_copy_ptr); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; -#define REGISTER_SGD_UPDATE_KERNEL(device, dtype, gtype) \ +#define REGISTER_SGD_UPDATE_KERNEL(device, dtype, gtype, ctype) \ REGISTER_USER_KERNEL("sgd_update") \ - .SetCreateFn>() \ + .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == device) \ && (user_op::HobDataType("model", 0) == GetDataType::value) \ && (user_op::HobDataType("model_diff", 0) == GetDataType::value)); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCPU, float, float); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // -#ifdef WITH_ROCM -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM +REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCPU, float, float, float16); +REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCPU, double, double, float16); +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float16, float16); +REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float, float16); +REGISTER_SGD_UPDATE_KERNEL(DeviceType::kCUDA, double, double, float16); +#endif // WITH_CUDA template user_op::InferTmpSizeFn GenInferTmpSizeFn() { @@ -205,6 +206,7 @@ class IndexedSlicesSGDUpdateKernel final : public user_op::OpKernel { const user_op::Tensor* model_diff_values = ctx->Tensor4ArgNameAndIndex("model_diff_values", 0); user_op::Tensor* model = ctx->Tensor4ArgNameAndIndex("model", 0); const auto weight_decay = ctx->Attr("weight_decay"); + const auto lr_scale = ctx->Attr("learning_rate_scale"); const int64_t num_indices = model_diff_indices->shape_view().elem_cnt(); const int64_t num_values = model_diff_values->shape_view().elem_cnt(); if (num_indices == 0) { @@ -226,7 +228,7 @@ class IndexedSlicesSGDUpdateKernel final : public user_op::OpKernel { model_diff_values->dptr(), buffer_manager.NumUniqueDiffIndicesPtr(), buffer_manager.UniqueDiffIndicesPtr(), buffer_manager.UniqueDiffValuesPtr(), buffer_manager.UniqueWorkspacePtr(), buffer_manager.UniqueWorkspaceBytes()); - MdUpdateUtilT::Update(ctx->stream(), weight_decay, num_indices, feature_size, + MdUpdateUtilT::Update(ctx->stream(), weight_decay, lr_scale, num_indices, feature_size, kernel_cache->lower(), kernel_cache->upper(), buffer_manager.NumUniqueDiffIndicesPtr(), learning_rate->dptr(), buffer_manager.UniqueDiffIndicesPtr(), @@ -265,7 +267,11 @@ class MomentumUpdateKernel final : public user_op::OpKernel, public user_op::Cud float l1 = ctx->Attr("l1"); float l2 = ctx->Attr("l2"); float beta = ctx->Attr("beta"); + const float dampening = ctx->Attr("dampening"); + const bool nesterov = ctx->Attr("nesterov"); + const bool maximize = ctx->Attr("maximize"); float weight_decay = ctx->Attr("weight_decay"); + const auto lr_scale = ctx->Attr("learning_rate_scale"); const user_op::Tensor* model_diff = ctx->Tensor4ArgNameAndIndex("model_diff", 0); user_op::Tensor* model = ctx->Tensor4ArgNameAndIndex("model", 0); @@ -290,8 +296,9 @@ class MomentumUpdateKernel final : public user_op::OpKernel, public user_op::Cud } MomentumUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, beta, - weight_decay, learning_rate_val, learning_rate_ptr, scale_by_ptr, skip_if_ptr, - model_diff->dptr(), model->mut_dptr(), momentum->mut_dptr()); + dampening, nesterov, maximize, weight_decay, learning_rate_val, lr_scale, learning_rate_ptr, + scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), + momentum->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -305,16 +312,11 @@ class MomentumUpdateKernel final : public user_op::OpKernel, public user_op::Cud REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class IndexedSlicesMomentumUpdateKernel final : public user_op::OpKernel { @@ -339,7 +341,11 @@ class IndexedSlicesMomentumUpdateKernel final : public user_op::OpKernel { user_op::Tensor* model = ctx->Tensor4ArgNameAndIndex("model", 0); user_op::Tensor* momentum = ctx->Tensor4ArgNameAndIndex("momentum", 0); const auto beta = ctx->Attr("beta"); + const float dampening = ctx->Attr("dampening"); + const bool nesterov = ctx->Attr("nesterov"); + const bool maximize = ctx->Attr("maximize"); const auto weight_decay = ctx->Attr("weight_decay"); + const float lr_scale = ctx->Attr("learning_rate_scale"); const int64_t num_indices = model_diff_indices->shape_view().elem_cnt(); const int64_t num_values = model_diff_values->shape_view().elem_cnt(); if (num_indices == 0) { @@ -363,11 +369,12 @@ class IndexedSlicesMomentumUpdateKernel final : public user_op::OpKernel { model_diff_values->dptr(), buffer_manager.NumUniqueDiffIndicesPtr(), buffer_manager.UniqueDiffIndicesPtr(), buffer_manager.UniqueDiffValuesPtr(), buffer_manager.UniqueWorkspacePtr(), buffer_manager.UniqueWorkspaceBytes()); - MdUpdateUtilT::Update( - ctx->stream(), beta, weight_decay, num_indices, feature_size, kernel_cache->lower(), - kernel_cache->upper(), buffer_manager.NumUniqueDiffIndicesPtr(), - learning_rate->dptr(), buffer_manager.UniqueDiffIndicesPtr(), - buffer_manager.UniqueDiffValuesPtr(), model->mut_dptr(), momentum->mut_dptr()); + MdUpdateUtilT::Update(ctx->stream(), beta, dampening, nesterov, maximize, weight_decay, + lr_scale, num_indices, feature_size, kernel_cache->lower(), + kernel_cache->upper(), buffer_manager.NumUniqueDiffIndicesPtr(), + learning_rate->dptr(), buffer_manager.UniqueDiffIndicesPtr(), + buffer_manager.UniqueDiffValuesPtr(), model->mut_dptr(), + momentum->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -389,7 +396,7 @@ class IndexedSlicesMomentumUpdateKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_INDEXED_SLICES_MOMENTUM_UPDATE_KERNEL, DEVICE_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -template +template class AdamUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AdamUpdateKernel() = default; @@ -411,6 +418,7 @@ class AdamUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra const auto weight_decay = ctx->Attr("weight_decay"); const bool amsgrad = ctx->Attr("amsgrad"); const bool do_bias_correction = ctx->Attr("do_bias_correction"); + const float lr_scale = ctx->Attr("learning_rate_scale"); T* max_v_ptr = nullptr; if (amsgrad) { @@ -459,35 +467,36 @@ class AdamUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra skip_if_ptr = skip_if->dptr(); } - AdamUpdateKernelUtil::Update( + C* model_copy_ptr = nullptr; + if (ctx->has_input("model_copy", 0)) { + user_op::Tensor* model_copy = ctx->Tensor4ArgNameAndIndex("model_copy", 0); + model_copy_ptr = model_copy->mut_dptr(); + } + + AdamUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, beta1, beta2, - epsilon, weight_decay, amsgrad, do_bias_correction, learning_rate_val, bias_correction1_val, - bias_correction2_val, learning_rate_ptr, scale_by_ptr, skip_if_ptr, bias_correction1_ptr, - bias_correction2_ptr, model_diff->dptr(), model->mut_dptr(), m->mut_dptr(), - v->mut_dptr(), max_v_ptr); + epsilon, weight_decay, amsgrad, do_bias_correction, learning_rate_val, lr_scale, + bias_correction1_val, bias_correction2_val, learning_rate_ptr, scale_by_ptr, skip_if_ptr, + bias_correction1_ptr, bias_correction2_ptr, model_diff->dptr(), model->mut_dptr(), + model_copy_ptr, m->mut_dptr(), v->mut_dptr(), max_v_ptr); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; -#define REGISTER_ADAM_UPDATE_KERNEL(device, dtype, gtype) \ +#define REGISTER_ADAM_UPDATE_KERNEL(device, dtype, gtype, ctype) \ REGISTER_USER_KERNEL("adam_update") \ - .SetCreateFn>() \ + .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == device) \ && (user_op::HobDataType("model", 0) == GetDataType::value) \ && (user_op::HobDataType("model_diff", 0) == GetDataType::value)); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCPU, float, float); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); +REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCPU, float, float, float16); +REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCPU, double, double, float16); +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16, float16); +REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float, float16); +REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, double, double, float16); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class AdagradUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { @@ -510,6 +519,7 @@ class AdagradUpdateKernel final : public user_op::OpKernel, public user_op::Cuda const float* learning_rate_ptr = nullptr; const int64_t train_step_val = ctx->Attr("train_step_val"); const int64_t* train_step_ptr = nullptr; + const float lr_scale = ctx->Attr("learning_rate_scale"); if (ctx->has_input("learning_rate", 0)) { const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); @@ -535,8 +545,9 @@ class AdagradUpdateKernel final : public user_op::OpKernel, public user_op::Cuda } AdagradUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, lr_decay, - epsilon, weight_decay, learning_rate_val, train_step_val, learning_rate_ptr, train_step_ptr, - scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), sum->mut_dptr()); + epsilon, weight_decay, learning_rate_val, lr_scale, train_step_val, learning_rate_ptr, + train_step_ptr, scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), + sum->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -550,14 +561,10 @@ class AdagradUpdateKernel final : public user_op::OpKernel, public user_op::Cuda REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_ADAGRAD_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class IndexedSlicesAdamUpdateKernel final : public user_op::OpKernel { @@ -608,6 +615,7 @@ class IndexedSlicesAdamUpdateKernel final : public user_op::OpKernel { const auto weight_decay = ctx->Attr("weight_decay"); const bool amsgrad = ctx->Attr("amsgrad"); const bool do_bias_correction = ctx->Attr("do_bias_correction"); + const float lr_scale = ctx->Attr("learning_rate_scale"); T* max_v_ptr = nullptr; if (amsgrad) { @@ -642,9 +650,9 @@ class IndexedSlicesAdamUpdateKernel final : public user_op::OpKernel { MdUpdateUtilT::Update( ctx->stream(), beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, - learning_rate_val, num_indices, feature_size, kernel_cache->lower(), kernel_cache->upper(), - buffer_manager.NumUniqueDiffIndicesPtr(), learning_rate_ptr, bias_correction1_ptr, - bias_correction2_ptr, buffer_manager.UniqueDiffIndicesPtr(), + learning_rate_val, lr_scale, num_indices, feature_size, kernel_cache->lower(), + kernel_cache->upper(), buffer_manager.NumUniqueDiffIndicesPtr(), learning_rate_ptr, + bias_correction1_ptr, bias_correction2_ptr, buffer_manager.UniqueDiffIndicesPtr(), buffer_manager.UniqueDiffValuesPtr(), model->mut_dptr(), m->mut_dptr(), v->mut_dptr(), max_v_ptr); } @@ -725,6 +733,7 @@ class LambUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra const auto beta2 = ctx->Attr("beta2"); const auto epsilon = ctx->Attr("epsilon"); const auto weight_decay = ctx->Attr("weight_decay"); + const auto lr_scale = ctx->Attr("learning_rate_scale"); const bool do_bias_correction = ctx->Attr("do_bias_correction"); const float bias_correction1_val = ctx->Attr("bias_correction1_val"); @@ -767,7 +776,7 @@ class LambUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra LambUpdateKernelUtil::Update( ctx->stream(), m->shape_view().elem_cnt(), scale, l1, l2, beta1, beta2, epsilon, - weight_decay, learning_rate_val, do_bias_correction, bias_correction1_val, + weight_decay, learning_rate_val, lr_scale, do_bias_correction, bias_correction1_val, bias_correction2_val, learning_rate_ptr, bias_correction1_ptr, bias_correction2_ptr, scale_by_ptr, skip_if_ptr, model_diff->dptr(), tbm.AdamDiffPtr(), model->mut_dptr(), m->mut_dptr(), v->mut_dptr(), tbm.NormBufferPtr()); @@ -794,16 +803,11 @@ user_op::InferTmpSizeFn LambGenInferTmpSizeFn() { REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_LAMB_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class BiasCorrectionFactorKernel final : public user_op::OpKernel, @@ -828,12 +832,9 @@ class BiasCorrectionFactorKernel final : public user_op::OpKernel, .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == device)); REGISTER_ADAM_BIAS_CORRECTION_FACTOR_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_ADAM_BIAS_CORRECTION_FACTOR_KERNEL(DeviceType::kCUDA) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_ADAM_BIAS_CORRECTION_FACTOR_KERNEL(DeviceType::kCUDA) -#endif // WITH_ROCM template class RmsPropUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { @@ -854,6 +855,7 @@ class RmsPropUpdateKernel final : public user_op::OpKernel, public user_op::Cuda const auto centered = ctx->Attr("centered"); const auto weight_decay = ctx->Attr("weight_decay"); const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); const float* learning_rate_ptr = nullptr; if (ctx->has_input("learning_rate", 0)) { const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); @@ -879,9 +881,9 @@ class RmsPropUpdateKernel final : public user_op::OpKernel, public user_op::Cuda } RmsPropUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, centered, - epsilon, weight_decay, decay_rate, learning_rate_val, learning_rate_ptr, scale_by_ptr, - skip_if_ptr, model_diff->dptr(), model->mut_dptr(), mean_square->mut_dptr(), - mean_gradient_ptr); + epsilon, weight_decay, decay_rate, learning_rate_val, lr_scale, learning_rate_ptr, + scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), + mean_square->mut_dptr(), mean_gradient_ptr); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -895,16 +897,11 @@ class RmsPropUpdateKernel final : public user_op::OpKernel, public user_op::Cuda REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_RMSPROP_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class LarsTmpBufferManager final { @@ -963,6 +960,7 @@ class LarsUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra const auto epsilon = ctx->Attr("epsilon"); const auto lars_coefficient = ctx->Attr("lars_coefficient"); const auto weight_decay = ctx->Attr("weight_decay"); + const auto lr_scale = ctx->Attr("learning_rate_scale"); const T* scale_by_ptr = nullptr; if (ctx->has_input("scale_by_tensor", 0)) { const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); @@ -978,9 +976,9 @@ class LarsUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra } LarsUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, momentum_beta, - epsilon, lars_coefficient, weight_decay, learning_rate->dptr(), scale_by_ptr, - skip_if_ptr, model_diff->dptr(), model->mut_dptr(), momentum->mut_dptr(), - tlm.DataTmpPtr(), tlm.ModelDiffPtr()); + epsilon, lars_coefficient, weight_decay, lr_scale, learning_rate->dptr(), + scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), + momentum->mut_dptr(), tlm.DataTmpPtr(), tlm.ModelDiffPtr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -1004,16 +1002,11 @@ user_op::InferTmpSizeFn LarsGenInferTmpSizeFn() { REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_LARS_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM template class FtrlUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { @@ -1040,6 +1033,7 @@ class FtrlUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra CHECK_EQ(weight_decay, static_cast(0.0)) << "Currently not support for setting weight decay. "; const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); const float* learning_rate_ptr = nullptr; if (ctx->has_input("learning_rate", 0)) { @@ -1062,9 +1056,9 @@ class FtrlUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra } FtrlUpdateKernelUtil::Update( ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, lr_power, - lambda1, lambda2, beta, weight_decay, learning_rate_val, learning_rate_ptr, scale_by_ptr, - skip_if_ptr, model_diff->dptr(), model->mut_dptr(), accumulate->mut_dptr(), - z->mut_dptr()); + lambda1, lambda2, beta, weight_decay, learning_rate_val, lr_scale, learning_rate_ptr, + scale_by_ptr, skip_if_ptr, model_diff->dptr(), model->mut_dptr(), + accumulate->mut_dptr(), z->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; @@ -1078,16 +1072,75 @@ class FtrlUpdateKernel final : public user_op::OpKernel, public user_op::CudaGra REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCPU, float, float); REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCPU, double, double); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, float, float); REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, double, double); #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); -REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, float, float); -REGISTER_FTRL_UPDATE_KERNEL(DeviceType::kCUDA, double, double); -#endif // WITH_ROCM + +template +class AdadeltaUpdateKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + AdadeltaUpdateKernel() = default; + ~AdadeltaUpdateKernel() override = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* model_diff = ctx->Tensor4ArgNameAndIndex("model_diff", 0); + user_op::Tensor* model = ctx->Tensor4ArgNameAndIndex("model", 0); + user_op::Tensor* square_avgs = ctx->Tensor4ArgNameAndIndex("square_avgs", 0); + user_op::Tensor* acc_deltas = ctx->Tensor4ArgNameAndIndex("acc_deltas", 0); + const auto scale = ctx->Attr("scale"); + const auto l1 = ctx->Attr("l1"); + const auto l2 = ctx->Attr("l2"); + const float rho = ctx->Attr("rho"); + const float epsilon = ctx->Attr("epsilon"); + const bool maximize = ctx->Attr("maximize"); + const float weight_decay = ctx->Attr("weight_decay"); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), model->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + AdadeltaUpdateKernelUtil::Update( + ctx->stream(), model->shape_view().elem_cnt(), static_cast(scale), l1, l2, rho, epsilon, + maximize, weight_decay, learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, + skip_if_ptr, model_diff->dptr(), model->mut_dptr(), square_avgs->mut_dptr(), + acc_deltas->mut_dptr()); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_ADADELTA_UPDATE_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("adadelta_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value)); + +REGISTER_ADADELTA_UPDATE_KERNEL(DeviceType::kCPU, float, float); +REGISTER_ADADELTA_UPDATE_KERNEL(DeviceType::kCPU, double, double); +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_ADADELTA_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); +REGISTER_ADADELTA_UPDATE_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_ADADELTA_UPDATE_KERNEL(DeviceType::kCUDA, double, double); +#endif // WITH_CUDA } // namespace diff --git a/oneflow/user/kernels/moving_average_min_max_observer_kernel.cu b/oneflow/user/kernels/moving_average_min_max_observer_kernel.cu index 2db5a2ef984797159cca6ec36b9ae56c133a9489..45a82b2cc401bcaba94dbc76a3cc018ddc948303 100644 --- a/oneflow/user/kernels/moving_average_min_max_observer_kernel.cu +++ b/oneflow/user/kernels/moving_average_min_max_observer_kernel.cu @@ -246,9 +246,9 @@ class GpuMovingAverageMinMaxObserverKernel final : public user_op::OpKernel { T* min_ptr = max_ptr + 1; int64_t* host_current_train_step_ptr = new int64_t[current_train_step->shape_view().elem_cnt()]; - OF_CUDA_CHECK(cudaMemcpy(host_current_train_step_ptr, current_train_step->dptr(), + OF_CUDA_CHECK(GPU(Memcpy)(host_current_train_step_ptr, current_train_step->dptr(), current_train_step->shape_view().elem_cnt() * sizeof(int64_t), - cudaMemcpyDefault)); + GPU(MemcpyDefault))); auto* cuda_stream = ctx->stream()->As(); if (*host_current_train_step_ptr <= stop_update_after_iters && is_training) { LAUNCH_CUDA_KERNEL((InitMaxMin), cuda_stream, 1, 0, 1, max_ptr, min_ptr); diff --git a/oneflow/user/kernels/moving_average_min_max_observer_kernel.hip.cpp b/oneflow/user/kernels/moving_average_min_max_observer_kernel.hip.cpp deleted file mode 100644 index 4feb42bff7dd4df202599887d8b644568689075b..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/moving_average_min_max_observer_kernel.hip.cpp +++ /dev/null @@ -1,317 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#include - -namespace oneflow { - -namespace { - -// NOTE(Liang Depeng): refer to -// https://stackoverflow.com/questions/17371275/implementing-max-reduce-in-cuda -template -__global__ void ReduceMaxMinPerLayer(const T* input_ptr, const int64_t elements, T* max_ptr, - T* min_ptr) { - extern __shared__ unsigned char shared_max_min_memory[]; - T* shared_max = reinterpret_cast(shared_max_min_memory); - T* shared_min = shared_max + blockDim.x; - - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - shared_max[tid] = -FLT_MAX; - shared_min[tid] = -FLT_MAX; - - while (gid < elements) { - shared_max[tid] = max(shared_max[tid], input_ptr[gid]); - shared_min[tid] = max(shared_min[tid], -input_ptr[gid]); - gid += gridDim.x * blockDim.x; - } - __syncthreads(); - gid = (blockDim.x * blockIdx.x) + tid; - for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s && gid < elements) { - shared_max[tid] = max(shared_max[tid], shared_max[tid + s]); - shared_min[tid] = max(shared_min[tid], shared_min[tid + s]); - } - __syncthreads(); - } - - if (tid == 0) { - cuda::atomic::Max(max_ptr, shared_max[0]); - cuda::atomic::Max(min_ptr, shared_min[0]); - } -} - -template -__global__ void InitMaxMin(const int64_t elements, T* max_ptr, T* min_ptr) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - max_ptr[gid] = -FLT_MAX; - min_ptr[gid] = -FLT_MAX; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointSymmetric(const int64_t elements, const double quantization_bit, - const float momentum, const T* max_ptr, const T* min_ptr, - T* moving_max_ptr, T* moving_min_ptr, T* scale, - T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T activation_max = max(fabs(max_ptr[gid]), fabs(min_ptr[gid])); - T denominator = static_cast(pow(2.0, quantization_bit - 1)) - 1; - - if (moving_max_ptr[gid] == 0) - moving_max_ptr[gid] = activation_max; - else - moving_max_ptr[gid] = moving_max_ptr[gid] * momentum + activation_max * (1 - momentum); - - // NOTE(Liang Depeng): symmetric quantization only use moving_max to calculate the scale - moving_min_ptr[gid] = moving_max_ptr[gid]; - - scale[gid] = moving_max_ptr[gid] / denominator; - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalFreezeScaleZeroPointSymmetric(const int64_t elements, - const double quantization_bit, - const float momentum, const T* moving_max_ptr, - T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T denominator = static_cast(pow(2.0, quantization_bit - 1)) - 1; - scale[gid] = moving_max_ptr[gid] / denominator; - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointAffine(const int64_t elements, const double quantization_bit, - const float momentum, const T* max_ptr, const T* min_ptr, - T* moving_max_ptr, T* moving_min_ptr, T* scale, - T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T denominator = static_cast(pow(2.0, quantization_bit)) - 1; - - if (moving_max_ptr[gid] == 0) - moving_max_ptr[gid] = max_ptr[gid]; - else - moving_max_ptr[gid] = moving_max_ptr[gid] * momentum + max_ptr[gid] * (1 - momentum); - - if (moving_min_ptr[gid] == 0) - moving_min_ptr[gid] = -min_ptr[gid]; - else - moving_min_ptr[gid] = moving_min_ptr[gid] * momentum + -min_ptr[gid] * (1 - momentum); - - T min = moving_min_ptr[gid]; - T s = (moving_max_ptr[gid] - min) / denominator; - - scale[gid] = s; - zero_point[gid] = -round(min / s); - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalFreezeScaleZeroPointAffine(const int64_t elements, const double quantization_bit, - const float momentum, const T* moving_max_ptr, - const T* moving_min_ptr, T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T denominator = static_cast(pow(2.0, quantization_bit)) - 1; - - T min = moving_min_ptr[gid]; - T s = (moving_max_ptr[gid] - min) / denominator; - - scale[gid] = s; - zero_point[gid] = -round(min / s); - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalScaleZeroPointCambricon(const int64_t elements, const double quantization_bit, - const float momentum, const T* max_ptr, const T* min_ptr, - T* moving_max_ptr, T* moving_min_ptr, T* scale, - T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T activation_max = max(fabs(max_ptr[gid]), fabs(min_ptr[gid])); - - if (moving_max_ptr[gid] == 0) - moving_max_ptr[gid] = activation_max; - else - moving_max_ptr[gid] = moving_max_ptr[gid] * momentum + activation_max * (1 - momentum); - - // NOTE(Liang Depeng): cambricon quantization only use moving_max to calculate the scale - moving_min_ptr[gid] = moving_max_ptr[gid]; - - scale[gid] = floor(log2(moving_max_ptr[gid])) - (quantization_bit - 2); - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -template -__global__ void CalFreezeScaleZeroPointCambricon(const int64_t elements, - const double quantization_bit, - const float momentum, const T* moving_max_ptr, - T* scale, T* zero_point) { - int64_t tid = threadIdx.x; - int64_t gid = (blockDim.x * blockIdx.x) + tid; - - while (gid < elements) { - T denominator = static_cast(pow(2.0, quantization_bit - 1)) - 1; - scale[gid] = floor(log2(moving_max_ptr[gid])) - (quantization_bit - 2); - zero_point[gid] = 0; - gid += gridDim.x * blockDim.x; - } -} - -ep::CudaLaunchConfig GetLaunchConfig(ep::CudaStream* stream, size_t thread_num, - size_t shared_mem_size) { - ep::CudaLaunchConfig config; - stream->InitLaunchConfigWithWaves(&config, thread_num, kCudaThreadsNumPerBlock, 1); - config.shared_mem_size = shared_mem_size; - return config; -} - -} // namespace - -#define LAUNCH_CUDA_KERNEL(func, stream, thread_num, shared_mem_size, ...) \ - (stream)->LaunchKernel(func, GetLaunchConfig((stream), thread_num, shared_mem_size), __VA_ARGS__); - -template -class GpuMovingAverageMinMaxObserverKernel final : public user_op::OpKernel { - public: - GpuMovingAverageMinMaxObserverKernel() = default; - ~GpuMovingAverageMinMaxObserverKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - const user_op::Tensor* current_train_step = - ctx->Tensor4ArgNameAndIndex("current_train_step", 0); - user_op::Tensor* moving_max = ctx->Tensor4ArgNameAndIndex("moving_max", 0); - user_op::Tensor* moving_min = ctx->Tensor4ArgNameAndIndex("moving_min", 0); - user_op::Tensor* scale = ctx->Tensor4ArgNameAndIndex("scale", 0); - user_op::Tensor* zero_point = ctx->Tensor4ArgNameAndIndex("zero_point", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const bool is_training = ctx->Attr("training"); - const int64_t stop_update_after_iters = ctx->Attr("stop_update_after_iters"); - const std::string quantization_scheme = ctx->Attr("quantization_scheme"); - const int32_t quantization_bit = ctx->Attr("quantization_bit"); - const float momentum = ctx->Attr("momentum"); - const std::string quantization_formula = ctx->Attr("quantization_formula"); - - int64_t elements = in->shape_view().elem_cnt(); - T* max_ptr = tmp_buffer->mut_dptr(); - T* min_ptr = max_ptr + 1; - - int64_t* host_current_train_step_ptr = new int64_t[current_train_step->shape_view().elem_cnt()]; - OF_CUDA_CHECK(hipMemcpy(host_current_train_step_ptr, current_train_step->dptr(), - current_train_step->shape_view().elem_cnt() * sizeof(int64_t), - hipMemcpyDefault)); - auto* cuda_stream = ctx->stream()->As(); - if (*host_current_train_step_ptr <= stop_update_after_iters && is_training) { - LAUNCH_CUDA_KERNEL((InitMaxMin), cuda_stream, 1, 0, 1, max_ptr, min_ptr); - LAUNCH_CUDA_KERNEL((ReduceMaxMinPerLayer), cuda_stream, elements, - kCudaThreadsNumPerBlock * 2 * sizeof(T), in->dptr(), elements, max_ptr, - min_ptr); - } - bool moving = (*host_current_train_step_ptr <= stop_update_after_iters) && is_training; - if (quantization_formula == "google") { - if (quantization_scheme == "symmetric") { - if (moving) { - LAUNCH_CUDA_KERNEL((CalScaleZeroPointSymmetric), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, max_ptr, min_ptr, - moving_max->mut_dptr(), moving_min->mut_dptr(), - scale->mut_dptr(), zero_point->mut_dptr()); - } else { - LAUNCH_CUDA_KERNEL((CalFreezeScaleZeroPointSymmetric), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, moving_max->dptr(), - scale->mut_dptr(), zero_point->mut_dptr()); - } - } else { // quantization_scheme == "affine" - if (moving) { - LAUNCH_CUDA_KERNEL((CalScaleZeroPointAffine), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, max_ptr, min_ptr, - moving_max->mut_dptr(), moving_min->mut_dptr(), - scale->mut_dptr(), zero_point->mut_dptr()); - } else { - LAUNCH_CUDA_KERNEL((CalFreezeScaleZeroPointAffine), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, moving_max->dptr(), - moving_min->dptr(), scale->mut_dptr(), - zero_point->mut_dptr()); - } - } - } else if (quantization_formula == "cambricon") { - if (moving) { - LAUNCH_CUDA_KERNEL((CalScaleZeroPointCambricon), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, max_ptr, min_ptr, - moving_max->mut_dptr(), moving_min->mut_dptr(), - scale->mut_dptr(), zero_point->mut_dptr()); - } else { - LAUNCH_CUDA_KERNEL((CalFreezeScaleZeroPointCambricon), cuda_stream, 1, 0, 1, - static_cast(quantization_bit), momentum, moving_max->dptr(), - scale->mut_dptr(), zero_point->mut_dptr()); - } - } else { - UNIMPLEMENTED(); - } - - delete[] host_current_train_step_ptr; - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_MOVING_AVERAGE_MIN_MAX_OBSERVER_KERNEL(dtype) \ - REGISTER_USER_KERNEL("moving_average_min_max_observer") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { return 2 * sizeof(dtype); }) - -REGISTER_MOVING_AVERAGE_MIN_MAX_OBSERVER_KERNEL(float); -REGISTER_MOVING_AVERAGE_MIN_MAX_OBSERVER_KERNEL(double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/multi_reduce_kernels.cu b/oneflow/user/kernels/multi_reduce_kernels.cu index 7de71f83751b07b68e4d7d8dbea8f6863ac60da7..7a68c9899fefed033f1f9c6af55b68b935264b1f 100644 --- a/oneflow/user/kernels/multi_reduce_kernels.cu +++ b/oneflow/user/kernels/multi_reduce_kernels.cu @@ -17,7 +17,12 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/fill.h" #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/device/cuda_util.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif #include namespace oneflow { @@ -42,7 +47,11 @@ __global__ void MultiBlockReduceGpu(TransformFn transform, const auto& param = pack_params.params[i]; CUDA_1D_KERNEL_LOOP(j, param.size) { t_out = reduce_fn(t_out, transform(param.data[j])); } } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_out = BlockReduce(temp_storage).Reduce(t_out, reduce_fn); if (threadIdx.x == 0) { out[blockIdx.x] = b_out; } @@ -97,9 +106,15 @@ struct MultiReduce { } size_t wksp_size = 0; auto DeviceReduce = [&](void* temp_storage) -> void { +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipcub::DeviceReduce::Reduce(temp_storage, wksp_size, temp, ret, total_num_blocks, + ReduceFn{}, init, + stream->As()->cuda_stream())); +#else OF_CUDA_CHECK(cub::DeviceReduce::Reduce(temp_storage, wksp_size, temp, ret, total_num_blocks, ReduceFn{}, init, stream->As()->cuda_stream())); +#endif }; DeviceReduce(nullptr); // NOTE(zwx): We have allocated the temp storage with the space diff --git a/oneflow/user/kernels/multi_reduce_kernels.hip.cpp b/oneflow/user/kernels/multi_reduce_kernels.hip.cpp deleted file mode 100644 index 85440cfa00924c334e572ae53c4e5c724879fa62..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/multi_reduce_kernels.hip.cpp +++ /dev/null @@ -1,142 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/multi_reduce_kernels.h" -#include "oneflow/core/ep/include/primitive/fill.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/device/cuda_util.h" -#include -#include - -namespace oneflow { - -namespace { - -constexpr int64_t kMultiReduceMaxPackSize = 64; - -template -struct MultiReduceParamsPack { - MultiReduceParam params[kMultiReduceMaxPackSize]; - size_t size; -}; - -template -__global__ void MultiBlockReduceGpu(TransformFn transform, - const MultiReduceParamsPack pack_params, const T init, - T* out) { - ReduceFn reduce_fn{}; - T t_out = init; - for (int i = 0; i < pack_params.size; ++i) { - const auto& param = pack_params.params[i]; - CUDA_1D_KERNEL_LOOP(j, param.size) { t_out = reduce_fn(t_out, transform(param.data[j])); } - } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_out = BlockReduce(temp_storage).Reduce(t_out, reduce_fn); - if (threadIdx.x == 0) { out[blockIdx.x] = b_out; } -} - -size_t InferTempStorageSize(user_op::InferContext* ctx) { - auto input_size = ctx->input_size("x"); - if (input_size == 0) { return 0; } - int64_t max_elem_cnt = 0; - int64_t pack_size = 0; - int32_t num_blocks = 0; - for (size_t i = 0; i < input_size; ++i) { - int64_t elem_cnt = ctx->InputShape("x", i).elem_cnt(); - max_elem_cnt = std::max(max_elem_cnt, elem_cnt); - pack_size++; - if (pack_size == kMultiReduceMaxPackSize || i == input_size - 1) { - CHECK_LT(max_elem_cnt, std::numeric_limits::max()); - num_blocks += BlocksNum4ThreadsNum(static_cast(max_elem_cnt)); - max_elem_cnt = 0; - pack_size = 0; - } - } - CHECK_LT(num_blocks, kCudaThreadsNumPerBlock * kCudaThreadsNumPerBlock * kCudaThreadsNumPerBlock) - << "Too much blocks needed for computing " << ctx->op_name() << ", should be less than " - << kCudaThreadsNumPerBlock << "*" << kCudaThreadsNumPerBlock << "*" << kCudaThreadsNumPerBlock - << ", but got " << num_blocks; - size_t elem_size = GetSizeOfDataType(ctx->InputDType("x", 0)); - return GetCudaAlignedSize(num_blocks * elem_size * 2); -} - -} // namespace - -template -struct MultiReduce { - void operator()(ep::Stream* stream, TransformFn transform, - const std::vector>& params, T init, T* ret, T* temp) { - CHECK_NOTNULL(temp); - int32_t total_num_blocks = 0; - for (size_t i = 0; i < params.size(); i += kMultiReduceMaxPackSize) { - MultiReduceParamsPack pack_params{}; - size_t max_elem_cnt = 0; - pack_params.size = std::min(kMultiReduceMaxPackSize, params.size() - i); - for (size_t j = 0; j < pack_params.size; ++j) { - pack_params.params[j] = params[i + j]; - max_elem_cnt = std::max(max_elem_cnt, pack_params.params[j].size); - } - int32_t num_blocks = BlocksNum4ThreadsNum(max_elem_cnt); - MultiBlockReduceGpu - <<As()->cuda_stream()>>>( - transform, pack_params, init, temp + total_num_blocks); - total_num_blocks += num_blocks; - } - size_t wksp_size = 0; - auto DeviceReduce = [&](void* temp_storage) -> void { - OF_CUDA_CHECK(hipcub::DeviceReduce::Reduce(temp_storage, wksp_size, temp, ret, total_num_blocks, - ReduceFn{}, init, - stream->As()->cuda_stream())); - }; - DeviceReduce(nullptr); - // NOTE(zwx): We have allocated the temp storage with the space - // that can hold all the elements to reduce, - // normally the `temp_storage_bytes` for hipcub::DeviceReduce shouldn't exceed it. - CHECK_LE(wksp_size, total_num_blocks * sizeof(T)) - << wksp_size << " size in bytes of temp storage is needed for doing hipcub::DeviceReduce, " - << "but only allocated " << total_num_blocks * sizeof(T); - DeviceReduce(temp + total_num_blocks); - } -}; - -#define REGISTER_MULTI_REDUCE_SUM_POW_ABS_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("multi_reduce_sum_pow_abs") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTempStorageSize); - -#define REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNEL(op_type_name, ximum_enum, dtype) \ - REGISTER_USER_KERNEL(op_type_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTempStorageSize); - -#define REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNELS(dtype) \ - REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNEL("multi_reduce_max_abs", Ximum::kMax, dtype) \ - REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNEL("multi_reduce_min_abs", Ximum::kMin, dtype) \ - REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNEL("local_multi_reduce_max_abs", Ximum::kMax, dtype) \ - REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNEL("local_multi_reduce_min_abs", Ximum::kMin, dtype) - -REGISTER_MULTI_REDUCE_SUM_POW_ABS_CUDA_KERNEL(float) -REGISTER_MULTI_REDUCE_SUM_POW_ABS_CUDA_KERNEL(double) - -REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNELS(float) -REGISTER_MULTI_REDUCE_XIMUM_ABS_CUDA_KERNELS(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/multi_tensor_model_update_kernel.cpp b/oneflow/user/kernels/multi_tensor_model_update_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..527f980fbe31515eabc3b201e9a29d5f206ea666 --- /dev/null +++ b/oneflow/user/kernels/multi_tensor_model_update_kernel.cpp @@ -0,0 +1,626 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/multi_tensor_model_update_kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +namespace oneflow { + +namespace { + +template +class MultiTensorSGDUpdateKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorSGDUpdateKernel() = default; + ~MultiTensorSGDUpdateKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const double scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + const float weight_decay = ctx->Attr("weight_decay"); + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<2> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorSGDUpdateKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, weight_decay, + learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, skip_if_ptr, + tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_sgd_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); +REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_KERNEL(DeviceType::kCUDA, double, double); +#endif + +template +class MultiTensorMomentumUpdateKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorMomentumUpdateKernel() = default; + ~MultiTensorMomentumUpdateKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const double scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + const float weight_decay = ctx->Attr("weight_decay"); + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + const float momentum = ctx->Attr("momentum"); + const float dampening = ctx->Attr("dampening"); + const bool nesterov = ctx->Attr("nesterov"); + const bool maximize = ctx->Attr("maximize"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<3> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[2][count] = + (ctx->Tensor4ArgNameAndIndex("momentum_buf", tensor_idx))->mut_dptr(); + + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorMomentumUpdateKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, weight_decay, + learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, skip_if_ptr, momentum, + dampening, nesterov, maximize, tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_momentum_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value) \ + && (user_op::HobDataType("momentum_buf", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); +REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); +#endif + +template +class MultiTensorAdamUpdateKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorAdamUpdateKernel() = default; + ~MultiTensorAdamUpdateKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const auto scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + + const float beta1 = ctx->Attr("beta1"); + const float beta2 = ctx->Attr("beta2"); + const float epsilon = ctx->Attr("epsilon"); + const float weight_decay = ctx->Attr("weight_decay"); + + const bool amsgrad = ctx->Attr("amsgrad"); + const bool do_bias_correction = ctx->Attr("do_bias_correction"); + if (amsgrad) { UNIMPLEMENTED() << "Multi Tensor Adam Update do not support amsgrad = True. "; } + + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + + const float bias_correction1_val = ctx->Attr("bias_correction1_val"); + const float* bias_correction1_ptr = nullptr; + if (ctx->has_input("bias_correction1", 0)) { + const user_op::Tensor* bias_correction1 = ctx->Tensor4ArgNameAndIndex("bias_correction1", 0); + CHECK_EQ(bias_correction1->shape_view().elem_cnt(), + 1); // Just for Lazy Optional Input Check. + bias_correction1_ptr = bias_correction1->dptr(); + } + + const float bias_correction2_val = ctx->Attr("bias_correction2_val"); + const float* bias_correction2_ptr = nullptr; + if (ctx->has_input("bias_correction2", 0)) { + const user_op::Tensor* bias_correction2 = ctx->Tensor4ArgNameAndIndex("bias_correction2", 0); + CHECK_EQ(bias_correction2->shape_view().elem_cnt(), + 1); // Just for Lazy Optional Input Check. + bias_correction2_ptr = bias_correction2->dptr(); + } + + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<4> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[2][count] = + (ctx->Tensor4ArgNameAndIndex("m", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[3][count] = + (ctx->Tensor4ArgNameAndIndex("v", tensor_idx))->mut_dptr(); + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorAdamUpdateKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, beta1, beta2, + epsilon, weight_decay, amsgrad, do_bias_correction, learning_rate_val, + bias_correction1_val, bias_correction2_val, lr_scale, learning_rate_ptr, scale_by_ptr, + skip_if_ptr, bias_correction1_ptr, bias_correction2_ptr, tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_adam_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float16); +REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_KERNEL(DeviceType::kCUDA, double, double); +#endif + +template +class MultiTensorSGDUpdateWithCastKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorSGDUpdateWithCastKernel() = default; + ~MultiTensorSGDUpdateWithCastKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const double scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + const float weight_decay = ctx->Attr("weight_decay"); + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<3> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[2][count] = + (ctx->Tensor4ArgNameAndIndex("model_copy", tensor_idx))->mut_dptr(); + + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorSGDUpdateWithCastKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, weight_decay, + learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, skip_if_ptr, + tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_WITH_CAST_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_sgd_update_with_cast") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_copy", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_SGD_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float16); +#endif + +template +class MultiTensorMomentumUpdateWithCastKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorMomentumUpdateWithCastKernel() = default; + ~MultiTensorMomentumUpdateWithCastKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const double scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + const float weight_decay = ctx->Attr("weight_decay"); + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + const float momentum = ctx->Attr("momentum"); + const float dampening = ctx->Attr("dampening"); + const bool nesterov = ctx->Attr("nesterov"); + const bool maximize = ctx->Attr("maximize"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<4> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[2][count] = + (ctx->Tensor4ArgNameAndIndex("momentum_buf", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[3][count] = + (ctx->Tensor4ArgNameAndIndex("model_copy", tensor_idx))->mut_dptr(); + + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorMomentumUpdateWithCastKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, weight_decay, + learning_rate_val, lr_scale, learning_rate_ptr, scale_by_ptr, skip_if_ptr, momentum, + dampening, nesterov, maximize, tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_WITH_CAST_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_momentum_update_with_cast") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value) \ + && (user_op::HobDataType("momentum_buf", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_copy", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_MOMENTUM_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float16); +#endif + +template +class MultiTensorAdamUpdateWithCastKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorAdamUpdateWithCastKernel() = default; + ~MultiTensorAdamUpdateWithCastKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const auto scale = ctx->Attr("scale"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + + const float beta1 = ctx->Attr("beta1"); + const float beta2 = ctx->Attr("beta2"); + const float epsilon = ctx->Attr("epsilon"); + const float weight_decay = ctx->Attr("weight_decay"); + + const bool amsgrad = ctx->Attr("amsgrad"); + const bool do_bias_correction = ctx->Attr("do_bias_correction"); + if (amsgrad) { UNIMPLEMENTED() << "Multi Tensor Adam Update do not support amsgrad = True. "; } + + const float* learning_rate_ptr = nullptr; + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float lr_scale = ctx->Attr("learning_rate_scale"); + + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + + const float bias_correction1_val = ctx->Attr("bias_correction1_val"); + const float* bias_correction1_ptr = nullptr; + if (ctx->has_input("bias_correction1", 0)) { + const user_op::Tensor* bias_correction1 = ctx->Tensor4ArgNameAndIndex("bias_correction1", 0); + CHECK_EQ(bias_correction1->shape_view().elem_cnt(), + 1); // Just for Lazy Optional Input Check. + bias_correction1_ptr = bias_correction1->dptr(); + } + + const float bias_correction2_val = ctx->Attr("bias_correction2_val"); + const float* bias_correction2_ptr = nullptr; + if (ctx->has_input("bias_correction2", 0)) { + const user_op::Tensor* bias_correction2 = ctx->Tensor4ArgNameAndIndex("bias_correction2", 0); + CHECK_EQ(bias_correction2->shape_view().elem_cnt(), + 1); // Just for Lazy Optional Input Check. + bias_correction2_ptr = bias_correction2->dptr(); + } + + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->data_type(), ctx->Tensor4ArgNameAndIndex("model", 0)->data_type()); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + + TensorTupleParams<5> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_diff", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[2][count] = + (ctx->Tensor4ArgNameAndIndex("m", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[3][count] = + (ctx->Tensor4ArgNameAndIndex("v", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[4][count] = + (ctx->Tensor4ArgNameAndIndex("model_copy", tensor_idx))->mut_dptr(); + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorAdamUpdateWithCastKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, static_cast(scale), l1, l2, beta1, beta2, + epsilon, weight_decay, amsgrad, do_bias_correction, learning_rate_val, + bias_correction1_val, bias_correction2_val, lr_scale, learning_rate_ptr, scale_by_ptr, + skip_if_ptr, bias_correction1_ptr, bias_correction2_ptr, tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_WITH_CAST_KERNEL(device, dtype, gtype) \ + REGISTER_USER_KERNEL("multi_tensor_adam_update_with_cast") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_diff", 0) == GetDataType::value) \ + && (user_op::HobDataType("model_copy", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float); +REGISTER_MULTI_TENSOR_UPDATE_ADAM_UPDATE_WITH_CAST_KERNEL(DeviceType::kCUDA, float, float16); +#endif + +template +class MultiTensorYoloV5WeightUpdateKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + MultiTensorYoloV5WeightUpdateKernel() = default; + ~MultiTensorYoloV5WeightUpdateKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const int64_t n_tensor = ctx->input_size("model"); + const float d = ctx->Attr("d"); + + TensorTupleParams<2> tensor_tuple_params{}; + int32_t count = 0; + int32_t total_elem_cnt = 0; + for (int tensor_idx = 0; tensor_idx < n_tensor; tensor_idx++) { + tensor_tuple_params.ptr[0][count] = + (ctx->Tensor4ArgNameAndIndex("model", tensor_idx))->mut_dptr(); + tensor_tuple_params.ptr[1][count] = + (ctx->Tensor4ArgNameAndIndex("model_update", tensor_idx))->mut_dptr(); + const int64_t tensor_elem_cnt = + ctx->Tensor4ArgNameAndIndex("model", tensor_idx)->shape_view().elem_cnt(); + tensor_tuple_params.sizes[count] = tensor_elem_cnt; + + count += 1; + total_elem_cnt += tensor_elem_cnt; + if (count == kMaxTuples || tensor_idx == n_tensor - 1) { + MultiTensorYoloV5WeightUpdateKernelUtil::Update( + ctx->stream(), total_elem_cnt, count, d, tensor_tuple_params); + count = 0; + total_elem_cnt = 0; + } + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } +}; + +#define REGISTER_MULTI_TENSOR_YOLOV5_WEIGHT_UPDATE_KERNEL(device, dtype) \ + REGISTER_USER_KERNEL("multi_tensor_yolov5_weight_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("model", 0) == GetDataType::value)); + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_MULTI_TENSOR_YOLOV5_WEIGHT_UPDATE_KERNEL(DeviceType::kCUDA, float); +#endif + +} // namespace + +} // namespace oneflow diff --git a/oneflow/user/kernels/multi_tensor_model_update_kernel_util.cu b/oneflow/user/kernels/multi_tensor_model_update_kernel_util.cu new file mode 100644 index 0000000000000000000000000000000000000000..4ef9538e7f202013e08d3b92c8f72827eeed87e6 --- /dev/null +++ b/oneflow/user/kernels/multi_tensor_model_update_kernel_util.cu @@ -0,0 +1,651 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/model_update_kernel_util.h" +#include "oneflow/user/kernels/multi_tensor_model_update_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { + +constexpr int kBlockSize = 256; +constexpr int kUnrollSize = 4; + +unsigned int ComputeGridSize(ep::Stream* stream, const int32_t block_size, const int64_t elem_cnt) { + auto* cuda_stream = stream->As(); + const int32_t max_threads_multi_process = + cuda_stream->device_properties().maxThreadsPerMultiProcessor; + const int32_t multi_processor_count = cuda_stream->device_properties().multiProcessorCount; + unsigned int blocks_per_sm = max_threads_multi_process / block_size; + unsigned int grid_size = ((elem_cnt + block_size - 1) / block_size); + grid_size = std::min((unsigned int)multi_processor_count * blocks_per_sm, grid_size); + return grid_size; +} + +template +__global__ void MultiTensorSGDUpdateGpu(int64_t num_tensor, T scale, const float l1, const float l2, + const float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, + TensorTupleParams tensor_tuple_params) { + if (skip_if != nullptr && *skip_if != 0) { return; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; + int64_t v_block_id = blockIdx.x; + for (int64_t tensor_idx = 0; tensor_idx < num_tensor; tensor_idx++) { + const int64_t tensor_elem_cnt = tensor_tuple_params.sizes[tensor_idx]; + T* model_ptr = (T*)tensor_tuple_params.ptr[0][tensor_idx]; + G* model_diff_ptr = (G*)tensor_tuple_params.ptr[1][tensor_idx]; + half* model_copy_ptr = nullptr; + if (N == 3) { model_copy_ptr = (half*)tensor_tuple_params.ptr[2][tensor_idx]; } + + for (int64_t i = v_block_id * blockDim.x * kUnrollSize + threadIdx.x; i < tensor_elem_cnt; + i += blockDim.x * gridDim.x * kUnrollSize) { + T model_val[kUnrollSize] = {0}; + G model_diff[kUnrollSize] = {0}; + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + model_val[ilp] = *(model_ptr + actual_idx); + model_diff[ilp] = *(model_diff_ptr + actual_idx); + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + T model_diff_t = CastScaleRegularizeGradientFunctor()( + model_diff[ilp], model_val[ilp], scale, l1, l2); + model_val[ilp] = + model_val[ilp] - learning_rate_val * (model_diff_t + weight_decay * model_val[ilp]); + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + *(model_ptr + actual_idx) = model_val[ilp]; + if (N == 3) { *(model_copy_ptr + actual_idx) = static_cast(model_val[ilp]); } + } + } + } + v_block_id -= tensor_tuple_params.block_offset[tensor_idx]; + if (v_block_id < 0) { v_block_id += gridDim.x; } + } +} + +template +struct MultiTensorSGDUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<2> tensor_tuple_params); +}; + +template +void MultiTensorSGDUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, TensorTupleParams<2> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorSGDUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, static_cast(scale), l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, tensor_tuple_params); +} + +template +struct MultiTensorSGDUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<2> tensor_tuple_params); +}; + +template +void MultiTensorSGDUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, TensorTupleParams<2> tensor_tuple_params) { + MultiTensorSGDUpdateKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, tensor_tuple_params); +} + +template struct MultiTensorSGDUpdateKernelUtil; +template struct MultiTensorSGDUpdateKernelUtil; +template struct MultiTensorSGDUpdateKernelUtil; + +template +__global__ void MultiTensorMomentumUpdateGpu( + int64_t num_tensor, T scale, const float l1, const float l2, const float weight_decay, + float learning_rate_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, const bool nesterov, + const bool maximize, TensorTupleParams tensor_tuple_params) { + if (skip_if != nullptr && *skip_if != 0) { return; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + learning_rate_val *= lr_scale; + int64_t v_block_id = blockIdx.x; + for (int64_t tensor_idx = 0; tensor_idx < num_tensor; tensor_idx++) { + const int64_t tensor_elem_cnt = tensor_tuple_params.sizes[tensor_idx]; + T* model_ptr = (T*)tensor_tuple_params.ptr[0][tensor_idx]; + G* model_diff_ptr = (G*)tensor_tuple_params.ptr[1][tensor_idx]; + T* momentum_buf_ptr = (T*)tensor_tuple_params.ptr[2][tensor_idx]; + half* model_copy_ptr = nullptr; + if (N == 4) { model_copy_ptr = (half*)tensor_tuple_params.ptr[3][tensor_idx]; } + + for (int64_t i = v_block_id * blockDim.x * kUnrollSize + threadIdx.x; i < tensor_elem_cnt; + i += blockDim.x * gridDim.x * kUnrollSize) { + T model_val[kUnrollSize] = {0}; + G model_diff[kUnrollSize] = {0}; + T momentum_buf[kUnrollSize] = {0}; + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + model_val[ilp] = *(model_ptr + actual_idx); + model_diff[ilp] = *(model_diff_ptr + actual_idx); + momentum_buf[ilp] = *(momentum_buf_ptr + actual_idx); + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + T model_diff_t = CastScaleRegularizeGradientFunctor()( + model_diff[ilp], model_val[ilp], scale, l1, l2); + + if (weight_decay != 0.f) { model_diff_t += weight_decay * model_val[ilp]; } + + momentum_buf[ilp] = momentum * momentum_buf[ilp] + (1.f - dampening) * model_diff_t; + + if (nesterov) + model_diff_t += momentum * momentum_buf[ilp]; + else + model_diff_t = momentum_buf[ilp]; + + T alpha = -learning_rate_val; + if (maximize) alpha = learning_rate_val; + model_val[ilp] += alpha * model_diff_t; + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + *(model_ptr + actual_idx) = model_val[ilp]; + *(momentum_buf_ptr + actual_idx) = momentum_buf[ilp]; + if (N == 4) { *(model_copy_ptr + actual_idx) = static_cast(model_val[ilp]); } + } + } + } + v_block_id -= tensor_tuple_params.block_offset[tensor_idx]; + if (v_block_id < 0) { v_block_id += gridDim.x; } + } +} + +template +struct MultiTensorMomentumUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<3> tensor_tuple_params); +}; + +template +void MultiTensorMomentumUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, TensorTupleParams<3> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorMomentumUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, static_cast(scale), l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, momentum, dampening, nesterov, maximize, + tensor_tuple_params); +} + +template +struct MultiTensorMomentumUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<3> tensor_tuple_params); +}; + +template +void MultiTensorMomentumUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, TensorTupleParams<3> tensor_tuple_params) { + MultiTensorMomentumUpdateKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, momentum, dampening, nesterov, maximize, + tensor_tuple_params); +} + +template struct MultiTensorMomentumUpdateKernelUtil; +template struct MultiTensorMomentumUpdateKernelUtil; +template struct MultiTensorMomentumUpdateKernelUtil; + +template +__global__ void MultiTensorAdamUpdateGpu(int64_t num_tensor, T scale, float l1, float l2, + float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, + const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float* bias_correction1_ptr, + const float* bias_correction2_ptr, + TensorTupleParams tensor_tuple_params) { + if (skip_if != nullptr && *skip_if != 0) { return; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } + if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } + + learning_rate_val *= lr_scale; + int64_t v_block_id = blockIdx.x; + for (int64_t tensor_idx = 0; tensor_idx < num_tensor; tensor_idx++) { + const int64_t tensor_elem_cnt = tensor_tuple_params.sizes[tensor_idx]; + T* model_ptr = (T*)tensor_tuple_params.ptr[0][tensor_idx]; + G* model_diff_ptr = (G*)tensor_tuple_params.ptr[1][tensor_idx]; + T* m_ptr = (T*)tensor_tuple_params.ptr[2][tensor_idx]; + T* v_ptr = (T*)tensor_tuple_params.ptr[3][tensor_idx]; + half* model_copy_ptr = nullptr; + if (N == 5) { model_copy_ptr = (half*)tensor_tuple_params.ptr[4][tensor_idx]; } + + for (int64_t i = v_block_id * blockDim.x * kUnrollSize + threadIdx.x; i < tensor_elem_cnt; + i += blockDim.x * gridDim.x * kUnrollSize) { + T model_val[kUnrollSize] = {0}; + T m_val[kUnrollSize] = {0}; + T v_val[kUnrollSize] = {0}; + G model_diff[kUnrollSize] = {0}; + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + model_val[ilp] = *(model_ptr + actual_idx); + m_val[ilp] = *(m_ptr + actual_idx); + v_val[ilp] = *(v_ptr + actual_idx); + model_diff[ilp] = *(model_diff_ptr + actual_idx); + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + T model_diff_t = CastScaleRegularizeGradientFunctor()( + model_diff[ilp], model_val[ilp], scale, l1, l2); + + m_val[ilp] = beta1 * m_val[ilp] + (1 - beta1) * model_diff_t; + v_val[ilp] = beta2 * v_val[ilp] + (1 - beta2) * model_diff_t * model_diff_t; + + T denom = (sqrt(v_val[ilp]) / sqrt(bias_correction2_val)) + epsilon; + const T step_size = learning_rate_val / bias_correction1_val; + model_val[ilp] = model_val[ilp] - step_size * (m_val[ilp] / denom) + - learning_rate_val * weight_decay * model_val[ilp]; + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + *(model_ptr + actual_idx) = model_val[ilp]; + *(m_ptr + actual_idx) = m_val[ilp]; + *(v_ptr + actual_idx) = v_val[ilp]; + if (N == 5) { *(model_copy_ptr + actual_idx) = static_cast(model_val[ilp]); } + } + } + } + v_block_id -= tensor_tuple_params.block_offset[tensor_idx]; + if (v_block_id < 0) { v_block_id += gridDim.x; } + } +} + +template +struct MultiTensorAdamUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<4> tensor_tuple_params); +}; + +template +void MultiTensorAdamUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction, float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float* bias_correction1, const float* bias_correction2, + TensorTupleParams<4> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorAdamUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, + learning_rate_val, bias_correction1_val, bias_correction2_val, lr_scale, learning_rate, + scale_by_ptr, skip_if, bias_correction1, bias_correction2, tensor_tuple_params); +} + +template +struct MultiTensorAdamUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<4> tensor_tuple_params); +}; + +template +void MultiTensorAdamUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction, float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float* bias_correction1, const float* bias_correction2, + TensorTupleParams<4> tensor_tuple_params) { + MultiTensorAdamUpdateKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, + do_bias_correction, learning_rate_val, bias_correction1_val, bias_correction2_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, bias_correction1, bias_correction2, + tensor_tuple_params); +} + +template struct MultiTensorAdamUpdateKernelUtil; +template struct MultiTensorAdamUpdateKernelUtil; +template struct MultiTensorAdamUpdateKernelUtil; + +template +struct MultiTensorSGDUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<3> tensor_tuple_params); +}; + +template +void MultiTensorSGDUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, TensorTupleParams<3> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorSGDUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, static_cast(scale), l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, tensor_tuple_params); +} + +template +struct MultiTensorSGDUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<3> tensor_tuple_params); +}; + +template +void MultiTensorSGDUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, TensorTupleParams<3> tensor_tuple_params) { + MultiTensorSGDUpdateWithCastKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, tensor_tuple_params); +} + +template struct MultiTensorSGDUpdateWithCastKernelUtil; +template struct MultiTensorSGDUpdateWithCastKernelUtil; + +template +struct MultiTensorMomentumUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<4> tensor_tuple_params); +}; + +template +void MultiTensorMomentumUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, TensorTupleParams<4> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorMomentumUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, static_cast(scale), l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, momentum, dampening, nesterov, maximize, + tensor_tuple_params); +} + +template +struct MultiTensorMomentumUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<4> tensor_tuple_params); +}; + +template +void MultiTensorMomentumUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float weight_decay, float learning_rate_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, TensorTupleParams<4> tensor_tuple_params) { + MultiTensorMomentumUpdateWithCastKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, weight_decay, learning_rate_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, momentum, dampening, nesterov, maximize, + tensor_tuple_params); +} + +template struct MultiTensorMomentumUpdateWithCastKernelUtil; +template struct MultiTensorMomentumUpdateWithCastKernelUtil; + +template +struct MultiTensorAdamUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<5> tensor_tuple_params); +}; + +template +void MultiTensorAdamUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction, float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float* bias_correction1, const float* bias_correction2, + TensorTupleParams<5> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorAdamUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, do_bias_correction, + learning_rate_val, bias_correction1_val, bias_correction2_val, lr_scale, learning_rate, + scale_by_ptr, skip_if, bias_correction1, bias_correction2, tensor_tuple_params); +} + +template +struct MultiTensorAdamUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<5> tensor_tuple_params); +}; + +template +void MultiTensorAdamUpdateWithCastKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, float l1, float l2, + float beta1, float beta2, float epsilon, float weight_decay, bool amsgrad, + bool do_bias_correction, float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float* bias_correction1, const float* bias_correction2, + TensorTupleParams<5> tensor_tuple_params) { + MultiTensorAdamUpdateWithCastKernelUtil::Update( + stream, elem_cnt, n_tensor, scale, l1, l2, beta1, beta2, epsilon, weight_decay, amsgrad, + do_bias_correction, learning_rate_val, bias_correction1_val, bias_correction2_val, lr_scale, + learning_rate, scale_by_ptr, skip_if, bias_correction1, bias_correction2, + tensor_tuple_params); +} + +template struct MultiTensorAdamUpdateWithCastKernelUtil; +template struct MultiTensorAdamUpdateWithCastKernelUtil; + +template +__global__ void MultiTensorYoloModelEmaUpdateGpu(int64_t num_tensor, const float d, + TensorTupleParams tensor_tuple_params) { + int64_t v_block_id = blockIdx.x; + for (int64_t tensor_idx = 0; tensor_idx < num_tensor; tensor_idx++) { + const int64_t tensor_elem_cnt = tensor_tuple_params.sizes[tensor_idx]; + T* model_ptr = (T*)tensor_tuple_params.ptr[0][tensor_idx]; + T* model_update_ptr = (T*)tensor_tuple_params.ptr[1][tensor_idx]; + + for (int64_t i = v_block_id * blockDim.x * kUnrollSize + threadIdx.x; i < tensor_elem_cnt; + i += blockDim.x * gridDim.x * kUnrollSize) { + T model_val[kUnrollSize] = {0}; + T model_update_val[kUnrollSize] = {0}; + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + model_val[ilp] = *(model_ptr + actual_idx); + model_update_val[ilp] = *(model_update_ptr + actual_idx); + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + model_val[ilp] *= d; + model_val[ilp] += (1 - d) * model_update_val[ilp]; + } + } + +#pragma unroll + for (int32_t ilp = 0; ilp < kUnrollSize; ilp++) { + int64_t actual_idx = i + ilp * blockDim.x; + if (actual_idx < tensor_elem_cnt) { + *(model_ptr + actual_idx) = model_val[ilp]; + *(model_update_ptr + actual_idx) = model_update_val[ilp]; + } + } + } + v_block_id -= tensor_tuple_params.block_offset[tensor_idx]; + if (v_block_id < 0) { v_block_id += gridDim.x; } + } +} + +template +struct MultiTensorYoloV5WeightUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, float d, + TensorTupleParams<2> tensor_tuple_params); +}; + +template<> +struct MultiTensorYoloV5WeightUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, float d, + TensorTupleParams<2> tensor_tuple_params); +}; + +template +void MultiTensorYoloV5WeightUpdateKernelUtil::Update( + ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, float d, + TensorTupleParams<2> tensor_tuple_params) { + const unsigned int grid_size = + ComputeGridSize(stream->As(), kBlockSize, elem_cnt); + for (int i = 0; i < n_tensor; i++) { + tensor_tuple_params.block_offset[i] = + ((tensor_tuple_params.sizes[i] + kBlockSize * kUnrollSize - 1) / (kBlockSize * kUnrollSize)) + % grid_size; + } + MultiTensorYoloModelEmaUpdateGpu + <<As()->cuda_stream()>>>( + n_tensor, d, tensor_tuple_params); +} + +template struct MultiTensorYoloV5WeightUpdateKernelUtil; + +} // namespace oneflow diff --git a/oneflow/user/kernels/multi_tensor_model_update_kernel_util.h b/oneflow/user/kernels/multi_tensor_model_update_kernel_util.h new file mode 100644 index 0000000000000000000000000000000000000000..5fb7cff6cc3ed9ac47ff784016cd50e5a2624c3a --- /dev/null +++ b/oneflow/user/kernels/multi_tensor_model_update_kernel_util.h @@ -0,0 +1,100 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_MULTI_TENSOR_MODEL_UPDATE_KERNEL_UTIL_H_ +#define ONEFLOW_USER_KERNELS_MULTI_TENSOR_MODEL_UPDATE_KERNEL_UTIL_H_ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/kernel_util.h" + +namespace oneflow { + +// Kernel arg size has 4K limit, but currently we set process 32 tensors in each kernel. +constexpr int kMaxTuples = 32; + +template +struct TensorTupleParams { + void* ptr[N][kMaxTuples]; + int64_t sizes[kMaxTuples]; + int32_t block_offset[kMaxTuples]; +}; + +template +struct MultiTensorSGDUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<2> tensor_tuple_params); +}; + +template +struct MultiTensorMomentumUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<3> tensor_tuple_params); +}; + +template +struct MultiTensorAdamUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<4> tensor_tuple_params); +}; + +template +struct MultiTensorSGDUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, TensorTupleParams<3> tensor_tuple_params); +}; + +template +struct MultiTensorMomentumUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float weight_decay, float learning_rate_val, + float lr_scale, const float* learning_rate, const T* scale_by_ptr, + const int64_t* skip_if, const float momentum, const float dampening, + const bool nesterov, const bool maximize, + TensorTupleParams<4> tensor_tuple_params); +}; + +template +struct MultiTensorAdamUpdateWithCastKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, T scale, + float l1, float l2, float beta1, float beta2, float epsilon, + float weight_decay, bool amsgrad, bool do_bias_correction, + float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, float lr_scale, const float* learning_rate, + const T* scale_by_ptr, const int64_t* skip_if, const float* bias_correction1, + const float* bias_correction2, TensorTupleParams<5> tensor_tuple_params); +}; + +template +struct MultiTensorYoloV5WeightUpdateKernelUtil { + static void Update(ep::Stream* stream, const int64_t elem_cnt, const int64_t n_tensor, float d, + TensorTupleParams<2> tensor_tuple_params); +}; + +} // namespace oneflow + +#endif diff --git a/oneflow/user/kernels/mutable_cast_once_kernel.cpp b/oneflow/user/kernels/mutable_cast_once_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d40a60add50bb1cbcf621be6df71373860d5de3b --- /dev/null +++ b/oneflow/user/kernels/mutable_cast_once_kernel.cpp @@ -0,0 +1,90 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/user/kernels/op_kernel_wrapper.h" + +namespace oneflow { + +namespace user_op { + +namespace { + +template +std::unique_ptr NewCastPrimitive(Context* ctx) { + const DataType in_data_type = ctx->TensorDesc4ArgNameAndIndex("in", 0)->data_type(); + const DataType out_data_type = ctx->TensorDesc4ArgNameAndIndex("out", 0)->data_type(); + return ep::primitive::NewPrimitive(ctx->device_type(), in_data_type, + out_data_type); +} + +class MutableCastOnceOpKernelState final : public OpKernelState { + public: + MutableCastOnceOpKernelState() : cast_once_flag_(false) {} + + void SetDone() { + if (!cast_once_flag_) { cast_once_flag_ = true; } + } + + bool IsDone() { return cast_once_flag_; } + + private: + bool cast_once_flag_ = false; +}; + +class MutableCastOnce final : public OpKernel { + public: + MutableCastOnce() = default; + ~MutableCastOnce() = default; + + std::shared_ptr CreateOpKernelState(KernelInitContext* ctx) const override { + return std::make_shared(); + } + + private: + void Compute(KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* cast_state = CHECK_NOTNULL(dynamic_cast(state)); + if (cast_state->IsDone()) { return; } + const Tensor* input_tensor = ctx->Tensor4ArgNameAndIndex("in", 0); + Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + const int64_t elem_cnt = input_tensor->shape_view().elem_cnt(); + CHECK_EQ(output_tensor->shape_view().elem_cnt(), elem_cnt); + auto cast_primitive = NewCastPrimitive(ctx); + CHECK(cast_primitive); + cast_primitive->Launch(ctx->stream(), input_tensor->dptr(), output_tensor->mut_dptr(), + elem_cnt); + cast_state->SetDone(); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +auto CastPrimitiveExists() { + return hob::make_custom("CastPrimitiveExists", [](const user_op::KernelRegContext& ctx) -> bool { + return NewCastPrimitive(&ctx).operator bool(); + }); +} + +REGISTER_USER_KERNEL("mutable_cast_once") + .SetCreateFn() + .SetIsMatchedHob(CastPrimitiveExists() == true); + +} // namespace + +} // namespace user_op + +} // namespace oneflow diff --git a/oneflow/user/kernels/nccl_logical_2d_sbp_kernels.cpp b/oneflow/user/kernels/nccl_logical_2d_sbp_kernels.cpp index b5ddd12b425d0f405d5a26e5a5423b17ab3df74b..a210e26b4ae93963e7774da5b4603d5a06485afc 100644 --- a/oneflow/user/kernels/nccl_logical_2d_sbp_kernels.cpp +++ b/oneflow/user/kernels/nccl_logical_2d_sbp_kernels.cpp @@ -19,15 +19,10 @@ limitations under the License. #include "oneflow/core/job/eager_nccl_comm_manager.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/ep/include/primitive/permute.h" -#ifdef WITH_CUDA #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#endif #include "oneflow/user/ops/nccl_logical_util.h" -#if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) +#if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) namespace oneflow { @@ -257,9 +252,9 @@ class NcclLogical2DSameDim0AllGatherNoncontinuous final : public user_op::OpKern }; size_t Infer2DSameDim0AllGatherNoncontinuousKernelTmpBufferSize(user_op::InferContext* ctx) { - const user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - return GetCudaAlignedSize(out_tensor->shape().elem_cnt() - * GetSizeOfDataType(out_tensor->data_type())); + const user_op::TensorDesc& out_tensor = ctx->OutputTensorDesc("out", 0); + return GetCudaAlignedSize(out_tensor.shape().elem_cnt() + * GetSizeOfDataType(out_tensor.data_type())); } template @@ -511,6 +506,9 @@ REGISTER_2D_SAME_DIM0_ALLGATHER_NONCONTINUOUS_KERNEL(int64_t) REGISTER_2D_SAME_DIM0_ALLGATHER_NONCONTINUOUS_KERNEL(float) REGISTER_2D_SAME_DIM0_ALLGATHER_NONCONTINUOUS_KERNEL(double) REGISTER_2D_SAME_DIM0_ALLGATHER_NONCONTINUOUS_KERNEL(float16) +#if defined(__CUDA_BF16_TYPES_EXIST__) +REGISTER_2D_SAME_DIM0_ALLGATHER_NONCONTINUOUS_KERNEL(nv_bfloat16) +#endif #define REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(dtype) \ REGISTER_USER_KERNEL("_nccl_logical_2D_same_dim0_all2all") \ @@ -527,6 +525,9 @@ REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(int64_t) REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(float) REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(double) REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(float16) +#if defined(__CUDA_BF16_TYPES_EXIST__) +REGISTER_2D_SAME_DIM0_ALL2ALL_KERNEL(nv_bfloat16) +#endif REGISTER_USER_KERNEL("_nccl_logical_2D_same_dim1_all_reduce") .SetCreateFn() diff --git a/oneflow/user/kernels/nccl_logical_kernels.cpp b/oneflow/user/kernels/nccl_logical_kernels.cpp index c7dda4dfb10cab60d3149f7c61f0f5be064b413c..e384ee945e10cc2d91a9adaab8d6b92d09ed3a91 100644 --- a/oneflow/user/kernels/nccl_logical_kernels.cpp +++ b/oneflow/user/kernels/nccl_logical_kernels.cpp @@ -19,12 +19,7 @@ limitations under the License. #include "oneflow/core/job/eager_nccl_comm_manager.h" #include "oneflow/core/job/parallel_desc.h" #include "oneflow/core/ep/include/primitive/permute.h" -#ifdef WITH_CUDA #include "oneflow/core/ep/cuda/cuda_stream.h" -#endif -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#endif #include "oneflow/user/ops/nccl_logical_util.h" #if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) @@ -281,9 +276,9 @@ class NcclLogicalAllGatherNoncontinuous final : public user_op::OpKernel { }; size_t InferAllGatherNoncontinuousKernelTmpBufferSize(user_op::InferContext* ctx) { - const user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - return GetCudaAlignedSize(out_tensor->shape().elem_cnt() - * GetSizeOfDataType(out_tensor->data_type())); + const user_op::TensorDesc& out_tensor = ctx->OutputTensorDesc("out", 0); + return GetCudaAlignedSize(out_tensor.shape().elem_cnt() + * GetSizeOfDataType(out_tensor.data_type())); } template @@ -353,9 +348,9 @@ class NcclLogicalReduceScatterNoncontinuous final : public user_op::OpKernel { }; size_t InferReduceScatterNoncontinuousKernelTmpBufferSize(user_op::InferContext* ctx) { - const user_op::TensorDesc* in_tensor = ctx->OutputTensorDesc("in", 0); - return GetCudaAlignedSize(in_tensor->shape().elem_cnt() - * GetSizeOfDataType(in_tensor->data_type())); + const user_op::TensorDesc& in_tensor = ctx->OutputTensorDesc("in", 0); + return GetCudaAlignedSize(in_tensor.shape().elem_cnt() + * GetSizeOfDataType(in_tensor.data_type())); } template @@ -533,6 +528,9 @@ REGISTER_ALLGATHER_NONCONTINUOUS_KERNEL(int64_t) REGISTER_ALLGATHER_NONCONTINUOUS_KERNEL(float) REGISTER_ALLGATHER_NONCONTINUOUS_KERNEL(double) REGISTER_ALLGATHER_NONCONTINUOUS_KERNEL(float16) +#if defined(__CUDA_BF16_TYPES_EXIST__) +REGISTER_ALLGATHER_NONCONTINUOUS_KERNEL(nv_bfloat16) +#endif #define REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(dtype) \ REGISTER_USER_KERNEL("_nccl_logical_reduce_scatter_noncontinuous") \ @@ -549,6 +547,9 @@ REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(int64_t) REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(float) REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(double) REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(float16) +#if defined(__CUDA_BF16_TYPES_EXIST__) +REGISTER_REDUCE_SCATTER_NONCONTINUOUS_KERNEL(nv_bfloat16) +#endif #define REGISTER_S2S_KERNEL(dtype) \ REGISTER_USER_KERNEL("_nccl_logical_s2s") \ @@ -565,6 +566,9 @@ REGISTER_S2S_KERNEL(int64_t) REGISTER_S2S_KERNEL(float) REGISTER_S2S_KERNEL(double) REGISTER_S2S_KERNEL(float16) +#if defined(__CUDA_BF16_TYPES_EXIST__) +REGISTER_S2S_KERNEL(nv_bfloat16) +#endif REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("_nccl_logical_all_reduce"); REGISTER_USER_KERNEL_UNIFIED_NCCL_COMM_INIT("_nccl_logical_reduce_scatter"); diff --git a/oneflow/user/kernels/nccl_logical_send_recv_kernel.cpp b/oneflow/user/kernels/nccl_logical_send_recv_kernel.cpp index b8b528d8fad364e223dcc2318b98e6032afba050..53cc3f63685451377391447d872849b5a29ca79d 100644 --- a/oneflow/user/kernels/nccl_logical_send_recv_kernel.cpp +++ b/oneflow/user/kernels/nccl_logical_send_recv_kernel.cpp @@ -26,6 +26,7 @@ limitations under the License. #include "oneflow/core/register/tensor_slice_copier.h" #include "oneflow/core/ep/include/primitive/memset.h" #include "oneflow/core/ep/include/primitive/add.h" +#include "oneflow/core/operator/nccl_send_recv_boxing_op_util.h" #if (defined(WITH_CUDA) && NCCL_VERSION_CODE > 2700) || defined(WITH_ROCM) @@ -87,7 +88,9 @@ NcclLogicalSendRecvState::NcclLogicalSendRecvState(user_op::KernelInitContext* c std::vector src_send_intersections; std::vector dst_recv_intersections; - GetRankSendRecvIntersection(parallel_id, parallel_desc_->hierarchy(), src_nd_sbp, dst_nd_sbp, + GetRankSendRecvIntersection(parallel_id, /*merge_parallel_desc=*/*parallel_desc_, + /*in_parallel_desc=*/*parallel_desc_, + /*out_parallel_desc=*/*parallel_desc_, src_nd_sbp, dst_nd_sbp, logical_shape, &src_send_intersections, &dst_recv_intersections); CHECK_EQ(src_send_intersections.size(), parallel_num); @@ -161,7 +164,7 @@ void NcclLogicalSendRecv::Compute(user_op::KernelComputeContext* ctx, user_op::O user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); ncclComm_t comm = kernel_state->comm(); - hipStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); const std::vector& send_elem_cnts = kernel_state->send_elem_cnts(); const std::vector& recv_elem_cnts = kernel_state->recv_elem_cnts(); const int64_t parallel_num = send_elem_cnts.size(); @@ -252,7 +255,7 @@ void NcclLogicalSendRecv::Compute(user_op::KernelComputeContext* ctx, user_op::O } size_t InferTmpBufferSize(user_op::InferContext* ctx) { - const Shape* out_shape = ctx->OutputShape("out", 0); + const Shape& out_shape = ctx->OutputShape("out", 0); const user_op::TensorDesc* logical_in_tensor = ctx->LogicalTensorDesc4ArgNameAndIndex("in", 0); const Shape& logical_shape = logical_in_tensor->shape(); const DataType data_type = logical_in_tensor->data_type(); @@ -264,7 +267,10 @@ size_t InferTmpBufferSize(user_op::InferContext* ctx) { std::vector src_send_intersections; std::vector dst_recv_intersections; - GetRankSendRecvIntersection(parallel_id, ctx->parallel_desc().hierarchy(), src_nd_sbp, dst_nd_sbp, + const auto& parallel_desc = ctx->parallel_desc(); + GetRankSendRecvIntersection(parallel_id, /*merge_parallel_desc=*/parallel_desc, + /*in_parallel_desc=*/parallel_desc, + /*out_parallel_desc=*/parallel_desc, src_nd_sbp, dst_nd_sbp, logical_shape, &src_send_intersections, &dst_recv_intersections); int64_t buf_count = 0; CHECK_EQ(src_send_intersections.size(), parallel_num); @@ -278,7 +284,7 @@ size_t InferTmpBufferSize(user_op::InferContext* ctx) { } if (NdSbpHasPartialParallel(src_nd_sbp)) { // Note: when src_nd_sbp has partial_sum, need a out_size buffer to copy and add to out. - buf_count += out_shape->elem_cnt(); + buf_count += out_shape.elem_cnt(); } return buf_count * GetSizeOfDataType(data_type); } diff --git a/oneflow/user/kernels/nd_index_slice_kernels.cpp b/oneflow/user/kernels/nd_index_slice_kernels.cpp index 6c7f44fd6770c81abf30a5fa149f06404ca45f97..dbd8c7f4513ec8faae3ce7c11f9baa3f72876f75 100644 --- a/oneflow/user/kernels/nd_index_slice_kernels.cpp +++ b/oneflow/user/kernels/nd_index_slice_kernels.cpp @@ -19,7 +19,7 @@ namespace oneflow { template struct GatherNdFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* dense, T* slices) const { DoGatherNd(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, dense, slices); @@ -28,7 +28,7 @@ struct GatherNdFunctor final { template struct ScatterNdAddFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const { DoScatterNdAdd(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, slices, dense); @@ -37,28 +37,41 @@ struct ScatterNdAddFunctor final { template struct ScatterNdUpdateFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const { DoScatterNdUpdate(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, slices, dense); } }; +template +struct ScatterNdUpdateWithStrideFunctor final { + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + const T* slices, T* dense) const { + DoScatterNdUpdateWithStride(args.num_slices * args.slice_size, args, indices, + slices, dense); + } +}; + template struct FillByNdIndexFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - T* dense, T value) const { + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, T* dense, + T value) const { DoFillByNdIndex(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, dense, value); } }; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - INSTANTIATE_ND_INDEX_SLICE_FUNCTORS, (DeviceType::kCPU), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ND_INDEX_SLICE_FUNCTORS, (DeviceType::kCPU), + ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ + BFLOAT16_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_ND_INDEX_SLICE_KERNELS, (DeviceType::kCPU), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ND_INDEX_SLICE_KERNELS, (DeviceType::kCPU), + ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ + BFLOAT16_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ + BOOL_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) } // namespace oneflow diff --git a/oneflow/user/kernels/nd_index_slice_kernels.cu b/oneflow/user/kernels/nd_index_slice_kernels.cu index 3e22651ab73a044951c4fb492f0eb2fa922d860f..8324b8dd12ce427c4c9beccf03acdf4d27365a8c 100644 --- a/oneflow/user/kernels/nd_index_slice_kernels.cu +++ b/oneflow/user/kernels/nd_index_slice_kernels.cu @@ -21,29 +21,34 @@ namespace oneflow { namespace { template -__global__ void CudaGatherNd(NdIndexSliceArgs args, const I* indices, const T* dense, - T* slices) { +__global__ void CudaGatherNd(NdIndexSliceArgs args, const I* indices, const T* dense, T* slices) { DoGatherNd(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, dense, slices); } template -__global__ void CudaScatterNdAdd(NdIndexSliceArgs args, const I* indices, const T* slices, +__global__ void CudaScatterNdAdd(NdIndexSliceArgs args, const I* indices, const T* slices, T* dense) { DoScatterNdAdd(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, slices, dense); } template -__global__ void CudaScatterNdUpdate(NdIndexSliceArgs args, const I* indices, const T* slices, +__global__ void CudaScatterNdUpdate(NdIndexSliceArgs args, const I* indices, const T* slices, T* dense) { DoScatterNdUpdate(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, slices, dense); } template -__global__ void CudaFillByNdIndex(NdIndexSliceArgs args, const I* indices, T* dense, - T value) { +__global__ void CudaScatterNdUpdateWithStride(NdIndexSliceArgs args, const I* indices, + const T* slices, T* dense) { + DoScatterNdUpdateWithStride(args.num_slices * args.slice_size, args, indices, + slices, dense); +} + +template +__global__ void CudaFillByNdIndex(NdIndexSliceArgs args, const I* indices, T* dense, T value) { DoFillByNdIndex(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, indices, dense, value); } @@ -52,7 +57,7 @@ __global__ void CudaFillByNdIndex(NdIndexSliceArgs args, const I* indices, template struct GatherNdFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* dense, T* slices) const { RUN_CUDA_KERNEL((CudaGatherNd), stream, args.num_slices * args.slice_size, args, indices, dense, slices); @@ -61,7 +66,7 @@ struct GatherNdFunctor final { template struct ScatterNdAddFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const { RUN_CUDA_KERNEL((CudaScatterNdAdd), stream, args.num_slices * args.slice_size, args, indices, slices, dense); @@ -70,17 +75,26 @@ struct ScatterNdAddFunctor final { template struct ScatterNdUpdateFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const { RUN_CUDA_KERNEL((CudaScatterNdUpdate), stream, args.num_slices * args.slice_size, args, indices, slices, dense); } }; +template +struct ScatterNdUpdateWithStrideFunctor final { + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + const T* slices, T* dense) const { + RUN_CUDA_KERNEL((CudaScatterNdUpdateWithStride), stream, + args.num_slices * args.slice_size, args, indices, slices, dense); + } +}; + template struct FillByNdIndexFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - T* dense, T value) const { + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, T* dense, + T value) const { RUN_CUDA_KERNEL((CudaFillByNdIndex), stream, args.num_slices * args.slice_size, args, indices, dense, value); } @@ -145,8 +159,6 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_TENSOR_GATHER_ND_ADD_KERNELS, (DeviceType::kCUDA), CUDA_ATOMIC_ADD_SUPPORTED_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700 && CUDA_VERSION >= 10000 - template<> struct DeviceAdd { __device__ __forceinline__ static void Invoke(const float16* x, float16* y) { @@ -160,6 +172,19 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ND_INDEX_SLICE_FUNCTORS, (DeviceTyp OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ND_INDEX_SLICE_KERNELS, (DeviceType::kCUDA), FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) +#if defined(__CUDA_BF16_TYPES_EXIST__) +template<> +struct DeviceAdd { + __device__ __forceinline__ static void Invoke(const bfloat16* x, bfloat16* y) { + cuda::atomic::Add(reinterpret_cast(y), + *(reinterpret_cast(x))); + } +}; +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ND_INDEX_SLICE_FUNCTORS, (DeviceType::kCUDA), + BFLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ND_INDEX_SLICE_KERNELS, (DeviceType::kCUDA), + BFLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) #endif } // namespace oneflow diff --git a/oneflow/user/kernels/nd_index_slice_kernels.h b/oneflow/user/kernels/nd_index_slice_kernels.h index 7df6eadcde8b54d4b61bbbe2a53c97a7985ecd98..e22b23ee8885dfba3f0924cf9694a710788eb3a4 100644 --- a/oneflow/user/kernels/nd_index_slice_kernels.h +++ b/oneflow/user/kernels/nd_index_slice_kernels.h @@ -17,6 +17,7 @@ limitations under the License. #define ONEFLOW_USER_KERNELS_ND_INDEX_SLICE_KERNELS_H_ #include "oneflow/user/kernels/nd_index_slice_util.h" +#include "oneflow/core/common/tensor_meta.h" namespace oneflow { @@ -73,8 +74,8 @@ void GatherNdKernel::Compute(user_op::KernelComputeContext* c const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); const user_op::Tensor* params = ctx->Tensor4ArgNameAndIndex("params", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - if (indices->shape_view().elem_cnt() == 0) { return; } - auto args = ConstructNdIndexSliceArgs(*params, *out, *indices); + if (params->shape_view().elem_cnt() == 0 || indices->shape_view().elem_cnt() == 0) { return; } + auto args = ConstructNdIndexSliceArgs(*params, *out, *indices); GatherNdFunctor()(ctx->stream(), args, indices->dptr(), params->dptr(), out->mut_dptr()); } @@ -87,7 +88,7 @@ void ScatterNdKernel::Compute(user_op::KernelComputeContext* size_t out_bytes_size = out->shape_view().elem_cnt() * GetSizeOfDataType(out->data_type()); Memset(ctx->stream(), out->mut_dptr(), 0, out_bytes_size); if (indices->shape_view().elem_cnt() == 0) { return; } - auto args = ConstructNdIndexSliceArgs(*out, *updates, *indices); + auto args = ConstructNdIndexSliceArgs(*out, *updates, *indices); ScatterNdAddFunctor()(ctx->stream(), args, indices->dptr(), updates->dptr(), out->mut_dptr()); } @@ -102,9 +103,15 @@ void TensorScatterNdUpdateKernel::Compute( size_t out_bytes_size = out->shape_view().elem_cnt() * GetSizeOfDataType(out->data_type()); Memcpy(ctx->stream(), out->mut_dptr(), params->dptr(), out_bytes_size); if (indices->shape_view().elem_cnt() == 0) { return; } - auto args = ConstructNdIndexSliceArgs(*params, *updates, *indices); - ScatterNdUpdateFunctor()(ctx->stream(), args, indices->dptr(), - updates->dptr(), out->mut_dptr()); + auto args = ConstructNdIndexSliceArgs(*params, *updates, *indices); + if (one::IsContiguous(params->shape_view(), params->stride()) + && one::IsContiguous(updates->shape_view(), updates->stride())) { + ScatterNdUpdateFunctor()(ctx->stream(), args, indices->dptr(), + updates->dptr(), out->mut_dptr()); + } else { + ScatterNdUpdateWithStrideFunctor()(ctx->stream(), args, indices->dptr(), + updates->dptr(), out->mut_dptr()); + } } template @@ -117,7 +124,7 @@ void TensorScatterNdAddKernel::Compute( size_t out_bytes_size = out->shape_view().elem_cnt() * GetSizeOfDataType(out->data_type()); Memcpy(ctx->stream(), out->mut_dptr(), params->dptr(), out_bytes_size); if (indices->shape_view().elem_cnt() == 0) { return; } - auto args = ConstructNdIndexSliceArgs(*params, *updates, *indices); + auto args = ConstructNdIndexSliceArgs(*params, *updates, *indices); ScatterNdAddFunctor()(ctx->stream(), args, indices->dptr(), updates->dptr(), out->mut_dptr()); } diff --git a/oneflow/user/kernels/nd_index_slice_kernels.hip.cpp b/oneflow/user/kernels/nd_index_slice_kernels.hip.cpp deleted file mode 100644 index 5f0cea1e0d38930a9548117eaf168fdf759d0fd9..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/nd_index_slice_kernels.hip.cpp +++ /dev/null @@ -1,166 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/nd_index_slice_kernels.h" -#include "oneflow/core/hip/atomic.hip.h" - -namespace oneflow { - -namespace { - -template -__global__ void CudaGatherNd(NdIndexSliceArgs args, const I* indices, const T* dense, - T* slices) { - DoGatherNd(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, args.dense_shape, - indices, dense, slices); -} - -template -__global__ void CudaScatterNdAdd(NdIndexSliceArgs args, const I* indices, const T* slices, - T* dense) { - DoScatterNdAdd(args.num_slices * args.slice_size, args.slice_size, - args.index_ndims, args.dense_shape, indices, slices, dense); -} - -template -__global__ void CudaScatterNdUpdate(NdIndexSliceArgs args, const I* indices, const T* slices, - T* dense) { - DoScatterNdUpdate(args.num_slices * args.slice_size, args.slice_size, - args.index_ndims, args.dense_shape, indices, slices, dense); -} - -template -__global__ void CudaFillByNdIndex(NdIndexSliceArgs args, const I* indices, T* dense, - T value) { - DoFillByNdIndex(args.num_slices * args.slice_size, args.slice_size, args.index_ndims, - args.dense_shape, indices, dense, value); -} - -} // namespace - -template -struct GatherNdFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - const T* dense, T* slices) const { - RUN_CUDA_KERNEL((CudaGatherNd), stream, args.num_slices * args.slice_size, args, indices, - dense, slices); - } -}; - -template -struct ScatterNdAddFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - const T* slices, T* dense) const { - RUN_CUDA_KERNEL((CudaScatterNdAdd), stream, args.num_slices * args.slice_size, args, - indices, slices, dense); - } -}; - -template -struct ScatterNdUpdateFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - const T* slices, T* dense) const { - RUN_CUDA_KERNEL((CudaScatterNdUpdate), stream, args.num_slices * args.slice_size, args, - indices, slices, dense); - } -}; - -template -struct FillByNdIndexFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - T* dense, T value) const { - RUN_CUDA_KERNEL((CudaFillByNdIndex), stream, args.num_slices * args.slice_size, args, - indices, dense, value); - } -}; - -template -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const T* x, T* y) { cuda::atomic::Add(y, *x); } -}; - -template<> -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const bool* x, bool* y) { *y += *x; } -}; - -template<> -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const uint8_t* x, uint8_t* y) { *y += *x; } -}; - -template<> -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const int8_t* x, int8_t* y) { *y += *x; } -}; - -template<> -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const int64_t* x, int64_t* y) { *y += *x; } -}; - -#define CUDA_ATOMIC_ADD_SUPPORTED_DATA_TYPE_SEQ \ - FLOATING_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - INSTANTIATE_GATHER_ND_FUNCTOR, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_SCATTER_ND_ADD_FUNCTOR, (DeviceType::kCUDA), - CUDA_ATOMIC_ADD_SUPPORTED_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_FILL_BY_ND_INDEX_FUNCTOR, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_GATHER_ND_KERNELS, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_SCATTER_ND_KERNELS, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCATTER_ND_LIKE_KERNELS, (DeviceType::kCUDA), - CUDA_ATOMIC_ADD_SUPPORTED_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE( - REGISTER_TENSOR_GATHER_ND_UPDATE_KERNELS, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_TENSOR_GATHER_ND_ADD_KERNELS, (DeviceType::kCUDA), - CUDA_ATOMIC_ADD_SUPPORTED_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -#if (defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700 && CUDA_VERSION >= 10000) || defined(__HIP_DEVICE_COMPILE__) - -template<> -struct DeviceAdd { - __device__ __forceinline__ static void Invoke(const float16* x, float16* y) { - cuda::atomic::Add(reinterpret_cast(y), *(reinterpret_cast(x))); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_ND_INDEX_SLICE_FUNCTORS, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_ND_INDEX_SLICE_KERNELS, (DeviceType::kCUDA), - FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -#endif - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/nd_index_slice_util.h b/oneflow/user/kernels/nd_index_slice_util.h index 22cc9c836a76d630f96ffaa04c7feb1c4566bae0..22caed6192969f3abdec8991299f8a97d805dfd9 100644 --- a/oneflow/user/kernels/nd_index_slice_util.h +++ b/oneflow/user/kernels/nd_index_slice_util.h @@ -16,57 +16,75 @@ limitations under the License. #ifndef ONEFLOW_USER_KERNELS_ND_INDEX_SLICE_UTIL_H_ #define ONEFLOW_USER_KERNELS_ND_INDEX_SLICE_UTIL_H_ +#include "oneflow/core/common/shape.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/ndarray/xpu_util.h" namespace oneflow { -template struct NdIndexSliceArgs { static const size_t kMaxDims = 8; - int64_t num_slices; - int64_t slice_size; - int64_t index_ndims; + int64_t num_slices; // The number of slices (indices_shape.Count(0, -1)) + int64_t slice_size; // The element_cnt of each slice (sliced_shape.Count(indices_num_axes-1)) + int64_t index_ndims; // The number of dims which are sliced (indices_shape.At(-1)) + int64_t dense_ndims; int64_t dense_shape[kMaxDims]; + int64_t dense_stride[kMaxDims]; + int64_t slices_ndims; + int64_t slices_shape[kMaxDims]; + int64_t slices_stride[kMaxDims]; }; -template -inline NdIndexSliceArgs ConstructNdIndexSliceArgs(const user_op::Tensor& dense, - const user_op::Tensor& slices, - const user_op::Tensor& indices) { - NdIndexSliceArgs args; - std::memset(&args, 0, sizeof(NdIndexSliceArgs)); +inline NdIndexSliceArgs ConstructNdIndexSliceArgs(const user_op::Tensor& dense, + const user_op::Tensor& slices, + const user_op::Tensor& indices) { + NdIndexSliceArgs args; + std::memset(&args, 0, sizeof(NdIndexSliceArgs)); args.num_slices = indices.shape_view().Count(0, indices.shape_view().NumAxes() - 1); args.index_ndims = indices.shape_view().At(indices.shape_view().NumAxes() - 1); args.slice_size = slices.shape_view().Count(indices.shape_view().NumAxes() - 1); + + args.dense_ndims = dense.shape_view().NumAxes(); FOR_RANGE(int64_t, i, 0, dense.shape_view().NumAxes()) { args.dense_shape[i] = dense.shape_view().At(i); + args.dense_stride[i] = dense.stride().at(i); + } + args.slices_ndims = slices.shape_view().NumAxes(); + FOR_RANGE(int64_t, i, 0, slices.stride().size()) { + args.slices_shape[i] = slices.shape_view().At(i); + args.slices_stride[i] = slices.stride().at(i); } return args; } template struct GatherNdFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* dense, T* slices) const; }; template struct ScatterNdAddFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const; }; template struct ScatterNdUpdateFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, + const T* slices, T* dense) const; +}; + +template +struct ScatterNdUpdateWithStrideFunctor final { + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, const T* slices, T* dense) const; }; template struct FillByNdIndexFunctor final { - void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, - T* dense, T value) const; + void operator()(ep::Stream* stream, const NdIndexSliceArgs& args, const I* indices, T* dense, + T value) const; }; template @@ -74,16 +92,36 @@ OF_DEVICE_FUNC int64_t OffsetInSliceToOffsetInDense(int64_t slice_size, int64_t const int64_t* dense_shape, const I* indices, int64_t n) { int64_t slice_idx = n / slice_size; - const I* cur_nd_index_ptr = indices + slice_idx * index_ndims; + const I* nd_index = indices + slice_idx * index_ndims; int64_t offset = 0; int64_t product = 1; + int64_t shifted_index = 0; for (int64_t i = index_ndims - 1; i >= 0; --i) { - offset += cur_nd_index_ptr[i] * product; +#if defined(__CUDACC__) || defined(__HIP_DEVICE_COMPILE__) + assert(nd_index[i] < dense_shape[i] && nd_index[i] >= -dense_shape[i] && "index out of bounds"); +#else + CHECK(nd_index[i] < dense_shape[i] && nd_index[i] >= -dense_shape[i]) + << "IndexError: index " << nd_index[i] << " is out of bounds for dimension " << i + << " with size " << dense_shape[i]; +#endif + shifted_index = nd_index[i] < 0 && nd_index[i] >= -dense_shape[i] ? nd_index[i] + dense_shape[i] + : nd_index[i]; + offset += shifted_index * product; product *= dense_shape[i]; } return offset * slice_size + n % slice_size; } +OF_DEVICE_FUNC int64_t GetMemoryOffset4ElementIdx(int64_t n, int64_t ndims, const int64_t* shape, + const int64_t* stride) { + int64_t offset = 0; + for (int64_t i = ndims - 1; i >= 0; --i) { + offset += n % shape[i] * stride[i]; + n /= shape[i]; + } + return offset; +} + template OF_DEVICE_FUNC void DoGatherNd(int64_t elem_cnt, int64_t slice_size, int64_t index_ndims, const int64_t* dense_shape, const I* indices, const T* dense, @@ -119,6 +157,22 @@ OF_DEVICE_FUNC void DoScatterNdUpdate(int64_t elem_cnt, int64_t slice_size, int6 } } +template +OF_DEVICE_FUNC void DoScatterNdUpdateWithStride(int64_t elem_cnt, const NdIndexSliceArgs& args, + const I* indices, const T* slices, T* dense) { + XPU_1D_KERNEL_LOOP(i, elem_cnt) { + // dense tensor memory offset + int64_t dense_index = OffsetInSliceToOffsetInDense(args.slice_size, args.index_ndims, + args.dense_shape, indices, i); + int64_t dense_mem_offset = GetMemoryOffset4ElementIdx(dense_index, args.dense_ndims, + args.dense_shape, args.dense_stride); + // update tensor memory offset + int64_t slice_mem_offset = + GetMemoryOffset4ElementIdx(i, args.slices_ndims, args.slices_shape, args.slices_stride); + dense[dense_mem_offset] = slices[slice_mem_offset]; + } +} + template OF_DEVICE_FUNC void DoFillByNdIndex(int64_t elem_cnt, int64_t slice_size, int64_t index_ndims, const int64_t* dense_shape, const I* indices, T* dense, diff --git a/oneflow/user/kernels/nll_kernel.cpp b/oneflow/user/kernels/nll_kernel.cpp index 20e8357acc17a0fabe3ee5b06f6c93f90e45ee9a..536590e0e21e642ed5ca5eaaa2896bb216f15042 100644 --- a/oneflow/user/kernels/nll_kernel.cpp +++ b/oneflow/user/kernels/nll_kernel.cpp @@ -179,7 +179,7 @@ REGISTER_NLL_KERNELS(DeviceType::kCPU, float, int64_t); REGISTER_NLL_KERNELS(DeviceType::kCPU, double, int32_t); REGISTER_NLL_KERNELS(DeviceType::kCPU, double, int64_t); -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_NLL_KERNELS(DeviceType::kCUDA, float, int32_t); REGISTER_NLL_KERNELS(DeviceType::kCUDA, float, int64_t); @@ -190,15 +190,4 @@ REGISTER_NLL_KERNELS(DeviceType::kCUDA, half, int64_t); #endif // WITH_CUDA -#ifdef WITH_ROCM - -REGISTER_NLL_KERNELS(DeviceType::kCUDA, float, int32_t); -REGISTER_NLL_KERNELS(DeviceType::kCUDA, float, int64_t); -REGISTER_NLL_KERNELS(DeviceType::kCUDA, double, int32_t); -REGISTER_NLL_KERNELS(DeviceType::kCUDA, double, int64_t); -REGISTER_NLL_KERNELS(DeviceType::kCUDA, half, int32_t); -REGISTER_NLL_KERNELS(DeviceType::kCUDA, half, int64_t); - -#endif // WITH_ROCM - } // namespace oneflow diff --git a/oneflow/user/kernels/nll_kernel_util.h b/oneflow/user/kernels/nll_kernel_util.h index e9d19a9a5aa29bcff7e73c73a063fb65deea9b86..25953d9b64fbc4a468ad45c211e8ae0779766fe1 100644 --- a/oneflow/user/kernels/nll_kernel_util.h +++ b/oneflow/user/kernels/nll_kernel_util.h @@ -18,10 +18,6 @@ limitations under the License. #include "oneflow/core/kernel/kernel_util.h" -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_stream.h" -#endif - namespace oneflow { template diff --git a/oneflow/user/kernels/nll_kernel_util.hip.cpp b/oneflow/user/kernels/nll_kernel_util.hip.cpp deleted file mode 100644 index 90c82b77803464d315e073553a20879326c4d1a4..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/nll_kernel_util.hip.cpp +++ /dev/null @@ -1,93 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/nll_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "hip/hip_runtime.h" - -namespace oneflow { - -namespace { - -template -__global__ void NLLForward(const int32_t num_samples, const K num_classes, const K class_start, - const K ignore_index, const T* input, const K* target, const T* weight, - T* out, T* out_weight) { - const T zero = GetZeroVal(); - const T one = GetOneVal(); - CUDA_1D_KERNEL_LOOP(i, num_samples) { - K label = target[i]; - T w = zero; - T y = zero; - if (label != ignore_index) { - label -= class_start; - if (label >= 0 && label < num_classes) { - w = weight ? weight[label] : one; - y = -(input[i * num_classes + label] * w); - } - } - out[i] = y; - out_weight[i] = w; - } -} - -template -__global__ void NLLBackward(const int32_t num_samples, const K num_classes, const K class_start, - const K ignore_index, const T* out_grad, const K* target, - const T* weight, T* in_grad) { - const T one = GetOneVal(); - const T zero = GetZeroVal(); - CUDA_1D_KERNEL_LOOP_T(K, i, num_samples * num_classes) { - const K n = i / num_classes; - const K idx = i - n * num_classes; - const K label = target[n]; - if (label != ignore_index && idx == label - class_start) { - in_grad[i] = out_grad[n] * (weight ? -weight[idx] : -one); - } else { - in_grad[i] = zero; - } - } -} - -} // namespace - -template -struct NLLKernelUtil { - static void Forward(ep::Stream* stream, const int32_t num_samples, const K num_classes, - const K class_start, const K ignore_index, const T* input, const K* target, - const T* weight, T* out, T* out_weight) { - NLLForward<<As()->cuda_stream()>>>(num_samples, num_classes, - class_start, ignore_index, input, - target, weight, out, out_weight); - } - - static void Backward(ep::Stream* stream, const int32_t num_samples, const K num_classes, - const K class_start, const K ignore_index, const T* out_grad, - const K* target, const T* weight, T* in_grad) { - NLLBackward<<As()->cuda_stream()>>>( - num_samples, num_classes, class_start, ignore_index, out_grad, target, weight, in_grad); - } -}; - -template struct NLLKernelUtil; -template struct NLLKernelUtil; -template struct NLLKernelUtil; -template struct NLLKernelUtil; -template struct NLLKernelUtil; -template struct NLLKernelUtil; - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/nms_kernel.cu b/oneflow/user/kernels/nms_kernel.cu index 8a1f1785e0eac7b5dce10b5f080bfb29dc4969a9..fa3984af8ab28a5b429c0fde92cd3ca5f1879e55 100644 --- a/oneflow/user/kernels/nms_kernel.cu +++ b/oneflow/user/kernels/nms_kernel.cu @@ -132,8 +132,8 @@ class NmsGpuKernel final : public user_op::OpKernel { && (user_op::HobDataType("out", 0) == DataType::kInt8) \ && (user_op::HobDataType("in", 0) == GetDataType::value)) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - Shape* in_shape = ctx->Shape4ArgNameAndIndex("in", 0); \ - int64_t num_boxes = in_shape->At(0); \ + const Shape& in_shape = ctx->Shape4ArgNameAndIndex("in", 0); \ + int64_t num_boxes = in_shape.At(0); \ int64_t blocks = CeilDiv(num_boxes, kBlockSize); \ return num_boxes * blocks * sizeof(int64_t); \ }); diff --git a/oneflow/user/kernels/nms_kernel.hip.cpp b/oneflow/user/kernels/nms_kernel.hip.cpp deleted file mode 100644 index 0be72a5ca6cd3b365af15f9d860b94dca46def1c..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/nms_kernel.hip.cpp +++ /dev/null @@ -1,145 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -constexpr int kBlockSize = sizeof(int64_t) * 8; - -template -__host__ __device__ __forceinline__ T CeilDiv(T a, T b) { - return (a + b - 1) / b; -} - -template -__host__ __device__ __forceinline__ T IoU(T const* const a, T const* const b) { - T interS = - max(min(a[2], b[2]) - max(a[0], b[0]), 0.f) * max(min(a[3], b[3]) - max(a[1], b[1]), 0.f); - T Sa = (a[2] - a[0]) * (a[3] - a[1]); - T Sb = (b[2] - b[0]) * (b[3] - b[1]); - return interS / (Sa + Sb - interS); -} - -template -__global__ void CalcSuppressionBitmaskMatrix(int num_boxes, float iou_threshold, const T* boxes, - int64_t* suppression_bmask_matrix) { - const int row = blockIdx.y; - const int col = blockIdx.x; - - if (row > col) return; - - const int row_size = min(num_boxes - row * kBlockSize, kBlockSize); - const int col_size = min(num_boxes - col * kBlockSize, kBlockSize); - - __shared__ T block_boxes[kBlockSize * 4]; - if (threadIdx.x < col_size) { - block_boxes[threadIdx.x * 4 + 0] = boxes[(kBlockSize * col + threadIdx.x) * 4 + 0]; - block_boxes[threadIdx.x * 4 + 1] = boxes[(kBlockSize * col + threadIdx.x) * 4 + 1]; - block_boxes[threadIdx.x * 4 + 2] = boxes[(kBlockSize * col + threadIdx.x) * 4 + 2]; - block_boxes[threadIdx.x * 4 + 3] = boxes[(kBlockSize * col + threadIdx.x) * 4 + 3]; - } - __syncthreads(); - - if (threadIdx.x < row_size) { - const int cur_box_idx = kBlockSize * row + threadIdx.x; - const T* cur_box_ptr = boxes + cur_box_idx * 4; - unsigned long long bits = 0; - int start = 0; - if (row == col) { start = threadIdx.x + 1; } - for (int i = start; i < col_size; i++) { - if (IoU(cur_box_ptr, block_boxes + i * 4) > iou_threshold) { bits |= 1Ull << i; } - } - suppression_bmask_matrix[cur_box_idx * gridDim.y + col] = bits; - } -} - -__global__ void ScanSuppression(int num_boxes, int num_blocks, int num_keep, - int64_t* suppression_bmask, int8_t* keep_mask) { - extern __shared__ int64_t remv[]; - remv[threadIdx.x] = 0; - for (int i = 0; i < num_boxes; ++i) { - int block_n = i / kBlockSize; - int block_i = i % kBlockSize; - if (!(remv[block_n] & (1Ull << block_i))) { - remv[threadIdx.x] |= suppression_bmask[i * num_blocks + threadIdx.x]; - if (threadIdx.x == block_n && num_keep > 0) { - keep_mask[i] = 1; - num_keep -= 1; - } - } - } -} - -} // namespace - -template -class NmsGpuKernel final : public user_op::OpKernel { - public: - NmsGpuKernel() = default; - ~NmsGpuKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* boxes_blob = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* keep_blob = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_blob = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const T* boxes = boxes_blob->dptr(); - int8_t* keep = keep_blob->mut_dptr(); - int64_t* suppression_mask = tmp_blob->mut_dptr(); - - const int num_boxes = boxes_blob->shape_view().At(0); - int num_keep = ctx->Attr("keep_n"); - if (num_keep <= 0 || num_keep > num_boxes) { num_keep = num_boxes; } - const int num_blocks = CeilDiv(num_boxes, kBlockSize); - Memset(ctx->stream(), suppression_mask, 0, - num_boxes * num_blocks * sizeof(int64_t)); - Memset(ctx->stream(), keep, 0, num_boxes * sizeof(int8_t)); - - dim3 blocks(num_blocks, num_blocks); - dim3 threads(kBlockSize); - CalcSuppressionBitmaskMatrix<<stream()->As()->cuda_stream()>>>( - num_boxes, ctx->Attr("iou_threshold"), boxes, suppression_mask); - ScanSuppression<<<1, num_blocks, num_blocks * sizeof(int64_t), - ctx->stream()->As()->cuda_stream()>>>( - num_boxes, num_blocks, num_keep, suppression_mask, keep); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_NMS_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("nms") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == DataType::kInt8) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - Shape* in_shape = ctx->Shape4ArgNameAndIndex("in", 0); \ - int64_t num_boxes = in_shape->At(0); \ - int64_t blocks = CeilDiv(num_boxes, kBlockSize); \ - return num_boxes * blocks * sizeof(int64_t); \ - }); - -REGISTER_NMS_CUDA_KERNEL(float) -REGISTER_NMS_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/nop_kernel.cpp b/oneflow/user/kernels/nop_kernel.cpp index f5e0ee86d98c49ec70301836c911a32c6c26be41..a8f813cf921379a147ca6c2808f2c66ece5df7e1 100644 --- a/oneflow/user/kernels/nop_kernel.cpp +++ b/oneflow/user/kernels/nop_kernel.cpp @@ -35,6 +35,8 @@ class NopKernel final : public user_op::OpKernel { REGISTER_USER_KERNEL(op_type_name).SetCreateFn().SetIsMatchedHob(user_op::HobTrue()); REGISTER_NOP_KERNEL("cast_to_tick") +REGISTER_NOP_KERNEL("acc_ctrl_tick") +REGISTER_NOP_KERNEL("repeat") } // namespace diff --git a/oneflow/user/kernels/normalization_kernel.cu b/oneflow/user/kernels/normalization_kernel.cu index 8589ca56239255afcd9cdc8fff26034ae502cdb7..16d2d74698f14d51fdad43bdeeeb564748bc30e7 100644 --- a/oneflow/user/kernels/normalization_kernel.cu +++ b/oneflow/user/kernels/normalization_kernel.cu @@ -22,34 +22,33 @@ limitations under the License. #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/cuda/cuda_stream.h" - -namespace oneflow { - -namespace { +#include "oneflow/core/device/cuda_pseudo_bfloat16.h" +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 +#include #if (CUDNN_VERSION >= 7401) #define BN_ENABLE_EX_API -class CudnnHandleWrapper { - public: - CudnnHandleWrapper() { OF_CUDNN_CHECK(cudnnCreate(&handle_)); } - ~CudnnHandleWrapper() { OF_CUDNN_CHECK(cudnnDestroy(handle_)); } - - cudnnHandle_t handle() const { return handle_; } +#endif - private: - cudnnHandle_t handle_; -}; +namespace oneflow { -cudnnHandle_t GetOrCreateCudnnHandle() { - static std::unordered_map device_id2handle; - int device_id; - OF_CUDA_CHECK(cudaGetDevice(&device_id)); +namespace { - // operator [] will create and insert a CudnnHandleWrapper - // if key `device_id` does not exist - return device_id2handle[device_id].handle(); +cudnnBatchNormMode_t getCudnnBatchNormMode(const int64_t dim) { + if (dim == 2) { + return CUDNN_BATCHNORM_PER_ACTIVATION; + } else if (ParseBooleanFromEnv("ONEFLOW_ENABLE_NHWC", false)) { + return CUDNN_BATCHNORM_SPATIAL_PERSISTENT; + } else { + // NOTE(Liang Depeng): The new CUDNN_BATCHNORM_SPATIAL_PERSISTENT mode was + // introduced in CuDNN 7 for performance optimization, but it results in + // accuracy losses in convolution models such as ResNeXt-101 and + // video R(2+1)D. We will fall back to the normal CUDNN_BATCHNORM_SPATIAL + return CUDNN_BATCHNORM_SPATIAL; + } } -#endif void InferDimSizeAndDataFormat(const ShapeView& x_shape, const int32_t axis, int32_t* n, int32_t* c, int32_t* h, int32_t* w, cudnnTensorFormat_t* format) { @@ -127,13 +126,14 @@ class CudnnTensorDescHelper final { size_t InferTrainWorkspaceSize(const ShapeView& x_shape, const DataType data_type, const int32_t axis) { #if defined(BN_ENABLE_EX_API) - const CudnnTensorDescHelper desc_helper(x_shape, data_type, axis, - CUDNN_BATCHNORM_SPATIAL_PERSISTENT); + cudnnBatchNormMode_t mode = getCudnnBatchNormMode(x_shape.NumAxes()); + const CudnnTensorDescHelper desc_helper(x_shape, data_type, axis, mode); size_t size_in_bytes; - cudnnHandle_t handle = GetOrCreateCudnnHandle(); + cudnnHandle_t handle = Singleton::Get()->Get(); OF_CUDNN_CHECK(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( - handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), - nullptr, desc_helper.xy_desc(), desc_helper.param_desc(), nullptr, &size_in_bytes)); + handle, mode, CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), + desc_helper.param_desc(), nullptr, &size_in_bytes)); + Singleton::Get()->Put(handle); return std::max(size_in_bytes, static_cast(1)); #else return 1; @@ -149,14 +149,14 @@ size_t InferTrainTmpSize(user_op::InferContext* ctx) { size_t InferGradWorkspaceSize(const ShapeView& x_shape, const DataType data_type, const int32_t axis) { #if defined(BN_ENABLE_EX_API) - const CudnnTensorDescHelper desc_helper(x_shape, data_type, axis, - CUDNN_BATCHNORM_SPATIAL_PERSISTENT); + cudnnBatchNormMode_t mode = getCudnnBatchNormMode(x_shape.NumAxes()); + const CudnnTensorDescHelper desc_helper(x_shape, data_type, axis, mode); size_t size_in_bytes; - cudnnHandle_t handle = GetOrCreateCudnnHandle(); + cudnnHandle_t handle = Singleton::Get()->Get(); OF_CUDNN_CHECK(cudnnGetBatchNormalizationBackwardExWorkspaceSize( - handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), - nullptr, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), desc_helper.param_desc(), - nullptr, &size_in_bytes)); + handle, mode, CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), + nullptr, desc_helper.xy_desc(), desc_helper.param_desc(), nullptr, &size_in_bytes)); + Singleton::Get()->Put(handle); return std::max(size_in_bytes, static_cast(1)); #else return 1; @@ -174,7 +174,6 @@ size_t InferGradTmpSize(user_op::InferContext* ctx) { return tmp_size; } -template class NormalizationInferenceKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -201,14 +200,14 @@ class NormalizationInferenceKernel final : public user_op::OpKernel, CHECK_GE(axis, 0); CHECK_LT(axis, x->shape_view().NumAxes()); - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - CUDNN_BATCHNORM_SPATIAL); + cudnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); desc_helper.CheckParamTensor(gamma); desc_helper.CheckParamTensor(beta); desc_helper.CheckParamTensor(moving_mean); desc_helper.CheckParamTensor(moving_variance); - const void* sp_alpha = CudnnSPOnePtr(); + const void* sp_alpha = CudnnSPOnePtr(data_type); const void* sp_beta; if (ctx->has_input("_add_to_output", 0)) { const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); @@ -217,14 +216,14 @@ class NormalizationInferenceKernel final : public user_op::OpKernel, Memcpy( ctx->stream(), y->mut_dptr(), add_to_output->dptr(), add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - sp_beta = CudnnSPOnePtr(); + sp_beta = CudnnSPOnePtr(data_type); } else { - sp_beta = CudnnSPZeroPtr(); + sp_beta = CudnnSPZeroPtr(data_type); } OF_CUDNN_CHECK(cudnnBatchNormalizationForwardInference( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL, sp_alpha, - sp_beta, desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), + ctx->stream()->As()->cudnn_handle(), mode, sp_alpha, sp_beta, + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), moving_mean->dptr(), moving_variance->dptr(), epsilon)); } @@ -232,47 +231,27 @@ class NormalizationInferenceKernel final : public user_op::OpKernel, bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_BN_INFERENCE_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value) \ - && (user_op::HobAttr("training") == false)) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }); - -REGISTER_BN_INFERENCE_KERNEL(float16) -REGISTER_BN_INFERENCE_KERNEL(float) -REGISTER_BN_INFERENCE_KERNEL(double) - -#undef REGISTER_BN_INFERENCE_KERNEL +REGISTER_USER_KERNEL("normalization") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobAttr("training") == false)) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); constexpr int64_t kCudaWarpSize = 32; template __global__ void ReluGpu(int64_t n, const T* x, T* y, int32_t* mask) { const int32_t lane_id = threadIdx.x % kCudaWarpSize; + const T zero = static_cast(0.f); CUDA_1D_KERNEL_LOOP(i, n) { const T x_val = x[i]; - const bool is_positive = (x_val > 0); - int32_t warp_mask = __ballot_sync(__activemask(), static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? x_val : 0; - } -} - -template<> -__global__ void ReluGpu(int64_t n, const half* x, half* y, int32_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - const half zero = __float2half(0.0f); - CUDA_1D_KERNEL_LOOP(i, n) { - const half x_val = x[i]; - const bool is_positive = __hgt(x_val, zero); + const bool is_positive = (x_val > zero); int32_t warp_mask = __ballot_sync(__activemask(), static_cast(is_positive)); if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } y[i] = is_positive ? x_val : zero; @@ -282,23 +261,10 @@ __global__ void ReluGpu(int64_t n, const half* x, half* y, int32_t* mask) template __global__ void AddReluGpu(int64_t n, const T* x, const T* addend, T* y, int32_t* mask) { const int32_t lane_id = threadIdx.x % kCudaWarpSize; + const T zero = static_cast(0.f); CUDA_1D_KERNEL_LOOP(i, n) { const T sum = x[i] + addend[i]; - const bool is_positive = (sum > 0); - int32_t warp_mask = __ballot_sync(__activemask(), static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? sum : 0; - } -} - -template<> -__global__ void AddReluGpu(int64_t n, const half* x, const half* addend, half* y, - int32_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - const half zero = __float2half(0.0f); - CUDA_1D_KERNEL_LOOP(i, n) { - const half sum = __hadd(x[i], addend[i]); - const bool is_positive = __hgt(sum, zero); + const bool is_positive = (sum > zero); int32_t warp_mask = __ballot_sync(__activemask(), static_cast(is_positive)); if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } y[i] = is_positive ? sum : zero; @@ -311,24 +277,12 @@ void Relu(ep::Stream* stream, int64_t n, const T* x, T* y, int32_t* mask) { stream->As()->cuda_stream()>>>(n, x, y, mask); } -template<> -void Relu(ep::Stream* stream, int64_t n, const float16* x, float16* y, int32_t* mask) { - Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); -} - template void AddRelu(ep::Stream* stream, int64_t n, const T* x, const T* addend, T* y, int32_t* mask) { AddReluGpu<<As()->cuda_stream()>>>(n, x, addend, y, mask); } -template<> -void AddRelu(ep::Stream* stream, int64_t n, const float16* x, const float16* addend, - float16* y, int32_t* mask) { - AddRelu(stream, n, reinterpret_cast(x), reinterpret_cast(addend), - reinterpret_cast(y), mask); -} - template __global__ void ReluBackwardGpu(int64_t n, const int32_t* mask, const T* dy, T* addend_diff) { int32_t lane_id = threadIdx.x % kCudaWarpSize; @@ -339,20 +293,92 @@ __global__ void ReluBackwardGpu(int64_t n, const int32_t* mask, const T* dy, T* } } +#if CUDA_VERSION >= 11000 + +template<> +__global__ void ReluBackwardGpu(int64_t n, const int32_t* mask, const nv_bfloat16* dy, + nv_bfloat16* addend_diff) { + int32_t lane_id = threadIdx.x % kCudaWarpSize; + CUDA_1D_KERNEL_LOOP(i, n) { + int32_t mask_val = mask[i / kCudaWarpSize]; + bool is_positive = mask_val & (1 << lane_id); + addend_diff[i] = static_cast(static_cast(is_positive)) * dy[i]; + } +} + +#endif + template void ReluBackward(ep::Stream* stream, int64_t n, const int32_t* mask, const T* dy, T* addend_diff) { ReluBackwardGpu<<As()->cuda_stream()>>>(n, mask, dy, addend_diff); } -template<> -void ReluBackward(ep::Stream* stream, int64_t n, const int32_t* mask, const float16* dy, - float16* addend_diff) { - ReluBackward(stream, n, mask, reinterpret_cast(dy), - reinterpret_cast(addend_diff)); +void Relu(ep::Stream* stream, int64_t n, const DataType data_type, const void* x, void* y, + int32_t* mask) { + if (data_type == kFloat) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kDouble) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kFloat16) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + Relu(stream, n, reinterpret_cast(x), + reinterpret_cast(y), mask); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } +} +void AddRelu(ep::Stream* stream, int64_t n, const DataType data_type, const void* x, + const void* addend, void* y, int32_t* mask) { + if (data_type == kFloat) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kDouble) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kFloat16) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), + reinterpret_cast(y), mask); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } +} +void ReluBackward(ep::Stream* stream, int64_t n, const DataType data_type, const int32_t* mask, + const void* dy, void* addend_diff) { + if (data_type == kFloat) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kDouble) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kFloat16) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } } -template class NormalizationTrainKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: NormalizationTrainKernel() = default; @@ -374,8 +400,8 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: CHECK_EQ(y->data_type(), data_type); CHECK_GE(axis, 0); CHECK_LT(axis, x->shape_view().NumAxes()); - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - CUDNN_BATCHNORM_SPATIAL_PERSISTENT); + cudnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); @@ -396,7 +422,7 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: desc_helper.CheckParamTensor(moving_variance); } - const void* sp_alpha = CudnnSPOnePtr(); + const void* sp_alpha = CudnnSPOnePtr(data_type); const void* sp_beta; if (ctx->has_input("_add_to_output", 0)) { const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); @@ -405,35 +431,35 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: Memcpy( ctx->stream(), y->mut_dptr(), add_to_output->dptr(), add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - sp_beta = CudnnSPOnePtr(); + sp_beta = CudnnSPOnePtr(data_type); } else { - sp_beta = CudnnSPZeroPtr(); + sp_beta = CudnnSPZeroPtr(data_type); } #if defined(BN_ENABLE_EX_API) size_t workspace_size; OF_CUDNN_CHECK(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), - desc_helper.param_desc(), nullptr, &workspace_size)); + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, + desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), desc_helper.param_desc(), nullptr, + &workspace_size)); size_t reserve_space_size; OF_CUDNN_CHECK(cudnnGetBatchNormalizationTrainingExReserveSpaceSize( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, nullptr, desc_helper.xy_desc(), &reserve_space_size)); + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, nullptr, + desc_helper.xy_desc(), &reserve_space_size)); auto* workspace = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); if (reserve_space_size == 0 && workspace_size <= workspace->shape_view().elem_cnt()) { OF_CUDNN_CHECK(cudnnBatchNormalizationForwardTrainingEx( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, sp_alpha, sp_beta, desc_helper.xy_desc(), x->dptr(), nullptr, - nullptr, desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, + sp_alpha, sp_beta, desc_helper.xy_desc(), x->dptr(), nullptr, nullptr, + desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), 1.0 - momentum, moving_mean ? moving_mean->mut_dptr() : NULL, moving_variance ? moving_variance->mut_dptr() : NULL, epsilon, mean->mut_dptr(), inv_variance->mut_dptr(), nullptr, workspace->mut_dptr(), workspace->shape_view().elem_cnt(), nullptr, 0)); } else { OF_CUDNN_CHECK(cudnnBatchNormalizationForwardTraining( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - sp_alpha, sp_beta, desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), + ctx->stream()->As()->cudnn_handle(), mode, sp_alpha, sp_beta, + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), 1.0 - momentum, moving_mean ? moving_mean->mut_dptr() : NULL, moving_variance ? moving_variance->mut_dptr() : NULL, epsilon, mean->mut_dptr(), @@ -441,8 +467,8 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: } #else OF_CUDNN_CHECK(cudnnBatchNormalizationForwardTraining( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - sp_alpha, sp_beta, desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), + ctx->stream()->As()->cudnn_handle(), mode, sp_alpha, sp_beta, + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), 1.0 - momentum, moving_mean ? moving_mean->mut_dptr() : NULL, moving_variance ? moving_variance->mut_dptr() : NULL, epsilon, mean->mut_dptr(), @@ -455,10 +481,11 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: auto* mask = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); if (ctx->has_input("addend", 0)) { const auto* addend = ctx->Tensor4ArgNameAndIndex("addend", 0); - AddRelu(ctx->stream(), elem_cnt, y->dptr(), addend->dptr(), y->mut_dptr(), + AddRelu(ctx->stream(), elem_cnt, data_type, y->dptr(), addend->dptr(), y->mut_dptr(), mask->mut_dptr()); } else { - Relu(ctx->stream(), elem_cnt, y->dptr(), y->mut_dptr(), mask->mut_dptr()); + Relu(ctx->stream(), elem_cnt, data_type, y->dptr(), y->mut_dptr(), + mask->mut_dptr()); } } } @@ -466,37 +493,24 @@ class NormalizationTrainKernel final : public user_op::OpKernel, public user_op: bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_BN_TRAIN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value) \ - && (user_op::HobAttr("training") == true)) \ - .SetInferTmpSizeFn(InferTrainTmpSize) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }); - -REGISTER_BN_TRAIN_KERNEL(float16) -REGISTER_BN_TRAIN_KERNEL(float) -REGISTER_BN_TRAIN_KERNEL(double) - -#define REGISTER_BN_ADD_RELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_add_relu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTrainTmpSize); - -REGISTER_BN_ADD_RELU_KERNEL(float16) -REGISTER_BN_ADD_RELU_KERNEL(float) -REGISTER_BN_ADD_RELU_KERNEL(double) +REGISTER_USER_KERNEL("normalization") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobAttr("training") == true)) + .SetInferTmpSizeFn(InferTrainTmpSize) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); + +REGISTER_USER_KERNEL("normalization_add_relu") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferTrainTmpSize); -template class NormalizationGradUserKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -525,9 +539,8 @@ class NormalizationGradUserKernel final : public user_op::OpKernel, CHECK_EQ(dx->data_type(), data_type); CHECK_GE(axis, 0); CHECK_LT(axis, x->shape_view().NumAxes()); - - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - CUDNN_BATCHNORM_SPATIAL_PERSISTENT); + cudnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); desc_helper.CheckParamTensor(gamma); desc_helper.CheckParamTensor(gamma_diff); desc_helper.CheckParamTensor(beta_diff); @@ -548,18 +561,18 @@ class NormalizationGradUserKernel final : public user_op::OpKernel, user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); if (ctx->has_output("addend_diff", 0)) { user_op::Tensor* addend_diff = ctx->Tensor4ArgNameAndIndex("addend_diff", 0); - ReluBackward(ctx->stream(), elem_cnt, mask->dptr(), dy->dptr(), - addend_diff->mut_dptr()); + ReluBackward(ctx->stream(), elem_cnt, data_type, mask->dptr(), dy->dptr(), + addend_diff->mut_dptr()); bn_workspace_ptr = tmp_buffer->mut_dptr(); bn_workspace_size = tmp_buffer->shape_view().elem_cnt(); bn_dy_ptr = addend_diff->dptr(); } else { const size_t tmp_buffer_size = tmp_buffer->shape_view().elem_cnt(); const size_t relu_dx_size = - GetCudaAlignedSize(dy->shape_view().elem_cnt() * GetSizeOfDataType(dy->data_type())); + GetCudaAlignedSize(dy->shape_view().elem_cnt() * GetSizeOfDataType(data_type)); CHECK_GE(tmp_buffer_size, relu_dx_size); - ReluBackward(ctx->stream(), elem_cnt, mask->dptr(), dy->dptr(), - reinterpret_cast(tmp_buffer->mut_dptr())); + ReluBackward(ctx->stream(), elem_cnt, data_type, mask->dptr(), dy->dptr(), + tmp_buffer->mut_dptr()); bn_workspace_ptr = tmp_buffer->mut_dptr() + relu_dx_size; bn_workspace_size = tmp_buffer_size - relu_dx_size; bn_dy_ptr = tmp_buffer->dptr(); @@ -571,34 +584,34 @@ class NormalizationGradUserKernel final : public user_op::OpKernel, #if defined(BN_ENABLE_EX_API) size_t workspace_size; OF_CUDNN_CHECK(cudnnGetBatchNormalizationBackwardExWorkspaceSize( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), nullptr, - desc_helper.xy_desc(), desc_helper.param_desc(), nullptr, &workspace_size)); + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, + desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), nullptr, desc_helper.xy_desc(), + desc_helper.param_desc(), nullptr, &workspace_size)); size_t reserve_space_size; OF_CUDNN_CHECK(cudnnGetBatchNormalizationTrainingExReserveSpaceSize( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, nullptr, desc_helper.xy_desc(), &reserve_space_size)); + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, nullptr, + desc_helper.xy_desc(), &reserve_space_size)); if (reserve_space_size == 0 && workspace_size <= bn_workspace_size) { OF_CUDNN_CHECK(cudnnBatchNormalizationBackwardEx( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CUDNN_BATCHNORM_OPS_BN, CudnnSPOnePtr(), CudnnSPZeroPtr(), CudnnSPOnePtr(), - CudnnSPZeroPtr(), desc_helper.xy_desc(), x->dptr(), nullptr, nullptr, + ctx->stream()->As()->cudnn_handle(), mode, CUDNN_BATCHNORM_OPS_BN, + CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), nullptr, nullptr, desc_helper.xy_desc(), bn_dy_ptr, nullptr, nullptr, desc_helper.xy_desc(), dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), nullptr, gamma_diff->mut_dptr(), beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr(), nullptr, bn_workspace_ptr, bn_workspace_size, nullptr, 0)); } else { OF_CUDNN_CHECK(cudnnBatchNormalizationBackward( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CudnnSPOnePtr(), CudnnSPZeroPtr(), CudnnSPOnePtr(), CudnnSPZeroPtr(), + ctx->stream()->As()->cudnn_handle(), mode, CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), bn_dy_ptr, desc_helper.xy_desc(), dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), gamma_diff->mut_dptr(), beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr())); } #else OF_CUDNN_CHECK(cudnnBatchNormalizationBackward( - ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - CudnnSPOnePtr(), CudnnSPZeroPtr(), CudnnSPOnePtr(), CudnnSPZeroPtr(), + ctx->stream()->As()->cudnn_handle(), mode, CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), bn_dy_ptr, desc_helper.xy_desc(), dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), gamma_diff->mut_dptr(), beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr())); @@ -608,27 +621,16 @@ class NormalizationGradUserKernel final : public user_op::OpKernel, bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_BN_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferGradTmpSize); - -REGISTER_BN_GRAD_KERNEL(float16) -REGISTER_BN_GRAD_KERNEL(float) -REGISTER_BN_GRAD_KERNEL(double) - -#define REGISTER_BN_ADD_RELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_add_relu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferGradTmpSize); +REGISTER_USER_KERNEL("normalization_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferGradTmpSize); -REGISTER_BN_ADD_RELU_GRAD_KERNEL(float16) -REGISTER_BN_ADD_RELU_GRAD_KERNEL(float) -REGISTER_BN_ADD_RELU_GRAD_KERNEL(double) +#define REGISTER_BN_ADD_RELU_GRAD_KERNEL(dtype) +REGISTER_USER_KERNEL("normalization_add_relu_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferGradTmpSize); #if (CUDNN_VERSION >= 7401) @@ -638,7 +640,7 @@ size_t InferFusedNormalizationAddReluTmpSize(user_op::InferContext* ctx) { const CudnnTensorDescHelper desc_helper(x.shape(), x.data_type(), axis, CUDNN_BATCHNORM_SPATIAL_PERSISTENT); size_t size_in_bytes; - cudnnHandle_t handle = GetOrCreateCudnnHandle(); + cudnnHandle_t handle = Singleton::Get()->Get(); CudnnActivationDesc activation_desc(CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0); cudnnBatchNormOps_t ops; cudnnTensorDescriptor_t z_desc; @@ -652,6 +654,7 @@ size_t InferFusedNormalizationAddReluTmpSize(user_op::InferContext* ctx) { OF_CUDNN_CHECK(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, ops, desc_helper.xy_desc(), z_desc, desc_helper.xy_desc(), desc_helper.param_desc(), activation_desc.Get(), &size_in_bytes)); + Singleton::Get()->Put(handle); return std::max(size_in_bytes, static_cast(1)); } @@ -661,7 +664,7 @@ size_t InferFusedNormalizationAddReluGradTmpSize(user_op::InferContext* ctx) { const CudnnTensorDescHelper desc_helper(x.shape(), x.data_type(), axis, CUDNN_BATCHNORM_SPATIAL_PERSISTENT); size_t size_in_bytes; - cudnnHandle_t handle = GetOrCreateCudnnHandle(); + cudnnHandle_t handle = Singleton::Get()->Get(); CudnnActivationDesc activation_desc(CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0); cudnnBatchNormOps_t ops; cudnnTensorDescriptor_t z_desc; @@ -676,10 +679,10 @@ size_t InferFusedNormalizationAddReluGradTmpSize(user_op::InferContext* ctx) { handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, ops, desc_helper.xy_desc(), desc_helper.xy_desc(), desc_helper.xy_desc(), z_desc, desc_helper.xy_desc(), desc_helper.param_desc(), activation_desc.Get(), &size_in_bytes)); + Singleton::Get()->Put(handle); return std::max(size_in_bytes, static_cast(1)); } -template class FusedNormalizationAddReluKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -748,26 +751,22 @@ class FusedNormalizationAddReluKernel final : public user_op::OpKernel, OF_CUDNN_CHECK(cudnnBatchNormalizationForwardTrainingEx( ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - ops, CudnnSPOnePtr(), CudnnSPZeroPtr(), desc_helper.xy_desc(), x->dptr(), z_desc, - z_ptr, desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), - beta->dptr(), 1.0 - momentum, moving_mean->mut_dptr(), moving_variance->mut_dptr(), epsilon, - mean->mut_dptr(), inv_variance->mut_dptr(), activation_desc.Get(), tmp_buffer->mut_dptr(), - workspace_size, reserve_space->mut_dptr(), reserve_space_size)); + ops, CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), + z_desc, z_ptr, desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), + gamma->dptr(), beta->dptr(), 1.0 - momentum, moving_mean->mut_dptr(), + moving_variance->mut_dptr(), epsilon, mean->mut_dptr(), inv_variance->mut_dptr(), + activation_desc.Get(), tmp_buffer->mut_dptr(), workspace_size, reserve_space->mut_dptr(), + reserve_space_size)); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_FUSED_BN_ADD_RELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferFusedNormalizationAddReluTmpSize); +REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferFusedNormalizationAddReluTmpSize); -REGISTER_FUSED_BN_ADD_RELU_KERNEL(float16) - -template class FusedNormalizationAddReluGradUserKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -839,26 +838,783 @@ class FusedNormalizationAddReluGradUserKernel final : public user_op::OpKernel, CHECK_GE(reserve_space_size, min_reserve_space_size); OF_CUDNN_CHECK(cudnnBatchNormalizationBackwardEx( ctx->stream()->As()->cudnn_handle(), CUDNN_BATCHNORM_SPATIAL_PERSISTENT, - ops, CudnnSPOnePtr(), CudnnSPZeroPtr(), CudnnSPOnePtr(), CudnnSPZeroPtr(), - desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->dptr(), desc_helper.xy_desc(), - dy->dptr(), dz_desc, dz_ptr, desc_helper.xy_desc(), dx->mut_dptr(), - desc_helper.param_desc(), gamma->dptr(), beta->dptr(), gamma_diff->mut_dptr(), - beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr(), activation_desc.Get(), - tmp_buffer->mut_dptr(), workspace_size, const_cast(reserve_space->dptr()), - reserve_space_size)); + ops, CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), + y->dptr(), desc_helper.xy_desc(), dy->dptr(), dz_desc, dz_ptr, desc_helper.xy_desc(), + dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), + gamma_diff->mut_dptr(), beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr(), + activation_desc.Get(), tmp_buffer->mut_dptr(), workspace_size, + const_cast(reserve_space->dptr()), reserve_space_size)); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferFusedNormalizationAddReluGradTmpSize); + +#endif + +} // namespace +} // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +#include + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/device/cudnn_util.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/device/cuda_pseudo_bfloat16.h" +// #if CUDA_VERSION >= 11000 +// #include +// #endif // CUDA_VERSION >= 11000 +#include "oneflow/core/hipdnn/hipdnn.h" + + +namespace oneflow { + +namespace { + +hipdnnBatchNormMode_t getCudnnBatchNormMode(const int64_t dim) { + if (dim == 2) { + return HIPDNN_BATCHNORM_PER_ACTIVATION; + } else if (ParseBooleanFromEnv("ONEFLOW_ENABLE_NHWC", false)) { + return HIPDNN_BATCHNORM_SPATIAL_PERSISTENT; + } else { + // NOTE(Liang Depeng): The new HIPDNN_BATCHNORM_SPATIAL_PERSISTENT mode was + // introduced in CuDNN 7 for performance optimization, but it results in + // accuracy losses in convolution models such as ResNeXt-101 and + // video R(2+1)D. We will fall back to the normal HIPDNN_BATCHNORM_SPATIAL + return HIPDNN_BATCHNORM_SPATIAL; + } +} + +void InferDimSizeAndDataFormat(const ShapeView& x_shape, const int32_t axis, int32_t* n, int32_t* c, + int32_t* h, int32_t* w, hipdnnTensorFormat_t* format) { + if (x_shape.Count(axis + 1) == 1) { + if (axis == 0) { + *n = 1; + *h = 1; + } else { + *n = x_shape.At(0); + *h = x_shape.Count(1, axis); + } + *w = 1; + *c = x_shape.At(axis); + *format = HIPDNN_TENSOR_NHWC; + } else { + *n = x_shape.Count(0, axis); + *c = x_shape.At(axis); + *h = x_shape.Count(axis + 1); + *w = 1; + *format = HIPDNN_TENSOR_NCHW; + } +} + +void InferXYCudnnTensorDesc(const ShapeView& xy_shape, const DataType& data_type, + const int32_t axis, hipdnnTensorDescriptor_t xy_desc) { + int32_t n, c, h, w; + hipdnnTensorFormat_t format; + InferDimSizeAndDataFormat(xy_shape, axis, &n, &c, &h, &w, &format); + OF_CUDNN_CHECK( + hipdnnSetTensor4dDescriptor(xy_desc, format, GetCudnnDataType(data_type), n, c, h, w)); +} + +void InferParamCudnnTensorDesc(const hipdnnTensorDescriptor_t xy_desc, hipdnnBatchNormMode_t mode, + hipdnnTensorDescriptor_t param_desc) { + OF_CUDNN_CHECK(hipdnnDeriveBNTensorDescriptor(param_desc, xy_desc, mode)); +} + +class CudnnTensorDescHelper final { + public: + OF_DISALLOW_COPY_AND_MOVE(CudnnTensorDescHelper); + CudnnTensorDescHelper(const ShapeView& xy_shape, const DataType& data_type, const int32_t axis, + hipdnnBatchNormMode_t mode) { + OF_CUDNN_CHECK(hipdnnCreateTensorDescriptor(&xy_desc_)); + InferXYCudnnTensorDesc(xy_shape, data_type, axis, xy_desc_); + OF_CUDNN_CHECK(hipdnnCreateTensorDescriptor(¶m_desc_)); + InferParamCudnnTensorDesc(xy_desc_, mode, param_desc_); + int n, c, h, w, n_stride, c_stride, h_stride, w_stride; + OF_CUDNN_CHECK(hipdnnGetTensor4dDescriptor(param_desc_, ¶m_data_type_, &n, &c, &h, &w, + &n_stride, &c_stride, &h_stride, &w_stride)); + param_size_ = c; + } + ~CudnnTensorDescHelper() { + OF_CUDNN_CHECK(hipdnnDestroyTensorDescriptor(param_desc_)); + OF_CUDNN_CHECK(hipdnnDestroyTensorDescriptor(xy_desc_)); + } + + hipdnnTensorDescriptor_t xy_desc() const { return xy_desc_; } + + hipdnnTensorDescriptor_t param_desc() const { return param_desc_; } + + void CheckParamTensor(const user_op::Tensor* tensor) const { + CHECK_NOTNULL(tensor); + CHECK_EQ(tensor->shape_view().NumAxes(), 1); + CHECK_EQ(tensor->shape_view().At(0), param_size_); + // CHECK_EQ(GetCudnnDataType(tensor->data_type()), param_data_type_); + } + + private: + hipdnnTensorDescriptor_t xy_desc_ = nullptr; + hipdnnTensorDescriptor_t param_desc_ = nullptr; + hipdnnDataType_t param_data_type_; + int32_t param_size_ = 0; +}; + +size_t InferTrainWorkspaceSize(const ShapeView& x_shape, const DataType data_type, + const int32_t axis) { + return 1; +} + +size_t InferTrainTmpSize(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("x", 0); + const auto axis = ctx->Attr("axis"); + return InferTrainWorkspaceSize(x.shape(), x.data_type(), axis); +} + +size_t InferGradWorkspaceSize(const ShapeView& x_shape, const DataType data_type, + const int32_t axis) { + return 1; +} + +size_t InferGradTmpSize(user_op::InferContext* ctx) { + const auto& dy = ctx->InputTensorDesc("dy", 0); + const auto axis = ctx->Attr("axis"); + size_t tmp_size = 0; + if (ctx->op_type_name() == "normalization_add_relu_grad" && !ctx->has_output("addend_diff", 0)) { + tmp_size += GetCudaAlignedSize(dy.shape().elem_cnt() * GetSizeOfDataType(dy.data_type())); + } + tmp_size += GetCudaAlignedSize(InferGradWorkspaceSize(dy.shape(), dy.data_type(), axis)); + return tmp_size; +} + +class NormalizationInferenceKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + NormalizationInferenceKernel() = default; + ~NormalizationInferenceKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const bool training = ctx->Attr("training"); + CHECK(!training); + const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); + auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); + auto* moving_mean = ctx->Tensor4ArgNameAndIndex("moving_mean", 0); + auto* moving_variance = ctx->Tensor4ArgNameAndIndex("moving_variance", 0); + const auto axis = ctx->Attr("axis"); + const auto epsilon = ctx->Attr("epsilon"); + + const DataType data_type = x->data_type(); + CHECK_EQ(x->shape_view(), y->shape_view()); + CHECK_EQ(y->data_type(), data_type); + CHECK_GE(axis, 0); + CHECK_LT(axis, x->shape_view().NumAxes()); + + hipdnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); + desc_helper.CheckParamTensor(gamma); + desc_helper.CheckParamTensor(beta); + desc_helper.CheckParamTensor(moving_mean); + desc_helper.CheckParamTensor(moving_variance); + + const void* sp_alpha = CudnnSPOnePtr(data_type); + const void* sp_beta; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), y->data_type()); + CHECK_EQ(add_to_output->shape_view(), y->shape_view()); + Memcpy( + ctx->stream(), y->mut_dptr(), add_to_output->dptr(), + add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); + sp_beta = CudnnSPOnePtr(data_type); + } else { + sp_beta = CudnnSPZeroPtr(data_type); + } + + OF_CUDNN_CHECK(hipdnnBatchNormalizationForwardInference( + ctx->stream()->As()->cudnn_handle(), mode, sp_alpha, sp_beta, + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), + desc_helper.param_desc(), gamma->dptr(), beta->dptr(), moving_mean->dptr(), + moving_variance->dptr(), epsilon)); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_FUSED_BN_ADD_RELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferFusedNormalizationAddReluGradTmpSize); +REGISTER_USER_KERNEL("normalization") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobAttr("training") == false)) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); + +constexpr int64_t kCudaWarpSize = 64; + +template +__global__ void ReluGpu(int64_t n, const T* x, T* y, int32_t* mask) { + const int32_t lane_id = threadIdx.x % kCudaWarpSize; + const T zero = static_cast(0.f); + CUDA_1D_KERNEL_LOOP(i, n) { + const T x_val = x[i]; + const bool is_positive = (x_val > zero); + int32_t warp_mask = __ballot(static_cast(is_positive)); + if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } + y[i] = is_positive ? x_val : zero; + } +} + +template +__global__ void AddReluGpu(int64_t n, const T* x, const T* addend, T* y, int32_t* mask) { + const int32_t lane_id = threadIdx.x % kCudaWarpSize; + const T zero = static_cast(0.f); + CUDA_1D_KERNEL_LOOP(i, n) { + const T sum = x[i] + addend[i]; + const bool is_positive = (sum > zero); + int32_t warp_mask = __ballot(static_cast(is_positive)); + if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } + y[i] = is_positive ? sum : zero; + } +} + +template +void Relu(ep::Stream* stream, int64_t n, const T* x, T* y, int32_t* mask) { + ReluGpu<<As()->cuda_stream()>>>(n, x, y, mask); +} + +template +void AddRelu(ep::Stream* stream, int64_t n, const T* x, const T* addend, T* y, int32_t* mask) { + AddReluGpu<<As()->cuda_stream()>>>(n, x, addend, y, mask); +} + +template +__global__ void ReluBackwardGpu(int64_t n, const int32_t* mask, const T* dy, T* addend_diff) { + int32_t lane_id = threadIdx.x % kCudaWarpSize; + CUDA_1D_KERNEL_LOOP(i, n) { + int32_t mask_val = mask[i / kCudaWarpSize]; + bool is_positive = mask_val & (1 << lane_id); + addend_diff[i] = static_cast(is_positive) * dy[i]; + } +} + +#if CUDA_VERSION >= 11000 + +template<> +__global__ void ReluBackwardGpu(int64_t n, const int32_t* mask, const nv_bfloat16* dy, + nv_bfloat16* addend_diff) { + int32_t lane_id = threadIdx.x % kCudaWarpSize; + CUDA_1D_KERNEL_LOOP(i, n) { + int32_t mask_val = mask[i / kCudaWarpSize]; + bool is_positive = mask_val & (1 << lane_id); + addend_diff[i] = static_cast(static_cast(is_positive)) * dy[i]; + } +} + +#endif + +template +void ReluBackward(ep::Stream* stream, int64_t n, const int32_t* mask, const T* dy, T* addend_diff) { + ReluBackwardGpu<<As()->cuda_stream()>>>(n, mask, dy, addend_diff); +} + +void Relu(ep::Stream* stream, int64_t n, const DataType data_type, const void* x, void* y, + int32_t* mask) { + if (data_type == kFloat) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kDouble) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kFloat16) { + Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + Relu(stream, n, reinterpret_cast(x), + reinterpret_cast(y), mask); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } +} +void AddRelu(ep::Stream* stream, int64_t n, const DataType data_type, const void* x, + const void* addend, void* y, int32_t* mask) { + if (data_type == kFloat) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kDouble) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kFloat16) { + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), reinterpret_cast(y), mask); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + AddRelu(stream, n, reinterpret_cast(x), + reinterpret_cast(addend), + reinterpret_cast(y), mask); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } +} +void ReluBackward(ep::Stream* stream, int64_t n, const DataType data_type, const int32_t* mask, + const void* dy, void* addend_diff) { + if (data_type == kFloat) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kDouble) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kFloat16) { + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); + } else if (data_type == kBFloat16) { +#if CUDA_VERSION >= 11000 + ReluBackward(stream, n, mask, reinterpret_cast(dy), + reinterpret_cast(addend_diff)); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } +} + +class NormalizationTrainKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + NormalizationTrainKernel() = default; + ~NormalizationTrainKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + if (ctx->op_type_name() == "normalization") { CHECK(ctx->Attr("training")); } + const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); + auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); + + const auto axis = ctx->Attr("axis"); + const auto epsilon = ctx->Attr("epsilon"); + const auto momentum = ctx->Attr("momentum"); + + const DataType data_type = x->data_type(); + CHECK_EQ(x->shape_view(), y->shape_view()); + CHECK_EQ(y->data_type(), data_type); + CHECK_GE(axis, 0); + CHECK_LT(axis, x->shape_view().NumAxes()); + hipdnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); + + const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); + auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + desc_helper.CheckParamTensor(gamma); + desc_helper.CheckParamTensor(beta); + desc_helper.CheckParamTensor(mean); + desc_helper.CheckParamTensor(inv_variance); + + user_op::Tensor* moving_mean = nullptr; + user_op::Tensor* moving_variance = nullptr; + if (ctx->has_input("moving_mean", 0)) { + CHECK(ctx->has_input("moving_variance", 0)); + moving_mean = ctx->Tensor4ArgNameAndIndex("moving_mean", 0); + moving_variance = ctx->Tensor4ArgNameAndIndex("moving_variance", 0); + desc_helper.CheckParamTensor(moving_mean); + desc_helper.CheckParamTensor(moving_variance); + } + const void* sp_alpha = CudnnSPOnePtr(data_type); + const void* sp_beta; + if (ctx->has_input("_add_to_output", 0)) { + const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); + CHECK_EQ(add_to_output->data_type(), y->data_type()); + CHECK_EQ(add_to_output->shape_view(), y->shape_view()); + Memcpy( + ctx->stream(), y->mut_dptr(), add_to_output->dptr(), + add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); + sp_beta = CudnnSPOnePtr(data_type); + } else { + sp_beta = CudnnSPZeroPtr(data_type); + } + + OF_CUDNN_CHECK(hipdnnBatchNormalizationForwardTraining( + ctx->stream()->As()->cudnn_handle(), mode, const_cast(sp_alpha), const_cast(sp_beta), + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), + desc_helper.param_desc(), const_cast(gamma->dptr()), const_cast(beta->dptr()), 1.0 - momentum, + moving_mean ? moving_mean->mut_dptr() : NULL, + moving_variance ? moving_variance->mut_dptr() : NULL, epsilon, mean->mut_dptr(), + inv_variance->mut_dptr())); + + if (ctx->op_type_name() == "normalization_add_relu") { + CHECK(!ctx->has_input("_add_to_output", 0)); + const int64_t elem_cnt = x->shape_view().elem_cnt(); + auto* mask = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); + if (ctx->has_input("addend", 0)) { + const auto* addend = ctx->Tensor4ArgNameAndIndex("addend", 0); + AddRelu(ctx->stream(), elem_cnt, data_type, y->dptr(), addend->dptr(), y->mut_dptr(), + mask->mut_dptr()); + } else { + Relu(ctx->stream(), elem_cnt, data_type, y->dptr(), y->mut_dptr(), + mask->mut_dptr()); + } + } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("normalization") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobAttr("training") == true)) + .SetInferTmpSizeFn(InferTrainTmpSize) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + if (ctx.has_input("_add_to_output", 0)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); + } + return Maybe::Ok(); + }); + +REGISTER_USER_KERNEL("normalization_add_relu") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferTrainTmpSize); + +class NormalizationGradUserKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + NormalizationGradUserKernel() = default; + ~NormalizationGradUserKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); + auto* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + auto* gamma_diff = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0); + auto* beta_diff = ctx->Tensor4ArgNameAndIndex("beta_diff", 0); + const auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const auto axis = ctx->Attr("axis"); + const auto epsilon = ctx->Attr("epsilon"); + + const DataType data_type = x->data_type(); + CHECK_EQ(dy->shape_view(), x->shape_view()); + CHECK_EQ(dy->data_type(), data_type); + CHECK_EQ(dx->shape_view(), x->shape_view()); + CHECK_EQ(dx->data_type(), data_type); + CHECK_GE(axis, 0); + CHECK_LT(axis, x->shape_view().NumAxes()); + hipdnnBatchNormMode_t mode = getCudnnBatchNormMode(x->shape_view().NumAxes()); + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, mode); + desc_helper.CheckParamTensor(gamma); + desc_helper.CheckParamTensor(gamma_diff); + desc_helper.CheckParamTensor(beta_diff); + desc_helper.CheckParamTensor(mean); + desc_helper.CheckParamTensor(inv_variance); + + void* bn_workspace_ptr; + size_t bn_workspace_size; + const void* bn_dy_ptr; + + if (ctx->op_type_name() == "normalization_grad") { + bn_workspace_ptr = tmp_buffer->mut_dptr(); + bn_workspace_size = tmp_buffer->shape_view().elem_cnt(); + bn_dy_ptr = dy->dptr(); + } else if (ctx->op_type_name() == "normalization_add_relu_grad") { + const int64_t elem_cnt = dy->shape_view().elem_cnt(); + const auto* mask = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + if (ctx->has_output("addend_diff", 0)) { + user_op::Tensor* addend_diff = ctx->Tensor4ArgNameAndIndex("addend_diff", 0); + ReluBackward(ctx->stream(), elem_cnt, data_type, mask->dptr(), dy->dptr(), + addend_diff->mut_dptr()); + bn_workspace_ptr = tmp_buffer->mut_dptr(); + bn_workspace_size = tmp_buffer->shape_view().elem_cnt(); + bn_dy_ptr = addend_diff->dptr(); + } else { + const size_t tmp_buffer_size = tmp_buffer->shape_view().elem_cnt(); + const size_t relu_dx_size = + GetCudaAlignedSize(dy->shape_view().elem_cnt() * GetSizeOfDataType(data_type)); + CHECK_GE(tmp_buffer_size, relu_dx_size); + ReluBackward(ctx->stream(), elem_cnt, data_type, mask->dptr(), dy->dptr(), + tmp_buffer->mut_dptr()); + bn_workspace_ptr = tmp_buffer->mut_dptr() + relu_dx_size; + bn_workspace_size = tmp_buffer_size - relu_dx_size; + bn_dy_ptr = tmp_buffer->dptr(); + } + } else { + UNIMPLEMENTED(); + } + + OF_CUDNN_CHECK(hipdnnBatchNormalizationBackward( + ctx->stream()->As()->cudnn_handle(), mode, CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), + desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), bn_dy_ptr, desc_helper.xy_desc(), + dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), gamma_diff->mut_dptr(), + beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr())); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("normalization_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferGradTmpSize); + +#define REGISTER_BN_ADD_RELU_GRAD_KERNEL(dtype) +REGISTER_USER_KERNEL("normalization_add_relu_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferGradTmpSize); + +#if (HIPDNN_VERSION >= 7401) + +size_t InferFusedNormalizationAddReluTmpSize(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("x", 0); + const auto axis = ctx->Attr("axis"); + const CudnnTensorDescHelper desc_helper(x.shape(), x.data_type(), axis, + HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); + size_t size_in_bytes; + hipdnnHandle_t handle = Singleton::Get()->Get(); + CudnnActivationDesc activation_desc(HIPDNN_ACTIVATION_RELU, HIPDNN_PROPAGATE_NAN, 0); + cudnnBatchNormOps_t ops; + hipdnnTensorDescriptor_t z_desc; + if (ctx->has_input("addend", 0)) { + ops = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION; + z_desc = desc_helper.xy_desc(); + } else { + ops = CUDNN_BATCHNORM_OPS_BN_ACTIVATION; + z_desc = nullptr; + } + OF_CUDNN_CHECK(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( + handle, HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, ops, desc_helper.xy_desc(), z_desc, + desc_helper.xy_desc(), desc_helper.param_desc(), activation_desc.Get(), &size_in_bytes)); + Singleton::Get()->Put(handle); + return std::max(size_in_bytes, static_cast(1)); +} + +size_t InferFusedNormalizationAddReluGradTmpSize(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("x", 0); + const auto axis = ctx->Attr("axis"); + const CudnnTensorDescHelper desc_helper(x.shape(), x.data_type(), axis, + HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); + size_t size_in_bytes; + hipdnnHandle_t handle = Singleton::Get()->Get(); + CudnnActivationDesc activation_desc(HIPDNN_ACTIVATION_RELU, HIPDNN_PROPAGATE_NAN, 0); + cudnnBatchNormOps_t ops; + hipdnnTensorDescriptor_t z_desc; + if (ctx->has_output("addend_diff", 0)) { + ops = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION; + z_desc = desc_helper.xy_desc(); + } else { + ops = CUDNN_BATCHNORM_OPS_BN_ACTIVATION; + z_desc = nullptr; + } + OF_CUDNN_CHECK(cudnnGetBatchNormalizationBackwardExWorkspaceSize( + handle, HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, ops, desc_helper.xy_desc(), desc_helper.xy_desc(), + desc_helper.xy_desc(), z_desc, desc_helper.xy_desc(), desc_helper.param_desc(), + activation_desc.Get(), &size_in_bytes)); + Singleton::Get()->Put(handle); + return std::max(size_in_bytes, static_cast(1)); +} + +class FusedNormalizationAddReluKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + FusedNormalizationAddReluKernel() = default; + ~FusedNormalizationAddReluKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); + auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); + auto* moving_mean = ctx->Tensor4ArgNameAndIndex("moving_mean", 0); + auto* moving_variance = ctx->Tensor4ArgNameAndIndex("moving_variance", 0); + const auto axis = ctx->Attr("axis"); + const auto epsilon = ctx->Attr("epsilon"); + const auto momentum = ctx->Attr("momentum"); + auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + auto* reserve_space = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); + auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + + const DataType data_type = x->data_type(); + CHECK_EQ(x->shape_view(), y->shape_view()); + CHECK_EQ(y->data_type(), data_type); + CHECK_GE(axis, 0); + CHECK_LT(axis, x->shape_view().NumAxes()); + + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, + HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); + desc_helper.CheckParamTensor(gamma); + desc_helper.CheckParamTensor(beta); + desc_helper.CheckParamTensor(moving_mean); + desc_helper.CheckParamTensor(moving_variance); + desc_helper.CheckParamTensor(mean); + desc_helper.CheckParamTensor(inv_variance); + + CudnnActivationDesc activation_desc(HIPDNN_ACTIVATION_RELU, HIPDNN_PROPAGATE_NAN, 0); + hipdnnTensorDescriptor_t z_desc; + const void* z_ptr; + cudnnBatchNormOps_t ops; + if (ctx->has_input("addend", 0)) { + z_desc = desc_helper.xy_desc(); + z_ptr = ctx->Tensor4ArgNameAndIndex("addend", 0)->dptr(); + ops = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION; + } else { + z_desc = nullptr; + z_ptr = nullptr; + ops = CUDNN_BATCHNORM_OPS_BN_ACTIVATION; + } + + size_t min_workspace_size; + OF_CUDNN_CHECK(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, desc_helper.xy_desc(), z_desc, desc_helper.xy_desc(), desc_helper.param_desc(), + activation_desc.Get(), &min_workspace_size)); + const size_t workspace_size = tmp_buffer->shape_view().elem_cnt(); + CHECK_GE(workspace_size, min_workspace_size); + size_t min_reserve_space_size; + OF_CUDNN_CHECK(cudnnGetBatchNormalizationTrainingExReserveSpaceSize( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, activation_desc.Get(), desc_helper.xy_desc(), &min_reserve_space_size)); + const size_t reserve_space_size = reserve_space->shape_view().elem_cnt(); + CHECK_GE(reserve_space_size, min_reserve_space_size); + + OF_CUDNN_CHECK(cudnnBatchNormalizationForwardTrainingEx( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), + z_desc, z_ptr, desc_helper.xy_desc(), y->mut_dptr(), desc_helper.param_desc(), + gamma->dptr(), beta->dptr(), 1.0 - momentum, moving_mean->mut_dptr(), + moving_variance->mut_dptr(), epsilon, mean->mut_dptr(), inv_variance->mut_dptr(), + activation_desc.Get(), tmp_buffer->mut_dptr(), workspace_size, reserve_space->mut_dptr(), + reserve_space_size)); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferFusedNormalizationAddReluTmpSize); + +class FusedNormalizationAddReluGradUserKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + FusedNormalizationAddReluGradUserKernel() = default; + ~FusedNormalizationAddReluGradUserKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); + auto* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); + const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); + auto* gamma_diff = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0); + auto* beta_diff = ctx->Tensor4ArgNameAndIndex("beta_diff", 0); + const auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); + const auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); + const auto* reserve_space = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); + auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const auto axis = ctx->Attr("axis"); + const auto epsilon = ctx->Attr("epsilon"); + + const DataType data_type = x->data_type(); + CHECK_EQ(dy->shape_view(), x->shape_view()); + CHECK_EQ(dy->data_type(), data_type); + CHECK_EQ(dx->shape_view(), x->shape_view()); + CHECK_EQ(dx->data_type(), data_type); + CHECK_GE(axis, 0); + CHECK_LT(axis, x->shape_view().NumAxes()); + + const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, + HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); + desc_helper.CheckParamTensor(gamma); + desc_helper.CheckParamTensor(beta); + desc_helper.CheckParamTensor(gamma_diff); + desc_helper.CheckParamTensor(beta_diff); + desc_helper.CheckParamTensor(mean); + desc_helper.CheckParamTensor(inv_variance); + + CudnnActivationDesc activation_desc(HIPDNN_ACTIVATION_RELU, HIPDNN_PROPAGATE_NAN, 0); + hipdnnTensorDescriptor_t dz_desc; + void* dz_ptr; + cudnnBatchNormOps_t ops; + if (ctx->has_output("addend_diff", 0)) { + dz_desc = desc_helper.xy_desc(); + dz_ptr = ctx->Tensor4ArgNameAndIndex("addend_diff", 0)->mut_dptr(); + ops = CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION; + } else { + dz_desc = nullptr; + dz_ptr = nullptr; + ops = CUDNN_BATCHNORM_OPS_BN_ACTIVATION; + } + + size_t min_workspace_size; + OF_CUDNN_CHECK(cudnnGetBatchNormalizationBackwardExWorkspaceSize( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, desc_helper.xy_desc(), desc_helper.xy_desc(), desc_helper.xy_desc(), dz_desc, + desc_helper.xy_desc(), desc_helper.param_desc(), activation_desc.Get(), + &min_workspace_size)); + const size_t workspace_size = tmp_buffer->shape_view().elem_cnt(); + CHECK_GE(workspace_size, min_workspace_size); + size_t min_reserve_space_size; + OF_CUDNN_CHECK(cudnnGetBatchNormalizationTrainingExReserveSpaceSize( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, activation_desc.Get(), desc_helper.xy_desc(), &min_reserve_space_size)); + const size_t reserve_space_size = reserve_space->shape_view().elem_cnt(); + CHECK_GE(reserve_space_size, min_reserve_space_size); + OF_CUDNN_CHECK(cudnnBatchNormalizationBackwardEx( + ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, + ops, CudnnSPOnePtr(data_type), CudnnSPZeroPtr(data_type), CudnnSPOnePtr(data_type), + CudnnSPZeroPtr(data_type), desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), + y->dptr(), desc_helper.xy_desc(), dy->dptr(), dz_desc, dz_ptr, desc_helper.xy_desc(), + dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), beta->dptr(), + gamma_diff->mut_dptr(), beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr(), + activation_desc.Get(), tmp_buffer->mut_dptr(), workspace_size, + const_cast(reserve_space->dptr()), reserve_space_size)); + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; -REGISTER_FUSED_BN_ADD_RELU_GRAD_KERNEL(float16) +REGISTER_USER_KERNEL("cudnn_fused_normalization_add_relu_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)) + .SetInferTmpSizeFn(InferFusedNormalizationAddReluGradTmpSize); #endif diff --git a/oneflow/user/kernels/normalization_kernel.hip.cpp b/oneflow/user/kernels/normalization_kernel.hip.cpp deleted file mode 100644 index e5ea5f09b1cfeebd408d89666a7056fe8e21a4f7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/normalization_kernel.hip.cpp +++ /dev/null @@ -1,534 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM - -#include -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cudnn_util.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "hip/hsa_detail/device_functions.h" - -namespace oneflow { - -namespace { - -void InferDimSizeAndDataFormat(const ShapeView& x_shape, const int32_t axis, int32_t* n, int32_t* c, - int32_t* h, int32_t* w, hipdnnTensorFormat_t* format) { - if (x_shape.Count(axis + 1) == 1) { - if (axis == 0) { - *n = 1; - *h = 1; - } else { - *n = x_shape.At(0); - *h = x_shape.Count(1, axis); - } - *w = 1; - *c = x_shape.At(axis); - // *format = HIPDNN_TENSOR_NHWC; - *format = HIPDNN_TENSOR_NCHW; - // std::cout << "don't surpport HIPDNN_TENSOR_NHWC, use HIPDNN_TENSOR_NCHW instead, maybe cause wrong results" << std::endl; - } else { - *n = x_shape.Count(0, axis); - *c = x_shape.At(axis); - *h = x_shape.Count(axis + 1); - *w = 1; - *format = HIPDNN_TENSOR_NCHW; - } -} - -void InferXYCudnnTensorDesc(const ShapeView& xy_shape, const DataType& data_type, - const int32_t axis, hipdnnTensorDescriptor_t xy_desc) { - int32_t n, c, h, w; - hipdnnTensorFormat_t format; - InferDimSizeAndDataFormat(xy_shape, axis, &n, &c, &h, &w, &format); - OF_CUDNN_CHECK( - hipdnnSetTensor4dDescriptor(xy_desc, format, GetCudnnDataType(data_type), n, c, h, w)); -} - -void InferParamCudnnTensorDesc(const hipdnnTensorDescriptor_t xy_desc, hipdnnBatchNormMode_t mode, - hipdnnTensorDescriptor_t param_desc) { - OF_CUDNN_CHECK(hipdnnDeriveBNTensorDescriptor(param_desc, xy_desc, mode)); -} - -class CudnnTensorDescHelper final { - public: - OF_DISALLOW_COPY_AND_MOVE(CudnnTensorDescHelper); - CudnnTensorDescHelper(const ShapeView& xy_shape, const DataType& data_type, const int32_t axis, - hipdnnBatchNormMode_t mode) { - OF_CUDNN_CHECK(hipdnnCreateTensorDescriptor(&xy_desc_)); - InferXYCudnnTensorDesc(xy_shape, data_type, axis, xy_desc_); - OF_CUDNN_CHECK(hipdnnCreateTensorDescriptor(¶m_desc_)); - InferParamCudnnTensorDesc(xy_desc_, mode, param_desc_); - int n, c, h, w, n_stride, c_stride, h_stride, w_stride; - OF_CUDNN_CHECK(hipdnnGetTensor4dDescriptor(param_desc_, ¶m_data_type_, &n, &c, &h, &w, - &n_stride, &c_stride, &h_stride, &w_stride)); - param_size_ = c; - } - ~CudnnTensorDescHelper() { - OF_CUDNN_CHECK(hipdnnDestroyTensorDescriptor(param_desc_)); - OF_CUDNN_CHECK(hipdnnDestroyTensorDescriptor(xy_desc_)); - } - - hipdnnTensorDescriptor_t xy_desc() const { return xy_desc_; } - - hipdnnTensorDescriptor_t param_desc() const { return param_desc_; } - - void CheckParamTensor(const user_op::Tensor* tensor) const { - CHECK_NOTNULL(tensor); - CHECK_EQ(tensor->shape_view().NumAxes(), 1); - CHECK_EQ(tensor->shape_view().At(0), param_size_); - // CHECK_EQ(GetCudnnDataType(tensor->data_type()), param_data_type_); - } - - private: - hipdnnTensorDescriptor_t xy_desc_ = nullptr; - hipdnnTensorDescriptor_t param_desc_ = nullptr; - hipdnnDataType_t param_data_type_; - int32_t param_size_ = 0; -}; - -size_t InferTrainWorkspaceSize(const ShapeView& x_shape, const DataType data_type, - const int32_t axis) { - return 1; -} - -size_t InferTrainTmpSize(user_op::InferContext* ctx) { - const auto& x = ctx->InputTensorDesc("x", 0); - const auto axis = ctx->Attr("axis"); - return InferTrainWorkspaceSize(x.shape(), x.data_type(), axis); -} - -size_t InferGradWorkspaceSize(const ShapeView& x_shape, const DataType data_type, - const int32_t axis) { - return 1; -} - -size_t InferGradTmpSize(user_op::InferContext* ctx) { - const auto& dy = ctx->InputTensorDesc("dy", 0); - const auto axis = ctx->Attr("axis"); - size_t tmp_size = 0; - if (ctx->op_type_name() == "normalization_add_relu_grad" && !ctx->has_output("addend_diff", 0)) { - tmp_size += GetCudaAlignedSize(dy.shape().elem_cnt() * GetSizeOfDataType(dy.data_type())); - } - tmp_size += GetCudaAlignedSize(InferGradWorkspaceSize(dy.shape(), dy.data_type(), axis)); - return tmp_size; -} - -template -class NormalizationInferenceKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - NormalizationInferenceKernel() = default; - ~NormalizationInferenceKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const bool training = ctx->Attr("training"); - CHECK(!training); - const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); - auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); - const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); - auto* moving_mean = ctx->Tensor4ArgNameAndIndex("moving_mean", 0); - auto* moving_variance = ctx->Tensor4ArgNameAndIndex("moving_variance", 0); - const auto axis = ctx->Attr("axis"); - const auto epsilon = ctx->Attr("epsilon"); - - const DataType data_type = x->data_type(); - CHECK_EQ(x->shape_view(), y->shape_view()); - CHECK_EQ(y->data_type(), data_type); - CHECK_GE(axis, 0); - CHECK_LT(axis, x->shape_view().NumAxes()); - - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - HIPDNN_BATCHNORM_SPATIAL); - desc_helper.CheckParamTensor(gamma); - desc_helper.CheckParamTensor(beta); - desc_helper.CheckParamTensor(moving_mean); - desc_helper.CheckParamTensor(moving_variance); - - const void* sp_alpha = CudnnSPOnePtr(); - const void* sp_beta; - if (ctx->has_input("_add_to_output", 0)) { - const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); - CHECK_EQ(add_to_output->data_type(), y->data_type()); - CHECK_EQ(add_to_output->shape_view(), y->shape_view()); - Memcpy( - ctx->stream(), y->mut_dptr(), add_to_output->dptr(), - add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - sp_beta = CudnnSPOnePtr(); - } else { - sp_beta = CudnnSPZeroPtr(); - } - - OF_CUDNN_CHECK(hipdnnBatchNormalizationForwardInference( - ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL, sp_alpha, - sp_beta, desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), - desc_helper.param_desc(), gamma->dptr(), beta->dptr(), moving_mean->dptr(), - moving_variance->dptr(), epsilon)); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_BN_INFERENCE_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value) \ - && (user_op::HobAttr("training") == false)) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }); - -REGISTER_BN_INFERENCE_KERNEL(float16) -REGISTER_BN_INFERENCE_KERNEL(float) -REGISTER_BN_INFERENCE_KERNEL(double) - -#undef REGISTER_BN_INFERENCE_KERNEL - -constexpr int64_t kCudaWarpSize = 64; - -template -__global__ void ReluGpu(int64_t n, const T* x, T* y, int64_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - CUDA_1D_KERNEL_LOOP(i, n) { - const T x_val = x[i]; - const bool is_positive = (x_val > 0); - int64_t warp_mask = __ballot(static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? x_val : 0; - } -} - -template<> -__global__ void ReluGpu(int64_t n, const half* x, half* y, int64_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - const half zero = __float2half(0.0f); - CUDA_1D_KERNEL_LOOP(i, n) { - const half x_val = x[i]; - const bool is_positive = __hgt(x_val, zero); - int64_t warp_mask = __ballot(static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? x_val : zero; - } -} - -template -__global__ void AddReluGpu(int64_t n, const T* x, const T* addend, T* y, int64_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - CUDA_1D_KERNEL_LOOP(i, n) { - const T sum = x[i] + addend[i]; - const bool is_positive = (sum > 0); - int64_t warp_mask = __ballot(static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? sum : 0; - } -} - -template<> -__global__ void AddReluGpu(int64_t n, const half* x, const half* addend, half* y, - int64_t* mask) { - const int32_t lane_id = threadIdx.x % kCudaWarpSize; - const half zero = __float2half(0.0f); - CUDA_1D_KERNEL_LOOP(i, n) { - const half sum = __hadd(x[i], addend[i]); - const bool is_positive = __hgt(sum, zero); - int64_t warp_mask = __ballot(static_cast(is_positive)); - if (lane_id == 0) { mask[i / kCudaWarpSize] = warp_mask; } - y[i] = is_positive ? sum : zero; - } -} - -template -void Relu(ep::Stream* stream, int64_t n, const T* x, T* y, int64_t* mask) { - ReluGpu<<As()->cuda_stream()>>>(n, x, y, mask); -} - -template<> -void Relu(ep::Stream* stream, int64_t n, const float16* x, float16* y, int64_t* mask) { - Relu(stream, n, reinterpret_cast(x), reinterpret_cast(y), mask); -} - -template -void AddRelu(ep::Stream* stream, int64_t n, const T* x, const T* addend, T* y, int64_t* mask) { - AddReluGpu<<As()->cuda_stream()>>>(n, x, addend, y, mask); -} - -template<> -void AddRelu(ep::Stream* stream, int64_t n, const float16* x, const float16* addend, - float16* y, int64_t* mask) { - AddRelu(stream, n, reinterpret_cast(x), reinterpret_cast(addend), - reinterpret_cast(y), mask); -} - -template -__global__ void ReluBackwardGpu(int64_t n, const int64_t* mask, const T* dy, T* addend_diff) { - int32_t lane_id = threadIdx.x % kCudaWarpSize; - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t mask_val = mask[i / kCudaWarpSize]; - bool is_positive = mask_val & (1 << lane_id); - addend_diff[i] = static_cast(is_positive) * dy[i]; - } -} - -template -void ReluBackward(ep::Stream* stream, int64_t n, const int64_t* mask, const T* dy, T* addend_diff) { - ReluBackwardGpu<<As()->cuda_stream()>>>(n, mask, dy, addend_diff); -} - -template<> -void ReluBackward(ep::Stream* stream, int64_t n, const int64_t* mask, const float16* dy, - float16* addend_diff) { - ReluBackward(stream, n, mask, reinterpret_cast(dy), - reinterpret_cast(addend_diff)); -} - -template -class NormalizationTrainKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - NormalizationTrainKernel() = default; - ~NormalizationTrainKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - if (ctx->op_type_name() == "normalization") { CHECK(ctx->Attr("training")); } - const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); - auto* y = ctx->Tensor4ArgNameAndIndex("y", 0); - - const auto axis = ctx->Attr("axis"); - const auto epsilon = ctx->Attr("epsilon"); - const auto momentum = ctx->Attr("momentum"); - - const DataType data_type = x->data_type(); - CHECK_EQ(x->shape_view(), y->shape_view()); - CHECK_EQ(y->data_type(), data_type); - CHECK_GE(axis, 0); - CHECK_LT(axis, x->shape_view().NumAxes()); - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); - - const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); - const auto* beta = ctx->Tensor4ArgNameAndIndex("beta", 0); - auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); - auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); - desc_helper.CheckParamTensor(gamma); - desc_helper.CheckParamTensor(beta); - desc_helper.CheckParamTensor(mean); - desc_helper.CheckParamTensor(inv_variance); - - user_op::Tensor* moving_mean = nullptr; - user_op::Tensor* moving_variance = nullptr; - if (ctx->has_input("moving_mean", 0)) { - CHECK(ctx->has_input("moving_variance", 0)); - moving_mean = ctx->Tensor4ArgNameAndIndex("moving_mean", 0); - moving_variance = ctx->Tensor4ArgNameAndIndex("moving_variance", 0); - desc_helper.CheckParamTensor(moving_mean); - desc_helper.CheckParamTensor(moving_variance); - } - - const void* sp_alpha = CudnnSPOnePtr(); - const void* sp_beta; - if (ctx->has_input("_add_to_output", 0)) { - const user_op::Tensor* add_to_output = ctx->Tensor4ArgNameAndIndex("_add_to_output", 0); - CHECK_EQ(add_to_output->data_type(), y->data_type()); - CHECK_EQ(add_to_output->shape_view(), y->shape_view()); - Memcpy( - ctx->stream(), y->mut_dptr(), add_to_output->dptr(), - add_to_output->shape_view().elem_cnt() * GetSizeOfDataType(add_to_output->data_type())); - sp_beta = CudnnSPOnePtr(); - } else { - sp_beta = CudnnSPZeroPtr(); - } - - OF_CUDNN_CHECK(hipdnnBatchNormalizationForwardTraining( - ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, - const_cast(sp_alpha), const_cast(sp_beta), desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), y->mut_dptr(), - desc_helper.param_desc(), const_cast(gamma->dptr()), const_cast(beta->dptr()), 1.0 - momentum, - moving_mean ? moving_mean->mut_dptr() : NULL, - moving_variance ? moving_variance->mut_dptr() : NULL, epsilon, mean->mut_dptr(), - inv_variance->mut_dptr())); - - if (ctx->op_type_name() == "normalization_add_relu") { - CHECK(!ctx->has_input("_add_to_output", 0)); - const int64_t elem_cnt = x->shape_view().elem_cnt(); - auto* mask = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); - if (ctx->has_input("addend", 0)) { - const auto* addend = ctx->Tensor4ArgNameAndIndex("addend", 0); - AddRelu(ctx->stream(), elem_cnt, y->dptr(), addend->dptr(), y->mut_dptr(), - mask->mut_dptr()); - } else { - Relu(ctx->stream(), elem_cnt, y->dptr(), y->mut_dptr(), mask->mut_dptr()); - } - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_BN_TRAIN_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value) \ - && (user_op::HobAttr("training") == true)) \ - .SetInferTmpSizeFn(InferTrainTmpSize) \ - .SetInplaceProposalFn([](const user_op::InferContext& ctx, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - if (ctx.has_input("_add_to_output", 0)) { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("y", 0, "_add_to_output", 0, true)); \ - } \ - return Maybe::Ok(); \ - }); - -REGISTER_BN_TRAIN_KERNEL(float16) -REGISTER_BN_TRAIN_KERNEL(float) -REGISTER_BN_TRAIN_KERNEL(double) - -#define REGISTER_BN_ADD_RELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_add_relu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTrainTmpSize); - -REGISTER_BN_ADD_RELU_KERNEL(float16) -REGISTER_BN_ADD_RELU_KERNEL(float) -REGISTER_BN_ADD_RELU_KERNEL(double) - -template -class NormalizationGradUserKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - NormalizationGradUserKernel() = default; - ~NormalizationGradUserKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const auto* x = ctx->Tensor4ArgNameAndIndex("x", 0); - auto* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - const auto* gamma = ctx->Tensor4ArgNameAndIndex("gamma", 0); - auto* gamma_diff = ctx->Tensor4ArgNameAndIndex("gamma_diff", 0); - auto* beta_diff = ctx->Tensor4ArgNameAndIndex("beta_diff", 0); - const auto* mean = ctx->Tensor4ArgNameAndIndex("mean", 0); - const auto* inv_variance = ctx->Tensor4ArgNameAndIndex("inv_variance", 0); - auto* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const auto axis = ctx->Attr("axis"); - const auto epsilon = ctx->Attr("epsilon"); - - const DataType data_type = x->data_type(); - CHECK_EQ(dy->shape_view(), x->shape_view()); - CHECK_EQ(dy->data_type(), data_type); - CHECK_EQ(dx->shape_view(), x->shape_view()); - CHECK_EQ(dx->data_type(), data_type); - CHECK_GE(axis, 0); - CHECK_LT(axis, x->shape_view().NumAxes()); - - const CudnnTensorDescHelper desc_helper(x->shape_view(), data_type, axis, - HIPDNN_BATCHNORM_SPATIAL_PERSISTENT); - desc_helper.CheckParamTensor(gamma); - desc_helper.CheckParamTensor(gamma_diff); - desc_helper.CheckParamTensor(beta_diff); - desc_helper.CheckParamTensor(mean); - desc_helper.CheckParamTensor(inv_variance); - - void* bn_workspace_ptr; - size_t bn_workspace_size; - const void* bn_dy_ptr; - - if (ctx->op_type_name() == "normalization_grad") { - bn_workspace_ptr = tmp_buffer->mut_dptr(); - bn_workspace_size = tmp_buffer->shape_view().elem_cnt(); - bn_dy_ptr = dy->dptr(); - } else if (ctx->op_type_name() == "normalization_add_relu_grad") { - const int64_t elem_cnt = dy->shape_view().elem_cnt(); - const auto* mask = ctx->Tensor4ArgNameAndIndex("reserve_space", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - if (ctx->has_output("addend_diff", 0)) { - user_op::Tensor* addend_diff = ctx->Tensor4ArgNameAndIndex("addend_diff", 0); - ReluBackward(ctx->stream(), elem_cnt, mask->dptr(), dy->dptr(), - addend_diff->mut_dptr()); - bn_workspace_ptr = tmp_buffer->mut_dptr(); - bn_workspace_size = tmp_buffer->shape_view().elem_cnt(); - bn_dy_ptr = addend_diff->dptr(); - } else { - const size_t tmp_buffer_size = tmp_buffer->shape_view().elem_cnt(); - const size_t relu_dx_size = - GetCudaAlignedSize(dy->shape_view().elem_cnt() * GetSizeOfDataType(dy->data_type())); - CHECK_GE(tmp_buffer_size, relu_dx_size); - ReluBackward(ctx->stream(), elem_cnt, mask->dptr(), dy->dptr(), - reinterpret_cast(tmp_buffer->mut_dptr())); - bn_workspace_ptr = tmp_buffer->mut_dptr() + relu_dx_size; - bn_workspace_size = tmp_buffer_size - relu_dx_size; - bn_dy_ptr = tmp_buffer->dptr(); - } - } else { - UNIMPLEMENTED(); - } - - OF_CUDNN_CHECK(hipdnnBatchNormalizationBackward( - ctx->stream()->As()->cudnn_handle(), HIPDNN_BATCHNORM_SPATIAL_PERSISTENT, - CudnnSPOnePtr(), CudnnSPZeroPtr(), CudnnSPOnePtr(), CudnnSPZeroPtr(), - desc_helper.xy_desc(), x->dptr(), desc_helper.xy_desc(), bn_dy_ptr, desc_helper.xy_desc(), - dx->mut_dptr(), desc_helper.param_desc(), gamma->dptr(), gamma_diff->mut_dptr(), - beta_diff->mut_dptr(), epsilon, mean->dptr(), inv_variance->dptr())); - - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_BN_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferGradTmpSize); - -REGISTER_BN_GRAD_KERNEL(float16) -REGISTER_BN_GRAD_KERNEL(float) -REGISTER_BN_GRAD_KERNEL(double) - -#define REGISTER_BN_ADD_RELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("normalization_add_relu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn(InferGradTmpSize); - -REGISTER_BN_ADD_RELU_GRAD_KERNEL(float16) -REGISTER_BN_ADD_RELU_GRAD_KERNEL(float) -REGISTER_BN_ADD_RELU_GRAD_KERNEL(double) - - -} // namespace -} // namespace oneflow - -#endif \ No newline at end of file diff --git a/oneflow/user/kernels/nvtx_range_kernel.cu b/oneflow/user/kernels/nvtx_range_kernel.cu index 9efe3f52a9221833b68c45034a34229bffd9b9e8..9f038e41f0a856a7dde5b82aceefcf1d12b1c6d7 100644 --- a/oneflow/user/kernels/nvtx_range_kernel.cu +++ b/oneflow/user/kernels/nvtx_range_kernel.cu @@ -16,6 +16,8 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" +#ifdef WITH_CUDA + #ifdef OF_ENABLE_PROFILER #include #endif // OF_ENABLE_PROFILER @@ -136,3 +138,130 @@ REGISTER_USER_KERNEL("nvtx_end") }); } // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +#ifdef OF_ENABLE_PROFILER +#include +#endif // OF_ENABLE_PROFILER + +namespace oneflow { + +namespace { + +#ifdef OF_ENABLE_PROFILER +static thread_local HashMap mark2range_id; +#endif + +} // namespace + +class NvtxOpKernelState final : public user_op::OpKernelState { + public: + NvtxOpKernelState() : counter_(0) { +#ifndef OF_ENABLE_PROFILER + LOG(WARNING) << "To use NVTX, run cmake with -DBUILD_PROFILER=ON"; +#endif + } + ~NvtxOpKernelState() override = default; + + int64_t counter() const { return counter_; } + void IncreaseCount() { counter_ += 1; } + + private: + int64_t counter_; +}; + +class NvtxStartKernel final : public user_op::OpKernel { + public: + NvtxStartKernel() = default; + ~NvtxStartKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const ShapeView& in_shape = in->shape_view(); + CHECK_EQ(out->shape_view(), in_shape); + const DataType in_data_type = in->data_type(); + CHECK_EQ(out->data_type(), in_data_type); + Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), + in_shape.elem_cnt() * GetSizeOfDataType(in_data_type)); +#ifdef OF_ENABLE_PROFILER + auto* kernel_state = dynamic_cast(state); + const std::string mark_prefix = ctx->Attr("mark_prefix"); + const std::string mark = mark_prefix + "-" + std::to_string(kernel_state->counter()); + roctx_range_id_t range_id = roctxRangeStartA(mark.c_str()); + CHECK(mark2range_id.emplace(mark, range_id).second); + kernel_state->IncreaseCount(); +#endif // OF_ENABLE_PROFILER + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("nvtx_start") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInplaceProposalFn([](const user_op::InferContext&, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, false)); + return Maybe::Ok(); + }); + +class NvtxEndKernel final : public user_op::OpKernel { + public: + NvtxEndKernel() = default; + ~NvtxEndKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + const ShapeView& in_shape = in->shape_view(); + CHECK_EQ(out->shape_view(), in_shape); + const DataType in_data_type = in->data_type(); + CHECK_EQ(out->data_type(), in_data_type); +#ifdef OF_ENABLE_PROFILER + auto* kernel_state = dynamic_cast(state); + const std::string mark_prefix = ctx->Attr("mark_prefix"); + const std::string mark = mark_prefix + "-" + std::to_string(kernel_state->counter()); + auto it = mark2range_id.find(mark.c_str()); + CHECK(it != mark2range_id.end()); + roctx_range_id_t range_id = it->second; + mark2range_id.erase(it); + roctxRangeStop(range_id); + Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), + in_shape.elem_cnt() * GetSizeOfDataType(in_data_type)); + kernel_state->IncreaseCount(); +#endif + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("nvtx_end") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) + .SetInplaceProposalFn([](const user_op::InferContext&, + user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, false)); + return Maybe::Ok(); + }); + +} // namespace oneflow + +#endif diff --git a/oneflow/user/kernels/nvtx_range_kernel.hip.cpp b/oneflow/user/kernels/nvtx_range_kernel.hip.cpp deleted file mode 100644 index 8f22f5fcc88d0f95cce7d0c7de5298d6bd7c6bd5..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/nvtx_range_kernel.hip.cpp +++ /dev/null @@ -1,138 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" - -#ifdef OF_ENABLE_PROFILER -#include -#endif // OF_ENABLE_PROFILER - -namespace oneflow { - -namespace { - -#ifdef OF_ENABLE_PROFILER -static thread_local HashMap mark2range_id; -#endif - -} // namespace - -class NvtxOpKernelState final : public user_op::OpKernelState { - public: - NvtxOpKernelState() : counter_(0) { -#ifndef OF_ENABLE_PROFILER - LOG(WARNING) << "To use NVTX, run cmake with -DBUILD_PROFILER=ON"; -#endif - } - ~NvtxOpKernelState() override = default; - - int64_t counter() const { return counter_; } - void IncreaseCount() { counter_ += 1; } - - private: - int64_t counter_; -}; - -class NvtxStartKernel final : public user_op::OpKernel { - public: - NvtxStartKernel() = default; - ~NvtxStartKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared(); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const ShapeView& in_shape = in->shape_view(); - CHECK_EQ(out->shape_view(), in_shape); - const DataType in_data_type = in->data_type(); - CHECK_EQ(out->data_type(), in_data_type); - Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), - in_shape.elem_cnt() * GetSizeOfDataType(in_data_type)); -#ifdef OF_ENABLE_PROFILER - auto* kernel_state = dynamic_cast(state); - const std::string mark_prefix = ctx->Attr("mark_prefix"); - const std::string mark = mark_prefix + "-" + std::to_string(kernel_state->counter()); - roctx_range_id_t range_id = roctxRangeStartA(mark.c_str()); - CHECK(mark2range_id.emplace(mark, range_id).second); - kernel_state->IncreaseCount(); -#endif // OF_ENABLE_PROFILER - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("nvtx_start") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) - .SetInplaceProposalFn([](const user_op::InferContext&, - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, false)); - return Maybe::Ok(); - }); - -class NvtxEndKernel final : public user_op::OpKernel { - public: - NvtxEndKernel() = default; - ~NvtxEndKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared(); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const ShapeView& in_shape = in->shape_view(); - CHECK_EQ(out->shape_view(), in_shape); - const DataType in_data_type = in->data_type(); - CHECK_EQ(out->data_type(), in_data_type); -#ifdef OF_ENABLE_PROFILER - auto* kernel_state = dynamic_cast(state); - const std::string mark_prefix = ctx->Attr("mark_prefix"); - const std::string mark = mark_prefix + "-" + std::to_string(kernel_state->counter()); - auto it = mark2range_id.find(mark.c_str()); - CHECK(it != mark2range_id.end()); - roctx_range_id_t range_id = it->second; - mark2range_id.erase(it); - roctxRangeStop(range_id); - Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), - in_shape.elem_cnt() * GetSizeOfDataType(in_data_type)); - kernel_state->IncreaseCount(); -#endif - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("nvtx_end") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) - .SetInplaceProposalFn([](const user_op::InferContext&, - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, false)); - return Maybe::Ok(); - }); - -} // namespace oneflow diff --git a/oneflow/user/kernels/one_embedding_data_shuffle.cuh b/oneflow/user/kernels/one_embedding_data_shuffle.cuh new file mode 100644 index 0000000000000000000000000000000000000000..7764848cc8a6d02a6c54692f8d188eae0d161916 --- /dev/null +++ b/oneflow/user/kernels/one_embedding_data_shuffle.cuh @@ -0,0 +1,529 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/device/nccl_util.h" +#include "oneflow/core/job/eager_nccl_comm_manager.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/user/kernels/gather_kernel_util.h" +#include "oneflow/user/kernels/unsorted_segment_sum_kernel_util.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/embedding/hash_functions.cuh" + +namespace oneflow { + +namespace data_shuffle { + +template +struct TableEntry { + K key; + uint32_t value; +}; + +template +__global__ void GenerateTableIds(int32_t elem_cnt, int32_t num_tables, U* table_ids) { + CUDA_1D_KERNEL_LOOP(i, elem_cnt) { table_ids[i] = i % num_tables; } +} + +namespace { + +constexpr uint32_t PADDING_REV_INDEX = 0xffffffff; + +template +__global__ void HashTableUniqueAndPartitionPairs( + const uint32_t table_capacity, const uint32_t num_keys, int32_t num_partition, + IDX* unique_counts, TableEntry* table, const K* keys, const V* values, + K* partitioned_unique_keys, V* partitioned_unique_values, IDX* reverse_index, + bool need_process_values, const bool has_padding_idx, const int64_t padding_idx) { + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_keys) { + IDX r_index_plus_one = 0; + const K key = keys[i]; + if (has_padding_idx && key == padding_idx) { + reverse_index[i] = PADDING_REV_INDEX; + } else { + size_t key_hash = HASH()(key); + uint32_t partition_id = key_hash % num_partition; + IDX* unique_count = unique_counts + partition_id; + K* unique_keys = partitioned_unique_keys + partition_id * num_keys; + uint32_t pos = key_hash % table_capacity; + const K key_hi = (key | 0x1); + const K key_lo = (key & 0x1); + uint32_t counter = 0; + while (r_index_plus_one == 0) { + bool prob_next = false; + K* key_ptr = &table[pos].key; + volatile uint32_t* table_value_ptr = &table[pos].value; + const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi); + if (old_key == 0) { + IDX unique_pos = cuda::atomic::Add(unique_count, 1); + r_index_plus_one = unique_pos + 1; + unique_keys[unique_pos] = key; + if (need_process_values) { + partitioned_unique_values[partition_id * num_keys + unique_pos] = values[i]; + } + *table_value_ptr = ((r_index_plus_one << 1U) | key_lo); + } else if (old_key == key_hi) { + const uint32_t value = *table_value_ptr; + if (value == 0) { + // do nothing + } else if ((value & 0x1) == key_lo) { + r_index_plus_one = (value >> 1U); + } else { + prob_next = true; + } + } else { + prob_next = true; + } + if (prob_next) { + pos += 1; + counter += 1; + if (pos >= table_capacity) { pos -= table_capacity; } + if (counter >= table_capacity) { TRAP(); } + } + } + reverse_index[i] = partition_id * num_keys + r_index_plus_one - 1; + } + } +} + +template +__global__ void ComputeOffset(int32_t n, IDX* value) { + IDX sum = 0; + for (int i = 0; i < n; ++i) { + IDX count = value[i]; + value[i] = sum; + sum += count; + } +} + +template +__global__ void ContiguousInverseUniquePartitionIndices(const int32_t num_ids, IDX* indices_offset, + IDX* inverse_ptr) { + CUDA_1D_KERNEL_LOOP(i, num_ids) { + int inverse_indice = inverse_ptr[i]; + int partition_id = inverse_indice / num_ids; + int partition_indice = inverse_indice - partition_id * num_ids; + int new_offset = indices_offset[partition_id]; + inverse_ptr[i] = new_offset + partition_indice; + } +} + +template +void ShuffleData(GPU(Stream_t) cuda_stream, ncclComm_t comm, DataType data_type, + const std::vector& send_offsets, + const std::vector& send_elem_cnt, const T* send_data, + const std::vector& recv_offsets, + const std::vector& recv_elem_cnt, T* recv_data) { + ncclDataType_t nccl_data_type = GetNcclDataType(data_type); + const int64_t parallel_num = send_offsets.size(); + OF_NCCL_CHECK(ncclGroupStart()); + for (int64_t i = 0; i < parallel_num; ++i) { + OF_NCCL_CHECK(ncclSend(send_data + send_offsets.at(i), send_elem_cnt.at(i), nccl_data_type, i, + comm, cuda_stream)); + OF_NCCL_CHECK(ncclRecv(recv_data + recv_offsets.at(i), recv_elem_cnt.at(i), nccl_data_type, i, + comm, cuda_stream)); + } + OF_NCCL_CHECK(ncclGroupEnd()); +} + +template +void MakeShuffleIdParams(const IDX* host_num_unique_matrix, const int64_t num_ids, + const int64_t row_size, int64_t parallel_id, int64_t parallel_num, + std::vector* scatter_offset_vec, + std::vector* scatter_elem_cnt_vec, + std::vector* gather_offset_vec, + std::vector* gather_elem_cnt_vec) { + scatter_offset_vec->resize(parallel_num); + scatter_elem_cnt_vec->resize(parallel_num); + gather_offset_vec->resize(parallel_num); + gather_elem_cnt_vec->resize(parallel_num); + int64_t gather_offset = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + const int64_t scatter_elem_cnt = + host_num_unique_matrix[parallel_id * parallel_num + i] * row_size; + const int64_t gather_elem_cnt = + host_num_unique_matrix[i * parallel_num + parallel_id] * row_size; + scatter_offset_vec->at(i) = i * num_ids * row_size; + scatter_elem_cnt_vec->at(i) = scatter_elem_cnt; + gather_offset_vec->at(i) = gather_offset; + gather_elem_cnt_vec->at(i) = gather_elem_cnt; + gather_offset += gather_elem_cnt; + } +} + +template +void MakeShuffleParams(const IDX* host_num_unique_matrix, const int64_t num_ids, + const int64_t row_size, int64_t parallel_id, int64_t parallel_num, + std::vector* scatter_offset_vec, + std::vector* scatter_elem_cnt_vec, + std::vector* gather_offset_vec, + std::vector* gather_elem_cnt_vec) { + scatter_offset_vec->resize(parallel_num); + scatter_elem_cnt_vec->resize(parallel_num); + gather_offset_vec->resize(parallel_num); + gather_elem_cnt_vec->resize(parallel_num); + int64_t gather_offset = 0; + int64_t scatter_offset = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + const int64_t scatter_elem_cnt = + host_num_unique_matrix[parallel_id * parallel_num + i] * row_size; + const int64_t gather_elem_cnt = + host_num_unique_matrix[i * parallel_num + parallel_id] * row_size; + scatter_offset_vec->at(i) = scatter_offset; + scatter_elem_cnt_vec->at(i) = scatter_elem_cnt; + gather_offset_vec->at(i) = gather_offset; + gather_elem_cnt_vec->at(i) = gather_elem_cnt; + scatter_offset += scatter_elem_cnt; + gather_offset += gather_elem_cnt; + } +} + +template +void ShuffleIdsAndTableIds(GPU(Stream_t) cuda_stream, ncclComm_t comm, int64_t parallel_id, + int64_t parallel_num, int64_t num_ids, DataType ids_data_type, + DataType table_ids_data_type, IDX* host_num_unique_matrix, + K* partitioned_unique_ids, U* partitioned_unique_table_ids, + K* received_ids, U* received_table_ids, int64_t* received_elem_cnt, + bool need_process_table_ids) { + std::vector send_offsets; + std::vector send_elem_cnt; + std::vector recv_offsets; + std::vector recv_elem_cnt; + MakeShuffleIdParams(host_num_unique_matrix, num_ids, 1, parallel_id, parallel_num, &send_offsets, + &send_elem_cnt, &recv_offsets, &recv_elem_cnt); + ShuffleData(cuda_stream, comm, ids_data_type, send_offsets, send_elem_cnt, partitioned_unique_ids, + recv_offsets, recv_elem_cnt, received_ids); + *received_elem_cnt = recv_offsets.at(parallel_num - 1) + recv_elem_cnt.at(parallel_num - 1); + if (need_process_table_ids) { + ShuffleData(cuda_stream, comm, table_ids_data_type, send_offsets, send_elem_cnt, + partitioned_unique_table_ids, recv_offsets, recv_elem_cnt, received_table_ids); + } +} + +template +__global__ void UnsortedSegmentHalfGpu(const IDX in_h2_elem_cnt, const IDX h2_inner_dim_size, + const IDX inner_dim_size, const half* data, + const K* segment_ids, const IDX num_segments, + half2* out_h2) { + CUDA_1D_KERNEL_LOOP_T(IDX, i, in_h2_elem_cnt) { + const IDX segment_id_idx = i / h2_inner_dim_size; + const IDX h2_inner_idx = i - segment_id_idx * h2_inner_dim_size; + const IDX inner_idx_0 = 2 * h2_inner_idx; + const IDX inner_idx_1 = inner_idx_0 + 1; + const half* data_row = data + segment_id_idx * inner_dim_size; + half2 val; +#ifdef WITH_ROCM + val.data.x = data_row[inner_idx_0]; + val.data.y = (inner_idx_1 >= inner_dim_size) ? static_cast(0) : data_row[inner_idx_1]; +#else + val.x = data_row[inner_idx_0]; + val.y = (inner_idx_1 >= inner_dim_size) ? static_cast(0) : data_row[inner_idx_1]; +#endif + const IDX idx = segment_ids[segment_id_idx]; + const IDX out_h2_offset = idx * h2_inner_dim_size + h2_inner_idx; + cuda::atomic::Add(out_h2 + out_h2_offset, val); + } +} + +template +struct UnsortedSegmentSumPad { + void operator()(ep::Stream* stream, const K* segment_ids, const T* data, int64_t num_segment_ids, + int64_t num_segments, int64_t inner_dim_size, int64_t padded_inner_dim_size, + T* out) const { + UNIMPLEMENTED(); + } +}; + +template +struct UnsortedSegmentSumPad { + void operator()(ep::Stream* stream, const K* segment_ids, const half* data, + int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, + int64_t padded_inner_dim_size, half* out) const { + const int64_t data_elem_cnt = num_segment_ids * inner_dim_size; + const int64_t out_elem_cnt = num_segments * padded_inner_dim_size; + CHECK_EQ(padded_inner_dim_size % 2, 0); + CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); + const int64_t h2_inner_dim_size = padded_inner_dim_size / 2; + const int64_t in_h2_elem_cnt = num_segment_ids * h2_inner_dim_size; + if (std::max(data_elem_cnt, out_elem_cnt) < GetMaxVal() / 2) { + UnsortedSegmentHalfGpu + <<As()->cuda_stream()>>>( + in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, + reinterpret_cast(out)); + } else { + UnsortedSegmentHalfGpu + <<As()->cuda_stream()>>>( + in_h2_elem_cnt, h2_inner_dim_size, inner_dim_size, data, segment_ids, num_segments, + reinterpret_cast(out)); + } + } +}; + +template +void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const T* data, + int64_t num_segment_ids, int64_t num_segments, int64_t inner_dim_size, + int64_t padded_inner_dim_size, T* out) { + if (inner_dim_size == padded_inner_dim_size) { + UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( + stream, segment_ids, data, num_segment_ids, num_segments, 1, inner_dim_size, 0, out); + } else { + CHECK_EQ(inner_dim_size + 1, padded_inner_dim_size); + UnsortedSegmentSumPad()(stream, segment_ids, data, num_segment_ids, num_segments, + inner_dim_size, padded_inner_dim_size, out); + } +} + +} // namespace + +template +void UniqueAndPartition(GPU(Stream_t) cuda_stream, int64_t num_ids, size_t capacity, + int64_t num_partition, const K* ids, const V* table_ids, + IDX* num_partitioned_unique_ids_ptr, K* partitioned_unique_ids, + V* partitioned_unique_table_ids, IDX* inverse_unique_partition_indices, + void* workspace_ptr, size_t workspace_bytes, bool need_process_table_ids, + const bool has_padding_idx, const int64_t padding_idx) { + size_t table_capacity_bytes = capacity * sizeof(TableEntry); + CHECK_GE(workspace_bytes, table_capacity_bytes); + OF_CUDA_CHECK(GPU(MemsetAsync)(workspace_ptr, 0, table_capacity_bytes, cuda_stream)); + OF_CUDA_CHECK( + GPU(MemsetAsync)(num_partitioned_unique_ids_ptr, 0, num_partition * sizeof(IDX), cuda_stream)); + HashTableUniqueAndPartitionPairs + <<>>( + capacity, num_ids, num_partition, num_partitioned_unique_ids_ptr, + reinterpret_cast*>(workspace_ptr), ids, table_ids, partitioned_unique_ids, + partitioned_unique_table_ids, inverse_unique_partition_indices, need_process_table_ids, + has_padding_idx, padding_idx); +} + +template +void ShuffleEmbeddings(GPU(Stream_t) cuda_stream, ncclComm_t comm, int64_t parallel_id, + int64_t parallel_num, int64_t num_ids, int64_t embedding_size, + DataType data_type, IDX* host_num_unique_matrix, + const T* reverse_unique_cur_rank_embeddings, T* received_embeddings) { + std::vector send_offsets; + std::vector send_elem_cnt; + std::vector recv_offsets; + std::vector recv_elem_cnt; + MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, + &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); + ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, + reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); +} + +// Quantized Version. +template +void ShuffleEmbeddings(GPU(Stream_t) cuda_stream, ncclComm_t comm, int64_t parallel_id, + int64_t parallel_num, int64_t num_ids, int64_t embedding_size, + DataType data_type, IDX* host_num_unique_matrix, + int8_t* reverse_unique_cur_rank_embeddings, int8_t* received_embeddings, + T* reverse_cur_rank_quantize_factor, T* recv_quantize_factor) { + std::vector send_offsets; + std::vector send_elem_cnt; + std::vector recv_offsets; + std::vector recv_elem_cnt; + // shuffle quantized_embedding + MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, + &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); + ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, + reverse_unique_cur_rank_embeddings, recv_offsets, recv_elem_cnt, received_embeddings); + // shuffle quantize_factor + MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, + parallel_num, &recv_offsets, &recv_elem_cnt, &send_offsets, &send_elem_cnt); + ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, + reverse_cur_rank_quantize_factor, recv_offsets, recv_elem_cnt, recv_quantize_factor); +} + +template +void ShuffleEmbeddingsGrad(GPU(Stream_t) cuda_stream, ncclComm_t comm, int64_t parallel_id, + int64_t parallel_num, int64_t num_ids, int64_t embedding_size, + DataType data_type, IDX* host_num_unique_matrix, + const T* unique_partition_embedding_grad, T* received_embeddings_grad) { + std::vector send_offsets; + std::vector send_elem_cnt; + std::vector recv_offsets; + std::vector recv_elem_cnt; + MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, + &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); + ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, + unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, + received_embeddings_grad); +} + +// Quantize Version. +template +void ShuffleEmbeddingsGrad(GPU(Stream_t) cuda_stream, ncclComm_t comm, int64_t parallel_id, + int64_t parallel_num, int64_t num_ids, int64_t embedding_size, + DataType data_type, IDX* host_num_unique_matrix, + int8_t* unique_partition_embedding_grad, + int8_t* received_embeddings_grad, T* cur_rank_quantize_factor, + T* received_cur_rank_quantize_factor) { + std::vector send_offsets; + std::vector send_elem_cnt; + std::vector recv_offsets; + std::vector recv_elem_cnt; + // Shuffle Embedding Grad. + MakeShuffleParams(host_num_unique_matrix, num_ids, embedding_size, parallel_id, parallel_num, + &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); + ShuffleData(cuda_stream, comm, DataType::kInt8, send_offsets, send_elem_cnt, + unique_partition_embedding_grad, recv_offsets, recv_elem_cnt, + received_embeddings_grad); + // Shuffle Quantize factor. + MakeShuffleParams(host_num_unique_matrix, num_ids, /*embedding_size=*/1, parallel_id, + parallel_num, &send_offsets, &send_elem_cnt, &recv_offsets, &recv_elem_cnt); + ShuffleData(cuda_stream, comm, data_type, send_offsets, send_elem_cnt, cur_rank_quantize_factor, + recv_offsets, recv_elem_cnt, received_cur_rank_quantize_factor); +} + +inline int64_t GetPaddedEmbeddingSize(DataType data_type, int64_t embedding_size) { + if (data_type == DataType::kFloat16 && embedding_size % 2 != 0) { + return embedding_size + 1; + } else { + return embedding_size; + } +} + +template +void UniquePartitionEmbeddingGrad(ep::Stream* stream, int64_t unique_partitioned_num_ids, + int64_t num_ids, int64_t embedding_size, + int64_t padded_embedding_size, const IDX* host_num_unique_matrix, + const T* embedding_grad, + const IDX* inverse_unique_partition_indices, + T* unique_partition_embedding_grad) { + const int64_t valid_value_size = unique_partitioned_num_ids * padded_embedding_size * sizeof(T); + OF_CUDA_CHECK(GPU(MemsetAsync)(unique_partition_embedding_grad, 0, valid_value_size, + stream->As()->cuda_stream())); + UnsortedSegmentSum(stream, inverse_unique_partition_indices, embedding_grad, num_ids, + unique_partitioned_num_ids, embedding_size, padded_embedding_size, + unique_partition_embedding_grad); +} + +template +void UniqueCurRankEmbeddingGrad(ep::Stream* stream, DataType data_type, int64_t cur_rank_num_ids, + int64_t num_unique, int64_t embedding_size, + int64_t padded_embedding_size, bool only_zero_valid_grad, + int64_t cur_rank_unique_embedding_grad_elem_cnt, + const T* cur_rank_embedding_grad, + const IDX* cur_rank_inverse_indices, + T* cur_rank_unique_embedding_grad, T* tmp_buffer) { + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); + // memset cur_rank_unique_embedding_grad, if only_zero_valid_grad, only memset valid data. + if (only_zero_valid_grad) { + OF_CUDA_CHECK(GPU(MemsetAsync)(cur_rank_unique_embedding_grad, 0, + num_unique * embedding_size * sizeof(T), cuda_stream)); + } else { + OF_CUDA_CHECK(GPU(MemsetAsync)(cur_rank_unique_embedding_grad, 0, + cur_rank_unique_embedding_grad_elem_cnt * sizeof(T), + cuda_stream)); + } + T* unsorted_segment_sum_out; + if (embedding_size != padded_embedding_size) { + unsorted_segment_sum_out = tmp_buffer; + size_t buffer_size = GetCudaAlignedSize(num_unique * padded_embedding_size * sizeof(T)); + OF_CUDA_CHECK(GPU(MemsetAsync)(unsorted_segment_sum_out, 0, buffer_size, cuda_stream)); + } else { + // cur_rank_unique_embedding_grad's has been memset, not need to memset again. + unsorted_segment_sum_out = cur_rank_unique_embedding_grad; + } + UnsortedSegmentSum(stream, cur_rank_inverse_indices, cur_rank_embedding_grad, + cur_rank_num_ids, num_unique, padded_embedding_size, + padded_embedding_size, unsorted_segment_sum_out); + if (embedding_size != padded_embedding_size) { + std::unique_ptr primitive = + ep::primitive::NewPrimitive(DeviceType::kCUDA, 2); + DimVector dst_shape = {num_unique, embedding_size}; + DimVector dst_pos_vec = {0, 0}; + DimVector src_shape = {num_unique, padded_embedding_size}; + DimVector src_pos_vec = {0, 0}; + DimVector extent_vec = {num_unique, embedding_size}; + primitive->Launch(stream, data_type, 2, cur_rank_unique_embedding_grad, dst_shape.data(), + dst_pos_vec.data(), unsorted_segment_sum_out, src_shape.data(), + src_pos_vec.data(), extent_vec.data()); + } +} + +template +struct IdShuffleDataPtrs { + const K* ids_ptr; + const U* table_ids_ptr; + IDX* num_partitioned_unique; + K* partitioned_unique_ids; + U* partitioned_unique_table_ids; + IDX* num_unique_matrix_ptr; + IDX* inverse_unique_partition_indices_ptr; + void* workspace_ptr; + size_t workspace_size; + K* received_ids; + U* received_table_ids; + IDX* cur_rank_num_unique_ptr; + K* cur_rank_unique_ids_ptr; + U* cur_rank_unique_table_ids_ptr; + IDX* cur_rank_inverse_indices_ptr; +}; + +template +void IdShuffle(ep::Stream* stream, ncclComm_t comm, const IdShuffleDataPtrs& data_ptrs, + int64_t num_ids, int64_t parallel_id, int64_t parallel_num, + DataType num_unique_matrix_dtype, DataType ids_dtype, DataType table_ids_dtype, + bool need_process_table_ids, const bool has_padding_idx, const int64_t padding_idx, + IDX* host_num_unique_matrix, IDX* host_num_keys) { + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); + size_t hash_table_capacity = parallel_num * num_ids; + UniqueAndPartition( + cuda_stream, num_ids, hash_table_capacity, parallel_num, data_ptrs.ids_ptr, + data_ptrs.table_ids_ptr, data_ptrs.num_partitioned_unique, data_ptrs.partitioned_unique_ids, + data_ptrs.partitioned_unique_table_ids, data_ptrs.inverse_unique_partition_indices_ptr, + data_ptrs.workspace_ptr, data_ptrs.workspace_size, need_process_table_ids, has_padding_idx, + padding_idx); + + OF_NCCL_CHECK(ncclAllGather(data_ptrs.num_partitioned_unique, data_ptrs.num_unique_matrix_ptr, + parallel_num, GetNcclDataType(num_unique_matrix_dtype), comm, + cuda_stream)); + + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_unique_matrix, data_ptrs.num_unique_matrix_ptr, + parallel_num * parallel_num * sizeof(IDX), GPU(MemcpyDefault), + cuda_stream)); + CHECK_JUST(stream->Sync()); + if (parallel_num > 1) { + // use num_partitioned_unique as indices_offset buffer, so should after ncclAllGather. + ComputeOffset<<<1, 1, 0, cuda_stream>>>(parallel_num, data_ptrs.num_partitioned_unique); + ContiguousInverseUniquePartitionIndices<<>>( + num_ids, data_ptrs.num_partitioned_unique, data_ptrs.inverse_unique_partition_indices_ptr); + } + int64_t received_elem_cnt = 0; + ShuffleIdsAndTableIds(cuda_stream, comm, parallel_id, parallel_num, num_ids, ids_dtype, + table_ids_dtype, host_num_unique_matrix, data_ptrs.partitioned_unique_ids, + data_ptrs.partitioned_unique_table_ids, data_ptrs.received_ids, + data_ptrs.received_table_ids, &received_elem_cnt, need_process_table_ids); + UniqueAndPartition( + cuda_stream, received_elem_cnt, hash_table_capacity, 1, data_ptrs.received_ids, + data_ptrs.received_table_ids, data_ptrs.cur_rank_num_unique_ptr, + data_ptrs.cur_rank_unique_ids_ptr, data_ptrs.cur_rank_unique_table_ids_ptr, + data_ptrs.cur_rank_inverse_indices_ptr, data_ptrs.workspace_ptr, data_ptrs.workspace_size, + need_process_table_ids, has_padding_idx, padding_idx); + if (!need_process_table_ids) { + OF_CUDA_CHECK(GPU(MemsetAsync)(data_ptrs.cur_rank_unique_table_ids_ptr, 0, + received_elem_cnt * sizeof(U), cuda_stream)); + } + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_keys, data_ptrs.cur_rank_num_unique_ptr, sizeof(IDX), + GPU(MemcpyDefault), cuda_stream)); + CHECK_JUST(stream->Sync()); +} + +} // namespace data_shuffle +} // namespace oneflow diff --git a/oneflow/user/kernels/one_embedding_embedding_gradient_shuffle_p2p_kernel.cu b/oneflow/user/kernels/one_embedding_embedding_gradient_shuffle_p2p_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..1a53fa850620944bd0fc37932c697e08845212f3 --- /dev/null +++ b/oneflow/user/kernels/one_embedding_embedding_gradient_shuffle_p2p_kernel.cu @@ -0,0 +1,361 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/embedding/embedding_manager.h" +#include "oneflow/core/control/ctrl_client.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +#ifdef WITH_CUDA +#include +#endif + +#if CUDA_VERSION >= 11030 + +namespace oneflow { + +namespace { + +template +struct alignas(sizeof(T) * pack_size) Pack { + T elem[pack_size]; +}; + +template +__device__ __inline__ void AtomicAdd(Pack* address, Pack val) { +#pragma unroll + for (int i = 0; i < pack_size; ++i) { + cuda::atomic::Add(reinterpret_cast(address) + i, static_cast(val.elem[i])); + } +} + +template<> +__device__ __inline__ void AtomicAdd(Pack* address, Pack val) { + half2 h2_val; + h2_val.x = static_cast(val.elem[0]); + h2_val.y = static_cast(val.elem[1]); + cuda::atomic::Add(reinterpret_cast(address), h2_val); +} + +template +struct Param { + const IDX* cur_rank_inverse_indices; + const Pack* unique_partitioned_embedding_grads[N]; + int32_t* is_kernel_start[N]; + const IDX* num_unique_matrix; + Pack* cur_rank_unique_embedding_grad_ptr; +}; + +template +__global__ void EmbeddingGradientShuffleCudaKernel(int64_t parallel_id, int64_t parallel_num, + int64_t embedding_num_pack, + Param param) { +#pragma unroll 1 + for (int i = 0; i < parallel_num; ++i) { + int rank_id = (parallel_id + i) % parallel_num; + IDX cur_rank_index_offset = 0; + for (int k = 0; k < rank_id; ++k) { + cur_rank_index_offset += param.num_unique_matrix[k * parallel_num + parallel_id]; + } + IDX in_index_offset = 0; + for (int k = 0; k < parallel_id; ++k) { + in_index_offset += param.num_unique_matrix[rank_id * parallel_num + k]; + } + const IDX* cur_rank_inverse_indices_ptr = + param.cur_rank_inverse_indices + cur_rank_index_offset; + const Pack* unique_partitioned_embedding_grad_ptr = + param.unique_partitioned_embedding_grads[rank_id] + in_index_offset * embedding_num_pack; + Pack* cur_rank_unique_embedding_grad_ptr = + param.cur_rank_unique_embedding_grad_ptr; + const int copy_cnt = + param.num_unique_matrix[rank_id * parallel_num + parallel_id] * embedding_num_pack; + CUDA_1D_KERNEL_LOOP_T(int, j, copy_cnt) { + int in_row_id = j / embedding_num_pack; + int col_id = j - in_row_id * embedding_num_pack; + int out_row_id = cur_rank_inverse_indices_ptr[in_row_id]; + Pack grad_val = unique_partitioned_embedding_grad_ptr[j]; + AtomicAdd(cur_rank_unique_embedding_grad_ptr + out_row_id * embedding_num_pack + col_id, + grad_val); + } + } +} + +template +__global__ void BarrierKernel(int32_t parallel_id, int32_t parallel_num, + Param param) { + int count = param.is_kernel_start[parallel_id][parallel_id]; + if (threadIdx.x < parallel_num) { + volatile int32_t* start_f = param.is_kernel_start[parallel_id]; + volatile int32_t* remote_start_f = param.is_kernel_start[threadIdx.x]; + start_f[threadIdx.x] = count + 1; + while (remote_start_f[parallel_id] < count + 1) {} + } +} + +struct IpcMemHandleOffset { + cudaIpcMemHandle_t handle; + int64_t offset; +}; + +void GetPtrs(user_op::KernelComputeContext* ctx, + std::vector* unique_partitioned_embedding_grad_ptr, + std::vector* is_kernel_start_ptr) { + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + unique_partitioned_embedding_grad_ptr->at(parallel_id) = + const_cast(ctx->Tensor4ArgNameAndIndex("embedding_grad", 0)->dptr()); + std::string name = ctx->op_name(); + { + std::vector push_handle_offset; + push_handle_offset.resize(2); + OF_CUDA_CHECK(cudaIpcGetMemHandle(&push_handle_offset.at(0).handle, + unique_partitioned_embedding_grad_ptr->at(parallel_id))); + OF_CUDA_CHECK(cudaIpcGetMemHandle(&push_handle_offset.at(1).handle, + is_kernel_start_ptr->at(parallel_id))); + cudaError_t (*func)(void*, CUpointer_attribute, CUdeviceptr); + OF_CUDA_CHECK( + cudaGetDriverEntryPoint("cuPointerGetAttribute", (void**)(&func), cudaEnableDefault)); + void* embedding_grad_base; + OF_CUDA_CHECK(func(&embedding_grad_base, CU_POINTER_ATTRIBUTE_RANGE_START_ADDR, + (CUdeviceptr)(unique_partitioned_embedding_grad_ptr->at(parallel_id)))); + push_handle_offset.at(0).offset = + reinterpret_cast(unique_partitioned_embedding_grad_ptr->at(parallel_id)) + - reinterpret_cast(embedding_grad_base); + push_handle_offset.at(1).offset = 0; + Singleton::Get()->PushKV( + name + std::to_string(parallel_id), + std::string(reinterpret_cast(push_handle_offset.data()), + 2 * sizeof(IpcMemHandleOffset))); + } + for (int64_t i = 0; i < parallel_num; ++i) { + std::string key = name + std::to_string(i); + if (parallel_id != i) { + std::vector handle_offset; + handle_offset.resize(2); + Singleton::Get()->PullKV(key, [i, &handle_offset](const std::string& val) { + memcpy(handle_offset.data(), val.data(), 2 * sizeof(IpcMemHandleOffset)); + }); + OF_CUDA_CHECK(cudaIpcOpenMemHandle(&unique_partitioned_embedding_grad_ptr->at(i), + handle_offset.at(0).handle, + cudaIpcMemLazyEnablePeerAccess)); + unique_partitioned_embedding_grad_ptr->at(i) = + reinterpret_cast(unique_partitioned_embedding_grad_ptr->at(i)) + + handle_offset.at(0).offset; + OF_CUDA_CHECK(cudaIpcOpenMemHandle(&is_kernel_start_ptr->at(i), handle_offset.at(1).handle, + cudaIpcMemLazyEnablePeerAccess)); + is_kernel_start_ptr->at(i) = + reinterpret_cast(is_kernel_start_ptr->at(i)) + handle_offset.at(1).offset; + } + } +} + +template +class DataShuffleKernelState final : public user_op::OpKernelState { + public: + explicit DataShuffleKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1), + parallel_desc_(ctx->parallel_desc()), + parallel_id_(ctx->parallel_ctx().parallel_id()) { + OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + int64_t parallel_num = parallel_desc_.parallel_num(); + unique_partitioned_embedding_grad_ptr_.resize(parallel_num); + is_kernel_start_ptr_.resize(parallel_num); + size_t is_kernel_start_size = GetCudaAlignedSize(parallel_num * sizeof(int32_t)); + OF_CUDA_CHECK(cudaMalloc(&is_kernel_start_ptr_.at(parallel_id_), is_kernel_start_size)); + OF_CUDA_CHECK(cudaMemset(is_kernel_start_ptr_.at(parallel_id_), 0, is_kernel_start_size)); + } + + ~DataShuffleKernelState() { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(cudaFree(is_kernel_start_ptr_.at(parallel_id_))); + } + + std::vector* UniquePartitionedEmbeddingGrads() { + return &unique_partitioned_embedding_grad_ptr_; + } + + std::vector* IsKernelStart() { return &is_kernel_start_ptr_; } + + private: + int device_index_; + ParallelDesc parallel_desc_; + int64_t parallel_id_; + std::vector unique_partitioned_embedding_grad_ptr_; + std::vector is_kernel_start_ptr_; +}; + +constexpr int pack_size = 2; + +template +__global__ void MemsetCurRankEmbeddingGrad(int64_t parallel_id, int64_t parallel_num, + int64_t vector_size, const uint32_t* num_unique_matrix, + T* dst) { + size_t count = 0; + for (int i = 0; i < parallel_num; ++i) { + count += num_unique_matrix[i * parallel_num + parallel_id] * vector_size; + } + const size_t pack_count = count / pack; + Pack pack_value; + for (int i = 0; i < pack; ++i) { pack_value.elem[i] = static_cast(0); } + auto* pack_dst = reinterpret_cast*>(dst); + CUDA_1D_KERNEL_LOOP_T(size_t, i, pack_count) { pack_dst[i] = pack_value; } + T* tail_dst = dst + pack_count * pack; + const size_t tail_count = count - pack_count * pack; + CUDA_1D_KERNEL_LOOP_T(size_t, i, tail_count) { tail_dst[i] = static_cast(0); } +} + +template +typename std::enable_if<(pack != 0), void>::type LaunchPackMemsetCurRankEmbeddingGrad( + cudaStream_t stream, const uint32_t* num_unique_matrix, T* ptr, int sm_count, + int64_t vector_size, int64_t parallel_id, int64_t parallel_num) { + MemsetCurRankEmbeddingGrad<<<2 * sm_count, 1024, 0, stream>>>( + parallel_id, parallel_num, vector_size, num_unique_matrix, ptr); +} + +template +typename std::enable_if<(pack == 0), void>::type LaunchPackMemsetCurRankEmbeddingGrad( + cudaStream_t stream, const uint32_t* num_unique_matrix, T* ptr, int sm_count, + int64_t vector_size, int64_t parallel_id, int64_t parallel_num) { + LOG(FATAL) << "wrong alignment"; +} + +template +void LaunchMemsetCurRankEmbeddingGrad(cudaStream_t stream, int sm_count, int64_t vector_size, + int64_t parallel_id, int64_t parallel_num, + const uint32_t* num_unique_matrix, T* ptr) { + auto uintptr = reinterpret_cast(ptr); + if (uintptr % 16 == 0) { + LaunchPackMemsetCurRankEmbeddingGrad( + stream, num_unique_matrix, ptr, sm_count, vector_size, parallel_id, parallel_num); + } else if (uintptr % 8 == 0) { + LaunchPackMemsetCurRankEmbeddingGrad(stream, num_unique_matrix, ptr, sm_count, + vector_size, parallel_id, parallel_num); + } else if (uintptr % 4 == 0) { + LaunchPackMemsetCurRankEmbeddingGrad(stream, num_unique_matrix, ptr, sm_count, + vector_size, parallel_id, parallel_num); + } else if (uintptr % 2 == 0) { + LaunchPackMemsetCurRankEmbeddingGrad(stream, num_unique_matrix, ptr, sm_count, + vector_size, parallel_id, parallel_num); + } else { + LaunchPackMemsetCurRankEmbeddingGrad(stream, num_unique_matrix, ptr, sm_count, + vector_size, parallel_id, parallel_num); + } +} + +} // namespace + +template +class EmbeddingGraidientShuffleP2PKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + EmbeddingGraidientShuffleP2PKernel() : current_iter_(0) {} + ~EmbeddingGraidientShuffleP2PKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + bool IsReadyForCapture(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + if (current_iter_ == 0) { + return false; + } else { + return true; + } + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + CHECK(!embedding::UseDynamicMemoryAllocation()); + CHECK(ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSE_EMBEDDING_INTERACTION", + false)); // only support skip last gather. + CHECK(ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ADD_ID_SHUFFLE_COPY_OUT", + true)); // when no identity, every time the cur_rank_inverse_indices + // will change becauseof regster num=2. + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); + const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); + const user_op::Tensor* cur_rank_inverse_indices = + ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); + user_op::Tensor* cur_rank_unique_embedding_grad = + ctx->Tensor4ArgNameAndIndex("cur_rank_unique_embedding_grad", 0); + + const int64_t embedding_size = ctx->Attr("embedding_size"); + const bool only_zero_valid_grad = ctx->Attr("only_zero_valid_grad"); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const int sm_count = + ctx->stream()->As()->device_properties().multiProcessorCount; + const bool skip_first_scatter = ctx->Attr("skip_first_scatter"); + CHECK(skip_first_scatter); + cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + if (current_iter_ == 0) { + GetPtrs(ctx, kernel_state->UniquePartitionedEmbeddingGrads(), kernel_state->IsKernelStart()); + } + CHECK_EQ(kernel_state->UniquePartitionedEmbeddingGrads()->at(parallel_id), + embedding_grad->dptr()); + Param param; + CHECK_EQ(embedding_size % pack_size, 0); + CHECK_LE(parallel_num, 8); + param.cur_rank_unique_embedding_grad_ptr = + reinterpret_cast*>(cur_rank_unique_embedding_grad->mut_dptr()); + for (int i = 0; i < parallel_num; ++i) { + param.unique_partitioned_embedding_grads[i] = reinterpret_cast*>( + kernel_state->UniquePartitionedEmbeddingGrads()->at(i)); + param.is_kernel_start[i] = reinterpret_cast(kernel_state->IsKernelStart()->at(i)); + } + param.cur_rank_inverse_indices = reinterpret_cast(cur_rank_inverse_indices->dptr()); + param.num_unique_matrix = reinterpret_cast(num_unique_matrix->dptr()); + int64_t embedding_num_pack = embedding_size / pack_size; + if (only_zero_valid_grad) { + LaunchMemsetCurRankEmbeddingGrad(cuda_stream, sm_count, embedding_size, parallel_id, + parallel_num, + reinterpret_cast(num_unique_matrix->dptr()), + cur_rank_unique_embedding_grad->mut_dptr()); + } else { + OF_CUDA_CHECK(cudaMemsetAsync( + cur_rank_unique_embedding_grad->mut_dptr(), 0, + cur_rank_unique_embedding_grad->shape_view().elem_cnt() * sizeof(T), cuda_stream)); + } + BarrierKernel<<<1, parallel_num, 0, cuda_stream>>>(parallel_id, parallel_num, param); + const int num_blocks = + 2 * ctx->stream()->As()->device_properties().multiProcessorCount; + EmbeddingGradientShuffleCudaKernel<<>>( + parallel_id, parallel_num, embedding_num_pack, param); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +REGISTER_USER_KERNEL("embedding_gradient_shuffle") + .SetCreateFn>() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobDataType("embedding_grad", 0) == DataType::kFloat16) + && (user_op::HobDataType("num_unique_matrix", 0) == DataType::kUInt32) + && (user_op::HobAttr("skip_first_scatter") == true) + && (embedding::UseEmbeddingGradientShuffleP2PKernel(DataType::kFloat16, + DataType::kUInt32))); + +} // namespace oneflow + +#endif // CUDA_VERSION >= 11030 diff --git a/oneflow/user/kernels/one_embedding_embedding_shuffle_p2p_kernel.cu b/oneflow/user/kernels/one_embedding_embedding_shuffle_p2p_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..14598fcaa92dc56417ce6dc3177e5c3b592df53d --- /dev/null +++ b/oneflow/user/kernels/one_embedding_embedding_shuffle_p2p_kernel.cu @@ -0,0 +1,375 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/embedding/embedding_manager.h" +#include "oneflow/core/control/ctrl_client.h" +#include "oneflow/core/kernel/cuda_graph_support.h" + +#ifdef WITH_CUDA +#include +#endif + +#if CUDA_VERSION >= 11030 + +namespace oneflow { + +namespace { + +template +struct alignas(sizeof(T) * pack_size) Pack { + T elem[pack_size]; +}; + +template +struct Param { + IDX* inverse_indices[N]; + Pack* unique_embeddings[N]; + int32_t* is_kernel_start[N]; + const IDX* num_unique_matrix; + Pack* embedding_ptr; +}; + +template +__global__ void EmbeddingShuffleCudaKernel(int parallel_id, int parallel_num, + int embedding_num_pack, + Param param) { +#pragma unroll 1 + for (int i = 0; i < parallel_num; ++i) { + int rank_id = (parallel_id + i) % parallel_num; + IDX out_index_offset = 0; + for (int k = 0; k < rank_id; ++k) { + out_index_offset += param.num_unique_matrix[parallel_id * parallel_num + k]; + } + IDX in_index_offset = 0; + for (int k = 0; k < parallel_id; ++k) { + in_index_offset += param.num_unique_matrix[k * parallel_num + rank_id]; + } + const IDX* inverse_indices_ptr = param.inverse_indices[rank_id] + in_index_offset; + const Pack* unique_embeddings_ptr = param.unique_embeddings[rank_id]; + Pack* embedding_ptr = param.embedding_ptr + out_index_offset * embedding_num_pack; + const int copy_cnt = + param.num_unique_matrix[parallel_id * parallel_num + rank_id] * embedding_num_pack; + CUDA_1D_KERNEL_LOOP_T(int, j, copy_cnt) { + int out_row_id = j / embedding_num_pack; + int in_row_id = inverse_indices_ptr[out_row_id]; + int col_id = j - out_row_id * embedding_num_pack; + embedding_ptr[j] = unique_embeddings_ptr[in_row_id * embedding_num_pack + col_id]; + } + } +} + +template +__global__ void EmbeddingShuffleCopyKernel(int parallel_id, int parallel_num, + int embedding_num_pack, + Param param) { +#pragma unroll 1 + for (int i = 0; i < parallel_num; ++i) { + int rank_id = (parallel_id + i) % parallel_num; + IDX out_index_offset = 0; + for (int k = 0; k < rank_id; ++k) { + out_index_offset += param.num_unique_matrix[parallel_id * parallel_num + k]; + } + IDX in_index_offset = 0; + for (int k = 0; k < parallel_id; ++k) { + in_index_offset += param.num_unique_matrix[k * parallel_num + rank_id]; + } + const Pack* unique_embeddings_ptr = + param.unique_embeddings[rank_id] + in_index_offset * embedding_num_pack; + Pack* embedding_ptr = param.embedding_ptr + out_index_offset * embedding_num_pack; + const int copy_cnt = + param.num_unique_matrix[parallel_id * parallel_num + rank_id] * embedding_num_pack; + CUDA_1D_KERNEL_LOOP_T(int, j, copy_cnt) { embedding_ptr[j] = unique_embeddings_ptr[j]; } + } +} + +template +__global__ void GatherKernel(int parallel_id, int parallel_num, int embedding_num_pack, + const IDX* num_unique_matrix, const IDX* inverse_indices, + const Pack* unique_embeddings, + Pack* gather_out_unique_embeddings) { + int cur_rank_num_ids = 0; + for (int i = 0; i < parallel_num; ++i) { + cur_rank_num_ids += num_unique_matrix[i * parallel_num + parallel_id]; + } + int out_cnt = cur_rank_num_ids * embedding_num_pack; + CUDA_1D_KERNEL_LOOP_T(int, i, out_cnt) { + int out_row_id = i / embedding_num_pack; + int in_row_id = inverse_indices[out_row_id]; + int col_id = i - out_row_id * embedding_num_pack; + gather_out_unique_embeddings[i] = unique_embeddings[in_row_id * embedding_num_pack + col_id]; + } +} + +template +__global__ void BarrierKernel(int32_t parallel_id, int32_t parallel_num, + Param param) { + int count = param.is_kernel_start[parallel_id][parallel_id]; + if (threadIdx.x < parallel_num) { + volatile int32_t* start_f = param.is_kernel_start[parallel_id]; + volatile int32_t* remote_start_f = param.is_kernel_start[threadIdx.x]; + start_f[threadIdx.x] = count + 1; + while (remote_start_f[parallel_id] < count + 1) {} + } +} + +struct IpcMemHandleOffset { + cudaIpcMemHandle_t handle; + int64_t offset; +}; + +bool DisableFuseGatherCopy() { + return ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_P2P_DISABLE_FUSE_GATHER_COPY", false); +} + +void GetPtrs(user_op::KernelComputeContext* ctx, std::vector* unique_embeddings_ptr, + std::vector* inverse_indices_ptr, std::vector* is_kernel_start_ptr) { + const int64_t num_ids = + ctx->TensorDesc4ArgNameAndIndex("inverse_unique_partition_indices", 0)->shape().elem_cnt(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + inverse_indices_ptr->at(parallel_id) = + const_cast(ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0)->dptr()); + if (DisableFuseGatherCopy()) { + unique_embeddings_ptr->at(parallel_id) = + ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr(); + } else { + unique_embeddings_ptr->at(parallel_id) = + const_cast(ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0)->dptr()); + } + + std::string name = ctx->op_name(); + { + std::vector push_handle_offset; + push_handle_offset.resize(3); + OF_CUDA_CHECK(cudaIpcGetMemHandle(&push_handle_offset.at(0).handle, + unique_embeddings_ptr->at(parallel_id))); + OF_CUDA_CHECK(cudaIpcGetMemHandle(&push_handle_offset.at(1).handle, + inverse_indices_ptr->at(parallel_id))); + OF_CUDA_CHECK(cudaIpcGetMemHandle(&push_handle_offset.at(2).handle, + is_kernel_start_ptr->at(parallel_id))); + + cudaError_t (*func)(void*, CUpointer_attribute, CUdeviceptr); + OF_CUDA_CHECK( + cudaGetDriverEntryPoint("cuPointerGetAttribute", (void**)(&func), cudaEnableDefault)); + void* unique_embeddings_base; + OF_CUDA_CHECK(func(&unique_embeddings_base, CU_POINTER_ATTRIBUTE_RANGE_START_ADDR, + (CUdeviceptr)(unique_embeddings_ptr->at(parallel_id)))); + push_handle_offset.at(0).offset = + reinterpret_cast(unique_embeddings_ptr->at(parallel_id)) + - reinterpret_cast(unique_embeddings_base); + void* inverse_indices_base; + OF_CUDA_CHECK(func(&inverse_indices_base, CU_POINTER_ATTRIBUTE_RANGE_START_ADDR, + (CUdeviceptr)(inverse_indices_ptr->at(parallel_id)))); + push_handle_offset.at(1).offset = reinterpret_cast(inverse_indices_ptr->at(parallel_id)) + - reinterpret_cast(inverse_indices_base); + push_handle_offset.at(2).offset = 0; + Singleton::Get()->PushKV( + name + std::to_string(parallel_id), + std::string(reinterpret_cast(push_handle_offset.data()), + 3 * sizeof(IpcMemHandleOffset))); + } + for (int64_t i = 0; i < parallel_num; ++i) { + std::string key = name + std::to_string(i); + if (parallel_id != i) { + std::vector handle_offset; + handle_offset.resize(3); + Singleton::Get()->PullKV(key, [i, &handle_offset](const std::string& val) { + memcpy(handle_offset.data(), val.data(), 3 * sizeof(IpcMemHandleOffset)); + }); + OF_CUDA_CHECK(cudaIpcOpenMemHandle(&unique_embeddings_ptr->at(i), handle_offset.at(0).handle, + cudaIpcMemLazyEnablePeerAccess)); + unique_embeddings_ptr->at(i) = + reinterpret_cast(unique_embeddings_ptr->at(i)) + handle_offset.at(0).offset; + + OF_CUDA_CHECK(cudaIpcOpenMemHandle(&inverse_indices_ptr->at(i), handle_offset.at(1).handle, + cudaIpcMemLazyEnablePeerAccess)); + inverse_indices_ptr->at(i) = + reinterpret_cast(inverse_indices_ptr->at(i)) + handle_offset.at(1).offset; + + OF_CUDA_CHECK(cudaIpcOpenMemHandle(&is_kernel_start_ptr->at(i), handle_offset.at(2).handle, + cudaIpcMemLazyEnablePeerAccess)); + is_kernel_start_ptr->at(i) = + reinterpret_cast(is_kernel_start_ptr->at(i)) + handle_offset.at(2).offset; + } + } +} + +template +class DataShuffleKernelState final : public user_op::OpKernelState { + public: + explicit DataShuffleKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1), + parallel_desc_(ctx->parallel_desc()), + parallel_id_(ctx->parallel_ctx().parallel_id()) { + OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + int64_t parallel_num = parallel_desc_.parallel_num(); + unique_embeddings_ptr_.resize(parallel_num); + inverse_indices_ptr_.resize(parallel_num); + is_kernel_start_ptr_.resize(parallel_num); + size_t is_kernel_start_size = GetCudaAlignedSize(parallel_num * sizeof(int32_t)); + OF_CUDA_CHECK(cudaMalloc(&is_kernel_start_ptr_.at(parallel_id_), is_kernel_start_size)); + OF_CUDA_CHECK(cudaMemset(is_kernel_start_ptr_.at(parallel_id_), 0, is_kernel_start_size)); + } + + ~DataShuffleKernelState() { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(cudaFree(is_kernel_start_ptr_.at(parallel_id_))); + } + + std::vector* UniqueEmbeddings() { return &unique_embeddings_ptr_; } + + std::vector* InverseIndices() { return &inverse_indices_ptr_; } + + std::vector* IsKernelStart() { return &is_kernel_start_ptr_; } + + private: + int device_index_; + ParallelDesc parallel_desc_; + int64_t parallel_id_; + std::vector unique_embeddings_ptr_; + std::vector inverse_indices_ptr_; + std::vector is_kernel_start_ptr_; +}; + +template +void LaunchKernel(user_op::KernelComputeContext* ctx, DataShuffleKernelState* kernel_state) { + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + const int64_t embedding_size = ctx->Attr("embedding_size"); + DataType data_type = embeddings->data_type(); + Param param; + CHECK_LE(parallel_num, 8); + param.embedding_ptr = reinterpret_cast*>(embeddings->mut_dptr()); + for (int i = 0; i < parallel_num; ++i) { + param.inverse_indices[i] = reinterpret_cast(kernel_state->InverseIndices()->at(i)); + param.unique_embeddings[i] = + reinterpret_cast*>(kernel_state->UniqueEmbeddings()->at(i)); + param.is_kernel_start[i] = reinterpret_cast(kernel_state->IsKernelStart()->at(i)); + } + param.num_unique_matrix = reinterpret_cast(num_unique_matrix->dptr()); + int64_t embedding_num_pack = embedding_size / pack_size; + cudaStream_t cuda_stream = ctx->stream()->As()->cuda_stream(); + BarrierKernel<<<1, parallel_num, 0, cuda_stream>>>(parallel_id, parallel_num, param); + const int num_blocks = + 2 * ctx->stream()->As()->device_properties().multiProcessorCount; + + if (DisableFuseGatherCopy()) { + CHECK_EQ(kernel_state->UniqueEmbeddings()->at(parallel_id), + ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->dptr()) + << parallel_id; + GatherKernel<<>>( + parallel_id, parallel_num, embedding_num_pack, param.num_unique_matrix, + param.inverse_indices[parallel_id], + reinterpret_cast*>( + ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0)->dptr()), + param.unique_embeddings[parallel_id]); + EmbeddingShuffleCopyKernel<<>>(parallel_id, parallel_num, + embedding_num_pack, param); + } else { + CHECK_EQ(kernel_state->UniqueEmbeddings()->at(parallel_id), + ctx->Tensor4ArgNameAndIndex("cur_rank_embeddings", 0)->dptr()) + << parallel_id; + EmbeddingShuffleCudaKernel<<>>(parallel_id, parallel_num, + embedding_num_pack, param); + } + if (!ctx->Attr("is_train")) { + BarrierKernel<<<1, parallel_num, 0, cuda_stream>>>( + parallel_id, parallel_num, + param); // if in eval, should add last barrier. + } +} + +} // namespace + +template +class EmbeddingShuffleP2PKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + EmbeddingShuffleP2PKernel() : current_iter_(0) {} + ~EmbeddingShuffleP2PKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + bool IsReadyForCapture(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const override { + if (current_iter_ == 0) { + return false; + } else { + return true; + } + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + CHECK(!embedding::UseDynamicMemoryAllocation()); + CHECK(ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSE_EMBEDDING_INTERACTION", + false)); // only support skip last gather. + CHECK(ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ADD_ID_SHUFFLE_COPY_OUT", + true)); // when no identity, every time the cur_rank_inverse_indices + // will change becauseof regster num=2. + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + const user_op::Tensor* cur_rank_inverse_indices = + ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); + const user_op::Tensor* inverse_unique_partition_indices = + ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); + const bool skip_last_gather = ctx->Attr("skip_last_gather"); + CHECK(skip_last_gather); + const int64_t embedding_size = ctx->Attr("embedding_size"); + if (current_iter_ == 0) { + GetPtrs(ctx, kernel_state->UniqueEmbeddings(), kernel_state->InverseIndices(), + kernel_state->IsKernelStart()); + } + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + CHECK_EQ(kernel_state->InverseIndices()->at(parallel_id), cur_rank_inverse_indices->dptr()) + << parallel_id; + if (embedding_size % 4 == 0) { + LaunchKernel(ctx, kernel_state); + } else if (embedding_size % 2 == 0) { + LaunchKernel(ctx, kernel_state); + } else { + LaunchKernel(ctx, kernel_state); + } + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +REGISTER_USER_KERNEL("embedding_shuffle") + .SetCreateFn>() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) + && (user_op::HobDataType("cur_rank_embeddings", 0) == DataType::kFloat16) + && (user_op::HobDataType("num_unique_matrix", 0) == DataType::kUInt32) + && (user_op::HobAttr("skip_last_gather") == true) + && (embedding::UseEmbeddingShuffleP2PKernel(DataType::kFloat16, + DataType::kUInt32))) + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { + return GetCudaAlignedSize(ctx->InputTensorDesc("cur_rank_embeddings", 0).shape().elem_cnt() + * sizeof(half)); + }); +} // namespace oneflow + +#endif // CUDA_VERSION >= 11030 diff --git a/oneflow/user/kernels/one_embedding_id_shuffle_p2p_kernel.cu b/oneflow/user/kernels/one_embedding_id_shuffle_p2p_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..0943a707b977f6d3f0e7423cc080b7e422012b1e --- /dev/null +++ b/oneflow/user/kernels/one_embedding_id_shuffle_p2p_kernel.cu @@ -0,0 +1,587 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/job/parallel_desc.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/embedding/hash_functions.cuh" +#include "oneflow/core/embedding/embedding_manager.h" +#include "oneflow/core/control/ctrl_client.h" + +namespace oneflow { + +namespace { + +template +struct TableEntry { + K key; + uint32_t value; +}; + +template +__global__ void HashTableUniqueAndPartitionPairs( + const uint32_t table_capacity, const uint32_t num_keys, int32_t num_partition, + IDX* unique_counts, TableEntry* table, const K* keys, const V* values, + K* partitioned_unique_keys, V* partitioned_unique_values, IDX* reverse_index, + bool need_process_values, int32_t* is_kernel_start) { + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, num_keys) { + IDX r_index_plus_one = 0; + const K key = keys[i]; + size_t key_hash = HASH()(key); + uint32_t partition_id = key_hash % num_partition; + IDX* unique_count = unique_counts + partition_id; + K* unique_keys = partitioned_unique_keys + partition_id * num_keys; + uint32_t pos = key_hash % table_capacity; + const K key_hi = (key | 0x1); + const K key_lo = (key & 0x1); + uint32_t counter = 0; + while (r_index_plus_one == 0) { + bool prob_next = false; + K* key_ptr = &table[pos].key; + volatile uint32_t* table_value_ptr = &table[pos].value; + const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi); + if (old_key == 0) { + IDX unique_pos = cuda::atomic::Add(unique_count, 1); + r_index_plus_one = unique_pos + 1; + unique_keys[unique_pos] = key; + if (need_process_values) { + partitioned_unique_values[partition_id * num_keys + unique_pos] = values[i]; + } + *table_value_ptr = ((r_index_plus_one << 1U) | key_lo); + } else if (old_key == key_hi) { + const uint32_t value = *table_value_ptr; + if (value == 0) { + // do nothing + } else if ((value & 0x1) == key_lo) { + r_index_plus_one = (value >> 1U); + } else { + prob_next = true; + } + } else { + prob_next = true; + } + if (prob_next) { + pos += 1; + counter += 1; + if (pos >= table_capacity) { pos -= table_capacity; } + if (counter >= table_capacity) { TRAP(); } + } + } + reverse_index[i] = partition_id * num_keys + r_index_plus_one - 1; + } +} + +template +struct Param { + IDX* num_unique[N]; + K* unique_ids[N]; + U* unique_table_ids[N]; + int32_t* is_kernel_start[N]; + IDX* num_unique_matrix; + int32_t* counter; +}; + +template +struct alignas(sizeof(T) * pack_size) Pack { + T elem[pack_size]; +}; + +template +__global__ void BarrierAndMemset(int32_t parallel_id, int32_t parallel_num, + Param param, Pack* workspace_ptr, + size_t workspace_num_pack, IDX* counter, int num_counter) { + int count; + if (blockIdx.x == 0) { + count = param.is_kernel_start[parallel_id][parallel_id]; + if (threadIdx.x < parallel_num) { + volatile int32_t* start_f = param.is_kernel_start[parallel_id]; + start_f[threadIdx.x] = count + 1; + } + } + Pack pack_value; + for (int i = 0; i < pack_size; ++i) { pack_value.elem[i] = static_cast(0); } + CUDA_1D_KERNEL_LOOP(i, workspace_num_pack) { workspace_ptr[i] = pack_value; } + int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; + if (global_thread_id < num_counter) { counter[global_thread_id] = 0; } + if (blockIdx.x == 0) { + if (threadIdx.x < parallel_num) { + volatile int32_t* remote_start_f = param.is_kernel_start[threadIdx.x]; + while (remote_start_f[parallel_id] < count + 1) {} + } + } +} + +template +__global__ void HashTableUniquePairs(const uint32_t table_capacity, const uint32_t num_ids, + int32_t parallel_num, int32_t parallel_id, IDX* unique_count, + TableEntry* table, Param param, + K* unique_keys, V* unique_values, IDX* reverse_index, + bool need_process_values) { +#pragma unroll 1 + for (int i = 0; i < parallel_num; ++i) { + int rank_id = (parallel_id + i) % parallel_num; + const IDX* num_uniques = param.num_unique[rank_id]; + CUDA_1D_KERNEL_LOOP_T(int, rank_index, num_uniques[parallel_id]) { + const IDX* num_uniques = param.num_unique[rank_id]; + // if (rank_index >= num_uniques[parallel_id]) { continue; } + const K* keys = param.unique_ids[rank_id]; + const V* values = param.unique_table_ids[rank_id]; + IDX index_offset = 0; + for (int k = 0; k < rank_id; ++k) { index_offset += param.num_unique[k][parallel_id]; } + IDX r_index_plus_one = 0; + const K key = keys[rank_index]; + size_t key_hash = HASH()(key); + uint32_t pos = key_hash % table_capacity; + const K key_hi = (key | 0x1); + const K key_lo = (key & 0x1); + uint32_t counter = 0; + while (r_index_plus_one == 0) { + bool prob_next = false; + K* key_ptr = &table[pos].key; + volatile uint32_t* table_value_ptr = &table[pos].value; + const K old_key = cuda::atomic::CAS(key_ptr, 0, key_hi); + if (old_key == 0) { + IDX unique_pos = cuda::atomic::Add(unique_count, 1); + r_index_plus_one = unique_pos + 1; + unique_keys[unique_pos] = key; + if (need_process_values) { unique_values[unique_pos] = values[rank_index]; } + *table_value_ptr = ((r_index_plus_one << 1U) | key_lo); + } else if (old_key == key_hi) { + const uint32_t value = *table_value_ptr; + if (value == 0) { + // do nothing + } else if ((value & 0x1) == key_lo) { + r_index_plus_one = (value >> 1U); + } else { + prob_next = true; + } + } else { + prob_next = true; + } + if (prob_next) { + pos += 1; + counter += 1; + if (pos >= table_capacity) { pos -= table_capacity; } + if (counter >= table_capacity) { TRAP(); } + } + } + reverse_index[rank_index + index_offset] = r_index_plus_one - 1; + if (rank_index < parallel_num) { + param.num_unique_matrix[i * parallel_num + rank_index] = param.num_unique[i][rank_index]; + } + } + } +} + +template +__global__ void GenerateTableIdsAndMemsetUniqueWorkspace(int32_t elem_cnt, int32_t num_tables, + U* table_ids, + Pack* workspace_ptr, + size_t workspace_num_pack, IDX* counter, + int num_counter) { + CUDA_1D_KERNEL_LOOP(i, elem_cnt) { table_ids[i] = i % num_tables; } + Pack pack_value; + for (int i = 0; i < pack_size; ++i) { pack_value.elem[i] = static_cast(0); } + CUDA_1D_KERNEL_LOOP(i, workspace_num_pack) { workspace_ptr[i] = pack_value; } + int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; + if (global_thread_id < num_counter) { counter[global_thread_id] = 0; } +} + +template +void UniqueAndPartition(GPU(Stream_t) cuda_stream, int64_t num_blocks, int64_t num_ids, + size_t capacity, int64_t num_partition, const K* ids, const V* table_ids, + IDX* num_partitioned_unique_ids_ptr, K* partitioned_unique_ids, + V* partitioned_unique_table_ids, IDX* inverse_unique_partition_indices, + void* workspace_ptr, size_t workspace_bytes, bool need_process_table_ids, + int32_t* is_kernel_start_ptr) { + size_t table_capacity_bytes = capacity * sizeof(TableEntry); + CHECK_GE(workspace_bytes, table_capacity_bytes); + HashTableUniqueAndPartitionPairs<<>>( + capacity, num_ids, num_partition, num_partitioned_unique_ids_ptr, + reinterpret_cast*>(workspace_ptr), ids, table_ids, partitioned_unique_ids, + partitioned_unique_table_ids, inverse_unique_partition_indices, need_process_table_ids, + is_kernel_start_ptr); +} + +enum class IdShuffleBufferType { kTableIds = 0, kWorkspace, kMaxType }; + +template +class IdShuffleTmpBufferManager final { + public: + OF_DISALLOW_COPY_AND_MOVE(IdShuffleTmpBufferManager); + IdShuffleTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t parallel_num, + bool need_table_ids, bool need_process_table_ids) + : offset_(0), + offsets_(static_cast(IdShuffleBufferType::kMaxType), -1), + sizes_(static_cast(IdShuffleBufferType::kMaxType)), + ptr_(ptr) { + const int64_t num_table_ids = need_process_table_ids ? num_ids : 0; + const size_t table_ids_bytes = need_table_ids ? num_ids * sizeof(U) : 0; + AllocBuffer(IdShuffleBufferType::kTableIds, table_ids_bytes); + const size_t hash_table_capacity = parallel_num * num_ids; + AllocBuffer(IdShuffleBufferType::kWorkspace, hash_table_capacity * sizeof(TableEntry)); + } + + template + T* Ptr(IdShuffleBufferType type) { + CHECK(ptr_ != nullptr); + int64_t offset = offsets_.at(static_cast(type)); + CHECK_NE(offset, -1); + return reinterpret_cast(reinterpret_cast(ptr_) + offset); + } + + int64_t Size(IdShuffleBufferType type) { return sizes_.at(static_cast(type)); } + + size_t TotalBufferSize() const { return offset_; } + + private: + void AllocBuffer(IdShuffleBufferType type, size_t size) { + const size_t type_id = static_cast(type); + CHECK_EQ(offsets_.at(type_id), -1); + offsets_.at(type_id) = offset_; + sizes_.at(type_id) = size; + offset_ += GetCudaAlignedSize(size); + } + size_t offset_; + std::vector offsets_; + std::vector sizes_; + void* ptr_; +}; + +template +class DataShuffleKernelState final : public user_op::OpKernelState { + public: + explicit DataShuffleKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1), + parallel_desc_(ctx->parallel_desc()), + parallel_id_(ctx->parallel_ctx().parallel_id()) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); + int64_t parallel_num = parallel_desc_.parallel_num(); +#ifdef WITH_ROCM + OF_CUDA_CHECK( + hipMallocHost(reinterpret_cast(&host_num_unique_matrix_), parallel_num * parallel_num * sizeof(IDX))); + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_cur_rank_num_unique_), sizeof(IDX))); +#else + OF_CUDA_CHECK( + cudaMallocHost(&host_num_unique_matrix_, parallel_num * parallel_num * sizeof(IDX))); + OF_CUDA_CHECK(cudaMallocHost(&host_cur_rank_num_unique_, sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = parallel_id_; + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); + const int64_t num_ids = ctx->TensorDesc4ArgNameAndIndex("ids", 0)->shape().elem_cnt(); + num_partitioned_unique_size_ = GetCudaAlignedSize(parallel_num * sizeof(IDX)); + partitioned_unique_ids_size_ = GetCudaAlignedSize(parallel_num * num_ids * sizeof(K)); + partitioned_unique_table_ids_size_ = GetCudaAlignedSize(parallel_num * num_ids * sizeof(U)); + is_kernel_start_size_ = GetCudaAlignedSize(parallel_num * sizeof(int32_t)); + size_t buffer_size = num_partitioned_unique_size_ + partitioned_unique_ids_size_ + + partitioned_unique_table_ids_size_ + is_kernel_start_size_; + buffer_ptrs_.resize(parallel_num); + GPU(Malloc)(&buffer_ptrs_.at(parallel_id), buffer_size); + GPU(Memset)(buffer_ptrs_.at(parallel_id), 0, buffer_size); + } + ~DataShuffleKernelState() { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(GPU(FreeHost)(host_cur_rank_num_unique_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_unique_matrix_)); + OF_CUDA_CHECK(GPU(Free)(buffer_ptrs_.at(parallel_id_))); + } + + std::vector* BufferPtrs() { return &buffer_ptrs_; } + + IDX* HostNumUniqueMatrix() { return host_num_unique_matrix_; } + + IDX* HostCurRankNumUnique() { return host_cur_rank_num_unique_; } + + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } + + IDX* NumPartitionedUnique(int64_t parallel_id) { + return reinterpret_cast(buffer_ptrs_.at(parallel_id)); + } + + K* PartitionedUniqueIds(int64_t parallel_id) { + return reinterpret_cast(reinterpret_cast(buffer_ptrs_.at(parallel_id)) + + num_partitioned_unique_size_); + } + + U* PartitionedUniqueTableIds(int64_t parallel_id) { + return reinterpret_cast(reinterpret_cast(buffer_ptrs_.at(parallel_id)) + + num_partitioned_unique_size_ + partitioned_unique_ids_size_); + } + + int32_t* IsKernelStart(int64_t parallel_id) { + return reinterpret_cast(reinterpret_cast(buffer_ptrs_.at(parallel_id)) + + num_partitioned_unique_size_ + partitioned_unique_ids_size_ + + partitioned_unique_table_ids_size_); + } + + private: + int device_index_; + ParallelDesc parallel_desc_; + int64_t parallel_id_; + IDX* host_num_unique_matrix_; + IDX* host_cur_rank_num_unique_; + std::vector buffer_ptrs_; + size_t num_partitioned_unique_size_; + size_t partitioned_unique_ids_size_; + size_t partitioned_unique_table_ids_size_; + size_t is_kernel_start_size_; + embedding::EmbeddingState* embedding_state_; +}; + +void GetPtrs(user_op::KernelComputeContext* ctx, std::vector* buffer_ptrs) { + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + std::string name = ctx->op_name(); + GPU(IpcMemHandle_t) handle; + OF_CUDA_CHECK(GPU(IpcGetMemHandle)(&handle, buffer_ptrs->at(parallel_id))); + Singleton::Get()->PushKV( + name + std::to_string(parallel_id), + std::string(reinterpret_cast(&handle), sizeof(GPU(IpcMemHandle_t)))); + for (int64_t i = 0; i < parallel_num; ++i) { + std::string key = name + std::to_string(i); + if (parallel_id != i) { + GPU(IpcMemHandle_t) handle; + Singleton::Get()->PullKV(key, [&handle](const std::string& val) { + memcpy(&handle, val.data(), sizeof(GPU(IpcMemHandle_t))); + }); + OF_CUDA_CHECK( + GPU(IpcOpenMemHandle)(&buffer_ptrs->at(i), handle, GPU(IpcMemLazyEnablePeerAccess))); + } + } +} + +template +__global__ void BarrierAndComputeOut(int32_t parallel_id, int32_t parallel_num, int32_t num_ids, + Param param, IDX* num_partitioned_unique, + IDX* inverse_ptr, IDX* num_unique_matrix, + IDX* host_num_unique_matrix, IDX* cur_rank_num_unique, + IDX* host_cur_rank_num_unique) { + int count; + if (blockIdx.x == 0) { + count = param.is_kernel_start[parallel_id][parallel_id]; + if (threadIdx.x < parallel_num) { + volatile int32_t* start_f = param.is_kernel_start[parallel_id]; + start_f[threadIdx.x] = count + 1; + } + } + if (parallel_num > 1) { + CUDA_1D_KERNEL_LOOP(i, num_ids) { + int inverse_indice = inverse_ptr[i]; + int partition_id = inverse_indice / num_ids; + int partition_indice = inverse_indice - partition_id * num_ids; + int new_offset = 0; + for (int k = 0; k < partition_id; ++k) { new_offset += num_partitioned_unique[k]; } + inverse_ptr[i] = new_offset + partition_indice; + } + } + int global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; + if (global_thread_id < parallel_num * parallel_num) { + host_num_unique_matrix[global_thread_id] = num_unique_matrix[global_thread_id]; + } + if (global_thread_id == 0) { + host_cur_rank_num_unique[global_thread_id] = cur_rank_num_unique[global_thread_id]; + } + if (blockIdx.x == 0) { + if (threadIdx.x < parallel_num) { + volatile int32_t* remote_start_f = param.is_kernel_start[threadIdx.x]; + while (remote_start_f[parallel_id] < count + 1) {} + } + } +} + +} // namespace + +template +class IdShuffleP2PKernel final : public user_op::OpKernel { + public: + IdShuffleP2PKernel() : current_iter_(0){}; + ~IdShuffleP2PKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + const user_op::Tensor* ids = ctx->Tensor4ArgNameAndIndex("ids", 0); + user_op::Tensor* num_unique_matrix = ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0); + user_op::Tensor* inverse_unique_partition_indices = + ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0); + user_op::Tensor* cur_rank_num_unique = ctx->Tensor4ArgNameAndIndex("cur_rank_num_unique", 0); + user_op::Tensor* cur_rank_unique_ids = ctx->Tensor4ArgNameAndIndex("cur_rank_unique_ids", 0); + user_op::Tensor* cur_rank_unique_table_ids = + ctx->Tensor4ArgNameAndIndex("cur_rank_unique_table_ids", 0); + user_op::Tensor* cur_rank_inverse_indices = + ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const int32_t num_tables = ctx->Attr("num_tables"); + const bool has_table_ids = ctx->has_input("table_ids", 0); + const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); + const bool need_process_table_ids = (has_table_ids || num_tables > 1); + const int64_t num_ids = ids->shape_view().elem_cnt(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + IdShuffleTmpBufferManager buffer_manager( + tmp_buffer->mut_dptr(), num_ids, parallel_num, need_gen_table_ids, need_process_table_ids); + CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize()); + if (current_iter_ == 0) { GetPtrs(ctx, kernel_state->BufferPtrs()); } + const int num_blocks = + 2 * ctx->stream()->As()->device_properties().multiProcessorCount; + IDX* num_partitioned_unique = kernel_state->NumPartitionedUnique(parallel_id); + K* partitioned_unique_ids = kernel_state->PartitionedUniqueIds(parallel_id); + U* partitioned_unique_table_ids = kernel_state->PartitionedUniqueTableIds(parallel_id); + IDX* num_unique_matrix_ptr = reinterpret_cast(num_unique_matrix->mut_dptr()); + size_t hash_table_capacity = parallel_num * num_ids; + void* workspace_ptr = buffer_manager.Ptr(IdShuffleBufferType::kWorkspace); + size_t workspace_size = buffer_manager.Size(IdShuffleBufferType::kWorkspace); + const U* table_ids_ptr; + bool skip_memset = false; + if (has_table_ids) { + const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); + table_ids_ptr = reinterpret_cast(table_ids->dptr()); + } else if (need_gen_table_ids) { + CHECK_EQ(workspace_size % 16, 0); + CHECK_EQ(reinterpret_cast(workspace_ptr) % 16, 0); + GenerateTableIdsAndMemsetUniqueWorkspace<<>>( + num_ids, num_tables, buffer_manager.template Ptr(IdShuffleBufferType::kTableIds), + reinterpret_cast*>(workspace_ptr), workspace_size / 16, + num_partitioned_unique, parallel_num); + table_ids_ptr = buffer_manager.template Ptr(IdShuffleBufferType::kTableIds); + skip_memset = true; + } else { + table_ids_ptr = nullptr; + } + if (!skip_memset) { + OF_CUDA_CHECK(GPU(MemsetAsync)(workspace_ptr, 0, workspace_size, cuda_stream)); + OF_CUDA_CHECK( + GPU(MemsetAsync)(num_partitioned_unique, 0, parallel_num * sizeof(IDX), cuda_stream)); + } + UniqueAndPartition( + cuda_stream, num_blocks, num_ids, hash_table_capacity, parallel_num, + reinterpret_cast(ids->dptr()), table_ids_ptr, num_partitioned_unique, + partitioned_unique_ids, partitioned_unique_table_ids, + reinterpret_cast(inverse_unique_partition_indices->mut_dptr()), workspace_ptr, + workspace_size, need_process_table_ids, kernel_state->IsKernelStart(parallel_id)); + + IDX* cur_rank_num_unique_ids_ptr = reinterpret_cast(cur_rank_num_unique->mut_dptr()); + Param param; + CHECK_LE(parallel_num, 8); + for (int i = 0; i < parallel_num; ++i) { + param.num_unique[i] = kernel_state->NumPartitionedUnique(i); + param.unique_ids[i] = kernel_state->PartitionedUniqueIds(i) + parallel_id * num_ids; + param.unique_table_ids[i] = + kernel_state->PartitionedUniqueTableIds(i) + parallel_id * num_ids; + param.is_kernel_start[i] = kernel_state->IsKernelStart(i); + } + param.num_unique_matrix = num_unique_matrix_ptr; + CHECK_EQ(workspace_size % 16, 0); + CHECK_EQ(reinterpret_cast(workspace_ptr) % 16, 0); + int workspace_num_pack = workspace_size / 16; + BarrierAndMemset<<>>( + parallel_id, parallel_num, param, reinterpret_cast*>(workspace_ptr), + workspace_num_pack, cur_rank_num_unique_ids_ptr, 1); + HashTableUniquePairs + <<>>( + hash_table_capacity, num_ids, parallel_num, parallel_id, cur_rank_num_unique_ids_ptr, + reinterpret_cast*>(workspace_ptr), param, + reinterpret_cast(cur_rank_unique_ids->mut_dptr()), + reinterpret_cast(cur_rank_unique_table_ids->mut_dptr()), + reinterpret_cast(cur_rank_inverse_indices->mut_dptr()), need_process_table_ids); + + IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); + IDX* host_cur_rank_num_unique = kernel_state->HostCurRankNumUnique(); + BarrierAndComputeOut<<>>( + parallel_id, parallel_num, num_ids, param, num_partitioned_unique, + reinterpret_cast(inverse_unique_partition_indices->mut_dptr()), num_unique_matrix_ptr, + host_num_unique_matrix, cur_rank_num_unique_ids_ptr, host_cur_rank_num_unique); + + if (!need_process_table_ids) { + OF_CUDA_CHECK(GPU(MemsetAsync)(cur_rank_unique_table_ids->mut_dptr(), 0, + cur_rank_unique_table_ids->shape_view().elem_cnt() * sizeof(U), + cuda_stream)); + } + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::vector num_unique_matrix_vec(parallel_num * parallel_num); + CHECK_JUST(ctx->stream()->Sync()); + std::memcpy(num_unique_matrix_vec.data(), host_num_unique_matrix, + parallel_num * parallel_num * sizeof(IDX)); + CHECK_EQ(sizeof(IDX), sizeof(uint32_t)) << "assume sizeof(IDX) equals to sizeof(uint32_t)"; + embedding_state->SetIdNumUniqueMatrix(num_unique_matrix_vec, current_iter_); + uint32_t final_num_unique = *host_cur_rank_num_unique; + embedding_state->SetIdFinalNumUnique(final_num_unique, current_iter_); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +#define ID_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ + OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) + +#define TABLE_ID_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ + OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ + OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) + +#define IDX_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) + +#define REGISTER_CUDA_ID_SHUFFLE_P2P_KERNEL(k_dtype_pair, table_id_dtype_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("id_shuffle") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("cur_rank_unique_table_ids", 0) \ + == OF_PP_PAIR_SECOND(table_id_dtype_pair)) \ + && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ + && ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_ID_SHUFFLE_USE_P2P", false)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ + const bool has_table_ids = ctx->has_input("table_ids", 0); \ + const int32_t num_tables = ctx->Attr("num_tables"); \ + const bool need_gen_table_ids = (!has_table_ids && num_tables > 1); \ + const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ + IdShuffleTmpBufferManager \ + buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_desc().parallel_num(), \ + need_gen_table_ids, need_process_table_ids); \ + return buffer_manager.TotalBufferSize(); \ + }); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ID_SHUFFLE_P2P_KERNEL, ID_DATA_TYPE_SEQ, + TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + +} // namespace oneflow + diff --git a/oneflow/user/kernels/one_embedding_kernels.cu b/oneflow/user/kernels/one_embedding_kernels.cu index 30d6c98bfe89670c5e2b85c884cdb5faa4cb213d..178035ee21a9c452ec7cec76e19b7b416b0dac38 100644 --- a/oneflow/user/kernels/one_embedding_kernels.cu +++ b/oneflow/user/kernels/one_embedding_kernels.cu @@ -18,12 +18,17 @@ limitations under the License. #include "oneflow/core/embedding/key_value_store.h" #include "oneflow/core/embedding/embedding_manager.h" #include "oneflow/core/device/cuda_util.h" -#include "oneflow/user/kernels/random_mask_generator.h" -#include "oneflow/core/framework/random_generator_impl.h" -#include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/ep/include/primitive/copy_nd.h" #include "oneflow/core/ep/include/primitive/cast.h" #include "oneflow/core/ep/include/device.h" +#include "oneflow/user/kernels/one_embedding_data_shuffle.cuh" +#ifdef WITH_ROCM +#include +#include +#else +#include +#include +#endif namespace oneflow { @@ -137,6 +142,23 @@ void ParseAndSetStateInitializerIndex(const std::string& state_initializer, } } +void ParseAndSetStepInitializerIndex(const int32_t num_tables, const int64_t line_size, + const int64_t embedding_size, + std::vector* initializer_params, + std::vector* initializer_index) { + if (line_size % embedding_size == 0) { return; } + nlohmann::json initializer; + initializer["type"] = "constant"; + initializer["value"] = 0.0; + int32_t offset = ParseJsonToUniqueInitializerVecAndReturnOffset(initializer, initializer_params); + int32_t col_start = line_size / embedding_size * embedding_size; + int32_t col_end = line_size; + CHECK_LE(col_end, line_size); + for (int32_t j = 0; j < num_tables; ++j) { + SetInitializerIndex(j, col_start, col_end, line_size, offset, initializer_index); + } +} + void ParseAndSetModelInitializerIndex(const nlohmann::json& tables, const std::vector& column_dims, const int32_t num_tables, const int32_t num_columns, @@ -176,6 +198,8 @@ void ParseInitializers(const int64_t line_size, const int64_t embedding_size, CHECK(tables.is_array()); const int32_t num_tables = tables.size(); initializer_index->resize(num_tables * line_size); + ParseAndSetStepInitializerIndex(num_tables, line_size, embedding_size, initializer_params, + initializer_index); ParseAndSetStateInitializerIndex(state_initializer, num_tables, line_size, embedding_size, initializer_params, initializer_index); ParseAndSetModelInitializerIndex(tables, column_dims, num_tables, num_columns, line_size, @@ -185,15 +209,22 @@ void ParseInitializers(const int64_t line_size, const int64_t embedding_size, template class EmbeddingKernelState final : public user_op::OpKernelState { public: - explicit EmbeddingKernelState(user_op::KernelInitContext* ctx) - : device_index_(-1), generator_(CHECK_JUST(one::MakeGenerator(DeviceType::kCUDA))) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); + explicit EmbeddingKernelState(user_op::KernelInitContext* ctx) : device_index_(-1) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_keys_), sizeof(IDX))); +#else OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); key_value_store_ = Singleton::Get()->GetKeyValueStore( - ctx->Attr("embedding_name"), ctx->parallel_ctx().parallel_id()); + embedding_name, parallel_id); uint32_t max_query_length = ctx->TensorDesc4ArgNameAndIndex("unique_ids", 0)->shape().elem_cnt(); key_value_store_->ReserveQueryLength(max_query_length); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); const int64_t embedding_size = ctx->Attr("embedding_size"); const int64_t line_size = ctx->Attr("line_size"); @@ -206,35 +237,43 @@ class EmbeddingKernelState final : public user_op::OpKernelState { &initializer_index); const size_t param_size_bytes = initializer_param.size() * sizeof(EmbeddingInitializer); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_param_), param_size_bytes)); +#else OF_CUDA_CHECK(cudaMallocHost(&host_initializer_param_, param_size_bytes)); +#endif std::memcpy(host_initializer_param_, initializer_param.data(), param_size_bytes); - OF_CUDA_CHECK(cudaMalloc(&device_initializer_param_, param_size_bytes)); - OF_CUDA_CHECK(cudaMemcpyAsync(device_initializer_param_, host_initializer_param_, - param_size_bytes, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_param_, param_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_param_, host_initializer_param_, + param_size_bytes, GPU(MemcpyDefault), ctx->stream()->As()->cuda_stream())); const size_t index_size_bytes = initializer_index.size() * sizeof(int8_t); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_index_), index_size_bytes)); +#else OF_CUDA_CHECK(cudaMallocHost(&host_initializer_index_, index_size_bytes)); +#endif std::memcpy(host_initializer_index_, initializer_index.data(), index_size_bytes); - OF_CUDA_CHECK(cudaMalloc(&device_initializer_index_, index_size_bytes)); - OF_CUDA_CHECK(cudaMemcpyAsync(device_initializer_index_, host_initializer_index_, - index_size_bytes, cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_index_, index_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_index_, host_initializer_index_, + index_size_bytes, GPU(MemcpyDefault), ctx->stream()->As()->cuda_stream())); } ~EmbeddingKernelState() override { CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(cudaFreeHost(host_num_keys_)); - OF_CUDA_CHECK(cudaFreeHost(host_initializer_param_)); - OF_CUDA_CHECK(cudaFree(device_initializer_param_)); - OF_CUDA_CHECK(cudaFreeHost(host_initializer_index_)); - OF_CUDA_CHECK(cudaFree(device_initializer_index_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_param_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_param_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_index_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_index_)); } void* HostNumKeys() { return host_num_keys_; } embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } - one::Generator* generator() { return generator_.get(); } + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } const int8_t* InitializerIndex() { return device_initializer_index_; } const EmbeddingInitializer* Initializers() { return device_initializer_param_; } @@ -242,88 +281,44 @@ class EmbeddingKernelState final : public user_op::OpKernelState { private: int device_index_; void* host_num_keys_; - std::shared_ptr generator_; embedding::KeyValueStore* key_value_store_; - + embedding::EmbeddingState* embedding_state_; EmbeddingInitializer* host_initializer_param_; EmbeddingInitializer* device_initializer_param_; int8_t* host_initializer_index_; int8_t* device_initializer_index_; }; -template class EmbeddingPutKernelState final : public user_op::OpKernelState { public: - explicit EmbeddingPutKernelState(user_op::KernelInitContext* ctx) : device_index_(-1) { - OF_CUDA_CHECK(cudaGetDevice(&device_index_)); - OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); + explicit EmbeddingPutKernelState(user_op::KernelInitContext* ctx) { + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); key_value_store_ = Singleton::Get()->GetKeyValueStore( - ctx->Attr("embedding_name"), ctx->parallel_ctx().parallel_id()); + embedding_name, parallel_id); uint32_t max_query_length = ctx->TensorDesc4ArgNameAndIndex("unique_ids", 0)->shape().elem_cnt(); key_value_store_->ReserveQueryLength(max_query_length); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); } - ~EmbeddingPutKernelState() override { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(cudaFreeHost(host_num_keys_)); - } + ~EmbeddingPutKernelState() override = default; - void* HostNumKeys() { return host_num_keys_; } embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } private: - int device_index_; - void* host_num_keys_; embedding::KeyValueStore* key_value_store_; + embedding::EmbeddingState* embedding_state_; }; -enum class EmbeddingBufferType { kNumMissing = 0, kMissingIndices, kValues, kMaxType }; - -class EmbeddingTmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(EmbeddingTmpBufferManager); - EmbeddingTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t value_byte_size, - const bool need_value_buffer) - : offset_(0), offsets_(static_cast(EmbeddingBufferType::kMaxType), -1), ptr_(ptr) { - AllocBuffer(EmbeddingBufferType::kNumMissing, sizeof(uint32_t)); - AllocBuffer(EmbeddingBufferType::kMissingIndices, num_ids * sizeof(uint32_t)); - if (need_value_buffer) { AllocBuffer(EmbeddingBufferType::kValues, num_ids * value_byte_size); } - } - - template - T* Ptr(EmbeddingBufferType type) { - CHECK(ptr_ != nullptr); - int64_t offset = offsets_.at(static_cast(type)); - CHECK_NE(offset, -1); - return reinterpret_cast(reinterpret_cast(ptr_) + offset); - } - - size_t TotalBufferSize() const { return offset_; } - - private: - void AllocBuffer(EmbeddingBufferType type, size_t size) { - const size_t type_id = static_cast(type); - CHECK_EQ(offsets_.at(type_id), -1); - offsets_.at(type_id) = offset_; - offset_ += GetCudaAlignedSize(size); - } - - size_t offset_; - std::vector offsets_; - void* ptr_; -}; - -template -__global__ void InitValueKernel(uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, - uint64_t inc_offset, const int32_t line_size, +template +__global__ void InitValueKernel(uint64_t seed, const int32_t line_size, const int32_t embedding_size, const EmbeddingInitializer* initializer_param, - const int8_t* initializer_index, const U* table_ids, - const uint32_t* num_missing_keys, const uint32_t* missing_indices, - T* values) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - curandStatePhilox4_32_10_t state; - curand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); + const int8_t* initializer_index, const K* unique_ids, + const U* table_ids, const uint32_t* num_missing_keys, + const uint32_t* missing_indices, T* values) { int64_t n = *num_missing_keys * line_size; CUDA_1D_KERNEL_LOOP(i, n) { int row = i / line_size; @@ -331,67 +326,41 @@ __global__ void InitValueKernel(uint64_t seed, one::CUDAGeneratorState* cuda_gen const uint32_t index = missing_indices[row]; const int64_t offset = index * line_size + col; const int32_t table_idx = table_ids[index]; + const K id = unique_ids[index]; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, id, col, &state); const int32_t initializer_idx = initializer_index[table_idx * line_size + col]; EmbeddingInitializer initializer = initializer_param[initializer_idx]; T value; if (initializer.type == InitializerType::kUniform) { const float low = initializer.uniform_param.low; const float high = initializer.uniform_param.high; - value = curand_uniform(&state) * (high - low) + low; + value = GPURAND(_uniform)(&state) * (high - low) + low; } else if (initializer.type == InitializerType::kNormal) { const float mean = initializer.normal_param.mean; const float std = initializer.normal_param.std; - value = curand_normal(&state) * std + mean; + value = GPURAND(_normal)(&state) * std + mean; } else if (initializer.type == InitializerType::kConstant) { value = initializer.constant_param.value; } else { - __trap(); + TRAP(); } values[offset] = value; } - __syncthreads(); - if (threadIdx.x == 0) { - int32_t new_counter = cuda::atomic::Add(&cuda_gen_state->dev_counter, 1) + 1; - if (new_counter == gridDim.x) { - cuda_gen_state->dev_counter = 0; // reset counter to zero - cuda_gen_state->dev_offset += inc_offset; // maintain the state of generator's dev_offset - } - } } -template -void LookupAndInitMissing(ep::Stream* stream, EmbeddingKernelState* embedding_state, - const int64_t num_ids, const int64_t embedding_size, - const int64_t line_size, const void* num_unique_ptr, - const void* unique_ids, const void* table_ids, T* values_ptr, - void* tmp_buffer_ptr, uint32_t* return_num_unique, - const bool put_to_kv_store) { - const auto& generator = embedding_state->generator(); - CHECK_NOTNULL(generator); - std::shared_ptr cuda_generator = - CHECK_JUST(generator->template Get(stream->device()->device_index())); - uint64_t seed = cuda_generator->current_seed(); - one::CUDAGeneratorState* cuda_gen_state = cuda_generator->cuda_gen_state(); - embedding::KeyValueStore* store = embedding_state->KeyValueStore(); - const EmbeddingInitializer* initializer_param = embedding_state->Initializers(); - const int8_t* initializer_index = embedding_state->InitializerIndex(); - bool need_value_buffer = (values_ptr == nullptr); - EmbeddingTmpBufferManager buffer_manager(tmp_buffer_ptr, num_ids, line_size * sizeof(T), - need_value_buffer); - void* host_num_keys = embedding_state->HostNumKeys(); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_keys, num_unique_ptr, sizeof(IDX), cudaMemcpyDefault, - stream->As()->cuda_stream())); - CHECK_JUST(stream->Sync()); - uint32_t num_unique = *reinterpret_cast(host_num_keys); - uint32_t* num_missing_ptr = - buffer_manager.template Ptr(EmbeddingBufferType::kNumMissing); - uint32_t* missing_indices = - buffer_manager.template Ptr(EmbeddingBufferType::kMissingIndices); - T* store_values = - need_value_buffer ? buffer_manager.template Ptr(EmbeddingBufferType::kValues) : values_ptr; - store->Get(stream, num_unique, unique_ids, store_values, num_missing_ptr, missing_indices); +template +void LookupAndInitMissing(ep::Stream* stream, uint64_t seed, embedding::KeyValueStore* store, + const EmbeddingInitializer* initializer_param, + const int8_t* initializer_index, void* host_num_keys, uint32_t num_unique, + const int64_t embedding_size, const int64_t line_size, + const bool put_to_store, const void* unique_ids, const void* table_ids, + void* num_missing_ptr, void* missing_indices, void* store_values) { + store->Get(stream, num_unique, unique_ids, store_values, + reinterpret_cast(num_missing_ptr), + reinterpret_cast(missing_indices)); CHECK_GE(sizeof(IDX), sizeof(uint32_t)); // host_num_keys's buffer size is sizeof(IDX) - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_keys, num_missing_ptr, sizeof(uint32_t), cudaMemcpyDefault, + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_keys, num_missing_ptr, sizeof(uint32_t), GPU(MemcpyDefault), stream->As()->cuda_stream())); CHECK_JUST(stream->Sync()); uint32_t num_missing = *reinterpret_cast(host_num_keys); @@ -399,15 +368,158 @@ void LookupAndInitMissing(ep::Stream* stream, EmbeddingKernelState* embeddi if (num_missing > 0) { const int64_t elem_cnt = num_missing * line_size; const int64_t num_blocks = BlocksNum4ThreadsNum(elem_cnt); - const uint64_t inc_offset = std::ceil(elem_cnt / num_blocks / kCudaThreadsNumPerBlock); - InitValueKernel + InitValueKernel <<As()->cuda_stream()>>>( - seed, cuda_gen_state, inc_offset, line_size, embedding_size, initializer_param, - initializer_index, reinterpret_cast(table_ids), num_missing_ptr, - missing_indices, store_values); + seed, line_size, embedding_size, initializer_param, initializer_index, + reinterpret_cast(unique_ids), reinterpret_cast(table_ids), + reinterpret_cast(num_missing_ptr), + reinterpret_cast(missing_indices), reinterpret_cast(store_values)); + } + if (put_to_store) { store->Put(stream, num_unique, unique_ids, store_values); } +} + +template +void LookupAndInitMissing(ep::Stream* stream, EmbeddingKernelState* kernel_state, + uint64_t seed, uint32_t num_unique, const int64_t embedding_size, + const int64_t line_size, const bool put_to_store, const void* unique_ids, + const void* table_ids, void* num_missing_ptr, void* missing_indices, + void* store_values) { + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + const EmbeddingInitializer* initializer_param = kernel_state->Initializers(); + const int8_t* initializer_index = kernel_state->InitializerIndex(); + void* host_num_keys = kernel_state->HostNumKeys(); + LookupAndInitMissing(stream, seed, store, initializer_param, initializer_index, + host_num_keys, num_unique, embedding_size, line_size, + put_to_store, unique_ids, table_ids, num_missing_ptr, + missing_indices, store_values); +} + +template +struct alignas(sizeof(T) * pack_size) Pack { + T elem[pack_size]; +}; + +template +__global__ void FusedInitSliceCast(const int32_t elem_cnt, uint64_t seed, const int32_t line_size, + const int32_t embedding_size, const int32_t line_num_pack, + const int32_t embedding_num_pack, + const EmbeddingInitializer* initializer_param, + const int8_t* initializer_index, const K* unique_ids, + const U* table_ids, const uint8_t* lookup_mask, + Pack* values, Pack* embeddings) { + CUDA_1D_KERNEL_LOOP(i, elem_cnt) { + int row = i / line_num_pack; + int col = i - row * line_num_pack; + Pack value_i; + if (!lookup_mask[row]) { + const int32_t table_idx = table_ids[row]; + const K id = unique_ids[row]; + GPURAND(StatePhilox4_32_10_t) state; + GPURAND(_init)(seed, id, col, &state); +#pragma unroll + for (int k = 0; k < pack_size; ++k) { + const int32_t initializer_idx = + initializer_index[table_idx * line_size + col * pack_size + k]; + EmbeddingInitializer initializer = initializer_param[initializer_idx]; + T value; + if (initializer.type == InitializerType::kUniform) { + const float low = initializer.uniform_param.low; + const float high = initializer.uniform_param.high; + value = GPURAND(_uniform)(&state) * (high - low) + low; + } else if (initializer.type == InitializerType::kNormal) { + const float mean = initializer.normal_param.mean; + const float std = initializer.normal_param.std; + value = GPURAND(_normal)(&state) * std + mean; + } else if (initializer.type == InitializerType::kConstant) { + value = initializer.constant_param.value; + } else { + TRAP(); + } + value_i.elem[k] = value; + } + values[i] = value_i; + } else { + value_i = values[i]; + } + if (embeddings != nullptr && col < embedding_num_pack) { + int64_t embedding_offset = row * embedding_num_pack + col; + Pack embedding_i; +#pragma unroll + for (int k = 0; k < pack_size; ++k) { embedding_i.elem[k] = static_cast(value_i.elem[k]); } + embeddings[embedding_offset] = embedding_i; + } + } +} + +template +void InitMissingAndSliceCast(GPU(Stream_t) cuda_stream, uint32_t num_unique, + const int64_t embedding_size, const int64_t line_size, uint64_t seed, + const EmbeddingInitializer* initializer_param, + const int8_t* initializer_index, const void* unique_ids, + const void* table_ids, const uint8_t* mask, T* values_ptr, + V* embeddings_ptr) { + int32_t pack_size; + if (embedding_size % 4 == 0 && line_size % 4 == 0) { + pack_size = 4; + } else if (embedding_size % 2 == 0 && line_size % 2 == 0) { + pack_size = 2; + } else { + pack_size = 1; + } + int32_t embedding_num_pack = embedding_size / pack_size; + int32_t line_num_pack = line_size / pack_size; + int64_t value_elem_cnt = num_unique * line_size; + int64_t value_elem_num_pack = value_elem_cnt / pack_size; + const int64_t num_blocks = BlocksNum4ThreadsNum(value_elem_num_pack); + if (pack_size == 4) { + FusedInitSliceCast<<>>( + value_elem_num_pack, seed, line_size, embedding_size, line_num_pack, embedding_num_pack, + initializer_param, initializer_index, reinterpret_cast(unique_ids), + reinterpret_cast(table_ids), mask, reinterpret_cast*>(values_ptr), + reinterpret_cast*>(embeddings_ptr)); + } else if (pack_size == 2) { + FusedInitSliceCast<<>>( + value_elem_num_pack, seed, line_size, embedding_size, line_num_pack, embedding_num_pack, + initializer_param, initializer_index, reinterpret_cast(unique_ids), + reinterpret_cast(table_ids), mask, reinterpret_cast*>(values_ptr), + reinterpret_cast*>(embeddings_ptr)); + } else { + FusedInitSliceCast<<>>( + value_elem_num_pack, seed, line_size, embedding_size, line_num_pack, embedding_num_pack, + initializer_param, initializer_index, reinterpret_cast(unique_ids), + reinterpret_cast(table_ids), mask, reinterpret_cast*>(values_ptr), + reinterpret_cast*>(embeddings_ptr)); + } +} + +template +void LookupAndFusedInitMissingSliceCast(ep::Stream* stream, EmbeddingKernelState* kernel_state, + uint64_t seed, uint32_t num_unique, + const int64_t embedding_size, const int64_t line_size, + DataType value_dtype, DataType embedding_dtype, + const void* unique_ids, const void* table_ids, + uint8_t* lookup_mask_ptr, void* values_ptr, + void* embeddings_ptr) { + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + const EmbeddingInitializer* initializer_param = kernel_state->Initializers(); + const int8_t* initializer_index = kernel_state->InitializerIndex(); + GPU(Stream_t) cuda_stream = stream->As()->cuda_stream(); + store->Get(stream, num_unique, unique_ids, values_ptr, lookup_mask_ptr); + if (embedding_dtype == value_dtype) { + InitMissingAndSliceCast( + cuda_stream, num_unique, embedding_size, line_size, seed, initializer_param, + initializer_index, reinterpret_cast(unique_ids), + reinterpret_cast(table_ids), lookup_mask_ptr, reinterpret_cast(values_ptr), + reinterpret_cast(embeddings_ptr)); + } else if (embedding_dtype == DataType::kFloat16) { + InitMissingAndSliceCast( + cuda_stream, num_unique, embedding_size, line_size, seed, initializer_param, + initializer_index, reinterpret_cast(unique_ids), + reinterpret_cast(table_ids), lookup_mask_ptr, reinterpret_cast(values_ptr), + reinterpret_cast(embeddings_ptr)); + } else { + UNIMPLEMENTED() << "Unimplemented data_type " << embedding_dtype; } - if (put_to_kv_store) { store->Put(stream, num_unique, unique_ids, store_values); } - *return_num_unique = num_unique; } template @@ -460,12 +572,89 @@ void CopyValuesToEmbeddings(ep::Stream* stream, int64_t num_unique, const int32_ } } +template +user_op::InferTmpSizeFn GenEmbeddingInferTmpSizeFn() { + return [](user_op::InferContext* ctx) { + size_t total_buffer_size = 0; + if (embedding::UseDynamicMemoryAllocation()) { return total_buffer_size; } + const user_op::TensorDesc& unique_ids = ctx->InputTensorDesc("unique_ids", 0); + int64_t num_ids = unique_ids.shape().elem_cnt(); + size_t num_missing_size = GetCudaAlignedSize(sizeof(uint32_t)); + size_t missing_indices_size = GetCudaAlignedSize(num_ids * sizeof(uint32_t)); + size_t value_buffer_size; + if (is_prefetch) { + size_t value_byte_size = ctx->Attr("line_size") * sizeof(T); + value_buffer_size = GetCudaAlignedSize(num_ids * value_byte_size); + } else { + value_buffer_size = 0; + } + total_buffer_size = num_missing_size + missing_indices_size + value_buffer_size; + return total_buffer_size; + }; +} + +class IdShuffleCopyOutKernelState final : public user_op::OpKernelState { + public: + explicit IdShuffleCopyOutKernelState(user_op::KernelInitContext* ctx) { + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); + } + ~IdShuffleCopyOutKernelState() override = default; + + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } + + private: + embedding::EmbeddingState* embedding_state_; +}; + +template +struct IdShuffleCopyOutParam { + uint32_t final_num_unique_ids; + const K* cur_rank_unique_ids; + K* out_cur_rank_unique_ids; + const U* cur_rank_unique_table_ids; + U* out_cur_rank_unique_table_ids; + uint32_t cur_rank_num_ids; + const IDX* cur_rank_inverse_indices; + IDX* out_cur_rank_inverse_indices; + uint32_t num_ids; + const IDX* inverse_unique_partition_indices; + IDX* out_inverse_unique_partition_indices; + uint32_t num_unique_matrix_cnt; + const IDX* num_unique_matrix; + IDX* out_num_unique_matrix; + const IDX* cur_rank_num_unique; + IDX* out_cur_rank_num_unique; +}; + +template +__global__ void CopyGpu(IdShuffleCopyOutParam param) { + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, param.final_num_unique_ids) { + param.out_cur_rank_unique_ids[i] = param.cur_rank_unique_ids[i]; + param.out_cur_rank_unique_table_ids[i] = param.cur_rank_unique_table_ids[i]; + } + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, param.cur_rank_num_ids) { + param.out_cur_rank_inverse_indices[i] = param.cur_rank_inverse_indices[i]; + } + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, param.num_ids) { + param.out_inverse_unique_partition_indices[i] = param.inverse_unique_partition_indices[i]; + } + CUDA_1D_KERNEL_LOOP_T(uint32_t, i, param.num_unique_matrix_cnt) { + param.out_num_unique_matrix[i] = param.num_unique_matrix[i]; + } + if (blockIdx.x * blockDim.x + threadIdx.x == 0) { + *param.out_cur_rank_num_unique = *param.cur_rank_num_unique; + } +} + } // namespace -template +template class EmbeddingPrefetchKernel final : public user_op::OpKernel { public: - EmbeddingPrefetchKernel() = default; + EmbeddingPrefetchKernel() : current_iter_(0){}; ~EmbeddingPrefetchKernel() override = default; std::shared_ptr CreateOpKernelState( @@ -477,27 +666,44 @@ class EmbeddingPrefetchKernel final : public user_op::OpKernel { using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); - + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::unique_ptr allocator = + embedding_state->NewTmpBufferAllocator(ctx); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int64_t embedding_size = ctx->Attr("embedding_size"); const int64_t line_size = ctx->Attr("line_size"); - uint32_t num_unique; - T* values_ptr = nullptr; - LookupAndInitMissing(ctx->stream(), embedding_state, - unique_ids->shape_view().elem_cnt(), embedding_size, line_size, - num_unique_ids->dptr(), unique_ids->dptr(), table_ids->dptr(), - values_ptr, tmp_buffer->mut_dptr(), &num_unique, true); + const int64_t seed = ctx->Attr("seed"); + void* num_missing_ptr; + allocator->Allocate(&num_missing_ptr, sizeof(uint32_t)); + void* missing_indices_ptr; + allocator->Allocate(&missing_indices_ptr, num_unique * sizeof(uint32_t)); + void* values_ptr; + allocator->Allocate(&values_ptr, num_unique * line_size * sizeof(T)); + LookupAndInitMissing( + ctx->stream(), kernel_state, seed, num_unique, embedding_size, line_size, true, + unique_ids->dptr(), table_ids->dptr(), num_missing_ptr, missing_indices_ptr, values_ptr); + allocator->Free(num_missing_ptr); + allocator->Free(missing_indices_ptr); + allocator->Free(values_ptr); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define EMBEDDING_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) +#define ID_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ + OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) + #define TABLE_ID_DATA_TYPE_SEQ \ OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) \ OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ @@ -510,30 +716,26 @@ class EmbeddingPrefetchKernel final : public user_op::OpKernel { OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) -#define REGISTER_CUDA_EMBEDDING_PREFETCH_KERNEL(t_dtype_pair, table_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("embedding_prefetch") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("table_ids", 0) == OF_PP_PAIR_SECOND(table_dtype_pair)) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& unique_ids = ctx->InputTensorDesc("unique_ids", 0); \ - EmbeddingTmpBufferManager buffer_manager( \ - nullptr, unique_ids.shape().elem_cnt(), \ - ctx->Attr("line_size") * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)), true); \ - return buffer_manager.TotalBufferSize(); \ - }); +#define REGISTER_CUDA_EMBEDDING_PREFETCH_KERNEL(t_dtype_pair, k_dtype_pair, table_dtype_pair, \ + idx_dtype_pair) \ + REGISTER_USER_KERNEL("embedding_prefetch") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("unique_ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("table_ids", 0) == OF_PP_PAIR_SECOND(table_dtype_pair)) \ + && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ + .SetInferTmpSizeFn(GenEmbeddingInferTmpSizeFn()); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_PREFETCH_KERNEL, EMBEDDING_DATA_TYPE_SEQ, - TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + ID_DATA_TYPE_SEQ, TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) -template +template class EmbeddingLookupKernel final : public user_op::OpKernel { public: - EmbeddingLookupKernel() = default; + EmbeddingLookupKernel() : current_iter_(0){}; ~EmbeddingLookupKernel() override = default; std::shared_ptr CreateOpKernelState( @@ -545,82 +747,106 @@ class EmbeddingLookupKernel final : public user_op::OpKernel { using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + std::unique_ptr allocator = + embedding_state->NewTmpBufferAllocator(ctx); + embedding_state->OnEmbeddingLookupStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const int64_t embedding_size = ctx->Attr("embedding_size"); const int64_t line_size = ctx->Attr("line_size"); - uint32_t num_unique; - LookupAndInitMissing( - ctx->stream(), embedding_state, unique_ids->shape_view().elem_cnt(), embedding_size, - line_size, num_unique_ids->dptr(), unique_ids->dptr(), table_ids->dptr(), - unique_values->mut_dptr(), tmp_buffer->mut_dptr(), &num_unique, false); - if (ctx->has_output("embeddings", 0)) { + const bool has_output_embeddings = ctx->has_output("embeddings", 0); + const int64_t seed = ctx->Attr("seed"); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + void* values_ptr = embedding_state->LookupUniqueValues(current_iter_); + if (has_output_embeddings && kernel_state->KeyValueStore()->IsFusionSupported()) { + void* embeddings_ptr = embedding_state->LookupEmbeddings(current_iter_); user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); - CopyValuesToEmbeddings(ctx->stream(), num_unique, embedding_size, line_size, - unique_values->data_type(), embeddings->data_type(), - unique_values->dptr(), embeddings->mut_dptr()); + void* lookup_mask_ptr; + allocator->Allocate(&lookup_mask_ptr, num_unique * sizeof(uint8_t)); + LookupAndFusedInitMissingSliceCast( + ctx->stream(), kernel_state, seed, num_unique, embedding_size, line_size, + unique_values->data_type(), embeddings->data_type(), unique_ids->dptr(), + table_ids->dptr(), reinterpret_cast(lookup_mask_ptr), values_ptr, + embeddings_ptr); + allocator->Free(lookup_mask_ptr); + } else { + void* num_missing_ptr; + allocator->Allocate(&num_missing_ptr, sizeof(uint32_t)); + void* missing_indices_ptr; + allocator->Allocate(&missing_indices_ptr, num_unique * sizeof(uint32_t)); + LookupAndInitMissing( + ctx->stream(), kernel_state, seed, num_unique, embedding_size, line_size, false, + unique_ids->dptr(), table_ids->dptr(), num_missing_ptr, missing_indices_ptr, values_ptr); + allocator->Free(num_missing_ptr); + allocator->Free(missing_indices_ptr); + if (has_output_embeddings) { + void* embeddings_ptr = embedding_state->LookupEmbeddings(current_iter_); + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + CopyValuesToEmbeddings(ctx->stream(), num_unique, embedding_size, line_size, + unique_values->data_type(), embeddings->data_type(), + reinterpret_cast(values_ptr), embeddings_ptr); + } } + embedding_state->OnEmbeddingLookupEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; -#define REGISTER_CUDA_EMBEDDING_LOOKUP_KERNEL(t_dtype_pair, table_dtype_pair, idx_dtype_pair) \ +#define REGISTER_CUDA_EMBEDDING_LOOKUP_KERNEL(t_dtype_pair, k_dtype_pair, table_dtype_pair, \ + idx_dtype_pair) \ REGISTER_USER_KERNEL("embedding_lookup") \ - .SetCreateFn>() \ + .SetCreateFn>() \ .SetIsMatchedHob( \ (user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("unique_values", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ + && (user_op::HobDataType("unique_ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ && (user_op::HobDataType("table_ids", 0) == OF_PP_PAIR_SECOND(table_dtype_pair)) \ && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& unique_ids = ctx->InputTensorDesc("unique_ids", 0); \ - EmbeddingTmpBufferManager buffer_manager( \ - nullptr, unique_ids.shape().elem_cnt(), \ - ctx->Attr("line_size") * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)), false); \ - return buffer_manager.TotalBufferSize(); \ - }); + .SetInferTmpSizeFn(GenEmbeddingInferTmpSizeFn()); OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_LOOKUP_KERNEL, EMBEDDING_DATA_TYPE_SEQ, - TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + ID_DATA_TYPE_SEQ, TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) template class EmbeddingPutKernel final : public user_op::OpKernel { public: - EmbeddingPutKernel() = default; + EmbeddingPutKernel() : current_iter_(0){}; ~EmbeddingPutKernel() override = default; std::shared_ptr CreateOpKernelState( user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); + return std::make_shared(ctx); } private: using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); - embedding::KeyValueStore* store = embedding_state->KeyValueStore(); + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingPutStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - - IDX* host_num_keys = reinterpret_cast(embedding_state->HostNumKeys()); - OF_CUDA_CHECK(cudaMemcpyAsync(host_num_keys, num_unique_ids->dptr(), sizeof(IDX), - cudaMemcpyDefault, - ctx->stream()->As()->cuda_stream())); - CHECK_JUST(ctx->stream()->Sync()); - - store->Put(ctx->stream(), *host_num_keys, unique_ids->dptr(), unique_embeddings->dptr()); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + store->Put(ctx->stream(), num_unique, unique_ids->dptr(), + embedding_state->EmbeddingPutUniqueEmbeddings(current_iter_)); + embedding_state->OnEmbeddingPutEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define REGISTER_CUDA_EMBEDDING_PUT_KERNEL(dtype, typeproto) \ @@ -631,4 +857,958 @@ class EmbeddingPutKernel final : public user_op::OpKernel { OF_PP_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_PUT_KERNEL, IDX_DATA_TYPE_SEQ) +template +class OneEmbeddingFusedSgdUpdatePutKernel final : public user_op::OpKernel { + public: + OneEmbeddingFusedSgdUpdatePutKernel() : current_iter_(0){}; + ~OneEmbeddingFusedSgdUpdatePutKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingFusedUpdatePutStart(ctx, current_iter_); + const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); + const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + const float* learning_rate_ptr = learning_rate->dptr(); + const auto scale = ctx->Attr("scale"); + uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + store->FusedHalfUpdatePut( + ctx->stream(), num_unique, unique_ids->dptr(), + embedding_state->EmbeddingFusedUpdatePutUniqueEmbeddings(current_iter_), + embedding_grad->dptr(), learning_rate_ptr, scale); + embedding_state->OnEmbeddingFusedUpdatePutEnd(ctx, current_iter_); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +#define REGISTER_CUDA_ONE_EMBEDDING_FUSED_SGD_UPDATE_PUT_KERNEL(dtype, typeproto) \ + REGISTER_USER_KERNEL("one_embedding_fused_sgd_update_put") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("num_unique_ids", 0) == typeproto) \ + && (user_op::HobDataType("unique_embeddings", 0) == DataType::kFloat) \ + && (user_op::HobDataType("embedding_grad", 0) == DataType::kFloat16)); + +OF_PP_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_FUSED_SGD_UPDATE_PUT_KERNEL, IDX_DATA_TYPE_SEQ) + +template +class IdShuffleCopyOutKernel final : public user_op::OpKernel { + public: + IdShuffleCopyOutKernel() : current_iter_(0){}; + ~IdShuffleCopyOutKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const std::vector& num_unique_matrix_vec = + embedding_state->GetIdNumUniqueMatrix(current_iter_); + uint32_t cur_rank_num_ids = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + cur_rank_num_ids += num_unique_matrix_vec.at(i * parallel_num + parallel_id); + } + IdShuffleCopyOutParam param; + param.final_num_unique_ids = num_unique; + param.cur_rank_unique_ids = + reinterpret_cast(ctx->Tensor4ArgNameAndIndex("cur_rank_unique_ids", 0)->dptr()); + param.out_cur_rank_unique_ids = + reinterpret_cast(ctx->Tensor4ArgNameAndIndex("out_cur_rank_unique_ids", 0)->mut_dptr()); + param.cur_rank_unique_table_ids = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("cur_rank_unique_table_ids", 0)->dptr()); + param.out_cur_rank_unique_table_ids = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("out_cur_rank_unique_table_ids", 0)->mut_dptr()); + param.cur_rank_num_ids = cur_rank_num_ids; + param.cur_rank_inverse_indices = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("cur_rank_inverse_indices", 0)->dptr()); + param.out_cur_rank_inverse_indices = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("out_cur_rank_inverse_indices", 0)->mut_dptr()); + param.num_ids = + ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0)->shape_view().elem_cnt(); + param.inverse_unique_partition_indices = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("inverse_unique_partition_indices", 0)->dptr()); + param.out_inverse_unique_partition_indices = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("out_inverse_unique_partition_indices", 0)->mut_dptr()); + param.num_unique_matrix_cnt = parallel_num * parallel_num; + param.num_unique_matrix = + reinterpret_cast(ctx->Tensor4ArgNameAndIndex("num_unique_matrix", 0)->dptr()); + param.out_num_unique_matrix = + reinterpret_cast(ctx->Tensor4ArgNameAndIndex("out_num_unique_matrix", 0)->mut_dptr()); + param.cur_rank_num_unique = + reinterpret_cast(ctx->Tensor4ArgNameAndIndex("cur_rank_num_unique", 0)->dptr()); + param.out_cur_rank_num_unique = reinterpret_cast( + ctx->Tensor4ArgNameAndIndex("out_cur_rank_num_unique", 0)->mut_dptr()); + + CopyGpu<<stream()->As()->cuda_stream()>>>(param); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +#define REGISTER_CUDA_ID_SHUFFLE_COPY_OUT_KERNEL(k_dtype_pair, table_id_dtype_pair, \ + idx_dtype_pair) \ + REGISTER_USER_KERNEL("id_shuffle_copy_out") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("cur_rank_unique_ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("cur_rank_unique_table_ids", 0) \ + == OF_PP_PAIR_SECOND(table_id_dtype_pair)) \ + && (user_op::HobDataType("num_unique_matrix", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ID_SHUFFLE_COPY_OUT_KERNEL, ID_DATA_TYPE_SEQ, + TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) + +enum class FusedEmbeddingBufferType { + // id shuffle + kNumPartitionedUnique = 0, + kPartitionedUniqueIds, + kReceivedIds, + kTableIds, + kPartitionedUniqueTableIds, + kReceivedTableIds, + kWorkspace, + kNumUniqueMatrix, + kInverseUniquePartitionIndices, + kCurRankNumUnique, + kCurRankUniqueIds, + kCurRankUniqueTableIds, + kCurRankInverseIndices, + // embedding lookup + kNumMissing, + kMissingIndices, + kCurRankUniqueValues, + kCurRankUniqueEmbeddings, + // embedding shuffle + kReverseUniqueCurRankEmbeddings, + kReceivedEmbeddings, + kMaxType +}; + +template +class FusedEmbeddingTmpBufferManager final { + public: + OF_DISALLOW_COPY_AND_MOVE(FusedEmbeddingTmpBufferManager); + FusedEmbeddingTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t parallel_num, + bool need_process_table_ids, int64_t line_size, + int64_t embedding_size, bool need_unique_values, + bool need_embeddings, DataType value_dtype, + DataType embedding_dtype) + : offset_(0), + offsets_(static_cast(FusedEmbeddingBufferType::kMaxType), -1), + sizes_(static_cast(FusedEmbeddingBufferType::kMaxType)), + ptr_(ptr) { + // id shuffle + const int64_t num_table_ids = need_process_table_ids ? num_ids : 0; + const size_t table_ids_bytes = need_process_table_ids ? num_ids * sizeof(U) : 0; + AllocBuffer(FusedEmbeddingBufferType::kNumPartitionedUnique, parallel_num * sizeof(IDX)); + size_t partitioned_ids_bytes = parallel_num * num_ids * sizeof(K); + AllocBuffer(FusedEmbeddingBufferType::kPartitionedUniqueIds, partitioned_ids_bytes); + AllocBuffer(FusedEmbeddingBufferType::kReceivedIds, partitioned_ids_bytes); + AllocBuffer(FusedEmbeddingBufferType::kTableIds, table_ids_bytes); + size_t partitioned_table_ids_bytes = parallel_num * num_table_ids * sizeof(U); + AllocBuffer(FusedEmbeddingBufferType::kPartitionedUniqueTableIds, partitioned_table_ids_bytes); + AllocBuffer(FusedEmbeddingBufferType::kReceivedTableIds, partitioned_table_ids_bytes); + const size_t hash_table_capacity = parallel_num * num_ids; + AllocBuffer(FusedEmbeddingBufferType::kWorkspace, + hash_table_capacity * sizeof(data_shuffle::TableEntry)); + size_t num_unique_matrix_bytes = parallel_num * parallel_num * sizeof(IDX); + AllocBuffer(FusedEmbeddingBufferType::kNumUniqueMatrix, num_unique_matrix_bytes); + size_t inverse_unique_partition_indices_bytes = num_ids * sizeof(IDX); + AllocBuffer(FusedEmbeddingBufferType::kInverseUniquePartitionIndices, + inverse_unique_partition_indices_bytes); + size_t cur_rank_num_ids = parallel_num * num_ids; + size_t cur_rank_num_table_ids = cur_rank_num_ids; + size_t cur_rank_num_unique_bytes = sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kCurRankNumUnique, cur_rank_num_unique_bytes); + size_t cur_rank_unique_ids_bytes = cur_rank_num_ids * sizeof(K); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueIds, cur_rank_unique_ids_bytes); + size_t cur_rank_unique_table_ids_bytes = cur_rank_num_table_ids * sizeof(U); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueTableIds, cur_rank_unique_table_ids_bytes); + size_t cur_rank_inverse_indices_bytes = cur_rank_num_ids * sizeof(IDX); + AllocBuffer(FusedEmbeddingBufferType::kCurRankInverseIndices, cur_rank_inverse_indices_bytes); + // embedding lookup + size_t num_missing_bytes = sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kNumMissing, num_missing_bytes); + size_t missing_indices_bytes = cur_rank_num_ids * sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kMissingIndices, missing_indices_bytes); + if (need_unique_values) { + size_t cur_rank_unique_values_bytes = + cur_rank_num_ids * line_size * GetSizeOfDataType(value_dtype); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueValues, cur_rank_unique_values_bytes); + } + if (need_embeddings) { + size_t cur_rank_unique_embeddings_bytes = + cur_rank_num_ids * embedding_size * GetSizeOfDataType(embedding_dtype); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueEmbeddings, + cur_rank_unique_embeddings_bytes); + } + // embedding shuffle + size_t reverse_unique_cur_rank_embeddings_bytes = + cur_rank_num_ids * embedding_size * GetSizeOfDataType(embedding_dtype); + AllocBuffer(FusedEmbeddingBufferType::kReverseUniqueCurRankEmbeddings, + reverse_unique_cur_rank_embeddings_bytes); + size_t received_embeddings_bytes = + cur_rank_num_ids * embedding_size * GetSizeOfDataType(embedding_dtype); + AllocBuffer(FusedEmbeddingBufferType::kReceivedEmbeddings, received_embeddings_bytes); + } + + template + T* Ptr(FusedEmbeddingBufferType type) const { + CHECK(ptr_ != nullptr); + int64_t offset = offsets_.at(static_cast(type)); + CHECK_NE(offset, -1); + return reinterpret_cast(reinterpret_cast(ptr_) + offset); + } + + int64_t Size(FusedEmbeddingBufferType type) const { return sizes_.at(static_cast(type)); } + + size_t TotalBufferSize() const { return offset_; } + + private: + void AllocBuffer(FusedEmbeddingBufferType type, size_t size) { + const size_t type_id = static_cast(type); + CHECK_EQ(offsets_.at(type_id), -1); + offsets_.at(type_id) = offset_; + sizes_.at(type_id) = size; + offset_ += GetCudaAlignedSize(size); + } + size_t offset_; + std::vector offsets_; + std::vector sizes_; + void* ptr_; +}; + +void MakeConstantInitializerAttr(const int64_t embedding_size, const int64_t line_size, + const std::vector& values, std::string* initializer_attr) { + if (embedding_size == line_size) { return; } + const int32_t num_states = line_size / embedding_size - 1; + CHECK_GT(num_states, 0) << "num_states " << num_states; + CHECK(values.size() == 0 || num_states == values.size()) + << "must set " << num_states << " optimizer states init value, but get " << values.size(); + nlohmann::json initializers; + for (int32_t i = 0; i < num_states; ++i) { + nlohmann::json initializer; + initializer["type"] = "constant"; + const float initial_value = values.size() > 0 ? values.at(i) : 0.0; + initializer["value"] = initial_value; + initializers.push_back(initializer); + } + *initializer_attr = initializers.dump(); +} + +template +class OneEmbeddingFusedLookupKernelState final : public user_op::OpKernelState { + public: + explicit OneEmbeddingFusedLookupKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1), + stream_name_(EagerNcclCommMgr::kDefaultStreamName), + parallel_desc_(ctx->parallel_desc()) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_keys_), sizeof(IDX))); + OF_CUDA_CHECK( + hipMallocHost(reinterpret_cast(&host_num_unique_matrix_), parallel_num * parallel_num * sizeof(IDX))); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); + OF_CUDA_CHECK( + cudaMallocHost(&host_num_unique_matrix_, parallel_num * parallel_num * sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + key_value_store_ = Singleton::Get()->GetKeyValueStore( + embedding_name, parallel_id); + uint32_t max_query_length = + ctx->TensorDesc4ArgNameAndIndex("ids", 0)->shape().elem_cnt() * parallel_num; + key_value_store_->ReserveQueryLength(max_query_length); + + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + // Note(guoran): This op have no optimizer info, so set embedding states initializer constant + // 0, which may make error in optimizer with initial_accumulator_value like adagrad and ftrl. + std::string state_initializer; + MakeConstantInitializerAttr(embedding_size, line_size, {}, &state_initializer); + + std::vector initializer_param; + std::vector initializer_index; + ParseInitializers(line_size, embedding_size, state_initializer, + ctx->Attr("embedding_tables"), &initializer_param, + &initializer_index); + + const size_t param_size_bytes = initializer_param.size() * sizeof(EmbeddingInitializer); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_param_), param_size_bytes)); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_initializer_param_, param_size_bytes)); +#endif + std::memcpy(host_initializer_param_, initializer_param.data(), param_size_bytes); + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_param_, param_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_param_, host_initializer_param_, + param_size_bytes, GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + + const size_t index_size_bytes = initializer_index.size() * sizeof(int8_t); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_index_), index_size_bytes)); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_initializer_index_, index_size_bytes)); +#endif + std::memcpy(host_initializer_index_, initializer_index.data(), index_size_bytes); + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_index_, index_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_index_, host_initializer_index_, + index_size_bytes, GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + } + ~OneEmbeddingFusedLookupKernelState() override { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_unique_matrix_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_param_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_param_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_index_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_index_)); + } + + ncclComm_t comm() { return GetOrCreate().comm; } + + IDX* HostNumUniqueMatrix() { return host_num_unique_matrix_; } + + IDX* HostNumKeys() { return host_num_keys_; } + + embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } + + const int8_t* InitializerIndex() { return device_initializer_index_; } + const EmbeddingInitializer* Initializers() { return device_initializer_param_; } + + private: + struct Comm { + Comm(ncclComm_t comm) : comm(comm) {} + ncclComm_t comm; + }; + + const Comm& GetOrCreate() { + if (!comm_) { Init(); } + return *comm_; + } + + void Init() { + std::set> device_set; + for (int64_t parallel_id = 0; parallel_id < parallel_desc_.parallel_num(); ++parallel_id) { + int64_t machine_id = CHECK_JUST(parallel_desc_.MachineId4ParallelId(parallel_id)); + int64_t device_id = CHECK_JUST(parallel_desc_.DeviceId4ParallelId(parallel_id)); + device_set.emplace(std::make_pair(machine_id, device_id)); + } + EagerNcclCommMgr* comm_mgr = CHECK_NOTNULL(Singleton::Get()); + ncclComm_t comm; + comm = comm_mgr->GetCommForDeviceAndStreamName(device_set, stream_name_); + comm_.reset(new Comm(comm)); + } + + int device_index_; + std::string stream_name_; + ParallelDesc parallel_desc_; + std::unique_ptr comm_; + IDX* host_num_keys_; + IDX* host_num_unique_matrix_; + embedding::KeyValueStore* key_value_store_; + + EmbeddingInitializer* host_initializer_param_; + EmbeddingInitializer* device_initializer_param_; + int8_t* host_initializer_index_; + int8_t* device_initializer_index_; +}; + +template +void LookupAndInitMissing(ep::Stream* stream, OneEmbeddingFusedLookupKernelState* kernel_state, + uint64_t seed, uint32_t num_unique, const int64_t embedding_size, + const int64_t line_size, const bool put_to_store, const void* unique_ids, + const void* table_ids, void* num_missing_ptr, void* missing_indices, + void* store_values) { + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + const EmbeddingInitializer* initializer_param = kernel_state->Initializers(); + const int8_t* initializer_index = kernel_state->InitializerIndex(); + void* host_num_keys = kernel_state->HostNumKeys(); + LookupAndInitMissing(stream, seed, store, initializer_param, initializer_index, + host_num_keys, num_unique, embedding_size, line_size, + put_to_store, unique_ids, table_ids, num_missing_ptr, + missing_indices, store_values); +} + +template +void SetIdShuffleDataPtrsParam(const void* ids_ptr, + const FusedEmbeddingTmpBufferManager& buffer_manager, + data_shuffle::IdShuffleDataPtrs* data_ptrs) { + data_ptrs->ids_ptr = reinterpret_cast(ids_ptr); + data_ptrs->table_ids_ptr = buffer_manager.template Ptr(FusedEmbeddingBufferType::kTableIds); + data_ptrs->num_partitioned_unique = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kNumPartitionedUnique); + data_ptrs->partitioned_unique_ids = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kPartitionedUniqueIds); + data_ptrs->partitioned_unique_table_ids = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kPartitionedUniqueTableIds); + data_ptrs->workspace_ptr = buffer_manager.Ptr(FusedEmbeddingBufferType::kWorkspace); + data_ptrs->workspace_size = buffer_manager.Size(FusedEmbeddingBufferType::kWorkspace); + data_ptrs->received_ids = buffer_manager.template Ptr(FusedEmbeddingBufferType::kReceivedIds); + data_ptrs->received_table_ids = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kReceivedTableIds); + data_ptrs->inverse_unique_partition_indices_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kInverseUniquePartitionIndices); + data_ptrs->num_unique_matrix_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kNumUniqueMatrix); + data_ptrs->cur_rank_num_unique_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankNumUnique); + data_ptrs->cur_rank_unique_ids_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueIds); + data_ptrs->cur_rank_unique_table_ids_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueTableIds); + data_ptrs->cur_rank_inverse_indices_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankInverseIndices); +} + +template +class OneEmbeddingFusedLookupKernel final : public user_op::OpKernel { + public: + OneEmbeddingFusedLookupKernel() = default; + ~OneEmbeddingFusedLookupKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + // IDX type is uint32_t, table_ids type is uint8_t. + DataType num_unique_matrix_dtype = DataType::kUInt32; + DataType table_ids_dtype = DataType::kUInt8; + CHECK_EQ(sizeof(IDX), GetSizeOfDataType(num_unique_matrix_dtype)); + CHECK_EQ(sizeof(U), GetSizeOfDataType(table_ids_dtype)); + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + const user_op::Tensor* ids = ctx->Tensor4ArgNameAndIndex("ids", 0); + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + const int32_t num_tables = ctx->Attr("num_tables"); + // default uint8_t as table_ids type, so num_tables can not greater than 256. + CHECK_LE(num_tables, 256) << num_tables; + const bool has_table_ids = ctx->has_input("table_ids", 0); + const bool need_process_table_ids = (has_table_ids || num_tables > 1); + const int64_t num_ids = ids->shape_view().elem_cnt(); + const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + DataType value_dtype = ctx->Attr("dtype"); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t padding_idx = ctx->Attr("padding_idx"); + const bool has_padding_idx = ctx->Attr("has_padding_idx"); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + bool need_unique_values = true; + bool need_embeddings = + (line_size != embedding_size) || (value_dtype != embeddings->data_type()); + FusedEmbeddingTmpBufferManager buffer_manager( + tmp_buffer->mut_dptr(), num_ids, parallel_num, need_process_table_ids, line_size, + embedding_size, need_unique_values, need_embeddings, value_dtype, embeddings->data_type()); + CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize()); + ncclComm_t comm = kernel_state->comm(); + IDX* host_num_unique_matrix = kernel_state->HostNumUniqueMatrix(); + IDX* host_num_keys = kernel_state->HostNumKeys(); + data_shuffle::IdShuffleDataPtrs data_ptrs; + SetIdShuffleDataPtrsParam(ids->dptr(), buffer_manager, &data_ptrs); + // overwrite data_ptrs.table_ids_ptr + if (need_process_table_ids) { + U* tmp_table_ids_ptr = buffer_manager.template Ptr(FusedEmbeddingBufferType::kTableIds); + data_ptrs.table_ids_ptr = tmp_table_ids_ptr; + if (has_table_ids) { + // use table_id default data_type uint8, if has input table_ids with different data_type, + // cast it to uint8. + const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); + if (table_ids->data_type() != table_ids_dtype) { + std::unique_ptr cast_primitive = + ep::primitive::NewPrimitive( + DeviceType::kCUDA, table_ids->data_type(), table_ids_dtype); + cast_primitive->Launch(ctx->stream(), table_ids->dptr(), tmp_table_ids_ptr, + table_ids->shape_view().elem_cnt()); + } else { + data_ptrs.table_ids_ptr = reinterpret_cast(table_ids->dptr()); + } + } else { + const int32_t num_tables = ctx->Attr("num_tables"); + data_shuffle::GenerateTableIds<<>>(num_ids, num_tables, tmp_table_ids_ptr); + } + } else { + data_ptrs.table_ids_ptr = nullptr; + } + + data_shuffle::IdShuffle(ctx->stream(), comm, data_ptrs, num_ids, parallel_id, parallel_num, + num_unique_matrix_dtype, ids->data_type(), table_ids_dtype, + need_process_table_ids, has_padding_idx, padding_idx, + host_num_unique_matrix, host_num_keys); + uint32_t num_unique = *host_num_keys; + + // lookup and put, if is_full_cache, not put to store. + uint32_t* num_missing_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kNumMissing); + uint32_t* missing_indices_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kMissingIndices); + void* values_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueValues); + T* cur_rank_embeddings_ptr = + need_embeddings + ? buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueEmbeddings) + : reinterpret_cast(values_ptr); + const bool is_full_cache = ctx->Attr("is_full_cache"); + const bool put_to_store = (!is_full_cache); + const int64_t seed = ctx->Attr("seed"); + LookupAndInitMissing( + ctx->stream(), kernel_state, seed, num_unique, embedding_size, line_size, put_to_store, + data_ptrs.cur_rank_unique_ids_ptr, data_ptrs.cur_rank_unique_table_ids_ptr, num_missing_ptr, + missing_indices_ptr, values_ptr); + if (need_embeddings) { + CopyValuesToEmbeddings(ctx->stream(), num_unique, embedding_size, line_size, value_dtype, + embeddings->data_type(), reinterpret_cast(values_ptr), + cur_rank_embeddings_ptr); + } + + // embedding shuffle + int64_t cur_rank_num_ids = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + cur_rank_num_ids += host_num_unique_matrix[i * parallel_num + parallel_id]; + } + int64_t unique_partitioned_num_ids = 0; + for (int64_t i = 0; i < parallel_num; ++i) { + unique_partitioned_num_ids += host_num_unique_matrix[parallel_id * parallel_num + i]; + } + T* reverse_unique_cur_rank_embeddings_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kReverseUniqueCurRankEmbeddings); + T* received_embeddings_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kReceivedEmbeddings); + GatherKernelUtilImpl::Forward( + ctx->stream(), data_ptrs.cur_rank_inverse_indices_ptr, cur_rank_num_ids, + cur_rank_embeddings_ptr, Shape({1, num_unique, embedding_size}), + reverse_unique_cur_rank_embeddings_ptr, 0); + + data_shuffle::ShuffleEmbeddings(cuda_stream, comm, parallel_id, parallel_num, num_ids, + embedding_size, embeddings->data_type(), host_num_unique_matrix, + reverse_unique_cur_rank_embeddings_ptr, + received_embeddings_ptr); + GatherKernelUtilImpl::Forward( + ctx->stream(), data_ptrs.inverse_unique_partition_indices_ptr, num_ids, + received_embeddings_ptr, Shape({1, unique_partitioned_num_ids, embedding_size}), + embeddings->mut_dptr(), 0); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +auto SingleDeviceKernel() { + return hob::make_custom("SingleDeviceKernel", [](const user_op::KernelRegContext& ctx) { + return (ctx.parallel_ctx().parallel_num() == 1); + }); +} + +// Note(guoran): Default use U type as uint8_t, IDX as uint32_t. Because table_ids is optional, so +// can not use it in hob, if has table_ids input and dtype is not uint8_t cast to uint8_t in kernel. +#define REGISTER_CUDA_ONE_EMBEDDING_FUSED_LOOKUP_KERNEL(k_dtype_pair, t_dtype_pair, v_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_fused_lookup") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ + && (user_op::HobAttr("dtype") == OF_PP_PAIR_SECOND(v_dtype_pair)) \ + && !SingleDeviceKernel()) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ + const user_op::TensorDesc& embeddings = ctx->OutputTensorDesc("embeddings", 0); \ + const bool has_table_ids = ctx->has_input("table_ids", 0); \ + const int32_t num_tables = ctx->Attr("num_tables"); \ + const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ + DataType value_dtype = ctx->Attr("dtype"); \ + const int64_t embedding_size = ctx->Attr("embedding_size"); \ + const int64_t line_size = ctx->Attr("line_size"); \ + bool need_embeddings = \ + (line_size != embedding_size) || (value_dtype != embeddings.data_type()); \ + FusedEmbeddingTmpBufferManager \ + buffer_manager(nullptr, ids.shape().elem_cnt(), ctx->parallel_ctx().parallel_num(), \ + need_process_table_ids, line_size, embedding_size, true, \ + need_embeddings, value_dtype, embeddings.data_type()); \ + return buffer_manager.TotalBufferSize(); \ + }); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_FUSED_LOOKUP_KERNEL, ID_DATA_TYPE_SEQ, + FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, EMBEDDING_DATA_TYPE_SEQ) + +template +class OneEmbeddingFusedLookupLocalKernelState final : public user_op::OpKernelState { + public: + explicit OneEmbeddingFusedLookupLocalKernelState(user_op::KernelInitContext* ctx) + : device_index_(-1) { + OF_CUDA_CHECK(GPU(GetDevice)(&device_index_)); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_num_keys_), sizeof(IDX))); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_num_keys_, sizeof(IDX))); +#endif + const std::string& embedding_name = ctx->Attr("embedding_name"); + key_value_store_ = Singleton::Get()->GetKeyValueStore( + embedding_name, parallel_id); + uint32_t max_query_length = ctx->TensorDesc4ArgNameAndIndex("ids", 0)->shape().elem_cnt(); + key_value_store_->ReserveQueryLength(max_query_length); + + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + // Note(guoran): This op have no optimizer info, so set embedding states initializer constant + // 0, which may make error in optimizer with initial_accumulator_value like adagrad and ftrl. + std::string state_initializer; + MakeConstantInitializerAttr(embedding_size, line_size, {}, &state_initializer); + + std::vector initializer_param; + std::vector initializer_index; + ParseInitializers(line_size, embedding_size, state_initializer, + ctx->Attr("embedding_tables"), &initializer_param, + &initializer_index); + + const size_t param_size_bytes = initializer_param.size() * sizeof(EmbeddingInitializer); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_param_), param_size_bytes)); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_initializer_param_, param_size_bytes)); +#endif + std::memcpy(host_initializer_param_, initializer_param.data(), param_size_bytes); + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_param_, param_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_param_, host_initializer_param_, + param_size_bytes, GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + + const size_t index_size_bytes = initializer_index.size() * sizeof(int8_t); +#ifdef WITH_ROCM + OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_index_), index_size_bytes)); +#else + OF_CUDA_CHECK(cudaMallocHost(&host_initializer_index_, index_size_bytes)); +#endif + std::memcpy(host_initializer_index_, initializer_index.data(), index_size_bytes); + OF_CUDA_CHECK(GPU(Malloc)(&device_initializer_index_, index_size_bytes)); + OF_CUDA_CHECK(GPU(MemcpyAsync)(device_initializer_index_, host_initializer_index_, + index_size_bytes, GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); + } + ~OneEmbeddingFusedLookupLocalKernelState() override { + CudaCurrentDeviceGuard guard(device_index_); + OF_CUDA_CHECK(GPU(FreeHost)(host_num_keys_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_param_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_param_)); + OF_CUDA_CHECK(GPU(FreeHost)(host_initializer_index_)); + OF_CUDA_CHECK(GPU(Free)(device_initializer_index_)); + } + + IDX* HostNumKeys() { return host_num_keys_; } + + embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } + + const int8_t* InitializerIndex() { return device_initializer_index_; } + const EmbeddingInitializer* Initializers() { return device_initializer_param_; } + + private: + int device_index_; + IDX* host_num_keys_; + embedding::KeyValueStore* key_value_store_; + + EmbeddingInitializer* host_initializer_param_; + EmbeddingInitializer* device_initializer_param_; + int8_t* host_initializer_index_; + int8_t* device_initializer_index_; +}; + +template +void LookupAndInitMissing(ep::Stream* stream, + OneEmbeddingFusedLookupLocalKernelState* kernel_state, uint64_t seed, + uint32_t num_unique, const int64_t embedding_size, + const int64_t line_size, const bool put_to_store, const void* unique_ids, + const void* table_ids, void* num_missing_ptr, void* missing_indices, + void* store_values) { + embedding::KeyValueStore* store = kernel_state->KeyValueStore(); + const EmbeddingInitializer* initializer_param = kernel_state->Initializers(); + const int8_t* initializer_index = kernel_state->InitializerIndex(); + void* host_num_keys = kernel_state->HostNumKeys(); + LookupAndInitMissing(stream, seed, store, initializer_param, initializer_index, + host_num_keys, num_unique, embedding_size, line_size, + put_to_store, unique_ids, table_ids, num_missing_ptr, + missing_indices, store_values); +} + +template +class FusedLocalEmbeddingTmpBufferManager final { + public: + OF_DISALLOW_COPY_AND_MOVE(FusedLocalEmbeddingTmpBufferManager); + FusedLocalEmbeddingTmpBufferManager(void* ptr, const int64_t num_ids, bool need_process_table_ids, + int64_t line_size, int64_t embedding_size, + bool need_embeddings, DataType value_dtype, + DataType embedding_dtype) + : offset_(0), + offsets_(static_cast(FusedEmbeddingBufferType::kMaxType), -1), + sizes_(static_cast(FusedEmbeddingBufferType::kMaxType)), + ptr_(ptr) { + // id shuffle + const size_t table_ids_bytes = need_process_table_ids ? num_ids * sizeof(U) : 0; + AllocBuffer(FusedEmbeddingBufferType::kTableIds, table_ids_bytes); + const size_t hash_table_capacity = num_ids; + AllocBuffer(FusedEmbeddingBufferType::kWorkspace, + hash_table_capacity * sizeof(data_shuffle::TableEntry)); + size_t cur_rank_num_ids = num_ids; + size_t cur_rank_num_table_ids = cur_rank_num_ids; + size_t cur_rank_num_unique_bytes = sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kCurRankNumUnique, cur_rank_num_unique_bytes); + size_t cur_rank_unique_ids_bytes = cur_rank_num_ids * sizeof(K); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueIds, cur_rank_unique_ids_bytes); + size_t cur_rank_unique_table_ids_bytes = cur_rank_num_table_ids * sizeof(U); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueTableIds, cur_rank_unique_table_ids_bytes); + size_t cur_rank_inverse_indices_bytes = cur_rank_num_ids * sizeof(IDX); + AllocBuffer(FusedEmbeddingBufferType::kCurRankInverseIndices, cur_rank_inverse_indices_bytes); + // embedding lookup + size_t num_missing_bytes = sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kNumMissing, num_missing_bytes); + size_t missing_indices_bytes = cur_rank_num_ids * sizeof(uint32_t); + AllocBuffer(FusedEmbeddingBufferType::kMissingIndices, missing_indices_bytes); + size_t cur_rank_unique_values_bytes = + cur_rank_num_ids * line_size * GetSizeOfDataType(value_dtype); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueValues, cur_rank_unique_values_bytes); + if (need_embeddings) { + size_t cur_rank_unique_embeddings_bytes = + cur_rank_num_ids * embedding_size * GetSizeOfDataType(embedding_dtype); + AllocBuffer(FusedEmbeddingBufferType::kCurRankUniqueEmbeddings, + cur_rank_unique_embeddings_bytes); + } + } + + template + T* Ptr(FusedEmbeddingBufferType type) const { + CHECK(ptr_ != nullptr); + int64_t offset = offsets_.at(static_cast(type)); + CHECK_NE(offset, -1); + return reinterpret_cast(reinterpret_cast(ptr_) + offset); + } + + int64_t Size(FusedEmbeddingBufferType type) const { return sizes_.at(static_cast(type)); } + + size_t TotalBufferSize() const { return offset_; } + + private: + void AllocBuffer(FusedEmbeddingBufferType type, size_t size) { + const size_t type_id = static_cast(type); + CHECK_EQ(offsets_.at(type_id), -1); + offsets_.at(type_id) = offset_; + sizes_.at(type_id) = size; + offset_ += GetCudaAlignedSize(size); + } + size_t offset_; + std::vector offsets_; + std::vector sizes_; + void* ptr_; +}; + +template +class OneEmbeddingFusedLookupLocalKernel final : public user_op::OpKernel { + public: + OneEmbeddingFusedLookupLocalKernel() = default; + ~OneEmbeddingFusedLookupLocalKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared>(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + // IDX type is uint32_t, table_ids type is uint8_t. + DataType num_unique_matrix_dtype = DataType::kUInt32; + DataType table_ids_dtype = DataType::kUInt8; + CHECK_EQ(sizeof(IDX), GetSizeOfDataType(num_unique_matrix_dtype)); + CHECK_EQ(sizeof(U), GetSizeOfDataType(table_ids_dtype)); + auto* kernel_state = dynamic_cast*>(state); + CHECK(kernel_state != nullptr); + const user_op::Tensor* ids = ctx->Tensor4ArgNameAndIndex("ids", 0); + user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); + const int32_t num_tables = ctx->Attr("num_tables"); + // default uint8_t as table_ids type, so num_tables can not greater than 256. + CHECK_LE(num_tables, 256) << num_tables; + const bool has_table_ids = ctx->has_input("table_ids", 0); + const bool need_process_table_ids = (has_table_ids || num_tables > 1); + const int64_t num_ids = ids->shape_view().elem_cnt(); + GPU(Stream_t) cuda_stream = ctx->stream()->As()->cuda_stream(); + DataType value_dtype = ctx->Attr("dtype"); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t padding_idx = ctx->Attr("padding_idx"); + const bool has_padding_idx = ctx->Attr("has_padding_idx"); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + bool need_embeddings = + (line_size != embedding_size) || (value_dtype != embeddings->data_type()); + FusedLocalEmbeddingTmpBufferManager buffer_manager( + tmp_buffer->mut_dptr(), num_ids, need_process_table_ids, line_size, embedding_size, + need_embeddings, value_dtype, embeddings->data_type()); + CHECK_GE(tmp_buffer->shape_view().elem_cnt(), buffer_manager.TotalBufferSize()); + IDX* host_num_keys = kernel_state->HostNumKeys(); + + const U* table_ids_ptr = nullptr; + if (need_process_table_ids) { + U* tmp_table_ids_ptr = buffer_manager.template Ptr(FusedEmbeddingBufferType::kTableIds); + table_ids_ptr = tmp_table_ids_ptr; + if (has_table_ids) { + // use table_id default data_type uint8, if has input table_ids with different data_type, + // cast it to uint8. + const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); + if (table_ids->data_type() != table_ids_dtype) { + std::unique_ptr cast_primitive = + ep::primitive::NewPrimitive( + DeviceType::kCUDA, table_ids->data_type(), table_ids_dtype); + cast_primitive->Launch(ctx->stream(), table_ids->dptr(), tmp_table_ids_ptr, + table_ids->shape_view().elem_cnt()); + } else { + table_ids_ptr = reinterpret_cast(table_ids->dptr()); + } + } else { + const int32_t num_tables = ctx->Attr("num_tables"); + data_shuffle::GenerateTableIds<<>>(num_ids, num_tables, tmp_table_ids_ptr); + } + } + IDX* num_unique_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankNumUnique); + K* unique_ids_ptr = buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueIds); + U* unique_table_ids_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueTableIds); + IDX* inverse_indices_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankInverseIndices); + void* workspace_ptr = buffer_manager.Ptr(FusedEmbeddingBufferType::kWorkspace); + const size_t workspace_bytes = buffer_manager.Size(FusedEmbeddingBufferType::kWorkspace); + int64_t hash_capacity = num_ids; + data_shuffle::UniqueAndPartition( + cuda_stream, num_ids, hash_capacity, 1, reinterpret_cast(ids->dptr()), + table_ids_ptr, num_unique_ptr, unique_ids_ptr, unique_table_ids_ptr, inverse_indices_ptr, + reinterpret_cast*>(workspace_ptr), workspace_bytes, + need_process_table_ids, has_padding_idx, padding_idx); + + OF_CUDA_CHECK(GPU(MemcpyAsync)(host_num_keys, num_unique_ptr, sizeof(IDX), GPU(MemcpyDefault), + cuda_stream)); + CHECK_JUST(ctx->stream()->Sync()); + + uint32_t num_unique = *host_num_keys; + + // lookup and put, if is_full_cache, not put to store. + uint32_t* num_missing_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kNumMissing); + uint32_t* missing_indices_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kMissingIndices); + void* values_ptr = + buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueValues); + T* cur_rank_embeddings_ptr = + need_embeddings + ? buffer_manager.template Ptr(FusedEmbeddingBufferType::kCurRankUniqueEmbeddings) + : reinterpret_cast(values_ptr); + const bool is_full_cache = ctx->Attr("is_full_cache"); + const bool put_to_store = (!is_full_cache); + const int64_t seed = ctx->Attr("seed"); + LookupAndInitMissing( + ctx->stream(), kernel_state, seed, num_unique, embedding_size, line_size, put_to_store, + unique_ids_ptr, unique_table_ids_ptr, num_missing_ptr, missing_indices_ptr, values_ptr); + if (need_embeddings) { + CopyValuesToEmbeddings(ctx->stream(), num_unique, embedding_size, line_size, value_dtype, + embeddings->data_type(), reinterpret_cast(values_ptr), + cur_rank_embeddings_ptr); + } + // gather + GatherKernelUtilImpl::Forward( + ctx->stream(), inverse_indices_ptr, num_ids, cur_rank_embeddings_ptr, + Shape({1, num_unique, embedding_size}), embeddings->mut_dptr(), 0); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +// Note(guoran): Default use U type as uint8_t, IDX as uint32_t. Because table_ids is optional, so +// can not use it in hob, if has table_ids input and dtype is not uint8_t cast to uint8_t in kernel. +#define REGISTER_CUDA_ONE_EMBEDDING_FUSED_LOOKUP_LOCAL_KERNEL(k_dtype_pair, t_dtype_pair, \ + v_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_fused_lookup") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("ids", 0) == OF_PP_PAIR_SECOND(k_dtype_pair)) \ + && (user_op::HobDataType("embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ + && (user_op::HobAttr("dtype") == OF_PP_PAIR_SECOND(v_dtype_pair)) \ + && SingleDeviceKernel()) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const user_op::TensorDesc& ids = ctx->InputTensorDesc("ids", 0); \ + const user_op::TensorDesc& embeddings = ctx->OutputTensorDesc("embeddings", 0); \ + const bool has_table_ids = ctx->has_input("table_ids", 0); \ + const int32_t num_tables = ctx->Attr("num_tables"); \ + const bool need_process_table_ids = (has_table_ids || num_tables > 1); \ + DataType value_dtype = ctx->Attr("dtype"); \ + const int64_t embedding_size = ctx->Attr("embedding_size"); \ + const int64_t line_size = ctx->Attr("line_size"); \ + bool need_embeddings = \ + (line_size != embedding_size) || (value_dtype != embeddings.data_type()); \ + FusedLocalEmbeddingTmpBufferManager \ + buffer_manager(nullptr, ids.shape().elem_cnt(), need_process_table_ids, line_size, \ + embedding_size, need_embeddings, value_dtype, embeddings.data_type()); \ + return buffer_manager.TotalBufferSize(); \ + }); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_FUSED_LOOKUP_LOCAL_KERNEL, + ID_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + EMBEDDING_DATA_TYPE_SEQ) + +class OneEmbeddingFusedLookupGradKernel final : public user_op::OpKernel { + public: + OneEmbeddingFusedLookupGradKernel() = default; + ~OneEmbeddingFusedLookupGradKernel() override = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + // do nothing + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("one_embedding_fused_lookup_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); + } // namespace oneflow diff --git a/oneflow/user/kernels/one_embedding_kernels.hip.cpp b/oneflow/user/kernels/one_embedding_kernels.hip.cpp deleted file mode 100644 index 59d3e50d2b1e16bb875bd44a84356e13448ffa92..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/one_embedding_kernels.hip.cpp +++ /dev/null @@ -1,634 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/embedding/key_value_store.h" -#include "oneflow/core/embedding/embedding_manager.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/user/kernels/random_mask_generator.h" -#include "oneflow/core/framework/random_generator_impl.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/include/primitive/copy_nd.h" -#include "oneflow/core/ep/include/primitive/cast.h" -#include "oneflow/core/ep/include/device.h" - -namespace oneflow { - -namespace { - -enum class InitializerType { kUniform, kNormal, kConstant }; - -struct EmbeddingInitializer { - InitializerType type; - union { - struct { - float low; - float high; - } uniform_param; - struct { - float mean; - float std; - } normal_param; - struct { - float value; - } constant_param; - }; - - bool operator==(const EmbeddingInitializer& rhs) const { - if (this->type != rhs.type) { return false; } - if (rhs.type == InitializerType::kUniform) { - return (this->uniform_param.low == rhs.uniform_param.low) - && (this->uniform_param.high == rhs.uniform_param.high); - } else if (rhs.type == InitializerType::kNormal) { - return (this->normal_param.mean == rhs.normal_param.mean) - && (this->normal_param.std == rhs.normal_param.std); - } else if (rhs.type == InitializerType::kConstant) { - return this->constant_param.value == rhs.constant_param.value; - } else { - UNIMPLEMENTED(); - return false; - } - } -}; - -void ParseInitializerFromJson(const nlohmann::json& initializer, - EmbeddingInitializer* embedding_initializer) { - CHECK(initializer.contains("type")); - CHECK(initializer["type"].is_string()); - std::string type = initializer["type"].get(); - if (type == "uniform") { - embedding_initializer->type = InitializerType::kUniform; - CHECK(initializer.contains("low")); - CHECK(initializer.contains("high")); - CHECK(initializer["low"].is_number()); - CHECK(initializer["high"].is_number()); - embedding_initializer->uniform_param.low = initializer["low"]; - embedding_initializer->uniform_param.high = initializer["high"]; - } else if (type == "normal") { - CHECK(initializer.contains("mean")); - CHECK(initializer.contains("std")); - CHECK(initializer["mean"].is_number()); - CHECK(initializer["std"].is_number()); - embedding_initializer->type = InitializerType::kNormal; - embedding_initializer->normal_param.mean = initializer["mean"]; - embedding_initializer->normal_param.std = initializer["std"]; - } else if (type == "constant") { - CHECK(initializer.contains("value")); - CHECK(initializer["value"].is_number()); - embedding_initializer->type = InitializerType::kConstant; - embedding_initializer->constant_param.value = initializer["value"]; - } else { - UNIMPLEMENTED() << "Unsupported initializer type"; - } -} - -int32_t ParseJsonToUniqueInitializerVecAndReturnOffset( - const nlohmann::json& initializer, std::vector* initializers) { - EmbeddingInitializer embedding_initializer; - ParseInitializerFromJson(initializer, &embedding_initializer); - for (int32_t i = 0; i < initializers->size(); ++i) { - if (initializers->at(i) == embedding_initializer) { return i; } - } - initializers->push_back(embedding_initializer); - return initializers->size() - 1; -} - -void SetInitializerIndex(int32_t row_id, int32_t col_start, int32_t col_end, int64_t line_size, - int8_t index, std::vector* initializer_index) { - int64_t row_offset = row_id * line_size; - for (int32_t col = col_start; col < col_end; ++col) { - initializer_index->at(row_offset + col) = index; - } -} - -void ParseAndSetStateInitializerIndex(const std::string& state_initializer, - const int32_t num_tables, const int64_t line_size, - const int64_t embedding_size, - std::vector* initializer_params, - std::vector* initializer_index) { - if (line_size == embedding_size) { return; } - CHECK(!state_initializer.empty()); - auto initializers = nlohmann::json::parse(state_initializer); - CHECK(initializers.is_array()); - const int num_states = line_size / embedding_size - 1; - CHECK_EQ(num_states, initializers.size()); - for (int32_t i = 0; i < num_states; ++i) { - int32_t offset = - ParseJsonToUniqueInitializerVecAndReturnOffset(initializers.at(i), initializer_params); - int32_t col_start = embedding_size + i * embedding_size; - int32_t col_end = col_start + embedding_size; - CHECK_LE(col_end, line_size); - for (int32_t j = 0; j < num_tables; ++j) { - SetInitializerIndex(j, col_start, col_end, line_size, offset, initializer_index); - } - } -} - -void ParseAndSetModelInitializerIndex(const nlohmann::json& tables, - const std::vector& column_dims, - const int32_t num_tables, const int32_t num_columns, - const int64_t line_size, const int64_t embedding_size, - std::vector* initializer_params, - std::vector* initializer_index) { - for (int32_t i = 0; i < num_tables; ++i) { - auto table = tables.at(i); - CHECK(table.contains("columns")); - auto columns = table["columns"]; - CHECK(columns.is_array()); - CHECK_EQ(num_columns, columns.size()) << "columns size must equal to num embedding dims"; - int32_t col_start = 0; - for (int k = 0; k < columns.size(); ++k) { - auto column = columns.at(k); - CHECK(column.contains("initializer")); - int32_t offset = - ParseJsonToUniqueInitializerVecAndReturnOffset(column["initializer"], initializer_params); - int32_t col_end = col_start + column_dims.at(k); - SetInitializerIndex(i, col_start, col_end, line_size, offset, initializer_index); - col_start = col_end; - } - CHECK_EQ(col_start, embedding_size); - } -} - -void ParseInitializers(const int64_t line_size, const int64_t embedding_size, - const std::string& state_initializer, const std::string& json_serialized, - std::vector* initializer_params, - std::vector* initializer_index) { - auto json_object = nlohmann::json::parse(json_serialized); - CHECK(json_object.contains("column_dims")); - std::vector column_dims = json_object["column_dims"]; - const int32_t num_columns = column_dims.size(); - CHECK(json_object.contains("tables")); - auto tables = json_object["tables"]; - CHECK(tables.is_array()); - const int32_t num_tables = tables.size(); - initializer_index->resize(num_tables * line_size); - ParseAndSetStateInitializerIndex(state_initializer, num_tables, line_size, embedding_size, - initializer_params, initializer_index); - ParseAndSetModelInitializerIndex(tables, column_dims, num_tables, num_columns, line_size, - embedding_size, initializer_params, initializer_index); -} - -template -class EmbeddingKernelState final : public user_op::OpKernelState { - public: - explicit EmbeddingKernelState(user_op::KernelInitContext* ctx) - : device_index_(-1), generator_(CHECK_JUST(one::MakeGenerator(DeviceType::kCUDA))) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - OF_CUDA_CHECK(hipMallocHost(&host_num_keys_, sizeof(IDX))); - key_value_store_ = Singleton::Get()->GetKeyValueStore( - ctx->Attr("embedding_name"), ctx->parallel_ctx().parallel_id()); - uint32_t max_query_length = - ctx->TensorDesc4ArgNameAndIndex("unique_ids", 0)->shape().elem_cnt(); - key_value_store_->ReserveQueryLength(max_query_length); - - const int64_t embedding_size = ctx->Attr("embedding_size"); - const int64_t line_size = ctx->Attr("line_size"); - const std::string& state_initializer = ctx->Attr("state_initializer"); - - std::vector initializer_param; - std::vector initializer_index; - ParseInitializers(line_size, embedding_size, state_initializer, - ctx->Attr("embedding_tables"), &initializer_param, - &initializer_index); - - const size_t param_size_bytes = initializer_param.size() * sizeof(EmbeddingInitializer); - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_param_), param_size_bytes)); - std::memcpy(host_initializer_param_, initializer_param.data(), param_size_bytes); - OF_CUDA_CHECK(hipMalloc(&device_initializer_param_, param_size_bytes)); - OF_CUDA_CHECK(hipMemcpyAsync(device_initializer_param_, host_initializer_param_, - param_size_bytes, hipMemcpyDefault, - ctx->stream()->As()->cuda_stream())); - - const size_t index_size_bytes = initializer_index.size() * sizeof(int8_t); - OF_CUDA_CHECK(hipMallocHost(reinterpret_cast(&host_initializer_index_), index_size_bytes)); - std::memcpy(host_initializer_index_, initializer_index.data(), index_size_bytes); - OF_CUDA_CHECK(hipMalloc(&device_initializer_index_, index_size_bytes)); - OF_CUDA_CHECK(hipMemcpyAsync(device_initializer_index_, host_initializer_index_, - index_size_bytes, hipMemcpyDefault, - ctx->stream()->As()->cuda_stream())); - } - ~EmbeddingKernelState() override { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipHostFree(host_num_keys_)); - OF_CUDA_CHECK(hipHostFree(host_initializer_param_)); - OF_CUDA_CHECK(hipFree(device_initializer_param_)); - OF_CUDA_CHECK(hipHostFree(host_initializer_index_)); - OF_CUDA_CHECK(hipFree(device_initializer_index_)); - } - - void* HostNumKeys() { return host_num_keys_; } - - embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } - - one::Generator* generator() { return generator_.get(); } - - const int8_t* InitializerIndex() { return device_initializer_index_; } - const EmbeddingInitializer* Initializers() { return device_initializer_param_; } - - private: - int device_index_; - void* host_num_keys_; - std::shared_ptr generator_; - embedding::KeyValueStore* key_value_store_; - - EmbeddingInitializer* host_initializer_param_; - EmbeddingInitializer* device_initializer_param_; - int8_t* host_initializer_index_; - int8_t* device_initializer_index_; -}; - -template -class EmbeddingPutKernelState final : public user_op::OpKernelState { - public: - explicit EmbeddingPutKernelState(user_op::KernelInitContext* ctx) : device_index_(-1) { - OF_CUDA_CHECK(hipGetDevice(&device_index_)); - OF_CUDA_CHECK(hipMallocHost(&host_num_keys_, sizeof(IDX))); - key_value_store_ = Singleton::Get()->GetKeyValueStore( - ctx->Attr("embedding_name"), ctx->parallel_ctx().parallel_id()); - uint32_t max_query_length = - ctx->TensorDesc4ArgNameAndIndex("unique_ids", 0)->shape().elem_cnt(); - key_value_store_->ReserveQueryLength(max_query_length); - } - ~EmbeddingPutKernelState() override { - CudaCurrentDeviceGuard guard(device_index_); - OF_CUDA_CHECK(hipHostFree(host_num_keys_)); - } - - void* HostNumKeys() { return host_num_keys_; } - embedding::KeyValueStore* KeyValueStore() { return key_value_store_; } - - private: - int device_index_; - void* host_num_keys_; - embedding::KeyValueStore* key_value_store_; -}; - -enum class EmbeddingBufferType { kNumMissing = 0, kMissingIndices, kValues, kMaxType }; - -class EmbeddingTmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(EmbeddingTmpBufferManager); - EmbeddingTmpBufferManager(void* ptr, const int64_t num_ids, const int64_t value_byte_size, - const bool need_value_buffer) - : offset_(0), offsets_(static_cast(EmbeddingBufferType::kMaxType), -1), ptr_(ptr) { - AllocBuffer(EmbeddingBufferType::kNumMissing, sizeof(uint32_t)); - AllocBuffer(EmbeddingBufferType::kMissingIndices, num_ids * sizeof(uint32_t)); - if (need_value_buffer) { AllocBuffer(EmbeddingBufferType::kValues, num_ids * value_byte_size); } - } - - template - T* Ptr(EmbeddingBufferType type) { - CHECK(ptr_ != nullptr); - int64_t offset = offsets_.at(static_cast(type)); - CHECK_NE(offset, -1); - return reinterpret_cast(reinterpret_cast(ptr_) + offset); - } - - size_t TotalBufferSize() const { return offset_; } - - private: - void AllocBuffer(EmbeddingBufferType type, size_t size) { - const size_t type_id = static_cast(type); - CHECK_EQ(offsets_.at(type_id), -1); - offsets_.at(type_id) = offset_; - offset_ += GetCudaAlignedSize(size); - } - - size_t offset_; - std::vector offsets_; - void* ptr_; -}; - -template -__global__ void InitValueKernel(uint64_t seed, one::CUDAGeneratorState* cuda_gen_state, - uint64_t inc_offset, const int32_t line_size, - const int32_t embedding_size, - const EmbeddingInitializer* initializer_param, - const int8_t* initializer_index, const U* table_ids, - const uint32_t* num_missing_keys, const uint32_t* missing_indices, - T* values) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandStatePhilox4_32_10_t state; - hiprand_init(seed, global_thread_id, cuda_gen_state->dev_offset, &state); - int64_t n = *num_missing_keys * line_size; - CUDA_1D_KERNEL_LOOP(i, n) { - int row = i / line_size; - int col = i - row * line_size; - const uint32_t index = missing_indices[row]; - const int64_t offset = index * line_size + col; - const int32_t table_idx = table_ids[index]; - const int32_t initializer_idx = initializer_index[table_idx * line_size + col]; - EmbeddingInitializer initializer = initializer_param[initializer_idx]; - T value; - if (initializer.type == InitializerType::kUniform) { - const float low = initializer.uniform_param.low; - const float high = initializer.uniform_param.high; - value = hiprand_uniform(&state) * (high - low) + low; - } else if (initializer.type == InitializerType::kNormal) { - const float mean = initializer.normal_param.mean; - const float std = initializer.normal_param.std; - value = hiprand_normal(&state) * std + mean; - } else if (initializer.type == InitializerType::kConstant) { - value = initializer.constant_param.value; - } else { - asm volatile("s_trap 0;"); - } - values[offset] = value; - } - __syncthreads(); - if (threadIdx.x == 0) { - int32_t new_counter = cuda::atomic::Add(&cuda_gen_state->dev_counter, 1) + 1; - if (new_counter == gridDim.x) { - cuda_gen_state->dev_counter = 0; // reset counter to zero - cuda_gen_state->dev_offset += inc_offset; // maintain the state of generator's dev_offset - } - } -} - -template -void LookupAndInitMissing(ep::Stream* stream, EmbeddingKernelState* embedding_state, - const int64_t num_ids, const int64_t embedding_size, - const int64_t line_size, const void* num_unique_ptr, - const void* unique_ids, const void* table_ids, T* values_ptr, - void* tmp_buffer_ptr, uint32_t* return_num_unique, - const bool put_to_kv_store) { - const auto& generator = embedding_state->generator(); - CHECK_NOTNULL(generator); - std::shared_ptr cuda_generator = - CHECK_JUST(generator->template Get(stream->device()->device_index())); - uint64_t seed = cuda_generator->current_seed(); - one::CUDAGeneratorState* cuda_gen_state = cuda_generator->cuda_gen_state(); - embedding::KeyValueStore* store = embedding_state->KeyValueStore(); - const EmbeddingInitializer* initializer_param = embedding_state->Initializers(); - const int8_t* initializer_index = embedding_state->InitializerIndex(); - bool need_value_buffer = (values_ptr == nullptr); - EmbeddingTmpBufferManager buffer_manager(tmp_buffer_ptr, num_ids, line_size * sizeof(T), - need_value_buffer); - void* host_num_keys = embedding_state->HostNumKeys(); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_keys, num_unique_ptr, sizeof(IDX), hipMemcpyDefault, - stream->As()->cuda_stream())); - CHECK_JUST(stream->Sync()); - uint32_t num_unique = *reinterpret_cast(host_num_keys); - uint32_t* num_missing_ptr = - buffer_manager.template Ptr(EmbeddingBufferType::kNumMissing); - uint32_t* missing_indices = - buffer_manager.template Ptr(EmbeddingBufferType::kMissingIndices); - T* store_values = - need_value_buffer ? buffer_manager.template Ptr(EmbeddingBufferType::kValues) : values_ptr; - store->Get(stream, num_unique, unique_ids, store_values, num_missing_ptr, missing_indices); - CHECK_GE(sizeof(IDX), sizeof(uint32_t)); // host_num_keys's buffer size is sizeof(IDX) - OF_CUDA_CHECK(hipMemcpyAsync(host_num_keys, num_missing_ptr, sizeof(uint32_t), hipMemcpyDefault, - stream->As()->cuda_stream())); - CHECK_JUST(stream->Sync()); - uint32_t num_missing = *reinterpret_cast(host_num_keys); - // init missing values - if (num_missing > 0) { - const int64_t elem_cnt = num_missing * line_size; - const int64_t num_blocks = BlocksNum4ThreadsNum(elem_cnt); - const uint64_t inc_offset = std::ceil(elem_cnt / num_blocks / kCudaThreadsNumPerBlock); - InitValueKernel - <<As()->cuda_stream()>>>( - seed, cuda_gen_state, inc_offset, line_size, embedding_size, initializer_param, - initializer_index, reinterpret_cast(table_ids), num_missing_ptr, - missing_indices, store_values); - } - if (put_to_kv_store) { store->Put(stream, num_unique, unique_ids, store_values); } - *return_num_unique = num_unique; -} - -template -__global__ void Copy2D(int64_t out_elem_cnt, const int32_t in_cols, const int32_t out_cols, - const T* in, U* out) { - CUDA_1D_KERNEL_LOOP(i, out_elem_cnt) { - const int32_t row = i / out_cols; - const int32_t col = i - row * out_cols; - const int64_t in_offset = row * in_cols + col; - out[i] = static_cast(in[in_offset]); - } -} - -template -void CopyValuesToEmbeddings(ep::Stream* stream, int64_t num_unique, const int32_t embedding_size, - const int32_t value_size, const DataType value_dtype, - const DataType embedding_dtype, const T* values, void* embeddings) { - bool need_cast = (value_dtype != embedding_dtype); - bool need_copy_nd = (embedding_size != value_size); - CHECK(need_cast || need_copy_nd); - if (need_cast && !need_copy_nd) { - const int64_t cast_elem_count = num_unique * embedding_size; - std::unique_ptr cast_primitive = - ep::primitive::NewPrimitive(DeviceType::kCUDA, value_dtype, - embedding_dtype); - cast_primitive->Launch(stream, values, embeddings, cast_elem_count); - } else if (!need_cast && need_copy_nd) { - const int32_t ndims = 2; - DimVector src_pos_vec(ndims, 0); - DimVector dst_pos_vec(ndims, 0); - DimVector src_shape = {num_unique, value_size}; - DimVector dst_shape = {num_unique, embedding_size}; - DimVector extent_shape = {num_unique, embedding_size}; - std::unique_ptr copy_nd_primitive = - ep::primitive::NewPrimitive(DeviceType::kCUDA, ndims); - CHECK(copy_nd_primitive); - copy_nd_primitive->Launch(stream, value_dtype, ndims, embeddings, dst_shape.data(), - dst_pos_vec.data(), values, src_shape.data(), src_pos_vec.data(), - extent_shape.data()); - } else { - const int64_t embedding_elem_cnt = num_unique * embedding_size; - if (embedding_dtype == DataType::kFloat16) { - Copy2D<<As()->cuda_stream()>>>( - embedding_elem_cnt, value_size, embedding_size, values, - reinterpret_cast(embeddings)); - } else { - UNIMPLEMENTED(); - } - } -} - -} // namespace - -template -class EmbeddingPrefetchKernel final : public user_op::OpKernel { - public: - EmbeddingPrefetchKernel() = default; - ~EmbeddingPrefetchKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); - - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); - const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int64_t embedding_size = ctx->Attr("embedding_size"); - const int64_t line_size = ctx->Attr("line_size"); - uint32_t num_unique; - T* values_ptr = nullptr; - LookupAndInitMissing(ctx->stream(), embedding_state, - unique_ids->shape_view().elem_cnt(), embedding_size, line_size, - num_unique_ids->dptr(), unique_ids->dptr(), table_ids->dptr(), - values_ptr, tmp_buffer->mut_dptr(), &num_unique, true); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define EMBEDDING_DATA_TYPE_SEQ OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) - -#define TABLE_ID_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ - OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) - -#define IDX_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) - -#define REGISTER_CUDA_EMBEDDING_PREFETCH_KERNEL(t_dtype_pair, table_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("embedding_prefetch") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("table_ids", 0) == OF_PP_PAIR_SECOND(table_dtype_pair)) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& unique_ids = ctx->InputTensorDesc("unique_ids", 0); \ - EmbeddingTmpBufferManager buffer_manager( \ - nullptr, unique_ids.shape().elem_cnt(), \ - ctx->Attr("line_size") * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)), true); \ - return buffer_manager.TotalBufferSize(); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_PREFETCH_KERNEL, EMBEDDING_DATA_TYPE_SEQ, - TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -template -class EmbeddingLookupKernel final : public user_op::OpKernel { - public: - EmbeddingLookupKernel() = default; - ~EmbeddingLookupKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); - const user_op::Tensor* table_ids = ctx->Tensor4ArgNameAndIndex("table_ids", 0); - user_op::Tensor* unique_values = ctx->Tensor4ArgNameAndIndex("unique_values", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int64_t embedding_size = ctx->Attr("embedding_size"); - const int64_t line_size = ctx->Attr("line_size"); - uint32_t num_unique; - LookupAndInitMissing( - ctx->stream(), embedding_state, unique_ids->shape_view().elem_cnt(), embedding_size, - line_size, num_unique_ids->dptr(), unique_ids->dptr(), table_ids->dptr(), - unique_values->mut_dptr(), tmp_buffer->mut_dptr(), &num_unique, false); - if (ctx->has_output("embeddings", 0)) { - user_op::Tensor* embeddings = ctx->Tensor4ArgNameAndIndex("embeddings", 0); - CopyValuesToEmbeddings(ctx->stream(), num_unique, embedding_size, line_size, - unique_values->data_type(), embeddings->data_type(), - unique_values->dptr(), embeddings->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_EMBEDDING_LOOKUP_KERNEL(t_dtype_pair, table_dtype_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("embedding_lookup") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("unique_values", 0) == OF_PP_PAIR_SECOND(t_dtype_pair)) \ - && (user_op::HobDataType("table_ids", 0) == OF_PP_PAIR_SECOND(table_dtype_pair)) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const user_op::TensorDesc& unique_ids = ctx->InputTensorDesc("unique_ids", 0); \ - EmbeddingTmpBufferManager buffer_manager( \ - nullptr, unique_ids.shape().elem_cnt(), \ - ctx->Attr("line_size") * sizeof(OF_PP_PAIR_FIRST(t_dtype_pair)), false); \ - return buffer_manager.TotalBufferSize(); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_LOOKUP_KERNEL, EMBEDDING_DATA_TYPE_SEQ, - TABLE_ID_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -template -class EmbeddingPutKernel final : public user_op::OpKernel { - public: - EmbeddingPutKernel() = default; - ~EmbeddingPutKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - return std::make_shared>(ctx); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - auto* embedding_state = dynamic_cast*>(state); - CHECK(embedding_state != nullptr); - embedding::KeyValueStore* store = embedding_state->KeyValueStore(); - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_ids = ctx->Tensor4ArgNameAndIndex("unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - - IDX* host_num_keys = reinterpret_cast(embedding_state->HostNumKeys()); - OF_CUDA_CHECK(hipMemcpyAsync(host_num_keys, num_unique_ids->dptr(), sizeof(IDX), - hipMemcpyDefault, - ctx->stream()->As()->cuda_stream())); - CHECK_JUST(ctx->stream()->Sync()); - - store->Put(ctx->stream(), *host_num_keys, unique_ids->dptr(), unique_embeddings->dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_EMBEDDING_PUT_KERNEL(dtype, typeproto) \ - REGISTER_USER_KERNEL("embedding_put") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == typeproto)); - -OF_PP_FOR_EACH_TUPLE(REGISTER_CUDA_EMBEDDING_PUT_KERNEL, IDX_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/one_embedding_update_kernels.cu b/oneflow/user/kernels/one_embedding_update_kernels.cu index fd5c0cddd66e1b1eaa56e08952520f9009686eec..fa1d371620ff5544b61ff6ba3f48c7f583404c83 100644 --- a/oneflow/user/kernels/one_embedding_update_kernels.cu +++ b/oneflow/user/kernels/one_embedding_update_kernels.cu @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/device/cuda_util.h" #include "oneflow/user/kernels/model_update_kernel_util.h" +#include "oneflow/core/embedding/embedding_manager.h" namespace oneflow { @@ -23,17 +24,18 @@ namespace { template __global__ void SGDUpdateKernel(const int64_t embedding_size, T scale, float l1, float l2, - float weight_decay, const IDX* num_unique_ids, - const float* learning_rate, const T* scale_by_ptr, - const T* down_scale_by_ptr, const int64_t* skip_if, - const G* model_diff, const T* model, T* updated_model) { + float weight_decay, float learning_rate_val, + const IDX* num_unique_ids, const float* learning_rate, + const T* scale_by_ptr, const T* down_scale_by_ptr, + const int64_t* skip_if, const G* model_diff, const T* model, + T* updated_model) { if (skip_if != nullptr && *skip_if != 0) { const int64_t n = *num_unique_ids * embedding_size; CUDA_1D_KERNEL_LOOP(i, n) { updated_model[i] = model[i]; } } else { if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } const int64_t n = *num_unique_ids * embedding_size; CUDA_1D_KERNEL_LOOP(i, n) { updated_model[i] = model[i]; @@ -55,17 +57,19 @@ __device__ void GetMomentumOffset(const int32_t line_size, const int32_t embeddi template __global__ void MomentumUpdateKernel(const int64_t line_size, const int64_t embedding_size, T scale, float l1, float l2, float weight_decay, float beta, - const IDX* num_unique_ids, const float* learning_rate, - const T* scale_by_ptr, const T* down_scale_by_ptr, - const int64_t* skip_if, const G* model_diff, - const T* unique_values, T* updated_unique_values) { + float dampening, bool nesterov, bool maximize, + float learning_rate_val, const IDX* num_unique_ids, + const float* learning_rate, const T* scale_by_ptr, + const T* down_scale_by_ptr, const int64_t* skip_if, + const G* model_diff, const T* unique_values, + T* updated_unique_values) { if (skip_if != nullptr && *skip_if != 0) { const int64_t n = *num_unique_ids * line_size; CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } } else { if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } const int64_t n = *num_unique_ids * embedding_size; CUDA_1D_KERNEL_LOOP(i, n) { int64_t model_offset; @@ -75,7 +79,7 @@ __global__ void MomentumUpdateKernel(const int64_t line_size, const int64_t embe updated_unique_values[momentum_offset] = unique_values[momentum_offset]; MomentumUpdateFunctor()(model_diff + i, updated_unique_values + model_offset, updated_unique_values + momentum_offset, scale, l1, l2, beta, - weight_decay, learning_rate_val); + dampening, nesterov, maximize, weight_decay, learning_rate_val); } } } @@ -93,7 +97,8 @@ __device__ void GetAdamOffset(const int32_t line_size, const int32_t embedding_s template __global__ void AdamUpdateKernel(const int32_t line_size, const int32_t embedding_size, T scale, float l1, float l2, float weight_decay, float beta1, float beta2, - float epsilon, const float* bias_correction1_ptr, + float epsilon, float learning_rate_val, float bias_correction1_val, + float bias_correction2_val, const float* bias_correction1_ptr, const float* bias_correction2_ptr, const IDX* num_unique_ids, const float* learning_rate, const T* scale_by_ptr, const T* down_scale_by_ptr, const int64_t* skip_if, @@ -108,11 +113,9 @@ __global__ void AdamUpdateKernel(const int32_t line_size, const int32_t embeddin } else { if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float bias_correction1_val = 1.0; - float bias_correction2_val = 1.0; if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } - float learning_rate_val = *learning_rate; + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } const int64_t n = *num_unique_ids * embedding_size; // The n is model_diff elem_cnt. CUDA_1D_KERNEL_LOOP(i, n) { @@ -131,22 +134,74 @@ __global__ void AdamUpdateKernel(const int32_t line_size, const int32_t embeddin } } +// Note(guoran): The SmartDecaySparseAdam is from +// https://github.com/pytorch/pytorch/blob/master/caffe2/sgd/adam_op.h#L57 +template +__global__ void SmartDecaySparseAdamUpdateKernel( + const int32_t line_size, const int32_t embedding_size, T scale, float l1, float l2, + float weight_decay, float beta1, float beta2, float epsilon, float learning_rate_val, + int64_t step_col_offset, const IDX* num_unique_ids, const float* learning_rate, + const int64_t* train_step_ptr, const T* scale_by_ptr, const T* down_scale_by_ptr, + const int64_t* skip_if, const G* model_diff, const T* unique_values, T* updated_unique_values) { + if (skip_if != nullptr && *skip_if != 0) { + const int64_t n = *num_unique_ids * line_size; + CUDA_1D_KERNEL_LOOP(i, n) { + // The n is the unique_values elem_cnt, so not need to use GetAdamOffset. + updated_unique_values[i] = unique_values[i]; + } + } else { + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } + if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } + const int64_t n = *num_unique_ids * embedding_size; + // The n is model_diff elem_cnt. + CUDA_1D_KERNEL_LOOP(i, n) { + const int32_t row = i / embedding_size; + const int32_t col = i - row * embedding_size; + int64_t model_offset = row * line_size + col; + int64_t m_offset = model_offset + embedding_size; + int64_t v_offset = model_offset + 2 * embedding_size; + int64_t step_offset = row * line_size + step_col_offset; + const T model_val = *(unique_values + model_offset); + const T m_val = *(unique_values + m_offset); + const T v_val = *(unique_values + v_offset); + T model_diff_t = + CastScaleRegularizeGradientFunctor()(*(model_diff + i), model_val, scale, l1, l2); + int64_t prev_step = *reinterpret_cast(unique_values + step_offset); + int64_t cur_step = *train_step_ptr + 1; + int64_t skip_step = cur_step - prev_step; + float catchup = 0.0; + if (skip_step > 1) { + catchup = m_val * beta1 * (1 - pow(beta1, skip_step - 1)) / (1 - beta1); + } + const T next_m = pow(beta1, skip_step) * m_val + (1 - beta1) * model_diff_t; + const T next_v = pow(beta2, skip_step) * v_val + (1 - beta2) * model_diff_t * model_diff_t; + updated_unique_values[m_offset] = next_m; + updated_unique_values[v_offset] = next_v; + updated_unique_values[model_offset] = + model_val - (learning_rate_val * (next_m + catchup)) / (sqrt(next_v) + epsilon); + if (col == 0) { *reinterpret_cast(updated_unique_values + step_offset) = cur_step; } + } + } +} + template __global__ void AdagradUpdateKernel(const int64_t line_size, const int64_t embedding_size, T scale, float l1, float l2, float weight_decay, float lr_decay, - float epsilon, const IDX* num_unique_ids, - const float* learning_rate, const int64_t* train_step_ptr, - const T* scale_by_ptr, const T* down_scale_by_ptr, - const int64_t* skip_if, const G* model_diff, - const T* unique_values, T* updated_unique_values) { + float epsilon, float learning_rate_val, int64_t train_step, + const IDX* num_unique_ids, const float* learning_rate, + const int64_t* train_step_ptr, const T* scale_by_ptr, + const T* down_scale_by_ptr, const int64_t* skip_if, + const G* model_diff, const T* unique_values, + T* updated_unique_values) { if (skip_if != nullptr && *skip_if != 0) { const int64_t n = *num_unique_ids * line_size; CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } } else { - int64_t train_step = *train_step_ptr + 1; + if (train_step_ptr != nullptr) { train_step = *train_step_ptr + 1; } if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } learning_rate_val = learning_rate_val / (1 + (train_step - 1) * lr_decay); const int64_t n = *num_unique_ids * embedding_size; CUDA_1D_KERNEL_LOOP(i, n) { @@ -175,17 +230,18 @@ __device__ void GetFtrlOffset(const int32_t line_size, const int32_t embedding_s template __global__ void FtrlUpdateKernel(const int32_t line_size, const int32_t embedding_size, T scale, float l1, float l2, float weight_decay, float lr_power, - float lambda1, float lambda2, float beta, + float lambda1, float lambda2, float beta, float learning_rate_val, const IDX* num_unique_ids, const float* learning_rate, - const T* down_scale_by_ptr, const int64_t* skip_if, - const G* model_diff, const T* unique_values, - T* updated_unique_values) { + const T* scale_by_ptr, const T* down_scale_by_ptr, + const int64_t* skip_if, const G* model_diff, + const T* unique_values, T* updated_unique_values) { if (skip_if != nullptr && *skip_if != 0) { const int64_t n = *num_unique_ids * line_size; CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } } else { + if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; + if (learning_rate != nullptr) { learning_rate_val = *learning_rate; } const int64_t n = *num_unique_ids * embedding_size; CUDA_1D_KERNEL_LOOP(i, n) { int64_t model_offset; @@ -203,6 +259,22 @@ __global__ void FtrlUpdateKernel(const int32_t line_size, const int32_t embeddin } } +class EmbeddingUpdateKernelState final : public user_op::OpKernelState { + public: + explicit EmbeddingUpdateKernelState(user_op::KernelInitContext* ctx) { + const std::string& embedding_name = ctx->Attr("embedding_name"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + embedding_state_ = Singleton::Get()->GetEmbeddingState( + embedding_name, parallel_id); + } + ~EmbeddingUpdateKernelState() override = default; + + embedding::EmbeddingState* EmbeddingState() { return embedding_state_; } + + private: + embedding::EmbeddingState* embedding_state_; +}; + } // namespace template @@ -211,29 +283,38 @@ class SgdEmbeddingUpdateKernel final : public user_op::OpKernel { SgdEmbeddingUpdateKernel() = default; ~SgdEmbeddingUpdateKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + private: using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); CHECK_EQ(line_size, embedding_size); const auto scale = ctx->Attr("scale"); const float l1 = ctx->Attr("l1"); const float l2 = ctx->Attr("l2"); const auto weight_decay = ctx->Attr("weight_decay"); - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } const T* scale_by_ptr = nullptr; if (ctx->has_input("scale_by_tensor", 0)) { const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); scale_by_ptr = scale_by_tensor->dptr(); } @@ -241,7 +322,6 @@ class SgdEmbeddingUpdateKernel final : public user_op::OpKernel { if (ctx->has_input("down_scale_by_tensor", 0)) { const user_op::Tensor* down_scale_by_tensor = ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); down_scale_by_ptr = down_scale_by_tensor->dptr(); } @@ -252,23 +332,32 @@ class SgdEmbeddingUpdateKernel final : public user_op::OpKernel { skip_if_ptr = skip_if->dptr(); } // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; SGDUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - embedding_size, scale, l1, l2, weight_decay, + <<stream()->As()->cuda_stream()>>>( + embedding_size, scale, l1, l2, weight_decay, learning_rate_val, reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); + down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings_ptr, + updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; #define IDX_DATA_TYPE_SEQ \ OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) -#define REGISTER_CUDA_SGD_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("sgd_embedding_update") \ +#define REGISTER_CUDA_ONE_EMBEDDING_SGD_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_sgd_update") \ .SetCreateFn< \ SgdEmbeddingUpdateKernel>() \ @@ -278,38 +367,47 @@ class SgdEmbeddingUpdateKernel final : public user_op::OpKernel { && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_SGD_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_SGD_UPDATE_KERNEL, + FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + IDX_DATA_TYPE_SEQ) template class MomentumEmbeddingUpdateKernel final : public user_op::OpKernel { public: - MomentumEmbeddingUpdateKernel() = default; + MomentumEmbeddingUpdateKernel() : current_iter_(0){}; ~MomentumEmbeddingUpdateKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + private: using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); CHECK_EQ(line_size, embedding_size * 2); const float l1 = ctx->Attr("l1"); const float l2 = ctx->Attr("l2"); const auto weight_decay = ctx->Attr("weight_decay"); const auto beta = ctx->Attr("beta"); + // TODO: Suppoprt dampening, nesterov, maximize in OneEmbeddingMomentumUpdate(zhengzekang). + const float dampening = 0.0; + const bool nesterov = false; + const bool maximize = false; const auto scale = ctx->Attr("scale"); const T* scale_by_ptr = nullptr; if (ctx->has_input("scale_by_tensor", 0)) { const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); scale_by_ptr = scale_by_tensor->dptr(); } @@ -317,12 +415,15 @@ class MomentumEmbeddingUpdateKernel final : public user_op::OpKernel { if (ctx->has_input("down_scale_by_tensor", 0)) { const user_op::Tensor* down_scale_by_tensor = ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); down_scale_by_ptr = down_scale_by_tensor->dptr(); } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } const int64_t* skip_if_ptr = nullptr; if (ctx->has_input("skip_if", 0)) { const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); @@ -330,51 +431,69 @@ class MomentumEmbeddingUpdateKernel final : public user_op::OpKernel { skip_if_ptr = skip_if->dptr(); } // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; MomentumUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - line_size, embedding_size, scale, l1, l2, weight_decay, beta, - reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); + <<stream()->As()->cuda_stream()>>>( + line_size, embedding_size, scale, l1, l2, weight_decay, beta, dampening, nesterov, + maximize, learning_rate_val, reinterpret_cast(num_unique_ids->dptr()), + learning_rate_ptr, scale_by_ptr, down_scale_by_ptr, skip_if_ptr, + embedding_grad->dptr(), unique_embeddings_ptr, updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; -#define REGISTER_CUDA_MOMENTUM_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("momentum_embedding_update") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ +#define REGISTER_CUDA_ONE_EMBEDDING_MOMENTUM_UPDATE_KERNEL(t_dtype_pair, g_type_pair, \ + idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_momentum_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ + && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_MOMENTUM_EMBEDDING_UPDATE_KERNEL, +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_MOMENTUM_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) template class AdamEmbeddingUpdateKernel final : public user_op::OpKernel { public: - AdamEmbeddingUpdateKernel() = default; + AdamEmbeddingUpdateKernel() : current_iter_(0){}; ~AdamEmbeddingUpdateKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + private: using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); user_op::Tensor* updated_unique_embeddings = ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); CHECK_EQ(line_size, embedding_size * 3); const float l1 = ctx->Attr("l1"); @@ -388,7 +507,6 @@ class AdamEmbeddingUpdateKernel final : public user_op::OpKernel { const T* scale_by_ptr = nullptr; if (ctx->has_input("scale_by_tensor", 0)) { const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); scale_by_ptr = scale_by_tensor->dptr(); } @@ -396,41 +514,56 @@ class AdamEmbeddingUpdateKernel final : public user_op::OpKernel { if (ctx->has_input("down_scale_by_tensor", 0)) { const user_op::Tensor* down_scale_by_tensor = ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); down_scale_by_ptr = down_scale_by_tensor->dptr(); } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } const int64_t* skip_if_ptr = nullptr; if (ctx->has_input("skip_if", 0)) { const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); skip_if_ptr = skip_if->dptr(); } + const float bias_correction1_val = ctx->Attr("bias_correction1_val"); const float* bias_correction1_ptr = nullptr; if (ctx->has_input("bias_correction1", 0)) { bias_correction1_ptr = ctx->Tensor4ArgNameAndIndex("bias_correction1", 0)->dptr(); } + const float bias_correction2_val = ctx->Attr("bias_correction2_val"); const float* bias_correction2_ptr = nullptr; if (ctx->has_input("bias_correction2", 0)) { bias_correction2_ptr = ctx->Tensor4ArgNameAndIndex("bias_correction2", 0)->dptr(); } // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; AdamUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( + <<stream()->As()->cuda_stream()>>>( line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, beta1, beta2, - epsilon, bias_correction1_ptr, bias_correction2_ptr, + epsilon, learning_rate_val, bias_correction1_val, bias_correction2_val, + bias_correction1_ptr, bias_correction2_ptr, reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); + down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings_ptr, + updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; -#define REGISTER_CUDA_ADAM_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("adam_embedding_update") \ +#define REGISTER_CUDA_ONE_EMBEDDING_ADAM_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_adam_update") \ .SetCreateFn< \ AdamEmbeddingUpdateKernel>() \ @@ -440,30 +573,152 @@ class AdamEmbeddingUpdateKernel final : public user_op::OpKernel { && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ADAM_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_ADAM_UPDATE_KERNEL, + FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + IDX_DATA_TYPE_SEQ) + +template +class SmartDecaySparseAdamEmbeddingUpdateKernel final : public user_op::OpKernel { + public: + SmartDecaySparseAdamEmbeddingUpdateKernel() : current_iter_(0){}; + ~SmartDecaySparseAdamEmbeddingUpdateKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); + const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); + const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); + user_op::Tensor* updated_unique_embeddings = + ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); + CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const float l1 = ctx->Attr("l1"); + const float l2 = ctx->Attr("l2"); + const auto weight_decay = ctx->Attr("weight_decay"); + const auto beta1 = ctx->Attr("beta1"); + const auto beta2 = ctx->Attr("beta2"); + const auto epsilon = ctx->Attr("epsilon"); + const bool do_bias_correction = ctx->Attr("do_bias_correction"); + const auto scale = ctx->Attr("scale"); + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } + const T* down_scale_by_ptr = nullptr; + if (ctx->has_input("down_scale_by_tensor", 0)) { + const user_op::Tensor* down_scale_by_tensor = + ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); + CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); + down_scale_by_ptr = down_scale_by_tensor->dptr(); + } + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const int64_t train_step_val = ctx->Attr("train_step_val"); + const int64_t* train_step_ptr = nullptr; + if (ctx->has_input("train_step", 0)) { + const user_op::Tensor* train_step = ctx->Tensor4ArgNameAndIndex("train_step", 0); + train_step_ptr = train_step->dptr(); + } + const int64_t* skip_if_ptr = nullptr; + if (ctx->has_input("skip_if", 0)) { + const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); + CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); + skip_if_ptr = skip_if->dptr(); + } + // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; + + const int64_t value_dtype_size = GetSizeOfDataType(updated_unique_embeddings->data_type()); + const int64_t step_dtype_size = sizeof(int64_t); + const int64_t model_and_states_bytes = embedding_size * 3 * value_dtype_size; + const int64_t align_to_step_size_bytes = + (model_and_states_bytes + step_dtype_size - 1) / step_dtype_size * step_dtype_size; + const int64_t step_col_offset = align_to_step_size_bytes / value_dtype_size; + const int64_t smart_decay_sparse_adam_line_size = + (align_to_step_size_bytes + step_dtype_size) / value_dtype_size; + CHECK_EQ(line_size, smart_decay_sparse_adam_line_size); + + SmartDecaySparseAdamUpdateKernel + <<stream()->As()->cuda_stream()>>>( + line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, beta1, beta2, + epsilon, learning_rate_val, step_col_offset, + reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, train_step_ptr, + scale_by_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), + unique_embeddings_ptr, updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; +}; + +#define REGISTER_CUDA_ONE_EMBEDDING_SMART_DECAY_SPARSE_ADAM_UPDATE_KERNEL( \ + t_dtype_pair, g_type_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_smart_decay_sparse_adam_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ + && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ + && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_SMART_DECAY_SPARSE_ADAM_UPDATE_KERNEL, + FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + IDX_DATA_TYPE_SEQ) template class AdagradEmbeddingUpdateKernel final : public user_op::OpKernel { public: - AdagradEmbeddingUpdateKernel() = default; + AdagradEmbeddingUpdateKernel() : current_iter_(0){}; ~AdagradEmbeddingUpdateKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + private: using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); user_op::Tensor* updated_unique_embeddings = ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); CHECK_EQ(line_size, embedding_size * 2); - const float l1 = ctx->Attr("l1"); const float l2 = ctx->Attr("l2"); const auto weight_decay = ctx->Attr("weight_decay"); @@ -473,7 +728,6 @@ class AdagradEmbeddingUpdateKernel final : public user_op::OpKernel { const T* scale_by_ptr = nullptr; if (ctx->has_input("scale_by_tensor", 0)) { const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); scale_by_ptr = scale_by_tensor->dptr(); } @@ -481,13 +735,21 @@ class AdagradEmbeddingUpdateKernel final : public user_op::OpKernel { if (ctx->has_input("down_scale_by_tensor", 0)) { const user_op::Tensor* down_scale_by_tensor = ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); down_scale_by_ptr = down_scale_by_tensor->dptr(); } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const int64_t* train_step_ptr = ctx->Tensor4ArgNameAndIndex("train_step", 0)->dptr(); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } + const int64_t train_step_val = ctx->Attr("train_step_val"); + const int64_t* train_step_ptr = nullptr; + if (ctx->has_input("train_step", 0)) { + const user_op::Tensor* train_step = ctx->Tensor4ArgNameAndIndex("train_step", 0); + train_step_ptr = train_step->dptr(); + } const int64_t* skip_if_ptr = nullptr; if (ctx->has_input("skip_if", 0)) { const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); @@ -495,53 +757,68 @@ class AdagradEmbeddingUpdateKernel final : public user_op::OpKernel { skip_if_ptr = skip_if->dptr(); } // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; AdagradUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( + <<stream()->As()->cuda_stream()>>>( line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, lr_decay, - epsilon, reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, - train_step_ptr, scale_by_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), - unique_embeddings->dptr(), updated_unique_embeddings->mut_dptr()); + epsilon, learning_rate_val, train_step_val, + reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, train_step_ptr, + scale_by_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), + unique_embeddings_ptr, updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; -#define REGISTER_CUDA_ADAGRAD_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("adagrad_embedding_update") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ +#define REGISTER_CUDA_ONE_EMBEDDING_ADAGRAD_UPDATE_KERNEL(t_dtype_pair, g_type_pair, \ + idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_adagrad_update") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ + && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ADAGRAD_EMBEDDING_UPDATE_KERNEL, +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_ADAGRAD_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) template class FtrlEmbeddingUpdateKernel final : public user_op::OpKernel { public: - FtrlEmbeddingUpdateKernel() = default; + FtrlEmbeddingUpdateKernel() : current_iter_(0){}; ~FtrlEmbeddingUpdateKernel() override = default; + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + return std::make_shared(ctx); + } + private: using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* kernel_state = dynamic_cast(state); + CHECK(kernel_state != nullptr); + embedding::EmbeddingState* embedding_state = kernel_state->EmbeddingState(); + embedding_state->OnEmbeddingUpdateStart(ctx, current_iter_); const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2) - << "The NumAxes of unique_embedding should be equal to 2. "; CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2) << "The NumAxes of embedding_grad should be equal to 2. "; - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); + const int64_t line_size = ctx->Attr("line_size"); + const int64_t embedding_size = ctx->Attr("embedding_size"); CHECK_EQ(line_size, embedding_size * 3) << "The line_size should be equal to 3 x embedding_size. "; const float l1 = 0.0; @@ -556,16 +833,25 @@ class FtrlEmbeddingUpdateKernel final : public user_op::OpKernel { const float lambda2 = ctx->Attr("lambda2"); const float beta = ctx->Attr("beta"); const double scale = ctx->Attr("scale"); + const T* scale_by_ptr = nullptr; + if (ctx->has_input("scale_by_tensor", 0)) { + const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); + CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); + scale_by_ptr = scale_by_tensor->dptr(); + } const T* down_scale_by_ptr = nullptr; if (ctx->has_input("down_scale_by_tensor", 0)) { const user_op::Tensor* down_scale_by_tensor = ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); down_scale_by_ptr = down_scale_by_tensor->dptr(); } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); + const float learning_rate_val = ctx->Attr("learning_rate_val"); + const float* learning_rate_ptr = nullptr; + if (ctx->has_input("learning_rate", 0)) { + const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); + learning_rate_ptr = learning_rate->dptr(); + } const int64_t* skip_if_ptr = nullptr; if (ctx->has_input("skip_if", 0)) { const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); @@ -573,18 +859,28 @@ class FtrlEmbeddingUpdateKernel final : public user_op::OpKernel { skip_if_ptr = skip_if->dptr(); } // update kernel + const T* unique_embeddings_ptr = + reinterpret_cast(embedding_state->EmbeddingUpdateUniqueEmbeddings(current_iter_)); + T* updated_unique_embeddings_ptr = reinterpret_cast( + embedding_state->EmbeddingUpdateUpdatedUniqueEmbeddings(current_iter_)); + const uint32_t num_unique = embedding_state->GetIdNumUnique(current_iter_); + const int64_t embedding_grad_elem_cnt = num_unique * embedding_size; FtrlUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( + <<stream()->As()->cuda_stream()>>>( line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, lr_power, - lambda1, lambda2, beta, reinterpret_cast(num_unique_ids->dptr()), - learning_rate_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), - unique_embeddings->dptr(), updated_unique_embeddings->mut_dptr()); + lambda1, lambda2, beta, learning_rate_val, + reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, + down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings_ptr, + updated_unique_embeddings_ptr); + embedding_state->OnEmbeddingUpdateEnd(ctx, current_iter_); + current_iter_++; } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + mutable int64_t current_iter_; }; -#define REGISTER_CUDA_FTRL_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("ftrl_embedding_update") \ +#define REGISTER_CUDA_ONE_EMBEDDING_FTRL_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ + REGISTER_USER_KERNEL("one_embedding_ftrl_update") \ .SetCreateFn< \ FtrlEmbeddingUpdateKernel>() \ @@ -593,7 +889,8 @@ class FtrlEmbeddingUpdateKernel final : public user_op::OpKernel { && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_FTRL_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ONE_EMBEDDING_FTRL_UPDATE_KERNEL, + FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, + IDX_DATA_TYPE_SEQ) } // namespace oneflow diff --git a/oneflow/user/kernels/one_embedding_update_kernels.hip.cpp b/oneflow/user/kernels/one_embedding_update_kernels.hip.cpp deleted file mode 100644 index a134dda162b6dd370cedec02d8ff4110911b5300..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/one_embedding_update_kernels.hip.cpp +++ /dev/null @@ -1,604 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/user/kernels/model_update_kernel_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void SGDUpdateKernel(const int64_t embedding_size, T scale, float l1, float l2, - float weight_decay, const IDX* num_unique_ids, - const float* learning_rate, const T* scale_by_ptr, - const T* down_scale_by_ptr, const int64_t* skip_if, - const G* model_diff, const T* model, T* updated_model) { - if (skip_if != nullptr && *skip_if != 0) { - const int64_t n = *num_unique_ids * embedding_size; - CUDA_1D_KERNEL_LOOP(i, n) { updated_model[i] = model[i]; } - } else { - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; - const int64_t n = *num_unique_ids * embedding_size; - CUDA_1D_KERNEL_LOOP(i, n) { - updated_model[i] = model[i]; - SGDUpdateFunctor()(model_diff + i, updated_model + i, scale, l1, l2, weight_decay, - learning_rate_val); - } - } -} - -__device__ void GetMomentumOffset(const int32_t line_size, const int32_t embedding_size, - int64_t model_diff_offset, int64_t* model_offset, - int64_t* momentum_offset) { - const int32_t row = model_diff_offset / embedding_size; - const int32_t col = model_diff_offset - row * embedding_size; - *model_offset = row * line_size + col; - *momentum_offset = *model_offset + embedding_size; -} - -template -__global__ void MomentumUpdateKernel(const int64_t line_size, const int64_t embedding_size, T scale, - float l1, float l2, float weight_decay, float beta, - const IDX* num_unique_ids, const float* learning_rate, - const T* scale_by_ptr, const T* down_scale_by_ptr, - const int64_t* skip_if, const G* model_diff, - const T* unique_values, T* updated_unique_values) { - if (skip_if != nullptr && *skip_if != 0) { - const int64_t n = *num_unique_ids * line_size; - CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } - } else { - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; - const int64_t n = *num_unique_ids * embedding_size; - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t model_offset; - int64_t momentum_offset; - GetMomentumOffset(line_size, embedding_size, i, &model_offset, &momentum_offset); - updated_unique_values[model_offset] = unique_values[model_offset]; - updated_unique_values[momentum_offset] = unique_values[momentum_offset]; - MomentumUpdateFunctor()(model_diff + i, updated_unique_values + model_offset, - updated_unique_values + momentum_offset, scale, l1, l2, beta, - weight_decay, learning_rate_val); - } - } -} - -__device__ void GetAdamOffset(const int32_t line_size, const int32_t embedding_size, - int64_t model_diff_offset, int64_t* model_offset, int64_t* m_offset, - int64_t* v_offset) { - const int32_t row = model_diff_offset / embedding_size; - const int32_t col = model_diff_offset - row * embedding_size; - *model_offset = row * line_size + col; - *m_offset = *model_offset + embedding_size; - *v_offset = *model_offset + 2 * embedding_size; -} - -template -__global__ void AdamUpdateKernel(const int32_t line_size, const int32_t embedding_size, T scale, - float l1, float l2, float weight_decay, float beta1, float beta2, - float epsilon, const float* bias_correction1_ptr, - const float* bias_correction2_ptr, const IDX* num_unique_ids, - const float* learning_rate, const T* scale_by_ptr, - const T* down_scale_by_ptr, const int64_t* skip_if, - const G* model_diff, const T* unique_values, - T* updated_unique_values) { - if (skip_if != nullptr && *skip_if != 0) { - const int64_t n = *num_unique_ids * line_size; - CUDA_1D_KERNEL_LOOP(i, n) { - // The n is the unique_values elem_cnt, so not need to use GetAdamOffset. - updated_unique_values[i] = unique_values[i]; - } - } else { - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float bias_correction1_val = 1.0; - float bias_correction2_val = 1.0; - if (bias_correction1_ptr != nullptr) { bias_correction1_val = *bias_correction1_ptr; } - if (bias_correction2_ptr != nullptr) { bias_correction2_val = *bias_correction2_ptr; } - float learning_rate_val = *learning_rate; - const int64_t n = *num_unique_ids * embedding_size; - // The n is model_diff elem_cnt. - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t model_offset; - int64_t m_offset; - int64_t v_offset; - GetAdamOffset(line_size, embedding_size, i, &model_offset, &m_offset, &v_offset); - updated_unique_values[model_offset] = unique_values[model_offset]; - updated_unique_values[m_offset] = unique_values[m_offset]; - updated_unique_values[v_offset] = unique_values[v_offset]; - AdamUpdateFunctor()(model_diff + i, updated_unique_values + model_offset, - updated_unique_values + m_offset, updated_unique_values + v_offset, - nullptr, scale, l1, l2, beta1, beta2, epsilon, weight_decay, false, - bias_correction1_val, bias_correction2_val, learning_rate_val); - } - } -} - -template -__global__ void AdagradUpdateKernel(const int64_t line_size, const int64_t embedding_size, T scale, - float l1, float l2, float weight_decay, float lr_decay, - float epsilon, const IDX* num_unique_ids, - const float* learning_rate, const int64_t* train_step_ptr, - const T* scale_by_ptr, const T* down_scale_by_ptr, - const int64_t* skip_if, const G* model_diff, - const T* unique_values, T* updated_unique_values) { - if (skip_if != nullptr && *skip_if != 0) { - const int64_t n = *num_unique_ids * line_size; - CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } - } else { - int64_t train_step = *train_step_ptr + 1; - if (scale_by_ptr != nullptr) { scale *= *scale_by_ptr; } - if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; - learning_rate_val = learning_rate_val / (1 + (train_step - 1) * lr_decay); - const int64_t n = *num_unique_ids * embedding_size; - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t model_offset; - int64_t sum_offset; - GetMomentumOffset(line_size, embedding_size, i, &model_offset, &sum_offset); - updated_unique_values[model_offset] = unique_values[model_offset]; - updated_unique_values[sum_offset] = unique_values[sum_offset]; - AdagradUpdateFunctor()(model_diff + i, updated_unique_values + model_offset, - updated_unique_values + sum_offset, scale, l1, l2, epsilon, - weight_decay, learning_rate_val); - } - } -} - -__device__ void GetFtrlOffset(const int32_t line_size, const int32_t embedding_size, - int64_t model_diff_offset, int64_t* model_offset, - int64_t* accumulate_offset, int64_t* z_offset) { - const int32_t row = model_diff_offset / embedding_size; - const int32_t col = model_diff_offset - row * embedding_size; - *model_offset = row * line_size + col; - *accumulate_offset = *model_offset + embedding_size; - *z_offset = *model_offset + 2 * embedding_size; -} - -template -__global__ void FtrlUpdateKernel(const int32_t line_size, const int32_t embedding_size, T scale, - float l1, float l2, float weight_decay, float lr_power, - float lambda1, float lambda2, float beta, - const IDX* num_unique_ids, const float* learning_rate, - const T* down_scale_by_ptr, const int64_t* skip_if, - const G* model_diff, const T* unique_values, - T* updated_unique_values) { - if (skip_if != nullptr && *skip_if != 0) { - const int64_t n = *num_unique_ids * line_size; - CUDA_1D_KERNEL_LOOP(i, n) { updated_unique_values[i] = unique_values[i]; } - } else { - if (down_scale_by_ptr != nullptr) { scale /= *down_scale_by_ptr; } - float learning_rate_val = *learning_rate; - const int64_t n = *num_unique_ids * embedding_size; - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t model_offset; - int64_t accumulate_offset; - int64_t z_offset; - GetFtrlOffset(line_size, embedding_size, i, &model_offset, &accumulate_offset, &z_offset); - updated_unique_values[model_offset] = unique_values[model_offset]; - updated_unique_values[accumulate_offset] = unique_values[accumulate_offset]; - updated_unique_values[z_offset] = unique_values[z_offset]; - FtrlUpdateFunctor()(model_diff + i, updated_unique_values + model_offset, - updated_unique_values + accumulate_offset, - updated_unique_values + z_offset, scale, l1, l2, lr_power, lambda1, - lambda2, beta, weight_decay, learning_rate_val); - } - } -} - -} // namespace - -template -class SgdEmbeddingUpdateKernel final : public user_op::OpKernel { - public: - SgdEmbeddingUpdateKernel() = default; - ~SgdEmbeddingUpdateKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); - CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); - CHECK_EQ(line_size, embedding_size); - const auto scale = ctx->Attr("scale"); - const float l1 = ctx->Attr("l1"); - const float l2 = ctx->Attr("l2"); - const auto weight_decay = ctx->Attr("weight_decay"); - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const T* scale_by_ptr = nullptr; - if (ctx->has_input("scale_by_tensor", 0)) { - const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); - scale_by_ptr = scale_by_tensor->dptr(); - } - const T* down_scale_by_ptr = nullptr; - if (ctx->has_input("down_scale_by_tensor", 0)) { - const user_op::Tensor* down_scale_by_tensor = - ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); - down_scale_by_ptr = down_scale_by_tensor->dptr(); - } - const int64_t* skip_if_ptr = nullptr; - if (ctx->has_input("skip_if", 0)) { - const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); - CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); - skip_if_ptr = skip_if->dptr(); - } - // update kernel - SGDUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - embedding_size, scale, l1, l2, weight_decay, - reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define IDX_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) - -#define REGISTER_CUDA_SGD_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("sgd_embedding_update") \ - .SetCreateFn< \ - SgdEmbeddingUpdateKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ - && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_SGD_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - // FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) -template -class MomentumEmbeddingUpdateKernel final : public user_op::OpKernel { - public: - MomentumEmbeddingUpdateKernel() = default; - ~MomentumEmbeddingUpdateKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); - CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); - CHECK_EQ(line_size, embedding_size * 2); - const float l1 = ctx->Attr("l1"); - const float l2 = ctx->Attr("l2"); - const auto weight_decay = ctx->Attr("weight_decay"); - const auto beta = ctx->Attr("beta"); - const auto scale = ctx->Attr("scale"); - const T* scale_by_ptr = nullptr; - if (ctx->has_input("scale_by_tensor", 0)) { - const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); - scale_by_ptr = scale_by_tensor->dptr(); - } - const T* down_scale_by_ptr = nullptr; - if (ctx->has_input("down_scale_by_tensor", 0)) { - const user_op::Tensor* down_scale_by_tensor = - ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); - down_scale_by_ptr = down_scale_by_tensor->dptr(); - } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const int64_t* skip_if_ptr = nullptr; - if (ctx->has_input("skip_if", 0)) { - const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); - CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); - skip_if_ptr = skip_if->dptr(); - } - // update kernel - MomentumUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - line_size, embedding_size, scale, l1, l2, weight_decay, beta, - reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_MOMENTUM_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("momentum_embedding_update") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ - && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_MOMENTUM_EMBEDDING_UPDATE_KERNEL, - // FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ, - IDX_DATA_TYPE_SEQ) - -template -class AdamEmbeddingUpdateKernel final : public user_op::OpKernel { - public: - AdamEmbeddingUpdateKernel() = default; - ~AdamEmbeddingUpdateKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); - CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); - CHECK_EQ(line_size, embedding_size * 3); - - const float l1 = ctx->Attr("l1"); - const float l2 = ctx->Attr("l2"); - const auto weight_decay = ctx->Attr("weight_decay"); - const auto beta1 = ctx->Attr("beta1"); - const auto beta2 = ctx->Attr("beta2"); - const auto epsilon = ctx->Attr("epsilon"); - const bool do_bias_correction = ctx->Attr("do_bias_correction"); - const auto scale = ctx->Attr("scale"); - const T* scale_by_ptr = nullptr; - if (ctx->has_input("scale_by_tensor", 0)) { - const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); - scale_by_ptr = scale_by_tensor->dptr(); - } - const T* down_scale_by_ptr = nullptr; - if (ctx->has_input("down_scale_by_tensor", 0)) { - const user_op::Tensor* down_scale_by_tensor = - ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); - down_scale_by_ptr = down_scale_by_tensor->dptr(); - } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const int64_t* skip_if_ptr = nullptr; - if (ctx->has_input("skip_if", 0)) { - const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); - CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); - skip_if_ptr = skip_if->dptr(); - } - const float* bias_correction1_ptr = nullptr; - if (ctx->has_input("bias_correction1", 0)) { - bias_correction1_ptr = ctx->Tensor4ArgNameAndIndex("bias_correction1", 0)->dptr(); - } - const float* bias_correction2_ptr = nullptr; - if (ctx->has_input("bias_correction2", 0)) { - bias_correction2_ptr = ctx->Tensor4ArgNameAndIndex("bias_correction2", 0)->dptr(); - } - // update kernel - AdamUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, beta1, beta2, - epsilon, bias_correction1_ptr, bias_correction2_ptr, - reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, scale_by_ptr, - down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), unique_embeddings->dptr(), - updated_unique_embeddings->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ADAM_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("adam_embedding_update") \ - .SetCreateFn< \ - AdamEmbeddingUpdateKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ - && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ADAM_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - // FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -template -class AdagradEmbeddingUpdateKernel final : public user_op::OpKernel { - public: - AdagradEmbeddingUpdateKernel() = default; - ~AdagradEmbeddingUpdateKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2); - CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2); - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); - CHECK_EQ(line_size, embedding_size * 2); - - const float l1 = ctx->Attr("l1"); - const float l2 = ctx->Attr("l2"); - const auto weight_decay = ctx->Attr("weight_decay"); - const auto lr_decay = ctx->Attr("lr_decay"); - const auto epsilon = ctx->Attr("epsilon"); - const auto scale = ctx->Attr("scale"); - const T* scale_by_ptr = nullptr; - if (ctx->has_input("scale_by_tensor", 0)) { - const user_op::Tensor* scale_by_tensor = ctx->Tensor4ArgNameAndIndex("scale_by_tensor", 0); - CHECK_EQ(scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(scale_by_tensor->shape_view().elem_cnt(), 1); - scale_by_ptr = scale_by_tensor->dptr(); - } - const T* down_scale_by_ptr = nullptr; - if (ctx->has_input("down_scale_by_tensor", 0)) { - const user_op::Tensor* down_scale_by_tensor = - ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); - down_scale_by_ptr = down_scale_by_tensor->dptr(); - } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const int64_t* train_step_ptr = ctx->Tensor4ArgNameAndIndex("train_step", 0)->dptr(); - const int64_t* skip_if_ptr = nullptr; - if (ctx->has_input("skip_if", 0)) { - const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); - CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); - skip_if_ptr = skip_if->dptr(); - } - // update kernel - AdagradUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, lr_decay, - epsilon, reinterpret_cast(num_unique_ids->dptr()), learning_rate_ptr, - train_step_ptr, scale_by_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), - unique_embeddings->dptr(), updated_unique_embeddings->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ADAGRAD_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("adagrad_embedding_update") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ - && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_ADAGRAD_EMBEDDING_UPDATE_KERNEL, - // FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, - FLOATING_DATA_TYPE_SEQ, FLOATING_DATA_TYPE_SEQ, - IDX_DATA_TYPE_SEQ) - -template -class FtrlEmbeddingUpdateKernel final : public user_op::OpKernel { - public: - FtrlEmbeddingUpdateKernel() = default; - ~FtrlEmbeddingUpdateKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* num_unique_ids = ctx->Tensor4ArgNameAndIndex("num_unique_ids", 0); - const user_op::Tensor* unique_embeddings = ctx->Tensor4ArgNameAndIndex("unique_embeddings", 0); - const user_op::Tensor* embedding_grad = ctx->Tensor4ArgNameAndIndex("embedding_grad", 0); - user_op::Tensor* updated_unique_embeddings = - ctx->Tensor4ArgNameAndIndex("updated_unique_embeddings", 0); - CHECK_EQ(unique_embeddings->shape_view().NumAxes(), 2) - << "The NumAxes of unique_embedding should be equal to 2. "; - CHECK_EQ(embedding_grad->shape_view().NumAxes(), 2) - << "The NumAxes of embedding_grad should be equal to 2. "; - const int64_t num_keys = unique_embeddings->shape_view().At(0); - const int64_t line_size = unique_embeddings->shape_view().At(1); - const int64_t embedding_size = embedding_grad->shape_view().At(1); - CHECK_EQ(line_size, embedding_size * 3) - << "The line_size should be equal to 3 x embedding_size. "; - const float l1 = 0.0; - const float l2 = 0.0; - const float weight_decay = ctx->Attr("weight_decay"); - // TODO(zhengzekang): Undefined behavior for ftrl optimizer with weight_decay in `abs(new_z_val) - // < lambda1` condition. - CHECK_EQ(weight_decay, static_cast(0.0)) - << "Currently not support for setting weight decay. "; - const float lr_power = ctx->Attr("lr_power"); - const float lambda1 = ctx->Attr("lambda1"); - const float lambda2 = ctx->Attr("lambda2"); - const float beta = ctx->Attr("beta"); - const double scale = ctx->Attr("scale"); - const T* down_scale_by_ptr = nullptr; - if (ctx->has_input("down_scale_by_tensor", 0)) { - const user_op::Tensor* down_scale_by_tensor = - ctx->Tensor4ArgNameAndIndex("down_scale_by_tensor", 0); - CHECK_EQ(down_scale_by_tensor->data_type(), unique_embeddings->data_type()); - CHECK_EQ(down_scale_by_tensor->shape_view().elem_cnt(), 1); - down_scale_by_ptr = down_scale_by_tensor->dptr(); - } - const user_op::Tensor* learning_rate = ctx->Tensor4ArgNameAndIndex("learning_rate", 0); - const float* learning_rate_ptr = learning_rate->dptr(); - const int64_t* skip_if_ptr = nullptr; - if (ctx->has_input("skip_if", 0)) { - const user_op::Tensor* skip_if = ctx->Tensor4ArgNameAndIndex("skip_if", 0); - CHECK_EQ(skip_if->shape_view().elem_cnt(), 1); - skip_if_ptr = skip_if->dptr(); - } - // update kernel - FtrlUpdateKernel - <<shape_view().elem_cnt()), kCudaThreadsNumPerBlock, - 0, ctx->stream()->As()->cuda_stream()>>>( - line_size, embedding_size, static_cast(scale), l1, l2, weight_decay, lr_power, - lambda1, lambda2, beta, reinterpret_cast(num_unique_ids->dptr()), - learning_rate_ptr, down_scale_by_ptr, skip_if_ptr, embedding_grad->dptr(), - unique_embeddings->dptr(), updated_unique_embeddings->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; -#define REGISTER_CUDA_FTRL_EMBEDDING_UPDATE_KERNEL(t_dtype_pair, g_type_pair, idx_dtype_pair) \ - REGISTER_USER_KERNEL("ftrl_embedding_update") \ - .SetCreateFn< \ - FtrlEmbeddingUpdateKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("num_unique_ids", 0) == OF_PP_PAIR_SECOND(idx_dtype_pair)) \ - && (user_op::HobDataType("embedding_grad", 0) == OF_PP_PAIR_SECOND(g_type_pair)) \ - && (user_op::HobDataType("unique_embeddings", 0) == OF_PP_PAIR_SECOND(t_dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_CUDA_FTRL_EMBEDDING_UPDATE_KERNEL, FLOATING_DATA_TYPE_SEQ, - // FLOATING_DATA_TYPE_SEQ HALF_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - FLOATING_DATA_TYPE_SEQ, IDX_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/one_hot_kernel.hip.cpp b/oneflow/user/kernels/one_hot_kernel.hip.cpp deleted file mode 100644 index 661a41b31dbcaae8e67c0eecc4e2b23c426dc9a3..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/one_hot_kernel.hip.cpp +++ /dev/null @@ -1,81 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -template -__global__ void OneHotEncodeGpu(int64_t elem_cnt, const int64_t depth, const T on_value, - const T off_value, const K* indices, T* out) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int64_t row = i / depth; - const int64_t col = i - row * depth; - const int64_t idx = indices[row]; - assert(idx >= 0 && idx < depth); - out[i] = (idx == col) ? on_value : off_value; - } -} - -} // namespace - -template -class GpuOneHotKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - GpuOneHotKernel() = default; - ~GpuOneHotKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* indices = ctx->Tensor4ArgNameAndIndex("indices", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const int64_t num_indices = indices->shape_view().elem_cnt(); - const int64_t depth = ctx->Attr("depth"); - const DataType dtype = ctx->Attr("dtype"); - const T on_value = IsFloatingDataType(dtype) - ? static_cast(ctx->Attr("floating_on_value")) - : static_cast(ctx->Attr("integer_on_value")); - const T off_value = IsFloatingDataType(dtype) - ? static_cast(ctx->Attr("floating_off_value")) - : static_cast(ctx->Attr("integer_off_value")); - RUN_CUDA_KERNEL((OneHotEncodeGpu), ctx->stream(), num_indices * depth, - num_indices * depth, depth, on_value, off_value, indices->dptr(), - out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_ONE_HOT_KERNEL(dtype, itype) \ - REGISTER_USER_KERNEL("one_hot").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("indices", 0) == GetDataType::value) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)); - -REGISTER_CUDA_ONE_HOT_KERNEL(int32_t, int32_t) -REGISTER_CUDA_ONE_HOT_KERNEL(int32_t, int64_t) -REGISTER_CUDA_ONE_HOT_KERNEL(int64_t, int32_t) -REGISTER_CUDA_ONE_HOT_KERNEL(int64_t, int64_t) -REGISTER_CUDA_ONE_HOT_KERNEL(float, int32_t) -REGISTER_CUDA_ONE_HOT_KERNEL(float, int64_t) -REGISTER_CUDA_ONE_HOT_KERNEL(double, int32_t) -REGISTER_CUDA_ONE_HOT_KERNEL(double, int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/p2p_comm_kernel.cpp b/oneflow/user/kernels/p2p_comm_kernel.cpp index 236a0a4f9daf4937a0f17846b94ee25cb134fe47..984ca798873b22c127f83d1f08c62485873de61c 100644 --- a/oneflow/user/kernels/p2p_comm_kernel.cpp +++ b/oneflow/user/kernels/p2p_comm_kernel.cpp @@ -15,16 +15,36 @@ limitations under the License. */ #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ccl/ccl.h" #include "oneflow/core/control/global_process_ctx.h" #include "oneflow/core/job/rank_group.h" #include "oneflow/core/framework/instructions_builder.h" +#include "oneflow/user/kernels/collective_communication/include/send.h" +#include "oneflow/user/kernels/collective_communication/include/recv.h" namespace oneflow { namespace { -template +namespace { + +auto SendCollectiveCommunicationExists() { + return hob::make_custom("SendCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsSendRegistered(device_type); + }); +} + +auto RecvCollectiveCommunicationExists() { + return hob::make_custom("RecvCollectiveCommunicationExists", + [=](const user_op::KernelRegContext& ctx) { + DeviceType device_type = ctx.device_type(); + return ccl::IsRecvRegistered(device_type); + }); +} + +} // namespace + class SendKernel final : public user_op::OpKernel { public: SendKernel() = default; @@ -34,13 +54,13 @@ class SendKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); const auto& dst_process_id = ctx->Attr("dst_process_id"); - CHECK_JUST(ccl::Send(in->dptr(), in->shape_view().elem_cnt(), in->data_type(), - dst_process_id, ctx->stream())); + std::unique_ptr send = + ccl::NewCollectiveCommunication(ctx->device_type(), in->data_type()); + send->Launch(ctx->stream(), in->dptr(), in->shape_view().elem_cnt(), dst_process_id); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template class RecvKernel final : public user_op::OpKernel { public: RecvKernel() = default; @@ -50,25 +70,18 @@ class RecvKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); const auto& src_process_id = ctx->Attr("src_process_id"); - CHECK_JUST(ccl::Recv(out->mut_dptr(), out->shape_view().elem_cnt(), - out->data_type(), src_process_id, ctx->stream())); + std::unique_ptr recv = + ccl::NewCollectiveCommunication(ctx->device_type(), out->data_type()); + recv->Launch(ctx->stream(), out->mut_dptr(), out->shape_view().elem_cnt(), src_process_id); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_KERNEL(device) \ - REGISTER_USER_KERNEL("send").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == device)); \ - REGISTER_USER_KERNEL("recv").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == device)); - -REGISTER_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_KERNEL(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM -REGISTER_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("send").SetCreateFn().SetIsMatchedHob( + SendCollectiveCommunicationExists()); + +REGISTER_USER_KERNEL("recv").SetCreateFn().SetIsMatchedHob( + RecvCollectiveCommunicationExists()); } // namespace } // namespace oneflow diff --git a/oneflow/user/kernels/pad2d_kernels.cpp b/oneflow/user/kernels/pad2d_kernels.cpp deleted file mode 100644 index cf8e57d2be9ebaef1e57bea1239ccfe7b5eda183..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/pad2d_kernels.cpp +++ /dev/null @@ -1,283 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/user/kernels/pad2d_kernels_util.h" - -namespace oneflow { - -namespace { - -template -T GetDtypeMatchedValue(double floating, int64_t integral); - -template<> -float16 GetDtypeMatchedValue(double floating, int64_t integral) { - return static_cast(floating); -} - -template<> -float GetDtypeMatchedValue(double floating, int64_t integral) { - return static_cast(floating); -} - -template<> -double GetDtypeMatchedValue(double floating, int64_t integral) { - return floating; -} - -template<> -int8_t GetDtypeMatchedValue(double floating, int64_t integral) { - return static_cast(integral); -} - -template<> -int32_t GetDtypeMatchedValue(double floating, int64_t integral) { - return static_cast(integral); -} - -template<> -int64_t GetDtypeMatchedValue(double floating, int64_t integral) { - return integral; -} - -} // namespace - -namespace user_op { - -template -class ReflectionPad2dKernel final : public OpKernel { - public: - ReflectionPad2dKernel() = default; - ~ReflectionPad2dKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const auto& padding = ctx->Attr>("padding"); - const int64_t ndims = x->shape_view().NumAxes(); - CHECK_EQ(padding.size(), ndims); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - const int64_t pad_left = padding[0]; - const int64_t pad_top = padding[2]; - - const int64_t n_batch = y->shape_view().At(n_idx); - const int64_t n_channel = y->shape_view().At(c_idx); - const int64_t y_height = y->shape_view().At(h_idx); - const int64_t y_width = y->shape_view().At(w_idx); - const int64_t x_height = x->shape_view().At(h_idx); - const int64_t x_width = x->shape_view().At(w_idx); - - IN_T* dest = y->mut_dptr(); - const IN_T* src = x->dptr(); - DimVector y_vector; - y->shape_view().ToDimVector(&y_vector); - NdIndexOffsetHelper index_helper(y_vector.data()); - - ReflectionPad2dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, - n_channel, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class ReflectionPad2dGradKernel final : public OpKernel { - public: - ReflectionPad2dGradKernel() = default; - ~ReflectionPad2dGradKernel() = default; - - private: - void Compute(KernelComputeContext* ctx) const override { - const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto& padding = ctx->Attr>("padding"); - const int64_t ndims = dy->shape_view().NumAxes(); - CHECK_EQ(padding.size(), ndims); - - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - int64_t pad_left = padding[0]; - int64_t pad_top = padding[2]; - int64_t n_batch = dy->shape_view().At(n_idx); - int64_t n_channel = dy->shape_view().At(c_idx); - int64_t dy_height = dy->shape_view().At(h_idx); - int64_t dy_width = dy->shape_view().At(w_idx); - int64_t dx_height = dx->shape_view().At(h_idx); - int64_t dx_width = dx->shape_view().At(w_idx); - - const IN_T* src = dy->dptr(); - IN_T* dest = dx->mut_dptr(); - DimVector dy_vector; - dy->shape_view().ToDimVector(&dy_vector); - NdIndexOffsetHelper index_helper(dy_vector.data()); - - size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); - Memset(ctx->stream(), dest, 0, out_bytes_size); - - ReflectionPad2dGradFunctor()(ctx->stream(), src, dest, index_helper, n_batch, - n_channel, dy_height, dy_width, dx_height, - dx_width, pad_left, pad_top); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_REFLECTION_PAD2D_KERNELS(device, dtype) \ - REGISTER_USER_KERNEL("reflection_pad2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("reflection_pad2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -#define REGISTER_REFLECTION_PAD2D_WITH_DEVICE(device) \ - REGISTER_REFLECTION_PAD2D_KERNELS(device, float) \ - REGISTER_REFLECTION_PAD2D_KERNELS(device, double) \ - REGISTER_REFLECTION_PAD2D_KERNELS(device, int32_t) - -REGISTER_REFLECTION_PAD2D_WITH_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_REFLECTION_PAD2D_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_REFLECTION_PAD2D_KERNELS(DeviceType::kCUDA, float16) -#endif -#ifdef WITH_ROCM -REGISTER_REFLECTION_PAD2D_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_REFLECTION_PAD2D_KERNELS(DeviceType::kCUDA, float16) -#endif - -template -class ReplicationPad2dKernel final : public OpKernel { - public: - ReplicationPad2dKernel() = default; - ~ReplicationPad2dKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const auto& padding = ctx->Attr>("padding"); - const int64_t ndims = x->shape_view().NumAxes(); - CHECK_EQ(padding.size(), ndims); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - const int64_t pad_left = padding[0]; - const int64_t pad_top = padding[2]; - - const int64_t n_batch = y->shape_view().At(n_idx); - const int64_t n_channel = y->shape_view().At(c_idx); - const int64_t y_height = y->shape_view().At(h_idx); - const int64_t y_width = y->shape_view().At(w_idx); - const int64_t x_height = x->shape_view().At(h_idx); - const int64_t x_width = x->shape_view().At(w_idx); - - IN_T* dest = y->mut_dptr(); - const IN_T* src = x->dptr(); - DimVector y_vector; - y->shape_view().ToDimVector(&y_vector); - NdIndexOffsetHelper index_helper(y_vector.data()); - - ReplicationPad2dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, - n_channel, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class ReplicationPad2dGradKernel final : public OpKernel { - public: - ReplicationPad2dGradKernel() = default; - ~ReplicationPad2dGradKernel() = default; - - private: - void Compute(KernelComputeContext* ctx) const override { - const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - const auto& padding = ctx->Attr>("padding"); - const int64_t ndims = dy->shape_view().NumAxes(); - CHECK_EQ(padding.size(), ndims); - - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - int64_t pad_left = padding[0]; - int64_t pad_top = padding[2]; - int64_t n_batch = dy->shape_view().At(n_idx); - int64_t n_channel = dy->shape_view().At(c_idx); - int64_t dy_height = dy->shape_view().At(h_idx); - int64_t dy_width = dy->shape_view().At(w_idx); - int64_t dx_height = dx->shape_view().At(h_idx); - int64_t dx_width = dx->shape_view().At(w_idx); - - const IN_T* src = dy->dptr(); - IN_T* dest = dx->mut_dptr(); - DimVector dy_vector; - dy->shape_view().ToDimVector(&dy_vector); - NdIndexOffsetHelper index_helper(dy_vector.data()); - - size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); - Memset(ctx->stream(), dest, 0, out_bytes_size); - - ReplicationPad2dGradFunctor()(ctx->stream(), src, dest, index_helper, - n_batch, n_channel, dy_height, dy_width, - dx_height, dx_width, pad_left, pad_top); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_REPLICATION_PAD2D_KERNELS(device, dtype) \ - REGISTER_USER_KERNEL("replication_pad2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("replication_pad2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -#define REGISTER_REPLICATION_PAD2D_WITH_DEVICE(device) \ - REGISTER_REPLICATION_PAD2D_KERNELS(device, float) \ - REGISTER_REPLICATION_PAD2D_KERNELS(device, double) \ - REGISTER_REPLICATION_PAD2D_KERNELS(device, int32_t) - -REGISTER_REPLICATION_PAD2D_WITH_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_REPLICATION_PAD2D_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_REPLICATION_PAD2D_KERNELS(DeviceType::kCUDA, float16) -#endif -#ifdef WITH_ROCM -REGISTER_REPLICATION_PAD2D_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_REPLICATION_PAD2D_KERNELS(DeviceType::kCUDA, float16) -#endif - -} // namespace user_op -} // namespace oneflow diff --git a/oneflow/user/kernels/pad2d_kernels_util.cpp b/oneflow/user/kernels/pad2d_kernels_util.cpp deleted file mode 100644 index 287ad430ebe30ce72da78dbadaa70169c198396e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/pad2d_kernels_util.cpp +++ /dev/null @@ -1,92 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/pad2d_kernels_util.h" -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -namespace user_op { - -template -struct ReflectionPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoReflectionPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, - x_height, x_width, pad_left, pad_top); - } -}; - -template -struct ReflectionPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoReflectionPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_FUNCTOR, (DeviceType::kCPU), - PADDING_DATA_TYPE_CPU_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_GRAD_FUNCTOR, (DeviceType::kCPU), - PADDING_DATA_TYPE_CPU_SEQ); - -template -struct ReplicationPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoReplicationPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, - y_width, x_height, x_width, pad_left, pad_top); - } -}; - -template -struct ReplicationPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoReplicationPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_FUNCTOR, (DeviceType::kCPU), - PADDING_DATA_TYPE_CPU_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_GRAD_FUNCTOR, (DeviceType::kCPU), - PADDING_DATA_TYPE_CPU_SEQ); - -} // namespace user_op -} // namespace oneflow diff --git a/oneflow/user/kernels/pad2d_kernels_util.cu b/oneflow/user/kernels/pad2d_kernels_util.cu deleted file mode 100644 index d1a3a63a2758826e434fe902f97f50d89fc530a2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/pad2d_kernels_util.cu +++ /dev/null @@ -1,213 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#ifdef WITH_CUDA -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/pad2d_kernels_util.h" -#include "oneflow/core/ep/cuda/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -template -__global__ void DoCUDAReflectionPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - DoReflectionPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, - x_height, x_width, pad_left, pad_top); -}; - -template -__global__ void DoCUDAReflectionPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - DoReflectionPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); -}; - -template -struct ReflectionPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReflectionPad2dFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t y_height, int64_t y_width, int64_t x_height, int64_t x_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); -} - -template -struct ReflectionPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, - dx_width, pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReflectionPad2dGradFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t dy_height, int64_t dy_width, int64_t dx_height, int64_t dx_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); -} - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_GRAD_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -template -__global__ void DoCUDAReplicationPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - DoReplicationPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, - x_height, x_width, pad_left, pad_top); -}; - -template -__global__ void DoCUDAReplicationPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - DoReplicationPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); -}; - -template -struct ReplicationPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReplicationPad2dFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t y_height, int64_t y_width, int64_t x_height, int64_t x_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); -} - -template -struct ReplicationPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, - dx_width, pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReplicationPad2dGradFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t dy_height, int64_t dy_width, int64_t dx_height, int64_t dx_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); -} - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_GRAD_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -} // namespace user_op -} // namespace oneflow - -#endif // WITH_CUDA diff --git a/oneflow/user/kernels/pad2d_kernels_util.h b/oneflow/user/kernels/pad2d_kernels_util.h deleted file mode 100644 index 0499286abc7abf8f83c9d49a5e04cadfa66c117a..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/pad2d_kernels_util.h +++ /dev/null @@ -1,250 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_USER_KERNELS_PAD2D_KERNELS_UTIL_H_ -#define ONEFLOW_USER_KERNELS_PAD2D_KERNELS_UTIL_H_ -#ifdef WITH_CUDA -#include "oneflow/core/cuda/atomic.cuh" -#endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/hip/atomic.hip.h" -#endif // WITH_ROCM -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/ndarray/xpu_util.h" - -namespace oneflow { - -#define PADDING_DATA_TYPE_CPU_SEQ \ - FLOATING_DATA_TYPE_SEQ \ - OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) - -#define PADDING_DATA_TYPE_CUDA_SEQ \ - FLOAT16_DATA_TYPE_SEQ \ - PADDING_DATA_TYPE_CPU_SEQ - -namespace user_op { - -template -struct DeviceAdd { - OF_DEVICE_FUNC static void Invoke(const T* x, T* y) { -#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - cuda::atomic::Add(y, *x); -#else - *y += *x; -#endif - }; -}; - -template -struct ReflectionPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top); -}; - -template -struct ReflectionPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top); -}; - -template -OF_DEVICE_FUNC void DoReflectionPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - XPU_1D_KERNEL_LOOP(k, elem_num) { - int64_t n, c, i, j, ip_x, ip_y; - int64_t coord_y[4]; - index_helper.OffsetToNdIndex(k, coord_y); - n = coord_y[0]; - c = coord_y[1]; - i = coord_y[2]; - j = coord_y[3]; - if (j < pad_left) { - ip_x = pad_left * 2 - j; - } else if (j >= pad_left && j < x_width + pad_left) { - ip_x = j; - } else { - ip_x = (x_width + pad_left - 1) * 2 - j; - } - - if (i < pad_top) { - ip_y = pad_top * 2 - i; - } else if (i >= pad_top && i < x_height + pad_top) { - ip_y = i; - } else { - ip_y = (x_height + pad_top - 1) * 2 - i; - } - ip_x = ip_x - pad_left; - ip_y = ip_y - pad_top; - int64_t dest_index = n * dest_num + c * y_width * y_height + i * y_width + j; - int64_t src_index = n * src_num + c * x_width * x_height + ip_y * x_width + ip_x; - dest[dest_index] = src[src_index]; - } -} - -template -OF_DEVICE_FUNC void DoReflectionPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - XPU_1D_KERNEL_LOOP(k, elem_num) { - int64_t n, c, i, j, ip_x, ip_y; - int64_t coord[4]; - index_helper.OffsetToNdIndex(k, coord); - n = coord[0]; - c = coord[1]; - i = coord[2]; - j = coord[3]; - if (j < pad_left) { - ip_x = pad_left * 2 - j; - } else if (j >= pad_left && j < dx_width + pad_left) { - ip_x = j; - } else { - ip_x = (dx_width + pad_left - 1) * 2 - j; - } - - if (i < pad_top) { - ip_y = pad_top * 2 - i; - } else if (i >= pad_top && i < dx_height + pad_top) { - ip_y = i; - } else { - ip_y = (dx_height + pad_top - 1) * 2 - i; - } - ip_x = ip_x - pad_left; - ip_y = ip_y - pad_top; - - int64_t src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j; - int64_t dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x; - DeviceAdd::Invoke(src + src_index, dest + dest_index); - } -} - -// macros for functors instantiate(used by pad2d_kernels_util.cu) -#define INSTANTIATE_REFLECTION_PAD2D_FUNCTOR(device_type_v, dtype_pair) \ - template struct ReflectionPad2dFunctor; - -#define INSTANTIATE_REFLECTION_PAD2D_GRAD_FUNCTOR(device_type_v, dtype_pair) \ - template struct ReflectionPad2dGradFunctor; - -template -struct ReplicationPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top); -}; - -template -struct ReplicationPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top); -}; - -template -OF_DEVICE_FUNC void DoReplicationPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - XPU_1D_KERNEL_LOOP(k, elem_num) { - int64_t n, c, i, j, ip_x, ip_y; - int64_t coord_y[4]; - index_helper.OffsetToNdIndex(k, coord_y); - n = coord_y[0]; - c = coord_y[1]; - i = coord_y[2]; - j = coord_y[3]; - if (j < pad_left) { - ip_x = pad_left; - } else if (j >= pad_left && j < x_width + pad_left) { - ip_x = j; - } else { - ip_x = x_width + pad_left - 1; - } - - if (i < pad_top) { - ip_y = pad_top; - } else if (i >= pad_top && i < x_height + pad_top) { - ip_y = i; - } else { - ip_y = x_height + pad_top - 1; - } - ip_x = ip_x - pad_left; - ip_y = ip_y - pad_top; - - int64_t dest_index = n * dest_num + c * y_width * y_height + i * y_width + j; - int64_t src_index = n * src_num + c * x_width * x_height + ip_y * x_width + ip_x; - dest[dest_index] = src[src_index]; - } -} - -template -OF_DEVICE_FUNC void DoReplicationPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - XPU_1D_KERNEL_LOOP(k, elem_num) { - int64_t n, c, i, j, ip_x, ip_y; - int64_t coord[4]; - index_helper.OffsetToNdIndex(k, coord); - n = coord[0]; - c = coord[1]; - i = coord[2]; - j = coord[3]; - if (j < pad_left) { - ip_x = pad_left; - } else if (j >= pad_left && j < dx_width + pad_left) { - ip_x = j; - } else { - ip_x = dx_width + pad_left - 1; - } - - if (i < pad_top) { - ip_y = pad_top; - } else if (i >= pad_top && i < dx_height + pad_top) { - ip_y = i; - } else { - ip_y = dx_height + pad_top - 1; - } - ip_x = ip_x - pad_left; - ip_y = ip_y - pad_top; - - int64_t src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j; - int64_t dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x; - DeviceAdd::Invoke(src + src_index, dest + dest_index); - } -} - -// macros for functors instantiate(used by pad2d_kernels_util.cu) -#define INSTANTIATE_REPLICATION_PAD2D_FUNCTOR(device_type_v, dtype_pair) \ - template struct ReplicationPad2dFunctor; - -#define INSTANTIATE_REPLICATION_PAD2D_GRAD_FUNCTOR(device_type_v, dtype_pair) \ - template struct ReplicationPad2dGradFunctor; - -} // namespace user_op -} // namespace oneflow - -#endif // ONEFLOW_USER_KERNELS_PAD2D_KERNELS_UTIL_H_ diff --git a/oneflow/user/kernels/pad2d_kernels_util.hip.cpp b/oneflow/user/kernels/pad2d_kernels_util.hip.cpp deleted file mode 100644 index 9c9bf9c2bf74d7a72c803456c1ca8821dfebc534..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/pad2d_kernels_util.hip.cpp +++ /dev/null @@ -1,214 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/pad2d_kernels_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -template -__global__ void DoCUDAReflectionPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - DoReflectionPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, - x_height, x_width, pad_left, pad_top); -}; - -template -__global__ void DoCUDAReflectionPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - DoReflectionPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); -}; - -template -struct ReflectionPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReflectionPad2dFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t y_height, int64_t y_width, int64_t x_height, int64_t x_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); -} - -template -struct ReflectionPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, - dx_width, pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReflectionPad2dGradFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t dy_height, int64_t dy_width, int64_t dx_height, int64_t dx_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); -} - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD2D_GRAD_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -template -__global__ void DoCUDAReplicationPad2d(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - DoReplicationPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, - x_height, x_width, pad_left, pad_top); -}; - -template -__global__ void DoCUDAReplicationPad2dGrad(const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper index_helper, - int64_t elem_num, int64_t src_num, int64_t dest_num, - int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - DoReplicationPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, - dy_width, dx_height, dx_width, pad_left, pad_top); -}; - -template -struct ReplicationPad2dFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t y_height, int64_t y_width, int64_t x_height, - int64_t x_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, - pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReplicationPad2dFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t y_height, int64_t y_width, int64_t x_height, int64_t x_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * y_height * y_width; - int64_t src_num = n_channel * x_height * x_width; - int64_t elem_num = n_batch * dest_num; - DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); -} - -template -struct ReplicationPad2dGradFunctor final { - void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, - int64_t n_channel, int64_t dy_height, int64_t dy_width, int64_t dx_height, - int64_t dx_width, int64_t pad_left, int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( - src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, - dx_width, pad_left, pad_top); - } -}; - -// float16 implementation -template<> -void ReplicationPad2dGradFunctor::operator()( - ep::Stream* stream, const float16* src, float16* dest, - const NdIndexOffsetHelper& index_helper, int64_t n_batch, int64_t n_channel, - int64_t dy_height, int64_t dy_width, int64_t dx_height, int64_t dx_width, int64_t pad_left, - int64_t pad_top) { - int64_t dest_num = n_channel * dx_height * dx_width; - int64_t src_num = n_channel * dy_height * dy_width; - int64_t elem_num = n_batch * src_num; - DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( - reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, - src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); -} - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD2D_GRAD_FUNCTOR, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - PADDING_DATA_TYPE_CUDA_SEQ); - -} // namespace user_op -} // namespace oneflow - -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/pad_kernel.cpp b/oneflow/user/kernels/pad_kernel.cpp index 7f4a1c793eb8716bf04ba432609d51e033aa4a10..48e38f829733809bdece620a8a85987e95ce8460 100644 --- a/oneflow/user/kernels/pad_kernel.cpp +++ b/oneflow/user/kernels/pad_kernel.cpp @@ -54,7 +54,7 @@ class PadKernel final : public OpKernel, public CudaGraphSupport { } Scalar value; - if (IsIntegralDataType(x->data_type())) { + if (IsIntegralDataType(x->data_type()) || x->data_type() == kBool) { value = Scalar(ctx->Attr("integral_constant_value")); } else { value = Scalar(ctx->Attr("floating_constant_value")); diff --git a/oneflow/user/kernels/partial_fc_sample_kernel.cu b/oneflow/user/kernels/partial_fc_sample_kernel.cu index 2a7b898c636b4fb581573e215b8745646ec07f36..1015d095791ba78ad8ad6414bcdf0a4845ddbacc 100644 --- a/oneflow/user/kernels/partial_fc_sample_kernel.cu +++ b/oneflow/user/kernels/partial_fc_sample_kernel.cu @@ -13,16 +13,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/common/balanced_splitter.h" #include "oneflow/user/kernels/gather_kernel_util.h" #include "oneflow/core/common/not_equal_to_previous_adjacent_iterator.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#include +#include +#else #include #include #include +#endif #include "oneflow/core/ep/cuda/cuda_stream.h" namespace oneflow { @@ -33,8 +40,13 @@ namespace { template int64_t GetCubSortPairsTempStorageSize(int64_t n) { size_t cub_sort_temp_store_size = 0; +#ifdef WITH_ROCM + OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, + nullptr, nullptr, nullptr, n))); +#else OF_CUDA_CHECK((cub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, nullptr, nullptr, nullptr, n))); +#endif size_t temp_store_size = GetCudaAlignedSize(cub_sort_temp_store_size); CHECK_GE(temp_store_size, 0); CHECK_LT(temp_store_size, static_cast(GetMaxVal())); @@ -45,8 +57,13 @@ template int64_t GetCubScanTempStorageSize(int64_t n) { size_t cub_scan_temp_store_size = 0; NotEqualToPreviousAdjacentIterator unique_counting_iter(nullptr, 0); +#ifdef WITH_ROCM + OF_CUDA_CHECK((hipcub::DeviceScan::InclusiveSum, K*>( + nullptr, cub_scan_temp_store_size, unique_counting_iter, nullptr, n))); +#else OF_CUDA_CHECK((cub::DeviceScan::InclusiveSum, K*>( nullptr, cub_scan_temp_store_size, unique_counting_iter, nullptr, n))); +#endif size_t temp_store_size = GetCudaAlignedSize(cub_scan_temp_store_size); CHECK_GE(temp_store_size, 0); CHECK_LT(temp_store_size, static_cast(GetMaxVal())); @@ -126,18 +143,18 @@ class TmpBufferManager final { void* ptr_; }; -__global__ void SetupKernel(int64_t seed, curandState* state) { +__global__ void SetupKernel(int64_t seed, GPURAND(State)* state) { const int id = blockIdx.x * blockDim.x + threadIdx.x; size_t local_seed = (static_cast(seed) + 0x9e3779b9U + (static_cast(id) << 6U) + (static_cast(id) >> 2U)); - curand_init(local_seed, 0, 0, &state[id]); + GPURAND(_init)(local_seed, 0, 0, &state[id]); } template -__global__ void GenerateGpu(curandState* state, const int64_t n, const int64_t max_val, K* buffer) { +__global__ void GenerateGpu(GPURAND(State)* state, const int64_t n, const int64_t max_val, K* buffer) { const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, n) { buffer[i] = static_cast(curand(&localState) % max_val); } + GPURAND(State) localState = state[id]; + CUDA_1D_KERNEL_LOOP(i, n) { buffer[i] = static_cast(GPURAND()(&localState) % max_val); } state[id] = localState; } @@ -148,14 +165,16 @@ class DistributedPartialFcSampleOpKernelState final : public user_op::OpKernelSt : lower_(lower), upper_(upper), num_sample_per_rank_(num_sample_per_rank) { CHECK_NOTNULL(stream); const int64_t num_classes = upper_ - lower_; - OF_CUDA_CHECK(cudaMalloc(&curand_states_, BlocksNum4ThreadsNum(num_classes) - * kCudaThreadsNumPerBlock * sizeof(curandState))); + OF_CUDA_CHECK(GPU(Malloc)(&curand_states_, BlocksNum4ThreadsNum(num_classes) + * kCudaThreadsNumPerBlock * sizeof(GPURAND(State)))); SetupKernel<<As()->cuda_stream()>>>(seed, curand_states_); } ~DistributedPartialFcSampleOpKernelState() { - cudaError_t ret = cudaFree(curand_states_); + GPU(Error_t) ret = GPU(Free)(curand_states_); +#ifdef WITH_CUDA if (ret != cudaErrorCudartUnloading) { OF_CUDA_CHECK(ret); } +#endif }; int64_t lower() const { return lower_; } @@ -173,7 +192,7 @@ class DistributedPartialFcSampleOpKernelState final : public user_op::OpKernelSt const int64_t lower_; const int64_t upper_; const int64_t num_sample_per_rank_; - curandState* curand_states_; + GPURAND(State)* curand_states_; }; template @@ -255,6 +274,16 @@ void MapLabel(ep::Stream* stream, const int64_t num_classes, const int64_t batch void* cub_tmp_storage_ptr, K* bound_index_ptr, K* bound_value_ptr) { IotaKernel<<As()->cuda_stream()>>>(batch_size, cub_sort_values_ptr); +#ifdef WITH_ROCM + OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs( + cub_tmp_storage_ptr, temp_storage_bytes, label_ptr, cub_sort_keys_out_ptr, + cub_sort_values_ptr, cub_sort_values_out_ptr, batch_size, 0, sizeof(K) * 8, + stream->As()->cuda_stream()))); + NotEqualToPreviousAdjacentIterator unique_counting_iter(cub_sort_keys_out_ptr, 0); + OF_CUDA_CHECK((hipcub::DeviceScan::InclusiveSum, K*>( + cub_tmp_storage_ptr, temp_storage_bytes, unique_counting_iter, cub_sort_values_ptr, + batch_size, stream->As()->cuda_stream()))); +#else OF_CUDA_CHECK((cub::DeviceRadixSort::SortPairs( cub_tmp_storage_ptr, temp_storage_bytes, label_ptr, cub_sort_keys_out_ptr, cub_sort_values_ptr, cub_sort_values_out_ptr, batch_size, 0, sizeof(K) * 8, @@ -263,6 +292,7 @@ void MapLabel(ep::Stream* stream, const int64_t num_classes, const int64_t batch OF_CUDA_CHECK((cub::DeviceScan::InclusiveSum, K*>( cub_tmp_storage_ptr, temp_storage_bytes, unique_counting_iter, cub_sort_values_ptr, batch_size, stream->As()->cuda_stream()))); +#endif GetPartionBound<<As()->cuda_stream()>>>( @@ -342,11 +372,19 @@ class DistributedPartialFcSampleGpuKernel final : public user_op::OpKernel { ctx->stream()->As()->cuda_stream()>>>( num_classes, buffer_manager.CubSortValuesPtr()); size_t temp_storage_bytes = buffer_manager.GetCubTmpStorageSize(); +#ifdef WITH_ROCM + OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs( + buffer_manager.CubTmpStoragePtr(), temp_storage_bytes, buffer_manager.CubSortKeysPtr(), + buffer_manager.CubSortKeysOutPtr(), buffer_manager.CubSortValuesPtr(), + buffer_manager.CubSortValuesOutPtr(), num_classes, 0, sizeof(K) * 8, + ctx->stream()->As()->cuda_stream()))); +#else OF_CUDA_CHECK((cub::DeviceRadixSort::SortPairs( buffer_manager.CubTmpStoragePtr(), temp_storage_bytes, buffer_manager.CubSortKeysPtr(), buffer_manager.CubSortKeysOutPtr(), buffer_manager.CubSortValuesPtr(), buffer_manager.CubSortValuesOutPtr(), num_classes, 0, sizeof(K) * 8, ctx->stream()->As()->cuda_stream()))); +#endif GetSampledLabel<<stream()->As()->cuda_stream()>>>( diff --git a/oneflow/user/kernels/partial_fc_sample_kernel.hip.cpp b/oneflow/user/kernels/partial_fc_sample_kernel.hip.cpp deleted file mode 100644 index 22c1798e822ef076d85dc76949951c045096a9ba..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/partial_fc_sample_kernel.hip.cpp +++ /dev/null @@ -1,431 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/user/kernels/gather_kernel_util.h" -#include "oneflow/core/common/not_equal_to_previous_adjacent_iterator.h" -#include -#include -#include -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -namespace { - -template -int64_t GetCubSortPairsTempStorageSize(int64_t n) { - size_t cub_sort_temp_store_size = 0; - OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, - nullptr, nullptr, nullptr, n))); - size_t temp_store_size = GetCudaAlignedSize(cub_sort_temp_store_size); - CHECK_GE(temp_store_size, 0); - CHECK_LT(temp_store_size, static_cast(GetMaxVal())); - return static_cast(temp_store_size); -} - -template -int64_t GetCubScanTempStorageSize(int64_t n) { - size_t cub_scan_temp_store_size = 0; - NotEqualToPreviousAdjacentIterator unique_counting_iter(nullptr, 0); - OF_CUDA_CHECK((hipcub::DeviceScan::InclusiveSum, K*>( - nullptr, cub_scan_temp_store_size, unique_counting_iter, nullptr, n))); - size_t temp_store_size = GetCudaAlignedSize(cub_scan_temp_store_size); - CHECK_GE(temp_store_size, 0); - CHECK_LT(temp_store_size, static_cast(GetMaxVal())); - return static_cast(temp_store_size); -} - -template -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(void* ptr, const int64_t device_num_class, const int64_t batch_size, - const int64_t parallel_num) - : ptr_(ptr) { - const int64_t buffer_elem_cnt = std::max(device_num_class, batch_size); - const size_t cub_sort_keys_bytes = GetCudaAlignedSize(buffer_elem_cnt * sizeof(K)); - const size_t cub_sort_values_bytes = GetCudaAlignedSize(buffer_elem_cnt * sizeof(K)); - const size_t cub_sort_keys_out_bytes = GetCudaAlignedSize(buffer_elem_cnt * sizeof(K)); - const size_t cub_sort_values_out_bytes = GetCudaAlignedSize(buffer_elem_cnt * sizeof(K)); - const size_t bound_index_bytes = GetCudaAlignedSize((parallel_num + 1) * sizeof(K)); - const size_t bound_value_bytes = GetCudaAlignedSize((parallel_num + 1) * sizeof(K)); - cub_tmp_storage_bytes_ = std::max(GetCubSortPairsTempStorageSize(buffer_elem_cnt), - GetCubScanTempStorageSize(batch_size)); - cub_sort_keys_offset_ = 0; - cub_sort_values_offset_ = cub_sort_keys_offset_ + cub_sort_keys_bytes; - cub_sort_keys_out_offset_ = cub_sort_values_offset_ + cub_sort_values_bytes; - cub_sort_values_out_offset_ = cub_sort_keys_out_offset_ + cub_sort_keys_out_bytes; - cub_tmp_storage_offset_ = cub_sort_values_out_offset_ + cub_sort_values_out_bytes; - bound_index_offset_ = cub_tmp_storage_offset_ + cub_tmp_storage_bytes_; - bound_value_offset_ = bound_index_offset_ + bound_index_bytes; - total_buffer_size_ = cub_sort_keys_bytes + cub_sort_values_bytes + cub_sort_keys_out_bytes - + cub_sort_values_out_bytes + cub_tmp_storage_bytes_ + bound_index_bytes - + bound_value_bytes; - } - ~TmpBufferManager() = default; - - size_t GetTotalBufferSize() const { return total_buffer_size_; } - size_t GetCubTmpStorageSize() const { return cub_tmp_storage_bytes_; } - K* CubSortKeysPtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + cub_sort_keys_offset_); - } - K* CubSortValuesPtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + cub_sort_values_offset_); - } - K* CubSortKeysOutPtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + cub_sort_keys_out_offset_); - } - K* CubSortValuesOutPtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + cub_sort_values_out_offset_); - } - void* CubTmpStoragePtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + cub_tmp_storage_offset_); - } - K* BoundIndexPtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + bound_index_offset_); - } - K* BoundValuePtr() const { - CHECK(ptr_ != nullptr); - return reinterpret_cast(reinterpret_cast(ptr_) + bound_value_offset_); - } - - private: - size_t cub_sort_keys_offset_; - size_t cub_sort_values_offset_; - size_t cub_sort_keys_out_offset_; - size_t cub_sort_values_out_offset_; - size_t cub_tmp_storage_offset_; - size_t bound_index_offset_; - size_t bound_value_offset_; - size_t cub_tmp_storage_bytes_; - size_t total_buffer_size_; - void* ptr_; -}; - -__global__ void SetupKernel(int64_t seed, hiprandState* state) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - size_t local_seed = (static_cast(seed) + 0x9e3779b9U + (static_cast(id) << 6U) - + (static_cast(id) >> 2U)); - hiprand_init(local_seed, 0, 0, &state[id]); -} - -template -__global__ void GenerateGpu(hiprandState* state, const int64_t n, const int64_t max_val, K* buffer) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState localState = state[id]; - CUDA_1D_KERNEL_LOOP(i, n) { buffer[i] = static_cast(hiprand(&localState) % max_val); } - state[id] = localState; -} - -class DistributedPartialFcSampleOpKernelState final : public user_op::OpKernelState { - public: - DistributedPartialFcSampleOpKernelState(ep::Stream* stream, int64_t lower, int64_t upper, - int64_t num_sample_per_rank, int64_t seed) - : lower_(lower), upper_(upper), num_sample_per_rank_(num_sample_per_rank) { - CHECK_NOTNULL(stream); - const int64_t num_classes = upper_ - lower_; - OF_CUDA_CHECK(hipMalloc(&curand_states_, BlocksNum4ThreadsNum(num_classes) - * kCudaThreadsNumPerBlock * sizeof(hiprandState))); - SetupKernel<<As()->cuda_stream()>>>(seed, curand_states_); - } - ~DistributedPartialFcSampleOpKernelState() { - hipError_t ret = hipFree(curand_states_); - if (ret != hipErrorDeinitialized) { OF_CUDA_CHECK(ret); } - }; - - int64_t lower() const { return lower_; } - int64_t upper() const { return upper_; } - int64_t num_sample_per_rank() const { return num_sample_per_rank_; } - - template - void GenRandom(ep::Stream* stream, const int64_t n, const int64_t max_val, K* buffer) { - GenerateGpu - <<As()->cuda_stream()>>>(curand_states_, n, max_val, buffer); - } - - private: - const int64_t lower_; - const int64_t upper_; - const int64_t num_sample_per_rank_; - hiprandState* curand_states_; -}; - -template -__global__ void IotaKernel(int64_t n, K* out) { - CUDA_1D_KERNEL_LOOP(i, n) { out[i] = static_cast(i); } -} - -template -__global__ void MarkPositive(const int64_t n, const int64_t offset, const int64_t num_classes, - const K* labels, K* out) { - CUDA_1D_KERNEL_LOOP(i, n) { - K label = labels[i] - offset; - if (label >= 0 && label < num_classes) { out[label] = label - num_classes; } - } -} - -template -__global__ void GetSampledLabel(const int64_t n, const int64_t offset, const K* label, - K* sampled_label) { - CUDA_1D_KERNEL_LOOP(i, n) { sampled_label[i] = label[i] + offset; } -} - -template -__global__ void GetLabelMap(const int64_t n, const int64_t parallel_num, - const int64_t num_sample_per_rank, const K* bound_index, - const K* bound_value, K* label_map) { - CUDA_1D_KERNEL_LOOP(i, n) { -#pragma unroll - for (int64_t j = 0; j < parallel_num; j++) { - if (i >= bound_index[j] && i < bound_index[j + 1]) { - label_map[i] = label_map[i] - bound_value[j] + j * num_sample_per_rank; - } - } - } -} - -template -__global__ void GetPartionBound(const int64_t n, const int64_t parallel_num, - const int64_t num_classes_per_rank, const K* key_ptr, - const K* value_ptr, K* bound_index, K* bound_value) { - CUDA_1D_KERNEL_LOOP(i, n) { - if (i != 0) { - const K cur_in = key_ptr[i] / num_classes_per_rank; - const K pre_in = key_ptr[i - 1] / num_classes_per_rank; - if (cur_in > pre_in) { - assert(cur_in < parallel_num); -#pragma unroll - for (int32_t j = pre_in + 1; j <= cur_in; ++j) { - bound_index[j] = static_cast(i); - bound_value[j] = value_ptr[i]; - } - } - } - } - CUDA_1D_KERNEL_LOOP(i, parallel_num + 1) { - const K first_in = key_ptr[0] / num_classes_per_rank; - const K last_in = key_ptr[n - 1] / num_classes_per_rank; - if (i <= first_in) { - bound_index[i] = 0; - bound_value[i] = value_ptr[0]; - } else if (i > last_in) { - bound_index[i] = n; - bound_value[i] = value_ptr[n - 1]; - } - } -} - -template -__global__ void GetMappedLabel(const int64_t n, const K* label_map_key, const K* label_map_value, - K* mapped_label) { - CUDA_1D_KERNEL_LOOP(i, n) { mapped_label[label_map_key[i]] = label_map_value[i]; } -} - -template -void MapLabel(ep::Stream* stream, const int64_t num_classes, const int64_t batch_size, - const int64_t lower_bound, const int64_t parallel_num, const int64_t num_sample, - size_t temp_storage_bytes, const K* label_ptr, K* mapped_label_ptr, - K* cub_sort_values_ptr, K* cub_sort_keys_out_ptr, K* cub_sort_values_out_ptr, - void* cub_tmp_storage_ptr, K* bound_index_ptr, K* bound_value_ptr) { - IotaKernel<<As()->cuda_stream()>>>(batch_size, cub_sort_values_ptr); - OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs( - cub_tmp_storage_ptr, temp_storage_bytes, label_ptr, cub_sort_keys_out_ptr, - cub_sort_values_ptr, cub_sort_values_out_ptr, batch_size, 0, sizeof(K) * 8, - stream->As()->cuda_stream()))); - NotEqualToPreviousAdjacentIterator unique_counting_iter(cub_sort_keys_out_ptr, 0); - OF_CUDA_CHECK((hipcub::DeviceScan::InclusiveSum, K*>( - cub_tmp_storage_ptr, temp_storage_bytes, unique_counting_iter, cub_sort_values_ptr, - batch_size, stream->As()->cuda_stream()))); - - GetPartionBound<<As()->cuda_stream()>>>( - batch_size, parallel_num, num_classes, cub_sort_keys_out_ptr, cub_sort_values_ptr, - bound_index_ptr, bound_value_ptr); - - GetLabelMap<<As()->cuda_stream()>>>( - batch_size, parallel_num, num_sample, bound_index_ptr, bound_value_ptr, cub_sort_values_ptr); - - GetMappedLabel<<As()->cuda_stream()>>>( - batch_size, cub_sort_values_out_ptr, cub_sort_values_ptr, mapped_label_ptr); -} - -} // namespace - -template -class DistributedPartialFcSampleGpuKernel final : public user_op::OpKernel { - public: - DistributedPartialFcSampleGpuKernel() = default; - ~DistributedPartialFcSampleGpuKernel() override = default; - - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - const SbpParallel& in_sbp = ctx->SbpParallel4ArgNameAndIndex("weight", 0); - const TensorDesc* in_logical_desc = ctx->LogicalTensorDesc4ArgNameAndIndex("weight", 0); - const int64_t class_num = in_logical_desc->shape().At(0); - const int64_t num_sample = ctx->Attr("num_sample"); - int64_t seed = ctx->Attr("seed"); - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - const int64_t num_sample_per_rank = RoundUp(num_sample, parallel_num) / parallel_num; - if (in_sbp.has_split_parallel() && in_sbp.split_parallel().axis() == 0 && parallel_num > 1) { - std::seed_seq seq{seed}; - std::vector seeds(parallel_num); - seq.generate(seeds.begin(), seeds.end()); - seed = seeds.at(ctx->parallel_ctx().parallel_id()); - CHECK(ctx->SbpParallel4ArgNameAndIndex("label", 0).has_broadcast_parallel()); - BalancedSplitter bs(class_num, parallel_num); - return std::make_shared( - ctx->stream(), bs.At(ctx->parallel_ctx().parallel_id()).begin(), - bs.At(ctx->parallel_ctx().parallel_id()).end(), num_sample_per_rank, seed); - } else { - return std::make_shared(ctx->stream(), 0, class_num, - num_sample_per_rank, seed); - } - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); - const user_op::Tensor* label = ctx->Tensor4ArgNameAndIndex("label", 0); - user_op::Tensor* mapped_label = ctx->Tensor4ArgNameAndIndex("mapped_label", 0); - user_op::Tensor* sampled_label = ctx->Tensor4ArgNameAndIndex("sampled_label", 0); - user_op::Tensor* sampled_weight = ctx->Tensor4ArgNameAndIndex("sampled_weight", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - const int64_t batch_size = label->shape_view().At(0); - const int64_t num_classes = weight->shape_view().At(0); - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - TmpBufferManager buffer_manager(tmp_buffer->mut_dptr(), num_classes, batch_size, - parallel_num); - - auto* kernel_state = dynamic_cast(state); - CHECK_NOTNULL(kernel_state); - CHECK_EQ(num_classes, kernel_state->upper() - kernel_state->lower()); - const int64_t lower_bound = kernel_state->lower(); - const int64_t num_sample = kernel_state->num_sample_per_rank(); - kernel_state->GenRandom(ctx->stream(), num_classes, num_classes, - buffer_manager.CubSortKeysPtr()); - MarkPositive<<stream()->As()->cuda_stream()>>>( - batch_size, lower_bound, num_classes, label->dptr(), buffer_manager.CubSortKeysPtr()); - IotaKernel<<stream()->As()->cuda_stream()>>>( - num_classes, buffer_manager.CubSortValuesPtr()); - size_t temp_storage_bytes = buffer_manager.GetCubTmpStorageSize(); - OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs( - buffer_manager.CubTmpStoragePtr(), temp_storage_bytes, buffer_manager.CubSortKeysPtr(), - buffer_manager.CubSortKeysOutPtr(), buffer_manager.CubSortValuesPtr(), - buffer_manager.CubSortValuesOutPtr(), num_classes, 0, sizeof(K) * 8, - ctx->stream()->As()->cuda_stream()))); - - GetSampledLabel<<stream()->As()->cuda_stream()>>>( - num_sample, lower_bound, buffer_manager.CubSortValuesOutPtr(), - sampled_label->mut_dptr()); - - GatherKernelUtilImpl::Forward( - ctx->stream(), buffer_manager.CubSortValuesOutPtr(), num_sample, weight->dptr(), - Shape({1, num_classes, weight->shape_view().Count(1)}), sampled_weight->mut_dptr(), 0); - - MapLabel(ctx->stream(), num_classes, batch_size, lower_bound, parallel_num, num_sample, - buffer_manager.GetCubTmpStorageSize(), label->dptr(), - mapped_label->mut_dptr(), buffer_manager.CubSortValuesPtr(), - buffer_manager.CubSortKeysOutPtr(), buffer_manager.CubSortValuesOutPtr(), - buffer_manager.CubTmpStoragePtr(), buffer_manager.BoundIndexPtr(), - buffer_manager.BoundValuePtr()); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_DISTRIBUTED_PARTIAL_FC_SAMPLE_CUDA_KERNEL(dtype_pair, ltype_pair) \ - REGISTER_USER_KERNEL("distributed_partial_fc_sample") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("label", 0) == OF_PP_PAIR_SECOND(ltype_pair)) \ - && (user_op::HobDataType("weight", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ - .SetInferTmpSizeFn([](oneflow::user_op::InferContext* ctx) { \ - const int64_t num_classes = ctx->InputTensorDesc("weight", 0).shape().At(0); \ - const int64_t batch_size = ctx->InputTensorDesc("label", 0).shape().At(0); \ - const int64_t parallel_num = ctx->parallel_ctx().parallel_num(); \ - TmpBufferManager buffer_manager(nullptr, num_classes, \ - batch_size, parallel_num); \ - return buffer_manager.GetTotalBufferSize(); \ - }); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DISTRIBUTED_PARTIAL_FC_SAMPLE_CUDA_KERNEL, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -template -class DistributedPartialFcSampleDisableBoxingGpuKernel final : public user_op::OpKernel { - public: - DistributedPartialFcSampleDisableBoxingGpuKernel() = default; - ~DistributedPartialFcSampleDisableBoxingGpuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache*) const override { - const user_op::Tensor* sampled_weight_diff = - ctx->Tensor4ArgNameAndIndex("sampled_weight_diff", 0); - const user_op::Tensor* sampled_label = ctx->Tensor4ArgNameAndIndex("sampled_label", 0); - user_op::Tensor* boxing_disabled_sampled_weight_diff = - ctx->Tensor4ArgNameAndIndex("boxing_disabled_sampled_weight_diff", 0); - user_op::Tensor* boxing_disabled_sampled_label = - ctx->Tensor4ArgNameAndIndex("boxing_disabled_sampled_label", 0); - Memcpy(ctx->stream(), boxing_disabled_sampled_weight_diff->mut_dptr(), - sampled_weight_diff->dptr(), - sampled_weight_diff->shape_view().elem_cnt() - * GetSizeOfDataType(sampled_weight_diff->data_type())); - Memcpy( - ctx->stream(), boxing_disabled_sampled_label->mut_dptr(), sampled_label->dptr(), - sampled_label->shape_view().elem_cnt() * GetSizeOfDataType(sampled_label->data_type())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_DISTRIBUTED_PARTIAL_FC_SAMPLE_DISABLE_BOXING_CUDA_KERNEL(dtype_pair, ltype_pair) \ - REGISTER_USER_KERNEL("distributed_partial_fc_sample_disable_boxing") \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("sampled_label", 0) == OF_PP_PAIR_SECOND(ltype_pair)) \ - && (user_op::HobDataType("sampled_weight_diff", 0) == OF_PP_PAIR_SECOND(dtype_pair))); -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_DISTRIBUTED_PARTIAL_FC_SAMPLE_DISABLE_BOXING_CUDA_KERNEL, - FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -} // namespace user_op -} // namespace oneflow -#endif \ No newline at end of file diff --git a/oneflow/user/kernels/pocketfft_hdronly.h b/oneflow/user/kernels/pocketfft_hdronly.h new file mode 100644 index 0000000000000000000000000000000000000000..55deb36abd9fbbdba36c08250b05234321ca7cd7 --- /dev/null +++ b/oneflow/user/kernels/pocketfft_hdronly.h @@ -0,0 +1,3406 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +/* +This file is part of pocketfft. + +Copyright (C) 2010-2021 Max-Planck-Society +Copyright (C) 2019-2020 Peter Bell + +For the odd-sized DCT-IV transforms: + Copyright (C) 2003, 2007-14 Matteo Frigo + Copyright (C) 2003, 2007-14 Massachusetts Institute of Technology + +Authors: Martin Reinecke, Peter Bell + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. +* Neither the name of the copyright holder nor the names of its contributors may + be used to endorse or promote products derived from this software without + specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef POCKETFFT_HDRONLY_H +#define POCKETFFT_HDRONLY_H + +#ifndef __cplusplus +#error This file is C++ and requires a C++ compiler. +#endif + +#if !(__cplusplus >= 201103L || _MSVC_LANG + 0L >= 201103L) +#error This file requires at least C++11 support. +#endif + +#ifndef POCKETFFT_CACHE_SIZE +#define POCKETFFT_CACHE_SIZE 0 +#endif + +#include +#include +#include +#include +#include +#include +#include +#if POCKETFFT_CACHE_SIZE != 0 +#include +#include +#endif + +#ifndef POCKETFFT_NO_MULTITHREADING +#include +#include +#include +#include +#include +#include +#include + +#ifdef POCKETFFT_PTHREADS +#include +#endif +#endif + +#if defined(__GNUC__) +#define POCKETFFT_NOINLINE __attribute__((noinline)) +#define POCKETFFT_RESTRICT __restrict__ +#elif defined(_MSC_VER) +#define POCKETFFT_NOINLINE __declspec(noinline) +#define POCKETFFT_RESTRICT __restrict +#else +#define POCKETFFT_NOINLINE +#define POCKETFFT_RESTRICT +#endif + +namespace pocketfft { + +namespace detail { +using std::ptrdiff_t; +using std::size_t; + +// Always use std:: for functions +template +T cos(T) = delete; +template +T sin(T) = delete; +template +T sqrt(T) = delete; + +using shape_t = std::vector; +using stride_t = std::vector; + +constexpr bool FORWARD = true, BACKWARD = false; + +// only enable vector support for gcc>=5.0 and clang>=5.0 +#ifndef POCKETFFT_NO_VECTORS +#define POCKETFFT_NO_VECTORS +#if defined(__INTEL_COMPILER) +// do nothing. This is necessary because this compiler also sets __GNUC__. +#elif defined(__clang__) +// AppleClang has their own version numbering +#ifdef __apple_build_version__ +#if (__clang_major__ > 9) || (__clang_major__ == 9 && __clang_minor__ >= 1) +#undef POCKETFFT_NO_VECTORS +#endif +#elif __clang_major__ >= 5 +#undef POCKETFFT_NO_VECTORS +#endif +#elif defined(__GNUC__) +#if __GNUC__ >= 5 +#undef POCKETFFT_NO_VECTORS +#endif +#endif +#endif + +template +struct VLEN { + static constexpr size_t val = 1; +}; + +#ifndef POCKETFFT_NO_VECTORS +#if (defined(__AVX512F__)) +template<> +struct VLEN { + static constexpr size_t val = 16; +}; +template<> +struct VLEN { + static constexpr size_t val = 8; +}; +#elif (defined(__AVX__)) +template<> +struct VLEN { + static constexpr size_t val = 8; +}; +template<> +struct VLEN { + static constexpr size_t val = 4; +}; +#elif (defined(__SSE2__)) +template<> +struct VLEN { + static constexpr size_t val = 4; +}; +template<> +struct VLEN { + static constexpr size_t val = 2; +}; +#elif (defined(__VSX__)) +template<> +struct VLEN { + static constexpr size_t val = 4; +}; +template<> +struct VLEN { + static constexpr size_t val = 2; +}; +#elif (defined(__ARM_NEON__) || defined(__ARM_NEON)) +template<> +struct VLEN { + static constexpr size_t val = 4; +}; +template<> +struct VLEN { + static constexpr size_t val = 2; +}; +#else +#define POCKETFFT_NO_VECTORS +#endif +#endif + +#if __cplusplus >= 201703L +inline void* aligned_alloc(size_t align, size_t size) { + // aligned_alloc() requires that the requested size is a multiple of "align" + void* ptr = ::aligned_alloc(align, (size + align - 1) & (~(align - 1))); + if (!ptr) throw std::bad_alloc(); + return ptr; +} +inline void aligned_dealloc(void* ptr) { free(ptr); } +#else // portable emulation +inline void* aligned_alloc(size_t align, size_t size) { + align = std::max(align, alignof(max_align_t)); + void* ptr = malloc(size + align); + if (!ptr) throw std::bad_alloc(); + void* res = reinterpret_cast((reinterpret_cast(ptr) & ~(uintptr_t(align - 1))) + + uintptr_t(align)); + (reinterpret_cast(res))[-1] = ptr; + return res; +} +inline void aligned_dealloc(void* ptr) { + if (ptr) free((reinterpret_cast(ptr))[-1]); +} +#endif + +template +class arr { + private: + T* p; + size_t sz; + +#if defined(POCKETFFT_NO_VECTORS) + static T* ralloc(size_t num) { + if (num == 0) return nullptr; + void* res = malloc(num * sizeof(T)); + if (!res) throw std::bad_alloc(); + return reinterpret_cast(res); + } + static void dealloc(T* ptr) { free(ptr); } +#else + static T* ralloc(size_t num) { + if (num == 0) return nullptr; + void* ptr = aligned_alloc(64, num * sizeof(T)); + return static_cast(ptr); + } + static void dealloc(T* ptr) { aligned_dealloc(ptr); } +#endif + + public: + arr() : p(0), sz(0) {} + arr(size_t n) : p(ralloc(n)), sz(n) {} + arr(arr&& other) : p(other.p), sz(other.sz) { + other.p = nullptr; + other.sz = 0; + } + ~arr() { dealloc(p); } + + void resize(size_t n) { + if (n == sz) return; + dealloc(p); + p = ralloc(n); + sz = n; + } + + T& operator[](size_t idx) { return p[idx]; } + const T& operator[](size_t idx) const { return p[idx]; } + + T* data() { return p; } + const T* data() const { return p; } + + size_t size() const { return sz; } +}; + +template +struct cmplx { + T r, i; + cmplx() {} + cmplx(T r_, T i_) : r(r_), i(i_) {} + void Set(T r_, T i_) { + r = r_; + i = i_; + } + void Set(T r_) { + r = r_; + i = T(0); + } + cmplx& operator+=(const cmplx& other) { + r += other.r; + i += other.i; + return *this; + } + template + cmplx& operator*=(T2 other) { + r *= other; + i *= other; + return *this; + } + template + cmplx& operator*=(const cmplx& other) { + T tmp = r * other.r - i * other.i; + i = r * other.i + i * other.r; + r = tmp; + return *this; + } + template + cmplx& operator+=(const cmplx& other) { + r += other.r; + i += other.i; + return *this; + } + template + cmplx& operator-=(const cmplx& other) { + r -= other.r; + i -= other.i; + return *this; + } + template + auto operator*(const T2& other) const -> cmplx { + return {r * other, i * other}; + } + template + auto operator+(const cmplx& other) const -> cmplx { + return {r + other.r, i + other.i}; + } + template + auto operator-(const cmplx& other) const -> cmplx { + return {r - other.r, i - other.i}; + } + template + auto operator*(const cmplx& other) const -> cmplx { + return {r * other.r - i * other.i, r * other.i + i * other.r}; + } + template + auto special_mul(const cmplx& other) const -> cmplx { + using Tres = cmplx; + return fwd ? Tres(r * other.r + i * other.i, i * other.r - r * other.i) + : Tres(r * other.r - i * other.i, r * other.i + i * other.r); + } +}; +template +inline void PM(T& a, T& b, T c, T d) { + a = c + d; + b = c - d; +} +template +inline void PMINPLACE(T& a, T& b) { + T t = a; + a += b; + b = t - b; +} +template +inline void MPINPLACE(T& a, T& b) { + T t = a; + a -= b; + b = t + b; +} +template +cmplx conj(const cmplx& a) { + return {a.r, -a.i}; +} +template +void special_mul(const cmplx& v1, const cmplx& v2, cmplx& res) { + res = fwd ? cmplx(v1.r * v2.r + v1.i * v2.i, v1.i * v2.r - v1.r * v2.i) + : cmplx(v1.r * v2.r - v1.i * v2.i, v1.r * v2.i + v1.i * v2.r); +} + +template +void ROT90(cmplx& a) { + auto tmp_ = a.r; + a.r = -a.i; + a.i = tmp_; +} +template +void ROTX90(cmplx& a) { + auto tmp_ = fwd ? -a.r : a.r; + a.r = fwd ? a.i : -a.i; + a.i = tmp_; +} + +// +// twiddle factor section +// +template +class sincos_2pibyn { + private: + using Thigh = typename std::conditional<(sizeof(T) > sizeof(double)), T, double>::type; + size_t N, mask, shift; + arr> v1, v2; + + static cmplx calc(size_t x, size_t n, Thigh ang) { + x <<= 3; + if (x < 4 * n) // first half + { + if (x < 2 * n) // first quadrant + { + if (x < n) return cmplx(std::cos(Thigh(x) * ang), std::sin(Thigh(x) * ang)); + return cmplx(std::sin(Thigh(2 * n - x) * ang), std::cos(Thigh(2 * n - x) * ang)); + } else // second quadrant + { + x -= 2 * n; + if (x < n) return cmplx(-std::sin(Thigh(x) * ang), std::cos(Thigh(x) * ang)); + return cmplx(-std::cos(Thigh(2 * n - x) * ang), std::sin(Thigh(2 * n - x) * ang)); + } + } else { + x = 8 * n - x; + if (x < 2 * n) // third quadrant + { + if (x < n) return cmplx(std::cos(Thigh(x) * ang), -std::sin(Thigh(x) * ang)); + return cmplx(std::sin(Thigh(2 * n - x) * ang), -std::cos(Thigh(2 * n - x) * ang)); + } else // fourth quadrant + { + x -= 2 * n; + if (x < n) return cmplx(-std::sin(Thigh(x) * ang), -std::cos(Thigh(x) * ang)); + return cmplx(-std::cos(Thigh(2 * n - x) * ang), -std::sin(Thigh(2 * n - x) * ang)); + } + } + } + + public: + POCKETFFT_NOINLINE sincos_2pibyn(size_t n) : N(n) { + constexpr auto pi = 3.141592653589793238462643383279502884197L; + Thigh ang = Thigh(0.25L * pi / n); + size_t nval = (n + 2) / 2; + shift = 1; + while ((size_t(1) << shift) * (size_t(1) << shift) < nval) ++shift; + mask = (size_t(1) << shift) - 1; + v1.resize(mask + 1); + v1[0].Set(Thigh(1), Thigh(0)); + for (size_t i = 1; i < v1.size(); ++i) v1[i] = calc(i, n, ang); + v2.resize((nval + mask) / (mask + 1)); + v2[0].Set(Thigh(1), Thigh(0)); + for (size_t i = 1; i < v2.size(); ++i) v2[i] = calc(i * (mask + 1), n, ang); + } + + cmplx operator[](size_t idx) const { + if (2 * idx <= N) { + auto x1 = v1[idx & mask], x2 = v2[idx >> shift]; + return cmplx(T(x1.r * x2.r - x1.i * x2.i), T(x1.r * x2.i + x1.i * x2.r)); + } + idx = N - idx; + auto x1 = v1[idx & mask], x2 = v2[idx >> shift]; + return cmplx(T(x1.r * x2.r - x1.i * x2.i), -T(x1.r * x2.i + x1.i * x2.r)); + } +}; + +struct util // hack to avoid duplicate symbols +{ + static POCKETFFT_NOINLINE size_t largest_prime_factor(size_t n) { + size_t res = 1; + while ((n & 1) == 0) { + res = 2; + n >>= 1; + } + for (size_t x = 3; x * x <= n; x += 2) + while ((n % x) == 0) { + res = x; + n /= x; + } + if (n > 1) res = n; + return res; + } + + static POCKETFFT_NOINLINE double cost_guess(size_t n) { + constexpr double lfp = 1.1; // penalty for non-hardcoded larger factors + size_t ni = n; + double result = 0.; + while ((n & 1) == 0) { + result += 2; + n >>= 1; + } + for (size_t x = 3; x * x <= n; x += 2) + while ((n % x) == 0) { + result += (x <= 5) ? double(x) : lfp * double(x); // penalize larger prime factors + n /= x; + } + if (n > 1) result += (n <= 5) ? double(n) : lfp * double(n); + return result * double(ni); + } + + /* returns the smallest composite of 2, 3, 5, 7 and 11 which is >= n */ + static POCKETFFT_NOINLINE size_t good_size_cmplx(size_t n) { + if (n <= 12) return n; + + size_t bestfac = 2 * n; + for (size_t f11 = 1; f11 < bestfac; f11 *= 11) + for (size_t f117 = f11; f117 < bestfac; f117 *= 7) + for (size_t f1175 = f117; f1175 < bestfac; f1175 *= 5) { + size_t x = f1175; + while (x < n) x *= 2; + for (;;) { + if (x < n) + x *= 3; + else if (x > n) { + if (x < bestfac) bestfac = x; + if (x & 1) break; + x >>= 1; + } else + return n; + } + } + return bestfac; + } + + /* returns the smallest composite of 2, 3, 5 which is >= n */ + static POCKETFFT_NOINLINE size_t good_size_real(size_t n) { + if (n <= 6) return n; + + size_t bestfac = 2 * n; + for (size_t f5 = 1; f5 < bestfac; f5 *= 5) { + size_t x = f5; + while (x < n) x *= 2; + for (;;) { + if (x < n) + x *= 3; + else if (x > n) { + if (x < bestfac) bestfac = x; + if (x & 1) break; + x >>= 1; + } else + return n; + } + } + return bestfac; + } + + static size_t prod(const shape_t& shape) { + size_t res = 1; + for (auto sz : shape) res *= sz; + return res; + } + + static POCKETFFT_NOINLINE void sanity_check(const shape_t& shape, const stride_t& stride_in, + const stride_t& stride_out, bool inplace) { + auto ndim = shape.size(); + if (ndim < 1) throw std::runtime_error("ndim must be >= 1"); + if ((stride_in.size() != ndim) || (stride_out.size() != ndim)) + throw std::runtime_error("stride dimension mismatch"); + if (inplace && (stride_in != stride_out)) throw std::runtime_error("stride mismatch"); + } + + static POCKETFFT_NOINLINE void sanity_check(const shape_t& shape, const stride_t& stride_in, + const stride_t& stride_out, bool inplace, + const shape_t& axes) { + sanity_check(shape, stride_in, stride_out, inplace); + auto ndim = shape.size(); + shape_t tmp(ndim, 0); + for (auto ax : axes) { + if (ax >= ndim) throw std::invalid_argument("bad axis number"); + if (++tmp[ax] > 1) throw std::invalid_argument("axis specified repeatedly"); + } + } + + static POCKETFFT_NOINLINE void sanity_check(const shape_t& shape, const stride_t& stride_in, + const stride_t& stride_out, bool inplace, + size_t axis) { + sanity_check(shape, stride_in, stride_out, inplace); + if (axis >= shape.size()) throw std::invalid_argument("bad axis number"); + } + +#ifdef POCKETFFT_NO_MULTITHREADING + static size_t thread_count(size_t /*nthreads*/, const shape_t& /*shape*/, size_t /*axis*/, + size_t /*vlen*/) { + return 1; + } +#else + static size_t thread_count(size_t nthreads, const shape_t& shape, size_t axis, size_t vlen) { + if (nthreads == 1) return 1; + size_t size = prod(shape); + size_t parallel = size / (shape[axis] * vlen); + if (shape[axis] < 1000) parallel /= 4; + size_t max_threads = nthreads == 0 ? std::thread::hardware_concurrency() : nthreads; + return std::max(size_t(1), std::min(parallel, max_threads)); + } +#endif +}; + +namespace threading { + +#ifdef POCKETFFT_NO_MULTITHREADING + +constexpr inline size_t thread_id() { return 0; } +constexpr inline size_t num_threads() { return 1; } + +template +void thread_map(size_t /* nthreads */, Func f) { + f(); +} + +#else + +inline size_t& thread_id() { + static thread_local size_t thread_id_ = 0; + return thread_id_; +} +inline size_t& num_threads() { + static thread_local size_t num_threads_ = 1; + return num_threads_; +} +static const size_t max_threads = std::max(1u, std::thread::hardware_concurrency()); + +class latch { + std::atomic num_left_; + std::mutex mut_; + std::condition_variable completed_; + using lock_t = std::unique_lock; + + public: + latch(size_t n) : num_left_(n) {} + + void count_down() { + lock_t lock(mut_); + if (--num_left_) return; + completed_.notify_all(); + } + + void wait() { + lock_t lock(mut_); + completed_.wait(lock, [this] { return is_ready(); }); + } + bool is_ready() { return num_left_ == 0; } +}; + +template +class concurrent_queue { + std::queue q_; + std::mutex mut_; + std::atomic size_; + using lock_t = std::lock_guard; + + public: + void push(T val) { + lock_t lock(mut_); + ++size_; + q_.push(std::move(val)); + } + + bool try_pop(T& val) { + if (size_ == 0) return false; + lock_t lock(mut_); + // Queue might have been emptied while we acquired the lock + if (q_.empty()) return false; + + val = std::move(q_.front()); + --size_; + q_.pop(); + return true; + } + + bool empty() const { return size_ == 0; } +}; + +// C++ allocator with support for over-aligned types +template +struct aligned_allocator { + using value_type = T; + template + aligned_allocator(const aligned_allocator&) {} + aligned_allocator() = default; + + T* allocate(size_t n) { + void* mem = aligned_alloc(alignof(T), n * sizeof(T)); + return static_cast(mem); + } + + void deallocate(T* p, size_t /*n*/) { aligned_dealloc(p); } +}; + +class thread_pool { + // A reasonable guess, probably close enough for most hardware + static constexpr size_t cache_line_size = 64; + struct alignas(cache_line_size) worker { + std::thread thread; + std::condition_variable work_ready; + std::mutex mut; + std::atomic_flag busy_flag = ATOMIC_FLAG_INIT; + std::function work; + + void worker_main(std::atomic& shutdown_flag, std::atomic& unscheduled_tasks, + concurrent_queue>& overflow_work) { + using lock_t = std::unique_lock; + bool expect_work = true; + while (!shutdown_flag || expect_work) { + std::function local_work; + if (expect_work || unscheduled_tasks == 0) { + lock_t lock(mut); + // Wait until there is work to be executed + work_ready.wait(lock, [&] { return (work || shutdown_flag); }); + local_work.swap(work); + expect_work = false; + } + + bool marked_busy = false; + if (local_work) { + marked_busy = true; + local_work(); + } + + if (!overflow_work.empty()) { + if (!marked_busy && busy_flag.test_and_set()) { + expect_work = true; + continue; + } + marked_busy = true; + + while (overflow_work.try_pop(local_work)) { + --unscheduled_tasks; + local_work(); + } + } + + if (marked_busy) busy_flag.clear(); + } + } + }; + + concurrent_queue> overflow_work_; + std::mutex mut_; + std::vector> workers_; + std::atomic shutdown_; + std::atomic unscheduled_tasks_; + using lock_t = std::lock_guard; + + void create_threads() { + lock_t lock(mut_); + size_t nthreads = workers_.size(); + for (size_t i = 0; i < nthreads; ++i) { + try { + auto* worker = &workers_[i]; + worker->busy_flag.clear(); + worker->work = nullptr; + worker->thread = std::thread( + [worker, this] { worker->worker_main(shutdown_, unscheduled_tasks_, overflow_work_); }); + } catch (...) { + shutdown_locked(); + throw; + } + } + } + + void shutdown_locked() { + shutdown_ = true; + for (auto& worker : workers_) worker.work_ready.notify_all(); + + for (auto& worker : workers_) + if (worker.thread.joinable()) worker.thread.join(); + } + + public: + explicit thread_pool(size_t nthreads) : workers_(nthreads) { create_threads(); } + + thread_pool() : thread_pool(max_threads) {} + + ~thread_pool() { shutdown(); } + + void submit(std::function work) { + lock_t lock(mut_); + if (shutdown_) throw std::runtime_error("Work item submitted after shutdown"); + + ++unscheduled_tasks_; + + // First check for any idle workers and wake those + for (auto& worker : workers_) + if (!worker.busy_flag.test_and_set()) { + --unscheduled_tasks_; + { + lock_t lock(worker.mut); + worker.work = std::move(work); + } + worker.work_ready.notify_one(); + return; + } + + // If no workers were idle, push onto the overflow queue for later + overflow_work_.push(std::move(work)); + } + + void shutdown() { + lock_t lock(mut_); + shutdown_locked(); + } + + void restart() { + shutdown_ = false; + create_threads(); + } +}; + +inline thread_pool& get_pool() { + static thread_pool pool; +#ifdef POCKETFFT_PTHREADS + static std::once_flag f; + std::call_once(f, [] { + pthread_atfork( + +[] { get_pool().shutdown(); }, // prepare + +[] { get_pool().restart(); }, // parent + +[] { get_pool().restart(); } // child + ); + }); +#endif + + return pool; +} + +/** Map a function f over nthreads */ +template +void thread_map(size_t nthreads, Func f) { + if (nthreads == 0) nthreads = max_threads; + + if (nthreads == 1) { + f(); + return; + } + + auto& pool = get_pool(); + latch counter(nthreads); + std::exception_ptr ex; + std::mutex ex_mut; + for (size_t i = 0; i < nthreads; ++i) { + pool.submit([&f, &counter, &ex, &ex_mut, i, nthreads] { + thread_id() = i; + num_threads() = nthreads; + try { + f(); + } catch (...) { + std::lock_guard lock(ex_mut); + ex = std::current_exception(); + } + counter.count_down(); + }); + } + counter.wait(); + if (ex) std::rethrow_exception(ex); +} + +#endif + +} // namespace threading + +// +// complex FFTPACK transforms +// + +template +class cfftp { + private: + struct fctdata { + size_t fct; + cmplx*tw, *tws; + }; + + size_t length; + arr> mem; + std::vector fact; + + void add_factor(size_t factor) { fact.push_back({factor, nullptr, nullptr}); } + + template + void pass2(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 2 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + CH(0, k, 0) = CC(0, 0, k) + CC(0, 1, k); + CH(0, k, 1) = CC(0, 0, k) - CC(0, 1, k); + } + else + for (size_t k = 0; k < l1; ++k) { + CH(0, k, 0) = CC(0, 0, k) + CC(0, 1, k); + CH(0, k, 1) = CC(0, 0, k) - CC(0, 1, k); + for (size_t i = 1; i < ido; ++i) { + CH(i, k, 0) = CC(i, 0, k) + CC(i, 1, k); + special_mul(CC(i, 0, k) - CC(i, 1, k), WA(0, i), CH(i, k, 1)); + } + } + } + +#define POCKETFFT_PREP3(idx) \ + T t0 = CC(idx, 0, k), t1, t2; \ + PM(t1, t2, CC(idx, 1, k), CC(idx, 2, k)); \ + CH(idx, k, 0) = t0 + t1; +#define POCKETFFT_PARTSTEP3a(u1, u2, twr, twi) \ + { \ + T ca = t0 + t1 * twr; \ + T cb{-t2.i * twi, t2.r * twi}; \ + PM(CH(0, k, u1), CH(0, k, u2), ca, cb); \ + } +#define POCKETFFT_PARTSTEP3b(u1, u2, twr, twi) \ + { \ + T ca = t0 + t1 * twr; \ + T cb{-t2.i * twi, t2.r * twi}; \ + special_mul(ca + cb, WA(u1 - 1, i), CH(i, k, u1)); \ + special_mul(ca - cb, WA(u2 - 1, i), CH(i, k, u2)); \ + } + template + void pass3(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + constexpr T0 tw1r = -0.5, tw1i = (fwd ? -1 : 1) * T0(0.8660254037844386467637231707529362L); + + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 3 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + POCKETFFT_PREP3(0) + POCKETFFT_PARTSTEP3a(1, 2, tw1r, tw1i) + } + else + for (size_t k = 0; k < l1; ++k) { + { + POCKETFFT_PREP3(0) + POCKETFFT_PARTSTEP3a(1, 2, tw1r, tw1i) + } + for (size_t i = 1; i < ido; ++i) { + POCKETFFT_PREP3(i) + POCKETFFT_PARTSTEP3b(1, 2, tw1r, tw1i) + } + } + } + +#undef POCKETFFT_PARTSTEP3b +#undef POCKETFFT_PARTSTEP3a +#undef POCKETFFT_PREP3 + + template + void pass4(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 4 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + T t1, t2, t3, t4; + PM(t2, t1, CC(0, 0, k), CC(0, 2, k)); + PM(t3, t4, CC(0, 1, k), CC(0, 3, k)); + ROTX90(t4); + PM(CH(0, k, 0), CH(0, k, 2), t2, t3); + PM(CH(0, k, 1), CH(0, k, 3), t1, t4); + } + else + for (size_t k = 0; k < l1; ++k) { + { + T t1, t2, t3, t4; + PM(t2, t1, CC(0, 0, k), CC(0, 2, k)); + PM(t3, t4, CC(0, 1, k), CC(0, 3, k)); + ROTX90(t4); + PM(CH(0, k, 0), CH(0, k, 2), t2, t3); + PM(CH(0, k, 1), CH(0, k, 3), t1, t4); + } + for (size_t i = 1; i < ido; ++i) { + T t1, t2, t3, t4; + T cc0 = CC(i, 0, k), cc1 = CC(i, 1, k), cc2 = CC(i, 2, k), cc3 = CC(i, 3, k); + PM(t2, t1, cc0, cc2); + PM(t3, t4, cc1, cc3); + ROTX90(t4); + CH(i, k, 0) = t2 + t3; + special_mul(t1 + t4, WA(0, i), CH(i, k, 1)); + special_mul(t2 - t3, WA(1, i), CH(i, k, 2)); + special_mul(t1 - t4, WA(2, i), CH(i, k, 3)); + } + } + } + +#define POCKETFFT_PREP5(idx) \ + T t0 = CC(idx, 0, k), t1, t2, t3, t4; \ + PM(t1, t4, CC(idx, 1, k), CC(idx, 4, k)); \ + PM(t2, t3, CC(idx, 2, k), CC(idx, 3, k)); \ + CH(idx, k, 0).r = t0.r + t1.r + t2.r; \ + CH(idx, k, 0).i = t0.i + t1.i + t2.i; + +#define POCKETFFT_PARTSTEP5a(u1, u2, twar, twbr, twai, twbi) \ + { \ + T ca, cb; \ + ca.r = t0.r + twar * t1.r + twbr * t2.r; \ + ca.i = t0.i + twar * t1.i + twbr * t2.i; \ + cb.i = twai * t4.r twbi * t3.r; \ + cb.r = -(twai * t4.i twbi * t3.i); \ + PM(CH(0, k, u1), CH(0, k, u2), ca, cb); \ + } + +#define POCKETFFT_PARTSTEP5b(u1, u2, twar, twbr, twai, twbi) \ + { \ + T ca, cb, da, db; \ + ca.r = t0.r + twar * t1.r + twbr * t2.r; \ + ca.i = t0.i + twar * t1.i + twbr * t2.i; \ + cb.i = twai * t4.r twbi * t3.r; \ + cb.r = -(twai * t4.i twbi * t3.i); \ + special_mul(ca + cb, WA(u1 - 1, i), CH(i, k, u1)); \ + special_mul(ca - cb, WA(u2 - 1, i), CH(i, k, u2)); \ + } + template + void pass5(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + constexpr T0 tw1r = T0(0.3090169943749474241022934171828191L), + tw1i = (fwd ? -1 : 1) * T0(0.9510565162951535721164393333793821L), + tw2r = T0(-0.8090169943749474241022934171828191L), + tw2i = (fwd ? -1 : 1) * T0(0.5877852522924731291687059546390728L); + + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 5 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + POCKETFFT_PREP5(0) + POCKETFFT_PARTSTEP5a(1, 4, tw1r, tw2r, +tw1i, +tw2i) + POCKETFFT_PARTSTEP5a(2, 3, tw2r, tw1r, +tw2i, -tw1i) + } + else + for (size_t k = 0; k < l1; ++k) { + { + POCKETFFT_PREP5(0) + POCKETFFT_PARTSTEP5a(1, 4, tw1r, tw2r, +tw1i, +tw2i) + POCKETFFT_PARTSTEP5a(2, 3, tw2r, tw1r, +tw2i, -tw1i) + } + for (size_t i = 1; i < ido; ++i) { + POCKETFFT_PREP5(i) + POCKETFFT_PARTSTEP5b(1, 4, tw1r, tw2r, +tw1i, +tw2i) + POCKETFFT_PARTSTEP5b(2, 3, tw2r, tw1r, +tw2i, -tw1i) + } + } + } + +#undef POCKETFFT_PARTSTEP5b +#undef POCKETFFT_PARTSTEP5a +#undef POCKETFFT_PREP5 + +#define POCKETFFT_PREP7(idx) \ + T t1 = CC(idx, 0, k), t2, t3, t4, t5, t6, t7; \ + PM(t2, t7, CC(idx, 1, k), CC(idx, 6, k)); \ + PM(t3, t6, CC(idx, 2, k), CC(idx, 5, k)); \ + PM(t4, t5, CC(idx, 3, k), CC(idx, 4, k)); \ + CH(idx, k, 0).r = t1.r + t2.r + t3.r + t4.r; \ + CH(idx, k, 0).i = t1.i + t2.i + t3.i + t4.i; + +#define POCKETFFT_PARTSTEP7a0(u1, u2, x1, x2, x3, y1, y2, y3, out1, out2) \ + { \ + T ca, cb; \ + ca.r = t1.r + x1 * t2.r + x2 * t3.r + x3 * t4.r; \ + ca.i = t1.i + x1 * t2.i + x2 * t3.i + x3 * t4.i; \ + cb.i = y1 * t7.r y2 * t6.r y3 * t5.r; \ + cb.r = -(y1 * t7.i y2 * t6.i y3 * t5.i); \ + PM(out1, out2, ca, cb); \ + } +#define POCKETFFT_PARTSTEP7a(u1, u2, x1, x2, x3, y1, y2, y3) \ + POCKETFFT_PARTSTEP7a0(u1, u2, x1, x2, x3, y1, y2, y3, CH(0, k, u1), CH(0, k, u2)) +#define POCKETFFT_PARTSTEP7(u1, u2, x1, x2, x3, y1, y2, y3) \ + { \ + T da, db; \ + POCKETFFT_PARTSTEP7a0(u1, u2, x1, x2, x3, y1, y2, y3, da, db) \ + special_mul(da, WA(u1 - 1, i), CH(i, k, u1)); \ + special_mul(db, WA(u2 - 1, i), CH(i, k, u2)); \ + } + + template + void pass7(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + constexpr T0 tw1r = T0(0.6234898018587335305250048840042398L), + tw1i = (fwd ? -1 : 1) * T0(0.7818314824680298087084445266740578L), + tw2r = T0(-0.2225209339563144042889025644967948L), + tw2i = (fwd ? -1 : 1) * T0(0.9749279121818236070181316829939312L), + tw3r = T0(-0.9009688679024191262361023195074451L), + tw3i = (fwd ? -1 : 1) * T0(0.433883739117558120475768332848359L); + + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 7 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + POCKETFFT_PREP7(0) + POCKETFFT_PARTSTEP7a(1, 6, tw1r, tw2r, tw3r, +tw1i, +tw2i, +tw3i) + POCKETFFT_PARTSTEP7a(2, 5, tw2r, tw3r, tw1r, +tw2i, -tw3i, -tw1i) + POCKETFFT_PARTSTEP7a(3, 4, tw3r, tw1r, tw2r, +tw3i, -tw1i, +tw2i) + } + else + for (size_t k = 0; k < l1; ++k) { + { + POCKETFFT_PREP7(0) + POCKETFFT_PARTSTEP7a(1, 6, tw1r, tw2r, tw3r, +tw1i, +tw2i, +tw3i) + POCKETFFT_PARTSTEP7a(2, 5, tw2r, tw3r, tw1r, +tw2i, -tw3i, -tw1i) + POCKETFFT_PARTSTEP7a(3, 4, tw3r, tw1r, tw2r, +tw3i, -tw1i, +tw2i) + } + for (size_t i = 1; i < ido; ++i) { + POCKETFFT_PREP7(i) + POCKETFFT_PARTSTEP7(1, 6, tw1r, tw2r, tw3r, +tw1i, +tw2i, +tw3i) + POCKETFFT_PARTSTEP7(2, 5, tw2r, tw3r, tw1r, +tw2i, -tw3i, -tw1i) + POCKETFFT_PARTSTEP7(3, 4, tw3r, tw1r, tw2r, +tw3i, -tw1i, +tw2i) + } + } + } + +#undef POCKETFFT_PARTSTEP7 +#undef POCKETFFT_PARTSTEP7a0 +#undef POCKETFFT_PARTSTEP7a +#undef POCKETFFT_PREP7 + + template + void ROTX45(T& a) const { + constexpr T0 hsqt2 = T0(0.707106781186547524400844362104849L); + if (fwd) { + auto tmp_ = a.r; + a.r = hsqt2 * (a.r + a.i); + a.i = hsqt2 * (a.i - tmp_); + } else { + auto tmp_ = a.r; + a.r = hsqt2 * (a.r - a.i); + a.i = hsqt2 * (a.i + tmp_); + } + } + template + void ROTX135(T& a) const { + constexpr T0 hsqt2 = T0(0.707106781186547524400844362104849L); + if (fwd) { + auto tmp_ = a.r; + a.r = hsqt2 * (a.i - a.r); + a.i = hsqt2 * (-tmp_ - a.i); + } else { + auto tmp_ = a.r; + a.r = hsqt2 * (-a.r - a.i); + a.i = hsqt2 * (tmp_ - a.i); + } + } + + template + void pass8(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 8 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + T a0, a1, a2, a3, a4, a5, a6, a7; + PM(a1, a5, CC(0, 1, k), CC(0, 5, k)); + PM(a3, a7, CC(0, 3, k), CC(0, 7, k)); + PMINPLACE(a1, a3); + ROTX90(a3); + + ROTX90(a7); + PMINPLACE(a5, a7); + ROTX45(a5); + ROTX135(a7); + + PM(a0, a4, CC(0, 0, k), CC(0, 4, k)); + PM(a2, a6, CC(0, 2, k), CC(0, 6, k)); + PM(CH(0, k, 0), CH(0, k, 4), a0 + a2, a1); + PM(CH(0, k, 2), CH(0, k, 6), a0 - a2, a3); + ROTX90(a6); + PM(CH(0, k, 1), CH(0, k, 5), a4 + a6, a5); + PM(CH(0, k, 3), CH(0, k, 7), a4 - a6, a7); + } + else + for (size_t k = 0; k < l1; ++k) { + { + T a0, a1, a2, a3, a4, a5, a6, a7; + PM(a1, a5, CC(0, 1, k), CC(0, 5, k)); + PM(a3, a7, CC(0, 3, k), CC(0, 7, k)); + PMINPLACE(a1, a3); + ROTX90(a3); + + ROTX90(a7); + PMINPLACE(a5, a7); + ROTX45(a5); + ROTX135(a7); + + PM(a0, a4, CC(0, 0, k), CC(0, 4, k)); + PM(a2, a6, CC(0, 2, k), CC(0, 6, k)); + PM(CH(0, k, 0), CH(0, k, 4), a0 + a2, a1); + PM(CH(0, k, 2), CH(0, k, 6), a0 - a2, a3); + ROTX90(a6); + PM(CH(0, k, 1), CH(0, k, 5), a4 + a6, a5); + PM(CH(0, k, 3), CH(0, k, 7), a4 - a6, a7); + } + for (size_t i = 1; i < ido; ++i) { + T a0, a1, a2, a3, a4, a5, a6, a7; + PM(a1, a5, CC(i, 1, k), CC(i, 5, k)); + PM(a3, a7, CC(i, 3, k), CC(i, 7, k)); + ROTX90(a7); + PMINPLACE(a1, a3); + ROTX90(a3); + PMINPLACE(a5, a7); + ROTX45(a5); + ROTX135(a7); + PM(a0, a4, CC(i, 0, k), CC(i, 4, k)); + PM(a2, a6, CC(i, 2, k), CC(i, 6, k)); + PMINPLACE(a0, a2); + CH(i, k, 0) = a0 + a1; + special_mul(a0 - a1, WA(3, i), CH(i, k, 4)); + special_mul(a2 + a3, WA(1, i), CH(i, k, 2)); + special_mul(a2 - a3, WA(5, i), CH(i, k, 6)); + ROTX90(a6); + PMINPLACE(a4, a6); + special_mul(a4 + a5, WA(0, i), CH(i, k, 1)); + special_mul(a4 - a5, WA(4, i), CH(i, k, 5)); + special_mul(a6 + a7, WA(2, i), CH(i, k, 3)); + special_mul(a6 - a7, WA(6, i), CH(i, k, 7)); + } + } + } + +#define POCKETFFT_PREP11(idx) \ + T t1 = CC(idx, 0, k), t2, t3, t4, t5, t6, t7, t8, t9, t10, t11; \ + PM(t2, t11, CC(idx, 1, k), CC(idx, 10, k)); \ + PM(t3, t10, CC(idx, 2, k), CC(idx, 9, k)); \ + PM(t4, t9, CC(idx, 3, k), CC(idx, 8, k)); \ + PM(t5, t8, CC(idx, 4, k), CC(idx, 7, k)); \ + PM(t6, t7, CC(idx, 5, k), CC(idx, 6, k)); \ + CH(idx, k, 0).r = t1.r + t2.r + t3.r + t4.r + t5.r + t6.r; \ + CH(idx, k, 0).i = t1.i + t2.i + t3.i + t4.i + t5.i + t6.i; + +#define POCKETFFT_PARTSTEP11a0(u1, u2, x1, x2, x3, x4, x5, y1, y2, y3, y4, y5, out1, out2) \ + { \ + T ca = t1 + t2 * x1 + t3 * x2 + t4 * x3 + t5 * x4 + t6 * x5, cb; \ + cb.i = y1 * t11.r y2 * t10.r y3 * t9.r y4 * t8.r y5 * t7.r; \ + cb.r = -(y1 * t11.i y2 * t10.i y3 * t9.i y4 * t8.i y5 * t7.i); \ + PM(out1, out2, ca, cb); \ + } +#define POCKETFFT_PARTSTEP11a(u1, u2, x1, x2, x3, x4, x5, y1, y2, y3, y4, y5) \ + POCKETFFT_PARTSTEP11a0(u1, u2, x1, x2, x3, x4, x5, y1, y2, y3, y4, y5, CH(0, k, u1), CH(0, k, u2)) +#define POCKETFFT_PARTSTEP11(u1, u2, x1, x2, x3, x4, x5, y1, y2, y3, y4, y5) \ + { \ + T da, db; \ + POCKETFFT_PARTSTEP11a0(u1, u2, x1, x2, x3, x4, x5, y1, y2, y3, y4, y5, da, db) \ + special_mul(da, WA(u1 - 1, i), CH(i, k, u1)); \ + special_mul(db, WA(u2 - 1, i), CH(i, k, u2)); \ + } + + template + void pass11(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa) const { + constexpr T0 tw1r = T0(0.8412535328311811688618116489193677L), + tw1i = (fwd ? -1 : 1) * T0(0.5406408174555975821076359543186917L), + tw2r = T0(0.4154150130018864255292741492296232L), + tw2i = (fwd ? -1 : 1) * T0(0.9096319953545183714117153830790285L), + tw3r = T0(-0.1423148382732851404437926686163697L), + tw3i = (fwd ? -1 : 1) * T0(0.9898214418809327323760920377767188L), + tw4r = T0(-0.6548607339452850640569250724662936L), + tw4i = (fwd ? -1 : 1) * T0(0.7557495743542582837740358439723444L), + tw5r = T0(-0.9594929736144973898903680570663277L), + tw5i = (fwd ? -1 : 1) * T0(0.2817325568414296977114179153466169L); + + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 11 * c)]; + }; + auto WA = [wa, ido](size_t x, size_t i) { return wa[i - 1 + x * (ido - 1)]; }; + + if (ido == 1) + for (size_t k = 0; k < l1; ++k) { + POCKETFFT_PREP11(0) + POCKETFFT_PARTSTEP11a(1, 10, tw1r, tw2r, tw3r, tw4r, tw5r, +tw1i, +tw2i, +tw3i, +tw4i, + +tw5i) POCKETFFT_PARTSTEP11a(2, 9, tw2r, tw4r, tw5r, tw3r, tw1r, + +tw2i, +tw4i, -tw5i, -tw3i, -tw1i) + POCKETFFT_PARTSTEP11a(3, 8, tw3r, tw5r, tw2r, tw1r, tw4r, +tw3i, -tw5i, -tw2i, +tw1i, + +tw4i) POCKETFFT_PARTSTEP11a(4, 7, tw4r, tw3r, tw1r, tw5r, tw2r, + +tw4i, -tw3i, +tw1i, +tw5i, -tw2i) + POCKETFFT_PARTSTEP11a(5, 6, tw5r, tw1r, tw4r, tw2r, tw3r, +tw5i, -tw1i, +tw4i, + -tw2i, +tw3i) + } + else + for (size_t k = 0; k < l1; ++k) { + { + POCKETFFT_PREP11(0) + POCKETFFT_PARTSTEP11a(1, 10, tw1r, tw2r, tw3r, tw4r, tw5r, +tw1i, +tw2i, +tw3i, +tw4i, + +tw5i) POCKETFFT_PARTSTEP11a(2, 9, tw2r, tw4r, tw5r, tw3r, tw1r, + +tw2i, +tw4i, -tw5i, -tw3i, -tw1i) + POCKETFFT_PARTSTEP11a(3, 8, tw3r, tw5r, tw2r, tw1r, tw4r, +tw3i, -tw5i, -tw2i, +tw1i, + +tw4i) POCKETFFT_PARTSTEP11a(4, 7, tw4r, tw3r, tw1r, tw5r, tw2r, + +tw4i, -tw3i, +tw1i, +tw5i, -tw2i) + POCKETFFT_PARTSTEP11a(5, 6, tw5r, tw1r, tw4r, tw2r, tw3r, +tw5i, -tw1i, +tw4i, + -tw2i, +tw3i) + } + for (size_t i = 1; i < ido; ++i) { + POCKETFFT_PREP11(i) + POCKETFFT_PARTSTEP11(1, 10, tw1r, tw2r, tw3r, tw4r, tw5r, +tw1i, +tw2i, +tw3i, +tw4i, + +tw5i) + POCKETFFT_PARTSTEP11(2, 9, tw2r, tw4r, tw5r, tw3r, tw1r, +tw2i, +tw4i, -tw5i, -tw3i, + -tw1i) + POCKETFFT_PARTSTEP11(3, 8, tw3r, tw5r, tw2r, tw1r, tw4r, +tw3i, -tw5i, -tw2i, +tw1i, + +tw4i) + POCKETFFT_PARTSTEP11(4, 7, tw4r, tw3r, tw1r, tw5r, tw2r, +tw4i, -tw3i, +tw1i, +tw5i, + -tw2i) + POCKETFFT_PARTSTEP11(5, 6, tw5r, tw1r, tw4r, tw2r, tw3r, +tw5i, -tw1i, +tw4i, -tw2i, + +tw3i) + } + } + } + +#undef POCKETFFT_PARTSTEP11 +#undef POCKETFFT_PARTSTEP11a0 +#undef POCKETFFT_PARTSTEP11a +#undef POCKETFFT_PREP11 + + template + void passg(size_t ido, size_t ip, size_t l1, T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const cmplx* POCKETFFT_RESTRICT wa, + const cmplx* POCKETFFT_RESTRICT csarr) const { + const size_t cdim = ip; + size_t ipph = (ip + 1) / 2; + size_t idl1 = ido * l1; + + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto CC = [cc, ido, cdim](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + cdim * c)]; + }; + auto CX = [cc, ido, l1](size_t a, size_t b, size_t c) -> T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto CX2 = [cc, idl1](size_t a, size_t b) -> T& { return cc[a + idl1 * b]; }; + auto CH2 = [ch, idl1](size_t a, size_t b) -> const T& { return ch[a + idl1 * b]; }; + + arr> wal(ip); + wal[0] = cmplx(1., 0.); + for (size_t i = 1; i < ip; ++i) wal[i] = cmplx(csarr[i].r, fwd ? -csarr[i].i : csarr[i].i); + + for (size_t k = 0; k < l1; ++k) + for (size_t i = 0; i < ido; ++i) CH(i, k, 0) = CC(i, 0, k); + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) + for (size_t k = 0; k < l1; ++k) + for (size_t i = 0; i < ido; ++i) PM(CH(i, k, j), CH(i, k, jc), CC(i, j, k), CC(i, jc, k)); + for (size_t k = 0; k < l1; ++k) + for (size_t i = 0; i < ido; ++i) { + T tmp = CH(i, k, 0); + for (size_t j = 1; j < ipph; ++j) tmp += CH(i, k, j); + CX(i, k, 0) = tmp; + } + for (size_t l = 1, lc = ip - 1; l < ipph; ++l, --lc) { + // j=0 + for (size_t ik = 0; ik < idl1; ++ik) { + CX2(ik, l).r = CH2(ik, 0).r + wal[l].r * CH2(ik, 1).r + wal[2 * l].r * CH2(ik, 2).r; + CX2(ik, l).i = CH2(ik, 0).i + wal[l].r * CH2(ik, 1).i + wal[2 * l].r * CH2(ik, 2).i; + CX2(ik, lc).r = -wal[l].i * CH2(ik, ip - 1).i - wal[2 * l].i * CH2(ik, ip - 2).i; + CX2(ik, lc).i = wal[l].i * CH2(ik, ip - 1).r + wal[2 * l].i * CH2(ik, ip - 2).r; + } + + size_t iwal = 2 * l; + size_t j = 3, jc = ip - 3; + for (; j < ipph - 1; j += 2, jc -= 2) { + iwal += l; + if (iwal > ip) iwal -= ip; + cmplx xwal = wal[iwal]; + iwal += l; + if (iwal > ip) iwal -= ip; + cmplx xwal2 = wal[iwal]; + for (size_t ik = 0; ik < idl1; ++ik) { + CX2(ik, l).r += CH2(ik, j).r * xwal.r + CH2(ik, j + 1).r * xwal2.r; + CX2(ik, l).i += CH2(ik, j).i * xwal.r + CH2(ik, j + 1).i * xwal2.r; + CX2(ik, lc).r -= CH2(ik, jc).i * xwal.i + CH2(ik, jc - 1).i * xwal2.i; + CX2(ik, lc).i += CH2(ik, jc).r * xwal.i + CH2(ik, jc - 1).r * xwal2.i; + } + } + for (; j < ipph; ++j, --jc) { + iwal += l; + if (iwal > ip) iwal -= ip; + cmplx xwal = wal[iwal]; + for (size_t ik = 0; ik < idl1; ++ik) { + CX2(ik, l).r += CH2(ik, j).r * xwal.r; + CX2(ik, l).i += CH2(ik, j).i * xwal.r; + CX2(ik, lc).r -= CH2(ik, jc).i * xwal.i; + CX2(ik, lc).i += CH2(ik, jc).r * xwal.i; + } + } + } + + // shuffling and twiddling + if (ido == 1) + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) + for (size_t ik = 0; ik < idl1; ++ik) { + T t1 = CX2(ik, j), t2 = CX2(ik, jc); + PM(CX2(ik, j), CX2(ik, jc), t1, t2); + } + else { + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) + for (size_t k = 0; k < l1; ++k) { + T t1 = CX(0, k, j), t2 = CX(0, k, jc); + PM(CX(0, k, j), CX(0, k, jc), t1, t2); + for (size_t i = 1; i < ido; ++i) { + T x1, x2; + PM(x1, x2, CX(i, k, j), CX(i, k, jc)); + size_t idij = (j - 1) * (ido - 1) + i - 1; + special_mul(x1, wa[idij], CX(i, k, j)); + idij = (jc - 1) * (ido - 1) + i - 1; + special_mul(x2, wa[idij], CX(i, k, jc)); + } + } + } + } + + template + void pass_all(T c[], T0 fct) const { + if (length == 1) { + c[0] *= fct; + return; + } + size_t l1 = 1; + arr ch(length); + T *p1 = c, *p2 = ch.data(); + + for (size_t k1 = 0; k1 < fact.size(); k1++) { + size_t ip = fact[k1].fct; + size_t l2 = ip * l1; + size_t ido = length / l2; + if (ip == 4) + pass4(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 8) + pass8(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 2) + pass2(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 3) + pass3(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 5) + pass5(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 7) + pass7(ido, l1, p1, p2, fact[k1].tw); + else if (ip == 11) + pass11(ido, l1, p1, p2, fact[k1].tw); + else { + passg(ido, ip, l1, p1, p2, fact[k1].tw, fact[k1].tws); + std::swap(p1, p2); + } + std::swap(p1, p2); + l1 = l2; + } + if (p1 != c) { + if (fct != 1.) + for (size_t i = 0; i < length; ++i) c[i] = ch[i] * fct; + else + std::copy_n(p1, length, c); + } else if (fct != 1.) + for (size_t i = 0; i < length; ++i) c[i] *= fct; + } + + public: + template + void exec(T c[], T0 fct, bool fwd) const { + fwd ? pass_all(c, fct) : pass_all(c, fct); + } + + private: + POCKETFFT_NOINLINE void factorize() { + size_t len = length; + while ((len & 7) == 0) { + add_factor(8); + len >>= 3; + } + while ((len & 3) == 0) { + add_factor(4); + len >>= 2; + } + if ((len & 1) == 0) { + len >>= 1; + // factor 2 should be at the front of the factor list + add_factor(2); + std::swap(fact[0].fct, fact.back().fct); + } + for (size_t divisor = 3; divisor * divisor <= len; divisor += 2) + while ((len % divisor) == 0) { + add_factor(divisor); + len /= divisor; + } + if (len > 1) add_factor(len); + } + + size_t twsize() const { + size_t twsize = 0, l1 = 1; + for (size_t k = 0; k < fact.size(); ++k) { + size_t ip = fact[k].fct, ido = length / (l1 * ip); + twsize += (ip - 1) * (ido - 1); + if (ip > 11) twsize += ip; + l1 *= ip; + } + return twsize; + } + + void comp_twiddle() { + sincos_2pibyn twiddle(length); + size_t l1 = 1; + size_t memofs = 0; + for (size_t k = 0; k < fact.size(); ++k) { + size_t ip = fact[k].fct, ido = length / (l1 * ip); + fact[k].tw = mem.data() + memofs; + memofs += (ip - 1) * (ido - 1); + for (size_t j = 1; j < ip; ++j) + for (size_t i = 1; i < ido; ++i) + fact[k].tw[(j - 1) * (ido - 1) + i - 1] = twiddle[j * l1 * i]; + if (ip > 11) { + fact[k].tws = mem.data() + memofs; + memofs += ip; + for (size_t j = 0; j < ip; ++j) fact[k].tws[j] = twiddle[j * l1 * ido]; + } + l1 *= ip; + } + } + + public: + POCKETFFT_NOINLINE cfftp(size_t length_) : length(length_) { + if (length == 0) throw std::runtime_error("zero-length FFT requested"); + if (length == 1) return; + factorize(); + mem.resize(twsize()); + comp_twiddle(); + } +}; + +// +// real-valued FFTPACK transforms +// + +template +class rfftp { + private: + struct fctdata { + size_t fct; + T0 *tw, *tws; + }; + + size_t length; + arr mem; + std::vector fact; + + void add_factor(size_t factor) { fact.push_back({factor, nullptr, nullptr}); } + + /* (a+ib) = conj(c+id) * (e+if) */ + template + inline void MULPM(T1& a, T1& b, T2 c, T2 d, T3 e, T3 f) const { + a = c * e + d * f; + b = c * f - d * e; + } + + template + void radf2(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto CH = [ch, ido](size_t a, size_t b, size_t c) -> T& { return ch[a + ido * (b + 2 * c)]; }; + + for (size_t k = 0; k < l1; k++) PM(CH(0, 0, k), CH(ido - 1, 1, k), CC(0, k, 0), CC(0, k, 1)); + if ((ido & 1) == 0) + for (size_t k = 0; k < l1; k++) { + CH(0, 1, k) = -CC(ido - 1, k, 1); + CH(ido - 1, 0, k) = CC(ido - 1, k, 0); + } + if (ido <= 2) return; + for (size_t k = 0; k < l1; k++) + for (size_t i = 2; i < ido; i += 2) { + size_t ic = ido - i; + T tr2, ti2; + MULPM(tr2, ti2, WA(0, i - 2), WA(0, i - 1), CC(i - 1, k, 1), CC(i, k, 1)); + PM(CH(i - 1, 0, k), CH(ic - 1, 1, k), CC(i - 1, k, 0), tr2); + PM(CH(i, 0, k), CH(ic, 1, k), ti2, CC(i, k, 0)); + } + } + +// a2=a+b; b2=i*(b-a); +#define POCKETFFT_REARRANGE(rx, ix, ry, iy) \ + { \ + auto t1 = rx + ry, t2 = ry - rx, t3 = ix + iy, t4 = ix - iy; \ + rx = t1; \ + ix = t3; \ + ry = t4; \ + iy = t2; \ + } + + template + void radf3(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 taur = -0.5, taui = T0(0.8660254037844386467637231707529362L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto CH = [ch, ido](size_t a, size_t b, size_t c) -> T& { return ch[a + ido * (b + 3 * c)]; }; + + for (size_t k = 0; k < l1; k++) { + T cr2 = CC(0, k, 1) + CC(0, k, 2); + CH(0, 0, k) = CC(0, k, 0) + cr2; + CH(0, 2, k) = taui * (CC(0, k, 2) - CC(0, k, 1)); + CH(ido - 1, 1, k) = CC(0, k, 0) + taur * cr2; + } + if (ido == 1) return; + for (size_t k = 0; k < l1; k++) + for (size_t i = 2; i < ido; i += 2) { + size_t ic = ido - i; + T di2, di3, dr2, dr3; + MULPM(dr2, di2, WA(0, i - 2), WA(0, i - 1), CC(i - 1, k, 1), + CC(i, k, 1)); // d2=conj(WA0)*CC1 + MULPM(dr3, di3, WA(1, i - 2), WA(1, i - 1), CC(i - 1, k, 2), + CC(i, k, 2)); // d3=conj(WA1)*CC2 + POCKETFFT_REARRANGE(dr2, di2, dr3, di3); + CH(i - 1, 0, k) = CC(i - 1, k, 0) + dr2; // c add + CH(i, 0, k) = CC(i, k, 0) + di2; + T tr2 = CC(i - 1, k, 0) + taur * dr2; // c add + T ti2 = CC(i, k, 0) + taur * di2; + T tr3 = taui * dr3; // t3 = taui*i*(d3-d2)? + T ti3 = taui * di3; + PM(CH(i - 1, 2, k), CH(ic - 1, 1, k), tr2, tr3); // PM(i) = t2+t3 + PM(CH(i, 2, k), CH(ic, 1, k), ti3, ti2); // PM(ic) = conj(t2-t3) + } + } + + template + void radf4(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 hsqt2 = T0(0.707106781186547524400844362104849L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto CH = [ch, ido](size_t a, size_t b, size_t c) -> T& { return ch[a + ido * (b + 4 * c)]; }; + + for (size_t k = 0; k < l1; k++) { + T tr1, tr2; + PM(tr1, CH(0, 2, k), CC(0, k, 3), CC(0, k, 1)); + PM(tr2, CH(ido - 1, 1, k), CC(0, k, 0), CC(0, k, 2)); + PM(CH(0, 0, k), CH(ido - 1, 3, k), tr2, tr1); + } + if ((ido & 1) == 0) + for (size_t k = 0; k < l1; k++) { + T ti1 = -hsqt2 * (CC(ido - 1, k, 1) + CC(ido - 1, k, 3)); + T tr1 = hsqt2 * (CC(ido - 1, k, 1) - CC(ido - 1, k, 3)); + PM(CH(ido - 1, 0, k), CH(ido - 1, 2, k), CC(ido - 1, k, 0), tr1); + PM(CH(0, 3, k), CH(0, 1, k), ti1, CC(ido - 1, k, 2)); + } + if (ido <= 2) return; + for (size_t k = 0; k < l1; k++) + for (size_t i = 2; i < ido; i += 2) { + size_t ic = ido - i; + T ci2, ci3, ci4, cr2, cr3, cr4, ti1, ti2, ti3, ti4, tr1, tr2, tr3, tr4; + MULPM(cr2, ci2, WA(0, i - 2), WA(0, i - 1), CC(i - 1, k, 1), CC(i, k, 1)); + MULPM(cr3, ci3, WA(1, i - 2), WA(1, i - 1), CC(i - 1, k, 2), CC(i, k, 2)); + MULPM(cr4, ci4, WA(2, i - 2), WA(2, i - 1), CC(i - 1, k, 3), CC(i, k, 3)); + PM(tr1, tr4, cr4, cr2); + PM(ti1, ti4, ci2, ci4); + PM(tr2, tr3, CC(i - 1, k, 0), cr3); + PM(ti2, ti3, CC(i, k, 0), ci3); + PM(CH(i - 1, 0, k), CH(ic - 1, 3, k), tr2, tr1); + PM(CH(i, 0, k), CH(ic, 3, k), ti1, ti2); + PM(CH(i - 1, 2, k), CH(ic - 1, 1, k), tr3, ti4); + PM(CH(i, 2, k), CH(ic, 1, k), tr4, ti3); + } + } + + template + void radf5(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 tr11 = T0(0.3090169943749474241022934171828191L), + ti11 = T0(0.9510565162951535721164393333793821L), + tr12 = T0(-0.8090169943749474241022934171828191L), + ti12 = T0(0.5877852522924731291687059546390728L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto CH = [ch, ido](size_t a, size_t b, size_t c) -> T& { return ch[a + ido * (b + 5 * c)]; }; + + for (size_t k = 0; k < l1; k++) { + T cr2, cr3, ci4, ci5; + PM(cr2, ci5, CC(0, k, 4), CC(0, k, 1)); + PM(cr3, ci4, CC(0, k, 3), CC(0, k, 2)); + CH(0, 0, k) = CC(0, k, 0) + cr2 + cr3; + CH(ido - 1, 1, k) = CC(0, k, 0) + tr11 * cr2 + tr12 * cr3; + CH(0, 2, k) = ti11 * ci5 + ti12 * ci4; + CH(ido - 1, 3, k) = CC(0, k, 0) + tr12 * cr2 + tr11 * cr3; + CH(0, 4, k) = ti12 * ci5 - ti11 * ci4; + } + if (ido == 1) return; + for (size_t k = 0; k < l1; ++k) + for (size_t i = 2, ic = ido - 2; i < ido; i += 2, ic -= 2) { + T di2, di3, di4, di5, dr2, dr3, dr4, dr5; + MULPM(dr2, di2, WA(0, i - 2), WA(0, i - 1), CC(i - 1, k, 1), CC(i, k, 1)); + MULPM(dr3, di3, WA(1, i - 2), WA(1, i - 1), CC(i - 1, k, 2), CC(i, k, 2)); + MULPM(dr4, di4, WA(2, i - 2), WA(2, i - 1), CC(i - 1, k, 3), CC(i, k, 3)); + MULPM(dr5, di5, WA(3, i - 2), WA(3, i - 1), CC(i - 1, k, 4), CC(i, k, 4)); + POCKETFFT_REARRANGE(dr2, di2, dr5, di5); + POCKETFFT_REARRANGE(dr3, di3, dr4, di4); + CH(i - 1, 0, k) = CC(i - 1, k, 0) + dr2 + dr3; + CH(i, 0, k) = CC(i, k, 0) + di2 + di3; + T tr2 = CC(i - 1, k, 0) + tr11 * dr2 + tr12 * dr3; + T ti2 = CC(i, k, 0) + tr11 * di2 + tr12 * di3; + T tr3 = CC(i - 1, k, 0) + tr12 * dr2 + tr11 * dr3; + T ti3 = CC(i, k, 0) + tr12 * di2 + tr11 * di3; + T tr5 = ti11 * dr5 + ti12 * dr4; + T ti5 = ti11 * di5 + ti12 * di4; + T tr4 = ti12 * dr5 - ti11 * dr4; + T ti4 = ti12 * di5 - ti11 * di4; + PM(CH(i - 1, 2, k), CH(ic - 1, 1, k), tr2, tr5); + PM(CH(i, 2, k), CH(ic, 1, k), ti5, ti2); + PM(CH(i - 1, 4, k), CH(ic - 1, 3, k), tr3, tr4); + PM(CH(i, 4, k), CH(ic, 3, k), ti4, ti3); + } + } + +#undef POCKETFFT_REARRANGE + + template + void radfg(size_t ido, size_t ip, size_t l1, T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa, const T0* POCKETFFT_RESTRICT csarr) const { + const size_t cdim = ip; + size_t ipph = (ip + 1) / 2; + size_t idl1 = ido * l1; + + auto CC = [cc, ido, cdim](size_t a, size_t b, size_t c) -> T& { + return cc[a + ido * (b + cdim * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto C1 = [cc, ido, l1](size_t a, size_t b, size_t c) -> T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto C2 = [cc, idl1](size_t a, size_t b) -> T& { return cc[a + idl1 * b]; }; + auto CH2 = [ch, idl1](size_t a, size_t b) -> T& { return ch[a + idl1 * b]; }; + + if (ido > 1) { + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 114 + { + size_t is = (j - 1) * (ido - 1), is2 = (jc - 1) * (ido - 1); + for (size_t k = 0; k < l1; ++k) // 113 + { + size_t idij = is; + size_t idij2 = is2; + for (size_t i = 1; i <= ido - 2; i += 2) // 112 + { + T t1 = C1(i, k, j), t2 = C1(i + 1, k, j), t3 = C1(i, k, jc), t4 = C1(i + 1, k, jc); + T x1 = wa[idij] * t1 + wa[idij + 1] * t2, x2 = wa[idij] * t2 - wa[idij + 1] * t1, + x3 = wa[idij2] * t3 + wa[idij2 + 1] * t4, x4 = wa[idij2] * t4 - wa[idij2 + 1] * t3; + PM(C1(i, k, j), C1(i + 1, k, jc), x3, x1); + PM(C1(i + 1, k, j), C1(i, k, jc), x2, x4); + idij += 2; + idij2 += 2; + } + } + } + } + + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 123 + for (size_t k = 0; k < l1; ++k) // 122 + MPINPLACE(C1(0, k, jc), C1(0, k, j)); + + // everything in C + // memset(ch,0,ip*l1*ido*sizeof(double)); + + for (size_t l = 1, lc = ip - 1; l < ipph; ++l, --lc) // 127 + { + for (size_t ik = 0; ik < idl1; ++ik) // 124 + { + CH2(ik, l) = C2(ik, 0) + csarr[2 * l] * C2(ik, 1) + csarr[4 * l] * C2(ik, 2); + CH2(ik, lc) = csarr[2 * l + 1] * C2(ik, ip - 1) + csarr[4 * l + 1] * C2(ik, ip - 2); + } + size_t iang = 2 * l; + size_t j = 3, jc = ip - 3; + for (; j < ipph - 3; j += 4, jc -= 4) // 126 + { + iang += l; + if (iang >= ip) iang -= ip; + T0 ar1 = csarr[2 * iang], ai1 = csarr[2 * iang + 1]; + iang += l; + if (iang >= ip) iang -= ip; + T0 ar2 = csarr[2 * iang], ai2 = csarr[2 * iang + 1]; + iang += l; + if (iang >= ip) iang -= ip; + T0 ar3 = csarr[2 * iang], ai3 = csarr[2 * iang + 1]; + iang += l; + if (iang >= ip) iang -= ip; + T0 ar4 = csarr[2 * iang], ai4 = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) // 125 + { + CH2(ik, l) += + ar1 * C2(ik, j) + ar2 * C2(ik, j + 1) + ar3 * C2(ik, j + 2) + ar4 * C2(ik, j + 3); + CH2(ik, lc) += + ai1 * C2(ik, jc) + ai2 * C2(ik, jc - 1) + ai3 * C2(ik, jc - 2) + ai4 * C2(ik, jc - 3); + } + } + for (; j < ipph - 1; j += 2, jc -= 2) // 126 + { + iang += l; + if (iang >= ip) iang -= ip; + T0 ar1 = csarr[2 * iang], ai1 = csarr[2 * iang + 1]; + iang += l; + if (iang >= ip) iang -= ip; + T0 ar2 = csarr[2 * iang], ai2 = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) // 125 + { + CH2(ik, l) += ar1 * C2(ik, j) + ar2 * C2(ik, j + 1); + CH2(ik, lc) += ai1 * C2(ik, jc) + ai2 * C2(ik, jc - 1); + } + } + for (; j < ipph; ++j, --jc) // 126 + { + iang += l; + if (iang >= ip) iang -= ip; + T0 ar = csarr[2 * iang], ai = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) // 125 + { + CH2(ik, l) += ar * C2(ik, j); + CH2(ik, lc) += ai * C2(ik, jc); + } + } + } + for (size_t ik = 0; ik < idl1; ++ik) // 101 + CH2(ik, 0) = C2(ik, 0); + for (size_t j = 1; j < ipph; ++j) // 129 + for (size_t ik = 0; ik < idl1; ++ik) // 128 + CH2(ik, 0) += C2(ik, j); + + // everything in CH at this point! + // memset(cc,0,ip*l1*ido*sizeof(double)); + + for (size_t k = 0; k < l1; ++k) // 131 + for (size_t i = 0; i < ido; ++i) // 130 + CC(i, 0, k) = CH(i, k, 0); + + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 137 + { + size_t j2 = 2 * j - 1; + for (size_t k = 0; k < l1; ++k) // 136 + { + CC(ido - 1, j2, k) = CH(0, k, j); + CC(0, j2 + 1, k) = CH(0, k, jc); + } + } + + if (ido == 1) return; + + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 140 + { + size_t j2 = 2 * j - 1; + for (size_t k = 0; k < l1; ++k) // 139 + for (size_t i = 1, ic = ido - i - 2; i <= ido - 2; i += 2, ic -= 2) // 138 + { + CC(i, j2 + 1, k) = CH(i, k, j) + CH(i, k, jc); + CC(ic, j2, k) = CH(i, k, j) - CH(i, k, jc); + CC(i + 1, j2 + 1, k) = CH(i + 1, k, j) + CH(i + 1, k, jc); + CC(ic + 1, j2, k) = CH(i + 1, k, jc) - CH(i + 1, k, j); + } + } + } + + template + void radb2(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 2 * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + + for (size_t k = 0; k < l1; k++) PM(CH(0, k, 0), CH(0, k, 1), CC(0, 0, k), CC(ido - 1, 1, k)); + if ((ido & 1) == 0) + for (size_t k = 0; k < l1; k++) { + CH(ido - 1, k, 0) = 2 * CC(ido - 1, 0, k); + CH(ido - 1, k, 1) = -2 * CC(0, 1, k); + } + if (ido <= 2) return; + for (size_t k = 0; k < l1; ++k) + for (size_t i = 2; i < ido; i += 2) { + size_t ic = ido - i; + T ti2, tr2; + PM(CH(i - 1, k, 0), tr2, CC(i - 1, 0, k), CC(ic - 1, 1, k)); + PM(ti2, CH(i, k, 0), CC(i, 0, k), CC(ic, 1, k)); + MULPM(CH(i, k, 1), CH(i - 1, k, 1), WA(0, i - 2), WA(0, i - 1), ti2, tr2); + } + } + + template + void radb3(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 taur = -0.5, taui = T0(0.8660254037844386467637231707529362L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 3 * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + + for (size_t k = 0; k < l1; k++) { + T tr2 = 2 * CC(ido - 1, 1, k); + T cr2 = CC(0, 0, k) + taur * tr2; + CH(0, k, 0) = CC(0, 0, k) + tr2; + T ci3 = 2 * taui * CC(0, 2, k); + PM(CH(0, k, 2), CH(0, k, 1), cr2, ci3); + } + if (ido == 1) return; + for (size_t k = 0; k < l1; k++) + for (size_t i = 2, ic = ido - 2; i < ido; i += 2, ic -= 2) { + T tr2 = CC(i - 1, 2, k) + CC(ic - 1, 1, k); // t2=CC(I) + conj(CC(ic)) + T ti2 = CC(i, 2, k) - CC(ic, 1, k); + T cr2 = CC(i - 1, 0, k) + taur * tr2; // c2=CC +taur*t2 + T ci2 = CC(i, 0, k) + taur * ti2; + CH(i - 1, k, 0) = CC(i - 1, 0, k) + tr2; // CH=CC+t2 + CH(i, k, 0) = CC(i, 0, k) + ti2; + T cr3 = taui * (CC(i - 1, 2, k) - CC(ic - 1, 1, k)); // c3=taui*(CC(i)-conj(CC(ic))) + T ci3 = taui * (CC(i, 2, k) + CC(ic, 1, k)); + T di2, di3, dr2, dr3; + PM(dr3, dr2, cr2, ci3); // d2= (cr2-ci3, ci2+cr3) = c2+i*c3 + PM(di2, di3, ci2, cr3); // d3= (cr2+ci3, ci2-cr3) = c2-i*c3 + MULPM(CH(i, k, 1), CH(i - 1, k, 1), WA(0, i - 2), WA(0, i - 1), di2, dr2); // ch = WA*d2 + MULPM(CH(i, k, 2), CH(i - 1, k, 2), WA(1, i - 2), WA(1, i - 1), di3, dr3); + } + } + + template + void radb4(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 sqrt2 = T0(1.414213562373095048801688724209698L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 4 * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + + for (size_t k = 0; k < l1; k++) { + T tr1, tr2; + PM(tr2, tr1, CC(0, 0, k), CC(ido - 1, 3, k)); + T tr3 = 2 * CC(ido - 1, 1, k); + T tr4 = 2 * CC(0, 2, k); + PM(CH(0, k, 0), CH(0, k, 2), tr2, tr3); + PM(CH(0, k, 3), CH(0, k, 1), tr1, tr4); + } + if ((ido & 1) == 0) + for (size_t k = 0; k < l1; k++) { + T tr1, tr2, ti1, ti2; + PM(ti1, ti2, CC(0, 3, k), CC(0, 1, k)); + PM(tr2, tr1, CC(ido - 1, 0, k), CC(ido - 1, 2, k)); + CH(ido - 1, k, 0) = tr2 + tr2; + CH(ido - 1, k, 1) = sqrt2 * (tr1 - ti1); + CH(ido - 1, k, 2) = ti2 + ti2; + CH(ido - 1, k, 3) = -sqrt2 * (tr1 + ti1); + } + if (ido <= 2) return; + for (size_t k = 0; k < l1; ++k) + for (size_t i = 2; i < ido; i += 2) { + T ci2, ci3, ci4, cr2, cr3, cr4, ti1, ti2, ti3, ti4, tr1, tr2, tr3, tr4; + size_t ic = ido - i; + PM(tr2, tr1, CC(i - 1, 0, k), CC(ic - 1, 3, k)); + PM(ti1, ti2, CC(i, 0, k), CC(ic, 3, k)); + PM(tr4, ti3, CC(i, 2, k), CC(ic, 1, k)); + PM(tr3, ti4, CC(i - 1, 2, k), CC(ic - 1, 1, k)); + PM(CH(i - 1, k, 0), cr3, tr2, tr3); + PM(CH(i, k, 0), ci3, ti2, ti3); + PM(cr4, cr2, tr1, tr4); + PM(ci2, ci4, ti1, ti4); + MULPM(CH(i, k, 1), CH(i - 1, k, 1), WA(0, i - 2), WA(0, i - 1), ci2, cr2); + MULPM(CH(i, k, 2), CH(i - 1, k, 2), WA(1, i - 2), WA(1, i - 1), ci3, cr3); + MULPM(CH(i, k, 3), CH(i - 1, k, 3), WA(2, i - 2), WA(2, i - 1), ci4, cr4); + } + } + + template + void radb5(size_t ido, size_t l1, const T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa) const { + constexpr T0 tr11 = T0(0.3090169943749474241022934171828191L), + ti11 = T0(0.9510565162951535721164393333793821L), + tr12 = T0(-0.8090169943749474241022934171828191L), + ti12 = T0(0.5877852522924731291687059546390728L); + + auto WA = [wa, ido](size_t x, size_t i) { return wa[i + x * (ido - 1)]; }; + auto CC = [cc, ido](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + 5 * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + + for (size_t k = 0; k < l1; k++) { + T ti5 = CC(0, 2, k) + CC(0, 2, k); + T ti4 = CC(0, 4, k) + CC(0, 4, k); + T tr2 = CC(ido - 1, 1, k) + CC(ido - 1, 1, k); + T tr3 = CC(ido - 1, 3, k) + CC(ido - 1, 3, k); + CH(0, k, 0) = CC(0, 0, k) + tr2 + tr3; + T cr2 = CC(0, 0, k) + tr11 * tr2 + tr12 * tr3; + T cr3 = CC(0, 0, k) + tr12 * tr2 + tr11 * tr3; + T ci4, ci5; + MULPM(ci5, ci4, ti5, ti4, ti11, ti12); + PM(CH(0, k, 4), CH(0, k, 1), cr2, ci5); + PM(CH(0, k, 3), CH(0, k, 2), cr3, ci4); + } + if (ido == 1) return; + for (size_t k = 0; k < l1; ++k) + for (size_t i = 2, ic = ido - 2; i < ido; i += 2, ic -= 2) { + T tr2, tr3, tr4, tr5, ti2, ti3, ti4, ti5; + PM(tr2, tr5, CC(i - 1, 2, k), CC(ic - 1, 1, k)); + PM(ti5, ti2, CC(i, 2, k), CC(ic, 1, k)); + PM(tr3, tr4, CC(i - 1, 4, k), CC(ic - 1, 3, k)); + PM(ti4, ti3, CC(i, 4, k), CC(ic, 3, k)); + CH(i - 1, k, 0) = CC(i - 1, 0, k) + tr2 + tr3; + CH(i, k, 0) = CC(i, 0, k) + ti2 + ti3; + T cr2 = CC(i - 1, 0, k) + tr11 * tr2 + tr12 * tr3; + T ci2 = CC(i, 0, k) + tr11 * ti2 + tr12 * ti3; + T cr3 = CC(i - 1, 0, k) + tr12 * tr2 + tr11 * tr3; + T ci3 = CC(i, 0, k) + tr12 * ti2 + tr11 * ti3; + T ci4, ci5, cr5, cr4; + MULPM(cr5, cr4, tr5, tr4, ti11, ti12); + MULPM(ci5, ci4, ti5, ti4, ti11, ti12); + T dr2, dr3, dr4, dr5, di2, di3, di4, di5; + PM(dr4, dr3, cr3, ci4); + PM(di3, di4, ci3, cr4); + PM(dr5, dr2, cr2, ci5); + PM(di2, di5, ci2, cr5); + MULPM(CH(i, k, 1), CH(i - 1, k, 1), WA(0, i - 2), WA(0, i - 1), di2, dr2); + MULPM(CH(i, k, 2), CH(i - 1, k, 2), WA(1, i - 2), WA(1, i - 1), di3, dr3); + MULPM(CH(i, k, 3), CH(i - 1, k, 3), WA(2, i - 2), WA(2, i - 1), di4, dr4); + MULPM(CH(i, k, 4), CH(i - 1, k, 4), WA(3, i - 2), WA(3, i - 1), di5, dr5); + } + } + + template + void radbg(size_t ido, size_t ip, size_t l1, T* POCKETFFT_RESTRICT cc, T* POCKETFFT_RESTRICT ch, + const T0* POCKETFFT_RESTRICT wa, const T0* POCKETFFT_RESTRICT csarr) const { + const size_t cdim = ip; + size_t ipph = (ip + 1) / 2; + size_t idl1 = ido * l1; + + auto CC = [cc, ido, cdim](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + cdim * c)]; + }; + auto CH = [ch, ido, l1](size_t a, size_t b, size_t c) -> T& { + return ch[a + ido * (b + l1 * c)]; + }; + auto C1 = [cc, ido, l1](size_t a, size_t b, size_t c) -> const T& { + return cc[a + ido * (b + l1 * c)]; + }; + auto C2 = [cc, idl1](size_t a, size_t b) -> T& { return cc[a + idl1 * b]; }; + auto CH2 = [ch, idl1](size_t a, size_t b) -> T& { return ch[a + idl1 * b]; }; + + for (size_t k = 0; k < l1; ++k) // 102 + for (size_t i = 0; i < ido; ++i) // 101 + CH(i, k, 0) = CC(i, 0, k); + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 108 + { + size_t j2 = 2 * j - 1; + for (size_t k = 0; k < l1; ++k) { + CH(0, k, j) = 2 * CC(ido - 1, j2, k); + CH(0, k, jc) = 2 * CC(0, j2 + 1, k); + } + } + + if (ido != 1) { + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 111 + { + size_t j2 = 2 * j - 1; + for (size_t k = 0; k < l1; ++k) + for (size_t i = 1, ic = ido - i - 2; i <= ido - 2; i += 2, ic -= 2) // 109 + { + CH(i, k, j) = CC(i, j2 + 1, k) + CC(ic, j2, k); + CH(i, k, jc) = CC(i, j2 + 1, k) - CC(ic, j2, k); + CH(i + 1, k, j) = CC(i + 1, j2 + 1, k) - CC(ic + 1, j2, k); + CH(i + 1, k, jc) = CC(i + 1, j2 + 1, k) + CC(ic + 1, j2, k); + } + } + } + for (size_t l = 1, lc = ip - 1; l < ipph; ++l, --lc) { + for (size_t ik = 0; ik < idl1; ++ik) { + C2(ik, l) = CH2(ik, 0) + csarr[2 * l] * CH2(ik, 1) + csarr[4 * l] * CH2(ik, 2); + C2(ik, lc) = csarr[2 * l + 1] * CH2(ik, ip - 1) + csarr[4 * l + 1] * CH2(ik, ip - 2); + } + size_t iang = 2 * l; + size_t j = 3, jc = ip - 3; + for (; j < ipph - 3; j += 4, jc -= 4) { + iang += l; + if (iang > ip) iang -= ip; + T0 ar1 = csarr[2 * iang], ai1 = csarr[2 * iang + 1]; + iang += l; + if (iang > ip) iang -= ip; + T0 ar2 = csarr[2 * iang], ai2 = csarr[2 * iang + 1]; + iang += l; + if (iang > ip) iang -= ip; + T0 ar3 = csarr[2 * iang], ai3 = csarr[2 * iang + 1]; + iang += l; + if (iang > ip) iang -= ip; + T0 ar4 = csarr[2 * iang], ai4 = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) { + C2(ik, l) += + ar1 * CH2(ik, j) + ar2 * CH2(ik, j + 1) + ar3 * CH2(ik, j + 2) + ar4 * CH2(ik, j + 3); + C2(ik, lc) += ai1 * CH2(ik, jc) + ai2 * CH2(ik, jc - 1) + ai3 * CH2(ik, jc - 2) + + ai4 * CH2(ik, jc - 3); + } + } + for (; j < ipph - 1; j += 2, jc -= 2) { + iang += l; + if (iang > ip) iang -= ip; + T0 ar1 = csarr[2 * iang], ai1 = csarr[2 * iang + 1]; + iang += l; + if (iang > ip) iang -= ip; + T0 ar2 = csarr[2 * iang], ai2 = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) { + C2(ik, l) += ar1 * CH2(ik, j) + ar2 * CH2(ik, j + 1); + C2(ik, lc) += ai1 * CH2(ik, jc) + ai2 * CH2(ik, jc - 1); + } + } + for (; j < ipph; ++j, --jc) { + iang += l; + if (iang > ip) iang -= ip; + T0 war = csarr[2 * iang], wai = csarr[2 * iang + 1]; + for (size_t ik = 0; ik < idl1; ++ik) { + C2(ik, l) += war * CH2(ik, j); + C2(ik, lc) += wai * CH2(ik, jc); + } + } + } + for (size_t j = 1; j < ipph; ++j) + for (size_t ik = 0; ik < idl1; ++ik) CH2(ik, 0) += CH2(ik, j); + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 124 + for (size_t k = 0; k < l1; ++k) PM(CH(0, k, jc), CH(0, k, j), C1(0, k, j), C1(0, k, jc)); + + if (ido == 1) return; + + for (size_t j = 1, jc = ip - 1; j < ipph; ++j, --jc) // 127 + for (size_t k = 0; k < l1; ++k) + for (size_t i = 1; i <= ido - 2; i += 2) { + CH(i, k, j) = C1(i, k, j) - C1(i + 1, k, jc); + CH(i, k, jc) = C1(i, k, j) + C1(i + 1, k, jc); + CH(i + 1, k, j) = C1(i + 1, k, j) + C1(i, k, jc); + CH(i + 1, k, jc) = C1(i + 1, k, j) - C1(i, k, jc); + } + + // All in CH + + for (size_t j = 1; j < ip; ++j) { + size_t is = (j - 1) * (ido - 1); + for (size_t k = 0; k < l1; ++k) { + size_t idij = is; + for (size_t i = 1; i <= ido - 2; i += 2) { + T t1 = CH(i, k, j), t2 = CH(i + 1, k, j); + CH(i, k, j) = wa[idij] * t1 - wa[idij + 1] * t2; + CH(i + 1, k, j) = wa[idij] * t2 + wa[idij + 1] * t1; + idij += 2; + } + } + } + } + + template + void copy_and_norm(T* c, T* p1, T0 fct) const { + if (p1 != c) { + if (fct != 1.) + for (size_t i = 0; i < length; ++i) c[i] = fct * p1[i]; + else + std::copy_n(p1, length, c); + } else if (fct != 1.) + for (size_t i = 0; i < length; ++i) c[i] *= fct; + } + + public: + template + void exec(T c[], T0 fct, bool r2hc) const { + if (length == 1) { + c[0] *= fct; + return; + } + size_t nf = fact.size(); + arr ch(length); + T *p1 = c, *p2 = ch.data(); + + if (r2hc) + for (size_t k1 = 0, l1 = length; k1 < nf; ++k1) { + size_t k = nf - k1 - 1; + size_t ip = fact[k].fct; + size_t ido = length / l1; + l1 /= ip; + if (ip == 4) + radf4(ido, l1, p1, p2, fact[k].tw); + else if (ip == 2) + radf2(ido, l1, p1, p2, fact[k].tw); + else if (ip == 3) + radf3(ido, l1, p1, p2, fact[k].tw); + else if (ip == 5) + radf5(ido, l1, p1, p2, fact[k].tw); + else { + radfg(ido, ip, l1, p1, p2, fact[k].tw, fact[k].tws); + std::swap(p1, p2); + } + std::swap(p1, p2); + } + else + for (size_t k = 0, l1 = 1; k < nf; k++) { + size_t ip = fact[k].fct, ido = length / (ip * l1); + if (ip == 4) + radb4(ido, l1, p1, p2, fact[k].tw); + else if (ip == 2) + radb2(ido, l1, p1, p2, fact[k].tw); + else if (ip == 3) + radb3(ido, l1, p1, p2, fact[k].tw); + else if (ip == 5) + radb5(ido, l1, p1, p2, fact[k].tw); + else + radbg(ido, ip, l1, p1, p2, fact[k].tw, fact[k].tws); + std::swap(p1, p2); + l1 *= ip; + } + + copy_and_norm(c, p1, fct); + } + + private: + void factorize() { + size_t len = length; + while ((len % 4) == 0) { + add_factor(4); + len >>= 2; + } + if ((len % 2) == 0) { + len >>= 1; + // factor 2 should be at the front of the factor list + add_factor(2); + std::swap(fact[0].fct, fact.back().fct); + } + for (size_t divisor = 3; divisor * divisor <= len; divisor += 2) + while ((len % divisor) == 0) { + add_factor(divisor); + len /= divisor; + } + if (len > 1) add_factor(len); + } + + size_t twsize() const { + size_t twsz = 0, l1 = 1; + for (size_t k = 0; k < fact.size(); ++k) { + size_t ip = fact[k].fct, ido = length / (l1 * ip); + twsz += (ip - 1) * (ido - 1); + if (ip > 5) twsz += 2 * ip; + l1 *= ip; + } + return twsz; + } + + void comp_twiddle() { + sincos_2pibyn twid(length); + size_t l1 = 1; + T0* ptr = mem.data(); + for (size_t k = 0; k < fact.size(); ++k) { + size_t ip = fact[k].fct, ido = length / (l1 * ip); + if (k < fact.size() - 1) // last factor doesn't need twiddles + { + fact[k].tw = ptr; + ptr += (ip - 1) * (ido - 1); + for (size_t j = 1; j < ip; ++j) + for (size_t i = 1; i <= (ido - 1) / 2; ++i) { + fact[k].tw[(j - 1) * (ido - 1) + 2 * i - 2] = twid[j * l1 * i].r; + fact[k].tw[(j - 1) * (ido - 1) + 2 * i - 1] = twid[j * l1 * i].i; + } + } + if (ip > 5) // special factors required by *g functions + { + fact[k].tws = ptr; + ptr += 2 * ip; + fact[k].tws[0] = 1.; + fact[k].tws[1] = 0.; + for (size_t i = 2, ic = 2 * ip - 2; i <= ic; i += 2, ic -= 2) { + fact[k].tws[i] = twid[i / 2 * (length / ip)].r; + fact[k].tws[i + 1] = twid[i / 2 * (length / ip)].i; + fact[k].tws[ic] = twid[i / 2 * (length / ip)].r; + fact[k].tws[ic + 1] = -twid[i / 2 * (length / ip)].i; + } + } + l1 *= ip; + } + } + + public: + POCKETFFT_NOINLINE rfftp(size_t length_) : length(length_) { + if (length == 0) throw std::runtime_error("zero-length FFT requested"); + if (length == 1) return; + factorize(); + mem.resize(twsize()); + comp_twiddle(); + } +}; + +// +// complex Bluestein transforms +// + +template +class fftblue { + private: + size_t n, n2; + cfftp plan; + arr> mem; + cmplx*bk, *bkf; + + template + void fft(cmplx c[], T0 fct) const { + arr> akf(n2); + + /* initialize a_k and FFT it */ + for (size_t m = 0; m < n; ++m) special_mul(c[m], bk[m], akf[m]); + auto zero = akf[0] * T0(0); + for (size_t m = n; m < n2; ++m) akf[m] = zero; + + plan.exec(akf.data(), 1., true); + + /* do the convolution */ + akf[0] = akf[0].template special_mul(bkf[0]); + for (size_t m = 1; m < (n2 + 1) / 2; ++m) { + akf[m] = akf[m].template special_mul(bkf[m]); + akf[n2 - m] = akf[n2 - m].template special_mul(bkf[m]); + } + if ((n2 & 1) == 0) akf[n2 / 2] = akf[n2 / 2].template special_mul(bkf[n2 / 2]); + + /* inverse FFT */ + plan.exec(akf.data(), 1., false); + + /* multiply by b_k */ + for (size_t m = 0; m < n; ++m) c[m] = akf[m].template special_mul(bk[m]) * fct; + } + + public: + POCKETFFT_NOINLINE fftblue(size_t length) + : n(length), + n2(util::good_size_cmplx(n * 2 - 1)), + plan(n2), + mem(n + n2 / 2 + 1), + bk(mem.data()), + bkf(mem.data() + n) { + /* initialize b_k */ + sincos_2pibyn tmp(2 * n); + bk[0].Set(1, 0); + + size_t coeff = 0; + for (size_t m = 1; m < n; ++m) { + coeff += 2 * m - 1; + if (coeff >= 2 * n) coeff -= 2 * n; + bk[m] = tmp[coeff]; + } + + /* initialize the zero-padded, Fourier transformed b_k. Add normalisation. */ + arr> tbkf(n2); + T0 xn2 = T0(1) / T0(n2); + tbkf[0] = bk[0] * xn2; + for (size_t m = 1; m < n; ++m) tbkf[m] = tbkf[n2 - m] = bk[m] * xn2; + for (size_t m = n; m <= (n2 - n); ++m) tbkf[m].Set(0., 0.); + plan.exec(tbkf.data(), 1., true); + for (size_t i = 0; i < n2 / 2 + 1; ++i) bkf[i] = tbkf[i]; + } + + template + void exec(cmplx c[], T0 fct, bool fwd) const { + fwd ? fft(c, fct) : fft(c, fct); + } + + template + void exec_r(T c[], T0 fct, bool fwd) { + arr> tmp(n); + if (fwd) { + auto zero = T0(0) * c[0]; + for (size_t m = 0; m < n; ++m) tmp[m].Set(c[m], zero); + fft(tmp.data(), fct); + c[0] = tmp[0].r; + std::copy_n(&tmp[1].r, n - 1, &c[1]); + } else { + tmp[0].Set(c[0], c[0] * 0); + std::copy_n(c + 1, n - 1, &tmp[1].r); + if ((n & 1) == 0) tmp[n / 2].i = T0(0) * c[0]; + for (size_t m = 1; 2 * m < n; ++m) tmp[n - m].Set(tmp[m].r, -tmp[m].i); + fft(tmp.data(), fct); + for (size_t m = 0; m < n; ++m) c[m] = tmp[m].r; + } + } +}; + +// +// flexible (FFTPACK/Bluestein) complex 1D transform +// + +template +class pocketfft_c { + private: + std::unique_ptr> packplan; + std::unique_ptr> blueplan; + size_t len; + + public: + POCKETFFT_NOINLINE pocketfft_c(size_t length) : len(length) { + if (length == 0) throw std::runtime_error("zero-length FFT requested"); + size_t tmp = (length < 50) ? 0 : util::largest_prime_factor(length); + if (tmp * tmp <= length) { + packplan = std::unique_ptr>(new cfftp(length)); + return; + } + double comp1 = util::cost_guess(length); + double comp2 = 2 * util::cost_guess(util::good_size_cmplx(2 * length - 1)); + comp2 *= 1.5; /* fudge factor that appears to give good overall performance */ + if (comp2 < comp1) // use Bluestein + blueplan = std::unique_ptr>(new fftblue(length)); + else + packplan = std::unique_ptr>(new cfftp(length)); + } + + template + POCKETFFT_NOINLINE void exec(cmplx c[], T0 fct, bool fwd) const { + packplan ? packplan->exec(c, fct, fwd) : blueplan->exec(c, fct, fwd); + } + + size_t length() const { return len; } +}; + +// +// flexible (FFTPACK/Bluestein) real-valued 1D transform +// + +template +class pocketfft_r { + private: + std::unique_ptr> packplan; + std::unique_ptr> blueplan; + size_t len; + + public: + POCKETFFT_NOINLINE pocketfft_r(size_t length) : len(length) { + if (length == 0) throw std::runtime_error("zero-length FFT requested"); + size_t tmp = (length < 50) ? 0 : util::largest_prime_factor(length); + if (tmp * tmp <= length) { + packplan = std::unique_ptr>(new rfftp(length)); + return; + } + double comp1 = 0.5 * util::cost_guess(length); + double comp2 = 2 * util::cost_guess(util::good_size_cmplx(2 * length - 1)); + comp2 *= 1.5; /* fudge factor that appears to give good overall performance */ + if (comp2 < comp1) // use Bluestein + blueplan = std::unique_ptr>(new fftblue(length)); + else + packplan = std::unique_ptr>(new rfftp(length)); + } + + template + POCKETFFT_NOINLINE void exec(T c[], T0 fct, bool fwd) const { + packplan ? packplan->exec(c, fct, fwd) : blueplan->exec_r(c, fct, fwd); + } + + size_t length() const { return len; } +}; + +// +// sine/cosine transforms +// + +template +class T_dct1 { + private: + pocketfft_r fftplan; + + public: + POCKETFFT_NOINLINE T_dct1(size_t length) : fftplan(2 * (length - 1)) {} + + template + POCKETFFT_NOINLINE void exec(T c[], T0 fct, bool ortho, int /*type*/, bool /*cosine*/) const { + constexpr T0 sqrt2 = T0(1.414213562373095048801688724209698L); + size_t N = fftplan.length(), n = N / 2 + 1; + if (ortho) { + c[0] *= sqrt2; + c[n - 1] *= sqrt2; + } + arr tmp(N); + tmp[0] = c[0]; + for (size_t i = 1; i < n; ++i) tmp[i] = tmp[N - i] = c[i]; + fftplan.exec(tmp.data(), fct, true); + c[0] = tmp[0]; + for (size_t i = 1; i < n; ++i) c[i] = tmp[2 * i - 1]; + if (ortho) { + c[0] *= sqrt2 * T0(0.5); + c[n - 1] *= sqrt2 * T0(0.5); + } + } + + size_t length() const { return fftplan.length() / 2 + 1; } +}; + +template +class T_dst1 { + private: + pocketfft_r fftplan; + + public: + POCKETFFT_NOINLINE T_dst1(size_t length) : fftplan(2 * (length + 1)) {} + + template + POCKETFFT_NOINLINE void exec(T c[], T0 fct, bool /*ortho*/, int /*type*/, bool /*cosine*/) const { + size_t N = fftplan.length(), n = N / 2 - 1; + arr tmp(N); + tmp[0] = tmp[n + 1] = c[0] * 0; + for (size_t i = 0; i < n; ++i) { + tmp[i + 1] = c[i]; + tmp[N - 1 - i] = -c[i]; + } + fftplan.exec(tmp.data(), fct, true); + for (size_t i = 0; i < n; ++i) c[i] = -tmp[2 * i + 2]; + } + + size_t length() const { return fftplan.length() / 2 - 1; } +}; + +template +class T_dcst23 { + private: + pocketfft_r fftplan; + std::vector twiddle; + + public: + POCKETFFT_NOINLINE T_dcst23(size_t length) : fftplan(length), twiddle(length) { + sincos_2pibyn tw(4 * length); + for (size_t i = 0; i < length; ++i) twiddle[i] = tw[i + 1].r; + } + + template + POCKETFFT_NOINLINE void exec(T c[], T0 fct, bool ortho, int type, bool cosine) const { + constexpr T0 sqrt2 = T0(1.414213562373095048801688724209698L); + size_t N = length(); + size_t NS2 = (N + 1) / 2; + if (type == 2) { + if (!cosine) + for (size_t k = 1; k < N; k += 2) c[k] = -c[k]; + c[0] *= 2; + if ((N & 1) == 0) c[N - 1] *= 2; + for (size_t k = 1; k < N - 1; k += 2) MPINPLACE(c[k + 1], c[k]); + fftplan.exec(c, fct, false); + for (size_t k = 1, kc = N - 1; k < NS2; ++k, --kc) { + T t1 = twiddle[k - 1] * c[kc] + twiddle[kc - 1] * c[k]; + T t2 = twiddle[k - 1] * c[k] - twiddle[kc - 1] * c[kc]; + c[k] = T0(0.5) * (t1 + t2); + c[kc] = T0(0.5) * (t1 - t2); + } + if ((N & 1) == 0) c[NS2] *= twiddle[NS2 - 1]; + if (!cosine) + for (size_t k = 0, kc = N - 1; k < kc; ++k, --kc) std::swap(c[k], c[kc]); + if (ortho) c[0] *= sqrt2 * T0(0.5); + } else { + if (ortho) c[0] *= sqrt2; + if (!cosine) + for (size_t k = 0, kc = N - 1; k < NS2; ++k, --kc) std::swap(c[k], c[kc]); + for (size_t k = 1, kc = N - 1; k < NS2; ++k, --kc) { + T t1 = c[k] + c[kc], t2 = c[k] - c[kc]; + c[k] = twiddle[k - 1] * t2 + twiddle[kc - 1] * t1; + c[kc] = twiddle[k - 1] * t1 - twiddle[kc - 1] * t2; + } + if ((N & 1) == 0) c[NS2] *= 2 * twiddle[NS2 - 1]; + fftplan.exec(c, fct, true); + for (size_t k = 1; k < N - 1; k += 2) MPINPLACE(c[k], c[k + 1]); + if (!cosine) + for (size_t k = 1; k < N; k += 2) c[k] = -c[k]; + } + } + + size_t length() const { return fftplan.length(); } +}; + +template +class T_dcst4 { + private: + size_t N; + std::unique_ptr> fft; + std::unique_ptr> rfft; + arr> C2; + + public: + POCKETFFT_NOINLINE T_dcst4(size_t length) + : N(length), + fft((N & 1) ? nullptr : new pocketfft_c(N / 2)), + rfft((N & 1) ? new pocketfft_r(N) : nullptr), + C2((N & 1) ? 0 : N / 2) { + if ((N & 1) == 0) { + sincos_2pibyn tw(16 * N); + for (size_t i = 0; i < N / 2; ++i) C2[i] = conj(tw[8 * i + 1]); + } + } + + template + POCKETFFT_NOINLINE void exec(T c[], T0 fct, bool /*ortho*/, int /*type*/, bool cosine) const { + size_t n2 = N / 2; + if (!cosine) + for (size_t k = 0, kc = N - 1; k < n2; ++k, --kc) std::swap(c[k], c[kc]); + if (N & 1) { + // The following code is derived from the FFTW3 function apply_re11() + // and is released under the 3-clause BSD license with friendly + // permission of Matteo Frigo and Steven G. Johnson. + + arr y(N); + { + size_t i = 0, m = n2; + for (; m < N; ++i, m += 4) y[i] = c[m]; + for (; m < 2 * N; ++i, m += 4) y[i] = -c[2 * N - m - 1]; + for (; m < 3 * N; ++i, m += 4) y[i] = -c[m - 2 * N]; + for (; m < 4 * N; ++i, m += 4) y[i] = c[4 * N - m - 1]; + for (; i < N; ++i, m += 4) y[i] = c[m - 4 * N]; + } + rfft->exec(y.data(), fct, true); + { + auto SGN = [](size_t i) { + constexpr T0 sqrt2 = T0(1.414213562373095048801688724209698L); + return (i & 2) ? -sqrt2 : sqrt2; + }; + c[n2] = y[0] * SGN(n2 + 1); + size_t i = 0, i1 = 1, k = 1; + for (; k < n2; ++i, ++i1, k += 2) { + c[i] = y[2 * k - 1] * SGN(i1) + y[2 * k] * SGN(i); + c[N - i1] = y[2 * k - 1] * SGN(N - i) - y[2 * k] * SGN(N - i1); + c[n2 - i1] = y[2 * k + 1] * SGN(n2 - i) - y[2 * k + 2] * SGN(n2 - i1); + c[n2 + i1] = y[2 * k + 1] * SGN(n2 + i + 2) + y[2 * k + 2] * SGN(n2 + i1); + } + if (k == n2) { + c[i] = y[2 * k - 1] * SGN(i + 1) + y[2 * k] * SGN(i); + c[N - i1] = y[2 * k - 1] * SGN(i + 2) + y[2 * k] * SGN(i1); + } + } + + // FFTW-derived code ends here + } else { + // even length algorithm from + // https://www.appletonaudio.com/blog/2013/derivation-of-fast-dct-4-algorithm-based-on-dft/ + arr> y(n2); + for (size_t i = 0; i < n2; ++i) { + y[i].Set(c[2 * i], c[N - 1 - 2 * i]); + y[i] *= C2[i]; + } + fft->exec(y.data(), fct, true); + for (size_t i = 0, ic = n2 - 1; i < n2; ++i, --ic) { + c[2 * i] = 2 * (y[i].r * C2[i].r - y[i].i * C2[i].i); + c[2 * i + 1] = -2 * (y[ic].i * C2[ic].r + y[ic].r * C2[ic].i); + } + } + if (!cosine) + for (size_t k = 1; k < N; k += 2) c[k] = -c[k]; + } + + size_t length() const { return N; } +}; + +// +// multi-D infrastructure +// + +template +std::shared_ptr get_plan(size_t length) { +#if POCKETFFT_CACHE_SIZE == 0 + return std::make_shared(length); +#else + constexpr size_t nmax = POCKETFFT_CACHE_SIZE; + static std::array, nmax> cache; + static std::array last_access{{0}}; + static size_t access_counter = 0; + static std::mutex mut; + + auto find_in_cache = [&]() -> std::shared_ptr { + for (size_t i = 0; i < nmax; ++i) + if (cache[i] && (cache[i]->length() == length)) { + // no need to update if this is already the most recent entry + if (last_access[i] != access_counter) { + last_access[i] = ++access_counter; + // Guard against overflow + if (access_counter == 0) last_access.fill(0); + } + return cache[i]; + } + + return nullptr; + }; + + { + std::lock_guard lock(mut); + auto p = find_in_cache(); + if (p) return p; + } + auto plan = std::make_shared(length); + { + std::lock_guard lock(mut); + auto p = find_in_cache(); + if (p) return p; + + size_t lru = 0; + for (size_t i = 1; i < nmax; ++i) + if (last_access[i] < last_access[lru]) lru = i; + + cache[lru] = plan; + last_access[lru] = ++access_counter; + } + return plan; +#endif +} + +class arr_info { + protected: + shape_t shp; + stride_t str; + + public: + arr_info(const shape_t& shape_, const stride_t& stride_) : shp(shape_), str(stride_) {} + size_t ndim() const { return shp.size(); } + size_t size() const { return util::prod(shp); } + const shape_t& shape() const { return shp; } + size_t shape(size_t i) const { return shp[i]; } + const stride_t& stride() const { return str; } + const ptrdiff_t& stride(size_t i) const { return str[i]; } +}; + +template +class cndarr : public arr_info { + protected: + const char* d; + + public: + cndarr(const void* data_, const shape_t& shape_, const stride_t& stride_) + : arr_info(shape_, stride_), d(reinterpret_cast(data_)) {} + const T& operator[](ptrdiff_t ofs) const { return *reinterpret_cast(d + ofs); } +}; + +template +class ndarr : public cndarr { + public: + ndarr(void* data_, const shape_t& shape_, const stride_t& stride_) + : cndarr::cndarr(const_cast(data_), shape_, stride_) {} + T& operator[](ptrdiff_t ofs) { + return *reinterpret_cast(const_cast(cndarr::d + ofs)); + } +}; + +template +class multi_iter { + private: + shape_t pos; + const arr_info &iarr, &oarr; + ptrdiff_t p_ii, p_i[N], str_i, p_oi, p_o[N], str_o; + size_t idim, rem; + + void advance_i() { + for (int i_ = int(pos.size()) - 1; i_ >= 0; --i_) { + auto i = size_t(i_); + if (i == idim) continue; + p_ii += iarr.stride(i); + p_oi += oarr.stride(i); + if (++pos[i] < iarr.shape(i)) return; + pos[i] = 0; + p_ii -= ptrdiff_t(iarr.shape(i)) * iarr.stride(i); + p_oi -= ptrdiff_t(oarr.shape(i)) * oarr.stride(i); + } + } + + public: + multi_iter(const arr_info& iarr_, const arr_info& oarr_, size_t idim_) + : pos(iarr_.ndim(), 0), + iarr(iarr_), + oarr(oarr_), + p_ii(0), + str_i(iarr.stride(idim_)), + p_oi(0), + str_o(oarr.stride(idim_)), + idim(idim_), + rem(iarr.size() / iarr.shape(idim)) { + auto nshares = threading::num_threads(); + if (nshares == 1) return; + if (nshares == 0) throw std::runtime_error("can't run with zero threads"); + auto myshare = threading::thread_id(); + if (myshare >= nshares) throw std::runtime_error("impossible share requested"); + size_t nbase = rem / nshares; + size_t additional = rem % nshares; + size_t lo = myshare * nbase + ((myshare < additional) ? myshare : additional); + size_t hi = lo + nbase + (myshare < additional); + size_t todo = hi - lo; + + size_t chunk = rem; + for (size_t i = 0; i < pos.size(); ++i) { + if (i == idim) continue; + chunk /= iarr.shape(i); + size_t n_advance = lo / chunk; + pos[i] += n_advance; + p_ii += ptrdiff_t(n_advance) * iarr.stride(i); + p_oi += ptrdiff_t(n_advance) * oarr.stride(i); + lo -= n_advance * chunk; + } + rem = todo; + } + void advance(size_t n) { + if (rem < n) throw std::runtime_error("underrun"); + for (size_t i = 0; i < n; ++i) { + p_i[i] = p_ii; + p_o[i] = p_oi; + advance_i(); + } + rem -= n; + } + ptrdiff_t iofs(size_t i) const { return p_i[0] + ptrdiff_t(i) * str_i; } + ptrdiff_t iofs(size_t j, size_t i) const { return p_i[j] + ptrdiff_t(i) * str_i; } + ptrdiff_t oofs(size_t i) const { return p_o[0] + ptrdiff_t(i) * str_o; } + ptrdiff_t oofs(size_t j, size_t i) const { return p_o[j] + ptrdiff_t(i) * str_o; } + size_t length_in() const { return iarr.shape(idim); } + size_t length_out() const { return oarr.shape(idim); } + ptrdiff_t stride_in() const { return str_i; } + ptrdiff_t stride_out() const { return str_o; } + size_t remaining() const { return rem; } +}; + +class simple_iter { + private: + shape_t pos; + const arr_info& arr; + ptrdiff_t p; + size_t rem; + + public: + simple_iter(const arr_info& arr_) : pos(arr_.ndim(), 0), arr(arr_), p(0), rem(arr_.size()) {} + void advance() { + --rem; + for (int i_ = int(pos.size()) - 1; i_ >= 0; --i_) { + auto i = size_t(i_); + p += arr.stride(i); + if (++pos[i] < arr.shape(i)) return; + pos[i] = 0; + p -= ptrdiff_t(arr.shape(i)) * arr.stride(i); + } + } + ptrdiff_t ofs() const { return p; } + size_t remaining() const { return rem; } +}; + +class rev_iter { + private: + shape_t pos; + const arr_info& arr; + std::vector rev_axis; + std::vector rev_jump; + size_t last_axis, last_size; + shape_t shp; + ptrdiff_t p, rp; + size_t rem; + + public: + rev_iter(const arr_info& arr_, const shape_t& axes) + : pos(arr_.ndim(), 0), + arr(arr_), + rev_axis(arr_.ndim(), 0), + rev_jump(arr_.ndim(), 1), + p(0), + rp(0) { + for (auto ax : axes) rev_axis[ax] = 1; + last_axis = axes.back(); + last_size = arr.shape(last_axis) / 2 + 1; + shp = arr.shape(); + shp[last_axis] = last_size; + rem = 1; + for (auto i : shp) rem *= i; + } + void advance() { + --rem; + for (int i_ = int(pos.size()) - 1; i_ >= 0; --i_) { + auto i = size_t(i_); + p += arr.stride(i); + if (!rev_axis[i]) + rp += arr.stride(i); + else { + rp -= arr.stride(i); + if (rev_jump[i]) { + rp += ptrdiff_t(arr.shape(i)) * arr.stride(i); + rev_jump[i] = 0; + } + } + if (++pos[i] < shp[i]) return; + pos[i] = 0; + p -= ptrdiff_t(shp[i]) * arr.stride(i); + if (rev_axis[i]) { + rp -= ptrdiff_t(arr.shape(i) - shp[i]) * arr.stride(i); + rev_jump[i] = 1; + } else + rp -= ptrdiff_t(shp[i]) * arr.stride(i); + } + } + ptrdiff_t ofs() const { return p; } + ptrdiff_t rev_ofs() const { return rp; } + size_t remaining() const { return rem; } +}; + +template +struct VTYPE {}; +template +using vtype_t = typename VTYPE::type; + +#ifndef POCKETFFT_NO_VECTORS +template<> +struct VTYPE { + using type = float __attribute__((vector_size(VLEN::val * sizeof(float)))); +}; +template<> +struct VTYPE { + using type = double __attribute__((vector_size(VLEN::val * sizeof(double)))); +}; +template<> +struct VTYPE { + using type = + long double __attribute__((vector_size(VLEN::val * sizeof(long double)))); +}; +#endif + +template +arr alloc_tmp(const shape_t& shape, size_t axsize, size_t elemsize) { + auto othersize = util::prod(shape) / axsize; + auto tmpsize = axsize * ((othersize >= VLEN::val) ? VLEN::val : 1); + return arr(tmpsize * elemsize); +} +template +arr alloc_tmp(const shape_t& shape, const shape_t& axes, size_t elemsize) { + size_t fullsize = util::prod(shape); + size_t tmpsize = 0; + for (size_t i = 0; i < axes.size(); ++i) { + auto axsize = shape[axes[i]]; + auto othersize = fullsize / axsize; + auto sz = axsize * ((othersize >= VLEN::val) ? VLEN::val : 1); + if (sz > tmpsize) tmpsize = sz; + } + return arr(tmpsize * elemsize); +} + +template +void copy_input(const multi_iter& it, const cndarr>& src, + cmplx>* POCKETFFT_RESTRICT dst) { + for (size_t i = 0; i < it.length_in(); ++i) + for (size_t j = 0; j < vlen; ++j) { + dst[i].r[j] = src[it.iofs(j, i)].r; + dst[i].i[j] = src[it.iofs(j, i)].i; + } +} + +template +void copy_input(const multi_iter& it, const cndarr& src, + vtype_t* POCKETFFT_RESTRICT dst) { + for (size_t i = 0; i < it.length_in(); ++i) + for (size_t j = 0; j < vlen; ++j) dst[i][j] = src[it.iofs(j, i)]; +} + +template +void copy_input(const multi_iter& it, const cndarr& src, T* POCKETFFT_RESTRICT dst) { + if (dst == &src[it.iofs(0)]) return; // in-place + for (size_t i = 0; i < it.length_in(); ++i) dst[i] = src[it.iofs(i)]; +} + +template +void copy_output(const multi_iter& it, const cmplx>* POCKETFFT_RESTRICT src, + ndarr>& dst) { + for (size_t i = 0; i < it.length_out(); ++i) + for (size_t j = 0; j < vlen; ++j) dst[it.oofs(j, i)].Set(src[i].r[j], src[i].i[j]); +} + +template +void copy_output(const multi_iter& it, const vtype_t* POCKETFFT_RESTRICT src, + ndarr& dst) { + for (size_t i = 0; i < it.length_out(); ++i) + for (size_t j = 0; j < vlen; ++j) dst[it.oofs(j, i)] = src[i][j]; +} + +template +void copy_output(const multi_iter& it, const T* POCKETFFT_RESTRICT src, ndarr& dst) { + if (src == &dst[it.oofs(0)]) return; // in-place + for (size_t i = 0; i < it.length_out(); ++i) dst[it.oofs(i)] = src[i]; +} + +template +struct add_vec { + using type = vtype_t; +}; +template +struct add_vec> { + using type = cmplx>; +}; +template +using add_vec_t = typename add_vec::type; + +template +POCKETFFT_NOINLINE void general_nd(const cndarr& in, ndarr& out, const shape_t& axes, T0 fct, + size_t nthreads, const Exec& exec, + const bool allow_inplace = true) { + std::shared_ptr plan; + + for (size_t iax = 0; iax < axes.size(); ++iax) { + size_t len = in.shape(axes[iax]); + if ((!plan) || (len != plan->length())) plan = get_plan(len); + + threading::thread_map(util::thread_count(nthreads, in.shape(), axes[iax], VLEN::val), [&] { + constexpr auto vlen = VLEN::val; + auto storage = alloc_tmp(in.shape(), len, sizeof(T)); + const auto& tin(iax == 0 ? in : out); + multi_iter it(tin, out, axes[iax]); +#ifndef POCKETFFT_NO_VECTORS + if (vlen > 1) + while (it.remaining() >= vlen) { + it.advance(vlen); + auto tdatav = reinterpret_cast*>(storage.data()); + exec(it, tin, out, tdatav, *plan, fct); + } +#endif + while (it.remaining() > 0) { + it.advance(1); + auto buf = allow_inplace && it.stride_out() == sizeof(T) + ? &out[it.oofs(0)] + : reinterpret_cast(storage.data()); + exec(it, tin, out, buf, *plan, fct); + } + }); // end of parallel region + fct = T0(1); // factor has been applied, use 1 for remaining axes + } +} + +struct ExecC2C { + bool forward; + + template + void operator()(const multi_iter& it, const cndarr>& in, ndarr>& out, + T* buf, const pocketfft_c& plan, T0 fct) const { + copy_input(it, in, buf); + plan.exec(buf, fct, forward); + copy_output(it, buf, out); + } +}; + +template +void copy_hartley(const multi_iter& it, const vtype_t* POCKETFFT_RESTRICT src, + ndarr& dst) { + for (size_t j = 0; j < vlen; ++j) dst[it.oofs(j, 0)] = src[0][j]; + size_t i = 1, i1 = 1, i2 = it.length_out() - 1; + for (i = 1; i < it.length_out() - 1; i += 2, ++i1, --i2) + for (size_t j = 0; j < vlen; ++j) { + dst[it.oofs(j, i1)] = src[i][j] + src[i + 1][j]; + dst[it.oofs(j, i2)] = src[i][j] - src[i + 1][j]; + } + if (i < it.length_out()) + for (size_t j = 0; j < vlen; ++j) dst[it.oofs(j, i1)] = src[i][j]; +} + +template +void copy_hartley(const multi_iter& it, const T* POCKETFFT_RESTRICT src, ndarr& dst) { + dst[it.oofs(0)] = src[0]; + size_t i = 1, i1 = 1, i2 = it.length_out() - 1; + for (i = 1; i < it.length_out() - 1; i += 2, ++i1, --i2) { + dst[it.oofs(i1)] = src[i] + src[i + 1]; + dst[it.oofs(i2)] = src[i] - src[i + 1]; + } + if (i < it.length_out()) dst[it.oofs(i1)] = src[i]; +} + +struct ExecHartley { + template + void operator()(const multi_iter& it, const cndarr& in, ndarr& out, T* buf, + const pocketfft_r& plan, T0 fct) const { + copy_input(it, in, buf); + plan.exec(buf, fct, true); + copy_hartley(it, buf, out); + } +}; + +struct ExecDcst { + bool ortho; + int type; + bool cosine; + + template + void operator()(const multi_iter& it, const cndarr& in, ndarr& out, T* buf, + const Tplan& plan, T0 fct) const { + copy_input(it, in, buf); + plan.exec(buf, fct, ortho, type, cosine); + copy_output(it, buf, out); + } +}; + +template +POCKETFFT_NOINLINE void general_r2c(const cndarr& in, ndarr>& out, size_t axis, + bool forward, T fct, size_t nthreads) { + auto plan = get_plan>(in.shape(axis)); + size_t len = in.shape(axis); + threading::thread_map(util::thread_count(nthreads, in.shape(), axis, VLEN::val), [&] { + constexpr auto vlen = VLEN::val; + auto storage = alloc_tmp(in.shape(), len, sizeof(T)); + multi_iter it(in, out, axis); +#ifndef POCKETFFT_NO_VECTORS + if (vlen > 1) + while (it.remaining() >= vlen) { + it.advance(vlen); + auto tdatav = reinterpret_cast*>(storage.data()); + copy_input(it, in, tdatav); + plan->exec(tdatav, fct, true); + for (size_t j = 0; j < vlen; ++j) out[it.oofs(j, 0)].Set(tdatav[0][j]); + size_t i = 1, ii = 1; + if (forward) + for (; i < len - 1; i += 2, ++ii) + for (size_t j = 0; j < vlen; ++j) + out[it.oofs(j, ii)].Set(tdatav[i][j], tdatav[i + 1][j]); + else + for (; i < len - 1; i += 2, ++ii) + for (size_t j = 0; j < vlen; ++j) + out[it.oofs(j, ii)].Set(tdatav[i][j], -tdatav[i + 1][j]); + if (i < len) + for (size_t j = 0; j < vlen; ++j) out[it.oofs(j, ii)].Set(tdatav[i][j]); + } +#endif + while (it.remaining() > 0) { + it.advance(1); + auto tdata = reinterpret_cast(storage.data()); + copy_input(it, in, tdata); + plan->exec(tdata, fct, true); + out[it.oofs(0)].Set(tdata[0]); + size_t i = 1, ii = 1; + if (forward) + for (; i < len - 1; i += 2, ++ii) out[it.oofs(ii)].Set(tdata[i], tdata[i + 1]); + else + for (; i < len - 1; i += 2, ++ii) out[it.oofs(ii)].Set(tdata[i], -tdata[i + 1]); + if (i < len) out[it.oofs(ii)].Set(tdata[i]); + } + }); // end of parallel region +} +template +POCKETFFT_NOINLINE void general_c2r(const cndarr>& in, ndarr& out, size_t axis, + bool forward, T fct, size_t nthreads) { + auto plan = get_plan>(out.shape(axis)); + size_t len = out.shape(axis); + threading::thread_map(util::thread_count(nthreads, in.shape(), axis, VLEN::val), [&] { + constexpr auto vlen = VLEN::val; + auto storage = alloc_tmp(out.shape(), len, sizeof(T)); + multi_iter it(in, out, axis); +#ifndef POCKETFFT_NO_VECTORS + if (vlen > 1) + while (it.remaining() >= vlen) { + it.advance(vlen); + auto tdatav = reinterpret_cast*>(storage.data()); + for (size_t j = 0; j < vlen; ++j) tdatav[0][j] = in[it.iofs(j, 0)].r; + { + size_t i = 1, ii = 1; + if (forward) + for (; i < len - 1; i += 2, ++ii) + for (size_t j = 0; j < vlen; ++j) { + tdatav[i][j] = in[it.iofs(j, ii)].r; + tdatav[i + 1][j] = -in[it.iofs(j, ii)].i; + } + else + for (; i < len - 1; i += 2, ++ii) + for (size_t j = 0; j < vlen; ++j) { + tdatav[i][j] = in[it.iofs(j, ii)].r; + tdatav[i + 1][j] = in[it.iofs(j, ii)].i; + } + if (i < len) + for (size_t j = 0; j < vlen; ++j) tdatav[i][j] = in[it.iofs(j, ii)].r; + } + plan->exec(tdatav, fct, false); + copy_output(it, tdatav, out); + } +#endif + while (it.remaining() > 0) { + it.advance(1); + auto tdata = reinterpret_cast(storage.data()); + tdata[0] = in[it.iofs(0)].r; + { + size_t i = 1, ii = 1; + if (forward) + for (; i < len - 1; i += 2, ++ii) { + tdata[i] = in[it.iofs(ii)].r; + tdata[i + 1] = -in[it.iofs(ii)].i; + } + else + for (; i < len - 1; i += 2, ++ii) { + tdata[i] = in[it.iofs(ii)].r; + tdata[i + 1] = in[it.iofs(ii)].i; + } + if (i < len) tdata[i] = in[it.iofs(ii)].r; + } + plan->exec(tdata, fct, false); + copy_output(it, tdata, out); + } + }); // end of parallel region +} + +struct ExecR2R { + bool r2h, forward; + + template + void operator()(const multi_iter& it, const cndarr& in, ndarr& out, T* buf, + const pocketfft_r& plan, T0 fct) const { + copy_input(it, in, buf); + if ((!r2h) && forward) + for (size_t i = 2; i < it.length_out(); i += 2) buf[i] = -buf[i]; + plan.exec(buf, fct, r2h); + if (r2h && (!forward)) + for (size_t i = 2; i < it.length_out(); i += 2) buf[i] = -buf[i]; + copy_output(it, buf, out); + } +}; + +template +void c2c(const shape_t& shape, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, bool forward, const std::complex* data_in, + std::complex* data_out, T fct, size_t nthreads = 1) { + if (util::prod(shape) == 0) return; + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + cndarr> ain(data_in, shape, stride_in); + ndarr> aout(data_out, shape, stride_out); + general_nd>(ain, aout, axes, fct, nthreads, ExecC2C{forward}); +} + +template +void dct(const shape_t& shape, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, int type, const T* data_in, T* data_out, T fct, bool ortho, + size_t nthreads = 1) { + if ((type < 1) || (type > 4)) throw std::invalid_argument("invalid DCT type"); + if (util::prod(shape) == 0) return; + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + cndarr ain(data_in, shape, stride_in); + ndarr aout(data_out, shape, stride_out); + const ExecDcst exec{ortho, type, true}; + if (type == 1) + general_nd>(ain, aout, axes, fct, nthreads, exec); + else if (type == 4) + general_nd>(ain, aout, axes, fct, nthreads, exec); + else + general_nd>(ain, aout, axes, fct, nthreads, exec); +} + +template +void dst(const shape_t& shape, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, int type, const T* data_in, T* data_out, T fct, bool ortho, + size_t nthreads = 1) { + if ((type < 1) || (type > 4)) throw std::invalid_argument("invalid DST type"); + if (util::prod(shape) == 0) return; + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + cndarr ain(data_in, shape, stride_in); + ndarr aout(data_out, shape, stride_out); + const ExecDcst exec{ortho, type, false}; + if (type == 1) + general_nd>(ain, aout, axes, fct, nthreads, exec); + else if (type == 4) + general_nd>(ain, aout, axes, fct, nthreads, exec); + else + general_nd>(ain, aout, axes, fct, nthreads, exec); +} + +template +void r2c(const shape_t& shape_in, const stride_t& stride_in, const stride_t& stride_out, + size_t axis, bool forward, const T* data_in, std::complex* data_out, T fct, + size_t nthreads = 1) { + if (util::prod(shape_in) == 0) return; + util::sanity_check(shape_in, stride_in, stride_out, false, axis); + cndarr ain(data_in, shape_in, stride_in); + shape_t shape_out(shape_in); + shape_out[axis] = shape_in[axis] / 2 + 1; + ndarr> aout(data_out, shape_out, stride_out); + general_r2c(ain, aout, axis, forward, fct, nthreads); +} + +template +void r2c(const shape_t& shape_in, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, bool forward, const T* data_in, std::complex* data_out, T fct, + size_t nthreads = 1) { + if (util::prod(shape_in) == 0) return; + util::sanity_check(shape_in, stride_in, stride_out, false, axes); + r2c(shape_in, stride_in, stride_out, axes.back(), forward, data_in, data_out, fct, nthreads); + if (axes.size() == 1) return; + shape_t shape_out(shape_in); + shape_out[axes.back()] = shape_in[axes.back()] / 2 + 1; + auto newaxes = shape_t{axes.begin(), --axes.end()}; + c2c(shape_out, stride_out, stride_out, newaxes, forward, data_out, data_out, T(1), nthreads); +} + +template +void c2r(const shape_t& shape_out, const stride_t& stride_in, const stride_t& stride_out, + size_t axis, bool forward, const std::complex* data_in, T* data_out, T fct, + size_t nthreads = 1) { + if (util::prod(shape_out) == 0) return; + util::sanity_check(shape_out, stride_in, stride_out, false, axis); + shape_t shape_in(shape_out); + shape_in[axis] = shape_out[axis] / 2 + 1; + cndarr> ain(data_in, shape_in, stride_in); + ndarr aout(data_out, shape_out, stride_out); + general_c2r(ain, aout, axis, forward, fct, nthreads); +} + +template +void c2r(const shape_t& shape_out, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, bool forward, const std::complex* data_in, T* data_out, T fct, + size_t nthreads = 1) { + if (util::prod(shape_out) == 0) return; + if (axes.size() == 1) + return c2r(shape_out, stride_in, stride_out, axes[0], forward, data_in, data_out, fct, + nthreads); + util::sanity_check(shape_out, stride_in, stride_out, false, axes); + auto shape_in = shape_out; + shape_in[axes.back()] = shape_out[axes.back()] / 2 + 1; + auto nval = util::prod(shape_in); + stride_t stride_inter(shape_in.size()); + stride_inter.back() = sizeof(cmplx); + for (int i = int(shape_in.size()) - 2; i >= 0; --i) + stride_inter[size_t(i)] = stride_inter[size_t(i + 1)] * ptrdiff_t(shape_in[size_t(i + 1)]); + arr> tmp(nval); + auto newaxes = shape_t{axes.begin(), --axes.end()}; + c2c(shape_in, stride_in, stride_inter, newaxes, forward, data_in, tmp.data(), T(1), nthreads); + c2r(shape_out, stride_inter, stride_out, axes.back(), forward, tmp.data(), data_out, fct, + nthreads); +} + +template +void r2r_fftpack(const shape_t& shape, const stride_t& stride_in, const stride_t& stride_out, + const shape_t& axes, bool real2hermitian, bool forward, const T* data_in, + T* data_out, T fct, size_t nthreads = 1) { + if (util::prod(shape) == 0) return; + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + cndarr ain(data_in, shape, stride_in); + ndarr aout(data_out, shape, stride_out); + general_nd>(ain, aout, axes, fct, nthreads, ExecR2R{real2hermitian, forward}); +} + +template +void r2r_separable_hartley(const shape_t& shape, const stride_t& stride_in, + const stride_t& stride_out, const shape_t& axes, const T* data_in, + T* data_out, T fct, size_t nthreads = 1) { + if (util::prod(shape) == 0) return; + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + cndarr ain(data_in, shape, stride_in); + ndarr aout(data_out, shape, stride_out); + general_nd>(ain, aout, axes, fct, nthreads, ExecHartley{}, false); +} + +template +void r2r_genuine_hartley(const shape_t& shape, const stride_t& stride_in, + const stride_t& stride_out, const shape_t& axes, const T* data_in, + T* data_out, T fct, size_t nthreads = 1) { + if (util::prod(shape) == 0) return; + if (axes.size() == 1) + return r2r_separable_hartley(shape, stride_in, stride_out, axes, data_in, data_out, fct, + nthreads); + util::sanity_check(shape, stride_in, stride_out, data_in == data_out, axes); + shape_t tshp(shape); + tshp[axes.back()] = tshp[axes.back()] / 2 + 1; + arr> tdata(util::prod(tshp)); + stride_t tstride(shape.size()); + tstride.back() = sizeof(std::complex); + for (size_t i = tstride.size() - 1; i > 0; --i) tstride[i - 1] = tstride[i] * ptrdiff_t(tshp[i]); + r2c(shape, stride_in, tstride, axes, true, data_in, tdata.data(), fct, nthreads); + cndarr> atmp(tdata.data(), tshp, tstride); + ndarr aout(data_out, shape, stride_out); + simple_iter iin(atmp); + rev_iter iout(aout, axes); + while (iin.remaining() > 0) { + auto v = atmp[iin.ofs()]; + aout[iout.ofs()] = v.r + v.i; + aout[iout.rev_ofs()] = v.r - v.i; + iin.advance(); + iout.advance(); + } +} + +} // namespace detail + +using detail::BACKWARD; +using detail::c2c; +using detail::c2r; +using detail::dct; +using detail::dst; +using detail::FORWARD; +using detail::r2c; +using detail::r2r_fftpack; +using detail::r2r_genuine_hartley; +using detail::r2r_separable_hartley; +using detail::shape_t; +using detail::stride_t; + +} // namespace pocketfft + +#undef POCKETFFT_NOINLINE +#undef POCKETFFT_RESTRICT + +#endif // POCKETFFT_HDRONLY_H diff --git a/oneflow/user/kernels/pocketfftplan.h b/oneflow/user/kernels/pocketfftplan.h new file mode 100644 index 0000000000000000000000000000000000000000..89a5a5ecf10c4d037c4bf8db544d0cb6d100ee44 --- /dev/null +++ b/oneflow/user/kernels/pocketfftplan.h @@ -0,0 +1,102 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "pocketfft_hdronly.h" +#include "oneflow/core/kernel/kernel.h" +using namespace pocketfft; + +namespace oneflow { +namespace { + +enum class FFT_EXCUTETYPE { R2C, C2C }; + +template +struct PocketFFtParams { + shape_t input_shape; + shape_t output_shape; + stride_t in_stridef; + stride_t out_stridef; + shape_t axes; + bool IsForward; + FFT_EXCUTETYPE excute_type; + IN fct; + PocketFFtParams() = default; + PocketFFtParams(const Shape& in_shape, const Shape& out_shape, const bool is_froward, const IN f, + FFT_EXCUTETYPE type) + : IsForward(is_froward), excute_type(type), fct(f) { + input_shape.resize(in_shape.size()); + output_shape.resize(out_shape.size()); + in_stridef.resize(input_shape.size()); + out_stridef.resize(output_shape.size()); + axes.resize(input_shape.size()); + + std::copy(in_shape.begin(), in_shape.end(), input_shape.begin()); + std::copy(out_shape.begin(), out_shape.end(), output_shape.begin()); + std::iota(axes.begin(), axes.end(), 0); + + size_t out_tmpf = sizeof(OUT); + size_t in_tmpf = sizeof(IN); + for (int i = input_shape.size() - 1; i >= 0; --i) { + in_stridef[i] = in_tmpf; + in_tmpf *= input_shape[i]; + out_stridef[i] = out_tmpf; + out_tmpf *= output_shape[i]; + } + } +}; + +template +class PocketFFtConfig { + public: + PocketFFtConfig(const PocketFFtConfig&) = delete; + PocketFFtConfig& operator=(PocketFFtConfig const&) = delete; + + explicit PocketFFtConfig(const PocketFFtParams& params) : fftparams(params) {} + + void excute(const IN* in, OUT* out, int64_t dims, int64_t batch, int64_t len) { + int64_t in_offset = len; + int64_t out_offset = len / 2 + 1; + for (int j = 0; j < dims; j++) { + for (int i = 0; i < batch; i++) { + const IN* data_in = in + j * batch * in_offset + i * in_offset; + OUT* data_out = out + j * batch * out_offset + i * out_offset; + switch (fftparams.excute_type) { + case FFT_EXCUTETYPE::R2C: + r2c(fftparams.input_shape, fftparams.in_stridef, fftparams.out_stridef, fftparams.axes, + fftparams.IsForward, data_in, data_out, fftparams.fct); + break; + + case FFT_EXCUTETYPE::C2C: + // c2c(fftparams.input_shape, fftparams.in_stridef, fftparams.out_stridef, + // fftparams.axes, fftparams.IsForward, in, + // out, fftparams.fct); + break; + default: break; + } + } + } + } + + private: + PocketFFtParams fftparams; +}; + +} // namespace + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/prelu_kernel.cu b/oneflow/user/kernels/prelu_kernel.cu index 48dc3c150f8edf9c35cd7b856508f797ba6e5bdc..d340ff60f8e18b04c9b4a93789be9c5c87bc61c8 100644 --- a/oneflow/user/kernels/prelu_kernel.cu +++ b/oneflow/user/kernels/prelu_kernel.cu @@ -246,7 +246,7 @@ void DispatchPreluForwardPackSize(ep::Stream* stream, const int64_t elem_cnt, int grid_size; const int pack_size = GetLaunchPackSize(inner_size); const int64_t pack_num = elem_cnt / pack_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); if (pack_size == 8) { PReluForwardMultiAlphaGpu <<As()->cuda_stream()>>>( @@ -284,7 +284,7 @@ void DispatchPreluBackwardPackSize(ep::Stream* stream, const int64_t elem_cnt, int grid_size; const int pack_size = GetLaunchPackSize(inner_size); const int64_t pack_num = elem_cnt / pack_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); if (pack_size == 8) { if (alpha_requires_grad) { @@ -351,7 +351,7 @@ void DispatchPreluBackwardSingleAlphaTail(ep::Stream* stream, const IndexType el constexpr int pack_size = cuda::elementwise::PackSize(); const int64_t pack_num = elem_cnt / pack_size; int grid_size; - cudaError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); + GPU(Error_t) err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); const int64_t tail_offset = pack_num * pack_size; const int64_t n_tail = elem_cnt - tail_offset; const bool tail = n_tail > 0 ? true : false; diff --git a/oneflow/user/kernels/prelu_kernel.hip.cpp b/oneflow/user/kernels/prelu_kernel.hip.cpp deleted file mode 100644 index 1613ff456e949c63369638a5a9a68cc6ced3dcd0..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/prelu_kernel.hip.cpp +++ /dev/null @@ -1,505 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -Shape CreatePreluLeftExtendedShape(const ShapeView& shape) { - DimVector dim_vec(shape.NumAxes()); - dim_vec.at(0) = 1LL; - dim_vec.at(1) = shape.At(1); - for (int i = 2; i < shape.NumAxes(); i++) { dim_vec.at(i) = 1LL; } - return Shape(std::move(dim_vec)); -} - -template -struct PreluForwardSingleAlphaFunctor { - OF_DEVICE_FUNC explicit PreluForwardSingleAlphaFunctor(const T alpha) : alpha(alpha) {} - __device__ T operator()(T x) const { return (x > static_cast(0.0)) ? x : (alpha * x); } - const T alpha; -}; - -template -struct PreluForwardSingleAlphaPtrFunctor { - OF_DEVICE_FUNC explicit PreluForwardSingleAlphaPtrFunctor(const T* alpha_ptr) - : alpha_ptr(alpha_ptr) {} - __device__ PreluForwardSingleAlphaFunctor operator()() const { - return PreluForwardSingleAlphaFunctor(*alpha_ptr); - } - const T* alpha_ptr; -}; - -template -__global__ void PReluBackwardSingleAlphaGpu(const IndexType elem_cnt, const int64_t n_tail, - const T* x, const T* alpha, const T* dy, T* dx, - T* alpha_diff, const T* tail_x, const T* tail_dy, - T* tail_dx, T* tail_alpha_diff) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - T zero_val = static_cast(0); - T alpha_val = alpha[0]; - - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - const LoadType* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec; - x_vec.storage = *x_load; - - const LoadType* dy_load = reinterpret_cast(dy + linear_index); - LoadPack dy_vec; - dy_vec.storage = *dy_load; - - LoadPack dx_vec; - T zero_val = static_cast(0.0); - if (alpha_requires_grad) { - LoadPack dalpha_vec; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - if (x_vec.elem[i] > zero_val) { - dx_vec.elem[i] = dy_vec.elem[i]; - dalpha_vec.elem[i] = zero_val; - } else { - dx_vec.elem[i] = dy_vec.elem[i] * alpha_val; - dalpha_vec.elem[i] = dy_vec.elem[i] * x_vec.elem[i]; - } - } - *(reinterpret_cast(dx + linear_index)) = dx_vec.storage; - *(reinterpret_cast(alpha_diff + linear_index)) = dalpha_vec.storage; - } else { -#pragma unroll - for (int i = 0; i < pack_size; i++) { - if (x_vec.elem[i] > zero_val) { - dx_vec.elem[i] = dy_vec.elem[i]; - } else { - dx_vec.elem[i] = dy_vec.elem[i] * alpha_val; - } - } - *(reinterpret_cast(dx + linear_index)) = dx_vec.storage; - } - } - - if (tail && global_thread_id < n_tail) { - const T tail_dy_val = tail_dy[global_thread_id]; - if (tail_x[global_thread_id] > zero_val) { - tail_dx[global_thread_id] = tail_dy_val; - if (alpha_requires_grad) { tail_alpha_diff[global_thread_id] = zero_val; } - } else { - tail_dx[global_thread_id] = alpha_val * tail_dy_val; - if (alpha_requires_grad) { - tail_alpha_diff[global_thread_id] = tail_x[global_thread_id] * tail_dy_val; - } - } - } -} - -template -__global__ void BroadcastPReluMultiAlphaNaiveForwardGpu(const int32_t elem_cnt, - const int32_t alpha_size, - const int32_t inner_size, const T* x, - const T* alpha, T* y) { - const T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - int32_t alpha_idx = (i / inner_size) % alpha_size; - y[i] = x_i > zero_val ? x_i : x_i * alpha[alpha_idx]; - } -} - -template -__global__ void PReluForwardMultiAlphaGpu(const IndexType elem_cnt, const IndexType alpha_size, - const IndexType inner_size, const T* x, const T* alpha, - T* y) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - T zero_val = static_cast(0); - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - IndexType alpha_idx = (linear_index / inner_size) % alpha_size; - - const LoadType* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec; - x_vec.storage = *x_load; - - LoadPack y_vec; - - T alpha_val = alpha[alpha_idx]; -#pragma unroll - for (int i = 0; i < pack_size; i++) { - y_vec.elem[i] = x_vec.elem[i] > zero_val ? x_vec.elem[i] : x_vec.elem[i] * alpha_val; - } - *(reinterpret_cast(y + linear_index)) = y_vec.storage; - } -} - -template -__global__ void BroadcastPReluMultiAlphaNaiveBackwardGpu(const int32_t elem_cnt, - const int32_t alpha_size, - const int32_t inner_size, const T* x, - const T* alpha, const T* dy, T* dx, - T* alpha_diff) { - const T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - const T dy_i = dy[i]; - int32_t alpha_i = (i / inner_size) % alpha_size; - if (x_i > zero_val) { - dx[i] = dy_i; - if (alpha_requires_grad) { alpha_diff[i] = zero_val; } - } else { - dx[i] = dy_i * alpha[alpha_i]; - if (alpha_requires_grad) { alpha_diff[i] = dy_i * x_i; } - } - } -} - -template -__global__ void PReluBackwardMultiAlphaGpu(const IndexType elem_cnt, const IndexType alpha_size, - const IndexType inner_size, const T* x, const T* alpha, - const T* dy, T* dx, T* alpha_diff) { - int32_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x; - - using LoadType = cuda::elementwise::PackType; - using LoadPack = cuda::elementwise::Pack; - T zero_val = static_cast(0); - for (int64_t linear_index = global_thread_id * pack_size; linear_index < elem_cnt; - linear_index += gridDim.x * blockDim.x * pack_size) { - IndexType alpha_idx = (linear_index / inner_size) % alpha_size; - - const LoadType* x_load = reinterpret_cast(x + linear_index); - LoadPack x_vec; - x_vec.storage = *x_load; - - const LoadType* dy_load = reinterpret_cast(dy + linear_index); - LoadPack dy_vec; - dy_vec.storage = *dy_load; - - LoadPack dx_vec; - T alpha_val = alpha[alpha_idx]; - if (alpha_requires_grad) { - LoadPack dalpha_vec; - T zero_val = static_cast(0.0); -#pragma unroll - for (int i = 0; i < pack_size; i++) { - if (x_vec.elem[i] > zero_val) { - dx_vec.elem[i] = dy_vec.elem[i]; - dalpha_vec.elem[i] = zero_val; - } else { - dx_vec.elem[i] = dy_vec.elem[i] * alpha_val; - dalpha_vec.elem[i] = dy_vec.elem[i] * x_vec.elem[i]; - } - } - *(reinterpret_cast(dx + linear_index)) = dx_vec.storage; - *(reinterpret_cast(alpha_diff + linear_index)) = dalpha_vec.storage; - } else { -#pragma unroll - for (int i = 0; i < pack_size; i++) { - if (x_vec.elem[i] > zero_val) { - dx_vec.elem[i] = dy_vec.elem[i]; - } else { - dx_vec.elem[i] = dy_vec.elem[i] * alpha_val; - } - } - *(reinterpret_cast(dx + linear_index)) = dx_vec.storage; - } - } -} - -constexpr int32_t kBlockSize = 256; - -template -int GetLaunchPackSize(const int64_t inner_size) { - constexpr int type_pack_size = cuda::elementwise::PackSize(); - for (int launch_pack_size = 8; launch_pack_size > 0; launch_pack_size /= 2) { - if (type_pack_size >= launch_pack_size && inner_size % launch_pack_size == 0) { - return launch_pack_size; - } - } - return 1; -} - -template -void DispatchPreluForwardPackSize(ep::Stream* stream, const int64_t elem_cnt, - const int64_t alpha_size, const int64_t inner_size, const T* x, - const T* alpha, T* y) { - int grid_size; - const int pack_size = GetLaunchPackSize(inner_size); - const int64_t pack_num = elem_cnt / pack_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - if (pack_size == 8) { - PReluForwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, y); - } else if (pack_size == 4) { - PReluForwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, y); - } else if (pack_size == 2) { - PReluForwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, y); - } else { - BroadcastPReluMultiAlphaNaiveForwardGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, y); - } -} - -template -void DispatchPreluForwardIndex(ep::Stream* stream, const int64_t elem_cnt, const int64_t alpha_size, - const int64_t inner_size, const T* x, const T* alpha, T* y) { - if (elem_cnt < GetMaxVal()) { - DispatchPreluForwardPackSize(stream, elem_cnt, alpha_size, inner_size, x, alpha, y); - } else { - DispatchPreluForwardPackSize(stream, elem_cnt, alpha_size, inner_size, x, alpha, y); - } -} - -template -void DispatchPreluBackwardPackSize(ep::Stream* stream, const int64_t elem_cnt, - const int64_t alpha_size, const int64_t inner_size, const T* x, - const T* alpha, const T* dy, T* dx, T* alpha_diff, - const bool alpha_requires_grad) { - int grid_size; - const int pack_size = GetLaunchPackSize(inner_size); - const int64_t pack_num = elem_cnt / pack_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - - if (pack_size == 8) { - if (alpha_requires_grad) { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } else { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } - } else if (pack_size == 4) { - if (alpha_requires_grad) { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } else { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } - } else if (pack_size == 2) { - if (alpha_requires_grad) { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } else { - PReluBackwardMultiAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } - - } else { - if (alpha_requires_grad) { - BroadcastPReluMultiAlphaNaiveBackwardGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } else { - BroadcastPReluMultiAlphaNaiveBackwardGpu - <<As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x, alpha, dy, dx, alpha_diff); - } - } -} - -template -void DispatchPreluBackwardIndex(ep::Stream* stream, const int64_t elem_cnt, - const int64_t alpha_size, const int64_t inner_size, const T* x, - const T* alpha, const T* dy, T* dx, T* alpha_diff, - const bool alpha_requires_grad) { - if (elem_cnt < GetMaxVal()) { - DispatchPreluBackwardPackSize(stream, elem_cnt, alpha_size, inner_size, x, alpha, - dy, dx, alpha_diff, alpha_requires_grad); - } else { - DispatchPreluBackwardPackSize(stream, elem_cnt, alpha_size, inner_size, x, alpha, - dy, dx, alpha_diff, alpha_requires_grad); - } -} - -template -void DispatchPreluBackwardSingleAlphaTail(ep::Stream* stream, const IndexType elem_cnt, const T* x, - const T* alpha, const T* dy, T* dx, T* alpha_diff, - const bool alpha_requires_grad) { - constexpr int pack_size = cuda::elementwise::PackSize(); - const int64_t pack_num = elem_cnt / pack_size; - int grid_size; - hipError_t err = cuda::elementwise::GetNumBlocks(pack_num, &grid_size); - const int64_t tail_offset = pack_num * pack_size; - const int64_t n_tail = elem_cnt - tail_offset; - const bool tail = n_tail > 0 ? true : false; - if (tail) { - if (alpha_requires_grad) { - PReluBackwardSingleAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, n_tail, x, alpha, dy, dx, alpha_diff, x + tail_offset, dy + tail_offset, - dx + tail_offset, alpha_diff + tail_offset); - } else { - PReluBackwardSingleAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, n_tail, x, alpha, dy, dx, alpha_diff, x + tail_offset, dy + tail_offset, - dx + tail_offset, alpha_diff + tail_offset); - } - } else { - if (alpha_requires_grad) { - PReluBackwardSingleAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, n_tail, x, alpha, dy, dx, alpha_diff, x + tail_offset, dy + tail_offset, - dx + tail_offset, alpha_diff + tail_offset); - } else { - PReluBackwardSingleAlphaGpu - <<As()->cuda_stream()>>>( - elem_cnt, n_tail, x, alpha, dy, dx, alpha_diff, x + tail_offset, dy + tail_offset, - dx + tail_offset, alpha_diff + tail_offset); - } - } -} - -template -void DispatchPreluBackwardSingleAlphaIndex(ep::Stream* stream, const int64_t elem_cnt, const T* x, - const T* alpha, const T* dy, T* dx, T* alpha_diff, - const bool alpha_requires_grad) { - if (elem_cnt < GetMaxVal()) { - DispatchPreluBackwardSingleAlphaTail(stream, elem_cnt, x, alpha, dy, dx, alpha_diff, - alpha_requires_grad); - } else { - DispatchPreluBackwardSingleAlphaTail(stream, elem_cnt, x, alpha, dy, dx, alpha_diff, - alpha_requires_grad); - } -} - -} // namespace - -template -class GpuPReluKernel final : public user_op::OpKernel { - public: - GpuPReluKernel() = default; - ~GpuPReluKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t elem_cnt = x->shape_view().elem_cnt(); - const int32_t batch = x->shape_view().At(0); - const int32_t channels = (x->shape_view().NumAxes() == 1) ? 1 : x->shape_view().At(1); - const int32_t alpha_size = alpha->shape_view().elem_cnt(); - const int32_t inner_size = elem_cnt / batch / channels; - - if (alpha_size == 1) { - OF_CUDA_CHECK((cuda::elementwise::UnaryWithFactory( - PreluForwardSingleAlphaPtrFunctor(reinterpret_cast(alpha->dptr())), elem_cnt, - reinterpret_cast(y->mut_dptr()), reinterpret_cast(x->dptr()), - ctx->stream()->As()->cuda_stream()))); - } else { - DispatchPreluForwardIndex( - ctx->stream(), elem_cnt, alpha_size, inner_size, reinterpret_cast(x->dptr()), - reinterpret_cast(alpha->dptr()), reinterpret_cast(y->mut_dptr())); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_PRELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("prelu").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); - -REGISTER_CUDA_PRELU_KERNEL(half) -REGISTER_CUDA_PRELU_KERNEL(float) -REGISTER_CUDA_PRELU_KERNEL(double) - -template -class GpuPReluGradKernel final : public user_op::OpKernel { - public: - GpuPReluGradKernel() = default; - ~GpuPReluGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - user_op::Tensor* alpha_diff = ctx->Tensor4ArgNameAndIndex("alpha_diff", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const bool alpha_requires_grad = ctx->Attr("alpha_requires_grad"); - const int32_t elem_cnt = x->shape_view().elem_cnt(); - T* broadcasted_alpha_diff = tmp_buffer->mut_dptr(); - T* reduce_sum_tmp_buf = reinterpret_cast(tmp_buffer->mut_dptr() - + GetCudaAlignedSize(elem_cnt * sizeof(T))); - - const Shape& left_extended_shape = CreatePreluLeftExtendedShape(ShapeView(x->shape_view())); - - const int32_t batch = x->shape_view().At(0); - const int32_t channels = (x->shape_view().NumAxes() == 1) ? 1 : x->shape_view().At(1); - const int32_t alpha_size = alpha->shape_view().elem_cnt(); - const int32_t inner_size = elem_cnt / batch / channels; - if (alpha_size == 1) { - DispatchPreluBackwardSingleAlphaIndex(ctx->stream(), elem_cnt, x->dptr(), - alpha->dptr(), dy->dptr(), dx->mut_dptr(), - broadcasted_alpha_diff, alpha_requires_grad); - } else { - DispatchPreluBackwardIndex(ctx->stream(), elem_cnt, alpha_size, inner_size, x->dptr(), - alpha->dptr(), dy->dptr(), dx->mut_dptr(), - broadcasted_alpha_diff, alpha_requires_grad); - } - if (alpha_requires_grad) { - NdarrayUtil::ReduceSum( - ctx->stream(), XpuVarNdarray(left_extended_shape, alpha_diff->mut_dptr()), - XpuVarNdarray(x->shape_view(), broadcasted_alpha_diff), - XpuVarNdarray(x->shape_view(), reduce_sum_tmp_buf)); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_PRELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("prelu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("x", 0); \ - const Shape& alpha_shape = ctx->InputShape("alpha", 0); \ - const int64_t tmp_buffer_size = \ - 2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(dtype)); \ - return tmp_buffer_size; \ - }); - -REGISTER_CUDA_PRELU_GRAD_KERNEL(half) -REGISTER_CUDA_PRELU_GRAD_KERNEL(float) -REGISTER_CUDA_PRELU_GRAD_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/quantization_kernel.hip.cpp b/oneflow/user/kernels/quantization_kernel.hip.cpp deleted file mode 100644 index 51fbbde42b07d80c6bf2ad44d35bc2ab2c700db6..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/quantization_kernel.hip.cpp +++ /dev/null @@ -1,159 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/kernel_util.hip.h" - -namespace oneflow { - -namespace { - -template -__global__ void QuantizationSymmetric(const T* in_ptr, const T* scale_ptr, const int64_t scale_size, - const int64_t elements, const int64_t panel_size, - const double quantization_bit, T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit - 1)) - 1; - T lower_bound = -upper_bound - 1; - - while (gid < elements) { - int64_t channel_index = gid / panel_size; - int64_t scale_idx = min(scale_size - 1, channel_index); - - T scale = scale_ptr[scale_idx]; - - T out = nearbyint(in_ptr[gid] / scale); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = out; - - gid += step; - } -} - -template -__global__ void QuantizationAffine(const T* in_ptr, const T* scale_ptr, const T* zero_point_ptr, - const int64_t scale_size, const int64_t elements, - const int64_t panel_size, const double quantization_bit, - T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit)) - 1; - T lower_bound = 0; - - while (gid < elements) { - int64_t channel_index = gid / panel_size; - int64_t scale_idx = min(scale_size - 1, channel_index); - - T scale = scale_ptr[scale_idx]; - T zero_point = zero_point_ptr[scale_idx]; - - T out = nearbyint(in_ptr[gid] / scale + zero_point); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = out; - - gid += step; - } -} - -template -__global__ void QuantizationCambricon(const T* in_ptr, const T* shift, const int64_t scale_size, - const int64_t elements, const int64_t panel_size, - const double quantization_bit, T* out_ptr) { - int64_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int64_t step = gridDim.x * blockDim.x; - - T upper_bound = static_cast(pow(2.0, quantization_bit - 1)) - 1; - T lower_bound = -upper_bound - 1; - - T scale = static_cast(pow(2.0, static_cast(shift[0]))); - - while (gid < elements) { - T out = nearbyint(in_ptr[gid] / scale); - out = out > upper_bound ? upper_bound : out; - out = out < lower_bound ? lower_bound : out; - out_ptr[gid] = out; - gid += step; - } -} - -} // namespace - -template -class GpuQuantizationKernel final : public user_op::OpKernel { - public: - GpuQuantizationKernel() = default; - ~GpuQuantizationKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - const user_op::Tensor* scale = ctx->Tensor4ArgNameAndIndex("scale", 0); - const user_op::Tensor* zero_point = ctx->Tensor4ArgNameAndIndex("zero_point", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - - const std::string quantization_scheme = ctx->Attr("quantization_scheme"); - const int32_t quantization_bit = ctx->Attr("quantization_bit"); - const std::string quantization_formula = ctx->Attr("quantization_formula"); - - const int64_t elements = in->shape_view().elem_cnt(); - const int64_t panel_size = in->shape_view().Count(1); - const int64_t scale_size = scale->shape_view().elem_cnt(); - - // round to even - auto origin_round_mode = std::fegetround(); - std::fesetround(FE_TONEAREST); - - if (quantization_formula == "google") { - if (quantization_scheme == "symmetric") { - RUN_CUDA_KERNEL((QuantizationSymmetric), ctx->stream(), elements, in->dptr(), - scale->dptr(), scale_size, elements, panel_size, quantization_bit, - out->mut_dptr()); - } else { // quantization_scheme == "affine" - RUN_CUDA_KERNEL((QuantizationAffine), ctx->stream(), elements, in->dptr(), - scale->dptr(), zero_point->dptr(), scale_size, elements, panel_size, - quantization_bit, out->mut_dptr()); - } - } else if (quantization_formula == "cambricon") { - RUN_CUDA_KERNEL((QuantizationCambricon), ctx->stream(), elements, in->dptr(), - scale->dptr(), scale_size, elements, panel_size, quantization_bit, - out->mut_dptr()); - } else { - UNIMPLEMENTED(); - } - - std::fesetround(origin_round_mode); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_QUANTIZATION_KERNEL(dtype) \ - REGISTER_USER_KERNEL("quantization") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_QUANTIZATION_KERNEL(float); -REGISTER_QUANTIZATION_KERNEL(double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/radix_sort.cuh b/oneflow/user/kernels/radix_sort.cuh index 023f42a72d285cd23c4718d2a0f3d893c507b7c0..bef1ccfb461532e0bf557630a838e80d9fbb57ba 100644 --- a/oneflow/user/kernels/radix_sort.cuh +++ b/oneflow/user/kernels/radix_sort.cuh @@ -16,6 +16,8 @@ limitations under the License. #ifndef ONEFLOW_USER_KERNELS_RADIX_SORT_CUH_ #define ONEFLOW_USER_KERNELS_RADIX_SORT_CUH_ +#ifdef WITH_CUDA + #include #include "oneflow/core/device/cuda_util.h" @@ -277,4 +279,271 @@ void SortKeysDescending(const KeyType* keys_ptr, int32_t num_row, int32_t num_co } // namespace oneflow +#endif // WITH_CUDA + +#ifdef WITH_ROCM + +#include +#include "oneflow/core/device/cuda_util.h" + +namespace oneflow { + +namespace { + +class MultiplyFunctor final { + public: + MultiplyFunctor(int32_t num_col) : num_col_(num_col) {} + __host__ __device__ __forceinline__ int32_t operator()(int32_t idx) const { + return idx * num_col_; + } + + private: + int32_t num_col_; +}; + +} // namespace + +template +size_t InferTempStorageForSortPairsAscending(int32_t num_row, int32_t num_col) { + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + size_t temp_storage_bytes = 0; + auto err = hipcub::DeviceSegmentedRadixSort::SortPairs( + /* d_temp_storage */ nullptr, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ nullptr, + /* d_keys_out */ nullptr, + /* d_values_in */ nullptr, + /* d_values_out */ nullptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ 0); + OF_CUDA_CHECK(err); + + return temp_storage_bytes; +} + +template +size_t InferTempStorageForSortPairsDescending(int32_t num_row, int32_t num_col) { + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + size_t temp_storage_bytes = 0; + auto err = + hipcub::DeviceSegmentedRadixSort::SortPairsDescending( + /* d_temp_storage */ nullptr, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ nullptr, + /* d_keys_out */ nullptr, + /* d_values_in */ nullptr, + /* d_values_out */ nullptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ 0); + OF_CUDA_CHECK(err); + + return temp_storage_bytes; +} + +template +size_t InferTempStorageForSortKeysAscending(int32_t num_row, int32_t num_col) { + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + size_t temp_storage_bytes = 0; + auto err = hipcub::DeviceSegmentedRadixSort::SortKeys( + /* d_temp_storage */ nullptr, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ nullptr, + /* d_keys_out */ nullptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ 0); + OF_CUDA_CHECK(err); + + return temp_storage_bytes; +} + +template +size_t InferTempStorageForSortKeysDescending(int32_t num_row, int32_t num_col) { + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + size_t temp_storage_bytes = 0; + auto err = hipcub::DeviceSegmentedRadixSort::SortKeysDescending( + /* d_temp_storage */ nullptr, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ nullptr, + /* d_keys_out */ nullptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ 0); + OF_CUDA_CHECK(err); + + return temp_storage_bytes; +} + +template +void SortPairsAscending(const KeyType* keys_ptr, const ValueType* values_ptr, int32_t num_row, + int32_t num_col, void* temp_storage_ptr, int32_t temp_storage_bytes, + KeyType* sorted_keys_ptr, ValueType* sorted_values_ptr, + hipStream_t stream) { + size_t rt_inferred_temp_storage_bytes = + InferTempStorageForSortPairsAscending(num_row, num_col); + CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); + + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + auto err = hipcub::DeviceSegmentedRadixSort::SortPairs( + /* d_temp_storage */ temp_storage_ptr, + /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, + /* d_keys_in */ keys_ptr, + /* d_keys_out */ sorted_keys_ptr, + /* d_values_in */ values_ptr, + /* d_values_out */ sorted_values_ptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ stream); + OF_CUDA_CHECK(err); +} + +template +void SortPairsDescending(const KeyType* keys_ptr, const ValueType* values_ptr, int32_t num_row, + int32_t num_col, void* temp_storage_ptr, int32_t temp_storage_bytes, + KeyType* sorted_keys_ptr, ValueType* sorted_values_ptr, + hipStream_t stream) { + size_t rt_inferred_temp_storage_bytes = + InferTempStorageForSortPairsDescending(num_row, num_col); + CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); + + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + auto err = hipcub::DeviceSegmentedRadixSort::SortPairsDescending( + /* d_temp_storage */ temp_storage_ptr, + /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, + /* d_keys_in */ keys_ptr, + /* d_keys_out */ sorted_keys_ptr, + /* d_values_in */ values_ptr, + /* d_values_out */ sorted_values_ptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ stream); + OF_CUDA_CHECK(err); +} + +template +void SortKeysAscending(const KeyType* keys_ptr, int32_t num_row, int32_t num_col, + void* temp_storage_ptr, int32_t temp_storage_bytes, KeyType* sorted_keys_ptr, + hipStream_t stream) { + size_t rt_inferred_temp_storage_bytes = + InferTempStorageForSortKeysAscending(num_row, num_col); + CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); + + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + auto err = hipcub::DeviceSegmentedRadixSort::SortKeys( + /* d_temp_storage */ temp_storage_ptr, + /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, + /* d_keys_in */ keys_ptr, + /* d_keys_out */ sorted_keys_ptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ stream); + OF_CUDA_CHECK(err); +} + +template +void SortKeysDescending(const KeyType* keys_ptr, int32_t num_row, int32_t num_col, + void* temp_storage_ptr, int32_t temp_storage_bytes, + KeyType* sorted_keys_ptr, hipStream_t stream) { + size_t rt_inferred_temp_storage_bytes = + InferTempStorageForSortKeysDescending(num_row, num_col); + CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); + + using SegmentOffsetIter = + hipcub::TransformInputIterator>; + + hipcub::CountingInputIterator counting_iter(0); + MultiplyFunctor multiply_functor(num_col); + SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); + + auto err = hipcub::DeviceSegmentedRadixSort::SortKeysDescending( + /* d_temp_storage */ temp_storage_ptr, + /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, + /* d_keys_in */ keys_ptr, + /* d_keys_out */ sorted_keys_ptr, + /* num_items */ num_row * num_col, + /* num_segments */ num_row, + /* d_begin_offsets */ segment_offset_iter, + /* d_end_offsets */ segment_offset_iter + 1, + /* begin_bit */ 0, + /* end_bit */ sizeof(KeyType) * 8, + /* stream */ stream); + OF_CUDA_CHECK(err); +} + +} // namespace oneflow + +#endif // WITH_ROCM + #endif // ONEFLOW_USER_KERNELS_RADIX_SORT_CUH_ diff --git a/oneflow/user/kernels/radix_sort.hip.h b/oneflow/user/kernels/radix_sort.hip.h deleted file mode 100644 index 8cdd903f4c651c09cc82ea567781e7ab1147b0bd..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/radix_sort.hip.h +++ /dev/null @@ -1,280 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef ONEFLOW_USER_KERNELS_RADIX_SORT_HIP_H_ -#define ONEFLOW_USER_KERNELS_RADIX_SORT_HIP_H_ - -#include -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { - -namespace { - -class MultiplyFunctor final { - public: - MultiplyFunctor(int32_t num_col) : num_col_(num_col) {} - __host__ __device__ __forceinline__ int32_t operator()(int32_t idx) const { - return idx * num_col_; - } - - private: - int32_t num_col_; -}; - -} // namespace - -template -size_t InferTempStorageForSortPairsAscending(int32_t num_row, int32_t num_col) { - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - size_t temp_storage_bytes = 0; - auto err = hipcub::DeviceSegmentedRadixSort::SortPairs( - /* d_temp_storage */ nullptr, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ nullptr, - /* d_keys_out */ nullptr, - /* d_values_in */ nullptr, - /* d_values_out */ nullptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ 0); - OF_CUDA_CHECK(err); - - return temp_storage_bytes; -} - -template -size_t InferTempStorageForSortPairsDescending(int32_t num_row, int32_t num_col) { - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - size_t temp_storage_bytes = 0; - auto err = - hipcub::DeviceSegmentedRadixSort::SortPairsDescending( - /* d_temp_storage */ nullptr, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ nullptr, - /* d_keys_out */ nullptr, - /* d_values_in */ nullptr, - /* d_values_out */ nullptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ 0); - OF_CUDA_CHECK(err); - - return temp_storage_bytes; -} - -template -size_t InferTempStorageForSortKeysAscending(int32_t num_row, int32_t num_col) { - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - size_t temp_storage_bytes = 0; - auto err = hipcub::DeviceSegmentedRadixSort::SortKeys( - /* d_temp_storage */ nullptr, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ nullptr, - /* d_keys_out */ nullptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ 0); - OF_CUDA_CHECK(err); - - return temp_storage_bytes; -} - -template -size_t InferTempStorageForSortKeysDescending(int32_t num_row, int32_t num_col) { - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - size_t temp_storage_bytes = 0; - auto err = hipcub::DeviceSegmentedRadixSort::SortKeysDescending( - /* d_temp_storage */ nullptr, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ nullptr, - /* d_keys_out */ nullptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ 0); - OF_CUDA_CHECK(err); - - return temp_storage_bytes; -} - -template -void SortPairsAscending(const KeyType* keys_ptr, const ValueType* values_ptr, int32_t num_row, - int32_t num_col, void* temp_storage_ptr, int32_t temp_storage_bytes, - KeyType* sorted_keys_ptr, ValueType* sorted_values_ptr, - hipStream_t stream) { - size_t rt_inferred_temp_storage_bytes = - InferTempStorageForSortPairsAscending(num_row, num_col); - CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); - - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - auto err = hipcub::DeviceSegmentedRadixSort::SortPairs( - /* d_temp_storage */ temp_storage_ptr, - /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, - /* d_keys_in */ keys_ptr, - /* d_keys_out */ sorted_keys_ptr, - /* d_values_in */ values_ptr, - /* d_values_out */ sorted_values_ptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ stream); - OF_CUDA_CHECK(err); -} - -template -void SortPairsDescending(const KeyType* keys_ptr, const ValueType* values_ptr, int32_t num_row, - int32_t num_col, void* temp_storage_ptr, int32_t temp_storage_bytes, - KeyType* sorted_keys_ptr, ValueType* sorted_values_ptr, - hipStream_t stream) { - size_t rt_inferred_temp_storage_bytes = - InferTempStorageForSortPairsDescending(num_row, num_col); - CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); - - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - auto err = hipcub::DeviceSegmentedRadixSort::SortPairsDescending( - /* d_temp_storage */ temp_storage_ptr, - /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, - /* d_keys_in */ keys_ptr, - /* d_keys_out */ sorted_keys_ptr, - /* d_values_in */ values_ptr, - /* d_values_out */ sorted_values_ptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ stream); - OF_CUDA_CHECK(err); -} - -template -void SortKeysAscending(const KeyType* keys_ptr, int32_t num_row, int32_t num_col, - void* temp_storage_ptr, int32_t temp_storage_bytes, KeyType* sorted_keys_ptr, - hipStream_t stream) { - size_t rt_inferred_temp_storage_bytes = - InferTempStorageForSortKeysAscending(num_row, num_col); - CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); - - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - auto err = hipcub::DeviceSegmentedRadixSort::SortKeys( - /* d_temp_storage */ temp_storage_ptr, - /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, - /* d_keys_in */ keys_ptr, - /* d_keys_out */ sorted_keys_ptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ stream); - OF_CUDA_CHECK(err); -} - -template -void SortKeysDescending(const KeyType* keys_ptr, int32_t num_row, int32_t num_col, - void* temp_storage_ptr, int32_t temp_storage_bytes, - KeyType* sorted_keys_ptr, hipStream_t stream) { - size_t rt_inferred_temp_storage_bytes = - InferTempStorageForSortKeysDescending(num_row, num_col); - CHECK_LE(rt_inferred_temp_storage_bytes, temp_storage_bytes); - - using SegmentOffsetIter = - hipcub::TransformInputIterator>; - - hipcub::CountingInputIterator counting_iter(0); - MultiplyFunctor multiply_functor(num_col); - SegmentOffsetIter segment_offset_iter(counting_iter, multiply_functor); - - auto err = hipcub::DeviceSegmentedRadixSort::SortKeysDescending( - /* d_temp_storage */ temp_storage_ptr, - /* temp_storage_bytes */ rt_inferred_temp_storage_bytes, - /* d_keys_in */ keys_ptr, - /* d_keys_out */ sorted_keys_ptr, - /* num_items */ num_row * num_col, - /* num_segments */ num_row, - /* d_begin_offsets */ segment_offset_iter, - /* d_end_offsets */ segment_offset_iter + 1, - /* begin_bit */ 0, - /* end_bit */ sizeof(KeyType) * 8, - /* stream */ stream); - OF_CUDA_CHECK(err); -} - -} // namespace oneflow - -#endif // ONEFLOW_USER_KERNELS_RADIX_SORT_HIP_H_ \ No newline at end of file diff --git a/oneflow/user/kernels/radix_sort_top_k_kernel.cu b/oneflow/user/kernels/radix_sort_top_k_kernel.cu index 6c43a0cd70447de4bdb47284da8306f41d3234a0..5f38b93dbc458790d7d4f5f4381541fc1c07d35e 100644 --- a/oneflow/user/kernels/radix_sort_top_k_kernel.cu +++ b/oneflow/user/kernels/radix_sort_top_k_kernel.cu @@ -100,9 +100,9 @@ class GpuRadixSortTopKKernel final : public user_op::OpKernel { buf_manager.TempStoragePtr(), buf_manager.TempStorageBytes(), buf_manager.SortedInPtr(), buf_manager.SortedIndicesPtr(), ctx->stream()->As()->cuda_stream()); - OF_CUDA_CHECK(cudaMemcpy2DAsync(out->mut_dptr(), k * sizeof(int64_t), + OF_CUDA_CHECK(GPU(Memcpy2DAsync)(out->mut_dptr(), k * sizeof(int64_t), buf_manager.SortedIndicesPtr(), instance_size * sizeof(int64_t), - k * sizeof(int64_t), instance_num, cudaMemcpyDefault, + k * sizeof(int64_t), instance_num, GPU(MemcpyDefault), ctx->stream()->As()->cuda_stream())); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } diff --git a/oneflow/user/kernels/radix_sort_top_k_kernel.hip.cpp b/oneflow/user/kernels/radix_sort_top_k_kernel.hip.cpp deleted file mode 100644 index f1ea0e3a9ffb6d6f56317d056465f95e2298996b..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/radix_sort_top_k_kernel.hip.cpp +++ /dev/null @@ -1,145 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/radix_sort.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -class TmpBufferManager final { - public: - OF_DISALLOW_COPY_AND_MOVE(TmpBufferManager); - TmpBufferManager(int64_t capacity, void* ptr, const ShapeView& in_shape) - : capacity_{capacity}, - sorted_in_elem_cnt_{in_shape.elem_cnt()}, - indices_elem_cnt_{sorted_in_elem_cnt_}, - sorted_indices_elem_cnt_{sorted_in_elem_cnt_} { - const int64_t sorted_in_aligned_bytes = GetCudaAlignedSize(sorted_in_elem_cnt_ * sizeof(T)); - const int64_t indices_aligned_bytes = GetCudaAlignedSize(indices_elem_cnt_ * sizeof(int64_t)); - const int64_t sorted_indices_aligned_bytes = indices_aligned_bytes; - sorted_in_ptr_ = reinterpret_cast(ptr); - indices_ptr_ = reinterpret_cast(reinterpret_cast(sorted_in_ptr_) - + sorted_in_aligned_bytes); - sorted_indices_ptr_ = - reinterpret_cast(reinterpret_cast(indices_ptr_) + indices_aligned_bytes); - temp_storage_ptr_ = reinterpret_cast(reinterpret_cast(sorted_indices_ptr_) - + sorted_indices_aligned_bytes); - temp_storage_bytes_ = - capacity_ - sorted_in_aligned_bytes - indices_aligned_bytes - sorted_indices_aligned_bytes; - CHECK_GE(temp_storage_bytes_, 0); - } - ~TmpBufferManager() = default; - - T* SortedInPtr() const { return sorted_in_ptr_; } - int64_t* IndicesPtr() const { return indices_ptr_; } - int64_t* SortedIndicesPtr() const { return sorted_indices_ptr_; } - void* TempStoragePtr() const { return temp_storage_ptr_; } - - int64_t TempStorageBytes() const { return temp_storage_bytes_; } - - private: - int64_t capacity_; - - T* sorted_in_ptr_; - int64_t* indices_ptr_; - int64_t* sorted_indices_ptr_; - void* temp_storage_ptr_; - - int64_t sorted_in_elem_cnt_; - int64_t indices_elem_cnt_; - int64_t sorted_indices_elem_cnt_; - int64_t temp_storage_bytes_; -}; - -__global__ void InitializeIndices(int64_t elem_cnt, int64_t* indices_ptr, int64_t instance_size) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { indices_ptr[i] = i % instance_size; }; -} - -} // namespace - -template -class GpuRadixSortTopKKernel final : public user_op::OpKernel { - public: - GpuRadixSortTopKKernel() = default; - ~GpuRadixSortTopKKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - if (in->shape_view().elem_cnt() == 0) { return; } - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - TmpBufferManager buf_manager(static_cast(tmp_buffer->shape_view().elem_cnt()), - tmp_buffer->mut_dptr(), in->shape_view()); - - const int64_t elem_cnt = in->shape_view().elem_cnt(); - const int64_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int64_t instance_num = elem_cnt / instance_size; - const int64_t k = std::min(static_cast(ctx->Attr("k")), instance_size); - InitializeIndices<<stream()->As()->cuda_stream()>>>( - elem_cnt, buf_manager.IndicesPtr(), instance_size); - SortPairsDescending(in->dptr(), buf_manager.IndicesPtr(), instance_num, instance_size, - buf_manager.TempStoragePtr(), buf_manager.TempStorageBytes(), - buf_manager.SortedInPtr(), buf_manager.SortedIndicesPtr(), - ctx->stream()->As()->cuda_stream()); - OF_CUDA_CHECK(hipMemcpy2DAsync(out->mut_dptr(), k * sizeof(int64_t), - buf_manager.SortedIndicesPtr(), instance_size * sizeof(int64_t), - k * sizeof(int64_t), instance_num, hipMemcpyDefault, - ctx->stream()->As()->cuda_stream())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(dtype) \ - REGISTER_USER_KERNEL("top_k") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobAttr("k") > 128) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - const int64_t elem_cnt = in_shape.elem_cnt(); \ - const int64_t instance_size = in_shape.dim_vec().back(); \ - const int64_t instance_num = elem_cnt / instance_size; \ - \ - /* Sorted In*/ \ - const int64_t sorted_in_aligned_bytes = GetCudaAlignedSize(elem_cnt * sizeof(dtype)); \ - /* Indices */ \ - const int64_t indices_aligned_bytes = GetCudaAlignedSize(elem_cnt * sizeof(int64_t)); \ - /* Sorted Indices */ \ - const int64_t sorted_indices_aligned_bytes = indices_aligned_bytes; \ - /* CUB Temp Storage */ \ - int64_t temp_storage_bytes = \ - InferTempStorageForSortPairsDescending(instance_num, instance_size); \ - \ - return sorted_in_aligned_bytes + indices_aligned_bytes + sorted_indices_aligned_bytes \ - + temp_storage_bytes; \ - }); - -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(float) -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(double) -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(uint8_t) -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(int8_t) -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(int32_t) -REGISTER_CUDA_RADIX_SORT_TOP_K_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/random_mask_generator.cu b/oneflow/user/kernels/random_mask_generator.cu index 3e52f90a32f978f2a348bf672013d86452ce822a..a097f3ef89cef3b1e4357dd549be0e389dde885d 100644 --- a/oneflow/user/kernels/random_mask_generator.cu +++ b/oneflow/user/kernels/random_mask_generator.cu @@ -29,13 +29,13 @@ union Pack { bool b_value[sizeof(PackType)]; }; -__device__ bool GenMask(curandState* state, const float rate) { - return curand_uniform(state) > rate; +__device__ bool GenMask(GPURAND(State)* state, const float rate) { + return GPURAND(_uniform)(state) > rate; } -__global__ void GenerateGpu(curandState* state, const int64_t n, const float rate, bool* mask) { +__global__ void GenerateGpu(GPURAND(State)* state, const int64_t n, const float rate, bool* mask) { const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState localState = state[id]; + GPURAND(State) localState = state[id]; PackType* pack_mask = reinterpret_cast(mask); Pack pack; CUDA_1D_KERNEL_LOOP(i, n / sizeof(PackType)) { diff --git a/oneflow/user/kernels/random_mask_generator.h b/oneflow/user/kernels/random_mask_generator.h index 104b887a4b05b640ed6b390453ee760c75d7b9b6..d76ad9b3f83d17b304e9f240fe78c26fe12f5dee 100644 --- a/oneflow/user/kernels/random_mask_generator.h +++ b/oneflow/user/kernels/random_mask_generator.h @@ -19,15 +19,15 @@ limitations under the License. #include "oneflow/core/common/data_type.h" #include "oneflow/core/ep/include/stream.h" #include "oneflow/core/framework/random_generator.h" -#ifdef WITH_CUDA -#include -#include -#endif #ifdef WITH_ROCM #include #include +#else +#include +#include #endif + namespace oneflow { template @@ -49,25 +49,7 @@ class RandomMaskGenerator final { std::shared_ptr generator_; }; -#ifdef WITH_CUDA -template<> -class RandomMaskGenerator final { - public: - OF_DISALLOW_COPY_AND_MOVE(RandomMaskGenerator); - RandomMaskGenerator(const std::shared_ptr& generator, - const int device_index = -1) { - generator_ = CHECK_JUST(generator->Get(device_index)); - } - ~RandomMaskGenerator() = default; - - void Generate(ep::Stream* stream, int64_t n, float rate, bool* mask); - - private: - std::shared_ptr generator_; -}; -#endif - -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) template<> class RandomMaskGenerator final { public: diff --git a/oneflow/user/kernels/random_mask_generator.hip.cpp b/oneflow/user/kernels/random_mask_generator.hip.cpp deleted file mode 100644 index c28b7e34add54e9fc141dc5fa29c5a6f2bbeac36..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/random_mask_generator.hip.cpp +++ /dev/null @@ -1,69 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/random_mask_generator.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -constexpr int32_t kMinPackPerThread = 2; - -using PackType = ulonglong2; - -union Pack { - PackType p_value; - bool b_value[sizeof(PackType)]; -}; - -__device__ bool GenMask(hiprandState* state, const float rate) { - return hiprand_uniform(state) > rate; -} - -__global__ void GenerateGpu(hiprandState* state, const int64_t n, const float rate, bool* mask) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState localState = state[id]; - PackType* pack_mask = reinterpret_cast(mask); - Pack pack; - CUDA_1D_KERNEL_LOOP(i, n / sizeof(PackType)) { -#pragma unroll - for (int j = 0; j < sizeof(PackType); ++j) { pack.b_value[j] = GenMask(&localState, rate); } - pack_mask[i] = pack.p_value; - } - const int32_t rem_cnt = n % sizeof(PackType); - const int32_t rem_offset = n - rem_cnt; - if (id < rem_cnt) { mask[id + rem_offset] = GenMask(&localState, rate); } - state[id] = localState; -} - -} // namespace - -void RandomMaskGenerator::Generate(ep::Stream* stream, const int64_t n, - const float rate, bool* mask) { - int32_t block_num = generator_->max_block_num(); - int32_t thread_num = generator_->max_thread_num(); - auto* curand_states = generator_->curand_states(); - const int32_t elem_cnt_per_block = thread_num * sizeof(PackType) * kMinPackPerThread; - const int32_t block_num_final = - std::min(static_cast((n + elem_cnt_per_block - 1) / elem_cnt_per_block), block_num); - GenerateGpu<<As()->cuda_stream()>>>( - curand_states, n, rate, mask); -} - -template class RandomMaskGenerator; - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/random_mask_like_kernel.cpp b/oneflow/user/kernels/random_mask_like_kernel.cpp index fd89ff72b23a0b2ff172362f646de233de1bcfd6..6364f316f1e7d70a977f41cd49ad9b2737720ac1 100644 --- a/oneflow/user/kernels/random_mask_like_kernel.cpp +++ b/oneflow/user/kernels/random_mask_like_kernel.cpp @@ -24,10 +24,7 @@ namespace { .SetIsMatchedHob(user_op::HobDeviceType() == device); REGISTER_RANDOM_MASK_LIKE_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_RANDOM_MASK_LIKE_KERNEL(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_RANDOM_MASK_LIKE_KERNEL(DeviceType::kCUDA) #endif } // namespace diff --git a/oneflow/user/kernels/random_seed_util.cpp b/oneflow/user/kernels/random_seed_util.cpp index 77c5c410a4664e6da56809f9bd3d67b35b168bee..3c6ca911542ea8be30186fda33abb4e8502d6cfa 100644 --- a/oneflow/user/kernels/random_seed_util.cpp +++ b/oneflow/user/kernels/random_seed_util.cpp @@ -18,47 +18,58 @@ limitations under the License. namespace oneflow { -Maybe GetOpKernelRandomSeed(const user_op::KernelInitContext* ctx) { +Maybe GetOpKernelRandomSeed(const user_op::KernelInitContext* ctx) { int64_t seed = ctx->Attr("seed"); if (!ctx->Attr("has_seed")) { seed = NewRandomSeed(); } return GetOpKernelRandomSeedInCurrentRank(ctx, seed); } -Maybe GetOpKernelRandomSeedInCurrentRank(const user_op::KernelInitContext* ctx, - int64_t init_seed) { - int64_t seed = init_seed; - int64_t parallel_num = ctx->parallel_ctx().parallel_num(); +// NOTE: Get random seed in current rank, and ensure that it will have same seed between +// broadcast sbp and it will be different between split sbp. +// +// It will scan nd_sbp from last axis to first axis(It likes the algorithm in NdIndexOffsetHelper). +// If sbp is broadcast, this axis will skip. +// If sbp is split, it will use rand_id to accumulate the offset. +Maybe GetRandomSeedForRank(const ParallelDesc& placement, const NdSbp& nd_sbp, + uint64_t init_seed, int64_t rank_id) { + uint64_t seed = init_seed; + const Shape& hierarchy = *placement.hierarchy(); + int64_t seed_idx = 0; + int64_t stride = 1; + for (int i = nd_sbp.sbp_parallel_size() - 1; i >= 0; --i) { + // coordinate at axis i + int coord = rank_id % hierarchy.At(i); + rank_id = (rank_id - coord) / hierarchy.At(i); + // coordinate reset to 0 if broadcast + if (nd_sbp.sbp_parallel(i).has_broadcast_parallel()) { + // do nothing + } else if (nd_sbp.sbp_parallel(i).has_split_parallel()) { + seed_idx += coord * stride; + stride *= hierarchy.At(i); + } else { + // other sbp is not allowed + return Error::RuntimeError() << "random source op only support broadcast or split"; + } + } + std::seed_seq seq{init_seed}; + std::vector seeds(stride); + seq.generate(seeds.begin(), seeds.end()); + seed = JUST(VectorAt(seeds, seed_idx)); + return seed; +} + +Maybe GetOpKernelRandomSeedInCurrentRank(const user_op::KernelInitContext* ctx, + uint64_t init_seed) { const auto& outputs = ctx->outputs(); CHECK_EQ(outputs.size(), 1); - if (parallel_num > 1) { - const Shape& hierarchy = *ctx->parallel_desc().hierarchy(); - int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex(JUST(VectorAt(outputs, 0)).first, - JUST(VectorAt(outputs, 0)).second); - std::vector coordinate(hierarchy.NumAxes()); - int64_t seed_idx = 0; - int64_t stride = 1; - for (int i = nd_sbp.sbp_parallel_size() - 1; i >= 0; --i) { - // coordinate at axis i - int coord = parallel_id % hierarchy.At(i); - parallel_id = (parallel_id - coord) / hierarchy.At(i); - // coordinate reset to 0 if broadcast - if (nd_sbp.sbp_parallel(i).has_broadcast_parallel()) { - // do nothing - } else if (nd_sbp.sbp_parallel(i).has_split_parallel()) { - seed_idx += coord * stride; - stride *= hierarchy.At(i); - } else { - // other sbp is not allowed - return Error::RuntimeError() << "random source op only support broadcast or split"; - } - } + if (ctx->parallel_ctx().parallel_num() == 1) { std::seed_seq seq{init_seed}; - std::vector seeds(stride); + std::vector seeds(1); seq.generate(seeds.begin(), seeds.end()); - seed = JUST(VectorAt(seeds, seed_idx)); + return seeds[0]; } - return seed; + return GetRandomSeedForRank(ctx->parallel_desc(), ctx->NdSbp4ArgNameAndIndex("out", 0), init_seed, + ctx->parallel_ctx().parallel_id()); } } // namespace oneflow diff --git a/oneflow/user/kernels/random_seed_util.h b/oneflow/user/kernels/random_seed_util.h index 7f22010886b874b19ebc8a457d3e57fd5ca3c8ac..233f8384d519e845b47dc8d21470a3e4dcf4ab4a 100644 --- a/oneflow/user/kernels/random_seed_util.h +++ b/oneflow/user/kernels/random_seed_util.h @@ -20,10 +20,12 @@ limitations under the License. namespace oneflow { -Maybe GetOpKernelRandomSeed(const user_op::KernelInitContext* ctx); +Maybe GetRandomSeedForRank(const ParallelDesc& placement, const NdSbp& nd_sbp, + uint64_t init_seed, int64_t rank_id); -Maybe GetOpKernelRandomSeedInCurrentRank(const user_op::KernelInitContext* ctx, - int64_t init_seed); +Maybe GetOpKernelRandomSeed(const user_op::KernelInitContext* ctx); +Maybe GetOpKernelRandomSeedInCurrentRank(const user_op::KernelInitContext* ctx, + uint64_t init_seed); } // namespace oneflow diff --git a/oneflow/user/kernels/randperm_kernel.cu b/oneflow/user/kernels/randperm_kernel.cu index e6a3166cc1df9b7800222d01bba94af6267c5bcb..b4b55566de3b7dd1fb516d7f60e125d64b98fba0 100644 --- a/oneflow/user/kernels/randperm_kernel.cu +++ b/oneflow/user/kernels/randperm_kernel.cu @@ -13,8 +13,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#ifdef WITH_ROCM +#include +#include +#else #include #include +#endif #include "oneflow/core/common/data_type.h" #include "oneflow/core/ep/include/stream.h" @@ -33,11 +38,11 @@ limitations under the License. namespace oneflow { __global__ void GeneKeysAndValues(const int32_t n, int32_t* values, int32_t* keys, - curandState* state) { + GPURAND(State)* state) { const int id = blockIdx.x * blockDim.x + threadIdx.x; - curandState local_state = state[id]; + GPURAND(State) local_state = state[id]; CUDA_1D_KERNEL_LOOP(i, n) { - keys[i] = curand(&local_state); + keys[i] = GPURAND()(&local_state); values[i] = i; } state[id] = local_state; @@ -65,8 +70,13 @@ namespace { template size_t GetCubSortPairsTempStorageSize(int64_t n) { size_t cub_sort_temp_store_size = 0; +#ifdef WITH_ROCM + OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, + nullptr, nullptr, nullptr, n))); +#else OF_CUDA_CHECK((cub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, nullptr, nullptr, nullptr, n))); +#endif size_t temp_store_size = GetCudaAlignedSize(cub_sort_temp_store_size); CHECK_GE(temp_store_size, 0) << "temp_store_size should >= 0."; CHECK_LT(temp_store_size, static_cast(GetMaxVal())) @@ -125,7 +135,7 @@ class GpuRandPermKernel final : public user_op::OpKernel { int32_t block_num = gpu_generator->max_block_num(); int32_t thread_num = gpu_generator->max_thread_num(); - curandState* curand_states = gpu_generator->curand_states(); + GPURAND(State)* curand_states = gpu_generator->curand_states(); // layout for tmp |...key(in and out,2xN)..|....value....|.... space for sort function....| // values are the desired indexes ,and keys are generated randomly. @@ -146,6 +156,20 @@ class GpuRandPermKernel final : public user_op::OpKernel { GeneKeysAndValues<<As()->cuda_stream()>>>( n, value_base, key_base, curand_states); if (cache == nullptr) { +#ifdef WITH_ROCM + auto err = hipcub::DeviceRadixSort::SortPairs( + /* d_temp_storage */ tmp_base, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ key_base, + /* d_keys_out */ key_base + n, + /* d_values_in */ value_base, + /* d_values_out */ output, + /* num_items */ n, + /* begin_bit */ 0, + /* end_bit */ sizeof(int32_t) * 8, + /* stream */ ctx->stream()->As()->cuda_stream()); + OF_CUDA_CHECK(err); +#else auto err = cub::DeviceRadixSort::SortPairs( /* d_temp_storage */ tmp_base, /* temp_storage_bytes */ temp_storage_bytes, @@ -158,7 +182,22 @@ class GpuRandPermKernel final : public user_op::OpKernel { /* end_bit */ sizeof(int32_t) * 8, /* stream */ ctx->stream()->As()->cuda_stream()); OF_CUDA_CHECK(err); +#endif } else { +#ifdef WITH_ROCM + auto err = hipcub::DeviceRadixSort::SortPairs( + /* d_temp_storage */ tmp_base, + /* temp_storage_bytes */ temp_storage_bytes, + /* d_keys_in */ key_base, + /* d_keys_out */ key_base + n, + /* d_values_in */ value_base, + /* d_values_out */ temp_buffer_base, + /* num_items */ n, + /* begin_bit */ 0, + /* end_bit */ sizeof(int32_t) * 8, + /* stream */ ctx->stream()->As()->cuda_stream()); + OF_CUDA_CHECK(err); +#else auto err = cub::DeviceRadixSort::SortPairs( /* d_temp_storage */ tmp_base, /* temp_storage_bytes */ temp_storage_bytes, @@ -171,6 +210,7 @@ class GpuRandPermKernel final : public user_op::OpKernel { /* end_bit */ sizeof(int32_t) * 8, /* stream */ ctx->stream()->As()->cuda_stream()); OF_CUDA_CHECK(err); +#endif const auto* randperm_cache = dynamic_cast(cache); auto len = randperm_cache->upper() - randperm_cache->lower(); const int64_t offset = randperm_cache->lower(); diff --git a/oneflow/user/kernels/randperm_kernel.hip.cpp b/oneflow/user/kernels/randperm_kernel.hip.cpp deleted file mode 100644 index 94dcf6c5eb8ee241db5d471da5040353f49d31da..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/randperm_kernel.hip.cpp +++ /dev/null @@ -1,201 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include -#include -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/ep/include/stream.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/framework/random_generator.h" -#include "oneflow/user/kernels/op_kernel_wrapper.h" -#include "oneflow/user/kernels/arange_kernel_util.h" -#include "oneflow/user/kernels/radix_sort.hip.h" -#include "oneflow/user/kernels/distributions/common.h" -#include "oneflow/core/ep/include/device.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/job/nd_sbp_util.h" -#include "oneflow/core/common/container_util.h" -#include "oneflow/core/register/tensor_slice_view.h" -#include "oneflow/core/device/cuda_util.h" - -namespace oneflow { -__global__ void GeneKeysAndValues(const int32_t n, int32_t* values, int32_t* keys, - hiprandState* state) { - const int id = blockIdx.x * blockDim.x + threadIdx.x; - hiprandState local_state = state[id]; - CUDA_1D_KERNEL_LOOP(i, n) { - keys[i] = hiprand(&local_state); - values[i] = i; - } - state[id] = local_state; -} - -__global__ void tempcopy2output(const int32_t n, const int32_t offset, int32_t* temp, - int32_t* output) { - CUDA_1D_KERNEL_LOOP(i, n) { output[i] = temp[offset + i]; } -} -class GpuRandPermKernelCache final : public user_op::OpKernelCache { - public: - GpuRandPermKernelCache(int32_t lower, int32_t upper) : lower_(lower), upper_(upper) {} - ~GpuRandPermKernelCache() override = default; - - int32_t lower() const { return lower_; } - int32_t upper() const { return upper_; } - - private: - const int32_t lower_; - const int32_t upper_; -}; - -namespace { - -template -size_t GetCubSortPairsTempStorageSize(int64_t n) { - size_t cub_sort_temp_store_size = 0; - OF_CUDA_CHECK((hipcub::DeviceRadixSort::SortPairs(nullptr, cub_sort_temp_store_size, nullptr, - nullptr, nullptr, nullptr, n))); - size_t temp_store_size = GetCudaAlignedSize(cub_sort_temp_store_size); - CHECK_GE(temp_store_size, 0) << "temp_store_size should >= 0."; - CHECK_LT(temp_store_size, static_cast(GetMaxVal())) - << "temp_store_size should < " << static_cast(GetMaxVal()); - return temp_store_size; -} - -} // namespace - -class GpuRandPermKernel final : public user_op::OpKernel { - public: - GpuRandPermKernel() = default; - ~GpuRandPermKernel() = default; - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - int64_t parallel_num = ctx->parallel_ctx().parallel_num(); - if (parallel_num > 1) { - const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); - const Shape& hierarchy = *ctx->parallel_desc().hierarchy(); - int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - int32_t n = ctx->Attr("n"); - const Shape& logical_shape = Shape({n}); - TensorSliceView view = - GetTensorSliceView4ParallelId(hierarchy, nd_sbp, logical_shape, parallel_id); - std::shared_ptr cache( - new GpuRandPermKernelCache(view.At(0).begin(), view.At(0).end())); - return cache; - } else { - return nullptr; - } - } - std::shared_ptr CreateOpKernelState( - user_op::KernelInitContext* ctx) const override { - const auto& generator = CHECK_JUST(one::MakeGenerator(kCUDA)); - generator->set_current_seed(ctx->Attr("seed")); - return std::make_shared(generator); - } - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, - const user_op::OpKernelCache* cache) const override { - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int32_t* output = out->mut_dptr(); - const int32_t n = ctx->Attr("n"); - if (n == 0) { return; } - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - auto* distribution_state = dynamic_cast(state); - CHECK_NOTNULL(distribution_state); - const auto& generator = distribution_state->generator(); - auto* stream = ctx->stream(); - const auto device_index = stream->device()->device_index(); - const auto& gpu_generator = CHECK_JUST(generator->Get(device_index)); - CHECK_NOTNULL(generator); - - int32_t block_num = gpu_generator->max_block_num(); - int32_t thread_num = gpu_generator->max_thread_num(); - hiprandState* curand_states = gpu_generator->curand_states(); - - // layout for tmp |...key(in and out,2xN)..|....value....|.... space for sort function....| - // values are the desired indexes ,and keys are generated randomly. - void* tmp = tmp_buffer->mut_dptr(); - int32_t* key_base = reinterpret_cast(tmp); - - const int32_t key_aligned_bytes = GetCudaAlignedSize(n * sizeof(int32_t)); - int32_t* value_base = - reinterpret_cast(reinterpret_cast(key_base) + 2 * key_aligned_bytes); - const int32_t indices_aligned_bytes = GetCudaAlignedSize(n * sizeof(int32_t)); - int32_t* temp_buffer_base = - reinterpret_cast(reinterpret_cast(value_base) + indices_aligned_bytes); - const int32_t temp_buffer_aligned_bytes = GetCudaAlignedSize(n * sizeof(int32_t)); - - void* tmp_base = reinterpret_cast(reinterpret_cast(temp_buffer_base) - + temp_buffer_aligned_bytes); - size_t temp_storage_bytes = GetCubSortPairsTempStorageSize(n); - GeneKeysAndValues<<As()->cuda_stream()>>>( - n, value_base, key_base, curand_states); - if (cache == nullptr) { - auto err = hipcub::DeviceRadixSort::SortPairs( - /* d_temp_storage */ tmp_base, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ key_base, - /* d_keys_out */ key_base + n, - /* d_values_in */ value_base, - /* d_values_out */ output, - /* num_items */ n, - /* begin_bit */ 0, - /* end_bit */ sizeof(int32_t) * 8, - /* stream */ ctx->stream()->As()->cuda_stream()); - OF_CUDA_CHECK(err); - } else { - auto err = hipcub::DeviceRadixSort::SortPairs( - /* d_temp_storage */ tmp_base, - /* temp_storage_bytes */ temp_storage_bytes, - /* d_keys_in */ key_base, - /* d_keys_out */ key_base + n, - /* d_values_in */ value_base, - /* d_values_out */ temp_buffer_base, - /* num_items */ n, - /* begin_bit */ 0, - /* end_bit */ sizeof(int32_t) * 8, - /* stream */ ctx->stream()->As()->cuda_stream()); - OF_CUDA_CHECK(err); - const auto* randperm_cache = dynamic_cast(cache); - auto len = randperm_cache->upper() - randperm_cache->lower(); - const int64_t offset = randperm_cache->lower(); - tempcopy2output<<stream()->As()->cuda_stream()>>>( - len, offset, temp_buffer_base, output); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; -REGISTER_USER_KERNEL("randperm") - .SetCreateFn() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const int32_t n = ctx->Attr("n"); - /* Sorted In */ - const int32_t sorted_in_aligned_bytes = 2 * GetCudaAlignedSize(n * sizeof(int32_t)); - /* Indices */ - const int32_t indices_aligned_bytes = GetCudaAlignedSize(n * sizeof(int32_t)); - const int32_t temp_aligned_bytes = GetCudaAlignedSize(n * sizeof(int32_t)); - - /* CUB Temp Storage */ - const int32_t temp_storage_bytes = GetCubSortPairsTempStorageSize(n); - - return sorted_in_aligned_bytes + indices_aligned_bytes + temp_storage_bytes - + temp_aligned_bytes; - }); -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/raw_reader_kernel.cpp b/oneflow/user/kernels/raw_reader_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3967cdf074d9ed81f660d5fdd17d84e1c2be55c4 --- /dev/null +++ b/oneflow/user/kernels/raw_reader_kernel.cpp @@ -0,0 +1,430 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/common/buffer.h" +#include "oneflow/core/embedding/posix_file.h" +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/common/channel.h" + +namespace oneflow { + +namespace { + +struct Block { + size_t file_index; + size_t offset_in_file; +}; + +struct BatchReaderRequest { + std::shared_ptr> blocks; + void* buffer{}; +}; + +class BatchReader { + public: + OF_DISALLOW_COPY_AND_MOVE(BatchReader); + BatchReader(std::vector>&& files, + std::vector&& blocks, size_t block_size_bytes, size_t num_workers) + : head_(0), + tail_(0), + files_(std::move(files)), + blocks_(blocks), + block_size_bytes_(block_size_bytes), + num_workers_(num_workers) { + for (size_t i = 0; i < num_workers_; ++i) { + Worker worker; + auto* sq = new Channel(); + auto* cq = new Channel(); + worker.sq.reset(sq); + worker.cq.reset(cq); + worker.thread = std::thread([sq, cq, this]() { + while (true) { + BatchReaderRequest request; + auto status = sq->Receive(&request); + if (status == kChannelStatusErrorClosed) { break; } + CHECK_EQ(status, kChannelStatusSuccess) << "channel error"; + size_t buffer_offset = 0; + for (size_t i = 0; i < request.blocks->size(); ++i) { + size_t block_index = request.blocks->at(i); + const Block& block = blocks_[block_index]; + size_t remaining = block_size_bytes_; + size_t file_index = block.file_index; + size_t file_offset = block.offset_in_file; + while (remaining != 0) { + const size_t bytes_to_read = + std::min(remaining, files_.at(file_index)->Size() - file_offset); + PCHECK(pread(files_[file_index]->fd(), + reinterpret_cast(request.buffer) + buffer_offset, + bytes_to_read, file_offset) + == bytes_to_read) + << "file read error"; + remaining -= bytes_to_read; + buffer_offset += bytes_to_read; + if (remaining != 0) { + file_index = (file_index + 1) % files_.size(); + file_offset = 0; + } + } + } + CHECK(cq->Send(std::move(request)) == kChannelStatusSuccess) << "channel error"; + } + }); + workers_.emplace_back(std::move(worker)); + } + } + ~BatchReader() { + for (auto& work : workers_) { work.Close(); } + } + + void SubmitRequest(BatchReaderRequest&& request) { + size_t worker_id = head_.fetch_add(1, std::memory_order_relaxed) % workers_.size(); + workers_.at(worker_id).sq->Send(std::move(request)); + } + void WaitCompleted(BatchReaderRequest* request) { + size_t worker_id = tail_.fetch_add(1, std::memory_order_relaxed) % workers_.size(); + workers_.at(worker_id).cq->Receive(request); + } + + private: + struct Worker { + std::thread thread; + std::unique_ptr> sq; + std::unique_ptr> cq; + void Close() { + sq->Close(); + cq->Close(); + thread.join(); + } + }; + std::atomic head_; + std::atomic tail_; + std::vector workers_; + std::vector> files_; + std::vector blocks_; + size_t block_size_bytes_; + size_t num_workers_; +}; + +size_t GetNumShards(const Shape& hierarchy, const NdSbp& nd_sbp) { + size_t num_shards = 1; + FOR_RANGE(size_t, i, 0, nd_sbp.sbp_parallel_size()) { + const auto& sbp_parallel = nd_sbp.sbp_parallel(i); + if (sbp_parallel.has_split_parallel()) { + num_shards *= hierarchy.At(sbp_parallel.split_parallel().axis()); + } + } + return num_shards; +} + +size_t GetShardIndex(const Shape& hierarchy, const NdSbp& nd_sbp, size_t rank) { + using index_helper_t = NdIndexOffsetHelper; + size_t ndim = hierarchy.NumAxes(); + CHECK_GT(ndim, 0) << "wrong hierarchy"; + CHECK_LE(ndim, SHAPE_MAX_AXIS_SIZE) << "wrong hierarchy"; + index_helper_t index_helper(hierarchy.dim_vec().data(), ndim); + int64_t nd_index[SHAPE_MAX_AXIS_SIZE] = {0}; + index_helper.OffsetToNdIndex(rank, nd_index); + size_t stride = 1; + size_t index = 0; + for (int i = ndim - 1; i >= 0; --i) { + const auto& sbp_parallel = nd_sbp.sbp_parallel(i); + if (sbp_parallel.has_split_parallel()) { + index += nd_index[i] * stride; + stride *= hierarchy.At(i); + } + } + return index; +} + +class BatchGenerator { + public: + OF_DISALLOW_COPY_AND_MOVE(BatchGenerator); + BatchGenerator() = default; + virtual ~BatchGenerator() = default; + + virtual void Next(size_t* blocks) = 0; +}; + +class SequentialBatchGenerator : public BatchGenerator { + public: + OF_DISALLOW_COPY_AND_MOVE(SequentialBatchGenerator); + SequentialBatchGenerator(size_t shard_index, size_t num_shards, size_t num_batches, + size_t num_blocks_per_batch) + : shard_index_(shard_index), + num_shards_(num_shards), + num_batches_(num_batches), + num_blocks_per_batch_(num_blocks_per_batch), + num_blocks_per_local_batch_(num_blocks_per_batch_ / num_shards_), + next_batch_index_(0) {} + ~SequentialBatchGenerator() override = default; + + void Next(size_t* blocks) override { + const size_t batch_index = next_batch_index_; + next_batch_index_ = (batch_index + 1) % num_batches_; + for (size_t i = 0; i < num_blocks_per_local_batch_; ++i) { + blocks[i] = + batch_index * num_blocks_per_batch_ + shard_index_ * num_blocks_per_local_batch_ + i; + } + } + + private: + size_t shard_index_; + size_t num_shards_; + size_t num_batches_; + size_t num_blocks_per_batch_; + size_t num_blocks_per_local_batch_; + size_t next_batch_index_; +}; + +class RandomShuffleBatchGenerator : public BatchGenerator { + public: + OF_DISALLOW_COPY_AND_MOVE(RandomShuffleBatchGenerator); + RandomShuffleBatchGenerator(size_t shard_index, size_t num_shards, size_t num_batches, + size_t num_blocks_per_batch, std::mt19937_64 generator) + : shard_index_(shard_index), + num_shards_(num_shards), + num_batches_(num_batches), + num_blocks_per_batch_(num_blocks_per_batch), + num_blocks_per_local_batch_(num_blocks_per_batch_ / num_shards_), + current_batch_pos_(0), + generator_(generator) { + batches_.resize(num_batches_); + std::iota(batches_.begin(), batches_.end(), 0); + } + ~RandomShuffleBatchGenerator() override = default; + + void Next(size_t* blocks) override { + size_t target_batch_pos = + generator_() % (batches_.size() - current_batch_pos_) + current_batch_pos_; + if (target_batch_pos != current_batch_pos_) { + std::swap(batches_[target_batch_pos], batches_[current_batch_pos_]); + } + const size_t batch_index = batches_[current_batch_pos_]; + for (size_t i = 0; i < num_blocks_per_local_batch_; ++i) { + blocks[i] = + batch_index * num_blocks_per_batch_ + shard_index_ * num_blocks_per_local_batch_ + i; + } + current_batch_pos_ = (current_batch_pos_ + 1) % batches_.size(); + if (current_batch_pos_ == 0) { shard_index_ = (shard_index_ + 1) % num_shards_; } + } + + private: + size_t shard_index_; + size_t num_shards_; + size_t num_batches_; + size_t num_blocks_per_batch_; + size_t num_blocks_per_local_batch_; + std::vector batches_; + size_t current_batch_pos_; + std::mt19937_64 generator_; +}; + +class RawReaderKernelState final : public user_op::OpKernelState { + public: + OF_DISALLOW_COPY_AND_MOVE(RawReaderKernelState); + explicit RawReaderKernelState(user_op::KernelInitContext* ctx) { + const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); + num_shards_ = GetNumShards(*ctx->parallel_desc().hierarchy(), nd_sbp); + shard_index_ = + GetShardIndex(*ctx->parallel_desc().hierarchy(), nd_sbp, ctx->parallel_ctx().parallel_id()); + batch_size_ = ctx->Attr("batch_size"); + CHECK_EQ(batch_size_ % num_shards_, 0) << "batch_size must be a multiple of num_shards"; + local_batch_size_ = batch_size_ / num_shards_; + random_shuffle_ = ctx->Attr("random_shuffle"); + block_size_ = ctx->Attr("shuffle_block_size"); + if (block_size_ <= 0 || !random_shuffle_) { block_size_ = local_batch_size_; } + CHECK_EQ(batch_size_ % block_size_, 0) << "batch_size must be a multiple of block_size"; + if (block_size_ > local_batch_size_) { block_size_ = local_batch_size_; } + const std::vector& filenames = ctx->Attr>("files"); + const Shape& instance_shape = ctx->Attr("shape"); + const size_t elem_cnt = instance_shape.elem_cnt(); + CHECK_GT(elem_cnt, 0) << "instance size must be greater than 0"; + DimVector dim_vec; + dim_vec.push_back(local_batch_size_); + for (int64_t i = 0; i < instance_shape.NumAxes(); ++i) { + dim_vec.push_back(instance_shape.At(i)); + } + out_shape_ = Shape(dim_vec); + data_type_ = ctx->Attr("data_type"); + instance_size_ = ctx->Attr("shape").elem_cnt() * GetSizeOfDataType(data_type_); + CHECK_GT(batch_size_, 0) << "batch size must be greater than 0"; + size_t num_instances = 0; + std::vector> files; + int flags = O_RDONLY; + if (ParseBooleanFromEnv("ONEFLOW_RAW_READER_FORCE_DIRECT_IO", false)) { flags |= O_DIRECT; } + for (const auto& filename : filenames) { + std::unique_ptr file(new embedding::PosixFile(filename, flags, 0644)); + if (file->Size() == 0) { continue; } + CHECK_EQ(file->Size() % instance_size_, 0) << "file_size must be a multiple of instance_size"; + num_instances += file->Size() / instance_size_; + files.emplace_back(std::move(file)); + } + if ((flags & O_DIRECT) != 0) { + num_batches_ = num_instances / batch_size_; + } else { + num_batches_ = RoundUp(num_instances, batch_size_) / batch_size_; + } + block_size_bytes_ = block_size_ * instance_size_; + local_batch_size_bytes_ = local_batch_size_ * instance_size_; + num_blocks_per_local_batch_ = local_batch_size_ / block_size_; + const size_t num_blocks = num_batches_ * (batch_size_ / block_size_); + size_t file_index = 0; + size_t offset_in_file = 0; + std::vector blocks; + for (size_t i = 0; i < num_blocks; ++i) { + blocks.emplace_back(Block{file_index, offset_in_file}); + size_t remaining = block_size_bytes_; + while (remaining != 0) { + if (files[file_index]->Size() - offset_in_file >= remaining) { + offset_in_file += remaining; + if (offset_in_file == files[file_index]->Size()) { offset_in_file = 0; } + remaining = 0; + } else { + remaining -= (files[file_index]->Size() - offset_in_file); + offset_in_file = 0; + file_index = (file_index + 1) % files.size(); + } + } + } + if (random_shuffle_) { + std::mt19937_64 generator; + generator.seed(ctx->Attr("seed")); + std::shuffle(blocks.begin(), blocks.end(), generator); + batch_generator_.reset(new RandomShuffleBatchGenerator( + shard_index_, num_shards_, num_batches_, batch_size_ / block_size_, generator)); + } else { + batch_generator_.reset(new SequentialBatchGenerator(shard_index_, num_shards_, num_batches_, + batch_size_ / block_size_)); + } + const size_t num_workers = ParseIntegerFromEnv("ONEFLOW_RAW_READER_NUM_WORKERS", 1); + batch_reader_.reset( + new BatchReader(std::move(files), std::move(blocks), block_size_bytes_, num_workers)); + prefetching_qd_ = ParseIntegerFromEnv("ONEFLOW_RAW_READER_PREFETCHING_QUEUE_DEPTH", 256); + for (size_t i = 0; i < prefetching_qd_; ++i) { + BatchReaderRequest request; + request.blocks = std::make_shared>(); + if (ctx->device_type() == DeviceType::kCPU) { + request.buffer = aligned_alloc(4096, RoundUp(local_batch_size_bytes_, 4096)); // NOLINT + } else if (ctx->device_type() == DeviceType::kCUDA) { +#if defined(WITH_CUDA) || defined(WITH_ROCM) + int dev = 0; + OF_CUDA_CHECK(GPU(GetDevice)(&dev)); + OF_CUDA_CHECK(NumaAwareCudaMallocHost(dev, &request.buffer, local_batch_size_bytes_)); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } + request.blocks = std::make_shared>(local_batch_size_ / block_size_); + batch_generator_->Next(request.blocks->data()); + batch_reader_->SubmitRequest(std::move(request)); + } + device_type_ = ctx->device_type(); + } + + ~RawReaderKernelState() { + for (size_t i = 0; i < prefetching_qd_; ++i) { + BatchReaderRequest request; + batch_reader_->WaitCompleted(&request); + if (device_type_ == DeviceType::kCPU) { + free(request.buffer); // NOLINT + } else if (device_type_ == DeviceType::kCUDA) { +#if defined(WITH_CUDA) || defined(WITH_ROCM) + OF_CUDA_CHECK(GPU(FreeHost)(request.buffer)); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED(); + } + } + } + + void Next(user_op::KernelComputeContext* ctx) { + auto* tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(tensor->data_type(), data_type_) << "data type mismatch"; + CHECK(tensor->shape_view() == ShapeView(out_shape_)) << "shape mismatch"; + BatchReaderRequest request; + batch_reader_->WaitCompleted(&request); + if (ctx->stream()->device_type() == DeviceType::kCPU) { + std::memcpy(tensor->mut_dptr(), request.buffer, local_batch_size_bytes_); + } else if (ctx->stream()->device_type() == DeviceType::kCUDA) { +#if defined(WITH_CUDA) || defined(WITH_ROCM) + OF_CUDA_CHECK(GPU(MemcpyAsync)(tensor->mut_dptr(), request.buffer, + local_batch_size_bytes_, GPU(MemcpyDefault), + ctx->stream()->As()->cuda_stream())); +#else + UNIMPLEMENTED(); +#endif + } else { + UNIMPLEMENTED() << "only support CPU or CUDA"; + } + CHECK_JUST(ctx->stream()->Sync()); + CHECK(request.blocks) << "blocks is NULL"; + CHECK_EQ(request.blocks->size(), num_blocks_per_local_batch_) << "blocks size mismatch"; + batch_generator_->Next(request.blocks->data()); + batch_reader_->SubmitRequest(std::move(request)); + } + + private: + size_t instance_size_; + size_t batch_size_; + size_t local_batch_size_; + size_t num_batches_; + size_t num_shards_; + size_t shard_index_; + size_t block_size_; + size_t block_size_bytes_; + size_t num_blocks_per_local_batch_; + size_t local_batch_size_bytes_; + bool random_shuffle_; + Shape out_shape_; + DataType data_type_; + std::unique_ptr batch_generator_; + std::unique_ptr batch_reader_; + DeviceType device_type_; + size_t prefetching_qd_; +}; + +} // namespace + +class RawReaderKernel final : public user_op::OpKernel { + public: + RawReaderKernel() = default; + ~RawReaderKernel() override = default; + + std::shared_ptr CreateOpKernelState( + user_op::KernelInitContext* ctx) const override { + std::shared_ptr state(new RawReaderKernelState(ctx)); + return state; + } + + private: + void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState* state, + const user_op::OpKernelCache*) const override { + auto* reader = CHECK_NOTNULL(dynamic_cast(state)); + reader->Next(ctx); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("raw_reader") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobTrue()); + +} // namespace oneflow diff --git a/oneflow/user/kernels/reduce_kernel.cpp b/oneflow/user/kernels/reduce_kernel.cpp index 5efa5bc6e7ffa265350efa38897ed6df4100504b..f930e25d58b2f3f1310adb43d360f41f7f709954 100644 --- a/oneflow/user/kernels/reduce_kernel.cpp +++ b/oneflow/user/kernels/reduce_kernel.cpp @@ -13,6 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/common/scalar.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/ndarray/ndarray_util.h" #include "oneflow/core/ndarray/xpu_var_ndarray.h" @@ -21,12 +22,9 @@ limitations under the License. #include "oneflow/core/ep/include/primitive/cast.h" #include "oneflow/core/ep/include/primitive/fill.h" -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/ep/cuda/cuda_device.h" #endif // WITH_CUDA -#ifdef WITH_ROCM -#include "oneflow/core/ep/rocm/cuda_device.h" -#endif // WITH_ROCM #include "oneflow/core/ep/include/primitive/matmul.h" namespace oneflow { @@ -60,6 +58,12 @@ std::unique_ptr NewReduceMatmulNoTransAPrimitive(Context* /*transpose_b=*/false); } +template +std::unique_ptr NewFillPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("output_tensor", 0)->data_type(); + return ep::primitive::NewPrimitive(ctx->device_type(), data_type); +} + auto ReduceMatmulTransAPrimitiveExists() { return hob::make_custom("ReduceMatmulTransAPrimitiveExists", [](const user_op::KernelRegContext& ctx) { @@ -74,6 +78,12 @@ auto ReduceMatmulNoTransAPrimitiveExists() { }); } +auto FillPrimitiveExists() { + return hob::make_custom("FillPrimitiveExists", [](const user_op::KernelRegContext& ctx) { + return NewFillPrimitive(&ctx).operator bool(); + }); +} + template class BinaryFunc, DeviceType device_type, typename T, typename K> class ReduceKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -86,12 +96,20 @@ class ReduceKernel final : public user_op::OpKernel, public user_op::CudaGraphSu user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("output_tensor", 0); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const auto& axis = ctx->Attr>("axis"); + const int32_t output_elem_cnt = output_tensor->shape_view().elem_cnt(); if (input_tensor->shape_view().elem_cnt() == 0) { if (output_tensor->shape_view().elem_cnt() != 0) { - Memset( - ctx->stream(), output_tensor->mut_dptr(), 0, - output_tensor->shape_view().elem_cnt() * GetSizeOfDataType(output_tensor->data_type())); + Scalar init_value = [&]() { + if (std::is_same, BinaryFuncAny>::value) { return Scalar(0); } + if (std::is_same, BinaryFuncAll>::value) { return Scalar(1); } + return Scalar(0); + }(); + CHECK_GE(output_elem_cnt, 0); + if (output_elem_cnt == 0) { return; } + std::unique_ptr fill = NewFillPrimitive(ctx); + CHECK(fill); + fill->Launch(ctx->stream(), output_tensor->mut_dptr(), init_value, output_elem_cnt); } return; } @@ -122,7 +140,8 @@ class ReduceKernel final : public user_op::OpKernel, public user_op::CudaGraphSu .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == device) \ && (user_op::HobDataType("input_tensor", 0) == GetDataType::value) \ - && (user_op::HobDataType("output_tensor", 0) == DataType::kBool)) \ + && (user_op::HobDataType("output_tensor", 0) == DataType::kBool) \ + && FillPrimitiveExists()) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ const Shape& in_shape = ctx->InputShape("input_tensor", 0); \ return in_shape.elem_cnt() * sizeof(dtype); \ @@ -133,7 +152,11 @@ class ReduceKernel final : public user_op::OpKernel, public user_op::CudaGraphSu REGISTER_REDUCE_XPU_KERNEL("reduce_min", BinaryFuncMin, device, dtype) \ REGISTER_REDUCE_XPU_KERNEL("reduce_max", BinaryFuncMax, device, dtype) +#define REGISTER_REDUCE_NANSUM_KERNELS(device, dtype) \ + REGISTER_REDUCE_XPU_KERNEL("reduce_nansum", BinaryFuncNanSum, device, dtype) + #define REGISTER_REDUCE_ARITHMETIC_KERNELS_BY_DEVICE(device) \ + REGISTER_REDUCE_ARITHMETIC_KERNELS(device, bool) \ REGISTER_REDUCE_ARITHMETIC_KERNELS(device, float) \ REGISTER_REDUCE_ARITHMETIC_KERNELS(device, double) \ REGISTER_REDUCE_ARITHMETIC_KERNELS(device, int8_t) \ @@ -141,12 +164,15 @@ class ReduceKernel final : public user_op::OpKernel, public user_op::CudaGraphSu REGISTER_REDUCE_ARITHMETIC_KERNELS(device, int32_t) \ REGISTER_REDUCE_ARITHMETIC_KERNELS(device, int64_t) +#define REGISTER_REDUCE_NANSUM_KERNELS_BY_DEVICE(device) \ + REGISTER_REDUCE_NANSUM_KERNELS(device, float) \ + REGISTER_REDUCE_NANSUM_KERNELS(device, double) + REGISTER_REDUCE_ARITHMETIC_KERNELS_BY_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_REDUCE_ARITHMETIC_KERNELS_BY_DEVICE(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM +REGISTER_REDUCE_NANSUM_KERNELS_BY_DEVICE(DeviceType::kCPU) +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_REDUCE_ARITHMETIC_KERNELS_BY_DEVICE(DeviceType::kCUDA) +REGISTER_REDUCE_NANSUM_KERNELS_BY_DEVICE(DeviceType::kCUDA) #endif #define REGISTER_REDUCE_SUM_KERNELS(device, dtype) \ @@ -160,13 +186,11 @@ REGISTER_REDUCE_ARITHMETIC_KERNELS_BY_DEVICE(DeviceType::kCUDA) REGISTER_REDUCE_SUM_KERNELS(device, int64_t) REGISTER_REDUCE_SUM_KERNELS_BY_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_REDUCE_SUM_KERNELS_BY_DEVICE(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_REDUCE_SUM_KERNELS_BY_DEVICE(DeviceType::kCUDA) #endif REGISTER_REDUCE_SUM_KERNELS(DeviceType::kCPU, float) +REGISTER_REDUCE_SUM_KERNELS(DeviceType::kCPU, float16) #define REGISTER_REDUCE_LOGICAL_KERNELS(device) \ REGISTER_REDUCE_LOGICAL_XPU_KERNEL("reduce_any", BinaryFuncAny, device, bool) \ @@ -185,7 +209,7 @@ REGISTER_REDUCE_SUM_KERNELS(DeviceType::kCPU, float) REGISTER_REDUCE_LOGICAL_XPU_KERNEL("reduce_all", BinaryFuncAll, device, int64_t) REGISTER_REDUCE_LOGICAL_KERNELS(DeviceType::kCPU) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_REDUCE_LOGICAL_KERNELS(DeviceType::kCUDA) namespace { @@ -221,6 +245,7 @@ class ReduceSumHalfKernel final : public user_op::OpKernel, public user_op::Cuda user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("output_tensor", 0); user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); const ShapeView& in_shape = input_tensor->shape_view(); + const DataType data_type = input_tensor->data_type(); bool is_axis_contiguous = false; int64_t outer_size = 0, inner_size = 0, reduce_size = 0; GetReduceSumLayout(axis, in_shape, &is_axis_contiguous, &outer_size, &inner_size, &reduce_size); @@ -229,19 +254,16 @@ class ReduceSumHalfKernel final : public user_op::OpKernel, public user_op::Cuda const int32_t m = (inner_size == 1) ? outer_size : inner_size; const int32_t n = 1; const int32_t k = reduce_size; - const float16* ones = nullptr; + const void* ones = nullptr; auto* cuda_device = dynamic_cast(ctx->stream()->device()); - if (cuda_device != nullptr) { - ones = - static_cast(cuda_device->GetConstOnes(DataType::kFloat16, reduce_size)); - } + if (cuda_device != nullptr) { ones = cuda_device->GetConstOnes(data_type, reduce_size); } if (ones == nullptr) { std::unique_ptr fill = ep::primitive::NewPrimitive(ctx->stream()->device_type(), - DataType::kFloat16); + data_type); CHECK(fill); fill->Launch(ctx->stream(), tmp_buffer->mut_dptr(), 1.0, reduce_size); - ones = tmp_buffer->dptr(); + ones = tmp_buffer->dptr(); } std::unique_ptr matmul; if (trans_a) { @@ -266,48 +288,54 @@ class ReduceSumHalfKernel final : public user_op::OpKernel, public user_op::Cuda CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, tmp_buffer->shape_view().elem_cnt()); auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); + ctx->device_type(), data_type, DataType::kFloat); CHECK(h2f); auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); + ctx->device_type(), DataType::kFloat, data_type); CHECK(f2h); - h2f->Launch(ctx->stream(), input_tensor->dptr(), in_tmp_buffer, in_shape.elem_cnt()); + h2f->Launch(ctx->stream(), input_tensor->dptr(), in_tmp_buffer, in_shape.elem_cnt()); NdarrayReduce::Reduce( ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), XpuVarNdarray(in_shape, in_tmp_buffer), XpuVarNdarray(in_shape, reduce_tmp_buffer)); - f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), + f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), output_tensor->shape_view().elem_cnt()); } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -REGISTER_USER_KERNEL("reduce_sum") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("output_tensor", 0) == GetDataType::value) - && ReduceMatmulTransAPrimitiveExists() - && ReduceMatmulNoTransAPrimitiveExists()) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputTensorDesc("input_tensor", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("output_tensor", 0)->shape(); - const auto& axis = RegularAxis(ctx->Attr>("axis")); - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, &inner_size, - &reduce_size); - size_t tmp_bytes = 0; - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(float16)); - } else { - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } - return tmp_bytes; - }); +#define REGISTER_REDUCE_SUM_HALF_KERNEL(dtype) \ + REGISTER_USER_KERNEL("reduce_sum") \ + .SetCreateFn() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("output_tensor", 0) == GetDataType::value) \ + && ReduceMatmulTransAPrimitiveExists() \ + && ReduceMatmulNoTransAPrimitiveExists()) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& in_shape = ctx->InputTensorDesc("input_tensor", 0).shape(); \ + const Shape& out_shape = ctx->OutputTensorDesc("output_tensor", 0).shape(); \ + const auto& axis = RegularAxis(ctx->Attr>("axis")); \ + bool is_axis_contiguous = false; \ + int64_t outer_size = 0, inner_size = 0, reduce_size = 0; \ + GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, \ + &inner_size, &reduce_size); \ + size_t tmp_bytes = 0; \ + if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { \ + tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(dtype)); \ + } else { \ + tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) \ + + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); \ + } \ + return tmp_bytes; \ + }); + +REGISTER_REDUCE_SUM_HALF_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_REDUCE_SUM_HALF_KERNEL(nv_bfloat16) +#endif class ReduceSumFloatCudaKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -379,194 +407,4 @@ REGISTER_USER_KERNEL("reduce_sum") #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_REDUCE_LOGICAL_KERNELS(DeviceType::kCUDA) - -namespace { - -std::vector RegularAxis(const std::vector& axis) { - std::vector regular_axis = axis; - std::sort(regular_axis.begin(), regular_axis.end()); - return regular_axis; -} - -void GetReduceSumLayout(const std::vector& axis, const ShapeView& in_shape, - bool* is_axis_contiguous, int64_t* outer_size, int64_t* inner_size, - int64_t* reduce_size) { - if (!axis.empty()) { - *is_axis_contiguous = ((axis.back() - axis.front() + 1) == axis.size()); - *outer_size = in_shape.Count(0, axis.front()); - *inner_size = in_shape.Count(axis.back() + 1); - *reduce_size = in_shape.Count(axis.front(), axis.back() + 1); - } -} - -} // namespace - -class ReduceSumHalfKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - ReduceSumHalfKernel() = default; - ~ReduceSumHalfKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - std::vector axis = RegularAxis(ctx->Attr>("axis")); - const user_op::Tensor* input_tensor = ctx->Tensor4ArgNameAndIndex("input_tensor", 0); - user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("output_tensor", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const ShapeView& in_shape = input_tensor->shape_view(); - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, in_shape, &is_axis_contiguous, &outer_size, &inner_size, &reduce_size); - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - bool trans_a = (inner_size != 1); - const int32_t m = (inner_size == 1) ? outer_size : inner_size; - const int32_t n = 1; - const int32_t k = reduce_size; - const float16* ones = nullptr; - auto* cuda_device = dynamic_cast(ctx->stream()->device()); - if (cuda_device != nullptr) { - ones = - static_cast(cuda_device->GetConstOnes(DataType::kFloat16, reduce_size)); - } - if (ones == nullptr) { - std::unique_ptr fill = - ep::primitive::NewPrimitive(ctx->stream()->device_type(), - DataType::kFloat16); - CHECK(fill); - fill->Launch(ctx->stream(), tmp_buffer->mut_dptr(), 1.0, reduce_size); - ones = tmp_buffer->dptr(); - } - std::unique_ptr matmul; - if (trans_a) { - matmul = NewReduceMatmulTransAPrimitive(ctx); - } else { - matmul = NewReduceMatmulNoTransAPrimitive(ctx); - } - matmul->Launch(ctx->stream(), m, n, k, 1.0, input_tensor->dptr(), ones, 0.0, - output_tensor->mut_dptr()); - } else { - const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); - float* in_tmp_buffer = tmp_buffer->mut_dptr(); - const size_t in_tmp_buffer_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - float* out_tmp_buffer = - reinterpret_cast(tmp_buffer->mut_dptr() + in_tmp_buffer_bytes); - const size_t out_tmp_buffer_bytes = - GetCudaAlignedSize(reduced_shape.elem_cnt() * sizeof(float)); - float* reduce_tmp_buffer = reinterpret_cast( - tmp_buffer->mut_dptr() + in_tmp_buffer_bytes + out_tmp_buffer_bytes); - const size_t reduce_tmp_buffer_bytes = - GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, - tmp_buffer->shape_view().elem_cnt()); - auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); - CHECK(h2f); - auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); - CHECK(f2h); - h2f->Launch(ctx->stream(), input_tensor->dptr(), in_tmp_buffer, in_shape.elem_cnt()); - - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), - XpuVarNdarray(in_shape, in_tmp_buffer), - XpuVarNdarray(in_shape, reduce_tmp_buffer)); - - f2h->Launch(ctx->stream(), out_tmp_buffer, output_tensor->mut_dptr(), - output_tensor->shape_view().elem_cnt()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("reduce_sum") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("output_tensor", 0) == GetDataType::value) - && ReduceMatmulTransAPrimitiveExists() - && ReduceMatmulNoTransAPrimitiveExists()) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputTensorDesc("input_tensor", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("output_tensor", 0)->shape(); - const auto& axis = RegularAxis(ctx->Attr>("axis")); - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, &inner_size, - &reduce_size); - size_t tmp_bytes = 0; - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(float16)); - } else { - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } - return tmp_bytes; - }); - -class ReduceSumFloatCudaKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - ReduceSumFloatCudaKernel() = default; - ~ReduceSumFloatCudaKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - std::vector axis = RegularAxis(ctx->Attr>("axis")); - const user_op::Tensor* input_tensor = ctx->Tensor4ArgNameAndIndex("input_tensor", 0); - user_op::Tensor* output_tensor = ctx->Tensor4ArgNameAndIndex("output_tensor", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const ShapeView& in_shape = input_tensor->shape_view(); - if (input_tensor->shape_view().elem_cnt() == 0) { - if (output_tensor->shape_view().elem_cnt() != 0) { - Memset( - ctx->stream(), output_tensor->mut_dptr(), 0, - output_tensor->shape_view().elem_cnt() * GetSizeOfDataType(output_tensor->data_type())); - } - return; - } - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, in_shape, &is_axis_contiguous, &outer_size, &inner_size, &reduce_size); - const float* ones = nullptr; - auto* cuda_device = dynamic_cast(ctx->stream()->device()); - if (cuda_device != nullptr) { - ones = static_cast(cuda_device->GetConstOnes(DataType::kFloat, reduce_size)); - } - if ((!axis.empty()) && in_shape.NumAxes() > 0 && is_axis_contiguous - && (outer_size == 1 || inner_size == 1) && ones != nullptr - && ParseBooleanFromEnv("ONEFLOW_KERNEL_REDUCE_SUM_USE_MATMUL", false)) { - ep::primitive::BlasTransposeType trans_a = (inner_size == 1) - ? ep::primitive::BlasTransposeType::N - : ep::primitive::BlasTransposeType::T; - ep::primitive::BlasTransposeType trans_b = ep::primitive::BlasTransposeType::N; - const int32_t m = (inner_size == 1) ? outer_size : inner_size; - const int32_t n = 1; - const int32_t k = reduce_size; - - auto matmul = ep::primitive::NewPrimitive( - DeviceType::kCUDA, DataType::kFloat, trans_a, trans_b); - CHECK(matmul); - matmul->Launch(ctx->stream(), m, n, k, 1.0, input_tensor->dptr(), ones, 0.0, - output_tensor->mut_dptr()); - } else { - const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, output_tensor->mut_dptr()), - XpuVarNdarray(input_tensor->shape_view(), input_tensor->dptr()), - XpuVarNdarray(tmp_buffer->shape_view(), tmp_buffer->mut_dptr())); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("reduce_sum") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("output_tensor", 0) == DataType::kFloat)) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputTensorDesc("input_tensor", 0).shape(); - return GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - }); - -#endif // WITH_ROCM - } // namespace oneflow diff --git a/oneflow/user/kernels/reduce_like_kernels.cpp b/oneflow/user/kernels/reduce_like_kernels.cpp index af4e66a4692bb9070b0dfff9116ff5848308296b..a072aaac1130f03bab29f1a8c154cf90584156ea 100644 --- a/oneflow/user/kernels/reduce_like_kernels.cpp +++ b/oneflow/user/kernels/reduce_like_kernels.cpp @@ -124,7 +124,7 @@ class ReduceSumLikeOpKernel final : public user_op::OpKernel, public user_op::Cu OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_REDUCE_SUM_LIKE_KERNEL, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ) -#if defined(WITH_CUDA) +#if defined(WITH_CUDA) || defined(WITH_ROCM) namespace { @@ -174,7 +174,7 @@ class ReduceSumLikeHalfKernel final : public user_op::OpKernel, public user_op:: const int32_t k = reduce_size; std::unique_ptr fill = ep::primitive::NewPrimitive(ctx->stream()->device_type(), - DataType::kFloat16); + tensor_x->data_type()); CHECK(fill); fill->Launch(ctx->stream(), tmp_buffer->mut_dptr(), 1.0, reduce_size); @@ -185,8 +185,8 @@ class ReduceSumLikeHalfKernel final : public user_op::OpKernel, public user_op:: matmul = NewReduceMatmulNoTransAPrimitive(ctx); } CHECK(matmul); - matmul->Launch(ctx->stream(), m, n, k, 1.0, tensor_x->dptr(), - tmp_buffer->dptr(), 0.0, tensor_y->mut_dptr()); + matmul->Launch(ctx->stream(), m, n, k, 1.0, tensor_x->dptr(), tmp_buffer->dptr(), 0.0, + tensor_y->mut_dptr()); } else { const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); @@ -203,19 +203,19 @@ class ReduceSumLikeHalfKernel final : public user_op::OpKernel, public user_op:: CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, tmp_buffer->shape_view().elem_cnt()); auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); + ctx->device_type(), tensor_x->data_type(), DataType::kFloat); CHECK(h2f); auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); + ctx->device_type(), DataType::kFloat, tensor_x->data_type()); CHECK(f2h); - h2f->Launch(ctx->stream(), tensor_x->dptr(), in_tmp_buffer, in_shape.elem_cnt()); + h2f->Launch(ctx->stream(), tensor_x->dptr(), in_tmp_buffer, in_shape.elem_cnt()); NdarrayReduce::Reduce( ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), XpuVarNdarray(in_shape, in_tmp_buffer), XpuVarNdarray(in_shape, reduce_tmp_buffer)); - f2h->Launch(ctx->stream(), out_tmp_buffer, tensor_y->mut_dptr(), + f2h->Launch(ctx->stream(), out_tmp_buffer, tensor_y->mut_dptr(), tensor_y->shape_view().elem_cnt()); } } @@ -223,163 +223,39 @@ class ReduceSumLikeHalfKernel final : public user_op::OpKernel, public user_op:: bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -REGISTER_USER_KERNEL("reduce_sum_like") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("y", 0) == GetDataType::value) - && ReduceMatmulTransAPrimitiveExists() - && ReduceMatmulNoTransAPrimitiveExists()) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputTensorDesc("x", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("y", 0)->shape(); - const auto& axis = RegularAxis(ctx->Attr>("axis")); - if (axis.empty()) { - size_t tmp_bytes = 0; - return tmp_bytes; - } - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, &inner_size, - &reduce_size); - size_t tmp_bytes = 0; - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(float16)); - } else { - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } - return tmp_bytes; - }); - +#define REGISTER_REDUCE_SUM_LIKE_HALF_KERNEL(dtype) \ + REGISTER_USER_KERNEL("reduce_sum_like") \ + .SetCreateFn() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("y", 0) == GetDataType::value) \ + && ReduceMatmulTransAPrimitiveExists() \ + && ReduceMatmulNoTransAPrimitiveExists()) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& in_shape = ctx->InputTensorDesc("x", 0).shape(); \ + const Shape& out_shape = ctx->OutputTensorDesc("y", 0).shape(); \ + const auto& axis = RegularAxis(ctx->Attr>("axis")); \ + if (axis.empty()) { \ + size_t tmp_bytes = 0; \ + return tmp_bytes; \ + } \ + bool is_axis_contiguous = false; \ + int64_t outer_size = 0, inner_size = 0, reduce_size = 0; \ + GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, \ + &inner_size, &reduce_size); \ + size_t tmp_bytes = 0; \ + if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { \ + tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(dtype)); \ + } else { \ + tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) \ + + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); \ + } \ + return tmp_bytes; \ + }); +REGISTER_REDUCE_SUM_LIKE_HALF_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_REDUCE_SUM_LIKE_HALF_KERNEL(nv_bfloat16) #endif -#if defined(WITH_ROCM) - -namespace { - -std::vector RegularAxis(const std::vector& axis) { - std::vector regular_axis = axis; - std::sort(regular_axis.begin(), regular_axis.end()); - return regular_axis; -} - -void GetReduceSumLayout(const std::vector& axis, const ShapeView& in_shape, - bool* is_axis_contiguous, int64_t* outer_size, int64_t* inner_size, - int64_t* reduce_size) { - *is_axis_contiguous = ((axis.back() - axis.front() + 1) == axis.size()); - *outer_size = in_shape.Count(0, axis.front()); - *inner_size = in_shape.Count(axis.back() + 1); - *reduce_size = in_shape.Count(axis.front(), axis.back() + 1); -} - -} // namespace - -class ReduceSumLikeHalfKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { - public: - ReduceSumLikeHalfKernel() = default; - ~ReduceSumLikeHalfKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - std::vector axis = RegularAxis(ctx->Attr>("axis")); - const user_op::Tensor* tensor_x = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* tensor_y = ctx->Tensor4ArgNameAndIndex("y", 0); - if (axis.empty()) { - CHECK_EQ(tensor_x->shape_view(), tensor_y->shape_view()); - Memcpy( - ctx->stream(), tensor_y->mut_dptr(), tensor_x->dptr(), - tensor_x->shape_view().elem_cnt() * GetSizeOfDataType(tensor_x->data_type())); - } else { - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const ShapeView& in_shape = tensor_x->shape_view(); - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, in_shape, &is_axis_contiguous, &outer_size, &inner_size, - &reduce_size); - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - bool trans_a = (inner_size != 1); - const int32_t m = (inner_size == 1) ? outer_size : inner_size; - const int32_t n = 1; - const int32_t k = reduce_size; - std::unique_ptr fill = - ep::primitive::NewPrimitive(ctx->stream()->device_type(), - DataType::kFloat16); - CHECK(fill); - fill->Launch(ctx->stream(), tmp_buffer->mut_dptr(), 1.0, reduce_size); - - std::unique_ptr matmul; - if (trans_a) { - matmul = NewReduceMatmulTransAPrimitive(ctx); - } else { - matmul = NewReduceMatmulNoTransAPrimitive(ctx); - } - CHECK(matmul); - matmul->Launch(ctx->stream(), m, n, k, 1.0, tensor_x->dptr(), - tmp_buffer->dptr(), 0.0, tensor_y->mut_dptr()); - - } else { - const Shape& reduced_shape = CreateReducedShape(in_shape, {axis.begin(), axis.end()}); - float* in_tmp_buffer = tmp_buffer->mut_dptr(); - const size_t in_tmp_buffer_bytes = GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - float* out_tmp_buffer = - reinterpret_cast(tmp_buffer->mut_dptr() + in_tmp_buffer_bytes); - const size_t out_tmp_buffer_bytes = - GetCudaAlignedSize(reduced_shape.elem_cnt() * sizeof(float)); - float* reduce_tmp_buffer = reinterpret_cast( - tmp_buffer->mut_dptr() + in_tmp_buffer_bytes + out_tmp_buffer_bytes); - const size_t reduce_tmp_buffer_bytes = - GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)); - CHECK_LE(in_tmp_buffer_bytes + out_tmp_buffer_bytes + reduce_tmp_buffer_bytes, - tmp_buffer->shape_view().elem_cnt()); - auto h2f = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat16, DataType::kFloat); - CHECK(h2f); - auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); - CHECK(f2h); - h2f->Launch(ctx->stream(), tensor_x->dptr(), in_tmp_buffer, in_shape.elem_cnt()); - - NdarrayReduce::Reduce( - ctx->stream(), XpuVarNdarray(reduced_shape, out_tmp_buffer), - XpuVarNdarray(in_shape, in_tmp_buffer), - XpuVarNdarray(in_shape, reduce_tmp_buffer)); - - f2h->Launch(ctx->stream(), out_tmp_buffer, tensor_y->mut_dptr(), - tensor_y->shape_view().elem_cnt()); - } - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("reduce_sum_like") - .SetCreateFn() - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) - && (user_op::HobDataType("y", 0) == GetDataType::value) - && ReduceMatmulTransAPrimitiveExists() - && ReduceMatmulNoTransAPrimitiveExists()) - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputTensorDesc("x", 0).shape(); - const Shape& out_shape = ctx->OutputTensorDesc("y", 0)->shape(); - const auto& axis = RegularAxis(ctx->Attr>("axis")); - if (axis.empty()) { - size_t tmp_bytes = 0; - return tmp_bytes; - } - bool is_axis_contiguous = false; - int64_t outer_size = 0, inner_size = 0, reduce_size = 0; - GetReduceSumLayout(axis, ShapeView(in_shape), &is_axis_contiguous, &outer_size, &inner_size, - &reduce_size); - size_t tmp_bytes = 0; - if (is_axis_contiguous && (outer_size == 1 || inner_size == 1)) { - tmp_bytes = GetCudaAlignedSize(reduce_size * sizeof(float16)); - } else { - tmp_bytes = (2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(float)) - + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float))); - } - return tmp_bytes; - }); - #endif } // namespace oneflow diff --git a/oneflow/user/kernels/reflection_pad_kernels.cpp b/oneflow/user/kernels/reflection_pad_kernels.cpp new file mode 100644 index 0000000000000000000000000000000000000000..82517f28331863749e0fd8fd8c0f53a16710f2b7 --- /dev/null +++ b/oneflow/user/kernels/reflection_pad_kernels.cpp @@ -0,0 +1,210 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/user/kernels/reflection_pad_kernels_util.h" + +namespace oneflow { +namespace user_op { + +template +class ReflectionPad1dKernel final : public OpKernel { + public: + ReflectionPad1dKernel() = default; + ~ReflectionPad1dKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t ndims = x->shape_view().NumAxes(); + CHECK_EQ(padding.size(), ndims - 1); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + const int64_t pad_left = padding[0]; + + const int64_t n_batch = y->shape_view().At(n_idx); + const int64_t n_channel = y->shape_view().At(c_idx); + const int64_t y_width = y->shape_view().At(w_idx); + const int64_t x_width = x->shape_view().At(w_idx); + + IN_T* dest = y->mut_dptr(); + const IN_T* src = x->dptr(); + DimVector y_vector; + y->shape_view().ToDimVector(&y_vector); + NdIndexOffsetHelper index_helper(y_vector.data()); + + ReflectionPad1dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, y_width, x_width, pad_left); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReflectionPad1dGradKernel final : public OpKernel { + public: + ReflectionPad1dGradKernel() = default; + ~ReflectionPad1dGradKernel() = default; + + private: + void Compute(KernelComputeContext* ctx) const override { + const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t ndims = dy->shape_view().NumAxes(); + CHECK_EQ(padding.size(), ndims - 1); + + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + const int64_t pad_left = padding[0]; + const int64_t n_batch = dy->shape_view().At(n_idx); + const int64_t n_channel = dy->shape_view().At(c_idx); + const int64_t dy_width = dy->shape_view().At(w_idx); + const int64_t dx_width = dx->shape_view().At(w_idx); + + const IN_T* src = dy->dptr(); + IN_T* dest = dx->mut_dptr(); + DimVector dy_vector; + dy->shape_view().ToDimVector(&dy_vector); + NdIndexOffsetHelper index_helper(dy_vector.data()); + + size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); + Memset(ctx->stream(), dest, 0, out_bytes_size); + + ReflectionPad1dGradFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, dy_width, dx_width, pad_left); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReflectionPad2dKernel final : public OpKernel { + public: + ReflectionPad2dKernel() = default; + ~ReflectionPad2dKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + const int64_t pad_left = padding[0]; + const int64_t pad_top = padding[2]; + + const int64_t n_batch = y->shape_view().At(n_idx); + const int64_t n_channel = y->shape_view().At(c_idx); + const int64_t y_height = y->shape_view().At(h_idx); + const int64_t y_width = y->shape_view().At(w_idx); + const int64_t x_height = x->shape_view().At(h_idx); + const int64_t x_width = x->shape_view().At(w_idx); + + IN_T* dest = y->mut_dptr(); + const IN_T* src = x->dptr(); + DimVector y_vector; + y->shape_view().ToDimVector(&y_vector); + NdIndexOffsetHelper index_helper(y_vector.data()); + + ReflectionPad2dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, y_height, y_width, x_height, x_width, + pad_left, pad_top); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReflectionPad2dGradKernel final : public OpKernel { + public: + ReflectionPad2dGradKernel() = default; + ~ReflectionPad2dGradKernel() = default; + + private: + void Compute(KernelComputeContext* ctx) const override { + const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto& padding = ctx->Attr>("padding"); + + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + int64_t pad_left = padding[0]; + int64_t pad_top = padding[2]; + int64_t n_batch = dy->shape_view().At(n_idx); + int64_t n_channel = dy->shape_view().At(c_idx); + int64_t dy_height = dy->shape_view().At(h_idx); + int64_t dy_width = dy->shape_view().At(w_idx); + int64_t dx_height = dx->shape_view().At(h_idx); + int64_t dx_width = dx->shape_view().At(w_idx); + + const IN_T* src = dy->dptr(); + IN_T* dest = dx->mut_dptr(); + DimVector dy_vector; + dy->shape_view().ToDimVector(&dy_vector); + NdIndexOffsetHelper index_helper(dy_vector.data()); + + size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); + Memset(ctx->stream(), dest, 0, out_bytes_size); + + ReflectionPad2dGradFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, dy_height, dy_width, dx_height, + dx_width, pad_left, pad_top); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_REFLECTION_PAD_ND_KERNELS(device, dtype) \ + REGISTER_USER_KERNEL("reflection_pad1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("reflection_pad1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("reflection_pad2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("reflection_pad2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +#define REGISTER_REFLECTION_PAD_ND_WITH_DEVICE(device) \ + REGISTER_REFLECTION_PAD_ND_KERNELS(device, float) \ + REGISTER_REFLECTION_PAD_ND_KERNELS(device, double) \ + REGISTER_REFLECTION_PAD_ND_KERNELS(device, int32_t) + +REGISTER_REFLECTION_PAD_ND_WITH_DEVICE(DeviceType::kCPU) +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_REFLECTION_PAD_ND_WITH_DEVICE(DeviceType::kCUDA) +REGISTER_REFLECTION_PAD_ND_KERNELS(DeviceType::kCUDA, float16) +#endif + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/reflection_pad_kernels_util.cpp b/oneflow/user/kernels/reflection_pad_kernels_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e7b8f92f99144603db1a07193b593987eec9ed9d --- /dev/null +++ b/oneflow/user/kernels/reflection_pad_kernels_util.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/reflection_pad_kernels_util.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { +namespace user_op { + +template +struct ReflectionPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoReflectionPad1d(src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, + pad_left); + } +}; + +template +struct ReflectionPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoReflectionPad1dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_width, + dx_width, pad_left); + } +}; + +template +struct ReflectionPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoReflectionPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, + x_height, x_width, pad_left, pad_top); + } +}; + +template +struct ReflectionPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoReflectionPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, + dy_width, dx_height, dx_width, pad_left, pad_top); + } +}; + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD_FUNCTOR, (DeviceType::kCPU), + PADDING_DATA_TYPE_CPU_SEQ); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD_GRAD_FUNCTOR, (DeviceType::kCPU), + PADDING_DATA_TYPE_CPU_SEQ); + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/reflection_pad_kernels_util.cu b/oneflow/user/kernels/reflection_pad_kernels_util.cu new file mode 100644 index 0000000000000000000000000000000000000000..a0a4774387804d29ae221dca7166ca125a19b733 --- /dev/null +++ b/oneflow/user/kernels/reflection_pad_kernels_util.cu @@ -0,0 +1,208 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/reflection_pad_kernels_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { +namespace user_op { + +template +__global__ void DoCUDAReflectionPad1d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_width, + const int64_t x_width, const int64_t pad_left) { + DoReflectionPad1d(src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, + pad_left); +}; + +template +__global__ void DoCUDAReflectionPad1dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_width, + const int64_t dx_width, const int64_t pad_left) { + DoReflectionPad1dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_width, + dx_width, pad_left); +}; + +template +__global__ void DoCUDAReflectionPad2d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_height, + const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + DoReflectionPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, + x_height, x_width, pad_left, pad_top); +}; + +template +__global__ void DoCUDAReflectionPad2dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_height, + const int64_t dy_width, const int64_t dx_height, + const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + DoReflectionPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, + dy_width, dx_height, dx_width, pad_left, pad_top); +}; + +template +struct ReflectionPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReflectionPad1d<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, pad_left); + } +}; + +// float16 implementation +template<> +void ReflectionPad1dFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReflectionPad1d<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, y_width, x_width, pad_left); +} + +template +struct ReflectionPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReflectionPad1dGrad<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, dy_width, dx_width, pad_left); + } +}; + +// float16 implementation +template<> +void ReflectionPad1dGradFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReflectionPad1dGrad<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, dy_width, dx_width, pad_left); +} + +template +struct ReflectionPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, + pad_left, pad_top); + } +}; + +// float16 implementation +template<> +void ReflectionPad2dFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReflectionPad2d<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); +} + +template +struct ReflectionPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, + dx_width, pad_left, pad_top); + } +}; + +// float16 implementation +template<> +void ReflectionPad2dGradFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReflectionPad2dGrad<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); +} + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD_FUNCTOR, + OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), + PADDING_DATA_TYPE_CUDA_SEQ); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REFLECTION_PAD_GRAD_FUNCTOR, + OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), + PADDING_DATA_TYPE_CUDA_SEQ); + +} // namespace user_op +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/reflection_pad_kernels_util.h b/oneflow/user/kernels/reflection_pad_kernels_util.h new file mode 100644 index 0000000000000000000000000000000000000000..33ed0cadbe4180ca9259945dfe2d3020b0f71a6d --- /dev/null +++ b/oneflow/user/kernels/reflection_pad_kernels_util.h @@ -0,0 +1,229 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_REFLECTION_PAD_KERNELS_UTIL_H_ +#define ONEFLOW_USER_KERNELS_REFLECTION_PAD_KERNELS_UTIL_H_ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/cuda/atomic.cuh" +#endif // WITH_CUDA +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/ndarray/xpu_util.h" + +namespace oneflow { + +#define PADDING_DATA_TYPE_CPU_SEQ \ + FLOATING_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) + +#define PADDING_DATA_TYPE_CUDA_SEQ \ + FLOAT16_DATA_TYPE_SEQ \ + PADDING_DATA_TYPE_CPU_SEQ + +namespace user_op { + +template +struct DeviceAdd { + OF_DEVICE_FUNC static void Invoke(const T* x, T* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + cuda::atomic::Add(y, *x); +#else + *y += *x; +#endif + }; +}; + +template +struct ReflectionPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left); +}; + +template +struct ReflectionPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left); +}; + +template +struct ReflectionPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top); +}; + +template +struct ReflectionPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top); +}; + +template +OF_DEVICE_FUNC void DoReflectionPad1d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_width, + const int64_t x_width, const int64_t pad_left) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, j, ip_x; + int64_t coord_y[3]; + index_helper.OffsetToNdIndex(k, coord_y); + n = coord_y[0]; + c = coord_y[1]; + j = coord_y[2]; + if (j < pad_left) { + ip_x = pad_left * 2 - j; + } else if (j >= pad_left && j < x_width + pad_left) { + ip_x = j; + } else { + ip_x = (x_width + pad_left - 1) * 2 - j; + } + + ip_x = ip_x - pad_left; + int64_t dest_index = n * dest_num + c * y_width + j; + int64_t src_index = n * src_num + c * x_width + ip_x; + dest[dest_index] = src[src_index]; + } +} + +template +OF_DEVICE_FUNC void DoReflectionPad1dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_width, + const int64_t dx_width, const int64_t pad_left) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, j, ip_x; + int64_t coord[3]; + index_helper.OffsetToNdIndex(k, coord); + n = coord[0]; + c = coord[1]; + j = coord[2]; + if (j < pad_left) { + ip_x = pad_left * 2 - j; + } else if (j >= pad_left && j < dx_width + pad_left) { + ip_x = j; + } else { + ip_x = (dx_width + pad_left - 1) * 2 - j; + } + + ip_x = ip_x - pad_left; + + int64_t src_index = n * src_num + c * dy_width + j; + int64_t dest_index = n * dest_num + c * dx_width + ip_x; + DeviceAdd::Invoke(src + src_index, dest + dest_index); + } +} + +template +OF_DEVICE_FUNC void DoReflectionPad2d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_height, + const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, i, j, ip_x, ip_y; + int64_t coord_y[4]; + index_helper.OffsetToNdIndex(k, coord_y); + n = coord_y[0]; + c = coord_y[1]; + i = coord_y[2]; + j = coord_y[3]; + if (j < pad_left) { + ip_x = pad_left * 2 - j; + } else if (j >= pad_left && j < x_width + pad_left) { + ip_x = j; + } else { + ip_x = (x_width + pad_left - 1) * 2 - j; + } + + if (i < pad_top) { + ip_y = pad_top * 2 - i; + } else if (i >= pad_top && i < x_height + pad_top) { + ip_y = i; + } else { + ip_y = (x_height + pad_top - 1) * 2 - i; + } + ip_x = ip_x - pad_left; + ip_y = ip_y - pad_top; + int64_t dest_index = n * dest_num + c * y_width * y_height + i * y_width + j; + int64_t src_index = n * src_num + c * x_width * x_height + ip_y * x_width + ip_x; + dest[dest_index] = src[src_index]; + } +} + +template +OF_DEVICE_FUNC void DoReflectionPad2dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_height, + const int64_t dy_width, const int64_t dx_height, + const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, i, j, ip_x, ip_y; + int64_t coord[4]; + index_helper.OffsetToNdIndex(k, coord); + n = coord[0]; + c = coord[1]; + i = coord[2]; + j = coord[3]; + if (j < pad_left) { + ip_x = pad_left * 2 - j; + } else if (j >= pad_left && j < dx_width + pad_left) { + ip_x = j; + } else { + ip_x = (dx_width + pad_left - 1) * 2 - j; + } + + if (i < pad_top) { + ip_y = pad_top * 2 - i; + } else if (i >= pad_top && i < dx_height + pad_top) { + ip_y = i; + } else { + ip_y = (dx_height + pad_top - 1) * 2 - i; + } + ip_x = ip_x - pad_left; + ip_y = ip_y - pad_top; + + int64_t src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j; + int64_t dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x; + DeviceAdd::Invoke(src + src_index, dest + dest_index); + } +} + +// macros for functors instantiate +#define INSTANTIATE_REFLECTION_PAD_FUNCTOR(device_type_v, dtype_pair) \ + template struct ReflectionPad1dFunctor; \ + template struct ReflectionPad2dFunctor; + +#define INSTANTIATE_REFLECTION_PAD_GRAD_FUNCTOR(device_type_v, dtype_pair) \ + template struct ReflectionPad1dGradFunctor; \ + template struct ReflectionPad2dGradFunctor; + +} // namespace user_op +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_REFLECTION_PAD_KERNELS_UTIL_H_ diff --git a/oneflow/user/kernels/repeat_interleave_kernel.cu b/oneflow/user/kernels/repeat_interleave_kernel.cu index 9a547be5014ed326960ba20e7dad48794047e2d9..ff071a6f8b4978ee90b0e89125f88c45e589d7da 100644 --- a/oneflow/user/kernels/repeat_interleave_kernel.cu +++ b/oneflow/user/kernels/repeat_interleave_kernel.cu @@ -43,6 +43,7 @@ class GpuRepeatInterLeaveKernel final : public user_op::OpKernel { ~GpuRepeatInterLeaveKernel() = default; private: + using user_op::OpKernel::Compute; void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); const user_op::Tensor* cumsum = ctx->Tensor4ArgNameAndIndex("cumsum", 0); diff --git a/oneflow/user/kernels/repeat_interleave_kernel.hip.cpp b/oneflow/user/kernels/repeat_interleave_kernel.hip.cpp deleted file mode 100644 index 3b3d3dcb3cb4be4b1f3ca284468aed94de2ee62e..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/repeat_interleave_kernel.hip.cpp +++ /dev/null @@ -1,73 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/roll_kernel_utils.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -#include - -namespace oneflow { - -namespace { - -template -__global__ void repeat_interleave(const T* in_ptr, const T* cumsum_ptr, T* out_ptr, - const int64_t num) { - CUDA_1D_KERNEL_LOOP(i, num) { - T end = cumsum_ptr[i]; - T size = in_ptr[i]; - T start = end - size; - for (T j = start; j < end; j++) { out_ptr[j] = i; } - } -} - -} // namespace - -template -class GpuRepeatInterLeaveKernel final : public user_op::OpKernel { - public: - GpuRepeatInterLeaveKernel() = default; - ~GpuRepeatInterLeaveKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - const user_op::Tensor* cumsum = ctx->Tensor4ArgNameAndIndex("cumsum", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const int64_t& repeat_num = ctx->Attr("repeat_num"); - const T* in_ptr = in->dptr(); - const T* cumsum_ptr = cumsum->dptr(); - T* out_ptr = out->mut_dptr(); - - repeat_interleave<<shape_view().At(0)), kCudaThreadsNumPerBlock, 0, - ctx->stream()->As()->cuda_stream()>>>( - in_ptr, cumsum_ptr, out_ptr, in->shape_view().At(0)); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_REPEAT_INTER_LEAVE_KERNEL(dtype) \ - REGISTER_USER_KERNEL("repeat_interleave") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_REPEAT_INTER_LEAVE_KERNEL(int32_t); -REGISTER_REPEAT_INTER_LEAVE_KERNEL(int64_t); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/repeat_kernel.cpp b/oneflow/user/kernels/repeat_kernel.cpp deleted file mode 100644 index 4ea643a787d97bf6f6eceab34ee31262541c86e3..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/repeat_kernel.cpp +++ /dev/null @@ -1,51 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" - -namespace oneflow { - -namespace { - -template -class RepeatKernel final : public user_op::OpKernel { - public: - RepeatKernel() = default; - ~RepeatKernel() override = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - CHECK_EQ(in->shape_view().elem_cnt(), out->shape_view().elem_cnt()); - CHECK_EQ(in->data_type(), out->data_type()); - Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), - in->shape_view().elem_cnt() * GetSizeOfDataType(in->data_type())); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_REPEAT_KERNEL(device) \ - REGISTER_USER_KERNEL("repeat").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == device)); - -OF_PP_FOR_EACH_TUPLE(REGISTER_REPEAT_KERNEL, DEVICE_TYPE_SEQ) - -#undef REGISTER_REPEAT_KERNEL - -} // namespace - -} // namespace oneflow diff --git a/oneflow/user/kernels/replication_pad_kernels.cpp b/oneflow/user/kernels/replication_pad_kernels.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9840d9f25a4d465bdd12fe90eac08bff9a439b0d --- /dev/null +++ b/oneflow/user/kernels/replication_pad_kernels.cpp @@ -0,0 +1,206 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/user/kernels/replication_pad_kernels_util.h" + +namespace oneflow { +namespace user_op { + +template +class ReplicationPad1dKernel final : public OpKernel { + public: + ReplicationPad1dKernel() = default; + ~ReplicationPad1dKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + const int64_t pad_left = padding[0]; + + const int64_t n_batch = y->shape_view().At(n_idx); + const int64_t n_channel = y->shape_view().At(c_idx); + const int64_t y_width = y->shape_view().At(w_idx); + const int64_t x_width = x->shape_view().At(w_idx); + + IN_T* dest = y->mut_dptr(); + const IN_T* src = x->dptr(); + DimVector y_vector; + y->shape_view().ToDimVector(&y_vector); + NdIndexOffsetHelper index_helper(y_vector.data()); + + ReplicationPad1dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, y_width, x_width, pad_left); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReplicationPad1dGradKernel final : public OpKernel { + public: + ReplicationPad1dGradKernel() = default; + ~ReplicationPad1dGradKernel() = default; + + private: + void Compute(KernelComputeContext* ctx) const override { + const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto& padding = ctx->Attr>("padding"); + + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + const int64_t pad_left = padding[0]; + const int64_t n_batch = dy->shape_view().At(n_idx); + const int64_t n_channel = dy->shape_view().At(c_idx); + const int64_t dy_width = dy->shape_view().At(w_idx); + const int64_t dx_width = dx->shape_view().At(w_idx); + + const IN_T* src = dy->dptr(); + IN_T* dest = dx->mut_dptr(); + DimVector dy_vector; + dy->shape_view().ToDimVector(&dy_vector); + NdIndexOffsetHelper index_helper(dy_vector.data()); + + size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); + Memset(ctx->stream(), dest, 0, out_bytes_size); + + ReplicationPad1dGradFunctor()( + ctx->stream(), src, dest, index_helper, n_batch, n_channel, dy_width, dx_width, pad_left); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReplicationPad2dKernel final : public OpKernel { + public: + ReplicationPad2dKernel() = default; + ~ReplicationPad2dKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + const int64_t pad_left = padding[0]; + const int64_t pad_top = padding[2]; + + const int64_t n_batch = y->shape_view().At(n_idx); + const int64_t n_channel = y->shape_view().At(c_idx); + const int64_t y_height = y->shape_view().At(h_idx); + const int64_t y_width = y->shape_view().At(w_idx); + const int64_t x_height = x->shape_view().At(h_idx); + const int64_t x_width = x->shape_view().At(w_idx); + + IN_T* dest = y->mut_dptr(); + const IN_T* src = x->dptr(); + DimVector y_vector; + y->shape_view().ToDimVector(&y_vector); + NdIndexOffsetHelper index_helper(y_vector.data()); + + ReplicationPad2dFunctor()(ctx->stream(), src, dest, index_helper, n_batch, + n_channel, y_height, y_width, x_height, x_width, + pad_left, pad_top); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +class ReplicationPad2dGradKernel final : public OpKernel { + public: + ReplicationPad2dGradKernel() = default; + ~ReplicationPad2dGradKernel() = default; + + private: + void Compute(KernelComputeContext* ctx) const override { + const Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const auto& padding = ctx->Attr>("padding"); + + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + const int64_t pad_left = padding[0]; + const int64_t pad_top = padding[2]; + const int64_t n_batch = dy->shape_view().At(n_idx); + const int64_t n_channel = dy->shape_view().At(c_idx); + const int64_t dy_height = dy->shape_view().At(h_idx); + const int64_t dy_width = dy->shape_view().At(w_idx); + const int64_t dx_height = dx->shape_view().At(h_idx); + const int64_t dx_width = dx->shape_view().At(w_idx); + + const IN_T* src = dy->dptr(); + IN_T* dest = dx->mut_dptr(); + DimVector dy_vector; + dy->shape_view().ToDimVector(&dy_vector); + NdIndexOffsetHelper index_helper(dy_vector.data()); + + size_t out_bytes_size = dx->shape_view().elem_cnt() * GetSizeOfDataType(dx->data_type()); + Memset(ctx->stream(), dest, 0, out_bytes_size); + + ReplicationPad2dGradFunctor()(ctx->stream(), src, dest, index_helper, + n_batch, n_channel, dy_height, dy_width, + dx_height, dx_width, pad_left, pad_top); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_REPLICATION_PAD_ND_KERNELS(device, dtype) \ + REGISTER_USER_KERNEL("replication_pad1d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("replication_pad1d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("replication_pad2d") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("y", 0) == GetDataType::value)); \ + REGISTER_USER_KERNEL("replication_pad2d_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device) \ + && (user_op::HobDataType("dx", 0) == GetDataType::value)); + +#define REGISTER_REPLICATION_PAD_ND_WITH_DEVICE(device) \ + REGISTER_REPLICATION_PAD_ND_KERNELS(device, float) \ + REGISTER_REPLICATION_PAD_ND_KERNELS(device, double) \ + REGISTER_REPLICATION_PAD_ND_KERNELS(device, int32_t) + +REGISTER_REPLICATION_PAD_ND_WITH_DEVICE(DeviceType::kCPU) +#if defined(WITH_CUDA) || defined(WITH_ROCM) +REGISTER_REPLICATION_PAD_ND_WITH_DEVICE(DeviceType::kCUDA) +REGISTER_REPLICATION_PAD_ND_KERNELS(DeviceType::kCUDA, float16) +#endif + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/replication_pad_kernels_util.cpp b/oneflow/user/kernels/replication_pad_kernels_util.cpp new file mode 100644 index 0000000000000000000000000000000000000000..18a00cb2d4ed69e2b5a6c682f17356d5b280ec15 --- /dev/null +++ b/oneflow/user/kernels/replication_pad_kernels_util.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/replication_pad_kernels_util.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { +namespace user_op { + +template +struct ReplicationPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoReplicationPad1d(src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, + pad_left); + } +}; + +template +struct ReplicationPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoReplicationPad1dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_width, + dx_width, pad_left); + } +}; + +template +struct ReplicationPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoReplicationPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, + y_width, x_height, x_width, pad_left, pad_top); + } +}; + +template +struct ReplicationPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoReplicationPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, + dy_width, dx_height, dx_width, pad_left, pad_top); + } +}; + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD_FUNCTOR, (DeviceType::kCPU), + PADDING_DATA_TYPE_CPU_SEQ); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD_GRAD_FUNCTOR, (DeviceType::kCPU), + PADDING_DATA_TYPE_CPU_SEQ); + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/replication_pad_kernels_util.cu b/oneflow/user/kernels/replication_pad_kernels_util.cu new file mode 100644 index 0000000000000000000000000000000000000000..55c9ff5a74eadaba53276505129e7d108ff667ef --- /dev/null +++ b/oneflow/user/kernels/replication_pad_kernels_util.cu @@ -0,0 +1,207 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/common/data_type.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/kernels/replication_pad_kernels_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" + +namespace oneflow { +namespace user_op { + +template +__global__ void DoCUDAReplicationPad1d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_width, + const int64_t x_width, const int64_t pad_left) { + DoReplicationPad1d(src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, + pad_left); +}; + +template +__global__ void DoCUDAReplicationPad1dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_width, + const int64_t dx_width, const int64_t pad_left) { + DoReplicationPad1dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_width, + dx_width, pad_left); +}; + +template +__global__ void DoCUDAReplicationPad2d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_height, + const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + DoReplicationPad2d(src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, + x_height, x_width, pad_left, pad_top); +}; + +template +__global__ void DoCUDAReplicationPad2dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_height, + const int64_t dy_width, const int64_t dx_height, + const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + DoReplicationPad2dGrad(src, dest, index_helper, elem_num, src_num, dest_num, dy_height, + dy_width, dx_height, dx_width, pad_left, pad_top); +}; + +template +struct ReplicationPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReplicationPad1d<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, y_width, x_width, pad_left); + } +}; + +// float16 implementation +template<> +void ReplicationPad1dFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, const int64_t pad_left) { + const int64_t dest_num = n_channel * y_width; + const int64_t src_num = n_channel * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReplicationPad1d<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, y_width, x_width, pad_left); +} + +template +struct ReplicationPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReplicationPad1dGrad<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, dy_width, dx_width, pad_left); + } +}; + +// float16 implementation +template<> +void ReplicationPad1dGradFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left) { + const int64_t dest_num = n_channel * dx_width; + const int64_t src_num = n_channel * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReplicationPad1dGrad<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, dy_width, dx_width, pad_left); +} + +template +struct ReplicationPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, y_height, y_width, x_height, x_width, + pad_left, pad_top); + } +}; + +// float16 implementation +template<> +void ReplicationPad2dFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, const int64_t pad_top) { + const int64_t dest_num = n_channel * y_height * y_width; + const int64_t src_num = n_channel * x_height * x_width; + const int64_t elem_num = n_batch * dest_num; + DoCUDAReplicationPad2d<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, y_height, y_width, x_height, x_width, pad_left, pad_top); +} + +template +struct ReplicationPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( + src, dest, index_helper, elem_num, src_num, dest_num, dy_height, dy_width, dx_height, + dx_width, pad_left, pad_top); + } +}; + +// float16 implementation +template<> +void ReplicationPad2dGradFunctor::operator()( + ep::Stream* stream, const float16* src, float16* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + const int64_t dest_num = n_channel * dx_height * dx_width; + const int64_t src_num = n_channel * dy_height * dy_width; + const int64_t elem_num = n_batch * src_num; + DoCUDAReplicationPad2dGrad<<As()->cuda_stream()>>>( + reinterpret_cast(src), reinterpret_cast(dest), index_helper, elem_num, + src_num, dest_num, dy_height, dy_width, dx_height, dx_width, pad_left, pad_top); +} + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD_FUNCTOR, + OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), + PADDING_DATA_TYPE_CUDA_SEQ); +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_REPLICATION_PAD_GRAD_FUNCTOR, + OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), + PADDING_DATA_TYPE_CUDA_SEQ); +} // namespace user_op +} // namespace oneflow + +#endif // WITH_CUDA diff --git a/oneflow/user/kernels/replication_pad_kernels_util.h b/oneflow/user/kernels/replication_pad_kernels_util.h new file mode 100644 index 0000000000000000000000000000000000000000..85cbd07dcf3674dc64481cbea71236c907f4afc3 --- /dev/null +++ b/oneflow/user/kernels/replication_pad_kernels_util.h @@ -0,0 +1,230 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#ifndef ONEFLOW_USER_KERNELS_REPLICATION_PAD_KERNELS_UTIL_H_ +#define ONEFLOW_USER_KERNELS_REPLICATION_PAD_KERNELS_UTIL_H_ +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#include "oneflow/core/cuda/atomic.cuh" +#endif // WITH_CUDA +#include "oneflow/core/common/nd_index_offset_helper.h" +#include "oneflow/core/ndarray/xpu_util.h" + +namespace oneflow { + +#define PADDING_DATA_TYPE_CPU_SEQ \ + FLOATING_DATA_TYPE_SEQ \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) + +#define PADDING_DATA_TYPE_CUDA_SEQ \ + FLOAT16_DATA_TYPE_SEQ \ + PADDING_DATA_TYPE_CPU_SEQ + +namespace user_op { + +template +struct DeviceAdd { + OF_DEVICE_FUNC static void Invoke(const T* x, T* y) { +#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) + cuda::atomic::Add(y, *x); +#else + *y += *x; +#endif + }; +}; + +template +struct ReplicationPad1dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_width, const int64_t x_width, + const int64_t pad_left); +}; + +template +struct ReplicationPad1dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_width, const int64_t dx_width, + const int64_t pad_left); +}; + +template +struct ReplicationPad2dFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t y_height, const int64_t y_width, + const int64_t x_height, const int64_t x_width, const int64_t pad_left, + const int64_t pad_top); +}; + +template +struct ReplicationPad2dGradFunctor final { + void operator()(ep::Stream* stream, const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, const int64_t n_batch, + const int64_t n_channel, const int64_t dy_height, const int64_t dy_width, + const int64_t dx_height, const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top); +}; + +template +OF_DEVICE_FUNC void DoReplicationPad1d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_width, + const int64_t x_width, const int64_t pad_left) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, j, ip_x; + int64_t coord_y[3]; + index_helper.OffsetToNdIndex(k, coord_y); + n = coord_y[0]; + c = coord_y[1]; + j = coord_y[2]; + if (j < pad_left) { + ip_x = pad_left; + } else if (j >= pad_left && j < x_width + pad_left) { + ip_x = j; + } else { + ip_x = x_width + pad_left - 1; + } + + ip_x = ip_x - pad_left; + int64_t dest_index = n * dest_num + c * y_width + j; + int64_t src_index = n * src_num + c * x_width + ip_x; + dest[dest_index] = src[src_index]; + } +} + +template +OF_DEVICE_FUNC void DoReplicationPad1dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_width, + const int64_t dx_width, const int64_t pad_left) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, j, ip_x; + int64_t coord[3]; + index_helper.OffsetToNdIndex(k, coord); + n = coord[0]; + c = coord[1]; + j = coord[2]; + if (j < pad_left) { + ip_x = pad_left; + } else if (j >= pad_left && j < dx_width + pad_left) { + ip_x = j; + } else { + ip_x = dx_width + pad_left - 1; + } + + ip_x = ip_x - pad_left; + + int64_t src_index = n * src_num + c * dy_width + j; + int64_t dest_index = n * dest_num + c * dx_width + ip_x; + DeviceAdd::Invoke(src + src_index, dest + dest_index); + } +} + +template +OF_DEVICE_FUNC void DoReplicationPad2d(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t y_height, + const int64_t y_width, const int64_t x_height, + const int64_t x_width, const int64_t pad_left, + const int64_t pad_top) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, i, j, ip_x, ip_y; + int64_t coord_y[4]; + index_helper.OffsetToNdIndex(k, coord_y); + n = coord_y[0]; + c = coord_y[1]; + i = coord_y[2]; + j = coord_y[3]; + if (j < pad_left) { + ip_x = pad_left; + } else if (j >= pad_left && j < x_width + pad_left) { + ip_x = j; + } else { + ip_x = x_width + pad_left - 1; + } + + if (i < pad_top) { + ip_y = pad_top; + } else if (i >= pad_top && i < x_height + pad_top) { + ip_y = i; + } else { + ip_y = x_height + pad_top - 1; + } + ip_x = ip_x - pad_left; + ip_y = ip_y - pad_top; + + int64_t dest_index = n * dest_num + c * y_width * y_height + i * y_width + j; + int64_t src_index = n * src_num + c * x_width * x_height + ip_y * x_width + ip_x; + dest[dest_index] = src[src_index]; + } +} + +template +OF_DEVICE_FUNC void DoReplicationPad2dGrad(const IN_T* src, IN_T* dest, + const NdIndexOffsetHelper& index_helper, + const int64_t elem_num, const int64_t src_num, + const int64_t dest_num, const int64_t dy_height, + const int64_t dy_width, const int64_t dx_height, + const int64_t dx_width, const int64_t pad_left, + const int64_t pad_top) { + XPU_1D_KERNEL_LOOP(k, elem_num) { + int64_t n, c, i, j, ip_x, ip_y; + int64_t coord[4]; + index_helper.OffsetToNdIndex(k, coord); + n = coord[0]; + c = coord[1]; + i = coord[2]; + j = coord[3]; + if (j < pad_left) { + ip_x = pad_left; + } else if (j >= pad_left && j < dx_width + pad_left) { + ip_x = j; + } else { + ip_x = dx_width + pad_left - 1; + } + + if (i < pad_top) { + ip_y = pad_top; + } else if (i >= pad_top && i < dx_height + pad_top) { + ip_y = i; + } else { + ip_y = dx_height + pad_top - 1; + } + ip_x = ip_x - pad_left; + ip_y = ip_y - pad_top; + + int64_t src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j; + int64_t dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x; + DeviceAdd::Invoke(src + src_index, dest + dest_index); + } +} + +// macros for functors instantiate(used by pad2d_kernels_util.cu) +#define INSTANTIATE_REPLICATION_PAD_FUNCTOR(device_type_v, dtype_pair) \ + template struct ReplicationPad1dFunctor; \ + template struct ReplicationPad2dFunctor; + +#define INSTANTIATE_REPLICATION_PAD_GRAD_FUNCTOR(device_type_v, dtype_pair) \ + template struct ReplicationPad1dGradFunctor; \ + template struct ReplicationPad2dGradFunctor; + +} // namespace user_op +} // namespace oneflow + +#endif // ONEFLOW_USER_KERNELS_REPLICATION_PAD_KERNELS_UTIL_H_ diff --git a/oneflow/user/kernels/rms_norm_gpu_kernel.cu b/oneflow/user/kernels/rms_norm_gpu_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..0fa71456cb7e6f4d3c962eb4086831612a9f8bc2 --- /dev/null +++ b/oneflow/user/kernels/rms_norm_gpu_kernel.cu @@ -0,0 +1,324 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include "oneflow/core/ep/include/primitive/fill.h" +#include "oneflow/core/ep/include/primitive/matmul.h" +#include "oneflow/core/cuda/rms_norm.cuh" +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 + +namespace oneflow { +namespace cuda { +namespace rms_norm { + +template +struct AffineStore { + AffineStore(DST* dst, const DST* weight, int32_t row_size) + : dst(dst), weight(weight), row_size(row_size) {} + + template + __device__ void store(const SRC* src, int32_t row, int32_t col) { + layer_norm::Pack dst_pack; + layer_norm::Pack weight_pack; + const int32_t offset = (row * row_size + col) / N; + const int32_t weight_offset = col / N; + if (affine) { + weight_pack.storage = + *(reinterpret_cast*>(weight) + weight_offset); + } +#pragma unroll + for (int i = 0; i < N; ++i) { + if (affine) { + dst_pack.elem[i] = static_cast(src[i]) * weight_pack.elem[i]; + } else { + dst_pack.elem[i] = static_cast(src[i]); + } + } + *(reinterpret_cast*>(dst) + offset) = dst_pack.storage; + } + + DST* dst; + const DST* weight; + int32_t row_size; +}; + +template +struct AffineLoad { + AffineLoad(const SRC* src, const SRC* weight, int32_t row_size) + : src(src), weight(weight), row_size(row_size) {} + + template + __device__ void load(DST* dst, int32_t row, int32_t col) const { + layer_norm::Pack src_pack; + layer_norm::Pack weight_pack; + const int32_t offset = (row * row_size + col) / N; + src_pack.storage = *(reinterpret_cast*>(src) + offset); + if (affine) { + const int32_t weight_offset = col / N; + weight_pack.storage = + *(reinterpret_cast*>(weight) + weight_offset); + } +#pragma unroll + for (int i = 0; i < N; ++i) { + if (affine) { + dst[i] = static_cast(src_pack.elem[i] * weight_pack.elem[i]); + } else { + dst[i] = static_cast(src_pack.elem[i]); + } + } + } + const SRC* src; + const SRC* weight; + int32_t row_size; +}; + +template +void DispatchRmsNormForwardAffine(ep::Stream* stream, const int64_t nrow, const int64_t ncol, + const double eps, const T* x_dptr, const T* w_dptr, T* y_dptr, + ComputeType* inv_rms) { + layer_norm::DirectLoad load(x_dptr, ncol); + AffineStore store(y_dptr, w_dptr, ncol); + OF_CUDA_CHECK((LaunchRmsNorm( + stream->As()->cuda_stream(), load, store, nrow, ncol, eps, inv_rms))); +} + +template +void RmsNormForward(ep::Stream* stream, const int64_t nrow, const int64_t ncol, const double eps, + const T* x_dptr, const T* w_dptr, T* y_dptr, ComputeType* inv_rms) { + if (w_dptr) { + DispatchRmsNormForwardAffine(stream, nrow, ncol, eps, x_dptr, w_dptr, + y_dptr, inv_rms); + } else { + DispatchRmsNormForwardAffine(stream, nrow, ncol, eps, x_dptr, w_dptr, + y_dptr, inv_rms); + } +} + +template +void DispatchRmsNormBackwardAffine(ep::Stream* stream, const int64_t nrow, const int64_t ncol, + const T* dy_dptr, const T* x_dptr, const T* weight_dptr, + const ComputeType* inv_rms, T* dx_ptr) { + layer_norm::DirectLoad load_x(x_dptr, ncol); + AffineLoad load_dy(dy_dptr, weight_dptr, ncol); + layer_norm::DirectStore store(dx_ptr, ncol); + OF_CUDA_CHECK((rms_norm::LaunchRmsNormGrad(stream->As()->cuda_stream(), nrow, + ncol, load_x, load_dy, store, inv_rms))); +} + +template +void RmsNormBackward(ep::Stream* stream, const int64_t nrow, const int64_t ncol, const T* dy_dptr, + const T* x_dptr, const T* weight_dptr, const ComputeType* inv_rms, + T* dx_dptr) { + if (weight_dptr) { + DispatchRmsNormBackwardAffine(stream, nrow, ncol, dy_dptr, x_dptr, + weight_dptr, inv_rms, dx_dptr); + } else { + DispatchRmsNormBackwardAffine(stream, nrow, ncol, dy_dptr, x_dptr, + weight_dptr, inv_rms, dx_dptr); + } +} + +} // namespace rms_norm + +template +class RmsNormKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + RmsNormKernel() = default; + ~RmsNormKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* inv_rms = ctx->Tensor4ArgNameAndIndex("inv_rms", 0); + const double eps = ctx->Attr("epsilon"); + const Shape& normalized_shape = ctx->Attr("normalized_shape"); + const int64_t ncol = normalized_shape.elem_cnt(); + const int64_t nrow = inv_rms->shape_view().elem_cnt(); + const T* weight_dptr = nullptr; + if (ctx->has_input("weight", 0)) { + const auto* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + CHECK_EQ(weight->shape_view().elem_cnt(), ncol); + weight_dptr = weight->dptr(); + } + CHECK_EQ(x->shape_view().elem_cnt(), ncol * nrow); + CHECK_LT(nrow * ncol, std::numeric_limits::max()) + << "The size of tensor exceeds int32 max limit. The kernel don't support large tensor."; + using ComputeType = typename layer_norm::DefaultComputeType::type; + rms_norm::RmsNormForward(ctx->stream(), nrow, ncol, eps, x->dptr(), weight_dptr, + y->mut_dptr(), inv_rms->mut_dptr()); + }; +}; + +#define REGISTER_RMS_NORM_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("rms_norm") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("x", 0) == GetDataType::value)); + +REGISTER_RMS_NORM_CUDA_KERNEL(float) +REGISTER_RMS_NORM_CUDA_KERNEL(double) +REGISTER_RMS_NORM_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_RMS_NORM_CUDA_KERNEL(nv_bfloat16) +#endif + +template +class RmsNormGradKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + RmsNormGradKernel() = default; + ~RmsNormGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* inv_rms = ctx->Tensor4ArgNameAndIndex("inv_rms", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + const int64_t nrow = inv_rms->shape_view().elem_cnt(); + const int64_t ncol = x->shape_view().elem_cnt() / nrow; + const T* weight_dptr = nullptr; + if (ctx->has_input("weight", 0)) { + const user_op::Tensor* weight = ctx->Tensor4ArgNameAndIndex("weight", 0); + CHECK_EQ(ncol, weight->shape_view().elem_cnt()); + weight_dptr = weight->dptr(); + } + CHECK_LT(nrow * ncol, std::numeric_limits::max()) + << "The size of tensor exceeds int32 max limit. The kernel don't support large tensor."; + using ComputeType = typename layer_norm::DefaultComputeType::type; + rms_norm::RmsNormBackward(ctx->stream(), nrow, ncol, dy->dptr(), x->dptr(), + weight_dptr, inv_rms->dptr(), dx->mut_dptr()); + }; +}; + +#define REGISTER_RMS_NORM_GRAD_CUDA_KERNEL(dtype) \ + REGISTER_USER_KERNEL("rms_norm_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)); + +REGISTER_RMS_NORM_GRAD_CUDA_KERNEL(float) +REGISTER_RMS_NORM_GRAD_CUDA_KERNEL(double) +REGISTER_RMS_NORM_GRAD_CUDA_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_RMS_NORM_GRAD_CUDA_KERNEL(nv_bfloat16) +#endif + +namespace { + +constexpr int kNProcPerThread = 4; + +} // namespace + +template +class RmsNormParamGradKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + RmsNormParamGradKernel() = default; + ~RmsNormParamGradKernel() = default; + + private: + using user_op::OpKernel::Compute; + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + const user_op::Tensor* inv_rms = ctx->Tensor4ArgNameAndIndex("inv_rms", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + user_op::Tensor* weight_grad = ctx->Tensor4ArgNameAndIndex("weight_grad", 0); + const int64_t nrow = inv_rms->shape_view().elem_cnt(); + const int64_t ncol = weight_grad->shape_view().elem_cnt(); + CHECK_LT(nrow * ncol, std::numeric_limits::max()) + << "The size of tensor exceeds int32 max limit. The kernel don't support large tensor."; + + // step 1: dx = dy * y and reduce partial rows in a block + const int block_dim_x = rms_norm::kWarpSize; + const int block_dim_y = rms_norm::kWarpSize / kNProcPerThread; + int grid_dim_x; + int grid_dim_y; + OF_CUDA_CHECK((rms_norm::GetGrid2Dim(nrow, ncol, block_dim_x, block_dim_y, + &grid_dim_x, &grid_dim_y))); + // tmp weight shape [grid_dim_y, ncol] (reduce nrow -> grid_dim_y) + size_t tmp_weight_grad_size = grid_dim_y * ncol; + T* tmp_weight_grad_dptr = reinterpret_cast(tmp_buffer->mut_dptr()); + using ComputeType = typename layer_norm::DefaultComputeType::type; + dim3 grid_dims(grid_dim_x, grid_dim_y); + dim3 block_dims(block_dim_x, block_dim_y); + rms_norm::RmsNormParamGrad + <<stream()->As()->cuda_stream()>>>( + nrow, ncol, dy->dptr(), x->dptr(), inv_rms->dptr(), + tmp_weight_grad_dptr); + + // step 2: reduce rows throught gemm to calculate weight grad + // fill ones matrix with shape (grid_dim_y, 1) + const int32_t m = ncol; + const int32_t n = 1; + const int32_t k = grid_dim_y; + const DataType data_type = dy->data_type(); + auto fill = ep::primitive::NewPrimitive( + ctx->stream()->device_type(), data_type); + CHECK(fill); + T* tmp_ones_dptr = tmp_buffer->mut_dptr() + tmp_weight_grad_size; + fill->Launch(ctx->stream(), tmp_ones_dptr, 1.0, k); + // tmp weight grad (grid_dim_y, ncol) (T) * tmp ones (grid_dim_y, 1) (N) + // -> weight grad (ncol, 1) + auto matmul = ep::primitive::NewPrimitive( + ctx->stream()->device_type(), data_type, ep::primitive::BlasTransposeType::T, + ep::primitive::BlasTransposeType::N); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, /*alpha*/ 1.0, tmp_weight_grad_dptr, tmp_ones_dptr, + /*beta*/ 0.0, weight_grad->mut_dptr()); + }; +}; + +template +size_t InferRmsNormParamGradTempBufferSize(user_op::InferContext* ctx) { + const auto& shape = ctx->InputTensorDesc("dy", 0).shape(); + const auto& b_shape = ctx->InputTensorDesc("inv_rms", 0).shape(); + const int64_t nrow = b_shape.elem_cnt(); + const int64_t ncol = shape.elem_cnt() / nrow; + const int block_dim_x = rms_norm::kWarpSize; + const int block_dim_y = rms_norm::kWarpSize / kNProcPerThread; + int grid_dim_x; + int grid_dim_y; + OF_CUDA_CHECK((rms_norm::GetGrid2Dim(nrow, ncol, block_dim_x, block_dim_y, + &grid_dim_x, &grid_dim_y))); + return (grid_dim_y * ncol + grid_dim_y) * sizeof(T); +} + +#define REGISTER_RMS_NORM_PARAM_GRAD_GPU_KERNEL(dtype) \ + REGISTER_USER_KERNEL("rms_norm_param_grad") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("dy", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn(InferRmsNormParamGradTempBufferSize); + +REGISTER_RMS_NORM_PARAM_GRAD_GPU_KERNEL(float) +REGISTER_RMS_NORM_PARAM_GRAD_GPU_KERNEL(double) +REGISTER_RMS_NORM_PARAM_GRAD_GPU_KERNEL(half) +#if CUDA_VERSION >= 11000 +REGISTER_RMS_NORM_PARAM_GRAD_GPU_KERNEL(nv_bfloat16) +#endif + +} // namespace cuda +} // namespace oneflow diff --git a/oneflow/user/kernels/roi_align_kernel.hip.cpp b/oneflow/user/kernels/roi_align_kernel.hip.cpp deleted file mode 100644 index f9492c67292c363827ff47760b8f30d9d0e1278a..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/roi_align_kernel.hip.cpp +++ /dev/null @@ -1,302 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__device__ T BilinearInterpolate(const T* channel_dptr, const int32_t height, const int32_t width, - T y, T x) { - if (y < -1.0 || y > height || x < -1.0 || x > width) { return 0; } - - if (y <= 0) { y = 0; } - if (x <= 0) { x = 0; } - int32_t y_low = static_cast(y); - int32_t x_low = static_cast(x); - int32_t y_high = 0; - int32_t x_high = 0; - - if (y_low >= height - 1) { - y_low = height - 1; - y_high = y_low; - y = static_cast(y_low); - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_low = width - 1; - x_high = x_low; - x = static_cast(x_low); - } else { - x_high = x_low + 1; - } - - const T ly = y - y_low; - const T lx = x - x_low; - const T hy = 1.f - ly; - const T hx = 1.f - lx; - - // https://en.wikipedia.org/wiki/Bilinear_interpolation - const int64_t q11 = y_low * width + x_low; - const int64_t q21 = y_low * width + x_high; - const int64_t q12 = y_high * width + x_low; - const int64_t q22 = y_high * width + x_high; - // no 1 / (x_high - x_low) * (y_high - y_low) because it will always be 1 in RoI Align - return (hy * hx) * channel_dptr[q11] + (hy * lx) * channel_dptr[q21] - + (ly * hx) * channel_dptr[q12] + (ly * lx) * channel_dptr[q22]; -} - -template -__device__ bool BilinearInterpolateDiff(const T bin_diff_avg, const int64_t height, - const int64_t width, T y, T x, T& diff11, T& diff21, - T& diff12, T& diff22, int32_t& x_low, int32_t& x_high, - int32_t& y_low, int32_t& y_high) { - if (y < -1.0 || y > height || x < -1.0 || x > width) { return false; } - - if (y <= 0) { y = 0; } - if (x <= 0) { x = 0; } - - y_low = static_cast(y); - x_low = static_cast(x); - - if (y_low >= height - 1) { - y_low = height - 1; - y_high = y_low; - y = static_cast(y_low); - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_low = width - 1; - x_high = x_low; - x = static_cast(x_low); - } else { - x_high = x_low + 1; - } - - const T ly = y - y_low; - const T lx = x - x_low; - const T hy = 1.f - ly; - const T hx = 1.f - lx; - - diff11 = bin_diff_avg * hy * hx; - diff21 = bin_diff_avg * hy * lx; - diff12 = bin_diff_avg * ly * hx; - diff22 = bin_diff_avg * ly * lx; - return true; -} - -template -__global__ void RoiAlignForward(const int64_t nthreads, const T* in_dptr, const T* rois_dptr, - const T spatial_scale, const int32_t sampling_ratio, - const int64_t channel_num, const int64_t height, - const int64_t width, const int64_t pooled_height, - const int64_t pooled_width, const bool aligned, T* out_dptr) { - const int64_t pooled_area = pooled_height * pooled_width; - const int64_t channel_pooled_area = channel_num * pooled_height * pooled_width; - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int64_t h = (index / pooled_width) % pooled_height; - const int64_t w = index % pooled_width; - const int64_t c = (index / pooled_area) % channel_num; - const int64_t r = index / channel_pooled_area; - const T* offset_rois_dptr = rois_dptr + r * 5; - const int64_t n = static_cast(offset_rois_dptr[0]); - const T align_offset = aligned ? static_cast(0.5) : static_cast(0.f); - const T roi_start_w = offset_rois_dptr[1] * spatial_scale - align_offset; - const T roi_start_h = offset_rois_dptr[2] * spatial_scale - align_offset; - const T roi_end_w = offset_rois_dptr[3] * spatial_scale - align_offset; - const T roi_end_h = offset_rois_dptr[4] * spatial_scale - align_offset; - T roi_height = roi_end_h - roi_start_h; - T roi_width = roi_end_w - roi_start_w; - // aligned == false is for compatibility. the argument "aligned" doesn't have the semantic of - // determining minimum roi size - if (aligned == false) { - roi_height = max(roi_height, static_cast(1.0)); - roi_width = max(roi_width, static_cast(1.0)); - } - const T bin_height = static_cast(roi_height) / static_cast(pooled_height); - const T bin_width = static_cast(roi_width) / static_cast(pooled_width); - const int32_t bin_grid_height = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); - const int32_t bin_grid_width = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); - const T count = max(bin_grid_height * bin_grid_width, 1); - const T* channel_dptr = in_dptr + (n * channel_num + c) * height * width; - T out_val = 0.0; - FOR_RANGE(int64_t, grid_i, 0, bin_grid_height) { - // + .5f for center position - T y = roi_start_h + h * bin_height - + static_cast(grid_i + 0.5f) * bin_height / static_cast(bin_grid_height); - FOR_RANGE(int64_t, grid_j, 0, bin_grid_width) { - T x = roi_start_w + w * bin_width - + static_cast(grid_j + 0.5f) * bin_width / static_cast(bin_grid_width); - out_val += BilinearInterpolate(channel_dptr, height, width, y, x); - } - } - out_dptr[index] = out_val / count; - } -} - -template -__global__ void RoiAlignBackward(const int64_t nthreads, const T* out_diff_dptr, const T* rois_dptr, - const T spatial_scale, const int32_t sampling_ratio, - const int64_t channel_num, const int64_t height, - const int64_t width, const int64_t pooled_height, - const int64_t pooled_width, const bool aligned, T* in_diff_dptr) { - const int64_t pooled_area = pooled_height * pooled_width; - const int64_t channel_pooled_area = channel_num * pooled_height * pooled_width; - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int64_t h = (index / pooled_width) % pooled_height; - const int64_t w = index % pooled_width; - const int64_t c = (index / pooled_area) % channel_num; - const int64_t r = index / channel_pooled_area; - const T* offset_rois_dptr = rois_dptr + r * 5; - const int64_t n = static_cast(offset_rois_dptr[0]); - const T align_offset = aligned ? static_cast(0.5) : static_cast(0.f); - const T roi_start_w = offset_rois_dptr[1] * spatial_scale - align_offset; - const T roi_start_h = offset_rois_dptr[2] * spatial_scale - align_offset; - const T roi_end_w = offset_rois_dptr[3] * spatial_scale - align_offset; - const T roi_end_h = offset_rois_dptr[4] * spatial_scale - align_offset; - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - // aligned == false is for compatibility. the argument "aligned" doesn't have the semantic of - // determining minimum roi size - if (aligned == false) { - roi_height = max(roi_height, static_cast(1.0)); - roi_width = max(roi_width, static_cast(1.0)); - } - const T bin_height = static_cast(roi_height) / static_cast(pooled_height); - const T bin_width = static_cast(roi_width) / static_cast(pooled_width); - const int32_t bin_grid_height = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_height / pooled_height); - const int32_t bin_grid_width = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); - - const T count = max(bin_grid_height * bin_grid_width, 1); - const T bin_diff_avg = out_diff_dptr[index] / count; - T* in_diff_channel_dptr = in_diff_dptr + (n * channel_num + c) * height * width; - FOR_RANGE(int64_t, grid_i, 0, bin_grid_height) { - // + .5f for center position - T y = roi_start_h + h * bin_height - + static_cast(grid_i + 0.5f) * bin_height / static_cast(bin_grid_height); - FOR_RANGE(int64_t, grid_j, 0, bin_grid_width) { - T x = roi_start_w + w * bin_width - + static_cast(grid_j + 0.5f) * bin_width / static_cast(bin_grid_width); - T diff11 = 0; - T diff21 = 0; - T diff12 = 0; - T diff22 = 0; - int32_t x_low = 0; - int32_t x_high = 0; - int32_t y_low = 0; - int32_t y_high = 0; - bool has_diff = BilinearInterpolateDiff(bin_diff_avg, height, width, y, x, diff11, diff21, - diff12, diff22, x_low, x_high, y_low, y_high); - if (has_diff) { - const int64_t q11 = y_low * width + x_low; - const int64_t q21 = y_low * width + x_high; - const int64_t q12 = y_high * width + x_low; - const int64_t q22 = y_high * width + x_high; - atomicAdd(in_diff_channel_dptr + q11, diff11); - atomicAdd(in_diff_channel_dptr + q21, diff21); - atomicAdd(in_diff_channel_dptr + q12, diff12); - atomicAdd(in_diff_channel_dptr + q22, diff22); - } - } - } - } -} - -} // namespace - -template -class RoIAlignKernel final : public user_op::OpKernel { - public: - RoIAlignKernel() = default; - ~RoIAlignKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_blob = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* rois_blob = ctx->Tensor4ArgNameAndIndex("rois", 0); - if (rois_blob->shape_view().elem_cnt() == 0) { return; } - user_op::Tensor* y_blob = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t pooled_h = ctx->Attr("pooled_h"); - const int32_t pooled_w = ctx->Attr("pooled_w"); - const float spatial_scale = ctx->Attr("spatial_scale"); - const int32_t sampling_ratio = ctx->Attr("sampling_ratio"); - const bool aligned = ctx->Attr("aligned"); - - const int64_t elem_cnt = y_blob->shape_view().elem_cnt(); - RoiAlignForward<<stream()->As()->cuda_stream()>>>( - elem_cnt, x_blob->dptr(), rois_blob->dptr(), spatial_scale, sampling_ratio, - x_blob->shape_view().At(1), x_blob->shape_view().At(2), x_blob->shape_view().At(3), - pooled_h, pooled_w, aligned, y_blob->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class RoIAlignGradKernel final : public user_op::OpKernel { - public: - RoIAlignGradKernel() = default; - ~RoIAlignGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_blob = ctx->Tensor4ArgNameAndIndex("dx", 0); - if (dx_blob == nullptr) { return; } - Memset(ctx->stream(), dx_blob->mut_dptr(), 0, - dx_blob->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_blob = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* rois_blob = ctx->Tensor4ArgNameAndIndex("rois", 0); - const int32_t pooled_h = ctx->Attr("pooled_h"); - const int32_t pooled_w = ctx->Attr("pooled_w"); - const float spatial_scale = ctx->Attr("spatial_scale"); - const int32_t sampling_ratio = ctx->Attr("sampling_ratio"); - const bool aligned = ctx->Attr("aligned"); - - const int64_t elem_cnt = dy_blob->shape_view().elem_cnt(); - if (elem_cnt > 0) { - RoiAlignBackward<<stream()->As()->cuda_stream()>>>( - elem_cnt, dy_blob->dptr(), rois_blob->dptr(), spatial_scale, sampling_ratio, - dx_blob->shape_view().At(1), dx_blob->shape_view().At(2), dx_blob->shape_view().At(3), - pooled_h, pooled_w, aligned, dx_blob->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -REGISTER_USER_KERNEL("roi_align") - .SetCreateFn>() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -REGISTER_USER_KERNEL("roi_align_grad") - .SetCreateFn>() - .SetIsMatchedHob(user_op::HobDeviceType() == DeviceType::kCUDA); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/roll_kernel.hip.cpp b/oneflow/user/kernels/roll_kernel.hip.cpp deleted file mode 100644 index dc6515697516cd5706990eadaa10b46713b80aef..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/roll_kernel.hip.cpp +++ /dev/null @@ -1,295 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/roll_kernel_utils.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void RollCudaKernel(const T* in_ptr, const SHIFTS shifts, const SHAPE shape, - const STRIDE stride, const int64_t elements, T* out_ptr) { - int32_t global_index = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (global_index < elements) { - int32_t shifted_global_index = - getShiftedIndex(global_index, shifts.val, shape.val, stride.val); - out_ptr[global_index] = in_ptr[shifted_global_index]; - global_index += step; - } -} - -template -struct GpuRollFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const SHIFTS shifts, const SHAPE shape, - const STRIDE stride, const int64_t elements, T* out_ptr) { - RollCudaKernel<<As()->cuda_stream()>>>( - in_ptr, shifts, shape, stride, elements, out_ptr); - } -}; - -template -struct GpuRollFunctor final { - void operator()(ep::Stream* stream, const float16* in_ptr, const SHIFTS shifts, const SHAPE shape, - const STRIDE stride, const int64_t elements, float16* out_ptr) { - RollCudaKernel<<As()->cuda_stream()>>>( - reinterpret_cast(in_ptr), shifts, shape, stride, elements, - reinterpret_cast(out_ptr)); - } -}; - -template -__global__ void RollFlattenCudaKernel(const T* in_ptr, const int64_t start, - const int64_t elem_count_minus_start, const int64_t elements, - T* out_ptr) { - int64_t global_index = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - - while (global_index < elements) { - int64_t source_idx = 0; - if (global_index >= elem_count_minus_start) { - source_idx = global_index - elem_count_minus_start; - } else { - source_idx = global_index + start; - } - out_ptr[global_index] = in_ptr[source_idx]; - - global_index += step; - } -} - -template -struct GpuRollFlattenFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const int64_t start, - const int64_t elem_count_minus_start, const int64_t elements, T* out_ptr) { - RollFlattenCudaKernel<<As()->cuda_stream()>>>( - in_ptr, start, elem_count_minus_start, elements, out_ptr); - } -}; - -template<> -void GpuRollFlattenFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const int64_t start, - const int64_t elem_count_minus_start, - const int64_t elements, float16* out_ptr) { - RollFlattenCudaKernel<<As()->cuda_stream()>>>( - reinterpret_cast(in_ptr), start, elem_count_minus_start, elements, - reinterpret_cast(out_ptr)); -} - -template -__global__ void Roll1DimCudaKernel(const T* in_ptr, const int32_t stride_x_size, - const int32_t stride, const int32_t size_minus_start, - const int32_t size_minus_start_x_stride, - const int32_t start_x_stride, const int64_t elements, - T* out_ptr) { - int32_t global_index = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - - while (global_index < elements) { - // roll dim idx is the index of linear_index along the rolling dimension. - int32_t roll_dim_idx = global_index % stride_x_size / stride; - // index into the source data to find appropriate value. - int32_t source_idx = 0; - if (roll_dim_idx >= size_minus_start) { - source_idx = global_index - size_minus_start_x_stride; - } else { - source_idx = global_index + start_x_stride; - } - out_ptr[global_index] = in_ptr[source_idx]; - - global_index += step; - } -} - -template -struct GpuRoll1DimFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const int32_t stride_x_size, - const int32_t stride, const int32_t size_minus_start, - const int32_t size_minus_start_x_stride, const int32_t start_x_stride, - const int64_t elements, T* out_ptr) { - Roll1DimCudaKernel<<As()->cuda_stream()>>>( - in_ptr, stride_x_size, stride, size_minus_start, size_minus_start_x_stride, start_x_stride, - elements, out_ptr); - } -}; - -template<> -void GpuRoll1DimFunctor::operator()(ep::Stream* stream, const float16* in_ptr, - const int32_t stride_x_size, const int32_t stride, - const int32_t size_minus_start, - const int32_t size_minus_start_x_stride, - const int32_t start_x_stride, const int64_t elements, - float16* out_ptr) { - Roll1DimCudaKernel<<As()->cuda_stream()>>>( - reinterpret_cast(in_ptr), stride_x_size, stride, size_minus_start, - size_minus_start_x_stride, start_x_stride, elements, reinterpret_cast(out_ptr)); -} - -} // namespace - -template -class GpuRollKernel final : public user_op::OpKernel { - public: - GpuRollKernel() = default; - ~GpuRollKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const std::vector& shifts = ctx->Attr>("shifts"); - const std::vector& dims = ctx->Attr>("dims"); - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - const int64_t elem_count = out->shape_view().elem_cnt(); - - if (dims[0] == -1) { - // NOTE(Liang Depeng): Borrow the implementation of pytorch and simplify to 1d array case. - int64_t start = (elem_count - shifts[0]) % elem_count; - if (start < 0) start = start + elem_count; - const int64_t elem_count_minus_start = elem_count - start; - GpuRollFlattenFunctor()(ctx->stream(), in_ptr, start, elem_count_minus_start, elem_count, - out_ptr); - } else { - SHAPE new_shape{}; - SHIFTS new_shifts{}; - int32_t num_axes = 0; - computeParams(in->shape_view(), shifts, dims, new_shifts.val, new_shape.val, &num_axes); - - STRIDE stride{}; - initStride(stride, new_shape, num_axes); - - if (dims.size() == 1) { - // NOTE(Liang Depeng): Borrow the implementation of pytorch - const int32_t size = new_shape.val[dims[0]]; - int32_t start = (size - new_shifts.val[dims[0]]) % size; - // Behavior of % is different in C++ vs Python for negative numbers. This - // corrects the difference. - if (start < 0) start = start + size; - - const int32_t stride_x_size = stride.val[dims[0]] * size; - const int32_t size_minus_start = size - start; - const int32_t size_minus_start_x_stride = size_minus_start * stride.val[dims[0]]; - const int32_t start_x_stride = start * stride.val[dims[0]]; - - GpuRoll1DimFunctor()(ctx->stream(), in_ptr, stride_x_size, stride.val[dims[0]], - size_minus_start, size_minus_start_x_stride, start_x_stride, - elem_count, out_ptr); - - } else { - transformShifts(new_shifts.val, new_shape.val, num_axes); - switch (num_axes) { - case 1: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 2: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 3: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 4: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 5: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 6: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 7: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 8: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 9: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, elem_count, - out_ptr); - break; - case 10: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 11: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 12: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 13: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 14: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 15: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - case 16: - GpuRollFunctor()(ctx->stream(), in_ptr, new_shifts, new_shape, stride, - elem_count, out_ptr); - break; - default: break; - } - } - } - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_ROLL_KERNEL(dtype) \ - REGISTER_USER_KERNEL("roll").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)) - -REGISTER_ROLL_KERNEL(float); -REGISTER_ROLL_KERNEL(double); -REGISTER_ROLL_KERNEL(float16); -REGISTER_ROLL_KERNEL(bool); -REGISTER_ROLL_KERNEL(uint8_t); -REGISTER_ROLL_KERNEL(int8_t); -REGISTER_ROLL_KERNEL(int32_t); -REGISTER_ROLL_KERNEL(int64_t); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/scalar_math_kernels.cpp b/oneflow/user/kernels/scalar_math_kernels.cpp index 83f15b5eac77901fcfca601eb65e72de27ca9193..e6b080b6f972a9acf4cd6a0e451b30d5e4f7b391 100644 --- a/oneflow/user/kernels/scalar_math_kernels.cpp +++ b/oneflow/user/kernels/scalar_math_kernels.cpp @@ -13,25 +13,61 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#include "oneflow/user/kernels/scalar_math_kernels.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" +#include "oneflow/core/common/scalar.h" namespace oneflow { -template class BIN_OP, typename T> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - DoScalarMath(elem_cnt, scalar, in, out); +namespace { + +template +std::unique_ptr NewBroadcastElementwiseBinaryPrimitive( + Context* ctx, ep::primitive::BinaryOp op) { + const user_op::TensorDesc* x = ctx->TensorDesc4ArgNameAndIndex("in", 0); + const user_op::TensorDesc* y = ctx->TensorDesc4ArgNameAndIndex("out", 0); + const int64_t ndims = y->shape().NumAxes(); + return ep::primitive::NewPrimitive( + ctx->device_type(), op, x->data_type(), y->data_type(), ndims); +} + +template +auto BroadcastElementwiseBinaryPrimitiveExists() { + return hob::make_custom("BroadcastElementwiseBinaryPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewBroadcastElementwiseBinaryPrimitive(&ctx, op).operator bool(); + }); +} + +template +std::unique_ptr +NewBroadcastElementwiseAttrBinaryPrimitive(Context* ctx, ep::primitive::BinaryOp op) { + const user_op::TensorDesc* x = ctx->TensorDesc4ArgNameAndIndex("x", 0); + const user_op::TensorDesc* dy = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const int64_t ndims = dy->shape().NumAxes(); + Scalar value; + if (ctx->template Attr("has_int_operand")) { + value = Scalar(ctx->template Attr("int_operand")); + } else if (ctx->template Attr("has_float_operand")) { + value = Scalar(ctx->template Attr("float_operand")); + } else { + UNIMPLEMENTED(); } -}; + return ep::primitive::NewPrimitive( + ctx->device_type(), op, x->data_type(), dy->data_type(), ndims, value); +} -template class BIN_OP, typename T> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - DoScalarReverseMath(elem_cnt, scalar, in, out); - } -}; +template +auto BroadcastElementwiseAttrBinaryPrimitiveExists() { + return hob::make_custom( + "BroadcastElementwiseBinaryAttrPrimitiveExists", [](const user_op::KernelRegContext& ctx) { + return NewBroadcastElementwiseAttrBinaryPrimitive(&ctx, op).operator bool(); + }); +} -template class BIN_OP, typename T> +} // namespace + +template class ScalarMathKernel final : public user_op::OpKernel { public: ScalarMathKernel() = default; @@ -41,21 +77,28 @@ class ScalarMathKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - T scalar_operand = static_cast(0); + Scalar value; if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); + value = Scalar(ctx->Attr("int_operand")); } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); + value = Scalar(ctx->Attr("float_operand")); } else { UNIMPLEMENTED(); } - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - int64_t elem_cnt = out->shape_view().elem_cnt(); if (elem_cnt != 0) { - ScalarMathFunctor()(ctx->stream(), elem_cnt, scalar_operand, in_ptr, - out_ptr); + const bool is_add_sub_0 = + (op == ep::primitive::BinaryOp::kAdd || op == ep::primitive::BinaryOp::kSub) + && value.Value() == 0.0; + const bool is_mul_div_1 = + (op == ep::primitive::BinaryOp::kMul || op == ep::primitive::BinaryOp::kDiv) + && value.Value() == 1.0; + if ((is_add_sub_0 || is_mul_div_1) && in->dptr() == out->dptr()) { return; } + std::unique_ptr primitive = + NewBroadcastElementwiseBinaryPrimitive(ctx, op); + CHECK(primitive); + primitive->Launch(ctx->stream(), in->shape_view().NumAxes(), in->shape_view().ptr(), + in->dptr(), value, out->mut_dptr()); } else { // For 0-d Tensor return; @@ -64,7 +107,7 @@ class ScalarMathKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -template class BIN_OP, typename T> +template class ScalarReverseMathKernel final : public user_op::OpKernel { public: ScalarReverseMathKernel() = default; @@ -74,21 +117,21 @@ class ScalarReverseMathKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - T scalar_operand = static_cast(0); + Scalar value; if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); + value = Scalar(ctx->Attr("int_operand")); } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); + value = Scalar(ctx->Attr("float_operand")); } else { UNIMPLEMENTED(); } - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - int64_t elem_cnt = out->shape_view().elem_cnt(); if (elem_cnt != 0) { - ScalarReverseMathFunctor()(ctx->stream(), elem_cnt, scalar_operand, - in_ptr, out_ptr); + std::unique_ptr primitive = + NewBroadcastElementwiseBinaryPrimitive(ctx, op); + CHECK(primitive); + primitive->Launch(ctx->stream(), value, in->shape_view().NumAxes(), in->shape_view().ptr(), + in->dptr(), out->mut_dptr()); } else { // For 0-d Tensor return; @@ -97,144 +140,70 @@ class ScalarReverseMathKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, kernel_name, binary_op, \ - input_dtype_pair) \ - REGISTER_USER_KERNEL(kernel_name) \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("in", 0) == OF_PP_PAIR_SECOND(input_dtype_pair))); - -#define REGISTER_SCALAR_MATH_KERNEL(device, dtype_pair) \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_add", BinaryFuncAdd, \ - dtype_pair); \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_floordiv", BinaryFuncFloorDiv, \ - dtype_pair); \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_fmod", BinaryFuncFMod, \ - dtype_pair); \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_mul", BinaryFuncMul, \ - dtype_pair); \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_div", BinaryFuncDiv, \ - dtype_pair); \ - REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(device, "scalar_pow", BinaryFuncPow, dtype_pair); - -#define REGISTER_UNARY_MATH_SCALAR_REVERSE_ELEMWISE_USER_KERNEL(device, kernel_name, binary_op, \ - input_dtype_pair) \ - REGISTER_USER_KERNEL(kernel_name) \ - .SetCreateFn< \ - ScalarReverseMathKernel>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("in", 0) == OF_PP_PAIR_SECOND(input_dtype_pair))); - -#define REGISTER_SCALAR_REVERSE_POW_KERNEL(device, dtype_pair) \ - REGISTER_UNARY_MATH_SCALAR_REVERSE_ELEMWISE_USER_KERNEL(device, "scalar_reverse_pow", \ - BinaryFuncPow, dtype_pair); - -// we register uint8_t, int8_t, int32_t, int64_t, float, double, float16. -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_MATH_KERNEL, (DeviceType::kCPU), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_REVERSE_POW_KERNEL, (DeviceType::kCPU), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) - -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_MATH_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_REVERSE_POW_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) -#endif // WITH_CUDA - -#ifdef WITH_ROCM -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_MATH_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SCALAR_REVERSE_POW_KERNEL, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ - FLOAT16_DATA_TYPE_SEQ) -#endif // WITH_ROCM - -template -class CpuScalarPowGradKernel final : public user_op::OpKernel { - public: - CpuScalarPowGradKernel() = default; - ~CpuScalarPowGradKernel() = default; - - private: - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); - } else { - UNIMPLEMENTED(); - } - - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - FOR_RANGE(int32_t, i, 0, elem_cnt) { - dx_ptr[i] = - scalar_operand * (std::pow(x_ptr[i], scalar_operand - static_cast(1))) * dy_ptr[i]; - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CPU_SCALAR_POW_GRAD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CPU_SCALAR_POW_GRAD_KERNEL(DeviceType::kCPU, float); -REGISTER_CPU_SCALAR_POW_GRAD_KERNEL(DeviceType::kCPU, double); - -template -class CpuScalarReversePowGradKernel final : public user_op::OpKernel { +#define SCALAR_MATH_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("scalar_add", ep::primitive::BinaryOp::kAdd) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_mul", ep::primitive::BinaryOp::kMul) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_div", ep::primitive::BinaryOp::kDiv) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_floordiv", ep::primitive::BinaryOp::kFloorDiv) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_truncdiv", ep::primitive::BinaryOp::kTruncDiv) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_fmod", ep::primitive::BinaryOp::kFmod) \ + OF_PP_MAKE_TUPLE_SEQ("scalar_pow", ep::primitive::BinaryOp::kPow) + +#define REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL(op_name, binary_op) \ + REGISTER_USER_KERNEL(op_name) \ + .SetCreateFn>() \ + .SetIsMatchedHob((BroadcastElementwiseBinaryPrimitiveExists())) \ + .SetInplaceProposalFn( \ + [](const user_op::InferContext& ctx, \ + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { \ + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, true)); \ + return Maybe::Ok(); \ + }); + +OF_PP_FOR_EACH_TUPLE(REGISTER_UNARY_MATH_SCALAR_ELEMWISE_USER_KERNEL, SCALAR_MATH_SEQ) + +#define REGISTER_UNARY_MATH_SCALAR_REVERSE_ELEMWISE_USER_KERNEL(op_name, binary_op) \ + REGISTER_USER_KERNEL(op_name).SetCreateFn>().SetIsMatchedHob( \ + (BroadcastElementwiseBinaryPrimitiveExists())); + +REGISTER_UNARY_MATH_SCALAR_REVERSE_ELEMWISE_USER_KERNEL("scalar_reverse_pow", + ep::primitive::BinaryOp::kPow) + +template +class ScalarPowGradKernel final : public user_op::OpKernel { public: - CpuScalarReversePowGradKernel() = default; - ~CpuScalarReversePowGradKernel() = default; + ScalarPowGradKernel() = default; + ~ScalarPowGradKernel() = default; private: void Compute(user_op::KernelComputeContext* ctx) const override { const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); + const int64_t elem_cnt = dx_tensor->shape_view().elem_cnt(); + if (elem_cnt != 0) { + std::unique_ptr primitive = + NewBroadcastElementwiseAttrBinaryPrimitive(ctx, op); + CHECK(primitive); + primitive->Launch(ctx->stream(), x_tensor->shape_view().NumAxes(), + x_tensor->shape_view().ptr(), x_tensor->dptr(), + dy_tensor->shape_view().NumAxes(), dy_tensor->shape_view().ptr(), + dy_tensor->dptr(), dx_tensor->mut_dptr()); } else { - UNIMPLEMENTED(); - } - - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - // NOTE: y = a^x ==>> dy/dx = a^x * lna - FOR_RANGE(int32_t, i, 0, elem_cnt) { - dx_ptr[i] = std::pow(scalar_operand, x_ptr[i]) * std::log(scalar_operand) * dy_ptr[i]; + // For 0-d Tensor + return; } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_CPU_SCALAR_REVERSE_POW_GRAD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_reverse_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); +#define REGISTER_BINARY_MATH_WITH_ATTR_ELEMWISE_USER_KERNEL(op_name, binary_op) \ + REGISTER_USER_KERNEL(op_name).SetCreateFn>().SetIsMatchedHob( \ + (BroadcastElementwiseAttrBinaryPrimitiveExists())); -REGISTER_CPU_SCALAR_REVERSE_POW_GRAD_KERNEL(DeviceType::kCPU, float); -REGISTER_CPU_SCALAR_REVERSE_POW_GRAD_KERNEL(DeviceType::kCPU, double); +REGISTER_BINARY_MATH_WITH_ATTR_ELEMWISE_USER_KERNEL("scalar_pow_grad", + ep::primitive::BinaryOp::kScalarBasePowerGrad); +REGISTER_BINARY_MATH_WITH_ATTR_ELEMWISE_USER_KERNEL("scalar_reverse_pow_grad", + ep::primitive::BinaryOp::kScalarExpPowerGrad); } // namespace oneflow diff --git a/oneflow/user/kernels/scalar_math_kernels.cu b/oneflow/user/kernels/scalar_math_kernels.cu deleted file mode 100644 index 877623c9df5f479766d0ada9efbeecec7409344f..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/scalar_math_kernels.cu +++ /dev/null @@ -1,223 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/scalar_math_kernels.h" -#include "oneflow/core/cuda/elementwise.cuh" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/ep/cuda/cuda_stream.h" - -namespace oneflow { - -template class Op, typename T> -struct UnaryByScalarFunctor { - __host__ __device__ explicit UnaryByScalarFunctor(T scalar) : scalar(scalar) {} - __device__ T operator()(T a) const { return Op::Invoke(a, scalar); } - const T scalar; -}; - -template class Op, typename T> -struct UnaryByScalarReverseFunctor { - __host__ __device__ explicit UnaryByScalarReverseFunctor(T scalar) : scalar(scalar) {} - __device__ T operator()(T a) const { return Op::Invoke(scalar, a); } - const T scalar; -}; - -template class Op> -struct UnaryByScalarFunctor { - __host__ __device__ explicit UnaryByScalarFunctor(half scalar) : scalar(scalar) {} - __device__ half operator()(half a) const { return Op::Invoke(a, scalar); } - const half scalar; -}; - -template class Op> -struct UnaryByScalarReverseFunctor { - __host__ __device__ explicit UnaryByScalarReverseFunctor(half scalar) : scalar(scalar) {} - __device__ half operator()(half a) const { return Op::Invoke(scalar, a); } - const half scalar; -}; - -template class BIN_OP, typename T> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary(UnaryByScalarFunctor(scalar), elem_cnt, out, - in, stream->As()->cuda_stream())); - } -}; - -template class BIN_OP> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, float16 scalar, const float16* in, - float16* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary( - UnaryByScalarFunctor(float16_2half(scalar)), elem_cnt, - reinterpret_cast(out), reinterpret_cast(in), - stream->As()->cuda_stream())); - } -}; - -template class BIN_OP, typename T> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary(UnaryByScalarReverseFunctor(scalar), elem_cnt, - out, in, stream->As()->cuda_stream())); - } -}; - -template class BIN_OP> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, float16 scalar, const float16* in, - float16* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary( - UnaryByScalarReverseFunctor(float16_2half(scalar)), elem_cnt, - reinterpret_cast(out), reinterpret_cast(in), - stream->As()->cuda_stream())); - } -}; - -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncAdd); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncFloorDiv); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncFMod); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncMul); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncDiv); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncPow); -INSTANTIATE_SCALAR_REVERSE_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncPow); - -template -struct ScalarPowGradFunctor { - OF_DEVICE_FUNC explicit ScalarPowGradFunctor(T exponent) : exponent(exponent) {} - __device__ T operator()(T x, T dy) const { - return exponent * (pow(x, exponent - static_cast(1.0))) * dy; - } - const T exponent; -}; - -template<> -struct ScalarPowGradFunctor { - OF_DEVICE_FUNC explicit ScalarPowGradFunctor(half exponent) : exponent(exponent) {} - __device__ half operator()(half x, half dy) const { - return __float2half(__half2float(exponent) - * (powf(__half2float(x), __half2float(exponent) - static_cast(1.0))) - * __half2float(dy)); - } - const half exponent; -}; - -template -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(T exponent) : exponent(exponent) {} - __device__ T operator()(T x, T dy) const { return pow(exponent, x) * log(exponent) * dy; } - const T exponent; -}; - -template<> -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(float exponent) : exponent(exponent) {} - __device__ float operator()(float x, float dy) const { - return powf(exponent, x) * logf(exponent) * dy; - } - const float exponent; -}; - -template<> -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(half exponent) : exponent(exponent) {} - __device__ half operator()(half x, half dy) const { - const float exp = __half2float(exponent); - return __float2half(exp * powf(exp, __half2float(x)) * logf(exp) * __half2float(dy)); - } - const half exponent; -}; - -template -class GpuScalarPowGradKernel final : public user_op::OpKernel { - public: - GpuScalarPowGradKernel() = default; - ~GpuScalarPowGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); - } else { - UNIMPLEMENTED(); - } - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - OF_CUDA_CHECK((oneflow::cuda::elementwise::Binary( - ScalarPowGradFunctor(scalar_operand), elem_cnt, dx_ptr, x_ptr, dy_ptr, - ctx->stream()->As()->cuda_stream()))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(DeviceType::kCUDA, double); - -template -class GpuScalarReversePowGradKernel final : public user_op::OpKernel { - public: - GpuScalarReversePowGradKernel() = default; - ~GpuScalarReversePowGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); - } else { - UNIMPLEMENTED(); - } - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - OF_CUDA_CHECK((oneflow::cuda::elementwise::Binary( - ScalarReversePowGradFunctor(scalar_operand), elem_cnt, dx_ptr, x_ptr, dy_ptr, - ctx->stream()->As()->cuda_stream()))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_reverse_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(DeviceType::kCUDA, double); - -} // namespace oneflow diff --git a/oneflow/user/kernels/scalar_math_kernels.h b/oneflow/user/kernels/scalar_math_kernels.h deleted file mode 100644 index 296e40ccddc57a80a26d35013d14730808876af0..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/scalar_math_kernels.h +++ /dev/null @@ -1,66 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifndef _ONEFLOW_USER_KERNELS_SCALAR_MATH_KERNELS_H_ -#define _ONEFLOW_USER_KERNELS_SCALAR_MATH_KERNELS_H_ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/binary_func.h" -#include "oneflow/core/ndarray/xpu_util.h" -#include "oneflow/core/common/data_type.h" - -namespace oneflow { - -#define INSTANTIATE_SCALAR_MATH_FUNCTORS(device_type, binary_op) \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; \ - template struct ScalarMathFunctor; - -#define INSTANTIATE_SCALAR_REVERSE_MATH_FUNCTORS(device_type, binary_op) \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; \ - template struct ScalarReverseMathFunctor; - -template class BIN_OP, typename T> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out); -}; - -template class UnaryFunctor, typename T> -OF_DEVICE_FUNC void DoScalarMath(const int64_t elem_cnt, const T scalar, const T* in, T* out) { - XPU_1D_KERNEL_LOOP(idx, elem_cnt) { out[idx] = UnaryFunctor::Invoke(in[idx], scalar); } -} - -template class BIN_OP, typename T> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out); -}; - -template class UnaryFunctor, typename T> -OF_DEVICE_FUNC void DoScalarReverseMath(const int64_t elem_cnt, const T scalar, const T* in, - T* out) { - XPU_1D_KERNEL_LOOP(idx, elem_cnt) { out[idx] = UnaryFunctor::Invoke(scalar, in[idx]); } -} - -} // namespace oneflow - -#endif // _ONEFLOW_USER_KERNELS_SCALAR_MATH_KERNELS_H_ diff --git a/oneflow/user/kernels/scalar_math_kernels.hip.cpp b/oneflow/user/kernels/scalar_math_kernels.hip.cpp deleted file mode 100644 index 6691786b91109b1bb4a9c199fe0830f86fc948f5..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/scalar_math_kernels.hip.cpp +++ /dev/null @@ -1,223 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/scalar_math_kernels.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -template class Op, typename T> -struct UnaryByScalarFunctor { - __host__ __device__ explicit UnaryByScalarFunctor(T scalar) : scalar(scalar) {} - __device__ T operator()(T a) const { return Op::Invoke(a, scalar); } - const T scalar; -}; - -template class Op, typename T> -struct UnaryByScalarReverseFunctor { - __host__ __device__ explicit UnaryByScalarReverseFunctor(T scalar) : scalar(scalar) {} - __device__ T operator()(T a) const { return Op::Invoke(scalar, a); } - const T scalar; -}; - -template class Op> -struct UnaryByScalarFunctor { - __host__ __device__ explicit UnaryByScalarFunctor(half scalar) : scalar(scalar) {} - __device__ half operator()(half a) const { return Op::Invoke(a, scalar); } - const half scalar; -}; - -template class Op> -struct UnaryByScalarReverseFunctor { - __host__ __device__ explicit UnaryByScalarReverseFunctor(half scalar) : scalar(scalar) {} - __device__ half operator()(half a) const { return Op::Invoke(scalar, a); } - const half scalar; -}; - -template class BIN_OP, typename T> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary(UnaryByScalarFunctor(scalar), elem_cnt, out, - in, stream->As()->cuda_stream())); - } -}; - -template class BIN_OP> -struct ScalarMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, float16 scalar, const float16* in, - float16* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary( - UnaryByScalarFunctor(float16_2half(scalar)), elem_cnt, - reinterpret_cast(out), reinterpret_cast(in), - stream->As()->cuda_stream())); - } -}; - -template class BIN_OP, typename T> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, const T scalar, const T* in, T* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary(UnaryByScalarReverseFunctor(scalar), elem_cnt, - out, in, stream->As()->cuda_stream())); - } -}; - -template class BIN_OP> -struct ScalarReverseMathFunctor final { - void operator()(ep::Stream* stream, const int64_t elem_cnt, float16 scalar, const float16* in, - float16* out) { - OF_CUDA_CHECK(cuda::elementwise::Unary( - UnaryByScalarReverseFunctor(float16_2half(scalar)), elem_cnt, - reinterpret_cast(out), reinterpret_cast(in), - stream->As()->cuda_stream())); - } -}; - -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncAdd); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncFloorDiv); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncFMod); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncMul); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncDiv); -INSTANTIATE_SCALAR_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncPow); -INSTANTIATE_SCALAR_REVERSE_MATH_FUNCTORS(DeviceType::kCUDA, BinaryFuncPow); - -template -struct ScalarPowGradFunctor { - OF_DEVICE_FUNC explicit ScalarPowGradFunctor(T exponent) : exponent(exponent) {} - __device__ T operator()(T x, T dy) const { - return exponent * (pow(x, exponent - static_cast(1.0))) * dy; - } - const T exponent; -}; - -template<> -struct ScalarPowGradFunctor { - OF_DEVICE_FUNC explicit ScalarPowGradFunctor(half exponent) : exponent(exponent) {} - __device__ half operator()(half x, half dy) const { - return __float2half(__half2float(exponent) - * (powf(__half2float(x), __half2float(exponent) - static_cast(1.0))) - * __half2float(dy)); - } - const half exponent; -}; - -template -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(T exponent) : exponent(exponent) {} - __device__ T operator()(T x, T dy) const { return pow(exponent, x) * log(exponent) * dy; } - const T exponent; -}; - -template<> -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(float exponent) : exponent(exponent) {} - __device__ float operator()(float x, float dy) const { - return powf(exponent, x) * logf(exponent) * dy; - } - const float exponent; -}; - -template<> -struct ScalarReversePowGradFunctor { - OF_DEVICE_FUNC explicit ScalarReversePowGradFunctor(half exponent) : exponent(exponent) {} - __device__ half operator()(half x, half dy) const { - const float exp = __half2float(exponent); - return __float2half(exp * powf(exp, __half2float(x)) * logf(exp) * __half2float(dy)); - } - const half exponent; -}; - -template -class GpuScalarPowGradKernel final : public user_op::OpKernel { - public: - GpuScalarPowGradKernel() = default; - ~GpuScalarPowGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); - } else { - UNIMPLEMENTED(); - } - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - OF_CUDA_CHECK((oneflow::cuda::elementwise::Binary( - ScalarPowGradFunctor(scalar_operand), elem_cnt, dx_ptr, x_ptr, dy_ptr, - ctx->stream()->As()->cuda_stream()))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_SCALAR_POW_BACKWARD_KERNEL(DeviceType::kCUDA, double); - -template -class GpuScalarReversePowGradKernel final : public user_op::OpKernel { - public: - GpuScalarReversePowGradKernel() = default; - ~GpuScalarReversePowGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - const T* x_ptr = x_tensor->dptr(); - const T* dy_ptr = dy_tensor->dptr(); - T* dx_ptr = dx_tensor->mut_dptr(); - T scalar_operand = static_cast(0); - if (ctx->Attr("has_int_operand")) { - scalar_operand = static_cast(ctx->Attr("int_operand")); - } else if (ctx->Attr("has_float_operand")) { - scalar_operand = static_cast(ctx->Attr("float_operand")); - } else { - UNIMPLEMENTED(); - } - const int32_t elem_cnt = x_tensor->shape_view().elem_cnt(); - OF_CUDA_CHECK((oneflow::cuda::elementwise::Binary( - ScalarReversePowGradFunctor(scalar_operand), elem_cnt, dx_ptr, x_ptr, dy_ptr, - ctx->stream()->As()->cuda_stream()))); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(device, dtype) \ - REGISTER_USER_KERNEL("scalar_reverse_pow_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(DeviceType::kCUDA, float); -REGISTER_CUDA_SCALAR_REVERSE_POW_BACKWARD_KERNEL(DeviceType::kCUDA, double); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/search_sorted_kernel.hip.cpp b/oneflow/user/kernels/search_sorted_kernel.hip.cpp deleted file mode 100644 index 2fcdabea46990b9eb70d65dc36ecf39dca7f0353..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/search_sorted_kernel.hip.cpp +++ /dev/null @@ -1,129 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/user/kernels/search_sorted_kernel_util.h" - -namespace oneflow { - -template -__global__ void DoSearchSortedLogical(int32_t instance_num, bool is_sequence_1d, - K values_shape_last, K sequence_shape_last, bool right, - const T* values_ptr, const T* sequence_ptr, K* out_ptr) { - CUDA_1D_KERNEL_LOOP(i, instance_num) { - K start_bd = is_sequence_1d ? 0 : i / values_shape_last * sequence_shape_last; - K end_bd = start_bd + sequence_shape_last; - K pos = !right - ? cus_lower_bound(start_bd, end_bd, values_ptr[i], sequence_ptr) - start_bd - : cus_upper_bound(start_bd, end_bd, values_ptr[i], sequence_ptr) - start_bd; - out_ptr[i] = pos; - } -} - -template -__global__ void DoSearchSortedScalarLogical(K sequence_shape_last, bool right, const T values, - const T* sequence_ptr, K* out_ptr) { - CUDA_1D_KERNEL_LOOP(i, 1) { - K pos = !right ? cus_lower_bound(0, sequence_shape_last, values, sequence_ptr) - : cus_upper_bound(0, sequence_shape_last, values, sequence_ptr); - out_ptr[0] = pos; - } -} - -template -class GpuSearchSortedKernel final : public user_op::OpKernel { - public: - GpuSearchSortedKernel() = default; - ~GpuSearchSortedKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* sorted_sequence = ctx->Tensor4ArgNameAndIndex("sorted_sequence", 0); - const user_op::Tensor* values = ctx->Tensor4ArgNameAndIndex("values", 0); - - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - const bool& right = ctx->Attr("right"); - const T* values_ptr = values->dptr(); - const T* sequence_ptr = sorted_sequence->dptr(); - K* out_ptr = out->mut_dptr(); - const int32_t instance_num = values->shape_view().elem_cnt(); - bool is_values_scalar = values->shape_view().NumAxes() == 0; - bool is_sequence_1d = (sorted_sequence->shape_view().NumAxes() == 1); - K values_shape_last = - is_values_scalar ? 1 : values->shape_view().At(values->shape_view().NumAxes() - 1); - K sequence_shape_last = - sorted_sequence->shape_view().At(sorted_sequence->shape_view().NumAxes() - 1); - RUN_CUDA_KERNEL((DoSearchSortedLogical), ctx->stream(), instance_num, instance_num, - is_sequence_1d, values_shape_last, sequence_shape_last, right, values_ptr, - sequence_ptr, out_ptr); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_GPU_SEARCH_SORTED_KERNEL(in_dtype, out_dtype) \ - REGISTER_USER_KERNEL("searchsorted") \ - .SetCreateFn< \ - GpuSearchSortedKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("sorted_sequence", 0) == OF_PP_PAIR_SECOND(in_dtype)) \ - && (user_op::HobDataType("values", 0) == OF_PP_PAIR_SECOND(in_dtype)) \ - && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(out_dtype))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GPU_SEARCH_SORTED_KERNEL, ARITHMETIC_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -template -class GpuSearchSortedScalarKernel final : public user_op::OpKernel { - public: - GpuSearchSortedScalarKernel() = default; - ~GpuSearchSortedScalarKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* sorted_sequence = ctx->Tensor4ArgNameAndIndex("sorted_sequence", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - - const bool& right = ctx->Attr("right"); - const T& values = static_cast(ctx->Attr("values")); - - const T* sequence_ptr = sorted_sequence->dptr(); - K* out_ptr = out->mut_dptr(); - K sequence_shape_last = sorted_sequence->shape_view().At(0); - RUN_CUDA_KERNEL((DoSearchSortedScalarLogical), ctx->stream(), 1, sequence_shape_last, - right, values, sequence_ptr, out_ptr); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_GPU_SEARCH_SORTED_SCALAR_KERNEL(in_dtype, out_dtype) \ - REGISTER_USER_KERNEL("searchsorted_scalar") \ - .SetCreateFn< \ - GpuSearchSortedScalarKernel>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("sorted_sequence", 0) == OF_PP_PAIR_SECOND(in_dtype)) \ - && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(out_dtype))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_GPU_SEARCH_SORTED_SCALAR_KERNEL, ARITHMETIC_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/search_sorted_kernel_util.h b/oneflow/user/kernels/search_sorted_kernel_util.h index cd0728ea071a0947d341e261c3c0369398c28dce..f3db7700234ae152c12d2a816e85f34382d71fc1 100644 --- a/oneflow/user/kernels/search_sorted_kernel_util.h +++ b/oneflow/user/kernels/search_sorted_kernel_util.h @@ -18,7 +18,6 @@ limitations under the License. template OF_DEVICE_FUNC K cus_lower_bound(K start, K end, const T val, const T* bd) { - const K orig_start = start; while (start < end) { const K mid = start + ((end - start) >> 1); const T mid_val = bd[mid]; @@ -33,7 +32,6 @@ OF_DEVICE_FUNC K cus_lower_bound(K start, K end, const T val, const T* bd) { template OF_DEVICE_FUNC K cus_upper_bound(K start, K end, const T val, const T* bd) { - const K orig_start = start; while (start < end) { const K mid = start + ((end - start) >> 1); const T mid_val = bd[mid]; diff --git a/oneflow/user/kernels/sigmoid_cross_entropy_kernel.hip.cpp b/oneflow/user/kernels/sigmoid_cross_entropy_kernel.hip.cpp deleted file mode 100644 index 237d71b6a9ebfed5cf4604205d168653b11da8b3..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sigmoid_cross_entropy_kernel.hip.cpp +++ /dev/null @@ -1,55 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/sigmoid_cross_entropy_kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { -template class Opt, typename PredT, typename LabelT> -struct ElemwiseSigmoidCrossEntropyGradFunctor final { - void operator()(ep::Stream* stream, int64_t n, PredT* prediction_diff, const PredT* prediction, - const LabelT* label, const PredT* loss_diff) { - OF_CUDA_CHECK(cuda::elementwise::Ternary(Opt(), n, prediction_diff, prediction, - label, loss_diff, - stream->As()->cuda_stream())); - } -}; - -template class Opt, typename PredT, typename LabelT> -struct ElemwiseSigmoidCrossEntropyFunctor final { - void operator()(ep::Stream* stream, int64_t n, PredT* loss, const PredT* prediction, - const LabelT* label) { - OF_CUDA_CHECK(cuda::elementwise::Binary(Opt(), n, loss, prediction, label, - stream->As()->cuda_stream())); - } -}; -} // namespace -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, float, int32_t) -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, double, int32_t) -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, float, int8_t) -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, double, int8_t) -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, float, float) -REGISTER_SIGMOID_CROSS_ENTROPY_KERNEL(DeviceType::kCUDA, double, double) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, float, int32_t) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, double, int32_t) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, float, int8_t) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, double, int8_t) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, float, float) -REGISTER_SIGMOID_CROSS_ENTROPY_GRAD_KERNEL(DeviceType::kCUDA, double, double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/slice_kernel.cpp b/oneflow/user/kernels/slice_kernel.cpp index 6f767a8683b73e67452a1e360abd37a0907e1d62..0108b08ae9a63fab6d100cd8a90259702d991837 100644 --- a/oneflow/user/kernels/slice_kernel.cpp +++ b/oneflow/user/kernels/slice_kernel.cpp @@ -208,7 +208,6 @@ SliceParams ConstructSliceParams(user_op::KernelComputeContext* ctx, const user_ } // namespace -// template template void WriteSlice(user_op::KernelComputeContext* ctx, const user_op::Tensor* src, user_op::Tensor* dst, const SliceContext& slice_ctx, @@ -251,65 +250,39 @@ void WriteSlice(user_op::KernelComputeContext* ctx, const user_op::Tensor* src, } SliceParams large_slice_param; + std::copy(large->stride().begin(), large->stride().end(), large_slice_param.stride); SliceParams small_slice_param; + std::copy(small->stride().begin(), small->stride().end(), small_slice_param.stride); ConstructSliceParamsLarge(slice_ctx, positive_start_vec, positive_stop_vec, step_attr, large->shape_view(), &large_slice_param); ConstructSliceParamsSmall(slice_ctx, positive_start_vec, positive_stop_vec, step_attr, small->shape_view(), &small_slice_param); CHECK_EQ(large_slice_param.elem_cnt(), small_slice_param.elem_cnt()); - - // const int64_t elem_cnt = large_slice_param.elem_cnt(); - // SliceIndexHelper entire_splitted_large_idx_cvtr(large_slice_param.dims); - // SliceIndexHelper sliced_splitted_large_idx_cvtr(large_slice_param.size); - // SliceIndexHelper entire_full_small_idx_cvtr(small_slice_param.dims); - // SliceIndexHelper sliced_full_small_idx_cvtr(small_slice_param.size); - // // Calculate the length of continuous part - // int cnt = 1; - // for (int i = NDIM - 1; i >= 0; i--) { - // if (large_slice_param.step[i] == 1) { cnt *= large_slice_param.size[i]; } - // if (!large_slice_param.IsFullSlice(i) || !small_slice_param.IsFullSlice(i)) { break; } - // } + if (large_slice_param.ndim == 0 && small_slice_param.ndim == 0) { + // Copy data directly for scalar tensor + AutoMemcpy(ctx->stream(), dst->mut_dptr(), src->dptr(), sizeof(T), src->mem_case(), + dst->mem_case()); + return; + } if (from_large_to_small) { if (small_slice_param.elem_cnt() == small->shape_view().elem_cnt()) { SliceKernelUtil::Forward(ctx->stream(), large_slice_param, src->dptr(), dst->mut_dptr()); } else { + AutoMemset(ctx->stream(), dst->mut_dptr(), 0, + dst->shape_view().elem_cnt() * GetSizeOfDataType(dst->data_type()), + dst->mem_case()); SliceKernelUtil::Forward(ctx->stream(), large_slice_param, small_slice_param, src->dptr(), dst->mut_dptr()); } } else { SliceKernelUtil::Forward(ctx->stream(), small_slice_param, large_slice_param, src->dptr(), dst->mut_dptr()); - // const auto* src_ptr = src->dptr(); - // auto* dst_ptr = dst->mut_dptr(); - // for (int i = 0; i < elem_cnt; i += cnt) { - // const int64_t large_offset = SliceOffsetToEntireOffset( - // i, large_slice_param, entire_splitted_large_idx_cvtr, sliced_splitted_large_idx_cvtr); - // const int64_t small_offset = SliceOffsetToEntireOffset( - // i, small_slice_param, entire_full_small_idx_cvtr, sliced_full_small_idx_cvtr); - // const int64_t src_offset = from_large_to_small ? large_offset : small_offset; - // const int64_t dst_offset = from_large_to_small ? small_offset : large_offset; - // AutoMemcpy(ctx->stream(), dst_ptr + dst_offset, src_ptr + src_offset, - // cnt * GetSizeOfDataType(src->data_type()), src->mem_case(), dst->mem_case()); } } -// #define MAKE_WRITE_SLICE_SWITCH_ENTRY(func_name, N, T) func_name -// DEFINE_STATIC_SWITCH_FUNC( -// void, WriteSlice, MAKE_WRITE_SLICE_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ), -// MAKE_DATA_TYPE_CTRV_SEQ(ARITHMETIC_DATA_TYPE_SEQ UNSIGNED_INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ -// #if defined(WITH_CUDA) -// HALF_DATA_TYPE_SEQ -// #endif -// #if defined(WITH_ROCM) -// HALF_DATA_TYPE_SEQ -// #endif -// )); -// #undef MAKE_WRITE_SLICE_SWITCH_ENTRY - -// template template -class SliceKernel final : public user_op::OpKernel { +class SliceKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: SliceKernel() = default; ~SliceKernel() = default; @@ -359,17 +332,11 @@ class SliceKernel final : public user_op::OpKernel { const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); const SliceContext& slice_ctx = dynamic_cast*>(cache)->Get(); - AutoMemset(ctx->stream(), y_tensor->mut_dptr(), 0, - y_tensor->shape_view().elem_cnt() * GetSizeOfDataType(y_tensor->data_type()), - y_tensor->mem_case()); - // SwitchWriteSlice(SwitchCase(y_tensor->shape_view().NumAxes(), y_tensor->data_type()), ctx, - // x_tensor, y_tensor, slice_ctx, true); WriteSlice(ctx, x_tensor, y_tensor, slice_ctx, /*from_large_to_small=*/true); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -// template template class SliceUpdateKernel final : public user_op::OpKernel { public: @@ -429,35 +396,12 @@ class SliceUpdateKernel final : public user_op::OpKernel { } const SliceContext& slice_ctx = dynamic_cast*>(cache)->Get(); - // SwitchWriteSlice(SwitchCase(value_tensor->shape_view().NumAxes(), value_tensor->data_type()), - // ctx, value_tensor, y_tensor, slice_ctx, false); WriteSlice(ctx, value_tensor, y_tensor, slice_ctx, /*from_large_to_small=*/false); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } }; -// #define REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(dtype) \ -// REGISTER_USER_KERNEL("slice_update") \ -// .SetCreateFn>() \ -// .SetIsMatchedHob(user_op::HobDataType("ref", 0) == GetDataType::value); \ -// REGISTER_USER_KERNEL("slice").SetCreateFn>().SetIsMatchedHob( \ -// user_op::HobDataType("x", 0) == GetDataType::value); - -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(float) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(double) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(int32_t) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(int64_t) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(int8_t) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(uint8_t) -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(bool) -// #ifdef WITH_CUDA -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(float16) -// #endif -// #ifdef WITH_ROCM -// REGISTER_SLICE_UPDATE_AND_SLICE_KERNELS(float16) -// #endif - template class SliceGradKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: @@ -478,31 +422,6 @@ class SliceGradKernel final : public user_op::OpKernel, public user_op::CudaGrap bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -// #define REGISTER_SLICE_GRAD_KERNEL(device, dtype) \ -// REGISTER_USER_KERNEL("slice_grad") \ -// .SetCreateFn>() \ -// .SetIsMatchedHob((user_op::HobDeviceType() == device) \ -// && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -// #define REGISTER_SLICE_GRAD_KERNEL_WITH_DEVICE(device) \ -// REGISTER_SLICE_GRAD_KERNEL(device, bool) \ -// REGISTER_SLICE_GRAD_KERNEL(device, float) \ -// REGISTER_SLICE_GRAD_KERNEL(device, double) \ -// REGISTER_SLICE_GRAD_KERNEL(device, int32_t) \ -// REGISTER_SLICE_GRAD_KERNEL(device, int64_t) \ -// REGISTER_SLICE_GRAD_KERNEL(device, int8_t) \ -// REGISTER_SLICE_GRAD_KERNEL(device, uint8_t) - -// REGISTER_SLICE_GRAD_KERNEL_WITH_DEVICE(DeviceType::kCPU) -// #ifdef WITH_CUDA -// REGISTER_SLICE_GRAD_KERNEL_WITH_DEVICE(DeviceType::kCUDA) -// REGISTER_SLICE_GRAD_KERNEL(DeviceType::kCUDA, float16) -// #endif -// #ifdef WITH_ROCM -// REGISTER_SLICE_GRAD_KERNEL_WITH_DEVICE(DeviceType::kCUDA) -// REGISTER_SLICE_GRAD_KERNEL(DeviceType::kCUDA, float16) -// #endif - #define REGISTER_SLICE_KERNEL(device, dtype) \ REGISTER_USER_KERNEL("slice").SetCreateFn>().SetIsMatchedHob( \ (user_op::HobDeviceType() == device) \ @@ -518,6 +437,7 @@ class SliceGradKernel final : public user_op::OpKernel, public user_op::CudaGrap #define REGISTER_SLICE_KERNEL_WITH_DEVICE(device) \ REGISTER_SLICE_KERNEL(device, bool) \ + REGISTER_SLICE_KERNEL(device, float16) \ REGISTER_SLICE_KERNEL(device, float) \ REGISTER_SLICE_KERNEL(device, double) \ REGISTER_SLICE_KERNEL(device, int32_t) \ @@ -526,13 +446,12 @@ class SliceGradKernel final : public user_op::OpKernel, public user_op::CudaGrap REGISTER_SLICE_KERNEL(device, uint8_t) REGISTER_SLICE_KERNEL_WITH_DEVICE(DeviceType::kCPU) -#ifdef WITH_CUDA +REGISTER_SLICE_KERNEL(DeviceType::kCPU, bfloat16) +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_SLICE_KERNEL_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_SLICE_KERNEL(DeviceType::kCUDA, float16) +#if CUDA_VERSION >= 11000 +REGISTER_SLICE_KERNEL(DeviceType::kCUDA, nv_bfloat16) #endif -#ifdef WITH_ROCM -REGISTER_SLICE_KERNEL_WITH_DEVICE(DeviceType::kCUDA) -REGISTER_SLICE_KERNEL(DeviceType::kCUDA, float16) #endif } // namespace oneflow diff --git a/oneflow/user/kernels/slice_util.cpp b/oneflow/user/kernels/slice_util.cpp index bd0c6f4a57acf9e7c8dc12175e23c1021e0286e1..cfc6842dbee8e405c736d40f92534a38fb9421cf 100644 --- a/oneflow/user/kernels/slice_util.cpp +++ b/oneflow/user/kernels/slice_util.cpp @@ -14,7 +14,8 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/user/kernels/slice_util.h" -#include "oneflow/core/common/switch_func.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/thread/thread_manager.h" namespace oneflow { @@ -65,10 +66,18 @@ struct SliceKernelUtil { int64_t elem_cnt = params.elem_cnt(); SliceIndexHelper entire_idx_cvtr(params.dims); SliceIndexHelper sliced_idx_cvtr(params.size); - FOR_RANGE(int, i, 0, elem_cnt) { + MultiThreadLoop(elem_cnt, [&](int64_t i) { int64_t offset = SliceOffsetToEntireOffset(i, params, entire_idx_cvtr, sliced_idx_cvtr); sliced[i] = entire[offset]; - } + }); + } + + template + static void SteppedMultiThreadLoop(size_t elem_cnt, size_t step, const DoEachT& DoEach) { + if (elem_cnt == 0) { return; } + CHECK_GT(step, 0); + CHECK_EQ(elem_cnt % step, 0); + MultiThreadLoop(elem_cnt / step, [&](size_t i) { DoEach(i * step); }); } template @@ -77,23 +86,36 @@ struct SliceKernelUtil { CHECK_EQ(entire_params.ndim, NDIM); CHECK_EQ(sliced_params.ndim, NDIM); int64_t elem_cnt = entire_params.elem_cnt(); - SliceIndexHelper entire_splitted_large_idx_cvtr(entire_params.dims); + SliceIndexHelper entire_splitted_large_idx_cvtr = + NdIndexStrideOffsetHelper(entire_params.stride); SliceIndexHelper sliced_splitted_large_idx_cvtr(entire_params.size); - SliceIndexHelper entire_full_small_idx_cvtr(sliced_params.dims); + SliceIndexHelper entire_full_small_idx_cvtr = + NdIndexStrideOffsetHelper(sliced_params.stride); SliceIndexHelper sliced_full_small_idx_cvtr(sliced_params.size); - // Calculate the length of continuous part + int cnt = 1; + int entire_target_stride = 1; + int sliced_target_stride = 1; + // Calculate the length of continuous part for (int i = NDIM - 1; i >= 0; i--) { - if (entire_params.step[i] == 1) { cnt *= entire_params.size[i]; } + if (entire_params.stride[i] != entire_target_stride + || sliced_params.stride[i] != sliced_target_stride) { + break; + } + entire_target_stride *= entire_params.size[i]; + sliced_target_stride *= sliced_params.size[i]; + if (sliced_params.step[i] == 1 && entire_params.step[i] == 1) { + cnt *= sliced_params.size[i]; + } if (!entire_params.IsFullSlice(i) || !sliced_params.IsFullSlice(i)) { break; } } - for (int i = 0; i < elem_cnt; i += cnt) { + SteppedMultiThreadLoop(elem_cnt, cnt, [&](int64_t i) { const int64_t entire_offset = SliceOffsetToEntireOffset( i, entire_params, entire_splitted_large_idx_cvtr, sliced_splitted_large_idx_cvtr); const int64_t sliced_offset = SliceOffsetToEntireOffset( i, sliced_params, entire_full_small_idx_cvtr, sliced_full_small_idx_cvtr); std::copy(entire + entire_offset, entire + entire_offset + cnt, sliced + sliced_offset); - } + }); } template @@ -103,10 +125,10 @@ struct SliceKernelUtil { int64_t elem_cnt = params.elem_cnt(); SliceIndexHelper entire_idx_cvtr(params.dims); SliceIndexHelper sliced_idx_cvtr(params.size); - FOR_RANGE(int, i, 0, elem_cnt) { + MultiThreadLoop(elem_cnt, [&](int64_t i) { int64_t offset = SliceOffsetToEntireOffset(i, params, entire_idx_cvtr, sliced_idx_cvtr); entire[offset] = sliced[i]; - } + }); } #define MAKE_SLICE_KERNEL_UTIL_SWITCH_ENTRY(func_name, N) \ @@ -122,5 +144,6 @@ struct SliceKernelUtil { }; INSTANTIATE_SLICE_KERNEL_UTIL_WITH_DEVICE(DeviceType::kCPU) +INSTANTIATE_SLICE_KERNEL_UTIL(DeviceType::kCPU, bfloat16) } // namespace oneflow diff --git a/oneflow/user/kernels/slice_util.cu b/oneflow/user/kernels/slice_util.cu index 15cf0d19fb036b9084c39ef605dfadd05d55f1f5..505436827e6437d42882a5b6a65cd6c7b5100cb2 100644 --- a/oneflow/user/kernels/slice_util.cu +++ b/oneflow/user/kernels/slice_util.cu @@ -16,6 +16,9 @@ limitations under the License. #include "oneflow/user/kernels/slice_util.h" #include "oneflow/core/common/switch_func.h" #include "oneflow/core/ep/cuda/cuda_stream.h" +#if CUDA_VERSION >= 11000 +#include +#endif // CUDA_VERSION >= 11000 namespace oneflow { @@ -78,9 +81,11 @@ void LaunchSliceForward(ep::Stream* stream, const SliceParams& entire_params, CHECK_EQ(sliced_params.ndim, NDIM); int64_t elem_cnt = entire_params.elem_cnt(); if (elem_cnt == 0) { return; } - SliceIndexHelper entire_splitted_large_idx_cvtr(entire_params.dims); + SliceIndexHelper entire_splitted_large_idx_cvtr = + NdIndexStrideOffsetHelper(entire_params.stride); SliceIndexHelper sliced_splitted_large_idx_cvtr(entire_params.size); - SliceIndexHelper entire_full_small_idx_cvtr(sliced_params.dims); + SliceIndexHelper entire_full_small_idx_cvtr = + NdIndexStrideOffsetHelper(sliced_params.stride); SliceIndexHelper sliced_full_small_idx_cvtr(sliced_params.size); SliceForwardGpu<<As()->cuda_stream()>>>( @@ -226,6 +231,8 @@ struct SliceKernelUtil { }; INSTANTIATE_SLICE_KERNEL_UTIL_WITH_DEVICE(DeviceType::kCUDA) -INSTANTIATE_SLICE_KERNEL_UTIL(DeviceType::kCUDA, float16) +#if CUDA_VERSION >= 11000 +INSTANTIATE_SLICE_KERNEL_UTIL(DeviceType::kCUDA, nv_bfloat16) +#endif } // namespace oneflow diff --git a/oneflow/user/kernels/slice_util.h b/oneflow/user/kernels/slice_util.h index e4020f603bd28ed85ea355efe4f5da4419c74284..f70bf437198785b0e0b6ebbd162297c3fef99d1f 100644 --- a/oneflow/user/kernels/slice_util.h +++ b/oneflow/user/kernels/slice_util.h @@ -42,6 +42,7 @@ constexpr size_t kSliceMaxDims = 8; struct SliceParams { int64_t ndim = 0; int64_t dims[kSliceMaxDims]{0}; + int64_t stride[kSliceMaxDims]{0}; int64_t start[kSliceMaxDims]{0}; int64_t step[kSliceMaxDims]{0}; int64_t size[kSliceMaxDims]{0}; @@ -62,12 +63,11 @@ struct SliceParams { return true; } - std::string ToString() { + std::string ToString() const { std::stringstream ss("SliceParams:"); for (int i = 0; i < ndim; ++i) { ss << "\n\tdim: " << i << ", start: " << start[i] << ", step: " << step[i] - // << ", size: " << size[i]; - << ", size: " << size[i] << ", dims: " << dims[i]; + << ", stride: " << stride[i] << ", size: " << size[i] << ", dims: " << dims[i]; } return ss.str(); } @@ -107,6 +107,7 @@ struct SliceKernelUtil { #define INSTANTIATE_SLICE_KERNEL_UTIL_WITH_DEVICE(device) \ INSTANTIATE_SLICE_KERNEL_UTIL(device, bool) \ + INSTANTIATE_SLICE_KERNEL_UTIL(device, float16) \ INSTANTIATE_SLICE_KERNEL_UTIL(device, float) \ INSTANTIATE_SLICE_KERNEL_UTIL(device, double) \ INSTANTIATE_SLICE_KERNEL_UTIL(device, int32_t) \ diff --git a/oneflow/user/kernels/slice_util.hip.cpp b/oneflow/user/kernels/slice_util.hip.cpp deleted file mode 100644 index a008c273a4bc0e8826c15237a98fb67775e298fa..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/slice_util.hip.cpp +++ /dev/null @@ -1,232 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/slice_util.h" -#include "oneflow/core/common/switch_func.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void SliceForwardGpu(const int n, SliceParams params, - SliceIndexHelper entire_idx_cvtr, - SliceIndexHelper sliced_idx_cvtr, const T* entire, - T* sliced) { - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t offset = SliceOffsetToEntireOffset(i, params, entire_idx_cvtr, sliced_idx_cvtr); - sliced[i] = entire[offset]; - } -} - -template -__global__ void SliceForwardGpu(const int n, SliceParams entire_params, SliceParams sliced_params, - SliceIndexHelper entire_splitted_large_idx_cvtr, - SliceIndexHelper sliced_splitted_large_idx_cvtr, - SliceIndexHelper entire_full_small_idx_cvtr, - SliceIndexHelper sliced_full_small_idx_cvtr, const T* entire, - T* sliced) { - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t entire_offset = SliceOffsetToEntireOffset( - i, entire_params, entire_splitted_large_idx_cvtr, sliced_splitted_large_idx_cvtr); - int64_t sliced_offset = SliceOffsetToEntireOffset( - i, sliced_params, entire_full_small_idx_cvtr, sliced_full_small_idx_cvtr); - sliced[sliced_offset] = entire[entire_offset]; - } -} - -template -__global__ void SliceBackwardGpu(const int n, SliceParams params, - SliceIndexHelper entire_idx_cvtr, - SliceIndexHelper sliced_idx_cvtr, T* entire, - const T* sliced) { - CUDA_1D_KERNEL_LOOP(i, n) { - int64_t offset = SliceOffsetToEntireOffset(i, params, entire_idx_cvtr, sliced_idx_cvtr); - entire[offset] = sliced[i]; - } -} - -template -void LaunchSliceForward(ep::Stream* stream, const SliceParams& params, const T* entire, T* sliced) { - CHECK_EQ(params.ndim, NDIM); - int64_t elem_cnt = params.elem_cnt(); - SliceIndexHelper entire_idx_cvtr(params.dims); - SliceIndexHelper sliced_idx_cvtr(params.size); - if (elem_cnt == 0) { return; } - SliceForwardGpu<<As()->cuda_stream()>>>( - elem_cnt, params, entire_idx_cvtr, sliced_idx_cvtr, entire, sliced); -} - -template -void LaunchSliceForward(ep::Stream* stream, const SliceParams& entire_params, - const SliceParams& sliced_params, const T* entire, T* sliced) { - CHECK_EQ(entire_params.ndim, NDIM); - CHECK_EQ(sliced_params.ndim, NDIM); - int64_t elem_cnt = entire_params.elem_cnt(); - if (elem_cnt == 0) { return; } - SliceIndexHelper entire_splitted_large_idx_cvtr(entire_params.dims); - SliceIndexHelper sliced_splitted_large_idx_cvtr(entire_params.size); - SliceIndexHelper entire_full_small_idx_cvtr(sliced_params.dims); - SliceIndexHelper sliced_full_small_idx_cvtr(sliced_params.size); - SliceForwardGpu<<As()->cuda_stream()>>>( - elem_cnt, entire_params, sliced_params, entire_splitted_large_idx_cvtr, - sliced_splitted_large_idx_cvtr, entire_full_small_idx_cvtr, sliced_full_small_idx_cvtr, - entire, sliced); -} - -template -void LaunchSliceBackward(ep::Stream* stream, const SliceParams& params, const T* sliced, - T* entire) { - CHECK_EQ(params.ndim, NDIM); - int64_t elem_cnt = params.elem_cnt(); - SliceIndexHelper entire_idx_cvtr(params.dims); - SliceIndexHelper sliced_idx_cvtr(params.size); - if (elem_cnt == 0) { return; } - SliceBackwardGpu<<As()->cuda_stream()>>>( - elem_cnt, params, entire_idx_cvtr, sliced_idx_cvtr, entire, sliced); -} - -template -struct SliceSwitchUtil final { -#define MAKE_SLICE_SWITCH_ENTRY(func_name, N) func_name -#define DEFINE_SLICE_SWITCH_UTIL_STATIC_METHOD(func_name) \ - DEFINE_STATIC_SWITCH_FUNC(void, func_name, MAKE_SLICE_SWITCH_ENTRY, MAKE_NDIM_CTRV_SEQ(DIM_SEQ)) - - DEFINE_SLICE_SWITCH_UTIL_STATIC_METHOD(LaunchSliceForward) - DEFINE_SLICE_SWITCH_UTIL_STATIC_METHOD(LaunchSliceBackward) -#undef DEFINE_SLICE_SWITCH_UTIL_STATIC_METHOD -#undef MAKE_SLICE_SWITCH_ENTRY -}; - -template -size_t GetPackSize(const SliceParams& params, const T* entire, const T* sliced) { - CHECK_GT(params.ndim, 0); - const int64_t last_dim = params.ndim - 1; - const int64_t mask = (params.dims[last_dim] * sizeof(T)) | (params.start[last_dim] * sizeof(T)) - | (params.size[last_dim] * sizeof(T)) - | static_cast(reinterpret_cast(entire)) - | static_cast(reinterpret_cast(sliced)); - if ((mask & 0xF) == 0) { - return 16; - } else if ((mask & 0x7) == 0) { - return 8; - } else if ((mask & 0x3) == 0) { - return 4; - } else if ((mask & 0x1) == 0) { - return 2; - } else { - return 1; - } -} - -template -void GetPackedParams(const SliceParams& params, const T* entire, const T* sliced, size_t* pack_size, - SliceParams* packed_params) { - CHECK_GT(params.ndim, 0); - const int64_t last_dim = params.ndim - 1; - if (params.step[last_dim] == 1) { - *pack_size = GetPackSize(params, entire, sliced); - CHECK_GE(*pack_size, sizeof(T)); - const int64_t elem_per_pack = *pack_size / sizeof(T); - *packed_params = params; - packed_params->dims[last_dim] /= elem_per_pack; - packed_params->start[last_dim] /= elem_per_pack; - packed_params->size[last_dim] /= elem_per_pack; - } else { - *pack_size = sizeof(T); - *packed_params = params; - } -} - -} // namespace - -template -struct SliceKernelUtil { - static void Forward(ep::Stream* stream, const SliceParams& params, const T* entire, T* sliced) { - SliceParams fold_slice_params = FoldContiguousFullSliceDimensions(params); - size_t pack_size; - SliceParams packed_params{}; - GetPackedParams(fold_slice_params, entire, sliced, &pack_size, &packed_params); - if (pack_size == 1) { - SliceSwitchUtil::SwitchLaunchSliceForward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(entire), reinterpret_cast(sliced)); - } else if (pack_size == 2) { - SliceSwitchUtil::SwitchLaunchSliceForward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(entire), reinterpret_cast(sliced)); - } else if (pack_size == 4) { - SliceSwitchUtil::SwitchLaunchSliceForward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(entire), reinterpret_cast(sliced)); - } else if (pack_size == 8) { - SliceSwitchUtil::SwitchLaunchSliceForward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(entire), reinterpret_cast(sliced)); - } else if (pack_size == 16) { - SliceSwitchUtil::SwitchLaunchSliceForward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(entire), reinterpret_cast(sliced)); - } else { - UNIMPLEMENTED(); - } - } - - static void Forward(ep::Stream* stream, const SliceParams& entire_params, - const SliceParams& sliced_params, const T* entire, T* sliced) { - SliceSwitchUtil::SwitchLaunchSliceForward(SwitchCase(entire_params.ndim), stream, - entire_params, sliced_params, entire, sliced); - } - - static void Backward(ep::Stream* stream, const SliceParams& params, const T* sliced, T* entire) { - SliceParams fold_slice_params = FoldContiguousFullSliceDimensions(params); - size_t pack_size; - SliceParams packed_params{}; - GetPackedParams(fold_slice_params, entire, sliced, &pack_size, &packed_params); - if (pack_size == 1) { - SliceSwitchUtil::SwitchLaunchSliceBackward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(sliced), reinterpret_cast(entire)); - } else if (pack_size == 2) { - SliceSwitchUtil::SwitchLaunchSliceBackward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(sliced), reinterpret_cast(entire)); - } else if (pack_size == 4) { - SliceSwitchUtil::SwitchLaunchSliceBackward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(sliced), reinterpret_cast(entire)); - } else if (pack_size == 8) { - SliceSwitchUtil::SwitchLaunchSliceBackward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(sliced), reinterpret_cast(entire)); - } else if (pack_size == 16) { - SliceSwitchUtil::SwitchLaunchSliceBackward( - SwitchCase(packed_params.ndim), stream, packed_params, - reinterpret_cast(sliced), reinterpret_cast(entire)); - } else { - UNIMPLEMENTED(); - } - } -}; - -INSTANTIATE_SLICE_KERNEL_UTIL_WITH_DEVICE(DeviceType::kCUDA) -INSTANTIATE_SLICE_KERNEL_UTIL(DeviceType::kCUDA, float16) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/smooth_l1_loss_kernel.hip.cpp b/oneflow/user/kernels/smooth_l1_loss_kernel.hip.cpp deleted file mode 100644 index 59d4eebe12bb5af5c60f00411be2f56a6282f8f7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/smooth_l1_loss_kernel.hip.cpp +++ /dev/null @@ -1,145 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/loss_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -namespace { - -using namespace loss; - -template -struct SmoothL1Functor { - float beta_; - float inv_beta_; - T half_of_one_; - SmoothL1Functor(float beta) - : beta_(beta), inv_beta_(static_cast(1.0 / beta)), half_of_one_(static_cast(0.5)) {} - - __device__ __forceinline__ T operator()(T input_val, T target_val) const { - const T abs_diff = abs(input_val - target_val); - if (abs_diff < beta_) { - return half_of_one_ * abs_diff * abs_diff * inv_beta_; - } else { - return abs_diff - half_of_one_ * beta_; - } - } -}; - -template<> -struct SmoothL1Functor { - half beta_; - half inv_beta_; - half zero_; - half half_of_one_; - SmoothL1Functor(float beta) - : beta_(__float2half(beta)), - inv_beta_(__float2half(static_cast(1.0 / beta))), - zero_(__float2half(0.f)), - half_of_one_(__float2half(0.5f)) {} - - __device__ __forceinline__ half operator()(half input_val, half target_val) const { - const half diff = input_val - target_val; - const half abs_diff = diff < zero_ ? __hneg(diff) : diff; - if (abs_diff < beta_) { - return half_of_one_ * abs_diff * abs_diff * inv_beta_; - } else { - return abs_diff - half_of_one_ * beta_; - } - } -}; - -template -struct SmoothL1GradFunctor { - float beta_; - float inv_beta_; - T zero_; - SmoothL1GradFunctor(float beta) - : beta_(beta), inv_beta_(static_cast(1.0 / beta)), zero_(GetZeroVal()) {} - - __device__ __forceinline__ T operator()(T input_val, T target_val, T dy_val) const { - const T diff = input_val - target_val; - const T abs_diff = abs(diff); - T dx_val; - if (abs_diff < beta_) { - dx_val = diff * inv_beta_; - } else { - dx_val = (diff > zero_) - (diff < zero_); - } - return dx_val * dy_val; - } -}; - -template<> -struct SmoothL1GradFunctor { - half beta_; - half inv_beta_; - half zero_; - half one_; - SmoothL1GradFunctor(float beta) - : beta_(__float2half(beta)), - inv_beta_(__float2half(static_cast(1.0 / beta))), - zero_(__float2half(0.f)), - one_(__float2half(1.f)) {} - - __device__ __forceinline__ half operator()(half input_val, half target_val, half dy_val) const { - const half diff = input_val - target_val; - const half abs_diff = diff < zero_ ? __hneg(diff) : diff; - half dx_val; - if (abs_diff < beta_) { - dx_val = diff * inv_beta_; - } else { - dx_val = (diff > zero_) - (diff < zero_); - } - return dx_val * dy_val; - } -}; - -template -class SmoothL1LossKernel : public SimpleLossKernel> { - public: - void ComputeOut(user_op::KernelComputeContext* ctx, int64_t elem_cnt, const T* input, - const T* target, T* out) const { - const float beta = ctx->Attr("beta"); - OF_CUDA_CHECK((cuda::elementwise::Binary(SmoothL1Functor(beta), elem_cnt, out, input, target, - ctx->stream()->As()->cuda_stream()))); - } -}; - -template -class SmoothL1LossGradKernel - : public SimpleLossGradKernel> { - public: - void ComputeOut(user_op::KernelComputeContext* ctx, int64_t elem_cnt, const T* input, - const T* target, const T* dy, T* dx) const { - const float beta = ctx->Attr("beta"); - OF_CUDA_CHECK( - (cuda::elementwise::Ternary(SmoothL1GradFunctor(beta), elem_cnt, dx, input, target, dy, - ctx->stream()->As()->cuda_stream()))); - } -}; - -} // namespace - -REGISTER_SIMPLE_LOSS_KERNEL_CUDA("smooth_l1_loss", SmoothL1LossKernel) -REGISTER_SIMPLE_LOSS_GRAD_KERNEL_CUDA("smooth_l1_loss_grad", SmoothL1LossGradKernel) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/softmax_cross_entropy_kernel.cpp b/oneflow/user/kernels/softmax_cross_entropy_kernel.cpp index 9805393f98aea14b10ea1e7f4696460f2ff5cd47..d528785e6ab1af8d826e7b8c10eee0ec2a5f6f15 100644 --- a/oneflow/user/kernels/softmax_cross_entropy_kernel.cpp +++ b/oneflow/user/kernels/softmax_cross_entropy_kernel.cpp @@ -14,11 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/user/kernels/softmax_cross_entropy_kernel.h" -#ifdef WITH_ROCM -#include "oneflow/core/kernel/kernel_util.hip.h" -#else #include "oneflow/core/kernel/kernel_util.cuh" -#endif namespace oneflow { namespace user_op { diff --git a/oneflow/user/kernels/softmax_cross_entropy_kernel.cu b/oneflow/user/kernels/softmax_cross_entropy_kernel.cu index cebb861e549e3872b61ac1f8628b2f4718c064c7..8033b67d1c70df52b8f9cfd164949361ccb2ace1 100644 --- a/oneflow/user/kernels/softmax_cross_entropy_kernel.cu +++ b/oneflow/user/kernels/softmax_cross_entropy_kernel.cu @@ -16,7 +16,12 @@ limitations under the License. #include "oneflow/user/kernels/softmax_cross_entropy_kernel.h" #include "oneflow/core/kernel/kernel_util.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif namespace oneflow { namespace user_op { @@ -28,7 +33,11 @@ constexpr int64_t kCrossEntropyGpuBlockSize = 128; template __global__ void ComputeEntropyGpu(const int64_t num_instances, const int64_t num_classes, const T* x, const T* labels, T* y) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; const int tid = threadIdx.x; for (int row = blockIdx.x; row < num_instances; row += gridDim.x) { @@ -42,14 +51,22 @@ __global__ void ComputeEntropyGpu(const int64_t num_instances, const int64_t num result += -label * SafeLog(prob); } __syncthreads(); +#ifdef WITH_ROCM + T row_reduce_result = BlockReduce(temp_storage).Reduce(result, hipcub::Sum()); +#else T row_reduce_result = BlockReduce(temp_storage).Reduce(result, cub::Sum()); +#endif if (0 == tid) { y[row] = row_reduce_result; } } } __global__ void ComputeEntropyGpuHalf(const int64_t num_instances, const int64_t num_classes, const half* x, const half* labels, half* y) { +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; const int tid = threadIdx.x; for (int row = blockIdx.x; row < num_instances; row += gridDim.x) { @@ -63,7 +80,11 @@ __global__ void ComputeEntropyGpuHalf(const int64_t num_instances, const int64_t result += -label * SafeLog(prob); } __syncthreads(); +#ifdef WITH_ROCM + float row_reduce_result = BlockReduce(temp_storage).Reduce(result, hipcub::Sum()); +#else float row_reduce_result = BlockReduce(temp_storage).Reduce(result, cub::Sum()); +#endif if (0 == tid) { y[row] = __float2half(row_reduce_result); } } } @@ -80,7 +101,7 @@ __global__ void ComputeDiffWithSoftmaxGpu(const int64_t elem_cnt, const int64_t __global__ void ComputeDiffWithSoftmaxGpuHalf(const int64_t elem_cnt, const int64_t num_classes, const half* prob, const half* labels, const half* dy, half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) +#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(WITH_ROCM) CUDA_1D_KERNEL_LOOP(i, elem_cnt) { const int32_t row_id = i / num_classes; dx[i] = __hmul(dy[row_id], __hsub(prob[i], labels[i])); @@ -103,7 +124,7 @@ template struct CrossEntropyKernelUtil { static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, const int64_t num_classes, const T* x, const T* labels, T* y) { - OF_CUDA_CHECK(cudaMemsetAsync(y, 0, sizeof(T) * num_instances, + OF_CUDA_CHECK(GPU(MemsetAsync)(y, 0, sizeof(T) * num_instances, stream->As()->cuda_stream())); ComputeEntropyGpu<<As()->cuda_stream()>>>(num_instances, num_classes, @@ -124,7 +145,7 @@ struct CrossEntropyKernelUtil { static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, const int64_t num_classes, const float16* x, const float16* labels, float16* y) { - OF_CUDA_CHECK(cudaMemsetAsync(y, 0, sizeof(float16) * num_instances, + OF_CUDA_CHECK(GPU(MemsetAsync)(y, 0, sizeof(float16) * num_instances, stream->As()->cuda_stream())); ComputeEntropyGpuHalf<<As()->cuda_stream()>>>( diff --git a/oneflow/user/kernels/softmax_cross_entropy_kernel.hip.cpp b/oneflow/user/kernels/softmax_cross_entropy_kernel.hip.cpp deleted file mode 100644 index 40e5cb1c1b1005a74fac64429eba81b37b2bb861..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/softmax_cross_entropy_kernel.hip.cpp +++ /dev/null @@ -1,156 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/softmax_cross_entropy_kernel.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { -namespace user_op { - -namespace { - -constexpr int64_t kCrossEntropyGpuBlockSize = 128; - -template -__global__ void ComputeEntropyGpu(const int64_t num_instances, const int64_t num_classes, - const T* x, const T* labels, T* y) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - const int tid = threadIdx.x; - for (int row = blockIdx.x; row < num_instances; row += gridDim.x) { - const int row_offset = row * num_classes; - const T* in_row = x + row_offset; - const T* label_row = labels + row_offset; - T result = 0; - for (int col = tid; col < num_classes; col += kCrossEntropyGpuBlockSize) { - T label = label_row[col]; - T prob = in_row[col]; - result += -label * SafeLog(prob); - } - __syncthreads(); - T row_reduce_result = BlockReduce(temp_storage).Reduce(result, hipcub::Sum()); - if (0 == tid) { y[row] = row_reduce_result; } - } -} - -__global__ void ComputeEntropyGpuHalf(const int64_t num_instances, const int64_t num_classes, - const half* x, const half* labels, half* y) { - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - const int tid = threadIdx.x; - for (int row = blockIdx.x; row < num_instances; row += gridDim.x) { - const int row_offset = row * num_classes; - const half* in_row = x + row_offset; - const half* label_row = labels + row_offset; - float result = 0; - for (int col = tid; col < num_classes; col += kCrossEntropyGpuBlockSize) { - float label = __half2float(label_row[col]); - float prob = __half2float(in_row[col]); - result += -label * SafeLog(prob); - } - __syncthreads(); - float row_reduce_result = BlockReduce(temp_storage).Reduce(result, hipcub::Sum()); - if (0 == tid) { y[row] = __float2half(row_reduce_result); } - } -} - -template -__global__ void ComputeDiffWithSoftmaxGpu(const int64_t elem_cnt, const int64_t num_classes, - const T* prob, const T* labels, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int32_t row_id = i / num_classes; - dx[i] = dy[row_id] * (prob[i] - labels[i]); - } -} - -__global__ void ComputeDiffWithSoftmaxGpuHalf(const int64_t elem_cnt, const int64_t num_classes, - const half* prob, const half* labels, const half* dy, - half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const int32_t row_id = i / num_classes; - dx[i] = __hmul(dy[row_id], __hsub(prob[i], labels[i])); - } -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} - -} // namespace - -int GetCrossEntropyNumBlocks(const int num_instances) { - return std::min(static_cast(num_instances), kCudaMaxBlocksNum); -} - -int GetCrossEntropyBlockSize() { return kCrossEntropyGpuBlockSize; } - -template -struct CrossEntropyKernelUtil { - static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const T* x, const T* labels, T* y) { - OF_CUDA_CHECK(hipMemsetAsync(y, 0, sizeof(T) * num_instances, - stream->As()->cuda_stream())); - ComputeEntropyGpu<<As()->cuda_stream()>>>(num_instances, num_classes, - x, labels, y); - } - - static void ComputeDiffWithSoftmax(ep::Stream* stream, const int64_t elem_cnt, - const int64_t num_classes, const T* prob, const T* labels, - const T* dy, T* dx) { - ComputeDiffWithSoftmaxGpu<<As()->cuda_stream()>>>( - elem_cnt, num_classes, prob, labels, dy, dx); - } -}; - -template<> -struct CrossEntropyKernelUtil { - static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const float16* x, const float16* labels, - float16* y) { - OF_CUDA_CHECK(hipMemsetAsync(y, 0, sizeof(float16) * num_instances, - stream->As()->cuda_stream())); - ComputeEntropyGpuHalf<<As()->cuda_stream()>>>( - num_instances, num_classes, reinterpret_cast(x), - reinterpret_cast(labels), reinterpret_cast(y)); - } - - static void ComputeDiffWithSoftmax(ep::Stream* stream, const int64_t elem_cnt, - const int64_t num_classes, const float16* prob, - const float16* labels, const float16* dy, float16* dx) { - ComputeDiffWithSoftmaxGpuHalf<<As()->cuda_stream()>>>( - elem_cnt, num_classes, reinterpret_cast(prob), - reinterpret_cast(labels), reinterpret_cast(dy), - reinterpret_cast(dx)); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SOFTMAX_CROSS_ENTROPY_KERNEL, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SOFTMAX_CROSS_ENTROPY_GRAD_KERNEL, - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/sort_kernel.hip.cpp b/oneflow/user/kernels/sort_kernel.hip.cpp deleted file mode 100644 index 1f6067f2f592e5ed242011a07ae506b90cffa1db..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sort_kernel.hip.cpp +++ /dev/null @@ -1,81 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/user/kernels/radix_sort.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -template -class GpuSortKernel final : public user_op::OpKernel { - public: - GpuSortKernel() = default; - ~GpuSortKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("in", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - - Memcpy(ctx->stream(), out->mut_dptr(), in->dptr(), - in->shape_view().elem_cnt() * sizeof(T)); - const int32_t instance_size = in->shape_view().At(in->shape_view().NumAxes() - 1); - const int32_t instance_num = in->shape_view().elem_cnt() / instance_size; - const std::string& direction = ctx->Attr("direction"); - if (direction == "ASCENDING") { - SortKeysAscending(in->dptr(), instance_num, instance_size, tmp_buffer->mut_dptr(), - tmp_buffer->shape_view().elem_cnt(), out->mut_dptr(), - ctx->stream()->As()->cuda_stream()); - } else if (direction == "DESCENDING") { - SortKeysDescending(in->dptr(), instance_num, instance_size, tmp_buffer->mut_dptr(), - tmp_buffer->shape_view().elem_cnt(), out->mut_dptr(), - ctx->stream()->As()->cuda_stream()); - } else { - UNIMPLEMENTED(); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_SORT_KERNEL(dtype) \ - REGISTER_USER_KERNEL("sort") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) -> size_t { \ - const Shape& in_shape = ctx->InputShape("in", 0); \ - const int32_t instance_size = in_shape.dim_vec().back(); \ - const int32_t instance_num = in_shape.elem_cnt() / instance_size; \ - const std::string& direction = ctx->Attr("direction"); \ - if (direction == "ASCENDING") { \ - return InferTempStorageForSortKeysAscending(instance_num, instance_size); \ - } else if (direction == "DESCENDING") { \ - return InferTempStorageForSortKeysDescending(instance_num, instance_size); \ - } else { \ - UNIMPLEMENTED(); \ - return 0; \ - } \ - }); - -REGISTER_CUDA_SORT_KERNEL(float) -REGISTER_CUDA_SORT_KERNEL(double) -REGISTER_CUDA_SORT_KERNEL(int32_t) -REGISTER_CUDA_SORT_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/sparse_cross_entropy_kernel.cpp b/oneflow/user/kernels/sparse_cross_entropy_kernel.cpp index 19b05f3c9a96cc35d09224a54f01831880e8cfd2..b1263ffb541f2f42bc5b2b2ca1a7a0a3f348eab6 100644 --- a/oneflow/user/kernels/sparse_cross_entropy_kernel.cpp +++ b/oneflow/user/kernels/sparse_cross_entropy_kernel.cpp @@ -122,12 +122,7 @@ class SparseCrossEntropyMsKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCrossEntropyKernel), ("sparse_cross_entropy"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCrossEntropyKernel), - ("sparse_cross_entropy"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCrossEntropyKernel), ("sparse_cross_entropy"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) @@ -136,13 +131,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCr ("sparse_cross_entropy_ms"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCrossEntropyMsKernel), - ("sparse_cross_entropy_ms"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_KERNEL, (SparseCrossEntropyMsKernel), ("sparse_cross_entropy_ms"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), @@ -243,13 +232,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, (SparseCrossEntropyGradKernel), ("sparse_cross_entropy_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, - (SparseCrossEntropyGradKernel), ("sparse_cross_entropy_grad"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, (SparseCrossEntropyGradKernel), ("sparse_cross_entropy_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), @@ -259,13 +242,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, (SparseCrossEntropyMsGradKernel), ("sparse_cross_entropy_ms_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, - (SparseCrossEntropyMsGradKernel), ("sparse_cross_entropy_ms_grad"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_CROSS_ENTROPY_GRAD_KERNEL, (SparseCrossEntropyMsGradKernel), ("sparse_cross_entropy_ms_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), diff --git a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cpp b/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cpp index 35a71b7705cc8d61466ae007cb1850321410b056..bf1b27668da58b07d69948214fb8aa10c6aa4b75 100644 --- a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cpp +++ b/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cpp @@ -14,11 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/user/kernels/sparse_cross_entropy_kernel_util.h" -#ifdef WITH_ROCM -#include "oneflow/core/kernel/kernel_util.hip.h" -#else #include "oneflow/core/kernel/kernel_util.cuh" -#endif namespace oneflow { namespace user_op { diff --git a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cu b/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cu index 8c7bcf93277ff9bfafa23954df9523fdfd1350a9..cd9401870a1fb76a3f4eebe56152aa35e7dc1243 100644 --- a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cu +++ b/oneflow/user/kernels/sparse_cross_entropy_kernel_util.cu @@ -39,7 +39,7 @@ template __global__ void ComputeEntropyGpuHalf(const int64_t num_instances, const int64_t num_classes, const int64_t depth, const int64_t lower_bound, const half* x, const K* labels, half* y) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) +#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(WITH_ROCM) CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { assert(labels[i] >= 0); assert(labels[i] < depth); @@ -72,7 +72,7 @@ template __global__ void ComputeDiffGpuHalf(const int64_t num_instances, const int64_t num_classes, const int64_t depth, const int64_t lower_bound, const half* x, const K* labels, const half* dy, half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) +#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(WITH_ROCM) CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { assert(labels[i] >= 0); assert(labels[i] < depth); @@ -136,7 +136,7 @@ __global__ void ComputeDiffWithSoftmaxGpuHalf2(const int64_t elem_cnt, const int const int64_t depth, const int64_t lower_bound, const half* prob, const K* labels, const half* dy, half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) +#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(WITH_ROCM) const int64_t h2_num_classes = num_classes / 2; const int64_t h2_elem_cnt = elem_cnt / 2; const auto* prob_h2 = reinterpret_cast(prob); @@ -150,8 +150,13 @@ __global__ void ComputeDiffWithSoftmaxGpuHalf2(const int64_t elem_cnt, const int const half2 prob_h2_i = prob_h2[i]; const half dy_row = dy[row_id]; half2 dx_h2_i; +#ifdef WITH_ROCM + dx_h2_i.data.x = __hmul(dy_row, __hsub(prob_h2_i.data.x, static_cast(label == 2 * h2_col_id))); + dx_h2_i.data.y = __hmul(dy_row, __hsub(prob_h2_i.data.y, static_cast(label == 2 * h2_col_id + 1))); +#else dx_h2_i.x = __hmul(dy_row, __hsub(prob_h2_i.x, static_cast(label == 2 * h2_col_id))); dx_h2_i.y = __hmul(dy_row, __hsub(prob_h2_i.y, static_cast(label == 2 * h2_col_id + 1))); +#endif dx_h2[i] = dx_h2_i; } #else diff --git a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.hip.cpp b/oneflow/user/kernels/sparse_cross_entropy_kernel_util.hip.cpp deleted file mode 100644 index 38c390371d30bf74cc0a03119b40b27bac0bba0f..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sparse_cross_entropy_kernel_util.hip.cpp +++ /dev/null @@ -1,267 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/sparse_cross_entropy_kernel_util.h" -#include "oneflow/core/kernel/kernel_util.hip.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -namespace { - -template -__global__ void ComputeEntropyGpu(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const T* x, - const K* labels, T* y) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { - assert(labels[i] >= 0); - assert(labels[i] < depth); - K label = labels[i] - lower_bound; - if (label >= 0 && label < num_classes) { y[i] = -SafeLog(x[i * num_classes + label]); } - } -} - -template -__global__ void ComputeEntropyGpuHalf(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const half* x, - const K* labels, half* y) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { - assert(labels[i] >= 0); - assert(labels[i] < depth); - K label = labels[i] - lower_bound; - if (label >= 0 && label < num_classes) { - y[i] = __float2half(-SafeLog(__half2float(x[i * num_classes + label]))); - } - } -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} - -template -__global__ void ComputeDiffGpu(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const T* x, - const K* labels, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { - assert(labels[i] >= 0); - assert(labels[i] < depth); - K label = labels[i] - lower_bound; - if (label >= 0 && label < num_classes) { - dx[i * num_classes + label] = -dy[i] / MaxWithLogThreshold(x[i * num_classes + label]); - } - } -} - -template -__global__ void ComputeDiffGpuHalf(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const half* x, - const K* labels, const half* dy, half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { - assert(labels[i] >= 0); - assert(labels[i] < depth); - K label = labels[i] - lower_bound; - if (label >= 0 && label < num_classes) { - dx[i * num_classes + label] = - __hneg(__hdiv(__float2half(dy[i]), MaxWithLogThreshold(x[i * num_classes + label]))); - } - } -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} - -template -__global__ void ComputeDiffWithSoftmaxGpu(const int64_t elem_cnt, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, - const T* prob, const K* labels, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(IndexType, i, elem_cnt) { - const IndexType row_id = i / num_classes; - const IndexType col_id = i - row_id * num_classes; - assert(labels[row_id] >= 0); - assert(labels[row_id] < depth); - K label = labels[row_id] - lower_bound; - if (label == col_id) { - dx[i] = dy[row_id] * (prob[i] - 1); - } else { - dx[i] = dy[row_id] * prob[i]; - } - } -} - -template -__global__ void ComputeDiffWithSoftmaxGpuHalf(const int64_t elem_cnt, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, - const half* prob, const K* labels, const half* dy, - half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - CUDA_1D_KERNEL_LOOP_T(IndexType, i, elem_cnt) { - // NOTE(chengcheng): int division ('/') of i will reduce performance of int64_t. - const IndexType row_id = i / num_classes; - const IndexType col_id = i - row_id * num_classes; - assert(labels[row_id] >= 0); - assert(labels[row_id] < depth); - K label = labels[row_id] - lower_bound; - if (label == col_id) { - dx[i] = __hmul(dy[row_id], __hsub(prob[i], __float2half(1.0))); - } else { - dx[i] = __hmul(dy[row_id], prob[i]); - } - } -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} - -template -__global__ void ComputeDiffWithSoftmaxGpuHalf2(const int64_t elem_cnt, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, - const half* prob, const K* labels, const half* dy, - half* dx) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - const int64_t h2_num_classes = num_classes / 2; - const int64_t h2_elem_cnt = elem_cnt / 2; - const auto* prob_h2 = reinterpret_cast(prob); - auto* dx_h2 = reinterpret_cast(dx); - CUDA_1D_KERNEL_LOOP_T(IndexType, i, h2_elem_cnt) { - const IndexType row_id = i / h2_num_classes; - const IndexType h2_col_id = i - row_id * h2_num_classes; - assert(labels[row_id] >= 0); - assert(labels[row_id] < depth); - K label = labels[row_id] - lower_bound; - const half2 prob_h2_i = prob_h2[i]; - const half dy_row = dy[row_id]; - half2 dx_h2_i; - dx_h2_i.data.x = __hmul(dy_row, __hsub(prob_h2_i.data.x, static_cast(label == 2 * h2_col_id))); - dx_h2_i.data.y = __hmul(dy_row, __hsub(prob_h2_i.data.y, static_cast(label == 2 * h2_col_id + 1))); - dx_h2[i] = dx_h2_i; - } -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} - -} // namespace - -template -struct SparseCrossEntropyKernelUtil { - static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const T* x, const K* labels, T* y) { - ComputeEntropyGpu<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, x, labels, y); - } - - static void ComputeDiff(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, const int64_t lower_bound, - const T* x, const K* labels, const T* dy, T* dx) { - ComputeDiffGpu<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, x, labels, dy, dx); - } - - static void ComputeDiffWithSoftmax(ep::Stream* stream, const int64_t elem_cnt, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const T* prob, const K* labels, - const T* dy, T* dx) { - if (elem_cnt < GetMaxVal() / 2) { - ComputeDiffWithSoftmaxGpu - <<As()->cuda_stream()>>>(elem_cnt, num_classes, depth, - lower_bound, prob, labels, dy, dx); - } else { - ComputeDiffWithSoftmaxGpu - <<As()->cuda_stream()>>>(elem_cnt, num_classes, depth, - lower_bound, prob, labels, dy, dx); - } - } -}; - -template -struct SparseCrossEntropyKernelUtil { - static void ComputeEntropy(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const float16* x, const K* labels, - float16* y) { - ComputeEntropyGpuHalf<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, reinterpret_cast(x), labels, - reinterpret_cast(y)); - } - - static void ComputeDiff(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, const int64_t lower_bound, - const float16* x, const K* labels, const float16* dy, float16* dx) { - ComputeDiffGpuHalf<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, reinterpret_cast(x), labels, - reinterpret_cast(dy), reinterpret_cast(dx)); - } - - static void ComputeDiffWithSoftmax(ep::Stream* stream, const int64_t elem_cnt, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const float16* prob, - const K* labels, const float16* dy, float16* dx) { - if (num_classes % 2 == 0) { - if (elem_cnt < GetMaxVal() / 2) { - ComputeDiffWithSoftmaxGpuHalf2 - <<As()->cuda_stream()>>>( - elem_cnt, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } else { - ComputeDiffWithSoftmaxGpuHalf2 - <<As()->cuda_stream()>>>( - elem_cnt, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } - } else { - if (elem_cnt < GetMaxVal() / 2) { - ComputeDiffWithSoftmaxGpuHalf - <<As()->cuda_stream()>>>( - elem_cnt, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } else { - ComputeDiffWithSoftmaxGpuHalf - <<As()->cuda_stream()>>>( - elem_cnt, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } - } - } -}; - -#define INSTANTIATE_SPARSE_CROSS_ENTROPY_KERNEL_UTIL_CUDA(data_type_pair, index_type_pair) \ - template struct SparseCrossEntropyKernelUtil< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(index_type_pair)>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_SPARSE_CROSS_ENTROPY_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ); -#undef INSTANTIATE_SPARSE_CROSS_ENTROPY_KERNEL_UTIL_CUDA - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cpp b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cpp index df9099a716bdc771fd1071a21f1ca17ba09ed91b..4190acdfd837089cbd2bfbbd094c6645a234d021 100644 --- a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cpp +++ b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cpp @@ -126,14 +126,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL, ("sparse_softmax_cross_entropy_ms"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL, - (SparseSoftmaxCrossEntropyMsKernel), - ("sparse_softmax_cross_entropy_ms"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL, (SparseSoftmaxCrossEntropyMsKernel), ("sparse_softmax_cross_entropy_ms"), @@ -232,14 +225,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERN ("sparse_softmax_cross_entropy_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERNEL, - (SparseSoftmaxCrossEntropyGradKernel), - ("sparse_softmax_cross_entropy_grad"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERNEL, (SparseSoftmaxCrossEntropyGradKernel), ("sparse_softmax_cross_entropy_grad"), @@ -251,14 +237,7 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERN ("sparse_softmax_cross_entropy_ms_grad"), OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCPU), FLOATING_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERNEL, - (SparseSoftmaxCrossEntropyMsGradKernel), - ("sparse_softmax_cross_entropy_ms_grad"), - OF_PP_MAKE_TUPLE_SEQ(DeviceType::kCUDA), - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_GRAD_KERNEL, (SparseSoftmaxCrossEntropyMsGradKernel), ("sparse_softmax_cross_entropy_ms_grad"), diff --git a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cu b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cu index abdddb3ffbdc5249d64d8c276fbb749386607e7a..3e859bce4f177185cbaa34cfc17bdb00a0fd1821 100644 --- a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cu +++ b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.cu @@ -72,7 +72,8 @@ ComputeSparseSoftmaxCrossEntropyResult(ep::Stream* stream, const int64_t num_ins const int64_t num_classes, const int64_t depth, const int64_t lower_bound, const K* labels, const T* prob, T* out) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) + +#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(WITH_ROCM) ComputeSparseSoftmaxCrossEntropyResultGpu <<As()->cuda_stream()>>>( diff --git a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.hip.cpp b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.hip.cpp deleted file mode 100644 index 869b28394c28eb71f7b1c89d0168e3bbe7b4312a..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel.hip.cpp +++ /dev/null @@ -1,131 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/sparse_cross_entropy_kernel_util.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { - -namespace { - -template -void ComputeProb(ep::Stream* stream, const int64_t row, const int64_t col, const T* in, T* prob) { - using ComputeType = typename cuda::softmax::DefaultComputeType::type; - cuda::softmax::DirectLoad load(in, col); - cuda::softmax::DirectStore store(prob, col); - OF_CUDA_CHECK((cuda::softmax::DispatchLogSoftmax( - stream->As()->cuda_stream(), load, store, row, col))); -} - -template<> -void ComputeProb(ep::Stream* stream, const int64_t row, const int64_t col, const float16* in, - float16* prob) { - cuda::softmax::DirectLoad load(reinterpret_cast(in), col); - cuda::softmax::DirectStore store(reinterpret_cast(prob), col); - OF_CUDA_CHECK((cuda::softmax::DispatchLogSoftmax( - stream->As()->cuda_stream(), load, store, row, col))); -} - -template -__global__ void ComputeSparseSoftmaxCrossEntropyResultGpu(const int64_t num_instances, - const int64_t num_classes, - const int64_t depth, - const int64_t lower_bound, - const K* labels, const T* prob, T* out) { - CUDA_1D_KERNEL_LOOP_T(int64_t, i, num_instances) { - assert(labels[i] >= 0); - assert(labels[i] < depth); - K label = labels[i] - lower_bound; - if (label >= 0 && label < num_classes) { out[i] = -prob[i * num_classes + label]; } - } -} -template -inline typename std::enable_if::value, void>::type -ComputeSparseSoftmaxCrossEntropyResult(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const K* labels, const T* prob, - T* out) { - ComputeSparseSoftmaxCrossEntropyResultGpu - <<As()->cuda_stream()>>>(num_instances, num_classes, depth, - lower_bound, labels, prob, out); -} -template -inline typename std::enable_if::value, void>::type -ComputeSparseSoftmaxCrossEntropyResult(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, - const int64_t lower_bound, const K* labels, const T* prob, - T* out) { -#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__) - ComputeSparseSoftmaxCrossEntropyResultGpu - <<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, labels, - reinterpret_cast(prob), reinterpret_cast(out)); -#else - printf("use half need nvcc arch >= 530"); - assert(false); -#endif /* __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)*/ -} -} // namespace - -template -class SparseSoftmaxCrossEntropyKernel final : public user_op::OpKernel, - public user_op::CudaGraphSupport { - public: - SparseSoftmaxCrossEntropyKernel() = default; - ~SparseSoftmaxCrossEntropyKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* prediction = ctx->Tensor4ArgNameAndIndex("prediction", 0); - const user_op::Tensor* label = ctx->Tensor4ArgNameAndIndex("label", 0); - user_op::Tensor* prob = ctx->Tensor4ArgNameAndIndex("prob", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - - const int64_t num_instances = label->shape_view().elem_cnt(); - CHECK_EQ(prediction->shape_view().elem_cnt() % num_instances, 0); - const int64_t num_classes = prediction->shape_view().elem_cnt() / num_instances; - const int64_t lower_bound = 0; - const int64_t depth = ctx->Attr("depth"); - - ComputeProb(ctx->stream(), num_instances, num_classes, prediction->dptr(), - prob->mut_dptr()); - ComputeSparseSoftmaxCrossEntropyResult(ctx->stream(), num_instances, num_classes, depth, - lower_bound, label->dptr(), prob->dptr(), - out->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL(dtype_pair, ltype_pair) \ - REGISTER_USER_KERNEL("sparse_softmax_cross_entropy") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("label", 0) == OF_PP_PAIR_SECOND(ltype_pair)) \ - && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(dtype_pair))); - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL, - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel_util.hip.cpp b/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel_util.hip.cpp deleted file mode 100644 index b2a004aacc681626addb470f19ebf072d669be66..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sparse_softmax_cross_entropy_kernel_util.hip.cpp +++ /dev/null @@ -1,134 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/sparse_softmax_cross_entropy_kernel_util.h" -#include "oneflow/core/hip/softmax.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { -namespace user_op { -namespace { - -template -__inline__ __device__ T Exp(T x); - -template<> -__inline__ __device__ float Exp(float x) { -#ifdef OF_SOFTMAX_USE_FAST_MATH - return __expf(x); -#else - return exp(x); -#endif -} - -template<> -__inline__ __device__ double Exp(double x) { - return exp(x); -} - -template<> -__inline__ __device__ half Exp(half x) { -#ifdef OF_SOFTMAX_USE_FAST_MATH - return __float2half(__expf(__half2float(x))); -#else - return __float2half(exp(__half2float(x))); -#endif -} - -template -__global__ void ComputeDiffGpu(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const T* prob, - const K* labels, const T* dy, T* dx) { - CUDA_1D_KERNEL_LOOP_T(IndexType, i, num_instances) { - const IndexType row_id = i / num_classes; - const IndexType col_id = i - row_id * num_classes; - assert(labels[row_id] >= 0); - assert(labels[row_id] < depth); - K label = labels[row_id] - lower_bound; - if (label == col_id) { - dx[i] = dy[row_id] * (Exp(prob[i]) - 1); - } else { - dx[i] = dy[row_id] * Exp(prob[i]); - } - } -} - -template -__global__ void ComputeDiffGpuHalf(const int64_t num_instances, const int64_t num_classes, - const int64_t depth, const int64_t lower_bound, const half* prob, - const K* labels, const half* dy, half* dx) { - CUDA_1D_KERNEL_LOOP_T(IndexType, i, num_instances) { - const IndexType row_id = i / num_classes; - const IndexType col_id = i - row_id * num_classes; - assert(labels[row_id] >= 0); - assert(labels[row_id] < depth); - K label = labels[row_id] - lower_bound; - if (label == col_id) { - dx[i] = __hmul(dy[row_id], __hsub(Exp(prob[i]), __float2half(1.0))); - } else { - dx[i] = __hmul(dy[row_id], Exp(prob[i])); - } - } -} - -} // namespace - -template -struct SparseSoftmaxCrossEntropyKernelUtil { - static void ComputeDiff(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, const int64_t lower_bound, - const T* prob, const K* labels, const T* dy, T* dx) { - if (num_instances < GetMaxVal() / 2) { - ComputeDiffGpu<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, prob, labels, dy, dx); - } else { - // NOTE(chengcheng): int division ('/') of i will reduce performance of int64_t. - ComputeDiffGpu<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, prob, labels, dy, dx); - } - } -}; - -template -struct SparseSoftmaxCrossEntropyKernelUtil { - static void ComputeDiff(ep::Stream* stream, const int64_t num_instances, - const int64_t num_classes, const int64_t depth, const int64_t lower_bound, - const float16* prob, const K* labels, const float16* dy, float16* dx) { - if (num_instances < GetMaxVal() / 2) { - ComputeDiffGpuHalf<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } else { - ComputeDiffGpuHalf<<As()->cuda_stream()>>>( - num_instances, num_classes, depth, lower_bound, reinterpret_cast(prob), - labels, reinterpret_cast(dy), reinterpret_cast(dx)); - } - } -}; - -#define INSTANTIATE_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL_UTIL_CUDA(data_type_pair, index_type_pair) \ - template struct SparseSoftmaxCrossEntropyKernelUtil< \ - DeviceType::kCUDA, OF_PP_PAIR_FIRST(data_type_pair), OF_PP_PAIR_FIRST(index_type_pair)>; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ); -#undef INSTANTIATE_SPARSE_SOFTMAX_CROSS_ENTROPY_KERNEL_UTIL_CUDA - -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/sqrt_square_sum_kernel.cpp b/oneflow/user/kernels/sqrt_square_sum_kernel.cpp index 8125097b49a352861f65261e0054708e8078bfd9..2226369dd0eec569a4970a95d0ebad30541cc51a 100644 --- a/oneflow/user/kernels/sqrt_square_sum_kernel.cpp +++ b/oneflow/user/kernels/sqrt_square_sum_kernel.cpp @@ -25,10 +25,7 @@ namespace user_op { int64_t getThreadNumBlocks(int64_t n) { int64_t num_blocks = 1; -#ifdef WITH_CUDA - num_blocks = BlocksNum4ThreadsNum(n); -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) num_blocks = BlocksNum4ThreadsNum(n); #endif return num_blocks; diff --git a/oneflow/user/kernels/sqrt_square_sum_kernel_util.cu b/oneflow/user/kernels/sqrt_square_sum_kernel_util.cu index a93f48e0ee23fc66b8ee3f6dad38411a8aa895ae..52e910dc0c3acbe30cc19f47e96f2c27d99d03e3 100644 --- a/oneflow/user/kernels/sqrt_square_sum_kernel_util.cu +++ b/oneflow/user/kernels/sqrt_square_sum_kernel_util.cu @@ -16,7 +16,12 @@ limitations under the License. #include "oneflow/user/kernels/sqrt_square_sum_kernel_util.h" #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif namespace oneflow { @@ -26,7 +31,11 @@ template __global__ void SqrtSquareSumForOneThreadBlock(int64_t n, const T* x, T* y) { T t_sum = 0; CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_sum = BlockReduce(temp_storage).Sum(t_sum); if (threadIdx.x == 0) { *y = sqrt(b_sum); } @@ -36,7 +45,11 @@ template __global__ void SqrtSumForMultiThreadBlock(int64_t n, const T* x, T* y) { T t_sum = 0; CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i]; } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_sum = BlockReduce(temp_storage).Sum(t_sum); if (threadIdx.x == 0) { *y = sqrt(b_sum); } @@ -46,7 +59,11 @@ template __global__ void SquareSumForMultiThreadBlock(int64_t n, const T* x, T* tmp) { T t_sum = 0; CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_sum = BlockReduce(temp_storage).Sum(t_sum); if (threadIdx.x == 0) { tmp[blockIdx.x] = b_sum; } diff --git a/oneflow/user/kernels/sqrt_square_sum_kernel_util.hip.cpp b/oneflow/user/kernels/sqrt_square_sum_kernel_util.hip.cpp deleted file mode 100644 index 7db4c19009a7b29157525d33eecc9191d1bb2a94..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/sqrt_square_sum_kernel_util.hip.cpp +++ /dev/null @@ -1,83 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/sqrt_square_sum_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace { - -template -__global__ void SqrtSquareSumForOneThreadBlock(int64_t n, const T* x, T* y) { - T t_sum = 0; - CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_sum = BlockReduce(temp_storage).Sum(t_sum); - if (threadIdx.x == 0) { *y = sqrt(b_sum); } -} - -template -__global__ void SqrtSumForMultiThreadBlock(int64_t n, const T* x, T* y) { - T t_sum = 0; - CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i]; } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_sum = BlockReduce(temp_storage).Sum(t_sum); - if (threadIdx.x == 0) { *y = sqrt(b_sum); } -} - -template -__global__ void SquareSumForMultiThreadBlock(int64_t n, const T* x, T* tmp) { - T t_sum = 0; - CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_sum = BlockReduce(temp_storage).Sum(t_sum); - if (threadIdx.x == 0) { tmp[blockIdx.x] = b_sum; } -} - -} // namespace - -template -struct SqrtSquareSumKernelUtil { - static void SqrtSquareSum(ep::Stream* stream, int64_t n, const T* x, T* y, T* tmp) { - const int32_t num_blocks = BlocksNum4ThreadsNum(n); - CHECK_GE(num_blocks, 0); - if (num_blocks == 1) { - SqrtSquareSumForOneThreadBlock - <<<1, kCudaThreadsNumPerBlock, 0, stream->As()->cuda_stream()>>>(n, x, y); - } else { - Memset(stream, y, 0, sizeof(T)); - SquareSumForMultiThreadBlock - <<As()->cuda_stream()>>>( - n, x, tmp); - SqrtSumForMultiThreadBlock - <<<1, kCudaThreadsNumPerBlock, 0, stream->As()->cuda_stream()>>>( - num_blocks, tmp, y); - } - } -}; - -#define INSTANTIATE_SQRT_SQUARE_SUM_KERNEL_UTIL_CUDA(type_cpp, type_proto) \ - template struct SqrtSquareSumKernelUtil; -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_SQRT_SQUARE_SUM_KERNEL_UTIL_CUDA, FLOATING_DATA_TYPE_SEQ); -#undef INSTANTIATE_SQRT_SQUARE_SUM_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/square_sum_kernel_util.cu b/oneflow/user/kernels/square_sum_kernel_util.cu index ad15975705fd4972e85045eee381437eb3abb7bf..e8479967da3f41c091e6e2fad00755acb7d851ef 100644 --- a/oneflow/user/kernels/square_sum_kernel_util.cu +++ b/oneflow/user/kernels/square_sum_kernel_util.cu @@ -16,7 +16,12 @@ limitations under the License. #include "oneflow/user/kernels/square_sum_kernel_util.h" #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/core/ep/cuda/cuda_stream.h" +#ifdef WITH_ROCM +#include "hip/hip_runtime.h" +#include +#else #include +#endif namespace oneflow { @@ -26,7 +31,11 @@ template __global__ void SquareSumGpu(int64_t n, const T* x, T* y) { T t_sum = 0; CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_sum = BlockReduce(temp_storage).Sum(t_sum); if (threadIdx.x == 0) { @@ -53,7 +62,11 @@ __global__ void MultiSquareSumGpu(const MultiSquareSumParams params, T* y) { const SquareSumParam param = params.params[i]; CUDA_1D_KERNEL_LOOP(j, param.count) { t_sum += param.ptr[j] * param.ptr[j]; } } +#ifdef WITH_ROCM + typedef hipcub::BlockReduce BlockReduce; +#else typedef cub::BlockReduce BlockReduce; +#endif __shared__ typename BlockReduce::TempStorage temp_storage; T b_sum = BlockReduce(temp_storage).Sum(t_sum); if (threadIdx.x == 0) { cuda::atomic::Add(y, b_sum); } diff --git a/oneflow/user/kernels/square_sum_kernel_util.hip.cpp b/oneflow/user/kernels/square_sum_kernel_util.hip.cpp deleted file mode 100644 index 490c6b9ead939066f93faae7ae11d8e0b9d22958..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/square_sum_kernel_util.hip.cpp +++ /dev/null @@ -1,105 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/square_sum_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace { - -template -__global__ void SquareSumGpu(int64_t n, const T* x, T* y) { - T t_sum = 0; - CUDA_1D_KERNEL_LOOP(i, n) { t_sum += x[i] * x[i]; } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_sum = BlockReduce(temp_storage).Sum(t_sum); - if (threadIdx.x == 0) { - if (ONE_BLOCK) { - *y = b_sum; - } else { - cuda::atomic::Add(y, b_sum); - } - } -} - -constexpr int64_t kMultiSquareSumMaxSize = 64; - -template -struct MultiSquareSumParams { - SquareSumParam params[kMultiSquareSumMaxSize]; - int32_t size; -}; - -template -__global__ void MultiSquareSumGpu(const MultiSquareSumParams params, T* y) { - T t_sum = 0; - for (int i = 0; i < params.size; ++i) { - const SquareSumParam param = params.params[i]; - CUDA_1D_KERNEL_LOOP(j, param.count) { t_sum += param.ptr[j] * param.ptr[j]; } - } - typedef hipcub::BlockReduce BlockReduce; - __shared__ typename BlockReduce::TempStorage temp_storage; - T b_sum = BlockReduce(temp_storage).Sum(t_sum); - if (threadIdx.x == 0) { cuda::atomic::Add(y, b_sum); } -} - -} // namespace - -template -struct SquareSumKernelUtil { - static void SquareSum(ep::Stream* stream, int64_t n, const T* x, T* y) { - const int32_t num_blocks = BlocksNum4ThreadsNum(n); - CHECK_GE(num_blocks, 0); - if (num_blocks == 0) { - Memset(stream, y, 0, sizeof(T)); - } else if (num_blocks == 1) { - SquareSumGpu - <<<1, kCudaThreadsNumPerBlock, 0, stream->As()->cuda_stream()>>>(n, x, y); - } else { - Memset(stream, y, 0, sizeof(T)); - SquareSumGpu - <<As()->cuda_stream()>>>( - n, x, y); - } - } - - static void MultiSquareSum(ep::Stream* stream, const std::vector>& params, - T* y) { - Memset(stream, y, 0, sizeof(T)); - for (int64_t start = 0; start < params.size(); start += kMultiSquareSumMaxSize) { - MultiSquareSumParams gpu_params{}; - int64_t max_count = 0; - gpu_params.size = std::min(start + kMultiSquareSumMaxSize, params.size()) - start; - for (int64_t i = 0; i < gpu_params.size; ++i) { - gpu_params.params[i] = params[start + i]; - max_count = std::max(max_count, gpu_params.params[i].count); - } - MultiSquareSumGpu<<As()->cuda_stream()>>>(gpu_params, y); - } - } -}; - -#define INSTANTIATE_SQUARE_SUM_KERNEL_UTIL_CUDA(type_cpp, type_proto) \ - template struct SquareSumKernelUtil; -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_SQUARE_SUM_KERNEL_UTIL_CUDA, FLOATING_DATA_TYPE_SEQ); -#undef INSTANTIATE_SQUARE_SUM_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/ssp_variable_proxy_kernel.cpp b/oneflow/user/kernels/ssp_variable_proxy_kernel.cpp index 4247a09e7362501caeba08d213536b951fcba809..2486a9bd4aa4a988bd6f362e2ae5c90a702735de 100644 --- a/oneflow/user/kernels/ssp_variable_proxy_kernel.cpp +++ b/oneflow/user/kernels/ssp_variable_proxy_kernel.cpp @@ -53,10 +53,7 @@ class SspVariableProxyKernel final : public user_op::OpKernel { }); REGISTER_SSP_VARIABLE_PROXY_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_SSP_VARIABLE_PROXY_KERNEL(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_SSP_VARIABLE_PROXY_KERNEL(DeviceType::kCUDA) #endif diff --git a/oneflow/user/kernels/stateful_opkernel.cpp b/oneflow/user/kernels/stateful_opkernel.cpp index b72a45620e2cc0aa2d4637c486726db6045e4262..492c1572a4b1d95f4898f05a3fec2e983e90cf36 100644 --- a/oneflow/user/kernels/stateful_opkernel.cpp +++ b/oneflow/user/kernels/stateful_opkernel.cpp @@ -1,901 +1,989 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/stateful_opkernel.h" -#include "oneflow/core/framework/attr_value_accessor.h" -#include "oneflow/core/framework/user_op_conf.h" -#include "oneflow/core/framework/user_op_registry_manager.h" -#include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/framework/attr_map.h" -#include "oneflow/core/rpc/include/global_process_ctx.h" -#include "oneflow/core/framework/consistent_tensor_infer_cache.h" -#include "oneflow/core/operator/operator.h" -#include "oneflow/core/profiler/profiler.h" -#include "oneflow/core/profiler/profile_manager.h" -#include "oneflow/core/profiler/event_recorder.h" -#include "oneflow/core/eager/call_context.h" - -namespace oneflow { -namespace one { - -class ConsistentTensorInferResult; - -using ArgVec = std::vector>; - -using EagerBlobObjectListRawPtr = const std::vector>*; -using ConsistentTensorInferResultRawPtr = const ConsistentTensorInferResult*; - -class ZeroCopyBaseContextHelper { - public: - ZeroCopyBaseContextHelper(const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : input_arg_tuple_(input_arg_tuple), output_arg_tuple_(output_arg_tuple) {} - -#define RETURN_IF_FOUND(inputs, outputs, post_action) \ - int32_t i = TryGetTensorTupleIndex(input_arg_tuple_->arg_name2bn_index2tensor_tuple_index(), \ - arg_name, index); \ - if (i >= 0) { return (inputs).at(i) post_action; } \ - i = TryGetTensorTupleIndex(output_arg_tuple_->arg_name2bn_index2tensor_tuple_index(), arg_name, \ - index); \ - if (i >= 0) { return (outputs).at(i) post_action; } - - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - const int32_t index) const { - RETURN_IF_FOUND(*call_ctx->inputs(), *call_ctx->outputs(), .get()); - return nullptr; - } - - user_op::Tensor* Tensor4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - const int32_t index) const { - RETURN_IF_FOUND(*call_ctx->inputs(), *call_ctx->outputs(), .get()); - if (arg_name == "tmp_buffer" && index == 0) { return call_ctx->mut_tmp_tensor(); } - return nullptr; - } - - const ConsistentTensorMeta* ConsistentTensorMeta4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - const int32_t index) const { - const auto& consistent_tensor_infer_result = call_ctx->consistent_tensor_infer_result(); - RETURN_IF_FOUND(consistent_tensor_infer_result->input_tensor_metas(), - consistent_tensor_infer_result->output_tensor_metas(), - .shared_from_symbol().get()); - return nullptr; - } - - Optional> parallel_desc(eager::CallContext* call_ctx) const { - const auto& consistent_tensor_infer_result = call_ctx->consistent_tensor_infer_result(); - if (!consistent_tensor_infer_result) { return Optional>(); } - if (!consistent_tensor_infer_result->input_tensor_metas().empty()) { - return consistent_tensor_infer_result->input_tensor_metas().at(0)->parallel_desc(); - } else if (!consistent_tensor_infer_result->output_tensor_metas().empty()) { - return consistent_tensor_infer_result->output_tensor_metas().at(0)->parallel_desc(); - } else { - UNIMPLEMENTED(); - return Optional>(); - } - } - - const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { - const auto& parallel_desc = this->parallel_desc(call_ctx); - if (parallel_desc.has_value()) { - const auto& parallel_desc_symbol = CHECK_JUST(parallel_desc); - return *CHECK_JUST(GetParallelContext4CurrentProcessCtx(parallel_desc_symbol)); - } else { - static ParallelContext single_device_parallel_ctx(MakeSingleDeviceParallelCtx()); - return single_device_parallel_ctx; - } - } - - const ArgVec& inputs() const { return input_arg_tuple_->indexed_arg_name_and_index(); } - const ArgVec& outputs() const { return output_arg_tuple_->indexed_arg_name_and_index(); } - - private: - static int32_t TryGetTensorTupleIndex(const std::unordered_map>& - arg_name2bn_index2tensor_tuple_index, - const std::string& arg_name, const int32_t arg_index) { - auto it = arg_name2bn_index2tensor_tuple_index.find(arg_name); - if (it != arg_name2bn_index2tensor_tuple_index.end()) { return it->second.at(arg_index); } - return -1; - } - - static ParallelContext MakeSingleDeviceParallelCtx() { - ParallelContext single_device_parallel_ctx; - single_device_parallel_ctx.set_parallel_id(0); - single_device_parallel_ctx.set_parallel_num(1); - return single_device_parallel_ctx; - } - - std::shared_ptr input_arg_tuple_; - std::shared_ptr output_arg_tuple_; -}; - -class UserKernelBaseContextHelper final : public ZeroCopyBaseContextHelper { - public: - UserKernelBaseContextHelper(DeviceType device_type, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : ZeroCopyBaseContextHelper(input_arg_tuple, output_arg_tuple), device_type_(device_type) {} - - ~UserKernelBaseContextHelper() = default; - - DeviceType device_type() const { return device_type_; } - const JobDesc& job_desc() const { - UNIMPLEMENTED(); - return *(const JobDesc*)nullptr; - } - - private: - const DeviceType device_type_; -}; - -class UserOpInferContextHelper final { - public: - UserOpInferContextHelper(const user_op::UserOpConfWrapper* user_op_conf, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : user_op_conf_(user_op_conf), - zero_copy_base_ctx_helper_(input_arg_tuple, output_arg_tuple) {} - - ~UserOpInferContextHelper() = default; - - const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - UNIMPLEMENTED(); - return nullptr; - } - - const user_op::TensorDesc& InputTensorDesc(eager::CallContext* call_ctx, - const std::string& arg_name, int32_t index) const { - return *CHECK_NOTNULL(TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)); - } - - user_op::TensorDesc* OutputTensorDesc(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - } - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - return zero_copy_base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - } - - const Shape& InputShape(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *Shape4ArgNameAndIndex(call_ctx, arg_name, index); - } - Shape* OutputShape(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return Shape4ArgNameAndIndex(call_ctx, arg_name, index); - } - Shape* Shape4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->mut_shape(); - } - const Stride& InputStride(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *Stride4ArgNameAndIndex(call_ctx, arg_name, index); - } - Stride* OutputStride(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return Stride4ArgNameAndIndex(call_ctx, arg_name, index); - } - Stride* Stride4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->mut_stride(); - } - const DataType& InputDType(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *Dtype4ArgNameAndIndex(call_ctx, arg_name, index); - } - DataType* OutputDType(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return Dtype4ArgNameAndIndex(call_ctx, arg_name, index); - } - DataType* Dtype4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->mut_data_type(); - } - bool InputIsDynamic(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *IsDynamic4ArgNameAndIndex(call_ctx, arg_name, index); - } - bool* OutputIsDynamic(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return IsDynamic4ArgNameAndIndex(call_ctx, arg_name, index); - } - bool* IsDynamic4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->mut_is_dynamic(); - } - - const ArgVec& inputs() const { return zero_copy_base_ctx_helper_.inputs(); } - const ArgVec& outputs() const { return zero_copy_base_ctx_helper_.outputs(); } - const JobDesc* job_desc() const { - UNIMPLEMENTED(); - return nullptr; - } - const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { - return zero_copy_base_ctx_helper_.parallel_ctx(call_ctx); - } - const ParallelDesc& parallel_desc(eager::CallContext* call_ctx) const { - return *CHECK_JUST(zero_copy_base_ctx_helper_.parallel_desc(call_ctx)); - } - const SbpParallel& SbpParallel4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, int32_t index) const { - const auto& nd_sbp = NdSbp4ArgNameAndIndex(call_ctx, arg_name, index); - CHECK_EQ(nd_sbp.sbp_parallel_size(), 1); - return nd_sbp.sbp_parallel(0); - } - const NdSbp& NdSbp4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *CHECK_NOTNULL(zero_copy_base_ctx_helper_.ConsistentTensorMeta4ArgNameAndIndex( - call_ctx, arg_name, index)) - ->nd_sbp(); - } - - int64_t parallel_num(eager::CallContext* call_ctx) const { - return parallel_ctx(call_ctx).parallel_num(); - } - - const std::string& input(const std::string& arg_name, int32_t index) const { - return user_op_conf().input(arg_name, index); - } - const std::string& output(const std::string& arg_name, int32_t index) const { - return user_op_conf().output(arg_name, index); - } - bool has_input(const std::string& arg_name, int32_t index) const { - return user_op_conf().has_input(arg_name, index); - } - bool has_output(const std::string& arg_name, int32_t index) const { - return user_op_conf().has_output(arg_name, index); - } - int32_t input_size(const std::string& arg_name) const { - return user_op_conf().input_size(arg_name); - } - int32_t output_size(const std::string& arg_name) const { - return user_op_conf().output_size(arg_name); - } - const std::string& op_name() const { return user_op_conf().op_name(); } - const std::string& op_type_name() const { return user_op_conf().op_type_name(); } - const std::string& op_loc() const { return user_op_conf_->op_conf().loc(); } - - const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } - const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, - const std::string& attr_name) const { - return call_ctx->composed_attrs().Attr4Name(attr_name); - } - - private: - user_op::TensorDesc* NonNullTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - user_op::TensorDesc* tensor_desc = TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - if (!tensor_desc) { LOG(FATAL) << "Arg (" << arg_name << "," << index << ") is not found"; } - return tensor_desc; - } - - const user_op::UserOpConfWrapper* user_op_conf_; - ZeroCopyBaseContextHelper zero_copy_base_ctx_helper_; -}; - -class UserOpInferContext : public user_op::InferContext { - public: - UserOpInferContext(const UserOpInferContextHelper* helper, eager::CallContext* call_ctx) - : helper_(helper), call_ctx_(call_ctx) {} - - ~UserOpInferContext() override = default; - - const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->LogicalTensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, - int32_t index) const override { - return helper_->InputTensorDesc(call_ctx_, arg_name, index); - } - user_op::TensorDesc* OutputTensorDesc(const std::string& arg_name, int32_t index) override { - return helper_->OutputTensorDesc(call_ctx_, arg_name, index); - } - user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { - return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - const Shape& InputShape(const std::string& arg_name, int32_t index) const override { - return helper_->InputShape(call_ctx_, arg_name, index); - } - Shape* OutputShape(const std::string& arg_name, int32_t index) override { - return helper_->OutputShape(call_ctx_, arg_name, index); - } - Shape* Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return helper_->Shape4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const Stride& InputStride(const std::string& arg_name, int32_t index) const override { - return helper_->InputStride(call_ctx_, arg_name, index); - } - Stride* OutputStride(const std::string& arg_name, int32_t index) override { - return helper_->OutputStride(call_ctx_, arg_name, index); - } - Stride* Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return helper_->Stride4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const DataType& InputDType(const std::string& arg_name, int32_t index) const override { - return helper_->InputDType(call_ctx_, arg_name, index); - } - DataType* OutputDType(const std::string& arg_name, int32_t index) override { - return helper_->OutputDType(call_ctx_, arg_name, index); - } - DataType* Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return helper_->Dtype4ArgNameAndIndex(call_ctx_, arg_name, index); - } - bool InputIsDynamic(const std::string& arg_name, int32_t index) const override { - return helper_->InputIsDynamic(call_ctx_, arg_name, index); - } - bool* OutputIsDynamic(const std::string& arg_name, int32_t index) override { - return helper_->OutputIsDynamic(call_ctx_, arg_name, index); - } - bool* IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return helper_->IsDynamic4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - const ArgVec& inputs() const override { return helper_->inputs(); } - const ArgVec& outputs() const override { return helper_->outputs(); } - const JobDesc* job_desc() const override { return helper_->job_desc(); } - const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } - const ParallelDesc& parallel_desc() const override { return helper_->parallel_desc(call_ctx_); } - const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->SbpParallel4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const NdSbp& NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { - return helper_->NdSbp4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - int64_t parallel_num() const override { return helper_->parallel_num(call_ctx_); } - - const std::string& input(const std::string& arg_name, int32_t index) const override { - return helper_->input(arg_name, index); - } - const std::string& output(const std::string& arg_name, int32_t index) const override { - return helper_->output(arg_name, index); - } - bool has_input(const std::string& arg_name, int32_t index) const override { - return helper_->has_input(arg_name, index); - } - bool has_output(const std::string& arg_name, int32_t index) const override { - return helper_->has_output(arg_name, index); - } - int32_t input_size(const std::string& arg_name) const override { - return helper_->input_size(arg_name); - } - int32_t output_size(const std::string& arg_name) const override { - return helper_->output_size(arg_name); - } - const std::string& op_name() const override { return helper_->op_name(); } - const std::string& op_type_name() const override { return helper_->op_type_name(); } - const std::string& op_loc() const override { return helper_->op_loc(); } - - private: - const std::shared_ptr& Attr4Name( - const std::string& attr_name) const override { - return helper_->Attr4Name(call_ctx_, attr_name); - } - - const UserOpInferContextHelper* helper_; - eager::CallContext* call_ctx_; -}; - -class UserKernelComputeContextHelper final { - public: - UserKernelComputeContextHelper(DeviceType device_type, - const user_op::UserOpConfWrapper* user_op_conf, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : user_op_conf_(user_op_conf), - base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} - - ~UserKernelComputeContextHelper() = default; - - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - } - - user_op::Tensor* Tensor4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return base_ctx_helper_.Tensor4ArgNameAndIndex(call_ctx, arg_name, index); - } - ep::Stream* stream(DeviceCtx* device_ctx) const { - CHECK(device_ctx); - return device_ctx->stream(); - } - - DeviceType device_type() const { return base_ctx_helper_.device_type(); } - const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { - return base_ctx_helper_.parallel_ctx(call_ctx); - } - - const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } - const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } - - const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } - const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, - const std::string& attr_name) const { - return call_ctx->composed_attrs().Attr4Name(attr_name); - } - - private: - const user_op::UserOpConfWrapper* user_op_conf_; - UserKernelBaseContextHelper base_ctx_helper_; -}; - -class UserKernelComputeContext final : public user_op::KernelComputeContext { - public: - UserKernelComputeContext(const UserKernelComputeContextHelper* helper, - eager::CallContext* call_ctx, DeviceCtx* device_ctx) - : helper_(helper), call_ctx_(call_ctx), device_ctx_(device_ctx) {} - - ~UserKernelComputeContext() = default; - - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - user_op::Tensor* Tensor4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { - return helper_->Tensor4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - ep::Stream* stream() override { return helper_->stream(device_ctx_); } - - DeviceType device_type() const override { return helper_->device_type(); } - - const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } - - const ArgVec& inputs() const override { return helper_->inputs(); } - const ArgVec& outputs() const override { return helper_->outputs(); } - - private: - const user_op::UserOpConfWrapper& user_op_conf() const override { - return helper_->user_op_conf(); - } - - const std::shared_ptr& Attr4Name( - const std::string& attr_name) const override { - return helper_->Attr4Name(call_ctx_, attr_name); - } - - const UserKernelComputeContextHelper* helper_; - eager::CallContext* call_ctx_; - DeviceCtx* device_ctx_; -}; - -class UserKernelRegContextHelper final { - public: - UserKernelRegContextHelper(DeviceType device_type, const user_op::UserOpConfWrapper* user_op_conf, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : user_op_conf_(user_op_conf), - base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} - ~UserKernelRegContextHelper() = default; - - DeviceType device_type() const { return base_ctx_helper_.device_type(); } - const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { - return base_ctx_helper_.parallel_ctx(call_ctx); - } - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - } - const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } - const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } - - const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } - - const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, - const std::string& attr_name) const { - return call_ctx->composed_attrs().Attr4Name(attr_name); - } - - private: - const user_op::UserOpConfWrapper* user_op_conf_; - UserKernelBaseContextHelper base_ctx_helper_; -}; - -class UserKernelRegContext final : public user_op::KernelRegContext { - public: - UserKernelRegContext(const UserKernelRegContextHelper* helper, eager::CallContext* call_ctx) - : helper_(helper), call_ctx_(call_ctx) {} - ~UserKernelRegContext() = default; - - DeviceType device_type() const override { return helper_->device_type(); } - const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const ArgVec& inputs() const override { return helper_->inputs(); } - const ArgVec& outputs() const override { return helper_->outputs(); } - - const user_op::UserOpConfWrapper& user_op_conf() const override { - return helper_->user_op_conf(); - } - - private: - const std::shared_ptr& Attr4Name( - const std::string& attr_name) const override { - return helper_->Attr4Name(call_ctx_, attr_name); - } - - const UserKernelRegContextHelper* helper_; - eager::CallContext* call_ctx_; -}; - -class UserKernelInitAndCacheContextHelper final { - public: - UserKernelInitAndCacheContextHelper(DeviceType device_type, - const user_op::UserOpConfWrapper* user_op_conf, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) - : user_op_conf_(user_op_conf), - base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} - - ~UserKernelInitAndCacheContextHelper() = default; - - ep::Stream* stream(DeviceCtx* device_ctx) const { - CHECK(device_ctx); - return device_ctx->stream(); - } - - DeviceType device_type() const { return base_ctx_helper_.device_type(); } - const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { - return base_ctx_helper_.parallel_ctx(call_ctx); - } - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); - } - const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, - int32_t index) const { - return base_ctx_helper_.ConsistentTensorMeta4ArgNameAndIndex(call_ctx, arg_name, index); - } - const SbpParallel& SbpParallel4ArgNameAndIndex(eager::CallContext* call_ctx, - const std::string& arg_name, int32_t index) const { - const auto& nd_sbp = NdSbp4ArgNameAndIndex(call_ctx, arg_name, index); - CHECK_EQ(nd_sbp.sbp_parallel_size(), 1); - return nd_sbp.sbp_parallel(0); - } - - const NdSbp& NdSbp4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, - int32_t index) const { - return *CHECK_NOTNULL( - base_ctx_helper_.ConsistentTensorMeta4ArgNameAndIndex(call_ctx, arg_name, index)) - ->nd_sbp(); - } - - const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } - const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } - const ParallelDesc& parallel_desc(eager::CallContext* call_ctx) const { - return *CHECK_JUST(base_ctx_helper_.parallel_desc(call_ctx)); - } - - const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, - const std::string& attr_name) const { - return call_ctx->composed_attrs().Attr4Name(attr_name); - } - - const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } - - private: - const user_op::UserOpConfWrapper* user_op_conf_; - UserKernelBaseContextHelper base_ctx_helper_; -}; - -class UserKernelInitAndCacheContext final : public user_op::KernelInitContext, - public user_op::KernelCacheContext { - public: - UserKernelInitAndCacheContext(const UserKernelInitAndCacheContextHelper* helper, - eager::CallContext* call_ctx, DeviceCtx* device_ctx) - : helper_(helper), call_ctx_(call_ctx), device_ctx_(device_ctx) {} - - ~UserKernelInitAndCacheContext() override = default; - - ep::Stream* stream() override { return helper_->stream(device_ctx_); } - - DeviceType device_type() const override { return helper_->device_type(); } - const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } - const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->LogicalTensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); - } - const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& arg_name, - int32_t index) const override { - return helper_->SbpParallel4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - const NdSbp& NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { - return helper_->NdSbp4ArgNameAndIndex(call_ctx_, arg_name, index); - } - - const ArgVec& inputs() const override { return helper_->inputs(); } - const ArgVec& outputs() const override { return helper_->outputs(); } - const ParallelDesc& parallel_desc() const override { return helper_->parallel_desc(call_ctx_); } - - private: - const std::shared_ptr& Attr4Name( - const std::string& attr_name) const override { - return helper_->Attr4Name(call_ctx_, attr_name); - } - - const user_op::UserOpConfWrapper& user_op_conf() const override { - return helper_->user_op_conf(); - } - - const UserKernelInitAndCacheContextHelper* helper_; - eager::CallContext* call_ctx_; - DeviceCtx* device_ctx_; -}; - -namespace { - -Maybe InitTensorTupleIndexes4Bns(const std::shared_ptr& op_conf, - const ArgVec& indexed_input_pairs, - const ArgVec& indexed_output_pairs, - std::vector* input_tuple_indexes4const_ibns, - std::vector* input_tuple_indexes4mut_ibns, - std::vector* output_tuple_indexes4mut_obns, - std::vector* output_tuple_indexes4mut2_obns) { - const auto* op_reg_val = - user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_conf->user_conf().op_type_name()); - CHECK_NOTNULL_OR_RETURN(op_reg_val); - - ArgModifierSignature arg_modifier_signature; - for (const auto& pair : indexed_input_pairs) { - const std::string ibn = GenRepeatedBn(pair.first, pair.second); - arg_modifier_signature.mutable_ibn2input_blob_modifier()->insert( - {ibn, user_op::InputArgModifier()}); - } - for (const auto& pair : indexed_output_pairs) { - const std::string obn = GenRepeatedBn(pair.first, pair.second); - arg_modifier_signature.mutable_obn2output_blob_modifier()->insert( - {obn, user_op::OutputArgModifier()}); - } - user_op::UserOpConfWrapper op_conf_wrapper(op_conf); - if (op_reg_val->input_arg_modify_fn) { - user_op::GetInputArgModifier GetInputArgModifierFn = - [&arg_modifier_signature](const std::string& in_arg_name, - int32_t in_arg_index) -> user_op::InputArgModifier* { - const std::string ibn = GenRepeatedBn(in_arg_name, in_arg_index); - auto* map = arg_modifier_signature.mutable_ibn2input_blob_modifier(); - return &map->at(ibn); - }; - JUST(op_reg_val->input_arg_modify_fn(GetInputArgModifierFn, op_conf_wrapper)); - } - if (op_reg_val->output_arg_modify_fn) { - user_op::GetOutputArgModifier GetOutputArgModifierFn = - [&arg_modifier_signature](const std::string& in_arg_name, - int32_t in_arg_index) -> user_op::OutputArgModifier* { - const std::string obn = GenRepeatedBn(in_arg_name, in_arg_index); - auto* map = arg_modifier_signature.mutable_obn2output_blob_modifier(); - return &map->at(obn); - }; - JUST(op_reg_val->output_arg_modify_fn(GetOutputArgModifierFn, op_conf_wrapper)); - } - - for (int i = 0; i < indexed_input_pairs.size(); i++) { - const auto& pair = indexed_input_pairs.at(i); - const std::string ibn = GenRepeatedBn(pair.first, pair.second); - if (arg_modifier_signature.ibn2input_blob_modifier().at(ibn).is_mutable()) { - input_tuple_indexes4mut_ibns->emplace_back(i); - } else { - input_tuple_indexes4const_ibns->emplace_back(i); - } - } - - for (int i = 0; i < indexed_output_pairs.size(); i++) { - const auto& pair = indexed_output_pairs.at(i); - const std::string obn = GenRepeatedBn(pair.first, pair.second); - if (arg_modifier_signature.obn2output_blob_modifier().at(obn).header_infered_before_compute()) { - output_tuple_indexes4mut_obns->emplace_back(i); - } else { - output_tuple_indexes4mut2_obns->emplace_back(i); - } - } - return Maybe::Ok(); -} - -} // namespace - -/* static */ Maybe StatefulOpKernel::New( - const std::shared_ptr& op_conf, const Symbol& stream, - const AttrMap& base_attrs, const std::shared_ptr& parallel_desc, - const std::shared_ptr& input_arg_tuple, - const std::shared_ptr& output_arg_tuple) { - auto opkernel = std::shared_ptr(new StatefulOpKernel()); - opkernel->base_attrs_ = base_attrs; - opkernel->op_conf_ = op_conf; - opkernel->user_op_conf_.reset(new user_op::UserOpConfWrapper(op_conf)); - opkernel->stream_ = stream; - opkernel->input_arg_tuple_ = input_arg_tuple; - opkernel->output_arg_tuple_ = output_arg_tuple; - opkernel->need_check_mem_case_ = true; - - const DeviceType device_type = CHECK_JUST(DeviceType4DeviceTag(op_conf->device_tag())); - const user_op::UserOpConfWrapper* user_op_conf = opkernel->user_op_conf_.get(); - opkernel->op_infer_ctx_helper_.reset( - new UserOpInferContextHelper(user_op_conf, input_arg_tuple, output_arg_tuple)); - - opkernel->init_and_cache_ctx_helper_.reset(new UserKernelInitAndCacheContextHelper( - device_type, opkernel->user_op_conf_.get(), opkernel->input_arg_tuple_, - opkernel->output_arg_tuple_)); - opkernel->compute_ctx_helper_.reset(new UserKernelComputeContextHelper( - device_type, user_op_conf, input_arg_tuple, output_arg_tuple)); - opkernel->reg_ctx_helper_.reset( - new UserKernelRegContextHelper(device_type, user_op_conf, input_arg_tuple, output_arg_tuple)); - const auto* op_reg_val = - user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(user_op_conf->op_type_name()); - CHECK_NOTNULL_OR_RETURN(op_reg_val); - if (op_reg_val->logical_tensor_desc_infer_fn) { - opkernel->tensor_desc_infer_fn_ = op_reg_val->logical_tensor_desc_infer_fn; - } else { - return Error::UnimplementedError(); - } - opkernel->data_type_infer_fn_ = op_reg_val->data_type_infer_fn; - - JUST(InitTensorTupleIndexes4Bns( - op_conf, input_arg_tuple->indexed_arg_name_and_index(), - output_arg_tuple->indexed_arg_name_and_index(), &opkernel->input_tuple_indexes4const_ibns_, - &opkernel->input_tuple_indexes4mut_ibns_, &opkernel->output_tuple_indexes4mut_obns_, - &opkernel->output_tuple_indexes4mut2_obns_)); - - return opkernel; -} - -StatefulOpKernel::~StatefulOpKernel() = default; - -size_t StatefulOpKernel::InferTmpSize(eager::CallContext* call_ctx, - const user_op::OpKernel* user_opkernel) const { - UserOpInferContext op_infer_ctx(op_infer_ctx_helper_.get(), call_ctx); - const auto& InferTmpSizeFn = GetInferTmpSizeFn(user_opkernel); - return InferTmpSizeFn(&op_infer_ctx); -} - -Maybe StatefulOpKernel::ChooseOpKernel(eager::CallContext* call_ctx, - const user_op::OpKernel** user_opkernel, - bool* need_temp_storage) { - OF_PROFILER_RANGE_GUARD("ChooseOpKernel"); - DataType primary_dtype = kInvalidDataType; - const auto& inputs = call_ctx->inputs(); - const auto& outputs = call_ctx->outputs(); - if (likely(!inputs->empty())) { - primary_dtype = (*inputs)[0]->data_type(); - } else if (likely(!outputs->empty())) { - primary_dtype = (*outputs)[0]->data_type(); - } else { - // do nothing - } - - UserKernelRegContext reg_ctx(reg_ctx_helper_.get(), call_ctx); - for (const auto& pair : dtype2cached_kernels_[primary_dtype]) { - if (likely(pair.first->is_matched_hob->get(reg_ctx))) { - *need_temp_storage = pair.first->need_temp_storage; - *user_opkernel = pair.second.get(); - return Maybe::Ok(); - } - } - - OF_PROFILER_RANGE_GUARD("fallback"); - - const auto& op_type_name = user_op_conf_->op_type_name(); - const auto* kernel_reg_val = - JUST(user_op::UserOpRegistryMgr::Get().GetOpKernelRegistryResult(op_type_name, reg_ctx)); - CHECK_NOTNULL(kernel_reg_val); - auto* kernel = kernel_reg_val->create_fn(); - dtype2cached_kernels_[primary_dtype].push_back( - {kernel_reg_val, std::shared_ptr(kernel)}); - - infer_tmp_size_fn_map_.emplace(kernel, &kernel_reg_val->infer_tmp_size_fn); - *need_temp_storage = kernel_reg_val->need_temp_storage; - *user_opkernel = kernel; - return Maybe::Ok(); -} - -void StatefulOpKernel::TryInitOpKernelStateAndCache(eager::CallContext* call_ctx, - DeviceCtx* device_ctx, - const user_op::OpKernel* op_kernel, - user_op::OpKernelState** state, - user_op::OpKernelCache** cache) { - UserKernelInitAndCacheContext init_and_cache_ctx(init_and_cache_ctx_helper_.get(), call_ctx, - device_ctx); - if (state != nullptr) { - auto it = op_kernel_state_map_.find(op_kernel); - if (it != op_kernel_state_map_.end()) { - *state = it->second.get(); - } else { - auto created_state = op_kernel->CreateOpKernelState(&init_and_cache_ctx); - op_kernel_state_map_.emplace(op_kernel, created_state); - *state = created_state.get(); - } - } - - { - auto& cache_in_map = op_kernel_cache_map_[op_kernel]; - op_kernel->InitOpKernelCacheWithFlags(&init_and_cache_ctx, - user_op::OpKernelCache::kAllMayChanged, &cache_in_map); - *cache = cache_in_map.get(); - } -} - -const user_op::InferTmpSizeFn& StatefulOpKernel::GetInferTmpSizeFn( - const user_op::OpKernel* op_kernel) const { - return *infer_tmp_size_fn_map_.at(op_kernel); -} - -user_op::TensorDescInferFn StatefulOpKernel::TensorDescInferFn() const { - return tensor_desc_infer_fn_; -} - -user_op::DataTypeInferFn StatefulOpKernel::DataTypeInferFn() const { return data_type_infer_fn_; } - -void StatefulOpKernel::Compute(eager::CallContext* call_ctx, DeviceCtx* device_ctx, - const user_op::OpKernel* user_opkernel, - user_op::OpKernelState* state, - const user_op::OpKernelCache* cache) const { - UserKernelComputeContext compute_context(compute_ctx_helper_.get(), call_ctx, device_ctx); - auto* compute_ctx = &compute_context; - OF_PROFILER_RANGE_GUARD("Compute"); - if (Singleton::Get()) { -#if defined(WITH_CUDA) || defined(WITH_ROCM) - const auto CalMemorySize = [compute_ctx](const one::ArgVec& args) -> int64_t { - const auto Func = [compute_ctx](int64_t mem_size, const auto& pair) { - const auto tensor = compute_ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); - return mem_size + tensor->shape_view().elem_cnt() * GetSizeOfDataType(tensor->data_type()); - }; - return std::accumulate(args.begin(), args.end(), static_cast(0), Func); - }; -#endif - auto er_guard = CHECK_JUST(profiler::EventRecorder::CreateKernelEventRecorder( - op_type_name(), -#if defined(WITH_CUDA) || defined(WITH_ROCM) - [compute_ctx, CalMemorySize]() -> int64_t { - return CalMemorySize(compute_ctx->inputs()) + CalMemorySize(compute_ctx->outputs()); - }, -#endif - [compute_ctx]() -> std::vector { - std::vector shapes; - for (const auto& pair : compute_ctx->inputs()) { - shapes.emplace_back( - compute_ctx->TensorDesc4ArgNameAndIndex(pair.first, pair.second)->shape()); - } - return shapes; - })); - user_opkernel->Compute(compute_ctx, state, cache); - } else { - user_opkernel->Compute(compute_ctx, state, cache); - } -} - -} // namespace one -} // namespace oneflow +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/stateful_opkernel.h" +#include "oneflow/core/framework/attr_value_accessor.h" +#include "oneflow/core/framework/user_op_conf.h" +#include "oneflow/core/framework/user_op_registry_manager.h" +#include "oneflow/core/eager/eager_blob_object.h" +#include "oneflow/core/framework/attr_map.h" +#include "oneflow/core/rpc/include/global_process_ctx.h" +#include "oneflow/core/framework/global_tensor_infer_cache.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/profiler/profiler.h" +#include "oneflow/core/profiler/profile_manager.h" +#include "oneflow/core/profiler/event_recorder.h" +#include "oneflow/core/eager/call_context.h" + +namespace oneflow { +namespace one { + +class GlobalTensorInferResult; + +using ArgVec = std::vector>; + +using EagerBlobObjectListRawPtr = const std::vector>*; +using GlobalTensorInferResultRawPtr = const GlobalTensorInferResult*; + +class ZeroCopyBaseContextHelper { + public: + ZeroCopyBaseContextHelper(const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : input_arg_tuple_(input_arg_tuple), output_arg_tuple_(output_arg_tuple) {} + +#define RETURN_IF_FOUND(inputs, outputs, post_action) \ + int32_t i = TryGetTensorTupleIndex(input_arg_tuple_->arg_name2bn_index2tensor_tuple_index(), \ + arg_name, index); \ + if (i >= 0) { return (inputs).at(i) post_action; } \ + i = TryGetTensorTupleIndex(output_arg_tuple_->arg_name2bn_index2tensor_tuple_index(), arg_name, \ + index); \ + if (i >= 0) { return (outputs).at(i) post_action; } + + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + const int32_t index) const { + RETURN_IF_FOUND(call_ctx->inputs(), call_ctx->outputs(), .get()); + return nullptr; + } + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + const int32_t index) const { + RETURN_IF_FOUND(call_ctx->inputs(), call_ctx->outputs(), .get()); + return nullptr; + } + + user_op::Tensor* Tensor4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + const int32_t index) const { + RETURN_IF_FOUND(call_ctx->inputs(), call_ctx->outputs(), .get()); + if (arg_name == "tmp_buffer" && index == 0) { return call_ctx->mut_tmp_tensor(); } + return nullptr; + } + + const GlobalTensorMeta* GlobalTensorMeta4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + const int32_t index) const { + const auto& global_tensor_infer_result = call_ctx->global_tensor_infer_result(); + RETURN_IF_FOUND(global_tensor_infer_result->input_tensor_metas(), + global_tensor_infer_result->output_tensor_metas(), .shared_from_symbol().get()); + return nullptr; + } + + Optional> parallel_desc(eager::CallContext* call_ctx) const { + const auto& global_tensor_infer_result = call_ctx->global_tensor_infer_result(); + if (!global_tensor_infer_result) { return Optional>(); } + if (!global_tensor_infer_result->input_tensor_metas().empty()) { + return global_tensor_infer_result->input_tensor_metas().at(0)->parallel_desc(); + } else if (!global_tensor_infer_result->output_tensor_metas().empty()) { + return global_tensor_infer_result->output_tensor_metas().at(0)->parallel_desc(); + } else { + UNIMPLEMENTED(); + return Optional>(); + } + } + + const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { + const auto& parallel_desc = this->parallel_desc(call_ctx); + if (parallel_desc.has_value()) { + const auto& parallel_desc_symbol = CHECK_JUST(parallel_desc); + return *CHECK_JUST(GetParallelContext4CurrentProcessCtx(parallel_desc_symbol)); + } else { + static ParallelContext single_device_parallel_ctx(MakeSingleDeviceParallelCtx()); + return single_device_parallel_ctx; + } + } + + const ArgVec& inputs() const { return input_arg_tuple_->indexed_arg_name_and_index(); } + const ArgVec& outputs() const { return output_arg_tuple_->indexed_arg_name_and_index(); } + + private: + static int32_t TryGetTensorTupleIndex(const std::unordered_map>& + arg_name2bn_index2tensor_tuple_index, + const std::string& arg_name, const int32_t arg_index) { + auto it = arg_name2bn_index2tensor_tuple_index.find(arg_name); + if (it != arg_name2bn_index2tensor_tuple_index.end()) { return it->second.at(arg_index); } + return -1; + } + + static ParallelContext MakeSingleDeviceParallelCtx() { + ParallelContext single_device_parallel_ctx; + single_device_parallel_ctx.set_parallel_id(0); + single_device_parallel_ctx.set_parallel_num(1); + return single_device_parallel_ctx; + } + + std::shared_ptr input_arg_tuple_; + std::shared_ptr output_arg_tuple_; +}; + +class UserKernelBaseContextHelper final : public ZeroCopyBaseContextHelper { + public: + UserKernelBaseContextHelper(DeviceType device_type, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : ZeroCopyBaseContextHelper(input_arg_tuple, output_arg_tuple), device_type_(device_type) {} + + ~UserKernelBaseContextHelper() = default; + + DeviceType device_type() const { return device_type_; } + const JobDesc& job_desc() const { + UNIMPLEMENTED(); + return *(const JobDesc*)nullptr; + } + + private: + const DeviceType device_type_; +}; + +class UserOpInferContextHelper final { + public: + UserOpInferContextHelper(const user_op::UserOpConfWrapper* user_op_conf, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : user_op_conf_(user_op_conf), + zero_copy_base_ctx_helper_(input_arg_tuple, output_arg_tuple) {} + + ~UserOpInferContextHelper() = default; + + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + UNIMPLEMENTED(); + return nullptr; + } + + const user_op::TensorDesc& InputTensorDesc(eager::CallContext* call_ctx, + const std::string& arg_name, int32_t index) const { + return *TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + const user_op::TensorDesc& OutputTensorDesc(eager::CallContext* call_ctx, + const std::string& arg_name, int32_t index) const { + return *TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(eager::CallContext* call_ctx, + const std::string& arg_name, int32_t index) const { + return MutTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return zero_copy_base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return zero_copy_base_ctx_helper_.MutTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + + const Shape& InputShape(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Shape4ArgNameAndIndex(call_ctx, arg_name, index); + } + const Shape& OutputShape(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Shape4ArgNameAndIndex(call_ctx, arg_name, index); + } + void SetOutputShape(eager::CallContext* call_ctx, const std::string& arg_name, int32_t index, + const Shape& shape) const { + SetShape4ArgNameAndIndex(call_ctx, arg_name, index, shape); + } + const Shape& Shape4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index).shape(); + } + void SetShape4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index, const Shape& shape) const { + return MutNonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->set_shape(shape); + } + const Stride& InputStride(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Stride4ArgNameAndIndex(call_ctx, arg_name, index); + } + const Stride& OutputStride(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Stride4ArgNameAndIndex(call_ctx, arg_name, index); + } + void SetOutputStride(eager::CallContext* call_ctx, const std::string& arg_name, int32_t index, + const Stride& stride) const { + return SetStride4ArgNameAndIndex(call_ctx, arg_name, index, stride); + } + const Stride& Stride4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index).stride(); + } + void SetStride4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index, const Stride& stride) const { + return MutNonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index)->set_stride(stride); + } + DataType InputDType(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Dtype4ArgNameAndIndex(call_ctx, arg_name, index); + } + DataType OutputDType(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return Dtype4ArgNameAndIndex(call_ctx, arg_name, index); + } + void SetOutputDType(eager::CallContext* call_ctx, const std::string& arg_name, int32_t index, + DataType data_type) const { + return SetDtype4ArgNameAndIndex(call_ctx, arg_name, index, data_type); + } + DataType Dtype4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index).data_type(); + } + void SetDtype4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index, DataType data_type) const { + return MutNonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index) + ->set_data_type(data_type); + } + bool InputIsDynamic(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return IsDynamic4ArgNameAndIndex(call_ctx, arg_name, index); + } + bool OutputIsDynamic(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return IsDynamic4ArgNameAndIndex(call_ctx, arg_name, index); + } + void SetOutputIsDynamic(eager::CallContext* call_ctx, const std::string& arg_name, int32_t index, + bool is_dynamic) const { + return SetIsDynamic4ArgNameAndIndex(call_ctx, arg_name, index, is_dynamic); + } + bool IsDynamic4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return NonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index).is_dynamic(); + } + void SetIsDynamic4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index, bool is_dynamic) const { + return MutNonNullTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index) + ->set_is_dynamic(is_dynamic); + } + + const ArgVec& inputs() const { return zero_copy_base_ctx_helper_.inputs(); } + const ArgVec& outputs() const { return zero_copy_base_ctx_helper_.outputs(); } + const JobDesc* job_desc() const { + UNIMPLEMENTED(); + return nullptr; + } + const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { + return zero_copy_base_ctx_helper_.parallel_ctx(call_ctx); + } + const ParallelDesc& parallel_desc(eager::CallContext* call_ctx) const { + return *CHECK_JUST(zero_copy_base_ctx_helper_.parallel_desc(call_ctx)); + } + const SbpParallel& SbpParallel4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, int32_t index) const { + const auto& nd_sbp = NdSbp4ArgNameAndIndex(call_ctx, arg_name, index); + CHECK_EQ(nd_sbp.sbp_parallel_size(), 1); + return nd_sbp.sbp_parallel(0); + } + const NdSbp& NdSbp4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return *CHECK_NOTNULL(zero_copy_base_ctx_helper_.GlobalTensorMeta4ArgNameAndIndex( + call_ctx, arg_name, index)) + ->nd_sbp(); + } + + int64_t parallel_num(eager::CallContext* call_ctx) const { + return parallel_ctx(call_ctx).parallel_num(); + } + + const std::string& input(const std::string& arg_name, int32_t index) const { + return user_op_conf().input(arg_name, index); + } + const std::string& output(const std::string& arg_name, int32_t index) const { + return user_op_conf().output(arg_name, index); + } + bool has_input(const std::string& arg_name, int32_t index) const { + return user_op_conf().has_input(arg_name, index); + } + bool has_output(const std::string& arg_name, int32_t index) const { + return user_op_conf().has_output(arg_name, index); + } + int32_t input_size(const std::string& arg_name) const { + return user_op_conf().input_size(arg_name); + } + int32_t output_size(const std::string& arg_name) const { + return user_op_conf().output_size(arg_name); + } + const std::string& op_name() const { return user_op_conf().op_name(); } + const std::string& op_type_name() const { return user_op_conf().op_type_name(); } + const std::string& op_loc() const { return user_op_conf_->op_conf().loc(); } + + const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } + const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, + const std::string& attr_name) const { + return call_ctx->composed_attrs().Attr4Name(attr_name); + } + + private: + const user_op::TensorDesc& NonNullTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + const user_op::TensorDesc* tensor_desc = TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + if (!tensor_desc) { LOG(FATAL) << "Arg (" << arg_name << "," << index << ") is not found"; } + return *tensor_desc; + } + user_op::TensorDesc* MutNonNullTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + user_op::TensorDesc* tensor_desc = MutTensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + if (!tensor_desc) { LOG(FATAL) << "Arg (" << arg_name << "," << index << ") is not found"; } + return tensor_desc; + } + + const user_op::UserOpConfWrapper* user_op_conf_; + ZeroCopyBaseContextHelper zero_copy_base_ctx_helper_; +}; + +class UserOpInferContext : public user_op::InferContext { + public: + UserOpInferContext(const UserOpInferContextHelper* helper, eager::CallContext* call_ctx) + : helper_(helper), call_ctx_(call_ctx) {} + + ~UserOpInferContext() override = default; + + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->LogicalTensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + const user_op::TensorDesc& InputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return helper_->InputTensorDesc(call_ctx_, arg_name, index); + } + const user_op::TensorDesc& OutputTensorDesc(const std::string& arg_name, + int32_t index) const override { + return helper_->OutputTensorDesc(call_ctx_, arg_name, index); + } + user_op::TensorDesc* MutOutputTensorDesc(const std::string& arg_name, int32_t index) override { + return helper_->MutOutputTensorDesc(call_ctx_, arg_name, index); + } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const { + return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + user_op::TensorDesc* MutTensorDesc4ArgNameAndIndex(const std::string& arg_name, int32_t index) { + return helper_->MutTensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + const Shape& InputShape(const std::string& arg_name, int32_t index) const override { + return helper_->InputShape(call_ctx_, arg_name, index); + } + const Shape& OutputShape(const std::string& arg_name, int32_t index) const override { + return helper_->OutputShape(call_ctx_, arg_name, index); + } + void SetOutputShape(const std::string& arg_name, int32_t index, const Shape& shape) override { + return helper_->SetOutputShape(call_ctx_, arg_name, index, shape); + } + const Shape& Shape4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->Shape4ArgNameAndIndex(call_ctx_, arg_name, index); + } + void SetShape4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Shape& shape) override { + return helper_->SetShape4ArgNameAndIndex(call_ctx_, arg_name, index, shape); + } + const Stride& InputStride(const std::string& arg_name, int32_t index) const override { + return helper_->InputStride(call_ctx_, arg_name, index); + } + const Stride& OutputStride(const std::string& arg_name, int32_t index) const override { + return helper_->InputStride(call_ctx_, arg_name, index); + } + void SetOutputStride(const std::string& arg_name, int32_t index, const Stride& stride) override { + return helper_->SetOutputStride(call_ctx_, arg_name, index, stride); + } + const Stride& Stride4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->Stride4ArgNameAndIndex(call_ctx_, arg_name, index); + } + void SetStride4ArgNameAndIndex(const std::string& arg_name, int32_t index, + const Stride& stride) override { + return helper_->SetStride4ArgNameAndIndex(call_ctx_, arg_name, index, stride); + } + DataType InputDType(const std::string& arg_name, int32_t index) const override { + return helper_->InputDType(call_ctx_, arg_name, index); + } + DataType OutputDType(const std::string& arg_name, int32_t index) const override { + return helper_->OutputDType(call_ctx_, arg_name, index); + } + void SetOutputDType(const std::string& arg_name, int32_t index, DataType data_type) override { + return helper_->SetOutputDType(call_ctx_, arg_name, index, data_type); + } + DataType Dtype4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->Dtype4ArgNameAndIndex(call_ctx_, arg_name, index); + } + void SetDtype4ArgNameAndIndex(const std::string& arg_name, int32_t index, + DataType data_type) override { + return helper_->SetDtype4ArgNameAndIndex(call_ctx_, arg_name, index, data_type); + } + bool InputIsDynamic(const std::string& arg_name, int32_t index) const override { + return helper_->InputIsDynamic(call_ctx_, arg_name, index); + } + bool OutputIsDynamic(const std::string& arg_name, int32_t index) const override { + return helper_->OutputIsDynamic(call_ctx_, arg_name, index); + } + void SetOutputIsDynamic(const std::string& arg_name, int32_t index, bool is_dynamic) override { + return helper_->SetOutputIsDynamic(call_ctx_, arg_name, index, is_dynamic); + } + bool IsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->IsDynamic4ArgNameAndIndex(call_ctx_, arg_name, index); + } + void SetIsDynamic4ArgNameAndIndex(const std::string& arg_name, int32_t index, + bool is_dynamic) override { + return helper_->SetIsDynamic4ArgNameAndIndex(call_ctx_, arg_name, index, is_dynamic); + } + + const ArgVec& inputs() const override { return helper_->inputs(); } + const ArgVec& outputs() const override { return helper_->outputs(); } + const JobDesc* job_desc() const override { return helper_->job_desc(); } + const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } + const ParallelDesc& parallel_desc() const override { return helper_->parallel_desc(call_ctx_); } + const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->SbpParallel4ArgNameAndIndex(call_ctx_, arg_name, index); + } + const NdSbp& NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->NdSbp4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + int64_t parallel_num() const override { return helper_->parallel_num(call_ctx_); } + + const std::string& input(const std::string& arg_name, int32_t index) const override { + return helper_->input(arg_name, index); + } + const std::string& output(const std::string& arg_name, int32_t index) const override { + return helper_->output(arg_name, index); + } + bool has_input(const std::string& arg_name, int32_t index) const override { + return helper_->has_input(arg_name, index); + } + bool has_output(const std::string& arg_name, int32_t index) const override { + return helper_->has_output(arg_name, index); + } + int32_t input_size(const std::string& arg_name) const override { + return helper_->input_size(arg_name); + } + int32_t output_size(const std::string& arg_name) const override { + return helper_->output_size(arg_name); + } + const std::string& op_name() const override { return helper_->op_name(); } + const std::string& op_type_name() const override { return helper_->op_type_name(); } + const std::string& op_loc() const override { return helper_->op_loc(); } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return helper_->Attr4Name(call_ctx_, attr_name); + } + + const UserOpInferContextHelper* helper_; + eager::CallContext* call_ctx_; +}; + +class UserKernelComputeContextHelper final { + public: + UserKernelComputeContextHelper(DeviceType device_type, + const user_op::UserOpConfWrapper* user_op_conf, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : user_op_conf_(user_op_conf), + base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} + + ~UserKernelComputeContextHelper() = default; + + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + + user_op::Tensor* Tensor4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return base_ctx_helper_.Tensor4ArgNameAndIndex(call_ctx, arg_name, index); + } + + DeviceType device_type() const { return base_ctx_helper_.device_type(); } + const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { + return base_ctx_helper_.parallel_ctx(call_ctx); + } + + const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } + const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } + + const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } + const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, + const std::string& attr_name) const { + return call_ctx->composed_attrs().Attr4Name(attr_name); + } + + private: + const user_op::UserOpConfWrapper* user_op_conf_; + UserKernelBaseContextHelper base_ctx_helper_; +}; + +class UserKernelComputeContext final : public user_op::KernelComputeContext { + public: + UserKernelComputeContext(const UserKernelComputeContextHelper* helper, + eager::CallContext* call_ctx, ep::Stream* stream) + : helper_(helper), call_ctx_(call_ctx), stream_(stream) {} + + ~UserKernelComputeContext() = default; + + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + user_op::Tensor* Tensor4ArgNameAndIndex(const std::string& arg_name, int32_t index) override { + return helper_->Tensor4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + ep::Stream* stream() override { + CHECK_NOTNULL(stream_); + return stream_; + } + + DeviceType device_type() const override { return helper_->device_type(); } + + const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } + + const ArgVec& inputs() const override { return helper_->inputs(); } + const ArgVec& outputs() const override { return helper_->outputs(); } + + private: + const user_op::UserOpConfWrapper& user_op_conf() const override { + return helper_->user_op_conf(); + } + + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return helper_->Attr4Name(call_ctx_, attr_name); + } + + const UserKernelComputeContextHelper* helper_; + eager::CallContext* call_ctx_; + ep::Stream* stream_; +}; + +class UserKernelRegContextHelper final { + public: + UserKernelRegContextHelper(DeviceType device_type, const user_op::UserOpConfWrapper* user_op_conf, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : user_op_conf_(user_op_conf), + base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} + ~UserKernelRegContextHelper() = default; + + DeviceType device_type() const { return base_ctx_helper_.device_type(); } + const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { + return base_ctx_helper_.parallel_ctx(call_ctx); + } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } + const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } + + const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } + + const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, + const std::string& attr_name) const { + return call_ctx->composed_attrs().Attr4Name(attr_name); + } + + private: + const user_op::UserOpConfWrapper* user_op_conf_; + UserKernelBaseContextHelper base_ctx_helper_; +}; + +class UserKernelRegContext final : public user_op::KernelRegContext { + public: + UserKernelRegContext(const UserKernelRegContextHelper* helper, eager::CallContext* call_ctx) + : helper_(helper), call_ctx_(call_ctx) {} + ~UserKernelRegContext() = default; + + DeviceType device_type() const override { return helper_->device_type(); } + const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + const ArgVec& inputs() const override { return helper_->inputs(); } + const ArgVec& outputs() const override { return helper_->outputs(); } + + const user_op::UserOpConfWrapper& user_op_conf() const override { + return helper_->user_op_conf(); + } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return helper_->Attr4Name(call_ctx_, attr_name); + } + + const UserKernelRegContextHelper* helper_; + eager::CallContext* call_ctx_; +}; + +class UserKernelInitAndCacheContextHelper final { + public: + UserKernelInitAndCacheContextHelper(DeviceType device_type, + const user_op::UserOpConfWrapper* user_op_conf, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) + : user_op_conf_(user_op_conf), + base_ctx_helper_(device_type, input_arg_tuple, output_arg_tuple) {} + + ~UserKernelInitAndCacheContextHelper() = default; + + DeviceType device_type() const { return base_ctx_helper_.device_type(); } + const ParallelContext& parallel_ctx(eager::CallContext* call_ctx) const { + return base_ctx_helper_.parallel_ctx(call_ctx); + } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return base_ctx_helper_.TensorDesc4ArgNameAndIndex(call_ctx, arg_name, index); + } + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, + int32_t index) const { + return base_ctx_helper_.GlobalTensorMeta4ArgNameAndIndex(call_ctx, arg_name, index); + } + const SbpParallel& SbpParallel4ArgNameAndIndex(eager::CallContext* call_ctx, + const std::string& arg_name, int32_t index) const { + const auto& nd_sbp = NdSbp4ArgNameAndIndex(call_ctx, arg_name, index); + CHECK_EQ(nd_sbp.sbp_parallel_size(), 1); + return nd_sbp.sbp_parallel(0); + } + + const NdSbp& NdSbp4ArgNameAndIndex(eager::CallContext* call_ctx, const std::string& arg_name, + int32_t index) const { + return *CHECK_NOTNULL( + base_ctx_helper_.GlobalTensorMeta4ArgNameAndIndex(call_ctx, arg_name, index)) + ->nd_sbp(); + } + + const ArgVec& inputs() const { return base_ctx_helper_.inputs(); } + const ArgVec& outputs() const { return base_ctx_helper_.outputs(); } + const ParallelDesc& parallel_desc(eager::CallContext* call_ctx) const { + return *CHECK_JUST(base_ctx_helper_.parallel_desc(call_ctx)); + } + + const std::shared_ptr& Attr4Name(eager::CallContext* call_ctx, + const std::string& attr_name) const { + return call_ctx->composed_attrs().Attr4Name(attr_name); + } + + const user_op::UserOpConfWrapper& user_op_conf() const { return *user_op_conf_; } + + private: + const user_op::UserOpConfWrapper* user_op_conf_; + UserKernelBaseContextHelper base_ctx_helper_; +}; + +class UserKernelInitAndCacheContext final : public user_op::KernelInitContext, + public user_op::KernelCacheContext { + public: + UserKernelInitAndCacheContext(const UserKernelInitAndCacheContextHelper* helper, + eager::CallContext* call_ctx, ep::Stream* stream) + : helper_(helper), call_ctx_(call_ctx), stream_(stream) {} + + ~UserKernelInitAndCacheContext() override = default; + + ep::Stream* stream() override { + CHECK_NOTNULL(stream_); + return stream_; + } + + DeviceType device_type() const override { return helper_->device_type(); } + const ParallelContext& parallel_ctx() const override { return helper_->parallel_ctx(call_ctx_); } + const user_op::TensorDesc* TensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->TensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + const user_op::TensorDesc* LogicalTensorDesc4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->LogicalTensorDesc4ArgNameAndIndex(call_ctx_, arg_name, index); + } + const SbpParallel& SbpParallel4ArgNameAndIndex(const std::string& arg_name, + int32_t index) const override { + return helper_->SbpParallel4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + const NdSbp& NdSbp4ArgNameAndIndex(const std::string& arg_name, int32_t index) const override { + return helper_->NdSbp4ArgNameAndIndex(call_ctx_, arg_name, index); + } + + const ArgVec& inputs() const override { return helper_->inputs(); } + const ArgVec& outputs() const override { return helper_->outputs(); } + const ParallelDesc& parallel_desc() const override { return helper_->parallel_desc(call_ctx_); } + + private: + const std::shared_ptr& Attr4Name( + const std::string& attr_name) const override { + return helper_->Attr4Name(call_ctx_, attr_name); + } + + const user_op::UserOpConfWrapper& user_op_conf() const override { + return helper_->user_op_conf(); + } + + const UserKernelInitAndCacheContextHelper* helper_; + eager::CallContext* call_ctx_; + ep::Stream* stream_; +}; + +namespace { + +Maybe InitTensorTupleIndexes4Bns( + const std::shared_ptr& op_conf, const ArgVec& indexed_input_pairs, + const ArgVec& indexed_output_pairs, OpArgsVector* input_tuple_indexes4const_ibns, + OpArgsVector* input_tuple_indexes4mut_ibns, + OpArgsVector* output_tuple_indexes4mut_obns, + OpArgsVector* output_tuple_indexes4mut2_obns, + small_vector* output_tuple_indexes2is_mut2_type) { + const auto* op_reg_val = + user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(op_conf->user_conf().op_type_name()); + CHECK_NOTNULL_OR_RETURN(op_reg_val); + + ArgModifierSignature arg_modifier_signature; + for (const auto& pair : indexed_input_pairs) { + const std::string ibn = GenRepeatedBn(pair.first, pair.second); + arg_modifier_signature.mutable_ibn2input_blob_modifier()->insert( + {ibn, user_op::InputArgModifier()}); + } + for (const auto& pair : indexed_output_pairs) { + const std::string obn = GenRepeatedBn(pair.first, pair.second); + arg_modifier_signature.mutable_obn2output_blob_modifier()->insert( + {obn, user_op::OutputArgModifier()}); + } + user_op::UserOpConfWrapper op_conf_wrapper(op_conf); + if (op_reg_val->input_arg_modify_fn) { + user_op::GetInputArgModifier GetInputArgModifierFn = + [&arg_modifier_signature](const std::string& in_arg_name, + int32_t in_arg_index) -> user_op::InputArgModifier* { + const std::string ibn = GenRepeatedBn(in_arg_name, in_arg_index); + auto* map = arg_modifier_signature.mutable_ibn2input_blob_modifier(); + return &map->at(ibn); + }; + JUST(op_reg_val->input_arg_modify_fn(GetInputArgModifierFn, op_conf_wrapper)); + } + if (op_reg_val->output_arg_modify_fn) { + user_op::GetOutputArgModifier GetOutputArgModifierFn = + [&arg_modifier_signature](const std::string& in_arg_name, + int32_t in_arg_index) -> user_op::OutputArgModifier* { + const std::string obn = GenRepeatedBn(in_arg_name, in_arg_index); + auto* map = arg_modifier_signature.mutable_obn2output_blob_modifier(); + return &map->at(obn); + }; + JUST(op_reg_val->output_arg_modify_fn(GetOutputArgModifierFn, op_conf_wrapper)); + } + + for (int i = 0; i < indexed_input_pairs.size(); i++) { + const auto& pair = indexed_input_pairs.at(i); + const std::string ibn = GenRepeatedBn(pair.first, pair.second); + if (arg_modifier_signature.ibn2input_blob_modifier().at(ibn).is_mutable()) { + input_tuple_indexes4mut_ibns->emplace_back(i); + } else { + input_tuple_indexes4const_ibns->emplace_back(i); + } + } + + for (int i = 0; i < indexed_output_pairs.size(); i++) { + const auto& pair = indexed_output_pairs.at(i); + const std::string obn = GenRepeatedBn(pair.first, pair.second); + if (arg_modifier_signature.obn2output_blob_modifier().at(obn).header_infered_before_compute()) { + output_tuple_indexes4mut_obns->emplace_back(i); + output_tuple_indexes2is_mut2_type->emplace_back(false); + } else { + output_tuple_indexes4mut2_obns->emplace_back(i); + output_tuple_indexes2is_mut2_type->emplace_back(true); + } + } + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe StatefulOpKernel::New( + const std::shared_ptr& op_conf, const Symbol& stream, + const AttrMap& base_attrs, const std::shared_ptr& parallel_desc, + const std::shared_ptr& input_arg_tuple, + const std::shared_ptr& output_arg_tuple) { + auto opkernel = std::shared_ptr(new StatefulOpKernel()); + opkernel->base_attrs_ = base_attrs; + opkernel->op_conf_ = op_conf; + opkernel->user_op_conf_.reset(new user_op::UserOpConfWrapper(op_conf)); + opkernel->stream_ = stream; + opkernel->input_arg_tuple_ = input_arg_tuple; + opkernel->output_arg_tuple_ = output_arg_tuple; + + const DeviceType device_type = CHECK_JUST(DeviceType4DeviceTag(op_conf->device_tag())); + const user_op::UserOpConfWrapper* user_op_conf = opkernel->user_op_conf_.get(); + opkernel->op_infer_ctx_helper_.reset( + new UserOpInferContextHelper(user_op_conf, input_arg_tuple, output_arg_tuple)); + + opkernel->init_and_cache_ctx_helper_.reset(new UserKernelInitAndCacheContextHelper( + device_type, opkernel->user_op_conf_.get(), opkernel->input_arg_tuple_, + opkernel->output_arg_tuple_)); + opkernel->compute_ctx_helper_.reset(new UserKernelComputeContextHelper( + device_type, user_op_conf, input_arg_tuple, output_arg_tuple)); + opkernel->reg_ctx_helper_.reset( + new UserKernelRegContextHelper(device_type, user_op_conf, input_arg_tuple, output_arg_tuple)); + const auto* op_reg_val = + user_op::UserOpRegistryMgr::Get().GetOpRegistryResult(user_op_conf->op_type_name()); + CHECK_NOTNULL_OR_RETURN(op_reg_val); + if (op_reg_val->logical_tensor_desc_infer_fn) { + opkernel->tensor_desc_infer_fn_ = op_reg_val->logical_tensor_desc_infer_fn; + } else { + return Error::UnimplementedError(); + } + opkernel->data_type_infer_fn_ = op_reg_val->data_type_infer_fn; + + JUST(InitTensorTupleIndexes4Bns( + op_conf, input_arg_tuple->indexed_arg_name_and_index(), + output_arg_tuple->indexed_arg_name_and_index(), &opkernel->input_tuple_indexes4const_ibns_, + &opkernel->input_tuple_indexes4mut_ibns_, &opkernel->output_tuple_indexes4mut_obns_, + &opkernel->output_tuple_indexes4mut2_obns_, &opkernel->output_tuple_indexes2is_mut2_type_)); + + return opkernel; +} + +StatefulOpKernel::~StatefulOpKernel() = default; + +size_t StatefulOpKernel::InferTmpSize(eager::CallContext* call_ctx, + const user_op::OpKernel* user_opkernel) const { + UserOpInferContext op_infer_ctx(op_infer_ctx_helper_.get(), call_ctx); + const auto& InferTmpSizeFn = GetInferTmpSizeFn(user_opkernel); + return InferTmpSizeFn(&op_infer_ctx); +} + +Maybe StatefulOpKernel::ChooseOpKernel(eager::CallContext* call_ctx, + const user_op::OpKernel** user_opkernel, + bool* need_temp_storage) { + DataType primary_dtype = kInvalidDataType; + const auto& inputs = call_ctx->inputs(); + const auto& outputs = call_ctx->outputs(); + if (likely(!inputs.empty())) { + primary_dtype = inputs[0]->data_type(); + } else if (likely(!outputs.empty())) { + primary_dtype = outputs[0]->data_type(); + } else { + // do nothing + } + + UserKernelRegContext reg_ctx(reg_ctx_helper_.get(), call_ctx); + for (const auto& pair : dtype2cached_kernels_[primary_dtype]) { + if (likely(pair.first->is_matched_hob->get(reg_ctx))) { + *need_temp_storage = pair.first->need_temp_storage; + *user_opkernel = pair.second.get(); + return Maybe::Ok(); + } + } + + OF_PROFILER_RANGE_GUARD("fallback"); + + const auto& op_type_name = user_op_conf_->op_type_name(); + const auto* kernel_reg_val = + JUST(user_op::UserOpRegistryMgr::Get().GetOpKernelRegistryResult(op_type_name, reg_ctx)); + CHECK_NOTNULL(kernel_reg_val); + auto* kernel = kernel_reg_val->create_fn(); + dtype2cached_kernels_[primary_dtype].push_back( + {kernel_reg_val, std::shared_ptr(kernel)}); + + infer_tmp_size_fn_map_.emplace(kernel, &kernel_reg_val->infer_tmp_size_fn); + *need_temp_storage = kernel_reg_val->need_temp_storage; + *user_opkernel = kernel; + return Maybe::Ok(); +} + +void StatefulOpKernel::TryInitOpKernelStateAndCache(eager::CallContext* call_ctx, + ep::Stream* stream, + const user_op::OpKernel* op_kernel, + user_op::OpKernelState** state, + user_op::OpKernelCache** cache) { + UserKernelInitAndCacheContext init_and_cache_ctx(init_and_cache_ctx_helper_.get(), call_ctx, + stream); + if (state != nullptr) { + auto it = op_kernel_state_map_.find(op_kernel); + if (it != op_kernel_state_map_.end()) { + *state = it->second.get(); + } else { + auto created_state = op_kernel->CreateOpKernelState(&init_and_cache_ctx); + op_kernel_state_map_.emplace(op_kernel, created_state); + *state = created_state.get(); + } + } + + { + auto& cache_in_map = op_kernel_cache_map_[op_kernel]; + op_kernel->InitOpKernelCacheWithFlags(&init_and_cache_ctx, + user_op::OpKernelCache::kAllMayChanged, &cache_in_map); + *cache = cache_in_map.get(); + } +} + +const user_op::InferTmpSizeFn& StatefulOpKernel::GetInferTmpSizeFn( + const user_op::OpKernel* op_kernel) const { + return *infer_tmp_size_fn_map_.at(op_kernel); +} + +user_op::TensorDescInferFn StatefulOpKernel::TensorDescInferFn() const { + return tensor_desc_infer_fn_; +} + +user_op::DataTypeInferFn StatefulOpKernel::DataTypeInferFn() const { return data_type_infer_fn_; } + +void StatefulOpKernel::Compute(eager::CallContext* call_ctx, ep::Stream* stream, + const user_op::OpKernel* user_opkernel, + user_op::OpKernelState* state, + const user_op::OpKernelCache* cache) const { + UserKernelComputeContext compute_context(compute_ctx_helper_.get(), call_ctx, stream); + auto* compute_ctx = &compute_context; + OF_PROFILER_RANGE_GUARD("Compute"); + if (Singleton::Get()) { +#if defined(WITH_CUDA) || defined(WITH_ROCM) + const auto CalMemorySize = [compute_ctx](const one::ArgVec& args) -> int64_t { + const auto Func = [compute_ctx](int64_t mem_size, const auto& pair) { + const auto tensor = compute_ctx->Tensor4ArgNameAndIndex(pair.first, pair.second); + return mem_size + tensor->shape_view().elem_cnt() * GetSizeOfDataType(tensor->data_type()); + }; + return std::accumulate(args.begin(), args.end(), static_cast(0), Func); + }; +#endif + auto er_guard = CHECK_JUST(profiler::EventRecorder::CreateKernelEventRecorder( + op_type_name(), +#if defined(WITH_CUDA) || defined(WITH_ROCM) + [compute_ctx, CalMemorySize]() -> int64_t { + return CalMemorySize(compute_ctx->inputs()) + CalMemorySize(compute_ctx->outputs()); + }, +#endif + [compute_ctx]() -> std::vector { + std::vector shapes; + for (const auto& pair : compute_ctx->inputs()) { + shapes.emplace_back( + compute_ctx->TensorDesc4ArgNameAndIndex(pair.first, pair.second)->shape()); + } + return shapes; + })); + user_opkernel->Compute(compute_ctx, state, cache); + } else { + user_opkernel->Compute(compute_ctx, state, cache); + } + CHECK_JUST(compute_ctx->stream()->GetAsyncError()); +} + +} // namespace one +} // namespace oneflow diff --git a/oneflow/user/kernels/stateful_opkernel.h b/oneflow/user/kernels/stateful_opkernel.h index 91eb58a326f1fc4161671645b59e55a4dbfa9a6e..32d1f165f318149f3dfbc685b9f745e091286d28 100644 --- a/oneflow/user/kernels/stateful_opkernel.h +++ b/oneflow/user/kernels/stateful_opkernel.h @@ -17,13 +17,14 @@ limitations under the License. #define ONEFLOW_USER_KERNELS_STATEFUL_OPKERNEL_H_ #include "oneflow/core/eager/eager_blob_object.h" -#include "oneflow/core/framework/tensor_meta.h" +#include "oneflow/core/common/tensor_meta.h" #include "oneflow/core/kernel/kernel.h" #include "oneflow/core/framework/op_kernel.h" #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/user_op_kernel_registry.h" #include "oneflow/core/framework/arg_tuple.h" #include "oneflow/core/framework/op_interpreter.h" +#include "oneflow/core/common/op_args_vector.h" namespace oneflow { @@ -58,25 +59,27 @@ class StatefulOpKernel final { const Symbol& stream() const { return stream_; } const std::shared_ptr& mem_case() const { return stream_->device()->mem_case(); } const std::string& op_type_name() const { return op_conf_->user_conf().op_type_name(); } - const std::vector& input_tuple_indexes4const_ibns() const { + const OpArgsVector& input_tuple_indexes4const_ibns() const { return input_tuple_indexes4const_ibns_; } - const std::vector& input_tuple_indexes4mut_ibns() const { + const OpArgsVector& input_tuple_indexes4mut_ibns() const { return input_tuple_indexes4mut_ibns_; } - const std::vector& output_tuple_indexes4mut_obns() const { + const OpArgsVector& output_tuple_indexes4mut_obns() const { return output_tuple_indexes4mut_obns_; } - const std::vector& output_tuple_indexes4mut2_obns() const { + const OpArgsVector& output_tuple_indexes4mut2_obns() const { return output_tuple_indexes4mut2_obns_; } + bool output_is_mut2_type(int64_t index) const { + return output_tuple_indexes2is_mut2_type_.at(index); + } + const AttrMap& base_attrs() const { return base_attrs_; } size_t InferTmpSize(eager::CallContext* call_ctx, const user_op::OpKernel* user_opkernel) const; - void set_need_check_mem_case(bool value) { need_check_mem_case_ = value; } - Maybe ChooseOpKernel(eager::CallContext* call_ctx, const user_op::OpKernel** user_opkernel, bool* need_temp_storage); @@ -86,14 +89,14 @@ class StatefulOpKernel final { friend struct vm::OpCallInstructionUtil; StatefulOpKernel() = default; - void Compute(eager::CallContext* call_ctx, DeviceCtx* device_ctx, + void Compute(eager::CallContext* call_ctx, ep::Stream* stream, const user_op::OpKernel* user_opkernel, user_op::OpKernelState* state, const user_op::OpKernelCache* cache) const; user_op::TensorDescInferFn TensorDescInferFn() const; user_op::DataTypeInferFn DataTypeInferFn() const; - void TryInitOpKernelStateAndCache(eager::CallContext* call_ctx, DeviceCtx* device_ctx, + void TryInitOpKernelStateAndCache(eager::CallContext* call_ctx, ep::Stream* stream, const user_op::OpKernel* op_kernel, user_op::OpKernelState** state, user_op::OpKernelCache** cache); @@ -101,8 +104,6 @@ class StatefulOpKernel final { return op_kernel_state_map_.at(opkernel).get(); } - bool need_check_mem_case() const { return need_check_mem_case_; } - const user_op::InferTmpSizeFn& GetInferTmpSizeFn(const user_op::OpKernel* op_kernel) const; std::shared_ptr op_conf_; @@ -115,7 +116,6 @@ class StatefulOpKernel final { std::unique_ptr compute_ctx_helper_; std::shared_ptr input_arg_tuple_; std::shared_ptr output_arg_tuple_; - bool need_check_mem_case_; user_op::TensorDescInferFn tensor_desc_infer_fn_; user_op::DataTypeInferFn data_type_infer_fn_; // NOTE: every device has its own stateful local opkernel instance, @@ -127,10 +127,11 @@ class StatefulOpKernel final { HashMap> op_kernel_state_map_; HashMap> op_kernel_cache_map_; HashMap infer_tmp_size_fn_map_; - std::vector input_tuple_indexes4const_ibns_; - std::vector input_tuple_indexes4mut_ibns_; - std::vector output_tuple_indexes4mut_obns_; - std::vector output_tuple_indexes4mut2_obns_; + OpArgsVector input_tuple_indexes4const_ibns_; + OpArgsVector input_tuple_indexes4mut_ibns_; + OpArgsVector output_tuple_indexes4mut_obns_; + OpArgsVector output_tuple_indexes4mut2_obns_; + OpArgsVector output_tuple_indexes2is_mut2_type_; }; } // namespace one diff --git a/oneflow/user/kernels/stft_kernel.cpp b/oneflow/user/kernels/stft_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6ac06238457393059abe78349b86b852298b8247 --- /dev/null +++ b/oneflow/user/kernels/stft_kernel.cpp @@ -0,0 +1,138 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "pocketfftplan.h" +using namespace pocketfft; +namespace oneflow { + +namespace { + +enum class fft_norm_mode { + none, // No normalization + by_root_n, // Divide by sqrt(signal_size) + by_n, // Divide by signal_size +}; + +template +T compute_fct(int64_t size, fft_norm_mode normalization) { + constexpr auto one = static_cast(1); + switch (normalization) { + case fft_norm_mode::none: return one; + case fft_norm_mode::by_n: return one / static_cast(size); + case fft_norm_mode::by_root_n: return one / std::sqrt(static_cast(size)); + } + return static_cast(0); +} +template +void convert_to_doublesized(const std::complex* in, std::complex* dst, size_t len, size_t n) { + size_t fact_len = 2 * len - 2; + for (int i = 0; i < n; i++) { + int index_x = i / fact_len; + int index_y = i % fact_len; + if (index_y == 0) { + dst[i] = in[index_x * len]; + } else if (index_y == len - 1) { + dst[i] = in[(index_x + 1) * len - 1]; + } else if (index_y < len - 1 && index_y > 0) { + dst[i] = in[index_x * len + index_y]; + } else { + auto index = (index_x + 2) * len - index_y - 2; + auto realvalue = in[index].real(); + dst[i].real(realvalue); + auto imagvalue = -in[index].imag(); + dst[i].imag(imagvalue); + } + } +} + +template +void comvert_to_real(const std::complex* in, T* out, size_t n) { + for (int i = 0; i < n; i++) { + out[2 * i] = in[i].real(); + out[2 * i + 1] = in[i].imag(); + } +} + +template +class StftCpuKernel final : public user_op::OpKernel { + public: + StftCpuKernel() = default; + ~StftCpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const auto normalized = ctx->Attr("normalized"); + const auto return_complex = ctx->Attr("return_complex"); + const bool onesized = ctx->Attr("onesided"); + + const ShapeView& input_shape = input->shape_view(); + const ShapeView& output_shape = output->shape_view(); + const auto output_elem_cnt = output_shape.elem_cnt() / 2; + + int64_t dims = input_shape.At(0); + int64_t batch = input_shape.At(1); + int64_t len = input_shape.back(); + const IN* data_in = input->dptr(); + IN* data_out = output->mut_dptr(); + auto normalization = normalized ? fft_norm_mode::by_root_n : fft_norm_mode::none; + PocketFFtParams params(Shape{len}, Shape{len}, true, + compute_fct(len, normalization) /*1.f*/, + FFT_EXCUTETYPE::R2C); + PocketFFtConfig config(params); + + OUT* out_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr()); + config.excute(data_in, out_tmp_buffer, dims, batch, len); + + if (!onesized) { + OUT* doublesided_tmp_buffer = + reinterpret_cast(tmp_buffer->mut_dptr()) + output_elem_cnt; + size_t last_dim_length = len / 2 + 1; + size_t elem_conut = output_elem_cnt; + convert_to_doublesized(out_tmp_buffer, doublesided_tmp_buffer, last_dim_length, + elem_conut); + out_tmp_buffer = doublesided_tmp_buffer; + } + + if (!return_complex) { comvert_to_real(out_tmp_buffer, data_out, output_elem_cnt); } + } + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +#define REGISTER_STFT_CPU_KERNEL(intype, outtype) \ + REGISTER_USER_KERNEL("stft") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == kCPU) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& output_shape = ctx->InputShape("output", 0); \ + const bool return_complex = ctx->Attr("return_complex"); \ + const bool onesided = ctx->Attr("onesided"); \ + int64_t output_elem_cnt = \ + return_complex ? output_shape.elem_cnt() : output_shape.elem_cnt() / 2; \ + const int64_t output_bytes = (output_elem_cnt * sizeof(outtype)); \ + return onesided ? output_bytes : 2 * output_bytes; \ + }); + +REGISTER_STFT_CPU_KERNEL(double, std::complex) +REGISTER_STFT_CPU_KERNEL(float, std::complex) + +} // namespace +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/stft_kernel.cu b/oneflow/user/kernels/stft_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..7d1bc2397ad010e9d6528efc1e4f497911a3adc9 --- /dev/null +++ b/oneflow/user/kernels/stft_kernel.cu @@ -0,0 +1,312 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#if __CUDACC_VER_MAJOR__ >= 11 +#define CUDA_SUPPORT_CUFFT +#endif + +#ifdef CUDA_SUPPORT_CUFFT + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include +#include +#include "oneflow/core/kernel/kernel.h" +#include "cufftplancache.h" +namespace oneflow { +namespace {} // namespace + +template +__global__ void convert_complex_to_real(IN* dst, const OUT* src, size_t n) { + CUDA_1D_KERNEL_LOOP(i, n) { + dst[2 * i] = src[i].x; + dst[2 * i + 1] = src[i].y; + }; +} + +double _fft_normalization_scale(const int32_t frame_length) { + return static_cast(1.0 / std::sqrt(frame_length)); +} + +template +__global__ void fft_apply_normalization(FFTTYPE* dst, const double normalization_scale, size_t n, + bool IsNormalized) { + if (!IsNormalized) { return; } + CUDA_1D_KERNEL_LOOP(i, n) { + dst[i].x *= normalization_scale; + dst[i].y *= normalization_scale; + }; +} + +// TODO(yzm):support doublesided +template +__global__ void convert_doublesided(const FFTTYPE* src, FFTTYPE* dst, size_t len, size_t n) { + size_t fact_len = 2 * len - 2; + CUDA_1D_KERNEL_LOOP(i, n) { + int index_x = i / fact_len; + int index_y = i % fact_len; + if (index_y == 0) { + dst[i] = src[index_x * len]; + } else if (index_y == len - 1) { + dst[i] = src[(index_x + 1) * len - 1]; + } else if (index_y < len - 1 && index_y > 0) { + dst[i] = src[index_x * len + index_y]; + } else { + auto index = (index_x + 2) * len - index_y - 2; + dst[i].x = src[index].x; + dst[i].y = -src[index].y; + } + } +} + +template +class StftGpuKernel final : public user_op::OpKernel { + public: + StftGpuKernel() = default; + ~StftGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const bool normalized = ctx->Attr("normalized"); + const bool onesided = ctx->Attr("onesided"); + const bool return_complex = ctx->Attr("return_complex"); + + const ShapeView& input_shape = input->shape_view(); + const ShapeView& output_shape = output->shape_view(); + + const Stride& input_stride = input->stride(); + const int out_elem_cnt = + return_complex ? output->shape_view().elem_cnt() : output->shape_view().elem_cnt() / 2; + + const IN* data_in = input->dptr(); + IN* data_out = output->mut_dptr(); + OUT* out_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr()); + + int32_t ndim = 1; + int32_t n_frames = static_cast(input_shape.At(1)); + int32_t fft_size = static_cast(input_shape.At(2)); + const Stride& in_stride = {input_stride.at(2), input_stride.at(1)}; + const Stride& out_stride = {1, fft_size / 2 + 1}; + const Shape& in_shape = {fft_size, n_frames}; + const Shape& out_shape = in_shape; + int32_t batch = n_frames; + int32_t rank[1] = {fft_size}; + CuFFtParams params(ndim, rank, in_stride, out_stride, in_shape, out_shape, batch); + CuFFtConfig config(params); + + int32_t in_offset = input_stride.at(0); + int32_t out_offset = n_frames * (fft_size / 2 + 1); + int32_t signal_groups_count = static_cast(input_shape.At(0)); + for (int32_t i = 0; i < signal_groups_count; i++) { + config.excute_plan(data_in + i * in_offset, out_tmp_buffer + i * out_offset); + } + + if (!onesided) { + size_t last_dim_length = fft_size / 2 + 1; + OUT* doublesided_tmp_buffer = + reinterpret_cast(tmp_buffer->mut_dptr()) + out_elem_cnt; + convert_doublesided<<stream()->As()->cuda_stream()>>>( + out_tmp_buffer, doublesided_tmp_buffer, last_dim_length, out_elem_cnt); + out_tmp_buffer = doublesided_tmp_buffer; + } + + const double normalization_scale = _fft_normalization_scale(input_shape.back()); + fft_apply_normalization<<stream()->As()->cuda_stream()>>>( + out_tmp_buffer, normalization_scale, out_elem_cnt, normalized); + + if (!return_complex) { + convert_complex_to_real<<stream()->As()->cuda_stream()>>>( + data_out, out_tmp_buffer, out_elem_cnt); + } else { + // TODO(yzm):support return_complex after oneflow supports complex numbers + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; +#define REGISTER_STFT_GPU_KERNEL(intype, outtype) \ + REGISTER_USER_KERNEL("stft") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& output_shape = ctx->InputShape("output", 0); \ + const bool return_complex = ctx->Attr("return_complex"); \ + const bool onesided = ctx->Attr("onesided"); \ + int64_t output_elem_cnt = \ + return_complex ? output_shape.elem_cnt() : output_shape.elem_cnt() / 2; \ + const int64_t output_bytes = GetCudaAlignedSize(output_elem_cnt * sizeof(outtype)); \ + return onesided ? output_bytes : 2 * output_bytes; \ + }); +REGISTER_STFT_GPU_KERNEL(float, cufftComplex) +REGISTER_STFT_GPU_KERNEL(double, cufftDoubleComplex) +} // namespace oneflow + +#endif + +#ifdef WITH_ROCM + +#include "hip/hip_runtime.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" +#include +#include "oneflow/core/kernel/kernel.h" +#include "cufftplancache.h" +namespace oneflow { +namespace {} // namespace + +template +__global__ void convert_complex_to_real(IN* dst, const OUT* src, size_t n) { + CUDA_1D_KERNEL_LOOP(i, n) { + dst[2 * i] = src[i].x; + dst[2 * i + 1] = src[i].y; + }; +} + +double _fft_normalization_scale(const int32_t frame_length) { + return static_cast(1.0 / std::sqrt(frame_length)); +} + +template +__global__ void fft_apply_normalization(FFTTYPE* dst, const double normalization_scale, size_t n, + bool IsNormalized) { + if (!IsNormalized) { return; } + CUDA_1D_KERNEL_LOOP(i, n) { + dst[i].x *= normalization_scale; + dst[i].y *= normalization_scale; + }; +} + +// TODO(yzm):support doublesided +template +__global__ void convert_doublesided(const FFTTYPE* src, FFTTYPE* dst, size_t len, size_t n) { + size_t fact_len = 2 * len - 2; + CUDA_1D_KERNEL_LOOP(i, n) { + int index_x = i / fact_len; + int index_y = i % fact_len; + if (index_y == 0) { + dst[i] = src[index_x * len]; + } else if (index_y == len - 1) { + dst[i] = src[(index_x + 1) * len - 1]; + } else if (index_y < len - 1 && index_y > 0) { + dst[i] = src[index_x * len + index_y]; + } else { + auto index = (index_x + 2) * len - index_y - 2; + dst[i].x = src[index].x; + dst[i].y = -src[index].y; + } + } +} + +template +class StftGpuKernel final : public user_op::OpKernel { + public: + StftGpuKernel() = default; + ~StftGpuKernel() = default; + + private: + using user_op::OpKernel::Compute; + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* input = ctx->Tensor4ArgNameAndIndex("input", 0); + user_op::Tensor* output = ctx->Tensor4ArgNameAndIndex("output", 0); + user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const bool normalized = ctx->Attr("normalized"); + const bool onesided = ctx->Attr("onesided"); + const bool return_complex = ctx->Attr("return_complex"); + + const ShapeView& input_shape = input->shape_view(); + const ShapeView& output_shape = output->shape_view(); + + const Stride& input_stride = input->stride(); + const int out_elem_cnt = + return_complex ? output->shape_view().elem_cnt() : output->shape_view().elem_cnt() / 2; + + const IN* data_in = input->dptr(); + IN* data_out = output->mut_dptr(); + OUT* out_tmp_buffer = reinterpret_cast(tmp_buffer->mut_dptr()); + + int32_t ndim = 1; + int32_t n_frames = static_cast(input_shape.At(1)); + int32_t fft_size = static_cast(input_shape.At(2)); + const Stride& in_stride = {input_stride.at(2), input_stride.at(1)}; + const Stride& out_stride = {1, fft_size / 2 + 1}; + const Shape& in_shape = {fft_size, n_frames}; + const Shape& out_shape = in_shape; + int32_t batch = n_frames; + int32_t rank[1] = {fft_size}; + CuFFtParams params(ndim, rank, in_stride, out_stride, in_shape, out_shape, batch); + CuFFtConfig config(params); + + int32_t in_offset = input_stride.at(0); + int32_t out_offset = n_frames * (fft_size / 2 + 1); + int32_t signal_groups_count = static_cast(input_shape.At(0)); + for (int32_t i = 0; i < signal_groups_count; i++) { + config.excute_plan(data_in + i * in_offset, out_tmp_buffer + i * out_offset); + } + + if (!onesided) { + size_t last_dim_length = fft_size / 2 + 1; + OUT* doublesided_tmp_buffer = + reinterpret_cast(tmp_buffer->mut_dptr()) + out_elem_cnt; + convert_doublesided<<stream()->As()->cuda_stream()>>>( + out_tmp_buffer, doublesided_tmp_buffer, last_dim_length, out_elem_cnt); + out_tmp_buffer = doublesided_tmp_buffer; + } + + const double normalization_scale = _fft_normalization_scale(input_shape.back()); + fft_apply_normalization<<stream()->As()->cuda_stream()>>>( + out_tmp_buffer, normalization_scale, out_elem_cnt, normalized); + + if (!return_complex) { + convert_complex_to_real<<stream()->As()->cuda_stream()>>>( + data_out, out_tmp_buffer, out_elem_cnt); + } else { + // TODO(yzm):support return_complex after oneflow supports complex numbers + } + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; +#define REGISTER_STFT_GPU_KERNEL(intype, outtype) \ + REGISTER_USER_KERNEL("stft") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ + && (user_op::HobDataType("input", 0) == GetDataType::value)) \ + .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ + const Shape& output_shape = ctx->InputShape("output", 0); \ + const bool return_complex = ctx->Attr("return_complex"); \ + const bool onesided = ctx->Attr("onesided"); \ + int64_t output_elem_cnt = \ + return_complex ? output_shape.elem_cnt() : output_shape.elem_cnt() / 2; \ + const int64_t output_bytes = GetCudaAlignedSize(output_elem_cnt * sizeof(outtype)); \ + return onesided ? output_bytes : 2 * output_bytes; \ + }); +REGISTER_STFT_GPU_KERNEL(float, hipfftComplex) +REGISTER_STFT_GPU_KERNEL(double, hipfftDoubleComplex) +} // namespace oneflow + + +#endif \ No newline at end of file diff --git a/oneflow/user/kernels/tensor_constant_kernel.cpp b/oneflow/user/kernels/tensor_constant_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..adbaaf3181862f6cab40a125159d75ea26e00a79 --- /dev/null +++ b/oneflow/user/kernels/tensor_constant_kernel.cpp @@ -0,0 +1,67 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/ep/include/primitive/tensor_fill.h" + +namespace oneflow { +namespace user_op { + +namespace { + +template +std::unique_ptr NewTensorFillPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("out", 0)->data_type(); + return ep::primitive::NewPrimitive(ctx->device_type(), + data_type); +} + +class TensorConstantKernel final : public OpKernel { + public: + TensorConstantKernel() = default; + ~TensorConstantKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const Tensor* value_tensor = ctx->Tensor4ArgNameAndIndex("in", 0); + Tensor* out_tensor = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK(value_tensor->shape_view().NumAxes() <= 1 && value_tensor->shape_view().elem_cnt() == 1) + << "Only scalar tensor as filled value is supported!"; + + const int64_t elem_cnt = out_tensor->shape_view().elem_cnt(); + CHECK_GE(elem_cnt, 0); + if (elem_cnt == 0) { return; } + std::unique_ptr tensor_fill = NewTensorFillPrimitive(ctx); + CHECK(tensor_fill); + tensor_fill->Launch(ctx->stream(), value_tensor->raw_dptr(), out_tensor->mut_dptr(), elem_cnt); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +auto TensorFillPrimitiveExists() { + return hob::make_custom("TensorFillPrimitiveExists", [](const user_op::KernelRegContext& ctx) { + return NewTensorFillPrimitive(&ctx).operator bool(); + }); +} + +REGISTER_USER_KERNEL("tensor_constant") + .SetCreateFn() + .SetIsMatchedHob(TensorFillPrimitiveExists() == true); + +} // namespace + +} // namespace user_op +} // namespace oneflow diff --git a/oneflow/user/kernels/tf_pool_gpu_kernel.cpp b/oneflow/user/kernels/tf_pool_gpu_kernel.cpp index 92582a5769e268a04c95cbe85f3a066c5092660a..3ed6660e100fc68091decc43619a62d81482a4d9 100644 --- a/oneflow/user/kernels/tf_pool_gpu_kernel.cpp +++ b/oneflow/user/kernels/tf_pool_gpu_kernel.cpp @@ -48,7 +48,7 @@ class GPUPoolOpKernelCache final : public user_op::OpKernelCache { GPUPoolOpKernelCache(const int32_t dim, const std::string& pooling_type, const ShapeView& x_shape, const ShapeView& y_shape, const std::string& data_format, const DataType& dtype, const Params3D& params_3d) - : dim_(dim), pooling_type_(pooling_type) { + : pooling_type_(pooling_type) { Reset(dim, pooling_type, x_shape, y_shape, data_format, dtype, params_3d); } ~GPUPoolOpKernelCache() = default; @@ -107,11 +107,9 @@ class GPUPoolOpKernelCache final : public user_op::OpKernelCache { std::unique_ptr x_desc_; std::unique_ptr y_desc_; std::unique_ptr pooling_desc_; - int32_t dim_; std::string pooling_type_; }; -template struct PoolGpuKernelUtil { static void FWCompute(user_op::KernelComputeContext* ctx, const GPUPoolOpKernelCache* gpu_pool_op_kernel_cache) { @@ -120,8 +118,8 @@ struct PoolGpuKernelUtil { CHECK(gpu_pool_op_kernel_cache != nullptr); OF_CUDNN_CHECK(cudnnPoolingForward( ctx->stream()->As()->cudnn_handle(), - gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(), - gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(), + gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(x->data_type()), + gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(x->data_type()), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), y->mut_dptr())); } @@ -134,17 +132,16 @@ struct PoolGpuKernelUtil { CHECK(gpu_pool_op_kernel_cache != nullptr); OF_CUDNN_CHECK(cudnnPoolingBackward( ctx->stream()->As()->cudnn_handle(), - gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(), + gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(y->data_type()), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), y->dptr(), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), dy->dptr(), - gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(), + gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(y->data_type()), gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), dx->mut_dptr())); } }; } // namespace -template class AvgPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool1DGpuKernel() = default; @@ -159,11 +156,10 @@ class AvgPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool1DGradGpuKernel() = default; @@ -176,11 +172,10 @@ class AvgPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::C } void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool2DGpuKernel() = default; @@ -195,11 +190,10 @@ class AvgPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool2DGradGpuKernel() = default; @@ -214,11 +208,10 @@ class AvgPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool3DGpuKernel() = default; @@ -233,11 +226,10 @@ class AvgPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool3DGradGpuKernel() = default; @@ -252,11 +244,10 @@ class AvgPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool1DGpuKernel() = default; @@ -271,11 +262,10 @@ class MaxPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool1DGradGpuKernel() = default; @@ -290,11 +280,10 @@ class MaxPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool2DGpuKernel() = default; @@ -309,11 +298,10 @@ class MaxPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool2DGradGpuKernel() = default; @@ -328,11 +316,10 @@ class MaxPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool3DGpuKernel() = default; @@ -347,11 +334,10 @@ class MaxPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool3DGradGpuKernel() = default; @@ -366,63 +352,46 @@ class MaxPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -#define REGISTER_POOL_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("tf_avg_pool_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_POOL_CUDA_KERNEL(float) -REGISTER_POOL_CUDA_KERNEL(double) -REGISTER_POOL_CUDA_KERNEL(float16) +REGISTER_USER_KERNEL("tf_avg_pool_1d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_1d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_2d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_2d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_3d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_3d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_1d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_1d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_2d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_2d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_3d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_3d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); } // namespace oneflow @@ -434,7 +403,7 @@ REGISTER_POOL_CUDA_KERNEL(float16) #include "oneflow/user/utils/pool_util.h" #include "oneflow/core/device/cudnn_util.h" #include "oneflow/core/kernel/cuda_graph_support.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" +#include "oneflow/core/ep/cuda/cuda_stream.h" namespace oneflow { @@ -463,7 +432,7 @@ class GPUPoolOpKernelCache final : public user_op::OpKernelCache { GPUPoolOpKernelCache(const int32_t dim, const std::string& pooling_type, const ShapeView& x_shape, const ShapeView& y_shape, const std::string& data_format, const DataType& dtype, const Params3D& params_3d) - : dim_(dim), pooling_type_(pooling_type) { + : pooling_type_(pooling_type) { Reset(dim, pooling_type, x_shape, y_shape, data_format, dtype, params_3d); } ~GPUPoolOpKernelCache() = default; @@ -522,11 +491,9 @@ class GPUPoolOpKernelCache final : public user_op::OpKernelCache { std::unique_ptr x_desc_; std::unique_ptr y_desc_; std::unique_ptr pooling_desc_; - int32_t dim_; std::string pooling_type_; }; -template struct PoolGpuKernelUtil { static void FWCompute(user_op::KernelComputeContext* ctx, const GPUPoolOpKernelCache* gpu_pool_op_kernel_cache) { @@ -535,8 +502,8 @@ struct PoolGpuKernelUtil { CHECK(gpu_pool_op_kernel_cache != nullptr); OF_CUDNN_CHECK(hipdnnPoolingForward( ctx->stream()->As()->cudnn_handle(), - gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(), - gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(), + gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(x->data_type()), + gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(x->data_type()), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), y->mut_dptr(), 0)); } @@ -549,17 +516,16 @@ struct PoolGpuKernelUtil { CHECK(gpu_pool_op_kernel_cache != nullptr); OF_CUDNN_CHECK(hipdnnPoolingBackward( ctx->stream()->As()->cudnn_handle(), - gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(), + gpu_pool_op_kernel_cache->cudnn_pooling_desc(), CudnnSPOnePtr(y->data_type()), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), y->dptr(), gpu_pool_op_kernel_cache->cudnn_y_tensor_desc(), dy->dptr(), - gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(), + gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), x->dptr(), CudnnSPZeroPtr(y->data_type()), gpu_pool_op_kernel_cache->cudnn_x_tensor_desc(), dx->mut_dptr())); } }; } // namespace -template class AvgPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool1DGpuKernel() = default; @@ -574,11 +540,10 @@ class AvgPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool1DGradGpuKernel() = default; @@ -591,11 +556,10 @@ class AvgPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::C } void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool2DGpuKernel() = default; @@ -610,11 +574,10 @@ class AvgPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool2DGradGpuKernel() = default; @@ -629,11 +592,10 @@ class AvgPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool3DGpuKernel() = default; @@ -648,11 +610,10 @@ class AvgPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class AvgPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: AvgPool3DGradGpuKernel() = default; @@ -667,11 +628,10 @@ class AvgPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool1DGpuKernel() = default; @@ -686,11 +646,10 @@ class MaxPool1DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool1DGradGpuKernel() = default; @@ -705,11 +664,10 @@ class MaxPool1DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool2DGpuKernel() = default; @@ -724,11 +682,10 @@ class MaxPool2DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool2DGradGpuKernel() = default; @@ -743,11 +700,10 @@ class MaxPool2DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool3DGpuKernel() = default; @@ -762,11 +718,10 @@ class MaxPool3DGpuKernel final : public user_op::OpKernel, public user_op::CudaG private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::FWCompute(ctx, dynamic_cast(cache)); }; }; -template class MaxPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { public: MaxPool3DGradGpuKernel() = default; @@ -781,63 +736,46 @@ class MaxPool3DGradGpuKernel final : public user_op::OpKernel, public user_op::C private: void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, const user_op::OpKernelCache* cache) const override { - PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); + PoolGpuKernelUtil::BWCompute(ctx, dynamic_cast(cache)); }; }; -#define REGISTER_POOL_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("tf_avg_pool_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_avg_pool_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("tf_max_pool_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)); - -REGISTER_POOL_CUDA_KERNEL(float) -REGISTER_POOL_CUDA_KERNEL(double) -REGISTER_POOL_CUDA_KERNEL(float16) +REGISTER_USER_KERNEL("tf_avg_pool_1d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_1d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_2d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_2d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_3d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_avg_pool_3d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_1d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_1d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_2d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_2d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_3d") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); +REGISTER_USER_KERNEL("tf_max_pool_3d_grad") + .SetCreateFn() + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA)); } // namespace oneflow diff --git a/oneflow/user/kernels/tf_prelu_kernel.hip.cpp b/oneflow/user/kernels/tf_prelu_kernel.hip.cpp deleted file mode 100644 index 2a27ca38bb0d0a6b465d073e8e4ef4db1bcfb3da..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/tf_prelu_kernel.hip.cpp +++ /dev/null @@ -1,254 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/ndarray/ndarray_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void BroadcastPReluForwardGpu(const int32_t elem_cnt, const int32_t alpha_size, - const int32_t inner_size, const T* x, const T* alpha, - T* y) { - T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - const T alpha_i = alpha[(i / inner_size) % alpha_size]; - y[i] = x_i > zero_val ? x_i : x_i * alpha_i; - } -} - -template -__global__ void BroadcastPReluBackwardGpu(const int32_t elem_cnt, const int32_t alpha_size, - const int32_t inner_size, const T* x, const T* alpha, - const T* dy, T* dx, T* alpha_diff) { - T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - const T dy_i = dy[i]; - const T alpha_i = alpha[(i / inner_size) % alpha_size]; - T dx_i = zero_val; - T alpha_diff_i = zero_val; - if (x_i > zero_val) { - dx_i = dy_i; - alpha_diff_i = zero_val; - } else { - dx_i = dy_i * alpha_i; - alpha_diff_i = dy_i * x_i; - } - dx[i] = dx_i; - alpha_diff[i] = alpha_diff_i; - } -} - -template -__global__ void ElemwisePReluForwardGpu(const int32_t elem_cnt, const T* x, const T* alpha, T* y) { - T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - const T alpha_i = alpha[i]; - y[i] = x_i > zero_val ? x_i : x_i * alpha_i; - } -} - -template -__global__ void ElemwisePReluBackwardGpu(const int32_t elem_cnt, const T* x, const T* alpha, - const T* dy, T* dx, T* alpha_diff) { - T zero_val = static_cast(0.0); - CUDA_1D_KERNEL_LOOP(i, elem_cnt) { - const T x_i = x[i]; - const T dy_i = dy[i]; - const T alpha_i = alpha[i]; - T dx_i = zero_val; - T alpha_diff_i = zero_val; - if (x_i > zero_val) { - dx_i = dy_i; - alpha_diff_i = zero_val; - } else { - dx_i = dy_i * alpha_i; - alpha_diff_i = dy_i * x_i; - } - dx[i] = dx_i; - alpha_diff[i] = alpha_diff_i; - } -} - -bool IsAlphaShapeContiguous(const ShapeView& alpha_shape, const ShapeView& x_shape) { - if (alpha_shape.elem_cnt() == 1) { return true; } - int64_t begin_idx = -1; - for (int64_t i = 0; i < alpha_shape.NumAxes(); ++i) { - if (alpha_shape.At(i) != 1) { - begin_idx = i; - break; - } - } - CHECK_NE(begin_idx, -1); - int64_t end_idx = -1; - for (int64_t i = alpha_shape.NumAxes(); i > 0; --i) { - if (alpha_shape.At(i - 1) != 1) { - end_idx = i; - break; - } - } - CHECK_NE(end_idx, -1); - if (alpha_shape.elem_cnt() == x_shape.Count(begin_idx + 1, end_idx + 1)) { - return true; - } else { - return false; - } -} - -int32_t GetOuterSize(const ShapeView& alpha_shape, const ShapeView& x_shape) { - int32_t outer_size = x_shape.At(0); - for (int32_t i = 0; i < alpha_shape.NumAxes(); ++i) { - if (alpha_shape.At(i) == 1) { - outer_size *= x_shape.At(i + 1); - } else { - break; - } - } - return outer_size; -} - -} // namespace - -template -class TfGpuPReluKernel final : public user_op::OpKernel { - public: - TfGpuPReluKernel() = default; - ~TfGpuPReluKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); - const int32_t elem_cnt = x->shape_view().elem_cnt(); - if (IsAlphaShapeContiguous(alpha->shape_view(), x->shape_view())) { - const int32_t outer_size = GetOuterSize(alpha->shape_view(), x->shape_view()); - const int32_t alpha_size = alpha->shape_view().elem_cnt(); - const int32_t inner_size = elem_cnt / outer_size / alpha_size; - BroadcastPReluForwardGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x->dptr(), alpha->dptr(), y->mut_dptr()); - } else { - user_op::Tensor* broadcasted_alpha = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const Shape& left_extended_shape = - CreateLeftExtendedShape(ShapeView(alpha->shape_view()), x->shape_view().NumAxes()); - NdarrayUtil::BroadcastTo( - ctx->stream(), XpuVarNdarray(x->shape_view(), broadcasted_alpha->mut_dptr()), - XpuVarNdarray(left_extended_shape, alpha->dptr())); - ElemwisePReluForwardGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, x->dptr(), broadcasted_alpha->dptr(), y->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_TF_CUDA_PRELU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("tf_prelu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("x", 0); \ - const Shape& alpha_shape = ctx->InputShape("alpha", 0); \ - const int64_t tmp_buffer_size = \ - IsAlphaShapeContiguous(alpha_shape, in_shape) \ - ? 0 \ - : GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(dtype)); \ - return tmp_buffer_size; \ - }); - -REGISTER_TF_CUDA_PRELU_KERNEL(half) -REGISTER_TF_CUDA_PRELU_KERNEL(float) -REGISTER_TF_CUDA_PRELU_KERNEL(double) - -template -class TfGpuPReluGradKernel final : public user_op::OpKernel { - public: - TfGpuPReluGradKernel() = default; - ~TfGpuPReluGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); - const user_op::Tensor* alpha = ctx->Tensor4ArgNameAndIndex("alpha", 0); - const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); - user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); - user_op::Tensor* alpha_diff = ctx->Tensor4ArgNameAndIndex("alpha_diff", 0); - user_op::Tensor* tmp_buffer = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - const int32_t elem_cnt = x->shape_view().elem_cnt(); - T* broadcasted_alpha_diff = tmp_buffer->mut_dptr(); - T* reduce_sum_tmp_buf = reinterpret_cast(tmp_buffer->mut_dptr() - + GetCudaAlignedSize(elem_cnt * sizeof(T))); - const Shape& left_extended_shape = - CreateLeftExtendedShape(ShapeView(alpha->shape_view()), x->shape_view().NumAxes()); - if (IsAlphaShapeContiguous(alpha->shape_view(), x->shape_view())) { - const int32_t outer_size = GetOuterSize(alpha->shape_view(), x->shape_view()); - const int32_t alpha_size = alpha->shape_view().elem_cnt(); - const int32_t inner_size = elem_cnt / outer_size / alpha_size; - BroadcastPReluBackwardGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, alpha_size, inner_size, x->dptr(), alpha->dptr(), dy->dptr(), - dx->mut_dptr(), broadcasted_alpha_diff); - } else { - T* broadcasted_alpha = reinterpret_cast(tmp_buffer->mut_dptr() - + 2 * GetCudaAlignedSize(elem_cnt * sizeof(T))); - - NdarrayUtil::BroadcastTo( - ctx->stream(), XpuVarNdarray(x->shape_view(), broadcasted_alpha), - XpuVarNdarray(left_extended_shape, alpha->dptr())); - - ElemwisePReluBackwardGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, x->dptr(), broadcasted_alpha, dy->dptr(), dx->mut_dptr(), - broadcasted_alpha_diff); - } - NdarrayUtil::ReduceSum( - ctx->stream(), XpuVarNdarray(left_extended_shape, alpha_diff->mut_dptr()), - XpuVarNdarray(x->shape_view(), broadcasted_alpha_diff), - XpuVarNdarray(x->shape_view(), reduce_sum_tmp_buf)); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_TF_CUDA_PRELU_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("tf_prelu_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape& in_shape = ctx->InputShape("x", 0); \ - const Shape& alpha_shape = ctx->InputShape("alpha", 0); \ - const int64_t tmp_buffer_size = \ - IsAlphaShapeContiguous(alpha_shape, in_shape) \ - ? 2 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(dtype)) \ - : 3 * GetCudaAlignedSize(in_shape.elem_cnt() * sizeof(dtype)); \ - return tmp_buffer_size; \ - }); - -REGISTER_TF_CUDA_PRELU_GRAD_KERNEL(half) -REGISTER_TF_CUDA_PRELU_GRAD_KERNEL(float) -REGISTER_TF_CUDA_PRELU_GRAD_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/throw_error_kernel.cpp b/oneflow/user/kernels/throw_error_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7942acd97cce242fb3fbadfe6be53fbcfcbbf7ab --- /dev/null +++ b/oneflow/user/kernels/throw_error_kernel.cpp @@ -0,0 +1,40 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +class ThrowErrorKernel final : public user_op::OpKernel { + public: + ThrowErrorKernel() = default; + ~ThrowErrorKernel() override = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + THROW(RuntimeError) << "throw error kernel"; + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +REGISTER_USER_KERNEL("throw_error") + .SetCreateFn() + .SetIsMatchedHob(user_op::HobTrue()); + +} // namespace + +} // namespace oneflow diff --git a/oneflow/user/kernels/to_contiguous_kernel.cpp b/oneflow/user/kernels/to_contiguous_kernel.cpp index 142107e936d4852fa4e96fb55a3fcb805aa0aabe..f2dc8d5e418cf97a4694cad644244a7b4fd30e5f 100644 --- a/oneflow/user/kernels/to_contiguous_kernel.cpp +++ b/oneflow/user/kernels/to_contiguous_kernel.cpp @@ -100,27 +100,26 @@ class ToContiguousKernel final : public user_op::OpKernel { bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_TO_CONTIGUOUS_KERNEL(device_type, T) \ - REGISTER_USER_KERNEL("to_contiguous") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == device_type) \ - && (user_op::HobDataType("in", 0) == GetDataType::value)); +#define REGISTER_TO_CONTIGUOUS_KERNEL(device_type, cpp_type, data_type) \ + REGISTER_USER_KERNEL("to_contiguous") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == device_type) \ + && (user_op::HobDataType("in", 0) == data_type)); -#define REGISTER_TO_CONTIGUOUS_CPU_KERNEL(T) REGISTER_TO_CONTIGUOUS_KERNEL(DeviceType::kCPU, T) -#define REGISTER_TO_CONTIGUOUS_CUDA_KERNEL(T) REGISTER_TO_CONTIGUOUS_KERNEL(DeviceType::kCUDA, T) +#define REGISTER_TO_CONTIGUOUS_CPU_KERNEL(cpp_type, data_type) \ + REGISTER_TO_CONTIGUOUS_KERNEL(DeviceType::kCPU, cpp_type, data_type) +#define REGISTER_TO_CONTIGUOUS_CUDA_KERNEL(cpp_type, data_type) \ + REGISTER_TO_CONTIGUOUS_KERNEL(DeviceType::kCUDA, cpp_type, data_type) #define REGISTER_TO_CONTIGUOUS_KERNEL_FOR_CPU_TYPES \ - OF_PP_FOR_EACH_TUPLE(REGISTER_TO_CONTIGUOUS_CPU_KERNEL, TO_CONTIGUOUS_TYPES) + OF_PP_FOR_EACH_TUPLE(REGISTER_TO_CONTIGUOUS_CPU_KERNEL, TO_CONTIGUOUS_CPU_TYPES) #define REGISTER_TO_CONTIGUOUS_KERNEL_FOR_CUDA_TYPES \ OF_PP_FOR_EACH_TUPLE(REGISTER_TO_CONTIGUOUS_CUDA_KERNEL, \ - TO_CONTIGUOUS_TYPES TO_CONTIGUOUS_CUDA_SPECIAL_TYPE) + TO_CONTIGUOUS_COMMON_TYPES TO_CONTIGUOUS_CUDA_SPECIAL_TYPE) REGISTER_TO_CONTIGUOUS_KERNEL_FOR_CPU_TYPES -#ifdef WITH_CUDA -REGISTER_TO_CONTIGUOUS_KERNEL_FOR_CUDA_TYPES -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_TO_CONTIGUOUS_KERNEL_FOR_CUDA_TYPES #endif diff --git a/oneflow/user/kernels/to_contiguous_kernel.cu b/oneflow/user/kernels/to_contiguous_kernel.cu index 56165adbec7782b3602ee72618d3d1b0cedaaf33..d5a3f9d0e542b787a1cb04d8a9117abef61bcbf9 100644 --- a/oneflow/user/kernels/to_contiguous_kernel.cu +++ b/oneflow/user/kernels/to_contiguous_kernel.cu @@ -124,7 +124,7 @@ struct ToContiguousUtil : ToContiguousUtilBase { const size_t ndims = contiguous_dim + 1; if (ndims == 0) { // 0-dim tensor - OF_CUDA_CHECK(cudaMemcpyAsync(out_dptr, in_dptr, block_size * dsize, cudaMemcpyDeviceToDevice, + OF_CUDA_CHECK(GPU(MemcpyAsync)(out_dptr, in_dptr, block_size * dsize, GPU(MemcpyDeviceToDevice), stream->As()->cuda_stream())); } else { bool is_same = true; @@ -136,8 +136,8 @@ struct ToContiguousUtil : ToContiguousUtilBase { } if (is_same) { // if input tensor's strides equals to output's, than just copy one memory-contiguous tensor - OF_CUDA_CHECK(cudaMemcpyAsync(out_dptr, in_dptr, element_count * dsize, - cudaMemcpyDeviceToDevice, + OF_CUDA_CHECK(GPU(MemcpyAsync)(out_dptr, in_dptr, element_count * dsize, + GPU(MemcpyDeviceToDevice), stream->As()->cuda_stream())); } else { if (element_count < GetMaxVal()) { @@ -152,9 +152,9 @@ struct ToContiguousUtil : ToContiguousUtilBase { } }; -#define INSTANTIATE_TO_CONTIGUOUS_UTILS_FOR_CUDA(T) \ - template struct ToContiguousUtil; +#define INSTANTIATE_TO_CONTIGUOUS_UTILS_FOR_CUDA(cpp_type, data_type) \ + template struct ToContiguousUtil; OF_PP_FOR_EACH_TUPLE(INSTANTIATE_TO_CONTIGUOUS_UTILS_FOR_CUDA, - TO_CONTIGUOUS_TYPES TO_CONTIGUOUS_CUDA_SPECIAL_TYPE) + TO_CONTIGUOUS_COMMON_TYPES TO_CONTIGUOUS_CUDA_SPECIAL_TYPE) } // namespace oneflow diff --git a/oneflow/user/kernels/to_contiguous_kernel.h b/oneflow/user/kernels/to_contiguous_kernel.h index 54eb1b5bc3945ddcddd735b5410e6e66c6af28ed..419f64fbc562675be18a615e49e0fadc1bc214e5 100644 --- a/oneflow/user/kernels/to_contiguous_kernel.h +++ b/oneflow/user/kernels/to_contiguous_kernel.h @@ -83,14 +83,29 @@ struct ToContiguousUtil : ToContiguousUtilBase { } // namespace oneflow -#define TO_CONTIGUOUS_TYPES \ - OF_PP_MAKE_TUPLE_SEQ(bool) \ - OF_PP_MAKE_TUPLE_SEQ(float) \ - OF_PP_MAKE_TUPLE_SEQ(double) \ - OF_PP_MAKE_TUPLE_SEQ(int32_t) \ - OF_PP_MAKE_TUPLE_SEQ(int64_t) \ - OF_PP_MAKE_TUPLE_SEQ(int8_t) \ - OF_PP_MAKE_TUPLE_SEQ(uint8_t) -#define TO_CONTIGUOUS_CUDA_SPECIAL_TYPE OF_PP_MAKE_TUPLE_SEQ(float16) - +#define TO_CONTIGUOUS_COMMON_TYPES \ + OF_PP_MAKE_TUPLE_SEQ(bool, DataType::kBool) \ + OF_PP_MAKE_TUPLE_SEQ(char, DataType::kChar) \ + OF_PP_MAKE_TUPLE_SEQ(int8_t, DataType::kInt8) \ + OF_PP_MAKE_TUPLE_SEQ(uint8_t, DataType::kUInt8) \ + OF_PP_MAKE_TUPLE_SEQ(int32_t, DataType::kInt32) \ + OF_PP_MAKE_TUPLE_SEQ(uint32_t, DataType::kUInt32) \ + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) \ + OF_PP_MAKE_TUPLE_SEQ(uint64_t, DataType::kUInt64) \ + OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) \ + OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) + +#define TO_CONTIGUOUS_CPU_TYPES \ + TO_CONTIGUOUS_COMMON_TYPES OF_PP_MAKE_TUPLE_SEQ(float16, DataType::kFloat16) \ + OF_PP_MAKE_TUPLE_SEQ(bfloat16, DataType::kBFloat16) + +#if defined(WITH_CUDA) || defined(WITH_ROCM) +#if CUDA_VERSION >= 11000 +#define TO_CONTIGUOUS_CUDA_SPECIAL_TYPE \ + OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) \ + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16) +#else +#define TO_CONTIGUOUS_CUDA_SPECIAL_TYPE OF_PP_MAKE_TUPLE_SEQ(half, DataType::kFloat16) +#endif // CUDA_VERSION >= 11000 +#endif // WITH_CUDA #endif // ONEFLOW_USER_KERNELS_TO_CONTIGUOUS_KERNEL_H_ diff --git a/oneflow/user/kernels/to_contiguous_kernel.hip.cpp b/oneflow/user/kernels/to_contiguous_kernel.hip.cpp deleted file mode 100644 index 72d23a89e1d940e3ca9b2e10cc0915914a9d18e7..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/to_contiguous_kernel.hip.cpp +++ /dev/null @@ -1,161 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/core/common/device_type.pb.h" -#include "oneflow/user/kernels/to_contiguous_kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include "oneflow/core/hip/elementwise.hip.h" - -namespace oneflow { - -namespace { - -constexpr int32_t kThreadWorkSize = 4; -constexpr int32_t kNumThreads = 32 * 4; -constexpr int32_t get_min_threads_num() { return kNumThreads; } -constexpr int32_t get_block_work_size() { return kThreadWorkSize * kNumThreads; } -constexpr int32_t get_num_blocks(int64_t elem_cnt) { - return (elem_cnt + get_block_work_size() - 1) / get_block_work_size(); -} - -struct StrideParam { - int32_t stride[SHAPE_MAX_AXIS_SIZE]; - - StrideParam(const int64_t* stride_vec, const size_t ndim) { - for (size_t i = 0; i < ndim; ++i) { stride[i] = stride_vec[i]; } - } -}; - -template -__device__ __forceinline__ IndexType compute_index(IndexType out_offset, - const StrideParam& out_params, - const StrideParam& in_params) { - IndexType in_offset = 0; - IndexType remaining = out_offset; - -#pragma unroll - for (size_t i = 0; i < ndim; ++i) { - const IndexType idx = static_cast(remaining / out_params.stride[i]); - remaining -= idx * out_params.stride[i]; - in_offset += idx * in_params.stride[i]; - } - return in_offset; -} - -template -__global__ void ToContiguousForwardGpuParallel(IndexType count, const StrideParam in_stride, - const StrideParam out_stride, const T* in_dptr, - T* out_dptr, const int32_t num_block_threads, - const int32_t thread_work_size, - const int32_t block_work_size) { - IndexType remaining = count - block_work_size * blockIdx.x; - IndexType idx = blockIdx.x; - IndexType thread_idx = threadIdx.x; -#pragma unroll - for (int32_t i = 0; i < thread_work_size; i++) { - if (thread_idx >= remaining) { return; } - IndexType out_idx = thread_idx + block_work_size * idx; - IndexType in_idx = compute_index(out_idx, out_stride, in_stride); - out_dptr[out_idx] = in_dptr[in_idx]; - thread_idx += num_block_threads; - } -} - -template -void LaunchToContiguousKernel(ep::Stream* stream, IndexType count, const size_t ndim, - IndexType block_size, const std::vector& in_stride, - const DimVector& out_stride, const char* in_dptr, char* out_dptr) { - const int32_t num_blocks = get_num_blocks(count); - constexpr int32_t num_threads = get_min_threads_num(); - constexpr int32_t block_work_size = get_block_work_size(); - StrideParam param_in_stride(in_stride.data(), ndim), param_out_stride(out_stride.data(), ndim); - - switch (ndim) { -#define TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(dim) \ - case dim: \ - ToContiguousForwardGpuParallel \ - <<As()->cuda_stream()>>>( \ - count, param_in_stride, param_out_stride, reinterpret_cast(in_dptr), \ - reinterpret_cast(out_dptr), num_threads, kThreadWorkSize, block_work_size); \ - break; - - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(1) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(2) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(3) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(4) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(5) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(6) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(7) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(8) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(9) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(10) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(11) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(12) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(13) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(14) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(15) - TO_CONTIGUOUS_FORWARD_GPU_PARALLEL(16) - default: break; -#undef TO_CONTIGUOUS_FORWARD_GPU_PARALLEL - } -} - -} // namespace - -template -struct ToContiguousUtil : ToContiguousUtilBase { - using ToContiguousUtilBase::ToContiguousUtilBase; - static constexpr size_t dsize = sizeof(T); - void operator()() { - int constant_memory_size = 0; - const size_t ndims = contiguous_dim + 1; - if (ndims == 0) { - // 0-dim tensor - OF_CUDA_CHECK(hipMemcpyAsync(out_dptr, in_dptr, block_size * dsize, hipMemcpyDeviceToDevice, - stream->As()->cuda_stream())); - } else { - bool is_same = true; - for (int64_t i = contiguous_dim; i != -1; --i) { - if (out_stride[i] != in_stride[i]) { - is_same = false; - break; - } - } - if (is_same) { - // if input tensor's strides equals to output's, than just copy one memory-contiguous tensor - OF_CUDA_CHECK(hipMemcpyAsync(out_dptr, in_dptr, element_count * dsize, - hipMemcpyDeviceToDevice, - stream->As()->cuda_stream())); - } else { - if (element_count < GetMaxVal()) { - LaunchToContiguousKernel(stream, element_count, ndims, block_size, in_stride, - out_stride, in_dptr, out_dptr); - } else { - LaunchToContiguousKernel(stream, element_count, ndims, block_size, in_stride, - out_stride, in_dptr, out_dptr); - } - } - } - } -}; - -#define INSTANTIATE_TO_CONTIGUOUS_UTILS_FOR_CUDA(T) \ - template struct ToContiguousUtil; -OF_PP_FOR_EACH_TUPLE(INSTANTIATE_TO_CONTIGUOUS_UTILS_FOR_CUDA, - TO_CONTIGUOUS_TYPES TO_CONTIGUOUS_CUDA_SPECIAL_TYPE) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/transpose_kernel.cpp b/oneflow/user/kernels/transpose_kernel.cpp index 889a96c184462f965e7f88195f2b336caf3184d8..58bdf58f32fa5ee1dc22dab44a3500257cd443dd 100644 --- a/oneflow/user/kernels/transpose_kernel.cpp +++ b/oneflow/user/kernels/transpose_kernel.cpp @@ -17,17 +17,31 @@ limitations under the License. #include "oneflow/core/kernel/kernel_util.h" #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/ep/include/primitive/permute.h" +#include "oneflow/core/ep/common/primitive/permute.h" + namespace oneflow { namespace user_op { namespace { -bool IsIdentity(const std::vector& perm) { - for (auto i = 0; i < perm.size(); i++) { - if (perm[i] != i) { return false; } + +bool IsIdentity(const ShapeView& in_shape, const std::vector& perm) { + constexpr int kMaxNumDims = 12; + CHECK_LE(in_shape.NumAxes(), kMaxNumDims); + CHECK_EQ(in_shape.NumAxes(), perm.size()); + + size_t simplified_num_dims{}; + int64_t simplified_src_dims[kMaxNumDims]{}; + int simplified_permutation[kMaxNumDims]{}; + ep::primitive::permute::SimplifyPermutation( + in_shape.NumAxes(), in_shape.ptr(), perm.data(), &simplified_num_dims, simplified_src_dims, + simplified_permutation); + for (int i = 0; i < simplified_num_dims; ++i) { + if (simplified_permutation[i] != i) { return false; } } return true; } + } // namespace template @@ -58,7 +72,7 @@ class TransposeKernel final : public OpKernel, public user_op::CudaGraphSupport int64_t elem_cnt = tensor_out->shape_view().elem_cnt(); if (elem_cnt != 0) { - if (IsIdentity(perm)) { + if (IsIdentity(in_shape, perm)) { // if permute vector is 0,1,...,n, do data copy directly AutoMemcpy(ctx->stream(), tensor_out->mut_dptr(), tensor_in->dptr(), elem_cnt * GetSizeOfDataType(dtype), tensor_out->mem_case(), @@ -84,7 +98,16 @@ auto PermutePrimitiveExists() { REGISTER_USER_KERNEL("transpose") .SetCreateFn() - .SetIsMatchedHob(PermutePrimitiveExists() == true); + .SetIsMatchedHob(PermutePrimitiveExists() == true) + .SetInplaceProposalFn([](const user_op::InferContext& ctx, + const user_op::AddInplaceArgPair& AddInplaceArgPairFn) -> Maybe { + const ShapeView input_shape(ctx.InputShape("input", 0)); + const auto& perm = ctx.Attr>("perm"); + if (IsIdentity(input_shape, perm)) { + OF_RETURN_IF_ERROR(AddInplaceArgPairFn("output", 0, "input", 0, false)); + } + return Maybe::Ok(); + }); } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/tril_kernel.cu b/oneflow/user/kernels/tril_kernel.cu index 9b7b0214cb33583086307059294bc8d50c63f652..4b547f801306553a10b83b32922932230802b882 100644 --- a/oneflow/user/kernels/tril_kernel.cu +++ b/oneflow/user/kernels/tril_kernel.cu @@ -67,8 +67,13 @@ __global__ void TrilWarpProcessRowGpu(const int64_t total_rows, const int6 const int64_t idx = i * h2_num_cols + col; const half2 x_val = x_h2[idx]; half2 y_val; +#ifdef WITH_ROCM + y_val.data.x = (2 * col) > row + diagonal ? static_cast(fill) : static_cast(x_val.data.x); + y_val.data.y = (2 * col + 1) > row + diagonal ? static_cast(fill) : static_cast(x_val.data.y); +#else y_val.x = (2 * col) > row + diagonal ? fill : x_val.x; y_val.y = (2 * col + 1) > row + diagonal ? fill : x_val.y; +#endif y_h2[idx] = y_val; } } @@ -122,8 +127,13 @@ __global__ void FusedScaleTrilWarpProcessRowGpu(const int64_t total_rows, const int64_t idx = i * h2_num_cols + col; const half2 scaled_x = __hmul2(h2_scale, x_h2[idx]); half2 y_val; +#ifdef WITH_ROCM + y_val.data.x = (2 * col) > row + diagonal ? static_cast(fill) : static_cast(scaled_x.data.x); + y_val.data.y = (2 * col + 1) > row + diagonal ? static_cast(fill) : static_cast(scaled_x.data.y); +#else y_val.x = (2 * col) > row + diagonal ? fill : scaled_x.x; y_val.y = (2 * col + 1) > row + diagonal ? fill : scaled_x.y; +#endif y_h2[idx] = y_val; } } diff --git a/oneflow/user/kernels/tril_kernel.hip.cpp b/oneflow/user/kernels/tril_kernel.hip.cpp deleted file mode 100644 index 817e8b0b12a68376b2e357c036f8a566eb33b188..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/tril_kernel.hip.cpp +++ /dev/null @@ -1,256 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void TrilGpu(const int64_t elem_cnt, const int64_t num_rows, const int64_t num_cols, - const int64_t diagonal, const T* x, const T fill, T* y) { - const int64_t matrix_size = num_rows * num_cols; - CUDA_1D_KERNEL_LOOP_T(int64_t, k, elem_cnt) { - const int64_t offset_in_matrix = k % matrix_size; - const int64_t i = offset_in_matrix / num_cols; - const int64_t j = offset_in_matrix - num_cols * i; - y[k] = j > i + diagonal ? fill : x[k]; - } -} - -template -__global__ void TrilWarpProcessRowGpu(const int64_t total_rows, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, const T* x, - const T fill, T* y) { - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < num_cols; col += kCudaWarpSize) { - const int64_t idx = i * num_cols + col; - y[idx] = col > row + diagonal ? fill : x[idx]; - } - } -} - -template<> -__global__ void TrilWarpProcessRowGpu(const int64_t total_rows, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, - const half* x, const half fill, half* y) { - const int64_t h2_num_cols = num_cols / 2; - const auto* x_h2 = reinterpret_cast(x); - auto* y_h2 = reinterpret_cast(y); - - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < h2_num_cols; col += kCudaWarpSize) { - const int64_t idx = i * h2_num_cols + col; - const half2 x_val = x_h2[idx]; - half2 y_val; - y_val.data.x = (2 * col) > row + diagonal ? fill : static_cast(x_val.data.x); - y_val.data.y = (2 * col + 1) > row + diagonal ? fill : static_cast(x_val.data.y); - y_h2[idx] = y_val; - } - } -} - -template -__global__ void FusedScaleTrilGpu(const int64_t elem_cnt, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, const T scale, - const T* x, const T fill, T* y) { - const int64_t matrix_size = num_rows * num_cols; - CUDA_1D_KERNEL_LOOP_T(int64_t, k, elem_cnt) { - const int64_t offset_in_matrix = k % matrix_size; - const int64_t i = offset_in_matrix / num_cols; - const int64_t j = offset_in_matrix - num_cols * i; - y[k] = j > i + diagonal ? fill : (scale * x[k]); - } -} - -template -__global__ void FusedScaleTrilWarpProcessRowGpu(const int64_t total_rows, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, - const T scale, const T* x, const T fill, T* y) { - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < num_cols; col += kCudaWarpSize) { - const int64_t idx = i * num_cols + col; - y[idx] = col > row + diagonal ? fill : (scale * x[idx]); - } - } -} - -template<> -__global__ void FusedScaleTrilWarpProcessRowGpu(const int64_t total_rows, - const int64_t num_rows, - const int64_t num_cols, - const int64_t diagonal, const half scale, - const half* x, const half fill, half* y) { - const int64_t h2_num_cols = num_cols / 2; - const auto* x_h2 = reinterpret_cast(x); - auto* y_h2 = reinterpret_cast(y); - const half2 h2_scale = __half2half2(scale); - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < h2_num_cols; col += kCudaWarpSize) { - const int64_t idx = i * h2_num_cols + col; - const half2 scaled_x = __hmul2(h2_scale, x_h2[idx]); - half2 y_val; - y_val.data.x = (2 * col) > row + diagonal ? fill : static_cast(scaled_x.data.x); - y_val.data.y = (2 * col + 1) > row + diagonal ? fill : static_cast(scaled_x.data.y); - y_h2[idx] = y_val; - } - } -} - -template -T GetAttrVal(bool is_floating_val, double floating_value, int64_t integer_value) { - return is_floating_val ? static_cast(floating_value) : static_cast(integer_value); -} - -template<> -half GetAttrVal(bool is_floating_val, double floating_value, int64_t integer_value) { - return is_floating_val ? __float2half(floating_value) : __float2half(integer_value); -} - -} // namespace - -template -class GpuTrilKernel final : public user_op::OpKernel { - public: - GpuTrilKernel() = default; - ~GpuTrilKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("in", 0); - const auto shape = x->shape_view(); - const auto diagonal = ctx->Attr("diagonal"); - const int64_t num_rows = shape.At(shape.NumAxes() - 2); - const int64_t num_cols = shape.At(shape.NumAxes() - 1); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("out", 0); - const int32_t elem_cnt = shape.elem_cnt(); - const T fill = GetAttrVal(ctx->Attr("is_floating_fill_value"), - ctx->Attr("floating_fill_value"), - ctx->Attr("integer_fill_value")); - if (num_cols % (kCudaWarpSize * 2) == 0) { - const int64_t total_rows = elem_cnt / num_cols; - TrilWarpProcessRowGpu<<stream()->As()->cuda_stream()>>>( - total_rows, num_rows, num_cols, diagonal, x->dptr(), fill, y->mut_dptr()); - } else { - TrilGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, num_rows, num_cols, diagonal, x->dptr(), fill, y->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_TRIL_KERNEL(dtype) \ - REGISTER_USER_KERNEL("tril") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, true)); \ - return Maybe::Ok(); \ - }); - -REGISTER_CUDA_TRIL_KERNEL(float) -REGISTER_CUDA_TRIL_KERNEL(double) -REGISTER_CUDA_TRIL_KERNEL(bool) -REGISTER_CUDA_TRIL_KERNEL(uint8_t) -REGISTER_CUDA_TRIL_KERNEL(int8_t) -REGISTER_CUDA_TRIL_KERNEL(int32_t) -REGISTER_CUDA_TRIL_KERNEL(int64_t) -REGISTER_CUDA_TRIL_KERNEL(half) - -template -class GpuFusedScaleTrilKernel final : public user_op::OpKernel { - public: - GpuFusedScaleTrilKernel() = default; - ~GpuFusedScaleTrilKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("in", 0); - const auto shape = x->shape_view(); - const auto diagonal = ctx->Attr("diagonal"); - const int32_t num_rows = shape.At(shape.NumAxes() - 2); - const int32_t num_cols = shape.At(shape.NumAxes() - 1); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("out", 0); - const int32_t elem_cnt = shape.elem_cnt(); - const T fill = GetAttrVal(ctx->Attr("is_floating_fill_value"), - ctx->Attr("floating_fill_value"), - ctx->Attr("integer_fill_value")); - const T scale = GetAttrVal(ctx->Attr("is_floating_scale_value"), - ctx->Attr("floating_scale_value"), - ctx->Attr("integer_scale_value")); - if (num_cols % (kCudaWarpSize * 2) == 0) { - const int64_t total_rows = elem_cnt / num_cols; - FusedScaleTrilWarpProcessRowGpu<<stream()->As()->cuda_stream()>>>( - total_rows, num_rows, num_cols, diagonal, scale, x->dptr(), fill, y->mut_dptr()); - } else { - FusedScaleTrilGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, num_rows, num_cols, diagonal, scale, x->dptr(), fill, y->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(dtype) \ - REGISTER_USER_KERNEL("fused_scale_tril") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, true)); \ - return Maybe::Ok(); \ - }); - -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(float) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(double) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(bool) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(uint8_t) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(int8_t) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(int32_t) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(int64_t) -REGISTER_CUDA_FUSED_SCALE_TRIL_KERNEL(half) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/triu_kernel.cpp b/oneflow/user/kernels/triu_kernel.cpp index f6dce625f32baae65ad6fa11a227b87b696a6c41..3a3a45cf174b7aa1a3332eab757a29499cb608a7 100644 --- a/oneflow/user/kernels/triu_kernel.cpp +++ b/oneflow/user/kernels/triu_kernel.cpp @@ -50,11 +50,13 @@ class CpuTriuKernel final : public user_op::OpKernel { (user_op::HobDeviceType() == DeviceType::kCPU) \ && (user_op::HobDataType("out", 0) == GetDataType::value)); +REGISTER_CPU_TRIU_KERNEL(float16) REGISTER_CPU_TRIU_KERNEL(float) REGISTER_CPU_TRIU_KERNEL(double) REGISTER_CPU_TRIU_KERNEL(uint8_t) REGISTER_CPU_TRIU_KERNEL(int8_t) REGISTER_CPU_TRIU_KERNEL(int32_t) REGISTER_CPU_TRIU_KERNEL(int64_t) +REGISTER_CPU_TRIU_KERNEL(bool) } // namespace oneflow diff --git a/oneflow/user/kernels/triu_kernel.cu b/oneflow/user/kernels/triu_kernel.cu index 93e53bc438832325a9bac414e8fe8479c128cb7c..477eb87be177ec286ce23726cacd3f8557287607 100644 --- a/oneflow/user/kernels/triu_kernel.cu +++ b/oneflow/user/kernels/triu_kernel.cu @@ -67,8 +67,13 @@ __global__ void TriuWarpProcessRowGpu(const int64_t total_rows, const int6 const int64_t idx = i * h2_num_cols + col; const half2 x_val = x_h2[idx]; half2 y_val; +#ifdef WITH_ROCM + y_val.data.x = (2 * col) < row + diagonal ? static_cast(0) : static_cast(x_val.data.x); + y_val.data.y = (2 * col + 1) < row + diagonal ? static_cast(0) : static_cast(x_val.data.y); +#else y_val.x = (2 * col) < row + diagonal ? static_cast(0) : x_val.x; y_val.y = (2 * col + 1) < row + diagonal ? static_cast(0) : x_val.y; +#endif y_h2[idx] = y_val; } } @@ -126,5 +131,6 @@ REGISTER_CUDA_TRIU_KERNEL(uint8_t) REGISTER_CUDA_TRIU_KERNEL(int8_t) REGISTER_CUDA_TRIU_KERNEL(int32_t) REGISTER_CUDA_TRIU_KERNEL(int64_t) +REGISTER_CUDA_TRIU_KERNEL(bool) } // namespace oneflow diff --git a/oneflow/user/kernels/triu_kernel.hip.cpp b/oneflow/user/kernels/triu_kernel.hip.cpp deleted file mode 100644 index 6ffb20c68b6fb5f2edffe9d7480d506bf1ff5202..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/triu_kernel.hip.cpp +++ /dev/null @@ -1,131 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/data_type.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/util/cuda_half_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void TriuGpu(const int64_t elem_cnt, const int64_t num_rows, const int64_t num_cols, - const int64_t diagonal, const T* x, T* y) { - const int64_t matrix_size = num_rows * num_cols; - CUDA_1D_KERNEL_LOOP_T(int64_t, k, elem_cnt) { - const int64_t offset_in_matrix = k % matrix_size; - const int64_t i = offset_in_matrix / num_cols; - const int64_t j = offset_in_matrix - num_cols * i; - y[k] = j < i + diagonal ? static_cast(0) : x[k]; - } -} - -template -__global__ void TriuWarpProcessRowGpu(const int64_t total_rows, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, const T* x, - T* y) { - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < num_cols; col += kCudaWarpSize) { - const int64_t idx = i * num_cols + col; - y[idx] = col < row + diagonal ? static_cast(0) : x[idx]; - } - } -} - -template<> -__global__ void TriuWarpProcessRowGpu(const int64_t total_rows, const int64_t num_rows, - const int64_t num_cols, const int64_t diagonal, - const half* x, half* y) { - const int64_t h2_num_cols = num_cols / 2; - const auto* x_h2 = reinterpret_cast(x); - auto* y_h2 = reinterpret_cast(y); - - const int64_t warp_id = (blockIdx.x * blockDim.x + threadIdx.x) / kCudaWarpSize; - const int64_t lan_id = threadIdx.x % kCudaWarpSize; - const int64_t num_warp = blockDim.x * gridDim.x / kCudaWarpSize; - for (int64_t i = warp_id; i < total_rows; i += num_warp) { - const int64_t row = i % num_rows; - for (int64_t col = lan_id; col < h2_num_cols; col += kCudaWarpSize) { - const int64_t idx = i * h2_num_cols + col; - const half2 x_val = x_h2[idx]; - half2 y_val; - y_val.data.x = (2 * col) < row + diagonal ? static_cast(0) : static_cast(x_val.data.x); - y_val.data.y = (2 * col + 1) < row + diagonal ? static_cast(0) : static_cast(x_val.data.y); - y_h2[idx] = y_val; - } - } -} - -} // namespace - -template -class GpuTriuKernel final : public user_op::OpKernel { - public: - GpuTriuKernel() = default; - ~GpuTriuKernel() override = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("in", 0); - const auto shape = x->shape_view(); - const auto diagonal = ctx->Attr("diagonal"); - const int64_t num_rows = shape.At(shape.NumAxes() - 2); - const int64_t num_cols = shape.At(shape.NumAxes() - 1); - user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("out", 0); - const int32_t elem_cnt = shape.elem_cnt(); - if (elem_cnt == 0) { return; } - if (num_cols % (kCudaWarpSize * 2) == 0) { - const int64_t total_rows = elem_cnt / num_cols; - TriuWarpProcessRowGpu<<stream()->As()->cuda_stream()>>>( - total_rows, num_rows, num_cols, diagonal, x->dptr(), y->mut_dptr()); - } else { - TriuGpu<<stream()->As()->cuda_stream()>>>( - elem_cnt, num_rows, num_cols, diagonal, x->dptr(), y->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_CUDA_TRIU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("triu") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("out", 0) == GetDataType::value)) \ - .SetInplaceProposalFn([](const user_op::InferContext&, \ - user_op::AddInplaceArgPair AddInplaceArgPairFn) -> Maybe { \ - OF_RETURN_IF_ERROR(AddInplaceArgPairFn("out", 0, "in", 0, true)); \ - return Maybe::Ok(); \ - }); - -REGISTER_CUDA_TRIU_KERNEL(half) -REGISTER_CUDA_TRIU_KERNEL(float) -REGISTER_CUDA_TRIU_KERNEL(double) -REGISTER_CUDA_TRIU_KERNEL(uint8_t) -REGISTER_CUDA_TRIU_KERNEL(int8_t) -REGISTER_CUDA_TRIU_KERNEL(int32_t) -REGISTER_CUDA_TRIU_KERNEL(int64_t) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/tuple_identity_kernel.cpp b/oneflow/user/kernels/tuple_identity_kernel.cpp index 3acf90ff29f96847ffdac7abf0c10bb011ef6b7b..ac311156eb64c7fe87f32cf1eca9b71ba600f338 100644 --- a/oneflow/user/kernels/tuple_identity_kernel.cpp +++ b/oneflow/user/kernels/tuple_identity_kernel.cpp @@ -50,10 +50,7 @@ class TupleIdentityKernel final : public user_op::OpKernel { .SetIsMatchedHob(user_op::HobDeviceType() == device); REGISTER_TUPLE_IDENTITY_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_TUPLE_IDENTITY_KERNEL(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_TUPLE_IDENTITY_KERNEL(DeviceType::kCUDA) #endif diff --git a/oneflow/user/kernels/two_stage_reduce_kernel.cpp b/oneflow/user/kernels/two_stage_reduce_kernel.cpp index c76eaa9749db89642b4e1d9de31fb8209319c45f..057012e21a08310f462507a37c01602a7cd1debd 100644 --- a/oneflow/user/kernels/two_stage_reduce_kernel.cpp +++ b/oneflow/user/kernels/two_stage_reduce_kernel.cpp @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/ndarray/xpu_var_ndarray.h" #include "oneflow/user/kernels/two_stage_reduce_kernel_util.h" #include "oneflow/core/ep/include/primitive/cast.h" +#include "oneflow/core/ep/include/primitive/broadcast_elementwise_binary.h" namespace oneflow { @@ -47,10 +48,13 @@ class ReduceDeviceStageKernel final : public OpKernel { ctx->stream(), XpuVarNdarray(out->shape_view(), out->mut_dptr()), XpuVarNdarray(in->shape_view(), in->dptr()), XpuVarNdarray(in->shape_view(), reduce_tmp_buf)); - NdarrayUtil::BroadcastEQ( - ctx->stream(), XpuVarNdarray(mask->shape_view(), mask->mut_dptr()), - XpuVarNdarray(in->shape_view(), in->dptr()), - XpuVarNdarray(out->shape_view(), out->dptr())); + auto bcast_eq = ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kEqual, in->data_type(), DataType::kBool, + in->shape_view().NumAxes()); + CHECK(bcast_eq); + bcast_eq->Launch(ctx->stream(), in->shape_view().NumAxes(), in->shape_view().ptr(), in->dptr(), + out->shape_view().NumAxes(), out->shape_view().ptr(), out->dptr(), + mask->mut_dptr()); auto cast = ep::primitive::NewPrimitive( ctx->device_type(), DataType::kInt8, DataType::kInt32); @@ -127,9 +131,9 @@ template user_op::InferTmpSizeFn GenDeviceStageGradInferTmpSizeFn() { return [](user_op::InferContext* ctx) { const Shape& out_diff_shape = ctx->InputShape("out_diff", 0); - const Shape* in_diff_shape = ctx->OutputShape("in_diff", 0); + const Shape& in_diff_shape = ctx->OutputShape("in_diff", 0); const size_t tmp_bytes = GetCudaAlignedSize(out_diff_shape.elem_cnt() * sizeof(T)); - const size_t broadcasted_tmp_bytes = GetCudaAlignedSize(in_diff_shape->elem_cnt() * sizeof(T)); + const size_t broadcasted_tmp_bytes = GetCudaAlignedSize(in_diff_shape.elem_cnt() * sizeof(T)); return tmp_bytes + broadcasted_tmp_bytes; }; } @@ -167,10 +171,13 @@ class ReduceGlobalStageKernel final : public OpKernel { XpuVarNdarray(in->shape_view(), in->dptr()), XpuVarNdarray(in->shape_view(), tmp_buffer->mut_dptr())); - NdarrayUtil::BroadcastEQ( - ctx->stream(), XpuVarNdarray(in->shape_view(), mask->mut_dptr()), - XpuVarNdarray(in->shape_view(), in->dptr()), - XpuVarNdarray(reduced_shape, out->dptr())); + auto bcast_eq = ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::BinaryOp::kEqual, in->data_type(), DataType::kBool, + in->shape_view().NumAxes()); + CHECK(bcast_eq); + bcast_eq->Launch(ctx->stream(), in->shape_view().NumAxes(), in->shape_view().ptr(), in->dptr(), + reduced_shape.NumAxes(), reduced_shape.dim_vec().data(), out->dptr(), + mask->mut_dptr()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; @@ -259,7 +266,7 @@ user_op::InferTmpSizeFn GenGlobalStageGradInferTmpSizeFn() { return [](user_op::InferContext* ctx) { const Shape& device_count_shape = ctx->InputShape("device_count", 0); const Shape& out_diff_shape = ctx->InputShape("out_diff", 0); - const Shape* in_diff_shape = ctx->OutputShape("in_diff", 0); + const Shape& in_diff_shape = ctx->OutputShape("in_diff", 0); const size_t device_count_with_mask_bytes = GetCudaAlignedSize(device_count_shape.elem_cnt() * sizeof(int32_t)); const size_t global_count_bytes = @@ -268,7 +275,7 @@ user_op::InferTmpSizeFn GenGlobalStageGradInferTmpSizeFn() { GetCudaAlignedSize(device_count_shape.elem_cnt() * sizeof(int32_t)); const size_t divided_buf_bytes = GetCudaAlignedSize(out_diff_shape.elem_cnt() * sizeof(T)); const size_t broadcasted_divided_buf_bytes = - GetCudaAlignedSize(in_diff_shape->elem_cnt() * sizeof(T)); + GetCudaAlignedSize(in_diff_shape.elem_cnt() * sizeof(T)); const size_t total_bytes = device_count_with_mask_bytes + global_count_bytes + reduce_sum_tmp_bytes + divided_buf_bytes + broadcasted_divided_buf_bytes; diff --git a/oneflow/user/kernels/two_stage_reduce_kernel_util.hip.cpp b/oneflow/user/kernels/two_stage_reduce_kernel_util.hip.cpp deleted file mode 100644 index a12653b67accdae32fd91ff0ab68dcdb4d6f3071..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/two_stage_reduce_kernel_util.hip.cpp +++ /dev/null @@ -1,67 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/user/kernels/two_stage_reduce_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -__global__ void DivideGpu(const int64_t n, const T* x, const K* count, T* y) { - CUDA_1D_KERNEL_LOOP(i, n) { y[i] = x[i] / count[i]; } -} - -template -__global__ void MaskGpu(const int64_t n, const T* x, const K* mask, T* y) { - CUDA_1D_KERNEL_LOOP(i, n) { y[i] = static_cast(mask[i]) * x[i]; } -} - -template -__global__ void ScaleGpu(const int64_t n, const T* x, const K* scale, T* y) { - CUDA_1D_KERNEL_LOOP(i, n) { y[i] = x[i] * static_cast(scale[i]); } -} - -} // namespace - -template -struct TwoStageReduceKernelUtil { - static void Divide(ep::Stream* stream, const int64_t n, const T* x, const K* count, T* y) { - DivideGpu<<As()->cuda_stream()>>>(n, x, count, y); - } - - static void Mask(ep::Stream* stream, const int64_t n, const T* x, const K* mask, T* y) { - MaskGpu<<As()->cuda_stream()>>>(n, x, mask, y); - } - - static void Scale(ep::Stream* stream, const int64_t n, const T* x, const K* scale, T* y) { - ScaleGpu<<As()->cuda_stream()>>>(n, x, scale, y); - } -}; - -#define INSTANTIATE_TWO_STAGE_REDUCE_KERNEL_UTIL_CUDA(data_type_pair, index_type_pair) \ - template struct TwoStageReduceKernelUtil; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_TWO_STAGE_REDUCE_KERNEL_UTIL_CUDA, - FLOATING_DATA_TYPE_SEQ INDEX_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ); -#undef INSTANTIATE_TWO_STAGE_REDUCE_KERNEL_UTIL_CUDA - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/unfold_kernel.cpp b/oneflow/user/kernels/unfold_kernel.cpp index d13dbe5591de5b3a818f1eb31702fabe53498d0f..8db4ac6c9279d5de792df5abaef2eabf2f092ac1 100644 --- a/oneflow/user/kernels/unfold_kernel.cpp +++ b/oneflow/user/kernels/unfold_kernel.cpp @@ -91,14 +91,10 @@ class UnfoldKernel final : public OpKernel { REGISTER_UNFOLD_KERNEL(DeviceType::kCPU, float) REGISTER_UNFOLD_KERNEL(DeviceType::kCPU, double) -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) REGISTER_UNFOLD_KERNEL(DeviceType::kCUDA, float) REGISTER_UNFOLD_KERNEL(DeviceType::kCUDA, double) #endif // WITH_CUDA -#ifdef WITH_ROCM -REGISTER_UNFOLD_KERNEL(DeviceType::kCUDA, float) -REGISTER_UNFOLD_KERNEL(DeviceType::kCUDA, double) -#endif // WITH_ROCM } // namespace user_op diff --git a/oneflow/user/kernels/unfold_kernel_util.cu b/oneflow/user/kernels/unfold_kernel_util.cu index 63c55ed8e5ca8f5a27eeb0effc523050cade8a8a..9b2748f51caaa5436ab092089aeb3626731d37ee 100644 --- a/oneflow/user/kernels/unfold_kernel_util.cu +++ b/oneflow/user/kernels/unfold_kernel_util.cu @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) #include "oneflow/core/framework/framework.h" #include "oneflow/core/cuda/elementwise.cuh" diff --git a/oneflow/user/kernels/unfold_kernel_util.hip.cpp b/oneflow/user/kernels/unfold_kernel_util.hip.cpp deleted file mode 100644 index 7831c8f4cd8974a305fc4e861cf0805cc6b683fc..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/unfold_kernel_util.hip.cpp +++ /dev/null @@ -1,70 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#ifdef WITH_ROCM -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/user/kernels/unfold_kernel_util.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace user_op { - -namespace { - -constexpr int kBlockSize = cuda::elementwise::kBlockSize; - -int GetNumBlocks(int64_t elem_cnt) { - int num_blocks = 0; - OF_CUDA_CHECK(cuda::elementwise::GetNumBlocks(elem_cnt, &num_blocks)); - return num_blocks; -} - -// NDIM range: (1, 2, 3) -// SDIM range: (1, 2), 1 indicates channels_last, 2 indicates channels_first -template -__global__ void CudaUnfoldForward(UnfoldParams params, const T* in, T* out) { - CUDA_1D_KERNEL_LOOP_T(INDEX_T, out_offset, params.out_elem_cnt) { - using ParamType = UnfoldParams; - INDEX_T in_index[ParamType::kInputNDim] = {0}; - INDEX_T out_index[ParamType::kOutputNDim] = {0}; - params.out_index_helper.OffsetToNdIndex(out_offset, out_index); - if (!UnfoldIndexTransform(params, out_index, in_index)) { - INDEX_T in_offset = params.in_index_helper.NdIndexToOffset(in_index); - out[out_offset] = in[in_offset]; - } else { - out[out_offset] = static_cast(kUnfoldPaddingValue); - } - } -} - -} // namespace - -template -struct UnfoldKernelUtil { - using ParamType = UnfoldParams; - static void Forward(ep::Stream* stream, const UnfoldParams* params, - const T* input_ptr, T* output_ptr) { - CudaUnfoldForward - <<out_elem_cnt), kBlockSize, 0, - stream->As()->cuda_stream()>>>(*params, input_ptr, output_ptr); - } -}; -INSTANTIATE_UNFOLD_KERNEL_UTIL_FOR_DEVICE(DeviceType::kCUDA) -} // namespace user_op -} // namespace oneflow -#endif // WITH_ROCM \ No newline at end of file diff --git a/oneflow/user/kernels/unfold_tensor_kernel.hip.cpp b/oneflow/user/kernels/unfold_tensor_kernel.hip.cpp deleted file mode 100644 index cc21d309bf00d74af3451a0f0538b520021a5f29..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/unfold_tensor_kernel.hip.cpp +++ /dev/null @@ -1,222 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/device/cuda_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/unfold_tensor_kernel_utils.h" - -namespace oneflow { - -namespace { - -const int32_t NDIMS = 16; -struct STRIDES { - int32_t val[NDIMS]; -}; - -template -__global__ void UnfoldTensorCudaKernel(const T* in_ptr, const STRIDES out_stride, - const STRIDES out_shape, const int32_t out_dims, - const int32_t elements, T* out_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = Offset(gid, out_stride.val, out_shape.val, out_dims - 1); - out_ptr[gid] = in_ptr[offset]; - gid += step; - } -} - -template -__global__ void UnfoldTensorGradCudaKernel(const T* dout_ptr, const STRIDES dout_stride, - const STRIDES dout_shape, const int32_t dout_dims, - const int32_t elements, T* din_ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - int32_t offset = Offset(gid, dout_stride.val, dout_shape.val, dout_dims - 1); - cuda::atomic::Add(&din_ptr[offset], dout_ptr[gid]); - gid += step; - } -} - -template -__global__ void InitPtr(const int32_t elements, T* ptr) { - int32_t gid = (blockDim.x * blockIdx.x) + threadIdx.x; - int32_t step = gridDim.x * blockDim.x; - while (gid < elements) { - ptr[gid] = static_cast(0); - gid += step; - } -} - -template -struct GpuUnfoldTensorFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, const STRIDES out_stride, - const STRIDES out_shape, const int32_t out_dims, const int32_t elements, - T* out_ptr) { - RUN_CUDA_KERNEL((UnfoldTensorCudaKernel), stream, elements, in_ptr, out_stride, out_shape, - out_dims, elements, out_ptr); - } -}; - -template -struct GpuUnfoldTensorGradFunctor final { - void operator()(ep::Stream* stream, const T* dout_ptr, const STRIDES dout_stride, - const STRIDES dout_shape, const int32_t dout_dims, const int32_t dout_elements, - const int32_t din_elements, T* din_ptr) { - RUN_CUDA_KERNEL((InitPtr), stream, din_elements, din_elements, din_ptr); - RUN_CUDA_KERNEL((UnfoldTensorGradCudaKernel), stream, dout_elements, dout_ptr, dout_stride, - dout_shape, dout_dims, dout_elements, din_ptr); - } -}; - -} // namespace - -template -class GpuUnfoldTensorKernel final : public user_op::OpKernel { - public: - GpuUnfoldTensorKernel() = default; - ~GpuUnfoldTensorKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("y", 0); - - const ShapeView& in_shape = in->shape_view(); - std::vector out_shape; - out_shape.resize(out->shape_view().NumAxes()); - for (int i = 0; i < out->shape_view().NumAxes(); ++i) { - out_shape[i] = out->shape_view().At(i); - } - const int32_t in_dims = in_shape.NumAxes(); - const int32_t out_dims = out_shape.size(); - const int32_t dimension = ctx->Attr("dimension"); - const int32_t step = ctx->Attr("step"); - - std::vector in_stride(in_dims, 1); - for (int32_t i = in_dims - 2; i >= 0; --i) { - in_stride[i] = in_shape.At(i + 1) * in_stride.at(i + 1); - } - - std::vector out_stride(in_dims + 1); - out_stride[in_dims] = in_dims == 0 ? 1 : in_stride[dimension]; - for (int d = 0; d < in_dims; ++d) { - if (d == dimension) { - out_stride[d] = step * in_stride[d]; - } else { - out_stride[d] = in_stride[d]; - } - } - - const T* in_ptr = in->dptr(); - T* out_ptr = out->mut_dptr(); - const int32_t out_size = out->shape_view().elem_cnt(); - - STRIDES out_stride_cuda; - for (int i = 0; i < out_dims; ++i) { out_stride_cuda.val[i] = out_stride[i]; } - STRIDES out_shape_cuda; - for (int i = 0; i < out_dims; ++i) { out_shape_cuda.val[i] = out_shape[i]; } - - GpuUnfoldTensorFunctor()(ctx->stream(), in_ptr, out_stride_cuda, out_shape_cuda, out_dims, - out_size, out_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UNFOLD_TENSOR_KERNEL(dtype) \ - REGISTER_USER_KERNEL("unfold_tensor") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)) - -REGISTER_UNFOLD_TENSOR_KERNEL(float); -REGISTER_UNFOLD_TENSOR_KERNEL(double); -REGISTER_UNFOLD_TENSOR_KERNEL(int32_t); -REGISTER_UNFOLD_TENSOR_KERNEL(int64_t); - -template -class GpuUnfoldTensorGradKernel final : public user_op::OpKernel { - public: - GpuUnfoldTensorGradKernel() = default; - ~GpuUnfoldTensorGradKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* dout = ctx->Tensor4ArgNameAndIndex("dy", 0); - const user_op::Tensor* in = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* din = ctx->Tensor4ArgNameAndIndex("dx", 0); - - const ShapeView& in_shape = in->shape_view(); - const int32_t in_dims = in_shape.NumAxes(); - std::vector din_stride(in_dims, 1); - for (int32_t i = in_dims - 2; i >= 0; --i) { - din_stride[i] = in_shape.At(i + 1) * din_stride.at(i + 1); - } - - std::vector dout_shape; - dout_shape.resize(dout->shape_view().NumAxes()); - for (int i = 0; i < dout->shape_view().NumAxes(); ++i) { - dout_shape[i] = dout->shape_view().At(i); - } - - const int32_t dout_dims = dout_shape.size(); - const int32_t dimension = ctx->Attr("dimension"); - const int32_t step = ctx->Attr("step"); - - std::vector dout_stride(in_dims + 1); - dout_stride[in_dims] = in_dims == 0 ? 1 : din_stride[dimension]; - for (int d = 0; d < in_dims; ++d) { - if (d == dimension) { - dout_stride[d] = step * din_stride[d]; - } else { - dout_stride[d] = din_stride[d]; - } - } - - STRIDES dout_stride_cuda; - for (int i = 0; i < dout_dims; ++i) { dout_stride_cuda.val[i] = dout_stride[i]; } - STRIDES dout_shape_cuda; - for (int i = 0; i < dout_dims; ++i) { dout_shape_cuda.val[i] = dout_shape[i]; } - - const T* dout_ptr = dout->dptr(); - T* din_ptr = din->mut_dptr(); - const int32_t dout_size = dout->shape_view().elem_cnt(); - const int32_t din_size = din->shape_view().elem_cnt(); - - GpuUnfoldTensorGradFunctor()(ctx->stream(), dout_ptr, dout_stride_cuda, dout_shape_cuda, - dout_dims, dout_size, din_size, din_ptr); - } - - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UNFOLD_TENSOR_GRAD_KERNEL(dtype) \ - REGISTER_USER_KERNEL("unfold_tensor_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("x", 0) == GetDataType::value)) - -REGISTER_UNFOLD_TENSOR_GRAD_KERNEL(float); -REGISTER_UNFOLD_TENSOR_GRAD_KERNEL(double); -REGISTER_UNFOLD_TENSOR_GRAD_KERNEL(int32_t); -REGISTER_UNFOLD_TENSOR_GRAD_KERNEL(int64_t); - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/unique_kernel.cpp b/oneflow/user/kernels/unique_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f1879c2f6f2a8ad5b8cc89baa93653d2bf5e3ba1 --- /dev/null +++ b/oneflow/user/kernels/unique_kernel.cpp @@ -0,0 +1,74 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/user/kernels/unique_kernel_util.h" +#include "oneflow/core/framework/framework.h" + +namespace oneflow { + +namespace { + +template +class UniqueKernel final : public user_op::OpKernel { + public: + UniqueKernel() = default; + ~UniqueKernel() = default; + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + const user_op::Tensor* x = ctx->Tensor4ArgNameAndIndex("x", 0); + user_op::Tensor* y = ctx->Tensor4ArgNameAndIndex("y", 0); + user_op::Tensor* idx = ctx->Tensor4ArgNameAndIndex("idx", 0); + user_op::Tensor* num_unique = ctx->Tensor4ArgNameAndIndex("num_unique", 0); + user_op::Tensor* tmp = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const bool& sorted = ctx->Attr("sorted"); + void* tmp_ptr = tmp ? tmp->mut_dptr() : nullptr; + int64_t tmp_size = tmp ? tmp->shape_view().elem_cnt() * GetSizeOfDataType(tmp->data_type()) : 0; + UniqueKernelUtil::Unique( + ctx->stream(), x->shape_view().elem_cnt(), x->dptr(), num_unique->mut_dptr(), + y->mut_dptr(), idx->mut_dptr(), tmp_ptr, tmp_size, sorted); + } + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } +}; + +template +user_op::InferTmpSizeFn GenInferTmpSizeFn() { + return [](user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("x", 0); + int64_t workspace_size_in_bytes = 0; + UniqueKernelUtil::GetUniqueWorkspaceSizeInBytes( + nullptr, x.shape().elem_cnt(), &workspace_size_in_bytes); + + return workspace_size_in_bytes; + }; +} + +#define REGISTER_UNIQUE_KERNEL(device_type_v, data_type_pair, indices_type_pair) \ + REGISTER_USER_KERNEL("unique") \ + .SetCreateFn>() \ + .SetIsMatchedHob( \ + (user_op::HobDeviceType() == device_type_v) \ + && (user_op::HobDataType("x", 0) == OF_PP_PAIR_SECOND(data_type_pair)) \ + && (user_op::HobDataType("idx", 0) == OF_PP_PAIR_SECOND(indices_type_pair))) \ + .SetInferTmpSizeFn(GenInferTmpSizeFn()); + +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_UNIQUE_KERNEL, DEVICE_TYPE_SEQ, ARITHMETIC_DATA_TYPE_SEQ, + INDEX_DATA_TYPE_SEQ) + +} // namespace + +} // namespace oneflow diff --git a/oneflow/user/kernels/unique_kernel_util.cpp b/oneflow/user/kernels/unique_kernel_util.cpp index 8bce12c2e81fe8e3f09b9554d4c12ce8e99c1a81..4af66c15e65bfd7736cf44771401fa68c65a0a71 100644 --- a/oneflow/user/kernels/unique_kernel_util.cpp +++ b/oneflow/user/kernels/unique_kernel_util.cpp @@ -20,16 +20,23 @@ namespace oneflow { template struct UniqueKernelUtil { static void Unique(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, - IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes) { + IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes, bool sorted) { UniqueKernelUtil::UniqueWithCounts( - stream, n, in, num_unique, unique_out, idx_out, nullptr, workspace, - workspace_size_in_bytes); + stream, n, in, num_unique, unique_out, idx_out, nullptr, workspace, workspace_size_in_bytes, + sorted); } static void UniqueWithCounts(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, IDX* count, void* workspace, - int64_t workspace_size_in_bytes) { + int64_t workspace_size_in_bytes, bool sorted) { + std::vector sorted_idx(n); + std::iota(sorted_idx.begin(), sorted_idx.end(), 0); + if (sorted) { + std::sort(sorted_idx.begin(), sorted_idx.end(), + [&in](size_t a, size_t b) { return in[a] < in[b]; }); + } + HashMap map; - FOR_RANGE(int64_t, i, 0, n) { + for (int64_t i : sorted_idx) { KEY in_i = in[i]; auto it = map.find(in_i); if (it == map.end()) { @@ -46,6 +53,7 @@ struct UniqueKernelUtil { } *num_unique = map.size(); } + static void GetUniqueWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, int64_t* workspace_size_in_bytes) { *workspace_size_in_bytes = 1; diff --git a/oneflow/user/kernels/unique_kernel_util.cu b/oneflow/user/kernels/unique_kernel_util.cu index 5639cf1d82435319a5874a5b637a02cf56fad6a1..7c9f60a0f3fca15b4e433adb98c84b9e53701ffa 100644 --- a/oneflow/user/kernels/unique_kernel_util.cu +++ b/oneflow/user/kernels/unique_kernel_util.cu @@ -30,10 +30,10 @@ constexpr cuda::unique::Flag kUniqueWithCountsFlag = template struct UniqueKernelUtil { static void Unique(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, - IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes); + IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes, bool sorted); static void UniqueWithCounts(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, IDX* count, void* workspace, - int64_t workspace_size_in_bytes); + int64_t workspace_size_in_bytes, bool sorted); static void GetUniqueWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, int64_t* workspace_size_in_bytes); static void GetUniqueWithCountsWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, @@ -41,11 +41,10 @@ struct UniqueKernelUtil { }; template -void UniqueKernelUtil::Unique(ep::Stream* stream, int64_t n, - const KEY* in, IDX* num_unique, - KEY* unique_out, IDX* idx_out, - void* workspace, - int64_t workspace_size_in_bytes) { +void UniqueKernelUtil::Unique( + ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, + void* workspace, int64_t workspace_size_in_bytes, + bool sorted /* not used, always return sorted output in CUDA,it`s the same as torch.unique*/) { OF_CUDA_CHECK((cuda::unique::Launch(kUniqueFlag, n, in, unique_out, num_unique, idx_out, nullptr, workspace, workspace_size_in_bytes, stream->As()->cuda_stream()))); @@ -54,7 +53,8 @@ void UniqueKernelUtil::Unique(ep::Stream* stream, i template void UniqueKernelUtil::UniqueWithCounts( ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, - IDX* count, void* workspace, int64_t workspace_size_in_bytes) { + IDX* count, void* workspace, int64_t workspace_size_in_bytes, + bool sorted /* not used, always return sorted output in CUDA,it`s the same as torch.unique*/) { OF_CUDA_CHECK((cuda::unique::Launch( kUniqueWithCountsFlag, n, in, unique_out, num_unique, idx_out, count, workspace, workspace_size_in_bytes, stream->As()->cuda_stream()))); diff --git a/oneflow/user/kernels/unique_kernel_util.h b/oneflow/user/kernels/unique_kernel_util.h index c8a0dfbc2fa4f60854e21a32a233bfeec003e070..177202b354a55bdb73c20a2cf3ca9d1d576938b3 100644 --- a/oneflow/user/kernels/unique_kernel_util.h +++ b/oneflow/user/kernels/unique_kernel_util.h @@ -24,10 +24,10 @@ namespace oneflow { template struct UniqueKernelUtil { static void Unique(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, - IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes); + IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes, bool sorted); static void UniqueWithCounts(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, IDX* count, void* workspace, - int64_t workspace_size_in_bytes); + int64_t workspace_size_in_bytes, bool sorted); static void GetUniqueWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, int64_t* workspace_size_in_bytes); static void GetUniqueWithCountsWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, diff --git a/oneflow/user/kernels/unique_kernel_util.hip.cpp b/oneflow/user/kernels/unique_kernel_util.hip.cpp deleted file mode 100644 index b69bba148ffd2235fd940ff931409183386466af..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/unique_kernel_util.hip.cpp +++ /dev/null @@ -1,86 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/unique_kernel_util.h" -#include "oneflow/core/hip/unique.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -constexpr cuda::unique::Flag kUniqueFlag = cuda::unique::kOutputInverseIndices; -constexpr cuda::unique::Flag kUniqueWithCountsFlag = - cuda::unique::kOutputInverseIndices | cuda::unique::kOutputCounts; - -} // namespace - -template -struct UniqueKernelUtil { - static void Unique(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, - IDX* idx_out, void* workspace, int64_t workspace_size_in_bytes); - static void UniqueWithCounts(ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, - KEY* unique_out, IDX* idx_out, IDX* count, void* workspace, - int64_t workspace_size_in_bytes); - static void GetUniqueWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, - int64_t* workspace_size_in_bytes); - static void GetUniqueWithCountsWorkspaceSizeInBytes(ep::Stream* stream, int64_t n, - int64_t* workspace_size_in_bytes); -}; - -template -void UniqueKernelUtil::Unique(ep::Stream* stream, int64_t n, - const KEY* in, IDX* num_unique, - KEY* unique_out, IDX* idx_out, - void* workspace, - int64_t workspace_size_in_bytes) { - OF_CUDA_CHECK((cuda::unique::Launch(kUniqueFlag, n, in, unique_out, num_unique, idx_out, - nullptr, workspace, workspace_size_in_bytes, - stream->As()->cuda_stream()))); -} - -template -void UniqueKernelUtil::UniqueWithCounts( - ep::Stream* stream, int64_t n, const KEY* in, IDX* num_unique, KEY* unique_out, IDX* idx_out, - IDX* count, void* workspace, int64_t workspace_size_in_bytes) { - OF_CUDA_CHECK((cuda::unique::Launch( - kUniqueWithCountsFlag, n, in, unique_out, num_unique, idx_out, count, workspace, - workspace_size_in_bytes, stream->As()->cuda_stream()))); -} - -template -void UniqueKernelUtil::GetUniqueWorkspaceSizeInBytes( - ep::Stream* stream, int64_t n, int64_t* workspace_size_in_bytes) { - size_t ws = 0; - OF_CUDA_CHECK((cuda::unique::GetWorkspaceSize(kUniqueFlag, n, &ws))); - *workspace_size_in_bytes = static_cast(ws); -} - -template -void UniqueKernelUtil::GetUniqueWithCountsWorkspaceSizeInBytes( - ep::Stream* stream, int64_t n, int64_t* workspace_size_in_bytes) { - size_t ws = 0; - OF_CUDA_CHECK((cuda::unique::GetWorkspaceSize(kUniqueWithCountsFlag, n, &ws))); - *workspace_size_in_bytes = static_cast(ws); -} - -#define INSTANTIATE_UNIQUE_KERNEL_UTIL_CUDA(key_type_pair, idx_type_pair) \ - template struct UniqueKernelUtil; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_UNIQUE_KERNEL_UTIL_CUDA, ARITHMETIC_DATA_TYPE_SEQ, - INDEX_DATA_TYPE_SEQ); -#undef INSTANTIATE_UNIQUE_KERNEL_UTIL_CUDA - -} // namespace oneflow diff --git a/oneflow/user/kernels/unique_with_counts_kernel.cpp b/oneflow/user/kernels/unique_with_counts_kernel.cpp index d15cb7a66b1d91e175874c9c5e26d522a91e901f..43d0d7120993ef0f54c293140213599126f1eaf5 100644 --- a/oneflow/user/kernels/unique_with_counts_kernel.cpp +++ b/oneflow/user/kernels/unique_with_counts_kernel.cpp @@ -34,11 +34,12 @@ class UniqueWithCountsKernel final : public user_op::OpKernel { user_op::Tensor* count = ctx->Tensor4ArgNameAndIndex("count", 0); user_op::Tensor* num_unique = ctx->Tensor4ArgNameAndIndex("num_unique", 0); user_op::Tensor* tmp = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); + const bool& sorted = ctx->Attr("sorted"); void* tmp_ptr = tmp ? tmp->mut_dptr() : nullptr; int64_t tmp_size = tmp ? tmp->shape_view().elem_cnt() * GetSizeOfDataType(tmp->data_type()) : 0; UniqueKernelUtil::UniqueWithCounts( ctx->stream(), x->shape_view().elem_cnt(), x->dptr(), num_unique->mut_dptr(), - y->mut_dptr(), idx->mut_dptr(), count->mut_dptr(), tmp_ptr, tmp_size); + y->mut_dptr(), idx->mut_dptr(), count->mut_dptr(), tmp_ptr, tmp_size, sorted); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; diff --git a/oneflow/user/kernels/unsorted_segment_sum_kernel.cpp b/oneflow/user/kernels/unsorted_segment_sum_kernel.cpp index b1efa715f1319e136ad90bca9f4053a17673bb5d..babf6ddd788010744e0f98ab752dde1238c07abc 100644 --- a/oneflow/user/kernels/unsorted_segment_sum_kernel.cpp +++ b/oneflow/user/kernels/unsorted_segment_sum_kernel.cpp @@ -18,6 +18,12 @@ limitations under the License. #include "oneflow/core/kernel/cuda_graph_support.h" #include "oneflow/core/job/nd_sbp_util.h" #include "oneflow/core/ep/include/primitive/cast.h" +#ifdef WITH_CUDA +#include +#endif +#ifdef WITH_ROCM +#include +#endif namespace oneflow { @@ -138,8 +144,8 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_UNSORTED_SEGMENT_SUM_KERNEL_CASE, DEVI OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_UNSORTED_SEGMENT_SUM_LIKE_KERNEL_CASE, DEVICE_TYPE_SEQ, UNSORTED_SEGMENT_SUM_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -template +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template class UnsortedSegmentSumHalfKernel final : public user_op::OpKernel { public: UnsortedSegmentSumHalfKernel() = default; @@ -172,14 +178,14 @@ class UnsortedSegmentSumHalfKernel final : public user_op::OpKernel { offset = sum_cache->lower(); } - UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( - ctx->stream(), segment_ids->dptr(), data->dptr(), num_segment_ids, num_segments, + UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( + ctx->stream(), segment_ids->dptr(), data->dptr(), num_segment_ids, num_segments, outer_dim_size, inner_dim_size, offset, tmp_buf->mut_dptr()); auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); + ctx->device_type(), DataType::kFloat, out->data_type()); CHECK(f2h); - f2h->Launch(ctx->stream(), tmp_buf->dptr(), out->mut_dptr(), + f2h->Launch(ctx->stream(), tmp_buf->dptr(), out->mut_dptr(), out->shape_view().elem_cnt()); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } @@ -187,14 +193,15 @@ class UnsortedSegmentSumHalfKernel final : public user_op::OpKernel { #define REGISTER_UNSORTED_SEGMENT_SUM_HALF_HALF_KERNEL(out_type, segment_ids_type, kernel_type) \ REGISTER_USER_KERNEL(kernel_type) \ - .SetCreateFn>() \ + .SetCreateFn>() \ .SetIsMatchedHob( \ (user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("segment_ids", 0) == OF_PP_PAIR_SECOND(segment_ids_type)) \ && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(out_type))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape* out_shape = ctx->OutputShape("out", 0); \ - return GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(float)); \ + const Shape& out_shape = ctx->OutputShape("out", 0); \ + return GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(float)); \ }); #define REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE(out_type, segment_ids_type) \ @@ -206,81 +213,15 @@ class UnsortedSegmentSumHalfKernel final : public user_op::OpKernel { OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE, FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) -#undef REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE - -#endif // WITH_CUDA - -#ifdef WITH_ROCM -template -class UnsortedSegmentSumHalfKernel final : public user_op::OpKernel { - public: - UnsortedSegmentSumHalfKernel() = default; - ~UnsortedSegmentSumHalfKernel() override = default; - - std::shared_ptr InitOpKernelCache( - user_op::KernelCacheContext* ctx) const override { - return CreateUnsortedSegmentSumOpKernelCache(ctx); - } - - private: - void Compute(user_op::KernelComputeContext* ctx, user_op::OpKernelState*, - const user_op::OpKernelCache* cache) const override { - const user_op::Tensor* data = ctx->Tensor4ArgNameAndIndex("data", 0); - const user_op::Tensor* segment_ids = ctx->Tensor4ArgNameAndIndex("segment_ids", 0); - int64_t axis = ctx->Attr("axis"); - user_op::Tensor* tmp_buf = ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0); - user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - int64_t outer_dim_size = out->shape_view().Count(0, axis); - int64_t num_segments = out->shape_view().At(axis); - int64_t inner_dim_size = out->shape_view().Count(axis + 1); - int64_t num_segment_ids = segment_ids->shape_view().elem_cnt(); - Memset(ctx->stream(), tmp_buf->mut_dptr(), 0, - out->shape_view().elem_cnt() * sizeof(float)); - int64_t offset = 0; - if (cache != nullptr) { - auto* sum_cache = dynamic_cast(cache); - CHECK_NOTNULL(sum_cache); - CHECK_EQ(out->shape_view().At(axis), sum_cache->upper() - sum_cache->lower()); - offset = sum_cache->lower(); - } - - UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( - ctx->stream(), segment_ids->dptr(), data->dptr(), num_segment_ids, num_segments, - outer_dim_size, inner_dim_size, offset, tmp_buf->mut_dptr()); - - auto f2h = ep::primitive::NewPrimitive( - ctx->device_type(), DataType::kFloat, DataType::kFloat16); - CHECK(f2h); - f2h->Launch(ctx->stream(), tmp_buf->dptr(), out->mut_dptr(), - out->shape_view().elem_cnt()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return true; } -}; - -#define REGISTER_UNSORTED_SEGMENT_SUM_HALF_HALF_KERNEL(out_type, segment_ids_type, kernel_type) \ - REGISTER_USER_KERNEL(kernel_type) \ - .SetCreateFn>() \ - .SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("segment_ids", 0) == OF_PP_PAIR_SECOND(segment_ids_type)) \ - && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(out_type))) \ - .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - const Shape* out_shape = ctx->OutputShape("out", 0); \ - return GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(float)); \ - }); - -#define REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE(out_type, segment_ids_type) \ - REGISTER_UNSORTED_SEGMENT_SUM_HALF_HALF_KERNEL(out_type, segment_ids_type, \ - ("unsorted_segment_sum")) \ - REGISTER_UNSORTED_SEGMENT_SUM_HALF_HALF_KERNEL(out_type, segment_ids_type, \ - ("unsorted_segment_sum_like")) - +#if CUDA_VERSION >= 11000 OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE, - FLOAT16_DATA_TYPE_SEQ, INDEX_DATA_TYPE_SEQ) + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16), + INDEX_DATA_TYPE_SEQ) +#endif #undef REGISTER_UNSORTED_SEGMENT_SUM_HALF_KERNEL_CASE -#endif // WITH_ROCM +#endif // WITH_CUDA } // namespace user_op diff --git a/oneflow/user/kernels/unsorted_segment_sum_kernel_util.cu b/oneflow/user/kernels/unsorted_segment_sum_kernel_util.cu index acb90798278678d6ff21edc149152bf6c88b527b..7226211ceb723d5288323353183ece5526d84ef8 100644 --- a/oneflow/user/kernels/unsorted_segment_sum_kernel_util.cu +++ b/oneflow/user/kernels/unsorted_segment_sum_kernel_util.cu @@ -34,9 +34,22 @@ __device__ __forceinline__ bool IsZero(half v) { return v == static_cast(0); } +#if CUDA_VERSION >= 11000 + +template<> +__device__ __forceinline__ bool IsZero(nv_bfloat16 v) { + return v == __float2bfloat16(0); +} + +#endif + template<> __device__ __forceinline__ bool IsZero(half2 v) { +#ifdef WITH_ROCM + return static_cast(v.data.x) == static_cast(0) && static_cast(v.data.y) == static_cast(0); +#else return v.x == static_cast(0) && v.y == static_cast(0); +#endif } template @@ -210,6 +223,12 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_UTIL_CUDA, OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA, OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat), UNSORTED_SEGMENT_SUM_INDEX_TYPE_SEQ, FLOAT16_DATA_TYPE_SEQ); +#if CUDA_VERSION >= 11000 +OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA, + OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat), + UNSORTED_SEGMENT_SUM_INDEX_TYPE_SEQ, + OF_PP_MAKE_TUPLE_SEQ(nv_bfloat16, DataType::kBFloat16)); +#endif #undef INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA diff --git a/oneflow/user/kernels/unsorted_segment_sum_kernel_util.hip.cpp b/oneflow/user/kernels/unsorted_segment_sum_kernel_util.hip.cpp deleted file mode 100644 index 6334ee726878918d6409069e4cee8979be186393..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/unsorted_segment_sum_kernel_util.hip.cpp +++ /dev/null @@ -1,222 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/user/kernels/unsorted_segment_sum_kernel_util.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/core/kernel/kernel.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" -#include - -namespace oneflow { - -namespace { - -template -__device__ __forceinline__ bool IsZero(T v) { - return v == 0; -} - -template<> -__device__ __forceinline__ bool IsZero(half v) { - return v == static_cast(0); -} - -template<> -__device__ __forceinline__ bool IsZero(half2 v) { - // return v.x == static_cast(0) && v.y == static_cast(0); - return v.data.x == 0 && v.data.y == 0; -} - -template -__global__ void UnsortedSegmentSumGpu(const IDX data_elem_cnt, - const NdIndexOffsetHelper in_helper, - const NdIndexOffsetHelper out_helper, const U* data, - const K* segment_ids, const IDX num_segments, - const IDX segment_id_offset, T* out) { - CUDA_1D_KERNEL_LOOP_T(IDX, i, data_elem_cnt) { - const U val = data[i]; - if (!IsZero(val)) { - IDX outer_idx, segment_id_idx, inner_idx; - in_helper.OffsetToNdIndex(i, outer_idx, segment_id_idx, inner_idx); - const K origin_idx = segment_ids[segment_id_idx]; - assert(origin_idx >= 0); - const IDX idx = origin_idx - segment_id_offset; - if (idx >= 0 && idx < num_segments) { - const int64_t out_offset = out_helper.NdIndexToOffset(outer_idx, idx, inner_idx); - if (out_offset >= 0) { cuda::atomic::Add(out + out_offset, static_cast(val)); } - } - } - } -} - -template -__global__ void UnsortedSegmentColSumGpu(const IDX data_elem_cnt, - const NdIndexOffsetHelper in_helper, - const NdIndexOffsetHelper out_helper, - const U* data, const K* segment_ids, - const IDX num_segments, const IDX segment_id_offset, - T* out) { - CUDA_1D_KERNEL_LOOP_T(IDX, i, data_elem_cnt) { - const U val = data[i]; - if (!IsZero(val)) { - IDX outer_idx, segment_id_idx; - in_helper.OffsetToNdIndex(i, outer_idx, segment_id_idx); - const K origin_idx = segment_ids[segment_id_idx]; - assert(origin_idx >= 0); - const IDX idx = origin_idx - segment_id_offset; - if (idx >= 0 && idx < num_segments) { - const int64_t out_offset = out_helper.NdIndexToOffset(outer_idx, idx); - if (out_offset >= 0) { cuda::atomic::Add(out + out_offset, static_cast(val)); } - } - } - } -} - -template -__global__ void UnsortedSegmentRowSumGpu(const IDX data_elem_cnt, - const NdIndexOffsetHelper in_helper, - const NdIndexOffsetHelper out_helper, - const U* data, const K* segment_ids, - const IDX num_segments, const IDX segment_id_offset, - T* out) { - CUDA_1D_KERNEL_LOOP_T(IDX, i, data_elem_cnt) { - const U val = data[i]; - if (!IsZero(val)) { - IDX segment_id_idx, inner_idx; - in_helper.OffsetToNdIndex(i, segment_id_idx, inner_idx); - const K origin_idx = segment_ids[segment_id_idx]; - assert(origin_idx >= 0); - const IDX idx = origin_idx - segment_id_offset; - if (idx >= 0 && idx < num_segments) { - const int64_t out_offset = out_helper.NdIndexToOffset(idx, inner_idx); - if (out_offset >= 0) { cuda::atomic::Add(out + out_offset, static_cast(val)); } - } - } - } -} - -template -void UnsortedSegmentSumUtil(ep::Stream* stream, const K* segment_ids, const U* data, - IDX num_segment_ids, IDX num_segments, IDX outer_dim_size, - IDX inner_dim_size, IDX segment_id_offset, T* out) { - const IDX data_elem_cnt = num_segment_ids * outer_dim_size * inner_dim_size; - if (inner_dim_size == 1) { - NdIndexOffsetHelper in_helper(outer_dim_size, num_segment_ids); - NdIndexOffsetHelper out_helper(outer_dim_size, num_segments); - UnsortedSegmentColSumGpu - <<As()->cuda_stream()>>>(data_elem_cnt, in_helper, out_helper, - data, segment_ids, num_segments, - segment_id_offset, out); - - } else if (outer_dim_size == 1) { - NdIndexOffsetHelper in_helper(num_segment_ids, inner_dim_size); - NdIndexOffsetHelper out_helper(num_segments, inner_dim_size); - UnsortedSegmentRowSumGpu - <<As()->cuda_stream()>>>(data_elem_cnt, in_helper, out_helper, - data, segment_ids, num_segments, - segment_id_offset, out); - - } else { - NdIndexOffsetHelper in_helper(outer_dim_size, num_segment_ids, inner_dim_size); - NdIndexOffsetHelper out_helper(outer_dim_size, num_segments, inner_dim_size); - UnsortedSegmentSumGpu - <<As()->cuda_stream()>>>(data_elem_cnt, in_helper, out_helper, - data, segment_ids, num_segments, - segment_id_offset, out); - } -} - -template -void DispatchDataType(ep::Stream* stream, const K* segment_ids, const U* data, - int64_t num_segment_ids, int64_t num_segments, int64_t outer_dim_size, - int64_t inner_dim_size, int64_t segment_id_offset, T* out) { - auto* cuda_stream = stream->As(); - if (std::is_same::value && std::is_same::value - && cuda_stream->device_properties().major >= 6 - && reinterpret_cast(data) % sizeof(half2) == 0 - && reinterpret_cast(out) % sizeof(half2) == 0 && inner_dim_size % 2 == 0) { - UnsortedSegmentSumUtil( - stream, segment_ids, reinterpret_cast(data), num_segment_ids, num_segments, - outer_dim_size, inner_dim_size / 2, segment_id_offset, reinterpret_cast(out)); - } else { - UnsortedSegmentSumUtil(stream, segment_ids, data, num_segment_ids, num_segments, - outer_dim_size, inner_dim_size, segment_id_offset, out); - } -} - -} // namespace - -template -struct UnsortedSegmentSumKernelUtil final { - static void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const U* data, - int64_t num_segment_ids, int64_t num_segments, - int64_t outer_dim_size, int64_t inner_dim_size, - int64_t segment_id_offset, T* out) { - const int64_t data_elem_cnt = num_segment_ids * outer_dim_size * inner_dim_size; - const int64_t out_elem_cnt = outer_dim_size * num_segments * inner_dim_size; - - if (std::max(data_elem_cnt, out_elem_cnt) < GetMaxVal() / 2) { - DispatchDataType(stream, segment_ids, data, num_segment_ids, num_segments, - outer_dim_size, inner_dim_size, segment_id_offset, out); - } else { - DispatchDataType(stream, segment_ids, data, num_segment_ids, num_segments, - outer_dim_size, inner_dim_size, segment_id_offset, out); - } - } -}; - -template -struct UnsortedSegmentSumKernelUtil final { - static void UnsortedSegmentSum(ep::Stream* stream, const K* segment_ids, const float16* data, - int64_t num_segment_ids, int64_t num_segments, - int64_t outer_dim_size, int64_t inner_dim_size, - int64_t segment_id_offset, float* out) { - UnsortedSegmentSumKernelUtil::UnsortedSegmentSum( - stream, segment_ids, reinterpret_cast(data), num_segment_ids, num_segments, - outer_dim_size, inner_dim_size, segment_id_offset, out); - } -}; - -#define INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_UTIL_CUDA(in_type_pair, index_type_pair) \ - template struct UnsortedSegmentSumKernelUtil; -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_UTIL_CUDA, - UNSORTED_SEGMENT_SUM_DATA_TYPE_SEQ, - UNSORTED_SEGMENT_SUM_INDEX_TYPE_SEQ); -#undef INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_UTIL_CUDA - -#define INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA(in_type_pair, index_type_pair, \ - out_type_pair) \ - template struct UnsortedSegmentSumKernelUtil; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA, - OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat), - UNSORTED_SEGMENT_SUM_INDEX_TYPE_SEQ, FLOAT16_DATA_TYPE_SEQ); - -#undef INITIATE_UNSORTED_SEGMENT_SUM_KERNEL_HALF_CUDA - -template struct UnsortedSegmentSumKernelUtil; -template struct UnsortedSegmentSumKernelUtil; -template struct UnsortedSegmentSumKernelUtil; - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/upsample_bicubic_2d_kernel.cu b/oneflow/user/kernels/upsample_bicubic_2d_kernel.cu index eabdaa4ea7e60f6ad701bc7dc0b95bccd93b1465..10e3e23f781aec7d8d943edf35a762f23573910d 100644 --- a/oneflow/user/kernels/upsample_bicubic_2d_kernel.cu +++ b/oneflow/user/kernels/upsample_bicubic_2d_kernel.cu @@ -18,6 +18,7 @@ limitations under the License. #include "oneflow/core/common/nd_index_offset_helper.h" #include "oneflow/core/cuda/atomic.cuh" #include "oneflow/user/kernels/upsample_kernel.h" +#include "oneflow/core/kernel/kernel_util.cuh" namespace oneflow { @@ -25,10 +26,11 @@ namespace { template __device__ void upsample_increment_value_bounded_cuda(T* data, int64_t width, int64_t height, - int64_t x, int64_t y, T value) { + int64_t element, int64_t x, int64_t y, + T value) { int64_t access_x = max(min(x, width - 1), static_cast(0)); int64_t access_y = max(min(y, height - 1), static_cast(0)); - cuda::atomic::Add(data + access_y * width + access_x, value); + FastAtomicAdd(data, access_y * width + access_x, element, value); } template @@ -110,8 +112,8 @@ __global__ void UpsampleBicubic2dBackward(const int64_t elem_cnt, const T* dy_dp for (int64_t i = 0; i < 4; i++) { for (int64_t j = 0; j < 4; j++) { - upsample_increment_value_bounded_cuda(in, in_width, in_height, input_x - 1 + i, - input_y - 1 + j, + upsample_increment_value_bounded_cuda(in, in_width, in_height, elem_cnt, + input_x - 1 + i, input_y - 1 + j, out_value * y_coeffs[j] * x_coeffs[i]); } } diff --git a/oneflow/user/kernels/upsample_bicubic_2d_kernel.hip.cpp b/oneflow/user/kernels/upsample_bicubic_2d_kernel.hip.cpp deleted file mode 100644 index 6525f417d8fa6b678b1c5f0ab74b6b3cc00b97c2..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/upsample_bicubic_2d_kernel.hip.cpp +++ /dev/null @@ -1,234 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/upsample_kernel.h" - -namespace oneflow { - -namespace { - -template -__device__ void upsample_increment_value_bounded_cuda(T* data, int64_t width, int64_t height, - int64_t x, int64_t y, T value) { - int64_t access_x = max(min(x, width - 1), static_cast(0)); - int64_t access_y = max(min(y, height - 1), static_cast(0)); - cuda::atomic::Add(data + access_y * width + access_x, value); -} - -template -__global__ void UpsampleBicubic2dForward(const int64_t elem_cnt, const T* in_dptr, - const int64_t nbatch, const int64_t channels, - const int64_t in_height, const int64_t in_width, - const int64_t out_height, const int64_t out_width, - const float scale_height, const float scale_width, - bool align_corners, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(idx, elem_cnt) { - const int output_x = idx % out_width; - const int output_y = idx / out_width; - - const T* in = in_dptr; - T* out = out_dptr; - - const T real_x = GetAreaPixel(scale_width, output_x, align_corners, /*cubic=*/true); - int64_t input_x = floor(1.0 * real_x); - const T t_x = real_x - input_x; - - const T real_y = GetAreaPixel(scale_height, output_y, align_corners, /*cubic=*/true); - int64_t input_y = floor(1.0 * real_y); - const T t_y = real_y - input_y; - - for (int64_t c = 0; c < channels * nbatch; c++) { - T coefficients[4]; - - // Interpolate 4 times in the x direction - for (int64_t i = 0; i < 4; i++) { - coefficients[i] = cubic_interp1d( - upsample_get_value_bounded(in, in_width, in_height, input_x - 1, input_y - 1 + i), - upsample_get_value_bounded(in, in_width, in_height, input_x + 0, input_y - 1 + i), - upsample_get_value_bounded(in, in_width, in_height, input_x + 1, input_y - 1 + i), - upsample_get_value_bounded(in, in_width, in_height, input_x + 2, input_y - 1 + i), - t_x); - } - - // Interpolate in the y direction using x interpolations - out[output_y * out_width + output_x] = cubic_interp1d( - coefficients[0], coefficients[1], coefficients[2], coefficients[3], t_y); - - // Move to next channel - in += in_width * in_height; - out += out_width * out_height; - } - } -} - -template -__global__ void UpsampleBicubic2dBackward(const int64_t elem_cnt, const T* dy_dptr, - const int64_t nbatch, const int64_t channels, - const int64_t in_height, const int64_t in_width, - const int64_t out_height, const int64_t out_width, - const float scale_height, const float scale_width, - bool align_corners, T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(idx, elem_cnt) { - const int output_x = idx % out_width; - const int output_y = idx / out_width; - - T* in = dx_dptr; - const T* out = dy_dptr; - - T real_x = GetAreaPixel(scale_width, output_x, align_corners, true); - int64_t input_x = floor(1.0 * real_x); - T t_x = real_x - input_x; - - T real_y = GetAreaPixel(scale_height, output_y, align_corners, true); - int64_t input_y = floor(1.0 * real_y); - T t_y = real_y - input_y; - - T x_coeffs[4]; - T y_coeffs[4]; - - get_cubic_upsample_coefficients(x_coeffs, t_x); - get_cubic_upsample_coefficients(y_coeffs, t_y); - - for (int64_t c = 0; c < channels * nbatch; c++) { - T out_value = out[output_y * out_width + output_x]; - - for (int64_t i = 0; i < 4; i++) { - for (int64_t j = 0; j < 4; j++) { - upsample_increment_value_bounded_cuda(in, in_width, in_height, input_x - 1 + i, - input_y - 1 + j, - out_value * y_coeffs[j] * x_coeffs[i]); - } - } - - in += in_width * in_height; - out += out_width * out_height; - } - } -} - -} // namespace - -template -class UpsampleBicubic2dGPUKernel final : public user_op::OpKernel { - public: - UpsampleBicubic2dGPUKernel() = default; - ~UpsampleBicubic2dGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const T* in_ptr = x_tensor->dptr(); - T* out_ptr = y_tensor->mut_dptr(); - const bool align_corners = ctx->Attr("align_corners"); - - const int nbatch = x_tensor->shape_view().At(0); - const int channels = x_tensor->shape_view().At(1); - const int64_t in_height = x_tensor->shape_view().At(2); - const int64_t in_width = x_tensor->shape_view().At(3); - const int64_t out_height = y_tensor->shape_view().At(2); - const int64_t out_width = y_tensor->shape_view().At(3); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - const int64_t elem_cnt = out_height * out_width; - - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), - x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - - RUN_CUDA_KERNEL((UpsampleBicubic2dForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), nbatch, channels, in_height, in_width, out_height, - out_width, scale_height, scale_width, align_corners, y_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleBicubic2dGradGPUKernel final : public user_op::OpKernel { - public: - UpsampleBicubic2dGradGPUKernel() = default; - ~UpsampleBicubic2dGradGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const bool align_corners = ctx->Attr("align_corners"); - - const int nbatch = dx_tensor->shape_view().At(0); - const int channels = dx_tensor->shape_view().At(1); - const int64_t in_height = dx_tensor->shape_view().At(2); - const int64_t in_width = dx_tensor->shape_view().At(3); - const int64_t out_height = dy_tensor->shape_view().At(2); - const int64_t out_width = dy_tensor->shape_view().At(3); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - const int64_t elem_cnt = out_height * out_width; - - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), - dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - - RUN_CUDA_KERNEL((UpsampleBicubic2dBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), nbatch, channels, in_height, in_width, out_height, - out_width, scale_height, scale_width, align_corners, - dx_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPLE_BICUBIC_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_bicubic_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_bicubic_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPLE_BICUBIC_CUDA_KERNEL(float) -REGISTER_UPSAMPLE_BICUBIC_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/upsample_bilinear_2d_kernel.cu b/oneflow/user/kernels/upsample_bilinear_2d_kernel.cu index 2dc3627d1b50054eabf1c9b17c705257e855cae8..0eee91930445a015f6c278f0517812a81bb167a6 100644 --- a/oneflow/user/kernels/upsample_bilinear_2d_kernel.cu +++ b/oneflow/user/kernels/upsample_bilinear_2d_kernel.cu @@ -23,6 +23,39 @@ namespace oneflow { namespace { +__device__ __forceinline__ void GetBilinearParamHalf(const bool align_corners, const int64_t h, + const int64_t w, const int64_t in_height, + const int64_t in_width, const double scale_h, + const double scale_w, + BilinearParam* params) { + half h1r; + if (align_corners) { + h1r = static_cast(scale_h * static_cast(h)); + } else { + h1r = h1r = static_cast((static_cast(h) + 0.5f) * scale_h - 0.5f); + h1r = h1r < static_cast(0.0) ? static_cast(0.0) : h1r; + } + const int64_t h1 = int(h1r); + const int64_t h1p = (h1 < in_height - 1) ? 1 : 0; + + half w1r; + if (align_corners) { + w1r = static_cast(scale_w * static_cast(w)); + } else { + w1r = static_cast((static_cast(w) + 0.5f) * scale_w - 0.5f); + w1r = w1r < static_cast(0.0) ? static_cast(0.0) : w1r; + } + const int64_t w1 = int(w1r); + const int64_t w1p = (w1 < in_width - 1) ? 1 : 0; + + params->top_h_index = h1; + params->bottom_h_index = h1 + h1p; + params->h_lerp = h1r - static_cast(h1 * 1.0); + params->left_w_index = w1; + params->right_w_index = w1 + w1p; + params->w_lerp = w1r - static_cast(w1 * 1.0); +} + template __global__ void UpsampleBilinear2DForward(const int64_t elem_cnt, const T* in_dptr, NdIndexOffsetHelper in_helper, @@ -47,6 +80,33 @@ __global__ void UpsampleBilinear2DForward(const int64_t elem_cnt, const T* in_dp } } +template<> +__global__ void UpsampleBilinear2DForward(const int64_t elem_cnt, const half* in_dptr, + NdIndexOffsetHelper in_helper, + NdIndexOffsetHelper out_helper, + const int64_t in_height, const int64_t in_width, + const half scale_h, const half scale_w, + const bool align_corners, half* out_dptr) { + CUDA_1D_KERNEL_LOOP(index, elem_cnt) { + int64_t n, c, h, w; + out_helper.OffsetToNdIndex(index, n, c, h, w); + BilinearParam params; + GetBilinearParamHalf(align_corners, h, w, in_height, in_width, scale_h, scale_w, ¶ms); + const int64_t top_offset = in_helper.NdIndexToOffset(n, c, params.top_h_index, 0); + const int64_t bottom_offset = in_helper.NdIndexToOffset(n, c, params.bottom_h_index, 0); + const half top_left = in_dptr[top_offset + params.left_w_index]; + const half top_right = in_dptr[top_offset + params.right_w_index]; + const half bottom_left = in_dptr[bottom_offset + params.left_w_index]; + const half bottom_right = in_dptr[bottom_offset + params.right_w_index]; + out_dptr[index] = + (static_cast(1.0) - params.h_lerp) + * ((static_cast(1.0) - params.w_lerp) * top_left + params.w_lerp * top_right) + + params.h_lerp + * ((static_cast(1.0) - params.w_lerp) * bottom_left + + params.w_lerp * bottom_right); + } +} + template __global__ void UpsampleBilinearBackward(const int64_t elem_cnt, const T* dy_dptr, NdIndexOffsetHelper dy_helper, @@ -77,6 +137,36 @@ __global__ void UpsampleBilinearBackward(const int64_t elem_cnt, const T* dy_dpt } } +template<> +__global__ void UpsampleBilinearBackward(const int64_t elem_cnt, const half* dy_dptr, + NdIndexOffsetHelper dy_helper, + NdIndexOffsetHelper dx_helper, + const int64_t dx_height, const int64_t dx_width, + const half scale_h, const half scale_w, + const bool align_corners, half* dx_dptr) { + CUDA_1D_KERNEL_LOOP(index, elem_cnt) { + int64_t n, c, h, w; + dy_helper.OffsetToNdIndex(index, n, c, h, w); + BilinearParam params; + GetBilinearParamHalf(align_corners, h, w, dx_height, dx_width, scale_h, scale_w, ¶ms); + const int64_t top_offset = dx_helper.NdIndexToOffset(n, c, params.top_h_index, 0); + const int64_t bottom_offset = dx_helper.NdIndexToOffset(n, c, params.bottom_h_index, 0); + const half dy = dy_dptr[index]; + const half dbottom = params.h_lerp * dy; + half* dx_dptr_bottom_offset = dx_dptr + bottom_offset; + cuda::atomic::Add(dx_dptr_bottom_offset + params.left_w_index, + static_cast((static_cast(1.0) - params.w_lerp) * dbottom)); + cuda::atomic::Add(dx_dptr_bottom_offset + params.right_w_index, + static_cast(params.w_lerp * dbottom)); + const half dtop = dy - dbottom; + half* dx_dptr_top_offset = dx_dptr + top_offset; + cuda::atomic::Add(dx_dptr_top_offset + params.left_w_index, + static_cast((static_cast(1.0) - params.w_lerp) * dtop)); + cuda::atomic::Add(dx_dptr_top_offset + params.right_w_index, + static_cast(params.w_lerp * dtop)); + } +} + } // namespace template @@ -183,6 +273,7 @@ class UpsampleBilinear2DGradGPUKernel final : public user_op::OpKernel { .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobDataType("dx", 0) == GetDataType::value)); +REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(half) REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(float) REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(double) diff --git a/oneflow/user/kernels/upsample_bilinear_2d_kernel.hip.cpp b/oneflow/user/kernels/upsample_bilinear_2d_kernel.hip.cpp deleted file mode 100644 index 1a4eb29870d3f1123402d6bf2f6644689c53ebf4..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/upsample_bilinear_2d_kernel.hip.cpp +++ /dev/null @@ -1,190 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/upsample_kernel.h" - -namespace oneflow { - -namespace { - -template -__global__ void UpsampleBilinear2DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int64_t in_height, const int64_t in_width, - const T scale_h, const T scale_w, - const bool align_corners, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h, w; - out_helper.OffsetToNdIndex(index, n, c, h, w); - BilinearParam params; - GetBilinearParam(align_corners, h, w, in_height, in_width, scale_h, scale_w, ¶ms); - const int64_t top_offset = in_helper.NdIndexToOffset(n, c, params.top_h_index, 0); - const int64_t bottom_offset = in_helper.NdIndexToOffset(n, c, params.bottom_h_index, 0); - const T top_left = in_dptr[top_offset + params.left_w_index]; - const T top_right = in_dptr[top_offset + params.right_w_index]; - const T bottom_left = in_dptr[bottom_offset + params.left_w_index]; - const T bottom_right = in_dptr[bottom_offset + params.right_w_index]; - out_dptr[index] = - (1 - params.h_lerp) * ((1 - params.w_lerp) * top_left + params.w_lerp * top_right) - + params.h_lerp * ((1 - params.w_lerp) * bottom_left + params.w_lerp * bottom_right); - } -} - -template -__global__ void UpsampleBilinearBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int64_t dx_height, const int64_t dx_width, - const T scale_h, const T scale_w, const bool align_corners, - T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h, w; - dy_helper.OffsetToNdIndex(index, n, c, h, w); - BilinearParam params; - GetBilinearParam(align_corners, h, w, dx_height, dx_width, scale_h, scale_w, ¶ms); - const int64_t top_offset = dx_helper.NdIndexToOffset(n, c, params.top_h_index, 0); - const int64_t bottom_offset = dx_helper.NdIndexToOffset(n, c, params.bottom_h_index, 0); - const T dy = dy_dptr[index]; - const T dbottom = params.h_lerp * dy; - T* dx_dptr_bottom_offset = dx_dptr + bottom_offset; - cuda::atomic::Add(dx_dptr_bottom_offset + params.left_w_index, - static_cast((1 - params.w_lerp) * dbottom)); - cuda::atomic::Add(dx_dptr_bottom_offset + params.right_w_index, - static_cast(params.w_lerp * dbottom)); - const T dtop = dy - dbottom; - T* dx_dptr_top_offset = dx_dptr + top_offset; - cuda::atomic::Add(dx_dptr_top_offset + params.left_w_index, - static_cast((1 - params.w_lerp) * dtop)); - cuda::atomic::Add(dx_dptr_top_offset + params.right_w_index, - static_cast(params.w_lerp * dtop)); - } -} - -} // namespace - -template -class UpsampleBilinear2DGPUKernel final : public user_op::OpKernel { - public: - UpsampleBilinear2DGPUKernel() = default; - ~UpsampleBilinear2DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const bool align_corners = ctx->Attr("align_corners"); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2), - y_tensor->shape_view().At(3)); - - const int64_t in_height = x_tensor->shape_view().At(2); - const int64_t in_width = x_tensor->shape_view().At(3); - const int64_t out_height = y_tensor->shape_view().At(2); - const int64_t out_width = y_tensor->shape_view().At(3); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), - x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - RUN_CUDA_KERNEL((UpsampleBilinear2DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, in_height, in_width, scale_height, - scale_width, align_corners, y_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleBilinear2DGradGPUKernel final : public user_op::OpKernel { - public: - UpsampleBilinear2DGradGPUKernel() = default; - ~UpsampleBilinear2DGradGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const bool align_corners = ctx->Attr("align_corners"); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - NdIndexOffsetHelper dy_helper( - dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), dy_tensor->shape_view().At(2), - dy_tensor->shape_view().At(3)); - NdIndexOffsetHelper dx_helper( - dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3)); - - const int64_t in_height = dx_tensor->shape_view().At(2); - const int64_t in_width = dx_tensor->shape_view().At(3); - const int64_t out_height = dy_tensor->shape_view().At(2); - const int64_t out_width = dy_tensor->shape_view().At(3); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), - dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - RUN_CUDA_KERNEL((UpsampleBilinearBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, in_height, in_width, scale_height, - scale_width, align_corners, dx_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_bilinear_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_bilinear_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(float) -REGISTER_UPSAMPLE_BILINEAR_2D_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/upsample_linear_1d_kernel.hip.cpp b/oneflow/user/kernels/upsample_linear_1d_kernel.hip.cpp deleted file mode 100644 index a949f1e13ce2729d19f87134f7492ad5d1ff05a8..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/upsample_linear_1d_kernel.hip.cpp +++ /dev/null @@ -1,163 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/upsample_kernel.h" - -namespace oneflow { - -namespace { - -template -__global__ void UpsampleLinear1DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int in_height, const double scale_factor, - bool align_corners, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h; - out_helper.OffsetToNdIndex(index, n, c, h); - const double h1r = GetLinearInputIndex(h, scale_factor, align_corners); - const int64_t h1 = h1r; - const int64_t h1p = (h1 < in_height - 1) ? 1 : 0; - const double h1lambda = h1r - h1; - const double h0lambda = static_cast(1.) - h1lambda; - out_dptr[index] = h0lambda * in_dptr[in_helper.NdIndexToOffset(n, c, h1)] - + h1lambda * in_dptr[in_helper.NdIndexToOffset(n, c, h1 + h1p)]; - } -} - -template -__global__ void UpsampleLinear1DBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int in_height, const double scale_factor, - bool align_corners, T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h; - dy_helper.OffsetToNdIndex(index, n, c, h); - const double h1r = GetLinearInputIndex(h, scale_factor, align_corners); - const int64_t h1 = h1r; - const int64_t h1p = (h1 < in_height - 1) ? 1 : 0; - const double h1lambda = h1r - h1; - const double h0lambda = static_cast(1.) - h1lambda; - - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, h1), h0lambda * dy_dptr[index]); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, h1 + h1p), - h1lambda * dy_dptr[index]); - } -} - -} // namespace - -template -class UpsampleLinear1DGPUKernel final : public user_op::OpKernel { - public: - UpsampleLinear1DGPUKernel() = default; - ~UpsampleLinear1DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const bool align_corners = ctx->Attr("align_corners"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2)); - const int64_t in_height = x_tensor->shape_view().At(2); - const int64_t out_height = y_tensor->shape_view().At(2); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("scale_factor"); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - } - if (in_height == out_height) { - Memcpy( - ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), - x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - RUN_CUDA_KERNEL((UpsampleLinear1DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, in_height, scale_height, - align_corners, y_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleLinearGrad1DGPUKernel final : public user_op::OpKernel { - public: - UpsampleLinearGrad1DGPUKernel() = default; - ~UpsampleLinearGrad1DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const bool align_corners = ctx->Attr("align_corners"); - - NdIndexOffsetHelper dy_helper(dy_tensor->shape_view().At(0), - dy_tensor->shape_view().At(1), - dy_tensor->shape_view().At(2)); - NdIndexOffsetHelper dx_helper(dx_tensor->shape_view().At(0), - dx_tensor->shape_view().At(1), - dx_tensor->shape_view().At(2)); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - const int64_t in_height = dx_tensor->shape_view().At(2); - const int64_t out_height = dy_tensor->shape_view().At(2); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("scale_factor"); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - } - if (in_height == out_height) { - Memcpy( - ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), - dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); - } else { - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - RUN_CUDA_KERNEL((UpsampleLinear1DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, in_height, scale_height, - align_corners, dx_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPLELINEAR1D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_linear_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_linear_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPLELINEAR1D_CUDA_KERNEL(float) -REGISTER_UPSAMPLELINEAR1D_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/upsample_nearest_kernel.cu b/oneflow/user/kernels/upsample_nearest_kernel.cu index d299150adae78c59038ca84642bde085c91f8fb6..bc7e5471ea36542f4fa014ebc0e9be89864048eb 100644 --- a/oneflow/user/kernels/upsample_nearest_kernel.cu +++ b/oneflow/user/kernels/upsample_nearest_kernel.cu @@ -16,8 +16,9 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/kernel/new_kernel_util.h" #include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/cuda/atomic.cuh" +#include "oneflow/core/kernel/kernel_util.cuh" #include "oneflow/user/kernels/upsample_kernel.h" +#include "oneflow/core/kernel/cuda_graph_support.h" namespace oneflow { @@ -47,7 +48,8 @@ __global__ void UpsampleNearest1DBackward(const int64_t elem_cnt, const T* dy_dp int64_t n, c, h; dy_helper.OffsetToNdIndex(index, n, c, h); const int64_t dx_h = GetNearestInputIndex(h, scale_factor, in_height); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, dx_h), dy_dptr[index]); + FastAtomicAdd(dx_dptr, dx_helper.NdIndexToOffset(n, c, dx_h), elem_cnt, + static_cast(dy_dptr[index])); } } @@ -66,6 +68,30 @@ __global__ void UpsampleNearest2DForward(const int64_t elem_cnt, const T* in_dpt } } +template +struct alignas(2 * sizeof(T)) Pack2X { + T x; + T y; +}; + +template +__global__ void UpsampleNearest2D2XForward(const int32_t in_elem_cnt, const T* in_dptr, + const int32_t in_height, const int32_t in_width, + T* out_dptr) { + const int32_t in_hw_size = in_width * in_height; + CUDA_1D_KERNEL_LOOP(index, in_elem_cnt) { + const T in_value = in_dptr[index]; + const int32_t nc_idx = index / in_hw_size; + const int32_t hw_off = index - nc_idx * in_hw_size; + const int32_t h = hw_off / in_width; + const int32_t w = hw_off - h * in_width; + Pack2X out_value{in_value, in_value}; + Pack2X* out_pack_dptr = reinterpret_cast*>(out_dptr); + out_pack_dptr[nc_idx * in_hw_size * 2 + h * 2 * in_width + w] = out_value; + out_pack_dptr[nc_idx * in_hw_size * 2 + (h * 2 + 1) * in_width + w] = out_value; + } +} + template __global__ void UpsampleNearest2DBackward(const int64_t elem_cnt, const T* dy_dptr, NdIndexOffsetHelper dy_helper, @@ -77,7 +103,32 @@ __global__ void UpsampleNearest2DBackward(const int64_t elem_cnt, const T* dy_dp dy_helper.OffsetToNdIndex(index, n, c, h, w); const int64_t dx_h = GetNearestInputIndex(h, scale_h, dx_height); const int64_t dx_w = GetNearestInputIndex(w, scale_w, dx_width); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, dx_h, dx_w), dy_dptr[index]); + FastAtomicAdd(dx_dptr, dx_helper.NdIndexToOffset(n, c, dx_h, dx_w), elem_cnt, + static_cast(dy_dptr[index])); + } +} + +template +__global__ void UpsampleNearest2D2XBackward(const int32_t in_elem_cnt, const T* dy_dptr, + const int32_t dx_height, const int32_t dx_width, + T* dx_dptr) { + const int32_t dx_hw_size = dx_height * dx_width; + CUDA_1D_KERNEL_LOOP(index, in_elem_cnt) { + T dx_value = 0.0; + const int32_t nc_idx = index / dx_hw_size; + const int32_t dx_hw_off = index - nc_idx * dx_hw_size; + const int32_t dx_h = dx_hw_off / dx_width; + const int32_t dx_w = dx_hw_off - dx_h * dx_width; + const Pack2X* dy_pack_dptr = reinterpret_cast*>(dy_dptr); + const Pack2X dy_pack_value1 = + dy_pack_dptr[nc_idx * dx_hw_size * 2 + dx_h * 2 * dx_width + dx_w]; + const Pack2X dy_pack_value2 = + dy_pack_dptr[nc_idx * dx_hw_size * 2 + (dx_h * 2 + 1) * dx_width + dx_w]; + dx_value += dy_pack_value1.x; + dx_value += dy_pack_value1.y; + dx_value += dy_pack_value2.x; + dx_value += dy_pack_value2.y; + dx_dptr[index] = dx_value; } } @@ -111,7 +162,8 @@ __global__ void UpsampleNearest3DBackward(const int64_t elem_cnt, const T* dy_dp const int64_t dx_h = GetNearestInputIndex(h, scale_h, in_height); const int64_t dx_w = GetNearestInputIndex(w, scale_w, in_width); const int64_t in_d = GetNearestInputIndex(d, scale_d, in_depth); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, in_d, dx_h, dx_w), dy_dptr[index]); + FastAtomicAdd(dx_dptr, dx_helper.NdIndexToOffset(n, c, in_d, dx_h, dx_w), elem_cnt, + static_cast(dy_dptr[index])); } } @@ -164,8 +216,6 @@ class UpsampleNearestGrad1DGPUKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); const std::vector output_size = ctx->Attr>("output_size"); double height_scale = ctx->Attr("scale_factor"); @@ -180,6 +230,8 @@ class UpsampleNearestGrad1DGPUKernel final : public user_op::OpKernel { ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); } else { + Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, + dx_tensor->shape_view().elem_cnt() * sizeof(T)); NdIndexOffsetHelper dy_helper(dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), dy_tensor->shape_view().At(2)); @@ -208,7 +260,8 @@ REGISTER_UPSAMPNEAREST1D_CUDA_KERNEL(float) REGISTER_UPSAMPNEAREST1D_CUDA_KERNEL(double) template -class UpsampleNearest2DGPUKernel final : public user_op::OpKernel { +class UpsampleNearest2DGPUKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { public: UpsampleNearest2DGPUKernel() = default; ~UpsampleNearest2DGPUKernel() = default; @@ -221,7 +274,8 @@ class UpsampleNearest2DGPUKernel final : public user_op::OpKernel { const std::vector output_size = ctx->Attr>("output_size"); double height_scale = ctx->Attr("height_scale"); double width_scale = ctx->Attr("width_scale"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); + const int64_t out_elem_cnt = y_tensor->shape_view().elem_cnt(); + const int64_t in_elem_cnt = x_tensor->shape_view().elem_cnt(); const int64_t in_height = x_tensor->shape_view().At(2); const int64_t in_width = x_tensor->shape_view().At(3); const int64_t out_height = y_tensor->shape_view().At(2); @@ -236,16 +290,18 @@ class UpsampleNearest2DGPUKernel final : public user_op::OpKernel { ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); } else { - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2), - y_tensor->shape_view().At(3)); - RUN_CUDA_KERNEL((UpsampleNearest2DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), 1.f / height_scale, 1.f / width_scale, - y_tensor->mut_dptr()); + const int64_t n = x_tensor->shape_view().At(0); + const int64_t c = x_tensor->shape_view().At(1); + if (out_height == 2 * in_height && out_width == 2 * in_width && in_elem_cnt <= 1 << 29) { + RUN_CUDA_KERNEL(UpsampleNearest2D2XForward, ctx->stream(), in_elem_cnt, in_elem_cnt, + x_tensor->dptr(), in_height, in_width, y_tensor->mut_dptr()); + } else { + NdIndexOffsetHelper in_helper(n, c, in_height, in_width); + NdIndexOffsetHelper out_helper(n, c, out_height, out_width); + RUN_CUDA_KERNEL((UpsampleNearest2DForward), ctx->stream(), out_elem_cnt, out_elem_cnt, + x_tensor->dptr(), in_helper, out_helper, in_height, in_width, + 1.f / height_scale, 1.f / width_scale, y_tensor->mut_dptr()); + } } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } @@ -262,13 +318,12 @@ class UpsampleNearest2DGradGPUKernel final : public user_op::OpKernel { void Compute(user_op::KernelComputeContext* ctx) const override { user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); const std::vector output_size = ctx->Attr>("output_size"); double height_scale = ctx->Attr("height_scale"); double width_scale = ctx->Attr("width_scale"); const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); + const int64_t in_elem_cnt = dx_tensor->shape_view().elem_cnt(); const int64_t in_height = dx_tensor->shape_view().At(2); const int64_t in_width = dx_tensor->shape_view().At(3); const int64_t out_height = dy_tensor->shape_view().At(2); @@ -282,16 +337,24 @@ class UpsampleNearest2DGradGPUKernel final : public user_op::OpKernel { ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); } else { - NdIndexOffsetHelper dy_helper( - dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), - dy_tensor->shape_view().At(2), dy_tensor->shape_view().At(3)); - NdIndexOffsetHelper dx_helper( - dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), - dx_tensor->shape_view().At(2), dx_tensor->shape_view().At(3)); - RUN_CUDA_KERNEL((UpsampleNearest2DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), 1.f / height_scale, 1.f / width_scale, - dx_tensor->mut_dptr()); + if (out_height == 2 * in_height && out_width == 2 * in_width && in_elem_cnt <= 1 << 29) { + RUN_CUDA_KERNEL(UpsampleNearest2D2XBackward, ctx->stream(), in_elem_cnt, in_elem_cnt, + dy_tensor->dptr(), dx_tensor->shape_view().At(2), + dx_tensor->shape_view().At(3), dx_tensor->mut_dptr()); + } else { + Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, + dx_tensor->shape_view().elem_cnt() * sizeof(T)); + NdIndexOffsetHelper dy_helper( + dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), + dy_tensor->shape_view().At(2), dy_tensor->shape_view().At(3)); + NdIndexOffsetHelper dx_helper( + dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), + dx_tensor->shape_view().At(2), dx_tensor->shape_view().At(3)); + RUN_CUDA_KERNEL((UpsampleNearest2DBackward), ctx->stream(), elem_cnt, elem_cnt, + dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), + dx_tensor->shape_view().At(3), 1.f / height_scale, 1.f / width_scale, + dx_tensor->mut_dptr()); + } } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } @@ -308,6 +371,7 @@ class UpsampleNearest2DGradGPUKernel final : public user_op::OpKernel { && (user_op::HobDataType("dx", 0) == GetDataType::value)); REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(float) +REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(half) REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(double) template diff --git a/oneflow/user/kernels/upsample_nearest_kernel.hip.cpp b/oneflow/user/kernels/upsample_nearest_kernel.hip.cpp deleted file mode 100644 index c007355fb7b29e1e70826f8f423f94b9545a7de4..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/upsample_nearest_kernel.hip.cpp +++ /dev/null @@ -1,412 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/upsample_kernel.h" - -namespace oneflow { - -namespace { - -template -__global__ void UpsampleNearest1DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int64_t in_height, const double scale_factor, - T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h; - out_helper.OffsetToNdIndex(index, n, c, h); - const int64_t in_h = GetNearestInputIndex(h, scale_factor, in_height); - out_dptr[index] = in_dptr[in_helper.NdIndexToOffset(n, c, in_h)]; - } -} - -template -__global__ void UpsampleNearest1DBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int64_t in_height, const double scale_factor, - T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h; - dy_helper.OffsetToNdIndex(index, n, c, h); - const int64_t dx_h = GetNearestInputIndex(h, scale_factor, in_height); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, dx_h), dy_dptr[index]); - } -} - -template -__global__ void UpsampleNearest2DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int64_t in_height, const int64_t in_width, - const double scale_h, const double scale_w, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h, w; - out_helper.OffsetToNdIndex(index, n, c, h, w); - const int64_t in_h = GetNearestInputIndex(h, scale_h, in_height); - const int64_t in_w = GetNearestInputIndex(w, scale_w, in_width); - out_dptr[index] = in_dptr[in_helper.NdIndexToOffset(n, c, in_h, in_w)]; - } -} - -template -__global__ void UpsampleNearest2DBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int64_t dx_height, const int64_t dx_width, - const double scale_h, const double scale_w, T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, h, w; - dy_helper.OffsetToNdIndex(index, n, c, h, w); - const int64_t dx_h = GetNearestInputIndex(h, scale_h, dx_height); - const int64_t dx_w = GetNearestInputIndex(w, scale_w, dx_width); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, dx_h, dx_w), dy_dptr[index]); - } -} - -template -__global__ void UpsampleNearest3DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int64_t in_depth, const int64_t in_height, - const int64_t in_width, const float scale_d, - const float scale_h, const float scale_w, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, d, h, w; - out_helper.OffsetToNdIndex(index, n, c, d, h, w); - const int64_t in_h = GetNearestInputIndex(h, scale_h, in_height); - const int64_t in_w = GetNearestInputIndex(w, scale_w, in_width); - const int64_t in_d = GetNearestInputIndex(d, scale_d, in_depth); - out_dptr[index] = in_dptr[in_helper.NdIndexToOffset(n, c, in_d, in_h, in_w)]; - } -} - -template -__global__ void UpsampleNearest3DBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int64_t in_depth, const int64_t in_height, - const int64_t in_width, const float scale_d, - const float scale_h, const float scale_w, T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, d, h, w; - dy_helper.OffsetToNdIndex(index, n, c, d, h, w); - const int64_t dx_h = GetNearestInputIndex(h, scale_h, in_height); - const int64_t dx_w = GetNearestInputIndex(w, scale_w, in_width); - const int64_t in_d = GetNearestInputIndex(d, scale_d, in_depth); - cuda::atomic::Add(dx_dptr + dx_helper.NdIndexToOffset(n, c, in_d, dx_h, dx_w), dy_dptr[index]); - } -} - -} // namespace - -template -class UpsampleNearest1DGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearest1DGPUKernel() = default; - ~UpsampleNearest1DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("scale_factor"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - const int64_t in_height = x_tensor->shape_view().At(2); - const int64_t out_height = y_tensor->shape_view().At(2); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - } - if (in_height == out_height) { - Memcpy( - ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), - x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); - } else { - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2)); - RUN_CUDA_KERNEL((UpsampleNearest1DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, x_tensor->shape_view().At(2), - 1.f / height_scale, y_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleNearestGrad1DGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearestGrad1DGPUKernel() = default; - ~UpsampleNearestGrad1DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("scale_factor"); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - const int64_t in_height = dx_tensor->shape_view().At(2); - const int64_t out_height = dy_tensor->shape_view().At(2); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - } - if (in_height == out_height) { - Memcpy( - ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), - dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); - } else { - NdIndexOffsetHelper dy_helper(dy_tensor->shape_view().At(0), - dy_tensor->shape_view().At(1), - dy_tensor->shape_view().At(2)); - NdIndexOffsetHelper dx_helper(dx_tensor->shape_view().At(0), - dx_tensor->shape_view().At(1), - dx_tensor->shape_view().At(2)); - RUN_CUDA_KERNEL((UpsampleNearest1DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), - 1.f / height_scale, dx_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPNEAREST1D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_nearest_1d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_nearest_1d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPNEAREST1D_CUDA_KERNEL(float) -REGISTER_UPSAMPNEAREST1D_CUDA_KERNEL(double) - -template -class UpsampleNearest2DGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearest2DGPUKernel() = default; - ~UpsampleNearest2DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - const int64_t in_height = x_tensor->shape_view().At(2); - const int64_t in_width = x_tensor->shape_view().At(3); - const int64_t out_height = y_tensor->shape_view().At(2); - const int64_t out_width = y_tensor->shape_view().At(3); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), y_tensor->mut_dptr(), x_tensor->dptr(), - x_tensor->shape_view().elem_cnt() * GetSizeOfDataType(x_tensor->data_type())); - } else { - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2), - y_tensor->shape_view().At(3)); - RUN_CUDA_KERNEL((UpsampleNearest2DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), 1.f / height_scale, 1.f / width_scale, - y_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleNearest2DGradGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearest2DGradGPUKernel() = default; - ~UpsampleNearest2DGradGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - const int64_t in_height = dx_tensor->shape_view().At(2); - const int64_t in_width = dx_tensor->shape_view().At(3); - const int64_t out_height = dy_tensor->shape_view().At(2); - const int64_t out_width = dy_tensor->shape_view().At(3); - if (!output_size.empty()) { - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - if (in_height == out_height && in_width == out_width) { - Memcpy( - ctx->stream(), dx_tensor->mut_dptr(), dy_tensor->dptr(), - dy_tensor->shape_view().elem_cnt() * GetSizeOfDataType(dy_tensor->data_type())); - } else { - NdIndexOffsetHelper dy_helper( - dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), - dy_tensor->shape_view().At(2), dy_tensor->shape_view().At(3)); - NdIndexOffsetHelper dx_helper( - dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), - dx_tensor->shape_view().At(2), dx_tensor->shape_view().At(3)); - RUN_CUDA_KERNEL((UpsampleNearest2DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), 1.f / height_scale, 1.f / width_scale, - dx_tensor->mut_dptr()); - } - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_nearest_2d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_nearest_2d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(float) -REGISTER_UPSAMPLE_NEAREST_2D_CUDA_KERNEL(double) - -template -class UpsampleNearest3DGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearest3DGPUKernel() = default; - ~UpsampleNearest3DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double depth_scale = ctx->Attr("depth_scale"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t in_depth = x_tensor->shape_view().At(2); - const int64_t in_height = x_tensor->shape_view().At(3); - const int64_t in_width = x_tensor->shape_view().At(4); - const int64_t out_depth = y_tensor->shape_view().At(2); - const int64_t out_height = y_tensor->shape_view().At(3); - const int64_t out_width = y_tensor->shape_view().At(4); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - if (!output_size.empty()) { - depth_scale = static_cast(out_depth) / static_cast(in_depth); - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), x_tensor->shape_view().At(4)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2), - y_tensor->shape_view().At(3), y_tensor->shape_view().At(4)); - RUN_CUDA_KERNEL((UpsampleNearest3DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), x_tensor->shape_view().At(4), 1.f / depth_scale, - 1.f / height_scale, 1.f / width_scale, y_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleNearestGrad3DGPUKernel final : public user_op::OpKernel { - public: - UpsampleNearestGrad3DGPUKernel() = default; - ~UpsampleNearestGrad3DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const std::vector output_size = ctx->Attr>("output_size"); - double depth_scale = ctx->Attr("depth_scale"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - const int64_t in_depth = dx_tensor->shape_view().At(2); - const int64_t in_height = dx_tensor->shape_view().At(3); - const int64_t in_width = dx_tensor->shape_view().At(4); - const int64_t out_depth = dy_tensor->shape_view().At(2); - const int64_t out_height = dy_tensor->shape_view().At(3); - const int64_t out_width = dy_tensor->shape_view().At(4); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - if (!output_size.empty()) { - depth_scale = static_cast(out_depth) / static_cast(in_depth); - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - NdIndexOffsetHelper dy_helper( - dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), dy_tensor->shape_view().At(2), - dy_tensor->shape_view().At(3), dy_tensor->shape_view().At(4)); - NdIndexOffsetHelper dx_helper( - dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), dx_tensor->shape_view().At(4)); - RUN_CUDA_KERNEL((UpsampleNearest3DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), dx_tensor->shape_view().At(4), 1.f / depth_scale, - 1.f / height_scale, 1.f / width_scale, dx_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPNEAREST3D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_nearest_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_nearest_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPNEAREST3D_CUDA_KERNEL(float) -REGISTER_UPSAMPNEAREST3D_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/upsample_trilinear_3d_kernel.hip.cpp b/oneflow/user/kernels/upsample_trilinear_3d_kernel.hip.cpp deleted file mode 100644 index 489312fbc5aca0f260c33da5eb597f187393c462..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/upsample_trilinear_3d_kernel.hip.cpp +++ /dev/null @@ -1,237 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" -#include "oneflow/core/common/nd_index_offset_helper.h" -#include "oneflow/core/hip/atomic.hip.h" -#include "oneflow/user/kernels/upsample_kernel.h" - -namespace oneflow { - -namespace { - -template -__global__ void UpsampleTrilinear3DForward(const int64_t elem_cnt, const T* in_dptr, - NdIndexOffsetHelper in_helper, - NdIndexOffsetHelper out_helper, - const int64_t in_depth, const int64_t in_height, - const int64_t in_width, const T rdepth, const T rheight, - const T rwidth, const bool align_corners, T* out_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, d, h, w; - out_helper.OffsetToNdIndex(index, n, c, d, h, w); - - const T t1r = GetAreaPixel(rdepth, d, align_corners); - const int64_t t1 = t1r; - const int64_t t1p = (t1 < in_depth - 1) ? 1 : 0; - const T t1lambda = t1r - t1; - const T t0lambda = static_cast(1.) - t1lambda; - - const T h1r = GetAreaPixel(rheight, h, align_corners); - const int64_t h1 = h1r; - const int64_t h1p = (h1 < in_height - 1) ? 1 : 0; - const T h1lambda = h1r - h1; - const T h0lambda = static_cast(1.) - h1lambda; - - const T w1r = GetAreaPixel(rwidth, w, align_corners); - const int64_t w1 = w1r; - const int64_t w1p = (w1 < in_width - 1) ? 1 : 0; - const T w1lambda = w1r - w1; - const T w0lambda = static_cast(1.) - w1lambda; - - const T* pos1 = &in_dptr[in_helper.NdIndexToOffset(n, c, t1, h1, w1)]; - - out_dptr[index] = - t0lambda - * (h0lambda * (w0lambda * pos1[0] + w1lambda * pos1[w1p]) - + h1lambda - * (w0lambda * pos1[h1p * in_width] + w1lambda * pos1[h1p * in_width + w1p])) - + t1lambda - * (h0lambda - * (w0lambda * pos1[t1p * in_height * in_width] - + w1lambda * pos1[t1p * in_height * in_width + w1p]) - + h1lambda - * (w0lambda * pos1[t1p * in_height * in_width + h1p * in_width] - + w1lambda * pos1[t1p * in_height * in_width + h1p * in_width + w1p])); - } -} - -template -__global__ void UpsampleTrilinear3DBackward(const int64_t elem_cnt, const T* dy_dptr, - NdIndexOffsetHelper dy_helper, - NdIndexOffsetHelper dx_helper, - const int64_t in_depth, const int64_t in_height, - const int64_t in_width, const T rdepth, const T rheight, - const T rwidth, const bool align_corners, T* dx_dptr) { - CUDA_1D_KERNEL_LOOP(index, elem_cnt) { - int64_t n, c, d, h, w; - dy_helper.OffsetToNdIndex(index, n, c, d, h, w); - - const T t1r = GetAreaPixel(rdepth, d, align_corners); - const int64_t t1 = t1r; - const int64_t t1p = (t1 < in_depth - 1) ? 1 : 0; - const T t1lambda = t1r - t1; - const T t0lambda = static_cast(1.) - t1lambda; - - const T h1r = GetAreaPixel(rheight, h, align_corners); - const int64_t h1 = h1r; - const int64_t h1p = (h1 < in_height - 1) ? 1 : 0; - const T h1lambda = h1r - h1; - const T h0lambda = static_cast(1.) - h1lambda; - - const T w1r = GetAreaPixel(rwidth, w, align_corners); - const int64_t w1 = w1r; - const int64_t w1p = (w1 < in_width - 1) ? 1 : 0; - const T w1lambda = w1r - w1; - const T w0lambda = static_cast(1.) - w1lambda; - - T* pos1 = &dx_dptr[dx_helper.NdIndexToOffset(n, c, t1, h1, w1)]; - const T* pos2 = &dy_dptr[index]; - - cuda::atomic::Add(pos1 + 0, t0lambda * h0lambda * w0lambda * pos2[0]); - cuda::atomic::Add(pos1 + w1p, t0lambda * h0lambda * w1lambda * pos2[0]); - cuda::atomic::Add(pos1 + h1p * in_width, t0lambda * h1lambda * w0lambda * pos2[0]); - cuda::atomic::Add(pos1 + h1p * in_width + w1p, t0lambda * h1lambda * w1lambda * pos2[0]); - cuda::atomic::Add(pos1 + t1p * in_height * in_width, t1lambda * h0lambda * w0lambda * pos2[0]); - cuda::atomic::Add(pos1 + t1p * in_height * in_width + w1p, - t1lambda * h0lambda * w1lambda * pos2[0]); - cuda::atomic::Add(pos1 + t1p * in_height * in_width + h1p * in_width, - t1lambda * h1lambda * w0lambda * pos2[0]); - cuda::atomic::Add(pos1 + t1p * in_height * in_width + h1p * in_width + w1p, - t1lambda * h1lambda * w1lambda * pos2[0]); - } -} - -} // namespace - -template -class UpsampleTrilinear3DGPUKernel final : public user_op::OpKernel { - public: - UpsampleTrilinear3DGPUKernel() = default; - ~UpsampleTrilinear3DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - const user_op::Tensor* x_tensor = ctx->Tensor4ArgNameAndIndex("x", 0); - user_op::Tensor* y_tensor = ctx->Tensor4ArgNameAndIndex("y", 0); - const bool align_corners = ctx->Attr("align_corners"); - const int64_t elem_cnt = y_tensor->shape_view().elem_cnt(); - NdIndexOffsetHelper in_helper( - x_tensor->shape_view().At(0), x_tensor->shape_view().At(1), x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), x_tensor->shape_view().At(4)); - NdIndexOffsetHelper out_helper( - y_tensor->shape_view().At(0), y_tensor->shape_view().At(1), y_tensor->shape_view().At(2), - y_tensor->shape_view().At(3), y_tensor->shape_view().At(4)); - - const int64_t in_depth = x_tensor->shape_view().At(2); - const int64_t in_height = x_tensor->shape_view().At(3); - const int64_t in_width = x_tensor->shape_view().At(4); - - const int64_t out_depth = y_tensor->shape_view().At(2); - const int64_t out_height = y_tensor->shape_view().At(3); - const int64_t out_width = y_tensor->shape_view().At(4); - - const std::vector output_size = ctx->Attr>("output_size"); - double depth_scale = ctx->Attr("depth_scale"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - if (!output_size.empty()) { - depth_scale = static_cast(out_depth) / static_cast(in_depth); - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - - const T scale_depth = GetAreaPixelScale(in_depth, out_depth, align_corners, depth_scale); - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - - RUN_CUDA_KERNEL((UpsampleTrilinear3DForward), ctx->stream(), elem_cnt, elem_cnt, - x_tensor->dptr(), in_helper, out_helper, x_tensor->shape_view().At(2), - x_tensor->shape_view().At(3), x_tensor->shape_view().At(4), scale_depth, - scale_height, scale_width, align_corners, y_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -template -class UpsampleTrilinearGrad3DGPUKernel final : public user_op::OpKernel { - public: - UpsampleTrilinearGrad3DGPUKernel() = default; - ~UpsampleTrilinearGrad3DGPUKernel() = default; - - private: - using user_op::OpKernel::Compute; - void Compute(user_op::KernelComputeContext* ctx) const override { - user_op::Tensor* dx_tensor = ctx->Tensor4ArgNameAndIndex("dx", 0); - - Memset(ctx->stream(), dx_tensor->mut_dptr(), 0, - dx_tensor->shape_view().elem_cnt() * sizeof(T)); - const user_op::Tensor* dy_tensor = ctx->Tensor4ArgNameAndIndex("dy", 0); - const bool align_corners = ctx->Attr("align_corners"); - const int64_t elem_cnt = dy_tensor->shape_view().elem_cnt(); - NdIndexOffsetHelper dy_helper( - dy_tensor->shape_view().At(0), dy_tensor->shape_view().At(1), dy_tensor->shape_view().At(2), - dy_tensor->shape_view().At(3), dy_tensor->shape_view().At(4)); - NdIndexOffsetHelper dx_helper( - dx_tensor->shape_view().At(0), dx_tensor->shape_view().At(1), dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), dx_tensor->shape_view().At(4)); - - const int64_t in_depth = dx_tensor->shape_view().At(2); - const int64_t in_height = dx_tensor->shape_view().At(3); - const int64_t in_width = dx_tensor->shape_view().At(4); - - const int64_t out_depth = dy_tensor->shape_view().At(2); - const int64_t out_height = dy_tensor->shape_view().At(3); - const int64_t out_width = dy_tensor->shape_view().At(4); - - const std::vector output_size = ctx->Attr>("output_size"); - double depth_scale = ctx->Attr("depth_scale"); - double height_scale = ctx->Attr("height_scale"); - double width_scale = ctx->Attr("width_scale"); - if (!output_size.empty()) { - depth_scale = static_cast(out_depth) / static_cast(in_depth); - height_scale = static_cast(out_height) / static_cast(in_height); - width_scale = static_cast(out_width) / static_cast(in_width); - } - - const T scale_depth = GetAreaPixelScale(in_depth, out_depth, align_corners, depth_scale); - const T scale_height = GetAreaPixelScale(in_height, out_height, align_corners, height_scale); - const T scale_width = GetAreaPixelScale(in_width, out_width, align_corners, width_scale); - - RUN_CUDA_KERNEL((UpsampleTrilinear3DBackward), ctx->stream(), elem_cnt, elem_cnt, - dy_tensor->dptr(), dy_helper, dx_helper, dx_tensor->shape_view().At(2), - dx_tensor->shape_view().At(3), dx_tensor->shape_view().At(4), scale_depth, - scale_height, scale_width, align_corners, dx_tensor->mut_dptr()); - } - bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } -}; - -#define REGISTER_UPSAMPTRILINEAR3D_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("upsample_trilinear_3d") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("y", 0) == GetDataType::value)); \ - REGISTER_USER_KERNEL("upsample_trilinear_3d_grad") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobDataType("dx", 0) == GetDataType::value)); - -REGISTER_UPSAMPTRILINEAR3D_CUDA_KERNEL(float) -REGISTER_UPSAMPTRILINEAR3D_CUDA_KERNEL(double) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/util_ops_kernels.cpp b/oneflow/user/kernels/util_ops_kernels.cpp index 00aebb25b8c7a2ef5be5221c3b71b9e8d7faf1a3..2c089211e238513fdbfee431442b0fea39e86c5a 100644 --- a/oneflow/user/kernels/util_ops_kernels.cpp +++ b/oneflow/user/kernels/util_ops_kernels.cpp @@ -17,32 +17,26 @@ limitations under the License. namespace oneflow { namespace user_op { +#define UTIL_OPS_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("isinf", ep::primitive::UnaryOp::kIsInf) \ + OF_PP_MAKE_TUPLE_SEQ("isnan", ep::primitive::UnaryOp::kIsNan) \ + OF_PP_MAKE_TUPLE_SEQ("isfinite", ep::primitive::UnaryOp::kIsFinite) -REGISTER_USER_KERNEL("isinf") - .SetCreateFn([]() { - return user_op::NewOpKernel( - "out", "in", [](user_op::KernelComputeContext* ctx) { - const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("in", 0); - const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("out", 0); - return ep::primitive::NewPrimitive( - ctx->device_type(), ep::primitive::UnaryOp::kIsInf, src->data_type(), - dst->data_type()); - }); - }) - .SetIsMatchedHob(UnaryPrimitiveExists(ep::primitive::UnaryOp::kIsInf, "out", "in")); - -REGISTER_USER_KERNEL("isnan") - .SetCreateFn([]() { - return user_op::NewOpKernel( - "out", "in", [](user_op::KernelComputeContext* ctx) { - const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("in", 0); - const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("out", 0); - return ep::primitive::NewPrimitive( - ctx->device_type(), ep::primitive::UnaryOp::kIsNan, src->data_type(), - dst->data_type()); - }); - }) - .SetIsMatchedHob(UnaryPrimitiveExists(ep::primitive::UnaryOp::kIsNan, "out", "in")); +#define RISTER_UTIL_OPS(op_name, op_kind) \ + REGISTER_USER_KERNEL(op_name) \ + .SetCreateFn([]() { \ + return user_op::NewOpKernel( \ + "out", "in", [](user_op::KernelComputeContext* ctx) { \ + const user_op::TensorDesc* src = ctx->TensorDesc4ArgNameAndIndex("in", 0); \ + const user_op::TensorDesc* dst = ctx->TensorDesc4ArgNameAndIndex("out", 0); \ + return ep::primitive::NewPrimitive( \ + ctx->device_type(), op_kind, src->data_type(), dst->data_type()); \ + }); \ + }) \ + .SetIsMatchedHob(UnaryPrimitiveExists(op_kind, "out", "in")); +OF_PP_FOR_EACH_TUPLE(RISTER_UTIL_OPS, UTIL_OPS_SEQ) +#undef RISTER_UTIL_OPS +#undef UTIL_OPS_SEQ } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/variance_kernel.cpp b/oneflow/user/kernels/variance_kernel.cpp index afa705a277bb7cb5b411fb4f1b3169d032823eae..0619323a5ac5d148afbcef8daf7b1b943dd57b65 100644 --- a/oneflow/user/kernels/variance_kernel.cpp +++ b/oneflow/user/kernels/variance_kernel.cpp @@ -21,7 +21,7 @@ limitations under the License. namespace oneflow { namespace user_op { -template +template class VarKernel final : public user_op::OpKernel { public: VarKernel() = default; @@ -35,78 +35,72 @@ class VarKernel final : public user_op::OpKernel { const T* in_ptr = input->dptr(); T* out_ptr = output->mut_dptr(); const std::vector axis = ctx->Attr>("dim"); - // only all dims cuda case will use tmp buffer. - T* tmp_buffer_ptr = - (axis.size() == input->shape_view().NumAxes() && DeviceType::kCUDA == device_type) - ? ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr() + const int64_t input_dim_element = input->shape_view().elem_cnt(); + int64_t axis_dim_element = 1; + for (int64_t i = 0; i < axis.size(); ++i) { + axis_dim_element *= input->shape_view().At(axis[i]); + } + // when computing the variance with all the elements, the implementation of cuda kernel may use + // tmp buffer for computation. + ComputeType* tmp_buffer_ptr = + (input_dim_element > 0 + && (axis.size() == input->shape_view().NumAxes() || input_dim_element == axis_dim_element) + && DeviceType::kCUDA == device_type) + ? ctx->Tensor4ArgNameAndIndex("tmp_buffer", 0)->mut_dptr() : nullptr; VarParamHelper param_helper(input->shape_view(), axis, unbiased); - VarFunctor()(ctx->stream(), in_ptr, out_ptr, tmp_buffer_ptr, - param_helper.param); + VarFunctor()(ctx->stream(), in_ptr, out_ptr, tmp_buffer_ptr, + param_helper.param); } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_VAR_CPU_KERNEL(dtype) \ - REGISTER_USER_KERNEL("var").SetCreateFn>().SetIsMatchedHob( \ - (user_op::HobDeviceType() == DeviceType::kCPU) \ - && (user_op::HobAttr("dtype") == GetDataType::value)); -REGISTER_VAR_CPU_KERNEL(float) -REGISTER_VAR_CPU_KERNEL(double) +#define REGISTER_VAR_CPU_KERNEL(dtype, compute_type) \ + REGISTER_USER_KERNEL("var") \ + .SetCreateFn>() \ + .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCPU) \ + && (user_op::HobAttr("dtype") == GetDataType::value)); +REGISTER_VAR_CPU_KERNEL(float, double) +REGISTER_VAR_CPU_KERNEL(double, double) +REGISTER_VAR_CPU_KERNEL(float16, double) +REGISTER_VAR_CPU_KERNEL(bfloat16, double) #undef REGISTER_VAR_CPU_KERNEL -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) +template size_t InferTmpBufferSize(user_op::InferContext* ctx) { const TensorDesc& input = ctx->InputTensorDesc("input", 0); const Shape& input_shape = input.shape(); const std::vector axis = ctx->Attr>("dim"); - if (axis.size() == input_shape.NumAxes()) { + const int64_t input_dim_element = input.shape().elem_cnt(); + int64_t axis_dim_element = 1; + for (int64_t i = 0; i < axis.size(); ++i) { axis_dim_element *= input.shape().At(axis[i]); } + if (input_dim_element > 0 + && (axis.size() == input_shape.NumAxes() || input_dim_element == axis_dim_element)) { return GetCudaAlignedSize( std::min(static_cast(std::ceil(std::sqrt(input.shape().elem_cnt()))), kCudaMaxBlocksNum) - * GetSizeOfDataType(input.data_type()) * 3); + * sizeof(ComputeType) * 3); } return 0; } -#define REGISTER_VAR_CUDA_KERNEL(dtype) \ +#define REGISTER_VAR_CUDA_KERNEL(dtype, compute_type) \ REGISTER_USER_KERNEL("var") \ - .SetCreateFn>() \ + .SetCreateFn>() \ .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ && (user_op::HobAttr("dtype") == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTmpBufferSize); + .SetInferTmpSizeFn(InferTmpBufferSize); -REGISTER_VAR_CUDA_KERNEL(float) -REGISTER_VAR_CUDA_KERNEL(double) +REGISTER_VAR_CUDA_KERNEL(float, double) +REGISTER_VAR_CUDA_KERNEL(double, double) +REGISTER_VAR_CUDA_KERNEL(half, double) +#if CUDA_VERSION >= 11000 +REGISTER_VAR_CUDA_KERNEL(nv_bfloat16, double) +#endif // CUDA_VERSION >= 11000 #undef REGISTER_VAR_CUDA_KERNEL -#endif -#ifdef WITH_ROCM - -size_t InferTmpBufferSize(user_op::InferContext* ctx) { - const TensorDesc& input = ctx->InputTensorDesc("input", 0); - const Shape& input_shape = input.shape(); - const std::vector axis = ctx->Attr>("dim"); - if (axis.size() == input_shape.NumAxes()) { - return GetCudaAlignedSize( - std::min(static_cast(std::ceil(std::sqrt(input.shape().elem_cnt()))), - kCudaMaxBlocksNum) - * GetSizeOfDataType(input.data_type()) * 3); - } - return 0; -} - -#define REGISTER_VAR_CUDA_KERNEL(dtype) \ - REGISTER_USER_KERNEL("var") \ - .SetCreateFn>() \ - .SetIsMatchedHob((user_op::HobDeviceType() == DeviceType::kCUDA) \ - && (user_op::HobAttr("dtype") == GetDataType::value)) \ - .SetInferTmpSizeFn(InferTmpBufferSize); - -REGISTER_VAR_CUDA_KERNEL(float) -REGISTER_VAR_CUDA_KERNEL(double) -#undef REGISTER_VAR_CUDA_KERNEL -#endif +#endif // WITH_CUDA } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/variance_kernel_util.cpp b/oneflow/user/kernels/variance_kernel_util.cpp index 3cc81f678f9151c6e10deb3c9de72e53a3da3419..e44d836a7940fbb79443a64365943e38a7ead815 100644 --- a/oneflow/user/kernels/variance_kernel_util.cpp +++ b/oneflow/user/kernels/variance_kernel_util.cpp @@ -19,9 +19,9 @@ namespace oneflow { namespace user_op { -template -struct VarFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, +template +struct VarFunctor final { + void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, ComputeType* tmp_buffer_ptr, const VarParam var_param) { // if var_param.parallel_num is 0, do nothing, return 0-size tensor if (IsNanOut(var_param)) { @@ -32,13 +32,15 @@ struct VarFunctor final { for (size_t i = 0; i < var_param.parallel_num; i++) { const size_t input_offset = LinearIndex2Offset( i, var_param.dim_size_in_caxis, var_param.stride_in_caxis, var_param.caxis_size); - ComputeVarUsingWelford(&in_ptr[input_offset], &out_ptr[i], var_param); + ComputeVarUsingWelford(&in_ptr[input_offset], &out_ptr[i], var_param); } } } }; -template struct VarFunctor; -template struct VarFunctor; +template struct VarFunctor; +template struct VarFunctor; +template struct VarFunctor; +template struct VarFunctor; } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/variance_kernel_util.cu b/oneflow/user/kernels/variance_kernel_util.cu index 7d1eb7550a45bbe0d7870cd0736e48d7b4a9b2f4..cb89d80dc25a9b8dc889aa5c2d767f188da2504c 100644 --- a/oneflow/user/kernels/variance_kernel_util.cu +++ b/oneflow/user/kernels/variance_kernel_util.cu @@ -26,16 +26,40 @@ __inline__ __device__ T Nan(); template<> __inline__ __device__ float Nan() { +#ifdef WITH_ROCM + return __int_as_float(0x7fffffffU); +#else return CUDART_NAN_F; +#endif } template<> __inline__ __device__ double Nan() { +#ifdef WITH_ROCM + return __longlong_as_double(0xfff8000000000000ULL); +#else return CUDART_NAN; +#endif } + +template<> +__inline__ __device__ half Nan() { +#ifdef WITH_ROCM + return half(__int_as_float(0x7fffffffU)); +#else + return half(CUDART_NAN_F); +#endif +} + +#if CUDA_VERSION >= 11000 +template<> +__inline__ __device__ nv_bfloat16 Nan() { + return nv_bfloat16(CUDART_NAN_F); +} +#endif } // namespace -template +template __global__ void ComputeVarUsingWelfordWrapper(const T* in_ptr, T* out_ptr, const VarParam var_param, bool is_nan) { if (is_nan) { @@ -44,22 +68,23 @@ __global__ void ComputeVarUsingWelfordWrapper(const T* in_ptr, T* out_ptr, const CUDA_1D_KERNEL_LOOP(i, var_param.parallel_num) { const size_t input_offset = LinearIndex2Offset( i, var_param.dim_size_in_caxis, var_param.stride_in_caxis, var_param.caxis_size); - ComputeVarUsingWelford(&in_ptr[input_offset], &out_ptr[i], var_param); + ComputeVarUsingWelford(&in_ptr[input_offset], &out_ptr[i], var_param); } } } namespace { -template -inline __device__ void WelfordReduce(const T* in_ptr, T* mean, T* m2, T* count, - const size_t total_elem_cnt, const size_t start, - const size_t step) { - T old_mean = 0.0; +template +inline __device__ void WelfordReduce(const T* in_ptr, ComputeType* mean, ComputeType* m2, + ComputeType* count, const size_t total_elem_cnt, + const size_t start, const size_t step) { + ComputeType old_mean = 0.0; for (size_t i = start; i < total_elem_cnt; i += step) { ++(*count); old_mean = *mean; - *mean += (in_ptr[i] - *mean) / *count; - *m2 += (in_ptr[i] - *mean) * (in_ptr[i] - old_mean); + *mean += (static_cast(in_ptr[i]) - *mean) / *count; + *m2 += (static_cast(in_ptr[i]) - *mean) + * (static_cast(in_ptr[i]) - old_mean); } } @@ -74,10 +99,10 @@ inline __device__ void WelfordCombine(const T* b_mean, const T* b_m2, const T* b __device__ int32_t done_block_count = 0; } // namespace -template -__global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, - const VarParam var_param) { - if (var_param.elem_cnt == 1 && var_param.unbiased == true) { +template +__global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, ComputeType* tmp_buffer_ptr, + const VarParam var_param, bool is_nan) { + if (is_nan) { if (blockIdx.x == 0 && threadIdx.x == 0) { *out_ptr = Nan(); } return; } @@ -86,14 +111,14 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p // tail element number in block size_t tail_elems = elems_per_block % blockDim.x; - T thread_mean = 0.0; - T thread_m2 = 0.0; - T thread_count = 0.0; + ComputeType thread_mean = 0.0; + ComputeType thread_m2 = 0.0; + ComputeType thread_count = 0.0; // every thread deal it's elems if (elems_per_thread > 0) { const size_t block_offset = blockIdx.x * elems_per_block; - WelfordReduce(&in_ptr[block_offset], &thread_mean, &thread_m2, &thread_count, - elems_per_block - tail_elems, threadIdx.x, blockDim.x); + WelfordReduce(&in_ptr[block_offset], &thread_mean, &thread_m2, &thread_count, + elems_per_block - tail_elems, threadIdx.x, blockDim.x); } // thread 0 of last block handles tail element between blocks if (blockIdx.x == gridDim.x - 1 && threadIdx.x == 0) { @@ -102,15 +127,16 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p // thread 0 deal tail elems if (tail_elems != 0 && threadIdx.x == 0) { const size_t tail_offset = blockIdx.x * elems_per_block + blockDim.x * elems_per_thread; - WelfordReduce(&in_ptr[tail_offset], &thread_mean, &thread_m2, &thread_count, tail_elems, - /*tail start=*/0, /*step=*/1); + WelfordReduce(&in_ptr[tail_offset], &thread_mean, &thread_m2, &thread_count, + tail_elems, + /*tail start=*/0, /*step=*/1); } - T block_mean = 0; - T block_m2 = 0; - T block_count = 0; - cuda::layer_norm::WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, &block_mean, - &block_m2, &block_count); + ComputeType block_mean = 0; + ComputeType block_m2 = 0; + ComputeType block_count = 0; + cuda::layer_norm::WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, + &block_mean, &block_m2, &block_count); if (gridDim.x == 1) { if (threadIdx.x == 0) { @@ -120,9 +146,9 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p return; } - T* tmp_mean_ptr = tmp_buffer_ptr; - T* tmp_m2_ptr = &tmp_mean_ptr[gridDim.x]; - T* tmp_count_ptr = &tmp_m2_ptr[gridDim.x]; + ComputeType* tmp_mean_ptr = tmp_buffer_ptr; + ComputeType* tmp_m2_ptr = &tmp_mean_ptr[gridDim.x]; + ComputeType* tmp_count_ptr = &tmp_m2_ptr[gridDim.x]; if (threadIdx.x == 0) { tmp_mean_ptr[blockIdx.x] = block_mean; tmp_m2_ptr[blockIdx.x] = block_m2; @@ -132,9 +158,9 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p if (threadIdx.x == 0) { is_last_block = atomicAdd(&done_block_count, 1) == gridDim.x - 1; } __syncthreads(); if (is_last_block) { - T last_block_thread_mean = 0; - T last_block_thread_m2 = 0; - T last_block_thread_count = 0; + ComputeType last_block_thread_mean = 0; + ComputeType last_block_thread_m2 = 0; + ComputeType last_block_thread_count = 0; const size_t welforddatas_per_thread = gridDim.x / blockDim.x; const size_t tail_welforddatas = gridDim.x % blockDim.x; @@ -151,12 +177,12 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p &last_block_thread_m2, &last_block_thread_count, tail_welforddatas, /*tail start=*/0, /*step=*/1); } - T final_mean = 0; - T final_m2 = 0; - T final_count = 0; - cuda::layer_norm::WelfordBlockAllReduce(last_block_thread_mean, last_block_thread_m2, - last_block_thread_count, &final_mean, &final_m2, - &final_count); + ComputeType final_mean = 0; + ComputeType final_m2 = 0; + ComputeType final_count = 0; + cuda::layer_norm::WelfordBlockAllReduce( + last_block_thread_mean, last_block_thread_m2, last_block_thread_count, &final_mean, + &final_m2, &final_count); if (threadIdx.x == 0) { *out_ptr = cuda::layer_norm::Div(final_m2, (var_param.unbiased ? final_count - 1 : final_count)); @@ -165,27 +191,32 @@ __global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_p } } -template -struct VarFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, +template +struct VarFunctor final { + void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, ComputeType* tmp_buffer_ptr, const VarParam var_param) { int grid_dim = 0; int block_dim = 0; SetGridDimAndBlockDim(var_param.elem_cnt, &grid_dim, &block_dim); if (var_param.parallel_num == 1) { - ComputeVarScalarOut + ComputeVarScalarOut <<As()->cuda_stream()>>>( - in_ptr, out_ptr, tmp_buffer_ptr, var_param); + in_ptr, out_ptr, tmp_buffer_ptr, var_param, IsNanOut(var_param)); } else { // if var_param.parallel_num is 0, do nothing, return 0-size tensor if (var_param.parallel_num == 0) { return; } - RUN_CUDA_KERNEL(ComputeVarUsingWelfordWrapper, stream, var_param.parallel_num, in_ptr, - out_ptr, var_param, IsNanOut(var_param)); + RUN_CUDA_KERNEL((ComputeVarUsingWelfordWrapper), stream, + var_param.parallel_num, in_ptr, out_ptr, var_param, IsNanOut(var_param)); } } }; -template struct VarFunctor; -template struct VarFunctor; +template struct VarFunctor; +template struct VarFunctor; +template struct VarFunctor; + +#if CUDA_VERSION >= 11000 +template struct VarFunctor; +#endif } // namespace user_op } // namespace oneflow diff --git a/oneflow/user/kernels/variance_kernel_util.h b/oneflow/user/kernels/variance_kernel_util.h index 8595ecaece55fcb1688774b34576e5b462669f22..99592baea199dfcbd655f5c1c9152243cda4b47b 100644 --- a/oneflow/user/kernels/variance_kernel_util.h +++ b/oneflow/user/kernels/variance_kernel_util.h @@ -116,20 +116,21 @@ class VarParamHelper final { std::vector caxis_; }; -template +template OF_DEVICE_FUNC void ComputeVarUsingWelford(const T* in_ptr, T* out_ptr, const VarParam& var_param) { size_t count = 0; // torch use double even for float data, so here float will result in accuracy error. - double mean = 0.0; - double old_mean = 0.0; - double m2 = 0.0; + ComputeType mean = 0.0; + ComputeType old_mean = 0.0; + ComputeType m2 = 0.0; for (size_t i = 0; i < var_param.elem_cnt; i++) { const size_t offset = LinearIndex2Offset(i, var_param.dim_size_in_axis, var_param.stride_in_axis, var_param.axis_size); count++; old_mean = mean; - mean += (in_ptr[offset] - mean) / count; - m2 += (in_ptr[offset] - mean) * (in_ptr[offset] - old_mean); + mean += (static_cast(in_ptr[offset]) - mean) / count; + m2 += (static_cast(in_ptr[offset]) - mean) + * (static_cast(in_ptr[offset]) - old_mean); } *out_ptr = m2 / (var_param.unbiased ? count - 1 : count); } @@ -140,7 +141,7 @@ OF_DEVICE_FUNC bool IsNanOut(const VarParam var_param) { return (var_param.elem_cnt == 0) || (var_param.elem_cnt == 1 && var_param.unbiased == true); } -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) void SetGridDimAndBlockDim(const size_t total_elem_cnt, int* grid_dim, int* block_dim) { // when total_elem_cnt > 2 * kCudaThreadsNumPerBlock, use two cuda kernel if (total_elem_cnt > (kCudaThreadsNumPerBlock << 1)) { @@ -152,33 +153,18 @@ void SetGridDimAndBlockDim(const size_t total_elem_cnt, int* grid_dim, int* bloc int32_t aligned_block_dim = (total_elem_cnt >= kCudaThreadsNumPerBlock) ? kCudaThreadsNumPerBlock - : (total_elem_cnt + kCudaWarpSize - 1) / kCudaWarpSize * kCudaWarpSize; + // avoid get block_dim = 0 when total_elem_cnt is 0 + : std::max((total_elem_cnt + kCudaWarpSize - 1) / kCudaWarpSize, 1) + * kCudaWarpSize; *block_dim = std::min(aligned_block_dim, kCudaThreadsNumPerBlock); } } #endif // WITH_CUDA -#ifdef WITH_ROCM -void SetGridDimAndBlockDim(const size_t total_elem_cnt, int* grid_dim, int* block_dim) { - // when total_elem_cnt > 2 * kCudaThreadsNumPerBlock, use two cuda kernel - if (total_elem_cnt > (kCudaThreadsNumPerBlock << 1)) { - *grid_dim = - std::min(static_cast(std::ceil(std::sqrt(total_elem_cnt))), kCudaMaxBlocksNum); - *block_dim = kCudaThreadsNumPerBlock; - } else { - *grid_dim = 1; - int32_t aligned_block_dim = - (total_elem_cnt >= kCudaThreadsNumPerBlock) - ? kCudaThreadsNumPerBlock - : (total_elem_cnt + kCudaWarpSize - 1) / kCudaWarpSize * kCudaWarpSize; - *block_dim = std::min(aligned_block_dim, kCudaThreadsNumPerBlock); - } -} -#endif // WITH_ROCM } // namespace -template +template struct VarFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, + void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, ComputeType* tmp_buffer_ptr, const VarParam var_param); }; diff --git a/oneflow/user/kernels/variance_kernel_util.hip.cpp b/oneflow/user/kernels/variance_kernel_util.hip.cpp deleted file mode 100644 index 44eb16479993ece31e0b151f2cd78bbea46c82b4..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/variance_kernel_util.hip.cpp +++ /dev/null @@ -1,192 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "hip/hip_runtime.h" -#include -#include "oneflow/user/kernels/variance_kernel_util.h" -#include "oneflow/core/hip/layer_norm.hip.h" - -namespace oneflow { -namespace user_op { - -namespace { -template -__inline__ __device__ T Nan(); - -template<> -__inline__ __device__ float Nan() { - return __int_as_float(0x7fffffffU); -} - -template<> -__inline__ __device__ double Nan() { - return __longlong_as_double(0xfff8000000000000ULL); -} -} // namespace - -template -__global__ void ComputeVarUsingWelfordWrapper(const T* in_ptr, T* out_ptr, const VarParam var_param, - bool is_nan) { - if (is_nan) { - CUDA_1D_KERNEL_LOOP(i, var_param.parallel_num) { out_ptr[i] = Nan(); } - } else { - CUDA_1D_KERNEL_LOOP(i, var_param.parallel_num) { - const size_t input_offset = LinearIndex2Offset( - i, var_param.dim_size_in_caxis, var_param.stride_in_caxis, var_param.caxis_size); - ComputeVarUsingWelford(&in_ptr[input_offset], &out_ptr[i], var_param); - } - } -} - -namespace { -template -inline __device__ void WelfordReduce(const T* in_ptr, T* mean, T* m2, T* count, - const size_t total_elem_cnt, const size_t start, - const size_t step) { - T old_mean = 0.0; - for (size_t i = start; i < total_elem_cnt; i += step) { - ++(*count); - old_mean = *mean; - *mean += (in_ptr[i] - *mean) / *count; - *m2 += (in_ptr[i] - *mean) * (in_ptr[i] - old_mean); - } -} - -template -inline __device__ void WelfordCombine(const T* b_mean, const T* b_m2, const T* b_count, T* mean, - T* m2, T* count, const size_t total_elem_cnt, - const size_t start, const size_t step) { - for (size_t i = start; i < total_elem_cnt; i += step) { - cuda::layer_norm::WelfordCombine(b_mean[i], b_m2[i], b_count[i], mean, m2, count); - } -} -__device__ int32_t done_block_count = 0; -} // namespace - -template -__global__ void ComputeVarScalarOut(const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, - const VarParam var_param) { - if (var_param.elem_cnt == 1 && var_param.unbiased == true) { - if (blockIdx.x == 0 && threadIdx.x == 0) { *out_ptr = Nan(); } - return; - } - const size_t elems_per_block = var_param.elem_cnt / gridDim.x; - const size_t elems_per_thread = elems_per_block / blockDim.x; - // tail element number in block - size_t tail_elems = elems_per_block % blockDim.x; - - T thread_mean = 0.0; - T thread_m2 = 0.0; - T thread_count = 0.0; - // every thread deal it's elems - if (elems_per_thread > 0) { - const size_t block_offset = blockIdx.x * elems_per_block; - WelfordReduce(&in_ptr[block_offset], &thread_mean, &thread_m2, &thread_count, - elems_per_block - tail_elems, threadIdx.x, blockDim.x); - } - // thread 0 of last block handles tail element between blocks - if (blockIdx.x == gridDim.x - 1 && threadIdx.x == 0) { - tail_elems += var_param.elem_cnt % gridDim.x; - } - // thread 0 deal tail elems - if (tail_elems != 0 && threadIdx.x == 0) { - const size_t tail_offset = blockIdx.x * elems_per_block + blockDim.x * elems_per_thread; - WelfordReduce(&in_ptr[tail_offset], &thread_mean, &thread_m2, &thread_count, tail_elems, - /*tail start=*/0, /*step=*/1); - } - - T block_mean = 0; - T block_m2 = 0; - T block_count = 0; - cuda::layer_norm::WelfordBlockAllReduce(thread_mean, thread_m2, thread_count, &block_mean, - &block_m2, &block_count); - - if (gridDim.x == 1) { - if (threadIdx.x == 0) { - *out_ptr = - cuda::layer_norm::Div(block_m2, (var_param.unbiased ? block_count - 1 : block_count)); - } - return; - } - - T* tmp_mean_ptr = tmp_buffer_ptr; - T* tmp_m2_ptr = &tmp_mean_ptr[gridDim.x]; - T* tmp_count_ptr = &tmp_m2_ptr[gridDim.x]; - if (threadIdx.x == 0) { - tmp_mean_ptr[blockIdx.x] = block_mean; - tmp_m2_ptr[blockIdx.x] = block_m2; - tmp_count_ptr[blockIdx.x] = block_count; - } - __shared__ bool is_last_block; - if (threadIdx.x == 0) { is_last_block = atomicAdd(&done_block_count, 1) == gridDim.x - 1; } - __syncthreads(); - if (is_last_block) { - T last_block_thread_mean = 0; - T last_block_thread_m2 = 0; - T last_block_thread_count = 0; - const size_t welforddatas_per_thread = gridDim.x / blockDim.x; - const size_t tail_welforddatas = gridDim.x % blockDim.x; - - if (welforddatas_per_thread > 0) { - WelfordCombine(tmp_mean_ptr, tmp_m2_ptr, tmp_count_ptr, &last_block_thread_mean, - &last_block_thread_m2, &last_block_thread_count, gridDim.x - tail_welforddatas, - threadIdx.x, blockDim.x); - } - // thread 0 deal tail welford data - if (tail_welforddatas != 0 && threadIdx.x == 0) { - const size_t last_block_tail_offset = blockDim.x * welforddatas_per_thread; - WelfordCombine(&tmp_mean_ptr[last_block_tail_offset], &tmp_m2_ptr[last_block_tail_offset], - &tmp_count_ptr[last_block_tail_offset], &last_block_thread_mean, - &last_block_thread_m2, &last_block_thread_count, tail_welforddatas, - /*tail start=*/0, /*step=*/1); - } - T final_mean = 0; - T final_m2 = 0; - T final_count = 0; - cuda::layer_norm::WelfordBlockAllReduce(last_block_thread_mean, last_block_thread_m2, - last_block_thread_count, &final_mean, &final_m2, - &final_count); - if (threadIdx.x == 0) { - *out_ptr = - cuda::layer_norm::Div(final_m2, (var_param.unbiased ? final_count - 1 : final_count)); - done_block_count = 0; - } - } -} - -template -struct VarFunctor final { - void operator()(ep::Stream* stream, const T* in_ptr, T* out_ptr, T* tmp_buffer_ptr, - const VarParam var_param) { - int grid_dim = 0; - int block_dim = 0; - SetGridDimAndBlockDim(var_param.elem_cnt, &grid_dim, &block_dim); - if (var_param.parallel_num == 1) { - ComputeVarScalarOut - <<As()->cuda_stream()>>>( - in_ptr, out_ptr, tmp_buffer_ptr, var_param); - } else { - // if var_param.parallel_num is 0, do nothing, return 0-size tensor - if (var_param.parallel_num == 0) { return; } - RUN_CUDA_KERNEL(ComputeVarUsingWelfordWrapper, stream, var_param.parallel_num, in_ptr, - out_ptr, var_param, IsNanOut(var_param)); - } - } -}; - -template struct VarFunctor; -template struct VarFunctor; -} // namespace user_op -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/vector_matrix_product_kernel.cpp b/oneflow/user/kernels/vector_matrix_product_kernel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..533b4b17977e3dab7c50e859f225ddb87e878598 --- /dev/null +++ b/oneflow/user/kernels/vector_matrix_product_kernel.cpp @@ -0,0 +1,186 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/framework/config_def.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/matmul.h" + +namespace oneflow { + +namespace { + +ep::primitive::BlasTransposeType GetBlasTransposeType(bool transpose) { + return transpose ? ep::primitive::BlasTransposeType::T : ep::primitive::BlasTransposeType::N; +} + +template +std::unique_ptr NewMemcpyPrimitive(Context* ctx) { + return ep::primitive::NewPrimitive( + ctx->device_type(), ep::primitive::MemcpyKind::kDtoD); +} + +std::unique_ptr NewMatmulPrimitive(DeviceType device_type, + DataType data_type, bool transpose_a, + bool transpose_b) { + const auto trans_a = GetBlasTransposeType(transpose_a); + const auto trans_b = GetBlasTransposeType(transpose_b); + return ep::primitive::NewPrimitive(device_type, data_type, trans_a, + trans_b); +} + +template +std::unique_ptr NewVectorMatrixProductPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("out", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, false, false); +} + +template +std::unique_ptr NewVectorMatrixProductGradAPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, false, true); +} + +template +std::unique_ptr NewVectorMatrixProductGradBPrimitive(Context* ctx) { + const DataType data_type = ctx->TensorDesc4ArgNameAndIndex("dx", 0)->data_type(); + return NewMatmulPrimitive(ctx->device_type(), data_type, true, false); +} + +auto VectorMatrixProductPrimitiveExists() { + return hob::make_custom("NewVectorMatrixProductPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewVectorMatrixProductPrimitive(&ctx).operator bool(); + }); +} + +auto VectorMatrixProductGradAPrimitiveExists() { + return hob::make_custom("NewVectorMatrixProductGradAPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewVectorMatrixProductGradAPrimitive(&ctx).operator bool(); + }); +} + +auto VectorMatrixProductGradBPrimitiveExists() { + return hob::make_custom("NewVectorMatrixProductGradBPrimitiveExists", + [](const user_op::KernelRegContext& ctx) { + return NewVectorMatrixProductGradBPrimitive(&ctx).operator bool(); + }); +} + +class VectorMatrixProductKernel final : public user_op::OpKernel, public user_op::CudaGraphSupport { + public: + VectorMatrixProductKernel() = default; + ~VectorMatrixProductKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + */ + const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); + CHECK_EQ(a->shape_view().NumAxes(), 1) << "A Numdims should be equal to 1. "; + const DataType data_type = a->data_type(); + const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); + CHECK_EQ(b->shape_view().NumAxes(), 2) << "B Numdims should be equal to 2. "; + CHECK_EQ(b->data_type(), data_type) << "Matrix A Datatype should be equal to Vector B"; + user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); + CHECK_EQ(out->shape_view().NumAxes(), 1) << "Out Numdims should be equal to 1. "; + CHECK_EQ(out->data_type(), data_type) << "Out Datatype should be equal to input's. "; + size_t m = 1; + size_t k = a->shape_view().At(0); + size_t n = b->shape_view().At(1); + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewVectorMatrixProductPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, a->dptr(), b->dptr(), beta, out->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("vector_matrix_product") + .SetCreateFn() + .SetIsMatchedHob(VectorMatrixProductPrimitiveExists()); + +class VectorMatrixProductGradAKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + VectorMatrixProductGradAKernel() = default; + ~VectorMatrixProductGradAKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + GradA = dy (n) matmul B_transpose(n, k) -> (1, n) matmul (n, k) + */ + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* b = ctx->Tensor4ArgNameAndIndex("b", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + size_t m = 1; + size_t k = dy->shape_view().At(0); + size_t n = b->shape_view().At(0); + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewVectorMatrixProductGradAPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, dy->dptr(), b->dptr(), beta, dx->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("vector_matrix_product_grad_a") + .SetCreateFn() + .SetIsMatchedHob(VectorMatrixProductGradAPrimitiveExists()); + +class VectorMatrixProductGradBKernel final : public user_op::OpKernel, + public user_op::CudaGraphSupport { + public: + VectorMatrixProductGradBKernel() = default; + ~VectorMatrixProductGradBKernel() = default; + + bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } + + private: + void Compute(user_op::KernelComputeContext* ctx) const override { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + GradB = a_transpose (k, 1) matmul dy (1, n) + */ + const user_op::Tensor* dy = ctx->Tensor4ArgNameAndIndex("dy", 0); + const user_op::Tensor* a = ctx->Tensor4ArgNameAndIndex("a", 0); + user_op::Tensor* dx = ctx->Tensor4ArgNameAndIndex("dx", 0); + size_t m = a->shape_view().At(0); + size_t k = 1; + size_t n = dy->shape_view().At(0); + const double alpha = 1.0; + double beta = 0.0; + auto matmul = NewVectorMatrixProductGradBPrimitive(ctx); + CHECK(matmul); + matmul->Launch(ctx->stream(), m, n, k, alpha, a->dptr(), dy->dptr(), beta, dx->mut_dptr()); + } +}; + +REGISTER_USER_KERNEL("vector_matrix_product_grad_b") + .SetCreateFn() + .SetIsMatchedHob(VectorMatrixProductGradBPrimitiveExists()); + +} // namespace + +} // namespace oneflow diff --git a/oneflow/user/kernels/where_kernel.cpp b/oneflow/user/kernels/where_kernel.cpp index 6c2e893491e5e861ed8ad7c4dea3fac5a2a14d7a..ed6d6777cc7d4e86ed40e811616d8287e47be8c0 100644 --- a/oneflow/user/kernels/where_kernel.cpp +++ b/oneflow/user/kernels/where_kernel.cpp @@ -191,13 +191,13 @@ class WhereScalarXYKernel final : public user_op::OpKernel { && (user_op::HobDataType("condition", 0) == OF_PP_PAIR_SECOND(ctype_pair)) \ && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - Shape* out_shape = ctx->OutputShape("out", 0); \ + const Shape& out_shape = ctx->OutputShape("out", 0); \ const size_t x_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ const size_t y_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ const size_t cond_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ return x_bytes + y_bytes + cond_bytes; \ }); @@ -209,11 +209,11 @@ class WhereScalarXYKernel final : public user_op::OpKernel { && (user_op::HobDataType("condition", 0) == OF_PP_PAIR_SECOND(ctype_pair)) \ && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - Shape* out_shape = ctx->OutputShape("out", 0); \ + const Shape& out_shape = ctx->OutputShape("out", 0); \ const size_t y_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ const size_t cond_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ return y_bytes + cond_bytes; \ }); @@ -225,11 +225,11 @@ class WhereScalarXYKernel final : public user_op::OpKernel { && (user_op::HobDataType("condition", 0) == OF_PP_PAIR_SECOND(ctype_pair)) \ && (user_op::HobDataType("out", 0) == OF_PP_PAIR_SECOND(dtype_pair))) \ .SetInferTmpSizeFn([](user_op::InferContext* ctx) { \ - Shape* out_shape = ctx->OutputShape("out", 0); \ + const Shape& out_shape = ctx->OutputShape("out", 0); \ const size_t x_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(dtype_pair))); \ const size_t cond_bytes = \ - GetCudaAlignedSize(out_shape->elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ + GetCudaAlignedSize(out_shape.elem_cnt() * sizeof(OF_PP_PAIR_FIRST(ctype_pair))); \ return x_bytes + cond_bytes; \ }); @@ -255,15 +255,12 @@ OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_WHERE_SCALAR_Y_KERNEL, DEVICE_TYPE_SEQ BOOL_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_WHERE_SCALAR_XY_KERNEL, DEVICE_TYPE_SEQ, - OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) - OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) - BOOL_DATA_TYPE_SEQ, + OF_PP_MAKE_TUPLE_SEQ(float, DataType::kFloat) + OF_PP_MAKE_TUPLE_SEQ(double, DataType::kDouble) + OF_PP_MAKE_TUPLE_SEQ(int64_t, DataType::kInt64) + BOOL_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -#ifdef WITH_CUDA -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_WHERE_KERNEL, (DeviceType::kCUDA), FLOAT16_DATA_TYPE_SEQ, - INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) -#endif -#ifdef WITH_ROCM +#if defined(WITH_CUDA) || defined(WITH_ROCM) OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(REGISTER_WHERE_KERNEL, (DeviceType::kCUDA), FLOAT16_DATA_TYPE_SEQ, INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) #endif diff --git a/oneflow/user/kernels/where_kernel_util.hip.cpp b/oneflow/user/kernels/where_kernel_util.hip.cpp deleted file mode 100644 index 276b1c1e92ce28a77c766de22b0185f3d7414eef..0000000000000000000000000000000000000000 --- a/oneflow/user/kernels/where_kernel_util.hip.cpp +++ /dev/null @@ -1,90 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/user/kernels/where_kernel_util.h" -#include "oneflow/core/hip/elementwise.hip.h" -#include "oneflow/core/ep/rocm/cuda_stream.h" - -namespace oneflow { - -namespace { - -template -struct WhereFunctor { - OF_DEVICE_FUNC T operator()(CondT cond, T lhs, T rhs) const { - return static_cast(cond) ? lhs : rhs; - } -}; - -template -struct WhereScalarXFunctor { - OF_DEVICE_FUNC explicit WhereScalarXFunctor(T scalar) : x_scalar(scalar) {} - OF_DEVICE_FUNC T operator()(CondT cond, T rhs) const { - return static_cast(cond) ? x_scalar : rhs; - } - const T x_scalar; -}; - -template -struct WhereScalarYFunctor { - OF_DEVICE_FUNC explicit WhereScalarYFunctor(T scalar) : y_scalar(scalar) {} - OF_DEVICE_FUNC T operator()(CondT cond, T lhs) const { - return static_cast(cond) ? lhs : y_scalar; - } - const T y_scalar; -}; - -template -struct WhereScalarXYFunctor { - OF_DEVICE_FUNC explicit WhereScalarXYFunctor(T x_scalar, T y_scalar) - : x_scalar(x_scalar), y_scalar(y_scalar) {} - OF_DEVICE_FUNC T operator()(CondT cond) const { - return static_cast(cond) ? x_scalar : y_scalar; - } - const T x_scalar; - const T y_scalar; -}; - -} // namespace - -template -struct WhereKernelUtil { - static void Where(ep::Stream* stream, const int64_t elem_cnt, const CondT* cond, const T* lhs, - const T* rhs, T* out) { - cuda::elementwise::Ternary(WhereFunctor(), elem_cnt, out, cond, lhs, rhs, - stream->As()->cuda_stream()); - } - static void WhereXScalar(ep::Stream* stream, const int64_t elem_cnt, const CondT* cond, - const T x_scalar, const T* rhs, T* out) { - cuda::elementwise::Binary(WhereScalarXFunctor(x_scalar), elem_cnt, out, cond, rhs, - stream->As()->cuda_stream()); - } - static void WhereYScalar(ep::Stream* stream, const int64_t elem_cnt, const CondT* cond, - const T* lhs, const T y_scalar, T* out) { - cuda::elementwise::Binary(WhereScalarYFunctor(y_scalar), elem_cnt, out, cond, lhs, - stream->As()->cuda_stream()); - } - static void WhereXYScalar(ep::Stream* stream, const int64_t elem_cnt, const CondT* cond, - const T x_scalar, const T y_scalar, T* out) { - cuda::elementwise::Unary(WhereScalarXYFunctor(x_scalar, y_scalar), elem_cnt, out, - cond, stream->As()->cuda_stream()); - } -}; - -OF_PP_SEQ_PRODUCT_FOR_EACH_TUPLE(INSTANTIATE_WHERE_FUNCTOR, (DeviceType::kCUDA), - ARITHMETIC_DATA_TYPE_SEQ FLOAT16_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ, - INT_DATA_TYPE_SEQ BOOL_DATA_TYPE_SEQ) - -} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/kernels/zero_like_kernel.cpp b/oneflow/user/kernels/zero_like_kernel.cpp index 675d374a265f250c76e9915059a62c91f52962c7..ade157b3a1384d22a93fb9a601dd3dfbcebcae32 100644 --- a/oneflow/user/kernels/zero_like_kernel.cpp +++ b/oneflow/user/kernels/zero_like_kernel.cpp @@ -14,11 +14,13 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#include "oneflow/core/kernel/new_kernel_util.h" +#include "oneflow/core/kernel/cuda_graph_support.h" +#include "oneflow/core/ep/include/primitive/memset.h" namespace oneflow { -template +namespace { + class ZeroLikeKernel final : public user_op::OpKernel { public: ZeroLikeKernel() = default; @@ -27,23 +29,21 @@ class ZeroLikeKernel final : public user_op::OpKernel { private: void Compute(user_op::KernelComputeContext* ctx) const override { user_op::Tensor* out = ctx->Tensor4ArgNameAndIndex("out", 0); - Memset(ctx->stream(), out->mut_dptr(), 0, - out->shape_view().elem_cnt() * GetSizeOfDataType(out->data_type())); + const int64_t elem_cnt = out->shape_view().elem_cnt(); + if (elem_cnt > 0) { + std::unique_ptr primitive = + ep::primitive::NewPrimitive(ctx->stream()->device_type()); + CHECK(primitive) << "Can not create Memset primitive for device type " + << ctx->stream()->device_type(); + primitive->Launch(ctx->stream(), out->mut_dptr(), 0, + elem_cnt * GetSizeOfDataType(out->data_type())); + } } bool AlwaysComputeWhenAllOutputsEmpty() const override { return false; } }; -#define REGISTER_ZERO_LIKE_KERNEL(device_type_v) \ - REGISTER_USER_KERNEL("zero_like") \ - .SetCreateFn>() \ - .SetIsMatchedHob(user_op::HobDeviceType() == device_type_v); - -REGISTER_ZERO_LIKE_KERNEL(DeviceType::kCPU) -#ifdef WITH_CUDA -REGISTER_ZERO_LIKE_KERNEL(DeviceType::kCUDA) -#endif -#ifdef WITH_ROCM -REGISTER_ZERO_LIKE_KERNEL(DeviceType::kCUDA) -#endif +REGISTER_USER_KERNEL("zero_like").SetCreateFn().SetIsMatchedHob(user_op::HobTrue()); + +} // namespace } // namespace oneflow diff --git a/oneflow/user/ops/acc_ctrl_tick_op.cpp b/oneflow/user/ops/acc_ctrl_tick_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..dbbecdf0e3c7b18660e26874fb5fa1b98dca9baf --- /dev/null +++ b/oneflow/user/ops/acc_ctrl_tick_op.cpp @@ -0,0 +1,80 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/operator/operator.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe AccCtrlTickOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, Shape({1})); + return Maybe::Ok(); +} + +/*static*/ Maybe AccCtrlTickOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe AccCtrlTickOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); +} + +/* static */ Maybe AccCtrlTickOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { + const NdSbp& in_dis_hint = ctx->NdSbpHint4InputArgNameAndIndex("in", 0); + const Shape& parallel_hierarchy = ctx->parallel_hierarchy(); + CHECK_EQ_OR_RETURN(in_dis_hint.sbp_parallel_size(), // NOLINT(maybe-need-error-msg) + parallel_hierarchy.NumAxes()); // NOLINT(maybe-need-error-msg) + + NdSbp* in_distribution = ctx->NdSbp4ArgNameAndIndex("in", 0); + NdSbp* out_distribution = ctx->NdSbp4ArgNameAndIndex("out", 0); + in_distribution->clear_sbp_parallel(); + out_distribution->clear_sbp_parallel(); + // in use hint + in_distribution->CopyFrom(in_dis_hint); + + for (int32_t i = 0; i < parallel_hierarchy.NumAxes(); ++i) { + // out dim1 = broadcast + out_distribution->add_sbp_parallel()->mutable_broadcast_parallel(); + } + return Maybe::Ok(); +} + +/* static */ Maybe AccCtrlTickOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe AccCtrlTickOp::InferOutputBlobTimeShape( + user_op::InferOutputBlobTimeShapeFnContext* ctx) { + const int32_t max_acc_num = ctx->user_op_conf().attr("max_acc_num"); + const Shape& in_time_shape = ctx->TimeShape4InputArgNameAndIndex("in", 0); + DimVector time_shape_dim_vec = in_time_shape.dim_vec(); // NOLINT(maybe-need-error-msg) + CHECK_OR_RETURN(!time_shape_dim_vec.empty()); // NOLINT(maybe-need-error-msg) + if (time_shape_dim_vec.back() == max_acc_num) { + time_shape_dim_vec.pop_back(); + } else if (time_shape_dim_vec.back() % max_acc_num == 0) { + time_shape_dim_vec.back() /= max_acc_num; + } else { + const int64_t elem_cnt = in_time_shape.elem_cnt(); + CHECK_EQ_OR_RETURN(elem_cnt % max_acc_num, 0); + time_shape_dim_vec.resize(1); + time_shape_dim_vec.back() = elem_cnt / max_acc_num; + } + *ctx->mut_output_blob_time_shape() = Shape(time_shape_dim_vec); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/acc_op.cpp b/oneflow/user/ops/acc_op.cpp index 92df9df8f8e0bb4975ab50de3acbb63006e599a3..d2f9ed7bd991e4bcb7e9094d94572541d4d5f29f 100644 --- a/oneflow/user/ops/acc_op.cpp +++ b/oneflow/user/ops/acc_op.cpp @@ -30,15 +30,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe AccOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } /*static*/ Maybe AccOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return AccOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe AccOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe AccOp::InferOutputBlobTimeShape( @@ -60,24 +60,4 @@ namespace oneflow { return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("acc").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) - -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("repeat") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr("repeat_num", ctx->FwOp().attr("max_acc_num")) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/adaptive_max_pool_op.cpp b/oneflow/user/ops/adaptive_max_pool_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..38ffe3cf0d203e8de9e0166fb093be439c5b1e15 --- /dev/null +++ b/oneflow/user/ops/adaptive_max_pool_op.cpp @@ -0,0 +1,129 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/ops/nn_util.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferFWTensorDesc(user_op::InferContext* ctx) { + std::vector output_size = ctx->Attr>("output_size"); + const Shape& x_shape = ctx->InputShape("x", 0); + DimVector out_shape(x_shape.NumAxes()); + out_shape[0] = x_shape.dim_vec()[0]; + out_shape[1] = x_shape.dim_vec()[1]; + for (int i = 2; i < out_shape.size(); ++i) { + out_shape[i] = output_size.size() > i - 2 ? output_size[i - 2] : output_size[0]; + } + + ctx->SetOutputShape("y", 0, Shape(out_shape)); + ctx->SetOutputShape("index", 0, Shape(out_shape)); + return Maybe::Ok(); +} + +Maybe InferBWTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("x", 0)); + return Maybe::Ok(); +} + +Maybe FwGetSbpFn(user_op::SbpContext* ctx) { + const user_op::TensorDesc& tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + // only for nchw + FOR_RANGE(int64_t, i, 0, std::min(2, (int)tensor.shape().NumAxes())) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Split(user_op::OpArg("y", 0), i) + .Split(user_op::OpArg("index", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe BwGetSbpFn(user_op::SbpContext* ctx) { + const user_op::TensorDesc& tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, std::min(2, (int)tensor.shape().NumAxes())) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Split(user_op::OpArg("dy", 0), i) + .Split(user_op::OpArg("dx", 0), i) + .Split(user_op::OpArg("index", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe InferFWDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("index", 0, DataType::kInt64); + return Maybe::Ok(); +} + +Maybe InferBWDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +} // namespace + +#define DEF_ADAPTIVE_MAX_POOL_OP(op_class_name_prefix) \ + /* static */ Maybe op_class_name_prefix##Op::InferLogicalTensorDesc( \ + user_op::InferContext* ctx) { \ + return InferFWTensorDesc(ctx); \ + } \ + \ + /*static*/ Maybe op_class_name_prefix##Op::InferPhysicalTensorDesc( \ + user_op::InferContext* ctx) { \ + return InferLogicalTensorDesc(ctx); \ + } \ + \ + /* static */ Maybe op_class_name_prefix##Op::GetSbp(user_op::SbpContext* ctx) { \ + return FwGetSbpFn(ctx); \ + } \ + \ + /* static */ Maybe op_class_name_prefix##Op::InferDataType(user_op::InferContext* ctx) { \ + return InferFWDataType(ctx); \ + } \ + \ + /* static */ Maybe op_class_name_prefix##GradOp::InferLogicalTensorDesc( \ + user_op::InferContext* ctx) { \ + return InferBWTensorDesc(ctx); \ + } \ + \ + /*static*/ \ + Maybe op_class_name_prefix##GradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ + return InferLogicalTensorDesc(ctx); \ + } \ + \ + /* static */ \ + Maybe op_class_name_prefix##GradOp::GetSbp(user_op::SbpContext* ctx) { \ + return BwGetSbpFn(ctx); \ + } \ + \ + /* static */ \ + Maybe op_class_name_prefix##GradOp::InferDataType(user_op::InferContext* ctx) { \ + return InferBWDataType(ctx); \ + } + +DEF_ADAPTIVE_MAX_POOL_OP(AdaptiveMaxPool1D); +DEF_ADAPTIVE_MAX_POOL_OP(AdaptiveMaxPool2D); +DEF_ADAPTIVE_MAX_POOL_OP(AdaptiveMaxPool3D); + +#undef DEF_ADAPTIVE_MAX_POOL_OP + +} // namespace oneflow diff --git a/oneflow/user/ops/adaptive_pool_op.cpp b/oneflow/user/ops/adaptive_pool_op.cpp index 935e644ea83de02363f9f78ef4907b97258692b9..523b728a522f2389ff23f476ade0814528944363 100644 --- a/oneflow/user/ops/adaptive_pool_op.cpp +++ b/oneflow/user/ops/adaptive_pool_op.cpp @@ -31,13 +31,13 @@ Maybe InferFWTensorDesc(user_op::InferContext* ctx) { out_shape[i] = output_size.size() > i - 2 ? output_size[i - 2] : output_size[0]; } - *ctx->OutputShape("y", 0) = Shape(out_shape); + ctx->SetOutputShape("y", 0, Shape(out_shape)); return Maybe::Ok(); } Maybe InferBWTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("x", 0)); return Maybe::Ok(); } @@ -63,12 +63,12 @@ Maybe BwGetSbpFn(user_op::SbpContext* ctx) { } Maybe InferFWDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe InferBWDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -118,58 +118,4 @@ DEF_ADAPTIVE_AVG_POOL_OP(AdaptiveAvgPool3D) #undef DEF_ADAPTIVE_AVG_POOL_OP -REGISTER_USER_OP_GRAD("adaptive_avg_pool1d") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto adaptive_avg_pool1d_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(adaptive_avg_pool1d_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("adaptive_avg_pool1d_grad") - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind( - user_op::OpArg("x", 0), - [&ctx, &adaptive_avg_pool1d_grad_op_name]() -> const std::string& { - return ctx->GetOp(adaptive_avg_pool1d_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("adaptive_avg_pool2d") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto adaptive_avg_pool2d_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(adaptive_avg_pool2d_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("adaptive_avg_pool2d_grad") - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind( - user_op::OpArg("x", 0), - [&ctx, &adaptive_avg_pool2d_grad_op_name]() -> const std::string& { - return ctx->GetOp(adaptive_avg_pool2d_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("adaptive_avg_pool3d") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto adaptive_avg_pool3d_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(adaptive_avg_pool3d_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("adaptive_avg_pool3d_grad") - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind( - user_op::OpArg("x", 0), - [&ctx, &adaptive_avg_pool3d_grad_op_name]() -> const std::string& { - return ctx->GetOp(adaptive_avg_pool3d_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/add_n_op.cpp b/oneflow/user/ops/add_n_op.cpp index c135a845c4e3843c6d34068adb8c1c91d3656ee6..b48eed2f0298ed3299416053297e231a7d367e6c 100644 --- a/oneflow/user/ops/add_n_op.cpp +++ b/oneflow/user/ops/add_n_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe AddNOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const auto& in_0 = ctx->InputTensorDesc("in", 0); - auto* out = ctx->OutputTensorDesc("out", 0); + auto* out = ctx->MutOutputTensorDesc("out", 0); CHECK_NOTNULL_OR_RETURN(out); // NOLINT(maybe-need-error-msg) for (const auto& pair : ctx->inputs()) { const auto& cur_in = ctx->InputTensorDesc(pair.first, pair.second); @@ -30,8 +30,8 @@ namespace oneflow { << "but got " << in_0.shape().elem_cnt() << " and " << cur_in.shape().elem_cnt(); } } - *out->mut_shape() = in_0.shape(); - *out->mut_is_dynamic() = in_0.is_dynamic(); + out->set_shape(in_0.shape()); + out->set_is_dynamic(in_0.is_dynamic()); return Maybe::Ok(); } @@ -50,7 +50,7 @@ namespace oneflow { /* static */ Maybe AddNOp::InferDataType(user_op::InferContext* ctx) { const auto& in_0 = ctx->InputTensorDesc("in", 0); - auto* out = ctx->OutputTensorDesc("out", 0); + auto* out = ctx->MutOutputTensorDesc("out", 0); CHECK_NOTNULL_OR_RETURN(out); // NOLINT(maybe-need-error-msg) for (const auto& pair : ctx->inputs()) { const auto& cur_in = ctx->InputTensorDesc(pair.first, pair.second); @@ -59,7 +59,7 @@ namespace oneflow { << " expected all tenser to have same type, but found " << DataType_Name(in_0.data_type()) << " and " << DataType_Name(cur_in.data_type()); } - *out->mut_data_type() = in_0.data_type(); + out->set_data_type(in_0.data_type()); return Maybe::Ok(); } @@ -71,15 +71,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("add_n").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - int32_t in_size = op.input_size("in"); - for (int i = 0; i < in_size; ++i) { - if (op.NeedGenGradTensor4OpInput("in", i)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", 0), "in", i); - } - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/affine_grid_op.cpp b/oneflow/user/ops/affine_grid_op.cpp index 24a8c9c0dd40af3f384c4a06c7afd9a47e6471bd..40b346e78735fa4195f4cdd4f19cb6e1cba94bbf 100644 --- a/oneflow/user/ops/affine_grid_op.cpp +++ b/oneflow/user/ops/affine_grid_op.cpp @@ -48,7 +48,7 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, /* static */ Maybe AffineGridOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& theta = ctx->InputTensorDesc("theta", 0); - user_op::TensorDesc* grid = ctx->OutputTensorDesc("grid", 0); + user_op::TensorDesc* grid = ctx->MutOutputTensorDesc("grid", 0); const Shape& size = ctx->Attr("size"); // Only support 2D or 3D affine grid with NCHW layout // For 2D grid: theta = { N, 2, 3 }, @@ -66,26 +66,27 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, is_2d_grid = true; } else if (theta.shape().At(1) == 3) { CHECK_EQ_OR_RETURN(theta.shape().At(2), 4) << "Theta shape MUST be (N, 2, 3) or (N, 3, 4)"; - CHECK_EQ_OR_RETURN(size.NumAxes(), 5) "Dimension of size MUST be 4, when 3d affine grid"; + CHECK_EQ_OR_RETURN(size.NumAxes(), 5) << "Dimension of size MUST be 4, when 3d affine grid"; CHECK_EQ_OR_RETURN(theta.shape().At(0), size.At(0)) << "Theta and size MUST have same batch dimension"; is_2d_grid = false; } else { CHECK_OR_RETURN(false) << "Theta MUST be 2D or 3D grid"; } - *grid->mut_is_dynamic() = theta.is_dynamic(); - Shape& grid_shape = *grid->mut_shape(); + grid->set_is_dynamic(theta.is_dynamic()); + Shape grid_shape; if (is_2d_grid) { grid_shape = {size.At(0), size.At(2), size.At(3), 2}; } else { grid_shape = {size.At(0), size.At(2), size.At(3), size.At(4), 3}; } + grid->set_shape(grid_shape); return Maybe::Ok(); } /*static*/ Maybe AffineGridOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& theta = ctx->InputTensorDesc("theta", 0); - user_op::TensorDesc* grid = ctx->OutputTensorDesc("grid", 0); + user_op::TensorDesc* grid = ctx->MutOutputTensorDesc("grid", 0); const Shape& size = ctx->Attr("size"); // Only support 2D or 3D affine grid with NCHW layout // For 2D grid: theta = { N, 2, 3 }, @@ -102,7 +103,7 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, is_2d_grid = true; } else if (theta_shape.At(1) == 3) { CHECK_EQ_OR_RETURN(theta_shape.At(2), 4) << "Theta shape MUST be (N, 2, 3) or (N, 3, 4)"; - CHECK_EQ_OR_RETURN(size.NumAxes(), 5) "Dimension of size MUST be 4, when 3d affine grid"; + CHECK_EQ_OR_RETURN(size.NumAxes(), 5) << "Dimension of size MUST be 4, when 3d affine grid"; is_2d_grid = false; } else { CHECK_OR_RETURN(false) << "Theta MUST be 2D or 3D grid"; @@ -118,16 +119,17 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, JUST(GetPhysicalShape(logical_shape, nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx())); N = physical_shape->At(0); } - CHECK_EQ_OR_RETURN(theta_shape.At(0), size.At(0)) + CHECK_EQ_OR_RETURN(theta_shape.At(0), N) << "The dimension 0 size of theta shape should be " << N << ", but got " << theta_shape.At(0); - *grid->mut_is_dynamic() = theta.is_dynamic(); - Shape& grid_shape = *grid->mut_shape(); + grid->set_is_dynamic(theta.is_dynamic()); + Shape grid_shape; if (is_2d_grid) { grid_shape = {N, size.At(2), size.At(3), 2}; } else { grid_shape = {N, size.At(2), size.At(3), size.At(4), 3}; } + grid->set_shape(grid_shape); return Maybe::Ok(); } @@ -145,7 +147,7 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, } /* static */ Maybe AffineGridOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("grid", 0) = ctx->InputDType("theta", 0); + ctx->SetOutputDType("grid", 0, ctx->InputDType("theta", 0)); return Maybe::Ok(); } @@ -153,9 +155,9 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, const user_op::TensorDesc& dgrid = ctx->InputTensorDesc("dgrid", 0); const Shape& size = ctx->Attr("size"); if (size.NumAxes() == 4) { - *(ctx->OutputTensorDesc("dtheta", 0)->mut_shape()) = {dgrid.shape().At(0), 2, 3}; + ctx->MutOutputTensorDesc("dtheta", 0)->set_shape(Shape({dgrid.shape().At(0), 2, 3})); } else if (size.NumAxes() == 5) { - *(ctx->OutputTensorDesc("dtheta", 0)->mut_shape()) = {dgrid.shape().At(0), 3, 4}; + ctx->MutOutputTensorDesc("dtheta", 0)->set_shape(Shape({dgrid.shape().At(0), 3, 4})); } else { CHECK_OR_RETURN(false) << "size MUST be 4D or 5D"; } @@ -180,26 +182,8 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, } /* static */ Maybe AffineGridGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dtheta", 0) = ctx->InputDType("dgrid", 0); + ctx->SetOutputDType("dtheta", 0, ctx->InputDType("dgrid", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("affine_grid") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("theta", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("affine_grid_grad") - .Input("dgrid", op.GetGradTensorWithOpOutput("grid", 0)) - .Output("dtheta") - .Attr("size", op.attr("size")) - .Attr("align_corners", op.attr("align_corners")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dtheta", 0), "theta", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/amp_white_identity_op.cpp b/oneflow/user/ops/amp_white_identity_op.cpp index 46a90141d8dda41602e38a7062e264c65a2b2d91..41e732229fd88e096f488391ffa74e17f24cf5e5 100644 --- a/oneflow/user/ops/amp_white_identity_op.cpp +++ b/oneflow/user/ops/amp_white_identity_op.cpp @@ -20,9 +20,9 @@ namespace oneflow { /* static */ Maybe AmpWhiteIdentityOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_shape() = in.shape(); - *out->mut_is_dynamic() = in.is_dynamic(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_shape(in.shape()); + out->set_is_dynamic(in.is_dynamic()); return Maybe::Ok(); } @@ -41,25 +41,33 @@ namespace oneflow { /* static */ Maybe AmpWhiteIdentityOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("amp_white_identity") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("amp_white_identity") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); +/* static */ Maybe AmpBlackIdentityOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_shape(in.shape()); + out->set_is_dynamic(in.is_dynamic()); + return Maybe::Ok(); +} + +/* static */ Maybe AmpBlackIdentityOp::GetSbp(user_op::SbpContext* ctx) { + const auto& in = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); + for (int i = 0; i < in.shape().NumAxes(); ++i) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe AmpBlackIdentityOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); + return Maybe::Ok(); +} } // namespace oneflow diff --git a/oneflow/user/ops/arange_op.cpp b/oneflow/user/ops/arange_op.cpp index 735853473767a69e23b53da04dcebfe2f9e79dbf..2a560c931129b213b705c1bf5679e2c1f4a09a4e 100644 --- a/oneflow/user/ops/arange_op.cpp +++ b/oneflow/user/ops/arange_op.cpp @@ -21,7 +21,6 @@ limitations under the License. namespace oneflow { /* static */ Maybe ArangeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); DataType dtype = ctx->Attr("dtype"); int64_t range_elem_cnt = 0; if (IsIntegralDataType(dtype)) { @@ -46,7 +45,7 @@ namespace oneflow { << "RuntimeError: upper bound and larger bound inconsistent with step sign"; range_elem_cnt = std::ceil(static_cast(float_limit - float_start) / float_delta); } - *out_shape = Shape({range_elem_cnt}); + ctx->SetOutputShape("out", 0, Shape({range_elem_cnt})); return Maybe::Ok(); } @@ -88,7 +87,7 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } @@ -105,7 +104,7 @@ namespace oneflow { } /* static */ Maybe ArangeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/arg_sort_op.cpp b/oneflow/user/ops/arg_sort_op.cpp index e4ca90915ff2999a13ba7ccb3461819f7e902156..895417898194046d5da56f5242154b980539564f 100644 --- a/oneflow/user/ops/arg_sort_op.cpp +++ b/oneflow/user/ops/arg_sort_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe ArgSortOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -48,7 +48,7 @@ namespace oneflow { } /* static */ Maybe ArgSortOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kInt32; + ctx->SetOutputDType("out", 0, DataType::kInt32); return Maybe::Ok(); } diff --git a/oneflow/user/ops/arg_where_op.cpp b/oneflow/user/ops/arg_where_op.cpp index 3ce31486a5031aa2f02158c015417d4f555b1278..893655d93a79da5cf8c9be218e4f52c9f36e0854 100644 --- a/oneflow/user/ops/arg_where_op.cpp +++ b/oneflow/user/ops/arg_where_op.cpp @@ -22,11 +22,11 @@ namespace { Maybe InferTensorDesc(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("input", 0); - user_op::TensorDesc* output_desc = ctx->OutputTensorDesc("output", 0); - *output_desc->mut_shape() = Shape({input_shape.elem_cnt(), input_shape.NumAxes()}); + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("output", 0); + output_desc->set_shape(Shape({input_shape.elem_cnt(), input_shape.NumAxes()})); output_desc->set_is_dynamic(true); - user_op::TensorDesc* output_size_desc = ctx->OutputTensorDesc("output_size", 0); - *output_size_desc->mut_shape() = Shape({1}); + user_op::TensorDesc* output_size_desc = ctx->MutOutputTensorDesc("output_size", 0); + output_size_desc->set_shape(Shape({1})); return Maybe::Ok(); } @@ -46,10 +46,10 @@ Maybe InferTensorDesc(user_op::InferContext* ctx) { /* static */ Maybe ArgwhereOp::InferDataType(user_op::InferContext* ctx) { const DataType dtype = ctx->Attr("dtype"); - user_op::TensorDesc* output_desc = ctx->OutputTensorDesc("output", 0); - *output_desc->mut_data_type() = dtype; - user_op::TensorDesc* output_size_desc = ctx->OutputTensorDesc("output_size", 0); - *output_size_desc->mut_data_type() = dtype; + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("output", 0); + output_desc->set_data_type(dtype); + user_op::TensorDesc* output_size_desc = ctx->MutOutputTensorDesc("output_size", 0); + output_size_desc->set_data_type(dtype); return Maybe::Ok(); } diff --git a/oneflow/user/ops/argmax_op.cpp b/oneflow/user/ops/argmax_op.cpp index 58c6581eb29f3c54a237c3b5970ef847ba511032..b69c57a1ac38cc28bf2dccd6719cb8fc44dcaa7e 100644 --- a/oneflow/user/ops/argmax_op.cpp +++ b/oneflow/user/ops/argmax_op.cpp @@ -21,7 +21,7 @@ namespace oneflow { /* static */ Maybe ArgmaxOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { auto dim_vec = ctx->InputShape("in", 0).dim_vec(); dim_vec.pop_back(); - *ctx->OutputShape("out", 0) = Shape(std::move(dim_vec)); + ctx->SetOutputShape("out", 0, Shape(std::move(dim_vec))); return Maybe::Ok(); } @@ -38,7 +38,7 @@ namespace oneflow { } /* static */ Maybe ArgmaxOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kInt64; + ctx->SetOutputDType("out", 0, DataType::kInt64); return Maybe::Ok(); } diff --git a/oneflow/user/ops/as_strided_op.cpp b/oneflow/user/ops/as_strided_op.cpp index c347a627f55acbbd74f4477f6ce1823489bd13a7..9a850a1e7f5a30bd6a801ad86f46bc819f4105b9 100644 --- a/oneflow/user/ops/as_strided_op.cpp +++ b/oneflow/user/ops/as_strided_op.cpp @@ -19,13 +19,13 @@ limitations under the License. namespace oneflow { /* static */ auto AsStridedOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { - const auto& size = ctx->Attr>("size"); - const auto& stride = ctx->Attr>("stride"); + const auto& size = ctx->Attr>("size"); + const auto& stride = ctx->Attr>("stride"); CHECK_EQ_OR_RETURN(size.size(), stride.size()) << "mismatch in length of strides and shape"; DimVector out_vec; out_vec.insert(out_vec.end(), size.cbegin(), size.cend()); - user_op::TensorDesc* output_desc = ctx->OutputTensorDesc("output", 0); - *output_desc->mut_shape() = Shape(out_vec); + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("output", 0); + output_desc->set_shape(Shape(out_vec)); return Maybe::Ok(); } /*static*/ auto AsStridedOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { @@ -35,15 +35,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ auto AsStridedOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } /* static */ auto AsStridedGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { const Shape& input_shape = ctx->InputShape("input", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = input_shape; + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(input_shape); return Maybe::Ok(); } /*static*/ auto AsStridedGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -54,29 +54,8 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ auto AsStridedGradOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("dx", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("as_strided") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - bool need_grad_weight = op.NeedGenGradTensor4OpInput("input", 0); - if (need_grad_weight) { - user_op::UserOpConfWrapperBuilder in_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper in_grad_op = - in_grad_builder.Op("as_strided_grad") - .Input("dy", op.GetGradTensorWithOpOutput("output", 0)) - .Input("input", op.input("input", 0)) - .Output("dx") - .Attr("size", op.attr>("size")) - .Attr("stride", op.attr>("stride")) - .Attr("storage_offset", op.attr("storage_offset")) - .Build(); - op.BindGradTensorWithOpInput(in_grad_op.output("dx", 0), "input", 0); - AddOp(in_grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/assign_op.cpp b/oneflow/user/ops/assign_op.cpp index c2b296dbca791aaab3ae9a751866ff9e24f05ce4..06ceb85a639463c8e10217036d0c2ced36fbe45d 100644 --- a/oneflow/user/ops/assign_op.cpp +++ b/oneflow/user/ops/assign_op.cpp @@ -68,7 +68,9 @@ Maybe InputArgModifierFn(const user_op::GetInputArgModifier& GetInputArgMo Maybe InferDataType_(user_op::InferContext* ctx) { const user_op::TensorDesc& ref_desc = ctx->InputTensorDesc("ref", 0); const user_op::TensorDesc& value_desc = ctx->InputTensorDesc("value", 0); - CHECK_OR_RETURN(ref_desc.data_type() == value_desc.data_type()); + CHECK_OR_RETURN(ref_desc.data_type() == value_desc.data_type()) + << Error::RuntimeError() << DataType_Name(ref_desc.data_type()) << " vs." + << DataType_Name(value_desc.data_type()); if (ctx->has_input("condition", 0)) { const user_op::TensorDesc& condition = ctx->InputTensorDesc("condition", 0); CHECK_OR_RETURN(IsIndexDataType(condition.data_type())); diff --git a/oneflow/user/ops/avg_pool_op.cpp b/oneflow/user/ops/avg_pool_op.cpp index e6d1521707da627461f3bc6b03a36967a7ecb155..da7ffbefd2296375adf9d523d02ffd2b5e6ae32f 100644 --- a/oneflow/user/ops/avg_pool_op.cpp +++ b/oneflow/user/ops/avg_pool_op.cpp @@ -22,12 +22,10 @@ namespace oneflow { namespace { typedef std::function(user_op::InferContext* ctx)> TensorDescInferFn; -typedef std::function(const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp)> - GenBackwardOpConfFn; TensorDescInferFn AvgPoolMakeForwardTensorDescInferFn(const int32_t dim) { return [dim](user_op::InferContext* ctx) -> Maybe { - const Shape* x_shape = ctx->Shape4ArgNameAndIndex("x", 0); + const Shape& x_shape = ctx->Shape4ArgNameAndIndex("x", 0); const std::string& data_format = ctx->Attr("data_format"); const std::vector& padding = ctx->Attr>("padding"); const std::vector& kernel_size = ctx->Attr>("kernel_size"); @@ -53,11 +51,11 @@ TensorDescInferFn AvgPoolMakeForwardTensorDescInferFn(const int32_t dim) { << "pad should be smaller than half of kernel size"; } - const AvgPoolParams3D params_3d(dim, *x_shape, data_format, padding, kernel_size, stride, + const AvgPoolParams3D params_3d(dim, x_shape, data_format, padding, kernel_size, stride, ceil_mode, count_include_pad, divisor_override); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); *y_desc = ctx->InputTensorDesc("x", 0); - *y_desc->mut_shape() = params_3d.GetYShape(); + y_desc->set_shape(params_3d.GetYShape()); return Maybe::Ok(); }; @@ -82,42 +80,37 @@ Maybe AvgPoolBackwardGetSbpFn(user_op::SbpContext* ctx) { return Maybe::Ok(); } -GenBackwardOpConfFn AvgPoolMakeBackwardOpConfFn(const int32_t dim) { - return [dim](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("avg_pool_" + std::to_string(dim) + "d_grad") - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("data_format", op.attr("data_format")) - .Attr("padding", op.attr>("padding")) - .Attr("kernel_size", op.attr>("kernel_size")) - .Attr("stride", op.attr>("stride")) - .Attr("ceil_mode", op.attr("ceil_mode")) - .Attr("count_include_pad", op.attr("count_include_pad")) - .Attr("divisor_override", op.attr("divisor_override")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); +// Logically computation cost of pool op is the product of output data amount and pool kernal data +// amount. After adding sbp, we just divide it by parallel number if output data is splitted because +// splitting input and using partial sum for output is not a valid sbp for this op for now. +Maybe GetComputationCost(user_op::ComputeComplexityFnContext* ctx, + const std::string& blob_name) { + const std::vector pool_size = ctx->Attr>("kernel_size"); + double logical_computation_cost = std::accumulate( + pool_size.begin(), pool_size.end(), ctx->Shape4ArgNameAndIndex(blob_name, 0).elem_cnt(), + std::multiplies()); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + const auto& nd_sbp_y = ctx->NdSbp4ArgNameAndIndex(blob_name, 0); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp_y.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp_y.sbp_parallel(dim_sbp).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(dim_sbp); } - return Maybe::Ok(); - }; + } + return logical_computation_cost; } Maybe BackwardTensorDescInferFn(user_op::InferContext* ctx) { - *ctx->OutputTensorDesc("dx", 0) = ctx->InputTensorDesc("x", 0); + *ctx->MutOutputTensorDesc("dx", 0) = ctx->InputTensorDesc("x", 0); return Maybe::Ok(); } Maybe FwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe BwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -135,6 +128,10 @@ Maybe BwInferDataType(user_op::InferContext* ctx) { } \ /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ return FwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##Op::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx, "y"); \ } IMPLEMENT_AVGPOOL_FUNCS(AvgPool1D, 1) @@ -154,6 +151,10 @@ IMPLEMENT_AVGPOOL_FUNCS(AvgPool3D, 3) } \ /*static*/ Maybe name##GradOp::InferDataType(user_op::InferContext* ctx) { \ return BwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##GradOp::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx, "dy"); \ } IMPLEMENT_AVGPOOL_BACKWARD_FUNCS(AvgPool1D) @@ -161,8 +162,4 @@ IMPLEMENT_AVGPOOL_BACKWARD_FUNCS(AvgPool2D) IMPLEMENT_AVGPOOL_BACKWARD_FUNCS(AvgPool3D) #undef IMPLEMENT_AVGPOOL_BACKWARD_FUNCS -REGISTER_USER_OP_GRAD("avg_pool_1d").SetGenBackwardOpConfFn(AvgPoolMakeBackwardOpConfFn(1)); -REGISTER_USER_OP_GRAD("avg_pool_2d").SetGenBackwardOpConfFn(AvgPoolMakeBackwardOpConfFn(2)); -REGISTER_USER_OP_GRAD("avg_pool_3d").SetGenBackwardOpConfFn(AvgPoolMakeBackwardOpConfFn(3)); - } // namespace oneflow diff --git a/oneflow/user/ops/batch_gather_op.cpp b/oneflow/user/ops/batch_gather_op.cpp index f61efbc61b630d26656909163c128015f6237fd0..72b0a841cf19fac9025d215401b69a8892289125 100644 --- a/oneflow/user/ops/batch_gather_op.cpp +++ b/oneflow/user/ops/batch_gather_op.cpp @@ -28,7 +28,7 @@ namespace oneflow { << Error::RuntimeError() << "The dimension of the indices tensor should be greater than zero, " << "but got " << indices.shape().NumAxes(); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_LE_OR_RETURN(indices.shape().dim_vec().size(), in.shape().dim_vec().size()) << Error::RuntimeError() << "The dimension of the input tensor should be greater than or equal to the dimension of " @@ -56,7 +56,7 @@ namespace oneflow { DimVector dim_vec(in.shape().dim_vec()); dim_vec.at(indices.shape().NumAxes() - 1) = indices.shape().dim_vec().back(); - *out->mut_shape() = Shape(dim_vec); + out->set_shape(Shape(dim_vec)); return Maybe::Ok(); } @@ -97,31 +97,9 @@ namespace oneflow { CHECK_OR_RETURN(IsIndexDataType(indices.data_type())) << Error::TypeError() << "The dtype of the indices tensor must be int32 or int64"; const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("batch_gather") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - bool need_grad_in = op.NeedGenGradTensor4OpInput("in", 0); - if (need_grad_in) { - const Shape in_shape = op.TensorDesc4ArgNameAndIndex("in", 0).shape(); - const Shape indices_shape = op.TensorDesc4ArgNameAndIndex("indices", 0).shape(); - - user_op::UserOpConfWrapperBuilder in_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper in_grad_op = - in_grad_builder.Op("unsorted_batch_segment_sum") - .Input("data", op.GetGradTensorWithOpOutput("out", 0)) - .Input("segment_ids", op.input("indices", 0)) - .Output("out") - .Attr("num_segments", in_shape.At(indices_shape.NumAxes() - 1)) - .Build(); - op.BindGradTensorWithOpInput(in_grad_op.output("out", 0), "in", 0); - AddOp(in_grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/batch_norm_backward_elemt_op.cpp b/oneflow/user/ops/batch_norm_backward_elemt_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4487d3d61dfc85b737abb5f5e327e2b13ea4eb2d --- /dev/null +++ b/oneflow/user/ops/batch_norm_backward_elemt_op.cpp @@ -0,0 +1,76 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +std::function(const std::string&)> MakeSetOutTensorDescFn(user_op::InferContext* ctx, + const Shape& shape) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_shape(shape); + } + return Maybe::Ok(); + }; +} + +std::function(const std::string&)> MakeSetOutDataTypeFn(user_op::InferContext* ctx, + DataType data_type) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_data_type(data_type); + } + return Maybe::Ok(); + }; +} + +} // namespace + +/* static */ Maybe BatchNormBackwardElemtOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const Shape& x_shape = x.shape(); + const auto SetOutTensorDesc = MakeSetOutTensorDescFn(ctx, x_shape); + JUST(SetOutTensorDesc("grad_in")); + return Maybe::Ok(); +} + +/*static*/ Maybe BatchNormBackwardElemtOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BatchNormBackwardElemtOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormBackwardElemtOp::InferDataType(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const auto data_type = x.data_type(); + const DataType out_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + const auto SetOutDataType = MakeSetOutDataTypeFn(ctx, out_data_type); + JUST(SetOutDataType("grad_in")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/batch_norm_backward_reduce_op.cpp b/oneflow/user/ops/batch_norm_backward_reduce_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2b7a9895b6b9ad1aef832377519e52cb15935a8c --- /dev/null +++ b/oneflow/user/ops/batch_norm_backward_reduce_op.cpp @@ -0,0 +1,87 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +std::function(const std::string&)> MakeSetOutTensorDescFn(user_op::InferContext* ctx, + const Shape& shape) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_shape(shape); + } + return Maybe::Ok(); + }; +} + +std::function(const std::string&)> MakeSetOutDataTypeFn(user_op::InferContext* ctx, + DataType data_type) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_data_type(data_type); + } + return Maybe::Ok(); + }; +} + +} // namespace + +/* static */ Maybe BatchNormBackwardReduceOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const Shape& x_shape = x.shape(); + const auto axis = ctx->Attr("axis"); + CHECK_GE_OR_RETURN(axis, 0) << "channel axis should be larger than 0"; + CHECK_LT_OR_RETURN(axis, x_shape.NumAxes()) + << "channel axis should be less than " << x_shape.NumAxes(); + const Shape param_shape({x_shape.At(axis)}); + const auto SetOutTensorDesc = MakeSetOutTensorDescFn(ctx, param_shape); + JUST(SetOutTensorDesc("sum_dy")); + JUST(SetOutTensorDesc("sum_dy_xmu")); + JUST(SetOutTensorDesc("grad_weight")); + JUST(SetOutTensorDesc("grad_bias")); + return Maybe::Ok(); +} + +/*static*/ Maybe BatchNormBackwardReduceOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BatchNormBackwardReduceOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormBackwardReduceOp::InferDataType(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const auto data_type = x.data_type(); + const DataType out_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + const auto SetOutDataType = MakeSetOutDataTypeFn(ctx, out_data_type); + JUST(SetOutDataType("sum_dy")); + JUST(SetOutDataType("sum_dy_xmu")); + JUST(SetOutDataType("grad_weight")); + JUST(SetOutDataType("grad_bias")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/batch_norm_elemt_op.cpp b/oneflow/user/ops/batch_norm_elemt_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..71eacf0d3350798f8c33117a456238c2e9ed2e74 --- /dev/null +++ b/oneflow/user/ops/batch_norm_elemt_op.cpp @@ -0,0 +1,74 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +std::function(const std::string&)> MakeSetOutTensorDescFn(user_op::InferContext* ctx, + const Shape& shape) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_shape(shape); + } + return Maybe::Ok(); + }; +} + +std::function(const std::string&)> MakeSetOutDataTypeFn(user_op::InferContext* ctx, + DataType data_type) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_data_type(data_type); + } + return Maybe::Ok(); + }; +} + +} // namespace + +/* static */ Maybe BatchNormElemtOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const Shape& x_shape = x.shape(); + const auto SetOutTensorDesc = MakeSetOutTensorDescFn(ctx, x_shape); + JUST(SetOutTensorDesc("output")); + return Maybe::Ok(); +} + +/*static*/ Maybe BatchNormElemtOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BatchNormElemtOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormElemtOp::InferDataType(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const auto data_type = x.data_type(); + const DataType out_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + const auto SetOutDataType = MakeSetOutDataTypeFn(ctx, out_data_type); + JUST(SetOutDataType("output")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/batch_norm_gather_stats_with_counts_op.cpp b/oneflow/user/ops/batch_norm_gather_stats_with_counts_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..dd13eea022f7d4223df54130061c6f87c8e1a794 --- /dev/null +++ b/oneflow/user/ops/batch_norm_gather_stats_with_counts_op.cpp @@ -0,0 +1,99 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +std::function(const std::string&)> MakeSetOutTensorDescFn(user_op::InferContext* ctx, + const Shape& shape) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_shape(shape); + } + return Maybe::Ok(); + }; +} + +std::function(const std::string&)> MakeSetOutDataTypeFn(user_op::InferContext* ctx, + DataType data_type) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_data_type(data_type); + } + return Maybe::Ok(); + }; +} + +} // namespace + +/* static */ Maybe BatchNormGatherStatsWithCountsOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const auto& mean = ctx->InputTensorDesc("mean", 0); + const Shape& mean_shape = mean.shape(); + const Shape param_shape({mean_shape.At(1)}); + const auto SetOutTensorDesc = MakeSetOutTensorDescFn(ctx, param_shape); + JUST(SetOutTensorDesc("global_mean")); + JUST(SetOutTensorDesc("global_invstd")); + return Maybe::Ok(); +} + +/*static*/ Maybe BatchNormGatherStatsWithCountsOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BatchNormGatherStatsWithCountsOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormGatherStatsWithCountsOp::InferDataType( + user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const auto data_type = x.data_type(); + const DataType out_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + const auto SetOutDataType = MakeSetOutDataTypeFn(ctx, out_data_type); + JUST(SetOutDataType("global_mean")); + JUST(SetOutDataType("global_invstd")); + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormGatherStatsWithCountsOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + if (conf.has_input("running_mean", 0)) { + CHECK_OR_RETURN(conf.has_input("running_var", 0)) + << "running_mean and running_var should be provided as inputs in the same time."; + user_op::InputArgModifier* running_mean_modifier = GetInputArgModifierFn("running_mean", 0); + CHECK_OR_RETURN(running_mean_modifier != nullptr) + << "input arg modifier of running_mean is null."; + running_mean_modifier->set_is_mutable(true); + running_mean_modifier->set_requires_grad(false); + user_op::InputArgModifier* running_var_modifier = GetInputArgModifierFn("running_var", 0); + CHECK_OR_RETURN(running_var_modifier != nullptr) + << "input arg modifier of running_var is null."; + running_var_modifier->set_is_mutable(true); + running_var_modifier->set_requires_grad(false); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/batch_norm_stats_op.cpp b/oneflow/user/ops/batch_norm_stats_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9237a9b95eabf2d1df4afdb950dfc9537e8abe75 --- /dev/null +++ b/oneflow/user/ops/batch_norm_stats_op.cpp @@ -0,0 +1,81 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +std::function(const std::string&)> MakeSetOutTensorDescFn(user_op::InferContext* ctx, + const Shape& shape) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_shape(shape); + } + return Maybe::Ok(); + }; +} + +std::function(const std::string&)> MakeSetOutDataTypeFn(user_op::InferContext* ctx, + DataType data_type) { + return [=](const std::string& bn) -> Maybe { + if (ctx->has_output(bn, 0)) { + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); + CHECK_OR_RETURN(tensor_desc != nullptr) << "output tensordesc of " << bn << " is null."; + tensor_desc->set_data_type(data_type); + } + return Maybe::Ok(); + }; +} + +} // namespace + +/* static */ Maybe BatchNormStatsOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const Shape& x_shape = x.shape(); + const auto axis = ctx->Attr("axis"); + CHECK_GE_OR_RETURN(axis, 0) << "channel axis should be larger than 0"; + CHECK_LT_OR_RETURN(axis, x_shape.NumAxes()) + << "channel axis should be less than " << x_shape.NumAxes(); + const Shape param_shape({x_shape.At(axis)}); + const auto SetOutTensorDesc = MakeSetOutTensorDescFn(ctx, param_shape); + JUST(SetOutTensorDesc("mean")); + JUST(SetOutTensorDesc("invstd")); + return Maybe::Ok(); +} + +/*static*/ Maybe BatchNormStatsOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BatchNormStatsOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe BatchNormStatsOp::InferDataType(user_op::InferContext* ctx) { + const auto& x = ctx->InputTensorDesc("input", 0); + const auto data_type = x.data_type(); + const DataType out_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + const auto SetOutDataType = MakeSetOutDataTypeFn(ctx, out_data_type); + JUST(SetOutDataType("mean")); + JUST(SetOutDataType("invstd")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/bernoulli_op.cpp b/oneflow/user/ops/bernoulli_op.cpp index 3068b83fd0c47a1f3b941a791e97b137e5123629..2db537a2ae962e0243529c1fa8f2085e5e0f4a62 100644 --- a/oneflow/user/ops/bernoulli_op.cpp +++ b/oneflow/user/ops/bernoulli_op.cpp @@ -19,9 +19,9 @@ limitations under the License. namespace oneflow { /* static */ Maybe BernoulliOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - *out_tensor->mut_shape() = in_tensor.shape(); + out_tensor->set_shape(in_tensor.shape()); return Maybe::Ok(); } @@ -38,8 +38,8 @@ namespace oneflow { } /* static */ Maybe BernoulliOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_data_type() = ctx->Attr("dtype"); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_data_type(ctx->Attr("dtype")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/bias_add_op.cpp b/oneflow/user/ops/bias_add_op.cpp index 77dfff37837066d6a13b6d1c26c99516d9338ffd..8b300ff2464b9f54eb5879fa4db5e261badc768a 100644 --- a/oneflow/user/ops/bias_add_op.cpp +++ b/oneflow/user/ops/bias_add_op.cpp @@ -35,8 +35,8 @@ namespace oneflow { << Error::RuntimeError() << "The size of tensor " << a_tensor_desc.shape().ToString() << " must match the size of tensor " << b_tensor_desc.shape().ToString() << " at dimension " << bias_add_axis; - *ctx->OutputShape("out", 0) = ctx->InputShape("a", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("a", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("a", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("a", 0)); return Maybe::Ok(); } @@ -64,34 +64,8 @@ namespace oneflow { } /* static */ Maybe BiasAddOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("a", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("a", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("bias_add") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("a", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", 0), "a", 0); - } - if (op.NeedGenGradTensor4OpInput("b", 0)) { - const int64_t num_axes = op.TensorDesc4ArgNameAndIndex("a", 0).shape().NumAxes(); - const int32_t bias_add_axis = op.attr("axis"); - std::vector reduce_axes_vec; - FOR_RANGE(int64_t, i, 0, num_axes) { - if (i != bias_add_axis) { reduce_axes_vec.emplace_back(i); } - } - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - auto grad_op = builder.Op("reduce_sum") - .Input("input_tensor", op.GetGradTensorWithOpOutput("out", 0)) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(grad_op); - op.BindGradTensorWithOpInput(grad_op.output("output_tensor", 0), "b", 0); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/binary_cross_entropy_op.cpp b/oneflow/user/ops/binary_cross_entropy_op.cpp index 0d328657660af893a9f3ea27f1597fd4ead86044..8c7cdda1fec83decd5f53da366e819fde20106d7 100644 --- a/oneflow/user/ops/binary_cross_entropy_op.cpp +++ b/oneflow/user/ops/binary_cross_entropy_op.cpp @@ -33,9 +33,9 @@ Maybe InferTensorDescFn_(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(weight_desc.shape(), input_desc.shape()); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *out_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(input_desc.is_dynamic()); + out_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -43,13 +43,17 @@ Maybe InferTensorDescFn_(user_op::InferContext* ctx) { Maybe InferDataType_(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(input_desc.data_type()) << ", but got " + << DataType_Name(target_desc.data_type()); if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); - CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(input_desc.data_type()) + << ", but got " << DataType_Name(weight_desc.data_type()); } - *ctx->OutputDType("out", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } @@ -67,22 +71,26 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(weight_desc.shape(), input_desc.shape()); } - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *dx_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(input_desc.is_dynamic()); + dx_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } Maybe InferGradDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(input_desc.data_type()) << ", but got " + << DataType_Name(target_desc.data_type()); if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); - CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(input_desc.data_type()) + << ", but got " << DataType_Name(weight_desc.data_type()); } - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -130,24 +138,4 @@ Maybe InferGradDataType(user_op::InferContext* ctx) { return InferGradDataType(ctx); } -REGISTER_USER_OP_GRAD("binary_cross_entropy") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("binary_cross_entropy_grad") - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx"); - if (op.user_op_conf().has_input("weight", 0)) { - builder.Input("weight", op.input("weight", 0)); - } - user_op::UserOpConfWrapper grad_op = builder.Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/binary_cross_entropy_with_logits_op.cpp b/oneflow/user/ops/binary_cross_entropy_with_logits_op.cpp index 0a124525a60e58fb1442d1ba02335c1835c243d8..f303be5ced9d0c7bc39d0b6cd947d2b672034722 100644 --- a/oneflow/user/ops/binary_cross_entropy_with_logits_op.cpp +++ b/oneflow/user/ops/binary_cross_entropy_with_logits_op.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/framework.h" #include "oneflow/user/ops/loss_op_util.h" #include "oneflow/core/framework/op_generated.h" @@ -24,7 +25,8 @@ Maybe InferTensorDescFn(user_op::InferContext* ctx) { const auto& input_desc = ctx->InputTensorDesc("input", 0); const auto& target_desc = ctx->InputTensorDesc("target", 0); CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); + CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) + << "Input shape should be equal to Target shape. "; if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); CHECK_EQ_OR_RETURN(weight_desc.is_dynamic(), input_desc.is_dynamic()); @@ -33,12 +35,10 @@ Maybe InferTensorDescFn(user_op::InferContext* ctx) { if (ctx->Attr("has_pos_weight")) { const auto& pos_weight_desc = ctx->InputTensorDesc("pos_weight", 0); CHECK_EQ_OR_RETURN(pos_weight_desc.is_dynamic(), input_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(pos_weight_desc.shape(), - Shape({input_desc.shape().At(input_desc.shape().NumAxes() - 1)})); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *out_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(input_desc.is_dynamic()); + out_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -46,16 +46,23 @@ Maybe InferTensorDescFn(user_op::InferContext* ctx) { Maybe InferDataType_(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_GE_OR_RETURN(DType::priority_order[input_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + CHECK_GE_OR_RETURN(DType::priority_order[target_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); - CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(weight_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(target_desc.data_type()) + << ", but got " << DataType_Name(weight_desc.data_type()); } if (ctx->Attr("has_pos_weight")) { const auto& pos_weight_desc = ctx->InputTensorDesc("pos_weight", 0); - CHECK_EQ_OR_RETURN(pos_weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(pos_weight_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(target_desc.data_type()) + << ", but got " << DataType_Name(pos_weight_desc.data_type()); } - *ctx->OutputDType("out", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("target", 0)); return Maybe::Ok(); } @@ -64,8 +71,10 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { const auto& target_desc = ctx->InputTensorDesc("target", 0); const auto& dy_desc = ctx->InputTensorDesc("dy", 0); CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); - CHECK_EQ_OR_RETURN(dy_desc.shape(), target_desc.shape()); + CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) + << "Input shape should be equal to Target shape. "; + CHECK_EQ_OR_RETURN(dy_desc.shape(), target_desc.shape()) + << "Dy shape should be equal to Target shape. "; if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); CHECK_EQ_OR_RETURN(weight_desc.is_dynamic(), input_desc.is_dynamic()); @@ -74,29 +83,34 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { if (ctx->Attr("has_pos_weight")) { const auto& pos_weight_desc = ctx->InputTensorDesc("pos_weight", 0); CHECK_EQ_OR_RETURN(pos_weight_desc.is_dynamic(), input_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(pos_weight_desc.shape(), - Shape({input_desc.shape().At(input_desc.shape().NumAxes() - 1)})); } - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *dx_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(input_desc.is_dynamic()); + dx_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } Maybe InferGradDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_GE_OR_RETURN(DType::priority_order[input_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + CHECK_GE_OR_RETURN(DType::priority_order[target_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); if (ctx->has_input("weight", 0)) { const auto& weight_desc = ctx->InputTensorDesc("weight", 0); - CHECK_EQ_OR_RETURN(weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(weight_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(weight_desc.data_type()) + << ", but got " << DataType_Name(target_desc.data_type()); } if (ctx->Attr("has_pos_weight")) { const auto& pos_weight_desc = ctx->InputTensorDesc("pos_weight", 0); - CHECK_EQ_OR_RETURN(pos_weight_desc.data_type(), input_desc.data_type()); + CHECK_EQ_OR_RETURN(pos_weight_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(target_desc.data_type()) + << ", but got " << DataType_Name(pos_weight_desc.data_type()); } - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } @@ -157,28 +171,4 @@ Maybe InferGradDataType(user_op::InferContext* ctx) { return InferGradDataType(ctx); } -REGISTER_USER_OP_GRAD("binary_cross_entropy_with_logits") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("binary_cross_entropy_with_logits_grad") - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx"); - if (op.user_op_conf().has_input("weight", 0)) { - builder.Input("weight", op.input("weight", 0)); - } - if (op.attr("has_pos_weight")) { - builder.Input("pos_weight", op.input("pos_weight", 0)) - .Attr("has_pos_weight", op.attr("has_pos_weight")); - } - user_op::UserOpConfWrapper grad_op = builder.Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/binary_cross_entropy_with_logits_reduce_mean_op.cpp b/oneflow/user/ops/binary_cross_entropy_with_logits_reduce_mean_op.cpp index d32d06fb8c1c90b7ea754ef080945d28c3ba76f5..35aaa0565f951fdfce2519c27c8cc8ce0837db06 100644 --- a/oneflow/user/ops/binary_cross_entropy_with_logits_reduce_mean_op.cpp +++ b/oneflow/user/ops/binary_cross_entropy_with_logits_reduce_mean_op.cpp @@ -14,6 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include "oneflow/core/framework/dtype.h" #include "oneflow/core/framework/framework.h" #include "oneflow/core/framework/op_generated.h" @@ -26,18 +27,20 @@ Maybe InferTensorDescFn(user_op::InferContext* ctx) { const auto& target_desc = ctx->InputTensorDesc("target", 0); CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) << "Input shape should be equal to Target shape. "; - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = false; - *out_desc->mut_shape() = Shape({}); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(false); + out_desc->set_shape(Shape({})); return Maybe::Ok(); } Maybe InferFwDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) - << "Input datatype should be equal to Target datatype. "; - *ctx->OutputDType("out", 0) = ctx->InputDType("input", 0); + CHECK_GE_OR_RETURN(DType::priority_order[input_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + CHECK_GE_OR_RETURN(DType::priority_order[target_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + ctx->SetOutputDType("out", 0, ctx->InputDType("target", 0)); return Maybe::Ok(); } @@ -47,18 +50,20 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { const auto& target_desc = ctx->InputTensorDesc("target", 0); CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) << "Input shape should be equal to Target shape. "; - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = false; - *dx_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(false); + dx_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } Maybe InferGradDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) - << "Input datatype should be equal to Target datatype. "; - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + CHECK_GE_OR_RETURN(DType::priority_order[input_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + CHECK_GE_OR_RETURN(DType::priority_order[target_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + ctx->SetOutputDType("dx", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } } // namespace @@ -123,21 +128,48 @@ Maybe InferGradDataType(user_op::InferContext* ctx) { return InferGradDataType(ctx); } -REGISTER_USER_OP_GRAD("binary_cross_entropy_with_logits_reduce_mean") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("binary_cross_entropy_with_logits_reduce_mean_grad") - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx"); - user_op::UserOpConfWrapper grad_op = builder.Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); +/* static */ Maybe FusedBCEReduceMeanFwBwOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const auto& input_desc = ctx->InputTensorDesc("input", 0); + const auto& target_desc = ctx->InputTensorDesc("target", 0); + CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) + << "Input shape should be equal to Target shape. "; + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(false); + out_desc->set_shape(Shape({})); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(false); + dx_desc->set_shape(input_desc.shape()); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedBCEReduceMeanFwBwOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe FusedBCEReduceMeanFwBwOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("input", 0), 0) + .Split(user_op::OpArg("target", 0), 0) + .PartialSum(user_op::OpArg("out", 0)) + .Split(user_op::OpArg("dx", 0), 0) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe FusedBCEReduceMeanFwBwOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); + const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); + CHECK_GE_OR_RETURN(DType::priority_order[input_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + CHECK_GE_OR_RETURN(DType::priority_order[target_desc.data_type()], + DType::priority_order[DType::Float16()->data_type()]); + DataType out_dtype = ctx->Attr("out_dtype"); + if (out_dtype == DataType::kInvalidDataType) { out_dtype = target_desc.data_type(); } + ctx->SetOutputDType("out", 0, out_dtype); + ctx->SetOutputDType("dx", 0, input_desc.data_type()); + return Maybe::Ok(); +} } // namespace oneflow diff --git a/oneflow/user/ops/bincount_op.cpp b/oneflow/user/ops/bincount_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4fbe408b61378b0b5a91c0c8640dac9ae60aaaf9 --- /dev/null +++ b/oneflow/user/ops/bincount_op.cpp @@ -0,0 +1,56 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferTensorDesc(user_op::InferContext* ctx) { + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("out", 0); + const int64_t size = ctx->Attr("size"); + output_desc->set_shape(Shape({size})); + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe BinCountOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferTensorDesc(ctx); +} + +/*static*/ Maybe BinCountOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe BinCountOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); +} + +/* static */ Maybe BinCountOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("in", 0); + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("out", 0); + if (ctx->has_input("weight", 0)) { + const user_op::TensorDesc& weight_desc = ctx->InputTensorDesc("weight", 0); + output_desc->set_data_type(weight_desc.data_type()); + } else { + output_desc->set_data_type(input_desc.data_type()); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/broadcast_div_grad_op.cpp b/oneflow/user/ops/broadcast_div_grad_op.cpp index c59b2436997f158d5dff218997d565e062cb151f..46a6f6fa0825b1231f2e69f26bc80ab327cbe29f 100644 --- a/oneflow/user/ops/broadcast_div_grad_op.cpp +++ b/oneflow/user/ops/broadcast_div_grad_op.cpp @@ -19,8 +19,8 @@ limitations under the License. namespace oneflow { /* static */ Maybe BroadcastDivGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dy", 0) = ctx->InputShape("y", 0); - *ctx->OutputIsDynamic("dy", 0) = ctx->InputIsDynamic("y", 0); + ctx->SetOutputShape("dy", 0, ctx->InputShape("y", 0)); + ctx->SetOutputIsDynamic("dy", 0, ctx->InputIsDynamic("y", 0)); return Maybe::Ok(); } @@ -67,7 +67,7 @@ namespace oneflow { } /* static */ Maybe BroadcastDivGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dy", 0) = ctx->InputDType("y", 0); + ctx->SetOutputDType("dy", 0, ctx->InputDType("y", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/broadcast_like_op.cpp b/oneflow/user/ops/broadcast_like_op.cpp index 1478378ea7fc3734e2eec0a0dcff0378a8afb228..a618216532dfc04ac73ab197e101fe443187e5e0 100644 --- a/oneflow/user/ops/broadcast_like_op.cpp +++ b/oneflow/user/ops/broadcast_like_op.cpp @@ -27,10 +27,6 @@ Maybe GetSbpSignatures(user_op::SbpContext* ctx) { int32_t x_num_axes = x_shape.NumAxes(); int32_t like_num_axes = like_shape.NumAxes(); const auto& reduced_axes = ctx->Attr>("broadcast_axes"); - if (x_num_axes != like_num_axes && (x_num_axes + reduced_axes.size() != like_num_axes)) { - return Error::RuntimeError() << "Can not broadcast shape " << x_shape.ToString() << " to " - << like_shape.ToString(); - } HashSet conf_axes; ReduceSbpUtil::GetRegularAxes(like_num_axes, reduced_axes, &conf_axes); auto IsReducedAxis = ReduceSbpUtil::MakePredicatorIsReducedAxis(conf_axes, like_num_axes); @@ -47,7 +43,7 @@ Maybe GetSbpSignatures(user_op::SbpContext* ctx) { ctx->NewBuilder() .Split(user_op::OpArg("x", 0), i - num_reduced_axis) .Split(user_op::OpArg("like", 0), i) - .Split(ctx->outputs(), i) + .Split(user_op::OpArg("y", 0), i) .Build(); } } @@ -66,11 +62,25 @@ Maybe GetSbpSignatures(user_op::SbpContext* ctx) { } bool IsAxesLegal(const AxisVector& axis_vec, const Shape& like_shape, const Shape& in_shape) { - Shape reduced_shape = CreateReducedShape(like_shape, axis_vec); + Shape reduced_like_shape = CreateReducedShape(like_shape, axis_vec); if (like_shape.NumAxes() > in_shape.NumAxes()) { - reduced_shape = reduced_shape.RemoveOnes(axis_vec); + std::vector in_shape_vec; + in_shape_vec.reserve(in_shape.NumAxes()); + std::vector like_shape_vec; + like_shape_vec.reserve(reduced_like_shape.NumAxes()); + for (const int64_t& dim : in_shape.dim_vec()) { + if (dim != 1) { in_shape_vec.emplace_back(dim); } + } + for (const int64_t& dim : reduced_like_shape.dim_vec()) { + if (dim != 1) { like_shape_vec.emplace_back(dim); } + } + if (in_shape_vec.size() > like_shape_vec.size()) { + return false; + } else { + return std::equal(in_shape_vec.begin(), in_shape_vec.end(), like_shape_vec.begin()); + } } - return reduced_shape.dim_vec() == in_shape.dim_vec(); + return reduced_like_shape.dim_vec() == in_shape.dim_vec(); } Maybe InferTensorDesc(user_op::InferContext* ctx) { @@ -78,12 +88,12 @@ Maybe InferTensorDesc(user_op::InferContext* ctx) { CHECK_OR_RETURN(!broadcast_axes.empty()); const Shape& in_shape = ctx->InputShape("x", 0); const Shape& like_shape = ctx->InputShape("like", 0); - Shape* out_shape = ctx->OutputShape("y", 0); - Stride* out_stride = ctx->OutputStride("y", 0); const AxisVector axis_vec = {broadcast_axes.begin(), broadcast_axes.end()}; - CHECK_OR_RETURN(IsAxesLegal(axis_vec, like_shape, in_shape)); - *out_shape = like_shape; - *out_stride = Stride(like_shape); + CHECK_OR_RETURN(IsAxesLegal(axis_vec, like_shape, in_shape)) + << Error::RuntimeError() << "Invalid input parameter: like shape:" << like_shape.ToString() + << ", in shape:" << in_shape.ToString() << ", axis_vec size:" << axis_vec.size(); + ctx->SetOutputShape("y", 0, like_shape); + ctx->SetOutputStride("y", 0, Stride(like_shape)); return Maybe::Ok(); } @@ -110,27 +120,8 @@ Maybe InferTensorDesc(user_op::InferContext* ctx) { } /* static */ Maybe BroadcastLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("like", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("broadcast_like") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto x_grad_op_name = ctx->FwOp().op_name() + "_x_grad"; - ctx->DefineOp(x_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("reduce_sum_like") - .InputBind("x", ctx->FwOp().output_grad("y", 0)) - .InputBind("like", ctx->FwOp().input("x", 0)) - .Output("y") - .Attr("axis", ctx->FwOp().attr>("broadcast_axes")) - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &x_grad_op_name]() -> const std::string& { - return ctx->GetOp(x_grad_op_name).output("y", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/broadcast_ops_grad.cpp b/oneflow/user/ops/broadcast_ops_grad.cpp deleted file mode 100644 index c3715d279ea5b81697d8a06d26a6e600f15a674b..0000000000000000000000000000000000000000 --- a/oneflow/user/ops/broadcast_ops_grad.cpp +++ /dev/null @@ -1,228 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" - -namespace oneflow { - -namespace { - -std::string CreateReduceSumLikeBlob(const std::string& in_lbn, const Shape& in_shape, - const std::string& like_lbn, const Shape& like_shape, - const std::string& op_name, const user_op::AddOpFn& AddOp) { - const Shape& left_extended_shape = - CreateLeftExtendedShape(ShapeView(like_shape), in_shape.NumAxes()); - if (in_shape == like_shape) { - return in_lbn; - } else if (in_shape == left_extended_shape) { - user_op::UserOpConfWrapperBuilder builder(op_name + "_grad_reshape_like"); - user_op::UserOpConfWrapper grad_op = builder.Op("reshape_like") - .Input("in", in_lbn) - .Input("like", like_lbn) - .Output("out") - .Build(); - AddOp(grad_op); - return grad_op.output("out", 0); - } else { - const AxisVector& broadcast_axis_vec = left_extended_shape.Axes4BroadcastTo(in_shape); - user_op::UserOpConfWrapperBuilder builder(op_name + "_grad_reduce_sum_like"); - user_op::UserOpConfWrapper grad_op = - builder.Op("reduce_sum_like") - .Input("x", in_lbn) - .Input("like", like_lbn) - .Attr>("axis", - {broadcast_axis_vec.begin(), broadcast_axis_vec.end()}) - .Output("y") - .Build(); - AddOp(grad_op); - return grad_op.output("y", 0); - } -} - -} // namespace - -REGISTER_USER_OP_GRAD("broadcast_add") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - const Shape& z_shape = op.TensorDesc4ArgNameAndIndex("z", 0).shape(); - const std::string& dz_lbn = op.GetGradTensorWithOpOutput("z", 0); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - const Shape& x_shape = op.TensorDesc4ArgNameAndIndex("x", 0).shape(); - const std::string& x_lbn = op.input("x", 0); - const std::string& out_lbn = - CreateReduceSumLikeBlob(dz_lbn, z_shape, x_lbn, x_shape, op.op_name() + "_x", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "x", 0); - } - if (op.NeedGenGradTensor4OpInput("y", 0)) { - const Shape& y_shape = op.TensorDesc4ArgNameAndIndex("y", 0).shape(); - const std::string& y_lbn = op.input("y", 0); - const std::string& out_lbn = - CreateReduceSumLikeBlob(dz_lbn, z_shape, y_lbn, y_shape, op.op_name() + "_y", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "y", 0); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_sub") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - const Shape& z_shape = op.TensorDesc4ArgNameAndIndex("z", 0).shape(); - const std::string& dz_lbn = op.GetGradTensorWithOpOutput("z", 0); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - const Shape& x_shape = op.TensorDesc4ArgNameAndIndex("x", 0).shape(); - const std::string& x_lbn = op.input("x", 0); - const std::string& out_lbn = - CreateReduceSumLikeBlob(dz_lbn, z_shape, x_lbn, x_shape, op.op_name() + "_x", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "x", 0); - } - if (op.NeedGenGradTensor4OpInput("y", 0)) { - user_op::UserOpConfWrapperBuilder scalar_mul_builder(op.op_name() + "_grad_y_mul"); - user_op::UserOpConfWrapper scalar_mul_op = scalar_mul_builder.Op("scalar_mul") - .Input("in", dz_lbn) - .Attr("has_int_operand", false) - .Attr("has_float_operand", true) - .Attr("int_operand", -1) - .Attr("float_operand", -1.0) - .Output("out") - .Build(); - AddOp(scalar_mul_op); - - const Shape& y_shape = op.TensorDesc4ArgNameAndIndex("y", 0).shape(); - const std::string& y_lbn = op.input("y", 0); - const std::string& out_lbn = CreateReduceSumLikeBlob( - scalar_mul_op.output("out", 0), z_shape, y_lbn, y_shape, op.op_name() + "_y", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "y", 0); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_mul") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - const Shape& z_shape = op.TensorDesc4ArgNameAndIndex("z", 0).shape(); - const std::string& dz_lbn = op.GetGradTensorWithOpOutput("z", 0); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder broadcast_mul_builder(op.op_name() + "_grad_x_mul"); - user_op::UserOpConfWrapper broadcast_mul_op = broadcast_mul_builder.Op("broadcast_mul") - .Input("x", dz_lbn) - .Input("y", op.input("y", 0)) - .Output("z") - .Build(); - AddOp(broadcast_mul_op); - const Shape& x_shape = op.TensorDesc4ArgNameAndIndex("x", 0).shape(); - const std::string& x_lbn = op.input("x", 0); - const std::string& out_lbn = CreateReduceSumLikeBlob( - broadcast_mul_op.output("z", 0), z_shape, x_lbn, x_shape, op.op_name() + "_x", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "x", 0); - } - if (op.NeedGenGradTensor4OpInput("y", 0)) { - user_op::UserOpConfWrapperBuilder broadcast_mul_builder(op.op_name() + "_grad_y_mul"); - user_op::UserOpConfWrapper broadcast_mul_op = broadcast_mul_builder.Op("broadcast_mul") - .Input("x", dz_lbn) - .Input("y", op.input("x", 0)) - .Output("z") - .Build(); - AddOp(broadcast_mul_op); - const Shape& y_shape = op.TensorDesc4ArgNameAndIndex("y", 0).shape(); - const std::string& y_lbn = op.input("y", 0); - const std::string& out_lbn = CreateReduceSumLikeBlob( - broadcast_mul_op.output("z", 0), z_shape, y_lbn, y_shape, op.op_name() + "_y", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "y", 0); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_div") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - const std::string& dz_lbn = op.GetGradTensorWithOpOutput("z", 0); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - const Shape& z_shape = op.TensorDesc4ArgNameAndIndex("z", 0).shape(); - user_op::UserOpConfWrapperBuilder broadcast_div_builder(op.op_name() + "_grad_x_div"); - user_op::UserOpConfWrapper broadcast_div_op = broadcast_div_builder.Op("broadcast_div") - .Input("x", dz_lbn) - .Input("y", op.input("y", 0)) - .Output("z") - .Build(); - AddOp(broadcast_div_op); - const Shape& x_shape = op.TensorDesc4ArgNameAndIndex("x", 0).shape(); - const std::string& x_lbn = op.input("x", 0); - const std::string& out_lbn = CreateReduceSumLikeBlob( - broadcast_div_op.output("z", 0), z_shape, x_lbn, x_shape, op.op_name() + "_x", AddOp); - op.BindGradTensorWithOpInput(out_lbn, "x", 0); - } - if (op.NeedGenGradTensor4OpInput("y", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_y_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("broadcast_div_grad") - .Input("y", op.input("y", 0)) - .Input("z", op.output("z", 0)) - .Input("dz", dz_lbn) - .Output("dy") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dy", 0), "y", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_pow") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - const std::string& dz_lbn = op.GetGradTensorWithOpOutput("z", 0); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_x_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("broadcast_pow_x_grad") - .Input("x", op.input("x", 0)) - .Input("y", op.input("y", 0)) - .Input("z", op.output("z", 0)) - .Input("dz", dz_lbn) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("y", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_y_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("broadcast_pow_y_grad") - .Input("x", op.input("x", 0)) - .Input("y", op.input("y", 0)) - .Input("z", op.output("z", 0)) - .Input("dz", dz_lbn) - .Output("dy") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dy", 0), "y", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_floor_mod") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("z", 0), "x", 0); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_fmod") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("z", 0), "x", 0); - } - return Maybe::Ok(); - }); -} // namespace oneflow diff --git a/oneflow/user/ops/broadcast_pow_grad_op.cpp b/oneflow/user/ops/broadcast_pow_grad_op.cpp deleted file mode 100644 index 21fa575b03b9291841b1291c9a59bbc2540f6877..0000000000000000000000000000000000000000 --- a/oneflow/user/ops/broadcast_pow_grad_op.cpp +++ /dev/null @@ -1,123 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/framework/op_generated.h" - -namespace oneflow { - -/* static */ Maybe BroadcastPowXGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("x", 0); - return Maybe::Ok(); -} - -/*static*/ Maybe BroadcastPowXGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); -} - -/* static */ Maybe BroadcastPowXGradOp::GetSbp(user_op::SbpContext* ctx) { - const Shape& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); - const Shape& y_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("y", 0).shape(); - const Shape& z_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("z", 0).shape(); - CHECK_LE_OR_RETURN(x_shape.NumAxes(), z_shape.NumAxes()); - CHECK_LE_OR_RETURN(y_shape.NumAxes(), z_shape.NumAxes()); - FOR_RANGE(int64_t, i, 0, z_shape.NumAxes()) { - const int64_t _axis = z_shape.NumAxes() - 1 - i; - if (z_shape.At(_axis) == x_shape.At(_axis) && z_shape.At(_axis) == y_shape.At(_axis)) { - ctx->NewBuilder() - .Split(user_op::OpArg("x", 0), _axis) - .Split(user_op::OpArg("y", 0), _axis) - .Split(user_op::OpArg("z", 0), _axis) - .Split(user_op::OpArg("dz", 0), _axis) - .Split(user_op::OpArg("dx", 0), _axis) - .Build(); - } - } - ctx->NewBuilder() - .Broadcast(user_op::OpArg("y", 0)) - .PartialSum(user_op::OpArg("z", 0)) - .Broadcast(user_op::OpArg("dz", 0)) - .Broadcast(user_op::OpArg("x", 0)) - .Broadcast(user_op::OpArg("dx", 0)) - .Build(); - ctx->NewBuilder() - .PartialSum(user_op::OpArg("y", 0)) - .Broadcast(user_op::OpArg("z", 0)) - .Broadcast(user_op::OpArg("dz", 0)) - .Broadcast(user_op::OpArg("x", 0)) - .Broadcast(user_op::OpArg("dx", 0)) - .Build(); - ctx->NewBuilder() - .Broadcast(user_op::OpArg("y", 0)) - .Broadcast(user_op::OpArg("z", 0)) - .PartialSum(user_op::OpArg("dz", 0)) - .Broadcast(user_op::OpArg("x", 0)) - .Broadcast(user_op::OpArg("dx", 0)) - .Build(); - return Maybe::Ok(); -} - -/* static */ Maybe BroadcastPowXGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); - return Maybe::Ok(); -} - -/* static */ Maybe BroadcastPowYGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dy", 0) = ctx->InputShape("y", 0); - *ctx->OutputIsDynamic("dy", 0) = ctx->InputIsDynamic("y", 0); - return Maybe::Ok(); -} - -/*static*/ Maybe BroadcastPowYGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); -} - -/* static */ Maybe BroadcastPowYGradOp::GetSbp(user_op::SbpContext* ctx) { - const Shape& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); - const Shape& z_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("z", 0).shape(); - CHECK_LE_OR_RETURN(x_shape.NumAxes(), z_shape.NumAxes()); - FOR_RANGE(int64_t, i, 0, z_shape.NumAxes()) { - const int64_t _axis = z_shape.NumAxes() - 1 - i; - if (z_shape.At(_axis) == x_shape.At(_axis)) { - ctx->NewBuilder() - .Split(user_op::OpArg("x", 0), _axis) - .Split(user_op::OpArg("z", 0), _axis) - .Split(user_op::OpArg("dz", 0), _axis) - .Split(user_op::OpArg("dy", 0), _axis) - .Build(); - } - } - ctx->NewBuilder() - .Broadcast(user_op::OpArg("x", 0)) - .PartialSum(user_op::OpArg("z", 0)) - .Broadcast(user_op::OpArg("dz", 0)) - .Broadcast(user_op::OpArg("dy", 0)) - .Build(); - ctx->NewBuilder() - .Broadcast(user_op::OpArg("x", 0)) - .Broadcast(user_op::OpArg("z", 0)) - .PartialSum(user_op::OpArg("dz", 0)) - .Broadcast(user_op::OpArg("dy", 0)) - .Build(); - return Maybe::Ok(); -} - -/* static */ Maybe BroadcastPowYGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dy", 0) = ctx->InputDType("y", 0); - return Maybe::Ok(); -} - -} // namespace oneflow diff --git a/oneflow/user/ops/buffer_op.cpp b/oneflow/user/ops/buffer_op.cpp index eb8abde1ee6f9e100af0d105bc41d52a878209be..32f1c44f2fd0302b93a0682e8d28ba7be3be8228 100644 --- a/oneflow/user/ops/buffer_op.cpp +++ b/oneflow/user/ops/buffer_op.cpp @@ -19,8 +19,8 @@ limitations under the License. namespace oneflow { /* static */ Maybe IdentityBufferOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -41,7 +41,7 @@ namespace oneflow { } /* static */ Maybe IdentityBufferOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/cast_like_op.cpp b/oneflow/user/ops/cast_like_op.cpp index c4d41a00be898ba32ed3de9c9ca671f457aeb187..72157bc6189b8ece3aee1f99f5824d2976de446e 100644 --- a/oneflow/user/ops/cast_like_op.cpp +++ b/oneflow/user/ops/cast_like_op.cpp @@ -19,8 +19,8 @@ limitations under the License. namespace oneflow { /* static */ Maybe CastLikeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -65,8 +65,8 @@ namespace oneflow { /* static */ Maybe CastLikeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& dtype_like_tensor_desc = ctx->InputTensorDesc("dtype_like", 0); - user_op::TensorDesc* output_tensor_desc = ctx->OutputTensorDesc("out", 0); - *output_tensor_desc->mut_data_type() = dtype_like_tensor_desc.data_type(); + user_op::TensorDesc* output_tensor_desc = ctx->MutOutputTensorDesc("out", 0); + output_tensor_desc->set_data_type(dtype_like_tensor_desc.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/cast_op.cpp b/oneflow/user/ops/cast_op.cpp index 0cbcd03ce5f372de342420ead2605797d8db66d1..5de13a8661e88b82479fd50c9c95f849c9ca498d 100644 --- a/oneflow/user/ops/cast_op.cpp +++ b/oneflow/user/ops/cast_op.cpp @@ -29,20 +29,20 @@ Maybe> MakeCastStream(const Symbol& in_device, << "cast op only support pin_memory in cpu device but got " << in_device->type(); // TODO:(zhaoluyang) Parsing pin-memory-device from python auto pin_device = JUST(Device::New("cuda")); - return Stream::New(pin_device, StreamRole::kPinnedCompute); + return Stream::New(pin_device, StreamType::kPinnedCompute); } - return Stream::New(out_device, StreamRole::kCompute); + return Stream::New(out_device, StreamType::kCompute); } } // namespace /* static */ Maybe CastOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& input_tensor_desc = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* output_tensor_desc = ctx->OutputTensorDesc("out", 0); - *output_tensor_desc->mut_shape() = input_tensor_desc.shape(); - *output_tensor_desc->mut_stride() = - input_tensor_desc.stride(); // output's stride should consistent with input's - *output_tensor_desc->mut_is_dynamic() = input_tensor_desc.is_dynamic(); + user_op::TensorDesc* output_tensor_desc = ctx->MutOutputTensorDesc("out", 0); + output_tensor_desc->set_shape(input_tensor_desc.shape()); + output_tensor_desc->set_stride( + input_tensor_desc.stride()); // output's stride should consistent with input's + output_tensor_desc->set_is_dynamic(input_tensor_desc.is_dynamic()); return Maybe::Ok(); } @@ -60,9 +60,8 @@ Maybe> MakeCastStream(const Symbol& in_device, } /* static */ Maybe CastOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* output_tensor_desc = ctx->OutputTensorDesc("out", 0); - DataType* dtype = output_tensor_desc->mut_data_type(); - *dtype = ctx->Attr("dtype"); + user_op::TensorDesc* output_tensor_desc = ctx->MutOutputTensorDesc("out", 0); + output_tensor_desc->set_data_type(ctx->Attr("dtype")); return Maybe::Ok(); } @@ -75,21 +74,4 @@ Maybe> MakeCastStream(const Symbol& in_device, return MakeCastStream(in_device, out_device, pin_memory); } -REGISTER_USER_OP_GRAD("cast").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - const DataType& dtype = op.TensorDesc4ArgNameAndIndex("in", 0).data_type(); - user_op::UserOpConfWrapper cast_grad_op = - builder.Op("cast") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("dtype", dtype) - .Build(); - op.BindGradTensorWithOpInput(cast_grad_op.output("out", 0), "in", 0); - AddOp(cast_grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/cast_to_static_shape_op.cpp b/oneflow/user/ops/cast_to_static_shape_op.cpp index 20843124a242a5044b6dbfe6af87b0c8723b7e0e..69f922e15cba0196e0e0b2da28a92f9739eb1449 100644 --- a/oneflow/user/ops/cast_to_static_shape_op.cpp +++ b/oneflow/user/ops/cast_to_static_shape_op.cpp @@ -20,8 +20,8 @@ namespace oneflow { /* static */ Maybe CastToStaticShapeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); - user_op::TensorDesc* output_desc = ctx->OutputTensorDesc("output", 0); - *output_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("output", 0); + output_desc->set_shape(input_desc.shape()); output_desc->set_is_dynamic(false); return Maybe::Ok(); } @@ -46,24 +46,8 @@ namespace oneflow { } /* static */ Maybe CastToStaticShapeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("cast_to_static_shape") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper identity_op = - builder.Op("identity") - .Input("in", op.GetGradTensorWithOpOutput("output", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(identity_op.output("out", 0), "input", 0); - AddOp(identity_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/cast_to_tick_op.cpp b/oneflow/user/ops/cast_to_tick_op.cpp index bb76f5887e6da4d926fe41baa1b92b855136b410..610e6f48a8b98f36cc4b8bc63dc64df056bc01c9 100644 --- a/oneflow/user/ops/cast_to_tick_op.cpp +++ b/oneflow/user/ops/cast_to_tick_op.cpp @@ -20,8 +20,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe CastToTickOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = Shape({1}); + ctx->SetOutputShape("out", 0, Shape({1})); return Maybe::Ok(); } @@ -53,7 +52,7 @@ namespace oneflow { } /* static */ Maybe CastToTickOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/categorical_ordinal_encode_op.cpp b/oneflow/user/ops/categorical_ordinal_encode_op.cpp index ca2b453382609d068c07f9e08bcefe74eea3bc2f..54a55653966ca2b52b0a0008e1fe36c21b84e5c0 100644 --- a/oneflow/user/ops/categorical_ordinal_encode_op.cpp +++ b/oneflow/user/ops/categorical_ordinal_encode_op.cpp @@ -26,7 +26,7 @@ namespace oneflow { const Shape& size_shape = ctx->InputShape("size", 0); CHECK_EQ_OR_RETURN(size_shape.NumAxes(), 1); CHECK_EQ_OR_RETURN(size_shape.elem_cnt(), 1); - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -39,7 +39,7 @@ namespace oneflow { const Shape& size_shape = ctx->InputShape("size", 0); CHECK_EQ_OR_RETURN(size_shape.NumAxes(), 1); CHECK_EQ_OR_RETURN(size_shape.elem_cnt(), 1); - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -68,11 +68,15 @@ namespace oneflow { } /* static */ Maybe CategoricalOrdinalEncodeOp::InferDataType(user_op::InferContext* ctx) { - const DataType& data_type = ctx->InputDType("in", 0); + DataType data_type = ctx->InputDType("in", 0); CHECK_OR_RETURN(IsIndexDataType(data_type)); - CHECK_EQ_OR_RETURN(ctx->InputDType("table", 0), data_type); - CHECK_EQ_OR_RETURN(ctx->InputDType("size", 0), data_type); - *ctx->OutputDType("out", 0) = data_type; + CHECK_EQ_OR_RETURN(ctx->InputDType("table", 0), data_type) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("table", 0)) + << ", but got " << DataType_Name(data_type); + CHECK_EQ_OR_RETURN(ctx->InputDType("size", 0), data_type) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("size", 0)) + << ", but got " << DataType_Name(data_type); + ctx->SetOutputDType("out", 0, data_type); return Maybe::Ok(); } diff --git a/oneflow/user/ops/celu_op.cpp b/oneflow/user/ops/celu_op.cpp index 60d4815243407e30472247585a22c912a4bec7ac..6f0ec1640b3f397c8301f28eb290fee3e33b8bdc 100644 --- a/oneflow/user/ops/celu_op.cpp +++ b/oneflow/user/ops/celu_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe CeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe CeluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe CeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,27 +65,11 @@ namespace oneflow { } /* static */ Maybe CeluGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("celu").SetBackwardOpConfGenFn( - [](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto celu_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(celu_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("celu_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("alpha", ctx->FwOp().attr("alpha")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &celu_grad_op_name]() -> const std::string& { - return ctx->GetOp(celu_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/clip_by_value_op.cpp b/oneflow/user/ops/clip_by_value_op.cpp index f216e077816a2292757f7807bb43330c9d55d9c9..1e328ee2c822183a673067b219bd9257d3d73967 100644 --- a/oneflow/user/ops/clip_by_value_op.cpp +++ b/oneflow/user/ops/clip_by_value_op.cpp @@ -21,7 +21,7 @@ namespace oneflow { namespace { Maybe InferClipTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("y", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } @@ -34,7 +34,7 @@ Maybe GetClipSbpSignature(user_op::SbpContext* ctx) { } Maybe InferClipGradTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } @@ -56,12 +56,12 @@ Maybe GetClipGradSbpSignature(user_op::SbpContext* ctx) { } Maybe InferClipTensorDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe InferClipGradDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -107,63 +107,4 @@ DEF_CLIP_BY_VALUE_OP(ClipByScalarMax) #undef DEF_CLIP_BY_VALUE_OP -REGISTER_USER_OP_GRAD("clip_by_scalar") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("clip_by_scalar_grad") - .Attr("floating_min", op.attr("floating_min")) - .Attr("integral_min", op.attr("integral_min")) - .Attr("floating_max", op.attr("floating_max")) - .Attr("integral_max", op.attr("integral_max")) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("clip_by_scalar_min") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("clip_by_scalar_min_grad") - .Attr("floating_min", op.attr("floating_min")) - .Attr("integral_min", op.attr("integral_min")) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("clip_by_scalar_max") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("clip_by_scalar_max_grad") - .Attr("floating_max", op.attr("floating_max")) - .Attr("integral_max", op.attr("integral_max")) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/coco_reader_op.cpp b/oneflow/user/ops/coco_reader_op.cpp index 9cea9d8c168b33f5ae7640a80328421b5f3b4657..8af7a26727974eb1a86084a703371608298557df 100644 --- a/oneflow/user/ops/coco_reader_op.cpp +++ b/oneflow/user/ops/coco_reader_op.cpp @@ -20,20 +20,20 @@ namespace oneflow { /* static */ Maybe COCOReaderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { int64_t batch_size = ctx->Attr("batch_size"); - user_op::TensorDesc* image_desc = ctx->OutputTensorDesc("image", 0); - *image_desc->mut_shape() = Shape({batch_size}); - user_op::TensorDesc* image_id_desc = ctx->OutputTensorDesc("image_id", 0); - *image_id_desc->mut_shape() = Shape({batch_size}); - user_op::TensorDesc* image_size_desc = ctx->OutputTensorDesc("image_size", 0); - *image_size_desc->mut_shape() = Shape({batch_size, 2}); - user_op::TensorDesc* bbox_desc = ctx->OutputTensorDesc("gt_bbox", 0); - *bbox_desc->mut_shape() = Shape({batch_size}); - user_op::TensorDesc* label_desc = ctx->OutputTensorDesc("gt_label", 0); - *label_desc->mut_shape() = Shape({batch_size}); - user_op::TensorDesc* segm_desc = ctx->OutputTensorDesc("gt_segm", 0); - *segm_desc->mut_shape() = Shape({batch_size}); - user_op::TensorDesc* segm_index_desc = ctx->OutputTensorDesc("gt_segm_index", 0); - *segm_index_desc->mut_shape() = Shape({batch_size}); + user_op::TensorDesc* image_desc = ctx->MutOutputTensorDesc("image", 0); + image_desc->set_shape(Shape({batch_size})); + user_op::TensorDesc* image_id_desc = ctx->MutOutputTensorDesc("image_id", 0); + image_id_desc->set_shape(Shape({batch_size})); + user_op::TensorDesc* image_size_desc = ctx->MutOutputTensorDesc("image_size", 0); + image_size_desc->set_shape(Shape({batch_size, 2})); + user_op::TensorDesc* bbox_desc = ctx->MutOutputTensorDesc("gt_bbox", 0); + bbox_desc->set_shape(Shape({batch_size})); + user_op::TensorDesc* label_desc = ctx->MutOutputTensorDesc("gt_label", 0); + label_desc->set_shape(Shape({batch_size})); + user_op::TensorDesc* segm_desc = ctx->MutOutputTensorDesc("gt_segm", 0); + segm_desc->set_shape(Shape({batch_size})); + user_op::TensorDesc* segm_index_desc = ctx->MutOutputTensorDesc("gt_segm_index", 0); + segm_index_desc->set_shape(Shape({batch_size})); return Maybe::Ok(); } @@ -59,25 +59,25 @@ namespace oneflow { device_batch_size /= split_num; } - user_op::TensorDesc* image_desc = ctx->OutputTensorDesc("image", 0); - *image_desc->mut_shape() = Shape({device_batch_size}); - user_op::TensorDesc* image_id_desc = ctx->OutputTensorDesc("image_id", 0); - *image_id_desc->mut_shape() = Shape({device_batch_size}); - user_op::TensorDesc* image_size_desc = ctx->OutputTensorDesc("image_size", 0); - *image_size_desc->mut_shape() = Shape({device_batch_size, 2}); - user_op::TensorDesc* bbox_desc = ctx->OutputTensorDesc("gt_bbox", 0); - *bbox_desc->mut_shape() = Shape({device_batch_size}); - user_op::TensorDesc* label_desc = ctx->OutputTensorDesc("gt_label", 0); - *label_desc->mut_shape() = Shape({device_batch_size}); - user_op::TensorDesc* segm_desc = ctx->OutputTensorDesc("gt_segm", 0); - *segm_desc->mut_shape() = Shape({device_batch_size}); - user_op::TensorDesc* segm_index_desc = ctx->OutputTensorDesc("gt_segm_index", 0); - *segm_index_desc->mut_shape() = Shape({device_batch_size}); + user_op::TensorDesc* image_desc = ctx->MutOutputTensorDesc("image", 0); + image_desc->set_shape(Shape({device_batch_size})); + user_op::TensorDesc* image_id_desc = ctx->MutOutputTensorDesc("image_id", 0); + image_id_desc->set_shape(Shape({device_batch_size})); + user_op::TensorDesc* image_size_desc = ctx->MutOutputTensorDesc("image_size", 0); + image_size_desc->set_shape(Shape({device_batch_size, 2})); + user_op::TensorDesc* bbox_desc = ctx->MutOutputTensorDesc("gt_bbox", 0); + bbox_desc->set_shape(Shape({device_batch_size})); + user_op::TensorDesc* label_desc = ctx->MutOutputTensorDesc("gt_label", 0); + label_desc->set_shape(Shape({device_batch_size})); + user_op::TensorDesc* segm_desc = ctx->MutOutputTensorDesc("gt_segm", 0); + segm_desc->set_shape(Shape({device_batch_size})); + user_op::TensorDesc* segm_index_desc = ctx->MutOutputTensorDesc("gt_segm_index", 0); + segm_index_desc->set_shape(Shape({device_batch_size})); return Maybe::Ok(); } /* static */ Maybe COCOReaderOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->outputs(), 0).Build(); + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), 0).Build(); return Maybe::Ok(); } @@ -120,20 +120,20 @@ namespace oneflow { } /* static */ Maybe COCOReaderOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* image_desc = ctx->OutputTensorDesc("image", 0); - *image_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* image_id_desc = ctx->OutputTensorDesc("image_id", 0); - *image_id_desc->mut_data_type() = DataType::kInt64; - user_op::TensorDesc* image_size_desc = ctx->OutputTensorDesc("image_size", 0); - *image_size_desc->mut_data_type() = DataType::kInt32; - user_op::TensorDesc* bbox_desc = ctx->OutputTensorDesc("gt_bbox", 0); - *bbox_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* label_desc = ctx->OutputTensorDesc("gt_label", 0); - *label_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* segm_desc = ctx->OutputTensorDesc("gt_segm", 0); - *segm_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* segm_index_desc = ctx->OutputTensorDesc("gt_segm_index", 0); - *segm_index_desc->mut_data_type() = DataType::kTensorBuffer; + user_op::TensorDesc* image_desc = ctx->MutOutputTensorDesc("image", 0); + image_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* image_id_desc = ctx->MutOutputTensorDesc("image_id", 0); + image_id_desc->set_data_type(DataType::kInt64); + user_op::TensorDesc* image_size_desc = ctx->MutOutputTensorDesc("image_size", 0); + image_size_desc->set_data_type(DataType::kInt32); + user_op::TensorDesc* bbox_desc = ctx->MutOutputTensorDesc("gt_bbox", 0); + bbox_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* label_desc = ctx->MutOutputTensorDesc("gt_label", 0); + label_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* segm_desc = ctx->MutOutputTensorDesc("gt_segm", 0); + segm_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* segm_index_desc = ctx->MutOutputTensorDesc("gt_segm_index", 0); + segm_index_desc->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } diff --git a/oneflow/user/ops/combined_margin_loss_op.cpp b/oneflow/user/ops/combined_margin_loss_op.cpp index 72854a539284a94abf9137d15415bcf7aa8c8d11..38ffb49b939f422e290e35ac41024d2f5cd4b01c 100644 --- a/oneflow/user/ops/combined_margin_loss_op.cpp +++ b/oneflow/user/ops/combined_margin_loss_op.cpp @@ -21,13 +21,13 @@ namespace oneflow { /* static */ Maybe CombinedMarginLossOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& label = ctx->InputTensorDesc("label", 0); - user_op::TensorDesc* theta = ctx->OutputTensorDesc("theta", 0); + user_op::TensorDesc* theta = ctx->MutOutputTensorDesc("theta", 0); CHECK_EQ_OR_RETURN(label.shape().At(0), x.shape().At(0)); CHECK_GE_OR_RETURN(x.shape().NumAxes(), 2); - *ctx->OutputShape("y", 0) = ctx->InputShape("x", 0); - *ctx->IsDynamic4ArgNameAndIndex("y", 0) = ctx->InputIsDynamic("x", 0); - *theta->mut_is_dynamic() = x.is_dynamic(); - *theta->mut_shape() = label.shape(); + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); + ctx->SetIsDynamic4ArgNameAndIndex("y", 0, ctx->InputIsDynamic("x", 0)); + theta->set_is_dynamic(x.is_dynamic()); + theta->set_shape(label.shape()); return Maybe::Ok(); } @@ -59,8 +59,8 @@ namespace oneflow { } /* static */ Maybe CombinedMarginLossOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("theta", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("theta", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -72,8 +72,8 @@ namespace oneflow { CHECK_EQ_OR_RETURN(label.shape().At(0), dy.shape().At(0)); CHECK_EQ_OR_RETURN(label.shape().At(0), theta.shape().At(0)); CHECK_GE_OR_RETURN(dy.shape().NumAxes(), 2); - *ctx->OutputShape("dx", 0) = ctx->InputShape("dy", 0); - *ctx->IsDynamic4ArgNameAndIndex("dx", 0) = ctx->InputIsDynamic("dy", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("dy", 0)); + ctx->SetIsDynamic4ArgNameAndIndex("dx", 0, ctx->InputIsDynamic("dy", 0)); return Maybe::Ok(); } @@ -99,29 +99,8 @@ namespace oneflow { } /* static */ Maybe CombinedMarginLossGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("combined_margin_loss") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("combined_margin_loss_grad") - .Input("label", op.input("label", 0)) - .Input("theta", op.output("theta", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("m1", op.attr("m1")) - .Attr("m2", op.attr("m2")) - .Attr("m3", op.attr("m3")) - .Attr("depth", op.attr("depth")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/comm_net_device_infer_util.cpp b/oneflow/user/ops/comm_net_device_infer_util.cpp index e32c40381ada8e5dcd9d0d65776cfce39c15558a..5261578c2945f74fff7c40c783174fda44d20633 100644 --- a/oneflow/user/ops/comm_net_device_infer_util.cpp +++ b/oneflow/user/ops/comm_net_device_infer_util.cpp @@ -17,22 +17,14 @@ limitations under the License. namespace oneflow { -Maybe SyncLaunched(user_op::DeviceAndStreamInferContext* ctx) { return false; } - -Maybe IsAsyncLaunched(user_op::DeviceAndStreamInferContext* ctx) { - return ctx->Attr("async_launch"); -} - namespace { -Maybe> RawGetNcclDevice(bool is_async_launced) { - StreamRole stream_role = - (is_async_launced ? StreamRole::kAsyncedLaunchedCommNet : StreamRole::kSyncedLaunchedCommNet); - return Stream::New(JUST(Device::New("cuda")), stream_role); +Maybe> RawGetNcclDevice() { + return Stream::New(JUST(Device::New("cuda")), StreamType::kCcl); } Maybe> RawGetCpuTransportDevice() { - return Stream::New(JUST(Device::New("cpu")), StreamRole::kSyncedLaunchedCommNet); + return Stream::New(JUST(Device::New("cpu")), StreamType::kCcl); } } // namespace diff --git a/oneflow/user/ops/comm_net_device_infer_util.h b/oneflow/user/ops/comm_net_device_infer_util.h index 09c3f7109f955c78af553e80c44f601ea5490eeb..9758c0ba20b2477a9bf746a627f6328f6137581a 100644 --- a/oneflow/user/ops/comm_net_device_infer_util.h +++ b/oneflow/user/ops/comm_net_device_infer_util.h @@ -24,26 +24,20 @@ limitations under the License. namespace oneflow { -Maybe SyncLaunched(user_op::DeviceAndStreamInferContext* ctx); - -Maybe IsAsyncLaunched(user_op::DeviceAndStreamInferContext* ctx); - -extern Maybe> (*GetNcclDevice)(bool is_async_launced); +extern Maybe> (*GetNcclDevice)(); extern Maybe> (*GetCpuTransportDevice)(); Maybe> DefaultGetOutputDeivce(user_op::DeviceAndStreamInferContext* ctx); -template (*GetIsAsyncLaunched)(user_op::DeviceAndStreamInferContext*), - Maybe> (*GetOutputDeivce)(user_op::DeviceAndStreamInferContext*) = +template> (*GetOutputDeivce)(user_op::DeviceAndStreamInferContext*) = DefaultGetOutputDeivce> Maybe> DeviceAndStreamInferFn(user_op::DeviceAndStreamInferContext* ctx) { Symbol output_device = JUST(GetOutputDeivce(ctx)); - if (ctx->outputs().size() > 0) { - *ctx->OutputTensorDevice4ArgNameAndIndex("out", 0) = output_device; + for (const auto& pair : ctx->outputs()) { + *ctx->OutputTensorDevice4ArgNameAndIndex(pair.first, pair.second) = output_device; } if (output_device->type() == "cuda") { - bool is_async_launched = JUST(GetIsAsyncLaunched(ctx)); - const auto& cuda_device = JUST(GetNcclDevice(is_async_launched)); + const auto& cuda_device = JUST(GetNcclDevice()); return cuda_device; } else if (output_device->type() == "cpu") { return JUST(GetCpuTransportDevice()); diff --git a/oneflow/user/ops/concat_op.cpp b/oneflow/user/ops/concat_op.cpp index b8d5e8782e5ecdb54a97f9328cba31a9f77b8de3..19d7bb6ac84b484754bdb2d426522270546fe5c1 100644 --- a/oneflow/user/ops/concat_op.cpp +++ b/oneflow/user/ops/concat_op.cpp @@ -18,35 +18,6 @@ limitations under the License. namespace oneflow { -namespace { - -Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) { - bool need_grad = false; - const int32_t in_size = op.input_size("in"); - FOR_RANGE(int32_t, i, 0, in_size) { - if (op.NeedGenGradTensor4OpInput("in", i)) { need_grad = true; } - } - if (need_grad) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder = builder.Op("split_like"); - FOR_RANGE(int32_t, i, 0, in_size) { builder = builder.Input("like", op.input("in", i)); } - user_op::UserOpConfWrapper grad_op = builder.Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out", in_size) - .Attr("axis", op.attr("axis")) - .Build(); - - FOR_RANGE(int32_t, i, 0, in_size) { - if (op.NeedGenGradTensor4OpInput("in", i)) { - op.BindGradTensorWithOpInput(grad_op.output("out", i), "in", i); - } - } - AddOp(grad_op); - } - return Maybe::Ok(); -} - -} // namespace - /* static */ Maybe ConcatOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& first_in_desc = ctx->InputTensorDesc("in", 0); const int64_t axis = ctx->Attr("axis"); @@ -54,12 +25,26 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& CHECK_LT_OR_RETURN(axis, first_in_desc.shape().NumAxes()); DimVector out_dim_vec = first_in_desc.shape().dim_vec(); out_dim_vec.at(axis) = 0; + int64_t first_axes = first_in_desc.shape().NumAxes(); + int64_t first_elemcnt = first_in_desc.shape().elem_cnt(); int64_t dynamic_dim_size = 0; for (const auto& in_arg_pair : ctx->inputs()) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); - CHECK_EQ_OR_RETURN(in_desc.shape().NumAxes(), first_in_desc.shape().NumAxes()); + if (first_elemcnt == 0 and first_axes == 1) { + if (in_desc.shape().elem_cnt() != 0 or in_desc.shape().NumAxes() != 1) { + out_dim_vec = in_desc.shape().dim_vec(); + out_dim_vec.at(axis) = 0; + first_axes = in_desc.shape().NumAxes(); + first_elemcnt = in_desc.shape().elem_cnt(); + } else { + continue; + } + } else if (in_desc.shape().elem_cnt() != 0 or in_desc.shape().NumAxes() != 1) { + CHECK_EQ_OR_RETURN(in_desc.shape().NumAxes(), first_axes); + } FOR_RANGE(int64_t, i, 0, in_desc.shape().NumAxes()) { + if (in_desc.shape().elem_cnt() == 0 and in_desc.shape().NumAxes() == 1) { continue; } if (i == axis) { if (in_desc.is_dynamic()) { dynamic_dim_size += in_desc.shape().At(i); @@ -72,7 +57,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& } } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); const int64_t max_dim_size = ctx->Attr("max_dim_size"); CHECK_LE_OR_RETURN(out_dim_vec.at(axis), max_dim_size); if (dynamic_dim_size == 0) { @@ -81,7 +66,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& out_desc->set_is_dynamic(true); out_dim_vec.at(axis) = max_dim_size; } - *out_desc->mut_shape() = Shape(out_dim_vec); + out_desc->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -105,10 +90,12 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& for (const auto& in_arg_pair : ctx->inputs()) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); - CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()); + CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(in_desc.data_type()) << ", but got " + << DataType_Name(first_in_desc.data_type()); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(first_in_desc.data_type()); return Maybe::Ok(); } @@ -118,6 +105,4 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("concat").SetGenBackwardOpConfFn(GenGradOp); - } // namespace oneflow diff --git a/oneflow/user/ops/constant_op.cpp b/oneflow/user/ops/constant_op.cpp index 62d9bdcc050d04a2793c1a9bd57082d2ad072778..e8aecf1f2f92f020e8550961822a8a48bda6703b 100644 --- a/oneflow/user/ops/constant_op.cpp +++ b/oneflow/user/ops/constant_op.cpp @@ -20,7 +20,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe ConstantOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape(ctx->Attr("shape").dim_vec()); + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); return Maybe::Ok(); } @@ -33,7 +33,7 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } @@ -46,7 +46,7 @@ namespace oneflow { } /* static */ Maybe ConstantOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/conv_op.cpp b/oneflow/user/ops/conv_op.cpp index 64940f4d2da4100d85af976c14f4c25164acc77e..b838f2ece4cfebe46f6bd85b7bf8cd15bba0659e 100644 --- a/oneflow/user/ops/conv_op.cpp +++ b/oneflow/user/ops/conv_op.cpp @@ -39,7 +39,7 @@ Maybe InferTensorDesc4Conv(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(NDims, strides.size()); CHECK_EQ_OR_RETURN(NDims, padding_before.size()); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); DimVector out_shape(NDims + 2); out_shape.at(0) = in.shape().At(0); const size_t c_dim = data_format == "channels_first" ? 1 : NDims + 1; @@ -48,8 +48,8 @@ Maybe InferTensorDesc4Conv(user_op::InferContext* ctx) { JUST(CalcConvOut(in.shape().At(idx_offset + i), kernel_size.at(i), dilation_rate.at(i), strides.at(i), padding_before.at(i), &out_shape.at(idx_offset + i))); } - *out->mut_is_dynamic() = in.is_dynamic(); - *out->mut_shape() = Shape(out_shape); + out->set_is_dynamic(in.is_dynamic()); + out->set_shape(Shape(out_shape)); } { @@ -87,7 +87,6 @@ Maybe InferTensorDesc4Conv(user_op::InferContext* ctx) { } Maybe GetSbpSignatures4Conv(user_op::SbpContext* ctx) { - // TODO(niuchong) : handle bias_multiplier bool has_bias = false; for (const auto& pair : ctx->inputs()) { if (pair.first == "bias") { @@ -99,6 +98,7 @@ Maybe GetSbpSignatures4Conv(user_op::SbpContext* ctx) { if (has_bias) { ctx->NewBuilder() + .Split(ctx->inputs(), 0) .Split(user_op::OpArg("in", 0), 0) .Broadcast(user_op::OpArg("weight", 0)) .Broadcast(user_op::OpArg("bias", 0)) @@ -106,6 +106,7 @@ Maybe GetSbpSignatures4Conv(user_op::SbpContext* ctx) { .Build(); } else { ctx->NewBuilder() + .Split(ctx->inputs(), 0) .Split(user_op::OpArg("in", 0), 0) .Broadcast(user_op::OpArg("weight", 0)) .Split(user_op::OpArg("out", 0), 0) @@ -114,6 +115,37 @@ Maybe GetSbpSignatures4Conv(user_op::SbpContext* ctx) { return Maybe::Ok(); } +/* +Example for conv2d: + +ComputationCost += ((k*k + k*k-1)*c + c-1 + bias?1:0) * out_channel * out_width * out_height * batch_size += (2*k*k*c - 1 + bias?1:0) * out_channel * out_width * out_height * batch_size +≈ 2*k*k*c * out_channel * out_width * out_height * batch_size +*/ +Maybe ConvComputationCost(user_op::ComputeComplexityFnContext* ctx) { + const std::vector kernel_size = ctx->Attr>("kernel_size"); + const std::string data_format = ctx->Attr("data_format"); + const user_op::TensorDesc* in = ctx->TensorDesc4ArgNameAndIndex("in", 0); + const size_t c_dim = data_format == "channels_first" ? 1 : in->shape().NumAxes() - 1; + const int32_t c = in->shape().At(c_dim); + const user_op::TensorDesc* out = ctx->TensorDesc4ArgNameAndIndex("out", 0); + double cost = + std::accumulate(kernel_size.begin(), kernel_size.end(), 1.0, std::multiplies()); + cost = cost * 2 * c; + cost *= std::accumulate(out->shape().dim_vec().begin(), out->shape().dim_vec().end(), 1.0, + std::multiplies()); + + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + const auto& nd_sbp_out = ctx->NdSbp4ArgNameAndIndex("out", 0); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp_out.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp_out.sbp_parallel(dim_sbp).has_split_parallel()) { + cost /= parallel_hierarchy->At(dim_sbp); + } + } + return cost; +} + template Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, const user_op::UserOpConfWrapper& conf) { @@ -160,74 +192,6 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, } } -Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp) { - const auto& padding_before = op.attr>("padding_before"); - std::string data_format = op.attr("data_format"); - std::vector kernel_size = op.attr>("kernel_size"); - std::vector strides = op.attr>("strides"); - std::vector dilation_rate = op.attr>("dilation_rate"); - int32_t groups = op.attr("groups"); - - int32_t ndims = kernel_size.size(); - CHECK_EQ_OR_RETURN(ndims, strides.size()); - CHECK_EQ_OR_RETURN(ndims, dilation_rate.size()); - - if (op.user_op_conf().has_input("bias", 0)) { - if (op.NeedGenGradTensor4OpInput("bias", 0)) { - auto bias_grad_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name() + "-BiasGrad") - .Op("conv_bias_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("bias_diff") - .Attr("data_format", data_format) - .Attr("num_spatial_dims", ndims) - .Build(); - op.BindGradTensorWithOpInput(bias_grad_op.output("bias_diff", 0), "bias", 0); - AddOp(bias_grad_op); - } - } - - if (op.NeedGenGradTensor4OpInput("weight", 0)) { - auto filter_grad_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name() + "-FilterGrad") - .Op("conv_filter_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("x", op.input("in", 0)) - .Output("filter_diff") - .Attr("num_spatial_dims", ndims) - .Attr>("padding_before", padding_before) - .Attr("data_format", data_format) - .Attr>("kernel_size", kernel_size) - .Attr>("strides", strides) - .Attr>("dilation_rate", dilation_rate) - .Attr("groups", groups) - .Build(); - op.BindGradTensorWithOpInput(filter_grad_op.output("filter_diff", 0), "weight", 0); - AddOp(filter_grad_op); - } - - if (op.NeedGenGradTensor4OpInput("in", 0)) { - auto data_grad_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name() + "-DataGrad") - .Op("conv_data_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("filter", op.input("weight", 0)) - .Input("x_like", op.input("in", 0)) - .Output("dx") - .Attr("num_spatial_dims", ndims) - .Attr>("padding_before", padding_before) - .Attr("data_format", data_format) - .Attr>("kernel_size", kernel_size) - .Attr>("strides", strides) - .Attr>("dilation_rate", dilation_rate) - .Attr("groups", groups) - .Build(); - op.BindGradTensorWithOpInput(data_grad_op.output("dx", 0), "in", 0); - AddOp(data_grad_op); - } - return Maybe::Ok(); -} - } // namespace /* static */ Maybe Conv1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { @@ -242,13 +206,18 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o return GetSbpSignatures4Conv(ctx); } +/* static */ Maybe Conv1DOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + return ConvComputationCost(ctx); +} + /* static */ Maybe Conv1DOp::CheckAttr(const user_op::UserOpDefWrapper& def, const user_op::UserOpConfWrapper& conf) { return CheckAttr_<1>(def, conf); } /* static */ Maybe Conv1DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -264,13 +233,18 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o return GetSbpSignatures4Conv(ctx); } +/* static */ Maybe Conv2DOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + return ConvComputationCost(ctx); +} + /* static */ Maybe Conv2DOp::CheckAttr(const user_op::UserOpDefWrapper& def, const user_op::UserOpConfWrapper& conf) { return CheckAttr_<2>(def, conf); } /* static */ Maybe Conv2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -286,13 +260,18 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o return GetSbpSignatures4Conv(ctx); } +/* static */ Maybe Conv3DOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + return ConvComputationCost(ctx); +} + /* static */ Maybe Conv3DOp::CheckAttr(const user_op::UserOpDefWrapper& def, const user_op::UserOpConfWrapper& conf) { return CheckAttr_<3>(def, conf); } /* static */ Maybe Conv3DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -308,8 +287,8 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o const user_op::TensorDesc& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); CHECK_EQ_OR_RETURN(add_to_output.shape(), x_like.shape()); } - *ctx->OutputShape("dx", 0) = ctx->InputShape("x_like", 0); - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("x_like", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x_like", 0)); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("x_like", 0)); return Maybe::Ok(); } @@ -337,15 +316,43 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o /* static */ Maybe ConvDataGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& x_like = ctx->InputTensorDesc("x_like", 0); - CHECK_EQ_OR_RETURN(x_like.data_type(), dy.data_type()); + CHECK_EQ_OR_RETURN(x_like.data_type(), dy.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(dy.data_type()) << ", but got " + << DataType_Name(x_like.data_type()); if (ctx->has_input("_add_to_output", 0)) { const user_op::TensorDesc& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); - CHECK_EQ_OR_RETURN(add_to_output.data_type(), x_like.data_type()); + CHECK_EQ_OR_RETURN(add_to_output.data_type(), x_like.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(add_to_output.data_type()) + << ", but got " << DataType_Name(x_like.data_type()); } - *ctx->OutputDType("dx", 0) = ctx->InputDType("x_like", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x_like", 0)); return Maybe::Ok(); } +/* static */ Maybe ConvDataGradOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + const std::vector kernel_size = ctx->Attr>("kernel_size"); + const user_op::TensorDesc* dx = ctx->TensorDesc4ArgNameAndIndex("dx", 0); + const user_op::TensorDesc* dy = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const size_t c_dim = + ctx->Attr("data_format") == "channels_first" ? 1 : dy->shape().NumAxes() - 1; + + double cost = + std::accumulate(kernel_size.begin(), kernel_size.end(), 1.0, std::multiplies()) + * std::accumulate(dx->shape().dim_vec().begin(), dx->shape().dim_vec().end(), 1.0, + std::multiplies()) + * 2.0 * dy->shape().At(c_dim); + + const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex("dx", 0); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp.sbp_parallel(dim_sbp).has_split_parallel()) { + cost /= parallel_hierarchy->At(dim_sbp); + } + } + return cost; +} + /* static */ Maybe ConvFilterGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); @@ -378,8 +385,8 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o filter_diff_dim_vec.emplace_back(x.shape().dim_vec().back() / groups); } - user_op::TensorDesc* filter_diff = ctx->OutputTensorDesc("filter_diff", 0); - *filter_diff->mut_shape() = Shape(filter_diff_dim_vec); + user_op::TensorDesc* filter_diff = ctx->MutOutputTensorDesc("filter_diff", 0); + filter_diff->set_shape(Shape(filter_diff_dim_vec)); filter_diff->set_is_dynamic(false); return Maybe::Ok(); @@ -406,15 +413,41 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o /* static */ Maybe ConvFilterGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); - CHECK_EQ_OR_RETURN(x.data_type(), dy.data_type()); - user_op::TensorDesc* filter_diff = ctx->OutputTensorDesc("filter_diff", 0); - *filter_diff->mut_data_type() = x.data_type(); + CHECK_EQ_OR_RETURN(x.data_type(), dy.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(dy.data_type()) << ", but got " + << DataType_Name(x.data_type()); + user_op::TensorDesc* filter_diff = ctx->MutOutputTensorDesc("filter_diff", 0); + filter_diff->set_data_type(x.data_type()); return Maybe::Ok(); } +/* static */ Maybe ConvFilterGradOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + const std::vector kernel_size = ctx->Attr>("kernel_size"); + const user_op::TensorDesc* dy = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const user_op::TensorDesc* x = ctx->TensorDesc4ArgNameAndIndex("x", 0); + const size_t c_dim = + ctx->Attr("data_format") == "channels_first" ? 1 : x->shape().NumAxes() - 1; + + double cost = + std::accumulate(kernel_size.begin(), kernel_size.end(), 1.0, std::multiplies()) + * std::accumulate(dy->shape().dim_vec().begin(), dy->shape().dim_vec().end(), 1.0, + std::multiplies()) + * 2.0 * x->shape().At(c_dim); + + const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex("dy", 0); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp.sbp_parallel(dim_sbp).has_split_parallel()) { + cost /= parallel_hierarchy->At(dim_sbp); + } + } + return cost; +} + /* static */ Maybe ConvBiasGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* bias_diff = ctx->OutputTensorDesc("bias_diff", 0); + user_op::TensorDesc* bias_diff = ctx->MutOutputTensorDesc("bias_diff", 0); int32_t num_spatial_dims = ctx->Attr("num_spatial_dims"); std::string data_format = ctx->Attr("data_format"); @@ -423,9 +456,9 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o CHECK_LE_OR_RETURN(num_spatial_dims, 3); CHECK_EQ_OR_RETURN(dy.shape().NumAxes(), num_spatial_dims + 2); if (data_format == "channels_first") { - *bias_diff->mut_shape() = Shape({dy.shape().At(1)}); + bias_diff->set_shape(Shape({dy.shape().At(1)})); } else if (data_format == "channels_last") { - *bias_diff->mut_shape() = Shape({dy.shape().At(dy.shape().NumAxes() - 1)}); + bias_diff->set_shape(Shape({dy.shape().At(dy.shape().NumAxes() - 1)})); } else { OF_UNIMPLEMENTED(); } @@ -456,13 +489,25 @@ Maybe GenerateBackwardOpConf4Conv(const user_op::UserOpWrapper& op, user_o /* static */ Maybe ConvBiasGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* bias_diff = ctx->OutputTensorDesc("bias_diff", 0); - *bias_diff->mut_data_type() = dy.data_type(); + user_op::TensorDesc* bias_diff = ctx->MutOutputTensorDesc("bias_diff", 0); + bias_diff->set_data_type(dy.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("conv1d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4Conv); -REGISTER_USER_OP_GRAD("conv2d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4Conv); -REGISTER_USER_OP_GRAD("conv3d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4Conv); +/* static */ Maybe ConvBiasGradOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + const user_op::TensorDesc* dy = ctx->TensorDesc4ArgNameAndIndex("dy", 0); + const std::string data_format = ctx->Attr("data_format"); + double cost = std::accumulate(dy->shape().dim_vec().begin(), dy->shape().dim_vec().end(), 1.0, + std::multiplies()); + const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex("dy", 0); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp.sbp_parallel(dim_sbp).has_split_parallel()) { + cost /= parallel_hierarchy->At(dim_sbp); + } + } + return cost; +} } // namespace oneflow diff --git a/oneflow/user/ops/copy_hd_op.cpp b/oneflow/user/ops/copy_hd_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6c88daf7262a47d4d8aca6681b4649709fa5a9df --- /dev/null +++ b/oneflow/user/ops/copy_hd_op.cpp @@ -0,0 +1,68 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/ops/nn_util.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/operator/operator.h" + +namespace oneflow { + +namespace { + +Maybe InferLogical(user_op::InferContext* ctx) { + UNIMPLEMENTED_THEN_RETURN() << "copy hd should only exist in physical graph"; +} + +Maybe InferPhysical(user_op::InferContext* ctx) { + *ctx->MutOutputTensorDesc("out", 0) = ctx->InputTensorDesc("in", 0); + return Maybe::Ok(); +} + +Maybe FwGetSbpFn(user_op::SbpContext* ctx) { return Maybe::Ok(); } + +Maybe InferFWDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + return Maybe::Ok(); +} + +} // namespace + +Maybe CopyD2HOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferLogical(ctx); +} + +Maybe CopyD2HOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferPhysical(ctx); +} + +Maybe CopyD2HOp::GetSbp(user_op::SbpContext* ctx) { return FwGetSbpFn(ctx); } + +Maybe CopyD2HOp::InferDataType(user_op::InferContext* ctx) { return InferFWDataType(ctx); } + +Maybe CopyH2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferLogical(ctx); +} + +Maybe CopyH2DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferPhysical(ctx); +} + +Maybe CopyH2DOp::GetSbp(user_op::SbpContext* ctx) { return FwGetSbpFn(ctx); } + +Maybe CopyH2DOp::InferDataType(user_op::InferContext* ctx) { return InferFWDataType(ctx); } + +} // namespace oneflow diff --git a/oneflow/user/ops/copy_op.cpp b/oneflow/user/ops/copy_op.cpp index 6b7d5f994f22f72cf1bbbe1cac4e35e098f27ffd..434ddf4b63b5e73387dc39f776dd0920c306d929 100644 --- a/oneflow/user/ops/copy_op.cpp +++ b/oneflow/user/ops/copy_op.cpp @@ -17,34 +17,43 @@ limitations under the License. #include "oneflow/core/framework/device.h" #include "oneflow/core/framework/stream.h" #include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/common/env_var/stream.h" namespace oneflow { namespace { +StreamType GetH2DStreamType() { + if (ThreadLocalEnvBool()) { + return StreamType::kHost2Device; + } else { + return StreamType::kCompute; + } +} + Maybe> MakeCopyStream(const Symbol& in_device, const Symbol& out_device, const bool pin_memory) { if (in_device->type() != "cpu" && out_device->type() == "cpu") { - return Stream::New(in_device, StreamRole::kDevice2Host); + return Stream::New(in_device, StreamType::kDevice2Host); } else if (in_device->type() == "cpu" && out_device->type() != "cpu") { const auto device = JUST(Device::New(out_device->type(), out_device->device_id())); - return Stream::New(device, StreamRole::kHost2Device); + return Stream::New(device, GetH2DStreamType()); } else if (in_device->type() == "cpu" && out_device->type() == "cpu" && pin_memory) { // TODO:(zhaoluyang) Parsing pin-memory-device from python auto pin_device = JUST(Device::New("cuda")); - return Stream::New(pin_device, StreamRole::kPinnedCompute); + return Stream::New(pin_device, StreamType::kPinnedCompute); } else { CHECK_EQ_OR_RETURN(in_device->type(), out_device->type()); - return Stream::New(out_device, StreamRole::kCompute); + return Stream::New(out_device, StreamType::kCompute); } } } // namespace /* static */ Maybe CopyOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputStride("out", 0) = ctx->InputStride("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputStride("out", 0, ctx->InputStride("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -65,7 +74,7 @@ Maybe> MakeCopyStream(const Symbol& in_device, } /* static */ Maybe CopyOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/count_not_finite_op.cpp b/oneflow/user/ops/count_not_finite_op.cpp index 20e752a0b2cb17a1e27cbe53920296b69a6bd9c0..d9b42dcbd1fda1889798df494ce42164884b851e 100644 --- a/oneflow/user/ops/count_not_finite_op.cpp +++ b/oneflow/user/ops/count_not_finite_op.cpp @@ -19,8 +19,8 @@ limitations under the License. namespace oneflow { /* static */ Maybe CountNotFiniteOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = Shape({1}); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(Shape({1})); return Maybe::Ok(); } @@ -37,14 +37,14 @@ namespace oneflow { } /* static */ Maybe CountNotFiniteOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_data_type() = DataType::kInt64; + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_data_type(DataType::kInt64); return Maybe::Ok(); } /* static */ Maybe MultiCountNotFiniteOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = Shape({1}); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(Shape({1})); return Maybe::Ok(); } @@ -68,10 +68,12 @@ namespace oneflow { const user_op::TensorDesc& first_x_desc = ctx->InputTensorDesc("x", 0); for (const auto& in_arg_pair : ctx->inputs()) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); - CHECK_EQ_OR_RETURN(x_desc.data_type(), first_x_desc.data_type()); + CHECK_EQ_OR_RETURN(x_desc.data_type(), first_x_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(first_x_desc.data_type()) + << ", but got " << DataType_Name(x_desc.data_type()); } - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_data_type() = DataType::kInt64; + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_data_type(DataType::kInt64); return Maybe::Ok(); } diff --git a/oneflow/user/ops/ctc_loss_op.cpp b/oneflow/user/ops/ctc_loss_op.cpp index b8dee1ad9ccff20aa3c78f170cf6a485d35f9be6..209371fe6557c3d31ad196b82cf3cb8c258f6cd0 100644 --- a/oneflow/user/ops/ctc_loss_op.cpp +++ b/oneflow/user/ops/ctc_loss_op.cpp @@ -31,12 +31,12 @@ namespace oneflow { } CHECK_EQ_OR_RETURN(input_lengths.shape().At(0), batch_size); CHECK_EQ_OR_RETURN(target_lengths.shape().At(0), batch_size); - CHECK_GE_OR_RETURN(ctx->Attr("blank"), 0); - CHECK_LT_OR_RETURN(ctx->Attr("blank"), log_probs.shape().At(2)); + CHECK_GE_OR_RETURN(ctx->Attr("blank"), 0); + CHECK_LT_OR_RETURN(ctx->Attr("blank"), log_probs.shape().At(2)); - *ctx->OutputShape("loss", 0) = Shape({batch_size}); - *ctx->OutputShape("alpha", 0) = - Shape({batch_size, log_probs.shape().At(0), 2 * max_target_length + 1}); + ctx->SetOutputShape("loss", 0, Shape({batch_size})); + ctx->SetOutputShape("alpha", 0, + Shape({batch_size, log_probs.shape().At(0), 2 * max_target_length + 1})); return Maybe::Ok(); } @@ -57,8 +57,8 @@ namespace oneflow { } /* static */ Maybe CtcLossOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("loss", 0) = ctx->InputDType("log_probs", 0); - *ctx->OutputDType("alpha", 0) = ctx->InputDType("log_probs", 0); + ctx->SetOutputDType("loss", 0, ctx->InputDType("log_probs", 0)); + ctx->SetOutputDType("alpha", 0, ctx->InputDType("log_probs", 0)); return Maybe::Ok(); } @@ -75,10 +75,10 @@ namespace oneflow { } CHECK_EQ_OR_RETURN(input_lengths.shape().At(0), batch_size); CHECK_EQ_OR_RETURN(target_lengths.shape().At(0), batch_size); - CHECK_GE_OR_RETURN(ctx->Attr("blank"), 0); - CHECK_LT_OR_RETURN(ctx->Attr("blank"), log_probs.shape().At(2)); + CHECK_GE_OR_RETURN(ctx->Attr("blank"), 0); + CHECK_LT_OR_RETURN(ctx->Attr("blank"), log_probs.shape().At(2)); - *ctx->OutputShape("grad", 0) = log_probs.shape(); + ctx->SetOutputShape("grad", 0, log_probs.shape()); return Maybe::Ok(); } @@ -101,7 +101,7 @@ namespace oneflow { } /* static */ Maybe CtcLossGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("grad", 0) = ctx->InputDType("log_probs", 0); + ctx->SetOutputDType("grad", 0, ctx->InputDType("log_probs", 0)); return Maybe::Ok(); } @@ -110,8 +110,8 @@ namespace oneflow { const user_op::TensorDesc& input_lengths = ctx->InputTensorDesc("input_lengths", 0); const int64_t batch_size = log_probs.shape().At(1); CHECK_EQ_OR_RETURN(batch_size, input_lengths.shape().At(0)); - *ctx->OutputShape("decoded", 0) = Shape({batch_size, log_probs.shape().At(0)}); - *ctx->OutputShape("neg_sum_logits", 0) = Shape({batch_size, 1}); + ctx->SetOutputShape("decoded", 0, Shape({batch_size, log_probs.shape().At(0)})); + ctx->SetOutputShape("neg_sum_logits", 0, Shape({batch_size, 1})); return Maybe::Ok(); } @@ -130,34 +130,9 @@ namespace oneflow { } /* static */ Maybe CtcGreedyDecoderOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("decoded", 0) = ctx->InputDType("input_lengths", 0); - *ctx->OutputDType("neg_sum_logits", 0) = ctx->InputDType("log_probs", 0); + ctx->SetOutputDType("decoded", 0, ctx->InputDType("input_lengths", 0)); + ctx->SetOutputDType("neg_sum_logits", 0, ctx->InputDType("log_probs", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("ctc_loss") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto ctc_loss_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(ctc_loss_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("ctc_loss_grad") - .InputBind("grad_out", ctx->FwOp().output_grad("loss", 0)) - .InputBind("log_probs", ctx->FwOp().input("log_probs", 0)) - .InputBind("targets", ctx->FwOp().input("targets", 0)) - .InputBind("input_lengths", ctx->FwOp().input("input_lengths", 0)) - .InputBind("target_lengths", ctx->FwOp().input("target_lengths", 0)) - .InputBind("loss", ctx->FwOp().output("loss", 0)) - .InputBind("alpha", ctx->FwOp().output("alpha", 0)) - .Attr("max_target_length", ctx->FwOp().attr("max_target_length")) - .Attr("blank", ctx->FwOp().attr("blank")) - .Attr("zero_infinity", ctx->FwOp().attr("zero_infinity")) - .Output("grad") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("log_probs", 0), - [&ctx, &ctc_loss_grad_op_name]() -> const std::string& { - return ctx->GetOp(ctc_loss_grad_op_name).output("grad", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/cublas_bias_add_relu_matmul_grad_op.cpp b/oneflow/user/ops/cublas_bias_add_relu_matmul_grad_op.cpp index ae09393bf854bdccb986b5ffa64b633b2eaf41a6..c03548fa2f9a8773fb6a216509134e699dca8d48 100644 --- a/oneflow/user/ops/cublas_bias_add_relu_matmul_grad_op.cpp +++ b/oneflow/user/ops/cublas_bias_add_relu_matmul_grad_op.cpp @@ -28,21 +28,23 @@ Maybe InferTensorDesc4FusedMatmulBackward(user_op::InferContext* ctx) { const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); const int64_t bias_size = weight_desc.shape().At(1); Shape d_grad_shape({dy_desc.shape().At(0), weight_desc.shape().At(1)}); - *ctx->OutputShape("d_grad", 0) = d_grad_shape; - *ctx->OutputShape("d_bias", 0) = Shape({bias_size}); + ctx->SetOutputShape("d_grad", 0, d_grad_shape); + ctx->SetOutputShape("d_bias", 0, Shape({bias_size})); return Maybe::Ok(); } Maybe InferDataType4MatmulBackward(user_op::InferContext* ctx) { const user_op::TensorDesc& weight_desc = ctx->InputTensorDesc("weight", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(weight_desc.data_type(), dy_desc.data_type()); + CHECK_EQ_OR_RETURN(weight_desc.data_type(), dy_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(dy_desc.data_type()) << ", but got " + << DataType_Name(weight_desc.data_type()); - user_op::TensorDesc* d_grad_desc = ctx->OutputTensorDesc("d_grad", 0); - user_op::TensorDesc* d_bias_desc = ctx->OutputTensorDesc("d_bias", 0); + user_op::TensorDesc* d_grad_desc = ctx->MutOutputTensorDesc("d_grad", 0); + user_op::TensorDesc* d_bias_desc = ctx->MutOutputTensorDesc("d_bias", 0); - *d_grad_desc->mut_data_type() = dy_desc.data_type(); - *d_bias_desc->mut_data_type() = dy_desc.data_type(); + d_grad_desc->set_data_type(dy_desc.data_type()); + d_bias_desc->set_data_type(dy_desc.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/cublas_fused_matmul_bias_add_grad_op.cpp b/oneflow/user/ops/cublas_fused_matmul_bias_add_grad_op.cpp index 8ae2e512d6257c77f135d94489c7bc64825388de..abeba0fd080f141db78d3433ebb3f5b4d5c16d4a 100644 --- a/oneflow/user/ops/cublas_fused_matmul_bias_add_grad_op.cpp +++ b/oneflow/user/ops/cublas_fused_matmul_bias_add_grad_op.cpp @@ -36,8 +36,8 @@ Maybe InferTensorDesc4MatmulBiasAddBackward(user_op::InferContext* ctx) { const int64_t bias_size = dy_desc.shape().At(1); Shape w_grad_shape({dy_desc.shape().At(1), x_desc.shape().At(1)}); - *ctx->OutputShape("w_grad", 0) = w_grad_shape; - *ctx->OutputShape("b_grad", 0) = Shape({bias_size}); + ctx->SetOutputShape("w_grad", 0, w_grad_shape); + ctx->SetOutputShape("b_grad", 0, Shape({bias_size})); return Maybe::Ok(); } @@ -45,13 +45,14 @@ Maybe InferDataType4MatmulBiasAddBackward(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); CHECK_EQ_OR_RETURN(x_desc.data_type(), dy_desc.data_type()) - << "x's datatype should be the same as y's datatype"; + << "InferDataType Failed. Expected " << DataType_Name(dy_desc.data_type()) << ", but got " + << DataType_Name(x_desc.data_type()); - user_op::TensorDesc* w_grad_desc = ctx->OutputTensorDesc("w_grad", 0); - user_op::TensorDesc* b_grad_desc = ctx->OutputTensorDesc("b_grad", 0); + user_op::TensorDesc* w_grad_desc = ctx->MutOutputTensorDesc("w_grad", 0); + user_op::TensorDesc* b_grad_desc = ctx->MutOutputTensorDesc("b_grad", 0); - *w_grad_desc->mut_data_type() = dy_desc.data_type(); - *b_grad_desc->mut_data_type() = dy_desc.data_type(); + w_grad_desc->set_data_type(dy_desc.data_type()); + b_grad_desc->set_data_type(dy_desc.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/cublas_fused_mlp_grad_op.cpp b/oneflow/user/ops/cublas_fused_mlp_grad_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c34c9d095b92ccc27a95576d74f1420ed3ac0b3e --- /dev/null +++ b/oneflow/user/ops/cublas_fused_mlp_grad_op.cpp @@ -0,0 +1,101 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferTensorDesc4FusedMatmulBackward(user_op::InferContext* ctx) { + const int64_t weight_num = ctx->input_size("weights"); + const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); + for (int idx = weight_num - 1; idx >= 0; idx--) { + const user_op::TensorDesc& weight_desc = ctx->InputTensorDesc("weights", idx); + ctx->SetOutputShape("d_weights", idx, weight_desc.shape()); + ctx->SetOutputShape("d_biases", idx, Shape({weight_desc.shape().At(0)})); + } + ctx->SetOutputShape("d_x", 0, x_desc.shape()); + return Maybe::Ok(); +} + +Maybe InferDataType4MatmulBackward(user_op::InferContext* ctx) { + const int64_t weight_num = ctx->input_size("weights"); + const int64_t dweight_num = ctx->output_size("d_weights"); + CHECK_EQ(weight_num, dweight_num) << "The number of weights and d_weights should be equal. "; + const int64_t dbias_size = ctx->output_size("d_biases"); + CHECK_EQ(weight_num, dbias_size) << "The number of d_biases should be equal to weight_num. " + "Because last layer's bias_grad is computed by ReduceSum. "; + const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); + for (int idx = weight_num - 1; idx >= 0; idx--) { + ctx->SetOutputDType("d_weights", idx, dy_desc.data_type()); + ctx->SetOutputDType("d_biases", idx, dy_desc.data_type()); + } + ctx->SetOutputDType("d_x", 0, dy_desc.data_type()); + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe CublasFusedMLPGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferTensorDesc4FusedMatmulBackward(ctx); +} + +/*static*/ Maybe CublasFusedMLPGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe CublasFusedMLPGradOp::GetSbp(user_op::SbpContext* ctx) { + auto builder = ctx->NewBuilder().Split(user_op::OpArg("x", 0), 0); + builder.Split(user_op::OpArg("dy", 0), 0); + for (int i = 0; i < ctx->user_op_conf().input_size("weights"); ++i) { + builder.Broadcast(user_op::OpArg("weights", i)); + } + for (int i = 0; i < ctx->user_op_conf().input_size("cublas_aux"); ++i) { + builder.Split(user_op::OpArg("cublas_aux", i), 0); + } + for (int i = 0; i < ctx->user_op_conf().input_size("hidden"); ++i) { + builder.Split(user_op::OpArg("hidden", i), 0); + } + + builder.Split(user_op::OpArg("d_x", 0), 0); + if (ParseBooleanFromEnv("ONEFLOW_ONE_EMBEDDING_FUSED_MLP_GRAD_OVERLAP_ALLREDUCE", false)) { + // FusedMLPGradKernel do allreduce for dbias and dweight, so here convert from PartialSum to + // Broadcast. + for (int i = 0; i < ctx->user_op_conf().output_size("d_biases"); ++i) { + builder.Broadcast(user_op::OpArg("d_biases", i)); + } + for (int i = 0; i < ctx->user_op_conf().output_size("d_weights"); ++i) { + builder.Broadcast(user_op::OpArg("d_weights", i)); + } + } else { + for (int i = 0; i < ctx->user_op_conf().output_size("d_biases"); ++i) { + builder.PartialSum(user_op::OpArg("d_biases", i)); + } + for (int i = 0; i < ctx->user_op_conf().output_size("d_weights"); ++i) { + builder.PartialSum(user_op::OpArg("d_weights", i)); + } + } + + builder.Build(); + return Maybe::Ok(); +} + +/* static */ Maybe CublasFusedMLPGradOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4MatmulBackward(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/cublas_fused_mlp_op.cpp b/oneflow/user/ops/cublas_fused_mlp_op.cpp index 65619b14d0c806dd2365983163d3472e5445a73d..f79f4bb9dd6870c59b0b1b2c2beff4839df2f7a3 100644 --- a/oneflow/user/ops/cublas_fused_mlp_op.cpp +++ b/oneflow/user/ops/cublas_fused_mlp_op.cpp @@ -65,12 +65,12 @@ Maybe InferTensorDesc4FusedMatmul(user_op::InferContext* ctx) { // Set Middle result shape. long cublas_aligned_aux_ld = AlignReluAuxLd(cublas_aux_ld); int64_t aux_size = cublas_aligned_aux_ld / 32; // Cause we use int32_t as dtype - *ctx->OutputShape("cublas_aux", idx) = Shape({m, aux_size}); - *ctx->OutputShape("hidden", idx) = Shape({m, n}); + ctx->SetOutputShape("cublas_aux", idx, Shape({m, aux_size})); + ctx->SetOutputShape("hidden", idx, Shape({m, n})); // Set for next layer. k = n; } - *ctx->OutputShape("out", 0) = {m, n}; + ctx->SetOutputShape("out", 0, Shape({m, n})); return Maybe::Ok(); } @@ -80,20 +80,22 @@ Maybe InferDataType4Matmul(user_op::InferContext* ctx) { for (const auto& in_arg_pair : ctx->inputs()) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); - CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()); + CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(first_in_desc.data_type()) + << ", but got " << DataType_Name(in_desc.data_type()); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(first_in_desc.data_type()); for (int32_t i = 0; i < ctx->output_size("hidden"); i++) { - user_op::TensorDesc* hidden_desc = ctx->OutputTensorDesc("hidden", i); - *hidden_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* hidden_desc = ctx->MutOutputTensorDesc("hidden", i); + hidden_desc->set_data_type(first_in_desc.data_type()); } for (int32_t i = 0; i < ctx->output_size("cublas_aux"); i++) { - user_op::TensorDesc* aux_desc = ctx->OutputTensorDesc("cublas_aux", i); - *aux_desc->mut_data_type() = DataType::kInt32; + user_op::TensorDesc* aux_desc = ctx->MutOutputTensorDesc("cublas_aux", i); + aux_desc->set_data_type(DataType::kInt32); } return Maybe::Ok(); @@ -133,119 +135,4 @@ Maybe InferDataType4Matmul(user_op::InferContext* ctx) { return InferDataType4Matmul(ctx); } -REGISTER_USER_OP_GRAD("cublas_fused_mlp") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - bool skip_final_activation = op.attr("skip_final_activation"); - int64_t weight_num = op.input_size("weights"); - - std::string last_bias_grad; - if (!skip_final_activation) { - // step1: use dy and final output to get last layer's relu grad. - user_op::UserOpConfWrapperBuilder relu_grad_builder(op.op_name() + "_relu_grad"); - user_op::UserOpConfWrapper relu_grad_op = - relu_grad_builder.Op("relu_grad") - .Input("y", op.output("out", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Build(); - AddOp(relu_grad_op); - last_bias_grad = relu_grad_op.output("dx", 0); - } else { - last_bias_grad = op.GetGradTensorWithOpOutput("out", 0); - } - - // step2: use reduce_sum to get last layer's bias grad. - // TODO: Currently Only support 2d fused_matmul. - // so here we hard encode bias reduce axis as 0. - std::vector reduce_axes_vec{0}; - user_op::UserOpConfWrapperBuilder bias_grad_builder(op.op_name() + "_bias_grad"); - user_op::UserOpConfWrapper bias_grad_op = bias_grad_builder.Op("reduce_sum") - .Input("input_tensor", last_bias_grad) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(bias_grad_op); - if (op.NeedGenGradTensor4OpInput("biases", weight_num - 1)) { - op.BindGradTensorWithOpInput(bias_grad_op.output("output_tensor", 0), "biases", - weight_num - 1); - } - std::string cublas_dy = last_bias_grad; - for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { - user_op::UserOpConfWrapperBuilder cublas_bias_add_relu_matmul_grad_builder( - op.op_name() + "_cublas_bias_add_relu_matmul_grad_" + std::to_string(hidden_layer_idx)); - user_op::UserOpConfWrapper cublas_bias_add_relu_matmul_grad_op = - cublas_bias_add_relu_matmul_grad_builder.Op("cublas_bias_add_relu_matmul_grad") - .Input("dy", cublas_dy) - .Input("weight", op.input("weights", hidden_layer_idx)) - .Input("aux", op.output("cublas_aux", hidden_layer_idx - 1)) - .Attr("alpha", 1.0) - .Output("d_grad") - .Output("d_bias") - .Build(); - AddOp(cublas_bias_add_relu_matmul_grad_op); - if (op.NeedGenGradTensor4OpInput("biases", hidden_layer_idx - 1)) { - op.BindGradTensorWithOpInput(cublas_bias_add_relu_matmul_grad_op.output("d_bias", 0), - "biases", - hidden_layer_idx - 1); // previous layers bias grad - } - - user_op::UserOpConfWrapperBuilder matmul_weight_grad_builder( - op.op_name() + "_matmul_a_grad_" + std::to_string(hidden_layer_idx)); - user_op::UserOpConfWrapper matmul_weight_grad_op = - matmul_weight_grad_builder.Op("matmul") - .Input("a", cublas_dy) - .Input("b", op.output("hidden", hidden_layer_idx - 1)) - .Output("out") - .Attr("transpose_a", true) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_weight_grad_op); - if (op.NeedGenGradTensor4OpInput("weights", hidden_layer_idx)) { - op.BindGradTensorWithOpInput(matmul_weight_grad_op.output("out", 0), "weights", - hidden_layer_idx); - } - // update dgrad - cublas_dy = cublas_bias_add_relu_matmul_grad_op.output("d_grad", 0); - } - - // For the first layer, we need to use 2 matmul to get grads. - std::string last_dy; - if (weight_num != 1) { last_dy = cublas_dy; } - // dx: - user_op::UserOpConfWrapperBuilder matmul_input_grad_builder(op.op_name() - + "_matmul_input_grad"); - user_op::UserOpConfWrapper matmul_input_grad_op = matmul_input_grad_builder.Op("matmul") - .Input("a", last_dy) - .Input("b", op.input("weights", 0)) - .Output("out") - .Attr("transpose_a", false) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_input_grad_op); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(matmul_input_grad_op.output("out", 0), "x", 0); - } - // dw: - user_op::UserOpConfWrapperBuilder matmul_weight_grad_builder(op.op_name() - + "_matmul_input_weight_grad"); - user_op::UserOpConfWrapper matmul_weight_grad_op = matmul_weight_grad_builder.Op("matmul") - .Input("a", last_dy) - .Input("b", op.input("x", 0)) - .Output("out") - .Attr("transpose_a", true) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_weight_grad_op); - if (op.NeedGenGradTensor4OpInput("weights", 0)) { - op.BindGradTensorWithOpInput(matmul_weight_grad_op.output("out", 0), "weights", 0); - } - - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/cum_ops.cpp b/oneflow/user/ops/cum_ops.cpp index 9e18afbd500e76be9f36cc65669bd7a1c07b53c7..be2cbb87fbdf1151ca7ea07eccccdee1615769ed 100644 --- a/oneflow/user/ops/cum_ops.cpp +++ b/oneflow/user/ops/cum_ops.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { Maybe CumsumOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("y", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } @@ -37,27 +37,12 @@ Maybe CumsumOp::GetSbp(user_op::SbpContext* ctx) { } Maybe CumsumOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("cumsum").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("cumsum_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("dim", op.attr("dim")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - Maybe CumProdOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("y", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } @@ -75,12 +60,12 @@ Maybe CumProdOp::GetSbp(user_op::SbpContext* ctx) { } Maybe CumProdOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe CumProdGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("dy", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("dy", 0)); return Maybe::Ok(); } @@ -90,31 +75,21 @@ Maybe CumProdGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { Maybe CumProdGradOp::GetSbp(user_op::SbpContext* ctx) { const auto& dy_tensor_desc = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); - for (auto i = 0; i < dy_tensor_desc.shape().NumAxes(); i++) { - ctx->NewBuilder().Split(user_op::OpArg("dy", 0), i).Split(user_op::OpArg("dx", 0), i).Build(); + auto dim = ctx->Attr("dim"); + for (auto i = dim + 1; i < dy_tensor_desc.shape().NumAxes(); i++) { + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), i) + .Split(user_op::OpArg("output", 0), i) + .Split(user_op::OpArg("input", 0), i) + .Split(user_op::OpArg("dx", 0), i) + .Build(); } return Maybe::Ok(); } Maybe CumProdGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("cumprod").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("cumprod_grad") - .Input("dy", op.GetGradTensorWithOpOutput("output", 0)) - .Output("dx") - .Attr("dim", op.attr("dim")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/data_shuffle_op.cpp b/oneflow/user/ops/data_shuffle_op.cpp index 9e422f62fe81d269609cc3c66056a58ed190728c..c0cdc1f5e607e2c9745d733f43ea75f363796569 100644 --- a/oneflow/user/ops/data_shuffle_op.cpp +++ b/oneflow/user/ops/data_shuffle_op.cpp @@ -33,10 +33,10 @@ namespace oneflow { CHECK_EQ_OR_RETURN(keys_shape.At(1), num_tables) << "keys cols must equal to num_tables"; } } - *ctx->OutputShape("num_unique", 0) = Shape({1}); - *ctx->OutputShape("unique_keys", 0) = Shape({keys_shape.elem_cnt()}); - *ctx->OutputShape("unique_values", 0) = Shape({keys_shape.elem_cnt()}); - *ctx->OutputShape("inverse_indices", 0) = keys_shape; + ctx->SetOutputShape("num_unique", 0, Shape({1})); + ctx->SetOutputShape("unique_keys", 0, Shape({keys_shape.elem_cnt()})); + ctx->SetOutputShape("unique_values", 0, Shape({keys_shape.elem_cnt()})); + ctx->SetOutputShape("inverse_indices", 0, keys_shape); return Maybe::Ok(); } @@ -49,13 +49,13 @@ namespace oneflow { } /* static */ Maybe UniqueKeyValuePairOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("num_unique", 0) = DataType::kInt32; - *ctx->OutputDType("unique_keys", 0) = ctx->InputDType("keys", 0); - *ctx->OutputDType("inverse_indices", 0) = DataType::kInt32; + ctx->SetOutputDType("num_unique", 0, DataType::kInt32); + ctx->SetOutputDType("unique_keys", 0, ctx->InputDType("keys", 0)); + ctx->SetOutputDType("inverse_indices", 0, DataType::kInt32); if (ctx->has_input("values", 0)) { - *ctx->OutputDType("unique_values", 0) = ctx->InputDType("values", 0); + ctx->SetOutputDType("unique_values", 0, ctx->InputDType("values", 0)); } else { - *ctx->OutputDType("unique_values", 0) = DataType::kInt32; + ctx->SetOutputDType("unique_values", 0, DataType::kUInt8); } return Maybe::Ok(); } @@ -75,12 +75,12 @@ namespace oneflow { } const int64_t num_ids = ids_shape.elem_cnt(); const int64_t parallel_num = ctx->parallel_num(); - *ctx->OutputShape("num_unique_matrix", 0) = Shape({parallel_num * parallel_num}); - *ctx->OutputShape("inverse_unique_partition_indices", 0) = ids_shape; - *ctx->OutputShape("cur_rank_num_unique", 0) = Shape({1}); - *ctx->OutputShape("cur_rank_unique_ids", 0) = Shape({num_ids * parallel_num}); - *ctx->OutputShape("cur_rank_inverse_indices", 0) = Shape({num_ids * parallel_num}); - *ctx->OutputShape("cur_rank_unique_table_ids", 0) = Shape({num_ids * parallel_num}); + ctx->SetOutputShape("num_unique_matrix", 0, Shape({parallel_num * parallel_num})); + ctx->SetOutputShape("inverse_unique_partition_indices", 0, ids_shape); + ctx->SetOutputShape("cur_rank_num_unique", 0, Shape({1})); + ctx->SetOutputShape("cur_rank_unique_ids", 0, Shape({num_ids * parallel_num})); + ctx->SetOutputShape("cur_rank_inverse_indices", 0, Shape({num_ids * parallel_num})); + ctx->SetOutputShape("cur_rank_unique_table_ids", 0, Shape({num_ids * parallel_num})); return Maybe::Ok(); } @@ -99,15 +99,15 @@ namespace oneflow { } /* static */ Maybe IdShuffleOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("num_unique_matrix", 0) = DataType::kUInt32; - *ctx->OutputDType("inverse_unique_partition_indices", 0) = DataType::kUInt32; - *ctx->OutputDType("cur_rank_num_unique", 0) = DataType::kUInt32; - *ctx->OutputDType("cur_rank_unique_ids", 0) = ctx->InputDType("ids", 0); - *ctx->OutputDType("cur_rank_inverse_indices", 0) = DataType::kUInt32; + ctx->SetOutputDType("num_unique_matrix", 0, DataType::kUInt32); + ctx->SetOutputDType("inverse_unique_partition_indices", 0, DataType::kUInt32); + ctx->SetOutputDType("cur_rank_num_unique", 0, DataType::kUInt32); + ctx->SetOutputDType("cur_rank_unique_ids", 0, ctx->InputDType("ids", 0)); + ctx->SetOutputDType("cur_rank_inverse_indices", 0, DataType::kUInt32); if (ctx->has_input("table_ids", 0)) { - *ctx->OutputDType("cur_rank_unique_table_ids", 0) = ctx->InputDType("table_ids", 0); + ctx->SetOutputDType("cur_rank_unique_table_ids", 0, ctx->InputDType("table_ids", 0)); } else { - *ctx->OutputDType("cur_rank_unique_table_ids", 0) = DataType::kUInt8; + ctx->SetOutputDType("cur_rank_unique_table_ids", 0, DataType::kUInt8); } return Maybe::Ok(); } @@ -118,16 +118,25 @@ namespace oneflow { const Shape& cur_rank_inverse_indices_shape = ctx->InputShape("cur_rank_inverse_indices", 0); const Shape& inverse_unique_partition_indices_shape = ctx->InputShape("inverse_unique_partition_indices", 0); + const int64_t embedding_size = ctx->Attr("embedding_size"); const int64_t num_ids = inverse_unique_partition_indices_shape.elem_cnt(); const int64_t parallel_num = ctx->parallel_num(); - CHECK_EQ_OR_RETURN(cur_rank_embeddings_shape.NumAxes(), 2); - CHECK_EQ_OR_RETURN(cur_rank_embeddings_shape.At(0), parallel_num * num_ids); - const int64_t embedding_size = cur_rank_embeddings_shape.At(1); + if (embedding::UseDynamicMemoryAllocation()) { + CHECK_EQ_OR_RETURN(cur_rank_embeddings_shape.elem_cnt(), 1) + << "if use dynamic memory allocation, cur_rank_embeddings elem_cnt should be 1."; + } else { + CHECK_EQ_OR_RETURN(cur_rank_embeddings_shape.NumAxes(), 2) + << "cur_rank_embeddings num_axes should be 2."; + CHECK_EQ_OR_RETURN(cur_rank_embeddings_shape.At(0), parallel_num * num_ids) + << " got " << cur_rank_embeddings_shape.At(0) << " and " << parallel_num * num_ids; + CHECK_EQ_OR_RETURN(embedding_size, cur_rank_embeddings_shape.At(1)) + << " got " << embedding_size << " and " << cur_rank_embeddings_shape.At(1); + } CHECK_EQ_OR_RETURN(num_unique_matrix_shape.elem_cnt(), parallel_num * parallel_num); CHECK_EQ_OR_RETURN(cur_rank_inverse_indices_shape.elem_cnt(), parallel_num * num_ids); DimVector out_dim_vec = inverse_unique_partition_indices_shape.dim_vec(); out_dim_vec.push_back(embedding_size); - *ctx->OutputShape("embeddings", 0) = Shape(out_dim_vec); + ctx->SetOutputShape("embeddings", 0, Shape(out_dim_vec)); return Maybe::Ok(); } @@ -136,11 +145,15 @@ namespace oneflow { } /* static */ Maybe EmbeddingShuffleOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Split(ctx->inputs(), 0) - .Broadcast(user_op::OpArg("num_unique_matrix", 0)) - .Split(ctx->outputs(), 0) - .Build(); + auto builder = ctx->NewBuilder() + .Split(ctx->inputs(), 0) + .Broadcast(user_op::OpArg("num_unique_matrix", 0)) + .Split(ctx->outputs(), 0); + if (embedding::UseDynamicMemoryAllocation()) { + builder.Broadcast(user_op::OpArg("cur_rank_embeddings", 0)).Build(); + } else { + builder.Split(user_op::OpArg("cur_rank_embeddings", 0), 0).Build(); + } return Maybe::Ok(); } @@ -148,7 +161,7 @@ namespace oneflow { CHECK_OR_RETURN(ctx->InputDType("num_unique_matrix", 0) == DataType::kUInt32); CHECK_OR_RETURN(ctx->InputDType("cur_rank_inverse_indices", 0) == DataType::kUInt32); CHECK_OR_RETURN(ctx->InputDType("inverse_unique_partition_indices", 0) == DataType::kUInt32); - *ctx->OutputDType("embeddings", 0) = ctx->InputDType("cur_rank_embeddings", 0); + ctx->SetOutputDType("embeddings", 0, ctx->InputDType("cur_rank_embeddings", 0)); return Maybe::Ok(); } @@ -167,7 +180,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(cur_rank_inverse_indices_shape.elem_cnt(), parallel_num * num_ids); DimVector out_dim_vec = cur_rank_inverse_indices_shape.dim_vec(); out_dim_vec.push_back(embedding_size); - *ctx->OutputShape("cur_rank_unique_embedding_grad", 0) = Shape(out_dim_vec); + ctx->SetOutputShape("cur_rank_unique_embedding_grad", 0, Shape(out_dim_vec)); return Maybe::Ok(); } @@ -189,7 +202,43 @@ namespace oneflow { CHECK_OR_RETURN(ctx->InputDType("num_unique_matrix", 0) == DataType::kUInt32); CHECK_OR_RETURN(ctx->InputDType("cur_rank_inverse_indices", 0) == DataType::kUInt32); CHECK_OR_RETURN(ctx->InputDType("inverse_unique_partition_indices", 0) == DataType::kUInt32); - *ctx->OutputDType("cur_rank_unique_embedding_grad", 0) = ctx->InputDType("embedding_grad", 0); + ctx->SetOutputDType("cur_rank_unique_embedding_grad", 0, ctx->InputDType("embedding_grad", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingGatherOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& in_shape = ctx->InputShape("in", 0); + const Shape& indices_shape = ctx->InputShape("indices", 0); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t num_ids = indices_shape.elem_cnt(); + const int64_t parallel_num = ctx->parallel_num(); + if (embedding::UseDynamicMemoryAllocation()) { + CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), 1) + << "if use dynamic memory allocation, in elem_cnt should be 1."; + } else { + CHECK_EQ_OR_RETURN(in_shape.NumAxes(), 2) << "in num_axes should be 2."; + CHECK_EQ_OR_RETURN(in_shape.At(0), parallel_num * num_ids) + << " got " << in_shape.At(0) << " and " << parallel_num * num_ids; + CHECK_EQ_OR_RETURN(embedding_size, in_shape.At(1)) + << " got " << embedding_size << " and " << in_shape.At(1); + } + DimVector out_dim_vec = indices_shape.dim_vec(); + out_dim_vec.push_back(embedding_size); + ctx->SetOutputShape("out", 0, Shape(out_dim_vec)); + return Maybe::Ok(); +} + +/*static*/ Maybe OneEmbeddingGatherOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe OneEmbeddingGatherOp::GetSbp(user_op::SbpContext* ctx) { + // Only used in parallel_num = 1. + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingGatherOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/deconv_op.cpp b/oneflow/user/ops/deconv_op.cpp index 43fd14bb16fbc142558a2157986549061d897c10..843bcff66d48282b45abf3b610058292b3638126 100644 --- a/oneflow/user/ops/deconv_op.cpp +++ b/oneflow/user/ops/deconv_op.cpp @@ -42,7 +42,7 @@ Maybe InferTensorDesc4DeConv(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(NDims, strides.size()); CHECK_EQ_OR_RETURN(NDims, output_padding.size()); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); DimVector out_shape(NDims + 2); out_shape.at(0) = in.shape().At(0); const size_t c_dim = data_format == "channels_first" ? 1 : NDims + 1; @@ -61,8 +61,8 @@ Maybe InferTensorDesc4DeConv(user_op::InferContext* ctx) { << "). Output size is too small"; } } - *out->mut_is_dynamic() = in.is_dynamic(); - *out->mut_shape() = Shape(out_shape); + out->set_is_dynamic(in.is_dynamic()); + out->set_shape(Shape(out_shape)); } { @@ -85,7 +85,7 @@ Maybe InferTensorDesc4DeConv(user_op::InferContext* ctx) { } Maybe InferDataType_(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -145,61 +145,6 @@ Maybe CheckAttr_(const user_op::UserOpDefWrapper& def, } } -Maybe GenerateBackwardOpConf4DeConv(const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - const std::string& data_format = op.attr("data_format"); - const auto& padding_before = op.attr>("padding_before"); - const auto& kernel_size = op.attr>("kernel_size"); - const auto& strides = op.attr>("strides"); - const auto& dilation_rate = op.attr>("dilation_rate"); - const Shape& weight_shape = op.TensorDesc4ArgNameAndIndex("weight", 0).shape(); - int32_t groups = op.attr("groups"); - - const int32_t ndims = kernel_size.size(); - CHECK_EQ_OR_RETURN(ndims, strides.size()); - CHECK_EQ_OR_RETURN(ndims, dilation_rate.size()); - - if (op.NeedGenGradTensor4OpInput("weight", 0)) { - auto filter_grad_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name() + "-FilterGrad") - .Op("conv_filter_grad") - .Input("dy", op.input("in", 0)) - .Input("x", op.GetGradTensorWithOpOutput("out", 0)) - .Output("filter_diff") - .Attr("num_spatial_dims", ndims) - .Attr>("padding_before", padding_before) - .Attr("data_format", data_format) - .Attr>("kernel_size", kernel_size) - .Attr>("strides", strides) - .Attr>("dilation_rate", dilation_rate) - .Attr("groups", groups) - .Build(); - op.BindGradTensorWithOpInput(filter_grad_op.output("filter_diff", 0), "weight", 0); - AddOp(filter_grad_op); - } - - if (op.NeedGenGradTensor4OpInput("in", 0)) { - std::string ndims_str = std::to_string(ndims); - auto data_grad_op = - user_op::UserOpConfWrapperBuilder("System-AutoGrad-" + op.op_name() + "-DataGrad") - .Op("conv" + ndims_str + "d") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("weight", op.input("weight", 0)) - .Output("out") - .Attr("filters", weight_shape.At(0)) - .Attr("data_format", data_format) - .Attr>("padding_before", padding_before) - .Attr>("kernel_size", kernel_size) - .Attr>("strides", strides) - .Attr>("dilation_rate", dilation_rate) - .Attr("groups", groups) - .Build(); - op.BindGradTensorWithOpInput(data_grad_op.output("out", 0), "in", 0); - AddOp(data_grad_op); - } - return Maybe::Ok(); -} - } // namespace /* static */ Maybe Deconv1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { @@ -265,8 +210,4 @@ Maybe GenerateBackwardOpConf4DeConv(const user_op::UserOpWrapper& op, return InferDataType_(ctx); } -REGISTER_USER_OP_GRAD("deconv1d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4DeConv); -REGISTER_USER_OP_GRAD("deconv2d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4DeConv); -REGISTER_USER_OP_GRAD("deconv3d").SetGenBackwardOpConfFn(GenerateBackwardOpConf4DeConv); - } // namespace oneflow diff --git a/oneflow/user/ops/deform_conv_op.cpp b/oneflow/user/ops/deform_conv_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b05fbdebbd2c16f13bc736287baaf61092dafa7b --- /dev/null +++ b/oneflow/user/ops/deform_conv_op.cpp @@ -0,0 +1,228 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe DeformConv2dOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& input_shape = ctx->InputShape("input", 0); + const Shape& weight_shape = ctx->InputShape("weight", 0); + const Shape& offset_shape = ctx->InputShape("offset", 0); + const Shape& mask_shape = ctx->InputShape("mask", 0); + const int32_t kW = weight_shape.at(3); + const int32_t kH = weight_shape.at(2); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const int32_t deformable_group = ctx->Attr("offset_groups"); + const bool use_mask = ctx->Attr("use_mask"); + bool has_bias = ctx->has_input("bias", 0); + if (has_bias) { + const Shape& bias_shape = ctx->InputShape("bias", 0); + std::cout << "bias_shape:" << bias_shape.ToString() << std::endl; + CHECK_EQ_OR_RETURN(bias_shape.At(0), weight_shape.At(0)); + } + CHECK_OR_RETURN(dW > 0 && dH > 0) + << Error::RuntimeError() << "The stride must be greater than 0,but got " << dW << " and " + << dH; + CHECK_OR_RETURN(kW > 0 && kH > 0) + << Error::RuntimeError() << "The weight must be greater than 0,but got " << kW << " and " + << kH; + + CHECK_OR_RETURN(padW >= 0 && padH >= 0) + << Error::RuntimeError() << "The pad must be greater than or equal to 0,but got " << padW + << " and " << padH; + CHECK_OR_RETURN(dilationW > 0 && dilationH > 0) + << Error::RuntimeError() << "The dilation must be greater than 0,but got " << dilationH + << " and " << dilationW; + + CHECK_EQ_OR_RETURN(input_shape.NumAxes(), 4); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(weight_shape.NumAxes(), 4); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(offset_shape.NumAxes(), 4); // NOLINT(maybe-need-error-msg) + if (use_mask) { CHECK_EQ_OR_RETURN(mask_shape.NumAxes(), 4); } // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(weight_shape.At(2), kH); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(weight_shape.At(3), kW); // NOLINT(maybe-need-error-msg) + + CHECK_EQ_OR_RETURN(offset_shape.At(1), deformable_group * 2 * kW * kH) + << Error::RuntimeError() << "offset.shape[1] is not valid: got: " << offset_shape.At(1) + << " ,expected: " << deformable_group * 2 * kW * kH; + + if (use_mask) { + CHECK_EQ_OR_RETURN(mask_shape.At(1), deformable_group * kW * kH) + << Error::RuntimeError() << "mask.shape[1] is not valid: got: " << mask_shape.At(1) + << " expected: " << deformable_group * kW * kH; + } + CHECK_EQ_OR_RETURN(offset_shape.At(0), input_shape.At(0)) + << Error::RuntimeError() << "invalid batch size of offset:got: " << offset_shape.At(0) + << " ,expected: " << input_shape.At(0); + + int64_t outputWidth = (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + int64_t outputHeight = (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + CHECK_OR_RETURN(outputWidth > 0 && outputHeight > 0) + << Error::RuntimeError() << "Calculated output size too small - out_h: " << outputHeight + << " ,out_w: " << outputWidth; + CHECK_OR_RETURN(offset_shape.At(2) == outputHeight && offset_shape.At(3) == outputWidth) + << Error::RuntimeError() << "invalid offset output dims: got ( " << offset_shape.At(2) << ", " + << offset_shape.At(3) << ")" + << ",expected: " + << "(" << outputHeight << ", " << outputWidth << ")"; + + if (use_mask) { + CHECK_OR_RETURN(mask_shape.At(2) == outputHeight && mask_shape.At(3) == outputWidth) + << Error::RuntimeError() << "invalid mask output dims: got ( " << mask_shape.At(2) << ", " + << mask_shape.At(3) << ")" + << ",expected: " + << "(" << outputHeight << ", " << outputWidth << ")"; + } + ctx->SetOutputShape("output", 0, + Shape({input_shape.At(0), weight_shape.At(0), outputHeight, outputWidth})); + ctx->SetOutputIsDynamic("output", 0, ctx->InputIsDynamic("input", 0)); + + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dInputGradOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const Shape& input_shape = ctx->InputShape("input", 0); + const Shape& weight_shape = ctx->InputShape("weight", 0); + const Shape& offset_shape = ctx->InputShape("offset", 0); + const Shape& output_grad_shape = ctx->InputShape("output_grad", 0); + const int32_t kW = weight_shape.at(3); + const int32_t kH = weight_shape.at(2); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + const bool use_mask = ctx->Attr("use_mask"); + CHECK_EQ_OR_RETURN(weight_shape.NumAxes(), 4); + + int64_t outputWidth = (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + int64_t outputHeight = (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + CHECK_EQ_OR_RETURN(output_grad_shape.At(2), outputHeight); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(output_grad_shape.At(3), outputWidth); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(output_grad_shape.At(1), weight_shape.At(0)); // NOLINT(maybe-need-error-msg) + ctx->SetOutputShape("input_grad", 0, ctx->InputShape("input", 0)); + ctx->SetOutputShape("offset_grad", 0, ctx->InputShape("offset", 0)); + ctx->SetOutputIsDynamic("input_grad", 0, ctx->InputIsDynamic("input", 0)); + ctx->SetOutputIsDynamic("offset_grad", 0, false); + + if (use_mask) { + ctx->SetOutputShape("mask_grad", 0, + Shape({offset_shape.At(0), offset_shape.At(1) / 2, offset_shape.At(2), + offset_shape.At(3)})); + ctx->SetOutputIsDynamic("mask_grad", 0, false); + } + return Maybe::Ok(); +} + +Maybe DeformConv2dParamGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& input_shape = ctx->InputShape("input", 0); + const Shape& output_grad_shape = ctx->InputShape("output_grad", 0); + const Shape& weight_shape = ctx->InputShape("weight", 0); + const int32_t kW = weight_shape.at(3); + const int32_t kH = weight_shape.at(2); + const int32_t dW = ctx->Attr("stride_w"); + const int32_t dH = ctx->Attr("stride_h"); + const int32_t padW = ctx->Attr("pad_w"); + const int32_t padH = ctx->Attr("pad_h"); + const int32_t dilationW = ctx->Attr("dilation_w"); + const int32_t dilationH = ctx->Attr("dilation_h"); + int64_t outputWidth = (input_shape.At(3) + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + int64_t outputHeight = (input_shape.At(2) + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + CHECK_EQ_OR_RETURN(output_grad_shape.At(2), outputHeight); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(output_grad_shape.At(3), outputWidth); // NOLINT(maybe-need-error-msg) + ctx->SetOutputShape("weight_grad", 0, ctx->InputShape("weight", 0)); + ctx->SetOutputIsDynamic("weight_grad", 0, false); + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe DeformConv2dInputGradOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe DeformConv2dParamGradOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe DeformConv2dOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dInputGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("input_grad", 0, ctx->InputDType("input", 0)); + ctx->SetOutputDType("offset_grad", 0, ctx->InputDType("offset", 0)); + const bool use_mask = ctx->Attr("use_mask"); + if (use_mask) { ctx->SetOutputDType("mask_grad", 0, ctx->InputDType("mask", 0)); } + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dParamGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("weight_grad", 0, ctx->InputDType("input", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("input", 0), 0) + .Split(user_op::OpArg("offset", 0), 0) + .Split(user_op::OpArg("mask", 0), 0) + .Broadcast(user_op::OpArg("weight", 0)) + .Split(user_op::OpArg("output", 0), 0) + .Build(); + + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dInputGradOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("output_grad", 0), 0) + .Split(user_op::OpArg("input", 0), 0) + .Split(user_op::OpArg("offset", 0), 0) + .Split(user_op::OpArg("mask", 0), 0) + .Broadcast(user_op::OpArg("weight", 0)) + .Split(user_op::OpArg("input_grad", 0), 0) + .Split(user_op::OpArg("offset_grad", 0), 0) + .Split(user_op::OpArg("mask_grad", 0), 0) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe DeformConv2dParamGradOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("output_grad", 0), 0) + .Broadcast(user_op::OpArg("weight", 0)) + .Split(user_op::OpArg("input", 0), 0) + .Split(user_op::OpArg("mask", 0), 0) + .Split(user_op::OpArg("offset", 0), 0) + .PartialSum(user_op::OpArg("weight_grad", 0)) + .Build(); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/diag_op.cpp b/oneflow/user/ops/diag_op.cpp index 3ea7d0ffd1d702a386cdf773fc30c9f88f9cd1d6..be331a45e06aa860140e6187bdee34b49bf48dcb 100644 --- a/oneflow/user/ops/diag_op.cpp +++ b/oneflow/user/ops/diag_op.cpp @@ -37,12 +37,13 @@ namespace oneflow { } else { out_dim_vec[0] = std::min(in_shape.At(0) + diagonal, in_shape.At(1)); } - CHECK_GT_OR_RETURN(out_dim_vec[0], 0); + // For 0-size Tensor. + CHECK_GE_OR_RETURN(out_dim_vec[0], 0); // NOLINT } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); out_desc->set_is_dynamic(false); - *out_desc->mut_shape() = Shape(out_dim_vec); + out_desc->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -56,15 +57,15 @@ namespace oneflow { } /* static */ Maybe DiagOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe DiagGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); const Shape& in_shape = in.shape(); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = Shape(in_shape.dim_vec()); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(Shape(in_shape.dim_vec())); return Maybe::Ok(); } @@ -78,25 +79,8 @@ namespace oneflow { } /* static */ Maybe DiagGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("diag").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) - -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("diag_grad") - .InputBind("in", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("diagonal", ctx->FwOp().attr("diagonal")) - .Output("dx") - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); -}); } // namespace oneflow diff --git a/oneflow/user/ops/diagonal_op.cpp b/oneflow/user/ops/diagonal_op.cpp index c7bed93b1726b4c76fa7fa3a1d1964eb775b0641..e8c4d698f95739644506cd108fa838f172bf7bd2 100644 --- a/oneflow/user/ops/diagonal_op.cpp +++ b/oneflow/user/ops/diagonal_op.cpp @@ -36,9 +36,9 @@ namespace oneflow { if (last_dim < 0) { last_dim = 0; } out_dim_vec.push_back(last_dim); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); out_desc->set_is_dynamic(false); - *out_desc->mut_shape() = Shape(out_dim_vec); + out_desc->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -52,15 +52,15 @@ namespace oneflow { } /* static */ Maybe DiagonalOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe DiagonalGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); const Shape& in_shape = in.shape(); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = Shape(in_shape.dim_vec()); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(Shape(in_shape.dim_vec())); return Maybe::Ok(); } @@ -74,27 +74,8 @@ namespace oneflow { } /* static */ Maybe DiagonalGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("diagonal") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("diagonal_grad") - .InputBind("in", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("offset", ctx->FwOp().attr("offset")) - .Output("dx") - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/dim_gather_op.cpp b/oneflow/user/ops/dim_gather_op.cpp index 4e9c23b663b87875ed598c847c46db41f4c9ad30..59537db0ebe1a7facf93f670c330727e75932688 100644 --- a/oneflow/user/ops/dim_gather_op.cpp +++ b/oneflow/user/ops/dim_gather_op.cpp @@ -37,8 +37,8 @@ namespace oneflow { CHECK_EQ_OR_RETURN(in.is_dynamic(), index.is_dynamic()); - user_op::TensorDesc* out = ctx->OutputTensorDesc("output", 0); - *out->mut_shape() = index.shape(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("output", 0); + out->set_shape(index.shape()); return Maybe::Ok(); } @@ -87,33 +87,9 @@ namespace oneflow { const user_op::TensorDesc& index = ctx->InputTensorDesc("index", 0); CHECK_OR_RETURN(IsIndexDataType(index.data_type())); const user_op::TensorDesc& in = ctx->InputTensorDesc("input", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("output", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("output", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("dim_gather") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto op_grad_name = ctx->FwOp().op_name() + "_grad"; - - ctx->DefineOp(op_grad_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder - .OpTypeName( - "dim_scatter_add_like") // dim_scatter_add_like(like, dim, index, src) -> output - .InputBind("index", ctx->FwOp().input("index", 0)) // scatter.index <- gather.index - .InputBind("src", - ctx->FwOp().output_grad("output", 0)) // scatter.src <- grad of gather.out - .InputBind("like", ctx->FwOp().input("input", 0)) - .Output("output") - .Attr("dim", ctx->FwOp().attr("dim")) - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("input", 0), - [&ctx, &op_grad_name]() -> const std::string& { - return ctx->GetOp(op_grad_name).output("output", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/dim_scatter_ops.cpp b/oneflow/user/ops/dim_scatter_ops.cpp index 60ef62837740c7446fd4d4968df5722bb6a5e5bd..f88287e909bb11e80e533b332720aecd4e04cea6 100644 --- a/oneflow/user/ops/dim_scatter_ops.cpp +++ b/oneflow/user/ops/dim_scatter_ops.cpp @@ -73,8 +73,8 @@ Maybe InferTensorDesc(user_op::InferContext* ctx) { CHECK_LE_OR_RETURN(index.shape().At(i), src.shape().At(i)); } - user_op::TensorDesc* out = ctx->OutputTensorDesc("output", 0); - *out->mut_shape() = input ? input->shape() : like->shape(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("output", 0); + out->set_shape(input ? input->shape() : like->shape()); return Maybe::Ok(); } @@ -96,8 +96,8 @@ Maybe InferScalarTensorDesc(user_op::InferContext* ctx) { CHECK_LE_OR_RETURN(index.shape().At(i), input.shape().At(i)); } - user_op::TensorDesc* out = ctx->OutputTensorDesc("output", 0); - *out->mut_shape() = input.shape(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("output", 0); + out->set_shape(input.shape()); return Maybe::Ok(); } @@ -181,59 +181,22 @@ Maybe InferDtype(user_op::InferContext* ctx) { const user_op::TensorDesc& index = ctx->InputTensorDesc("index", 0); CHECK_OR_RETURN(IsIndexDataType(index.data_type())); if (ctx->has_input("input", 0)) { - CHECK_EQ_OR_RETURN(ctx->InputDType("input", 0), ctx->InputDType("src", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("input", 0), ctx->InputDType("src", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("src", 0)) + << ", but got " << DataType_Name(ctx->InputDType("input", 0)); } else { - CHECK_EQ_OR_RETURN(ctx->InputDType("like", 0), ctx->InputDType("src", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("like", 0), ctx->InputDType("src", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("like", 0)) + << ", but got " << DataType_Name(ctx->InputDType("src", 0)); } - *ctx->OutputDType("output", 0) = ctx->InputDType("src", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("src", 0)); return Maybe::Ok(); } Maybe InferScalarDtype(user_op::InferContext* ctx) { const user_op::TensorDesc& index = ctx->InputTensorDesc("index", 0); CHECK_OR_RETURN(IsIndexDataType(index.data_type())); - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); - return Maybe::Ok(); -} - -Maybe ScatterBackward(user_op::BackwardOpConfContext* ctx) { - const user_op::TensorDesc& src = ctx->FwOp().TensorDesc4ArgNameAndIndex("src", 0); - const user_op::TensorDesc& index = ctx->FwOp().TensorDesc4ArgNameAndIndex("index", 0); - const int64_t ndim = src.shape().NumAxes(); - - FOR_RANGE(int64_t, i, 0, ndim) { - if (index.shape().At(i) != src.shape().At(i)) { - UNIMPLEMENTED() << "The backward pass is implemented only for src.shape == index.shape.\n"; - } - } - - const auto op_src_grad_name = ctx->FwOp().op_name() + "_src_grad"; - ctx->DefineOp(op_src_grad_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("dim_gather") - .InputBind("index", ctx->FwOp().input("index", 0)) - .InputBind("input", ctx->FwOp().output_grad("output", 0)) - .Output("output") - .Attr("dim", ctx->FwOp().attr("dim")) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("src", 0), - [&ctx, &op_src_grad_name]() -> const std::string& { - return ctx->GetOp(op_src_grad_name).output("output", 0); - }); - const auto op_input_grad_name = ctx->FwOp().op_name() + "_input_grad"; - ctx->DefineOp(op_input_grad_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("dim_scatter_update_scalar") - .InputBind("index", ctx->FwOp().input("index", 0)) - .InputBind("input", ctx->FwOp().output_grad("output", 0)) - .Output("output") - .Attr("dim", ctx->FwOp().attr("dim")) - .Attr("src_scalar", static_cast(0.0)) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("input", 0), - [&ctx, &op_input_grad_name]() -> const std::string& { - return ctx->GetOp(op_input_grad_name).output("output", 0); - }); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } @@ -304,28 +267,6 @@ Maybe ScatterBackward(user_op::BackwardOpConfContext* ctx) { return InferScalarDtype(ctx); \ } -#define REGISTER_SCATTER_GRAD(optypename) \ - REGISTER_USER_OP_GRAD(optypename).SetBackwardOpConfGenFn(ScatterBackward); - -#define REGISTER_SCATTER_SCALAR_GRAD(optypename) \ - REGISTER_USER_OP_GRAD(optypename) \ - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { \ - const auto op_input_grad_name = ctx->FwOp().op_name() + "_input_grad"; \ - ctx->DefineOp(op_input_grad_name, [&ctx](user_op::BackwardOpBuilder& builder) { \ - return builder.OpTypeName("dim_scatter_update_scalar") \ - .InputBind("index", ctx->FwOp().input("index", 0)) \ - .InputBind("input", ctx->FwOp().output_grad("output", 0)) \ - .Output("output") \ - .Attr("dim", ctx->FwOp().attr("dim")) \ - .Attr("src_scalar", static_cast(0.0)) \ - .Build(); \ - }); \ - ctx->FwOp().InputGradBind(user_op::OpArg("input", 0), \ - [&ctx, &op_input_grad_name]() -> const std::string& { \ - return ctx->GetOp(op_input_grad_name).output("output", 0); \ - }); \ - return Maybe::Ok(); \ - }); DEF_SCATTER_OP(DimScatterAddOp); DEF_SCATTER_OP(DimScatterUpdateOp); DEF_SCATTER_OP(DimScatterMulOp); @@ -334,9 +275,4 @@ DEF_SCATTER_SCALAR_OP(DimScatterUpdateScalarOp); DEF_SCATTER_SCALAR_OP(DimScatterAddScalarOp); DEF_SCATTER_SCALAR_OP(DimScatterMulScalarOp); -REGISTER_SCATTER_GRAD("dim_scatter_add"); -REGISTER_SCATTER_GRAD("dim_scatter_update"); - -REGISTER_SCATTER_SCALAR_GRAD("dim_scatter_update_scalar"); - } // namespace oneflow diff --git a/oneflow/user/ops/distributions/exponential_op.cpp b/oneflow/user/ops/distributions/exponential_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ecb9a3ad63d98b0a7f63d0670cc624f82da8a3f2 --- /dev/null +++ b/oneflow/user/ops/distributions/exponential_op.cpp @@ -0,0 +1,62 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/job/nd_sbp_util.h" + +namespace oneflow { + +/* static */ Maybe ExponentialOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& shape = ctx->Attr("out_shape"); + DimVector dim_vec; + if (shape.NumAxes() > 0) { + dim_vec.insert(dim_vec.end(), shape.dim_vec().cbegin(), shape.dim_vec().cend()); + } + ctx->SetOutputShape("out", 0, Shape(dim_vec)); + return Maybe::Ok(); +} + +/*static*/ Maybe ExponentialOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + const Shape& parallel_hierarchy = *ctx->parallel_desc().hierarchy(); + const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); + const Shape& logical_shape = ctx->Attr("out_shape"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const auto tensor_slice_view = + GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); + const Shape& physical_shape = tensor_slice_view.shape(); + + ctx->SetOutputShape("out", 0, physical_shape); + return Maybe::Ok(); +} + +/* static */ Maybe ExponentialOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe ExponentialOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { + SbpParallel default_sbp; + default_sbp.mutable_broadcast_parallel(); + return user_op::InferNdSbp4SrcOp(ctx, default_sbp); +} + +/* static */ Maybe ExponentialOp::InferDataType(user_op::InferContext* ctx) { + auto dtype = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, dtype); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/distributions/multinomial_with_replacement_op.cpp b/oneflow/user/ops/distributions/multinomial_with_replacement_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..aee591ce5a9b47dff858092c592076fb70d40265 --- /dev/null +++ b/oneflow/user/ops/distributions/multinomial_with_replacement_op.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe MultinomialWithReplacementOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + int32_t num_samples = ctx->Attr("num_samples"); + const Shape& x_shape = ctx->InputShape("x", 0); + if (x_shape.NumAxes() == 1) { + ctx->SetOutputShape("out", 0, Shape({num_samples})); + } else { + ctx->SetOutputShape("out", 0, Shape({x_shape.At(0), num_samples})); + } + return Maybe::Ok(); +} + +/*static*/ Maybe MultinomialWithReplacementOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultinomialWithReplacementOp::GetSbp(user_op::SbpContext* ctx) { + const Shape& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); + if (x_shape.NumAxes() == 2) { + ctx->NewBuilder().Split(ctx->inputs(), 0).Split(ctx->outputs(), 0).Build(); + } + return Maybe::Ok(); +} + +/* static */ Maybe MultinomialWithReplacementOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, DataType::kInt64); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/distributions/normal_op.cpp b/oneflow/user/ops/distributions/normal_op.cpp index 736a70e5d0b156fe199e3e732af5688aca9b831a..f0896e05c47074fbbb89eb3d3e69d6c91f313eb7 100644 --- a/oneflow/user/ops/distributions/normal_op.cpp +++ b/oneflow/user/ops/distributions/normal_op.cpp @@ -21,9 +21,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe NormalOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); - const Shape& shape = ctx->Attr("shape"); - *out_shape = shape; + ctx->SetOutputShape("out", 0, ctx->Attr("shape")); return Maybe::Ok(); } @@ -36,7 +34,7 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } @@ -53,7 +51,7 @@ namespace oneflow { /* static */ Maybe NormalOp::InferDataType(user_op::InferContext* ctx) { auto dtype = ctx->Attr("dtype"); - *ctx->OutputDType("out", 0) = dtype; + ctx->SetOutputDType("out", 0, dtype); return Maybe::Ok(); } diff --git a/oneflow/user/ops/distributions/uniform_int_op.cpp b/oneflow/user/ops/distributions/uniform_int_op.cpp index f01bb710f3cc6a6bcc8352cce9b9e814b7c00683..ba63e3888f07d75c0c042de6e7546b23632f515f 100644 --- a/oneflow/user/ops/distributions/uniform_int_op.cpp +++ b/oneflow/user/ops/distributions/uniform_int_op.cpp @@ -20,13 +20,12 @@ limitations under the License. namespace oneflow { /* static */ Maybe UniformIntOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); const Shape& shape = ctx->Attr("shape"); DimVector dim_vec; if (shape.NumAxes() > 0) { dim_vec.insert(dim_vec.end(), shape.dim_vec().cbegin(), shape.dim_vec().cend()); } - *out_shape = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -39,7 +38,7 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } @@ -56,7 +55,7 @@ namespace oneflow { /* static */ Maybe UniformIntOp::InferDataType(user_op::InferContext* ctx) { auto dtype = ctx->Attr("dtype"); - *ctx->OutputDType("out", 0) = dtype; + ctx->SetOutputDType("out", 0, dtype); return Maybe::Ok(); } diff --git a/oneflow/user/ops/distributions/uniform_op.cpp b/oneflow/user/ops/distributions/uniform_op.cpp index b7d566aac49c2f3b96ffb384d9339dc5351aaeaf..1de90cb178fb4218b3cb22909df3ed4ac2c8b313 100644 --- a/oneflow/user/ops/distributions/uniform_op.cpp +++ b/oneflow/user/ops/distributions/uniform_op.cpp @@ -20,13 +20,12 @@ limitations under the License. namespace oneflow { /* static */ Maybe UniformOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); const Shape& shape = ctx->Attr("shape"); DimVector dim_vec; if (shape.NumAxes() > 0) { dim_vec.insert(dim_vec.end(), shape.dim_vec().cbegin(), shape.dim_vec().cend()); } - *out_shape = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -39,12 +38,16 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } /* static */ Maybe UniformOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + const Shape& logical_shape = ctx->Attr("shape"); + int64_t num_axes = logical_shape.NumAxes(); + for (int i = 0; i < num_axes; ++i) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), i).Build(); + } return Maybe::Ok(); } @@ -56,8 +59,13 @@ namespace oneflow { /* static */ Maybe UniformOp::InferDataType(user_op::InferContext* ctx) { auto dtype = ctx->Attr("dtype"); - *ctx->OutputDType("out", 0) = dtype; + ctx->SetOutputDType("out", 0, dtype); return Maybe::Ok(); } +/* static */ Maybe UniformOp::DumpNdSbpSignatureForOpConfFn(const NdSbpSignature& nd_sbp_sig, + OperatorConf* op_conf) { + return user_op::SetSrcOpNdSbp(nd_sbp_sig, "out_0", op_conf); +} + } // namespace oneflow diff --git a/oneflow/user/ops/dot_op.cpp b/oneflow/user/ops/dot_op.cpp index 080a8cff539445cb0629e5154bcf78c316f510d6..b91b8f752fb11f9c783288056a4a8dc08ae5125c 100644 --- a/oneflow/user/ops/dot_op.cpp +++ b/oneflow/user/ops/dot_op.cpp @@ -28,7 +28,7 @@ namespace oneflow { CHECK_OR_RETURN(x.shape().NumAxes() == 1) << Error::RuntimeError() << "1D tensors expected, but got " << x.shape().NumAxes() << "D tensors"; - *ctx->OutputShape("out", 0) = Shape({}); + ctx->SetOutputShape("out", 0, Shape({})); return Maybe::Ok(); } @@ -52,36 +52,8 @@ namespace oneflow { CHECK_OR_RETURN(x.data_type() == y.data_type()) << Error::RuntimeError() << "expected both vectors to have same dtype, but found " << DataType_Name(x.data_type()) << " and " << DataType_Name(y.data_type()); - *ctx->OutputDType("out", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("dot").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_mul") - .Input("x", op.input("y", 0)) - .Input("scalar", op.GetGradTensorWithOpOutput("out", 0)) - .Output("y") - .Build(); - - op.BindGradTensorWithOpInput(grad_op.output("y", 0), "x", 0); - } - - if (op.NeedGenGradTensor4OpInput("y", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_mul") - .Input("x", op.input("x", 0)) - .Input("scalar", op.GetGradTensorWithOpOutput("out", 0)) - .Output("y") - .Build(); - - op.BindGradTensorWithOpInput(grad_op.output("y", 0), "y", 0); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/dropout_op.cpp b/oneflow/user/ops/dropout_op.cpp index c23d2ef28af21f2b76d95aeedf1ad759e1db126b..10f8b384ed7a724d8767d221d32f20456f69e115 100644 --- a/oneflow/user/ops/dropout_op.cpp +++ b/oneflow/user/ops/dropout_op.cpp @@ -20,9 +20,9 @@ namespace oneflow { /* static */ Maybe DropoutOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - *ctx->OutputShape("out", 0) = in_shape; - *ctx->OutputShape("mask", 0) = in_shape; - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, in_shape); + ctx->SetOutputShape("mask", 0, in_shape); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -46,15 +46,15 @@ namespace oneflow { } /* static */ Maybe DropoutOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - *ctx->OutputDType("mask", 0) = DataType::kBool; + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + ctx->SetOutputDType("mask", 0, DataType::kBool); return Maybe::Ok(); } /* static */ Maybe DropoutGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - *ctx->OutputShape("dx", 0) = dy_shape; - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("dy", 0); + ctx->SetOutputShape("dx", 0, dy_shape); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("dy", 0)); CHECK_EQ_OR_RETURN(ctx->InputShape("mask", 0), dy_shape); return Maybe::Ok(); } @@ -83,13 +83,15 @@ namespace oneflow { } /* static */ Maybe DropoutGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); - CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kBool) << ", but got " + << DataType_Name(ctx->InputDType("mask", 0)); return Maybe::Ok(); } /* static */ Maybe RandomMaskLikeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("like", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("like", 0)); return Maybe::Ok(); } @@ -117,28 +119,8 @@ namespace oneflow { } /* static */ Maybe RandomMaskLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kBool; + ctx->SetOutputDType("out", 0, DataType::kBool); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("dropout").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - const float rate = op.attr("rate"); - float scale = 0.0f; // When dropout rate = 1.0, we set scale as zero. - if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } - user_op::UserOpConfWrapper dropout_grad_op = - builder.Op("dropout_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("mask", op.output("mask", 0)) - .Output("dx") - .Attr("scale", scale) - .Build(); - op.BindGradTensorWithOpInput(dropout_grad_op.output("dx", 0), "in", 0); - AddOp(dropout_grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/eager_b_to_s_op.cpp b/oneflow/user/ops/eager_b_to_s_op.cpp index c4727500c28e20f14fa05975331b929fe8b2be53..c3bd6ff8b99870a81991f62883ead5b9085a4488 100644 --- a/oneflow/user/ops/eager_b_to_s_op.cpp +++ b/oneflow/user/ops/eager_b_to_s_op.cpp @@ -24,7 +24,7 @@ limitations under the License. namespace oneflow { -// Can only be called in mirrored TODO: move this comment to ods +// Can only be called in local TODO: move this comment to ods /* static */ Maybe EagerBToSOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& shape = ctx->Attr("shape"); const std::string& out_parallel_conf_txt = ctx->Attr("out_parallel_conf"); @@ -39,7 +39,7 @@ namespace oneflow { int64_t parallel_id = opt_parallel_id->value_or(0); dim_vec[out_split_axis] = bs.At(parallel_id).size(); } - *ctx->OutputShape("out", 0) = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -48,21 +48,21 @@ namespace oneflow { } /* static */ Maybe EagerBToSOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerBToSOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerBToSOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerBToSOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_nccl_ops.cpp b/oneflow/user/ops/eager_nccl_ops.cpp index bd4cdda136758d8a4ff5ca653f707c4a0810149d..f3755048dab1c262cbe80cfc5c836f197e4384d9 100644 --- a/oneflow/user/ops/eager_nccl_ops.cpp +++ b/oneflow/user/ops/eager_nccl_ops.cpp @@ -16,6 +16,7 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/common/balanced_splitter.h" #include "oneflow/core/common/decorator.h" +#include "oneflow/core/common/container_util.h" #include "oneflow/core/framework/device.h" #include "oneflow/user/ops/comm_net_device_infer_util.h" #include "oneflow/core/framework/op_generated.h" @@ -23,54 +24,61 @@ limitations under the License. namespace oneflow { -/* static */ Maybe EagerNcclAllReduceOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); +/* static */ Maybe EagerCclAllReduceOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } -/*static*/ Maybe EagerNcclAllReduceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe EagerCclAllReduceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe EagerNcclAllReduceOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe EagerCclAllReduceOp::GetSbp(user_op::SbpContext* ctx) { ctx->NewBuilder().PartialSum(user_op::OpArg("in", 0)).Broadcast(user_op::OpArg("out", 0)).Build(); return Maybe::Ok(); } -/* static */ Maybe EagerNcclAllReduceOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); +/* static */ Maybe EagerCclAllReduceOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -/* static */ Maybe> EagerNcclAllReduceOp::InferDeviceAndStream( +/* static */ Maybe> EagerCclAllReduceOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&IsAsyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } -/* static */ Maybe EagerNcclBroadcastOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); +/* static */ Maybe EagerCclBroadcastOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + size_t size = ctx->input_size("in"); + const std::vector& shape_list = ctx->Attr>("shape_list"); + CHECK_EQ_OR_RETURN(size, ctx->output_size("out")) + << "the size of input tensor tuple should equal the size of output tensor tuple."; + for (int i = 0; i < size; ++i) { ctx->SetOutputShape("out", i, JUST(VectorAt(shape_list, i))); } return Maybe::Ok(); } -/*static*/ Maybe EagerNcclBroadcastOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe EagerCclBroadcastOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe EagerNcclBroadcastOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().PartialSum(user_op::OpArg("in", 0)).Broadcast(user_op::OpArg("out", 0)).Build(); - ctx->NewBuilder().Broadcast(user_op::OpArg("in", 0)).Broadcast(user_op::OpArg("out", 0)).Build(); - ctx->NewBuilder().Split(user_op::OpArg("in", 0), 0).Broadcast(user_op::OpArg("out", 0)).Build(); +/* static */ Maybe EagerCclBroadcastOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().PartialSum(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + ctx->NewBuilder().Split(ctx->inputs(), 0).Broadcast(ctx->outputs()).Build(); return Maybe::Ok(); } -/* static */ Maybe EagerNcclBroadcastOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); +/* static */ Maybe EagerCclBroadcastOp::InferDataType(user_op::InferContext* ctx) { + size_t size = ctx->input_size("in"); + CHECK_EQ_OR_RETURN(size, ctx->output_size("out")) + << "the size of input tensor tuple should equal the size of output tensor tuple."; + for (int i = 0; i < size; ++i) { ctx->SetOutputDType("out", i, ctx->InputDType("in", i)); } return Maybe::Ok(); } -/* static */ Maybe> EagerNcclBroadcastOp::InferDeviceAndStream( +/* static */ Maybe> EagerCclBroadcastOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&IsAsyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe EagerNcclTouchOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { @@ -92,62 +100,60 @@ namespace oneflow { /* static */ Maybe> EagerNcclTouchOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&IsAsyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } -/* static */ Maybe EagerNcclReduceOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); +/* static */ Maybe EagerCclReduceOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } -/*static*/ Maybe EagerNcclReduceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe EagerCclReduceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe EagerNcclReduceOp::GetSbp(user_op::SbpContext* ctx) { - UNIMPLEMENTED_THEN_RETURN() << "consistent tensor are not supported"; +/* static */ Maybe EagerCclReduceOp::GetSbp(user_op::SbpContext* ctx) { + UNIMPLEMENTED_THEN_RETURN() << "global tensor are not supported"; } -/* static */ Maybe EagerNcclReduceOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); +/* static */ Maybe EagerCclReduceOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -/* static */ Maybe> EagerNcclReduceOp::InferDeviceAndStream( +/* static */ Maybe> EagerCclReduceOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } -/* static */ Maybe EagerNcclReduceScatterOp::InferLogicalTensorDesc( +/* static */ Maybe EagerCclReduceScatterOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } -/* static */ Maybe EagerNcclReduceScatterOp::InferPhysicalTensorDesc( +/* static */ Maybe EagerCclReduceScatterOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - const int64_t& parallel_num = ctx->parallel_ctx().parallel_num(); - if (parallel_num > 1) { - const Shape& parallel_hierarchy = *ctx->parallel_desc().hierarchy(); - const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); - const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); - const auto tensor_slice_view = - GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, in_shape, parallel_id); - const Shape& physical_shape = tensor_slice_view.shape(); - *out_shape = physical_shape; - } else { - *out_shape = in_shape; - } + const auto& input_shape = ctx->InputShape("in", 0); + const auto& shape = ctx->Attr("output_shape"); + Symbol parallel_desc = + JUST(TxtStringToPlacement(ctx->Attr("parallel_conf"))); + CHECK_EQ_OR_RETURN(input_shape.elem_cnt(), shape.elem_cnt() * parallel_desc->parallel_num()) + << Error::RuntimeError() + << "output tensor size must be equal to world_size times input tensor size"; + CHECK_EQ_OR_RETURN(ctx->InputDType("in", 0), ctx->Attr("output_dtype")) + << Error::RuntimeError() << "output tensor must have the same type as input tensor"; + ctx->SetOutputShape("out", 0, ctx->Attr("output_shape")); + ctx->SetOutputDType("out", 0, ctx->Attr("output_dtype")); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } -/* static */ Maybe EagerNcclReduceScatterOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe EagerCclReduceScatterOp::GetSbp(user_op::SbpContext* ctx) { return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); } -/* static */ Maybe EagerNcclReduceScatterOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { +/* static */ Maybe EagerCclReduceScatterOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { const NdSbp& in_dis_hint = ctx->NdSbpHint4InputArgNameAndIndex("in", 0); NdSbp* in_nd_sbp = ctx->NdSbp4ArgNameAndIndex("in", 0); NdSbp* out_nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); @@ -168,31 +174,44 @@ namespace oneflow { return Maybe::Ok(); } -/* static */ Maybe EagerNcclReduceScatterOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); +/* static */ Maybe EagerCclReduceScatterOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -/* static */ Maybe> EagerNcclReduceScatterOp::InferDeviceAndStream( +/* static */ Maybe> EagerCclReduceScatterOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } -/* static */ Maybe EagerNcclAllGatherOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); +/* static */ Maybe EagerCclAllGatherOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } -/*static*/ Maybe EagerNcclAllGatherOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); +/*static*/ Maybe EagerCclAllGatherOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + const auto& input_shape = ctx->InputShape("in", 0); + const auto& shape = ctx->Attr("output_shape"); + Symbol parallel_desc = + JUST(TxtStringToPlacement(ctx->Attr("parallel_conf"))); + CHECK_EQ_OR_RETURN(input_shape.elem_cnt() * parallel_desc->parallel_num(), shape.elem_cnt()) + << Error::RuntimeError() + << "output tensor size must be equal to world_size times input tensor size"; + CHECK_EQ_OR_RETURN(ctx->InputDType("in", 0), ctx->Attr("output_dtype")) + << Error::RuntimeError() << Error::RuntimeError() + << "output tensor must have the same type as input tensor"; + ctx->SetOutputShape("out", 0, ctx->Attr("output_shape")); + ctx->SetOutputDType("out", 0, ctx->Attr("output_dtype")); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); + return Maybe::Ok(); } -/* static */ Maybe EagerNcclAllGatherOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe EagerCclAllGatherOp::GetSbp(user_op::SbpContext* ctx) { return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); } -/* static */ Maybe EagerNcclAllGatherOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { +/* static */ Maybe EagerCclAllGatherOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { const NdSbp& in_dis_hint = ctx->NdSbpHint4InputArgNameAndIndex("in", 0); NdSbp* in_nd_sbp = ctx->NdSbp4ArgNameAndIndex("in", 0); NdSbp* out_nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); @@ -215,19 +234,19 @@ namespace oneflow { return Maybe::Ok(); } -/* static */ Maybe EagerNcclAllGatherOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); +/* static */ Maybe EagerCclAllGatherOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -/* static */ Maybe> EagerNcclAllGatherOp::InferDeviceAndStream( +/* static */ Maybe> EagerCclAllGatherOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe EagerNcclS2sOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -261,13 +280,13 @@ namespace oneflow { } /* static */ Maybe EagerNcclS2sOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerNcclS2sOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_p_to_b_op.cpp b/oneflow/user/ops/eager_p_to_b_op.cpp index 92f2e35c0a57173c88aff5f395c613857e016b56..a405c2608fd69ef2a610d094e0b59523e172d82a 100644 --- a/oneflow/user/ops/eager_p_to_b_op.cpp +++ b/oneflow/user/ops/eager_p_to_b_op.cpp @@ -22,9 +22,9 @@ limitations under the License. #include "oneflow/core/framework/op_generated.h" namespace oneflow { -// Can only be called in mirrored +// Can only be called in local /* static */ Maybe EagerPToBOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape(ctx->Attr("shape").dim_vec()); + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); return Maybe::Ok(); } @@ -33,21 +33,21 @@ namespace oneflow { } /* static */ Maybe EagerPToBOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_s_to_b op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_s_to_b op doesn't support global tensor!"; } /* static */ Maybe EagerPToBOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_s_to_b op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_s_to_b op doesn't support global tensor!"; } /* static */ Maybe EagerPToBOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerPToBOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_p_to_s_op.cpp b/oneflow/user/ops/eager_p_to_s_op.cpp index 8f51b51b68e1d04bdc4f10f218168f35057d9368..808a3c4f81ce941c98d90fcf7454c909674ad63e 100644 --- a/oneflow/user/ops/eager_p_to_s_op.cpp +++ b/oneflow/user/ops/eager_p_to_s_op.cpp @@ -38,7 +38,7 @@ namespace oneflow { int64_t parallel_id = opt_parallel_id->value_or(0); dim_vec[out_split_axis] = bs.At(parallel_id).size(); } - *ctx->OutputShape("out", 0) = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -47,21 +47,21 @@ namespace oneflow { } /* static */ Maybe EagerPToSOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerPToSOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerPToSOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerPToSOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_s_to_b_op.cpp b/oneflow/user/ops/eager_s_to_b_op.cpp index 595a2f686e53db1ddce12b94c2d0660ae7b98a2e..a4291c40903a7e63675797a0119f560a2d7887af 100644 --- a/oneflow/user/ops/eager_s_to_b_op.cpp +++ b/oneflow/user/ops/eager_s_to_b_op.cpp @@ -24,7 +24,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe EagerSToBOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape(ctx->Attr("shape").dim_vec()); + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); return Maybe::Ok(); } @@ -33,21 +33,21 @@ namespace oneflow { } /* static */ Maybe EagerSToBOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_s_to_b op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_s_to_b op doesn't support global tensor!"; } /* static */ Maybe EagerSToBOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_s_to_b op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_s_to_b op doesn't support global tensor!"; } /* static */ Maybe EagerSToBOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerSToBOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_s_to_p_op.cpp b/oneflow/user/ops/eager_s_to_p_op.cpp index 09cd9b33fa995539eb4107be9a990d4ec192a173..274a1bc61c1a5a162ffc457767c79dd4a5d70773 100644 --- a/oneflow/user/ops/eager_s_to_p_op.cpp +++ b/oneflow/user/ops/eager_s_to_p_op.cpp @@ -24,7 +24,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe EagerSToPOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape(ctx->Attr("shape").dim_vec()); + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); return Maybe::Ok(); } @@ -33,21 +33,21 @@ namespace oneflow { } /* static */ Maybe EagerSToPOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerSToPOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_b_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_b_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerSToPOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerSToPOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_s_to_s_op.cpp b/oneflow/user/ops/eager_s_to_s_op.cpp index 280fe7e6ca4ef2dce7d5d73cf50d753cdcc3db87..c5d867c2b63e78957c86a158f450a491bb9cd53d 100644 --- a/oneflow/user/ops/eager_s_to_s_op.cpp +++ b/oneflow/user/ops/eager_s_to_s_op.cpp @@ -38,7 +38,7 @@ namespace oneflow { int64_t parallel_id = opt_parallel_id->value_or(0); dim_vec[out_split_axis] = bs.At(parallel_id).size(); } - *ctx->OutputShape("out", 0) = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -47,21 +47,21 @@ namespace oneflow { } /* static */ Maybe EagerNaiveSToSOp::GetSbp(user_op::SbpContext* ctx) { - return Error::TypeError() << "eager_naive_s_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_naive_s_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerNaiveSToSOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { - return Error::TypeError() << "eager_naive_s_to_s op doesn't support consistent tensor!"; + return Error::TypeError() << "eager_naive_s_to_s op doesn't support global tensor!"; } /* static */ Maybe EagerNaiveSToSOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerNaiveSToSOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/eager_symmetric_s_to_p_op.cpp b/oneflow/user/ops/eager_symmetric_s_to_p_op.cpp index 1767d96e9f41a6ec818202d20a6d2189aea289af..9908257d2959b45ebff2bb6b125f7e0602436b91 100644 --- a/oneflow/user/ops/eager_symmetric_s_to_p_op.cpp +++ b/oneflow/user/ops/eager_symmetric_s_to_p_op.cpp @@ -22,7 +22,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe EagerSymmetricSToPOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -65,13 +65,13 @@ namespace oneflow { } /* static */ Maybe EagerSymmetricSToPOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> EagerSymmetricSToPOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/elementwise_maximum_minimum_ops.cpp b/oneflow/user/ops/elementwise_maximum_minimum_ops.cpp index 7a143bb4ecd22cfe96eded2a638375136e0a80d3..4ee596a944d9d163a0038af5a08520abb5c27a70 100644 --- a/oneflow/user/ops/elementwise_maximum_minimum_ops.cpp +++ b/oneflow/user/ops/elementwise_maximum_minimum_ops.cpp @@ -47,59 +47,28 @@ Maybe InferTensorDesc_(InferContext* ctx) { CHECK_EQ_OR_RETURN(tensor_x.shape().At(i), tensor_y.shape().At(i)); } - TensorDesc* tensor_dx = ctx->OutputTensorDesc("dx", 0); - TensorDesc* tensor_dy = ctx->OutputTensorDesc("dy", 0); + TensorDesc* tensor_dx = ctx->MutOutputTensorDesc("dx", 0); + TensorDesc* tensor_dy = ctx->MutOutputTensorDesc("dy", 0); - if (tensor_dx) { *tensor_dx->mut_shape() = tensor_x.shape(); } + if (tensor_dx) { tensor_dx->set_shape(tensor_x.shape()); } - if (tensor_dy) { *tensor_dy->mut_shape() = tensor_y.shape(); } + if (tensor_dy) { tensor_dy->set_shape(tensor_y.shape()); } return Maybe::Ok(); } Maybe InferDataType_(InferContext* ctx) { const TensorDesc& tensor_dz = ctx->InputTensorDesc("dz", 0); - TensorDesc* tensor_dx = ctx->OutputTensorDesc("dx", 0); - TensorDesc* tensor_dy = ctx->OutputTensorDesc("dy", 0); + TensorDesc* tensor_dx = ctx->MutOutputTensorDesc("dx", 0); + TensorDesc* tensor_dy = ctx->MutOutputTensorDesc("dy", 0); - if (tensor_dx) { *tensor_dx->mut_data_type() = tensor_dz.data_type(); } + if (tensor_dx) { tensor_dx->set_data_type(tensor_dz.data_type()); } - if (tensor_dy) { *tensor_dy->mut_data_type() = tensor_dz.data_type(); } + if (tensor_dy) { tensor_dy->set_data_type(tensor_dz.data_type()); } return Maybe::Ok(); } -user_op::BackwardOpConfGenFn MakeGenBackwardOpFn(const std::string& op_type_name) { - return [=](user_op::BackwardOpConfContext* ctx) -> Maybe { - const bool x_need_grad = ctx->FwOp().NeedGenGradTensor4OpInput("x", 0); - const bool y_need_grad = ctx->FwOp().NeedGenGradTensor4OpInput("y", 0); - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - - auto BuildGradOp = [&](user_op::BackwardOpBuilder& builder) -> user_op::UserOpConfWrapper { - builder.OpTypeName(op_type_name + "_backward") - .InputBind("dz", ctx->FwOp().output_grad("z", 0)) - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("y", ctx->FwOp().input("y", 0)); - if (x_need_grad) { builder.Output("dx"); } - if (y_need_grad) { builder.Output("dy"); } - return builder.Build(); - }; - ctx->DefineOp(grad_op_name, BuildGradOp); - if (x_need_grad) { - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), [&]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dx", 0); - }); - } - - if (y_need_grad) { - ctx->FwOp().InputGradBind(user_op::OpArg("y", 0), [&]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dy", 0); - }); - } - return Maybe::Ok(); - }; -} - } // namespace #define DEF_ELEMENTWISE_XIMUM_FW_OP(op_class_name_prefix) \ @@ -141,14 +110,9 @@ user_op::BackwardOpConfGenFn MakeGenBackwardOpFn(const std::string& op_type_name return InferDataType_(ctx); \ } -#define REGISTER_ELEMENTWISE_XIMUM_GRAD(op_type_name) \ - REGISTER_USER_OP_GRAD(op_type_name) \ - .SetBackwardOpConfGenFn(MakeGenBackwardOpFn(std::string(op_type_name))); - #define REGISTER_ELEMENTWISE_XIMUM_OP(op_type_name, op_class_name_prefix) \ DEF_ELEMENTWISE_XIMUM_FW_OP(op_class_name_prefix); \ - DEF_ELEMENTWISE_XIMUM_BW_OP(op_class_name_prefix); \ - REGISTER_ELEMENTWISE_XIMUM_GRAD(op_type_name); + DEF_ELEMENTWISE_XIMUM_BW_OP(op_class_name_prefix); REGISTER_ELEMENTWISE_XIMUM_OP("elementwise_maximum", ElementwiseMaximum); REGISTER_ELEMENTWISE_XIMUM_OP("elementwise_minimum", ElementwiseMinimum); diff --git a/oneflow/user/ops/elu_op.cpp b/oneflow/user/ops/elu_op.cpp index 9de85d3465562e0910766d2441e31c840c864d99..6e5547b2c2f9517e864f7f9f8f94a50c1cbe0df6 100644 --- a/oneflow/user/ops/elu_op.cpp +++ b/oneflow/user/ops/elu_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe EluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe EluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe EluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,27 +65,11 @@ namespace oneflow { } /* static */ Maybe EluGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("elu").SetBackwardOpConfGenFn( - [](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto elu_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(elu_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("elu_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("alpha", ctx->FwOp().attr("alpha")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &elu_grad_op_name]() -> const std::string& { - return ctx->GetOp(elu_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/embedding_op.cpp b/oneflow/user/ops/embedding_op.cpp index 5d124cac67481edb8ef985ee6239f35f853864c6..f5264c1d4a714b3c834acd2a3d640f38f980268b 100644 --- a/oneflow/user/ops/embedding_op.cpp +++ b/oneflow/user/ops/embedding_op.cpp @@ -20,7 +20,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe EmbeddingRenormOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -33,7 +33,7 @@ namespace oneflow { } /*static*/ Maybe EmbeddingRenormOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -46,8 +46,8 @@ namespace oneflow { indices_shape.dim_vec().cend()); out_dim_vec.push_back(weight_shape.At(1)); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = Shape(out_dim_vec); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -73,7 +73,7 @@ namespace oneflow { } /*static*/ Maybe EmbeddingOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("weight", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("weight", 0)); return Maybe::Ok(); } @@ -87,8 +87,8 @@ namespace oneflow { /* static */ Maybe EmbeddingGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& weight_shape = ctx->InputShape("weight", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = weight_shape; + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(weight_shape); return Maybe::Ok(); } @@ -125,27 +125,10 @@ namespace oneflow { /*static*/ Maybe EmbeddingGradOp::InferDataType(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(ctx->InputDType("weight", 0), ctx->InputDType("dy", 0)) - << "input grad has different type with weight"; - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("weight", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("embedding") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto embedding_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(embedding_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("embedding_grad") - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .InputBind("weight", ctx->FwOp().input("weight", 0)) - .InputBind("indices", ctx->FwOp().input("indices", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("weight", 0), - [&ctx, &embedding_grad_op_name]() -> const std::string& { - return ctx->GetOp(embedding_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/empty_op.cpp b/oneflow/user/ops/empty_op.cpp index 92582ad145d475279b42d5f15ae45c32d89decd0..8bca331f787e50f27c3798f97dbd205045e39c82 100644 --- a/oneflow/user/ops/empty_op.cpp +++ b/oneflow/user/ops/empty_op.cpp @@ -30,16 +30,16 @@ Maybe> MakeEmptyStream(const Symbol& out_device, const bo << "empty op only support pin_memory in cpu device but got " << out_device->type(); // TODO:(zhaoluyang) Parsing pin-memory-device from python auto pin_device = JUST(Device::New("cuda")); - return Stream::New(pin_device, StreamRole::kPinnedCompute); + return Stream::New(pin_device, StreamType::kPinnedCompute); } - return Stream::New(out_device, StreamRole::kCompute); + return Stream::New(out_device, StreamType::kCompute); } } // namespace /* static */ Maybe EmptyOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape(ctx->Attr("shape").dim_vec()); - *ctx->OutputStride("out", 0) = Stride(Shape(ctx->Attr("shape").dim_vec())); + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); + ctx->SetOutputStride("out", 0, Stride(Shape(ctx->Attr("shape").dim_vec()))); return Maybe::Ok(); } @@ -52,8 +52,8 @@ Maybe> MakeEmptyStream(const Symbol& out_device, const bo GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; - *ctx->OutputStride("out", 0) = Stride(physical_shape); + ctx->SetOutputShape("out", 0, physical_shape); + ctx->SetOutputStride("out", 0, Stride(physical_shape)); return Maybe::Ok(); } @@ -66,7 +66,7 @@ Maybe> MakeEmptyStream(const Symbol& out_device, const bo } /* static */ Maybe EmptyOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/erfinv_op.cpp b/oneflow/user/ops/erfinv_op.cpp index 708e50c89c624f9f071a7cc709124142d62059ad..1a7a95c4b9a7bef1d32406f342d56a6291570337 100644 --- a/oneflow/user/ops/erfinv_op.cpp +++ b/oneflow/user/ops/erfinv_op.cpp @@ -20,8 +20,7 @@ namespace oneflow { /* static */ Maybe ErfInvOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); - Shape* y_shape = ctx->OutputShape("y", 0); - *y_shape = x_shape; + ctx->SetOutputShape("y", 0, x_shape); return Maybe::Ok(); } @@ -38,7 +37,7 @@ namespace oneflow { } /* static */ Maybe ErfInvOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/expand_dims_op.cpp b/oneflow/user/ops/expand_dims_op.cpp index f5031f7a1b36432f959d252411a5401b8082e9dd..fccccf1997fed3090d6e47aa07c0c7870d3c113b 100644 --- a/oneflow/user/ops/expand_dims_op.cpp +++ b/oneflow/user/ops/expand_dims_op.cpp @@ -31,13 +31,12 @@ int32_t TransformNegativeAxisToPositive(int32_t axis, const int32_t num_axes) { /* static */ Maybe ExpandDimsOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); const int32_t axis = TransformNegativeAxisToPositive(ctx->Attr("axis"), in_shape.NumAxes()); auto dim_vec = in_shape.dim_vec(); dim_vec.insert(dim_vec.begin() + axis, 1); - *out_shape = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } @@ -65,25 +64,8 @@ int32_t TransformNegativeAxisToPositive(int32_t axis, const int32_t num_axes) { } /* static */ Maybe ExpandDimsOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("expand_dims") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("reshape_like") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/expand_op.cpp b/oneflow/user/ops/expand_op.cpp index 9e8cfd5c2eff2d08860e3897abf4c0e21856ea2e..9b32afae85087431c6f979750b9623691de94e88 100644 --- a/oneflow/user/ops/expand_op.cpp +++ b/oneflow/user/ops/expand_op.cpp @@ -14,105 +14,83 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/expand_kernel_utils.h" #include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/job/nd_sbp_util.h" namespace oneflow { -/* static */ Maybe ExpandOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& input_shape = ctx->InputShape("in", 0); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - - std::vector in_shape; - in_shape.resize(input_shape.NumAxes()); - for (int i = 0; i < input_shape.NumAxes(); ++i) { in_shape[i] = input_shape.At(i); } - - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForFp(in_shape, logical_expand_shape, out_shape, stride)); - - Shape* output_shape = ctx->OutputShape("out", 0); - DimVector dim_vec(out_shape.begin(), out_shape.end()); - *output_shape = Shape(dim_vec); - +namespace { + +Maybe InferExpandOutputStride(const Shape& input_shape, const Stride& input_stride, + const Shape& expand_shape, Stride* output_stride) { + CHECK_EQ_OR_RETURN(input_shape.size(), input_stride.size()); // NOLINT(maybe-need-error-msg) + size_t lpad = expand_shape.size() - input_shape.size(); + CHECK_GE_OR_RETURN(lpad, 0); // NOLINT(maybe-need-error-msg) + + output_stride->resize(expand_shape.size(), 0); + for (int i = expand_shape.size() - 1; i >= 0; --i) { + int64_t dim = i < lpad ? 1 : input_shape[i - lpad]; + if (dim == expand_shape[i]) { + if (i >= lpad) { + output_stride->at(i) = input_stride[i - lpad]; + } else if (i < expand_shape.size() - 1) { + output_stride->at(i) = output_stride->at(i + 1) * expand_shape[i + 1]; + } + } else { + CHECK_EQ_OR_RETURN(dim, 1); // NOLINT(maybe-need-error-msg) + } + } + // NOTE: expand op only can output contiguous stride, + // because lazy don't support to_contiguous op for now + *output_stride = Stride(expand_shape); return Maybe::Ok(); } -/*static*/ Maybe ExpandOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); -} +} // namespace -/* static */ Maybe ExpandOp::GetSbp(user_op::SbpContext* ctx) { - const std::vector& logical_in_shape = - ctx->Attr>("logical_in_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - std::vector logical_out_shape; - std::vector stride; - CHECK_JUST( - getOutShapeAndStride(logical_in_shape, logical_expand_shape, logical_out_shape, stride)); - - int offset = logical_out_shape.size() - logical_in_shape.size(); - FOR_RANGE(int64_t, i, 0, logical_in_shape.size()) { - if (logical_in_shape[i] == logical_out_shape[i + offset]) { - ctx->NewBuilder() - .Split(user_op::OpArg("in", 0), i) - .Split(user_op::OpArg("out", 0), i + offset) - .Build(); - } - } +/* static */ Maybe ExpandOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& input_shape = ctx->InputShape("in", 0); + const Stride& input_stride = ctx->InputStride("in", 0); + const Shape& expand_shape = ctx->Attr("expand_shape"); - ctx->NewBuilder() - .PartialSum(user_op::OpArg("in", 0)) - .PartialSum(user_op::OpArg("out", 0)) - .Build(); - return Maybe::Ok(); -} + ctx->SetOutputShape("out", 0, expand_shape); -/* static */ Maybe ExpandOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + Stride output_stride; + JUST(InferExpandOutputStride(input_shape, input_stride, expand_shape, &output_stride)); + ctx->SetOutputStride("out", 0, output_stride); return Maybe::Ok(); } -/* static */ Maybe ExpandGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe ExpandOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("in", 0); - const std::vector& logical_out_shape = - ctx->Attr>("logical_out_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); - - std::vector in_shape; - in_shape.resize(input_shape.NumAxes()); - for (int i = 0; i < input_shape.NumAxes(); ++i) { in_shape[i] = input_shape.At(i); } - std::vector out_shape; - std::vector stride; - CHECK_JUST(getOutShapeAndStrideForBp(logical_out_shape, logical_expand_shape, in_shape, out_shape, - stride)); - - Shape* output_shape = ctx->OutputShape("out", 0); - DimVector dim_vec(out_shape.begin(), out_shape.end()); - *output_shape = Shape(dim_vec); + const Stride& input_stride = ctx->InputStride("in", 0); + + const auto& global_expand_shape = ctx->Attr("expand_shape"); + const auto& output_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); + const auto& device_mesh = *ctx->parallel_desc().hierarchy(); + const auto& rank = ctx->parallel_ctx().parallel_id(); + const auto local_view = + GetTensorSliceView4ParallelId(device_mesh, output_sbp, global_expand_shape, rank); + const auto& local_expand_shape = local_view.shape(); + ctx->SetOutputShape("out", 0, local_expand_shape); + + Stride output_stride; + JUST(InferExpandOutputStride(input_shape, input_stride, local_expand_shape, &output_stride)); + ctx->SetOutputStride("out", 0, output_stride); return Maybe::Ok(); } -/*static*/ Maybe ExpandGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); -} - -/* static */ Maybe ExpandGradOp::GetSbp(user_op::SbpContext* ctx) { - const user_op::TensorDesc& input_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); - const std::vector& logical_out_shape = - ctx->Attr>("logical_out_shape"); - const std::vector& logical_expand_shape = - ctx->Attr>("logical_expand_shape"); +/* static */ Maybe ExpandOp::GetSbp(user_op::SbpContext* ctx) { + const auto& global_in_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0).shape(); + const auto& global_expand_shape = ctx->Attr("expand_shape"); + size_t lpad = global_expand_shape.size() - global_in_shape.size(); + CHECK_GE_OR_RETURN(lpad, 0); // NOLINT(maybe-need-error-msg) - int offset = input_tensor.shape().NumAxes() - logical_out_shape.size(); - FOR_RANGE(int64_t, i, 0, logical_out_shape.size()) { - if (logical_out_shape[i] == input_tensor.shape().At(i + offset)) { + for (size_t i = 0; i < global_in_shape.size(); ++i) { + if (global_in_shape[i] == global_expand_shape[i + lpad]) { ctx->NewBuilder() - .Split(user_op::OpArg("in", 0), i + offset) - .Split(user_op::OpArg("out", 0), i) + .Split(user_op::OpArg("in", 0), i) + .Split(user_op::OpArg("out", 0), i + lpad) .Build(); } } @@ -121,31 +99,13 @@ namespace oneflow { .PartialSum(user_op::OpArg("in", 0)) .PartialSum(user_op::OpArg("out", 0)) .Build(); - return Maybe::Ok(); -} -/* static */ Maybe ExpandGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("expand").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("expand_grad") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr>("logical_out_shape", - op.attr>("logical_in_shape")) - .Attr>("logical_expand_shape", - op.attr>("logical_expand_shape")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } +/* static */ Maybe ExpandOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); -}); +} } // namespace oneflow diff --git a/oneflow/user/ops/eye_op.cpp b/oneflow/user/ops/eye_op.cpp index 077758b24527d309db4de67370eb02b16a97005f..2a27a4ec2cff10d18d8ee32840ef11b1acd5dc90 100644 --- a/oneflow/user/ops/eye_op.cpp +++ b/oneflow/user/ops/eye_op.cpp @@ -21,7 +21,7 @@ namespace oneflow { /* static */ Maybe EyeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { int64_t rows = ctx->Attr("rows"); int64_t cols = ctx->Attr("cols"); - *ctx->OutputShape("out", 0) = Shape({rows, cols}); + ctx->SetOutputShape("out", 0, Shape({rows, cols})); return Maybe::Ok(); } @@ -35,7 +35,7 @@ namespace oneflow { } /* static */ Maybe EyeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/fake_quantization_op.cpp b/oneflow/user/ops/fake_quantization_op.cpp index fbe6a7d8ca62c0161dd8a17c1613260e42b0c811..527934dddd8d330c439d89bb7a944abeace5517a 100644 --- a/oneflow/user/ops/fake_quantization_op.cpp +++ b/oneflow/user/ops/fake_quantization_op.cpp @@ -30,7 +30,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(zero_point_shape.elem_cnt(), in_shape.At(0)); } - *ctx->OutputShape("out", 0) = in_shape; + ctx->SetOutputShape("out", 0, in_shape); return Maybe::Ok(); } @@ -104,24 +104,8 @@ namespace oneflow { } /* static */ Maybe FakeQuantizationOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fake_quantization") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper identity_op = - builder.Op("identity") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(identity_op.output("out", 0), "in", 0); - AddOp(identity_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fill_op.cpp b/oneflow/user/ops/fill_op.cpp index d342712d14a9893d8eb8df56e5ef3a6aab2a83af..59edd1877cb1dd59570316a70d01e58b1af5bfdb 100644 --- a/oneflow/user/ops/fill_op.cpp +++ b/oneflow/user/ops/fill_op.cpp @@ -20,10 +20,8 @@ namespace oneflow { /* static */ Maybe FillOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; - Stride* out_stride = ctx->OutputStride("out", 0); - *out_stride = ctx->InputStride("in", 0); + ctx->SetOutputShape("out", 0, in_shape); + ctx->SetOutputStride("out", 0, ctx->InputStride("in", 0)); return Maybe::Ok(); } @@ -40,16 +38,14 @@ namespace oneflow { } /* static */ Maybe FillOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe FillTensorOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; - Stride* out_stride = ctx->OutputStride("out", 0); - *out_stride = ctx->InputStride("in", 0); + ctx->SetOutputShape("out", 0, in_shape); + ctx->SetOutputStride("out", 0, ctx->InputStride("in", 0)); return Maybe::Ok(); } @@ -66,61 +62,17 @@ namespace oneflow { .Split(user_op::OpArg("out", 0), i) .Build(); } + ctx->NewBuilder() + .PartialSum(user_op::OpArg("in", 0)) + .PartialSum(user_op::OpArg("value", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); return Maybe::Ok(); } /* static */ Maybe FillTensorOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fill_").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name()); - user_op::UserOpConfWrapper grad_op = - builder.Op("fill_") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("floating_value", 0.) - .Attr("is_floating_value", true) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("fill_tensor_") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("value", 0)) { - const int64_t num_axes = op.TensorDesc4ArgNameAndIndex("in", 0).shape().NumAxes(); - std::vector axes_vec(num_axes); - std::iota(axes_vec.begin(), axes_vec.end(), 0); - user_op::UserOpConfWrapperBuilder builder(op.op_name()); - auto grad_op = builder.Op("reduce_sum") - .Input("input_tensor", op.GetGradTensorWithOpOutput("out", 0)) - .Output("output_tensor") - .Attr("axis", axes_vec) - .Attr("keepdims", false) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "value", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name()); - user_op::UserOpConfWrapper grad_op = - builder.Op("fill_") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("floating_value", 0.) - .Attr("is_floating_value", true) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/flatten_op.cpp b/oneflow/user/ops/flatten_op.cpp deleted file mode 100644 index 7ac839b479c585762a27a5f24defdf692cab5374..0000000000000000000000000000000000000000 --- a/oneflow/user/ops/flatten_op.cpp +++ /dev/null @@ -1,105 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/framework/framework.h" -#include "oneflow/core/framework/op_generated.h" - -namespace oneflow { - -/* static */ Maybe FlattenOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const int32_t start_dim = ctx->Attr("start_dim"); - const int32_t end_dim = ctx->Attr("end_dim"); - const user_op::TensorDesc& in_tensor_desc = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor_desc = ctx->OutputTensorDesc("out", 0); - const Shape& in_shape = ExpandDimIf0D(in_tensor_desc.shape()); - CHECK_GE_OR_RETURN(start_dim, 0); - CHECK_LT_OR_RETURN(start_dim, in_shape.NumAxes()); - const int32_t true_end_dim = end_dim < 0 ? end_dim + in_shape.NumAxes() : end_dim; - CHECK_GE_OR_RETURN(true_end_dim, 0); - CHECK_LT_OR_RETURN(true_end_dim, in_shape.NumAxes()); - CHECK_LE_OR_RETURN(start_dim, true_end_dim); - - *out_tensor_desc->mut_is_dynamic() = in_tensor_desc.is_dynamic(); - - Shape* out_shape = out_tensor_desc->mut_shape(); - - DimVector dim_vec; - - for (int i = 0; i < start_dim; ++i) { dim_vec.emplace_back(in_shape.At(i)); } - int64_t flatten_dim = 1; - for (int i = start_dim; i <= true_end_dim; ++i) { flatten_dim *= in_shape.At(i); } - dim_vec.emplace_back(flatten_dim); - for (int i = true_end_dim + 1; i < in_shape.NumAxes(); ++i) { - dim_vec.emplace_back(in_shape.At(i)); - } - - *out_shape = Shape(dim_vec); - CHECK_EQ_OR_RETURN(out_shape->elem_cnt(), in_shape.elem_cnt()); - return Maybe::Ok(); -} - -/*static*/ Maybe FlattenOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); -} - -/* static */ Maybe FlattenOp::GetSbp(user_op::SbpContext* ctx) { - const auto& in_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0).shape(); - const int32_t start_dim = ctx->Attr("start_dim"); - const int32_t end_dim = ctx->Attr("end_dim"); - - CHECK_GE_OR_RETURN(start_dim, 0); - CHECK_LT_OR_RETURN(start_dim, in_shape.NumAxes()); - const int32_t true_end_dim = end_dim < 0 ? end_dim + in_shape.NumAxes() : end_dim; - CHECK_GE_OR_RETURN(true_end_dim, 0); - CHECK_LT_OR_RETURN(true_end_dim, in_shape.NumAxes()); - CHECK_LE_OR_RETURN(start_dim, true_end_dim); - - for (int i = 0; i <= start_dim; ++i) { - ctx->NewBuilder().Split(user_op::OpArg("in", 0), i).Split(user_op::OpArg("out", 0), i).Build(); - } - const int32_t diff = true_end_dim - start_dim; - for (int i = true_end_dim + 1; i < in_shape.NumAxes(); ++i) { - ctx->NewBuilder() - .Split(user_op::OpArg("in", 0), i) - .Split(user_op::OpArg("out", 0), i - diff) - .Build(); - } - - ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(ctx->outputs()).Build(); - return Maybe::Ok(); -} - -/* static */ Maybe FlattenOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("flatten").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper reshape_grad_op = - builder.Op("reshape_like") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(reshape_grad_op.output("out", 0), "in", 0); - AddOp(reshape_grad_op); - } - return Maybe::Ok(); -}); - -} // namespace oneflow diff --git a/oneflow/user/ops/flip_op.cpp b/oneflow/user/ops/flip_op.cpp index c407c9580da40c90911730e828458d7e6bd82525..50d5a8a1109da1ff5d3ae3cda4bdaa79a54ecfb1 100644 --- a/oneflow/user/ops/flip_op.cpp +++ b/oneflow/user/ops/flip_op.cpp @@ -24,8 +24,8 @@ namespace oneflow { const std::vector dims = ctx->Attr>("dims"); CHECK_OR_RETURN(dims.size() <= input_dims) << "len of dims must less than len of input tensor"; for (auto x : dims) { CHECK_OR_RETURN(x < input_dims) << "dims parameter is illegal."; } - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = x_desc.shape(); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(x_desc.shape()); return Maybe::Ok(); } /*static*/ auto FlipOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { @@ -49,7 +49,7 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ auto FlipOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/fused_bias_add_op.cpp b/oneflow/user/ops/fused_bias_add_op.cpp index 46f9394ff18b709e8db4853521f3ec48c0d5921d..b27cc83bdcd6540126bd1507b9695a9cf9278ef2 100644 --- a/oneflow/user/ops/fused_bias_add_op.cpp +++ b/oneflow/user/ops/fused_bias_add_op.cpp @@ -27,8 +27,8 @@ namespace oneflow { CHECK_GE_OR_RETURN(bias_add_axis, 0); CHECK_LT_OR_RETURN(bias_add_axis, a_tensor_desc.shape().NumAxes()); CHECK_EQ_OR_RETURN(a_tensor_desc.shape().At(bias_add_axis), b_tensor_desc.shape().At(0)); - *ctx->OutputShape("out", 0) = a_tensor_desc.shape(); - *ctx->OutputIsDynamic("out", 0) = a_tensor_desc.is_dynamic(); + ctx->SetOutputShape("out", 0, a_tensor_desc.shape()); + ctx->SetOutputIsDynamic("out", 0, a_tensor_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedBiasAddGeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -37,7 +37,7 @@ namespace oneflow { } /*static*/ auto FusedBiasAddGeluOp::InferDataType(user_op::InferContext* ctx) -> Maybe { const auto& a_tensor_desc = ctx->InputTensorDesc("a", 0); - *ctx->OutputDType("out", 0) = a_tensor_desc.data_type(); + ctx->SetOutputDType("out", 0, a_tensor_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedBiasAddGeluOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { @@ -67,8 +67,8 @@ namespace oneflow { CHECK_GE_OR_RETURN(bias_add_axis, 0); CHECK_LT_OR_RETURN(bias_add_axis, a_tensor_desc.shape().NumAxes()); CHECK_EQ_OR_RETURN(a_tensor_desc.shape().At(bias_add_axis), b_tensor_desc.shape().At(0)); - *ctx->OutputShape("dx", 0) = a_tensor_desc.shape(); - *ctx->OutputIsDynamic("dx", 0) = a_tensor_desc.is_dynamic(); + ctx->SetOutputShape("dx", 0, a_tensor_desc.shape()); + ctx->SetOutputIsDynamic("dx", 0, a_tensor_desc.is_dynamic()); return Maybe::Ok(); } @@ -78,7 +78,7 @@ namespace oneflow { } /*static*/ auto FusedBiasAddGeluGradOp::InferDataType(user_op::InferContext* ctx) -> Maybe { const auto& a_tensor_desc = ctx->InputTensorDesc("a", 0); - *ctx->OutputDType("dx", 0) = a_tensor_desc.data_type(); + ctx->SetOutputDType("dx", 0, a_tensor_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedBiasAddGeluGradOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { @@ -102,45 +102,6 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_bias_add_gelu") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("a", 0) || op.NeedGenGradTensor4OpInput("b", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_gelu_grad"); - user_op::UserOpConfWrapper bias_add_gelu_grad_op = - builder.Op("fused_bias_add_gelu_grad") - .Input("a", op.input("a", 0)) - .Input("b", op.input("b", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Attr("axis", op.attr("axis")) - .Output("dx") - .Build(); - AddOp(bias_add_gelu_grad_op); - - if (op.NeedGenGradTensor4OpInput("a", 0)) { - op.BindGradTensorWithOpInput(bias_add_gelu_grad_op.output("dx", 0), "a", 0); - } - if (op.NeedGenGradTensor4OpInput("b", 0)) { - const int64_t num_axes = op.TensorDesc4ArgNameAndIndex("a", 0).shape().NumAxes(); - const int32_t bias_add_axis = op.attr("axis"); - std::vector reduce_axes_vec; - FOR_RANGE(int64_t, i, 0, num_axes) { - if (i != bias_add_axis) { reduce_axes_vec.emplace_back(i); } - } - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - auto grad_op = builder.Op("reduce_sum") - .Input("input_tensor", bias_add_gelu_grad_op.output("dx", 0)) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(grad_op); - op.BindGradTensorWithOpInput(grad_op.output("output_tensor", 0), "b", 0); - } - } - return Maybe::Ok(); - }); - /*static*/ auto FusedBiasAddMaskScaleOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { const auto& a_tensor_desc = ctx->InputTensorDesc("a", 0); @@ -152,8 +113,8 @@ REGISTER_USER_OP_GRAD("fused_bias_add_gelu") CHECK_LT_OR_RETURN(bias_add_axis, a_tensor_desc.shape().NumAxes()); CHECK_EQ_OR_RETURN(a_tensor_desc.shape().At(bias_add_axis), b_tensor_desc.shape().At(0)); CHECK_EQ_OR_RETURN(a_tensor_desc.shape(), mask_tensor_desc.shape()); - *ctx->OutputShape("out", 0) = a_tensor_desc.shape(); - *ctx->OutputIsDynamic("out", 0) = a_tensor_desc.is_dynamic(); + ctx->SetOutputShape("out", 0, a_tensor_desc.shape()); + ctx->SetOutputIsDynamic("out", 0, a_tensor_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedBiasAddMaskScaleOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -162,7 +123,7 @@ REGISTER_USER_OP_GRAD("fused_bias_add_gelu") } /*static*/ auto FusedBiasAddMaskScaleOp::InferDataType(user_op::InferContext* ctx) -> Maybe { const auto& a_tensor_desc = ctx->InputTensorDesc("a", 0); - *ctx->OutputDType("out", 0) = a_tensor_desc.data_type(); + ctx->SetOutputDType("out", 0, a_tensor_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedBiasAddMaskScaleOp::ModifyInputArg( @@ -191,67 +152,4 @@ REGISTER_USER_OP_GRAD("fused_bias_add_gelu") return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_bias_add_mask_scale") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("a", 0) || op.NeedGenGradTensor4OpInput("b", 0)) { - float scale = op.attr("scale"); - if (scale != 1.0) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_dropout_grad"); - user_op::UserOpConfWrapper dropout_grad_op = - builder.Op("dropout_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("mask", op.input("mask", 0)) - .Output("dx") - .Attr("scale", scale) - .Build(); - AddOp(dropout_grad_op); - - if (op.NeedGenGradTensor4OpInput("a", 0)) { - op.BindGradTensorWithOpInput(dropout_grad_op.output("dx", 0), "a", 0); - } - if (op.NeedGenGradTensor4OpInput("b", 0)) { - const int64_t num_axes = op.TensorDesc4ArgNameAndIndex("a", 0).shape().NumAxes(); - const int32_t bias_add_axis = op.attr("axis"); - std::vector reduce_axes_vec; - FOR_RANGE(int64_t, i, 0, num_axes) { - if (i != bias_add_axis) { reduce_axes_vec.emplace_back(i); } - } - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - auto grad_op = builder.Op("reduce_sum") - .Input("input_tensor", dropout_grad_op.output("dx", 0)) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(grad_op); - op.BindGradTensorWithOpInput(grad_op.output("output_tensor", 0), "b", 0); - } - } else { - // When dropout_prob = 0.0, scale = 1.0, here we directly use out grad. - if (op.NeedGenGradTensor4OpInput("a", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", 0), "a", 0); - } - if (op.NeedGenGradTensor4OpInput("b", 0)) { - const int64_t num_axes = op.TensorDesc4ArgNameAndIndex("a", 0).shape().NumAxes(); - const int32_t bias_add_axis = op.attr("axis"); - std::vector reduce_axes_vec; - FOR_RANGE(int64_t, i, 0, num_axes) { - if (i != bias_add_axis) { reduce_axes_vec.emplace_back(i); } - } - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - auto grad_op = builder.Op("reduce_sum") - .Input("input_tensor", op.GetGradTensorWithOpOutput("out", 0)) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(grad_op); - op.BindGradTensorWithOpInput(grad_op.output("output_tensor", 0), "b", 0); - } - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_bias_add_scale_mask_softmax_dropout_op.cpp b/oneflow/user/ops/fused_bias_add_scale_mask_softmax_dropout_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8b69aad85fed7438946f5bb09d35f00763af369e --- /dev/null +++ b/oneflow/user/ops/fused_bias_add_scale_mask_softmax_dropout_op.cpp @@ -0,0 +1,231 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +bool CheckBroadcastable(const Shape& shape, const Shape& broadcast_shape) { + int left_pad = broadcast_shape.size() - shape.size(); + if (left_pad < 0) { return false; } + for (int i = 0; i < shape.size(); ++i) { + int j = i + left_pad; + if (shape[i] != 1 && shape[i] != broadcast_shape[j]) { return false; } + } + return true; +} + +bool CheckBroadcastAndSimplifyDims(const Shape& shape, const Shape& broadcast_shape, + int& simplified_ndim, int64_t* simplified_dims) { + int lpad = broadcast_shape.size() - shape.size(); + if (lpad < 0) { return false; } + simplified_ndim = 0; + bool prev_broadcast = false; + for (int i = 0; i < broadcast_shape.size(); ++i) { + int64_t dim = (i < lpad) ? 1 : shape[i - lpad]; + int64_t broadcast_dim = broadcast_shape[i]; + if (dim != 1 && dim != broadcast_dim) { return false; } + bool broadcast = (dim == 1 && broadcast_dim != 1); + if (simplified_ndim > 0 && broadcast == prev_broadcast) { + // fold to prev dim + simplified_dims[simplified_ndim - 1] *= dim; + } else { + simplified_dims[simplified_ndim] = dim; + simplified_ndim += 1; + } + prev_broadcast = broadcast; + } + return true; +} + +// return lpad +int GetBroadcastDims(const Shape& shape, const Shape& broadcast_shape, + HashSet& broadcast_dims) { + int lpad = broadcast_shape.size() - shape.size(); + if (lpad < 0) { return lpad; } + for (int i = 0; i < broadcast_shape.size(); ++i) { + if (i < lpad) { + broadcast_dims.insert(i); + } else { + int j = i - lpad; + if (shape[j] == 1 && shape[j] != broadcast_shape[i]) { broadcast_dims.insert(i); } + if (shape[j] != 1 && shape[j] != broadcast_shape[i]) { return -1; } + } + } + return lpad; +} + +} // namespace + +Maybe FusedBiasAddScaleMaskSoftmaxDropoutOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const Shape& bias_shape = ctx->InputShape("bias", 0); + const Shape& mask_shape = ctx->InputShape("mask", 0); + const Shape& dropout_mask_shape = ctx->InputShape("dropout_mask", 0); + + CHECK_GE_OR_RETURN(x_shape.size(), 2) << Error::RuntimeError() << "x has at least 2 dimensions"; + CHECK_EQ_OR_RETURN(x_shape.back(), mask_shape.back()) + << " Last dimension of x and mask should be equal, which is softmax dimension."; + CHECK_EQ_OR_RETURN(dropout_mask_shape, x_shape) + << Error::RuntimeError() << "dropout_mask shape " << dropout_mask_shape.ToString() + << " should be equal to x shape " << x_shape.ToString(); + + int simplified_bias_ndim = 0; + int simplified_mask_ndim = 0; + DimVector simplified_bias_dims(x_shape.size()); + DimVector simplified_mask_dims(x_shape.size()); + CHECK_OR_RETURN(CheckBroadcastAndSimplifyDims(bias_shape, x_shape, simplified_bias_ndim, + simplified_bias_dims.data())) + << Error::RuntimeError() << "bias shape " << bias_shape.ToString() + << " could not be broadcast to x shape " << x_shape.ToString(); + CHECK_OR_RETURN(CheckBroadcastAndSimplifyDims(mask_shape, x_shape, simplified_mask_ndim, + simplified_mask_dims.data())) + << Error::RuntimeError() << "mask shape " << mask_shape.ToString() + << " could not be broadcast to x shape " << x_shape.ToString(); + CHECK_GT_OR_RETURN(simplified_bias_ndim, 0); // NOLINT(maybe-need-error-msg) + CHECK_GT_OR_RETURN(simplified_mask_ndim, 0); // NOLINT(maybe-need-error-msg) + // (1, ) -> (K, ) + // (M, 1) -> (M, N) + // (1, N) -> (M, N) + // (M, 1, N) -> (M, K, N) + if ((simplified_bias_ndim == 2 && simplified_bias_dims[0] != 1) || simplified_bias_ndim > 2) { + return Error::RuntimeError() + << "bias only support (1, N)->(M, N) broadcast, but got bias shape " + << bias_shape.ToString() << " broadcast to x shape " << x_shape.ToString(); + } + + if (simplified_mask_ndim > 3 || (simplified_mask_ndim == 3 && simplified_mask_dims[1] != 1)) { + return Error::RuntimeError() << "mask support (M, 1)->(M, N) or (1, N)->(M, N) or (M, 1, " + "N)->(M, K, N) broadcast, but got mask shape " + << mask_shape.ToString() << " broadcast to x shape " + << x_shape.ToString(); + } + + ctx->SetOutputShape("y", 0, x_shape); + ctx->SetOutputShape("softmax_y", 0, x_shape); + ctx->SetOutputIsDynamic("y", 0, ctx->InputIsDynamic("x", 0)); + ctx->SetOutputIsDynamic("softmax_y", 0, ctx->InputIsDynamic("x", 0)); + return Maybe::Ok(); +} + +Maybe FusedBiasAddScaleMaskSoftmaxDropoutOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +Maybe FusedBiasAddScaleMaskSoftmaxDropoutOp::InferDataType(user_op::InferContext* ctx) { + const DataType x_dtype = ctx->InputDType("x", 0); + const DataType bias_dtype = ctx->InputDType("bias", 0); + const DataType mask_dtype = ctx->InputDType("mask", 0); + const DataType dropout_mask_dtype = ctx->InputDType("dropout_mask", 0); + + CHECK_EQ_OR_RETURN(bias_dtype, x_dtype) + << Error::RuntimeError() << "Expected bias data type " << DataType_Name(x_dtype) + << ", but got " << DataType_Name(bias_dtype); + CHECK_OR_RETURN(IsBoolDataType(mask_dtype) || IsIntegralDataType(mask_dtype)) + << Error::RuntimeError() << "Expected mask data type to be bool or integer, but got " + << DataType_Name(mask_dtype); + CHECK_OR_RETURN(IsBoolDataType(dropout_mask_dtype)) + << Error::RuntimeError() << "Expected dropout_mask data type to be bool, but got " + << DataType_Name(dropout_mask_dtype); + + ctx->SetOutputDType("y", 0, x_dtype); + ctx->SetOutputDType("softmax_y", 0, x_dtype); + return Maybe::Ok(); +} + +Maybe FusedBiasAddScaleMaskSoftmaxDropoutOp::GetSbp(user_op::SbpContext* ctx) { + const Shape& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); + const Shape& bias_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("bias", 0).shape(); + const Shape& mask_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("mask", 0).shape(); + const Shape& dropout_mask_shape = + ctx->LogicalTensorDesc4InputArgNameAndIndex("dropout_mask", 0).shape(); + + CHECK_GE_OR_RETURN(x_shape.size(), 2) << Error::RuntimeError() << "x has at least 2 dimensions"; + CHECK_EQ_OR_RETURN(dropout_mask_shape, x_shape) + << Error::RuntimeError() << "dropout_mask_shape shape " << dropout_mask_shape.ToString() + << " should be equal to x shape " << x_shape.ToString(); + + HashSet bias_broadcast_dims; + HashSet mask_broadcast_dims; + int bias_lpad = GetBroadcastDims(bias_shape, x_shape, bias_broadcast_dims); + int mask_lpad = GetBroadcastDims(mask_shape, x_shape, mask_broadcast_dims); + + CHECK_GE_OR_RETURN(bias_lpad, 0) + << Error::RuntimeError() << "bias shape " << bias_shape.ToString() + << " could not be broadcast to x shape " << x_shape.ToString(); + CHECK_GE_OR_RETURN(mask_lpad, 0) + << Error::RuntimeError() << "mask shape " << mask_shape.ToString() + << " could not be broadcast to x shape " << x_shape.ToString(); + + std::vector split_args = { + {"x", 0}, + {"dropout_mask", 0}, + {"y", 0}, + {"softmax_y", 0}, + }; + + for (int i = 0; i < x_shape.size(); ++i) { + bool bias_can_split = (bias_broadcast_dims.find(i) == bias_broadcast_dims.end()); + bool mask_can_split = (mask_broadcast_dims.find(i) == mask_broadcast_dims.end()); + if (bias_can_split && mask_can_split) { + CHECK_GE_OR_RETURN(i, bias_lpad); // NOLINT(maybe-need-error-msg) + CHECK_GE_OR_RETURN(i, mask_lpad); // NOLINT(maybe-need-error-msg) + ctx->NewBuilder() + .Split(split_args, i) + .Split(user_op::OpArg("bias", 0), i - bias_lpad) + .Split(user_op::OpArg("mask", 0), i - mask_lpad) + .Build(); + } else if (bias_can_split) { + CHECK_GE_OR_RETURN(i, bias_lpad); // NOLINT(maybe-need-error-msg) + ctx->NewBuilder() + .Split(split_args, i) + .Split(user_op::OpArg("bias", 0), i - bias_lpad) + .Broadcast(user_op::OpArg("mask", 0)) + .Build(); + } else if (mask_can_split) { + CHECK_GE_OR_RETURN(i, mask_lpad); // NOLINT(maybe-need-error-msg) + ctx->NewBuilder() + .Split(split_args, i) + .Broadcast(user_op::OpArg("bias", 0)) + .Split(user_op::OpArg("mask", 0), i - mask_lpad) + .Build(); + } else { + ctx->NewBuilder() + .Split(split_args, i) + .Broadcast(user_op::OpArg("bias", 0)) + .Broadcast(user_op::OpArg("mask", 0)) + .Build(); + } + } + return Maybe::Ok(); +} + +Maybe FusedBiasAddScaleMaskSoftmaxDropoutOp::ModifyInputArg( + const user_op::GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { + user_op::InputArgModifier* mask_modifier = GetInputArgModifierFn("mask", 0); + user_op::InputArgModifier* dropout_mask_modifier = GetInputArgModifierFn("dropout_mask", 0); + CHECK_OR_RETURN(mask_modifier != nullptr) << " cannot find mask input."; + CHECK_OR_RETURN(dropout_mask_modifier != nullptr) << " cannot find dropout mask input."; + mask_modifier->set_requires_grad(false); + dropout_mask_modifier->set_requires_grad(false); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_cast_scale_op.cpp b/oneflow/user/ops/fused_cast_scale_op.cpp index 816a10efb06d55ddbf2a47d0283b3be7fdf1f249..0038c9541d5a263ccc5292cd1ca6fc72717d21d6 100644 --- a/oneflow/user/ops/fused_cast_scale_op.cpp +++ b/oneflow/user/ops/fused_cast_scale_op.cpp @@ -23,9 +23,9 @@ Maybe FusedCastScaleOp::InferLogicalTensorDesc(user_op::InferContext* ctx) const user_op::TensorDesc& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); CHECK_EQ_OR_RETURN(scale_by_tensor.shape().NumAxes(), 1); CHECK_EQ_OR_RETURN(scale_by_tensor.shape().At(0), 1); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_is_dynamic() = x.is_dynamic(); - *y->mut_shape() = x.shape(); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_is_dynamic(x.is_dynamic()); + y->set_shape(x.shape()); return Maybe::Ok(); } @@ -35,8 +35,8 @@ Maybe FusedCastScaleOp::InferPhysicalTensorDesc(user_op::InferContext* ctx Maybe FusedCastScaleOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_data_type() = scale_by_tensor.data_type(); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(scale_by_tensor.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/fused_center_op.cpp b/oneflow/user/ops/fused_center_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3cc9a5cc1023c18ce10ab37533037e91c5c58abc --- /dev/null +++ b/oneflow/user/ops/fused_center_op.cpp @@ -0,0 +1,223 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedCenterOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y2.shape()); + + user_op::TensorDesc* rho = ctx->MutOutputTensorDesc("rho2", 0); + rho->set_is_dynamic(b1_x1.is_dynamic()); + rho->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedCenterOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedCenterOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedCenterOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y2.data_type()); + + user_op::TensorDesc* rho = ctx->MutOutputTensorDesc("rho2", 0); + rho->set_data_type(b1_x1.data_type()); + return Maybe::Ok(); +} + +Maybe FusedCenterOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("rho2", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedCenterGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + const user_op::TensorDesc& rho2_diff = ctx->InputTensorDesc("rho2_diff", 0); + + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), rho2_diff.shape()); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y2_diff->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedCenterGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedCenterGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedCenterGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + const user_op::TensorDesc& rho2_diff = ctx->InputTensorDesc("rho2_diff", 0); + + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), rho2_diff.data_type()); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_data_type(b1_x1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedCenterGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("rho2_diff", 0), i) + .Split(user_op::OpArg("b1_x1_diff", 0), i) + .Split(user_op::OpArg("b1_x2_diff", 0), i) + .Split(user_op::OpArg("b1_y1_diff", 0), i) + .Split(user_op::OpArg("b1_y2_diff", 0), i) + .Split(user_op::OpArg("b2_x1_diff", 0), i) + .Split(user_op::OpArg("b2_x2_diff", 0), i) + .Split(user_op::OpArg("b2_y1_diff", 0), i) + .Split(user_op::OpArg("b2_y2_diff", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_cross_feature_interaction_op.cpp b/oneflow/user/ops/fused_cross_feature_interaction_op.cpp index 0dfce53893df9d944fc10062d61e75945718ad7a..2b0d027af4d20bea8975d06e1ba8fd3ad6417014 100644 --- a/oneflow/user/ops/fused_cross_feature_interaction_op.cpp +++ b/oneflow/user/ops/fused_cross_feature_interaction_op.cpp @@ -24,11 +24,11 @@ namespace oneflow { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& weight_shape = ctx->InputShape("weight", 0); CHECK_EQ_OR_RETURN(x_shape.At(1), weight_shape.At(1)) << "Matmul K dims should be equal. "; - *ctx->OutputShape("matmul_result", 0) = Shape({x_shape.At(0), weight_shape.At(0)}); + ctx->SetOutputShape("matmul_result", 0, Shape({x_shape.At(0), weight_shape.At(0)})); const Shape& x0_shape = ctx->InputShape("x0", 0); const Shape& bias_shape = ctx->InputShape("bias", 0); CHECK_EQ_OR_RETURN(bias_shape.At(0), x0_shape.At(1)) << "Bias dim should be equal to X0 dim1. "; - *ctx->OutputShape("out", 0) = x0_shape; + ctx->SetOutputShape("out", 0, x0_shape); return Maybe::Ok(); } @@ -50,8 +50,8 @@ namespace oneflow { } /* static */ Maybe FusedCrossFeatureInteractionOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("matmul_result", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("matmul_result", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -59,10 +59,10 @@ namespace oneflow { user_op::InferContext* ctx) { const Shape& x0_shape = ctx->InputShape("x0", 0); const Shape& weight_shape = ctx->InputShape("weight", 0); - *ctx->OutputShape("dx0", 0) = x0_shape; - *ctx->OutputShape("dw", 0) = weight_shape; - *ctx->OutputShape("dx", 0) = x0_shape; - *ctx->OutputShape("dbias", 0) = Shape({x0_shape.At(1)}); + ctx->SetOutputShape("dx0", 0, x0_shape); + ctx->SetOutputShape("dw", 0, weight_shape); + ctx->SetOutputShape("dx", 0, x0_shape); + ctx->SetOutputShape("dbias", 0, Shape({x0_shape.At(1)})); return Maybe::Ok(); } @@ -89,10 +89,10 @@ namespace oneflow { /* static */ Maybe FusedCrossFeatureInteractionV1GradOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("dx0", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dw", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dbias", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx0", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dw", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dbias", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -100,10 +100,10 @@ namespace oneflow { user_op::InferContext* ctx) { const Shape& x0_shape = ctx->InputShape("x0", 0); const Shape& weight_shape = ctx->InputShape("weight", 0); - *ctx->OutputShape("dx0", 0) = x0_shape; - *ctx->OutputShape("dw", 0) = weight_shape; - *ctx->OutputShape("dx", 0) = x0_shape; - *ctx->OutputShape("dbias", 0) = Shape({x0_shape.At(1)}); + ctx->SetOutputShape("dx0", 0, x0_shape); + ctx->SetOutputShape("dw", 0, weight_shape); + ctx->SetOutputShape("dx", 0, x0_shape); + ctx->SetOutputShape("dbias", 0, Shape({x0_shape.At(1)})); return Maybe::Ok(); } @@ -131,51 +131,11 @@ namespace oneflow { /* static */ Maybe FusedCrossFeatureInteractionV2GradOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("dx0", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dw", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("dbias", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx0", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dw", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("dbias", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_cross_feature_interaction") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - if (op.attr("interaction_mode") == "vector") { - builder.Op("fused_cross_feature_interaction_v1_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("weight", op.input("weight", 0)) - .Input("x", op.input("x", 0)) - .Input("x0", op.input("x0", 0)) - .Input("matmul_result", op.output("matmul_result", 0)); - } else if (op.attr("interaction_mode") == "matrix") { - builder.Op("fused_cross_feature_interaction_v2_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("weight", op.input("weight", 0)) - .Input("bias", op.input("bias", 0)) - .Input("x", op.input("x", 0)) - .Input("x0", op.input("x0", 0)) - .Input("matmul_result", op.output("matmul_result", 0)); - } else { - UNIMPLEMENTED(); - } - builder.Output("dx", 0).Output("dw", 0).Output("dx0", 0).Output("dbias", 0); - auto grad_op = builder.Build(); - AddOp(grad_op); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - } - if (op.NeedGenGradTensor4OpInput("weight", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dw", 0), "weight", 0); - } - if (op.NeedGenGradTensor4OpInput("x0", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dx0", 0), "x0", 0); - } - if (op.NeedGenGradTensor4OpInput("bias", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dbias", 0), "bias", 0); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_dot_feature_interaction_op.cpp b/oneflow/user/ops/fused_dot_feature_interaction_op.cpp index 0b091e0aee1e2f95fef8b99f77aa7edb30c8cabd..19ea29cd44095d7e44d816545a91a8d560843914 100644 --- a/oneflow/user/ops/fused_dot_feature_interaction_op.cpp +++ b/oneflow/user/ops/fused_dot_feature_interaction_op.cpp @@ -36,9 +36,24 @@ namespace oneflow { } const std::string& pooling = ctx->Attr("pooling"); if (pooling == "sum") { - *ctx->OutputShape("out", 0) = Shape({batch_size, vector_size}); + ctx->SetOutputShape("out", 0, Shape({batch_size, vector_size})); return Maybe::Ok(); } + if (ctx->has_input("sparse_feature", 0)) { + CHECK_OR_RETURN(pooling == "none") << "only none pooling support sparse feature."; + CHECK_OR_RETURN(ctx->has_input("sparse_indices", 0)) + << "if input sparse_feature exists, must have input sparse_indices."; + const Shape& sparse_feature_shape = ctx->InputShape("sparse_feature", 0); + const Shape& sparse_indices_shape = ctx->InputShape("sparse_indices", 0); + CHECK_EQ_OR_RETURN(sparse_indices_shape.NumAxes(), 2) + << "sparse_indices num_axes must be 2, but get " << sparse_indices_shape.NumAxes(); + CHECK_EQ_OR_RETURN(sparse_indices_shape.At(0), batch_size) + << "get " << sparse_indices_shape.At(0) << " and " << batch_size; + CHECK_EQ_OR_RETURN(sparse_feature_shape.At(sparse_feature_shape.NumAxes() - 1), vector_size) + << "get " << sparse_feature_shape.At(sparse_feature_shape.NumAxes() - 1) << " and " + << vector_size; + features_concated_dim += sparse_indices_shape.At(1); + } const bool self_interaction = ctx->Attr("self_interaction"); const int32_t output_padding = ctx->Attr("output_padding"); const int64_t interaction_dim = self_interaction @@ -51,7 +66,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(output_concat_shape.At(0), batch_size); out_dim += output_concat_shape.At(1); } - *ctx->OutputShape("out", 0) = Shape({batch_size, out_dim}); + ctx->SetOutputShape("out", 0, Shape({batch_size, out_dim})); return Maybe::Ok(); } @@ -61,21 +76,34 @@ namespace oneflow { } /* static */ Maybe FusedDotFeatureInteractionOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->inputs(), 0).Split(ctx->outputs(), 0).Build(); + auto builder = ctx->NewBuilder().Split(ctx->inputs(), 0).Split(ctx->outputs(), 0); + if (ctx->user_op_conf().has_input("num_valid_sparse_feature", 0)) { + builder.Broadcast(user_op::OpArg("num_valid_sparse_feature", 0)); + } + builder.Build(); return Maybe::Ok(); } /* static */ Maybe FusedDotFeatureInteractionOp::InferDataType(user_op::InferContext* ctx) { const int64_t feature_input_size = ctx->input_size("features"); CHECK_GE_OR_RETURN(feature_input_size, 1); - const auto& first_feature_dtype = ctx->InputDType("features", 0); + DataType first_feature_dtype = ctx->InputDType("features", 0); for (int64_t i = 1; i < feature_input_size; ++i) { - CHECK_EQ_OR_RETURN(first_feature_dtype, ctx->InputDType("features", i)); + CHECK_EQ_OR_RETURN(first_feature_dtype, ctx->InputDType("features", i)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("features", i)) + << ", but got " << DataType_Name(first_feature_dtype); } if (ctx->has_input("output_concat", 0)) { - CHECK_EQ_OR_RETURN(first_feature_dtype, ctx->InputDType("output_concat", 0)); + CHECK_EQ_OR_RETURN(first_feature_dtype, ctx->InputDType("output_concat", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("output_concat", 0)) + << ", but got " << DataType_Name(first_feature_dtype); } - *ctx->OutputDType("out", 0) = first_feature_dtype; + if (ctx->has_input("sparse_feature", 0)) { + CHECK_EQ_OR_RETURN(first_feature_dtype, ctx->InputDType("sparse_feature", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("sparse_feature", 0)) + << ", but got " << DataType_Name(first_feature_dtype); + } + ctx->SetOutputDType("out", 0, first_feature_dtype); return Maybe::Ok(); } @@ -86,11 +114,14 @@ namespace oneflow { CHECK_EQ_OR_RETURN(ctx->output_size("features_grad"), ctx->input_size("features")) << "features_grad and features must have same size"; for (int64_t i = 0; i < ctx->output_size("features_grad"); ++i) { - *ctx->OutputShape("features_grad", i) = ctx->InputShape("features", i); + ctx->SetOutputShape("features_grad", i, ctx->InputShape("features", i)); } if (ctx->has_output("output_concat_grad", 0)) { const int32_t output_concat_grad_dim = ctx->Attr("output_concat_grad_dim"); - *ctx->OutputShape("output_concat_grad", 0) = Shape({batch_size, output_concat_grad_dim}); + ctx->SetOutputShape("output_concat_grad", 0, Shape({batch_size, output_concat_grad_dim})); + } + if (ctx->has_output("sparse_feature_grad", 0)) { + ctx->SetOutputShape("sparse_feature_grad", 0, ctx->InputShape("sparse_feature", 0)); } return Maybe::Ok(); } @@ -101,53 +132,27 @@ namespace oneflow { } /* static */ Maybe FusedDotFeatureInteractionGradOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->inputs(), 0).Split(ctx->outputs(), 0).Build(); + auto builder = ctx->NewBuilder().Split(ctx->inputs(), 0).Split(ctx->outputs(), 0); + if (ctx->user_op_conf().has_input("num_valid_sparse_feature", 0)) { + builder.Broadcast(user_op::OpArg("num_valid_sparse_feature", 0)); + } + builder.Build(); return Maybe::Ok(); } /* static */ Maybe FusedDotFeatureInteractionGradOp::InferDataType( user_op::InferContext* ctx) { - const auto& dy_dtype = ctx->InputDType("dy", 0); + DataType dy_dtype = ctx->InputDType("dy", 0); for (int64_t i = 0; i < ctx->output_size("features_grad"); ++i) { - *ctx->OutputDType("features_grad", i) = dy_dtype; + ctx->SetOutputDType("features_grad", i, dy_dtype); } if (ctx->has_output("output_concat_grad", 0)) { - *ctx->OutputDType("output_concat_grad", 0) = dy_dtype; + ctx->SetOutputDType("output_concat_grad", 0, dy_dtype); + } + if (ctx->has_output("sparse_feature_grad", 0)) { + ctx->SetOutputDType("sparse_feature_grad", 0, dy_dtype); } return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_dot_feature_interaction") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("fused_dot_feature_interaction_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Attr("self_interaction", op.attr("self_interaction")) - .Attr("pooling", op.attr("pooling")); - for (int64_t i = 0; i < op.input_size("features"); ++i) { - builder.Input("features", op.input("features", i)); - } - if (op.user_op_conf().has_input("output_concat", 0)) { - builder.Output("output_concat_grad") - .Attr("output_concat_grad_dim", - op.TensorDesc4ArgNameAndIndex("output_concat", 0).shape().At(1)); - } - builder.Output("features_grad", op.input_size("features")); - auto grad_op = builder.Build(); - AddOp(grad_op); - - for (int64_t i = 0; i < op.input_size("features"); ++i) { - if (op.NeedGenGradTensor4OpInput("features", i)) { - op.BindGradTensorWithOpInput(grad_op.output("features_grad", i), "features", i); - } - } - if (op.user_op_conf().has_input("output_concat", 0)) { - if (op.NeedGenGradTensor4OpInput("output_concat", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("output_concat_grad", 0), "output_concat", 0); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_get_boundding_boxes_coord_op.cpp b/oneflow/user/ops/fused_get_boundding_boxes_coord_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..dcb4187f2c7434d37c35c712b046b6884e1cb6a4 --- /dev/null +++ b/oneflow/user/ops/fused_get_boundding_boxes_coord_op.cpp @@ -0,0 +1,217 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetBounddingBoxesCoordOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& x1 = ctx->InputTensorDesc("x1", 0); + Shape x1_shape = x1.shape(); + + user_op::TensorDesc* b1_x1 = ctx->MutOutputTensorDesc("b1_x1", 0); + b1_x1->set_is_dynamic(x1.is_dynamic()); + b1_x1->set_shape(x1_shape); + + user_op::TensorDesc* b1_x2 = ctx->MutOutputTensorDesc("b1_x2", 0); + b1_x2->set_is_dynamic(x1.is_dynamic()); + b1_x2->set_shape(x1_shape); + + user_op::TensorDesc* b1_y1 = ctx->MutOutputTensorDesc("b1_y1", 0); + b1_y1->set_is_dynamic(x1.is_dynamic()); + b1_y1->set_shape(x1_shape); + + user_op::TensorDesc* b1_y2 = ctx->MutOutputTensorDesc("b1_y2", 0); + b1_y2->set_is_dynamic(x1.is_dynamic()); + b1_y2->set_shape(x1_shape); + + user_op::TensorDesc* b2_x1 = ctx->MutOutputTensorDesc("b2_x1", 0); + b2_x1->set_is_dynamic(x1.is_dynamic()); + b2_x1->set_shape(x1_shape); + + user_op::TensorDesc* b2_x2 = ctx->MutOutputTensorDesc("b2_x2", 0); + b2_x2->set_is_dynamic(x1.is_dynamic()); + b2_x2->set_shape(x1_shape); + + user_op::TensorDesc* b2_y1 = ctx->MutOutputTensorDesc("b2_y1", 0); + b2_y1->set_is_dynamic(x1.is_dynamic()); + b2_y1->set_shape(x1_shape); + + user_op::TensorDesc* b2_y2 = ctx->MutOutputTensorDesc("b2_y2", 0); + b2_y2->set_is_dynamic(x1.is_dynamic()); + b2_y2->set_shape(x1_shape); + + return Maybe::Ok(); +} + +Maybe FusedGetBounddingBoxesCoordOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetBounddingBoxesCoordOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetBounddingBoxesCoordOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& x1 = ctx->InputTensorDesc("x1", 0); + + user_op::TensorDesc* b1_x1 = ctx->MutOutputTensorDesc("b1_x1", 0); + b1_x1->set_data_type(x1.data_type()); + + user_op::TensorDesc* b1_x2 = ctx->MutOutputTensorDesc("b1_x2", 0); + b1_x2->set_data_type(x1.data_type()); + + user_op::TensorDesc* b1_y1 = ctx->MutOutputTensorDesc("b1_y1", 0); + b1_y1->set_data_type(x1.data_type()); + + user_op::TensorDesc* b1_y2 = ctx->MutOutputTensorDesc("b1_y2", 0); + b1_y2->set_data_type(x1.data_type()); + + user_op::TensorDesc* b2_x1 = ctx->MutOutputTensorDesc("b2_x1", 0); + b2_x1->set_data_type(x1.data_type()); + + user_op::TensorDesc* b2_x2 = ctx->MutOutputTensorDesc("b2_x2", 0); + b2_x2->set_data_type(x1.data_type()); + + user_op::TensorDesc* b2_y1 = ctx->MutOutputTensorDesc("b2_y1", 0); + b2_y1->set_data_type(x1.data_type()); + + user_op::TensorDesc* b2_y2 = ctx->MutOutputTensorDesc("b2_y2", 0); + b2_y2->set_data_type(x1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetBounddingBoxesCoordOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("x1", 0); + FOR_RANGE(int64_t, i, 0, x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("x1", 0), i) + .Split(user_op::OpArg("y1", 0), i) + .Split(user_op::OpArg("w1", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("x2", 0), i) + .Split(user_op::OpArg("y2", 0), i) + .Split(user_op::OpArg("w2", 0), i) + .Split(user_op::OpArg("h2", 0), i) + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetBounddingBoxesCoordGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1_diff = ctx->InputTensorDesc("b1_x1_diff", 0); + + user_op::TensorDesc* x1_diff = ctx->MutOutputTensorDesc("x1_diff", 0); + x1_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + x1_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* y1_diff = ctx->MutOutputTensorDesc("y1_diff", 0); + y1_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + y1_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* w1_diff = ctx->MutOutputTensorDesc("w1_diff", 0); + w1_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + w1_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* h1_diff = ctx->MutOutputTensorDesc("h1_diff", 0); + h1_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + h1_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* x2_diff = ctx->MutOutputTensorDesc("x2_diff", 0); + x2_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + x2_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* y2_diff = ctx->MutOutputTensorDesc("y2_diff", 0); + y2_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + y2_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* w2_diff = ctx->MutOutputTensorDesc("w2_diff", 0); + w2_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + w2_diff->set_shape(b1_x1_diff.shape()); + + user_op::TensorDesc* h2_diff = ctx->MutOutputTensorDesc("h2_diff", 0); + h2_diff->set_is_dynamic(b1_x1_diff.is_dynamic()); + h2_diff->set_shape(b1_x1_diff.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetBounddingBoxesCoordGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetBounddingBoxesCoordGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetBounddingBoxesCoordGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1_diff = ctx->InputTensorDesc("b1_x1_diff", 0); + + user_op::TensorDesc* x1_diff = ctx->MutOutputTensorDesc("x1_diff", 0); + x1_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* y1_diff = ctx->MutOutputTensorDesc("y1_diff", 0); + y1_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* w1_diff = ctx->MutOutputTensorDesc("w1_diff", 0); + w1_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* h1_diff = ctx->MutOutputTensorDesc("h1_diff", 0); + h1_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* x2_diff = ctx->MutOutputTensorDesc("x2_diff", 0); + x2_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* y2_diff = ctx->MutOutputTensorDesc("y2_diff", 0); + y2_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* w2_diff = ctx->MutOutputTensorDesc("w2_diff", 0); + w2_diff->set_data_type(b1_x1_diff.data_type()); + + user_op::TensorDesc* h2_diff = ctx->MutOutputTensorDesc("h2_diff", 0); + h2_diff->set_data_type(b1_x1_diff.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetBounddingBoxesCoordGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1_diff = + ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1_diff", 0); + FOR_RANGE(int64_t, i, 0, b1_x1_diff.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1_diff", 0), i) + .Split(user_op::OpArg("b1_x2_diff", 0), i) + .Split(user_op::OpArg("b1_y1_diff", 0), i) + .Split(user_op::OpArg("b1_y2_diff", 0), i) + .Split(user_op::OpArg("b2_x1_diff", 0), i) + .Split(user_op::OpArg("b2_x2_diff", 0), i) + .Split(user_op::OpArg("b2_y1_diff", 0), i) + .Split(user_op::OpArg("b2_y2_diff", 0), i) + .Split(user_op::OpArg("x1_diff", 0), i) + .Split(user_op::OpArg("y1_diff", 0), i) + .Split(user_op::OpArg("w1_diff", 0), i) + .Split(user_op::OpArg("h1_diff", 0), i) + .Split(user_op::OpArg("x2_diff", 0), i) + .Split(user_op::OpArg("y2_diff", 0), i) + .Split(user_op::OpArg("w2_diff", 0), i) + .Split(user_op::OpArg("h2_diff", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_get_ciou_diagonal_angle_op.cpp b/oneflow/user/ops/fused_get_ciou_diagonal_angle_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c3244eba9edf9f5cd288bcac33376e8f9fbf2d33 --- /dev/null +++ b/oneflow/user/ops/fused_get_ciou_diagonal_angle_op.cpp @@ -0,0 +1,155 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetCiouDiagonalAngleOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + const user_op::TensorDesc& h1 = ctx->InputTensorDesc("h1", 0); + const user_op::TensorDesc& w2 = ctx->InputTensorDesc("w2", 0); + const user_op::TensorDesc& h2 = ctx->InputTensorDesc("h2", 0); + + CHECK_EQ_OR_RETURN(w1.shape(), h1.shape()); + CHECK_EQ_OR_RETURN(w1.shape(), w2.shape()); + CHECK_EQ_OR_RETURN(w1.shape(), h2.shape()); + + user_op::TensorDesc* v = ctx->MutOutputTensorDesc("v", 0); + v->set_is_dynamic(w1.is_dynamic()); + v->set_shape(w1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouDiagonalAngleOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetCiouDiagonalAngleOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetCiouDiagonalAngleOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + const user_op::TensorDesc& h1 = ctx->InputTensorDesc("h1", 0); + const user_op::TensorDesc& w2 = ctx->InputTensorDesc("w2", 0); + const user_op::TensorDesc& h2 = ctx->InputTensorDesc("h2", 0); + + CHECK_EQ_OR_RETURN(w1.data_type(), h1.data_type()); + CHECK_EQ_OR_RETURN(w1.data_type(), w2.data_type()); + CHECK_EQ_OR_RETURN(w1.data_type(), h2.data_type()); + + user_op::TensorDesc* v = ctx->MutOutputTensorDesc("v", 0); + v->set_data_type(w1.data_type()); + return Maybe::Ok(); +} + +Maybe FusedGetCiouDiagonalAngleOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("w1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("w1", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("w2", 0), i) + .Split(user_op::OpArg("h2", 0), i) + .Split(user_op::OpArg("v", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetCiouDiagonalAngleGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + const user_op::TensorDesc& h1 = ctx->InputTensorDesc("h1", 0); + const user_op::TensorDesc& w2 = ctx->InputTensorDesc("w2", 0); + const user_op::TensorDesc& h2 = ctx->InputTensorDesc("h2", 0); + const user_op::TensorDesc& v_diff = ctx->InputTensorDesc("v_diff", 0); + + CHECK_EQ_OR_RETURN(w1.shape(), h1.shape()); + CHECK_EQ_OR_RETURN(w1.shape(), w2.shape()); + CHECK_EQ_OR_RETURN(w1.shape(), h2.shape()); + CHECK_EQ_OR_RETURN(w1.shape(), v_diff.shape()); + + user_op::TensorDesc* w1_diff = ctx->MutOutputTensorDesc("w1_diff", 0); + w1_diff->set_is_dynamic(w1.is_dynamic()); + w1_diff->set_shape(w1.shape()); + + user_op::TensorDesc* h1_diff = ctx->MutOutputTensorDesc("h1_diff", 0); + h1_diff->set_is_dynamic(w1.is_dynamic()); + h1_diff->set_shape(w1.shape()); + + user_op::TensorDesc* w2_diff = ctx->MutOutputTensorDesc("w2_diff", 0); + w2_diff->set_is_dynamic(w1.is_dynamic()); + w2_diff->set_shape(w1.shape()); + + user_op::TensorDesc* h2_diff = ctx->MutOutputTensorDesc("h2_diff", 0); + h2_diff->set_is_dynamic(w1.is_dynamic()); + h2_diff->set_shape(w1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouDiagonalAngleGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetCiouDiagonalAngleGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetCiouDiagonalAngleGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + const user_op::TensorDesc& h1 = ctx->InputTensorDesc("h1", 0); + const user_op::TensorDesc& w2 = ctx->InputTensorDesc("w2", 0); + const user_op::TensorDesc& h2 = ctx->InputTensorDesc("h2", 0); + const user_op::TensorDesc& v_diff = ctx->InputTensorDesc("v_diff", 0); + + CHECK_EQ_OR_RETURN(w1.data_type(), h1.data_type()); + CHECK_EQ_OR_RETURN(w1.data_type(), w2.data_type()); + CHECK_EQ_OR_RETURN(w1.data_type(), h2.data_type()); + CHECK_EQ_OR_RETURN(w1.data_type(), v_diff.data_type()); + + user_op::TensorDesc* w1_diff = ctx->MutOutputTensorDesc("w1_diff", 0); + w1_diff->set_is_dynamic(w1.is_dynamic()); + w1_diff->set_data_type(w1.data_type()); + + user_op::TensorDesc* h1_diff = ctx->MutOutputTensorDesc("h1_diff", 0); + h1_diff->set_is_dynamic(w1.is_dynamic()); + h1_diff->set_data_type(w1.data_type()); + + user_op::TensorDesc* w2_diff = ctx->MutOutputTensorDesc("w2_diff", 0); + w2_diff->set_is_dynamic(w1.is_dynamic()); + w2_diff->set_data_type(w1.data_type()); + + user_op::TensorDesc* h2_diff = ctx->MutOutputTensorDesc("h2_diff", 0); + h2_diff->set_is_dynamic(w1.is_dynamic()); + h2_diff->set_data_type(w1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouDiagonalAngleGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& w1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("w1", 0); + FOR_RANGE(int64_t, i, 0, w1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("w1", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("w2", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("v_diff", 0), i) + .Split(user_op::OpArg("w1_diff", 0), i) + .Split(user_op::OpArg("h1_diff", 0), i) + .Split(user_op::OpArg("w2_diff", 0), i) + .Split(user_op::OpArg("h2_diff", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_get_ciou_result_op.cpp b/oneflow/user/ops/fused_get_ciou_result_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..55b224925f3ea6d35a5902eb53989f9d711dafc1 --- /dev/null +++ b/oneflow/user/ops/fused_get_ciou_result_op.cpp @@ -0,0 +1,127 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetCiouResultOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& v = ctx->InputTensorDesc("v", 0); + + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_is_dynamic(v.is_dynamic()); + y->set_shape(v.shape()); + + user_op::TensorDesc* ahpha = ctx->MutOutputTensorDesc("alpha", 0); + ahpha->set_is_dynamic(v.is_dynamic()); + ahpha->set_shape(v.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouResultOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetCiouResultOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetCiouResultOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& v = ctx->InputTensorDesc("v", 0); + + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(v.data_type()); + + user_op::TensorDesc* alpha = ctx->MutOutputTensorDesc("alpha", 0); + alpha->set_data_type(v.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouResultOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& v = ctx->LogicalTensorDesc4InputArgNameAndIndex("v", 0); + FOR_RANGE(int64_t, i, 0, v.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("v", 0), i) + .Split(user_op::OpArg("iou", 0), i) + .Split(user_op::OpArg("rho2", 0), i) + .Split(user_op::OpArg("c2", 0), i) + .Split(user_op::OpArg("y", 0), i) + .Split(user_op::OpArg("alpha", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetCiouResultGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + + user_op::TensorDesc* dv = ctx->MutOutputTensorDesc("dv", 0); + dv->set_is_dynamic(dy.is_dynamic()); + dv->set_shape(dy.shape()); + + user_op::TensorDesc* diou = ctx->MutOutputTensorDesc("diou", 0); + diou->set_is_dynamic(dy.is_dynamic()); + diou->set_shape(dy.shape()); + + user_op::TensorDesc* drho2 = ctx->MutOutputTensorDesc("drho2", 0); + drho2->set_is_dynamic(dy.is_dynamic()); + drho2->set_shape(dy.shape()); + + user_op::TensorDesc* dc2 = ctx->MutOutputTensorDesc("dc2", 0); + dc2->set_is_dynamic(dy.is_dynamic()); + dc2->set_shape(dy.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouResultGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetCiouResultGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetCiouResultGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + + user_op::TensorDesc* dv = ctx->MutOutputTensorDesc("dv", 0); + dv->set_data_type(dy.data_type()); + + user_op::TensorDesc* diou = ctx->MutOutputTensorDesc("diou", 0); + diou->set_data_type(dy.data_type()); + + user_op::TensorDesc* drho2 = ctx->MutOutputTensorDesc("drho2", 0); + drho2->set_data_type(dy.data_type()); + + user_op::TensorDesc* dc2 = ctx->MutOutputTensorDesc("dc2", 0); + dc2->set_data_type(dy.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetCiouResultGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& dy = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); + FOR_RANGE(int64_t, i, 0, dy.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), i) + .Split(user_op::OpArg("alpha", 0), i) + .Split(user_op::OpArg("rho2", 0), i) + .Split(user_op::OpArg("c2", 0), i) + .Split(user_op::OpArg("dv", 0), i) + .Split(user_op::OpArg("diou", 0), i) + .Split(user_op::OpArg("drho2", 0), i) + .Split(user_op::OpArg("dc2", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_get_convex_diagonal_squared_op.cpp b/oneflow/user/ops/fused_get_convex_diagonal_squared_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..f6962b60ba29472c27bb1be9a7d523542f32bb16 --- /dev/null +++ b/oneflow/user/ops/fused_get_convex_diagonal_squared_op.cpp @@ -0,0 +1,161 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetConvexDiagonalSquaredOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + + user_op::TensorDesc* c2 = ctx->MutOutputTensorDesc("c2", 0); + c2->set_is_dynamic(b1_x1.is_dynamic()); + c2->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetConvexDiagonalSquaredOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetConvexDiagonalSquaredOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetConvexDiagonalSquaredOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + + user_op::TensorDesc* c2 = ctx->MutOutputTensorDesc("c2", 0); + c2->set_data_type(b1_x1.data_type()); + return Maybe::Ok(); +} + +Maybe FusedGetConvexDiagonalSquaredOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("c2", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetConvexDiagonalSquaredGradOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y2_diff->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetConvexDiagonalSquaredGradOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return FusedGetConvexDiagonalSquaredGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetConvexDiagonalSquaredGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_data_type(b1_x1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetConvexDiagonalSquaredGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("c2_diff", 0), i) + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("b1_x1_diff", 0), i) + .Split(user_op::OpArg("b1_x2_diff", 0), i) + .Split(user_op::OpArg("b2_x1_diff", 0), i) + .Split(user_op::OpArg("b2_x2_diff", 0), i) + .Split(user_op::OpArg("b1_y1_diff", 0), i) + .Split(user_op::OpArg("b1_y2_diff", 0), i) + .Split(user_op::OpArg("b2_y1_diff", 0), i) + .Split(user_op::OpArg("b2_y2_diff", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_get_intersection_area_op.cpp b/oneflow/user/ops/fused_get_intersection_area_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8bfc42b78635725c8cceecee8e735dae8611cc9a --- /dev/null +++ b/oneflow/user/ops/fused_get_intersection_area_op.cpp @@ -0,0 +1,223 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetIntersectionAreaOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y2.shape()); + + user_op::TensorDesc* inter = ctx->MutOutputTensorDesc("inter", 0); + inter->set_is_dynamic(b1_x1.is_dynamic()); + inter->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetIntersectionAreaOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetIntersectionAreaOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetIntersectionAreaOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y2.data_type()); + + user_op::TensorDesc* inter = ctx->MutOutputTensorDesc("inter", 0); + inter->set_data_type(b1_x1.data_type()); + return Maybe::Ok(); +} + +Maybe FusedGetIntersectionAreaOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("inter", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetIntersectionAreaGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + const user_op::TensorDesc& inter_diff = ctx->InputTensorDesc("inter_diff", 0); + + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b1_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_x2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y1.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), b2_y2.shape()); + CHECK_EQ_OR_RETURN(b1_x1.shape(), inter_diff.shape()); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_x2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b1_y2_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y1_diff->set_shape(b1_x1.shape()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_is_dynamic(b1_x1.is_dynamic()); + b2_y2_diff->set_shape(b1_x1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetIntersectionAreaGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetIntersectionAreaGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetIntersectionAreaGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->InputTensorDesc("b1_x1", 0); + const user_op::TensorDesc& b1_x2 = ctx->InputTensorDesc("b1_x2", 0); + const user_op::TensorDesc& b1_y1 = ctx->InputTensorDesc("b1_y1", 0); + const user_op::TensorDesc& b1_y2 = ctx->InputTensorDesc("b1_y2", 0); + const user_op::TensorDesc& b2_x1 = ctx->InputTensorDesc("b2_x1", 0); + const user_op::TensorDesc& b2_x2 = ctx->InputTensorDesc("b2_x2", 0); + const user_op::TensorDesc& b2_y1 = ctx->InputTensorDesc("b2_y1", 0); + const user_op::TensorDesc& b2_y2 = ctx->InputTensorDesc("b2_y2", 0); + const user_op::TensorDesc& inter_diff = ctx->InputTensorDesc("inter_diff", 0); + + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b1_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_x2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y1.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), b2_y2.data_type()); + CHECK_EQ_OR_RETURN(b1_x1.data_type(), inter_diff.data_type()); + + user_op::TensorDesc* b1_x1_diff = ctx->MutOutputTensorDesc("b1_x1_diff", 0); + b1_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_x2_diff = ctx->MutOutputTensorDesc("b1_x2_diff", 0); + b1_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x1_diff = ctx->MutOutputTensorDesc("b2_x1_diff", 0); + b2_x1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_x2_diff = ctx->MutOutputTensorDesc("b2_x2_diff", 0); + b2_x2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y1_diff = ctx->MutOutputTensorDesc("b1_y1_diff", 0); + b1_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b1_y2_diff = ctx->MutOutputTensorDesc("b1_y2_diff", 0); + b1_y2_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y1_diff = ctx->MutOutputTensorDesc("b2_y1_diff", 0); + b2_y1_diff->set_data_type(b1_x1.data_type()); + + user_op::TensorDesc* b2_y2_diff = ctx->MutOutputTensorDesc("b2_y2_diff", 0); + b2_y2_diff->set_data_type(b1_x1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetIntersectionAreaGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& b1_x1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("b1_x1", 0); + FOR_RANGE(int64_t, i, 0, b1_x1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("b1_x1", 0), i) + .Split(user_op::OpArg("b1_x2", 0), i) + .Split(user_op::OpArg("b1_y1", 0), i) + .Split(user_op::OpArg("b1_y2", 0), i) + .Split(user_op::OpArg("b2_x1", 0), i) + .Split(user_op::OpArg("b2_x2", 0), i) + .Split(user_op::OpArg("b2_y1", 0), i) + .Split(user_op::OpArg("b2_y2", 0), i) + .Split(user_op::OpArg("inter_diff", 0), i) + .Split(user_op::OpArg("b1_x1_diff", 0), i) + .Split(user_op::OpArg("b1_x2_diff", 0), i) + .Split(user_op::OpArg("b1_y1_diff", 0), i) + .Split(user_op::OpArg("b1_y2_diff", 0), i) + .Split(user_op::OpArg("b2_x1_diff", 0), i) + .Split(user_op::OpArg("b2_x2_diff", 0), i) + .Split(user_op::OpArg("b2_y1_diff", 0), i) + .Split(user_op::OpArg("b2_y2_diff", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_get_iou_op.cpp b/oneflow/user/ops/fused_get_iou_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..70eee4ff4b3616ffc1e0855a130d62f5621d54dc --- /dev/null +++ b/oneflow/user/ops/fused_get_iou_op.cpp @@ -0,0 +1,114 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe FusedGetIouOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + + user_op::TensorDesc* iou = ctx->MutOutputTensorDesc("iou", 0); + iou->set_is_dynamic(w1.is_dynamic()); + iou->set_shape(w1.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetIouOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetIouOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetIouOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& w1 = ctx->InputTensorDesc("w1", 0); + + user_op::TensorDesc* iou = ctx->MutOutputTensorDesc("iou", 0); + iou->set_data_type(w1.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetIouOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& w1 = ctx->LogicalTensorDesc4InputArgNameAndIndex("w1", 0); + FOR_RANGE(int64_t, i, 0, w1.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("w1", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("w2", 0), i) + .Split(user_op::OpArg("h2", 0), i) + .Split(user_op::OpArg("inter", 0), i) + .Split(user_op::OpArg("iou", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe FusedGetIouGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& diou = ctx->InputTensorDesc("diou", 0); + + user_op::TensorDesc* dw1 = ctx->MutOutputTensorDesc("dw1", 0); + dw1->set_is_dynamic(diou.is_dynamic()); + dw1->set_shape(diou.shape()); + + user_op::TensorDesc* dh1 = ctx->MutOutputTensorDesc("dh1", 0); + dh1->set_is_dynamic(diou.is_dynamic()); + dh1->set_shape(diou.shape()); + + user_op::TensorDesc* dinter = ctx->MutOutputTensorDesc("dinter", 0); + dinter->set_is_dynamic(diou.is_dynamic()); + dinter->set_shape(diou.shape()); + + return Maybe::Ok(); +} + +Maybe FusedGetIouGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return FusedGetIouGradOp::InferLogicalTensorDesc(ctx); +} + +Maybe FusedGetIouGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& diou = ctx->InputTensorDesc("diou", 0); + + user_op::TensorDesc* dw1 = ctx->MutOutputTensorDesc("dw1", 0); + dw1->set_data_type(diou.data_type()); + + user_op::TensorDesc* dh1 = ctx->MutOutputTensorDesc("dh1", 0); + dh1->set_data_type(diou.data_type()); + + user_op::TensorDesc* dinter = ctx->MutOutputTensorDesc("dinter", 0); + dinter->set_data_type(diou.data_type()); + + return Maybe::Ok(); +} + +Maybe FusedGetIouGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& dy = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); + FOR_RANGE(int64_t, i, 0, dy.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("diou", 0), i) + .Split(user_op::OpArg("w1", 0), i) + .Split(user_op::OpArg("h1", 0), i) + .Split(user_op::OpArg("w2", 0), i) + .Split(user_op::OpArg("h2", 0), i) + .Split(user_op::OpArg("inter", 0), i) + .Split(user_op::OpArg("dw1", 0), i) + .Split(user_op::OpArg("dh1", 0), i) + .Split(user_op::OpArg("dinter", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_glu_op.cpp b/oneflow/user/ops/fused_glu_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2b446c9874cd242aaf34db5e49d3c45495396968 --- /dev/null +++ b/oneflow/user/ops/fused_glu_op.cpp @@ -0,0 +1,174 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ auto FusedGluOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + // check existance of optional args + bool is_split_mode = false; + if (ctx->user_op_conf().has_input("v", 0) && ctx->user_op_conf().has_input("c", 0)) { + is_split_mode = true; + } else { + CHECK(!(ctx->user_op_conf().has_input("v", 0) || ctx->user_op_conf().has_input("c", 0))) + << "\'v\' and \'c\' should have consistant existance"; + } + + // data parallelism + for (int64_t i = 0; i < ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape().NumAxes() - 1; + ++i) { + if (is_split_mode) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Broadcast(user_op::OpArg("w", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .Broadcast(user_op::OpArg("v", 0)) + .Broadcast(user_op::OpArg("c", 0)) + .Split(ctx->outputs(), i) + .Build(); + } else { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Broadcast(user_op::OpArg("w", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .Split(ctx->outputs(), i) + .Build(); + } + } + + // model parallelism + if (is_split_mode) { + ctx->NewBuilder() + .Broadcast(user_op::OpArg("x", 0)) + .Split(user_op::OpArg("w", 0), 0) + .Split(user_op::OpArg("b", 0), 0) + .Split(user_op::OpArg("v", 0), 0) + .Split(user_op::OpArg("c", 0), 0) + .Split(ctx->outputs(), + ctx->LogicalTensorDesc4InputArgNameAndIndex("y", 0).shape().NumAxes() - 1) + .Build(); + } + + return Maybe::Ok(); +} + +/* static */ auto FusedGluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + // obtain input shape + const Shape& x_shape = ctx->InputShape("x", 0); + const Shape& w_shape = ctx->InputShape("w", 0); + const Shape& b_shape = ctx->InputShape("b", 0); + + // check existance of optional args + bool is_split_mode = false; + if (ctx->has_input("v", 0) && ctx->has_input("c", 0)) { + is_split_mode = true; + } else { + CHECK(!(ctx->has_input("v", 0) || ctx->has_input("c", 0))); + } + + // check dimensions of x, w and b + CHECK_GE_OR_RETURN(x_shape.NumAxes(), 2) + << "number of axes of \'x\' should have be greater than 1, yet get " << x_shape.NumAxes(); + CHECK_EQ_OR_RETURN(w_shape.NumAxes(), 2) + << "number of axes of \'w\' should have be equal to 2, yet get " << w_shape.NumAxes(); + CHECK_EQ_OR_RETURN(b_shape.NumAxes(), 1) + << "number of axes of \'b\' should have be equal to 1, yet get " << b_shape.NumAxes(); + + // check input shapes of w and b + size_t x_num_axes = x_shape.NumAxes(); + CHECK_EQ_OR_RETURN(w_shape.At(1), x_shape.At(x_num_axes - 1)) + << "dimension 1 of \'w\'(" << w_shape.At(1) + << ") is not consistant with the last dimension of \'x\'(" << x_shape.At(x_num_axes - 1) + << ")"; + CHECK_EQ_OR_RETURN(b_shape.At(0), w_shape.At(0)) + << "dimension 0 of \'b\'(" << b_shape.At(0) + << ") is not consistant with dimension 0 of \'w\'(" << w_shape.At(0) << ")"; + if (!is_split_mode) { + CHECK_EQ_OR_RETURN(w_shape.At(1) % 2, 0) << "dimension 1 of \'w\' is not divisible by 2"; + } + + // check both dimensions and input shapes of v and c (optional) + if (is_split_mode) { + const Shape& v_shape = ctx->InputShape("v", 0); + const Shape& c_shape = ctx->InputShape("c", 0); + + CHECK_EQ_OR_RETURN(v_shape.NumAxes(), 2) + << "number of axes of \'v\' should have be equal to 2, yet get " << v_shape.NumAxes(); + CHECK_EQ_OR_RETURN(c_shape.NumAxes(), 1) + << "number of axes of \'c\' should have be equal to 1, yet get " << c_shape.NumAxes(); + + CHECK_OR_RETURN(v_shape == w_shape) << "the shape of \'v\' is not consistant with \'w\'"; + CHECK_OR_RETURN(c_shape == b_shape) << "the shape of \'c\' is not consistant with \'b\'"; + } + + // set shape of the output tensor y + Shape y_shape = x_shape; // borrow from input shape + size_t y_num_axes = x_num_axes; + if (is_split_mode) { + y_shape.Set(y_num_axes - 1, w_shape.At(0)); + } else { + y_shape.Set(y_num_axes - 1, w_shape.At(0) / 2); + } + user_op::TensorDesc* y_tensor = ctx->MutOutputTensorDesc("y", 0); + y_tensor->set_shape(y_shape); + + // set shape of the output tensors of both matmul_wx and matmul_vx + Shape matmul_wx_shape = x_shape; // borrow from input shape + matmul_wx_shape.Set(x_num_axes - 1, w_shape.At(0)); + user_op::TensorDesc* matmul_wx_tensor = ctx->MutOutputTensorDesc("matmul_wx", 0); + matmul_wx_tensor->set_shape(matmul_wx_shape); + if (is_split_mode) { + user_op::TensorDesc* matmul_vx_tensor = ctx->MutOutputTensorDesc("matmul_vx", 0); + matmul_vx_tensor->set_shape(y_shape); + } + + return Maybe::Ok(); +} + +/* static */ auto FusedGluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferLogicalTensorDesc(ctx); +} + +/* static */ auto FusedGluOp::InferDataType(user_op::InferContext* ctx) -> Maybe { + // obtain input data types + DataType x_dtype = ctx->InputDType("x", 0); + + // check types of x, w and b + CHECK_EQ_OR_RETURN(ctx->InputDType("w", 0), x_dtype) + << "data type of \'w\' is not consitant with \'x\'"; + CHECK_EQ_OR_RETURN(ctx->InputDType("b", 0), x_dtype) + << "data type of \'b\' is not consitant with \'x\'"; + + bool is_split_mode = ctx->has_input("v", 0) && ctx->has_input("c", 0); + + // check types of v and c (optional) + if (is_split_mode) { + CHECK_EQ_OR_RETURN(ctx->InputDType("v", 0), x_dtype) + << "data type of \'v\' is not consitant with \'x\'"; + CHECK_EQ_OR_RETURN(ctx->InputDType("c", 0), x_dtype) + << "data type of \'c\' is not consitant with \'x\'"; + } + + // set output data type + ctx->SetOutputDType("y", 0, x_dtype); + ctx->SetOutputDType("matmul_wx", 0, x_dtype); + if (is_split_mode) { ctx->SetOutputDType("matmul_vx", 0, x_dtype); } + + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_gru_cell_op.cpp b/oneflow/user/ops/fused_gru_cell_op.cpp index b9b6b7063f1bf2df8f0681b15ad9c6a99462c8bc..0eb390149be8663cc55542f810bcbe8a91b9e953 100644 --- a/oneflow/user/ops/fused_gru_cell_op.cpp +++ b/oneflow/user/ops/fused_gru_cell_op.cpp @@ -14,15 +14,14 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/expand_kernel_utils.h" #include "oneflow/core/framework/op_generated.h" namespace oneflow { /* static */ Maybe FusedGruCellOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& hx_shape = ctx->InputShape("hx", 0); - *ctx->OutputShape("hy", 0) = hx_shape; - *ctx->OutputShape("workspace", 0) = Shape({hx_shape.At(0), hx_shape.At(1) * 5}); + ctx->SetOutputShape("hy", 0, hx_shape); + ctx->SetOutputShape("workspace", 0, Shape({hx_shape.At(0), hx_shape.At(1) * 5})); return Maybe::Ok(); } @@ -60,23 +59,23 @@ namespace oneflow { } /* static */ Maybe FusedGruCellOp::InferDataType(user_op::InferContext* ctx) { - const oneflow::DataType& in_types = ctx->InputDType("hx", 0); - *ctx->OutputDType("hy", 0) = in_types; - *ctx->OutputDType("workspace", 0) = in_types; + DataType in_types = ctx->InputDType("hx", 0); + ctx->SetOutputDType("hy", 0, in_types); + ctx->SetOutputDType("workspace", 0, in_types); return Maybe::Ok(); } /* static */ Maybe FusedGruCellGradOp ::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& grad_hy_shape = ctx->InputShape("grad_hy", 0); DimVector dim_vec({grad_hy_shape.At(0), grad_hy_shape.At(1) * 3}); - *ctx->OutputShape("grad_input_gates", 0) = Shape(dim_vec); - *ctx->OutputShape("grad_hidden_gates", 0) = Shape(dim_vec); + ctx->SetOutputShape("grad_input_gates", 0, Shape(dim_vec)); + ctx->SetOutputShape("grad_hidden_gates", 0, Shape(dim_vec)); - if (ctx->has_output("grad_hx", 0)) { *ctx->OutputShape("grad_hx", 0) = grad_hy_shape; } + if (ctx->has_output("grad_hx", 0)) { ctx->SetOutputShape("grad_hx", 0, grad_hy_shape); } if (ctx->has_output("grad_input_bias", 0) && ctx->has_output("grad_hidden_bias", 0)) { - *ctx->OutputShape("grad_input_bias", 0) = Shape({grad_hy_shape.At(1) * 3}); - *ctx->OutputShape("grad_hidden_bias", 0) = Shape({grad_hy_shape.At(1) * 3}); + ctx->SetOutputShape("grad_input_bias", 0, Shape({grad_hy_shape.At(1) * 3})); + ctx->SetOutputShape("grad_hidden_bias", 0, Shape({grad_hy_shape.At(1) * 3})); } return Maybe::Ok(); @@ -117,65 +116,17 @@ namespace oneflow { } /* static */ Maybe FusedGruCellGradOp ::InferDataType(user_op::InferContext* ctx) { - const oneflow::DataType& in_types = ctx->InputDType("grad_hy", 0); - *ctx->OutputDType("grad_input_gates", 0) = in_types; - *ctx->OutputDType("grad_hidden_gates", 0) = in_types; - if (ctx->has_output("grad_hx", 0)) { *ctx->OutputDType("grad_hx", 0) = in_types; } - if (ctx->has_output("grad_input_bias", 0)) { *ctx->OutputDType("grad_input_bias", 0) = in_types; } + DataType in_types = ctx->InputDType("grad_hy", 0); + ctx->SetOutputDType("grad_input_gates", 0, in_types); + ctx->SetOutputDType("grad_hidden_gates", 0, in_types); + if (ctx->has_output("grad_hx", 0)) { ctx->SetOutputDType("grad_hx", 0, in_types); } + if (ctx->has_output("grad_input_bias", 0)) { + ctx->SetOutputDType("grad_input_bias", 0, in_types); + } if (ctx->has_output("grad_hidden_bias", 0)) { - *ctx->OutputDType("grad_hidden_bias", 0) = in_types; + ctx->SetOutputDType("grad_hidden_bias", 0, in_types); } return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_gru_cell") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - builder.OpTypeName("fused_gru_cell_grad") - .InputBind("grad_hy", ctx->FwOp().output_grad("hy", 0)) - .InputBind("workspace", ctx->FwOp().output("workspace", 0)) - .Output("grad_input_gates") - .Output("grad_hidden_gates"); - - if (ctx->FwOp().NeedGenGradTensor4OpInput("hx", 0)) { builder.Output("grad_hx"); } - - if (ctx->FwOp().user_op_conf().has_input("input_bias", 0) - && ctx->FwOp().user_op_conf().has_input("hidden_bias", 0)) { - builder.Output("grad_input_bias"); - builder.Output("grad_hidden_bias"); - } - return builder.Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("input_gates", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_input_gates", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("hidden_gates", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_hidden_gates", 0); - }); - - if (ctx->FwOp().NeedGenGradTensor4OpInput("hx", 0)) { - ctx->FwOp().InputGradBind(user_op::OpArg("hx", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_hx", 0); - }); - } - - if (ctx->FwOp().user_op_conf().has_input("input_bias", 0) - && ctx->FwOp().user_op_conf().has_input("hidden_bias", 0)) { - ctx->FwOp().InputGradBind(user_op::OpArg("input_bias", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_input_bias", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("hidden_bias", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_hidden_bias", 0); - }); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_lstm_cell_op.cpp b/oneflow/user/ops/fused_lstm_cell_op.cpp index 5ce8add4f7b7bf3a1d385b4177e673e172bc338c..c58c83e8c4f1573f7900c24f7efdb4dbcd3e463a 100644 --- a/oneflow/user/ops/fused_lstm_cell_op.cpp +++ b/oneflow/user/ops/fused_lstm_cell_op.cpp @@ -14,16 +14,15 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" -#include "oneflow/user/kernels/expand_kernel_utils.h" #include "oneflow/core/framework/op_generated.h" namespace oneflow { /* static */ Maybe FusedLstmCellOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& cx_shape = ctx->InputShape("cx", 0); - *ctx->OutputShape("hy", 0) = cx_shape; - *ctx->OutputShape("cy", 0) = cx_shape; - *ctx->OutputShape("workspace", 0) = ctx->InputShape("input_gates", 0); + ctx->SetOutputShape("hy", 0, cx_shape); + ctx->SetOutputShape("cy", 0, cx_shape); + ctx->SetOutputShape("workspace", 0, ctx->InputShape("input_gates", 0)); return Maybe::Ok(); } @@ -63,20 +62,22 @@ namespace oneflow { } /* static */ Maybe FusedLstmCellOp::InferDataType(user_op::InferContext* ctx) { - const oneflow::DataType& in_types = ctx->InputDType("cx", 0); - *ctx->OutputDType("hy", 0) = in_types; - *ctx->OutputDType("cy", 0) = in_types; - *ctx->OutputDType("workspace", 0) = in_types; + DataType in_types = ctx->InputDType("cx", 0); + ctx->SetOutputDType("hy", 0, in_types); + ctx->SetOutputDType("cy", 0, in_types); + ctx->SetOutputDType("workspace", 0, in_types); return Maybe::Ok(); } /* static */ Maybe FusedLstmCellGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("grad_gates", 0) = ctx->InputShape("workspace", 0); + ctx->SetOutputShape("grad_gates", 0, ctx->InputShape("workspace", 0)); - if (ctx->has_output("grad_cx", 0)) { *ctx->OutputShape("grad_cx", 0) = ctx->InputShape("cx", 0); } + if (ctx->has_output("grad_cx", 0)) { + ctx->SetOutputShape("grad_cx", 0, ctx->InputShape("cx", 0)); + } if (ctx->has_output("grad_bias", 0)) { - *ctx->OutputShape("grad_bias", 0) = Shape({ctx->InputShape("workspace", 0).At(1)}); + ctx->SetOutputShape("grad_bias", 0, Shape({ctx->InputShape("workspace", 0).At(1)})); } return Maybe::Ok(); @@ -117,61 +118,11 @@ namespace oneflow { } /* static */ Maybe FusedLstmCellGradOp::InferDataType(user_op::InferContext* ctx) { - const oneflow::DataType& in_types = ctx->InputDType("grad_hy", 0); - *ctx->OutputDType("grad_gates", 0) = in_types; - if (ctx->has_output("grad_cx", 0)) { *ctx->OutputDType("grad_cx", 0) = in_types; } - if (ctx->has_output("grad_bias", 0)) { *ctx->OutputDType("grad_bias", 0) = in_types; } + DataType in_types = ctx->InputDType("grad_hy", 0); + ctx->SetOutputDType("grad_gates", 0, in_types); + if (ctx->has_output("grad_cx", 0)) { ctx->SetOutputDType("grad_cx", 0, in_types); } + if (ctx->has_output("grad_bias", 0)) { ctx->SetOutputDType("grad_bias", 0, in_types); } return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_lstm_cell") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - builder.OpTypeName("fused_lstm_cell_grad") - .InputBind("grad_hy", ctx->FwOp().output_grad("hy", 0)) - .InputBind("grad_cy", ctx->FwOp().output_grad("cy", 0)) - .InputBind("cx", ctx->FwOp().input("cx", 0)) - .InputBind("cy", ctx->FwOp().output("cy", 0)) - .InputBind("workspace", ctx->FwOp().output("workspace", 0)) - .Output("grad_gates"); - - if (ctx->FwOp().NeedGenGradTensor4OpInput("cx", 0)) { builder.Output("grad_cx"); } - - if (ctx->FwOp().user_op_conf().has_input("input_bias", 0) - && ctx->FwOp().user_op_conf().has_input("hidden_bias", 0)) { - builder.Output("grad_bias"); - } - return builder.Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("input_gates", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_gates", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("hidden_gates", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_gates", 0); - }); - - if (ctx->FwOp().NeedGenGradTensor4OpInput("cx", 0)) { - ctx->FwOp().InputGradBind(user_op::OpArg("cx", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_cx", 0); - }); - } - - if (ctx->FwOp().user_op_conf().has_input("input_bias", 0)) { - ctx->FwOp().InputGradBind(user_op::OpArg("input_bias", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_bias", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("hidden_bias", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("grad_bias", 0); - }); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_matmul_bias_add_relu_dropout_op.cpp b/oneflow/user/ops/fused_matmul_bias_add_relu_dropout_op.cpp index c473ba7ea577ba8754e7131ee5e0b1156979c686..022159776c8f63d05040cd84d197b67a2d6ace36 100644 --- a/oneflow/user/ops/fused_matmul_bias_add_relu_dropout_op.cpp +++ b/oneflow/user/ops/fused_matmul_bias_add_relu_dropout_op.cpp @@ -65,12 +65,12 @@ Maybe InferTensorDesc4FusedMatmul(user_op::InferContext* ctx) { // Set Middle result shape. long cublas_aligned_aux_ld = AlignReluAuxLd(cublas_aux_ld); int64_t aux_size = cublas_aligned_aux_ld / 32; // Cause we use int32_t as dtype - *ctx->OutputShape("cublas_aux", idx) = Shape({m, aux_size}); - *ctx->OutputShape("hidden", idx) = Shape({m, n}); + ctx->SetOutputShape("cublas_aux", idx, Shape({m, aux_size})); + ctx->SetOutputShape("hidden", idx, Shape({m, n})); // Set for next layer. k = n; } - *ctx->OutputShape("out", 0) = {m, n}; + ctx->SetOutputShape("out", 0, Shape({m, n})); return Maybe::Ok(); } @@ -81,20 +81,21 @@ Maybe InferDataType4Matmul(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) - << "The Input's datatype should be equal. "; + << "InferDataType Failed. Expected " << DataType_Name(in_desc.data_type()) << ", but got " + << DataType_Name(first_in_desc.data_type()); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(first_in_desc.data_type()); for (int32_t i = 0; i < ctx->output_size("hidden"); i++) { - user_op::TensorDesc* hidden_desc = ctx->OutputTensorDesc("hidden", i); - *hidden_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* hidden_desc = ctx->MutOutputTensorDesc("hidden", i); + hidden_desc->set_data_type(first_in_desc.data_type()); } for (int32_t i = 0; i < ctx->output_size("cublas_aux"); i++) { - user_op::TensorDesc* aux_desc = ctx->OutputTensorDesc("cublas_aux", i); - *aux_desc->mut_data_type() = DataType::kInt32; + user_op::TensorDesc* aux_desc = ctx->MutOutputTensorDesc("cublas_aux", i); + aux_desc->set_data_type(DataType::kInt32); } return Maybe::Ok(); @@ -136,128 +137,4 @@ Maybe InferDataType4Matmul(user_op::InferContext* ctx) { return InferDataType4Matmul(ctx); } -REGISTER_USER_OP_GRAD("fused_matmul_bias_add_relu_dropout") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - bool skip_final_activation = op.attr("skip_final_activation"); - const std::vector dropout_rate_list = op.attr>("dropout_rate_list"); - float scale = 1.0; - float rate = 0.0; - int64_t weight_num = op.input_size("weights"); - - std::string last_bias_grad; - if (!skip_final_activation || (dropout_rate_list[weight_num - 1] != 0.0f)) { - // step1: Get last layer's relu+dropout grad. - rate = dropout_rate_list[weight_num - 1]; - if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } - user_op::UserOpConfWrapperBuilder relu_grad_builder(op.op_name() - + "fused_relu_dropout_grad"); - user_op::UserOpConfWrapper relu_dropout_grad_op = - relu_grad_builder.Op("fused_relu_dropout_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("mask", op.output("cublas_aux", weight_num - 1)) - .Attr("scale", scale) - .Output("dx") - .Build(); - AddOp(relu_dropout_grad_op); - last_bias_grad = relu_dropout_grad_op.output("dx", 0); - } else { - last_bias_grad = op.GetGradTensorWithOpOutput("out", 0); - } - - // step2: Get last layer's bias grad. - std::vector reduce_axes_vec{0}; - user_op::UserOpConfWrapperBuilder bias_grad_builder(op.op_name() + "_bias_grad"); - user_op::UserOpConfWrapper bias_grad_op = bias_grad_builder.Op("reduce_sum") - .Input("input_tensor", last_bias_grad) - .Output("output_tensor") - .Attr("axis", reduce_axes_vec) - .Attr("keepdims", false) - .Build(); - AddOp(bias_grad_op); - if (op.NeedGenGradTensor4OpInput("biases", weight_num - 1)) { - op.BindGradTensorWithOpInput(bias_grad_op.output("output_tensor", 0), "biases", - weight_num - 1); - } - std::string cublas_dy = last_bias_grad; - - for (int32_t hidden_layer_idx = weight_num - 1; hidden_layer_idx > 0; hidden_layer_idx--) { - rate = dropout_rate_list[hidden_layer_idx - 1]; - scale = 1.0; - if (rate < 1.0f) { scale = 1.0f / (1.0f - rate); } - user_op::UserOpConfWrapperBuilder cublas_bias_add_relu_matmul_grad_builder( - op.op_name() + "_cublas_bias_add_relu_matmul_grad_" + std::to_string(hidden_layer_idx)); - user_op::UserOpConfWrapper cublas_bias_add_relu_matmul_grad_op = - cublas_bias_add_relu_matmul_grad_builder.Op("cublas_bias_add_relu_matmul_grad") - .Input("dy", cublas_dy) - .Input("weight", op.input("weights", hidden_layer_idx)) - .Input("aux", op.output("cublas_aux", hidden_layer_idx - 1)) - .Attr("alpha", scale) - .Output("d_grad") - .Output("d_bias") - .Build(); - AddOp(cublas_bias_add_relu_matmul_grad_op); - if (op.NeedGenGradTensor4OpInput("biases", hidden_layer_idx - 1)) { - op.BindGradTensorWithOpInput(cublas_bias_add_relu_matmul_grad_op.output("d_bias", 0), - "biases", - hidden_layer_idx - 1); // previous layers bias grad - } - - user_op::UserOpConfWrapperBuilder matmul_weight_grad_builder( - op.op_name() + "_matmul_a_grad_" + std::to_string(hidden_layer_idx)); - user_op::UserOpConfWrapper matmul_weight_grad_op = - matmul_weight_grad_builder.Op("matmul") - .Input("a", cublas_dy) - .Input("b", op.output("hidden", hidden_layer_idx - 1)) - .Output("out") - .Attr("transpose_a", true) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_weight_grad_op); - if (op.NeedGenGradTensor4OpInput("weights", hidden_layer_idx)) { - op.BindGradTensorWithOpInput(matmul_weight_grad_op.output("out", 0), "weights", - hidden_layer_idx); - } - // update dgrad - cublas_dy = cublas_bias_add_relu_matmul_grad_op.output("d_grad", 0); - } - - // For the first layer, we need to use 2 matmul to get grads. - std::string last_dy = last_bias_grad; - if (weight_num != 1) { last_dy = cublas_dy; } - // dx: - user_op::UserOpConfWrapperBuilder matmul_input_grad_builder(op.op_name() - + "_matmul_input_grad"); - user_op::UserOpConfWrapper matmul_input_grad_op = matmul_input_grad_builder.Op("matmul") - .Input("a", last_dy) - .Input("b", op.input("weights", 0)) - .Output("out") - .Attr("transpose_a", false) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_input_grad_op); - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(matmul_input_grad_op.output("out", 0), "x", 0); - } - // dw: - user_op::UserOpConfWrapperBuilder matmul_weight_grad_builder(op.op_name() - + "_matmul_input_weight_grad"); - user_op::UserOpConfWrapper matmul_weight_grad_op = matmul_weight_grad_builder.Op("matmul") - .Input("a", last_dy) - .Input("b", op.input("x", 0)) - .Output("out") - .Attr("transpose_a", true) - .Attr("transpose_b", false) - .Attr("alpha", 1.0) - .Build(); - AddOp(matmul_weight_grad_op); - if (op.NeedGenGradTensor4OpInput("weights", 0)) { - op.BindGradTensorWithOpInput(matmul_weight_grad_op.output("out", 0), "weights", 0); - } - - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_matmul_bias_op.cpp b/oneflow/user/ops/fused_matmul_bias_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..08240441e90efdcd62c54d4db9f864403f387e65 --- /dev/null +++ b/oneflow/user/ops/fused_matmul_bias_op.cpp @@ -0,0 +1,156 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.pb.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/infer_util.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferTensorDesc4FusedMatmulBias(user_op::InferContext* ctx) { + const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); + /* + x: (m_i, ... m_1, k) + weight: (n, k) need transpose + bias: (n) + */ + + CHECK_GE_OR_RETURN(x_desc.shape().NumAxes(), 2); + const int64_t k = x_desc.shape().At(x_desc.shape().NumAxes() - 1); + + const user_op::TensorDesc& weight_desc = ctx->InputTensorDesc("weight", 0); + const user_op::TensorDesc& bias_desc = ctx->InputTensorDesc("bias", 0); + CHECK_EQ_OR_RETURN(weight_desc.shape().NumAxes(), 2); + CHECK_EQ_OR_RETURN(bias_desc.shape().NumAxes(), 1); + + const int64_t n = weight_desc.shape().At(0); + + CHECK_EQ_OR_RETURN(bias_desc.shape().At(0), n); + CHECK_EQ_OR_RETURN(weight_desc.shape().At(1), k); + + Shape out_shape = x_desc.shape(); + out_shape[x_desc.shape().NumAxes() - 1] = n; + ctx->SetOutputShape("out", 0, out_shape); + + if (ctx->has_input("_add_to_output", 0)) { + const user_op::TensorDesc& _add_to_output_desc = ctx->InputTensorDesc("_add_to_output", 0); + CHECK_EQ_OR_RETURN(_add_to_output_desc.shape(), out_shape); + } + + return Maybe::Ok(); +} + +Maybe InferDataType4MatmulBias(user_op::InferContext* ctx) { + const user_op::TensorDesc& first_in_desc = ctx->InputTensorDesc("x", 0); + + for (const auto& in_arg_pair : ctx->inputs()) { + const user_op::TensorDesc& in_desc = + ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); + CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(first_in_desc.data_type()) + << ", but got " << DataType_Name(in_desc.data_type()); + } + + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(first_in_desc.data_type()); + + if (ctx->has_input("_add_to_output", 0)) { + CHECK_EQ_OR_RETURN(ctx->InputDType("_add_to_output", 0), out_desc->data_type()) + << "InferDataType Failed. _add_to_output Expected " << DataType_Name(out_desc->data_type()) + << ", but got " << DataType_Name(ctx->InputDType("_add_to_output", 0)); + } + + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe FusedMatmulBiasOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferTensorDesc4FusedMatmulBias(ctx); +} + +/*static*/ Maybe FusedMatmulBiasOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe FusedMatmulBiasOp::GetSbp(user_op::SbpContext* ctx) { + // (b, m, k) * (n, k) + const auto& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); + + const int64_t x_num_axes = x_shape.NumAxes(); + + const int64_t out_num_axes = x_num_axes; + const int32_t k_x_axis = x_num_axes - 1; + + std::vector out_and_add_to_output_args; + out_and_add_to_output_args.emplace_back("out", 0); + if (ctx->user_op_conf().has_input("_add_to_output", 0)) { + out_and_add_to_output_args.emplace_back("_add_to_output", 0); + } + + for (int i = 0; i < x_shape.NumAxes() - 1; i++) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Broadcast(user_op::OpArg("weight", 0)) + .Broadcast(user_op::OpArg("bias", 0)) + .Split(out_and_add_to_output_args, i) + .Build(); + } + + // B x S(n_axis) -> S(n_axis) + ctx->NewBuilder() + .Broadcast(user_op::OpArg("x", 0)) + .Split(user_op::OpArg("weight", 0), 0) + .Split(user_op::OpArg("bias", 0), 0) + .Split(out_and_add_to_output_args, out_num_axes - 1) + .Build(); + + // S(x_k_axis) x S(w_k_axis) -> P + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), k_x_axis) + .Split(user_op::OpArg("weight", 0), 1) + .PartialSum(user_op::OpArg("bias", 0)) + .PartialSum(out_and_add_to_output_args) + .Build(); + + // P x B -> P + ctx->NewBuilder() + .PartialSum(user_op::OpArg("x", 0)) + .Broadcast(user_op::OpArg("weight", 0)) + .PartialSum(user_op::OpArg("bias", 0)) + .PartialSum(out_and_add_to_output_args) + .Build(); + + // B x P -> P + ctx->NewBuilder() + .Broadcast(user_op::OpArg("x", 0)) + .PartialSum(user_op::OpArg("weight", 0)) + .PartialSum(user_op::OpArg("bias", 0)) + .PartialSum(out_and_add_to_output_args) + .Build(); + + return Maybe::Ok(); +} + +/* static */ Maybe FusedMatmulBiasOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4MatmulBias(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_multi_head_attention_inference_op.cpp b/oneflow/user/ops/fused_multi_head_attention_inference_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..ac10d0d9968edbc42d9929f0da7e2c0bdeacfb4b --- /dev/null +++ b/oneflow/user/ops/fused_multi_head_attention_inference_op.cpp @@ -0,0 +1,108 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ auto FusedMultiHeadAttentionInferenceOp::InferDataType(user_op::InferContext* ctx) + -> Maybe { + DataType query_type = ctx->InputDType("query", 0); + DataType key_type = ctx->InputDType("key", 0); + DataType value_type = ctx->InputDType("value", 0); + CHECK_EQ_OR_RETURN(key_type, query_type); + CHECK_EQ_OR_RETURN(value_type, query_type); + ctx->SetOutputDType("out", 0, query_type); + return Maybe::Ok(); +} + +/*static*/ auto FusedMultiHeadAttentionInferenceOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) -> Maybe { + const int64_t num_heads = ctx->Attr("num_heads"); + CHECK_GE_OR_RETURN(num_heads, 1); + const Shape& query_shape = ctx->InputShape("query", 0); + CHECK_EQ_OR_RETURN(query_shape.NumAxes(), 3); + const int64_t batch_size = query_shape.At(0); + const int64_t query_seq_len = query_shape.At(1); + const int64_t query_slice_start = ctx->Attr("query_hidden_slice_start"); + CHECK_GE_OR_RETURN(query_slice_start, 0); + CHECK_LT_OR_RETURN(query_slice_start, query_shape.At(2)); + int64_t query_slice_end = ctx->Attr("query_hidden_slice_end"); + if (query_slice_end == -1) { + query_slice_end = query_shape.At(2); + } else { + CHECK_GT_OR_RETURN(query_slice_end, 0); + CHECK_LE_OR_RETURN(query_slice_end, query_shape.At(2)); + } + CHECK_LT_OR_RETURN(query_slice_start, query_slice_end); + const int64_t query_hidden_size = query_slice_end - query_slice_start; + CHECK_EQ_OR_RETURN(query_hidden_size % num_heads, 0); + + const Shape& key_shape = ctx->InputShape("key", 0); + CHECK_EQ_OR_RETURN(key_shape.NumAxes(), 3); + CHECK_EQ_OR_RETURN(key_shape.At(0), batch_size); + const int64_t kv_seq_len = key_shape.At(1); + const int64_t key_slice_start = ctx->Attr("key_hidden_slice_start"); + CHECK_GE_OR_RETURN(key_slice_start, 0); + CHECK_LT_OR_RETURN(key_slice_start, key_shape.At(2)); + int64_t key_slice_end = ctx->Attr("key_hidden_slice_end"); + if (key_slice_end == -1) { + key_slice_end = key_shape.At(2); + } else { + CHECK_GT_OR_RETURN(key_slice_end, 0); + CHECK_LE_OR_RETURN(key_slice_end, key_shape.At(2)); + } + CHECK_LT_OR_RETURN(key_slice_start, key_slice_end); + const int64_t key_hidden_size = key_slice_end - key_slice_start; + CHECK_EQ_OR_RETURN(key_hidden_size, query_hidden_size); + + const Shape& value_shape = ctx->InputShape("value", 0); + CHECK_EQ_OR_RETURN(value_shape.NumAxes(), 3); + CHECK_EQ_OR_RETURN(value_shape.At(0), batch_size); + CHECK_EQ(value_shape.At(1), kv_seq_len); + const int64_t value_slice_start = ctx->Attr("value_hidden_slice_start"); + CHECK_GE_OR_RETURN(value_slice_start, 0); + CHECK_LT_OR_RETURN(value_slice_start, value_shape.At(2)); + int64_t value_slice_end = ctx->Attr("value_hidden_slice_end"); + if (value_slice_end == -1) { + value_slice_end = value_shape.At(2); + } else { + CHECK_GT_OR_RETURN(value_slice_end, 0); + CHECK_LE_OR_RETURN(value_slice_end, value_shape.At(2)); + } + CHECK_LT_OR_RETURN(value_slice_start, value_slice_end); + const int64_t value_hidden_size = value_slice_end - value_slice_start; + CHECK_EQ_OR_RETURN(value_hidden_size % num_heads, 0); + + ctx->SetOutputShape("out", 0, Shape({batch_size, query_seq_len, value_hidden_size})); + return Maybe::Ok(); +} +/*static*/ auto FusedMultiHeadAttentionInferenceOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) -> Maybe { + return FusedMultiHeadAttentionInferenceOp::InferLogicalTensorDesc(ctx); +} +/*static*/ auto FusedMultiHeadAttentionInferenceOp::GetSbp(user_op::SbpContext* ctx) + -> Maybe { + ctx->NewBuilder() + .Split(user_op::OpArg("query", 0), 0) + .Split(user_op::OpArg("key", 0), 0) + .Split(user_op::OpArg("value", 0), 0) + .Split(user_op::OpArg("out", 0), 0) + .Build(); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/fused_relu_dropout_grad_op.cpp b/oneflow/user/ops/fused_relu_dropout_grad_op.cpp index 14101dd16c500c3dd0a23877b8db2affa6438486..97d0b1f06e2556917324f13194c2305818eb5b42 100644 --- a/oneflow/user/ops/fused_relu_dropout_grad_op.cpp +++ b/oneflow/user/ops/fused_relu_dropout_grad_op.cpp @@ -25,12 +25,12 @@ namespace oneflow { namespace { Maybe InferTensorDesc4FusedReluDropoutGrad(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("dy", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("dy", 0)); return Maybe::Ok(); } Maybe InferDataType4FusedReluDropoutGrad(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/fused_scale_mask_softmax_dropout_op.cpp b/oneflow/user/ops/fused_scale_mask_softmax_dropout_op.cpp index eabeed57b062d9c3c5205fc0da64102b35ccc2d1..7030386a4f395d339ecea9860a3ebc9132bddd2c 100644 --- a/oneflow/user/ops/fused_scale_mask_softmax_dropout_op.cpp +++ b/oneflow/user/ops/fused_scale_mask_softmax_dropout_op.cpp @@ -27,10 +27,10 @@ namespace oneflow { CHECK_EQ_OR_RETURN(x_desc.shape().At(x_shape.NumAxes() - 1), mask_desc.shape().At(mask_shape.NumAxes() - 1)) << " last dim of x and mask is not equal."; - *ctx->OutputShape("y", 0) = x_desc.shape(); - *ctx->OutputIsDynamic("y", 0) = x_desc.is_dynamic(); - *ctx->OutputShape("softmax_y", 0) = x_desc.shape(); - *ctx->OutputIsDynamic("softmax_y", 0) = x_desc.is_dynamic(); + ctx->SetOutputShape("y", 0, x_desc.shape()); + ctx->SetOutputIsDynamic("y", 0, x_desc.is_dynamic()); + ctx->SetOutputShape("softmax_y", 0, x_desc.shape()); + ctx->SetOutputIsDynamic("softmax_y", 0, x_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxDropoutOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -41,9 +41,11 @@ namespace oneflow { -> Maybe { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& mask_desc = ctx->InputTensorDesc("mask", 0); - CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) << " mask dtype only support bool."; - *ctx->OutputDType("y", 0) = x_desc.data_type(); - *ctx->OutputDType("softmax_y", 0) = x_desc.data_type(); + CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kBool) << ", but got " + << DataType_Name(mask_desc.data_type()); + ctx->SetOutputDType("y", 0, x_desc.data_type()); + ctx->SetOutputDType("softmax_y", 0, x_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxDropoutOp::ModifyInputArg( @@ -95,9 +97,9 @@ namespace oneflow { CHECK_EQ_OR_RETURN(dy_desc.shape().At(dy_desc.shape().NumAxes() - 1), mask_desc.shape().At(mask_desc.shape().NumAxes() - 1)) << " last dim of y and mask is not equal."; - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = dy_desc.shape(); - *dx_desc->mut_is_dynamic() = dy_desc.is_dynamic(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(dy_desc.shape()); + dx_desc->set_is_dynamic(dy_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxDropoutGradOp::InferPhysicalTensorDesc( @@ -110,10 +112,13 @@ namespace oneflow { const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& mask_desc = ctx->InputTensorDesc("mask", 0); CHECK_EQ_OR_RETURN(dy_desc.data_type(), softmax_y_desc.data_type()) - << " dy and softmax_y dtype must equal"; - CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) << " mask dtype only support bool."; - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_data_type() = dy_desc.data_type(); + << "InferDataType Failed. Expected " << DataType_Name(softmax_y_desc.data_type()) + << ", but got " << DataType_Name(dy_desc.data_type()); + CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kBool) << ", but got " + << DataType_Name(mask_desc.data_type()); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_data_type(dy_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxDropoutGradOp::GetSbp(user_op::SbpContext* ctx) @@ -145,25 +150,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_scale_mask_softmax_dropout") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("fused_scale_mask_softmax_dropout_grad") - .Input("softmax_y", op.output("softmax_y", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("mask", op.input("mask", 0)) - .Input("dropout_mask", op.input("dropout_mask", 0)) - .Output("dx") - .Attr("scale_value", op.attr("scale_value")) - .Attr("dropout_scale_value", op.attr("dropout_scale_value")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_scale_mask_softmax_op.cpp b/oneflow/user/ops/fused_scale_mask_softmax_op.cpp index 235e897db476e094271bb5f493f9584d2132ba2d..2f5af4a1423b85120e2f1c2326c289f4301fa5c2 100644 --- a/oneflow/user/ops/fused_scale_mask_softmax_op.cpp +++ b/oneflow/user/ops/fused_scale_mask_softmax_op.cpp @@ -27,8 +27,8 @@ namespace oneflow { CHECK_EQ_OR_RETURN(x_desc.shape().At(x_shape.NumAxes() - 1), mask_desc.shape().At(mask_shape.NumAxes() - 1)) << " last dim of x and mask is not equal."; - *ctx->OutputShape("y", 0) = x_desc.shape(); - *ctx->OutputIsDynamic("y", 0) = x_desc.is_dynamic(); + ctx->SetOutputShape("y", 0, x_desc.shape()); + ctx->SetOutputIsDynamic("y", 0, x_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -39,7 +39,7 @@ namespace oneflow { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& mask_desc = ctx->InputTensorDesc("mask", 0); CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) << " mask dtype only support bool."; - *ctx->OutputDType("y", 0) = x_desc.data_type(); + ctx->SetOutputDType("y", 0, x_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxOp::ModifyInputArg( @@ -83,9 +83,9 @@ namespace oneflow { CHECK_EQ_OR_RETURN(y_desc.shape().At(y_desc.shape().NumAxes() - 1), mask_desc.shape().At(mask_desc.shape().NumAxes() - 1)) << " last dim of y and mask is not equal."; - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = dy_desc.shape(); - *dx_desc->mut_is_dynamic() = dy_desc.is_dynamic(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(dy_desc.shape()); + dx_desc->set_is_dynamic(dy_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -99,8 +99,8 @@ namespace oneflow { const user_op::TensorDesc& mask_desc = ctx->InputTensorDesc("mask", 0); CHECK_EQ_OR_RETURN(dy_desc.data_type(), y_desc.data_type()) << " dy and y dtype must equal"; CHECK_EQ_OR_RETURN(mask_desc.data_type(), DataType::kBool) << " mask dtype only support bool."; - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_data_type() = dy_desc.data_type(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_data_type(dy_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedScaleMaskSoftmaxGradOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { @@ -129,22 +129,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_scale_mask_softmax") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("fused_scale_mask_softmax_grad") - .Input("y", op.output("y", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("mask", op.input("mask", 0)) - .Output("dx") - .Attr("scale_value", op.attr("scale_value")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_scale_tril_softmax_mask_scale_op.cpp b/oneflow/user/ops/fused_scale_tril_softmax_mask_scale_op.cpp index 20dead6c8d754e4012ffc9a3c2c1495d27c4f467..a8900d76f315acc34ef9bcac2814a2e12291bcf1 100644 --- a/oneflow/user/ops/fused_scale_tril_softmax_mask_scale_op.cpp +++ b/oneflow/user/ops/fused_scale_tril_softmax_mask_scale_op.cpp @@ -20,10 +20,10 @@ namespace oneflow { /*static*/ auto FusedTrilScaleSoftmaxMaskScaleOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - *ctx->OutputShape("y", 0) = x_desc.shape(); - *ctx->OutputIsDynamic("y", 0) = x_desc.is_dynamic(); - *ctx->OutputShape("softmax_y", 0) = x_desc.shape(); - *ctx->OutputIsDynamic("softmax_y", 0) = x_desc.is_dynamic(); + ctx->SetOutputShape("y", 0, x_desc.shape()); + ctx->SetOutputIsDynamic("y", 0, x_desc.is_dynamic()); + ctx->SetOutputShape("softmax_y", 0, x_desc.shape()); + ctx->SetOutputIsDynamic("softmax_y", 0, x_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedTrilScaleSoftmaxMaskScaleOp::InferPhysicalTensorDesc( @@ -33,8 +33,8 @@ namespace oneflow { /*static*/ auto FusedTrilScaleSoftmaxMaskScaleOp::InferDataType(user_op::InferContext* ctx) -> Maybe { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - *ctx->OutputDType("y", 0) = x_desc.data_type(); - *ctx->OutputDType("softmax_y", 0) = x_desc.data_type(); + ctx->SetOutputDType("y", 0, x_desc.data_type()); + ctx->SetOutputDType("softmax_y", 0, x_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedTrilScaleSoftmaxMaskScaleOp::ModifyInputArg( @@ -63,10 +63,10 @@ namespace oneflow { user_op::InferContext* ctx) -> Maybe { const user_op::TensorDesc& softmax_y_desc = ctx->InputTensorDesc("softmax_y", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); CHECK_OR_RETURN(dy_desc.shape() == softmax_y_desc.shape()); - *dx_desc->mut_shape() = dy_desc.shape(); - *dx_desc->mut_is_dynamic() = dy_desc.is_dynamic(); + dx_desc->set_shape(dy_desc.shape()); + dx_desc->set_is_dynamic(dy_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ auto FusedTrilScaleSoftmaxMaskScaleGradOp::InferPhysicalTensorDesc( @@ -77,9 +77,9 @@ namespace oneflow { -> Maybe { const user_op::TensorDesc& softmax_y_desc = ctx->InputTensorDesc("softmax_y", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); CHECK_OR_RETURN(dy_desc.data_type() == softmax_y_desc.data_type()); - *dx_desc->mut_data_type() = dy_desc.data_type(); + dx_desc->set_data_type(dy_desc.data_type()); return Maybe::Ok(); } /*static*/ auto FusedTrilScaleSoftmaxMaskScaleGradOp::GetSbp(user_op::SbpContext* ctx) @@ -97,25 +97,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_tril_scale_softmax_mask_scale") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("fused_tril_scale_softmax_mask_scale_grad") - .Input("softmax_y", op.output("softmax_y", 0)) - .Input("mask", op.input("mask", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("diagonal", op.attr("diagonal")) - .Attr("tril_scale_value", op.attr("tril_scale_value")) - .Attr("mask_scale_value", op.attr("mask_scale_value")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_self_attention_query_mul_key_and_value_ops.cpp b/oneflow/user/ops/fused_self_attention_query_mul_key_and_value_ops.cpp index 232a78189c9339cf475d2265b206a216b1704189..e94cabeb28e489d3595215af5266587663705dda 100644 --- a/oneflow/user/ops/fused_self_attention_query_mul_key_and_value_ops.cpp +++ b/oneflow/user/ops/fused_self_attention_query_mul_key_and_value_ops.cpp @@ -20,9 +20,9 @@ namespace oneflow { /*static*/ auto FusedSelfAttentionQueryMulKeyAndValueOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - const DataType& dtype = ctx->InputDType("hidden_states", 0); - *ctx->OutputDType("query_mul_key", 0) = dtype; - *ctx->OutputDType("value", 0) = dtype; + DataType dtype = ctx->InputDType("hidden_states", 0); + ctx->SetOutputDType("query_mul_key", 0, dtype); + ctx->SetOutputDType("value", 0, dtype); return Maybe::Ok(); } /*static*/ auto FusedSelfAttentionQueryMulKeyAndValueOp::InferLogicalTensorDesc( @@ -41,8 +41,8 @@ namespace oneflow { CHECK_EQ_OR_RETURN(hidden_size % (head_size * 3), 0); int64_t num_heads = hidden_size / (head_size * 3); - *ctx->OutputShape("query_mul_key", 0) = Shape({batch_size, num_heads, seq_len, seq_len}); - *ctx->OutputShape("value", 0) = Shape({batch_size, num_heads, seq_len, head_size}); + ctx->SetOutputShape("query_mul_key", 0, Shape({batch_size, num_heads, seq_len, seq_len})); + ctx->SetOutputShape("value", 0, Shape({batch_size, num_heads, seq_len, head_size})); return Maybe::Ok(); } @@ -67,9 +67,11 @@ namespace oneflow { /*static*/ auto FusedSelfAttentionQueryMulKeyAndValueGradOp::InferDataType( user_op::InferContext* ctx) -> Maybe { - const DataType& dtype = ctx->InputDType("query_mul_key_grad", 0); - CHECK_EQ_OR_RETURN(ctx->InputDType("value_grad", 0), dtype); - *ctx->OutputDType("hidden_states_grad", 0) = dtype; + DataType dtype = ctx->InputDType("query_mul_key_grad", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("value_grad", 0), dtype) + << "InferDataType Failed. Expected " << DataType_Name(dtype) << ", but got " + << DataType_Name(ctx->InputDType("value_grad", 0)); + ctx->SetOutputDType("hidden_states_grad", 0, dtype); return Maybe::Ok(); } /*static*/ auto FusedSelfAttentionQueryMulKeyAndValueGradOp::InferLogicalTensorDesc( @@ -98,7 +100,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(qmk_grad_shape.At(2), seq_len); CHECK_EQ_OR_RETURN(qmk_grad_shape.At(3), seq_len); - *ctx->OutputShape("hidden_states_grad", 0) = h_shape; + ctx->SetOutputShape("hidden_states_grad", 0, h_shape); return Maybe::Ok(); } /*static*/ auto FusedSelfAttentionQueryMulKeyAndValueGradOp::InferPhysicalTensorDesc( @@ -122,24 +124,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_self_attention_query_mul_key_and_value") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - std::string grad_op_name = ctx->FwOp().op_name() + "_grad"; - - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("fused_self_attention_query_mul_key_and_value_grad") - .InputBind("hidden_states", ctx->FwOp().input("hidden_states", 0)) - .InputBind("query_mul_key_grad", ctx->FwOp().output_grad("query_mul_key", 0)) - .InputBind("value_grad", ctx->FwOp().output_grad("value", 0)) - .Output("hidden_states_grad") - .Attr("alpha", ctx->FwOp().attr("alpha")) - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("hidden_states", 0), [=]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("hidden_states_grad", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/fused_weighted_sum_op.cpp b/oneflow/user/ops/fused_weighted_sum_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a414e6caf4693dc84de3ac2e6b96a30f4dd13168 --- /dev/null +++ b/oneflow/user/ops/fused_weighted_sum_op.cpp @@ -0,0 +1,71 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe FusedWeightedSumOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const auto& in_0 = ctx->InputTensorDesc("in", 0); + auto* out = ctx->MutOutputTensorDesc("out", 0); + for (int64_t i = 1; i < ctx->input_size("in"); ++i) { + const auto& cur_in = ctx->InputTensorDesc("in", i); + CHECK_EQ_OR_RETURN(in_0.shape(), cur_in.shape()) + << Error::RuntimeError() + << "inconsistent tensor size, expected all tensor to have the same shape, " + << "but got " << in_0.shape().DebugStr() << " and " << cur_in.shape().DebugStr(); + } + out->set_shape(in_0.shape()); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedWeightedSumOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe FusedWeightedSumOp::GetSbp(user_op::SbpContext* ctx) { + const int64_t num_axes = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0).shape().NumAxes(); + for (int64_t i = 0; i < num_axes; ++i) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(user_op::OpArg("out", 0), i).Build(); + } + ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(user_op::OpArg("out", 0)).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe FusedWeightedSumOp::InferDataType(user_op::InferContext* ctx) { + const auto& in_0 = ctx->InputTensorDesc("in", 0); + auto* out = ctx->MutOutputTensorDesc("out", 0); + const DataType data_type = in_0.data_type(); + for (int64_t i = 1; i < ctx->input_size("in"); ++i) { + const auto& cur_in = ctx->InputTensorDesc("in", i); + CHECK_EQ_OR_RETURN(cur_in.data_type(), data_type) + << Error::RuntimeError() << ctx->op_name() + << " expected all tenser to have same type, but found " << DataType_Name(cur_in.data_type()) + << " and " << DataType_Name(data_type); + } + out->set_data_type(data_type); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedWeightedSumOp::CheckAttr(const user_op::UserOpDefWrapper&, + const user_op::UserOpConfWrapper& op_conf) { + CHECK_OR_RETURN(op_conf.input_size("in") >= 2) + << Error::RuntimeError() + << "The number of input tensors should be greater than or equal to 2"; + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/gather_op.cpp b/oneflow/user/ops/gather_op.cpp index 34fd62b74d5347466bad27d6a39dc326c6b53234..5fe91f9b5e0ddc79769bbe63e35d3cc0001bc3fd 100644 --- a/oneflow/user/ops/gather_op.cpp +++ b/oneflow/user/ops/gather_op.cpp @@ -25,7 +25,7 @@ namespace oneflow { const user_op::TensorDesc& indices = ctx->InputTensorDesc("indices", 0); // For 0-dim Tensor CHECK_GE_OR_RETURN(indices.shape().NumAxes(), 0); // NOLINT - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); DimVector dim_vec; dim_vec.insert(dim_vec.end(), in.shape().dim_vec().cbegin(), @@ -34,7 +34,7 @@ namespace oneflow { indices.shape().dim_vec().cend()); dim_vec.insert(dim_vec.end(), in.shape().dim_vec().cbegin() + axis + 1, in.shape().dim_vec().end()); - *out->mut_shape() = Shape(dim_vec); + out->set_shape(Shape(dim_vec)); out->set_is_dynamic(indices.is_dynamic() || in.is_dynamic()); return Maybe::Ok(); } @@ -83,29 +83,10 @@ namespace oneflow { /*static*/ auto GatherOp::InferDataType(user_op::InferContext* ctx) -> Maybe { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); const user_op::TensorDesc& indices = ctx->InputTensorDesc("indices", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(IsIndexDataType(indices.data_type())); - *out->mut_data_type() = in.data_type(); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("gather").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - bool need_grad_in = op.NeedGenGradTensor4OpInput("in", 0); - if (need_grad_in) { - user_op::UserOpConfWrapperBuilder in_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper in_grad_op = - in_grad_builder.Op("unsorted_segment_sum_like") - .Input("data", op.GetGradTensorWithOpOutput("out", 0)) - .Input("segment_ids", op.input("indices", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Attr("axis", op.attr("axis")) - .Build(); - op.BindGradTensorWithOpInput(in_grad_op.output("out", 0), "in", 0); - AddOp(in_grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/gelu_op.cpp b/oneflow/user/ops/gelu_op.cpp index 39f12592c23a9efc2ae1abc719dea7f9650a86a6..efa9e3a33901c5f0d466db4ff50c11847a80f410 100644 --- a/oneflow/user/ops/gelu_op.cpp +++ b/oneflow/user/ops/gelu_op.cpp @@ -18,39 +18,73 @@ limitations under the License. namespace oneflow { -/*static*/ auto GeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; +namespace { + +Maybe InferGeluTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } -/*static*/ auto GeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { - return GeluOp::InferLogicalTensorDesc(ctx); + +Maybe InferGeluDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + return Maybe::Ok(); } -/*static*/ auto GeluOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + +Maybe GetGeluSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); FOR_RANGE(int64_t, i, 0, in_tensor.shape().NumAxes()) { - ctx->NewBuilder().Split(user_op::OpArg("in", 0), i).Split(user_op::OpArg("out", 0), i).Build(); + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); } return Maybe::Ok(); } + +} // namespace + +/*static*/ auto GeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluTensorDesc(ctx); +} +/*static*/ auto GeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluTensorDesc(ctx); +} /*static*/ auto GeluOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - return Maybe::Ok(); + return InferGeluDataType(ctx); } +/*static*/ auto GeluOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { return GetGeluSbp(ctx); } -/*static*/ auto GeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { +/*static*/ auto FastGeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluTensorDesc(ctx); +} +/*static*/ auto FastGeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluTensorDesc(ctx); +} +/*static*/ auto FastGeluOp::InferDataType(user_op::InferContext* ctx) -> Maybe { + return InferGeluDataType(ctx); +} +/*static*/ auto FastGeluOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + return GetGeluSbp(ctx); +} + +namespace { + +Maybe InferGeluGradTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == x_shape) + << "InferTensorDesc failed (" << ctx->op_name() << "). Expected x shape " + << x_shape.ToString() << " to be equal to dy shape " << dy_shape.ToString(); + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } -/*static*/ auto GeluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { - return GeluGradOp::InferLogicalTensorDesc(ctx); + +Maybe InferGeluGradDataType(user_op::InferContext* ctx) { + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); } -/*static*/ auto GeluGradOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + +Maybe GetGeluGradSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); FOR_RANGE(int64_t, i, 0, x_tensor.shape().NumAxes()) { ctx->NewBuilder() @@ -66,25 +100,101 @@ namespace oneflow { .Build(); return Maybe::Ok(); } + +} // namespace + +/*static*/ auto GeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluGradTensorDesc(ctx); +} +/*static*/ auto GeluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluGradTensorDesc(ctx); +} /*static*/ auto GeluGradOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + return InferGeluGradDataType(ctx); +} +/*static*/ auto GeluGradOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + return GetGeluGradSbp(ctx); +} + +/*static*/ auto FastGeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluGradTensorDesc(ctx); +} +/*static*/ auto FastGeluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) -> Maybe { + return InferGeluGradTensorDesc(ctx); +} +/*static*/ auto FastGeluGradOp::InferDataType(user_op::InferContext* ctx) -> Maybe { + return InferGeluGradDataType(ctx); +} +/*static*/ auto FastGeluGradOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { + return GetGeluGradSbp(ctx); +} + +/*static*/ Maybe FusedFastGeluMulOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& in_shape = ctx->InputShape("in", 0); + const Shape& m_shape = ctx->InputShape("multiplier", 0); + CHECK_OR_RETURN(ctx->InputShape("multiplier", 0) == in_shape) + << "Expected multiplier shape " << in_shape.ToString() << ", but got " << m_shape.ToString(); + ctx->SetOutputShape("out", 0, in_shape); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedFastGeluMulOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe FusedFastGeluMulOp::InferDataType(user_op::InferContext* ctx) { + const DataType in_dtype = ctx->InputDType("in", 0); + const DataType m_dtype = ctx->InputDType("multiplier", 0); + CHECK_EQ_OR_RETURN(m_dtype, in_dtype) + << "Expected multiplier data type " << DataType_Name(in_dtype) << ", but got " + << DataType_Name(m_dtype); + ctx->SetOutputDType("out", 0, in_dtype); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedFastGeluMulOp::GetSbp(user_op::SbpContext* ctx) { + return GetGeluSbp(ctx); +} + +/*static*/ Maybe FusedFastGeluMulGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& in_shape = ctx->InputShape("in", 0); + const Shape& out_diff_shape = ctx->InputShape("out_diff", 0); + const Shape& m_shape = ctx->InputShape("multiplier", 0); + CHECK_EQ_OR_RETURN(out_diff_shape, in_shape); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(m_shape, in_shape); // NOLINT(maybe-need-error-msg) + ctx->SetOutputShape("in_diff", 0, in_shape); + ctx->SetOutputShape("multiplier_diff", 0, m_shape); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("gelu").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("gelu_grad") - .Input("x", op.input("in", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "in", 0); - AddOp(grad_op); +/*static*/ Maybe FusedFastGeluMulGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe FusedFastGeluMulGradOp::InferDataType(user_op::InferContext* ctx) { + const DataType in_dtype = ctx->InputDType("in", 0); + const DataType out_diff_dtype = ctx->InputDType("out_diff", 0); + const DataType m_dtype = ctx->InputDType("multiplier", 0); + CHECK_EQ_OR_RETURN(out_diff_dtype, in_dtype); // NOLINT(maybe-need-error-msg) + CHECK_EQ_OR_RETURN(m_dtype, in_dtype); // NOLINT(maybe-need-error-msg) + ctx->SetOutputDType("in_diff", 0, in_dtype); + ctx->SetOutputDType("multiplier_diff", 0, m_dtype); + return Maybe::Ok(); +} + +/*static*/ Maybe FusedFastGeluMulGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& in = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); + FOR_RANGE(int64_t, i, 0, in.shape().NumAxes()) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); } + ctx->NewBuilder() + .Broadcast(user_op::OpArg("in", 0)) + .Broadcast(user_op::OpArg("multiplier", 0)) + .PartialSum(user_op::OpArg("out_diff", 0)) + .PartialSum(user_op::OpArg("in_diff", 0)) + .PartialSum(user_op::OpArg("multiplier_diff", 0)) + .Build(); return Maybe::Ok(); -}); +} } // namespace oneflow diff --git a/oneflow/user/ops/generate_random_batch_permutation_indices_op.cpp b/oneflow/user/ops/generate_random_batch_permutation_indices_op.cpp index 73b7dcb52eb2973f28ca73fa8c94ccc7eb3700ab..b5135e7a75853c26a20586bfa539bd7311245c13 100644 --- a/oneflow/user/ops/generate_random_batch_permutation_indices_op.cpp +++ b/oneflow/user/ops/generate_random_batch_permutation_indices_op.cpp @@ -21,7 +21,7 @@ namespace oneflow { /*static*/ auto GenerateRandomBatchPermutationIndicesOp::InferLogicalTensorDesc( user_op::InferContext* ctx) -> Maybe { - *ctx->OutputShape("y", 0) = Shape({ctx->InputShape("x", 0).At(0)}); + ctx->SetOutputShape("y", 0, Shape({ctx->InputShape("x", 0).At(0)})); return Maybe::Ok(); } /*static*/ auto GenerateRandomBatchPermutationIndicesOp::InferPhysicalTensorDesc( @@ -39,7 +39,7 @@ namespace oneflow { } /*static*/ auto GenerateRandomBatchPermutationIndicesOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("y", 0) = DataType::kInt32; + ctx->SetOutputDType("y", 0, DataType::kInt32); return Maybe::Ok(); } diff --git a/oneflow/user/ops/gpt_data_loader_op.cpp b/oneflow/user/ops/gpt_data_loader_op.cpp index b6a1e56f926cf1c0c1c5565b42de6c628dc843d2..41fb3adf3ed7786757b244743ea2184aa9d7b94e 100644 --- a/oneflow/user/ops/gpt_data_loader_op.cpp +++ b/oneflow/user/ops/gpt_data_loader_op.cpp @@ -22,13 +22,13 @@ namespace oneflow { -> Maybe { int64_t batch_size = ctx->Attr("batch_size"); int64_t sample_len = ctx->Attr("seq_length") + ctx->Attr("label_length"); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = Shape({batch_size, sample_len}); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(Shape({batch_size, sample_len})); return Maybe::Ok(); } /*static*/ auto MegatronGptMmapDataLoaderOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputTensorDesc("out", 0)->mut_data_type() = ctx->Attr("dtype"); + ctx->MutOutputTensorDesc("out", 0)->set_data_type(ctx->Attr("dtype")); return Maybe::Ok(); } /*static*/ auto MegatronGptMmapDataLoaderOp::GetSbp(user_op::SbpContext* ctx) -> Maybe { diff --git a/oneflow/user/ops/grid_sample_op.cpp b/oneflow/user/ops/grid_sample_op.cpp index febe10c65ad6d4c666a19629b474730ae548b2b5..c0ce96b1f41e2b1a8a6375c2f0c8abe185c07a64 100644 --- a/oneflow/user/ops/grid_sample_op.cpp +++ b/oneflow/user/ops/grid_sample_op.cpp @@ -47,7 +47,7 @@ Maybe GridSampleOp::CheckAttr(const user_op::UserOpDefWrapper& def, /*static*/ auto GridSampleOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { const user_op::TensorDesc& input = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& grid = ctx->InputTensorDesc("grid", 0); - user_op::TensorDesc& output = *(ctx->OutputTensorDesc("output", 0)); + user_op::TensorDesc& output = *(ctx->MutOutputTensorDesc("output", 0)); // Only support 4D or 5D input with NCHW layout // For 4D grid: input = { N, C, H_in, W_in }, // grid = { N, H_out, W_out, 2 } @@ -73,13 +73,13 @@ Maybe GridSampleOp::CheckAttr(const user_op::UserOpDefWrapper& def, } else { CHECK_OR_RETURN(false) << "MUST be 4D or 5D input"; } - *output.mut_is_dynamic() = grid.is_dynamic(); + output.set_is_dynamic(grid.is_dynamic()); if (is_4d_input) { - *(output.mut_shape()) = {input_shape.At(0), input_shape.At(1), grid_shape.At(1), - grid_shape.At(2)}; + output.set_shape( + Shape({input_shape.At(0), input_shape.At(1), grid_shape.At(1), grid_shape.At(2)})); } else { - *(output.mut_shape()) = {input_shape.At(0), input_shape.At(1), grid_shape.At(1), - grid_shape.At(2), grid_shape.At(3)}; + output.set_shape(Shape({input_shape.At(0), input_shape.At(1), grid_shape.At(1), + grid_shape.At(2), grid_shape.At(3)})); } return Maybe::Ok(); } @@ -100,7 +100,7 @@ Maybe GridSampleOp::CheckAttr(const user_op::UserOpDefWrapper& def, return Maybe::Ok(); } /*static*/ auto GridSampleOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } @@ -111,8 +111,8 @@ Maybe GridSampleGradOp::CheckAttr(const user_op::UserOpDefWrapper& def, /*static*/ auto GridSampleGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) -> Maybe { - *(ctx->OutputTensorDesc("dinput", 0)->mut_shape()) = ctx->InputTensorDesc("input", 0).shape(); - *(ctx->OutputTensorDesc("dgrid", 0)->mut_shape()) = ctx->InputTensorDesc("grid", 0).shape(); + ctx->MutOutputTensorDesc("dinput", 0)->set_shape(ctx->InputTensorDesc("input", 0).shape()); + ctx->MutOutputTensorDesc("dgrid", 0)->set_shape(ctx->InputTensorDesc("grid", 0).shape()); return Maybe::Ok(); } /*static*/ auto GridSampleGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) @@ -137,37 +137,9 @@ Maybe GridSampleGradOp::CheckAttr(const user_op::UserOpDefWrapper& def, return Maybe::Ok(); } /*static*/ auto GridSampleGradOp::InferDataType(user_op::InferContext* ctx) -> Maybe { - *ctx->OutputDType("dinput", 0) = ctx->InputDType("input", 0); - *ctx->OutputDType("dgrid", 0) = ctx->InputDType("grid", 0); + ctx->SetOutputDType("dinput", 0, ctx->InputDType("input", 0)); + ctx->SetOutputDType("dgrid", 0, ctx->InputDType("grid", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("grid_sample") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0) || op.NeedGenGradTensor4OpInput("grid", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("grid_sample_grad") - .Input("doutput", op.GetGradTensorWithOpOutput("output", 0)) - .Input("input", op.input("input", 0)) - .Input("grid", op.input("grid", 0)) - .Output("dinput") - .Output("dgrid") - .Attr("interpolation_mode", op.attr("interpolation_mode")) - .Attr("padding_mode", op.attr("padding_mode")) - .Attr("align_corners", op.attr("align_corners")) - .Build(); - - if (op.NeedGenGradTensor4OpInput("input", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dinput", 0), "input", 0); - } - if (op.NeedGenGradTensor4OpInput("grid", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dgrid", 0), "grid", 0); - } - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/group_norm_op.cpp b/oneflow/user/ops/group_norm_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..db97f13adf32b1ae403f9db2e6124d30264b23a8 --- /dev/null +++ b/oneflow/user/ops/group_norm_op.cpp @@ -0,0 +1,193 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +oneflow::DataType InferGnParamDataType(const DataType x_data_type) { + return (x_data_type == DataType::kFloat16 || x_data_type == DataType::kBFloat16) + ? DataType::kFloat + : x_data_type; +} + +} // namespace + +/* static */ Maybe GroupNormOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + user_op::TensorDesc* mean = ctx->MutOutputTensorDesc("mean", 0); + user_op::TensorDesc* inv_variance = ctx->MutOutputTensorDesc("inv_variance", 0); + const bool affine = ctx->Attr("affine"); + const int32_t num_groups = ctx->Attr("num_groups"); + const int64_t batch_size = x.shape().At(0); + const std::string& data_format = ctx->Attr("data_format"); + CHECK_GT_OR_RETURN(x.shape().NumAxes(), 2); + int64_t channel_size = 0; + if (data_format == "channels_first") { + channel_size = x.shape().At(1); + } else if (data_format == "channels_last") { + channel_size = x.shape().At(x.shape().NumAxes() - 1); + } else { + UNIMPLEMENTED_THEN_RETURN(); + } + y->set_shape(x.shape()); + y->set_is_dynamic(x.is_dynamic()); + if (affine) { + const user_op::TensorDesc& gamma = ctx->InputTensorDesc("gamma", 0); + CHECK_EQ_OR_RETURN(gamma.shape().At(0), channel_size); + const user_op::TensorDesc& beta = ctx->InputTensorDesc("beta", 0); + CHECK_EQ_OR_RETURN(beta.shape().At(0), channel_size); + } + CHECK_EQ_OR_RETURN(channel_size % num_groups, 0) << "Channels should be divisble by num_groups. "; + mean->set_shape(Shape({batch_size, num_groups})); + *inv_variance = *mean; + return Maybe::Ok(); +} + +/*static*/ Maybe GroupNormOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe GroupNormOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(ctx->inputs(), 0) + .Split(ctx->outputs(), 0) + .Broadcast(user_op::OpArg("gamma", 0)) + .Broadcast(user_op::OpArg("beta", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe GroupNormOp::InferDataType(user_op::InferContext* ctx) { + const bool affine = ctx->Attr("affine"); + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(x.data_type()); + if (affine) { + const user_op::TensorDesc& gamma = ctx->InputTensorDesc("gamma", 0); + CHECK_EQ_OR_RETURN(gamma.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(gamma.data_type()); + const user_op::TensorDesc& beta = ctx->InputTensorDesc("beta", 0); + CHECK_EQ_OR_RETURN(beta.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(beta.data_type()); + } + user_op::TensorDesc* mean = ctx->MutOutputTensorDesc("mean", 0); + user_op::TensorDesc* inv_variance = ctx->MutOutputTensorDesc("inv_variance", 0); + mean->set_data_type(InferGnParamDataType(x.data_type())); + inv_variance->set_data_type(mean->data_type()); + return Maybe::Ok(); +} + +// GroupNorm Grad +/* static */ Maybe GroupNormGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + const user_op::TensorDesc& mean = ctx->InputTensorDesc("mean", 0); + const user_op::TensorDesc& inv_variance = ctx->InputTensorDesc("inv_variance", 0); + const int32_t num_groups = ctx->Attr("num_groups"); + user_op::TensorDesc* dx = ctx->MutOutputTensorDesc("dx", 0); + CHECK_EQ_OR_RETURN(dy.shape(), x.shape()); + const Shape& gn_param_shape = Shape({x.shape().At(0), num_groups}); + CHECK_EQ_OR_RETURN(mean.shape(), gn_param_shape); + CHECK_EQ_OR_RETURN(inv_variance.shape(), gn_param_shape); + dx->set_shape(dy.shape()); + dx->set_is_dynamic(dy.is_dynamic()); + return Maybe::Ok(); +} + +/*static*/ Maybe GroupNormGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe GroupNormGradOp::GetSbp(user_op::SbpContext* ctx) { + std::vector broadcast_args; + if (ctx->user_op_conf().has_input("gamma", 0)) { + broadcast_args.emplace_back(user_op::OpArg("gamma", 0)); + } + + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), 0) + .Split(user_op::OpArg("x", 0), 0) + .Split(user_op::OpArg("mean", 0), 0) + .Split(user_op::OpArg("inv_variance", 0), 0) + .Split(ctx->outputs(), 0) + .Broadcast(broadcast_args) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe GroupNormGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + CHECK_EQ_OR_RETURN(dy.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(dy.data_type()); + const user_op::TensorDesc& mean = ctx->InputTensorDesc("mean", 0); + const user_op::TensorDesc& inv_variance = ctx->InputTensorDesc("inv_variance", 0); + const DataType& gn_param_data_type = InferGnParamDataType(x.data_type()); + CHECK_EQ_OR_RETURN(mean.data_type(), gn_param_data_type) + << "InferDataType Failed. Expected " << DataType_Name(gn_param_data_type) << ", but got " + << DataType_Name(mean.data_type()); + CHECK_EQ_OR_RETURN(inv_variance.data_type(), gn_param_data_type) + << "InferDataType Failed. Expected " << DataType_Name(gn_param_data_type) << ", but got " + << DataType_Name(inv_variance.data_type()); + user_op::TensorDesc* dx = ctx->MutOutputTensorDesc("dx", 0); + dx->set_data_type(dy.data_type()); + return Maybe::Ok(); +} + +// GroupNorm Param Grad +/* static */ Maybe GroupNormParamGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + user_op::TensorDesc* dgamma = ctx->MutOutputTensorDesc("dgamma", 0); + user_op::TensorDesc* dbeta = ctx->MutOutputTensorDesc("dbeta", 0); + const int64_t channel_size = x.shape().At(1); + dgamma->set_shape(Shape{channel_size}); + dbeta->set_shape(Shape{channel_size}); + return Maybe::Ok(); +} + +/*static*/ Maybe GroupNormParamGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe GroupNormParamGradOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), 0) + .Split(user_op::OpArg("x", 0), 0) + .Split(user_op::OpArg("mean", 0), 0) + .Split(user_op::OpArg("inv_variance", 0), 0) + .PartialSum(ctx->outputs()) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe GroupNormParamGradOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + user_op::TensorDesc* dgamma = ctx->MutOutputTensorDesc("dgamma", 0); + user_op::TensorDesc* dbeta = ctx->MutOutputTensorDesc("dbeta", 0); + dgamma->set_data_type(dy.data_type()); + dbeta->set_data_type(dy.data_type()); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/grouped_matmul_bias_op.cpp b/oneflow/user/ops/grouped_matmul_bias_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c3a4424f8da84c64a53ede5bc37e01b1edc38c27 --- /dev/null +++ b/oneflow/user/ops/grouped_matmul_bias_op.cpp @@ -0,0 +1,94 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/data_type.pb.h" +#include "oneflow/core/common/just.h" +#include "oneflow/core/common/maybe.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/infer_util.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe GroupedMatmulBiasOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const int64_t input_size = ctx->input_size("xs"); + CHECK_EQ_OR_RETURN(ctx->input_size("weights"), input_size); + const bool has_biases = ctx->has_input("biases", 0); + if (has_biases) { CHECK_EQ_OR_RETURN(ctx->input_size("biases"), input_size); } + CHECK_EQ_OR_RETURN(ctx->output_size("ys"), input_size); + + const DataType data_type = ctx->InputTensorDesc("xs", 0).data_type(); + for (int64_t i = 0; i < input_size; ++i) { + const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("xs", i); + CHECK_EQ_OR_RETURN(x_desc.data_type(), data_type); + CHECK_GE_OR_RETURN(x_desc.shape().NumAxes(), 2); + const int64_t k = x_desc.shape().At(x_desc.shape().NumAxes() - 1); + const user_op::TensorDesc& weight_desc = ctx->InputTensorDesc("weights", i); + CHECK_EQ_OR_RETURN(weight_desc.shape().NumAxes(), 2); + CHECK_EQ_OR_RETURN(weight_desc.shape().At(1), k); + const int64_t n = weight_desc.shape().At(0); + if (has_biases) { + const user_op::TensorDesc& bias_desc = ctx->InputTensorDesc("biases", i); + CHECK_EQ_OR_RETURN(bias_desc.shape().NumAxes(), 1); + CHECK_EQ_OR_RETURN(bias_desc.shape().At(0), n); + } + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("ys", i); + y_desc->set_data_type(data_type); + DimVector out_dim_vec = x_desc.shape().dim_vec(); + out_dim_vec.back() = n; + y_desc->set_shape(Shape(out_dim_vec)); + } + return Maybe::Ok(); +} + +/*static*/ Maybe GroupedMatmulBiasOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe GroupedMatmulBiasOp::GetSbp(user_op::SbpContext* ctx) { + auto builder = ctx->NewBuilder(); + for (int64_t i = 0; i < ctx->user_op_conf().input_size("xs"); ++i) { + builder.Split(user_op::OpArg("xs", i), 0); + } + for (int i = 0; i < ctx->user_op_conf().input_size("weights"); ++i) { + builder.Broadcast(user_op::OpArg("weights", i)); + } + for (int i = 0; i < ctx->user_op_conf().input_size("biases"); ++i) { + builder.Broadcast(user_op::OpArg("biases", i)); + } + for (int i = 0; i < ctx->user_op_conf().output_size("ys"); ++i) { + builder.Split(user_op::OpArg("ys", i), 0); + } + builder.Build(); + return Maybe::Ok(); +} + +/* static */ Maybe GroupedMatmulBiasOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& first_in_desc = ctx->InputTensorDesc("xs", 0); + for (const auto& in_arg_pair : ctx->inputs()) { + const user_op::TensorDesc& in_desc = + ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); + CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(first_in_desc.data_type()) + << ", but got " << DataType_Name(in_desc.data_type()); + } + for (int32_t i = 0; i < ctx->output_size("ys"); i++) { + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("ys", i); + y_desc->set_data_type(first_in_desc.data_type()); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/hardshrink_op.cpp b/oneflow/user/ops/hardshrink_op.cpp index 21fdae26a17dd076e98eb73e4029a4df38eed326..4afda4ccfea31a60a22ee6f60b29f22fe371cb91 100644 --- a/oneflow/user/ops/hardshrink_op.cpp +++ b/oneflow/user/ops/hardshrink_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe HardShrinkOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe HardShrinkOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe HardShrinkGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("y", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == y_shape) << "The shape of y_grad and y must be same."; - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -67,26 +66,10 @@ namespace oneflow { /* static */ Maybe HardShrinkGradOp::InferDataType(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("y", 0)) - << "The dtype of y_grad and y must be same."; - *ctx->OutputDType("dx", 0) = ctx->InputDType("y", 0); + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("y", 0)) << ", but got " + << DataType_Name(ctx->InputDType("dy", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("y", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("hardshrink") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto hardshrink_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(hardshrink_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hardshrink_grad") - .InputBind("y", ctx->FwOp().output("y", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("lambd", ctx->FwOp().attr("lambd")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &hardshrink_grad_op_name]() -> const std::string& { - return ctx->GetOp(hardshrink_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/hardsigmoid_op.cpp b/oneflow/user/ops/hardsigmoid_op.cpp index 887614425acf4a49e051c0c76523ca44e744c712..7f5581d91d73c292b294b0ed19cfeb2911c9f0e2 100644 --- a/oneflow/user/ops/hardsigmoid_op.cpp +++ b/oneflow/user/ops/hardsigmoid_op.cpp @@ -19,9 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe HardsigmoidOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -38,16 +36,15 @@ namespace oneflow { } /* static */ Maybe HardsigmoidOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe HardsigmoidGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -68,26 +65,11 @@ namespace oneflow { } /* static */ Maybe HardsigmoidGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("hardsigmoid") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto hardsigmoid_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(hardsigmoid_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hardsigmoid_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &hardsigmoid_grad_op_name]() -> const std::string& { - return ctx->GetOp(hardsigmoid_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/hardswish_op.cpp b/oneflow/user/ops/hardswish_op.cpp index f7dfbc5c870512e6a43204b5e5cb51e3c6bd6eae..d54320456068943541bc43e9cadb21cc1cbdedf7 100644 --- a/oneflow/user/ops/hardswish_op.cpp +++ b/oneflow/user/ops/hardswish_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe HardswishOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe HardswishOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe HardswishGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,26 +65,11 @@ namespace oneflow { } /* static */ Maybe HardswishGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("hardswish") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto hardswish_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(hardswish_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hardswish_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &hardswish_grad_op_name]() -> const std::string& { - return ctx->GetOp(hardswish_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/hardtanh_op.cpp b/oneflow/user/ops/hardtanh_op.cpp index 2d5208c7b0b0597bdff7a7a84111f3435379bca0..d8bda4d7293823696d89332060d82c5ff3c2e13b 100644 --- a/oneflow/user/ops/hardtanh_op.cpp +++ b/oneflow/user/ops/hardtanh_op.cpp @@ -19,9 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe HardtanhOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); double min_val = ctx->Attr("min_val"); double max_val = ctx->Attr("max_val"); CHECK_LE_OR_RETURN(min_val, max_val); @@ -41,16 +39,15 @@ namespace oneflow { } /* static */ Maybe HardtanhOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe HardtanhGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("y", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == y_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); double min_val = ctx->Attr("min_val"); double max_val = ctx->Attr("max_val"); CHECK_LE_OR_RETURN(min_val, max_val); @@ -74,28 +71,11 @@ namespace oneflow { } /* static */ Maybe HardtanhGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("y", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("y", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("y", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("hardtanh") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto hardtanh_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(hardtanh_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hardtanh_grad") - .InputBind("y", ctx->FwOp().output("out", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("min_val", ctx->FwOp().attr("min_val")) - .Attr("max_val", ctx->FwOp().attr("max_val")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &hardtanh_grad_op_name]() -> const std::string& { - return ctx->GetOp(hardtanh_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/hierarchical_parallel_cast_op.cpp b/oneflow/user/ops/hierarchical_parallel_cast_op.cpp index 7ddad5a603f166df95113a681fcb754c481ebd3a..dce9dd96255950e1e29f8d8613747fd773981a57 100644 --- a/oneflow/user/ops/hierarchical_parallel_cast_op.cpp +++ b/oneflow/user/ops/hierarchical_parallel_cast_op.cpp @@ -21,8 +21,8 @@ namespace oneflow { /* static */ Maybe HierarchicalParallelCastOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -50,15 +50,31 @@ namespace oneflow { return Maybe::Ok(); } +/* static */ Maybe HierarchicalParallelCastOp::GetNdSbpSignatureList( + user_op::GetNdSbpSignatureListContext* ctx) { + const auto& conf = ctx->Attr>("nd_sbp"); + NdSbpSignature nd_sbp_signature; + for (const std::string& sbp_str : conf) { + SbpParallel sbp_parallel; + CHECK_OR_RETURN(ParseSbpParallelFromString(sbp_str, &sbp_parallel)); + *(*nd_sbp_signature.mutable_bn_in_op2nd_sbp())[GenRepeatedBn("in", 0)].add_sbp_parallel() = + sbp_parallel; + *(*nd_sbp_signature.mutable_bn_in_op2nd_sbp())[GenRepeatedBn("out", 0)].add_sbp_parallel() = + sbp_parallel; + } + ctx->AddNdSbpSignature(nd_sbp_signature); + return Maybe::Ok(); +} + /* static */ Maybe HierarchicalParallelCastOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe HierarchicalParallelCastLikeOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -84,48 +100,8 @@ namespace oneflow { } /* static */ Maybe HierarchicalParallelCastLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("hierarchical_parallel_cast") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - if (ctx->FwOp().NeedGenGradTensor4OpInput("in", 0)) { - const auto& grad_mode = ctx->FwOp().attr("grad_mode"); - if (grad_mode == "identity") { - ctx->FwOp().BindGradTensorWithOpInput(ctx->FwOp().GetGradTensorWithOpOutput("out", 0), - "in", 0); - } else if (grad_mode == "manual") { - const std::string grad_op_name = "System-AutoGrad-" + ctx->FwOp().op_name(); - ctx->DefineOp(grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hierarchical_parallel_cast") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr>( - "nd_sbp", ctx->FwOp().attr>("grad_nd_sbp")) - .Attr>("grad_nd_sbp", std::vector()) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - } else if (grad_mode == "restore") { - const std::string grad_op_name = "System-AutoGrad-" + ctx->FwOp().op_name(); - ctx->DefineOp(grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("hierarchical_parallel_cast_like") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .InputBind("like", ctx->FwOp().input("in", 0)) - .Output("out") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - } else { - UNIMPLEMENTED(); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/identity_op.cpp b/oneflow/user/ops/identity_op.cpp index 538abeb5dde3da2f77f8e591fd828c0f7ba161d4..c597c98151461d76707f6f13fb48e7abd05731e4 100644 --- a/oneflow/user/ops/identity_op.cpp +++ b/oneflow/user/ops/identity_op.cpp @@ -19,8 +19,8 @@ limitations under the License. namespace oneflow { /* static */ Maybe IdentityOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -41,24 +41,8 @@ namespace oneflow { } /* static */ Maybe IdentityOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("identity") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper identity_op = - builder.Op("identity") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(identity_op.output("out", 0), "in", 0); - AddOp(identity_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/image_batch_align_op.cpp b/oneflow/user/ops/image_batch_align_op.cpp index 0563281485b3f8cf925f1fa7d76b90ffddf53b6d..3fadc49af4cc09b0e63138b175cd2b39c133c723 100644 --- a/oneflow/user/ops/image_batch_align_op.cpp +++ b/oneflow/user/ops/image_batch_align_op.cpp @@ -36,8 +36,8 @@ bool PowerOfTwo(T x) { DimVector dim_vec(shape_attr.NumAxes() + 1); dim_vec.at(0) = in_desc.shape().elem_cnt(); FOR_RANGE(int64_t, i, 0, shape_attr.NumAxes()) { dim_vec.at(i + 1) = shape_attr.At(i); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = Shape(dim_vec); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(Shape(dim_vec)); out_desc->set_is_dynamic(dynamic_out); return Maybe::Ok(); } @@ -90,8 +90,8 @@ bool PowerOfTwo(T x) { /* static */ Maybe ImageBatchAlignOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.data_type() == DataType::kTensorBuffer); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = ctx->Attr("data_type"); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(ctx->Attr("data_type")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/image_decode_op.cpp b/oneflow/user/ops/image_decode_op.cpp index cd308ce528eff382088cfede3ef0257d826baa8d..fb1b5f3d2f415d30e9a23f438119f63a7a899926 100644 --- a/oneflow/user/ops/image_decode_op.cpp +++ b/oneflow/user/ops/image_decode_op.cpp @@ -21,8 +21,8 @@ namespace oneflow { /* static */ Maybe ImageDecodeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.shape().NumAxes() == 1 && in_desc.shape().At(0) >= 1); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = in_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(in_desc.shape()); return Maybe::Ok(); } @@ -58,8 +58,8 @@ namespace oneflow { /* static */ Maybe ImageDecodeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.data_type() == DataType::kTensorBuffer); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = DataType::kTensorBuffer; + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } diff --git a/oneflow/user/ops/image_object_preprocess_ops.cpp b/oneflow/user/ops/image_object_preprocess_ops.cpp index 5fd2cb99f384762e03dd397ecb09774f3bf61040..273fd3bf6542d4ad2a09902bc4519aab7bf017e0 100644 --- a/oneflow/user/ops/image_object_preprocess_ops.cpp +++ b/oneflow/user/ops/image_object_preprocess_ops.cpp @@ -35,8 +35,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& flip_code_desc = ctx->InputTensorDesc("flip_code", 0); CHECK_EQ_OR_RETURN(flip_code_desc.shape().elem_cnt(), N); - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -50,8 +50,10 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ImageFlipOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in_desc.data_type(), DataType::kTensorBuffer); - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + CHECK_EQ_OR_RETURN(in_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(in_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -66,8 +68,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& flip_code_desc = ctx->InputTensorDesc("flip_code", 0); CHECK_EQ_OR_RETURN(flip_code_desc.shape().elem_cnt(), N); - *ctx->OutputShape("out", 0) = ctx->InputShape("bbox", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("bbox", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("bbox", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("bbox", 0)); return Maybe::Ok(); } @@ -81,12 +83,18 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ObjectBboxFlipOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& bbox_desc = ctx->InputTensorDesc("bbox", 0); - CHECK_EQ_OR_RETURN(bbox_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(bbox_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(bbox_desc.data_type()); const user_op::TensorDesc& image_size_desc = ctx->InputTensorDesc("image_size", 0); - CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32); + CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(image_size_desc.data_type()); const user_op::TensorDesc& flip_code_desc = ctx->InputTensorDesc("flip_code", 0); - CHECK_EQ_OR_RETURN(flip_code_desc.data_type(), DataType::kInt8); - *ctx->OutputDType("out", 0) = ctx->InputDType("bbox", 0); + CHECK_EQ_OR_RETURN(flip_code_desc.data_type(), DataType::kInt8) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt8) << ", but got " + << DataType_Name(flip_code_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("bbox", 0)); return Maybe::Ok(); } @@ -98,8 +106,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& scale_desc = ctx->InputTensorDesc("scale", 0); CHECK_EQ_OR_RETURN(scale_desc.shape().elem_cnt(), N * 2); - *ctx->OutputShape("out", 0) = ctx->InputShape("bbox", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("bbox", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("bbox", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("bbox", 0)); return Maybe::Ok(); } @@ -113,10 +121,14 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ObjectBboxScaleOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& bbox_desc = ctx->InputTensorDesc("bbox", 0); - CHECK_EQ_OR_RETURN(bbox_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(bbox_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(bbox_desc.data_type()); const user_op::TensorDesc& scale_desc = ctx->InputTensorDesc("scale", 0); - CHECK_EQ_OR_RETURN(scale_desc.data_type(), DataType::kFloat); - *ctx->OutputDType("out", 0) = ctx->InputDType("bbox", 0); + CHECK_EQ_OR_RETURN(scale_desc.data_type(), DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(scale_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("bbox", 0)); return Maybe::Ok(); } @@ -132,8 +144,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& flip_code_desc = ctx->InputTensorDesc("flip_code", 0); CHECK_EQ_OR_RETURN(flip_code_desc.shape().elem_cnt(), N); - *ctx->OutputShape("out", 0) = ctx->InputShape("poly", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("poly", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("poly", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("poly", 0)); return Maybe::Ok(); } @@ -149,12 +161,18 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ObjectSegmentationPolygonFlipOp::InferDataType( user_op::InferContext* ctx) { const user_op::TensorDesc& poly_desc = ctx->InputTensorDesc("poly", 0); - CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(poly_desc.data_type()); const user_op::TensorDesc& image_size_desc = ctx->InputTensorDesc("image_size", 0); - CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32); + CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(image_size_desc.data_type()); const user_op::TensorDesc& flip_code_desc = ctx->InputTensorDesc("flip_code", 0); - CHECK_EQ_OR_RETURN(flip_code_desc.data_type(), DataType::kInt8); - *ctx->OutputDType("out", 0) = ctx->InputDType("poly", 0); + CHECK_EQ_OR_RETURN(flip_code_desc.data_type(), DataType::kInt8) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt8) << ", but got " + << DataType_Name(flip_code_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("poly", 0)); return Maybe::Ok(); } @@ -167,8 +185,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& scale_desc = ctx->InputTensorDesc("scale", 0); CHECK_EQ_OR_RETURN(scale_desc.shape().elem_cnt(), N * 2); - *ctx->OutputShape("out", 0) = ctx->InputShape("poly", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("poly", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("poly", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("poly", 0)); return Maybe::Ok(); } @@ -184,18 +202,22 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ObjectSegmentationPolygonScaleOp::InferDataType( user_op::InferContext* ctx) { const user_op::TensorDesc& poly_desc = ctx->InputTensorDesc("poly", 0); - CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(poly_desc.data_type()); const user_op::TensorDesc& scale_desc = ctx->InputTensorDesc("scale", 0); - CHECK_EQ_OR_RETURN(scale_desc.data_type(), DataType::kFloat); - *ctx->OutputDType("out", 0) = ctx->InputDType("poly", 0); + CHECK_EQ_OR_RETURN(scale_desc.data_type(), DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(scale_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("poly", 0)); return Maybe::Ok(); } /* static */ Maybe ImageNormalizeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_EQ_OR_RETURN(in_desc.shape().NumAxes(), 1); - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -209,8 +231,10 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ImageNormalizeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in_desc.data_type(), DataType::kTensorBuffer); - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + CHECK_EQ_OR_RETURN(in_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(in_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -227,8 +251,8 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& image_size_desc = ctx->InputTensorDesc("image_size", 0); CHECK_EQ_OR_RETURN(image_size_desc.shape().elem_cnt(), N * 2); - *ctx->OutputShape("out", 0) = ctx->InputShape("poly", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("poly", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("poly", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("poly", 0)); return Maybe::Ok(); } @@ -244,12 +268,18 @@ Maybe ImageObjectGetSbp(user_op::SbpContext* ctx) { /* static */ Maybe ObjectSegmentationPolygonToMaskOp::InferDataType( user_op::InferContext* ctx) { const user_op::TensorDesc& poly_desc = ctx->InputTensorDesc("poly", 0); - CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(poly_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(poly_desc.data_type()); const user_op::TensorDesc& poly_index_desc = ctx->InputTensorDesc("poly_index", 0); - CHECK_EQ_OR_RETURN(poly_index_desc.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(poly_index_desc.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(poly_desc.data_type()); const user_op::TensorDesc& image_size_desc = ctx->InputTensorDesc("image_size", 0); - CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32); - *ctx->OutputDType("out", 0) = ctx->InputDType("poly", 0); + CHECK_EQ_OR_RETURN(image_size_desc.data_type(), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(image_size_desc.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("poly", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/image_preprocess_ops.cpp b/oneflow/user/ops/image_preprocess_ops.cpp index 00c6d419c8b11ef3a83a7c0a8e100154b816bdf2..eb9f0f3d42ab38aecd3a19c20190d59657128c58 100644 --- a/oneflow/user/ops/image_preprocess_ops.cpp +++ b/oneflow/user/ops/image_preprocess_ops.cpp @@ -31,7 +31,7 @@ namespace oneflow { CHECK_OR_RETURN(mirror_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) == mirror_tensor.shape().At(0)); } - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); int64_t N = in_tensor.shape().At(0); int64_t H = ctx->Attr("crop_h"); int64_t W = ctx->Attr("crop_w"); @@ -42,9 +42,9 @@ namespace oneflow { CHECK_OR_RETURN(in_tensor.shape().NumAxes() == 1); std::string output_layout = ctx->Attr("output_layout"); if (output_layout == "NCHW") { - *out_tensor->mut_shape() = Shape({N, C, H, W}); + out_tensor->set_shape(Shape({N, C, H, W})); } else if (output_layout == "NHWC") { - *out_tensor->mut_shape() = Shape({N, H, W, C}); + out_tensor->set_shape(Shape({N, H, W, C})); } else { return Error::CheckFailedError() << "output_layout: " << output_layout << " is not supported"; } @@ -64,18 +64,24 @@ namespace oneflow { /* static */ Maybe CropMirrorNormalizeFromTensorbufferOp::InferDataType( user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in_tensor.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(in_tensor.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(in_tensor.data_type()); bool has_mirror = ctx->has_input("mirror", 0); if (has_mirror) { const user_op::TensorDesc& mirror_tensor = ctx->InputTensorDesc("mirror", 0); - CHECK_EQ_OR_RETURN(mirror_tensor.data_type(), DataType::kInt8); + CHECK_EQ_OR_RETURN(mirror_tensor.data_type(), DataType::kInt8) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt8) << ", but got " + << DataType_Name(mirror_tensor.data_type()); } - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); DataType output_dtype = ctx->Attr("output_dtype"); CHECK_EQ_OR_RETURN(output_dtype, - DataType::kFloat); // only support float now; for float16 in future - *out_tensor->mut_data_type() = output_dtype; + DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(output_dtype); // only support float now; for float16 in future + out_tensor->set_data_type(output_dtype); return Maybe::Ok(); } @@ -89,7 +95,7 @@ namespace oneflow { CHECK_OR_RETURN(mirror_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) == mirror_tensor.shape().At(0)); } - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); int64_t N = in_tensor.shape().At(0); int64_t H = ctx->Attr("crop_h"); int64_t W = ctx->Attr("crop_w"); @@ -106,9 +112,9 @@ namespace oneflow { } std::string output_layout = ctx->Attr("output_layout"); if (output_layout == "NCHW") { - *out_tensor->mut_shape() = Shape({N, C, H, W}); + out_tensor->set_shape(Shape({N, C, H, W})); } else if (output_layout == "NHWC") { - *out_tensor->mut_shape() = Shape({N, H, W, C}); + out_tensor->set_shape(Shape({N, H, W, C})); } else { return Error::CheckFailedError() << "output_layout: " << output_layout << " is not supported"; } @@ -128,24 +134,30 @@ namespace oneflow { /* static */ Maybe CropMirrorNormalizeFromUint8Op::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in_tensor.data_type(), DataType::kUInt8); + CHECK_EQ_OR_RETURN(in_tensor.data_type(), DataType::kUInt8) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kUInt8) << ", but got " + << DataType_Name(in_tensor.data_type()); bool has_mirror = ctx->has_input("mirror", 0); if (has_mirror) { const user_op::TensorDesc& mirror_tensor = ctx->InputTensorDesc("mirror", 0); - CHECK_EQ_OR_RETURN(mirror_tensor.data_type(), DataType::kInt8); + CHECK_EQ_OR_RETURN(mirror_tensor.data_type(), DataType::kInt8) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt8) << ", but got " + << DataType_Name(mirror_tensor.data_type()); } - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); DataType output_dtype = ctx->Attr("output_dtype"); CHECK_EQ_OR_RETURN(output_dtype, - DataType::kFloat); // only support float now; for float16 in future - *out_tensor->mut_data_type() = output_dtype; + DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(output_dtype); // only support float now; for float16 in future + out_tensor->set_data_type(output_dtype); return Maybe::Ok(); } /* static */ Maybe CoinFlipOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); int64_t batch_size = ctx->Attr("batch_size"); - *out_tensor->mut_shape() = Shape({batch_size}); + out_tensor->set_shape(Shape({batch_size})); return Maybe::Ok(); } @@ -159,7 +171,7 @@ namespace oneflow { const auto tensor_slice_view = GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } @@ -202,16 +214,16 @@ namespace oneflow { } /* static */ Maybe CoinFlipOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_data_type() = DataType::kInt8; + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_data_type(DataType::kInt8); return Maybe::Ok(); } /* static */ Maybe ImageRandomCropOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_shape() = in_tensor.shape(); - *out_tensor->mut_is_dynamic() = in_tensor.is_dynamic(); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_shape(in_tensor.shape()); + out_tensor->set_is_dynamic(in_tensor.is_dynamic()); return Maybe::Ok(); } @@ -234,7 +246,7 @@ namespace oneflow { /* static */ Maybe ImageRandomCropOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kTensorBuffer); - *ctx->OutputDType("out", 0) = in_tensor.data_type(); + ctx->SetOutputDType("out", 0, in_tensor.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/image_resize_ops.cpp b/oneflow/user/ops/image_resize_ops.cpp index fe6f351ecafa863810e16f10f1b5d585da341644..95900fca9319dbec7359d64f2c64cfa9467b9dd5 100644 --- a/oneflow/user/ops/image_resize_ops.cpp +++ b/oneflow/user/ops/image_resize_ops.cpp @@ -27,12 +27,12 @@ namespace oneflow { int64_t target_height = ctx->Attr("target_height"); int64_t channels = ctx->Attr("channels"); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_shape() = Shape({batch_size, target_height, target_width, channels}); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_shape(Shape({batch_size, target_height, target_width, channels})); out_tensor->set_is_dynamic(in_tensor.is_dynamic()); - user_op::TensorDesc* scale_tensor = ctx->OutputTensorDesc("scale", 0); - *scale_tensor->mut_shape() = Shape({batch_size, 2}); + user_op::TensorDesc* scale_tensor = ctx->MutOutputTensorDesc("scale", 0); + scale_tensor->set_shape(Shape({batch_size, 2})); scale_tensor->set_is_dynamic(in_tensor.is_dynamic()); return Maybe::Ok(); @@ -77,10 +77,10 @@ namespace oneflow { /* static */ Maybe ImageResizeToFixedOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kTensorBuffer); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_data_type() = ctx->Attr("data_type"); - user_op::TensorDesc* scale_tensor = ctx->OutputTensorDesc("scale", 0); - *scale_tensor->mut_data_type() = DataType::kFloat; + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_data_type(ctx->Attr("data_type")); + user_op::TensorDesc* scale_tensor = ctx->MutOutputTensorDesc("scale", 0); + scale_tensor->set_data_type(DataType::kFloat); return Maybe::Ok(); } @@ -88,12 +88,12 @@ namespace oneflow { user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.shape().NumAxes() == 1 && in_desc.shape().At(0) > 0); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = in_desc.shape(); - user_op::TensorDesc* size_desc = ctx->OutputTensorDesc("size", 0); - *size_desc->mut_shape() = in_desc.shape(); - user_op::TensorDesc* scale_desc = ctx->OutputTensorDesc("scale", 0); - *scale_desc->mut_shape() = in_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(in_desc.shape()); + user_op::TensorDesc* size_desc = ctx->MutOutputTensorDesc("size", 0); + size_desc->set_shape(in_desc.shape()); + user_op::TensorDesc* scale_desc = ctx->MutOutputTensorDesc("scale", 0); + scale_desc->set_shape(in_desc.shape()); return Maybe::Ok(); } @@ -132,12 +132,12 @@ namespace oneflow { /* static */ Maybe ImageResizeKeepAspectRatioOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.data_type() == DataType::kTensorBuffer); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* size_desc = ctx->OutputTensorDesc("size", 0); - *size_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* scale_desc = ctx->OutputTensorDesc("scale", 0); - *scale_desc->mut_data_type() = DataType::kTensorBuffer; + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* size_desc = ctx->MutOutputTensorDesc("size", 0); + size_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* scale_desc = ctx->MutOutputTensorDesc("scale", 0); + scale_desc->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } diff --git a/oneflow/user/ops/image_target_resize_op.cpp b/oneflow/user/ops/image_target_resize_op.cpp index 49d7db09479f13030ca3dcba909957bc50a9d132..2ab7093f2f61ff560fedc4f5ad4280f57f7b58d5 100644 --- a/oneflow/user/ops/image_target_resize_op.cpp +++ b/oneflow/user/ops/image_target_resize_op.cpp @@ -21,12 +21,12 @@ namespace oneflow { /* static */ Maybe ImageTargetResizeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.shape().NumAxes() == 1 && in_desc.shape().At(0) >= 1); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = in_desc.shape(); - user_op::TensorDesc* size_desc = ctx->OutputTensorDesc("size", 0); - *size_desc->mut_shape() = Shape({in_desc.shape().elem_cnt(), 2}); - user_op::TensorDesc* scale_desc = ctx->OutputTensorDesc("scale", 0); - *scale_desc->mut_shape() = Shape({in_desc.shape().elem_cnt(), 2}); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(in_desc.shape()); + user_op::TensorDesc* size_desc = ctx->MutOutputTensorDesc("size", 0); + size_desc->set_shape(Shape({in_desc.shape().elem_cnt(), 2})); + user_op::TensorDesc* scale_desc = ctx->MutOutputTensorDesc("scale", 0); + scale_desc->set_shape(Shape({in_desc.shape().elem_cnt(), 2})); return Maybe::Ok(); } @@ -61,12 +61,12 @@ namespace oneflow { /* static */ Maybe ImageTargetResizeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(in_desc.data_type() == DataType::kTensorBuffer); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = DataType::kTensorBuffer; - user_op::TensorDesc* size_desc = ctx->OutputTensorDesc("size", 0); - *size_desc->mut_data_type() = DataType::kInt32; - user_op::TensorDesc* scale_desc = ctx->OutputTensorDesc("scale", 0); - *scale_desc->mut_data_type() = DataType::kFloat; + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(DataType::kTensorBuffer); + user_op::TensorDesc* size_desc = ctx->MutOutputTensorDesc("size", 0); + size_desc->set_data_type(DataType::kInt32); + user_op::TensorDesc* scale_desc = ctx->MutOutputTensorDesc("scale", 0); + scale_desc->set_data_type(DataType::kFloat); return Maybe::Ok(); } diff --git a/oneflow/user/ops/in_top_k_op.cpp b/oneflow/user/ops/in_top_k_op.cpp index 3d76b2b9110cf750dcd6017eb55d37bfb8530c5b..0630aff15c5c4670ae3c6db642bd09fa04568acb 100644 --- a/oneflow/user/ops/in_top_k_op.cpp +++ b/oneflow/user/ops/in_top_k_op.cpp @@ -21,13 +21,13 @@ namespace oneflow { /* static */ Maybe InTopKOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& targets = ctx->InputTensorDesc("targets", 0); const user_op::TensorDesc& predictions = ctx->InputTensorDesc("predictions", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_EQ_OR_RETURN(targets.shape().NumAxes(), 1); CHECK_EQ_OR_RETURN(predictions.shape().NumAxes(), 2); const bool is_dynamic = targets.is_dynamic(); CHECK_EQ_OR_RETURN(is_dynamic, predictions.is_dynamic()); out->set_is_dynamic(is_dynamic); - *out->mut_shape() = targets.shape(); + out->set_shape(targets.shape()); return Maybe::Ok(); } @@ -44,9 +44,11 @@ namespace oneflow { const user_op::TensorDesc& targets = ctx->InputTensorDesc("targets", 0); CHECK_OR_RETURN(IsIndexDataType(targets.data_type())); const user_op::TensorDesc& predictions = ctx->InputTensorDesc("predictions", 0); - CHECK_EQ_OR_RETURN(predictions.data_type(), DataType::kFloat); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = kBool; + CHECK_EQ_OR_RETURN(predictions.data_type(), DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(predictions.data_type()); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(kBool); return Maybe::Ok(); } diff --git a/oneflow/user/ops/index_add_op.cpp b/oneflow/user/ops/index_add_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3f8810f7c38b4b998bb36749b55c404c746a2b25 --- /dev/null +++ b/oneflow/user/ops/index_add_op.cpp @@ -0,0 +1,42 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe IndexAddOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& input_shape = ctx->InputShape("input", 0); + ctx->SetOutputShape("output", 0, input_shape); + ctx->SetOutputStride("output", 0, ctx->InputStride("input", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe IndexAddOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe IndexAddOp::GetSbp(user_op::SbpContext* ctx) { + // TODO(yangzhimin): support more valid sbp signature. + return Maybe::Ok(); +} + +/* static */ Maybe IndexAddOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/indexed_slices_reduce_sum_op.cpp b/oneflow/user/ops/indexed_slices_reduce_sum_op.cpp index 5b61c8ff2ba6ad069f28f2a7b5979930acae7f00..82948b6b35c5b684e23f33dda24b87db92fd97ca 100644 --- a/oneflow/user/ops/indexed_slices_reduce_sum_op.cpp +++ b/oneflow/user/ops/indexed_slices_reduce_sum_op.cpp @@ -29,14 +29,14 @@ namespace oneflow { const int64_t n = x_indices.shape().elem_cnt(); const int64_t m = x_values.shape().elem_cnt() / n; - user_op::TensorDesc* y_indices = ctx->OutputTensorDesc("y_indices", 0); - user_op::TensorDesc* y_values = ctx->OutputTensorDesc("y_values", 0); + user_op::TensorDesc* y_indices = ctx->MutOutputTensorDesc("y_indices", 0); + user_op::TensorDesc* y_values = ctx->MutOutputTensorDesc("y_values", 0); *y_indices = x_indices; - *y_indices->mut_shape() = Shape({n}); + y_indices->set_shape(Shape({n})); *y_values = x_values; - *y_values->mut_shape() = Shape({n, m}); - user_op::TensorDesc* num_unique = ctx->OutputTensorDesc("num_unique", 0); - *num_unique->mut_shape() = Shape({1}); + y_values->set_shape(Shape({n, m})); + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_shape(Shape({1})); return Maybe::Ok(); } @@ -52,8 +52,8 @@ namespace oneflow { /* static */ Maybe IndexedSlicesReduceSumOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& x_indices = ctx->InputTensorDesc("x_indices", 0); CHECK_OR_RETURN(IsIndexDataType(x_indices.data_type())); - user_op::TensorDesc* num_unique = ctx->OutputTensorDesc("num_unique", 0); - *num_unique->mut_data_type() = DataType::kInt64; + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_data_type(DataType::kInt64); return Maybe::Ok(); } diff --git a/oneflow/user/ops/inv_op.cpp b/oneflow/user/ops/inv_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5956f4a53fb614db9977a61a83c5171273bfd140 --- /dev/null +++ b/oneflow/user/ops/inv_op.cpp @@ -0,0 +1,43 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ Maybe InvOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe InvOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe InvOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& x = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, x.shape().NumAxes() - 2) { + ctx->NewBuilder().Split(user_op::OpArg("x", 0), i).Split(user_op::OpArg("y", 0), i).Build(); + } + return Maybe::Ok(); +} + +/*static*/ Maybe InvOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/kl_div_op.cpp b/oneflow/user/ops/kl_div_op.cpp index cb58f29764b159050ece4e0b3a4ac36782826413..eec9f36cfe8d0a1bd8ed7fec51a321e09c267907 100644 --- a/oneflow/user/ops/kl_div_op.cpp +++ b/oneflow/user/ops/kl_div_op.cpp @@ -26,9 +26,9 @@ Maybe KlInferTensorDescFn(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()); CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *out_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(input_desc.is_dynamic()); + out_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -36,9 +36,11 @@ Maybe KlInferTensorDescFn(user_op::InferContext* ctx) { Maybe KlInferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(target_desc.data_type()) << ", but got " + << DataType_Name(input_desc.data_type()); - *ctx->OutputDType("out", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } @@ -51,9 +53,9 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); CHECK_EQ_OR_RETURN(dy_desc.shape(), target_desc.shape()); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *dx_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(input_desc.is_dynamic()); + dx_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -61,9 +63,11 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { Maybe InferGradDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(target_desc.data_type()) << ", but got " + << DataType_Name(input_desc.data_type()); - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -123,22 +127,4 @@ Maybe InferGradDataType(user_op::InferContext* ctx) { return InferGradDataType(ctx); } -REGISTER_USER_OP_GRAD("kl_div_loss") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("kl_div_loss_grad") - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/l1_l2_regularize_gradient_op.cpp b/oneflow/user/ops/l1_l2_regularize_gradient_op.cpp index 05affa224047af2db0a263afa53d9642f6274916..8ddb72018733c04d70eb6bd8b6389f02e10fcde6 100644 --- a/oneflow/user/ops/l1_l2_regularize_gradient_op.cpp +++ b/oneflow/user/ops/l1_l2_regularize_gradient_op.cpp @@ -24,8 +24,8 @@ Maybe InferTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& model = ctx->InputTensorDesc("model", 0); const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", 0); CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()); - *ctx->OutputShape("out", 0) = ctx->InputShape("model", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("model", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("model", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("model", 0)); return Maybe::Ok(); } @@ -56,8 +56,10 @@ Maybe GetSbpSignatures(user_op::SbpContext* ctx) { /* static */ Maybe L1L2RegularizeGradientOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& model = ctx->InputTensorDesc("model", 0); const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", 0); - CHECK_EQ_OR_RETURN(model_diff.data_type(), model.data_type()); - *ctx->OutputDType("out", 0) = ctx->InputDType("model", 0); + CHECK_EQ_OR_RETURN(model_diff.data_type(), model.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(model.data_type()) << ", but got " + << DataType_Name(model_diff.data_type()); + ctx->SetOutputDType("out", 0, ctx->InputDType("model", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/l2_normalize_op.cpp b/oneflow/user/ops/l2_normalize_op.cpp index d1723c41c977af6facac925c0f4bc4378cc0b336..e557c99dba26928ac35b312afed8063570141dd3 100644 --- a/oneflow/user/ops/l2_normalize_op.cpp +++ b/oneflow/user/ops/l2_normalize_op.cpp @@ -20,16 +20,15 @@ namespace oneflow { /* static */ Maybe L2NormalizeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); - Shape* y_shape = ctx->OutputShape("y", 0); - Shape* square_x_sum_shape = ctx->OutputShape("square_x_sum", 0); const int32_t axis = ctx->Attr("axis"); const float epsilon = ctx->Attr("epsilon"); CHECK_GE_OR_RETURN(axis, 0); CHECK_LT_OR_RETURN(axis, x_shape.NumAxes()); CHECK_GT_OR_RETURN(epsilon, 0); - *y_shape = x_shape; - *square_x_sum_shape = x_shape; - square_x_sum_shape->Set(axis, 1); + ctx->SetOutputShape("y", 0, x_shape); + Shape square_x_sum_shape = x_shape; + square_x_sum_shape.Set(axis, 1); + ctx->SetOutputShape("square_x_sum", 0, square_x_sum_shape); return Maybe::Ok(); } @@ -53,8 +52,13 @@ namespace oneflow { } /* static */ Maybe L2NormalizeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("square_x_sum", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + DataType x_dtype = ctx->InputDType("x", 0); + DataType square_x_sum_dtype = x_dtype; + if (x_dtype == DataType::kFloat16 || x_dtype == DataType::kBFloat16) { + square_x_sum_dtype = DataType::kFloat; + } + ctx->SetOutputDType("square_x_sum", 0, square_x_sum_dtype); + ctx->SetOutputDType("y", 0, x_dtype); return Maybe::Ok(); } @@ -62,7 +66,6 @@ namespace oneflow { const Shape& dy_shape = ctx->InputShape("dy", 0); const Shape& y_shape = ctx->InputShape("y", 0); const Shape& square_x_sum_shape = ctx->InputShape("square_x_sum", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); const int32_t axis = ctx->Attr("axis"); const float epsilon = ctx->Attr("epsilon"); CHECK_EQ_OR_RETURN(dy_shape, y_shape); @@ -76,7 +79,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(square_x_sum_shape.At(i), dy_shape.At(i)); } } - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -101,30 +104,14 @@ namespace oneflow { } /* static */ Maybe L2NormalizeGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("dy", 0)); - CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("square_x_sum", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("y", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("square_x_sum", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("square_x_sum", 0)) + << ", but got " << DataType_Name(ctx->InputDType("y", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("l2_normalize") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("l2_normalize_grad") - .Input("y", op.output("y", 0)) - .Input("square_x_sum", op.output("square_x_sum", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("axis", op.attr("axis")) - .Attr("epsilon", op.attr("epsilon")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/layer_norm_op.cpp b/oneflow/user/ops/layer_norm_op.cpp index 3ae2765b362c2598ebfac86f4cb0c3d3959e4f37..55eec5bb2a9330176b342753258ad8b7bba66d71 100644 --- a/oneflow/user/ops/layer_norm_op.cpp +++ b/oneflow/user/ops/layer_norm_op.cpp @@ -36,22 +36,24 @@ Shape InferBnParamShape(const Shape& x_shape, const int64_t begin_norm_axis) { } oneflow::DataType InferBnParamDataType(const DataType x_data_type) { - return x_data_type == DataType::kFloat16 ? DataType::kFloat : x_data_type; + return (x_data_type == DataType::kFloat16 || x_data_type == DataType::kBFloat16) + ? DataType::kFloat + : x_data_type; } } // namespace /* static */ Maybe LayerNormOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - user_op::TensorDesc* mean = ctx->OutputTensorDesc("mean", 0); - user_op::TensorDesc* inv_variance = ctx->OutputTensorDesc("inv_variance", 0); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + user_op::TensorDesc* mean = ctx->MutOutputTensorDesc("mean", 0); + user_op::TensorDesc* inv_variance = ctx->MutOutputTensorDesc("inv_variance", 0); const bool center = ctx->Attr("center"); const bool scale = ctx->Attr("scale"); const int64_t begin_params_axis = ShiftNegativeAxisIfNeed(x.shape(), ctx->Attr("begin_params_axis")); - *y->mut_shape() = x.shape(); - *y->mut_is_dynamic() = x.is_dynamic(); + y->set_shape(x.shape()); + y->set_is_dynamic(x.is_dynamic()); DimVector param_shape_dim_vec; param_shape_dim_vec.insert(param_shape_dim_vec.end(), x.shape().dim_vec().cbegin() + begin_params_axis, @@ -71,7 +73,7 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { return Error::RuntimeError() << "begin_norm_axis must equal to begin_params_axis, but got " << begin_norm_axis << " vs " << begin_params_axis; } - *mean->mut_shape() = InferBnParamShape(x.shape(), begin_norm_axis); + mean->set_shape(InferBnParamShape(x.shape(), begin_norm_axis)); *inv_variance = *mean; return Maybe::Ok(); } @@ -99,21 +101,25 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { /* static */ Maybe LayerNormOp::InferDataType(user_op::InferContext* ctx) { const bool center = ctx->Attr("center"); const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_data_type() = x.data_type(); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(x.data_type()); if (center) { const user_op::TensorDesc& beta = ctx->InputTensorDesc("beta", 0); - CHECK_EQ_OR_RETURN(beta.data_type(), x.data_type()); + CHECK_EQ_OR_RETURN(beta.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(beta.data_type()); } const bool scale = ctx->Attr("scale"); if (scale) { const user_op::TensorDesc& gamma = ctx->InputTensorDesc("gamma", 0); - CHECK_EQ_OR_RETURN(gamma.data_type(), x.data_type()); + CHECK_EQ_OR_RETURN(gamma.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(gamma.data_type()); } - user_op::TensorDesc* mean = ctx->OutputTensorDesc("mean", 0); - user_op::TensorDesc* inv_variance = ctx->OutputTensorDesc("inv_variance", 0); - *mean->mut_data_type() = InferBnParamDataType(x.data_type()); - *inv_variance->mut_data_type() = mean->data_type(); + user_op::TensorDesc* mean = ctx->MutOutputTensorDesc("mean", 0); + user_op::TensorDesc* inv_variance = ctx->MutOutputTensorDesc("inv_variance", 0); + mean->set_data_type(InferBnParamDataType(x.data_type())); + inv_variance->set_data_type(mean->data_type()); return Maybe::Ok(); } @@ -122,15 +128,15 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& mean = ctx->InputTensorDesc("mean", 0); const user_op::TensorDesc& inv_variance = ctx->InputTensorDesc("inv_variance", 0); - user_op::TensorDesc* dx = ctx->OutputTensorDesc("dx", 0); + user_op::TensorDesc* dx = ctx->MutOutputTensorDesc("dx", 0); CHECK_EQ_OR_RETURN(dy.shape(), x.shape()); const int64_t begin_norm_axis = ctx->Attr("begin_norm_axis"); CHECK_GT_OR_RETURN(begin_norm_axis, 0); const Shape& bn_param_shape = InferBnParamShape(x.shape(), begin_norm_axis); CHECK_EQ_OR_RETURN(mean.shape(), bn_param_shape); CHECK_EQ_OR_RETURN(inv_variance.shape(), bn_param_shape); - *dx->mut_shape() = dy.shape(); - *dx->mut_is_dynamic() = dy.is_dynamic(); + dx->set_shape(dy.shape()); + dx->set_is_dynamic(dy.is_dynamic()); if (ctx->has_input("_add_to_output", 0)) { const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); CHECK_EQ_OR_RETURN(add_to_output.shape(), dx->shape()); @@ -161,17 +167,25 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { /* static */ Maybe LayerNormGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); - CHECK_EQ_OR_RETURN(dy.data_type(), x.data_type()); + CHECK_EQ_OR_RETURN(dy.data_type(), x.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(x.data_type()) << ", but got " + << DataType_Name(dy.data_type()); const user_op::TensorDesc& mean = ctx->InputTensorDesc("mean", 0); const user_op::TensorDesc& inv_variance = ctx->InputTensorDesc("inv_variance", 0); - const DataType& bn_param_data_type = InferBnParamDataType(x.data_type()); - CHECK_EQ_OR_RETURN(mean.data_type(), bn_param_data_type); - CHECK_EQ_OR_RETURN(inv_variance.data_type(), bn_param_data_type); - user_op::TensorDesc* dx = ctx->OutputTensorDesc("dx", 0); - *dx->mut_data_type() = dy.data_type(); + DataType bn_param_data_type = InferBnParamDataType(x.data_type()); + CHECK_EQ_OR_RETURN(mean.data_type(), bn_param_data_type) + << "InferDataType Failed. Expected " << DataType_Name(bn_param_data_type) << ", but got " + << DataType_Name(mean.data_type()); + CHECK_EQ_OR_RETURN(inv_variance.data_type(), bn_param_data_type) + << "InferDataType Failed. Expected " << DataType_Name(bn_param_data_type) << ", but got " + << DataType_Name(inv_variance.data_type()); + user_op::TensorDesc* dx = ctx->MutOutputTensorDesc("dx", 0); + dx->set_data_type(dy.data_type()); if (ctx->has_input("_add_to_output", 0)) { const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); - CHECK_EQ_OR_RETURN(add_to_output.data_type(), dx->data_type()); + CHECK_EQ_OR_RETURN(add_to_output.data_type(), dx->data_type()) + << "InferDataType Failed. Expected " << DataType_Name(dx->data_type()) << ", but got " + << DataType_Name(add_to_output.data_type()); } return Maybe::Ok(); } @@ -200,12 +214,12 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { dy.shape().dim_vec().cend()); const Shape param_shape(param_shape_dim_vec); if (has_beta_diff) { - user_op::TensorDesc* beta_diff = ctx->OutputTensorDesc("beta_diff", 0); - *beta_diff->mut_shape() = param_shape; + user_op::TensorDesc* beta_diff = ctx->MutOutputTensorDesc("beta_diff", 0); + beta_diff->set_shape(param_shape); } if (has_gamma_diff) { - user_op::TensorDesc* gamma_diff = ctx->OutputTensorDesc("gamma_diff", 0); - *gamma_diff->mut_shape() = param_shape; + user_op::TensorDesc* gamma_diff = ctx->MutOutputTensorDesc("gamma_diff", 0); + gamma_diff->set_shape(param_shape); } return Maybe::Ok(); } @@ -237,67 +251,14 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) { const bool has_gamma_diff = has_tensor("gamma_diff"); const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); if (has_beta_diff) { - user_op::TensorDesc* beta_diff = ctx->OutputTensorDesc("beta_diff", 0); - *beta_diff->mut_data_type() = dy.data_type(); + user_op::TensorDesc* beta_diff = ctx->MutOutputTensorDesc("beta_diff", 0); + beta_diff->set_data_type(dy.data_type()); } if (has_gamma_diff) { - user_op::TensorDesc* gamma_diff = ctx->OutputTensorDesc("gamma_diff", 0); - *gamma_diff->mut_data_type() = dy.data_type(); + user_op::TensorDesc* gamma_diff = ctx->MutOutputTensorDesc("gamma_diff", 0); + gamma_diff->set_data_type(dy.data_type()); } return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("layer_norm") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - const bool center = op.attr("center"); - const bool scale = op.attr("scale"); - const bool has_beta = center; - const bool has_gamma = scale; - const bool has_beta_diff = has_beta && op.NeedGenGradTensor4OpInput("beta", 0); - const bool has_gamma_diff = has_gamma && op.NeedGenGradTensor4OpInput("gamma", 0); - const Shape& x_shape = op.TensorDesc4ArgNameAndIndex("x", 0).shape(); - const int64_t begin_norm_axis = - ShiftNegativeAxisIfNeed(x_shape, op.attr("begin_norm_axis")); - const int64_t begin_params_axis = - ShiftNegativeAxisIfNeed(x_shape, op.attr("begin_params_axis")); - if (has_beta_diff || has_gamma_diff) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_param_grad"); - auto grad_op_builder = builder.Op("layer_norm_param_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Input("mean", op.output("mean", 0)) - .Input("inv_variance", op.output("inv_variance", 0)) - .Attr("begin_params_axis", begin_params_axis); - if (has_beta_diff) { grad_op_builder.Output("beta_diff"); } - if (has_gamma_diff) { grad_op_builder.Output("gamma_diff"); } - auto grad_op = grad_op_builder.Build(); - if (has_beta_diff) { - op.BindGradTensorWithOpInput(grad_op.output("beta_diff", 0), "beta", 0); - } - if (has_gamma_diff) { - op.BindGradTensorWithOpInput(grad_op.output("gamma_diff", 0), "gamma", 0); - } - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("layer_norm_grad") - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("mean", op.output("mean", 0)) - .Input("inv_variance", op.output("inv_variance", 0)) - .Output("dx") - .Attr("begin_norm_axis", begin_norm_axis) - .Attr("epsilon", op.attr("epsilon")); - if (op.user_op_conf().has_input("gamma", 0)) { - builder.Input("gamma", op.input("gamma", 0)); - } - user_op::UserOpConfWrapper grad_op = builder.Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/leaky_relu_op.cpp b/oneflow/user/ops/leaky_relu_op.cpp index 09d8b318c54fb643039fdb8721d231a6e948d20b..bb67147a0717dd55e9de507aa7067e168d304770 100644 --- a/oneflow/user/ops/leaky_relu_op.cpp +++ b/oneflow/user/ops/leaky_relu_op.cpp @@ -19,9 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe LeakyReluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& x_shape = ctx->InputShape("x", 0); - Shape* y_shape = ctx->OutputShape("y", 0); - *y_shape = x_shape; + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } @@ -38,16 +36,15 @@ namespace oneflow { } /* static */ Maybe LeakyReluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } /* static */ Maybe LeakyReluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -73,26 +70,11 @@ namespace oneflow { } /* static */ Maybe LeakyReluGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("leaky_relu") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("leaky_relu_grad") - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("alpha", op.attr("alpha")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/linalg_cross_op.cpp b/oneflow/user/ops/linalg_cross_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..31c6a2b187ff3552f02ec70ddddde8df2493bcc5 --- /dev/null +++ b/oneflow/user/ops/linalg_cross_op.cpp @@ -0,0 +1,51 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ Maybe LinalgCrossOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("input", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe LinalgCrossOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe LinalgCrossOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& input = ctx->LogicalTensorDesc4InputArgNameAndIndex("input", 0); + const int64_t num_axes = input.shape().NumAxes(); + const int64_t dim = ctx->Attr("dim"); + + FOR_RANGE(int64_t, i, 0, num_axes) { + if (i == dim) continue; + ctx->NewBuilder() + .Split(user_op::OpArg("input", 0), i) + .Split(user_op::OpArg("other", 0), i) + .Split(user_op::OpArg("out", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +/*static*/ Maybe LinalgCrossOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("input", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/ops/log_softmax_op.cpp b/oneflow/user/ops/log_softmax_op.cpp index d8cffbf7460965a3cccd59bc0e47169fab11e4ce..961d9f82164da4767bbe5f3ff53d25141c2c7837 100644 --- a/oneflow/user/ops/log_softmax_op.cpp +++ b/oneflow/user/ops/log_softmax_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe LogSoftmaxOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("prob", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("prob", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -39,16 +39,15 @@ namespace oneflow { } /* static */ Maybe LogSoftmaxOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("prob", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("prob", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe LogSoftmaxGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("prob", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == y_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -69,26 +68,11 @@ namespace oneflow { } /* static */ Maybe LogSoftmaxGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("prob", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("prob", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("prob", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("prob", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("prob", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("log_softmax") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper logsoftmax_grad_op = - builder.Op("log_softmax_grad") - .Input("prob", op.output("prob", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("prob", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(logsoftmax_grad_op.output("dx", 0), "in", 0); - AddOp(logsoftmax_grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/logical_not_op.cpp b/oneflow/user/ops/logical_not_op.cpp index 730b13a9eababf43c1a1fd530adbe68ff33cc745..8225d819aee0e32d2cad5d699115857c6f32e94f 100644 --- a/oneflow/user/ops/logical_not_op.cpp +++ b/oneflow/user/ops/logical_not_op.cpp @@ -21,7 +21,7 @@ namespace oneflow { namespace { Maybe InferDataTypeLogicalNot(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = DataType::kBool; + ctx->SetOutputDType("y", 0, DataType::kBool); return Maybe::Ok(); } diff --git a/oneflow/user/ops/loss_op_util.cpp b/oneflow/user/ops/loss_op_util.cpp index 5b8714df93d394b7d6e667ad8d8f680fbac8343e..a2df016381343b918f4680ac995d73a9e82cef7d 100644 --- a/oneflow/user/ops/loss_op_util.cpp +++ b/oneflow/user/ops/loss_op_util.cpp @@ -27,7 +27,7 @@ user_op::GetSbpFn GenLossForwardDefaultGetSbpFn( .Split(user_op::OpArg("target", 0), 0) .Split(user_op::OpArg("out", 0), 0); if (ctx->user_op_conf().has_input("weight", 0)) { - builder.Broadcast(user_op::OpArg("weight", 0)); + builder.Split(user_op::OpArg("weight", 0), 0); } f(builder, ctx); builder.Build(); @@ -45,7 +45,7 @@ user_op::GetSbpFn GenLossBackwardDefaultGetSbpFn( .Split(user_op::OpArg("dx", 0), 0) .Split(user_op::OpArg("dy", 0), 0); if (ctx->user_op_conf().has_input("weight", 0)) { - builder.Broadcast(user_op::OpArg("weight", 0)); + builder.Split(user_op::OpArg("weight", 0), 0); } f(builder, ctx); builder.Build(); diff --git a/oneflow/user/ops/masked_fill_op.cpp b/oneflow/user/ops/masked_fill_op.cpp index 327ce994ded034f280a7e068d70e8bb693f43a70..b7dac334df58a440e25d9e814623032c5118ae8d 100644 --- a/oneflow/user/ops/masked_fill_op.cpp +++ b/oneflow/user/ops/masked_fill_op.cpp @@ -22,14 +22,14 @@ namespace { Maybe InferMaskedFillTensorDesc(user_op::InferContext* ctx) { const Shape& mask_shape = ctx->InputShape("mask", 0); - *ctx->OutputShape("out", 0) = mask_shape; + ctx->SetOutputShape("out", 0, mask_shape); return Maybe::Ok(); } Maybe InferMaskedFillDataType(user_op::InferContext* ctx) { - const DataType& mask_dtype = ctx->InputDType("mask", 0); + DataType mask_dtype = ctx->InputDType("mask", 0); CHECK_OR_RETURN(IsIntegralDataType(mask_dtype) || IsBoolDataType(mask_dtype)); - *ctx->OutputDType("out", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/math_binary_broadcast_ops.cpp b/oneflow/user/ops/math_binary_broadcast_ops.cpp index 0c4ef770ac370b55ea36070fa80c8fdf7254121b..26c686b53869f64205f20a4541465921e62deb32 100644 --- a/oneflow/user/ops/math_binary_broadcast_ops.cpp +++ b/oneflow/user/ops/math_binary_broadcast_ops.cpp @@ -31,26 +31,26 @@ bool IsZeroDimTensor(const user_op::TensorDesc* tensor) { return tensor->shape() Maybe InferTensorDescBinaryBroadcastNormal(user_op::InferContext* ctx) { const user_op::TensorDesc& tensor_x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& tensor_y = ctx->InputTensorDesc("y", 0); - user_op::TensorDesc* tensor_z = ctx->OutputTensorDesc("z", 0); + user_op::TensorDesc* tensor_z = ctx->MutOutputTensorDesc("z", 0); size_t output_num_axes = std::max(tensor_x.shape().NumAxes(), tensor_y.shape().NumAxes()); if (IsZeroDimTensor(&tensor_x)) { - *ctx->OutputShape("z", 0) = ctx->InputShape("y", 0); - *ctx->OutputIsDynamic("z", 0) = ctx->InputIsDynamic("y", 0); + ctx->SetOutputShape("z", 0, ctx->InputShape("y", 0)); + ctx->SetOutputIsDynamic("z", 0, ctx->InputIsDynamic("y", 0)); } else if (IsZeroDimTensor(&tensor_y)) { - *ctx->OutputShape("z", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("z", 0) = ctx->InputIsDynamic("x", 0); + ctx->SetOutputShape("z", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("z", 0, ctx->InputIsDynamic("x", 0)); } else if (IsScalarTensor(&tensor_x)) { - *ctx->OutputShape("z", 0) = ctx->InputShape("y", 0); - *ctx->OutputIsDynamic("z", 0) = ctx->InputIsDynamic("y", 0); + ctx->SetOutputShape("z", 0, ctx->InputShape("y", 0)); + ctx->SetOutputIsDynamic("z", 0, ctx->InputIsDynamic("y", 0)); } else if (IsScalarTensor(&tensor_y)) { - *ctx->OutputShape("z", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("z", 0) = ctx->InputIsDynamic("x", 0); + ctx->SetOutputShape("z", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("z", 0, ctx->InputIsDynamic("x", 0)); } else { const auto& x_shape = CreateLeftExtendedShape(ShapeView(tensor_x.shape()), output_num_axes); const auto& y_shape = CreateLeftExtendedShape(ShapeView(tensor_y.shape()), output_num_axes); - *ctx->OutputShape("z", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("z", 0) = ctx->InputIsDynamic("x", 0); + ctx->SetOutputShape("z", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("z", 0, ctx->InputIsDynamic("x", 0)); Shape out_shape(x_shape); FOR_RANGE(int64_t, i, 0, x_shape.NumAxes()) { if (x_shape.At(i) != 1 && y_shape.At(i) != 1 && x_shape.At(i) != y_shape.At(i)) { @@ -62,7 +62,7 @@ Maybe InferTensorDescBinaryBroadcastNormal(user_op::InferContext* ctx) { ? 0 : std::max(x_shape.At(i), y_shape.At(i))); } - *tensor_z->mut_shape() = out_shape; + tensor_z->set_shape(out_shape); } tensor_z->set_is_dynamic(tensor_x.is_dynamic() || tensor_y.is_dynamic()); return Maybe::Ok(); @@ -75,16 +75,20 @@ Maybe InferTensorDescBinaryBroadcastLogical(user_op::InferContext* ctx) { Maybe InferDataTypeBinaryBroadcastNormal(user_op::InferContext* ctx) { const user_op::TensorDesc& tensor_x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& tensor_y = ctx->InputTensorDesc("y", 0); - CHECK_EQ_OR_RETURN(tensor_x.data_type(), tensor_y.data_type()); // NOLINT(maybe-need-error-msg) - *ctx->OutputDType("z", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(tensor_x.data_type(), tensor_y.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(tensor_x.data_type()) << ", but got " + << DataType_Name(tensor_y.data_type()); + ctx->SetOutputDType("z", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe InferDataTypeBinaryBroadcastLogical(user_op::InferContext* ctx) { const user_op::TensorDesc& tensor_x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& tensor_y = ctx->InputTensorDesc("y", 0); - CHECK_EQ_OR_RETURN(tensor_x.data_type(), tensor_y.data_type()); // NOLINT(maybe-need-error-msg) - *ctx->OutputDType("z", 0) = DataType::kBool; + CHECK_EQ_OR_RETURN(tensor_x.data_type(), tensor_y.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(tensor_x.data_type()) << ", but got " + << DataType_Name(tensor_y.data_type()); + ctx->SetOutputDType("z", 0, DataType::kBool); return Maybe::Ok(); } @@ -100,6 +104,15 @@ void GenPartialSbpSign(user_op::SbpContext* ctx) { .Build(); } +template<> +void GenPartialSbpSign(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .PartialSum(user_op::OpArg("x", 0)) + .PartialSum(user_op::OpArg("y", 0)) + .PartialSum(user_op::OpArg("z", 0)) + .Build(); +} + template<> void GenPartialSbpSign(user_op::SbpContext* ctx) { ctx->NewBuilder() diff --git a/oneflow/user/ops/math_binary_broadcast_seq.h b/oneflow/user/ops/math_binary_broadcast_seq.h index 4dc820c0fc880833ff19f928f0271d38f6d965a4..12496b20b4643b23e8cbe05dc0db90c6dbc3c11f 100644 --- a/oneflow/user/ops/math_binary_broadcast_seq.h +++ b/oneflow/user/ops/math_binary_broadcast_seq.h @@ -31,16 +31,18 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ("broadcast_fmod", FMod) \ OF_PP_MAKE_TUPLE_SEQ("broadcast_pow", Pow) -#define MATH_BINARY_BROADCAST_LOGICAL_FUNC_SEQ \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_equal", EQ) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_not_equal", NE) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_greater", GT) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_greater_equal", GE) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_less", LT) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_less_equal", LE) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_and", AND) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_or", OR) \ - OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_xor", XOR) +#define MATH_BINARY_BROADCAST_LOGICAL_FUNC_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_equal", EQ) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_not_equal", NE) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_greater", GT) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_greater_equal", GE) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_less", LT) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_less_equal", LE) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_and", AND) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_or", OR) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_logical_xor", XOR) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_isclose_eq_nan", IEN) \ + OF_PP_MAKE_TUPLE_SEQ("broadcast_isclose_neq_nan", INN) #define MATH_BINARY_BROADCAST_FUNC_SEQ_ODS \ OF_PP_MAKE_TUPLE_SEQ(BroadcastAddOp, Add) \ @@ -53,16 +55,18 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ(BroadcastFmodOp, FMod) \ OF_PP_MAKE_TUPLE_SEQ(BroadcastPowOp, Pow) -#define MATH_BINARY_BROADCAST_LOGICAL_FUNC_SEQ_ODS \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastEqualOp, EQ) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastNotEqualOp, NE) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastGreaterOp, GT) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastGreaterEqualOp, GE) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastLessOp, LT) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastLessEqualOp, LE) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalAndOp, AND) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalOrOp, OR) \ - OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalXorOp, XOR) +#define MATH_BINARY_BROADCAST_LOGICAL_FUNC_SEQ_ODS \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastEqualOp, EQ) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastNotEqualOp, NE) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastGreaterOp, GT) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastGreaterEqualOp, GE) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastLessOp, LT) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastLessEqualOp, LE) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalAndOp, AND) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalOrOp, OR) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastLogicalXorOp, XOR) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastIsCloseEqualNanOp, IEN) \ + OF_PP_MAKE_TUPLE_SEQ(BroadcastIsCloseNotEqualNanOp, INN) } // namespace oneflow diff --git a/oneflow/user/ops/math_binary_elementwise_ops.cpp b/oneflow/user/ops/math_binary_elementwise_ops.cpp index ec6e71f82deba5d68ef1b2e387e7ef36d46361e9..1ec1633725b0d85b6b8b851641353f799b98750a 100644 --- a/oneflow/user/ops/math_binary_elementwise_ops.cpp +++ b/oneflow/user/ops/math_binary_elementwise_ops.cpp @@ -38,37 +38,7 @@ namespace oneflow { \ MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##XGradOp); \ \ - MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##YGradOp); \ - \ - REGISTER_USER_OP_GRAD(math_binary_elementwise_type) \ - .SetGenBackwardOpConfFn( \ - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { \ - if (op.NeedGenGradTensor4OpInput("x", 0)) { \ - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_x_grad"); \ - user_op::UserOpConfWrapper binary_grad_op = \ - builder.Op(std::string("") + math_binary_elementwise_type + "_x_grad") \ - .Input("x", op.input("x", 0)) \ - .Input("y", op.input("y", 0)) \ - .Input("dz", op.GetGradTensorWithOpOutput("z", 0)) \ - .Output("dx") \ - .Build(); \ - op.BindGradTensorWithOpInput(binary_grad_op.output("dx", 0), "x", 0); \ - AddOp(binary_grad_op); \ - } \ - if (op.NeedGenGradTensor4OpInput("y", 0)) { \ - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_y_grad"); \ - user_op::UserOpConfWrapper binary_grad_op = \ - builder.Op(std::string("") + math_binary_elementwise_type + "_y_grad") \ - .Input("x", op.input("x", 0)) \ - .Input("y", op.input("y", 0)) \ - .Input("dz", op.GetGradTensorWithOpOutput("z", 0)) \ - .Output("dy") \ - .Build(); \ - op.BindGradTensorWithOpInput(binary_grad_op.output("dy", 0), "y", 0); \ - AddOp(binary_grad_op); \ - } \ - return Maybe::Ok(); \ - }); + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##YGradOp); OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_BINARY_ELEMENTWISE_OP_AND_GRAD, MATH_BINARY_ELEMENTWISE_FUNC_SEQ_ODS) diff --git a/oneflow/user/ops/math_binary_elementwise_seq.h b/oneflow/user/ops/math_binary_elementwise_seq.h index 4cdc682d687c1f2fb5a69f6529f257960a3f7d09..e3c1f40824b81741053b52d8c50586e62848399b 100644 --- a/oneflow/user/ops/math_binary_elementwise_seq.h +++ b/oneflow/user/ops/math_binary_elementwise_seq.h @@ -24,6 +24,7 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ("pow", Pow) \ OF_PP_MAKE_TUPLE_SEQ("atan2", Atan2) \ OF_PP_MAKE_TUPLE_SEQ("floordiv", FloorDiv) \ + OF_PP_MAKE_TUPLE_SEQ("truncdiv", TruncDiv) \ OF_PP_MAKE_TUPLE_SEQ("xdivy", Xdivy) \ OF_PP_MAKE_TUPLE_SEQ("xlogy", Xlogy) @@ -31,6 +32,7 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ("pow", Pow) \ OF_PP_MAKE_TUPLE_SEQ("atan2", Atan2) \ OF_PP_MAKE_TUPLE_SEQ("floordiv", Floordiv) \ + OF_PP_MAKE_TUPLE_SEQ("truncdiv", Truncdiv) \ OF_PP_MAKE_TUPLE_SEQ("xdivy", Xdivy) \ OF_PP_MAKE_TUPLE_SEQ("xlogy", Xlogy) diff --git a/oneflow/user/ops/math_unary_elementwise_op.cpp b/oneflow/user/ops/math_unary_elementwise_op.cpp index 64af43f6316bf491491217ee661bfdb9aeb7d0b0..8f9e840b4cd85927fd31adc34f367caa48ec913e 100644 --- a/oneflow/user/ops/math_unary_elementwise_op.cpp +++ b/oneflow/user/ops/math_unary_elementwise_op.cpp @@ -33,27 +33,30 @@ namespace oneflow { return user_op::TensorDescInferFnUtil::UnchangedDataType(ctx); \ } -#define REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD(math_unary_elementwise_type, func_prefix) \ - MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##Op) \ - MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##GradOp) \ - REGISTER_USER_OP_GRAD(math_unary_elementwise_type) \ - .SetGenBackwardOpConfFn( \ - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { \ - if (op.NeedGenGradTensor4OpInput("x", 0)) { \ - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); \ - user_op::UserOpConfWrapper unary_grad_op = \ - builder.Op(std::string("") + math_unary_elementwise_type + "_grad") \ - .Input("x", op.input("x", 0)) \ - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) \ - .Output("dx") \ - .Build(); \ - op.BindGradTensorWithOpInput(unary_grad_op.output("dx", 0), "x", 0); \ - AddOp(unary_grad_op); \ - } \ - return Maybe::Ok(); \ - }); - -OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD, - MATH_UNARY_ELEMENTWISE_FUNC_SEQ_ODS) +#define REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_DY_X(math_unary_elementwise_type, \ + func_prefix) \ + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##Op) \ + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##GradOp) + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_DY_X, + MATH_UNARY_ELEMENTWISE_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ) + +#define REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_DY_Y(math_unary_elementwise_type, \ + func_prefix) \ + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##Op) \ + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##GradOp) + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_DY_Y, + MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_DY_Y_SEQ) + +#define REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_FILL(math_unary_elementwise_type, \ + func_prefix) \ + MATH_ELEMENTWISE_DEFAULT_SET_FUNC(func_prefix##Op) + +OF_PP_FOR_EACH_TUPLE(REGISTER_MATH_UNARY_ELEMENTWISE_OP_AND_GRAD_WITH_FILL, + MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_FILL_SEQ) + +// Negative's grad function = negative(dy), so here register negative op separately. +MATH_ELEMENTWISE_DEFAULT_SET_FUNC(NegativeOp) } // namespace oneflow diff --git a/oneflow/user/ops/math_unary_elementwise_seq.h b/oneflow/user/ops/math_unary_elementwise_seq.h index 27e2f95a26c0d89ed3891604d4e7959f84625633..56deabc476cd0e89236e30efb0b59e16bf5ab309 100644 --- a/oneflow/user/ops/math_unary_elementwise_seq.h +++ b/oneflow/user/ops/math_unary_elementwise_seq.h @@ -39,6 +39,7 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ("lgamma", Lgamma) \ OF_PP_MAKE_TUPLE_SEQ("log", Log) \ OF_PP_MAKE_TUPLE_SEQ("log2", Log2) \ + OF_PP_MAKE_TUPLE_SEQ("log10", Log10) \ OF_PP_MAKE_TUPLE_SEQ("log1p", Log1p) \ OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", LogSigmoid) \ OF_PP_MAKE_TUPLE_SEQ("negative", Negative) \ @@ -56,41 +57,44 @@ namespace oneflow { OF_PP_MAKE_TUPLE_SEQ("tan", Tan) \ OF_PP_MAKE_TUPLE_SEQ("not_equal_zero", NotEqualZero) -#define MATH_UNARY_ELEMENTWISE_FUNC_SEQ_ODS \ - OF_PP_MAKE_TUPLE_SEQ("abs", Abs) \ - OF_PP_MAKE_TUPLE_SEQ("acos", Acos) \ - OF_PP_MAKE_TUPLE_SEQ("acosh", Acosh) \ - OF_PP_MAKE_TUPLE_SEQ("asin", Asin) \ - OF_PP_MAKE_TUPLE_SEQ("asinh", Asinh) \ - OF_PP_MAKE_TUPLE_SEQ("atan", Atan) \ - OF_PP_MAKE_TUPLE_SEQ("atanh", Atanh) \ - OF_PP_MAKE_TUPLE_SEQ("ceil", Ceil) \ - OF_PP_MAKE_TUPLE_SEQ("cos", Cos) \ - OF_PP_MAKE_TUPLE_SEQ("cosh", Cosh) \ - OF_PP_MAKE_TUPLE_SEQ("erf", Erf) \ - OF_PP_MAKE_TUPLE_SEQ("erfc", Erfc) \ - OF_PP_MAKE_TUPLE_SEQ("exp", Exp) \ - OF_PP_MAKE_TUPLE_SEQ("expm1", Expm1) \ - OF_PP_MAKE_TUPLE_SEQ("floor", Floor) \ - OF_PP_MAKE_TUPLE_SEQ("lgamma", Lgamma) \ - OF_PP_MAKE_TUPLE_SEQ("log", Log) \ - OF_PP_MAKE_TUPLE_SEQ("log2", Log2) \ - OF_PP_MAKE_TUPLE_SEQ("log1p", Log1p) \ - OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", LogSigmoid) \ - OF_PP_MAKE_TUPLE_SEQ("negative", Negative) \ - OF_PP_MAKE_TUPLE_SEQ("reciprocal", Reciprocal) \ - OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan", ReciprocalNoNan) \ - OF_PP_MAKE_TUPLE_SEQ("rint", Rint) \ - OF_PP_MAKE_TUPLE_SEQ("round", Round) \ - OF_PP_MAKE_TUPLE_SEQ("rsqrt", Rsqrt) \ - OF_PP_MAKE_TUPLE_SEQ("sigmoid_v2", SigmoidV2) \ - OF_PP_MAKE_TUPLE_SEQ("sign", Sign) \ - OF_PP_MAKE_TUPLE_SEQ("sin", Sin) \ - OF_PP_MAKE_TUPLE_SEQ("sinh", Sinh) \ - OF_PP_MAKE_TUPLE_SEQ("sqrt", Sqrt) \ - OF_PP_MAKE_TUPLE_SEQ("square", Square) \ - OF_PP_MAKE_TUPLE_SEQ("tan", Tan) \ - OF_PP_MAKE_TUPLE_SEQ("not_equal_zero", NotEqualZero) +#define MATH_UNARY_ELEMENTWISE_PRIMITIVE_FUNC_BWD_WITH_DY_X_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("abs", Abs) \ + OF_PP_MAKE_TUPLE_SEQ("acos", Acos) \ + OF_PP_MAKE_TUPLE_SEQ("acosh", Acosh) \ + OF_PP_MAKE_TUPLE_SEQ("asin", Asin) \ + OF_PP_MAKE_TUPLE_SEQ("asinh", Asinh) \ + OF_PP_MAKE_TUPLE_SEQ("atan", Atan) \ + OF_PP_MAKE_TUPLE_SEQ("atanh", Atanh) \ + OF_PP_MAKE_TUPLE_SEQ("cos", Cos) \ + OF_PP_MAKE_TUPLE_SEQ("cosh", Cosh) \ + OF_PP_MAKE_TUPLE_SEQ("erf", Erf) \ + OF_PP_MAKE_TUPLE_SEQ("erfc", Erfc) \ + OF_PP_MAKE_TUPLE_SEQ("exp", Exp) \ + OF_PP_MAKE_TUPLE_SEQ("expm1", Expm1) \ + OF_PP_MAKE_TUPLE_SEQ("log", Log) \ + OF_PP_MAKE_TUPLE_SEQ("lgamma", Lgamma) \ + OF_PP_MAKE_TUPLE_SEQ("log2", Log2) \ + OF_PP_MAKE_TUPLE_SEQ("log10", Log10) \ + OF_PP_MAKE_TUPLE_SEQ("log1p", Log1p) \ + OF_PP_MAKE_TUPLE_SEQ("log_sigmoid", LogSigmoid) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal", Reciprocal) \ + OF_PP_MAKE_TUPLE_SEQ("reciprocal_no_nan", ReciprocalNoNan) \ + OF_PP_MAKE_TUPLE_SEQ("rsqrt", Rsqrt) \ + OF_PP_MAKE_TUPLE_SEQ("sin", Sin) \ + OF_PP_MAKE_TUPLE_SEQ("sinh", Sinh) \ + OF_PP_MAKE_TUPLE_SEQ("sqrt", Sqrt) \ + OF_PP_MAKE_TUPLE_SEQ("square", Square) \ + OF_PP_MAKE_TUPLE_SEQ("tan", Tan) + +#define MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_DY_Y_SEQ OF_PP_MAKE_TUPLE_SEQ("sigmoid", Sigmoid) + +#define MATH_UNARY_ELEMENTWISE_FUNC_BWD_WITH_FILL_SEQ \ + OF_PP_MAKE_TUPLE_SEQ("not_equal_zero", NotEqualZero) \ + OF_PP_MAKE_TUPLE_SEQ("sign", Sign) \ + OF_PP_MAKE_TUPLE_SEQ("rint", Rint) \ + OF_PP_MAKE_TUPLE_SEQ("round", Round) \ + OF_PP_MAKE_TUPLE_SEQ("floor", Floor) \ + OF_PP_MAKE_TUPLE_SEQ("ceil", Ceil) } // namespace oneflow diff --git a/oneflow/user/ops/matmul_op.cpp b/oneflow/user/ops/matmul_op.cpp index 9c10d7538fe9465a932148d5255f2e36f28dca48..ced7f3e6ef4e3c477b947f4ee907b738e7c76c8d 100644 --- a/oneflow/user/ops/matmul_op.cpp +++ b/oneflow/user/ops/matmul_op.cpp @@ -34,10 +34,10 @@ Maybe InferTensorDesc4Matmul(user_op::InferContext* ctx) { for (int i = 0; i < num_axes - 2; ++i) { CHECK_EQ_OR_RETURN(a.shape().At(i), b.shape().At(i)); } } - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); - *ctx->OutputShape("out", 0) = ctx->InputShape("a", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("a", 0); + Shape output = ctx->InputShape("a", 0); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("a", 0)); int64_t m, n, k; // tensor a (no trans): m*k, tensor b (no trans): k*n if (!transpose_a) { @@ -54,8 +54,9 @@ Maybe InferTensorDesc4Matmul(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(k, b.shape().At(num_axes - 1)); n = b.shape().At(num_axes - 2); } - out->mut_shape()->Set(num_axes - 2, m); - out->mut_shape()->Set(num_axes - 1, n); + output.Set(num_axes - 2, m); + output.Set(num_axes - 1, n); + out->set_shape(output); if (ctx->has_input("_add_to_output", 0)) { const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); CHECK_EQ_OR_RETURN(add_to_output.shape(), out->shape()); @@ -64,80 +65,45 @@ Maybe InferTensorDesc4Matmul(user_op::InferContext* ctx) { } Maybe InferDataType4Matmul(user_op::InferContext* ctx) { - const DataType& dtype = ctx->InputDType("a", 0); - CHECK_EQ_OR_RETURN(ctx->InputDType("b", 0), dtype); + DataType dtype = ctx->InputDType("a", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("b", 0), dtype) + << "InferDataType Failed. Expected " << DataType_Name(dtype) << ", but got " + << DataType_Name(ctx->InputDType("b", 0)); if (ctx->has_input("_add_to_output", 0)) { - CHECK_EQ_OR_RETURN(ctx->InputDType("_add_to_output", 0), dtype); + CHECK_EQ_OR_RETURN(ctx->InputDType("_add_to_output", 0), dtype) + << "InferDataType Failed. Expected " << DataType_Name(dtype) << ", but got " + << DataType_Name(ctx->InputDType("_add_to_output", 0)); } - *ctx->OutputDType("out", 0) = dtype; + ctx->SetOutputDType("out", 0, dtype); return Maybe::Ok(); } -void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - const bool transpose_a = op.attr("transpose_a"); - const bool transpose_b = op.attr("transpose_b"); - const double alpha = op.attr("alpha"); - auto HandleGradOp = [&](user_op::UserOpConfWrapper&& grad_op, - std::string&& input_arg_name) -> void { - op.BindGradTensorWithOpInput(grad_op.output("out", 0), input_arg_name, 0); - AddOp(grad_op); - }; - - if (op.NeedGenGradTensor4OpInput("a", 0)) { - if (transpose_a) { - user_op::UserOpConfWrapper grad_a_op = - user_op::UserOpConfWrapperBuilder(op.op_name() + "_grad_a") - .Op(op_type_name) - .Input("a", op.input("b", 0)) - .Input("b", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("transpose_a", transpose_b) - .Attr("transpose_b", true) - .Attr("alpha", alpha) - .Build(); - HandleGradOp(std::move(grad_a_op), "a"); - } else { - user_op::UserOpConfWrapper grad_a_op = - user_op::UserOpConfWrapperBuilder(op.op_name() + "_grad_a") - .Op(op_type_name) - .Input("a", op.GetGradTensorWithOpOutput("out", 0)) - .Input("b", op.input("b", 0)) - .Output("out") - .Attr("transpose_a", false) - .Attr("transpose_b", !transpose_b) - .Attr("alpha", alpha) - .Build(); - HandleGradOp(std::move(grad_a_op), "a"); - } +// Theoretically computation cost of matrix multiplication is the products of the number of matrix +// and first dimension of matrix a, second dimension of matrix a, second dimension of matrix +// b. If there is any splitting sbp parallel, the computation cost will be divided by number of +// machines. If we use S(1) at matrix a and S(0) at matrix b, then it will be P at output matrix. +// This is why we don't use SbpParallel at output matrix. +Maybe GetComputationCost(user_op::ComputeComplexityFnContext* ctx) { + bool transpose_b = ctx->Attr("transpose_b"); + const Shape& shape_b = ctx->Shape4ArgNameAndIndex("b", 0); + int64_t n = 0; + if (!transpose_b) { + n = shape_b.At(shape_b.NumAxes() - 1); + } else { + n = shape_b.At(shape_b.NumAxes() - 2); } - if (op.NeedGenGradTensor4OpInput("b", 0)) { - if (transpose_b) { - user_op::UserOpConfWrapper grad_b_op = - user_op::UserOpConfWrapperBuilder(op.op_name() + "_grad_b") - .Op(op_type_name) - .Input("a", op.GetGradTensorWithOpOutput("out", 0)) - .Input("b", op.input("a", 0)) - .Output("out") - .Attr("transpose_a", true) - .Attr("transpose_b", transpose_a) - .Attr("alpha", alpha) - .Build(); - HandleGradOp(std::move(grad_b_op), "b"); - } else { - user_op::UserOpConfWrapper grad_b_op = - user_op::UserOpConfWrapperBuilder(op.op_name() + "_grad_b") - .Op(op_type_name) - .Input("a", op.input("a", 0)) - .Input("b", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("transpose_a", !transpose_a) - .Attr("transpose_b", false) - .Attr("alpha", alpha) - .Build(); - HandleGradOp(std::move(grad_b_op), "b"); + + double logical_computation_cost = 2 * ctx->Shape4ArgNameAndIndex("a", 0).elem_cnt() * n; + const auto& nd_sbp_a = ctx->NdSbp4ArgNameAndIndex("a", 0); + const auto& nd_sbp_b = ctx->NdSbp4ArgNameAndIndex("b", 0); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + for (int32_t sbp_dim = 0; sbp_dim < nd_sbp_a.sbp_parallel_size(); sbp_dim++) { + if (nd_sbp_a.sbp_parallel(sbp_dim).has_split_parallel() + || nd_sbp_b.sbp_parallel(sbp_dim).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(sbp_dim); } } + return logical_computation_cost; } } // namespace @@ -150,6 +116,10 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return InferLogicalTensorDesc(ctx); } +/*static*/ Maybe MatmulOp::GetComputeComplexity(user_op::ComputeComplexityFnContext* ctx) { + return GetComputationCost(ctx); +} + /* static */ Maybe MatmulOp::GetSbp(user_op::SbpContext* ctx) { // (m, k_a) * (k_b, n) where k_a == k_b int32_t m_axis = -1; @@ -207,6 +177,8 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return InferDataType4Matmul(ctx); } +// BatchMatmul + /* static */ Maybe BatchMatmulOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { return InferTensorDesc4Matmul(ctx); } @@ -272,38 +244,74 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return Maybe::Ok(); } +/*static*/ Maybe BatchMatmulOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + return GetComputationCost(ctx); +} + /* static */ Maybe BatchMatmulOp::InferDataType(user_op::InferContext* ctx) { return InferDataType4Matmul(ctx); } +// BroadcastMatmul + /* static */ Maybe BroadcastMatmulOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { bool transpose_a = ctx->Attr("transpose_a"); bool transpose_b = ctx->Attr("transpose_b"); const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - - // NOTE: support broadcast b to a for now - // TODO(zwx): support broadcast a to b - CHECK_GT_OR_RETURN(a.shape().NumAxes(), b.shape().NumAxes()); - CHECK_EQ_OR_RETURN(b.shape().NumAxes(), 2); - // NOTE: don't support transpose_a for now - CHECK_OR_RETURN(!transpose_a); - - DimVector out_dim_vec(a.shape().NumAxes() - 1); - FOR_RANGE(int64_t, i, 0, out_dim_vec.size()) { out_dim_vec[i] = a.shape().At(i); } - int64_t k = a.shape().At(a.shape().NumAxes() - 1); - int64_t n = -1; + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + + const int64_t num_a_dims = a.shape().NumAxes(); + const int64_t num_b_dims = b.shape().NumAxes(); + const size_t num_max_batch_dims = std::max(num_a_dims, num_b_dims) - 2; + auto MakeGetBatchDim = [num_max_batch_dims](size_t num_dims, const Shape& shape_dim) { + const int64_t num_batch_dims = num_dims - 2; + const int64_t num_padding_dims = num_max_batch_dims - num_batch_dims; + return [num_padding_dims, shape_dim](size_t index) { + return index < num_padding_dims ? 1 : shape_dim.At(index - num_padding_dims); + }; + }; + auto GetABatchDim = MakeGetBatchDim(num_a_dims, a.shape()); + auto GetBBatchDim = MakeGetBatchDim(num_b_dims, b.shape()); + + DimVector out_dim_vec(std::max(num_a_dims, num_b_dims)); + FOR_RANGE(int64_t, i, 0, out_dim_vec.size() - 2) { + // Set broadcast shape + // m k k n + // For example: A(16, 1, 4, 8) B(1, 8, 8, 6) + // We First set the previous batch dims to broadcasted shape: C(16, 8) + // Then we emplace back m, n -> C(16, 8, 4, 6) + const int64_t a_batch_dim = GetABatchDim(i); + const int64_t b_batch_dim = GetBBatchDim(i); + CHECK(((a_batch_dim != 1 && b_batch_dim == 1) || (a_batch_dim == 1 && b_batch_dim != 1) + || (a_batch_dim == b_batch_dim))) + << "Batch Dims could not broadcast, please check. "; + out_dim_vec[i] = std::max(a_batch_dim, b_batch_dim); + } + int64_t m = 0; + int64_t n = 0; + int64_t k = 0; // tensor a (no trans): batch_dims*m*k, tensor b (no trans): batch_dims*k*n + if (!transpose_a) { + m = a.shape().At(num_a_dims - 2); + k = a.shape().At(num_a_dims - 1); + } else { + m = a.shape().At(num_a_dims - 1); + k = a.shape().At(num_a_dims - 2); + } if (!transpose_b) { - CHECK_EQ_OR_RETURN(k, b.shape().At(b.shape().NumAxes() - 2)); - n = b.shape().At(b.shape().NumAxes() - 1); + CHECK_EQ_OR_RETURN(k, b.shape().At(num_b_dims - 2)) + << "K dim should be equal to b.shape().At(num_b_dims - 2). "; + n = b.shape().At(num_b_dims - 1); } else { - CHECK_EQ_OR_RETURN(k, b.shape().At(b.shape().NumAxes() - 1)); - n = b.shape().At(b.shape().NumAxes() - 2); + CHECK_EQ_OR_RETURN(k, b.shape().At(num_b_dims - 1)) + << "K dim should be equal to b.shape().At(num_b_dims - 1). "; + n = b.shape().At(num_b_dims - 2); } - out_dim_vec.emplace_back(n); - *out->mut_shape() = Shape(out_dim_vec); + out_dim_vec.at(num_max_batch_dims) = m; + out_dim_vec.at(num_max_batch_dims + 1) = n; + out->set_shape(Shape(out_dim_vec)); if (ctx->has_input("_add_to_output", 0)) { const user_op::TensorDesc& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); @@ -322,18 +330,31 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us // (b, m, k) * (n, k) when transpose_b is true bool transpose_a = ctx->Attr("transpose_a"); bool transpose_b = ctx->Attr("transpose_b"); - CHECK_OR_RETURN(!transpose_a); const auto& a_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("a", 0).shape(); - int32_t k_a_axis = a_shape.NumAxes() - 1; + const auto& b_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("b", 0).shape(); + + const int64_t a_num_axes = a_shape.NumAxes(); + const int64_t b_num_axes = b_shape.NumAxes(); + + int32_t m_a_axis = -1; + int32_t k_a_axis = -1; int32_t k_b_axis = -1; int32_t n_axis = -1; + + if (transpose_a) { + m_a_axis = a_num_axes - 1; + k_a_axis = a_num_axes - 2; + } else { + m_a_axis = a_num_axes - 2; + k_a_axis = a_num_axes - 1; + } if (transpose_b) { - k_b_axis = 1; - n_axis = 0; + k_b_axis = b_num_axes - 1; + n_axis = b_num_axes - 2; } else { - k_b_axis = 0; - n_axis = 1; + k_b_axis = b_num_axes - 2; + n_axis = b_num_axes - 1; } std::vector out_and_add_to_output_args; @@ -342,32 +363,76 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us out_and_add_to_output_args.emplace_back("_add_to_output", 0); } - // S(b or m axis) x B -> S(b or m axis) - for (int64_t i = 0; i < a_shape.NumAxes() - 1; ++i) { - ctx->NewBuilder() - .Split(user_op::OpArg("a", 0), i) - .Broadcast(user_op::OpArg("b", 0)) - .Split(out_and_add_to_output_args, i) - .Build(); + const int64_t a_batch_dims = a_num_axes - 2; + const int64_t b_batch_dims = b_num_axes - 2; + const int64_t max_num_axes = std::max(a_num_axes, b_num_axes); + const size_t num_max_batch_dims = max_num_axes - 2; + auto MakeGetBatchDim = [num_max_batch_dims](size_t num_dims, const Shape& shape_dim) { + const int64_t num_batch_dims = num_dims - 2; + const int64_t num_padding_dims = num_max_batch_dims - num_batch_dims; + return [num_padding_dims, shape_dim](size_t index) { + return index < num_padding_dims ? 1 : shape_dim.At(index - num_padding_dims); + }; + }; + auto GetABatchDim = MakeGetBatchDim(a_num_axes, a_shape); + auto GetBBatchDim = MakeGetBatchDim(b_num_axes, b_shape); + + for (int i = 0; i < num_max_batch_dims; i++) { + const int64_t a_batch_dim = GetABatchDim(i); + const int64_t b_batch_dim = GetBBatchDim(i); + + if (a_batch_dim == b_batch_dim && a_batch_dim != 1) { + // S(b axis) x S(b axis) -> S(b axis) + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), i - (num_max_batch_dims - a_batch_dims)) + .Split(user_op::OpArg("b", 0), i - (num_max_batch_dims - b_batch_dims)) + .Split(out_and_add_to_output_args, i) + .Build(); + } else if (a_batch_dim == 1 && b_batch_dim != 1) { + // B x S(b axis) -> S(b axis) + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .Split(user_op::OpArg("b", 0), i - (num_max_batch_dims - b_batch_dims)) + .Split(out_and_add_to_output_args, i) + .Build(); + } else if (b_batch_dim == 1 && a_batch_dim != 1) { + // S(b axis) x B -> S(b axis) + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), i - (num_max_batch_dims - a_batch_dims)) + .Broadcast(user_op::OpArg("b", 0)) + .Split(out_and_add_to_output_args, i) + .Build(); + } } + + // S(m axis) x B -> S(m axis) + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), m_a_axis) + .Broadcast(user_op::OpArg("b", 0)) + .Split(out_and_add_to_output_args, max_num_axes - 2) + .Build(); + // B x S(n_axis) -> S(n_axis) ctx->NewBuilder() .Broadcast(user_op::OpArg("a", 0)) .Split(user_op::OpArg("b", 0), n_axis) - .Split(out_and_add_to_output_args, a_shape.NumAxes() - 1) + .Split(out_and_add_to_output_args, max_num_axes - 1) .Build(); + // S(a_k_axis) x S(b_k_axis) -> P ctx->NewBuilder() .Split(user_op::OpArg("a", 0), k_a_axis) .Split(user_op::OpArg("b", 0), k_b_axis) .PartialSum(out_and_add_to_output_args) .Build(); + // P x B -> P ctx->NewBuilder() .PartialSum(user_op::OpArg("a", 0)) .Broadcast(user_op::OpArg("b", 0)) .PartialSum(out_and_add_to_output_args) .Build(); + // B x P -> P ctx->NewBuilder() .Broadcast(user_op::OpArg("a", 0)) @@ -381,19 +446,23 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return InferDataType4Matmul(ctx); } +/*static*/ Maybe BroadcastMatmulOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + return GetComputationCost(ctx); +} + /* static */ Maybe BroadcastMatmulGradBOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_EQ_OR_RETURN(a.shape().NumAxes(), b.shape().NumAxes()); for (int i = 0; i < a.shape().NumAxes() - 1; ++i) { CHECK_EQ_OR_RETURN(a.shape().At(i), b.shape().At(i)); } - - *out->mut_shape() = - Shape({a.shape().At(a.shape().NumAxes() - 1), b.shape().At(b.shape().NumAxes() - 1)}); + out->set_shape( + Shape({a.shape().At(a.shape().NumAxes() - 1), b.shape().At(b.shape().NumAxes() - 1)})); if (ctx->has_input("_add_to_output", 0)) { const user_op::TensorDesc& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); @@ -407,16 +476,31 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return InferLogicalTensorDesc(ctx); } +/*static*/ Maybe BroadcastMatmulGradBOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + const Shape& shape_a = ctx->Shape4ArgNameAndIndex("a", 0); + int64_t n = shape_a.At(shape_a.NumAxes() - 2); + + double logical_computation_cost = 2 * ctx->Shape4ArgNameAndIndex("b", 0).elem_cnt() * n; + const auto& nd_sbp_a = ctx->NdSbp4ArgNameAndIndex("a", 0); + const auto& nd_sbp_b = ctx->NdSbp4ArgNameAndIndex("b", 0); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + for (int32_t sbp_dim = 0; sbp_dim < nd_sbp_a.sbp_parallel_size(); sbp_dim++) { + if (nd_sbp_a.sbp_parallel(sbp_dim).has_split_parallel() + || nd_sbp_b.sbp_parallel(sbp_dim).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(sbp_dim); + } + } + return logical_computation_cost; +} /* static */ Maybe BroadcastMatmulGradBOp::GetSbp(user_op::SbpContext* ctx) { const auto& a_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("a", 0).shape(); int64_t last_axis = a_shape.NumAxes() - 1; - std::vector out_and_add_to_output_args; out_and_add_to_output_args.emplace_back("out", 0); if (ctx->user_op_conf().has_input("_add_to_output", 0)) { out_and_add_to_output_args.emplace_back("_add_to_output", 0); } - // S(b or m axis) x S(b or m axis) -> P for (int64_t i = 0; i < last_axis; ++i) { ctx->NewBuilder() @@ -425,7 +509,6 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us .PartialSum(out_and_add_to_output_args) .Build(); } - // (b, m, k) * (b, m, n) -> (k, n) [transpose a] // S(k) x B -> S(0) or B x S(n) -> S(1) // (b, m, n) * (b, m, k) -> (n, k) [transpose a] @@ -440,7 +523,6 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us .Split(user_op::OpArg("b", 0), last_axis) .Split(out_and_add_to_output_args, 1) .Build(); - return Maybe::Ok(); } @@ -448,67 +530,4 @@ void GenBackwardOpConf4Matmul(const std::string& op_type_name, const user_op::Us return InferDataType4Matmul(ctx); } -REGISTER_USER_OP_GRAD("matmul").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - GenBackwardOpConf4Matmul("matmul", op, AddOp); - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("batch_matmul") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - GenBackwardOpConf4Matmul("batch_matmul", op, AddOp); - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("broadcast_matmul") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - bool transpose_a = ctx->FwOp().attr("transpose_a"); - bool transpose_b = ctx->FwOp().attr("transpose_b"); - double alpha = ctx->FwOp().attr("alpha"); - CHECK_OR_RETURN(!transpose_a); - - std::string a_grad_op_name = ctx->FwOp().op_name() + "_a_grad"; - ctx->DefineOp(a_grad_op_name, - [&](user_op::BackwardOpBuilder& builder) -> user_op::UserOpConfWrapper { - return builder.OpTypeName("broadcast_matmul") - .InputBind("a", ctx->FwOp().output_grad("out", 0)) - .InputBind("b", ctx->FwOp().input("b", 0)) - .Attr("transpose_a", transpose_a) - .Attr("transpose_b", !transpose_b) - .Attr("alpha", alpha) - .Output("out") - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("a", 0), [&]() -> const std::string& { - return ctx->GetOp(a_grad_op_name).output("out", 0); - }); - - std::string b_grad_op_name = ctx->FwOp().op_name() + "_b_grad"; - ctx->DefineOp(b_grad_op_name, - [&](user_op::BackwardOpBuilder& builder) -> user_op::UserOpConfWrapper { - if (!transpose_b) { - return builder.OpTypeName("broadcast_matmul_grad_b") - .InputBind("a", ctx->FwOp().input("a", 0)) - .InputBind("b", ctx->FwOp().output_grad("out", 0)) - .Attr("alpha", alpha) - .Output("out") - .Build(); - } else { - return builder.OpTypeName("broadcast_matmul_grad_b") - .InputBind("a", ctx->FwOp().output_grad("out", 0)) - .InputBind("b", ctx->FwOp().input("a", 0)) - .Attr("alpha", alpha) - .Output("out") - .Build(); - } - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("b", 0), [&]() -> const std::string& { - return ctx->GetOp(b_grad_op_name).output("out", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/matrix_vector_product_op.cpp b/oneflow/user/ops/matrix_vector_product_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..21431369a3b6e5f96766b15e22f6ca891c0f8dd0 --- /dev/null +++ b/oneflow/user/ops/matrix_vector_product_op.cpp @@ -0,0 +1,190 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferTensorDesc4MatrixVectorProduct(user_op::InferContext* ctx) { + const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); + const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); + int64_t m = a.shape().At(0); + int64_t k = a.shape().At(1); + CHECK_EQ_OR_RETURN(k, b.shape().At(0)) << "Dim K should be equal to vector b's dim0. "; + ctx->SetOutputShape("out", 0, Shape({m})); + return Maybe::Ok(); +} + +Maybe InferDataType4MatrixVectorProduct(user_op::InferContext* ctx) { + DataType dtype = ctx->InputDType("a", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("b", 0), dtype) + << "InferDataType Failed. Expected " << DataType_Name(dtype) << ", but got " + << DataType_Name(ctx->InputDType("b", 0)); + ctx->SetOutputDType("out", 0, dtype); + return Maybe::Ok(); +} + +Maybe InferTensorDesc4MatrixVectorProductGradA(user_op::InferContext* ctx) { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + GradA = dy (m) matmul B(k) -> (m, 1) (k, 1)_transpose + */ + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); + int64_t m = dy.shape().At(0); + int64_t n = b.shape().At(0); + ctx->SetOutputShape("dx", 0, Shape({m, n})); + return Maybe::Ok(); +} + +Maybe InferTensorDesc4MatrixVectorProductGradB(user_op::InferContext* ctx) { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + GradB = dy_transpose (1, m) matmul A(m, k) + */ + const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); + int64_t n = a.shape().At(1); + ctx->SetOutputShape("dx", 0, Shape({n})); + return Maybe::Ok(); +} + +Maybe InferDataType4Grad(user_op::InferContext* ctx) { + DataType dtype = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, dtype); + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe MatrixVectorProductOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferTensorDesc4MatrixVectorProduct(ctx); +} + +/*static*/ Maybe MatrixVectorProductOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MatrixVectorProductOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), 0) + .Broadcast(user_op::OpArg("b", 0)) + .Split(user_op::OpArg("out", 0), 0) + .Build(); + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), 1) + .Split(user_op::OpArg("b", 0), 0) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("a", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .PartialSum(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MatrixVectorProductOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4MatrixVectorProduct(ctx); +} + +/* static */ Maybe MatrixVectorProductGradAOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferTensorDesc4MatrixVectorProductGradA(ctx); +} + +/*static*/ Maybe MatrixVectorProductGradAOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MatrixVectorProductGradAOp::GetSbp(user_op::SbpContext* ctx) { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + GradA = dy (m) matmul B(k) -> (m, 1) (k, 1)_transpose + */ + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), 0) + .Broadcast(user_op::OpArg("b", 0)) + .Split(user_op::OpArg("dx", 0), 0) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("dy", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MatrixVectorProductGradAOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4Grad(ctx); +} + +/* static */ Maybe MatrixVectorProductGradBOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferTensorDesc4MatrixVectorProductGradB(ctx); +} + +/*static*/ Maybe MatrixVectorProductGradBOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MatrixVectorProductGradBOp::GetSbp(user_op::SbpContext* ctx) { + /* + A(m, k) matmul B(k) -> (m, k) matmul (k, 1) -> (m, 1) -> (m) + dy = (m, ) + GradB = dy_transpose (1, m) matmul A(m, k) + */ + ctx->NewBuilder() + .Broadcast(user_op::OpArg("dy", 0)) + .Split(user_op::OpArg("a", 0), 1) + .Split(user_op::OpArg("dx", 0), 0) + .Build(); + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), 0) + .Split(user_op::OpArg("a", 0), 0) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("dy", 0)) + .Broadcast(user_op::OpArg("a", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("a", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MatrixVectorProductGradBOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4Grad(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/max_pool_op.cpp b/oneflow/user/ops/max_pool_op.cpp index 8d4d20bc7975d7b52b3f5c44dc61f2533a248bf1..4e2c1e2f06846111053c48f57d549af59e33fc06 100644 --- a/oneflow/user/ops/max_pool_op.cpp +++ b/oneflow/user/ops/max_pool_op.cpp @@ -22,8 +22,6 @@ namespace oneflow { namespace { typedef std::function(user_op::InferContext* ctx)> TensorDescInferFn; -typedef std::function(const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp)> - GenBackwardOpConfFn; TensorDescInferFn MaxPoolMakeForwardTensorDescInferFn(const int32_t dim) { return [dim](user_op::InferContext* ctx) -> Maybe { @@ -47,15 +45,14 @@ TensorDescInferFn MaxPoolMakeForwardTensorDescInferFn(const int32_t dim) { const MaxPoolParams3D params_3d(dim, x_shape, data_format, padding, kernel_size, stride, dilation, return_indices, ceil_mode); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); *y_desc = ctx->InputTensorDesc("x", 0); - *y_desc->mut_shape() = params_3d.GetYShape(); + y_desc->set_shape(params_3d.GetYShape()); - user_op::TensorDesc* indice_desc = ctx->OutputTensorDesc("indice", 0); - *indice_desc = *ctx->OutputTensorDesc("y", 0); - *indice_desc->mut_shape() = *y_desc->mut_shape(); - DataType* dtype = indice_desc->mut_data_type(); - *dtype = kInt64; + user_op::TensorDesc* indice_desc = ctx->MutOutputTensorDesc("indice", 0); + *indice_desc = *ctx->MutOutputTensorDesc("y", 0); + indice_desc->set_shape(y_desc->shape()); + indice_desc->set_data_type(kInt64); return Maybe::Ok(); }; } @@ -85,43 +82,37 @@ Maybe MaxPoolBackwardGetSbpFn(user_op::SbpContext* ctx) { return Maybe::Ok(); } -GenBackwardOpConfFn MaxPoolMakeBackwardOpConfFn(const int32_t dim) { - return [dim](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("max_pool_" + std::to_string(dim) + "d_grad") - .Input("x", op.input("x", 0)) - .Input("indice", op.output("indice", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("data_format", op.attr("data_format")) - .Attr("padding", op.attr>("padding")) - .Attr("kernel_size", op.attr>("kernel_size")) - .Attr("stride", op.attr>("stride")) - .Attr("dilation", op.attr>("dilation")) - .Attr("return_indices", op.attr("return_indices")) - .Attr("ceil_mode", op.attr("ceil_mode")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); +// Logically computation cost of pool op is the product of output data amount and pool kernal data +// amount. After adding sbp, we just divide it by parallel number if output data is splitted because +// splitting input and using partial sum for output is not a valid sbp for this op for now. +Maybe GetComputationCost(user_op::ComputeComplexityFnContext* ctx, + const std::string& blob_name) { + const std::vector& pool_size = ctx->Attr>("kernel_size"); + double logical_computation_cost = std::accumulate( + pool_size.begin(), pool_size.end(), ctx->Shape4ArgNameAndIndex(blob_name, 0).elem_cnt(), + std::multiplies()); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + const auto& nd_sbp_y = ctx->NdSbp4ArgNameAndIndex(blob_name, 0); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp_y.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp_y.sbp_parallel(dim_sbp).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(dim_sbp); } - return Maybe::Ok(); - }; + } + return logical_computation_cost; } Maybe BackwardTensorDescInferFn(user_op::InferContext* ctx) { - *ctx->OutputTensorDesc("dx", 0) = ctx->InputTensorDesc("x", 0); + *ctx->MutOutputTensorDesc("dx", 0) = ctx->InputTensorDesc("x", 0); return Maybe::Ok(); } Maybe FwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe BwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } } // namespace @@ -138,6 +129,10 @@ Maybe BwInferDataType(user_op::InferContext* ctx) { } \ /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ return FwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##Op::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx, "y"); \ } IMPLEMENT_MAXPOOL_FUNCS(MaxPool1D, 1) @@ -157,6 +152,10 @@ IMPLEMENT_MAXPOOL_FUNCS(MaxPool3D, 3) } \ /*static*/ Maybe name##GradOp::InferDataType(user_op::InferContext* ctx) { \ return BwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##GradOp::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx, "dy"); \ } IMPLEMENT_MAXPOOL_BACKWARD_FUNCS(MaxPool1D) @@ -164,22 +163,4 @@ IMPLEMENT_MAXPOOL_BACKWARD_FUNCS(MaxPool2D) IMPLEMENT_MAXPOOL_BACKWARD_FUNCS(MaxPool3D) #undef IMPLEMENT_MAXPOOL_BACKWARD_FUNCS -REGISTER_USER_OP_GRAD("max_pool_1d").SetGenBackwardOpConfFn(MaxPoolMakeBackwardOpConfFn(1)); -REGISTER_USER_OP_GRAD("max_pool_2d").SetGenBackwardOpConfFn(MaxPoolMakeBackwardOpConfFn(2)); -REGISTER_USER_OP_GRAD("max_pool_3d").SetGenBackwardOpConfFn(MaxPoolMakeBackwardOpConfFn(3)); - -#define IMPLEMENT_AVGPOOL_FUNCS(name, ndim) \ - /*static*/ Maybe name##Op::GetSbp(user_op::SbpContext* ctx) { \ - return AvgPoolForwardGetSbpFn(ctx); \ - } \ - /*static*/ Maybe name##Op::InferLogicalTensorDesc(user_op::InferContext* ctx) { \ - return AvgPoolMakeForwardTensorDescInferFn(ndim)(ctx); \ - } \ - /*static*/ Maybe name##Op::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ - return InferLogicalTensorDesc(ctx); \ - } \ - /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ - return FwInferDataType(ctx); \ - } - } // namespace oneflow diff --git a/oneflow/user/ops/max_unpool_op.cpp b/oneflow/user/ops/max_unpool_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..94d33e83a77fd8f67c6d094ae5e7b545e7038494 --- /dev/null +++ b/oneflow/user/ops/max_unpool_op.cpp @@ -0,0 +1,128 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/shape.h" +#include "oneflow/user/kernels/max_unpool_kernel_util.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +typedef std::function(user_op::InferContext* ctx)> TensorDescInferFn; + +TensorDescInferFn MaxUnpoolMakeForwardTensorDescInferFn(const int32_t dim) { + return [dim](user_op::InferContext* ctx) -> Maybe { + const Shape& x_shape = ctx->InputShape("x", 0); + const std::vector& padding = ctx->Attr>("padding"); + const std::vector& kernel_size = ctx->Attr>("kernel_size"); + const std::vector& stride = ctx->Attr>("stride"); + Shape output_shape = Shape(); + if (ctx->Attr("has_output_size")) { + output_shape = ctx->Attr("output_size"); + } else { + const MaxUnpoolParams3D params_3d(dim, x_shape, padding, kernel_size, stride); + output_shape = params_3d.GetYShape(); + } + + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + *y_desc = ctx->InputTensorDesc("x", 0); + y_desc->set_shape(output_shape); + + return Maybe::Ok(); + }; +} + +Maybe MaxUnpoolForwardGetSbpFn(user_op::SbpContext* ctx) { + const user_op::TensorDesc& tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, std::min(2L, tensor.shape().NumAxes() - 2)) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Split(user_op::OpArg("indices", 0), i) + .Split(user_op::OpArg("y", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe MaxUnpoolBackwardGetSbpFn(user_op::SbpContext* ctx) { + const user_op::TensorDesc& tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, std::min(2L, tensor.shape().NumAxes())) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Split(user_op::OpArg("indices", 0), i) + .Split(user_op::OpArg("dy", 0), i) + .Split(user_op::OpArg("dx", 0), i) + .Build(); + } + return Maybe::Ok(); +} + +Maybe BackwardTensorDescInferFn(user_op::InferContext* ctx) { + *ctx->MutOutputTensorDesc("dx", 0) = ctx->InputTensorDesc("x", 0); + return Maybe::Ok(); +} + +Maybe FwInferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +Maybe BwInferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} +} // namespace + +#define IMPLEMENT_MAXUNPOOL_FUNCS(name, dim) \ + /*static*/ Maybe name##Op::GetSbp(user_op::SbpContext* ctx) { \ + return MaxUnpoolForwardGetSbpFn(ctx); \ + } \ + /*static*/ Maybe name##Op::InferLogicalTensorDesc(user_op::InferContext* ctx) { \ + return MaxUnpoolMakeForwardTensorDescInferFn(dim)(ctx); \ + } \ + /*static*/ Maybe name##Op::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ + return InferLogicalTensorDesc(ctx); \ + } \ + /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ + return FwInferDataType(ctx); \ + } + +IMPLEMENT_MAXUNPOOL_FUNCS(MaxUnpool1D, 1) +IMPLEMENT_MAXUNPOOL_FUNCS(MaxUnpool2D, 2) +IMPLEMENT_MAXUNPOOL_FUNCS(MaxUnpool3D, 3) +#undef IMPLEMENT_MAXUNPOOL_FUNCS + +#define IMPLEMENT_MAXUNPOOL_BACKWARD_FUNCS(name) \ + /*static*/ Maybe name##GradOp::GetSbp(user_op::SbpContext* ctx) { \ + return MaxUnpoolBackwardGetSbpFn(ctx); \ + } \ + /*static*/ Maybe name##GradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { \ + return BackwardTensorDescInferFn(ctx); \ + } \ + /*static*/ Maybe name##GradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ + return InferLogicalTensorDesc(ctx); \ + } \ + /*static*/ Maybe name##GradOp::InferDataType(user_op::InferContext* ctx) { \ + return BwInferDataType(ctx); \ + } + +IMPLEMENT_MAXUNPOOL_BACKWARD_FUNCS(MaxUnpool1D) +IMPLEMENT_MAXUNPOOL_BACKWARD_FUNCS(MaxUnpool2D) +IMPLEMENT_MAXUNPOOL_BACKWARD_FUNCS(MaxUnpool3D) +#undef IMPLEMENT_MAXUNPOOL_BACKWARD_FUNCS + +} // namespace oneflow diff --git a/oneflow/user/ops/median_op.cpp b/oneflow/user/ops/median_op.cpp index 5ca4689b037f48aa397b096cd9d00328333295dc..c08f31b29f568ea050fdc5af9705ea964cc0ef68 100644 --- a/oneflow/user/ops/median_op.cpp +++ b/oneflow/user/ops/median_op.cpp @@ -28,93 +28,15 @@ namespace oneflow { } /*static*/ Maybe MedianOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& ones_shape = {1}; - *ctx->OutputShape("output", 0) = ones_shape.RemoveOnes({0}); + ctx->SetOutputShape("output", 0, ones_shape.RemoveOnes({0})); return Maybe::Ok(); } /*static*/ Maybe MedianOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe MedianOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } -Maybe GenerateBackwardOpConf4Median(const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - const auto& input = op.arg_tensor_desc("input", 0); - std::vector axis(input.shape().NumAxes()); - std::iota(axis.begin(), axis.end(), 0); - - user_op::UserOpConfWrapperBuilder broadcast_out_builder(op.op_name() + "_grad_broadcast_out"); - user_op::UserOpConfWrapper broadcast_out_op = broadcast_out_builder.Op("broadcast_like") - .Input("x", op.output("output", 0)) - .Input("like", op.input("input", 0)) - .Attr("broadcast_axes", axis) - .Output("y") - .Build(); - AddOp(broadcast_out_op); - - user_op::UserOpConfWrapperBuilder broadcast_eq_builder(op.op_name() + "_grad_broadcast_eq"); - user_op::UserOpConfWrapper broadcast_eq_op = broadcast_eq_builder.Op("broadcast_equal") - .Input("x", op.input("input", 0)) - .Input("y", broadcast_out_op.output("y", 0)) - .Output("z") - .Build(); - AddOp(broadcast_eq_op); - - user_op::UserOpConfWrapperBuilder cast_mask_builder(op.op_name() + "_grad_cast_mask"); - user_op::UserOpConfWrapper cast_mask_op = cast_mask_builder.Op("cast_like") - .Input("in", broadcast_eq_op.output("z", 0)) - .Input("dtype_like", op.input("input", 0)) - .Output("out") - .Build(); - AddOp(cast_mask_op); - - user_op::UserOpConfWrapperBuilder reduce_sum_mask_builder(op.op_name() - + "_grad_reduce_sum_mask"); - user_op::UserOpConfWrapper reduce_sum_mask_op = - reduce_sum_mask_builder.Op("reduce_sum") - .Input("input_tensor", cast_mask_op.output("out", 0)) - .Output("output_tensor") - .Attr("axis", axis) - .Attr("keepdims", op.attr("keepdims")) - .Build(); - AddOp(reduce_sum_mask_op); - - user_op::UserOpConfWrapperBuilder divide_count_builder(op.op_name() + "_grad_divide_count"); - user_op::UserOpConfWrapper divide_count_op = - divide_count_builder.Op("broadcast_div") - .Input("x", op.GetGradTensorWithOpOutput("output", 0)) - .Input("y", reduce_sum_mask_op.output("output_tensor", 0)) - .Output("z") - .Build(); - AddOp(divide_count_op); - - user_op::UserOpConfWrapperBuilder broadcast_divided_dy_builder(op.op_name() - + "_grad_broadcast_divided_dy"); - user_op::UserOpConfWrapper broadcast_divided_dy_op = - broadcast_divided_dy_builder.Op("broadcast_like") - .Input("x", divide_count_op.output("z", 0)) - .Input("like", op.input("input", 0)) - .Attr("broadcast_axis", axis) - .Output("y") - .Build(); - AddOp(broadcast_divided_dy_op); - - user_op::UserOpConfWrapperBuilder multiply_mask_builder(op.op_name() + "_grad_multiply_mask"); - user_op::UserOpConfWrapper multiply_mask_op = - multiply_mask_builder.Op("broadcast_mul") - .Input("x", broadcast_divided_dy_op.output("y", 0)) - .Input("y", cast_mask_op.output("out", 0)) - .Output("z") - .Build(); - AddOp(multiply_mask_op); - op.BindGradTensorWithOpInput(multiply_mask_op.output("z", 0), "input", 0); - } - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("median").SetGenBackwardOpConfFn(GenerateBackwardOpConf4Median); - } // namespace oneflow diff --git a/oneflow/user/ops/median_with_indices_op.cpp b/oneflow/user/ops/median_with_indices_op.cpp index d9d0d67273502393951ea5b5ba6ea3e6bee1422b..e0237e7cafe418c759dd5d7aa4c7b7c07cffe2db 100644 --- a/oneflow/user/ops/median_with_indices_op.cpp +++ b/oneflow/user/ops/median_with_indices_op.cpp @@ -31,70 +31,18 @@ namespace oneflow { } /*static*/ Maybe MedianWithIndicesOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("input", 0); - Shape* values_shape = ctx->OutputShape("values", 0); - Shape* indices_shape = ctx->OutputShape("indices", 0); const Shape& reduce_shape = CreateReducedShape(input_shape, {-1}); - *values_shape = reduce_shape.RemoveOnes({-1}); - *indices_shape = reduce_shape.RemoveOnes({-1}); + ctx->SetOutputShape("values", 0, reduce_shape.RemoveOnes({-1})); + ctx->SetOutputShape("indices", 0, reduce_shape.RemoveOnes({-1})); return Maybe::Ok(); } /*static*/ Maybe MedianWithIndicesOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe MedianWithIndicesOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("values", 0) = ctx->InputDType("input", 0); - *ctx->OutputDType("indices", 0) = DataType::kInt64; + ctx->SetOutputDType("values", 0, ctx->InputDType("input", 0)); + ctx->SetOutputDType("indices", 0, DataType::kInt64); return Maybe::Ok(); } -Maybe GenerateBackwardOpConf4MedianWithIndices(const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - const auto& input = op.arg_tensor_desc("input", 0); - user_op::UserOpConfWrapperBuilder expand_indices_builder(op.op_name() + "_grad_expand_indices"); - user_op::UserOpConfWrapper expand_indices_op = expand_indices_builder.Op("expand_dims") - .Input("in", op.output("indices", 0)) - .Attr("axis", -1) - .Output("out") - .Build(); - AddOp(expand_indices_op); - - user_op::UserOpConfWrapperBuilder expand_dout_builder(op.op_name() + "_grad_expand_dout"); - user_op::UserOpConfWrapper expand_dout_op = - expand_dout_builder.Op("expand_dims") - .Input("in", op.GetGradTensorWithOpOutput("output", 0)) - .Attr("axis", -1) - .Output("out") - .Build(); - AddOp(expand_dout_op); - - bool is_integral = IsIntegralDataType(input.data_type()); - user_op::UserOpConfWrapperBuilder zeros_builder(op.op_name() + "_grad_zeros"); - user_op::UserOpConfWrapper zeros_op = zeros_builder.Op("constant") - .Attr("shape", input.shape()) - .Attr("dtype", input.data_type()) - .Attr("is_floating_value", is_integral ? false : true) - .Output("out") - .Build(); - AddOp(zeros_op); - - user_op::UserOpConfWrapperBuilder dim_scatter_update_builder(op.op_name() - + "_grad_dim_scatter_update"); - user_op::UserOpConfWrapper dim_scatter_update_op = - dim_scatter_update_builder.Op("dim_scatter_update") - .Input("input", zeros_op.output("out", 0)) - .Input("index", expand_indices_op.output("out", 0)) - .Input("src", expand_dout_op.output("out", 0)) - .Attr("dim", input.shape().NumAxes()) - .Output("output") - .Build(); - AddOp(dim_scatter_update_op); - op.BindGradTensorWithOpInput(dim_scatter_update_op.output("output", 0), "input", 0); - } - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("median_with_indices") - .SetGenBackwardOpConfFn(GenerateBackwardOpConf4MedianWithIndices); - } // namespace oneflow diff --git a/oneflow/user/ops/min_max_observer_op.cpp b/oneflow/user/ops/min_max_observer_op.cpp index 3d7f186c3786790a8f8b68aeff9ebb025c426cab..c1f2ba715f68432dcaa2fb07e156d1913905c9f5 100644 --- a/oneflow/user/ops/min_max_observer_op.cpp +++ b/oneflow/user/ops/min_max_observer_op.cpp @@ -23,16 +23,16 @@ namespace oneflow { if (ctx->Attr("quantization_formula") == "google") { if (ctx->Attr("per_layer_quantization") == true) { - *ctx->OutputShape("scale", 0) = Shape({1}); - *ctx->OutputShape("zero_point", 0) = Shape({1}); + ctx->SetOutputShape("scale", 0, Shape({1})); + ctx->SetOutputShape("zero_point", 0, Shape({1})); } else { // NOTE(Liang Depeng): For now per-channel quantization only support axis 0 - *ctx->OutputShape("scale", 0) = Shape({in_shape.At(0)}); - *ctx->OutputShape("zero_point", 0) = Shape({in_shape.At(0)}); + ctx->SetOutputShape("scale", 0, Shape({in_shape.At(0)})); + ctx->SetOutputShape("zero_point", 0, Shape({in_shape.At(0)})); } } else { // quantization_formula == "cambricon" - *ctx->OutputShape("scale", 0) = Shape({1}); - *ctx->OutputShape("zero_point", 0) = Shape({1}); + ctx->SetOutputShape("scale", 0, Shape({1})); + ctx->SetOutputShape("zero_point", 0, Shape({1})); } return Maybe::Ok(); } @@ -70,8 +70,8 @@ namespace oneflow { } /* static */ Maybe MinMaxObserverOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("scale", 0) = ctx->InputDType("in", 0); - *ctx->OutputDType("zero_point", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("scale", 0, ctx->InputDType("in", 0)); + ctx->SetOutputDType("zero_point", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/mish_op.cpp b/oneflow/user/ops/mish_op.cpp index bee4ebb18a864d02e34abc107ede1d791ae31028..87a87929c48196eef4d9f027dd31d4ae705146bf 100644 --- a/oneflow/user/ops/mish_op.cpp +++ b/oneflow/user/ops/mish_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe MishOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe MishOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe MishGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,26 +65,11 @@ namespace oneflow { } /* static */ Maybe MishGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("mish").SetBackwardOpConfGenFn( - [](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto mish_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(mish_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("mish_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &mish_grad_op_name]() -> const std::string& { - return ctx->GetOp(mish_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/model_update_ops.cpp b/oneflow/user/ops/model_update_ops.cpp index 7bc13f941523f713a96bddec731cdab9e9c0c637..e893a5498ea18c94d20425973d13b5a9f56dc839 100644 --- a/oneflow/user/ops/model_update_ops.cpp +++ b/oneflow/user/ops/model_update_ops.cpp @@ -30,7 +30,9 @@ Maybe CheckShapeLike(const user_op::TensorDesc* tensor_desc, } Maybe CheckDataTypeLike(const user_op::TensorDesc* tensor_desc, const user_op::TensorDesc* like) { - CHECK_EQ_OR_RETURN(tensor_desc->data_type(), like->data_type()); + CHECK_EQ_OR_RETURN(tensor_desc->data_type(), like->data_type()) + << "InferDataType Failed. Expected " << DataType_Name(tensor_desc->data_type()) + << ", but got " << DataType_Name(like->data_type()); return Maybe::Ok(); } @@ -41,7 +43,9 @@ Maybe CheckScalarShape(const user_op::TensorDesc* tensor_desc) { return Maybe::Ok(); } Maybe CheckScalarDataType(const user_op::TensorDesc* tensor_desc, const DataType data_type) { - CHECK_EQ_OR_RETURN(tensor_desc->data_type(), data_type); + CHECK_EQ_OR_RETURN(tensor_desc->data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(tensor_desc->data_type()) + << ", but got " << DataType_Name(data_type); return Maybe::Ok(); } @@ -81,7 +85,9 @@ Maybe CheckIndexedSlicesModelDiffDataType(const user_op::TensorDesc* model const user_op::TensorDesc* model_diff_indices, const user_op::TensorDesc* model_diff_values) { CHECK_OR_RETURN(IsIndexDataType(model_diff_indices->data_type())); - CHECK_EQ_OR_RETURN(model->data_type(), model_diff_values->data_type()); + CHECK_EQ_OR_RETURN(model->data_type(), model_diff_values->data_type()) + << "InferDataType Failed. Expected " << DataType_Name(model->data_type()) << ", but got " + << DataType_Name(model_diff_values->data_type()); return Maybe::Ok(); } @@ -93,6 +99,10 @@ Maybe InferSGDUpdateTensorDesc(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(model_diff.shape(), shape); } JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("model_copy", 0)) { + CHECK_EQ_OR_RETURN(ctx->InputTensorDesc("model_copy", 0).shape(), shape) + << "Model copy shape should be equal to Model shape. "; + } if (ctx->has_input("scale_by_tensor", 0)) { const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); JUST(CheckScalarShape(&scale_by_tensor)); @@ -182,6 +192,10 @@ Maybe InferAdamUpdateTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& v = ctx->InputTensorDesc("v", 0); JUST(CheckShapeLike(&v, &model)); JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("model_copy", 0)) { + CHECK_EQ_OR_RETURN(ctx->InputTensorDesc("model_copy", 0).shape(), shape) + << "Model copy shape should be equal to Model shape. "; + } if (ctx->has_input("scale_by_tensor", 0)) { const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); JUST(CheckScalarShape(&scale_by_tensor)); @@ -300,6 +314,28 @@ Maybe InferFtrlUpdateDataType(user_op::InferContext* ctx) { return Maybe::Ok(); } +Maybe InferAdadeltaUpdateTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", 0); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", 0); + const user_op::TensorDesc& square_avgs = ctx->InputTensorDesc("square_avgs", 0); + const user_op::TensorDesc& acc_deltas = ctx->InputTensorDesc("acc_deltas", 0); + JUST(CheckShapeLike(&model_diff, &model)); + JUST(CheckShapeLike(&square_avgs, &model)); + JUST(CheckShapeLike(&acc_deltas, &model)); + JUST(CheckLearningRateShape(ctx)); + return Maybe::Ok(); +} + +Maybe InferAdadeltaUpdateDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", 0); + const user_op::TensorDesc& square_avgs = ctx->InputTensorDesc("square_avgs", 0); + const user_op::TensorDesc& acc_deltas = ctx->InputTensorDesc("acc_deltas", 0); + JUST(CheckDataTypeLike(&square_avgs, &model)); + JUST(CheckDataTypeLike(&acc_deltas, &model)); + JUST(CheckLearningRateDataType(ctx)); + return Maybe::Ok(); +} + Maybe SetInputArgModifierMutable(const user_op::GetInputArgModifier& GetInputArgModifierFn, const std::string& arg_name, int32_t arg_index) { user_op::InputArgModifier* arg_modifier = GetInputArgModifierFn(arg_name, arg_index); @@ -316,6 +352,9 @@ Maybe AdamInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArg if (conf.has_input("max_v", 0)) { JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "max_v", 0)); } + if (conf.has_input("model_copy", 0)) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_copy", 0)); + } return Maybe::Ok(); } @@ -337,6 +376,9 @@ Maybe LambInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArg Maybe SgdInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", 0)); + if (conf.has_input("model_copy", 0)) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_copy", 0)); + } return Maybe::Ok(); } @@ -387,6 +429,14 @@ Maybe FtrlInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArg return Maybe::Ok(); } +Maybe AdadeltaInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", 0)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "square_avgs", 0)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "acc_deltas", 0)); + return Maybe::Ok(); +} + Maybe InferRmsPropUpdateTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& model = ctx->InputTensorDesc("model", 0); @@ -468,11 +518,14 @@ Maybe InferLarsUpdateDataType(user_op::InferContext* ctx) { /* static */ Maybe SgdUpdateOp::GetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& model = ctx->LogicalTensorDesc4InputArgNameAndIndex("model", 0); FOR_RANGE(int64_t, axis, 0, model.shape().NumAxes()) { - ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Split(user_op::OpArg("model", 0), axis) - .Split(user_op::OpArg("model_diff", 0), axis) - .Build(); + auto builder = ctx->NewBuilder() + .Broadcast(ctx->inputs()) + .Split(user_op::OpArg("model", 0), axis) + .Split(user_op::OpArg("model_diff", 0), axis); + if (ctx->user_op_conf().has_input("model_copy", 0)) { + builder.Split(user_op::OpArg("model_copy", 0), axis); + } + builder.Build(); } return Maybe::Ok(); } @@ -615,7 +668,11 @@ Maybe InferLarsUpdateDataType(user_op::InferContext* ctx) { split_args.emplace_back("m", 0); split_args.emplace_back("v", 0); if (ctx->user_op_conf().has_input("max_v", 0)) { split_args.emplace_back("max_v", 0); } - ctx->NewBuilder().Broadcast(ctx->inputs()).Split(split_args, axis).Build(); + auto builder = ctx->NewBuilder().Broadcast(ctx->inputs()).Split(split_args, axis); + if (ctx->user_op_conf().has_input("model_copy", 0)) { + builder.Split(user_op::OpArg("model_copy", 0), axis); + } + builder.Build(); } return Maybe::Ok(); } @@ -731,7 +788,7 @@ Maybe InferLarsUpdateDataType(user_op::InferContext* ctx) { /* static */ Maybe AdamBiasCorrectionFactorOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("train_step", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("train_step", 0)); return Maybe::Ok(); } @@ -745,7 +802,7 @@ Maybe InferLarsUpdateDataType(user_op::InferContext* ctx) { } /* static */ Maybe AdamBiasCorrectionFactorOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kFloat; + ctx->SetOutputDType("out", 0, DataType::kFloat); return Maybe::Ok(); } @@ -851,4 +908,35 @@ Maybe InferLarsUpdateDataType(user_op::InferContext* ctx) { return InferFtrlUpdateDataType(ctx); } +/* static */ Maybe AdadeltaUpdateOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return AdadeltaInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe AdadeltaUpdateOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferAdadeltaUpdateTensorDesc(ctx); +} + +/*static*/ Maybe AdadeltaUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe AdadeltaUpdateOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& model = ctx->LogicalTensorDesc4InputArgNameAndIndex("model", 0); + FOR_RANGE(int64_t, axis, 0, model.shape().NumAxes()) { + ctx->NewBuilder() + .Broadcast(ctx->inputs()) + .Split(user_op::OpArg("model", 0), axis) + .Split(user_op::OpArg("model_diff", 0), axis) + .Split(user_op::OpArg("square_avgs", 0), axis) + .Split(user_op::OpArg("acc_deltas", 0), axis) + .Build(); + } + return Maybe::Ok(); +} + +/* static */ Maybe AdadeltaUpdateOp::InferDataType(user_op::InferContext* ctx) { + return InferAdadeltaUpdateDataType(ctx); +} + } // namespace oneflow diff --git a/oneflow/user/ops/moving_average_min_max_observer_op.cpp b/oneflow/user/ops/moving_average_min_max_observer_op.cpp index 434865f2d5998cc5f092789a6b74d0d2f85321ea..8adec92a98bfaf637af9d5455e59c76dea5ab23f 100644 --- a/oneflow/user/ops/moving_average_min_max_observer_op.cpp +++ b/oneflow/user/ops/moving_average_min_max_observer_op.cpp @@ -31,8 +31,8 @@ namespace oneflow { CHECK_OR_RETURN(current_train_step.NumAxes() == 1 && current_train_step.At(0) == 1); - *ctx->OutputShape("scale", 0) = Shape({1}); - *ctx->OutputShape("zero_point", 0) = Shape({1}); + ctx->SetOutputShape("scale", 0, Shape({1})); + ctx->SetOutputShape("zero_point", 0, Shape({1})); return Maybe::Ok(); } @@ -87,8 +87,8 @@ namespace oneflow { } /* static */ Maybe MovingAverageMinMaxObserverOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("scale", 0) = ctx->InputDType("in", 0); - *ctx->OutputDType("zero_point", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("scale", 0, ctx->InputDType("in", 0)); + ctx->SetOutputDType("zero_point", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/multi_reduce_ops.cpp b/oneflow/user/ops/multi_reduce_ops.cpp index 58ceca4ff10b4f7f34c503e38f2c6194a6be13bd..cdaee7a7e650656f3e01cc57c73cba591f950cb9 100644 --- a/oneflow/user/ops/multi_reduce_ops.cpp +++ b/oneflow/user/ops/multi_reduce_ops.cpp @@ -23,17 +23,17 @@ namespace { Maybe InferMultiReduceOpShape(user_op::InferContext* ctx) { CHECK_GT_OR_RETURN(ctx->input_size("x"), 0) << ctx->op_name() << "must have at least 1 input"; - *ctx->OutputShape("y", 0) = Shape({}); + ctx->SetOutputShape("y", 0, Shape({})); return Maybe::Ok(); } Maybe InferMultiReduceOpDataType(user_op::InferContext* ctx) { - const auto& x_0_dtype = ctx->InputDType("x", 0); + DataType x_0_dtype = ctx->InputDType("x", 0); for (size_t i = 1; i < ctx->input_size("x"); ++i) { CHECK_EQ_OR_RETURN(ctx->InputDType("x", i), x_0_dtype) << ctx->op_name() << ": the " << i << " th input has the different data type with others"; } - *ctx->OutputDType("y", 0) = x_0_dtype; + ctx->SetOutputDType("y", 0, x_0_dtype); return Maybe::Ok(); } @@ -67,13 +67,13 @@ Maybe InferLocalMultiReduceOpLogicalShape(user_op::InferContext* ctx) { for (int64_t i = 0; i < rank_mesh->NumAxes(); ++i) { if (any_nd_sbp.sbp_parallel(i).has_split_parallel()) { split_num *= rank_mesh->At(i); } } - *ctx->OutputShape("y", 0) = Shape({split_num}); + ctx->SetOutputShape("y", 0, Shape({split_num})); return Maybe::Ok(); } Maybe InferLocalMultiReduceOpPhysicalShape(user_op::InferContext* ctx) { CHECK_GT_OR_RETURN(ctx->input_size("x"), 0) << ctx->op_name() << "must have at least 1 input"; - *ctx->OutputShape("y", 0) = Shape({1}); + ctx->SetOutputShape("y", 0, Shape({1})); return Maybe::Ok(); } diff --git a/oneflow/user/ops/multi_tensor_model_update_ops.cpp b/oneflow/user/ops/multi_tensor_model_update_ops.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e54392da49b835e93a10de7c5c44399c5db3bece --- /dev/null +++ b/oneflow/user/ops/multi_tensor_model_update_ops.cpp @@ -0,0 +1,513 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/infer_util.h" +#include "oneflow/core/framework/user_op_conf.h" +#include "oneflow/core/framework/user_op_registry.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe CheckShapeLike(const user_op::TensorDesc* tensor_desc, + const user_op::TensorDesc* like) { + CHECK_EQ_OR_RETURN(tensor_desc->shape(), like->shape()) + << "Tensordesc shape should be equal to Like shape. "; + return Maybe::Ok(); +} + +Maybe CheckDataTypeLike(const user_op::TensorDesc* tensor_desc, + const user_op::TensorDesc* like) { + CHECK_EQ_OR_RETURN(tensor_desc->data_type(), like->data_type()) + << "Tensordesc DataType should be equal to Like DataType. "; + return Maybe::Ok(); +} + +Maybe CheckScalarShape(const user_op::TensorDesc* tensor_desc) { + CHECK_OR_RETURN(tensor_desc->shape().NumAxes() == 0 + || (tensor_desc->shape().NumAxes() == 1 && tensor_desc->shape().At(0) == 1)) + << tensor_desc->shape().DebugStr(); + return Maybe::Ok(); +} + +Maybe CheckScalarDataType(const user_op::TensorDesc* tensor_desc, const DataType data_type) { + CHECK_EQ_OR_RETURN(tensor_desc->data_type(), data_type) + << "TensorDesc DataType should be equal to Scalar DataType. "; + return Maybe::Ok(); +} + +Maybe CheckLearningRateShape(user_op::InferContext* ctx) { + if (ctx->has_input("learning_rate", 0)) { + const user_op::TensorDesc& learning_rate = ctx->InputTensorDesc("learning_rate", 0); + JUST(CheckScalarShape(&learning_rate)); + } + return Maybe::Ok(); +} +Maybe CheckLearningRateDataType(user_op::InferContext* ctx) { + if (ctx->has_input("learning_rate", 0)) { + const user_op::TensorDesc& learning_rate = ctx->InputTensorDesc("learning_rate", 0); + JUST(CheckScalarDataType(&learning_rate, DataType::kFloat)); + } + return Maybe::Ok(); +} + +Maybe SetInputArgModifierMutable(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const std::string& arg_name, int32_t arg_index) { + user_op::InputArgModifier* arg_modifier = GetInputArgModifierFn(arg_name, arg_index); + CHECK_NOTNULL_OR_RETURN(arg_modifier) << "Arg Modifier should not be null. "; + arg_modifier->set_is_mutable(true); + return Maybe::Ok(); +} + +Maybe InferSGDUpdateTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model Diff shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe InferSGDUpdateDataType(user_op::InferContext* ctx) { + JUST(CheckLearningRateDataType(ctx)); + const user_op::TensorDesc& first_model_desc = ctx->InputTensorDesc("model", 0); + const int64_t input_size = ctx->input_size("model"); + for (int64_t i = 0; i < input_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + CHECK_EQ(model.data_type(), first_model_desc.data_type()) << "Model DataType should be equal. "; + } + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarDataType(&scale_by_tensor, first_model_desc.data_type())); + } + return Maybe::Ok(); +} + +Maybe SgdInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + } + return Maybe::Ok(); +} + +Maybe InferMomentumUpdateTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + const user_op::TensorDesc& momentum_buf = ctx->InputTensorDesc("momentum_buf", i); + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model Diff shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(momentum_buf.shape(), model.shape()) + << "Momentum buf shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe InferMomentumUpdateDataType(user_op::InferContext* ctx) { + JUST(CheckLearningRateDataType(ctx)); + const user_op::TensorDesc& first_model_desc = ctx->InputTensorDesc("model", 0); + const int64_t input_size = ctx->input_size("model"); + for (int64_t i = 0; i < input_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& momentum_buf = ctx->InputTensorDesc("momentum_buf", i); + CHECK_EQ(model.data_type(), first_model_desc.data_type()) << "Model DataType should be equal. "; + CHECK_EQ(momentum_buf.data_type(), first_model_desc.data_type()) + << "Momentum buf DataType should be equal. "; + } + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarDataType(&scale_by_tensor, first_model_desc.data_type())); + } + return Maybe::Ok(); +} + +Maybe MomentumInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "momentum_buf", i)); + } + return Maybe::Ok(); +} + +Maybe InferAdamUpdateTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + const user_op::TensorDesc& m = ctx->InputTensorDesc("m", i); + const user_op::TensorDesc& v = ctx->InputTensorDesc("v", i); + + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model Diff shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(m.shape(), model.shape()) << "m shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(v.shape(), model.shape()) << "v shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe InferAdamUpdateDataType(user_op::InferContext* ctx) { // todo + JUST(CheckLearningRateDataType(ctx)); + const user_op::TensorDesc& first_model_desc = ctx->InputTensorDesc("model", 0); + const int64_t input_size = ctx->input_size("model"); + for (int64_t i = 0; i < input_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& m = ctx->InputTensorDesc("m", i); + const user_op::TensorDesc& v = ctx->InputTensorDesc("v", i); + CHECK_EQ(model.data_type(), first_model_desc.data_type()) << "Model DataType should be equal. "; + CHECK_EQ(m.data_type(), first_model_desc.data_type()) << "m DataType should be equal. "; + CHECK_EQ(v.data_type(), first_model_desc.data_type()) << "v DataType should be equal. "; + } + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarDataType(&scale_by_tensor, first_model_desc.data_type())); + } + return Maybe::Ok(); +} + +Maybe AdamInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "m", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "v", i)); + } + return Maybe::Ok(); +} + +Maybe InferSGDUpdateWithCastTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_copy = ctx->InputTensorDesc("model_copy", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model diff shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(model_copy.shape(), model.shape()) + << "Model copy shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe SgdWithCastInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_copy", i)); + } + return Maybe::Ok(); +} + +Maybe InferMomentumUpdateWithCastTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_copy = ctx->InputTensorDesc("model_copy", i); + const user_op::TensorDesc& momentum_buf = ctx->InputTensorDesc("momentum_buf", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model diff shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(momentum_buf.shape(), model.shape()) + << "Momentum buf shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(model_copy.shape(), model.shape()) + << "Model copy shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe MomentumWithCastInputArgModifyFn( + const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "momentum_buf", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_copy", i)); + } + return Maybe::Ok(); +} + +Maybe InferAdamUpdateWithCastTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_diff = ctx->InputTensorDesc("model_diff", i); + const user_op::TensorDesc& model_copy = ctx->InputTensorDesc("model_copy", i); + const user_op::TensorDesc& m = ctx->InputTensorDesc("m", i); + const user_op::TensorDesc& v = ctx->InputTensorDesc("v", i); + + CHECK_EQ_OR_RETURN(model_diff.shape(), model.shape()) + << "Model diff shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(model_copy.shape(), model.shape()) + << "Model copy shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(m.shape(), model.shape()) << "m shape should be equal to Model shape. "; + CHECK_EQ_OR_RETURN(v.shape(), model.shape()) << "v shape should be equal to Model shape. "; + } + JUST(CheckLearningRateShape(ctx)); + if (ctx->has_input("scale_by_tensor", 0)) { + const auto& scale_by_tensor = ctx->InputTensorDesc("scale_by_tensor", 0); + JUST(CheckScalarShape(&scale_by_tensor)); + } + return Maybe::Ok(); +} + +Maybe AdamWithCastInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_copy", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "m", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "v", i)); + } + return Maybe::Ok(); +} + +Maybe InferYoloV5WeightUpdateTensorDesc(user_op::InferContext* ctx) { + const int64_t weight_size = ctx->input_size("model"); + for (int i = 0; i < weight_size; i++) { + const user_op::TensorDesc& model_i = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_update_i = ctx->InputTensorDesc("model_update", i); + CHECK_EQ_OR_RETURN(model_update_i.shape(), model_i.shape()) + << "All Model shape should be equal to model_update shape."; + } + return Maybe::Ok(); +} + +Maybe InferYoloV5WeightUpdateDataType(user_op::InferContext* ctx) { + JUST(CheckLearningRateDataType(ctx)); + const user_op::TensorDesc& first_model_desc = ctx->InputTensorDesc("model", 0); + const int64_t input_size = ctx->input_size("model"); + for (int64_t i = 0; i < input_size; i++) { + const user_op::TensorDesc& model = ctx->InputTensorDesc("model", i); + const user_op::TensorDesc& model_update_i = ctx->InputTensorDesc("model_update", i); + CHECK_EQ(model.data_type(), first_model_desc.data_type()) << "Model DataType should be equal. "; + CHECK_EQ(model_update_i.data_type(), first_model_desc.data_type()) + << "Model DataType should be equal to model_update DataType."; + } + return Maybe::Ok(); +} + +Maybe YoloV5WeightInputArgModifyFn(const user_op::GetInputArgModifier& GetInputArgModifierFn, + const user_op::UserOpConfWrapper& conf) { + for (int64_t i = 0; i < conf.input_size("model"); i++) { + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model", i)); + JUST(SetInputArgModifierMutable(GetInputArgModifierFn, "model_update", i)); + } + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe MultiTensorSgdUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferSGDUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorSgdUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorSgdUpdateOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorSgdUpdateOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return SgdInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorSgdUpdateOp::InferDataType(user_op::InferContext* ctx) { + return InferSGDUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorMomentumUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferMomentumUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorMomentumUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorMomentumUpdateOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorMomentumUpdateOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return MomentumInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorMomentumUpdateOp::InferDataType(user_op::InferContext* ctx) { + return InferMomentumUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorAdamUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferAdamUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorAdamUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorAdamUpdateOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorAdamUpdateOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return AdamInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorAdamUpdateOp::InferDataType(user_op::InferContext* ctx) { + return InferAdamUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorSgdUpdateWithCastOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferSGDUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorSgdUpdateWithCastOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorSgdUpdateWithCastOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorSgdUpdateWithCastOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return SgdWithCastInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorSgdUpdateWithCastOp::InferDataType(user_op::InferContext* ctx) { + return InferSGDUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorMomentumUpdateWithCastOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferMomentumUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorMomentumUpdateWithCastOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorMomentumUpdateWithCastOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorMomentumUpdateWithCastOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return MomentumWithCastInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorMomentumUpdateWithCastOp::InferDataType( + user_op::InferContext* ctx) { + return InferMomentumUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorAdamUpdateWithCastOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferAdamUpdateWithCastTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorAdamUpdateWithCastOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorAdamUpdateWithCastOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorAdamUpdateWithCastOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return AdamWithCastInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorAdamUpdateWithCastOp::InferDataType( + user_op::InferContext* ctx) { + return InferAdamUpdateDataType(ctx); +} + +/* static */ Maybe MultiTensorYoloV5WeightUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferYoloV5WeightUpdateTensorDesc(ctx); +} + +/*static*/ Maybe MultiTensorYoloV5WeightUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MultiTensorYoloV5WeightUpdateOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MultiTensorYoloV5WeightUpdateOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { + return YoloV5WeightInputArgModifyFn(GetInputArgModifierFn, conf); +} + +/* static */ Maybe MultiTensorYoloV5WeightUpdateOp::InferDataType( + user_op::InferContext* ctx) { + return InferYoloV5WeightUpdateDataType(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/mutable_cast_once_op.cpp b/oneflow/user/ops/mutable_cast_once_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3e40ff61838c25962bbc0e155f39ce6a23f54b18 --- /dev/null +++ b/oneflow/user/ops/mutable_cast_once_op.cpp @@ -0,0 +1,48 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe MutableCastOnceOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& input_tensor_desc = ctx->InputTensorDesc("in", 0); + user_op::TensorDesc* output_tensor_desc = ctx->MutOutputTensorDesc("out", 0); + output_tensor_desc->set_shape(input_tensor_desc.shape()); + output_tensor_desc->set_is_dynamic(input_tensor_desc.is_dynamic()); + return Maybe::Ok(); +} + +/*static*/ Maybe MutableCastOnceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe MutableCastOnceOp::GetSbp(user_op::SbpContext* ctx) { + const auto& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); + for (int i = 0; i < in_tensor.shape().NumAxes(); ++i) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe MutableCastOnceOp::InferDataType(user_op::InferContext* ctx) { + user_op::TensorDesc* output_tensor_desc = ctx->MutOutputTensorDesc("out", 0); + output_tensor_desc->set_data_type(ctx->Attr("dtype")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/narrow_op.cpp b/oneflow/user/ops/narrow_op.cpp index a8569c6784e2264a8390b9aed11fc9bcb899729d..866f6363534312bdc7aa56ff503ecbd45956e531 100644 --- a/oneflow/user/ops/narrow_op.cpp +++ b/oneflow/user/ops/narrow_op.cpp @@ -29,14 +29,14 @@ namespace oneflow { CHECK_GE_OR_RETURN(length, 0); // length should be input size if split the full slice dimension if (start == 0 && length > in.shape().At(dim)) { length = in.shape().At(dim); } - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); DimVector dim_vec; dim_vec.insert(dim_vec.end(), in.shape().dim_vec().cbegin(), in.shape().dim_vec().cbegin() + dim); dim_vec.insert(dim_vec.end(), length); dim_vec.insert(dim_vec.end(), in.shape().dim_vec().cbegin() + dim + 1, in.shape().dim_vec().end()); - *out->mut_shape() = Shape(dim_vec); + out->set_shape(Shape(dim_vec)); return Maybe::Ok(); } @@ -72,8 +72,8 @@ namespace oneflow { /* static */ Maybe NarrowOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } @@ -83,7 +83,7 @@ namespace oneflow { const int64_t ndim = dy_shape.NumAxes(); CHECK_EQ_OR_RETURN(like_shape.NumAxes(), ndim); - *ctx->OutputShape("dx", 0) = like_shape; + ctx->SetOutputShape("dx", 0, like_shape); return Maybe::Ok(); } @@ -131,26 +131,8 @@ namespace oneflow { } /* static */ Maybe NarrowGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("narrow").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder in_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper in_grad_op = in_grad_builder.Op("narrow_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Attr("dim", op.attr("dim")) - .Attr("start", op.attr("start")) - .Attr("length", op.attr("length")) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(in_grad_op.output("dx", 0), "in", 0); - AddOp(in_grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/nccl_logical_2d_sbp_ops.cpp b/oneflow/user/ops/nccl_logical_2d_sbp_ops.cpp index f8bf37f2771a46443705fc2a43ef5255f8973a99..a342097f333a787e581b55b5377da1c0d1b6a856 100644 --- a/oneflow/user/ops/nccl_logical_2d_sbp_ops.cpp +++ b/oneflow/user/ops/nccl_logical_2d_sbp_ops.cpp @@ -23,8 +23,8 @@ namespace oneflow { /* static */ Maybe _ncclLogical_2DSameDim0AllReduceOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -54,19 +54,19 @@ namespace oneflow { /* static */ Maybe _ncclLogical_2DSameDim0AllReduceOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogical_2DSameDim0AllReduceOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogical_2DSameDim1AllReduceOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -96,19 +96,19 @@ namespace oneflow { /* static */ Maybe _ncclLogical_2DSameDim1AllReduceOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogical_2DSameDim1AllReduceOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogical_2DSameDim0AllGatherOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -139,19 +139,19 @@ namespace oneflow { /* static */ Maybe _ncclLogical_2DSameDim0AllGatherOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogical_2DSameDim0AllGatherOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogical_2DSameDim0AllGatherNoncontinuousOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -183,20 +183,20 @@ namespace oneflow { /* static */ Maybe _ncclLogical_2DSameDim0AllGatherNoncontinuousOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogical_2DSameDim0AllGatherNoncontinuousOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogical_2DSameDim0All2allOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -226,13 +226,13 @@ _ncclLogical_2DSameDim0AllGatherNoncontinuousOp::InferDeviceAndStream( /* static */ Maybe _ncclLogical_2DSameDim0All2allOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogical_2DSameDim0All2allOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/nccl_logical_ops.cpp b/oneflow/user/ops/nccl_logical_ops.cpp index 5f157516389602a0b9b547cca6d854d48622bfaf..d45abcc8c8279272a2d284baf693adda9a7690ff 100644 --- a/oneflow/user/ops/nccl_logical_ops.cpp +++ b/oneflow/user/ops/nccl_logical_ops.cpp @@ -23,8 +23,8 @@ namespace oneflow { /* static */ Maybe _ncclLogicalAllReduceOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -51,19 +51,19 @@ namespace oneflow { } /* static */ Maybe _ncclLogicalAllReduceOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalAllReduceOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalReduceScatterOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -92,19 +92,19 @@ namespace oneflow { } /* static */ Maybe _ncclLogicalReduceScatterOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalReduceScatterOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalAllGatherOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -132,19 +132,19 @@ namespace oneflow { } /* static */ Maybe _ncclLogicalAllGatherOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalAllGatherOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalAllGatherNoncontinuousOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -174,19 +174,19 @@ namespace oneflow { /* static */ Maybe _ncclLogicalAllGatherNoncontinuousOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalAllGatherNoncontinuousOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalReduceScatterNoncontinuousOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -220,18 +220,18 @@ namespace oneflow { /* static */ Maybe _ncclLogicalReduceScatterNoncontinuousOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalReduceScatterNoncontinuousOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalS2sOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -258,19 +258,19 @@ namespace oneflow { } /* static */ Maybe _ncclLogicalS2sOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalS2sOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } /* static */ Maybe _ncclLogicalSendRecvOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -291,13 +291,13 @@ namespace oneflow { } /* static */ Maybe _ncclLogicalSendRecvOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe> _ncclLogicalSendRecvOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/nd_index_slice_ops.cpp b/oneflow/user/ops/nd_index_slice_ops.cpp index 2fa17d2d3901068da372589b808763c54c37f659..49c5d4fdcbae416cc2894332cc05ca4803769216 100644 --- a/oneflow/user/ops/nd_index_slice_ops.cpp +++ b/oneflow/user/ops/nd_index_slice_ops.cpp @@ -42,12 +42,12 @@ Maybe InferScatterNdTensorDesc(user_op::InferContext* ctx) { const Shape& updates_shape = ctx->InputShape("updates", 0); const Shape& params_shape = ctx->Attr("shape"); JUST(CheckScatterNdShape(params_shape, indices_shape, updates_shape)); - *ctx->OutputShape("out", 0) = params_shape; + ctx->SetOutputShape("out", 0, params_shape); return Maybe::Ok(); } Maybe InferScatterNdDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("updates", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("updates", 0)); return Maybe::Ok(); } @@ -56,12 +56,12 @@ Maybe InferScatterNdLikeTensorDesc(user_op::InferContext* ctx) { const Shape& updates_shape = ctx->InputShape("updates", 0); const Shape& like_shape = ctx->InputShape("like", 0); JUST(CheckScatterNdShape(like_shape, indices_shape, updates_shape)); - *ctx->OutputShape("out", 0) = like_shape; + ctx->SetOutputShape("out", 0, like_shape); return Maybe::Ok(); } Maybe InferScatterNdLikeDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("updates", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("updates", 0)); return Maybe::Ok(); } @@ -70,12 +70,13 @@ Maybe InferTensorScatterNdOptTensorDesc(user_op::InferContext* ctx) { const Shape& updates_shape = ctx->InputShape("updates", 0); const Shape& indices_shape = ctx->InputShape("indices", 0); JUST(CheckScatterNdShape(params_shape, indices_shape, updates_shape)); - *ctx->OutputShape("out", 0) = params_shape; + ctx->SetOutputShape("out", 0, params_shape); + ctx->SetOutputStride("out", 0, ctx->InputStride("params", 0)); return Maybe::Ok(); } Maybe InferTensorScatterNdOptDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("params", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("params", 0)); return Maybe::Ok(); } @@ -122,7 +123,11 @@ Maybe GetTensorScatterNdOptSbpSignatures(user_op::SbpContext* ctx) { FOR_RANGE(int64_t, i, index_ndims, params_shape.NumAxes()) { out_shape_vec.emplace_back(params_shape.At(i)); } - *ctx->OutputShape("out", 0) = Shape(out_shape_vec); + const Shape& out_shape = Shape(out_shape_vec); + bool is_out_of_bounds = params_shape.Count(0) == 0 && out_shape.Count(0) != 0; + CHECK_OR_RETURN(!is_out_of_bounds) + << Error::IndexError() << "The index is out of bounds for dimension with size 0"; + ctx->SetOutputShape("out", 0, out_shape); return Maybe::Ok(); } @@ -168,7 +173,7 @@ Maybe GetTensorScatterNdOptSbpSignatures(user_op::SbpContext* ctx) { } /* static */ Maybe GatherNdOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("params", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("params", 0)); return Maybe::Ok(); } @@ -315,93 +320,4 @@ Maybe GetTensorScatterNdOptSbpSignatures(user_op::SbpContext* ctx) { return InferTensorScatterNdOptDataType(ctx); } -REGISTER_USER_OP_GRAD("gather_nd") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("params", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scatter_nd_like") - .Input("like", op.input("params", 0)) - .Input("updates", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("indices", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "params", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scatter_nd") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("updates", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("gather_nd") - .Input("params", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("indices", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "updates", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("tensor_scatter_nd_update") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("updates", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_updates_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("gather_nd") - .Input("params", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("indices", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "updates", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("params", 0)) { - user_op::UserOpConfWrapperBuilder zero_grad_builder(op.op_name() + "_zero_updates"); - user_op::UserOpConfWrapper zero_grad_op = zero_grad_builder.Op("zero_like") - .Input("like", op.input("updates", 0)) - .Output("out") - .Build(); - AddOp(zero_grad_op); - user_op::UserOpConfWrapperBuilder grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - grad_builder.Op("tensor_scatter_nd_update") - .Input("params", op.GetGradTensorWithOpOutput("out", 0)) - .Input("updates", zero_grad_op.output("out", 0)) - .Input("indices", op.input("indices", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "params", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("tensor_scatter_nd_add") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("updates", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_updates_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("gather_nd") - .Input("params", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("indices", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "updates", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("params", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", 0), "params", 0); - } - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/nll_op.cpp b/oneflow/user/ops/nll_op.cpp index 1afffc2c16bfc7dbfe155ff9179be3e4210a04cf..7f9035539b730b68d02f551ed4f009e83bc50ed1 100644 --- a/oneflow/user/ops/nll_op.cpp +++ b/oneflow/user/ops/nll_op.cpp @@ -22,15 +22,15 @@ namespace oneflow { CHECK_OR_RETURN(IsIndexDataType(ctx->InputDType("target", 0))) << ctx->op_name() << ": expected target being integer type"; - auto input_dtype = ctx->InputDType("input", 0); + DataType input_dtype = ctx->InputDType("input", 0); if (ctx->has_input("weight", 0)) { - auto weight_dtype = ctx->InputDType("weight", 0); + DataType weight_dtype = ctx->InputDType("weight", 0); CHECK_EQ_OR_RETURN(weight_dtype, input_dtype) << ctx->op_name() << ": expected weight dtype " << input_dtype << ", but got " << weight_dtype; } - *ctx->OutputDType("output", 0) = input_dtype; - *ctx->OutputDType("out_weight", 0) = input_dtype; + ctx->SetOutputDType("output", 0, input_dtype); + ctx->SetOutputDType("out_weight", 0, input_dtype); return Maybe::Ok(); } @@ -61,13 +61,13 @@ namespace oneflow { << weight_desc.shape().ToString(); } - user_op::TensorDesc* output_desc = ctx->OutputTensorDesc("output", 0); - *output_desc->mut_is_dynamic() = is_dynamic; - *output_desc->mut_shape() = Shape({N}); + user_op::TensorDesc* output_desc = ctx->MutOutputTensorDesc("output", 0); + output_desc->set_is_dynamic(is_dynamic); + output_desc->set_shape(Shape({N})); - user_op::TensorDesc* out_weight_desc = ctx->OutputTensorDesc("out_weight", 0); - *out_weight_desc->mut_is_dynamic() = is_dynamic; - *out_weight_desc->mut_shape() = Shape({N}); + user_op::TensorDesc* out_weight_desc = ctx->MutOutputTensorDesc("out_weight", 0); + out_weight_desc->set_is_dynamic(is_dynamic); + out_weight_desc->set_shape(Shape({N})); return Maybe::Ok(); } @@ -115,7 +115,7 @@ namespace oneflow { CHECK_OR_RETURN(IsIndexDataType(ctx->InputDType("target", 0))) << ctx->op_name() << ": expected target being integer type"; - auto input_dtype = ctx->InputDType("input", 0); + DataType input_dtype = ctx->InputDType("input", 0); CHECK_EQ_OR_RETURN(ctx->InputDType("out_grad", 0), input_dtype) << ctx->op_name() << ": expected out_grad dtype " << input_dtype << ", got " << ctx->InputDType("out_grad", 0); @@ -126,7 +126,7 @@ namespace oneflow { << ctx->InputDType("weight", 0); } - *ctx->OutputDType("in_grad", 0) = input_dtype; + ctx->SetOutputDType("in_grad", 0, input_dtype); return Maybe::Ok(); } @@ -159,9 +159,9 @@ namespace oneflow { << weight_desc.shape().ToString(); } - user_op::TensorDesc* in_grad_desc = ctx->OutputTensorDesc("in_grad", 0); - *in_grad_desc->mut_is_dynamic() = is_dynamic; - *in_grad_desc->mut_shape() = input_desc.shape(); + user_op::TensorDesc* in_grad_desc = ctx->MutOutputTensorDesc("in_grad", 0); + in_grad_desc->set_is_dynamic(is_dynamic); + in_grad_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -193,24 +193,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("nll").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder.Op("nll_grad") - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Input("out_grad", op.GetGradTensorWithOpOutput("output", 0)) - .Output("in_grad") - .Attr("ignore_index", op.attr("ignore_index")); - if (op.user_op_conf().has_input("weight", 0)) { - builder.Input("weight", op.input("weight", 0)); - } - auto grad_op = builder.Build(); - op.BindGradTensorWithOpInput(grad_op.output("in_grad", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/nms_op.cpp b/oneflow/user/ops/nms_op.cpp index 1d9c0e295374207771cbc16e1ef480020950cf10..e49236c7e8cf53d27000a00c0bdf37527884720b 100644 --- a/oneflow/user/ops/nms_op.cpp +++ b/oneflow/user/ops/nms_op.cpp @@ -21,12 +21,12 @@ namespace oneflow { namespace { Maybe InferNmsTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape({ctx->InputShape("in", 0).At(0)}); + ctx->SetOutputShape("out", 0, Shape({ctx->InputShape("in", 0).At(0)})); return Maybe::Ok(); } Maybe InferNmsDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kInt8; + ctx->SetOutputDType("out", 0, DataType::kInt8); return Maybe::Ok(); } diff --git a/oneflow/user/ops/normalization_op.cpp b/oneflow/user/ops/normalization_op.cpp index 8d0db30cffd90cc7e1f860f9a49abcff26880339..e10cc809e81e601592cd8468e8aea8d308f02c86 100644 --- a/oneflow/user/ops/normalization_op.cpp +++ b/oneflow/user/ops/normalization_op.cpp @@ -19,6 +19,10 @@ limitations under the License. #include "oneflow/core/device/cuda_util.h" #include "oneflow/core/device/cudnn_util.h" #endif +#ifdef WITH_ROCM +#include "oneflow/core/device/cuda_util.h" +#include "oneflow/core/device/cudnn_util.h" +#endif namespace oneflow { @@ -40,7 +44,9 @@ std::function(const std::string&)> MakeCheckParamDataTypeFn(user_op: return [=](const std::string& bn) -> Maybe { if (ctx->has_input(bn, 0)) { const auto& tensor_desc = ctx->InputTensorDesc(bn, 0); - CHECK_EQ_OR_RETURN(tensor_desc.data_type(), data_type); + CHECK_EQ_OR_RETURN(tensor_desc.data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(tensor_desc.data_type()) + << ", but got " << DataType_Name(data_type); } return Maybe::Ok(); }; @@ -50,9 +56,9 @@ std::function(const std::string&)> MakeSetParamTensorDescFn(user_op: const Shape& shape) { return [=](const std::string& bn) -> Maybe { if (ctx->has_output(bn, 0)) { - auto* tensor_desc = ctx->OutputTensorDesc(bn, 0); + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); CHECK_OR_RETURN(tensor_desc != nullptr); - *tensor_desc->mut_shape() = shape; + tensor_desc->set_shape(shape); } return Maybe::Ok(); }; @@ -62,9 +68,9 @@ std::function(const std::string&)> MakeSetParamDataTypeFn(user_op::I DataType data_type) { return [=](const std::string& bn) -> Maybe { if (ctx->has_output(bn, 0)) { - auto* tensor_desc = ctx->OutputTensorDesc(bn, 0); + auto* tensor_desc = ctx->MutOutputTensorDesc(bn, 0); CHECK_OR_RETURN(tensor_desc != nullptr); - *tensor_desc->mut_data_type() = data_type; + tensor_desc->set_data_type(data_type); } return Maybe::Ok(); }; @@ -133,15 +139,19 @@ user_op::TensorDescInferFn MakeFwTensorDescInferFn( const Shape& x_shape = x.shape(); if (ctx->has_input("addend", 0)) { const auto& addend = ctx->InputTensorDesc("addend", 0); - CHECK_EQ_OR_RETURN(addend.data_type(), data_type); + CHECK_EQ_OR_RETURN(addend.data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(addend.data_type()) << ", but got " + << DataType_Name(data_type); CHECK_EQ_OR_RETURN(addend.shape(), x_shape); } if (ctx->has_input("_add_to_output", 0)) { const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); - CHECK_EQ_OR_RETURN(add_to_output.data_type(), data_type); + CHECK_EQ_OR_RETURN(add_to_output.data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(add_to_output.data_type()) + << ", but got " << DataType_Name(data_type); CHECK_EQ_OR_RETURN(add_to_output.shape(), x_shape); } - *ctx->OutputTensorDesc("y", 0) = x; + *ctx->MutOutputTensorDesc("y", 0) = x; const auto axis = ctx->Attr("axis"); CHECK_GE_OR_RETURN(axis, 0); CHECK_LT_OR_RETURN(axis, x_shape.NumAxes()); @@ -159,7 +169,7 @@ user_op::TensorDescInferFn MakeFwTensorDescInferFn( JUST(SetParamTensorDesc("inv_variance")); if (ctx->has_output("reserve_space", 0)) { CHECK_OR_RETURN(reserve_space_infer_fn); - reserve_space_infer_fn(ctx, &x, ctx->OutputTensorDesc("reserve_space", 0)); + reserve_space_infer_fn(ctx, &x, ctx->MutOutputTensorDesc("reserve_space", 0)); } return Maybe::Ok(); }; @@ -173,14 +183,20 @@ user_op::DataTypeInferFn MakeFwDataTypeInferFn( const auto data_type = x.data_type(); if (ctx->has_input("addend", 0)) { const auto& addend = ctx->InputTensorDesc("addend", 0); - CHECK_EQ_OR_RETURN(addend.data_type(), data_type); + CHECK_EQ_OR_RETURN(addend.data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(data_type) << ", but got " + << DataType_Name(addend.data_type()); } if (ctx->has_input("_add_to_output", 0)) { const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0); - CHECK_EQ_OR_RETURN(add_to_output.data_type(), data_type); + CHECK_EQ_OR_RETURN(add_to_output.data_type(), data_type) + << "InferDataType Failed. Expected " << DataType_Name(data_type) << ", but got " + << DataType_Name(add_to_output.data_type()); } - *ctx->OutputTensorDesc("y", 0) = x; - const DataType param_data_type = data_type == DataType::kFloat16 ? DataType::kFloat : data_type; + *ctx->MutOutputTensorDesc("y", 0) = x; + const DataType param_data_type = + (data_type == DataType::kFloat16 || data_type == DataType::kBFloat16) ? DataType::kFloat + : data_type; const auto CheckParamDataType = MakeCheckParamDataTypeFn(ctx, param_data_type); const auto SetParamDataType = MakeSetParamDataTypeFn(ctx, param_data_type); if (ctx->has_input("moving_mean", 0)) { @@ -195,7 +211,7 @@ user_op::DataTypeInferFn MakeFwDataTypeInferFn( JUST(SetParamDataType("inv_variance")); if (ctx->has_output("reserve_space", 0)) { CHECK_OR_RETURN(reserve_space_infer_fn); - reserve_space_infer_fn(ctx, &x, ctx->OutputTensorDesc("reserve_space", 0)); + reserve_space_infer_fn(ctx, &x, ctx->MutOutputTensorDesc("reserve_space", 0)); } return Maybe::Ok(); }; @@ -258,8 +274,7 @@ user_op::DataTypeInferFn MakeFwDataTypeInferFn() { CHECK_EQ_OR_RETURN(reserve_space_bits % split_num, 0); reserve_space_bits = reserve_space_bits / split_num; } - *reserve_space->mut_shape() = - Shape({static_cast(RoundUp(reserve_space_bits, 32) / 32)}); + reserve_space->set_shape(Shape({static_cast(RoundUp(reserve_space_bits, 32) / 32)})); return Maybe::Ok(); })(ctx); } @@ -269,8 +284,8 @@ user_op::DataTypeInferFn MakeFwDataTypeInferFn() { return MakeFwTensorDescInferFn([](user_op::InferContext* ctx, const user_op::TensorDesc* x, user_op::TensorDesc* reserve_space) -> Maybe { const auto& x_desc = ctx->InputTensorDesc("x", 0); - *reserve_space->mut_shape() = - Shape({static_cast(RoundUp(x_desc.shape().elem_cnt(), 32) / 32)}); + reserve_space->set_shape( + Shape({static_cast(RoundUp(x_desc.shape().elem_cnt(), 32) / 32)})); return Maybe::Ok(); })(ctx); } @@ -287,7 +302,7 @@ user_op::DataTypeInferFn MakeFwDataTypeInferFn() { /* static */ Maybe NormalizationAddReluOp::InferDataType(user_op::InferContext* ctx) { return MakeFwDataTypeInferFn([](user_op::InferContext* ctx, const user_op::TensorDesc* x, user_op::TensorDesc* reserve_space) -> Maybe { - *reserve_space->mut_data_type() = DataType::kInt32; + reserve_space->set_data_type(DataType::kInt32); return Maybe::Ok(); })(ctx); } @@ -298,14 +313,13 @@ namespace { void InferCudnnReserveSpaceSize(DataType data_type, cudnnBatchNormOps_t ops, int64_t n, int64_t c, int64_t h, int64_t w, size_t* reserve_space_size) { - cudnnHandle_t cudnn_handle; - OF_CUDNN_CHECK(cudnnCreate(&cudnn_handle)); + cudnnHandle_t cudnn_handle = Singleton::Get()->Get(); CudnnTensorDesc xy_desc(CUDNN_TENSOR_NHWC, data_type, n, c, h, w); CudnnActivationDesc activation_desc(CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0); OF_CUDNN_CHECK(cudnnGetBatchNormalizationTrainingExReserveSpaceSize( cudnn_handle, CUDNN_BATCHNORM_SPATIAL_PERSISTENT, ops, activation_desc.Get(), xy_desc.Get(), reserve_space_size)); - OF_CUDNN_CHECK(cudnnDestroy(cudnn_handle)); + Singleton::Get()->Put(cudnn_handle); } } // namespace @@ -344,7 +358,7 @@ void InferCudnnReserveSpaceSize(DataType data_type, cudnnBatchNormOps_t ops, int size_t reserve_space_size; InferCudnnReserveSpaceSize(x->data_type(), ops, n, c, h, w, &reserve_space_size); reserve_space_size = std::max(reserve_space_size, GetOneVal()); - *reserve_space->mut_shape() = Shape({static_cast(reserve_space_size)}); + reserve_space->set_shape(Shape({static_cast(reserve_space_size)})); return Maybe::Ok(); })(ctx); } @@ -369,7 +383,7 @@ void InferCudnnReserveSpaceSize(DataType data_type, cudnnBatchNormOps_t ops, int size_t reserve_space_size; InferCudnnReserveSpaceSize(x->data_type(), ops, n, c, h, w, &reserve_space_size); reserve_space_size = std::max(reserve_space_size, GetOneVal()); - *reserve_space->mut_shape() = Shape({static_cast(reserve_space_size)}); + reserve_space->set_shape(Shape({static_cast(reserve_space_size)})); return Maybe::Ok(); })(ctx); } @@ -387,7 +401,7 @@ void InferCudnnReserveSpaceSize(DataType data_type, cudnnBatchNormOps_t ops, int user_op::InferContext* ctx) { return MakeFwDataTypeInferFn([](user_op::InferContext* ctx, const user_op::TensorDesc* x, user_op::TensorDesc* reserve_space) -> Maybe { - *reserve_space->mut_data_type() = DataType::kChar; + reserve_space->set_data_type(DataType::kChar); return Maybe::Ok(); })(ctx); } @@ -435,8 +449,8 @@ Maybe BwTensorDescInferFn(user_op::InferContext* ctx) { const user_op::TensorDesc& y = ctx->InputTensorDesc("y", 0); CHECK_EQ_OR_RETURN(y.shape(), x_shape); } - *ctx->OutputTensorDesc("dx", 0) = x; - if (ctx->has_output("addend_diff", 0)) { *ctx->OutputTensorDesc("addend_diff", 0) = x; } + *ctx->MutOutputTensorDesc("dx", 0) = x; + if (ctx->has_output("addend_diff", 0)) { *ctx->MutOutputTensorDesc("addend_diff", 0) = x; } const Shape param_shape({x_shape.At(ctx->Attr("axis"))}); const auto CheckParamTensorDesc = MakeCheckParamTensorDescFn(ctx, param_shape); const auto SetParamTensorDesc = MakeSetParamTensorDescFn(ctx, param_shape); @@ -453,14 +467,19 @@ Maybe BwDataTypeInferFn(user_op::InferContext* ctx) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); const DataType x_type = x.data_type(); const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(dy.data_type(), x_type); + CHECK_EQ_OR_RETURN(dy.data_type(), x_type) + << "InferDataType Failed. Expected " << DataType_Name(x_type) << ", but got " + << DataType_Name(dy.data_type()); if (ctx->has_input("y", 0)) { const user_op::TensorDesc& y = ctx->InputTensorDesc("y", 0); - CHECK_EQ_OR_RETURN(y.data_type(), x_type); + CHECK_EQ_OR_RETURN(y.data_type(), x_type) + << "InferDataType Failed. Expected " << DataType_Name(x_type) << ", but got " + << DataType_Name(y.data_type()); } - *ctx->OutputTensorDesc("dx", 0) = x; - if (ctx->has_output("addend_diff", 0)) { *ctx->OutputTensorDesc("addend_diff", 0) = x; } - const DataType param_data_type = x_type == DataType::kFloat16 ? DataType::kFloat : x_type; + *ctx->MutOutputTensorDesc("dx", 0) = x; + if (ctx->has_output("addend_diff", 0)) { *ctx->MutOutputTensorDesc("addend_diff", 0) = x; } + const DataType param_data_type = + (x_type == DataType::kFloat16 || x_type == DataType::kBFloat16) ? DataType::kFloat : x_type; const auto CheckParamDataType = MakeCheckParamDataTypeFn(ctx, param_data_type); const auto SetParamDataType = MakeSetParamDataTypeFn(ctx, param_data_type); JUST(CheckParamDataType("mean")); @@ -579,210 +598,4 @@ Maybe BwGetSbpFn(user_op::SbpContext* ctx) { } #endif - -REGISTER_USER_OP_GRAD("normalization") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const bool is_training = ctx->FwOp().attr("training"); - const bool is_fp16 = ctx->FwOp().arg_tensor_desc("y", 0).data_type() == DataType::kFloat16; - - std::string mean; - std::string inv_variance; - if (ctx->FwOp().user_op_conf().has_input("moving_variance", 0)) { - // calculate inv_variance from moving_variance - const auto var_add_eps_op_name = - "System-AutoGrad-" + ctx->FwOp().op_name() + "-VarianceAddEpsilon"; - ctx->DefineOp(var_add_eps_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("scalar_add") - .InputBind("in", ctx->FwOp().input("moving_variance", 0)) - .Attr("has_float_operand", true) - .Attr("has_int_operand", false) - .Attr("int_operand", static_cast(0)) - .Attr("float_operand", static_cast(ctx->FwOp().attr("epsilon"))) - .Output("out") - .Build(); - }); - - const auto var_rsqrt_op_name = - "System-AutoGrad-" + ctx->FwOp().op_name() + "-VarianceRsqrt"; - ctx->DefineOp(var_rsqrt_op_name, - [&ctx, &var_add_eps_op_name](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("rsqrt") - .InputBind("x", ctx->GetOp(var_add_eps_op_name).output("out", 0)) - .Output("y") - .Build(); - }); - mean = ctx->FwOp().input("moving_mean", 0); - inv_variance = ctx->GetOp(var_rsqrt_op_name).output("y", 0); - } else { - mean = ctx->FwOp().output("mean", 0); - inv_variance = ctx->FwOp().output("inv_variance", 0); - } - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx, &is_training, &mean, - &inv_variance](user_op::BackwardOpBuilder& builder) { - builder.OpTypeName("normalization_grad") - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .InputBind("gamma", ctx->FwOp().input("gamma", 0)) - .Attr("axis", ctx->FwOp().attr("axis")) - .Attr("epsilon", ctx->FwOp().attr("epsilon")) - .Output("gamma_diff") - .Output("beta_diff") - .Output("dx"); - if (is_training) { - builder.InputBind("mean", ctx->FwOp().output("mean", 0)) - .InputBind("inv_variance", ctx->FwOp().output("inv_variance", 0)); - } else { - builder.InputBind("mean", mean).InputBind("inv_variance", inv_variance); - } - return builder.Build(); - }); - - // calculate dx manually as cudnn cannot be used in evaluation mode - // reference: https://github.com/pytorch/pytorch/issues/4284 - const auto axis = ctx->FwOp().attr("axis"); - const auto BroadcastMulAtAxisOpDefine = - [&ctx, &axis](std::function scale_bn_func, - std::function input_bn_func, const std::string& name) { - // local variable(scale_bn_func) need to be captured by value - const auto reshape_op_name = "System-AutoGrad-" + name + "-Reshape"; - ctx->DefineOp(reshape_op_name, - [&ctx, &axis, scale_bn_func](user_op::BackwardOpBuilder& builder) { - DimVector broadcast_dim_vec; - const auto& in_shape = ctx->FwOp().arg_tensor_desc("x", 0).shape(); - FOR_RANGE(size_t, i, 0, in_shape.NumAxes()) { - if (i != axis) { - broadcast_dim_vec.emplace_back(1); - } else { - broadcast_dim_vec.emplace_back(in_shape.At(axis)); - } - } - const Shape broadcast_shape(broadcast_dim_vec); - - return builder.OpTypeName("reshape") - .InputBind("in", scale_bn_func()) - .Attr("shape", broadcast_shape) - .Output("out") - .Build(); - }); - - // local variable(reshape_op_name/input_bn_func) need to be captured by value - const auto mul_op_name = "System-AutoGrad-" + name + "-BroadcastMul"; - ctx->DefineOp(mul_op_name, [&ctx, reshape_op_name, - input_bn_func](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("broadcast_mul") - .InputBind("x", ctx->GetOp(reshape_op_name).output("out", 0)) - .InputBind("y", input_bn_func()) - .Output("z") - .Build(); - }); - }; - - const auto dy_h2f_cast_op_name = "System-AutoGrad-" + ctx->FwOp().op_name() + "-Cast-dy-h2f"; - ctx->DefineOp(dy_h2f_cast_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("cast") - .Input("in", ctx->FwOp().output_grad("y", 0)) - .Output("out") - .Attr("dtype", ctx->FwOp().arg_tensor_desc("gamma", 0).data_type()) - .Build(); - }); - - const std::string mul_gamma_name = ctx->FwOp().op_name() + "_out_grad_mul_gamma"; - const auto dy_mul_gamma_op_name = "System-AutoGrad-" + mul_gamma_name + "-BroadcastMul"; - BroadcastMulAtAxisOpDefine([&ctx]() { return ctx->FwOp().input("gamma", 0); }, - [&ctx, &is_fp16, &dy_h2f_cast_op_name]() { - if (is_fp16) { - return ctx->GetOp(dy_h2f_cast_op_name).output("out", 0); - } else { - return ctx->FwOp().output_grad("y", 0); - } - }, - mul_gamma_name); - - const std::string mul_inv_var_name = ctx->FwOp().op_name() + "_out_grad_mul_inv_var"; - const auto dy_mul_inv_var_op_name = "System-AutoGrad-" + mul_inv_var_name + "-BroadcastMul"; - BroadcastMulAtAxisOpDefine([&ctx, &inv_variance]() { return inv_variance; }, - [&ctx, &dy_mul_gamma_op_name]() { - return ctx->GetOp(dy_mul_gamma_op_name).output("z", 0); - }, - mul_inv_var_name); - - const auto dx_f2h_cast_op_name = "System-AutoGrad-" + ctx->FwOp().op_name() + "-Cast-dx-f2h"; - ctx->DefineOp(dx_f2h_cast_op_name, - [&ctx, &dy_mul_inv_var_op_name](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("cast") - .InputBind("in", ctx->GetOp(dy_mul_inv_var_op_name).output("z", 0)) - .Output("out") - .Attr("dtype", DataType::kFloat16) - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &is_training, &is_fp16, &grad_op_name, &dx_f2h_cast_op_name, - &dy_mul_inv_var_op_name]() -> const std::string& { - if (is_training) { - return ctx->GetOp(grad_op_name).output("dx", 0); - } else { - if (is_fp16) { - return ctx->GetOp(dx_f2h_cast_op_name).output("out", 0); - } else { - return ctx->GetOp(dy_mul_inv_var_op_name).output("z", 0); - } - } - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("gamma", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("gamma_diff", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("beta", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("beta_diff", 0); - }); - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("normalization_add_relu") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - builder.OpTypeName("normalization_add_relu_grad") - .InputBind("x", ctx->FwOp().input("x", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .InputBind("gamma", ctx->FwOp().input("gamma", 0)) - .InputBind("beta", ctx->FwOp().input("beta", 0)) - .InputBind("reserve_space", ctx->FwOp().output("reserve_space", 0)) - .InputBind("mean", ctx->FwOp().output("mean", 0)) - .InputBind("inv_variance", ctx->FwOp().output("inv_variance", 0)) - .InputBind("y", ctx->FwOp().output("y", 0)) - .Attr("axis", ctx->FwOp().attr("axis")) - .Attr("epsilon", ctx->FwOp().attr("epsilon")) - .Output("gamma_diff") - .Output("beta_diff") - .Output("dx"); - if (ctx->FwOp().input_size("addend") != 0) { builder.Output("addend_diff"); } - return builder.Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dx", 0); - }); - if (ctx->FwOp().user_op_conf().has_input("addend", 0)) { - ctx->FwOp().InputGradBind(user_op::OpArg("addend", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("addend_diff", 0); - }); - } - ctx->FwOp().InputGradBind(user_op::OpArg("gamma", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("gamma_diff", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("beta", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("beta_diff", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/nvtx_range_op.cpp b/oneflow/user/ops/nvtx_range_op.cpp index 0f2bd54b2e67d70e94412437ab954b5fcee7711b..c6ad328749834387de6cb80c4d82d72b7edc3d37 100644 --- a/oneflow/user/ops/nvtx_range_op.cpp +++ b/oneflow/user/ops/nvtx_range_op.cpp @@ -19,11 +19,11 @@ limitations under the License. namespace oneflow { -#ifdef WITH_CUDA +#if defined(WITH_CUDA) || defined(WITH_ROCM) /* static */ Maybe NvtxStartOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -44,13 +44,13 @@ namespace oneflow { } /* static */ Maybe NvtxStartOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe NvtxEndOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } @@ -71,7 +71,7 @@ namespace oneflow { } /* static */ Maybe NvtxEndOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -111,38 +111,4 @@ namespace oneflow { #endif // WITH_CUDA -REGISTER_USER_OP_GRAD("nvtx_start") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper nvtx_end_op = - builder.Op("nvtx_end") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("mark_prefix", op.attr("mark_prefix") + "-bw") - .Build(); - op.BindGradTensorWithOpInput(nvtx_end_op.output("out", 0), "in", 0); - AddOp(nvtx_end_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("nvtx_end") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper nvtx_start_op = - builder.Op("nvtx_start") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("mark_prefix", op.attr("mark_prefix") + "-bw") - .Build(); - op.BindGradTensorWithOpInput(nvtx_start_op.output("out", 0), "in", 0); - AddOp(nvtx_start_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/ofrecord_decoder_ops.cpp b/oneflow/user/ops/ofrecord_decoder_ops.cpp index 02ccf542062a49672aebcd7ada422934b1f8e648..0fbf46721fef296833f959706ebf86ec2c3e0f45 100644 --- a/oneflow/user/ops/ofrecord_decoder_ops.cpp +++ b/oneflow/user/ops/ofrecord_decoder_ops.cpp @@ -21,13 +21,13 @@ namespace oneflow { /* static */ Maybe OfrecordRawDecoderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) >= 1); Shape conf_shape = ctx->Attr("shape"); DimVector dim_vec(1 + conf_shape.NumAxes()); dim_vec[0] = in_tensor.shape().At(0); for (int i = 1; i < dim_vec.size(); ++i) { dim_vec[i] = conf_shape.At(i - 1); } - *out_tensor->mut_shape() = Shape(dim_vec); + out_tensor->set_shape(Shape(dim_vec)); return Maybe::Ok(); } @@ -50,18 +50,18 @@ namespace oneflow { /* static */ Maybe OfrecordRawDecoderOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kOFRecord); - *out_tensor->mut_data_type() = ctx->Attr("data_type"); + out_tensor->set_data_type(ctx->Attr("data_type")); return Maybe::Ok(); } /* static */ Maybe OfrecordBytesDecoderOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_is_dynamic() = in.is_dynamic(); - *out->mut_shape() = in.shape(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_is_dynamic(in.is_dynamic()); + out->set_shape(in.shape()); return Maybe::Ok(); } @@ -83,18 +83,18 @@ namespace oneflow { /* static */ Maybe OfrecordBytesDecoderOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in.data_type() == DataType::kOFRecord); - *out->mut_data_type() = DataType::kTensorBuffer; + out->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } /* static */ Maybe OfrecordImageDecoderOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) >= 1); - *out_tensor->mut_shape() = in_tensor.shape(); + out_tensor->set_shape(in_tensor.shape()); return Maybe::Ok(); } @@ -117,18 +117,18 @@ namespace oneflow { /* static */ Maybe OfrecordImageDecoderOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kOFRecord); - *out_tensor->mut_data_type() = DataType::kTensorBuffer; + out_tensor->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } /* static */ Maybe OfrecordImageDecoderRandomCropOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) >= 1); - *out_tensor->mut_shape() = in_tensor.shape(); + out_tensor->set_shape(in_tensor.shape()); return Maybe::Ok(); } @@ -153,9 +153,9 @@ namespace oneflow { /* static */ Maybe OfrecordImageDecoderRandomCropOp::InferDataType( user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kOFRecord); - *out_tensor->mut_data_type() = DataType::kTensorBuffer; + out_tensor->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } diff --git a/oneflow/user/ops/ofrecord_image_classification_reader_op.cpp b/oneflow/user/ops/ofrecord_image_classification_reader_op.cpp index 800ec2d27e01a673546ca662ec9c7855dc1d0ad1..d1af891364baf4ade68632d7196e5860456fc9fa 100644 --- a/oneflow/user/ops/ofrecord_image_classification_reader_op.cpp +++ b/oneflow/user/ops/ofrecord_image_classification_reader_op.cpp @@ -20,18 +20,18 @@ namespace oneflow { /* static */ Maybe OfrecordImageClassificationReaderOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { - user_op::TensorDesc* image_tensor = ctx->OutputTensorDesc("image", 0); - user_op::TensorDesc* label_tensor = ctx->OutputTensorDesc("label", 0); + user_op::TensorDesc* image_tensor = ctx->MutOutputTensorDesc("image", 0); + user_op::TensorDesc* label_tensor = ctx->MutOutputTensorDesc("label", 0); int32_t batch_size = ctx->Attr("batch_size"); - *image_tensor->mut_shape() = Shape({batch_size}); - *label_tensor->mut_shape() = Shape({batch_size}); + image_tensor->set_shape(Shape({batch_size})); + label_tensor->set_shape(Shape({batch_size})); return Maybe::Ok(); } /* static */ Maybe OfrecordImageClassificationReaderOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { - user_op::TensorDesc* image_tensor = ctx->OutputTensorDesc("image", 0); - user_op::TensorDesc* label_tensor = ctx->OutputTensorDesc("label", 0); + user_op::TensorDesc* image_tensor = ctx->MutOutputTensorDesc("image", 0); + user_op::TensorDesc* label_tensor = ctx->MutOutputTensorDesc("label", 0); int32_t local_batch_size = ctx->Attr("batch_size"); int64_t parallel_num = ctx->parallel_ctx().parallel_num(); @@ -45,13 +45,13 @@ namespace oneflow { CHECK_EQ_OR_RETURN(local_batch_size % split_num, 0); local_batch_size /= split_num; } - *image_tensor->mut_shape() = Shape({local_batch_size}); - *label_tensor->mut_shape() = Shape({local_batch_size}); + image_tensor->set_shape(Shape({local_batch_size})); + label_tensor->set_shape(Shape({local_batch_size})); return Maybe::Ok(); } /* static */ Maybe OfrecordImageClassificationReaderOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->outputs(), 0).Build(); + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), 0).Build(); return Maybe::Ok(); } @@ -68,8 +68,8 @@ namespace oneflow { /* static */ Maybe OfrecordImageClassificationReaderOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("image", 0) = DataType::kTensorBuffer; - *ctx->OutputDType("label", 0) = DataType::kTensorBuffer; + ctx->SetOutputDType("image", 0, DataType::kTensorBuffer); + ctx->SetOutputDType("label", 0, DataType::kTensorBuffer); return Maybe::Ok(); } diff --git a/oneflow/user/ops/ofrecord_reader_op.cpp b/oneflow/user/ops/ofrecord_reader_op.cpp index 6d40f5f92bdc46bb38000e4b23900a8579fb407e..9e75e8a212f84cdd6e3f91ee9f9a91c7c1848128 100644 --- a/oneflow/user/ops/ofrecord_reader_op.cpp +++ b/oneflow/user/ops/ofrecord_reader_op.cpp @@ -14,18 +14,18 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "oneflow/core/framework/framework.h" +#include "oneflow/core/operator/operator.h" #include "oneflow/core/framework/op_generated.h" namespace oneflow { - /* static */ Maybe OFRecordReaderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); - *out_tensor->mut_shape() = Shape({ctx->Attr("batch_size")}); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_shape(Shape({ctx->Attr("batch_size")})); return Maybe::Ok(); } /* static */ Maybe OFRecordReaderOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); int32_t batch_size = ctx->Attr("batch_size"); int64_t parallel_num = ctx->parallel_ctx().parallel_num(); if (parallel_num > 1) { @@ -38,15 +38,36 @@ namespace oneflow { CHECK_EQ_OR_RETURN(batch_size % split_num, 0); batch_size /= split_num; } - *out_tensor->mut_shape() = Shape({batch_size}); + out_tensor->set_shape(Shape({batch_size})); return Maybe::Ok(); } /* static */ Maybe OFRecordReaderOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->outputs(), 0).Build(); + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), 0).Build(); return Maybe::Ok(); } +/* static */ Maybe OFRecordReaderOp::GetNdSbpSignatureList( + user_op::GetNdSbpSignatureListContext* ctx) { + NdSbpSignature nd_sbp_signature; + SbpParallel split_sbp_parallel; + split_sbp_parallel.mutable_split_parallel()->set_axis(0); + for (int32_t dim_sbp = 0; dim_sbp < ctx->parallel_hierarchy().NumAxes(); dim_sbp++) { + *(*nd_sbp_signature.mutable_bn_in_op2nd_sbp())[GenRepeatedBn("out", 0)].add_sbp_parallel() = + split_sbp_parallel; + } + ctx->AddNdSbpSignature(nd_sbp_signature); + return Maybe::Ok(); +} + +/* static */ Maybe OFRecordReaderOp::GetComputeComplexity( + user_op::ComputeComplexityFnContext* ctx) { + // Don't support broadcast. + return double(ctx->Shape4ArgNameAndIndex("out", 0).elem_cnt() + * GetSizeOfDataType(DataType::kOFRecord)) + / ctx->parallel_desc().hierarchy()->elem_cnt(); +} + /* static */ Maybe OFRecordReaderOp::ModifyOutputArg( const GetOutputArgModifier& GetOutputArgModifierFn, const user_op::UserOpConfWrapper& conf) { user_op::OutputArgModifier* out_modifier = GetOutputArgModifierFn("out", 0); @@ -64,7 +85,7 @@ namespace oneflow { } /* static */ Maybe OFRecordReaderOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kOFRecord; + ctx->SetOutputDType("out", 0, DataType::kOFRecord); return Maybe::Ok(); } diff --git a/oneflow/user/ops/one_embedding_ops.cpp b/oneflow/user/ops/one_embedding_ops.cpp index 4da21db60f782929ab1930ef577a4cabb3c43a05..4e5cd7af62a94f7821dc8d78310e04ebb987a1e5 100644 --- a/oneflow/user/ops/one_embedding_ops.cpp +++ b/oneflow/user/ops/one_embedding_ops.cpp @@ -16,10 +16,11 @@ limitations under the License. #include "oneflow/core/framework/framework.h" #include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/embedding/embedding_manager.h" namespace oneflow { -/* static */ Maybe EmbeddingLookupPlaceholderOp::InferLogicalTensorDesc( +/* static */ Maybe OneEmbeddingFusedLookupOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const Shape& ids_shape = ctx->InputShape("ids", 0); if (ctx->has_input("table_ids", 0)) { @@ -29,16 +30,16 @@ namespace oneflow { DimVector out_dim_vec = ids_shape.dim_vec(); const int64_t embedding_size = ctx->Attr("embedding_size"); out_dim_vec.push_back(embedding_size); - *ctx->OutputShape("embeddings", 0) = Shape(out_dim_vec); + ctx->SetOutputShape("embeddings", 0, Shape(out_dim_vec)); return Maybe::Ok(); } -/*static*/ Maybe EmbeddingLookupPlaceholderOp::InferPhysicalTensorDesc( +/*static*/ Maybe OneEmbeddingFusedLookupOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe EmbeddingLookupPlaceholderOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe OneEmbeddingFusedLookupOp::GetSbp(user_op::SbpContext* ctx) { auto builder = ctx->NewBuilder() .Broadcast(user_op::OpArg("shadow", 0)) .Split(user_op::OpArg("ids", 0), 0) @@ -50,7 +51,7 @@ namespace oneflow { return Maybe::Ok(); } -/* static */ Maybe EmbeddingLookupPlaceholderOp::ModifyInputArg( +/* static */ Maybe OneEmbeddingFusedLookupOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper& conf) { user_op::InputArgModifier* shadow = GetInputArgModifierFn("shadow", 0); CHECK_OR_RETURN(shadow != nullptr) << "shadow is nullptr"; @@ -66,22 +67,22 @@ namespace oneflow { return Maybe::Ok(); } -/* static */ Maybe EmbeddingLookupPlaceholderOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("embeddings", 0) = ctx->InputDType("shadow", 0); +/* static */ Maybe OneEmbeddingFusedLookupOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("embeddings", 0, ctx->InputDType("shadow", 0)); return Maybe::Ok(); } -/* static */ Maybe EmbeddingUpdatePlaceholderOp::InferLogicalTensorDesc( +/* static */ Maybe OneEmbeddingFusedLookupGradOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { return Maybe::Ok(); } -/*static*/ Maybe EmbeddingUpdatePlaceholderOp::InferPhysicalTensorDesc( +/*static*/ Maybe OneEmbeddingFusedLookupGradOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe EmbeddingUpdatePlaceholderOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe OneEmbeddingFusedLookupGradOp::GetSbp(user_op::SbpContext* ctx) { ctx->NewBuilder() .Split(user_op::OpArg("ids", 0), 0) .Split(user_op::OpArg("embedding_grad", 0), 0) @@ -89,25 +90,10 @@ namespace oneflow { return Maybe::Ok(); } -/* static */ Maybe EmbeddingUpdatePlaceholderOp::InferDataType(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingFusedLookupGradOp::InferDataType(user_op::InferContext* ctx) { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_update"); - user_op::UserOpConfWrapper grad_op = - builder.Op("embedding_update_placeholder") - .Input("ids", op.input("ids", 0)) - .Input("embedding_grad", op.GetGradTensorWithOpOutput("embeddings", 0)) - .Attr("key_value_store_options", - op.attr("key_value_store_options")) - .Build(); - AddOp(grad_op); - return Maybe::Ok(); - }); - /* static */ Maybe EmbeddingPrefetchOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& num_unique_ids_shape = ctx->InputShape("num_unique_ids", 0); const Shape& unique_ids_shape = ctx->InputShape("unique_ids", 0); @@ -115,7 +101,7 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") CHECK_EQ_OR_RETURN(unique_ids_shape, table_ids_shape) << "table_ids shape must equal to ids shape"; CHECK_EQ_OR_RETURN(num_unique_ids_shape.elem_cnt(), 1); - *ctx->OutputShape("context", 0) = num_unique_ids_shape; + ctx->SetOutputShape("context", 0, num_unique_ids_shape); return Maybe::Ok(); } @@ -134,7 +120,7 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") } /* static */ Maybe EmbeddingPrefetchOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("context", 0) = ctx->InputDType("num_unique_ids", 0); + ctx->SetOutputDType("context", 0, ctx->InputDType("num_unique_ids", 0)); return Maybe::Ok(); } @@ -150,15 +136,24 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") CHECK_NE_OR_RETURN(embedding_size, 0); CHECK_NE_OR_RETURN(line_size, 0); CHECK_GE_OR_RETURN(line_size, embedding_size); - CHECK_EQ_OR_RETURN(line_size % embedding_size, 0); + const bool use_dynamic_memory_allocation = embedding::UseDynamicMemoryAllocation(); if (ctx->has_output("embeddings", 0)) { - DimVector embeddings_dim_vec = unique_ids_shape.dim_vec(); - embeddings_dim_vec.push_back(embedding_size); - *ctx->OutputShape("embeddings", 0) = Shape(embeddings_dim_vec); + if (use_dynamic_memory_allocation) { + ctx->SetOutputShape("embeddings", 0, Shape({1})); + } else { + DimVector embeddings_dim_vec = unique_ids_shape.dim_vec(); + embeddings_dim_vec.push_back(embedding_size); + ctx->SetOutputShape("embeddings", 0, Shape(embeddings_dim_vec)); + } + } + if (use_dynamic_memory_allocation) { + ctx->SetOutputShape("unique_values", 0, Shape({1})); + } else { + DimVector unique_values_dim_vec = unique_ids_shape.dim_vec(); + unique_values_dim_vec.push_back(line_size); + ctx->SetOutputShape("unique_values", 0, Shape(unique_values_dim_vec)); } - DimVector unique_values_dim_vec = unique_ids_shape.dim_vec(); - unique_values_dim_vec.push_back(line_size); - *ctx->OutputShape("unique_values", 0) = Shape(unique_values_dim_vec); + return Maybe::Ok(); } @@ -170,19 +165,31 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") auto builder = ctx->NewBuilder() .Broadcast(user_op::OpArg("num_unique_ids", 0)) .Split(user_op::OpArg("unique_ids", 0), 0) - .Split(user_op::OpArg("table_ids", 0), 0) - .Split(ctx->outputs(), 0); + .Split(user_op::OpArg("table_ids", 0), 0); if (ctx->user_op_conf().has_input("context", 0)) { builder.Broadcast(user_op::OpArg("context", 0)); } + const bool use_dynamic_memory_allocation = embedding::UseDynamicMemoryAllocation(); + if (use_dynamic_memory_allocation) { + builder.Broadcast(user_op::OpArg("unique_values", 0)); + } else { + builder.Split(user_op::OpArg("unique_values", 0), 0); + } + if (ctx->user_op_conf().has_output("embeddings", 0)) { + if (use_dynamic_memory_allocation) { + builder.Broadcast(user_op::OpArg("embeddings", 0)); + } else { + builder.Split(user_op::OpArg("embeddings", 0), 0); + } + } builder.Build(); return Maybe::Ok(); } /* static */ Maybe EmbeddingLookupOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("unique_values", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("unique_values", 0, ctx->Attr("dtype")); if (ctx->has_output("embeddings", 0)) { - *ctx->OutputDType("embeddings", 0) = ctx->Attr("embeddings_dtype"); + ctx->SetOutputDType("embeddings", 0, ctx->Attr("embeddings_dtype")); } return Maybe::Ok(); } @@ -196,11 +203,14 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") } /* static */ Maybe EmbeddingPutOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_ids", 0), 0) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Build(); + auto builder = ctx->NewBuilder() + .Broadcast(user_op::OpArg("num_unique_ids", 0)) + .Split(user_op::OpArg("unique_ids", 0), 0); + if (embedding::UseDynamicMemoryAllocation()) { + builder.Broadcast(user_op::OpArg("unique_embeddings", 0)).Build(); + } else { + builder.Split(user_op::OpArg("unique_embeddings", 0), 0).Build(); + } return Maybe::Ok(); } @@ -209,179 +219,336 @@ REGISTER_USER_OP_GRAD("embedding_lookup_placeholder") } Maybe CheckDataShape(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputShape("learning_rate", 0), Shape({1})); - if (ctx->has_input("down_scale_by_tensor", 0)) { + if (ctx->has_input("learning_rate", 0)) { CHECK_EQ_OR_RETURN(ctx->InputShape("learning_rate", 0), Shape({1})); } + if (ctx->has_input("down_scale_by_tensor", 0)) { + CHECK_EQ_OR_RETURN(ctx->InputShape("down_scale_by_tensor", 0), Shape({1})); + } CHECK_EQ_OR_RETURN(ctx->InputShape("num_unique_ids", 0), Shape({1})); const Shape& embedding_grad_shape = ctx->InputShape("embedding_grad", 0); CHECK_EQ_OR_RETURN(embedding_grad_shape.NumAxes(), 2); const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.NumAxes(), 2); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(0), embedding_grad_shape.At(0)); + if (embedding::UseDynamicMemoryAllocation()) { + CHECK_EQ_OR_RETURN(unique_embeddings_shape.elem_cnt(), 1) + << "if use dynamic memory allocation, unique_embeddings elem_cnt should be 1."; + } else { + CHECK_EQ_OR_RETURN(unique_embeddings_shape.NumAxes(), 2) + << "unique_embeddings num_axes should be 2."; + CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(0), embedding_grad_shape.At(0)) + << "got " << unique_embeddings_shape.At(0) << " and " << embedding_grad_shape.At(0); + } return Maybe::Ok(); } Maybe CheckDataType(user_op::InferContext* ctx) { - const DataType learning_rate_dtype = ctx->InputDType("learning_rate", 0); - CHECK_EQ_OR_RETURN(learning_rate_dtype, DataType::kFloat); + if (ctx->has_input("learning_rate", 0)) { + const DataType learning_rate_dtype = ctx->InputDType("learning_rate", 0); + CHECK_EQ_OR_RETURN(learning_rate_dtype, DataType::kFloat) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kFloat) << ", but got " + << DataType_Name(learning_rate_dtype); + } if (ctx->has_input("down_scale_by_tensor", 0)) { CHECK_EQ_OR_RETURN(ctx->InputDType("down_scale_by_tensor", 0), - ctx->InputDType("unique_embeddings", 0)); + ctx->InputDType("unique_embeddings", 0)) + << "InferDataType Failed. Expected " + << DataType_Name(ctx->InputDType("unique_embeddings", 0)) << ", but got " + << DataType_Name(ctx->InputDType("down_scale_by_tensor", 0)); + } + return Maybe::Ok(); +} + +Maybe GetEmbeddingUpdateSbp(user_op::SbpContext* ctx) { + auto builder = ctx->NewBuilder() + .Broadcast(ctx->inputs()) + .Broadcast(user_op::OpArg("num_unique_ids", 0)) + .Split(user_op::OpArg("embedding_grad", 0), 0); + if (embedding::UseDynamicMemoryAllocation()) { + builder.Broadcast(user_op::OpArg("unique_embeddings", 0)) + .Broadcast(user_op::OpArg("updated_unique_embeddings", 0)) + .Build(); + } else { + builder.Split(user_op::OpArg("unique_embeddings", 0), 0) + .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) + .Build(); + } + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingFusedSgdUpdatePutOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return Maybe::Ok(); +} + +/*static*/ Maybe OneEmbeddingFusedSgdUpdatePutOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe OneEmbeddingFusedSgdUpdatePutOp::GetSbp(user_op::SbpContext* ctx) { + auto builder = ctx->NewBuilder() + .Broadcast(user_op::OpArg("learning_rate", 0)) + .Broadcast(user_op::OpArg("num_unique_ids", 0)) + .Split(user_op::OpArg("unique_ids", 0), 0) + .Split(user_op::OpArg("embedding_grad", 0), 0); + if (embedding::UseDynamicMemoryAllocation()) { + builder.Broadcast(user_op::OpArg("unique_embeddings", 0)).Build(); + } else { + builder.Split(user_op::OpArg("unique_embeddings", 0), 0).Build(); } return Maybe::Ok(); } -/* static */ Maybe SgdEmbeddingUpdateOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingFusedSgdUpdatePutOp::InferDataType( + user_op::InferContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingSgdUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + CHECK_EQ_OR_RETURN(line_size, embedding_size) + << "when use SGD optimizer, line_size should equals to embedding_size, but get line_size: " + << line_size << " embedding_size: " << embedding_size + << ", please set size_factor of store_options to 1."; const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(1), ctx->InputShape("embedding_grad", 0).At(1)); - *ctx->OutputShape("updated_unique_embeddings", 0) = unique_embeddings_shape; + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); return Maybe::Ok(); } -/*static*/ Maybe SgdEmbeddingUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe OneEmbeddingSgdUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe SgdEmbeddingUpdateOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Split(user_op::OpArg("embedding_grad", 0), 0) - .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) - .Build(); +/* static */ Maybe OneEmbeddingSgdUpdateOp::GetSbp(user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); return Maybe::Ok(); } -/* static */ Maybe SgdEmbeddingUpdateOp::InferDataType(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingSgdUpdateOp::InferDataType(user_op::InferContext* ctx) { JUST(CheckDataType(ctx)); - *ctx->OutputDType("updated_unique_embeddings", 0) = ctx->InputDType("unique_embeddings", 0); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); return Maybe::Ok(); } -/* static */ Maybe MomentumEmbeddingUpdateOp::InferLogicalTensorDesc( +/* static */ Maybe OneEmbeddingMomentumUpdateOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + CHECK_EQ_OR_RETURN(line_size, embedding_size * 2) + << "when using Momentum optimizer, line_size should equals to embedding_size * 2, but get " + "line_size: " + << line_size << " embedding_size: " << embedding_size + << ", please set size_factor of store_options to 2."; const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(1), 2 * ctx->InputShape("embedding_grad", 0).At(1)) - << "please adjust size_factor of MultiTableEmbedding's store_options to 2"; - *ctx->OutputShape("updated_unique_embeddings", 0) = unique_embeddings_shape; + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); return Maybe::Ok(); } -/*static*/ Maybe MomentumEmbeddingUpdateOp::InferPhysicalTensorDesc( +/*static*/ Maybe OneEmbeddingMomentumUpdateOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe MomentumEmbeddingUpdateOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Split(user_op::OpArg("embedding_grad", 0), 0) - .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) - .Build(); +/* static */ Maybe OneEmbeddingMomentumUpdateOp::GetSbp(user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); return Maybe::Ok(); } -/* static */ Maybe MomentumEmbeddingUpdateOp::InferDataType(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingMomentumUpdateOp::InferDataType(user_op::InferContext* ctx) { JUST(CheckDataType(ctx)); - *ctx->OutputDType("updated_unique_embeddings", 0) = ctx->InputDType("unique_embeddings", 0); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); return Maybe::Ok(); } -/* static */ Maybe AdamEmbeddingUpdateOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingAdamUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + CHECK_EQ_OR_RETURN(line_size, embedding_size * 3) + << "when using Adam optimizer, line_size should equals to embedding_size * 3, but get " + "line_size: " + << line_size << " embedding_size: " << embedding_size + << ", please set size_factor of store_options to 3."; const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(1), 3 * ctx->InputShape("embedding_grad", 0).At(1)) - << "please adjust size_factor of MultiTableEmbedding's store_options to 3"; - *ctx->OutputShape("updated_unique_embeddings", 0) = unique_embeddings_shape; + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); return Maybe::Ok(); } -/*static*/ Maybe AdamEmbeddingUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe OneEmbeddingAdamUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe AdamEmbeddingUpdateOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Split(user_op::OpArg("embedding_grad", 0), 0) - .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) - .Build(); +/* static */ Maybe OneEmbeddingAdamUpdateOp::GetSbp(user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); return Maybe::Ok(); } -/* static */ Maybe AdamEmbeddingUpdateOp::InferDataType(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingAdamUpdateOp::InferDataType(user_op::InferContext* ctx) { JUST(CheckDataType(ctx)); - *ctx->OutputDType("updated_unique_embeddings", 0) = ctx->InputDType("unique_embeddings", 0); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); return Maybe::Ok(); } -/* static */ Maybe AdagradEmbeddingUpdateOp::InferLogicalTensorDesc( +/* static */ Maybe OneEmbeddingSmartDecaySparseAdamUpdateOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + const int64_t value_dtype_size = GetSizeOfDataType(ctx->InputDType("unique_embeddings", 0)); + const int64_t step_dtype_size = sizeof(int64_t); + const int64_t model_and_states_bytes = embedding_size * 3 * value_dtype_size; + const int64_t align_to_step_size_bytes = + (model_and_states_bytes + step_dtype_size - 1) / step_dtype_size * step_dtype_size; + const int64_t smart_decay_sparse_adam_line_size = + (align_to_step_size_bytes + step_dtype_size) / value_dtype_size; + CHECK_EQ_OR_RETURN(line_size, smart_decay_sparse_adam_line_size) + << "when using SmartDecayAdam optimizer with embedding_size " << embedding_size + << ", storage_dim should equals to " << smart_decay_sparse_adam_line_size + << ", but got " + "storage_dim: " + << line_size << ", please set storage_dim of store_options to " + << smart_decay_sparse_adam_line_size; const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(1), 2 * ctx->InputShape("embedding_grad", 0).At(1)) - << "please adjust size_factor of MultiTableEmbedding's store_options to 2"; - *ctx->OutputShape("updated_unique_embeddings", 0) = unique_embeddings_shape; + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); return Maybe::Ok(); } -/*static*/ Maybe AdagradEmbeddingUpdateOp::InferPhysicalTensorDesc( +/*static*/ Maybe OneEmbeddingSmartDecaySparseAdamUpdateOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe AdagradEmbeddingUpdateOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Split(user_op::OpArg("embedding_grad", 0), 0) - .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) - .Build(); +/* static */ Maybe OneEmbeddingSmartDecaySparseAdamUpdateOp::GetSbp( + user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingSmartDecaySparseAdamUpdateOp::InferDataType( + user_op::InferContext* ctx) { + JUST(CheckDataType(ctx)); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingAdagradUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + CHECK_EQ_OR_RETURN(line_size, embedding_size * 2) + << "when using Adagrad optimizer, line_size should equals to embedding_size * 2, but get " + "line_size: " + << line_size << " embedding_size: " << embedding_size + << ", please set size_factor of store_options to 2."; + const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); + return Maybe::Ok(); +} + +/*static*/ Maybe OneEmbeddingAdagradUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe OneEmbeddingAdagradUpdateOp::GetSbp(user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); return Maybe::Ok(); } -/* static */ Maybe AdagradEmbeddingUpdateOp::InferDataType(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingAdagradUpdateOp::InferDataType(user_op::InferContext* ctx) { JUST(CheckDataType(ctx)); - *ctx->OutputDType("updated_unique_embeddings", 0) = ctx->InputDType("unique_embeddings", 0); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); return Maybe::Ok(); } -/* static */ Maybe FtrlEmbeddingUpdateOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { +/* static */ Maybe OneEmbeddingFtrlUpdateOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { JUST(CheckDataShape(ctx)); + const int64_t embedding_size = ctx->Attr("embedding_size"); + const int64_t line_size = ctx->Attr("line_size"); + CHECK_NE_OR_RETURN(embedding_size, 0) << "should set attr embedding_size"; + CHECK_NE_OR_RETURN(line_size, 0) << "should set attr line_size"; + CHECK_EQ_OR_RETURN(line_size, embedding_size * 3) + << "when using Ftrl optimizer, line_size should equals to embedding_size * 3, but get " + "line_size: " + << line_size << " embedding_size: " << embedding_size + << ", please set size_factor of store_options to 3."; const Shape& unique_embeddings_shape = ctx->InputShape("unique_embeddings", 0); - CHECK_EQ_OR_RETURN(unique_embeddings_shape.At(1), 3 * ctx->InputShape("embedding_grad", 0).At(1)) - << "please adjust size_factor of MultiTableEmbedding's store_options to 3"; - *ctx->OutputShape("updated_unique_embeddings", 0) = unique_embeddings_shape; + ctx->SetOutputShape("updated_unique_embeddings", 0, unique_embeddings_shape); return Maybe::Ok(); } -/*static*/ Maybe FtrlEmbeddingUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { +/*static*/ Maybe OneEmbeddingFtrlUpdateOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } -/* static */ Maybe FtrlEmbeddingUpdateOp::GetSbp(user_op::SbpContext* ctx) { +/* static */ Maybe OneEmbeddingFtrlUpdateOp::GetSbp(user_op::SbpContext* ctx) { + JUST(GetEmbeddingUpdateSbp(ctx)); + return Maybe::Ok(); +} + +/* static */ Maybe OneEmbeddingFtrlUpdateOp::InferDataType(user_op::InferContext* ctx) { + JUST(CheckDataType(ctx)); + ctx->SetOutputDType("updated_unique_embeddings", 0, ctx->InputDType("unique_embeddings", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe IdShuffleCopyOutOp::GetSbp(user_op::SbpContext* ctx) { ctx->NewBuilder() - .Broadcast(ctx->inputs()) - .Broadcast(user_op::OpArg("num_unique_ids", 0)) - .Split(user_op::OpArg("unique_embeddings", 0), 0) - .Split(user_op::OpArg("embedding_grad", 0), 0) - .Split(user_op::OpArg("updated_unique_embeddings", 0), 0) + .Split(ctx->inputs(), 0) + .Split(ctx->outputs(), 0) + .Broadcast(user_op::OpArg("num_unique_matrix", 0)) + .Broadcast(user_op::OpArg("out_num_unique_matrix", 0)) + .Broadcast(user_op::OpArg("cur_rank_num_unique", 0)) + .Broadcast(user_op::OpArg("out_cur_rank_num_unique", 0)) .Build(); return Maybe::Ok(); } -/* static */ Maybe FtrlEmbeddingUpdateOp::InferDataType(user_op::InferContext* ctx) { - JUST(CheckDataType(ctx)); - *ctx->OutputDType("updated_unique_embeddings", 0) = ctx->InputDType("unique_embeddings", 0); +/*static*/ Maybe IdShuffleCopyOutOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out_num_unique_matrix", 0, ctx->InputShape("num_unique_matrix", 0)); + ctx->SetOutputShape("out_inverse_unique_partition_indices", 0, + ctx->InputShape("inverse_unique_partition_indices", 0)); + ctx->SetOutputShape("out_cur_rank_num_unique", 0, ctx->InputShape("cur_rank_num_unique", 0)); + ctx->SetOutputShape("out_cur_rank_unique_ids", 0, ctx->InputShape("cur_rank_unique_ids", 0)); + ctx->SetOutputShape("out_cur_rank_unique_table_ids", 0, + ctx->InputShape("cur_rank_unique_table_ids", 0)); + ctx->SetOutputShape("out_cur_rank_inverse_indices", 0, + ctx->InputShape("cur_rank_inverse_indices", 0)); + return Maybe::Ok(); +} +/*static*/ Maybe IdShuffleCopyOutOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe IdShuffleCopyOutOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out_num_unique_matrix", 0, ctx->InputDType("num_unique_matrix", 0)); + ctx->SetOutputDType("out_inverse_unique_partition_indices", 0, + ctx->InputDType("inverse_unique_partition_indices", 0)); + ctx->SetOutputDType("out_cur_rank_num_unique", 0, ctx->InputDType("cur_rank_num_unique", 0)); + ctx->SetOutputDType("out_cur_rank_unique_ids", 0, ctx->InputDType("cur_rank_unique_ids", 0)); + ctx->SetOutputDType("out_cur_rank_unique_table_ids", 0, + ctx->InputDType("cur_rank_unique_table_ids", 0)); + ctx->SetOutputDType("out_cur_rank_inverse_indices", 0, + ctx->InputDType("cur_rank_inverse_indices", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/one_hot_op.cpp b/oneflow/user/ops/one_hot_op.cpp index 0928eeb3cbf1bdf4cfa38539584369559b37965d..d85fcfcc98ecdcc1b7a96d4973ea420801b86ad9 100644 --- a/oneflow/user/ops/one_hot_op.cpp +++ b/oneflow/user/ops/one_hot_op.cpp @@ -26,11 +26,11 @@ namespace oneflow { // For 0-dim Tensor CHECK_GE_OR_RETURN(indices_desc.shape().NumAxes(), 0) << "indices dim must be great or equal than 0"; - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = indices_desc.is_dynamic(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(indices_desc.is_dynamic()); DimVector dim_vec = indices_desc.shape().dim_vec(); dim_vec.emplace_back(depth); - *out_desc->mut_shape() = Shape(dim_vec); + out_desc->set_shape(Shape(dim_vec)); return Maybe::Ok(); } @@ -62,9 +62,9 @@ namespace oneflow { /* static */ Maybe OneHotOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& indices_desc = ctx->InputTensorDesc("indices", 0); CHECK_OR_RETURN(IsIndexDataType(indices_desc.data_type())); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); DataType dtype = ctx->Attr("dtype"); - *out_desc->mut_data_type() = dtype; + out_desc->set_data_type(dtype); return Maybe::Ok(); } diff --git a/oneflow/user/ops/onerec_decoder_op.cpp b/oneflow/user/ops/onerec_decoder_op.cpp index 6057dd6486c51859683cdb2e305a1ca46d8dbc69..8414b9e8dfeab87acd4473e6e4a6a7adc24b0a9c 100644 --- a/oneflow/user/ops/onerec_decoder_op.cpp +++ b/oneflow/user/ops/onerec_decoder_op.cpp @@ -20,13 +20,13 @@ namespace oneflow { /* static */ Maybe OnerecDecoderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.shape().NumAxes() == 1 && in_tensor.shape().At(0) >= 1); const Shape& static_shape = ctx->Attr("static_shape"); DimVector dim_vec(1 + static_shape.NumAxes()); dim_vec[0] = in_tensor.shape().At(0); FOR_RANGE(int64_t, i, 1, dim_vec.size()) { dim_vec[i] = static_shape.At(i - 1); } - *out_tensor->mut_shape() = Shape(dim_vec); + out_tensor->set_shape(Shape(dim_vec)); out_tensor->set_is_dynamic(ctx->Attr("is_dynamic")); return Maybe::Ok(); } @@ -51,7 +51,7 @@ namespace oneflow { /* static */ Maybe OnerecDecoderOp::ModifyOutputArg( const GetOutputArgModifier& GetOutputArgModifierFn, const user_op::UserOpConfWrapper& conf) { // NOTE(yaochi): refer to tensor_buffer_to_list_of_tensors - // In order to support consistent tensor, set set_header_infered_before_compute to false + // In order to support global tensor, set set_header_infered_before_compute to false // only when is_dynamic == true if (conf.attr("is_dynamic")) { FOR_RANGE(int64_t, i, 0, conf.output_size("out")) { @@ -65,9 +65,9 @@ namespace oneflow { /* static */ Maybe OnerecDecoderOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(in_tensor.data_type() == DataType::kTensorBuffer); - *out_tensor->mut_data_type() = ctx->Attr("data_type"); + out_tensor->set_data_type(ctx->Attr("data_type")); return Maybe::Ok(); } diff --git a/oneflow/user/ops/onerec_reader_op.cpp b/oneflow/user/ops/onerec_reader_op.cpp index 7a53d7d584a3fa4048fc6d09ed001d5c13381d23..2d756ab5c591cab142eeb25c80c61b47ee7bc6a9 100644 --- a/oneflow/user/ops/onerec_reader_op.cpp +++ b/oneflow/user/ops/onerec_reader_op.cpp @@ -19,19 +19,19 @@ limitations under the License. namespace oneflow { /*static*/ Maybe OneRecReaderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_tensor = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); int64_t batch_size = ctx->Attr("batch_size"); - *out_tensor->mut_shape() = Shape({batch_size}); + out_tensor->set_shape(Shape({batch_size})); return Maybe::Ok(); } /*static*/ Maybe OneRecReaderOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kTensorBuffer; + ctx->SetOutputDType("out", 0, DataType::kTensorBuffer); return Maybe::Ok(); } /*static*/ Maybe OneRecReaderOp::GetSbp(user_op::SbpContext* ctx) { - ctx->NewBuilder().Split(ctx->outputs(), 0).Build(); + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), 0).Build(); return Maybe::Ok(); } diff --git a/oneflow/user/ops/ones_like_op.cpp b/oneflow/user/ops/ones_like_op.cpp index c64eefc2a0fe5d6130face315402b80f2415ace6..f69ce8ce631b8ed71facdab970eed9db7f35e88c 100644 --- a/oneflow/user/ops/ones_like_op.cpp +++ b/oneflow/user/ops/ones_like_op.cpp @@ -33,15 +33,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe OnesLikeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("like", 0); - *ctx->OutputStride("out", 0) = ctx->InputStride("like", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("like", 0)); + ctx->SetOutputStride("out", 0, ctx->InputStride("like", 0)); return Maybe::Ok(); } /*static*/ Maybe OnesLikeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return OnesLikeOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe OnesLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("like", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("like", 0)); return Maybe::Ok(); } /*static*/ Maybe OnesLikeOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { diff --git a/oneflow/user/ops/p2p_comm_op.cpp b/oneflow/user/ops/p2p_comm_op.cpp index 0c6998bdb87b970d405005e563af8d2b5ea7f3ae..692076026d5a7b844c49145a634277d87aab0252 100644 --- a/oneflow/user/ops/p2p_comm_op.cpp +++ b/oneflow/user/ops/p2p_comm_op.cpp @@ -33,7 +33,7 @@ namespace oneflow { } /*static*/ Maybe> SendOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched>(ctx); + return DeviceAndStreamInferFn(ctx); } namespace { @@ -48,19 +48,19 @@ Maybe> GetRecvOutputDeivce(user_op::DeviceAndStreamInferContext* /*static*/ Maybe RecvOp::GetSbp(user_op::SbpContext* ctx) { UNIMPLEMENTED_THEN_RETURN(); } /*static*/ Maybe RecvOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->Attr("shape"); + ctx->SetOutputShape("out", 0, ctx->Attr("shape")); return Maybe::Ok(); } /*static*/ Maybe RecvOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return SendOp::InferLogicalTensorDesc(ctx); + return RecvOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe RecvOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->Attr("dtype"); + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); return Maybe::Ok(); } /*static*/ Maybe> RecvOp::InferDeviceAndStream( user_op::DeviceAndStreamInferContext* ctx) { - return DeviceAndStreamInferFn<&SyncLaunched, &GetRecvOutputDeivce>(ctx); + return DeviceAndStreamInferFn<&GetRecvOutputDeivce>(ctx); } } // namespace oneflow diff --git a/oneflow/user/ops/pack_op.cpp b/oneflow/user/ops/pack_op.cpp index b5ae5c75a747a4d8a16df3387d361b7a837a9fb0..5fcfff15cb150a3cd247b6f61e97a03fdaab774c 100644 --- a/oneflow/user/ops/pack_op.cpp +++ b/oneflow/user/ops/pack_op.cpp @@ -31,18 +31,18 @@ namespace oneflow { } /*static*/ Maybe PackOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); - const Shape& in_shape = in_desc.shape(); const int32_t pack_num = ctx->Attr("pack_num"); CHECK_GT_OR_RETURN(pack_num, 0); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = in_desc.is_dynamic(); - if (in_shape.NumAxes() > 0) { - *out_desc->mut_shape() = in_shape; - out_desc->mut_shape()->Set(0, in_shape.At(0) * pack_num); + Shape out_shape = in_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(in_desc.is_dynamic()); + if (out_shape.NumAxes() > 0) { + out_shape.Set(0, out_shape.At(0) * pack_num); + out_desc->set_shape(out_shape); } else { // NOTE(chengcheng): for Scalar input pack - CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), 1); - *out_desc->mut_shape() = Shape({pack_num}); + CHECK_EQ_OR_RETURN(out_shape.elem_cnt(), 1); + out_desc->set_shape(Shape({pack_num})); } return Maybe::Ok(); } @@ -51,7 +51,7 @@ namespace oneflow { return PackOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe PackOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe PackOp::InferOutputBlobTimeShape( @@ -66,24 +66,4 @@ namespace oneflow { return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("pack").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) - -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("unpack") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr("unpack_num", ctx->FwOp().attr("pack_num")) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/pad_op.cpp b/oneflow/user/ops/pad_op.cpp index d1d020ed355251cabecd93eedc868e0ba176c0bc..af186ebd45c40b99a0abefaf7ee874286c1ca12d 100644 --- a/oneflow/user/ops/pad_op.cpp +++ b/oneflow/user/ops/pad_op.cpp @@ -40,40 +40,15 @@ namespace oneflow { FOR_RANGE(int64_t, i, 0, x_shape.NumAxes()) { y_dim_vec[i] = x_shape.At(i) + padding_before[i] + padding_after[i]; } - *ctx->OutputShape("y", 0) = Shape(y_dim_vec); + ctx->SetOutputShape("y", 0, Shape(y_dim_vec)); return Maybe::Ok(); } /*static*/ Maybe PadOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return PadOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe PadOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("pad").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - std::vector padding_before = op.attr>("padding_before"); - std::vector padding_after = op.attr>("padding_after"); - for (int i = 0; i < padding_before.size(); i++) { - padding_before[i] = -padding_before[i]; - padding_after[i] = -padding_after[i]; - } - user_op::UserOpConfWrapper grad_op = - builder.Op("pad") - .Input("x", op.GetGradTensorWithOpOutput("y", 0)) - .Output("y") - .Attr("floating_constant_value", static_cast(0.0)) - .Attr("integral_constant_value", static_cast(0)) - .Attr("padding_before", padding_before) - .Attr("padding_after", padding_after) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("y", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/padding_ops.cpp b/oneflow/user/ops/padding_ops.cpp deleted file mode 100644 index 41ef1da54ea914f573e5dac940c03f2d16524daa..0000000000000000000000000000000000000000 --- a/oneflow/user/ops/padding_ops.cpp +++ /dev/null @@ -1,232 +0,0 @@ -/* -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -*/ -#include "oneflow/core/common/balanced_splitter.h" -#include "oneflow/core/framework/framework.h" -#include "oneflow/user/ops/nn_util.h" -#include "oneflow/core/framework/op_generated.h" - -namespace oneflow { - -namespace { - -Maybe GetOpSbpSignature(user_op::SbpContext* ctx) { - const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); - const int64_t input_dims = x_tensor.shape().NumAxes(); - CHECK_EQ_OR_RETURN(input_dims, 4); - // NOTE(Liang Depeng): assume data format is NCHW. - const int64_t first_two_dims = input_dims - 2; - FOR_RANGE(int64_t, i, 0, first_two_dims) { - ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); - } - return Maybe::Ok(); -} - -Maybe GetOpGradSbpSignature(user_op::SbpContext* ctx) { - const user_op::TensorDesc& dy_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); - const int64_t grad_dims = dy_tensor.shape().NumAxes(); - CHECK_EQ_OR_RETURN(grad_dims, 4); - const int64_t first_two_dims = grad_dims - 2; - FOR_RANGE(int64_t, i, 0, first_two_dims) { - ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); - } - return Maybe::Ok(); -} - -} // namespace - -/*static*/ Maybe ReflectionPad2DOp::GetSbp(user_op::SbpContext* ctx) { - return GetOpSbpSignature(ctx); -} -/*static*/ Maybe ReflectionPad2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& x_shape = ctx->InputShape("x", 0); - const auto& padding = ctx->Attr>("padding"); - CHECK_EQ_OR_RETURN(padding.size(), x_shape.NumAxes()); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - // Ensure the padding size is less than the input dimension. - CHECK_LT_OR_RETURN(padding[0], x_shape.At(w_idx)); - CHECK_LT_OR_RETURN(padding[1], x_shape.At(w_idx)); - CHECK_LT_OR_RETURN(padding[2], x_shape.At(h_idx)); - CHECK_LT_OR_RETURN(padding[3], x_shape.At(h_idx)); - - DimVector y_dim_vec(x_shape.NumAxes()); - const int64_t h_x = x_shape.At(h_idx); - const int64_t w_x = x_shape.At(w_idx); - - y_dim_vec[n_idx] = x_shape.At(n_idx); - y_dim_vec[c_idx] = x_shape.At(c_idx); - y_dim_vec[h_idx] = h_x + padding[2] + padding[3]; - y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; - - *ctx->OutputShape("y", 0) = Shape(y_dim_vec); - return Maybe::Ok(); -} -/*static*/ Maybe ReflectionPad2DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return ReflectionPad2DOp::InferLogicalTensorDesc(ctx); -} -/*static*/ Maybe ReflectionPad2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); - return Maybe::Ok(); -} -/*static*/ Maybe ReflectionPad2DOp::ModifyInputArg( - const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { - user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); - CHECK_NOTNULL_OR_RETURN(x_modifier); - x_modifier->set_requires_grad(true); - return Maybe::Ok(); -} - -/*static*/ Maybe ReflectionPad2DGradOp::GetSbp(user_op::SbpContext* ctx) { - return GetOpGradSbpSignature(ctx); -} -/*static*/ Maybe ReflectionPad2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& dy_shape = ctx->InputShape("dy", 0); - const auto& padding = ctx->Attr>("padding"); - CHECK_EQ_OR_RETURN(padding.size(), dy_shape.NumAxes()); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - DimVector dx_dim_vec(dy_shape.NumAxes()); - int64_t h_dy = dy_shape.At(h_idx); - int64_t w_dy = dy_shape.At(w_idx); - - dx_dim_vec[n_idx] = dy_shape.At(0); - dx_dim_vec[c_idx] = dy_shape.At(1); - dx_dim_vec[h_idx] = h_dy - padding[2] - padding[3]; - dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; - - *ctx->OutputShape("dx", 0) = Shape(dx_dim_vec); - return Maybe::Ok(); -} -/*static*/ Maybe ReflectionPad2DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return ReflectionPad2DGradOp::InferLogicalTensorDesc(ctx); -} -/*static*/ Maybe ReflectionPad2DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("reflection_pad2d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("reflection_pad2d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("padding", op.attr>("padding")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -/*static*/ Maybe ReplicationPad2DOp::GetSbp(user_op::SbpContext* ctx) { - return GetOpSbpSignature(ctx); -} -/*static*/ Maybe ReplicationPad2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& x_shape = ctx->InputShape("x", 0); - const auto& padding = ctx->Attr>("padding"); - CHECK_EQ_OR_RETURN(padding.size(), x_shape.NumAxes()); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - DimVector y_dim_vec(x_shape.NumAxes()); - const int64_t h_x = x_shape.At(h_idx); - const int64_t w_x = x_shape.At(w_idx); - - y_dim_vec[n_idx] = x_shape.At(n_idx); - y_dim_vec[c_idx] = x_shape.At(c_idx); - y_dim_vec[h_idx] = h_x + padding[2] + padding[3]; - y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; - - *ctx->OutputShape("y", 0) = Shape(y_dim_vec); - return Maybe::Ok(); -} -/*static*/ Maybe ReplicationPad2DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return ReplicationPad2DOp::InferLogicalTensorDesc(ctx); -} -/*static*/ Maybe ReplicationPad2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); - return Maybe::Ok(); -} -/*static*/ Maybe ReplicationPad2DOp::ModifyInputArg( - const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { - user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); - CHECK_NOTNULL_OR_RETURN(x_modifier); - x_modifier->set_requires_grad(true); - return Maybe::Ok(); -} - -/*static*/ Maybe ReplicationPad2DGradOp::GetSbp(user_op::SbpContext* ctx) { - return GetOpGradSbpSignature(ctx); -} -/*static*/ Maybe ReplicationPad2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& dy_shape = ctx->InputShape("dy", 0); - const auto& padding = ctx->Attr>("padding"); - CHECK_EQ_OR_RETURN(padding.size(), dy_shape.NumAxes()); - const int64_t n_idx = 0; - const int64_t c_idx = 1; - const int64_t h_idx = 2; - const int64_t w_idx = 3; - - DimVector dx_dim_vec(dy_shape.NumAxes()); - int64_t h_dy = dy_shape.At(h_idx); - int64_t w_dy = dy_shape.At(w_idx); - - dx_dim_vec[n_idx] = dy_shape.At(0); - dx_dim_vec[c_idx] = dy_shape.At(1); - dx_dim_vec[h_idx] = h_dy - padding[2] - padding[3]; - dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; - - *ctx->OutputShape("dx", 0) = Shape(dx_dim_vec); - return Maybe::Ok(); -} -/*static*/ Maybe ReplicationPad2DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return ReplicationPad2DGradOp::InferLogicalTensorDesc(ctx); -} -/*static*/ Maybe ReplicationPad2DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("replication_pad2d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("replication_pad2d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("padding", op.attr>("padding")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -} // namespace oneflow diff --git a/oneflow/user/ops/parallel_cast_op.cpp b/oneflow/user/ops/parallel_cast_op.cpp index 9d25b9504debd71628ef1654df04b77995016a87..2df62761c98364c10d1636ec4ac18b9b1477ec24 100644 --- a/oneflow/user/ops/parallel_cast_op.cpp +++ b/oneflow/user/ops/parallel_cast_op.cpp @@ -23,15 +23,15 @@ namespace oneflow { return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); } /*static*/ Maybe ParallelCastOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } /*static*/ Maybe ParallelCastOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return ParallelCastOp::InferLogicalTensorDesc(ctx); } /*static*/ Maybe ParallelCastOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe ParallelCastOp::InferSbpSignature(user_op::InferSbpSignatureFnContext* ctx) { @@ -60,29 +60,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("parallel_cast") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - if (ctx->FwOp().NeedGenGradTensor4OpInput("in", 0)) { - const auto& grad_sbp_parallel_str = ctx->FwOp().attr("grad_sbp_parallel"); - if (grad_sbp_parallel_str.empty()) { - ctx->FwOp().BindGradTensorWithOpInput(ctx->FwOp().GetGradTensorWithOpOutput("out", 0), - "in", 0); - } else { - CHECK_OR_RETURN(IsValidSbpParallelString(grad_sbp_parallel_str)); - const std::string grad_op_name = "System-AutoGrad-" + ctx->FwOp().op_name(); - ctx->DefineOp(grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("parallel_cast") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr("sbp_parallel", grad_sbp_parallel_str) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/partial_fc_sample_op.cpp b/oneflow/user/ops/partial_fc_sample_op.cpp index 1798e91fe6dbc5c8a46332a10101ede369a1645c..32008f76b52c8000fc84bcc74460973515526bb9 100644 --- a/oneflow/user/ops/partial_fc_sample_op.cpp +++ b/oneflow/user/ops/partial_fc_sample_op.cpp @@ -33,17 +33,19 @@ namespace oneflow { const int64_t num_sample = ctx->Attr("num_sample"); const user_op::TensorDesc& weight = ctx->InputTensorDesc("weight", 0); const user_op::TensorDesc& label = ctx->InputTensorDesc("label", 0); - user_op::TensorDesc* mapped_label = ctx->OutputTensorDesc("mapped_label", 0); - user_op::TensorDesc* sampled_weight = ctx->OutputTensorDesc("sampled_weight", 0); - user_op::TensorDesc* sampled_label = ctx->OutputTensorDesc("sampled_label", 0); - *mapped_label->mut_shape() = label.shape(); - *mapped_label->mut_is_dynamic() = label.is_dynamic(); - *sampled_weight->mut_shape() = weight.shape(); - sampled_weight->mut_shape()->Set(0, num_sample); - *sampled_weight->mut_is_dynamic() = weight.is_dynamic(); - *sampled_label->mut_shape() = label.shape(); - sampled_label->mut_shape()->Set(0, num_sample); - *sampled_label->mut_is_dynamic() = label.is_dynamic(); + user_op::TensorDesc* mapped_label = ctx->MutOutputTensorDesc("mapped_label", 0); + user_op::TensorDesc* sampled_weight = ctx->MutOutputTensorDesc("sampled_weight", 0); + user_op::TensorDesc* sampled_label = ctx->MutOutputTensorDesc("sampled_label", 0); + mapped_label->set_shape(label.shape()); + mapped_label->set_is_dynamic(label.is_dynamic()); + Shape sampled_weight_shape = weight.shape(); + sampled_weight_shape.Set(0, num_sample); + sampled_weight->set_shape(sampled_weight_shape); + sampled_weight->set_is_dynamic(weight.is_dynamic()); + Shape sampled_label_shape = label.shape(); + sampled_label_shape.Set(0, num_sample); + sampled_label->set_shape(sampled_label_shape); + sampled_label->set_is_dynamic(label.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe DistributedPartialFcSampleOp::InferPhysicalTensorDesc( @@ -54,23 +56,25 @@ namespace oneflow { const int64_t num_sample_per_rank = num_sample / parallel_num; const user_op::TensorDesc& weight = ctx->InputTensorDesc("weight", 0); const user_op::TensorDesc& label = ctx->InputTensorDesc("label", 0); - user_op::TensorDesc* mapped_label = ctx->OutputTensorDesc("mapped_label", 0); - user_op::TensorDesc* sampled_weight = ctx->OutputTensorDesc("sampled_weight", 0); - user_op::TensorDesc* sampled_label = ctx->OutputTensorDesc("sampled_label", 0); - *mapped_label->mut_shape() = label.shape(); - *mapped_label->mut_is_dynamic() = label.is_dynamic(); - *sampled_weight->mut_shape() = weight.shape(); - sampled_weight->mut_shape()->Set(0, num_sample_per_rank); - *sampled_weight->mut_is_dynamic() = weight.is_dynamic(); - *sampled_label->mut_shape() = label.shape(); - sampled_label->mut_shape()->Set(0, num_sample_per_rank); - *sampled_label->mut_is_dynamic() = label.is_dynamic(); + user_op::TensorDesc* mapped_label = ctx->MutOutputTensorDesc("mapped_label", 0); + user_op::TensorDesc* sampled_weight = ctx->MutOutputTensorDesc("sampled_weight", 0); + user_op::TensorDesc* sampled_label = ctx->MutOutputTensorDesc("sampled_label", 0); + mapped_label->set_shape(label.shape()); + mapped_label->set_is_dynamic(label.is_dynamic()); + Shape sampled_weight_shape = weight.shape(); + sampled_weight_shape.Set(0, num_sample_per_rank); + sampled_weight->set_shape(sampled_weight_shape); + sampled_weight->set_is_dynamic(weight.is_dynamic()); + Shape sampled_label_shape = label.shape(); + sampled_label_shape.Set(0, num_sample_per_rank); + sampled_label->set_shape(sampled_label_shape); + sampled_label->set_is_dynamic(label.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe DistributedPartialFcSampleOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("mapped_label", 0) = ctx->InputDType("label", 0); - *ctx->OutputDType("sampled_weight", 0) = ctx->InputDType("weight", 0); - *ctx->OutputDType("sampled_label", 0) = ctx->InputDType("label", 0); + ctx->SetOutputDType("mapped_label", 0, ctx->InputDType("label", 0)); + ctx->SetOutputDType("sampled_weight", 0, ctx->InputDType("weight", 0)); + ctx->SetOutputDType("sampled_label", 0, ctx->InputDType("label", 0)); return Maybe::Ok(); } /*static*/ Maybe DistributedPartialFcSampleOp::ModifyInputArg( @@ -93,74 +97,42 @@ namespace oneflow { /*static*/ Maybe DistributedPartialFcSampleDisableBoxingOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { user_op::TensorDesc* boxing_disabled_sampled_weight_diff = - ctx->OutputTensorDesc("boxing_disabled_sampled_weight_diff", 0); - *boxing_disabled_sampled_weight_diff->mut_shape() = ctx->InputShape("sampled_weight_diff", 0); - CHECK_EQ_OR_RETURN(boxing_disabled_sampled_weight_diff->shape().At(0) % ctx->parallel_num(), 0); - boxing_disabled_sampled_weight_diff->mut_shape()->Set( - 0, boxing_disabled_sampled_weight_diff->shape().At(0) / ctx->parallel_num()); - *boxing_disabled_sampled_weight_diff->mut_is_dynamic() = - ctx->InputIsDynamic("sampled_weight_diff", 0); + ctx->MutOutputTensorDesc("boxing_disabled_sampled_weight_diff", 0); + Shape boxing_disabled_sampled_weight_diff_shape = ctx->InputShape("sampled_weight_diff", 0); + CHECK_EQ_OR_RETURN(boxing_disabled_sampled_weight_diff_shape.At(0) % ctx->parallel_num(), 0); + boxing_disabled_sampled_weight_diff_shape.Set( + 0, boxing_disabled_sampled_weight_diff_shape.At(0) / ctx->parallel_num()); + boxing_disabled_sampled_weight_diff->set_shape(boxing_disabled_sampled_weight_diff_shape); + boxing_disabled_sampled_weight_diff->set_is_dynamic( + ctx->InputIsDynamic("sampled_weight_diff", 0)); user_op::TensorDesc* boxing_disabled_sampled_label = - ctx->OutputTensorDesc("boxing_disabled_sampled_label", 0); - *boxing_disabled_sampled_label->mut_shape() = ctx->InputShape("sampled_label", 0); - CHECK_EQ_OR_RETURN(boxing_disabled_sampled_label->shape().At(0) % ctx->parallel_num(), 0); - boxing_disabled_sampled_label->mut_shape()->Set( - 0, boxing_disabled_sampled_label->shape().At(0) / ctx->parallel_num()); - *boxing_disabled_sampled_label->mut_is_dynamic() = ctx->InputIsDynamic("sampled_label", 0); + ctx->MutOutputTensorDesc("boxing_disabled_sampled_label", 0); + Shape boxing_disabled_sampled_label_shape = ctx->InputShape("sampled_label", 0); + ; + CHECK_EQ_OR_RETURN(boxing_disabled_sampled_label_shape.At(0) % ctx->parallel_num(), 0); + boxing_disabled_sampled_label_shape.Set( + 0, boxing_disabled_sampled_label_shape.At(0) / ctx->parallel_num()); + boxing_disabled_sampled_label->set_shape(boxing_disabled_sampled_label_shape); + boxing_disabled_sampled_label->set_is_dynamic(ctx->InputIsDynamic("sampled_label", 0)); return Maybe::Ok(); } /*static*/ Maybe DistributedPartialFcSampleDisableBoxingOp::InferPhysicalTensorDesc( user_op::InferContext* ctx) { - *ctx->OutputShape("boxing_disabled_sampled_weight_diff", 0) = - ctx->InputShape("sampled_weight_diff", 0); - *ctx->OutputIsDynamic("boxing_disabled_sampled_weight_diff", 0) = - ctx->InputIsDynamic("sampled_weight_diff", 0); - *ctx->OutputShape("boxing_disabled_sampled_label", 0) = ctx->InputShape("sampled_label", 0); - *ctx->OutputIsDynamic("boxing_disabled_sampled_label", 0) = - ctx->InputIsDynamic("sampled_label", 0); + ctx->SetOutputShape("boxing_disabled_sampled_weight_diff", 0, + ctx->InputShape("sampled_weight_diff", 0)); + ctx->SetOutputIsDynamic("boxing_disabled_sampled_weight_diff", 0, + ctx->InputIsDynamic("sampled_weight_diff", 0)); + ctx->SetOutputShape("boxing_disabled_sampled_label", 0, ctx->InputShape("sampled_label", 0)); + ctx->SetOutputIsDynamic("boxing_disabled_sampled_label", 0, + ctx->InputIsDynamic("sampled_label", 0)); return Maybe::Ok(); } /*static*/ Maybe DistributedPartialFcSampleDisableBoxingOp::InferDataType( user_op::InferContext* ctx) { - *ctx->OutputDType("boxing_disabled_sampled_weight_diff", 0) = - ctx->InputDType("sampled_weight_diff", 0); - *ctx->OutputDType("boxing_disabled_sampled_label", 0) = ctx->InputDType("sampled_label", 0); + ctx->SetOutputDType("boxing_disabled_sampled_weight_diff", 0, + ctx->InputDType("sampled_weight_diff", 0)); + ctx->SetOutputDType("boxing_disabled_sampled_label", 0, ctx->InputDType("sampled_label", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("distributed_partial_fc_sample") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto disable_boxing_op_name = ctx->FwOp().op_name() + "_disable_boxing"; - ctx->DefineOp(disable_boxing_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("distributed_partial_fc_sample_disable_boxing") - .InputBind("sampled_weight_diff", ctx->FwOp().output_grad("sampled_weight", 0)) - .InputBind("sampled_label", ctx->FwOp().output("sampled_label", 0)) - .Output("boxing_disabled_sampled_weight_diff") - .Output("boxing_disabled_sampled_label") - .Build(); - }); - const auto unsorted_segment_sum_like_op_name = - ctx->FwOp().op_name() + "_grad_unsorted_segment_sum_like"; - ctx->DefineOp(unsorted_segment_sum_like_op_name, [&ctx, &disable_boxing_op_name]( - user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("unsorted_segment_sum_like") - .InputBind( - "data", - ctx->GetOp(disable_boxing_op_name).output("boxing_disabled_sampled_weight_diff", 0)) - .InputBind( - "segment_ids", - ctx->GetOp(disable_boxing_op_name).output("boxing_disabled_sampled_label", 0)) - .InputBind("like", ctx->FwOp().input("weight", 0)) - .Output("out") - .Attr("axis", static_cast(0)) - .Build(); - }); - ctx->FwOp().InputGradBind( - user_op::OpArg("weight", 0), - [&ctx, &unsorted_segment_sum_like_op_name]() -> const std::string& { - return ctx->GetOp(unsorted_segment_sum_like_op_name).output("out", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/pinned_identity_op.cpp b/oneflow/user/ops/pinned_identity_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..84a1fb7270f41864e336c2abc2c3fe67a889ba51 --- /dev/null +++ b/oneflow/user/ops/pinned_identity_op.cpp @@ -0,0 +1,48 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe PinnedIdentityOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe PinnedIdentityOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe PinnedIdentityOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); + FOR_RANGE(int64_t, i, 0, in_tensor.shape().NumAxes()) { + ctx->NewBuilder().Split(user_op::OpArg("in", 0), i).Split(user_op::OpArg("out", 0), i).Build(); + } + ctx->NewBuilder() + .PartialSum(user_op::OpArg("in", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe PinnedIdentityOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/prelu_op.cpp b/oneflow/user/ops/prelu_op.cpp index 6cd352ba5ba8377b63cfef4bcf23b05a0e9d4628..6a753a5ac76145e393c86115af268c7a6310cfb8 100644 --- a/oneflow/user/ops/prelu_op.cpp +++ b/oneflow/user/ops/prelu_op.cpp @@ -40,17 +40,16 @@ namespace oneflow { } /*static*/ Maybe PreluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); - Shape* y_shape = ctx->OutputShape("y", 0); const Shape& alpha_shape = ctx->InputShape("alpha", 0); CHECK_EQ_OR_RETURN(alpha_shape.NumAxes(), 1); - *y_shape = x_shape; + ctx->SetOutputShape("y", 0, x_shape); return Maybe::Ok(); } /*static*/ Maybe PreluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe PreluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -91,54 +90,21 @@ namespace oneflow { /*static*/ Maybe PreluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - Shape* alpha_diff_shape = ctx->OutputShape("alpha_diff", 0); const Shape& alpha_shape = ctx->InputShape("alpha", 0); CHECK_EQ_OR_RETURN(alpha_shape.NumAxes(), 1); CHECK_OR_RETURN((alpha_shape.At(0) == x_shape.At(1)) || (alpha_shape.At(0) == 1)); CHECK_EQ_OR_RETURN(dy_shape, x_shape); - *dx_shape = x_shape; - *alpha_diff_shape = alpha_shape; + ctx->SetOutputShape("dx", 0, x_shape); + ctx->SetOutputShape("alpha_diff", 0, alpha_shape); return Maybe::Ok(); } /*static*/ Maybe PreluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe PreluGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("alpha_diff", 0) = ctx->InputDType("alpha", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("alpha_diff", 0, ctx->InputDType("alpha", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("prelu").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0) || op.NeedGenGradTensor4OpInput("alpha", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("prelu_grad") - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("alpha", op.input("alpha", 0)) - .Output("dx") - .Output("alpha_diff") - .Attr("alpha_requires_grad", op.NeedGenGradTensor4OpInput("alpha", 0)) - .Build(); - AddOp(grad_op); - - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - } - if (op.NeedGenGradTensor4OpInput("alpha", 0)) { - auto alpha_identity_op = user_op::UserOpConfWrapperBuilder(op.op_name() + "_alpha_identity") - .Op("identity") - .Input("in", grad_op.output("alpha_diff", 0)) - .Output("out") - .Build(); - AddOp(alpha_identity_op); - op.BindGradTensorWithOpInput(alpha_identity_op.output("out", 0), "alpha", 0); - } - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/quantization_op.cpp b/oneflow/user/ops/quantization_op.cpp index 2396a1a16850952ae84c35d87016f2dfe85b83dd..e0299fec1b351cda290d2babffa0e9e232c16c42 100644 --- a/oneflow/user/ops/quantization_op.cpp +++ b/oneflow/user/ops/quantization_op.cpp @@ -68,14 +68,14 @@ namespace oneflow { CHECK_EQ_OR_RETURN(zero_point_shape.elem_cnt(), in_shape.At(0)); } - *ctx->OutputShape("out", 0) = in_shape; + ctx->SetOutputShape("out", 0, in_shape); return Maybe::Ok(); } /*static*/ Maybe QuantizationOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe QuantizationOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe QuantizationOp::ModifyInputArg( diff --git a/oneflow/user/ops/quick_gelu_op.cpp b/oneflow/user/ops/quick_gelu_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..039831bc5cd8d71f5fced1b1b2c6c48ebb80c720 --- /dev/null +++ b/oneflow/user/ops/quick_gelu_op.cpp @@ -0,0 +1,82 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ Maybe QuickGeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe QuickGeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe QuickGeluOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe QuickGeluOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, in_tensor.shape().NumAxes()) { + ctx->NewBuilder().Split(user_op::OpArg("x", 0), i).Split(user_op::OpArg("y", 0), i).Build(); + } + return Maybe::Ok(); +} + +/*static*/ Maybe QuickGeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const Shape& dy_shape = ctx->InputShape("dy", 0); + CHECK_OR_RETURN(dy_shape == x_shape) + << "InferTensorDesc failed (" << ctx->op_name() << "). Expected x shape " + << x_shape.ToString() << " to be equal to dy shape " << dy_shape.ToString(); + ctx->SetOutputShape("dx", 0, dy_shape); + return Maybe::Ok(); +} + +/*static*/ Maybe QuickGeluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/*static*/ Maybe QuickGeluGradOp::InferDataType(user_op::InferContext* ctx) { + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe QuickGeluGradOp::GetSbp(user_op::SbpContext* ctx) { + const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + FOR_RANGE(int64_t, i, 0, x_tensor.shape().NumAxes()) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Split(user_op::OpArg("dy", 0), i) + .Split(user_op::OpArg("dx", 0), i) + .Build(); + } + ctx->NewBuilder() + .Broadcast(user_op::OpArg("x", 0)) + .PartialSum(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/randperm_op.cpp b/oneflow/user/ops/randperm_op.cpp index aa6103a2f0d539fd44155d8bba1815ac0c87fdfa..3a2f76cff276df134adebfb79a378c6a86ff06e7 100644 --- a/oneflow/user/ops/randperm_op.cpp +++ b/oneflow/user/ops/randperm_op.cpp @@ -27,10 +27,11 @@ namespace oneflow { } /*static*/ Maybe RandpermOp::GetSbp(user_op::SbpContext* ctx) { return Maybe::Ok(); } /*static*/ Maybe RandpermOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - Shape* out_shape = ctx->OutputShape("out", 0); int32_t n = ctx->Attr("n"); - CHECK_GE_OR_RETURN(n, 0); - *out_shape = Shape({n}); + CHECK_GE_OR_RETURN(n, 0) << Error::RuntimeError() + << "Trying to create tensor with negative dimension " << n << ":" + << " [" << n << "]"; + ctx->SetOutputShape("out", 0, Shape({n})); return Maybe::Ok(); } /*static*/ Maybe RandpermOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -43,13 +44,13 @@ namespace oneflow { GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); const Shape& physical_shape = tensor_slice_view.shape(); - *ctx->OutputShape("out", 0) = physical_shape; + ctx->SetOutputShape("out", 0, physical_shape); return Maybe::Ok(); } /*static*/ Maybe RandpermOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kInt32; + ctx->SetOutputDType("out", 0, DataType::kInt32); return Maybe::Ok(); } diff --git a/oneflow/user/ops/raw_reader_op.cpp b/oneflow/user/ops/raw_reader_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..90e53a6f6a915ccb1af0186e0347480bbb92fa58 --- /dev/null +++ b/oneflow/user/ops/raw_reader_op.cpp @@ -0,0 +1,74 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe RawReaderOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& instance_shape = ctx->Attr("shape"); + const int32_t batch_size = ctx->Attr("batch_size"); + DimVector dim_vec; + dim_vec.push_back(batch_size); + for (int64_t i = 0; i < instance_shape.NumAxes(); ++i) { + dim_vec.push_back(instance_shape.At(i)); + } + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + out_tensor->set_shape(Shape(dim_vec)); + return Maybe::Ok(); +} + +/* static */ Maybe RawReaderOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + user_op::TensorDesc* out_tensor = ctx->MutOutputTensorDesc("out", 0); + int32_t batch_size = ctx->Attr("batch_size"); + int64_t parallel_num = ctx->parallel_ctx().parallel_num(); + if (parallel_num > 1) { + int64_t split_num = 1; + const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); + const Shape& hierarchy = *ctx->parallel_desc().hierarchy(); + for (int32_t i = 0; i < nd_sbp.sbp_parallel_size(); ++i) { + if (nd_sbp.sbp_parallel(i).has_split_parallel()) { split_num *= hierarchy.At(i); } + } + CHECK_EQ_OR_RETURN(batch_size % split_num, 0) << "batch_size must be a multiple of shard num"; + batch_size /= split_num; + } + const Shape& instance_shape = ctx->Attr("shape"); + DimVector dim_vec; + dim_vec.push_back(batch_size); + for (int64_t i = 0; i < instance_shape.NumAxes(); ++i) { + dim_vec.push_back(instance_shape.At(i)); + } + out_tensor->set_shape(Shape({dim_vec})); + return Maybe::Ok(); +} + +/* static */ Maybe RawReaderOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Split(ctx->outputs(), 0).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe RawReaderOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { + SbpParallel default_sbp; + default_sbp.mutable_split_parallel()->set_axis(0); + return user_op::InferNdSbp4SrcOp(ctx, default_sbp); +} + +/* static */ Maybe RawReaderOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->Attr("data_type")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/reduce_like_ops.cpp b/oneflow/user/ops/reduce_like_ops.cpp index 898e81ade46ac3c4fe457dbee0aa643c5355fcab..ce88a9758fa1440ffde4d1c820ba1193ed29ff1a 100644 --- a/oneflow/user/ops/reduce_like_ops.cpp +++ b/oneflow/user/ops/reduce_like_ops.cpp @@ -22,20 +22,21 @@ namespace oneflow { /*static*/ Maybe ReduceSumLikeOp::GetSbp(user_op::SbpContext* ctx) { int32_t num_axes = 0; HashSet conf_axes; - { - const auto& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); - num_axes = in_tensor.shape().NumAxes(); - const auto& reduced_axes = ctx->Attr>("axis"); - ReduceSbpUtil::GetRegularAxes(num_axes, reduced_axes, &conf_axes); - } + + const auto& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + num_axes = in_tensor.shape().NumAxes(); + const auto& reduced_axes = ctx->Attr>("axis"); + ReduceSbpUtil::GetRegularAxes(num_axes, reduced_axes, &conf_axes); + const auto& like_num_axes = ctx->LogicalTensorDesc4InputArgNameAndIndex("like", 0).shape().NumAxes(); const bool keep_dims = (num_axes == like_num_axes); - if (!keep_dims) { CHECK_EQ_OR_RETURN(conf_axes.size(), num_axes - like_num_axes); } auto IsReducedAxis = ReduceSbpUtil::MakePredicatorIsReducedAxis(conf_axes, num_axes); int64_t num_reduced_axes = 0; FOR_RANGE(int64_t, i, 0, num_axes) { - if (IsReducedAxis(i)) { + if (in_tensor.shape().at(i) == 1) { + num_reduced_axes += 1; + } else if (IsReducedAxis(i)) { ctx->NewBuilder() .Split(user_op::OpArg("x", 0), i) .Broadcast(user_op::OpArg("like", 0)) @@ -67,26 +68,29 @@ namespace oneflow { const user_op::TensorDesc& x_tensor = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& like_tensor = ctx->InputTensorDesc("like", 0); const auto& axis = ctx->Attr>("axis"); - if (axis.empty()) { CHECK_EQ_OR_RETURN(x_tensor.shape(), like_tensor.shape()); } - user_op::TensorDesc* y_tensor = ctx->OutputTensorDesc("y", 0); - *y_tensor->mut_shape() = like_tensor.shape(); - *y_tensor->mut_is_dynamic() = like_tensor.is_dynamic(); + if (axis.empty()) { + CHECK_EQ_OR_RETURN(x_tensor.shape(), like_tensor.shape()) + << Error::RuntimeError() + << "The shape of the x tensor must be consistent to the shape of the like tensor" + << " when the input axis list is empty"; + } + + user_op::TensorDesc* y_tensor = ctx->MutOutputTensorDesc("y", 0); + y_tensor->set_shape(like_tensor.shape()); + y_tensor->set_is_dynamic(like_tensor.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe ReduceSumLikeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ReduceSumLikeOp::InferDataType(user_op::InferContext* ctx) { - const user_op::TensorDesc& x_tensor = ctx->InputTensorDesc("x", 0); - const user_op::TensorDesc& like_tensor = ctx->InputTensorDesc("like", 0); - CHECK_EQ_OR_RETURN(x_tensor.data_type(), like_tensor.data_type()); - *ctx->OutputDType("y", 0) = like_tensor.data_type(); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } /*static*/ Maybe ReduceSumLikeOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* like_arg_modifier = GetInputArgModifierFn("like", 0); - CHECK_OR_RETURN(like_arg_modifier != nullptr); + CHECK_OR_RETURN(like_arg_modifier != nullptr); // NOLINT(maybe-need-error-msg) like_arg_modifier->set_requires_grad(false); return Maybe::Ok(); } diff --git a/oneflow/user/ops/reduce_ops.cpp b/oneflow/user/ops/reduce_ops.cpp index 5ac0a70038c475da40105947a9d1cec8fdd0298e..e4c2a0bb67853826b24b3ef013d3fcfaab915ea2 100644 --- a/oneflow/user/ops/reduce_ops.cpp +++ b/oneflow/user/ops/reduce_ops.cpp @@ -23,32 +23,32 @@ namespace oneflow { Maybe InferTensorDescFn(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("input_tensor", 0); const auto& reduce_axes = ctx->Attr>("axis"); - Shape* output_shape = ctx->OutputShape("output_tensor", 0); - Stride* output_stride = ctx->OutputStride("output_tensor", 0); + Shape output_shape; // For 0-dim Tensor if (reduce_axes.empty()) { - *output_shape = input_shape; + output_shape = input_shape; } else { const AxisVector reduce_axes_vec = {reduce_axes.begin(), reduce_axes.end()}; const Shape& reduce_shape = CreateReducedShape(input_shape, reduce_axes_vec); const bool keepdims = ctx->Attr("keepdims"); if (keepdims) { - *output_shape = reduce_shape; + output_shape = reduce_shape; } else { - *output_shape = reduce_shape.RemoveOnes(reduce_axes_vec); + output_shape = reduce_shape.RemoveOnes(reduce_axes_vec); } } - *output_stride = Stride(*output_shape); + ctx->SetOutputShape("output_tensor", 0, output_shape); + ctx->SetOutputStride("output_tensor", 0, Stride(output_shape)); return Maybe::Ok(); } Maybe InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output_tensor", 0) = ctx->InputDType("input_tensor", 0); + ctx->SetOutputDType("output_tensor", 0, ctx->InputDType("input_tensor", 0)); return Maybe::Ok(); } Maybe InferLogicalDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output_tensor", 0) = DataType::kBool; + ctx->SetOutputDType("output_tensor", 0, DataType::kBool); return Maybe::Ok(); } @@ -110,102 +110,7 @@ IMPLEMENT_REDUCE_OP_FUNCS(ReduceMin, BinaryFuncMin, oneflow::InferDataType) IMPLEMENT_REDUCE_OP_FUNCS(ReduceMax, BinaryFuncMax, oneflow::InferDataType) IMPLEMENT_REDUCE_OP_FUNCS(ReduceSum, BinaryFuncSum, oneflow::InferDataType) IMPLEMENT_REDUCE_OP_FUNCS(ReduceProd, BinaryFuncProd, oneflow::InferDataType) +IMPLEMENT_REDUCE_OP_FUNCS(ReduceNanSum, BinaryFuncNanSum, oneflow::InferDataType) #undef IMPLEMENT_REDUCE_OP_FUNCS -REGISTER_USER_OP_GRAD("reduce_sum") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input_tensor", 0)) { - const auto& axes = op.attr>("axis"); - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper reduce_sum_grad_op = - builder.Op("broadcast_like") - .Input("x", op.GetGradTensorWithOpOutput("output_tensor", 0)) - .Input("like", op.input("input_tensor", 0)) - .Attr("broadcast_axes", axes) - .Output("y") - .Build(); - op.BindGradTensorWithOpInput(reduce_sum_grad_op.output("y", 0), "input_tensor", 0); - AddOp(reduce_sum_grad_op); - } - return Maybe::Ok(); - }); - -Maybe GenerateBackwardOpConf4ReduceMaxMin(const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - if (op.NeedGenGradTensor4OpInput("input_tensor", 0)) { - const auto& axes = op.attr>("axis"); - - user_op::UserOpConfWrapperBuilder broadcast_out_builder(op.op_name() + "_grad_broadcast_out"); - user_op::UserOpConfWrapper broadcast_out_op = broadcast_out_builder.Op("broadcast_like") - .Input("x", op.output("output_tensor", 0)) - .Input("like", op.input("input_tensor", 0)) - .Attr("broadcast_axes", axes) - .Output("y") - .Build(); - AddOp(broadcast_out_op); - - user_op::UserOpConfWrapperBuilder broadcast_eq_builder(op.op_name() + "_grad_broadcast_eq"); - user_op::UserOpConfWrapper broadcast_eq_op = broadcast_eq_builder.Op("broadcast_equal") - .Input("x", op.input("input_tensor", 0)) - .Input("y", broadcast_out_op.output("y", 0)) - .Output("z") - .Build(); - AddOp(broadcast_eq_op); - - user_op::UserOpConfWrapperBuilder cast_mask_builder(op.op_name() + "_grad_cast_mask"); - user_op::UserOpConfWrapper cast_mask_op = cast_mask_builder.Op("cast_like") - .Input("in", broadcast_eq_op.output("z", 0)) - .Input("dtype_like", op.input("input_tensor", 0)) - .Output("out") - .Build(); - AddOp(cast_mask_op); - - user_op::UserOpConfWrapperBuilder reduce_sum_mask_builder(op.op_name() - + "_grad_reduce_sum_mask"); - user_op::UserOpConfWrapper reduce_sum_mask_op = - reduce_sum_mask_builder.Op("reduce_sum") - .Input("input_tensor", cast_mask_op.output("out", 0)) - .Output("output_tensor") - .Attr("axis", axes) - .Attr("keepdims", op.attr("keepdims")) - .Build(); - AddOp(reduce_sum_mask_op); - - user_op::UserOpConfWrapperBuilder divide_count_builder(op.op_name() + "_grad_divide_count"); - user_op::UserOpConfWrapper divide_count_op = - divide_count_builder.Op("broadcast_div") - .Input("x", op.GetGradTensorWithOpOutput("output_tensor", 0)) - .Input("y", reduce_sum_mask_op.output("output_tensor", 0)) - .Output("z") - .Build(); - AddOp(divide_count_op); - - user_op::UserOpConfWrapperBuilder broadcast_divided_dy_builder(op.op_name() - + "_grad_broadcast_divided_dy"); - user_op::UserOpConfWrapper broadcast_divided_dy_op = - broadcast_divided_dy_builder.Op("broadcast_like") - .Input("x", divide_count_op.output("z", 0)) - .Input("like", op.input("input_tensor", 0)) - .Attr("broadcast_axes", axes) - .Output("y") - .Build(); - AddOp(broadcast_divided_dy_op); - - user_op::UserOpConfWrapperBuilder multiply_mask_builder(op.op_name() + "_grad_multiply_mask"); - user_op::UserOpConfWrapper multiply_mask_op = - multiply_mask_builder.Op("broadcast_mul") - .Input("x", broadcast_divided_dy_op.output("y", 0)) - .Input("y", cast_mask_op.output("out", 0)) - .Output("z") - .Build(); - AddOp(multiply_mask_op); - op.BindGradTensorWithOpInput(multiply_mask_op.output("z", 0), "input_tensor", 0); - } - return Maybe::Ok(); -} - -REGISTER_USER_OP_GRAD("reduce_max").SetGenBackwardOpConfFn(GenerateBackwardOpConf4ReduceMaxMin); -REGISTER_USER_OP_GRAD("reduce_min").SetGenBackwardOpConfFn(GenerateBackwardOpConf4ReduceMaxMin); - } // namespace oneflow diff --git a/oneflow/user/ops/reflection_pad_op.cpp b/oneflow/user/ops/reflection_pad_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..422a85fee778d72b59de9a3445367bb7277950ff --- /dev/null +++ b/oneflow/user/ops/reflection_pad_op.cpp @@ -0,0 +1,183 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/balanced_splitter.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/ops/nn_util.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +template +Maybe GetOpSbpSignature(user_op::SbpContext* ctx) { + const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + const int64_t input_dims = x_tensor.shape().NumAxes(); + const int64_t split_dims = input_dims - (ndim - 2); + FOR_RANGE(int64_t, i, 0, split_dims) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +template +Maybe GetOpGradSbpSignature(user_op::SbpContext* ctx) { + const user_op::TensorDesc& dy_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); + const int64_t grad_dims = dy_tensor.shape().NumAxes(); + const int64_t split_dims = grad_dims - (ndim - 2); + FOR_RANGE(int64_t, i, 0, split_dims) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +} // namespace + +/*static*/ Maybe ReflectionPad1DOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpSbpSignature<3>(ctx); +} +/*static*/ Maybe ReflectionPad1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + DimVector y_dim_vec(x_shape.NumAxes()); + const int64_t w_x = x_shape.At(w_idx); + + y_dim_vec[n_idx] = x_shape.At(n_idx); + y_dim_vec[c_idx] = x_shape.At(c_idx); + y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; + + ctx->SetOutputShape("y", 0, Shape(y_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad1DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReflectionPad1DOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReflectionPad1DOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad1DOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { + user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); + CHECK_NOTNULL_OR_RETURN(x_modifier); // NOLINT + x_modifier->set_requires_grad(true); + return Maybe::Ok(); +} + +/*static*/ Maybe ReflectionPad1DGradOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpGradSbpSignature<3>(ctx); +} +/*static*/ Maybe ReflectionPad1DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& dy_shape = ctx->InputShape("dy", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + DimVector dx_dim_vec(dy_shape.NumAxes()); + int64_t w_dy = dy_shape.At(w_idx); + + dx_dim_vec[n_idx] = dy_shape.At(0); + dx_dim_vec[c_idx] = dy_shape.At(1); + dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; + + ctx->SetOutputShape("dx", 0, Shape(dx_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad1DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReflectionPad1DGradOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReflectionPad1DGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe ReflectionPad2DOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpSbpSignature<4>(ctx); +} +/*static*/ Maybe ReflectionPad2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + DimVector y_dim_vec(x_shape.NumAxes()); + const int64_t h_x = x_shape.At(h_idx); + const int64_t w_x = x_shape.At(w_idx); + + y_dim_vec[n_idx] = x_shape.At(n_idx); + y_dim_vec[c_idx] = x_shape.At(c_idx); + y_dim_vec[h_idx] = h_x + padding[2] + padding[3]; + y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; + + ctx->SetOutputShape("y", 0, Shape(y_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad2DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReflectionPad2DOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReflectionPad2DOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad2DOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { + user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); + CHECK_NOTNULL_OR_RETURN(x_modifier); // NOLINT + x_modifier->set_requires_grad(true); + return Maybe::Ok(); +} + +/*static*/ Maybe ReflectionPad2DGradOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpGradSbpSignature<4>(ctx); +} +/*static*/ Maybe ReflectionPad2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& dy_shape = ctx->InputShape("dy", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + DimVector dx_dim_vec(dy_shape.NumAxes()); + int64_t h_dy = dy_shape.At(h_idx); + int64_t w_dy = dy_shape.At(w_idx); + + dx_dim_vec[n_idx] = dy_shape.At(0); + dx_dim_vec[c_idx] = dy_shape.At(1); + dx_dim_vec[h_idx] = h_dy - padding[2] - padding[3]; + dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; + + ctx->SetOutputShape("dx", 0, Shape(dx_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReflectionPad2DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReflectionPad2DGradOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReflectionPad2DGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/relu_op.cpp b/oneflow/user/ops/relu_op.cpp index 52fb55fdc22580061ecb778d5e16c380d9644f29..aabca9411d546919ca47c9a95d16871155b5c560 100644 --- a/oneflow/user/ops/relu_op.cpp +++ b/oneflow/user/ops/relu_op.cpp @@ -26,16 +26,14 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe ReluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputShape("x", 0); - Shape* out_shape = ctx->OutputShape("y", 0); - *out_shape = in_shape; + ctx->SetOutputShape("y", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe ReluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ReluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -53,40 +51,21 @@ namespace oneflow { /*static*/ Maybe ReluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("y", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == y_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == y_shape) + << Error::RuntimeError() << "Tensors y and dy must have the same shape"; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } /*static*/ Maybe ReluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ReluGradOp::InferDataType(user_op::InferContext* ctx) { - const DataType& data_type = ctx->InputDType("y", 0); - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), data_type); - *ctx->OutputDType("dx", 0) = data_type; + DataType data_type = ctx->InputDType("y", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), data_type) + << "InferDataType Failed. Expected " << DataType_Name(data_type) << ", but got " + << DataType_Name(ctx->InputDType("dy", 0)); + ctx->SetOutputDType("dx", 0, data_type); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("relu").SetBackwardOpConfGenFn( - [](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto relu_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(relu_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("relu_grad") - .InputBind("y", ctx->FwOp().output("y", 0)) - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &relu_grad_op_name]() -> const std::string& { - return ctx->GetOp(relu_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/repeat_interleave_op.cpp b/oneflow/user/ops/repeat_interleave_op.cpp index ec77a9efe3bca4ccb2d4f529c2561ca38cee80be..00a0193d0220433401aa80165352dd839c3c3e17 100644 --- a/oneflow/user/ops/repeat_interleave_op.cpp +++ b/oneflow/user/ops/repeat_interleave_op.cpp @@ -36,15 +36,15 @@ namespace oneflow { } /*static*/ Maybe Repeat_InterLeaveOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const int64_t repeat_num = ctx->Attr("repeat_num"); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = Shape({repeat_num}); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_shape(Shape({repeat_num})); return Maybe::Ok(); } /*static*/ Maybe Repeat_InterLeaveOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe Repeat_InterLeaveOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/repeat_op.cpp b/oneflow/user/ops/repeat_op.cpp index 2b087308603e96bae32035609787b092efb2c688..d462f9d833f3afbff7cd668c28656979c6098011 100644 --- a/oneflow/user/ops/repeat_op.cpp +++ b/oneflow/user/ops/repeat_op.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/framework/framework.h" #include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/operator/operator.h" namespace oneflow { @@ -30,15 +31,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe RepeatOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); return Maybe::Ok(); } /*static*/ Maybe RepeatOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe RepeatOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe RepeatOp::InferOutputBlobTimeShape( @@ -49,24 +50,4 @@ namespace oneflow { return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("repeat").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) - -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("acc") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr("max_acc_num", ctx->FwOp().attr("repeat_num")) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/replication_pad_op.cpp b/oneflow/user/ops/replication_pad_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..58354087980c31e9b9d4857936884cb0065a291f --- /dev/null +++ b/oneflow/user/ops/replication_pad_op.cpp @@ -0,0 +1,187 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/common/balanced_splitter.h" +#include "oneflow/core/framework/framework.h" +#include "oneflow/user/ops/nn_util.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +template +Maybe GetOpSbpSignature(user_op::SbpContext* ctx) { + const user_op::TensorDesc& x_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0); + const int64_t input_dims = x_tensor.shape().NumAxes(); + const int64_t first_two_dims = input_dims - (ndim - 2); + FOR_RANGE(int64_t, i, 0, first_two_dims) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +template +Maybe GetOpGradSbpSignature(user_op::SbpContext* ctx) { + const user_op::TensorDesc& dy_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("dy", 0); + const int64_t grad_dims = dy_tensor.shape().NumAxes(); + CHECK_EQ_OR_RETURN(grad_dims, ndim); // NOLINT + const int64_t first_two_dims = grad_dims - (ndim - 2); + FOR_RANGE(int64_t, i, 0, first_two_dims) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +} // namespace + +/*static*/ Maybe ReplicationPad1DOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpSbpSignature<3>(ctx); +} +/*static*/ Maybe ReplicationPad1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const auto& padding = ctx->Attr>("padding"); + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + DimVector y_dim_vec(x_shape.NumAxes()); + const int64_t w_x = x_shape.At(w_idx); + + y_dim_vec[n_idx] = x_shape.At(n_idx); + y_dim_vec[c_idx] = x_shape.At(c_idx); + y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; + + ctx->SetOutputShape("y", 0, Shape(y_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad1DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReplicationPad1DOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReplicationPad1DOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad1DOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { + user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); + CHECK_NOTNULL_OR_RETURN(x_modifier); + x_modifier->set_requires_grad(true); + return Maybe::Ok(); +} + +/*static*/ Maybe ReplicationPad1DGradOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpGradSbpSignature<3>(ctx); +} +/*static*/ Maybe ReplicationPad1DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& dy_shape = ctx->InputShape("dy", 0); + const auto& padding = ctx->Attr>("padding"); + CHECK_EQ_OR_RETURN(padding.size(), dy_shape.NumAxes() - 1); // NOLINT + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t w_idx = 2; + + DimVector dx_dim_vec(dy_shape.NumAxes()); + int64_t w_dy = dy_shape.At(w_idx); + + dx_dim_vec[n_idx] = dy_shape.At(0); + dx_dim_vec[c_idx] = dy_shape.At(1); + dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; + + ctx->SetOutputShape("dx", 0, Shape(dx_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad1DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReplicationPad1DGradOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReplicationPad1DGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe ReplicationPad2DOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpSbpSignature<4>(ctx); +} +/*static*/ Maybe ReplicationPad2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const auto& padding = ctx->Attr>("padding"); + CHECK_EQ_OR_RETURN(padding.size(), x_shape.NumAxes()); // NOLINT + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + DimVector y_dim_vec(x_shape.NumAxes()); + const int64_t h_x = x_shape.At(h_idx); + const int64_t w_x = x_shape.At(w_idx); + + y_dim_vec[n_idx] = x_shape.At(n_idx); + y_dim_vec[c_idx] = x_shape.At(c_idx); + y_dim_vec[h_idx] = h_x + padding[2] + padding[3]; + y_dim_vec[w_idx] = w_x + padding[0] + padding[1]; + + ctx->SetOutputShape("y", 0, Shape(y_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad2DOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReplicationPad2DOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReplicationPad2DOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad2DOp::ModifyInputArg( + const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { + user_op::InputArgModifier* x_modifier = GetInputArgModifierFn("x", 0); + CHECK_NOTNULL_OR_RETURN(x_modifier); // NOLINT + x_modifier->set_requires_grad(true); + return Maybe::Ok(); +} + +/*static*/ Maybe ReplicationPad2DGradOp::GetSbp(user_op::SbpContext* ctx) { + return GetOpGradSbpSignature<4>(ctx); +} +/*static*/ Maybe ReplicationPad2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& dy_shape = ctx->InputShape("dy", 0); + const auto& padding = ctx->Attr>("padding"); + CHECK_EQ_OR_RETURN(padding.size(), dy_shape.NumAxes()); // NOLINT + const int64_t n_idx = 0; + const int64_t c_idx = 1; + const int64_t h_idx = 2; + const int64_t w_idx = 3; + + DimVector dx_dim_vec(dy_shape.NumAxes()); + int64_t h_dy = dy_shape.At(h_idx); + int64_t w_dy = dy_shape.At(w_idx); + + dx_dim_vec[n_idx] = dy_shape.At(0); + dx_dim_vec[c_idx] = dy_shape.At(1); + dx_dim_vec[h_idx] = h_dy - padding[2] - padding[3]; + dx_dim_vec[w_idx] = w_dy - padding[0] - padding[1]; + + ctx->SetOutputShape("dx", 0, Shape(dx_dim_vec)); + return Maybe::Ok(); +} +/*static*/ Maybe ReplicationPad2DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return ReplicationPad2DGradOp::InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe ReplicationPad2DGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/reshape_like_op.cpp b/oneflow/user/ops/reshape_like_op.cpp index 923e31899da52f24ac96e464e9e98850f63b4457..f057f5834ba41e82ec36964deb42b350efee3c6d 100644 --- a/oneflow/user/ops/reshape_like_op.cpp +++ b/oneflow/user/ops/reshape_like_op.cpp @@ -33,58 +33,34 @@ namespace oneflow { .PartialSum(user_op::OpArg("out", 0)) .Build(); user_op::UserOpSbpSignatureBuilder builder = ctx->NewBuilder(); - return ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures( - in_shape, like_shape, {{"in", 0}}, {{"like", 0}, {"out", 0}}, ctx->parallel_num(), &builder); + return ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures(in_shape, like_shape, {{"in", 0}}, + {{"like", 0}, {"out", 0}}, + ctx->hierarchy_value(), &builder); } /*static*/ Maybe ReshapeLikeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); const Shape& like_shape = ctx->InputShape("like", 0); - CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), like_shape.elem_cnt()); - *ctx->OutputShape("out", 0) = like_shape; + CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), like_shape.elem_cnt()) + << Error::RuntimeError() + << "The element number of the in tensor must be equal to the element number of the " + "like tensor, " + << "but got " << in_shape.elem_cnt() << " and " << like_shape.elem_cnt(); + ctx->SetOutputShape("out", 0, like_shape); return Maybe::Ok(); } /*static*/ Maybe ReshapeLikeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ReshapeLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe ReshapeLikeOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* like_modifier = GetInputArgModifierFn("like", 0); - CHECK_NOTNULL_OR_RETURN(like_modifier); + CHECK_NOTNULL_OR_RETURN(like_modifier); // NOLINT(maybe-need-error-msg) like_modifier->set_requires_grad(false); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("reshape_like") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - const auto& in_desc = op.TensorDesc4ArgNameAndIndex("in", 0); - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - if (in_desc.is_dynamic()) { - user_op::UserOpConfWrapper reshape_grad_op = - builder.Op("reshape_like") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(reshape_grad_op.output("out", 0), "in", 0); - AddOp(reshape_grad_op); - } else { - user_op::UserOpConfWrapper reshape_grad_op = - builder.Op("reshape") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Attr("shape", in_desc.shape()) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(reshape_grad_op.output("out", 0), "in", 0); - AddOp(reshape_grad_op); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/reshape_op.cpp b/oneflow/user/ops/reshape_op.cpp index 42dbba80b5e249d8cc690a6234bab998261e5169..916b66089f5864fead1b77cf0ee7748be1cb6b82 100644 --- a/oneflow/user/ops/reshape_op.cpp +++ b/oneflow/user/ops/reshape_op.cpp @@ -27,31 +27,31 @@ namespace oneflow { const auto& outshape = JUST(ReshapeUserOpUtil::GetLogicalOutBlobShape(in_shape, shape)); user_op::UserOpSbpSignatureBuilder builder = ctx->NewBuilder(); return ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures( - in_shape, *outshape, {{"in", 0}}, {{"out", 0}}, ctx->parallel_num(), &builder); + in_shape, *outshape, {{"in", 0}}, {{"out", 0}}, ctx->hierarchy_value(), &builder); } /*static*/ Maybe ReshapeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { Shape shape = ctx->Attr("shape"); const user_op::TensorDesc& in_tensor_desc = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor_desc = ctx->MutOutputTensorDesc("out", 0); const Shape& in_shape = in_tensor_desc.shape(); - Shape* out_shape = out_tensor_desc->mut_shape(); - Stride* out_stride = out_tensor_desc->mut_stride(); - CHECK_OR_RETURN(in_tensor_desc.is_dynamic() == false); - *out_tensor_desc->mut_data_type() = in_tensor_desc.data_type(); + CHECK_OR_RETURN(in_tensor_desc.is_dynamic() == false); // NOLINT(maybe-need-error-msg) + out_tensor_desc->set_data_type(in_tensor_desc.data_type()); if (in_shape.NumAxes() == 0 || shape.NumAxes() == 0) { // NOTE(chengcheng): input/output Scalar // do nothing } else { - CHECK_GE_OR_RETURN(shape.NumAxes(), 1); - CHECK_GE_OR_RETURN(in_shape.NumAxes(), 1); + CHECK_GE_OR_RETURN(shape.NumAxes(), 1); // NOLINT(maybe-need-error-msg) + CHECK_GE_OR_RETURN(in_shape.NumAxes(), 1); // NOLINT(maybe-need-error-msg) + int need_infer_axis = -1; size_t count = 1; for (int i = 0; i < shape.NumAxes(); ++i) { if (shape.At(i) == -1) { CHECK_EQ_OR_RETURN(need_infer_axis, -1) - << "Shape " << shape.ToString() << " has more than 1 axis that needs to be infered."; + << Error::RuntimeError() << "Shape " << shape.ToString() + << " has more than 1 axis that needs to be infered"; need_infer_axis = i; } else { count *= shape.At(i); @@ -59,29 +59,36 @@ namespace oneflow { } if (need_infer_axis != -1) { shape.Set(need_infer_axis, in_shape.elem_cnt() / count); } } - *out_shape = shape; - *out_stride = Stride(shape); - CHECK_EQ_OR_RETURN(out_shape->elem_cnt(), in_shape.elem_cnt()); + out_tensor_desc->set_shape(shape); + out_tensor_desc->set_stride(Stride(shape)); + // For 0-size tensor, we don't need to check whether the input and output tensors have the same + // element size. + if (in_shape.elem_cnt() > 0) { + CHECK_EQ_OR_RETURN(shape.elem_cnt(), in_shape.elem_cnt()) + << Error::RuntimeError() << "Reshape infer ERROR! in op_name: " << ctx->op_name() + << " input shape is : " << in_shape.ToString() + << " , output shape is : " << shape.ToString() + << " , and reshape shape conf is : " << ctx->Attr("shape").ToString() + << " op_loc: " << ctx->op_loc(); + } + return Maybe::Ok(); } /*static*/ Maybe ReshapeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { Shape logical_shape = ctx->Attr("shape"); const user_op::TensorDesc& in_tensor_desc = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out_tensor_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_tensor_desc = ctx->MutOutputTensorDesc("out", 0); const Shape& in_shape = in_tensor_desc.shape(); - Shape* out_shape = out_tensor_desc->mut_shape(); - Stride* out_stride = out_tensor_desc->mut_stride(); - *out_tensor_desc->mut_shape() = in_tensor_desc.shape(); - *out_tensor_desc->mut_stride() = Stride(in_tensor_desc.shape()); - *out_tensor_desc->mut_is_dynamic() = in_tensor_desc.is_dynamic(); + out_tensor_desc->set_stride(Stride(in_tensor_desc.shape())); + out_tensor_desc->set_is_dynamic(in_tensor_desc.is_dynamic()); if (in_shape.NumAxes() == 0 || logical_shape.NumAxes() == 0) { // NOTE(chengcheng): input/output Scalar // do nothing } else { - CHECK_GE_OR_RETURN(logical_shape.NumAxes(), 1); - CHECK_GE_OR_RETURN(in_shape.NumAxes(), 1); + CHECK_GE_OR_RETURN(logical_shape.NumAxes(), 1); // NOLINT(maybe-need-error-msg) + CHECK_GE_OR_RETURN(in_shape.NumAxes(), 1); // NOLINT(maybe-need-error-msg) const auto& in_nd_sbp = ctx->NdSbp4ArgNameAndIndex("in", 0); const Shape in_logical_shape = *JUST(GetLogicalShape(in_shape, in_nd_sbp, ctx->parallel_desc())); @@ -90,8 +97,8 @@ namespace oneflow { for (int i = 0; i < logical_shape.NumAxes(); ++i) { if (logical_shape.At(i) == -1) { CHECK_EQ_OR_RETURN(need_infer_axis, -1) - << "Shape " << logical_shape.ToString() - << " has more than 1 axis that needs to be infered."; + << Error::RuntimeError() << "Shape " << logical_shape.ToString() + << " has more than 1 axis that needs to be infered"; need_infer_axis = i; } else { count *= logical_shape.At(i); @@ -102,53 +109,22 @@ namespace oneflow { } } const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); - *out_shape = - *JUST(GetPhysicalShape(logical_shape, nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx())); - *out_stride = Stride(*out_shape); - CHECK_EQ_OR_RETURN(out_shape->elem_cnt(), in_shape.elem_cnt()) - << " Reshape infer ERROR! in op_name: " << ctx->op_name() + out_tensor_desc->set_shape( + *JUST(GetPhysicalShape(logical_shape, nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx()))); + out_tensor_desc->set_stride(Stride(out_tensor_desc->shape())); + CHECK_EQ_OR_RETURN(out_tensor_desc->shape().elem_cnt(), in_shape.elem_cnt()) + << Error::RuntimeError() << " Reshape infer ERROR! in op_name: " << ctx->op_name() << " input shape is : " << in_shape.ToString() - << " , output shape is : " << out_shape->ToString() << " , output logical shape is " - << logical_shape.ToString() - << " , And reshape shape conf is : " << ctx->Attr("shape").ToString() + << " , output shape is : " << out_tensor_desc->shape().ToString() + << " , output logical shape is " << logical_shape.ToString() + << " , and reshape shape conf is : " << ctx->Attr("shape").ToString() << " op_loc: " << ctx->op_loc(); return Maybe::Ok(); } /*static*/ Maybe ReshapeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("reshape").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - const auto& in_desc = op.TensorDesc4ArgNameAndIndex("in", 0); - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - if (in_desc.is_dynamic()) { - user_op::UserOpConfWrapper reshape_grad_op = - builder.Op("reshape_like") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(reshape_grad_op.output("out", 0), "in", 0); - AddOp(reshape_grad_op); - } else { - user_op::UserOpConfWrapper reshape_grad_op = - builder.Op("reshape") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Attr("shape", in_desc.shape()) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(reshape_grad_op.output("out", 0), "in", 0); - AddOp(reshape_grad_op); - } - } - return Maybe::Ok(); -}); - -} // namespace } // namespace oneflow diff --git a/oneflow/user/ops/reshape_user_op_util.cpp b/oneflow/user/ops/reshape_user_op_util.cpp index a7cb0a22b16d2eaa15612fb448acc5539e4d58cc..a5597e4ccabe365a6d7acd1d3690c5e9c8fc2d67 100644 --- a/oneflow/user/ops/reshape_user_op_util.cpp +++ b/oneflow/user/ops/reshape_user_op_util.cpp @@ -26,10 +26,11 @@ Maybe ReshapeUserOpUtil::GetLogicalOutBlobShape(const Shape& in_shape, int64_t dim = reshape.At(axis); if (dim == -1) { return Error::RuntimeError() - << "cannot reshape tensor of 0 elements into shape " << reshape.DebugStr() + << "Cannot reshape tensor of 0 elements into shape " << reshape.DebugStr() << " because the unspecified dimension size -1 can be any value and is ambiguous"; } else if (dim < 0) { - return Error::RuntimeError() << "invalid shape dimension " << dim; + return Error::RuntimeError() << "Invalid shape dimension " << dim + << ", the shape dimension can not to be less than 0"; } } return std::make_shared(reshape); @@ -42,24 +43,33 @@ Maybe ReshapeUserOpUtil::GetLogicalOutBlobShape(const Shape& in_shape, int64_t dim = reshape.At(axis); dim_vec.emplace_back(dim); if (dim == -1) { - CHECK_OR_RETURN(has_minus_1 == false) << "only one `-1' supported"; + CHECK_OR_RETURN(has_minus_1 == false) + << Error::RuntimeError() + << "There are multiple '-1' in the shape list, only one '-1' can be inferred"; has_minus_1 = true; minus_1_axis = axis; } else if (dim > 0) { - CHECK_LE_OR_RETURN(dim, in_shape.elem_cnt()) << "invalid axis: " << axis << ", dim: " << dim; + CHECK_LE_OR_RETURN(dim, in_shape.elem_cnt()) + << Error::RuntimeError() << "Invalid axis: " << axis << ", dim: " << dim; total_elem_dim_exclude_minus_1 *= dim; CHECK_LE_OR_RETURN(total_elem_dim_exclude_minus_1, in_shape.elem_cnt()) - << "element number in reshape_conf is bigger than input blob"; + << Error::RuntimeError() + << "Element number in reshape_conf must be less than or equal to input blob, " + << "but got " << total_elem_dim_exclude_minus_1 << " and " << in_shape.elem_cnt(); } else { OF_UNIMPLEMENTED() << "only positive number or -1 supported"; } } - CHECK_EQ_OR_RETURN(in_shape.elem_cnt() % total_elem_dim_exclude_minus_1, 0); + CHECK_EQ_OR_RETURN(in_shape.elem_cnt() % total_elem_dim_exclude_minus_1, 0) + << Error::RuntimeError() + << "Element number in input blob must be an integer multiple of reshape_conf, " + << "but got " << in_shape.elem_cnt() << " and " << total_elem_dim_exclude_minus_1; if (has_minus_1) { dim_vec[minus_1_axis] = in_shape.elem_cnt() / total_elem_dim_exclude_minus_1; } else { CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), total_elem_dim_exclude_minus_1) - << "input blob's element number not equals reshape_conf"; + << "Element number in input blob must be equal to reshape_conf, " + << "but got " << in_shape.elem_cnt() << " and " << total_elem_dim_exclude_minus_1; } return std::make_shared(dim_vec); } @@ -73,7 +83,8 @@ Maybe ReshapeUserOpUtil::Squeeze(const Shape& origin, Shape* shape, << "Trying to suqeeze tensor with negative dimension " << dim << " : " << origin.DebugStr(); if (dim == 1) { continue; } - CHECK_OR_RETURN(squeezed_axis2origin_axis->emplace(dim_vec.size(), axis).second); + CHECK_OR_RETURN(squeezed_axis2origin_axis->emplace(dim_vec.size(), axis).second) + << "emplace error"; // NOLINT(maybe-need-error-msg) dim_vec.emplace_back(dim); } *shape = Shape(dim_vec); @@ -81,41 +92,66 @@ Maybe ReshapeUserOpUtil::Squeeze(const Shape& origin, Shape* shape, } Maybe ReshapeUserOpUtil::GetGroupStartInAxis2OutAxis( - const Shape& in_shape, const Shape& out_shape, const int64_t parallel_num, + const Shape& in_shape, const Shape& out_shape, const int64_t hierarchy_value, HashMap* group_start_in_axis2out_axis) { - CHECK_GE_OR_RETURN(in_shape.NumAxes(), 0); // support 0D tensor - CHECK_GE_OR_RETURN(out_shape.NumAxes(), 0); // support 0D tensor - CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), out_shape.elem_cnt()); - int in_axis = in_shape.NumAxes() - 1; - int out_axis = out_shape.NumAxes() - 1; - while (in_axis >= 0 && out_axis >= 0) { - if (in_shape.Count(in_axis) < out_shape.Count(out_axis)) { - --in_axis; - } else if (in_shape.Count(in_axis) > out_shape.Count(out_axis)) { - --out_axis; - } else { + CHECK_GE_OR_RETURN(in_shape.NumAxes(), 0) + << Error::RuntimeError() + << "The dimension of input tensor must be greater than or equal to zero, " + << "but got " << in_shape.NumAxes(); // support 0D tensor + CHECK_GE_OR_RETURN(out_shape.NumAxes(), 0) + << Error::RuntimeError() + << "The dimension of output tensor must be greater than or equal to zero, " + << "but got " << out_shape.NumAxes(); // support 0D tensor + CHECK_EQ_OR_RETURN(in_shape.elem_cnt(), out_shape.elem_cnt()) + << Error::RuntimeError() + << "The element number of input tensor must be equal to output tensor, " + << "but got " << in_shape.elem_cnt() << " and " << out_shape.elem_cnt(); + // Initialization + // shape_count is the product of the axis length in [start_axis, end) + int64_t in_shape_count = 1; + int64_t out_shape_count = 1; + int64_t in_axis = in_shape.NumAxes(); + int64_t out_axis = out_shape.NumAxes(); + // Move forward functions + auto Move2NextAxis = [](const Shape& shape, int64_t* axis, int64_t* shape_count) { + (*axis)--; + if (*axis >= 0) { *shape_count *= shape.At(*axis); } + }; + auto MoveInAxis = [&] { Move2NextAxis(in_shape, &in_axis, &in_shape_count); }; + auto MoveOutAxis = [&] { Move2NextAxis(out_shape, &out_axis, &out_shape_count); }; + // Move the first step + MoveInAxis(); + MoveOutAxis(); + // At the last step, both in_axis == out_axis == 0 + // Then they would move to -1 simultaneously. + while (in_axis >= 0) { + if (in_shape_count == out_shape_count) { + // Record split axises if (in_shape.At(in_axis) == out_shape.At(out_axis) - || (in_shape.Count(in_axis) % parallel_num == 0 - && out_shape.Count(out_axis) % parallel_num == 0)) { + || (in_shape.At(in_axis) % hierarchy_value == 0 + && out_shape.At(out_axis) % hierarchy_value == 0)) { (*group_start_in_axis2out_axis)[in_axis] = out_axis; } - --in_axis; - --out_axis; + // Move forward + MoveInAxis(); + MoveOutAxis(); + } else if (in_shape_count < out_shape_count) { + MoveInAxis(); + } else { + // in_shape_count > out_shape_count + MoveOutAxis(); } } - CHECK_GE_OR_RETURN(in_axis, -1); - CHECK_GE_OR_RETURN(out_axis, -1); - CHECK_LE_OR_RETURN(in_axis, 0); - CHECK_LE_OR_RETURN(out_axis, 0); - CHECK_EQ_OR_RETURN(in_axis == 0 && out_axis == 0, false); return Maybe::Ok(); } Maybe ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures( - const Shape& in_shape, const Shape& out_shape, std::vector in_args, - std::vector out_args, const int64_t parallel_num, + const Shape& in_shape, const Shape& out_shape, const std::vector& in_args, + const std::vector& out_args, const int64_t hierarchy_value, user_op::UserOpSbpSignatureBuilder* builder) { - if (in_shape.NumAxes() == 0) { return Maybe::Ok(); } + if (in_shape.NumAxes() == 0 || in_shape.elem_cnt() == 0) { + return Maybe::Ok(); + } // 0D/0Size tensor only support b2b HashMap squeezed_group_start_in_axis2out_axis; HashMap in_squeezed_axis2original_axis; HashMap out_squeezed_axis2original_axis; @@ -126,7 +162,7 @@ Maybe ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures( JUST(ReshapeUserOpUtil::Squeeze(out_shape, &squeezed_out_shape, &out_squeezed_axis2original_axis)); JUST(ReshapeUserOpUtil::GetGroupStartInAxis2OutAxis(squeezed_in_shape, squeezed_out_shape, - parallel_num, + hierarchy_value, &squeezed_group_start_in_axis2out_axis)); } for (const auto& pair : squeezed_group_start_in_axis2out_axis) { @@ -138,96 +174,4 @@ Maybe ReshapeUserOpUtil::GetReshapeUserOpSbpSignatures( return Maybe::Ok(); } -namespace { - -Maybe GetInputNdSbp(user_op::InferNdSbpFnContext* ctx, const user_op::OpArg& in_arg, - NdSbp* distribution) { - *distribution = ctx->NdSbpHint4InputArgNameAndIndex(in_arg.name(), in_arg.index()); - const auto& constraints = ctx->nd_sbp_constraints(); - if (constraints.bn_in_op2nd_sbp_size() != 0) { - const auto it = - constraints.bn_in_op2nd_sbp().find(GenRepeatedBn(in_arg.name(), in_arg.index())); - if (it != constraints.bn_in_op2nd_sbp().end()) { *distribution = it->second; } - } - return Maybe::Ok(); -} - -Maybe ApplySbpParallel(const SbpParallel& sbp, const int64_t parallel_num, Shape* shape) { - if (sbp.has_split_parallel()) { - const int64_t axis = sbp.split_parallel().axis(); - CHECK_EQ_OR_RETURN(shape->At(axis) % parallel_num, 0); - shape->Set(axis, shape->At(axis) / parallel_num); - } - return Maybe::Ok(); -} - -} // namespace - -Maybe ReshapeUserOpUtil::InferNdSbp(user_op::InferNdSbpFnContext* ctx, - const Shape& logical_in_shape, - const Shape& logical_out_shape) { - const std::string& op_type_name = ctx->user_op_conf().op_type_name(); - CHECK_OR_RETURN(op_type_name == "reshape" || op_type_name == "reshape_like"); - const bool is_reshape_like = (op_type_name == "reshape_like"); - std::vector in_args({{"in", 0}}); - if (is_reshape_like) { in_args.emplace_back(user_op::OpArg("like", 0)); } - HashMap ibn2nd_sbp; - ibn2nd_sbp.reserve(in_args.size()); - for (const auto& arg : in_args) { - NdSbp* in_distribution = ctx->NdSbp4ArgNameAndIndex(arg.name(), arg.index()); - JUST(GetInputNdSbp(ctx, arg, in_distribution)); - CHECK_OR_RETURN( - ibn2nd_sbp.emplace(GenRepeatedBn(arg.name(), arg.index()), *in_distribution).second); - } - NdSbp* out_distribution = ctx->NdSbp4ArgNameAndIndex("out", 0); - - Shape in_shape = logical_in_shape; - Shape out_shape = logical_out_shape; - const Shape& parallel_hierarchy = ctx->parallel_hierarchy(); - for (int64_t i = 0; i < parallel_hierarchy.NumAxes(); ++i) { - SbpSignatureList sbp_sig_list; - user_op::UserOpSbpSignatureBuilder builder(&sbp_sig_list); - builder.Broadcast(in_args).Broadcast(user_op::OpArg("out", 0)).Build(); - if (is_reshape_like) { - builder.PartialSum(user_op::OpArg("like", 0)) - .Broadcast(user_op::OpArg("in", 0)) - .Broadcast(user_op::OpArg("out", 0)) - .Build(); - builder.Broadcast(user_op::OpArg("like", 0)) - .PartialSum(user_op::OpArg("in", 0)) - .PartialSum(user_op::OpArg("out", 0)) - .Build(); - JUST(GetReshapeUserOpSbpSignatures(in_shape, out_shape, {{"in", 0}}, - {{"like", 0}, {"out", 0}}, parallel_hierarchy.At(i), - &builder)); - } else { - JUST(GetReshapeUserOpSbpSignatures(in_shape, out_shape, {{"in", 0}}, {{"out", 0}}, - parallel_hierarchy.At(i), &builder)); - } - - const SbpSignature* matched_sbp_signature = nullptr; - for (const auto& sbp_signature : sbp_sig_list.sbp_signature()) { - bool all_match = true; - for (const auto& in_arg : in_args) { - std::string ibn = GenRepeatedBn(in_arg.name(), in_arg.index()); - if (sbp_signature.bn_in_op2sbp_parallel().at(ibn) != ibn2nd_sbp.at(ibn).sbp_parallel(i)) { - all_match = false; - break; - } - } - if (all_match) { - matched_sbp_signature = &sbp_signature; - break; - } - } - CHECK_OR_RETURN(matched_sbp_signature != nullptr); - SbpParallel out_sbp = matched_sbp_signature->bn_in_op2sbp_parallel().at("out_0"); - JUST(ApplySbpParallel(matched_sbp_signature->bn_in_op2sbp_parallel().at("in_0"), - parallel_hierarchy.At(i), &in_shape)); - JUST(ApplySbpParallel(out_sbp, parallel_hierarchy.At(i), &out_shape)); - *(out_distribution->add_sbp_parallel()) = out_sbp; - } - return Maybe::Ok(); -} - } // namespace oneflow diff --git a/oneflow/user/ops/reshape_user_op_util.h b/oneflow/user/ops/reshape_user_op_util.h index 8178803fbc1e7aa5b991c1b6d260f29239f15259..b1fa968f3d0910229a7d69774b329ea9a4c47d0a 100644 --- a/oneflow/user/ops/reshape_user_op_util.h +++ b/oneflow/user/ops/reshape_user_op_util.h @@ -26,15 +26,13 @@ struct ReshapeUserOpUtil { static Maybe Squeeze(const Shape& origin, Shape* shape, HashMap* squeezed_axis2origin_axis); static Maybe GetGroupStartInAxis2OutAxis(const Shape& in_shape, const Shape& out_shape, - const int64_t parallel_num, + const int64_t hierarchy_value, HashMap* group_start_in_axis2out_axis); static Maybe GetReshapeUserOpSbpSignatures(const Shape& in_shape, const Shape& out_shape, - std::vector in_args, - std::vector out_args, - const int64_t parallel_num, + const std::vector& in_args, + const std::vector& out_args, + const int64_t hierarchy_value, user_op::UserOpSbpSignatureBuilder* builder); - static Maybe InferNdSbp(user_op::InferNdSbpFnContext* ctx, const Shape& logical_in_shape, - const Shape& logical_out_shape); }; } // namespace oneflow diff --git a/oneflow/user/ops/rms_norm_op.cpp b/oneflow/user/ops/rms_norm_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9c22f2beec6ba5b924961a5031ad10ebb72c82dc --- /dev/null +++ b/oneflow/user/ops/rms_norm_op.cpp @@ -0,0 +1,156 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/* static */ Maybe RmsNormOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& x_shape = ctx->InputShape("x", 0); + const Shape& normalized_shape = ctx->Attr("normalized_shape"); + if (ctx->has_input("weight", 0)) { + const Shape& w_shape = ctx->InputShape("weight", 0); + CHECK_EQ_OR_RETURN(w_shape, normalized_shape) + << "expected weight shape " << normalized_shape.ToString() << ", got " + << w_shape.ToString(); + } + CHECK_LE_OR_RETURN(normalized_shape.size(), x_shape.size()) + << "invalid normalized shape " << normalized_shape.ToString() << " with input shape " + << x_shape.ToString(); + size_t batch_ndim = x_shape.size() - normalized_shape.size(); + DimVector batch_dims(batch_ndim); + for (int i = 0; i < x_shape.size(); ++i) { + if (i < batch_ndim) { + batch_dims[i] = x_shape[i]; + } else { + CHECK_EQ_OR_RETURN(normalized_shape[i - batch_ndim], x_shape[i]) + << "invalid normalized shape " << normalized_shape.ToString() << " with input shape " + << x_shape.ToString(); + } + } + ctx->SetOutputShape("y", 0, x_shape); + ctx->SetOutputShape("inv_rms", 0, Shape{batch_dims}); + return Maybe::Ok(); +} + +/*static*/ Maybe RmsNormOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe RmsNormOp::InferDataType(user_op::InferContext* ctx) { + DataType x_dtype = ctx->InputDType("x", 0); + if (ctx->has_input("weight", 0)) { + DataType w_dtype = ctx->InputDType("weight", 0); + CHECK_EQ_OR_RETURN(w_dtype, x_dtype) + << "RmsNormOp " << ctx->op_name() << " has different input dtype " << DataType_Name(x_dtype) + << " and param dtype " << DataType_Name(w_dtype); + } + ctx->SetOutputDType("y", 0, x_dtype); + + DataType rms_dtype = x_dtype; + if (x_dtype == DataType::kFloat16 || x_dtype == DataType::kBFloat16) { + rms_dtype = DataType::kFloat; + } + ctx->SetOutputDType("inv_rms", 0, rms_dtype); + return Maybe::Ok(); +} + +/* static */ Maybe RmsNormOp::GetSbp(user_op::SbpContext* ctx) { + const Shape& x_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("x", 0).shape(); + const Shape& normalized_shape = ctx->Attr("normalized_shape"); + size_t batch_ndim = x_shape.size() - normalized_shape.size(); + for (int i = 0; i < batch_ndim; ++i) { + ctx->NewBuilder() + .Split(user_op::OpArg("x", 0), i) + .Broadcast(user_op::OpArg("weight", 0)) + .Split(ctx->outputs(), i) + .Build(); + } + return Maybe::Ok(); +} + +/* static */ Maybe RmsNormGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& shape = ctx->InputShape("dy", 0); + CHECK_EQ_OR_RETURN(ctx->InputShape("x", 0), shape); // NOLINT(maybe-need-error-msg) + // No need to check weight and inv_rms legality which should be guaranteed by forward op + ctx->SetOutputShape("dx", 0, shape); + return Maybe::Ok(); +} + +/*static*/ Maybe RmsNormGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe RmsNormGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe RmsNormGradOp::GetSbp(user_op::SbpContext* ctx) { + std::vector split_args = {user_op::OpArg("dy", 0), user_op::OpArg("x", 0), + user_op::OpArg("inv_rms", 0)}; + std::vector broadcast_args; + if (ctx->user_op_conf().has_input("weight", 0)) { broadcast_args.emplace_back("weight", 0); } + const Shape& b_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("inv_rms", 0).shape(); + for (int i = 0; i < b_shape.size(); ++i) { + ctx->NewBuilder() + .Split(split_args, i) + .Broadcast(broadcast_args) + .Split(ctx->outputs(), i) + .Build(); + } + return Maybe::Ok(); +} + +/* static */ Maybe RmsNormParamGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& shape = ctx->InputShape("dy", 0); + CHECK_EQ_OR_RETURN(ctx->InputShape("x", 0), shape); // NOLINT(maybe-need-error-msg) + const Shape& b_shape = ctx->InputShape("inv_rms", 0); + + CHECK_LE_OR_RETURN(b_shape.size(), shape.size()) + << "invalid inv_rms shape " << b_shape.ToString() << " with dy shape " << shape.ToString(); + size_t n_ndim = shape.size() - b_shape.size(); + DimVector n_shape_vec(n_ndim); + for (int i = 0; i < shape.size(); ++i) { + if (i < b_shape.size()) { + CHECK_EQ_OR_RETURN(b_shape[i], shape[i]) << "invalid inv_rms shape " << b_shape.ToString() + << " with dy shape " << shape.ToString(); + } else { + n_shape_vec[i - b_shape.size()] = shape[i]; + } + } + ctx->SetOutputShape("weight_grad", 0, Shape{n_shape_vec}); + return Maybe::Ok(); +} + +/*static*/ Maybe RmsNormParamGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe RmsNormParamGradOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("weight_grad", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +/* static */ Maybe RmsNormParamGradOp::GetSbp(user_op::SbpContext* ctx) { + const Shape& b_shape = ctx->LogicalTensorDesc4InputArgNameAndIndex("inv_rms", 0).shape(); + for (int i = 0; i < b_shape.size(); ++i) { + ctx->NewBuilder().Split(ctx->inputs(), i).PartialSum(ctx->outputs()).Build(); + } + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/roc_auc_score_op.cpp b/oneflow/user/ops/roc_auc_score_op.cpp index 9a7e68ed52410901e0739424c4bc881db8219fea..65171cb8cf5c6ad038894ee2c6979d3cc24d883b 100644 --- a/oneflow/user/ops/roc_auc_score_op.cpp +++ b/oneflow/user/ops/roc_auc_score_op.cpp @@ -19,13 +19,13 @@ limitations under the License. namespace oneflow { /* static */ Maybe RocAucScoreOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); const Shape& pred_shape = ctx->InputTensorDesc("pred", 0).shape(); const Shape& label_shape = ctx->InputTensorDesc("label", 0).shape(); CHECK_EQ_OR_RETURN(pred_shape.elem_cnt(), label_shape.elem_cnt()) << "pred and label MUST have same element count."; out_desc->set_is_dynamic(false); - *out_desc->mut_shape() = Shape({1}); + out_desc->set_shape(Shape({1})); return Maybe::Ok(); } @@ -38,7 +38,7 @@ namespace oneflow { } /* static */ Maybe RocAucScoreOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kFloat; + ctx->SetOutputDType("out", 0, DataType::kFloat); const user_op::TensorDesc& label = ctx->InputTensorDesc("label", 0); CHECK_OR_RETURN(IsFloatingDataType(label.data_type()) || IsIntegralDataType(label.data_type())) << "Input `label` data type " << DataType_Name(label.data_type()) << " is not supported."; diff --git a/oneflow/user/ops/roi_align_op.cpp b/oneflow/user/ops/roi_align_op.cpp index c2a45e6eedc9257499ed6f6f8d5677e19d996262..04080cf7c3ffee4414ad6d0d5e8cf4de3839ea33 100644 --- a/oneflow/user/ops/roi_align_op.cpp +++ b/oneflow/user/ops/roi_align_op.cpp @@ -32,28 +32,34 @@ namespace oneflow { const int32_t pooled_h = ctx->Attr("pooled_h"); const int32_t pooled_w = ctx->Attr("pooled_w"); // x: feature map (N, C, H, W) - CHECK_EQ(x_shape.NumAxes(), 4); + CHECK_EQ_OR_RETURN(x_shape.NumAxes(), 4) + << Error::RuntimeError() << "The dimension of x tensor must be equal to 4, " + << "but got " << x_shape.NumAxes(); // rois: (R, 5) - CHECK_EQ(rois_shape.NumAxes(), 2); - CHECK_EQ(rois_shape.At(1), 5); + CHECK_EQ_OR_RETURN(rois_shape.NumAxes(), 2) + << Error::RuntimeError() << "The dimension of rois tensor must be equal to 2, " + << "but got " << rois_shape.NumAxes(); + CHECK_EQ_OR_RETURN(rois_shape.At(1), 5) + << Error::RuntimeError() << "The size of rois tensor must be equal to 5 at dimension 1, " + << "but got " << rois_shape.At(1); // y: (R, C, pool_h, pool_w) - *ctx->OutputShape("y", 0) = Shape({rois_shape.At(0), x_shape.At(1), pooled_h, pooled_w}); + ctx->SetOutputShape("y", 0, Shape({rois_shape.At(0), x_shape.At(1), pooled_h, pooled_w})); return Maybe::Ok(); } /*static*/ Maybe RoiAlignOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe RoiAlignOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } /*static*/ Maybe RoiAlignOp::ModifyInputArg(const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* roi_modifier = GetInputArgModifierFn("rois", 0); - CHECK_OR_RETURN(roi_modifier != nullptr); + CHECK_OR_RETURN(roi_modifier != nullptr); // NOLINT(maybe-need-error-msg) roi_modifier->set_requires_grad(false); user_op::InputArgModifier* feat_modifier = GetInputArgModifierFn("x", 0); - CHECK_OR_RETURN(feat_modifier != nullptr); + CHECK_OR_RETURN(feat_modifier != nullptr); // NOLINT(maybe-need-error-msg) feat_modifier->set_requires_grad(true); return Maybe::Ok(); } @@ -74,51 +80,34 @@ namespace oneflow { const int32_t pooled_h = ctx->Attr("pooled_h"); const int32_t pooled_w = ctx->Attr("pooled_w"); // x: feature map (N, C, H, W) - CHECK_EQ_OR_RETURN(x_like_shape.NumAxes(), 4); + CHECK_EQ_OR_RETURN(x_like_shape.NumAxes(), 4) + << Error::RuntimeError() << "The dimension of x_like tensor must be equal to 4, " + << "but got " << x_like_shape.NumAxes(); + // rois: (R, 5) - CHECK_EQ_OR_RETURN(rois_shape.NumAxes(), 2); - CHECK_EQ_OR_RETURN(rois_shape.At(1), 5); + CHECK_EQ_OR_RETURN(rois_shape.NumAxes(), 2) + << Error::RuntimeError() << "The dimension of rois tensor must be equal to 2, " + << "but got " << rois_shape.NumAxes(); + CHECK_EQ_OR_RETURN(rois_shape.At(1), 5) + << Error::RuntimeError() << "The size of rois tensor must be equal to 5 " + << "at dimension 1, " + << "but got " << rois_shape.At(1); // y: (R, C, pool_h, pool_w) const Shape& y_shape = Shape({rois_shape.At(0), x_like_shape.At(1), pooled_h, pooled_w}); - CHECK_EQ_OR_RETURN(y_shape, dy_shape); - *ctx->OutputShape("dx", 0) = x_like_shape; + CHECK_EQ_OR_RETURN(y_shape, dy_shape) + << Error::RuntimeError() << "Tensors y and dy must have same shape"; + ctx->SetOutputShape("dx", 0, x_like_shape); return Maybe::Ok(); } /*static*/ Maybe RoiAlignGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe RoiAlignGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x_like", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x_like", 0); - return Maybe::Ok(); -} + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x_like", 0)) + << Error::TypeError() << "The dy tensor and x_like tensor must have same type"; -namespace { - -Maybe GenerateBackwardOpConf4RoiAlign(const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("roi_align_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x_like", op.input("x", 0)) - .Input("rois", op.input("rois", 0)) - .Attr("pooled_h", op.attr("pooled_h")) - .Attr("pooled_w", op.attr("pooled_w")) - .Attr("spatial_scale", op.attr("spatial_scale")) - .Attr("sampling_ratio", op.attr("sampling_ratio")) - .Attr("aligned", op.attr("aligned")) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } + ctx->SetOutputDType("dx", 0, ctx->InputDType("x_like", 0)); return Maybe::Ok(); } -} // namespace - -REGISTER_USER_OP_GRAD("roi_align").SetGenBackwardOpConfFn(GenerateBackwardOpConf4RoiAlign); - } // namespace oneflow diff --git a/oneflow/user/ops/roll_op.cpp b/oneflow/user/ops/roll_op.cpp index b07077d814b117f41303db74f561abd72d7bf7f4..d3544aebc5571c53c997eceba38d453d6dbd9680 100644 --- a/oneflow/user/ops/roll_op.cpp +++ b/oneflow/user/ops/roll_op.cpp @@ -22,8 +22,8 @@ namespace oneflow { const user_op::TensorDesc& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); const std::vector& dims = ctx->Attr>("dims"); - CHECK_GT_OR_RETURN(dims.size(), 0); - + CHECK_GT_OR_RETURN(dims.size(), 0) + << Error::RuntimeError() << "The input list of dims doesn't allow to be empty"; // NOTE(Liang Depeng): (dims.size == 1 && dims[0] == -1) means that user call flow.roll with // dims == None if (dims[0] != -1) { @@ -45,37 +45,15 @@ namespace oneflow { } /*static*/ Maybe RollOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - *ctx->OutputShape("out", 0) = in_shape; + ctx->SetOutputShape("out", 0, in_shape); return Maybe::Ok(); } /*static*/ Maybe RollOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe RollOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("roll").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - std::vector shifts = op.attr>("shifts"); - - // NOTE(Liang Depeng): reverse the roll process - for (int i = 0; i < shifts.size(); ++i) { shifts[i] *= -1; } - - user_op::UserOpConfWrapper grad_op = - builder.Op("roll") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr>("shifts", shifts) - .Attr>("dims", op.attr>("dims")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/same_padding_op.cpp b/oneflow/user/ops/same_padding_op.cpp index e643232ba667690987fcdb4a6f30ae593a716833..25c0c9e5b0b5d54747f63b07898dd7d303e1d72f 100644 --- a/oneflow/user/ops/same_padding_op.cpp +++ b/oneflow/user/ops/same_padding_op.cpp @@ -35,18 +35,27 @@ namespace oneflow { } /*static*/ Maybe SamePaddingOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = x_desc.shape(); - *y_desc->mut_is_dynamic() = x_desc.is_dynamic(); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(x_desc.shape()); + y_desc->set_is_dynamic(x_desc.is_dynamic()); const std::string& data_format = ctx->Attr("data_format"); const auto& kernel_size = ctx->Attr>("kernel_size"); const auto& strides = ctx->Attr>("strides"); const auto& dilation_rate = ctx->Attr>("dilation_rate"); const size_t idx_offset = IdxOffset(data_format); const int32_t num_spatial_dims = x_desc.shape().NumAxes() - 2; - CHECK_EQ_OR_RETURN(num_spatial_dims, kernel_size.size()); - CHECK_EQ_OR_RETURN(num_spatial_dims, strides.size()); - CHECK_EQ_OR_RETURN(num_spatial_dims, dilation_rate.size()); + CHECK_EQ_OR_RETURN(num_spatial_dims, kernel_size.size()) + << Error::RuntimeError() + << "The dimension of x tensor must be equal to the size of kernel_size array plus 2, " + << "but got " << num_spatial_dims << " and " << kernel_size.size(); + CHECK_EQ_OR_RETURN(num_spatial_dims, strides.size()) + << Error::RuntimeError() + << "The dimension of x tensor must be equal to the size of strides array plus 2, " + << "but got " << num_spatial_dims << " and " << strides.size(); + CHECK_EQ_OR_RETURN(num_spatial_dims, dilation_rate.size()) + << Error::RuntimeError() + << "The dimension of x tensor must be equal to the size of dilation_rate array plus 2, " + << "but got " << num_spatial_dims << " and " << dilation_rate.size(); DimVector y_dim_vec(x_desc.shape().dim_vec()); for (int32_t i = 0; i < num_spatial_dims; ++i) { int32_t padding_small = 0; @@ -55,14 +64,14 @@ namespace oneflow { strides.at(i), &padding_small, &padding_large)); y_dim_vec[idx_offset + i] = x_desc.shape().At(idx_offset + i) + padding_small + padding_large; } - *y_desc->mut_shape() = Shape(y_dim_vec); + y_desc->set_shape(Shape(y_dim_vec)); return Maybe::Ok(); } /*static*/ Maybe SamePaddingOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SamePaddingOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -99,43 +108,16 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SamePaddingGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x_like", 0); - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("x_like", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x_like", 0)); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("x_like", 0)); return Maybe::Ok(); } /*static*/ Maybe SamePaddingGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SamePaddingGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x_like", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x_like", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("same_padding") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - const std::string& padding = op.attr("padding"); - const std::string& data_format = op.attr("data_format"); - const auto& kernel_size = op.attr>("kernel_size"); - const auto& strides = op.attr>("strides"); - const auto& dilation_rate = op.attr>("dilation_rate"); - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("same_padding_grad") - .Input("x_like", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("padding", padding) - .Attr("data_format", data_format) - .Attr>("kernel_size", kernel_size) - .Attr>("strides", strides) - .Attr>("dilation_rate", dilation_rate) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/scalar_by_tensor_op.cpp b/oneflow/user/ops/scalar_by_tensor_op.cpp index a384f9e70f5cfd4189a2bbc8e787d5a92ad273cd..fbe9eb645398e1d7e4ee9c00eee4c506bce39d30 100644 --- a/oneflow/user/ops/scalar_by_tensor_op.cpp +++ b/oneflow/user/ops/scalar_by_tensor_op.cpp @@ -23,19 +23,21 @@ namespace { Maybe TensorDescInferFn(user_op::InferContext* ctx) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& scalar = ctx->InputTensorDesc("scalar", 0); - CHECK_EQ_OR_RETURN(scalar.shape().elem_cnt(), 1) << "op: " << ctx->op_name(); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_shape() = x.shape(); - *y->mut_is_dynamic() = x.is_dynamic(); + CHECK_EQ_OR_RETURN(scalar.shape().elem_cnt(), 1) + << Error::RuntimeError() << "The input scalar tensor is not a scalar"; + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(x.shape()); + y->set_is_dynamic(x.is_dynamic()); return Maybe::Ok(); } Maybe DataTypeInferFn(user_op::InferContext* ctx) { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& scalar = ctx->InputTensorDesc("scalar", 0); - CHECK_EQ_OR_RETURN(x.data_type(), scalar.data_type()); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_data_type() = x.data_type(); + CHECK_EQ_OR_RETURN(x.data_type(), scalar.data_type()) + << Error::TypeError() << "Tensors x and scalar have different type"; + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(x.data_type()); return Maybe::Ok(); } @@ -147,128 +149,4 @@ GetSbpFn MakeGetSbpFn(GetSbpFn extra) { return DataTypeInferFn(ctx); } -REGISTER_USER_OP_GRAD("scalar_add_by_tensor") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("y", 0), "x", 0); - } - if (op.NeedGenGradTensor4OpInput("scalar", 0)) { - std::vector axes_vec(op.TensorDesc4ArgNameAndIndex("y", 0).shape().NumAxes()); - std::iota(axes_vec.begin(), axes_vec.end(), 0); - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "scalar_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("reduce_sum") - .Input("input_tensor", op.GetGradTensorWithOpOutput("y", 0)) - .Output("output_tensor") - .Attr("axis", axes_vec) - .Attr("keepdims", false) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("output_tensor", 0), "scalar", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_sub_by_tensor") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("y", 0), "x", 0); - } - if (op.NeedGenGradTensor4OpInput("scalar", 0)) { - std::vector axes_vec(op.TensorDesc4ArgNameAndIndex("y", 0).shape().NumAxes()); - std::iota(axes_vec.begin(), axes_vec.end(), 0); - user_op::UserOpConfWrapperBuilder builder0(op.op_name() + "scalar_grad_reduce_sum"); - user_op::UserOpConfWrapper scalar_grad_reduce_sum_op = - builder0.Op("reduce_sum") - .Input("input_tensor", op.GetGradTensorWithOpOutput("y", 0)) - .Output("output_tensor") - .Attr("axis", axes_vec) - .Attr("keepdims", false) - .Build(); - user_op::UserOpConfWrapperBuilder builder1(op.op_name() + "scalar_grad_scalar_mul"); - user_op::UserOpConfWrapper scalar_grad_scalar_mul_op = - builder1.Op("scalar_mul") - .Input("in", scalar_grad_reduce_sum_op.output("output_tensor", 0)) - .Output("out") - .Attr("has_float_operand", true) - .Attr("has_int_operand", false) - .Attr("float_operand", static_cast(-1)) - .Attr("int_operand", static_cast(-1)) - .Build(); - op.BindGradTensorWithOpInput(scalar_grad_scalar_mul_op.output("out", 0), "scalar", 0); - AddOp(scalar_grad_reduce_sum_op); - AddOp(scalar_grad_scalar_mul_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_mul_by_tensor") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("scalar_mul_by_tensor") - .Input("x", op.GetGradTensorWithOpOutput("y", 0)) - .Input("scalar", op.input("scalar", 0)) - .Output("y") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("y", 0), "x", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("scalar", 0)) { - int64_t num_axes = op.TensorDesc4ArgNameAndIndex("y", 0).shape().NumAxes(); - user_op::UserOpConfWrapperBuilder builder0(op.op_name() + "scalar_grad_multiply"); - user_op::UserOpConfWrapper scalar_grad_multiply_op = - builder0.Op("broadcast_mul") - .Input("x", op.GetGradTensorWithOpOutput("y", 0)) - .Input("y", op.input("x", 0)) - .Output("z") - .Build(); - std::vector axes_vec(num_axes); - std::iota(axes_vec.begin(), axes_vec.end(), 0); - user_op::UserOpConfWrapperBuilder builder1(op.op_name() + "scalar_grad_reduce_sum"); - user_op::UserOpConfWrapper scalar_grad_reduce_sum_op = - builder1.Op("reduce_sum") - .Input("input_tensor", scalar_grad_multiply_op.output("z", 0)) - .Output("output_tensor") - .Attr("axis", axes_vec) - .Attr("keepdims", false) - .Build(); - op.BindGradTensorWithOpInput(scalar_grad_reduce_sum_op.output("output_tensor", 0), "scalar", - 0); - AddOp(scalar_grad_multiply_op); - AddOp(scalar_grad_reduce_sum_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_div_by_tensor") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("scalar_div_by_tensor") - .Input("x", op.GetGradTensorWithOpOutput("y", 0)) - .Input("scalar", op.input("scalar", 0)) - .Output("y") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("y", 0), "x", 0); - AddOp(grad_op); - } - if (op.NeedGenGradTensor4OpInput("scalar", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "scalar_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("broadcast_div_grad") - .Input("dz", op.GetGradTensorWithOpOutput("y", 0)) - .Input("z", op.output("y", 0)) - .Input("y", op.input("scalar", 0)) - .Output("dy") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dy", 0), "scalar", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/scalar_logical_op.cpp b/oneflow/user/ops/scalar_logical_op.cpp index 8c0786c2804a586f19e06f89c291f7d9acf658b4..5c5b6c302a0572bc440ae06e944981ba2d5e0bbc 100644 --- a/oneflow/user/ops/scalar_logical_op.cpp +++ b/oneflow/user/ops/scalar_logical_op.cpp @@ -27,15 +27,15 @@ namespace oneflow { return Maybe::Ok(); \ } \ /*static*/ Maybe name##Op::InferLogicalTensorDesc(user_op::InferContext* ctx) { \ - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); \ - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); \ + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); \ + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); \ return Maybe::Ok(); \ } \ /*static*/ Maybe name##Op::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ return InferLogicalTensorDesc(ctx); \ } \ /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ - *ctx->OutputDType("out", 0) = DataType::kBool; \ + ctx->SetOutputDType("out", 0, DataType::kBool); \ return Maybe::Ok(); \ } diff --git a/oneflow/user/ops/scalar_math_op.cpp b/oneflow/user/ops/scalar_math_op.cpp index b13a9bc60b3970aaf82d756ef39b9dfec52432d9..0d0ebba514d971d7be2a3c120ae391f0626142a7 100644 --- a/oneflow/user/ops/scalar_math_op.cpp +++ b/oneflow/user/ops/scalar_math_op.cpp @@ -42,20 +42,21 @@ Maybe GetSbp4ScalarMul(user_op::SbpContext* ctx) { #define IMPLEMENT_SCALAR_MATH_OP_FUNCS(op_name, get_sbp_fn) \ /*static*/ Maybe op_name##Op::GetSbp(user_op::SbpContext* ctx) { return get_sbp_fn(ctx); } \ /*static*/ Maybe op_name##Op::InferLogicalTensorDesc(user_op::InferContext* ctx) { \ - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); \ - *ctx->OutputIsDynamic("out", 0) = ctx->InputIsDynamic("in", 0); \ + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); \ + ctx->SetOutputIsDynamic("out", 0, ctx->InputIsDynamic("in", 0)); \ return Maybe::Ok(); \ } \ /*static*/ Maybe op_name##Op::InferPhysicalTensorDesc(user_op::InferContext* ctx) { \ return InferLogicalTensorDesc(ctx); \ } \ /*static*/ Maybe op_name##Op::InferDataType(user_op::InferContext* ctx) { \ - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); \ + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); \ return Maybe::Ok(); \ } IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarAdd, GetSbp4ScalarMath) IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarFloordiv, GetSbp4ScalarMath) +IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarTruncdiv, GetSbp4ScalarMath) IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarFmod, GetSbp4ScalarMath) IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarMul, GetSbp4ScalarMul) IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarDiv, GetSbp4ScalarMul) @@ -71,15 +72,16 @@ IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarReversePow, GetSbp4ScalarMath) return Maybe::Ok(); } /*static*/ Maybe ScalarPowGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe ScalarPowGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ScalarPowGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << Error::TypeError() << "Tensors dy and x must have same type"; + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -91,107 +93,17 @@ IMPLEMENT_SCALAR_MATH_OP_FUNCS(ScalarReversePow, GetSbp4ScalarMath) return Maybe::Ok(); } /*static*/ Maybe ScalarReversePowGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe ScalarReversePowGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ScalarReversePowGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("x", 0), ctx->InputDType("dy", 0)) + << Error::TypeError() << "Tensors dy and x must have same type"; + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("scalar_add") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", 0), "in", 0); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_mul") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_mul") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("has_int_operand", op.attr("has_int_operand")) - .Attr("int_operand", op.attr_or_default("int_operand", 0)) - .Attr("has_float_operand", op.attr("has_float_operand")) - .Attr("float_operand", op.attr_or_default("float_operand", 0.0)) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_div") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_div") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("has_int_operand", op.attr("has_int_operand")) - .Attr("int_operand", op.attr_or_default("int_operand", 0)) - .Attr("has_float_operand", op.attr("has_float_operand")) - .Attr("float_operand", op.attr_or_default("float_operand", 0.0)) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_pow") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_pow_grad") - .Input("x", op.input("in", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Attr("has_int_operand", op.attr("has_int_operand")) - .Attr("int_operand", op.attr_or_default("int_operand", 0)) - .Attr("has_float_operand", op.attr("has_float_operand")) - .Attr("float_operand", op.attr_or_default("float_operand", 0.0)) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("scalar_reverse_pow") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("scalar_reverse_pow_grad") - .Input("x", op.input("in", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Attr("has_int_operand", op.attr("has_int_operand")) - .Attr("int_operand", op.attr_or_default("int_operand", 0)) - .Attr("has_float_operand", op.attr("has_float_operand")) - .Attr("float_operand", op.attr_or_default("float_operand", 0.0)) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/search_sorted_op.cpp b/oneflow/user/ops/search_sorted_op.cpp index 368114c17ec588e6a1f1dbdf325a17d9fcea2e9b..08794dd80850470ecd6e687a6dea57fa643b750c 100644 --- a/oneflow/user/ops/search_sorted_op.cpp +++ b/oneflow/user/ops/search_sorted_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe SearchSortedOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("values", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("values", 0)); return Maybe::Ok(); } @@ -46,15 +46,15 @@ namespace oneflow { /* static */ Maybe SearchSortedOp::InferDataType(user_op::InferContext* ctx) { const bool& out_int32 = ctx->Attr("out_int32"); if (out_int32) { - *ctx->OutputDType("out", 0) = DataType::kInt32; + ctx->SetOutputDType("out", 0, DataType::kInt32); } else { - *ctx->OutputDType("out", 0) = DataType::kInt64; + ctx->SetOutputDType("out", 0, DataType::kInt64); } return Maybe::Ok(); } /* static */ Maybe SearchSortedScalarOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = Shape({}); + ctx->SetOutputShape("out", 0, Shape({})); return Maybe::Ok(); } @@ -74,9 +74,9 @@ namespace oneflow { /* static */ Maybe SearchSortedScalarOp::InferDataType(user_op::InferContext* ctx) { const bool& out_int32 = ctx->Attr("out_int32"); if (out_int32) { - *ctx->OutputDType("out", 0) = DataType::kInt32; + ctx->SetOutputDType("out", 0, DataType::kInt32); } else { - *ctx->OutputDType("out", 0) = DataType::kInt64; + ctx->SetOutputDType("out", 0, DataType::kInt64); } return Maybe::Ok(); } diff --git a/oneflow/user/ops/selu_op.cpp b/oneflow/user/ops/selu_op.cpp index f336f3620339cfee4af3c53535ce4b7bfa4092b7..92694590e550b1a83144212153e773c5637cc155 100644 --- a/oneflow/user/ops/selu_op.cpp +++ b/oneflow/user/ops/selu_op.cpp @@ -26,14 +26,14 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SeluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SeluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SeluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -51,38 +51,19 @@ namespace oneflow { /*static*/ Maybe SeluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == x_shape) + << Error::RuntimeError() << "Tensors dy and x must be the same shape"; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } /*static*/ Maybe SeluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SeluGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << Error::TypeError() << "Tensors dy and x must have same type"; + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("selu").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) { - const auto selu_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(selu_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("selu_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &selu_grad_op_name]() -> const std::string& { - return ctx->GetOp(selu_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/sigmoid_cross_entropy_op.cpp b/oneflow/user/ops/sigmoid_cross_entropy_op.cpp index 1928b5ab5c25e2c5ceb8190a334d6ad0e604d28d..2772fac43d43048dc4e9b0ab27fd86eef4803bc3 100644 --- a/oneflow/user/ops/sigmoid_cross_entropy_op.cpp +++ b/oneflow/user/ops/sigmoid_cross_entropy_op.cpp @@ -33,17 +33,19 @@ namespace oneflow { /*static*/ Maybe SigmoidCrossEntropyOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()); - user_op::TensorDesc* loss_desc = ctx->OutputTensorDesc("loss", 0); - *loss_desc->mut_shape() = prediction_desc.shape(); - *loss_desc->mut_is_dynamic() = prediction_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()) + << Error::RuntimeError() << "The size of label " << label_desc.shape() + << " must match the size of prediction " << prediction_desc.shape(); + user_op::TensorDesc* loss_desc = ctx->MutOutputTensorDesc("loss", 0); + loss_desc->set_shape(prediction_desc.shape()); + loss_desc->set_is_dynamic(prediction_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe SigmoidCrossEntropyOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SigmoidCrossEntropyOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("loss", 0) = ctx->InputDType("prediction", 0); + ctx->SetOutputDType("loss", 0, ctx->InputDType("prediction", 0)); return Maybe::Ok(); } /*static*/ Maybe SigmoidCrossEntropyOp::ModifyInputArg( @@ -71,11 +73,15 @@ namespace oneflow { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); const user_op::TensorDesc& loss_diff_desc = ctx->InputTensorDesc("loss_diff", 0); - CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()); - CHECK_EQ_OR_RETURN(loss_diff_desc.shape(), prediction_desc.shape()); - user_op::TensorDesc* prediction_diff = ctx->OutputTensorDesc("prediction_diff", 0); - *prediction_diff->mut_shape() = prediction_desc.shape(); - *prediction_diff->mut_is_dynamic() = prediction_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()) + << Error::RuntimeError() << "The size of label " << label_desc.shape() + << " must match the size of prediction " << prediction_desc.shape(); + CHECK_EQ_OR_RETURN(loss_diff_desc.shape(), prediction_desc.shape()) + << Error::RuntimeError() << "The size of loss_diff " << loss_diff_desc.shape() + << " must match the size of prediction " << prediction_desc.shape(); + user_op::TensorDesc* prediction_diff = ctx->MutOutputTensorDesc("prediction_diff", 0); + prediction_diff->set_shape(prediction_desc.shape()); + prediction_diff->set_is_dynamic(prediction_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe SigmoidCrossEntropyGradOp::InferPhysicalTensorDesc( @@ -83,7 +89,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SigmoidCrossEntropyGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("prediction_diff", 0) = ctx->InputDType("prediction", 0); + ctx->SetOutputDType("prediction_diff", 0, ctx->InputDType("prediction", 0)); return Maybe::Ok(); } /*static*/ Maybe SigmoidCrossEntropyGradOp::ModifyInputArg( @@ -93,21 +99,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("sigmoid_cross_entropy") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("prediction", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("sigmoid_cross_entropy_grad") - .Input("prediction", op.input("prediction", 0)) - .Input("label", op.input("label", 0)) - .Input("loss_diff", op.GetGradTensorWithOpOutput("loss", 0)) - .Output("prediction_diff") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("prediction_diff", 0), "prediction", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/silu_op.cpp b/oneflow/user/ops/silu_op.cpp index 8aa50309ce0f3466700e9dcfb87ff7a7046f279a..2ce0f325f5a35afc133ccfe50b104f1cdfab6d73 100644 --- a/oneflow/user/ops/silu_op.cpp +++ b/oneflow/user/ops/silu_op.cpp @@ -26,14 +26,14 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SiluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SiluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SiluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -51,38 +51,21 @@ namespace oneflow { /*static*/ Maybe SiluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == x_shape) << Error::RuntimeError() << "The size of dy " << dy_shape + << " must match the size of x " << x_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } /*static*/ Maybe SiluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SiluGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << Error::TypeError() << "dy and x are expected to have the same dtype, but found " + << DataType_Name(ctx->InputDType("dy", 0)) << " and " + << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("silu").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) { - const auto silu_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(silu_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("silu_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &silu_grad_op_name]() -> const std::string& { - return ctx->GetOp(silu_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/slice_op.cpp b/oneflow/user/ops/slice_op.cpp index 3c547ec593bb4bce8448540899487dab20cd5df2..8eb405b04eba73c1c090bff60f3719a64152d33e 100644 --- a/oneflow/user/ops/slice_op.cpp +++ b/oneflow/user/ops/slice_op.cpp @@ -37,11 +37,17 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const auto& stop_vec = ctx->Attr>("stop"); const auto& step_vec = ctx->Attr>("step"); CHECK_EQ_OR_RETURN(start_vec.size(), ndim) - << "start_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of start list must be equal to the dimension of ref tensor, " + << "but got " << start_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(stop_vec.size(), ndim) - << "stop_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of stop list must be equal to the dimension of ref tensor, " + << "but got " << stop_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(step_vec.size(), ndim) - << "step_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of step list must be equal to the dimension of ref tensor, " + << "but got " << step_vec.size() << " and " << ndim; FOR_RANGE(int64_t, axis, 0, ndim) { ctx->NewBuilder() @@ -67,34 +73,51 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const auto& start_vec = ctx->Attr>("start"); const auto& stop_vec = ctx->Attr>("stop"); const auto& step_vec = ctx->Attr>("step"); - CHECK_OR_RETURN(!ref_desc.is_dynamic()); + CHECK_OR_RETURN(!ref_desc.is_dynamic()) + << Error::RuntimeError() << "The ref tensor is not dynamic"; FOR_RANGE(size_t, i, 0, step_vec.size()) { const int64_t step = step_vec.at(i); const int64_t start = start_vec.at(i); const int64_t stop = stop_vec.at(i); - CHECK_GT_OR_RETURN(step, 0) << "slice_update step must be greater than 0"; - CHECK_GE_OR_RETURN(start, 0) << "slice_update start must be greater or equal to 0"; - CHECK_GE_OR_RETURN(stop, 0) << "slice_update stop must be greater or equal than 0"; - CHECK_LE_OR_RETURN(start, stop) << "slice_update start must be less or equal than stop"; + CHECK_GT_OR_RETURN(step, 0) << Error::RuntimeError() + << "The step list elements must be greater than 0, " + << "but got " << step << " at index " << i; + + CHECK_GE_OR_RETURN(start, 0) << Error::RuntimeError() + << "The start list elements must be greater than or equal to 0, " + << "but got " << start << " at index " << i; + CHECK_GE_OR_RETURN(stop, 0) << Error::RuntimeError() + << "The stop list elements must be greater than or equal to 0, " + << "but got " << stop << " at index " << i; + CHECK_LE_OR_RETURN(start, stop) << Error::RuntimeError() + << "The element in start list must be less than or equal to " + "the element in stop list at index " + << i << ", but got " << start << " and " << stop; CHECK_EQ_OR_RETURN((stop - start + step - 1) / step, value_shape.At(i)) - << "slice_update slice tuple size must equal to value tensor shape, but got " << start - << ":" << stop << ":" << step << " vs " << value_shape.At(i) << " at dim " - << "i"; + << Error::RuntimeError() + << "The size of slice tuple must be equal to the size of value tensor at dimension " << i + << ", but got " << (stop - start + step - 1) / step << " and " << value_shape.At(i); } - auto* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = ref_desc.shape(); - *y_desc->mut_is_dynamic() = ref_desc.is_dynamic(); + auto* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(ref_desc.shape()); + y_desc->set_is_dynamic(ref_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe SliceUpdateOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { - return InferLogicalTensorDesc(ctx); + const user_op::TensorDesc& ref_desc = ctx->InputTensorDesc("ref", 0); + auto* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(ref_desc.shape()); + y_desc->set_is_dynamic(ref_desc.is_dynamic()); + return Maybe::Ok(); } /*static*/ Maybe SliceUpdateOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& ref_desc = ctx->InputTensorDesc("ref", 0); const user_op::TensorDesc& value_desc = ctx->InputTensorDesc("value", 0); - CHECK_OR_RETURN(ref_desc.data_type() == value_desc.data_type()); - auto* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_data_type() = ref_desc.data_type(); + CHECK_OR_RETURN(ref_desc.data_type() == value_desc.data_type()) + << Error::TypeError() << "Tensors ref and value must have same type"; + auto* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_data_type(ref_desc.data_type()); + y_desc->set_stride(ref_desc.stride()); return Maybe::Ok(); } @@ -136,14 +159,23 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const int64_t step = step_vec.at(i); const int64_t start = start_vec.at(i); const int64_t stop = stop_vec.at(i); - CHECK_GT_OR_RETURN(step, 0) << "Slice step must be greater than 0"; - CHECK_GE_OR_RETURN(start, 0) << "Slice start must be greater or equal to 0"; - CHECK_GE_OR_RETURN(stop, 0) << "Slice stop must be greater or equal to 0"; - CHECK_LE_OR_RETURN(start, stop) << "Slice start must be less or equal to stop"; + CHECK_GT_OR_RETURN(step, 0) << Error::RuntimeError() + << "The step list elements must be greater than 0, " + << "but got " << step << " at index " << i; + CHECK_GE_OR_RETURN(start, 0) << Error::RuntimeError() + << "The start list elements must be greater than or equal to 0, " + << "but got " << start << " at index " << i; + CHECK_GE_OR_RETURN(stop, 0) << Error::RuntimeError() + << "The stop list elements must be greater than or equal to 0, " + << "but got " << stop << " at index " << i; + CHECK_LE_OR_RETURN(start, stop) << Error::RuntimeError() + << "The element in start list must be less than or equal to " + "the element in stop list at index " + << i << ", but got " << start << " and " << stop; const int64_t diff = stop - start - 1; dim_vec[i] = diff / step + 1; } - *ctx->OutputShape("y", 0) = Shape(dim_vec); + ctx->SetOutputShape("y", 0, Shape(dim_vec)); return Maybe::Ok(); } /*static*/ Maybe SliceOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -171,11 +203,11 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); const TensorSliceView& slice_view = GetTensorSliceView4ParallelId(parallel_hierarchy, y_nd_sbp, logical_shape, parallel_id); - *ctx->OutputShape("y", 0) = Shape(slice_view.shape()); + ctx->SetOutputShape("y", 0, Shape(slice_view.shape())); return Maybe::Ok(); } /*static*/ Maybe SliceOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -186,12 +218,17 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const auto& stop_vec = ctx->Attr>("stop"); const auto& step_vec = ctx->Attr>("step"); CHECK_EQ_OR_RETURN(start_vec.size(), ndim) - << "start_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of start list must be equal to the dimension of ref tensor, " + << "but got " << start_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(stop_vec.size(), ndim) - << "stop_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of stop list must be equal to the dimension of ref tensor, " + << "but got " << stop_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(step_vec.size(), ndim) - << "step_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; - + << Error::RuntimeError() + << "The size of step list must be equal to the dimension of ref tensor, " + << "but got " << step_vec.size() << " and " << ndim; FOR_RANGE(int, i, 0, ndim) { if (IsFullSlice(start_vec[i], stop_vec[i], step_vec[i], like_shape.At(i))) { ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); @@ -210,32 +247,38 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { const int64_t ndim = dy_shape.NumAxes(); CHECK_EQ_OR_RETURN(start_vec.size(), ndim) - << "start_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of start list must be equal to the dimension of ref tensor, " + << "but got " << start_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(stop_vec.size(), ndim) - << "stop_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; + << Error::RuntimeError() + << "The size of stop list must be equal to the dimension of ref tensor, " + << "but got " << stop_vec.size() << " and " << ndim; CHECK_EQ_OR_RETURN(step_vec.size(), ndim) - << "step_vec's dim not equal to ref shape's dim: " << start_vec.size() << " vs " << ndim; - *ctx->OutputShape("dx", 0) = like_shape; + << Error::RuntimeError() + << "The size of step list must be equal to the dimension of ref tensor, " + << "but got " << step_vec.size() << " and " << ndim; + ctx->SetOutputShape("dx", 0, like_shape); return Maybe::Ok(); } /*static*/ Maybe SliceGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { Shape logical_shape = ctx->Attr("like_shape"); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = dy_desc.is_dynamic(); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(dy_desc.is_dynamic()); const auto& nd_sbp = ctx->NdSbp4ArgNameAndIndex("dx", 0); - *(dx_desc->mut_shape()) = - *JUST(GetPhysicalShape(logical_shape, nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx())); + dx_desc->set_shape( + *JUST(GetPhysicalShape(logical_shape, nd_sbp, ctx->parallel_desc(), ctx->parallel_ctx()))); int dx_ndim = dx_desc->shape().NumAxes(); int dy_ndim = dy_desc.shape().NumAxes(); CHECK_EQ_OR_RETURN(dx_ndim, dy_ndim) - << "Output dimension (" << dx_ndim << ") should equal to the input dimension (" << dy_ndim - << ") for slice backward."; + << Error::RuntimeError() << "The output dimension (" << dx_ndim + << ") should be equal to the input dimension (" << dy_ndim << ") for slice backward"; return Maybe::Ok(); } /*static*/ Maybe SliceGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } /*static*/ Maybe SliceGradOp::ModifyInputArg(const GetInputArgModifier& GetInputArgModifierFn, @@ -245,70 +288,4 @@ bool IsFullSlice(int64_t start, int64_t stop, int64_t step, int64_t size) { return Maybe::Ok(); } -namespace { - -Maybe GenSliceUpdateGradOp(user_op::BackwardOpConfContext* ctx) { - // value grad - const std::string update_grad_op_name = ctx->FwOp().op_name() + "_value_grad"; - ctx->DefineOp(update_grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("slice") - .InputBind("x", ctx->FwOp().output_grad("y", 0)) - .Attr("start", ctx->FwOp().attr>("start")) - .Attr("stop", ctx->FwOp().attr>("stop")) - .Attr("step", ctx->FwOp().attr>("step")) - .Output("y") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("value", 0), [&]() -> const std::string& { - return ctx->GetOp(update_grad_op_name).output("y", 0); - }); - - // ref grad - const std::string zero_grad_op_name = ctx->FwOp().op_name() + "_zero_grad"; - ctx->DefineOp(zero_grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("zero_like") - .InputBind("like", ctx->FwOp().input("value", 0)) - .Output("out") - .Build(); - }); - const std::string x_grad_op_name = ctx->FwOp().op_name() + "_x_grad"; - ctx->DefineOp(x_grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("slice_update") - .InputBind("ref", ctx->FwOp().output_grad("y", 0)) - .InputBind("value", ctx->GetOp(zero_grad_op_name).output("out", 0)) - .Attr("start", ctx->FwOp().attr>("start")) - .Attr("stop", ctx->FwOp().attr>("stop")) - .Attr("step", ctx->FwOp().attr>("step")) - .Output("y") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("ref", 0), [&]() -> const std::string& { - return ctx->GetOp(x_grad_op_name).output("y", 0); - }); - return Maybe::Ok(); -} - -Maybe GenSliceGradOp(user_op::BackwardOpConfContext* ctx) { - const std::string ref_grad_op_name = ctx->FwOp().op_name() + "_x_grad"; - ctx->DefineOp(ref_grad_op_name, [&](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("slice_grad") - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .Attr("like_shape", ctx->FwOp().arg_tensor_desc("x", 0).shape()) - .Attr("start", ctx->FwOp().attr>("start")) - .Attr("stop", ctx->FwOp().attr>("stop")) - .Attr("step", ctx->FwOp().attr>("step")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), [&]() -> const std::string& { - return ctx->GetOp(ref_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); -} - -} // namespace - -REGISTER_USER_OP_GRAD("slice_update").SetBackwardOpConfGenFn(GenSliceUpdateGradOp); -REGISTER_USER_OP_GRAD("slice").SetBackwardOpConfGenFn(GenSliceGradOp); - } // namespace oneflow diff --git a/oneflow/user/ops/smooth_l1_loss_op.cpp b/oneflow/user/ops/smooth_l1_loss_op.cpp index 025895cb2d775b5bc9873af12eadc8feeccb13d6..c52e8825fe7538b8be5a02c783304224a70100a2 100644 --- a/oneflow/user/ops/smooth_l1_loss_op.cpp +++ b/oneflow/user/ops/smooth_l1_loss_op.cpp @@ -29,13 +29,20 @@ namespace oneflow { /*static*/ Maybe SmoothL1LossOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const auto& input_desc = ctx->InputTensorDesc("input", 0); const auto& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); - CHECK_GE_OR_RETURN(ctx->Attr("beta"), 0); - - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *out_desc->mut_shape() = input_desc.shape(); + CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()) + << Error::RuntimeError() + << "input and target are expected to have the same dynamic property, but found " + << input_desc.is_dynamic() << " and " << target_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) + << Error::RuntimeError() << "The size of input " << input_desc.shape() + << " must match the size of target " << target_desc.shape(); + CHECK_GE_OR_RETURN(ctx->Attr("beta"), 0) + << Error::RuntimeError() << "beta must be greater than or equal to 0, but found it to be " + << ctx->Attr("beta"); + + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(input_desc.is_dynamic()); + out_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -45,16 +52,18 @@ namespace oneflow { /*static*/ Maybe SmoothL1LossOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << Error::TypeError() << "input and target are expected to have the same dtype, but found " + << DataType_Name(input_desc.data_type()) << " and " << DataType_Name(target_desc.data_type()); - *ctx->OutputDType("out", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } /*static*/ Maybe SmoothL1LossOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* target_modifier = GetInputArgModifierFn("target", 0); - CHECK_OR_RETURN(target_modifier != nullptr); + CHECK_OR_RETURN(target_modifier != nullptr); // NOLINT(maybe-need-error-msg) target_modifier->set_requires_grad(false); return Maybe::Ok(); } @@ -75,15 +84,24 @@ namespace oneflow { const auto& input_desc = ctx->InputTensorDesc("input", 0); const auto& target_desc = ctx->InputTensorDesc("target", 0); const auto& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()); - CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()); - CHECK_EQ_OR_RETURN(dy_desc.shape(), target_desc.shape()); - - CHECK_GE_OR_RETURN(ctx->Attr("beta"), 0); - - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_is_dynamic() = input_desc.is_dynamic(); - *dx_desc->mut_shape() = input_desc.shape(); + CHECK_EQ_OR_RETURN(input_desc.is_dynamic(), target_desc.is_dynamic()) + << Error::RuntimeError() + << "input and target are expected to have the same dynamic property, but found " + << input_desc.is_dynamic() << " and " << target_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(input_desc.shape(), target_desc.shape()) + << Error::RuntimeError() << "The size of input " << input_desc.shape() + << " must match the size of target " << target_desc.shape(); + CHECK_EQ_OR_RETURN(dy_desc.shape(), target_desc.shape()) + << Error::RuntimeError() << "The size of dy " << dy_desc.shape() + << " must match the size of target " << target_desc.shape(); + + CHECK_GE_OR_RETURN(ctx->Attr("beta"), 0) + << Error::RuntimeError() << "beta must be greater than or equal to 0, but found it to be " + << ctx->Attr("beta"); + + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_is_dynamic(input_desc.is_dynamic()); + dx_desc->set_shape(input_desc.shape()); return Maybe::Ok(); } @@ -93,30 +111,13 @@ namespace oneflow { /*static*/ Maybe SmoothL1LossGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& input_desc = ctx->InputTensorDesc("input", 0); const user_op::TensorDesc& target_desc = ctx->InputTensorDesc("target", 0); - CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()); + CHECK_EQ_OR_RETURN(input_desc.data_type(), target_desc.data_type()) + << Error::TypeError() << "input and target are expected to have the same dtype, but found " + << DataType_Name(input_desc.data_type()) << " and " << DataType_Name(target_desc.data_type()); - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("smooth_l1_loss") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("smooth_l1_loss_grad") - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Input("input", op.input("input", 0)) - .Input("target", op.input("target", 0)) - .Output("dx") - .Attr("beta", op.attr("beta")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "input", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/softmax_cross_entropy_op.cpp b/oneflow/user/ops/softmax_cross_entropy_op.cpp index aa42ab0ee40f7003c1e3c4cee092eeb1d8889a8f..f1546cf739b06589ba275aca3175cb822b959e78 100644 --- a/oneflow/user/ops/softmax_cross_entropy_op.cpp +++ b/oneflow/user/ops/softmax_cross_entropy_op.cpp @@ -35,19 +35,27 @@ namespace oneflow { /*static*/ Maybe SoftmaxCrossEntropyOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_EQ_OR_RETURN(prediction_desc.is_dynamic(), label_desc.is_dynamic()); - CHECK_GE_OR_RETURN(prediction_desc.shape().NumAxes(), 2); - CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()); + CHECK_EQ_OR_RETURN(prediction_desc.is_dynamic(), label_desc.is_dynamic()) + << Error::RuntimeError() + << "prediction and label are expected to have the same dynamic property, but found " + << prediction_desc.is_dynamic() << " and " << label_desc.is_dynamic(); + CHECK_GE_OR_RETURN(prediction_desc.shape().NumAxes(), 2) + << Error::RuntimeError() + << "The dimension of prediction must be greater than or equal to 2, but found " + << prediction_desc.shape().NumAxes(); + CHECK_EQ_OR_RETURN(label_desc.shape(), prediction_desc.shape()) + << Error::RuntimeError() << "The size of label " << label_desc.shape() + << " must match the size of prediction " << prediction_desc.shape(); const int64_t num_out_axes = prediction_desc.shape().NumAxes() - 1; DimVector out_dim_vector; FOR_RANGE(int64_t, i, 0, num_out_axes) { out_dim_vector.emplace_back(prediction_desc.shape().At(i)); } - *ctx->OutputShape("prob", 0) = ctx->InputShape("prediction", 0); - *ctx->OutputIsDynamic("prob", 0) = ctx->InputIsDynamic("prediction", 0); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = prediction_desc.is_dynamic(); - *out_desc->mut_shape() = Shape(out_dim_vector); + ctx->SetOutputShape("prob", 0, ctx->InputShape("prediction", 0)); + ctx->SetOutputIsDynamic("prob", 0, ctx->InputIsDynamic("prediction", 0)); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(prediction_desc.is_dynamic()); + out_desc->set_shape(Shape(out_dim_vector)); return Maybe::Ok(); } /*static*/ Maybe SoftmaxCrossEntropyOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -56,10 +64,14 @@ namespace oneflow { /*static*/ Maybe SoftmaxCrossEntropyOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_EQ_OR_RETURN(label_desc.data_type(), prediction_desc.data_type()); - *ctx->OutputDType("prob", 0) = ctx->InputDType("prediction", 0); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = prediction_desc.data_type(); + CHECK_EQ_OR_RETURN(label_desc.data_type(), prediction_desc.data_type()) + << Error::TypeError() + << "label and prediction are expected to have the same dtype, but found " + << DataType_Name(label_desc.data_type()) << " and " + << DataType_Name(prediction_desc.data_type()); + ctx->SetOutputDType("prob", 0, ctx->InputDType("prediction", 0)); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(prediction_desc.data_type()); return Maybe::Ok(); } /*static*/ Maybe SoftmaxCrossEntropyOp::ModifyInputArg( @@ -86,15 +98,28 @@ namespace oneflow { const user_op::TensorDesc& prob_desc = ctx->InputTensorDesc("prob", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(prob_desc.is_dynamic(), label_desc.is_dynamic()); - CHECK_GE_OR_RETURN(prob_desc.shape().NumAxes(), 2); - CHECK_EQ_OR_RETURN(dy_desc.shape().NumAxes(), prob_desc.shape().NumAxes() - 1); + CHECK_EQ_OR_RETURN(prob_desc.is_dynamic(), label_desc.is_dynamic()) + << Error::RuntimeError() + << "prob and label are expected to have the same dynamic property, but found " + << prob_desc.is_dynamic() << " and " << label_desc.is_dynamic(); + CHECK_GE_OR_RETURN(prob_desc.shape().NumAxes(), 2) + << Error::RuntimeError() + << "The dimension of prob must be greater than or equal to 2, but found " + << prob_desc.shape().NumAxes(); + CHECK_EQ_OR_RETURN(dy_desc.shape().NumAxes(), prob_desc.shape().NumAxes() - 1) + << Error::RuntimeError() + << "The dimension of dy is expected to be less than that of prob by 1, but found " + << dy_desc.shape().NumAxes() << " and " << prob_desc.shape().NumAxes() - 1; FOR_RANGE(int64_t, i, 0, dy_desc.shape().NumAxes()) { - CHECK_EQ_OR_RETURN(dy_desc.shape().At(i), label_desc.shape().At(i)); + CHECK_EQ_OR_RETURN(dy_desc.shape().At(i), label_desc.shape().At(i)) + << Error::RuntimeError() << "The size of dy (" << dy_desc.shape().At(i) + << ") must match the size of label (" << label_desc.shape().At(i) << ") at dimension " << i; } - CHECK_EQ_OR_RETURN(label_desc.shape(), prob_desc.shape()); - *ctx->OutputShape("prediction_diff", 0) = ctx->InputShape("prob", 0); - *ctx->OutputIsDynamic("prediction_diff", 0) = ctx->InputIsDynamic("prob", 0); + CHECK_EQ_OR_RETURN(label_desc.shape(), prob_desc.shape()) + << Error::RuntimeError() << "The size of label " << label_desc.shape() + << " must match the size of prob " << prob_desc.shape(); + ctx->SetOutputShape("prediction_diff", 0, ctx->InputShape("prob", 0)); + ctx->SetOutputIsDynamic("prediction_diff", 0, ctx->InputIsDynamic("prob", 0)); return Maybe::Ok(); } /*static*/ Maybe SoftmaxCrossEntropyGradOp::InferPhysicalTensorDesc( @@ -105,28 +130,14 @@ namespace oneflow { const user_op::TensorDesc& prob_desc = ctx->InputTensorDesc("prob", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(label_desc.data_type(), prob_desc.data_type()); - CHECK_EQ_OR_RETURN(dy_desc.data_type(), prob_desc.data_type()); - *ctx->OutputDType("prediction_diff", 0) = ctx->InputDType("prob", 0); + CHECK_EQ_OR_RETURN(label_desc.data_type(), prob_desc.data_type()) + << Error::TypeError() << "label and prob are expected to have the same dtype, but found " + << DataType_Name(label_desc.data_type()) << " and " << DataType_Name(prob_desc.data_type()); + CHECK_EQ_OR_RETURN(dy_desc.data_type(), prob_desc.data_type()) + << Error::TypeError() << "dy and prob are expected to have the same dtype, but found " + << DataType_Name(dy_desc.data_type()) << " and " << DataType_Name(prob_desc.data_type()); + ctx->SetOutputDType("prediction_diff", 0, ctx->InputDType("prob", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("softmax_cross_entropy") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("prediction", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("softmax_cross_entropy_grad") - .Input("prob", op.output("prob", 0)) - .Input("label", op.input("label", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("prediction_diff") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("prediction_diff", 0), "prediction", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/softmax_op.cpp b/oneflow/user/ops/softmax_op.cpp index d460508d7830e68ebd9f655d75522b192573f5f5..8ddfb02a7d18323d83b51ea1eaf9606b58da2efc 100644 --- a/oneflow/user/ops/softmax_op.cpp +++ b/oneflow/user/ops/softmax_op.cpp @@ -29,17 +29,32 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SoftmaxOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SoftmaxOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SoftmaxOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } +// Logically computation cost of pool op is the product of output data amount and pool kernal data +// amount. After adding sbp, we just divide it by parallel number if output data is splitted because +// splitting input and using partial sum for output is not a valid sbp for this op for now. +/*static*/ Maybe SoftmaxOp::GetComputeComplexity(user_op::ComputeComplexityFnContext* ctx) { + double logical_computation_cost = ctx->Shape4ArgNameAndIndex("in", 0).elem_cnt() * 10; + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + const auto& nd_sbp_in = ctx->NdSbp4ArgNameAndIndex("in", 0); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp_in.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp_in.sbp_parallel(dim_sbp).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(dim_sbp); + } + } + return logical_computation_cost; +} + /*static*/ Maybe SoftmaxGradOp::GetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& y_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("y", 0); FOR_RANGE(int64_t, axis, 0, y_tensor.shape().NumAxes() - 1) { @@ -54,38 +69,21 @@ namespace oneflow { /*static*/ Maybe SoftmaxGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("y", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == y_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == y_shape) << Error::RuntimeError() << "The size of dy " << dy_shape + << " must match the size of y " << y_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } /*static*/ Maybe SoftmaxGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SoftmaxGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("y", 0), ctx->InputDType("dy", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("y", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("y", 0)) + << Error::TypeError() << "dy and y are expected to have the same dtype, but found " + << DataType_Name(ctx->InputDType("dy", 0)) << " and " + << DataType_Name(ctx->InputDType("y", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("y", 0)); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("softmax").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper softmax_grad_op = - builder.Op("softmax_grad") - .Input("y", op.output("out", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(softmax_grad_op.output("dx", 0), "in", 0); - AddOp(softmax_grad_op); - } - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/softplus_op.cpp b/oneflow/user/ops/softplus_op.cpp index bd596517588c7bbe941118e494d9ba9acd2b1206..daab406b9ee25cbef44100433dc4d17618a3addc 100644 --- a/oneflow/user/ops/softplus_op.cpp +++ b/oneflow/user/ops/softplus_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe SoftplusOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,16 @@ namespace oneflow { } /* static */ Maybe SoftplusOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe SoftplusGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == x_shape) << Error::RuntimeError() << "The size of dy " << dy_shape + << " must match the size of x " << x_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,27 +66,12 @@ namespace oneflow { } /* static */ Maybe SoftplusGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << Error::TypeError() << "dy and x are expected to have the same dtype, but found " + << DataType_Name(ctx->InputDType("dy", 0)) << " and " + << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("softplus") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto softplus_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(softplus_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("softplus_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("beta", ctx->FwOp().attr("beta")) - .Attr("threshold", ctx->FwOp().attr("threshold")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &softplus_grad_op_name]() -> const std::string& { - return ctx->GetOp(softplus_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/softshrink_op.cpp b/oneflow/user/ops/softshrink_op.cpp index abfd8732458cfcfee69d83fdb7c6dacb599a3d4d..20ad9e61b6ae545c8dabe9322f5e96a2e89fecbe 100644 --- a/oneflow/user/ops/softshrink_op.cpp +++ b/oneflow/user/ops/softshrink_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe SoftShrinkOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,16 @@ namespace oneflow { } /* static */ Maybe SoftShrinkOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe SoftShrinkGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& y_shape = ctx->InputShape("y", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == y_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == y_shape) << Error::RuntimeError() << "The size of dy " << dy_shape + << " must match the size of y " << y_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,26 +66,12 @@ namespace oneflow { } /* static */ Maybe SoftShrinkGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("y", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("y", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("y", 0)) + << Error::TypeError() << "dy and y are expected to have the same dtype, but found " + << DataType_Name(ctx->InputDType("dy", 0)) << " and " + << DataType_Name(ctx->InputDType("y", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("y", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("softshrink") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto softshrink_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(softshrink_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("softshrink_grad") - .InputBind("y", ctx->FwOp().output("y", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("alpha", ctx->FwOp().attr("alpha")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &softshrink_grad_op_name]() -> const std::string& { - return ctx->GetOp(softshrink_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/softsign_op.cpp b/oneflow/user/ops/softsign_op.cpp index e3d180efcba9d72ca0888d00fb654f2486c4b02a..8f55c7295b32483813eac18501d188cc9aec1cf4 100644 --- a/oneflow/user/ops/softsign_op.cpp +++ b/oneflow/user/ops/softsign_op.cpp @@ -26,14 +26,14 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SoftsignOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SoftsignOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SoftsignOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } @@ -51,38 +51,21 @@ namespace oneflow { /*static*/ Maybe SoftsignGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); - CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + CHECK_OR_RETURN(dy_shape == x_shape) << Error::RuntimeError() << "The size of dy " << dy_shape + << " must match the size of x " << x_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } /*static*/ Maybe SoftsignGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SoftsignGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << Error::TypeError() << "dy and x are expected to have the same dtype, but found " + << DataType_Name(ctx->InputDType("dy", 0)) << " and " + << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -namespace { - -REGISTER_USER_OP_GRAD("softsign").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) { - const auto softsign_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(softsign_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("softsign_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &softsign_grad_op_name]() -> const std::string& { - return ctx->GetOp(softsign_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); -}); - -} // namespace - } // namespace oneflow diff --git a/oneflow/user/ops/sort_op.cpp b/oneflow/user/ops/sort_op.cpp index cbcbaa07e4833e512ffac2c67674714ed804dbd5..e072ac1b74b0b43c8ca0437e1fc4c2f1a7fc6ab2 100644 --- a/oneflow/user/ops/sort_op.cpp +++ b/oneflow/user/ops/sort_op.cpp @@ -28,20 +28,23 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SortOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SortOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SortOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /*static*/ Maybe SortOp::CheckAttr(const user_op::UserOpDefWrapper&, const user_op::UserOpConfWrapper& op_conf) { const std::string& direction = op_conf.attr("direction"); - CHECK_OR_RETURN(direction == "ASCENDING" || direction == "DESCENDING"); + CHECK_OR_RETURN(direction == "ASCENDING" || direction == "DESCENDING") + << Error::RuntimeError() + << "The input direction parameter value is expected to be ASCENDING or DESCENDING, " + << "but found it to be " << direction; return Maybe::Ok(); } diff --git a/oneflow/user/ops/sparse_cross_entropy_op.cpp b/oneflow/user/ops/sparse_cross_entropy_op.cpp index adf9acdebfd94e8a14ee805d0a4168c2695a9bb1..80c1794df6c3c8ac57ac70d0cfbe1c818fdd23a8 100644 --- a/oneflow/user/ops/sparse_cross_entropy_op.cpp +++ b/oneflow/user/ops/sparse_cross_entropy_op.cpp @@ -22,12 +22,24 @@ namespace { Maybe CheckPredictionLabelDesc(const user_op::TensorDesc* prediction_desc, const user_op::TensorDesc* label_desc) { - CHECK_EQ_OR_RETURN(prediction_desc->is_dynamic(), label_desc->is_dynamic()); - CHECK_GE_OR_RETURN(prediction_desc->shape().NumAxes(), 2); + CHECK_EQ_OR_RETURN(prediction_desc->is_dynamic(), label_desc->is_dynamic()) + << Error::RuntimeError() + << "prediction and label are expected to have the same dynamic property, but found " + << prediction_desc->is_dynamic() << " and " << label_desc->is_dynamic(); + CHECK_GE_OR_RETURN(prediction_desc->shape().NumAxes(), 2) + << Error::RuntimeError() + << "The dimension of prediction must be greater than or equal to 2, but found " + << prediction_desc->shape().NumAxes(); const int64_t num_out_axes = prediction_desc->shape().NumAxes() - 1; - CHECK_EQ_OR_RETURN(label_desc->shape().NumAxes(), num_out_axes); + CHECK_EQ_OR_RETURN(label_desc->shape().NumAxes(), num_out_axes) + << Error::RuntimeError() + << "The dimension of label is expected to be less than that of prediction by 1, but found " + << label_desc->shape().NumAxes() << " and " << num_out_axes; FOR_RANGE(int64_t, i, 0, num_out_axes) { - CHECK_EQ_OR_RETURN(prediction_desc->shape().At(i), label_desc->shape().At(i)); + CHECK_EQ_OR_RETURN(prediction_desc->shape().At(i), label_desc->shape().At(i)) + << Error::RuntimeError() << "The size of prediction (" << prediction_desc->shape().At(i) + << ") must match the size of label (" << label_desc->shape().At(i) << ") at dimension " + << i; } return Maybe::Ok(); } @@ -36,9 +48,9 @@ Maybe InferTensorDescFn(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); JUST(CheckPredictionLabelDesc(&prediction_desc, &label_desc)); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = prediction_desc.is_dynamic(); - *out_desc->mut_shape() = label_desc.shape(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(prediction_desc.is_dynamic()); + out_desc->set_shape(label_desc.shape()); return Maybe::Ok(); } @@ -47,18 +59,22 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); JUST(CheckPredictionLabelDesc(&prediction_desc, &label_desc)); - CHECK_EQ_OR_RETURN(dy_desc.shape(), label_desc.shape()); - *ctx->OutputShape("prediction_diff", 0) = prediction_desc.shape(); - *ctx->OutputIsDynamic("prediction_diff", 0) = prediction_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(dy_desc.shape(), label_desc.shape()) + << Error::RuntimeError() << "The size of dy " << dy_desc.shape() + << " must match the size of label " << label_desc.shape(); + ctx->SetOutputShape("prediction_diff", 0, prediction_desc.shape()); + ctx->SetOutputIsDynamic("prediction_diff", 0, prediction_desc.is_dynamic()); return Maybe::Ok(); } Maybe InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = prediction_desc.data_type(); + CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())) + << Error::TypeError() << "The dtype of label must be integer, but found " + << DataType_Name(label_desc.data_type()); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(prediction_desc.data_type()); return Maybe::Ok(); } @@ -66,27 +82,14 @@ Maybe InferDataTypeGrad(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())); - CHECK_EQ_OR_RETURN(dy_desc.data_type(), prediction_desc.data_type()); - *ctx->OutputDType("prediction_diff", 0) = prediction_desc.data_type(); - return Maybe::Ok(); -} - -Maybe GenBackwardOpConf4SparseCrossEntropy(const std::string& op_type_name, - const user_op::UserOpWrapper& op, - const user_op::AddOpFn& AddOp) { - if (op.NeedGenGradTensor4OpInput("prediction", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op(op_type_name) - .Input("prediction", op.input("prediction", 0)) - .Input("label", op.input("label", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("prediction_diff") - .Attr("depth", op.attr("depth")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("prediction_diff", 0), "prediction", 0); - AddOp(grad_op); - } + CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())) + << Error::TypeError() << "The dtype of label must be integer, but found " + << DataType_Name(label_desc.data_type()); + CHECK_EQ_OR_RETURN(dy_desc.data_type(), prediction_desc.data_type()) + << Error::TypeError() << "dy and prediction are expected to have the same dtype, but found " + << DataType_Name(dy_desc.data_type()) << " and " + << DataType_Name(prediction_desc.data_type()); + ctx->SetOutputDType("prediction_diff", 0, prediction_desc.data_type()); return Maybe::Ok(); } @@ -112,7 +115,7 @@ Maybe GenBackwardOpConf4SparseCrossEntropy(const std::string& op_type_name /*static*/ Maybe SparseCrossEntropyOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* label_modifier = GetInputArgModifierFn("label", 0); - CHECK_OR_RETURN(label_modifier != nullptr); + CHECK_OR_RETURN(label_modifier != nullptr); // NOLINT(maybe-need-error-msg) label_modifier->set_requires_grad(false); return Maybe::Ok(); } @@ -144,7 +147,7 @@ Maybe GenBackwardOpConf4SparseCrossEntropy(const std::string& op_type_name /*static*/ Maybe SparseCrossEntropyMsOp::ModifyInputArg( const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { user_op::InputArgModifier* label_modifier = GetInputArgModifierFn("label", 0); - CHECK_OR_RETURN(label_modifier != nullptr); + CHECK_OR_RETURN(label_modifier != nullptr); // NOLINT(maybe-need-error-msg) label_modifier->set_requires_grad(false); return Maybe::Ok(); } @@ -199,16 +202,4 @@ Maybe GenBackwardOpConf4SparseCrossEntropy(const std::string& op_type_name return InferDataTypeGrad(ctx); } -REGISTER_USER_OP_GRAD("sparse_cross_entropy") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - return GenBackwardOpConf4SparseCrossEntropy("sparse_cross_entropy_grad", op, AddOp); - }); - -REGISTER_USER_OP_GRAD("sparse_cross_entropy_ms") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - return GenBackwardOpConf4SparseCrossEntropy("sparse_cross_entropy_ms_grad", op, AddOp); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/sparse_softmax_cross_entropy_op.cpp b/oneflow/user/ops/sparse_softmax_cross_entropy_op.cpp index 5550e1caae885d2d87825d19674b97e377c07601..cee569f5a8d50aa428b1b7d0d7326d832beff896 100644 --- a/oneflow/user/ops/sparse_softmax_cross_entropy_op.cpp +++ b/oneflow/user/ops/sparse_softmax_cross_entropy_op.cpp @@ -23,19 +23,30 @@ namespace { Maybe InferTensorDescFn(user_op::InferContext* ctx) { const user_op::TensorDesc& prediction_desc = ctx->InputTensorDesc("prediction", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_EQ_OR_RETURN(prediction_desc.is_dynamic(), label_desc.is_dynamic()); - CHECK_GE_OR_RETURN(prediction_desc.shape().NumAxes(), 2); + CHECK_EQ_OR_RETURN(prediction_desc.is_dynamic(), label_desc.is_dynamic()) + << Error::RuntimeError() + << "prediction and label are expected to have the same dynamic property, but found " + << prediction_desc.is_dynamic() << " and " << label_desc.is_dynamic(); + CHECK_GE_OR_RETURN(prediction_desc.shape().NumAxes(), 2) + << Error::RuntimeError() + << "The dimension of prediction must be greater than or equal to 2, but found " + << prediction_desc.shape().NumAxes(); const int64_t num_out_axes = prediction_desc.shape().NumAxes() - 1; - CHECK_EQ_OR_RETURN(label_desc.shape().NumAxes(), num_out_axes); + CHECK_EQ_OR_RETURN(label_desc.shape().NumAxes(), num_out_axes) + << Error::RuntimeError() + << "The dimension of label is expected to be less than that of prediction by 1, but found " + << label_desc.shape().NumAxes() << " and " << num_out_axes; FOR_RANGE(int64_t, i, 0, num_out_axes) { - CHECK_EQ_OR_RETURN(prediction_desc.shape().At(i), label_desc.shape().At(i)); + CHECK_EQ_OR_RETURN(prediction_desc.shape().At(i), label_desc.shape().At(i)) + << Error::RuntimeError() << "The size of prediction (" << prediction_desc.shape().At(i) + << ") must match the size of label (" << label_desc.shape().At(i) << ") at dimension " << i; } - *ctx->OutputIsDynamic("prob", 0) = prediction_desc.is_dynamic(); + ctx->SetOutputIsDynamic("prob", 0, prediction_desc.is_dynamic()); // 'prob' is just for compute prediction's grad, prob's grad will be ignored - *ctx->OutputShape("prob", 0) = prediction_desc.shape(); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_is_dynamic() = prediction_desc.is_dynamic(); - *out_desc->mut_shape() = label_desc.shape(); + ctx->SetOutputShape("prob", 0, prediction_desc.shape()); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_is_dynamic(prediction_desc.is_dynamic()); + out_desc->set_shape(label_desc.shape()); return Maybe::Ok(); } @@ -43,24 +54,39 @@ Maybe InferGradTensorDescFn(user_op::InferContext* ctx) { const user_op::TensorDesc& prob_desc = ctx->InputTensorDesc("prob", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(prob_desc.is_dynamic(), label_desc.is_dynamic()); - CHECK_GE_OR_RETURN(prob_desc.shape().NumAxes(), 2); + CHECK_EQ_OR_RETURN(prob_desc.is_dynamic(), label_desc.is_dynamic()) + << Error::RuntimeError() + << "prob and label are expected to have the same dynamic property, but found " + << prob_desc.is_dynamic() << " and " << label_desc.is_dynamic(); + CHECK_GE_OR_RETURN(prob_desc.shape().NumAxes(), 2) + << Error::RuntimeError() + << "The dimension of prob must be greater than or equal to 2, but found " + << prob_desc.shape().NumAxes(); const int64_t num_out_axes = prob_desc.shape().NumAxes() - 1; - CHECK_EQ_OR_RETURN(label_desc.shape().NumAxes(), num_out_axes); + CHECK_EQ_OR_RETURN(label_desc.shape().NumAxes(), num_out_axes) + << Error::RuntimeError() + << "The dimension of label is expected to be less than that of prediction by 1, but found " + << label_desc.shape().NumAxes() << " and " << num_out_axes; FOR_RANGE(int64_t, i, 0, num_out_axes) { - CHECK_EQ_OR_RETURN(prob_desc.shape().At(i), label_desc.shape().At(i)); + CHECK_EQ_OR_RETURN(prob_desc.shape().At(i), label_desc.shape().At(i)) + << Error::RuntimeError() << "The size of prob (" << prob_desc.shape().At(i) + << ") must match the size of label (" << label_desc.shape().At(i) << ") at dimension " << i; } - CHECK_EQ_OR_RETURN(dy_desc.shape(), label_desc.shape()); - *ctx->OutputShape("prediction_diff", 0) = prob_desc.shape(); - *ctx->OutputIsDynamic("prediction_diff", 0) = prob_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(dy_desc.shape(), label_desc.shape()) + << Error::RuntimeError() << "The size of dy " << dy_desc.shape() + << " must match the size of label " << label_desc.shape(); + ctx->SetOutputShape("prediction_diff", 0, prob_desc.shape()); + ctx->SetOutputIsDynamic("prediction_diff", 0, prob_desc.is_dynamic()); return Maybe::Ok(); } Maybe InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); - CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())); - *ctx->OutputDType("prob", 0) = ctx->InputDType("prediction", 0); - *ctx->OutputDType("out", 0) = ctx->InputDType("prediction", 0); + CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())) + << Error::TypeError() << "The dtype of label must be integer, but found " + << DataType_Name(label_desc.data_type()); + ctx->SetOutputDType("prob", 0, ctx->InputDType("prediction", 0)); + ctx->SetOutputDType("out", 0, ctx->InputDType("prediction", 0)); return Maybe::Ok(); } @@ -68,10 +94,13 @@ Maybe InferDataTypeGrad(user_op::InferContext* ctx) { const user_op::TensorDesc& prob_desc = ctx->InputTensorDesc("prob", 0); const user_op::TensorDesc& label_desc = ctx->InputTensorDesc("label", 0); CHECK_OR_RETURN(IsIndexDataType(label_desc.data_type())) - << label_desc.data_type() << " is not index data type, op name: " << ctx->op_name(); + << Error::TypeError() << "The dtype of label must be integer, but found " + << DataType_Name(label_desc.data_type()); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - CHECK_EQ_OR_RETURN(dy_desc.data_type(), prob_desc.data_type()); - *ctx->OutputDType("prediction_diff", 0) = prob_desc.data_type(); + CHECK_EQ_OR_RETURN(dy_desc.data_type(), prob_desc.data_type()) + << Error::TypeError() << "dy and prob are expected to have the same dtype, but found " + << DataType_Name(dy_desc.data_type()) << " and " << DataType_Name(prob_desc.data_type()); + ctx->SetOutputDType("prediction_diff", 0, prob_desc.data_type()); return Maybe::Ok(); } @@ -136,24 +165,6 @@ Maybe GetSbpFn(user_op::SbpContext* ctx) { return Maybe::Ok(); } -Maybe GenBackwardOpConf4SparseSoftmaxCrossEntropy(const std::string& op_type_name, - const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - if (op.NeedGenGradTensor4OpInput("prediction", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op(op_type_name) - .Input("prob", op.output("prob", 0)) - .Input("label", op.input("label", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("out", 0)) - .Output("prediction_diff") - .Attr("depth", op.attr("depth")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("prediction_diff", 0), "prediction", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -} - } // namespace #define IMPLEMENT_SPAESE_SOFTMAX_CROSS_ENTROPY_OP_FUNCS(op_name, sbp_sig) \ @@ -170,7 +181,7 @@ Maybe GenBackwardOpConf4SparseSoftmaxCrossEntropy(const std::string& op_ty /*static*/ Maybe op_name##Op::ModifyInputArg( \ const GetInputArgModifier& GetInputArgModifierFn, const user_op::UserOpConfWrapper&) { \ user_op::InputArgModifier* label_modifier = GetInputArgModifierFn("label", 0); \ - CHECK_OR_RETURN(label_modifier != nullptr); \ + CHECK_OR_RETURN(label_modifier != nullptr); /* NOLINT(maybe-need-error-msg) */ \ label_modifier->set_requires_grad(false); \ return Maybe::Ok(); \ } @@ -198,18 +209,4 @@ IMPLEMENT_SPAESE_SOFTMAX_CROSS_ENTROPY_GRAD_OP_FUNCS(SparseSoftmaxCrossEntropyMs AddGradMsSignature); #undef IMPLEMENT_SPAESE_SOFTMAX_CROSS_ENTROPY_GRAD_OP_FUNCS -REGISTER_USER_OP_GRAD("sparse_softmax_cross_entropy") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - return GenBackwardOpConf4SparseSoftmaxCrossEntropy("sparse_softmax_cross_entropy_grad", op, - AddOp); - }); - -REGISTER_USER_OP_GRAD("sparse_softmax_cross_entropy_ms") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - return GenBackwardOpConf4SparseSoftmaxCrossEntropy("sparse_softmax_cross_entropy_ms_grad", op, - AddOp); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/split_like_op.cpp b/oneflow/user/ops/split_like_op.cpp index 2e0505b48143f4c20115a0672c4945c5379c3884..78e7312dd70bc959adad06bbda8482b1cf038017 100644 --- a/oneflow/user/ops/split_like_op.cpp +++ b/oneflow/user/ops/split_like_op.cpp @@ -68,12 +68,18 @@ namespace oneflow { int64_t static_dim_size = 0; const int64_t in_num_axes = ctx->InputTensorDesc("in", 0).shape().NumAxes(); const int64_t like_num_axes = ctx->InputTensorDesc("like", 0).shape().NumAxes(); - CHECK_LE_OR_RETURN(like_num_axes, in_num_axes); - CHECK_LT_OR_RETURN(axis, like_num_axes); + CHECK_LE_OR_RETURN(like_num_axes, in_num_axes) + << Error::RuntimeError() << "The dimension of like (" << like_num_axes + << ") should be less than or equal to input (" << in_num_axes << ")"; + CHECK_LT_OR_RETURN(axis, like_num_axes) + << Error::RuntimeError() << "The axis (" << axis + << ") should be less than the dimension of like (" << like_num_axes << ")"; FOR_RANGE(int32_t, i, 0, ctx->outputs().size()) { const user_op::TensorDesc& like_i_desc = ctx->InputTensorDesc("like", i); - user_op::TensorDesc* out_i_desc = ctx->OutputTensorDesc("out", i); - CHECK_EQ_OR_RETURN(like_i_desc.shape().NumAxes(), like_num_axes); + user_op::TensorDesc* out_i_desc = ctx->MutOutputTensorDesc("out", i); + CHECK_EQ_OR_RETURN(like_i_desc.shape().NumAxes(), like_num_axes) + << Error::RuntimeError() << "The dimension of like_i (" << like_i_desc.shape().NumAxes() + << ") must match the dimension of the first like (" << like_num_axes << ")"; FOR_RANGE(int64_t, j, 0, like_num_axes) { if (j == axis) { if (like_i_desc.is_dynamic()) { @@ -82,20 +88,29 @@ namespace oneflow { static_dim_size += like_i_desc.shape().At(j); } } else { - CHECK_EQ_OR_RETURN(in_desc.shape().At(j), like_i_desc.shape().At(j)); + CHECK_EQ_OR_RETURN(in_desc.shape().At(j), like_i_desc.shape().At(j)) + << Error::RuntimeError() << "The size of input (" << in_desc.shape().At(j) + << ") must match the size of like_i (" << like_i_desc.shape().At(j) << ") at dimension " + << j; } } DimVector out_i_dim_vec = like_i_desc.shape().dim_vec(); FOR_RANGE(int64_t, j, like_num_axes, in_num_axes) { out_i_dim_vec.emplace_back(in_desc.shape().At(j)); } - *out_i_desc->mut_shape() = Shape(out_i_dim_vec); + out_i_desc->set_shape(Shape(out_i_dim_vec)); out_i_desc->set_is_dynamic(like_i_desc.is_dynamic()); } if (dynamic_dim_size == 0) { - CHECK_EQ_OR_RETURN(static_dim_size, in_desc.shape().At(axis)); + CHECK_EQ_OR_RETURN(static_dim_size, in_desc.shape().At(axis)) + << Error::RuntimeError() << "In non-dynamic shape situation, the total size of like (" + << static_dim_size << ") should be equal to the size of input (" << in_desc.shape().At(axis) + << ") at dimension " << axis; } else { - CHECK_LE_OR_RETURN(static_dim_size, in_desc.shape().At(axis)); + CHECK_LE_OR_RETURN(static_dim_size, in_desc.shape().At(axis)) + << Error::RuntimeError() << "In dynamic shape situation, the total size of like (" + << static_dim_size << ") should be less than or equal to the size of input (" + << in_desc.shape().At(axis) << ") at dimension " << axis; } return Maybe::Ok(); } @@ -105,8 +120,8 @@ namespace oneflow { /*static*/ Maybe SplitLikeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); FOR_RANGE(int32_t, i, 0, ctx->outputs().size()) { - user_op::TensorDesc* out_i_desc = ctx->OutputTensorDesc("out", i); - *out_i_desc->mut_data_type() = in_desc.data_type(); + user_op::TensorDesc* out_i_desc = ctx->MutOutputTensorDesc("out", i); + out_i_desc->set_data_type(in_desc.data_type()); } return Maybe::Ok(); } @@ -114,7 +129,7 @@ namespace oneflow { const user_op::UserOpConfWrapper& user_op_conf) { FOR_RANGE(int32_t, i, 0, user_op_conf.input_size("like")) { user_op::InputArgModifier* like_modifier = GetInputArgModifierFn("like", i); - CHECK_NOTNULL_OR_RETURN(like_modifier); + CHECK_NOTNULL_OR_RETURN(like_modifier); // NOLINT(maybe-need-error-msg) like_modifier->set_requires_grad(false); } return Maybe::Ok(); @@ -122,50 +137,11 @@ namespace oneflow { /*static*/ Maybe SplitLikeOp::CheckAttr(const user_op::UserOpDefWrapper&, const user_op::UserOpConfWrapper& op_conf) { - CHECK_OR_RETURN(op_conf.input_size("like") >= 1); - CHECK_OR_RETURN(op_conf.output_size("out") >= 1); + CHECK_OR_RETURN(op_conf.input_size("like") >= 1) + << Error::RuntimeError() << "The number of like should be greater than or equal to 1"; + CHECK_OR_RETURN(op_conf.output_size("out") >= 1) + << Error::RuntimeError() << "The number of output should be greater than or equal to 1"; return Maybe::Ok(); } -namespace { - -Maybe GenGradOp(const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp) { - const int64_t axis = op.attr("axis"); - const int32_t out_size = op.output_size("out"); - int64_t max_dim_size = 0; - FOR_RANGE(int32_t, i, 0, out_size) { - max_dim_size += op.TensorDesc4ArgNameAndIndex("like", i).shape().At(axis); - } - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder = builder.Op("concat"); - FOR_RANGE(int32_t, i, 0, out_size) { - std::string out_diff_lbn; - if (op.HasGradTensor4OpOutput("out", i)) { - out_diff_lbn = op.GetGradTensorWithOpOutput("out", i); - } else { - auto zero_like_op = user_op::UserOpConfWrapperBuilder(op.op_name() + "_grad_zero_like_out_" - + std::to_string(i)) - .Op("zero_like") - .Input("like", op.output("out", i)) - .Output("out") - .Build(); - AddOp(zero_like_op); - out_diff_lbn = zero_like_op.output("out", 0); - } - builder = builder.Input("in", out_diff_lbn); - } - user_op::UserOpConfWrapper grad_op = - builder.Output("out").Attr("axis", axis).Attr("max_dim_size", max_dim_size).Build(); - - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -} - -} // namespace - -REGISTER_USER_OP_GRAD("split_like").SetGenBackwardOpConfFn(GenGradOp); - } // namespace oneflow diff --git a/oneflow/user/ops/sqrt_square_sum_op.cpp b/oneflow/user/ops/sqrt_square_sum_op.cpp index f8c6b43ca5bb80cfaf99bb8aa0b4fa67e34a6560..1eb4672afbafc00f3381ba35bcc2ec5d00e8aa31 100644 --- a/oneflow/user/ops/sqrt_square_sum_op.cpp +++ b/oneflow/user/ops/sqrt_square_sum_op.cpp @@ -26,15 +26,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SqrtSquareSumOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_shape() = Shape({}); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(Shape({})); return Maybe::Ok(); } /*static*/ Maybe SqrtSquareSumOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SqrtSquareSumOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/square_sum_op.cpp b/oneflow/user/ops/square_sum_op.cpp index c97d3219046ecef5f1dfb44954697e2e83ebf647..1fe93e52363af1a0799f2ed31f28cc8a0061c9ab 100644 --- a/oneflow/user/ops/square_sum_op.cpp +++ b/oneflow/user/ops/square_sum_op.cpp @@ -26,15 +26,15 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe SquareSumOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_shape() = Shape({1}); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(Shape({1})); return Maybe::Ok(); } /*static*/ Maybe SquareSumOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SquareSumOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -50,8 +50,8 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe MultiSquareSumOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_shape() = Shape({1}); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(Shape({1})); return Maybe::Ok(); } /*static*/ Maybe MultiSquareSumOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -59,17 +59,21 @@ namespace oneflow { } /*static*/ Maybe MultiSquareSumOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& x_0 = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); for (int64_t i = 1; i < ctx->input_size("x"); ++i) { const user_op::TensorDesc& x_i = ctx->InputTensorDesc("x", i); - CHECK_EQ_OR_RETURN(x_i.data_type(), x_0.data_type()); + CHECK_EQ_OR_RETURN(x_i.data_type(), x_0.data_type()) + << Error::TypeError() + << "All tensors are expected to have the same dtype, but found at least two dtypes, " + << DataType_Name(x_i.data_type()) << " and " << DataType_Name(x_0.data_type()); } - *y->mut_data_type() = x_0.data_type(); + y->set_data_type(x_0.data_type()); return Maybe::Ok(); } /*static*/ Maybe MultiSquareSumOp::CheckAttr(const user_op::UserOpDefWrapper&, const user_op::UserOpConfWrapper& op_conf) { - CHECK_OR_RETURN(op_conf.input_size("x") >= 1); + CHECK_OR_RETURN(op_conf.input_size("x") >= 1) + << Error::RuntimeError() << "The number of x should be greater than or equal to 1"; return Maybe::Ok(); } } // namespace oneflow diff --git a/oneflow/user/ops/squeeze_op.cpp b/oneflow/user/ops/squeeze_op.cpp index d6c9cb111a4bab5e2d4c47f9d98d6f5dd28e42c4..1a0a89f1723480578751c2fb12983bb100df4d57 100644 --- a/oneflow/user/ops/squeeze_op.cpp +++ b/oneflow/user/ops/squeeze_op.cpp @@ -63,7 +63,6 @@ Maybe CheckAndLabelAxesToSqueezeMinusOne(const AxisVector& axes, DimVector } /*static*/ Maybe SqueezeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); AxisVector fixed_axes_vec; JUST(TransformNegativeAxesToPositive(ctx->Attr>("axes"), in_shape.NumAxes(), &fixed_axes_vec)); @@ -71,30 +70,15 @@ Maybe CheckAndLabelAxesToSqueezeMinusOne(const AxisVector& axes, DimVector DimVector dim_vec = in_shape.dim_vec(); JUST(CheckAndLabelAxesToSqueezeMinusOne(fixed_axes_vec, &dim_vec)); dim_vec.erase(std::remove(dim_vec.begin(), dim_vec.end(), -1), dim_vec.end()); - *out_shape = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } /*static*/ Maybe SqueezeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SqueezeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("squeeze").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("reshape_like") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("like", op.input("in", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/ssp_variable_proxy_op.cpp b/oneflow/user/ops/ssp_variable_proxy_op.cpp index 9a5a31262a73d82ec074f46dd9d3dbadf75b00fe..b5584de6638dbca47f3a0097bc9e08f2452efbfc 100644 --- a/oneflow/user/ops/ssp_variable_proxy_op.cpp +++ b/oneflow/user/ops/ssp_variable_proxy_op.cpp @@ -31,16 +31,16 @@ namespace oneflow { } /*static*/ Maybe SspVariableProxyOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& var_shape = ctx->InputShape("var", 0); - *ctx->OutputShape("ref", 0) = var_shape; - *ctx->OutputShape("value", 0) = var_shape; + ctx->SetOutputShape("ref", 0, var_shape); + ctx->SetOutputShape("value", 0, var_shape); return Maybe::Ok(); } /*static*/ Maybe SspVariableProxyOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe SspVariableProxyOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("ref", 0) = ctx->InputDType("var", 0); - *ctx->OutputDType("value", 0) = ctx->InputDType("var", 0); + ctx->SetOutputDType("ref", 0, ctx->InputDType("var", 0)); + ctx->SetOutputDType("value", 0, ctx->InputDType("var", 0)); return Maybe::Ok(); } /*static*/ Maybe SspVariableProxyOp::ModifyOutputArg( diff --git a/oneflow/user/ops/stack_op.cpp b/oneflow/user/ops/stack_op.cpp index 1dd129081bdc2c0e5927d139ddb09292c2780e76..fdb6b5506179943e0ac11df88e2cefb3b1f603c0 100644 --- a/oneflow/user/ops/stack_op.cpp +++ b/oneflow/user/ops/stack_op.cpp @@ -18,41 +18,13 @@ limitations under the License. namespace oneflow { -namespace { - -Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) { - bool need_grad = false; - const int32_t in_size = op.input_size("in"); - FOR_RANGE(int32_t, i, 0, in_size) { - if (op.NeedGenGradTensor4OpInput("in", i)) { need_grad = true; } - } - if (need_grad) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - builder = builder.Op("stack_grad"); - FOR_RANGE(int32_t, i, 0, in_size) { builder = builder.Input("like", op.input("in", i)); } - user_op::UserOpConfWrapper grad_op = builder.Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out", in_size) - .Attr("axis", op.attr("axis")) - .Build(); - - FOR_RANGE(int32_t, i, 0, in_size) { - if (op.NeedGenGradTensor4OpInput("in", i)) { - op.BindGradTensorWithOpInput(grad_op.output("out", i), "in", i); - } - } - AddOp(grad_op); - } - return Maybe::Ok(); -} - -} // namespace - /* static */ Maybe StackOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& first_in_desc = ctx->InputTensorDesc("in", 0); const int64_t axis = ctx->Attr("axis"); CHECK_GE_OR_RETURN(axis, 0) << "The axis should be greater than or equal to 0."; const int64_t in_num_axes = first_in_desc.shape().NumAxes(); - CHECK_LE_OR_RETURN(axis, in_num_axes) "The axis should be less than or equal to input num axes."; + CHECK_LE_OR_RETURN(axis, in_num_axes) + << "The axis should be less than or equal to input num axes."; DimVector out_dim_vec(in_num_axes + 1); for (int i = 0; i < in_num_axes + 1; i++) { if (i == axis) { @@ -85,7 +57,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& } } } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); const int64_t max_dim_size = ctx->Attr("max_dim_size"); CHECK_LE_OR_RETURN(out_dim_vec.at(axis), max_dim_size) << "The out shape at axis " << axis << " should be less equal to " << max_dim_size; @@ -95,7 +67,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& out_desc->set_is_dynamic(true); out_dim_vec.at(axis) = max_dim_size; } - *out_desc->mut_shape() = Shape(out_dim_vec); + out_desc->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } @@ -128,10 +100,11 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& const user_op::TensorDesc& in_desc = ctx->InputTensorDesc(in_arg_pair.first, in_arg_pair.second); CHECK_EQ_OR_RETURN(in_desc.data_type(), first_in_desc.data_type()) - << "The input's data type should be equal to first input's data type. "; + << "InferDataType Failed. Expected " << DataType_Name(first_in_desc.data_type()) + << ", but got " << DataType_Name(in_desc.data_type()); } - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_data_type() = first_in_desc.data_type(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + out_desc->set_data_type(first_in_desc.data_type()); return Maybe::Ok(); } @@ -146,14 +119,21 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& const auto axis = ctx->Attr("axis"); const int64_t like_num_axes = ctx->LogicalTensorDesc4InputArgNameAndIndex("like", 0).shape().NumAxes(); - FOR_RANGE(int64_t, i, 0, like_num_axes) { - if (i == axis || i < 1) { continue; } - ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i - 1).Build(); - } std::vector like_arg_vec; const size_t like_arg_size = ctx->outputs().size(); like_arg_vec.reserve(like_arg_size); FOR_RANGE(int32_t, i, 0, like_arg_size) { like_arg_vec.emplace_back("like", i); } + FOR_RANGE(int64_t, i, 0, like_num_axes) { + if (i >= axis) { + ctx->NewBuilder() + .Split(like_arg_vec, i) + .Split(ctx->outputs(), i) + .Split(user_op::OpArg("in", 0), i + 1) + .Build(); + } else { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + } ctx->NewBuilder() .PartialSum(user_op::OpArg("in", 0)) .PartialSum(like_arg_vec) @@ -184,7 +164,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& << "The axis should be less equal than num axes of `like` tensor. "; FOR_RANGE(int32_t, i, 0, ctx->outputs().size()) { const user_op::TensorDesc& like_i_desc = ctx->InputTensorDesc("like", i); - user_op::TensorDesc* out_i_desc = ctx->OutputTensorDesc("out", i); + user_op::TensorDesc* out_i_desc = ctx->MutOutputTensorDesc("out", i); CHECK_EQ_OR_RETURN(like_i_desc.shape().NumAxes(), like_num_axes) << "The num axes of `like` tensor at index " << i << " should be equal to first `like` tensor. "; @@ -210,7 +190,7 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& } } DimVector out_i_dim_vec = like_i_desc.shape().dim_vec(); - *out_i_desc->mut_shape() = Shape(out_i_dim_vec); + out_i_desc->set_shape(Shape(out_i_dim_vec)); out_i_desc->set_is_dynamic(like_i_desc.is_dynamic()); } if (dynamic_dim_size == 0) { @@ -230,8 +210,8 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& /*static*/ Maybe StackGradOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); FOR_RANGE(int32_t, i, 0, ctx->outputs().size()) { - user_op::TensorDesc* out_i_desc = ctx->OutputTensorDesc("out", i); - *out_i_desc->mut_data_type() = in_desc.data_type(); + user_op::TensorDesc* out_i_desc = ctx->MutOutputTensorDesc("out", i); + out_i_desc->set_data_type(in_desc.data_type()); } return Maybe::Ok(); } @@ -254,6 +234,4 @@ Maybe GenGradOp(const user_op::UserOpWrapper& op, const user_op::AddOpFn& return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("stack").SetGenBackwardOpConfFn(GenGradOp); - } // namespace oneflow diff --git a/oneflow/user/ops/stft_op.cpp b/oneflow/user/ops/stft_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..bc8a4b372da908ddc09cbc00b39e16619b7806dc --- /dev/null +++ b/oneflow/user/ops/stft_op.cpp @@ -0,0 +1,82 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ + +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +const Stride InferOutputStride(const Shape& in_shape, bool onesided = true, + bool return_complex = false) { + // TODO(yzm):support return_complex + int last_dim_size = in_shape.At(2); + if (onesided) { last_dim_size = last_dim_size / 2 + 1; } + Stride out_stride(in_shape.NumAxes(), 0); + if (in_shape.At(0) == 1) { + out_stride = {2, 2 * last_dim_size, 1}; + } else { + out_stride = {last_dim_size * 2 * in_shape.At(1), 2, 2 * last_dim_size, 1}; + } + return out_stride; +} + +const Shape InferOutputShape(const Shape& in_shape, bool onesided = true, + bool return_complex = false) { + // TODO(yzm):support return_complex + Shape out_shape; + int last_dim_size = in_shape.At(2); + if (onesided) { last_dim_size = last_dim_size / 2 + 1; } + if (in_shape.At(0) == 1) { + out_shape = {last_dim_size, in_shape.At(1), 2}; + } else { + out_shape = {in_shape.At(0), last_dim_size, in_shape.At(1), 2}; + } + return out_shape; +} + +/* static */ Maybe StftOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const Shape& in_shape = ctx->InputShape("input", 0); + const bool onesided = ctx->Attr("onesided"); + + const Stride& out_stride = InferOutputStride(in_shape, onesided); + const Shape& out_shape = InferOutputShape(in_shape, onesided); + + ctx->SetOutputStride("output", 0, out_stride); + ctx->SetOutputShape("output", 0, out_shape); + ctx->SetOutputIsDynamic("output", 0, ctx->InputIsDynamic("input", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe StftOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe StftOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe StftOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe StftOp::GetComputeComplexity(user_op::ComputeComplexityFnContext* ctx) { + // TODO: add ComputeComplexityFun + return 0.0; +} + +} // namespace oneflow \ No newline at end of file diff --git a/oneflow/user/ops/tanh_op.cpp b/oneflow/user/ops/tanh_op.cpp index caf89a63ac9f491f0bb77b226b5a60294a490fd1..d83242f220bf049bf36d8bafbe9a827f11cb6363 100644 --- a/oneflow/user/ops/tanh_op.cpp +++ b/oneflow/user/ops/tanh_op.cpp @@ -44,20 +44,4 @@ namespace oneflow { return user_op::TensorDescInferFnUtil::UnchangedDataType(ctx); } -REGISTER_USER_OP_GRAD("tanh").SetGenBackwardOpConfFn( - [](const user_op::UserOpWrapper& op, const user_op::AddOpFn& AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper unary_grad_op = - builder.Op((std::string("") + "tanh" + "_grad")) - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Build(); - op.BindGradTensorWithOpInput(unary_grad_op.output("dx", 0), "x", 0); - AddOp(unary_grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/tensor_buffer_ops.cpp b/oneflow/user/ops/tensor_buffer_ops.cpp index 80b1c5c99ff9571cffeb0186e5968c0b2dc51d73..bda1871d55afb3763b3b6ff8bcbcf5a85dbd6d74 100644 --- a/oneflow/user/ops/tensor_buffer_ops.cpp +++ b/oneflow/user/ops/tensor_buffer_ops.cpp @@ -27,13 +27,13 @@ namespace oneflow { } /*static*/ Maybe TensorBufferToTensorOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); out->set_is_dynamic(in.is_dynamic()); const auto& instance_shape = ctx->Attr("instance_shape"); DimVector dim_vec; dim_vec.insert(dim_vec.end(), in.shape().dim_vec().cbegin(), in.shape().dim_vec().cend()); dim_vec.insert(dim_vec.end(), instance_shape.dim_vec().cbegin(), instance_shape.dim_vec().cend()); - *out->mut_shape() = Shape(dim_vec); + out->set_shape(Shape(dim_vec)); return Maybe::Ok(); } /*static*/ Maybe TensorBufferToTensorOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -41,9 +41,9 @@ namespace oneflow { } /*static*/ Maybe TensorBufferToTensorOp::InferDataType(user_op::InferContext* ctx) { const auto data_type = ctx->Attr("dtype"); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(IsPODDataType(data_type)); - *out->mut_data_type() = data_type; + out->set_data_type(data_type); return Maybe::Ok(); } @@ -61,12 +61,12 @@ namespace oneflow { const Shape& in_shape = in.shape(); const auto& instance_dims = ctx->Attr("instance_dims"); CHECK_LT_OR_RETURN(instance_dims, in_shape.NumAxes()); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); out->set_is_dynamic(in.is_dynamic()); DimVector out_dim_vec; out_dim_vec.insert(out_dim_vec.end(), in_shape.dim_vec().cbegin(), in_shape.dim_vec().cend() - instance_dims); - *out->mut_shape() = Shape(out_dim_vec); + out->set_shape(Shape(out_dim_vec)); return Maybe::Ok(); } /*static*/ Maybe TensorToTensorBufferOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -75,8 +75,8 @@ namespace oneflow { /*static*/ Maybe TensorToTensorBufferOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); CHECK_OR_RETURN(IsPODDataType(in.data_type())); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = DataType::kTensorBuffer; + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } @@ -84,14 +84,14 @@ namespace oneflow { return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); } /*static*/ Maybe GenTensorBufferOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); const Shape& shape = ctx->Attr("shape"); const int64_t num_tensor_buffers = shape.elem_cnt(); const std::vector& shape_list = ctx->Attr>("shape_list"); const std::vector& value_list = ctx->Attr>("value_list"); CHECK_EQ_OR_RETURN(num_tensor_buffers, shape_list.size()); CHECK_EQ_OR_RETURN(num_tensor_buffers, value_list.size()); - *out->mut_shape() = shape; + out->set_shape(shape); out->set_is_dynamic(ctx->Attr("dynamic_out")); return Maybe::Ok(); } @@ -99,8 +99,8 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe GenTensorBufferOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = DataType::kTensorBuffer; + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(DataType::kTensorBuffer); return Maybe::Ok(); } @@ -116,8 +116,8 @@ namespace oneflow { const bool dynamic_out = ctx->Attr("dynamic_out"); int64_t num_tensor_buffers = in.shape().elem_cnt(); for (int64_t i = 0; i < num_tensor_buffers; ++i) { - user_op::TensorDesc* out_i = ctx->OutputTensorDesc("out", i); - *out_i->mut_shape() = out_shape; + user_op::TensorDesc* out_i = ctx->MutOutputTensorDesc("out", i); + out_i->set_shape(out_shape); out_i->set_is_dynamic(dynamic_out); } return Maybe::Ok(); @@ -128,13 +128,15 @@ namespace oneflow { } /*static*/ Maybe TensorBufferToListOfTensorsOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(in.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(in.data_type()); const DataType out_dtype = ctx->Attr("out_dtype"); CHECK_OR_RETURN(IsPODDataType(out_dtype)); int64_t num_tensor_buffers = ctx->outputs().size(); for (int64_t i = 0; i < num_tensor_buffers; ++i) { - user_op::TensorDesc* out_i = ctx->OutputTensorDesc("out", i); - *out_i->mut_data_type() = out_dtype; + user_op::TensorDesc* out_i = ctx->MutOutputTensorDesc("out", i); + out_i->set_data_type(out_dtype); } return Maybe::Ok(); } @@ -168,8 +170,8 @@ namespace oneflow { const bool dynamic_out = ctx->Attr("dynamic_out"); int64_t num_tensor_buffers = in.shape().elem_cnt(); for (int64_t i = 0; i < num_tensor_buffers; ++i) { - user_op::TensorDesc* out_i = ctx->OutputTensorDesc("out", i); - *out_i->mut_shape() = out_shapes[i]; + user_op::TensorDesc* out_i = ctx->MutOutputTensorDesc("out", i); + out_i->set_shape(out_shapes[i]); out_i->set_is_dynamic(dynamic_out); } return Maybe::Ok(); @@ -180,13 +182,15 @@ namespace oneflow { } /*static*/ Maybe TensorBufferToListOfTensorsV2Op::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - CHECK_EQ_OR_RETURN(in.data_type(), DataType::kTensorBuffer); + CHECK_EQ_OR_RETURN(in.data_type(), DataType::kTensorBuffer) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kTensorBuffer) << ", but got " + << DataType_Name(in.data_type()); const std::vector& out_dtypes = ctx->Attr>("out_dtypes"); int64_t num_tensor_buffers = ctx->outputs().size(); for (int64_t i = 0; i < num_tensor_buffers; ++i) { CHECK_OR_RETURN(IsPODDataType(out_dtypes[i])); - user_op::TensorDesc* out_i = ctx->OutputTensorDesc("out", i); - *out_i->mut_data_type() = out_dtypes[i]; + user_op::TensorDesc* out_i = ctx->MutOutputTensorDesc("out", i); + out_i->set_data_type(out_dtypes[i]); } return Maybe::Ok(); } diff --git a/oneflow/user/ops/tensor_constant_op.cpp b/oneflow/user/ops/tensor_constant_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9ed4553a61af62f43c2adfd9a2a215ada4b105ad --- /dev/null +++ b/oneflow/user/ops/tensor_constant_op.cpp @@ -0,0 +1,55 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/job/nd_sbp_util.h" + +namespace oneflow { + +/* static */ Maybe TensorConstantOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, Shape(ctx->Attr("shape").dim_vec())); + return Maybe::Ok(); +} + +/*static*/ Maybe TensorConstantOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + const Shape& parallel_hierarchy = *ctx->parallel_desc().hierarchy(); + const NdSbp& nd_sbp = ctx->NdSbp4ArgNameAndIndex("out", 0); + const Shape& logical_shape = ctx->Attr("shape"); + const int64_t parallel_id = ctx->parallel_ctx().parallel_id(); + const auto tensor_slice_view = + GetTensorSliceView4ParallelId(parallel_hierarchy, nd_sbp, logical_shape, parallel_id); + const Shape& physical_shape = tensor_slice_view.shape(); + + ctx->SetOutputShape("out", 0, physical_shape); + return Maybe::Ok(); +} + +/* static */ Maybe TensorConstantOp::GetSbp(user_op::SbpContext* ctx) { + return Maybe::Ok(); +} + +/* static */ Maybe TensorConstantOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { + SbpParallel default_sbp; + default_sbp.mutable_broadcast_parallel(); + return user_op::InferNdSbp4SrcOp(ctx, default_sbp); +} + +/* static */ Maybe TensorConstantOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, ctx->Attr("dtype")); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/tf_pool_op.cpp b/oneflow/user/ops/tf_pool_op.cpp index 39afc8478b8c35b0d8dd0020f992a66ea0216633..babd61c3d02ab5b566bbd00c711482b710c15b34 100644 --- a/oneflow/user/ops/tf_pool_op.cpp +++ b/oneflow/user/ops/tf_pool_op.cpp @@ -21,9 +21,25 @@ namespace oneflow { namespace { +// Logically computation cost of pool op is the product of output data amount and pool kernal data +// amount. After adding sbp, we just divide it by parallel number if output data is splitted because +// splitting input and using partial sum for output is not a valid sbp for this op for now. +Maybe GetComputationCost(user_op::ComputeComplexityFnContext* ctx) { + const std::vector pool_size = ctx->Attr>("pool_size"); + double logical_computation_cost = + std::accumulate(pool_size.begin(), pool_size.end(), + ctx->Shape4ArgNameAndIndex("y", 0).elem_cnt(), std::multiplies()); + const auto& parallel_hierarchy = ctx->parallel_desc().hierarchy(); + const auto& nd_sbp_y = ctx->NdSbp4ArgNameAndIndex("y", 0); + for (int32_t dim_sbp = 0; dim_sbp < nd_sbp_y.sbp_parallel_size(); dim_sbp++) { + if (nd_sbp_y.sbp_parallel(dim_sbp).has_split_parallel()) { + logical_computation_cost /= parallel_hierarchy->At(dim_sbp); + } + } + return logical_computation_cost; +} + typedef std::function(user_op::InferContext* ctx)> TensorDescInferFn; -typedef std::function(const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp)> - GenBackwardOpConfFn; TensorDescInferFn MakeFwTensorDescInferFn(const int32_t dim) { return [dim](user_op::InferContext* ctx) -> Maybe { @@ -43,26 +59,26 @@ TensorDescInferFn MakeFwTensorDescInferFn(const int32_t dim) { const Params3D params_3d(dim, x_shape, data_format, padding, padding_before, padding_after, pool_size, strides, ceil_mode); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); - *y_desc->mut_shape() = params_3d.GetYShape(); - *y_desc->mut_is_dynamic() = ctx->InputIsDynamic("x", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); + y_desc->set_shape(params_3d.GetYShape()); + y_desc->set_is_dynamic(ctx->InputIsDynamic("x", 0)); return Maybe::Ok(); }; } Maybe BwTensorDescInferFn(user_op::InferContext* ctx) { - *ctx->OutputShape("dx", 0) = ctx->InputShape("x", 0); - *ctx->OutputIsDynamic("dx", 0) = ctx->InputIsDynamic("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); + ctx->SetOutputIsDynamic("dx", 0, ctx->InputIsDynamic("x", 0)); return Maybe::Ok(); } Maybe FwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } Maybe BwInferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -87,31 +103,6 @@ Maybe BwGetSbpFn(user_op::SbpContext* ctx) { return Maybe::Ok(); } -GenBackwardOpConfFn MakeGenBackwardOpConfFn(const std::string& mode, const int32_t dim) { - return [mode, dim](const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op(mode + "_pool_" + std::to_string(dim) + "d_grad") - .Input("x", op.input("x", 0)) - .Input("y", op.output("y", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Output("dx") - .Attr("data_format", op.attr("data_format")) - .Attr("padding", op.attr("padding")) - .Attr("padding_before", op.attr>("padding_before")) - .Attr("padding_after", op.attr>("padding_after")) - .Attr("pool_size", op.attr>("pool_size")) - .Attr("strides", op.attr>("strides")) - .Attr("ceil_mode", op.attr("ceil_mode")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }; -} - } // namespace #define IMPLEMENT_TF_POOL_FUNCS(name, dim) \ @@ -124,6 +115,10 @@ GenBackwardOpConfFn MakeGenBackwardOpConfFn(const std::string& mode, const int32 } \ /*static*/ Maybe name##Op::InferDataType(user_op::InferContext* ctx) { \ return FwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##Op::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx); \ } IMPLEMENT_TF_POOL_FUNCS(TfAvgPool1D, 1) @@ -146,6 +141,10 @@ IMPLEMENT_TF_POOL_FUNCS(TfMaxPool3D, 3) } \ /*static*/ Maybe name##GradOp::InferDataType(user_op::InferContext* ctx) { \ return BwInferDataType(ctx); \ + } \ + /*static*/ Maybe name##GradOp::GetComputeComplexity( \ + user_op::ComputeComplexityFnContext* ctx) { \ + return GetComputationCost(ctx); \ } IMPLEMENT_TF_POOL_BACKWARD_FUNCS(TfAvgPool1D) @@ -156,18 +155,4 @@ IMPLEMENT_TF_POOL_BACKWARD_FUNCS(TfMaxPool2D) IMPLEMENT_TF_POOL_BACKWARD_FUNCS(TfMaxPool3D) #undef IMPLEMENT_TF_POOL_BACKWARD_FUNCS -REGISTER_USER_OP_GRAD("tf_avg_pool_1d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_avg", 1)); -REGISTER_USER_OP_GRAD("tf_avg_pool_2d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_avg", 2)); -REGISTER_USER_OP_GRAD("tf_avg_pool_3d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_avg", 3)); - -REGISTER_USER_OP_GRAD("tf_max_pool_1d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_max", 1)); -REGISTER_USER_OP_GRAD("tf_max_pool_2d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_max", 2)); -REGISTER_USER_OP_GRAD("tf_max_pool_3d") - .SetGenBackwardOpConfFn(MakeGenBackwardOpConfFn("tf_max", 3)); - } // namespace oneflow diff --git a/oneflow/user/ops/tf_prelu_op.cpp b/oneflow/user/ops/tf_prelu_op.cpp index b4880e201e78c22e213a34af8b91fe5f6143be4b..860185fe1f7abc70b91e05407835820f37c435c4 100644 --- a/oneflow/user/ops/tf_prelu_op.cpp +++ b/oneflow/user/ops/tf_prelu_op.cpp @@ -39,22 +39,22 @@ namespace oneflow { } /*static*/ Maybe TfPreluOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const Shape& alpha_shape = ctx->InputShape("alpha", 0); CHECK_EQ_OR_RETURN(x_desc.shape().NumAxes(), alpha_shape.NumAxes() + 1); FOR_RANGE(int64_t, i, 1, x_desc.shape().NumAxes()) { CHECK_OR_RETURN((alpha_shape.At(i - 1) == x_desc.shape().At(i)) || (alpha_shape.At(i - 1) == 1)); } - *y_desc->mut_shape() = x_desc.shape(); - *y_desc->mut_is_dynamic() = x_desc.is_dynamic(); + y_desc->set_shape(x_desc.shape()); + y_desc->set_is_dynamic(x_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe TfPreluOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe TfPreluOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -91,7 +91,7 @@ namespace oneflow { /*static*/ Maybe TfPreluGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); const user_op::TensorDesc& dy_desc = ctx->InputTensorDesc("dy", 0); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); const user_op::TensorDesc& alpha_desc = ctx->InputTensorDesc("alpha", 0); CHECK_EQ_OR_RETURN(x_desc.shape().NumAxes(), alpha_desc.shape().NumAxes() + 1); FOR_RANGE(int64_t, i, 1, x_desc.shape().NumAxes()) { @@ -99,51 +99,22 @@ namespace oneflow { || (alpha_desc.shape().At(i - 1) == 1)); } CHECK_EQ_OR_RETURN(dy_desc.shape(), x_desc.shape()); - CHECK_EQ_OR_RETURN(dy_desc.data_type(), x_desc.data_type()); - *dx_desc->mut_shape() = x_desc.shape(); - *dx_desc->mut_is_dynamic() = x_desc.is_dynamic(); - *ctx->OutputShape("alpha_diff", 0) = alpha_desc.shape(); - *ctx->OutputIsDynamic("alpha_diff", 0) = alpha_desc.is_dynamic(); + CHECK_EQ_OR_RETURN(dy_desc.data_type(), x_desc.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(x_desc.data_type()); + dx_desc->set_shape(x_desc.shape()); + dx_desc->set_is_dynamic(x_desc.is_dynamic()); + ctx->SetOutputShape("alpha_diff", 0, alpha_desc.shape()); + ctx->SetOutputIsDynamic("alpha_diff", 0, alpha_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe TfPreluGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe TfPreluGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); - *ctx->OutputDType("alpha_diff", 0) = ctx->InputDType("alpha", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); + ctx->SetOutputDType("alpha_diff", 0, ctx->InputDType("alpha", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("tf_prelu") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0) || op.NeedGenGradTensor4OpInput("alpha", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("tf_prelu_grad") - .Input("x", op.input("x", 0)) - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("alpha", op.input("alpha", 0)) - .Output("dx") - .Output("alpha_diff") - .Build(); - AddOp(grad_op); - - if (op.NeedGenGradTensor4OpInput("x", 0)) { - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - } - if (op.NeedGenGradTensor4OpInput("alpha", 0)) { - auto alpha_identity_op = - user_op::UserOpConfWrapperBuilder(op.op_name() + "_alpha_identity") - .Op("identity") - .Input("in", grad_op.output("alpha_diff", 0)) - .Output("out") - .Build(); - AddOp(alpha_identity_op); - op.BindGradTensorWithOpInput(alpha_identity_op.output("out", 0), "alpha", 0); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/threshold_op.cpp b/oneflow/user/ops/threshold_op.cpp index 3cf10ab9dae8ab59c013469519380cf99cf0abdd..bef399ccfacaae171e3980f30e404c5b430a2e62 100644 --- a/oneflow/user/ops/threshold_op.cpp +++ b/oneflow/user/ops/threshold_op.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe ThresholdOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -36,16 +36,15 @@ namespace oneflow { } /* static */ Maybe ThresholdOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } /* static */ Maybe ThresholdGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(dy_shape == x_shape); - *dx_shape = dy_shape; + ctx->SetOutputShape("dx", 0, dy_shape); return Maybe::Ok(); } @@ -66,26 +65,11 @@ namespace oneflow { } /* static */ Maybe ThresholdGradOp::InferDataType(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)); - *ctx->OutputDType("dx", 0) = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("dy", 0), ctx->InputDType("x", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("dy", 0)) + << ", but got " << DataType_Name(ctx->InputDType("x", 0)); + ctx->SetOutputDType("dx", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("threshold") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto threshold_grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(threshold_grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("threshold_grad") - .InputBind("x", ctx->FwOp().input("in", 0)) - .InputBind("dy", ctx->FwOp().output_grad("out", 0)) - .Attr("threshold_val", ctx->FwOp().attr("threshold_val")) - .Output("dx") - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), - [&ctx, &threshold_grad_op_name]() -> const std::string& { - return ctx->GetOp(threshold_grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/throw_error_op.cpp b/oneflow/user/ops/throw_error_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..56e48908d78a1c27817b124087bea85ed657f082 --- /dev/null +++ b/oneflow/user/ops/throw_error_op.cpp @@ -0,0 +1,32 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +Maybe ThrowErrorOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return user_op::TensorDescInferFnUtil::Unchanged(ctx); +} +Maybe ThrowErrorOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); +} +Maybe ThrowErrorOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/to_contiguous_op.cpp b/oneflow/user/ops/to_contiguous_op.cpp index 95a80c3e1b614454febc18a5e65db6ad1b611538..8835aa148014e7a8a1312997d4695e482eb8a7ca 100644 --- a/oneflow/user/ops/to_contiguous_op.cpp +++ b/oneflow/user/ops/to_contiguous_op.cpp @@ -24,15 +24,15 @@ namespace oneflow { } /*static*/ Maybe ToContiguousOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); - *ctx->OutputStride("out", 0) = Stride(in_desc.shape()); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + ctx->SetOutputStride("out", 0, Stride(in_desc.shape())); return Maybe::Ok(); } /*static*/ Maybe ToContiguousOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ToContiguousOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/top_k_op.cpp b/oneflow/user/ops/top_k_op.cpp index 0bcf295d5bd83ca7764e307515a5544fc0dba97d..2046d430d38bc7c052625b1504e836a9a8373a02 100644 --- a/oneflow/user/ops/top_k_op.cpp +++ b/oneflow/user/ops/top_k_op.cpp @@ -28,18 +28,18 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe TopKOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - const Shape& in_shape = ctx->InputShape("in", 0); - Shape* out_shape = ctx->OutputShape("out", 0); - *out_shape = in_shape; - out_shape->Set(in_shape.NumAxes() - 1, std::min(ctx->Attr("k"), - static_cast(in_shape.dim_vec().back()))); + Shape out_shape = ctx->InputShape("in", 0); + out_shape.Set( + out_shape.NumAxes() - 1, + std::min(ctx->Attr("k"), static_cast(out_shape.dim_vec().back()))); + ctx->SetOutputShape("out", 0, out_shape); return Maybe::Ok(); } /*static*/ Maybe TopKOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe TopKOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kInt64; + ctx->SetOutputDType("out", 0, DataType::kInt64); return Maybe::Ok(); } diff --git a/oneflow/user/ops/transpose_ops.cpp b/oneflow/user/ops/transpose_ops.cpp index 9d8130e6efb826fe36aaa9576d72e4575ce88810..503df6070257f499e13bd0dc1f78ea7a33d2c96b 100644 --- a/oneflow/user/ops/transpose_ops.cpp +++ b/oneflow/user/ops/transpose_ops.cpp @@ -44,44 +44,24 @@ void CheckIsPerm(const std::vector& perm) { } /*static*/ Maybe TransposeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in_tensor_desc = ctx->InputTensorDesc("input", 0); - user_op::TensorDesc* out_tensor_desc = ctx->OutputTensorDesc("output", 0); + user_op::TensorDesc* out_tensor_desc = ctx->MutOutputTensorDesc("output", 0); const Shape& in_shape = in_tensor_desc.shape(); - Shape* out_shape = out_tensor_desc->mut_shape(); + Shape out_shape = in_tensor_desc.shape(); const auto& perm = ctx->Attr>("perm"); CHECK_EQ_OR_RETURN(perm.size(), in_shape.NumAxes()); CheckIsPerm(perm); // if (perm.at(0) != 0) { CHECK_OR_RETURN(!in_tensor_desc->is_dynamic()); } - *out_tensor_desc->mut_shape() = in_tensor_desc.shape(); - *out_tensor_desc->mut_is_dynamic() = in_tensor_desc.is_dynamic(); - FOR_RANGE(size_t, i, 0, perm.size()) { out_shape->Set(i, in_shape.At(perm[i])); } + out_tensor_desc->set_is_dynamic(in_tensor_desc.is_dynamic()); + FOR_RANGE(size_t, i, 0, perm.size()) { out_shape.Set(i, in_shape.At(perm[i])); } + out_tensor_desc->set_shape(out_shape); return Maybe::Ok(); } /*static*/ Maybe TransposeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe TransposeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("transpose") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("input", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - const auto& tmp = op.attr>("perm"); - std::vector perm; - perm.resize(tmp.size()); - FOR_RANGE(int32_t, i, 0, tmp.size()) { perm.at(tmp.at(i)) = i; } - user_op::UserOpConfWrapper transpose_grad_op = - builder.Op("transpose") - .Input("input", op.GetGradTensorWithOpOutput("output", 0)) - .Output("output") - .Attr>("perm", perm) - .Build(); - op.BindGradTensorWithOpInput(transpose_grad_op.output("output", 0), "input", 0); - AddOp(transpose_grad_op); - } - return Maybe::Ok(); - }); } // namespace oneflow diff --git a/oneflow/user/ops/tril_op.cpp b/oneflow/user/ops/tril_op.cpp index 933727beef0875ad46ba3c2428f501311241ddf4..4178ea9d62c3de260ef512fd746316bf394d211f 100644 --- a/oneflow/user/ops/tril_op.cpp +++ b/oneflow/user/ops/tril_op.cpp @@ -36,10 +36,10 @@ namespace oneflow { } /*static*/ Maybe TrilOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_GE_OR_RETURN(in.shape().NumAxes(), 2); - *out->mut_shape() = in.shape(); - *out->mut_is_dynamic() = in.is_dynamic(); + out->set_shape(in.shape()); + out->set_is_dynamic(in.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe TrilOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -47,26 +47,11 @@ namespace oneflow { } /*static*/ Maybe TrilOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("tril").SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = builder.Op("tril") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("diagonal", op.attr("diagonal")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -}); - /*static*/ Maybe FusedScaleTrilOp::GetSbp(user_op::SbpContext* ctx) { const user_op::TensorDesc& in = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); FOR_RANGE(int64_t, i, 0, in.shape().NumAxes() - 2) { @@ -85,10 +70,10 @@ REGISTER_USER_OP_GRAD("tril").SetGenBackwardOpConfFn([](const user_op::UserOpWra } /*static*/ Maybe FusedScaleTrilOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_GE_OR_RETURN(in.shape().NumAxes(), 2); - *out->mut_shape() = in.shape(); - *out->mut_is_dynamic() = in.is_dynamic(); + out->set_shape(in.shape()); + out->set_is_dynamic(in.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe FusedScaleTrilOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -96,29 +81,9 @@ REGISTER_USER_OP_GRAD("tril").SetGenBackwardOpConfFn([](const user_op::UserOpWra } /*static*/ Maybe FusedScaleTrilOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("fused_scale_tril") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("fused_scale_tril") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Output("out") - .Attr("diagonal", op.attr("diagonal")) - .Attr("floating_scale_value", op.attr("floating_scale_value")) - .Attr("integer_scale_value", op.attr("integer_scale_value")) - .Attr("is_floating_scale_value", op.attr("is_floating_scale_value")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("out", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/triu_op.cpp b/oneflow/user/ops/triu_op.cpp index 00448d7f58510ab25cff856673768bb720c07d2a..69f3722af433354dca59afc6c5a489c073bc633d 100644 --- a/oneflow/user/ops/triu_op.cpp +++ b/oneflow/user/ops/triu_op.cpp @@ -31,10 +31,10 @@ namespace oneflow { } /*static*/ Maybe TriuOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_GE_OR_RETURN(in.shape().NumAxes(), 2); - *out->mut_shape() = in.shape(); - *out->mut_is_dynamic() = in.is_dynamic(); + out->set_shape(in.shape()); + out->set_is_dynamic(in.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe TriuOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -42,8 +42,8 @@ namespace oneflow { } /*static*/ Maybe TriuOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("in", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); - *out->mut_data_type() = in.data_type(); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); + out->set_data_type(in.data_type()); return Maybe::Ok(); } diff --git a/oneflow/user/ops/trunc_op.cpp b/oneflow/user/ops/trunc_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..a061090ac0b5ed4dc2ca343e57edc732c2ef61bd --- /dev/null +++ b/oneflow/user/ops/trunc_op.cpp @@ -0,0 +1,50 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ Maybe TruncOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::SplitForEachAxis(ctx); +} +/*static*/ Maybe TruncOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return user_op::TensorDescInferFnUtil::Unchanged(ctx); +} +/*static*/ Maybe TruncOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe TruncOp::InferDataType(user_op::InferContext* ctx) { + return user_op::TensorDescInferFnUtil::UnchangedDataType(ctx); +} + +/* static */ Maybe TruncGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return user_op::TensorDescInferFnUtil::Unchanged(ctx); +} + +/*static*/ Maybe TruncGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe TruncGradOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::SplitForEachAxis(ctx); +} + +/* static */ Maybe TruncGradOp::InferDataType(user_op::InferContext* ctx) { + return user_op::TensorDescInferFnUtil::UnchangedDataType(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/tuple_identity_op.cpp b/oneflow/user/ops/tuple_identity_op.cpp index dd98f2fef74628f2651b9d6ac9b8c860b5fa1566..329e1134a656591bf72f4160bd9b8b9c5c528acf 100644 --- a/oneflow/user/ops/tuple_identity_op.cpp +++ b/oneflow/user/ops/tuple_identity_op.cpp @@ -26,8 +26,8 @@ namespace oneflow { const int64_t in_size = ctx->input_size("in"); CHECK_EQ_OR_RETURN(ctx->output_size("out"), in_size); for (int64_t i = 0; i < in_size; ++i) { - *ctx->OutputShape("out", i) = ctx->InputShape("in", i); - *ctx->IsDynamic4ArgNameAndIndex("out", i) = ctx->InputIsDynamic("in", i); + ctx->SetOutputShape("out", i, ctx->InputShape("in", i)); + ctx->SetIsDynamic4ArgNameAndIndex("out", i, ctx->InputIsDynamic("in", i)); } return Maybe::Ok(); } @@ -37,7 +37,7 @@ namespace oneflow { /*static*/ Maybe TupleIdentityOp::InferDataType(user_op::InferContext* ctx) { const int64_t in_size = ctx->input_size("in"); CHECK_EQ_OR_RETURN(ctx->output_size("out"), in_size); - for (int64_t i = 0; i < in_size; ++i) { *ctx->OutputDType("out", i) = ctx->InputDType("in", i); } + for (int64_t i = 0; i < in_size; ++i) { ctx->SetOutputDType("out", i, ctx->InputDType("in", i)); } return Maybe::Ok(); } /*static*/ Maybe TupleIdentityOp::InferSbpSignature( @@ -70,16 +70,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("tuple_identity") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - int32_t in_size = op.input_size("in"); - for (int i = 0; i < in_size; ++i) { - if (op.NeedGenGradTensor4OpInput("in", i)) { - op.BindGradTensorWithOpInput(op.GetGradTensorWithOpOutput("out", i), "in", i); - } - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/two_stage_reduce_ops.cpp b/oneflow/user/ops/two_stage_reduce_ops.cpp index 9fbb79e1da03d9aaf4d7d857e392b8824a30fdd2..92dfca74aade1e4704f6004c7712733249c1f644 100644 --- a/oneflow/user/ops/two_stage_reduce_ops.cpp +++ b/oneflow/user/ops/two_stage_reduce_ops.cpp @@ -23,9 +23,9 @@ namespace oneflow { namespace { Maybe InferReduceDeviceStageDtypeFn(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - *ctx->OutputDType("mask", 0) = DataType::kBool; - *ctx->OutputDType("count", 0) = DataType::kInt32; + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + ctx->SetOutputDType("mask", 0, DataType::kBool); + ctx->SetOutputDType("count", 0, DataType::kInt32); return Maybe::Ok(); } @@ -33,9 +33,9 @@ Maybe InferReduceDeviceStageLogicalTensorDescFn(user_op::InferContext* ctx const Shape& input_shape = ctx->InputShape("in", 0); const auto& axis = ctx->Attr>("axis"); const int64_t num_axes = input_shape.NumAxes(); - Shape* output_shape = ctx->OutputShape("out", 0); + Shape output_shape; if (axis.empty()) { - *output_shape = Shape::Ones(num_axes); + output_shape = Shape::Ones(num_axes); } else { const ParallelDesc& parallel_desc = ctx->parallel_desc(); const NdSbp& in_nd_sbp = ctx->NdSbp4ArgNameAndIndex("in", 0); @@ -60,11 +60,11 @@ Maybe InferReduceDeviceStageLogicalTensorDescFn(user_op::InferContext* ctx } } } - *output_shape = Shape(dim_vec); + output_shape = Shape(dim_vec); } - - *ctx->OutputShape("mask", 0) = input_shape; - *ctx->OutputShape("count", 0) = *output_shape; + ctx->SetOutputShape("out", 0, output_shape); + ctx->SetOutputShape("mask", 0, input_shape); + ctx->SetOutputShape("count", 0, output_shape); return Maybe::Ok(); } @@ -72,38 +72,46 @@ Maybe InferReduceDeviceStageLogicalTensorDescFn(user_op::InferContext* ctx Maybe InferReduceDeviceStagePhysicalTensorDescFn(user_op::InferContext* ctx) { const Shape& input_shape = ctx->InputShape("in", 0); const auto& axis = ctx->Attr>("axis"); - Shape* output_shape = ctx->OutputShape("out", 0); + Shape output_shape; if (axis.empty()) { - *output_shape = Shape::Ones(input_shape.NumAxes()); + output_shape = Shape::Ones(input_shape.NumAxes()); } else { const AxisVector axis_vec = {axis.begin(), axis.end()}; const Shape& reduced_shape = CreateReducedShape(input_shape, axis_vec); - *output_shape = reduced_shape; + output_shape = reduced_shape; } - *ctx->OutputShape("mask", 0) = input_shape; - *ctx->OutputShape("count", 0) = *output_shape; + ctx->SetOutputShape("out", 0, output_shape); + ctx->SetOutputShape("mask", 0, input_shape); + ctx->SetOutputShape("count", 0, output_shape); + ; return Maybe::Ok(); } Maybe InferReduceDeviceStageGradDtypeFn(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool); - CHECK_EQ_OR_RETURN(ctx->InputDType("count", 0), DataType::kInt32); - *ctx->OutputDType("in_diff", 0) = ctx->InputDType("out_diff", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kBool) << ", but got " + << DataType_Name(ctx->InputDType("mask", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("count", 0), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(ctx->InputDType("count", 0)); + ctx->SetOutputDType("in_diff", 0, ctx->InputDType("out_diff", 0)); return Maybe::Ok(); } Maybe InferReduceDeviceStageGradTensorDescFn(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(ctx->InputShape("out_diff", 0), ctx->InputShape("count", 0)); - *ctx->OutputShape("in_diff", 0) = ctx->InputShape("mask", 0); + ctx->SetOutputShape("in_diff", 0, ctx->InputShape("mask", 0)); return Maybe::Ok(); } Maybe InferReduceGlobalStageDtypeFn(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("device_count", 0), DataType::kInt32); - *ctx->OutputDType("out", 0) = ctx->InputDType("in", 0); - *ctx->OutputDType("mask", 0) = DataType::kBool; + CHECK_EQ_OR_RETURN(ctx->InputDType("device_count", 0), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(ctx->InputDType("device_count", 0)); + ctx->SetOutputDType("out", 0, ctx->InputDType("in", 0)); + ctx->SetOutputDType("mask", 0, DataType::kBool); return Maybe::Ok(); } @@ -114,33 +122,38 @@ Maybe InferReduceGlobalStageTensorDescFn(user_op::InferContext* ctx) { CHECK_EQ_OR_RETURN(input_shape, device_count_shape); const auto& axis = ctx->Attr>("axis"); bool keepdims = ctx->Attr("keepdims"); - Shape* output_shape = ctx->OutputShape("out", 0); + Shape output_shape; if (axis.empty()) { if (keepdims) { - *output_shape = Shape::Ones(input_shape.NumAxes()); + output_shape = Shape::Ones(input_shape.NumAxes()); } else { - *output_shape = Shape({1}); + output_shape = Shape({1}); } } else { const AxisVector axis_vec = {axis.begin(), axis.end()}; const Shape& reduced_shape = CreateReducedShape(input_shape, axis_vec); if (keepdims) { - *output_shape = reduced_shape; + output_shape = reduced_shape; } else { - *output_shape = reduced_shape.RemoveOnes(axis_vec); + output_shape = reduced_shape.RemoveOnes(axis_vec); } } - *ctx->OutputShape("mask", 0) = input_shape; + ctx->SetOutputShape("out", 0, output_shape); + ctx->SetOutputShape("mask", 0, input_shape); return Maybe::Ok(); } Maybe InferReduceGlobalStageGradDtypeFn(user_op::InferContext* ctx) { - CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool); - CHECK_EQ_OR_RETURN(ctx->InputDType("device_count", 0), DataType::kInt32); + CHECK_EQ_OR_RETURN(ctx->InputDType("mask", 0), DataType::kBool) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kBool) << ", but got " + << DataType_Name(ctx->InputDType("mask", 0)); + CHECK_EQ_OR_RETURN(ctx->InputDType("device_count", 0), DataType::kInt32) + << "InferDataType Failed. Expected " << DataType_Name(DataType::kInt32) << ", but got " + << DataType_Name(ctx->InputDType("device_count", 0)); - *ctx->OutputDType("in_diff", 0) = ctx->InputDType("out_diff", 0); + ctx->SetOutputDType("in_diff", 0, ctx->InputDType("out_diff", 0)); return Maybe::Ok(); } @@ -149,7 +162,7 @@ Maybe InferReduceGlobalStageGradTensorDescFn(user_op::InferContext* ctx) { const Shape& mask_shape = ctx->InputShape("mask", 0); const Shape& device_count_shape = ctx->InputShape("device_count", 0); CHECK_EQ_OR_RETURN(device_count_shape, mask_shape); - *ctx->OutputShape("in_diff", 0) = mask_shape; + ctx->SetOutputShape("in_diff", 0, mask_shape); return Maybe::Ok(); } @@ -235,34 +248,6 @@ IMPLEMENT_REDUCE_DEVICE_STAGE_USER_GRAD_OP_FUNCS(ReduceMinDeviceStage) IMPLEMENT_REDUCE_DEVICE_STAGE_USER_GRAD_OP_FUNCS(ReduceMaxDeviceStage) #undef IMPLEMENT_REDUCE_DEVICE_STAGE_USER_GRAD_OP_FUNCS -Maybe GenBackwardOpConf4ReduceDeviceStage(const std::string& op_type_name, - const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op(op_type_name) - .Input("mask", op.output("mask", 0)) - .Input("count", op.output("count", 0)) - .Input("out_diff", op.GetGradTensorWithOpOutput("out", 0)) - .Output("in_diff") - .Attr("axis", op.attr>("axis")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("in_diff", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -} - -#define REGISTER_REDUCE_DEVICE_STAGE_USER_OP_GRAD(op_type_name, grad_op_type_name) \ - REGISTER_USER_OP_GRAD(op_type_name) \ - .SetGenBackwardOpConfFn( \ - [](const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp) -> Maybe { \ - return GenBackwardOpConf4ReduceDeviceStage(grad_op_type_name, op, AddOp); \ - }); -REGISTER_REDUCE_DEVICE_STAGE_USER_OP_GRAD("reduce_min_device_stage", "reduce_min_device_stage_grad") -REGISTER_REDUCE_DEVICE_STAGE_USER_OP_GRAD("reduce_max_device_stage", "reduce_max_device_stage_grad") - #define IMPLEMENT_REDUCE_GLOBAL_STAGE_OP_FUNCS(op_name) \ /*static*/ Maybe op_name##Op::GetSbp(user_op::SbpContext* ctx) { \ ctx->NewBuilder() \ @@ -317,33 +302,4 @@ IMPLEMENT_REDUCE_GLOBAL_STAGE_GRAD_OP_FUNCS(ReduceMinGlobalStage) IMPLEMENT_REDUCE_GLOBAL_STAGE_GRAD_OP_FUNCS(ReduceMaxGlobalStage) #undef IMPLEMENT_REDUCE_GLOBAL_STAGE_GRAD_OP_FUNCS -Maybe GenBackwardOpConf4ReduceGlobalStage(const std::string& op_type_name, - const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) { - if (op.NeedGenGradTensor4OpInput("in", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op(op_type_name) - .Input("mask", op.output("mask", 0)) - .Input("device_count", op.input("device_count", 0)) - .Input("out_diff", op.GetGradTensorWithOpOutput("out", 0)) - .Output("in_diff") - .Attr("axis", op.attr>("axis")) - .Attr("keepdims", op.attr("keepdims")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("in_diff", 0), "in", 0); - AddOp(grad_op); - } - return Maybe::Ok(); -} - -#define REGISTER_REDUCE_GLOBAL_STAGE_USER_OP_GRAD(op_type_name, grad_op_type_name) \ - REGISTER_USER_OP_GRAD(op_type_name) \ - .SetGenBackwardOpConfFn( \ - [](const user_op::UserOpWrapper& op, user_op::AddOpFn AddOp) -> Maybe { \ - return GenBackwardOpConf4ReduceGlobalStage(grad_op_type_name, op, AddOp); \ - }); -REGISTER_REDUCE_GLOBAL_STAGE_USER_OP_GRAD("reduce_min_global_stage", "reduce_min_global_stage_grad") -REGISTER_REDUCE_GLOBAL_STAGE_USER_OP_GRAD("reduce_max_global_stage", "reduce_max_global_stage_grad") - } // namespace oneflow diff --git a/oneflow/user/ops/unfold_fold_op.cpp b/oneflow/user/ops/unfold_fold_op.cpp index 0560561604cdf930e1b1c609b199b2d04638b924..4d1a0470acaead3c4612ad2417969ccd8268891d 100644 --- a/oneflow/user/ops/unfold_fold_op.cpp +++ b/oneflow/user/ops/unfold_fold_op.cpp @@ -58,12 +58,12 @@ Maybe UnfoldTensorDescInferFn(user_op::InferContext* ctx) { * std::accumulate(kernel_size.begin(), kernel_size.end(), 1, std::multiplies()); y_shape.at(2) = std::accumulate(dhw_shape.begin(), dhw_shape.end(), 1, std::multiplies()); - *ctx->OutputShape("y", 0) = Shape(y_shape); + ctx->SetOutputShape("y", 0, Shape(y_shape)); return Maybe::Ok(); } Maybe SetUnfoldDTypeFn(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -118,12 +118,12 @@ Maybe FoldTensorDescInferFn(user_op::InferContext* ctx) { y_shape.at(2) = output_size[0]; y_shape.at(3) = output_size[1]; - *ctx->OutputShape("y", 0) = Shape(y_shape); + ctx->SetOutputShape("y", 0, Shape(y_shape)); return Maybe::Ok(); } Maybe FoldDTypeFn(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/unfold_tensor_op.cpp b/oneflow/user/ops/unfold_tensor_op.cpp index 52d1c068e6b234bb5aaae26dd1770e0059533808..416f85782c4e4bc3440dbfedb55b0aef37ae9013 100644 --- a/oneflow/user/ops/unfold_tensor_op.cpp +++ b/oneflow/user/ops/unfold_tensor_op.cpp @@ -57,14 +57,14 @@ namespace oneflow { out_shape.at(d) = in_size_at_d; } } - *ctx->OutputShape("y", 0) = Shape(out_shape); + ctx->SetOutputShape("y", 0, Shape(out_shape)); return Maybe::Ok(); } /*static*/ Maybe UnfoldTensorOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UnfoldTensorOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -86,37 +86,16 @@ namespace oneflow { /*static*/ Maybe UnfoldTensorGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& in = ctx->InputTensorDesc("x", 0); const Shape& in_shape = in.shape(); - user_op::TensorDesc* dx_desc = ctx->OutputTensorDesc("dx", 0); - *dx_desc->mut_shape() = Shape(in_shape.dim_vec()); + user_op::TensorDesc* dx_desc = ctx->MutOutputTensorDesc("dx", 0); + dx_desc->set_shape(Shape(in_shape.dim_vec())); return Maybe::Ok(); } /*static*/ Maybe UnfoldTensorGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UnfoldTensorGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("unfold_tensor") - .SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("unfold_tensor_grad") - .InputBind("dy", ctx->FwOp().output_grad("y", 0)) - .InputBind("x", ctx->FwOp().input("x", 0)) - .Attr("dimension", ctx->FwOp().attr("dimension")) - .Attr("size", ctx->FwOp().attr("size")) - .Attr("step", ctx->FwOp().attr("step")) - .Output("dx") - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("dx", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/unique_op.cpp b/oneflow/user/ops/unique_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..46ad8fa4be4cb54b26e28dcd95767798dd04fbd3 --- /dev/null +++ b/oneflow/user/ops/unique_op.cpp @@ -0,0 +1,58 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +/*static*/ Maybe UniqueOp::GetSbp(user_op::SbpContext* ctx) { + return user_op::GetSbpFnUtil::DefaultBroadcastToBroadcast(ctx); +} +/*static*/ Maybe UniqueOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + CHECK_EQ_OR_RETURN(x.shape().NumAxes(), 1); + + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(x.shape()); + y->set_is_dynamic(x.is_dynamic()); + + user_op::TensorDesc* idx = ctx->MutOutputTensorDesc("idx", 0); + idx->set_shape(x.shape()); + idx->set_is_dynamic(x.is_dynamic()); + + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_shape(Shape({1})); + return Maybe::Ok(); +} +/*static*/ Maybe UniqueOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} +/*static*/ Maybe UniqueOp::InferDataType(user_op::InferContext* ctx) { + const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); + auto out_idx = ctx->Attr("out_idx"); + CHECK_OR_RETURN(IsIndexDataType(out_idx)); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(x.data_type()); + + user_op::TensorDesc* idx = ctx->MutOutputTensorDesc("idx", 0); + idx->set_data_type(out_idx); + + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_data_type(out_idx); + return Maybe::Ok(); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/unique_with_counts_op.cpp b/oneflow/user/ops/unique_with_counts_op.cpp index ea0c120dfa78b076a2d8ce25c8d47d58908dcd0c..cc2c212a85dc9af83d9c1a88de2e13ad5f31a26e 100644 --- a/oneflow/user/ops/unique_with_counts_op.cpp +++ b/oneflow/user/ops/unique_with_counts_op.cpp @@ -25,20 +25,20 @@ namespace oneflow { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); CHECK_EQ_OR_RETURN(x.shape().NumAxes(), 1); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_shape() = x.shape(); - *y->mut_is_dynamic() = x.is_dynamic(); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_shape(x.shape()); + y->set_is_dynamic(x.is_dynamic()); - user_op::TensorDesc* idx = ctx->OutputTensorDesc("idx", 0); - *idx->mut_shape() = x.shape(); - *idx->mut_is_dynamic() = x.is_dynamic(); + user_op::TensorDesc* idx = ctx->MutOutputTensorDesc("idx", 0); + idx->set_shape(x.shape()); + idx->set_is_dynamic(x.is_dynamic()); - user_op::TensorDesc* count = ctx->OutputTensorDesc("count", 0); - *count->mut_shape() = x.shape(); - *count->mut_is_dynamic() = x.is_dynamic(); + user_op::TensorDesc* count = ctx->MutOutputTensorDesc("count", 0); + count->set_shape(x.shape()); + count->set_is_dynamic(x.is_dynamic()); - user_op::TensorDesc* num_unique = ctx->OutputTensorDesc("num_unique", 0); - *num_unique->mut_shape() = Shape({1}); + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_shape(Shape({1})); return Maybe::Ok(); } /*static*/ Maybe UniqueWithCountsOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -48,16 +48,16 @@ namespace oneflow { const user_op::TensorDesc& x = ctx->InputTensorDesc("x", 0); auto out_idx = ctx->Attr("out_idx"); CHECK_OR_RETURN(IsIndexDataType(out_idx)); - user_op::TensorDesc* y = ctx->OutputTensorDesc("y", 0); - *y->mut_data_type() = x.data_type(); + user_op::TensorDesc* y = ctx->MutOutputTensorDesc("y", 0); + y->set_data_type(x.data_type()); - user_op::TensorDesc* idx = ctx->OutputTensorDesc("idx", 0); - *idx->mut_data_type() = out_idx; + user_op::TensorDesc* idx = ctx->MutOutputTensorDesc("idx", 0); + idx->set_data_type(out_idx); - user_op::TensorDesc* count = ctx->OutputTensorDesc("count", 0); - *count->mut_data_type() = out_idx; - user_op::TensorDesc* num_unique = ctx->OutputTensorDesc("num_unique", 0); - *num_unique->mut_data_type() = out_idx; + user_op::TensorDesc* count = ctx->MutOutputTensorDesc("count", 0); + count->set_data_type(out_idx); + user_op::TensorDesc* num_unique = ctx->MutOutputTensorDesc("num_unique", 0); + num_unique->set_data_type(out_idx); return Maybe::Ok(); } diff --git a/oneflow/user/ops/unpack_op.cpp b/oneflow/user/ops/unpack_op.cpp index b0b4ee12f044b7ac37d0fd816ed98115b30d38b8..a3780900b95f0c778c51b47b2b25c44f768b4c65 100644 --- a/oneflow/user/ops/unpack_op.cpp +++ b/oneflow/user/ops/unpack_op.cpp @@ -35,19 +35,20 @@ namespace oneflow { CHECK_GT_OR_RETURN(in_shape.NumAxes(), 0); const auto unpack_num = ctx->Attr("unpack_num"); CHECK_EQ_OR_RETURN(in_shape.At(0) % unpack_num, 0); - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); - *out_desc->mut_shape() = in_desc.shape(); - out_desc->mut_shape()->Set(0, in_shape.At(0) / unpack_num); - *out_desc->mut_is_dynamic() = in_desc.is_dynamic(); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); + Shape out_shape = in_desc.shape(); + out_shape.Set(0, in_shape.At(0) / unpack_num); + out_desc->set_shape(out_shape); + out_desc->set_is_dynamic(in_desc.is_dynamic()); return Maybe::Ok(); } /*static*/ Maybe UnpackOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UnpackOp::InferDataType(user_op::InferContext* ctx) { - user_op::TensorDesc* out_desc = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out_desc = ctx->MutOutputTensorDesc("out", 0); const user_op::TensorDesc& in_desc = ctx->InputTensorDesc("in", 0); - *out_desc->mut_data_type() = in_desc.data_type(); + out_desc->set_data_type(in_desc.data_type()); return Maybe::Ok(); } /*static*/ Maybe UnpackOp::InferOutputBlobTimeShape( @@ -59,20 +60,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("unpack").SetBackwardOpConfGenFn([](user_op::BackwardOpConfContext* ctx) - -> Maybe { - const auto grad_op_name = ctx->FwOp().op_name() + "_grad"; - ctx->DefineOp(grad_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("pack") - .InputBind("in", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Attr("pack_num", ctx->FwOp().attr("unpack_num")) - .Build(); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("in", 0), [&ctx, &grad_op_name]() -> const std::string& { - return ctx->GetOp(grad_op_name).output("out", 0); - }); - return Maybe::Ok(); -}); - } // namespace oneflow diff --git a/oneflow/user/ops/unsorted_batch_segment_sum_op.cpp b/oneflow/user/ops/unsorted_batch_segment_sum_op.cpp index fa6b1ac22c35ac6dd92a194b37a643181fda7ced..80797056317b8356b053c23d8232490c9f49e10a 100644 --- a/oneflow/user/ops/unsorted_batch_segment_sum_op.cpp +++ b/oneflow/user/ops/unsorted_batch_segment_sum_op.cpp @@ -46,7 +46,7 @@ namespace oneflow { CHECK_EQ_OR_RETURN(segment_ids.is_dynamic(), data.is_dynamic()); const int64_t num_segments = ctx->Attr("num_segments"); CHECK_GE_OR_RETURN(num_segments, 1); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); FOR_RANGE(int64_t, i, 0, segment_ids.shape().NumAxes() - 1) { CHECK_EQ_OR_RETURN(segment_ids.shape().At(i), data.shape().At(i)); @@ -54,7 +54,7 @@ namespace oneflow { DimVector dim_vec(data.shape().dim_vec()); dim_vec.at(segment_ids.shape().NumAxes() - 1) = num_segments; - *out->mut_shape() = Shape(dim_vec); + out->set_shape(Shape(dim_vec)); return Maybe::Ok(); } /*static*/ Maybe UnsortedBatchSegmentSumOp::InferPhysicalTensorDesc( @@ -64,9 +64,9 @@ namespace oneflow { /*static*/ Maybe UnsortedBatchSegmentSumOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& data = ctx->InputTensorDesc("data", 0); const user_op::TensorDesc& segment_ids = ctx->InputTensorDesc("segment_ids", 0); - user_op::TensorDesc* out = ctx->OutputTensorDesc("out", 0); + user_op::TensorDesc* out = ctx->MutOutputTensorDesc("out", 0); CHECK_OR_RETURN(IsIndexDataType(segment_ids.data_type())); - *out->mut_data_type() = data.data_type(); + out->set_data_type(data.data_type()); return Maybe::Ok(); } /*static*/ Maybe UnsortedBatchSegmentSumOp::ModifyInputArg( @@ -77,22 +77,4 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("unsorted_batch_segment_sum") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - bool need_grad_data = op.NeedGenGradTensor4OpInput("data", 0); - if (need_grad_data) { - user_op::UserOpConfWrapperBuilder data_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper data_grad_op = - data_grad_builder.Op("batch_gather") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("segment_ids", 0)) - .Output("out") - .Build(); - op.BindGradTensorWithOpInput(data_grad_op.output("out", 0), "data", 0); - AddOp(data_grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/unsorted_segment_sum_op.cpp b/oneflow/user/ops/unsorted_segment_sum_op.cpp index 5df5e81e451cf5b8168775f251a13212b5905ca3..f550780fdc0607cd98b19589b6c003eb2508567b 100644 --- a/oneflow/user/ops/unsorted_segment_sum_op.cpp +++ b/oneflow/user/ops/unsorted_segment_sum_op.cpp @@ -52,7 +52,6 @@ namespace oneflow { const Shape& data_shape = ctx->InputShape("data", 0); const int64_t axis = ctx->Attr("axis"); const int64_t num_segments = ctx->Attr("num_segments"); - Shape* out_shape = ctx->OutputShape("out", 0); const Shape& segment_ids_shape = ctx->InputShape("segment_ids", 0); DimVector dim_vec; @@ -61,7 +60,7 @@ namespace oneflow { dim_vec.emplace_back(num_segments); dim_vec.insert(dim_vec.end(), data_shape.dim_vec().cbegin() + axis + segment_ids_shape.NumAxes(), data_shape.dim_vec().end()); - *out_shape = Shape(dim_vec); + ctx->SetOutputShape("out", 0, Shape(dim_vec)); return Maybe::Ok(); } /*static*/ Maybe UnsortedSegmentSumOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { @@ -69,7 +68,7 @@ namespace oneflow { } /*static*/ Maybe UnsortedSegmentSumOp::InferDataType(user_op::InferContext* ctx) { CHECK_OR_RETURN(IsIndexDataType(ctx->InputDType("segment_ids", 0))); - *ctx->OutputDType("out", 0) = ctx->InputDType("data", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("data", 0)); return Maybe::Ok(); } /*static*/ Maybe UnsortedSegmentSumOp::ModifyInputArg( @@ -80,25 +79,6 @@ namespace oneflow { return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("unsorted_segment_sum") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - bool need_grad_data = op.NeedGenGradTensor4OpInput("data", 0); - if (need_grad_data) { - user_op::UserOpConfWrapperBuilder data_grad_builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper data_grad_op = - data_grad_builder.Op("gather") - .Input("in", op.GetGradTensorWithOpOutput("out", 0)) - .Input("indices", op.input("segment_ids", 0)) - .Output("out") - .Attr("axis", op.attr("axis")) - .Build(); - op.BindGradTensorWithOpInput(data_grad_op.output("out", 0), "data", 0); - AddOp(data_grad_op); - } - return Maybe::Ok(); - }); - /*static*/ Maybe UnsortedSegmentSumLikeOp::GetSbp(user_op::SbpContext* ctx) { const int64_t data_num_axes = ctx->LogicalTensorDesc4InputArgNameAndIndex("data", 0).shape().NumAxes(); @@ -163,8 +143,8 @@ REGISTER_USER_OP_GRAD("unsorted_segment_sum") FOR_RANGE(int64_t, i, axis + 1, like_shape.NumAxes()) { CHECK_EQ_OR_RETURN(like_shape.At(i), data_shape.At(i + segment_ids_shape.NumAxes() - 1)); } - *ctx->OutputShape("out", 0) = ctx->InputShape("like", 0); - *ctx->IsDynamic4ArgNameAndIndex("out", 0) = ctx->InputIsDynamic("like", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("like", 0)); + ctx->SetIsDynamic4ArgNameAndIndex("out", 0, ctx->InputIsDynamic("like", 0)); return Maybe::Ok(); } /*static*/ Maybe UnsortedSegmentSumLikeOp::InferPhysicalTensorDesc( @@ -174,9 +154,11 @@ REGISTER_USER_OP_GRAD("unsorted_segment_sum") /*static*/ Maybe UnsortedSegmentSumLikeOp::InferDataType(user_op::InferContext* ctx) { const user_op::TensorDesc& data = ctx->InputTensorDesc("data", 0); const user_op::TensorDesc& like = ctx->InputTensorDesc("like", 0); - CHECK_EQ_OR_RETURN(data.data_type(), like.data_type()); + CHECK_EQ_OR_RETURN(data.data_type(), like.data_type()) + << "InferDataType Failed. Expected " << DataType_Name(like.data_type()) << ", but got " + << DataType_Name(data.data_type()); CHECK_OR_RETURN(IsIndexDataType(ctx->InputDType("segment_ids", 0))); - *ctx->OutputDType("out", 0) = ctx->InputDType("like", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("data", 0)); return Maybe::Ok(); } /*static*/ Maybe UnsortedSegmentSumLikeOp::ModifyInputArg( diff --git a/oneflow/user/ops/upsample_op.cpp b/oneflow/user/ops/upsample_op.cpp index e1d05c1b097603b357380508f9fc4bf7d49a3f0a..6e2ec3a2253d413c1e52f7ba2001210e96bebdb2 100644 --- a/oneflow/user/ops/upsample_op.cpp +++ b/oneflow/user/ops/upsample_op.cpp @@ -24,7 +24,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleLinear1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double scale_factor = ctx->Attr("scale_factor"); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" @@ -32,10 +32,10 @@ namespace oneflow { << "upsample_linear_1d only supports NCH"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0]}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(scale_factor * x_desc.shape().At(2))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(scale_factor * x_desc.shape().At(2))})); } return Maybe::Ok(); } @@ -43,7 +43,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleLinear1DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -53,17 +53,17 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest1DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double scale_factor = ctx->Attr("scale_factor"); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && x_desc.shape().NumAxes() == 3) << "upsample_nearest_1d only supports NCH"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0]}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(scale_factor * x_desc.shape().At(2))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(scale_factor * x_desc.shape().At(2))})); } return Maybe::Ok(); } @@ -71,7 +71,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest1DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -81,7 +81,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double height_scale = ctx->Attr("height_scale"); const double width_scale = ctx->Attr("width_scale"); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" @@ -89,12 +89,12 @@ namespace oneflow { << "upsample_nearest_2d only supports NCHW"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = - Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]}); + y_desc->set_shape( + Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(height_scale * x_desc.shape().At(2)), - static_cast(width_scale * x_desc.shape().At(3))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(height_scale * x_desc.shape().At(2)), + static_cast(width_scale * x_desc.shape().At(3))})); } return Maybe::Ok(); } @@ -102,7 +102,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -112,7 +112,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleBilinear2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double height_scale = ctx->Attr("height_scale"); const double width_scale = ctx->Attr("width_scale"); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" @@ -120,12 +120,12 @@ namespace oneflow { << "upsample_bilinear_2d only supports NCHW"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = - Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]}); + y_desc->set_shape( + Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(height_scale * x_desc.shape().At(2)), - static_cast(width_scale * x_desc.shape().At(3))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(height_scale * x_desc.shape().At(2)), + static_cast(width_scale * x_desc.shape().At(3))})); } return Maybe::Ok(); } @@ -133,7 +133,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleBilinear2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -143,7 +143,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleBicubic2DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double height_scale = ctx->Attr("height_scale"); const double width_scale = ctx->Attr("width_scale"); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" @@ -151,12 +151,12 @@ namespace oneflow { << "upsample_bicubic_2d only supports NCHW"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = - Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]}); + y_desc->set_shape( + Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], output_size[1]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(height_scale * x_desc.shape().At(2)), - static_cast(width_scale * x_desc.shape().At(3))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(height_scale * x_desc.shape().At(2)), + static_cast(width_scale * x_desc.shape().At(3))})); } return Maybe::Ok(); } @@ -164,7 +164,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleBicubic2DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -174,7 +174,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest3DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double depth_scale = ctx->Attr("depth_scale"); const double height_scale = ctx->Attr("height_scale"); const double width_scale = ctx->Attr("width_scale"); @@ -183,13 +183,13 @@ namespace oneflow { << "upsample_nearest_3d only supports NCDHW"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], - output_size[1], output_size[2]}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], + output_size[1], output_size[2]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(depth_scale * x_desc.shape().At(2)), - static_cast(height_scale * x_desc.shape().At(3)), - static_cast(width_scale * x_desc.shape().At(4))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(depth_scale * x_desc.shape().At(2)), + static_cast(height_scale * x_desc.shape().At(3)), + static_cast(width_scale * x_desc.shape().At(4))})); } return Maybe::Ok(); } @@ -197,7 +197,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest3DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -207,7 +207,7 @@ namespace oneflow { } /*static*/ Maybe UpsampleTrilinear3DOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const user_op::TensorDesc& x_desc = ctx->InputTensorDesc("x", 0); - user_op::TensorDesc* y_desc = ctx->OutputTensorDesc("y", 0); + user_op::TensorDesc* y_desc = ctx->MutOutputTensorDesc("y", 0); const double depth_scale = ctx->Attr("depth_scale"); const double height_scale = ctx->Attr("height_scale"); const double width_scale = ctx->Attr("width_scale"); @@ -216,13 +216,13 @@ namespace oneflow { << "upsample_trilinear_3d only supports NCDHW"; std::vector output_size = ctx->Attr>("output_size"); if (output_size.size()) { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], - output_size[1], output_size[2]}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), output_size[0], + output_size[1], output_size[2]})); } else { - *y_desc->mut_shape() = Shape({x_desc.shape().At(0), x_desc.shape().At(1), - static_cast(depth_scale * x_desc.shape().At(2)), - static_cast(height_scale * x_desc.shape().At(3)), - static_cast(width_scale * x_desc.shape().At(4))}); + y_desc->set_shape(Shape({x_desc.shape().At(0), x_desc.shape().At(1), + static_cast(depth_scale * x_desc.shape().At(2)), + static_cast(height_scale * x_desc.shape().At(3)), + static_cast(width_scale * x_desc.shape().At(4))})); } return Maybe::Ok(); } @@ -230,7 +230,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleTrilinear3DOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("y", 0) = ctx->InputDType("x", 0); + ctx->SetOutputDType("y", 0, ctx->InputDType("x", 0)); return Maybe::Ok(); } @@ -244,18 +244,17 @@ namespace oneflow { } /*static*/ Maybe UpsampleLinear1DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 3) << "upsample_linear_1d_grad only supports NCH"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleLinear1DGradOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleLinear1DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -269,11 +268,10 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest1DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 3) << "upsample_nearest_1d_grad only supports NCH"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleNearest1DGradOp::InferPhysicalTensorDesc( @@ -281,7 +279,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest1DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -295,11 +293,10 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 4) << "upsample_nearest_2d_grad only supports NCHW"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleNearest2DGradOp::InferPhysicalTensorDesc( @@ -307,7 +304,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest2DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -322,11 +319,10 @@ namespace oneflow { /*static*/ Maybe UpsampleBilinear2DGradOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 4) << "upsample_bilinear_2d_grad only supports NCHW"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleBilinear2DGradOp::InferPhysicalTensorDesc( @@ -334,7 +330,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleBilinear2DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -348,11 +344,10 @@ namespace oneflow { } /*static*/ Maybe UpsampleBicubic2DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 4) << "upsample_bicubic_2d_grad only supports NCHW"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleBicubic2DGradOp::InferPhysicalTensorDesc( @@ -360,7 +355,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleBicubic2DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -374,11 +369,10 @@ namespace oneflow { } /*static*/ Maybe UpsampleNearest3DGradOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 5) << "upsample_nearest_3d_grad only supports NCDHW"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleNearest3DGradOp::InferPhysicalTensorDesc( @@ -386,7 +380,7 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleNearest3DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } @@ -401,11 +395,10 @@ namespace oneflow { /*static*/ Maybe UpsampleTrilinear3DGradOp::InferLogicalTensorDesc( user_op::InferContext* ctx) { const Shape& dy_shape = ctx->InputShape("dy", 0); - Shape* dx_shape = ctx->OutputShape("dx", 0); CHECK_OR_RETURN(ctx->Attr("data_format") == "channels_first" && dy_shape.NumAxes() == 5) << "upsample_trilinear_3d_grad only supports NCDHW"; - *dx_shape = ctx->InputShape("x", 0); + ctx->SetOutputShape("dx", 0, ctx->InputShape("x", 0)); return Maybe::Ok(); } /*static*/ Maybe UpsampleTrilinear3DGradOp::InferPhysicalTensorDesc( @@ -413,159 +406,8 @@ namespace oneflow { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe UpsampleTrilinear3DGradOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("dx", 0) = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, ctx->InputDType("dy", 0)); return Maybe::Ok(); } -REGISTER_USER_OP_GRAD("upsample_linear_1d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_linear_1d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("scale_factor", op.attr("scale_factor")) - .Attr("align_corners", op.attr("align_corners")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_nearest_1d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_nearest_1d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("scale_factor", op.attr("scale_factor")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_nearest_2d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_nearest_2d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("height_scale", op.attr("height_scale")) - .Attr("width_scale", op.attr("width_scale")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_bilinear_2d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_bilinear_2d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("height_scale", op.attr("height_scale")) - .Attr("width_scale", op.attr("width_scale")) - .Attr("align_corners", op.attr("align_corners")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_bicubic_2d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_bicubic_2d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("height_scale", op.attr("height_scale")) - .Attr("width_scale", op.attr("width_scale")) - .Attr("align_corners", op.attr("align_corners")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_nearest_3d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_nearest_3d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("depth_scale", op.attr("depth_scale")) - .Attr("height_scale", op.attr("height_scale")) - .Attr("width_scale", op.attr("width_scale")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - -REGISTER_USER_OP_GRAD("upsample_trilinear_3d") - .SetGenBackwardOpConfFn([](const user_op::UserOpWrapper& op, - user_op::AddOpFn AddOp) -> Maybe { - if (op.NeedGenGradTensor4OpInput("x", 0)) { - user_op::UserOpConfWrapperBuilder builder(op.op_name() + "_grad"); - user_op::UserOpConfWrapper grad_op = - builder.Op("upsample_trilinear_3d_grad") - .Input("dy", op.GetGradTensorWithOpOutput("y", 0)) - .Input("x", op.input("x", 0)) - .Output("dx") - .Attr("depth_scale", op.attr("depth_scale")) - .Attr("height_scale", op.attr("height_scale")) - .Attr("width_scale", op.attr("width_scale")) - .Attr("align_corners", op.attr("align_corners")) - .Attr("output_size", op.attr>("output_size")) - .Attr("data_format", op.attr("data_format")) - .Build(); - op.BindGradTensorWithOpInput(grad_op.output("dx", 0), "x", 0); - AddOp(grad_op); - } - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/util_ops.cpp b/oneflow/user/ops/util_ops.cpp index 0be4ce5f115a70b1a22e4d34a606c4c43920b09f..b6f051f7b3ec0a0de78af5a1b00349ed77372538 100644 --- a/oneflow/user/ops/util_ops.cpp +++ b/oneflow/user/ops/util_ops.cpp @@ -19,7 +19,7 @@ limitations under the License. namespace oneflow { /* static */ Maybe IsNanOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -38,12 +38,12 @@ namespace oneflow { } /* static */ Maybe IsNanOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kBool; + ctx->SetOutputDType("out", 0, DataType::kBool); return Maybe::Ok(); } /* static */ Maybe IsInfOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("in", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); return Maybe::Ok(); } @@ -62,7 +62,31 @@ namespace oneflow { } /* static */ Maybe IsInfOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = DataType::kBool; + ctx->SetOutputDType("out", 0, DataType::kBool); + return Maybe::Ok(); +} + +/* static */ Maybe IsFiniteOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + ctx->SetOutputShape("out", 0, ctx->InputShape("in", 0)); + return Maybe::Ok(); +} + +/*static*/ Maybe IsFiniteOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe IsFiniteOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder().Broadcast(ctx->inputs()).Broadcast(ctx->outputs()).Build(); + const auto& in_tensor = ctx->LogicalTensorDesc4InputArgNameAndIndex("in", 0); + for (int i = 0; i < in_tensor.shape().NumAxes(); ++i) { + ctx->NewBuilder().Split(ctx->inputs(), i).Split(ctx->outputs(), i).Build(); + } + ctx->NewBuilder().PartialSum(ctx->inputs()).PartialSum(ctx->outputs()).Build(); + return Maybe::Ok(); +} + +/* static */ Maybe IsFiniteOp::InferDataType(user_op::InferContext* ctx) { + ctx->SetOutputDType("out", 0, DataType::kBool); return Maybe::Ok(); } diff --git a/oneflow/user/ops/variance_op.cpp b/oneflow/user/ops/variance_op.cpp index c1e578e694722f2f3d901c33dc92e52d16e1339b..1d1269efd58e809b9b605e516a36cce49cc50109 100644 --- a/oneflow/user/ops/variance_op.cpp +++ b/oneflow/user/ops/variance_op.cpp @@ -27,12 +27,13 @@ Maybe VarOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { const AxisVector reduce_axes_vec = {reduce_axes.begin(), reduce_axes.end()}; const Shape& reduce_shape = CreateReducedShape(input_shape, reduce_axes_vec); const bool keepdim = ctx->Attr("keepdim"); - Shape* output_shape = ctx->OutputShape("output", 0); + Shape output_shape; if (keepdim) { - *output_shape = reduce_shape; + output_shape = reduce_shape; } else { - *output_shape = reduce_shape.RemoveOnes(reduce_axes_vec); + output_shape = reduce_shape.RemoveOnes(reduce_axes_vec); } + ctx->SetOutputShape("output", 0, output_shape); return Maybe::Ok(); } @@ -41,7 +42,7 @@ Maybe VarOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { } Maybe VarOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("output", 0) = ctx->InputDType("input", 0); + ctx->SetOutputDType("output", 0, ctx->InputDType("input", 0)); return Maybe::Ok(); } diff --git a/oneflow/user/ops/vector_matrix_product_op.cpp b/oneflow/user/ops/vector_matrix_product_op.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5c5e45389923d7b3a61ff2f023bd39fd16ab6699 --- /dev/null +++ b/oneflow/user/ops/vector_matrix_product_op.cpp @@ -0,0 +1,194 @@ +/* +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +*/ +#include "oneflow/core/framework/framework.h" +#include "oneflow/core/framework/op_generated.h" + +namespace oneflow { + +namespace { + +Maybe InferTensorDesc4VectorMatrixProduct(user_op::InferContext* ctx) { + const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); + const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); + int64_t k = a.shape().At(0); + CHECK_EQ_OR_RETURN(k, b.shape().At(0)) << "Dim K should be equal to vector b's dim0. "; + int64_t n = b.shape().At(1); + ctx->SetOutputShape("out", 0, Shape({n})); + return Maybe::Ok(); +} + +Maybe InferDataType4VectorMatrixProduct(user_op::InferContext* ctx) { + DataType dtype = ctx->InputDType("a", 0); + CHECK_EQ_OR_RETURN(ctx->InputDType("b", 0), dtype) + << "Matrix A datatype should be equal to Vector B. "; + ctx->SetOutputDType("out", 0, dtype); + return Maybe::Ok(); +} + +Maybe InferTensorDesc4VectorMatrixProductGradA(user_op::InferContext* ctx) { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + GradA = dy (n) matmul B_transpose(n, k) -> (1, n) matmul (n, k) + */ + const user_op::TensorDesc& b = ctx->InputTensorDesc("b", 0); + int64_t k = b.shape().At(0); + ctx->SetOutputShape("dx", 0, Shape({k})); + return Maybe::Ok(); +} + +Maybe InferTensorDesc4VectorMatrixProductGradB(user_op::InferContext* ctx) { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + GradB = a (k, 1) matmul dy (1, n) + */ + const user_op::TensorDesc& dy = ctx->InputTensorDesc("dy", 0); + const user_op::TensorDesc& a = ctx->InputTensorDesc("a", 0); + int64_t k = a.shape().At(0); + int64_t n = dy.shape().At(0); + ctx->SetOutputShape("dx", 0, Shape({k, n})); + return Maybe::Ok(); +} + +Maybe InferDataType4Grad(user_op::InferContext* ctx) { + DataType dtype = ctx->InputDType("dy", 0); + ctx->SetOutputDType("dx", 0, dtype); + return Maybe::Ok(); +} + +} // namespace + +/* static */ Maybe VectorMatrixProductOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { + return InferTensorDesc4VectorMatrixProduct(ctx); +} + +/*static*/ Maybe VectorMatrixProductOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe VectorMatrixProductOp::GetSbp(user_op::SbpContext* ctx) { + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .Split(user_op::OpArg("b", 0), 1) + .Split(user_op::OpArg("out", 0), 0) + .Build(); + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), 0) + .Split(user_op::OpArg("b", 0), 0) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("a", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .PartialSum(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("out", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe VectorMatrixProductOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4VectorMatrixProduct(ctx); +} + +/* static */ Maybe VectorMatrixProductGradAOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferTensorDesc4VectorMatrixProductGradA(ctx); +} + +/*static*/ Maybe VectorMatrixProductGradAOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe VectorMatrixProductGradAOp::GetSbp(user_op::SbpContext* ctx) { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + GradA = dy (n) matmul B_transpose(n, k) -> (1, n) matmul (n, k) + */ + ctx->NewBuilder() + .Broadcast(user_op::OpArg("dy", 0)) + .Split(user_op::OpArg("b", 0), 0) + .Split(user_op::OpArg("dx", 0), 0) + .Build(); + ctx->NewBuilder() + .Split(user_op::OpArg("dy", 0), 0) + .Split(user_op::OpArg("b", 0), 1) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("dy", 0)) + .Broadcast(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("b", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe VectorMatrixProductGradAOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4Grad(ctx); +} + +/* static */ Maybe VectorMatrixProductGradBOp::InferLogicalTensorDesc( + user_op::InferContext* ctx) { + return InferTensorDesc4VectorMatrixProductGradB(ctx); +} + +/*static*/ Maybe VectorMatrixProductGradBOp::InferPhysicalTensorDesc( + user_op::InferContext* ctx) { + return InferLogicalTensorDesc(ctx); +} + +/* static */ Maybe VectorMatrixProductGradBOp::GetSbp(user_op::SbpContext* ctx) { + /* + A(k, ) matmul B(k, n) -> (1, k) matmul (k, n) -> (1, n) -> (n) + A(k, ) -> (1, k) + GradB = a_transpose (k, 1) matmul dy (1, n) + */ + ctx->NewBuilder() + .Split(user_op::OpArg("a", 0), 0) + .Broadcast(user_op::OpArg("dy", 0)) + .Split(user_op::OpArg("dx", 0), 0) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .Split(user_op::OpArg("dy", 0), 0) + .Split(user_op::OpArg("dx", 0), 1) + .Build(); + ctx->NewBuilder() + .Broadcast(user_op::OpArg("a", 0)) + .PartialSum(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + ctx->NewBuilder() + .PartialSum(user_op::OpArg("a", 0)) + .Broadcast(user_op::OpArg("dy", 0)) + .PartialSum(user_op::OpArg("dx", 0)) + .Build(); + return Maybe::Ok(); +} + +/* static */ Maybe VectorMatrixProductGradBOp::InferDataType(user_op::InferContext* ctx) { + return InferDataType4Grad(ctx); +} + +} // namespace oneflow diff --git a/oneflow/user/ops/where_op.cpp b/oneflow/user/ops/where_op.cpp index e49ffb19fe6128d18100b1ec3e736acf5834563d..6bf9bf415f9fbc5990db3e2b41b9fe7aee419058 100644 --- a/oneflow/user/ops/where_op.cpp +++ b/oneflow/user/ops/where_op.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "oneflow/core/framework/framework.h" #include "oneflow/core/framework/op_generated.h" +#include "oneflow/core/framework/dtype.h" namespace oneflow { @@ -81,11 +82,11 @@ Maybe InferWhereTensorDesc(user_op::InferContext* ctx) { const Shape& x_shape = ctx->InputShape("x", 0); const Shape& y_shape = ctx->InputShape("y", 0); if (x_shape == y_shape && y_shape == cond_shape) { - *ctx->OutputShape("out", 0) = cond_shape; + ctx->SetOutputShape("out", 0, cond_shape); } else { Shape max_shape = *JUST(GetBroadcastShape(cond_shape, x_shape)); max_shape = *JUST(GetBroadcastShape(max_shape, y_shape)); - *ctx->OutputShape("out", 0) = max_shape; + ctx->SetOutputShape("out", 0, max_shape); } return Maybe::Ok(); } @@ -94,10 +95,10 @@ Maybe InferWhereXScalarTensorDesc(user_op::InferContext* ctx) { const Shape& cond_shape = ctx->InputShape("condition", 0); const Shape& y_shape = ctx->InputShape("y", 0); if (cond_shape == y_shape) { - *ctx->OutputShape("out", 0) = cond_shape; + ctx->SetOutputShape("out", 0, cond_shape); } else { Shape max_shape = *JUST(GetBroadcastShape(cond_shape, y_shape)); - *ctx->OutputShape("out", 0) = max_shape; + ctx->SetOutputShape("out", 0, max_shape); } return Maybe::Ok(); } @@ -106,16 +107,16 @@ Maybe InferWhereYScalarTensorDesc(user_op::InferContext* ctx) { const Shape& cond_shape = ctx->InputShape("condition", 0); const Shape& x_shape = ctx->InputShape("x", 0); if (cond_shape == x_shape) { - *ctx->OutputShape("out", 0) = cond_shape; + ctx->SetOutputShape("out", 0, cond_shape); } else { Shape max_shape = *JUST(GetBroadcastShape(cond_shape, x_shape)); - *ctx->OutputShape("out", 0) = max_shape; + ctx->SetOutputShape("out", 0, max_shape); } return Maybe::Ok(); } Maybe InferWhereXYScalarTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("condition", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("condition", 0)); return Maybe::Ok(); } @@ -209,11 +210,13 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie return InferLogicalTensorDesc(ctx); } /*static*/ Maybe WhereOp::InferDataType(user_op::InferContext* ctx) { - const DataType& cond_dtype = ctx->InputDType("condition", 0); + DataType cond_dtype = ctx->InputDType("condition", 0); CHECK_OR_RETURN(IsBoolDataType(cond_dtype) || IsIntegralDataType(cond_dtype)); - const DataType& x_dtype = ctx->InputDType("x", 0); - CHECK_EQ_OR_RETURN(x_dtype, ctx->InputDType("y", 0)); - *ctx->OutputDType("out", 0) = x_dtype; + DataType x_dtype = ctx->InputDType("x", 0); + CHECK_EQ_OR_RETURN(x_dtype, ctx->InputDType("y", 0)) + << "InferDataType Failed. Expected " << DataType_Name(ctx->InputDType("y", 0)) << ", but got " + << DataType_Name(x_dtype); + ctx->SetOutputDType("out", 0, x_dtype); return Maybe::Ok(); } /*static*/ Maybe WhereOp::ModifyInputArg(const GetInputArgModifier& f, @@ -231,9 +234,9 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie return InferLogicalTensorDesc(ctx); } /*static*/ Maybe WhereScalarXOp::InferDataType(user_op::InferContext* ctx) { - const DataType& cond_dtype = ctx->InputDType("condition", 0); + DataType cond_dtype = ctx->InputDType("condition", 0); CHECK_OR_RETURN(IsBoolDataType(cond_dtype) || IsIntegralDataType(cond_dtype)); - const DataType& y_dtype = ctx->InputDType("y", 0); + DataType y_dtype = ctx->InputDType("y", 0); if (ctx->Attr("has_int_operand")) { CHECK_EQ_OR_RETURN(y_dtype, GetDataType::value) << "expected scalar type " << GetDataType::value << "but found " << y_dtype; @@ -244,7 +247,7 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie CHECK_EQ_OR_RETURN(y_dtype, GetDataType::value) << "expected scalar type " << GetDataType::value << "but found " << y_dtype; } - *ctx->OutputDType("out", 0) = y_dtype; + ctx->SetOutputDType("out", 0, y_dtype); return Maybe::Ok(); } /*static*/ Maybe WhereScalarXOp::ModifyInputArg(const GetInputArgModifier& f, @@ -262,9 +265,9 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie return InferLogicalTensorDesc(ctx); } /*static*/ Maybe WhereScalarYOp::InferDataType(user_op::InferContext* ctx) { - const DataType& cond_dtype = ctx->InputDType("condition", 0); + DataType cond_dtype = ctx->InputDType("condition", 0); CHECK_OR_RETURN(IsBoolDataType(cond_dtype) || IsIntegralDataType(cond_dtype)); - const DataType& x_dtype = ctx->InputDType("x", 0); + DataType x_dtype = ctx->InputDType("x", 0); if (ctx->Attr("has_int_operand")) { CHECK_EQ_OR_RETURN(x_dtype, GetDataType::value) << "expected scalar type " << GetDataType::value << "but found " << x_dtype; @@ -275,7 +278,7 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie CHECK_EQ_OR_RETURN(x_dtype, GetDataType::value) << "expected scalar type " << GetDataType::value << "but found " << x_dtype; } - *ctx->OutputDType("out", 0) = x_dtype; + ctx->SetOutputDType("out", 0, x_dtype); return Maybe::Ok(); } /*static*/ Maybe WhereScalarYOp::ModifyInputArg(const GetInputArgModifier& f, @@ -293,14 +296,16 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie return InferLogicalTensorDesc(ctx); } /*static*/ Maybe WhereScalarXyOp::InferDataType(user_op::InferContext* ctx) { - const DataType& cond_dtype = ctx->InputDType("condition", 0); + DataType cond_dtype = ctx->InputDType("condition", 0); CHECK_OR_RETURN(IsBoolDataType(cond_dtype) || IsIntegralDataType(cond_dtype)); if (ctx->Attr("has_x_bool_operand") && ctx->Attr("has_y_bool_operand")) { - *ctx->OutputDType("out", 0) = GetDataType::value; + ctx->SetOutputDType("out", 0, GetDataType::value); } else if (ctx->Attr("has_x_int_operand") && ctx->Attr("has_y_int_operand")) { - *ctx->OutputDType("out", 0) = GetDataType::value; + ctx->SetOutputDType("out", 0, GetDataType::value); } else if (ctx->Attr("has_x_float_operand") && ctx->Attr("has_y_float_operand")) { - *ctx->OutputDType("out", 0) = GetDataType::value; + // Align with PyTorch's where op output datatype when x and y is all Scalar. + ctx->SetOutputDType("out", 0, GetDefaultDType()->data_type()); + } else { UNIMPLEMENTED(); } @@ -311,45 +316,4 @@ Maybe GetWhereInputArgModify(const GetInputArgModifier& GetInputArgModifie return GetWhereInputArgModify(f, conf); } -REGISTER_USER_OP_GRAD("where").SetBackwardOpConfGenFn( - [](user_op::BackwardOpConfContext* ctx) -> Maybe { - const auto zero_op_name = ctx->FwOp().op_name() + "_zero_grad"; - ctx->DefineOp(zero_op_name, [&ctx](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("zero_like") - .InputBind("like", ctx->FwOp().input("x", 0)) - .Output("out") - .Build(); - }); - - const auto x_grad_op_name = ctx->FwOp().op_name() + "_x_grad"; - ctx->DefineOp(x_grad_op_name, [&ctx, &zero_op_name](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("where") - .InputBind("condition", ctx->FwOp().input("condition", 0)) - .InputBind("x", ctx->FwOp().output_grad("out", 0)) - .InputBind("y", ctx->GetOp(zero_op_name).output("out", 0)) - .Output("out") - .Build(); - }); - - const auto y_grad_op_name = ctx->FwOp().op_name() + "_y_grad"; - ctx->DefineOp(y_grad_op_name, [&ctx, &zero_op_name](user_op::BackwardOpBuilder& builder) { - return builder.OpTypeName("where") - .InputBind("condition", ctx->FwOp().input("condition", 0)) - .InputBind("x", ctx->GetOp(zero_op_name).output("out", 0)) - .InputBind("y", ctx->FwOp().output_grad("out", 0)) - .Output("out") - .Build(); - }); - - ctx->FwOp().InputGradBind(user_op::OpArg("x", 0), - [&ctx, &x_grad_op_name]() -> const std::string& { - return ctx->GetOp(x_grad_op_name).output("out", 0); - }); - ctx->FwOp().InputGradBind(user_op::OpArg("y", 0), - [&ctx, &y_grad_op_name]() -> const std::string& { - return ctx->GetOp(y_grad_op_name).output("out", 0); - }); - return Maybe::Ok(); - }); - } // namespace oneflow diff --git a/oneflow/user/ops/zero_like_op.cpp b/oneflow/user/ops/zero_like_op.cpp index ad6487796846226b7043ee2976c01a220b57e78b..46bb3e5a1d6c7e8eedf167b58ad2471456f491dc 100644 --- a/oneflow/user/ops/zero_like_op.cpp +++ b/oneflow/user/ops/zero_like_op.cpp @@ -33,14 +33,14 @@ namespace oneflow { return Maybe::Ok(); } /*static*/ Maybe ZeroLikeOp::InferLogicalTensorDesc(user_op::InferContext* ctx) { - *ctx->OutputShape("out", 0) = ctx->InputShape("like", 0); + ctx->SetOutputShape("out", 0, ctx->InputShape("like", 0)); return Maybe::Ok(); } /*static*/ Maybe ZeroLikeOp::InferPhysicalTensorDesc(user_op::InferContext* ctx) { return InferLogicalTensorDesc(ctx); } /*static*/ Maybe ZeroLikeOp::InferDataType(user_op::InferContext* ctx) { - *ctx->OutputDType("out", 0) = ctx->InputDType("like", 0); + ctx->SetOutputDType("out", 0, ctx->InputDType("like", 0)); return Maybe::Ok(); } /*static*/ Maybe ZeroLikeOp::InferNdSbp(user_op::InferNdSbpFnContext* ctx) { diff --git a/oneflow/user/utils/pool_util.cpp b/oneflow/user/utils/pool_util.cpp index 088dacc751b1d3a30084016c0d82d506e25d3752..a813437fa5eac684d6f18d67a91d52570ed77115 100644 --- a/oneflow/user/utils/pool_util.cpp +++ b/oneflow/user/utils/pool_util.cpp @@ -18,36 +18,6 @@ limitations under the License. namespace oneflow { -namespace { - -std::vector Get3DVec(const std::vector& original_vec, int32_t NDims) { - std::vector vec; - FOR_RANGE(uint8_t, dim, 0, 3) { - int64_t index = static_cast(dim) - (3 - NDims); - if (index < 0) { - vec.emplace_back(1); - } else { - vec.emplace_back(original_vec.at(index)); - } - } - return vec; -} - -std::vector Get3DPadVec(const std::vector& original_vec, int32_t NDims) { - std::vector vec; - FOR_RANGE(uint8_t, dim, 0, 3) { - int64_t index = static_cast(dim) - (3 - NDims); - if (index < 0) { - vec.emplace_back(0); - } else { - vec.emplace_back(original_vec.at(index)); - } - } - return vec; -} - -} // namespace - Params3D::Params3D(const int32_t dim, const ShapeView& x_shape, const std::string& data_format, const std::string& padding, const std::vector& padding_before, const std::vector& padding_after, const std::vector& pool_size, @@ -55,8 +25,8 @@ Params3D::Params3D(const int32_t dim, const ShapeView& x_shape, const std::strin : dim_(dim), pool_size_3d_(Get3DVec(pool_size, dim)), strides_3d_(Get3DVec(strides, dim)), - padding_before_3d_(Get3DPadVec(padding_before, dim)), - padding_after_3d_(Get3DPadVec(padding_after, dim)), + padding_before_3d_(Get3DVec(padding_before, dim)), + padding_after_3d_(Get3DVec(padding_after, dim)), data_format_(data_format), padding_(padding), ceil_mode_(ceil_mode) { diff --git a/oneflow/user/utils/pool_util.h b/oneflow/user/utils/pool_util.h index 4deed023f1f417e9b98c2e869442a28c5f184185..1c377ed51aab062bc5c925c8b42105bef177ecbe 100644 --- a/oneflow/user/utils/pool_util.h +++ b/oneflow/user/utils/pool_util.h @@ -57,6 +57,22 @@ class Params3D { int64_t channel_num_; }; +enum class Get3DVecType { kPad, kNonPad }; + +template +std::vector Get3DVec(const std::vector& original_vec, int32_t NDims) { + std::vector vec; + FOR_RANGE(uint8_t, dim, 0, 3) { + int64_t index = static_cast(dim) - (3 - NDims); + if (index < 0) { + vec.emplace_back(static_cast(get_3d_vec_type)); // kPad -> 0, kNonPad -> 1 + } else { + vec.emplace_back(original_vec.at(index)); + } + } + return vec; +} + } // namespace oneflow #endif // ONEFLOW_USER_UTILS_POOL_UTIL_H_ diff --git a/python/oneflow/_C/__init__.py b/python/oneflow/_C/__init__.py index 8f20b0ca79f3784d9538340704d26e1999767ba9..69b2ae422ca047f7a27cb7dac3faa0d9c55f6db8 100644 --- a/python/oneflow/_C/__init__.py +++ b/python/oneflow/_C/__init__.py @@ -14,3 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ from oneflow._oneflow_internal._C import * + + +def allclose(input, other, atol=1e-08, rtol=1e-05, equal_nan=False): + return isclose(input, other, atol, rtol, equal_nan).all().item() diff --git a/python/oneflow/__init__.py b/python/oneflow/__init__.py index 48c15e1132217c00778c5a0736ec926564b4d250..2cd93f1e795fa7ee38f25e83a796f238194564e0 100644 --- a/python/oneflow/__init__.py +++ b/python/oneflow/__init__.py @@ -18,13 +18,22 @@ import os import sys import collections +# https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#cuda-environment-variables +if "CUDA_MODULE_LOADING" not in os.environ: + os.environ["CUDA_MODULE_LOADING"] = "LAZY" + import oneflow._oneflow_internal +oneflow_python_base_dir = os.path.dirname(os.path.realpath(__file__)) +oneflow._oneflow_internal.InitPythonPathsToBeKeptAndFilteredForDebugging( + oneflow_python_base_dir +) oneflow._oneflow_internal.InitNumpyCAPI() oneflow._oneflow_internal.CheckAndClearRegistryFlag() Size = oneflow._oneflow_internal.Size device = oneflow._oneflow_internal.device placement = oneflow._oneflow_internal.placement + locals()["dtype"] = oneflow._oneflow_internal.dtype locals()["bool"] = oneflow._oneflow_internal.bool locals()["float16"] = oneflow._oneflow_internal.float16 @@ -72,6 +81,7 @@ from oneflow._C import amin from oneflow._C import atanh from oneflow._C import atanh as arctanh from oneflow._C import batch_matmul as bmm +from oneflow._C import baddbmm from oneflow._C import broadcast_like from oneflow._C import chunk from oneflow._C import split @@ -84,11 +94,15 @@ from oneflow._C import greater_equal from oneflow._C import greater_equal as ge from oneflow._C import log from oneflow._C import log2 +from oneflow._C import log10 from oneflow._C import logical_and from oneflow._C import logical_or from oneflow._C import logical_xor from oneflow._C import logical_not -from oneflow._C import gelu +from oneflow._C import logaddexp +from oneflow._C import quantile +from oneflow._C import gelu_with_approximate as gelu +from oneflow._C import quick_gelu from oneflow._C import mish from oneflow._C import repeat from oneflow._C import repeat_interleave @@ -96,16 +110,16 @@ from oneflow._C import tile from oneflow._C import sigmoid from oneflow._C import tanh from oneflow._C import as_strided +from oneflow._C import as_strided_ from oneflow._C import silu from oneflow._C import selu from oneflow._C import softshrink from oneflow._C import softsign from oneflow._C import cast -from oneflow._C import ones_like -from oneflow._C import zeros_like from oneflow._C import diag from oneflow._C import log1p from oneflow._C import add +from oneflow._C import addcdiv from oneflow._C import div, div_ from oneflow._C import addcmul from oneflow._C import floor, floor_ @@ -124,7 +138,7 @@ from oneflow._C import atan from oneflow._C import atan as arctan from oneflow._C import atan2 from oneflow._C import ceil -from oneflow._C import clamp, clamp_ +from oneflow._C import clamp, clamp_, clamp_min, clamp_min_, clamp_max, clamp_max_ from oneflow._C import clip, clip_ from oneflow._C import cos from oneflow._C import cosh @@ -134,9 +148,9 @@ from oneflow._C import erfc from oneflow._C import expm1 from oneflow._C import fmod from oneflow._C import flatten +from oneflow._C import topk from oneflow._C import in_top_k -from oneflow._C import log -from oneflow._C import log2 +from oneflow._C import lgamma from oneflow._C import minimum from oneflow._C import maximum from oneflow._C import max @@ -148,18 +162,21 @@ from oneflow._C import reduce_sum as sum from oneflow._C import reduce_mean as mean from oneflow._C import reduce_all as all from oneflow._C import reduce_any as any +from oneflow._C import reduce_nansum as nansum +from oneflow._C import logsumexp from oneflow._C import rsqrt from oneflow._C import sqrt from oneflow._C import square from oneflow._C import matmul from oneflow._C import mm -from oneflow._C import mv +from oneflow._C import matrix_vector_product as mv from oneflow._C import bernoulli from oneflow._C import round from oneflow._C import softplus from oneflow._C import threshold from oneflow._C import tril from oneflow._C import triu +from oneflow._C import trunc from oneflow._C import pad from oneflow._C import transpose from oneflow._C import relu @@ -169,8 +186,10 @@ from oneflow._C import log_softmax from oneflow._C import argmax from oneflow._C import argmin from oneflow._C import std +from oneflow._C import stft from oneflow._C import var -from oneflow._C import stack +from oneflow._C import stack, hstack, vstack, dstack, column_stack, row_stack +from oneflow._C import atleast_1d, atleast_2d, atleast_3d from oneflow._C import squeeze from oneflow._C import narrow from oneflow._C import unsqueeze @@ -178,11 +197,13 @@ from oneflow._C import permute from oneflow._C import select from oneflow._C import unbind from oneflow._C import tensor_split +from oneflow._C import hann_window from oneflow._C import hsplit from oneflow._C import vsplit from oneflow._C import concat from oneflow._C import concat as cat from oneflow._C import dim_gather as gather +from oneflow._C import deform_conv2d from oneflow._C import gather_nd from oneflow._C import roi_align from oneflow._C import decode_onerec @@ -197,16 +218,29 @@ from oneflow._C import amax from oneflow._C import swapdims from oneflow._C import t from oneflow._C import masked_fill +from oneflow._C import masked_fill_ from oneflow._C import equal -from oneflow._C import equal as eq +from oneflow._C import broadcast_equal as eq from oneflow._C import not_equal from oneflow._C import not_equal as ne from oneflow._C import less as lt from oneflow._C import less_equal as le from oneflow._C import searchsorted +from oneflow._C import flip from oneflow._C import index_select from oneflow._C import isnan from oneflow._C import isinf +from oneflow._C import isfinite +from oneflow._C import inv as inverse +from oneflow._C import iinfo, finfo +from oneflow._C import multinomial +from oneflow._C import linalg_cross as cross +from oneflow._C import bincount +from oneflow._C import isclose +from oneflow._C import allclose +from oneflow._C import index_add, index_add_ +from oneflow._C import sort + from oneflow._oneflow_internal import _set_num_threads as set_num_threads from . import sbp @@ -229,15 +263,9 @@ __oneflow_global_unique_env = env_util.GetEnv() session_ctx.NewDefaultSession(__oneflow_global_unique_env) oneflow._oneflow_internal.RegisterGILForeignLockHelper() -oneflow._oneflow_internal.InitDefaultConsistentTransportTokenScope() - -oneflow._oneflow_internal.EnableEagerEnvironment(True) -from oneflow.framework import python_callback - -oneflow._oneflow_internal.RegisterGlobalForeignCallback( - python_callback.global_python_callback -) -del python_callback +oneflow._oneflow_internal.autograd.graph.register_saved_tensors_hook_manager() +oneflow._oneflow_internal.RegisterStackGetter() +oneflow._oneflow_internal.InitDefaultGlobalTransportTokenScope() class ExitHook: @@ -270,8 +298,8 @@ hook = ExitHook() def atexit_hook(hook): - oneflow.framework.session_context.TryCloseDefaultSession() __oneflow_global_unique_env.switch_to_shutting_down(hook.is_normal_exit()) + oneflow.framework.session_context.TryCloseDefaultSession() atexit.register(atexit_hook, hook) @@ -281,9 +309,17 @@ del ExitHook del atexit del oneflow +# default dtype +from oneflow.framework.dtype import ( + set_default_dtype, + set_default_tensor_type, + get_default_dtype, + is_floating_point, +) + import oneflow._C from oneflow._C import tensor, batch_gather -from oneflow._C import from_numpy +from oneflow._C import from_numpy, from_dlpack from oneflow.autograd import ( enable_grad, @@ -311,9 +347,11 @@ from oneflow.framework.generator import ( # NOTE(chengcheng) oneflow.Model is unavailable now. # from oneflow.framework.model import Model -import oneflow.utils.torch +import oneflow.utils.tensor +import oneflow.utils.global_view from oneflow.framework.tensor import Tensor from oneflow.framework.tensor import is_nonzero +from oneflow._oneflow_internal import to_dlpack from oneflow.framework.type_tensor import * from oneflow.framework.tensor import zero_ @@ -327,15 +365,21 @@ from oneflow.nn.modules.einsum import einsum_op as einsum from oneflow.nn.modules.is_tensor import is_tensor_op as is_tensor from oneflow.nn.modules.arange import arange_op as arange from oneflow.nn.modules.linspace import linspace_op as linspace +from oneflow.nn.modules.logspace import logspace_op as logspace from oneflow.nn.modules.argsort import argsort_op as argsort from oneflow.nn.modules.argwhere import argwhere_op as argwhere from oneflow.nn.modules.constant import ones_op as ones from oneflow.nn.modules.constant import zeros_op as zeros +from oneflow.nn.modules.constant import zeros_like_op as zeros_like +from oneflow.nn.modules.constant import ones_like_op as ones_like from oneflow.nn.modules.constant import full_op as full +from oneflow.nn.modules.constant import full_like_op as full_like from oneflow.nn.modules.constant import new_ones_op as new_ones from oneflow.nn.modules.constant import new_zeros_op as new_zeros +from oneflow.nn.modules.constant import new_full_op as new_full from oneflow.nn.modules.empty import empty_op as empty from oneflow.nn.modules.empty import new_empty_op as new_empty +from oneflow.nn.modules.empty import empty_like_op as empty_like from oneflow.nn.modules.dataset import tensor_buffer_to_list_of_tensors from oneflow._C import movedim from oneflow.nn.modules.expand import expand_op as expand @@ -343,30 +387,30 @@ from oneflow.nn.modules.distributed_partial_fc_sample import ( distributed_partial_fc_sample_op as distributed_partial_fc_sample, ) from oneflow.nn.modules.roll import roll_op as roll -from oneflow.nn.modules.flip import flip_op as flip -from oneflow.nn.modules.tensor_ops import is_floating_point from oneflow.nn.modules.masked_select import masked_select_op as masked_select from oneflow.nn.modules.math_ops import addmm_op as addmm -from oneflow.nn.modules.math_ops import topk_op as topk from oneflow.nn.modules.nonzero import nonzero_op as nonzero from oneflow.nn.modules.nms import nms_op as nms from oneflow.nn.modules.numel import numel_op as numel from oneflow.nn.modules.meshgrid import meshgrid_op as meshgrid +from oneflow.nn.modules.unique import unique_op as unique from oneflow._C import normal from oneflow._C import rand from oneflow._C import randn +from oneflow._C import randn_like from oneflow._C import randint +from oneflow._C import randint_like from oneflow._C import randperm from oneflow.nn.modules.reshape import reshape_op as reshape from oneflow.nn.modules.reshape import view_op as view from oneflow.nn.modules.slice import slice_op as slice from oneflow.nn.modules.slice import slice_update_op as slice_update -from oneflow.nn.modules.sort import sort_op as sort from oneflow.nn.modules.tensor_buffer import gen_tensor_buffer from oneflow.nn.modules.tensor_buffer import ( tensor_buffer_to_tensor_op as tensor_buffer_to_tensor, ) from oneflow.nn.modules.tensordot import tensordot +from oneflow.nn.modules.norm import norm from oneflow.nn.modules.as_tensor import as_tensor from oneflow.nn.modules.tensor_buffer import tensor_to_tensor_buffer from oneflow.nn.modules.global_cast import local_to_global_op as local_to_global @@ -375,46 +419,53 @@ from oneflow.nn.modules.global_cast import to_global_op as to_global from oneflow.nn.modules.global_cast import to_local_op as to_local from oneflow.nn.modules.where import where_op as where from oneflow.nn.modules.scatter import * -from oneflow.ops.stateful_ops import StatefulOp as stateful_op -from oneflow.ops.initializer_util import constant_initializer -from oneflow.ops.initializer_util import glorot_normal_initializer -from oneflow.ops.initializer_util import ( - glorot_normal_initializer as xavier_normal_initializer, -) -from oneflow.ops.initializer_util import glorot_uniform_initializer -from oneflow.ops.initializer_util import ( - glorot_uniform_initializer as xavier_uniform_initializer, +from oneflow.nn.modules.broadcast_ops import ( + broadcast_tensors, + broadcast_shapes, + broadcast_to, ) -from oneflow.ops.initializer_util import ( - kaiming_initializer, - ones_initializer, - random_normal_initializer, - random_uniform_initializer, - truncated_normal_initializer, - variance_scaling_initializer, - zeros_initializer, +from oneflow.ops.stateful_ops import StatefulOp as stateful_op + +# autocast +from oneflow._oneflow_internal import ( + is_autocast_enabled, + set_autocast_enabled, + get_autocast_gpu_dtype, + get_autocast_cpu_dtype, + set_autocast_gpu_dtype, + set_autocast_cpu_dtype, + is_autocast_cache_enabled, + set_autocast_cache_enabled, + clear_autocast_cache, ) +from oneflow.amp.autocast_mode import * +from oneflow.jit import * from . import ( autograd, distributed, + distributions, linalg, optim, comm, boxing, backends, amp, + hub, ) import oneflow.utils.data -import oneflow.comm import oneflow.framework.docstr as docstr import oneflow.cuda import oneflow.multiprocessing +import oneflow.asyncs import oneflow.one_embedding import oneflow.profiler +import oneflow.mock_torch if oneflow._oneflow_internal.flags.with_mlir(): oneflow_internal_path = oneflow._oneflow_internal.__file__ - if os.getenv("ONEFLOW_MLIR_ENABLE_CODEGEN_FUSERS"): + if os.getenv("ONEFLOW_MLIR_ENABLE_CODEGEN_FUSERS") or os.getenv( + "ONEFLOW_MLIR_FUSE_KERNEL_LAUNCH" + ): print("MLIR JIT engine will load:", oneflow_internal_path, file=sys.stderr) oneflow._oneflow_internal.ir.load_jit_shared_lib(oneflow_internal_path) diff --git a/python/oneflow/_utils.py b/python/oneflow/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9b59e2fdbafa2771c4bb40487c3ed5817e3e9f9f --- /dev/null +++ b/python/oneflow/_utils.py @@ -0,0 +1,108 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import sys +import traceback +import oneflow as flow + + +class KeyErrorMessage(str): + r"""str subclass that returns itself in repr""" + + def __repr__(self): + return self + + +class ExceptionWrapper(object): + r"""Wraps an exception plus traceback to communicate across threads""" + + def __init__(self, exc_info=None, where="in background"): + # It is important that we don't store exc_info, see + # NOTE [ Python Traceback Reference Cycle Problem ] + if exc_info is None: + exc_info = sys.exc_info() + self.exc_type = exc_info[0] + self.exc_msg = "".join(traceback.format_exception(*exc_info)) + self.where = where + + def reraise(self): + r"""Reraises the wrapped exception in the current thread""" + # Format a message such as: "Caught ValueError in DataLoader worker + # process 2. Original Traceback:", followed by the traceback. + msg = "Caught {} {}.\nOriginal {}".format( + self.exc_type.__name__, self.where, self.exc_msg + ) + if self.exc_type == KeyError: + # KeyError calls repr() on its argument (usually a dict key). This + # makes stack traces unreadable. It will not be changed in Python + # (https://bugs.python.org/issue2651), so we work around it. + msg = KeyErrorMessage(msg) + elif getattr(self.exc_type, "message", None): + # Some exceptions have first argument as non-str but explicitly + # have message field + raise self.exc_type(message=msg) + raise self.exc_type(msg) + + +def _flatten_dense_tensors(tensors): + """Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of + same dense type. + + The api is referenced from https://github.com/pytorch/pytorch/blob/master/torch/_utils.py#L437 + + Since inputs are dense, the resulting tensor will be a concatenated 1D + buffer. Element-wise operation on this buffer will be equivalent to + operating individually. + + Args: + tensors (Iterable[Tensor]): dense tensors to flatten. + + Returns: + A contiguous 1D buffer containing input tensors. + """ + if len(tensors) == 1: + return flow._C.flatten(tensors[0]) + else: + flatten_tensors = [] + for tensor in tensors: + flatten_tensors.append(flow.flatten(tensor)) + return flow.cat(flatten_tensors, 0) + + +def _unflatten_dense_tensors(flat, tensors): + """View a flat buffer using the sizes of tensors. Assume that tensors are of + same dense type, and that flat is given by _flatten_dense_tensors. + + The api is referenced from https://github.com/pytorch/pytorch/blob/master/torch/_utils.py#L474 + + Args: + flat (Tensor): flattened dense tensors to unflatten. + tensors (Iterable[Tensor]): dense tensors whose sizes will be used to + unflatten flat. + + Returns: + Unflattened dense tensors with sizes same as tensors and values from + flat. + """ + outputs = [] + offset = 0 + for tensor in tensors: + numel = tensor.numel() + if numel == 0: + outputs.append(flow.zeros_like(tensor)) + else: + outputs.append(flow.narrow(flat, 0, offset, numel).view(tensor.size())) + offset += numel + return outputs diff --git a/python/oneflow/amp/__init__.py b/python/oneflow/amp/__init__.py index 94bc5710a744b0ac75a16c01a8c0f7c76bba50a4..dce71a355d6aef0d45edb09164e2197503a0698e 100644 --- a/python/oneflow/amp/__init__.py +++ b/python/oneflow/amp/__init__.py @@ -15,3 +15,4 @@ limitations under the License. """ from .grad_scaler import GradScaler from .grad_scaler import StaticGradScaler +from .autocast_mode import * diff --git a/python/oneflow/amp/autocast_mode.py b/python/oneflow/amp/autocast_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..3fbaf42956621b758d2be421353d1240bc5e0eee --- /dev/null +++ b/python/oneflow/amp/autocast_mode.py @@ -0,0 +1,193 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import functools +import warnings +from typing import Any, Optional + +import oneflow as flow +import oneflow._oneflow_internal.lazy_mode as lazy_mode + + +__all__ = ["autocast_decorator", "autocast"] + + +def autocast_decorator(autocast_instance, func): + @functools.wraps(func) + def decorate_autocast(*args, **kwargs): + with autocast_instance: + return func(*args, **kwargs) + + return decorate_autocast + + +class autocast(object): + r""" + Note: + The following doc was origined by pytorch, see + https://github.com/pytorch/pytorch/blob/master/torch/amp/autocast_mode.py#L19-L179 + + Instances of :class:`autocast` serve as context managers or decorators that + allow regions of your script to run in mixed precision. + + In these regions, ops run in an op-specific dtype chosen by autocast + to improve performance while maintaining accuracy. + + When entering an autocast-enabled region, Tensors may be any type. + You should not call ``half()`` or ``bfloat16()`` on your model(s) or inputs when using autocasting. + + :class:`autocast` should wrap only the forward pass(es) of your network, including the loss + computation(s). Backward passes under autocast are not recommended. + Backward ops run in the same type that autocast used for corresponding forward ops. + + Example for CUDA Devices:: + + # Creates model and optimizer in default precision + model = Net().cuda() + optimizer = optim.SGD(model.parameters(), ...) + + for input, target in data: + optimizer.zero_grad() + + # Enables autocasting for the forward pass (model + loss) + with oneflow.autocast(device_type="cuda"): + output = model(input) + loss = loss_fn(output, target) + + # Exits the context manager before backward() + loss.backward() + optimizer.step() + + + :class:`autocast` can also be used as a decorator, e.g., on the ``forward`` method of your model:: + + class AutocastModel(nn.Module): + ... + @oneflow.autocast(device_type="cuda") + def forward(self, input): + ... + + Floating-point Tensors produced in an autocast-enabled region may be ``float16``. + After returning to an autocast-disabled region, using them with floating-point + Tensors of different dtypes may cause type mismatch errors. If so, cast the Tensor(s) + produced in the autocast region back to ``float32`` (or other dtype if desired). + If a Tensor from the autocast region is already ``float32``, the cast is a no-op, + and incurs no additional overhead. + CUDA Example:: + + # Creates some tensors in default dtype (here assumed to be float32) + a_float32 = oneflow.rand((8, 8), device="cuda") + b_float32 = oneflow.rand((8, 8), device="cuda") + c_float32 = oneflow.rand((8, 8), device="cuda") + d_float32 = oneflow.rand((8, 8), device="cuda") + + with oneflow.autocast(device_type="cuda"): + # oneflow.mm is on autocast's list of ops that should run in float16. + # Inputs are float32, but the op runs in float16 and produces float16 output. + # No manual casts are required. + e_float16 = oneflow.mm(a_float32, b_float32) + # Also handles mixed input types + f_float16 = oneflow.mm(d_float32, e_float16) + + # After exiting autocast, calls f_float16.float() to use with d_float32 + g_float32 = oneflow.mm(d_float32, f_float16.float()) + + CPU Training Example:: + + # Creates model and optimizer in default precision + model = Net() + optimizer = optim.SGD(model.parameters(), ...) + + for epoch in epochs: + for input, target in data: + optimizer.zero_grad() + + # Runs the forward pass with autocasting. + with oneflow.autocast(device_type="cpu", dtype=oneflow.bfloat16): + output = model(input) + loss = loss_fn(output, target) + + loss.backward() + optimizer.step() + + + CPU Inference Example:: + + # Creates model in default precision + model = Net().eval() + + with oneflow.autocast(device_type="cpu", dtype=oneflow.bfloat16): + for input in data: + # Runs the forward pass with autocasting. + output = model(input) + + The autocast state is thread-local. If you want it enabled in a new thread, the context manager or decorator + must be invoked in that thread. + + Args: + device_type(str, required): Whether to use 'cuda' or 'cpu' device + enabled(bool, optional): Whether autocasting should be enabled in the region. + Default: ``True`` + dtype(oneflow_dtype, optional): Whether to use oneflow.float16 or oneflow.bfloat16. + cache_enabled(bool, optional): Whether the weight cache inside autocast should be enabled. + Default: ``True`` + """ + + def __init__( + self, + device_type: str, + dtype: Optional[flow.dtype] = None, + enabled: bool = True, + cache_enabled: Optional[bool] = None, + ): + self.device = device_type + if self.device == "cuda": + self.fast_dtype = flow.get_autocast_gpu_dtype() + elif self.device == "cpu": + self.fast_dtype = flow.get_autocast_cpu_dtype() + else: + raise RuntimeError( + "User specified autocast device_type must be 'cuda' or 'cpu'" + ) + self.cache_enabled = flow.is_autocast_cache_enabled() + + if dtype is not None: + self.fast_dtype = dtype + if cache_enabled is not None: + self.cache_enabled = cache_enabled + + if self.device == "cpu": + warnings.warn( + "CPU autocast is not supported currently. Disabling autocast." + ) + enabled = False + if lazy_mode.is_enabled(): + warnings.warn( + "Autocast is not supported for lazy mode. Disabling autocast." + ) + enabled = False + self.enabled = enabled + + def __enter__(self): + self.autocast_mode = flow._oneflow_internal.AutoCastMode( + self.device, self.fast_dtype, self.enabled, self.cache_enabled + ) + return self + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any): + del self.autocast_mode + + def __call__(self, func): + return autocast_decorator(self, func) diff --git a/python/oneflow/asyncs/__init__.py b/python/oneflow/asyncs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..37ae7b86324dc184a9935925dc2df576bce6accc --- /dev/null +++ b/python/oneflow/asyncs/__init__.py @@ -0,0 +1,16 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from .thread import Thread, thread diff --git a/python/oneflow/asyncs/thread.py b/python/oneflow/asyncs/thread.py new file mode 100644 index 0000000000000000000000000000000000000000..9cb75a74395bb1aca1daf401fe4a68d539681a30 --- /dev/null +++ b/python/oneflow/asyncs/thread.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow._oneflow_internal + +Thread = oneflow._oneflow_internal.AsyncThread + + +class thread: + r"""Context-manager to pick worker thread. + By default, all opkernels are excuted/launched in worker thread 0. Within this context, opkernels can be excuted/launched in the worker thread indicated by `thread_global_id`. + This context manager is thread local; it will not affect ops in other threads. + Also functions as a decorator. (Make sure to instantiate with parenthesis.) + + Args: + worker_thread: a worker thread create with oneflow.asyncs.Thread. + + For example: + + .. code-block:: python + >>> import oneflow as flow + >>> with flow.asyncs.thread(flow.asyncs.Thread()): + ... print(flow.ones(2, 2)) + ... + tensor([[1., 1.], + [1., 1.]], dtype=oneflow.float32) + """ + + def __init__(self, worker_thread: Thread): + self.stream_set_ = oneflow._oneflow_internal.StreamSet(worker_thread) + self.worker_thread_ = worker_thread + + def __enter__(self): + self.guard_ = oneflow._oneflow_internal.StreamGuard(self.stream_set_) + + def __exit__(self, type, value, traceback): + del self.guard_ diff --git a/python/oneflow/autograd/__init__.py b/python/oneflow/autograd/__init__.py index ddee0ebf0db623d223fc39b007b56c541d013372..85b2327cb35036a27b2c342498dab1690fdebd13 100644 --- a/python/oneflow/autograd/__init__.py +++ b/python/oneflow/autograd/__init__.py @@ -23,6 +23,7 @@ from oneflow.autograd.autograd_mode import ( is_grad_enabled, no_grad, ) +from . import graph __all__ = [ "backward", diff --git a/python/oneflow/autograd/autograd.py b/python/oneflow/autograd/autograd.py index 3aa0b2a95b518a22cb4d8ab85d42aa8cc546bfa9..1c694e2b04aa39e7c424d2548cabad0aed6a7d68 100644 --- a/python/oneflow/autograd/autograd.py +++ b/python/oneflow/autograd/autograd.py @@ -30,11 +30,11 @@ def grad( create_graph: bool = False, ) -> Tuple[Tensor]: r""" + Computes and returns the sum of gradients of outputs with respect to the inputs. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.autograd.grad.html. - Computes and returns the sum of gradients of outputs with respect to the inputs. - The graph is differentiated using the chain rule. ``grad_outputs`` should be a sequence of length matching ``outputs``, containing the "vector" in the Jacobian-vector product. (``None`` is an acceptable value for that tensor don't require gradient.) @@ -73,11 +73,11 @@ def backward( create_graph: bool = False, ) -> None: r""" + Computes the sum of gradients of given tensors with respect to graph leaves. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.autograd.backward.html. - Computes the sum of gradients of given tensors with respect to graph leaves. - The graph is differentiated using the chain rule. If any of ``tensors`` are non-scalar (i.e. their data has more than one element) and require gradient, then the Jacobian-vector product would be computed, in this case the function additionally requires specifying ``grad_tensors``. diff --git a/python/oneflow/autograd/graph.py b/python/oneflow/autograd/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..925b55401a60f237debd8bada7b3978d05c8004f --- /dev/null +++ b/python/oneflow/autograd/graph.py @@ -0,0 +1,97 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# This file is mostly copied from PyTorch + +import oneflow as flow +from typing import Callable, Any + + +class saved_tensors_hooks: + """Context-manager that sets a pair of pack / unpack hooks for saved tensors. + + Use this context-manager to define how intermediary results of an operation + should be packed before saving, and unpacked on retrieval. + + In that context, the ``pack_hook`` function will be called everytime an + operation saves a tensor for backward (this includes intermediary results + saved using + :func:`~oneflow.autograd.function.save_for_backward` but + also those recorded by a OneFlow-defined operation). The output of + ``pack_hook`` is then stored in the computation graph instead of the + original tensor. + + The ``unpack_hook`` is called when the saved tensor needs to be accessed, + namely when executing :func:`oneflow.Tensor.backward()` or + :func:`oneflow.autograd.grad()`. It takes as argument the *packed* object + returned by ``pack_hook`` and should return a tensor which has the same + content as the original tensor (passed as input to the corresponding + ``pack_hook``). + + The hooks should have the following signatures: + + pack_hook(tensor: Tensor) -> Any + + unpack_hook(Any) -> Tensor + + where the return value of ``pack_hook`` is a valid input to ``unpack_hook``. + + In general, you want ``unpack_hook(pack_hook(t))`` to be equal to ``t`` in terms + of value, size, dtype and device. + + Example:: + + >>> def pack_hook(x): + ... print("Packing", x) + ... return x + >>> + >>> def unpack_hook(x): + ... print("Unpacking", x) + ... return x + >>> + >>> a = flow.ones(5, requires_grad=True) + >>> b = flow.ones(5, requires_grad=True) * 2 + >>> with flow.autograd.graph.saved_tensors_hooks(pack_hook, unpack_hook): + ... y = a * b + Packing tensor([1., 1., 1., 1., 1.]) + Packing tensor([2., 2., 2., 2., 2.]) + >>> y.sum().backward() + Unpacking tensor([1., 1., 1., 1., 1.]) + Unpacking tensor([2., 2., 2., 2., 2.]) + + .. warning :: + Performing an inplace operation on the input to either hooks may lead + to undefined behavior. + + .. warning :: + Only one pair of hooks is allowed at a time. When recursively nesting this + context-manager, only the inner-most pair of hooks will be applied. + """ + + def __init__( + self, + pack_hook: Callable[["flow.Tensor"], Any], + unpack_hook: Callable[[Any], "flow.Tensor"], + ): + self.pack_hook = pack_hook + self.unpack_hook = unpack_hook + + def __enter__(self): + flow._oneflow_internal.autograd.graph.append_new_hooks( + self.pack_hook, self.unpack_hook + ) + + def __exit__(self, *args: Any): + flow._oneflow_internal.autograd.graph.pop_hooks() diff --git a/python/oneflow/backends/__init__.py b/python/oneflow/backends/__init__.py index 1a4789513d39cec957eec51ee37a10746d3dd86c..28b0060d590945301b060404a42415c86b9cae0d 100644 --- a/python/oneflow/backends/__init__.py +++ b/python/oneflow/backends/__init__.py @@ -14,3 +14,4 @@ See the License for the specific language governing permissions and limitations under the License. """ from . import cudnn +from . import mps diff --git a/python/oneflow/backends/cudnn/__init__.py b/python/oneflow/backends/cudnn/__init__.py index e52955659244a62a06a82379bff4fa2aba82d4ff..8061215858d5a5b49a8daa01f04b8ff229e22b08 100644 --- a/python/oneflow/backends/cudnn/__init__.py +++ b/python/oneflow/backends/cudnn/__init__.py @@ -19,3 +19,6 @@ from oneflow.framework.config_util import ( from oneflow.framework.config_util import ( api_enable_cudnn_fused_normalization_add_relu as enable_fused_normalization_add_relu, ) +from oneflow.framework.config_util import ( + api_enable_cudnn_conv_heuristic_search_algo as enable_conv_heuristic_search_algo, +) diff --git a/python/oneflow/backends/mps/__init__.py b/python/oneflow/backends/mps/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8f576bf1e53015c541f8e35a2358290a2fc542df --- /dev/null +++ b/python/oneflow/backends/mps/__init__.py @@ -0,0 +1,21 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +__all__ = ["is_available"] + + +def is_available() -> bool: + return False diff --git a/python/oneflow/comm/__init__.py b/python/oneflow/comm/__init__.py index 82c8267e2d2e9887b0e3225b7da8a35d7798ba3f..fdc37c027c1c34b35636c92bd3fdc46844a8f352 100644 --- a/python/oneflow/comm/__init__.py +++ b/python/oneflow/comm/__init__.py @@ -15,6 +15,8 @@ limitations under the License. """ from oneflow.comm.comm_ops import all_reduce from oneflow.comm.comm_ops import all_gather +from oneflow.comm.comm_ops import all_gather_into_tensor +from oneflow.comm.comm_ops import reduce_scatter_tensor from oneflow.comm.comm_ops import broadcast from oneflow.comm.comm_ops import scatter from oneflow.comm.comm_ops import reduce diff --git a/python/oneflow/comm/comm_ops.py b/python/oneflow/comm/comm_ops.py index 93f47931d78880b21dea97ab85948ae942f46472..2ba6e4be5769397901e201055b5f1764fe2c0752 100644 --- a/python/oneflow/comm/comm_ops.py +++ b/python/oneflow/comm/comm_ops.py @@ -52,13 +52,7 @@ def all_reduce(tensor): assert isinstance(tensor, flow._oneflow_internal.Tensor) assert tensor.device.index == flow.env.get_local_rank() assert tensor.is_local - device_type = tensor.device.type - placement = flow.env.all_device_placement(device_type) - result = tensor.to_global(placement=placement, sbp=flow.sbp.partial_sum).to_global( - placement=placement, sbp=flow.sbp.broadcast - ) - - tensor.data = result.to_local() + flow._C.local_all_reduce(tensor, inplace=True) def all_gather(tensor_list, tensor): @@ -107,7 +101,7 @@ def all_gather(tensor_list, tensor): assert tensor.is_local tensor = tensor.expand(*([1] + list(tensor.shape))) device_type = tensor.device.type - placement = flow.env.all_device_placement(device_type) + placement = flow.placement.all(device_type) tensor = ( tensor.to_global(placement=placement, sbp=flow.sbp.split(0)) .to_global(placement=placement, sbp=flow.sbp.broadcast) @@ -119,6 +113,76 @@ def all_gather(tensor_list, tensor): tensor_list[i] = tensor[i] +def all_gather_into_tensor(output_tensor, input_tensor): + """ + Gather tensors from all ranks and put them in a single output tensor. + + Args: + output_tensor (Tensor): Output tensor to accommodate tensor elements + from all ranks. It must be correctly sized to have one of the + following forms: + (i) a concatenation of all the input tensors along the primary + dimension; for definition of "concatenation", see ``oneflow.cat()``; + (ii) a stack of all the input tensors along the primary dimension; + for definition of "stack", see ``oneflow.stack()``. + Examples below may better explain the supported output forms. + input_tensor (Tensor): Tensor to be gathered from current rank. + The input tensors in this API must have the same size across all ranks. + + For example: + + .. code-block:: python + + >>> # We have 1 process groups, 2 ranks. + >>> # All tensors below are of flow.int64 dtype and on CUDA devices. + >>> import oneflow as flow + >>> tensor_in = flow.tensor([[1, 2, 3], [4, 5, 6]], dtype=flow.int64, device="cuda") + flow.env.get_rank() * 6 + >>> tensor_in # doctest: +ONLY_CHECK_RANK_0 + tensor([[1, 2, 3], + [4, 5, 6]], device='cuda:0', dtype=oneflow.int64) + >>> # Output in concatenation form + >>> tensor_out = flow.zeros(4, 3, dtype=flow.int64, device="cuda") + >>> flow.comm.all_gather_into_tensor(tensor_out, tensor_in) + >>> # result on rank0 + >>> tensor_out # doctest: +ONLY_CHECK_RANK_0 + tensor([[ 1, 2, 3], + [ 4, 5, 6], + [ 7, 8, 9], + [10, 11, 12]], device='cuda:0', dtype=oneflow.int64) + >>> # result on rank1 + >>> tensor_out # doctest: +ONLY_CHECK_RANK_1 + tensor([[ 1, 2, 3], + [ 4, 5, 6], + [ 7, 8, 9], + [10, 11, 12]], device='cuda:1', dtype=oneflow.int64) + >>> # Output in stack form + >>> tensor_out2 = flow.zeros(2, 3, 2, dtype=flow.int64, device="cuda") + >>> flow.comm.all_gather_into_tensor(tensor_out2, tensor_in) + >>> # result on rank0 + >>> tensor_out2 # doctest: +ONLY_CHECK_RANK_0 + tensor([[[ 1, 2], + [ 3, 4], + [ 5, 6]], + + [[ 7, 8], + [ 9, 10], + [11, 12]]], device='cuda:0', dtype=oneflow.int64) + >>> # result on rank1 + >>> tensor_out2 # doctest: +ONLY_CHECK_RANK_1 + tensor([[[ 1, 2], + [ 3, 4], + [ 5, 6]], + + [[ 7, 8], + [ 9, 10], + [11, 12]]], device='cuda:1', dtype=oneflow.int64) + + """ + assert output_tensor.is_local + assert input_tensor.is_local + flow._C.local_all_gather(output_tensor, input_tensor) + + def broadcast(tensor, src): """ Broadcasts the tensor to the whole group. @@ -153,7 +217,7 @@ def broadcast(tensor, src): assert isinstance(src, int) assert isinstance(tensor, flow._oneflow_internal.Tensor) assert tensor.is_local - flow._C.broadcast(tensor, src_rank=src, inplace=True) + flow._C.comm_broadcast(tensor, src_rank=src, inplace=True) def scatter(tensor, scatter_list=None, src=0): @@ -257,7 +321,7 @@ def barrier(): Synchronizes all processes. """ - flow._oneflow_internal.eager.Sync() + flow._oneflow_internal.eager.ClusterSync() def reduce_scatter(output, input_list): @@ -275,7 +339,7 @@ def reduce_scatter(output, input_list): assert len(input_list) == flow.env.get_world_size() output_shape = output.shape device_type = output.device.type - placement = flow.env.all_device_placement(device_type) + placement = flow.placement.all(device_type) reduced_tensor_list = [] for tensor in input_list: assert tensor.is_local @@ -287,6 +351,65 @@ def reduce_scatter(output, input_list): output.data = reduced_tensor_list[flow.env.get_rank()] +def reduce_scatter_tensor(output_tensor, input_tensor): + """ + Reduces, then scatters a tensor to all ranks. + + Args: + output (Tensor): Output tensor. It should have the same size across all + ranks. + input (Tensor): Input tensor to be reduced and scattered. Its size + should be output tensor size times the world size. The input tensor + can have one of the following shapes: + (i) a concatenation of the output tensors along the primary + dimension, or + (ii) a stack of the output tensors along the primary dimension. + For definition of "concatenation", see ``oneflow.cat()``. + For definition of "stack", see ``oneflow.stack()``. + + For example: + + .. code-block:: python + + >>> # We have 1 process groups, 2 ranks. + >>> # All tensors below are of flow.int64 dtype and on CUDA devices. + >>> import oneflow as flow + >>> tensor_in = flow.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=flow.int64, device="cuda") + >>> tensor_in # doctest: +ONLY_CHECK_RANK_0 + tensor([[ 1, 2, 3], + [ 4, 5, 6], + [ 7, 8, 9], + [10, 11, 12]], device='cuda:0', dtype=oneflow.int64) + >>> # Output in concatenation form + >>> tensor_out = flow.zeros(2, 3, dtype=flow.int64, device="cuda") + >>> flow.comm.reduce_scatter_tensor(tensor_out, tensor_in) + >>> # result on rank0 + >>> tensor_out # doctest: +ONLY_CHECK_RANK_0 + tensor([[ 2, 4, 6], + [ 8, 10, 12]], device='cuda:0', dtype=oneflow.int64) + >>> # result on rank1 + >>> tensor_out # doctest: +ONLY_CHECK_RANK_1 + tensor([[14, 16, 18], + [20, 22, 24]], device='cuda:1', dtype=oneflow.int64) + >>> # Output in stack form + >>> tensor_in2 = tensor_in.reshape(2, 3, 2) + >>> tensor_out2 = flow.zeros(2, 3, dtype=flow.int64, device="cuda") + >>> flow.comm.reduce_scatter_tensor(tensor_out2, tensor_in2) + >>> # result on rank0 + >>> tensor_out2 # doctest: +ONLY_CHECK_RANK_0 + tensor([[ 2, 4, 6], + [ 8, 10, 12]], device='cuda:0', dtype=oneflow.int64) + >>> # result on rank1 + >>> tensor_out2 # doctest: +ONLY_CHECK_RANK_1 + tensor([[14, 16, 18], + [20, 22, 24]], device='cuda:1', dtype=oneflow.int64) + + """ + assert output_tensor.is_local + assert input_tensor.is_local + flow._C.local_reduce_scatter(output_tensor, input_tensor) + + def gather(tensor, gather_list=None, dst=0): """ Gathers a list of tensors in a single process. @@ -305,7 +428,7 @@ def gather(tensor, gather_list=None, dst=0): dtype = tensor.dtype tensor = tensor.expand(*([1] + list(shape))) device_type = tensor.device.type - placement = flow.env.all_device_placement(device_type) + placement = flow.placement.all(device_type) tensor = tensor.to_global(placement=placement, sbp=flow.sbp.split(0)).to_global( placement=placement, sbp=flow.sbp.broadcast ) diff --git a/python/oneflow/cuda/__init__.py b/python/oneflow/cuda/__init__.py index b703750f367478634985147e0d8e47d619d54e98..5be75cc015ab81f9e7d09a6dc3c17b02d11b3223 100644 --- a/python/oneflow/cuda/__init__.py +++ b/python/oneflow/cuda/__init__.py @@ -18,7 +18,7 @@ import oneflow as flow from oneflow.cuda.type_tensor import * from oneflow.cuda._utils import _get_device_index -from typing import Union, Any +from typing import Optional, Tuple, Union, Any def is_available() -> bool: @@ -38,11 +38,66 @@ def current_device() -> int: return flow._oneflow_internal.GetCudaDeviceIndex() +def get_device_properties(device: Union[flow.device, str, int] = None): + r"""Gets the properties of a device. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.get_device_properties.html. + + Args: + device(oneflow.device or str or int): device for which to return the properties of the device. + + Returns: + the properties of the device. + """ + device = _get_device_index(device, optional=True) + return flow._oneflow_internal._get_device_properties(device) + + +def get_device_capability( + device: Optional[Union[flow.device, str, int]] = None +) -> Tuple[int, int]: + r"""Gets the cuda capability of a device. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.get_device_capability.html. + + Args: + device (oneflow.device or int or str, optional): device for which to return the + device capability. It uses the current device, given by + :func:`~oneflow.cuda.current_device`, if :attr:`device` is ``None`` + (default). + + Returns: + tuple(int, int): the major and minor cuda capability of the device + """ + device_prop = get_device_properties(device) + return device_prop.major, device_prop.minor + + +def get_device_name(device: Optional[Union[flow.device, str, int]] = None) -> str: + r"""Gets the name of a device. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.get_device_name.html. + + Args: + device (oneflow.device or int or str, optional): device for which to return the + name. It uses the current device, given by :func:`~oneflow.cuda.current_device`, + if :attr:`device` is ``None`` (default). + + Returns: + str: the name of the device + """ + return get_device_properties(device).name + + def manual_seed_all(seed) -> None: - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.cuda.manual_seed_all.html. + r"""Sets the seed for generating random numbers on all GPUs. - Sets the seed for generating random numbers on all GPUs. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.manual_seed_all.html. + It's safe to call this function if CUDA is not available; in that case, it is silently ignored. @@ -54,10 +109,11 @@ def manual_seed_all(seed) -> None: def manual_seed(seed: int) -> None: - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.cuda.manual_seed.html. + r"""Sets the seed for generating random numbers for the current GPU. - Sets the seed for generating random numbers for the current GPU. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.manual_seed.html. + It's safe to call this function if CUDA is not available; in that case, it is silently ignored. @@ -74,10 +130,11 @@ def manual_seed(seed: int) -> None: def set_device(device: Union[flow.device, str, int]) -> None: - r"""The documentation is referenced from: - https://pytorch.org/docs/stable/generated/torch.cuda.set_device.html. + r"""Sets the current device. - Sets the current device. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.cuda.set_device.html. + Usage of this function is discouraged in favor of :attr:`device`. In most cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable. @@ -85,11 +142,16 @@ def set_device(device: Union[flow.device, str, int]) -> None: device (flow.device or int): selected device. This function is a no-op if this argument is negative. """ - if flow.env.get_world_size() > 0: - raise ValueError("set_device() function is disabled in multi-device setting") device_idx = _get_device_index(device) - if device_idx >= 0: - flow._oneflow_internal.SetCudaDeviceIndex(device_idx) + if device_idx < 0: + return + if flow.env.get_world_size() > 0: + if device_idx == flow.env.get_local_rank(): + return + raise ValueError( + "Setting cuda device to a device whose index does not equal to the local rank is not supported." + ) + flow._oneflow_internal.SetCudaDeviceIndex(device_idx) def synchronize(device: Union[flow.device, str, int, None] = None) -> None: diff --git a/python/oneflow/cuda/amp/__init__.py b/python/oneflow/cuda/amp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fe7b620524a1d76a75f1a43728b05d276e70f9d6 --- /dev/null +++ b/python/oneflow/cuda/amp/__init__.py @@ -0,0 +1,16 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from .autocast_mode import autocast diff --git a/python/oneflow/cuda/amp/autocast_mode.py b/python/oneflow/cuda/amp/autocast_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..8fa074aa258a5ac86e3fbc0282c7d9687d75f02f --- /dev/null +++ b/python/oneflow/cuda/amp/autocast_mode.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow +from typing import Any, Optional + + +class autocast(flow.amp.autocast_mode.autocast): + r""" + See :class:`oneflow.autocast`. + ``oneflow.cuda.amp.autocast(args...)`` is equivalent to ``oneflow.autocast("cuda", args...)`` + """ + + def __init__( + self, + enabled: bool = True, + dtype: Optional[flow.dtype] = None, + cache_enabled: Optional[bool] = None, + ): + super().__init__( + "cuda", enabled=enabled, dtype=dtype, cache_enabled=cache_enabled + ) + + def __enter__(self): + return super().__enter__() + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any): # type: ignore[override] + return super().__exit__(exc_type, exc_val, exc_tb) + + def __call__(self, func): + return super().__call__(func) diff --git a/python/oneflow/distributed/constants.py b/python/oneflow/distributed/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..b53a970474e8d5d2c033a8ea26e37b09197998da --- /dev/null +++ b/python/oneflow/distributed/constants.py @@ -0,0 +1,20 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# Just for alignment with pytorch, not really useful + +from datetime import timedelta + +default_pg_timeout = timedelta(milliseconds=30 * 60 * 1000) diff --git a/python/oneflow/distributed/launch.py b/python/oneflow/distributed/launch.py index 9a0b1b1b4e560a0e39e658a1738292b1e441c788..c7e2c48ac1d18db52db9791495239c6c410c141d 100644 --- a/python/oneflow/distributed/launch.py +++ b/python/oneflow/distributed/launch.py @@ -108,6 +108,12 @@ def main(): current_env["MASTER_PORT"] = str(args.master_port) current_env["WORLD_SIZE"] = str(dist_world_size) + if args.master_port is None or args.master_port >= 2 ** 16: + raise ValueError( + f"The port number of the master endpoint '{args.master_addr}:{args.master_port}' must be an integer " + "between 0 and 65536." + ) + if "OMP_NUM_THREADS" not in os.environ and args.nproc_per_node > 1: current_env["OMP_NUM_THREADS"] = str(1) print( @@ -122,11 +128,14 @@ def main(): ) processes: List[Any] = [] - if os.path.exists(args.logdir): - if not os.path.isdir(args.logdir): - raise ValueError("argument --logdir must be a path to a directory.") - else: - os.mkdir(os.path.join(os.getcwd(), args.logdir)) + + if ( + args.redirect_stdout_and_stderr + and os.path.exists(args.logdir) + and not os.path.isdir(args.logdir) + ): + raise ValueError("argument --logdir must be a path to a directory.") + subprocess_file_handles = [] for local_rank in range(0, args.nproc_per_node): dist_rank = args.nproc_per_node * args.node_rank + local_rank @@ -149,9 +158,9 @@ def main(): log_directory_path = os.path.join( os.getcwd(), args.logdir, f"local_rank_{local_rank}" ) - os.makedirs(log_directory_path, exist_ok=True) current_env["GLOG_log_dir"] = log_directory_path if args.redirect_stdout_and_stderr: + os.makedirs(log_directory_path, exist_ok=True) node_rank = args.node_rank stdout_handle = open(os.path.join(log_directory_path, stdout_filename), "w") stderr_handle = open(os.path.join(log_directory_path, stderr_filename), "w") diff --git a/python/oneflow/distributions/__init__.py b/python/oneflow/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6777f56ae81cef1fb8d395e8f1d8dcb0f9ac2641 --- /dev/null +++ b/python/oneflow/distributions/__init__.py @@ -0,0 +1,93 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +r""" +The documentation is referenced from: +https://github.com/pytorch/pytorch/blob/master/torch/distributions/__init__.py + +The ``distributions`` package contains parameterizable probability distributions +and sampling functions. This allows the construction of stochastic computation +graphs and stochastic gradient estimators for optimization. This package +generally follows the design of the `TensorFlow Distributions`_ package. + +.. _`TensorFlow Distributions`: + https://arxiv.org/abs/1711.10604 + +It is not possible to directly backpropagate through random samples. However, +there are two main methods for creating surrogate functions that can be +backpropagated through. These are the score function estimator/likelihood ratio +estimator/REINFORCE and the pathwise derivative estimator. REINFORCE is commonly +seen as the basis for policy gradient methods in reinforcement learning, and the +pathwise derivative estimator is commonly seen in the reparameterization trick +in variational autoencoders. Whilst the score function only requires the value +of samples :math:`f(x)`, the pathwise derivative requires the derivative +:math:`f'(x)`. The next sections discuss these two in a reinforcement learning +example. For more details see +`Gradient Estimation Using Stochastic Computation Graphs`_ . + +.. _`Gradient Estimation Using Stochastic Computation Graphs`: + https://arxiv.org/abs/1506.05254 + +Score function +^^^^^^^^^^^^^^ + +When the probability density function is differentiable with respect to its +parameters, we only need :meth:`~oneflow.distributions.Distribution.sample` and +:meth:`~oneflow.distributions.Distribution.log_prob` to implement REINFORCE: + +.. math:: + + \Delta\theta = \alpha r \frac{\partial\log p(a|\pi^\theta(s))}{\partial\theta} + +where :math:`\theta` are the parameters, :math:`\alpha` is the learning rate, +:math:`r` is the reward and :math:`p(a|\pi^\theta(s))` is the probability of +taking action :math:`a` in state :math:`s` given policy :math:`\pi^\theta`. + +In practice we would sample an action from the output of a network, apply this +action in an environment, and then use ``log_prob`` to construct an equivalent +loss function. Note that we use a negative because optimizers use gradient +descent, whilst the rule above assumes gradient ascent. With a categorical +policy, the code for implementing REINFORCE would be as follows:: + + probs = policy_network(state) + # Note that this is equivalent to what used to be called multinomial + m = Categorical(probs) + action = m.sample() + next_state, reward = env.step(action) + loss = -m.log_prob(action) * reward + loss.backward() + +Pathwise derivative +^^^^^^^^^^^^^^^^^^^ + +The other way to implement these stochastic/policy gradients would be to use the +reparameterization trick from the +:meth:`~oneflow.distributions.Distribution.rsample` method, where the +parameterized random variable can be constructed via a parameterized +deterministic function of a parameter-free random variable. The reparameterized +sample therefore becomes differentiable. The code for implementing the pathwise +derivative would be as follows:: + + params = policy_network(state) + m = Normal(*params) + # Any distribution with .has_rsample == True could work based on the application + action = m.rsample() + next_state, reward = env.step(action) # Assuming that reward is differentiable + loss = -reward + loss.backward() +""" + +from .distribution import Distribution +from .categorical import Categorical diff --git a/python/oneflow/distributions/categorical.py b/python/oneflow/distributions/categorical.py new file mode 100644 index 0000000000000000000000000000000000000000..c0f15c514e1a7e515d50a7968cb4b49a26559736 --- /dev/null +++ b/python/oneflow/distributions/categorical.py @@ -0,0 +1,120 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow +from oneflow.distributions.distribution import Distribution +from oneflow.distributions.utils import probs_to_logits, logits_to_probs + +# NOTE(Liang Depeng): modified from +# https://github.com/pytorch/pytorch/blob/master/torch/distributions/categorical.py + +__all__ = ["Categorical"] + + +class Categorical(Distribution): + r""" + Creates a categorical distribution parameterized by either :attr:`probs` or + :attr:`logits` (but not both). + + .. note:: + It is equivalent to the distribution that :func:`oneflow.multinomial` + samples from. + + Samples are integers from :math:`\{0, \ldots, K-1\}` where `K` is ``probs.size(-1)``. + If `probs` is 1-dimensional with length-`K`, each element is the relative probability + of sampling the class at that index. + If `probs` is N-dimensional, the first N-1 dimensions are treated as a batch of + relative probability vectors. + + .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum, + and it will be normalized to sum to 1 along the last dimension. :attr:`probs` + will return this normalized value. + The `logits` argument will be interpreted as unnormalized log probabilities + and can therefore be any real number. It will likewise be normalized so that + the resulting probabilities sum to 1 along the last dimension. :attr:`logits` + will return this normalized value. + + Args: + probs (Tensor): event probabilities + logits (Tensor): event log probabilities (unnormalized) + + See also: :func:`oneflow.multinomial` + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> gen = flow.manual_seed(0) + >>> m = flow.distributions.categorical.Categorical(flow.tensor([ 0.25, 0.25, 0.25, 0.25 ])) + >>> m.sample() # equal probability of 0, 1, 2, 3 + tensor(3, dtype=oneflow.int64) + """ + has_enumerate_support = True + + def __init__(self, probs=None, logits=None, validate_args=None): + if (probs is None) == (logits is None): + raise ValueError( + "Either `probs` or `logits` must be specified, but not both." + ) + assert validate_args is None + + if probs is not None: + if probs.dim() < 1: + raise ValueError("`probs` parameter must be at least one-dimensional.") + self.probs = probs / probs.sum(-1, keepdim=True) + else: + if logits.dim() < 1: + raise ValueError("`logits` parameter must be at least one-dimensional.") + self.logits = logits + # Normalize + + import math + + def logsumexp(t): + if t.numel() != 0: + maxes = flow.max(t, dim=-1, keepdim=True)[0] + maxes.masked_fill_(flow.abs(maxes) == math.inf, 0) + result = flow.sum(flow.exp(t - maxes), dim=-1, keepdim=True) + return flow.log(result) + maxes + else: + return flow.log(flow.sum(t, dim=-1, keepdim=True)) + + self.probs = logits_to_probs(logits - logsumexp(logits)) + + self._param = self.probs if probs is not None else self.logits + self._num_events = self._param.size()[-1] + batch_shape = ( + self._param.size()[:-1] if self._param.ndimension() > 1 else flow.Size() + ) + super(Categorical, self).__init__(batch_shape, validate_args=validate_args) + + def logits(self): + return probs_to_logits(self.probs) + + def probs(self): + return logits_to_probs(self.logits) + + def sample(self, sample_shape=flow.Size()): + if not isinstance(sample_shape, flow.Size): + sample_shape = flow.Size(sample_shape) + probs_2d = self.probs.reshape(-1, self._num_events) + samples_2d = flow.multinomial(probs_2d, sample_shape.numel(), True).T + return samples_2d.reshape(self._extended_shape(sample_shape)) + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/distributions/distribution.py b/python/oneflow/distributions/distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..80405fbdb66d0c4cace56eb368e271032d55f8ab --- /dev/null +++ b/python/oneflow/distributions/distribution.py @@ -0,0 +1,306 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +import warnings +from typing import Dict, Optional, Any + +# NOTE(Liang Depeng): Modified from +# https://github.com/pytorch/pytorch/blob/master/torch/distributions/distribution.py + +__all__ = ["Distribution"] + + +class Distribution(object): + r""" + Distribution is the abstract base class for probability distributions. + """ + + has_rsample = False + has_enumerate_support = False + _validate_args = __debug__ + + @staticmethod + def set_default_validate_args(value): + """ + Sets whether validation is enabled or disabled. + + The default behavior mimics Python's ``assert`` statement: validation + is on by default, but is disabled if Python is run in optimized mode + (via ``python -O``). Validation may be expensive, so you may want to + disable it once a model is working. + + Args: + value (bool): Whether to enable validation. + """ + if value not in [True, False]: + raise ValueError + Distribution._validate_args = value + + def __init__( + self, batch_shape=oneflow.Size(), event_shape=oneflow.Size(), validate_args=None + ): + self._batch_shape = batch_shape + self._event_shape = event_shape + assert validate_args is None, "only support validate_args=None for now." + super(Distribution, self).__init__() + + def expand(self, batch_shape, _instance=None): + """ + Returns a new distribution instance (or populates an existing instance + provided by a derived class) with batch dimensions expanded to + `batch_shape`. This method calls :class:`~oneflow.Tensor.expand` on + the distribution's parameters. As such, this does not allocate new + memory for the expanded distribution instance. Additionally, + this does not repeat any args checking or parameter broadcasting in + `__init__.py`, when an instance is first created. + + Args: + batch_shape (oneflow.Size): the desired expanded size. + _instance: new instance provided by subclasses that + need to override `.expand`. + + Returns: + New distribution instance with batch dimensions expanded to + `batch_size`. + """ + raise NotImplementedError + + @property + def batch_shape(self): + """ + Returns the shape over which parameters are batched. + """ + return self._batch_shape + + @property + def event_shape(self): + """ + Returns the shape of a single sample (without batching). + """ + return self._event_shape + + @property + def mean(self): + """ + Returns the mean of the distribution. + """ + raise NotImplementedError + + @property + def mode(self): + """ + Returns the mode of the distribution. + """ + raise NotImplementedError(f"{self.__class__} does not implement mode") + + @property + def variance(self): + """ + Returns the variance of the distribution. + """ + raise NotImplementedError + + @property + def stddev(self): + """ + Returns the standard deviation of the distribution. + """ + return self.variance.sqrt() + + def sample(self, sample_shape=oneflow.Size()): + """ + Generates a sample_shape shaped sample or sample_shape shaped batch of + samples if the distribution parameters are batched. + """ + with oneflow.no_grad(): + return self.rsample(sample_shape) + + def rsample(self, sample_shape=oneflow.Size()): + """ + Generates a sample_shape shaped reparameterized sample or sample_shape + shaped batch of reparameterized samples if the distribution parameters + are batched. + """ + raise NotImplementedError + + def sample_n(self, n): + """ + Generates n samples or n batches of samples if the distribution + parameters are batched. + """ + warnings.warn( + "sample_n will be deprecated. Use .sample((n,)) instead", UserWarning + ) + return self.sample(oneflow.Size((n,))) + + def log_prob(self, value): + """ + Returns the log of the probability density/mass function evaluated at + `value`. + + Args: + value (Tensor): + """ + raise NotImplementedError + + def cdf(self, value): + """ + Returns the cumulative density/mass function evaluated at + `value`. + + Args: + value (Tensor): + """ + raise NotImplementedError + + def icdf(self, value): + """ + Returns the inverse cumulative density/mass function evaluated at + `value`. + + Args: + value (Tensor): + """ + raise NotImplementedError + + def enumerate_support(self, expand=True): + """ + Returns tensor containing all values supported by a discrete + distribution. The result will enumerate over dimension 0, so the shape + of the result will be `(cardinality,) + batch_shape + event_shape` + (where `event_shape = ()` for univariate distributions). + + Note that this enumerates over all batched tensors in lock-step + `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens + along dim 0, but with the remaining batch dimensions being + singleton dimensions, `[[0], [1], ..`. + + To iterate over the full Cartesian product use + `itertools.product(m.enumerate_support())`. + + Args: + expand (bool): whether to expand the support over the + batch dims to match the distribution's `batch_shape`. + + Returns: + Tensor iterating over dimension 0. + """ + raise NotImplementedError + + def entropy(self): + """ + Returns entropy of distribution, batched over batch_shape. + + Returns: + Tensor of shape batch_shape. + """ + raise NotImplementedError + + def perplexity(self): + """ + Returns perplexity of distribution, batched over batch_shape. + + Returns: + Tensor of shape batch_shape. + """ + return oneflow.exp(self.entropy()) + + def _extended_shape(self, sample_shape=oneflow.Size()): + """ + Returns the size of the sample returned by the distribution, given + a `sample_shape`. Note, that the batch and event shapes of a distribution + instance are fixed at the time of construction. If this is empty, the + returned shape is upcast to (1,). + + Args: + sample_shape (oneflow.Size): the size of the sample to be drawn. + """ + if not isinstance(sample_shape, oneflow.Size): + sample_shape = oneflow.Size(sample_shape) + return sample_shape + self._batch_shape + self._event_shape + + def _validate_sample(self, value): + """ + Argument validation for distribution methods such as `log_prob`, + `cdf` and `icdf`. The rightmost dimensions of a value to be + scored via these methods must agree with the distribution's batch + and event shapes. + + Args: + value (Tensor): the tensor whose log probability is to be + computed by the `log_prob` method. + Raises + ValueError: when the rightmost dimensions of `value` do not match the + distribution's batch and event shapes. + """ + if not isinstance(value, oneflow.Tensor): + raise ValueError("The value argument to log_prob must be a Tensor") + + event_dim_start = len(value.size()) - len(self._event_shape) + if value.size()[event_dim_start:] != self._event_shape: + raise ValueError( + "The right-most size of value must match event_shape: {} vs {}.".format( + value.size(), self._event_shape + ) + ) + + actual_shape = value.size() + expected_shape = self._batch_shape + self._event_shape + for i, j in zip(reversed(actual_shape), reversed(expected_shape)): + if i != 1 and j != 1 and i != j: + raise ValueError( + "Value is not broadcastable with batch_shape+event_shape: {} vs {}.".format( + actual_shape, expected_shape + ) + ) + try: + support = self.support + except NotImplementedError: + warnings.warn( + f"{self.__class__} does not define `support` to enable " + + "sample validation. Please initialize the distribution with " + + "`validate_args=False` to turn off validation." + ) + return + assert support is not None + valid = support.check(value) + if not valid.all(): + raise ValueError( + "Expected value argument " + f"({type(value).__name__} of shape {tuple(value.shape)}) " + f"to be within the support ({repr(support)}) " + f"of the distribution {repr(self)}, " + f"but found invalid values:\n{value}" + ) + + def _get_checked_instance(self, cls, _instance=None): + if _instance is None and type(self).__init__ != cls.__init__: + raise NotImplementedError( + "Subclass {} of {} that defines a custom __init__ method " + "must also define a custom .expand() method.".format( + self.__class__.__name__, cls.__name__ + ) + ) + return self.__new__(type(self)) if _instance is None else _instance + + def __repr__(self): + return self.__class__.__name__ + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/distributions/utils.py b/python/oneflow/distributions/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b074cfba75ab68afe85e647892fd8a7282a996d9 --- /dev/null +++ b/python/oneflow/distributions/utils.py @@ -0,0 +1,61 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from functools import update_wrapper +from numbers import Number +import oneflow as flow +import oneflow.nn.functional as F +from typing import Dict, Any + +# NOTE(Liang Depeng): modified from +# https://github.com/pytorch/pytorch/blob/master/torch/distributions/utils.py + +euler_constant = 0.57721566490153286060 # Euler Mascheroni Constant + + +def logits_to_probs(logits, is_binary=False): + r""" + Converts a tensor of logits into probabilities. Note that for the + binary case, each value denotes log odds, whereas for the + multi-dimensional case, the values along the last dimension denote + the log probabilities (possibly unnormalized) of the events. + """ + if is_binary: + return flow.sigmoid(logits) + return F.softmax(logits, dim=-1) + + +def clamp_probs(probs): + eps = flow.finfo(probs.dtype).eps + return probs.clamp(min=eps, max=1 - eps) + + +def probs_to_logits(probs, is_binary=False): + r""" + Converts a tensor of probabilities into logits. For the binary case, + this denotes the probability of occurrence of the event indexed by `1`. + For the multi-dimensional case, the values along the last dimension + denote the probabilities of occurrence of each of the events. + """ + ps_clamped = clamp_probs(probs) + if is_binary: + return flow.log(ps_clamped) - flow.log1p(-ps_clamped) + return flow.log(ps_clamped) + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/env.py b/python/oneflow/env.py index 9afdcb22cb3c246724591cee9ac4e5279667509a..661f2a587eb011b52f356690cc1640bad0584d32 100644 --- a/python/oneflow/env.py +++ b/python/oneflow/env.py @@ -85,3 +85,9 @@ def rdma_is_initialized(): """ return oneflow._oneflow_internal.RDMAIsInitialized() + + +def destory_rdma(): + """Destory RDMA in the current envirment. + """ + return oneflow._oneflow_internal.DestoryRDMA() diff --git a/python/oneflow/framework/c_api_util.py b/python/oneflow/framework/c_api_util.py index 2de84808bf60b0d309fd34eefac21c89c419287d..cbe30fb4307fc83c45b13d81a2eac9f1f5cfc90c 100644 --- a/python/oneflow/framework/c_api_util.py +++ b/python/oneflow/framework/c_api_util.py @@ -30,7 +30,6 @@ import oneflow.core.record.record_pb2 as record_util import oneflow.core.register.logical_blob_id_pb2 as logical_blob_id_util from oneflow.core.framework.config_def_pb2 import ConfigDef from oneflow.core.job.inter_user_job_info_pb2 import InterUserJobInfo -from oneflow.core.serving.saved_model_pb2 import SavedModel def CurrentResource(): @@ -50,19 +49,6 @@ def GetEnvContext(env_proto): return env_ctx -def InitLazyGlobalSession(config_proto): - assert type(config_proto) is job_set_pb.ConfigProto - config_proto_str = text_format.MessageToString(config_proto) - oneflow._oneflow_internal.InitLazyGlobalSession(config_proto_str) - - -def GetInterUserJobInfo(): - inter_user_job_info = oneflow._oneflow_internal.GetSerializedInterUserJobInfo() - ret = InterUserJobInfo() - ret.ParseFromString(inter_user_job_info) - return ret - - def JobBuildAndInferCtx_Open(job_name): job_name = str(job_name) oneflow._oneflow_internal.JobBuildAndInferCtx_Open(job_name) @@ -74,12 +60,6 @@ def CurJobBuildAndInferCtx_SetJobConf(job_config_proto): oneflow._oneflow_internal.CurJobBuildAndInferCtx_SetJobConf(job_config_proto_str) -def CurJobBuildAndInferCtx_SetTrainConf(train_config): - assert type(train_config) is job_conf_pb.TrainConf - train_config_str = text_format.MessageToString(train_config) - oneflow._oneflow_internal.CurJobBuildAndInferCtx_SetTrainConf(train_config_str) - - def InferOpConf(op_conf_proto, upstream_signature): serialized_op_conf = str(text_format.MessageToString(op_conf_proto)) serialized_upstream_sig = str(text_format.MessageToString(upstream_signature)) @@ -110,123 +90,6 @@ def CheckAndCompleteUserOpConf(op_conf_proto): return text_format.Parse(new_op_conf, op_conf_util.OperatorConf()) -def CurJobBuildAndInferCtx_AddAndInferConsistentOp(op_conf_proto): - serialized_op_conf = str(text_format.MessageToString(op_conf_proto)) - add_and_infer = ( - oneflow._oneflow_internal.CurJobBuildAndInferCtx_AddAndInferConsistentOp - ) - op_attribute_str = add_and_infer(serialized_op_conf) - return text_format.Parse(op_attribute_str, op_attribute_pb.OpAttribute()) - - -def CurJobBuildAndInferCtx_AddAndInferMirroredOp(op_conf_proto): - serialized_op_conf = str(text_format.MessageToString(op_conf_proto)) - add_and_infer = ( - oneflow._oneflow_internal.CurJobBuildAndInferCtx_AddAndInferMirroredOp - ) - op_attribute_str = add_and_infer(serialized_op_conf) - return text_format.Parse(op_attribute_str, op_attribute_pb.OpAttribute()) - - -def CurJobBuildAndInferCtx_AddLossLogicalBlobName(lbn): - lbn = str(lbn) - oneflow._oneflow_internal.CurJobBuildAndInferCtx_AddLossLogicalBlobName(lbn) - - -def CurJobBuildAndInferCtx_AddLbiAndDiffWatcherUuidPair(lbi_and_uuid): - serialized = str(text_format.MessageToString(lbi_and_uuid)) - oneflow._oneflow_internal.CurJobBuildAndInferCtx_AddLbiAndDiffWatcherUuidPair( - serialized - ) - - -def JobBuildAndInferCtx_IsMirroredBlob(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - return oneflow._oneflow_internal.JobBuildAndInferCtx_IsMirroredBlob(job_name, lbn) - - -def JobBuildAndInferCtx_MirroredBlobGetNumSubLbi(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - return oneflow._oneflow_internal.JobBuildAndInferCtx_MirroredBlobGetNumSubLbi( - job_name, lbn - ) - - -def JobBuildAndInferCtx_MirroredBlobGetSubLbi(job_name, lbn, index): - job_name = str(job_name) - lbn = str(lbn) - ret = oneflow._oneflow_internal.JobBuildAndInferCtx_MirroredBlobGetSerializedSubLbi( - job_name, lbn, index - ) - return text_format.Parse(ret, logical_blob_id_util.LogicalBlobId()) - - -def JobBuildAndInferCtx_GetStaticShape(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - axis_str = oneflow._oneflow_internal.JobBuildAndInferCtx_GetSerializedIdListAsStaticShape( - job_name, lbn - ) - int_list = text_format.Parse(axis_str, record_util.Int64List()) - return tuple(map(int, int_list.value)) - - -def JobBuildAndInferCtx_GetDataType(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - dtype = oneflow._oneflow_internal.JobBuildAndInferCtx_GetDataType(job_name, lbn) - return int(dtype) - - -def JobBuildAndInferCtx_IsDynamic(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - ret = oneflow._oneflow_internal.JobBuildAndInferCtx_IsDynamic(job_name, lbn) - return ret - - -def JobBuildAndInferCtx_DisableBoxing(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - ret = oneflow._oneflow_internal.JobBuildAndInferCtx_DisableBoxing(job_name, lbn) - return ret - - -def JobBuildAndInferCtx_GetSplitAxisFromProducerView(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - split_axis_str = oneflow._oneflow_internal.JobBuildAndInferCtx_GetSplitAxisFromProducerView( - job_name, lbn - ) - split_axis = text_format.Parse(split_axis_str, dtype_util.OptInt64()) - if split_axis.HasField("value"): - return split_axis.value - return None - - -def JobBuildAndInferCtx_GetParallelConfFromProducerView(job_name, lbn): - job_name = str(job_name) - lbn = str(lbn) - GetParallelConf = ( - oneflow._oneflow_internal.JobBuildAndInferCtx_GetSerializedParallelConfFromProducerView - ) - serialized_parallel_conf = GetParallelConf(job_name, lbn) - parallel_conf = text_format.Parse( - serialized_parallel_conf, placement_pb.ParallelConf() - ) - return parallel_conf - - -def GetMachine2DeviceIdListOFRecordFromParallelConf(parallel_conf): - serialized_parallel_conf = str(parallel_conf) - ofrecord = oneflow._oneflow_internal.GetMachine2DeviceIdListOFRecordFromParallelConf( - serialized_parallel_conf - ) - return text_format.Parse(ofrecord, record_util.OFRecord()) - - def GetFunctionConfigDef(): func_config_def = oneflow._oneflow_internal.GetFunctionConfigDef() return text_format.Parse(func_config_def, ConfigDef()) @@ -237,28 +100,8 @@ def GetScopeConfigDef(): return text_format.Parse(scope_config_def, ConfigDef()) -def GetInterfaceOpAttributes(): - op_attributes = oneflow._oneflow_internal.GetSerializedInterfaceOpAttributes() - return text_format.Parse(op_attributes, op_attribute_pb.OpAttributeList()) - - -def GetJobSet(): - job_set = oneflow._oneflow_internal.GetSerializedJobSet() - ret = job_set_pb.JobSet() - ret.ParseFromString(job_set) - return ret - - def GetCurrentJob(): serialized_job = oneflow._oneflow_internal.GetSerializedCurrentJob() ret = job_pb.Job() ret.ParseFromString(serialized_job) return ret - - -def LoadSavedModel(saved_model_meta_file, is_prototxt_file): - serialized_saved_model = oneflow._oneflow_internal.LoadSavedModel( - saved_model_meta_file, is_prototxt_file - ) - saved_model = text_format.Parse(serialized_saved_model, SavedModel()) - return saved_model diff --git a/python/oneflow/framework/check_point_v2.py b/python/oneflow/framework/check_point_v2.py index 766e0247483907a731e0c7b42730f38f17826b8e..f6c774b61a7dfa322a4c2153b9acd1d504f2b00c 100644 --- a/python/oneflow/framework/check_point_v2.py +++ b/python/oneflow/framework/check_point_v2.py @@ -19,6 +19,8 @@ import warnings from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union from pathlib import Path import pickle +import json +from collections import OrderedDict import numpy as np from google.protobuf import text_format @@ -31,7 +33,9 @@ import oneflow.framework.dtype as dtype_util import oneflow.framework.id_util as id_util from oneflow.framework.tensor import Tensor import oneflow.nn.graph.graph as graph_util +from oneflow.nn.graph.util import ArgsTree import pickle +from oneflow.nn.graph import GraphTensor SNAPSHOT_DONE_FILENAME = "snapshot_done" META_INFO_FILENAME = "meta" @@ -122,13 +126,25 @@ def _save_tensor_to_disk(tensor: "oneflow.Tensor", dir_name: Union[str, Path]) - ValueContainer = Union[FileBackendVariableBlob, np.ndarray, "oneflow.Tensor"] +def smart_to( + tensor: "oneflow.Tensor", dest: Optional[Union[str, flow.device, flow.placement]] +) -> "oneflow.Tensor": + if dest is None: + return tensor + if isinstance(dest, (str, flow.device)): + return tensor.to(device=dest) + else: + return tensor.to_global(placement=dest) + + def _LoadSingleVariable( - path: Optional[str], global_src_rank: Optional[int] = None + path: Optional[str], + global_src_rank: Optional[int] = None, + map_location: Optional[Union[flow.device, flow.placement]] = None, ) -> "flow.Tensor": if global_src_rank is not None: rank = flow.env.get_rank() if rank == global_src_rank: - assert isinstance(path, str) file_backed_blob = FileBackendVariableBlob(path) loaded = flow.tensor(file_backed_blob.numpy(), dtype=file_backed_blob.dtype) else: @@ -136,10 +152,9 @@ def _LoadSingleVariable( loaded = loaded.to_global( flow.placement("cpu", [global_src_rank]), flow.sbp.broadcast ) - return loaded - - assert isinstance(path, str) - return flow.tensor(FileBackendVariableBlob(path).numpy()) + else: + loaded = flow.tensor(FileBackendVariableBlob(path).numpy()) + return smart_to(loaded, map_location) def _broadcast_py_object(obj, src: int = 0): @@ -170,10 +185,17 @@ def tensor_getstate(self): rel_dir_name = f"global_tensor_{self.global_id()}" abs_dir_name = save_load_path / rel_dir_name - tensor = self.to_global( - sbp=flow.sbp.broadcast, - placement=flow.placement("cpu", [global_src_dsk_rank]), - ).to_local() + # Boxing to cpu firstly to avoid extra gpu memory usage + tensor = ( + self.to_global( + sbp=self.sbp, placement=flow.placement("cpu", self.placement.ranks) + ) + .to_global( + sbp=flow.sbp.broadcast, + placement=flow.placement("cpu", [global_src_dsk_rank]), + ) + .to_local() + ) if global_src_dsk_rank is None or global_src_dsk_rank == flow.env.get_rank(): _save_tensor_to_disk(tensor, abs_dir_name) @@ -201,8 +223,12 @@ def tensor_setstate(self, pickle_dict): assert isinstance(save_load_path, Path) rel_dir_name = pickle_dict["path"] abs_dir_name = save_load_path / rel_dir_name - self.__init__(_LoadSingleVariable(str(abs_dir_name), global_src_dsk_rank)) + tmp_tensor = _LoadSingleVariable( + str(abs_dir_name), global_src_dsk_rank, map_location + ) + self.__init__(tmp_tensor) else: + assert map_location is None if "placement" in pickle_dict: return self.__init__( flow.tensor( @@ -240,8 +266,29 @@ def RegisterMethods(): flow._oneflow_internal.placement.__setstate__ = placement_setstate +load_methods = [] + + +def load_if(condition): + def decorator(func): + load_methods.append((condition, func)) + return func + + return decorator + + +def is_dir_and_no_pickle_file(path: Path, support_pytorch_format: bool): + if path.is_dir(): + pickle_path = path / PICKLE_FILENAME + return not pickle_path.exists() + return False + + +@load_if(is_dir_and_no_pickle_file) def legacy_load( - path: Union[str, Path], global_src_rank: Optional[int] = None, + path: Path, + global_src_rank: Optional[int] = None, + map_location: Optional[Union[str, flow.device]] = None, ) -> Dict[str, "flow.Tensor"]: assert os.path.isdir(path), "Directory {} doesn't exist!".format(path) rank = flow.env.get_rank() @@ -257,7 +304,7 @@ def legacy_load( for f in all_files: var_dir = os.path.join(path, f) try: - var_dict[f] = _LoadSingleVariable(var_dir, global_src_rank) + var_dict[f] = _LoadSingleVariable(var_dir, global_src_rank, map_location) except FileNotFoundError: warnings.warn( f"'{var_dir}' does not have valid tensor data. Please check it if it is unexpected.", @@ -267,29 +314,115 @@ def legacy_load( @contextmanager -def tensor_pickling_context(path: Path, global_src_dst_rank: Optional[int]): +def tensor_pickling_context(path: Path, global_src_dst_rank: Optional[int], mp): global save_load_path global global_src_dsk_rank + global map_location global_src_dsk_rank = global_src_dst_rank save_load_path = path + map_location = mp try: yield finally: global_src_dsk_rank = None save_load_path = None + map_location = None + + +def is_file_and_support_pytorch_format( + path: Path, support_pytorch_format: bool +) -> bool: + return path.is_file() and support_pytorch_format + + +@load_if(is_file_and_support_pytorch_format) +def load_from_pytorch_file( + path: Path, + global_src_rank=None, + map_location: Optional[Union[str, flow.device]] = None, +): + with flow.mock_torch.disable(): + import torch + + if global_src_rank is None or global_src_rank == flow.env.get_rank(): + torch_obj = torch.load(path, map_location="cpu") + + def torch_tensor_to_flow(x): + if isinstance(x, torch.Tensor): + return flow.utils.tensor.from_torch(x) + else: + return x + + flow_obj = ArgsTree(torch_obj).map_leaf(torch_tensor_to_flow) + else: + flow_obj = None + if global_src_rank is not None: + flow_obj = flow.utils.global_view.to_global( + flow_obj, + placement=flow.placement("cpu", [global_src_rank]), + sbp=flow.sbp.broadcast, + warn_on_non_tensor_leaf=False, + ) + + flow_obj = ArgsTree(flow_obj).map_leaf(lambda x: smart_to(x, map_location)) + return flow_obj + + +def is_dir_and_has_pickle_file(path: Path, support_pytorch_format: bool) -> bool: + if path.is_dir(): + pickle_path = path / PICKLE_FILENAME + return pickle_path.exists() + return False + + +@load_if(is_dir_and_has_pickle_file) +def load_oneflow_pickle( + path: Path, + global_src_rank: Optional[int] = None, + map_location: Optional[Union[str, flow.device, flow.placement]] = None, +): + rank = flow.env.get_rank() + pickle_path = path / PICKLE_FILENAME + if global_src_rank is not None: + if rank == global_src_rank: + pickle_bytes = pickle_path.read_bytes() + _broadcast_py_object(pickle_bytes, global_src_rank) + else: + pickle_bytes = _broadcast_py_object(None, global_src_rank) + else: + pickle_bytes = pickle_path.read_bytes() + + if map_location is not None: + assert isinstance( + map_location, (str, flow.device, flow.placement) + ), "'map_location' only supports str, device or placement." + with tensor_pickling_context(path, global_src_rank, map_location): + res = pickle.loads(pickle_bytes) + assert res["protocol_version"] == PROTOCOL_VERSION + return res["data"] -def load(path: str, global_src_rank: Optional[int] = None,) -> Any: +def load( + path: str, + global_src_rank: Optional[int] = None, + map_location: Optional[Union[str, flow.device, flow.placement]] = None, + *, + support_pytorch_format: bool = True, +) -> Any: r"""Loads an object saved with oneflow.save() from a directory. Args: path (str): The directory containing the object - global_src_rank (int, optional): The source rank for - loading global tensors. When specified, only the + global_src_rank (int, optional): The source rank for + loading global tensors. When specified, only the process whose rank == global_src_rank will really read the files in `path`, and tensors in the loaded - object will be consistent with placement = + object will be consistent with placement = `flow.placement('cuda', [global_src_rank])` + map_location (str, flow.device or flow.placement, optional): + indicates the location where all tensors should be loaded. + support_pytorch_format (bool, optional): whether to support + loading the file saved by `torch.save`. Default: True Returns: The loaded object @@ -297,29 +430,55 @@ def load(path: str, global_src_rank: Optional[int] = None,) -> Any: path: Path = Path(path) rank = flow.env.get_rank() if global_src_rank is None or global_src_rank == rank: - assert path.is_dir(), "Directory {} doesn't exist!".format(path) - pickle_path = path / PICKLE_FILENAME - is_legacy = not pickle_path.exists() - if global_src_rank is not None: - _broadcast_py_object(is_legacy, global_src_rank) - else: - is_legacy = _broadcast_py_object(None, global_src_rank) - if is_legacy: - return legacy_load(path, global_src_rank) - - if global_src_rank is not None: - if rank == global_src_rank: - pickle_bytes = pickle_path.read_bytes() - _broadcast_py_object(pickle_bytes, global_src_rank) + for i, (condition, load) in enumerate(load_methods): + if condition(path, support_pytorch_format): + if global_src_rank is not None: + _broadcast_py_object(i, global_src_rank) + break else: - pickle_bytes = _broadcast_py_object(None, global_src_rank) + raise NotImplementedError("No valid load method found for {}".format(path)) else: - pickle_bytes = pickle_path.read_bytes() + i = _broadcast_py_object(None, global_src_rank) + load = load_methods[i][1] - with tensor_pickling_context(path, global_src_rank): - res = pickle.loads(pickle_bytes) - assert res["protocol_version"] == PROTOCOL_VERSION - return res["data"] + return load(path, global_src_rank, map_location) # type: ignore + + +def save_one_embedding_info(state_dict: Any, path: Union[str, Path]) -> None: + path: Path = Path(path) + + _embedding_info_dict = {"embedding": []} + os.makedirs(path, exist_ok=True) + + _save_one_embedding_info_flag = False + + for module in state_dict.keys(): + if not isinstance(state_dict[module], OrderedDict): + continue + for module_key in state_dict[module].keys(): + _info_dict = {} + if "OneEmbeddingKeyValueOptions" in module_key: + if not _save_one_embedding_info_flag: + _save_one_embedding_info_flag = True + + module_key_prefix = module_key.rstrip("OneEmbeddingKeyValueOptions") + + _embedding_info_dict["embedding"].append( + { + "snapshot": state_dict["module"][ + module_key_prefix + "OneEmbeddingSnapshot" + ], + "kv_options": json.loads( + state_dict["module"][ + module_key_prefix + "OneEmbeddingKeyValueOptions" + ] + ), + } + ) + + if _save_one_embedding_info_flag: + with open(os.path.join(path, "one_embedding_options.json"), "w") as f: + f.write(json.dumps(_embedding_info_dict, indent=4)) def save( @@ -330,9 +489,9 @@ def save( Args: obj: The object to be saved path (str): The directory in which the object is saved - global_dst_rank (int, optional): The destination rank for + global_dst_rank (int, optional): The destination rank for saving global tensors. When specified, whole tensors - will be saved by the process whose rank == + will be saved by the process whose rank == global_src_rank, while other processes will not do any disk I/O. """ @@ -345,16 +504,21 @@ def save( path.mkdir(exist_ok=True) - serialized_job = str(text_format.MessageToString(graph._forward_job_proto)) + serialized_job = graph._forward_job_proto.SerializeToString() oneflow._oneflow_internal.nn.graph.SaveJobToIR(serialized_job, str(path)) for x in graph._state(): - _save_tensor_to_disk(x.origin, path / f"{x.name_prefix}{x.name}") + _save_tensor_to_disk( + x.to(Tensor), + path / f"{x.to(GraphTensor).name_prefix}{x.to(GraphTensor).name}", + ) + + save_one_embedding_info(obj.state_dict(), path) return obj = {"protocol_version": PROTOCOL_VERSION, "data": obj} - with tensor_pickling_context(path, global_dst_rank): + with tensor_pickling_context(path, global_dst_rank, None): pickled_bytes = pickle.dumps(obj) def write_to_path(path): @@ -378,3 +542,4 @@ def save( save_load_path = None global_src_dsk_rank = None +map_location = None diff --git a/python/oneflow/framework/config_util.py b/python/oneflow/framework/config_util.py index c40b2c38b2dd23f72f31242406a723e631a2161b..4a08bec67b4c456a7bfcb1f0949608940fc66d60 100644 --- a/python/oneflow/framework/config_util.py +++ b/python/oneflow/framework/config_util.py @@ -84,62 +84,6 @@ def api_load_library(val: str) -> None: oneflow._oneflow_internal.LoadLibrary(val) -def api_machine_num(val: int) -> None: - """Set available number of machine/node for running job. - - Args: - val (int): available number of machines - """ - attrs, type_ = api_attrs_and_type[api_machine_num] - _set_resource_attr(attrs, val, type_) - - -def api_gpu_device_num(val: int) -> None: - """Set number of GPUs on each machine to run oneflow on. - - Args: - val (int): number of GPUs. It is identical on every machine. In other words, - you can't specify different number of GPUs you would like to use on each machine. - """ - - print( - "'gpu_device_num' has been deprecated, has no effect and will be removed in the future." - ) - - -def api_cpu_device_num(val: int) -> None: - """Set number of CPUs on each machine to run oneflow on. Usually you don't need to set this. - - Args: - val (int): number of CPUs. It is identical on every machine. - """ - - attrs, type_ = api_attrs_and_type[api_cpu_device_num] - _set_resource_attr(attrs, val, type_) - - -def api_comm_net_worker_num(val: int) -> None: - """Set up the workers number in epoll mode network, - If use RDMA mode network, then doesn't need. - - Args: - val (int): number of workers - """ - attrs, type_ = api_attrs_and_type[api_comm_net_worker_num] - _set_resource_attr(attrs, val, type_) - - -def api_max_mdsave_worker_num(val: int) -> None: - """Set up max number of workers for mdsave process. - - Args: - val (int): max number of workers - """ - - attrs, type_ = api_attrs_and_type[api_max_mdsave_worker_num] - _set_resource_attr(attrs, val, type_) - - def api_numa_aware_cuda_malloc_host(val: bool = True) -> None: """Whether or not let numa know that cuda allocated host's memory. @@ -151,31 +95,8 @@ def api_numa_aware_cuda_malloc_host(val: bool = True) -> None: ) -def api_compute_thread_pool_size(val: int) -> None: - """Set up the size of compute thread pool - - Args: - val (int): size of thread pool - """ - - attrs, type_ = api_attrs_and_type[api_compute_thread_pool_size] - _set_resource_attr(attrs, val, type_) - - -def api_reserved_host_mem_mbyte(val: int) -> None: - """Set up the memory size of reserved host - - Args: - val (int): memory size, e.g. 1024(mb) - """ - - attrs, type_ = api_attrs_and_type[api_reserved_host_mem_mbyte] - _set_resource_attr(attrs, val, type_) - - def api_reserved_device_mem_mbyte(val: int) -> None: """Set up the memory size of reserved device - Args: val (int): memory size, e.g. 1024(mb) """ @@ -195,41 +116,14 @@ def api_enable_cudnn_fused_normalization_add_relu(val: bool) -> None: _set_resource_attr(attrs, val, type_) -def api_enable_debug_mode(val: bool) -> None: - """Whether use debug mode or not. +def api_enable_cudnn_conv_heuristic_search_algo(val: bool) -> None: + """Whether enable cudnn conv operatioin to use heuristic search algorithm. Args: - val (bool): True or False + val (bool): whether enable or not, the default value is true. """ - attrs, type_ = api_attrs_and_type[api_enable_debug_mode] - _set_resource_attr(attrs, val, type_) - - -def api_legacy_model_io_enabled(): - sess = session_ctx.GetDefaultSession() - return sess.config_proto.resource.enable_legacy_model_io - - -def api_enable_legacy_model_io(val: bool = True): - """Whether or not use legacy model io. - - Args: - val ([type]): True or False - """ - - attrs, type_ = api_attrs_and_type[api_enable_legacy_model_io] - _set_resource_attr(attrs, val, type_) - - -def api_enable_model_io_v2(val: bool): - """Whether or not use version2 of model input/output function. - - Args: - val ([type]): True or False - """ - - attrs, type_ = api_attrs_and_type[api_enable_model_io_v2] + attrs, type_ = api_attrs_and_type[api_enable_cudnn_conv_heuristic_search_algo] _set_resource_attr(attrs, val, type_) @@ -244,41 +138,6 @@ def api_enable_fusion(val: bool = True) -> None: _set_resource_attr(attrs, val, type_) -def api_num_callback_threads(val: int) -> None: - """Set up number of callback threads for boxing process. - Boxing is used to convert between different parallel properties of logical tensor - - Args: - val (int): number of callback threads - """ - - attrs, type_ = api_attrs_and_type[api_num_callback_threads] - _set_resource_attr(attrs, val, type_) - - -def api_enable_tensor_float_32_compute(val: bool = True) -> None: - """Whether or not to enable Tensor-float-32 on supported GPUs - - Args: - val (bool, optional): True or False. Defaults to True. - """ - attrs, type_ = api_attrs_and_type[api_enable_tensor_float_32_compute] - _set_resource_attr(attrs, val, type_) - if not val: - os.environ["ONEFLOW_EP_CUDA_ENABLE_TF32_EXECUTION"] = "0" - - -def api_enable_mem_chain_merge(val: bool = True) -> None: - """Whether or not to enable MemChain merge. - - Args: - val (bool, optional): True or False. Defaults to True. - """ - - attrs, type_ = api_attrs_and_type[api_enable_mem_chain_merge] - _set_resource_attr(attrs, val, type_) - - def api_nccl_use_compute_stream(val: bool = False) -> None: """Whether or not nccl use compute stream to reuse nccl memory and speedup @@ -357,7 +216,7 @@ def api_nccl_fusion_reduce_scatter(val: bool) -> None: def api_nccl_fusion_all_gather(val: bool) -> None: - """Whether or not use nccl fusion during all gather progress + """Whether or not use nccl fusion during all gather progress Args: val (bool): True or False @@ -423,24 +282,16 @@ def api_nccl_enable_mixed_fusion(val: bool) -> None: api_attrs_and_type = { - api_machine_num: ("machine_num", int), - api_comm_net_worker_num: ("comm_net_worker_num", int), - api_max_mdsave_worker_num: ("max_mdsave_worker_num", int), - api_cpu_device_num: ("cpu_device_num", int), - api_compute_thread_pool_size: ("compute_thread_pool_size", int), - api_reserved_host_mem_mbyte: ("reserved_host_mem_mbyte", int), api_reserved_device_mem_mbyte: ("reserved_device_mem_mbyte", int), api_enable_cudnn_fused_normalization_add_relu: ( ["cudnn_conf", "enable_cudnn_fused_normalization_add_relu"], bool, ), - api_enable_debug_mode: ("enable_debug_mode", bool), - api_enable_legacy_model_io: ("enable_legacy_model_io", bool), - api_enable_model_io_v2: ("enable_legacy_model_io_v2", bool), + api_enable_cudnn_conv_heuristic_search_algo: ( + ["cudnn_conf", "cudnn_conv_heuristic_search_algo"], + bool, + ), api_enable_fusion: (["collective_boxing_conf", "enable_fusion"], bool), - api_num_callback_threads: (["collective_boxing_conf", "num_callback_threads"], int), - api_enable_tensor_float_32_compute: ("enable_tensor_float_32_compute", bool), - api_enable_mem_chain_merge: ("enable_mem_chain_merge", bool), api_nccl_use_compute_stream: ("nccl_use_compute_stream", bool), api_disable_group_boxing_by_dst_parallel: ( "disable_group_boxing_by_dst_parallel", diff --git a/python/oneflow/framework/docstr/__init__.py b/python/oneflow/framework/docstr/__init__.py index 00b89e9656009366d6c75912c15c863289c2f700..822675cecacb2cf7cfc30f40939c61cb8fa0c4e7 100644 --- a/python/oneflow/framework/docstr/__init__.py +++ b/python/oneflow/framework/docstr/__init__.py @@ -74,4 +74,12 @@ from .tensordot import * from .searchsorted import * from .amin import * from .deconv import * +from .inv import * from .logical_ops import * +from .distance import * +from .addcdiv import * +from .hann_window import * +from .convolution import * +from .linalg import * +from .index_add import * +from .baddbmm import * diff --git a/python/oneflow/framework/docstr/activation.py b/python/oneflow/framework/docstr/activation.py index 174577d3a9a1e3705dd8c0f629ebca185dac5e10..78ef57685bedec99b5b97ab677ae48a7569df464 100644 --- a/python/oneflow/framework/docstr/activation.py +++ b/python/oneflow/framework/docstr/activation.py @@ -75,10 +75,23 @@ add_docstr( r""" gelu(x: Tensor) -> Tensor - The equation is: + Applies the Gaussian Error Linear Units function: - .. math:: - out = 0.5 * x * (1 + tanh(\sqrt{\frac{2}{\pi}} * (x + 0.044715x^{3}))) + .. math:: \\text{GELU}(x) = x * \Phi(x) + + where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution. + + When the approximate argument is 'tanh', Gelu is estimated with: + + .. math:: \\text{GELU}(x) = 0.5 * x * (1 + \\text{Tanh}(\sqrt(2 / \pi) * (x + 0.044715 * x^3))) + + Args: + input (oneflow.Tensor): Input Tensor + approximate (string, optional): the gelu approximation algorithm to use: + ``'none'`` | ``'tanh'``. Default: ``'none'`` + + Returns: + oneflow.Tensor: A Tensor has same shape as the input. For example: @@ -101,6 +114,28 @@ add_docstr( ) +add_docstr( + oneflow._C.quick_gelu, + r""" + quick_gelu(x: Tensor) -> Tensor + + Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs + + .. math:: + \\text{QuickGELU}(x) = x * \\sigma(1.702x) = x * \\frac{1}{1 + \\exp(-1.702x)} + + Args: + input (oneflow.Tensor): Input Tensor + + Returns: + oneflow.Tensor: A Tensor has same shape as the input. + + See + :class:`~oneflow.nn.QuickGELU` for more details. + + """, +) + add_docstr( oneflow._C.softmax, r""" @@ -129,6 +164,37 @@ add_docstr( """, ) +add_docstr( + oneflow._C.gumbel_softmax, + r""" + gumbel_softmax(x: Tensor, dim: int, tau: float = 1.0, hard: bool = False) -> Tensor + + Solve the problem that the output values of argmax do not reflect the probability distribution of the model's output. + Compensates for the fact that the argmax cannot participate in gradient back-propagation. + + Gumbel is defined as: + + .. math:: + Gumbel_i = -log(-log(U_i)),\ U_i \sim U(0,1) + + Add Noise ~ Gumbel: + + .. math:: + In = (In + Noise) / tau + + Calculate Softmax value: + + .. math:: + gumbel\_softmax(In)=\frac{e^{In_i/tau}}{\sum_{j=1}^n{e^{In_j/tau}}},i=1,2,3...n + + Parameters: + x (oneflow.Tensor): the input Tensor. + dim (int, Tuple[int]): the dimension to softmax. + tau (double): the input tensor of Softmax should obey the Gumbel(x, tau). + hard (bool): if `hard=True`, the output tensor will be one-hot. + """, +) + add_docstr( oneflow.softplus, r""" @@ -397,7 +463,11 @@ add_docstr( """ selu(x: Tensor) -> Tensor - Applies element-wise function :math:`\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`, with :math:`\alpha=1.6732632423543772848170429916717` and :math:`scale=1.0507009873554804934193349852946`. + Applies element-wise function + + .. math:: + + \text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))`, with :math:`\alpha=1.6732632423543772848170429916717` and :math:`scale=1.0507009873554804934193349852946`. See :class:`~oneflow.nn.SELU` for more details. @@ -474,3 +544,36 @@ add_docstr( tensor([-0.3161, 0.0000, 0.5000], dtype=oneflow.float32) """, ) + +add_docstr( + oneflow._C.threshold, + """ + threshold(input: Tensor, threshold: float, value: float) -> Tensor + + Thresholds each element of the input Tensor. + + See :class:`~oneflow.nn.Threshold` for more details. + """, +) + +add_docstr( + oneflow._C.hardshrink, + """ + hardshrink(input: Tensor, lambd: float=0.5, inplace: bool=False) -> Tensor + + Applies the hard shrinkage function in an element-wise manner. + + See :class:`~oneflow.nn.Hardshrink` for more details. + """, +) + +add_docstr( + oneflow._C.softshrink, + """ + softshrink(input: Tensor, lambd: float=0.5, inplace: bool=False) -> Tensor + + Applies the soft shrinkage function in an element-wise manner. + + See :class:`~oneflow.nn.Softshrink` for more details. + """, +) diff --git a/python/oneflow/framework/docstr/addcdiv.py b/python/oneflow/framework/docstr/addcdiv.py new file mode 100644 index 0000000000000000000000000000000000000000..d170f426c7c8c9fe36488b63eba03cebfceb0926 --- /dev/null +++ b/python/oneflow/framework/docstr/addcdiv.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.addcdiv, + r""" + addcdiv(input, tensor1, tensor2, *, value=1) -> Tensor + + This function is equivalent to PyTorch’s addcdiv function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.addcdiv.html. + + Performs the element-wise division of :attr:`tensor1` by :attr:`tensor2`, + multiply the result by the scalar :attr:`value` and add it to :attr:`input`. + + .. math:: + \text{out}_i = \text{input}_i + \text{value} \times \frac{\text{tensor1}_i}{\text{tensor2}_i} + + + The shapes of :attr:`input`, :attr:`tensor1`, and :attr:`tensor2` must be + `broadcastable`. + + For inputs of type `FloatTensor` or `DoubleTensor`, :attr:`value` must be + a real number, otherwise an integer. + + Args: + input (Tensor): the tensor to be added + tensor1 (Tensor): the numerator tensor + tensor2 (Tensor): the denominator tensor + + Keyword args: + value (Number, optional): multiplier for :math:`\text{{tensor1}} / \text{{tensor2}}` + + Example:: + + >>> import oneflow as flow + >>> input = flow.tensor([ 0.3810, 1.2774, -0.2972, -0.3719]) + >>> tensor1 = flow.tensor([0.8032, 0.2930, -0.8113, -0.2308]) + >>> tensor2 = flow.tensor([[0.5], [1]]) + >>> output = flow.addcdiv(input, tensor1, tensor2) + >>> output.shape + oneflow.Size([2, 4]) + """, +) diff --git a/python/oneflow/framework/docstr/amax.py b/python/oneflow/framework/docstr/amax.py index 3140727040748fa264eb20e0416828ce99b888b4..d4df5fbb1de1ffb54e9db354d22be1915f054b8b 100644 --- a/python/oneflow/framework/docstr/amax.py +++ b/python/oneflow/framework/docstr/amax.py @@ -22,7 +22,9 @@ add_docstr( """ oneflow.amax(input, dim=None, keepdim=False) -> Tensor - This function is equivalent to PyTorch’s amax function. It returns the maximum along a dimension. + Returns the maximum along a dimension. + + This function is equivalent to PyTorch’s amax function. Args: input (oneflow.Tensor): the input Tensor. diff --git a/python/oneflow/framework/docstr/amin.py b/python/oneflow/framework/docstr/amin.py index 2fdf98110c9c316c1c3aaa63836a1c9b98cd9a41..7ddb623135793bd7ac83ddd36a90be9412ce7f5f 100644 --- a/python/oneflow/framework/docstr/amin.py +++ b/python/oneflow/framework/docstr/amin.py @@ -21,13 +21,13 @@ add_docstr( """ amin(input, dim, keepdim=False) -> Tensor - This function is equivalent to PyTorch’s amin function. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.amin.html. - Returns the minimum value of each slice of the `input` tensor in the given dimension(s) `dim`. If `keepdim` is `True`, the output tensor is of the same size as `input` except in the dimension(s) `dim` where it is of size 1. Otherwise, `dim` is squeezed (see :func:`oneflow.squeeze`), resulting in the output tensor having 1 (or `len(dim)`) fewer dimension(s). + This function is equivalent to PyTorch’s amin function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.amin.html. + Parameters: input (oneflow.Tensor): the input Tensor. dim (int, Tuple[int]): the dimension or dimensions to reduce. diff --git a/python/oneflow/framework/docstr/array_ops.py b/python/oneflow/framework/docstr/array_ops.py index 20e5fb8853eaa220c59f5d6538435e20b176c54c..e44d69b8ee18bb229a1d8223a604eecc9f8d276e 100644 --- a/python/oneflow/framework/docstr/array_ops.py +++ b/python/oneflow/framework/docstr/array_ops.py @@ -268,6 +268,116 @@ add_docstr( """, ) +add_docstr( + oneflow.atleast_1d, + r""" + oneflow.atleast_1d(*tensors) -> Tensor or List[Tensor] + + Returns a 1-dimensional view of each input tensor with zero dimensions. Input tensors with one or more dimensions are returned as-is. + + The interface is consistent with PyTorch. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.atleast_1d.html. + + Args: + tensors (List[oneflow.Tensor] or oneflow.Tensor): Tensor or list of tensors to be reshaped + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.randn(1) + >>> flow.atleast_1d(x).shape + oneflow.Size([1]) + >>> x = flow.tensor(0) + >>> x.shape + oneflow.Size([]) + >>> flow.atleast_1d(x).shape + oneflow.Size([1]) + + """, +) + +add_docstr( + oneflow.atleast_2d, + r""" + oneflow.atleast_2d(*tensors) -> Tensor or List[Tensor] + + Returns a 2-dimensional view of each input tensor with zero dimensions. Input tensors with two or more dimensions are returned as-is. + + The interface is consistent with PyTorch. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.atleast_2d.html. + + + Args: + tensors (List[oneflow.Tensor] or oneflow.Tensor): Tensor or list of tensors to be reshaped + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor(0) + >>> x.shape + oneflow.Size([]) + >>> flow.atleast_2d(x).shape + oneflow.Size([1, 1]) + >>> x = flow.randn(3) + >>> flow.atleast_2d(x).shape + oneflow.Size([1, 3]) + >>> x = flow.randn(3, 3) + >>> flow.atleast_2d(x).shape + oneflow.Size([3, 3]) + + """, +) + +add_docstr( + oneflow.atleast_3d, + r""" + oneflow.atleast_3d(*tensors) -> Tensor or List[Tensor] + + Returns a 3-dimensional view of each input tensor with zero dimensions. Input tensors with three or more dimensions are returned as-is. + + The interface is consistent with PyTorch. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.atleast_3d.html. + + Args: + tensors (List[oneflow.Tensor] or oneflow.Tensor): Tensor or list of tensors to be reshaped + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor(0) + >>> flow.atleast_3d(x).shape + oneflow.Size([1, 1, 1]) + >>> x = flow.randn(3) + >>> flow.atleast_3d(x).shape + oneflow.Size([1, 3, 1]) + >>> x = flow.randn(3, 4) + >>> flow.atleast_3d(x).shape + oneflow.Size([3, 4, 1]) + >>> x = flow.randn(3, 4, 5) + >>> flow.atleast_3d(x).shape + oneflow.Size([3, 4, 5]) + + """, +) + add_docstr( oneflow.stack, r"""Concatenates a sequence of tensors along a new dimension. @@ -299,6 +409,176 @@ add_docstr( """, ) +add_docstr( + oneflow.hstack, + r""" + oneflow.hstack(tensors) -> Tensor + + Stack tensors in :attr:`tensors` horizontally (column wise). + + This is equivalent to concatenation tensors in :attr:`tensors` along the first axis for 1-D tensors, and along the second axis for all other tensors. + + When there are tensors with dimension less than 1, these tensors will be reshaped by ``oneflow.atleast_1d()`` to 1-dims tensors before stacking. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.hstack.html. + + Args: + tensors: (List[oneflow.Tensor]): sequence of tensors to stack + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.randn(5, 2) + >>> x2 = flow.randn(5, 3) + >>> flow.hstack([x1, x2]).shape + oneflow.Size([5, 5]) + >>> x = flow.randn(5) + >>> flow.hstack([x, x]).shape + oneflow.Size([10]) + """, +) + +add_docstr( + oneflow.vstack, + r""" + oneflow.vstack(tensors) -> Tensor + + Stack tensors in :attr:`tensors` vertically (row wise). + + This is equivalent to concatenation tensors in :attr:`tensors` along the first axis. + + When there are tensors with dimension less than 2, these tensors will be reshaped by ``oneflow.atleast_2d()`` to 2-D tensors before stacking. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.vstack.html. + + Args: + tensors: (List[oneflow.Tensor]): sequence of tensors to stack + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.randn(2, 5) + >>> x2 = flow.randn(3, 5) + >>> flow.vstack([x1, x2]).shape + oneflow.Size([5, 5]) + >>> x = flow.randn(5) + >>> flow.vstack([x, x]).shape + oneflow.Size([2, 5]) + """, +) + +add_docstr( + oneflow.dstack, + r""" + oneflow.dstack(tensors) -> Tensor + + Stack tensors in :attr:`tensors` depthwish (along third axis). + + This is equivalent to concatenation tensors in :attr:`tensors` along the third axis after 1-D and 2-D tensors have been reshaped by ``oneflow.atleast_3d()``. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.dstack.html. + + Args: + tensors: (List[oneflow.Tensor]): sequence of tensors to stack + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.randn(2, 3, 4) + >>> x2 = flow.randn(2, 3, 2) + >>> flow.dstack([x1, x2]).shape + oneflow.Size([2, 3, 6]) + >>> x = flow.randn(6, 4) + >>> flow.dstack([x, x]).shape + oneflow.Size([6, 4, 2]) + """, +) + +add_docstr( + oneflow.column_stack, + r""" + oneflow.column_stack(tensors) -> Tensor + + Creates a new tensor by horizontally stacking the tensors in :attr:`tensors`. + + Equivalent to :code:`oneflow.hstack(tensors)`, tensors with dimensions less than 2 will be reshaped to :code:`(t.numel(), 1)` before being stacked horizontally. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.column_stack.html. + + Args: + tensors: (List[oneflow.Tensor]): sequence of tensors to stack + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.randn(5) + >>> x2 = flow.randn(5) + >>> flow.column_stack([x1, x2]).shape + oneflow.Size([5, 2]) + >>> x1 = flow.randn(2, 5) + >>> x2 = flow.randn(2, 2) + >>> flow.column_stack([x1, x2]).shape + oneflow.Size([2, 7]) + + """, +) + +add_docstr( + oneflow.row_stack, + r""" + oneflow.row_stack(tensors) -> Tensor + + Alias of ``oneflow.vstack()``. + + Stack tensors in :attr:`tensors` vertically (row wise). + + This is equivalent to concatenation tensors in :attr:`tensors` along the first axis. + + When there are tensors with dimension less than 2, these tensors will be reshaped by ``oneflow.atleast_2d()`` to 2-D tensors before stacking. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.row_stack.html. + + Args: + tensors: (List[oneflow.Tensor]): sequence of tensors to stack + + Returns: + A `Tensor` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.randn(2, 5) + >>> x2 = flow.randn(3, 5) + >>> flow.vstack([x1, x2]).shape + oneflow.Size([5, 5]) + >>> x = flow.randn(5) + >>> flow.vstack([x, x]).shape + oneflow.Size([2, 5]) + """, +) + add_docstr( oneflow.squeeze, r"""This operator removes the specified dimention which size is 1 of the input Tensor. @@ -441,3 +721,41 @@ add_docstr( """, ) + +add_docstr( + oneflow.bincount, + r"""oneflow.bincount(input, weights=None, minlength=0) → Tensor + + The interface is consistent with PyTorch. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.bincount.html. + + Count the frequency of each value in an array of non-negative ints. + + The number of bins (size 1) is one larger than the largest value in ``input`` unless ``input`` is empty, + in which case the result is a tensor of size 0. If ``minlength`` is specified, + the number of bins is at least ``minlength`` and if ``input`` is empty, + then the result is tensor of size ``minlength`` filled with zeros. + If ``n`` is the value at position ``i``, ``out[n] += weights[i]`` if ``weights`` is specified else ``out[n] += 1``. + + Args: + input (oneflow.Tensor): 1-d int Tensor + weights (oneflow.Tensor): optional, weight for each value in the input tensor. Should be of same size as input tensor. + minlength (int): optional, minimum number of bins. Should be non-negative. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor([1, 2, 4, 6]) + >>> flow.bincount(x) + tensor([0, 1, 1, 0, 1, 0, 1], dtype=oneflow.int64) + >>> x = flow.tensor([1, 2, 1]) + >>> weights = flow.tensor([0.1, 0.2, 0.15]) + >>> flow.bincount(x, weights=weights) + tensor([0.0000, 0.2500, 0.2000], dtype=oneflow.float32) + >>> flow.bincount(x, weights=weights, minlength=4) + tensor([0.0000, 0.2500, 0.2000, 0.0000], dtype=oneflow.float32) + """, +) diff --git a/python/oneflow/framework/docstr/as_tensor.py b/python/oneflow/framework/docstr/as_tensor.py index d4a1e9db8d58d0bbc09eaab00e16d646cc723ce4..11a9b73ff438d45e30d380f8b0a41902c71b1eef 100644 --- a/python/oneflow/framework/docstr/as_tensor.py +++ b/python/oneflow/framework/docstr/as_tensor.py @@ -20,8 +20,6 @@ add_docstr( oneflow.as_tensor, r""" as_tensor(data, dtype=None, device=None) -> Tensor - - The interface is consistent with PyTorch. Converts data into a tensor, sharing data and preserving autograd history if possible. @@ -29,6 +27,8 @@ add_docstr( If data is a NumPy array (an ndarray) with the same dtype and device then a tensor is constructed using oneflow.from_numpy. + The interface is consistent with PyTorch. + Args: data (array_like): Initial data for the tensor. Can be a list, tuple, NumPy ``ndarray``, scalar, and other types. dtype (oneflow.dtype, optional): the desired data type of returned tensor. Default: if ``None``, infers data type from data. diff --git a/python/oneflow/framework/docstr/autograd.py b/python/oneflow/framework/docstr/autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..25bc511823b88b9d7b5761efb16f6e26026a14e7 --- /dev/null +++ b/python/oneflow/framework/docstr/autograd.py @@ -0,0 +1,31 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr +from oneflow._oneflow_internal.autograd.Function import FunctionCtx + +add_docstr( + FunctionCtx.saved_tensors, "Get saved tensors in ctx.", +) + +add_docstr( + FunctionCtx.save_for_backward, + "Saves given tensors for a future call to ``backward()``.", +) + +add_docstr( + FunctionCtx.mark_non_differentiable, "Marks outputs as non-differentiable.", +) diff --git a/python/oneflow/framework/docstr/baddbmm.py b/python/oneflow/framework/docstr/baddbmm.py new file mode 100644 index 0000000000000000000000000000000000000000..9aaa38918ff40ef480d1b4b51248fd4c03d7c7c4 --- /dev/null +++ b/python/oneflow/framework/docstr/baddbmm.py @@ -0,0 +1,67 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.baddbmm, + r""" + baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=None) -> Tensor + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.baddbmm.html. + + Performs a batch matrix-matrix product of matrices in :attr:`batch1` and :attr:`batch2`. + :attr:`input` is added to the final result. + + :attr:`batch1` and :attr:`batch2` must be 3-D tensors each containing the same + number of matrices. + + If :attr:`batch1` is a :math:`(b \times n \times m)` tensor, :attr:`batch2` is a + :math:`(b \times m \times p)` tensor, then :attr:`input` must be + broadcastable with a + :math:`(b \times n \times p)` tensor and :attr:`out` will be a + :math:`(b \times n \times p)` tensor. + + .. math:: + \text{out}_i = \beta\ \text{input}_i + \alpha\ (\text{batch1}_i \mathbin{@} \text{batch2}_i) + + If :attr:`beta` is 0, then :attr:`input` will be ignored, and `nan` and `inf` in it will not be propagated. + + For inputs of type `FloatTensor` or `DoubleTensor`, arguments :attr:`beta` and + :attr:`alpha` must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): the tensor to be added + batch1 (Tensor): the first batch of matrices to be multiplied + batch2 (Tensor): the second batch of matrices to be multiplied + + Keyword args: + beta (Number, optional): multiplier for :attr:`input` (:math:`\beta`) + alpha (Number, optional): multiplier for :math:`\text{{batch1}} \mathbin{{@}} \text{{batch2}}` (:math:`\alpha`) + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> input = flow.randn(10, 3, 5) + >>> batch1 = flow.randn(10, 3, 4) + >>> batch2 = flow.randn(10, 4, 5) + >>> of_out = flow.baddbmm(input, batch1, batch2) + >>> of_out.shape + oneflow.Size([10, 3, 5]) + """, +) diff --git a/python/oneflow/framework/docstr/clamp.py b/python/oneflow/framework/docstr/clamp.py index f5d350631b72d6b243684bddc1c2a7a391830c75..f19fa77c6ab96fac95050a9dc35429ef74cc41a0 100644 --- a/python/oneflow/framework/docstr/clamp.py +++ b/python/oneflow/framework/docstr/clamp.py @@ -66,6 +66,88 @@ add_docstr( """, ) +add_docstr( + oneflow.clamp_min, + """ + Clamp all elements in :attr:`input` which are less than :attr:`min` to :attr:`min` and return + a resulting tensor: + + .. math:: + y_i = \max(min, x_i) + + If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, args :attr:`min` + must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): the input tensor. + min (Number): lower-bound of the range to be clamped to. + out (Tensor, optional): the output tensor. + + For example: + + + .. code-block:: python + + >>> import oneflow as flow + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_min(input, min=-0.5) + >>> output + tensor([ 0.2000, 0.6000, -0.5000, -0.3000], dtype=oneflow.float32) + + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_min(input, min=-2) + >>> output + tensor([ 0.2000, 0.6000, -1.5000, -0.3000], dtype=oneflow.float32) + + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_min(input, min=1) + >>> output + tensor([1., 1., 1., 1.], dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow.clamp_max, + """ + Clamp all elements in :attr:`input` which are greater than :attr:`max` to :attr:`max` and return + a resulting tensor: + + .. math:: + y_i = \min(max, x_i) + + If :attr:`input` is of type `FloatTensor` or `DoubleTensor`, args :attr:`max` + must be real numbers, otherwise they should be integers. + + Args: + input (Tensor): the input tensor. + max (Number): upper-bound of the range to be clamped to. + out (Tensor, optional): the output tensor. + + For example: + + + .. code-block:: python + + >>> import oneflow as flow + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_max(input, max=-0.5) + >>> output + tensor([-0.5000, -0.5000, -1.5000, -0.5000], dtype=oneflow.float32) + + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_max(input, max=-2) + >>> output + tensor([-2., -2., -2., -2.], dtype=oneflow.float32) + + >>> input = flow.Tensor([0.2, 0.6, -1.5, -0.3]) + >>> output = flow.clamp_max(input, max=1) + >>> output + tensor([ 0.2000, 0.6000, -1.5000, -0.3000], dtype=oneflow.float32) + + """, +) + add_docstr( oneflow.clip, """ diff --git a/python/oneflow/framework/docstr/comparison.py b/python/oneflow/framework/docstr/comparison.py index b83eed022f5244419edcaf129318bffc66066955..6c4f82aba29574306feee28dbae16e881e40e239 100644 --- a/python/oneflow/framework/docstr/comparison.py +++ b/python/oneflow/framework/docstr/comparison.py @@ -105,6 +105,40 @@ add_docstr( """, ) +add_docstr( + oneflow.equal, + """equal(input, other) -> bool + + `True` if two tensors have the same size and elements, `False` otherwise. + + Args: + input (oneflow.Tensor): the tensor to compare + other (oneflow.Tensor): the target to compare + + Returns: + A boolean value + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + + >>> input = flow.tensor(np.array([2, 3, 4, 5]), dtype=flow.float32) + >>> other = flow.tensor(np.array([2, 3, 4, 1]), dtype=flow.float32) + + >>> y = flow.equal(input, other) + >>> y + False + + >>> y = flow.equal(input, input) + >>> y + True + + """, +) + add_docstr( oneflow.ne, """ne(input, other) -> Tensor @@ -197,3 +231,83 @@ add_docstr( """, ) + +add_docstr( + oneflow.isclose, + r"""isclose(input, other, atol=1e-08, rtol=1e-05, equal_nan=False) -> Tensor + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.isclose.html + + Returns a new tensor with boolean elements representing if each element of + :attr:`input` is "close" to the corresponding element of :attr:`other`. + Closeness is defined as: + + .. math:: + \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert + + Args: + input (oneflow.Tensor): first tensor to compare + other (oneflow.Tensor): second tensor to compare + atol (float, optional): absolute tolerance. Default: 1e-08 + rtol (float, optional): relative tolerance. Default: 1e-05 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Returns: + oneflow.Tensor: A Tensor with bool type. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + + >>> flow.isclose(flow.tensor((1., 2, 3)), flow.tensor((1 + 1e-10, 3, 4))) + tensor([ True, False, False], dtype=oneflow.bool) + + >>> flow.isclose(flow.tensor((float('inf'), 4)), flow.tensor((float('inf'), 6)), rtol=.5) + tensor([True, True], dtype=oneflow.bool) + + """, +) + +add_docstr( + oneflow.allclose, + r"""allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.allclose.html + + This function checks if :attr:`input` and :attr:`other` satisfy the condition: + + .. math:: + \lvert \text{input} - \text{other} \rvert \leq \texttt{atol} + \texttt{rtol} \times \lvert \text{other} \rvert + + elementwise, for all elements of :attr:`input` and :attr:`other`. The behaviour of this function is analogous to + `numpy.allclose `_ + + Args: + input (oneflow.Tensor): first tensor to compare + other (oneflow.Tensor): second tensor to compare + atol (float, optional): absolute tolerance. Default: 1e-08 + rtol (float, optional): relative tolerance. Default: 1e-05 + equal_nan (bool, optional): if ``True``, then two ``NaN`` s will be considered equal. Default: ``False`` + + Returns: + oneflow.Tensor: A Tensor with bool type. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + + >>> flow.allclose(flow.tensor([10000., 1e-07]), flow.tensor([10000.1, 1e-08])) + False + >>> flow.allclose(flow.tensor([10000., 1e-08]), flow.tensor([10000.1, 1e-09])) + True + >>> flow.allclose(flow.tensor([1.0, float('nan')]), flow.tensor([1.0, float('nan')])) + False + >>> flow.allclose(flow.tensor([1.0, float('nan')]), flow.tensor([1.0, float('nan')]), equal_nan=True) + True + + """, +) diff --git a/python/oneflow/framework/docstr/constant.py b/python/oneflow/framework/docstr/constant.py index 6d84b58cc00be5e259d3d657a655d853d6dbfa61..b72d05278662e7bc498b649f46a8a630423e4417 100644 --- a/python/oneflow/framework/docstr/constant.py +++ b/python/oneflow/framework/docstr/constant.py @@ -19,11 +19,21 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.ones_like, """ + ones_like(input, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.ones_like.html. + Returns a tensor filled with the scalar value 1, with the same size as input. flow.ones_like(input) is equivalent to flow.ones(input.shape, dtype=input.dtype) Args: - other(Tensor): The size of input will determine size of the output tensor. + input(Tensor): The size of input will determine size of the output tensor. + dtype (flow.dtype, optional): the desired type of returned tensor. Default: if None, same flow.dtype as this tensor. + device (flow.device, optional): the desired device of returned tensor. Default: if None, same flow.device as this tensor. + placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: @@ -42,11 +52,21 @@ add_docstr( add_docstr( oneflow.zeros_like, """ + zeros_like(input, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.zeros_like.html. + Returns a tensor filled with the scalar value 0, with the same size as input. flow.zeros_like(input) is equivalent to flow.zeros(input.shape, dtype=input.dtype) Args: - other(Tensor): The size of input will determine size of the output tensor. + input(Tensor): The size of input will determine size of the output tensor. + dtype (flow.dtype, optional): the desired type of returned tensor. Default: if None, same flow.dtype as this tensor. + device (flow.device, optional): the desired device of returned tensor. Default: if None, same flow.device as this tensor. + placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: @@ -67,6 +87,9 @@ add_docstr( """ new_ones(x, size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.new_ones.html. + Returns a Tensor of size size filled with 1. By default, the returned Tensor has the same oneflow.dtype and oneflow.device as this tensor. Args: @@ -91,3 +114,79 @@ add_docstr( [1., 1.]], dtype=oneflow.float32) """, ) + +add_docstr( + oneflow.empty, + """ + empty(*size, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False, pin_memory=False) -> Tensor + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.empty.html. + + Returns a tensor filled with uninitialized data. + The shape of the tensor is defined by the variable argument ``size``. + + Args: + size (int... or oneflow.Size): Defining the shape of the output tensor. + Can be a variable number of arguments or a collection like a list or tuple or oneflow.Size. + dtype (flow.dtype, optional): The desired data type of returned tensor. Default: ``flow.float32``. + device (oneflow.device, optional): The desired device of returned local tensor. If None, uses the + current device. + placement (flow.placement, optional): The desired device of returned global tensor. If None, will + construct local tensor. + sbp (flow.sbp or List[flow.sbp], optional): The desired sbp of returned global tensor. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> y = flow.empty(4, 5) # construct local empty tensor + >>> y.shape + oneflow.Size([4, 5]) + >>> y.is_global + False + >>> placement = flow.placement("cpu", ranks=[0]) + >>> y = flow.empty(4, 5, placement=placement, sbp=flow.sbp.broadcast) # construct consistent empty tensor + >>> y.is_global + True + + """, +) + +add_docstr( + oneflow.empty_like, + """ + empty_like(input, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.empty_like.html. + + Returns an uninitialized tensor with the same size as :attr:`input`. + ``oneflow.empty_like(input)`` is equivalent to + ``oneflow.empty(input.size(), dtype=input.dtype, device=input.device)``. + + Args: + input(Tensor): The size of input will determine size of the output tensor. + dtype (flow.dtype, optional): The desired data type of returned tensor. Default: ``flow.float32``. + device (oneflow.device, optional): The desired device of returned local tensor. If None, uses the + current device. + placement (flow.placement, optional): The desired device of returned global tensor. If None, will + construct local tensor. + sbp (flow.sbp or List[flow.sbp], optional): The desired sbp of returned global tensor. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.randn(2, 3) + >>> y = flow.empty_like(x) + >>> y.shape + oneflow.Size([2, 3]) + + """, +) diff --git a/python/oneflow/framework/docstr/conv.py b/python/oneflow/framework/docstr/conv.py index d1be5c1c4fcc048a607fe7b245e6f4d2618549f9..14eebb3b69146f8f87f1ddcdf323054fb5ddbba2 100644 --- a/python/oneflow/framework/docstr/conv.py +++ b/python/oneflow/framework/docstr/conv.py @@ -21,11 +21,11 @@ add_docstr( r""" conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv1d.html. - Applies a 1D convolution over an input signal composed of several input planes. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv1d.html. + See :class:`~oneflow.nn.Conv1d` for details and output shape. Args: @@ -58,11 +58,11 @@ add_docstr( r""" conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv2d.html. - Applies a 2D convolution over an input image composed of several input planes. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv2d.html. + See :class:`~oneflow.nn.Conv2d` for details and output shape. Args: @@ -96,11 +96,10 @@ add_docstr( r""" conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv3d.html. - Applies a 3D convolution over an input image composed of several input planes. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv3d.html. See :class:`~oneflow.nn.Conv3d` for details and output shape. diff --git a/python/oneflow/framework/docstr/convolution.py b/python/oneflow/framework/docstr/convolution.py new file mode 100644 index 0000000000000000000000000000000000000000..ee0e3d5950a97d064d148ec5b9f7f78f7f8c1fff --- /dev/null +++ b/python/oneflow/framework/docstr/convolution.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.nn.functional.fold, + r""" + fold(input, output_size, kernel_size, dilation=1, padding=0, stride=1) + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.fold.html. + + Combines an array of sliding local blocks into a large containing tensor. + + .. warning:: + Currently, only 3-D input tensors (batched image-like tensors) are supported, and only unbatched (3D) + or batched (4D) image-like output tensors are supported. + + See :class:`oneflow.nn.Fold` for details. + """, +) + +add_docstr( + oneflow.nn.functional.unfold, + r""" + unfold(input, kernel_size, dilation=1, padding=0, stride=1) + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.unfold.html. + + Extracts sliding local blocks from a batched input tensor. + + .. warning:: + Currently, only 4-D input tensors (batched image-like tensors) are supported. + + .. warning:: + + More than one element of the unfolded tensor may refer to a single + memory location. As a result, in-place operations (especially ones that + are vectorized) may result in incorrect behavior. If you need to write + to the tensor, please clone it first. + + + See :class:`oneflow.nn.Unfold` for details. + """, +) diff --git a/python/oneflow/framework/docstr/ctc_decode.py b/python/oneflow/framework/docstr/ctc_decode.py index 72ccf573e79861c49d14bd9b46007e540f935efc..0a9b4d21ef800591f67eda4a2c34380b907e406c 100644 --- a/python/oneflow/framework/docstr/ctc_decode.py +++ b/python/oneflow/framework/docstr/ctc_decode.py @@ -18,7 +18,10 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow._C.ctc_greedy_decoder, - """Performs greedy decoding on the logits given in input (best path). + """ + ctc_greedy_decoder(log_probs: Tensor, input_lengths: Tensor, merge_repeated: bool=True) -> Tensor + + Performs greedy decoding on the logits given in input (best path). Args: log_probs(oneflow.Tensor): A Tensor of shape [input_length, batch_size, num_labels]. The logarithmized probabilities of the outputs (e.g. obtained with flow.nn.logsoftmax()). diff --git a/python/oneflow/framework/docstr/deconv.py b/python/oneflow/framework/docstr/deconv.py index 2b2219595d946960bbd41804726fc6451ece45fa..9578899af0a922bbda6099177afce1cf842c468e 100644 --- a/python/oneflow/framework/docstr/deconv.py +++ b/python/oneflow/framework/docstr/deconv.py @@ -21,9 +21,9 @@ add_docstr( r""" conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.functional.conv_transpose1d.html - Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called “deconvolution”. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv_transpose1d.html See :class:`~oneflow.nn.ConvTranspose1d` for details and output shape. @@ -57,10 +57,10 @@ add_docstr( r""" conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.functional.conv_transpose3d.html - Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv_transpose3d.html + See :class:`~oneflow.nn.ConvTranspose2d` for details and output shape. Args: @@ -93,10 +93,10 @@ add_docstr( r""" conv_transpose3d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.functional.conv_transpose3d.html - Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv_transpose3d.html + See :class:`~oneflow.nn.ConvTranspose3d` for details and output shape. Args: diff --git a/python/oneflow/framework/docstr/distance.py b/python/oneflow/framework/docstr/distance.py index ef7f4385eda182aaa0652b959264d6520ff4710f..cdc259ad2d1bc5c3d760da41f9190f1642d36e8c 100644 --- a/python/oneflow/framework/docstr/distance.py +++ b/python/oneflow/framework/docstr/distance.py @@ -19,15 +19,15 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow._C.cosine_similarity, r""" - cosine_similarity(x1, x2, dim=1, eps=1e-8) -> Tensor - - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.functional.cosine_similarity.html#torch.nn.functional.cosine_similarity + cosine_similarity(x1: Tensor, x2: Tensor, dim: int=1, eps: float=1e-8) -> Tensor Returns cosine similarity between ``x1`` and ``x2``, computed along dim. ``x1`` and ``x2`` must be broadcastable to a common shape. ``dim`` refers to the dimension in this common shape. Dimension ``dim`` of the output is squeezed (see :func:`oneflow.squeeze`), resulting in the output tensor having 1 fewer dimension. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.cosine_similarity.html + .. math :: \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)} @@ -49,3 +49,38 @@ add_docstr( >>> output = F.cosine_similarity(input1, input2) """, ) + +add_docstr( + oneflow._C.pairwise_distance, + r""" + pairwise_distance(x1: Tensor, x2: Tensor, dim: float=2.0, eps: float=1e-6, keepdim: bool=False) -> Tensor + Computes the pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm: + + .. math :: + \left \| x \right \| _p = (\sum_{i=1}^n \left | x_i \right |^p )^{\frac{1}{p}} + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.PairwiseDistance.html. + + Args: + x1 (Tensor): First input. + x2 (Tensor): Second input. + p (real): the norm degree. Default: 2 + eps (float, optional): Small value to avoid division by zero. Default: 1e-6 + keepdim (bool, optional): Determines whether or not to keep the vector dimension. Default: False + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x1 = flow.arange(12).reshape(3, 4) + >>> x2 = flow.arange(12).reshape(3, 4) + >>> output = flow.nn.functional.pairwise_distance(x1, x2, p=2) + >>> output + tensor([2.0000e-06, 2.0000e-06, 2.0000e-06], dtype=oneflow.float32) + >>> output.shape + oneflow.Size([3]) + + """, +) diff --git a/python/oneflow/framework/docstr/dropout.py b/python/oneflow/framework/docstr/dropout.py index 3c1318bd11716ed39480842fa581625e48804eb9..13bba47356479a53f74d00e15ece44576fac6324 100644 --- a/python/oneflow/framework/docstr/dropout.py +++ b/python/oneflow/framework/docstr/dropout.py @@ -21,13 +21,13 @@ add_docstr( """ dropout(x: Tensor, p: float = 0.5, training: bool = True, generator :Generator = None, *, addend: Tensor) -> Tensor - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout.html. - During training, randomly zeroes some of the elements of the input tensor with probability :attr:`p` using samples from a Bernoulli distribution. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout.html. + Args: x(Tensor): A Tensor which will be applyed dropout. p(float): probability of an element to be zeroed. Default: 0.5 @@ -97,3 +97,329 @@ add_docstr( """, ) + +add_docstr( + oneflow._C.dropout1d, + r""" + dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout1d.html. + + Randomly zero out entire channels (a channel is a 1D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 1D tensor :math:`\text{input}[i, j]`) of the input tensor). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + See :class:`~oneflow.nn.Dropout1d` for details. + + Args: + p: probability of a channel to be zeroed. Default: 0.5 + training: apply dropout if is ``True``. Default: ``True`` + """, +) + +add_docstr( + oneflow._C.dropout2d, + r""" + dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout2d.html. + + Randomly zero out entire channels (a channel is a 2D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 2D tensor :math:`\text{input}[i, j]`) of the input tensor). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + See :class:`~oneflow.nn.Dropout2d` for details. + + Args: + p: probability of a channel to be zeroed. Default: 0.5 + training: apply dropout if is ``True``. Default: ``True`` + """, +) + +add_docstr( + oneflow._C.dropout3d, + r""" + dropout1d(x: Tensor, p: float = 0.5, training: bool = True) -> Tensor + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.dropout3d.html. + + Randomly zero out entire channels (a channel is a 3D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 3D tensor :math:`\text{input}[i, j]`) of the input tensor). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + See :class:`~oneflow.nn.Dropout3d` for details. + + Args: + p: probability of a channel to be zeroed. Default: 0.5 + training: apply dropout if is ``True``. Default: ``True`` + """, +) + +add_docstr( + oneflow.nn.Dropout, + """ + During training, randomly zeroes some of the elements of the input + tensor with probability :attr:`p` using samples from a Bernoulli + distribution. Each channel will be zeroed out independently on every forward + call. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Dropout.html. + + This has proven to be an effective technique for regularization and + preventing the co-adaptation of neurons as described in the paper + "Improving neural networks by preventing co-adaptation of feature + detectors". + + Furthermore, the outputs are scaled by a factor of :math:`\\frac{1}{1-p}` during + training. This means that during evaluation the module simply computes an + identity function. + + Additionally, we can pass an extra Tensor `addend` which shape is consistent with input Tensor. + The `addend` Tensor will be add in result after dropout, it is very useful in model's residual connection structure. + + Args: + p: probability of an element to be zeroed. Default: 0.5 + inplace: If set to ``True``, will do this operation in-place. Default: ``False`` + generator: A pseudorandom number generator for sampling + + Shape: + - Input: :math:`(*)`. Input can be of any shape + - Output: :math:`(*)`. Output is of the same shape as input + + For example: + + example 1: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> m = flow.nn.Dropout(p=0) + >>> arr = np.array( + ... [ + ... [-0.7797, 0.2264, 0.2458, 0.4163], + ... [0.4299, 0.3626, -0.4892, 0.4141], + ... [-1.4115, 1.2183, -0.5503, 0.6520], + ... ] + ... ) + >>> x = flow.Tensor(arr) + >>> y = m(x) + >>> y #doctest: +ELLIPSIS + tensor([[-0.7797, 0.2264, 0.2458, 0.4163], + [ 0.4299, 0.3626, -0.4892, 0.4141], + [-1.4115, 1.2183, -0.5503, 0.6520]], dtype=oneflow.float32) + + example 2: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> m = flow.nn.Dropout(p=0) + >>> arr = np.array( + ... [ + ... [-0.7797, 0.2264, 0.2458, 0.4163], + ... [0.4299, 0.3626, -0.4892, 0.4141], + ... [-1.4115, 1.2183, -0.5503, 0.6520], + ... ] + ... ) + >>> x = flow.Tensor(arr) + >>> addend = flow.ones((3, 4), dtype=flow.float32) + >>> y = m(x, addend=addend) + >>> y #doctest: +ELLIPSIS + tensor([[ 0.2203, 1.2264, 1.2458, 1.4163], + [ 1.4299, 1.3626, 0.5108, 1.4141], + [-0.4115, 2.2183, 0.4497, 1.6520]], dtype=oneflow.float32) + + .. _Improving neural networks by preventing co-adaptation of feature + detectors: https://arxiv.org/abs/1207.0580 + """, +) + +add_docstr( + oneflow.nn.Dropout1d, + """ + Randomly zero out entire channels (a channel is a 1D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 1D tensor :math:`\text{input}[i, j]`). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Dropout1d.html. + + Usually the input comes from :class:`nn.Conv1d` modules. + + As described in the paper + `Efficient Object Localization Using Convolutional Networks`_ , + if adjacent pixels within feature maps are strongly correlated + (as is normally the case in early convolution layers) then i.i.d. dropout + will not regularize the activations and will otherwise just result + in an effective learning rate decrease. + + In this case, :func:`oneflow.nn.Dropout1d` will help promote independence between + feature maps and should be used instead. + + Args: + p (float, optional): probability of an element to be zero-ed. + inplace (bool, optional): If set to ``True``, will do this operation + in-place + + Shape: + - Input: :math:`(N, C, L)` or :math:`(C, L)`. + - Output: :math:`(N, C, L)` or :math:`(C, L)` (same shape as input). + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> m = flow.nn.Dropout1d(p=0) + >>> arr = np.array( + ... [ + ... [-0.7797, 0.2264, 0.2458, 0.4163], + ... [0.4299, 0.3626, -0.4892, 0.4141], + ... [-1.4115, 1.2183, -0.5503, 0.6520], + ... ] + ... ) + >>> x = flow.Tensor(arr) + >>> y = m(x) + >>> y #doctest: +ELLIPSIS + tensor([[-0.7797, 0.2264, 0.2458, 0.4163], + [ 0.4299, 0.3626, -0.4892, 0.4141], + [-1.4115, 1.2183, -0.5503, 0.6520]], dtype=oneflow.float32) + + .. _Efficient Object Localization Using Convolutional Networks: + https://arxiv.org/abs/1411.4280 + """, +) + +add_docstr( + oneflow.nn.Dropout2d, + """ + Randomly zero out entire channels (a channel is a 2D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 2D tensor :math:`\text{input}[i, j]`). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Dropout2d.html. + + Usually the input comes from :class:`nn.Conv2d` modules. + + As described in the paper + `Efficient Object Localization Using Convolutional Networks`_ , + if adjacent pixels within feature maps are strongly correlated + (as is normally the case in early convolution layers) then i.i.d. dropout + will not regularize the activations and will otherwise just result + in an effective learning rate decrease. + + In this case, :func:`oneflow.nn.Dropout2d` will help promote independence between + feature maps and should be used instead. + + Args: + p (float, optional): probability of an element to be zero-ed. + inplace (bool, optional): If set to ``True``, will do this operation + in-place + + Shape: + - Input: :math:`(N, C, H, W)` or :math:`(C, H, W)`. + - Output: :math:`(N, C, H, W)` or :math:`(C, H, W)` (same shape as input). + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> m = flow.nn.Dropout2d(p=0) + >>> arr = np.array( + ... [ + ... [-0.7797, 0.2264, 0.2458, 0.4163], + ... [0.4299, 0.3626, -0.4892, 0.4141], + ... [-1.4115, 1.2183, -0.5503, 0.6520], + ... ] + ... ) + >>> x = flow.Tensor(arr) + >>> y = m(x) + >>> y #doctest: +ELLIPSIS + tensor([[-0.7797, 0.2264, 0.2458, 0.4163], + [ 0.4299, 0.3626, -0.4892, 0.4141], + [-1.4115, 1.2183, -0.5503, 0.6520]], dtype=oneflow.float32) + + .. _Efficient Object Localization Using Convolutional Networks: + https://arxiv.org/abs/1411.4280 + """, +) + +add_docstr( + oneflow.nn.Dropout3d, + """ + Randomly zero out entire channels (a channel is a 3D feature map, + e.g., the :math:`j`-th channel of the :math:`i`-th sample in the + batched input is a 3D tensor :math:`\text{input}[i, j]`). + Each channel will be zeroed out independently on every forward call with + probability :attr:`p` using samples from a Bernoulli distribution. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Dropout2d.html. + + Usually the input comes from :class:`nn.Conv3d` modules. + + As described in the paper + `Efficient Object Localization Using Convolutional Networks`_ , + if adjacent pixels within feature maps are strongly correlated + (as is normally the case in early convolution layers) then i.i.d. dropout + will not regularize the activations and will otherwise just result + in an effective learning rate decrease. + + In this case, :func:`oneflow.nn.Dropout3d` will help promote independence between + feature maps and should be used instead. + + Args: + p (float, optional): probability of an element to be zeroed. + inplace (bool, optional): If set to ``True``, will do this operation + in-place + + Shape: + - Input: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)`. + - Output: :math:`(N, C, D, H, W)` or :math:`(C, D, H, W)` (same shape as input). + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> m = flow.nn.Dropout3d(p=0) + >>> arr = np.array( + ... [ + ... [-0.7797, 0.2264, 0.2458, 0.4163], + ... [0.4299, 0.3626, -0.4892, 0.4141], + ... [-1.4115, 1.2183, -0.5503, 0.6520], + ... ] + ... ) + >>> x = flow.Tensor(arr) + >>> y = m(x) + >>> y #doctest: +ELLIPSIS + tensor([[-0.7797, 0.2264, 0.2458, 0.4163], + [ 0.4299, 0.3626, -0.4892, 0.4141], + [-1.4115, 1.2183, -0.5503, 0.6520]], dtype=oneflow.float32) + + .. _Efficient Object Localization Using Convolutional Networks: + https://arxiv.org/abs/1411.4280 + """, +) diff --git a/python/oneflow/framework/docstr/hann_window.py b/python/oneflow/framework/docstr/hann_window.py new file mode 100644 index 0000000000000000000000000000000000000000..0e4be6437adda5c26116a295a8959f901f83fdbf --- /dev/null +++ b/python/oneflow/framework/docstr/hann_window.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.hann_window, + r""" + hann_window(window_length, periodic=True, *, device=None, placement=None, sbp=None, dtype=None, requires_grad=False) -> Tensor + + This function is equivalent to PyTorch’s hann_window function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.hann_window.html. + + Hann window function. + + .. math:: + w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = + \sin^2 \left( \frac{\pi n}{N - 1} \right), + + where :math:`N` is the full window size. + + The input :attr:`window_length` is a positive integer controlling the + returned window size. :attr:`periodic` flag determines whether the returned + window trims off the last duplicate value from the symmetric window. Therefore, if :attr:`periodic` is true, the :math:`N` in + above formula is in fact :math:`\text{window_length} + 1`. Also, we always have + ``oneflow.hann_window(L, periodic=True)`` equal to + ``oneflow.hann_window(L + 1, periodic=False)[:-1])``. + + .. note:: + If :attr:`window_length` :math:`=1`, the returned window contains a single value 1. + + Arguments: + window_length (int): the size of returned window + periodic (bool, optional): If True, returns a window to be used as periodic + function. If False, return a symmetric window. + + Keyword args: + dtype (oneflow.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see oneflow.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + device (oneflow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type + placement (oneflow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + Returns: + Tensor: A 1-D tensor of size :math:`(\text{{window_length}},)` containing the window + + """, +) diff --git a/python/oneflow/framework/docstr/index_add.py b/python/oneflow/framework/docstr/index_add.py new file mode 100644 index 0000000000000000000000000000000000000000..625aeb8f8dad4953d7d7a7f061da4d50e4c3548d --- /dev/null +++ b/python/oneflow/framework/docstr/index_add.py @@ -0,0 +1,87 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.Tensor.index_add_, + r""" + index_add_(dim, index, source, *, alpha=1) -> Tensor + + The interface is consistent with PyTorch. + + Accumulate the elements of :attr:`alpha` times ``source`` into the :attr:`self` + tensor by adding to the indices in the order given in :attr:`index`. For example, + if ``dim == 0``, ``index[i] == j``, and ``alpha=-1``, then the ``i``\ th row of + ``source`` is subtracted from the ``j``\ th row of :attr:`self`. + + The :attr:`dim`\ th dimension of ``source`` must have the same size as the + length of :attr:`index` (which must be a vector), and all other dimensions must + match :attr:`self`, or an error will be raised. + + For a 3-D tensor the output is given as:: + + self[index[i], :, :] += alpha * src[i, :, :] # if dim == 0 + self[:, index[i], :] += alpha * src[:, i, :] # if dim == 1 + self[:, :, index[i]] += alpha * src[:, :, i] # if dim == 2 + + Args: + dim (int): dimension along which to index + index (Tensor): indices of ``source`` to select from, + should have dtype either `oneflow.int64` or `oneflow.int32` + source (Tensor): the tensor containing values to add + + Keyword args: + alpha (Number): the scalar multiplier for ``source`` + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.ones(5, 3) + >>> t = flow.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=flow.float) + >>> index = flow.tensor([0, 4, 2]) + >>> x.index_add_(0, index, t) + tensor([[ 2., 3., 4.], + [ 1., 1., 1.], + [ 8., 9., 10.], + [ 1., 1., 1.], + [ 5., 6., 7.]], dtype=oneflow.float32) + >>> x.index_add_(0, index, t, alpha=-1) + tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]], dtype=oneflow.float32) + """, +) + +add_docstr( + oneflow._C.index_add, + r""" + index_add(input, dim, index, source, *, alpha=1, out=None) -> Tensor + + See :meth:`oneflow.Tensor.index_add_` for function description. + """, +) + +add_docstr( + oneflow._C.index_add_, + r""" + index_add_(dim, index, source, *, alpha=1) -> Tensor + + Out-of-place version of :meth:`oneflow.Tensor.index_add_`. + """, +) diff --git a/python/oneflow/framework/docstr/index_select.py b/python/oneflow/framework/docstr/index_select.py index 193c25796ff62ef7f1d0f47a0010993c8b3fe984..79a7c585a03dc236a5c812946c4b2d7f7d0bc772 100644 --- a/python/oneflow/framework/docstr/index_select.py +++ b/python/oneflow/framework/docstr/index_select.py @@ -21,9 +21,6 @@ add_docstr( """ input.index_select(dim, index) -> Tensor - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.index_select.html. - Select values along an axis specified by `dim`. :attr:`index` must be an Int32 Tensor with 1-D. @@ -31,6 +28,9 @@ add_docstr( value of :attr:`index` must be in the range of the dim-th of input. Note that ``input`` and ``index`` do not broadcast against each other. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.index_select.html. + Args: input (Tensor): the source tensor dim (int): the axis along which to index diff --git a/python/oneflow/framework/docstr/inv.py b/python/oneflow/framework/docstr/inv.py new file mode 100644 index 0000000000000000000000000000000000000000..65f27dd5af62c6e2c2ca4726407614dc278465a0 --- /dev/null +++ b/python/oneflow/framework/docstr/inv.py @@ -0,0 +1,135 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + + +add_docstr( + oneflow.linalg.inv, + """linalg.inv(A) -> Tensor + + Computes the inverse of a square matrix if it exists. + Throws a `RuntimeError` if the matrix is not invertible. + + Letting :math:`\mathbb{K}` be :math:`\mathbb{R}` or :math:`\mathbb{C}`, + for a matrix :math:`A \in \mathbb{K}^{n \times n}`, + its **inverse matrix** :math:`A^{-1} \in \mathbb{K}^{n \times n}` (if it exists) is defined as + + .. math:: + + A^{-1}A = AA^{-1} = \mathrm{I}_n + + where :math:`\mathrm{I}_n` is the `n`-dimensional identity matrix. + + The inverse matrix exists if and only if :math:`A` is `invertible`_. In this case, + the inverse is unique. + + Supports input of float, double, cfloat and cdouble dtypes. + Also supports batches of matrices, and if :attr:`A` is a batch of matrices + then the output has the same batch dimensions. + + Args: + A (Tensor): tensor of shape `(*, n, n)` where `*` is zero or more batch dimensions + consisting of invertible matrices. + + Raises: + RuntimeError: if the matrix :attr:`A` or any matrix in the batch of matrices :attr:`A` is not invertible. + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> A = flow.tensor([[ 1.3408, -0.7788, 1.0551, -0.5866], + ... [ 0.8480, 0.8350, 0.9781, -0.1297], + ... [-0.0881, -0.6142, -0.3833, 0.3232], + ... [ 1.2841, 0.7517, -0.3849, 0.2515]]) + >>> flow.linalg.inv(A) + tensor([[ 0.3105, -0.0811, 0.1288, 0.5169], + ... [-0.3457, 0.1716, -0.7133, 0.1987], + ... [-0.0593, 1.1706, 0.8694, -0.6516], + ... [-0.6427, 1.6923, 2.8049, -0.2541]], dtype=oneflow.float32) + + >>> A = flow.tensor([[[ 0.6144, 0.1027, -0.1353], + ... [-1.4415, -0.6731, 0.3723], + ... [ 0.4069, -0.8940, 1.4056]], + ... [[-1.1891, -0.3897, -1.5015], + ... [ 0.3028, 1.1040, 0.2600], + ... [-1.6970, 0.4238, 0.9146]]]) + >>> flow.linalg.inv(A) + tensor([[[ 1.6830, 0.0644, 0.1449], + ... [-5.9755, -2.5206, 0.0925], + ... [-4.2879, -1.6219, 0.7283]], + ... + ... [[-0.2370, 0.0737, -0.4100], + ... [ 0.1892, 0.9579, 0.0384], + ... [-0.5274, -0.3070, 0.3148]]], dtype=oneflow.float32) + + .. _invertible: + https://en.wikipedia.org/wiki/Invertible_matrix#The_invertible_matrix_theorem + + .. + Feature Stage of Operator [linalg.inv]. + - Maintainer List [@simonJJJ] + - Current Stage [pre Alpha] + - Alpha Stage Check List [ ] + - API(Compatible with PyTorch 1.11, anything incompatible must be noted in API Doc.)[Yes] + - Doc(API Doc must be provided and showed normally on the web page.)[Yes] + - Functionality and its' Test [ ] + - Functionality is highly compatiable with PyTorch 1.11. [Yes] + - eager local [Yes] [@simonJJJ] + - forward [Yes] + - backward [Yes] + - gpu [Yes] + - cpu [Yes] + - graph local [ ] [@simonJJJ] + - forward [Yes] + - backward [ ] + - gpu [Yes] + - cpu [Yes] + - Exception Handling + - Exception Message and Hint must be provided [Yes] + - Beta Stage Check List [ ] + - API(High compatibility with PyTorch 1.11, shouldn't have anything incompatible for a naive reason.)[ ] + - Doc(Same standard as Alpha Stage)[Yes] + - Functionality and its' Test [ ] + - eager global [Yes] [@simonJJJ] + - forward [Yes] + - backward [Yes] + - gpu [Yes] + - cpu [Yes] + - graph gloal [Yes] + - forward [Yes] + - backward [ ] + - gpu [Yes] + - cpu [Yes] + - Performance and Scalability(Must be evaluated.)[ ] + - CUDA kernel [ ] + - CPU kernel [ ] + - N nodes M devices [ ] + - Exception Handling [Yes] + - Exception Message and Hint must be provided [Yes] + - Try you best to do Exception Recovery [Yes] + - Stable Stage Check List [ ] + - API(Same standard as Beta Stage)[ ] + - Doc(Same standard as Beta Stage)[ ] + - Functionality and its' Test [ ] + - fp16 and AMP [ ] + - NHWC [ ] + - Performance and Scalability(Must be evaluated.)[ ] + - Exception Handling [ ] + """, +) diff --git a/python/oneflow/framework/docstr/is_floating_point.py b/python/oneflow/framework/docstr/is_floating_point.py index 6e14e3174db91343b368fede13ddc117669186a9..1490e0d4e44d62cf521f6a5a8252549a95f441a5 100644 --- a/python/oneflow/framework/docstr/is_floating_point.py +++ b/python/oneflow/framework/docstr/is_floating_point.py @@ -18,7 +18,7 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.is_floating_point, - r"""Returns True if the data type of input is a floating point data type i.e., one of flow.float64, flow.float32, flow.float16. + r"""Returns True if the data type of input is a floating point data type i.e., one of `oneflow.float64` , `oneflow.float32` , `oneflow.float16`, and `oneflow.bfloat16`. Args: input (Tensor): the input tensor. diff --git a/python/oneflow/framework/docstr/linalg.py b/python/oneflow/framework/docstr/linalg.py new file mode 100644 index 0000000000000000000000000000000000000000..ca401150ef9fb8796939ef927e1f81c1378845dc --- /dev/null +++ b/python/oneflow/framework/docstr/linalg.py @@ -0,0 +1,101 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.linalg.cross, + """linalg.cross(input, other, dim=-1) -> Tensor + + Computes the cross product of two 3-dimensional vectors. + + Supports input of float and double dtypes. + Also supports batches of vectors, for which it computes the product along the dimension dim. + In this case, the output has the same batch dimensions as the inputs broadcast to a common shape. + + The documentation is referenced from: https://pytorch.org/docs/1.11/generated/torch.linalg.cross.html + + Args: + input (Tensor): the first input tensor. + other (Tensor): the second input tensor. + dim (int, optional): the dimension along which to take the cross-product. Default: `-1` + + Raises: + RuntimeError: If after broadcasting ``input.size(dim) != 3`` or ``other.size(dim) != 3``. + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> a = flow.tensor([[ -0.3956, 1.1455, 1.6895], + ... [ -0.5849, 1.3672, 0.3599], + ... [ -1.1626, 0.7180, -0.0521], + ... [ -0.1339, 0.9902, -2.0225]]) + >>> b = flow.tensor([[ -0.0257, -1.4725, -1.2251], + ... [ -1.1479, -0.7005, -1.9757], + ... [ -1.3904, 0.3726, -1.1836], + ... [ -0.9688, -0.7153, 0.2159]]) + >>> flow.linalg.cross(a, b) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4491, -1.5687, 1.9791], + [-0.8304, -1.3036, 0.5651], + [-1.2329, 1.9883, 1.0551]], dtype=oneflow.float32) + """, +) + +add_docstr( + oneflow.cross, + """cross(input, other, dim=None) -> Tensor + + Returns the cross product of vectors in dimension `dim` of `input` and `other`. + + Supports input of float and double dtypes. + Also supports batches of vectors, for which it computes the product along the dimension `dim`. + In this case, the output has the same batch dimensions as the inputs. + + If `dim` is not given, it defaults to the first dimension found with the size 3. Note that this might be unexpected. + + The documentation is referenced from: https://pytorch.org/docs/1.11/generated/torch.cross.html + + .. warning:: + This function may change in a future PyTorch release to match the default behaviour in :func:`oneflow.linalg.cross`. We recommend using :func:`oneflow.linalg.cross`. + + Args: + input (Tensor): the first input tensor. + other (Tensor): the second input tensor. + dim (int, optional): the dimension to take the cross-product in. Default: `None` + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> a = flow.tensor([[ -0.3956, 1.1455, 1.6895], + ... [ -0.5849, 1.3672, 0.3599], + ... [ -1.1626, 0.7180, -0.0521], + ... [ -0.1339, 0.9902, -2.0225]]) + >>> b = flow.tensor([[ -0.0257, -1.4725, -1.2251], + ... [ -1.1479, -0.7005, -1.9757], + ... [ -1.3904, 0.3726, -1.1836], + ... [ -0.9688, -0.7153, 0.2159]]) + >>> flow.cross(a, b) + tensor([[ 1.0844, -0.5281, 0.6120], + [-2.4491, -1.5687, 1.9791], + [-0.8304, -1.3036, 0.5651], + [-1.2329, 1.9883, 1.0551]], dtype=oneflow.float32) + """, +) diff --git a/python/oneflow/framework/docstr/logaddexp.py b/python/oneflow/framework/docstr/logaddexp.py new file mode 100644 index 0000000000000000000000000000000000000000..9a44a47b82ce2581f1100bd898b4b5701d57072a --- /dev/null +++ b/python/oneflow/framework/docstr/logaddexp.py @@ -0,0 +1,50 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.logaddexp, + """ + logaddexp(input, other, *, out=None) -> Tensor + + The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.logaddexp.html. + + Logarithm of the sum of exponentiations of the inputs. + + Calculates pointwise :math:`\log\left(e^x + e^y\right)`. This function is useful + in statistics where the calculated probabilities of events may be so small as to + exceed the range of normal floating point numbers. In such cases the logarithm + of the calculated probability is stored. This function allows adding + probabilities stored in such a fashion. + + Args: + input (oneflow.Tensor): the input Tensor. + other (oneflow.Tensor): the second input Tensor. + out (oneflow.Tensor, optional): the output Tensor. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> flow.logaddexp(flow.tensor([-1.0]), flow.tensor([-1.0, -2, -3])) + tensor([-0.3069, -0.6867, -0.8731], dtype=oneflow.float32) + >>> flow.logaddexp(flow.tensor([-100.0, -200, -300]), flow.tensor([-1.0, -2, -3])) + tensor([-1., -2., -3.], dtype=oneflow.float32) + + """, +) diff --git a/python/oneflow/framework/docstr/loss.py b/python/oneflow/framework/docstr/loss.py index 9fab34beaddd20cf5a94aee1eb5dc604fdd1da75..76075ff959d7a79205f92cdc3559cff01b888107 100644 --- a/python/oneflow/framework/docstr/loss.py +++ b/python/oneflow/framework/docstr/loss.py @@ -19,15 +19,15 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow._C.triplet_margin_loss, r""" - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.triplet_margin_loss.html. - Creates a criterion that measures the triplet loss given an input tensors :math:`x1`, :math:`x2`, :math:`x3` and a margin with a value greater than :math:`0`. This is used for measuring a relative similarity between samples. A triplet is composed by `a`, `p` and `n` (i.e., `anchor`, `positive examples` and `negative examples` respectively). The shapes of all input tensors should be :math:`(N, D)`. - + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.triplet_margin_loss.html. + The distance swap is described in detail in the paper `Learning shallow convolutional feature descriptors with triplet losses `__ by V. Balntas, E. Riba et al. @@ -81,10 +81,13 @@ add_docstr( add_docstr( oneflow._C.cross_entropy, r""" - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.cross_entropy.html. + cross_entropy(input, target, weight=None, ignore_index=-100, reduction="mean", label_smoothing=0.0) See :class:`~oneflow.nn.CrossEntropyLoss` for details. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.cross_entropy.html. + + Args: input (Tensor) : :math:`(N, C)` where `C = number of classes` or :math:`(N, C, H, W)` in case of 2D Loss, or :math:`(N, C, d_1, d_2, ..., d_K)` where :math:`K \geq 1` @@ -107,6 +110,11 @@ add_docstr( elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average` and :attr:`reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override :attr:`reduction`. Default: ``'mean'`` + label_smoothing (float, optinoal): A float in [0.0, 1.0]. Specifies the amount + of smoothing when computing the loss, where 0.0 means no smoothing. + The targets become a mixture of the original ground truth and a uniform + distribution as described in `Rethinking the Inception Architecture for Computer Vision `_. + Default: :math:`0.0`. For example: @@ -122,3 +130,196 @@ add_docstr( """, ) + +add_docstr( + oneflow._C.l1_loss, + r""" + l1_loss(input, target, reduction="mean") -> Tensor + + This operator computes the L1 loss between each element in input and target. + + see :class:`~oneflow.nn.L1Loss` for details. + + Args: + input (Tensor): The input Tensor. + target (Tensor): The target Tensor. + reduction (string, optional): The reduce type, it can be one of "none", "mean", "sum". Defaults to "mean". + + Examples:: + + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> input = flow.randn(3, 4, requires_grad=True) + >>> target = flow.rand(3, 4, requires_grad=False) + >>> loss = F.l1_loss(input, target) + >>> loss.backward() + + """, +) + +add_docstr( + oneflow._C.mse_loss, + r""" + mse_loss(input, target, reduction="mean") -> Tensor + + This operator computes the mean squared error (squared L2 norm) + loss between each element in input and target. + + see :class:`~oneflow.nn.MSELoss` for details. + + Args: + input (Tensor): The input Tensor. + target (Tensor): The target Tensor. + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, + ``'mean'``: the sum of the output will be divided by the number of + elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` + + Examples:: + + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> input = flow.randn(3, 4, requires_grad=True) + >>> target = flow.rand(3, 4, requires_grad=False) + >>> loss = F.mse_loss(input, target) + >>> loss.backward() + + """, +) + +add_docstr( + oneflow._C.smooth_l1_loss, + """ + smooth_l1_loss(input: Tensor, target: Tensor, size_average: bool=True, reduce: bool=True, reduction: str='mean', beta: float=1.0) -> Tensor + + Function that uses a squared term if the absolute + element-wise error falls below beta and an L1 term otherwise. + + See :class:`~oneflow.nn.SmoothL1Loss` for details. + """, +) + +add_docstr( + oneflow._C.binary_cross_entropy_loss, + r""" + binary_cross_entropy(input, target, weight=None, reduction='mean') + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.binary_cross_entropy.html. + + Function that measures the Binary Cross Entropy between the target and input probabilities. + + See :class:`~oneflow.nn.BCELoss` for details. + + Args: + input: Tensor of arbitrary shape as probabilities. + target: Tensor of the same shape as input with values between 0 and 1. + weight (Tensor, optional): a manual rescaling weight + if provided it's repeated to match input tensor shape + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, + ``'mean'``: the sum of the output will be divided by the number of + elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average` + and :attr:`reduce` are in the process of being deprecated, and in the meantime, + specifying either of those two args will override :attr:`reduction`. Default: ``'mean'`` + + Examples:: + + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> input = flow.randn(3, 2, requires_grad=True) + >>> target = flow.rand(3, 2, requires_grad=False) + >>> loss = F.binary_cross_entropy(flow.sigmoid(input), target) + >>> loss.backward() + """, +) + +add_docstr( + oneflow._C.binary_cross_entropy_with_logits_loss, + r""" + binary_cross_entropy_with_logits(input, target, weight=None, reduction='mean', pos_weight=None) + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.binary_cross_entropy_with_logits.html. + + Function that measures Binary Cross Entropy between target and input logits. + + See :class:`~oneflow.nn.BCEWithLogitsLoss` for details. + + Args: + input: Tensor of arbitrary shape as unnormalized scores (often referred to as logits). + target: Tensor of the same shape as input with values between 0 and 1 + weight (Tensor, optional): a manual rescaling weight + if provided it's repeated to match input tensor shape + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, + ``'mean'``: the sum of the output will be divided by the number of + elements in the output, ``'sum'``: the output will be summed. Note: :attr:`size_average` + and :attr:`reduce` are in the process of being deprecated, and in the meantime, + specifying either of those two args will override :attr:`reduction`. Default: ``'mean'`` + pos_weight (Tensor, optional): a weight of positive examples. + Must be a vector with length equal to the number of classes. + + Examples:: + + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> input = flow.randn(3, requires_grad=True) + >>> target = flow.randn(3) + >>> target[target >= 0] = 1 + >>> target[target < 0] = 0 + >>> loss = F.binary_cross_entropy_with_logits(input, target) + >>> loss.backward() + """, +) + +add_docstr( + oneflow._C.kl_div_loss, + r""" + kl_div_loss(input, target, reduction="mean", log_target=False) + + `The Kullback-Leibler divergence loss measure `_ + + See :class:`~oneflow.nn.KLDivLoss` for details. + + Args: + reduction (string, optional): Specifies the reduction to apply to the output: + ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``. + ``'none'``: no reduction will be applied. + ``'batchmean'``: the sum of the output will be divided by batchsize. + ``'sum'``: the output will be summed. + ``'mean'``: the output will be divided by the number of elements in the output. + Default: ``'mean'`` + log_target (bool, optional): Specifies whether `target` is passed in the log space. + Default: ``False`` + + .. note:: + :attr:`reduction` = ``'mean'`` doesn't return the true kl divergence value, please use + :attr:`reduction` = ``'batchmean'`` which aligns with KL math definition. + In the next major release, ``'mean'`` will be changed to be the same as ``'batchmean'``. + + Shape: + - Input: :math:`(N, *)` where :math:`*` means, any number of additional + dimensions + - Target: :math:`(N, *)`, same shape as the input + - Output: scalar by default. If :attr:``reduction`` is ``'none'``, then :math:`(N, *)`, + the same shape as the input + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + >>> input = flow.tensor([-0.9021705, 0.08798598, 1.04686249], dtype=flow.float32) + >>> target = flow.tensor([1.22386942, -0.89729659, 0.01615712], dtype=flow.float32) + >>> out = flow.nn.functional.kl_div(input, target, reduction="none", log_target=False) + >>> out + tensor([ 1.3514, 0.0000, -0.0836], dtype=oneflow.float32) + >>> out = flow.nn.functional.kl_div(input, target, reduction="mean", log_target=False) + >>> out + tensor(0.4226, dtype=oneflow.float32) + >>> out = flow.nn.functional.kl_div(input, target, reduction="sum", log_target=True) + >>> out + tensor(5.7801, dtype=oneflow.float32) + + """, +) diff --git a/python/oneflow/framework/docstr/math_ops.py b/python/oneflow/framework/docstr/math_ops.py index ab057b4949aca16d67150605c52b38c3871237c3..2ae556d029d3415318dade00ddadd367a553fa7c 100644 --- a/python/oneflow/framework/docstr/math_ops.py +++ b/python/oneflow/framework/docstr/math_ops.py @@ -754,7 +754,7 @@ add_docstr( >>> input = flow.tensor(arr, dtype=flow.float32) >>> output = flow.cosh(input).numpy() >>> output - array([1.0133467, 1.7859949, 1.2535787, 1.2804903], dtype=float32) + array([1.0133467, 1.7859949, 1.2535788, 1.2804903], dtype=float32) """, ) @@ -830,12 +830,12 @@ add_docstr( >>> x = flow.tensor(np.array([0, -1., 10.]), dtype=flow.float32) >>> out = flow.erfc(x) >>> out - tensor([1.0000e+00, 1.8427e+00, 2.8026e-45], dtype=oneflow.float32) + tensor([1.0000e+00, 1.8427e+00, 1.4013e-45], dtype=oneflow.float32) >>> x = flow.tensor(np.array([[0, -1., 10.], [5, 7, 0.8]]), dtype=flow.float32) >>> out = flow.erfc(x) >>> out - tensor([[1.0000e+00, 1.8427e+00, 2.8026e-45], + tensor([[1.0000e+00, 1.8427e+00, 1.4013e-45], [1.5375e-12, 4.1838e-23, 2.5790e-01]], dtype=oneflow.float32) """, @@ -876,11 +876,11 @@ add_docstr( >>> print(y.shape) oneflow.Size([2, 2, 3]) >>> print(y.numpy()) - [[[6.3890562e+00 5.3598152e+01 4.0242880e+02] + [[[6.3890562e+00 5.3598148e+01 4.0242880e+02] [1.0956332e+03 2.9799580e+03 8.1020840e+03]] [[2.2025465e+04 5.9873141e+04 1.6275380e+05] - [4.4241238e+05 1.2026032e+06 3.2690165e+06]]] + [4.4241241e+05 1.2026032e+06 3.2690162e+06]]] """, @@ -971,6 +971,34 @@ add_docstr( """, ) +add_docstr( + oneflow.log10, + """ + oneflow.log10(input) -> Tensor + + Returns a new tensor with the natural logarithm to the base 10 of the elements of :attr:`input`. + + .. math:: + y_{i} = \\log10_{e} (x_{i}) + + Args: + input (Tensor): the input tensor. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.ones(3, 3) * 10 + >>> output = flow.log10(x) + >>> output + tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]], dtype=oneflow.float32) + + """, +) + add_docstr( oneflow.minimum, r"""Computes the element-wise minimum of x and y. @@ -1072,9 +1100,12 @@ add_docstr( ... [ 0.3180, -0.6993, 1.0436, 0.0438, 0.2270], ... [-0.2751, 0.7303, 0.2192, 0.3321, 0.2488], ... [ 1.0778, -1.9510, 0.7048, 0.4742, -0.7125]]) - >>> flow.median(a, 1) - (tensor([-0.3982, 0.2270, 0.2488, 0.4742], dtype=oneflow.float32), tensor([1, 4, 4, 3], dtype=oneflow.int64)) - + >>> result=flow.median(a, 1) + >>> result.values + tensor([-0.3982, 0.2270, 0.2488, 0.4742], dtype=oneflow.float32) + >>> result.indices + tensor([1, 4, 4, 3], dtype=oneflow.int64) + .. Feature Stage of Operator [index_select]. - Maintainer List [@simonJJJ] @@ -1134,16 +1165,16 @@ add_docstr( When exponent is a scalar value, the operation applied is: .. math:: - \\text{out}_i = x_i ^ \\text{exponent} -\u200b + \text{out}_i = x_i ^ \text{exponent} + When exponent is a tensor, the operation applied is: .. math:: - \\text{out}_i = x_i ^ {\\text{exponent}_i} + \text{out}_i = x_i ^ {\text{exponent}_i} Args: - - input (Tensor): the input tensor. - - exponent (int, float, Tensor): the exponent. + input (Tensor): the input tensor. + exponent (int, float, Tensor): the exponent. Returns: Tensor: The result of variance on the specified axis of input Tensor @@ -1279,8 +1310,6 @@ add_docstr( r""" mv(input, vec) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.mv.html. - Performs a matrix-vector product of the matrix :attr:`input` and the vector :attr:`vec`. If :attr:`input` is a :math:`(n \times m)` tensor, :attr:`vec` is a @@ -1288,6 +1317,8 @@ add_docstr( .. note:: This function does not broadcast. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.mv.html. + Args: input (oneflow.Tensor): matrix to be matrix multiplied vec (oneflow.Tensor): vector to be matrix multiplied @@ -1311,8 +1342,6 @@ add_docstr( oneflow.mm, r""" mm(input, mat2) -> Tensor - - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.mm.html. Performs a matrix multiplication of the matrices :attr:`input` and :attr:`mat2`. @@ -1322,6 +1351,8 @@ add_docstr( .. note:: This function does not broadcast. For broadcasting matrix products, see :func:`oneflow.matmul`. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.mm.html. + Args: input (oneflow.Tensor): the first matrix to be matrix multiplied mat2 (oneflow.Tensor): the second matrix to be matrix multiplied @@ -1345,6 +1376,10 @@ add_docstr( add_docstr( oneflow.round, r"""This operator rounds the value of Blob to the nearest integer. + + .. note:: + This function implements the "round half to even" to break ties when a number is equidistant from two integers (e.g. `round(2.5)` is 2). + Args: input (oneflow.Tensor): A Tensor Returns: @@ -1528,6 +1563,7 @@ add_docstr( add_docstr( oneflow.as_strided, r""" + as_strided(input, size, stride, storage_offset=None) -> Tensor Create a view of an existing oneflow.Tensor input with specified size, stride and storage_offset. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.as_strided.html. @@ -1562,7 +1598,7 @@ add_docstr( Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to input. The documentation is referenced from: - https://pytorch.org/docs/stable/generated/torch.addcmul.html + https://pytorch.org/docs/1.10/generated/torch.addcmul.html .. math:: \text{out}_i = \text{input}_i + value \times\ \text{tensor1}_i \times\ \text{tensor2}_i @@ -1673,25 +1709,22 @@ add_docstr( add_docstr( oneflow.hsplit, r""" - Splits input, a tensor with one or more dimensions, into multiple tensors horizontally according to indices_or_sections. - Each split is a view of input. - If input is one dimensional this is equivalent to calling oneflow.tensor_split(input, indices_or_sections, dim=0) - (the split dimension is zero), and if input has two or more dimensions it’s equivalent to calling - oneflow.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), except that if indices_or_sections - is an integer it must evenly divide the split dimension or a runtime error will be thrown. + hsplit(input, indices_or_sections) -> List of Tensors + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.hsplit.html. + Splits `input`, a tensor with one or more dimensions, into multiple tensors horizontally according to `indices_or_sections`. + Each split is a view of `input`. + + If `input` is one dimensional this is equivalent to calling oneflow.tensor_split(input, indices_or_sections, dim=0) + (the split dimension is zero), and if `input` has two or more dimensions it’s equivalent to calling + oneflow.tensor_split(input, indices_or_sections, dim=1) (the split dimension is 1), except that if `indices_or_sections` + is an integer it must evenly divide the split dimension or a runtime error will be thrown. + Args: input (Tensor): the input tensor. - indices_or_sections (int or a list): If indices_or_sections is an integer n , input is split into n sections - along dimension dim.If input is divisible by n along dimension dim, each section will be of equal size, - input.size (dim) / n. If input is not divisible by n, the sizes of the first int(input.size(dim) % n). - sections will have size int(input.size(dim) / n) + 1, and the rest will have size int(input.size(dim) / n). - If indices_or_sections is a list or tuple of ints, then input is split along dimension dim at each of the indices in - the list, tuple or tensor. For instance, indices_or_sections=[2, 3] and dim=0 would result in the tensors - input[:2], input[2:3], and input[3:].If indices_or_sections is a tensor, it must be a zero-dimensional or - one-dimensional long tensor on the CPU. + indices_or_sections (int or a list): See argument in :func:`oneflow.tensor_split()`. Returns: oneflow.TensorTuple: the output TensorTuple. @@ -1816,3 +1849,26 @@ add_docstr( tensor([1, 2, 6], dtype=oneflow.int64) """, ) + + +add_docstr( + oneflow.trunc, + r"""trunc(input) -> Tensor + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.trunc.html + + Returns a new tensor with the truncated integer values of + the elements of :attr:`input`. + + Args: + input(Tensor): the input tensor. + + Example:: + + >>> import oneflow as flow + >>> a = flow.tensor([ 3.4742, 0.5466, -0.8008, -0.9079]) + >>> flow.trunc(a) + tensor([3., 0., -0., -0.], dtype=oneflow.float32) + """, +) diff --git a/python/oneflow/framework/docstr/meshgrid.py b/python/oneflow/framework/docstr/meshgrid.py index f1ab069b9fa845ce2b346e443b337ab9f29ccce4..5afe2b15506ef4cae894c9b9c871938279953f8f 100644 --- a/python/oneflow/framework/docstr/meshgrid.py +++ b/python/oneflow/framework/docstr/meshgrid.py @@ -18,13 +18,14 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.meshgrid, - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/_modules/torch/functional.html#meshgrid. - + """ Take :math:`N` tensors, each of which can be either scalar or 1-dimensional vector, and create :math:`N` N-dimensional grids, where the :math:`i` :sup:`th` grid is defined by expanding the :math:`i` :sup:`th` input over dimensions defined by other inputs. + + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.meshgrid.html#torch.meshgrid Args: tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be diff --git a/python/oneflow/framework/docstr/oneflow.py b/python/oneflow/framework/docstr/oneflow.py index 4ba6f9808b878657dde70ace4bb6611de05d3000..ce7e9292cb2e6fa8a90c66ea5a7b7cf95dc76813 100644 --- a/python/oneflow/framework/docstr/oneflow.py +++ b/python/oneflow/framework/docstr/oneflow.py @@ -27,3 +27,56 @@ add_docstr( """, ) + +add_docstr( + oneflow.get_default_dtype, + """oneflow.get_default_dtype() -> oneflow._oneflow_internal.dtype + + Returns the default floating point dtype. + + Returns: + oneflow.dtype: The default floating point dtype. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> flow.set_default_dtype(flow.float32) + >>> flow.get_default_dtype() + oneflow.float32 + >>> flow.set_default_dtype(flow.float64) + >>> flow.get_default_dtype() + oneflow.float64 + >>> flow.set_default_tensor_type(flow.FloatTensor) + >>> flow.get_default_dtype() + oneflow.float32 + """, +) + +add_docstr( + oneflow.set_default_dtype, + """oneflow.set_default_dtype() -> None + + Sets the default floating point type for those source operators which create Tensor. + + The default floating point type is ``oneflow.float32``. + + Args: + dtype (oneflow.dtype): The floating point dtype. + + For example: + + .. code-block:: python + + >>> import oneflow + >>> oneflow.set_default_dtype(oneflow.float64) + >>> x = oneflow.randn(2, 3) + >>> x.dtype + oneflow.float64 + >>> oneflow.set_default_dtype(oneflow.float32) + >>> x = oneflow.randn(2, 3) + >>> x.dtype + oneflow.float32 + """, +) diff --git a/python/oneflow/framework/docstr/pooling.py b/python/oneflow/framework/docstr/pooling.py index f5190fbdd52f629416021511e7d09e31aedcdd37..eec932a6dbeeeb562140d61abcd569f2f9d4fb3f 100644 --- a/python/oneflow/framework/docstr/pooling.py +++ b/python/oneflow/framework/docstr/pooling.py @@ -94,3 +94,110 @@ add_docstr( >>> output = flow.nn.functional.adaptive_avg_pool3d(input, (2, 2, 2)) """, ) + +add_docstr( + oneflow._C.avg_pool1d, + """ + avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor + + Applies a 1D average pooling over an input signal composed of several input planes. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.avg_pool1d.html + + See :class:`~oneflow.nn.AvgPool1d` for details and output shape. + + Args: + input: input tensor of shape :math:`(\\text{minibatch} , \\text{in_channels} , iW)` + kernel_size: the size of the window. Can be a single number or a tuple `(kW,)` + stride: the stride of the window. Can be a single number or a tuple `(sW,)`. Default: :attr:`kernel_size` + padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padW,)`. Default: 0 + ceil_mode: when True, will use `ceil` instead of `floor` to compute the output shape. Default: ``False`` + count_include_pad: when True, will include the zero-padding in the averaging calculation. Default: ``True`` + + Examples:: + + >>> # pool of square window of size=3, stride=2 + >>> import oneflow + >>> input = oneflow.tensor([[[1, 2, 3, 4, 5, 6, 7]]], dtype=oneflow.float32) + >>> oneflow.nn.functional.avg_pool1d(input, kernel_size=3, stride=2) + tensor([[[2., 4., 6.]]], dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow._C.avg_pool2d, + """ + avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=0) -> Tensor + + Applies 2D average-pooling operation in :math:`kH \\times kW` regions by step size :math:`sH \\times sW` steps. The number of output features is equal to the number of input planes. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.avg_pool2d.html. + + See :class:`~oneflow.nn.AvgPool2d` for details and output shape. + + Args: + input: input tensor :math:`(\\text{minibatch} , \\text{in_channels} , iH , iW)` + kernel_size: size of the pooling region. Can be a single number or a tuple `(kH, kW)` + stride: stride of the pooling operation. Can be a single number or a tuple `(sH, sW)`. Default: :attr:`kernel_size` + padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padH, padW)`. Default: 0 + ceil_mode: when True, will use `ceil` instead of `floor` in the formula to compute the output shape. Default: ``False`` + count_include_pad: when True, will include the zero-padding in the averaging calculation. Default: ``True`` + divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: 0 + """, +) + +add_docstr( + oneflow._C.avg_pool3d, + """ + avg_pool3d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=0) -> Tensor + + Applies 3D average-pooling operation in :math:`kT \\times kH \\times kW` regions by step size :math:`sT \\times sH \\times sW` steps. The number of output features is equal to :math:`\\lfloor\\frac{\\text{input planes}}{sT}\\rfloor`. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.avg_pool3d.html + + See :class:`~oneflow.nn.AvgPool3d` for details and output shape. + + Args: + input: input tensor :math:`(\\text{minibatch} , \\text{in_channels} , iT \\times iH , iW)` + kernel_size: size of the pooling region. Can be a single number or a tuple `(kT, kH, kW)` + stride: stride of the pooling operation. Can be a single number or a tuple `(sT, sH, sW)`. Default: :attr:`kernel_size` + padding: implicit zero paddings on both sides of the input. Can be a single number or a tuple `(padT, padH, padW)`, Default: 0 + ceil_mode: when True, will use `ceil` instead of `floor` in the formula to compute the output shape + count_include_pad: when True, will include the zero-padding in the averaging calculation + divisor_override: if specified, it will be used as divisor, otherwise size of the pooling region will be used. Default: 0 + """, +) + +add_docstr( + oneflow._C.max_unpool1d, + """ + max_unpool1d(input, indices, kernel_size, stride=None, padding=0, output_size=None) -> Tensor + + Computes a partial inverse of ``MaxPool1d``. + + See :class:`MaxUnpool1d` for details. + """, +) + +add_docstr( + oneflow._C.max_unpool2d, + """ + max_unpool2d(input, indices, kernel_size, stride=None, padding=0, output_size=None) -> Tensor + + Computes a partial inverse of ``MaxPool2d``. + + See :class:`MaxUnpool2d` for details. + """, +) + +add_docstr( + oneflow._C.max_unpool3d, + """ + max_unpool3d(input, indices, kernel_size, stride=None, padding=0, output_size=None) -> Tensor + + Computes a partial inverse of ``MaxPool3d``. + + See :class:`MaxUnpool3d` for details. + """, +) diff --git a/python/oneflow/framework/docstr/quantile.py b/python/oneflow/framework/docstr/quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..16a1da132a1d0e12c01ae9529717acd8a29620da --- /dev/null +++ b/python/oneflow/framework/docstr/quantile.py @@ -0,0 +1,66 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +from oneflow.framework.docstr.utils import add_docstr + +add_docstr( + oneflow.quantile, + """ + quantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) -> Tensor + + The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.quantile.html. + + Computes the q-th quantiles of each row of the :attr:`input` tensor along the dimension :attr:`dim`. + + To compute the quantile, we map q in [0, 1] to the range of indices [0, n] to find the location + of the quantile in the sorted input. If the quantile lies between two data points ``a < b`` with + indices ``i`` and ``j`` in the sorted order, result is computed according to the given + :attr:`interpolation` method as follows: + + - ``linear``: ``a + (b - a) * fraction``, where ``fraction`` is the fractional part of the computed quantile index. + - ``lower``: ``a``. + - ``higher``: ``b``. + - ``nearest``: ``a`` or ``b``, whichever's index is closer to the computed quantile index (rounding down for .5 fractions). + - ``midpoint``: ``(a + b) / 2``. + + If :attr:`q` is a 1D tensor, the first dimension of the output represents the quantiles and has size + equal to the size of :attr:`q`, the remaining dimensions are what remains from the reduction. + + .. note:: + By default :attr:`dim` is ``None`` resulting in the :attr:`input` tensor being flattened before computation. + + Args: + input (oneflow.Tensor): the input Tensor. + q (float or oneflow.Tensor): a scalar or 1D tensor of values in the range [0, 1]. + dim (int, optional): the dimension to reduce. Default is None. + keepdim (bool, optional): whether the output tensor has dim retained or not. Default is False + interpolation (str, optional): interpolation method to use when the desired quantile lies between two data points. + Can be ``linear``, ``lower``, ``higher``, ``midpoint`` and ``nearest``. + Default is ``linear``. + out (oneflow.Tensor, optional): the output Tensor. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> a = flow.randn(2, 4) + >>> out = flow.quantile(a, 0.6, dim=1, interpolation='linear') + >>> out + tensor([-1.0091, 0.3997], dtype=oneflow.float64) + + """, +) diff --git a/python/oneflow/framework/docstr/random.py b/python/oneflow/framework/docstr/random.py index 9dc9a3c316e39623f4656c29d65db9a512d7f3ba..5f97528eac9ff455fcd77d7d68fd59147168610a 100644 --- a/python/oneflow/framework/docstr/random.py +++ b/python/oneflow/framework/docstr/random.py @@ -19,12 +19,13 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.bernoulli, """ - bernoulli(x, *, generator=None, out=None) + bernoulli(input, p, *, generator=None, out=None) This operator returns a Tensor with binaray random numbers (0 / 1) from a Bernoulli distribution. Args: - x (Tensor): the input tensor of probability values for the Bernoulli distribution + input (Tensor): the input tensor of probability values for the Bernoulli distribution + p (float, optional): the probability for the Bernoulli distribution. If specified, Bernoulli distribution will use p for sampling, not input generator (Generator, optional): a pseudorandom number generator for sampling out (Tensor, optional): the output tensor. @@ -52,6 +53,16 @@ add_docstr( tensor([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]], dtype=oneflow.float32) + >>> y = flow.bernoulli(x, 1) + >>> y + tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]], dtype=oneflow.float32) + >>> y = flow.bernoulli(x, p=0) + >>> y + tensor([[0., 0., 0.], + [0., 0., 0.], + [0., 0., 0.]], dtype=oneflow.float32) """, ) @@ -97,6 +108,46 @@ add_docstr( """, ) + +add_docstr( + oneflow._C.randn_like, + """ + randn_like(input, *, dtype=None, generator=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + Returns a tensor with the same size as `input` that is filled with random numbers from a normal distribution with mean 0 and variance 1. + flow.randn_like(input) is equivalent to flow.randn(input.size(), dtype=input.dtype, device=input.device). + + Args: + input (oneflow.Tensor): the size of ``input`` will determine size of the output tensor. + dtype (flow.dtype, optional): The desired data type of returned tensor. defaults to the dtype of `input`. + generator (flow.Generator, optional): a pseudorandom number generator for sampling + device (flow.device, optional): The desired device of returned local tensor. If None, defaults to the device of `input`. + placement (flow.placement, optional): The desired device of returned global tensor. If None, will + construct local tensor. + sbp (flow.sbp, optional): The desired sbp of returned global tensor. It must be equal with the + numbers of placement, If None, will construct local tensor. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.randn(3,3) # construct local tensor + >>> y = flow.randn_like(x) + >>> y.shape + oneflow.Size([3, 3]) + >>> y.is_global + False + >>> placement = flow.placement("cpu", ranks=[0]) + >>> sbp = flow.sbp.broadcast + >>> z = flow.randn_like(y, placement=placement, sbp=sbp) # construct global tensor + >>> z.is_global + True + + """, +) + add_docstr( oneflow._C.rand, """ @@ -169,7 +220,8 @@ add_docstr( >>> import oneflow as flow >>> generator = flow.Generator() - >>> generator.manual_seed(0) + >>> generator.manual_seed(0) #doctest: +ELLIPSIS + >>> y = flow.normal(0, 1, 5, generator=generator) >>> y tensor([2.2122, 1.1631, 0.7740, 0.4838, 1.0434], dtype=oneflow.float32) @@ -185,6 +237,9 @@ add_docstr( The shape of the tensor is defined by the variable argument ``size``. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.randint.html. + Args: low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. high (int): One above the highest integer to be drawn from the distribution. @@ -192,13 +247,13 @@ add_docstr( Can be a variable number of arguments or a collection like a list or tuple or oneflow.Size. Keyword args: - dtype (flow.dtype, optional): The desired data type of returned tensor. Default: ``flow.int64``. - generator (flow.Generator, optional) – a pseudorandom number generator for sampling - device (flow.device, optional): The desired device of returned local tensor. If None, uses the + dtype (oneflow.dtype, optional): The desired data type of returned tensor. Default: ``flow.int64``. + generator (oneflow.Generator, optional) – a pseudorandom number generator for sampling + device (oneflow.device, optional): The desired device of returned local tensor. If None, uses the current device. - placement (flow.placement, optional): The desired device of returned global tensor. If None, will + placement (oneflow.placement, optional): The desired device of returned global tensor. If None, will construct local tensor. - sbp (flow.sbp, optional): The desired sbp of returned global tensor. It must be equal with the + sbp (oneflow.sbp, optional): The desired sbp of returned global tensor. It must be equal with the numbers of placement. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. @@ -208,7 +263,8 @@ add_docstr( >>> import oneflow as flow >>> generator = flow.Generator() - >>> generator.manual_seed(0) + >>> generator.manual_seed(0) #doctest: +ELLIPSIS + >>> y = flow.randint(0, 5, (3,3), generator=generator) # construct local tensor >>> y tensor([[2, 2, 3], @@ -224,6 +280,56 @@ add_docstr( """, ) +add_docstr( + oneflow._C.randint_like, + """ + randint_like(input, low=0, high, size, *, dtype=None, generator=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive). + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.randint_like.html. + + Args: + input (oneflow.Tensor): the size of ``input`` will determine size of the output tensor. + low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. + high (int): One above the highest integer to be drawn from the distribution. + + + Keyword args: + dtype (oneflow.dtype, optional): The desired data type of returned tensor. Default: ``flow.int64``. + generator (oneflow.Generator, optional) – a pseudorandom number generator for sampling + device (oneflow.device, optional): The desired device of returned local tensor. If None, uses the + current device. + placement (oneflow.placement, optional): The desired device of returned global tensor. If None, will + construct local tensor. + sbp (oneflow.sbp, optional): The desired sbp of returned global tensor. It must be equal with the + numbers of placement. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> generator = flow.Generator() + >>> generator.manual_seed(0) #doctest: +ELLIPSIS + + >>> x = flow.randn(2, 2, generator=generator) + >>> y = flow.randint_like(x, 0, 5, generator=generator) # construct local tensor + >>> y + tensor([[3, 4], + [2, 4]], dtype=oneflow.int64) + >>> y.is_global + False + >>> placement = flow.placement("cpu", ranks=[0]) + >>> y = flow.randint_like(x, 0, 5, generator=generator, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor + >>> y.is_global + True + + """, +) + add_docstr( oneflow._C.randperm, r""" @@ -251,7 +357,8 @@ add_docstr( >>> import oneflow as flow >>> generator = flow.Generator() - >>> generator.manual_seed(0) + >>> generator.manual_seed(0) #doctest: +ELLIPSIS + >>> y = flow.randperm(5, generator=generator) # construct local tensor >>> y tensor([2, 4, 3, 0, 1], dtype=oneflow.int64) @@ -264,3 +371,55 @@ add_docstr( """, ) + +add_docstr( + oneflow.multinomial, + """ + multinomial(input, num_samples, replacement=False, generator=None) -> LongTensor + + Returns a tensor where each row contains :attr:`num_samples` indices sampled + from the multinomial probability distribution located in the corresponding row + of tensor :attr:`input`. + + .. note:: + The rows of :attr:`input` do not need to sum to one (in which case we use + the values as weights), but must be non-negative, finite and have + a non-zero sum. + + Indices are ordered from left to right according to when each was sampled + (first samples are placed in first column). + + If :attr:`input` is a vector, :attr:`out` is a vector of size :attr:`num_samples`. + + If :attr:`input` is a matrix with `m` rows, :attr:`out` is an matrix of shape + :math:`(m x num\_samples)`. + + If replacement is ``True``, samples are drawn with replacement. + + If not, they are drawn without replacement, which means that when a + sample index is drawn for a row, it cannot be drawn again for that row. + + .. note:: + When drawn without replacement, :attr:`num_samples` must be lower than + number of non-zero elements in :attr:`input` (or the min number of non-zero + elements in each row of :attr:`input` if it is a matrix). + + Args: + input (Tensor): the input tensor containing probabilities + num_samples (int): number of samples to draw + replacement (bool, optional): whether to draw with replacement or not + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> gen = flow.manual_seed(0) + >>> weights = flow.tensor([0, 10, 3, 0], dtype=flow.float) # create a tensor of weights + >>> flow.multinomial(weights, 2) + tensor([1, 2], dtype=oneflow.int64) + >>> flow.multinomial(weights, 4, replacement=True) + tensor([1, 2, 1, 1], dtype=oneflow.int64) + + """, +) diff --git a/python/oneflow/framework/docstr/reduce_ops.py b/python/oneflow/framework/docstr/reduce_ops.py index 11f8dc4a1267757aa5465a70f5dd5e6bcd4a9fdf..4b9aebd359f661f1844ee3ff4ca4eae8f228895d 100644 --- a/python/oneflow/framework/docstr/reduce_ops.py +++ b/python/oneflow/framework/docstr/reduce_ops.py @@ -43,10 +43,10 @@ add_docstr( >>> input = flow.Tensor([[4, 1, 5], [2, 6, 3]]) >>> flow.max(input) tensor(6., dtype=oneflow.float32) - >>> (values, indices) = flow.max(input, dim=1) - >>> values + >>> result = flow.max(input, dim=1) + >>> result.values tensor([5., 6.], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([2, 1], dtype=oneflow.int64) """, @@ -79,10 +79,10 @@ add_docstr( >>> input = flow.Tensor([[4, 1, 5], [2, 6, 3]]) >>> flow.min(input) tensor(1., dtype=oneflow.float32) - >>> (values, indices) = flow.min(input, dim=1) - >>> values + >>> result = flow.min(input, dim=1) + >>> result.values tensor([1., 2.], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([1, 0], dtype=oneflow.int64) """, @@ -248,3 +248,82 @@ add_docstr( """, ) + +add_docstr( + oneflow.nansum, + r"""oneflow.nansum(input, dim, keepdim=False, *, dtype=None) -> Tensor + + Returns the sum of each row of the ``input`` tensor in the given dimension ``dim``, + treating Not a Numbers (NaNs) as zero. If ``dim`` is a list of dimensions, + reduce over all of them. + + If ``keepdim`` is ``True``, the output tensor is of the same size as ``input`` except + in the dimension(s) ``dim`` where it is of size 1. + Otherwise, ``dim`` is squeezed (see :class:`oneflow.squeeze()`), + resulting in the output tensor having 1 (or ``len(dim)``) fewer dimension(s). + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nansum.html. + + Args: + input (oneflow.Tensor): the Input Tensor + dim (int, optional): the dimension to reduce. Default: ``None`` + keepdim (bool, optional): whether the output tensor has ``dim`` retained or not. Default: `False` + dtype (oneflow.dtype, optional): the desired data type of returned tensor. + If specified, the input tensor is casted to dtype before the operation is performed. + This is useful for preventing data type overflows. Default: ``None``. + + Example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor([1., 2., float("nan")]) + >>> flow.nansum(x) + tensor(3., dtype=oneflow.float32) + >>> x = flow.tensor([[1., float("nan")], [float("nan"), 2]]) + >>> flow.nansum(x, dim=1) + tensor([1., 2.], dtype=oneflow.float32) + >>> x = flow.tensor([float("nan") for i in range(3)]) + >>> flow.nansum(x) + tensor(0., dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow.logsumexp, + r""" + oneflow.logsumexp(input, dim, keepdim=False) -> Tensor + + Returns the log of summed exponentials of each row of the :attr:`input` + tensor in the given dimension :attr:`dim`. The computation is numerically + stabilized. + + For summation index :math:`j` given by `dim` and other indices :math:`i`, the result is + + .. math:: + \text{logsumexp}(x)_{{i}} = \log \sum_j \exp(x_{{ij}}) + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.12/generated/torch.logsumexp.html. + + Args: + input (oneflow.Tensor): the Input Tensor + dim (int or tuple of ints): the dimension or dimensions to reduce. + keepdim (bool, optional): whether the output tensor has dim retained or not. Default: `False` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + + >>> input = flow.Tensor([[1, 2, 3], [4, 5, 6]]) + >>> flow.logsumexp(input, 0) + tensor([4.0486, 5.0486, 6.0486], dtype=oneflow.float32) + >>> flow.logsumexp(input, 1) + tensor([3.4076, 6.4076], dtype=oneflow.float32) + + """, +) diff --git a/python/oneflow/framework/docstr/repeat_interleave.py b/python/oneflow/framework/docstr/repeat_interleave.py index ec7b14676e760f442cbd30f217122225dc9f8ccd..ca9ef45123c46278cfc822a48b5218479a317890 100644 --- a/python/oneflow/framework/docstr/repeat_interleave.py +++ b/python/oneflow/framework/docstr/repeat_interleave.py @@ -21,14 +21,14 @@ add_docstr( """ repeat_interleave(input, repeats, dim=None, *, output_size=None) -> Tensor - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.repeat_interleave.html - Repeat elements of a tensor. .. warning:: This is different from :meth:`oneflow.Tensor.repeat` but similar to ``numpy.repeat``. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.repeat_interleave.html + Args: input (oneflow.Tensor): the input Tensor. repeats (Tensor or int): The number of repetitions for each element. diff --git a/python/oneflow/framework/docstr/searchsorted.py b/python/oneflow/framework/docstr/searchsorted.py index 712bca64983ec79e2a83b2333f5a07c1e7f605b9..7e16f73718b19eaed5a5db5aaa02bef74feba0f4 100644 --- a/python/oneflow/framework/docstr/searchsorted.py +++ b/python/oneflow/framework/docstr/searchsorted.py @@ -21,8 +21,6 @@ add_docstr( """ searchsorted() -> oneflow.Tensor - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.searchsorted.html?highlight=searchsorted - Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices, the order of the corresponding innermost dimension within sorted_sequence would be preserved. Return a new tensor with the same size as values. If right is False @@ -40,6 +38,8 @@ add_docstr( sorted_sequence[m][n]...[l][i] ================= ========= ========================================================================== + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.searchsorted.html + Args: sorted_sequence (Tensor): N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension. diff --git a/python/oneflow/framework/docstr/sort.py b/python/oneflow/framework/docstr/sort.py index a7ada375107f1857a6a3dba40e65d5fa278a6b1b..f0bedc0680afe47bc3745a2cf7942c10d7091ccb 100644 --- a/python/oneflow/framework/docstr/sort.py +++ b/python/oneflow/framework/docstr/sort.py @@ -38,25 +38,25 @@ add_docstr( >>> import numpy as np >>> x = np.array([[1, 3, 8, 7, 2], [1, 9, 4, 3, 2]], dtype=np.float32) >>> input = flow.Tensor(x) - >>> (values, indices) = flow.sort(input) - >>> values + >>> result = flow.sort(input) + >>> result.values tensor([[1., 2., 3., 7., 8.], [1., 2., 3., 4., 9.]], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([[0, 4, 1, 3, 2], [0, 4, 3, 2, 1]], dtype=oneflow.int32) - >>> (values, indices) = flow.sort(input, descending=True) - >>> values + >>> result = flow.sort(input, descending=True) + >>> result.values tensor([[8., 7., 3., 2., 1.], [9., 4., 3., 2., 1.]], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([[2, 3, 1, 4, 0], [1, 2, 3, 4, 0]], dtype=oneflow.int32) - >>> (values, indices) = flow.sort(input, dim=0) - >>> values + >>> result = flow.sort(input, dim=0) + >>> result.values tensor([[1., 3., 4., 3., 2.], [1., 9., 8., 7., 2.]], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([[0, 0, 1, 1, 0], [1, 1, 0, 0, 1]], dtype=oneflow.int32) diff --git a/python/oneflow/framework/docstr/tensor.py b/python/oneflow/framework/docstr/tensor.py index d808b0ad0a08d4a017b117dc969cbe23beb0e54b..359f08627398ba870b64da480487fb91ce571ebf 100644 --- a/python/oneflow/framework/docstr/tensor.py +++ b/python/oneflow/framework/docstr/tensor.py @@ -83,11 +83,11 @@ add_docstr( add_docstr( oneflow.Tensor.device, - r""" + r""" + Is the :class:`oneflow.device` where this Tensor is, which is invalid for global tensor. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.device.html. - - Is the :class:`oneflow.device` where this Tensor is, which is invalid for global tensor. """, ) @@ -239,7 +239,7 @@ add_docstr( """ Tensor.new_zeros(size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor - Returns a Tensor of size size filled with 0. By default, the returned Tensor has the same torch.dtype and torch.device as this tensor. + Returns a Tensor of size size filled with 0. By default, the returned Tensor has the same oneflow.dtype, oneflow.device or oneflow.placement and oneflow.sbp as this tensor. Args: size (int...): a list, tuple, or flow.Size of integers defining the shape of the output tensor. @@ -264,6 +264,37 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.new_full, + """ + Tensor.new_full(size, fill_value, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + Returns a Tensor of size size filled with fill_value. By default, the returned Tensor has the same oneflow.dtype, oneflow.device or oneflow.placement and oneflow.sbp as this tensor. + + Args: + fill_value (scalar): the number to fill the output tensor with. + size (int...): a list, tuple, or flow.Size of integers defining the shape of the output tensor. + dtype (flow.dtype, optional): the desired type of returned tensor. Default: if None, same flow.dtype as this tensor. + device (flow.device, optional): the desired device of returned tensor. Default: if None, same flow.device as this tensor. + placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> tensor = flow.ones((2,), dtype=flow.float64) + >>> tensor.new_full((3, 4), 3.141592) + tensor([[3.1416, 3.1416, 3.1416, 3.1416], + [3.1416, 3.1416, 3.1416, 3.1416], + [3.1416, 3.1416, 3.1416, 3.1416]], dtype=oneflow.float64) + """, +) + add_docstr( oneflow.Tensor.storage_offset, """ @@ -305,6 +336,7 @@ add_docstr( check_meta (bool, optional): indicates whether to check meta information when createing global tensor from local tensor. Only can be set to False when the shape and dtype of the input local tensor on each rank are the same. If set to False, the execution of local_to_global can be accelerated. Default: True + copy (bool, optional): When copy is set, the returned global tensor takes the replication of this tensor as its local component in the current rank. Default: False .. code-block:: python @@ -351,6 +383,7 @@ add_docstr( tensor in the backward pass. If None, the grad tensor sbp will be infered automatically. Default: None check_meta (bool, optional): indicates whether to check meta information. If set to True, check the consistency of the input meta information (placement and sbp) on each rank. Default: False + copy (bool, optional): When copy is set, a new Tensor is created even when the Tensor already matches the desired conversion. Default: False .. code-block:: python @@ -408,6 +441,9 @@ add_docstr( global tensor. Default: None check_meta (bool, optional): indicates whether to check meta information. If set to True, check the input meta information on each rank. Default: True if this tensor is a local tensor, False if this tensor is a global tensor + copy (bool, optional): When copy is set, copy occurres in this operation. For local tensor, the returned global tensor takes the + replication of this tensor as its local component in the current rank. For global tensor, a new Tensor is created even when + the Tensor already matches the desired conversion. Default: False For local tensor: @@ -467,14 +503,17 @@ add_docstr( add_docstr( oneflow.Tensor.to_local, """ - Tensor.to_local() -> Tensor + Tensor.to_local(**kwargs) -> Tensor Returns the local component of this global tensor in the current rank. + Keyword Args: + copy (bool, optional): When copy is set, a new replicated tensor of the local component of this global tensor in the current rank is returned. Default: False + Note: This tensor should be a global tensor, and it returns a empty tensor if there is no local component in the current rank. - No copy occurred in this operation. + No copy occurred in this operation if copy is not set. For example: @@ -547,16 +586,22 @@ add_docstr( add_docstr( oneflow.Tensor.squeeze, """ + Tensor.squeeze(dim=None) -> Tensor See :func:`oneflow.squeeze` """, ) add_docstr( - oneflow.Tensor.unfold, + oneflow.Tensor.squeeze_, """ - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.unfold.html. + Tensor.squeeze_(dim=None) -> Tensor + In-place version of :func:`oneflow.Tensor.squeeze` + """, +) +add_docstr( + oneflow.Tensor.unfold, + """ Returns a view of the original tensor which contains all slices of `size` size from `self` tensor in the dimension `dimension`. @@ -567,6 +612,9 @@ add_docstr( An additional dimension of size `size` is appended in the returned tensor. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.unfold.html. + Args: dimension (int): dimension in which unfolding happens size (int): the size of each slice that is unfolded @@ -627,10 +675,39 @@ add_docstr( add_docstr( oneflow.Tensor.unsqueeze, """ + Tensor.unsqueeze(dim) -> Tensor + See :func:`oneflow.unsqueeze` """, ) +add_docstr( + oneflow.Tensor.unsqueeze_, + """ + Tensor.unsqueeze_(dim) -> Tensor + + In-place version of :func:`oneflow.Tensor.unsqueeze` + """, +) + +add_docstr( + oneflow.Tensor.as_strided, + """ + Tensor.as_strided(size, stride, storage_offset=None) -> Tensor + + See :func:`oneflow.as_strided` + """, +) + +add_docstr( + oneflow.Tensor.as_strided_, + """ + Tensor.as_strided_(size, stride, storage_offset=None) -> Tensor + + In-place version of :func:`oneflow.Tensor.as_strided` + """, +) + add_docstr( oneflow.Tensor.permute, """ @@ -718,10 +795,7 @@ add_docstr( add_docstr( oneflow.Tensor.backward, """ - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.backward.html. - - Computes the gradient of current tensor w.r.t. graph leaves. + Computes the gradient of current tensor `w.r.t.` graph leaves. The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying gradient. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. self. @@ -732,6 +806,9 @@ add_docstr( Note: When inputs are provided and a given input is not a leaf, the current implementation will call its grad_fn (though it is not strictly needed to get this gradients). It is an implementation detail on which the user should not rely. See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.backward.html. + Args: gradient (Tensor or None): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless create_graph is True. None values can be specified for scalar Tensors or ones that don’t require grad. If a None value would be acceptable then this argument is optional. @@ -758,8 +835,6 @@ add_docstr( add_docstr( oneflow.Tensor.is_leaf, r""" - Compatible with PyTorch. - All Tensors that have ``requires_grad`` which is ``False`` will be leaf Tensors by convention. For Tensor that have ``requires_grad`` which is ``True``, they will be leaf Tensors if they @@ -768,6 +843,8 @@ add_docstr( Only leaf Tensors will have their ``grad`` populated during a call to ``backward()``. To get ``grad`` populated for non-leaf Tensors, you can use ``retain_grad()``. + Compatible with PyTorch. + For example: .. code-block:: python @@ -791,15 +868,17 @@ add_docstr( add_docstr( oneflow.Tensor.requires_grad, r""" - Compatible with PyTorch. - Is ``True`` if gradient need to be computed for this Tensor, ``False`` otherwise. + + Compatible with PyTorch. """, ) add_docstr( oneflow.Tensor.requires_grad_, r"""oneflow.Tensor.requires_grad_(requires_grad=True) -> Tensor + Sets this tensor’s requires_grad attribute in-place. Returns this tensor. + Compatible with PyTorch. Args: @@ -855,10 +934,10 @@ add_docstr( add_docstr( oneflow.Tensor.retain_grad, r""" - Compatible with PyTorch. - Enables this Tensor to have their ``grad`` populated during ``backward()``. This is a no-op for leaf tensors. + + Compatible with PyTorch. """, ) @@ -925,6 +1004,20 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.addcdiv, + """ + See :func:`oneflow.addcdiv` + """, +) + +add_docstr( + oneflow.Tensor.addcdiv_, + """ + In-place version of :func:`oneflow.Tensor.addcdiv` + """, +) + add_docstr( oneflow.Tensor.dim, """ @@ -990,6 +1083,13 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.equal, + """ + See :func:`oneflow.equal` + """, +) + add_docstr( oneflow.Tensor.lt, """ @@ -1012,25 +1112,32 @@ add_docstr( ) add_docstr( - oneflow.Tensor.fill_, + oneflow.Tensor.neg, """ - Tensor.fill_(value) → Tensor + See :func:`oneflow.neg` + """, +) - Fills `self` tensor with the specified value. +add_docstr( + oneflow.Tensor.norm, + """ + See :func:`oneflow.norm` """, ) add_docstr( - oneflow.Tensor.ge, + oneflow.Tensor.fill_, """ - See :func:`oneflow.ge` + Tensor.fill_(value) → Tensor + + Fills `self` tensor with the specified value. """, ) add_docstr( - oneflow.Tensor.gelu, + oneflow.Tensor.ge, """ - See :func:`oneflow.gelu` + See :func:`oneflow.ge` """, ) @@ -1215,6 +1322,13 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.asinh, + """ + See :func:`oneflow.asinh` + """, +) + add_docstr( oneflow.Tensor.arcsin, """ @@ -1238,6 +1352,13 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.sin_, + """ + See :func:`oneflow.sin_` + """, +) + add_docstr( oneflow.Tensor.cos, """ @@ -1259,6 +1380,20 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.log2, + """ + See :func:`oneflow.log2` + """, +) + +add_docstr( + oneflow.Tensor.log10, + """ + See :func:`oneflow.log10` + """, +) + add_docstr( oneflow.Tensor.ndim, """ @@ -1339,10 +1474,10 @@ add_docstr( add_docstr( oneflow.Tensor.size, """ - The interface is consistent with PyTorch. - Returns the size of the self tensor. If dim is not specified, the returned value is a oneflow.Size, a subclass of tuple. If dim is specified, returns an int holding the size of that dimension. + The interface is consistent with PyTorch. + Args: idx (int, optional): The dimension for which to retrieve the size. @@ -1415,14 +1550,12 @@ add_docstr( add_docstr( oneflow.Tensor.copy_, """ - The interface is consistent with PyTorch. - - Tensor.copy_(src, non_blocking=False) → Tensor - Copies the elements from src into self tensor and returns self. The src tensor must be broadcastable with the self tensor. It may be of a different data type or reside on a different device. + The interface is consistent with PyTorch. + Args: src (Tensor): the source tensor to copy from @@ -1554,6 +1687,19 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.cumprod, + """ + See :func:`oneflow.cumprod` + """, +) + +add_docstr( + oneflow.Tensor.cumsum, + """ + See :func:`oneflow.cumsum` + """, +) add_docstr( oneflow.Tensor.repeat, @@ -1576,9 +1722,9 @@ add_docstr( add_docstr( oneflow.Tensor.t, """ - Tensor.t() → Tensor - See :func:`oneflow.t` + + Tensor.t() → Tensor """, ) @@ -1641,6 +1787,13 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.logsumexp, + """ + See :func:`oneflow.logsumexp` + """, +) + add_docstr( oneflow.Tensor.masked_fill, """ @@ -1648,6 +1801,13 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.masked_fill_, + """ + In-place version of :meth:`oneflow.Tensor.masked_fill`. + """, +) + add_docstr( oneflow.Tensor.masked_select, """ @@ -1725,6 +1885,27 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.maximum, + """ + See :func:`oneflow.maximum` + """, +) + +add_docstr( + oneflow.Tensor.median, + """ + See :func:`oneflow.median` + """, +) + +add_docstr( + oneflow.Tensor.minimum, + """ + See :func:`oneflow.minimum` + """, +) + add_docstr( oneflow.Tensor.sum, """ @@ -1796,9 +1977,6 @@ add_docstr( add_docstr( oneflow.Tensor.view, """ - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.view.html. - Returns a new tensor with the same data as the :attr:`self` tensor but of a different :attr:`shape`. @@ -1819,6 +1997,9 @@ add_docstr( returns a view if the shapes are compatible, and copies (equivalent to calling :meth:`contiguous`) otherwise. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.view.html. + Args: input: A Tensor. *shape: flow.Size or int... @@ -1890,9 +2071,31 @@ add_docstr( """, ) +add_docstr( + oneflow.Tensor.bool, + r"""``Tensor.bool()`` is equivalent to ``Tensor.to(oneflow.bool)``. See :class:`oneflow.Tensor.to()`. + + Args: + input (Tensor): the input tensor. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + + >>> input = flow.tensor(np.random.randn(1, 2, 3), dtype=flow.float32) + >>> input = input.bool() + >>> input.dtype + oneflow.bool + + """, +) + add_docstr( oneflow.Tensor.int, - r"""`Tensor.int()` is equivalent to `Tensor.to(flow.int32)`. See to(). + r"""``Tensor.int()`` is equivalent to ``Tensor.to(flow.int32)``. See :class:`oneflow.Tensor.to()`. Args: input (Tensor): the input tensor. @@ -1913,7 +2116,7 @@ add_docstr( add_docstr( oneflow.Tensor.long, - r"""`Tensor.long()` is equivalent to `Tensor.to(flow.int64)`. See to(). + r"""``Tensor.long()`` is equivalent to ``Tensor.to(flow.int64)``. See :class:`oneflow.Tensor.to()`. Args: input (Tensor): the input tensor. @@ -1934,7 +2137,7 @@ add_docstr( add_docstr( oneflow.Tensor.float, - r"""`Tensor.float()` is equivalent to `Tensor.to(flow.float32)`. See to(). + r"""``Tensor.float()`` is equivalent to ``Tensor.to(flow.float32)``. See :class:`oneflow.Tensor.to()`. Args: input (Tensor): the input tensor. @@ -1955,7 +2158,7 @@ add_docstr( add_docstr( oneflow.Tensor.double, - r"""`Tensor.double()` is equivalent to `Tensor.to(flow.float64)`. See to(). + r"""``Tensor.double()`` is equivalent to ``Tensor.to(flow.float64)``. See :class:`oneflow.Tensor.to()`. Args: input (Tensor): the input tensor. @@ -2106,8 +2309,12 @@ add_docstr( add_docstr( oneflow.Tensor.type, - r"""Returns the type if dtype is not provided, else casts this object to the specified type. - If this is already of the correct type, no copy is performed and the original object is returned. + r""" + type(dtype=None, non_blocking=False, **kwargs) -> str or Tensor + + Returns the type if dtype is not provided, else casts this object to the specified type. + + If this is already of the correct type, no copy is performed and the original object is returned. Args: dtype (oneflow.dtype or oneflow.tensortype or string, optional): The desired type. @@ -2131,3 +2338,149 @@ add_docstr( tensor([1., 2.], dtype=oneflow.float16) """, ) + +add_docstr( + oneflow.Tensor.scatter, + """ + See :func:`oneflow.scatter` + """, +) + +add_docstr( + oneflow.Tensor.scatter_, + """ + Inplace version of :func:`oneflow.Tensor.scatter` + """, +) + +add_docstr( + oneflow.Tensor.scatter_add, + """ + See :func:`oneflow.scatter_add` + """, +) + +add_docstr( + oneflow.Tensor.scatter_add_, + """ + Inplace version of :func:`oneflow.Tensor.scatter_add` + """, +) + +add_docstr( + oneflow.Tensor.cross, + """ + See :func:`oneflow.cross` + """, +) + +add_docstr( + oneflow.Tensor.nansum, + """ + See :func:`oneflow.nansum` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor([1., 2., float("nan")]) + >>> x.nansum() + tensor(3., dtype=oneflow.float32) + >>> x = flow.tensor([[1., float("nan")], [float("nan"), 2]]) + >>> x.nansum(dim=1, keepdim=True) + tensor([[1.], + [2.]], dtype=oneflow.float32) + """, +) + +add_docstr( + oneflow.Tensor.bincount, + """ + See :func:`oneflow.bincount` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.Tensor([0, 2, 3]).int() + >>> x.bincount() + tensor([1, 0, 1, 1], dtype=oneflow.int64) + >>> weight = flow.Tensor([0.1, 0.2, 0.3]) + >>> x.bincount(weight) + tensor([0.1000, 0.0000, 0.2000, 0.3000], dtype=oneflow.float32) + >>> x.bincount(weight, minlength=5) + tensor([0.1000, 0.0000, 0.2000, 0.3000, 0.0000], dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow.Tensor.bernoulli, + """ + See :func:`oneflow.bernoulli` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.Tensor([1, 1, 1]) + >>> x.bernoulli() + tensor([1., 1., 1.], dtype=oneflow.float32) + >>> x.bernoulli(p=0.0) + tensor([0., 0., 0.], dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow.Tensor.bernoulli_, + """ + The inplace version of :func:`oneflow.Tensor.bernoulli_`. + + See :func:`oneflow.Tensor.bernoulli` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.Tensor([1, 1, 1]) + >>> x.bernoulli_(p=0.0) + tensor([0., 0., 0.], dtype=oneflow.float32) + >>> x + tensor([0., 0., 0.], dtype=oneflow.float32) + + """, +) + +add_docstr( + oneflow.Tensor.broadcast_to, + """ + See :func:`oneflow.broadcast_to` + """, +) + +add_docstr( + oneflow.Tensor.unique, + """ + See :func:`oneflow.unique` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor([3, 1, 2, 0 ,2]) + >>> x.unique() + tensor([0, 1, 2, 3], dtype=oneflow.int64) + >>> x, indices = x.unique(return_inverse=True) + >>> indices + tensor([3, 1, 2, 0, 2], dtype=oneflow.int32) + >>> x, counts = x.unique(return_counts=True) + >>> counts + tensor([1, 1, 1, 1], dtype=oneflow.int32) + """, +) diff --git a/python/oneflow/framework/docstr/tensor_attributes.py b/python/oneflow/framework/docstr/tensor_attributes.py index 20c69fce5fd06e4be4ba3e5e057a9e591d15bd3a..a142ef614c839e9a85482fba61ab3122c9cc8c73 100644 --- a/python/oneflow/framework/docstr/tensor_attributes.py +++ b/python/oneflow/framework/docstr/tensor_attributes.py @@ -14,14 +14,14 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow -from oneflow.framework.docstr.utils import add_docstr +from oneflow.framework.docstr.utils import add_docstr, reset_docstr oneflow.device.__doc__ = r""" - The documentation is referenced from: - https://pytorch.org/docs/1.10/tensor_attributes.html#torch.torch.device. - A :class:`oneflow.device` is an object representing the device on which a :class:`oneflow.Tensor` is or will be allocated. + The documentation is referenced from: + https://pytorch.org/docs/1.10/tensor_attributes.html#torch.torch.device. + The :class:`oneflow.device` contains a device type ('cpu' or 'cuda') and optional device ordinal for the device type. If the device ordinal is not present, this object will always represent the current device for the device type. @@ -92,6 +92,29 @@ oneflow.placement.__doc__ = r""" """ +reset_docstr( + oneflow.placement.all, + r""" + oneflow.placement.all(device_type) -> oneflow.placement + + Returns a placement that contains all available devices. + + Args: + device_type (str): cuda or cpu + + For examples: + + .. code-block:: python + + # Runs on 4 ranks + import oneflow as flow + + p = flow.placement.all("cuda") # oneflow.placement(type="cuda", ranks=[0, 1, 2, 3]) + p = flow.placement.all("cpu") # oneflow.placement(type="cpu", ranks=[0, 1, 2, 3]) + + """, +) + oneflow.sbp.sbp.__doc__ = r""" A ``oneflow.sbp`` is an object representing that how the data of the global tensor is distributed across the ranks of the ``Tensor`` placement. diff --git a/python/oneflow/framework/docstr/tile.py b/python/oneflow/framework/docstr/tile.py index 8ffff360fbee7f5695ab6ee30c1f1ad5e50eaab2..263d6fc6ce828a9779f8a210677c869fa6bac0e8 100644 --- a/python/oneflow/framework/docstr/tile.py +++ b/python/oneflow/framework/docstr/tile.py @@ -21,10 +21,6 @@ add_docstr( """ tile(input, dims) -> Tensor - The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.tile.html. - Constructs a tensor by repeating the elements of ``input``. The ``dims`` argument specifies the number of repetitions in each dimension. @@ -39,6 +35,10 @@ add_docstr( .. note:: This function is similar to NumPy’s tile function. + + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.tile.html. Args: input (oneflow.Tensor): the tensor whose elements to repeat. diff --git a/python/oneflow/framework/docstr/topk.py b/python/oneflow/framework/docstr/topk.py index d51f78219cd719a73a511ca2fd0e37949e3a3b3f..9c7f055f1a41a45fd96aedd2c6b3f9254e7146b1 100644 --- a/python/oneflow/framework/docstr/topk.py +++ b/python/oneflow/framework/docstr/topk.py @@ -38,27 +38,27 @@ add_docstr( >>> import oneflow as flow >>> import numpy as np >>> x = np.array([[1, 3, 8, 7, 2], [1, 9, 4, 3, 2]], dtype=np.float32) - >>> (values, indices) = flow.topk(flow.Tensor(x), k=3, dim=1) - >>> values + >>> result = flow.topk(flow.Tensor(x), k=3, dim=1) + >>> result.values tensor([[8., 7., 3.], [9., 4., 3.]], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([[2, 3, 1], [1, 2, 3]], dtype=oneflow.int64) - >>> values.shape + >>> result.values.shape oneflow.Size([2, 3]) - >>> indices.shape + >>> result.indices.shape oneflow.Size([2, 3]) - >>> (values, indices) = flow.topk(flow.Tensor(x), k=2, dim=1, largest=False) - >>> values + >>> result = flow.topk(flow.Tensor(x), k=2, dim=1, largest=False) + >>> result.values tensor([[1., 2.], [1., 2.]], dtype=oneflow.float32) - >>> indices + >>> result.indices tensor([[0, 4], [0, 4]], dtype=oneflow.int64) - >>> values.shape + >>> result.values.shape oneflow.Size([2, 2]) - >>> indices.shape + >>> result.indices.shape oneflow.Size([2, 2]) """, diff --git a/python/oneflow/framework/docstr/unbind.py b/python/oneflow/framework/docstr/unbind.py index 76313f7e542d7da23908b33b1586dfbbdf11fe6a..db90bb4c05c50ab0be7065b5e61c8950e9951620 100644 --- a/python/oneflow/framework/docstr/unbind.py +++ b/python/oneflow/framework/docstr/unbind.py @@ -19,11 +19,12 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.unbind, """ - This function is equivalent to PyTorch's unbind function. Removes a tensor dimension. Returns a tuple of all slices along a given dimension, already without it. - + + This function is equivalent to PyTorch's unbind function. + Args: x(Tensor): the tensor to unbind dim(int): dimension to remove diff --git a/python/oneflow/framework/docstr/util_ops.py b/python/oneflow/framework/docstr/util_ops.py index b737a28fc8e7f084be29ea5bc74002f856ad2797..c325c2d3c0bfedb82f8ed549894ba3bb20df96c7 100644 --- a/python/oneflow/framework/docstr/util_ops.py +++ b/python/oneflow/framework/docstr/util_ops.py @@ -19,6 +19,11 @@ from oneflow.framework.docstr.utils import add_docstr add_docstr( oneflow.isnan, """ + isnan(input) -> Tensor + + This function is equivalent to PyTorch’s isnan function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.isnan.html?highlight=isnan#torch.isnan + Returns a new tensor with boolean elements representing if each element of input is NaN or not. Args: @@ -39,6 +44,11 @@ add_docstr( add_docstr( oneflow.isinf, """ + isinf(input) -> Tensor + + This function is equivalent to PyTorch’s isinf function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.isinf.html?highlight=isinf#torch.isinf + Tests if each element of input is infinite (positive or negative infinity) or not. Args: @@ -55,3 +65,28 @@ add_docstr( """, ) + +add_docstr( + oneflow.isfinite, + """ + isfinite(input) -> Tensor + + This function is equivalent to PyTorch’s isfinite function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.isfinite.html?highlight=isfinite#torch.isfinite + + Returns a new tensor with boolean elements representing if each element is finite or not. + + Args: + input(Tensor): the input tensor. + + Returns: + A boolean tensor that is True where input is finite and False elsewhere. + + Example:: + + >>> import oneflow as flow + >>> flow.isfinite(flow.tensor([1, float('inf'), 2, float('-inf'), float('nan')])) + tensor([ True, False, True, False, False], dtype=oneflow.bool) + + """, +) diff --git a/python/oneflow/framework/dtype.py b/python/oneflow/framework/dtype.py index c2e44fba2181f05a974b39b838e05c6af4a75ff7..000781e9d9dc77539aa47d60b262ad0d11a799f8 100644 --- a/python/oneflow/framework/dtype.py +++ b/python/oneflow/framework/dtype.py @@ -18,6 +18,10 @@ import numpy as np import oneflow import oneflow._oneflow_internal import oneflow.core.common.data_type_pb2 as data_type_pb2 +from oneflow._oneflow_internal import ( + set_default_dtype, + get_default_dtype, +) _dtypes = [ oneflow.bool, @@ -45,7 +49,10 @@ def convert_proto_dtype_to_oneflow_dtype(proto_dtype): _ONEFLOW_DTYPE_TO_NUMPY_DTYPE = { - oneflow.bool: np.bool, + # >> np_bool = np.array([1,2], dtype=bool).dtype + # >> np_bool == bool + # True + oneflow.bool: bool, oneflow.float: np.float32, oneflow.float16: np.float16, oneflow.float32: np.float32, @@ -73,3 +80,49 @@ def convert_numpy_dtype_to_oneflow_dtype(numpy_dtype: np.dtype): del data_type_pb2 del np + + +def set_default_tensor_type(tensor_type): + """Sets the default floating point type for those source operators which create Tensor. + + The default floating point type is ``oneflow.FloatTensor``. + + Args: + tensor_type (type or string): The floating point tensor type or its name. + + For example: + + .. code-block:: python + + >>> import oneflow + >>> oneflow.set_default_tensor_type(oneflow.FloatTensor) + >>> x = oneflow.ones(2, 3) + >>> x.dtype + oneflow.float32 + >>> oneflow.set_default_tensor_type("oneflow.DoubleTensor") + >>> x = oneflow.ones(2, 3) + >>> x.dtype + oneflow.float64 + >>> oneflow.set_default_tensor_type(oneflow.FloatTensor) + >>> x = oneflow.tensor([1.0, 2]) + >>> x.dtype + oneflow.float32 + """ + + def _import_dotted_name(name): + """ + This function quotes from: https://github.com/pytorch/pytorch/blob/master/torch/_utils.py + """ + components = name.split(".") + obj = __import__(components[0]) + for component in components[1:]: + obj = getattr(obj, component) + return obj + + if isinstance(tensor_type, str): + tensor_type = _import_dotted_name(tensor_type) + oneflow._oneflow_internal.set_default_tensor_type(tensor_type) + + +def is_floating_point(input): + return input.is_floating_point() diff --git a/python/oneflow/framework/env_util.py b/python/oneflow/framework/env_util.py index d216dfe88e39c5110e3c0aaf6a5e31a097a79885..299cb3372b7dd4d51641c5cea8f5621eb73208d4 100644 --- a/python/oneflow/framework/env_util.py +++ b/python/oneflow/framework/env_util.py @@ -17,6 +17,7 @@ import os import socket import traceback from contextlib import closing +import warnings import oneflow._oneflow_internal import oneflow.core.control.ctrl_bootstrap_pb2 as ctrl_bootstrap_pb @@ -31,6 +32,9 @@ def api_all_device_placement(device_type: str) -> oneflow._oneflow_internal.plac Returns a placement that contains all available devices. + Note: + It is recommended to use `oneflow.placement.all` instead of this function. + Args: device_type (str): cuda or cpu @@ -45,7 +49,7 @@ def api_all_device_placement(device_type: str) -> oneflow._oneflow_internal.plac p = flow.env.all_device_placement("cpu") # oneflow.placement(type="cpu", ranks=[0, 1, 2, 3]) """ - return oneflow._oneflow_internal.AllDevicePlacement(device_type) + return oneflow.placement.all(device_type) def check_non_localhost_proxy_and_print_warning(): @@ -153,19 +157,21 @@ def _FindFreePort(): def HasAllMultiClientEnvVars(): - env_var_names = ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK", "LOCAL_RANK"] - env_var_values = [os.getenv(x) for x in env_var_names] - has_no_env_vars = not any(env_var_values) - has_all_env_vars = all(env_var_values) - assert has_no_env_vars or has_all_env_vars, list(zip(env_var_names, env_var_values)) - return has_all_env_vars + env_var_names = ["MASTER_ADDR", "MASTER_PORT", "WORLD_SIZE", "RANK"] + env_var_without_value = [x for x in env_var_names if os.getenv(x) is None] + env_var_with_value = [x for x in env_var_names if os.getenv(x) is not None] + if len(env_var_with_value) != 0 and len(env_var_without_value) != 0: + warnings.warn( + f"Among four environment variables required for distributed training, only {', '.join('`{0}`'.format(x) for x in env_var_with_value)} are set, but {', '.join('`{0}`'.format(x) for x in env_var_without_value)} are not set." + ) + return len(env_var_without_value) == 0 def SetDefaultMultiClientEnvVars(): os.environ["MASTER_ADDR"] = "127.0.0.1" os.environ["MASTER_PORT"] = str(_FindFreePort()) os.environ["WORLD_SIZE"] = "1" - os.environ["RANK"] = "0" + os.environ["RANK"] = "-1" os.environ["LOCAL_RANK"] = "0" @@ -173,7 +179,6 @@ def _UpdateDefaultEnvProtoByMultiClientEnvVars(env_proto): assert HasAllMultiClientEnvVars() def str2int(env_config): - assert env_config.isdigit() return int(env_config) bootstrap_conf = ctrl_bootstrap_pb.BootstrapConf() @@ -188,9 +193,11 @@ def _UpdateDefaultEnvProtoByMultiClientEnvVars(env_proto): if os.getenv("GLOG_log_dir"): cpp_logging_conf.log_dir = os.getenv("GLOG_log_dir") if os.getenv("GLOG_logtostderr"): - cpp_logging_conf.logtostderr = int(os.getenv("GLOG_logtostderr")) + cpp_logging_conf.logtostderr = str2int(os.getenv("GLOG_logtostderr")) if os.getenv("GLOG_logbuflevel"): - cpp_logging_conf.logbuflevel = os.getenv("GLOG_logbuflevel") + cpp_logging_conf.logbuflevel = str2int(os.getenv("GLOG_logbuflevel")) + if os.getenv("GLOG_minloglevel"): + cpp_logging_conf.minloglevel = str2int(os.getenv("GLOG_minloglevel")) env_proto.cpp_logging_conf.CopyFrom(cpp_logging_conf) diff --git a/python/oneflow/framework/function_util.py b/python/oneflow/framework/function_util.py index a92de7ca1bd62316bfb894b13d779e6caeef1bb8..82336da91cb971eb40fb18e1244e31434135cd09 100644 --- a/python/oneflow/framework/function_util.py +++ b/python/oneflow/framework/function_util.py @@ -115,14 +115,6 @@ def _MakeLeafJobConfigCall(method): return lambda self, *argv, **kwarg: method(self.function_desc, *argv, **kwarg) -def _RunEagerJob(session, function_desc, *args): - return session.TryInit().EagerRun(function_desc, *args) - - -def _RunLazyJob(session, job_func, *args, **kwargs): - return session.TryInit().LazyRun(job_func, *args, **kwargs) - - @oneflow_function_config("default_data_type") def set_default_data_type(func_desc, value): """Set default data type for job diff --git a/python/oneflow/framework/generator.py b/python/oneflow/framework/generator.py index 064f405e0d71a6100f9c948abae7c9467c92be7a..4572aec777ab2ff0f911cd391b64ccafeef00aed 100644 --- a/python/oneflow/framework/generator.py +++ b/python/oneflow/framework/generator.py @@ -24,11 +24,12 @@ def create_generator(device=None): def seed() -> int: - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.seed.html. - + r""" Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.seed.html. """ seed = default_generator.seed() oneflow._oneflow_internal.manual_seed(seed) @@ -36,12 +37,13 @@ def seed() -> int: def manual_seed(seed): - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.manual_seed.html. - + r""" Sets the seed for generating random numbers. Returns a `oneflow.Generator` object. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.manual_seed.html. + Args: seed (int): The desired seed. Value must be within the inclusive range `[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]`. Otherwise, a RuntimeError @@ -53,11 +55,13 @@ def manual_seed(seed): def initial_seed() -> int: - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/_modules/torch/random.html. - + r""" Returns the initial seed for generating random numbers as a Python `long`. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/_modules/torch/random.html. + """ return default_generator.initial_seed() @@ -72,11 +76,12 @@ def _setstate(self, state_dict): def get_rng_state(): - r"""The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.get_rng_state.html. - + r""" Sets the random number generator state. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.get_rng_state.html. + .. note: This function only works for CPU. For CUDA, please use oneflow.manual_seed(seed), which works for both CPU and CUDA. @@ -87,11 +92,12 @@ def get_rng_state(): def set_rng_state(state): - """The documentation is referenced from: + """ + Returns the random number generator state as a `oneflow.ByteTensor`. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.set_rng_state.html. - - Returns the random number generator state as a `oneflow.ByteTensor`. """ return oneflow.default_generator.set_state(state) diff --git a/python/oneflow/framework/graph_build_util.py b/python/oneflow/framework/graph_build_util.py index d6d4ba6a7034075317a2ee28ec5e57ace3042916..cbd7b34d7a00a1456efaccceec2bf610a8025eff 100644 --- a/python/oneflow/framework/graph_build_util.py +++ b/python/oneflow/framework/graph_build_util.py @@ -27,6 +27,7 @@ import oneflow.framework.c_api_util as c_api_util import oneflow.framework.scope_util as scope_util import oneflow.framework.session_context as session_context from oneflow.framework.tensor import Tensor +from oneflow.nn.graph.proxy import GraphBlockType import oneflow._oneflow_internal._C as _C lazy_mode = oneflow._oneflow_internal.lazy_mode @@ -38,11 +39,12 @@ def graph_build_context(config_proto, session): assert type(config_proto) is job_conf_pb.JobConfigProto, type(config_proto) config_proto_str = text_format.MessageToString(config_proto) new_scope = oneflow._oneflow_internal.MakeInitialScope( - config_proto_str, oneflow.placement("cpu", [0]), False, # is_mirrored + config_proto_str, oneflow.placement("cpu", [0]), False, # is_local ) graph_scope = _make_new_graph_scope(new_scope, config_proto.job_name) + oneflow._oneflow_internal.eager.Sync() with lazy_mode.guard(True): with JobBuildAndInferCtx(config_proto): with BlockScopeContext(prev_scope, graph_scope): @@ -88,19 +90,30 @@ class BlockScopeContext(object): class DebugScopeContext(object): - def __init__(self, s_level, v_level=0, mode=False, max_py_stack_depth=2): + def __init__( + self, + s_level, + v_level=0, + mode=False, + max_py_stack_depth=2, + only_user_py_stack=True, + ): self._prev_v = oneflow._oneflow_internal.GetFLAGS_v() self._prev_logtostderr = oneflow._oneflow_internal.GetFLAGS_alsologtostderr() self._prev_mode = oneflow._oneflow_internal.GetGraphDebugMode() self._prev_max_py_stack_depth = ( oneflow._oneflow_internal.GetGraphDebugMaxPyStackDepth() ) + self._prev_only_user_py_stack = ( + oneflow._oneflow_internal.GetGraphDebugOnlyUserPyStack() + ) self._v = max(v_level, self._prev_v) self._mode = mode self._s = s_level self._max_py_stack_depth = max( max_py_stack_depth, self._prev_max_py_stack_depth ) + self._only_user_py_stack = only_user_py_stack def __enter__(self): oneflow._oneflow_internal.SetFLAGS_v(self._v) @@ -108,6 +121,7 @@ class DebugScopeContext(object): if self._s == 0 and self._v >= 1: oneflow._oneflow_internal.SetFLAGS_alsologtostderr(True) oneflow._oneflow_internal.SetGraphDebugMaxPyStackDepth(self._max_py_stack_depth) + oneflow._oneflow_internal.SetGraphDebugOnlyUserPyStack(self._only_user_py_stack) def __exit__(self, exc_type, exc_val, exc_tb): if self._s == 0 and self._v >= 1: @@ -117,6 +131,9 @@ class DebugScopeContext(object): oneflow._oneflow_internal.SetGraphDebugMaxPyStackDepth( self._prev_max_py_stack_depth ) + oneflow._oneflow_internal.SetGraphDebugOnlyUserPyStack( + self._prev_only_user_py_stack + ) def _make_new_scope(prev_scope, scope_proto_str_setter): @@ -149,14 +166,15 @@ def _make_new_graph_scope(prev_scope, graph_name): return _make_new_scope(prev_scope, scope_proto_str_setter) -def make_new_block_scope(prev_scope, block): +def make_new_blockgraph_scope(prev_scope, graph_block): assert prev_scope is not None - assert block is not None + assert graph_block is not None attr_dict = dict() - if block.config.stage_id is not None: - attr_dict["pipeline_stage_id_hint"] = block.config.stage_id - if block.config.activation_checkpointing is not None: - attr_dict["checkpointing"] = block.config.activation_checkpointing + if graph_block.stage_id is not None: + attr_dict["pipeline_stage_id_hint"] = graph_block.stage_id + if graph_block.type == GraphBlockType.MODULE: + if graph_block.activation_checkpointing is not None: + attr_dict["checkpointing"] = graph_block.activation_checkpointing name2default = session_context.GetDefaultSession().scope_attr_name2default_val @@ -174,10 +192,28 @@ def make_new_block_scope(prev_scope, block): ) # append name prefix scope_proto.ClearField("scope_op_name_prefixes") - scope_proto.scope_op_name_prefixes.append(block.name_prefix + block.name) + scope_proto.scope_op_name_prefixes.append( + graph_block.name_prefix + graph_block.name + ) # set module name - if isinstance(block, oneflow.nn.graph.block.ModuleBlock): - scope_proto.module_name = block.name_prefix + block.name + if graph_block.type == GraphBlockType.MODULE: + scope_proto.module_name = graph_block.name_prefix + graph_block.name + return str(text_format.MessageToString(scope_proto)) + + return _make_new_scope(prev_scope, scope_proto_str_setter) + + +def make_new_name_scope(prev_scope, name): + assert prev_scope is not None + + def scope_proto_str_setter(serialized_scope_proto: str): + scope_proto = text_format.Parse( + serialized_scope_proto, scope_pb2_util.ScopeProto() + ) + # append name prefix + scope_proto.ClearField("scope_op_name_prefixes") + scope_proto.scope_op_name_prefixes.append(name) + scope_proto.module_name = name return str(text_format.MessageToString(scope_proto)) return _make_new_scope(prev_scope, scope_proto_str_setter) @@ -233,5 +269,4 @@ def build_graph_output(op_name, out): op_name, output_conf_str, ["in_0"], ["out_0"] ) fake_eager_out = _C.dispatch_fetch_output(output_op, out) - return fake_eager_out diff --git a/python/oneflow/framework/hob.py b/python/oneflow/framework/hob.py index ad0c9a2a0a2d611d91fd3694a5ed2e62f2108d4f..d3e64b7f22ff5262ac2097a2be0e6e8a6550c348 100644 --- a/python/oneflow/framework/hob.py +++ b/python/oneflow/framework/hob.py @@ -13,55 +13,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -import oneflow -import oneflow._oneflow_internal -import oneflow.framework.runtime_mode as rt_mode -import oneflow.framework.session_context as session_ctx from oneflow.support.high_order_bool import bool_functor -@bool_functor("Current mode is %s" % rt_mode.NORMAL_MODE) -def in_normal_mode(ctx): - return rt_mode.CurrentMode() == rt_mode.NORMAL_MODE - - +"""Example: @bool_functor("Current mode is %s" % rt_mode.GLOBAL_MODE) def in_global_mode(ctx): return rt_mode.CurrentMode() == rt_mode.GLOBAL_MODE - - -@bool_functor("Current mode is %s" % rt_mode.DEVICE_MODE) -def in_device_mode(ctx): - return rt_mode.CurrentMode() == rt_mode.DEVICE_MODE - - -@bool_functor("Any global function defined") -def any_global_function_defined(ctx): - assert in_normal_mode(ctx) - return session_ctx.GetDefaultSession().AnyGlobalFunctionDefined() - - -@bool_functor("Eager execution enabled") -def eager_execution_enabled(ctx): - return oneflow._oneflow_internal.EagerExecutionEnabled() - - -@bool_functor("Session initialized") -def session_initialized(ctx): - assert in_normal_mode(ctx) - return session_ctx.GetDefaultSession().is_running - - -@bool_functor("Current global function is trainable") -def is_trainable(ctx): - assert in_global_mode(ctx) - if oneflow._oneflow_internal.EagerExecutionEnabled(): - return session_ctx.GetDefaultSession().CurrentEagerGlobalFunctionDesc() - else: - job_name = oneflow._oneflow_internal.JobBuildAndInferCtx_GetCurrentJobName() - return session_ctx.GetDefaultSession().GetFunctionDesc(job_name) - - -@bool_functor("Current machine is master") -def is_current_machine_master(ctx): - return oneflow._oneflow_internal.CurrentMachineId() == 0 +""" diff --git a/python/oneflow/framework/job_instance.py b/python/oneflow/framework/job_instance.py deleted file mode 100644 index e5da48d59c4901e4b063f852a5c299df41767e09..0000000000000000000000000000000000000000 --- a/python/oneflow/framework/job_instance.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import sys -import traceback - -import oneflow._oneflow_internal -import oneflow.framework.ofblob as ofblob - - -def MakeUserJobInstance(job_name, finish_cb=None): - return MakeJobInstance(job_name, finish_cb=finish_cb) - - -def MakePullJobInstance(job_name, op_name, pull_cb, finish_cb=None): - return MakeJobInstance( - job_name, - sole_output_op_name_in_user_job=op_name, - pull_cb=pull_cb, - finish_cb=finish_cb, - ) - - -def MakePushJobInstance(job_name, op_name, push_cb, finish_cb=None): - return MakeJobInstance( - job_name, - sole_input_op_name_in_user_job=op_name, - push_cb=push_cb, - finish_cb=finish_cb, - ) - - -def MakeArgPassJobInstance(job_name, src_op_name, dst_op_name, finish_cb=None): - return MakeJobInstance( - job_name, - sole_output_op_name_in_user_job=src_op_name, - sole_input_op_name_in_user_job=dst_op_name, - finish_cb=finish_cb, - ) - - -def MakeJobInstance(*arg, **kw): - def _DoNothing(): - pass - - if "finish_cb" not in kw or kw["finish_cb"] is None: - kw["finish_cb"] = _DoNothing - job_instance = JobInstance(*arg, **kw) - global _flying_job_instance - _flying_job_instance[id(job_instance)] = job_instance - - def DereferenceJobInstance(job_instance): - global _flying_job_instance - del _flying_job_instance[id(job_instance)] - - job_instance.AddPostFinishCallback(DereferenceJobInstance) - return job_instance - - -class JobInstance(oneflow._oneflow_internal.JobInstance): - def __init__( - self, - job_name, - sole_input_op_name_in_user_job=None, - sole_output_op_name_in_user_job=None, - push_cb=None, - pull_cb=None, - finish_cb=None, - ): - oneflow._oneflow_internal.JobInstance.__init__(self) - self.thisown = 0 - self.job_name_ = str(job_name) - self.sole_input_op_name_in_user_job_ = str(sole_input_op_name_in_user_job) - self.sole_output_op_name_in_user_job_ = str(sole_output_op_name_in_user_job) - self.push_cb_ = push_cb - self.pull_cb_ = pull_cb - self.finish_cb_ = finish_cb - self.post_finish_cbs_ = [] - - def job_name(self): - try: - return self.job_name_ - except Exception as e: - print(traceback.format_exc()) - raise e - - def sole_input_op_name_in_user_job(self): - try: - return self.sole_input_op_name_in_user_job_ - except Exception as e: - print(traceback.format_exc()) - raise e - - def sole_output_op_name_in_user_job(self): - try: - return self.sole_output_op_name_in_user_job_ - except Exception as e: - print(traceback.format_exc()) - raise e - - def PushBlob(self, of_blob_ptr): - try: - self.push_cb_(ofblob.OfBlob(of_blob_ptr)) - except Exception as e: - print(traceback.format_exc()) - raise e - - def PullBlob(self, of_blob_ptr): - try: - self.pull_cb_(ofblob.OfBlob(of_blob_ptr)) - except Exception as e: - print(traceback.format_exc()) - raise e - - def Finish(self): - try: - self.finish_cb_() - except Exception as e: - print(traceback.format_exc()) - raise e - finally: - try: - for post_finish_cb in self.post_finish_cbs_: - post_finish_cb(self) - except Exception as e: - print(traceback.format_exc()) - raise e - - def AddPostFinishCallback(self, cb): - self.post_finish_cbs_.append(cb) - - -_flying_job_instance = {} diff --git a/python/oneflow/framework/model.py b/python/oneflow/framework/model.py index ba9093638ee9ae6d68b03b9ae32a3e01a03fd49a..7bef513e1c07c7a87a607e7359e6c0eb20811256 100644 --- a/python/oneflow/framework/model.py +++ b/python/oneflow/framework/model.py @@ -33,8 +33,8 @@ import oneflow._oneflow_internal import oneflow.framework.dtype as dtype_util from oneflow.framework.function_util import FunctionConfig as ExecutionConfig from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module -from oneflow.nn.optimizer.optimizer import Optimizer as OOPOptimizer +from oneflow.nn.modules.module import Module +from oneflow.optim.optimizer import Optimizer as OOPOptimizer class DataModule(Module): diff --git a/python/oneflow/framework/multi_client_session.py b/python/oneflow/framework/multi_client_session.py index 64d5304999b9aa170078719b1504e72768c2e9d8..f4bcdd04152abd01b15410363d07c7e7db60633d 100644 --- a/python/oneflow/framework/multi_client_session.py +++ b/python/oneflow/framework/multi_client_session.py @@ -35,8 +35,6 @@ class MultiClientSession(object): self._id = sess_id self._env = env assert self._env is not None - # TODO(strint): Remove old session. - self._internal_sess = oneflow._oneflow_internal.RegsiterSession(sess_id) # New a MultiClientSessionContext self._session_ctx = oneflow._oneflow_internal.SessionContext(self._env._env_cxt) self.config_proto_ = self._make_config_proto() @@ -46,6 +44,12 @@ class MultiClientSession(object): self._update_scope_attr_name2defaultVal() self.status_ = self.Status.CREATED + def __del__(self): + if self._env.is_shutting_down(): + # After python shutting down, it's not safe to call oneflow + return + self._TryClose() + def TryInit(self): self._check_status(self.Status.CREATED, self.Status.INITED) if self.status_ == self.Status.CREATED: @@ -55,9 +59,29 @@ class MultiClientSession(object): def _TryClose(self): if self.status_ != self.Status.CLOSED: - oneflow._oneflow_internal.ClearSessionById(self.id) + oneflow._oneflow_internal.eager.Sync() + self._session_ctx.try_close() self.status_ = self.Status.CLOSED + def Reset(self): + if self.status_ == self.Status.CLOSED: + return + + if self.status_ == self.Status.CREATED or self.status_ == self.Status.INITED: + self.config_proto_ = self._make_config_proto() + self.function_flag_name2default_val_ = {} + self._update_function_flag_name2defaultVal() + self.scope_attr_name2default_val_ = {} + self._update_scope_attr_name2defaultVal() + + if self.status_ == self.Status.INITED: + # ensure all graphs using _session_ctx are finished + oneflow._oneflow_internal.eager.Sync() + + self._session_ctx.try_close() + config_proto_str = text_format.MessageToString(self.config_proto) + self._session_ctx.try_init(config_proto_str) + @property def status(self): return self.status_ @@ -87,9 +111,6 @@ class MultiClientSession(object): def is_running(self): return self.status_ == self.Status.INITED - def AnyGlobalFunctionDefined(self): - return False - def _check_status(self, *status): check_success = False for stat in status: @@ -123,9 +144,3 @@ class MultiClientSession(object): self._check_status(self.Status.INITED) config_proto_str = text_format.MessageToString(resource_config) self._session_ctx.update_resource(config_proto_str) - - def __del__(self): - if self._env.is_shutting_down(): - # After python shutting down, it's not safe to call oneflow - return - self._TryClose() diff --git a/python/oneflow/framework/ofblob.py b/python/oneflow/framework/ofblob.py deleted file mode 100644 index 259142d1586ea04e8586f068c8dbe83fd95a3bc2..0000000000000000000000000000000000000000 --- a/python/oneflow/framework/ofblob.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import collections -from functools import reduce - -import numpy as np -from google.protobuf import text_format - -import oneflow as flow -import oneflow._oneflow_internal -from oneflow.framework.dtype import convert_proto_dtype_to_oneflow_dtype -from oneflow.support.box import Box - - -class OfBlob(object): - def __init__(self, of_blob_ptr): - self.of_blob_ptr_ = of_blob_ptr - - @property - def dtype(self): - return convert_proto_dtype_to_oneflow_dtype( - oneflow._oneflow_internal.Ofblob_GetDataType(self.of_blob_ptr_) - ) - - @property - def static_shape(self): - num_axes = oneflow._oneflow_internal.OfBlob_NumAxes(self.of_blob_ptr_) - dst_ndarray = np.ndarray(num_axes, dtype=np.int64) - oneflow._oneflow_internal.OfBlob_CopyStaticShapeTo( - self.of_blob_ptr_, dst_ndarray - ) - return tuple(dst_ndarray.tolist()) - - @property - def shape(self): - num_axes = oneflow._oneflow_internal.OfBlob_NumAxes(self.of_blob_ptr_) - dst_ndarray = np.zeros(num_axes, dtype=np.int64) - oneflow._oneflow_internal.OfBlob_CopyShapeTo(self.of_blob_ptr_, dst_ndarray) - return tuple(dst_ndarray.tolist()) - - def set_shape(self, shape): - assert isinstance(shape, (list, tuple)) - assert len(shape) == oneflow._oneflow_internal.OfBlob_NumAxes(self.of_blob_ptr_) - oneflow._oneflow_internal.OfBlob_CopyShapeFrom( - self.of_blob_ptr_, np.array(shape, dtype=np.int64) - ) - - @property - def num_axes(self): - return oneflow._oneflow_internal.OfBlob_NumAxes(self.of_blob_ptr_) - - @property - def is_dynamic(self): - return oneflow._oneflow_internal.OfBlob_IsDynamic(self.of_blob_ptr_) - - def CopyToNdarray(self): - return self._CopyToNdarray() - - def CopyFromNdarray(self, src_ndarray): - if self.is_dynamic: - self.set_shape(src_ndarray.shape) - else: - shape_tensor = np.zeros(self.num_axes, dtype=np.int64) - oneflow._oneflow_internal.OfBlob_CopyShapeTo( - self.of_blob_ptr_, shape_tensor - ) - shape = tuple(shape_tensor.tolist()) - assert src_ndarray.shape == shape - return self._CopyBodyFromNdarray(src_ndarray) - - def _CopyBodyFromNdarray(self, src_ndarray): - method_name = oneflow._oneflow_internal.Dtype_GetOfBlobCopyFromBufferFuncName( - oneflow._oneflow_internal.deprecated.GetProtoDtype4OfDtype(self.dtype) - ) - copy_method = getattr(oneflow._oneflow_internal, method_name) - copy_method(self.of_blob_ptr_, src_ndarray) - - def _CopyToNdarray(self): - method_name = oneflow._oneflow_internal.Dtype_GetOfBlobCopyToBufferFuncName( - oneflow._oneflow_internal.deprecated.GetProtoDtype4OfDtype(self.dtype) - ) - copy_method = getattr(oneflow._oneflow_internal, method_name) - shape_tensor = np.zeros(self.num_axes, dtype=np.int64) - oneflow._oneflow_internal.OfBlob_CopyShapeTo(self.of_blob_ptr_, shape_tensor) - shape = tuple(shape_tensor.tolist()) - tensor = np.zeros( - shape, dtype=flow.convert_oneflow_dtype_to_numpy_dtype(self.dtype) - ) - copy_method(self.of_blob_ptr_, tensor) - return tensor diff --git a/python/oneflow/framework/python_callback.py b/python/oneflow/framework/python_callback.py deleted file mode 100644 index aea57b9058a499025f22e286fe0940d8eabc1b3e..0000000000000000000000000000000000000000 --- a/python/oneflow/framework/python_callback.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import traceback - -import oneflow._oneflow_internal -import oneflow.framework.ofblob as ofblob - - -def GetIdForRegisteredCallback(cb): - assert callable(cb) - global unique_id2handler - unique_id2handler[id(cb)] = cb - return id(cb) - - -def DeleteRegisteredCallback(cb): - global unique_id2handler - assert id(cb) in unique_id2handler - del unique_id2handler[id(cb)] - - -class PythonCallback(oneflow._oneflow_internal.ForeignCallback): - def __init__(self): - oneflow._oneflow_internal.ForeignCallback.__init__(self) - - def OfBlobCall(self, unique_id, of_blob_ptr): - try: - _WatcherHandler(unique_id, of_blob_ptr) - except Exception as e: - print(traceback.format_exc()) - raise e - - def RemoveForeignCallback(self, unique_id): - global unique_id2handler - try: - del unique_id2handler[unique_id] - except Exception as e: - print(traceback.format_exc()) - raise e - - -def _WatcherHandler(unique_id, of_blob_ptr): - global unique_id2handler - assert unique_id in unique_id2handler - handler = unique_id2handler[unique_id] - assert callable(handler) - handler(ofblob.OfBlob(of_blob_ptr)) - - -unique_id2handler = {} -global_python_callback = PythonCallback() diff --git a/python/oneflow/framework/runtime_mode.py b/python/oneflow/framework/runtime_mode.py deleted file mode 100644 index 0e063b035e2201dc89e4a9e7e03f19b8d267937a..0000000000000000000000000000000000000000 --- a/python/oneflow/framework/runtime_mode.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from contextlib import contextmanager - -NORMAL_MODE = "NORMAL_MODE" -GLOBAL_MODE = "GLOBAL_MODE" -DEVICE_MODE = "DEVICE_MODE" - - -def CurrentMode(): - return mode_statck[0] - - -def IsValidMode(mode): - return mode == NORMAL_MODE or mode == GLOBAL_MODE or mode == DEVICE_MODE - - -@contextmanager -def ModeScope(mode): - global mode_statck - mode_statck.insert(0, mode) - try: - yield - finally: - mode_statck.pop(0) - - -mode_statck = [NORMAL_MODE] diff --git a/python/oneflow/framework/session_context.py b/python/oneflow/framework/session_context.py index 0d67cd29ea8a1af2bd2296e6e90b250bb911f8f8..de2b6bb94ba86587f788225cc473e279c0860e8b 100644 --- a/python/oneflow/framework/session_context.py +++ b/python/oneflow/framework/session_context.py @@ -34,7 +34,9 @@ def GetDefaultSession(): def NewDefaultSession(env): - new_default_sess = MultiClientSession(env, oneflow._oneflow_internal.NewSessionId()) + session_id = oneflow._oneflow_internal.NewSessionId() + assert oneflow._oneflow_internal.RegsterSessionId(session_id) + new_default_sess = MultiClientSession(env, session_id) global _sess_id2sess assert new_default_sess.id not in _sess_id2sess _sess_id2sess[new_default_sess.id] = new_default_sess @@ -46,6 +48,7 @@ def TryCloseDefaultSession(): assert default_sess_id in _sess_id2sess if default_sess_id in _sess_id2sess: del _sess_id2sess[default_sess_id] + assert oneflow._oneflow_internal.ClearSessionId(default_sess_id) def try_init_default_session(func): diff --git a/python/oneflow/framework/tensor.py b/python/oneflow/framework/tensor.py index ec6f46bc424c18c1d45549e22587314e26e49ae3..e6741d38f3bdd25d3e6477bae21801a1f9ec4499 100644 --- a/python/oneflow/framework/tensor.py +++ b/python/oneflow/framework/tensor.py @@ -13,9 +13,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ +from numbers import Number import oneflow as flow import oneflow.framework.tensor_str as tensor_str -import oneflow.ops.initializer_util as initializer_util import oneflow._oneflow_internal.lazy_mode as lazy_mode import numpy as np @@ -30,18 +30,10 @@ def _ndim(self): def _backward(self, gradient=None, retain_graph=False, create_graph=False): - if not lazy_mode.is_enabled(): - flow.autograd.backward(self, gradient, retain_graph, create_graph) - else: + if lazy_mode.is_enabled(): assert ( self.is_lazy ), "nn.Graph only accept lazy tensor to call backward() in lazy mode." - assert ( - self.shape.numel() == 1 - ), " loss_tensor.backward(), loss_tensor must be a scalar in nn.Graph, please use loss_tensor.sum() or loss_tensor.mean() to make it a scalar tensor." - assert ( - gradient is None - ), "nn.Graph donot accept 'gradient' argument in backward() at the moment." assert ( not retain_graph ), "nn.Graph donot accept 'retain_graph' argument in backward() at the moment." @@ -49,6 +41,7 @@ def _backward(self, gradient=None, retain_graph=False, create_graph=False): not create_graph ), "nn.Graph donot accept 'create_graph' argument in backward() at the moment." flow._oneflow_internal.nn.graph.AddTensorAsGraphLoss(self) + flow.autograd.backward(self, gradient, retain_graph, create_graph) def _str(self): @@ -69,7 +62,7 @@ def _eq(self, other): elif self is None or other is None: return False else: - return flow._C.equal(self, other) + return flow._C.broadcast_equal(self, other) def _cuda(self, device: Union[int, str, flow.device] = None): @@ -81,7 +74,9 @@ def _cuda(self, device: Union[int, str, flow.device] = None): def _norm(self, p=None, dim=None, keepdim=False, dtype=None): - return flow._C.norm(self, p, dim, keepdim, dtype=dtype) + if type(p) == str or dim != None: + return flow._C.norm(self, p, dim, keepdim, dtype=dtype) + return flow._C.norm(self, p, dim, keepdim, dtype=dtype, for_norm=True) def is_nonzero(input): @@ -176,11 +171,6 @@ def _scalar_int(self): return self.numpy().astype(np.int64).item() -def _item(self): - assert self.numel() == 1, "Only a Tensor with 1 element can be converted to Scalar" - return self.numpy().item() - - def _new_empty( self, *size, dtype=None, device=None, placement=None, sbp=None, requires_grad=False, ): @@ -188,13 +178,7 @@ def _new_empty( def _new_ones( - self, - size=None, - dtype=None, - device=None, - placement=None, - sbp=None, - requires_grad=False, + self, *size, dtype=None, device=None, placement=None, sbp=None, requires_grad=False, ): return flow.new_ones(self, size, dtype, device, placement, sbp, requires_grad) @@ -205,82 +189,77 @@ def _new_zeros( return flow.new_zeros(self, size, dtype, device, placement, sbp, requires_grad) +def _squeeze_inplace(self, dim=None): + return flow._C.squeeze_(self, dim=dim) + + +def _unsqueeze_inplace(self, dim=None): + return flow._C.unsqueeze_(self, dim=dim) + + +def _new_full( + self, + size, + fill_value, + dtype=None, + device=None, + placement=None, + sbp=None, + requires_grad=False, +): + return flow.new_full( + self, size, fill_value, dtype, device, placement, sbp, requires_grad + ) + + def _mm(self, mat2): return flow._C.mm(self, mat2) def _mv(self, vec): - return flow._C.mv(self, vec) + return flow._C.matrix_vector_product(self, vec) -def _argsort(self, dim=None, descending=None): +def _argsort(self, dim=-1, descending=None): return flow.argsort(self, dim=dim, descending=descending) -def _split(self, split_size_or_sections=None, dim=0): - return flow._C.split(self, split_size_or_sections, dim) +def _uniform(self, a=0, b=1): + return flow.nn.init.uniform_(self, a, b) -def _uniform(self, a=0, b=1): - if isinstance(a, Tensor): - assert a.ndim == 0 and a.nelement() == 1, "a must be a number or scalar tensor!" - a = a.numpy().item() - if isinstance(b, Tensor): - assert b.ndim == 0 and b.nelement() == 1, "b must be a number or scalar tensor!" - b = b.numpy().item() - initializer_conf = flow.random_uniform_initializer( - minval=a, maxval=b, dtype=self.dtype - ) - return _init_by_initializer_conf(self, initializer_conf) +def _exponential(self, lambd=1.0, generator=None): + return flow._C.exponential_(self, lambd, generator) def _trunc_normal_( self, mean=0.0, std=1.0, a=-2.0, b=2.0, ): - initializer_conf = flow.truncated_normal_initializer(mean=mean, stddev=std) - res = _init_by_initializer_conf(self, initializer_conf) - res = flow.clamp(res, min=a, max=b) - return res + return flow.nn.init.trunc_normal_(self, mean=mean, std=std, a=a, b=b) def _kaiming_uniform( self, a=0, mode="fan_in", nonlinearity="leaky_relu", *, data_format="NCHW" ): - initializer_conf = flow.kaiming_initializer( - shape=self.shape, - distribution="random_uniform", - mode=mode, - nonlinearity=nonlinearity, - negative_slope=a, - data_format=data_format, + return flow.nn.init.kaiming_uniform_( + self, a=a, mode=mode, nonlinearity=nonlinearity, data_format=data_format ) - return _init_by_initializer_conf(self, initializer_conf) def _kaiming_normal( self, a=0, mode="fan_in", nonlinearity="leaky_relu", *, data_format="NCHW" ): - initializer_conf = flow.kaiming_initializer( - shape=self.shape, - distribution="random_normal", - mode=mode, - nonlinearity=nonlinearity, - negative_slope=a, - data_format=data_format, + return flow.nn.init.kaiming_normal_( + self, a=a, mode=mode, nonlinearity=nonlinearity, data_format=data_format ) - return _init_by_initializer_conf(self, initializer_conf) -def _xavier_normal(self, gain=1.0, *, data_format="NCHW"): - assert gain == 1.0, "Only gain == 1.0 is supported now" - initializer_conf = flow.xavier_normal_initializer(data_format=data_format) - return _init_by_initializer_conf(self, initializer_conf) +def _xavier_normal(self, gain=1.0): + return flow.nn.init.xavier_normal_(self, gain=gain, data_format=data_format) -def _xavier_uniform(self, gain=1.0, *, data_format="NCHW"): - assert gain == 1.0, "Only gain == 1.0 is supported now" - initializer_conf = flow.xavier_uniform_initializer(data_format=data_format) - return _init_by_initializer_conf(self, initializer_conf) +def _xavier_uniform(self, gain=1.0): + return flow.nn.init.xavier_uniform_(self, gain=gain, data_format=data_format) def _orthogonal(self, gain=1.0): @@ -303,24 +282,7 @@ def _orthogonal(self, gain=1.0): def _normal(self, mean=0, std=1): - if self.is_global: - src_tensor = flow.normal(mean, std, self.shape) - src_tensor = src_tensor.to_global( - placement=self.placement, - sbp=tuple(flow.sbp.broadcast for _ in range(len(self.sbp))), - ) - self.copy_(src_tensor) - return self - else: - return flow.normal( - mean, - std, - self.size(), - out=self, - dtype=self.dtype, - device=self.device, - requires_grad=self.requires_grad, - ) + return flow.nn.init.normal_(self, mean=mean, std=std) def _fill(self, value): @@ -328,36 +290,11 @@ def _fill(self, value): def _copy_from_numpy_to_eager_local_tensor(eager_local_tensor, np_arr): - method_name = eager_local_tensor._get_copy_mirrored_tensor_from_numpy_func_name() - copy_from_numpy = getattr(eager_local_tensor, method_name) assert np_arr.dtype == flow.convert_oneflow_dtype_to_numpy_dtype( eager_local_tensor.dtype ) assert np_arr.shape == tuple(eager_local_tensor.shape) - copy_from_numpy(np_arr) - - -def _init_by_initializer_conf(tensor, initializer_conf, random_seed=None): - if random_seed is None: - random_seed = flow.default_generator.initial_seed() - shape = tuple(tensor.shape) - initializer = initializer_util.GetInitializer(initializer_conf, random_seed, shape) - - np_arr = initializer_util.generate_values_by_initializer( - initializer, shape, tensor.dtype - ) - if tensor.is_global: - src_tensor = flow.tensor(np_arr) - src_tensor = src_tensor.to_global( - placement=tensor.placement, - sbp=tuple(flow.sbp.broadcast for _ in range(len(tensor.sbp))), - ) - tensor.copy_(src_tensor) - else: - _copy_from_numpy_to_eager_local_tensor( - tensor, np_arr, - ) - return tensor + eager_local_tensor._copy_from_numpy(np_arr) def _copy(self, other: Union[Tensor, np.ndarray]): @@ -375,7 +312,13 @@ def _copy(self, other: Union[Tensor, np.ndarray]): not other.is_global ), "Only local tensor can be assigned to local tensor." if self.device == other.device: - flow._C.assign_local_tensor(self, other) + other = flow._C.broadcast_like(other, self) + if not self.is_contiguous(): + # NOTE: slice_update support non-contiguous input tensor + with flow.no_grad(): + self[...] = other + else: + flow._C.assign_local_tensor(self, other) return # Possibility 2: `other` is a numpy array, or `self` and `other` are tensors on different devices/placements. @@ -401,10 +344,6 @@ def _copy(self, other: Union[Tensor, np.ndarray]): _copy_from_numpy_to_eager_local_tensor(self, other) -def _flip(self, dims): - return flow.flip(self, dims) - - def _format(self, format_spec): if self.dim() == 0: return self.numpy().tolist().__format__(format_spec) @@ -463,10 +402,6 @@ def _T(self): return flow._C.T(self) -def _topk(self, k, dim: int = None, largest: bool = True, sorted: bool = True): - return flow.topk(self, k, dim, largest, sorted) - - def _nms(boxes, scores, iou_threshold: float): return flow.nms(boxes, scores, iou_threshold) @@ -487,33 +422,26 @@ def _sort(self, dim: int = -1, descending: bool = False): return flow.sort(self, dim, descending) -def _type_as(self, target): - return self.to(dtype=target.dtype) - - def _where(self, x=None, y=None): return flow.where(self, x, y) -def _is_floating_point(self): - return flow.is_floating_point(self) - - def _numpy(self): assert ( not self.is_lazy - ), "tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor." + ), "tensor.numpy() is not allowed to be called in nn.Graph.build(*args) or be called by lazy tensor." if self.dtype == flow.tensor_buffer: shapes, dtypes = self._tensor_buffer_shapes_and_dtypes tensors = flow.tensor_buffer_to_list_of_tensors(self, shapes, dtypes) return [t.numpy() for t in tensors] + # TODO: support bfloat16 to numpy in C++ + if self.dtype == flow.bfloat16: + self = self.to(flow.float32) if self.is_global: self_cpu_placement = flow.placement("cpu", self.placement.ranks) self = ( self.to_global(placement=self_cpu_placement) - .to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.broadcast - ) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast) .to_local() ) assert self.is_local @@ -568,6 +496,79 @@ def _cumprod(self, dim, dtype=None): return flow._C.cumprod(self, dim, dtype=dtype) +def _inv(self): + return flow._C.inv(self) + + +def _trunc(self): + """trunc() -> Tensor + + See :func:`oneflow.trunc` + """ + return flow._C.trunc(self) + + +def _cross(self, other, dim=None): + return flow._C.cross(self, other, dim) + + +def _scatter(self, dim, index, src, *, reduce=""): + return flow._C.scatter(self, dim, index, src, reduce=reduce, inplace=False) + + +def _scatter_inplace(self, dim, index, src, *, reduce=None): + return flow._C.scatter(self, dim, index, src, reduce=reduce, inplace=True) + + +def _scatter_add(self, dim, index, src): + return flow._C.scatter_add(self, dim, index, src, inplace=False) + + +def _scatter_add_inplace(self, dim, index, src): + return flow._C.scatter_add(self, dim, index, src, inplace=True) + + +def _contains(self, element): + r"""Check if `element` is present in tensor + + Args: + element (Tensor or scalar): element to be checked + for presence in current tensor" + """ + if isinstance(element, (flow.Tensor, Number)): + # type hint doesn't understand the __contains__ result array + return (element == self).any().item() # type: ignore[union-attr] + + raise RuntimeError( + "Tensor.__contains__ only supports Tensor or scalar, but you passed in a %s." + % type(element) + ) + + +def _allclose(self, other, atol=1e-08, rtol=1e-05, equal_nan=False): + return flow._C.allclose(self, other, atol, rtol, equal_nan) + + +def _index_add(self, dim, index, source, alpha=1): + return flow._C.index_add(self, dim, index, source, alpha) + + +def _index_add_inplace(self, dim, index, source, alpha=1): + return flow._C.index_add_(self, dim, index, source, alpha) + + +def _as_strided(self, size, stride, storage_offset=0): + return flow._C.as_strided(self, size, stride, storage_offset) + + +def _as_strided_inplace(self, size, stride, storage_offset=0): + return flow._C.as_strided_(self, size, stride, storage_offset) + + +def _logaddexp(self, other): + return flow._C.logaddexp(self, other) + + def RegisterMethods(): Tensor.ndim = property(_ndim) Tensor.numpy = _numpy @@ -578,6 +579,7 @@ def RegisterMethods(): Tensor.backward = _backward Tensor.__str__ = _str Tensor.__repr__ = _repr + Tensor.__contains__ = _contains Tensor.__bool__ = is_nonzero Tensor.__iadd__ = _iadd Tensor.addmm = _addmm @@ -587,6 +589,7 @@ def RegisterMethods(): Tensor.__int__ = _scalar_int Tensor.__array__ = _numpy Tensor.uniform_ = _uniform + Tensor.exponential_ = _exponential Tensor.trunc_normal_ = _trunc_normal_ Tensor.kaiming_uniform_ = _kaiming_uniform Tensor.kaiming_normal_ = _kaiming_normal @@ -601,28 +604,25 @@ def RegisterMethods(): Tensor.argwhere = _argwhere Tensor.expand = _expand Tensor.expand_as = _expand_as - Tensor.flip = _flip Tensor.new_empty = _new_empty Tensor.new_ones = _new_ones Tensor.new_zeros = _new_zeros + Tensor.new_full = _new_full + Tensor.squeeze_ = _squeeze_inplace + Tensor.unsqueeze_ = _unsqueeze_inplace Tensor.where = _where Tensor.mm = _mm Tensor.norm = _norm Tensor.repeat = _repeat Tensor.repeat_interleave = _repeat_interleave Tensor.tile = _tile - Tensor.split = _split Tensor.to = _to Tensor.gather = _gather Tensor.T = property(_T) Tensor.masked_select = _masked_select Tensor.eq = _eq - Tensor.item = _item Tensor.sort = _sort - Tensor.type_as = _type_as Tensor.tolist = _tolist - Tensor.is_floating_point = _is_floating_point - Tensor.topk = _topk Tensor.nms = _nms Tensor.nonzero = _nonzero Tensor.prod = _prod @@ -632,6 +632,19 @@ def RegisterMethods(): Tensor.cumsum = _cumsum Tensor.cumprod = _cumprod Tensor.mv = _mv + Tensor.inverse = _inv + Tensor.trunc = _trunc + Tensor.cross = _cross + Tensor.scatter = _scatter + Tensor.scatter_ = _scatter_inplace + Tensor.scatter_add = _scatter_add + Tensor.scatter_add_ = _scatter_add_inplace + Tensor.allclose = _allclose + Tensor.index_add = _index_add + Tensor.index_add_ = _index_add_inplace + Tensor.as_strided = _as_strided + Tensor.as_strided_ = _as_strided_inplace + Tensor.logaddexp = _logaddexp def register_tensor_op(op_name): diff --git a/python/oneflow/framework/tensor_str.py b/python/oneflow/framework/tensor_str.py index 6f2bdefe6eec2018072db21686bd63157f28fe01..bdd4fa108a5012cf084bd169a8afeea8386f439f 100644 --- a/python/oneflow/framework/tensor_str.py +++ b/python/oneflow/framework/tensor_str.py @@ -22,7 +22,6 @@ import math import numpy as np from typing import Optional import oneflow as flow -from oneflow.framework.tensor_str_util import slice_wrapper from oneflow.framework.tensor_str_util import _autoset_linewidth from oneflow.framework.tensor_str_util import _try_convert_to_local_tensor @@ -216,10 +215,10 @@ def _vector_str(self, indent, summarize, formatter1): if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems: left_values = _try_convert_to_local_tensor( - slice_wrapper(self, [0, PRINT_OPTS.edgeitems, 1]) + self[: PRINT_OPTS.edgeitems] ).tolist() right_values = _try_convert_to_local_tensor( - slice_wrapper(self, [self.size(0) - PRINT_OPTS.edgeitems, self.size(0), 1]) + self[-PRINT_OPTS.edgeitems :] ).tolist() data = ( [_val_formatter(val) for val in left_values] @@ -249,30 +248,18 @@ def _tensor_str_with_formatter(self, indent, summarize, formatter1): if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems: slices = ( [ - _tensor_str_with_formatter( - slice_wrapper(self, [i, i + 1, 1]), - indent + 1, - summarize, - formatter1, - ) + _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1,) for i in range(0, PRINT_OPTS.edgeitems) ] + ["..."] + [ - _tensor_str_with_formatter( - slice_wrapper(self, [i, i + 1, 1]), - indent + 1, - summarize, - formatter1, - ) + _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1,) for i in range(self.shape[0] - PRINT_OPTS.edgeitems, self.shape[0]) ] ) else: slices = [ - _tensor_str_with_formatter( - slice_wrapper(self, [i, i + 1, 1]), indent + 1, summarize, formatter1 - ) + _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1) for i in range(0, self.size(0)) ] @@ -312,31 +299,26 @@ def get_summarized_data(self): if dim == 1: if self.size(0) > 2 * PRINT_OPTS.edgeitems: return flow.cat( - ( - slice_wrapper(self, [0, PRINT_OPTS.edgeitems, 1]), - slice_wrapper( - self, [self.size(0) - PRINT_OPTS.edgeitems, self.size(0), 1] - ), - ) + (self[: PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems :]) ) else: return self if self.size(0) > 2 * PRINT_OPTS.edgeitems: - start = [ - slice_wrapper(self, [i, i + 1, 1]) for i in range(0, PRINT_OPTS.edgeitems) - ] + start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)] end = [ - slice_wrapper(self, [i, i + 1, 1]) - for i in range(self.shape[0] - PRINT_OPTS.edgeitems, self.shape[0]) + self[i] for i in range(self.shape[0] - PRINT_OPTS.edgeitems, self.shape[0]) ] return flow.stack([get_summarized_data(x) for x in (start + end)]) else: - return flow.stack( - [ - get_summarized_data(slice_wrapper(self, [i, i + 1, 1])) - for i in range(len(self)) - ] - ) + return flow.stack([get_summarized_data(x) for x in self]) + + +def _format_tensor_on_cpu(tensor): + if tensor.is_global: + device = tensor.placement.type + else: + device = tensor.device.type + return device != "cpu" and device != "cuda" def _gen_tensor_str_template(tensor, is_meta): @@ -364,16 +346,10 @@ def _gen_tensor_str_template(tensor, is_meta): tensor_str = "..." suffixes.append("size=" + str(tuple(tensor.shape))) else: - if tensor.is_global: - if tensor.placement.type != "cpu" and tensor.placement.type != "cuda": - tensor_str = _tensor_str(tensor.detach().to("cpu"), indent) - else: - tensor_str = _tensor_str(tensor, indent) + if _format_tensor_on_cpu(tensor): + tensor_str = _tensor_str(tensor.detach().to("cpu"), indent) else: - if tensor.device.type != "cpu" and tensor.device.type != "cuda": - tensor_str = _tensor_str(tensor.detach().to("cpu"), indent) - else: - tensor_str = _tensor_str(tensor, indent) + tensor_str = _tensor_str(tensor, indent) suffixes.append("dtype=" + str(tensor.dtype)) if tensor.grad_fn is not None: diff --git a/python/oneflow/framework/tensor_str_util.py b/python/oneflow/framework/tensor_str_util.py index 742990a9e398baa556b91bb225c9d1cb33c07321..8e92e5e655857b69e89a6fb4113a4a85c0f0555b 100644 --- a/python/oneflow/framework/tensor_str_util.py +++ b/python/oneflow/framework/tensor_str_util.py @@ -18,22 +18,6 @@ import oneflow as flow from typing import Optional, Tuple -def slice_wrapper(tensor, slice_tuple: Tuple[int, int, int]): - with flow.no_grad(): - ndim = tensor.ndim - slice_tuple_list = [slice_tuple] + [[None, None, None]] * (ndim - 1) - # If tensor is global_tensor - # input is s0, output is p - # input is b, output is b - # input is p, output is p - # so 'to b' is not needed here - tensor = flow.slice(tensor, slice_tuple_list) - # TODO(): flow.sequeeze will fail in some global tensor case - if tensor.shape[0] == 1 and ndim > 1: - tensor = tensor.reshape(list(tensor.shape[1:])) - return tensor - - def _autoset_linewidth(): # os.terminal_size(columns, lines), # columns represents width of the terminal window in characters @@ -48,7 +32,6 @@ def _autoset_linewidth(): def _try_convert_to_local_tensor(tensor): if tensor.is_global: tensor = tensor.to_global( - placement=flow.env.all_device_placement(tensor.placement.type), - sbp=flow.sbp.broadcast, + placement=flow.placement.all(tensor.placement.type), sbp=flow.sbp.broadcast, ).to_local() return tensor diff --git a/python/oneflow/framework/unittest.py b/python/oneflow/framework/unittest.py index ab5aa20ded6f9a97f1cffd62e701531f432ceb00..2fbfc3ac8c5f4c1954492c9915f10b6db5ad6646 100644 --- a/python/oneflow/framework/unittest.py +++ b/python/oneflow/framework/unittest.py @@ -34,6 +34,9 @@ import oneflow.sysconfig from oneflow.core.job.env_pb2 import EnvProto +RESET_SESSION_COUNT = 0 + + def register_test_cases( scope: Dict[str, Any], directory: str, @@ -182,7 +185,16 @@ def call(conn=None, cmd=None, msg=None): return conn.recv().decode() -TestCase = unittest.TestCase +class TestCase(unittest.TestCase): + def tearDown(self): + global RESET_SESSION_COUNT + reset_session_period = int( + os.environ.get("ONEFLOW_TEST_RESET_SESSION_PERIOD", "10") + ) + RESET_SESSION_COUNT += 1 + if RESET_SESSION_COUNT >= reset_session_period: + oneflow.framework.session_context.GetDefaultSession().Reset() + RESET_SESSION_COUNT = 0 def skip_unless(n, d): diff --git a/python/oneflow/hub.py b/python/oneflow/hub.py new file mode 100644 index 0000000000000000000000000000000000000000..44c85a0c1f209b0c3a8a4d9fdfea15f6e1af4aa5 --- /dev/null +++ b/python/oneflow/hub.py @@ -0,0 +1,821 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# This file was copyed from https://github.com/pytorch/pytorch/blob/master/torch/hub.py and consistent with oneflow. + +import errno +import hashlib +import json +import os +import re +import shutil +import sys +import tempfile +import oneflow as flow +import warnings +import zipfile +from pathlib import Path +from typing import Dict, Optional, Any +from urllib.error import HTTPError +from urllib.request import urlopen, Request +from urllib.parse import urlparse # noqa: F401 + +try: + from tqdm.auto import ( + tqdm, + ) # automatically select proper tqdm submodule if available +except ImportError: + try: + from tqdm import tqdm + except ImportError: + # fake tqdm if it's not installed + class tqdm(object): # type: ignore[no-redef] + def __init__( + self, + total=None, + disable=False, + unit=None, + unit_scale=None, + unit_divisor=None, + ): + self.total = total + self.disable = disable + self.n = 0 + # ignore unit, unit_scale, unit_divisor; they're just for real tqdm + + def update(self, n): + if self.disable: + return + + self.n += n + if self.total is None: + sys.stderr.write("\r{0:.1f} bytes".format(self.n)) + else: + sys.stderr.write( + "\r{0:.1f}%".format(100 * self.n / float(self.total)) + ) + sys.stderr.flush() + + def close(self): + self.disable = True + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.disable: + return + + sys.stderr.write("\n") + + +__all__ = [ + "download_url_to_file", + "get_dir", + "help", + "list", + "load", + "load_state_dict_from_url", + "set_dir", +] + +# matches bfd8deac from resnet18-bfd8deac.pth +HASH_REGEX = re.compile(r"-([a-f0-9]*)\.") + +_TRUSTED_REPO_OWNERS = "oneflow" +ENV_GITHUB_TOKEN = "GITHUB_TOKEN" +ENV_ONEFLOW_HOME = "ONEFLOW_HOME" +ENV_XDG_CACHE_HOME = "XDG_CACHE_HOME" +DEFAULT_CACHE_DIR = "~/.cache" +VAR_DEPENDENCY = "dependencies" +MODULE_HUBCONF = "hubconf.py" +READ_DATA_CHUNK = 8192 +_hub_dir = None + + +# Copied from tools/shared/module_loader to be included in oneflow package +def _import_module(name, path): + import importlib.util + from importlib.abc import Loader + + spec = importlib.util.spec_from_file_location(name, path) + assert spec is not None + module = importlib.util.module_from_spec(spec) + assert isinstance(spec.loader, Loader) + spec.loader.exec_module(module) + return module + + +def _remove_if_exists(path): + if os.path.exists(path): + if os.path.isfile(path): + os.remove(path) + else: + shutil.rmtree(path) + + +def _git_archive_link(repo_owner, repo_name, ref): + # See https://docs.github.com/en/rest/reference/repos#download-a-repository-archive-zip + return f"https://github.com/{repo_owner}/{repo_name}/zipball/{ref}" + + +def _load_attr_from_module(module, func_name): + # Check if callable is defined in the module + if func_name not in dir(module): + return None + return getattr(module, func_name) + + +def _get_oneflow_home(): + oneflow_home = os.path.expanduser( + os.getenv( + ENV_ONEFLOW_HOME, + os.path.join(os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), "oneflow"), + ) + ) + return oneflow_home + + +def _parse_repo_info(github): + if ":" in github: + repo_info, ref = github.split(":") + else: + repo_info, ref = github, None + repo_owner, repo_name = repo_info.split("/") + + if ref is None: + # The ref wasn't specified by the user, so we need to figure out the + # default branch: main or master. Our assumption is that if main exists + # then it's the default branch, otherwise it's master. + try: + with urlopen(f"https://github.com/{repo_owner}/{repo_name}/tree/main/"): + ref = "main" + except HTTPError as e: + if e.code == 404: + ref = "master" + else: + raise + return repo_owner, repo_name, ref + + +def _read_url(url): + with urlopen(url) as r: + return r.read().decode(r.headers.get_content_charset("utf-8")) + + +def _validate_not_a_forked_repo(repo_owner, repo_name, ref): + # Use urlopen to avoid depending on local git. + headers = {"Accept": "application/vnd.github.v3+json"} + token = os.environ.get(ENV_GITHUB_TOKEN) + if token is not None: + headers["Authorization"] = f"token {token}" + for url_prefix in ( + f"https://api.github.com/repos/{repo_owner}/{repo_name}/branches", + f"https://api.github.com/repos/{repo_owner}/{repo_name}/tags", + ): + page = 0 + while True: + page += 1 + url = f"{url_prefix}?per_page=100&page={page}" + response = json.loads(_read_url(Request(url, headers=headers))) + # Empty response means no more data to process + if not response: + break + for br in response: + if br["name"] == ref or br["commit"]["sha"].startswith(ref): + return + + raise ValueError( + f"Cannot find {ref} in https://github.com/{repo_owner}/{repo_name}. " + "If it's a commit from a forked repo, please call hub.load() with forked repo directly." + ) + + +def _get_cache_or_reload( + github, force_reload, trust_repo, calling_fn, verbose=True, skip_validation=False +): + # Setup hub_dir to save downloaded files + hub_dir = get_dir() + if not os.path.exists(hub_dir): + os.makedirs(hub_dir) + # Parse github repo information + repo_owner, repo_name, ref = _parse_repo_info(github) + # Github allows branch name with slash '/', + # this causes confusion with path on both Linux and Windows. + # Backslash is not allowed in Github branch name so no need to + # to worry about it. + normalized_br = ref.replace("/", "_") + # Github renames folder repo-v1.x.x to repo-1.x.x + # We don't know the repo name before downloading the zip file + # and inspect name from it. + # To check if cached repo exists, we need to normalize folder names. + owner_name_branch = "_".join([repo_owner, repo_name, normalized_br]) + repo_dir = os.path.join(hub_dir, owner_name_branch) + # Check that the repo is in the trusted list + _check_repo_is_trusted( + repo_owner, + repo_name, + owner_name_branch, + trust_repo=trust_repo, + calling_fn=calling_fn, + ) + + use_cache = (not force_reload) and os.path.exists(repo_dir) + + if use_cache: + if verbose: + sys.stderr.write("Using cache found in {}\n".format(repo_dir)) + else: + # Validate the tag/branch is from the original repo instead of a forked repo + if not skip_validation: + _validate_not_a_forked_repo(repo_owner, repo_name, ref) + + cached_file = os.path.join(hub_dir, normalized_br + ".zip") + _remove_if_exists(cached_file) + + try: + url = _git_archive_link(repo_owner, repo_name, ref) + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + download_url_to_file(url, cached_file, progress=False) + except HTTPError as err: + if err.code == 300: + # Getting a 300 Multiple Choices error likely means that the ref is both a tag and a branch + # in the repo. This can be disambiguated by explicitely using refs/heads/ or refs/tags + # See https://git-scm.com/book/en/v2/Git-Internals-Git-References + # Here, we do the same as git: we throw a warning, and assume the user wanted the branch + warnings.warn( + f"The ref {ref} is ambiguous. Perhaps it is both a tag and a branch in the repo? " + "OneFlowhub will now assume that it's a branch. " + "You can disambiguate tags and branches by explicitly passing refs/heads/branch_name or " + "refs/tags/tag_name as the ref. That might require using skip_validation=True." + ) + disambiguated_branch_ref = f"refs/heads/{ref}" + url = _git_archive_link( + repo_owner, repo_name, ref=disambiguated_branch_ref + ) + download_url_to_file(url, cached_file, progress=False) + else: + raise + + with zipfile.ZipFile(cached_file) as cached_zipfile: + extraced_repo_name = cached_zipfile.infolist()[0].filename + extracted_repo = os.path.join(hub_dir, extraced_repo_name) + _remove_if_exists(extracted_repo) + # Unzip the code and rename the base folder + cached_zipfile.extractall(hub_dir) + + _remove_if_exists(cached_file) + _remove_if_exists(repo_dir) + shutil.move(extracted_repo, repo_dir) # rename the repo + + return repo_dir + + +def _check_repo_is_trusted( + repo_owner, repo_name, owner_name_branch, trust_repo, calling_fn="load" +): + hub_dir = get_dir() + filepath = os.path.join(hub_dir, "trusted_list") + + if not os.path.exists(filepath): + Path(filepath).touch() + with open(filepath, "r") as file: + trusted_repos = tuple(line.strip() for line in file) + + # To minimize friction of introducing the new trust_repo mechanism, we consider that + # if a repo was already downloaded by oneflowhub, then it is already trusted (even if it's not in the allowlist) + trusted_repos_legacy = next(os.walk(hub_dir))[1] + + owner_name = "_".join([repo_owner, repo_name]) + is_trusted = ( + owner_name in trusted_repos + or owner_name_branch in trusted_repos_legacy + or repo_owner in _TRUSTED_REPO_OWNERS + ) + + # TODO: Remove `None` option in 1.14 and change the default to "check" + if trust_repo is None: + if not is_trusted: + warnings.warn( + "You are about to download and run code from an untrusted repository. In a future release, this won't " + "be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., " + "trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, " + f"or {calling_fn}(..., trust_repo=True), which will assume that the prompt is to be answered with " + f"'yes'. You can also use {calling_fn}(..., trust_repo='check') which will only prompt for " + f"confirmation if the repo is not already trusted. This will eventually be the default behaviour" + ) + return + + if (trust_repo is False) or (trust_repo == "check" and not is_trusted): + response = input( + f"The repository {owner_name} does not belong to the list of trusted repositories and as such cannot be downloaded. " + "Do you trust this repository and wish to add it to the trusted list of repositories (y/N)?" + ) + if response.lower() in ("y", "yes"): + if is_trusted: + print("The repository is already trusted.") + elif response.lower() in ("n", "no", ""): + raise Exception("Untrusted repository.") + else: + raise ValueError(f"Unrecognized response {response}.") + + # At this point we're sure that the user trusts the repo (or wants to trust it) + if not is_trusted: + with open(filepath, "a") as file: + file.write(owner_name + "\n") + + +def _check_module_exists(name): + import importlib.util + + return importlib.util.find_spec(name) is not None + + +def _check_dependencies(m): + dependencies = _load_attr_from_module(m, VAR_DEPENDENCY) + + if dependencies is not None: + missing_deps = [pkg for pkg in dependencies if not _check_module_exists(pkg)] + if len(missing_deps): + raise RuntimeError( + "Missing dependencies: {}".format(", ".join(missing_deps)) + ) + + +def _load_entry_from_hubconf(m, model): + if not isinstance(model, str): + raise ValueError("Invalid input: model should be a string of function name") + + # Note that if a missing dependency is imported at top level of hubconf, it will + # throw before this function. It's a chicken and egg situation where we have to + # load hubconf to know what're the dependencies, but to import hubconf it requires + # a missing package. This is fine, Python will throw proper error message for users. + _check_dependencies(m) + + func = _load_attr_from_module(m, model) + + if func is None or not callable(func): + raise RuntimeError("Cannot find callable {} in hubconf".format(model)) + + return func + + +def get_dir(): + """ + Get the OneFlow Hub cache directory used for storing downloaded models & weights. + If :func:`~oneflow.hub.set_dir` is not called, default path is ``$ONEFLOW_HOME/hub`` where + environment variable ``$ONEFLOW_HOME`` defaults to ``$XDG_CACHE_HOME/oneflow``. + ``$XDG_CACHE_HOME`` follows the X Design Group specification of the Linux + filesystem layout, with a default value ``~/.cache`` if the environment + variable is not set. + """ + # Issue warning to move data if old env is set + if os.getenv("ONEFLOW_HUB"): + warnings.warn("ONEFLOW_HUB is deprecated, please use env ONEFLOW_HOME instead") + + if _hub_dir is not None: + return _hub_dir + return os.path.join(_get_oneflow_home(), "hub") + + +def set_dir(d): + """ + Optionally set the OneFlow Hub directory used to save downloaded models & weights. + + Args: + d (str): path to a local folder to save downloaded models & weights. + """ + global _hub_dir + _hub_dir = os.path.expanduser(d) + + +def list(github, force_reload=False, skip_validation=False, trust_repo=None): + """ + List all callable entrypoints available in the repo specified by ``github``. + + Args: + github (str): a string with format "repo_owner/repo_name[:ref]" with an optional + ref (tag or branch). If ``ref`` is not specified, the default branch is assumed to be ``main`` if + it exists, and otherwise ``master``. Example: ' Oneflow-Inc/vision:0.2.0' + force_reload (bool, optional): whether to discard the existing cache and force a fresh download. + Default is ``False``. + skip_validation (bool, optional): if ``False``, oneflowhub will check that the branch or commit + specified by the ``github`` argument properly belongs to the repo owner. This will make + requests to the GitHub API; you can specify a non-default GitHub token by setting the + ``GITHUB_TOKEN`` environment variable. Default is ``False``. + trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. + This parameter was introduced in v1.12 and helps ensuring that users + only run code from repos that they trust. + - If ``False``, a prompt will ask the user whether the repo should be trusted. + + - If ``True``, the repo will be added to the trusted list and loaded without + requiring explicit confirmation. + + - If ``"check"``, the repo will be checked against the list of + trusted repos in the cache. If it is not present in that list, the + behaviour will fall back onto the ``trust_repo=False`` option. + + - If ``None``, this will raise a warning, inviting the user to set + ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This + is only present for backward compatibility and will be removed in + v1.14. + + Default is ``None`` and will eventually change to ``"check"`` in v1.14. + + Returns: + list: The available callables entrypoint + + For example: + + >>> entrypoints = oneflow.hub.list('Oneflow-Inc/vision', force_reload=True) + + """ + repo_dir = _get_cache_or_reload( + github, + force_reload, + trust_repo, + "list", + verbose=True, + skip_validation=skip_validation, + ) + + sys.path.insert(0, repo_dir) + + hubconf_path = os.path.join(repo_dir, MODULE_HUBCONF) + hub_module = _import_module(MODULE_HUBCONF, hubconf_path) + + sys.path.remove(repo_dir) + + # We take functions starts with '_' as internal helper functions + entrypoints = [ + f + for f in dir(hub_module) + if callable(getattr(hub_module, f)) and not f.startswith("_") + ] + + return entrypoints + + +def help(github, model, force_reload=False, skip_validation=False, trust_repo=None): + """ + Show the docstring of entrypoint ``model``. + + Args: + github (str): a string with format with an optional + ref (a tag or a branch). If ``ref`` is not specified, the default branch is assumed + to be ``main`` if it exists, and otherwise ``master``. + Example: 'Oneflow-Inc/vision:0.2.0' + model (str): a string of entrypoint name defined in repo's ``hubconf.py`` + force_reload (bool, optional): whether to discard the existing cache and force a fresh download. + Default is ``False``. + skip_validation (bool, optional): if ``False``, oneflowhub will check that the ref + specified by the ``github`` argument properly belongs to the repo owner. This will make + requests to the GitHub API; you can specify a non-default GitHub token by setting the + ``GITHUB_TOKEN`` environment variable. Default is ``False``. + trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. + This parameter was introduced in v1.12 and helps ensuring that users + only run code from repos that they trust. + + - If ``False``, a prompt will ask the user whether the repo should + be trusted. + + - If ``True``, the repo will be added to the trusted list and loaded + without requiring explicit confirmation. + + - If ``"check"``, the repo will be checked against the list of + trusted repos in the cache. If it is not present in that list, the + behaviour will fall back onto the ``trust_repo=False`` option. + + - If ``None``: this will raise a warning, inviting the user to set + ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This + is only present for backward compatibility and will be removed in + v1.14. + + Default is ``None`` and will eventually change to ``"check"`` in v1.14. + + For example: + >>> print(oneflow.hub.help('Oneflow-Inc/vision', 'resnet18', force_reload=True)) + """ + repo_dir = _get_cache_or_reload( + github, + force_reload, + trust_repo, + "help", + verbose=True, + skip_validation=skip_validation, + ) + + sys.path.insert(0, repo_dir) + + hubconf_path = os.path.join(repo_dir, MODULE_HUBCONF) + hub_module = _import_module(MODULE_HUBCONF, hubconf_path) + + sys.path.remove(repo_dir) + + entry = _load_entry_from_hubconf(hub_module, model) + + return entry.__doc__ + + +def load( + repo_or_dir, + model, + *args, + source="github", + trust_repo=None, + force_reload=False, + verbose=True, + skip_validation=False, + **kwargs, +): + """ + Load a model from a github repo or a local directory. + Note: Loading a model is the typical use case, but this can also be used to + for loading other objects such as tokenizers, loss functions, etc. + If ``source`` is 'github', ``repo_or_dir`` is expected to be + of the form ``repo_owner/repo_name[:ref]`` with an optional + ref (a tag or a branch). + If ``source`` is 'local', ``repo_or_dir`` is expected to be a + path to a local directory. + + Args: + repo_or_dir (str): If ``source`` is 'github', + this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with + an optional ref (tag or branch), for example 'Oneflow-Inc/vision:0.2.0'. If ``ref`` is not specified, + the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. + If ``source`` is 'local' then it should be a path to a local directory. + model (str): the name of a callable (entrypoint) defined in the + repo/dir's ``hubconf.py``. + *args (optional): the corresponding args for callable ``model``. + source (str, optional): 'github' or 'local'. Specifies how + ``repo_or_dir`` is to be interpreted. Default is 'github'. + trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. + This parameter was introduced in v1.12 and helps ensuring that users + only run code from repos that they trust. + + - If ``False``, a prompt will ask the user whether the repo should + be trusted. + + - If ``True``, the repo will be added to the trusted list and loaded + without requiring explicit confirmation. + + - If ``"check"``, the repo will be checked against the list of + trusted repos in the cache. If it is not present in that list, the + behaviour will fall back onto the ``trust_repo=False`` option. + + - If ``None``: this will raise a warning, inviting the user to set + ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This + is only present for backward compatibility and will be removed in + v1.14. + + Default is ``None`` and will eventually change to ``"check"`` in v1.14. + force_reload (bool, optional): whether to force a fresh download of + the github repo unconditionally. Does not have any effect if + ``source = 'local'``. Default is ``False``. + verbose (bool, optional): If ``False``, mute messages about hitting + local caches. Note that the message about first download cannot be + muted. Does not have any effect if ``source = 'local'``. + Default is ``True``. + skip_validation (bool, optional): if ``False``, oneflowhub will check that the branch or commit + specified by the ``github`` argument properly belongs to the repo owner. This will make + requests to the GitHub API; you can specify a non-default GitHub token by setting the + ``GITHUB_TOKEN`` environment variable. Default is ``False``. + **kwargs (optional): the corresponding kwargs for callable ``model``. + + Returns: + The output of the ``model`` callable when called with the given + ``*args`` and ``**kwargs``. + + For example: + >>> # from a github repo + >>> repo = 'Oneflow-Inc/vision' + >>> model = oneflow.hub.load(repo, 'resnet50', weights='ResNet50_Weights.IMAGENET1K_V1') + >>> # from a local directory + >>> path = '/some/local/path/oneflow/vision' + >>> # xdoctest: +SKIP + >>> model = oneflow.hub.load(path, 'resnet50', weights='ResNet50_Weights.DEFAULT') + """ + source = source.lower() + + if source not in ("github", "local"): + raise ValueError( + f'Unknown source: "{source}". Allowed values: "github" | "local".' + ) + + if source == "github": + repo_or_dir = _get_cache_or_reload( + repo_or_dir, + force_reload, + trust_repo, + "load", + verbose=verbose, + skip_validation=skip_validation, + ) + + model = _load_local(repo_or_dir, model, *args, **kwargs) + return model + + +def _load_local(hubconf_dir, model, *args, **kwargs): + """ + Load a model from a local directory with a ``hubconf.py``. + + Args: + hubconf_dir (str): path to a local directory that contains a + ``hubconf.py``. + model (str): name of an entrypoint defined in the directory's + ``hubconf.py``. + *args (optional): the corresponding args for callable ``model``. + **kwargs (optional): the corresponding kwargs for callable ``model``. + + Returns: + a single model with corresponding pretrained weights. + + For example: + >>> # xdoctest: +SKIP("stub local path") + >>> path = '/some/local/path/oneflow/vision' + >>> model = _load_local(path, 'resnet50', weights='ResNet50_Weights.IMAGENET1K_V1') + """ + sys.path.insert(0, hubconf_dir) + + hubconf_path = os.path.join(hubconf_dir, MODULE_HUBCONF) + hub_module = _import_module(MODULE_HUBCONF, hubconf_path) + + entry = _load_entry_from_hubconf(hub_module, model) + model = entry(*args, **kwargs) + + sys.path.remove(hubconf_dir) + + return model + + +def download_url_to_file(url, dst, hash_prefix=None, progress=True): + """Download object at the given URL to a local path. + + Args: + url (str): URL of the object to download + dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` + hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. + Default: None + progress (bool, optional): whether or not to display a progress bar to stderr + Default: True + + For example: + >>> # xdoctest: +REQUIRES(POSIX) + >>> oneflow.hub.download_url_to_file('https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip', '/tmp/temporary_file') + """ + file_size = None + req = Request(url, headers={"User-Agent": "oneflow.hub"}) + u = urlopen(req) + meta = u.info() + if hasattr(meta, "getheaders"): + content_length = meta.getheaders("Content-Length") + else: + content_length = meta.get_all("Content-Length") + if content_length is not None and len(content_length) > 0: + file_size = int(content_length[0]) + + # We deliberately save it in a temp file and move it after + # download is complete. This prevents a local working checkpoint + # being overridden by a broken download. + dst = os.path.expanduser(dst) + dst_dir = os.path.dirname(dst) + f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir) + + try: + if hash_prefix is not None: + sha256 = hashlib.sha256() + with tqdm( + total=file_size, + disable=not progress, + unit="B", + unit_scale=True, + unit_divisor=1024, + ) as pbar: + while True: + buffer = u.read(8192) + if len(buffer) == 0: + break + f.write(buffer) + if hash_prefix is not None: + sha256.update(buffer) + pbar.update(len(buffer)) + + f.close() + if hash_prefix is not None: + digest = sha256.hexdigest() + if digest[: len(hash_prefix)] != hash_prefix: + raise RuntimeError( + 'invalid hash value (expected "{}", got "{}")'.format( + hash_prefix, digest + ) + ) + shutil.move(f.name, dst) + finally: + f.close() + if os.path.exists(f.name): + os.remove(f.name) + + +# Hub used to support automatically extracts from zipfile manually compressed by users. +# We should remove this support since zipfile is now default zipfile format for oneflow.save(). +def _is_legacy_zip_format(filename): + if zipfile.is_zipfile(filename): + return True + else: + return False + + +def _legacy_zip_load(filename, model_dir, map_location): + # Note: extractall() defaults to overwrite file if exists. No need to clean up beforehand. + # We deliberately don't handle tarfile here since our legacy serialization format was in tar. + # E.g. resnet18-5c106cde.pth which is widely used. + with zipfile.ZipFile(filename) as f: + members = f.infolist() + f.extractall(model_dir) + extraced_name = members[0].filename + extracted_file = os.path.join(model_dir, extraced_name) + return flow.load(extracted_file, map_location=map_location) + + +def load_state_dict_from_url( + url: str, + model_dir: Optional[str] = None, + map_location=None, + progress: bool = True, + check_hash: bool = False, + file_name: Optional[str] = None, +) -> Dict[str, Any]: + """Loads the OneFlow serialized object at the given URL. + If downloaded file is a zip file, it will be automatically + decompressed. + If the object is already present in `model_dir`, it's deserialized and + returned. + The default value of ``model_dir`` is ``/checkpoints`` where + ``hub_dir`` is the directory returned by :func:`~oneflow.hub.get_dir`. + + Args: + url (str): URL of the object to download + model_dir (str, optional): directory in which to save the object + map_location (optional): a function or a dict specifying how to remap storage locations (see oneflow.load) + progress (bool, optional): whether or not to display a progress bar to stderr. + Default: True + check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention + ``filename-.ext`` where ```` is the first eight or more + digits of the SHA256 hash of the contents of the file. The hash is used to + ensure unique names and to verify the contents of the file. + Default: False + file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. + + For example: + >>> state_dict = oneflow.hub.load_state_dict_from_url('https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip') + """ + # Issue warning to move data if old env is set + if os.getenv("ONEFLOW_MODEL_ZOO"): + warnings.warn( + "ONEFLOW_MODEL_ZOO is deprecated, please use env ONEFLOW_HOME instead" + ) + + if model_dir is None: + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, "checkpoints") + + try: + os.makedirs(model_dir) + except OSError as e: + if e.errno == errno.EEXIST: + # Directory already exists, ignore. + pass + else: + # Unexpected OSError, re-raise. + raise + + parts = urlparse(url) + filename = os.path.basename(parts.path) + if file_name is not None: + filename = file_name + cached_file = os.path.join(model_dir, filename) + if not os.path.exists(cached_file): + sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) + hash_prefix = None + if check_hash: + r = HASH_REGEX.search(filename) # r is Optional[Match[str]] + hash_prefix = r.group(1) if r else None + download_url_to_file(url, cached_file, hash_prefix, progress=progress) + + if _is_legacy_zip_format(cached_file): + return _legacy_zip_load(cached_file, model_dir, map_location) + + return flow.load(cached_file, map_location=map_location) diff --git a/python/oneflow/ir/ast_gen_transformer.py b/python/oneflow/ir/ast_gen_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ee55d8b758e5724eebfffb73267deee75b3349f4 --- /dev/null +++ b/python/oneflow/ir/ast_gen_transformer.py @@ -0,0 +1,165 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow +import ast + + +class ASTTransformer(ast.NodeTransformer): + def visit_arg(self, node: ast.arg): + node.ast = oneflow._oneflow_internal.ir.arg_(node.arg) + return node + + def visit_arguments(self, node: ast.arguments): + for arg in node.args: + self.visit(arg) + + list = [arg.ast for arg in node.args] + node.ast = oneflow._oneflow_internal.ir.arguments_(list) + return node + + def visit_FunctionDef(self, node: ast.FunctionDef): + for arg in node.body: + self.visit(arg) + + body = [arg.ast for arg in node.body] + self.visit(node.args) + node.ast = oneflow._oneflow_internal.ir.FunctionDef_( + "get_lr", node.args.ast, body + ) + return node + + def visit_Return(self, node: ast.Return): + self.visit(node.value) + + node.ast = oneflow._oneflow_internal.ir.Return_(node.value.ast) + return node + + def visit_Assign(self, node: ast.Assign): + self.visit(node.value) + for arg in node.targets: + self.visit(arg) + + targets = [arg.ast for arg in node.targets] + node.ast = oneflow._oneflow_internal.ir.Assign_(targets, node.value.ast) + return node + + def visit_If(self, node: ast.If): + self.visit(node.test) + for arg in node.body: + self.visit(arg) + + if node.orelse: + for arg in node.orelse: + self.visit(arg) + + test = node.test.ast + body = [arg.ast for arg in node.body] + orelse = [arg.ast for arg in node.orelse] + node.ast = oneflow._oneflow_internal.ir.If_(test, body, orelse) + return node + + def visit_Raise(self, node: ast.Raise): + print(ast.dump(node)) + raise "not suport yet now" + + def visit_Assert(self, node: ast.Assert): + print(ast.dump(node)) + raise "not suport yet now" + + def visit_Expr(self, node: ast.Expr): + print(ast.dump(node)) + raise "not suport yet now" + + def visit_BoolOp(self, node: ast.BoolOp): + print(ast.dump(node)) + raise "not suport yet now" + + def visit_BinOp(self, node: ast.BinOp): + self.visit(node.left) + self.visit(node.right) + + left = node.left.ast + right = node.right.ast + + def get_op(op: ast.operator): + list = [ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow] + res = 1 + for elem in list: + if isinstance(op, elem): + return res + res += 1 + + op = get_op(node.op) + + node.ast = oneflow._oneflow_internal.ir.BinOp_(left, op, right) + return node + + def visit_Lambda(self, node: ast.Lambda): + print(ast.dump(node)) + raise "not suport yet now" + + def visit_Compare(self, node: ast.Compare): + self.visit(node.left) + + for arg in node.comparators: + self.visit(arg) + + left = node.left.ast + comparators = [arg.ast for arg in node.comparators] + + def get_op(op: ast.operator): + list = [ast.Eq, ast.NotEq, ast.Lt, ast.LtE, ast.Gt, ast.GtE] + res = 1 + for elem in list: + if isinstance(op, elem): + return res + res += 1 + + ops = [get_op(arg) for arg in node.ops] + + node.ast = oneflow._oneflow_internal.ir.Compare_(left, ops, comparators) + return node + + def visit_Call(self, node: ast.Call): + self.visit(node.func) + + for arg in node.args: + self.visit(arg) + + func = node.func.ast + args = [arg.ast for arg in node.args] + + node.ast = oneflow._oneflow_internal.ir.Call_(func, args) + return node + + def visit_Constant(self, node: ast.Constant): + node.ast = oneflow._oneflow_internal.ir.Constant_(node.value) + return node + + def visit_Num(self, node: ast.Num): + node.ast = oneflow._oneflow_internal.ir.Num_(node.value) + return node + + def visit_Attribute(self, node: ast.Attribute): + self.visit(node.value) + value = node.value.ast + + node.ast = oneflow._oneflow_internal.ir.Attribute_(value, node.attr) + return node + + def visit_Name(self, node: ast.Name): + node.ast = oneflow._oneflow_internal.ir.Name_(node.id) + return node diff --git a/python/oneflow/ir/bisect_transformer.py b/python/oneflow/ir/bisect_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..6d17d228b6f64820e3968ccd5a67383560bbfe9b --- /dev/null +++ b/python/oneflow/ir/bisect_transformer.py @@ -0,0 +1,55 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import ast +from bisect import bisect + + +class BisectTransformer(ast.NodeTransformer): + def visit_FunctionDef(self, node: ast.FunctionDef): + self.body_index = 0 + self.body = node.body + for stmt in node.body: + self.visit(stmt) + self.body_index += 1 + return node + + def visit_Call(self, node: ast.Call): + if isinstance(node.func, ast.Attribute): + func: ast.Attribute = node.func + if func.value.id == "bisect": + bisect_x_list = ["bisect_right", "bisect_left"] + if func.attr in bisect_x_list: + op = ast.LtE + if func.attr == "bisect_right": + op = ast.Lt + if not isinstance(node.args[0], ast.List): + raise "only support bisect.bisect_right(list, x)" + ls = node.args[0].elts + cmp = node.args[1] + index = 0 + for i in ls[::-1]: + test = ast.Compare(cmp, [op()], [i]) + assign = ast.Assign( + [ast.Name("tmp")], ast.Constant(len(ls) - index - 1, None) + ) + if "orelse" in locals(): + orelse = ast.If(test, [assign], [orelse]) + else: + orelse = ast.If(test, [assign], []) + index += 1 + self.body.insert(self.body_index, orelse) + return ast.Name("tmp") + return node diff --git a/python/oneflow/ir/lr_jit.py b/python/oneflow/ir/lr_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..3cde1e852f6d22da15925c2b76572e5e4689cd46 --- /dev/null +++ b/python/oneflow/ir/lr_jit.py @@ -0,0 +1,111 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import ast +import textwrap +import inspect +import oneflow + +import unittest +import oneflow.unittest + +from ast_gen_transformer import ASTTransformer +from math_params_transformer import MathParamsTransformer +from self_params_transformer import SelfParamsTransformer +from bisect_transformer import BisectTransformer + + +def lr_jit_register(lr_obj, is_dump=False): + _id = lr_obj.__class__.__name__ + # load source txt + _src = textwrap.dedent(inspect.getsource(lr_obj.get_lr)) + _ast = ast.parse(_src).body[0] + + # transform param self + transformer = SelfParamsTransformer(lr_obj) + transformer.visit(_ast) + + # transform for bisect lib + transformer = BisectTransformer() + transformer.visit(_ast) + + # transform for math lib + transformer = MathParamsTransformer() + transformer.visit(_ast) + + # feed transformed as to C++ + transformer = ASTTransformer() + transformer.visit(_ast) + + oneflow._oneflow_internal.ir.compile_and_register_lr_jit(_id, _ast.ast, is_dump) + return _id + + +def _test_current_lr_jit(test_case): + from oneflow.nn.optimizer.constant_lr import ConstantLR + from oneflow.nn.optimizer.cosine_annealing_lr import CosineAnnealingLR + from oneflow.nn.optimizer.cosine_decay_lr import CosineDecayLR + from oneflow.nn.optimizer.exponential_lr import ExponentialLR + from oneflow.nn.optimizer.lambda_lr import LambdaLR + from oneflow.nn.optimizer.linear_lr import LinearLR + from oneflow.nn.optimizer.multistep_lr import MultiStepLR + from oneflow.nn.optimizer.polynomial_lr import PolynomialLR + from oneflow.nn.optimizer.sequential_lr import SequentialLR + from oneflow.nn.optimizer.step_lr import StepLR + from oneflow.nn.optimizer.warmup_lr import WarmupLR + + from oneflow.optim import SGD + from oneflow.nn import Parameter + import numpy as np + + param = Parameter(oneflow.ones(3, 4)) + optimizer = SGD([param], lr=0.001) + + lr_jit = oneflow._oneflow_internal.ir.create_global_lr_jit() + + lr_obj_list = [ + # WarmupLR(optimizer), + StepLR(optimizer, 5), + # SequentialLR(optimizer), + PolynomialLR(optimizer, 5), + MultiStepLR(optimizer, [10, 20, 30]), + LinearLR(optimizer), + # LambdaLR(optimizer, [lambda step: 0.95 * step]), + ExponentialLR(optimizer, 1.1), + CosineDecayLR(optimizer, 10), + CosineAnnealingLR(optimizer, 50), + ConstantLR(optimizer), + ] + + for lr_obj in lr_obj_list: + id_ = lr_jit_register(lr_obj, False) + + ls = [[0.005, 5], [0.01, 10], [0.02, 21]] + for elem in ls: + base_lr = elem[0] + step = elem[1] + lr = lr_obj.get_lr(base_lr, step) + lr_jit = oneflow._oneflow_internal.ir.get_lr(id_, base_lr, step) + test_case.assertTrue(np.isclose(lr, lr_jit)) + + +@oneflow.unittest.skip_unless_1n1d() +class TestCurrentLRJIT(oneflow.unittest.TestCase): + def test_current_lr_jit(test_case): + _test_current_lr_jit(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/ir/math_params_transformer.py b/python/oneflow/ir/math_params_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..81538ba8ae4371a8c4fa814a40925307325285ea --- /dev/null +++ b/python/oneflow/ir/math_params_transformer.py @@ -0,0 +1,29 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import ast + + +class MathParamsTransformer(ast.NodeTransformer): + def visit_Attribute(self, node): + import math + + list = ["pi"] + if node.value.id == "math": + if node.attr in list: + _name = node.attr + _attr = getattr(math, _name) + return ast.Constant(_attr, None) + return node diff --git a/python/oneflow/ir/self_params_transformer.py b/python/oneflow/ir/self_params_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..719bbeaee6882cee474f92d66711821cd52fe369 --- /dev/null +++ b/python/oneflow/ir/self_params_transformer.py @@ -0,0 +1,38 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import ast + + +class SelfParamsTransformer(ast.NodeTransformer): + def __init__(self, lr_obj): + super().__init__() + self.lr_obj = lr_obj + + def visit_Attribute(self, node): + if node.value.id == "self": + _name = node.attr + _attr = getattr(self.lr_obj, _name) + if isinstance(_attr, list): + ls = [ast.Constant(elem, None) for elem in _attr] + return ast.List(ls) + return ast.Constant(_attr, None) + return node + + def visit_arguments(self, node: ast.arguments): + for index, item in enumerate(node.args): + if item.arg == "self": + node.args.pop(index) + return node diff --git a/python/oneflow/jit/__init__.py b/python/oneflow/jit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a76b1ccf076167bf8a163a34d00d115e4bd37c46 --- /dev/null +++ b/python/oneflow/jit/__init__.py @@ -0,0 +1,16 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from oneflow.jit._script import script, ignore diff --git a/python/oneflow/jit/_script.py b/python/oneflow/jit/_script.py new file mode 100644 index 0000000000000000000000000000000000000000..b7d43a3368f0b94ba9a3bc8b0b5aaa7dc6751a90 --- /dev/null +++ b/python/oneflow/jit/_script.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import warnings +from typing import Any, Dict, List, Set, Tuple, Union, Callable + + +def script( + obj, + optimize=None, + _frames_up=0, + _rcb=None, + example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None, +): + warnings.warn( + "The oneflow.jit.script interface is just to align the torch.jit.script interface and has no practical significance." + ) + return obj + + +def ignore(drop=False, **kwargs): + warnings.warn( + "The oneflow.jit.ignore interface is just to align the torch.jit.ignore interface and has no practical significance." + ) + + def decorator(fn): + return fn + + return decorator diff --git a/python/oneflow/linalg.py b/python/oneflow/linalg.py index 66c065732237e4ade147455238c2db24fa8a0da5..b547bd5755aa37f8d232b7073f09dfcbecff932f 100644 --- a/python/oneflow/linalg.py +++ b/python/oneflow/linalg.py @@ -26,3 +26,18 @@ def vector_norm(self, ord=2, dim=None, keepdim=False, dtype=None): def matrix_norm(self, ord="fro", dim=(-2, -1), keepdim=False, dtype=None): return flow._C.matrix_norm(self, ord, dim, keepdim, dtype=dtype) + + +def inv(self): + return flow._C.inv(self) + + +def diagonal(self, input, offset=0, dim1=-2, dim2=-1): + """ + Alias for :func:`oneflow.diagonal` with defaults :attr:`dim1`\ `= -2`, :attr:`dim2`\ `= -1`. + """ + return flow._C.diagonal(self, input, offset=offset, dim1=dim1, dim2=dim2) + + +def cross(input, other, dim=-1): + return flow._C.linalg_cross(input, other, dim=dim) diff --git a/python/oneflow/mock_torch/__init__.py b/python/oneflow/mock_torch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3e9d8bb24274e01701cad4936ee40dd2805f2a --- /dev/null +++ b/python/oneflow/mock_torch/__init__.py @@ -0,0 +1,184 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from inspect import ismodule, currentframe +from types import ModuleType +from typing import Any +from importlib.abc import MetaPathFinder, Loader +from importlib.machinery import ModuleSpec +from importlib.util import find_spec, module_from_spec +import sys +from contextlib import contextmanager + +_first_init = True + +error_msg = """ is not implemented, please submit an issue at +'https://github.com/Oneflow-Inc/oneflow/issues' including the log information of the error, the +minimum reproduction code, and the system information.""" + +# module wrapper with checks for existence of methods +class ModuleWrapper(ModuleType): + def __init__(self, module): + self.module = module + + def __getattr__(self, name: str) -> Any: + if not hasattr(self.module, name): + if name == "__path__": + return None + if name == "__all__": + return [attr for attr in dir(self.module) if not attr.startswith("_")] + raise NotImplementedError(self.module.__name__ + "." + name + error_msg) + attr = getattr(self.module, name) + if ismodule(attr): + return ModuleWrapper(attr) + else: + return attr + + +def _is_torch(s: str): + return s == "torch" or s.startswith("torch.") + + +class OneflowImporter(MetaPathFinder, Loader): + def __init__(self): + # module_from_spec will try to call the loader's create_module, resulting in infinite recursion + self.in_create_module = False + self.enable = False + # both __init__.py of oneflow and torch can't be executed multiple times, so we use a cache + self.enable_mod_cache = {} + self.disable_mod_cache = {} + + def find_spec(self, fullname, path, target=None): + if _is_torch(fullname): # don't touch modules other than torch + # for first import of real torch, we use default meta path finders, not our own + if not self.enable and self.disable_mod_cache.get(fullname) is None: + return None + return ModuleSpec(fullname, self) + return None + + def find_module(self, fullname, path=None): + spec = self.find_spec(fullname, path) + return spec + + def create_module(self, spec): + if self.in_create_module: + return None + self.in_create_module = True + if self.enable: + oneflow_mod_fullname = "oneflow" + spec.name[len("torch") :] + if ( + sys.modules.get(oneflow_mod_fullname) is None + and self.enable_mod_cache.get(spec.name) is None + ): + # get actual oneflow module + real_spec = find_spec(oneflow_mod_fullname) + if real_spec is None: + raise NotImplementedError(oneflow_mod_fullname + error_msg) + real_mod = module_from_spec(real_spec) + real_spec.loader.exec_module(real_mod) + else: + real_mod = sys.modules.get(oneflow_mod_fullname) + if real_mod is None: + real_mod = self.enable_mod_cache[spec.name] + self.in_create_module = False + return real_mod + else: + torch_full_name = spec.name + real_mod = self.disable_mod_cache[torch_full_name] + self.in_create_module = False + return real_mod + + def exec_module(self, module): + fullname = "torch" + module.__name__[len("oneflow") :] + if self.enable: + module = ModuleWrapper(module) + sys.modules[fullname] = module + globals()[fullname] = module + + def _enable(self, globals): + global _first_init + if _first_init: + _first_init = False + self.enable = False # deal with previously imported torch + sys.meta_path.insert(0, self) + self._enable(globals) + return + if self.enable: # already enabled + return + for k, v in sys.modules.copy().items(): + if _is_torch(k): + self.disable_mod_cache.update({k: v}) + del sys.modules[k] + try: + del globals[k] + except KeyError: + pass + for k, v in self.enable_mod_cache.items(): + sys.modules.update({k: v}) + globals.update({k: v}) + self.enable = True + + def _disable(self, globals): + if not self.enable: # already disabled + return + for k, v in sys.modules.copy().items(): + if _is_torch(k): + self.enable_mod_cache.update({k: v}) + del sys.modules[k] + try: + del globals[k] + except KeyError: + pass + for k, v in self.disable_mod_cache.items(): + sys.modules.update({k: v}) + globals.update({k: v}) + self.enable = False + + +_importer = OneflowImporter() + + +class enable: + def __init__(self): + self.enable = _importer.enable + if self.enable: + return + globals = currentframe().f_back.f_globals + self.globals = globals + _importer._enable(globals) + + def __enter__(self): + pass + + def __exit__(self, exception_type, exception_value, traceback): + if not self.enable: + _importer._disable(self.globals) + + +class disable: + def __init__(self): + self.enable = _importer.enable + if not self.enable: + return + globals = currentframe().f_back.f_globals + self.globals = globals + _importer._disable(globals) + + def __enter__(self): + pass + + def __exit__(self, exception_type, exception_value, traceback): + if self.enable: + _importer._enable(self.globals) diff --git a/python/oneflow/mock_torch/__main__.py b/python/oneflow/mock_torch/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..13a4519046caa662ed00c768ee0301aebd509fec --- /dev/null +++ b/python/oneflow/mock_torch/__main__.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import argparse +from pathlib import Path +import os + +parser = argparse.ArgumentParser() +parser.add_argument( + "mock", + choices=["enable", "disable"], + help="enable/disable mocking 'import torch', default is enable", + nargs="?", + default="enable", +) +args = parser.parse_args() + +torch_env = Path(__file__).parent + + +def main(): + if args.mock == "enable": + print("export PYTHONPATH=" + str(torch_env) + ":$PYTHONPATH") + elif args.mock == "disable": + paths = os.environ["PYTHONPATH"].rstrip(":").split(":") + paths = [x for x in paths if x != str(torch_env)] + path = ":".join(paths) + print("export PYTHONPATH=" + path) + + +if __name__ == "__main__": + main() diff --git a/python/oneflow/mock_torch/torch/__init__.py b/python/oneflow/mock_torch/torch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..883244dd7ed9e204c8d0202de2f31e5f4fac1078 --- /dev/null +++ b/python/oneflow/mock_torch/torch/__init__.py @@ -0,0 +1,31 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import sys +import oneflow +from oneflow.mock_torch import ( + ModuleWrapper, + OneflowImporter, +) + + +def __getattr__(name: str): + return ModuleWrapper(oneflow).__getattr__(name) + + +# register importer in meta path +oneflow_importer = OneflowImporter() +oneflow_importer.enable = True +sys.meta_path.insert(0, oneflow_importer) diff --git a/python/oneflow/multiprocessing/reductions.py b/python/oneflow/multiprocessing/reductions.py index 1b18b1b6fb2a86b33c60415593f8d4c10ad201ce..d857fda845c0d1f439173a7c453d4a4795a8fc9e 100644 --- a/python/oneflow/multiprocessing/reductions.py +++ b/python/oneflow/multiprocessing/reductions.py @@ -42,8 +42,12 @@ def rebuild_empty_tensor(shape, dtype, requires_grad): def rebuild_shm_tensor(shm, shape, dtype, requires_grad): def delete_shm(): - shm.close() try: + # For unknown reasons delete_shm called in dataloader may fail + # with "StopIteration". + # An example is when dataloader is wrapped in a generator like + # `log_every`. + shm.close() shm.unlink() except: pass diff --git a/python/oneflow/nn/__init__.py b/python/oneflow/nn/__init__.py index 26ebaf65fe4b0b0d01b1e342b93d5a6c9af6ced2..4e6c961ec89a7713789ce2fc92e22b6a2ff6b1e8 100644 --- a/python/oneflow/nn/__init__.py +++ b/python/oneflow/nn/__init__.py @@ -13,12 +13,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ +from .modules import * from oneflow.nn.graph import Graph -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.activation import ( ELU, CELU, GELU, + QuickGELU, GLU, Hardsigmoid, Hardshrink, @@ -43,7 +45,12 @@ from oneflow.nn.modules.activation import ( ) from oneflow.nn.modules.all_reduce import AllReduce -from oneflow.nn.modules.batchnorm import BatchNorm1d, BatchNorm2d, BatchNorm3d +from oneflow.nn.modules.batchnorm import ( + BatchNorm1d, + BatchNorm2d, + BatchNorm3d, + SyncBatchNorm, +) from oneflow.nn.modules.batchnorm_fused import ( FusedBatchNorm1d, FusedBatchNorm2d, @@ -66,7 +73,7 @@ from oneflow.nn.modules.conv import ( ConvTranspose2d, ConvTranspose3d, ) -from oneflow.nn.modules.distance import CosineSimilarity +from oneflow.nn.modules.distance import CosineSimilarity, PairwiseDistance from oneflow.nn.modules.min_max_observer import MinMaxObserver from oneflow.nn.modules.moving_average_min_max_observer import ( MovingAverageMinMaxObserver, @@ -91,9 +98,10 @@ from oneflow.nn.modules.dataset import ( OFRecordBytesDecoder, GPTIndexedBinDataReader, OneRecReader, + RawReader, ) -from oneflow.nn.modules.dropout import Dropout +from oneflow.nn.modules.dropout import Dropout, Dropout1d, Dropout2d, Dropout3d from oneflow.nn.modules.flatten import Flatten from oneflow.nn.modules.instancenorm import ( InstanceNorm1d, @@ -115,12 +123,14 @@ from oneflow.nn.modules.loss import ( CombinedMarginLoss, TripletMarginLoss, ) -from oneflow.nn.modules.normalization import GroupNorm, LayerNorm +from oneflow.nn.modules.normalization import GroupNorm, LayerNorm, RMSLayerNorm, RMSNorm from oneflow.nn.modules.padding import ( ConstantPad1d, ConstantPad2d, ConstantPad3d, + ReflectionPad1d, ReflectionPad2d, + ReplicationPad1d, ReplicationPad2d, ZeroPad2d, ) @@ -132,9 +142,15 @@ from oneflow.nn.modules.pooling import ( MaxPool1d, MaxPool2d, MaxPool3d, + MaxUnpool1d, + MaxUnpool2d, + MaxUnpool3d, AdaptiveAvgPool1d, AdaptiveAvgPool2d, AdaptiveAvgPool3d, + AdaptiveMaxPool1d, + AdaptiveMaxPool2d, + AdaptiveMaxPool3d, ) from oneflow.nn.modules.sparse import Embedding from oneflow.nn.modules.upsampling import ( @@ -152,10 +168,14 @@ from . import functional from . import parallel from oneflow.nn.modules.rnn import ( + RNNCellBase, RNNCell, LSTMCell, GRUCell, + RNNBase, RNN, LSTM, GRU, ) + +from oneflow.nn.qat.conv import QatConv1d, QatConv2d, QatConv3d diff --git a/python/oneflow/nn/functional/__init__.py b/python/oneflow/nn/functional/__init__.py index e9520059ecce399ee93c3b53a424f7d9cf5d95ee..dbf5f986330de9cfd082f8dc988f5e86dddd2c25 100644 --- a/python/oneflow/nn/functional/__init__.py +++ b/python/oneflow/nn/functional/__init__.py @@ -29,10 +29,16 @@ from oneflow._C import avg_pool3d from .functional_maxpool import max_pool1d from .functional_maxpool import max_pool2d from .functional_maxpool import max_pool3d +from .functional_maxpool import adaptive_max_pool1d +from .functional_maxpool import adaptive_max_pool2d +from .functional_maxpool import adaptive_max_pool3d from oneflow._C import adaptive_avg_pool1d from oneflow._C import adaptive_avg_pool2d from oneflow._C import adaptive_avg_pool3d -from oneflow._C import cosine_similarity +from oneflow._C import max_unpool1d +from oneflow._C import max_unpool2d +from oneflow._C import max_unpool3d +from oneflow._C import cosine_similarity, pairwise_distance from oneflow._C import relu from oneflow._C import hardtanh from oneflow._C import hardsigmoid @@ -45,7 +51,8 @@ from oneflow._C import selu from oneflow._C import sigmoid from oneflow._C import softshrink from oneflow._C import prelu -from oneflow._C import gelu +from oneflow._C import gelu_with_approximate as gelu +from oneflow._C import quick_gelu from oneflow._C import glu from oneflow._C import logsigmoid from oneflow._C import log_softmax @@ -57,14 +64,27 @@ from oneflow._C import threshold from oneflow._C import silu from oneflow._C import mish from oneflow.nn.modules.normalization import layer_norm -from oneflow._C import dropout +from oneflow._C import dropout, dropout1d, dropout2d, dropout3d from oneflow._C import smooth_l1_loss -from oneflow._C import pad +from .functional_pad import pad +from .functional_batch_norm import batch_norm from oneflow._C import triplet_margin_loss from oneflow._C import ctc_greedy_decoder from oneflow._C import one_hot from oneflow._C import normalize +from oneflow._C import mse_loss +from oneflow._C import l1_loss from oneflow._C import cross_entropy +from oneflow._C import binary_cross_entropy_loss as binary_cross_entropy +from oneflow._C import ( + binary_cross_entropy_with_logits_loss as binary_cross_entropy_with_logits, +) from oneflow.nn.modules.sparse import embedding from oneflow.nn.modules.linear import linear from oneflow.nn.modules.activation import relu6 +from oneflow.nn.modules.upsampling import Upsample as upsample +from oneflow._C import unfold +from oneflow._C import fold +from .functional_deform_conv import deform_conv2d +from oneflow._C import kl_div_loss as kl_div +from oneflow._C import gumbel_softmax diff --git a/python/oneflow/nn/functional/functional_batch_norm.py b/python/oneflow/nn/functional/functional_batch_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..fbcf665a995ba92be34ed344740a5bd037122fd7 --- /dev/null +++ b/python/oneflow/nn/functional/functional_batch_norm.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +from typing import List, Optional + +from oneflow.framework.tensor import Tensor +import oneflow as flow + + +def batch_norm( + input: Tensor, + running_mean: Optional[Tensor], + running_var: Optional[Tensor], + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + training: bool = False, + momentum: float = 0.1, + eps: float = 1e-5, +) -> Tensor: + r"""Applies Batch Normalization for each channel across a batch of data. + + See :class:`~oneflow.nn.BatchNorm1d`, :class:`~oneflow.nn.BatchNorm2d`, + :class:`~oneflow.nn.BatchNorm3d` for details. + """ + if input.ndim == 4 and os.getenv("ONEFLOW_ENABLE_NHWC") == "1": + axis = 3 + else: + axis = 1 + + return flow._C.normalization( + input, running_mean, running_var, weight, bias, axis, eps, momentum, training, + ) diff --git a/python/oneflow/nn/functional/functional_deform_conv.py b/python/oneflow/nn/functional/functional_deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..188381fc8dfdfb9ebdb3fe6d955bf917bf2f96fc --- /dev/null +++ b/python/oneflow/nn/functional/functional_deform_conv.py @@ -0,0 +1,122 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from typing import Optional, Tuple, Union + +import oneflow as flow +from oneflow.framework.tensor import Tensor + + +def deform_conv2d( + input: Tensor, + offset: Tensor, + weight: Tensor, + bias: Optional[Tensor] = None, + stride: Tuple[int, int] = (1, 1), + padding: Tuple[int, int] = (0, 0), + dilation: Tuple[int, int] = (1, 1), + mask: Optional[Tensor] = None, +) -> Tensor: + r""" + Performs Deformable Convolution v2, described in + `Deformable ConvNets v2: More Deformable, Better Results + `__ if :attr:`mask` is not ``None`` and + Performs Deformable Convolution, described in + `Deformable Convolutional Networks + `__ if :attr:`mask` is ``None``. + + Args: + input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor + offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]): + offsets to be applied for each position in the convolution kernel. + weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]): convolution weights, + split into groups of size (in_channels // groups) + bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None + stride (int or Tuple[int, int]): distance between convolution centers. Default: 1 + padding (int or Tuple[int, int]): height/width of padding of zeroes around + each image. Default: 0 + dilation (int or Tuple[int, int]): the spacing between kernel elements. Default: 1 + mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): + masks to be applied for each position in the convolution kernel. Default: None + + Returns: + Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution + + Examples:: + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> input = flow.rand(4, 3, 10, 10) + >>> kh, kw = 3, 3 + >>> weight = flow.rand(5, 3, kh, kw) + >>> # offset and mask should have the same spatial size as the output + >>> # of the convolution. In this case, for an input of 10, stride of 1 + >>> # and kernel size of 3, without padding, the output size is 8 + >>> offset = flow.rand(4, 2 * kh * kw, 8, 8) + >>> mask = flow.rand(4, kh * kw, 8, 8) + >>> out = F.deform_conv2d(input, offset, weight, mask=mask) + >>> out.size() + oneflow.Size([4, 5, 8, 8]) + """ + use_mask = mask is not None + if mask is None: + mask = flow.zeros((input.shape[0], 0), dtype=input.dtype).to(input.device) + stride_h = stride[0] + stride_w = stride[1] + pad_h = padding[0] + pad_w = padding[1] + dil_h = dilation[0] + dil_w = dilation[1] + weights_h, weights_w = weight.shape[-2:] + + # TODO(yzm): Support rectangle convolution + if weights_h != weights_w: + raise NotImplementedError("Rectangle convolution is not supported currently.") + + if use_mask and len(mask.shape) != 4: + raise RuntimeError("The dimension of mask tensor weight must be 4") + if len(input.shape) != 4: + raise RuntimeError("The dimension of input tensor weight must be 4") + if len(weight.shape) != 4: + raise RuntimeError("The dimension of weight tensor weight must be 4") + if len(offset.shape) != 4: + raise RuntimeError("The dimension of offset tensor weight must be 4") + + _, n_in_channels, _, _ = input.shape + n_offset_grps = offset.shape[1] // (2 * weights_h * weights_w) + n_weight_grps = n_in_channels // weight.shape[1] + + if n_offset_grps == 0: + raise RuntimeError( + "The shape of the offset tensor at dimension 1 is not valid. It should " + "be a multiple of 2 * weight.size[2] * weight.size[3].\n" + f"Got offset.shape[1]={offset.shape[1]}, while 2 * weight.size[2] * weight.size[3]={2 * weights_h * weights_w}" + ) + + return flow._C.deform_conv2d( + input, + weight, + offset, + mask, + bias, + stride_h, + stride_w, + pad_h, + pad_w, + dil_h, + dil_w, + n_weight_grps, + n_offset_grps, + use_mask, + ) diff --git a/python/oneflow/nn/functional/functional_maxpool.py b/python/oneflow/nn/functional/functional_maxpool.py index 5a3b8a97a8c9e09c0d1eb892a326bb435596cd28..63eadee46911f1634c831642e830c948cdc91058 100644 --- a/python/oneflow/nn/functional/functional_maxpool.py +++ b/python/oneflow/nn/functional/functional_maxpool.py @@ -27,6 +27,29 @@ def max_pool1d( ceil_mode=False, data_format="channels_first", ): + r""" + max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False,ceil_mode=False, data_format="channels_first") + + Applies a 1D max pooling over an input signal composed of several input + planes. + + The documentation is referenced from: https://pytorch.org/docs/master/generated/torch.nn.functional.max_pool1d.html. + + .. note:: + The order of :attr:`ceil_mode` and :attr:`return_indices` is different from + what seen in :class:`~oneflow.nn.MaxPool1d`, and will change in a future release. + + See :class:`~oneflow.nn.MaxPool1d` for details. + + Args: + input: input tensor of shape :math:`(\text{minibatch} , \text{in_channels} , iW)`, minibatch dim optional. + kernel_size: the size of the window. Can be a single number or a tuple `(kW,)` + stride: the stride of the window. Can be a single number or a tuple `(sW,)`. Default: :attr:`kernel_size` + padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. + dilation: The stride between elements within a sliding window, must be > 0. + return_indices: If ``True``, will return the argmax along with the max values.Useful for :class:`oneflow.nn.functional.max_unpool1d` later. + ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + """ _max_pool_out = oneflow._C.max_pool1d( x, kernel_size, @@ -53,6 +76,29 @@ def max_pool2d( ceil_mode=False, data_format="channels_first", ): + r""" + max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False,data_format="channels_first") + + Applies a 2D max pooling over an input signal composed of several input + planes. + + The documentation is referenced from: https://pytorch.org/docs/master/generated/torch.nn.functional.max_pool2d.html. + + .. note:: + The order of :attr:`ceil_mode` and :attr:`return_indices` is different from + what seen in :class:`~oneflow.nn.MaxPool2d`, and will change in a future release. + + See :class:`~oneflow.nn.MaxPool2d` for details. + + Args: + input: input tensor :math:`(\text{minibatch} , \text{in_channels} , iH , iW)`, minibatch dim optional. + kernel_size: size of the pooling region. Can be a single number or a tuple `(kH, kW)` + stride: stride of the pooling operation. Can be a single number or a tuple `(sH, sW)`. Default: :attr:`kernel_size` + padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. + dilation: The stride between elements within a sliding window, must be > 0. + return_indices: If ``True``, will return the argmax along with the max values.Useful for :class:`oneflow.nn.functional.max_unpool2d` later. + ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + """ _max_pool_out = oneflow._C.max_pool2d( x, kernel_size, @@ -79,6 +125,29 @@ def max_pool3d( ceil_mode=False, data_format="channels_first", ): + r""" + max_pool3d(input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False, data_format="channels_first") + + Applies a 3D max pooling over an input signal composed of several input + planes. + + The documentation is referenced from: https://pytorch.org/docs/master/generated/torch.nn.functional.max_pool3d.html. + + .. note:: + The order of :attr:`ceil_mode` and :attr:`return_indices` is different from + what seen in :class:`~oneflow.nn.MaxPool3d`, and will change in a future release. + + See :class:`~oneflow.nn.MaxPool3d` for details. + + Args: + input: input tensor :math:`(\text{minibatch} , \text{in_channels} , iD, iH , iW)`, minibatch dim optional. + kernel_size: size of the pooling region. Can be a single number or a tuple `(kT, kH, kW)` + stride: stride of the pooling operation. Can be a single number or a tuple `(sT, sH, sW)`. Default: :attr:`kernel_size` + padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. + dilation: The stride between elements within a sliding window, must be > 0. + return_indices: If ``True``, will return the argmax along with the max values.Useful for :class:`~oneflow.nn.functional.max_unpool3d` later. + ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. + """ _max_pool_out = oneflow._C.max_pool3d( x, kernel_size, @@ -93,3 +162,70 @@ def max_pool3d( return _max_pool_out else: return _max_pool_out[0] + + +def adaptive_max_pool1d(input, output_size, return_indices: bool = False): + r"""Applies a 1D adaptive max pooling over an input signal composed of + several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.adaptive_max_pool1d.html + + See :class:`~oneflow.nn.AdaptiveMaxPool1d` for details and output shape. + + Args: + output_size: the target output size (single integer) + return_indices: whether to return pooling indices. Default: ``False`` + + """ + + _out = oneflow._C.adaptive_max_pool1d(input, output_size) + if return_indices: + return _out + else: + return _out[0] + + +def adaptive_max_pool2d(input, output_size, return_indices: bool = False): + r"""Applies a 2D adaptive max pooling over an input signal composed of + several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.adaptive_max_pool2d.html + + See :class:`~oneflow.nn.AdaptiveMaxPool2d` for details and output shape. + + Args: + output_size: the target output size (single integer or + double-integer tuple) + return_indices: whether to return pooling indices. Default: ``False`` + + """ + _out = oneflow._C.adaptive_max_pool2d(input, output_size) + if return_indices: + return _out + else: + return _out[0] + + +def adaptive_max_pool3d(input, output_size, return_indices: bool = False): + r"""Applies a 3D adaptive max pooling over an input signal composed of + several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.adaptive_max_pool3d.html + + See :class:`~oneflow.nn.AdaptiveMaxPool3d` for details and output shape. + + Args: + output_size: the target output size (single integer or + triple-integer tuple) + return_indices: whether to return pooling indices. Default: ``False`` + + """ + + _out = oneflow._C.adaptive_max_pool3d(input, output_size) + if return_indices: + return _out + else: + return _out[0] diff --git a/python/oneflow/nn/functional/functional_pad.py b/python/oneflow/nn/functional/functional_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..8204bfe471cb00c93cc19e1e98c0b97e3daa4be1 --- /dev/null +++ b/python/oneflow/nn/functional/functional_pad.py @@ -0,0 +1,126 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from typing import List +from oneflow.framework.tensor import Tensor +import oneflow as flow + + +def pad( + input: Tensor, pad: List[int], mode: str = "constant", value: float = 0.0 +) -> Tensor: + r"""Pads tensor. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.functional.pad.html. + + Padding size: + The padding size by which to pad some dimensions of :attr:`input` + are described starting from the last dimension and moving forward. + :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor` dimensions + of ``input`` will be padded. + For example, to pad only the last dimension of the input tensor, then + :attr:`pad` has the form + :math:`(\text{padding_left}, \text{padding_right})`; + to pad the last 2 dimensions of the input tensor, then use + :math:`(\text{padding_left}, \text{padding_right},` + :math:`\text{padding_top}, \text{padding_bottom})`; + to pad the last 3 dimensions, use + :math:`(\text{padding_left}, \text{padding_right},` + :math:`\text{padding_top}, \text{padding_bottom}` + :math:`\text{padding_front}, \text{padding_back})`. + + Padding mode: + See :class:`oneflow.nn.ConstantPad2d`, :class:`oneflow.nn.ReflectionPad2d`, and + :class:`oneflow.nn.ReplicationPad2d` for concrete examples on how each of the + padding modes works. Constant padding is implemented for arbitrary dimensions. + Replicate and reflection padding is implemented for padding the last 3 + dimensions of 5D input tensor, or the last 2 dimensions of 4D input + tensor, or the last dimension of 3D input tensor. + + Note: + When using the CUDA backend, this operation may induce nondeterministic + behaviour in its backward pass that is not easily switched off. + + Args: + input (Tensor): N-dimensional tensor + pad (tuple): m-elements tuple, where + :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even. + mode: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. + Default: ``'constant'`` + value: fill value for ``'constant'`` padding. Default: ``0`` + + Examples:: + + >>> import oneflow as flow + >>> import oneflow.nn.functional as F + >>> t4d = flow.empty(3, 3, 4, 2) + >>> p1d = (1, 1) + >>> out = F.pad(t4d, p1d) + >>> out.size() + oneflow.Size([3, 3, 4, 4]) + + """ + assert len(pad) % 2 == 0, "Padding length must be divisible by 2" + assert len(pad) // 2 <= input.dim(), "Padding length too large" + if mode == "constant": + return flow._C.pad(input, pad, mode="constant", value=value) + else: + assert ( + value == 0.0 + ), 'Padding mode "{}"" doesn\'t take in value argument'.format(mode) + if len(pad) == 2 and (input.dim() == 2 or input.dim() == 3): + if mode == "reflect": + return flow._C.pad(input, pad, mode="reflect") + elif mode == "replicate": + return flow._C.pad(input, pad, mode="replicate") + elif mode == "circular": + raise NotImplementedError( + "1D circular padding are not supported for now" + ) + else: + raise NotImplementedError + + elif len(pad) == 4 and (input.dim() == 3 or input.dim() == 4): + if mode == "reflect": + return flow._C.pad(input, pad, mode="reflect") + elif mode == "replicate": + return flow._C.pad(input, pad, mode="replicate") + elif mode == "circular": + raise NotImplementedError( + "2D circular padding are not supported for now" + ) + else: + raise NotImplementedError + + elif len(pad) == 6 and (input.dim() == 4 or input.dim() == 5): + if mode == "reflect": + raise NotImplementedError( + "3D reflect padding are not supported for now" + ) + elif mode == "replicate": + raise NotImplementedError( + "3D replicate padding are not supported for now" + ) + elif mode == "circular": + raise NotImplementedError( + "3D circular padding are not supported for now" + ) + else: + raise NotImplementedError + else: + raise NotImplementedError( + "Only 2D, 3D, 4D, 5D padding with non-constant padding are supported for now" + ) diff --git a/python/oneflow/nn/graph/__init__.py b/python/oneflow/nn/graph/__init__.py index 958bf9bf8ce06c2888657c824a57fef6ac669173..1f8400b6f6d1e56b2064d2ff23aafd551fb46b71 100644 --- a/python/oneflow/nn/graph/__init__.py +++ b/python/oneflow/nn/graph/__init__.py @@ -14,4 +14,6 @@ See the License for the specific language governing permissions and limitations under the License. """ from .graph import Graph -from .block import Block +from .proxy import Proxy +from .graph_block import GraphModule +from .graph_block import GraphTensor diff --git a/python/oneflow/nn/graph/block.py b/python/oneflow/nn/graph/block.py deleted file mode 100644 index fa38031ebb146cb544120f23ea1e22a7a5ba5d97..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/graph/block.py +++ /dev/null @@ -1,763 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from collections import OrderedDict -from functools import partial -from typing import Iterator, Optional, Set, Union, List -import weakref - -import oneflow._C -import oneflow._oneflow_internal -from oneflow.framework import graph_build_util -from oneflow.env import get_rank -from oneflow.framework.tensor import Tensor, TensorTuple -from oneflow.nn.module import Module -from oneflow.nn.modules.container import * -from oneflow.nn.utils.container import * -from oneflow.nn.parameter import Parameter -from oneflow.nn.graph.block_config import BlockConfig -from oneflow.nn.graph.util import ( - add_indent, - ArgsTree, - operators_repr, - seq_to_func_return, -) - - -def get_block_cls(item): - if isinstance(item, Sequential): - return SequentialBlock - elif isinstance(item, ModuleList): - return ModuleListBlock - elif isinstance(item, ModuleDict): - return ModuleDictBlock - elif isinstance(item, ParameterList): - return ParameterListBlock - elif isinstance(item, ParameterDict): - return ParameterDictBlock - elif isinstance(item, Module): - return ModuleBlock - elif isinstance(item, Tensor): - return TensorBlock - else: - raise NotImplementedError() - - -class BlockType: - NONE = "NONE" - MODULE = "MODULE" - PARAMETER = "PARAMETER" - BUFFER = "BUFFER" - - -class Block(object): - def __init__( - self, - prefix: str = "", - name: str = "", - belonged_graph: weakref.ProxyTypes = None, - ): - self._name = name - self._name_prefix = prefix - self._type = BlockType.NONE - self._origin = None - self._scope = None - self._prev_scope = None - assert belonged_graph is None or isinstance(belonged_graph, weakref.ProxyTypes) - self._belonged_graph = belonged_graph - self.config = BlockConfig() - - @property - def name(self): - return self._name - - @property - def name_prefix(self): - return self._name_prefix - - @property - def type(self): - return self._type - - @property - def prev_scope(self): - if self._prev_scope is None: - self._prev_scope = oneflow._oneflow_internal.GetCurrentScope() - return self._prev_scope - - @property - def scope(self): - if self._scope is None: - self._scope = graph_build_util.make_new_block_scope(self.prev_scope, self) - return self._scope - - def scope_context(self): - return graph_build_util.BlockScopeContext(self.prev_scope, self.scope) - - -class ModuleBlock(Block): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: Module = None, - belonged_graph: weakref.ProxyTypes = None, - ): - assert not isinstance(origin, Block) - super().__init__(prefix, name, belonged_graph) - self._debug = False - self._debug_min_s_level = 2 - self._debug_max_v_level = 0 - self._debug_max_py_stack_depth = 2 - self._type = BlockType.MODULE - self._is_executing_forward = False - self._modules = OrderedDict() - self._parameters = OrderedDict() - self._buffers = OrderedDict() - self._args_repr = [] - self._outs_repr = [] - self.set_origin(origin) - - @property - def origin(self): - return self._origin - - def set_origin(self, origin): - self._origin = origin - if origin is None: - return - assert isinstance(origin, Module) - for (n, m) in list(origin.named_children()): - self.__setattr__( - n, - get_block_cls(m)( - self._name_prefix + self._name + ".", n, m, self._belonged_graph - ), - ) - for (n, p) in list(origin.named_parameters("", False)): - self.__setattr__( - n, get_block_cls(p)(self._name_prefix + self._name + ".", n, p) - ) - for (n, b) in list(origin.named_buffers("", False)): - self.__setattr__( - n, get_block_cls(b)(self._name_prefix + self._name + ".", n, b) - ) - - def debug( - self, - v_level: int = 0, - *, - ranks: Optional[Union[int, List[int]]] = None, - max_py_stack_depth: int = 2, - ) -> None: - assert isinstance(v_level, int) - assert isinstance(max_py_stack_depth, int) - - if ranks is None: - rank_list = [0] - elif isinstance(ranks, int): - rank_list = [ranks] - elif isinstance(ranks, list): - rank_list = ranks - else: - raise ValueError("ranks must be int or List[int].") - - my_rank = get_rank() - if -1 in rank_list or my_rank in rank_list: - self._debug = v_level >= 0 - if self._debug: - self._debug_min_s_level = 0 - self._debug_max_v_level = max(0, v_level) - self._debug_max_py_stack_depth = max_py_stack_depth - - if self._type == BlockType.MODULE: - - def _set_child(d): - for (_, n) in d.items(): - n.debug( - v_level, ranks=ranks, max_py_stack_depth=max_py_stack_depth - ) - - _set_child(self._modules) - - def __call__(self, *args, **kwargs): - assert self._type == BlockType.MODULE - self.__print(0, 1, self._shallow_repr()) - - args_tree = ArgsTree( - (args, kwargs), True, "_" + self.name_prefix + self.name + "_input", None - ) - - for (name, arg) in args_tree.iter_named_nodes(): - if arg.is_leaf(): - arg_value = arg.value() - meta_repr_str = ( - arg_value._meta_repr() - if isinstance(arg_value, Tensor) - else str(type(arg_value)) - ) - in_str = "(INPUT:" + name + ":" + meta_repr_str + ")" - if not isinstance(arg_value, Tensor): - in_str = "[WARNING]" + in_str - self._args_repr.append(in_str) - self.__print(0, 1, in_str) - - def _print_state(d): - for (_, n) in d.items(): - self.__print(0, 1, n._shallow_repr()) - - _print_state(self._parameters) - _print_state(self._buffers) - - # NOTE: The original nn.Moudle's __call__ method is ignored, which means - # that hooks of nn.Modules are ignored. It is not recommended - # to use hooks of nn.Module in nn.Graph for the moment. - # result = self._origin.__class__.__call__(self, *args) - with graph_build_util.DebugScopeContext( - self._debug_min_s_level, - self._debug_max_v_level, - self._debug, - self._debug_max_py_stack_depth, - ): - result = self.__block_forward(*args, **kwargs) - - outputs = () - if not (type(result) is tuple or type(result) is list): - outputs = (result,) - else: - outputs = result - - args_tree = ArgsTree( - (outputs, {}), True, "_" + self.name_prefix + self.name + "_output", None - ) - - for (name, arg) in args_tree.iter_named_nodes(): - if arg.is_leaf(): - arg_value = arg.value() - meta_repr_str = ( - arg_value._meta_repr() - if isinstance(arg_value, Tensor) - else str(type(arg_value)) - ) - out_str = "(OUTPUT:" + name + ":" + meta_repr_str + ")" - if not isinstance(arg_value, Tensor): - out_str = "[WARNING]" + out_str - self._outs_repr.append(out_str) - self.__print(0, 1, out_str) - - return result - - def __block_forward(self, *args, **kwargs): - self._is_executing_forward = True - args, kwargs = self.__pre_forward_map(*args, **kwargs) - with self.scope_context(): - result = self._origin.__class__.forward(self, *args, **kwargs) - self._is_executing_forward = False - return result - - def __pre_forward_map(self, *args, **kwargs): - # Insert identity op when doing activation checkpointing or pipeline execution. - # Identity op outside activation checkpointing scope will be the endpoint of an activation checkpointing segment. - # Identity op as the first op of a pipeline stage will make backward op depends on the identity op within the stage, - # otherwise the backward op may depends the op in former stage which will make graph creates unnessary buffers. - if self.config._stage_placement is not None: - - def insert_to_global(t): - assert isinstance(t, Tensor) - return t.to_global(placement=self.config._stage_placement) - - args, kwargs = self.__map_io( - "input", insert_to_global, "insert_to_global", *args, **kwargs - ) - - if self.config.activation_checkpointing or ( - self.config.stage_id is not None and self.config.stage_id >= 0 - ): - - def insert_identity(t): - assert isinstance(t, Tensor) - return oneflow._C.identity(t) - - args, kwargs = self.__map_io( - "input", insert_identity, "insert_identity", *args, **kwargs - ) - - return args, kwargs - - def add_module(self, name: str, module: Optional[Module]) -> None: - self.__setattr__( - name, - get_block_cls(module)( - self._name_prefix + self._name + ".", name, module, self._belonged_graph - ), - ) - - def register_parameter(self, name: str, param: Optional[Parameter]) -> None: - self.__setattr__( - name, - get_block_cls(param)(self._name_prefix + self._name + ".", name, param), - ) - - def modules(self, memo: Optional[Set["Block"]] = None) -> Iterator["Block"]: - assert self._type == BlockType.MODULE - if memo is None: - memo = set() - if self not in memo: - memo.add(self) - yield self - for (name, module) in self._modules.items(): - if module is None: - continue - for m in module.modules(memo): - yield m - - def __map_io(self, io_type, func, func_desc, *args, **kwargs): - assert isinstance(func_desc, str) - assert io_type in ("input", "output") - mapped_args = [] - - def map_tensor(item): - assert isinstance(item, Tensor) - return func(item) - - args_tree = ArgsTree( - (args, kwargs), - True, - "_" + self.name_prefix + self.name + "_" + io_type, - None, - ) - - def leaf_node_fn(leaf_node): - arg = leaf_node.value() - name = leaf_node.prefix() + "_" + leaf_node.name() - is_tensor, repr_str = self.__io_tensor_check_and_gen(arg, io_type, name) - if is_tensor: - self.__print( - 0, - 1, - f"{repr_str} is a Tensor, {func_desc} transformation has been done.", - ) - return map_tensor(arg) - else: - self.__print( - 0, - 0, - f"{repr_str} is not a Tensor, {func_desc} transformation will be ignored.", - ) - return arg - - out = args_tree.map_leaf(leaf_node_fn) - mapped_args = out[0] - mapped_kwargs = out[1] - return mapped_args, mapped_kwargs - - def __io_tensor_check_and_gen(self, item, io_type, name): - assert io_type in ("input", "output") - if isinstance(item, Tensor): - repr_str = ( - "(" + io_type.upper() + ":" + name + ":" + item._meta_repr() + ")" - ) - return True, repr_str - else: - repr_str = ( - "[WARNING](" - + io_type.upper() - + ":" - + name - + ":" - + str(type(item)) - + ")" - ) - return False, repr_str - - def __members(self, get_members_fn, recurse=True) -> Iterator["Block"]: - assert self._type == BlockType.MODULE - memo = set() - modules = self.modules() if recurse else [self] - for module in modules: - members = get_members_fn(module) - for (k, v) in members: - if v is None or v in memo: - continue - memo.add(v) - yield v - - def parameters(self, recurse: bool = True) -> Iterator["Block"]: - assert self._type == BlockType.MODULE - gen = self.__members(lambda module: module._parameters.items(), recurse=recurse) - for elem in gen: - yield elem - - def buffers(self, recurse: bool = True) -> Iterator["Block"]: - assert self._type == BlockType.MODULE - gen = self.__members(lambda module: module._buffers.items(), recurse=recurse) - for elem in gen: - yield elem - - def __setattr__(self, name: str, value=None) -> None: - if value is None or not isinstance(value, Block): - self.__dict__[name] = value - else: - dicts_or_sets = ( - self.__dict__, - self._modules, - self._parameters, - self._buffers, - ) - for d in dicts_or_sets: - if name in d: - raise AttributeError( - "'{}' object has duplicated attribute named '{}'".format( - self._name, name - ) - ) - if value.type == BlockType.MODULE: - self._modules[name] = value - elif value.type == BlockType.PARAMETER: - self._parameters[name] = value - elif value.type == BlockType.BUFFER: - self._buffers[name] = value - else: - raise AttributeError( - "'{}' object are not allowed to set attribute named '{}'".format( - type(self).__name__, name - ) - ) - - def __getattr__(self, name: str): - if name in self.__dict__: - return self.__dict__[name] - # support get module - if "_modules" in self.__dict__: - modules = self.__dict__["_modules"] - if name in modules: - return modules[name] - # support get parameter - p_state = self._get_from_states(name, "_parameters") - if p_state is not None: - return p_state - # support get buffer - b_state = self._get_from_states(name, "_buffers") - if b_state is not None: - return b_state - # support none parameter or buffer - if name in self._origin._parameters: - p_none = self._origin._parameters[name] - assert p_none is None - return None - if name in self._origin._buffers: - b_none = self._origin._buffers[name] - assert b_none is None - return None - # support get normal attr - if name in self._origin.__dict__: - return self._origin.__dict__[name] - # support get function - if hasattr(self._origin, name): - return partial(getattr(self._origin.__class__, name), self) - raise AttributeError( - "'{}' '{}' object '{}' in nn.Graph has no attribute '{}'".format( - self._type, type(self).__name__, self._name_prefix + self.name, name - ) - ) - - def _get_from_states(self, name, states_name): - if states_name not in self.__dict__: - return None - - _states = self.__dict__[states_name] - if name not in _states: - return None - - _s_block = _states[name] - if graph_build_util.lazy_mode.is_enabled(): - _s_block.try_build() - return _s_block.lazy_origin - elif ( - not graph_build_util.lazy_mode.is_enabled() - ) and self._is_executing_forward: - # eager and inside nn.Graph.build() - return _s_block.origin - else: - # outside nn.Graph.build() - return _s_block - - def __repr__(self): - lines = None - child_lines = [] - if (self.config is not None) and (not self.config._is_null): - child_lines.append(add_indent(repr(self.config), 2)) - if len(self._args_repr) > 0: - for in_str in self._args_repr: - input_str = add_indent(in_str, 2) - child_lines.append(input_str) - - def _append_child(d): - for (_, n) in d.items(): - n_str = repr(n) - n_str = add_indent(n_str, 2) - child_lines.append(n_str) - - _append_child(self._parameters) - _append_child(self._buffers) - _append_child(self._modules) - - for op_str in self._ops_repr(): - child_lines.append(add_indent(op_str, 2)) - - if len(self._outs_repr) > 0: - for out_str in self._outs_repr: - output_str = add_indent(out_str, 2) - child_lines.append(output_str) - - if len(child_lines) > 0: - lines = child_lines - - main_str = self._shallow_repr() + ": (" - if lines is not None: - main_str += "\n " + "\n ".join(lines) + "\n" - main_str += ")" - return main_str - - def _shallow_repr(self): - shallow_repr = ( - "(" - + self._type - + ":" - + self._name_prefix - + self._name - + ":" - + self._origin._shallow_repr() - + ")" - ) - return shallow_repr - - def _ops_repr(self): - r"""Generate operators' string representation of this module - """ - assert self._belonged_graph, ( - "ModuleBlock: " - + self._name_prefix - + self.name - + "'s belonged graph is not set." - ) - - if self._belonged_graph.is_compiled: - if self._belonged_graph._compiled_graph_proto is not None: - module_conf = self._belonged_graph._compiled_graph_proto.module_name2module_conf[ - self.name_prefix + self.name - ] - return operators_repr( - module_conf.ops, self._belonged_graph._compiled_graph_proto - ) - - return [] - - def __print(self, s_level=2, v_level=0, msg: str = ""): - r"""Do print according to info level. - """ - assert isinstance(s_level, int) - assert isinstance(v_level, int) - assert isinstance(msg, str) - if s_level >= self._debug_min_s_level: - if (s_level > 0) or (s_level == 0 and v_level <= self._debug_max_v_level): - print(msg, flush=True) - - -class LazyBuilder(object): - def __init__(self, name: str = None, method=None): - self.name = name - self.method = method - self.result = None - self.finished = False - - def try_build(self, block=None): - if not self.finished: - assert self.name is not None - assert self.method is not None - assert self.result is None - with block.scope_context(): - self.result = self.method() - self.finished = True - - -class TensorBlock(Block): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: Union[Parameter, Tensor] = None, - belonged_graph: weakref.ProxyTypes = None, - ): - assert not isinstance(origin, Block) - super().__init__(prefix, name, belonged_graph) - if isinstance(origin, Parameter): - self._type = BlockType.PARAMETER - elif isinstance(origin, Tensor): - self._type = BlockType.BUFFER - else: - raise NotImplementedError() - self._lazy_origin_builder = LazyBuilder() - self.build_finished = False - self.set_origin(origin) - - @property - def origin(self): - return self._origin - - def set_origin(self, origin): - self._origin = origin - - @property - def lazy_origin(self): - assert ( - self._type == BlockType.PARAMETER or self._type == BlockType.BUFFER - ), "Only Parameter or Buffer Block has lazy_origin" - return self._lazy_origin_builder.result - - def lazy_origin_builder(self): - assert ( - self._type == BlockType.PARAMETER or self._type == BlockType.BUFFER - ), "Only Parameter or Buffer Block has lazy_origin_builder" - return self._lazy_origin_builder - - def set_lazy_origin_builder(self, builder=None): - assert ( - self._type == BlockType.PARAMETER or self._type == BlockType.BUFFER - ), "Only Parameter or Buffer Block has lazy_origin_builder" - self._lazy_origin_builder = builder - - def try_build(self): - if not self.build_finished: - self._lazy_origin_builder.try_build(self) - self.build_finished = True - - def __repr__(self): - lines = None - main_str = self._shallow_repr() + ": (" - if lines is not None: - main_str += "\n " + "\n ".join(lines) + "\n" - main_str += ")" - return main_str - - def _shallow_repr(self): - shallow_repr = ( - "(" - + self._type - + ":" - + self._name_prefix - + self._name - + ":" - + self._origin._meta_repr() - + ")" - ) - return shallow_repr - - -class SequentialBlock(get_seq(ModuleBlock)): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: Sequential = None, - belonged_graph: weakref.ProxyTypes = None, - ): - super().__init__() - self._name_prefix = prefix - self._name = name - self._belonged_graph = belonged_graph - self.set_origin(origin) - - -class ModuleListBlock(get_list(ModuleBlock)): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: ModuleList = None, - belonged_graph: weakref.ProxyTypes = None, - ): - super().__init__() - self._name_prefix = prefix - self._name = name - self._belonged_graph = belonged_graph - self.set_origin(origin) - # MoudleList is a container without forward() method, - # so it will not be executed or has an execution config. - self.config = None - - -class ModuleDictBlock(get_dict(ModuleBlock)): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: ModuleDict = None, - belonged_graph: weakref.ProxyTypes = None, - ): - super().__init__() - self._name_prefix = prefix - self._name = name - self._belonged_graph = belonged_graph - self.set_origin(origin) - - -class ParameterListBlock(get_para_list(ModuleBlock)): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: ParameterList = None, - belonged_graph: weakref.ProxyTypes = None, - ): - super().__init__() - self._name_prefix = prefix - self._name = name - self._belonged_graph = belonged_graph - self.set_origin(origin) - self._is_executing_forward = True - - def __getitem__(self, idx): - assert isinstance(idx, int) - idx = self._get_abs_string_index(idx) - key = str(idx) - p_state = self._get_from_states(key, "_parameters") - if p_state is not None: - return p_state - else: - raise AttributeError("ParameterList dosen't contain ", key) - - -class ParameterDictBlock(get_para_dict(ModuleBlock)): - def __init__( - self, - prefix: str = "", - name: str = "", - origin: ParameterDict = None, - belonged_graph: weakref.ProxyTypes = None, - ): - super().__init__() - self._name_prefix = prefix - self._name = name - self._belonged_graph = belonged_graph - self.set_origin(origin) - self._is_executing_forward = True - - def __getitem__(self, key: str): - p_state = self._get_from_states(key, "_parameters") - if p_state is not None: - return p_state - else: - raise AttributeError("ParameterDict dosen't contain key ", key) diff --git a/python/oneflow/nn/graph/block_config.py b/python/oneflow/nn/graph/block_config.py deleted file mode 100644 index 313f65f9a68a4163b2468b6536932f88838be8c0..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/graph/block_config.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - - -class BlockConfig(object): - r"""Configurations on Module Block in nn.Graph. - - When an nn.Module is added into an nn.Graph, it is wrapped into a ModuleBlock. You can set or get optimization configs on an nn.Module with it's `ModuleBlock.config`. - """ - - def __init__(self): - self._is_null = True - self._stage_id = None - self._stage_placement = None - self._activation_checkpointing = None - - # NOTE(lixiang): For the normal display of docstr, the API Doc of the get and set methods are written together in the stage_id function. - @property - def stage_id(self): - r"""Set/Get stage id of nn.Module/ModuleBlock in pipeline parallelism. - - When calling stage_id(value: int = None), set different module's stage id to hint the graph - preparing right num of buffers in pipeline. (Not Recommended, for easy and efficient pipeline - parallelism experience, please use config.set_stage(stage_id, placement)) - """ - return self._stage_id - - @stage_id.setter - def stage_id(self, value: int = None): - r"""Set stage id of Block in pipeline parallelism. - Set different module's stage id to hint the graph preparing right num of buffers in pipeline. - """ - print( - "Warning: `config.stage_id = i` is deprecated, please use \n", - " config.set_stage(i, placement) for easy and efficient Pipeline parallel experience.", - ) - - self._is_null = False - self._stage_id = value - - def set_stage(self, stage_id: int = None, placement=None): - r"""Set stage id and placement of nn.Module/ModuleBlock in pipeline parallelism. - - Args: - stage_id (int): stage id of this module. - placement (flow.placement): the placement of all tensor in this module. - - Note: - There will be automatically do tensor.to_global(placement) for all input tensor of - this module. So there is no need to write to_global() in the module forward when using - Pipeline Parallelism which is not recommended. - - For example: - - .. code-block:: python - - # m_stage0 and m_stage1 are the two pipeline stages of the network, respectively. - # We can set Stage ID and Placement by using Module.config.set_stage() - # The Stage ID is numbered starting from 0 and increasing by 1. - # The Placement is all tensors placement of this module. - P_0 = flow.placement(type = "cuda", ranks = [0, 1]) - P_1 = flow.placement(type = "cuda", ranks = [2, 3]) - self.module_pipeline.m_stage0.config.set_stage(stage_id = 0, placement = P0) - self.module_pipeline.m_stage1.config.set_stage(stage_id = 1, placement = P1) - - """ - - self._is_null = False - self._stage_id = stage_id - self._stage_placement = placement - - # NOTE(lixiang): For the normal display of docstr, the API Doc of the get and set methods are written together in the activation_checkpointing function. - @property - def activation_checkpointing(self): - r"""Set/Get whether do activation checkpointing in this nn.Module. - - For example: - - .. code-block:: python - - import oneflow as flow - - class Graph(flow.nn.Graph): - def __init__(self): - super().__init__() - self.linear1 = flow.nn.Linear(3, 5, False) - self.linear2 = flow.nn.Linear(5, 8, False) - self.linear1.config.activation_checkpointing = True - self.linear2.config.activation_checkpointing = True - - def build(self, x): - y_pred = self.linear1(x) - y_pred = self.linear2(y_pred) - return y_pred - - graph = Graph() - - """ - return self._activation_checkpointing - - @activation_checkpointing.setter - def activation_checkpointing(self, value: bool = False): - r"""Set whether do activation checkpointing in this Block. - """ - self._is_null = False - self._activation_checkpointing = value - - def __repr__(self): - main_str = ( - "(" - + "CONFIG" - + ":config:" - + self.__class__.__name__ - + "(" - + ( - ("stage_id=" + str(self.stage_id) + ", ") - if self.stage_id is not None - else "" - ) - + ( - ( - "activation_checkpointing=" - + str(self.activation_checkpointing) - + ", " - ) - if self.activation_checkpointing is not None - else "" - ) - + "))" - ) - return main_str diff --git a/python/oneflow/nn/graph/graph.py b/python/oneflow/nn/graph/graph.py index f197010a2f1978217569ee2fd18ffd96f4013537..59f56f6e19832dbdf36351aa76863bafa6e22c21 100644 --- a/python/oneflow/nn/graph/graph.py +++ b/python/oneflow/nn/graph/graph.py @@ -15,12 +15,13 @@ limitations under the License. """ import logging import os +import sys import time import inspect +import weakref from collections import OrderedDict from functools import partial from typing import Dict, Optional, Union, List, Callable -import weakref from google.protobuf import text_format import oneflow @@ -34,19 +35,26 @@ from oneflow.env import get_rank from oneflow.framework.multi_client_session import MultiClientSession from oneflow.framework.tensor import Tensor, TensorTuple from oneflow.framework.tensor_tuple_util import convert_to_tensor_tuple -from oneflow.nn.graph.block import Block, BlockType, get_block_cls +from oneflow.nn.graph.proxy import ( + Proxy, + GraphBlockType, + get_proxy_cls, + GraphModule, + GraphTensor, +) from oneflow.nn.graph.graph_config import GraphConfig from oneflow.nn.graph.optimizer import OptDict, VariableConfig from oneflow.nn.graph.util import ( add_indent, ArgsTree, operators_repr, + GraphIR, seq_to_func_return, sys_exc_error_msg, ) -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.optimizer.lr_scheduler import LRScheduler -from oneflow.nn.optimizer.optimizer import Optimizer +from oneflow.optim.optimizer import Optimizer class Graph(object): @@ -103,7 +111,7 @@ class Graph(object): .. code-block:: python >>> import oneflow as flow - >>> class SubclassGraph(flow.nn.Graph): + >>> class CustomGraph(flow.nn.Graph): ... def __init__(self): ... super().__init__() # MUST be called ... # Then define the graph attributes @@ -122,6 +130,20 @@ class Graph(object): self._is_compiled = False # Default is local view self._is_global_view = False + + self._debug = False + self._debug_min_s_level = 2 + self._debug_max_v_level = 0 + self._debug_max_py_stack_depth = 2 + self._debug_op_repr_with_py_stack = False + self._debug_only_user_py_stack = True + self._outputs_buffer_size = 2 + self._cur_index_of_ouputs_buffer = 0 + + # For graph level op rewrite + self._unique_global_op_dict = dict() + self._unique_identity_op_dict = dict() + # forward graph job proto self._forward_job_proto = None # forward, backward and optimized graph job proto @@ -131,12 +153,7 @@ class Graph(object): self._job_id = None self._args_repr = [] self._outs_repr = [] - self._debug = False - self._debug_min_s_level = 2 - self._debug_max_v_level = 0 - self._debug_max_py_stack_depth = 2 - self._outputs_buffer_size = 2 - self._cur_index_of_ouputs_buffer = 0 + self._oneflow_internal_graph_ir__ = None self._session = session_ctx.GetDefaultSession() assert type(self._session) is MultiClientSession @@ -339,10 +356,10 @@ class Graph(object): destination._metadata = OrderedDict() # Get states from sub module block for name, block in self._blocks.items(): - assert block.type == BlockType.MODULE + assert block.to(GraphModule).type == GraphBlockType.MODULE sub_destination = OrderedDict() sub_destination._metadata = OrderedDict() - module = block.origin + module = block.to(Module) if module is not None: module.state_dict( sub_destination, "", keep_vars=False, @@ -357,7 +374,7 @@ class Graph(object): assert len(additional_var_names) == len(additional_var_tensors) for i in range(len(additional_var_names)): additional_tensor = additional_var_tensors[i] - if not self._is_global_view: + if not self._is_global_view and additional_tensor.is_global: additional_tensor = additional_tensor.to_local() destination[additional_var_names[i]] = additional_tensor else: @@ -392,7 +409,7 @@ class Graph(object): for name, item in state_dict.items(): if name in self._blocks: # 1 load parameter/buffer to Modules - self._blocks[name].origin.load_state_dict(item, strict) + self._blocks[name].to(Module).load_state_dict(item, strict) else: # 2 store other state to CNNGraph, CNNGraph load them after job pass assert isinstance(item, Tensor) @@ -416,10 +433,12 @@ class Graph(object): def debug( self, - v_level: int = 0, + v_level: int = -1, *, ranks: Optional[Union[int, List[int]]] = None, max_py_stack_depth: int = 2, + only_user_py_stack=True, + op_repr_with_py_stack=False, ) -> None: r"""Open or close debug mode of the graph. @@ -439,6 +458,10 @@ class Graph(object): Use ``max_py_stack_depth`` to specify the max Python stack depth for the debug information. + Use ``only_user_py_stack`` to only print the operators' locations which are from users' code or models. + + Use ``op_repr_with_py_stack`` to print operators' locations when printing nn.Graph's repr. + For example: .. code-block:: python @@ -451,13 +474,17 @@ class Graph(object): v_level (int): choose verbose debug info level, default v_level is 0, max v_level is 3. v_level can be set to -1 to close the debug mode. ranks (int or list(int)): choose ranks to print the debug information. Default rank ``0``. You can choose any valid rank. Ranks equals ``-1`` means debug on all ranks. - max_py_stack_depth(int): the maximum depth for the Python stack debug information. Default: ``2`` + max_py_stack_depth(int): the maximum depth for the Python stack debug information. Default: ``2``. + only_user_py_stack(bool): only to print the operators' locations from users' code. Default: ``True``. + op_repr_with_py_stack(bool): print operators' locations when printing nn.Graph's repr. Default: ``False``. """ assert isinstance(v_level, int) assert v_level >= -1, "The min verbose debug info level is -1." assert v_level <= 3, "The max verbose debug info level is 3." assert max_py_stack_depth >= 0, "The min max stack depth is 0." assert isinstance(max_py_stack_depth, int) + assert isinstance(only_user_py_stack, bool) + assert isinstance(op_repr_with_py_stack, bool) if ranks is None: rank_list = [0] @@ -475,10 +502,18 @@ class Graph(object): self._debug_min_s_level = 0 self._debug_max_v_level = max(0, v_level) for name, block in self._blocks.items(): - assert block.type == BlockType.MODULE - block.debug(v_level, ranks=ranks, max_py_stack_depth=max_py_stack_depth) + assert block.to(GraphModule).type == GraphBlockType.MODULE + block.to(GraphModule).debug( + v_level, + ranks=ranks, + max_py_stack_depth=max_py_stack_depth, + only_user_py_stack=only_user_py_stack, + op_repr_with_py_stack=op_repr_with_py_stack, + ) self._debug_max_py_stack_depth = max_py_stack_depth + self._debug_op_repr_with_py_stack = op_repr_with_py_stack + self._debug_only_user_py_stack = only_user_py_stack def __repr__(self): r"""For printing the graph structure. @@ -535,7 +570,13 @@ class Graph(object): """ if self._is_compiled and self._compiled_graph_proto is not None: module_conf = self._compiled_graph_proto.module_name2module_conf[self.name] - return operators_repr(module_conf.ops, self._compiled_graph_proto) + if self._oneflow_internal_graph_ir__ is None: + self._oneflow_internal_graph_ir__ = GraphIR(self._compiled_graph_proto) + return operators_repr( + module_conf.ops, + self._oneflow_internal_graph_ir__, + self._debug_op_repr_with_py_stack, + ) return [] @@ -618,7 +659,7 @@ class Graph(object): def __ensure_state_tensors_contiguous(self): for state_block in self._state(): - state_tensor = state_block.origin + state_tensor = state_block.to(Tensor) if not state_tensor.is_contiguous(): state_tensor.contiguous_() @@ -628,18 +669,21 @@ class Graph(object): state_op_names = [] for state_block in self._state(): - state_tensor = state_block.origin + state_tensor = state_block.to(Tensor) # If any state tensor is global tensor, graph is in global view. if state_tensor.is_global: self._is_global_view = True if state_tensor in state_tensor_set: continue - op_name = state_block.name_prefix + state_block.name + op_name = ( + state_block.to(GraphTensor).name_prefix + + state_block.to(GraphTensor).name + ) state_tensor_set.add(state_tensor) state_tensors.append(state_tensor) state_op_names.append(op_name) - if state_block.type == BlockType.PARAMETER: + if state_block.to(GraphTensor).type == GraphBlockType.PARAMETER: self._variables_conf[state_tensor] = VariableConfig(op_name) self._state_tensor_tuple = convert_to_tensor_tuple(state_tensors) @@ -661,14 +705,17 @@ class Graph(object): def _create_states_builder(self): state2lazy_builder = dict() for state_block in self._state(): - state_tensor = state_block.origin - op_name = state_block.name_prefix + state_block.name + state_tensor = state_block.to(Tensor) + op_name = ( + state_block.to(GraphTensor).name_prefix + + state_block.to(GraphTensor).name + ) if state_tensor in state2lazy_builder: # Differe tensor block shares the same tensor, so they need to share the same # builder. state_block.set_lazy_origin_builder(state2lazy_builder[state_tensor]) else: - if state_block.type == BlockType.PARAMETER: + if state_block.to(GraphTensor).type == GraphBlockType.PARAMETER: assert state_tensor in self._variables_conf state_config = self._variables_conf[state_tensor] op_name = state_config.name @@ -684,22 +731,35 @@ class Graph(object): ) state2lazy_builder[state_tensor] = state_block.lazy_origin_builder() + def _mark_variable_gradients(self): + variable = [] + gradients = [] + for state_block in self._state(): + if ( + state_block.to(GraphTensor).type == GraphBlockType.PARAMETER + and state_block.to(Tensor).grad is not None + and state_block.to(Tensor).grad.is_lazy + ): + variable.append(state_block.to(Tensor)) + gradients.append(state_block.to(Tensor).grad) + oneflow._oneflow_internal.nn.graph.MarkVariableGradients(variable, gradients) + @staticmethod - def to_graph(func): - """Make a function to do static graph run with nn.Graph. + def trace(func): + """Trace a function to do static graph and run with nn.Graph. - After decorating a function with ``to_graph``, the function is turned into a naive `nn.Graph`. + After decorating a function with ``trace``, the function is turned into a naive `nn.Graph`. Note: This is just a quick way to run a simple function with nn.Graph. - If you want to do training or model save/load, customize a nn.Graph class instead, donot use ``to_graph``. + If you want to do training or model save/load, customize a nn.Graph class instead, donot use ``trace``. For example: .. code-block:: python >>> import oneflow as flow - >>> @flow.nn.Graph.to_graph + >>> @flow.nn.Graph.trace ... def test_func(x): ... return x * 2 >>> input = flow.tensor((1, 2), dtype=flow.float32) @@ -708,14 +768,14 @@ class Graph(object): tensor([2., 4.], dtype=oneflow.float32) .. - Feature Stage of Feature [to_graph]. + Feature Stage of Feature [trace]. - Maintainer List [@strint] - Current Stage [Pre-alpha, note that this is an experimental feature and maybe removed without notice.] """ assert inspect.isfunction( func - ), f"nn.Graph.to_graph only support function currently, so {func} must be a function." + ), f"nn.Graph.trace only support function currently, so {func} must be a function." graph_cls_name = func.__name__ + "_graph" def init(self): @@ -751,6 +811,7 @@ class Graph(object): self._debug_max_v_level, self._debug, self._debug_max_py_stack_depth, + self._debug_only_user_py_stack, ): outputs = self.__build_graph(*args, **kwargs) build_graph_end = time.perf_counter() @@ -794,6 +855,7 @@ class Graph(object): self._debug_max_v_level, self._debug, self._debug_max_py_stack_depth, + self._debug_only_user_py_stack, ): self._c_nn_graph.complie_and_init_runtime() # Get compiled job @@ -811,7 +873,8 @@ class Graph(object): + "s." + "\n", ) - except: + except Exception as e: + print(e, file=sys.stderr) self.__print( 2, 0, "[ERROR]" + self._shallow_repr() + " building plan got error." ) @@ -875,6 +938,9 @@ class Graph(object): # Save forward graph job proto self._forward_job_proto = c_api_util.GetCurrentJob() + if self.training: + self._mark_variable_gradients() + self.__print( 0, 1, @@ -890,26 +956,25 @@ class Graph(object): ) modules_has_training = False for item in self._blocks.values(): - if item._origin.training: + if item.to(Module).training: modules_has_training = True break if ( modules_has_training or self.training or self._is_global_view ) and enable_mlir_inference_opt: + log_for_mlir_inference_opt = lambda extra_info: logging.warning( + f"environment variable ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION will be ignored {extra_info}." + ) if self.training: - logging.warning( - "environment variable ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION will be ignored in training mode." - ) + log_for_mlir_inference_opt("in training mode") if modules_has_training and not self.training: - logging.warning( - "environment variable ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION will be ignored when not all modules in graph are in eval mode. " + log_for_mlir_inference_opt( + "when not all modules in graph are in eval mode" ) if self._is_global_view: - logging.warning( - "environment variable ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION will be ignored in global mode. " - ) + log_for_mlir_inference_opt("in global mode") enable_mlir_inference_opt = False del os.environ["ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION"] oneflow._oneflow_internal.FillVariableTensorMgr( @@ -964,6 +1029,10 @@ class Graph(object): state_op_names, self._state_tensor_tuple ) + # Clear useless dict used in graph build. + self._unique_global_op_dict.clear() + self._unique_identity_op_dict.clear() + # Always pack outputs to remain type of outputs return ( self._full_job_proto, @@ -1265,7 +1334,7 @@ class Graph(object): return self.__map_io(io_type, func, *args, **kwargs) - def _add_block(self, name: str, module: Module = None) -> None: + def _add_module(self, name: str, module: Module = None) -> None: r"""Adds module to the graph as a block so that the module will be called in nn.Graph.build. @@ -1273,8 +1342,8 @@ class Graph(object): name (str): name of the child block. The child block can be accessed from this graph using the given name. module (Module): child module to be added to the graph. - Just assign nn.Module in nn.Graph, _add_block will be called to add the - module as a Block: + Just assign nn.Module in nn.Graph, _add_module will be called to add the + module as a ProxyModule: For example: @@ -1293,9 +1362,10 @@ class Graph(object): The block can be accessed as an attribute using the given name. >>> g = LinearGraph() - >>> print(repr(g.linear)) + >>> print(g.linear) (MODULE:linear:Linear(in_features=3, out_features=8, bias=False)): ( (PARAMETER:linear.weight:tensor(..., size=(8, 3), dtype=oneflow.float32, requires_grad=True)): () + (GraphModule:linear()): () ) """ if "_name" not in self.__dict__: @@ -1315,13 +1385,13 @@ class Graph(object): elif name == "": raise KeyError('module name can\'t be empty string ""') - self._blocks[name] = get_block_cls(module)( - "", name, module, weakref.proxy(self) + self._blocks[name] = get_proxy_cls(module)( + module, "", name, weakref.proxy(self) ) def __setattr__(self, name: str, value=None): if isinstance(value, Module): - self._add_block(name, value) + self._add_module(name, value) elif isinstance(value, Optimizer): raise AttributeError( "'{}' nn.Graph is not allowed to set Optimizer attribute named '{}'. " @@ -1366,7 +1436,7 @@ class Graph(object): os.environ[ "ONEFLOW_MLIR_ENABLE_INFERENCE_OPTIMIZATION" ] = self.env_enable_mlir_inference_opt - oneflow._oneflow_internal.ClearVariableTensorMgr() + oneflow._oneflow_internal.ResetVariableTensorMgr() def __ensure_input_tensors_contiguous(self, *args, **kwargs): args_tree = ArgsTree((args, kwargs), False) diff --git a/python/oneflow/nn/graph/graph_block.py b/python/oneflow/nn/graph/graph_block.py new file mode 100644 index 0000000000000000000000000000000000000000..9216ce7bc13bb4b80b327b05f1785c44e01c8c21 --- /dev/null +++ b/python/oneflow/nn/graph/graph_block.py @@ -0,0 +1,400 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import weakref +from collections import OrderedDict +from typing import Iterator, Optional, Set, Union, List + +import oneflow._oneflow_internal +from oneflow.env import get_rank +from oneflow.framework import graph_build_util +from oneflow.nn.graph.util import ( + add_indent, + operators_repr, + GraphIR, +) + + +class GraphBlockType: + NONE = "NONE" + MODULE = "MODULE" + PARAMETER = "PARAMETER" + BUFFER = "BUFFER" + + +# Module or Tensor are both treated as Block. +class GraphBlock(object): + def __init__( + self, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + belonged_proxy: weakref.ProxyTypes = None, + block_graph_type: GraphBlockType = GraphBlockType.NONE, + ): + self._name = name + self._name_prefix = prefix + self._type = block_graph_type + self._scope = None + self._prev_scope = None + assert belonged_graph is None or isinstance(belonged_graph, weakref.ProxyTypes) + self._belonged_graph = belonged_graph + assert belonged_proxy is None or isinstance(belonged_proxy, weakref.ProxyTypes) + self._belonged_proxy = belonged_proxy + + @property + def name(self): + return self._name + + @property + def name_prefix(self): + return self._name_prefix + + @property + def type(self): + return self._type + + @property + def prev_scope(self): + if self._prev_scope is None: + self._prev_scope = oneflow._oneflow_internal.GetCurrentScope() + return self._prev_scope + + @property + def scope(self): + if self._scope is None: + self._scope = graph_build_util.make_new_blockgraph_scope( + self.prev_scope, self + ) + return self._scope + + def scope_context(self): + return graph_build_util.BlockScopeContext(self.prev_scope, self.scope) + + +class GraphModule(GraphBlock): + r"""GraphModule is the graph representation of a nn.Module in a nn.Graph. + + When an nn.Module is added into an nn.Graph, it is wrapped into a ProxyModule. The ProxyModule has a GraphModule inside it. + You can get and set the GraphModule to enable graph optimization on the nn.Module. + """ + + def __init__( + self, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + belonged_proxy: weakref.ProxyTypes = None, + ): + super().__init__( + prefix, name, belonged_graph, belonged_proxy, GraphBlockType.MODULE + ) + self._is_null = True + self._stage_id = None + self._stage_placement = None + self._activation_checkpointing = None + + self._debug = False + self._debug_min_s_level = 2 + self._debug_max_v_level = 0 + self._debug_max_py_stack_depth = 2 + self._debug_only_user_py_stack = True + self._debug_op_repr_with_py_stack = False + self._is_executing_forward = False + self._args_repr = [] + self._outs_repr = [] + + def set_stage(self, stage_id: int = None, placement=None): + r"""Set stage id and placement of nn.Module in pipeline parallelism. + + Args: + stage_id (int): stage id of this module. + placement (flow.placement): the placement of all tensor in this module. + + Note: + There will be automatically do tensor.to_global(placement) for all input tensor of + this module. So there is no need to write to_global() in the module forward when using + Pipeline Parallelism which is not recommended. + + For example: + + .. code-block:: python + + # module0 and module1 are two nn.Module in a nn.Graph. + # When a nn.Module is added into a nn.Graph, it is wrapped into a ProxyModule. + # We can set Stage ID and Placement by using ProxyModule.to(GraphModule).set_stage() + # The Stage ID is numbered starting from 0 and increasing by 1. + # The Placement is all tensors placement of this module. + import oneflow as flow + from oneflow.nn.graph import GraphModule + P_0 = flow.placement(type = "cuda", ranks = [0, 1]) + P_1 = flow.placement(type = "cuda", ranks = [2, 3]) + self.module0.to(GraphModule).set_stage(stage_id = 0, placement = P0) + self.module1.to(GraphModule).set_stage(stage_id = 1, placement = P1) + + """ + + self._is_null = False + self._stage_id = stage_id + self._stage_placement = placement + + # NOTE(lixiang): For the normal display of docstr, the API Doc of the get and set methods are written together in the stage_id function. + @property + def stage_id(self): + r"""Set/Get stage id of nn.Module/GraphModule in pipeline parallelism. + When calling stage_id(value: int = None), set different module's stage id to hint the graph + preparing right num of buffers in pipeline. (Not Recommended, for easy and efficient pipeline + parallelism experience, please use set_stage(stage_id, placement)) + """ + return self._stage_id + + @stage_id.setter + def stage_id(self, value: int = None): + r"""Set stage id of Module in pipeline parallelism. + Set different module's stage id to hint the graph preparing right num of buffers in pipeline. + """ + print( + "Warning: `stage_id = i` is deprecated, please use \n", + " set_stage(i, placement) for easy and efficient Pipeline parallel experience.", + ) + + self._is_null = False + self._stage_id = value + + @property + def stage_placement(self): + return self._stage_placement + + # NOTE(lixiang): For the normal display of docstr, the API Doc of the get and set methods are written together in the activation_checkpointing function. + @property + def activation_checkpointing(self): + r"""Set/Get whether do activation checkpointing in this nn.Module. + + For example: + + .. code-block:: python + + import oneflow as flow + from oneflow.nn.graph import GraphModule + + class Graph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear1 = flow.nn.Linear(3, 5, False) + self.linear2 = flow.nn.Linear(5, 8, False) + self.linear1.to(GraphModule).activation_checkpointing = True + self.linear2.to(GraphModule).activation_checkpointing = True + + def build(self, x): + y_pred = self.linear1(x) + y_pred = self.linear2(y_pred) + return y_pred + + graph = Graph() + + """ + return self._activation_checkpointing + + @activation_checkpointing.setter + def activation_checkpointing(self, mode: bool = False): + r"""Set whether do activation checkpointing in this Module. + """ + self._is_null = False + self._activation_checkpointing = mode + + def _config_repr(self): + main_str = ( + "(" + + self.__class__.__name__ + + "(" + + ( + ("stage_id=" + str(self.stage_id) + ", ") + if self.stage_id is not None + else "" + ) + + ( + ( + "activation_checkpointing=" + + str(self.activation_checkpointing) + + ", " + ) + if self.activation_checkpointing is not None + else "" + ) + + "))" + ) + return main_str + + def debug( + self, + v_level: int = 0, + *, + ranks: Optional[Union[int, List[int]]] = None, + max_py_stack_depth: int = 2, + only_user_py_stack=True, + op_repr_with_py_stack=False, + ) -> None: + assert isinstance(v_level, int) + assert isinstance(max_py_stack_depth, int) + assert isinstance(only_user_py_stack, bool) + assert isinstance(op_repr_with_py_stack, bool) + + if ranks is None: + rank_list = [0] + elif isinstance(ranks, int): + rank_list = [ranks] + elif isinstance(ranks, list): + rank_list = ranks + else: + raise ValueError("ranks must be int or List[int].") + + my_rank = get_rank() + if -1 in rank_list or my_rank in rank_list: + self._debug = v_level >= 0 + if self._debug: + self._debug_min_s_level = 0 + self._debug_max_v_level = max(0, v_level) + + self._debug_max_py_stack_depth = max_py_stack_depth + self._debug_only_user_py_stack = only_user_py_stack + self._debug_op_repr_with_py_stack = op_repr_with_py_stack + + if self._type == GraphBlockType.MODULE: + + def _set_child(d): + for (_, n) in d.items(): + n.to(GraphModule).debug( + v_level, + ranks=ranks, + max_py_stack_depth=max_py_stack_depth, + only_user_py_stack=only_user_py_stack, + op_repr_with_py_stack=op_repr_with_py_stack, + ) + + assert self._belonged_proxy is not None and isinstance( + self._belonged_proxy, weakref.ProxyTypes + ) + _set_child(self._belonged_proxy._modules) + + def _ops_repr(self): + r"""Generate operators' string representation of this GraphModule + """ + assert self._belonged_graph, ( + "ProxyModule: " + + self._name_prefix + + self.name + + "'s belonged graph is not set." + ) + + if self._belonged_graph.is_compiled: + if self._belonged_graph._compiled_graph_proto is not None: + module_conf = self._belonged_graph._compiled_graph_proto.module_name2module_conf[ + self.name_prefix + self.name + ] + if self._belonged_graph._oneflow_internal_graph_ir__ is None: + self._belonged_graph._oneflow_internal_graph_ir__ = GraphIR( + self._belonged_graph._compiled_graph_proto + ) + return operators_repr( + module_conf.ops, + self._belonged_graph._oneflow_internal_graph_ir__, + self._debug_op_repr_with_py_stack, + ) + + return [] + + def _shallow_repr(self): + main_str = ( + "(" + + self.__class__.__name__ + + ":" + + self._name_prefix + + self._name + + "(" + + ( + ("stage_id=" + str(self.stage_id) + ", ") + if self.stage_id is not None + else "" + ) + + ( + ( + "activation_checkpointing=" + + str(self.activation_checkpointing) + + ", " + ) + if self.activation_checkpointing is not None + else "" + ) + + "))" + ) + return main_str + + def _repr_lines(self): + child_lines = [] + for op_str in self._ops_repr(): + child_lines.append(add_indent(op_str, 2)) + return child_lines + + def __repr__(self): + lines = None + child_lines = self._repr_lines() + if len(child_lines) > 0: + lines = child_lines + + main_str = self._shallow_repr() + ": (" + if lines is not None: + main_str += "\n " + "\n ".join(lines) + "\n" + main_str += ")" + return main_str + + +class GraphTensor(GraphBlock): + r"""GraphTensor is the graph representation of a Tensor in a nn.Graph. + """ + + def __init__( + self, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + belonged_proxy: weakref.ProxyTypes = None, + tensor_graph_type: GraphBlockType = GraphBlockType.NONE, + ): + super().__init__( + prefix, name, belonged_graph, belonged_proxy, tensor_graph_type + ) + self._stage_id = None + self._stage_placement = None + + def set_stage(self, stage_id: int = None, placement=None): + self._stage_id = stage_id + self._stage_placement = placement + + @property + def stage_id(self): + return self._stage_id + + @stage_id.setter + def stage_id(self, value: int = None): + print( + "Warning: `stage_id = i` is deprecated, please use \n", + " set_stage(i, placement) for easy and efficient Pipeline parallel experience.", + ) + + self._stage_id = value + + @property + def stage_placement(self): + return self._stage_placement diff --git a/python/oneflow/nn/graph/graph_config.py b/python/oneflow/nn/graph/graph_config.py index 0f3e3273764a09c191d32bb70260fcc77552475d..46e866ddf5fd463879b3dd0ebadbe70dd9d983f9 100644 --- a/python/oneflow/nn/graph/graph_config.py +++ b/python/oneflow/nn/graph/graph_config.py @@ -20,6 +20,7 @@ from collections import OrderedDict import oneflow.boxing.nccl as nccl_config from oneflow.nn.graph.optimizer import OptDict import oneflow.core.job.job_conf_pb2 as job_conf_pb +import oneflow as flow class GraphConfig(object): @@ -46,7 +47,7 @@ class GraphConfig(object): return False raise NotImplementedError - def enable_amp(self, mode: bool = True): + def enable_amp(self, mode: bool = True, *, dtype: flow.dtype = flow.float16): r"""If set to true, then graph will use mixed precision mode, it means use both float16 and float32 during model training. For example: @@ -68,9 +69,14 @@ class GraphConfig(object): Args: mode (bool, optional): The default vaule is True. + """ assert type(mode) is bool + assert dtype in (flow.float16, flow.bfloat16) self.proto.enable_auto_mixed_precision = mode + self.proto.mixed_precision_data_type = flow._oneflow_internal.deprecated.GetProtoDtype4OfDtype( + dtype + ) def set_zero_redundancy_optimizer_mode(self, mode: str = "distributed_split"): raise RuntimeError( @@ -110,18 +116,18 @@ class GraphConfig(object): Args: mode (bool): if set to true, optimizer states of Data Parallel will be sharded across devices. - stage (int): optimization stage, range from 1 to 3. - shard_min_size (int): min size of a shard of an optimizer state. + stage (int): optimization stage, range from 1 to 3. + shard_min_size (int): min size (element count) of a shard of an optimizer state. shard_restore_level (int): level to restore sharded parameter to whole parameter for consumer operators, level 0 is no restore, level 1 is soft restore, level 2 is hard restore. Note that this paremeter is at pre-alpha stage. """ if not mode: self.proto.optimizer_placement_optimization_mode = "none" return - assert stage >= 1 and stage <= 3, "ZeRO stage must range form 1 to 3." + assert stage >= 1 and stage <= 3, "ZeRO stage must range from 1 to 3." assert ( shard_min_size > 0 ), "ZeRO min size of a sharded optimizer state must > 0." - assert stage >= 1 and stage <= 3, "ZeRO stage must range form 1 to 3." + assert stage >= 1 and stage <= 3, "ZeRO stage must range from 1 to 3." if stage >= 1: self.proto.optimizer_placement_optimization_mode = "distributed_split" self.proto.optimizer_placement_optimization_threshold = shard_min_size @@ -172,7 +178,7 @@ class GraphConfig(object): self.bn1 = flow.nn.BatchNorm1d(100) self.config.allow_fuse_add_to_output(True) def build(self, x): - bn = self.bn1(x) + bn = self.bn1(x) out = bn + x return out @@ -185,7 +191,7 @@ class GraphConfig(object): def allow_fuse_cast_scale(self, mode: bool = True): r"""If set to true, try to fuse cast and scalar_mul_by_tensor to improve performance. - + For example: .. code-block:: python @@ -234,6 +240,15 @@ class GraphConfig(object): value (int): num of steps. """ self.proto.num_gradient_accumulation_steps = value + if value > 1: + # NOTE(chengcheng): when use gradient accumulation, optimizer nccl allreduce can NOT + # overlap with backward, so nccl use compute stream is optimization without negative + # effects. + nccl_config.enable_use_compute_stream(True) + + # TODO(chengcheng): hotfix.(just for now), logical chain has some bugs in OneEmmbedding, + # just using logical chain in acc on. + os.environ["ENABLE_LOGICAL_CHAIN"] = "true" def set_outputs_buffer_size(self, value: int = 2): r"""Set the outputs buffer size of ``nn.Graph``. @@ -256,6 +271,9 @@ class GraphConfig(object): def enable_cudnn_conv_heuristic_search_algo(self, mode: bool = True): r""" Whether enable cudnn conv operatioin to use heuristic search algorithm. + Note: + It is recommended to use `flow.backends.cudnn.enable_conv_heuristic_search_algo(False)` instead of this function. + For example: .. code-block:: python @@ -272,20 +290,117 @@ class GraphConfig(object): return self.m(x) graph = Graph() - + Args: mode (bool, optional): The default vaule is True. """ self.proto.cudnn_conv_heuristic_search_algo = mode - def enable_straighten_algorithm(self, mode: bool = True): + def enable_straighten_algorithm(self, mode: str = "MemoryFirst"): r""" Whether enable the straighten algorithm. - If using nccl compute stream, turning it on might not speed up the training. - If not using nccl compute stream, turning it on might slow down data parallelism by 0.6% and slow down model parallelism by 6%. - Considering memory, enabling the straighten algorithm is forbidden with one machine/device only, and not recommended under pipeline parallelism. + straighten_algorithm_tag 1: Disable + Disable the straighten algorithm in the task graph. + Would use the original topography order for executing task nodes. + + straighten_algorithm_tag 2: SpeedFirst + Under the second configuration, the straighten algorithm would try to speed up the training as much as possible. + If using nccl compute stream, setting the tag to 2 might not speed up the training. + If not using nccl compute stream, setting the tag to 2 might speed up data parallelism by 0.6% and model parallelism by 6%. + Considering memory, enabling the straighten algorithm is forbidden with one machine/device only, and not recommended under pipeline parallelism. + + straighten_algorithm_tag 3: MemoryFirst + Under the third configuration, the straighten algorithm would try to compress memory as much as possible. + It might save up to 13% of the memory for some models. + And might save nothing for some models. + + straighten_algorithm_tag 4: OverlapCpuGpu + Under the forth configuration, the straighten algorithm would try to run the cpu nodes and gpu nodes alternately. + Such procedure would reduce the gaps of the execution on gpus. + It might speed up the training by 2%. + If no cpu nodes exist, the straighten_algorithm_tag would be switch to 3 automatically. + """ + assert ( + mode == "Disable" + or mode == "SpeedFirst" + or mode == "MemoryFirst" + or mode == "OverlapCpuGpu" + ) + if mode == "Disable": + self.proto.straighten_algorithm_tag_in_task_graph = 1 + elif mode == "SpeedFirst": + self.proto.straighten_algorithm_tag_in_task_graph = 2 + elif mode == "MemoryFirst": + self.proto.straighten_algorithm_tag_in_task_graph = 3 + else: + self.proto.straighten_algorithm_tag_in_task_graph = 4 + + def enable_compress_memory(self, mode: bool = True): + """If true, then the graph will try its best to find the minimum memory allocation strategy. + This process might take several minutes for a small graph and half an hour for a large one. + The compressed memory would be closed to the lower bound of the peak memory. + It benefits a lot if you need to train a lot of batches. + + Args: + mode (bool, optional): [description]. Default is True. + """ + self.proto.enable_compress_memory = mode + + def enable_auto_parallel(self, mode: bool = True): + """If true, then graph will use the auto parallel algorithm to select a parallelism strategy. + + Args: + mode (bool, optional): [description]. Default is True. + """ + self.proto.enable_auto_parallel = mode + + def enable_auto_parallel_ignore_user_sbp_config(self, mode: bool = True): + """If true, it will ignore all user configurations of SBP. + + Args: + mode (bool, optional): [description]. Default is True. + """ + self.proto.enable_auto_parallel_ignore_user_sbp_config = mode + + def set_auto_parallel_computation_cost_ratio(self, ratio): + """ + Set coefficient of computation cost in auto-parallel algorithm. + """ + self.proto.auto_parallel_computation_cost_ratio = ratio + + def set_auto_parallel_wait_time(self, cost): + """ + Set wait time for auto-parallel algorithm. + + wait time: An auto-parallel parameter. Describe the mutable extra time it will take when + communication between devices occurs. It will be added to the copy cost and may get reduced + when cover by computation cost. + """ + self.proto.auto_parallel_wait_time = cost + + def enable_auto_parallel_trunk_algo(self, mode: bool = True): + """ + Find the trunk of the SBP graph, then reduce the wait time for tributaries. + """ + self.proto.enable_auto_parallel_trunk_algo = mode + + def enable_auto_parallel_sbp_collector(self, mode: bool = True): + """ + Use \"sbp collector\" to create \"sbp proxy\" for nodes with multiple downstream operators. + """ + self.proto.enable_auto_parallel_sbp_collector = mode + + def enable_multi_tensor_update(self, mode: bool = True): + """ + Enable Multi Tensor Update Pass, it will merge small optimizer kernels to reduce kernel launch overhead. + """ + self.proto.enable_multi_tensor_update = mode + + def enable_fused_model_update_cast(self, mode: bool = True): + """ + This option only works in AMP Mode, it will fuse optimizer update and model weights cast to half precision operation. """ - self.proto.enable_straighten_algorithm_in_task_graph = mode + self.proto.enable_fused_model_update_cast = mode def _generate_optimizer_and_variable_configs( self, opt_dict: OptDict = None, variables_conf: OrderedDict = None, diff --git a/python/oneflow/nn/graph/optimizer.py b/python/oneflow/nn/graph/optimizer.py index 9e636561af7d1f63833cbdfee6e76a7f5c6a32aa..4f4ecbd62d99a7f7c73de0e2c4f2c8eeff8b6b1f 100644 --- a/python/oneflow/nn/graph/optimizer.py +++ b/python/oneflow/nn/graph/optimizer.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from oneflow.nn.optimizer.optimizer import Optimizer +from oneflow.optim.optimizer import Optimizer from oneflow.nn.optimizer.lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/graph/proxy.py b/python/oneflow/nn/graph/proxy.py new file mode 100644 index 0000000000000000000000000000000000000000..f261b40e56000e6e73cef4a437a4276045fe4a52 --- /dev/null +++ b/python/oneflow/nn/graph/proxy.py @@ -0,0 +1,791 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from typing import Iterator, Optional, Set, Union, List +import weakref +import types + +import oneflow._C +import oneflow._oneflow_internal +from oneflow.framework import graph_build_util +from oneflow.framework.tensor import Tensor, TensorTuple +from oneflow.nn.modules.module import Module +from oneflow.nn.modules.container import * +from oneflow.nn.utils.container import * +from oneflow.nn.parameter import Parameter +from oneflow.nn.graph.graph_block import ( + GraphBlockType, + GraphBlock, + GraphModule, + GraphTensor, +) +from oneflow.nn.graph.util import ( + add_indent, + ArgsTree, + seq_to_func_return, +) + + +def get_proxy_cls(item): + if isinstance(item, Sequential): + return ProxySequential + elif isinstance(item, ModuleList): + return ProxyModuleList + elif isinstance(item, ModuleDict): + return ProxyModuleDict + elif isinstance(item, ParameterList): + return ProxyParameterList + elif isinstance(item, ParameterDict): + return ProxyParameterDict + elif isinstance(item, Module): + return ProxyModule + elif isinstance(item, Tensor): + return ProxyTensor + else: + raise NotImplementedError() + + +class Proxy(object): + def __init__(self): + """ An ecution proxy of nn.Module or Tensor. + + A proxy contains the original data(nn.Module or Tensor) and a graph representation of the original data. + """ + # The original data + self._oneflow_internal_origin__ = None + # The graph representation of the original data + self._oneflow_internal_graphblock__ = None + + def to(self, *args, **kwargs): + """ + """ + if len(args) == 1 and issubclass(args[0], GraphBlock): + return self._oneflow_internal_graphblock__ + elif len(args) == 1 and (args[0] is Module or args[0] is Tensor): + return self._oneflow_internal_origin__ + else: + self._oneflow_internal_origin__.to(*args, **kwargs) + + +class ProxyModule(Proxy): + def __init__( + self, + origin: Module = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + assert not isinstance(origin, Proxy) + super().__init__() + self._oneflow_internal_graphblock__ = GraphModule( + prefix, name, belonged_graph, weakref.proxy(self) + ) + self._modules = OrderedDict() + self._parameters = OrderedDict() + self._buffers = OrderedDict() + + self._oneflow_internal_graphblock__set_origin(origin) + + def _oneflow_internal_graphblock__set_origin(self, origin): + self._oneflow_internal_origin__ = origin + if origin is None: + return + assert isinstance(origin, Module) + for (n, m) in origin._modules.items(): + self.__setattr__( + n, + get_proxy_cls(m)( + m, + self.to(GraphModule)._name_prefix + + self.to(GraphModule)._name + + ".", + n, + self.to(GraphModule)._belonged_graph, + ), + ) + for (n, p) in list(origin.named_parameters("", False)): + self.__setattr__( + n, + get_proxy_cls(p)( + p, + self.to(GraphTensor)._name_prefix + + self.to(GraphTensor)._name + + ".", + n, + ), + ) + for (n, b) in list(origin.named_buffers("", False)): + self.__setattr__( + n, + get_proxy_cls(b)( + b, + self.to(GraphTensor)._name_prefix + + self.to(GraphTensor)._name + + ".", + n, + ), + ) + + def __call__(self, *args, **kwargs): + assert self.to(GraphModule)._type == GraphBlockType.MODULE + self.__print(0, 1, self._shallow_repr()) + + args_tree = ArgsTree( + (args, kwargs), + True, + "_" + + self.to(GraphModule).name_prefix + + self.to(GraphModule).name + + "_input", + None, + ) + + for (name, arg) in args_tree.iter_named_nodes(): + if arg.is_leaf(): + arg_value = arg.value() + meta_repr_str = ( + arg_value._meta_repr() + if isinstance(arg_value, Tensor) + else str(type(arg_value)) + ) + in_str = "(INPUT:" + name + ":" + meta_repr_str + ")" + if not isinstance(arg_value, Tensor): + in_str = "[WARNING]" + in_str + self.to(GraphModule)._args_repr.append(in_str) + self.__print(0, 1, in_str) + + def _print_state(d): + for (_, n) in d.items(): + self.__print(0, 1, n._shallow_repr()) + + _print_state(self._parameters) + _print_state(self._buffers) + + # NOTE: The original nn.Moudle's __call__ method is ignored, which means + # that hooks of nn.Modules are ignored. It is not recommended + # to use hooks of nn.Module in nn.Graph for the moment. + with graph_build_util.DebugScopeContext( + self.to(GraphModule)._debug_min_s_level, + self.to(GraphModule)._debug_max_v_level, + self.to(GraphModule)._debug, + self.to(GraphModule)._debug_max_py_stack_depth, + self.to(GraphModule)._debug_only_user_py_stack, + ): + result = self.__block_forward(*args, **kwargs) + + outputs = () + if not (type(result) is tuple or type(result) is list): + outputs = (result,) + else: + outputs = result + + args_tree = ArgsTree( + (outputs, {}), + True, + "_" + + self.to(GraphModule).name_prefix + + self.to(GraphModule).name + + "_output", + None, + ) + + for (name, arg) in args_tree.iter_named_nodes(): + if arg.is_leaf(): + arg_value = arg.value() + meta_repr_str = ( + arg_value._meta_repr() + if isinstance(arg_value, Tensor) + else str(type(arg_value)) + ) + out_str = "(OUTPUT:" + name + ":" + meta_repr_str + ")" + if not isinstance(arg_value, Tensor): + out_str = "[WARNING]" + out_str + self.to(GraphModule)._outs_repr.append(out_str) + self.__print(0, 1, out_str) + + return result + + def __block_forward(self, *args, **kwargs): + self.to(GraphModule)._is_executing_forward = True + args, kwargs = self.__pre_forward_map(*args, **kwargs) + with self.to(GraphModule).scope_context(): + # "Instance method __func__ is the function object", "when an instance method object is called, + # the underlying function __func__ is called, inserting the class instance __self__ in front of + # the argument list." + # Reference: https://docs.python.org/3/reference/datamodel.html + unbound_forward_of_module_instance = self.to(Module).forward.__func__ + result = unbound_forward_of_module_instance(self, *args, **kwargs) + self.to(GraphModule)._is_executing_forward = False + return result + + def __pre_forward_map(self, *args, **kwargs): + # Insert identity op when doing activation checkpointing or pipeline execution. + # Identity op outside activation checkpointing scope will be the endpoint of an activation checkpointing segment. + # Identity op as the first op of a pipeline stage will make backward op depends on the identity op within the stage, + # otherwise the backward op may depends the op in former stage which will make graph creates unnessary buffers. + if self.to(GraphModule)._stage_placement is not None: + + def insert_to_global(t): + assert isinstance(t, Tensor) + return self.__get_or_create_global( + t, self.to(GraphModule)._stage_placement + ) + + args, kwargs = self.__map_io( + "input", insert_to_global, "insert_to_global", *args, **kwargs + ) + + if self.to(GraphModule).activation_checkpointing or ( + self.to(GraphModule).stage_id is not None + and self.to(GraphModule).stage_id >= 0 + ): + + def insert_identity(t): + assert isinstance(t, Tensor) + return self.__get_or_create_identity(t) + + args, kwargs = self.__map_io( + "input", insert_identity, "insert_identity", *args, **kwargs + ) + + return args, kwargs + + def __get_or_create_global(self, input_tensor: Tensor = None, placement=None): + assert input_tensor is not None + assert placement is not None + key = str(id(input_tensor)) + str(placement) + + # input_tensor + placement -> unique_global_tensor + if key not in self.to(GraphModule)._belonged_graph._unique_global_op_dict: + # store input tensor to avoid tensor id recycle + self.to(GraphModule)._belonged_graph._unique_global_op_dict[key] = ( + input_tensor.to_global(placement=placement), + input_tensor, + ) + + return self.to(GraphModule)._belonged_graph._unique_global_op_dict[key][0] + + def __get_or_create_identity(self, input_tensor: Tensor = None): + assert input_tensor is not None + key = input_tensor + + # input_tensor(with placement) -> unique_identity_tensor + # When placement is different, the input tensor(output tensor of __get_or_create_global) is different, so the + # key can use only input tensor. + if key not in self.to(GraphModule)._belonged_graph._unique_identity_op_dict: + # Reuse current module name for indentity op + ident_name_scope = graph_build_util.make_new_name_scope( + self.to(GraphModule).prev_scope, + self.to(GraphModule).name_prefix + self.to(GraphModule).name, + ) + with graph_build_util.BlockScopeContext( + self.to(GraphModule).prev_scope, ident_name_scope + ): + # store input tensor to avoid tensor id recycle + self.to(GraphModule)._belonged_graph._unique_identity_op_dict[ + key + ] = oneflow._C.identity(input_tensor) + + return self.to(GraphModule)._belonged_graph._unique_identity_op_dict[key] + + def add_module(self, name: str, module: Optional[Module]) -> None: + if isinstance(module, Module): + self.__setattr__( + name, + get_block_cls(module)( + module, + self.to(GraphModule)._name_prefix + + self.to(GraphModule)._name + + ".", + name, + self.to(GraphModule)._belonged_graph, + ), + ) + elif isinstance(module, Proxy): + self.__setattr__(name, module) + + def register_parameter(self, name: str, param: Optional[Parameter]) -> None: + self.__setattr__( + name, + get_proxy_cls(param)( + param, + self.to(GraphModule)._name_prefix + self.to(GraphModule)._name + ".", + name, + ), + ) + + def modules(self, memo: Optional[Set["Proxy"]] = None) -> Iterator["Proxy"]: + assert self.to(GraphModule)._type == GraphBlockType.MODULE + if memo is None: + memo = set() + if self not in memo: + memo.add(self) + yield self + for (name, module) in self._modules.items(): + if module is None: + continue + for m in module.modules(memo): + yield m + + def __map_io(self, io_type, func, func_desc, *args, **kwargs): + assert isinstance(func_desc, str) + assert io_type in ("input", "output") + mapped_args = [] + + def map_tensor(item): + assert isinstance(item, Tensor) + return func(item) + + args_tree = ArgsTree( + (args, kwargs), + True, + "_" + + self.to(GraphModule).name_prefix + + self.to(GraphModule).name + + "_" + + io_type, + None, + ) + + def leaf_node_fn(leaf_node): + arg = leaf_node.value() + name = leaf_node.prefix() + "_" + leaf_node.name() + is_tensor, repr_str = self.__io_tensor_check_and_gen(arg, io_type, name) + if is_tensor: + self.__print( + 0, + 1, + f"{repr_str} is a Tensor, {func_desc} transformation has been done.", + ) + return map_tensor(arg) + else: + self.__print( + 0, + 0, + f"{repr_str} is not a Tensor, {func_desc} transformation will be ignored.", + ) + return arg + + out = args_tree.map_leaf(leaf_node_fn) + mapped_args = out[0] + mapped_kwargs = out[1] + return mapped_args, mapped_kwargs + + def __io_tensor_check_and_gen(self, item, io_type, name): + assert io_type in ("input", "output") + if isinstance(item, Tensor): + repr_str = ( + "(" + io_type.upper() + ":" + name + ":" + item._meta_repr() + ")" + ) + return True, repr_str + else: + repr_str = ( + "[WARNING](" + + io_type.upper() + + ":" + + name + + ":" + + str(type(item)) + + ")" + ) + return False, repr_str + + def __members(self, get_members_fn, recurse=True) -> Iterator["Proxy"]: + assert self.to(GraphModule)._type == GraphBlockType.MODULE + memo = set() + modules = self.modules() if recurse else [self] + for module in modules: + members = get_members_fn(module) + for (k, v) in members: + if v is None or v in memo: + continue + memo.add(v) + yield v + + def parameters(self, recurse: bool = True) -> Iterator["Proxy"]: + assert self.to(GraphModule)._type == GraphBlockType.MODULE + gen = self.__members(lambda module: module._parameters.items(), recurse=recurse) + for elem in gen: + yield elem + + def buffers(self, recurse: bool = True) -> Iterator["Proxy"]: + assert self.to(GraphModule)._type == GraphBlockType.MODULE + gen = self.__members(lambda module: module._buffers.items(), recurse=recurse) + for elem in gen: + yield elem + + def __setattr__(self, name: str, value=None) -> None: + if value is None or not isinstance(value, Proxy): + self.__dict__[name] = value + else: + dicts_or_sets = ( + self.__dict__, + self._modules, + self._parameters, + self._buffers, + ) + for d in dicts_or_sets: + if name in d: + raise AttributeError( + "'{}' object has duplicated attribute named '{}'".format( + self.to(GraphModule)._name, name + ) + ) + if value.to(GraphModule).type == GraphBlockType.MODULE: + self._modules[name] = value + elif value.to(GraphTensor).type == GraphBlockType.PARAMETER: + self._parameters[name] = value + elif value.to(GraphTensor).type == GraphBlockType.BUFFER: + self._buffers[name] = value + else: + raise AttributeError( + "'{}' object are not allowed to set attribute named '{}'".format( + type(self).__name__, name + ) + ) + + def __getattr__(self, name: str): + if name in self.__dict__: + return self.__dict__[name] + # support get module + if "_modules" in self.__dict__: + modules = self.__dict__["_modules"] + if name in modules: + return modules[name] + # support get parameter + p_state = self._get_from_states(name, "_parameters") + if p_state is not None: + return p_state + # support get buffer + b_state = self._get_from_states(name, "_buffers") + if b_state is not None: + return b_state + # support none parameter or buffer + if name in self.to(Module)._parameters: + p_none = self.to(Module)._parameters[name] + assert p_none is None + return None + if name in self.to(Module)._buffers: + b_none = self.to(Module)._buffers[name] + assert b_none is None + return None + if hasattr(self.to(Module), name): + # support getting normal attr from the nn.Module + attr = getattr(self.to(Module), name) + if isinstance(attr, types.MethodType): + # If the attr is MethodType, rebind the method to self + attr = types.MethodType(attr.__func__, self) + return attr + raise AttributeError( + "'{}' '{}' object '{}' in nn.Graph has no attribute '{}'".format( + self.to(GraphModule)._type, + type(self).__name__, + self.to(GraphModule)._name_prefix + self.to(GraphModule).name, + name, + ) + ) + + def _get_from_states(self, name, states_name): + if states_name not in self.__dict__: + return None + + _states = self.__dict__[states_name] + if name not in _states: + return None + + _s_block = _states[name] + if graph_build_util.lazy_mode.is_enabled(): + _s_block.try_build() + return _s_block.lazy_origin + elif (not graph_build_util.lazy_mode.is_enabled()) and self.to( + GraphModule + )._is_executing_forward: + # eager and inside nn.Graph.build() + return _s_block.to(Tensor) + else: + # outside nn.Graph.build() + # eager and inside nn.Graph.build() + return _s_block + + def __repr__(self): + lines = None + child_lines = [] + if len(self.to(GraphModule)._args_repr) > 0: + for in_str in self.to(GraphModule)._args_repr: + input_str = add_indent(in_str, 2) + child_lines.append(input_str) + + def _append_child(d): + for (_, n) in d.items(): + n_str = repr(n) + n_str = add_indent(n_str, 2) + child_lines.append(n_str) + + _append_child(self._parameters) + _append_child(self._buffers) + _append_child(self._modules) + + if len(self.to(GraphModule)._outs_repr) > 0: + for out_str in self.to(GraphModule)._outs_repr: + output_str = add_indent(out_str, 2) + child_lines.append(output_str) + + child_lines.append(add_indent(repr(self.to(GraphModule)), 2)) + + if len(child_lines) > 0: + lines = child_lines + + main_str = self._shallow_repr() + ": (" + if lines is not None: + main_str += "\n " + "\n ".join(lines) + "\n" + main_str += ")" + return main_str + + def _shallow_repr(self): + shallow_repr = ( + "(" + + self.to(GraphModule)._type + + ":" + + self.to(GraphModule)._name_prefix + + self.to(GraphModule)._name + + ":" + + self._oneflow_internal_origin__._shallow_repr() + + ")" + ) + return shallow_repr + + def __print(self, s_level=2, v_level=0, msg: str = ""): + r"""Do print according to info level. + """ + assert isinstance(s_level, int) + assert isinstance(v_level, int) + assert isinstance(msg, str) + if s_level >= self.to(GraphModule)._debug_min_s_level: + if (s_level > 0) or ( + s_level == 0 and v_level <= self.to(GraphModule)._debug_max_v_level + ): + print(msg, flush=True) + + +class LazyBuilder(object): + def __init__(self, name: str = None, method=None): + self.name = name + self.method = method + self.result = None + self.finished = False + + def try_build(self, block=None): + if not self.finished: + assert self.name is not None + assert self.method is not None + assert self.result is None + with block.to(GraphTensor).scope_context(): + self.result = self.method() + self.finished = True + + +class ProxyTensor(Proxy): + def __init__( + self, + origin: Union[Parameter, Tensor] = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + assert not isinstance(origin, Proxy) + if isinstance(origin, Parameter): + self._oneflow_internal_graphblock__ = GraphTensor( + prefix, + name, + belonged_graph, + weakref.proxy(self), + GraphBlockType.PARAMETER, + ) + elif isinstance(origin, Tensor): + self._oneflow_internal_graphblock__ = GraphTensor( + prefix, name, belonged_graph, weakref.proxy(self), GraphBlockType.BUFFER + ) + else: + raise NotImplementedError() + self._lazy_origin_builder = LazyBuilder() + self.build_finished = False + self._oneflow_internal_graphblock__set_origin(origin) + + def _oneflow_internal_graphblock__set_origin(self, origin): + self._oneflow_internal_origin__ = origin + + @property + def lazy_origin(self): + assert ( + self.to(GraphTensor)._type == GraphBlockType.PARAMETER + or self.to(GraphTensor)._type == GraphBlockType.BUFFER + ), "Only Parameter or Buffer Proxy has lazy_origin" + return self._lazy_origin_builder.result + + def lazy_origin_builder(self): + assert ( + self.to(GraphTensor)._type == GraphBlockType.PARAMETER + or self.to(GraphTensor)._type == GraphBlockType.BUFFER + ), "Only Parameter or Buffer Proxy has lazy_origin_builder" + return self._lazy_origin_builder + + def set_lazy_origin_builder(self, builder=None): + assert ( + self.to(GraphTensor)._type == GraphBlockType.PARAMETER + or self.to(GraphTensor)._type == GraphBlockType.BUFFER + ), "Only Parameter or Buffer Proxy has lazy_origin_builder" + self._lazy_origin_builder = builder + + def try_build(self): + if not self.build_finished: + self._lazy_origin_builder.try_build(self) + self.build_finished = True + + def __repr__(self): + lines = None + main_str = self._shallow_repr() + ": (" + if lines is not None: + main_str += "\n " + "\n ".join(lines) + "\n" + main_str += ")" + return main_str + + def _shallow_repr(self): + shallow_repr = ( + "(" + + self.to(GraphTensor)._type + + ":" + + self.to(GraphTensor)._name_prefix + + self.to(GraphTensor)._name + + ":" + + self._oneflow_internal_origin__._meta_repr() + + ")" + ) + return shallow_repr + + +class ProxySequential(get_seq(ProxyModule)): + def __init__( + self, + origin: Sequential = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + super().__init__() + self.to(GraphModule)._name_prefix = prefix + self.to(GraphModule)._name = name + self.to(GraphModule)._belonged_graph = belonged_graph + self.to(GraphModule)._belonged_block = weakref.proxy(self) + self._oneflow_internal_graphblock__set_origin(origin) + + +class ProxyModuleList(get_list(ProxyModule)): + def __init__( + self, + origin: ModuleList = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + if isinstance(origin, ModuleList): + super().__init__() + self.to(GraphModule)._name_prefix = prefix + self.to(GraphModule)._name = name + self.to(GraphModule)._belonged_graph = belonged_graph + self._oneflow_internal_graphblock__set_origin(origin) + # MoudleList is a container without forward() method, + + elif isinstance(origin, list): + super().__init__(origin) + first = origin[0] + new_name = "_idx" + new_list = [] + for item in origin: + new_name += "-" + item.to(GraphModule).name + new_list.append(item.to(Module)) + new_module_list = ModuleList(new_list) + self.to(GraphModule)._name_prefix = ( + first.to(GraphModule).name_prefix + first.to(GraphModule).name + ) + self.to(GraphModule)._name = new_name + self.to(GraphModule)._belonged_graph = first.to(GraphModule)._belonged_graph + self._oneflow_internal_origin__ = new_module_list + + +class ProxyModuleDict(get_dict(ProxyModule)): + def __init__( + self, + origin: ModuleDict = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + super().__init__() + self.to(GraphModule)._name_prefix = prefix + self.to(GraphModule)._name = name + self.to(GraphModule)._belonged_graph = belonged_graph + self.to(GraphModule)._belonged_block = weakref.proxy(self) + self._oneflow_internal_graphblock__set_origin(origin) + + +class ProxyParameterList(get_para_list(ProxyModule)): + def __init__( + self, + origin: ParameterList = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + super().__init__() + self.to(GraphModule)._name_prefix = prefix + self.to(GraphModule)._name = name + self.to(GraphModule)._belonged_graph = belonged_graph + self.to(GraphModule)._belonged_block = weakref.proxy(self) + self._oneflow_internal_graphblock__set_origin(origin) + self.to(GraphModule)._is_executing_forward = True + + def __getitem__(self, idx): + assert isinstance(idx, int) + idx = self._get_abs_string_index(idx) + key = str(idx) + p_state = self._get_from_states(key, "_parameters") + if p_state is not None: + return p_state + else: + raise AttributeError("ParameterList dosen't contain ", key) + + +class ProxyParameterDict(get_para_dict(ProxyModule)): + def __init__( + self, + origin: ParameterDict = None, + prefix: str = "", + name: str = "", + belonged_graph: weakref.ProxyTypes = None, + ): + super().__init__() + self.to(GraphModule)._name_prefix = prefix + self.to(GraphModule)._name = name + self.to(GraphModule)._belonged_graph = belonged_graph + self.to(GraphModule)._belonged_block = weakref.proxy(self) + self._oneflow_internal_graphblock__set_origin(origin) + self.to(GraphModule)._is_executing_forward = True + + def __getitem__(self, key: str): + p_state = self._get_from_states(key, "_parameters") + if p_state is not None: + return p_state + else: + raise AttributeError("ParameterDict dosen't contain key ", key) diff --git a/python/oneflow/nn/graph/util.py b/python/oneflow/nn/graph/util.py index caa1c905f5f1dac70edc2736f007ce54117e0307..d6ecf5ed4abde744c204206f60411096bda3371a 100644 --- a/python/oneflow/nn/graph/util.py +++ b/python/oneflow/nn/graph/util.py @@ -16,7 +16,7 @@ limitations under the License. import sys from string import Template from collections import OrderedDict -from typing import Callable, Dict, Union, List, Tuple +from typing import Callable, Dict, Union, List, Tuple, Optional import google.protobuf as protobuf from google.protobuf import text_format @@ -145,25 +145,82 @@ def _get_iden_op_io_repr(op_conf, bn2nd_sbp, lbn2blob_desc): return input_sig_str, output_sig_str +def _get_input_op_io_repr(op_conf, bn2nd_sbp, lbn2blob_desc): + op_input_conf = op_conf.input_conf + output_lbn = op_conf.name + "/" + op_input_conf.out + nd_sbp = bn2nd_sbp[op_input_conf.out] + output_sig_str = ( + output_lbn + + ":" + + _nd_sbp2repr(nd_sbp) + + ", " + + _blob_desc_repr(lbn2blob_desc[output_lbn]) + ) + return "", output_sig_str + + +def _get_output_op_io_repr(op_conf, bn2nd_sbp, lbn2blob_desc): + op_output_conf = op_conf.output_conf + input_lbn = getattr(op_output_conf, "in") + output_lbn = op_conf.name + "/" + op_output_conf.out + + input_sig_str = ( + input_lbn + + ":" + + _nd_sbp2repr(bn2nd_sbp["in"]) + + ", " + + _blob_desc_repr(lbn2blob_desc[output_lbn]) + ) + + nd_sbp = bn2nd_sbp[op_output_conf.out] + output_sig_str = ( + output_lbn + + ":" + + _nd_sbp2repr(nd_sbp) + + ", " + + _blob_desc_repr(lbn2blob_desc[output_lbn]) + ) + return input_sig_str, output_sig_str + + +class GraphIR(object): + def __init__(self, g_proto: job_pb.Job): + assert g_proto is not None and isinstance(g_proto, job_pb.Job) + self._graph_proto = g_proto + self._op2conf = None + self._op2placement = None + + def get_op_conf(self, op_name: str) -> Optional[op_conf_util.OperatorConf]: + if self._op2conf is None: + self._op2conf = dict() + for op_conf in self._graph_proto.net.op: + self._op2conf[op_conf.name] = op_conf + if op_name not in self._op2conf: + return None + return self._op2conf[op_name] + + def get_op_placement(self, op_name: str) -> Optional[oneflow.placement]: + if self._op2placement is None: + self._op2placement = dict() + for group in self._graph_proto.placement.placement_group: + parallel_conf = group.parallel_conf + for op_name in group.op_set.op_name: + self._op2placement[op_name] = oneflow.placement( + proto_str=text_format.MessageToString(parallel_conf) + ) + if op_name not in self._op2placement: + return None + return self._op2placement[op_name] + + def operators_repr( - ops: protobuf.pyext._message.RepeatedCompositeContainer, graph_proto: job_pb.Job + ops: protobuf.pyext._message.RepeatedCompositeContainer, + graph_ir: GraphIR, + show_op_loc: bool, ) -> List[str]: r"""Generate operators' string representation of this module """ - if len(ops) > 0: - op_confs = dict() - for op_conf in graph_proto.net.op: - op_confs[op_conf.name] = op_conf - - op2placement = dict() - for group in graph_proto.placement.placement_group: - parallel_conf = group.parallel_conf - for op_name in group.op_set.op_name: - op2placement[op_name] = str( - oneflow.placement( - proto_str=text_format.MessageToString(parallel_conf) - ) - ) + graph_proto = graph_ir._graph_proto def _op_signature(op: op_conf_util.OperatorConf) -> Tuple[bool, str]: bn2nd_sbp = graph_proto.job_parallel_view_conf.op_name2nd_sbp_signature_conf[ @@ -173,8 +230,8 @@ def operators_repr( signature_template = Template( op.name + "($input) -> ($output)" - + ":placement=(" - + op2placement[op.name] + + ", placement=(" + + str(graph_ir.get_op_placement(op.name)) + ")" ) input_sig_str = "..." @@ -193,6 +250,14 @@ def operators_repr( input_sig_str, output_sig_str = _get_iden_op_io_repr( op, bn2nd_sbp, lbn2blob_desc ) + elif op.HasField("input_conf"): + input_sig_str, output_sig_str = _get_input_op_io_repr( + op, bn2nd_sbp, lbn2blob_desc + ) + elif op.HasField("output_conf"): + input_sig_str, output_sig_str = _get_output_op_io_repr( + op, bn2nd_sbp, lbn2blob_desc + ) elif op.name.startswith("System-"): return False, "" @@ -200,15 +265,19 @@ def operators_repr( op_str += signature_template.substitute( input=input_sig_str, output=output_sig_str ) + + if show_op_loc and op.loc: + op_str += ", location=(" + op.loc + ")" + op_str += ")" return True, op_str ops_strs = [] for op in ops: - if op not in op_confs: + op_conf = graph_ir.get_op_conf(op) + if op_conf is None: continue - op_conf = op_confs[op] assert isinstance(op_conf, op_conf_util.OperatorConf) got_repr, op_str = _op_signature(op_conf) if got_repr: @@ -243,13 +312,25 @@ def seq_to_func_return(seq, need_unpack=False): return seq +def _is_raw_type(value, raw_type): + # Special case for namedtuple return types + # For example, max(x, dim=1) return oneflow.return_types.max(values=..., indices=...) + if ( + raw_type == tuple + and isinstance(value, tuple) + and type(value).__module__ == "oneflow.return_types" + ): + return True + return type(value) is raw_type + + class NamedArg(object): r""" The class for wrapping over the input/output argument and associating each input/output argument with a prefix and name. The input/output argument can be viewed as a tree. NamedArg basically wraps over each tree node on this tree. The recursive structure of the input/output arguments are kept, for example: - iuput = [1, {key: "value" }] will be constructed into: + input = [1, {key: "value" }] will be constructed into: named_input = NamedArg([NamedArg(1), NamedArg({key: NamedArg("value")})]) """ @@ -277,13 +358,14 @@ class NamedArg(object): def is_leaf(self): assert self._is_value_set, "self._value is not set yet" return not ( - isinstance(self._value, dict) - or isinstance(self._value, tuple) - or isinstance(self._value, list) + _is_raw_type(self._value, dict) + or _is_raw_type(self._value, OrderedDict) + or _is_raw_type(self._value, tuple) + or _is_raw_type(self._value, list) ) def set_value(self, value): - assert not isinstance(value, NamedArg), "cannot accept value of type NamedArg" + assert not _is_raw_type(value, NamedArg), "cannot accept value of type NamedArg" self._value = value self._is_value_set = True @@ -292,11 +374,11 @@ class NamedArg(object): repr_str += "(name: " + self._name repr_str += ", idx: " + str(self._global_index) repr_str += ", type: " - if isinstance(self._value, tuple): + if _is_raw_type(self._value, tuple): repr_str += "TUPLE" - elif isinstance(self._value, list): + elif _is_raw_type(self._value, list): repr_str += "LIST" - elif isinstance(self._value, dict): + elif _is_raw_type(self._value, dict) or _is_raw_type(self._value, OrderedDict): repr_str += "DICT" elif isinstance(self._value, Tensor): repr_str += "TENSOR" @@ -307,9 +389,10 @@ class NamedArg(object): if isinstance(self._value, Tensor): repr_str += ", value: " + self._value._meta_repr() elif ( - isinstance(self._value, dict) - or isinstance(self._value, list) - or isinstance(self._value, tuple) + _is_raw_type(self._value, dict) + or _is_raw_type(self._value, OrderedDict) + or _is_raw_type(self._value, list) + or _is_raw_type(self._value, tuple) ): pass else: @@ -326,11 +409,6 @@ class ArgsTree(object): root_prefix: str = "", root_name: str = None, ) -> None: - assert ( - isinstance(io_args, dict) - or isinstance(io_args, tuple) - or isinstance(io_args, list) - ), "input/output arguments must be one of those types" self._io_args = io_args self._gen_name = gen_name @@ -363,14 +441,16 @@ class ArgsTree(object): stack.append(args_to_iter) while len(stack) > 0: curr = stack.pop() - if isinstance(curr, NamedArg): + if _is_raw_type(curr, NamedArg): curr_value = curr.value() else: curr_value = curr - if isinstance(curr_value, list) or isinstance(curr_value, tuple): + if _is_raw_type(curr_value, list) or _is_raw_type(curr_value, tuple): children = curr_value - elif isinstance(curr_value, dict): + elif _is_raw_type(curr_value, dict) or _is_raw_type( + curr_value, OrderedDict + ): children = list(curr_value.values()) else: children = None @@ -390,7 +470,7 @@ class ArgsTree(object): arg = NamedArg(prefix, name, self._next_global_index) self._next_global_index += 1 - if isinstance(value, list) or isinstance(value, tuple): + if _is_raw_type(value, list) or _is_raw_type(value, tuple): def construct_func(enum): (i, v) = enum @@ -400,7 +480,7 @@ class ArgsTree(object): arg.set_value(value.__class__(map(construct_func, enumerate(value)))) - elif isinstance(value, dict): + elif _is_raw_type(value, dict) or _is_raw_type(value, OrderedDict): def construct_func(enum): i, (key, v) = enum @@ -430,18 +510,18 @@ class ArgsTree(object): return self._execute_mapping(args_to_map, map_function) def _execute_mapping(self, value, map_function): - if isinstance(value, tuple) or isinstance(value, list): + if _is_raw_type(value, tuple) or _is_raw_type(value, list): mapped_value = value.__class__( map(lambda x: self._execute_mapping(x, map_function), value) ) - elif isinstance(value, dict): + elif _is_raw_type(value, dict) or _is_raw_type(value, OrderedDict): mapped_value = value.__class__( map( lambda x: (x[0], self._execute_mapping(x[1], map_function)), value.items(), ) ) - elif isinstance(value, NamedArg): + elif _is_raw_type(value, NamedArg): if value.is_leaf(): # only map the leaf: TENSOR/NONE/OPAQUE mapped_value = map_function(value) else: diff --git a/python/oneflow/nn/init.py b/python/oneflow/nn/init.py index 49c85bb5c9c5dd4eb7a2cc36a193645da2be4c05..b9d31e3eb014083c2898ad5a804cff72e5cfc271 100644 --- a/python/oneflow/nn/init.py +++ b/python/oneflow/nn/init.py @@ -14,30 +14,77 @@ See the License for the specific language governing permissions and limitations under the License. """ import os +import math + +import numpy as np import oneflow as flow -from oneflow.ops.initializer_util import CalcGain +from oneflow.ops.util.initializer_util import ( + calc_gain as calculate_gain, + calc_fan, + get_data_format, +) +from oneflow.framework.tensor import Tensor +import oneflow.framework.dtype as dtype_util -def calculate_gain(nonlinearity, param=None): - return CalcGain(nonlinearity, param) +def uniform_(tensor, a=0.0, b=1.0): + r""" + + Fills the input Tensor with values drawn from the uniform + distribution :math:`\mathcal{U}(a, b)`. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. -def uniform_(tensor, a=0.0, b=1.0): + Args: + tensor: an n-dimensional `oneflow.Tensor` + a: the lower bound of the uniform distribution + b: the upper bound of the uniform distribution + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.uniform_(w) + """ + assert a <= b, "b must be greater than or equal to a,but got {%d} vs {%d}" % (b, a) with flow.no_grad(): - return tensor.uniform_(a, b) + return flow._C.uniform_(tensor, a, b) def normal_(tensor, mean=0.0, std=1.0): + r""" + + Fills the input Tensor with values drawn from the normal + distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + + Args: + tensor: an n-dimensional `oneflow.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.normal_(w) + """ with flow.no_grad(): - return tensor.normal_(mean, std) + if tensor.is_local: + return flow.normal(mean=mean, std=std, size=tensor.shape, out=tensor) + else: + return flow.normal( + mean=mean, + std=std, + size=tensor.shape, + out=tensor, + placement=tensor.placement, + sbp=tensor.sbp, + ) def xavier_uniform_(tensor, gain=1.0, *, data_format="NCHW"): r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. - Fills the input `Tensor` with values according to the method described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010), using a uniform @@ -47,25 +94,27 @@ def xavier_uniform_(tensor, gain=1.0, *, data_format="NCHW"): .. math:: a = \text{gain} \times \sqrt{\frac{6}{\text{fan_in} + \text{fan_out}}} + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + Also known as Glorot initialization. Args: - tensor: an n-dimensional `flow.Tensor` + tensor: an n-dimensional `oneflow.Tensor` gain: an optional scaling factor Examples: >>> w = flow.empty(3, 5) >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu')) """ - with flow.no_grad(): - return tensor.xavier_uniform_(gain, data_format=data_format) + fan = calc_fan(tensor.shape, "fan_sum", get_data_format(data_format)) + std = gain * math.sqrt(2.0 / fan) + bound = math.sqrt(3.0) * std + return uniform_(tensor, -bound, bound) def xavier_normal_(tensor, gain=1.0, *, data_format="NCHW"): r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. - Fills the input `Tensor` with values according to the method described in `Understanding the difficulty of training deep feedforward neural networks` - Glorot, X. & Bengio, Y. (2010), using a normal @@ -75,31 +124,37 @@ def xavier_normal_(tensor, gain=1.0, *, data_format="NCHW"): .. math:: \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan_in} + \text{fan_out}}} + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + Also known as Glorot initialization. Args: - tensor: an n-dimensional `flow.Tensor` + tensor: an n-dimensional `oneflow.Tensor` gain: an optional scaling factor Examples: >>> w = flow.empty(3, 5) >>> nn.init.xavier_normal_(w) """ - with flow.no_grad(): - return tensor.xavier_normal_(gain, data_format=data_format) + if os.getenv("ONEFLOW_ENABLE_NHWC") == "1": + data_format = "NHWC" + fan = calc_fan(tensor.shape, "fan_sum", get_data_format(data_format)) + std = gain * math.sqrt(2.0 / fan) + return normal_(tensor, 0.0, std) def orthogonal_(tensor, gain=1.0): r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/nn.init.html. - Fills the input `Tensor` with a (semi) orthogonal matrix, as described in `Exact solutions to the nonlinear dynamics of learning in deep linear neural networks` - Saxe, A. et al. (2013). The input tensor must have at least 2 dimensions, and for tensors with more than 2 dimensions the trailing dimensions are flattened. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + Args: tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2` gain: optional scaling factor @@ -116,9 +171,6 @@ def kaiming_uniform_( tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", *, data_format="NCHW" ): r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. - Fills the input `Tensor` with values according to the method described in `Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification` - He, K. et al. (2015), using a @@ -127,11 +179,14 @@ def kaiming_uniform_( .. math:: \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan_mode}}} + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. Also known as He initialization. Args: - tensor: an n-dimensional `flow.Tensor` + tensor: an n-dimensional `oneflow.Tensor` a: the negative slope of the rectifier used after this layer (only used with ``'leaky_relu'``) mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` @@ -145,17 +200,19 @@ def kaiming_uniform_( >>> w = flow.empty(3, 5) >>> nn.init.kaiming_uniform_(w, mode='fan_in', nonlinearity='relu') """ - with flow.no_grad(): - return tensor.kaiming_uniform_(a, mode, nonlinearity, data_format=data_format) + if os.getenv("ONEFLOW_ENABLE_NHWC") == "1": + data_format = "NHWC" + fan = calc_fan(tensor.shape, mode, get_data_format(data_format)) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + bound = math.sqrt(3.0) * std + return uniform_(tensor, -bound, bound) def kaiming_normal_( tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", *, data_format="NCHW" ): - r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. - + r""" Fills the input `Tensor` with values according to the method described in `Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification` - He, K. et al. (2015), using a @@ -165,10 +222,13 @@ def kaiming_normal_( .. math:: \text{std} = \frac{\text{gain}}{\sqrt{\text{fan_mode}}} + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + Also known as He initialization. Args: - tensor: an n-dimensional `flow.Tensor` + tensor: an n-dimensional `oneflow.Tensor` a: the negative slope of the rectifier used after this layer (only used with ``'leaky_relu'``) mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'`` @@ -184,28 +244,98 @@ def kaiming_normal_( """ if os.getenv("ONEFLOW_ENABLE_NHWC") == "1": data_format = "NHWC" - with flow.no_grad(): - return tensor.kaiming_normal_(a, mode, nonlinearity, data_format=data_format) + assert mode in ["fan_in", "fan_out"] + fan = calc_fan(tensor.shape, mode, get_data_format(data_format)) + gain = calculate_gain(nonlinearity, a) + std = gain / math.sqrt(fan) + return normal_(tensor, 0.0, std) def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): with flow.no_grad(): - return tensor.trunc_normal_(mean, std, a, b) + return tensor.normal_(mean, std).clamp_(a, b) def constant_(tensor, val): + r""" + + Fills the input Tensor with the value :math:`\text{val}`. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + + Args: + tensor: an n-dimensional `oneflow.Tensor` + val: the value to fill the tensor with + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.constant_(w, 0.3) + """ with flow.no_grad(): - return tensor.fill_(val) + tensor[...] = val + return tensor def ones_(tensor): + r""" + + Fills the input Tensor with the scalar value `1`. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + + Args: + tensor: an n-dimensional `oneflow.Tensor` + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.ones_(w) + """ with flow.no_grad(): - return tensor.fill_(1) + return constant_(tensor, 1) def zeros_(tensor): + r""" + + Fills the input Tensor with the scalar value `0`. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + + Args: + tensor: an n-dimensional `oneflow.Tensor` + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.zeros_(w) + """ + with flow.no_grad(): + return constant_(tensor, 0) + + +def eye_(tensor): + r""" + + Fills the 2-dimensional input `Tensor` with the identity + matrix. Preserves the identity of the inputs in `Linear` layers, where as + many inputs are preserved as possible. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/nn.init.html. + + Args: + tensor: a 2-dimensional `oneflow.Tensor` + + Examples: + >>> w = flow.empty(3, 5) + >>> nn.init.eye_(w) + """ + if tensor.ndimension() != 2: + raise ValueError("Only tensors with 2 dimensions are supported") with flow.no_grad(): - return tensor.fill_(0) + return flow._C.eye_(tensor) def _calculate_fan_in_and_fan_out(tensor): diff --git a/python/oneflow/nn/modules/__init__.py b/python/oneflow/nn/modules/__init__.py index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..3a0b3915837488ab0566d7386b8a13cd8b71d81d 100644 --- a/python/oneflow/nn/modules/__init__.py +++ b/python/oneflow/nn/modules/__init__.py @@ -0,0 +1,16 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from .module import Module diff --git a/python/oneflow/nn/modules/activation.py b/python/oneflow/nn/modules/activation.py index 39dafaec5aed3b827165d536de5ea2653669f012..d16841c25fa1112593a89d92bb37c49bf988b080 100644 --- a/python/oneflow/nn/modules/activation.py +++ b/python/oneflow/nn/modules/activation.py @@ -17,7 +17,7 @@ import warnings from typing import Optional import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class PReLU(Module): @@ -221,14 +221,8 @@ class Tanh(Module): class ELU(Module): - """Applies the element-wise function: - - .. math:: - - \\text{ELU}(x) = \\begin{cases} - x & \\text{ if } x \\gt 0 \\\\ - \\alpha*(exp(x)-1) & \\text{ if } x \\le 0 \\\\ - \\end{cases} + """Applies the element-wise function + :math:`\\text{ELU}(x) = \\begin{cases}x & \\text{ if } x \\gt 0 \\\\\\alpha*(exp(x)-1) & \\text{ if } x \\le 0 \\\\\\end{cases}` Args: alpha: the :math:`\\alpha` value for the ELU formulation. Default: 1.0 @@ -325,18 +319,28 @@ class CELU(Module): class GELU(Module): - """Gelu activation operator. + """ + GELU(approximate='none') -> Tensor - The equation is: + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.GELU.html. - .. math:: - out = 0.5 * x * (1 + tanh(\\sqrt{\\frac{2}{\\pi}} * (x + 0.044715x^{3}))) + Applies the Gaussian Error Linear Units function: + + .. math:: \\text{GELU}(x) = x * \Phi(x) + + where :math:`\Phi(x)` is the Cumulative Distribution Function for Gaussian Distribution. + + When the approximate argument is 'tanh', Gelu is estimated with: + + .. math:: \\text{GELU}(x) = 0.5 * x * (1 + \\text{Tanh}(\sqrt(2 / \pi) * (x + 0.044715 * x^3))) Args: - x (oneflow.Tensor): Input Tensor + input (oneflow.Tensor): Input Tensor + approximate (string, optional): the gelu approximation algorithm to use: + ``'none'`` | ``'tanh'``. Default: ``'none'`` Returns: - oneflow.Tensor: A Tensor. + oneflow.Tensor: A Tensor has same shape as the input. For example: @@ -355,11 +359,52 @@ class GELU(Module): """ + def __init__(self, approximate: str = "none"): + super().__init__() + self.approximate = approximate + + def forward(self, input): + if self.approximate == "none" or self.approximate == "tanh": + return flow._C.gelu_with_approximate(input, self.approximate) + else: + raise NotImplementedError + + +class QuickGELU(Module): + """ + QuickGELU() -> Tensor + + Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs + + .. math:: + \\text{QuickGELU}(x) = x * \\sigma(1.702x) = x * \\frac{1}{1 + \\exp(-1.702x)} + + Args: + input (oneflow.Tensor): Input Tensor + + Returns: + oneflow.Tensor: A Tensor has same shape as the input. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + + >>> input = flow.Tensor([-0.5, 0, 0.5]) + >>> gelu = flow.nn.QuickGELU() + + >>> out = gelu(input) + >>> out + tensor([-0.1496, 0.0000, 0.3504], dtype=oneflow.float32) + + """ + def __init__(self): super().__init__() def forward(self, x): - return flow._C.gelu(x) + return flow._C.quick_gelu(x) class Sigmoid(Module): @@ -685,8 +730,8 @@ class Softplus(Module): class Hardswish(Module): - """Applies the hardswish function, element-wise, as described in the paper: - `Searching for MobileNetV3`_. + """Applies the hardswish function, element-wise, as described in the paper `Searching for MobileNetV3 + `__. .. math:: \\text{Hardswish}(x) = \\begin{cases} @@ -715,9 +760,7 @@ class Hardswish(Module): >>> out = hardswish(input) >>> out tensor([-0.2083, 0.0000, 0.2917], dtype=oneflow.float32) - - .. _`Searching for MobileNetV3`: - https://arxiv.org/abs/1905.02244 + """ def __init__(self, inplace: bool = False): @@ -994,9 +1037,6 @@ class SELU(Module): class Softshrink(Module): r""" - The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Softshrink.html. - The Softshrink activation. The formula is: @@ -1010,6 +1050,9 @@ class Softshrink(Module): 0, & \text{ otherwise } \end{cases} + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Softshrink.html. + Args: lambd: the :math:`\lambda` value for the Softshrink formulation. Default: 0.5 inplace: can optionally do the operation in-place. Default: ``False`` diff --git a/python/oneflow/nn/modules/all_reduce.py b/python/oneflow/nn/modules/all_reduce.py index 69b87e72906c0e4b50fd277caa03214b2eb3d4af..d2d512d12dae430502966c96f803a611b44ff488 100644 --- a/python/oneflow/nn/modules/all_reduce.py +++ b/python/oneflow/nn/modules/all_reduce.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from typing import Sequence @@ -23,11 +23,11 @@ class AllReduce(Module): def __init__(self, parallel_conf_str: str): super().__init__() self._op = ( - flow.stateful_op("eager_nccl_all_reduce").Input("in").Output("out").Build() + flow.stateful_op("eager_ccl_all_reduce").Input("in").Output("out").Build() ) self.parallel_conf = parallel_conf_str def forward(self, x): assert x.device.type == "cuda" assert x.device.index == flow.env.get_local_rank() - return flow._C.dispatch_eager_nccl_all_reduce(self._op, parallel_conf) + return flow._C.dispatch_eager_ccl_all_reduce(self._op, parallel_conf) diff --git a/python/oneflow/nn/modules/arange.py b/python/oneflow/nn/modules/arange.py index 2694b743710261fa202aae73739813d0ea1e5cba..bd1a568355c6148995a575225e509464e0e71db8 100644 --- a/python/oneflow/nn/modules/arange.py +++ b/python/oneflow/nn/modules/arange.py @@ -32,7 +32,7 @@ def arange_op( elif flow.is_tensor(start): # support start as a Scalar Tensor assert len(start.shape) == 0, "start must be a Scalar" - start = int(start.numpy()) + start = start.item() if end is None: end = start @@ -40,7 +40,7 @@ def arange_op( elif flow.is_tensor(end): # support end as a Scalar Tensor assert len(end.shape) == 0, "end must be a Scalar" - end = int(end.numpy()) + end = end.item() if placement is None: if isinstance(device, str): diff --git a/python/oneflow/nn/modules/argsort.py b/python/oneflow/nn/modules/argsort.py index 1d155f86487e02d3759a3d4fa996a2819888aa24..1715516e969bb23ac42eb9c90309cf444cc19af0 100644 --- a/python/oneflow/nn/modules/argsort.py +++ b/python/oneflow/nn/modules/argsort.py @@ -15,7 +15,7 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.ops.transpose_util import ( get_inversed_perm, get_perm_when_transpose_axis_to_last_dim, diff --git a/python/oneflow/nn/modules/argwhere.py b/python/oneflow/nn/modules/argwhere.py index 2cce02941c35b09697ddce1845d72b592e0ac7b3..288d54a710eadd3b820c672abcd7a53ca6cfbe49 100644 --- a/python/oneflow/nn/modules/argwhere.py +++ b/python/oneflow/nn/modules/argwhere.py @@ -19,7 +19,7 @@ import numpy as np import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def argwhere_op(input, dtype: Optional[flow.dtype] = flow.int32): diff --git a/python/oneflow/nn/modules/batchnorm.py b/python/oneflow/nn/modules/batchnorm.py index 06df2fb4dd991a91361f443c26bfb187f3ba983d..363d18728e12eacb7859160cf72098eee864bd19 100644 --- a/python/oneflow/nn/modules/batchnorm.py +++ b/python/oneflow/nn/modules/batchnorm.py @@ -17,7 +17,8 @@ from typing import Union import os import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module +from oneflow.autograd import Function class _NormBase(Module): @@ -81,7 +82,17 @@ class _NormBase(Module): ): if self.track_running_stats: num_batches_tracked_key = prefix + "num_batches_tracked" - state_dict[num_batches_tracked_key] = flow.tensor(0, dtype=flow.long) + if not num_batches_tracked_key in state_dict: + if self.running_mean.is_global: + sbp = self.running_mean.sbp + placement = self.running_mean.placement + state_dict[num_batches_tracked_key] = flow.tensor( + 0, dtype=flow.long + ).to_global(sbp=sbp, placement=placement) + else: + state_dict[num_batches_tracked_key] = flow.tensor( + 0, dtype=flow.long + ) super(_NormBase, self)._load_from_state_dict( state_dict, prefix, @@ -147,7 +158,7 @@ class BatchNorm1d(_BatchNorm): the mini-batches and :math:`\\gamma` and :math:`\\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\\gamma` are set to 1 and the elements of :math:`\\beta` are set to 0. The standard-deviation is calculated - via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. + via the biased estimator, equivalent to `oneflow.var(input, unbiased=False)`. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during @@ -224,7 +235,7 @@ class BatchNorm2d(_BatchNorm): the mini-batches and :math:`\\gamma` and :math:`\\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\\gamma` are set to 1 and the elements of :math:`\\beta` are set to 0. The standard-deviation is calculated - via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. + via the biased estimator, equivalent to `oneflow.var(input, unbiased=False)`. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during @@ -311,7 +322,7 @@ class BatchNorm3d(_BatchNorm): the mini-batches and :math:`\gamma` and :math:`\beta` are learnable parameter vectors of size `C` (where `C` is the input size). By default, the elements of :math:`\gamma` are set to 1 and the elements of :math:`\beta` are set to 0. The standard-deviation is calculated - via the biased estimator, equivalent to `torch.var(input, unbiased=False)`. + via the biased estimator, equivalent to `oneflow.var(input, unbiased=False)`. Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during @@ -374,6 +385,361 @@ class BatchNorm3d(_BatchNorm): raise ValueError("expected 5D input (got {}D input)".format(input.ndim)) +global_eps = 0.1 +global_momentum = 0.1 +global_world_size = 1 +global_axis = 1 + + +class SyncBatchNormFunction(flow.autograd.Function): + @staticmethod + def forward(self, input, weight, bias, running_mean, running_var): + assert input.is_local, "SyncBatchNorm does not support global tensor as input." + + if not input.is_contiguous(): + input = input.contiguous() + if weight is not None: + weight = weight.contiguous() + + size = int(input.numel() // input.size(1)) + if size == 1 and global_world_size < 2: + raise ValueError( + "Expected more than 1 value per channel when training, got input size {}".format( + size + ) + ) + + num_channels = input.shape[global_axis] + if input.numel() > 0: + # calculate mean/invstd for input. + mean, invstd = flow._C.batch_norm_stats(input, global_axis, global_eps) + + count = flow.full( + (1,), + input.numel() // input.size(global_axis), + dtype=mean.dtype, + device=mean.device, + ) + + # C, C, 1 -> (2C + 1) + combined = flow.cat([mean, invstd, count], dim=0) + else: + # for empty input, set stats and the count to zero. The stats with + # zero count will be filtered out later when computing global mean + # & invstd, but they still needs to participate the all_gather + # collective communication to unblock other peer processes. + combined = flow.zeros( + 2 * num_channels + 1, dtype=input.dtype, device=input.device + ) + + # Use allgather instead of allreduce because count could be different across + # ranks, simple all reduce op can not give correct results. + # batch_norm_gather_stats_with_counts calculates global mean & invstd based on + # all gathered mean, invstd and count. + # world_size * (2C + 1) + combined_size = combined.numel() + combined_flat = flow.empty( + global_world_size, + combined_size, + dtype=combined.dtype, + device=combined.device, + ) + flow.comm.all_gather_into_tensor(combined_flat, combined) + # world_size * (2C + 1) -> world_size * C, world_size * C, world_size * 1 + mean_all, invstd_all, count_all = flow.split(combined_flat, num_channels, dim=1) + + # remove stats from empty inputs + mask = count_all.squeeze(-1) >= 1 + count_all = count_all[mask] + mean_all = mean_all[mask] + invstd_all = invstd_all[mask] + + # calculate global mean & invstd + mean, invstd = flow._C.batch_norm_gather_stats_with_counts( + input, + mean_all, + invstd_all, + running_mean, + running_var, + global_momentum, + global_eps, + count_all.view(-1), + ) + + self.save_for_backward(input, weight, mean, invstd, count_all.to(flow.int32)) + + # apply element-wise normalization + if input.numel() > 0: + return flow._C.batch_norm_elemt( + input, weight, bias, mean, invstd, global_axis, global_eps + ) + else: + return flow.zeros(*(input.shape), dtype=input.dtype, device=input.device) + + @staticmethod + def backward(self, grad_output): + if not grad_output.is_contiguous(): + grad_output = grad_output.contiguous() + saved_input, weight, mean, invstd, count_tensor = self.saved_tensors + grad_input = grad_weight = grad_bias = None + + channel_axis = 1 + if os.getenv("ONEFLOW_ENABLE_NHWC") == "1": + if saved_input.dim() == 3: + channel_axis = 2 + elif saved_input.dim() == 4: + channel_axis = 3 + elif saved_input.dim() == 5: + channel_axis = 4 + + # calculate local stats as well as grad_weight / grad_bias + sum_dy, sum_dy_xmu, grad_weight, grad_bias = flow._C.batch_norm_backward_reduce( + grad_output, saved_input, mean, invstd, channel_axis + ) + + # synchronizing stats used to calculate input gradient. + num_channels = sum_dy.shape[0] + combined = flow.cat([sum_dy, sum_dy_xmu], dim=0) + flow.comm.all_reduce(combined) + sum_dy, sum_dy_xmu = flow.split(combined, num_channels) + + # backward pass for gradient calculation + grad_input = flow._C.batch_norm_backward_elemt( + grad_output, + saved_input, + mean, + invstd, + weight, + sum_dy, + sum_dy_xmu, + count_tensor, + channel_axis, + ) + + # synchronizing of grad_weight / grad_bias is not needed as distributed + # training would handle all reduce. + return grad_input, grad_weight, grad_bias, None, None + + +class SyncBatchNorm(_BatchNorm): + r"""Applies Batch Normalization over a N-Dimensional input (a mini-batch of [N-2]D inputs + with additional channel dimension) as described in the paper + `Batch Normalization: Accelerating Deep Network Training by Reducing + Internal Covariate Shift `__ . + + .. math:: + + y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta + + The mean and standard-deviation are calculated per-dimension over all + mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` + are learnable parameter vectors of size `C` (where `C` is the input size). + By default, the elements of :math:`\gamma` are sampled from + :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. + The standard-deviation is calculated via the biased estimator, equivalent to + `oneflow.var(input, unbiased=False)`. + + Also by default, during training this layer keeps running estimates of its + computed mean and variance, which are then used for normalization during + evaluation. The running estimates are kept with a default :attr:`momentum` + of 0.1. + + If :attr:`track_running_stats` is set to ``False``, this layer then does not + keep running estimates, and batch statistics are instead used during + evaluation time as well. + + .. note:: + This :attr:`momentum` argument is different from one used in optimizer + classes and the conventional notion of momentum. Mathematically, the + update rule for running statistics here is + :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, + where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the + new observed value. + + Because the Batch Normalization is done for each channel in the ``C`` dimension, computing + statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch + Normalization or Spatio-temporal Batch Normalization. + + Currently :class:`SyncBatchNorm` only supports + :class:`~oneflow.nn.DistributedDataParallel` (DDP) with single GPU per process. Use + :meth:`oneflow.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert + :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping + Network with DDP. + + Args: + num_features: :math:`C` from an expected input of size + :math:`(N, C, +)` + eps: a value added to the denominator for numerical stability. + Default: ``1e-5`` + momentum: the value used for the running_mean and running_var + computation. Can be set to ``None`` for cumulative moving average + (i.e. simple average). Default: 0.1 + affine: a boolean value that when set to ``True``, this module has + learnable affine parameters. Default: ``True`` + track_running_stats: a boolean value that when set to ``True``, this + module tracks the running mean and variance, and when set to ``False``, + this module does not track such statistics, and initializes statistics + buffers :attr:`running_mean` and :attr:`running_var` as ``None``. + When these buffers are ``None``, this module always uses batch statistics. + in both training and eval modes. Default: ``True`` + + Shape: + - Input: :math:`(N, C, +)` + - Output: :math:`(N, C, +)` (same shape as input) + + .. note:: + Synchronization of batchnorm statistics occurs only while training, i.e. + synchronization is disabled when ``model.eval()`` is set or if + ``self.training`` is otherwise ``False``. + + Examples:: + + >>> import oneflow as flow + + >>> bn = flow.nn.BatchNorm2d(100) + >>> sync_bn = flow.nn.SyncBatchNorm.convert_sync_batchnorm(bn).cuda() + >>> input = flow.randn(20, 100, 35, 45, device="cuda") + >>> output = sync_bn(input) + """ + + def __init__( + self, + num_features: int, + eps: float = 1e-5, + momentum: float = 0.1, + affine: bool = True, + track_running_stats: bool = True, + ) -> None: + super().__init__(num_features, eps, momentum, affine, track_running_stats) + + def _check_input_dim(self, input): + if input.dim() < 2: + raise ValueError( + "expected at least 2D input (got {}D input)".format(input.dim()) + ) + if os.getenv("ONEFLOW_ENABLE_NHWC") == "1": + if input.dim() == 3: + self.channel_axis = 2 + elif input.dim() == 4: + self.channel_axis = 3 + elif input.dim() == 5: + self.channel_axis = 4 + + def _check_non_zero_input_channels(self, input): + if input.size(1) == 0: + raise ValueError( + "SyncBatchNorm number of input channels should be non-zero" + ) + + def forward(self, input): + # currently only GPU input is supported + if not input.is_cuda: + raise ValueError("SyncBatchNorm expected input tensor to be on GPU") + + self._check_input_dim(input) + self._check_non_zero_input_channels(input) + + if self.momentum is None: + exponential_average_factor = 0.0 + else: + exponential_average_factor = self.momentum + + if self.training and self.track_running_stats: + assert self.num_batches_tracked is not None + self.num_batches_tracked.add_(1) + if self.momentum is None: # use cumulative moving average + exponential_average_factor = 1.0 / self.num_batches_tracked.item() + else: # use exponential moving average + exponential_average_factor = self.momentum + + r""" + Decide whether the mini-batch stats should be used for normalization rather than the buffers. + Mini-batch stats are used in training mode, and in eval mode when buffers are None. + """ + if self.training: + bn_training = True + else: + bn_training = (self.running_mean is None) and (self.running_var is None) + + # Don't sync batchnorm stats in inference mode (model.eval()). + need_sync = bn_training and self.training + if need_sync: + need_sync = flow.env.get_world_size() > 1 + + # # fallback to framework BN when synchronization is not necessary + if not need_sync: + return flow._C.normalization( + input, + self.running_mean, + self.running_var, + self.weight, + self.bias, + axis=self.channel_axis, + epsilon=self.eps, + momentum=exponential_average_factor, + is_training=bn_training, + ) + else: + assert bn_training + global global_eps + global global_momentum + global global_world_size + global global_axis + global_eps = self.eps + global_momentum = exponential_average_factor + global_world_size = flow.env.get_world_size() + global_axis = self.channel_axis + assert ( + self.track_running_stats + ), "`track_running_stats` should be True when using SyncBatchNorm." + return SyncBatchNormFunction.apply( + input, self.weight, self.bias, self.running_mean, self.running_var, + ) + + @classmethod + def convert_sync_batchnorm(cls, module): + r"""Helper function to convert all :attr:`BatchNorm*D` layers in the model to + :class:`oneflow.nn.SyncBatchNorm` layers. + + Args: + module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers + + Returns: + The original :attr:`module` with the converted :class:`oneflow.nn.SyncBatchNorm` + layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, + a new :class:`oneflow.nn.SyncBatchNorm` layer object will be returned + instead. + + Example:: + + >>> import oneflow as flow + + >>> module = flow.nn.Sequential( flow.nn.Linear(20, 100), flow.nn.BatchNorm1d(100)).cuda() + >>> sync_bn_module = flow.nn.SyncBatchNorm.convert_sync_batchnorm(module) + + """ + module_output = module + if isinstance(module, flow.nn.modules.batchnorm._BatchNorm): + module_output = flow.nn.SyncBatchNorm( + module.num_features, + module.eps, + module.momentum, + module.affine, + module.track_running_stats, + ) + if module.affine: + with flow.no_grad(): + module_output.weight = module.weight + module_output.bias = module.bias + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + for name, child in module.named_children(): + module_output.add_module(name, cls.convert_sync_batchnorm(child)) + del module + return module_output + + if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/batchnorm_fused.py b/python/oneflow/nn/modules/batchnorm_fused.py index 228bb36d577b04c933d88d8e8499462bae80daeb..efc11568b7dd465f821900a9e9ced4e8b6b1ac04 100644 --- a/python/oneflow/nn/modules/batchnorm_fused.py +++ b/python/oneflow/nn/modules/batchnorm_fused.py @@ -17,7 +17,7 @@ from typing import Union import os import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class _FusedNormBase(Module): diff --git a/python/oneflow/nn/modules/broadcast_ops.py b/python/oneflow/nn/modules/broadcast_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..96e04b32186701febea00e65c6d03136bf3206d3 --- /dev/null +++ b/python/oneflow/nn/modules/broadcast_ops.py @@ -0,0 +1,118 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow +from oneflow.nn.modules.utils import _single, _handle_size_arg + + +def broadcast_shapes(*shapes): + r"""broadcast_shapes(*shapes) -> Size + + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.broadcast_shapes.html. + + Similar to :func:`oneflow.broadcast_tensors` but for shapes. + + This is equivalent to ``flow.broadcast_tensors(*map(flow.empty, shapes))[0].shape`` + but avoids the need create to intermediate tensors. + This is useful for broadcasting tensors of common batch shape but different rightmost shape, + e.g. to broadcast mean vectors with covariance matrices. + + Args: + \*shapes (flow.Size): Shapes of tensors. + + Returns: + A shape compatible with all input shapes. + + Raises: + RuntimeError: If shapes are incompatible. + + Example:: + + >>> import oneflow as flow + >>> flow.broadcast_shapes((2,), (3, 1), (1, 1, 1)) + oneflow.Size([1, 3, 2]) + """ + shapes = _single(shapes) + return flow._C.broadcast_shapes(shapes) + + +def broadcast_tensors(*tensors): + r"""broadcast_tensors(*tensors) -> List of Tensors + + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.broadcast_tensors.html. + + Broadcasts the given tensors according to ``broadcasting-semantics``. + + Args: + *tensors: any number of tensors of the same type + + .. warning:: + + More than one element of a broadcasted tensor may refer to a single + memory location. As a result, in-place operations (especially ones that + are vectorized) may result in incorrect behavior. If you need to write + to the tensors, please clone them first. + + Example:: + + >>> import oneflow as flow + >>> x = flow.arange(3).view(1, 3) + >>> y = flow.arange(2).view(2, 1) + >>> a, b = flow.broadcast_tensors(x, y) + >>> a.size() + oneflow.Size([2, 3]) + >>> a + tensor([[0, 1, 2], + [0, 1, 2]], dtype=oneflow.int64) + """ + tensors = _single(tensors) + return flow._C.broadcast_tensors(tensors) + + +def broadcast_to(input, shape): + r"""broadcast_to(input, shape) -> Tensors + + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.broadcast_to.html. + + Broadcasts ``input`` to the shape ``shape``. Equivalent to calling ``input.expand(shape)``. See :func:`oneflow.expand` for details. + + Args: + input (oneflow.Tensor): the input tensor. + shape (list, tuple, or oneflow.Size): the new shape. + + Example:: + + >>> import oneflow as flow + >>> x = flow.tensor([1, 2, 3]) + >>> flow.broadcast_to(x, (3, 3)) + tensor([[1, 2, 3], + [1, 2, 3], + [1, 2, 3]], dtype=oneflow.int64) + """ + shape = _handle_size_arg(shape) + shape = _single(shape) + return flow._C.broadcast_to(input, shape) + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/constant.py b/python/oneflow/nn/modules/constant.py index c09f30d44726e2b15a51003e0329ee3d1c49e5a9..87874a5b8f423023cedfbc9991be8cddaf0777bb 100644 --- a/python/oneflow/nn/modules/constant.py +++ b/python/oneflow/nn/modules/constant.py @@ -14,11 +14,12 @@ See the License for the specific language governing permissions and limitations under the License. """ from typing import List, Optional, Union +import numpy as np import oneflow as flow from oneflow.framework.tensor import register_tensor_op from oneflow.nn.common_types import _size_any_t -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.utils import _single, _handle_size_arg @@ -28,7 +29,7 @@ class _ConstantBase(Module): size: Union[_size_any_t, flow.Size], value: Union[float, int], dtype: Optional[flow.dtype], - device: Union[flow.device, str] = None, + device: Union[flow.device, int, str] = None, placement: flow.placement = None, sbp: Union[flow.sbp.sbp, List[flow.sbp.sbp]] = None, requires_grad: bool = False, @@ -39,12 +40,14 @@ class _ConstantBase(Module): size, (int, tuple, list, flow.Size) ), "shape should be int or tuple int!" self.device = device + if isinstance(self.device, int): + self.device = flow.device("cuda", self.device) if isinstance(self.device, str): self.device = flow.device(self.device) self.requires_grad = requires_grad size = _single(size) if dtype is None: - dtype = flow.float32 + dtype = flow.get_default_dtype() if placement is None: if device is None: self.device = flow.device("cpu") @@ -68,21 +71,115 @@ class _ConstantBase(Module): def forward(self): if self.placement is not None: - res = flow._C.global_constant( - self.shape, - self.value, - dtype=self.dtype, - placement=self.placement, - sbp=self.sbp, - ) + if isinstance(self.value, flow.Tensor): + assert ( + self.value.ndim <= 1 and self.value.numel() == 1 + ), "Only tensor with single element or scalar tensor are supported as value!" + res = flow._C.global_tensor_constant( + self.shape, + self.value, + dtype=self.dtype, + placement=self.placement, + sbp=self.sbp, + ) + else: + res = flow._C.global_constant( + self.shape, + self.value, + dtype=self.dtype, + placement=self.placement, + sbp=self.sbp, + ) else: - res = flow._C.constant( - self.shape, self.value, dtype=self.dtype, device=self.device - ) + if isinstance(self.value, flow.Tensor): + assert ( + self.value.ndim <= 1 and self.value.numel() == 1 + ), "Only tensor with single element or scalar tensor are supported as value!" + res = flow._C.tensor_constant( + self.shape, self.value, dtype=self.dtype, device=self.device + ) + else: + res = flow._C.constant( + self.shape, self.value, dtype=self.dtype, device=self.device + ) res.requires_grad = self.requires_grad return res +def _handle_meta_args( + input, + size: Union[_size_any_t, List[int], flow.Size, None] = None, + dtype: Optional[flow.dtype] = None, + device: Union[flow.device, str, None] = None, + placement: flow.placement = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, + requires_grad: bool = False, +): + if isinstance(device, str): + device = flow.device(device) + if size is None or len(size) == 0: + new_size = input.shape + else: + new_size = _handle_size_arg(size) + if dtype is None: + new_dtype = input.dtype + else: + new_dtype = dtype + new_device = device + new_placement = placement + new_sbp = sbp + new_requires_grad = requires_grad + + if new_device is not None: + assert ( + new_placement is None + ), "argument 'placement' must be None when argument 'device' exist" + assert ( + new_sbp is None + ), "argument 'sbp' must be None when argument 'device' exist" + elif new_device is None and new_placement is None and new_sbp is None: + new_device = input.device if input.is_local else None + new_placement = input.placement if input.is_global else None + new_sbp = input.sbp if input.is_global else None + else: + if new_placement is None and new_sbp is not None: + assert ( + input.is_global + ), "argument 'placement' must not be None when argument 'sbp' exist and Tensor is local" + new_placement = input.placement + elif new_placement is not None and new_sbp is None: + assert ( + input.is_global + ), "argument 'sbp' must not be None when argument 'placement' exist and Tensor is local" + new_sbp = input.sbp + assert isinstance( + new_size, (int, tuple, list, flow.Size) + ), f"argument 'size' must be tuple of ints, not %s" % (type(new_size)) + assert isinstance( + new_dtype, flow.dtype + ), f"argument 'dtype' must be flow.dtype, not %s" % (type(new_dtype)) + if new_placement is not None: + assert isinstance( + new_placement, flow.placement + ), f"argument 'placement' must be flow.placement, not %s" % ( + type(new_placement) + ) + assert isinstance( + new_sbp, (flow.sbp.sbp, tuple) + ), f"argument 'sbp' must be flow.sbp.sbp, not %s" % (type(new_sbp)) + else: + assert isinstance( + new_device, (str, flow.device) + ), f"argument 'device' must be flow.device, not %s" % (type(new_device)) + assert isinstance( + new_requires_grad, bool + ), f"argument 'requires_grad' must be bool, not %s" % (type(new_requires_grad)) + + return new_size, new_dtype, new_device, new_placement, new_sbp, new_requires_grad + + class Ones(_ConstantBase): def __init__( self, @@ -101,7 +198,9 @@ def ones_op( dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, - sbp: flow._oneflow_internal.sbp.sbp = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, requires_grad: bool = False, ): """ @@ -140,6 +239,29 @@ def ones_op( return Ones(size, dtype, device, placement, sbp, requires_grad)() +def ones_like_op( + input, + dtype: Optional[flow.dtype] = None, + device: Union[flow.device, str, None] = None, + placement: flow.placement = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, + requires_grad: bool = False, +): + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(input, None, dtype, device, placement, sbp, requires_grad) + return Ones( + new_size, new_dtype, new_device, new_placement, new_sbp, new_requires_grad + )() + + class Zeros(_ConstantBase): def __init__( self, @@ -158,7 +280,9 @@ def zeros_op( dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, - sbp: flow._oneflow_internal.sbp.sbp = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, requires_grad: bool = False, ): """ @@ -192,6 +316,29 @@ def zeros_op( return Zeros(size, dtype, device, placement, sbp, requires_grad)() +def zeros_like_op( + input, + dtype: Optional[flow.dtype] = None, + device: Union[flow.device, str, None] = None, + placement: flow.placement = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, + requires_grad: bool = False, +): + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(input, None, dtype, device, placement, sbp, requires_grad) + return Zeros( + new_size, new_dtype, new_device, new_placement, new_sbp, new_requires_grad + )() + + class Full(_ConstantBase): def __init__( self, @@ -208,11 +355,13 @@ class Full(_ConstantBase): def full_op( size: Union[_size_any_t, flow.Size], - value: Union[float, int], + fill_value: Union[float, int], dtype: Optional[flow.dtype] = None, device: Union[flow.device, str, None] = None, placement: flow.placement = None, - sbp: flow._oneflow_internal.sbp.sbp = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, requires_grad: bool = False, ): """ @@ -222,10 +371,10 @@ def full_op( Args: size(int...): a list, tuple, or oneflow.Size of integers defining the shape of the output tensor. fill_value(Scalar): the value to fill the output tensor with. - dtype (flow.dtype, optional): the desired data type of returned tensor. - device (flow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type - placement (flow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. - sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + dtype (oneflow.dtype, optional): the desired data type of returned tensor. + device (oneflow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type + placement (oneflow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. For example: @@ -241,64 +390,104 @@ def full_op( tensor([[5., 5., 5.], [5., 5., 5.]], dtype=oneflow.float32) >>> placement = flow.placement("cpu", ranks=[0]) - >>> y = flow.full((2,3),5.0, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor + >>> y = flow.full((2,3), 5.0, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor >>> y.is_global True """ size = _handle_size_arg(size) + if not isinstance(fill_value, (int, float, flow.Tensor)): + # handle numpy scalar dtype + assert isinstance( + fill_value.dtype, (np.dtype) + ), "fill_value must be python scalar or numpy scalar." + fill_value = fill_value.item() if dtype is None: - dtype = flow.tensor(value).dtype - return Full(size, value, dtype, device, placement, sbp, requires_grad)() + dtype = flow.tensor(fill_value).dtype + return Full(size, fill_value, dtype, device, placement, sbp, requires_grad)() + + +def full_like_op( + input, + fill_value, + dtype: Optional[flow.dtype] = None, + device: Union[flow.device, str, None] = None, + placement: flow.placement = None, + sbp: Union[ + flow._oneflow_internal.sbp.sbp, List[flow._oneflow_internal.sbp.sbp], None + ] = None, + requires_grad: bool = False, +): + """ + full_like(input, fill_value, \*, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + Returns a tensor with the same size as :attr:`input` filled with :attr:`fill_value`. + ``oneflow.full_like(input, fill_value)`` is equivalent to + ``oneflow.full(input.size(), fill_value, dtype=input.dtype, device=input.device)``. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.full_like.html. + + Args: + input(oneflow.Tensor) + fill_value(Scalar): the value to fill the output tensor with. + dtype (oneflow.dtype, optional): the desired data type of returned tensor. + device (oneflow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type + placement (oneflow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.randn(2, 3) + >>> y = flow.full_like(x, 2.0) + >>> y + tensor([[2., 2., 2.], + [2., 2., 2.]], dtype=oneflow.float32) + >>> y = flow.full_like(x, 2, dtype=flow.int32) + >>> y + tensor([[2, 2, 2], + [2, 2, 2]], dtype=oneflow.int32) + >>> placement = flow.placement("cpu", ranks=[0]) + >>> y = flow.full_like(x, 5.0, placement=placement, sbp=flow.sbp.broadcast) # construct global tensor + >>> y.is_global + True + + """ + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(input, None, dtype, device, placement, sbp, requires_grad) + return Full( + new_size, + fill_value, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + )() def new_ones_op( x, size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False ): - if isinstance(device, str): - device = flow.device(device) - if size != None: - size = _single(size) - new_size = size - new_dtype = dtype - new_device = device - new_placement = placement - new_sbp = sbp - new_requires_grad = requires_grad - if size is None: - new_size = x.shape - if dtype is None: - new_dtype = x.dtype - if device is None: - new_device = x.device if x.is_local else None - if placement is None: - new_placement = x.placement if x.is_global else None - if sbp is None: - new_sbp = x.sbp if x.is_global else None - if new_placement is not None: - assert device is None - assert new_sbp is not None - assert isinstance( - new_size, (int, tuple, flow.Size) - ), f"size parameter not correct, please check!" - assert isinstance( - new_dtype, flow.dtype - ), f"dtype parameter not correct, please check!" + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(x, size, dtype, device, placement, sbp, requires_grad) if new_placement is not None: - assert isinstance( - new_placement, flow.placement - ), f"device parameter not correct, please check!" - assert isinstance( - new_sbp, flow.sbp.sbp - ), f"device parameter not correct, please check!" - else: - assert isinstance( - new_device, (str, flow.device) - ), f"device parameter not correct, please check!" - assert isinstance( - new_requires_grad, bool - ), f"requires_grad parameter not correct, please check!" - if placement is not None: res = flow._C.global_constant( new_size, 1.0, dtype=new_dtype, placement=placement, sbp=sbp ) @@ -311,61 +500,55 @@ def new_ones_op( def new_zeros_op( x, size=None, dtype=None, device=None, placement=None, sbp=None, requires_grad=False ): - if isinstance(device, str): - device = flow.device(device) - if size is None or len(size) == 0: - new_size = x.shape - else: - new_size = _handle_size_arg(size) - new_dtype = dtype - new_device = device - new_placement = placement - new_sbp = sbp - new_requires_grad = requires_grad - - if dtype is None: - new_dtype = x.dtype - if device is None: - new_device = x.device if x.is_local else None - if placement is None: - new_placement = x.placement if x.is_global else None - if sbp is None: - new_sbp = x.sbp if x.is_global else None - if new_placement is not None: - assert ( - device is None - ), "argument 'device' must be None when argument 'placement' exist" - assert ( - new_sbp is not None - ), "argument 'sbp' must not be None when argument 'placement' exist" - assert isinstance( - new_size, (int, tuple, list, flow.Size) - ), f"argument 'size' must be tuple of ints, not %s" % (type(new_size)) - assert isinstance( - new_dtype, flow.dtype - ), f"argument 'dtype' must be flow.dtype, not %s" % (type(new_dtype)) + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(x, size, dtype, device, placement, sbp, requires_grad) if new_placement is not None: - assert isinstance( - new_placement, flow.placement - ), f"argument 'placement' must be flow.placement, not %s" % ( - type(new_placement) + res = flow._C.global_constant( + new_size, 0.0, dtype=new_dtype, placement=new_placement, sbp=new_sbp ) - assert isinstance( - new_sbp, (flow.sbp.sbp, tuple) - ), f"argument 'sbp' must be flow.sbp.sbp, not %s" % (type(new_sbp)) else: - assert isinstance( - new_device, (str, flow.device) - ), f"argument 'device' must be flow.device, not %s" % (type(new_device)) - assert isinstance( - new_requires_grad, bool - ), f"argument 'requires_grad' must be bool, not %s" % (type(new_requires_grad)) + res = flow._C.constant(new_size, 0.0, dtype=new_dtype, device=new_device) + res.requires_grad = new_requires_grad + return res + + +def new_full_op( + x, + size, + fill_value, + dtype=None, + device=None, + placement=None, + sbp=None, + requires_grad=False, +): + size = _handle_size_arg(size) + ( + new_size, + new_dtype, + new_device, + new_placement, + new_sbp, + new_requires_grad, + ) = _handle_meta_args(x, size, dtype, device, placement, sbp, requires_grad) + if flow.is_tensor(fill_value): + assert ( + len(fill_value.size()) == 0 + ), "new_full(): argument 'fill_value' must be Number, not Tensor" + fill_value = fill_value.item() + if new_placement is not None: res = flow._C.global_constant( - new_size, 0.0, dtype=new_dtype, placement=new_placement, sbp=new_sbp + new_size, fill_value, dtype=new_dtype, placement=new_placement, sbp=new_sbp ) else: - res = flow._C.constant(new_size, 0.0, dtype=new_dtype, device=new_device) + res = flow._C.constant(new_size, fill_value, dtype=new_dtype, device=new_device) res.requires_grad = new_requires_grad return res diff --git a/python/oneflow/nn/modules/container.py b/python/oneflow/nn/modules/container.py index 400eb2a67c6712a1708ae9f07dc9c28c11c25ce4..54186368b801b3b17f1638e92e7eade4be152ece 100644 --- a/python/oneflow/nn/modules/container.py +++ b/python/oneflow/nn/modules/container.py @@ -14,11 +14,15 @@ See the License for the specific language governing permissions and limitations under the License. """ from oneflow.nn.utils.container import * -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Sequential(get_seq(Module)): """A sequential container. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Sequential.html?#torch.nn.Sequential. + Modules will be added to it in the order they are passed in the constructor. Alternatively, an ordered dict of modules can also be passed in. @@ -54,18 +58,212 @@ class Sequential(get_seq(Module)): class ModuleList(get_list(Module)): + """Holds submodules in a list. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ModuleList.html?#torch.nn.ModuleList. + + :class:`~oneflow.nn.ModuleList` can be indexed like a regular Python list, but + modules it contains are properly registered, and will be visible by all + :class:`~oneflow.nn.Module` methods. + + Args: + modules (iterable, optional): an iterable of modules to add + + .. code-block:: python + + >>> import oneflow.nn as nn + + >>> class MyModule(nn.Module): + ... def __init__(self): + ... super(MyModule, self).__init__() + ... self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)]) + ... def forward(self, x): + ... # ModuleList can act as an iterable, or be indexed using ints + ... for i, l in enumerate(self.linears): + ... x = self.linears[i // 2](x) + l(x) + ... return x + + >>> model = MyModule() + >>> model.linears + ModuleList( + (0): Linear(in_features=10, out_features=10, bias=True) + (1): Linear(in_features=10, out_features=10, bias=True) + (2): Linear(in_features=10, out_features=10, bias=True) + (3): Linear(in_features=10, out_features=10, bias=True) + (4): Linear(in_features=10, out_features=10, bias=True) + (5): Linear(in_features=10, out_features=10, bias=True) + (6): Linear(in_features=10, out_features=10, bias=True) + (7): Linear(in_features=10, out_features=10, bias=True) + (8): Linear(in_features=10, out_features=10, bias=True) + (9): Linear(in_features=10, out_features=10, bias=True) + ) + + + """ + pass class ModuleDict(get_dict(Module)): + """Holds submodules in a dictionary. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ModuleDict.html?#torch.nn.ModuleDict. + + :class:`~oneflow.nn.ModuleDict` can be indexed like a regular Python dictionary, + but modules it contains are properly registered, and will be visible by all + :class:`~oneflow.nn.Module` methods. + + :class:`~oneflow.nn.ModuleDict` is an **ordered** dictionary that respects + + * the order of insertion, and + + * in :meth:`~oneflow.nn.ModuleDict.update`, the order of the merged + ``OrderedDict``, ``dict`` (started from Python 3.6) or another + :class:`~oneflow.nn.ModuleDict` (the argument to + :meth:`~oneflow.nn.ModuleDict.update`). + + Note that :meth:`~oneflow.nn.ModuleDict.update` with other unordered mapping + types (e.g., Python's plain ``dict`` before Python version 3.6) does not + preserve the order of the merged mapping. + + Args: + modules (iterable, optional): a mapping (dictionary) of (string: module) + or an iterable of key-value pairs of type (string, module) + + .. code-block:: python + + >>> import oneflow.nn as nn + + >>> class MyModule(nn.Module): + ... def __init__(self): + ... super(MyModule, self).__init__() + ... self.choices = nn.ModuleDict({ + ... 'conv': nn.Conv2d(10, 10, 3), + ... 'pool': nn.MaxPool2d(3) + ... }) + ... self.activations = nn.ModuleDict([ + ... ['lrelu', nn.LeakyReLU()], + ... ['prelu', nn.PReLU()] + ... ]) + + ... def forward(self, x, choice, act): + ... x = self.choices[choice](x) + ... x = self.activations[act](x) + ... return x + + >>> model = MyModule() + >>> model.choices + ModuleDict( + (conv): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1)) + (pool): MaxPool2d(kernel_size=(3, 3), stride=(3, 3), padding=(0, 0), dilation=(1, 1)) + ) + """ + pass class ParameterList(get_para_list(Module)): + """Holds parameters in a list. + + :class:`~oneflow.nn.ParameterList` can be indexed like a regular Python + list, but parameters it contains are properly registered, and will be + visible by all :class:`~oneflow.nn.Module` methods. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ParameterList.html?#torch.nn.ParameterList. + + Args: + parameters (iterable, optional): an iterable of :class:`~oneflow.nn.Parameter` to add + + .. code-block:: python + + >>> import oneflow as flow + >>> import oneflow.nn as nn + + >>> class MyModule(nn.Module): + ... def __init__(self): + ... super(MyModule, self).__init__() + ... self.params = nn.ParameterList([nn.Parameter(flow.randn(10, 10)) for i in range(10)]) + ... + ... def forward(self, x): + ... # ParameterList can act as an iterable, or be indexed using ints + ... for i, p in enumerate(self.params): + ... x = self.params[i // 2].mm(x) + p.mm(x) + ... return x + + >>> model = MyModule() + >>> model.params + ParameterList( + (0): Parameter containing: [ of size 10x10] + (1): Parameter containing: [ of size 10x10] + (2): Parameter containing: [ of size 10x10] + (3): Parameter containing: [ of size 10x10] + (4): Parameter containing: [ of size 10x10] + (5): Parameter containing: [ of size 10x10] + (6): Parameter containing: [ of size 10x10] + (7): Parameter containing: [ of size 10x10] + (8): Parameter containing: [ of size 10x10] + (9): Parameter containing: [ of size 10x10] + ) + """ + pass class ParameterDict(get_para_dict(Module)): + """ + Holds parameters in a dictionary. + + ParameterDict can be indexed like a regular Python dictionary, but parameters it + contains are properly registered, and will be visible by all Module methods. + + :class:`~oneflow.nn.ParameterDict` is an **ordered** dictionary that respects + + * the order of insertion, and + + * in :meth:`~oneflow.nn.ParameterDict.update`, the order of the merged ``OrderedDict`` + or another :class:`~oneflow.nn.ParameterDict` (the argument to + :meth:`~oneflow.nn.ParameterDict.update`). + + Note that :meth:`~oneflow.nn.ParameterDict.update` with other unordered mapping + types (e.g., Python's plain ``dict``) does not preserve the order of the + merged mapping. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ParameterDict.html?#torch.nn.ParameterDict. + + Args: + parameters (iterable, optional): a mapping (dictionary) of + (string : :class:`~oneflow.nn.Parameter`) or an iterable of key-value pairs + of type (string, :class:`~oneflow.nn.Parameter`) + + .. code-block:: python + + >>> import oneflow as flow + >>> import oneflow.nn as nn + + >>> class MyModule(nn.Module): + ... def __init__(self): + ... super(MyModule, self).__init__() + ... self.params = nn.ParameterDict({ + ... 'left': nn.Parameter(flow.randn(5, 10)), + ... 'right': nn.Parameter(flow.randn(5, 10)) + ... }) + ... + ... def forward(self, x, choice): + ... x = self.params[choice].mm(x) + ... return x + + >>> model = MyModule() + >>> model.params + ParameterDict( + (left): Parameter containing: [ of size 5x10] + (right): Parameter containing: [ of size 5x10] + ) + """ + pass diff --git a/python/oneflow/nn/modules/conv.py b/python/oneflow/nn/modules/conv.py index e8453d6eff18051ea82932f18ce65ee7a61dca84..c07a9ee699dcfc4550ef9d5a4223c3d34d02bf85 100644 --- a/python/oneflow/nn/modules/conv.py +++ b/python/oneflow/nn/modules/conv.py @@ -19,7 +19,7 @@ import os import oneflow as flow from oneflow.nn import init from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.utils import _pair, _single, _triple from typing import Union @@ -95,12 +95,12 @@ def get_padding(padding, kernel_size, dilation, stride): class Conv1d(Module): - """The interface is consistent with PyTorch. + """Applies a 1D convolution over an input signal composed of several input + planes. + + The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Conv1d.html. - Applies a 1D convolution over an input signal composed of several input - planes. - In the simplest case, the output value of the layer with input size :math:`(N, C_{\\text{in}}, L)` and output :math:`(N, C_{\\text{out}}, L_{\\text{out}})` can be precisely described as: @@ -230,11 +230,11 @@ class Conv1d(Module): bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) - def forward(self, x): + def _conv_forward(self, x, weight, bias): return flow._C.conv1d( x, - self.weight, - self.bias, + weight, + bias, stride=self.stride, padding=self.padding, dilation=self.dilation, @@ -242,6 +242,9 @@ class Conv1d(Module): channel_pos=self.channel_pos, ) + def forward(self, x): + return self._conv_forward(x, self.weight, self.bias) + def extra_repr(self): s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}" if self.padding != (0,) * len(self.padding): @@ -258,11 +261,10 @@ class Conv1d(Module): class Conv2d(Module): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Conv2d.html. - - Applies a 2D convolution over an input signal composed of several input + """Applies a 2D convolution over an input signal composed of several input planes. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Conv2d.html. In the simplest case, the output value of the layer with input size :math:`(N, C_{\\text{in}}, H, W)` and output :math:`(N, C_{\\text{out}}, H_{\\text{out}}, W_{\\text{out}})` @@ -430,7 +432,7 @@ class Conv2d(Module): bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) - def forward(self, x): + def _conv_forward(self, x, weight, bias): if self.channel_pos == "channels_first": in_channel_axis = 1 else: @@ -441,8 +443,8 @@ class Conv2d(Module): ) return flow._C.conv2d( x, - self.weight, - self.bias, + weight, + bias, stride=self.stride, padding=self.padding, dilation=self.dilation, @@ -450,6 +452,9 @@ class Conv2d(Module): channel_pos=self.channel_pos, ) + def forward(self, x): + return self._conv_forward(x, self.weight, self.bias) + def extra_repr(self): s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}" if self.padding != (0,) * len(self.padding): @@ -466,11 +471,11 @@ class Conv2d(Module): class Conv3d(Module): - r"""The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Conv3d.html. - - Applies a 3D convolution over an input signal composed of several input + r"""Applies a 3D convolution over an input signal composed of several input planes. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Conv3d.html. In the simplest case, the output value of the layer with input size :math:`(N, C_{in}, D, H, W)` and output :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` can be precisely described as: @@ -606,13 +611,13 @@ class Conv3d(Module): bound = 1 / math.sqrt(fan_in) init.uniform_(self.bias, -bound, bound) - def forward(self, x): + def _conv_forward(self, x, weight, bias): if x.shape[1] != self.in_channels: raise ValueError("The input channels should be equal to self.in_channels") return flow._C.conv3d( x, - self.weight, - self.bias, + weight, + bias, stride=self.stride, padding=self.padding, dilation=self.dilation, @@ -620,6 +625,9 @@ class Conv3d(Module): channel_pos=self.channel_pos, ) + def forward(self, x): + return self._conv_forward(x, self.weight, self.bias) + def extra_repr(self): s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}" if self.padding != (0,) * len(self.padding): diff --git a/python/oneflow/nn/modules/dataset.py b/python/oneflow/nn/modules/dataset.py index 15b3805e000f3fef71bb1af8afcfe7149cd66bf6..243d3319bc878baebc5a7f2c100d5ae8e2a147f8 100644 --- a/python/oneflow/nn/modules/dataset.py +++ b/python/oneflow/nn/modules/dataset.py @@ -25,12 +25,12 @@ import oneflow._oneflow_internal._C as _C from oneflow.framework.tensor import Tensor from oneflow.framework.scope_util import current_scope from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t, _size_any_t -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.utils import _pair, _reverse_repeat_tuple, _single, _triple import oneflow.framework.id_util as id_util -def mirrored_gen_random_seed(seed=None): +def local_gen_random_seed(seed=None): if seed is None: seed = -1 has_seed = False @@ -88,7 +88,7 @@ class OFRecordReader(Module): self.sbp = sbp - (self.seed, self.has_seed) = mirrored_gen_random_seed(random_seed) + (self.seed, self.has_seed) = local_gen_random_seed(random_seed) self._op = flow.stateful_op("OFRecordReader").Output("out").Build() def forward(self): @@ -166,6 +166,36 @@ class OFRecordRawDecoder(Module): class CoinFlip(Module): + r""" + CoinFlip(batch_size=1, random_seed=None, probability=0.5, device=None, placement=None, sbp=None) + + Generates random boolean values following a bernoulli distribution. + + The probability of generating a value 1 (true) is determined by the ``probability`` argument. + + The shape of the generated data can be either specified explicitly with a ``shape`` argument, + or chosen to match the shape of the input, if provided. If none are present, a single value per + sample is generated. + + The documentation is referenced from: + https://docs.nvidia.com/deeplearning/dali/user-guide/docs/supported_ops_legacy.html#nvidia.dali.ops.CoinFlip. + + Args: + batch_size (int, optional): Maximum batch size of the pipeline. Negative values for this parameter + are invalid - the default value may only be used with serialized pipeline (the value stored in + serialized pipeline is used instead). In most cases, the actual batch size of the pipeline will be + equal to the maximum one. Default: 1 + random_seed (int, optional): Random seed. Default: None + probability (float, optional): Probability of value 1. Default: 0.5 + device (oneflow.device, optional): Desired device of returned tensor. Default: if None, uses the + current device for the default tensor type. + placement (oneflow.placement, optional): Desired placement of returned global tensor. + Default: if None, the returned tensor is local one using the argument `device`. + sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional): Desired sbp descriptor of returned + global tensor. Default: if None, the returned tensor is local one using the argument `device`. + + """ + def __init__( self, batch_size: int = 1, @@ -204,7 +234,7 @@ class CoinFlip(Module): self.sbp = sbp - (self.seed, self.has_seed) = mirrored_gen_random_seed(random_seed) + (self.seed, self.has_seed) = local_gen_random_seed(random_seed) self._op = flow.stateful_op("coin_flip").Output("out").Build() @@ -232,6 +262,41 @@ class CoinFlip(Module): class CropMirrorNormalize(Module): + r""" + CropMirrorNormalize(color_space="BGR", output_layout="NCHW", crop_h=0, crop_w=0, crop_pos_y=0.5, crop_pos_x=0.5, mean= [0.0], std= [1.0], output_dtype=oneflow.float) + + Performs fused cropping, normalization, format conversion + (NHWC to NCHW) if desired, and type casting. + + Normalization takes the input images and produces the output by using the following formula: + + .. math:: + output = (input - mean) / std + + .. note:: + If no cropping arguments are specified, only mirroring and normalization will occur. + + This operator allows sequence inputs and supports volumetric data. + + The documentation is referenced from: + https://docs.nvidia.com/deeplearning/dali/user-guide/docs/supported_ops_legacy.html#nvidia.dali.ops.CropMirrorNormalize. + + Args: + color_space (str, optional): The color space of the input image. Default: "BGR" + output_layout (str, optional): Tensor data layout for the output. Default: "NCHW" + crop_h (int, optional): Cropping the window height (in pixels). Default: 0 + crop_w (int, optional): Cropping window width (in pixels). Default: 0 + crop_pos_y (float, optional): Normalized (0.0 - 1.0) vertical position of the start of the cropping + window (typically, the upper left corner). Default: 0.5 + crop_pos_x (float, optional): Normalized (0.0 - 1.0) horizontal position of the cropping window + (upper left corner). Default: 0.5 + mean (float or list of float, optional): Mean pixel values for image normalization. Default: [0.0], + std (float or list of float, optional): Standard deviation values for image normalization. + Default: [1.0] + output_dtype (oneflow.dtype, optional): Output data type. Default: ``oneflow.float`` + + """ + def __init__( self, color_space: str = "BGR", @@ -376,7 +441,7 @@ class OFRecordImageDecoderRandomCrop(Module): self.num_attempts = num_attempts self.random_area = random_area self.random_aspect_ratio = random_aspect_ratio - (self.seed, self.has_seed) = mirrored_gen_random_seed(random_seed) + (self.seed, self.has_seed) = local_gen_random_seed(random_seed) self._op = ( flow.stateful_op("ofrecord_image_decoder_random_crop") .Input("in") @@ -990,7 +1055,7 @@ class OneRecReader(Module): batch_size=10, shuffle=True, shuffle_mode="batch", - placement=flow.env.all_device_placement("cpu") , + placement=flow.placement.all("cpu") , sbp=[flow.sbp.split(0)], ) readdata_2 = onerec_reader2() @@ -1142,6 +1207,63 @@ class GPTIndexedBinDataReader(Module): return output +class RawReader(Module): + def __init__( + self, + files: List[str], + shape: Sequence[int], + dtype: flow.dtype, + batch_size: int, + random_shuffle: bool = True, + shuffle_block_size: int = 0, + random_seed: Optional[int] = None, + placement: flow.placement = None, + sbp: Union[flow.sbp.sbp, List[flow.sbp.sbp]] = None, + ): + + super().__init__() + + _handle_shuffle_args(self, random_shuffle, random_seed) + _handle_distributed_args(self, None, placement, sbp) + + self.files = files + self.shape = shape + self.dtype = dtype + self.batch_size = batch_size + self.shuffle_block_size = shuffle_block_size + + self.op = flow.stateful_op("raw_reader").Output("out").Build() + + def forward(self): + if self.placement is None: + + output = _C.dispatch_raw_reader( + self.op, + files=self.files, + shape=self.shape, + data_type=self.dtype, + batch_size=self.batch_size, + random_shuffle=self.shuffle, + shuffle_block_size=self.shuffle_block_size, + random_seed=self.random_seed, + device=self.device, + ) + else: + output = _C.dispatch_raw_reader( + self.op, + files=self.files, + shape=self.shape, + data_type=self.dtype, + batch_size=self.batch_size, + random_shuffle=self.shuffle, + shuffle_block_size=self.shuffle_block_size, + random_seed=self.random_seed, + placement=self.placement, + sbp=self.sbp, + ) + return output + + def _handle_distributed_args(module, device, placement, sbp): module.placement = placement if placement is None: diff --git a/python/oneflow/nn/modules/distance.py b/python/oneflow/nn/modules/distance.py index 8962790397069ab4f49bf4e70d2482fa63b37f76..42719d28c1dd1a23e128882a75d0aa14bf1e384f 100644 --- a/python/oneflow/nn/modules/distance.py +++ b/python/oneflow/nn/modules/distance.py @@ -15,20 +15,21 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from typing import Optional class CosineSimilarity(Module): - r"""The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity - + r""" Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along `dim`. .. math :: \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.CosineSimilarity.html#torch.nn.CosineSimilarity + Args: dim (int, optional): Dimension where cosine similarity is computed. Default: 1 eps (float, optional): Small value to avoid division by zero. @@ -60,6 +61,57 @@ class CosineSimilarity(Module): return flow._C.cosine_similarity(x1, x2, self.dim, self.eps) +class PairwiseDistance(Module): + r"""Computes the pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm: + + .. math :: + \left \| x \right \| _p = (\sum_{i=1}^n \left | x_i \right |^p )^{\frac{1}{p}} + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.PairwiseDistance.html. + + Args: + p (real): the norm degree. Default: 2 + eps (float, optional): Small value to avoid division by zero. Default: 1e-6 + keepdim (bool, optional): Determines whether or not to keep the vector dimension. Default: False + + Shape: + - Input1: :math:`(N, D)` or :math:`(D)`, where N = batch dimension and D = vector dimension + - Input2: :math:`(N, D)` or :math:`(D)`, same shape as the input1 + - Output: :math:`(N)` or :math:`()` based on input dimension. If keepdim is True, then :math:`(N, 1)` or :math:`(1)` based on input dimension. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> pdist = flow.nn.PairwiseDistance(p=2) + >>> x1 = flow.arange(12).reshape(3, 4) + >>> x2 = flow.arange(12).reshape(3, 4) + >>> pdist(x1, x2) + tensor([2.0000e-06, 2.0000e-06, 2.0000e-06], dtype=oneflow.float32) + >>> pdist(x1, x2).shape + oneflow.Size([3]) + + """ + + def __init__( + self, + p: Optional[float] = 2.0, + eps: Optional[float] = 1e-06, + keepdim: Optional[bool] = False, + ) -> None: + super().__init__() + self.p = p + self.eps = eps + self.keepdim = keepdim + + def forward(self, x1: Tensor, x2: Tensor) -> Tensor: + return flow._C.pairwise_distance( + x1, x2, p=self.p, eps=self.eps, keepdim=self.keepdim + ) + + if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/distributed_partial_fc_sample.py b/python/oneflow/nn/modules/distributed_partial_fc_sample.py index 30670e4e34e3858f5b8bf33988acdb40ac3c1961..2f82286e2d0bf4fcb1a850f8d13e1e8e4f0925dd 100644 --- a/python/oneflow/nn/modules/distributed_partial_fc_sample.py +++ b/python/oneflow/nn/modules/distributed_partial_fc_sample.py @@ -16,7 +16,7 @@ limitations under the License. import warnings import oneflow as flow import oneflow._oneflow_internal._C as _C -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class DistributedPariticalFCSample(Module): diff --git a/python/oneflow/nn/modules/dropout.py b/python/oneflow/nn/modules/dropout.py index ed4883af25feccf80e523c3c38895bc730fa1d11..0b49f5ad9c5088f33faed5727c66a877509b1991 100644 --- a/python/oneflow/nn/modules/dropout.py +++ b/python/oneflow/nn/modules/dropout.py @@ -18,7 +18,7 @@ import sys import oneflow as flow import oneflow.framework.id_util as id_util -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class _DropoutNd(Module): @@ -40,80 +40,6 @@ class _DropoutNd(Module): class Dropout(_DropoutNd): - """During training, randomly zeroes some of the elements of the input - tensor with probability :attr:`p` using samples from a Bernoulli - distribution. Each channel will be zeroed out independently on every forward - call. - - This has proven to be an effective technique for regularization and - preventing the co-adaptation of neurons as described in the paper - "Improving neural networks by preventing co-adaptation of feature - detectors". - - Furthermore, the outputs are scaled by a factor of :math:`\\frac{1}{1-p}` during - training. This means that during evaluation the module simply computes an - identity function. - - Additionally, we can pass an extra Tensor `addend` which shape is consistent with input Tensor. - The `addend` Tensor will be add in result after dropout, it is very useful in model's residual connection structure. - - Args: - p: probability of an element to be zeroed. Default: 0.5 - inplace: If set to ``True``, will do this operation in-place. Default: ``False`` - generator: A pseudorandom number generator for sampling - - Shape: - - Input: :math:`(*)`. Input can be of any shape - - Output: :math:`(*)`. Output is of the same shape as input - - For example: - - example 1: - - .. code-block:: python - - >>> import numpy as np - >>> import oneflow as flow - - >>> m = flow.nn.Dropout(p=0) - >>> arr = np.array( - ... [ - ... [-0.7797, 0.2264, 0.2458, 0.4163], - ... [0.4299, 0.3626, -0.4892, 0.4141], - ... [-1.4115, 1.2183, -0.5503, 0.6520], - ... ] - ... ) - >>> x = flow.Tensor(arr) - >>> y = m(x) - >>> y #doctest: +ELLIPSIS - tensor([[-0.7797, 0.2264, 0.2458, 0.4163], - [ 0.4299, 0.3626, -0.4892, 0.4141], - [-1.4115, 1.2183, -0.5503, 0.6520]], dtype=oneflow.float32) - - example 2: - - .. code-block:: python - - >>> import numpy as np - >>> import oneflow as flow - - >>> m = flow.nn.Dropout(p=0) - >>> arr = np.array( - ... [ - ... [-0.7797, 0.2264, 0.2458, 0.4163], - ... [0.4299, 0.3626, -0.4892, 0.4141], - ... [-1.4115, 1.2183, -0.5503, 0.6520], - ... ] - ... ) - >>> x = flow.Tensor(arr) - >>> addend = flow.ones((3, 4), dtype=flow.float32) - >>> y = m(x, addend=addend) - >>> y #doctest: +ELLIPSIS - tensor([[ 0.2203, 1.2264, 1.2458, 1.4163], - [ 1.4299, 1.3626, 0.5108, 1.4141], - [-0.4115, 2.2183, 0.4497, 1.6520]], dtype=oneflow.float32) - """ - def __init__(self, p: float = 0.5, inplace: bool = False, generator=None): _DropoutNd.__init__(self, p, inplace) self.p = p @@ -132,6 +58,21 @@ class Dropout(_DropoutNd): ) +class Dropout1d(Dropout): + def forward(self, x, addend=None): + return flow._C.dropout1d(x, self.p, self.training) + + +class Dropout2d(Dropout): + def forward(self, x, addend=None): + return flow._C.dropout2d(x, self.p, self.training) + + +class Dropout3d(Dropout): + def forward(self, x, addend=None): + return flow._C.dropout3d(x, self.p, self.training) + + if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/empty.py b/python/oneflow/nn/modules/empty.py index a8f8b0c4fd1fc9c08aa448b13c50de756574f441..7338ff7f54443845b66119f86335bfb13f6bc560 100644 --- a/python/oneflow/nn/modules/empty.py +++ b/python/oneflow/nn/modules/empty.py @@ -32,48 +32,13 @@ def empty_op( requires_grad: bool = False, pin_memory: bool = False, ): - """ - Returns a tensor filled with uninitialized data. - The shape of the tensor is defined by the variable argument ``size``. - - Args: - size (int... or oneflow.Size): Defining the shape of the output tensor. - Can be a variable number of arguments or a collection like a list or tuple or oneflow.Size. - dtype (flow.dtype, optional): The desired data type of returned tensor. Default: ``flow.float32``. - device (oneflow.device, optional): The desired device of returned local tensor. If None, uses the - current device. - placement (flow.placement, optional): The desired device of returned global tensor. If None, will - construct local tensor. - sbp (flow.sbp or List[flow.sbp], optional): The desired sbp of returned global tensor. - requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. - pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False. - - For example: - - .. code-block:: python - - >>> import oneflow as flow - >>> y = flow.empty(4, 5) # construct local empty tensor - >>> y.shape - oneflow.Size([4, 5]) - >>> y.is_global - False - >>> placement = flow.placement("cpu", ranks=[0]) - >>> y = flow.empty(4, 5, placement=placement, sbp=flow.sbp.broadcast) # construct consistent empty tensor - >>> y.is_global - True - - """ assert size is not None, "shape must not be None" shape = _single(_handle_size_arg(size)) if dtype is None: - dtype = flow.float32 - if placement is None: - if device is None: - device = flow.device("cpu") - else: + dtype = flow.get_default_dtype() + if placement is not None: assert ( device is None ), "argument 'device' must be None when argument 'placement' exist" @@ -105,6 +70,33 @@ def empty_op( return tensor +def empty_like_op( + input, + dtype: Optional[flow.dtype] = None, + device: Union[flow.device, str, None] = None, + placement: flow.placement = None, + sbp: flow._oneflow_internal.sbp.sbp = None, + requires_grad: bool = False, +): + new_size = _single(_handle_size_arg(input.size())) + if placement is None and input.is_global and input.placement is not None: + placement = input.placement + if sbp is None and input.is_global and input.sbp is not None: + sbp = input.sbp + if dtype is None: + dtype = input.dtype + if placement is None and device is None: + device = input.device + return empty_op( + new_size, + dtype=dtype, + device=device, + placement=placement, + sbp=sbp, + requires_grad=requires_grad, + ) + + def new_empty_op( x, size, dtype=None, device=None, placement=None, sbp=None, requires_grad=False ): diff --git a/python/oneflow/nn/modules/fake_quantization.py b/python/oneflow/nn/modules/fake_quantization.py index e0f5395fd0f7793cfed06f6e385bfac1912e4227..0cda1bb6abbb0129ab58726501d9805da957e1ac 100644 --- a/python/oneflow/nn/modules/fake_quantization.py +++ b/python/oneflow/nn/modules/fake_quantization.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class FakeQuantization(Module): diff --git a/python/oneflow/nn/modules/flatten.py b/python/oneflow/nn/modules/flatten.py index 3ba6b43b6349b8123008ff687745aade4b64e803..4d634578f416df0ab8639d86484f384a77174ef6 100644 --- a/python/oneflow/nn/modules/flatten.py +++ b/python/oneflow/nn/modules/flatten.py @@ -15,7 +15,7 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Flatten(Module): diff --git a/python/oneflow/nn/modules/flip.py b/python/oneflow/nn/modules/flip.py deleted file mode 100644 index a7634a1164ba98359848a202d8600a1683e61779..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/modules/flip.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import oneflow as flow -from oneflow.framework.tensor import register_tensor_op - - -def flip_op(input, dims): - assert isinstance(dims, (int, list, tuple)), f"dims must be int, list or tuple" - if isinstance(dims, int): - dims = [dims] - - input_len = len(input.shape) - assert len(dims) <= input_len, f"len of dims must less than len of input tensor" - new_dims = [] - for i in dims: - if i < 0: - i += input_len - assert ( - i < input_len - ), f"IndexError: Dimension out of range (expected to be in range of {input_len}, but got {i})" - new_dims.append(i) - return flow._C.flip(input, new_dims) - - -if __name__ == "__main__": - import doctest - - doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/fold.py b/python/oneflow/nn/modules/fold.py index 738dd7a0801600a4b70239a52bafa4c0a8db42f5..dce0b465ace3b34dd15fb854faa4d4050aa4fcbc 100644 --- a/python/oneflow/nn/modules/fold.py +++ b/python/oneflow/nn/modules/fold.py @@ -15,11 +15,128 @@ limitations under the License. """ import oneflow as flow from oneflow.nn.common_types import _size_2_t -from oneflow.nn.module import Module -from oneflow.nn.modules.utils import _pair +from oneflow.nn.modules.module import Module class Fold(Module): + r""" + Fold(output_size, kernel_size, dilation=1, padding=0, stride=1) + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Fold.html. + + Combines an array of sliding local blocks into a large containing + tensor, it also called `col2img` + + Consider a batched :attr:`input` tensor containing sliding local blocks, + e.g., patches of images, of shape :math:`(N, C \times \prod(\text{kernel_size}), L)`, + where :math:`N` is batch dimension, :math:`C \times \prod(\text{kernel_size})` + is the number of values within a block (a block has :math:`\prod(\text{kernel_size})` + spatial locations each containing a :math:`C`-channeled vector), and + :math:`L` is the total number of blocks. (This is exactly the + same specification as the output shape of :class:`~oneflow.nn.Unfold`.) This + operation combines these local blocks into the large :attr:`output` tensor + of shape :math:`(N, C, \text{output_size}[0], \text{output_size}[1], \dots)` + by summing the overlapping values. Similar to :class:`~oneflow.nn.Unfold`, the + arguments must satisfy + + .. math:: + L = \prod_d \left\lfloor\frac{\text{output_size}[d] + 2 \times \text{padding}[d] % + - \text{dilation}[d] \times (\text{kernel_size}[d] - 1) - 1}{\text{stride}[d]} + 1\right\rfloor, + + where :math:`d` is over all spatial dimensions. + + * :attr:`output_size` describes the spatial shape of the large containing + tensor of the sliding local blocks. It is useful to resolve the ambiguity + when multiple input shapes map to same number of sliding blocks, e.g., + with ``stride > 0``. + + The :attr:`padding`, :attr:`stride` and :attr:`dilation` arguments specify + how the sliding blocks are retrieved. + + * :attr:`stride` controls the stride for the sliding blocks. + + * :attr:`padding` controls the amount of implicit zero-paddings on both + sides for :attr:`padding` number of points for each dimension before + reshaping. + + * :attr:`dilation` controls the spacing between the kernel points; also known as + the à trous algorithm. + + Args: + output_size (int or tuple): the shape of the spatial dimensions of the + output (i.e., ``output.sizes()[2:]``) + kernel_size (int or tuple): the size of the sliding blocks + stride (int or tuple): the stride of the sliding blocks in the input + spatial dimensions. Default: 1 + padding (int or tuple, optional): implicit zero padding to be added on + both sides of input. Default: 0 + dilation (int or tuple, optional): a parameter that controls the + stride of elements within the + neighborhood. Default: 1 + + * If :attr:`output_size`, :attr:`kernel_size`, :attr:`dilation`, + :attr:`padding` or :attr:`stride` is an int or a tuple of length 1 then + their values will be replicated across all spatial dimensions. + + * For the case of two output spatial dimensions this operation is sometimes + called ``col2im``. + + .. note:: + :class:`~oneflow.nn.Fold` calculates each combined value in the resulting + large tensor by summing all values from all containing blocks. + :class:`~oneflow.nn.Unfold` extracts the values in the local blocks by + copying from the large tensor. So, if the blocks overlap, they are not + inverses of each other. + + In general, folding and unfolding operations are related as + follows. Consider :class:`~oneflow.nn.Fold` and + :class:`~oneflow.nn.Unfold` instances created with the same + parameters: + + >>> fold_params = dict(kernel_size=..., dilation=..., padding=..., stride=...) + >>> fold = nn.Fold(output_size=..., **fold_params) + >>> unfold = nn.Unfold(**fold_params) + + Then for any (supported) ``input`` tensor the following + equality holds: + + :: + + fold(unfold(input)) == divisor * input + + where ``divisor`` is a tensor that depends only on the shape + and dtype of the ``input``: + + >>> input_ones = oneflow.ones(input.shape, dtype=input.dtype) + >>> divisor = fold(unfold(input_ones)) + + When the ``divisor`` tensor contains no zero elements, then + ``fold`` and ``unfold`` operations are inverses of each + other (up to constant divisor). + + .. warning:: + Currently, only unbatched (3D) or batched (4D) image-like output tensors are supported. + + Shape: + - Input: :math:`(N, C \times \prod(\text{kernel_size}), L)` or :math:`(C \times \prod(\text{kernel_size}), L)` + - Output: :math:`(N, C, \text{output_size}[0], \text{output_size}[1], \dots)` + or :math:`(C, \text{output_size}[0], \text{output_size}[1], \dots)` as described above + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + + >>> x_tensor = flow.Tensor(np.random.randn(1, 9, 16)) + >>> fold = flow.nn.Fold(output_size=(4, 4), kernel_size=3, padding=1) + >>> out = fold(x_tensor) + >>> out.shape + oneflow.Size([1, 1, 4, 4]) + + """ + def __init__( self, output_size: _size_2_t, @@ -28,62 +145,22 @@ class Fold(Module): padding: _size_2_t = 0, stride: _size_2_t = 1, ) -> None: - r"""Combines an array of sliding local blocks into a large containing - tensor, it also called `col2img`. - - Consider a batched :attr:`input` tensor containing sliding local blocks, - e.g., patches of images, of shape :math:`(N, C \times \prod(\text{kernel\_size}), L)`, - where :math:`N` is batch dimension, :math:`C \times \prod(\text{kernel\_size})` - is the number of values within a block (a block has :math:`\prod(\text{kernel\_size})` - spatial locations each containing a :math:`C`-channeled vector), and - :math:`L` is the total number of blocks. (This is exactly the - same specification as the output shape of :class:`~torch.nn.Unfold`.) This - operation combines these local blocks into the large :attr:`output` tensor - of shape :math:`(N, C, \text{output\_size}[0], \text{output\_size}[1], \dots)` - by summing the overlapping values. Similar to :class:`~torch.nn.Unfold`, the - arguments must satisfy - - .. math:: - L = \prod_d \left\lfloor\frac{\text{output\_size}[d] + 2 \times \text{padding}[d] % - - \text{dilation}[d] \times (\text{kernel\_size}[d] - 1) - 1}{\text{stride}[d]} + 1\right\rfloor, - - Args: - output_size (_size_2_t): The spatial dimension of output tensor. - kernel_size (_size_2_t): The size of kernel. - dilation (_size_2_t, optional): The dilation rate. Defaults to 1. - padding (_size_2_t, optional): The padding value. Defaults to 0. - stride (_size_2_t, optional): The stride of sliding window. Defaults to 1. - - For example: - - .. code-block:: python - - >>> import oneflow as flow - >>> import numpy as np - - >>> x_tensor = flow.Tensor(np.random.randn(1, 9, 16)) - >>> fold = flow.nn.Fold(output_size=(4, 4), kernel_size=3, padding=1) - >>> out = fold(x_tensor) - >>> out.shape - oneflow.Size([1, 1, 4, 4]) - - """ super(Fold, self).__init__() self.output_size = output_size - self.kernel_size = _pair(kernel_size) - self.dilation = _pair(dilation) - self.padding = _pair(padding) - self.stride = _pair(stride) + self.kernel_size = kernel_size + self.dilation = dilation + self.padding = padding + self.stride = stride def forward(self, input): return flow._C.fold( input, - "channels_first", self.output_size, self.kernel_size, self.dilation, self.padding, self.stride, + "channels_first", ) def extra_repr(self) -> str: @@ -96,72 +173,140 @@ class Fold(Module): class Unfold(Module): - def __init__( - self, - kernel_size: _size_2_t, - dilation: _size_2_t = 1, - padding: _size_2_t = 0, - stride: _size_2_t = 1, - ) -> None: - r"""This op extracts elements in a local window from input tensor, it also called `img2col`. + r""" + Unfold(kernel_size, dilation=1, padding=0, stride=1) + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Unfold.html. + + This op extracts elements in a local window from input tensor, it also called `img2col`. + + Consider a batched :attr:`input` tensor of shape :math:`(N, C, *)`, + where :math:`N` is the batch dimension, :math:`C` is the channel dimension, + and :math:`*` represent arbitrary spatial dimensions. This operation flattens + each sliding :attr:`kernel_size`-sized block within the spatial dimensions + of :attr:`input` into a column (i.e., last dimension) of a 3-D :attr:`output` + tensor of shape :math:`(N, C \times \prod(\text{kernel_size}), L)`, where + :math:`C \times \prod(\text{kernel_size})` is the total number of values + within each block (a block has :math:`\prod(\text{kernel_size})` spatial + locations each containing a :math:`C`-channeled vector), and :math:`L` is + the total number of such blocks: + + .. math:: + L = \prod_d \left\lfloor\frac{\text{spatial_size}[d] + 2 \times \text{padding}[d] % + - \text{dilation}[d] \times (\text{kernel_size}[d] - 1) - 1}{\text{stride}[d]} + 1\right\rfloor, + + where :math:`\text{spatial_size}` is formed by the spatial dimensions + of :attr:`input` (:math:`*` above), and :math:`d` is over all spatial + dimensions. + + Therefore, indexing :attr:`output` at the last dimension (column dimension) + gives all values within a certain block. + + The :attr:`padding`, :attr:`stride` and :attr:`dilation` arguments specify + how the sliding blocks are retrieved. + + * :attr:`stride` controls the stride for the sliding blocks. + + * :attr:`padding` controls the amount of implicit zero-paddings on both + sides for :attr:`padding` number of points for each dimension before + reshaping. + + * :attr:`dilation` controls the spacing between the kernel points; also known as + the à trous algorithm. + + Args: + kernel_size (int or tuple): the size of the sliding blocks + stride (int or tuple, optional): the stride of the sliding blocks in the input + spatial dimensions. Default: 1 + padding (int or tuple, optional): implicit zero padding to be added on + both sides of input. Default: 0 + dilation (int or tuple, optional): a parameter that controls the + stride of elements within the + neighborhood. Default: 1 + + * If :attr:`kernel_size`, :attr:`dilation`, :attr:`padding` or + :attr:`stride` is an int or a tuple of length 1, their values will be + replicated across all spatial dimensions. + + * For the case of two input spatial dimensions this operation is sometimes + called ``im2col``. - Consider a batched :attr:`input` tensor of shape :math:`(N, C, *)`, - where :math:`N` is the batch dimension, :math:`C` is the channel dimension, - and :math:`*` represent arbitrary spatial dimensions. This operation flattens - each sliding :attr:`kernel_size`-sized block within the spatial dimensions - of :attr:`input` into a column (i.e., last dimension) of a 3-D :attr:`output` - tensor of shape :math:`(N, C \times \prod(\text{kernel\_size}), L)`, where - :math:`C \times \prod(\text{kernel\_size})` is the total number of values - within each block (a block has :math:`\prod(\text{kernel\_size})` spatial - locations each containing a :math:`C`-channeled vector), and :math:`L` is - the total number of such blocks: + .. note:: + :class:`~oneflow.nn.Fold` calculates each combined value in the resulting + large tensor by summing all values from all containing blocks. + :class:`~oneflow.nn.Unfold` extracts the values in the local blocks by + copying from the large tensor. So, if the blocks overlap, they are not + inverses of each other. - .. math:: - L = \prod_d \left\lfloor\frac{\text{spatial\_size}[d] + 2 \times \text{padding}[d] % - - \text{dilation}[d] \times (\text{kernel\_size}[d] - 1) - 1}{\text{stride}[d]} + 1\right\rfloor, + In general, folding and unfolding operations are related as + follows. Consider :class:`~oneflow.nn.Fold` and + :class:`~oneflow.nn.Unfold` instances created with the same + parameters: - where :math:`\text{spatial\_size}` is formed by the spatial dimensions - of :attr:`input` (:math:`*` above), and :math:`d` is over all spatial - dimensions. + >>> fold_params = dict(kernel_size=..., dilation=..., padding=..., stride=...) + >>> fold = nn.Fold(output_size=..., **fold_params) + >>> unfold = nn.Unfold(**fold_params) - Therefore, indexing :attr:`output` at the last dimension (column dimension) - gives all values within a certain block. + Then for any (supported) ``input`` tensor the following + equality holds: + :: + fold(unfold(input)) == divisor * input - Args: - kernel_size (_size_2_t): The size of kernel. - dilation (_size_2_t, optional): The dilation rate. Defaults to 1. - padding (_size_2_t, optional): The padding value. Defaults to 0. - stride (_size_2_t, optional): The stride of sliding window. Defaults to 1. + where ``divisor`` is a tensor that depends only on the shape + and dtype of the ``input``: - For example: + >>> input_ones = oneflow.ones(input.shape, dtype=input.dtype) + >>> divisor = fold(unfold(input_ones)) - .. code-block:: python + When the ``divisor`` tensor contains no zero elements, then + ``fold`` and ``unfold`` operations are inverses of each + other (up to constant divisor). - >>> import oneflow as flow - >>> import numpy as np + .. warning:: + Currently, only 4-D input tensors (batched image-like tensors) are + supported. - >>> x_tensor = flow.Tensor(np.random.randn(1, 1, 4, 4)) - >>> unfold = flow.nn.Unfold(kernel_size=3, padding=1) - >>> out = unfold(x_tensor) - >>> out.shape - oneflow.Size([1, 9, 16]) + Shape: + - Input: :math:`(N, C, *)` + - Output: :math:`(N, C \times \prod(\text{kernel_size}), L)` as described above - """ + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + + >>> x_tensor = flow.Tensor(np.random.randn(1, 1, 4, 4)) + >>> unfold = flow.nn.Unfold(kernel_size=3, padding=1) + >>> out = unfold(x_tensor) + >>> out.shape + oneflow.Size([1, 9, 16]) + + """ + + def __init__( + self, + kernel_size: _size_2_t, + dilation: _size_2_t = 1, + padding: _size_2_t = 0, + stride: _size_2_t = 1, + ) -> None: super(Unfold, self).__init__() - self.kernel_size = _pair(kernel_size) - self.dilation = _pair(dilation) - self.padding = _pair(padding) - self.stride = _pair(stride) + self.kernel_size = kernel_size + self.dilation = dilation + self.padding = padding + self.stride = stride def forward(self, input): return flow._C.unfold( input, - "channels_first", self.kernel_size, self.dilation, self.padding, self.stride, + "channels_first", ) def extra_repr(self) -> str: @@ -169,9 +314,3 @@ class Unfold(Module): "kernel_size={kernel_size}, dilation={dilation}, padding={padding}," " stride={stride}".format(**self.__dict__) ) - - -if __name__ == "__main__": - import doctest - - doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/fused_mlp.py b/python/oneflow/nn/modules/fused_mlp.py index 5efa1deda5d7597cb668f86cd5ec7e4fe813c34f..be4f46015466f7b254dce1b072428cba9e3a1989 100644 --- a/python/oneflow/nn/modules/fused_mlp.py +++ b/python/oneflow/nn/modules/fused_mlp.py @@ -18,7 +18,7 @@ import math import oneflow as flow from oneflow.framework.tensor import Tensor from oneflow.nn.init import _calculate_fan_in_and_fan_out -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from typing import Tuple diff --git a/python/oneflow/nn/modules/global_cast.py b/python/oneflow/nn/modules/global_cast.py index fa62cdb7046778e40cb25fc42ddbe859d9002d00..063f0d1e6b10af9cd5e282d85e2c5331bf8fe361 100644 --- a/python/oneflow/nn/modules/global_cast.py +++ b/python/oneflow/nn/modules/global_cast.py @@ -15,7 +15,7 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import register_tensor_op, Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def _check_sbp(sbp): @@ -34,7 +34,11 @@ def _check_sbp(sbp): return sbp -def local_to_global_op(input, placement=None, sbp=None, *, check_meta=True): +def local_to_global_op(input, placement=None, sbp=None, *, check_meta=True, copy=False): + # Convert None to a tensor with shape 0, in order to input it into flow._C.to_global. + if input is None: + input = flow.tensor(()) + assert isinstance(input, Tensor) assert input.is_local, "input must be a local tensor" if placement is None or sbp is None: @@ -48,11 +52,11 @@ def local_to_global_op(input, placement=None, sbp=None, *, check_meta=True): sbp = _check_sbp(sbp) grad_sbp = tuple() - return flow._C.to_global(input, placement, sbp, grad_sbp, check_meta) + return flow._C.to_global(input, placement, sbp, grad_sbp, check_meta, copy) def global_to_global_op( - input, placement=None, sbp=None, *, grad_sbp=None, check_meta=False + input, placement=None, sbp=None, *, grad_sbp=None, check_meta=False, copy=False ): assert isinstance(input, Tensor) assert input.is_global, "input must be a global tensor" @@ -71,7 +75,7 @@ def global_to_global_op( grad_sbp = _check_sbp(grad_sbp) if grad_sbp is None: grad_sbp = tuple() - return flow._C.to_global(input, placement, sbp, grad_sbp, check_meta) + return flow._C.to_global(input, placement, sbp, grad_sbp, check_meta, copy) def to_global_op(input, placement=None, sbp=None, **kwargs): @@ -85,6 +89,6 @@ def to_global_op(input, placement=None, sbp=None, **kwargs): return local_to_global_op(input=input, placement=placement, sbp=sbp, **kwargs) -def to_local_op(input): +def to_local_op(input, *, copy=False): assert input.is_global, "Expected global tensor for to_local but got local tensor!" - return flow._C.to_local(input) + return flow._C.to_local(input, copy) diff --git a/python/oneflow/nn/modules/instancenorm.py b/python/oneflow/nn/modules/instancenorm.py index 34b4dfb7fbe01334a9c77320ebc87d6dc57cb58d..c928d4d359f18caea5763dd64c749a748d112036 100644 --- a/python/oneflow/nn/modules/instancenorm.py +++ b/python/oneflow/nn/modules/instancenorm.py @@ -57,9 +57,7 @@ class _InstanceNorm(_NormBase): class InstanceNorm1d(_InstanceNorm): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm1d.html. - + """ Applies Instance Normalization over a 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization @@ -100,6 +98,9 @@ class InstanceNorm1d(_InstanceNorm): transform, while :class:`InstanceNorm1d` usually don't apply affine transform. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm1d.html. + Args: num_features: :math:`C` from an expected input of size :math:`(N, C, L)` or :math:`L` from input of size :math:`(N, L)` @@ -143,9 +144,7 @@ class InstanceNorm1d(_InstanceNorm): class InstanceNorm2d(_InstanceNorm): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm2d.html. - + """ Applies Instance Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization @@ -186,6 +185,9 @@ class InstanceNorm2d(_InstanceNorm): transform, while :class:`InstanceNorm2d` usually don't apply affine transform. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm2d.html. + Args: num_features: :math:`C` from an expected input of size :math:`(N, C, H, W)` @@ -225,9 +227,7 @@ class InstanceNorm2d(_InstanceNorm): class InstanceNorm3d(_InstanceNorm): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm3d.html. - + """ Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper `Instance Normalization: The Missing Ingredient for Fast Stylization @@ -268,6 +268,9 @@ class InstanceNorm3d(_InstanceNorm): transform, while :class:`InstanceNorm3d` usually don't apply affine transform. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.InstanceNorm3d.html. + Args: num_features: :math:`C` from an expected input of size :math:`(N, C, D, H, W)` diff --git a/python/oneflow/nn/modules/interpolate.py b/python/oneflow/nn/modules/interpolate.py index e9b168ae0608b07498565358e4c064aa7a2ddc87..004f61d1cf1a28ebf25a85b0fd0732919893e44c 100644 --- a/python/oneflow/nn/modules/interpolate.py +++ b/python/oneflow/nn/modules/interpolate.py @@ -19,7 +19,7 @@ from typing import Optional, Tuple, Union import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Interpolate(Module): diff --git a/python/oneflow/nn/modules/linear.py b/python/oneflow/nn/modules/linear.py index 041441bf0f416401fe12eade72ba65b6e071a829..c717a1599e0385b92761e66d63fc0476cbf8911e 100644 --- a/python/oneflow/nn/modules/linear.py +++ b/python/oneflow/nn/modules/linear.py @@ -18,7 +18,8 @@ import math import oneflow as flow from oneflow.framework.tensor import Tensor from oneflow.nn.init import _calculate_fan_in_and_fan_out -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module +import os class Identity(Module): @@ -81,7 +82,7 @@ class Linear(Module): >>> import numpy as np >>> import oneflow as flow - + >>> m = flow.nn.Linear(20, 30, False) >>> input = flow.Tensor(np.random.randn(128, 20)) @@ -91,12 +92,29 @@ class Linear(Module): """ - def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None: + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + device=None, + dtype=None, + ) -> None: super().__init__() self.in_features = in_features self.out_features = out_features - self.weight = flow.nn.Parameter(flow.Tensor(out_features, in_features)) - self.bias = flow.nn.Parameter(flow.Tensor(out_features)) if bias else None + self.weight = flow.nn.Parameter( + flow.Tensor(out_features, in_features).to(dtype=dtype, device=device) + ) + self.bias = ( + flow.nn.Parameter(flow.Tensor(out_features).to(dtype=dtype, device=device)) + if bias + else None + ) + self.use_fused_matmul_bias = ( + self.bias is not None + and os.getenv("ONEFLOW_KERNEL_ENABLE_FUSED_LINEAR") == "1" + ) self.reset_parameters() def reset_parameters(self) -> None: @@ -107,10 +125,13 @@ class Linear(Module): flow.nn.init.uniform_(self.bias, -bound, bound) def forward(self, x): - res = flow._C.matmul(x, self.weight, transpose_a=False, transpose_b=True) - if self.bias is not None: - res += self.bias - return res + if self.use_fused_matmul_bias: + return flow._C.fused_matmul_bias(x, self.weight, self.bias) + else: + res = flow._C.matmul(x, self.weight, transpose_a=False, transpose_b=True) + if self.bias is not None: + res += self.bias + return res def extra_repr(self) -> str: return "in_features={}, out_features={}, bias={}".format( @@ -129,7 +150,7 @@ def linear(input, weight, bias=None): - Weight: :math:`(out\_features, in\_features)` - Bias: :math:`(out\_features)` - Output: :math:`(N, *, out\_features)` - + For example: .. code-block:: python @@ -142,7 +163,7 @@ def linear(input, weight, bias=None): >>> output = flow.nn.functional.linear(input, weight) >>> output.size() oneflow.Size([128, 30]) - + """ res = flow._C.matmul(input, weight, transpose_a=False, transpose_b=True) if bias is not None: diff --git a/python/oneflow/nn/modules/linspace.py b/python/oneflow/nn/modules/linspace.py index 9c8b902ff40d4bd1c3bced9d66b6ec2d9bc31ebf..9962104485ec539d8652aa4ef0b8481d73bee3c0 100644 --- a/python/oneflow/nn/modules/linspace.py +++ b/python/oneflow/nn/modules/linspace.py @@ -19,9 +19,9 @@ import oneflow as flow def linspace_op( - start: float, - end: float, - steps: int, + start: Union[float, flow.Tensor], + end: Union[float, flow.Tensor], + steps: Union[int, flow.Tensor], dtype: flow.dtype = flow.float32, device: Union[str, flow.device] = None, placement: flow.placement = None, @@ -60,6 +60,35 @@ def linspace_op( tensor([ 3.0000, 4.7500, 6.5000, 8.2500, 10.0000], dtype=oneflow.float32) """ + + def is_scalar(tensor): + return tensor.ndim == 0 and tensor.nelement() == 1 + + if isinstance(start, flow.Tensor): + if not is_scalar(start): + raise TypeError( + "linspace(): argument 'start' (position 1) must be Number, not Tensor" + ) + start = start.item() + if isinstance(end, flow.Tensor): + if not is_scalar(end): + raise TypeError( + "linspace(): argument 'end' (position 2) must be Number, not Tensor" + ) + end = end.item() + if isinstance(steps, flow.Tensor): + if not is_scalar(steps): + raise TypeError( + "linspace(): argument 'steps' (position 3) must be Number, not Tensor" + ) + if flow.is_floating_point(steps): + raise TypeError( + "linspace(): argument 'steps' (position 3) must be int, not Tensor (with dtype: " + + str(steps.dtype) + + ")" + ) + steps = steps.item() + if start == end: return flow.full((steps,), start * 1.0) step = 1.0 diff --git a/python/oneflow/nn/modules/logspace.py b/python/oneflow/nn/modules/logspace.py new file mode 100644 index 0000000000000000000000000000000000000000..b645852f9de3788887f2afbbdce7b09288f828e6 --- /dev/null +++ b/python/oneflow/nn/modules/logspace.py @@ -0,0 +1,92 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from cgitb import reset +from typing import List, Optional, Union +import math +import oneflow as flow + + +def logspace_op( + start: float, + end: float, + steps: int, + base: Optional[float] = 10.0, + dtype: flow.dtype = None, + device: Union[str, flow.device] = None, + placement: flow.placement = None, + sbp: Union[flow.sbp.sbp, List[flow.sbp.sbp]] = None, + requires_grad: bool = False, +): + r""" + logspace(start, end, steps, base=10.0, *, dtype=None, device=None, placement=None, sbp=None, requires_grad=False) -> Tensor + + This function is equivalent to PyTorch’s logspace function. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.logspace.html. + + Creates a one-dimensional tensor of size :attr:`steps` whose values are evenly + spaced from :math:`{{\text{{base}}}}^{{\text{{start}}}}` to + :math:`{{\text{{base}}}}^{{\text{{end}}}}`, inclusive, on a logarithmic scale + with base :attr:`base`. That is, the values are: + + .. math:: + (\text{base}^{\text{start}}, + \text{base}^{(\text{start} + \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \ldots, + \text{base}^{(\text{start} + (\text{steps} - 2) * \frac{\text{end} - \text{start}}{ \text{steps} - 1})}, + \text{base}^{\text{end}}) + + Args: + start (float): the starting value for the set of points + end (float): the ending value for the set of points + steps (int): size of the constructed tensor + base (float, optional): base of the logarithm function. Default: ``10.0``. + + Keyword arguments: + dtype (oneflow.dtype, optional): the data type to perform the computation in. + Default: if None, uses the global default dtype (see oneflow.get_default_dtype()) + when both :attr:`start` and :attr:`end` are real, + and corresponding complex dtype when either is complex. + device (oneflow.device, optional): the desired device of returned tensor. Default: if None, uses the current device for the default tensor type + placement (oneflow.placement, optional): the desired placement of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + sbp (oneflow.sbp.sbp or tuple of oneflow.sbp.sbp, optional): the desired sbp descriptor of returned global tensor. Default: if None, the returned tensor is local one using the argument `device`. + requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. + + Example:: + + >>> import oneflow as flow + >>> flow.logspace(start=-10, end=10, steps=2) + tensor([1.0000e-10, 1.0000e+10], dtype=oneflow.float32) + >>> flow.logspace(start=0.1, end=1.0, steps=5) + tensor([ 1.2589, 2.1135, 3.5481, 5.9566, 10.0000], dtype=oneflow.float32) + >>> flow.logspace(start=0.1, end=1.0, steps=1) + tensor([1.2589], dtype=oneflow.float32) + >>> flow.logspace(start=2, end=2, steps=1, base=2) + tensor([4.], dtype=oneflow.float32) + + """ + # TODO: Migrate to C++ + indice = flow.linspace( + start=start, + end=end, + steps=steps, + dtype=dtype, + device=device, + placement=placement, + sbp=sbp, + ) + res = flow.pow(base, indice) + res.requires_grad = requires_grad + return res diff --git a/python/oneflow/nn/modules/loss.py b/python/oneflow/nn/modules/loss.py index 40ec59efe24b181c818055b01092f340bdb597f2..e039d4d51d68c12686ca804674470f6d1022cfc7 100644 --- a/python/oneflow/nn/modules/loss.py +++ b/python/oneflow/nn/modules/loss.py @@ -17,7 +17,7 @@ from typing import Optional import oneflow as flow from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.constant import _ConstantBase @@ -99,35 +99,104 @@ class L1Loss(_Loss): class CrossEntropyLoss(_WeightedLoss): - """This criterion combines :class:`~flow.nn.LogSoftmax` and :class:`~flow.nn.NLLLoss` in one single class. + r""" + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.CrossEntropyLoss.html. + + This criterion combines :class:`~flow.nn.LogSoftmax` and :class:`~flow.nn.NLLLoss` in one single class. It is useful when training a classification problem with `C` classes. + If provided, the optional argument `weight` should be a 1D Tensor assigning weight to each of the classes. + This is particularly useful when you have an unbalanced training set. The `input` is expected to contain raw, unnormalized scores for each class. - `input` has to be a Tensor of size either :math:`(minibatch, C)` or :math:`(minibatch, C, d_1, d_2, ..., d_K)` - with :math:`K \\geq 1` for the `K`-dimensional case (described later). - - This criterion expects a class index in the range :math:`[0, C-1]` as the - `target` for each value of a 1D tensor of size `minibatch`; + with :math:`K \geq 1` for the `K`-dimensional case (described later). + + The target that this criterion expects should contain either: + + - Class indices in the range :math:`[0, C)` where :math:`C` is the number of classes; if + `ignore_index` is specified, this loss also accepts this class index (this index + may not necessarily be in the class range). The unreduced (i.e. with :attr:`reduction` + set to ``'none'``) loss for this case can be described as: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = - w_{y_n} \log \frac{\exp(x_{n,y_n})}{\sum_{c=1}^C \exp(x_{n,c})} + \cdot \mathbb{1}\{y_n \not= \text{ignore_index}\} + + where :math:`x` is the input, :math:`y` is the target, :math:`w` is the weight, + :math:`C` is the number of classes, and :math:`N` spans the minibatch dimension as well as + :math:`d_1, ..., d_k` for the `K`-dimensional case. If + :attr:`reduction` is not ``'none'`` (default ``'mean'``), then + + .. math:: + \ell(x, y) = \begin{cases} + \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n} \cdot \mathbb{1}\{y_n \not= \text{ignore_index}\}} l_n, & + \text{if reduction} = \text{'mean';}\\ + \sum_{n=1}^N l_n, & + \text{if reduction} = \text{'sum'.} + \end{cases} + + Note that this case is equivalent to the combination of :class:`~torch.nn.LogSoftmax` and + :class:`~torch.nn.NLLLoss`. + + - Probabilities for each class; useful when labels beyond a single class per minibatch item + are required, such as for blended labels, label smoothing, etc. The unreduced (i.e. with + :attr:`reduction` set to ``'none'``) loss for this case can be described as: + + .. math:: + \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad + l_n = - \sum_{c=1}^C w_c \log \frac{\exp(x_{n,c})}{\sum_{i=1}^C \exp(x_{n,i})} y_{n,c} + + where :math:`x` is the input, :math:`y` is the target, :math:`w` is the weight, + :math:`C` is the number of classes, and :math:`N` spans the minibatch dimension as well as + :math:`d_1, ..., d_k` for the `K`-dimensional case. If + :attr:`reduction` is not ``'none'`` (default ``'mean'``), then + + .. math:: + \ell(x, y) = \begin{cases} + \frac{\sum_{n=1}^N l_n}{N}, & + \text{if reduction} = \text{'mean';}\\ + \sum_{n=1}^N l_n, & + \text{if reduction} = \text{'sum'.} + \end{cases} - The loss can be described as: - - .. math:: - \\text{loss}(x, class) = -\\log\\left(\\frac{\\exp(x[class])}{\\sum_j \\exp(x[j])}\\right) - = -x[class] + \\log\\left(\\sum_j \\exp(x[j])\\right) - - Can also be used for higher dimension inputs, such as 2D images, by providing - an input of size :math:`(minibatch, C, d_1, d_2, ..., d_K)` with :math:`K \\geq 1`, - where :math:`K` is the number of dimensions, and a target of appropriate shape - (see below). Args: + weight (oneflow.Tensor, optional): a manual rescaling weight given to each class. + If given, has to be a Tensor of size `C` + ignore_index (int, optional): Specifies a target value that is ignored and does not + contribute to the input gradient. When ``reduction`` is ``mean``, the loss is averaged + over non-ignored targets. Note that ``ignore_index`` is only applicable when the target + contains class indices. reduction (string, optional): Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the weighted mean of the output is taken, ``'sum'``: the output will be summed. Default: ``'mean'`` + label_smoothing (float, optinoal): A float in [0.0, 1.0]. Specifies the amount + of smoothing when computing the loss, where 0.0 means no smoothing. + The targets become a mixture of the original ground truth and a uniform + distribution as described in `Rethinking the Inception Architecture for Computer Vision `_. + Default: :math:`0.0`. + + Shape: + - Input: Shape ::math:`(N, C)` or :math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1` + in the case of `K`-dimensional loss. + - Target: If containing class indices, shape :math:`(N)` or :math:`(N, d_1, d_2, ..., d_K)` with + :math:`K \geq 1` in the case of K-dimensional loss where each value should be between :math:`[0, C)`. + If containing class probabilities, same shape as the input and each value should be between :math:`[0, 1]`. + - Output: If reduction is 'none', same shape as the target. Otherwise, scalar. + + where: + + .. math:: + \begin{aligned} + C ={} & \text{number of classes} \\ + N ={} & \text{batch size} \\ + \end{aligned} + For example: @@ -150,6 +219,18 @@ class CrossEntropyLoss(_WeightedLoss): >>> out_mean = flow.nn.CrossEntropyLoss(reduction="mean")(input, target) >>> out_mean tensor(0.7590, dtype=oneflow.float32) + >>> out_ignore_0 = flow.nn.CrossEntropyLoss(reduction="none", ignore_index=0)(input, target) + >>> out_ignore_0 + tensor([0.0000, 1.1167, 0.3583], dtype=oneflow.float32) + >>> out_label_smoothing = flow.nn.CrossEntropyLoss(reduction="none", label_smoothing=0.5)(input, target) + >>> out_label_smoothing + tensor([1.0586, 1.1654, 0.8864], dtype=oneflow.float32) + >>> probs = flow.tensor([[ 0.99495536, 0.28255007, -0.2775054 ], + ... [ 0.42397153, 0.01075112, 0.56527734], + ... [ 0.72356546, -0.1304398 , 0.4068744 ]], dtype=flow.float32) + >>> out = flow.nn.CrossEntropyLoss()(input, probs) + >>> out + tensor(1.3305, dtype=oneflow.float32) """ @@ -158,13 +239,24 @@ class CrossEntropyLoss(_WeightedLoss): weight: Optional[Tensor] = None, ignore_index: int = -100, reduction: str = "mean", + label_smoothing: float = 0.0, ) -> None: super(CrossEntropyLoss, self).__init__(weight, reduction) self.ignore_index = ignore_index + self.label_smoothing = label_smoothing + if self.label_smoothing < 0.0 or self.label_smoothing > 1.0: + raise ValueError( + "label_smoothing must be between 0.0 and 1.0. Got: ", label_smoothing + ) def forward(self, input, target): return flow._C.cross_entropy( - input, target, self.weight, self.ignore_index, self.reduction + input, + target, + self.weight, + self.ignore_index, + self.reduction, + self.label_smoothing, ) @@ -335,10 +427,7 @@ class NLLLoss(_WeightedLoss): class KLDivLoss(_Loss): - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.KLDivLoss.html. - + """ The Kullback-Leibler divergence loss measure `Kullback-Leibler divergence`_ is a useful distance measure for continuous @@ -375,6 +464,10 @@ class KLDivLoss(_Loss): .. _`kullback-leibler divergence`: https://en.wikipedia.org/wiki/Kullback-Leibler_divergence + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.KLDivLoss.html. + Args: reduction (string, optional): Specifies the reduction to apply to the output: ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``. @@ -422,7 +515,12 @@ class KLDivLoss(_Loss): """ def __init__(self, reduction: str = "mean", log_target: bool = False) -> None: - super(KLDivLoss, self).__init__(reduction) + if reduction == "batchmean": + super(KLDivLoss, self).__init__("sum") + self.reduction = "batchmean" + else: + super(KLDivLoss, self).__init__(reduction) + self.log_target = log_target def forward(self, input: Tensor, target: Tensor) -> Tensor: @@ -430,10 +528,7 @@ class KLDivLoss(_Loss): class MSELoss(_Loss): - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.MSELoss.html. - + """ Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input :math:`x` and target :math:`y`. @@ -460,6 +555,10 @@ class MSELoss(_Loss): The division by :math:`n` can be avoided if one sets ``reduction = 'sum'``. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.MSELoss.html. + Args: reduction (string, optional): Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, diff --git a/python/oneflow/nn/modules/masked_select.py b/python/oneflow/nn/modules/masked_select.py index 2d213af38d49c7b3640f622a4fe92e853de9ead5..50fcaac2a2d6a7cc98e58352e4b39c9f7f9a9b73 100644 --- a/python/oneflow/nn/modules/masked_select.py +++ b/python/oneflow/nn/modules/masked_select.py @@ -14,7 +14,6 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow as flow -from oneflow.framework.tensor import register_tensor_op def masked_select_op(input, mask): @@ -49,10 +48,26 @@ def masked_select_op(input, mask): "global" if mask.is_global else "local", ) ) - res = flow._C.mul(input, mask) - - indices = flow.argwhere(res) - gather_res = flow._C.gather_nd(res, indices) + broadcast_shape = [] + input_shape_len = len(input.shape) + mask_shape_len = len(mask.shape) + input_shape = [input.shape[i] for i in range(input_shape_len)] + input_shape.reverse() + mask_shape = [mask.shape[i] for i in range(mask_shape_len)] + mask_shape.reverse() + for i in range(max(input_shape_len, mask_shape_len)): + if i < input_shape_len and i < mask_shape_len: + broadcast_shape.append(max(input_shape[i], mask_shape[i])) + elif i < input_shape_len: + broadcast_shape.append(input_shape[i]) + else: + broadcast_shape.append(mask_shape[i]) + broadcast_shape.reverse() + broadcast_input = input.expand(broadcast_shape) + broadcast_mask = mask.expand(broadcast_shape) + + indices = flow.argwhere(broadcast_mask) + gather_res = flow._C.gather_nd(broadcast_input, indices) return gather_res.flatten() diff --git a/python/oneflow/nn/modules/math_ops.py b/python/oneflow/nn/modules/math_ops.py index adbf43a200210b3fb29dec4037a830308d9ee188..a16ddf4555f82be980156024d8fa893e24247691 100644 --- a/python/oneflow/nn/modules/math_ops.py +++ b/python/oneflow/nn/modules/math_ops.py @@ -18,7 +18,7 @@ from typing import Optional, Sequence, Union import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.utils import _check_axis from oneflow.ops.transpose_util import ( get_inversed_perm, @@ -255,45 +255,6 @@ def addmm_op(input, mat1, mat2, alpha=1, beta=1): return addmm(input, mat1, mat2, alpha, beta) -class Topk(Module): - def __init__( - self, k, dim: int = None, largest: bool = True, sorted: bool = True - ) -> None: - super().__init__() - self.k = k - self.sorted = sorted - self.dim = dim - self.largest = largest - - def forward(self, input): - if self.dim == None: - self.dim = -1 - num_axes = len(input.shape) - axis = self.dim if self.dim >= 0 else self.dim + num_axes - assert 0 <= axis < num_axes, "axis out of range" - if axis == num_axes - 1: - if self.largest: - indices = flow._C.top_k(input, self.k) - else: - neg_input = flow.mul(input, -1) - indices = flow._C.top_k(neg_input, self.k) - return (flow.gather(input, axis, indices), indices) - else: - perm = get_perm_when_transpose_axis_to_last_dim(num_axes, axis) - x = flow._C.transpose(input, perm=perm) - if self.largest: - indices = flow._C.top_k(x, self.k) - else: - neg_input = flow.mul(x, -1) - indices = flow._C.top_k(neg_input, self.k) - indices = flow._C.transpose(indices, perm=get_inversed_perm(perm)) - return (flow.gather(input, axis, indices), indices) - - -def topk_op(input, k, dim: int = None, largest: bool = True, sorted: bool = True): - return Topk(k=k, dim=dim, largest=largest, sorted=sorted)(input) - - if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/min_max_observer.py b/python/oneflow/nn/modules/min_max_observer.py index ef2950ee8bb051569377208ccd7688b529ba6d74..d1043bde3351722396cfdf48058f400b97998043 100644 --- a/python/oneflow/nn/modules/min_max_observer.py +++ b/python/oneflow/nn/modules/min_max_observer.py @@ -15,7 +15,7 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class MinMaxObserver(Module): diff --git a/python/oneflow/nn/module.py b/python/oneflow/nn/modules/module.py similarity index 82% rename from python/oneflow/nn/module.py rename to python/oneflow/nn/modules/module.py index 7e8b4f25ea02ab22a6ccd7c538b44ee33e212596..01508b6812463d81f03ef271b48f5f6768ef8bb7 100644 --- a/python/oneflow/nn/module.py +++ b/python/oneflow/nn/modules/module.py @@ -15,7 +15,18 @@ limitations under the License. """ import itertools from collections import OrderedDict, namedtuple -from typing import Callable, Dict, Iterator, List, Optional, Set, Tuple, TypeVar, Union +from typing import ( + Callable, + Dict, + Iterator, + List, + Optional, + Set, + Tuple, + TypeVar, + Union, + overload, +) import traceback import warnings @@ -56,7 +67,7 @@ class Module(object): This class is consistent with PyTorch. The documentation is referenced from: - https://pytorch.org/docs/stable/generated/torch.nn.Module.html. + https://pytorch.org/docs/1.10/generated/torch.nn.Module.html. Your models should also subclass this class. @@ -100,11 +111,48 @@ class Module(object): self._state_dict_hooks = OrderedDict() self._load_state_dict_pre_hooks = OrderedDict() self._modules = OrderedDict() + self._is_ddp_module = False + self._oneflow_internal_module_tensor_applied_dict__ = None + + def __getstate__(self): + if not self._is_ddp_module: + if ( + len(self._backward_hooks) > 0 + or len(self._forward_hooks) > 0 + or len(self._forward_pre_hooks) > 0 + or len(self._state_dict_hooks) > 0 + or len(self._load_state_dict_pre_hooks) > 0 + ): + warnings.warn("The module hooks will not be remained after serializing") + + state = self.__dict__.copy() + del state["_backward_hooks"] + del state["_forward_hooks"] + del state["_forward_pre_hooks"] + del state["_state_dict_hooks"] + del state["_load_state_dict_pre_hooks"] + return state + + def __setstate__(self, state): + self.__dict__.update(state) + self._backward_hooks = OrderedDict() + self._forward_hooks = OrderedDict() + self._forward_pre_hooks = OrderedDict() + self._state_dict_hooks = OrderedDict() + self._load_state_dict_pre_hooks = OrderedDict() + if hasattr(self, "_is_ddp_module") and self._is_ddp_module: + # flow.nn.parallel.DistributedDataParallel updates the module inplace + flow.nn.parallel.DistributedDataParallel(self, broadcast_parameters=False) def forward(self, *args, **kwargs): raise NotImplementedError() def __call__(self, *args, **kwargs): + if flow._oneflow_internal.lazy_mode.is_enabled(): + warnings.warn( + self._shallow_repr() + + " is called in a nn.Graph, but not registered into a nn.Graph." + ) for hook in itertools.chain(self._forward_pre_hooks.values()): result = hook(self, args) if result is not None: @@ -324,6 +372,17 @@ class Module(object): else: object.__setattr__(self, name, value) + def __delattr__(self, name): + if name in self._parameters: + del self._parameters[name] + elif name in self._buffers: + del self._buffers[name] + self._non_persistent_buffers_set.discard(name) + elif name in self._modules: + del self._modules[name] + else: + super().__delattr__(name) + def _named_members(self, get_members_fn, prefix="", recurse=True): memo = set() modules = self.named_modules(prefix=prefix) if recurse else [(prefix, self)] @@ -610,6 +669,29 @@ class Module(object): """ return self.train(False) + def requires_grad_(self: T, requires_grad: bool = True) -> T: + r"""Change if autograd should record operations on parameters in this + module. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.Module.html?highlight=requires_grad_#torch.nn.Module.requires_grad_. + + This method sets the parameters' :attr:`requires_grad` attributes + in-place. + + This method is helpful for freezing part of the module for finetuning + or training parts of a model individually (e.g., GAN training). + + Args: + requires_grad (bool): whether autograd should record operations on + parameters in this module. Default: ``True``. + + Returns: + Module: self + """ + for p in self.parameters(): + p.requires_grad_(requires_grad) + return self + def zero_grad(self, set_to_none: bool = False) -> None: r""" zero_grad(set_to_none=False) @@ -688,6 +770,24 @@ class Module(object): ) ) continue + if ( + isinstance(input_param, flow.Tensor) + and input_param.is_global != param.is_global + ): + if param.is_global: + help_msg = "Maybe you need to convert the checkpoint param to global, or set global_src_rank=0 when using flow.load to load model's state_dict" + else: + help_msg = "Maybe you need to convert your model to global." + error_msgs.append( + 'local / global mismatch for "{}": param from checkpoint is {} tensor, but the param in current model is {} tensor. {}'.format( + key, + "global" if input_param.is_global else "local", + "global" if param.is_global else "local", + help_msg, + ) + ) + continue + try: with flow.no_grad(): param.copy_(input_param) @@ -885,14 +985,18 @@ class Module(object): """ self._forward_hooks[len(self._forward_hooks)] = hook - def _apply(self, fn, applied_dict=None): + def _apply(self, fn): # A dict to store tensors that has already been applied. # There is no need to apply multiple times on a same tensor. - if applied_dict is None: - applied_dict = dict() + if self._oneflow_internal_module_tensor_applied_dict__ is None: + self._oneflow_internal_module_tensor_applied_dict__ = dict() for module in self.children(): - module._apply(fn, applied_dict) + module._oneflow_internal_module_tensor_applied_dict__ = ( + self._oneflow_internal_module_tensor_applied_dict__ + ) + module._apply(fn) + module._oneflow_internal_module_tensor_applied_dict__ = None def can_use_assign_copy(tensor, tensor_applied): return tensor.is_local == tensor_applied.is_local @@ -902,7 +1006,7 @@ class Module(object): continue need_apply = False - if param not in applied_dict: + if param not in self._oneflow_internal_module_tensor_applied_dict__: need_apply = True assert isinstance(param, Parameter) assert param.is_leaf @@ -917,12 +1021,16 @@ class Module(object): grad_applied.requires_grad = param.grad.requires_grad param_applied.grad = grad_applied else: - param_applied = applied_dict[param] + param_applied = self._oneflow_internal_module_tensor_applied_dict__[ + param + ] if can_use_assign_copy(param_applied, param): if need_apply: self._parameters[key].data = param_applied - applied_dict[param] = param_applied + self._oneflow_internal_module_tensor_applied_dict__[ + param + ] = param_applied else: # The parameter's data has already been set when it can use assign copy. pass @@ -930,18 +1038,28 @@ class Module(object): if need_apply: new_param = Parameter(param_applied, param.requires_grad) self._parameters[key] = new_param - applied_dict[param] = new_param + self._oneflow_internal_module_tensor_applied_dict__[ + param + ] = new_param else: - self._parameters[key] = applied_dict[param] + self._parameters[ + key + ] = self._oneflow_internal_module_tensor_applied_dict__[param] for (key, buf) in self._buffers.items(): if buf is not None: - if buf not in applied_dict: + if buf not in self._oneflow_internal_module_tensor_applied_dict__: buf_applied = fn(buf) self._buffers[key] = buf_applied - applied_dict[buf] = buf_applied + self._oneflow_internal_module_tensor_applied_dict__[ + buf + ] = buf_applied else: - self._buffers[key] = applied_dict[buf] + self._buffers[ + key + ] = self._oneflow_internal_module_tensor_applied_dict__[buf] + + self._oneflow_internal_module_tensor_applied_dict__ = None return self def apply(self: T, fn: Callable[["Module"], None]) -> T: @@ -989,14 +1107,41 @@ class Module(object): fn(self) return self - def to(self, device: Optional[Union[str, flow.device]] = None): - r""" - to(device=None) - - Moves the parameters and buffers. + @overload + def to( + self: T, + device: Optional[Union[int, str, flow.device]] = ..., + dtype: Optional[flow.dtype] = ..., + ) -> T: + ... + + @overload + def to(self: T, dtype: flow.dtype) -> T: + ... + + @overload + def to(self: T, tensor: Tensor) -> T: + ... - Its signature is similar to :meth:`oneflow.Tensor.to`. - The parameters and buffers will be moved :attr:`device`, if that is given. + def to(self, *args, **kwargs): + r"""Moves and/or casts the parameters and buffers. + + This can be called as + + .. function:: to(device=None, dtype=None) + :noindex: + + .. function:: to(dtype) + :noindex: + + .. function:: to(tensor) + :noindex: + + Its signature is similar to :meth:`oneflow.Tensor.to`, but only accepts + floating point :attr:`dtype`\ s. In addition, this method will + only cast the floating point parameters and buffers to :attr:`dtype` + (if given). The integral parameters and buffers will be moved + :attr:`device`, if that is given, but with dtypes unchanged. See below for examples. @@ -1006,6 +1151,10 @@ class Module(object): Args: device (:class:`oneflow.device`): the desired device of the parameters and buffers in this module + dtype (:class:`oneflow.dtype`): the desired floating point dtype of + the parameters and buffers in this module + tensor (oneflow.Tensor): Tensor whose dtype and device are the desired + dtype and device for all parameters and buffers in this module Returns: Module: self @@ -1017,11 +1166,19 @@ class Module(object): >>> linear = nn.Linear(2, 2) >>> linear.weight.device device(type='cpu', index=0) + >>> linear.weight.dtype + oneflow.float32 + >>> linear.to(flow.double) + Linear(in_features=2, out_features=2, bias=True) + >>> linear.weight.dtype + oneflow.float64 >>> gpu1 = flow.device("cuda:1") - >>> linear.to(gpu1) + >>> linear.to(gpu1, dtype=flow.half) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight.device device(type='cuda', index=1) + >>> linear.weight.dtype + oneflow.float16 >>> cpu = flow.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) @@ -1030,8 +1187,46 @@ class Module(object): """ + device = None + dtype = None + if len(args) + len(kwargs) == 2: + device = kwargs.pop("device", None) or args[0] + dtype = kwargs.pop("dtype", None) or args[1] + elif len(args) + len(kwargs) == 1: + if len(args) == 1: + arg = args[0] + if isinstance(arg, Tensor): + device = arg.device + dtype = arg.dtype + elif isinstance(arg, flow.dtype): + dtype = arg + device = None + elif isinstance(arg, (flow.device, str, int)): + dtype = None + device = arg + else: + raise ValueError(f"Unsupported parameters in module.to: {arg}") + else: + device = kwargs.pop("device", None) + dtype = kwargs.pop("dtype", None) + tensor = kwargs.pop("tensor", None) + if tensor is not None: + device = tensor.device + dtype = tensor.dtype + else: + raise ValueError( + f"Unsupported parameters in module.to: {args} and {kwargs}" + ) + + if dtype is not None: + if not dtype.is_floating_point: + raise TypeError( + "nn.Module.to only accepts floating point " + "dtypes, but got desired dtype={}".format(dtype) + ) + def convert(t): - return t.to(device) + return t.to(device, dtype if t.is_floating_point() else None) return self._apply(convert) @@ -1046,6 +1241,12 @@ class Module(object): return self._apply(convert) + def to_local(self): + def convert(t): + return t.to_local() + + return self._apply(convert) + def cpu(self: T) -> T: r""" cpu() diff --git a/python/oneflow/nn/modules/moving_average_min_max_observer.py b/python/oneflow/nn/modules/moving_average_min_max_observer.py index 4e67a557aa7d5365038a85ab73fb49fae0f1235b..962293492367fd642ffc690418be134a16fbad0a 100644 --- a/python/oneflow/nn/modules/moving_average_min_max_observer.py +++ b/python/oneflow/nn/modules/moving_average_min_max_observer.py @@ -16,7 +16,7 @@ limitations under the License. import numpy as np import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class MovingAverageMinMaxObserver(Module): @@ -73,7 +73,6 @@ class MovingAverageMinMaxObserver(Module): input(oneflow.Tensor): the input value(s), in ``oneflow.float32``. current_train_step_tensor(oneflow.Tensor): record train step for quantionzation aware training. stop_update_after_iters(int): stop record train step for quantionzation aware training when train iter greater than stop_update_after_iters. - training (bool): Is the model in training state. Defaults to False. quantization_formula (str): Support "google" or "cambricon". quantization_bit (int): Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8. quantization_scheme (str): "symmetric" or "affine", quantize to signed / unsigned integer. Defaults to "symmetric". @@ -105,7 +104,7 @@ class MovingAverageMinMaxObserver(Module): >>> quantization_scheme = "symmetric" >>> quantization_formula = "google" - >>> moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver(training=True, stop_update_after_iters=1, + >>> moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver(stop_update_after_iters=1, ... quantization_formula=quantization_formula, quantization_bit=quantization_bit, ... quantization_scheme=quantization_scheme, momentum=momentum, ... ) @@ -119,32 +118,25 @@ class MovingAverageMinMaxObserver(Module): def __init__( self, - training: bool = False, - stop_update_after_iters: int = 0, + stop_update_after_iters: int = 1, quantization_formula: str = "google", quantization_bit: int = 8, quantization_scheme: str = "symmetric", - momentum: float = 0, + momentum: float = 0.95, ) -> None: super().__init__() - self.training = training self.quantization_formula = quantization_formula self.stop_update_after_iters = stop_update_after_iters self.quantization_bit = quantization_bit self.quantization_scheme = quantization_scheme self.momentum = momentum - if training == True: - self.register_buffer("moving_max", flow.Tensor(1)) - self.register_buffer("moving_min", flow.Tensor(1)) - else: - self.register_parameter("moving_max", None) - self.register_parameter("moving_min", None) + self.register_buffer("moving_max", flow.Tensor(1)) + self.register_buffer("moving_min", flow.Tensor(1)) self.reset_running_stats() def reset_running_stats(self) -> None: - if self.training: - self.moving_max.fill_(0) - self.moving_min.fill_(0) + self.moving_max.fill_(0) + self.moving_min.fill_(0) def forward(self, input, current_train_step): return flow._C.moving_average_min_max_observer( diff --git a/python/oneflow/nn/modules/nms.py b/python/oneflow/nn/modules/nms.py index 0137f2b177d7381d5ffbb63bf0818e7b928d0411..7fdb64f0087853822f3c611f4bfdc775c3d8b576 100644 --- a/python/oneflow/nn/modules/nms.py +++ b/python/oneflow/nn/modules/nms.py @@ -15,7 +15,7 @@ limitations under the License. """ import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def nms_op(boxes, scores, iou_threshold: float): diff --git a/python/oneflow/nn/modules/nonzero.py b/python/oneflow/nn/modules/nonzero.py index ae49b56e80471f63e48f4a8675c5369fd0a83331..73648a2795422b800c0a119ae5c2857b0e20cf99 100644 --- a/python/oneflow/nn/modules/nonzero.py +++ b/python/oneflow/nn/modules/nonzero.py @@ -19,19 +19,14 @@ import numpy as np import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def nonzero_op(input, as_tuple=False): - if as_tuple and not input.ndim: - input = input.unsqueeze(0) - (res, size) = flow._C.argwhere(input, dtype=flow.int64) - slice_tup_list = [[0, int(size.numpy()), 1]] - res = flow.slice(res, slice_tup_list=slice_tup_list) if as_tuple: - return tuple([flow._C.transpose(res, [1, 0])[x] for x in range(res.shape[1])]) + return flow._C.nonzero(input, as_tuple) else: - return res + return flow._C.nonzero(input, as_tuple)[0] if __name__ == "__main__": diff --git a/python/oneflow/nn/modules/norm.py b/python/oneflow/nn/modules/norm.py new file mode 100644 index 0000000000000000000000000000000000000000..3297d3d3d969ac32e5e381056f4afd2d198743a5 --- /dev/null +++ b/python/oneflow/nn/modules/norm.py @@ -0,0 +1,112 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow + + +def norm(input, p="fro", dim=None, keepdim=False, dtype=None): + """ + Returns the matrix norm or vector norm of a given tensor. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.norm.html. + + .. warning:: + + Use :func:`oneflow.linalg.norm`, instead, or :func:`oneflow.linalg.vector_norm` + when computing vector norms and :func:`oneflow.linalg.matrix_norm` when + computing matrix norms. Note, however, the signature for these functions + is slightly different than the signature for oneflow.norm. + + Args: + input (Tensor): The input tensor. Its data type must be either a floating + point or complex type. For complex inputs, the norm is calculated using the + absolute value of each element. If the input is complex and neither + :attr:`dtype` nor :attr:`out` is specified, the result's data type will + be the corresponding floating point type (e.g. float if :attr:`input` is + complexfloat). + + p (int, float, inf, -inf, 'fro', 'nuc', optional): the order of norm. Default: ``'fro'`` + The following norms can be calculated: + + ====== ============== ========================== + ord matrix norm vector norm + ====== ============== ========================== + 'fro' Frobenius norm -- + 'nuc' nuclear norm -- + Number -- sum(abs(x)**p)**(1./p) + ====== ============== ========================== + + The vector norm can be calculated across any number of dimensions. + The corresponding dimensions of :attr:`input` are flattened into + one dimension, and the norm is calculated on the flattened + dimension. + + Frobenius norm produces the same result as ``p=2`` in all cases + except when :attr:`dim` is a list of three or more dims, in which + case Frobenius norm throws an error. + + Nuclear norm can only be calculated across exactly two dimensions. + + dim (int, tuple of ints, list of ints, optional): + Specifies which dimension or dimensions of :attr:`input` to + calculate the norm across. If :attr:`dim` is ``None``, the norm will + be calculated across all dimensions of :attr:`input`. If the norm + type indicated by :attr:`p` does not support the specified number of + dimensions, an error will occur. + keepdim (bool, optional): whether the output tensors have :attr:`dim` + retained or not. Ignored if :attr:`dim` = ``None`` and + :attr:`out` = ``None``. Default: ``False`` + dtype (:class:`oneflow.dtype`, optional): the desired data type of + returned tensor. If specified, the input tensor is casted to + :attr:`dtype` while performing the operation. Default: None. + + .. note:: + Even though ``p='fro'`` supports any number of dimensions, the true + mathematical definition of Frobenius norm only applies to tensors with + exactly two dimensions. :func:`oneflow.linalg.norm` with ``ord='fro'`` aligns + with the mathematical definition, since it can only be applied across + exactly two dimensions. + + Example:: + + >>> import oneflow as flow + >>> a = flow.arange(9, dtype= flow.float) - 4 + >>> b = a.reshape((3, 3)) + >>> flow.norm(a) + tensor(7.7460, dtype=oneflow.float32) + >>> flow.norm(b) + tensor(7.7460, dtype=oneflow.float32) + >>> flow.norm(a, float('inf')) + tensor(4., dtype=oneflow.float32) + >>> flow.norm(b, float('inf')) + tensor(9., dtype=oneflow.float32) + >>> c = flow.tensor([[ 1, 2, 3],[-1, 1, 4]] , dtype= flow.float) + >>> flow.norm(c, dim=0) + tensor([1.4142, 2.2361, 5.0000], dtype=oneflow.float32) + >>> flow.norm(c, dim=1) + tensor([3.7417, 4.2426], dtype=oneflow.float32) + >>> flow.norm(c, p=1, dim=1) + tensor([6., 6.], dtype=oneflow.float32) + >>> d = flow.arange(8, dtype= flow.float).reshape(2,2,2) + >>> flow.norm(d, dim=(1,2)) + tensor([ 3.7417, 11.2250], dtype=oneflow.float32) + >>> flow.norm(d[0, :, :]), flow.norm(d[1, :, :]) + (tensor(3.7417, dtype=oneflow.float32), tensor(11.2250, dtype=oneflow.float32)) + """ + if type(p) == str or dim != None: + return flow._C.norm(input=input, ord=p, dim=dim, keepdim=keepdim, dtype=dtype) + return flow._C.norm( + input=input, ord=p, dim=dim, keepdim=keepdim, dtype=dtype, for_norm=True + ) diff --git a/python/oneflow/nn/modules/normalization.py b/python/oneflow/nn/modules/normalization.py index b2324c97b3cb660f101c443b19e3197c050a976e..d1eb6432968c1a46df55fc907c0ce2494d28ec6e 100644 --- a/python/oneflow/nn/modules/normalization.py +++ b/python/oneflow/nn/modules/normalization.py @@ -13,21 +13,19 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ +import warnings from typing import Tuple, Union import oneflow as flow from oneflow.framework.tensor import Tensor from oneflow.nn import init -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module _shape_t = Union[int, Tuple[int], flow._oneflow_internal.Size] class GroupNorm(Module): - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.GroupNorm.html. - + """ Applies Group Normalization over a mini-batch of inputs as described in the paper `Group Normalization `__ @@ -46,6 +44,10 @@ class GroupNorm(Module): This layer uses statistics computed from input data in both training and evaluation modes. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.GroupNorm.html. + Args: num_groups (int): number of groups to separate the channels into num_channels (int): number of channels expected in input @@ -110,22 +112,28 @@ class GroupNorm(Module): assert ( input.shape[1] == self.num_channels ), "The channels of input tensor must equal num_channels" - origin_shape = input.shape - reshape_to_1d = flow.reshape( - input, shape=[origin_shape[0], self.num_groups, -1] - ) - mean = flow.mean(reshape_to_1d, dim=2, keepdim=True) - variance = flow.var(reshape_to_1d, dim=2, unbiased=False, keepdim=True) - normalized = (reshape_to_1d - mean) / flow.sqrt(variance + self.eps) - normalized = flow.reshape( - normalized, shape=[origin_shape[0], self.num_channels, -1] - ) - if self.weight is not None: - normalized = normalized * self.weight.reshape(1, self.num_channels, 1) - if self.bias is not None: - normalized = normalized + self.bias.reshape(1, self.num_channels, 1) - res = flow.reshape(normalized, shape=tuple(input.shape)) - return res + + if input.is_cuda: + return flow._C.group_norm( + input, self.weight, self.bias, self.affine, self.num_groups, self.eps + ) + else: + origin_shape = input.shape + reshape_to_1d = flow.reshape( + input, shape=[origin_shape[0], self.num_groups, -1] + ) + mean = flow.mean(reshape_to_1d, dim=2, keepdim=True) + variance = flow.var(reshape_to_1d, dim=2, unbiased=False, keepdim=True) + normalized = (reshape_to_1d - mean) / flow.sqrt(variance + self.eps) + normalized = flow.reshape( + normalized, shape=[origin_shape[0], self.num_channels, -1] + ) + if self.weight is not None: + normalized = normalized * self.weight.reshape(1, self.num_channels, 1) + if self.bias is not None: + normalized = normalized + self.bias.reshape(1, self.num_channels, 1) + res = flow.reshape(normalized, shape=tuple(input.shape)) + return res def extra_repr(self) -> str: return "{num_groups}, {num_channels}, eps={eps}, affine={affine}".format( @@ -317,6 +325,171 @@ class LayerNorm(Module): ) +class RMSLayerNorm(Module): + """ + Construct a layernorm module in the T5 style. No bias and no subtraction of mean. + + T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean + + Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated + + w/o mean and there is no bias. Additionally we want to make sure that the accumulation for + + half-precision inputs is done in fp32. + + Args: + hidden_size (int): number of features in the hidden state + eps: a value added to the denominator for numerical stability. Default: 1e-6 + + Shape: + - Input: :math:`(N, *)` + - Output: :math:`(N, *)` (same shape as input) + + For example: + + .. code-block:: python + + >>> import oneflow as flow + + >>> x = flow.randn(2, 4, 3) + >>> m = flow.nn.RMSLayerNorm(3) + >>> y = m(x) + >>> y.size() + oneflow.Size([2, 4, 3]) + + """ + + def __init__(self, hidden_size, eps=1e-6): + warnings.warn( + f"nn.RMSLayerNorm has been deprecated. Please use nn.RMSNorm instead." + ) + + super().__init__() + self.weight = flow.nn.Parameter(flow.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + return flow._C.rms_layer_norm(hidden_states, self.weight, self.variance_epsilon) + + +class RMSNorm(Module): + """Applies Root Mean Square Layer Normalization over a mini-batch of inputs as described in + the paper `Root Mean Square Layer Normalization `__ + + .. math:: + y = \\frac{x}{\\mathrm{RMS}[x]} \\mathrm{weight},\\text{ where }\\mathrm{RMS}[x] = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n} x^{2}} + + There is no bias and no subtraction of mean with RMS Layer Normalization, + and it only scales and doesn't shift. + + The root mean squre are calculated separately over the last + certain number dimensions which have to be of the shape specified by + :attr:`normalized_shape`. + :math:`\\weight` is learnable affine transform parameters of + :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``. + + .. note:: + Like Layer Normalization, Root Mean Square Layer Normalization applies per-element scale + with :attr:`elementwise_affine`. + + This layer uses statistics computed from input data in both training and + evaluation modes. + + Args: + normalized_shape (int or list or oneflow.Size): input shape from an expected input of size + + .. math:: + [* \\times \\text{normalized_shape}[0] \\times \\text{normalized_shape}[1] \\times \\ldots \\times \\text{normalized_shape}[-1]] + + If a single integer is used, it is treated as a singleton list, and this module will + + normalize over the last dimension which is expected to be of that specific size. + + eps: a value added to the denominator for numerical stability. Default: 1e-5 + elementwise_affine: a boolean value that when set to ``True``, this module + has learnable per-element affine parameters initialized to ones (for weights). + Default: ``True``. + + Shape: + - Input: :math:`(N, *)` + - Output: :math:`(N, *)` (same shape as input) + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + + >>> input_arr = np.array( + ... [ + ... [ + ... [[-0.16046895, -1.03667831], [-0.34974465, 0.26505867]], + ... [[-1.24111986, -0.53806001], [1.72426331, 0.43572459]], + ... ], + ... [ + ... [[-0.77390957, -0.42610624], [0.16398858, -1.35760343]], + ... [[1.07541728, 0.11008703], [0.26361224, -0.48663723]], + ... ], + ... ], + ... dtype=np.float32, + ... ) + + >>> x = flow.Tensor(input_arr, device="cuda") + >>> m = flow.nn.RMSNorm(2).to(device="cuda") + >>> y = m(x).numpy() + >>> y + array([[[[-0.21632987, -1.3975569 ], + [-1.127044 , 0.8541454 ]], + + [[-1.2975204 , -0.5625112 ], + [ 1.3711083 , 0.34648165]]], + + + [[[-1.2388322 , -0.6820876 ], + [ 0.16959298, -1.4040003 ]], + + [[ 1.4068495 , 0.14401469], + [ 0.6735778 , -1.2434478 ]]]], dtype=float32) + + """ + + _constants__ = ["normalized_shape", "eps", "elementwise_affine"] + normalized_shape: Tuple[int, ...] + eps: float + elementwise_affine: bool + + def __init__( + self, + normalized_shape: _shape_t, + eps: float = 1e-05, + elementwise_affine: bool = True, + device=None, + dtype=None, + ): + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + if isinstance(normalized_shape, int): + normalized_shape = (normalized_shape,) + self.normalized_shape = tuple(normalized_shape) + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = flow.nn.Parameter( + flow.ones(*self.normalized_shape, **factory_kwargs) + ) + else: + self.register_parameter("weight", None) + + def forward(self, x): + return flow._C.rms_norm(x, self.weight, self.normalized_shape, self.eps) + + def extra_repr(self) -> str: + return "{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}".format( + **self.__dict__ + ) + + if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/padding.py b/python/oneflow/nn/modules/padding.py index 4aaf66537bc92d219e1d9ee61523275a4621f0b2..ce29a01319a1421436409d518d1870ef06f2821f 100644 --- a/python/oneflow/nn/modules/padding.py +++ b/python/oneflow/nn/modules/padding.py @@ -13,27 +13,91 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from typing import Union, Sequence +from typing import Union import oneflow as flow -from oneflow.nn.common_types import _size_4_t -from oneflow.nn.module import Module -from oneflow.nn.modules.utils import _quadruple +from oneflow.nn.common_types import _size_2_t, _size_4_t +from oneflow.nn.modules.module import Module +from oneflow.nn.modules.utils import _pair, _quadruple -class ReplicationPad2d(Module): - """The interface is consistent with PyTorch. +class ReplicationPad1d(Module): + r""" + ReplicationPad1d(padding) + + Pads the input tensor using replication of the input boundary. + + The interface is consistent with PyTorch. The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.ReplicationPad2d.html. + https://pytorch.org/docs/1.10/generated/torch.nn.ReplicationPad1d.html. + + For `N`-dimensional padding, use :func:`oneflow.nn.functional.pad()`. + + Args: + padding (int, tuple): the size of the padding. If is `int`, uses the same + padding in all boundaries. If a 2-`tuple`, uses + (:math:`\text{padding_left}`, :math:`\text{padding_right}`) + + Shape: + - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. + - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where + + :math:`W_{out} = W_{in} + \text{padding_left} + \text{padding_right}` + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + >>> m = flow.nn.ReplicationPad1d((2, 2)) + >>> input = flow.tensor(np.arange(18).reshape((2, 3, 3)).astype(np.float32)) + >>> out = m(input) + >>> out + tensor([[[ 0., 0., 0., 1., 2., 2., 2.], + [ 3., 3., 3., 4., 5., 5., 5.], + [ 6., 6., 6., 7., 8., 8., 8.]], + + [[ 9., 9., 9., 10., 11., 11., 11.], + [12., 12., 12., 13., 14., 14., 14.], + [15., 15., 15., 16., 17., 17., 17.]]], dtype=oneflow.float32) + + """ + + def __init__(self, padding: _size_4_t): + super().__init__() + if isinstance(padding, tuple): + assert len(padding) == 2, ValueError("Padding length must be 2") + boundary = [*padding] + elif isinstance(padding, int): + boundary = _pair(padding) + else: + raise ValueError("padding must be in or list or tuple!") + self.padding = boundary + + def forward(self, x): + return flow._C.pad(x, pad=self.padding, mode="replicate") + + def extra_repr(self) -> str: + return "{}".format(self.padding) + + +class ReplicationPad2d(Module): + """ + ReplicationPad2d(padding) Pads the input tensor using the replication of the input boundary. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.ReplicationPad2d.html. + Args: padding (Union[int, tuple, list]): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\\mathrm{padding_{left}}`, :math:`\\mathrm{padding_{right}}`, :math:`\\mathrm{padding_{top}}`, :math:`\\mathrm{padding_{bottom}}`) Shape: - - Input: :math:`(N, C, H_{in}, W_{in})` - - Output: :math:`(N, C, H_{out}, W_{out})` where + - Input: :math:`(N, C, H_{\\text{in}}, W_{\\text{in}})` or :math:`(C, H_{in}, W_{in})` + - Output: :math:`(N, C, H_{\\text{out}}, W_{\\text{out}})` or :math:`(C, H_{out}, W_{out})` where :math:`H_{out} = H_{in} + \\mathrm{padding_{top}} + \\mathrm{padding_{bottom}}` @@ -84,14 +148,76 @@ class ReplicationPad2d(Module): return "{}".format(self.padding) -class ReflectionPad2d(Module): - """The interface is consistent with PyTorch. +class ReflectionPad1d(Module): + """ + ReflectionPad1d(padding) + + This operator pads the input tensor using the reflection of the input boundary. + + The interface is consistent with PyTorch. The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.ReflectionPad2d.html. + https://pytorch.org/docs/1.10/generated/torch.nn.ReflectionPad1d.html. + Args: + padding (Union[int,tuple]): The size or bundary of padding, if is `int` uses the same padding in all dimension; if 4-dims `tuple`, uses :math:`(\\text{padding}_{\\text{left}}, \\text{padding}_{\\text{right}}, \\text{padding}_{\\text{top}}, \\text{padding}_{\\text{bottom}} )` + + Returns: + Tensor: Returns a new tensor which is result of the reflection padding of the input tensor. + + Shape: + - Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`. + - Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where + + :math:`W_{out} = W_{in} + \\text{padding_left} + \\text{padding_right}` + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> import numpy as np + >>> input = flow.tensor(np.arange(18).reshape((2, 3, 3)).astype(np.float32)) + >>> m = flow.nn.ReflectionPad1d((2, 2)) + >>> out = m(input) + >>> out + tensor([[[ 2., 1., 0., 1., 2., 1., 0.], + [ 5., 4., 3., 4., 5., 4., 3.], + [ 8., 7., 6., 7., 8., 7., 6.]], + + [[11., 10., 9., 10., 11., 10., 9.], + [14., 13., 12., 13., 14., 13., 12.], + [17., 16., 15., 16., 17., 16., 15.]]], dtype=oneflow.float32) + + """ + + def __init__(self, padding: _size_2_t) -> None: + super().__init__() + if isinstance(padding, tuple): + assert len(padding) == 2, ValueError("Padding length must be 2") + boundary = [*padding] + elif isinstance(padding, int): + boundary = _pair(padding) + else: + raise ValueError("padding must be in or list or tuple!") + self.padding = boundary + + def forward(self, x): + return flow._C.pad(x, pad=self.padding, mode="reflect") + + def extra_repr(self) -> str: + return "{}".format(self.padding) + + +class ReflectionPad2d(Module): + """ + ReflectionPad2d(padding) This operator pads the input tensor using the reflection of the input boundary. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.ReflectionPad2d.html. + Args: padding (Union[int,tuple]): The size or bundary of padding, if is `int` uses the same padding in all dimension; if 4-dims `tuple`, uses :math:`(\\text{padding}_{\\text{left}}, \\text{padding}_{\\text{right}}, \\text{padding}_{\\text{top}}, \\text{padding}_{\\text{bottom}} )` @@ -99,8 +225,8 @@ class ReflectionPad2d(Module): Tensor: Returns a new tensor which is result of the reflection padding of the input tensor. Shape: - - Input: :math:`(N, C, H_{\\text{in}}, W_{\\text{in}})` - - Output: :math:`(N, C, H_{\\text{out}}, W_{\\text{out}})` where + - Input: :math:`(N, C, H_{\\text{in}}, W_{\\text{in}})` or :math:`(C, H_{in}, W_{in})` + - Output: :math:`(N, C, H_{\\text{out}}, W_{\\text{out}})` or :math:`(C, H_{out}, W_{out})` where :math:`H_{\\text{out}} = H_{\\text{in}} + \\text{padding}_{\\text{top}} + \\text{padding}_{\\text{bottom}}` @@ -149,7 +275,11 @@ class ReflectionPad2d(Module): class ConstantPad1d(Module): - """Pads the input tensor boundaries with a constant value. + """ + ConstantPad1d(padding) + + Pads the input tensor boundaries with a constant value. + The interface is consistent with PyTorch, and referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ConstantPad1d.html. @@ -203,13 +333,16 @@ class ConstantPad1d(Module): class ConstantPad2d(Module): - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.ConstantPad2d.html. + """ + ConstantPad2d(padding) This operator pads the input with constant value that user specifies. User can set the amount of padding by setting the parameter `paddings`. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.ConstantPad2d.html. + Args: padding (int, tuple, list): the size of the padding. If is `int`, uses the same padding in all boundaries. @@ -268,7 +401,10 @@ class ConstantPad2d(Module): class ConstantPad3d(Module): - """Pads the input tensor boundaries with a constant value. + """ + ConstantPad3d(padding) + + Pads the input tensor boundaries with a constant value. The interface is consistent with PyTorch, and referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.ConstantPad3d.html. @@ -339,12 +475,15 @@ class ConstantPad3d(Module): class ZeroPad2d(Module): - """The interface is consistent with PyTorch. - The documentation is referenced from: - https://pytorch.org/docs/1.10/generated/torch.nn.ZeroPad2d.html. + """ + ZeroPad2d(padding) Pads the input tensor boundaries with zero. User can set the amount of padding by setting the parameter `paddings`. + The interface is consistent with PyTorch. + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.ZeroPad2d.html. + Args: padding (Union[int, tuple]): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (:math:`\\mathrm{padding_{left}}`, :math:`\\mathrm{padding_{right}}`, :math:`\\mathrm{padding_{top}}`, :math:`\\mathrm{padding_{bottom}}`) diff --git a/python/oneflow/nn/modules/pixelshuffle.py b/python/oneflow/nn/modules/pixelshuffle.py index 4da11616858192f6dd23b8d9c9803b960c213572..a0c19335a706bc44cd4bef2498018b1a2c7f7084 100644 --- a/python/oneflow/nn/modules/pixelshuffle.py +++ b/python/oneflow/nn/modules/pixelshuffle.py @@ -17,7 +17,7 @@ from typing import Optional import oneflow as flow from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class PixelShufflev2(Module): diff --git a/python/oneflow/nn/modules/pooling.py b/python/oneflow/nn/modules/pooling.py index 87b3011e034a79f780f24a3aa600e0b0f46ee487..384f1d8dfaba2bd163da491592a5417f18f36251 100644 --- a/python/oneflow/nn/modules/pooling.py +++ b/python/oneflow/nn/modules/pooling.py @@ -13,12 +13,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from typing import Optional +from typing import Optional, Union, List import os import oneflow as flow from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.nn.modules.utils import ( _generate_output_size, _getint, @@ -29,11 +29,11 @@ from oneflow.nn.modules.utils import ( class MaxPool1d(Module): - r"""The interface is consistent with PyTorch. + r"""Applies a 1D max pooling over an input signal composed of several input planes. + + The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxPool1d.html. - Applies a 1D max pooling over an input signal composed of several input planes. - In the simplest case, the output value of the layer with input size :math:`(N, C, L)` and output :math:`(N, C, L_{out})` can be precisely described as: @@ -43,7 +43,7 @@ class MaxPool1d(Module): If :attr:`padding` is non-zero, then the input is implicitly padded with minimum value on both sides for :attr:`padding` number of points. :attr:`dilation` is the stride between the elements within the - sliding window. This `link`_ has a nice visualization of the pooling parameters. + sliding window. This link has a nice visualization of the pooling parameters. Note: When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding @@ -55,7 +55,6 @@ class MaxPool1d(Module): padding: Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. dilation: The stride between elements within a sliding window, must be > 0. return_indices: If ``True``, will return the argmax along with the max values. - Useful for :class:`torch.nn.MaxUnpool1d` later ceil_mode: If ``True``, will use `ceil` instead of `floor` to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window. @@ -163,11 +162,11 @@ def calc_pool_padding(padding, dhw_offset, ndims): class MaxPool2d(Module): - r"""The interface is consistent with PyTorch. + r"""Applies a 2D max pooling over an input signal composed of several input planes. + + The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxPool2d.html. - Applies a 2D max pooling over an input signal composed of several input planes. - In the simplest case, the output value of the layer with input size :math:`(N, C, H, W)`, output :math:`(N, C, H_{out}, W_{out})` and :attr:`kernel_size` :math:`(kH, kW)` can be precisely described as: @@ -181,7 +180,7 @@ class MaxPool2d(Module): If :attr:`padding` is non-zero, then the input is implicitly minimum value padded on both sides for :attr:`padding` number of points. :attr:`dilation` controls the spacing between the kernel points. - It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. + It is harder to describe, but this link has a nice visualization of what :attr:`dilation` does. Note: When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding @@ -278,11 +277,11 @@ class MaxPool2d(Module): class MaxPool3d(Module): - r"""The interface is consistent with PyTorch. + r"""Applies a 3D max pooling over an input signal composed of several input planes. + + The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxPool3d.html. - Applies a 3D max pooling over an input signal composed of several input planes. - In the simplest case, the output value of the layer with input size :math:`(N, C, D, H, W)`, output :math:`(N, C, D_{out}, H_{out}, W_{out})` and :attr:`kernel_size` :math:`(kD, kH, kW)` can be precisely described as: @@ -296,7 +295,7 @@ class MaxPool3d(Module): If :attr:`padding` is non-zero, then the input is implicitly minimum value on both sides for :attr:`padding` number of points. :attr:`dilation` controls the spacing between the kernel points. - It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. + It is harder to describe, but this link has a nice visualization of what :attr:`dilation` does. Note: When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding @@ -861,6 +860,413 @@ def adaptive_avg_pool3d(input, output_size): return AdaptiveAvgPool3d(output_size)(input) +class _AdaptiveMaxPoolNd(Module): + def __init__(self, output_size, return_indices: bool = False) -> None: + super(_AdaptiveMaxPoolNd, self).__init__() + self.output_size = output_size + self.return_indices = return_indices + + def extra_repr(self) -> str: + return "output_size={}".format(self.output_size) + + +class AdaptiveMaxPool1d(_AdaptiveMaxPoolNd): + r"""Applies a 1D adaptive max pooling over an input signal composed of several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.AdaptiveMaxPool1d.html. + + The output size is :math:`L_{out}`, for any input size. + The number of output features is equal to the number of input planes. + + Args: + output_size: the target output size :math:`L_{out}`. + return_indices: if ``True``, will return the indices along with the outputs. + Default: ``False`` + + Shape: + - Input: :math:`(N, C, L_{in})`. + - Output: :math:`(N, C, L_{out})`, where :math:`L_{out}=\text{output_size}`. + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> # target output size of 5 + >>> m = flow.nn.AdaptiveMaxPool1d(5) + >>> input = flow.randn(1, 64, 8) + >>> output = m(input) + >>> print(output.shape) + oneflow.Size([1, 64, 5]) + + """ + + def forward(self, input): + self.output_size = _single(self.output_size) + assert ( + len(input.shape) == 3 and len(self.output_size) == 1 + ), "the length of 'output_size' does not match the input size, 1 expected" + new_output_size = _generate_output_size(input.shape, self.output_size) + return flow.nn.functional.adaptive_max_pool1d( + input, self.output_size, self.return_indices + ) + + +class AdaptiveMaxPool2d(_AdaptiveMaxPoolNd): + r"""Applies a 2D adaptive max pooling over an input signal composed of several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.AdaptiveMaxPool2d.html. + + The output is of size :math:`H_{out} \times W_{out}`, for any input size. + The number of output features is equal to the number of input planes. + + Args: + output_size: the target output size of the image of the form :math:`H_{out} \times W_{out}`. + Can be a tuple :math:`(H_{out}, W_{out})` or a single :math:`H_{out}` for a + square image :math:`H_{out} \times H_{out}`. :math:`H_{out}` and :math:`W_{out}` + should be a ``int``. + return_indices: if ``True``, will return the indices along with the outputs. + Default: ``False`` + + Shape: + - Input: :math:`(N, C, H_{in}, W_{in})`. + - Output: :math:`(N, C, H_{out}, W_{out})`, where + :math:`(H_{out}, W_{out})=\text{output_size}`. + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> import oneflow.nn as nn + >>> # target output size of 5x7 + >>> m = nn.AdaptiveMaxPool2d((5,7)) + >>> input = flow.randn(1, 64, 8, 9) + >>> output = m(input) + >>> print(output.shape) + oneflow.Size([1, 64, 5, 7]) + >>> # target output size of 7x7 (square) + >>> m = nn.AdaptiveMaxPool2d(7) + >>> input = flow.randn(1, 64, 10, 9) + >>> output = m(input) + >>> print(output.shape) + oneflow.Size([1, 64, 7, 7]) + """ + + def forward(self, input): + self.output_size = _pair(self.output_size) + assert ( + len(input.shape) == 4 + ), f"expected 4-dimensional tensor, but got {len(input.shape)}-dimensional tensor" + new_output_size = _generate_output_size(input.shape, self.output_size) + return flow.nn.functional.adaptive_max_pool2d( + input, self.output_size, self.return_indices + ) + + +class AdaptiveMaxPool3d(_AdaptiveMaxPoolNd): + r"""Applies a 3D adaptive max pooling over an input signal composed of several input planes. + + The documentation is referenced from: + https://pytorch.org/docs/1.10/generated/torch.nn.AdaptiveMaxPool3d.html. + + The output is of size :math:`D_{out} \times H_{out} \times W_{out}`, for any input size. + The number of output features is equal to the number of input planes. + + Args: + output_size: the target output size of the image of the form :math:`D_{out} \times H_{out} \times W_{out}`. + Can be a tuple :math:`(D_{out}, H_{out}, W_{out})` or a single + :math:`D_{out}` for a cube :math:`D_{out} \times D_{out} \times D_{out}`. + :math:`D_{out}`, :math:`H_{out}` and :math:`W_{out}` should be a + ``int``. + + return_indices: if ``True``, will return the indices along with the outputs. + Default: ``False`` + + Shape: + - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`. + - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})`, + where :math:`(D_{out}, H_{out}, W_{out})=\text{output_size}`. + + Examples: + + .. code-block:: python + + >>> import oneflow as flow + >>> import oneflow.nn as nn + >>> # target output size of 5x7x9 + >>> m = nn.AdaptiveMaxPool3d((5,7,9)) + >>> input = flow.randn(1, 64, 8, 9, 10) + >>> output = m(input) + >>> print(output.shape) + oneflow.Size([1, 64, 5, 7, 9]) + >>> # target output size of 7x7x7 (cube) + >>> m = nn.AdaptiveMaxPool3d(7) + >>> input = flow.randn(1, 64, 10, 9, 8) + >>> output = m(input) + >>> print(output.shape) + oneflow.Size([1, 64, 7, 7, 7]) + """ + + def forward(self, input): + self.output_size = _triple(self.output_size) + assert ( + len(input.shape) == 5 + ), f"expected 5-dimensional tensor, but got {len(input.shape)}-dimensional tensor" + new_output_size = _generate_output_size(input.shape, self.output_size) + return flow.nn.functional.adaptive_max_pool3d( + input, self.output_size, self.return_indices + ) + + +class MaxUnpool1d(Module): + r"""Computes a partial inverse of :class:`MaxPool1d`. + + :class:`MaxPool1d` is not fully invertible, since the non-maximal values are lost. + + :class:`MaxUnpool1d` takes in as input the output of :class:`MaxPool1d` + including the indices of the maximal values and computes a partial inverse + in which all non-maximal values are set to zero. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxUnpool1d.html. + + .. note:: :class:`MaxPool1d` can map several input sizes to the same output + sizes. Hence, the inversion process can get ambiguous. + To accommodate this, you can provide the needed output size + as an additional argument :attr:`output_size` in the forward call. + See the Inputs and Example below. + + Args: + kernel_size (int or tuple): Size of the max pooling window. + stride (int or tuple): Stride of the max pooling window. + It is set to :attr:`kernel_size` by default. + padding (int or tuple): Padding that was added to the input + + Inputs: + - `input`: the input Tensor to invert + - `indices`: the indices given out by :class:`~oneflow.nn.MaxPool1d` + - `output_size` (optional): the targeted output size + + Shape: + - Input: :math:`(N, C, H_{in})`. + - Output: :math:`(N, C, H_{out})`, where + + .. math:: + H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0] + + or as given by :attr:`output_size` in the call operator + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> pool = flow.nn.MaxPool1d(2, stride=2, return_indices=True) + >>> unpool = flow.nn.MaxUnpool1d(2, stride=2) + >>> input = flow.tensor([[[1., 2, 3, 4, 5, 6, 7, 8]]]) + >>> output, indices = pool(input) + >>> unpool(output, indices) + tensor([[[0., 2., 0., 4., 0., 6., 0., 8.]]], dtype=oneflow.float32) + >>> # Example showcasing the use of output_size + >>> input = flow.tensor([[[1., 2, 3, 4, 5, 6, 7, 8, 9]]]) + >>> output, indices = pool(input) + >>> unpool(output, indices, output_size=input.size()) + tensor([[[0., 2., 0., 4., 0., 6., 0., 8., 0.]]], dtype=oneflow.float32) + >>> unpool(output, indices) + tensor([[[0., 2., 0., 4., 0., 6., 0., 8.]]], dtype=oneflow.float32) + + .. note:: When `indices` contains elements out of the `output_size` range, + an RuntimeError will be raised on the cpu and an indeterminate + result will be calculated on the cuda. + + """ + + def __init__( + self, + kernel_size: _size_1_t, + stride: Optional[_size_1_t] = None, + padding: Optional[_size_1_t] = 0, + ): + super().__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + + def forward(self, x, indices, output_size=None): + return flow._C.max_unpool1d( + x, indices, self.kernel_size, self.stride, self.padding, output_size + ) + + +class MaxUnpool2d(Module): + r"""Computes a partial inverse of :class:`MaxPool2d`. + + :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. + + :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` + including the indices of the maximal values and computes a partial inverse + in which all non-maximal values are set to zero. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxUnpool2d.html. + + .. note:: :class:`MaxPool2d` can map several input sizes to the same output + sizes. Hence, the inversion process can get ambiguous. + To accommodate this, you can provide the needed output size + as an additional argument :attr:`output_size` in the forward call. + See the Inputs and Example below. + + Args: + kernel_size (int or tuple): Size of the max pooling window. + stride (int or tuple): Stride of the max pooling window. + It is set to :attr:`kernel_size` by default. + padding (int or tuple): Padding that was added to the input + + Inputs: + - `input`: the input Tensor to invert + - `indices`: the indices given out by :class:`~oneflow.nn.MaxPool2d` + - `output_size` (optional): the targeted output size + + Shape: + - Input: :math:`(N, C, H_{in}, W_{in})` . + - Output: :math:`(N, C, H_{out}, W_{out})`, where + + .. math:: + H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} + + .. math:: + W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} + + or as given by :attr:`output_size` in the call operator + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> pool = flow.nn.MaxPool2d(2, stride=2, return_indices=True) + >>> unpool = flow.nn.MaxUnpool2d(2, stride=2) + >>> input = flow.tensor([[[[ 1., 2, 3, 4], + ... [ 5, 6, 7, 8], + ... [ 9, 10, 11, 12], + ... [13, 14, 15, 16]]]]) + >>> output, indices = pool(input) + >>> unpool(output, indices) # doctest: +SKIP + tensor([[[[ 0., 0., 0., 0.], + [ 0., 6., 0., 8.], + [ 0., 0., 0., 0.], + [ 0., 14., 0., 16.]]]], dtype=oneflow.float32) + >>> # specify a different output size than input size + >>> unpool(output, indices, output_size=flow.Size([1, 1, 5, 5])) # doctest: +SKIP + tensor([[[[ 0., 0., 0., 0., 0.], + [ 6., 0., 8., 0., 0.], + [ 0., 0., 0., 14., 0.], + [16., 0., 0., 0., 0.], + [ 0., 0., 0., 0., 0.]]]], dtype=oneflow.float32) + + .. note:: When `indices` contains elements out of the `output_size` range, + an RuntimeError will be raised on the cpu and an indeterminate + result will be calculated on the cuda. + """ + + def __init__( + self, + kernel_size: _size_2_t, + stride: Optional[_size_2_t] = None, + padding: Optional[_size_2_t] = 0, + ): + super().__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + + def forward(self, x, indices, output_size=None): + return flow._C.max_unpool2d( + x, indices, self.kernel_size, self.stride, self.padding, output_size + ) + + +class MaxUnpool3d(Module): + r"""Computes a partial inverse of :class:`MaxPool3d`. + + :class:`MaxPool3d` is not fully invertible, since the non-maximal values are lost. + :class:`MaxUnpool3d` takes in as input the output of :class:`MaxPool3d` + including the indices of the maximal values and computes a partial inverse + in which all non-maximal values are set to zero. + + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.MaxPool3d.html. + + .. note:: :class:`MaxPool3d` can map several input sizes to the same output + sizes. Hence, the inversion process can get ambiguous. + To accommodate this, you can provide the needed output size + as an additional argument :attr:`output_size` in the forward call. + See the Inputs section below. + + Args: + kernel_size (int or tuple): Size of the max pooling window. + stride (int or tuple): Stride of the max pooling window. + It is set to :attr:`kernel_size` by default. + padding (int or tuple): Padding that was added to the input + + Inputs: + - `input`: the input Tensor to invert + - `indices`: the indices given out by :class:`~oneflow.nn.MaxPool3d` + - `output_size` (optional): the targeted output size + + Shape: + - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`. + - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})`, where + + .. math:: + D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} + + .. math:: + H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} + + .. math:: + W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]} + + or as given by :attr:`output_size` in the call operator + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> # pool of square window of size=3, stride=2 + >>> pool = flow.nn.MaxPool3d(3, stride=2, return_indices=True) + >>> unpool = flow.nn.MaxUnpool3d(3, stride=2) + >>> output, indices = pool(flow.randn(20, 16, 51, 33, 15)) + >>> unpooled_output = unpool(output, indices) + >>> unpooled_output.size() + oneflow.Size([20, 16, 51, 33, 15]) + + .. note:: When `indices` contains elements out of the `output_size` range, + an RuntimeError will be raised on the cpu and an indeterminate + result will be calculated on the cuda. + """ + + def __init__( + self, + kernel_size: _size_3_t, + stride: Optional[_size_3_t] = None, + padding: Optional[_size_3_t] = 0, + ): + super().__init__() + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + + def forward(self, x, indices, output_size=None): + return flow._C.max_unpool3d( + x, indices, self.kernel_size, self.stride, self.padding, output_size + ) + + if __name__ == "__main__": import doctest diff --git a/python/oneflow/nn/modules/quantization.py b/python/oneflow/nn/modules/quantization.py index 2a90eaf2ece8dfcd0b3df06829e60edb68520c57..ea5d871625cdeb7610cb4405ce03bd7e3bbd52dd 100644 --- a/python/oneflow/nn/modules/quantization.py +++ b/python/oneflow/nn/modules/quantization.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Quantization(Module): diff --git a/python/oneflow/nn/modules/reshape.py b/python/oneflow/nn/modules/reshape.py index 354039e4506aa36b60bfcbf0197d3cb8de123088..baa8b71fe1ce31f00f1c7346c687a30c5cbdc28c 100644 --- a/python/oneflow/nn/modules/reshape.py +++ b/python/oneflow/nn/modules/reshape.py @@ -17,7 +17,7 @@ from typing import Sequence import oneflow as flow from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def _input_args_is_int(args): diff --git a/python/oneflow/nn/modules/rnn.py b/python/oneflow/nn/modules/rnn.py index d4d0cb6e09abc359e5efa60dcc8a97ccf18e075a..94f69d18516da5d7575f93a5e1e2adc65e31dbc1 100644 --- a/python/oneflow/nn/modules/rnn.py +++ b/python/oneflow/nn/modules/rnn.py @@ -251,9 +251,7 @@ class RNNBase(nn.Module): class RNN(RNNBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.RNN.html. - + r""" Applies a multi-layer Elman RNN with \tanhtanh or \text{ReLU}ReLU non-linearity to an input sequence. For each element in the input sequence, each layer computes the following function: @@ -268,6 +266,9 @@ class RNN(RNNBase): previous layer at time `t-1` or the initial hidden state at time `0`. If :attr:`nonlinearity` is ``'relu'``, then :math:`\text{ReLU}` is used instead of :math:`\tanh`. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.RNN.html. + Args: input_size: The number of expected features in the input `x` hidden_size: The number of features in the hidden state `h` @@ -295,13 +296,14 @@ class RNN(RNNBase): state for each element in the batch. Defaults to zeros if not provided. where: + .. math:: \begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ - H_{in} ={} & \text{input\_size} \\ - H_{out} ={} & \text{hidden\_size} + H_{in} ={} & \text{input_size} \\ + H_{out} ={} & \text{hidden_size} \end{aligned} Outputs: output, h_n @@ -487,9 +489,7 @@ class RNN(RNNBase): class LSTM(RNNBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/rnn.html#LSTM. - + r""" Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence. For each element in the input sequence, each layer computes the following @@ -526,6 +526,9 @@ class LSTM(RNNBase): of LSTM network will be of different shape as well. See Inputs/Outputs sections below for exact dimensions of all variables. You can find more details in https://arxiv.org/abs/1402.1128. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/rnn.html#LSTM. + Args: input_size: The number of expected features in the input `x` hidden_size: The number of features in the hidden state `h` @@ -787,9 +790,7 @@ class LSTM(RNNBase): class GRU(RNNBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/rnn.html#GRU. - + r""" Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence. For each element in the input sequence, each layer computes the following @@ -800,7 +801,7 @@ class GRU(RNNBase): \begin{array}{ll} r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ - n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ + n_t = \\tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) * n_t + z_t * h_{(t-1)} \end{array} @@ -815,9 +816,10 @@ class GRU(RNNBase): dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random variable which is :math:`0` with probability :attr:`dropout`. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/rnn.html#GRU. + Args: - input_size: The number of expected features in the input `x` - hidden_size: The number of features in the hidden state `h` num_layers: Number of recurrent layers. E.g., setting ``num_layers=2`` would mean stacking two GRUs together to form a `stacked GRU`, with the second GRU taking in outputs of the first GRU and @@ -839,7 +841,7 @@ class GRU(RNNBase): the input sequence. * **h_0**: tensor of shape :math:`(D * \text{num\_layers}, N, H_{out})` containing the initial hidden state for each element in the batch. Defaults to zeros if not provided. - + where: .. math:: @@ -1047,9 +1049,7 @@ class RNNCellBase(nn.Module): class RNNCell(RNNCellBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.RNNCell.html. - + r""" An Elman RNN cell with tanh or ReLU non-linearity. .. math:: @@ -1058,6 +1058,9 @@ class RNNCell(RNNCellBase): If :attr:`nonlinearity` is `'relu'`, then ReLU is used in place of tanh. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.RNNCell.html. + Args: input_size: The number of expected features in the input `x` hidden_size: The number of features in the hidden state `h` @@ -1169,9 +1172,7 @@ class RNNCell(RNNCellBase): class LSTMCell(RNNCellBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.LSTMCell.html. - + r""" A long short-term memory (LSTM) cell. .. math:: @@ -1187,6 +1188,9 @@ class LSTMCell(RNNCellBase): where :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.LSTMCell.html. + Args: input_size: The number of expected features in the input `x` hidden_size: The number of features in the hidden state `h` @@ -1286,9 +1290,7 @@ class LSTMCell(RNNCellBase): class GRUCell(RNNCellBase): - """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.GRUCell.html. - + r""" A gated recurrent unit (GRU) cell .. math:: @@ -1302,6 +1304,9 @@ class GRUCell(RNNCellBase): where :math:`\sigma` is the sigmoid function, and :math:`*` is the Hadamard product. + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.GRUCell.html. + Args: input_size: The number of expected features in the input `x` hidden_size: The number of features in the hidden state `h` @@ -1340,6 +1345,7 @@ class GRUCell(RNNCellBase): For example: .. code-block:: python + >>> import oneflow as flow >>> import oneflow.nn as nn @@ -1349,6 +1355,7 @@ class GRUCell(RNNCellBase): >>> hx = rnn(input[0], hx) >>> hx.size() oneflow.Size([3, 20]) + """ def __init__( diff --git a/python/oneflow/nn/modules/scatter.py b/python/oneflow/nn/modules/scatter.py index 4cce6f1d52eaaa7efa3e104a45ecc8130ac915c2..23bd735594d94d00a7b9ba5dc64995e46ea41d80 100644 --- a/python/oneflow/nn/modules/scatter.py +++ b/python/oneflow/nn/modules/scatter.py @@ -16,12 +16,12 @@ limitations under the License. import oneflow as flow from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module __all__ = ["scatter", "scatter_add", "scatter_nd", "tensor_scatter_nd_update"] -def scatter(input, dim, index, src): +def scatter(input, dim, index, src, *, reduce=None): r"""This operator writes the elements specified by `index` along with the axis `dim` from the `src` into the `input`. @@ -35,14 +35,39 @@ def scatter(input, dim, index, src): input, index and src (if it is a Tensor) should all have the same number of dimensions. It is also required that index.shape(d) <= src.shape(d) for all dimensions d, - and that index.shape(d) <= self.shape(d) for all dimensions d != dim. + and that index.shape(d) <= input.shape(d) for all dimensions d != dim. Note that index and src do not broadcast. + .. warning:: + When indices are not unique, the behavior is non-deterministic (one of the values from src will be picked arbitrarily) + and the gradient will be incorrect (it will be propagated to all locations in the source that correspond to the same index)! + + .. note:: + The backward pass is implemented only for ``src.shape == index.shape``. + + Additionally accepts an optional ``reduce`` argument that allows specification of an optional reduction operation, + which is applied to all values in the tensor ``src`` into ``input`` at the indicies specified in the ``index``. + For each value in ``src``, the reduction operation is applied to an index in ``input`` which is specified by its index in ``src`` for ``dimension != dim`` + and by the corresponding value in ``index`` for ``dimension = dim``. + + Given a 3-D tensor and reduction using the multiplication operation, input is updated as: + + .. code-block:: python + + input[index[i][j][k]][j][k] *= src[i][j][k] # if dim == 0 + input[i][index[i][j][k]][k] *= src[i][j][k] # if dim == 1 + input[i][j][index[i][j][k]] *= src[i][j][k] # if dim == 2 + + Reducing with the addition operation is the same as using :func:`oneflow.scatter_add()`. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.Tensor.scatter\_.html. + Args: input (Tensor): The input blob. dim (int): The axis along which to index index (Tensor): The index blob of elements to scatter. src (Tensor or float): The source blob whose elements will be scatterd and updated to output. + reduce (str, optional): Reduction operation to apply, can be either ``add`` or ``multiply``. Returns: Tensor: The scatterd Tensor. @@ -64,8 +89,7 @@ def scatter(input, dim, index, src): [ 2., 2., 2., 2., 2.]], dtype=oneflow.float32) """ - - return flow._C.scatter(input, dim, index, src) + return flow._C.scatter(input, dim, index, src, reduce=reduce) def scatter_add(input, dim, index, src): diff --git a/python/oneflow/nn/modules/sort.py b/python/oneflow/nn/modules/sort.py deleted file mode 100644 index 6d5d6970fa946fbf0833944231bf97b819f4d91b..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/modules/sort.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import oneflow as flow -from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module -from oneflow.ops.transpose_util import ( - get_inversed_perm, - get_perm_when_transpose_axis_to_last_dim, -) - - -def sort_op(input, dim: int = -1, descending: bool = False): - num_dims = len(input.shape) - dim = dim if dim >= 0 else dim + num_dims - direction = "DESCENDING" if descending else "ASCENDING" - assert 0 <= dim < num_dims, "dim out of range" - if dim == num_dims - 1: - indices = flow._C.arg_sort(input, direction) - return (flow.gather(input, dim, indices), indices) - else: - perm = get_perm_when_transpose_axis_to_last_dim(num_dims, dim) - x = flow._C.transpose(input, perm=perm) - indices = flow._C.arg_sort(x, direction) - indices = flow._C.transpose(indices, perm=get_inversed_perm(perm)) - return (flow.gather(input, dim, indices), indices) - - -if __name__ == "__main__": - import doctest - - doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/sparse.py b/python/oneflow/nn/modules/sparse.py index b8eb4d50b9af56afc83d06d9ad321362dca8e95b..3bb67ba5dd8fcb2275258288eb13fa697a64cf04 100644 --- a/python/oneflow/nn/modules/sparse.py +++ b/python/oneflow/nn/modules/sparse.py @@ -17,7 +17,7 @@ from typing import List, Optional, Tuple import oneflow as flow from oneflow.framework.tensor import Tensor -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Embedding(Module): @@ -154,6 +154,20 @@ class Embedding(Module): with flow.no_grad(): self.weight[self.padding_idx] = 0 + def extra_repr(self) -> str: + s = "{num_embeddings}, {embedding_dim}" + if self.padding_idx is not None: + s += ", padding_idx={padding_idx}" + if self.max_norm is not None: + s += ", max_norm={max_norm}" + if self.norm_type != 2: + s += ", norm_type={norm_type}" + if self.scale_grad_by_freq is not False: + s += ", scale_grad_by_freq={scale_grad_by_freq}" + if self.sparse is not False: + s += ", sparse=True" + return s.format(**self.__dict__) + def forward(self, indices): if self.max_norm is not None: with flow.no_grad(): diff --git a/python/oneflow/nn/modules/tensor_ops.py b/python/oneflow/nn/modules/tensor_ops.py deleted file mode 100644 index 0af5c017bfa58a40ce6d499612b31c1c94dea226..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/modules/tensor_ops.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from typing import Union - -import oneflow as flow -from oneflow.framework.tensor import register_tensor_op - - -def is_floating_point(input): - if input.dtype in (flow.float, flow.float16, flow.float32, flow.float64): - return True - return False - - -if __name__ == "__main__": - import doctest - - doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/trigonometric_ops.py b/python/oneflow/nn/modules/trigonometric_ops.py index c4fe61125c64ab75ed11e29574e8e4e3069b996a..bef4a0ecd0d4f059bd70380c014f50044450402b 100644 --- a/python/oneflow/nn/modules/trigonometric_ops.py +++ b/python/oneflow/nn/modules/trigonometric_ops.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module from oneflow.framework.tensor import register_tensor_op diff --git a/python/oneflow/nn/modules/unique.py b/python/oneflow/nn/modules/unique.py new file mode 100644 index 0000000000000000000000000000000000000000..b4d0cefde6ca3796a82435f92a52266160c99ba8 --- /dev/null +++ b/python/oneflow/nn/modules/unique.py @@ -0,0 +1,84 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow + + +def unique_op( + input, sorted=True, return_inverse=False, return_counts=False, dtype=flow.int +): + r""" + Returns the unique elements of the input tensor. + + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.unique.html. + + Args: + input (Tensor): The input tensor. + sorted (bool): Whether to sort the unique elements in ascending order before returning as output. + return_inverse (bool): Whether to also return the indices for where elements in the original input ended up in the returned unique list. + return_counts (bool): Whether to also return the counts for each unique element. + dtype (flow.dtype): Dtype of the returned indices and counts. + + Returns: + oneflow.Tensor or List of oneflow.Tensor: + + - **output** (Tensor): the output list of unique scalar elements. + + - **inverse_indices** (Tensor): (optional) if return_inverse is True, + there will be an additional returned tensor (same shape as input) representing + the indices for where elements in the original input map to in the output; + otherwise, this function will only return a single tensor. + + - **counts** (Tensor): (optional) if return_counts is True, there will be an additional + returned tensor (same shape as output or output.size(dim), if dim was specified) + representing the number of occurrences for each unique value or tensor. + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> x = flow.tensor([3, 1, 2, 0 ,2]) + >>> flow.unique(x) + tensor([0, 1, 2, 3], dtype=oneflow.int64) + >>> flow.unique(x, sorted=False) + tensor([3, 1, 2, 0], dtype=oneflow.int64) + >>> results, indices = flow.unique(x, return_inverse=True) + >>> indices + tensor([3, 1, 2, 0, 2], dtype=oneflow.int32) + >>> results, counts = flow.unique(x, return_counts=True) + >>> counts + tensor([1, 1, 2, 1], dtype=oneflow.int32) + >>> results, indices = flow.unique(x, return_inverse=True, dtype=flow.long) + >>> indices + tensor([3, 1, 2, 0, 2], dtype=oneflow.int64) + + """ + if not return_inverse and not return_counts: + return flow._C.unique(input, sorted, dtype=dtype) + else: + return flow._C.unique( + input, + sorted, + return_inverse=return_inverse, + return_counts=return_counts, + dtype=dtype, + ) + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/modules/upsampling.py b/python/oneflow/nn/modules/upsampling.py index ff22f9cd125abaa098ae6e6ec44f0f08b77cddae..f93f18eff801bb301e462010bea2e8033b894d9e 100644 --- a/python/oneflow/nn/modules/upsampling.py +++ b/python/oneflow/nn/modules/upsampling.py @@ -16,14 +16,11 @@ limitations under the License. from typing import Optional, Tuple, Union import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module class Upsample(Module): - """The interface is consistent with PyTorch. - - The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/upsampling.html. - + """ Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data. The input data is assumed to be of the form @@ -37,6 +34,9 @@ class Upsample(Module): One can either give a :attr:`scale_factor` or the target output :attr:`size` to calculate the output size. (You cannot give both, as it is ambiguous) + The interface is consistent with PyTorch. + The documentation is referenced from: https://pytorch.org/docs/1.10/_modules/torch/nn/modules/upsampling.html. + Args: size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int], optional): output spatial sizes diff --git a/python/oneflow/nn/optimizer/adadelta.py b/python/oneflow/nn/optimizer/adadelta.py new file mode 100644 index 0000000000000000000000000000000000000000..8c1486ab5ab1832f0716f1191bb0992f10ec542c --- /dev/null +++ b/python/oneflow/nn/optimizer/adadelta.py @@ -0,0 +1,197 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import collections +import math +from typing import Callable, Dict, Iterator, List, Tuple, Union + +import oneflow as flow +from oneflow.optim.optimizer import Optimizer, ParamGroup +from oneflow.nn.parameter import Parameter + + +class Adadelta(Optimizer): + r"""Implements Adadelta Optimizer. + + The formula is: + + .. math:: + + & v_{t} = v_{t-1} * rho + g_{t}^2 * (1 - rho) + + & delta = \frac{\sqrt{u_{t-1} + \epsilon}}{\sqrt{v_{t} + \epsilon}} * g_{t} + + & u_{t} = u_{t-1} * rho + delta^2*(1 - rho) + + & x_{t} = x_{t-1} - lr * delta + + Args: + params (Union[Iterator[Parameter], List[Dict]]): iterable of parameters to optimize or dicts defining parameter groups + lr (float, optional): The learning rate. Defaults to 0.001. + rho (float, optional): The decay factor of learning rate. Defaults to 0.0. + eps (float, optional): A small constant terms added to the denominator to improve numerical stability. Defaults to 1e-10. + weight_decay (float, optional): The weight decay. Defaults to 0. + maximize (bool, optional): maximize the params based on the objective, instead of minimizing. Defaults False. + + For example: + + Example 1: + + .. code-block:: python + + # Assume net is a custom model. + adadelta = flow.optim.Adadelta(net.parameters(), lr=1e-3) + + for epoch in range(epochs): + # Read data, Compute the loss and so on. + # ... + loss.backward() + adadelta.step() + adadelta.zero_grad() + + Example 2: + + .. code-block:: python + + # Assume net is a custom model. + adadelta = flow.optim.Adadelta( + [ + { + "params": net.parameters(), + "lr": learning_rate, + "clip_grad_max_norm": 0.5, + "clip_grad_norm_type": 2.0, + } + ], + ) + + for epoch in range(epochs): + # Read data, Compute the loss and so on. + # ... + loss.backward() + adadelta.clip_grad() + adadelta.step() + adadelta.zero_grad() + + If you want to use clip_grad, you can refer this example. + + For more details of `clip_grad_max_norm` and `clip_grad_norm_type`, you can refer to :func:`oneflow.nn.utils.clip_grad_norm_`. + + """ + + def __init__( + self, + params: Union[Iterator[Parameter], List[Dict]], + lr: float = 1.0, + rho: float = 0.9, + eps: float = 1e-6, + weight_decay: float = 0, + maximize: bool = False, + ): + assert lr >= 0.0, f"Invalid learning rate: {lr}" + assert weight_decay >= 0.0, f"Invalid weight_decay value: {weight_decay}" + assert eps >= 0.0, f"Invalid epsilon value: {eps}" + assert 1.0 >= rho >= 0.0, f"Invalid rho value: {rho}" + assert ( + not maximize + ), f"In Graph Mode, weight decay has been added to Variable, it cause different result with Eager Mode when maximize = True" + options = dict() + options["lr"] = lr + options["rho"] = rho + options["eps"] = eps + options["maximize"] = maximize + options["weight_decay"] = weight_decay + super().__init__(params, options) + + for param_group in self.param_groups: + for param in param_group.parameters: + assert param.is_leaf, "parameters must be leaf tensor" + self._state[param] = dict() + self._state[param]["square_avgs"] = flow.zeros_like(param) + self._state[param]["acc_deltas"] = flow.zeros_like(param) + + self._op = ( + flow.stateful_op("adadelta_update") + .Input("model") + .Input("model_diff") + .Input("square_avgs") + .Input("acc_deltas") + .Build() + ) + + def step(self, closure: Callable = None): + """Performs a single optimization step. + + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + with flow.no_grad(): + loss = None + if closure is not None: + loss = closure() + for param_group in self.param_groups: + kwargs = { + "learning_rate": param_group["lr"], + "l2": param_group["weight_decay"], + "rho": param_group["rho"], + "epsilon": param_group["eps"], + "maximize": param_group["maximize"], + } + for param in param_group.parameters: + if param.grad is None: + continue + square_avgs_tensor = self._state[param]["square_avgs"] + acc_deltas_tensor = self._state[param]["acc_deltas"] + flow._C.dispatch_adadelta_update( + self._op, + (param, param.grad, square_avgs_tensor, acc_deltas_tensor), + **kwargs, + ) + + self._state["step"] = self._state["step"] + 1 + return loss + + def _generate_conf_for_graph(self, train_conf, vars_conf): + new_opt_confs = [] + for param_group in self.param_groups: + optimizer_conf = train_conf.optimizer_conf.add() + + lr = ( + param_group["initial_lr"] + if "initial_lr" in param_group + else param_group["lr"] + ) + l2 = param_group["weight_decay"] + rho = param_group["rho"] + epsilon = param_group["eps"] + maximize = param_group["maximize"] + + optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) + + optimizer_conf.adadelta_conf.rho = rho + optimizer_conf.adadelta_conf.epsilon = epsilon + optimizer_conf.adadelta_conf.maximize = maximize + + self._generate_grad_clip_conf_for_optim_conf(param_group, optimizer_conf) + + for param in param_group.parameters: + vars_conf[param].l2 = l2 + if param.requires_grad: + optimizer_conf.variable_op_names.append(vars_conf[param].name) + + new_opt_confs.append(optimizer_conf) + return new_opt_confs diff --git a/python/oneflow/nn/optimizer/adagrad.py b/python/oneflow/nn/optimizer/adagrad.py index db75b15592b32556ebd0a0a934c4e97e48929b0a..5eb8adac5f5a47f20f5d428bc47a9df2820b9d54 100644 --- a/python/oneflow/nn/optimizer/adagrad.py +++ b/python/oneflow/nn/optimizer/adagrad.py @@ -18,7 +18,7 @@ import math from typing import Callable, Dict, Iterator, List, Tuple, Union import oneflow as flow -from oneflow.nn.optimizer.optimizer import Optimizer, ParamGroup +from oneflow.optim.optimizer import Optimizer, ParamGroup from oneflow.nn.parameter import Parameter @@ -176,6 +176,8 @@ class Adagrad(Optimizer): epsilon = param_group["eps"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) + optimizer_conf.adagrad_conf.initial_accumulator_value = ( initial_accumulator_value ) diff --git a/python/oneflow/nn/optimizer/adam.py b/python/oneflow/nn/optimizer/adam.py index 3b237c610b6f60763ddbcb30b0aca54615943593..6a7f82aa8b4884e3d7f31691b8ecb297aebab187 100644 --- a/python/oneflow/nn/optimizer/adam.py +++ b/python/oneflow/nn/optimizer/adam.py @@ -13,11 +13,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ +import warnings import math from typing import Callable, Dict, Iterator, List, Tuple, Union import oneflow as flow -from oneflow.nn.optimizer.optimizer import Optimizer, ParamGroup +from oneflow.optim.optimizer import Optimizer, ParamGroup from oneflow.nn.parameter import Parameter @@ -52,7 +53,9 @@ class Adam(Optimizer): numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (bool, optional): whether to use the AMSGrad variant of this algorithm. (default: False) - do_bias_correction (bool, optional): Whether do bias correction (default: True) + do_bias_correction (bool, optional): whether to do bias correction (default: True) + fused (bool, optional): whether to divide all the parameters into several groups, then + update each group of parameters with the fused kernel. (default: False) .. _Adam\\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 @@ -116,6 +119,7 @@ class Adam(Optimizer): weight_decay: float = 0, amsgrad: bool = False, do_bias_correction: bool = True, + fused: bool = False, ): assert lr >= 0.0, f"Invalid learning rate: {lr}" assert eps >= 0.0, f"Invalid epsilon value: {eps}" @@ -135,6 +139,7 @@ class Adam(Optimizer): options["bias_correction1"] = 1.0 options["bias_correction2"] = 1.0 options["do_bias_correction"] = do_bias_correction + options["fused"] = fused super().__init__(params, options) for param_group in self.param_groups: @@ -142,6 +147,14 @@ class Adam(Optimizer): assert param.is_leaf, "parameters must be leaf tensor" self._state[param] = dict() + if param_group["fused"] and param_group["amsgrad"]: + warnings.warn("Fused Adam is not supported when amsgrad=True.") + param_group["fused"] = False + + if param_group["fused"] and not param.is_cuda: + warnings.warn("Fused Adam only support cuda parameters.") + param_group["fused"] = False + self._op_with_amsgrad = ( flow.stateful_op("adam_update") .Input("model") @@ -161,6 +174,86 @@ class Adam(Optimizer): .Build() ) + def _single_tensor_update(self, param_group): + kwargs = { + "learning_rate": param_group["lr"], + "bias_correction1": param_group["bias_correction1"], + "bias_correction2": param_group["bias_correction2"], + "l2": param_group["weight_decay"], + "beta1": param_group["betas"][0], + "beta2": param_group["betas"][1], + "epsilon": param_group["eps"], + "do_bias_correction": param_group["do_bias_correction"], + "amsgrad": param_group["amsgrad"], + } + for param in param_group.parameters: + if param.grad is None: + continue + if "exp_avg" not in self._state[param]: + self._state[param]["exp_avg"] = flow.zeros_like(param) + if "exp_avg_sq" not in self._state[param]: + self._state[param]["exp_avg_sq"] = flow.zeros_like(param) + if param_group["amsgrad"]: + if "max_exp_avg_sq" not in self._state[param]: + self._state[param]["max_exp_avg_sq"] = flow.zeros_like(param) + + m_tensor = self._state[param]["exp_avg"] + v_tensor = self._state[param]["exp_avg_sq"] + + if param_group["amsgrad"]: + max_v_tensor = self._state[param]["max_exp_avg_sq"] + flow._C.dispatch_adam_update( + self._op_with_amsgrad, + (param, param.grad, m_tensor, v_tensor, max_v_tensor), + **kwargs, + ) + else: + flow._C.dispatch_adam_update( + self._op_without_amsgrad, + (param, param.grad, m_tensor, v_tensor), + **kwargs, + ) + + def _fused_update(self, param_group): + param_list = [] + param_grad_list = [] + m_tensor_list = [] + v_tensor_list = [] + + for param in param_group.parameters: + if param.grad is None: + continue + + if "exp_avg" not in self._state[param]: + self._state[param]["exp_avg"] = flow.zeros_like(param) + if "exp_avg_sq" not in self._state[param]: + self._state[param]["exp_avg_sq"] = flow.zeros_like(param) + if param_group["amsgrad"]: + if "max_exp_avg_sq" not in self._state[param]: + self._state[param]["max_exp_avg_sq"] = flow.zeros_like(param) + + param_list.append(param) + param_grad_list.append(param.grad) + m_tensor_list.append(self._state[param]["exp_avg"]) + v_tensor_list.append(self._state[param]["exp_avg_sq"]) + + flow._C.multi_tensor_adam_update( + model=param_list, + model_diff=param_grad_list, + m=m_tensor_list, + v=v_tensor_list, + learning_rate_val=param_group["lr"], + l2=param_group["weight_decay"], + beta1=param_group["betas"][0], + beta2=param_group["betas"][1], + bias_correction1_val=param_group["bias_correction1"], + bias_correction2_val=param_group["bias_correction2"], + do_bias_correction=param_group["do_bias_correction"], + scale=1.0, + weight_decay=0.0, + epsilon=param_group["eps"], + ) + def step(self, closure: Callable = None): """Performs a single optimization step. @@ -182,46 +275,10 @@ class Adam(Optimizer): param_group["betas"][1], self._state["step"] + 1 ) - kwargs = { - "learning_rate": param_group["lr"], - "bias_correction1": param_group["bias_correction1"], - "bias_correction2": param_group["bias_correction2"], - "l2": param_group["weight_decay"], - "beta1": param_group["betas"][0], - "beta2": param_group["betas"][1], - "epsilon": param_group["eps"], - "do_bias_correction": param_group["do_bias_correction"], - "amsgrad": param_group["amsgrad"], - } - for param in param_group.parameters: - if param.grad is None: - continue - if "exp_avg" not in self._state[param]: - self._state[param]["exp_avg"] = flow.zeros_like(param) - if "exp_avg_sq" not in self._state[param]: - self._state[param]["exp_avg_sq"] = flow.zeros_like(param) - if param_group["amsgrad"]: - if "max_exp_avg_sq" not in self._state[param]: - self._state[param]["max_exp_avg_sq"] = flow.zeros_like( - param - ) - - m_tensor = self._state[param]["exp_avg"] - v_tensor = self._state[param]["exp_avg_sq"] - - if param_group["amsgrad"]: - max_v_tensor = self._state[param]["max_exp_avg_sq"] - flow._C.dispatch_adam_update( - self._op_with_amsgrad, - (param, param.grad, m_tensor, v_tensor, max_v_tensor), - **kwargs, - ) - else: - flow._C.dispatch_adam_update( - self._op_without_amsgrad, - (param, param.grad, m_tensor, v_tensor), - **kwargs, - ) + if param_group["fused"]: + self._fused_update(param_group) + else: + self._single_tensor_update(param_group) self._state["step"] += 1 @@ -244,7 +301,9 @@ class Adam(Optimizer): epsilon = param_group["eps"] do_bias_correction = param_group["do_bias_correction"] amsgrad = param_group["amsgrad"] + optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) optimizer_conf.adam_conf.beta1 = beta1 optimizer_conf.adam_conf.beta2 = beta2 diff --git a/python/oneflow/nn/optimizer/adamw.py b/python/oneflow/nn/optimizer/adamw.py index 93be4c49e37164d3df9536fab8704a37a6645aa8..ad3f0526c0f9454885ad3e85da3c3e4c8d140134 100644 --- a/python/oneflow/nn/optimizer/adamw.py +++ b/python/oneflow/nn/optimizer/adamw.py @@ -13,12 +13,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -import collections +import warnings import math from typing import Callable, Dict, Iterator, List, Tuple, Union import oneflow as flow -from oneflow.nn.optimizer.optimizer import Optimizer, ParamGroup +from oneflow.optim.optimizer import Optimizer, ParamGroup from oneflow.nn.parameter import Parameter @@ -56,7 +56,9 @@ class AdamW(Optimizer): numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (In the equation is λ, default: 0) amsgrad (bool, optional): whether to use the AMSGrad variant of this algorithm. (default: False) - do_bias_correction (bool, optional): Whether do bias correction (default: True) + do_bias_correction (bool, optional): whether to do bias correction (default: True) + fused (bool, optional): whether to divide all the parameters into several groups, then + update each group of parameters with the fused kernel. (default: False) .. _Adam\\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 @@ -119,6 +121,7 @@ class AdamW(Optimizer): weight_decay: float = 0, amsgrad: bool = False, do_bias_correction: bool = True, + fused: bool = False, ): assert lr >= 0.0, f"Invalid learning rate: {lr}" assert eps >= 0.0, f"Invalid epsilon value: {eps}" @@ -138,6 +141,7 @@ class AdamW(Optimizer): options["bias_correction2"] = 1.0 options["do_bias_correction"] = do_bias_correction options["amsgrad"] = amsgrad + options["fused"] = fused super().__init__(params, options) for param_group in self.param_groups: @@ -145,6 +149,14 @@ class AdamW(Optimizer): assert param.is_leaf, "parameters must be leaf tensor" self._state[param] = dict() + if param_group["fused"] and param_group["amsgrad"]: + warnings.warn("Fused Adamw is not supported when amsgrad=True.") + param_group["fused"] = False + + if param_group["fused"] and not param.is_cuda: + warnings.warn("Fused Adamw only support cuda parameters.") + param_group["fused"] = False + self._op_with_amsgrad = ( flow.stateful_op("adam_update") .Input("model") @@ -163,6 +175,87 @@ class AdamW(Optimizer): .Build() ) + def _single_tensor_update(self, param_group): + kwargs = { + "learning_rate": param_group["lr"], + "bias_correction1": param_group["bias_correction1"], + "bias_correction2": param_group["bias_correction2"], + "weight_decay": param_group["weight_decay"], + "beta1": param_group["betas"][0], + "beta2": param_group["betas"][1], + "epsilon": param_group["eps"], + "do_bias_correction": param_group["do_bias_correction"], + "amsgrad": param_group["amsgrad"], + } + + for param in param_group.parameters: + if param.grad is None: + continue + + if "exp_avg" not in self._state[param]: + self._state[param]["exp_avg"] = flow.zeros_like(param) + if "exp_avg_sq" not in self._state[param]: + self._state[param]["exp_avg_sq"] = flow.zeros_like(param) + if param_group["amsgrad"]: + if "max_exp_avg_sq" not in self._state[param]: + self._state[param]["max_exp_avg_sq"] = flow.zeros_like(param) + m_tensor = self._state[param]["exp_avg"] + v_tensor = self._state[param]["exp_avg_sq"] + + if param_group["amsgrad"]: + max_v_tensor = self._state[param]["max_exp_avg_sq"] + flow._C.dispatch_adam_update( + self._op_with_amsgrad, + (param, param.grad, m_tensor, v_tensor, max_v_tensor), + **kwargs, + ) + else: + flow._C.dispatch_adam_update( + self._op_without_amsgrad, + (param, param.grad, m_tensor, v_tensor), + **kwargs, + ) + + def _fused_update(self, param_group): + param_list = [] + param_grad_list = [] + m_tensor_list = [] + v_tensor_list = [] + + for param in param_group.parameters: + if param.grad is None: + continue + + if "exp_avg" not in self._state[param]: + self._state[param]["exp_avg"] = flow.zeros_like(param) + if "exp_avg_sq" not in self._state[param]: + self._state[param]["exp_avg_sq"] = flow.zeros_like(param) + if param_group["amsgrad"]: + if "max_exp_avg_sq" not in self._state[param]: + self._state[param]["max_exp_avg_sq"] = flow.zeros_like(param) + + param_list.append(param) + param_grad_list.append(param.grad) + m_tensor_list.append(self._state[param]["exp_avg"]) + v_tensor_list.append(self._state[param]["exp_avg_sq"]) + + flow._C.multi_tensor_adam_update( + model=param_list, + model_diff=param_grad_list, + m=m_tensor_list, + v=v_tensor_list, + learning_rate_val=param_group["lr"], + l2=0.0, + beta1=param_group["betas"][0], + beta2=param_group["betas"][1], + bias_correction1_val=param_group["bias_correction1"], + bias_correction2_val=param_group["bias_correction2"], + do_bias_correction=param_group["do_bias_correction"], + scale=1.0, + weight_decay=param_group["weight_decay"], + epsilon=param_group["eps"], + ) + def step(self, closure: Callable = None): """Performs a single optimization step. @@ -174,6 +267,7 @@ class AdamW(Optimizer): loss = None if closure is not None: loss = closure() + for param_group in self.param_groups: if param_group["do_bias_correction"]: param_group["bias_correction1"] = 1.0 - math.pow( @@ -183,47 +277,10 @@ class AdamW(Optimizer): param_group["betas"][1], self._state["step"] + 1 ) - kwargs = { - "learning_rate": param_group["lr"], - "bias_correction1": param_group["bias_correction1"], - "bias_correction2": param_group["bias_correction2"], - "weight_decay": param_group["weight_decay"], - "beta1": param_group["betas"][0], - "beta2": param_group["betas"][1], - "epsilon": param_group["eps"], - "do_bias_correction": param_group["do_bias_correction"], - "amsgrad": param_group["amsgrad"], - } - - for param in param_group.parameters: - if param.grad is None: - continue - - if "exp_avg" not in self._state[param]: - self._state[param]["exp_avg"] = flow.zeros_like(param) - if "exp_avg_sq" not in self._state[param]: - self._state[param]["exp_avg_sq"] = flow.zeros_like(param) - if param_group["amsgrad"]: - if "max_exp_avg_sq" not in self._state[param]: - self._state[param]["max_exp_avg_sq"] = flow.zeros_like( - param - ) - m_tensor = self._state[param]["exp_avg"] - v_tensor = self._state[param]["exp_avg_sq"] - - if param_group["amsgrad"]: - max_v_tensor = self._state[param]["max_exp_avg_sq"] - flow._C.dispatch_adam_update( - self._op_with_amsgrad, - (param, param.grad, m_tensor, v_tensor, max_v_tensor), - **kwargs, - ) - else: - flow._C.dispatch_adam_update( - self._op_without_amsgrad, - (param, param.grad, m_tensor, v_tensor), - **kwargs, - ) + if param_group["fused"]: + self._fused_update(param_group) + else: + self._single_tensor_update(param_group) self._state["step"] += 1 return loss @@ -245,6 +302,7 @@ class AdamW(Optimizer): amsgrad = param_group["amsgrad"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) optimizer_conf.adam_conf.beta1 = beta1 optimizer_conf.adam_conf.beta2 = beta2 diff --git a/python/oneflow/nn/optimizer/constant_lr.py b/python/oneflow/nn/optimizer/constant_lr.py index 25768d5075e3d32fbaaa0830c1b656d11a5e40bf..bf5606e6d6d9f88bd08086d1d4f4d19893f07de7 100644 --- a/python/oneflow/nn/optimizer/constant_lr.py +++ b/python/oneflow/nn/optimizer/constant_lr.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/cosine_annealing_lr.py b/python/oneflow/nn/optimizer/cosine_annealing_lr.py index f6aaf02ff3247e18536f63a070eac05912d99144..e6c0d586c4f3ec7b2f90eacde7c46d233fdcdf00 100644 --- a/python/oneflow/nn/optimizer/cosine_annealing_lr.py +++ b/python/oneflow/nn/optimizer/cosine_annealing_lr.py @@ -15,14 +15,12 @@ limitations under the License. """ import math -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler class CosineAnnealingLR(LRScheduler): r""" - The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html. - Set the learning rate of each parameter group using a cosine annealing schedule, where :math:`\eta_{max}` is set to the initial lr and :math:`T_{cur}` is the number of epochs since the last restart in SGDR: @@ -50,6 +48,8 @@ class CosineAnnealingLR(LRScheduler): `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only implements the cosine annealing part of SGDR, and not the restarts. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html. + Args: optimizer (Optimizer): Wrapped optimizer. T_max (int): Maximum number of iterations. diff --git a/python/oneflow/nn/optimizer/cosine_annealing_warm_restarts.py b/python/oneflow/nn/optimizer/cosine_annealing_warm_restarts.py index 8647ff7e3280db08a076a1469c8a59ac64eb3718..d5f79578cf46ddbd29c29901b6770712d57eb5f6 100644 --- a/python/oneflow/nn/optimizer/cosine_annealing_warm_restarts.py +++ b/python/oneflow/nn/optimizer/cosine_annealing_warm_restarts.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ import math -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/cosine_decay_lr.py b/python/oneflow/nn/optimizer/cosine_decay_lr.py index 60a4d248ad2fc3c34fd136aa2027470756e9831d..7ec5a4f8af26c308e679de41d1b239ab0c294ba9 100644 --- a/python/oneflow/nn/optimizer/cosine_decay_lr.py +++ b/python/oneflow/nn/optimizer/cosine_decay_lr.py @@ -15,7 +15,7 @@ limitations under the License. """ import math -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/exponential_lr.py b/python/oneflow/nn/optimizer/exponential_lr.py index 62f8c8a1388b69fb2d0f71ca9a9acde380011f01..3bbb79d777479f4c92ca31d9b2812b6b41645d4a 100644 --- a/python/oneflow/nn/optimizer/exponential_lr.py +++ b/python/oneflow/nn/optimizer/exponential_lr.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/lamb.py b/python/oneflow/nn/optimizer/lamb.py index a5fdecfd30a6aeb2f373de4ec5f131a9fa227b7c..2a6ce0ff7f2995f5f2ed64aa67aa6d1d475147d3 100644 --- a/python/oneflow/nn/optimizer/lamb.py +++ b/python/oneflow/nn/optimizer/lamb.py @@ -17,7 +17,7 @@ from typing import Callable, Dict, Iterator, List, Union, Tuple import math import oneflow as flow -from oneflow.nn.optimizer.optimizer import Optimizer +from oneflow.optim.optimizer import Optimizer from oneflow.nn.parameter import Parameter @@ -226,6 +226,7 @@ class LAMB(Optimizer): epsilon = param_group["eps"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) optimizer_conf.lamb_conf.beta1 = beta1 optimizer_conf.lamb_conf.beta2 = beta2 diff --git a/python/oneflow/nn/optimizer/linear_lr.py b/python/oneflow/nn/optimizer/linear_lr.py index 79e3a661207639863cba125edbfab143edcc2128..47e601f07770636fc61d406439999d6e9f37ecc7 100644 --- a/python/oneflow/nn/optimizer/linear_lr.py +++ b/python/oneflow/nn/optimizer/linear_lr.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler @@ -80,7 +80,7 @@ class LinearLR(LRScheduler): step / self.total_iters ) else: - multiplier = 1 + multiplier = self.end_factor return base_lr * multiplier diff --git a/python/oneflow/nn/optimizer/lr_scheduler.py b/python/oneflow/nn/optimizer/lr_scheduler.py index 7ad6eb7636f7dc12c15b6a6953103a6b5337b16c..60237e980ab075e6b3de116cd526e13274942849 100644 --- a/python/oneflow/nn/optimizer/lr_scheduler.py +++ b/python/oneflow/nn/optimizer/lr_scheduler.py @@ -13,7 +13,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer class LRScheduler(object): diff --git a/python/oneflow/nn/optimizer/multistep_lr.py b/python/oneflow/nn/optimizer/multistep_lr.py index bc0624fea7f8713b3d85df00d522f4b5f18c5fa5..3c9fbfade17ff286b8362de40facff2862dbb0a8 100644 --- a/python/oneflow/nn/optimizer/multistep_lr.py +++ b/python/oneflow/nn/optimizer/multistep_lr.py @@ -15,14 +15,14 @@ limitations under the License. """ import bisect -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler class MultiStepLR(LRScheduler): """ - Decays the learning rate of each parameter group by gamma once the number of step - reaches one of the milestones. Notice that such decay can happen simultaneously with + Decays the learning rate of each parameter group by gamma once the number of step + reaches one of the milestones. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler.When last_step=-1, sets initial lr as lr. Args: diff --git a/python/oneflow/nn/optimizer/reduce_lr_on_plateau.py b/python/oneflow/nn/optimizer/reduce_lr_on_plateau.py index 22d41066a7dc82b8b9b7093183d39160c9787ad9..2f9049eb62a8699759279eadb6190d8ca5f76463 100644 --- a/python/oneflow/nn/optimizer/reduce_lr_on_plateau.py +++ b/python/oneflow/nn/optimizer/reduce_lr_on_plateau.py @@ -14,7 +14,7 @@ See the License for the specific language governing permissions and limitations under the License. """ from math import inf -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer class ReduceLROnPlateau(object): diff --git a/python/oneflow/nn/optimizer/rmsprop.py b/python/oneflow/nn/optimizer/rmsprop.py index ec71cc0a070067155d7d014214243666658cd5b5..c8d9d0378a0292d816bcd7f5d50abbbe08e011ef 100644 --- a/python/oneflow/nn/optimizer/rmsprop.py +++ b/python/oneflow/nn/optimizer/rmsprop.py @@ -17,7 +17,7 @@ import collections from typing import Callable, Dict, Iterator, List, Union import oneflow as flow -from oneflow.nn.optimizer.optimizer import Optimizer, ParamGroup +from oneflow.optim.optimizer import Optimizer, ParamGroup from oneflow.nn.parameter import Parameter @@ -230,6 +230,7 @@ class RMSprop(Optimizer): epslion = param_group["eps"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) optimizer_conf.rmsprop_conf.decay_rate = decay_rate optimizer_conf.rmsprop_conf.centered = centered diff --git a/python/oneflow/nn/optimizer/sequential_lr.py b/python/oneflow/nn/optimizer/sequential_lr.py index ec50727aa8f5c8a384519c4050e497f211fe7e60..849026d5b1e6008faae2a8cee285a3c8f00c2a90 100644 --- a/python/oneflow/nn/optimizer/sequential_lr.py +++ b/python/oneflow/nn/optimizer/sequential_lr.py @@ -15,7 +15,7 @@ limitations under the License. """ import bisect from typing import Sequence, Union -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/sgd.py b/python/oneflow/nn/optimizer/sgd.py index 44ac3f9b18ea28be4e03e17c4692a4f4bd3b5521..fd39b08adaf4cb78e57bc1e648a0de898eeab81f 100644 --- a/python/oneflow/nn/optimizer/sgd.py +++ b/python/oneflow/nn/optimizer/sgd.py @@ -13,13 +13,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -import collections +import warnings from typing import Callable, Dict, Iterator, List, Union import oneflow as flow from oneflow.nn.parameter import Parameter -from .optimizer import Optimizer, ParamGroup +from ...optim.optimizer import Optimizer, ParamGroup class SGD(Optimizer): @@ -48,6 +48,8 @@ class SGD(Optimizer): lr (float, optional): learning rate (default: 1e-3) momentum (float, optional): Momentum factor (default: 0.0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0.0) + fused (bool, optional): whether to divide all the parameters into several groups, then + update each group of parameters with the fused kernel. (default: False) For example: @@ -100,15 +102,27 @@ class SGD(Optimizer): params: Union[Iterator[Parameter], List[Dict]], lr: float = 0.001, momentum: float = 0.0, + dampening: float = 0.0, weight_decay: float = 0.0, + nesterov: bool = False, + maximize: bool = False, + fused: bool = False, ): assert lr >= 0.0, f"Invalid learning rate: {lr}" assert momentum >= 0.0, f"Invalid momentum: {momentum}" assert weight_decay >= 0.0, f"Invalid weight_decay: {weight_decay}" + if maximize: + warnings.warn( + "Only Momentum > 0.0, param `maximize` takes effect. ", FutureWarning, + ) options = dict() options["lr"] = lr options["momentum"] = momentum + options["dampening"] = dampening options["weight_decay"] = weight_decay + options["nesterov"] = nesterov + options["maximize"] = maximize + options["fused"] = fused super().__init__(params, options) for param_group in self.param_groups: @@ -116,6 +130,10 @@ class SGD(Optimizer): assert param.is_leaf, "parameters must be leaf tensor" self._state[param] = dict() + if param_group["fused"] and not param.is_cuda: + warnings.warn("Fused SGD only support cuda parameters.") + param_group["fused"] = False + self._momentum_sgd = ( flow.stateful_op("momentum_update") .Input("model") @@ -127,9 +145,78 @@ class SGD(Optimizer): flow.stateful_op("sgd_update").Input("model").Input("model_diff").Build() ) + def _single_tensor_update(self, param_group): + lr = param_group["lr"] + l2 = param_group["weight_decay"] + for param in param_group.parameters: + if param.grad is None: + continue + if param_group["momentum"] == 0.0: + # TODO: Support param `maximize` in Naive SGD Optimizer. (zhengzekang) + flow._C.dispatch_sgd_update( + self._sgd, (param, param.grad), learning_rate=lr, l2=l2 + ) + else: + if "momentum_buf" not in self._state[param]: + self._state[param]["momentum_buf"] = flow.zeros_like(param) + momentum_buf = self._state[param]["momentum_buf"] + beta = param_group["momentum"] + dampening = param_group["dampening"] + nesterov = param_group["nesterov"] + maximize = param_group["maximize"] + flow._C.dispatch_momentum_update( + self._momentum_sgd, + (param, param.grad, momentum_buf), + learning_rate=lr, + l2=l2, + beta=beta, + dampening=dampening, + nesterov=nesterov, + maximize=maximize, + ) + + def _fused_update(self, param_group): + use_momentum = param_group["momentum"] != 0 + param_list = [] + param_grad_list = [] + if use_momentum: + momentum_buf_list = [] + + for param in param_group.parameters: + if param.grad is None: + continue + param_list.append(param) + param_grad_list.append(param.grad) + + if use_momentum: + if "momentum_buf" not in self._state[param]: + self._state[param]["momentum_buf"] = flow.zeros_like(param) + momentum_buf_list.append(self._state[param]["momentum_buf"]) + + if not use_momentum: + flow._C.multi_tensor_sgd_update( + model=param_list, + model_diff=param_grad_list, + scale=1.0, + weight_decay=param_group["weight_decay"], + learning_rate_val=param_group["lr"], + ) + else: + flow._C.multi_tensor_momentum_update( + model=param_list, + model_diff=param_grad_list, + momentum_buf=momentum_buf_list, + scale=1.0, + weight_decay=param_group["weight_decay"], + learning_rate_val=param_group["lr"], + momentum=param_group["momentum"], + dampening=param_group["dampening"], + nesterov=param_group["nesterov"], + maximize=param_group["maximize"], + ) + def step(self, closure: Callable = None): """Performs a single optimization step. - Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. @@ -138,30 +225,15 @@ class SGD(Optimizer): loss = None if closure is not None: loss = closure() + for param_group in self.param_groups: - lr = param_group["lr"] - l2 = param_group["weight_decay"] - for param in param_group.parameters: - if param.grad is None: - continue - if param_group["momentum"] == 0.0: - flow._C.dispatch_sgd_update( - self._sgd, (param, param.grad), learning_rate=lr, l2=l2 - ) - else: - if "momentum_buf" not in self._state[param]: - self._state[param]["momentum_buf"] = flow.zeros_like(param) - momentum_buf = self._state[param]["momentum_buf"] - beta = param_group["momentum"] - flow._C.dispatch_momentum_update( - self._momentum_sgd, - (param, param.grad, momentum_buf), - learning_rate=lr, - l2=l2, - beta=beta, - ) - self._state["step"] = self._state["step"] + 1 - return loss + if param_group["fused"]: + self._fused_update(param_group) + else: + self._single_tensor_update(param_group) + + self._state["step"] = self._state["step"] + 1 + return loss def _generate_conf_for_graph(self, train_conf, vars_conf): new_opt_confs = [] @@ -174,12 +246,21 @@ class SGD(Optimizer): ) beta = param_group["momentum"] l2 = param_group["weight_decay"] + dampening = param_group["dampening"] + nesterov = param_group["nesterov"] + maximize = param_group["maximize"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) + if beta == 0: optimizer_conf.naive_conf.SetInParent() else: optimizer_conf.momentum_conf.beta = beta + # Only Momentum Optimizer support these params. + optimizer_conf.momentum_conf.dampening = dampening + optimizer_conf.momentum_conf.nesterov = nesterov + optimizer_conf.momentum_conf.maximize = maximize self._generate_grad_clip_conf_for_optim_conf(param_group, optimizer_conf) diff --git a/python/oneflow/nn/optimizer/step_lr.py b/python/oneflow/nn/optimizer/step_lr.py index 41f451d6f038c8ef849686884c1249b27f5ad758..76410afcdace83d1cf948d5cb24d631365ce7c9e 100644 --- a/python/oneflow/nn/optimizer/step_lr.py +++ b/python/oneflow/nn/optimizer/step_lr.py @@ -15,7 +15,7 @@ limitations under the License. """ import math -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler diff --git a/python/oneflow/nn/optimizer/warmup_lr.py b/python/oneflow/nn/optimizer/warmup_lr.py index d0475af78aeec7c90fef0ff1d6bc85542edb9cdc..07feed153afcea7710f096335f4024e0cbffd05f 100644 --- a/python/oneflow/nn/optimizer/warmup_lr.py +++ b/python/oneflow/nn/optimizer/warmup_lr.py @@ -15,7 +15,7 @@ limitations under the License. """ import numpy as np from typing import Union -from .optimizer import Optimizer +from ...optim.optimizer import Optimizer from .lr_scheduler import LRScheduler from .sequential_lr import SequentialLR from .constant_lr import ConstantLR diff --git a/python/oneflow/nn/parallel/__init__.py b/python/oneflow/nn/parallel/__init__.py index a23bd029886dbc705d3cfa335415862abb576dfd..83716614297c9dc1e2b61cc4fda34d384d29ed58 100644 --- a/python/oneflow/nn/parallel/__init__.py +++ b/python/oneflow/nn/parallel/__init__.py @@ -13,6 +13,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -from .ddp import DistributedDataParallel +from .distributed import DistributedDataParallel __all__ = ["DistributedDataParallel"] diff --git a/python/oneflow/nn/parallel/ddp.py b/python/oneflow/nn/parallel/ddp.py deleted file mode 100644 index 95081b57a4c5fbff2b73e21bb8261af0a93d9eb3..0000000000000000000000000000000000000000 --- a/python/oneflow/nn/parallel/ddp.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import warnings -from collections import OrderedDict - -import oneflow as flow -from oneflow.support.env_var_util import parse_boolean_from_env -from oneflow.framework.tensor_tuple_util import convert_to_tensor_tuple - - -def grad_setting_fn(module, param): - def grad_setting(grad): - if param.grad is None: - start = module._param_grad_offset_in_bucket[param] - bucket_index = module._bucket_index[param] - bucket_tensor = module._bucket_tensors[bucket_index] - param.grad = flow._C.slice_view_1d_contiguous( - bucket_tensor, start, start + param.numel() - ).view(param.shape) - param._is_grad_acc_inplace = True - return grad - - return grad_setting - - -def allreduce_fn(module, param): - ddp_state_for_reversed_params = module._ddp_state_for_reversed_params - buckets = module._buckets - bucket_tensors = module._bucket_tensors - - def allreduce(grad): - ddp_state_for_reversed_params[param][0] = True - for index, bucket in enumerate(buckets): - deleted = all(ddp_state_for_reversed_params[x][1] for x in bucket) - if deleted: - continue - - assert not any(ddp_state_for_reversed_params[x][1] for x in bucket) - - all_params_in_bucket_ready = all( - ddp_state_for_reversed_params[x][0] for x in bucket - ) - if all_params_in_bucket_ready: - for x in bucket: - ddp_state_for_reversed_params[x][1] = True - # NOTE(jianhao)(higher-order-grad): - # local allreduce doesn't have gradient function, higher-order grad may be unsupported - flow._C.local_all_reduce(bucket_tensors[index], inplace=True) - else: - break - - return allreduce - - -def DistributedDataParallel( - module: "flow.nn.Module", *, broadcast_buffers: bool = True, bucket_size: int = 10 -): - assert all(x.dtype == flow.float32 for x in module.parameters()) - if parse_boolean_from_env("ONEFLOW_DISABLE_VIEW", False): - warnings.warn( - "because the environment variable 'ONEFLOW_DISABLE_VIEW' is set to true, so the view mechanism is disabled, and we will set bucket_size = 1" - ) - bucket_size = 1 - world_size = flow.env.get_world_size() - with flow.no_grad(): - for x in module.parameters(): - requires_grad = x.requires_grad - flow._C.broadcast(x, inplace=True) - # TODO: fix the bug that x's requires_grad is discarded - # after flow._C.broadcast - x.requires_grad_(requires_grad) - - all_grad_size = sum([x.numel() for x in module.parameters()]) - if all_grad_size > 0: - device = list(module.parameters())[0].device - assert all(x.device == device for x in module.parameters()) - reversed_param_list = list( - reversed(list([param for param in module.parameters() if param.requires_grad])) - ) - module._param_grad_offset_in_bucket = {} - - def numel_in_bucket(tensor: flow.Tensor): - def align(x: int, unit_size: int): - return (x + (unit_size - 1)) // unit_size * unit_size - - # tensor memory should be align to 512 bytes for cuda operations, - # 4 is the bytes of a float number - # TODO(jianhao): expose the `kCudaMemAllocAlignSize` from C++ to - # avoid this hardcoded "512" - return align(tensor.numel(), 512 // 4) - - offset_in_bucket = 0 - with flow.no_grad(): - for i, param in enumerate(reversed_param_list): - assert param.is_leaf - if i % bucket_size == 0: - offset_in_bucket = 0 - module._param_grad_offset_in_bucket[param] = offset_in_bucket - offset_in_bucket += numel_in_bucket(param) - - module._bucket_index = { - x: i // bucket_size for i, x in enumerate(reversed_param_list) - } - module._buckets = [ - reversed_param_list[i : i + bucket_size] - for i in range(0, len(reversed_param_list), bucket_size) - ] - - bucket_elems = 0 - module._bucket_tensors = [] - for b in module._buckets: - bucket_elems = sum([numel_in_bucket(x) for x in b]) - module._bucket_tensors.append( - flow.zeros(bucket_elems, dtype=flow.float32, device=device) - ) - - ddp_state_for_reversed_params = OrderedDict( - reversed([(x, [False, False]) for x in module.parameters() if x.requires_grad]) - ) - module._ddp_state_for_reversed_params = ddp_state_for_reversed_params - # The gradient shoule be averaged by all the nodes, so besides allreduce, - # a division by world_size is required. - # Use x * (1 / world_size) instead of x / world_size for two reasons: - # 1. multiplication is faster than division - # 2. An inplace operation is needed here (for allreduce grouping) - # But we do not have inplace division in oneflow. - mul_factor = 1 / world_size - - def inplace_mul_and_return_none(x): - x.mul_(mul_factor) - return None - - for param in module.parameters(): - if param.requires_grad: - param.register_hook(grad_setting_fn(module, param)) - param._register_post_grad_accumulation_hook(inplace_mul_and_return_none) - param._register_post_grad_accumulation_hook(allreduce_fn(module, param)) - - def post_forward_hook(module, input, output): - ddp_state_for_reversed_params = module._ddp_state_for_reversed_params - for state in ddp_state_for_reversed_params.values(): - state[0], state[1] = False, False - if isinstance(output, (tuple, list)): - if isinstance(output[0], dict): - # For List[Dict[Tensor]] return type. - out_key_list = [] - out_val_list = [] - for out in output: - out_keys = list(out.keys()) - out_values = list(out.values()) - out_key_list.append(out_keys) - out_val_list.extend(out_values) - out_values = flow._C.select_top_n( - convert_to_tensor_tuple( - [*out_val_list, *ddp_state_for_reversed_params.keys()] - ), - n=len(out_val_list), - ) - output = [] - for i, keys in enumerate(out_key_list): - output.append( - dict(zip(keys, out_values[i * len(keys) : (i + 1) * len(keys)])) - ) - return output - else: - # For List[Tensor] return type. - output = flow._C.select_top_n( - convert_to_tensor_tuple( - [*output, *ddp_state_for_reversed_params.keys()] - ), - n=len(output), - ) - elif isinstance(output, dict): - # For Dict[Tensor] return type. - out_keys = list(output.keys()) - out_values = list(output.values()) - out_values = flow._C.select_top_n( - convert_to_tensor_tuple( - [*out_values, *ddp_state_for_reversed_params.keys()] - ), - n=len(out_values), - ) - return dict(zip(out_keys, out_values)) - else: - # For Tensor return type. - output = flow._C.select_top_n( - convert_to_tensor_tuple( - [output, *ddp_state_for_reversed_params.keys()] - ), - n=1, - )[0] - return output - - module.register_forward_hook(post_forward_hook) - - if broadcast_buffers: - - def pre_forward_hook(module, input): - with flow.no_grad(): - buffers = list(module.buffers()) - if len(buffers) > 0: - flow._C.stream_touch(buffers) # for reusing soft syncs - for x in buffers: - flow._C.broadcast(x, inplace=True) - - module.register_forward_pre_hook(pre_forward_hook) - - return module diff --git a/python/oneflow/nn/parallel/distributed.py b/python/oneflow/nn/parallel/distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..000527bebb692090e45234086635955caa5d1964 --- /dev/null +++ b/python/oneflow/nn/parallel/distributed.py @@ -0,0 +1,225 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import warnings +from collections import OrderedDict + +import oneflow as flow +from oneflow.support.env_var_util import parse_boolean_from_env +from oneflow.framework.tensor_tuple_util import convert_to_tensor_tuple + + +def grad_setting_fn(module, param): + def grad_setting(grad): + if param.grad is None: + start = module._param_grad_offset_in_bucket[param] + bucket_index = module._bucket_index[param] + bucket_tensor = module._bucket_tensors[bucket_index] + param.grad = flow._C.slice_view_1d_contiguous( + bucket_tensor, start, start + param.numel() + ).view(param.shape) + return grad + + return grad_setting + + +def allreduce_fn(module, param): + ddp_state_for_reversed_params = module._ddp_state_for_reversed_params + buckets = module._buckets + bucket_tensors = module._bucket_tensors + + def allreduce(grad): + ddp_state_for_reversed_params[param][0] = True + for index, bucket in enumerate(buckets): + deleted = all(ddp_state_for_reversed_params[x][1] for x in bucket) + if deleted: + continue + + assert not any(ddp_state_for_reversed_params[x][1] for x in bucket) + + all_params_in_bucket_ready = all( + ddp_state_for_reversed_params[x][0] for x in bucket + ) + if all_params_in_bucket_ready: + for x in bucket: + ddp_state_for_reversed_params[x][1] = True + # NOTE(jianhao)(higher-order-grad): + # local allreduce doesn't have gradient function, higher-order grad may be unsupported + flow._C.local_all_reduce(bucket_tensors[index], inplace=True) + else: + break + + return allreduce + + +def DistributedDataParallel( + module: "flow.nn.Module", + *, + broadcast_buffers: bool = True, + broadcast_parameters: bool = True, + bucket_size: int = 10 +): + assert all(x.dtype == flow.float32 for x in module.parameters()) + if parse_boolean_from_env("ONEFLOW_DISABLE_VIEW", False): + warnings.warn( + "because the environment variable 'ONEFLOW_DISABLE_VIEW' is set to true, so the view mechanism is disabled, and we will set bucket_size = 1" + ) + bucket_size = 1 + world_size = flow.env.get_world_size() + if broadcast_parameters: + with flow.no_grad(): + for x in module.parameters(): + requires_grad = x.requires_grad + flow._C.comm_broadcast(x, inplace=True) + # TODO: fix the bug that x's requires_grad is discarded + # after flow._C.comm_broadcast + x.requires_grad_(requires_grad) + + all_grad_size = sum([x.numel() for x in module.parameters()]) + if all_grad_size > 0: + device = list(module.parameters())[0].device + assert all(x.device == device for x in module.parameters()) + reversed_param_list = list( + reversed(list([param for param in module.parameters() if param.requires_grad])) + ) + module._param_grad_offset_in_bucket = {} + + def numel_in_bucket(tensor: flow.Tensor): + def align(x: int, unit_size: int): + return (x + (unit_size - 1)) // unit_size * unit_size + + # tensor memory should be align to 512 bytes for cuda operations, + # 4 is the bytes of a float number + return align(tensor.numel(), flow._oneflow_internal.max_alignment_size() // 4) + + offset_in_bucket = 0 + with flow.no_grad(): + for i, param in enumerate(reversed_param_list): + assert param.is_leaf + if i % bucket_size == 0: + offset_in_bucket = 0 + module._param_grad_offset_in_bucket[param] = offset_in_bucket + offset_in_bucket += numel_in_bucket(param) + + module._bucket_index = { + x: i // bucket_size for i, x in enumerate(reversed_param_list) + } + module._buckets = [ + reversed_param_list[i : i + bucket_size] + for i in range(0, len(reversed_param_list), bucket_size) + ] + + bucket_elems = 0 + module._bucket_tensors = [] + for b in module._buckets: + bucket_elems = sum([numel_in_bucket(x) for x in b]) + module._bucket_tensors.append( + flow.zeros(bucket_elems, dtype=flow.float32, device=device) + ) + + ddp_state_for_reversed_params = OrderedDict( + reversed([(x, [False, False]) for x in module.parameters() if x.requires_grad]) + ) + module._ddp_state_for_reversed_params = ddp_state_for_reversed_params + # The gradient shoule be averaged by all the nodes, so besides allreduce, + # a division by world_size is required. + # Use x * (1 / world_size) instead of x / world_size for two reasons: + # 1. multiplication is faster than division + # 2. An inplace operation is needed here (for allreduce grouping) + # But we do not have inplace division in oneflow. + mul_factor = 1 / world_size + + def inplace_mul_and_return_none(x): + x.mul_(mul_factor) + return None + + for param in module.parameters(): + if param.requires_grad: + param.register_hook(grad_setting_fn(module, param)) + param._register_post_grad_accumulation_hook(inplace_mul_and_return_none) + param._register_post_grad_accumulation_hook(allreduce_fn(module, param)) + + def post_forward_hook(module, input, output): + ddp_state_for_reversed_params = module._ddp_state_for_reversed_params + for state in ddp_state_for_reversed_params.values(): + state[0], state[1] = False, False + if isinstance(output, (tuple, list)): + if isinstance(output[0], dict): + # For List[Dict[Tensor]] return type. + out_key_list = [] + out_val_list = [] + for out in output: + out_keys = list(out.keys()) + out_values = list(out.values()) + out_key_list.append(out_keys) + out_val_list.extend(out_values) + out_values = flow._C.select_top_n( + convert_to_tensor_tuple( + [*out_val_list, *ddp_state_for_reversed_params.keys()] + ), + n=len(out_val_list), + ) + output = [] + for i, keys in enumerate(out_key_list): + output.append( + dict(zip(keys, out_values[i * len(keys) : (i + 1) * len(keys)])) + ) + return output + else: + # For List[Tensor] return type. + output = flow._C.select_top_n( + convert_to_tensor_tuple( + [*output, *ddp_state_for_reversed_params.keys()] + ), + n=len(output), + ) + elif isinstance(output, dict): + # For Dict[Tensor] return type. + out_keys = list(output.keys()) + out_values = list(output.values()) + out_values = flow._C.select_top_n( + convert_to_tensor_tuple( + [*out_values, *ddp_state_for_reversed_params.keys()] + ), + n=len(out_values), + ) + return dict(zip(out_keys, out_values)) + else: + # For Tensor return type. + output = flow._C.select_top_n( + convert_to_tensor_tuple( + [output, *ddp_state_for_reversed_params.keys()] + ), + n=1, + )[0] + buffers = list(module.buffers()) + if len(buffers) > 0: + flow._C.stream_touch(buffers) + return output + + module.register_forward_hook(post_forward_hook) + + if broadcast_buffers: + + def pre_forward_hook(module, input): + with flow.no_grad(): + buffers = list(module.buffers()) + flow._C.comm_broadcast(buffers, inplace=True) + + module.register_forward_pre_hook(pre_forward_hook) + + module._is_ddp_module = True + + return module diff --git a/python/oneflow/experimental/__init__.py b/python/oneflow/nn/qat/__init__.py similarity index 100% rename from python/oneflow/experimental/__init__.py rename to python/oneflow/nn/qat/__init__.py diff --git a/python/oneflow/nn/qat/conv.py b/python/oneflow/nn/qat/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..ce4298a9012526beeebdd37488c0b9cbf698beb7 --- /dev/null +++ b/python/oneflow/nn/qat/conv.py @@ -0,0 +1,412 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow +from oneflow import nn as nn +from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t +from typing import Union + + +def get_conv_fake_quantized( + input, input_observer, current_train_step, weight, weight_observer, fake_quantizer +): + in_scale, in_zero_point = input_observer(input, current_train_step) + input_fake_quanted = fake_quantizer(input, in_scale, in_zero_point) + w_scale, w_zero_point = weight_observer(weight) + weight_fake_quanted = fake_quantizer(weight, w_scale, w_zero_point) + return input_fake_quanted, weight_fake_quanted + + +def init_conv_fake_quants( + self, + quantization_formula: str = "google", + quantization_bit: int = 8, + quantization_scheme: str = "symmetric", + weight_quant_per_layer: bool = True, + input_quant_momentum: float = 0.95, +): + self.input_min_max_observer = nn.MovingAverageMinMaxObserver( + stop_update_after_iters=1, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + momentum=input_quant_momentum, + ) + self.register_buffer("current_train_step", flow.zeros(1, dtype=flow.int64,)) + self.weight_min_max_observer = nn.MinMaxObserver( + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + per_layer_quantization=weight_quant_per_layer, + ) + self.fake_quantizer = nn.FakeQuantization( + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + ) + + +class QatConv1d(nn.Conv1d): + r"""A Conv1d module attached with `nn.MinMaxObserver`, `nn.MovingAverageMinMaxObserver` and `nn.FakeQuantization` modules for weight and input, + used for quantization aware training. + + The parameters of QatConv1d are the same as :class:`~oneflow.nn.Conv1d` with some extra parameters for fake quantization, + see :class:`~oneflow.nn.MinMaxObserver`, :class:`~oneflow.nn.MovingAverageMinMaxObserver` and :class:`~oneflow.nn.FakeQuantization` for more details. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int, tuple or str, optional): Padding added to both sides of + the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel + elements. Default: 1 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If ``True``, adds a learnable bias to the + output. Default: ``True`` + padding_mode (string, optional): ``'zeros'``. Default: ``'zeros'`` + quantization_formula (str): Support "google" or "cambricon". + quantization_bit (int): Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8. + quantization_scheme (str): "symmetric" or "affine", quantize to signed / unsigned integer. Defaults to "symmetric". + weight_quant_per_layer (bool): True or False, means per-layer / per-channel for weight quantization. Defaults to True. + input_quant_momentum (float): Smoothing parameter for exponential moving average operation for input quantization. Defaults to 0.95. + + Shape: + - Input: :math:`(N, C_{in}, L_{in})` + - Output: :math:`(N, C_{out}, L_{out})` where + + .. math:: + L_{out} = \\left\\lfloor\\frac{L_{in} + 2 \\times \\text{padding} - \\text{dilation} + \\times (\\text{kernel\\_size} - 1) - 1}{\\text{stride}} + 1\\right\\rfloor + + Attributes: + weight (Tensor): the learnable weights of the module of shape + :math:`(\\text{out\\_channels}, + \\frac{\\text{in\\_channels}}{\\text{groups}}, \\text{kernel\\_size})`. + The values of these weights are sampled from + :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where + :math:`k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}` + bias (Tensor): the learnable bias of the module of shape + (out_channels). If :attr:`bias` is ``True``, then the values of these weights are + sampled from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where + :math:`k = \\frac{groups}{C_\\text{in} * \\text{kernel\\_size}}` + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + >>> import oneflow.nn as nn + + >>> arr = np.random.randn(20, 16, 50) + >>> input = flow.Tensor(arr) + >>> m = nn.QatConv1d(16, 33, 3, stride=2, quantization_formula="google", quantization_bit=8, quantization_scheme="symmetric") + >>> output = m(input) + + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: _size_1_t, + stride: _size_1_t = 1, + padding: Union[str, _size_1_t] = 0, + dilation: _size_1_t = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + quantization_formula: str = "google", + quantization_bit: int = 8, + quantization_scheme: str = "symmetric", + weight_quant_per_layer: bool = True, + input_quant_momentum: float = 0.95, + ): + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode, + ) + self.channel_pos = "channels_first" + init_conv_fake_quants( + self, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ) + + def forward(self, x): + fake_quan_input, fake_quan_weight = get_conv_fake_quantized( + x, + self.input_min_max_observer, + self.current_train_step, + self.weight, + self.weight_min_max_observer, + self.fake_quantizer, + ) + return self._conv_forward(fake_quan_input, fake_quan_weight, self.bias) + + +class QatConv2d(nn.Conv2d): + r"""A Conv2d module attached with `nn.MinMaxObserver`, `nn.MovingAverageMinMaxObserver` and `nn.FakeQuantization` modules for weight and input, + used for quantization aware training. + + The parameters of QatConv2d are the same as :class:`~oneflow.nn.Conv2d` with some extra parameters for fake quantization, + see :class:`~oneflow.nn.MinMaxObserver`, :class:`~oneflow.nn.MovingAverageMinMaxObserver` and :class:`~oneflow.nn.FakeQuantization` for more details. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If ``True``, adds a learnable bias to the + output. Default: ``True`` + padding_mode (string, optional): ``'zeros'``. Default: ``'zeros'`` + quantization_formula (str): Support "google" or "cambricon". + quantization_bit (int): Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8. + quantization_scheme (str): "symmetric" or "affine", quantize to signed / unsigned integer. Defaults to "symmetric". + weight_quant_per_layer (bool): True or False, means per-layer / per-channel for weight quantization. Defaults to True. + input_quant_momentum (float): Smoothing parameter for exponential moving average operation for input quantization. Defaults to 0.95. + + + Shape: + - Input: :math:`(N, C_{in}, H_{in}, W_{in})` + - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where + + .. math:: + H_{out} = \\left\\lfloor\\frac{H_{in} + 2 \\times \\text{padding}[0] - \\text{dilation}[0] + \\times (\\text{kernel_size}[0] - 1) - 1}{\\text{stride}[0]} + 1\\right\\rfloor + + .. math:: + W_{out} = \\left\\lfloor\\frac{W_{in} + 2 \\times \\text{padding}[1] - \\text{dilation}[1] + \\times (\\text{kernel_size}[1] - 1) - 1}{\\text{stride}[1]} + 1\\right\\rfloor + + Attr: + - weight (Tensor): the learnable weights of the module of shape + :math:`(\\text{out_channels}, \\frac{\\text{in_channels}}{\\text{groups}},` + :math:`\\text{kernel_size[0]}, \\text{kernel_size[1]})`. + The values of these weights are sampled from + :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where + :math:`k = \\frac{groups}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel_size}[i]}` + + - bias (Tensor): the learnable bias of the module of shape + (out_channels). If :attr:`bias` is ``True``, + then the values of these weights are + sampled from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where + :math:`k = \\frac{groups}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel_size}[i]}` + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + >>> import oneflow.nn as nn + + >>> arr = np.random.randn(20, 16, 50, 100) + >>> input = flow.Tensor(arr) + >>> m = nn.QatConv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1), quantization_formula="google", quantization_bit=8, quantization_scheme="symmetric") + >>> output = m(input) + + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: _size_2_t, + stride: _size_2_t = 1, + padding: Union[str, _size_2_t] = 0, + dilation: _size_2_t = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + quantization_formula: str = "google", + quantization_bit: int = 8, + quantization_scheme: str = "symmetric", + weight_quant_per_layer: bool = True, + input_quant_momentum: float = 0.95, + ): + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode, + ) + self.channel_pos = "channels_first" + init_conv_fake_quants( + self, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ) + + def forward(self, x): + fake_quan_input, fake_quan_weight = get_conv_fake_quantized( + x, + self.input_min_max_observer, + self.current_train_step, + self.weight, + self.weight_min_max_observer, + self.fake_quantizer, + ) + return self._conv_forward(fake_quan_input, fake_quan_weight, self.bias) + + +class QatConv3d(nn.Conv3d): + r"""A Conv3d module attached with `nn.MinMaxObserver`, `nn.MovingAverageMinMaxObserver` and `nn.FakeQuantization` modules for weight and input, + used for quantization aware training. + + The parameters of QatConv3d are the same as :class:`~oneflow.nn.Conv3d` with some extra parameters for fake quantization, + see :class:`~oneflow.nn.MinMaxObserver`, :class:`~oneflow.nn.MovingAverageMinMaxObserver` and :class:`~oneflow.nn.FakeQuantization` for more details. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the convolving kernel + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int, tuple or str, optional): Padding added to all six sides of + the input. Default: 0 + dilation (int or tuple, optional): Spacing between kernel elements. Default: 1 + groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 + bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True`` + padding_mode (string, optional): ``'zeros'``. Default: ``'zeros'`` + quantization_formula (str): Support "google" or "cambricon". + quantization_bit (int): Quantize input to uintX / intX, X can be in range [2, 8]. Defaults to 8. + quantization_scheme (str): "symmetric" or "affine", quantize to signed / unsigned integer. Defaults to "symmetric". + weight_quant_per_layer (bool): True or False, means per-layer / per-channel for weight quantization. Defaults to True. + input_quant_momentum (float): Smoothing parameter for exponential moving average operation for input quantization. Defaults to 0.95. + + + Shape: + - Input: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})` + - Output: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})` where + + .. math:: + D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] + \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor + + .. math:: + H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] + \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor + + .. math:: + W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] + \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor + + Attributes: + weight (Tensor): the learnable weights of the module of shape + :math:`(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},` + :math:`\text{kernel\_size[0]}, \text{kernel\_size[1]}, \text{kernel\_size[2]})`. + The values of these weights are sampled from + :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where + :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` + bias (Tensor): the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``, + then the values of these weights are + sampled from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where + :math:`k = \frac{groups}{C_\text{in} * \prod_{i=0}^{2}\text{kernel\_size}[i]}` + + For example: + + .. code-block:: python + + >>> import numpy as np + >>> import oneflow as flow + >>> import oneflow.nn as nn + + >>> arr = np.random.randn(1, 2, 5, 5, 5) + >>> input = flow.Tensor(arr) + >>> m = nn.QatConv3d(2, 4, kernel_size=3, stride=1, quantization_formula="google", quantization_bit=8, quantization_scheme="symmetric") + >>> output = m(input) + + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: _size_3_t, + stride: _size_3_t = 1, + padding: Union[str, _size_3_t] = 0, + dilation: _size_3_t = 1, + groups: int = 1, + bias: bool = True, + padding_mode: str = "zeros", + quantization_formula: str = "google", + quantization_bit: int = 8, + quantization_scheme: str = "symmetric", + weight_quant_per_layer: bool = True, + input_quant_momentum: float = 0.95, + ): + super().__init__( + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode, + ) + self.channel_pos = "channels_first" + init_conv_fake_quants( + self, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ) + + def forward(self, x): + fake_quan_input, fake_quan_weight = get_conv_fake_quantized( + x, + self.input_min_max_observer, + self.current_train_step, + self.weight, + self.weight_min_max_observer, + self.fake_quantizer, + ) + return self._conv_forward(fake_quan_input, fake_quan_weight, self.bias) + + +if __name__ == "__main__": + import doctest + + doctest.testmod(raise_on_error=True) diff --git a/python/oneflow/nn/utils/clip_grad.py b/python/oneflow/nn/utils/clip_grad.py index ad53c203e04ed5c945c3059dac3666b1624f96c8..17ce77843976c0aeb46990b1dc83b5d4d7d252b1 100644 --- a/python/oneflow/nn/utils/clip_grad.py +++ b/python/oneflow/nn/utils/clip_grad.py @@ -22,7 +22,7 @@ import oneflow as flow from oneflow.framework.tensor import Tensor from oneflow.framework.tensor import register_tensor_op -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module _tensor_or_tensors = Union[Tensor, Iterable[Tensor]] @@ -94,7 +94,7 @@ def clip_grad_norm_( if parameters[0].is_global: assert all( [p.is_global for p in parameters] - ), "All parameters must be consistent tensor." + ), "All parameters must be global tensor." sbp_broadcast = [flow.sbp.broadcast for _ in parameters[0].sbp] param0_placement = parameters[0].placement if norm_type == float("inf"): diff --git a/python/oneflow/nn/utils/container.py b/python/oneflow/nn/utils/container.py index cead7b951888a747f8059166219bcd2628b023b5..61c906f0d437175be38a23dc9773ae82abd258d1 100644 --- a/python/oneflow/nn/utils/container.py +++ b/python/oneflow/nn/utils/container.py @@ -32,7 +32,7 @@ from typing import ( ) import oneflow as flow -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module T = TypeVar("T") @@ -171,7 +171,7 @@ def get_list(T): self.add_module(str(len(self)), module) return self - def extend(self: T, modules: Iterable[Module]) -> T: + def extend(self: T, modules: Iterable[T]) -> T: """Appends modules from a Python iterable to the end of the list. Arguments: diff --git a/python/oneflow/nn/utils/rnn.py b/python/oneflow/nn/utils/rnn.py index bb366905cc3308e8ca8aa7365c1ece1815bc9e97..7717ec8f4528c28e533babf77db6e83db659652c 100644 --- a/python/oneflow/nn/utils/rnn.py +++ b/python/oneflow/nn/utils/rnn.py @@ -45,7 +45,7 @@ def invert_permutation(permutation: Optional[Tensor]) -> Optional[Tensor]: class PackedSequence(object): """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.PackedSequence.html. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.PackedSequence.html. Holds the data and list of :attr:`batch_sizes` of a packed sequence. @@ -209,7 +209,7 @@ def pack_padded_sequence( enforce_sorted: bool = True, ) -> PackedSequence: """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.pack_padded_sequence.html. Packs a Tensor containing padded sequences of variable length. @@ -264,7 +264,7 @@ def pad_packed_sequence( total_length: Optional[int] = None, ) -> Tuple[Tensor, Tensor]: """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pad_packed_sequence.html. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.pad_packed_sequence.html. Pads a packed batch of variable length sequences. @@ -278,8 +278,6 @@ def pad_packed_sequence( :attr:`total_length` is useful to implement the ``pack sequence -> recurrent network -> unpack sequence`` pattern in a :class:`~oneflow.nn.Module` wrapped in :class:`~oneflow.nn.DataParallel`. - See :ref:`this FAQ section ` for - details. Args: sequence (PackedSequence): batch to pad @@ -362,6 +360,11 @@ def pad_packed_sequence( device=sequence.data.device, requires_grad=sequence.data.requires_grad, ) + # `padded_output` is leaf tensor which needs to be transformed into non-leaf tensor + # when it requires grad by calling the `clone` method before the following + # in-place operation to avoid runtime check error . + if padded_output.requires_grad == True: + padded_output = padded_output.clone() # This will be modified at every iteration, but we reserve memory for it now. tmp_view_size = output_size # == [-1, -1, *sequence.data.size()[1:]] @@ -390,7 +393,7 @@ def pad_packed_sequence( prev_batch_size = batch_size if batch_first: - permute_dims = (1, 0) + permute_dims = [1, 0] for i in range(2, padded_output.ndim): permute_dims.append(i) padded_output = padded_output.permute(permute_dims) @@ -411,7 +414,7 @@ def pad_sequence( padding_value: float = 0.0, ) -> Tensor: """The interface is consistent with PyTorch. - The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pad_sequence.html. + The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.utils.rnn.pad_sequence.html. Pad a list of variable length Tensors with ``padding_value`` diff --git a/python/oneflow/nn/utils/weight_norm.py b/python/oneflow/nn/utils/weight_norm.py index 38688c1e3447c1ad1c3b272353d6c407fd9acc0a..d3b0ba89572b9ddf1d88bc003476cb94656f0b60 100644 --- a/python/oneflow/nn/utils/weight_norm.py +++ b/python/oneflow/nn/utils/weight_norm.py @@ -18,7 +18,7 @@ limitations under the License. import oneflow as flow from oneflow.framework.tensor import Tensor from typing import Any, TypeVar -from oneflow.nn.module import Module +from oneflow.nn.modules.module import Module def _norm_except_dim_0(v: Tensor): diff --git a/python/oneflow/one_embedding.py b/python/oneflow/one_embedding.py index 0583f437d2a8cf3bbda4117b817b135b5eb11b88..d8e35e4d4d53e74cc5f067126bcfac43238dc74c 100644 --- a/python/oneflow/one_embedding.py +++ b/python/oneflow/one_embedding.py @@ -15,8 +15,8 @@ limitations under the License. """ from typing import Callable, Dict, Iterator, List, Union import oneflow as flow -from oneflow.nn.module import Module -from oneflow.nn.optimizer.optimizer import Optimizer +from oneflow.nn.modules.module import Module +from oneflow.optim.optimizer import Optimizer from oneflow.nn.parameter import Parameter import json import datetime @@ -25,19 +25,23 @@ from oneflow._oneflow_internal import PersistentTableReader from oneflow._oneflow_internal import PersistentTableWriter import numpy as np import traceback +from oneflow import nn +import oneflow.framework.graph_build_util as graph_build_util def _check_initializer(initializer): assert isinstance(initializer, dict) assert initializer.__contains__("type") initializer_type = initializer["type"] - assert initializer_type in ["uniform", "normal"] + assert initializer_type in ["uniform", "normal", "constant"] if initializer_type == "uniform": assert initializer.__contains__("low") assert initializer.__contains__("high") elif initializer_type == "normal": assert initializer.__contains__("mean") assert initializer.__contains__("std") + elif initializer_type == "constant": + assert initializer.__contains__("value") else: raise NotImplementedError("unsupported initializer_type") @@ -89,7 +93,11 @@ def _init( key_value_store_options["value_type_size"] = value_type_size key_value_store_options["value_type"] = str(dtype) scale_factor = store_options["size_factor"] - key_value_store_options["storage_dim"] = scale_factor * embedding_dim + storage_dim = store_options["storage_dim"] + if storage_dim != -1: + key_value_store_options["storage_dim"] = storage_dim + else: + key_value_store_options["storage_dim"] = scale_factor * embedding_dim # kv store assert store_options.__contains__("kv_store") kv_store = store_options["kv_store"] @@ -171,6 +179,8 @@ class Embedding(Module): tables, store_options, default_initializer=None, + padding_idx=None, + seed=0, ): super().__init__() self.dtype = dtype @@ -185,6 +195,13 @@ class Embedding(Module): store_options, default_initializer, ) + self.storage_dim = key_value_store_options["storage_dim"] + self.embedding_name = key_value_store_options["name"] + self.seed = seed + self.is_full_cache = ( + len(key_value_store_options["kv_store"]["caches"]) > 0 + and key_value_store_options["kv_store"]["caches"][0]["policy"] == "full" + ) self.key_value_store_options = json.dumps(key_value_store_options) self.embedding_tables = json.dumps(embedding_tables) self.num_tables = len(embedding_tables["tables"]) @@ -194,9 +211,13 @@ class Embedding(Module): self.handler = OneEmbeddingHandler( self.key_value_store_options, self.local_rank, self.rank_id, self.world_size ) + self.shadow = flow.nn.Parameter(flow.Tensor(1)) + self.padding_idx = padding_idx + self.embedding = None def _save_to_state_dict(self, destination, prefix, keep_vars): + super()._save_to_state_dict(destination, prefix, keep_vars) snapshot_timestamp_tensor = flow.tensor( datetime.datetime.now().timestamp(), dtype=flow.float64, device="cuda" ) @@ -210,7 +231,10 @@ class Embedding(Module): "%Y-%m-%d-%H-%M-%S-%f" ) self.handler.SaveSnapshot(snapshot_timestamp_str) - destination[prefix + "OneEmbedding"] = snapshot_timestamp_str + destination[prefix + "OneEmbeddingSnapshot"] = snapshot_timestamp_str + destination[ + prefix + "OneEmbeddingKeyValueOptions" + ] = self.key_value_store_options def _load_from_state_dict( self, @@ -222,7 +246,7 @@ class Embedding(Module): unexpected_keys, error_msgs, ): - key = prefix + "OneEmbedding" + key = prefix + "OneEmbeddingSnapshot" if key in state_dict: saved_snapshot_name = state_dict[key] try: @@ -280,20 +304,256 @@ class Embedding(Module): flow.tensor: the result of embedding lookup """ assert self.key_type == ids.dtype, "ids data_type must equals key_type" - return flow._C.one_embedding_lookup( + embedding = flow._C.one_embedding_fused_lookup( self.shadow, ids, table_ids, self.dtype, + self.embedding_name, + self.storage_dim, self.embedding_dim, + self.is_full_cache, self.num_tables, self.embedding_tables, - self.key_value_store_options, + self.padding_idx, + self.seed, + ) + if embedding.requires_grad and not graph_build_util.lazy_mode.is_enabled(): + if self.embedding is not None: + raise ValueError( + "You are training without set embedding optimizer, Please add flow.one_embedding.Optimizer after optimizer." + ) + + self.embedding = embedding + self.embedding.retain_grad() + self.ids = ids + self.table_ids = table_ids + return embedding + + def shuffle_and_lookup(self, state_initializer): + embedding_grad = self.embedding.grad + if self.world_size > 1: + ( + num_unique_matrix, + inverse_unique_partition_indices, + cur_rank_num_unique, + cur_rank_unique_ids, + cur_rank_unique_table_ids, + cur_rank_inverse_indices, + ) = flow._C.one_embedding_id_shuffle( + self.ids, self.table_ids, self.num_tables, self.embedding_name + ) + unique_values = flow._C.one_embedding_lookup( + cur_rank_num_unique, + cur_rank_unique_ids, + cur_rank_unique_table_ids, + self.dtype, + self.dtype, + self.storage_dim, + self.embedding_dim, + self.embedding_name, + self.embedding_tables, + state_initializer, + seed=self.seed, + ) + cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle( + embedding_grad, + num_unique_matrix, + cur_rank_inverse_indices, + inverse_unique_partition_indices, + self.embedding_name, + ) + else: + ( + cur_rank_num_unique, + cur_rank_unique_ids, + cur_rank_unique_table_ids, + inverse_indices, + ) = flow._C.one_embedding_unique_key_value_pair( + self.ids, self.table_ids, self.num_tables, self.embedding_name + ) + unique_values = flow._C.one_embedding_lookup( + cur_rank_num_unique, + cur_rank_unique_ids, + cur_rank_unique_table_ids, + self.dtype, + self.dtype, + self.storage_dim, + self.embedding_dim, + self.embedding_name, + self.embedding_tables, + state_initializer, + seed=self.seed, + ) + cur_rank_unique_embedding_grad = flow._C.unsorted_segment_sum( + embedding_grad, + inverse_indices, + axis=0, + num_segments=unique_values.shape[0], + ) + self.embedding = None + return ( + cur_rank_num_unique, + cur_rank_unique_ids, + unique_values, + cur_rank_unique_embedding_grad, + ) + + def sgd_update(self, param_group, step): + lr = param_group["lr"] + l2 = param_group["weight_decay"] + momentum = param_group["momentum"] + ( + cur_rank_num_unique, + cur_rank_unique_ids, + unique_values, + cur_rank_unique_embedding_grad, + ) = self.shuffle_and_lookup("") + updated_values = flow._C.one_embedding_sgd_update( + cur_rank_num_unique, + unique_values, + cur_rank_unique_embedding_grad, + learning_rate_val=lr, + scale=1.0, + weight_decay=l2, + momentum=momentum, + line_size=self.storage_dim, + embedding_size=self.embedding_dim, + embedding_name=self.embedding_name, + ) + flow._C.one_embedding_embedding_put( + cur_rank_num_unique, + cur_rank_unique_ids, + updated_values, + self.embedding_name, + self.storage_dim, + ) + + def adam_update(self, param_group, step): + line_size = self.storage_dim + embedding_size = self.embedding_dim + lr = param_group["lr"] + # not adjust, because it has been set in optimizer's step + bias_correction1 = param_group["bias_correction1"] + bias_correction2 = param_group["bias_correction2"] + l2 = param_group["weight_decay"] + beta1 = param_group["betas"][0] + beta2 = param_group["betas"][1] + epsilon = param_group["eps"] + do_bias_correction = param_group["do_bias_correction"] + amsgrad = param_group["amsgrad"] + assert amsgrad == False, "one_embedding's adam not support amsgrad" + state_initializer = [make_constant_initializer(0), make_constant_initializer(0)] + ( + cur_rank_num_unique, + cur_rank_unique_ids, + unique_values, + cur_rank_unique_embedding_grad, + ) = self.shuffle_and_lookup(json.dumps(state_initializer)) + updated_values = flow._C.one_embedding_adam_update( + cur_rank_num_unique, + unique_values, + cur_rank_unique_embedding_grad, + learning_rate_val=lr, + scale=1.0, + weight_decay=l2, + beta1=beta1, + beta2=beta2, + bias_correction1_val=bias_correction1, + bias_correction2_val=bias_correction2, + epsilon=epsilon, + do_bias_correction=do_bias_correction, + line_size=line_size, + embedding_size=embedding_size, + embedding_name=self.embedding_name, + ) + flow._C.one_embedding_embedding_put( + cur_rank_num_unique, + cur_rank_unique_ids, + updated_values, + self.embedding_name, + line_size, + ) + + def adagrad_update(self, param_group, step): + lr = param_group["lr"] + l2 = param_group["weight_decay"] + epsilon = param_group["eps"] + lr_decay = param_group["lr_decay"] + initial_accumulator_value = param_group["initial_accumulator_value"] + state_initializer = [make_constant_initializer(initial_accumulator_value)] + ( + cur_rank_num_unique, + cur_rank_unique_ids, + unique_values, + cur_rank_unique_embedding_grad, + ) = self.shuffle_and_lookup(json.dumps(state_initializer)) + updated_values = flow._C.one_embedding_adagrad_update( + cur_rank_num_unique, + unique_values, + cur_rank_unique_embedding_grad, + train_step_val=step + 1, + learning_rate_val=lr, + scale=1.0, + weight_decay=l2, + lr_decay=lr_decay, + epsilon=epsilon, + line_size=self.storage_dim, + embedding_size=self.embedding_dim, + embedding_name=self.embedding_name, + ) + flow._C.one_embedding_embedding_put( + cur_rank_num_unique, + cur_rank_unique_ids, + updated_values, + self.embedding_name, + self.storage_dim, + ) + + def ftrl_update(self, param_group, step): + lr = param_group["lr"] + l2 = param_group["weight_decay"] + lr_power = param_group["lr_power"] + lambda1 = param_group["lambda1"] + lambda2 = param_group["lambda2"] + beta = param_group["beta"] + initial_accumulator_value = param_group["initial_accumulator_value"] + state_initializer = [ + make_constant_initializer(initial_accumulator_value), + make_constant_initializer(initial_accumulator_value), + ] + ( + cur_rank_num_unique, + cur_rank_unique_ids, + unique_values, + cur_rank_unique_embedding_grad, + ) = self.shuffle_and_lookup(json.dumps(state_initializer)) + updated_values = flow._C.one_embedding_ftrl_update( + cur_rank_num_unique, + unique_values, + cur_rank_unique_embedding_grad, + learning_rate_val=lr, + scale=1.0, + weight_decay=l2, + lr_power=lr_power, + lambda1=lambda1, + lambda2=lambda2, + beta=beta, + line_size=self.storage_dim, + embedding_size=self.embedding_dim, + embedding_name=self.embedding_name, + ) + flow._C.one_embedding_embedding_put( + cur_rank_num_unique, + cur_rank_unique_ids, + updated_values, + self.embedding_name, + self.storage_dim, ) def make_device_mem_store_options( - persistent_path, capacity, size_factor=1, physical_block_size=512 + persistent_path, capacity, size_factor=1, storage_dim=-1, physical_block_size=4096 ): """make GPU only store_options param of MultiTableEmbedding @@ -301,7 +561,8 @@ def make_device_mem_store_options( persistent_path (str, list): persistent storage path of Embedding. If passed a str, current rank Embedding will be saved in path/rank_id-num_ranks path. If passed a list, the list length must equals num_ranks, each elem of list represent the path of rank_id Embedding. capacity (int): total capacity of Embedding size_factor (int, optional): store size factor of embedding_dim, if SGD update, and momentum = 0, should be 1, if momentum > 0, it should be 2. if Adam, should be 3. Defaults to 1. - physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 512. + storage_dim (int, optional): number of elements in embedding storage, if set storage_dim, the size_factor param will be invalid. if SGD update, and momentum = 0, storage_dim should be embedding_size*1, if momentum > 0, storage_dim should be embedding_size*2. if Adam, storage_dim should be embedding_size*3. Defaults to -1. + physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 4096. Returns: dict: GPU only store_options param of MultiTableEmbedding @@ -327,6 +588,7 @@ def make_device_mem_store_options( }, }, "size_factor": size_factor, + "storage_dim": storage_dim, } return options @@ -336,7 +598,8 @@ def make_cached_ssd_store_options( persistent_path, capacity=None, size_factor=1, - physical_block_size=512, + storage_dim=-1, + physical_block_size=4096, host_cache_budget_mb=0, ): """make SSD use GPU and host as cache store_options param of MultiTableEmbedding. If cache_budget_mb > 0 and host_cache_budget_mb > 0, use GPU and host memory as multi-level cache. @@ -346,7 +609,8 @@ def make_cached_ssd_store_options( persistent_path (str, list): persistent storage path of Embedding, must use fast SSD because of frequently random disk access during training. If passed a str, current rank Embedding will be saved in path/rank_id-num_ranks path. If passed a list, the list length must equals num_ranks, each elem of list represent the path of rank_id Embedding. capacity (int): total capacity of Embedding size_factor (int, optional): store size factor of embedding_dim, if SGD update, and momentum = 0, should be 1, if momentum > 0, it should be 2. if Adam, should be 3. Defaults to 1. - physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 512. + storage_dim (int, optional): number of elements in embedding storage, if set storage_dim, the size_factor param will be invalid. if SGD update, and momentum = 0, storage_dim should be embedding_size*1, if momentum > 0, storage_dim should be embedding_size*2. if Adam, storage_dim should be embedding_size*3. Defaults to -1. + physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 4096. host_cache_budget_mb (int): the MB budget of host memory as cache per rank. Defaults to 0. Returns: @@ -398,12 +662,18 @@ def make_cached_ssd_store_options( }, }, "size_factor": size_factor, + "storage_dim": storage_dim, } return options def make_cached_host_mem_store_options( - cache_budget_mb, persistent_path, capacity, size_factor=1, physical_block_size=512, + cache_budget_mb, + persistent_path, + capacity, + size_factor=1, + storage_dim=-1, + physical_block_size=4096, ): """make host use GPU as cache store_options param of MultiTableEmbedding @@ -412,7 +682,8 @@ def make_cached_host_mem_store_options( persistent_path (str, list): persistent storage path of Embedding. If passed a str, current rank Embedding will be saved in path/rank_id-num_ranks path. If passed a list, the list length must equals num_ranks, each elem of list represent the path of rank_id Embedding. capacity (int): total capacity of Embedding size_factor (int, optional): store size factor of embedding_dim, if SGD update, and momentum = 0, should be 1, if momentum > 0, it should be 2. if Adam, should be 3. Defaults to 1. - physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 512. + storage_dim (int, optional): number of elements in embedding storage, if set storage_dim, the size_factor param will be invalid. if SGD update, and momentum = 0, storage_dim should be embedding_size*1, if momentum > 0, storage_dim should be embedding_size*2. if Adam, storage_dim should be embedding_size*3. Defaults to -1. + physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 4096. Returns: dict: host use GPU as cache store_options param of MultiTableEmbedding @@ -443,6 +714,7 @@ def make_cached_host_mem_store_options( }, }, "size_factor": size_factor, + "storage_dim": storage_dim, } return options @@ -491,11 +763,32 @@ def make_normal_initializer(mean, std): return {"type": "normal", "mean": mean, "std": std} +def make_constant_initializer(value): + """make constant initializer param of make_table_options + + Args: + constant (float): A python scalar. value to generate. + + Returns: + dict: initializer param of make_table_options + + For example: + + .. code-block:: python + + >>> import oneflow as flow + >>> initializer = flow.one_embedding.make_constant_initializer(value=0) + >>> # pass the initializer to flow.one_embedding.make_table_options + >>> # ... + """ + return {"type": "constant", "value": value} + + def make_table_options(param): """make table param of Embedding tables Args: - param (dict or list): param can be initializer or list of column_option. initializer can be made by make_uniform_initializer or make_normal_initializer, column options can be made by make_column_options + param (dict or list): param can be initializer or list of column_option. initializer can be made by make_uniform_initializer or make_normal_initializer or make_constant_initializer, column options can be made by make_column_options Returns: dict: table param of Embedding tables @@ -545,7 +838,9 @@ class MultiTableEmbedding(Embedding): tables (list): list of table param which can be made by flow.one_embedding.make_table_options store_options (dict): store option of Embedding default_initializer (dict, optional): if tables param is None, use default_initializer to initialize table. Defaults to None. - + padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient; + therefore, the embedding vector at :attr:`padding_idx` is not updated during training, + the embedding vector at :attr:`padding_idx` will default to all zeros. For example: .. code-block:: python @@ -619,6 +914,8 @@ class MultiTableEmbedding(Embedding): tables, store_options, default_initializer=None, + padding_idx=None, + seed=0, ): assert isinstance(embedding_dim, int) super().__init__( @@ -629,6 +926,8 @@ class MultiTableEmbedding(Embedding): tables, store_options, default_initializer, + padding_idx, + seed, ) @@ -643,7 +942,9 @@ class MultiTableMultiColumnEmbedding(Embedding): tables (list): list of table param which can be made by flow.one_embedding.make_table_options store_options (dict): store option of Embedding default_initializer (dict, optional): if tables param is None, use default_initializer to initialize table. Defaults to None. - + padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient; + therefore, the embedding vector at :attr:`padding_idx` is not updated during training, + the embedding vector at :attr:`padding_idx` will default to all zeros. For example: .. code-block:: python @@ -721,6 +1022,8 @@ class MultiTableMultiColumnEmbedding(Embedding): tables, store_options, default_initializer=None, + padding_idx=None, + seed=0, ): if isinstance(embedding_dim, (list, tuple)): for dim in embedding_dim: @@ -736,6 +1039,8 @@ class MultiTableMultiColumnEmbedding(Embedding): tables, store_options, default_initializer, + padding_idx, + seed, ) @@ -745,18 +1050,16 @@ class Ftrl(Optimizer): The formula is: .. math:: - - & accumlator_{i+1} = accumlator_{i} + grad * grad - - & sigma = (accumulator_{i+1}^{lr\_power} - accumulator_{i}^{lr\_power}) / learning\_rate - - & z_{i+1} = z_{i} + grad - sigma * param_{i} - - \text{} - param_{i+1} = \begin{cases} - 0 & \text{ if } |z_{i+1}| < \lambda_1 \\ - -(\frac{\beta+accumlator_{i+1}^{lr\_power}}{learning\_rate} + \lambda_2)*(z_{i+1} - sign(z_{i+1})*\lambda_1) & \text{ otherwise } \\ - \end{cases} + \begin{align} + accumlator_{i+1} = accumlator_{i} + grad * grad \\ + sigma = (accumulator_{i+1}^{lr\_power} - accumulator_{i}^{lr\_power}) / learning\_rate \\ + z_{i+1} = z_{i} + grad - sigma * param_{i} \\ + \text{} + param_{i+1} = \begin{cases} + 0 & \text{ if } |z_{i+1}| < \lambda_1 \\ + -(\frac{\beta+accumlator_{i+1}^{lr\_power}}{learning\_rate} + \lambda_2)*(z_{i+1} - sign(z_{i+1})*\lambda_1) & \text{ otherwise } \\ + \end{cases} + \end{align} Example 1: @@ -806,7 +1109,6 @@ class Ftrl(Optimizer): options["lambda2"] = lambda2 options["beta"] = beta super().__init__(params, options) - # print("initial accumulator value is: ", options["initial_accumulator_value"]) for param_group in self.param_groups: for param in param_group.parameters: assert param.is_leaf, "parameters must be leaf tensor" @@ -881,6 +1183,8 @@ class Ftrl(Optimizer): beta = param_group["beta"] optimizer_conf.base_learning_rate = lr + self._generate_lr_scale_for_optim_conf(param_group, optimizer_conf) + optimizer_conf.ftrl_conf.initial_accumulator_value = ( initial_accumulator_value ) @@ -905,7 +1209,7 @@ class Ftrl(Optimizer): def make_persistent_table_reader( - paths, snapshot_name, key_type, value_type, storage_dim, physical_block_size=512, + paths, snapshot_name, key_type, value_type, storage_dim, physical_block_size=4096, ): r"""Creates a reader for reading persistent table. @@ -915,7 +1219,7 @@ def make_persistent_table_reader( key_type (flow.dtype): the data type of key value_type (flow.dtype): the data type of value storage_dim (int): number of elements in each value - physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 512 + physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 4096 """ return PersistentTableReader( paths, @@ -929,7 +1233,7 @@ def make_persistent_table_reader( def make_persistent_table_writer( - paths, snapshot_name, key_type, value_type, storage_dim, physical_block_size=512, + paths, snapshot_name, key_type, value_type, storage_dim, physical_block_size=4096, ): r"""Creates a writer for writing persistent table. @@ -939,7 +1243,7 @@ def make_persistent_table_writer( key_type (flow.dtype): the data type of key value_type (flow.dtype): the data type of value storage_dim (int): number of elements in each value - physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 512 + physical_block_size (int, optional): physical_block_size should be sector size. Defaults to 4096 """ return PersistentTableWriter( paths, @@ -950,3 +1254,56 @@ def make_persistent_table_writer( 4 * 1024, physical_block_size, ) + + +class SmartDecayAdam(flow.nn.optimizer.adam.Adam): + """Implements SmartDecayAdam algorithm. + The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. + For Sparse Embedding Table in OneEmbedding, implement the SmartDecayAdam algorithm. + For other models, it is same as Adam. + """ + + def _generate_conf_for_graph(self, train_conf, vars_conf): + new_opt_confs = super()._generate_conf_for_graph(train_conf, vars_conf) + for opt_conf in new_opt_confs: + opt_conf.adam_conf.smart_decay = True + + +class Optimizer(Optimizer): + def __init__( + self, optimizer: Optimizer, embeddings: List[Embedding], + ): + self.optimizer = optimizer + self.embeddings = embeddings + self.param_groups = optimizer.param_groups + self._default_options = optimizer._default_options + self._state = optimizer._state + self.embedding_param_group_dict = {} + for embedding in self.embeddings: + for group in self.param_groups: + param_set = set() + for param in group.parameters: + param_set.add(param) + if embedding.shadow in param_set: + self.embedding_param_group_dict[embedding.embedding_name] = group + if not embedding.embedding_name in self.embedding_param_group_dict: + raise ValueError("embedding must in optimizers param_group") + + def step(self, closure: Callable = None): + step = self.optimizer._state["step"] + for embedding in self.embeddings: + param_group = self.embedding_param_group_dict[embedding.embedding_name] + if type(self.optimizer) is flow.nn.optimizer.sgd.SGD: + embedding.sgd_update(param_group, step) + elif type(self.optimizer) is flow.nn.optimizer.adam.Adam: + embedding.adam_update(param_group, step) + elif type(self.optimizer) is flow.nn.optimizer.adagrad.Adagrad: + embedding.adagrad_update(param_group, step) + elif type(self.optimizer) is flow.one_embedding.Ftrl: + embedding.ftrl_update(param_group, step) + else: + raise NotImplementedError("only support sgd, adam, adagrad and ftrl") + self.optimizer.step() + + def _generate_conf_for_graph(self, train_conf, vars_conf): + return self.optimizer._generate_conf_for_graph(train_conf, vars_conf) diff --git a/python/oneflow/ops/initializer_util.py b/python/oneflow/ops/initializer_util.py deleted file mode 100644 index 2a21173eb953e3d7d528e304710253e69dfcbedd..0000000000000000000000000000000000000000 --- a/python/oneflow/ops/initializer_util.py +++ /dev/null @@ -1,1219 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import functools -import math -from typing import Optional, Sequence, Union - -import numpy as np - -import oneflow as flow -import oneflow.core.job.initializer_conf_pb2 as initializer_conf_util -import oneflow.core.operator.op_conf_pb2 as op_conf_util -import oneflow.framework.dtype as dtype_util - - -def constant_initializer( - value: float = 0, dtype: flow.dtype = flow.float -) -> initializer_conf_util.InitializerConf: - """Initializer that generates blob with constant values. - - Args: - value (float, optional): A Python scalar. All elements of the initialized variable . Defaults to 0. - dtype (flow.dtype, optional): Default data type. Defaults to flow.float. - - Raises: - NotImplementedError: Do not support such data type. - - Returns: - initializer_conf_util.InitializerConf: An InitializerConf object. - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def constant_Job() -> None: - init = flow.constant_initializer(2.5) - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - constant_Job() - - # out [2.5 2.5 2.5] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_constant_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.constant_initializer(0.01) - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_constant_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - initializer = initializer_conf_util.InitializerConf() - if dtype in [flow.float, flow.double]: - setattr(initializer.constant_conf, "value", float(value)) - elif dtype in [flow.int8, flow.int32, flow.int64]: - setattr(initializer.constant_int_conf, "value", int(value)) - else: - raise NotImplementedError("Do not support such data type") - return initializer - - -def zeros_initializer( - dtype: flow.dtype = flow.float, -) -> initializer_conf_util.InitializerConf: - """Initializer that generates blobs initialized to 0 - - Args: - dtype (flow.dtype, optional): Default data type. Defaults to flow.float. - - Returns: - initializer_conf_util.InitializerConf: constant_initializer - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def zeros_Job() -> None: - init = flow.zeros_initializer() - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - zeros_Job() - - # out [0. 0. 0.] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_zero_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.zeros_initializer() - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_zero_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - return constant_initializer(0.0, dtype) - - -def ones_initializer( - dtype: flow.dtype = flow.float, -) -> initializer_conf_util.InitializerConf: - """Initializer that generates blobs initialized to 1. - - Args: - dtype (flow.dtype, optional): Default data type. Defaults to flow.float. - - Returns: - initializer_conf_util.InitializerConf: constant_initializer - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def ones_Job() -> None: - init = flow.ones_initializer() - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - ones_Job() - - # out [1. 1. 1.] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_one_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.ones_initializer() - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_one_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - return constant_initializer(1.0, dtype) - - -def random_uniform_initializer( - minval: float = 0, maxval: float = 1, dtype: flow.dtype = flow.float -) -> initializer_conf_util.InitializerConf: - """Initializer that generates blobs with a uniform distribution. - - Args: - minval (float, optional): A python scalar. Lower bound of the range of random values to generate. Defaults to 0. - maxval (float, optional): A python scalar. Upper bound of the range of random values to generate. Defaults to 1. - dtype (flow.dtype, optional): Default data type. Defaults to flow.float. - - Raises: - NotImplementedError: Do not support such data type. - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def random_uniform_Job() -> None: - init = flow.random_uniform_initializer(minval=0, maxval=0.5) - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - random_uniform_Job() - - # out [0.07557311 0.3943565 0.31875622] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_random_uniform_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.random_uniform_initializer(minval=0, maxval=0.5) - - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_random_uniform_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - assert minval <= maxval - initializer = initializer_conf_util.InitializerConf() - if dtype in [flow.float, flow.double]: - setattr(initializer.random_uniform_conf, "min", float(minval)) - setattr(initializer.random_uniform_conf, "max", float(maxval)) - elif dtype in [flow.int8, flow.int32, flow.int64]: - setattr(initializer.random_uniform_int_conf, "min", int(minval)) - setattr(initializer.random_uniform_int_conf, "max", int(maxval)) - else: - raise NotImplementedError("Do not support such data type") - return initializer - - -def random_normal_initializer( - mean: float = 0.0, - stddev: float = 1.0, - seed: Optional[int] = None, - dtype: Optional[flow.dtype] = None, -) -> initializer_conf_util.InitializerConf: - """Initializer that generates blob with a normal distribution. - - Args: - mean (float, optional): A python scalar. Mean of the random values to generate.. Defaults to 0.0. - stddev (float, optional): A python scalar. Standard deviation of the random values to generate. Defaults to 1.0. - seed (Optional[int], optional): None. Not support yet. Defaults to None. - dtype (Optional[flow.dtype], optional): . Defaults to None. - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def random_normal_Job() -> None: - init = flow.random_normal_initializer(mean=1, stddev=1) - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - random_normal_Job() - - # out [1.4190257 2.7663114 1.7114428] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_random_normal_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.random_normal_initializer(mean=0, stddev=1) - - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_random_normal_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - assert seed is None - assert dtype is None - if seed is not None: - assert name is not None - initializer = initializer_conf_util.InitializerConf() - setattr(initializer.random_normal_conf, "mean", float(mean)) - setattr(initializer.random_normal_conf, "std", float(stddev)) - return initializer - - -def truncated_normal_initializer( - mean: float = 0.0, stddev: float = 1.0 -) -> initializer_conf_util.InitializerConf: - """Initializer that generates a truncated normal distribution. - - Args: - mean (float, optional): A scalar (float). Defaults to 0.0. - stddev (float, optional): A scalar (float). Defaults to 1.0. - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def truncated_normal_Job() -> None: - init = flow.truncated_normal_initializer(mean=1, stddev=1) - blob = flow.get_variable( - "blob-weight", - shape=(3, ), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - truncated_normal_Job() - - # out [1.8303236 0.09787154 0.83049864] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_truncated_normal_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.truncated_normal_initializer(mean=0, stddev=1) - - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_truncated_normal_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - initializer = initializer_conf_util.InitializerConf() - setattr(initializer.truncated_normal_conf, "mean", float(mean)) - setattr(initializer.truncated_normal_conf, "std", float(stddev)) - return initializer - - -def glorot_uniform_initializer( - data_format: str = "", -) -> initializer_conf_util.InitializerConf: - """Initializer that generates a Xavier uniform distribution. - - It also can be called as `oneflow.glorot_uniform_initializer`. - - The equation is: - - .. math:: - - W\\sim U(-\\sqrt{\\frac{{6}}{{n_j+n_{j+1}}}},\\sqrt{\\frac{{6}}{{n_j+n_{j+1}}}}) - - :math:`U` means uniform distribution - - :math:`n_j` means the amount of Nth layer parameters - - Args: - data_format (str, optional): The data format. Defaults to "". - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def xavier_uniform_Job() -> None: - init = flow.xavier_uniform_initializer() - blob = flow.get_variable( - "blob-weight", - shape=(3, 3), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - xavier_uniform_Job() - - # out [[-0.14424723 -0.9532095 -0.08723891] - # [-0.8011227 -0.29729813 -0.26769108] - # [ 0.9208976 -0.5971756 -0.15077025]] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_xavier_uniform_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.xavier_uniform_initializer() - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_xavier_uniform_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - return variance_scaling_initializer(1.0, "fan_avg", "random_uniform", data_format) - - -def glorot_normal_initializer( - data_format: str = "", -) -> initializer_conf_util.InitializerConf: - """Initializer that generates a Xavier normal distribution. - - It also can be called as `oneflow.glorot_normal_initializer`. - - The equation is: - - .. math:: - - W\\sim N(0, \\sqrt{\\frac{{2}}{{n_j+n_{j+1}}}}) - - :math:`N` means normal distribution - - :math:`n_j` means the amount of Nth layer parameters - - Args: - data_format (str, optional): The data format. Defaults to "". - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def xavier_normal_Job() -> None: - init = flow.xavier_normal_initializer() - blob = flow.get_variable( - "blob-weight", - shape=(3, 3), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - xavier_normal_Job() - - # out [[ 0.5908121 -0.10804518 -0.6148571 ] - # [ 1.4007381 -0.08172473 0.36579943] - # [-0.6461796 -0.15923311 0.33653972]] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_xavier_normal_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.xavier_normal_initializer() - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_xavier_normal_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - return variance_scaling_initializer(1.0, "fan_avg", "random_normal", data_format) - - -def variance_scaling_initializer( - scale: float = 1.0, - mode: str = "fan_in", - distribution: str = "truncated_normal", - data_format: str = "", -) -> initializer_conf_util.InitializerConf: - """Initializer that generates a truncated normal distribution or a random normal distribution or a random uniform distribution with a scale adapting to it. - - When the distribution is "truncated_normal" - - The equation is: - - .. math:: - - W\\sim N(0, \\sqrt{\\frac{{scale}}{{n}}}) - - If mode is "fan_in", the "n" is the number of input units in the weight Blob. - - If mode is "fan_out", the "n" is the number of output units in the weight Blob. - - if mode is "fan_avg", the "n" is the average of the number of input and output units in the weight Blob - - Args: - scale (float, optional): Scaling factor (positive float). Defaults to 1.0. - mode (str, optional): One of "fan_in", "fan_out", "fan_avg". Defaults to "fan_in". - distribution (str, optional): Random distribution to use. One of "truncated_normal",. Defaults to "truncated_normal". - data_format (str, optional): A string be one of "N...C" or "NC...". Defaults to "". - - Returns: - initializer_conf_util.InitializerConf: Initial configuration - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def variance_scale_Job() -> None: - init = flow.variance_scaling_initializer(scale=2.0, mode="fan_avg") - blob = flow.get_variable( - "blob-weight", - shape=(3, 3), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - variance_scale_Job() - - # out [[-0.13931477 0.12266728 -0.9434968 ] - # [-0.49665168 0.10231158 -0.19194333] - # [-0.7902896 -1.7034698 -0.38695997]] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_variance_scaling_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.variance_scaling_initializer(mode="fan_out") - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_variance_scaling_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - initializer = initializer_conf_util.InitializerConf() - setattr(initializer.variance_scaling_conf, "scale", float(scale)) - setattr( - initializer.variance_scaling_conf, "variance_norm", _get_variance_norm(mode) - ) - setattr( - initializer.variance_scaling_conf, - "distribution", - _get_random_distribution(distribution), - ) - setattr( - initializer.variance_scaling_conf, "data_format", _get_data_format(data_format) - ) - return initializer - - -def kaiming_initializer( - shape: Sequence[int], - distribution: str = "random_normal", - mode: str = "fan_in", - nonlinearity: str = "leaky_relu", - negative_slope: float = 0.0, - data_format: str = "NCHW", -) -> None: - """Initialize weight according to the method described in `Delving deep into - rectifiers: Surpassing human-level performance on ImageNet classification` - - He, K. et al. (2015), using a normal or uniform distribution. - - When distribution is "random_normal" - - The equation is: - - .. math:: - - W \\sim N(0, \\sqrt{\\frac{{2}}{{n}}}) - - When distribution is "random_uniform" - - The equation is: - - .. math:: - - W \\sim U(-\\sqrt{\\frac{{6}}{{n}}}, \\sqrt{\\frac{{6}}{{n}}}) - - If mode is "fan_in", the "n" is the number of input units in the weight Blob. - - If mode is "fan_out", the "n" is the number of output units in the weight Blob. - - if mode is "fan_avg", the "n" is the average of the number of input and output units in the weight Blob - - Args: - shape (Sequence[int]): Blob shape. - distribution (str, optional): 'random_normal' or 'random_uniform'. Defaults to "random_normal". - mode (str, optional): 'fan_in', 'fan_out' or 'fan_avg'. Defaults to "fan_in". - nonlinearity (str, optional): None, 'tanh', 'sigmoid', 'relu' or 'leaky_relu'. Defaults to "leaky_relu". - negative_slope (float, optional): The negative slope of leaky_relu. Defaults to 0.0. - data_format (str, optional): 'NCHW', 'NHWC'. Defaults to "NCHW". - - Raises: - NotImplementedError: Only support normal and uniform distribution - - Returns: - [type]: flow.random_normal_initializer or flow.random_uniform_initializer - - For example: - - Example 1: - - .. code-block:: python - - import oneflow as flow - import oneflow.typing as tp - - - def watch_handler(y: tp.Numpy): - print("out", y) - - - @flow.global_function() - def kaiming_Job() -> None: - init = flow.kaiming_initializer(shape=(3, 3), - mode="fan_avg", - nonlinearity="relu") - blob = flow.get_variable( - "blob-weight", - shape=(3, 3), - initializer=init, - trainable=True - ) - flow.watch(blob, watch_handler) - - - checkpoint = flow.train.CheckPoint() - checkpoint.init() - kaiming_Job() - - # out [[ 0.54521346 0.32585594 1.3474437 ] - # [ 0.30729076 -0.19158769 0.2709008 ] - # [-0.95830524 -0.05093324 0.28178614]] - - Example 2: - - .. code-block:: python - - import oneflow as flow - import numpy as np - import oneflow.typing as tp - - - @flow.global_function() - def conv2d_kaiming_Job(x: tp.Numpy.Placeholder((1, 256, 32, 32)) - ) -> tp.Numpy: - initializer = flow.kaiming_initializer(shape=(1, 256, 32, 32)) - conv2d = flow.layers.conv2d( - x, - filters=128, - kernel_size=3, - strides=1, - padding='SAME', - kernel_initializer=initializer, - name="Conv2d" - ) - return conv2d - - - x = np.random.randn(1, 256, 32, 32).astype(np.float32) - out = conv2d_kaiming_Job(x) - - # out.shape (1, 128, 32, 32) - - """ - assert isinstance(shape, (tuple, flow.Size)) - assert len(shape) >= 2 - elem_cnt = functools.reduce(lambda a, b: a * b, shape, 1) - assert elem_cnt > 0 - assert distribution in ["random_normal", "random_uniform"] - assert mode in ["fan_in", "fan_out", "fan_avg"] - assert nonlinearity in [None, "tanh", "sigmoid", "relu", "leaky_relu"] - assert data_format in ["NCHW", "NHWC"] - fan = _CalcFan(shape, mode, _get_data_format(data_format)) - gain = CalcGain(nonlinearity, negative_slope) - std = gain / math.sqrt(fan) - if distribution == "random_normal": - return flow.random_normal_initializer(0.0, std) - elif distribution == "random_uniform": - bound = math.sqrt(3.0) * std - return flow.random_uniform_initializer(-bound, bound) - else: - raise NotImplementedError("Only support normal and uniform distribution") - - -def _get_variance_norm(mode): - if mode.lower() == "fan_in": - return initializer_conf_util.kFanIn - elif mode.lower() == "fan_out": - return initializer_conf_util.kFanOut - elif mode.lower() == "fan_avg": - return initializer_conf_util.kAverage - else: - raise ValueError("Invalid variance_norm") - - -def _get_random_distribution(distribution): - if distribution.lower() == "truncated_normal": - return initializer_conf_util.kTruncatedNormal - elif distribution.lower() == "random_normal": - return initializer_conf_util.kRandomNormal - elif distribution.lower() == "random_uniform": - return initializer_conf_util.kRandomUniform - else: - raise ValueError("Invalid random_distribution") - - -def _get_data_format(data_format): - assert isinstance(data_format, str), "data_format must be a string" - if data_format.startswith("NC"): - return "channels_first" - elif data_format.startswith("N") and data_format.endswith("C"): - return "channels_last" - else: - assert data_format == "", ValueError( - 'data_format must be "N...C" or "NC..." or ""' - ) - return "" - - -def _CalcFan(shape, mode, data_format): - if len(shape) == 2: - fan_in = shape[1] - fan_out = shape[0] - else: - fan_in = 1.0 - for dim in shape[1:]: - fan_in *= dim - fan_out = shape[0] - if data_format == "channels_first": - for dim in shape[2:]: - fan_out *= dim - elif data_format == "channels_last": - for dim in shape[1:-1]: - fan_out *= dim - else: - raise NotImplementedError( - "Only support 'channels_first' and 'channels_last' data format" - ) - if mode == "fan_avg": - return (float(fan_in) + float(fan_out)) / 2 - elif mode == "fan_in": - return float(fan_in) - elif mode == "fan_out": - return float(fan_out) - else: - raise NotImplementedError("Only support 'fan_in', 'fan_out' and 'fan_avg' mode") - - -def CalcGain(nonlinearity, param): - linear_fns = [ - "linear", - "conv1d", - "conv2d", - "conv3d", - "conv_transpose1d", - "conv_transpose2d", - "conv_transpose3d", - ] - if nonlinearity in linear_fns or nonlinearity == "sigmoid": - return 1 - elif nonlinearity == "tanh": - return 5.0 / 3 - elif nonlinearity == "relu": - return math.sqrt(2.0) - elif nonlinearity == "leaky_relu": - if param is None: - negative_slope = 0.01 - elif ( - not isinstance(param, bool) - and isinstance(param, int) - or isinstance(param, float) - ): - negative_slope = param - else: - raise ValueError("negative_slope {} not a valid number".format(param)) - return math.sqrt(2.0 / (1 + negative_slope ** 2)) - elif nonlinearity == "selu": - return 3.0 / 4 - else: - raise ValueError("Unsupported nonlinearity {}".format(nonlinearity)) - - -_init_map = {} - - -def register_initializer(flow_initializer): - def deco(func): - _init_map[flow_initializer] = func - return func - - return deco - - -def GetInitializer(initializer_conf, random_seed, var_blob_shape): - f = None - for m in _init_map: - if initializer_conf.HasField(m): - f = _init_map[m] - break - assert f is not None, initializer_conf - return f(getattr(initializer_conf, m), random_seed, var_blob_shape) - - -@register_initializer("constant_conf") -@register_initializer("constant_int_conf") -def ConstantInitializerImpl( - initializer_conf: Union[ - initializer_conf_util.ConstantInitializerConf, - initializer_conf_util.ConstantIntInitializerConf, - ], - random_seed: int, - var_blob_shape: Sequence[int], -): - return lambda length: np.full((length,), initializer_conf.value) - - -@register_initializer("random_normal_conf") -def RandomNormalInitializerImpl( - initializer_conf: initializer_conf_util.RandomNormalInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - rng = np.random.default_rng(random_seed) - return lambda length: rng.normal( - loc=initializer_conf.mean, scale=initializer_conf.std, size=length - ) - - -@register_initializer("random_uniform_conf") -def RandomUniformInitializerImpl( - initializer_conf: initializer_conf_util.RandomUniformIntInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - rng = np.random.default_rng(random_seed) - return lambda length: rng.uniform( - low=initializer_conf.min, - high=np.nextafter(initializer_conf.max, float("inf")), - size=length, - ) - - -@register_initializer("random_uniform_int_conf") -def RandomUniformIntInitializerImpl( - initializer_conf: initializer_conf_util.RandomUniformIntInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - rng = np.random.default_rng(random_seed) - return lambda length: rng.integers( - low=initializer_conf.min, high=initializer_conf.max, size=length - ) - - -def RngTruncatedNormal(mean, std, length, rng): - truncated_value = 2 * std - data = np.empty(length) - generated = 0 - ratio = 1.2 - while generated < length: - remaining = length - generated - norm = rng.normal(mean, std, size=int(remaining * ratio)) - truncated = norm[np.abs(norm - mean) < truncated_value][:remaining] - data[generated : generated + len(truncated)] = truncated - generated += len(truncated) - return data - - -@register_initializer("truncated_normal_conf") -def TruncatedNormalInitializerImpl( - initializer_conf: initializer_conf_util.TruncatedNormalInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - rng = np.random.default_rng(random_seed) - return lambda length: RngTruncatedNormal( - initializer_conf.mean, initializer_conf.std, length, rng - ) - - -def GenInitialFan(initializer_conf, var_blob_shape: Sequence[int]): - variance_norm = initializer_conf.variance_norm - data_format = initializer_conf.data_format - fan_in = np.prod(var_blob_shape[1:]).astype(np.int32).item() - fan_out = var_blob_shape[0] - if data_format == "channel_first": - fan_out *= np.prod(var_blob_shape[2:]).astype(np.int32).item() - else: - fan_out *= np.prod(var_blob_shape[1:-1]).astype(np.int32).item() - if variance_norm == initializer_conf_util.kAverage: - fan = (fan_in + fan_out) / 2 - elif variance_norm == initializer_conf_util.kFanIn: - fan = fan_in - elif variance_norm == initializer_conf_util.kFanOut: - fan = fan_out - else: - raise NotImplemented() - return fan - - -@register_initializer("variance_scaling_conf") -def VarianceScalingInitializerImpl( - initializer_conf: initializer_conf_util.VarianceScalingInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - scale = initializer_conf.scale / GenInitialFan(initializer_conf, var_blob_shape) - distribution = initializer_conf.distribution - rng = np.random.default_rng(random_seed) - if distribution == initializer_conf_util.kTruncatedNormal: - stddev = math.sqrt(scale) / 0.8796256610342398 - return lambda length: RngTruncatedNormal(0, stddev, length, rng) - elif distribution == initializer_conf_util.kRandomNormal: - stddev = math.sqrt(scale) - return lambda length: rng.normal(0, stddev, size=length) - elif distribution == initializer_conf_util.kRandomUniform: - limit = math.sqrt(3.0 * scale) - return lambda length: rng.uniform(low=-limit, high=limit, size=length) - else: - raise NotImplemented() - - -@register_initializer("empty_conf") -def EmptyInitializerImpl( - initializer_conf: initializer_conf_util.EmptyInitializerConf, - random_seed: int, - var_blob_shape: Sequence[int], -): - return None - - -def _elem_cnt(shape): - return np.prod(shape).astype(int).item() - - -def generate_values_by_initializer(initializer, shape, dtype): - np_dtype = np.dtype(dtype_util.convert_oneflow_dtype_to_numpy_dtype(dtype)) - length = _elem_cnt(shape) - return np.array(initializer(length)).astype(np_dtype).reshape(shape) diff --git a/python/oneflow/ops/util/__init__.py b/python/oneflow/ops/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/python/oneflow/ops/util/initializer_util.py b/python/oneflow/ops/util/initializer_util.py new file mode 100644 index 0000000000000000000000000000000000000000..1aff9f186bc477567e2474d2e4aa83ba6d172869 --- /dev/null +++ b/python/oneflow/ops/util/initializer_util.py @@ -0,0 +1,115 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import functools +import math +from typing import Optional, Sequence, Union + +import numpy as np + +import oneflow as flow +import oneflow.core.job.initializer_conf_pb2 as initializer_conf_util +import oneflow.core.operator.op_conf_pb2 as op_conf_util +import oneflow.framework.dtype as dtype_util + + +def get_random_distribution(distribution): + if distribution.lower() == "truncated_normal": + return initializer_conf_util.kTruncatedNormal + elif distribution.lower() == "random_normal": + return initializer_conf_util.kRandomNormal + elif distribution.lower() == "random_uniform": + return initializer_conf_util.kRandomUniform + else: + raise ValueError("Invalid random_distribution") + + +def get_data_format(data_format): + assert isinstance(data_format, str), "data_format must be a string" + if data_format.startswith("NC"): + return "channels_first" + elif data_format.startswith("N") and data_format.endswith("C"): + return "channels_last" + else: + assert data_format == "", ValueError( + 'data_format must be "N...C" or "NC..." or ""' + ) + return "" + + +def calc_fan(shape, mode, data_format): + assert ( + len(shape) >= 2 + ), "Fan in and fan out can out be computed for tensor with fewer 2 dimensions" + if len(shape) == 2: + fan_in = shape[1] + fan_out = shape[0] + else: + fan_in = 1.0 + for dim in shape[1:]: + fan_in *= dim + fan_out = shape[0] + if data_format == "channels_first": + for dim in shape[2:]: + fan_out *= dim + elif data_format == "channels_last": + for dim in shape[1:-1]: + fan_out *= dim + else: + raise NotImplementedError( + "Only support 'channels_first' and 'channels_last' data format" + ) + if mode == "fan_sum": + return float(fan_in) + float(fan_out) + elif mode == "fan_in": + return float(fan_in) + elif mode == "fan_out": + return float(fan_out) + else: + raise NotImplementedError("Only support 'fan_in', 'fan_out' and 'fan_sum' mode") + + +def calc_gain(nonlinearity, param=None): + linear_fns = [ + "linear", + "conv1d", + "conv2d", + "conv3d", + "conv_transpose1d", + "conv_transpose2d", + "conv_transpose3d", + ] + if nonlinearity in linear_fns or nonlinearity == "sigmoid": + return 1 + elif nonlinearity == "tanh": + return 5.0 / 3 + elif nonlinearity == "relu": + return math.sqrt(2.0) + elif nonlinearity == "leaky_relu": + if param is None: + negative_slope = 0.01 + elif ( + not isinstance(param, bool) + and isinstance(param, int) + or isinstance(param, float) + ): + negative_slope = param + else: + raise ValueError("negative_slope {} not a valid number".format(param)) + return math.sqrt(2.0 / (1 + negative_slope ** 2)) + elif nonlinearity == "selu": + return 3.0 / 4 + else: + raise ValueError("Unsupported nonlinearity {}".format(nonlinearity)) diff --git a/python/oneflow/optim/__init__.py b/python/oneflow/optim/__init__.py index a4f995d3c5dbeb21665f722dba4ab34e0ee20c7d..dbe60dd16b8e23e82dde6bb03394ff3b1d766285 100644 --- a/python/oneflow/optim/__init__.py +++ b/python/oneflow/optim/__init__.py @@ -15,10 +15,11 @@ limitations under the License. """ from oneflow.nn.optimizer.adam import Adam from oneflow.nn.optimizer.adamw import AdamW -from oneflow.nn.optimizer.optimizer import Optimizer +from oneflow.optim.optimizer import Optimizer from oneflow.nn.optimizer.rmsprop import RMSprop from oneflow.nn.optimizer.sgd import SGD from oneflow.nn.optimizer.adagrad import Adagrad from oneflow.nn.optimizer.lamb import LAMB +from oneflow.nn.optimizer.adadelta import Adadelta from . import lr_scheduler diff --git a/python/oneflow/nn/optimizer/optimizer.py b/python/oneflow/optim/optimizer.py similarity index 75% rename from python/oneflow/nn/optimizer/optimizer.py rename to python/oneflow/optim/optimizer.py index d7269df12de98d83d7120c8ae632b6eee2748f31..f1d6653c42482e17c145c90ff2720bd4a061949d 100644 --- a/python/oneflow/nn/optimizer/optimizer.py +++ b/python/oneflow/optim/optimizer.py @@ -20,7 +20,7 @@ from itertools import chain from typing import Any, Callable, Dict, Union from oneflow.framework.tensor import Tensor -from oneflow.nn.graph.block import TensorBlock +from oneflow.nn.graph.proxy import ProxyTensor from oneflow.nn.parameter import Parameter from oneflow.nn.utils.clip_grad import clip_grad_norm_ import oneflow as flow @@ -30,19 +30,19 @@ class ParamGroup(object): def __init__( self, parameters: Dict[str, Any], default_options: Dict, ): - # ParamGroup must be constructed by Dict["params": parameters: List[Parameter, Tensor or TensorBlock], "...": ...] + # ParamGroup must be constructed by Dict["params": parameters: List[Parameter, Tensor or ProxyTensor], "...": ...] assert isinstance(parameters, dict) and "params" in parameters assert not isinstance(parameters["params"], (Parameter, Tensor)) self._parameters = list() for p in parameters["params"]: if isinstance(p, (Parameter, Tensor)): self._parameters.append(p) - elif isinstance(p, TensorBlock): + elif isinstance(p, ProxyTensor): # Add parameter from nn.Graph - self._parameters.append(p.origin) + self._parameters.append(p.to(Tensor)) else: raise ValueError( - "parameters in ParamGroup must be Tensor or TensorBlock." + "parameters in ParamGroup must be Tensor or ProxyTensor." ) self._options = deepcopy(default_options) @@ -77,6 +77,11 @@ class ParamGroup(object): def items(self): return self.__dict__.items() + def __repr__(self): + res = self.options + res["params"] = self.parameters + return str(res) + @property def options(self): return self._options @@ -107,6 +112,16 @@ def _decorate_step(step): return decorated_step +class _RequiredParameter(object): + """Singleton class representing a required parameter for an Optimizer.""" + + def __repr__(self): + return "" + + +required = _RequiredParameter() + + class Optimizer(object): def __init__(self, parameters, options): self.param_groups = list() @@ -119,7 +134,86 @@ class Optimizer(object): self.step = _decorate_step(self.step) def add_param_group(self, param_group) -> None: - raise NotImplementedError() + r""" + + Add a param group to the :class:`Optimizer` s `param_groups`. + This can be useful when fine tuning a pre-trained network as frozen layers can be made + trainable and added to the :class:`Optimizer` as training progresses. + + Args: + param_group (dict): Specifies what Tensors should be optimized along with group + specific optimization options. + + Example: + + >>> import oneflow + >>> import oneflow.optim as optim + >>> w1 = oneflow.ones(3, 3) + >>> w1.requires_grad = True + >>> w2 = oneflow.ones(3, 3) + >>> w2.requires_grad = True + >>> o = optim.SGD([w1]) + >>> o.param_groups[0] + {'lr': 0.001, 'momentum': 0.0, 'dampening': 0.0, 'weight_decay': 0.0, 'nesterov': False, 'maximize': False, 'params': [tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]], dtype=oneflow.float32, requires_grad=True)]} + >>> o.add_param_group({'params': w2}) + >>> o.param_groups[1] + {'lr': 0.001, 'momentum': 0.0, 'dampening': 0.0, 'weight_decay': 0.0, 'nesterov': False, 'maximize': False, 'params': [tensor([[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]], dtype=oneflow.float32, requires_grad=True)]} + + """ + assert isinstance(param_group, dict), "param group must be a dict" + + params = param_group["params"] + if isinstance(params, flow.Tensor): + param_group["params"] = [params] + elif isinstance(params, set): + raise TypeError( + "optimizer parameters need to be organized in ordered collections, but " + "the ordering of tensors in sets will change between runs. Please use a list instead." + ) + else: + param_group["params"] = list(params) + + for param in param_group["params"]: + if not isinstance(param, flow.Tensor): + raise TypeError( + "optimizer can only optimize Tensors, " + "but one of the params is " + type(param) + ) + if not param.is_leaf: + raise ValueError("can't optimize a non-leaf Tensor") + + for name, default in self._default_options.items(): + if default is required and name not in param_group: + raise ValueError( + "parameter group didn't specify a value of required optimization parameter " + + name + ) + else: + param_group.setdefault(name, default) + params = param_group["params"] + if len(params) != len(set(params)): + warnings.warn( + "optimizer contains a parameter group with duplicate parameters; " + "in future, this will cause an error; ", + stacklevel=3, + ) + + param_set = set() + for group in self.param_groups: + param_set.update(set(group.parameters)) + + if not param_set.isdisjoint(set(param_group["params"])): + raise ValueError("some parameters appear in more than one parameter group") + + self.param_groups.append(ParamGroup(param_group, self._default_options)) + + for param in param_group["params"]: + assert param.is_leaf, "parameters must be leaf tensor" + self._state[param] = dict() def load_state_dict(self, state_dict) -> None: r""" @@ -197,7 +291,7 @@ class Optimizer(object): def state_dict(self): r""" - Returns the state of the optimizer as a :class:`dict`. + Returns the state of the optimizer as a :py:class:`dict`. It contains two entries: @@ -248,7 +342,7 @@ class Optimizer(object): """ raise NotImplementedError() - def clip_grad(self): + def clip_grad(self, error_if_nonfinite: bool = False): r"""Clips gradient norm of an iterable of parameters. The norm is computed over all gradients together, as if they were concatenated into a single vector. @@ -258,6 +352,11 @@ class Optimizer(object): You can also refer the code in :func:`oneflow.nn.utils.clip_grad_norm_` + Args: + error_if_nonfinite (bool): if True, an error is thrown if the total + norm of the gradients from :attr:``parameters`` is ``nan``, + ``inf``, or ``-inf``. Default: False (will switch to True in the future) + """ for param_group in self.param_groups: if param_group._enable_clip_grad: @@ -265,7 +364,7 @@ class Optimizer(object): param_group.parameters, param_group["clip_grad_max_norm"], param_group["clip_grad_norm_type"], - True, + error_if_nonfinite, ) else: warnings.warn( @@ -273,7 +372,7 @@ class Optimizer(object): ) def zero_grad(self, set_to_none: bool = False): - """Sets the gradients of all optimized torch.Tensor s to zero. + """Sets the gradients of all optimized :class:`oneflow.Tensor` s to zero. Args: set_to_none (bool): instead of setting to zero, set the grads to None. @@ -293,11 +392,7 @@ class Optimizer(object): """ for param_group in self.param_groups: for param in param_group.parameters: - if param.grad is not None: - if set_to_none: - param.grad = None - else: - param.grad.zero_() + param._zero_grad_(set_to_none) def _parse_input_parameters(self, parameters): """ @@ -339,6 +434,13 @@ class Optimizer(object): clip_grad_norm.max_norm = max_norm clip_grad_norm.norm_type = norm_type + def _generate_lr_scale_for_optim_conf(self, param_group, optimizer_conf): + if "lr_scale" not in param_group: + return + + lr_scale = float(param_group["lr_scale"]) + optimizer_conf.lr_scale = lr_scale + @property def support_sparse(self): """Whether the Optimizer support sparse update. diff --git a/python/oneflow/saved_model.py b/python/oneflow/saved_model.py deleted file mode 100644 index 3f5a89b1aecbc325fcdf856f39672647a8906c46..0000000000000000000000000000000000000000 --- a/python/oneflow/saved_model.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from oneflow.serving.saved_model_builder import ( - GraphBuilder, - ModelBuilder, - SignatureBuilder, -) diff --git a/python/oneflow/serving/__init__.py b/python/oneflow/serving/__init__.py deleted file mode 100644 index bcbfb38a4afd9e4ecb9c2abf8e60ae8214ae07de..0000000000000000000000000000000000000000 --- a/python/oneflow/serving/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -from oneflow.serving.inference_session import ( - InferenceSession, - ModelVersionPolicy, - SessionOption, -) diff --git a/python/oneflow/serving/inference_session.py b/python/oneflow/serving/inference_session.py deleted file mode 100644 index b04667d430339b741a1b8420e0b9ad7974b5b13a..0000000000000000000000000000000000000000 --- a/python/oneflow/serving/inference_session.py +++ /dev/null @@ -1,440 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import asyncio -import contextlib -import enum -import inspect -import os - -import google.protobuf.text_format as text_format -import numpy as np - -import oneflow as flow -import oneflow._oneflow_internal -import oneflow.core.job.job_conf_pb2 as job_conf_proto -import oneflow.core.job.job_set_pb2 as job_set_util -import oneflow.core.operator.interface_blob_conf_pb2 as interface_blob_conf_proto -import oneflow.core.serving.saved_model_pb2 as saved_model_pb -import oneflow.framework.c_api_util as c_api_util -import oneflow.framework.compile_context as compile_ctx -import oneflow.framework.dtype as dtype_util -import oneflow.framework.input_blob_def as input_blob_util -import oneflow.framework.job_instance as job_instance_util -import oneflow.framework.runtime_mode as runtime_mode -import oneflow.framework.scope_util as scope_util - - -def _is_int(val): - try: - num = int(val) - except ValueError: - return False - return True - - -def _find_model_latest_version(saved_model_dir): - version_dirs = [] - for f in os.listdir(saved_model_dir): - if os.path.isdir(os.path.join(saved_model_dir, f)) and _is_int(f): - version_dirs.append(f) - version_dirs.sort(reverse=True, key=lambda x: int(x)) - return version_dirs[0] - - -def _need_check_device_tag(op_conf): - if op_conf.HasField("return_conf"): - return False - return op_conf.HasField("device_tag") - - -class ModelVersionPolicy(enum.Enum): - LATEST = 1 - - -class SessionOption(object): - def __init__(self): - self.device_tag = "cuda" - self.device_num = 1 - self.is_mirrored_view = False - - -class InferenceSession(object): - class SessionStatus(enum.Enum): - OPEN = 1 - RUNNING = 2 - CLOSED = 3 - - def __init__(self, option=None): - if option is None: - self.option_ = SessionOption() - else: - assert isinstance(option, SessionOption) - self.option_ = option - self.is_mirrored_ = self.option_.is_mirrored_view - self.checkpoint_path_ = None - self.config_proto_ = None - self.job_name2job_conf_ = {} - self.inter_user_job_info_ = None - self.cur_job_name_ = None - self.inferface_name2info_ = {} - self.output_name2future_ = {} - self.job_futures_ = [] - self.status_ = None - self._init_event_loop() - self.init() - - def __del__(self): - if self.status_ != self.SessionStatus.CLOSED: - self.close() - - def _init_event_loop(self): - self.event_loop_ = asyncio.get_event_loop() - if self.event_loop_.is_closed(): - asyncio.set_event_loop(asyncio.new_event_loop()) - self.event_loop_ = asyncio.get_event_loop() - - def init(self): - raise NotImplementedError("InferenceSession is deprecated.") - if not oneflow._oneflow_internal.IsSessionInited(): - self._make_config_proto() - # session_util._TryCompleteConfigProto(self.config_proto_) - c_api_util.InitLazyGlobalSession(self.config_proto_) - self.status_ = self.SessionStatus.OPEN - - def close(self): - self.event_loop_.run_until_complete(self.wait_for_all_jobs_finished()) - self.event_loop_.close() - if self.status_ == self.SessionStatus.RUNNING: - oneflow._oneflow_internal.StopLazyGlobalSession() - oneflow._oneflow_internal.DestroyLazyGlobalSession() - elif self.status_ == self.SessionStatus.OPEN: - oneflow._oneflow_internal.DestroyLazyGlobalSession() - else: - pass - self.status_ = self.SessionStatus.CLOSED - - def _check_status(self, *status): - check_success = False - for stat in status: - if self.status_ == stat: - check_success = True - break - if check_success is False: - caller_func_name = inspect.stack()[1].function - allowed_status = ",".join(status) - raise ValueError( - "The calling to {} is only allowed when status is {}, current status is {}".format( - caller_func_name, allowed_status, self.status_ - ) - ) - - def _make_config_proto(self): - if self.config_proto_ is None: - config_proto = job_set_util.ConfigProto() - config_proto.resource.SetInParent() - config_proto.session_id = 0 - self.config_proto_ = config_proto - # self.config_proto_ = session_util._GetDefaultConfigProto() - if self.option_.device_tag == "cuda": - pass - elif self.option_.device_tag == "cpu": - self.config_proto_.resource.cpu_device_num = self.option_.device_num - else: - raise NotImplementedError( - "not supported device tag {}".format(self.option_.device_tag) - ) - self.config_proto_.resource.enable_legacy_model_io = True - - def set_checkpoint_path(self, checkpoint_path): - self._check_status(self.SessionStatus.OPEN) - self.checkpoint_path_ = checkpoint_path - - def set_job_signature(self, job_name, signature): - assert isinstance(signature, job_conf_proto.JobSignatureDef) - job_conf = self._get_job_conf(job_name) - job_conf.signature.CopyFrom(signature) - - def set_job_batch_size(self, job_name, batch_size): - self._check_status(self.SessionStatus.OPEN) - job_conf = self._get_job_conf(job_name) - for (_, mut_input_def) in job_conf.signature.inputs.items(): - mut_shape = mut_input_def.blob_conf.shape - mut_shape.dim[0] = batch_size - - def _get_job_conf(self, job_name): - if job_name in self.job_name2job_conf_: - return self.job_name2job_conf_[job_name] - else: - job_conf = job_conf_proto.JobConfigProto() - job_conf.job_name = job_name - job_conf.predict_conf.SetInParent() - self.job_name2job_conf_[job_name] = job_conf - return job_conf - - @contextlib.contextmanager - def open(self, job_name, signature=None, batch_size=None): - self._check_status(self.SessionStatus.OPEN) - c_api_util.JobBuildAndInferCtx_Open(job_name) - if signature is not None: - self.set_job_signature(job_name, signature) - if isinstance(batch_size, int): - self.set_job_batch_size(job_name, batch_size) - job_conf = self._get_job_conf(job_name) - c_api_util.CurJobBuildAndInferCtx_SetJobConf(job_conf) - # NOTE(chengcheng): placement_util is unavailable. - # tag_and_dev_ids = placement_util.GetDefaultMachineDeviceIds( - # self.config_proto_.resource - # ) - assert type(job_conf) is job_conf_proto.JobConfigProto, type(job_conf) - serialized_job_conf_str = text_format.MessageToString(job_conf) - scope = oneflow._oneflow_internal.MakeInitialScope( - serialized_job_conf_str, flow.placement("cpu", [0]), self.is_mirrored_ - ) - with runtime_mode.ModeScope(runtime_mode.GLOBAL_MODE): - with scope_util.ScopeContext(scope): - self.cur_job_name_ = job_name - yield self - self.cur_job_name_ = None - oneflow._oneflow_internal.JobBuildAndInferCtx_Close() - - def compile(self, op_list): - self._check_status(self.SessionStatus.OPEN) - scope = scope_util.current_scope() - device_tag = scope.device_parallel_desc_symbol.device_tag - for op_conf in op_list: - if _need_check_device_tag(op_conf) and op_conf.device_tag != device_tag: - print( - "WARNING: the device_tag of op {} is not equal to the device_tag of seesion's current scope ({} vs. {}), which may cause the op graph to be incompatible".format( - op_conf.name, op_conf.device_tag, device_tag - ) - ) - compile_ctx.CurJobAddOp(op_conf) - oneflow._oneflow_internal.CurJobBuildAndInferCtx_Complete() - oneflow._oneflow_internal.CurJobBuildAndInferCtx_Rebuild() - - def launch(self): - self._check_status(self.SessionStatus.OPEN) - oneflow._oneflow_internal.StartLazyGlobalSession() - self.inter_user_job_info_ = c_api_util.GetInterUserJobInfo() - self._run_load_checkpoint_job() - self.status_ = self.SessionStatus.RUNNING - - def load_saved_model( - self, - saved_model_dir, - model_version=ModelVersionPolicy.LATEST, - saved_model_meta_file_basename="saved_model", - graph_name=None, - signature_name=None, - ): - if not os.path.isdir(saved_model_dir): - raise ValueError("{} is not a valid directory".format(saved_model_dir)) - if isinstance(model_version, int): - pass - elif model_version == ModelVersionPolicy.LATEST: - model_version = _find_model_latest_version(saved_model_dir) - else: - raise NotImplementedError - saved_model_path = os.path.join(saved_model_dir, str(model_version)) - if not os.path.isdir(saved_model_path): - raise ValueError( - "version {} of saved model in dir {} do not exist".format( - model_version, saved_model_dir - ) - ) - subfiles = list(os.listdir(saved_model_path)) - saved_model_meta_pb_filename = saved_model_meta_file_basename + ".pb" - saved_model_meta_prototxt_filename = ( - saved_model_meta_file_basename + ".prototxt" - ) - saved_model_proto = saved_model_pb.SavedModel() - if saved_model_meta_pb_filename in subfiles: - saved_model_meta_file_path = os.path.join( - saved_model_path, saved_model_meta_pb_filename - ) - with open(saved_model_meta_file_path, "rb") as f: - saved_model_proto.ParseFromString(f.read()) - elif saved_model_meta_prototxt_filename in subfiles: - saved_model_meta_file_path = os.path.join( - saved_model_path, saved_model_meta_prototxt_filename - ) - with open(saved_model_meta_file_path, "rt") as f: - text_format.Merge(f.read(), saved_model_proto) - else: - raise ValueError( - "saved model meta file {} do not exist in {}".format( - saved_model_meta_file_basename, saved_model_path - ) - ) - self.set_checkpoint_path( - os.path.join(saved_model_path, saved_model_proto.checkpoint_dir) - ) - signature = None - if graph_name is None: - graph_name = saved_model_proto.default_graph_name - elif graph_name not in saved_model_proto.graphs: - raise ValueError("graph {} do not exist".format(graph_name)) - graph_def = saved_model_proto.graphs[graph_name] - if signature_name is None and graph_def.HasField("default_signature_name"): - signature_name = graph_def.default_signature_name - if signature_name is not None: - if signature_name not in graph_def.signatures: - raise ValueError("signature {} do not exist".format(signature_name)) - else: - signature = graph_def.signatures[signature_name] - with self.open(graph_name, signature): - self.compile(graph_def.op_list) - - def print_job_set(self): - self._check_status(self.SessionStatus.OPEN, self.SessionStatus.RUNNING) - job_set = c_api_util.GetJobSet() - for job in job_set.job: - print("job_name:", job.job_conf.job_name) - for op_conf in job.net.op: - print("\top_name:", op_conf.name) - - def list_jobs(self): - self._check_status(self.SessionStatus.RUNNING) - return list(self.job_name2job_conf_.keys()) - - def list_inputs(self): - self._check_status(self.SessionStatus.RUNNING) - input_names = [] - for ( - input_name, - _, - ) in self.inter_user_job_info_.input_or_var_op_name2push_job_name.items(): - input_names.append(input_name) - return tuple(input_names) - - def list_outputs(self): - self._check_status(self.SessionStatus.RUNNING) - output_names = [] - for ( - output_name, - _, - ) in self.inter_user_job_info_.output_or_var_op_name2pull_job_name.items(): - output_names.append(output_name) - return tuple(output_names) - - def input_info(self, input_name, job_name=None): - return self._get_op_blob_info(job_name, input_name, "out") - - def output_info(self, output_name, job_name=None): - return self._get_op_blob_info(job_name, output_name, "in") - - def _get_op_blob_info(self, job_name, op_name, blob_name): - self._check_status(self.SessionStatus.OPEN, self.SessionStatus.RUNNING) - if op_name in self.inferface_name2info_: - return self.inferface_name2info_[op_name] - job_name = job_name or self.cur_job_name_ - if job_name is None: - raise ValueError("please specify job_name") - lbn = oneflow._oneflow_internal.JobBuildAndInferCtx_GetOpBlobLbn( - job_name, op_name, blob_name - ) - shape = c_api_util.JobBuildAndInferCtx_GetStaticShape(job_name, lbn) - dtype = c_api_util.JobBuildAndInferCtx_GetDataType(job_name, lbn) - dtype = dtype_util.convert_proto_dtype_to_oneflow_dtype(dtype) - info = dict(shape=shape, dtype=dtype) - self.inferface_name2info_[op_name] = info - return info - - def run(self, job_name, **kwargs): - self._check_status(self.SessionStatus.RUNNING) - return self.event_loop_.run_until_complete(self.async_run(job_name, **kwargs)) - - async def async_run(self, job_name, **kwargs): - self._check_status(self.SessionStatus.RUNNING) - self._run_push_jobs(**kwargs) - job_inst = job_instance_util.MakeUserJobInstance(job_name) - self._run_job(job_inst) - output_futures = tuple(self._run_pull_jobs(job_name).values()) - return await asyncio.gather(*output_futures) - - def _run_job(self, job_inst): - future = self.event_loop_.create_future() - - def job_finish_cb(_): - self.event_loop_.call_soon_threadsafe(future.set_result, None) - - job_inst.AddPostFinishCallback(job_finish_cb) - oneflow._oneflow_internal.LaunchJob(job_inst) - self.job_futures_.append(future) - - def _run_push_jobs(self, **kwargs): - for ( - input_name, - push_job_name, - ) in self.inter_user_job_info_.input_or_var_op_name2push_job_name.items(): - if input_name not in kwargs: - raise ValueError('input "{}" is absent'.format(input_name)) - input_numpy = kwargs[input_name] - if not isinstance(input_numpy, np.ndarray): - raise ValueError('input "{}" requires numpy.ndarray'.format(input_name)) - push_fn = input_blob_util._MakePushNdarrayCallback(input_numpy) - push_job_inst = job_instance_util.MakePushJobInstance( - push_job_name, input_name, push_fn - ) - self._run_job(push_job_inst) - - def _run_pull_jobs(self, user_job_name): - output_futures = {} - for ( - output_name, - pull_job_name, - ) in self.inter_user_job_info_.output_or_var_op_name2pull_job_name.items(): - future = self.event_loop_.create_future() - pull_fn = self._make_pull_job_cb(output_name, user_job_name, future) - pull_job_inst = job_instance_util.MakePullJobInstance( - pull_job_name, output_name, pull_fn - ) - self._run_job(pull_job_inst) - output_futures[output_name] = future - return output_futures - - def _make_pull_job_cb(self, output_name, user_job_name, future): - output_lbn = oneflow._oneflow_internal.JobBuildAndInferCtx_GetOpBlobLbn( - user_job_name, output_name, "out" - ) - split_axis = c_api_util.JobBuildAndInferCtx_GetSplitAxisFromProducerView( - user_job_name, output_lbn - ) - - def pull_fn(ofblob): - ndarray = ofblob.CopyToNdarray() - self.event_loop_.call_soon_threadsafe(future.set_result, ndarray) - - return pull_fn - - def _run_load_checkpoint_job(self): - if self.checkpoint_path_ is None: - raise ValueError("checkpoint path not set") - - def copy_model_load_path(ofblob): - ofblob.CopyFromNdarray( - np.frombuffer(self.checkpoint_path_.encode("ascii"), dtype=np.int8) - ) - - load_checkpoint_job_inst = job_instance_util.MakeJobInstance( - self.inter_user_job_info_.global_model_load_job_name, - push_cb=copy_model_load_path, - ) - self._run_job(load_checkpoint_job_inst) - - async def wait_for_all_jobs_finished(self): - await asyncio.gather(*self.job_futures_) - self.job_futures_ = [] diff --git a/python/oneflow/serving/saved_model_builder.py b/python/oneflow/serving/saved_model_builder.py deleted file mode 100644 index 4378b46fcf63c37c01a6736fe63279bd36c41091..0000000000000000000000000000000000000000 --- a/python/oneflow/serving/saved_model_builder.py +++ /dev/null @@ -1,312 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import os -import typing - -from google.protobuf import text_format - -import oneflow as flow -import oneflow._oneflow_internal -import oneflow.core.job.job_conf_pb2 as job_conf_pb -import oneflow.core.job.sbp_parallel_pb2 as sbp_parallel_pb -import oneflow.core.operator.interface_blob_conf_pb2 as interface_blob_conf_pb -import oneflow.core.register.logical_blob_id_pb2 as logical_blob_id_pb -import oneflow.core.serving.saved_model_pb2 as saved_model_pb -import oneflow.framework.c_api_util as c_api_util -import oneflow.framework.session_context as session_ctx - - -class ModelBuilder(object): - DEFAULT_CHECKPOINT_DIR = "variables" - DEFAULT_SAVED_MODEL_FILE_BASENAME = "saved_model" - - def __init__(self, save_path: str): - if not isinstance(save_path, str): - raise ValueError( - "param 'save_path' must be str, but got {}".format(save_path) - ) - self.version_ = None - self.checkpoint_dir_ = self.DEFAULT_CHECKPOINT_DIR - self.saved_model_dir_ = save_path - self.saved_model_pb_filename_ = "{}.pb".format( - self.DEFAULT_SAVED_MODEL_FILE_BASENAME - ) - self.saved_model_pbtxt_filename_ = "{}.prototxt".format( - self.DEFAULT_SAVED_MODEL_FILE_BASENAME - ) - self.saved_model_proto_ = saved_model_pb.SavedModel() - self.graph_builders_ = {} - - @property - def proto(self): - return self.saved_model_proto_ - - def ModelName(self, model_name: str): - assert isinstance(model_name, str) - self.proto.name = model_name - return self - - def Version(self, version: int): - assert isinstance(version, int) - self.version_ = version - return self - - def AddFunction(self, func): - func_name = func.__name__ - if func_name in self.graph_builders_: - raise ValueError("function with name {} already exists".format(func_name)) - graph_builder = GraphBuilder(func_name, self) - self.graph_builders_[func_name] = graph_builder - if not self.proto.HasField("default_graph_name"): - self.proto.default_graph_name = func_name - return graph_builder - - def _check_input_output_name_conflict(self): - name_set = set() - lbn_set = set() - - def check_name_conflict(name, interface_def): - if name in name_set: - raise ValueError("input conflict, {} already exist".format(name)) - name_set.add(name) - lbn = Lbi2Lbn(interface_def.lbi) - if lbn in lbn_set: - raise ValueError( - "input conflict, {} already bind to other input".format(lbn) - ) - lbn_set.add(lbn) - - for (_, graph_def) in self.proto.graphs.items(): - for (_, signature_def) in graph_def.signatures.items(): - for (input_name, input_def) in signature_def.inputs.items(): - check_name_conflict(input_name, input_def) - for (output_name, output_def) in signature_def.outputs.items(): - check_name_conflict(output_name, output_def) - - @session_ctx.try_init_default_session - def Save(self, save_model_before_graph_complete: bool = True): - self._check_input_output_name_conflict() - for (_, graph_builder) in self.graph_builders_.items(): - if not graph_builder.finished: - graph_builder.Finish() - sess = session_ctx.GetDefaultSession() - for (graph_name, graph_def) in self.proto.graphs.items(): - job = sess.Job( - graph_name - if save_model_before_graph_complete - else graph_name + "_after_complete" - ) - graph_def.op_list.extend(list(job.net.op)) - if not os.path.exists(self.saved_model_dir_): - os.makedirs(self.saved_model_dir_) - if self.version_ is None: - raise ValueError("model version is not set") - version_dir = os.path.join(self.saved_model_dir_, str(self.version_)) - if os.path.exists(version_dir): - raise ValueError( - 'Directory of model "{}" version "{}" already exist.'.format( - self.saved_model_dir_, self.version_ - ) - ) - os.makedirs(version_dir) - self.proto.version = self.version_ - checkpoint_path = os.path.join(version_dir, self.checkpoint_dir_) - flow.checkpoint.save(checkpoint_path) - self.proto.checkpoint_dir = self.checkpoint_dir_ - saved_model_pb_path = os.path.join(version_dir, self.saved_model_pb_filename_) - with open(saved_model_pb_path, "wb") as writer: - writer.write(self.saved_model_proto_.SerializeToString()) - saved_model_pbtxt_path = os.path.join( - version_dir, self.saved_model_pbtxt_filename_ - ) - with open(saved_model_pbtxt_path, "wt") as writer: - writer.write(text_format.MessageToString(self.saved_model_proto_)) - - -class GraphBuilder(object): - def __init__(self, name: str, model_builder: typing.Optional[ModelBuilder] = None): - if not isinstance(name, str): - raise ValueError("param 'name' must be str, but got {}".format(name)) - if not isinstance(model_builder, ModelBuilder) and model_builder is not None: - raise ValueError( - "param 'model_builder' must be a type of ModelBuilder or None" - ) - if model_builder is not None: - if name in model_builder.proto.graphs: - raise ValueError( - "graph function ({}) is already added to model ({})".format( - name, model_builder.proto.name - ) - ) - self.proto_ = model_builder.proto.graphs[name] - self.owner_ = model_builder - else: - self.proto_ = saved_model_pb.GraphDef() - self.owner_ = None - self.name_ = name - self.finished_ = False - self.signature_builders_ = {} - - @property - def name(self): - return self.name_ - - @property - def proto(self): - return self.proto_ - - @property - def finished(self): - return self.finished_ - - def AddSignature(self, signature_name: str): - assert isinstance(signature_name, str) - if signature_name in self.signature_builders_: - raise ValueError("signature name {} already exists".format(signature_name)) - signature_builder = SignatureBuilder(signature_name, self) - self.signature_builders_[signature_name] = signature_builder - if not self.proto.HasField("default_signature_name"): - self.proto.default_signature_name = signature_name - return signature_builder - - def Finish(self): - assert self.finished is False - for (_, signature_def) in self.proto.signatures.items(): - for (_, input_def) in signature_def.inputs.items(): - input_lbn = Lbi2Lbn(input_def.lbi) - oneflow._oneflow_internal.JobBuildAndInferCtx_CheckLbnValidAndExist( - self.name, input_lbn - ) - GetInterfaceBlobConf(self.name, input_lbn, input_def.blob_conf) - for (_, output_def) in signature_def.outputs.items(): - oneflow._oneflow_internal.JobBuildAndInferCtx_CheckLbnValidAndExist( - self.name, Lbi2Lbn(output_def.lbi) - ) - self.finished_ = True - - def OwnerModelBuilder(self): - return self.owner_ - - def AsDefault(self): - if self.owner_ is not None: - self.owner_.proto.default_graph_name = self.name - return self - - -class SignatureBuilder(object): - def __init__(self, name: str, graph_builder: typing.Optional[GraphBuilder] = None): - if not isinstance(name, str): - raise ValueError("param 'name' must be str, but got {}".format(name)) - if not isinstance(graph_builder, GraphBuilder) and graph_builder is not None: - raise ValueError( - "param 'graph_builder' must be a type of GraphBuilder or None" - ) - if graph_builder is not None: - if name in graph_builder.proto.signatures: - raise ValueError( - "signature ({}) already exist in graph ({})".format( - name, graph_builder.name - ) - ) - self.proto_ = graph_builder.proto.signatures[name] - self.owner_ = graph_builder - else: - self.proto_ = job_conf_pb.JobSignatureDef() - self.owner_ = None - self.name_ = name - - @property - def name(self): - return self.name_ - - @property - def proto(self): - return self.proto_ - - def Input(self, input_name: str, lbn: str): - assert isinstance(input_name, str) - assert isinstance(lbn, str) - assert "/" in lbn - if input_name in self.proto.inputs: - raise ValueError( - "input_name ({}) already exist in signature ({}) of graph ({})".format( - input_name, self.name, self.graph_builder_.name - ) - ) - input_def = self.proto.inputs[input_name] - Lbn2Lbi(lbn, input_def.lbi) - return self - - def Output(self, output_name: str, lbn: str): - assert isinstance(output_name, str) - assert isinstance(lbn, str) - assert "/" in lbn - if output_name in self.proto.outputs: - raise ValueError( - "output_name ({}) already exist in signature ({}) of graph ({})".format( - output_name, self.name, self.graph_builder_.name - ) - ) - output_def = self.proto.outputs[output_name] - Lbn2Lbi(lbn, output_def.lbi) - return self - - def OwnerGraphBuilder(self): - return self.owner_ - - def AsDefault(self): - if self.owner_ is not None: - self.owner_.proto.default_signature_name = self.name - return self - - -def GetInterfaceBlobConf(job_name, lbn, blob_conf=None): - assert isinstance(job_name, str) - assert isinstance(lbn, str) - if blob_conf is None: - blob_conf = interface_blob_conf_pb.InterfaceBlobConf() - else: - assert isinstance(blob_conf, interface_blob_conf_pb.InterfaceBlobConf) - shape = c_api_util.JobBuildAndInferCtx_GetStaticShape(job_name, lbn) - dtype = c_api_util.JobBuildAndInferCtx_GetDataType(job_name, lbn) - split_axis = c_api_util.JobBuildAndInferCtx_GetSplitAxisFromProducerView( - job_name, lbn - ) - is_dynamic = c_api_util.JobBuildAndInferCtx_IsDynamic(job_name, lbn) - blob_conf.shape.dim.extend(shape) - blob_conf.data_type = dtype - if split_axis is not None: - sbp_parallel = sbp_parallel_pb.SbpParallel() - sbp_parallel.split_parallel.axis = split_axis - blob_conf.nd_sbp.sbp_parallel.extend([sbp_parallel]) - blob_conf.is_dynamic = is_dynamic - return blob_conf - - -def Lbn2Lbi(lbn, lbi=None): - assert isinstance(lbn, str) - assert "/" in lbn, 'invalid lbn "{}"'.format(lbn) - [op_name, blob_name] = lbn.split("/") - if lbi is None: - lbi = logical_blob_id_pb.LogicalBlobId() - lbi.op_name = op_name - lbi.blob_name = blob_name - return lbi - - -def Lbi2Lbn(lbi): - assert isinstance(lbi, logical_blob_id_pb.LogicalBlobId) - return "{}/{}".format(lbi.op_name, lbi.blob_name) diff --git a/python/oneflow/test/README.md b/python/oneflow/test/README.md index 7ada2be57d5faab4be950643ca2ac9c81045e604..f72aadc3c023221315c25a06e19621bbfc6e04cb 100644 --- a/python/oneflow/test/README.md +++ b/python/oneflow/test/README.md @@ -1,456 +1,782 @@ ## Ops Version : Alpha -|op name | Doc Test | Compatiable/Completeness Test | Exception | -| ------------------------- | ------------- | ----------------------------- | --------- | -| oneflow.Tensor | [oneflow.tensor](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L20) | [tensor_init](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L161) | [tensordot_neg_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L25) | -| oneflow.BoolTensor | | | | -| oneflow.ByteTensor | | | | -| oneflow.CharTensor | | | | -| oneflow.DoubleTensor | | | | -| oneflow.FloatTensor | | | | -| oneflow.HalfTensor | | | | -| oneflow.IntTensor | | | | -| oneflow.LongTensor | | | | -| oneflow.Size | [oneflow.Tensor.size](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1319) | | [splitwithsize_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L239) | -| oneflow.abs | [oneflow.Tensor.abs](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L628) | [abs_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_abs.py#L27) | | -| oneflow.acos | [oneflow.Tensor.acos](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L635) | [acos_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L348) | | -| oneflow.acosh | [oneflow.Tensor.acosh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L649) | [acosh_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L368) | | -| oneflow.adaptive_avg_pool1d | | | | -| oneflow.adaptive_avg_pool2d | | | | -| oneflow.adaptive_avg_pool3d | | | | -| oneflow.add | [oneflow.Tensor.add](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1163) | [padding_idx](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sparse.py#L140) | [add_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L27) | -| oneflow.addmm | [oneflow.Tensor.addmm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1170) | [addmm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_addmm.py#L60) | | -| oneflow.any | [oneflow.any](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L219) | [any_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_reduce.py#L52) | | -| oneflow.arange | [oneflow.arange](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/arange.py#L20) | [arange](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_arange.py#L58) | | -| oneflow.arccos | [oneflow.Tensor.arccos](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L642) | [arccos_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L338) | | -| oneflow.arccosh | [oneflow.Tensor.arccosh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L656) | [arccosh_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L358) | | -| oneflow.arcsin | [oneflow.Tensor.arcsin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1205) | | | -| oneflow.arcsinh | [oneflow.Tensor.arcsinh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1212) | | | -| oneflow.arctan | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L663) | [arctan_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L438) | | -| oneflow.arctanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L663) | [arctanh_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L460) | | -| oneflow.argmax | [oneflow.argmax](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L139) | [argmax](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_argmax.py#L83) | [argmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L22) | -| oneflow.argmin | [oneflow.argmin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L169) | [argmin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_argmin.py#L34) | | -| oneflow.argsort | [oneflow.Tensor.argsort](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L684) | [argsort](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_argsort.py#L37) | | -| oneflow.argwhere | [oneflow.Tensor.argwhere](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L691) | [argwhere](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L672) | | -| oneflow.as_strided | | | | -| oneflow.as_tensor | | | | -| oneflow.asin | [oneflow.Tensor.asin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1198) | | | -| oneflow.asinh | [oneflow.asinh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L318) | | | -| oneflow.atan | [oneflow.atan2](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/trigonometric_ops.py#L21) | [atanh_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L410) | | -| oneflow.atan2 | [oneflow.atan2](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/trigonometric_ops.py#L21) | | | -| oneflow.atanh | [oneflow.Tensor.atanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L698) | [atanh_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L410) | | -| oneflow.autograd | | [autograd_interface](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L81) | | -| oneflow.batch_gather | | | | -| oneflow.bernoulli | [oneflow.bernoulli](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L20) | [bernoulli](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_bernoulli.py#L49) | | -| oneflow.bfloat16 | | | | -| oneflow.bmm | [oneflow.bmm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/bmm.py#L20) | [bmm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_bmm.py#L93) | [bmm_exception_dim_not_right](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_bmm.py#L25) | -| oneflow.bool | | [bool_add](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_add.py#L212) | | -| oneflow.boxing | | | | -| oneflow.broadcast_like | | | | -| oneflow.cast | [oneflow.Tensor.cast](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L901) | [broadcast_mul](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_mul.py#L193) | [broadcast_like_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L28) | -| oneflow.cat | [oneflow.cat](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L333) | [scatter_nd](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_scatter_nd.py#L56) | [concat_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L37) | -| oneflow.ceil | [oneflow.Tensor.ceil](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1653) | [ceil_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_ceil.py#L29) | | -| oneflow.char | | | | -| oneflow.chunk | [oneflow.Tensor.chunk](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L859) | [chunk](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_chunk.py#L37) | [chunk_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L254) | -| oneflow.clamp | [oneflow.clamp](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L20) | [clamp](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L96) | | -| oneflow.clamp_ | | | | -| oneflow.clip | [oneflow.clip](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L70) | [clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L152) | | -| oneflow.clip_ | | | | -| oneflow.concat | | [concat](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_concat.py#L124) | [concat_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L37) | -| oneflow.constant_initializer | | | | -| oneflow.convert_oneflow_dtype_to_numpy_dtype | | | | -| oneflow.cos | [oneflow.Tensor.acos](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L635) | [cos](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L88) | [cosine_similarity_not_floating_type](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_cosine_similarity.py#L24) | -| oneflow.cosh | [oneflow.Tensor.acosh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L649) | [arccosh_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L358) | | -| oneflow.cumprod | [oneflow.cumprod](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1723) | [cumprod](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_cum_ops.py#L38) | | -| oneflow.cumsum | [oneflow.cumsum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1690) | [cumsum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_cumsum.py#L37) | | -| oneflow.device | [oneflow.Tensor.device](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L85) | | [device_type](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_device.py#L25) | -| oneflow.diag | [oneflow.diagonal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L20) | [diag](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_diag.py#L35) | [diagonal_index_error1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L204) | -| oneflow.diagonal | [oneflow.diagonal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L20) | [diagonal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_diagonal.py#L44) | [diagonal_index_error1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L204) | -| oneflow.distributed_partial_fc_sample | | | | -| oneflow.div | [oneflow.Tensor.div_](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1071) | [div](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L501) | [div_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L63) | -| oneflow.div_ | | | | -| oneflow.dot | [oneflow.dot](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1370) | [tensordot_intdim](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_tensordot.py#L28) | [tensordot_neg_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L25) | -| oneflow.double | [oneflow.Tensor.double](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1936) | [double](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L200) | | -| oneflow.dtype | | | | -| oneflow.dtypes | | | | -| oneflow.einsum | [oneflow.einsum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/einsum.py#L20) | [einsum_alphaflod_usecase11](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase11.py#L38) | | -| oneflow.empty | | [empty_consistent](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_empty.py#L76) | | -| oneflow.eq | [oneflow.Tensor.requires_grad](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L778) | [eq](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_eq.py#L38) | | -| oneflow.equal | | | | -| oneflow.erf | [oneflow.Tensor.erf](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L941) | [erf](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_erf.py#L35) | | -| oneflow.erfc | [oneflow.Tensor.erfc](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L950) | [erfc](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_erfc.py#L35) | | -| oneflow.erfinv | [oneflow.Tensor.erfinv](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L959) | [erfinv_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L700) | | -| oneflow.erfinv_ | | | | -| oneflow.exp | [oneflow.Tensor.expand](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L130) | [expm1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_expm1.py#L35) | [expand_dim_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L78) | -| oneflow.expand | [oneflow.Tensor.expand](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L130) | [expand_compare_with_numpy](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_expand.py#L206) | [expand_dim_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L78) | -| oneflow.expm1 | [oneflow.Tensor.expm1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1660) | [expm1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_expm1.py#L35) | | -| oneflow.eye | [oneflow.eye](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1529) | [eye](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L50) | | -| oneflow.flatten | [oneflow.flatten](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/flatten.py#L20) | [flatten_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_flatten.py#L71) | | -| oneflow.flip | [oneflow.Tensor.flip](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L169) | [flip](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_flip.py#L40) | | -| oneflow.float | [oneflow.Tensor.float](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1915) | [float](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L186) | | -| oneflow.float16 | | | | -| oneflow.float32 | | | | -| oneflow.float64 | | | | -| oneflow.floor | [oneflow.Tensor.floor](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L162) | [floor](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_floor.py#L49) | | -| oneflow.floor_ | | | | -| oneflow.floor_divide | | | | -| oneflow.fmod | [oneflow.Tensor.fmod](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1583) | [fmod_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L885) | | -| oneflow.from_numpy | | | | -| oneflow.full | | [full_with_random_data_int](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_constant.py#L126) | | -| oneflow.gather | [oneflow.gather](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L367) | [gather_1n4d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_comm_ops.py#L106) | [gather_index_type_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L120) | -| oneflow.gather_nd | | | | -| oneflow.ge | [oneflow.arange](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/arange.py#L20) | [generator_manual_seed](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L72) | [get_sbp_with_invalid_axis](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L24) | -| oneflow.gelu | [oneflow.Tensor.gelu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1017) | [gelu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L149) | | -| oneflow.glorot_normal_initializer | | | | -| oneflow.glorot_uniform_initializer | | | | -| oneflow.grad_enable | | | | -| oneflow.greater | [oneflow.greater](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/comparison.py#L21) | [greater](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_greater.py#L44) | | -| oneflow.greater_equal | | | | -| oneflow.gt | [oneflow.Tensor.gt](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1035) | | | -| oneflow.half | [oneflow.Tensor.half](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1449) | [half](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1065) | | -| oneflow.hsplit | [oneflow.hsplit](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1606) | | | -| oneflow.in_top_k | | | | -| oneflow.index_select | | | | -| oneflow.int | [oneflow.Tensor.int](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1873) | [randint](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_randint.py#L99) | | -| oneflow.int32 | | | | -| oneflow.int64 | | | | -| oneflow.int8 | | | | -| oneflow.is_floating_point | | | | -| oneflow.is_grad_enabled | | | | -| oneflow.is_nonzero | | | | -| oneflow.is_tensor | | | | -| oneflow.kaiming_initializer | | | | -| oneflow.le | [oneflow.tile](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tile.py#L20) | [less_equal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_less_equal.py#L84) | [reflect_pad_size_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L107) | -| oneflow.linalg_flow | | | | -| oneflow.linalg_matrix_norm | | | | -| oneflow.linalg_norm | | | | -| oneflow.linalg_vector_norm | | | | -| oneflow.linspace | | [linspace_int_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_linspace.py#L32) | | -| oneflow.log | [oneflow.Tensor.logical_not](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L512) | [logical_or](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_or.py#L58) | | -| oneflow.log1p | [oneflow.Tensor.log1p](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1042) | [log1p_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_log1p.py#L31) | | -| oneflow.log2 | [oneflow.log2](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L948) | [log2_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L808) | | -| oneflow.log_softmax | | | | -| oneflow.logical_and | | | | -| oneflow.logical_not | | | | -| oneflow.logical_or | | | | -| oneflow.logical_xor | | | | -| oneflow.long | [oneflow.Tensor.long](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1894) | [long](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L144) | | -| oneflow.lt | [oneflow.Tensor.lt](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L980) | [multi_input](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_function.py#L54) | [multi_input_with_diff_device](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_multi_input_with_diff_device_or_placement.py#L27) | -| oneflow.manual_seed | | | | -| oneflow.masked_fill | | | | -| oneflow.masked_select | | | | -| oneflow.matmul | [oneflow.Tensor.matmul](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L600) | [matmul](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_matmul.py#L42) | [matmul_dimension_error1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L220) | -| oneflow.max | [oneflow.max](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L20) | [maxpool1d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_maxpool.py#L155) | [argmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L22) | -| oneflow.maximum | [oneflow.maximum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L997) | [maximum_minimum_with_same_input](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_maximum_minimum.py#L93) | | -| oneflow.mean | [oneflow.mean](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L123) | [mean](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_mean.py#L33) | | -| oneflow.meshgrid | [oneflow.meshgrid](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/meshgrid.py#L20) | [meshgrid](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_meshgrid.py#L68) | [meshgrid_tensors_scalar_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L276) | -| oneflow.min | [oneflow.min](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L56) | [argmin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_argmin.py#L34) | | -| oneflow.minimum | [oneflow.minimum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L975) | | | -| oneflow.mish | [oneflow.Tensor.mish](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1049) | [mish_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L189) | | -| oneflow.movedim | [oneflow.movedim](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1428) | [movedim](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_movedim.py#L37) | | -| oneflow.mul | [oneflow.Tensor.matmul](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L600) | [mul_with_scalar](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_mul.py#L47) | [matmul_dimension_error1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L220) | -| oneflow.narrow | [oneflow.narrow](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor_ops.py#L20) | [narrow](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_narrow.py#L35) | [narrow_dim_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L178) | -| oneflow.ne | [oneflow.comm.send](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/comm.py#L20) | [generator_manual_seed](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L72) | [onehot_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L375) | -| oneflow.neg | [oneflow.Tensor.negative](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1085) | [negative](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_negative.py#L31) | | -| oneflow.negative | [oneflow.Tensor.negative](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1085) | [negative](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_negative.py#L31) | | -| oneflow.new_ones | | | | -| oneflow.nms | [oneflow.Tensor.nms](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1674) | [nms](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_nms.py#L50) | | -| oneflow.no_grad | | | | -| oneflow.nonzero | [oneflow.Tensor.nonzero](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1681) | [nonzero](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_nozero.py#L31) | | -| oneflow.not_equal | | | | -| oneflow.numel | [oneflow.Tensor.numel](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L194) | | | -| oneflow.one_embedding | | | | -| oneflow.ones | [oneflow.ones_like](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L20) | [ones_like](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_ones_like.py#L53) | | -| oneflow.ones_initializer | | | | -| oneflow.ones_like | | | | -| oneflow.pad | | [padding_idx](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sparse.py#L140) | [pad_size_attribute_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L89) | -| oneflow.permute | [oneflow.permute](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor_ops.py#L82) | [permute2d_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_contiguous.py#L40) | | -| oneflow.placement | [oneflow.Tensor.placement](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L95) | | | -| oneflow.pow | [oneflow.Tensor.pow](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1128) | [pow_float_scalar_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L163) | | -| oneflow.prod | [oneflow.prod](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L154) | [cumprod](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_cum_ops.py#L38) | | -| oneflow.randint | | [randint](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_randint.py#L99) | | -| oneflow.randn | | [randn](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_randn.py#L102) | | -| oneflow.random_normal_initializer | | | | -| oneflow.random_uniform_initializer | | | | -| oneflow.randperm | | [randperm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_randperm.py#L86) | | -| oneflow.reciprocal | [oneflow.Tensor.reciprocal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1156) | | | -| oneflow.relu | [oneflow.Tensor.relu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1135) | [relu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L124) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | -| oneflow.repeat | [oneflow.Tensor.repeat](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1538) | | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | -| oneflow.reshape | [oneflow.Tensor.reshape](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1753) | [reshape](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_reshape.py#L86) | [reshape_exception_only_one_dim_infered](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reshape.py#L25) | -| oneflow.roi_align | | | | -| oneflow.roll | [oneflow.Tensor.roll](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1142) | | [roll_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L112) | -| oneflow.round | [oneflow.Tensor.round](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1149) | [round_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L722) | | -| oneflow.rsqrt | [oneflow.Tensor.rsqrt](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1256) | [rsqrt_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L136) | | -| oneflow.save | | [save_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L179) | | -| oneflow.sbp | [oneflow.Tensor.sbp](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L102) | [sbp_symbol](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sbp_symbol.py#L23) | | -| oneflow.scatter | | [scatter_nd](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_scatter_nd.py#L56) | | -| oneflow.scatter_add | | | | -| oneflow.select | [oneflow.select](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1399) | | [ApplySelectIndexing_input_dim_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensor_index.py#L37) | -| oneflow.selu | [oneflow.Tensor.selu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1284) | [selu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L199) | | -| oneflow.set_num_threads | | | | -| oneflow.set_printoptions | | | | -| oneflow.set_rng_state | | | | -| oneflow.sigmoid | [oneflow.Tensor.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1291) | [sigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L154) | | -| oneflow.sign | [oneflow.Tensor.sign](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1298) | [sign](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sign.py#L45) | | -| oneflow.silu | [oneflow.Tensor.silu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1305) | [silu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L194) | | -| oneflow.sin | [oneflow.Tensor.asin](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1198) | [cosine_decay_lr](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_lr_scheduler.py#L82) | [cosine_similarity_not_floating_type](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_cosine_similarity.py#L24) | -| oneflow.sin_ | | | | -| oneflow.sinh | [oneflow.Tensor.arcsinh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1212) | | | -| oneflow.slice | | [slice](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_slice.py#L151) | [PrepareSliceIndices_indices_amount_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensor_index.py#L22) | -| oneflow.slice_update | | | | -| oneflow.softmax | [oneflow.Tensor.softmax](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1333) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L415) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | -| oneflow.softplus | [oneflow.Tensor.softplus](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1340) | [softplus_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L209) | | -| oneflow.softshrink | | [softshrink_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L214) | | -| oneflow.softsign | [oneflow.Tensor.softsign](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1347) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L710) | | -| oneflow.sort | [oneflow.Tensor.argsort](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L684) | [argsort](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_argsort.py#L37) | | -| oneflow.split | [oneflow.Tensor.split](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L866) | | [split_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L224) | -| oneflow.sqrt | [oneflow.Tensor.sqrt](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L520) | [sqrt_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L109) | | -| oneflow.square | [oneflow.Tensor.square](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L527) | [square_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L146) | | -| oneflow.squeeze | [oneflow.unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor_ops.py#L50) | [unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L68) | [squeeze_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L106) | -| oneflow.stack | [oneflow.stack](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L272) | [stack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L28) | [stack_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L62) | -| oneflow.stateful_op | | | | -| oneflow.std | [oneflow.Tensor.std](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L534) | [std_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_std.py#L26) | | -| oneflow.sub | [oneflow.Tensor.sub_](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1078) | [sub](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sub.py#L96) | | -| oneflow.sum | [oneflow.sum](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/reduce_ops.py#L92) | [einsum_alphaflod_usecase11](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase11.py#L38) | | -| oneflow.support | | | | -| oneflow.swapaxes | [oneflow.Tensor.swapaxes](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L880) | [swapaxes_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_swapaxes.py#L31) | | -| oneflow.t | [oneflow.permute](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor_ops.py#L82) | [greter_equal](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_greater_equal.py#L88) | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | -| oneflow.tan | [oneflow.atan2](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/trigonometric_ops.py#L21) | [constant_warmup_cosine_annealing](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_lr_scheduler.py#L446) | | -| oneflow.tanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L663) | [tanh_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L134) | | -| oneflow.tensor_buffer | | | | -| oneflow.tensor_buffer_to_list_of_tensors | | | | -| oneflow.tensor_buffer_to_tensor | | | | -| oneflow.tensor_scatter_nd_update | | | | -| oneflow.tensor_split | | | | -| oneflow.tensor_to_tensor_buffer | | | | -| oneflow.tile | [oneflow.tile](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tile.py#L20) | | [tile_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L431) | -| oneflow.to_global | | | | -| oneflow.to_local | | | | -| oneflow.topk | [oneflow.Tensor.topk](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1667) | | | -| oneflow.transpose | [oneflow.transpose](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L245) | [transpose_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_contiguous.py#L32) | | -| oneflow.tril | [oneflow.tril](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L84) | [tril_without_diag](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_tril.py#L26) | | -| oneflow.triu | [oneflow.triu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L114) | [triu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_triu.py#L47) | | -| oneflow.truncated_normal_initializer | | | | -| oneflow.uint8 | | | | -| oneflow.unsqueeze | [oneflow.unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor_ops.py#L50) | [unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L68) | | -| oneflow.var | [oneflow.Tensor.var](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L541) | | | -| oneflow.variance_scaling_initializer | | | | -| oneflow.version | | | | -| oneflow.view | [oneflow.Tensor.view](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1776) | [view](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_view.py#L79) | [view_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L166) | -| oneflow.vsplit | [oneflow.vsplit](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1649) | | | -| oneflow.where | [oneflow.Tensor.argwhere](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L691) | [where](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_where.py#L196) | | -| oneflow.xavier_normal_initializer | | | | -| oneflow.xavier_uniform_initializer | | | | -| oneflow.zero_ | | | | -| oneflow.zeros | [oneflow.zeros_like](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L43) | [zeros_](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L944) | | -| oneflow.zeros_initializer | | | | -| oneflow.zeros_like | | | | -| oneflow.optim.Adagrad | | [adagrad](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L197) | | -| oneflow.optim.Adam | | [adamw](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adamw.py#L244) | | -| oneflow.optim.AdamW | | [adamw](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adamw.py#L244) | | -| oneflow.optim.LAMB | | [lambda_lr](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_lr_scheduler.py#L199) | | -| oneflow.optim.RMSprop | | [rmsprop](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_rmsprop.py#L228) | | -| oneflow.optim.SGD | | [sgd](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_sgd.py#L194) | | -| oneflow.optim.lr_scheduler.ChainedScheduler | | | | -| oneflow.optim.lr_scheduler.ConstantLR | | | | -| oneflow.optim.lr_scheduler.CosineAnnealingLR | | | | -| oneflow.optim.lr_scheduler.CosineAnnealingWarmRestarts | | | | -| oneflow.optim.lr_scheduler.CosineDecayLR | | | | -| oneflow.optim.lr_scheduler.ExponentialLR | | | | -| oneflow.optim.lr_scheduler.LambdaLR | | | | -| oneflow.optim.lr_scheduler.LinearLR | | | | -| oneflow.optim.lr_scheduler.MultiStepLR | | | | -| oneflow.optim.lr_scheduler.PolynomialLR | | | | -| oneflow.optim.lr_scheduler.ReduceLROnPlateau | | | | -| oneflow.optim.lr_scheduler.SequentialLR | | | | -| oneflow.optim.lr_scheduler.StepLR | | | | -| oneflow.optim.lr_scheduler.WarmUpLR | | | | -| oneflow.nn.AdaptiveAvgPool1d | | | | -| oneflow.nn.AdaptiveAvgPool2d | | | | -| oneflow.nn.AdaptiveAvgPool3d | | | | -| oneflow.nn.AllReduce | | | | -| oneflow.nn.AvgPool1d | | [avgpool1d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_avgpool.py#L28) | | -| oneflow.nn.AvgPool2d | | [avgpool2d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_avgpool.py#L44) | | -| oneflow.nn.AvgPool3d | | [avgpool3d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_avgpool.py#L61) | | -| oneflow.nn.BCELoss | | | | -| oneflow.nn.BCEWithLogitsLoss | | | | -| oneflow.nn.BatchNorm1d | | [batchnorm1d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L34) | | -| oneflow.nn.BatchNorm2d | | [batchnorm2d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L52) | | -| oneflow.nn.BatchNorm3d | | [batchnorm3d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L70) | | -| oneflow.nn.CELU | | [celu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L144) | [celu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L47) | -| oneflow.nn.COCOReader | | | | -| oneflow.nn.CTCLoss | | | [ctcloss_reduction_type_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L62) | -| oneflow.nn.CoinFlip | | | | -| oneflow.nn.CombinedMarginLoss | | | | -| oneflow.nn.ConstantPad1d | | [constantpad1d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_constantpad.py#L32) | | -| oneflow.nn.ConstantPad2d | | [ConstantPad2d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_zeropad2d.py#L96) | | -| oneflow.nn.ConstantPad3d | | [constantpad3d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_constantpad.py#L64) | | -| oneflow.nn.Conv1d | | [conv1d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_conv1d.py#L422) | | -| oneflow.nn.Conv2d | | [conv2d_default_init](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_conv2d.py#L1568) | | -| oneflow.nn.Conv3d | | | | -| oneflow.nn.ConvTranspose1d | | | | -| oneflow.nn.ConvTranspose2d | | | | -| oneflow.nn.ConvTranspose3d | | | | -| oneflow.nn.CropMirrorNormalize | | | | -| oneflow.nn.CrossEntropyLoss | | | | -| oneflow.nn.DistributedPariticalFCSample | | | | -| oneflow.nn.Dropout | | [dropout_p01](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_dropout.py#L44) | | -| oneflow.nn.ELU | [oneflow.Tensor.gelu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1017) | [relu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L124) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | -| oneflow.nn.Embedding | | [embedding](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_sparse.py#L45) | | -| oneflow.nn.FakeQuantization | | | | -| oneflow.nn.Flatten | [oneflow.flatten](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/flatten.py#L20) | [flatten_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_flatten.py#L71) | | -| oneflow.nn.Fold | [oneflow.Tensor.unfold](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L555) | [fold_with_random_data_1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_fold.py#L28) | | -| oneflow.nn.FusedBatchNorm1d | | | | -| oneflow.nn.FusedBatchNorm2d | | | | -| oneflow.nn.FusedBatchNorm3d | | | | -| oneflow.nn.FusedMLP | | | | -| oneflow.nn.GELU | [oneflow.Tensor.gelu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1017) | [gelu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L149) | | -| oneflow.nn.GLU | | [glu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_glu.py#L37) | [glu_scalar_tensor_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L57) | -| oneflow.nn.GPTIndexedBinDataReader | | | | -| oneflow.nn.GRU | | [gru_cell](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_rnn_cell.py#L218) | | -| oneflow.nn.GroupNorm | | [groupnorm](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_groupnorm.py#L332) | | -| oneflow.nn.Hardsigmoid | | [hardsigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L159) | | -| oneflow.nn.Hardswish | | [hardswish_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L174) | | -| oneflow.nn.Hardtanh | | [hardtanh_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L179) | | -| oneflow.nn.Identity | | [identity_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_linear.py#L217) | | -| oneflow.nn.InstanceNorm1d | | | | -| oneflow.nn.InstanceNorm2d | | | | -| oneflow.nn.InstanceNorm3d | | | | -| oneflow.nn.KLDivLoss | | | | -| oneflow.nn.L1Loss | | | | -| oneflow.nn.LSTM | | [lstm_cell](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_rnn_cell.py#L200) | | -| oneflow.nn.LayerNorm | | | [layernorm_exception_input_shape_not_match](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_layernorm.py#L25) | -| oneflow.nn.LeakyReLU | | [leakyrelu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L184) | | -| oneflow.nn.Linear | | [linear_forward](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_linear.py#L163) | | -| oneflow.nn.LogSigmoid | | [logsigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L169) | | -| oneflow.nn.LogSoftmax | | [logsoftmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L439) | | -| oneflow.nn.MSELoss | | | | -| oneflow.nn.MarginRankingLoss | | | | -| oneflow.nn.MaxPool1d | | [maxpool1d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_maxpool.py#L155) | | -| oneflow.nn.MaxPool2d | | [maxpool2d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_maxpool.py#L177) | | -| oneflow.nn.MaxPool3d | | [maxpool3d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_maxpool.py#L199) | | -| oneflow.nn.MinMaxObserver | | | | -| oneflow.nn.Mish | [oneflow.Tensor.mish](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1049) | [mish_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L189) | | -| oneflow.nn.Module | [oneflow.nn.Module.to_consistent](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/module.py#L20) | [module_to_global](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to_consistent.py#L30) | | -| oneflow.nn.ModuleDict | | [moduledict](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L310) | | -| oneflow.nn.ModuleList | | | | -| oneflow.nn.MovingAverageMinMaxObserver | | | | -| oneflow.nn.NLLLoss | | | | -| oneflow.nn.PReLU | | [prelu_4dim_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_prelu.py#L32) | [prelu_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L38) | -| oneflow.nn.Parameter | | [parameter](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L98) | | -| oneflow.nn.ParameterDict | | | | -| oneflow.nn.ParameterList | | | | -| oneflow.nn.PixelShuffle | | | | -| oneflow.nn.Quantization | | | | -| oneflow.nn.RNN | | [rnn_relu_cell](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_rnn_cell.py#L206) | | -| oneflow.nn.ReLU | [oneflow.Tensor.relu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1135) | [relu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L124) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | -| oneflow.nn.ReLU6 | | [relu6_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L129) | | -| oneflow.nn.ReflectionPad2d | | | | -| oneflow.nn.ReplicationPad2d | | [ReplicationPad2d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_replicationpad2d.py#L104) | | -| oneflow.nn.SELU | [oneflow.Tensor.selu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1284) | [selu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L199) | | -| oneflow.nn.Sequential | | | | -| oneflow.nn.SiLU | [oneflow.Tensor.silu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1305) | [silu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L194) | | -| oneflow.nn.Sigmoid | [oneflow.Tensor.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1291) | [sigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L154) | | -| oneflow.nn.SmoothL1Loss | | | | -| oneflow.nn.Softmax | [oneflow.Tensor.softmax](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1333) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L415) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | -| oneflow.nn.Softplus | [oneflow.Tensor.softplus](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1340) | [softplus_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L209) | | -| oneflow.nn.Softshrink | | [softshrink_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L214) | | -| oneflow.nn.Softsign | [oneflow.Tensor.softsign](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1347) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L710) | | -| oneflow.nn.Tanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L663) | [tanh_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L134) | | -| oneflow.nn.TripletMarginLoss | | | | -| oneflow.nn.Unfold | [oneflow.Tensor.unfold](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L555) | [unfold_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_unfold.py#L28) | | -| oneflow.nn.UpsamplingBilinear2d | | | | -| oneflow.nn.UpsamplingNearest2d | | | | -| oneflow.nn.ZeroPad2d | | | | -| oneflow.nn.functional.adaptive_avg_pool1d | | | | -| oneflow.nn.functional.adaptive_avg_pool2d | | | | -| oneflow.nn.functional.adaptive_avg_pool3d | | | | -| oneflow.nn.functional.affine_grid | | | | -| oneflow.nn.functional.avg_pool1d | | | | -| oneflow.nn.functional.avg_pool2d | | | | -| oneflow.nn.functional.avg_pool3d | | | | -| oneflow.nn.functional.celu | | [celu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L144) | [celu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L47) | -| oneflow.nn.functional.conv1d | | [conv1d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_conv1d.py#L422) | | -| oneflow.nn.functional.conv2d | | [conv2d_default_init](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_conv2d.py#L1568) | | -| oneflow.nn.functional.conv3d | | | | -| oneflow.nn.functional.cross_entropy | | | | -| oneflow.nn.functional.ctc_greedy_decoder | | | | -| oneflow.nn.functional.dropout | | [dropout_p01](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_dropout.py#L44) | | -| oneflow.nn.functional.elu | [oneflow.Tensor.gelu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1017) | [relu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L124) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | -| oneflow.nn.functional.embedding | | [embedding](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_sparse.py#L45) | | -| oneflow.nn.functional.functional_maxpool | | | | -| oneflow.nn.functional.gelu | [oneflow.Tensor.gelu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1017) | [gelu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L149) | | -| oneflow.nn.functional.glu | | [glu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_glu.py#L37) | [glu_scalar_tensor_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L57) | -| oneflow.nn.functional.grid_sample | | | | -| oneflow.nn.functional.hardsigmoid | | [hardsigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L159) | | -| oneflow.nn.functional.hardswish | | [hardswish_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L174) | | -| oneflow.nn.functional.hardtanh | | [hardtanh_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L179) | | -| oneflow.nn.functional.interpolate | | [interpolate](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_interpolate.py#L658) | | -| oneflow.nn.functional.layer_norm | | | | -| oneflow.nn.functional.leaky_relu | | | | -| oneflow.nn.functional.linear | | [linear_forward](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_linear.py#L163) | | -| oneflow.nn.functional.log_softmax | | | | -| oneflow.nn.functional.logsigmoid | | [logsigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L169) | | -| oneflow.nn.functional.max_pool1d | | | | -| oneflow.nn.functional.max_pool2d | | | | -| oneflow.nn.functional.max_pool3d | | | | -| oneflow.nn.functional.mish | [oneflow.Tensor.mish](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1049) | [mish_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L189) | | -| oneflow.nn.functional.normalize | | [normalize_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_normalize.py#L36) | [l2normalize_axis_error1](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L192) | -| oneflow.nn.functional.one_hot | | | | -| oneflow.nn.functional.pad | | [padding_idx](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_sparse.py#L140) | [pad_size_attribute_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L89) | -| oneflow.nn.functional.prelu | | [prelu_4dim_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_prelu.py#L32) | [prelu_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L38) | -| oneflow.nn.functional.relu | [oneflow.Tensor.relu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1135) | [relu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L124) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | -| oneflow.nn.functional.relu6 | | [relu6_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L129) | | -| oneflow.nn.functional.selu | [oneflow.Tensor.selu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1284) | [selu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L199) | | -| oneflow.nn.functional.sigmoid | [oneflow.Tensor.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1291) | [sigmoid_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L154) | | -| oneflow.nn.functional.silu | [oneflow.Tensor.silu](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1305) | [silu_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L194) | | -| oneflow.nn.functional.smooth_l1_loss | | | | -| oneflow.nn.functional.softmax | [oneflow.Tensor.softmax](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1333) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L415) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | -| oneflow.nn.functional.softplus | [oneflow.Tensor.softplus](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1340) | [softplus_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L209) | | -| oneflow.nn.functional.softshrink | | [softshrink_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L214) | | -| oneflow.nn.functional.softsign | [oneflow.Tensor.softsign](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1347) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L710) | | -| oneflow.nn.functional.sparse_softmax_cross_entropy | | | | -| oneflow.nn.functional.tanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L663) | [tanh_module](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_consistent_activation.py#L134) | | -| oneflow.nn.functional.triplet_margin_loss | | | | -| oneflow.nn.functional.upsample | | [upsample2d](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_upsample.py#L357) | | -| oneflow.nn.init.CalcGain | | | | -| oneflow.nn.init.calculate_gain | | | | -| oneflow.nn.init.constant_ | | | | -| oneflow.nn.init.flow | [oneflow.comm.send](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/comm.py#L20) | [flow_erf_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_erf.py#L33) | | -| oneflow.nn.init.kaiming_normal_ | | | | -| oneflow.nn.init.kaiming_uniform_ | | | | -| oneflow.nn.init.normal_ | | | | -| oneflow.nn.init.ones_ | | | | -| oneflow.nn.init.os | [oneflow.transpose](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L245) | [cos](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L88) | [cross_entropy_reduction_type_error](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L50) | -| oneflow.nn.init.trunc_normal_ | | | | -| oneflow.nn.init.uniform_ | | | | -| oneflow.nn.init.xavier_normal_ | | | | -| oneflow.nn.init.xavier_uniform_ | | | | -| oneflow.nn.init.zeros_ | | | | -| oneflow.nn.init.adagrad | | [adagrad](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L197) | | -| oneflow.nn.init.adam | | [adamw](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adamw.py#L244) | | -| oneflow.nn.init.adamw | | [adamw](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adamw.py#L244) | | -| oneflow.nn.init.chained_scheduler | | | | -| oneflow.nn.init.constant_lr | | | | -| oneflow.nn.init.cosine_annealing_lr | | | | -| oneflow.nn.init.cosine_annealing_warm_restarts | | | | -| oneflow.nn.init.cosine_decay_lr | | | | -| oneflow.nn.init.exponential_lr | | | | -| oneflow.nn.init.lamb | | [lambda_lr](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_lr_scheduler.py#L199) | | -| oneflow.nn.init.lambda_lr | | | | -| oneflow.nn.init.linear_lr | | | | -| oneflow.nn.init.lr_scheduler | | | | -| oneflow.nn.init.multistep_lr | | | | -| oneflow.nn.init.polynomial_lr | | | | -| oneflow.nn.init.reduce_lr_on_plateau | | | | -| oneflow.nn.init.rmsprop | | [rmsprop](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_rmsprop.py#L228) | | -| oneflow.nn.init.sequential_lr | | | | -| oneflow.nn.init.sgd | | [sgd](https://github.com/Oneflow-Inc/oneflow/blob/64503e09ab90bd7e47e0682df217996daeec220d/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_sgd.py#L194) | | -| oneflow.nn.init.step_lr | | | | -| oneflow.nn.init.warmup_lr | | | | +| Op Name | Doc Test | Compatiable/Completeness Test | Exception | Performance Test | +| ------------------------- | ------------- | ----------------------------- | --------- | ---------------- | +| oneflow.autograd.backward | [oneflow.Tensor.backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L727) | [unsqueeze_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L54) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.autograd.grad | [oneflow.Tensor.grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L753) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.autograd.no_grad | | [no_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L62) | | | +| oneflow.autograd.enable_grad | | [enable_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L50) | | | +| oneflow.autograd.set_grad_enabled | | [set_grad_enabled](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L74) | | | +| oneflow.autograd.inference_mode | | [inference_mode](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L27) | | | +| oneflow.Tensor.grad | [oneflow.Tensor.grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L753) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.requires_grad | [oneflow.Tensor.requires_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L800) | [requires_grad_tensor_inplace_and_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L170) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.is_leaf | [oneflow.Tensor.is_leaf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L767) | | | | +| oneflow.Tensor.backward | [oneflow.Tensor.backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L727) | [unsqueeze_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L54) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.detach | | [tensor_detach](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L91) | | | +| oneflow.Tensor.register_hook | [oneflow.Tensor.register_hook](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L833) | [tensor_register_hook](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L446) | | | +| oneflow.Tensor.retain_grad | [oneflow.Tensor.retain_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L866) | [retain_grad_for_leaf_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L178) | | | +| oneflow.autograd.Function.forward | | [eye_forward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L27) | | | +| oneflow.autograd.Function.backward | [oneflow.Tensor.backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L727) | [unsqueeze_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L54) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.autograd.Function.apply | | [module_apply](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L161) | | | +| oneflow.autograd.autograd_function.FunctionAutoGradCaptureState.mark_non_differentiable | | | | | +| oneflow.autograd.autograd_function.FunctionAutoGradCaptureState.save_for_backward | | | | | +| oneflow.autograd.autograd_function.FunctionAutoGradCaptureState.saved_tensors | | | | | +| oneflow.cuda.is_available | | | | | +| oneflow.cuda.device_count | | | | | +| oneflow.cuda.current_device | | | | | +| oneflow.cuda.set_device | | | | | +| oneflow.cuda.synchronize | | | | | +| oneflow.cuda.manual_seed_all | | | | | +| oneflow.cuda.manual_seed | | [generator_manual_seed](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L72) | | | +| oneflow.cuda.HalfTensor | | | | | +| oneflow.cuda.FloatTensor | | | | | +| oneflow.cuda.DoubleTensor | | | | | +| oneflow.cuda.BoolTensor | | | | | +| oneflow.cuda.ByteTensor | | | | | +| oneflow.cuda.CharTensor | | | | | +| oneflow.cuda.IntTensor | | | | | +| oneflow.cuda.LongTensor | | | | | +| oneflow.cuda.empty_cache | | | | | +| oneflow.nn.functional.conv1d | [oneflow._C.conv1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L20) | [conv1d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L128) | | | +| oneflow.nn.functional.conv2d | [oneflow._C.conv2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L57) | [conv2d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L134) | | done | +| oneflow.nn.functional.conv3d | [oneflow._C.conv3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L95) | [conv3d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L140) | | | +| oneflow.nn.functional.conv_transpose1d | | | | | +| oneflow.nn.functional.conv_transpose2d | | | | done | +| oneflow.nn.functional.conv_transpose3d | | | | | +| oneflow.nn.functional.fold | [oneflow.nn.functional.fold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/convolution.py#L20) | [fold_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_fold.py#L25) | | done | +| oneflow.nn.functional.unfold | [oneflow.Tensor.unfold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L563) | [unfold_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unfold_tensor.py#L30) | | | +| oneflow.nn.functional.avg_pool1d | [oneflow._C.avg_pool1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L99) | | | | +| oneflow.nn.functional.avg_pool2d | [oneflow._C.avg_pool2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L129) | | | done | +| oneflow.nn.functional.avg_pool3d | [oneflow._C.avg_pool3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L151) | | | | +| oneflow.nn.functional.max_pool1d | | | | | +| oneflow.nn.functional.max_pool2d | | | | done | +| oneflow.nn.functional.max_pool3d | | | | | +| oneflow.nn.functional.adaptive_avg_pool1d | [oneflow._C.adaptive_avg_pool1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L20) | | | done | +| oneflow.nn.functional.adaptive_avg_pool2d | [oneflow._C.adaptive_avg_pool2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L48) | | | done | +| oneflow.nn.functional.adaptive_avg_pool3d | [oneflow._C.adaptive_avg_pool3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L74) | | | done | +| oneflow.nn.functional.threshold | [oneflow._C.threshold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L496) | [softplus_threshold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L533) | | done | +| oneflow.nn.functional.relu | [oneflow.relu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L50) | [relu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L33) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | done | +| oneflow.nn.functional.hardtanh | [oneflow._C.hardtanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L363) | [hardtanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L618) | | done | +| oneflow.nn.functional.hardswish | [oneflow._C.hardswish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L316) | [hardswish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L590) | | done | +| oneflow.nn.functional.relu6 | | [relu6_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L73) | | done | +| oneflow.nn.functional.elu | [oneflow._C.elu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L385) | [elu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L165) | | done | +| oneflow.nn.functional.selu | [oneflow.selu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L409) | [selu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L754) | | done | +| oneflow.nn.functional.celu | [oneflow._C.celu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L468) | [celu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L203) | [celu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L47) | done | +| oneflow.nn.functional.leaky_relu | [oneflow._C.leaky_relu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L373) | | | done | +| oneflow.nn.functional.prelu | [oneflow._C.prelu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L20) | [prelu_4dim_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_prelu.py#L32) | [prelu_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L38) | | +| oneflow.nn.functional.glu | [oneflow._C.glu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L436) | [glu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_glu.py#L37) | [glu_scalar_tensor_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L57) | done | +| oneflow.nn.functional.gelu | [oneflow.gelu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L74) | [gelu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L253) | | done | +| oneflow.nn.functional.logsigmoid | [oneflow._C.logsigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L177) | [logsigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L484) | | done | +| oneflow.nn.functional.hardshrink | [oneflow._C.hardshrink](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L507) | [hardshrink_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L857) | | done | +| oneflow.nn.functional.softsign | [oneflow._C.softsign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L207) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L782) | | done | +| oneflow.nn.functional.softplus | [oneflow.softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L146) | [softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_softplus.py#L43) | | done | +| oneflow.nn.functional.softmax | [oneflow._C.softmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L118) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L436) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | done | +| oneflow.nn.functional.softshrink | [oneflow._C.softshrink](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L518) | [softshrink_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L895) | | done | +| oneflow.nn.functional.log_softmax | [oneflow._C.log_softmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L132) | | | done | +| oneflow.nn.functional.tanh | [oneflow.tanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L163) | [tanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L106) | | done | +| oneflow.nn.functional.sigmoid | [oneflow.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L338) | [sigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L281) | [hard_sigmoid_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L87) | done | +| oneflow.nn.functional.hardsigmoid | [oneflow._C.hardsigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L298) | [hardsigmoid_inplace](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L336) | | done | +| oneflow.nn.functional.silu | [oneflow.silu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L237) | [silu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L726) | | done | +| oneflow.nn.functional.mish | [oneflow.mish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L267) | [mish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L698) | | done | +| oneflow.nn.functional.layer_norm | [oneflow.nn.functional.layer_norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/normalization.py#L20) | [t5_layer_norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_t5_layernorm.py#L55) | | | +| oneflow.nn.functional.normalize | [oneflow._C.normalize](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L268) | [functional_normalize](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_normalize.py#L54) | | | +| oneflow.nn.functional.linear | | [interpolate_linear_1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_interpolate.py#L27) | | | +| oneflow.nn.functional.dropout | [oneflow._C.dropout](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L20) | [dropout_p01](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_dropout.py#L44) | | done | +| oneflow.nn.functional.dropout1d | [oneflow._C.dropout1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L102) | [dropout1d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L309) | | | +| oneflow.nn.functional.dropout2d | [oneflow._C.dropout2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L124) | [dropout2d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L316) | | | +| oneflow.nn.functional.dropout3d | [oneflow._C.dropout3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L146) | [dropout3d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L323) | | | +| oneflow.nn.functional.embedding | | [one_embedding_adagrad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_embedding_adagrad.py#L174) | | | +| oneflow.nn.functional.one_hot | [oneflow._C.one_hot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/onehot.py#L20) | [one_hot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_hot.py#L27) | | | +| oneflow.nn.functional.cosine_similarity | [oneflow._C.cosine_similarity](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/distance.py#L20) | | [cosine_similarity_not_floating_type](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_cosine_similarity.py#L24) | done | +| oneflow.nn.functional.pairwise_distance | [oneflow._C.pairwise_distance](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/distance.py#L54) | [pairwise_distance_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_pairwise_distance.py#L27) | | | +| oneflow.nn.functional.sparse_softmax_cross_entropy | | [eager_global_sparse_softmax_cross_entropy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sparse_softmax_cross_entropy.py#L131) | [sparse_softmax_cross_entropy_prediction_numaxes_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_sparse_softmax_cross_entropy_op.py#L23) | | +| oneflow.nn.functional.cross_entropy | [oneflow._C.cross_entropy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/loss.py#L82) | [eager_global_sparse_softmax_cross_entropy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sparse_softmax_cross_entropy.py#L131) | [sparse_cross_entropy_prediction_numaxes_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_sparse_cross_entropy_op.py#L23) | | +| oneflow.nn.functional.l1_loss | [oneflow._C.l1_loss](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/loss.py#L130) | [l1_loss_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_loss.py#L277) | [smooth_l1_loss_shape_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_smooth_l1_loss_op.py#L23) | | +| oneflow.nn.functional.mse_loss | [oneflow._C.mse_loss](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/loss.py#L156) | [mse_loss_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_loss.py#L328) | | | +| oneflow.nn.functional.smooth_l1_loss | [oneflow._C.smooth_l1_loss](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/loss.py#L186) | [smooth_l1_loss_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_loss.py#L308) | [smooth_l1_loss_shape_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_smooth_l1_loss_op.py#L23) | | +| oneflow.nn.functional.triplet_margin_loss | [oneflow._C.triplet_margin_loss](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/loss.py#L20) | | [triplet_margin_loss_reduce_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L255) | | +| oneflow.nn.functional.binary_cross_entropy | | [nn_functional_binary_cross_entropy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_loss.py#L245) | | | +| oneflow.nn.functional.binary_cross_entropy_with_logits | | [nn_functional_binary_cross_entropy_with_logits](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_loss.py#L269) | | | +| oneflow.nn.functional.pad | [oneflow._C.pad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/vision.py#L20) | [pad_1d_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_pad.py#L25) | [pad_size_attribute_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L89) | | +| oneflow.nn.functional.interpolate | | [interpolate_linear_1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_interpolate.py#L27) | | | +| oneflow.nn.functional.upsample | | [upsample_bilinear_align_corners](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_upsample.py#L338) | | | +| oneflow.nn.functional.grid_sample | | [grid_sample_4d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_grid_sample.py#L31) | | done | +| oneflow.nn.functional.affine_grid | | [affine_grid_2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_affine_grid.py#L31) | | done | +| oneflow.nn.functional.ctc_greedy_decoder | [oneflow._C.ctc_greedy_decoder](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/ctc_decode.py#L20) | [ctc_greedy_decoder](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_ctc_greedy_decoder.py#L111) | | | +| oneflow.Tensor.new_empty | [oneflow.Tensor.new_empty](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L201) | [new_empty](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_empty.py#L52) | | | +| oneflow.Tensor.new_ones | [oneflow.Tensor.new_ones](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L229) | [flow_new_ones_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant.py#L107) | | | +| oneflow.Tensor.new_zeros | [oneflow.Tensor.new_zeros](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L238) | [new_zeros](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant.py#L129) | | | +| oneflow.Tensor.new_tensor | | [new_tensor_local_mode_with_default_args](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_new_tensor.py#L25) | | | +| oneflow.Tensor.is_cuda | [oneflow.Tensor.is_cuda](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2071) | | | | +| oneflow.Tensor.is_global | [oneflow.Tensor.is_global](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L109) | | | | +| oneflow.Tensor.device | [oneflow.Tensor.device](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L85) | [non_default_device](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_randperm.py#L133) | [device_type](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_device.py#L25) | | +| oneflow.Tensor.grad | [oneflow.Tensor.grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L753) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.ndim | [oneflow.Tensor.ndim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1315) | [abs_with_ndim_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_abs.py#L34) | | | +| oneflow.Tensor.abs | [oneflow.abs](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L20) | [abs_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_abs.py#L27) | | done | +| oneflow.Tensor.acos | [oneflow.acos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L509) | [acos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L122) | | | +| oneflow.Tensor.acosh | [oneflow.acosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L535) | [acosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L138) | | | +| oneflow.Tensor.add | [oneflow.add](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L41) | [scatter_add_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_scatter_ops.py#L57) | [add_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L27) | done | +| oneflow.Tensor.add_ | [oneflow.Tensor.add_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1222) | [scatter_add_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_scatter_ops.py#L57) | [add_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L27) | | +| oneflow.Tensor.addcdiv | [oneflow.Tensor.addcdiv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L939) | [addcdiv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_addcdiv.py#L25) | | done | +| oneflow.Tensor.addcdiv_ | [oneflow.Tensor.addcdiv_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L946) | [tensor_addcdiv_inplace](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_addcdiv.py#L49) | | | +| oneflow.Tensor.addcmul | [oneflow.addcmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1558) | [addcmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_addcmul.py#L37) | | done | +| oneflow.Tensor.addcmul_ | [oneflow.Tensor.addcmul_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1236) | [tensor_addcmul_inplace](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_addcmul.py#L50) | | | +| oneflow.Tensor.addmm | [oneflow.Tensor.addmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1215) | [addmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_addmm.py#L60) | | done | +| oneflow.Tensor.all | [oneflow.Tensor.all](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1822) | [flow_var_all_dim_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_var.py#L27) | | | +| oneflow.Tensor.amin | [oneflow.Tensor.amin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2167) | [amin_with_negative_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_amin.py#L34) | | done | +| oneflow.Tensor.amax | [oneflow.Tensor.amax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L911) | [amax_with_negative_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_amax.py#L35) | | done | +| oneflow.Tensor.any | [oneflow.Tensor.any](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1831) | [any_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_reduce.py#L52) | | | +| oneflow.Tensor.arccos | [oneflow.Tensor.arccos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L664) | [arccos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L114) | | | +| oneflow.Tensor.arccosh | [oneflow.Tensor.arccosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L678) | [arccosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L130) | | | +| oneflow.Tensor.arcsin | [oneflow.Tensor.arcsin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1257) | [flow_arcsin_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L230) | | | +| oneflow.Tensor.arcsinh | [oneflow.Tensor.arcsinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1264) | [flow_arcsinh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L247) | | | +| oneflow.Tensor.arctan | [oneflow.Tensor.arctan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1343) | [flow_arctan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L274) | | | +| oneflow.Tensor.arctanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L685) | [flow_arctanh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L296) | | | +| oneflow.Tensor.argmax | [oneflow.Tensor.argmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L692) | [argmax_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmax.py#L29) | [argmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L22) | done | +| oneflow.Tensor.argmin | [oneflow.Tensor.argmin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L699) | [argmin_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmin.py#L29) | | | +| oneflow.Tensor.argsort | [oneflow.Tensor.argsort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L706) | [argsort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_argsort.py#L37) | | done | +| oneflow.Tensor.argwhere | [oneflow.Tensor.argwhere](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L713) | [argwhere_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argwhere.py#L50) | | | +| oneflow.Tensor.asin | [oneflow.asin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L285) | [flow_asin_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L223) | | | +| oneflow.Tensor.asinh | [oneflow.asinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L318) | [flow_asinh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L240) | | | +| oneflow.Tensor.atan | [oneflow.atan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L353) | [flow_atan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L267) | | | +| oneflow.Tensor.atan2 | [oneflow.Tensor.atan2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L123) | [atan2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L155) | | | +| oneflow.Tensor.atanh | [oneflow.atanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L564) | [flow_atanh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L289) | | | +| oneflow.Tensor.backward | [oneflow.Tensor.backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L727) | [unsqueeze_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L54) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.bmm | [oneflow.Tensor.bmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L876) | [bmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_bmm.py#L93) | [bmm_exception_dim_not_right](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_bmm.py#L25) | | +| oneflow.Tensor.byte | [oneflow.Tensor.byte](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2159) | [byte](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1234) | | | +| oneflow.Tensor.cast | [oneflow.cast](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/cast.py#L20) | [cast_float2int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cast.py#L28) | [add_broad_cast_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L37) | | +| oneflow.Tensor.ceil | [oneflow.ceil](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L378) | [ceil_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_ceil.py#L29) | | | +| oneflow.Tensor.chunk | [oneflow.Tensor.chunk](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L883) | [flow_chunk_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_chunk.py#L46) | [chunk_0_dim_input_exception](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_chunk.py#L25) | | +| oneflow.Tensor.clamp | [oneflow.clamp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L20) | [clamp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L96) | | | +| oneflow.Tensor.clamp_ | [oneflow.Tensor.clamp_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1548) | [clamp_scalar_min](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L47) | | | +| oneflow.Tensor.clip | [oneflow.clip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L152) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | | | +| oneflow.Tensor.clip_ | [oneflow.Tensor.clip_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1562) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | | | +| oneflow.Tensor.clone | | [clone_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_clone.py#L24) | | | +| oneflow.Tensor.contiguous | | [tensor_scatter_nd_update_with_non_contiguous_input](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_scatter_nd_update.py#L40) | | | +| oneflow.Tensor.copy_ | [oneflow.Tensor.copy_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1468) | [copy_broadcast_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_copy.py#L30) | | | +| oneflow.Tensor.cos | [oneflow.cos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L712) | [global_cos_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L65) | | | +| oneflow.Tensor.cosh | [oneflow.cosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L736) | | | | +| oneflow.Tensor.cpu | [oneflow.Tensor.cpu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1569) | [from_torch_cpu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_from_torch.py#L26) | | | +| oneflow.Tensor.cuda | [oneflow.Tensor.cuda](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1587) | [cuda](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L110) | | | +| oneflow.Tensor.cumprod | [oneflow.cumprod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1788) | [cumprod_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cumprod.py#L25) | | done | +| oneflow.Tensor.cumsum | [oneflow.cumsum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1755) | [cumsum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cumsum.py#L37) | | done | +| oneflow.Tensor.data | | [swapdims_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_swapdims.py#L32) | [normal_data_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L278) | | +| oneflow.Tensor.dot | [oneflow.dot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1438) | [dot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L903) | [dot_shape_error_msg](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_dot.py#L24) | done | +| oneflow.Tensor.detach | | [tensor_detach](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_2.py#L91) | | | +| oneflow.Tensor.placement | [oneflow.Tensor.placement](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L95) | [eager_boxing_with_same_placement_p_to_s1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing.py#L3093) | [multi_input_with_diff_placement](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_multi_input_with_diff_device_or_placement.py#L42) | | +| oneflow.Tensor.sbp | [oneflow.Tensor.sbp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L102) | [eager_global_cast_with_same_placement_and_sbp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing.py#L3205) | [get_sbp_with_invalid_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L24) | | +| oneflow.Tensor.diag | [oneflow.Tensor.diag](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L932) | [diag_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_diag.py#L26) | | done | +| oneflow.Tensor.diagonal | [oneflow.Tensor.diagonal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1294) | [diagonal_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_diagonal.py#L24) | [diagonal_index_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L204) | done | +| oneflow.Tensor.dim | [oneflow.Tensor.dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L953) | [cosine_similartiy_module_with_nonequal_dim_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cosine_similarity.py#L53) | [glu_dim_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L67) | | +| oneflow.Tensor.div | [oneflow.div](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L143) | [div_grad_grad_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_higher_derivative_div.py#L26) | [div_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L81) | done | +| oneflow.Tensor.div_ | [oneflow.Tensor.div_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1116) | [div_grad_grad_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_higher_derivative_div.py#L26) | [div_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L81) | | +| oneflow.Tensor.double | [oneflow.Tensor.double](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2041) | [double](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L211) | | | +| oneflow.Tensor.dtype | | [out_grad_with_different_dtype](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L113) | [sparse_cross_entropy_label_dtype_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_sparse_cross_entropy_op.py#L53) | | +| oneflow.Tensor.element_size | [oneflow.Tensor.element_size](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L962) | | | | +| oneflow.Tensor.eq | [oneflow.Tensor.eq](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1011) | [eq_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_eq.py#L25) | | done | +| oneflow.Tensor.erf | [oneflow.erf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L763) | [flow_erf_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_erf.py#L33) | | done | +| oneflow.Tensor.erfc | [oneflow.erfc](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L810) | [erfc_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_erfc.py#L25) | | done | +| oneflow.Tensor.erfinv | [oneflow.Tensor.erfinv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L997) | [flow_erfinv_with_inf_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_erfinv.py#L30) | | done | +| oneflow.Tensor.erfinv_ | [oneflow.Tensor.erfinv_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1004) | [flow_erfinv_with_inf_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_erfinv.py#L30) | | | +| oneflow.Tensor.exp | [oneflow.exp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L476) | [exp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L72) | | | +| oneflow.Tensor.expand | [oneflow.Tensor.expand](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L130) | [expand_new_dims_broadcast](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_expand_op.py#L28) | [expand_dim_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L78) | | +| oneflow.Tensor.expand_as | [oneflow.Tensor.expand_as](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L139) | | | | +| oneflow.Tensor.expm1 | [oneflow.expm1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L845) | [expm1_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_expm1.py#L29) | | done | +| oneflow.Tensor.fill_ | [oneflow.Tensor.fill_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1053) | [masked_fill_with_0dim_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_masked_fill.py#L35) | | done | +| oneflow.Tensor.flatten | [oneflow.Tensor.flatten](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L155) | [to_global_flatten_hierarchy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L30) | | done | +| oneflow.Tensor.flip | [oneflow.Tensor.flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L169) | [image_flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_image_flip.py#L70) | | done | +| oneflow.Tensor.float | [oneflow.Tensor.float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2020) | [logical_xor_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_xor.py#L37) | | | +| oneflow.Tensor.floor | [oneflow.floor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L100) | [floor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_floor.py#L35) | | done | +| oneflow.Tensor.floor_ | [oneflow.floor_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L135) | [flow_floor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_floor.py#L57) | | | +| oneflow.Tensor.floor_divide | | | | | +| oneflow.Tensor.fmod | [oneflow.fmod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L890) | [flow_fmod_element_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1021) | | done | +| oneflow.Tensor.gather | [oneflow.Tensor.gather](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1531) | [gather_nd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_gather_nd.py#L85) | [gather_index_type_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L120) | done | +| oneflow.Tensor.ge | [oneflow.Tensor.ge](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1062) | | | | +| oneflow.Tensor.get_device | [oneflow.Tensor.get_device](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1069) | | | | +| oneflow.Tensor.grad_fn | [oneflow.Tensor.grad_fn](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L760) | [parameter_grad_fn_none](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_parameter.py#L29) | | | +| oneflow.Tensor.gt | [oneflow.Tensor.gt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1080) | | | done | +| oneflow.Tensor.half | [oneflow.Tensor.half](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1520) | [module_to_half](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to_half.py#L25) | | | +| oneflow.Tensor.in_top_k | [oneflow.Tensor.in_top_k](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L176) | [in_top_k_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_in_top_k.py#L82) | [in_top_k_num_equal_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L389) | | +| oneflow.Tensor.index_select | [oneflow.Tensor.index_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L185) | [index_select_by_random](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_index_select.py#L30) | [index_select_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L330) | | +| oneflow.Tensor.int | [oneflow.Tensor.int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1978) | [logical_xor_int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_xor.py#L27) | [tensordot_too_large_int_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L35) | | +| oneflow.Tensor.is_contiguous | [oneflow.Tensor.is_contiguous](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2062) | | | | +| oneflow.Tensor.is_floating_point | [oneflow.is_floating_point](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/is_floating_point.py#L20) | [is_floating_point](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_ops.py#L176) | | | +| oneflow.Tensor.is_lazy | [oneflow.Tensor.is_lazy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L116) | | | | +| oneflow.Tensor.is_leaf | [oneflow.Tensor.is_leaf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L767) | | | | +| oneflow.Tensor.isinf | [oneflow.Tensor.isinf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2152) | [isinf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_util_ops.py#L33) | | | +| oneflow.Tensor.isnan | [oneflow.Tensor.isnan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2145) | [isnan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_util_ops.py#L24) | | | +| oneflow.Tensor.item | [oneflow.Tensor.item](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2087) | [tensordot_single_item_tensor_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensordot.py#L105) | | | +| oneflow.Tensor.le | [oneflow.Tensor.le](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1025) | | | | +| oneflow.Tensor.log | [oneflow.log](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L923) | [log](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L56) | | | +| oneflow.Tensor.log1p | [oneflow.log1p](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L455) | [log1p_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_log1p.py#L31) | | | +| oneflow.Tensor.log2 | [oneflow.log2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L948) | [log2_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L971) | | | +| oneflow.Tensor.logical_and | [oneflow.Tensor.logical_and](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1677) | [logical_and](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_and.py#L58) | | | +| oneflow.Tensor.logical_or | [oneflow.Tensor.logical_or](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1687) | [logical_or](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_or.py#L58) | | | +| oneflow.Tensor.logical_not | [oneflow.Tensor.logical_not](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L520) | [logical_not](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_not.py#L43) | | | +| oneflow.Tensor.logical_xor | [oneflow.Tensor.logical_xor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1698) | [logical_xor_int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_xor.py#L27) | | | +| oneflow.Tensor.long | [oneflow.Tensor.long](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1999) | [long](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L145) | | | +| oneflow.Tensor.lt | [oneflow.Tensor.lt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1018) | | | | +| oneflow.Tensor.masked_fill | [oneflow.Tensor.masked_fill](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1708) | [masked_fill](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_masked_fill.py#L58) | | | +| oneflow.Tensor.masked_select | [oneflow.Tensor.masked_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1715) | [masked_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_masked_select.py#L87) | | | +| oneflow.Tensor.matmul | [oneflow.matmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1249) | [fused_matmul_op](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cublas_fused_mlp.py#L173) | [matmul_dimension_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L220) | | +| oneflow.Tensor.mm | [oneflow.mm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1311) | [flow_mm_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_matmul.py#L69) | [mm_not_2dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_mm.py#L24) | | +| oneflow.Tensor.mv | [oneflow.mv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1278) | [flow_mv_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_matmul.py#L78) | [mv_not_matrix](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_mv.py#L23) | done | +| oneflow.Tensor.max | [oneflow.Tensor.max](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1774) | [moving_average_min_max_observer](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_moving_average_max_min_observer.py#L83) | | | +| oneflow.Tensor.maximum | [oneflow.maximum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L997) | [broadcast_maximum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maximum_minimum.py#L32) | | | +| oneflow.Tensor.median | [oneflow.median](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1019) | [median](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_median.py#L48) | [median_exception_dim_out_of_range](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_median.py#L25) | | +| oneflow.Tensor.mean | [oneflow.Tensor.mean](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1840) | [mean](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_mean.py#L70) | [normalization_moving_mean_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L317) | | +| oneflow.Tensor.min | [oneflow.Tensor.min](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1783) | [moving_average_min_max_observer](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_moving_average_max_min_observer.py#L83) | | | +| oneflow.Tensor.minimum | [oneflow.minimum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L975) | [broadcast_minimum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maximum_minimum.py#L50) | | | +| oneflow.Tensor.mish | [oneflow.mish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L267) | [mish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L698) | | done | +| oneflow.Tensor.mul | [oneflow.mul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L186) | [einsum_eltwise_mul_then_reduce_sum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_eltwise_mul_then_reduce_sum.py#L40) | | | +| oneflow.Tensor.mul_ | [oneflow.Tensor.mul_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1108) | [fused_matmul_op](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cublas_fused_mlp.py#L173) | [matmul_dimension_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L220) | | +| oneflow.Tensor.narrow | [oneflow.Tensor.narrow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L629) | [narrow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_narrow.py#L35) | [narrow_dim_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L178) | | +| oneflow.Tensor.ndimension | | | | | +| oneflow.Tensor.ne | [oneflow.Tensor.ne](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1032) | [ne](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_ne.py#L31) | | | +| oneflow.Tensor.neg | [oneflow.Tensor.neg](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1039) | [flow_split_sizes_neg_dim_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_split.py#L63) | [tensordot_neg_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L25) | | +| oneflow.Tensor.negative | [oneflow.negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L428) | [argmax_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmax.py#L29) | [repeat_interleave_negative_tensor_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L58) | | +| oneflow.Tensor.nelement | [oneflow.Tensor.nelement](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1137) | [tensor_nelement](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L552) | | | +| oneflow.Tensor.nonzero | [oneflow.nonzero](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/nonzero.py#L20) | [nonzero](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_nonzero.py#L51) | | | +| oneflow.Tensor.norm | [oneflow.linalg.norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L160) | [clip_grad_norm_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L50) | | | +| oneflow.Tensor.normal_ | [oneflow.Tensor.normal_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1154) | [eager_boxing_normal_1d_exhaustive_testing](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing_exhaustive.py#L113) | [normal_data_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L278) | | +| oneflow.Tensor.numel | [oneflow.Tensor.numel](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L194) | [tensor_numel](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L558) | | | +| oneflow.Tensor.numpy | [oneflow.Tensor.numpy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1163) | [dropout_numpy_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L29) | [numpy_type](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_pad.py#L32) | | +| oneflow.Tensor.permute | [oneflow.Tensor.permute](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L643) | [einsum_batch_permute](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_batch_permute.py#L42) | | | +| oneflow.Tensor.pow | [oneflow.pow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1132) | [pow_with_scalar](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L96) | | | +| oneflow.Tensor.prod | [oneflow.Tensor.prod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1849) | [prod_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_reduce.py#L59) | | | +| oneflow.Tensor.reciprocal | [oneflow.reciprocal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L226) | [flow_reciprocal_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_reciprocal.py#L32) | | | +| oneflow.Tensor.register_hook | [oneflow.Tensor.register_hook](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L833) | [tensor_register_hook](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L446) | | | +| oneflow.Tensor.relu | [oneflow.relu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L50) | [relu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L33) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | done | +| oneflow.Tensor.repeat | [oneflow.Tensor.repeat](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1622) | [flow_tensor_repeat_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_repeat.py#L27) | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | | +| oneflow.Tensor.repeat_interleave | [oneflow.Tensor.repeat_interleave](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1631) | [flow_int_repeat_interleave_dim_none](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_repeat_interleave.py#L29) | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | | +| oneflow.Tensor.requires_grad | [oneflow.Tensor.requires_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L800) | [requires_grad_tensor_inplace_and_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L170) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.requires_grad_ | [oneflow.Tensor.requires_grad_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L809) | [requires_grad_tensor_inplace_and_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L170) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.Tensor.reshape | [oneflow.Tensor.reshape](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1858) | [reshape_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_reshape.py#L27) | [reshape_like_size_match_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reshape_like_op.py#L24) | done | +| oneflow.Tensor.reshape_as | [oneflow.Tensor.reshape_as](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1865) | [reshape_as_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1181) | | | +| oneflow.Tensor.retain_grad | [oneflow.Tensor.retain_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L866) | [retain_grad_for_leaf_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L178) | | | +| oneflow.Tensor.roll | [oneflow.Tensor.roll](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1187) | [roll](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_roll.py#L27) | [roll_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L112) | | +| oneflow.Tensor.round | [oneflow.round](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1346) | [flow_round_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_round.py#L30) | | | +| oneflow.Tensor.rsqrt | [oneflow.rsqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1173) | [rsqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L80) | | | +| oneflow.Tensor.selu | [oneflow.selu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L409) | [selu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L754) | | done | +| oneflow.Tensor.shape | | [randn_tuple_shape](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_randn.py#L62) | [layernorm_exception_input_shape_not_match](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_layernorm.py#L25) | | +| oneflow.Tensor.sigmoid | [oneflow.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L338) | [sigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L281) | [hard_sigmoid_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L87) | done | +| oneflow.Tensor.sign | [oneflow.sign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L589) | [sign_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sign.py#L25) | | | +| oneflow.Tensor.silu | [oneflow.silu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L237) | [silu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L726) | | done | +| oneflow.Tensor.sin | [oneflow.sin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L618) | [global_sin_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L59) | | | +| oneflow.Tensor.sin_ | [oneflow.sin_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L648) | [global_sin_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L59) | | | +| oneflow.Tensor.sinh | [oneflow.sinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L656) | [sinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L23) | | | +| oneflow.Tensor.size | [oneflow.Tensor.size](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1392) | [unsqueeze_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_unsqueeze.py#L62) | [local_to_global_with_invalid_size](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L75) | | +| oneflow.Tensor.softmax | [oneflow._C.softmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L118) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L436) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | done | +| oneflow.Tensor.softplus | [oneflow.softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L146) | [softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_softplus.py#L43) | | done | +| oneflow.Tensor.softsign | [oneflow._C.softsign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L207) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L782) | | done | +| oneflow.Tensor.sort | [oneflow.Tensor.sort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1947) | [sort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_sort.py#L69) | | | +| oneflow.Tensor.split | [oneflow.Tensor.split](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L890) | [eager_boxing_2d_special_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing_exhaustive.py#L146) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.Tensor.sqrt | [oneflow.sqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1198) | [sqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L64) | | | +| oneflow.Tensor.square | [oneflow.square](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1224) | [inv_random_square_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_inv.py#L39) | [inv_exception_not_square_matrix](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_inv.py#L34) | | +| oneflow.Tensor.squeeze | [oneflow.Tensor.squeeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L556) | [squeeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_squeeze.py#L94) | [squeeze_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L106) | | +| oneflow.Tensor.std | [oneflow.std](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1371) | [std_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_std.py#L26) | | | +| oneflow.Tensor.storage_offset | [oneflow.Tensor.storage_offset](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L268) | | | | +| oneflow.Tensor.stride | | [flow_as_strided_with_stride](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_as_stride.py#L49) | | | +| oneflow.Tensor.sum | [oneflow.Tensor.sum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1813) | [sum_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_sum.py#L29) | [reduce_sum_like_empty_axis_case_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reduce_like_ops.py#L24) | | +| oneflow.Tensor.swapaxes | [oneflow.Tensor.swapaxes](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L904) | [swapaxes_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_swapaxes.py#L31) | | | +| oneflow.Tensor.swapdims | [oneflow.Tensor.swapdims](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L918) | [swapdims_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_swapdims.py#L32) | | | +| oneflow.Tensor.sub | [oneflow.sub](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L246) | [global_sub](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sub.py#L50) | | | +| oneflow.Tensor.sub_ | [oneflow.Tensor.sub_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1123) | [global_sub_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sub.py#L56) | | | +| oneflow.Tensor.tan | [oneflow.tan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L687) | [flow_tan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L257) | | | +| oneflow.Tensor.tanh | [oneflow.tanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L163) | [tanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L106) | | done | +| oneflow.Tensor.tile | [oneflow.tile](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tile.py#L20) | [flow_tile_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tile.py#L27) | [tile_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L431) | | +| oneflow.Tensor.to | [oneflow.Tensor.to](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1485) | [dummy_module_to](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to.py#L58) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.Tensor.local_to_global | [oneflow.Tensor.local_to_global](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L286) | [local_to_global_2d_sbp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L85) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.Tensor.global_to_global | [oneflow.Tensor.global_to_global](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L334) | [cuda_global_to_global_cpu_s2b](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L210) | [global_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L51) | | +| oneflow.Tensor.to_global | [oneflow.Tensor.to_global](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L381) | [to_global_flatten_hierarchy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L30) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.Tensor.to_local | [oneflow.Tensor.to_local](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L473) | | [call_to_local_for_local_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L65) | | +| oneflow.Tensor.to_consistent | [oneflow.Tensor.to_consistent](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L466) | | | | +| oneflow.Tensor.tolist | [oneflow.Tensor.tolist](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2108) | [tolist](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L257) | | | +| oneflow.Tensor.topk | [oneflow.Tensor.topk](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1751) | [flow_topk_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L306) | | | +| oneflow.Tensor.transpose | [oneflow.Tensor.transpose](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L513) | [einsum_matrix_transpose](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_matrix_transpose.py#L35) | | | +| oneflow.Tensor.tril | [oneflow.Tensor.tril](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1441) | [fused_scale_tril](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_fused_scale_tril.py#L78) | | | +| oneflow.Tensor.triu | [oneflow.Tensor.triu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1448) | [triu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_triu.py#L47) | | | +| oneflow.Tensor.type_as | [oneflow.Tensor.type_as](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1954) | [type_as](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_ops.py#L165) | | | +| oneflow.Tensor.type | [oneflow.Tensor.type](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2192) | [type_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_type_tensor.py#L74) | [cosine_similarity_not_floating_type](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_cosine_similarity.py#L24) | | +| oneflow.Tensor.t | [oneflow.Tensor.t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1640) | [global_tensor_scatter_nd_update_t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L140) | [t_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L439) | | +| oneflow.Tensor.T | [oneflow.Tensor.t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1640) | [global_tensor_scatter_nd_update_t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L140) | [t_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L439) | | +| oneflow.Tensor.unbind | [oneflow.Tensor.unbind](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L897) | [unbind_flow_with_random_data1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unbind.py#L32) | [unbind_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L248) | | +| oneflow.Tensor.unfold | [oneflow.Tensor.unfold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L563) | [unfold_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unfold_tensor.py#L30) | | | +| oneflow.Tensor.uniform_ | [oneflow.Tensor.uniform_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1455) | | | | +| oneflow.Tensor.unsqueeze | [oneflow.Tensor.unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L636) | [unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L68) | | | +| oneflow.Tensor.var | [oneflow.var](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1407) | [module_to_with_var_reuse](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to.py#L93) | | | +| oneflow.Tensor.view | [oneflow.Tensor.view](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1881) | [view](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_view.py#L79) | [view_exception](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_view.py#L25) | | +| oneflow.Tensor.view_as | [oneflow.Tensor.view_as](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1931) | | | | +| oneflow.Tensor.where | [oneflow.Tensor.where](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2129) | [where](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_where.py#L196) | | | +| oneflow.Tensor.zero_ | [oneflow.Tensor.zero_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2136) | [nonzero_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_nonzero.py#L64) | | | +| oneflow.Tensor.nms | [oneflow.Tensor.nms](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1758) | [nms](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_nms.py#L50) | | | +| oneflow.Tensor.pin_memory | [oneflow.Tensor.pin_memory](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2174) | [tensor_pin_memory](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_pin_memory.py#L33) | | | +| oneflow.Tensor.is_pinned | [oneflow.Tensor.is_pinned](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2183) | [tensor_is_pinned](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_pin_memory.py#L76) | | | +| oneflow.nn.Parameter | | [ddp_with_partial_requires_grad_parameter](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_ddp.py#L225) | [direction_parameter_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_arg_sort_op.py#L23) | | +| oneflow.nn.Module | [oneflow.nn.Module.to_consistent](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/module.py#L20) | [dummy_module](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to.py#L45) | | | +| oneflow.nn.Sequential | | | | | +| oneflow.nn.ModuleList | | | | | +| oneflow.nn.ModuleDict | | [moduledict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L353) | | | +| oneflow.nn.ParameterList | | | | | +| oneflow.nn.ParameterDict | | | | | +| oneflow.nn.Module.add_module | | | | | +| oneflow.nn.Module.apply | | [module_apply](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L161) | | | +| oneflow.nn.Module.buffers | | | | | +| oneflow.nn.Module.children | | | | | +| oneflow.nn.Module.cpu | [oneflow.Tensor.cpu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1569) | [from_torch_cpu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_from_torch.py#L26) | | | +| oneflow.nn.Module.cuda | [oneflow.Tensor.cuda](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1587) | [cuda](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L110) | | | +| oneflow.nn.Module.double | [oneflow.Tensor.double](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2041) | [double](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_ops.py#L211) | | | +| oneflow.nn.Module.train | | [train_eval](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L121) | | | +| oneflow.nn.Module.eval | | [dropout_eval_p01](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_dropout.py#L33) | [normalization_eval_need_moving_statistic_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L347) | | +| oneflow.nn.Module.extra_repr | | | | | +| oneflow.nn.Module.float | [oneflow.Tensor.float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2020) | [logical_xor_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_xor.py#L37) | | | +| oneflow.nn.Module.forward | | [eye_forward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L27) | | | +| oneflow.nn.Module.load_state_dict | | [load_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L63) | | | +| oneflow.nn.Module.modules | | | | | +| oneflow.nn.Module.named_buffers | | | | | +| oneflow.nn.Module.named_children | | | | | +| oneflow.nn.Module.named_modules | | | | | +| oneflow.nn.Module.named_parameters | | | | | +| oneflow.nn.Module.parameters | | | | | +| oneflow.nn.Module.register_buffer | | | | | +| oneflow.nn.Module.register_forward_hook | | | | | +| oneflow.nn.Module.register_forward_pre_hook | | | | | +| oneflow.nn.Module.register_parameter | | | | | +| oneflow.nn.Module.requires_grad_ | [oneflow.Tensor.requires_grad_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L809) | [requires_grad_tensor_inplace_and_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd.py#L170) | [non_requires_grad_tensor_backward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_autograd.py#L24) | | +| oneflow.nn.Module.state_dict | | [load_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L63) | | | +| oneflow.nn.Module.to | [oneflow.Tensor.to](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1485) | [dummy_module_to](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to.py#L58) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.nn.Module.zero_grad | | | | | +| oneflow.nn.Conv1d | [oneflow._C.conv1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L20) | [conv1d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L128) | | | +| oneflow.nn.Conv2d | [oneflow._C.conv2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L57) | [conv2d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L134) | | | +| oneflow.nn.Conv3d | [oneflow._C.conv3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/conv.py#L95) | [conv3d_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_higher_derivative_conv.py#L140) | | | +| oneflow.nn.ConvTranspose1d | | | | | +| oneflow.nn.ConvTranspose2d | | | | | +| oneflow.nn.ConvTranspose3d | | | | | +| oneflow.nn.Unfold | [oneflow.Tensor.unfold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L563) | [unfold_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unfold_tensor.py#L30) | | | +| oneflow.nn.Fold | [oneflow.nn.functional.fold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/convolution.py#L20) | [fold_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_fold.py#L25) | | | +| oneflow.nn.MaxPool1d | | [maxpool1d_functional](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maxpool.py#L28) | | | +| oneflow.nn.MaxPool2d | | [maxpool2d_functional](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maxpool.py#L51) | | | +| oneflow.nn.MaxPool3d | | [maxpool3d_functional](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maxpool.py#L75) | | | +| oneflow.nn.AdaptiveAvgPool1d | | | | | +| oneflow.nn.AdaptiveAvgPool2d | | | | | +| oneflow.nn.AdaptiveAvgPool3d | | | | | +| oneflow.nn.AvgPool1d | | [adaptive_avgpool1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_adaptive_pool.py#L39) | | | +| oneflow.nn.AvgPool2d | | [adaptive_avgpool2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_adaptive_pool.py#L53) | | | +| oneflow.nn.AvgPool3d | | [adaptive_avgpool3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_adaptive_pool.py#L72) | | | +| oneflow.nn.ConstantPad1d | | [constantpad1d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant_pad.py#L32) | | | +| oneflow.nn.ConstantPad2d | | [ConstantPad2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_zeropad2d.py#L96) | | | +| oneflow.nn.ConstantPad3d | | [constantpad3d_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant_pad.py#L64) | | | +| oneflow.nn.ReflectionPad1d | | | | | +| oneflow.nn.ReflectionPad2d | | | | | +| oneflow.nn.ReplicationPad1d | | | | | +| oneflow.nn.ReplicationPad2d | | [ReplicationPad2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_replication_pad.py#L104) | | | +| oneflow.nn.ZeroPad2d | | [global_ZeroPad2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_zeropad2d.py#L37) | | | +| oneflow.nn.ELU | [oneflow._C.elu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L385) | [elu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L165) | | | +| oneflow.nn.Hardshrink | [oneflow._C.hardshrink](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L507) | [hardshrink_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L857) | | | +| oneflow.nn.Hardsigmoid | [oneflow._C.hardsigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L298) | [hardsigmoid_inplace](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L336) | | | +| oneflow.nn.Hardswish | [oneflow._C.hardswish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L316) | [hardswish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L590) | | | +| oneflow.nn.Hardtanh | [oneflow._C.hardtanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L363) | [hardtanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L618) | | | +| oneflow.nn.LeakyReLU | | [leakyrelu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L656) | | | +| oneflow.nn.LogSigmoid | [oneflow._C.logsigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L177) | [logsigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L484) | | | +| oneflow.nn.PReLU | [oneflow._C.prelu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L20) | [prelu_4dim_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_prelu.py#L32) | [prelu_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L38) | | +| oneflow.nn.ReLU | [oneflow.relu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L50) | [relu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L33) | [relu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L29) | | +| oneflow.nn.ReLU6 | | [relu6_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L73) | | | +| oneflow.nn.SELU | [oneflow.selu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L409) | [selu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L754) | | | +| oneflow.nn.CELU | [oneflow._C.celu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L468) | [celu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L203) | [celu_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L47) | | +| oneflow.nn.GELU | [oneflow.gelu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L74) | [gelu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L253) | | | +| oneflow.nn.SiLU | [oneflow.silu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L237) | [silu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L726) | | | +| oneflow.nn.Sigmoid | [oneflow.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L338) | [sigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L281) | [hard_sigmoid_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L87) | | +| oneflow.nn.Mish | [oneflow.mish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L267) | [mish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L698) | | | +| oneflow.nn.Softplus | [oneflow.softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L146) | [softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_softplus.py#L43) | | | +| oneflow.nn.Softshrink | [oneflow._C.softshrink](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L518) | [softshrink_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L895) | | | +| oneflow.nn.Softsign | [oneflow._C.softsign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L207) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L782) | | | +| oneflow.nn.Tanh | [oneflow.tanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L163) | [tanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L106) | | | +| oneflow.nn.Threshold | [oneflow._C.threshold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L496) | [softplus_threshold](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L533) | | | +| oneflow.nn.GLU | [oneflow._C.glu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L436) | [glu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_glu.py#L37) | [glu_scalar_tensor_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L57) | | +| oneflow.nn.Softmax | [oneflow._C.softmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L118) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L436) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | | +| oneflow.nn.LogSoftmax | | [logsoftmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L460) | | | +| oneflow.nn.BatchNorm1d | | [batchnorm1d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L34) | | | +| oneflow.nn.BatchNorm2d | | [batchnorm2d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L52) | | | +| oneflow.nn.BatchNorm3d | | [batchnorm3d_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_batchnorm.py#L70) | | | +| oneflow.nn.FusedBatchNorm1d | | | | | +| oneflow.nn.FusedBatchNorm2d | | | | | +| oneflow.nn.FusedBatchNorm3d | | | | | +| oneflow.nn.GroupNorm | | [groupnorm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_groupnorm.py#L332) | | | +| oneflow.nn.InstanceNorm1d | | [instancenorm1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_instancenorm.py#L29) | | | +| oneflow.nn.InstanceNorm2d | | [instancenorm2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_instancenorm.py#L71) | | | +| oneflow.nn.InstanceNorm3d | | [instancenorm3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_instancenorm.py#L141) | | | +| oneflow.nn.LayerNorm | | [t5_layernorm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_t5_layernorm.py#L83) | [layernorm_exception_input_shape_not_match](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_layernorm.py#L25) | | +| oneflow.nn.RMSLayerNorm | | | | | +| oneflow.nn.RNN | | [rnn_relu_cell](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_rnn_cell.py#L206) | | | +| oneflow.nn.LSTM | | [lstm_cell](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_rnn_cell.py#L200) | | | +| oneflow.nn.GRU | | [gru_cell](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_rnn_cell.py#L218) | | | +| oneflow.nn.RNNCell | | | | | +| oneflow.nn.LSTMCell | | | | | +| oneflow.nn.GRUCell | | | | | +| oneflow.nn.Identity | | [identity](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_linear.py#L113) | | | +| oneflow.nn.Linear | | [interpolate_linear_1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_interpolate.py#L27) | | | +| oneflow.nn.Dropout | [oneflow._C.dropout](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L20) | [dropout_p01](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_dropout.py#L44) | | | +| oneflow.nn.Dropout1d | [oneflow._C.dropout1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L102) | [dropout1d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L309) | | | +| oneflow.nn.Dropout2d | [oneflow._C.dropout2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L124) | [dropout2d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L316) | | | +| oneflow.nn.Dropout3d | [oneflow._C.dropout3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dropout.py#L146) | [dropout3d_p0](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dropout.py#L323) | | | +| oneflow.nn.Embedding | | [one_embedding_adagrad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_embedding_adagrad.py#L174) | | | +| oneflow.nn.CosineSimilarity | | | | | +| oneflow.nn.PairwiseDistance | | | | | +| oneflow.nn.BCELoss | | | | | +| oneflow.nn.BCEWithLogitsLoss | | | | | +| oneflow.nn.CTCLoss | | | [ctcloss_reduction_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L62) | | +| oneflow.nn.CombinedMarginLoss | | | | | +| oneflow.nn.CrossEntropyLoss | | | | | +| oneflow.nn.KLDivLoss | | | | | +| oneflow.nn.L1Loss | | | | | +| oneflow.nn.MSELoss | | | | | +| oneflow.nn.MarginRankingLoss | | | | | +| oneflow.nn.NLLLoss | | | | | +| oneflow.nn.SmoothL1Loss | | | | | +| oneflow.nn.TripletMarginLoss | | | | | +| oneflow.nn.PixelShuffle | | | | | +| oneflow.nn.Upsample | | [upsample_bilinear_align_corners](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_upsample.py#L338) | | | +| oneflow.nn.UpsamplingBilinear2d | | [UpsamplingBilinear2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_upsample.py#L97) | | | +| oneflow.nn.UpsamplingNearest2d | | [UpsamplingNearest2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_upsample.py#L74) | | | +| oneflow.nn.parallel.DistributedDataParallel | | | | | +| oneflow.nn.COCOReader | | | | | +| oneflow.nn.CoinFlip | | | | | +| oneflow.nn.CropMirrorNormalize | | | | | +| oneflow.nn.OFRecordBytesDecoder | | [OFRecordBytesDecoder](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_dataset.py#L351) | | | +| oneflow.nn.OFRecordImageDecoder | | | | | +| oneflow.nn.OFRecordImageDecoderRandomCrop | | | | | +| oneflow.nn.OFRecordRawDecoder | | | | | +| oneflow.nn.OFRecordReader | | | | | +| oneflow.nn.MinMaxObserver | | | | | +| oneflow.nn.MovingAverageMinMaxObserver | | | | | +| oneflow.nn.FakeQuantization | | | | | +| oneflow.nn.QatConv1d | | | | | +| oneflow.nn.QatConv2d | | | | | +| oneflow.nn.QatConv3d | | | | | +| oneflow.nn.utils.clip_grad_norm_ | | [clip_grad_norm_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L50) | | | +| oneflow.nn.utils.clip_grad_value_ | | [clip_grad_value_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L79) | | | +| oneflow.nn.utils.weight_norm | | [weight_norm_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_weight_norm.py#L150) | | | +| oneflow.nn.utils.remove_weight_norm | | | | | +| oneflow.nn.utils.rnn.PackedSequence | | | | | +| oneflow.nn.utils.rnn.pack_padded_sequence | | | | | +| oneflow.nn.utils.rnn.pad_packed_sequence | | | | | +| oneflow.nn.utils.rnn.pad_sequence | | | | | +| oneflow.nn.utils.rnn.pack_sequence | | | | | +| oneflow.nn.Flatten | [oneflow.Tensor.flatten](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L155) | [to_global_flatten_hierarchy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L30) | | | +| oneflow.nn.FakeQuantization | | | | | +| oneflow.nn.MinMaxObserver | | | | | +| oneflow.nn.MovingAverageMinMaxObserver | | | | | +| oneflow.nn.Quantization | | | | | +| oneflow.BoolTensor | | | | | +| oneflow.ByteTensor | | | | | +| oneflow.CharTensor | | | | | +| oneflow.DoubleTensor | | | | | +| oneflow.FloatTensor | | | | | +| oneflow.HalfTensor | | | | | +| oneflow.IntTensor | | | | | +| oneflow.LongTensor | | | | | +| oneflow.is_tensor | | [ellipsis_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_indexing2.py#L900) | [rol_align_rois_tensor_dimension_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_roi_align_op.py#L34) | | +| oneflow.is_floating_point | [oneflow.is_floating_point](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/is_floating_point.py#L20) | [is_floating_point](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_ops.py#L176) | | | +| oneflow.is_nonzero | | | | | +| oneflow.numel | [oneflow.Tensor.numel](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L194) | [tensor_numel](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L558) | | | +| oneflow.set_printoptions | | | | | +| oneflow.tensor | [oneflow.tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L20) | [type_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_type_tensor.py#L74) | [call_to_local_for_local_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L65) | | +| oneflow.as_tensor | [oneflow.as_tensor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/as_tensor.py#L20) | [reshape_as_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1181) | | | +| oneflow.as_strided | [oneflow.as_strided](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1529) | [flow_as_strided_with_stride](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_as_stride.py#L49) | | | +| oneflow.from_numpy | [oneflow.from_numpy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L55) | [copy_to_and_from_numpy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L73) | | | +| oneflow.zeros | | [zeros_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_zeros_like.py#L27) | | | +| oneflow.zeros_like | [oneflow.zeros_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L53) | [zeros_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_zeros_like.py#L27) | | | +| oneflow.ones | | [ones_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_ones_like.py#L27) | | | +| oneflow.ones_like | [oneflow.ones_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L20) | [ones_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_ones_like.py#L27) | | | +| oneflow.randint_like | [oneflow._C.randint_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L242) | [consistent_randint_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_randint_like.py#L27) | | | +| oneflow.masked_fill | [oneflow.Tensor.masked_fill](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1708) | [masked_fill](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_masked_fill.py#L58) | | | +| oneflow.new_ones | [oneflow.Tensor.new_ones](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L229) | [flow_new_ones_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant.py#L107) | | | +| oneflow.arange | [oneflow.arange](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/arange.py#L20) | [arange](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_arange.py#L63) | | done | +| oneflow.linspace | | [global_linspace](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_linspace.py#L26) | | | +| oneflow.eye | [oneflow.eye](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1597) | [eye_forward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L27) | | done | +| oneflow.empty | [oneflow.empty](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L119) | [slice_empty](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_slice.py#L51) | [reduce_sum_like_empty_axis_case_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reduce_like_ops.py#L24) | | +| oneflow.empty_like | [oneflow.empty_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/constant.py#L160) | | | | +| oneflow.full | | [global_full](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_full.py#L27) | | | +| oneflow.full_like | | [full_like_with_random_data_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_constant.py#L154) | | | +| oneflow.tensor_scatter_nd_update | | [global_tensor_scatter_nd_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L128) | [tensor_scatter_nd_update_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L156) | | +| oneflow.logspace | | [logspace_int_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logspace.py#L26) | | | +| oneflow.argwhere | [oneflow.Tensor.argwhere](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L713) | [argwhere_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argwhere.py#L50) | | | +| oneflow.atleast_1d | [oneflow.atleast_1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L272) | [atleast_1d_with_list_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_atleast.py#L28) | | | +| oneflow.atleast_2d | [oneflow.atleast_2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L306) | [atleast_2d_with_list_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_atleast.py#L43) | | | +| oneflow.atleast_3d | [oneflow.atleast_3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L344) | [atleast_3d_with_list_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_atleast.py#L59) | | | +| oneflow.cat | [oneflow.cat](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L613) | [cat_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_concat.py#L138) | | | +| oneflow.column_stack | [oneflow.column_stack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L513) | [column_stack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L48) | | | +| oneflow.concat | | [concat_with_input_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_concat.py#L164) | [concat_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L37) | | +| oneflow.chunk | [oneflow.Tensor.chunk](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L883) | [flow_chunk_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_chunk.py#L46) | [chunk_0_dim_input_exception](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_chunk.py#L25) | | +| oneflow.dstack | [oneflow.dstack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L481) | [dstack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L115) | | | +| oneflow.expand | [oneflow.Tensor.expand](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L130) | [expand_new_dims_broadcast](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_expand_op.py#L28) | [expand_dim_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L78) | | +| oneflow.gather | [oneflow.Tensor.gather](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1531) | [gather_nd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_gather_nd.py#L85) | [gather_index_type_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L120) | done | +| oneflow.gather_nd | [oneflow.gather_nd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L685) | [gather_nd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_gather_nd.py#L85) | | | +| oneflow.batch_gather | [oneflow.batch_gather](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L199) | [batch_gather](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_batch_gather.py#L74) | | | +| oneflow.hsplit | [oneflow.hsplit](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1674) | [flow_hsplit_vec](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_hsplit.py#L27) | | | +| oneflow.hstack | [oneflow.hstack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L413) | [hstack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L80) | | | +| oneflow.vsplit | [oneflow.vsplit](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1714) | [flow_vsplit_vec](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_vsplit.py#L27) | | | +| oneflow.vstack | [oneflow.vstack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L447) | [vstack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L98) | | | +| oneflow.index_select | [oneflow.Tensor.index_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L185) | [index_select_by_random](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_index_select.py#L30) | [index_select_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L330) | | +| oneflow.masked_select | [oneflow.Tensor.masked_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1715) | [masked_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_masked_select.py#L87) | | | +| oneflow.movedim | [oneflow.movedim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1496) | [flow_movedim_with_vector](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_movedim.py#L27) | | | +| oneflow.narrow | [oneflow.Tensor.narrow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L629) | [narrow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_narrow.py#L35) | [narrow_dim_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L178) | | +| oneflow.nonzero | [oneflow.nonzero](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/nonzero.py#L20) | [nonzero](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_nonzero.py#L51) | | | +| oneflow.permute | [oneflow.Tensor.permute](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L643) | [einsum_batch_permute](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_batch_permute.py#L42) | | | +| oneflow.repeat | [oneflow.Tensor.repeat](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1622) | [flow_tensor_repeat_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_repeat.py#L27) | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | | +| oneflow.reshape | [oneflow.Tensor.reshape](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1858) | [reshape_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_reshape.py#L27) | [reshape_like_size_match_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reshape_like_op.py#L24) | done | +| oneflow.row_stack | [oneflow.row_stack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L547) | [row_stack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L64) | | | +| oneflow.select | [oneflow.select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1467) | [flow_select](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_select.py#L28) | [index_select_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L330) | | +| oneflow.scatter | | [global_tensor_scatter_nd_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L128) | [tensor_scatter_nd_update_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L156) | | +| oneflow.scatter_add | | [scatter_add_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_scatter_ops.py#L57) | | | +| oneflow.scatter_nd | | [global_tensor_scatter_nd_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L128) | [tensor_scatter_nd_update_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L156) | | +| oneflow.slice | | [slice_grad_grad_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_higher_derivative_slice.py#L38) | [slice_update_start_list_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_slice_op.py#L23) | | +| oneflow.slice_update | | [slice_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_slice_update.py#L120) | [slice_update_start_list_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_slice_op.py#L23) | | +| oneflow.split | [oneflow.Tensor.split](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L890) | [eager_boxing_2d_special_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing_exhaustive.py#L146) | [local_to_global_with_invalid_split_axis](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_local_global_convert_error.py#L39) | | +| oneflow.squeeze | [oneflow.Tensor.squeeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L556) | [squeeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_squeeze.py#L94) | [squeeze_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L106) | | +| oneflow.stack | [oneflow.stack](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/array_ops.py#L382) | [stack_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_stack.py#L28) | [stack_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L62) | | +| oneflow.swapaxes | [oneflow.Tensor.swapaxes](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L904) | [swapaxes_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_swapaxes.py#L31) | | | +| oneflow.swapdims | [oneflow.Tensor.swapdims](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L918) | [swapdims_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_swapdims.py#L32) | | | +| oneflow.t | [oneflow.Tensor.t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1640) | [global_tensor_scatter_nd_update_t](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L140) | [t_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L439) | | +| oneflow.tile | [oneflow.tile](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tile.py#L20) | [flow_tile_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tile.py#L27) | [tile_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L431) | | +| oneflow.transpose | [oneflow.Tensor.transpose](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L513) | [einsum_matrix_transpose](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_matrix_transpose.py#L35) | | | +| oneflow.unbind | [oneflow.Tensor.unbind](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L897) | [unbind_flow_with_random_data1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unbind.py#L32) | [unbind_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L248) | | +| oneflow.unsqueeze | [oneflow.Tensor.unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L636) | [unsqueeze](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_unsqueeze.py#L68) | | | +| oneflow.where | [oneflow.Tensor.where](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2129) | [where](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_where.py#L196) | | | +| oneflow.tensor_split | [oneflow.tensor_split](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1634) | [flow_tensor_split_vec](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensor_split.py#L27) | | | +| oneflow.seed | | [generator_manual_seed](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L72) | | | +| oneflow.manual_seed | | [generator_manual_seed](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L72) | | | +| oneflow.initial_seed | | | | | +| oneflow.get_rng_state | | [get_rng_state](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L137) | | | +| oneflow.set_rng_state | | [set_rng_state](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_generator.py#L148) | | | +| oneflow.bernoulli | [oneflow.bernoulli](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L20) | [bernoulli](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_bernoulli.py#L56) | | | +| oneflow.normal | [oneflow._C.normal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L154) | [eager_boxing_normal_1d_exhaustive_testing](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing_exhaustive.py#L113) | [normal_data_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L278) | | +| oneflow.rand | [oneflow._C.rand](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L112) | [0d_rand](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_rand.py#L45) | | | +| oneflow.randint | [oneflow._C.randint](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L191) | [global_randint](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_randint.py#L27) | | | +| oneflow.randn | [oneflow._C.randn](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L71) | [randn](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_randn.py#L103) | | | +| oneflow.randperm | [oneflow._C.randperm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/random.py#L291) | [global_randperm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_randperm.py#L26) | [randperm_n_value_err_mes](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_randperm_op.py#L24) | | +| oneflow.save | | [save_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L222) | | | +| oneflow.load | | [resnet18_load_weight_compatibile](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_resnet_load_torch_weight_compatibile.py#L30) | | | +| oneflow.set_num_threads | [oneflow.set_num_threads](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/oneflow.py#L20) | | | | +| oneflow.no_grad | | [no_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L62) | | | +| oneflow.set_grad_enabled | | [set_grad_enabled](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L74) | | | +| oneflow.enable_grad | | [enable_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L50) | | | +| oneflow.is_grad_enabled | | | | | +| oneflow.inference_mode | | [inference_mode](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_autograd_mode.py#L27) | | | +| oneflow.abs | [oneflow.abs](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L20) | [abs_with_0_size_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_abs.py#L27) | | done | +| oneflow.acos | [oneflow.acos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L509) | [acos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L122) | | | +| oneflow.acosh | [oneflow.acosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L535) | [acosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L138) | | | +| oneflow.arccos | [oneflow.Tensor.arccos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L664) | [arccos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L114) | | | +| oneflow.arccosh | [oneflow.Tensor.arccosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L678) | [arccosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L130) | | | +| oneflow.add | [oneflow.add](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L41) | [scatter_add_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_scatter_ops.py#L57) | [add_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L27) | done | +| oneflow.addcdiv | [oneflow.Tensor.addcdiv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L939) | [addcdiv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_addcdiv.py#L25) | | done | +| oneflow.addcmul | [oneflow.addcmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1558) | [addcmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_addcmul.py#L37) | | done | +| oneflow.asin | [oneflow.asin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L285) | [flow_asin_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L223) | | | +| oneflow.asinh | [oneflow.asinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L318) | [flow_asinh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L240) | | | +| oneflow.arcsin | [oneflow.Tensor.arcsin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1257) | [flow_arcsin_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L230) | | | +| oneflow.arcsinh | [oneflow.Tensor.arcsinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1264) | [flow_arcsinh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L247) | | | +| oneflow.atan | [oneflow.atan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L353) | [flow_atan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L267) | | | +| oneflow.atanh | [oneflow.atanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L564) | [flow_atanh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L289) | | | +| oneflow.arctan | [oneflow.Tensor.arctan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1343) | [flow_arctan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L274) | | | +| oneflow.arctanh | [oneflow.Tensor.arctanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L685) | [flow_arctanh_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L296) | | | +| oneflow.atan2 | [oneflow.Tensor.atan2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L123) | [atan2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L155) | | | +| oneflow.ceil | [oneflow.ceil](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L378) | [ceil_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_ceil.py#L29) | | | +| oneflow.clamp | [oneflow.clamp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L20) | [clamp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L96) | | | +| oneflow.clamp_min | [oneflow.clamp_min](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L70) | [clamp_min_none_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L119) | | | +| oneflow.clamp_max | [oneflow.clamp_max](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L111) | [clamp_max_none_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clamp.py#L126) | | | +| oneflow.clip | [oneflow.clip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/clamp.py#L152) | [adagrad_clip_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adagrad.py#L213) | | | +| oneflow.cos | [oneflow.cos](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L712) | [global_cos_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L65) | | | +| oneflow.cosh | [oneflow.cosh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L736) | | | | +| oneflow.div | [oneflow.div](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L143) | [div_grad_grad_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_higher_derivative_div.py#L26) | [div_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L81) | done | +| oneflow.erf | [oneflow.erf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L763) | [flow_erf_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_erf.py#L33) | | done | +| oneflow.erfc | [oneflow.erfc](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L810) | [erfc_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_erfc.py#L25) | | done | +| oneflow.erfinv | [oneflow.Tensor.erfinv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L997) | [flow_erfinv_with_inf_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_erfinv.py#L30) | | done | +| oneflow.exp | [oneflow.exp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L476) | [exp](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L72) | | | +| oneflow.expm1 | [oneflow.expm1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L845) | [expm1_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_expm1.py#L29) | | done | +| oneflow.floor | [oneflow.floor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L100) | [floor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_floor.py#L35) | | done | +| oneflow.floor_ | [oneflow.floor_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L135) | [flow_floor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_floor.py#L57) | | | +| oneflow.fmod | [oneflow.fmod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L890) | [flow_fmod_element_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L1021) | | done | +| oneflow.gelu | [oneflow.gelu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L74) | [gelu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L253) | | done | +| oneflow.log | [oneflow.log](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L923) | [log](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L56) | | | +| oneflow.log1p | [oneflow.log1p](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L455) | [log1p_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_log1p.py#L31) | | | +| oneflow.log2 | [oneflow.log2](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L948) | [log2_tensor_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L971) | | | +| oneflow.logical_and | [oneflow.Tensor.logical_and](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1677) | [logical_and](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_and.py#L58) | | | +| oneflow.logical_not | [oneflow.Tensor.logical_not](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L520) | [logical_not](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_not.py#L43) | | | +| oneflow.logical_or | [oneflow.Tensor.logical_or](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1687) | [logical_or](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_or.py#L58) | | | +| oneflow.logical_xor | [oneflow.Tensor.logical_xor](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1698) | [logical_xor_int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_xor.py#L27) | | | +| oneflow.mish | [oneflow.mish](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L267) | [mish_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L698) | | done | +| oneflow.mul | [oneflow.mul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L186) | [einsum_eltwise_mul_then_reduce_sum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_eltwise_mul_then_reduce_sum.py#L40) | | | +| oneflow.neg | [oneflow.Tensor.neg](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1039) | [flow_split_sizes_neg_dim_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_split.py#L63) | [tensordot_neg_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L25) | | +| oneflow.negative | [oneflow.negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L428) | [argmax_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmax.py#L29) | [repeat_interleave_negative_tensor_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L58) | | +| oneflow.pow | [oneflow.pow](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1132) | [pow_with_scalar](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L96) | | | +| oneflow.reciprocal | [oneflow.reciprocal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L226) | [flow_reciprocal_list_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_reciprocal.py#L32) | | | +| oneflow.round | [oneflow.round](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1346) | [flow_round_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_round.py#L30) | | | +| oneflow.rsqrt | [oneflow.rsqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1173) | [rsqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L80) | | | +| oneflow.selu | [oneflow.selu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L409) | [selu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L754) | | done | +| oneflow.softmax | [oneflow._C.softmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L118) | [softmax_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L436) | [softmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L109) | done | +| oneflow.softplus | [oneflow.softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L146) | [softplus](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_softplus.py#L43) | | done | +| oneflow.softsign | [oneflow._C.softsign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L207) | [softsign_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L782) | | done | +| oneflow.silu | [oneflow.silu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L237) | [silu_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L726) | | done | +| oneflow.sigmoid | [oneflow.sigmoid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L338) | [sigmoid_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L281) | [hard_sigmoid_inplace_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_activation.py#L87) | done | +| oneflow.sign | [oneflow.sign](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L589) | [sign_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sign.py#L25) | | | +| oneflow.sin | [oneflow.sin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L618) | [global_sin_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L59) | | | +| oneflow.sinh | [oneflow.sinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L656) | [sinh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L23) | | | +| oneflow.sin_ | [oneflow.sin_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L648) | [global_sin_grad_grad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_op_higher_derivative.py#L59) | | | +| oneflow.sqrt | [oneflow.sqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1198) | [sqrt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_math_ops.py#L64) | | | +| oneflow.square | [oneflow.square](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1224) | [inv_random_square_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_inv.py#L39) | [inv_exception_not_square_matrix](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_inv.py#L34) | | +| oneflow.sub | [oneflow.sub](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L246) | [global_sub](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_sub.py#L50) | | | +| oneflow.tan | [oneflow.tan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L687) | [flow_tan_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L257) | | | +| oneflow.tanh | [oneflow.tanh](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/activation.py#L163) | [tanh_module_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L106) | | done | +| oneflow.floor_divide | | | | | +| oneflow.argmax | [oneflow.Tensor.argmax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L692) | [argmax_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmax.py#L29) | [argmax_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L22) | done | +| oneflow.argmin | [oneflow.Tensor.argmin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L699) | [argmin_axis_negative](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_argmin.py#L29) | | | +| oneflow.amax | [oneflow.Tensor.amax](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L911) | [amax_with_negative_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_amax.py#L35) | | done | +| oneflow.amin | [oneflow.Tensor.amin](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2167) | [amin_with_negative_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_amin.py#L34) | | done | +| oneflow.any | [oneflow.Tensor.any](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1831) | [any_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_reduce.py#L52) | | | +| oneflow.max | [oneflow.Tensor.max](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1774) | [moving_average_min_max_observer](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_moving_average_max_min_observer.py#L83) | | | +| oneflow.min | [oneflow.Tensor.min](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1783) | [moving_average_min_max_observer](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_moving_average_max_min_observer.py#L83) | | | +| oneflow.mean | [oneflow.Tensor.mean](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1840) | [mean](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_mean.py#L70) | [normalization_moving_mean_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L317) | | +| oneflow.median | [oneflow.median](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1019) | [median](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_median.py#L48) | [median_exception_dim_out_of_range](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_median.py#L25) | | +| oneflow.prod | [oneflow.Tensor.prod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1849) | [prod_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_logical_reduce.py#L59) | | | +| oneflow.std | [oneflow.std](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1371) | [std_flow_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_std.py#L26) | | | +| oneflow.sum | [oneflow.Tensor.sum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1813) | [sum_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_sum.py#L29) | [reduce_sum_like_empty_axis_case_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reduce_like_ops.py#L24) | | +| oneflow.var | [oneflow.var](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1407) | [module_to_with_var_reuse](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module_to.py#L93) | | | +| oneflow.norm | [oneflow.linalg.norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L160) | [clip_grad_norm_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L50) | | | +| oneflow.all | [oneflow.Tensor.all](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1822) | [flow_var_all_dim_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_var.py#L27) | | | +| oneflow.argsort | [oneflow.Tensor.argsort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L706) | [argsort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_argsort.py#L37) | | done | +| oneflow.eq | [oneflow.Tensor.eq](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1011) | [eq_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_eq.py#L25) | | done | +| oneflow.equal | | [softmax_module_with_batch_size_equal_1024](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_activation.py#L464) | [concat_dim_equal_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L44) | | +| oneflow.gt | [oneflow.Tensor.gt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1080) | | | done | +| oneflow.isinf | [oneflow.Tensor.isinf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2152) | [isinf](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_util_ops.py#L33) | | | +| oneflow.isnan | [oneflow.Tensor.isnan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L2145) | [isnan](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_util_ops.py#L24) | | | +| oneflow.le | [oneflow.Tensor.le](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1025) | | | | +| oneflow.lt | [oneflow.Tensor.lt](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1018) | | | | +| oneflow.ne | [oneflow.Tensor.ne](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1032) | [ne](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_ne.py#L31) | | | +| oneflow.sort | [oneflow.Tensor.sort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1947) | [sort](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_sort.py#L69) | | | +| oneflow.topk | [oneflow.Tensor.topk](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1751) | [flow_topk_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_math_ops.py#L306) | | | +| oneflow.ge | [oneflow.Tensor.ge](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1062) | | | | +| oneflow.greater | [oneflow.greater](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/comparison.py#L21) | [greater_normal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_greater.py#L29) | | | +| oneflow.greater_equal | [oneflow.greater_equal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/comparison.py#L49) | [greater_equal_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_greater_equal.py#L25) | | | +| oneflow.maximum | [oneflow.maximum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L997) | [broadcast_maximum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maximum_minimum.py#L32) | | | +| oneflow.minimum | [oneflow.minimum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L975) | [broadcast_minimum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_maximum_minimum.py#L50) | | | +| oneflow.not_equal | | | | | +| oneflow.hann_window | [oneflow.hann_window](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/hann_window.py#L20) | [global_hann_window](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_hann_window.py#L26) | [hann_window_dtype_not_support](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_hann_window.py#L25) | done | +| oneflow.adaptive_avg_pool1d | [oneflow._C.adaptive_avg_pool1d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L20) | | | done | +| oneflow.adaptive_avg_pool2d | [oneflow._C.adaptive_avg_pool2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L48) | | | done | +| oneflow.adaptive_avg_pool3d | [oneflow._C.adaptive_avg_pool3d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/pooling.py#L74) | | | done | +| oneflow.broadcast_like | [oneflow.broadcast_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/broadcast_like.py#L20) | [broadcast_like](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_broadcast_like.py#L161) | [broadcast_like_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L28) | | +| oneflow.cast | [oneflow.cast](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/cast.py#L20) | [cast_float2int](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cast.py#L28) | [add_broad_cast_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_binary_functor_exception.py#L37) | | +| oneflow.cumprod | [oneflow.cumprod](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1788) | [cumprod_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cumprod.py#L25) | | done | +| oneflow.cumsum | [oneflow.cumsum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1755) | [cumsum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cumsum.py#L37) | | done | +| oneflow.decode_onerec | [oneflow.decode_onerec](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/dataset.py#L20) | | | | +| oneflow.diag | [oneflow.Tensor.diag](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L932) | [diag_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_diag.py#L26) | | done | +| oneflow.diagonal | [oneflow.Tensor.diagonal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1294) | [diagonal_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_diagonal.py#L24) | [diagonal_index_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L204) | done | +| oneflow.einsum | [oneflow.einsum](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/einsum.py#L20) | [einsum_alphaflod_usecase11](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_einsum_alphaflod_usecase11.py#L38) | | | +| oneflow.flatten | [oneflow.Tensor.flatten](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L155) | [to_global_flatten_hierarchy](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_cast.py#L30) | | done | +| oneflow.flip | [oneflow.Tensor.flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L169) | [image_flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_image_flip.py#L70) | | done | +| oneflow.in_top_k | [oneflow.Tensor.in_top_k](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L176) | [in_top_k_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_in_top_k.py#L82) | [in_top_k_num_equal_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L389) | | +| oneflow.meshgrid | [oneflow.meshgrid](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/meshgrid.py#L20) | [meshgrid_forawd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_meshgrid.py#L29) | [meshgrid_tensors_scalar_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L276) | | +| oneflow.nms | [oneflow.Tensor.nms](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1758) | [nms](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_nms.py#L50) | | | +| oneflow.roc_auc_score | [oneflow.roc_auc_score](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/roc_auc_score.py#L20) | [roc_auc_score](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_roc_auc_score.py#L52) | | | +| oneflow.roll | [oneflow.Tensor.roll](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1187) | [roll](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_roll.py#L27) | [roll_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L112) | | +| oneflow.searchsorted | [oneflow.searchsorted](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/searchsorted.py#L20) | | | | +| oneflow.tensordot | [oneflow.tensordot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensordot.py#L20) | [tensordot_intdim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_tensordot.py#L28) | [tensordot_neg_dims_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_tensordot.py#L25) | | +| oneflow.tril | [oneflow.Tensor.tril](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1441) | [fused_scale_tril](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_fused_scale_tril.py#L78) | | | +| oneflow.repeat_interleave | [oneflow.Tensor.repeat_interleave](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1631) | [flow_int_repeat_interleave_dim_none](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_repeat_interleave.py#L29) | [repeat_interleave_index_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_repeat_interleave.py#L25) | | +| oneflow.triu | [oneflow.Tensor.triu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1448) | [triu](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_triu.py#L47) | | | +| oneflow.addmm | [oneflow.Tensor.addmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1215) | [addmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_addmm.py#L60) | | done | +| oneflow.bmm | [oneflow.Tensor.bmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L876) | [bmm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_bmm.py#L93) | [bmm_exception_dim_not_right](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_bmm.py#L25) | | +| oneflow.dot | [oneflow.dot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1438) | [dot](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/tensor/test_tensor_part_1.py#L903) | [dot_shape_error_msg](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_dot.py#L24) | done | +| oneflow.matmul | [oneflow.matmul](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1249) | [fused_matmul_op](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cublas_fused_mlp.py#L173) | [matmul_dimension_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L220) | | +| oneflow.mm | [oneflow.mm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1311) | [flow_mm_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_matmul.py#L69) | [mm_not_2dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_mm.py#L24) | | +| oneflow.mv | [oneflow.mv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/math_ops.py#L1278) | [flow_mv_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_matmul.py#L78) | [mv_not_matrix](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_mv.py#L23) | done | +| oneflow.env.all_device_placement | | | | | +| oneflow.env.get_world_size | | | | | +| oneflow.env.get_rank | | | | | +| oneflow.env.get_local_rank | | | | | +| oneflow.env.get_node_size | | | | | +| oneflow.env.init_rdma | | | | | +| oneflow.env.rdma_is_initialized | | | | | +| oneflow.comm.all_reduce | | [all_reduce_1n2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm_ops.py#L31) | | | +| oneflow.comm.all_gather | | [all_gather_1n2d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm_ops.py#L48) | | | +| oneflow.comm.all_to_all | | [all_to_all_1n4d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm_ops.py#L148) | | | +| oneflow.comm.broadcast | | [cosine_similartiy_broadcast_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_cosine_similarity.py#L45) | [cosine_similarity_broadcast](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_cosine_similarity.py#L34) | | +| oneflow.comm.barrier | | | | | +| oneflow.comm.gather | [oneflow.Tensor.gather](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1531) | [gather_nd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_gather_nd.py#L85) | [gather_index_type_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L120) | done | +| oneflow.comm.reduce | | [min_reduce_random_dim](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_min.py#L28) | [reduce_sum_like_empty_axis_case_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_reduce_like_ops.py#L24) | | +| oneflow.comm.reduce_scatter | | [reduce_scatter_1n4d](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm_ops.py#L167) | | | +| oneflow.comm.recv | [oneflow.comm.recv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/comm.py#L32) | [send_recv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm.py#L28) | | | +| oneflow.comm.scatter | | [global_tensor_scatter_nd_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py#L128) | [tensor_scatter_nd_update_runtime_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L156) | | +| oneflow.comm.send | [oneflow.comm.send](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/comm.py#L20) | [send_recv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_comm.py#L28) | | | +| oneflow.linalg.norm | [oneflow.linalg.norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L160) | [clip_grad_norm_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_clip_grad.py#L50) | | | +| oneflow.linalg.vector_norm | [oneflow.linalg.vector_norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L21) | [vector_norm_only_zero_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_norm.py#L318) | | | +| oneflow.linalg.matrix_norm | [oneflow.linalg.matrix_norm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L88) | | | | +| oneflow.linalg.diagonal | [oneflow.Tensor.diagonal](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1294) | [diagonal_impl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_diagonal.py#L24) | [diagonal_index_error1](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_array_functor.py#L204) | done | +| oneflow.linalg.inv | [oneflow.linalg.inv](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/inv.py#L21) | [inv_3by3_with_random_data](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_inv.py#L27) | [inv_exception_dim_short](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_inv.py#L25) | done | +| oneflow.optim.Optimizer.add_param_group | | [sgd_add_param_group](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_add_param_group.py#L44) | [sgd_add_param_group_not_unique](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_optim_add_param_group.py#L23) | | +| oneflow.optim.Optimizer.load_state_dict | | [load_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L63) | | | +| oneflow.optim.Optimizer.state_dict | | [load_state_dict](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_module.py#L63) | | | +| oneflow.optim.Optimizer.step | | [arange_step_prarm](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_arange.py#L35) | [slice_update_step_list_err](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_slice_op.py#L49) | | +| oneflow.optim.Optimizer.zero_grad | | | | | +| oneflow.optim.Adagrad | | [one_embedding_adagrad](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_embedding_adagrad.py#L174) | | | +| oneflow.optim.Adam | | [multi_tensor_adam_update](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_multi_tensor_adam_update.py#L157) | | | +| oneflow.optim.AdamW | | [adamw](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_adamw.py#L244) | | | +| oneflow.optim.LAMB | | [lamb](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_lamb.py#L157) | | | +| oneflow.optim.RMSprop | | [rmsprop](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_optim_rmsprop.py#L228) | | | +| oneflow.optim.SGD | | [one_embedding_sgd](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_embedding_sgd.py#L190) | [sgd_add_param_group_not_unique](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_optim_add_param_group.py#L23) | | +| oneflow.optim.lr_scheduler.CosineAnnealingLR | | | | | +| oneflow.optim.lr_scheduler.CosineDecayLR | | | | | +| oneflow.optim.lr_scheduler.ExponentialLR | | | | | +| oneflow.optim.lr_scheduler.LambdaLR | | | | | +| oneflow.optim.lr_scheduler.MultiStepLR | | | | | +| oneflow.optim.lr_scheduler.PolynomialLR | | | | | +| oneflow.optim.lr_scheduler.ReduceLROnPlateau | | | | | +| oneflow.optim.lr_scheduler.StepLR | | | | | +| oneflow.optim.lr_scheduler.ConstantLR | | | | | +| oneflow.optim.lr_scheduler.LinearLR | | | | | +| oneflow.optim.lr_scheduler.ChainedScheduler | | | | | +| oneflow.optim.lr_scheduler.SequentialLR | | | | | +| oneflow.optim.lr_scheduler.CosineAnnealingWarmRestarts | | | | | +| oneflow.one_embedding.make_table_options | | | | | +| oneflow.one_embedding.make_table | | | | | +| oneflow.one_embedding.make_uniform_initializer | | | | | +| oneflow.one_embedding.make_normal_initializer | | | | | +| oneflow.one_embedding.make_device_mem_store_options | | | | | +| oneflow.one_embedding.make_cached_ssd_store_options | | | | | +| oneflow.one_embedding.make_cached_host_mem_store_options | | | | | +| oneflow.one_embedding.MultiTableEmbedding | | | | | +| oneflow.one_embedding.MultiTableEmbedding.forward | | [eye_forward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L27) | | | +| oneflow.one_embedding.MultiTableEmbedding.save_snapshot | | | | | +| oneflow.one_embedding.MultiTableEmbedding.load_snapshot | | | | | +| oneflow.one_embedding.MultiTableMultiColumnEmbedding | | | | | +| oneflow.one_embedding.MultiTableMultiColumnEmbedding.forward | | [eye_forward](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eye.py#L27) | | | +| oneflow.one_embedding.MultiTableMultiColumnEmbedding.save_snapshot | | | | | +| oneflow.one_embedding.MultiTableMultiColumnEmbedding.load_snapshot | | | | | +| oneflow.one_embedding.make_persistent_table_reader | | | | | +| oneflow.one_embedding.make_persistent_table_writer | | | | | +| oneflow.one_embedding.Ftrl | | [ftrl](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_embedding_ftrl.py#L191) | | | +| oneflow.nn.init.calculate_gain | | | | | +| oneflow.nn.init.uniform_ | [oneflow.Tensor.uniform_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1455) | | | | +| oneflow.nn.init.normal_ | [oneflow.Tensor.normal_](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L1154) | [eager_boxing_normal_1d_exhaustive_testing](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_eager_boxing_exhaustive.py#L113) | [normal_data_type_error](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/exceptions/test_nn_functor.py#L278) | | +| oneflow.nn.init.constant_ | | [constant_global](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_constant.py#L99) | | | +| oneflow.nn.init.ones_ | | [ones_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_ones_like.py#L27) | | | +| oneflow.nn.init.zeros_ | | [zeros_like_float](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_global_zeros_like.py#L27) | | | +| oneflow.nn.init.xavier_uniform_ | | | | | +| oneflow.nn.init.xavier_normal_ | | | | | +| oneflow.nn.init.kaiming_uniform_ | | | | | +| oneflow.nn.init.kaiming_normal_ | | | | | +| oneflow.nn.init.trunc_normal_ | | | | | +| oneflow.nn.init.orthogonal_ | | | | | +| oneflow.nn.image.Resize | | [image_resize_to_fixed_size](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_image_resize.py#L192) | | | +| oneflow.nn.image.batch_align | | [image_batch_align](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_image_batch_align.py#L52) | | | +| oneflow.nn.image.decode | | [read_decode](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_one_rec_ops.py#L78) | | | +| oneflow.nn.image.flip | [oneflow.Tensor.flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/tensor.py#L169) | [image_flip](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_image_flip.py#L70) | | done | +| oneflow.nn.image.normalize | [oneflow._C.normalize](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/framework/docstr/norm.py#L268) | [functional_normalize](https://github.com/Oneflow-Inc/oneflow/blob/5d4308ecd3c72dafe29634f964a103694e4dea5b/python/oneflow/test/../../../python/oneflow/test/modules/test_normalize.py#L54) | | | +| oneflow.utils.data.random_split | | | | | ## Test Data Summary -- OneFlow Total API Number: ====================>446 -- Doc Test Ratio: ====================>36.32% = 162 / 446 -- Compatiable/Completeness Test Ratio: ====================>49.33% = 220 / 446 -- Exception Test Ratio: ====================>13.23% = 59 / 446 +- OneFlow Total API Number: 771 +- Doc Test Ratio: 63.81% (492 / 771) +- Compatiable/Completeness Test Ratio: 73.80% (569 / 771) +- Exception Test Ratio: 19.71% (152 / 771) +- Performance Test Ratio: 15.56% (120 / 771) diff --git a/python/oneflow/test/exceptions/test_array_functor.py b/python/oneflow/test/exceptions/test_array_functor.py index 595768957b191e70c9890c41f5c02a39642bcb44..74ddb8c16d993fb336bc3018678b520d0ea3fc5e 100644 --- a/python/oneflow/test/exceptions/test_array_functor.py +++ b/python/oneflow/test/exceptions/test_array_functor.py @@ -34,6 +34,14 @@ class TestArrayError(flow.unittest.TestCase): "The expanded size of the tensor" in str(context.exception) ) + def test_broadcast_like_numaxes_runtime_error(test_case): + with test_case.assertRaises(Exception) as context: + x = flow.ones((2, 2, 2), dtype=flow.float32, requires_grad=True) + like = flow.ones((2, 2), dtype=flow.float32, requires_grad=True) + y = flow._C.broadcast_like(x, like) + print(str(context.exception)) + test_case.assertTrue("The number of sizes provided" in str(context.exception)) + def test_concat_index_error(test_case): with test_case.assertRaises(Exception) as context: x1 = flow.ones((2, 2), dtype=flow.float32, requires_grad=True) diff --git a/python/oneflow/test/exceptions/test_autograd.py b/python/oneflow/test/exceptions/test_autograd.py new file mode 100644 index 0000000000000000000000000000000000000000..d1efc468a5a81dddbb264f4bc9d240e0b4503981 --- /dev/null +++ b/python/oneflow/test/exceptions/test_autograd.py @@ -0,0 +1,37 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import re +import unittest + +import oneflow as flow +import oneflow.unittest + + +class TestAutograd(flow.unittest.TestCase): + def test_non_requires_grad_tensor_backward(test_case): + x = flow.ones(4, 4) + with test_case.assertRaises(Exception) as context: + x.backward() + test_case.assertIsNotNone( + re.search( + r"\nRuntimeError: element \d of tensors does not require grad and does not have a grad_fn", + str(context.exception), + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_binary_functor_exception.py b/python/oneflow/test/exceptions/test_binary_functor_exception.py index 69f37e50b1792f864db06c02bd41925def8957a6..4634eff6d574557a33c16a77cb535bedb24e5eca 100644 --- a/python/oneflow/test/exceptions/test_binary_functor_exception.py +++ b/python/oneflow/test/exceptions/test_binary_functor_exception.py @@ -40,7 +40,16 @@ class TestBinaryFunctorError(flow.unittest.TestCase): y = flow.ones((2, 4)) x.add_(y) test_case.assertTrue( - "Can not expand shape (2,4) to (2,3)" in str(context.exception) + "Tensor with shape (2,3) doesn't match the broadcast shape in an inplace operation" + in str(context.exception) + ) + + with test_case.assertRaises(RuntimeError) as context: + x = flow.ones((3, 3)) + y = flow.ones((2, 3, 3)) + x.add_(y) + test_case.assertTrue( + "Can not expand origin shape (2,3,3) to (3,3)" in str(context.exception) ) with test_case.assertRaises(RuntimeError) as context: @@ -57,7 +66,16 @@ class TestBinaryFunctorError(flow.unittest.TestCase): y = flow.ones((2, 4)) x.mul_(y) test_case.assertTrue( - "Can not expand shape (2,4) to (2,3)" in str(context.exception) + "Tensor with shape (2,3) doesn't match the broadcast shape in an inplace operation" + in str(context.exception) + ) + + with test_case.assertRaises(RuntimeError) as context: + x = flow.ones((3, 3)) + y = flow.ones((2, 3, 3)) + x.mul_(y) + test_case.assertTrue( + "Can not expand origin shape (2,3,3) to (3,3)" in str(context.exception) ) def test_div_inplace_runtime_error(test_case): @@ -75,7 +93,16 @@ class TestBinaryFunctorError(flow.unittest.TestCase): y = flow.ones((2, 4)) x.div_(y) test_case.assertTrue( - "Can not expand shape (2,4) to (2,3)" in str(context.exception) + "Tensor with shape (2,3) doesn't match the broadcast shape in an inplace operation" + in str(context.exception) + ) + + with test_case.assertRaises(RuntimeError) as context: + x = flow.ones((3, 3)) + y = flow.ones((2, 3, 3)) + x.div_(y) + test_case.assertTrue( + "Can not expand origin shape (2,3,3) to (3,3)" in str(context.exception) ) diff --git a/python/oneflow/test/exceptions/test_broadcast_ops.py b/python/oneflow/test/exceptions/test_broadcast_ops.py index 0173112448a89312e9a8386abb24501f208c612b..276123e4c42ba61a0ef48b5c697ccad7b2c7bee0 100644 --- a/python/oneflow/test/exceptions/test_broadcast_ops.py +++ b/python/oneflow/test/exceptions/test_broadcast_ops.py @@ -55,6 +55,32 @@ class TestBroadcastOps(flow.unittest.TestCase): in str(ctx.exception) ) + def test_broadcast_shapes(test_case): + with test_case.assertRaises(RuntimeError) as ctx: + y = flow.broadcast_shapes((2,), (3, 3), (1, 1, 1)) + test_case.assertTrue( + "input and other can't be broadcasted to a single shape." + in str(ctx.exception) + ) + with test_case.assertRaises(RuntimeError) as ctx: + y = flow.broadcast_shapes() + test_case.assertTrue("shapes should not be empty." in str(ctx.exception)) + + def test_broadcast_tensors(test_case): + with test_case.assertRaises(RuntimeError) as ctx: + y, z = flow.broadcast_tensors(flow.ones(2, 3), flow.ones(4, 3)) + test_case.assertTrue( + "input and other can't be broadcasted to a single shape." + in str(ctx.exception) + ) + with test_case.assertRaises(RuntimeError) as ctx: + y = flow.broadcast_tensors() + test_case.assertTrue("tensors should not be empty." in str(ctx.exception)) + + def test_broadcast_to(test_case): + # see flow.expand, because broadcast_to is an alias of flow.expand + pass + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/exceptions/test_deform_conv2d_op.py b/python/oneflow/test/exceptions/test_deform_conv2d_op.py new file mode 100644 index 0000000000000000000000000000000000000000..bded6b0625a5334c4cf59f5044a38d3962298548 --- /dev/null +++ b/python/oneflow/test/exceptions/test_deform_conv2d_op.py @@ -0,0 +1,144 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + + +class TestDeformConv(flow.unittest.TestCase): + def test_deform_conv2d_invalid_input_sizes(test_case): + input = flow.randn(2, 5, 1) + weight = flow.randn(2, 5, 1, 1) + offset = flow.randn(2, 5, 1, 1) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight) + test_case.assertTrue( + "The dimension of input tensor weight must be " in str(ctx.exception) + ) + + def test_deform_conv2d_invalid_offset_sizes(test_case): + input = flow.randn(2, 5, 1, 1) + weight = flow.randn(2, 5, 1, 1) + offset = flow.randn(2, 5, 1) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight) + test_case.assertTrue( + "The dimension of offset tensor weight must be " in str(ctx.exception) + ) + + def test_deform_conv2d_invalid_weight_sizes(test_case): + input = flow.randn(2, 5, 1, 1) + weight = flow.randn(2, 5, 5) + offset = flow.randn(2, 3, 1, 1) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight) + test_case.assertTrue( + "The dimension of weight tensor weight must be " in str(ctx.exception) + ) + + def test_deform_conv2d_invalid_mask_sizes(test_case): + input = flow.randn(2, 5, 1, 1) + weight = flow.randn(2, 4, 1, 1) + offset = flow.randn(2, 3, 1, 1) + mask = flow.randn(2, 3, 1) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight, mask=mask) + test_case.assertTrue( + "The dimension of mask tensor weight must be" in str(ctx.exception) + ) + + def test_deform_conv2d_invalid_dilation_parm(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d( + input, offset, weight, dilation=(-1, 0) + ) + test_case.assertTrue("The dilation must be greater than" in str(ctx.exception)) + + def test_deform_conv2d_invalid_pad_parm(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d( + input, offset, weight, padding=(-1, 0) + ) + test_case.assertTrue("The pad must be greater than" in str(ctx.exception)) + + def test_deform_conv2d_invalid_stride_parm(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d( + input, offset, weight, stride=(-1, 0) + ) + test_case.assertTrue("The stride must be greater than" in str(ctx.exception)) + + def test_deform_conv2d_invalid_offset_shape(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 9, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight) + test_case.assertTrue( + "The shape of the offset tensor at dimension 1 is not valid" + in str(ctx.exception) + ) + + def test_deform_conv2d_invalid_batch_size(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(3, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight) + test_case.assertTrue("invalid batch size of offset" in str(ctx.exception)) + + def test_deform_conv2d_invalid_mask_shape(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + mask = flow.randn(4, 1, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d(input, offset, weight, mask=mask) + test_case.assertTrue("mask.shape[1] is not valid" in str(ctx.exception)) + + def test_deform_conv2d_invalid_output_size(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d( + input, offset, weight, dilation=(10, 10) + ) + test_case.assertTrue("Calculated output size too small" in str(ctx.exception)) + + def test_deform_conv2d_invalid_offset_output_dims(test_case): + input = flow.randn(4, 3, 10, 10) + weight = flow.randn(5, 3, 3, 3) + offset = flow.randn(4, 18, 8, 8) + with test_case.assertRaises(RuntimeError) as ctx: + out = flow.nn.functional.deform_conv2d( + input, offset, weight, dilation=(2, 2) + ) + test_case.assertTrue("invalid offset output dims" in str(ctx.exception)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_error_reported_in_thread.py b/python/oneflow/test/exceptions/test_error_reported_in_thread.py new file mode 100644 index 0000000000000000000000000000000000000000..f217944924bc216b8c2a05e9eb751fba208e52cd --- /dev/null +++ b/python/oneflow/test/exceptions/test_error_reported_in_thread.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import subprocess +import sys +import tempfile +import os +import unittest +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +def test_error_reported_in_thread(): + for env_name in ["ONEFLOW_DEBUG", "ONEFLOW_PYTHON_STACK_GETTER"]: + env = os.environ.copy() + env[env_name] = "1" + # Run a new process to capture the error output + p = subprocess.run( + [sys.executable, "throw_error.py"], + capture_output=True, + cwd=os.path.dirname(os.path.realpath(__file__)), + env=env, + ) + assert p.returncode != 0 + error_msg = p.stderr.decode("utf-8") + print(error_msg) + assert ( + """File "throw_error.py", line 19, in g + flow._C.throw_error(x) + File "throw_error.py", line 23, in f + g(x) + File "throw_error.py", line 26, in + f(x)""" + in error_msg + ) + + +@flow.unittest.skip_unless_1n1d() +def test_python_stack_getter_disabled(): + # Run a new process to capture the error output + p = subprocess.run( + [sys.executable, "throw_error.py"], + capture_output=True, + cwd=os.path.dirname(os.path.realpath(__file__)), + ) + assert p.returncode != 0 + error_msg = p.stderr.decode("utf-8") + assert "No Python stack available." in error_msg + assert "ONEFLOW_DEBUG" in error_msg + assert "ONEFLOW_PYTHON_STACK_GETTER" in error_msg diff --git a/python/oneflow/test/exceptions/test_global_branch_error_local_to_global_with_split_sbp.py b/python/oneflow/test/exceptions/test_global_branch_error_local_to_global_with_split_sbp.py index 29399b4b5e7394277555cfbfa2288c250061c10e..c3af542a7012bfe0edb39fceddc7df8251312471 100644 --- a/python/oneflow/test/exceptions/test_global_branch_error_local_to_global_with_split_sbp.py +++ b/python/oneflow/test/exceptions/test_global_branch_error_local_to_global_with_split_sbp.py @@ -33,7 +33,7 @@ class TestLocalToGlobalBranchError(flow.unittest.TestCase): try: os.environ["ONEFLOW_TIMEOUT_SECONDS"] = "2" data = flow.rand(2, dtype=flow.float32) - placement = flow.env.all_device_placement("cuda") + placement = flow.placement.all("cuda") sbp = flow.sbp.split(0) if flow.env.get_rank() == 0: global_data = data.to_global(placement=placement, sbp=sbp) diff --git a/python/oneflow/test/exceptions/test_global_branch_error_with_global_mean.py b/python/oneflow/test/exceptions/test_global_branch_error_with_global_mean.py index 805ab7c15a8ca7f1ca8ba83081aecd52698aba2e..93c145f3544ddeb5d777350f1cb803465a22aec3 100644 --- a/python/oneflow/test/exceptions/test_global_branch_error_with_global_mean.py +++ b/python/oneflow/test/exceptions/test_global_branch_error_with_global_mean.py @@ -33,7 +33,7 @@ class TestGlobalMeanBranchError(flow.unittest.TestCase): try: os.environ["ONEFLOW_TIMEOUT_SECONDS"] = "2" data = flow.rand(2, dtype=flow.float32) - placement = flow.env.all_device_placement("cuda") + placement = flow.placement.all("cuda") sbp = flow.sbp.split(0) global_data = data.to_global(placement=placement, sbp=sbp) if flow.env.get_rank() == 0: diff --git a/python/oneflow/test/exceptions/test_hann_window.py b/python/oneflow/test/exceptions/test_hann_window.py new file mode 100644 index 0000000000000000000000000000000000000000..f0cac3b1d468c0dd7e3c11ec9a475a65afb4cf26 --- /dev/null +++ b/python/oneflow/test/exceptions/test_hann_window.py @@ -0,0 +1,36 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestHannWindow(flow.unittest.TestCase): + def test_hann_window_dtype_not_support(test_case): + window_length = 8 + dtype = flow.int64 + with test_case.assertRaises(RuntimeError) as ctx: + x = flow.hann_window(window_length, dtype=dtype) + test_case.assertTrue( + "hann_window expects floating point dtypes, got: " in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_inv.py b/python/oneflow/test/exceptions/test_inv.py new file mode 100644 index 0000000000000000000000000000000000000000..b103a8a744fe0956127290a67c192411256e5532 --- /dev/null +++ b/python/oneflow/test/exceptions/test_inv.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestInv(flow.unittest.TestCase): + def test_inv_exception_dim_short(test_case): + x = flow.tensor((2, 2)) + with test_case.assertRaises(RuntimeError) as ctx: + y = flow.linalg.inv(x) + test_case.assertTrue( + "linalg.inv: The input tensor must be at least 2 dimensions." + in str(ctx.exception) + ) + + def test_inv_exception_not_square_matrix(test_case): + x = flow.randn(2, 3, 2) + with test_case.assertRaises(RuntimeError) as ctx: + y = flow.linalg.inv(x) + test_case.assertTrue( + "RuntimeError: linalg.inv: A must be batches of square matrices, but they are 3 by 2 matrices" + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_linalg.py b/python/oneflow/test/exceptions/test_linalg.py new file mode 100644 index 0000000000000000000000000000000000000000..b65f699b48c22971dbbfcd68a64252df816dfb87 --- /dev/null +++ b/python/oneflow/test/exceptions/test_linalg.py @@ -0,0 +1,54 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + + +class TestLinalgCross(flow.unittest.TestCase): + def test_cross_has_no_3_error(test_case): + a = flow.randn(4, 2) + b = flow.randn(4, 2) + with test_case.assertRaises(RuntimeError) as ctx: + flow.cross(a, b) + test_case.assertTrue( + "RuntimeError: no dimension of size 3 in input." in str(ctx.exception) + ) + + def test_linalg_cross_has_no_3_error(test_case): + a = flow.randn(4, 2) + b = flow.randn(4, 2) + with test_case.assertRaises(RuntimeError) as ctx: + flow.linalg.cross(a, b) + test_case.assertTrue( + "RuntimeError: the size of the specified dimension(which is -1) is not 3." + in str(ctx.exception) + ) + + def test_linalg_cross_broadcast_error(test_case): + a = flow.randn(4) + b = flow.randn(4, 2) + with test_case.assertRaises(RuntimeError) as ctx: + flow.linalg.cross(a, b) + test_case.assertTrue( + "RuntimeError: input and other can't be broadcasted to a single shape. [input's shape: (1,4), other's shape: (4,2)]." + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_local_global_convert_error.py b/python/oneflow/test/exceptions/test_local_global_convert_error.py index eac0acba4c7741cb481b1b704fedeff5df58b86c..96f0db35c33a4b23f46705dc178cc1c8cb3bb4b7 100644 --- a/python/oneflow/test/exceptions/test_local_global_convert_error.py +++ b/python/oneflow/test/exceptions/test_local_global_convert_error.py @@ -39,9 +39,7 @@ class TestModule(flow.unittest.TestCase): def test_local_to_global_with_invalid_split_axis(test_case): x = flow.tensor([1, 2, 3, 4]) with test_case.assertRaises(RuntimeError) as ctx: - y = x.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.split(1) - ) + y = x.to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.split(1)) test_case.assertTrue( "Split axis out of range (expected to be in range of [0, 1), but got 1!" in str(ctx.exception) @@ -50,9 +48,7 @@ class TestModule(flow.unittest.TestCase): @flow.unittest.skip_unless_1n1d() def test_global_to_global_with_invalid_split_axis(test_case): x = flow.tensor( - [1, 2, 3, 4], - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, + [1, 2, 3, 4], placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast, ) with test_case.assertRaises(RuntimeError) as ctx: y = x.to_global(sbp=flow.sbp.split(1)) @@ -78,9 +74,7 @@ class TestModule(flow.unittest.TestCase): else: x = flow.Tensor(4, 4) # size(4, 4) with test_case.assertRaises(RuntimeError) as ctx: - y = x.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.split(0) - ) + y = x.to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.split(0)) test_case.assertTrue( "Sizes of tensors in dimension 0 must be same or match balanced split distribution. " "See https://github.com/Oneflow-Inc/oneflow/blob/master/oneflow/core/common/balanced_splitter.h " @@ -88,9 +82,7 @@ class TestModule(flow.unittest.TestCase): ) with test_case.assertRaises(RuntimeError) as ctx: - y = x.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.split(1) - ) + y = x.to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.split(1)) test_case.assertTrue( "Sizes of tensors must match except in dimension 1. Expected size 2 but got size 4 for tensor on rank 1!" in str(ctx.exception) diff --git a/python/oneflow/test/exceptions/test_nn_functor.py b/python/oneflow/test/exceptions/test_nn_functor.py index 33f7bdf0142d510a411394f8e9f51eef1cd2884b..801c9c352156af2738a6dc555f43cb3a283ea7b7 100644 --- a/python/oneflow/test/exceptions/test_nn_functor.py +++ b/python/oneflow/test/exceptions/test_nn_functor.py @@ -110,7 +110,7 @@ class TestPadError(flow.unittest.TestCase): out = flow._C.pad(x, (4, 4, 4, 4), mode="reflect") test_case.assertTrue( - "padding size should be less than the corresponding input dimension!" + "Padding size should be less than the corresponding input dimension, but got:" in str(ctx.exception) ) @@ -219,28 +219,17 @@ class TestLossBaseFunctorError(flow.unittest.TestCase): class TestMatmulError(flow.unittest.TestCase): def test_matmul_dimension_error1(test_case): with test_case.assertRaises(Exception) as ctx: - x = flow.ones((4,), dtype=flow.float32) + x = flow.ones((), dtype=flow.float32) w = flow.ones((4, 4), dtype=flow.float32) out = flow._C.matmul(x, w, False, False, 1.0) - test_case.assertTrue("Tensor a's dim should >= 2" in str(ctx.exception)) + test_case.assertTrue("Tensor a's dim should >= 1" in str(ctx.exception)) def test_matmul_dimension_error2(test_case): with test_case.assertRaises(Exception) as ctx: x = flow.ones((4, 4), dtype=flow.float32) - w = flow.ones((4,), dtype=flow.float32) + w = flow.ones((), dtype=flow.float32) out = flow._C.matmul(x, w, False, False, 1.0) - test_case.assertTrue("Tensor b's dim should >= 2" in str(ctx.exception)) - - def test_matmul_dimension_error3(test_case): - with test_case.assertRaises(Exception) as ctx: - x = flow.ones((4, 1, 2, 1), dtype=flow.float32) - w = flow.ones((4, 4, 4), dtype=flow.float32) - out = flow._C.matmul(x, w, False, False, 1.0) - - test_case.assertTrue( - "Not support number of dimensions of a being less than number of dimensions of b!" - in str(ctx.exception) - ) + test_case.assertTrue("Tensor b's dim should >= 1" in str(ctx.exception)) class TestPixelShuffleError(flow.unittest.TestCase): diff --git a/python/oneflow/test/exceptions/test_optim_add_param_group.py b/python/oneflow/test/exceptions/test_optim_add_param_group.py new file mode 100644 index 0000000000000000000000000000000000000000..085c15a6f9927a90749c41cde792ec617f7c3519 --- /dev/null +++ b/python/oneflow/test/exceptions/test_optim_add_param_group.py @@ -0,0 +1,40 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestSgdAddParamGroup(flow.unittest.TestCase): + def test_sgd_add_param_group_not_unique(test_case): + with test_case.assertRaises(Exception) as exp: + w1 = flow.ones(3, 3) + w1.requires_grad = True + w2 = flow.ones(3, 3) + w2.requires_grad = True + o = flow.optim.SGD([w1]) + o.add_param_group({"params": w2}) + o.add_param_group({"params": w2}) + print(str(exp.exception)) + test_case.assertTrue( + "some parameters appear in more than one parameter group" + in str(exp.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_pad.py b/python/oneflow/test/exceptions/test_pad.py index ba76b19194575be97ea1534809389a1acd90dfbd..d6acf0796ac22aac83338127122bff3d658483f1 100644 --- a/python/oneflow/test/exceptions/test_pad.py +++ b/python/oneflow/test/exceptions/test_pad.py @@ -26,8 +26,7 @@ class TestPad(flow.unittest.TestCase): with test_case.assertRaises(TypeError) as exp: F.pad(torch.randn(2, 2)) test_case.assertTrue( - "pad(): argument 'x' must be tensor, not " - in str(exp.exception) + "pad() missing 1 required positional argument: 'pad'" in str(exp.exception) ) def test_numpy_type(test_case): @@ -36,8 +35,7 @@ class TestPad(flow.unittest.TestCase): with test_case.assertRaises(TypeError) as exp: F.pad(np.random.randn(2, 2)) test_case.assertTrue( - "pad(): argument 'x' must be tensor, not " - in str(exp.exception) + "pad() missing 1 required positional argument: 'pad'" in str(exp.exception) ) diff --git a/python/oneflow/test/exceptions/test_placement.py b/python/oneflow/test/exceptions/test_placement.py new file mode 100644 index 0000000000000000000000000000000000000000..8aec030e7653171db70f45a15adb2d3006a7545b --- /dev/null +++ b/python/oneflow/test/exceptions/test_placement.py @@ -0,0 +1,37 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestPlacement(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_inconsistent_placement(test_case): + x = flow.randn(2, 3) + if flow.env.get_rank() == 0: + placement = flow.placement("cpu", [0, 1]) + else: + placement = flow.placement("cpu", [0]) + sbp = flow.sbp.split(1) + with test_case.assertRaises(RuntimeError) as ctx: + x_global = x.to_global(placement=placement, sbp=sbp) + test_case.assertTrue("Inconsistent parallel description" in str(ctx.exception)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_randperm_op.py b/python/oneflow/test/exceptions/test_randperm_op.py new file mode 100644 index 0000000000000000000000000000000000000000..2a62bf4101e5bd7de3939c6cc33f3ecd3240a502 --- /dev/null +++ b/python/oneflow/test/exceptions/test_randperm_op.py @@ -0,0 +1,33 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +class TestRandpermOp(flow.unittest.TestCase): + def test_randperm_n_value_err_mes(test_case): + with test_case.assertRaises(RuntimeError) as ctx: + a = flow.randperm(-1) + test_case.assertTrue( + "Trying to create tensor with negative dimension" in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_reduce_like_ops.py b/python/oneflow/test/exceptions/test_reduce_like_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..8de8504923da32206f755292eec0b74e20d190bb --- /dev/null +++ b/python/oneflow/test/exceptions/test_reduce_like_ops.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +class TestReduceSumLikeOps(flow.unittest.TestCase): + def test_reduce_sum_like_empty_axis_case_err(test_case): + a = flow.tensor([1, 1]) + b = flow.tensor([1, 1, 1]) + with test_case.assertRaises(RuntimeError) as ctx: + flow._C.reduce_sum_like(a, b, []) + test_case.assertTrue( + "The shape of the x tensor must be consistent to the shape of the like tensor" + in str(ctx.exception) + ) + + def test_reduce_sum_like_type_err(test_case): + a = flow.tensor([1, 1], dtype=flow.int64) + b = flow.tensor([1, 1], dtype=flow.float64) + with test_case.assertRaises(TypeError) as ctx: + flow._C.reduce_sum_like(a, b, [1]) + test_case.assertTrue( + "Tensors x and like must have the same type" in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_reshape.py b/python/oneflow/test/exceptions/test_reshape.py index cb36028dc9b86e655c205bbfd49a49beb832e5b9..d4cd37236b7a3b927df16df5ebf57e6b1dc90985 100644 --- a/python/oneflow/test/exceptions/test_reshape.py +++ b/python/oneflow/test/exceptions/test_reshape.py @@ -22,6 +22,26 @@ import oneflow.unittest @flow.unittest.skip_unless_1n1d() class TestModule(flow.unittest.TestCase): + def test_reshape_exception_invalid_dim(test_case): + # torch exception and messge: + # + # RuntimeError: Invalid shape dimension -2 + # + x = flow.tensor((2, 2)) + with test_case.assertRaises(RuntimeError) as ctx: + y = x.reshape((-2, 4)) + test_case.assertTrue("Invalid shape dimension -2" in str(ctx.exception)) + + def test_reshape_exception_invalid_size(test_case): + # torch exception and messge: + # + # RuntimeError: shape '[2, 3, 5]' is invalid for input of size 24 + # + x = flow.arange(24).reshape(2, 3, 4) + with test_case.assertRaises(RuntimeError) as ctx: + y = x.reshape((2, 3, 5)) + test_case.assertTrue("is invalid for input of size 24" in str(ctx.exception)) + def test_reshape_exception_only_one_dim_infered(test_case): # torch exception and messge: # diff --git a/python/oneflow/test/exceptions/test_reshape_like_op.py b/python/oneflow/test/exceptions/test_reshape_like_op.py new file mode 100644 index 0000000000000000000000000000000000000000..cd60fa54e3745a3587696d351f2c33d8457aeb07 --- /dev/null +++ b/python/oneflow/test/exceptions/test_reshape_like_op.py @@ -0,0 +1,36 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +class TestReshapeLikeOp(flow.unittest.TestCase): + def test_reshape_like_size_match_err(test_case): + a = flow.tensor([1, 1]) + b = flow.tensor([[1, 1, 1], [1, 1, 1]]) + with test_case.assertRaises(RuntimeError) as ctx: + flow._C.reshape_like(a, b) + test_case.assertTrue( + "The element number of the in tensor must be equal to the element number of the like tensor" + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_roi_align_op.py b/python/oneflow/test/exceptions/test_roi_align_op.py new file mode 100644 index 0000000000000000000000000000000000000000..442d55fbe0e36193fdfdd3c466f2347e4e0348bc --- /dev/null +++ b/python/oneflow/test/exceptions/test_roi_align_op.py @@ -0,0 +1,56 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + + +class TestRoiAlignOp(flow.unittest.TestCase): + def test_rol_align_x_tensor_dimension_err(test_case): + x = flow.randn(2, 3, 64) + rois = flow.randn(2, 3, 64, 64) + with test_case.assertRaises(RuntimeError) as ctx: + flow.roi_align(x, rois, 2.0, 14, 14, 2, True) + test_case.assertTrue( + "The dimension of x tensor must be equal to 4, but got" + in str(ctx.exception) + ) + + def test_rol_align_rois_tensor_dimension_err(test_case): + x = flow.randn(2, 3, 64, 5) + rois = flow.randn(2, 3, 64, 64) + with test_case.assertRaises(RuntimeError) as ctx: + flow.roi_align(x, rois, 2.0, 14, 14, 2, True) + test_case.assertTrue( + "The dimension of rois tensor must be equal to 2, but got" + in str(ctx.exception) + ) + + def test_rol_align_rois_tensor_size_err(test_case): + x = flow.randn(2, 3, 64, 5) + rois = flow.randn(2, 3) + with test_case.assertRaises(RuntimeError) as ctx: + flow.roi_align(x, rois, 2.0, 14, 14, 2, True) + test_case.assertTrue( + "The size of rois tensor must be equal to 5 at dimension 1, but got" + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_save_load.py b/python/oneflow/test/exceptions/test_save_load.py new file mode 100644 index 0000000000000000000000000000000000000000..58832e3e5590cea0bd99f7aa718b2f1b18923b55 --- /dev/null +++ b/python/oneflow/test/exceptions/test_save_load.py @@ -0,0 +1,37 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import tempfile + +import oneflow as flow +import oneflow.unittest +import torch + + +class TestSaveLoad(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n1d() + def test_support_pytorch_with_global_src_rank(test_case): + conv_torch = torch.nn.Conv2d(3, 3, 3) + conv_flow = flow.nn.Conv2d(3, 3, 3) + with tempfile.NamedTemporaryFile() as f: + torch.save(conv_torch.state_dict(), f.name) + with test_case.assertRaises(NotImplementedError) as ctx: + conv_flow.load_state_dict(flow.load(f.name, support_pytorch=False)) + test_case.assertTrue("No valid load method found" in str(ctx.exception)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_saved_tensor_hooks.py b/python/oneflow/test/exceptions/test_saved_tensor_hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..f1186f196b32f371ff1ce662bd17c7671a42c78e --- /dev/null +++ b/python/oneflow/test/exceptions/test_saved_tensor_hooks.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest + +import oneflow as flow +import oneflow.unittest + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestSavedTensorHooks(flow.unittest.TestCase): + def test_unpack_returns_non_tensor(test_case): + x = flow.ones(1, 2, 3).to("cuda").requires_grad_() + y = flow.zeros(1, 2, 3).to("cuda").requires_grad_() + + def pack(x): + return x + + def unpack(x): + return 0 + + with flow.autograd.graph.saved_tensors_hooks(pack, unpack): + z = x * y + with test_case.assertRaises(Exception) as exp: + z.sum().backward() + test_case.assertTrue( + "unpack_hook should return a Tensor, but got `` instead" + in str(exp.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_slice_op.py b/python/oneflow/test/exceptions/test_slice_op.py new file mode 100644 index 0000000000000000000000000000000000000000..a590794466e2233fb73f4018301b57a44f70bea8 --- /dev/null +++ b/python/oneflow/test/exceptions/test_slice_op.py @@ -0,0 +1,150 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +import numpy as np + + +class TestSlice(flow.unittest.TestCase): + def test_slice_update_start_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([[1], [2]]) + value = flow.tensor([[1], [2]]) + start = [-1] + stop = [1] + step = [1] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "The start list elements must be greater than or equal to 0, but got" + in str(context.exception) + ) + + def test_slice_update_stop_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([[1], [2]]) + value = flow.tensor([[1], [2]]) + start = [1] + stop = [-1] + step = [1] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "The stop list elements must be greater than or equal to 0" + in str(context.exception) + ) + + def test_slice_update_step_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([[1], [2]]) + value = flow.tensor([[1], [2]]) + start = [1] + stop = [1] + step = [0] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "The step list elements must be greater than 0, but got" + in str(context.exception) + ) + + def test_slice_update_start_and_stop_compare_value_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([[1], [2]]) + value = flow.tensor([[1], [2]]) + start = [2] + stop = [1] + step = [1] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "The element in start list must be less than or equal to the element in stop list at index" + in str(context.exception) + ) + + def test_slice_update_turple_size_match_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([1, 2]) + value = flow.tensor([1, 2]) + start = [1, 2, 3] + stop = [1, 2, 3] + step = [1, 2, 3] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "The size of slice tuple must be equal to the size of value tensor at dimension" + in str(context.exception) + ) + + def test_slice_update_type_err(test_case): + with test_case.assertRaises(TypeError) as context: + ref = flow.tensor([1], dtype=flow.int64) + value = flow.tensor([0.545], dtype=flow.float32) + start = [1] + stop = [2] + step = [1] + flow._C.slice_update(ref, value, start, stop, step) + test_case.assertTrue( + "Tensors ref and value must have same type" in str(context.exception) + ) + + def test_slice_start_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([1]) + start = [-1] + stop = [1] + step = [1] + flow._C.slice(ref, start, stop, step) + test_case.assertTrue( + "The start list elements must be greater than or equal to 0, but got " + in str(context.exception) + ) + + def test_slice_stop_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([1]) + start = [1] + stop = [-1] + step = [1] + flow._C.slice(ref, start, stop, step) + test_case.assertTrue( + "The stop list elements must be greater than or equal to 0, but got " + in str(context.exception) + ) + + def test_slice_step_list_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([1]) + start = [1] + stop = [1] + step = [-1] + flow._C.slice(ref, start, stop, step) + test_case.assertTrue( + "The step list elements must be greater than 0, but got " + in str(context.exception) + ) + + def test_slice_start_and_stop_compare_value_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + ref = flow.tensor([1]) + start = [2] + stop = [1] + step = [1] + flow._C.slice(ref, start, stop, step) + test_case.assertTrue( + "The element in start list must be less than or equal to the element in stop list at index " + in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_smooth_l1_loss_op.py b/python/oneflow/test/exceptions/test_smooth_l1_loss_op.py new file mode 100644 index 0000000000000000000000000000000000000000..6bcd7008fbb7f3cb81cca697352f19d094e204be --- /dev/null +++ b/python/oneflow/test/exceptions/test_smooth_l1_loss_op.py @@ -0,0 +1,57 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSmoothL1LossError(flow.unittest.TestCase): + def test_smooth_l1_loss_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + input = flow.randn(10) + target = flow.randn(11) + reduction = "mean" + beta = 1.0 + flow._C.smooth_l1_loss(input, target, beta, reduction) + test_case.assertTrue("must match the size of target" in str(context.exception)) + + def test_smooth_l1_loss_beta_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + input = flow.randn(10) + target = flow.randn(10) + reduction = "mean" + beta = -1.0 + flow._C.smooth_l1_loss(input, target, beta, reduction) + test_case.assertTrue( + "beta must be greater than or equal to 0" in str(context.exception) + ) + + def test_smooth_l1_loss_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + input = flow.randn(10, dtype=flow.float32) + target = flow.randn(10, dtype=flow.float64) + reduction = "mean" + beta = 1.0 + flow._C.smooth_l1_loss(input, target, beta, reduction) + test_case.assertTrue( + "input and target are expected to have the same dtype" + in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_softmax_cross_entropy_op.py b/python/oneflow/test/exceptions/test_softmax_cross_entropy_op.py new file mode 100644 index 0000000000000000000000000000000000000000..94628021e085bc7b5de2c82244436943336cb567 --- /dev/null +++ b/python/oneflow/test/exceptions/test_softmax_cross_entropy_op.py @@ -0,0 +1,115 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSoftmaxCrossEntropyError(flow.unittest.TestCase): + def test_softmax_cross_entropy_prediction_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10) + label = flow.randn(1, 10) + flow._C.softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "The dimension of prediction must be greater than or equal to 2, but found" + in str(context.exception) + ) + + def test_softmax_cross_entropy_prediction_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(1, 10) + label = flow.randn(1, 11) + flow._C.softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "must match the size of prediction" in str(context.exception) + ) + + def test_softmax_cross_entropy_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + prediction = flow.randn(1, 10, dtype=flow.float32) + label = flow.randn(1, 10, dtype=flow.float64) + flow._C.softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "label and prediction are expected to have the same dtype, but found" + in str(context.exception) + ) + + def test_softmax_cross_entropy_grad_prob_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + dy = flow.randn(10, 5) + label = flow.randn(10, 10, 5) + prob = flow.randn(10) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + test_case.assertTrue( + "The dimension of prob must be greater than or equal to 2, but found " + in str(context.exception) + ) + + def test_softmax_cross_entropy_grad_dy_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + dy = flow.randn(10, 10, 5) + label = flow.randn(10, 10, 5) + prob = flow.randn(10, 10, 5) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + test_case.assertTrue( + "The dimension of dy is expected to be less than that of prob by 1, but found" + in str(context.exception) + ) + + def test_softmax_cross_entropy_grad_dy_i_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + dy = flow.randn(10, 8) + label = flow.randn(10, 10, 5) + prob = flow.randn(10, 10, 5) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + test_case.assertTrue("must match the size of label" in str(context.exception)) + + def test_softmax_cross_entropy_grad_prob_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + dy = flow.randn(10, 10) + label = flow.randn(10, 10, 5) + prob = flow.randn(10, 10, 6) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + test_case.assertTrue("must match the size of prob" in str(context.exception)) + + def test_softmax_cross_entropy_grad_label_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + dy = flow.randn(10, 10, dtype=flow.float64) + label = flow.randn(10, 10, 5, dtype=flow.float32) + prob = flow.randn(10, 10, 5, dtype=flow.float64) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + test_case.assertTrue( + "label and prob are expected to have the same dtype, but found" + in str(context.exception) + ) + + def test_softmax_cross_entropy_grad_dy_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + dy = flow.randn(10, 10, dtype=flow.float32) + label = flow.randn(10, 10, 5, dtype=flow.float64) + prob = flow.randn(10, 10, 5, dtype=flow.float64) + flow._C.softmax_cross_entropy_grad(dy, label, prob) + print(str(context.exception)) + test_case.assertTrue( + "dy and prob are expected to have the same dtype, but found" + in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_sparse_cross_entropy_op.py b/python/oneflow/test/exceptions/test_sparse_cross_entropy_op.py new file mode 100644 index 0000000000000000000000000000000000000000..132e8b0e9650727942d8e79bf3effb9a4103d5d8 --- /dev/null +++ b/python/oneflow/test/exceptions/test_sparse_cross_entropy_op.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSparseCrossEntropyError(flow.unittest.TestCase): + def test_sparse_cross_entropy_prediction_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10) + label = flow.randint(0, 10, (10, 10), dtype=flow.int64) + depth = 10 + flow._C.sparse_cross_entropy(prediction, label, depth) + test_case.assertTrue( + "The dimension of prediction must be greater than or equal to 2, but found" + in str(context.exception) + ) + + def test_sparse_cross_entropy_label_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randint(0, 10, (10, 10, 5), dtype=flow.int64) + depth = 10 + flow._C.sparse_cross_entropy(prediction, label, depth) + test_case.assertTrue( + "The dimension of label is expected to be less than that of prediction by 1" + in str(context.exception) + ) + + def test_sparse_cross_entropy_prediction_i_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randint(0, 10, (10, 5), dtype=flow.int64) + depth = 10 + flow._C.sparse_cross_entropy(prediction, label, depth) + test_case.assertTrue(" must match the size of label" in str(context.exception)) + + def test_sparse_cross_entropy_label_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randn((10, 10), dtype=flow.float32) + depth = 10 + flow._C.sparse_cross_entropy(prediction, label, depth) + test_case.assertTrue( + "The dtype of label must be integer, but found" in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_sparse_softmax_cross_entropy_op.py b/python/oneflow/test/exceptions/test_sparse_softmax_cross_entropy_op.py new file mode 100644 index 0000000000000000000000000000000000000000..f890d28b82a185bae7b5a5af2bfade6c92885625 --- /dev/null +++ b/python/oneflow/test/exceptions/test_sparse_softmax_cross_entropy_op.py @@ -0,0 +1,61 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSparseSoftmaxCrossEntropyError(flow.unittest.TestCase): + def test_sparse_softmax_cross_entropy_prediction_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10) + label = flow.randint(0, 10, (10, 10), dtype=flow.int64) + flow._C.sparse_softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "The dimension of prediction must be greater than or equal to 2, but found" + in str(context.exception) + ) + + def test_sparse_softmax_cross_entropy_label_numaxes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randint(0, 10, (10, 10, 5), dtype=flow.int64) + flow._C.sparse_softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "The dimension of label is expected to be less than that of prediction by 1" + in str(context.exception) + ) + + def test_sparse_softmax_cross_entropy_prediction_i_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randint(0, 10, (10, 9), dtype=flow.int64) + flow._C.sparse_softmax_cross_entropy(prediction, label) + test_case.assertTrue("must match the size of label" in str(context.exception)) + + def test_sparse_softmax_cross_entropy_label_dtype_err(test_case): + with test_case.assertRaises(TypeError) as context: + prediction = flow.randn(10, 10, 5) + label = flow.randn(10, 10, dtype=flow.float32) + flow._C.sparse_softmax_cross_entropy(prediction, label) + test_case.assertTrue( + "The dtype of label must be integer, but found " in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_split_like_op.py b/python/oneflow/test/exceptions/test_split_like_op.py new file mode 100644 index 0000000000000000000000000000000000000000..fccf5fbef878269f447b86a515bdfddaf28ff428 --- /dev/null +++ b/python/oneflow/test/exceptions/test_split_like_op.py @@ -0,0 +1,73 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSplitLikeError(flow.unittest.TestCase): + def test_split_like_like_axes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + x = flow.randn(4, 4) + like = (flow.randn(2, 4, 4), flow.randn(2, 4, 4)) + axis = 0 + flow._C.split_like(x, like, axis) + test_case.assertTrue( + ") should be less than or equal to input (" in str(context.exception) + ) + + def test_split_like_split_axes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + x = flow.randn(4, 4) + like = (flow.randn(2, 4), flow.randn(2, 4)) + axis = 3 + flow._C.split_like(x, like, axis) + test_case.assertTrue( + "should be less than the dimension of like" in str(context.exception) + ) + + def test_split_like_like_i_axes_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + x = flow.randn(4, 4) + like = (flow.randn(2, 4), flow.randn(2)) + axis = 0 + flow._C.split_like(x, like, axis) + test_case.assertTrue( + "must match the dimension of the first like" in str(context.exception) + ) + + def test_split_like_x_i_shape_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + x = flow.randn(4, 4) + like = (flow.randn(2, 4), flow.randn(2, 3)) + axis = 0 + flow._C.split_like(x, like, axis) + test_case.assertTrue("must match the size of like_i" in str(context.exception)) + + def test_split_like_non_dynamic_static_dim_err(test_case): + with test_case.assertRaises(RuntimeError) as context: + x = flow.randn(4, 4) + like = (flow.randn(2, 4), flow.randn(3, 4)) + axis = 0 + flow._C.split_like(x, like, axis) + test_case.assertTrue( + "shape situation, the total size of like" in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_stft_op.py b/python/oneflow/test/exceptions/test_stft_op.py new file mode 100644 index 0000000000000000000000000000000000000000..c013f78045d21974ef38483f7fc8891d0254446d --- /dev/null +++ b/python/oneflow/test/exceptions/test_stft_op.py @@ -0,0 +1,118 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +import numpy as np + + +@flow.unittest.skip_unless_1n1d() +class TestModule(flow.unittest.TestCase): + def test_stft_illegal_input_dim(test_case): + np_tensor = np.arange(1, 13, dtype=float).reshape(2, 2, 3) + + with test_case.assertRaises(RuntimeError) as ctx: + x_flow = flow.tensor(np_tensor) + flow.stft( + x_flow, + n_fft=4, + center=True, + onesided=True, + return_complex=False, + normalized=False, + ) + test_case.assertTrue("Expected a 1D or 2D tensor,but got" in str(ctx.exception)) + + def test_stft_illegal_nfft(test_case): + np_tensor = np.arange(1, 13, dtype=float).reshape(4, 3) + win_tensor = np.arange(1, 5, dtype=float) + + with test_case.assertRaises(RuntimeError) as ctx: + x_flow = flow.tensor(np_tensor) + flow_win = flow.tensor(win_tensor) + + flow.stft( + x_flow, + n_fft=-1, + window=flow_win, + center=True, + onesided=True, + return_complex=False, + normalized=False, + ) + test_case.assertTrue("Expected 0 < n_fft <" in str(ctx.exception)) + + def test_stft_illegal_hop_length(test_case): + np_tensor = np.arange(1, 13, dtype=float).reshape(4, 3) + + with test_case.assertRaises(RuntimeError) as ctx: + x_flow = flow.tensor(np_tensor) + + flow.stft( + x_flow, + n_fft=4, + hop_length=-1, + center=True, + onesided=True, + return_complex=False, + normalized=False, + ) + test_case.assertTrue("Expected hop_length > 0, but got" in str(ctx.exception)) + + def test_stft_illegal_win_length(test_case): + np_tensor = np.arange(1, 13, dtype=float).reshape(4, 3) + + with test_case.assertRaises(RuntimeError) as ctx: + x_flow = flow.tensor(np_tensor) + + flow.stft( + x_flow, + n_fft=4, + win_length=-1, + center=True, + onesided=True, + return_complex=False, + normalized=False, + ) + test_case.assertTrue( + "Expected 0 < win_length <=n_fft ,but got" in str(ctx.exception) + ) + + def test_stft_illegal_window(test_case): + np_tensor = np.arange(1, 13, dtype=float).reshape(2, 6) + win_tensor = np.arange(1, 10, dtype=float) + + with test_case.assertRaises(RuntimeError) as ctx: + x_flow = flow.tensor(np_tensor) + flow_win = flow.tensor(win_tensor) + + flow.stft( + x_flow, + n_fft=4, + window=flow_win, + center=True, + onesided=True, + return_complex=False, + normalized=False, + ) + test_case.assertTrue( + "Expected a 1D window tensor of size equal to win_length=" + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_to_consistent_error.py b/python/oneflow/test/exceptions/test_to_consistent_error.py deleted file mode 100644 index 3ed62ea5d99454fcce4b204e90e1374d2895e0e0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/exceptions/test_to_consistent_error.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import os -import numpy as np -import time -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@flow.unittest.skip_unless_1n2d() -class TestToConsistentError(flow.unittest.TestCase): - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_tensor_to_consistent(self): - with self.assertRaises(Exception) as context: - data = flow.rand(2, dtype=flow.float32) - placement = flow.env.all_device_placement("cuda") - sbp = flow.sbp.split(0) - global_data = data.to_consistent(placement=placement, sbp=sbp) - - self.assertTrue( - ".to_consistent has been removed, please use .to_global instead" - in str(context.exception) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_tensor_is_consistent(self): - with self.assertRaises(Exception) as context: - data = flow.rand(2, dtype=flow.float32) - print(data.is_consistent()) - - self.assertTrue( - ".is_consistent has been removed, please use .is_global instead" - in str(context.exception) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_module_to_consistent(self): - with self.assertRaises(Exception) as context: - m = flow.nn.Conv2d(1, 1, 1) - placement = flow.env.all_device_placement("cuda") - sbp = flow.sbp.split(0) - m.to_consistent(placement=placement, sbp=sbp) - - self.assertTrue( - ".to_consistent has been removed, please use .to_global instead" - in str(context.exception) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/exceptions/test_to_global_error.py b/python/oneflow/test/exceptions/test_to_global_error.py new file mode 100644 index 0000000000000000000000000000000000000000..844089369b08807d25118192b003c2610e66059d --- /dev/null +++ b/python/oneflow/test/exceptions/test_to_global_error.py @@ -0,0 +1,70 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import os +import numpy as np +import time +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n2d() +class TestToGlobalError(flow.unittest.TestCase): + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_tensor_to_consistent(self): + with self.assertRaises(Exception) as context: + data = flow.rand(2, dtype=flow.float32) + placement = flow.placement.all("cuda") + sbp = flow.sbp.split(0) + global_data = data.to_consistent(placement=placement, sbp=sbp) + + self.assertTrue( + ".to_consistent has been removed, please use .to_global instead" + in str(context.exception) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_tensor_is_global(self): + with self.assertRaises(Exception) as context: + data = flow.rand(2, dtype=flow.float32) + print(data.is_consistent()) + + self.assertTrue( + ".is_consistent has been removed, please use .is_global instead" + in str(context.exception) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_module_to_consistent(self): + with self.assertRaises(Exception) as context: + m = flow.nn.Conv2d(1, 1, 1) + placement = flow.placement.all("cuda") + sbp = flow.sbp.split(0) + m.to_consistent(placement=placement, sbp=sbp) + + self.assertTrue( + ".to_consistent has been removed, please use .to_global instead" + in str(context.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/test_view.py b/python/oneflow/test/exceptions/test_view.py new file mode 100644 index 0000000000000000000000000000000000000000..ee8153d0e98de19fa07f30142ce198c5086c358b --- /dev/null +++ b/python/oneflow/test/exceptions/test_view.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestModule(flow.unittest.TestCase): + def test_view_exception(test_case): + # torch exception and messge: + # + # RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead. + # + a = flow.arange(9).reshape(3, 3) + b = a.permute(1, 0) + with test_case.assertRaises(RuntimeError) as ctx: + print(b.view(9)) + test_case.assertTrue( + "view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead." + in str(ctx.exception) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/exceptions/throw_error.py b/python/oneflow/test/exceptions/throw_error.py new file mode 100644 index 0000000000000000000000000000000000000000..01d86396aad5dde46d389efacb19a63d894dc23a --- /dev/null +++ b/python/oneflow/test/exceptions/throw_error.py @@ -0,0 +1,33 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +# This file is intended to be run in +# python/oneflow/test/exceptions/test_error_reported_in_thread.py + +import oneflow as flow + + +def g(x): + flow._C.throw_error(x) + + +def f(x): + x = x.relu() + g(x) + + +x = flow.ones(3, 3, 4) +f(x) diff --git a/python/oneflow/test/expensive/test_compatibility.py b/python/oneflow/test/expensive/test_compatibility.py index 0157f09b7be1ef9ea23904e685048a138e433654..0e1d6b237333df7774ccb17ac82001de3c37ea1a 100644 --- a/python/oneflow/test/expensive/test_compatibility.py +++ b/python/oneflow/test/expensive/test_compatibility.py @@ -64,10 +64,11 @@ class TestApiCompatibility(flow.unittest.TestCase): # test_case, "pytorch_rexnet.py", "rexnetv1_1_0", "cuda", 16, 224 # ) - def test_rexnetv1_lite_compatibility(test_case): - do_test_train_loss_oneflow_pytorch( - test_case, "pytorch_rexnetv1_lite.py", "rexnet_lite_1_0", "cuda", 16, 224 - ) + # TODO(): support non-contiguous inplace add + # def test_rexnetv1_lite_compatibility(test_case): + # do_test_train_loss_oneflow_pytorch( + # test_case, "pytorch_rexnetv1_lite.py", "rexnet_lite_1_0", "cuda", 16, 224 + # ) # def test_res2net_compatibility(test_case): # do_test_train_loss_oneflow_pytorch( @@ -134,15 +135,16 @@ class TestApiCompatibility(flow.unittest.TestCase): test_case, "pytorch_uniformer.py", "uniformer_small", "cuda", 8, 224, ) - def test_swin_transformer_compatibility(test_case): - do_test_train_loss_oneflow_pytorch( - test_case, - "pytorch_swin_transformer.py", - "swin_tiny_patch4_window7_224", - "cuda", - 8, - 224, - ) + # TODO(): support non-contiguous inplace add + # def test_swin_transformer_compatibility(test_case): + # do_test_train_loss_oneflow_pytorch( + # test_case, + # "pytorch_swin_transformer.py", + # "swin_tiny_patch4_window7_224", + # "cuda", + # 8, + # 224, + # ) def test_senet_compatibility(test_case): do_test_train_loss_oneflow_pytorch( diff --git a/python/oneflow/test/expensive/test_dynamic_allocation_gradient_shuffle.py b/python/oneflow/test/expensive/test_dynamic_allocation_gradient_shuffle.py new file mode 100644 index 0000000000000000000000000000000000000000..00a02db3c09099dfc8848c8b39032dba33c2e75e --- /dev/null +++ b/python/oneflow/test/expensive/test_dynamic_allocation_gradient_shuffle.py @@ -0,0 +1,214 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "1" +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict +import numpy as np +import oneflow as flow + + +def round_half_away_from_zero(x): + sign = np.sign(x) + abs_val = np.abs(x) + abs_val += 0.5 + floor_val = np.floor(abs_val) + out = floor_val * sign + return out + + +def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding_size): + batch_size = 512 + num_tables = 26 + ids = np.random.randint(0, 1000, (batch_size, num_tables), dtype=np.int64) + enable_quantized_comm = enable_quantize and embedding_size < 1025 + if enable_quantized_comm: + np_tolerance = 0.5 + os.environ["ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM"] = "1" + ids = np.arange(batch_size * num_tables, dtype=np.int64) + np.random.shuffle(ids) + else: + if fp16: + np_tolerance = 1e-2 + else: + np_tolerance = 1e-4 + os.environ["ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM"] = "0" + + table_ids = ( + ids % num_tables + ) # same id must have same table id, so in this case get table_ids from ids + embedding_grad = np.random.uniform( + low=-1, high=1, size=(batch_size, num_tables, embedding_size) + ).astype(np.float32) + ids_tensor = flow.tensor(ids, requires_grad=False).to("cuda") + table_ids_tensor = flow.tensor(table_ids.astype(np.int32), requires_grad=False).to( + "cuda" + ) + embedding_grad_tensor = flow.tensor(embedding_grad, requires_grad=False).to("cuda") + + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, ids, table_ids, embedding_grad): + ( + num_unique_matrix, + inverse_unique_partition_indices, + _, + cur_rank_unique_ids, + _, + cur_rank_inverse_indices, + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") + if fp16: + embedding_grad = flow.cast(embedding_grad, flow.float16) + cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle( + embedding_grad, + num_unique_matrix, + cur_rank_inverse_indices, + inverse_unique_partition_indices, + "test", + ) + if fp16: + cur_rank_unique_embedding_grad = flow.cast( + cur_rank_unique_embedding_grad, flow.float32 + ) + return ( + cur_rank_unique_embedding_grad, + flow.cast(cur_rank_unique_ids, flow.int32), + flow.cast(cur_rank_inverse_indices, flow.int32), + flow.cast(inverse_unique_partition_indices, flow.int32), + ) + + graph = TestGraph() + ( + cur_rank_unique_embedding_grad, + cur_rank_unique_ids, + cur_rank_inverse_indices, + inverse_unique_partition_indices, + ) = graph(ids_tensor, table_ids_tensor, embedding_grad_tensor) + np_unique_ids, np_inverse = np.unique(ids, return_inverse=True) + np_num_unique = np_unique_ids.size + np_cur_rank_unique_embedding_grad = np.zeros( + cur_rank_unique_embedding_grad.shape, dtype=np.float32 + ).reshape(-1, embedding_size) + + embedding_grad = embedding_grad.reshape(-1, embedding_size) + if fp16: + embedding_grad = embedding_grad.astype(np.float16) + for k in range(np_num_unique): + np_data = sum(embedding_grad[np.where(ids.flatten() == np_unique_ids[k])[0]]) + # Quantize Embedding Gradient. + if enable_quantized_comm: + abs_max_factor = np.max(np.abs(np_data)) + int8_factor = np.full(abs_max_factor.shape, 127.0, dtype=np.float32) + quantize_factor = int8_factor / abs_max_factor + np_data = np_data * quantize_factor + np_data = round_half_away_from_zero(np_data) + np_data = np_data.astype(np.int8) + np_data = np_data.astype(np.float32) + dequantize_factor = abs_max_factor / int8_factor + np_data = np_data * dequantize_factor + + np_cur_rank_unique_embedding_grad[k, :] = np_data + + reversed_ids = cur_rank_unique_ids[cur_rank_inverse_indices][ + inverse_unique_partition_indices + ] + test_case.assertTrue(np.array_equal(reversed_ids.numpy(), ids)) + of_cur_rank_embedding_grad = cur_rank_unique_embedding_grad[ + cur_rank_inverse_indices + ][inverse_unique_partition_indices] + of_cur_rank_embedding_grad = flow.reshape( + of_cur_rank_embedding_grad, (-1, embedding_size) + ) + np_cur_rank_embedding_grad = np_cur_rank_unique_embedding_grad[np_inverse] + if fp16: + np_cur_rank_embedding_grad = np_cur_rank_embedding_grad.astype(np.float32) + + test_case.assertTrue( + np.allclose( + of_cur_rank_embedding_grad.numpy().flatten(), + np_cur_rank_embedding_grad.flatten(), + atol=np_tolerance, + rtol=np_tolerance, + ) + ) + + +def _test_unique_key_value(test_case, has_table_id, num_tables): + batch_size = 128 + ids = np.random.randint(0, 1000, (batch_size, num_tables), dtype=np.int64) + if has_table_id: + table_ids = ( + ids % num_tables + ) # same id must have same table id, so in this case get table_ids from ids + table_ids_tensor = flow.tensor( + table_ids.astype(np.int32), requires_grad=False + ).to("cuda") + else: + table_ids_tensor = None + ids_tensor = flow.tensor(ids, requires_grad=False).to("cuda") + + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, ids, table_ids): + ( + num_unique, + unique_ids, + unique_table_ids, + inverse_indices, + ) = flow._C.one_embedding_unique_key_value_pair(ids, table_ids, num_tables) + return ( + flow.cast(num_unique, flow.int32), + flow.cast(unique_ids, flow.int32), + flow.cast(unique_table_ids, flow.int32), + flow.cast(inverse_indices, flow.int32), + ) + + graph = TestGraph() + (num_unique, unique_ids, unique_table_ids, inverse_indices,) = graph( + ids_tensor, table_ids_tensor + ) + np_unique_ids, np_inverse = np.unique(ids, return_inverse=True) + np_num_unique = np_unique_ids.size + test_case.assertTrue(np.array_equal(np_num_unique, num_unique[0])) + reversed_ids = unique_ids[inverse_indices] + test_case.assertTrue(np.array_equal(reversed_ids.numpy(), ids)) + if has_table_id: + reversed_table_ids = unique_table_ids[inverse_indices] + test_case.assertTrue(np.array_equal(reversed_table_ids.numpy(), table_ids)) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class DataShuffleTestCase(flow.unittest.TestCase): + def test_embedding_gradient_shuffle(test_case): + arg_dict = OrderedDict() + arg_dict["enable_quantize"] = [True, False] + arg_dict["fp16"] = [True, False] + arg_dict["embedding_size"] = [128, 17] + for kwargs in GenArgDict(arg_dict): + _test_embedding_gradient_shuffle(test_case, **kwargs) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/expensive/test_id_shuffle.py b/python/oneflow/test/expensive/test_id_shuffle.py index bd6b3f3c8912f6ee1b6cf8fdd8a83e59e3db2bf0..fb3e510343c701c6a481f9e2c36ebeefc3b558a4 100644 --- a/python/oneflow/test/expensive/test_id_shuffle.py +++ b/python/oneflow/test/expensive/test_id_shuffle.py @@ -14,14 +14,16 @@ See the License for the specific language governing permissions and limitations under the License. """ +import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import unittest from collections import OrderedDict from oneflow.test_utils.test_util import GenArgDict import numpy as np import oneflow as flow -from oneflow.test_utils.automated_test_util import * - def _test_id_shuffle(test_case, has_table_id, num_tables): batch_size = 512 @@ -49,7 +51,7 @@ def _test_id_shuffle(test_case, has_table_id, num_tables): cur_rank_unique_ids, cur_rank_unique_table_ids, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") return ( flow.cast(num_unique_matrix, flow.int32), flow.cast(inverse_unique_partition_indices, flow.int32), @@ -156,13 +158,14 @@ def _test_embedding_shuffle(test_case, dtype, enable_quantize): cur_rank_unique_ids, _, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") unique_embeddings = flow._C.gather(data, cur_rank_unique_ids, axis=0) embeddings = flow._C.one_embedding_embedding_shuffle( unique_embeddings, num_unique_matrix, cur_rank_inverse_indices, inverse_unique_partition_indices, + "test", ) return embeddings @@ -219,7 +222,7 @@ def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding cur_rank_unique_ids, _, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") if fp16: embedding_grad = flow.cast(embedding_grad, flow.float16) cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle( @@ -227,6 +230,7 @@ def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding num_unique_matrix, cur_rank_inverse_indices, inverse_unique_partition_indices, + "test", ) if fp16: cur_rank_unique_embedding_grad = flow.cast( @@ -319,7 +323,9 @@ def _test_unique_key_value(test_case, has_table_id, num_tables): unique_ids, unique_table_ids, inverse_indices, - ) = flow._C.one_embedding_unique_key_value_pair(ids, table_ids, num_tables) + ) = flow._C.one_embedding_unique_key_value_pair( + ids, table_ids, num_tables, "test" + ) return ( flow.cast(num_unique, flow.int32), flow.cast(unique_ids, flow.int32), @@ -351,7 +357,7 @@ class DataShuffleTestCase(flow.unittest.TestCase): for kwargs in GenArgDict(arg_dict): _test_id_shuffle(test_case, **kwargs) - def _test_embedding_shuffle(test_case): + def test_embedding_shuffle(test_case): arg_dict = OrderedDict() arg_dict["dtype"] = [flow.float32, flow.float16] arg_dict["enable_quantize"] = [True, False] @@ -359,7 +365,7 @@ class DataShuffleTestCase(flow.unittest.TestCase): for kwargs in GenArgDict(arg_dict): _test_embedding_shuffle(test_case, **kwargs) - def _test_embedding_gradient_shuffle(test_case): + def test_embedding_gradient_shuffle(test_case): arg_dict = OrderedDict() arg_dict["enable_quantize"] = [True, False] arg_dict["fp16"] = [True, False] @@ -367,7 +373,7 @@ class DataShuffleTestCase(flow.unittest.TestCase): for kwargs in GenArgDict(arg_dict): _test_embedding_gradient_shuffle(test_case, **kwargs) - def _test_unique_key_value(test_case): + def test_unique_key_value(test_case): arg_dict = OrderedDict() arg_dict["has_table_id"] = [True, False] arg_dict["num_tables"] = [13, 26, 1] diff --git a/python/oneflow/test/modules/test_id_shuffle_global.py b/python/oneflow/test/expensive/test_id_shuffle_global.py similarity index 98% rename from python/oneflow/test/modules/test_id_shuffle_global.py rename to python/oneflow/test/expensive/test_id_shuffle_global.py index 872eb7f0e04fe8e5cbfe435054dbf1ab7def2f46..60c2e6b495d84f8c406ede50d41a09059f98b86b 100644 --- a/python/oneflow/test/modules/test_id_shuffle_global.py +++ b/python/oneflow/test/expensive/test_id_shuffle_global.py @@ -14,14 +14,16 @@ See the License for the specific language governing permissions and limitations under the License. """ +import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import unittest from collections import OrderedDict from oneflow.test_utils.test_util import GenArgDict import numpy as np import oneflow as flow -from oneflow.test_utils.automated_test_util import * - parallel_num = 2 max_id = 1000 @@ -57,7 +59,7 @@ def _test_id_shuffle(test_case, has_table_id, num_tables): cur_rank_unique_ids, cur_rank_unique_table_ids, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") return ( flow.cast(num_unique_matrix, flow.int32), flow.cast(inverse_unique_partition_indices, flow.int32), @@ -136,6 +138,7 @@ def round_half_away_from_zero(x): def embedding_shuffle_quantize(np_data, np_dtype): + # When use float16, ComputeType is set to as Float. np_reduce_data = np_data.astype(np.float32) abs_max_factor = np.max(np.abs(np_reduce_data), axis=2) @@ -191,13 +194,14 @@ def _test_embedding_shuffle(test_case, dtype, enable_quantize): cur_rank_unique_ids, _, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") unique_embeddings = flow._C.gather(data, cur_rank_unique_ids, axis=0) embeddings = flow._C.one_embedding_embedding_shuffle( unique_embeddings, flow._C.identity(num_unique_matrix), flow._C.identity(cur_rank_inverse_indices), flow._C.identity(inverse_unique_partition_indices), + "test", ) return embeddings @@ -251,7 +255,7 @@ def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding cur_rank_unique_ids, _, cur_rank_inverse_indices, - ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables) + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") if fp16: embedding_grad = flow.cast(embedding_grad, flow.float16) cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle( @@ -259,6 +263,7 @@ def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding num_unique_matrix, cur_rank_inverse_indices, inverse_unique_partition_indices, + "test", ) if fp16: cur_rank_unique_embedding_grad = flow.cast( diff --git a/python/oneflow/test/expensive/test_oneembedding.py b/python/oneflow/test/expensive/test_oneembedding.py new file mode 100644 index 0000000000000000000000000000000000000000..0f6db7147651c1941507a7a7faf631109abdaacc --- /dev/null +++ b/python/oneflow/test/expensive/test_oneembedding.py @@ -0,0 +1,164 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os + +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict +import numpy as np +import oneflow as flow +import oneflow.nn as nn +import tempfile +import hashlib + + +class OneEmbedding(nn.Module): + def __init__( + self, + test_id, + embedding_vec_size, + persistent_path, + table_size_array, + size_factor, + ): + assert table_size_array is not None + vocab_size = sum(table_size_array) + + scales = np.sqrt(1 / np.array(table_size_array)) + tables = [ + flow.one_embedding.make_table( + flow.one_embedding.make_uniform_initializer(low=-scale, high=scale) + ) + for scale in scales + ] + store_options = flow.one_embedding.make_device_mem_store_options( + persistent_path=persistent_path, + capacity=vocab_size, + size_factor=size_factor, + ) + + super(OneEmbedding, self).__init__() + self.one_embedding = flow.one_embedding.MultiTableEmbedding( + f"oneembedding_{test_id}", + embedding_dim=embedding_vec_size, + dtype=flow.float, + key_type=flow.int64, + tables=tables, + store_options=store_options, + ) + + def forward(self, ids): + return self.one_embedding.forward(ids) + + +class TestModule(nn.Module): + def __init__( + self, + test_id, + embedding_vec_size, + persistent_path, + table_size_array, + size_factor, + ): + super(TestModule, self).__init__() + self.embedding = OneEmbedding( + test_id, embedding_vec_size, persistent_path, table_size_array, size_factor + ) + self.mlp = nn.Linear(embedding_vec_size, 1) + + def forward(self, inputs) -> flow.Tensor: + embedding = self.embedding(inputs) + logits = self.mlp(embedding).mean(dim=1) + return logits + + +class TrainGraph(flow.nn.Graph): + def __init__( + self, module, loss, optimizer, amp=False, + ): + super(TrainGraph, self).__init__() + self.module = module + self.loss = loss + self.add_optimizer(optimizer) + if amp: + self.config.enable_amp(True) + + def build(self, labels, features): + logits = self.module(features.to("cuda")) + loss = self.loss(logits, labels.to("cuda")) + reduce_loss = flow.mean(loss) + reduce_loss.backward() + return reduce_loss.to("cpu") + + +def _test_one_embedding( + test_case, batch_size, table_size_array, embedding_size, test_opt +): + test_str = str([batch_size, table_size_array, embedding_size, test_opt]) + test_hash = hashlib.sha256(test_str.encode("utf-8")).hexdigest() + + def np_to_global(np): + t = flow.from_numpy(np) + return t.to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.split(0)) + + with tempfile.TemporaryDirectory() as persistent_path: + size_factor = 3 if test_opt == "Adam" else 1 + module = TestModule( + test_hash, embedding_size, persistent_path, table_size_array, size_factor + ) + module.to_global(flow.placement.all("cuda"), flow.sbp.broadcast) + + if test_opt == "Adam": + opt = flow.optim.Adam(module.parameters(), lr=0.1) + elif test_opt == "SGD": + opt = flow.optim.SGD(module.parameters(), lr=0.1) + else: + assert False + + loss = flow.nn.BCEWithLogitsLoss(reduction="none").to("cuda") + + train_graph = TrainGraph(module, loss, opt) + + module.train() + for step in range(1, 101): + labels = np.random.randint(2, size=(batch_size, 1)).astype(np.float32) + features = np.random.randint( + sum(table_size_array), size=(batch_size, len(table_size_array)) + ) + labels = np_to_global(labels) + features = np_to_global(features) + loss = train_graph(labels, features) + test_case.assertFalse(np.isnan(loss.numpy())) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class OneEmbeddingTestCase(flow.unittest.TestCase): + def test_one_embedding(test_case): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [32, 4096] + arg_dict["table_size_array"] = [ + [32, 65536, 100, 7], + [32768, 10000, 17, 3, 686], + ] + arg_dict["embedding_size"] = [128, 17] + arg_dict["test_opt"] = ["SGD", "Adam"] + for kwargs in GenArgDict(arg_dict): + _test_one_embedding(test_case, **kwargs) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/expensive/test_oneembedding_padding_idx.py b/python/oneflow/test/expensive/test_oneembedding_padding_idx.py new file mode 100644 index 0000000000000000000000000000000000000000..09d6cb5389c0d30cbf002bdaf924b63053bcbc41 --- /dev/null +++ b/python/oneflow/test/expensive/test_oneembedding_padding_idx.py @@ -0,0 +1,206 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os + +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict +import numpy as np +import oneflow as flow +import oneflow.nn as nn +import tempfile +import hashlib +import random + + +class OneEmbedding(nn.Module): + def __init__( + self, + test_id, + embedding_vec_size, + persistent_path, + table_size_array, + size_factor, + padding_idx, + ): + assert table_size_array is not None + vocab_size = sum(table_size_array) + + scales = np.sqrt(1 / np.array(table_size_array)) + tables = [ + flow.one_embedding.make_table( + flow.one_embedding.make_uniform_initializer(low=-scale, high=scale) + ) + for scale in scales + ] + store_options = flow.one_embedding.make_device_mem_store_options( + persistent_path=persistent_path, + capacity=vocab_size, + size_factor=size_factor, + ) + + super(OneEmbedding, self).__init__() + self.one_embedding = flow.one_embedding.MultiTableEmbedding( + f"oneembedding_{test_id}", + embedding_dim=embedding_vec_size, + dtype=flow.float, + key_type=flow.int64, + tables=tables, + store_options=store_options, + padding_idx=padding_idx, + ) + + def forward(self, ids): + return self.one_embedding.forward(ids) + + +class TestModule(nn.Module): + def __init__( + self, + test_id, + embedding_vec_size, + persistent_path, + table_size_array, + size_factor, + padding_idx, + ): + super(TestModule, self).__init__() + self.embedding = OneEmbedding( + test_id, + embedding_vec_size, + persistent_path, + table_size_array, + size_factor, + padding_idx=padding_idx, + ) + + def forward(self, inputs) -> flow.Tensor: + embedding = self.embedding(inputs) + return embedding + + +class TrainGraph(flow.nn.Graph): + def __init__( + self, module, loss, optimizer, amp=False, + ): + super(TrainGraph, self).__init__() + self.module = module + self.loss = loss + self.add_optimizer(optimizer) + if amp: + self.config.enable_amp(True) + + def build(self, labels, features): + embedding = self.module(features.to("cuda")) + reduce_loss = flow.mean(embedding) + reduce_loss.backward() + return embedding.to("cpu") + + +def _test_one_embedding_padding_idx( + test_case, batch_size, table_size_array, embedding_size, test_opt, padding_idx +): + test_str = str([batch_size, table_size_array, embedding_size, test_opt]) + test_hash = hashlib.sha256(test_str.encode("utf-8")).hexdigest() + + def np_to_global(np): + t = flow.from_numpy(np) + return t.to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.split(0)) + + with tempfile.TemporaryDirectory() as persistent_path: + size_factor = 3 if test_opt == "Adam" else 1 + module = TestModule( + test_hash, + embedding_size, + persistent_path, + table_size_array, + size_factor, + padding_idx, + ) + module.to_global(flow.placement.all("cuda"), flow.sbp.broadcast) + + if test_opt == "Adam": + opt = flow.optim.Adam(module.parameters(), lr=0.1) + elif test_opt == "SGD": + opt = flow.optim.SGD(module.parameters(), lr=0.1) + else: + assert False + + loss = flow.nn.BCEWithLogitsLoss(reduction="none").to("cuda") + + train_graph = TrainGraph(module, loss, opt) + + module.train() + + padding_num = random.randint(0, batch_size - 1) + labels = np.random.randint(2, size=(batch_size, 1)).astype(np.float32) + padding_feature = np.full( + (len(table_size_array)), fill_value=padding_idx + ).astype(np.int64) + + features = np.random.randint( + sum(table_size_array), size=(batch_size, len(table_size_array)) + ) + padding_feature_idx = np.random.randint(batch_size, size=(padding_num,)) + for i in range(padding_num): + idx = int(padding_feature_idx[i]) + features[idx] = padding_feature + + labels = np_to_global(labels) + features = np_to_global(features) + embedding_val = train_graph(labels, features) + for i in range(padding_feature_idx.size): + idx = int(padding_feature_idx[i]) + test_case.assertTrue( + np.array_equal( + embedding_val[idx].numpy(), + np.zeros((len(table_size_array), embedding_size), dtype=np.float32), + ) + ) + + # Infer again to check the embedding in padding_idx is not updated. + embedding_val = train_graph(labels, features) + for i in range(padding_feature_idx.size): + idx = int(padding_feature_idx[i]) + test_case.assertTrue( + np.array_equal( + embedding_val[idx].numpy(), + np.zeros((len(table_size_array), embedding_size), dtype=np.float32), + ) + ) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class OneEmbeddingWithPaddingIdxTestCase(flow.unittest.TestCase): + def test_one_embedding_padding_idx(test_case): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [32] + arg_dict["table_size_array"] = [ + [32, 64, 32, 32], + ] + arg_dict["embedding_size"] = [12] + arg_dict["test_opt"] = ["SGD"] + arg_dict["padding_idx"] = [2] + os.environ["ONEFLOW_TIMEOUT_SECONDS"] = "300" + for kwargs in GenArgDict(arg_dict): + _test_one_embedding_padding_idx(test_case, **kwargs) + os.environ["ONEFLOW_TIMEOUT_SECONDS"] = "90" + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/expensive/test_rnn_utils.py b/python/oneflow/test/expensive/test_rnn_utils.py index c837542128ed038b57c48fd1a9f7dcf7c291a2b3..80571553eeb29be0e700fda8599b7ab026e1305f 100644 --- a/python/oneflow/test/expensive/test_rnn_utils.py +++ b/python/oneflow/test/expensive/test_rnn_utils.py @@ -31,6 +31,7 @@ def _test_rnn_utils_pack_padded_sequence(test_case, device): input_size = random.randint(10, 200) max_seq_len = random.randint(10, 500) batch_size = random.randint(10, 500) + requires_grad = np.random.rand() > 0.5 padded_inputs = np.zeros((max_seq_len, batch_size, input_size)) lengths = [] lengths.append(max_seq_len) @@ -42,9 +43,11 @@ def _test_rnn_utils_pack_padded_sequence(test_case, device): padded_inputs[0 : lengths[i], i : i + 1, :] = i + 1 inputs = flow.from_numpy(padded_inputs).to(device) + inputs.requires_grad = requires_grad flow_res = flow_rnn_utils.pack_padded_sequence(inputs, lengths) torch_inputs = torch.from_numpy(padded_inputs).to(device) + torch_inputs.requires_grad = requires_grad torch_res = torch_rnn_utils.pack_padded_sequence(torch_inputs, lengths) test_case.assertTrue( @@ -70,6 +73,10 @@ def _test_rnn_utils_pack_padded_sequence(test_case, device): flow_res, batch_first=False ) + if requires_grad: + torch_seq_unpacked.sum().backward() + flow_seq_unpacked.sum().backward() + test_case.assertTrue( np.allclose( torch_seq_unpacked.cpu().detach().numpy(), @@ -86,6 +93,11 @@ def _test_rnn_utils_pack_padded_sequence(test_case, device): ) ) + if requires_grad: + test_case.assertTrue( + np.allclose(inputs.grad.cpu().numpy(), torch_inputs.grad.cpu().numpy()) + ) + def _test_rnn_utils_pad_sequence(test_case, device): input_size = random.randint(10, 200) diff --git a/python/oneflow/test/expensive/test_session_reset_stream_index_state.py b/python/oneflow/test/expensive/test_session_reset_stream_index_state.py new file mode 100644 index 0000000000000000000000000000000000000000..dc5b557bfcc45ab12e394ce291222a3abfb34334 --- /dev/null +++ b/python/oneflow/test/expensive/test_session_reset_stream_index_state.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +from random import shuffle +import numpy as np +from oneflow.test_utils.test_util import GenArgList +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest +import oneflow.framework.session_context as session_ctx + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestSessionResetStreamIndexStateMock1(flow.unittest.TestCase): + @autotest(n=1000, auto_backward=False) + def test_mock_1(test_case): + device = gpu_device() + x = random_tensor().to(device) + return torch.nn.functional.relu(x) + + def setUp(self): + session_ctx.GetDefaultSession().Reset() + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestSessionResetStreamIndexStateMock2(flow.unittest.TestCase): + @autotest(n=1000, auto_backward=False) + def test_mock_2(test_case): + device = gpu_device() + input_size = random() + m = torch.nn.Linear(in_features=input_size, out_features=random()) + m.to(device) + x = random_tensor(ndim=2, dim1=input_size).to(device) + y = m(x) + return y + + def setUp(self): + # If not reset session, will raise + # F20221201 13:55:30.758263 2023961 stream_id.h:33] Check failed: stream_index <= kMaxStreamIndex (4096 vs. 4095) + session_ctx.GetDefaultSession().Reset() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/expensive/test_tensor_str.py b/python/oneflow/test/expensive/test_tensor_str.py index 2417de9d88930fc97932f2358c4d7bb596a69fee..528ada19c6bd4539109ffbbacecad256afdfb1a0 100644 --- a/python/oneflow/test/expensive/test_tensor_str.py +++ b/python/oneflow/test/expensive/test_tensor_str.py @@ -135,7 +135,7 @@ def _test_global_tensor_str_2d(test_case, device): x = flow.ones((10, 10), placement=placement, sbp=[flow.sbp.partial_sum]) tensor_str = str(x) - test_case.assertTrue("2." in tensor_str) + test_case.assertTrue("1." in tensor_str) x = flow.ones((100, 100), placement=placement, sbp=[flow.sbp.split(0)]) tensor_str = str(x) diff --git a/python/oneflow/test/gen_ops_process.py b/python/oneflow/test/gen_ops_process.py index 6c6930f0bfccf17801790e1344a7da9af86645a6..2e0eee6b624a1b3aec4d619c1c65303b92014139 100644 --- a/python/oneflow/test/gen_ops_process.py +++ b/python/oneflow/test/gen_ops_process.py @@ -15,489 +15,120 @@ limitations under the License. """ import os import subprocess +import glob +import re -from numpy import triu_indices -import oneflow as flow -import oneflow.nn as nn -api_list = [ - "Tensor", # flow.xxx - "BoolTensor", - "ByteTensor", - "CharTensor", - "DoubleTensor", - "FloatTensor", - "HalfTensor", - "IntTensor", - "LongTensor", - "Size", - "abs", - "acos", - "acosh", - "adaptive_avg_pool1d", - "adaptive_avg_pool2d", - "adaptive_avg_pool3d", - "add", - "addmm", - "any", - "arange", - "arccos", - "arccosh", - "arcsin", - "arcsinh", - "arctan", - "arctanh", - "argmax", - "argmin", - "argsort", - "argwhere", - "as_strided", - "as_tensor", - "asin", - "asinh", - "atan", - "atan2", - "atanh", - "autograd", - "batch_gather", - "bernoulli", - "bfloat16", - "bmm", - "bool", - "boxing", - "broadcast_like", - "cast", - "cat", - "ceil", - "char", - "chunk", - "clamp", - "clamp_", - "clip", - "clip_", - "concat", - "constant_initializer", - "convert_oneflow_dtype_to_numpy_dtype", - "cos", - "cosh", - "cumprod", - "cumsum", - "device", - "diag", - "diagonal", - "distributed_partial_fc_sample", - "div", - "div_", - "dot", - "double", - "dtype", - "dtypes", - "einsum", - "empty", - "eq", - "equal", - "erf", - "erfc", - "erfinv", - "erfinv_", - "exp", - "expand", - "expm1", - "eye", - "flatten", - "flip", - "float", - "float16", - "float32", - "float64", - "floor", - "floor_", - "floor_divide", - "fmod", - "from_numpy", - "full", - "gather", - "gather_nd", - "ge", - "gelu", - "glorot_normal_initializer", - "glorot_uniform_initializer", - "grad_enable", - "greater", - "greater_equal", - "gt", - "half", - "hsplit", - "in_top_k", - "index_select", - "int", - "int32", - "int64", - "int8", - "is_floating_point", - "is_grad_enabled", - "is_nonzero", - "is_tensor", - "kaiming_initializer", - "le", - "linalg_flow", - "linalg_matrix_norm", - "linalg_norm", - "linalg_vector_norm", - "linspace", - "log", - "log1p", - "log2", - "log_softmax", - "logical_and", - "logical_not", - "logical_or", - "logical_xor", - "long", - "lt", - "manual_seed", - "masked_fill", - "masked_select", - "matmul", - "max", - "maximum", - "mean", - "meshgrid", - "min", - "minimum", - "mish", - "movedim", - "mul", - "narrow", - "ne", - "neg", - "negative", - "new_ones", - "nms", - "no_grad", - "nonzero", - "not_equal", - "numel", - "one_embedding", - "ones", - "ones_initializer", - "ones_like", - "pad", - "permute", - "placement", - "pow", - "prod", - "randint", - "randn", - "random_normal_initializer", - "random_uniform_initializer", - "randperm", - "reciprocal", - "relu", - "repeat", - "reshape", - "roi_align", - "roll", - "round", - "rsqrt", - "save", - "sbp", - "scatter", - "scatter_add", - "select", - "selu", - "set_num_threads", - "set_printoptions", - "set_rng_state", - "sigmoid", - "sign", - "silu", - "sin", - "sin_", - "sinh", - "slice", - "slice_update", - "softmax", - "softplus", - "softshrink", - "softsign", - "sort", - "split", - "sqrt", - "square", - "squeeze", - "stack", - "stateful_op", - "std", - "sub", - "sum", - "support", - "swapaxes", - "t", - "tan", - "tanh", - "tensor_buffer", - "tensor_buffer_to_list_of_tensors", - "tensor_buffer_to_tensor", - "tensor_scatter_nd_update", - "tensor_split", - "tensor_to_tensor_buffer", - "tile", - "to_global", - "to_local", - "topk", - "transpose", - "tril", - "triu", - "truncated_normal_initializer", - "uint8", - "unsqueeze", - "var", - "variance_scaling_initializer", - "version", - "view", - "vsplit", - "where", - "xavier_normal_initializer", - "xavier_uniform_initializer", - "zero_", - "zeros", - "zeros_initializer", - "zeros_like", - "Adagrad", # oneflow.optim.xxx - "Adam", - "AdamW", - "LAMB", - "RMSprop", - "SGD", - "ChainedScheduler", # oneflow.optim.lr_scheduler.xxx - "ConstantLR", - "CosineAnnealingLR", - "CosineAnnealingWarmRestarts", - "CosineDecayLR", - "ExponentialLR", - "LambdaLR", - "LinearLR", - "MultiStepLR", - "PolynomialLR", - "ReduceLROnPlateau", - "SequentialLR", - "StepLR", - "WarmUpLR", - "AdaptiveAvgPool1d", # oneflow.nn.xxx - "AdaptiveAvgPool2d", - "AdaptiveAvgPool3d", - "AllReduce", - "AvgPool1d", - "AvgPool2d", - "AvgPool3d", - "BCELoss", - "BCEWithLogitsLoss", - "BatchNorm1d", - "BatchNorm2d", - "BatchNorm3d", - "CELU", - "COCOReader", - "CTCLoss", - "CoinFlip", - "CombinedMarginLoss", - "ConstantPad1d", - "ConstantPad2d", - "ConstantPad3d", - "Conv1d", - "Conv2d", - "Conv3d", - "ConvTranspose1d", - "ConvTranspose2d", - "ConvTranspose3d", - "CropMirrorNormalize", - "CrossEntropyLoss", - "DistributedPariticalFCSample", - "Dropout", - "ELU", - "Embedding", - "FakeQuantization", - "Flatten", - "Fold", - "FusedBatchNorm1d", - "FusedBatchNorm2d", - "FusedBatchNorm3d", - "FusedMLP", - "GELU", - "GLU", - "GPTIndexedBinDataReader", - "GRU", - "GroupNorm", - "Hardsigmoid", - "Hardswish", - "Hardtanh", - "Identity", - "InstanceNorm1d", - "InstanceNorm2d", - "InstanceNorm3d", - "KLDivLoss", - "L1Loss", - "LSTM", - "LayerNorm", - "LeakyReLU", - "Linear", - "LogSigmoid", - "LogSoftmax", - "MSELoss", - "MarginRankingLoss", - "MaxPool1d", - "MaxPool2d", - "MaxPool3d", - "MinMaxObserver", - "Mish", - "Module", - "ModuleDict", - "ModuleList", - "MovingAverageMinMaxObserver", - "NLLLoss", - "PReLU", - "Parameter", - "ParameterDict", - "ParameterList", - "PixelShuffle", - "Quantization", - "RNN", - "ReLU", - "ReLU6", - "ReflectionPad2d", - "ReplicationPad2d", - "SELU", - "Sequential", - "SiLU", - "Sigmoid", - "SmoothL1Loss", - "Softmax", - "Softplus", - "Softshrink", - "Softsign", - "Tanh", - "TripletMarginLoss", - "Unfold", - "UpsamplingBilinear2d", - "UpsamplingNearest2d", - "ZeroPad2d", - "adaptive_avg_pool1d", # oneflow.nn.functional.xxx - "adaptive_avg_pool2d", - "adaptive_avg_pool3d", - "affine_grid", - "avg_pool1d", - "avg_pool2d", - "avg_pool3d", - "celu", - "conv1d", - "conv2d", - "conv3d", - "cross_entropy", - "ctc_greedy_decoder", - "dropout", - "elu", - "embedding", - "functional_maxpool", - "gelu", - "glu", - "grid_sample", - "hardsigmoid", - "hardswish", - "hardtanh", - "interpolate", - "layer_norm", - "leaky_relu", - "linear", - "log_softmax", - "logsigmoid", - "max_pool1d", - "max_pool2d", - "max_pool3d", - "mish", - "normalize", - "one_hot", - "pad", - "prelu", - "relu", - "relu6", - "selu", - "sigmoid", - "silu", - "smooth_l1_loss", - "softmax", - "softplus", - "softshrink", - "softsign", - "sparse_softmax_cross_entropy", - "tanh", - "triplet_margin_loss", - "upsample", - "CalcGain", # flow.nn.init.xxx - "calculate_gain", - "constant_", - "flow", - "kaiming_normal_", - "kaiming_uniform_", - "normal_", - "ones_", - "os", - "trunc_normal_", - "uniform_", - "xavier_normal_", - "xavier_uniform_", - "zeros_", - "adagrad", # flow.nn.optimizer.xxx - "adam", - "adamw", - "chained_scheduler", - "constant_lr", - "cosine_annealing_lr", - "cosine_annealing_warm_restarts", - "cosine_decay_lr", - "exponential_lr", - "lamb", - "lambda_lr", - "linear_lr", - "lr_scheduler", - "multistep_lr", - "polynomial_lr", - "reduce_lr_on_plateau", - "rmsprop", - "sequential_lr", - "sgd", - "step_lr", - "warmup_lr", -] +def get_api(rst_dir): + """ + Extract operator names from rst files. -dir_list = [ - ["../../../python/oneflow/framework/docstr"], - ["../../../python/oneflow/test/modules", "../../../python/oneflow/test/tensor"], - ["../../../python/oneflow/test/exceptions"], -] -num_cols = 4 + `currentmodule` is not regarded as operators. + `autoclass` and `automodule` are regarded as operators in the absence of `members`. + """ + op_files = glob.glob(rst_dir + "/*.rst") + op_files.remove(rst_dir + "/graph.rst") + op_files.remove(rst_dir + "/index.rst") + api_list = [] + api_str = "" + for op_file in op_files: + with open(op_file, "r") as f: + line = f.readline() + pre = "" + while line: + skip = False + if ".. currentmodule::" in line: + pre = line.strip().replace(".. currentmodule::", "") + "." + elif ".. autofunction::" in line: + if "oneflow" not in line: + api_str += pre + api_str += line.replace(".. autofunction::", "") + elif ( + ".. autosummary::" in line + or ".. autoclass::" in line + or ":toctree:" in line + or ":nosignatures:" in line + or ":template:" in line + ): + if ":nosignatures:" in line: + line = f.readline() + if ":template:" in line: + line = f.readline() + line = f.readline() + while line and len(line.replace(" ", "")) > 1: + if "oneflow" not in line: + api_str += pre + api_str += line + line = f.readline() + elif ".. automodule::" in line: + pre_a = line.replace(".. automodule:: ", "") + line = f.readline() + skip = True + if ":members:" in line and len(line) > 14: + pre_a = pre_a.strip() + "." + api_str += pre_a + line.replace(":members:", "") + line = f.readline() + while ( + line and ":" not in line and len(line.replace(" ", "")) > 1 + ): + api_str += pre_a + line + line = f.readline() + if not skip: + line = f.readline() -test_func_list = list() -file_func_map = dict() -file_func_map_list = [] + api_list = api_str.strip().replace(" ", "").replace(",", "").split("\n") + return api_list + + +def get_profile_func(path): + """ + Iterate through files under `path` to find out all operator names, + and update code links to file_func_map_list by file_func_map. + """ + files = os.listdir(path) + commit_bytes = subprocess.check_output(["git", "rev-parse", "HEAD"]) + commit_str = commit_bytes.decode("utf-8").replace("\n", "") + result_profile_func_list = [] + for file in files: + if file != "log" and not os.path.isdir(file) and file.find("__pycache__") == -1: + f = open(os.path.join(path, file)) + last_line = "" + iter_f = iter(f) + line_num = 1 + for line in iter_f: + line = line.strip() + match = re.fullmatch(r"^@profile\((.+)\)$", line) + if match: + tem_profile = match.group(1) + tem_profile_name = tem_profile.split(".")[-1] + result_profile_func_list.append(tem_profile_name) + + return result_profile_func_list def get_test_func(path): + """ + Iterate through files under `path` to find out all operator names, + and update code links to file_func_map_list by file_func_map. + """ files = os.listdir(path) commit_bytes = subprocess.check_output(["git", "rev-parse", "HEAD"]) commit_str = commit_bytes.decode("utf-8").replace("\n", "") result_func_list = [] for file in files: - if not os.path.isdir(file) and file.find("__pycache__") == -1: - f = open(path + "/" + file) + if file != "log" and not os.path.isdir(file) and file.find("__pycache__") == -1: + f = open(os.path.join(path, file)) last_line = "" iter_f = iter(f) line_num = 1 for line in iter_f: line = line.strip() - if line.startswith("def test_") and line.endswith("(test_case):"): - result_func_list.append(line[9:-12]) - file_func_map[line[9:-12]] = ( - f" [{line[9:-12]}](" + rem = re.match("def .*?(test_.*)\(test_case.*", line) + if rem and "#" not in line: + func_name = rem.group(1).replace("_test_", "").replace("test_", "") + result_func_list.append(func_name) + file_func_map[func_name] = ( + f" [{func_name}](" + "https://github.com/Oneflow-Inc/oneflow/blob/" + commit_str + "/python/oneflow/test/" @@ -523,27 +154,43 @@ def get_test_func(path): return result_func_list -for i in range(0, len(dir_list)): - tmp_func_list = list() - file_func_map = dict() - for path in dir_list[i]: - tmp_func_list.extend(get_test_func(path)) - test_func_list.append(tmp_func_list) - file_func_map_list.append(file_func_map) +def pure_match(x, y): + """ + Check whether x contains y. + The purpose of identifying "." is to accurately match operator documents. + For example, if we make pos = x.find(y) while y = clip_, either oneflow.Tensor.clip or oneflow.Tensor.clip_ is right. -def pure_match(x, y): + Besides, identifying "_" is important. + For example, if we make pos = x.find(y) while y = squeeze, either test of squeeze or unsqueeze is right. + """ x = x.lower() - x = x.split("_")[0] y = y.lower() - pos = x.find(y) - if pos != -1: - return True + pos = -1 + if "." in x: + x = x.split(".") + for i in x: + if i == y: + pos = 1 + break + elif "_" in y: + pos = x.find(y) else: - return False + x = x.split("_") + for i in x: + if i == y: + pos = 1 + break + return pos != -1 def match_test_func(func, func_list): + """ + func: operator name + func_list: names of all operators + + Check whether func_list contains func. If yes, return matching content, or else return "". + """ match_res = "" for i in range(len(func_list)): if pure_match(func_list[i], func): @@ -552,67 +199,86 @@ def match_test_func(func, func_list): return match_res -result_list = [] -result_list.append(f"## Ops Version : Alpha") -result_list.append(f"") -result_list.append(f"") -table_head = f"|op name | Doc Test | Compatiable/Completeness Test | Exception |" -result_list.append(table_head) -result_list.append( - f"| ------------------------- | ------------- | ----------------------------- | --------- |" -) +if __name__ == "__main__": + api_list = get_api("../../../docs/source") + dir_list = [ + ["../../../python/oneflow/framework/docstr"], + ["../../../python/oneflow/test/modules", "../../../python/oneflow/test/tensor"], + ["../../../python/oneflow/test/exceptions"], + ] + num_cols = 4 + test_func_list = list() + test_profile_list = list() + file_func_map = dict() + file_func_map_list = [] + + for i in range(0, len(dir_list)): + tmp_func_list = list() + tmp_profile_list = list() + file_func_map = dict() + for path in dir_list[i]: + tmp_func_list.extend(get_test_func(path)) + tmp_profile_list.extend(get_profile_func(path)) + test_func_list.append(tmp_func_list) + test_profile_list.extend(tmp_profile_list) + file_func_map_list.append(file_func_map) -cnt0 = 0 -cnt1 = 0 -cnt2 = 0 + result_list = [] + result_list.append(f"## Ops Version : Alpha") + result_list.append(f"") + result_list.append(f"") + table_head = f"| Op Name | Doc Test | Compatiable/Completeness Test | Exception | Performance Test |" + result_list.append(table_head) + result_list.append( + f"| ------------------------- | ------------- | ----------------------------- | --------- | ---------------- |" + ) -pre = "" + cnt0 = 0 # the number of doc_test + cnt1 = 0 # the number of compatiable_completeness_test + cnt2 = 0 # the number of exception_test + cnt3 = 0 # the number of profile_test -for name in api_list: - if name == "Tensor": - pre = "oneflow." - elif name == "Adagrad": - pre = "oneflow.optim." - elif name == "ChainedScheduler": - pre = "oneflow.optim.lr_scheduler." - elif name == "AdaptiveAvgPool1d": - pre = "oneflow.nn." - elif name == "adaptive_avg_pool1d" and pre == "oneflow.nn.": - pre = "oneflow.nn.functional." - elif name == "CalcGain": - pre = "oneflow.nn.init." - table_line = f"| {pre+name} |" - for i in range(3): - match_name = match_test_func(name, test_func_list[i]) - if match_name != "": - if i == 0: - cnt0 += 1 - elif i == 1: - cnt1 += 1 - else: - cnt2 += 1 - table_line += file_func_map_list[i][match_name] + for name in api_list: + table_line = f"| {name} |" + name = name.split(".")[-1] + for i in range(3): + match_name = match_test_func(name, test_func_list[i]) + if match_name != "": + if i == 0: + cnt0 += 1 + elif i == 1: + cnt1 += 1 + else: + cnt2 += 1 + table_line += file_func_map_list[i][match_name] + table_line += " |" + if name in test_profile_list: + table_line += " done " + cnt3 += 1 table_line += " |" - result_list.append(table_line) - -doc_test_ratio = cnt0 * 1.0 / len(api_list) -compatiable_completeness_test_ratio = cnt1 * 1.0 / len(api_list) -exception_test_ratio = cnt2 * 1.0 / len(api_list) -result_list.append(f"## Test Data Summary") + result_list.append(table_line) -result_list.append(f"- OneFlow Total API Number: ====================>{len(api_list)}") -result_list.append( - f"- Doc Test Ratio: ====================>{100*doc_test_ratio:.2f}% = {cnt0} / {len(api_list)}" -) -result_list.append( - f"- Compatiable/Completeness Test Ratio: ====================>{100*compatiable_completeness_test_ratio:.2f}% = {cnt1} / {len(api_list)}" -) -result_list.append( - f"- Exception Test Ratio: ====================>{100*exception_test_ratio:.2f}% = {cnt2} / {len(api_list)}" -) + doc_test_ratio = cnt0 / len(api_list) + compatiable_completeness_test_ratio = cnt1 / len(api_list) + exception_test_ratio = cnt2 / len(api_list) + performance_test_ratio = cnt3 / len(api_list) -f = open("./README.md", "w") -for line in result_list: - f.write(line + "\n") -f.close() + result_list.append(f"## Test Data Summary") + result_list.append(f"- OneFlow Total API Number: {len(api_list)}") + result_list.append( + f"- Doc Test Ratio: {100*doc_test_ratio:.2f}% ({cnt0} / {len(api_list)})" + ) + result_list.append( + f"- Compatiable/Completeness Test Ratio: {100*compatiable_completeness_test_ratio:.2f}% ({cnt1} / {len(api_list)})" + ) + result_list.append( + f"- Exception Test Ratio: {100*exception_test_ratio:.2f}% ({cnt2} / {len(api_list)})" + ) + result_list.append( + f"- Performance Test Ratio: {100*performance_test_ratio:.2f}% ({cnt3} / {len(api_list)})" + ) + f = open("./README.md", "w") + for line in result_list: + f.write(line + "\n") + f.close() diff --git a/python/oneflow/test/graph/test_alexnet_auto_parallel.py b/python/oneflow/test/graph/test_alexnet_auto_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..7698ab9d7bf689c3115eef76ab5b088b26fd1be8 --- /dev/null +++ b/python/oneflow/test/graph/test_alexnet_auto_parallel.py @@ -0,0 +1,223 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import time +import unittest +import argparse + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from alexnet_model import alexnet +import flowvision as vision +import flowvision.transforms as transforms + + +def load_data_fashion_mnist( + batch_size, + resize=None, + root="./data-test/fashion-mnist", + download=True, + source_url=None, + num_workers=0, +): + """Download the Fashion-MNIST dataset and then load into memory.""" + root = os.path.expanduser(root) + trans = [] + if resize: + trans.append(transforms.Resize(resize)) + trans.append(transforms.ToTensor()) + transform = transforms.Compose(trans) + + mnist_train = vision.datasets.FashionMNIST( + root=root, + train=True, + transform=transform, + download=download, + source_url=source_url, + ) + mnist_test = vision.datasets.FashionMNIST( + root=root, + train=False, + transform=transform, + download=download, + source_url=source_url, + ) + + train_iter = flow.utils.data.DataLoader( + mnist_train, batch_size, shuffle=True, num_workers=num_workers + ) + test_iter = flow.utils.data.DataLoader( + mnist_test, batch_size, shuffle=False, num_workers=num_workers + ) + return train_iter, test_iter + + +def _parse_args(): + parser = argparse.ArgumentParser("flags for train alexnet") + parser.add_argument( + "--load_checkpoint", type=str, default="", help="load checkpoint" + ) + parser.add_argument( + "--ofrecord_path", + type=str, + default="/dataset/imagenette/ofrecord", + help="dataset path", + ) + # training hyper-parameters + parser.add_argument( + "--learning_rate", type=float, default=0.02, help="learning rate" + ) + parser.add_argument("--mom", type=float, default=0.9, help="momentum") + parser.add_argument("--epochs", type=int, default=1, help="training epochs") + parser.add_argument("--batch_size", type=int, default=128, help="val batch size") + + return parser.parse_known_args() + + +def _test_alexnet_graph(test_case, args, placement, sbp): + data_dir = os.path.join( + os.getenv("ONEFLOW_TEST_CACHE_DIR", "./data-test"), "fashion-mnist-lenet" + ) + source_url = "https://oneflow-public.oss-cn-beijing.aliyuncs.com/datasets/mnist/Fashion-MNIST/" + train_iter, test_iter = load_data_fashion_mnist( + batch_size=args.batch_size, + root=data_dir, + download=True, + source_url=source_url, + num_workers=0, + resize=(112, 112), + ) + + # oneflow init + start_t = time.time() + alexnet_module = alexnet(num_classes=10) + end_t = time.time() + print("init time : {}".format(end_t - start_t)) + + alexnet_module.to_global(placement, sbp) + + of_cross_entropy = flow.nn.CrossEntropyLoss().to_global(placement, sbp) + + of_sgd = flow.optim.SGD( + alexnet_module.parameters(), lr=args.learning_rate, momentum=args.mom + ) + + class AlexNetGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.alexnet = alexnet_module + self.cross_entropy = of_cross_entropy + self.add_optimizer(of_sgd) + self.config.enable_auto_parallel(True) + self.config.enable_auto_parallel_ignore_user_sbp_config(True) + self.config.enable_auto_parallel_trunk_algo(True) + self.config.enable_auto_parallel_sbp_collector(True) + + def build(self, image, label): + logits = self.alexnet(image) + loss = self.cross_entropy(logits, label) + loss.backward() + return loss + + alexnet_graph = AlexNetGraph() + + class AlexNetEvalGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.alexnet = alexnet_module + self.config.enable_auto_parallel(True) + self.config.enable_auto_parallel_ignore_user_sbp_config(True) + self.config.enable_auto_parallel_trunk_algo(True) + self.config.enable_auto_parallel_sbp_collector(True) + + def build(self, image): + with flow.no_grad(): + logits = self.alexnet(image) + predictions = logits.softmax() + return predictions + + alexnet_eval_graph = AlexNetEvalGraph() + + of_losses = [] + print_interval = 20 + + acc = 0.0 + for epoch in range(args.epochs): + alexnet_module.train() + + for i, (image, label) in enumerate(train_iter): + # oneflow graph train + if image.shape[0] != args.batch_size: + # drop last batch + break + start_t = time.time() + image = image.to_global(placement, sbp).expand(args.batch_size, 3, 112, 112) + label = label.to_global(placement, sbp) + loss = alexnet_graph(image, label) + end_t = time.time() + if i % print_interval == 0: + l = loss.numpy() + of_losses.append(l) + if flow.env.get_rank() == 0: + print( + "epoch {} train iter {}/{} oneflow loss {}, train time : {}".format( + epoch, i, len(train_iter), l, end_t - start_t + ) + ) + if flow.env.get_rank() == 0: + print("epoch %d train done, start validation" % epoch) + + alexnet_module.eval() + correct_of = 0.0 + total_of = 0.0 + for image, label in test_iter: + # oneflow graph eval + if image.shape[0] != args.batch_size: + # drop last batch + break + start_t = time.time() + image = image.to_global(placement, sbp).expand(args.batch_size, 3, 112, 112) + predictions = alexnet_eval_graph(image) + of_predictions = predictions.numpy() + clsidxs = np.argmax(of_predictions, axis=1) + + label_nd = label.numpy() + + for i in range(args.batch_size): + total_of += 1 + if clsidxs[i] == label_nd[i]: + correct_of += 1 + end_t = time.time() + acc = correct_of / total_of + + if flow.env.get_rank() == 0: + print("epoch %d, oneflow top1 val acc: %f" % (epoch, acc)) + # test_case.assertTrue(acc > 0.50) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestAlexnetAutoParallel(oneflow.unittest.TestCase): + def test_alexnet_auto_parallel_1d_sbp(test_case): + args, unknown_args = _parse_args() + placement = flow.placement.all("cuda") + sbp = [flow.sbp.broadcast,] * len(placement.ranks.shape) + _test_alexnet_graph(test_case, args, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_alexnet_graph.py b/python/oneflow/test/graph/test_alexnet_graph.py index 628e2a79238cce931430a9a08d529aaa1e6d465b..55b5fddaeac2270583589d5e767937aee362db8a 100644 --- a/python/oneflow/test/graph/test_alexnet_graph.py +++ b/python/oneflow/test/graph/test_alexnet_graph.py @@ -100,7 +100,7 @@ def _test_alexnet_graph_repr(test_case, args): print("repr(alexnet_graph) before run: \n", repr(alexnet_graph)) # debug graph build - alexnet_graph.debug(1) + alexnet_graph.debug(1, op_repr_with_py_stack=True, max_py_stack_depth=4) alexnet_module.train() image, label = train_data_loader() diff --git a/python/oneflow/test/graph/test_comb1to2d.py b/python/oneflow/test/graph/test_comb1to2d.py index eae8c04ec1de5628bcfa91a73fb4af6c9984e6d9..3dbe82f10b9d888c31aab5f2495cfe2d4d71a429 100644 --- a/python/oneflow/test/graph/test_comb1to2d.py +++ b/python/oneflow/test/graph/test_comb1to2d.py @@ -32,7 +32,6 @@ class _TestModuleDiffHierarchy(nn.Module): flow.sbp.split(0), flow.sbp.split(1), flow.sbp.split(2), - flow.sbp.split(3), ] for sbp1 in sbp_1ds: @@ -63,7 +62,6 @@ class _TestModuleDiffPlacement(nn.Module): flow.sbp.split(0), flow.sbp.split(1), flow.sbp.split(2), - flow.sbp.split(3), ] for sbp1 in sbp_1ds: @@ -101,18 +99,21 @@ class _TestGraph(nn.Graph): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestLazyAllSbpCombinationTesting(flow.unittest.TestCase): def test_lazy_boxing_2d_all_combination(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "0" x = flow.ones( 4, 12, 4, - 12, sbp=[flow.sbp.broadcast, flow.sbp.broadcast], placement=flow.placement( type="cuda", ranks=np.array(range(4)).reshape(2, 2) ), ) + flow.boxing.nccl.enable_use_compute_stream(False) + model_diff_hierarchy = _TestModuleDiffHierarchy() graph_diff_hierarchy = _TestGraph(model_diff_hierarchy) y = graph_diff_hierarchy(x) diff --git a/python/oneflow/test/graph/test_comb2d.py b/python/oneflow/test/graph/test_comb2d.py index 7b746017bdb4d91576486674fee69581ac49cf38..b42d535c2f052f6b0c1c49697392438a5ecfbf40 100644 --- a/python/oneflow/test/graph/test_comb2d.py +++ b/python/oneflow/test/graph/test_comb2d.py @@ -23,6 +23,8 @@ import numpy as np import oneflow.unittest +flow.boxing.nccl.enable_use_compute_stream(False) + class _TestModule(nn.Module): def forward(self, x): @@ -32,7 +34,6 @@ class _TestModule(nn.Module): flow.sbp.split(0), flow.sbp.split(1), flow.sbp.split(2), - flow.sbp.split(3), ] y = x @@ -40,6 +41,9 @@ class _TestModule(nn.Module): for sbp2 in sbp_1ds: for sbp3 in sbp_1ds: + # in this case, use intra group boxing + if sbp1 == sbp3: + continue for sbp4 in sbp_1ds: # (2, 2) -> (2, 2) x = x.to_global(sbp=[sbp1, sbp2]) @@ -62,6 +66,9 @@ class _TestGraph(nn.Graph): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestLazyAllSbpCombinationTesting(flow.unittest.TestCase): def test_lazy_boxing_2d_all_combination(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "0" + model = _TestModule() graph = _TestGraph(model) @@ -69,7 +76,6 @@ class TestLazyAllSbpCombinationTesting(flow.unittest.TestCase): 4, 4, 4, - 4, sbp=[flow.sbp.broadcast, flow.sbp.broadcast], placement=flow.placement( type="cuda", ranks=np.array(range(4)).reshape(2, 2) diff --git a/python/oneflow/test/graph/test_free_tensor_not_in_job.py b/python/oneflow/test/graph/test_free_tensor_not_in_job.py index 327d843e04db6157ba9304c1bb03f445550b6d88..e6d8c8974b8b480781adb275710e32db1c708a6a 100644 --- a/python/oneflow/test/graph/test_free_tensor_not_in_job.py +++ b/python/oneflow/test/graph/test_free_tensor_not_in_job.py @@ -24,7 +24,7 @@ import oneflow.nn as nn def get_bn_graph(): model = nn.BatchNorm1d(6) model.eval() - model.to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) + model.to_global(flow.placement.all("cpu"), flow.sbp.broadcast) class Testgraph(flow.nn.Graph): def __init__(self, model): @@ -42,7 +42,7 @@ def get_bn_graph(): class TestFreeTensorNotInJob(flow.unittest.TestCase): def test_free_tensor_not_in_job(test_case): x = flow.randn(1, 6, 2).to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.split(0) + placement=flow.placement.all("cpu"), sbp=flow.sbp.split(0) ) y = get_bn_graph()(x) test_case.assertEqual(y.size(), (1, 6, 2)) diff --git a/python/oneflow/test/graph/test_gbc1to2d.py b/python/oneflow/test/graph/test_gbc1to2d.py new file mode 100644 index 0000000000000000000000000000000000000000..18c996d1cbc083dff05b87c17aa70ef092edafa9 --- /dev/null +++ b/python/oneflow/test/graph/test_gbc1to2d.py @@ -0,0 +1,95 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import oneflow +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + +import time +import os + + +def _test_general_basic_communication_1d_to_2d(test_case, src_nd_sbp, dst_nd_sbp): + # can not process p in dst + if flow.sbp.partial_sum() in dst_nd_sbp: + return + + # input + placement_x = flow.placement("cuda", ranks=[0, 1, 2]) + placement_y = flow.placement("cuda", ranks=[[3, 0], [1, 2]]) + local_np = np.arange(4 * 14).reshape(4, 14) + x = flow.tensor(local_np, sbp=src_nd_sbp, placement=placement_x) + + # check eager boxing + eager_out = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + test_case.assertTrue(np.array_equal(eager_out.numpy(), x.numpy())) + + # check graph boxing + flow.boxing.nccl.enable_use_compute_stream(False) + + class TestGeneralBasicCommunicationGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + return y + + graph = TestGeneralBasicCommunicationGraph() + y = graph(x) + out_np = y.numpy() + in_np = x.numpy() + test_case.assertTrue(np.array_equal(out_np, in_np)) + + +def gen_nd_sbp_1d(): + sbp_list = [ + flow.sbp.partial_sum(), + flow.sbp.broadcast(), + flow.sbp.split(0), + flow.sbp.split(1), + ] + return sbp_list + + +def gen_nd_sbp_2d(): + nd_sbp_list = [] + for sbp0 in gen_nd_sbp_1d(): + for sbp1 in gen_nd_sbp_1d(): + nd_sbp_list.append([sbp0, sbp1]) + return nd_sbp_list + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGeneralBasicCommunication(flow.unittest.TestCase): + def test_general_basic_communication(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "1" + + arg_dict = OrderedDict() + arg_dict["src_nd_sbp"] = gen_nd_sbp_1d() + arg_dict["dst_nd_sbp"] = gen_nd_sbp_2d() + for arg in GenArgList(arg_dict): + _test_general_basic_communication_1d_to_2d(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_gbc2d.py b/python/oneflow/test/graph/test_gbc2d.py new file mode 100644 index 0000000000000000000000000000000000000000..b52149ef105ed4030180f7aba0833f313bcf3d29 --- /dev/null +++ b/python/oneflow/test/graph/test_gbc2d.py @@ -0,0 +1,106 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import oneflow +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + +import time +import os + + +def _test_general_basic_communication_same_placement(test_case, src_nd_sbp, dst_nd_sbp): + # can not process p in dst + if flow.sbp.partial_sum() in dst_nd_sbp: + return + + # skip src == dst + if src_nd_sbp == dst_nd_sbp: + return + + # in this case, use intra group boxing + if src_nd_sbp[0] == dst_nd_sbp[0]: + return + + # in this case, use inter group boxing + if ( + src_nd_sbp[1] == dst_nd_sbp[1] + and src_nd_sbp[0] != src_nd_sbp[1] + and dst_nd_sbp[0] != dst_nd_sbp[1] + ): + return + + # input + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + local_np = np.arange(4 * 5).reshape(4, 5) + x = flow.tensor(local_np, sbp=src_nd_sbp, placement=placement) + + # check eager boxing + eager_out = x.to_global(sbp=dst_nd_sbp, placement=placement) + test_case.assertTrue(np.array_equal(eager_out.numpy(), x.numpy())) + + # check graph boxing + flow.boxing.nccl.enable_use_compute_stream(False) + + class TestGeneralBasicCommunicationGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = x.to_global(sbp=dst_nd_sbp, placement=placement) + return y + + graph = TestGeneralBasicCommunicationGraph() + y = graph(x) + out_np = y.numpy() + in_np = x.numpy() + test_case.assertTrue(np.array_equal(out_np, in_np)) + + +def gen_nd_sbp(): + sbp_list = [ + flow.sbp.partial_sum(), + flow.sbp.broadcast(), + flow.sbp.split(0), + flow.sbp.split(1), + ] + nd_sbp_list = [] + for sbp0 in sbp_list: + for sbp1 in sbp_list: + nd_sbp_list.append([sbp0, sbp1]) + return nd_sbp_list + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGeneralBasicCommunication(flow.unittest.TestCase): + def test_general_basic_communication(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "1" + + arg_dict = OrderedDict() + arg_dict["src_nd_sbp"] = gen_nd_sbp() + arg_dict["dst_nd_sbp"] = gen_nd_sbp() + for arg in GenArgList(arg_dict): + _test_general_basic_communication_same_placement(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_gbc2to1d.py b/python/oneflow/test/graph/test_gbc2to1d.py new file mode 100644 index 0000000000000000000000000000000000000000..49ae148030bb5cb01b0a09032c0e384d4c5a2158 --- /dev/null +++ b/python/oneflow/test/graph/test_gbc2to1d.py @@ -0,0 +1,95 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import oneflow +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + +import time +import os + + +def _test_general_basic_communication_2d_to_1d(test_case, src_nd_sbp, dst_nd_sbp): + # can not process p in dst + if flow.sbp.partial_sum() == dst_nd_sbp: + return + + # input + placement_x = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + placement_y = flow.placement("cuda", ranks=[0, 3, 4]) + local_np = np.arange(13 * 5).reshape(13, 5) + x = flow.tensor(local_np, sbp=src_nd_sbp, placement=placement_x) + + # check eager boxing + eager_out = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + test_case.assertTrue(np.array_equal(eager_out.numpy(), x.numpy())) + + # check graph boxing + flow.boxing.nccl.enable_use_compute_stream(False) + + class TestGeneralBasicCommunicationGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + return y + + graph = TestGeneralBasicCommunicationGraph() + y = graph(x) + out_np = y.numpy() + in_np = x.numpy() + test_case.assertTrue(np.array_equal(out_np, in_np)) + + +def gen_nd_sbp_1d(): + sbp_list = [ + flow.sbp.partial_sum(), + flow.sbp.broadcast(), + flow.sbp.split(0), + flow.sbp.split(1), + ] + return sbp_list + + +def gen_nd_sbp_2d(): + nd_sbp_list = [] + for sbp0 in gen_nd_sbp_1d(): + for sbp1 in gen_nd_sbp_1d(): + nd_sbp_list.append([sbp0, sbp1]) + return nd_sbp_list + + +@flow.unittest.skip_unless_2n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGeneralBasicCommunication(flow.unittest.TestCase): + def test_general_basic_communication(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "1" + + arg_dict = OrderedDict() + arg_dict["src_nd_sbp"] = gen_nd_sbp_2d() + arg_dict["dst_nd_sbp"] = gen_nd_sbp_1d() + for arg in GenArgList(arg_dict): + _test_general_basic_communication_2d_to_1d(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_gbc2to2d.py b/python/oneflow/test/graph/test_gbc2to2d.py new file mode 100644 index 0000000000000000000000000000000000000000..8716b244a6bb36439ae57fa8137e6f0a19524dd7 --- /dev/null +++ b/python/oneflow/test/graph/test_gbc2to2d.py @@ -0,0 +1,94 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import oneflow +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + +import time +import os + + +def _test_general_basic_communication_2d_to_2d(test_case, src_nd_sbp, dst_nd_sbp): + # can not process p in dst + if flow.sbp.partial_sum() in dst_nd_sbp: + return + + if dst_nd_sbp[0] == dst_nd_sbp[1] and src_nd_sbp[0] == src_nd_sbp[1]: + return + + # input + placement_x = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + placement_y = flow.placement("cuda", ranks=[[0, 3, 4], [2, 5, 6]]) + local_np = np.arange(12 * 12).reshape(12, 12) + x = flow.tensor(local_np, sbp=src_nd_sbp, placement=placement_x) + + # check eager boxing + eager_out = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + test_case.assertTrue(np.array_equal(eager_out.numpy(), x.numpy())) + + # check graph boxing + flow.boxing.nccl.enable_use_compute_stream(False) + + class TestGeneralBasicCommunicationGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, x): + y = x.to_global(sbp=dst_nd_sbp, placement=placement_y) + return y + + graph = TestGeneralBasicCommunicationGraph() + y = graph(x) + out_np = y.numpy() + in_np = x.numpy() + test_case.assertTrue(np.array_equal(out_np, in_np)) + + +def gen_nd_sbp(): + sbp_list = [ + flow.sbp.partial_sum(), + flow.sbp.broadcast(), + flow.sbp.split(0), + flow.sbp.split(1), + ] + nd_sbp_list = [] + for sbp0 in sbp_list: + for sbp1 in sbp_list: + nd_sbp_list.append([sbp0, sbp1]) + return nd_sbp_list + + +@flow.unittest.skip_unless_2n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGeneralBasicCommunication(flow.unittest.TestCase): + def test_general_basic_communication(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "1" + + arg_dict = OrderedDict() + arg_dict["src_nd_sbp"] = gen_nd_sbp() + arg_dict["dst_nd_sbp"] = gen_nd_sbp() + for arg in GenArgList(arg_dict): + _test_general_basic_communication_2d_to_2d(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph.py b/python/oneflow/test/graph/test_graph.py index 3cb7e6b235cbca7a82ed9c634a0d303fb4e30b3b..2a8f114d10a8eed3b07008a0ac835b31412331f9 100644 --- a/python/oneflow/test/graph/test_graph.py +++ b/python/oneflow/test/graph/test_graph.py @@ -22,6 +22,7 @@ import numpy as np import oneflow import oneflow as flow +from oneflow.nn.graph import GraphModule, GraphTensor import oneflow.framework.graph_build_util as graph_build_util import oneflow.framework.scope_util as scope_util import oneflow.unittest @@ -71,19 +72,19 @@ class TestGraph(flow.unittest.TestCase): return self.m(x) g = CustomGraphNestedModule() - test_case.assertTrue(isinstance(g.m, flow.nn.graph.Block)) - test_case.assertEqual(g.m.type, "MODULE") - test_case.assertEqual(g.m.name, "m") - test_case.assertTrue(isinstance(g.m.dummy_buff, flow.nn.graph.Block)) - test_case.assertEqual(g.m.dummy_buff.type, "BUFFER") - test_case.assertTrue(isinstance(g.m.layer.conv1, flow.nn.graph.Block)) - test_case.assertEqual(g.m.layer.conv1.name, "conv1") - test_case.assertEqual(g.m.layer.conv1.name_prefix, "m.layer.") - test_case.assertTrue(isinstance(g.m.layer.conv1.weight, flow.nn.graph.Block)) - test_case.assertEqual(g.m.layer.conv1.weight.type, "PARAMETER") - g.m.layer.conv1._is_executing_forward = True + test_case.assertTrue(isinstance(g.m, flow.nn.graph.Proxy)) + test_case.assertEqual(g.m.to(GraphModule).type, "MODULE") + test_case.assertEqual(g.m.to(GraphModule).name, "m") + test_case.assertTrue(isinstance(g.m.dummy_buff, flow.nn.graph.Proxy)) + test_case.assertEqual(g.m.dummy_buff.to(GraphTensor).type, "BUFFER") + test_case.assertTrue(isinstance(g.m.layer.conv1, flow.nn.graph.Proxy)) + test_case.assertEqual(g.m.layer.conv1.to(GraphModule).name, "conv1") + test_case.assertEqual(g.m.layer.conv1.to(GraphModule).name_prefix, "m.layer.") + test_case.assertTrue(isinstance(g.m.layer.conv1.weight, flow.nn.graph.Proxy)) + test_case.assertEqual(g.m.layer.conv1.weight.to(GraphTensor).type, "PARAMETER") + g.m.layer.conv1.to(GraphModule)._is_executing_forward = True test_case.assertTrue(isinstance(g.m.layer.conv1.weight, flow.Tensor)) - g.m.layer.conv1._is_executing_forward = False + g.m.layer.conv1.to(GraphModule)._is_executing_forward = False test_case.assertEqual(g.m.layer.conv1.kernel_size, (5, 5)) z = g.build(x) test_case.assertTrue(np.array_equal(y.numpy(), z.numpy())) @@ -191,7 +192,8 @@ class TestGraph(flow.unittest.TestCase): scope = scope_util.current_scope() scope_proto = graph_build_util.scope_to_proto(scope) test_case.assertEqual( - scope_proto.parent_scope_symbol_id, self.prev_scope.symbol_id + scope_proto.parent_scope_symbol_id, + self.to(flow.nn.graph.GraphModule).prev_scope.symbol_id, ) ck_bool = scope_proto.attr_name2attr_value["checkpointing"] test_case.assertEqual(ck_bool.WhichOneof("value"), None) @@ -199,7 +201,10 @@ class TestGraph(flow.unittest.TestCase): "pipeline_stage_id_hint" ].at_int64 test_case.assertEqual(stage_int, 1) - name = self.name_prefix + self.name + name = ( + self.to(flow.nn.graph.GraphModule).name_prefix + + self.to(flow.nn.graph.GraphModule).name + ) prefixes = [] for prefix in scope_proto.scope_op_name_prefixes: prefixes.append(prefix) @@ -207,7 +212,7 @@ class TestGraph(flow.unittest.TestCase): test_case.assertEqual(name, name_in_scope) b = self.dummy_buff dummy_buff_scope_proto = graph_build_util.scope_to_proto( - self._buffers["dummy_buff"].scope + self._buffers["dummy_buff"].to(flow.nn.graph.GraphTensor).scope ) test_case.assertEqual( dummy_buff_scope_proto.parent_scope_symbol_id, scope.symbol_id @@ -236,14 +241,15 @@ class TestGraph(flow.unittest.TestCase): def __init__(self): super().__init__() self.m = m - self.m.layer0.config.stage_id = 0 - self.m.layer0.config.activation_checkpointing = True - self.m.layer1.config.stage_id = 1 + self.m.layer0.to(GraphModule).set_stage(stage_id=0) + self.m.layer0.to(GraphModule).activation_checkpointing = True + self.m.layer1.to(GraphModule).set_stage(stage_id=1) def build(self, x, y): return self.m(x, y) g = CustomGraphBlockScope() + print(g) x = np.ones((1, 1, 10, 10)) x = flow.tensor(x, dtype=flow.float32) y = np.ones((16, 36)) @@ -261,7 +267,7 @@ class TestGraph(flow.unittest.TestCase): def __init__(self): super().__init__() self.linear = linear - # creat optimizer in nn.Graph and add parameter from ModuleBlock + # creat optimizer in nn.Graph and add parameter from ProxyModule self.add_optimizer( flow.optim.SGD(self.linear.parameters(), lr=0.001, momentum=0.9) ) diff --git a/python/oneflow/test/graph/test_graph_activation_checkpoint.py b/python/oneflow/test/graph/test_graph_activation_checkpoint.py index cee58c93dcce87b3917133929ea76b0b50a45b74..d8f1f2e942f5b173960baa2356ae07a7712a3c30 100644 --- a/python/oneflow/test/graph/test_graph_activation_checkpoint.py +++ b/python/oneflow/test/graph/test_graph_activation_checkpoint.py @@ -25,6 +25,7 @@ import oneflow as flow import oneflow.framework.graph_build_util as graph_build_util import oneflow.framework.scope_util as scope_util import oneflow.unittest +from oneflow.nn.graph import GraphModule @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @@ -71,8 +72,8 @@ class TestGraphActivationCheckpoint(flow.unittest.TestCase): self.loss_fn = loss_fn # Add an optimizer self.add_optimizer(optimizer) - self.model.config.activation_checkpointing = True - self.model1.config.activation_checkpointing = True + self.model.to(GraphModule).activation_checkpointing = True + self.model1.to(GraphModule).activation_checkpointing = True def build(self, x, y): y_pred = self.model(x) @@ -93,9 +94,7 @@ class TestGraphActivationCheckpoint(flow.unittest.TestCase): find_check_point = False for value in op.user_conf.input.values(): if ( - re.search( - "OneFlow-System-Checkpointing-Fake-Fw-Op", str(value), re.I - ) + re.search("Sys-Checkpointing-Fake-Fw-Op", str(value), re.I) is not None ): find_check_point = True @@ -104,15 +103,13 @@ class TestGraphActivationCheckpoint(flow.unittest.TestCase): # Check having insert identity op and first fake op of a segment has indentity grad as it's ctrl in op if ( re.search( - "OneFlow-System-Checkpointing-Fake-Fw-Op_model.model.0-matmul*", - op.name, - re.I, + "Sys-Checkpointing-Fake-Fw-Op_model.model.0-matmul*", op.name, re.I, ) is not None ): find_ctrl = False for name in op.ctrl_in_op_name: - if re.search("identity-.*_grad", str(name), re.I) is not None: + if re.search("identity", str(name), re.I) is not None: find_ctrl = True print(name) test_case.assertTrue(find_ctrl) diff --git a/python/oneflow/test/graph/test_graph_asymmetric_io.py b/python/oneflow/test/graph/test_graph_asymmetric_io.py index d93edff8baa485dd37201b512d408994c74e6cef..67dfc231733b0e5d71c1a377811b8c9d1a65e277 100644 --- a/python/oneflow/test/graph/test_graph_asymmetric_io.py +++ b/python/oneflow/test/graph/test_graph_asymmetric_io.py @@ -23,13 +23,13 @@ import oneflow.unittest @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() -class TestConsistentAsymmetricGraph(oneflow.unittest.TestCase): +class TestGlobalAsymmetricGraph(oneflow.unittest.TestCase): def test_global_asymmetric_graph_gpu(test_case): Broadcast = [flow.sbp.broadcast] Placement_rank_0 = flow.placement("cuda", ranks=[0]) Placement_rank_1 = flow.placement("cuda", ranks=[1]) - class MyConsistentAsymmetricModule(flow.nn.Module): + class MyGlobalAsymmetricModule(flow.nn.Module): def __init__(self): super().__init__() self.linear1 = flow.nn.Linear(3, 8, False) @@ -74,7 +74,7 @@ class TestConsistentAsymmetricGraph(oneflow.unittest.TestCase): local_out = my_local_module(local_x, local_y) # print("eager_local_out: ", local_out) - my_module = MyConsistentAsymmetricModule() + my_module = MyGlobalAsymmetricModule() x = local_x.to_global(placement=Placement_rank_0, sbp=Broadcast) y = local_y.to_global(placement=Placement_rank_0, sbp=Broadcast) diff --git a/python/oneflow/test/graph/test_graph_block.py b/python/oneflow/test/graph/test_graph_block.py index 8a316fd7987741e5577c38196386ef24710f7c97..cd8c1468f687878376f69bd6160374e815a013c7 100644 --- a/python/oneflow/test/graph/test_graph_block.py +++ b/python/oneflow/test/graph/test_graph_block.py @@ -15,10 +15,13 @@ limitations under the License. """ import os import unittest +import types +import warnings import numpy as np import oneflow as flow +import oneflow.nn as nn import oneflow.unittest import oneflow.framework.graph_build_util as graph_build_util import oneflow.framework.scope_util as scope_util @@ -54,6 +57,33 @@ class TestGraphBlock(flow.unittest.TestCase): out = g(x) test_case.assertTrue(np.array_equal(x.numpy(), out.numpy())) + def test_module_has_special_attr(test_case): + class CustomModuleHasSpecialAttr(flow.nn.Module): + def __init__(self): + super().__init__() + self.config = 1 + self.name = "test_name" + + def forward(self, x): + test_case.assertEqual(self.config, 1) + test_case.assertEqual(self.name, "test_name") + test_case.assertEqual(self.to(nn.graph.GraphModule).name, "m") + return x + + class CustomGraphHasSpecialAttr(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = CustomModuleHasSpecialAttr() + + def build(self, x): + return self.m(x) + + g = CustomGraphHasSpecialAttr() + x = np.ones((10, 10)) + x = flow.tensor(x, dtype=flow.float32) + out = g(x) + test_case.assertTrue(np.array_equal(x.numpy(), out.numpy())) + def test_block_with_parameter(test_case): device = "cuda" linear = flow.nn.Linear(3, 8) @@ -87,7 +117,7 @@ class TestGraphBlock(flow.unittest.TestCase): return self._forward_impl(x) def _forward_impl(self, x): - test_case.assertTrue(isinstance(self.linear, flow.nn.graph.Block)) + test_case.assertTrue(isinstance(self.linear, flow.nn.graph.Proxy)) return self.linear(x) class LinearTrainGraph(flow.nn.Graph): @@ -107,6 +137,65 @@ class TestGraphBlock(flow.unittest.TestCase): linear_t_g(x) + def test_block_with_not_registered_module(test_case): + device = "cuda" + linear = flow.nn.Linear(3, 8) + linear = linear.to(device) + flow.nn.init.constant_(linear.weight, 2.068758) + flow.nn.init.constant_(linear.bias, 0.23) + + x = flow.tensor( + [ + [-0.94630778, -0.83378579, -0.87060891], + [2.0289922, -0.28708987, -2.18369248], + [0.35217619, -0.67095644, -1.58943879], + [0.08086036, -1.81075924, 1.20752494], + [0.8901075, -0.49976737, -1.07153746], + [-0.44872912, -1.07275683, 0.06256855], + [-0.22556897, 0.74798368, 0.90416439], + [0.48339456, -2.32742195, -0.59321527], + ], + dtype=flow.float32, + device=device, + requires_grad=False, + ) + + class CustomModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.dict = {"lin": linear} + + def forward(self, x): + return self._forward_impl(x) + + def _forward_impl(self, x): + test_case.assertTrue(isinstance(self.dict["lin"], flow.nn.Module)) + return self.dict["lin"](x) + + class LinearTrainGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = CustomModule() + + def build(self, x): + out = self.m(x) + out = out.sum() + return out + + linear_t_g = LinearTrainGraph() + + with warnings.catch_warnings(record=True) as w: + # Here will print: + # UserWarning: Linear(in_features=3, out_features=8, bias=True) is called in a nn.Graph, but not registered into a nn.Graph. + linear_t_g(x) + + test_case.assertTrue(len(w) == 1) + test_case.assertTrue(issubclass(w[-1].category, UserWarning)) + test_case.assertTrue( + "is called in a nn.Graph, but not registered into a nn.Graph" + in str(w[-1].message) + ) + def test_block_with_seq_container(test_case): class SubModule0(flow.nn.Module): def __init__(self): @@ -137,7 +226,7 @@ class TestGraphBlock(flow.unittest.TestCase): def __init__(self): super().__init__() self.linears = flow.nn.Sequential(*list_of_m) - self.linears.config.activation_checkpointing = True + self.linears.to(nn.graph.GraphModule).activation_checkpointing = True def build(self, x): x = self.linears(x) @@ -185,9 +274,11 @@ class TestGraphBlock(flow.unittest.TestCase): super().__init__() self.linears = flow.nn.ModuleList(list_of_m) # NOTE: ModuleList doesn't have config. - # self.linears.config.activation_checkpointing = True + # self.linears.to(GraphModule).activation_checkpointing = True for i, _ in enumerate(self.linears): - self.linears[i].config.activation_checkpointing = True + self.linears[i].to( + nn.graph.GraphModule + ).activation_checkpointing = True def build(self, x): # ModuleList can act as an iterable, or be indexed using ints @@ -206,6 +297,39 @@ class TestGraphBlock(flow.unittest.TestCase): # print(module_list_g) test_case.assertTrue(np.array_equal(output_m.numpy(), output_g.numpy())) + def test_module_list_slice(test_case): + class ModuleListSlice(nn.Module): + def __init__(self,): + super().__init__() + linear1 = nn.Linear(5, 5, bias=False) + linear2 = nn.Linear(5, 5, bias=False) + linear3 = nn.Linear(5, 5, bias=False) + self.modulelist = nn.ModuleList([linear1, linear2, linear3]) + + def forward(self, x): + sliced_m = self.modulelist[1:] + test_case.assertEqual(len(sliced_m), 2) + y = sliced_m[1](x) + return y + + class GraphModuleListSlice(nn.Graph): + def __init__(self, m): + super().__init__() + self.m = m + + def build(self, x): + return self.m(x) + + in_tensor = flow.randn(5, 5) + + m = ModuleListSlice() + eager_out = m(in_tensor) + + g = GraphModuleListSlice(m) + graph_out = g(in_tensor) + + test_case.assertTrue(np.array_equal(eager_out.numpy(), graph_out.numpy())) + def test_block_with_dict_container(test_case): class SubModule0(flow.nn.Module): def __init__(self, out): @@ -239,9 +363,11 @@ class TestGraphBlock(flow.unittest.TestCase): self.linears = flow.nn.ModuleDict(dict_of_m) # NOTE: ModuleDict doesn't have config. - # self.linears.config.activation_checkpointing = True + # self.linears.to(GraphModule).activation_checkpointing = True for k, _ in self.linears.items(): - self.linears[k].config.activation_checkpointing = True + self.linears[k].to( + nn.graph.GraphModule + ).activation_checkpointing = True def build(self, x): # ModuleDict can act as an iterable, or get using key @@ -259,6 +385,63 @@ class TestGraphBlock(flow.unittest.TestCase): # print(module_dict_g) test_case.assertTrue(np.array_equal(output_m.numpy(), output_g.numpy())) + def test_block_with_dict_container_nto1(test_case): + class SubModule0(flow.nn.Module): + def __init__(self, out): + super().__init__() + self.linear = flow.nn.Linear(10, out, False) + + def forward(self, x): + if graph_build_util.lazy_mode.is_enabled(): + scope = scope_util.current_scope() + scope_proto = graph_build_util.scope_to_proto(scope) + ck_bool = scope_proto.attr_name2attr_value["checkpointing"].at_bool + test_case.assertEqual(ck_bool, True) + out = self.linear(x) + return out + + sub_m = SubModule0(10) + dict_of_m = {"0": sub_m, "1": sub_m} + + class ModuleDictModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.linears = flow.nn.ModuleDict(dict_of_m) + + def forward(self, x): + x = self.linears["0"](x) + x = self.linears["1"](x) + return x + + class ModuleDictGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linears = flow.nn.ModuleDict(dict_of_m) + + # NOTE: ModuleDict doesn't have config. + # self.linears.to(GraphModule).activation_checkpointing = True + for k, _ in self.linears.items(): + self.linears[k].to( + nn.graph.GraphModule + ).activation_checkpointing = True + + def build(self, x): + # ModuleDict can act as an iterable, or get using key + x = self.linears["0"](x) + x = self.linears["1"](x) + return x + + module_dict_m = ModuleDictModule() + module_dict_g = ModuleDictGraph() + + input = flow.tensor(np.random.randn(4, 10), dtype=flow.float32) + output_m = module_dict_m(input) + output_g = module_dict_g(input) + print(module_dict_g) + + # print(module_dict_g) + test_case.assertTrue(np.array_equal(output_m.numpy(), output_g.numpy())) + def test_block_with_para_list_container(test_case): list_of_p = [flow.nn.Parameter(flow.randn(10, 10)) for i in range(2)] @@ -330,6 +513,60 @@ class TestGraphBlock(flow.unittest.TestCase): # print(para_dict_g) test_case.assertTrue(np.array_equal(output_m.numpy(), output_g.numpy())) + def test_mixin_module(test_case): + class ModuleMixin(flow.nn.Module): + def __init__(self): + super().__init__() + self._dtype = flow.float32 + + @property + def dtype(self): + return self._dtype + + class ConfigMixin: + def hello_from_cfg(self): + return "hello_from_cfg" + + @property + def property_from_cfg(self): + return 128 + + class MixedModule(ModuleMixin, ConfigMixin): + def __init__(self): + super().__init__() + + def forward(self, x): + test_case.assertEqual(self.dtype, flow.float32) + test_case.assertEqual(self.hello_from_cfg(), "hello_from_cfg") + test_case.assertEqual(self.property_from_cfg, 128) + return x + + mixedm = MixedModule() + + class GraphConfigMixin(object): + @property + def hello_from_graph(self): + return "hello_from_gcfg" + + def mixin_get_name(self): + return self.name + + class MixinGraph(flow.nn.Graph, GraphConfigMixin): + def __init__(self): + super().__init__() + self.m = mixedm + + def build(self, x): + test_case.assertEqual(self.hello_from_graph, "hello_from_gcfg") + test_case.assertEqual(self.mixin_get_name(), self.name) + return self.m(x) + + g = MixinGraph() + x = np.ones((10, 10)) + x = flow.tensor(x, dtype=flow.float32) + out = g(x) + test_case.assertTrue(np.array_equal(x.numpy(), out.numpy())) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/graph/test_graph_clip_grad_norm.py b/python/oneflow/test/graph/test_graph_clip_grad_norm.py index ee8effd43cd280fdf9d86327918eb6af98e2a1fd..f4d701b98426f54d8d5b23490189f36f6bbbe81b 100644 --- a/python/oneflow/test/graph/test_graph_clip_grad_norm.py +++ b/python/oneflow/test/graph/test_graph_clip_grad_norm.py @@ -18,6 +18,7 @@ import unittest import numpy as np import oneflow as flow +from oneflow.nn.graph import GraphModule import oneflow.unittest @@ -69,8 +70,8 @@ class MyGraph(flow.nn.Graph): and module2.param.is_global and module1.param.placement != module2.param.placement ): - self.m1.config.stage_id = 0 - self.m2.config.stage_id = 1 + self.m1.to(GraphModule).set_stage(0) + self.m2.to(GraphModule).set_stage(1) if optimizer is not None: self.add_optimizer(optimizer) @@ -258,15 +259,19 @@ def _compare_with_eager( if parallel_mode is None: loss_b = graph_loss.numpy() - grad1_b = graph.m1.origin.param.numpy() - grad2_b = graph.m2.origin.param.numpy() + grad1_b = graph.m1.to(flow.nn.Module).param.numpy() + grad2_b = graph.m2.to(flow.nn.Module).param.numpy() else: ranks = np.array(range(flow.env.get_world_size())) placement = flow.placement(device, ranks) loss_b = graph_loss.to_global(placement, flow.sbp.broadcast).to_local().numpy() - grad1_b = graph.m1.origin.param.to_global(placement, flow.sbp.broadcast) + grad1_b = graph.m1.to(flow.nn.Module).param.to_global( + placement, flow.sbp.broadcast + ) grad1_b = grad1_b.to_local().numpy() - grad2_b = graph.m2.origin.param.to_global(placement, flow.sbp.broadcast) + grad2_b = graph.m2.to(flow.nn.Module).param.to_global( + placement, flow.sbp.broadcast + ) grad2_b = grad2_b.to_local().numpy() # compare diff --git a/python/oneflow/test/graph/test_graph_debug.py b/python/oneflow/test/graph/test_graph_debug.py index b51bd460a6f16b99b0a95ad3377dc82c7d89a475..ccc6ffb5fff454b7347f1972a82305d080b3ff2d 100644 --- a/python/oneflow/test/graph/test_graph_debug.py +++ b/python/oneflow/test/graph/test_graph_debug.py @@ -20,6 +20,7 @@ import numpy as np import oneflow as flow import oneflow.unittest +from oneflow.nn.graph import GraphModule rank = flow.env.get_rank() @@ -48,11 +49,11 @@ def _graph_debug(test_case, v_level=0, ranks=None, max_py_stack_depth=2): -1 in rank_list or rank in rank_list ) and v_level >= 0: # v_level == -1 means debug mode is closed test_case.assertTrue(d_g._debug) - test_case.assertTrue(d_g.m._debug) + test_case.assertTrue(d_g.m.to(GraphModule)._debug) print(f"ranks {ranks} rank {rank} debug is opened.") else: test_case.assertTrue(not d_g._debug) - test_case.assertTrue(not d_g.m._debug) + test_case.assertTrue(not d_g.m.to(GraphModule)._debug) print(f"ranks {ranks} rank {rank} debug is closed.") diff --git a/python/oneflow/test/graph/test_graph_free_eager_tensor.py b/python/oneflow/test/graph/test_graph_free_eager_tensor.py index 67d2db1363905059056e28a3febfe2904f3effc5..4196f7523a53604a64e5aef827e209674b234965 100644 --- a/python/oneflow/test/graph/test_graph_free_eager_tensor.py +++ b/python/oneflow/test/graph/test_graph_free_eager_tensor.py @@ -181,14 +181,14 @@ class TestGraphWithEagerTensorCaught(oneflow.unittest.TestCase): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() -class ConsistentFreeEagerTensorGraphTestCase(oneflow.unittest.TestCase): +class GlobalFreeEagerTensorGraphTestCase(oneflow.unittest.TestCase): def test_global_eager_tensor_to(test_case): rank = flow.env.get_rank() placement = flow.placement("cpu", ranks=[0, 1]) t_l = flow.tensor([1.0, 2.0], dtype=flow.float32) t = t_l.to_global(placement=placement, sbp=flow.sbp.broadcast) - class ConsistentEagerTensorToModule(flow.nn.Module): + class GlobalEagerTensorToModule(flow.nn.Module): def __init__(self): super().__init__() @@ -198,9 +198,9 @@ class ConsistentFreeEagerTensorGraphTestCase(oneflow.unittest.TestCase): t = t.to("cuda") return t - e_m = ConsistentEagerTensorToModule() + e_m = GlobalEagerTensorToModule() - class ConsistentEagerTensorToGraph(flow.nn.Graph): + class GlobalEagerTensorToGraph(flow.nn.Graph): def __init__(self): super().__init__() self.e_m = e_m @@ -208,7 +208,7 @@ class ConsistentFreeEagerTensorGraphTestCase(oneflow.unittest.TestCase): def build(self): return self.e_m() - e_g = ConsistentEagerTensorToGraph() + e_g = GlobalEagerTensorToGraph() graph_out = e_g().to_local() print("g ", graph_out.numpy()) test_case.assertTrue( diff --git a/python/oneflow/test/graph/test_graph_inplace_add.py b/python/oneflow/test/graph/test_graph_inplace_add.py index 336929ef7ae6d1ad71c899dd1446857425dc822e..3ccc70eb89e3126af7278bfbc342e670b5618e2b 100644 --- a/python/oneflow/test/graph/test_graph_inplace_add.py +++ b/python/oneflow/test/graph/test_graph_inplace_add.py @@ -50,7 +50,7 @@ class TestLocalInplace(oneflow.unittest.TestCase): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() -class TestConsistentInplace(oneflow.unittest.TestCase): +class TestGlobalInplace(oneflow.unittest.TestCase): def test_graph_inplace_gpu(test_case): x = flow.randn( 10, diff --git a/python/oneflow/test/graph/test_graph_io_check.py b/python/oneflow/test/graph/test_graph_io_check.py index 2d2216676047dad65d402dcaf5537893a578002e..44c8a14b841d98bdd6d6cc2e5ff96d7efcdae3d7 100644 --- a/python/oneflow/test/graph/test_graph_io_check.py +++ b/python/oneflow/test/graph/test_graph_io_check.py @@ -13,6 +13,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ +import warnings +from collections import OrderedDict +from dataclasses import dataclass, fields +from typing import Any, Tuple +from collections import OrderedDict import os import unittest import sys @@ -23,6 +28,91 @@ import oneflow as flow import oneflow.unittest from oneflow.framework.tensor import Tensor, TensorTuple from oneflow.nn.graph.util import ArgsTree +from oneflow.nn.graph import GraphModule + + +class BaseOutput(OrderedDict): + def __post_init__(self): + class_fields = fields(self) + + # Safety and consistency checks + if not len(class_fields): + raise ValueError(f"{self.__class__.__name__} has no fields.") + + first_field = getattr(self, class_fields[0].name) + other_fields_are_none = all( + getattr(self, field.name) is None for field in class_fields[1:] + ) + + if other_fields_are_none and isinstance(first_field, dict): + for key, value in first_field.items(): + self[key] = value + else: + for field in class_fields: + v = getattr(self, field.name) + if v is not None: + self[field.name] = v + + def __delitem__(self, *args, **kwargs): + raise Exception( + f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance." + ) + + def setdefault(self, *args, **kwargs): + raise Exception( + f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance." + ) + + def pop(self, *args, **kwargs): + raise Exception( + f"You cannot use ``pop`` on a {self.__class__.__name__} instance." + ) + + def update(self, *args, **kwargs): + raise Exception( + f"You cannot use ``update`` on a {self.__class__.__name__} instance." + ) + + def __getitem__(self, k): + if isinstance(k, str): + inner_dict = {k: v for (k, v) in self.items()} + if ( + self.__class__.__name__ + in ["StableDiffusionPipelineOutput", "ImagePipelineOutput"] + and k == "sample" + ): + warnings.warn( + "The keyword 'samples' is deprecated and will be removed in version 0.4.0. Please use `.images` or" + " `'images'` instead.", + DeprecationWarning, + ) + return inner_dict["images"] + return inner_dict[k] + else: + return self.to_tuple()[k] + + def __setattr__(self, name, value): + if name in self.keys() and value is not None: + # Don't call self.__setitem__ to avoid recursion errors + super().__setitem__(name, value) + super().__setattr__(name, value) + + def __setitem__(self, key, value): + # Will raise a KeyException if needed + super().__setitem__(key, value) + # Don't call self.__setattr__ to avoid recursion errors + super().__setattr__(key, value) + + def to_tuple(self) -> Tuple[Any]: + """ + Convert self to a tuple containing all the attributes/keys that are not `None`. + """ + return tuple(self[k] for k in self.keys()) + + +@dataclass +class CustomDataClass(BaseOutput): + sample: flow.Tensor @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @@ -69,6 +159,32 @@ class TestGraphIOCheck(flow.unittest.TestCase): test_case.assertEqual(ret[0][2], "mapped_str") test_case.assertEqual(id(ret[1]["kw"]), id(t4)) + def test_custom_class(test_case): + x = np.ones((2, 2)) + x = flow.tensor(x, dtype=flow.float32) + ordered_d = CustomDataClass(sample=x) + + def fn(*args, **kwargs): + inp = (args, kwargs) + print("origin: ", inp) + + args_tree = ArgsTree(inp, True, "Graph_0", None) + + for (name, arg) in args_tree.iter_named_nodes(): + print(name, repr(arg)) + + def leaf_fn(arg): + if isinstance(arg.value(), dict): + return "replaced" + return arg.value() + + m_v = args_tree.map_leaf(leaf_fn) + print("mapped:", m_v) + return m_v[0], m_v[1] + + ret = fn(ordered_d) + print(ret) + def test_non_tensor_types_of_module(test_case): class CustomModuleIOCheck(flow.nn.Module): def __init__(self): @@ -81,7 +197,7 @@ class TestGraphIOCheck(flow.unittest.TestCase): def __init__(self): super().__init__() self.m = CustomModuleIOCheck() - self.m.config.activation_checkpointing = True + self.m.to(GraphModule).activation_checkpointing = True def build(self, t, lt, n, **kwargs): rt, rlt, n, ri, rs, dic = self.m(t, lt, n, 1, "2", **kwargs) diff --git a/python/oneflow/test/graph/test_graph_linear.py b/python/oneflow/test/graph/test_graph_linear.py index bb95b371d1d6d8cb7c077f21d126a1a9d06b019b..aabfd5791d6faa920859fe2551ace6a1a8244639 100644 --- a/python/oneflow/test/graph/test_graph_linear.py +++ b/python/oneflow/test/graph/test_graph_linear.py @@ -54,6 +54,7 @@ def _test_linear_graph(test_case, device): return self.my_linear(x) linear_g = LinearGraph() + linear_g.debug(0) of_lazy_out = linear_g(x) test_case.assertTrue(np.array_equal(of_lazy_out.numpy(), of_eager_out.numpy())) @@ -82,7 +83,7 @@ def _test_linear_graph_func(test_case, device): np_out = np.matmul(input_arr, np_weight) test_case.assertTrue(np.allclose(of_eager_out.numpy(), np_out, 1e-05, 1e-05)) - @flow.nn.Graph.to_graph + @flow.nn.Graph.trace def linear_func(x): return linear(x) diff --git a/python/oneflow/test/graph/test_graph_linear_train.py b/python/oneflow/test/graph/test_graph_linear_train.py index 01841051c788b51fe3773445ad5b165ea4d2e85d..0a1b080c35f617566a7d467792b480dbcb754a72 100644 --- a/python/oneflow/test/graph/test_graph_linear_train.py +++ b/python/oneflow/test/graph/test_graph_linear_train.py @@ -100,7 +100,10 @@ def _test_linear_train_graph(test_case, device): def one_iter(): of_graph_out = linear_t_g(x) print(linear_t_g.linear) - return of_graph_out.numpy(), linear_t_g.linear.weight.origin.numpy() + return ( + of_graph_out.numpy(), + linear_t_g.linear.weight.to(flow.Tensor).numpy(), + ) check_list = [] for i in range(iter_num): diff --git a/python/oneflow/test/graph/test_graph_lr_scale.py b/python/oneflow/test/graph/test_graph_lr_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..3d9c68d94c5e22cc878425e1cbbc1bfbeb1bf7e5 --- /dev/null +++ b/python/oneflow/test/graph/test_graph_lr_scale.py @@ -0,0 +1,201 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict + + +class _Block(flow.nn.Module): + def __init__(self, feats, device=None, placement=None): + super().__init__() + ones = flow.ones(feats) + if placement is not None: + ones = ones.to_global(placement=placement, sbp=flow.sbp.broadcast()) + elif device is not None: + ones = ones.to(device) + self.param = flow.nn.Parameter(ones) + + def forward(self, x): + return x + self.param + + +class _MyModule(flow.nn.Module): + def __init__(self, feats, depth, device=None, placement=None): + super().__init__() + self.layers = flow.nn.ModuleList( + [ + _Block(feats=feats, device=device, placement=placement) + for i in range(depth) + ] + ) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + return x + + +class _MyGraph(flow.nn.Graph): + def __init__(self, model, optimizer, lr_scheduler): + super().__init__() + self.m = model + self.add_optimizer(optimizer, lr_sch=lr_scheduler) + + def build(self, input): + out = self.m(input) + out.sum().backward() + return out + + +def _lrs_param_groups(model, base_scale): + param_groups = [] + for i, layer in enumerate(model.layers): + this_scale = base_scale ** (i + 1) + param_group = {"params": layer.parameters(), "lr_scale": this_scale} + param_groups.append(param_group) + + return param_groups + + +def _rand_input(shape, device=None, placement=None, requires_grad=False): + input = flow.tensor(np.random.rand(*shape).astype(np.float32)) + if placement is not None: + input = input.to_global(placement=placement, sbp=flow.sbp.split(0)) + elif device is not None: + input = input.to(device) + if requires_grad: + input.requires_grad_() + return input + + +def _test_lrs(test_case, **kwargs): + verbose = kwargs.pop("verbose", False) + if verbose: + print(f"#### kwargs={kwargs}") + + batch_size = kwargs.pop("batch_size", 4) + feats = kwargs.pop("feats", 768) + depth = kwargs.pop("depth", 3) + lr = kwargs.pop("lr", 1.0) + base_scale = kwargs.pop("base_scale", 0.1) + device_type = kwargs.pop("device_type", "cuda") + placement = kwargs.pop("placement", None) + graph_mode = kwargs.pop("graph_mode", True) + + model = _MyModule(feats=feats, depth=depth, device=device_type, placement=placement) + param_groups = _lrs_param_groups(model, base_scale=base_scale) + optimizer = flow.optim.SGD(param_groups, lr=lr) + lr_scheduler = flow.optim.lr_scheduler.ConstantLR( + optimizer, factor=1.0, total_iters=100 + ) + model_graph = _MyGraph(model, optimizer, lr_scheduler) + + input = _rand_input( + (batch_size, feats), device=device_type, placement=placement, requires_grad=True + ) + t_params = [] + + if graph_mode: + for i in range(depth): + origin_p = model.layers[i].param.numpy() + init_grad = float(batch_size * flow.env.get_world_size()) + t_params.append(origin_p - float(init_grad) * lr * (base_scale ** (i + 1))) + ret = model_graph(input) + else: + for i in range(depth): + origin_p = model.layers[i].param.numpy() + init_grad = float(batch_size * flow.env.get_world_size()) + t_params.append(origin_p - float(init_grad) * lr) + + optimizer.zero_grad() + ret = model(input) + ret.sum().backward() + optimizer.step() + lr_scheduler.step() + + if verbose: + print("#### input") + print(input) + # sync + np_ret = ret.numpy() + print("#### ret") + print(np_ret) + + for i in range(depth): + np_param = model.layers[i].param.numpy() + print(f"#### layer{i} param") + print(np_param) + + print("#### grad") + print(input.grad) + + for i in range(depth): + np_param = model.layers[i].param.numpy() + t_param = t_params[i] + test_case.assertTrue( + np.allclose(np_param, t_param), f"\n{np_param}\n vs. \n{t_param}" + ) + + +@flow.unittest.skip_unless_1n1d() +class LRScaleTest(flow.unittest.TestCase): + def test_lr_scale(self): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [2, 4] + arg_dict["feats"] = [10, 13] + arg_dict["depth"] = [3, 4] + arg_dict["lr"] = [1.0, 0.1] + arg_dict["base_scale"] = [0.1, 0.2] + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["is_global"] = [True, False] + arg_dict["graph_mode"] = [True, False] + + for arg in GenArgDict(arg_dict): + is_global = arg.pop("is_global", True) + if is_global: + device_type = arg.pop("device_type", "cuda") + arg["placement"] = flow.placement.all(device_type) + + # arg["verbose"] = True + _test_lrs(self, **arg) + + +@flow.unittest.skip_unless_1n2d() +class LRScaleParallelTest(flow.unittest.TestCase): + def test_lr_scale_parallel(self): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [2, 4] + arg_dict["feats"] = [5, 10] + arg_dict["depth"] = [3, 4] + arg_dict["lr"] = [1.0, 0.1] + arg_dict["base_scale"] = [0.1, 0.2] + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["graph_mode"] = [True, False] + + for arg in GenArgDict(arg_dict): + device_type = arg.pop("device_type", "cuda") + arg["placement"] = flow.placement.all(device_type) + # arg["verbose"] = True + _test_lrs(self, **arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_lr_scheduler.py b/python/oneflow/test/graph/test_graph_lr_scheduler.py index dbb13e561fad13deb31c6e7f4262f49f94522aa8..93302e57509e43394995fa0023467bf47ad1a851 100644 --- a/python/oneflow/test/graph/test_graph_lr_scheduler.py +++ b/python/oneflow/test/graph/test_graph_lr_scheduler.py @@ -111,10 +111,12 @@ def _compare_graph_lr_scheduler_with_eager(test_case, **kwargs): ret = graph(_rand_input()) ret.numpy() # sync for graph finishing - lr_log_file = glob.glob("log/*/train_step2lr.csv")[0] + pid = os.getpid() + lr_log_file = glob.glob(f"log/*/{pid}-train_step2lr.csv")[0] lrs = _get_graph_lrs_from_log(lr_log_file) lrs = lrs[:iters] + optimizer.zero_grad(set_to_none=True) eager_lrs = [lr_scheduler.get_last_lr()[0]] for _ in range(iters): ret = module(_rand_input()) @@ -155,6 +157,17 @@ class TestGraphLRSchedulerWithEager(flow.unittest.TestCase): total_iters=10, ) + def test_linear_lr_end_factor(self): + _compare_graph_lr_scheduler_with_eager( + self, + base_lr=0.1, + iters=20, + lr_scheduler=flow.optim.lr_scheduler.LinearLR, + start_factor=0.1, + end_factor=0.9, + total_iters=10, + ) + def test_step_lr(self): _compare_graph_lr_scheduler_with_eager( self, diff --git a/python/oneflow/test/graph/test_graph_lr_with_warmup.py b/python/oneflow/test/graph/test_graph_lr_with_warmup.py index 974fefa2085f21206c8fe8d62d3e67b66d68e3de..9c3d4ba4a1efd64c45c480f089b9d76ebb050661 100644 --- a/python/oneflow/test/graph/test_graph_lr_with_warmup.py +++ b/python/oneflow/test/graph/test_graph_lr_with_warmup.py @@ -110,7 +110,10 @@ def _test_linear_graph_train_with_lr_sch( def one_iter(): of_graph_out = linear_t_g(x) - return of_graph_out.numpy(), linear_t_g.linear.weight.origin.numpy() + return ( + of_graph_out.numpy(), + linear_t_g.linear.weight.to(flow.Tensor).numpy(), + ) check_list = [] for i in range(iter_num): diff --git a/python/oneflow/test/graph/test_graph_lrs.py b/python/oneflow/test/graph/test_graph_lrs.py index 76fcd4c60bc0a502923ada93fb1d8228a79af794..1c13cfb14317f3533f45bf4bef7ffb80491f337b 100644 --- a/python/oneflow/test/graph/test_graph_lrs.py +++ b/python/oneflow/test/graph/test_graph_lrs.py @@ -111,7 +111,10 @@ def _test_linear_graph_train_with_lr_sch( def one_iter(): of_graph_out = linear_t_g(x) - return of_graph_out.numpy(), linear_t_g.linear.weight.origin.numpy() + return ( + of_graph_out.numpy(), + linear_t_g.linear.weight.to(flow.Tensor).numpy(), + ) check_list = [] for i in range(iter_num): diff --git a/python/oneflow/test/graph/test_graph_masked_fill.py b/python/oneflow/test/graph/test_graph_masked_fill.py new file mode 100644 index 0000000000000000000000000000000000000000..2a461a5bca0432bc4baba6ece9e06fb77b16bd21 --- /dev/null +++ b/python/oneflow/test/graph/test_graph_masked_fill.py @@ -0,0 +1,67 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import random + +import oneflow as flow +from oneflow import nn +import oneflow.unittest +from test_util import generate_graph + + +@flow.unittest.skip_unless_1n1d() +class TestMaskedFillGraph(flow.unittest.TestCase): + def test_masked_fill_graph(test_case): + k = random.randint(1, 10) + model = nn.Sequential(nn.Linear(k, k)) + optimizer = flow.optim.SGD(model.parameters(), lr=1e-3) + loss_fn = nn.MSELoss() + + class MaskedFillGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + self.model = model + self.loss_fn = loss_fn + self.add_optimizer(optimizer) + + def build(self, input, mask): + output = self.model(input) + output = flow.masked_fill(output, mask > 0.5, 0.5) + loss = self.loss_fn(output, input) + loss.backward() + return loss + + input = flow.randn(k, k).requires_grad_() + mask = flow.randn(k, k) + model = MaskedFillGraph() + return model(input, mask) + + def test_masked_fill_by_generate_graph(test_case): + k = random.randint(1, 10) + input = flow.randn(k, k) + mask = flow.randn(k, k) + + masked_fill_fn = lambda: flow.masked_fill(input, mask > 0.5, 0.5) + y_eager = masked_fill_fn() + masked_fill_graph = generate_graph(masked_fill_fn) + y_lazy = masked_fill_graph() + test_case.assertTrue(np.array_equal(y_eager.numpy(), y_lazy.numpy())) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_non_contiguous_tensors.py b/python/oneflow/test/graph/test_graph_non_contiguous_tensors.py index 395b1411dda5a2791f0958ed18c4992ae0ffb285..f10ac6d3207709a8518fd1a26c5447a2f6dfbbb1 100644 --- a/python/oneflow/test/graph/test_graph_non_contiguous_tensors.py +++ b/python/oneflow/test/graph/test_graph_non_contiguous_tensors.py @@ -66,9 +66,11 @@ def _test_graph_non_contiguous_tensors(test_case, device): graph_contiguous_tensors = GraphTestContiguousTensors() graph_non_contiguous_tensors = GraphTestNonContiguousTensors() - test_case.assertTrue(graph_contiguous_tensors.model.weight.origin.is_contiguous()) + test_case.assertTrue( + graph_contiguous_tensors.model.weight.to(flow.Tensor).is_contiguous() + ) test_case.assertFalse( - graph_non_contiguous_tensors.model.weight.origin.is_contiguous() + graph_non_contiguous_tensors.model.weight.to(flow.Tensor).is_contiguous() ) inp = flow.tensor( diff --git a/python/oneflow/test/graph/test_graph_normal_inplace.py b/python/oneflow/test/graph/test_graph_normal_inplace.py new file mode 100644 index 0000000000000000000000000000000000000000..15770a239883f2b1bdf8d3d14249e5038ad270ef --- /dev/null +++ b/python/oneflow/test/graph/test_graph_normal_inplace.py @@ -0,0 +1,111 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import numpy as np +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +_fn_param_local = { + "normal": lambda data: flow.normal( + size=data.shape, mean=0.0, std=1.0, out=data + ), # NOTE(lixiang): source op that can be inplaced. +} + + +_fn_param_global = { + "normal": lambda data, placement, sbp: flow.normal( + size=data.shape, mean=0.0, std=1.0, out=data, placement=placement, sbp=sbp, + ), +} + + +def _test_data_local(test_case, device, fn): + + data_1 = flow.zeros([16, 64, 128, 128]).to(device) + data_2 = flow.zeros([16, 64, 128, 128]).to(device) + + class NormalGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + fn(data_1).to(device) + return data_1 + + model = NormalGraph() + lazy_x = model() + fn(data_2) + + test_case.assertTrue(lazy_x.numpy().sum() != 0) + test_case.assertTrue(data_2.numpy().sum() != 0) + + +def _test_data_global(test_case, data_1, data_2, placement, sbp, fn): + class GlobalNormalGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + flow.manual_seed(233) + fn(data_1, placement, sbp) + return data_1 + + model = GlobalNormalGraph() + lazy_x = model() + + flow.manual_seed(233) + fn(data_2, placement, sbp) + + test_case.assertTrue( + np.array_equal(lazy_x.to_local().numpy(), data_2.to_local().numpy()) + ) + + +class TestNormalOpInplaceData(flow.unittest.TestCase): + @oneflow.unittest.skip_unless_1n1d() + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_normal_op_data_local_with_eager_and_lazy(test_case): + + for device in ["cuda", "cpu"]: + for _, fn in _fn_param_local.items(): + _test_data_local(test_case, device, fn=fn) + + @globaltest + def test_normal_op_data_consistent_with_eager_and_lazy(test_case): + + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2, except_partial_sum=True): + + data_1 = flow.empty([8, 64, 128, 128]).to_global( + placement=placement, sbp=sbp + ) + data_2 = flow.empty([8, 64, 128, 128]).to_global( + placement=placement, sbp=sbp + ) + + for _, fn in _fn_param_global.items(): + _test_data_global(test_case, data_1, data_2, placement, sbp, fn=fn) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_optim_adadelta.py b/python/oneflow/test/graph/test_graph_optim_adadelta.py new file mode 100644 index 0000000000000000000000000000000000000000..1a8f2247f1497558e6ed8a2c2575e26211280526 --- /dev/null +++ b/python/oneflow/test/graph/test_graph_optim_adadelta.py @@ -0,0 +1,253 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +import copy + +from test_util import GenArgList +from optimizer_test_util import clip_grad_norm_np + +import oneflow as flow + + +def compare_with_numpy_adadelta( + test_case, + device, + x_shape, + learning_rate, + train_iters, + rho, + eps, + maximize, + weight_decay, +): + random_grad_seq = [] + for _ in range(train_iters): + random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + init_value = np.random.uniform(size=x_shape).astype(np.float32) + + class CustomModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.para0 = flow.nn.Parameter( + flow.Tensor(init_value, device=flow.device(device)) + ) + + def forward(self, mask): + return self.para0 * mask + + simp_module = CustomModule() + simp_module.to(device) + simp_module.train() + + adadelta0 = flow.optim.Adadelta( + [ + { + "params": simp_module.parameters(), + "lr": learning_rate, + "weight_decay": weight_decay, + } + ], + rho=rho, + eps=eps, + maximize=maximize, + ) + + class CustomAdadeltaGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = simp_module + self.add_optimizer(adadelta0) + + def build(self, mask_tensor): + loss = flow.sum(self.m(mask_tensor)) + loss.backward() + return loss + + of_res_list = [] + adadelta_graph = CustomAdadeltaGraph() + + for i in range(train_iters): + mask_tensor = flow.tensor( + random_grad_seq[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + adadelta_x = adadelta_graph(mask_tensor) + + of_res_list.append(copy.copy(simp_module.para0.numpy())) + + np_res_list = [] + + def train_by_numpy(): + x = init_value + square_avgs = np.zeros_like(x) + acc_deltas = np.zeros_like(x) + + def np_train_one_iter(grad): + grad = grad if not maximize else -grad + grad = grad + weight_decay * x + new_square_avgs = square_avgs * rho + (1.0 - rho) * grad * grad + std = np.sqrt(new_square_avgs + eps) + delta = np.sqrt(acc_deltas + eps) / std * grad + new_acc_deltas = acc_deltas * rho + delta * delta * (1 - rho) + param = x - learning_rate * delta + return (param, new_square_avgs, new_acc_deltas) + + for i in range(1, train_iters + 1): + (x, square_avgs, acc_deltas) = np_train_one_iter(random_grad_seq[i - 1]) + np_res_list.append(x) + return x + + train_by_numpy() + + test_case.assertTrue(np.allclose(of_res_list, np_res_list, rtol=1e-4, atol=1e-4)) + + +def compare_with_numpy_adadelta_clip_grad( + test_case, + device, + x_shape, + learning_rate, + train_iters, + rho, + eps, + maximize, + weight_decay, + clip_grad_max_norm, + clip_grad_norm_type, +): + random_grad_seq = [] + for _ in range(train_iters): + random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + init_value = np.random.uniform(size=x_shape).astype(np.float32) + + class CustomModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.para0 = flow.nn.Parameter( + flow.tensor(init_value, device=flow.device(device)) + ) + + def forward(self, mask): + return self.para0 * mask + + simp_module = CustomModule() + simp_module.to(device) + simp_module.train() + + adadelta0 = flow.optim.Adadelta( + [ + { + "params": simp_module.parameters(), + "lr": learning_rate, + "weight_decay": weight_decay, + "clip_grad_max_norm": clip_grad_max_norm, + "clip_grad_norm_type": clip_grad_norm_type, + } + ], + rho=rho, + eps=eps, + maximize=maximize, + ) + + class CustomAdadeltaGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = simp_module + self.add_optimizer(adadelta0) + + def build(self, mask_tensor): + loss = flow.sum(self.m(mask_tensor)) + loss.backward() + return loss + + of_res_list = [] + adadelta_graph = CustomAdadeltaGraph() + + for i in range(train_iters): + mask_tensor = flow.tensor( + random_grad_seq[i], requires_grad=False, device=flow.device(device) + ) + adadelta_x = adadelta_graph(mask_tensor) + + of_res_list.append(copy.copy(simp_module.para0.numpy())) + + np_res_list = [] + + def train_by_numpy(): + x = init_value + square_avgs = np.zeros_like(x) + acc_deltas = np.zeros_like(x) + + def np_train_one_iter(grad): + total_norm, grad = clip_grad_norm_np( + grad, clip_grad_max_norm, clip_grad_norm_type + ) + grad = grad if not maximize else -grad + grad = grad + weight_decay * x + new_square_avgs = square_avgs * rho + (1.0 - rho) * grad * grad + std = np.sqrt(new_square_avgs + eps) + delta = np.sqrt(acc_deltas + eps) / std * grad + new_acc_deltas = acc_deltas * rho + delta * delta * (1 - rho) + param = x - learning_rate * delta + return (param, new_square_avgs, new_acc_deltas) + + for i in range(1, train_iters + 1): + (x, square_avgs, acc_deltas) = np_train_one_iter(random_grad_seq[i - 1]) + np_res_list.append(x) + return x + + train_by_numpy() + test_case.assertTrue(np.allclose(of_res_list, np_res_list, rtol=1e-4, atol=1e-4)) + + +@flow.unittest.skip_unless_1n1d() +class TestAdadelta(flow.unittest.TestCase): + def test_adadelta(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["x_shape"] = [(10,)] + arg_dict["learning_rate"] = [1, 1e-3] + arg_dict["train_iters"] = [10] + arg_dict["rho"] = [0.9] + arg_dict["eps"] = [1e-6] + arg_dict["maximize"] = [False] + arg_dict["weight_decay"] = [0.1] + + for arg in GenArgList(arg_dict): + compare_with_numpy_adadelta(test_case, *arg) + + def test_adadelta_clip_grad(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["x_shape"] = [(10,)] + arg_dict["learning_rate"] = [1, 1e-3] + arg_dict["train_iters"] = [10] + arg_dict["rho"] = [0.9] + arg_dict["eps"] = [1e-6] + arg_dict["maximize"] = [False] + arg_dict["weight_decay"] = [0.1] + arg_dict["clip_grad_max_norm"] = [1.0] + arg_dict["clip_grad_norm_type"] = [2.0] + for arg in GenArgList(arg_dict): + compare_with_numpy_adadelta_clip_grad(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_optim_sgd.py b/python/oneflow/test/graph/test_graph_optim_sgd.py index 2d2adc240f94caa71073c7a403ffbf4bebf9db87..ef20b17c397cb995156e1fca9fb1b615744c6cd4 100644 --- a/python/oneflow/test/graph/test_graph_optim_sgd.py +++ b/python/oneflow/test/graph/test_graph_optim_sgd.py @@ -25,7 +25,16 @@ import oneflow as flow def compare_with_numpy_sgd( - test_case, device, x_shape, learning_rate, train_iters, momentum, weight_decay + test_case, + device, + x_shape, + learning_rate, + train_iters, + momentum, + dampening, + nesterov, + maximize, + weight_decay, ): random_grad_seq = [] for _ in range(train_iters): @@ -51,10 +60,13 @@ def compare_with_numpy_sgd( { "params": simp_module.parameters(), "lr": learning_rate, - "momentum": momentum, "weight_decay": weight_decay, } ], + momentum=momentum, + dampening=dampening, + nesterov=nesterov, + maximize=maximize, ) class CustomSGDGraph(flow.nn.Graph): @@ -85,8 +97,23 @@ def compare_with_numpy_sgd( def np_train_one_iter(grad): grad = grad + weight_decay * x - v = momentum * vt - learning_rate * grad - param = x + v + if momentum > 0.0: + next_momentum = momentum * vt + (1 - dampening) * grad + v = next_momentum + + if nesterov: + grad += momentum * next_momentum + else: + grad = next_momentum + + alpha = -learning_rate + if maximize: + alpha = learning_rate + next_model = x + alpha * grad + param = next_model + else: + v = learning_rate * grad + param = x - v return (param, v) for i in range(train_iters): @@ -103,6 +130,9 @@ def compare_with_numpy_sgd_clip_grad( x_shape, learning_rate, momentum, + dampening, + nesterov, + maximize, weight_decay, clip_grad_max_norm, clip_grad_norm_type, @@ -132,12 +162,15 @@ def compare_with_numpy_sgd_clip_grad( { "params": simp_module.parameters(), "lr": learning_rate, - "momentum": momentum, "weight_decay": weight_decay, "clip_grad_max_norm": clip_grad_max_norm, "clip_grad_norm_type": clip_grad_norm_type, } - ] + ], + momentum=momentum, + dampening=dampening, + nesterov=nesterov, + maximize=maximize, ) class CustomSGDGraph(flow.nn.Graph): @@ -171,8 +204,23 @@ def compare_with_numpy_sgd_clip_grad( grad, clip_grad_max_norm, clip_grad_norm_type ) grad = grad + weight_decay * x - v = momentum * vt - learning_rate * grad - param = x + v + if momentum > 0.0: + next_momentum = momentum * vt + (1 - dampening) * grad + v = next_momentum + + if nesterov: + grad += momentum * next_momentum + else: + grad = next_momentum + + alpha = -learning_rate + if maximize: + alpha = learning_rate + next_model = x + alpha * grad + param = next_model + else: + v = learning_rate * grad + param = x - v return (param, v) for i in range(train_iters): @@ -185,7 +233,7 @@ def compare_with_numpy_sgd_clip_grad( @flow.unittest.skip_unless_1n1d() -class TestCpuSGD(flow.unittest.TestCase): +class TestGraphSGD(flow.unittest.TestCase): def test_sgd(test_case): arg_dict = OrderedDict() arg_dict["device"] = ["cpu", "cuda"] @@ -193,6 +241,9 @@ class TestCpuSGD(flow.unittest.TestCase): arg_dict["learning_rate"] = [1, 1e-3] arg_dict["train_iters"] = [10] arg_dict["momentum"] = [0.9, 0.8] + arg_dict["dampening"] = [0.0, 0.9] + arg_dict["nesterov"] = [True, False] + arg_dict["maximize"] = [True, False] arg_dict["weight_decay"] = [0.001, 0.0] for arg in GenArgList(arg_dict): compare_with_numpy_sgd(test_case, *arg) @@ -203,6 +254,9 @@ class TestCpuSGD(flow.unittest.TestCase): arg_dict["x_shape"] = [(10,)] arg_dict["learning_rate"] = [1, 0.1] arg_dict["momentum"] = [0.0, 0.9] + arg_dict["dampening"] = [0.0, 0.9] + arg_dict["nesterov"] = [True, False] + arg_dict["maximize"] = [True, False] arg_dict["weight_decay"] = [0.0, 0.9] arg_dict["clip_grad_max_norm"] = [1.0] arg_dict["clip_grad_norm_type"] = [2.0] diff --git a/python/oneflow/test/graph/test_graph_pipeline.py b/python/oneflow/test/graph/test_graph_pipeline.py index 097bb691614a87ca10fd96ee3453f702dfcb47d7..a0a3e0ef41bf98b1566d6f7c0d6b9dd4911a01a8 100644 --- a/python/oneflow/test/graph/test_graph_pipeline.py +++ b/python/oneflow/test/graph/test_graph_pipeline.py @@ -20,6 +20,7 @@ import numpy as np import oneflow as flow import oneflow.unittest +from oneflow.nn.graph import GraphModule rank = flow.env.get_rank() @@ -140,10 +141,10 @@ def _train_with_graph(iter_num=3): self.train_data_loader = train_data_loader self.pp_m = pp_m # Set different module's stage id to hint the graph preparing right num of buffers in pipeline. - self.pp_m.stage_0_m.config.stage_id = 0 - self.pp_m.stage_1_m.config.stage_id = 1 - self.pp_m.stage_2_m.config.stage_id = 2 - self.pp_m.stage_3_m.config.stage_id = 3 + self.pp_m.stage_0_m.to(GraphModule).set_stage(0) + self.pp_m.stage_1_m.to(GraphModule).set_stage(1) + self.pp_m.stage_2_m.to(GraphModule).set_stage(2) + self.pp_m.stage_3_m.to(GraphModule).set_stage(3) self.mseloss = flow.nn.MSELoss("sum") self.add_optimizer(sgd) # Let graph to do gradient accumulatioin, pipline execution depends on gradient accumulatioin. diff --git a/python/oneflow/test/graph/test_graph_pipeline_delay.py b/python/oneflow/test/graph/test_graph_pipeline_delay.py index 6ac93d754c535ddc2ddfec2f3176c7362e3c5001..02d2fd066ae77d9d4476dd386859a8604f774f4a 100644 --- a/python/oneflow/test/graph/test_graph_pipeline_delay.py +++ b/python/oneflow/test/graph/test_graph_pipeline_delay.py @@ -20,6 +20,7 @@ import numpy as np import oneflow as flow import oneflow.unittest +from oneflow.nn.graph import GraphModule def _test_graph_pipeline_delay_output(test_case): @@ -67,8 +68,8 @@ def _test_graph_pipeline_delay_output(test_case): def __init__(self): super().__init__() self.pp_m = pp_m - self.pp_m.layer_0.config.stage_id = 0 - self.pp_m.layer_1.config.stage_id = 1 + self.pp_m.layer_0.to(GraphModule).stage_id = 0 + self.pp_m.layer_1.to(GraphModule).stage_id = 1 self.config.set_gradient_accumulation_steps(4) self.add_optimizer(of_sgd) diff --git a/python/oneflow/test/graph/test_graph_reshape_acc.py b/python/oneflow/test/graph/test_graph_reshape_acc.py index efdc26eb3c7fafbb246f2986b64866825843a8c3..1832d79ba5e6ba7f3d1e60789896270ec04499c9 100644 --- a/python/oneflow/test/graph/test_graph_reshape_acc.py +++ b/python/oneflow/test/graph/test_graph_reshape_acc.py @@ -19,6 +19,7 @@ import numpy as np import oneflow as flow import oneflow.unittest +from oneflow.nn.graph import GraphModule def _test_graph_reshape_acc(test_case): @@ -69,8 +70,8 @@ def _test_graph_reshape_acc(test_case): def __init__(self): super().__init__() self.pp_m = pp_m - self.pp_m.layer_0.config.stage_id = 0 - self.pp_m.layer_1.config.stage_id = 1 + self.pp_m.layer_0.to(GraphModule).set_stage(0) + self.pp_m.layer_1.to(GraphModule).set_stage(1) self.loss_fn = flow.nn.CrossEntropyLoss() self.config.set_gradient_accumulation_steps(2) self.add_optimizer(sgd) diff --git a/python/oneflow/test/graph/test_graph_save_load_global_b_s.py b/python/oneflow/test/graph/test_graph_save_load_global_b_s.py new file mode 100644 index 0000000000000000000000000000000000000000..d90a6ba558a54871bd9b8d01b8180bca18f3e8c8 --- /dev/null +++ b/python/oneflow/test/graph/test_graph_save_load_global_b_s.py @@ -0,0 +1,821 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import numpy as np +import tempfile + +import oneflow as flow +import oneflow.unittest +from oneflow.nn.graph import GraphModule + + +def _test_linear_graph_save_load_global_broadcast( + test_case, model_tensor_placement, model_file_placement +): + """Data parallelism on 2 ranks. + """ + B = flow.sbp.broadcast + S0 = flow.sbp.split(0) + + def train_with_graph(call_cnt=0, state_dict_dir=None, last_state_dict=None): + linear = flow.nn.Linear(3, 8) + linear = linear.to(flow.device(model_tensor_placement.type)) + flow.nn.init.constant_(linear.weight, 2.068758) + flow.nn.init.constant_(linear.bias, 0.23) + linear.to_global(placement=model_tensor_placement, sbp=B) + of_sgd = flow.optim.SGD(linear.parameters(), lr=0.001, momentum=0.9) + + x = flow.tensor( + [ + [-0.94630778, -0.83378579, -0.87060891], + [2.0289922, -0.28708987, -2.18369248], + [0.35217619, -0.67095644, -1.58943879], + [0.08086036, -1.81075924, 1.20752494], + [0.8901075, -0.49976737, -1.07153746], + [-0.44872912, -1.07275683, 0.06256855], + [-0.22556897, 0.74798368, 0.90416439], + [0.48339456, -2.32742195, -0.59321527], + ], + dtype=flow.float32, + device=flow.device(model_tensor_placement.type), + requires_grad=False, + ) + x = x.to_global(placement=model_tensor_placement, sbp=S0) + + class LinearTrainGraphGlobal(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear = linear + self.add_optimizer(of_sgd) + + def build(self, x): + out = self.linear(x) + out = out.sum() + out.backward() + return out + + linear_t_g = LinearTrainGraphGlobal() + cur_rank = flow.env.get_rank() + if call_cnt == 1: + if cur_rank in model_file_placement.ranks: + local_state_dict = flow.load(state_dict_dir) + else: + local_state_dict = None + + global_state_dict = flow.utils.global_view.to_global( + local_state_dict, placement=model_file_placement, sbp=B + ) + linear_t_g.load_state_dict(global_state_dict) + + if cur_rank == 0: # Ignore None on rank 1 + # Check state in module has been loaded. + test_case.assertTrue( + np.array_equal( + global_state_dict["linear"]["weight"].to_local().numpy(), + linear.weight.to_local().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + global_state_dict["linear"]["bias"].to_local().numpy(), + linear.bias.to_local().numpy(), + ) + ) + # Get state dict before compile is allowed. + init_state_dict = linear_t_g.state_dict() + + of_graph_out = linear_t_g(x) + iter0_state_dict = linear_t_g.state_dict() + + # Load the model and check + if call_cnt == 1: + # Check additional variable state initialized in job has been loaded. + # TrainStep's placement is only on rank 0, so it needs to be broadcast to all ranks before check. + cur_train_step = ( + iter0_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=B) + .to_local() + .numpy()[0] + ) + test_case.assertEqual(3, cur_train_step) + test_case.assertTrue( + cur_train_step + == last_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=B) + .to_local() + .numpy()[0] + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["linear"]["weight"].to_local().numpy(), + last_state_dict["linear"]["weight"].to_local().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["linear"]["bias"].to_local().numpy(), + last_state_dict["linear"]["bias"].to_local().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["linear.weight-momentum"].to_local().numpy(), + last_state_dict["linear.weight-momentum"].to_local().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["linear.bias-momentum"].to_local().numpy(), + last_state_dict["linear.bias-momentum"].to_local().numpy(), + ) + ) + + of_graph_out = linear_t_g(x) + of_graph_out.numpy() + iter1_state_dict = linear_t_g.state_dict() + + # Save the model + if call_cnt == 0: + # Transfer the state dict to model_file_placement + model_file_state_dict = flow.utils.global_view.to_global( + iter1_state_dict, placement=model_file_placement, sbp=B + ) + + # Get the local component and save it on model_file_placement's rank(s) + if cur_rank in model_file_placement.ranks: + iter1_local_dict = flow.utils.global_view.to_local( + model_file_state_dict + ) + flow.save(iter1_local_dict, state_dict_dir) + + of_graph_out = linear_t_g(x) + of_graph_out.numpy() + iter2_state_dict = linear_t_g.state_dict() + return iter2_state_dict + + rank_id = flow.env.get_rank() + with tempfile.TemporaryDirectory( + prefix="graph_save_load_global_" + str(rank_id) + ) as state_dict_dir: + iter2_state_dict = train_with_graph(0, state_dict_dir) + train_with_graph(1, state_dict_dir, iter2_state_dict) + + +def _test_graph_save_load_global_split_2( + test_case, model_tensor_placement, model_file_placement +): + """Pipeline parallelism on 2 ranks. + """ + P0 = flow.placement(model_tensor_placement.type, ranks=[0]) + P1 = flow.placement(model_tensor_placement.type, ranks=[1]) + BROADCAST = flow.sbp.broadcast + + def get_sbp(state_dict, tensor): + if tensor is state_dict["System-Train-TrainStep"]: + return BROADCAST + if tensor is state_dict["module_pipeline"]["m_stage1.linear.weight"]: + return flow.sbp.split(1) + if tensor is state_dict["module_pipeline"]["m_stage1.linear.bias"]: + return BROADCAST + return flow.sbp.split(0) + + class Stage0Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(16, 8) + self.relu = flow.nn.ReLU() + + def forward(self, x): + return self.relu(self.linear(x)) + + class Stage1Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(8, 1) + + def forward(self, x): + return self.linear(x) + + class PipelineModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.m_stage0 = Stage0Module() + self.m_stage1 = Stage1Module() + + self.m_stage0.to_global(placement=P0, sbp=BROADCAST) + self.m_stage1.to_global(placement=P1, sbp=BROADCAST) + + def forward(self, x): + out_stage0 = self.m_stage0(x) + in_stage1 = out_stage0.to_global(placement=P1, sbp=BROADCAST) + out_stage1 = self.m_stage1(in_stage1) + return out_stage1 + + class PipelineGraph(flow.nn.Graph): + def __init__(self, module_pipleine): + super().__init__() + self.module_pipeline = module_pipleine + self.module_pipeline.m_stage0.to(GraphModule).set_stage(0, P0) + self.module_pipeline.m_stage1.to(GraphModule).set_stage(1, P1) + self.config.set_gradient_accumulation_steps(2) + self.add_optimizer( + flow.optim.SGD(self.module_pipeline.parameters(), lr=0.001) + ) + + def build(self, x): + out = self.module_pipeline(x) + out = out.sum() + out.backward() + return out + + def train_with_graph(call_cnt=0, state_dict_dir=None, last_state_dict=None): + # A fixed input with shape [2, 16] + x = flow.tensor( + [ + [ + 0.4286, + 0.7402, + 0.4161, + 0.6103, + 0.7394, + 1.1330, + -0.2311, + -0.1013, + 0.8537, + 0.9757, + -0.9842, + 0.3839, + -0.5551, + -0.8832, + 0.7820, + 0.7421, + ], + [ + -0.1581, + -1.0319, + 1.8430, + 0.3576, + 0.7288, + -0.6912, + 0.9966, + 1.0840, + -1.1760, + 1.5683, + -0.2098, + -1.6439, + -2.7049, + 0.1949, + 1.6377, + 0.0745, + ], + ], + dtype=oneflow.float32, + placement=P0, + sbp=BROADCAST, + ) + + module_pipleine = PipelineModule() + graph_model = PipelineGraph(module_pipleine) + cur_rank = flow.env.get_rank() + + if call_cnt == 1: + if cur_rank in model_file_placement.ranks: + local_state_dict = flow.load(state_dict_dir) + else: + local_state_dict = None + + # test sbp_for_special_keys + global_state_dict = flow.utils.global_view.to_global( + local_state_dict, placement=model_file_placement, sbp=get_sbp, + ) + graph_model.load_state_dict(global_state_dict) + + if cur_rank == 0: + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + module_pipleine.m_stage0.linear.weight.to_local().numpy()[ + :4 + ], # The first half of shape (8, 16) + ) + ) + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + module_pipleine.m_stage0.linear.bias.to_local().numpy()[ + :4 + ], # The first half of shape (8,) + ) + ) + if cur_rank == 1: + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + module_pipleine.m_stage1.linear.weight.to_local().numpy()[ + :, 4: + ], # The second half of shape (1, 8) + ) + ) + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + module_pipleine.m_stage1.linear.bias.to_local().numpy(), + ) + ) + + graph_model(x) + iter0_state_dict = graph_model.state_dict() + + if call_cnt == 1: + # TrainStep + cur_train_step = ( + iter0_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=BROADCAST) + .to_local() + .numpy()[0] + ) + test_case.assertEqual(3, cur_train_step) + test_case.assertTrue( + cur_train_step + == last_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=BROADCAST) + .to_local() + .numpy()[0] + ) + + # Weight & bias + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + ) + ) + + graph_model(x) + iter1_state_dict = graph_model.state_dict() + + if call_cnt == 0: + model_file_state_dict = flow.utils.global_view.to_global( + iter1_state_dict, placement=model_file_placement, sbp=get_sbp, + ) + if flow.env.get_rank() in model_file_placement.ranks: + flow.save( + flow.utils.global_view.to_local(model_file_state_dict), + state_dict_dir, + ) + + graph_model(x) + iter2_state_dict = graph_model.state_dict() + return iter2_state_dict + + rank_id = flow.env.get_rank() + with tempfile.TemporaryDirectory( + prefix="graph_save_load_global_" + str(rank_id) + ) as state_dict_dir: + iter2_state_dict = train_with_graph(0, state_dict_dir) + train_with_graph(1, state_dict_dir, iter2_state_dict) + + +def _test_graph_save_load_global_split_4( + test_case, model_tensor_placement, model_file_placement +): + """Pipeline parallelism on 4 ranks. + """ + P0 = flow.placement(model_tensor_placement.type, ranks=[0]) + P1 = flow.placement(model_tensor_placement.type, ranks=[1]) + P2 = flow.placement(model_tensor_placement.type, ranks=[2]) + P3 = flow.placement(model_tensor_placement.type, ranks=[3]) + BROADCAST = flow.sbp.broadcast + + def get_sbp(state_dict, tensor): + if tensor is state_dict["System-Train-TrainStep"]: + return BROADCAST + if tensor is state_dict["module_pipeline"]["m_stage3.linear.weight"]: + return flow.sbp.split(1) + if tensor is state_dict["module_pipeline"]["m_stage3.linear.bias"]: + return BROADCAST + return flow.sbp.split(0) + + class Stage0Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(16, 8) + self.relu = flow.nn.ReLU() + + def forward(self, x): + return self.relu(self.linear(x)) + + class Stage1Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(8, 4) + self.relu = flow.nn.ReLU() + + def forward(self, x): + return self.relu(self.linear(x)) + + class Stage2Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(4, 2) + self.relu = flow.nn.ReLU() + + def forward(self, x): + return self.relu(self.linear(x)) + + class Stage3Module(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(2, 1) + + def forward(self, x): + return self.linear(x) + + class PipelineModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.m_stage0 = Stage0Module() + self.m_stage1 = Stage1Module() + self.m_stage2 = Stage2Module() + self.m_stage3 = Stage3Module() + + self.m_stage0.to_global(placement=P0, sbp=BROADCAST) + self.m_stage1.to_global(placement=P1, sbp=BROADCAST) + self.m_stage2.to_global(placement=P2, sbp=BROADCAST) + self.m_stage3.to_global(placement=P3, sbp=BROADCAST) + + def forward(self, x): + out_stage0 = self.m_stage0(x) + + in_stage1 = out_stage0.to_global(placement=P1, sbp=BROADCAST) + out_stage1 = self.m_stage1(in_stage1) + + in_stage2 = out_stage1.to_global(placement=P2, sbp=BROADCAST) + out_stage2 = self.m_stage2(in_stage2) + + in_stage3 = out_stage2.to_global(placement=P3, sbp=BROADCAST) + out_stage3 = self.m_stage3(in_stage3) + + return out_stage3 + + class PipelineGraph(flow.nn.Graph): + def __init__(self, module_pipleine): + super().__init__() + self.module_pipeline = module_pipleine + self.module_pipeline.m_stage0.to(GraphModule).set_stage(0, P0) + self.module_pipeline.m_stage1.to(GraphModule).set_stage(1, P1) + self.module_pipeline.m_stage2.to(GraphModule).set_stage(2, P2) + self.module_pipeline.m_stage3.to(GraphModule).set_stage(3, P3) + self.config.set_gradient_accumulation_steps(2) + self.add_optimizer( + flow.optim.SGD(self.module_pipeline.parameters(), lr=0.001) + ) + + def build(self, x): + out = self.module_pipeline(x) + out = out.sum() + out.backward() + return out + + def train_with_graph(call_cnt=0, state_dict_dir=None, last_state_dict=None): + # A fixed input with shape [2, 16] + x = flow.tensor( + [ + [ + 0.4286, + 0.7402, + 0.4161, + 0.6103, + 0.7394, + 1.1330, + -0.2311, + -0.1013, + 0.8537, + 0.9757, + -0.9842, + 0.3839, + -0.5551, + -0.8832, + 0.7820, + 0.7421, + ], + [ + -0.1581, + -1.0319, + 1.8430, + 0.3576, + 0.7288, + -0.6912, + 0.9966, + 1.0840, + -1.1760, + 1.5683, + -0.2098, + -1.6439, + -2.7049, + 0.1949, + 1.6377, + 0.0745, + ], + ], + dtype=flow.float32, + placement=P0, + sbp=BROADCAST, + ) + + module_pipleine = PipelineModule() + graph_model = PipelineGraph(module_pipleine) + cur_rank = flow.env.get_rank() + + if call_cnt == 1: + if cur_rank in model_file_placement.ranks: + local_state_dict = flow.load(state_dict_dir) + else: + local_state_dict = None + + # test sbp_for_special_keys + global_state_dict = flow.utils.global_view.to_global( + local_state_dict, placement=model_file_placement, sbp=get_sbp, + ) + graph_model.load_state_dict(global_state_dict) + + if cur_rank == 0: + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + module_pipleine.m_stage0.linear.weight.to_local().numpy()[ + :4 + ], # The first half of shape (8, 16) + ) + ) + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + module_pipleine.m_stage0.linear.bias.to_local().numpy()[ + :4 + ], # The first half of shape (8,) + ) + ) + if cur_rank == 1: + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + module_pipleine.m_stage1.linear.weight.to_local().numpy()[ + 2:, : + ], # The second half of shape (4, 8) + ) + ) + test_case.assertTrue( + np.array_equal( + global_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + module_pipleine.m_stage1.linear.bias.to_local().numpy()[ + 2: + ], # The second half if shape (4,) + ) + ) + + graph_model(x) + iter0_state_dict = graph_model.state_dict() + + if call_cnt == 1: + # TrainStep + cur_train_step = ( + iter0_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=BROADCAST) + .to_local() + .numpy()[0] + ) + test_case.assertEqual(3, cur_train_step) + test_case.assertTrue( + cur_train_step + == last_state_dict["System-Train-TrainStep"] + .to_global(placement=model_tensor_placement, sbp=BROADCAST) + .to_local() + .numpy()[0] + ) + + # Weight & bias + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage0.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage0.linear.bias"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage1.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage1.linear.bias"] + .to_local() + .numpy(), + ) + ) + + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage2.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage2.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage2.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage2.linear.bias"] + .to_local() + .numpy(), + ) + ) + + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage3.linear.weight"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage3.linear.weight"] + .to_local() + .numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + iter0_state_dict["module_pipeline"]["m_stage3.linear.bias"] + .to_local() + .numpy(), + last_state_dict["module_pipeline"]["m_stage3.linear.bias"] + .to_local() + .numpy(), + ) + ) + + graph_model(x) + iter1_state_dict = graph_model.state_dict() + + if call_cnt == 0: + model_file_state_dict = flow.utils.global_view.to_global( + iter1_state_dict, placement=model_file_placement, sbp=get_sbp, + ) + if flow.env.get_rank() in model_file_placement.ranks: + flow.save( + flow.utils.global_view.to_local(model_file_state_dict), + state_dict_dir, + ) + + graph_model(x) + iter2_state_dict = graph_model.state_dict() + return iter2_state_dict + + rank_id = flow.env.get_rank() + with tempfile.TemporaryDirectory( + prefix="graph_save_load_global_" + str(rank_id) + ) as state_dict_dir: + iter2_state_dict = train_with_graph(0, state_dict_dir) + train_with_graph(1, state_dict_dir, iter2_state_dict) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class TestGraphSaveLoadGlobal2d(oneflow.unittest.TestCase): + def test_linear_graph_save_load_gpu_1_broadcast(test_case): + _test_linear_graph_save_load_global_broadcast( + test_case, + model_tensor_placement=flow.placement("cuda", ranks=[0, 1]), + model_file_placement=flow.placement("cpu", ranks=[0]), + ) + + def test_linear_graph_save_load_cpu_1_broadcast(test_case): + _test_linear_graph_save_load_global_broadcast( + test_case, + model_tensor_placement=flow.placement("cpu", ranks=[0, 1]), + model_file_placement=flow.placement("cpu", ranks=[0]), + ) + + def test_graph_save_load_gpu_2_split(test_case): + _test_graph_save_load_global_split_2( + test_case, + model_tensor_placement=flow.placement("cuda", ranks=[0, 1]), + model_file_placement=flow.placement("cpu", ranks=[0, 1]), + ) + + def test_graph_save_load_cpu_2_split(test_case): + _test_graph_save_load_global_split_2( + test_case, + model_tensor_placement=flow.placement("cpu", ranks=[0, 1]), + model_file_placement=flow.placement("cpu", ranks=[0, 1]), + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n4d() +class TestGraphSaveLoadGlobal4d(oneflow.unittest.TestCase): + def test_graph_save_load_gpu_2_split_2_none(test_case): + _test_graph_save_load_global_split_4( + test_case, + model_tensor_placement=flow.placement("cuda", ranks=[0, 1, 2, 3]), + model_file_placement=flow.placement("cpu", ranks=[0, 1]), + ) + + def test_graph_save_load_cpu_2_split_2_none(test_case): + _test_graph_save_load_global_split_4( + test_case, + model_tensor_placement=flow.placement("cpu", ranks=[0, 1, 2, 3]), + model_file_placement=flow.placement("cpu", ranks=[0, 1]), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_scalar.py b/python/oneflow/test/graph/test_graph_scalar.py index ebd4efc38b9568e8f5801e670521937bea3d2e21..612f70e9944989fa7fa625a12b75541eef9166fe 100644 --- a/python/oneflow/test/graph/test_graph_scalar.py +++ b/python/oneflow/test/graph/test_graph_scalar.py @@ -142,7 +142,6 @@ def _test_scalar_global_train_graph(test_case, placement): x = x.to_global(placement=placement, sbp=sbp_b) of_lazy_out = scalar_g(x) lazy_out_list.append(of_lazy_out) - for i in range(3): test_case.assertTrue( np.array_equal( diff --git a/python/oneflow/test/graph/test_graph_with_global.py b/python/oneflow/test/graph/test_graph_with_global.py new file mode 100644 index 0000000000000000000000000000000000000000..47a81f46967b927a14eca7c154cd541d3f023390 --- /dev/null +++ b/python/oneflow/test/graph/test_graph_with_global.py @@ -0,0 +1,283 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import os +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.nn.graph import GraphModule +import oneflow.utils.global_view as global_view +from oneflow.utils.global_view import global_mode + + +def _test_linear_train_graph_with_ddp(test_case): + def train_with_graph(iter_num=1): + PC = flow.placement("cpu", ranks=[0, 1]) + P = flow.placement("cuda", ranks=[0, 1]) + B = flow.sbp.broadcast + S0 = flow.sbp.split(0) + + linear_dp = flow.nn.Linear(800, 400, bias=False) + linear_dp = linear_dp.to_global(placement=P, sbp=B) + flow.nn.init.constant_(linear_dp.weight, 2.068758) + + of_sgd = flow.optim.SGD( + [{"params": linear_dp.parameters()}], lr=0.001, momentum=0.9, + ) + + x = flow.ones((6, 800), placement=PC, sbp=S0) + + class LinearTrainGraphWithDDP(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear_dp = linear_dp + self.add_optimizer(of_sgd) + + def build(self, x): + x = x.to_global(placement=P) + out = self.linear_dp(x) + loss = out.sum() + loss.backward() + return out + + class LinearEvalGraphWithDDP(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear_dp = linear_dp + + def build(self, x): + x = x.to_global(placement=P) + out = self.linear_dp(x) + return out + + linear_t_g = LinearTrainGraphWithDDP() + # linear_t_g.debug(1) + linear_e_g = LinearEvalGraphWithDDP() + # linear_e_g.debug(1) + + result_check_list = [] + + def one_train_iter(iter_cnt=0): + out = linear_t_g(x) + result_check_list.append(out) + + # if iter_cnt == 0: + # if flow.env.get_rank() == 0: + # import traceback + + # try: + # print(linear_t_g) + # except: + # print(traceback.format_exc()) + + def one_eval_iter(iter_cnt=0): + out = linear_e_g(x) + result_check_list.append(out) + + for i in range(iter_num): + one_train_iter(i) + + # In evaluation graph, paramters's sbp are flow.sbp.split(0). + # But their consumer will consum them as flow.sbp.broadcast. + one_eval_iter() + + return result_check_list + + def train_with_graph_ddp(iter_num=1): + PC = flow.placement("cpu", ranks=[0, 1]) + P = flow.placement("cuda", ranks=[0, 1]) + B = flow.sbp.broadcast + S0 = flow.sbp.split(0) + + linear_dp = flow.nn.Linear(800, 400, bias=False) + linear_dp = linear_dp.to_global(placement=P, sbp=B) + flow.nn.init.constant_(linear_dp.weight, 2.068758) + + of_sgd = flow.optim.SGD( + [{"params": linear_dp.parameters()}], lr=0.001, momentum=0.9, + ) + + with global_mode(True, placement=PC, sbp=S0): + x = flow.ones((6, 800), placement=PC, sbp=S0) + + class LinearTrainGraphWithDDP(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear_dp = linear_dp + self.add_optimizer(of_sgd) + + def build(self, x): + # This is ok + # x = x.to("cuda") + + # This is ok + # x = x.to_global(placement=P) + + # This is not ok + # x = x.to(device) + + with global_mode(True, placement=P, sbp=B): + # Test global tensor to device + device = self.linear_dp.weight.device + + x = x.to(device) + + out = self.linear_dp(x) + + # Test randn source op + sample = flow.randn(out.shape, device="cpu").to(device) + out = out + sample * 100 + + # Test disable global_mode while passing placement and sbp + with global_mode(False, placement=P, sbp=B): + out = out - sample * 100 + cur_global_mode = global_view.current_global_mode() + test_case.assertFalse(cur_global_mode.is_enabled) + + loss = out.sum() + loss.backward() + return out + + class LinearEvalGraphWithDDP(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear_dp = linear_dp + + def build(self, x): + with global_mode(True, placement=P, sbp=B): + device = self.linear_dp.weight.device + + x = x.to(device) + + out = self.linear_dp(x) + + # Test randn source op + sample = flow.randn(out.shape, device="cpu").to(device) + out = out + sample * 100 + out = out - sample * 100 + + return out + + linear_t_g = LinearTrainGraphWithDDP() + # linear_t_g.debug(1) + linear_e_g = LinearEvalGraphWithDDP() + # linear_e_g.debug(1) + + result_check_list = [] + + def one_train_iter(iter_cnt=0): + out = linear_t_g(x) + result_check_list.append(out) + + # if iter_cnt == 0: + # if flow.env.get_rank() == 0: + # import traceback + + # try: + # print(linear_t_g) + # except: + # print(traceback.format_exc()) + + def one_eval_iter(iter_cnt=0): + out = linear_e_g(x) + result_check_list.append(out) + + for i in range(iter_num): + one_train_iter(i) + + # In evaluation graph, paramters's sbp are flow.sbp.split(0). + # But their consumer will consum them as flow.sbp.broadcast. + one_eval_iter() + + return result_check_list + + iter_num = 2 + graph_check_list = train_with_graph(iter_num) + graph_ddp_check_list = train_with_graph_ddp(iter_num) + test_case.assertEqual(len(graph_check_list), iter_num + 1) + test_case.assertEqual(len(graph_ddp_check_list), iter_num + 1) + for i in range(iter_num + 1): + test_case.assertTrue( + np.allclose( + graph_check_list[i].numpy(), + graph_ddp_check_list[i].numpy(), + rtol=1e-5, + atol=1e-5, + ), + f"current index {i} \n base {graph_check_list[i].numpy()} \n ddp {graph_ddp_check_list[i].numpy()} \n diff {graph_ddp_check_list[i].numpy() - graph_check_list[i].numpy()}", + ) + + +def _test_global_mode(test_case): + P = flow.placement("cuda", ranks=[0, 1]) + B = flow.sbp.broadcast + + class GlobalModeGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + with global_mode(True, placement=P, sbp=B): + # Test global mode meta data + cur_global_mode = global_view.current_global_mode() + test_case.assertTrue(cur_global_mode.is_enabled) + test_case.assertEqual(cur_global_mode.placement, P) + test_case.assertEqual(cur_global_mode.sbp[0], B) + + # Test global mode source op + randn_out = flow.randn((2, 2)) + rand_out = flow.rand((2, 2)) + randint_out = flow.randint(-100, 100, (2, 2)) + randperm_out = flow.randperm(5) + arange_out = flow.arange(10) + empty_out = flow.empty((1, 2)) + tensor_out = flow.tensor([[1, 2, 4, 5], [4, 3, 2, 9]], dtype=flow.int) + hann_window_out = flow.hann_window(8, dtype=flow.float) + + test_case.assertTrue(not global_view.current_global_mode().is_enabled) + + return { + "randn_out": randn_out, + "rand_out": rand_out, + "randint_out": randint_out, + "randperm_out": randperm_out, + "arange_out": arange_out, + "empty_out": empty_out, + "tensor_out": tensor_out, + "hann_window_out": hann_window_out, + } + + global_graph = GlobalModeGraph() + out = global_graph() + for k, v in out.items(): + test_case.assertEqual(v.is_global, True, k) + test_case.assertEqual(v.placement, P, k) + test_case.assertEqual(v.sbp[0], B, k) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class TestLinearTrainGraphWithDDP(oneflow.unittest.TestCase): + def test_linear_train_graph_with_ddp(test_case): + _test_linear_train_graph_with_ddp(test_case) + + def test_global_mode(test_case): + _test_global_mode(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_graph_zero.py b/python/oneflow/test/graph/test_graph_zero.py index 4dc9f10bc479570642a47b3538376dd6fbead486..338bd63002a4712dc8ba2da595ab9ce5cad702bd 100644 --- a/python/oneflow/test/graph/test_graph_zero.py +++ b/python/oneflow/test/graph/test_graph_zero.py @@ -19,6 +19,7 @@ import numpy as np import oneflow as flow import oneflow.unittest +from oneflow.nn.graph import GraphModule def _test_linear_train_graph_with_zero(test_case, zero_stage=1): @@ -164,9 +165,9 @@ def _test_linear_train_graph_2d_with_zero(test_case, zero_stage=1): def __init__(self): super().__init__() self.mixed_linear0 = mixed_linear0 - self.mixed_linear0.config.activation_checkpointing = True + self.mixed_linear0.to(GraphModule).activation_checkpointing = True self.mixed_linear1 = mixed_linear1 - self.mixed_linear1.config.activation_checkpointing = True + self.mixed_linear1.to(GraphModule).activation_checkpointing = True self.add_optimizer(of_sgd) self.config.enable_amp(True) @@ -211,7 +212,8 @@ def _test_linear_train_graph_2d_with_zero(test_case, zero_stage=1): for state in linear_t_g._state(): test_case.assertEqual( - state.origin.sbp, (oneflow.sbp.split(dim=0), oneflow.sbp.split(dim=0)) + state.to(flow.Tensor).sbp, + (oneflow.sbp.split(dim=0), oneflow.sbp.split(dim=0)), ) # In evaluation graph, paramters's sbp are flow.sbp.split(0). diff --git a/python/oneflow/test/graph/test_long_add_n_pass.py b/python/oneflow/test/graph/test_long_add_n_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..1792a6c9371b43e0fdfb4b339a4a52e8a18395d7 --- /dev/null +++ b/python/oneflow/test/graph/test_long_add_n_pass.py @@ -0,0 +1,121 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import argparse +import numpy as np +import os +import time +import unittest + +import oneflow as flow +import oneflow.unittest + + +def _test_long_add_n_graph(test_case, device): + input_arr = np.array( + [ + [-0.94630778, -0.83378579, -0.87060891], + [2.0289922, -0.28708987, -2.18369248], + [0.35217619, -0.67095644, -1.58943879], + [0.08086036, -1.81075924, 1.20752494], + [0.8901075, -0.49976737, -1.07153746], + [-0.44872912, -1.07275683, 0.06256855], + [-0.22556897, 0.74798368, 0.90416439], + [0.48339456, -2.32742195, -0.59321527], + ], + dtype=np.float32, + ) + x0 = flow.tensor(input_arr, device=device) + x1 = flow.tensor(input_arr, device=device) + x2 = flow.tensor(input_arr, device=device) + x3 = flow.tensor(input_arr, device=device) + x4 = flow.tensor(input_arr, device=device) + x5 = flow.tensor(input_arr, device=device) + x6 = flow.tensor(input_arr, device=device) + x7 = flow.tensor(input_arr, device=device) + x8 = flow.tensor(input_arr, device=device) + x9 = flow.tensor(input_arr, device=device) + + class AddNGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + # Deprecated `temp = x0 + x0` to avoid unstable test + # enable this after fix https://github.com/Oneflow-Inc/oneflow/issues/9431 + # temp = x0 + x0 + temp = x0 + temp = temp + x1 # test add_n1(add_n0(...), ...) + temp = temp + temp # test add_n1(add_n0(...), add_n0(...)) + temp = temp + x2 + temp = temp + x3 + temp = temp + x4 + temp = temp + x5 + temp = temp + x6 + temp = temp + x7 + other_add_n = x8 + x9 + temp = temp + other_add_n # test add_n2(add_n0(), add_n1()) + return temp + + add_n_g = AddNGraph() + of_lazy_out = add_n_g() + test_case.assertTrue(np.allclose(input_arr * 12, of_lazy_out.numpy(), 1e-05, 1e-05)) + + +def _test_add_n_consume_multi_add_n_graph(test_case, device): + input_arr = np.array( + [ + [-0.94630778, -0.83378579, -0.87060891], + [2.0289922, -0.28708987, -2.18369248], + [0.35217619, -0.67095644, -1.58943879], + [0.08086036, -1.81075924, 1.20752494], + [0.8901075, -0.49976737, -1.07153746], + [-0.44872912, -1.07275683, 0.06256855], + [-0.22556897, 0.74798368, 0.90416439], + [0.48339456, -2.32742195, -0.59321527], + ], + dtype=np.float32, + ) + x0 = flow.tensor(input_arr, device=device) + + class AddNGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + temp = x0 + x0 + temp = temp + temp + return temp + + add_n_g = AddNGraph() + of_lazy_out = add_n_g() + test_case.assertTrue(np.allclose(input_arr * 4, of_lazy_out.numpy(), 1e-05, 1e-05)) + + +@unittest.skip("fail on ci") +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestLongAddNGraph(oneflow.unittest.TestCase): + def test_add_n(test_case): + device = "cuda" + _test_long_add_n_graph(test_case, device) + + def test_consume_multi_add_n(test_case): + device = "cuda" + _test_add_n_consume_multi_add_n_graph(test_case, device) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_modify_module_forward.py b/python/oneflow/test/graph/test_modify_module_forward.py new file mode 100644 index 0000000000000000000000000000000000000000..958af5973000610ef01bd9334ac6d2cce816bfec --- /dev/null +++ b/python/oneflow/test/graph/test_modify_module_forward.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import argparse +import numpy as np +import os +import time +import unittest +from types import MethodType + +import oneflow as flow +import oneflow.unittest +from oneflow import nn + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestModifyForwardOfModule(oneflow.unittest.TestCase): + def test_modify_forward(test_case): + def forward2(self, x): + return x + 1 + + class Model1(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + + class ForwardModifiedGraph(nn.Graph): + def __init__(self, model): + super().__init__() + self.model = model + self.model.eval() + + def build(self, x): + return self.model(x) + + test_model = Model1() + test_model.forward = MethodType(forward2, test_model) + eval_graph_model1 = ForwardModifiedGraph(model=test_model) + + input_tensor = flow.tensor([0.0], requires_grad=True) + + eager_out = test_model(input_tensor) + graph_out = eval_graph_model1(input_tensor) + test_case.assertTrue(np.array_equal(graph_out.numpy(), eager_out.numpy())) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_multi_tensor_adam_update_with_cast.py b/python/oneflow/test/graph/test_multi_tensor_adam_update_with_cast.py new file mode 100644 index 0000000000000000000000000000000000000000..1a9f5fb830223dc1e5a3b8e11894939acd2e870e --- /dev/null +++ b/python/oneflow/test/graph/test_multi_tensor_adam_update_with_cast.py @@ -0,0 +1,194 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +import copy +import os + +from test_util import GenArgList + +import oneflow as flow + + +def compare_with_numpy_adam( + test_case, + device, + x_shape, + tensor_num, + learning_rate, + train_iters, + betas, + weight_decay, + eps, + do_bias_correction, + amsgrad, +): + random_weight_seq = [] + init_value_seq = [] + + for _ in range(train_iters): + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_weight_seq.append(random_grad_seq_per_iter) + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + + class CustomModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.add_parameters() + + def add_parameters(self) -> None: + for idx in range(tensor_num): + self.register_parameter( + f"param_{idx}", + flow.nn.Parameter( + flow.tensor(init_value_seq[idx], device=flow.device(device)) + ), + ) + + def param(self, i): + return getattr(self, f"param_{i}") + + def forward(self, mask_list): + out = 0 + for idx in range(tensor_num): + out += flow._C.matmul(self.param(idx), mask_list[idx]) + + return out + + simp_module = CustomModule() + simp_module.to(device) + simp_module.train() + + adam0 = flow.optim.Adam( + [ + { + "params": simp_module.parameters(), + "lr": learning_rate, + "betas": betas, + "eps": eps, + "weight_decay": weight_decay, + }, + ], + do_bias_correction=do_bias_correction, + amsgrad=amsgrad, + ) + + class CustomAdamGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = simp_module + self.add_optimizer(adam0) + self.config.enable_amp(True) + self.config.allow_fuse_model_update_ops(True) + self.config.enable_multi_tensor_update(True) + self.config.enable_fused_model_update_cast(True) + + def build(self, mask_tensor_list): + loss = flow.sum(self.m(mask_tensor_list)) + loss.backward() + return loss + + of_res_list = [] + adam_graph = CustomAdamGraph() + for i in range(train_iters): + mask_tensor_list = [] + for idx in range(tensor_num): + mask_tensor_list.append( + flow.tensor( + random_weight_seq[i][idx], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + ) + adam_x = adam_graph(mask_tensor_list) + of_res_list.append([]) + for idx in range(tensor_num): + of_res_list[i].append(copy.copy(simp_module.param(idx).numpy())) + + np_res_list = [] + + def train_by_numpy(): + x = init_value_seq + m = [] + v = [] + for idx in range(tensor_num): + m.append(np.zeros_like(x[idx])) + v.append(np.zeros_like(x[idx])) + beta1 = betas[0] + beta2 = betas[1] + + ones = np.ones(x_shape).astype(np.float32) + + def train_one_iter(step, weight): + for i in range(tensor_num): + transposed_weight = np.transpose(weight[i], (1, 0)) + grad = np.matmul(ones, transposed_weight) + grad = grad + weight_decay * x[i] + + bias_correction1 = 1.0 + bias_correction2 = 1.0 + + if do_bias_correction: + bias_correction1 = 1.0 - np.power(beta1, step) + bias_correction2 = 1.0 - np.power(beta2, step) + + m[i] = beta1 * m[i] + (1 - beta1) * grad + v[i] = beta2 * v[i] + (1 - beta2) * grad * grad + denom = np.sqrt(v[i]) / np.sqrt(bias_correction2) + eps + + x[i] = x[i] - ((learning_rate / bias_correction1) * m[i] / denom) + return (x, m, v) + + for i in range(1, train_iters + 1): + x, m, v = train_one_iter(i, random_weight_seq[i - 1]) + np_res_list.append(copy.copy(x)) + + train_by_numpy() + for i in range(tensor_num): + test_case.assertTrue( + np.allclose(np_res_list[i], of_res_list[i], rtol=1e-3, atol=1e-3) + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestMultiTensorAdam(flow.unittest.TestCase): + def test_multi_tensor_adam(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda"] + arg_dict["x_shape"] = [(4, 4)] + arg_dict["tensor_num"] = [4, 6] + arg_dict["learning_rate"] = [1, 1e-3] + arg_dict["train_iters"] = [10] + arg_dict["betas"] = [(0.99, 0.9)] + arg_dict["weight_decay"] = [0.0, 1e-3] + arg_dict["eps"] = [1e-5] + arg_dict["do_bias_correction"] = [True, False] + arg_dict["amsgrad"] = [False] # Multi tensor update do not support amsgrad + for arg in GenArgList(arg_dict): + compare_with_numpy_adam(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_multi_tensor_sgd_update_with_cast.py b/python/oneflow/test/graph/test_multi_tensor_sgd_update_with_cast.py new file mode 100644 index 0000000000000000000000000000000000000000..b92826168c246f5086cbe5a3499a519bb6787ef0 --- /dev/null +++ b/python/oneflow/test/graph/test_multi_tensor_sgd_update_with_cast.py @@ -0,0 +1,156 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +import copy +import os + +from test_util import GenArgList + +import oneflow as flow + + +def compare_with_numpy_sgd( + test_case, device, x_shape, tensor_num, learning_rate, train_iters, weight_decay +): + random_weight_seq = [] + init_value_seq = [] + + for _ in range(train_iters): + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_weight_seq.append(random_grad_seq_per_iter) + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + + class CustomModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.add_parameters() + + def add_parameters(self) -> None: + for idx in range(tensor_num): + self.register_parameter( + f"param_{idx}", + flow.nn.Parameter( + flow.tensor(init_value_seq[idx], device=flow.device(device)) + ), + ) + + def param(self, i): + return getattr(self, f"param_{i}") + + def forward(self, mask_list): + out = 0 + for idx in range(tensor_num): + out += flow._C.matmul(self.param(idx), mask_list[idx]) + + return out + + simp_module = CustomModule() + simp_module.to(device) + simp_module.train() + + sgd0 = flow.optim.SGD( + [ + { + "params": simp_module.parameters(), + "lr": learning_rate, + "weight_decay": weight_decay, + } + ], + ) + + class CustomSGDGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.m = simp_module + self.add_optimizer(sgd0) + self.config.enable_amp(True) + self.config.allow_fuse_model_update_ops(True) + self.config.enable_multi_tensor_update(True) + self.config.enable_fused_model_update_cast(True) + + def build(self, mask_tensor_list): + loss = flow.sum(self.m(mask_tensor_list)) + loss.backward() + return loss + + of_res_list = [] + sgd_graph = CustomSGDGraph() + for i in range(train_iters): + mask_tensor_list = [] + for idx in range(tensor_num): + mask_tensor_list.append( + flow.tensor( + random_weight_seq[i][idx], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + ) + sgd_x = sgd_graph(mask_tensor_list) + of_res_list.append([]) + for idx in range(tensor_num): + of_res_list[i].append(copy.copy(simp_module.param(idx).numpy())) + + np_res_list = [] + + def train_by_numpy(): + x = init_value_seq + ones = np.ones(x_shape).astype(np.float32) + + def train_one_iter(weight): + for i in range(tensor_num): + transposed_weight = np.transpose(weight[i], (1, 0)) + grad = np.matmul(ones, transposed_weight) + grad = grad + weight_decay * x[i] + x[i] = x[i] - learning_rate * grad + return x + + for i in range(train_iters): + x = train_one_iter(random_weight_seq[i]) + np_res_list.append(copy.copy(x)) + + train_by_numpy() + for i in range(tensor_num): + test_case.assertTrue( + np.allclose(np_res_list[i], of_res_list[i], rtol=1e-3, atol=1e-3) + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestMultiTensorSGD(flow.unittest.TestCase): + def test_multi_tensor_sgd(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda"] + arg_dict["x_shape"] = [(4, 4)] + arg_dict["tensor_num"] = [4, 6] + arg_dict["learning_rate"] = [1, 1e-3] + arg_dict["train_iters"] = [10] + arg_dict["weight_decay"] = [0.0, 1e-3] + for arg in GenArgList(arg_dict): + compare_with_numpy_sgd(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_nccl_logical_send_recv.py b/python/oneflow/test/graph/test_nccl_logical_send_recv.py index 9b6b90750d8e66ff706e59e0034c3d94c3649c0a..e533d81cc3ac6995c8a87b8445b38ce16c03a393 100644 --- a/python/oneflow/test/graph/test_nccl_logical_send_recv.py +++ b/python/oneflow/test/graph/test_nccl_logical_send_recv.py @@ -22,12 +22,9 @@ import oneflow as flow import oneflow.unittest from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import * import time import os -os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "1" - def _test_nccl_logical_send_recv_2d(test_case, src_nd_sbp, dst_nd_sbp): # can not process p in dst @@ -103,6 +100,7 @@ def gen_2d_sbp(): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestNcclLogicalSendRecv2D(flow.unittest.TestCase): def test_nccl_logical_send_recv_2d(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "1" arg_dict = OrderedDict() arg_dict["src_nd_sbp"] = gen_2d_sbp() arg_dict["dst_nd_sbp"] = gen_2d_sbp() @@ -175,6 +173,7 @@ def gen_1d_sbp(): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestNcclLogicalSendRecv1D(flow.unittest.TestCase): def test_nccl_logical_send_recv_1d(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "1" arg_dict = OrderedDict() arg_dict["src_nd_sbp"] = gen_1d_sbp() arg_dict["dst_nd_sbp"] = gen_1d_sbp() diff --git a/python/oneflow/test/graph/test_neq_device_process_num.py b/python/oneflow/test/graph/test_neq_device_process_num.py index 7073c6a54521f5df11f4c2aa1ce55f50c1622552..f3d004ca8f294d2327176a1f5a049b8e5938238c 100644 --- a/python/oneflow/test/graph/test_neq_device_process_num.py +++ b/python/oneflow/test/graph/test_neq_device_process_num.py @@ -23,6 +23,7 @@ import oneflow import oneflow as flow import oneflow.unittest import oneflow.sysconfig +from oneflow.nn.graph import GraphModule @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @@ -89,8 +90,8 @@ class TestGraphNeqDeviceProcessNum(flow.unittest.TestCase): def __init__(self): super().__init__() self.module_pipeline = module_pipeline - self.module_pipeline.m_stage0.config.stage_id = 0 - self.module_pipeline.m_stage1.config.stage_id = 1 + self.module_pipeline.m_stage0.to(GraphModule).set_stage(0) + self.module_pipeline.m_stage1.to(GraphModule).set_stage(1) self.loss_fn = flow.nn.CrossEntropyLoss(reduction="none") self.config.set_gradient_accumulation_steps(2) self.add_optimizer(sgd) diff --git a/python/oneflow/test/graph/test_optimization_conf.py b/python/oneflow/test/graph/test_optimization_conf.py index 6291a7ce698cfc706339fd08d00fe55799d34de9..2f01be985bf48fcc20b80d76cd621c17a351b2d6 100644 --- a/python/oneflow/test/graph/test_optimization_conf.py +++ b/python/oneflow/test/graph/test_optimization_conf.py @@ -15,11 +15,8 @@ limitations under the License. """ import os import unittest - -import numpy as np - +import oneflow.framework.session_context as session_ctx import oneflow as flow -import oneflow.framework.graph_build_util as graph_build_util import oneflow.unittest import oneflow.framework.config_util as config_util import oneflow.framework.attr_util as attr_util @@ -48,6 +45,7 @@ class TestGraphWithSysConf(flow.unittest.TestCase): flow.backends.cudnn.set_reserved_mem_mbytes(1000) flow.backends.cudnn.enable_fused_normalization_add_relu(True) + flow.backends.cudnn.enable_conv_heuristic_search_algo(False) flow.utils.load_library("") @@ -96,7 +94,7 @@ class TestGraphWithSysConf(flow.unittest.TestCase): attr_value = random.choice([True, False]) attrs_and_values_to_check.append((attrs, attr_value)) else: - assert False, "unsupported type!" + raise TypeError("Unsupported type!") api(attr_value) num_api_tested += 1 @@ -117,11 +115,18 @@ class TestGraphWithSysConf(flow.unittest.TestCase): print("number of APIs tested: " + str(num_api_tested)) + # save the resource config before running random resource api tests + session = session_ctx.GetDefaultSession() + prev_resource_config = session.resource + for i in range(5): test_resource_config_update_apis_eagerly_automatically() print("optimization conf after session init: \n", g._optimization_conf_proto) + # restore the resource config + session.update_resource_eagerly(prev_resource_config) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/graph/test_to_consistent.py b/python/oneflow/test/graph/test_to_consistent.py deleted file mode 100644 index 90763f7b932f56f12a59ca3cb90fad02ba72f440..0000000000000000000000000000000000000000 --- a/python/oneflow/test/graph/test_to_consistent.py +++ /dev/null @@ -1,536 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import os -import numpy as np - -import oneflow as flow -import oneflow.unittest - - -x = np.array( - [ - [ - 0.21490018, - 0.22043167, - 0.1605895, - 0.25424683, - 0.12975895, - 0.49967155, - 0.04753795, - 0.7518577, - 0.38964537, - 0.01955934, - ], - [ - 0.16392729, - 0.41410774, - 0.05424517, - 0.7668146, - 0.08050849, - 0.5763975, - 0.42364502, - 0.4950619, - 0.9608427, - 0.11889187, - ], - ] -) - -y = np.array( - [ - [ - 0.9903706, - 0.11213686, - 0.29525927, - 0.79380244, - 0.70357895, - 0.6950597, - 0.52552456, - 0.32304054, - 0.6997739, - 0.15671141, - ], - [ - 0.76867193, - 0.59983397, - 0.07774717, - 0.07815815, - 0.30385414, - 0.7366552, - 0.4607681, - 0.40554753, - 0.8290172, - 0.8405671, - ], - [ - 0.8900324, - 0.5274955, - 0.80989295, - 0.71331054, - 0.8076364, - 0.94833183, - 0.04778554, - 0.23992656, - 0.57683426, - 0.81757474, - ], - ] -) - - -class MyModule1(flow.nn.Module): - def __init__(self, weight): - assert isinstance(weight, flow._oneflow_internal.Tensor) - super().__init__() - self.weight = flow.nn.Parameter(weight) - self.activation = flow.nn.ReLU() - - def forward(self, x): - # print(f"x shape: {x.shape}, placement: {x.placement}, sbp: {x.sbp}") - # print( - # f"weight shape: {self.weight.shape}, placement: {self.weight.placement}, sbp: {self.weight.sbp}" - # ) - y = flow._C.matmul(x, self.weight, transpose_b=True) - # print(f"y shape: {y.shape}, placement: {y.placement}, sbp: {y.sbp}") - if y.is_global: - y = y.to_global(sbp=flow.sbp.broadcast) - # print(f"post y shape: {y.shape}, placement: {y.placement}, sbp: {y.sbp}") - return self.activation(y) - - -class MyModule2(flow.nn.Module): - def __init__(self, weight): - assert isinstance(weight, flow._oneflow_internal.Tensor) - super().__init__() - self.weight = flow.nn.Parameter(weight) - self.activation = flow.nn.ReLU() - - def forward(self, x): - # print(f"weight shape: {self.weight.shape}, placement: {self.weight.placement}, sbp: {self.weight.sbp}") - if self.weight.is_global: - y = self.weight.to_global(grad_sbp=flow.sbp.broadcast) - z = flow._C.matmul(y, x, transpose_b=True) - out = self.activation(z).sum() - if self.weight.is_global: - out = out.to_global(sbp=flow.sbp.broadcast) - return out - - -class MyModule3(flow.nn.Module): - def __init__(self, transpose_a=False, transpose_b=False): - super().__init__() - self.activation = flow.nn.ReLU() - self.transpose_a = transpose_a - self.transpose_b = transpose_b - - def forward(self, x, y): - z = flow._C.matmul(x, y, self.transpose_a, self.transpose_b) - if z.is_global: - z = z.to_global(sbp=flow.sbp.broadcast) - return self.activation(z) - - -class ConsistentToModule(flow.nn.Module): - def __init__(self, device="cuda"): - super().__init__() - self.device = device - - def forward(self, x): - return x.to(self.device) - - -class FreeTensorModule(flow.nn.Module): - def __init__(self, shape, placement, sbp): - super().__init__() - self.shape = shape - self.placement = placement - self.sbp = sbp - - def forward(self, x): - y = flow.ones( - self.shape, dtype=flow.float32, placement=self.placement, sbp=self.sbp - ) - return flow._C.matmul(x, y, transpose_b=True) - - -class ToPlacementModule(flow.nn.Module): - def __init__(self, placement): - super().__init__() - self.placement = placement - - def forward(self, x): - return x.to_global(placement=self.placement) - - -class MyGraph(flow.nn.Graph): - def __init__(self, module, optimizer=None): - super().__init__() - self.module = module - if optimizer is not None: - self.add_optimizer(optimizer) - - def build(self, *arg): - y = self.module(*arg) - if self.config.training: - y.backward() - return y - - -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -@flow.unittest.skip_unless_1n2d() -class ToGlobalGraphTestCase(oneflow.unittest.TestCase): - # @unittest.skipIf(True, "") - def test_fwd_P2B(test_case): - """ compare eager fwd and lazy bwd - """ - rank = flow.env.get_rank() - # pid = os.getpid() - # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_fwd_P2B") - - local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - - z = flow._C.matmul( - flow.cat([local_x, local_x], dim=1), - flow.cat([local_y, local_y], dim=1), - transpose_b=True, - ) - z = flow._C.relu(z) - # print(f"z shape: {z.shape}, device: {z.device}") - # print(z.numpy()) - - placement = flow.placement("cuda", ranks=[0, 1]) - sbp = flow.sbp.split(1) - c_x = local_x.to_global(placement=placement, sbp=sbp) - c_y = local_y.to_global(placement=placement, sbp=sbp) - - # print(f"c_x shape: {c_x.shape}, placement: {c_x.placement}, sbp: {c_x.sbp}") - # print(f"c_y shape: {c_y.shape}, placement: {c_y.placement}, sbp: {c_y.sbp}") - - m = MyModule1(c_y) - g = MyGraph(m) - - g_z = g(c_x) - # print(f"g_z shape: {g_z.shape}, placement: {g_z.placement}, sbp: {g_z.sbp}") - # print(g_z.to_local().numpy()) - test_case.assertTrue(np.allclose(z.numpy(), g_z.to_local().numpy())) - - # @unittest.skipIf(True, "") - def test_bwd_P2B(test_case): - """ compare eager bwd and lazy bwd - """ - rank = flow.env.get_rank() - # pid = os.getpid() - # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_bwd_P2B") - - local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - - z = flow._C.matmul( - local_y, flow.cat([local_x, local_x], dim=0), transpose_b=True, - ) - z = flow._C.relu(z) - z = z.sum() - - placement = flow.placement("cuda", ranks=[0, 1]) - c_x = local_x.to_global(placement=placement, sbp=flow.sbp.split(0)) - c_y = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) - - m = MyModule2(c_y) - optimizer = flow.optim.SGD(m.parameters(), lr=1.0) - g = MyGraph(m, optimizer) - - g_z = g(c_x) - # print(f"g_z shape: {g_z.shape}, placement: {g_z.placement}, sbp: {g_z.sbp}") - test_case.assertTrue(g_z.is_global) - test_case.assertTrue(g_z.sbp[0] == flow.sbp.broadcast) - # S(1) -> B not supported yet - # c_z = g_z.to_global(sbp=flow.sbp.broadcast) - # print(f"c_z shape: {c_z.shape}, placement: {c_z.placement}, sbp: {c_z.sbp}") - test_case.assertTrue(np.allclose(z.numpy(), g_z.to_local().numpy())) - - e_y = c_y.detach() - # print(f"e_y shape: {e_y.shape}, placement: {e_y.placement}, sbp: {e_y.sbp}") - e_m = MyModule2(e_y) - e_z = e_m(c_x) - # print(f"e_z shape: {e_z.shape}, placement: {e_z.placement}, sbp: {e_z.sbp}") - e_z.backward() - - test_case.assertTrue( - np.allclose(c_y.to_local().numpy(), e_y.to_local().numpy()) - ) - - # @unittest.skipIf(True, "") - def test_multi_graph(test_case): - """ compare two lazy fwd - """ - rank = flow.env.get_rank() - # pid = os.getpid() - # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_multi_graph") - - local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) - - placement = flow.placement("cuda", ranks=[0, 1]) - x1 = local_x.to_global(placement=placement, sbp=flow.sbp.broadcast) - y1 = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) - # B * B -> B -> B - m1 = MyModule3(transpose_b=True) - g1 = MyGraph(m1) - - slice_obj = slice( - int(rank * local_x.shape[0] / 2), int((rank + 1) * local_x.shape[0] / 2) - ) - x2 = local_x[slice_obj, :] - x2 = x2.to_global(placement=placement, sbp=flow.sbp.split(0)) - y2 = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) - # S(0) * B -> S(0) -> B - m2 = MyModule3(transpose_b=True) - g2 = MyGraph(m2) - - x3 = local_x[ - :, int(rank * local_x.shape[1] / 2) : int((rank + 1) * local_x.shape[1] / 2) - ] - x3 = x3.to_global(placement=placement, sbp=flow.sbp.split(1)) - y3 = local_y[ - :, int(rank * local_y.shape[1] / 2) : int((rank + 1) * local_y.shape[1] / 2) - ] - y3 = y3.to_global(placement=placement, sbp=flow.sbp.split(1)) - # S(1) * S(0) -> P -> B - m3 = MyModule3(transpose_b=True) - g3 = MyGraph(m3) - - z1 = g1(x1, y1) - # print(f"z1 shape: {z1.shape}, placement: {z1.placement}, sbp: {z1.sbp}") - # print(z1.to_local().numpy()) - z2 = g2(x2, y2) - # print(f"z2 shape: {z2.shape}, placement: {z2.placement}, sbp: {z2.sbp}") - # print(z2.to_local().numpy()) - z3 = g3(x3, y3) - # print(f"z3 shape: {z3.shape}, placement: {z3.placement}, sbp: {z3.sbp}") - # print(z3.to_local().numpy()) - - test_case.assertTrue(np.allclose(z1.to_local().numpy(), z2.to_local().numpy())) - test_case.assertTrue(np.allclose(z1.to_local().numpy(), z3.to_local().numpy())) - - # @unittest.skipIf(True, "") - def test_global_to(test_case): - c_x = flow.ones( - (4, 3), placement=flow.placement("cpu", ranks=[0, 1]), sbp=flow.sbp.split(0) - ) - - global_to = ConsistentToModule("cuda") - g_global_to = MyGraph(global_to) - - e = global_to(c_x) - test_case.assertTrue(e.is_cuda) - test_case.assertTrue(e.is_global) - test_case.assertTrue(e.sbp[0] == flow.sbp.split(0)) - - g = g_global_to(c_x) - test_case.assertTrue(g.is_cuda) - test_case.assertTrue(g.is_global) - test_case.assertTrue(g.sbp[0] == flow.sbp.split(0)) - - test_case.assertTrue(np.allclose(e.to_local().numpy(), g.to_local().numpy())) - - # @unittest.skipIf(True, "") - def test_free_tensor_to_global(test_case): - local_x = flow.tensor(x, dtype=flow.float32, device="cpu") - placement = flow.placement("cuda", ranks=[0, 1]) - c_x = local_x.to_global(placement, flow.sbp.split(0)) - - m = FreeTensorModule((3, 10), placement, flow.sbp.broadcast) - g = MyGraph(m) - - eager_out = m(c_x) - test_case.assertTrue(eager_out.is_cuda) - test_case.assertTrue(eager_out.is_global) - test_case.assertTrue(eager_out.sbp[0] == flow.sbp.split(0)) - - graph_out = g(c_x) - test_case.assertTrue(graph_out.is_cuda) - test_case.assertTrue(graph_out.is_global) - test_case.assertTrue(graph_out.sbp[0] == flow.sbp.split(0)) - - test_case.assertTrue( - np.allclose(eager_out.to_local().numpy(), graph_out.to_local().numpy()) - ) - - # @unittest.skipIf(True, "") - def test_to_placement(test_case): - rank = flow.env.get_rank() - # pid = os.getpid() - # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_to_placement") - - if rank == 0: - x = flow.ones((2, 3), dtype=flow.float32) - elif rank == 1: - x = flow.empty(tuple()) - else: - raise ValueError - - c_x = x.to_global( - placement=flow.placement("cpu", ranks=[0]), sbp=flow.sbp.broadcast - ) - # print(f"c_x shape: {c_x.shape}, placement: {c_x.placement}, sbp: {c_x.sbp}") - - p1 = flow.placement("cpu", ranks=[0, 1]) - m1 = ToPlacementModule(p1) - g1 = MyGraph(m1) - y1 = g1(c_x) - - # print(f"y1 shape: {y1.shape}, placement: {y1.placement}, sbp: {y1.sbp}") - test_case.assertTrue(y1.placement == p1) - test_case.assertTrue(y1.sbp[0] == flow.sbp.broadcast) - test_case.assertTrue(y1.to_local().numpy().mean() == 1.0) - - p2 = flow.placement("cuda", ranks=[0, 1]) - m2 = ToPlacementModule(p2) - g2 = MyGraph(m2) - y2 = g2(y1) - - # print(f"y2 shape: {y2.shape}, placement: {y2.placement}, sbp: {y2.sbp}") - test_case.assertTrue(y2.placement == p2) - test_case.assertTrue(y2.sbp[0] == flow.sbp.broadcast) - test_case.assertTrue(y2.to_local().numpy().mean() == 1.0) - - # @unittest.skipIf(True, "") - def test_to_dtype(test_case): - x = flow.ones((2, 3), dtype=flow.int32, device="cpu") - - placement = flow.placement("cpu", ranks=[0, 1]) - c_x = flow.ones( - (2, 3), dtype=flow.int32, placement=placement, sbp=flow.sbp.broadcast - ) - - class CastModule(flow.nn.Module): - def __init__(self, dtype): - super().__init__() - self.dtype = dtype - - def forward(self, x): - return x.to(dtype=self.dtype) - - m = CastModule(flow.float32) - g = MyGraph(m) - - e_x = m(x) - e_c_x = m(c_x) - # NOTE(chengcheng): - # There are two BUG in this test script: - # 1. first call and second call input tensor meta is NOT same - # 2. nn.Graph NOT support local input with multi-rank yet. - # g_x = g(x) - g_c_x = g(c_x) - - test_case.assertTrue(e_x.dtype == flow.float32) - # test_case.assertTrue(g_x.dtype == flow.float32) - test_case.assertTrue(e_c_x.dtype == flow.float32) - test_case.assertTrue(g_c_x.dtype == flow.float32) - - -class MyModule5(flow.nn.Module): - def __init__(self, transpose_a=False, transpose_b=False, sbp=[]): - super().__init__() - self.transpose_a = transpose_a - self.transpose_b = transpose_b - self.sbp = sbp - - def forward(self, x, y): - z = flow._C.matmul(x, y, self.transpose_a, self.transpose_b) - assert z.is_global - assert len(z.sbp) == len(self.sbp) - return z.to_global(sbp=self.sbp) - - -@unittest.skipIf(True, "") -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -@flow.unittest.skip_unless_1n4d() -class ToGlobal2DGraphTestCase(oneflow.unittest.TestCase): - def test_matmul(test_case): - placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) - x = flow.ones( - (4, 6), placement=placement, sbp=[flow.sbp.split(0), flow.sbp.split(1)] - ) - y = flow.ones( - (4, 6), placement=placement, sbp=[flow.sbp.broadcast, flow.sbp.split(1)] - ) - z = flow._C.matmul(x, y, transpose_b=True) - print(f"z shape: {z.shape}, placement: {z.placement}, sbp: {z.sbp}") - - # m = MyModule5(transpose_b=True, sbp=[flow.sbp.split(0), flow.sbp.broadcast]) - # z = m(x, y) - # print(f"z shape: {z.shape}, placement: {z.placement}, sbp: {z.sbp}") - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestLazy1dTo2dConsistent(flow.unittest.TestCase): - def test_lazy_1d_to_2d_sbp(test_case): - P_1d = flow.placement( - device_type="cuda", device_ids={0: range(4)}, hierarchy=(4,) - ) - P_2d = flow.placement( - device_type="cuda", device_ids={0: range(4)}, hierarchy=(2, 2) - ) - B = flow.sbp.broadcast - - class Test1dTo2dModule(flow.nn.Module): - def forward(self, x): - return x.to_global(placement=P_2d, sbp=[B, B]) - - class Test1dTo2dGraph(flow.nn.Graph): - def __init__(self, model): - super().__init__() - self.model = model - - def build(self, x): - return self.model(x) - - class Test2dTo1dModule(flow.nn.Module): - def forward(self, x): - return x.to_global(placement=P_1d, sbp=[B]) - - class Test2dTo1dGraph(flow.nn.Graph): - def __init__(self, model): - super().__init__() - self.model = model - - def build(self, x): - return self.model(x) - - model_1d_to_2d = Test1dTo2dModule() - graph_1d_to_2d = Test1dTo2dGraph(model_1d_to_2d) - - x = flow.zeros(4, 4, 4, 4, sbp=[B, B], placement=P_2d) - x = x.to_global(placement=P_1d, sbp=[B]) - test_case.assertTrue(x.sbp == (B,)) - test_case.assertTrue(x.placement == P_1d) - y = graph_1d_to_2d(x) - test_case.assertTrue(y.sbp == (B, B)) - test_case.assertTrue(y.placement == P_2d) - - model_2d_to_1d = Test2dTo1dModule() - graph_2d_to_1d = Test2dTo1dGraph(model_2d_to_1d) - z = graph_2d_to_1d(y) - test_case.assertTrue(z.sbp == x.sbp) - test_case.assertTrue(z.placement == x.placement) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/graph/test_to_global.py b/python/oneflow/test/graph/test_to_global.py new file mode 100644 index 0000000000000000000000000000000000000000..125113707f966bb59ff8c9950655ad18828a3a00 --- /dev/null +++ b/python/oneflow/test/graph/test_to_global.py @@ -0,0 +1,536 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import os +import numpy as np + +import oneflow as flow +import oneflow.unittest + + +x = np.array( + [ + [ + 0.21490018, + 0.22043167, + 0.1605895, + 0.25424683, + 0.12975895, + 0.49967155, + 0.04753795, + 0.7518577, + 0.38964537, + 0.01955934, + ], + [ + 0.16392729, + 0.41410774, + 0.05424517, + 0.7668146, + 0.08050849, + 0.5763975, + 0.42364502, + 0.4950619, + 0.9608427, + 0.11889187, + ], + ] +) + +y = np.array( + [ + [ + 0.9903706, + 0.11213686, + 0.29525927, + 0.79380244, + 0.70357895, + 0.6950597, + 0.52552456, + 0.32304054, + 0.6997739, + 0.15671141, + ], + [ + 0.76867193, + 0.59983397, + 0.07774717, + 0.07815815, + 0.30385414, + 0.7366552, + 0.4607681, + 0.40554753, + 0.8290172, + 0.8405671, + ], + [ + 0.8900324, + 0.5274955, + 0.80989295, + 0.71331054, + 0.8076364, + 0.94833183, + 0.04778554, + 0.23992656, + 0.57683426, + 0.81757474, + ], + ] +) + + +class MyModule1(flow.nn.Module): + def __init__(self, weight): + assert isinstance(weight, flow._oneflow_internal.Tensor) + super().__init__() + self.weight = flow.nn.Parameter(weight) + self.activation = flow.nn.ReLU() + + def forward(self, x): + # print(f"x shape: {x.shape}, placement: {x.placement}, sbp: {x.sbp}") + # print( + # f"weight shape: {self.weight.shape}, placement: {self.weight.placement}, sbp: {self.weight.sbp}" + # ) + y = flow._C.matmul(x, self.weight, transpose_b=True) + # print(f"y shape: {y.shape}, placement: {y.placement}, sbp: {y.sbp}") + if y.is_global: + y = y.to_global(sbp=flow.sbp.broadcast) + # print(f"post y shape: {y.shape}, placement: {y.placement}, sbp: {y.sbp}") + return self.activation(y) + + +class MyModule2(flow.nn.Module): + def __init__(self, weight): + assert isinstance(weight, flow._oneflow_internal.Tensor) + super().__init__() + self.weight = flow.nn.Parameter(weight) + self.activation = flow.nn.ReLU() + + def forward(self, x): + # print(f"weight shape: {self.weight.shape}, placement: {self.weight.placement}, sbp: {self.weight.sbp}") + if self.weight.is_global: + y = self.weight.to_global(grad_sbp=flow.sbp.broadcast) + z = flow._C.matmul(y, x, transpose_b=True) + out = self.activation(z).sum() + if self.weight.is_global: + out = out.to_global(sbp=flow.sbp.broadcast) + return out + + +class MyModule3(flow.nn.Module): + def __init__(self, transpose_a=False, transpose_b=False): + super().__init__() + self.activation = flow.nn.ReLU() + self.transpose_a = transpose_a + self.transpose_b = transpose_b + + def forward(self, x, y): + z = flow._C.matmul(x, y, self.transpose_a, self.transpose_b) + if z.is_global: + z = z.to_global(sbp=flow.sbp.broadcast) + return self.activation(z) + + +class GlobalToModule(flow.nn.Module): + def __init__(self, device="cuda"): + super().__init__() + self.device = device + + def forward(self, x): + return x.to(self.device) + + +class FreeTensorModule(flow.nn.Module): + def __init__(self, shape, placement, sbp): + super().__init__() + self.shape = shape + self.placement = placement + self.sbp = sbp + + def forward(self, x): + y = flow.ones( + self.shape, dtype=flow.float32, placement=self.placement, sbp=self.sbp + ) + return flow._C.matmul(x, y, transpose_b=True) + + +class ToPlacementModule(flow.nn.Module): + def __init__(self, placement): + super().__init__() + self.placement = placement + + def forward(self, x): + return x.to_global(placement=self.placement) + + +class MyGraph(flow.nn.Graph): + def __init__(self, module, optimizer=None): + super().__init__() + self.module = module + if optimizer is not None: + self.add_optimizer(optimizer) + + def build(self, *arg): + y = self.module(*arg) + if self.config.training: + y.backward() + return y + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class ToGlobalGraphTestCase(oneflow.unittest.TestCase): + # @unittest.skipIf(True, "") + def test_fwd_P2B(test_case): + """ compare eager fwd and lazy bwd + """ + rank = flow.env.get_rank() + # pid = os.getpid() + # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_fwd_P2B") + + local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + + z = flow._C.matmul( + flow.cat([local_x, local_x], dim=1), + flow.cat([local_y, local_y], dim=1), + transpose_b=True, + ) + z = flow._C.relu(z) + # print(f"z shape: {z.shape}, device: {z.device}") + # print(z.numpy()) + + placement = flow.placement("cuda", ranks=[0, 1]) + sbp = flow.sbp.split(1) + c_x = local_x.to_global(placement=placement, sbp=sbp) + c_y = local_y.to_global(placement=placement, sbp=sbp) + + # print(f"c_x shape: {c_x.shape}, placement: {c_x.placement}, sbp: {c_x.sbp}") + # print(f"c_y shape: {c_y.shape}, placement: {c_y.placement}, sbp: {c_y.sbp}") + + m = MyModule1(c_y) + g = MyGraph(m) + + g_z = g(c_x) + # print(f"g_z shape: {g_z.shape}, placement: {g_z.placement}, sbp: {g_z.sbp}") + # print(g_z.to_local().numpy()) + test_case.assertTrue(np.allclose(z.numpy(), g_z.to_local().numpy())) + + # @unittest.skipIf(True, "") + def test_bwd_P2B(test_case): + """ compare eager bwd and lazy bwd + """ + rank = flow.env.get_rank() + # pid = os.getpid() + # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_bwd_P2B") + + local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + + z = flow._C.matmul( + local_y, flow.cat([local_x, local_x], dim=0), transpose_b=True, + ) + z = flow._C.relu(z) + z = z.sum() + + placement = flow.placement("cuda", ranks=[0, 1]) + c_x = local_x.to_global(placement=placement, sbp=flow.sbp.split(0)) + c_y = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) + + m = MyModule2(c_y) + optimizer = flow.optim.SGD(m.parameters(), lr=1.0) + g = MyGraph(m, optimizer) + + g_z = g(c_x) + # print(f"g_z shape: {g_z.shape}, placement: {g_z.placement}, sbp: {g_z.sbp}") + test_case.assertTrue(g_z.is_global) + test_case.assertTrue(g_z.sbp[0] == flow.sbp.broadcast) + # S(1) -> B not supported yet + # c_z = g_z.to_global(sbp=flow.sbp.broadcast) + # print(f"c_z shape: {c_z.shape}, placement: {c_z.placement}, sbp: {c_z.sbp}") + test_case.assertTrue(np.allclose(z.numpy(), g_z.to_local().numpy())) + + e_y = c_y.detach() + # print(f"e_y shape: {e_y.shape}, placement: {e_y.placement}, sbp: {e_y.sbp}") + e_m = MyModule2(e_y) + e_z = e_m(c_x) + # print(f"e_z shape: {e_z.shape}, placement: {e_z.placement}, sbp: {e_z.sbp}") + e_z.backward() + + test_case.assertTrue( + np.allclose(c_y.to_local().numpy(), e_y.to_local().numpy()) + ) + + # @unittest.skipIf(True, "") + def test_multi_graph(test_case): + """ compare two lazy fwd + """ + rank = flow.env.get_rank() + # pid = os.getpid() + # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_multi_graph") + + local_x = flow.tensor(x, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + local_y = flow.tensor(y, dtype=flow.float32, device=flow.device(f"cuda:{rank}")) + + placement = flow.placement("cuda", ranks=[0, 1]) + x1 = local_x.to_global(placement=placement, sbp=flow.sbp.broadcast) + y1 = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) + # B * B -> B -> B + m1 = MyModule3(transpose_b=True) + g1 = MyGraph(m1) + + slice_obj = slice( + int(rank * local_x.shape[0] / 2), int((rank + 1) * local_x.shape[0] / 2) + ) + x2 = local_x[slice_obj, :] + x2 = x2.to_global(placement=placement, sbp=flow.sbp.split(0)) + y2 = local_y.to_global(placement=placement, sbp=flow.sbp.broadcast) + # S(0) * B -> S(0) -> B + m2 = MyModule3(transpose_b=True) + g2 = MyGraph(m2) + + x3 = local_x[ + :, int(rank * local_x.shape[1] / 2) : int((rank + 1) * local_x.shape[1] / 2) + ] + x3 = x3.to_global(placement=placement, sbp=flow.sbp.split(1)) + y3 = local_y[ + :, int(rank * local_y.shape[1] / 2) : int((rank + 1) * local_y.shape[1] / 2) + ] + y3 = y3.to_global(placement=placement, sbp=flow.sbp.split(1)) + # S(1) * S(0) -> P -> B + m3 = MyModule3(transpose_b=True) + g3 = MyGraph(m3) + + z1 = g1(x1, y1) + # print(f"z1 shape: {z1.shape}, placement: {z1.placement}, sbp: {z1.sbp}") + # print(z1.to_local().numpy()) + z2 = g2(x2, y2) + # print(f"z2 shape: {z2.shape}, placement: {z2.placement}, sbp: {z2.sbp}") + # print(z2.to_local().numpy()) + z3 = g3(x3, y3) + # print(f"z3 shape: {z3.shape}, placement: {z3.placement}, sbp: {z3.sbp}") + # print(z3.to_local().numpy()) + + test_case.assertTrue(np.allclose(z1.to_local().numpy(), z2.to_local().numpy())) + test_case.assertTrue(np.allclose(z1.to_local().numpy(), z3.to_local().numpy())) + + # @unittest.skipIf(True, "") + def test_global_to(test_case): + c_x = flow.ones( + (4, 3), placement=flow.placement("cpu", ranks=[0, 1]), sbp=flow.sbp.split(0) + ) + + global_to = GlobalToModule("cuda") + g_global_to = MyGraph(global_to) + + e = global_to(c_x) + test_case.assertTrue(e.is_cuda) + test_case.assertTrue(e.is_global) + test_case.assertTrue(e.sbp[0] == flow.sbp.split(0)) + + g = g_global_to(c_x) + test_case.assertTrue(g.is_cuda) + test_case.assertTrue(g.is_global) + test_case.assertTrue(g.sbp[0] == flow.sbp.split(0)) + + test_case.assertTrue(np.allclose(e.to_local().numpy(), g.to_local().numpy())) + + # @unittest.skipIf(True, "") + def test_free_tensor_to_global(test_case): + local_x = flow.tensor(x, dtype=flow.float32, device="cpu") + placement = flow.placement("cuda", ranks=[0, 1]) + c_x = local_x.to_global(placement, flow.sbp.split(0)) + + m = FreeTensorModule((3, 10), placement, flow.sbp.broadcast) + g = MyGraph(m) + + eager_out = m(c_x) + test_case.assertTrue(eager_out.is_cuda) + test_case.assertTrue(eager_out.is_global) + test_case.assertTrue(eager_out.sbp[0] == flow.sbp.split(0)) + + graph_out = g(c_x) + test_case.assertTrue(graph_out.is_cuda) + test_case.assertTrue(graph_out.is_global) + test_case.assertTrue(graph_out.sbp[0] == flow.sbp.split(0)) + + test_case.assertTrue( + np.allclose(eager_out.to_local().numpy(), graph_out.to_local().numpy()) + ) + + # @unittest.skipIf(True, "") + def test_to_placement(test_case): + rank = flow.env.get_rank() + # pid = os.getpid() + # print(f"[{pid}][{rank}] ToGlobalGraphTestCase.test_to_placement") + + if rank == 0: + x = flow.ones((2, 3), dtype=flow.float32) + elif rank == 1: + x = flow.empty(tuple()) + else: + raise ValueError + + c_x = x.to_global( + placement=flow.placement("cpu", ranks=[0]), sbp=flow.sbp.broadcast + ) + # print(f"c_x shape: {c_x.shape}, placement: {c_x.placement}, sbp: {c_x.sbp}") + + p1 = flow.placement("cpu", ranks=[0, 1]) + m1 = ToPlacementModule(p1) + g1 = MyGraph(m1) + y1 = g1(c_x) + + # print(f"y1 shape: {y1.shape}, placement: {y1.placement}, sbp: {y1.sbp}") + test_case.assertTrue(y1.placement == p1) + test_case.assertTrue(y1.sbp[0] == flow.sbp.broadcast) + test_case.assertTrue(y1.to_local().numpy().mean() == 1.0) + + p2 = flow.placement("cuda", ranks=[0, 1]) + m2 = ToPlacementModule(p2) + g2 = MyGraph(m2) + y2 = g2(y1) + + # print(f"y2 shape: {y2.shape}, placement: {y2.placement}, sbp: {y2.sbp}") + test_case.assertTrue(y2.placement == p2) + test_case.assertTrue(y2.sbp[0] == flow.sbp.broadcast) + test_case.assertTrue(y2.to_local().numpy().mean() == 1.0) + + # @unittest.skipIf(True, "") + def test_to_dtype(test_case): + x = flow.ones((2, 3), dtype=flow.int32, device="cpu") + + placement = flow.placement("cpu", ranks=[0, 1]) + c_x = flow.ones( + (2, 3), dtype=flow.int32, placement=placement, sbp=flow.sbp.broadcast + ) + + class CastModule(flow.nn.Module): + def __init__(self, dtype): + super().__init__() + self.dtype = dtype + + def forward(self, x): + return x.to(dtype=self.dtype) + + m = CastModule(flow.float32) + g = MyGraph(m) + + e_x = m(x) + e_c_x = m(c_x) + # NOTE(chengcheng): + # There are two BUG in this test script: + # 1. first call and second call input tensor meta is NOT same + # 2. nn.Graph NOT support local input with multi-rank yet. + # g_x = g(x) + g_c_x = g(c_x) + + test_case.assertTrue(e_x.dtype == flow.float32) + # test_case.assertTrue(g_x.dtype == flow.float32) + test_case.assertTrue(e_c_x.dtype == flow.float32) + test_case.assertTrue(g_c_x.dtype == flow.float32) + + +class MyModule5(flow.nn.Module): + def __init__(self, transpose_a=False, transpose_b=False, sbp=[]): + super().__init__() + self.transpose_a = transpose_a + self.transpose_b = transpose_b + self.sbp = sbp + + def forward(self, x, y): + z = flow._C.matmul(x, y, self.transpose_a, self.transpose_b) + assert z.is_global + assert len(z.sbp) == len(self.sbp) + return z.to_global(sbp=self.sbp) + + +@unittest.skipIf(True, "") +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n4d() +class ToGlobal2DGraphTestCase(oneflow.unittest.TestCase): + def test_matmul(test_case): + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + x = flow.ones( + (4, 6), placement=placement, sbp=[flow.sbp.split(0), flow.sbp.split(1)] + ) + y = flow.ones( + (4, 6), placement=placement, sbp=[flow.sbp.broadcast, flow.sbp.split(1)] + ) + z = flow._C.matmul(x, y, transpose_b=True) + print(f"z shape: {z.shape}, placement: {z.placement}, sbp: {z.sbp}") + + # m = MyModule5(transpose_b=True, sbp=[flow.sbp.split(0), flow.sbp.broadcast]) + # z = m(x, y) + # print(f"z shape: {z.shape}, placement: {z.placement}, sbp: {z.sbp}") + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestLazy1dTo2dGlobal(flow.unittest.TestCase): + def test_lazy_1d_to_2d_sbp(test_case): + P_1d = flow.placement( + device_type="cuda", device_ids={0: range(4)}, hierarchy=(4,) + ) + P_2d = flow.placement( + device_type="cuda", device_ids={0: range(4)}, hierarchy=(2, 2) + ) + B = flow.sbp.broadcast + + class Test1dTo2dModule(flow.nn.Module): + def forward(self, x): + return x.to_global(placement=P_2d, sbp=[B, B]) + + class Test1dTo2dGraph(flow.nn.Graph): + def __init__(self, model): + super().__init__() + self.model = model + + def build(self, x): + return self.model(x) + + class Test2dTo1dModule(flow.nn.Module): + def forward(self, x): + return x.to_global(placement=P_1d, sbp=[B]) + + class Test2dTo1dGraph(flow.nn.Graph): + def __init__(self, model): + super().__init__() + self.model = model + + def build(self, x): + return self.model(x) + + model_1d_to_2d = Test1dTo2dModule() + graph_1d_to_2d = Test1dTo2dGraph(model_1d_to_2d) + + x = flow.zeros(4, 4, 4, 4, sbp=[B, B], placement=P_2d) + x = x.to_global(placement=P_1d, sbp=[B]) + test_case.assertTrue(x.sbp == (B,)) + test_case.assertTrue(x.placement == P_1d) + y = graph_1d_to_2d(x) + test_case.assertTrue(y.sbp == (B, B)) + test_case.assertTrue(y.placement == P_2d) + + model_2d_to_1d = Test2dTo1dModule() + graph_2d_to_1d = Test2dTo1dGraph(model_2d_to_1d) + z = graph_2d_to_1d(y) + test_case.assertTrue(z.sbp == x.sbp) + test_case.assertTrue(z.placement == x.placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/graph/test_tvm_frontend_dependency_on_graph.py b/python/oneflow/test/graph/test_tvm_frontend_dependency_on_graph.py index d83848b6de95ce6174965610ad1ffcd097c8a3ab..347d883f7b20fe6a1666f8c135725179fcfc4e6f 100644 --- a/python/oneflow/test/graph/test_tvm_frontend_dependency_on_graph.py +++ b/python/oneflow/test/graph/test_tvm_frontend_dependency_on_graph.py @@ -109,7 +109,7 @@ class TestConvertDependency(flow.unittest.TestCase): size_where = 3 p_size = re.compile(r"size=\(.*?\)", re.S) - p_type = re.compile(r"dtype=.*?\)", re.S) + p_type = re.compile(r"(dtype=.*?)[,|\)]", re.S) types = ["INPUT", "PARAMETER", "BUFFER", "OUTPUT"] num_nodes = {} diff --git a/python/oneflow/test/misc/test_env_cuda.py b/python/oneflow/test/misc/test_env_cuda.py index 0c67965bb541b0e20c8e4a0730ea75e3d170a592..8947068a59044939925097b5c52e050568d69eb2 100644 --- a/python/oneflow/test/misc/test_env_cuda.py +++ b/python/oneflow/test/misc/test_env_cuda.py @@ -16,7 +16,9 @@ limitations under the License. import os import unittest import oneflow as flow +from oneflow.test_utils.automated_test_util.generators import nothing, oneof import oneflow.unittest +import torch @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @@ -47,6 +49,12 @@ class TestEnv(flow.unittest.TestCase): with test_case.assertRaisesRegex(ValueError, "Expected a cuda device, but"): flow.cuda.synchronize("cpu") + def test_cuda_get_device_name(test_case): + return torch.cuda.get_device_name(oneof(0, nothing())) + + def test_cuda_get_device_capability(test_case): + return torch.cuda.get_device_capability(oneof(0, nothing())) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/misc/test_mock_scope.py b/python/oneflow/test/misc/test_mock_scope.py new file mode 100644 index 0000000000000000000000000000000000000000..23b76d65b1332662a3ae19f6676ada0de1149308 --- /dev/null +++ b/python/oneflow/test/misc/test_mock_scope.py @@ -0,0 +1,120 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +import oneflow.mock_torch as mock + +""" +enable & disable mode hold a dict[str, ModuleType] like sys.modules, the keys start with 'torch'. +The two modes don't interfere with each other, sys.modules and global scope are replaced on switch. +""" + + +def _import_both(): + with mock.enable(): + import torch + import torch.nn + import torch.version + with mock.disable(): + import torch + import torch.nn + import torch.version + + +class TestMock(flow.unittest.TestCase): + def test_with(test_case): + _import_both() + with mock.enable(): + test_case.assertEqual(torch.__package__, "oneflow") + test_case.assertEqual(torch.nn.__package__, "oneflow.nn") + test_case.assertEqual(torch.version.__version__, flow.__version__) + with mock.disable(): + test_case.assertEqual(torch.__package__, "torch") + test_case.assertEqual(torch.nn.__package__, "torch.nn") + test_case.assertEqual(torch.version.__version__, torch.__version__) + + def test_simple(test_case): + _import_both() + mock.enable() + test_case.assertEqual(torch.__package__, "oneflow") + test_case.assertEqual(torch.nn.__package__, "oneflow.nn") + test_case.assertEqual(torch.version.__version__, flow.__version__) + + mock.disable() + + test_case.assertEqual(torch.__package__, "torch") + test_case.assertEqual(torch.nn.__package__, "torch.nn") + test_case.assertEqual(torch.version.__version__, torch.__version__) + + def test_import_from(test_case): + _import_both() + mock.enable() + from torch import nn + from torch.version import __version__ + + test_case.assertEqual(nn.__package__, "oneflow.nn") + test_case.assertEqual(__version__, flow.__version__) + + mock.disable() + from torch import nn + from torch.version import __version__ + + test_case.assertEqual(nn.__package__, "torch.nn") + test_case.assertEqual(__version__, torch.__version__) + + def test_error(test_case): + mock.enable() + with test_case.assertRaises(Exception) as context: + from torch import noexist + test_case.assertTrue( + "oneflow.noexist is not implemented" in str(context.exception) + ) + mock.disable() + with test_case.assertRaises(Exception) as context: + from torch import noexist + test_case.assertTrue( + "cannot import name 'noexist' from 'torch'" in str(context.exception) + ) + + def test_nested_with(test_case): + _import_both() + with mock.enable(): + test_case.assertEqual(torch.__package__, "oneflow") + with mock.disable(): + test_case.assertEqual(torch.__package__, "torch") + test_case.assertEqual(torch.__package__, "oneflow") + with mock.disable(): + test_case.assertEqual(torch.__package__, "torch") + with mock.enable(): + test_case.assertEqual(torch.__package__, "oneflow") + test_case.assertEqual(torch.__package__, "torch") + + def test_noop_disable(test_case): + with mock.disable(): + import torch + + test_case.assertEqual(torch.__package__, "torch") + + def test_3rd_party(test_case): + with mock.enable(): + from test_mock_simple import f + + test_case.assertEqual(f(), "oneflow") + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/misc/test_mock_simple.py b/python/oneflow/test/misc/test_mock_simple.py new file mode 100644 index 0000000000000000000000000000000000000000..0d1d2ab1c131e1af4ee7f64cce6ad39e0bc5aa66 --- /dev/null +++ b/python/oneflow/test/misc/test_mock_simple.py @@ -0,0 +1,22 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import torch + +print(torch.__file__.find("mock_torch") != -1) + + +def f(): + return torch.__package__ diff --git a/python/oneflow/test/misc/test_np_dtype_converter.py b/python/oneflow/test/misc/test_np_dtype_converter.py new file mode 100644 index 0000000000000000000000000000000000000000..48870b14e89f00a685b6e6e9a84872eec1d70370 --- /dev/null +++ b/python/oneflow/test/misc/test_np_dtype_converter.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestNpDtypeConverter(flow.unittest.TestCase): + def test_np_dtype_converter(test_case): + for flow_dtype in flow.dtypes(): + if flow_dtype in [flow.record, flow.tensor_buffer, flow.bfloat16]: + continue + np_dtype = flow.convert_oneflow_dtype_to_numpy_dtype(flow_dtype) + test_case.assertEqual( + flow.framework.dtype.convert_numpy_dtype_to_oneflow_dtype(np_dtype), + flow_dtype, + ) + + # Test whether dtype conversion works with arr.dtype + np_arr = np.array([1, 2], dtype=np_dtype) + test_case.assertEqual(np_arr.dtype, np_dtype) + flow_tensor = flow.tensor([1, 2], dtype=flow_dtype) + test_case.assertEqual(flow_tensor.dtype, flow_dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/misc/test_placement.py b/python/oneflow/test/misc/test_placement.py new file mode 100644 index 0000000000000000000000000000000000000000..0d57678075bc6c4eb50d1fbf3481e25e8e8866c7 --- /dev/null +++ b/python/oneflow/test/misc/test_placement.py @@ -0,0 +1,36 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestPlacement(flow.unittest.TestCase): + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_placement_all_cuda(test_case): + placement = flow.placement.all("cuda") + test_case.assertEqual(placement.device_tag, "cuda") + test_case.assertEqual(placement.ranks, range(flow.env.get_world_size())) + + def test_placement_all_cpu(test_case): + placement = flow.placement.all("cpu") + test_case.assertEqual(placement.device_tag, "cpu") + test_case.assertEqual(placement.ranks, range(flow.env.get_world_size())) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/misc/test_stack_getter.py b/python/oneflow/test/misc/test_stack_getter.py new file mode 100644 index 0000000000000000000000000000000000000000..d877bfe02d955486b96546284a2d931511bb5776 --- /dev/null +++ b/python/oneflow/test/misc/test_stack_getter.py @@ -0,0 +1,50 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os + +os.environ["ONEFLOW_PYTHON_STACK_GETTER"] = "1" +import oneflow as flow +import oneflow.unittest + + +def h(): + return g() + + +def g(): + res = f() + res = f() + res = f() + res = f() + res = f() + res = f() + return res + + +def f(): + return flow._oneflow_internal.GetCurrentStack() + + +@flow.unittest.skip_unless_1n1d() +def test_stack_getter(): + stack = h() + assert "flow._oneflow_internal.GetCurrentStack" in stack + assert "g()" in stack + assert "f()" in stack + + +if __name__ == "__main__": + test_stack_getter() diff --git a/python/oneflow/test/modules/fused_dot_feature_interaction.py b/python/oneflow/test/modules/fused_dot_feature_interaction.py deleted file mode 100644 index 6712f0415a8321a65025eea55038f4753a1273df..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/fused_dot_feature_interaction.py +++ /dev/null @@ -1,43 +0,0 @@ -import numpy as np -import oneflow as flow - -def fused_dot_feature_interaction(x, - y, - self_interaction=False, - output_padding=0, - output_concat=None, - dtype=flow.float32 - ): - # (bs, es) = x.shape - (bs, dims, es) = y.shape - - if self_interaction: - offset = 1 - else: - offset = 0 - li = flow.tensor([i for i in range(dims + 1) for j in range(i + offset)]) - lj = flow.tensor([j for i in range(dims + 1) for j in range(i + offset)]) - T = flow.cat( - [ - flow.reshape(x, (bs, 1, es)), - y, - ], - dim=1, - ) - Z = flow.matmul(T, T, transpose_b=True) - # gather_nd not support half, so cast to float32 - Z = flow.cast(Z, flow.float32) - Zflat = Z[:, li, lj] - Zflat = flow.cast(Zflat, dtype) - if output_concat is not None: - R = flow.cat([output_concat, Zflat], dim=1) - else: - R = Zflat - if output_padding != 0: - padding_tensor = flow.tensor( - np.zeros((bs, output_padding)).astype(np.float32), - device="cuda", - requires_grad=False, - ) - R = flow.cat([R, padding_tensor], dim=1) - return R diff --git a/python/oneflow/test/modules/optimizer_test_util.py b/python/oneflow/test/modules/optimizer_test_util.py index 4a137e24b04b87b1425b85d7f1c3371b8f67d268..f9c9bf0c36a3fa9d188771ab181e7a771fa4c250 100644 --- a/python/oneflow/test/modules/optimizer_test_util.py +++ b/python/oneflow/test/modules/optimizer_test_util.py @@ -17,19 +17,31 @@ import numpy as np def clip_grad_norm_np(np_grad, max_norm, norm_type): + np_grad_is_list = True + if isinstance(np_grad, np.ndarray): + np_grad_is_list = False + np_grad = [np_grad] + max_norm = float(max_norm) norm_type = float(norm_type) if norm_type == float("inf"): total_norm = np.max(np.abs(np_grad)) - if norm_type == float("-inf"): + elif norm_type == float("-inf"): total_norm = np.min(np.abs(np_grad)) - elif norm_type == 0: - total_norm = np.sum(np.stack([np.sum(np_grad != 0)]) != 0) else: - total_norm = np_grad - for i in range(np_grad.ndim, 0, -1): - total_norm = np.linalg.norm(total_norm, norm_type, axis=i - 1) + norms = np_grad + total_norm = [] + for i, norm in enumerate(norms): + for j in range(np_grad[i].ndim, 0, -1): + norm = np.linalg.norm(norm, norm_type, axis=j - 1) + total_norm.append(norm) + total_norm = np.linalg.norm(np.array(total_norm, dtype=np.float32), norm_type) + clip_coef = max_norm / (total_norm + 1e-6) if clip_coef < 1: - np_grad = np_grad * clip_coef + for grad in np_grad: + grad *= clip_coef + + if not np_grad_is_list: + np_grad = np_grad[0] return total_norm, np_grad diff --git a/python/oneflow/test/modules/sync_batchnorm_test_util.py b/python/oneflow/test/modules/sync_batchnorm_test_util.py new file mode 100644 index 0000000000000000000000000000000000000000..089b190123bb9a1e1be2a77aa9e1b02fe6886eed --- /dev/null +++ b/python/oneflow/test/modules/sync_batchnorm_test_util.py @@ -0,0 +1,84 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow as flow + +ONEREC_URL = ( + "https://oneflow-public.oss-cn-beijing.aliyuncs.com/sync_bn_test_datas.tar.gz" +) +MD5 = "537ff00fb47be8be90df75f47a883b76" + + +def md5(fname): + import hashlib + + hash_md5 = hashlib.md5() + with open(fname, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + result = hash_md5.hexdigest() + print("md5", fname, result) + return result + + +def download_file(out_path: str, url): + import requests + from tqdm import tqdm + + resp = requests.get(url=url, stream=True) + MB = 1024 ** 2 + size = int(resp.headers["Content-Length"]) / MB + print("File size: %.4f MB, downloading..." % size) + with open(out_path, "wb") as f: + for data in tqdm( + iterable=resp.iter_content(MB), total=size, unit="m", desc=out_path + ): + f.write(data) + print("Done!") + + +def ensure_datas(): + import os + import pathlib + + data_dir = os.path.join( + os.getenv("ONEFLOW_TEST_CACHE_DIR", "./data-test"), "sync_bn" + ) + file_path = pathlib.Path(data_dir) / ONEREC_URL.split("/")[-1] + absolute_file_path = str(file_path.absolute()) + + if flow.env.get_rank() == 0: + file_path.parent.mkdir(parents=True, exist_ok=True) + if file_path.exists(): + if MD5 != md5(absolute_file_path): + file_path.unlink() + download_file(absolute_file_path, ONEREC_URL) + else: + download_file(str(absolute_file_path), ONEREC_URL) + assert MD5 == md5(absolute_file_path) + + import tarfile + + my_tar = tarfile.open(str(absolute_file_path)) + my_tar.extractall(data_dir) # specify which folder to extract to + my_tar.close() + + flow.comm.barrier() + + return os.path.join( + os.getenv("ONEFLOW_TEST_CACHE_DIR", "./data-test"), + "sync_bn", + "sync_bn_test_datas", + ) diff --git a/python/oneflow/test/modules/test_abs.py b/python/oneflow/test/modules/test_abs.py index aed873f4c57c37923e534e89b0aa7715e96671eb..f906dca5d0a9c27b341a0b4b65bd963d574ee6d8 100644 --- a/python/oneflow/test/modules/test_abs.py +++ b/python/oneflow/test/modules/test_abs.py @@ -37,6 +37,11 @@ class TestAbsModule(flow.unittest.TestCase): y = torch.abs(x) return y + @profile(torch.abs) + def profile_abs(test_case): + torch.abs(torch.ones(1, 128, 28, 28)) + torch.abs(torch.ones(16, 128, 28, 28)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_activation.py b/python/oneflow/test/modules/test_activation.py index b57d869e63e89630ac7222dbc2563b77b9b7af3c..b64708bba8a7536a76769f27ced62d22aac5ed9b 100644 --- a/python/oneflow/test/modules/test_activation.py +++ b/python/oneflow/test/modules/test_activation.py @@ -18,6 +18,7 @@ import unittest from collections import OrderedDict import numpy as np +import torch as pytorch from oneflow.test_utils.automated_test_util import * from scipy import special @@ -62,7 +63,9 @@ class TestReLUModule(flow.unittest.TestCase): @profile(torch.nn.functional.relu) def profile_relu(test_case): torch.nn.functional.relu(torch.ones(1, 128, 28, 28)) + torch.nn.functional.relu(torch.ones(1, 128, 28, 28), inplace=True) torch.nn.functional.relu(torch.ones(16, 128, 28, 28)) + torch.nn.functional.relu(torch.ones(16, 128, 28, 28), inplace=True) @flow.unittest.skip_unless_1n1d() @@ -97,11 +100,6 @@ class TestReLU6Module(flow.unittest.TestCase): y = m(x) return y - @profile(torch.nn.functional.relu6) - def profile_relu6(test_case): - torch.nn.functional.relu6(torch.ones(1, 128, 28, 28)) - torch.nn.functional.relu6(torch.ones(16, 128, 28, 28)) - @flow.unittest.skip_unless_1n1d() class TestTanh(flow.unittest.TestCase): @@ -156,10 +154,10 @@ class TestTanh(flow.unittest.TestCase): y = torch.tanh(x) return y - @profile(torch.tanh) + @profile(torch.nn.functional.tanh) def profile_tanh(test_case): - torch.tanh(torch.ones(1, 128, 28, 28)) - torch.tanh(torch.ones(16, 128, 28, 28)) + torch.nn.functional.tanh(torch.ones(1, 128, 28, 28)) + torch.nn.functional.tanh(torch.ones(16, 128, 28, 28)) @flow.unittest.skip_unless_1n1d() @@ -194,6 +192,11 @@ class TestELUModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.elu) + def profile_elu(test_case): + torch.nn.functional.elu(torch.ones(1, 128, 28, 28), 1.0) + torch.nn.functional.elu(torch.ones(16, 128, 28, 28), 1.0) + @flow.unittest.skip_unless_1n1d() class TestCELUModule(flow.unittest.TestCase): @@ -237,6 +240,13 @@ class TestCELUModule(flow.unittest.TestCase): m(y) return y + @profile(torch.nn.functional.celu) + def profile_celu(test_case): + torch.nn.functional.celu(torch.ones(1, 128, 28, 28)) + torch.nn.functional.celu(torch.ones(1, 128, 28, 28), inplace=True) + torch.nn.functional.celu(torch.ones(16, 128, 28, 28)) + torch.nn.functional.celu(torch.ones(16, 128, 28, 28), inplace=True) + @flow.unittest.skip_unless_1n1d() class TestGelu(flow.unittest.TestCase): @@ -260,9 +270,53 @@ class TestGelu(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.gelu) + def profile_gelu(test_case): + torch.nn.functional.gelu(torch.ones(1, 128, 28, 28)) + torch.nn.functional.gelu(torch.ones(16, 128, 28, 28)) + + +@unittest.skipIf( + float(pytorch.__version__[:4]) < 1.12, + f"need pytorch version >= 1.12, got {pytorch.__version__}", +) +@flow.unittest.skip_unless_1n1d() +class TestFastGelu(flow.unittest.TestCase): + @autotest(n=5) + def test_fast_gelu(test_case): + device = random_device() + x = random_tensor().to(device) + y = torch.nn.functional.gelu(x, approximate="tanh") + return y + + @autotest(n=5) + def test_fast_gelu_fp16(test_case): + x = random_tensor().to(device=gpu_device(), dtype=torch.float16) + y = torch.nn.functional.gelu(x, approximate="tanh") + return y + + @autotest(n=5) + def test_fast_gelu_scalar(test_case): + x = random_tensor(ndim=0).to(device=random_device()) + y = torch.nn.functional.gelu(x, approximate="tanh") + return y + + @profile(torch.nn.functional.gelu) + def profile_fast_gelu(test_case): + torch.nn.functional.gelu(torch.ones(1, 128, 28, 28), approximate="tanh") + torch.nn.functional.gelu(torch.ones(16, 128, 28, 28), approximate="tanh") + @flow.unittest.skip_unless_1n1d() class TestSigmoidModule(flow.unittest.TestCase): + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @autotest(n=5, atol=1e-3, check_dtype=True) + def test_sigmoid_flow_with_half_data(test_case): + device = gpu_device() + x = random_tensor().to(device=device, dtype=torch.float16) + y = torch.sigmoid(x) + return y + @autotest(n=5) def test_sigmoid_module_with_random_data(test_case): m = torch.nn.Sigmoid() @@ -391,6 +445,13 @@ class TestHardsigmoidModule(flow.unittest.TestCase): y = torch.nn.functional.hardsigmoid(x, random_bool()) return y + @profile(torch.nn.functional.hardsigmoid) + def profile_hardsigmoid(test_case): + torch.nn.functional.hardsigmoid(torch.ones(1, 128, 28, 28)) + torch.nn.functional.hardsigmoid(torch.ones(1, 128, 28, 28), inplace=True) + torch.nn.functional.hardsigmoid(torch.ones(16, 128, 28, 28)) + torch.nn.functional.hardsigmoid(torch.ones(16, 128, 28, 28), inplace=True) + def do_test_softmax(batch_size: int, log_softmax: bool = False): num_dims = random(low=1, high=5).to(int) @@ -451,6 +512,11 @@ class TestLogSoftmaxModule(flow.unittest.TestCase): def test_softmax_module_with_batch_size_equal_10240(test_case): return do_test_softmax(batch_size=10240, log_softmax=True) + @profile(torch.nn.functional.log_softmax) + def profile_logsoftmax(test_case): + torch.nn.functional.log_softmax(torch.ones(1, 128, 28, 28)) + torch.nn.functional.log_softmax(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestLogSigmoidModule(flow.unittest.TestCase): @@ -474,6 +540,11 @@ class TestLogSigmoidModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.logsigmoid) + def profile_logsigmoid(test_case): + torch.nn.functional.logsigmoid(torch.ones(1, 128, 28, 28)) + torch.nn.functional.logsigmoid(torch.ones(16, 128, 28, 28)) + def numpy_softplus(x, beta, threshold): return np.where( @@ -547,6 +618,11 @@ class TestSoftplusModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.softplus) + def profile_softplus(test_case): + torch.nn.functional.softplus(torch.ones(1, 128, 28, 28)) + torch.nn.functional.softplus(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestHardswishModule(flow.unittest.TestCase): @@ -570,6 +646,11 @@ class TestHardswishModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.hardswish) + def profile_hardswish(test_case): + torch.nn.functional.hardswish(torch.ones(1, 128, 28, 28)) + torch.nn.functional.hardswish(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestHardtanhModule(flow.unittest.TestCase): @@ -599,6 +680,15 @@ class TestHardtanhModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.hardtanh) + def profile_hardtanh(test_case): + torch.nn.functional.hardtanh( + torch.ones(1, 128, 28, 28), min_val=-1.0, max_val=1.0 + ) + torch.nn.functional.hardtanh( + torch.ones(16, 128, 28, 28), min_val=-1.0, max_val=1.0 + ) + @flow.unittest.skip_unless_1n1d() class TestLeakyReLUModule(flow.unittest.TestCase): @@ -634,6 +724,13 @@ class TestLeakyReLUModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.leaky_relu) + def profile_leaky_relu(test_case): + torch.nn.functional.leaky_relu(torch.ones(1, 128, 28, 28), 0.1) + torch.nn.functional.leaky_relu(torch.ones(1, 128, 28, 28), 0.1, inplace=True) + torch.nn.functional.leaky_relu(torch.ones(16, 128, 28, 28), 0.1) + torch.nn.functional.leaky_relu(torch.ones(16, 128, 28, 28), 0.1, inplace=True) + @flow.unittest.skip_unless_1n1d() class TestMishModule(flow.unittest.TestCase): @@ -657,6 +754,11 @@ class TestMishModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.mish) + def profile_mish(test_case): + torch.nn.functional.mish(torch.ones(1, 128, 28, 28)) + torch.nn.functional.mish(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestSiluModule(flow.unittest.TestCase): @@ -680,6 +782,11 @@ class TestSiluModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.silu) + def profile_silu(test_case): + torch.nn.functional.silu(torch.ones(1, 128, 28, 28)) + torch.nn.functional.silu(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestSeluModule(flow.unittest.TestCase): @@ -703,6 +810,11 @@ class TestSeluModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.selu) + def profile_selu(test_case): + torch.nn.functional.selu(torch.ones(1, 128, 28, 28)) + torch.nn.functional.selu(torch.ones(16, 128, 28, 28)) + @unittest.skip("still have error in ci test") class TestSoftsignModule(flow.unittest.TestCase): @@ -716,6 +828,12 @@ class TestSoftsignModule(flow.unittest.TestCase): y = m(x) return y + #'Ran 1 test in 0.000s',return a blank table + @profile(torch.nn.functional.softsign) + def profile_softsign(test_case): + torch.nn.functional.softsign(torch.ones(1, 128, 28, 28)) + torch.nn.functional.softsign(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestReluFunction(flow.unittest.TestCase): @@ -750,6 +868,11 @@ class TestRelu6Function(flow.unittest.TestCase): y = torch.nn.functional.relu6(x) return y + @profile(torch.nn.functional.relu6) + def profile_relu6(test_case): + torch.nn.functional.relu6(torch.ones(1, 128, 28, 28)) + torch.nn.functional.relu6(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestLogSigmoidFunction(flow.unittest.TestCase): @@ -800,6 +923,11 @@ class TestHardshrinkModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.hardshrink) + def profile_hardshrink(test_case): + torch.nn.functional.hardshrink(torch.ones(1, 128, 28, 28)) + torch.nn.functional.hardshrink(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestSoftshrinkModule(flow.unittest.TestCase): @@ -833,6 +961,11 @@ class TestSoftshrinkModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.softshrink) + def profile_softshrink(test_case): + torch.nn.functional.softshrink(torch.ones(1, 128, 28, 28)) + torch.nn.functional.softshrink(torch.ones(16, 128, 28, 28)) + @flow.unittest.skip_unless_1n1d() class TestThresholdModule(flow.unittest.TestCase): @@ -872,6 +1005,15 @@ class TestThresholdModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.threshold) + def profile_threshold(test_case): + torch.nn.functional.threshold( + torch.ones(1, 128, 28, 28), threshold=0.1, value=20 + ) + torch.nn.functional.threshold( + torch.ones(16, 128, 28, 28), threshold=0.1, value=20 + ) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_adaptive_max_pool.py b/python/oneflow/test/modules/test_adaptive_max_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..ea7de428a467409f49a0b048d392c96e6370278d --- /dev/null +++ b/python/oneflow/test/modules/test_adaptive_max_pool.py @@ -0,0 +1,110 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.nn.common_types import _size_1_t +from packaging import version +import torch as torch_original +from typing import Union, Tuple +import numpy as np + + +from oneflow.test_utils.automated_test_util import * + +NoneType = type(None) + + +@flow.unittest.skip_unless_1n1d() +class TestAdaptiveMaxPool(flow.unittest.TestCase): + @autotest(n=5) + def test_adaptive_maxpool1d(test_case): + m = torch.nn.AdaptiveMaxPool1d(output_size=random().to(_size_1_t)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=3).to(device) + y = m(x) + return y + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_adaptive_maxpool2d_manually(test_case): + def _test_adaptive_max_pool_nd(input_shape, output_shape, m1, m2): + input_np = np.random.rand(2, 3, *input_shape) + input_pt = torch_original.tensor( + input_np, device="cuda", requires_grad=True + ) + input_of = flow.tensor(input_np, device="cuda", requires_grad=True) + + m_pt = m1(output_shape, True) + m_of = m2(output_shape, True) + + output_pt = m_pt(input_pt) + output_of = m_of(input_of) + + sum_pt = torch_original.sum(output_pt[0]) + sum_of = flow.sum(output_of[0]) + + sum_pt.backward() + sum_of.backward() + + test_case.assertTrue( + np.array_equal( + output_pt[0].detach().cpu().numpy(), + output_of[0].detach().cpu().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal( + output_pt[1].detach().cpu().numpy(), + output_of[1].detach().cpu().numpy(), + ) + ) + test_case.assertTrue( + np.array_equal(input_pt.grad.cpu().numpy(), input_of.grad.cpu().numpy()) + ) + + _test_adaptive_max_pool_nd( + (10, 11), + (3, 4), + torch_original.nn.AdaptiveMaxPool2d, + flow.nn.AdaptiveMaxPool2d, + ) + _test_adaptive_max_pool_nd( + (10, 11, 12), + (3, 4, 5), + torch_original.nn.AdaptiveMaxPool3d, + flow.nn.AdaptiveMaxPool3d, + ) + + @profile(torch.nn.functional.adaptive_max_pool1d) + def profile_adaptive_max_pool1d(test_case): + torch.nn.functional.adaptive_max_pool1d(torch.ones(1, 64, 8), 5) + + @profile(torch.nn.functional.adaptive_max_pool2d) + def profile_adaptive_max_pool2d(test_case): + torch.nn.functional.adaptive_max_pool2d(torch.ones(1, 64, 10, 9), 7) + torch.nn.functional.adaptive_max_pool2d(torch.ones(1, 64, 8, 9), (5, 7)) + + @profile(torch.nn.functional.adaptive_max_pool3d) + def profile_adaptive_max_pool3d(test_case): + torch.nn.functional.adaptive_max_pool3d(torch.ones(1, 64, 8, 9, 10), (5, 7, 9)) + torch.nn.functional.adaptive_max_pool3d(torch.ones(1, 64, 10, 9, 8), 7) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_adaptive_pool.py b/python/oneflow/test/modules/test_adaptive_pool.py index 74ba47a289c87f9b15fa97fb13cf1900683a97d1..8b3810c52ecf510a62242a9e2bbf1da8034d077d 100644 --- a/python/oneflow/test/modules/test_adaptive_pool.py +++ b/python/oneflow/test/modules/test_adaptive_pool.py @@ -45,6 +45,10 @@ class TestAdaptiveAvgPool(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.adaptive_avg_pool1d) + def profile_adaptive_avg_pool1d(test_case): + torch.nn.functional.adaptive_avg_pool1d(torch.ones(1, 64, 8), 5) + @autotest(n=5) def test_adaptive_avgpool2d(test_case): m = torch.nn.AdaptiveAvgPool2d(output_size=random().to(_size_2_opt_t_not_none)) @@ -55,6 +59,11 @@ class TestAdaptiveAvgPool(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.adaptive_avg_pool2d) + def profile_adaptive_avg_pool2d(test_case): + torch.nn.functional.adaptive_avg_pool2d(torch.ones(1, 64, 10, 9), 7) + torch.nn.functional.adaptive_avg_pool2d(torch.ones(1, 64, 8, 9), (5, 7)) + @unittest.skipIf( version.parse(torch_original.__version__) < version.parse("1.10.0"), "GPU version 'nn.AdaptiveAvgPool3d' has a bug in PyTorch before '1.10.0'", @@ -69,6 +78,11 @@ class TestAdaptiveAvgPool(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.adaptive_avg_pool3d) + def profile_adaptive_avg_pool3d(test_case): + torch.nn.functional.adaptive_avg_pool3d(torch.ones(1, 64, 8, 9, 10), (5, 7, 9)) + torch.nn.functional.adaptive_avg_pool3d(torch.ones(1, 64, 10, 9, 8), 7) + @flow.unittest.skip_unless_1n1d() class TestAdaptiveAvgPoolFunctional(flow.unittest.TestCase): diff --git a/python/oneflow/test/modules/test_add.py b/python/oneflow/test/modules/test_add.py index c1794a8648366380be58969a33cc9fda3ad20826..fe60389e6994f3e4bcec951c5a2cba59ab2be4a6 100644 --- a/python/oneflow/test/modules/test_add.py +++ b/python/oneflow/test/modules/test_add.py @@ -156,6 +156,17 @@ def _test_inplace_add(test_case, shape, device): test_case.assertTrue(np.allclose(of_x.grad.numpy(), np.ones(shape), 1e-05, 1e-05)) +def _test_inplace_add_with_type_promotion(test_case, shape, device): + x = flow.tensor( + np.random.randn(*shape), device=flow.device(device), dtype=flow.float16 + ) + y = flow.tensor( + np.random.randn(*shape), device=flow.device(device), dtype=flow.float32 + ) + x += y + test_case.assertTrue(x.dtype == flow.float16) + + @flow.unittest.skip_unless_1n1d() class TestAddModule(flow.unittest.TestCase): def test_add(test_case): @@ -164,6 +175,7 @@ class TestAddModule(flow.unittest.TestCase): _test_add_forward, _test_add_backward, _test_inplace_add, + _test_inplace_add_with_type_promotion, ] arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)] arg_dict["device"] = ["cpu", "cuda"] @@ -254,6 +266,29 @@ class TestAddModule(flow.unittest.TestCase): z3 = torch.add(s, x3, alpha=alpha) return z1, z2, z3 + @profile(torch.add) + def profile_add(test_case): + torch.add(torch.ones(100), 20) + torch.add(torch.ones(100), torch.ones(100, 1), alpha=10) + + @autotest(n=3) + def test_non_contiguous_inplace_add(test_case): + device = random_device() + x = random_tensor(2, 2, 4).to(device) + y = x + 1 + y = y[:, 1:3] + y += random_tensor(2, 2, 2).to(device) + return y + + @autotest(n=5) + def test_scalar_add_with_random_devices(test_case): + x1_device = random_device() + x2_device = random_device() + x1 = random_tensor(2, 2, 3).to(x1_device).mean() + x2 = random_tensor(2, 2, 3).to(x2_device) + y = x1 + x2 + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_addcdiv.py b/python/oneflow/test/modules/test_addcdiv.py new file mode 100644 index 0000000000000000000000000000000000000000..642c3302c744c0d5b03f468f5496e79c538751db --- /dev/null +++ b/python/oneflow/test/modules/test_addcdiv.py @@ -0,0 +1,70 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestAddcdiv(flow.unittest.TestCase): + @autotest(n=5) + def test_addcdiv(test_case): + device = random_device() + ndim = random(2, 4).to(int).value() + shape = [random(2, 4) for i in range(ndim)] + input = random_tensor(ndim, *shape).to(device) + tensor1 = random_tensor(ndim, *shape).to(device) + tensor2 = random_tensor(ndim, *shape).to(device) + value = random(2, 4).to(int) + output = torch.addcdiv(input, tensor1, tensor2, value=value) + return output + + @autotest(n=5) + def test_tensor_addcdiv(test_case): + device = random_device() + ndim = random(2, 4).to(int).value() + shape = [random(2, 4) for i in range(ndim)] + input = random_tensor(ndim, *shape).to(device) + tensor1 = random_tensor(ndim, *shape).to(device) + tensor2 = random_tensor(ndim, *shape).to(device) + value = random(2, 4).to(int) + output = input.addcdiv(tensor1, tensor2, value=value) + return output + + @autotest(n=5) + def test_tensor_addcdiv_inplace(test_case): + device = random_device() + ndim = random(2, 4).to(int).value() + shape = [random(2, 4) for i in range(ndim)] + input = random_tensor(ndim, *shape).to(device) + input = input + 1.0 + tensor1 = random_tensor(ndim, *shape).to(device) + tensor2 = random_tensor(ndim, *shape).to(device) + value = random(2, 4).to(int) + input.addcdiv_(tensor1, tensor2, value=value) + return input + + @profile(torch.addcdiv) + def profile_addcdiv(test_case): + t = torch.ones(1, 3) + t1 = torch.ones(3, 1) + t2 = torch.ones(1, 3) + torch.addcdiv(t, t1, t2, value=0.1) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_addcmul.py b/python/oneflow/test/modules/test_addcmul.py index c66a99048f2f3a1777e47a6e6bb086423becb8b9..efa438218d785045489123bc05ee5f5fd7adbd94 100644 --- a/python/oneflow/test/modules/test_addcmul.py +++ b/python/oneflow/test/modules/test_addcmul.py @@ -60,6 +60,13 @@ class TestAddcmul(flow.unittest.TestCase): input.addcmul_(tensor1, tensor2, value=value) return input + @profile(torch.addcmul) + def profile_addcmul(test_case): + input = torch.ones(100, 12, 13) + tensor1 = torch.ones(100, 12, 13) + tensor2 = torch.ones(100, 12, 13) + torch.addcmul(input, tensor1, tensor2, value=2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_addmm.py b/python/oneflow/test/modules/test_addmm.py index aac30b7a6c62bdf8ac33eba287dc4a85860f6747..dc1f6300b2fd1803048bde5123f841401219ce64 100644 --- a/python/oneflow/test/modules/test_addmm.py +++ b/python/oneflow/test/modules/test_addmm.py @@ -97,6 +97,14 @@ class TestAddmm(flow.unittest.TestCase): ) return y + @profile(torch.addmm) + def profile_addmm(test_case): + input = torch.ones(2, 3) + mat1 = torch.ones(2, 3) + mat2 = torch.ones(3, 3) + torch.addmm(input, mat1, mat2) + torch.addmm(input, mat1, mat2, alpha=1, beta=2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_affine_grid.py b/python/oneflow/test/modules/test_affine_grid.py index 3ab40641cbaeb207dedeb02c5391ceed97d988e8..e531f80faa3800800742a820f0b83073d5f4adf7 100644 --- a/python/oneflow/test/modules/test_affine_grid.py +++ b/python/oneflow/test/modules/test_affine_grid.py @@ -119,6 +119,13 @@ class TestAffineGrid(flow.unittest.TestCase): ).to(device) return output + @profile(torch.nn.functional.affine_grid) + def profile_affine_grid(test_case): + input = torch.tensor(np.arange(1.0, 7).reshape((1, 2, 3)), dtype=torch.float32) + torch.nn.functional.affine_grid( + input, torch.Size([1, 1, 2, 2]), align_corners=True + ) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_allclose.py b/python/oneflow/test/modules/test_allclose.py new file mode 100644 index 0000000000000000000000000000000000000000..b0f81af1da737e5baa344d2a315ac9192bf11850 --- /dev/null +++ b/python/oneflow/test/modules/test_allclose.py @@ -0,0 +1,81 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + +rtol = 1e-3 + + +def _perturbate(x): + shape = x.oneflow.shape + device = x.device + diff = ( + random_tensor(len(shape), *shape, low=-1, high=1, requires_grad=False).to( + device + ) + * rtol + * 2 + ) + return x + diff + + +@flow.unittest.skip_unless_1n1d() +class TestAllClose(flow.unittest.TestCase): + @autotest(n=10, auto_backward=False, check_graph=False) + def test_allclose_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = torch.allclose(x1, x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_allclose_with_0dim_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = torch.allclose(x1, x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_tensor_allclose_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = x1.allclose(x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_allclose_broadcast(test_case): + device = random_device() + x1 = random_tensor(2, 2, 8, requires_grad=False).to(device) + x2 = _perturbate(x1[:, :1]) + y = torch.allclose(x1, x2, rtol=rtol) + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_amax.py b/python/oneflow/test/modules/test_amax.py index 189d9572c5d93f1a25ef60e92ab60a1a4f9f09e1..79bfbeebd1eec796afed0e45eec272552b3dfd44 100644 --- a/python/oneflow/test/modules/test_amax.py +++ b/python/oneflow/test/modules/test_amax.py @@ -131,6 +131,15 @@ class TestAmax(flow.unittest.TestCase): y = torch.amax(x, dim=tuple(dim), keepdim=random().to(bool)) return y + @profile(torch.amax) + def profile_amax(test_case): + input1 = torch.ones(4, 4) + input2 = torch.ones(100, 100) + torch.amax(input1, 1) + torch.amax(input1, 1, True) + torch.amax(input2, 1) + torch.amax(input2, 1, True) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_amin.py b/python/oneflow/test/modules/test_amin.py index 55c26529487fd78f79d5344f523b3006719c5d1b..a69b77c873c5c6162d06cea47a7a027375e9d693 100644 --- a/python/oneflow/test/modules/test_amin.py +++ b/python/oneflow/test/modules/test_amin.py @@ -88,7 +88,7 @@ def _test_amin_not_keepdim(test_case, device): @flow.unittest.skip_unless_1n1d() -class TestAmax(flow.unittest.TestCase): +class TestAmin(flow.unittest.TestCase): def test_amin(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [ @@ -130,6 +130,15 @@ class TestAmax(flow.unittest.TestCase): y = torch.amin(x, dim=tuple(dim), keepdim=random().to(bool)) return y + @profile(torch.amin) + def profile_amin(test_case): + input1 = torch.ones(4, 4) + input2 = torch.ones(100, 100) + torch.amin(input1, 1) + torch.amin(input1, 1, True) + torch.amin(input2, 1) + torch.amin(input2, 1, True) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_arange.py b/python/oneflow/test/modules/test_arange.py index a47f7b6aecbb82ffd4daa314133b6d61dc9ec434..b9a1456afc9a7d0fb828e8219058e389b13674f4 100644 --- a/python/oneflow/test/modules/test_arange.py +++ b/python/oneflow/test/modules/test_arange.py @@ -56,6 +56,12 @@ def _test_arange_input_tensor_type(test_case, device): x = flow.tensor([[1, 2], [3, 4]], dtype=flow.int64).to(device) y = flow.arange(start=flow.min(x), end=flow.max(x), device=device) test_case.assertTrue(np.allclose(y.numpy(), np.arange(1, 4))) + if device == "cuda": + x = flow.tensor([[1, 2], [3, 4]], dtype=flow.int64).to(device) + y = flow.arange( + start=flow.min(x), end=flow.max(x), device=device, dtype=flow.float16 + ) + test_case.assertTrue(np.allclose(y.numpy(), np.arange(1, 4))) @flow.unittest.skip_unless_1n1d() @@ -77,7 +83,7 @@ class TestArange(flow.unittest.TestCase): def test_arange_with_random_data(test_case): start = random().to(int) end = start + random().to(int) - step = random(0, end - start).to(int) + step = random(1, end - start + 1).to(int) x = torch.arange(start=start, end=end, step=step) device = random_device() x.to(device) @@ -87,12 +93,21 @@ class TestArange(flow.unittest.TestCase): def test_arange_with_float_delta(test_case): start = random().to(int) end = start + random().to(int) - step = random(0, end - start).to(float) + step = random(1, end - start + 1).to(float) x = torch.arange(start=start, end=end, step=step) device = random_device() x.to(device) return x + @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True) + def test_arange_input_float_scalar_tensor(test_case): + start = random().to(float) + end = start + random().to(float) + x = torch.arange(start=torch.tensor(start), end=torch.tensor(end)) + device = random_device() + x.to(device) + return x + def test_global_naive(test_case): placement = flow.placement("cpu", ranks=[0]) sbp = (flow.sbp.broadcast,) @@ -100,6 +115,13 @@ class TestArange(flow.unittest.TestCase): test_case.assertEqual(x.sbp, sbp) test_case.assertEqual(x.placement, placement) + @profile(torch.arange) + def profile_arange(test_case): + torch.arange(5) + torch.arange(100000) + torch.arange(1, 4) + torch.arange(1, 2.5, 0.5) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_argmax.py b/python/oneflow/test/modules/test_argmax.py index ea13d25ef17dda9ca9396c40c9031932d6d53eda..9b499c011a0b57489296689c3117394e8a153c01 100644 --- a/python/oneflow/test/modules/test_argmax.py +++ b/python/oneflow/test/modules/test_argmax.py @@ -109,11 +109,3 @@ class TestArgmax(flow.unittest.TestCase): if __name__ == "__main__": unittest.main() - # input = flow.tensor( - # np.random.randn(5, 10), dtype=flow.float32, device=flow.device("cuda") - # ) - # axis = 0 - # of_out = input.argmax(dim=axis) - # np_out = np.argmax(input.numpy(), axis=axis) - # print(of_out) - # print(np_out) diff --git a/python/oneflow/test/modules/test_argsort.py b/python/oneflow/test/modules/test_argsort.py index 618c367f5569e63a79e1907300d22874e08a6027..61103ff169c5dd869b2649f26979dc3f46035022 100644 --- a/python/oneflow/test/modules/test_argsort.py +++ b/python/oneflow/test/modules/test_argsort.py @@ -32,9 +32,13 @@ def _test_argsort(test_case, data_shape, axis, descending, data_type, device): dtype=type_name_to_flow_type[data_type], device=flow.device(device), ) - of_out = flow.argsort(input, dim=axis, descending=descending) np_input = -input.numpy() if descending else input.numpy() - np_out = np.argsort(np_input, axis=axis) + if axis is not None: + of_out = flow.argsort(input, dim=axis, descending=descending) + np_out = np.argsort(np_input, axis=axis) + else: + of_out = flow.argsort(input, descending=descending) + np_out = np.argsort(np_input) test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) @@ -44,9 +48,13 @@ def _test_tensor_argsort(test_case, data_shape, axis, descending, data_type, dev dtype=type_name_to_flow_type[data_type], device=flow.device(device), ) - of_out = input.argsort(dim=axis, descending=descending) np_input = -input.numpy() if descending else input.numpy() - np_out = np.argsort(np_input, axis=axis) + if axis is not None: + of_out = input.argsort(dim=axis, descending=descending) + np_out = np.argsort(np_input, axis=axis) + else: + of_out = input.argsort(descending=descending) + np_out = np.argsort(np_input) test_case.assertTrue(np.array_equal(of_out.numpy().shape, np_out.shape)) test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) @@ -57,7 +65,7 @@ class TestArgsort(flow.unittest.TestCase): arg_dict = OrderedDict() arg_dict["test_fun"] = [_test_argsort, _test_tensor_argsort] arg_dict["data_shape"] = [(2, 6, 5, 4), (3, 4, 8)] - arg_dict["axis"] = [-1, 0, 2] + arg_dict["axis"] = [-1, 0, 2, None] arg_dict["descending"] = [True, False] arg_dict["data_type"] = ["double", "float32", "int32"] arg_dict["device"] = ["cpu", "cuda"] @@ -81,6 +89,11 @@ class TestArgsort(flow.unittest.TestCase): ) return y + @profile(torch.argsort) + def profile_argsort(test_case): + torch.argsort(torch.ones(10, 10), dim=1) + torch.argsort(torch.ones(1000, 1000), dim=1) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_argwhere.py b/python/oneflow/test/modules/test_argwhere.py index 0d8483cfcdeb7e9102d8e31861bc824e6db0fe6d..cde32eadbfc34d52bd8405b82509ac5457356823 100644 --- a/python/oneflow/test/modules/test_argwhere.py +++ b/python/oneflow/test/modules/test_argwhere.py @@ -22,8 +22,9 @@ from oneflow.test_utils.test_util import GenArgList import oneflow as flow import oneflow.unittest - +from packaging import version from oneflow.test_utils.automated_test_util import * +import torch as torch_original def _test_argwhere(test_case, shape, device): @@ -53,6 +54,17 @@ class TestArgwhere(flow.unittest.TestCase): y = torch.argwhere(x) return y + has_pytorch_1_11 = version.parse(torch_original.__version__) >= version.parse( + "1.11.0" + ) + + @unittest.skipIf( + not has_pytorch_1_11, "torch.argwhere only exists in PyTorch >= 1.11.0" + ) + @profile(torch.argwhere if has_pytorch_1_11 else None) + def profile_argwhere(test_case): + torch.argwhere(torch.ones(3, 3, 100, 100)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_as_stride.py b/python/oneflow/test/modules/test_as_stride.py deleted file mode 100644 index 3a88ef28a34d19c9ee98a3008e712033d2c017b8..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_as_stride.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import numpy as np -from random import shuffle - -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@flow.unittest.skip_unless_1n1d() -class TestAsStrided(flow.unittest.TestCase): - @autotest(check_graph=True) - def test_flow_AsStrided(test_case): - device = random_device() - ndim = np.random.randint(3, 6) - dim0 = np.random.randint(2, 4) - dim1 = np.random.randint(2, 4) - dim2 = np.random.randint(2, 4) - dim3 = np.random.randint(2, 4) - dim4 = np.random.randint(2, 4) - if ndim == 3: - x = random_tensor(3, dim0, dim1, dim2) - elif ndim == 4: - x = random_tensor(4, dim0, dim1, dim2, dim3) - elif ndim == 5: - x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) - x = x.to(device) - storage_offset = random(0, 3).to(int) - z = torch.as_strided(x, (2, 2, 3), (1, 1, 2), storage_offset) - return z - - # TODO:(zhaoluyang) some bug in as_strided backward to be fixed - @autotest(n=10, auto_backward=False, check_graph=False) - def test_flow_as_strided_with_stride(test_case): - device = random_device() - dim0 = np.random.randint(2, 4) - dim1 = np.random.randint(2, 4) - dim2 = np.random.randint(2, 4) - dim3 = np.random.randint(2, 4) - x = random_tensor(4, dim0, dim1, dim2, dim3) - x = x.to(device) - storage_offset = random(0, 3).to(int) - perm = [0, 1, 2, 3] - shuffle(perm) - y = x.permute(perm) - z = torch.as_strided(y, (2, 2, 3), (1, 1, 2), storage_offset) - return z - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_as_strided.py b/python/oneflow/test/modules/test_as_strided.py new file mode 100644 index 0000000000000000000000000000000000000000..66f9bfea05d30d9a3d12b2e87ca89ed8b916d88d --- /dev/null +++ b/python/oneflow/test/modules/test_as_strided.py @@ -0,0 +1,164 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +from random import shuffle + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestAsStrided(flow.unittest.TestCase): + @autotest(n=10) + def test_flow_AsStrided(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + dim4 = np.random.randint(2, 4) + if ndim == 3: + x = random_tensor(3, dim0, dim1, dim2) + elif ndim == 4: + x = random_tensor(4, dim0, dim1, dim2, dim3) + elif ndim == 5: + x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) + x = x.to(device) + storage_offset = random(0, 3).to(int) + z = torch.as_strided(x, (2, 2, 3), (1, 1, 2), storage_offset) + return z + + @autotest(n=5) + def test_tensor_as_strided(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + dim4 = np.random.randint(2, 4) + if ndim == 3: + x = random_tensor(3, dim0, dim1, dim2) + elif ndim == 4: + x = random_tensor(4, dim0, dim1, dim2, dim3) + elif ndim == 5: + x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) + x = x.to(device) + storage_offset = random(0, 3).to(int) + y = x.as_strided((2, 2, 3), (1, 1, 2), storage_offset) + return y + + @autotest(n=10) + def test_flow_as_strided_tensor_method(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + x = random_tensor(ndim, *[np.random.randint(2, 4) for _ in range(ndim)]) + x = x.to(device) + storage_offset = random(0, 3).to(int) + z = x.as_strided((2, 2, 3), (1, 1, 2), storage_offset) + return z + + @autotest(n=10) + def test_flow_as_strided_with_stride(test_case): + device = random_device() + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + x = random_tensor(4, dim0, dim1, dim2, dim3) + x = x.to(device) + storage_offset = random(0, 3).to(int) + perm = [0, 1, 2, 3] + shuffle(perm) + y = x.permute(perm) + z = torch.as_strided(y, (2, 2, 3), (1, 1, 2), storage_offset) + return z + + @autotest(n=5, auto_backward=False) + def test_flow_as_strided_bool(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + dim4 = np.random.randint(2, 4) + if ndim == 3: + x = random_tensor(3, dim0, dim1, dim2) + elif ndim == 4: + x = random_tensor(4, dim0, dim1, dim2, dim3) + elif ndim == 5: + x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) + x = x.to(device) + x = x.to(torch.bool) + storage_offset = random(0, 3).to(int) + z = torch.as_strided(x, (2, 2, 3), (1, 1, 2), storage_offset) + return z + + @autotest(n=5, auto_backward=False) + def test_flow_as_strided_int8(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + dim4 = np.random.randint(2, 4) + if ndim == 3: + x = random_tensor(3, dim0, dim1, dim2) + elif ndim == 4: + x = random_tensor(4, dim0, dim1, dim2, dim3) + elif ndim == 5: + x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) + x = x.to(device) + x = x.to(torch.int8) + storage_offset = random(0, 3).to(int) + z = torch.as_strided(x, (2, 2, 3), (1, 1, 2), storage_offset) + return z + + @autotest(n=5, auto_backward=False) + def test_flow_as_strided_uint8(test_case): + device = random_device() + ndim = np.random.randint(3, 6) + dim0 = np.random.randint(2, 4) + dim1 = np.random.randint(2, 4) + dim2 = np.random.randint(2, 4) + dim3 = np.random.randint(2, 4) + dim4 = np.random.randint(2, 4) + if ndim == 3: + x = random_tensor(3, dim0, dim1, dim2) + elif ndim == 4: + x = random_tensor(4, dim0, dim1, dim2, dim3) + elif ndim == 5: + x = random_tensor(5, dim0, dim1, dim2, dim3, dim4) + x = x.to(device) + x = x.to(torch.uint8) + storage_offset = random(0, 3).to(int) + z = torch.as_strided(x, (2, 2, 3), (1, 1, 2), storage_offset) + return z + + @profile(torch.as_strided) + def profile_as_strided(test_case): + input = torch.ones(10, 10, 128, 128) + torch.as_strided(input, (10, 3, 128, 128), (1, 1, 1, 1)) + torch.as_strided(input, (10, 3, 128, 128), (1, 1, 1, 1), 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_as_tensor.py b/python/oneflow/test/modules/test_as_tensor.py index eb20240f658e6d5fe7d0f05b19d14ab6cb4ca573..9b3186c0c83b22be49ad8c5df9bbcb2bc61c913f 100644 --- a/python/oneflow/test/modules/test_as_tensor.py +++ b/python/oneflow/test/modules/test_as_tensor.py @@ -28,7 +28,6 @@ numpy_dtype_to_oneflow_dtype_dict = { np.int64: flow.int64, np.int8: flow.int8, np.uint8: flow.uint8, - np.bool: flow.bool, np.float64: flow.float64, np.float32: flow.float32, np.float16: flow.float16, diff --git a/python/oneflow/test/modules/test_asyncs_thread.py b/python/oneflow/test/modules/test_asyncs_thread.py new file mode 100644 index 0000000000000000000000000000000000000000..c851c455300a387c4bbcf17d9bb06a4f067a47c8 --- /dev/null +++ b/python/oneflow/test/modules/test_asyncs_thread.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import unittest +from collections import OrderedDict + +import numpy as np + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestLocalThread(flow.unittest.TestCase): + def test_stream(test_case): + with flow.asyncs.thread(flow.asyncs.Thread()): + test_case.assertEqual(flow.ones(1)[0], 1) + + +@flow.unittest.skip_unless_1n2d() +class TestGlobalThread(flow.unittest.TestCase): + def test_cpu_stream(test_case): + threads = [flow.asyncs.Thread() for i in range(7)] + iter_and_threads = [(i, threads[i % 7]) for i in range(30)] + for i, thread in iter_and_threads: + with flow.asyncs.thread(thread): + placement = flow.placement("cpu", [0, 1]) + tensor = flow.ones(2, placement=placement, sbp=flow.sbp.split(0)) + test_case.assertEqual(tensor[0], 1) + test_case.assertEqual(tensor[1], 1) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_stream(test_case): + threads = [flow.asyncs.Thread() for i in range(7)] + iter_and_threads = [(i, threads[i % 7]) for i in range(200)] + tensors = [] + dim = 0 + for i, thread in iter_and_threads: + dim += 1 + with flow.asyncs.thread(thread): + placement = flow.placement("cuda", [0, 1]) + ones = flow.ones(2 * dim, placement=placement, sbp=flow.sbp.split(0)) + tensors.append(ones.to_global(sbp=flow.sbp.broadcast) + i) + for i, tensor in enumerate(tensors): + test_case.assertEqual(tensor[0], 1 + i) + test_case.assertEqual(tensor[int(tensor.shape[0] / 2)], 1 + i) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_atleast.py b/python/oneflow/test/modules/test_atleast.py new file mode 100644 index 0000000000000000000000000000000000000000..01d4e4901a9e08ec778705f3ad9068fa39ea263a --- /dev/null +++ b/python/oneflow/test/modules/test_atleast.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestAtLeast(flow.unittest.TestCase): + @autotest(n=5) + def test_atleast_1d_with_list_random_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=2).to(device) + out = torch.atleast_1d([x, y]) + return out + + @autotest(n=5) + def test_atleast_1d_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=random(low=0, high=3).to(int)).to(device) + out = torch.atleast_1d(x) + return out + + @autotest(n=5) + def test_atleast_2d_with_list_random_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=1).to(device) + z = random_tensor(ndim=3).to(device) + out = torch.atleast_2d([x, y, z]) + return out + + @autotest(n=5) + def test_atleast_2d_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=random(low=0, high=4).to(int)).to(device) + out = torch.atleast_2d(x) + return out + + @autotest(n=5) + def test_atleast_3d_with_list_random_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=1).to(device) + z = random_tensor(ndim=2).to(device) + p = random_tensor(ndim=4).to(device) + out = torch.atleast_3d([x, y, z, p]) + return out + + @autotest(n=5) + def test_atleast_3d_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=random(low=0, high=5).to(int)).to(device) + out = torch.atleast_3d(x) + return out + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_autograd.py b/python/oneflow/test/modules/test_autograd.py index 974b4284ade6d67d53e9e65a6cbef2dcace02bdd..6d9786181a4aae3a178fcef6516e552e48ac0c0a 100644 --- a/python/oneflow/test/modules/test_autograd.py +++ b/python/oneflow/test/modules/test_autograd.py @@ -21,7 +21,6 @@ import numpy as np import oneflow as flow import oneflow.unittest from oneflow.test_utils.automated_test_util import * - from oneflow.test_utils.test_util import GenArgList @@ -149,6 +148,84 @@ class TestAutograd(flow.unittest.TestCase): z.sum().backward() return (x.grad, y.grad) + def test_autograd_set_acc_grad_and_backward(test_case): + for _ in range(5): + ndim = 2 + dims = [random(1, 5).to(int).value() for _ in range(ndim)] + x = torch.randn(*dims).requires_grad_() + np_arr = np.random.rand(*dims) + init_grad = torch.tensor(np_arr).to(x.dtype) + x.pytorch.grad = init_grad.pytorch + x.oneflow.grad = init_grad.oneflow + + x.sum().backward() + test_case.assertTrue( + np.allclose( + x.grad.oneflow.numpy(), x.grad.pytorch.cpu().detach().numpy() + ) + ) + + @autotest(n=1, check_graph=False) + def test_requires_grad_tensor_inplace_and_backward(test_case): + random_shape = [random(1, 10).to(int) for _ in range(4)] + x = random_tensor(4, *random_shape, requires_grad=False) + y = random_tensor(4, *random_shape, requires_grad=True) + x += y + return x + + @autotest(n=1, check_graph=False) + def test_retain_grad_for_leaf_tensor(test_case): + random_shape = [random(1, 10).to(int) for _ in range(4)] + x = random_tensor(4, *random_shape, requires_grad=True) + y = x * 2 + x.retain_grad() + return y + + @autotest(n=1, auto_backward=False, check_graph=False) + def test_run_backward_and_grad_for_same_tensor(test_case): + random_shape = [random(1, 10).to(int) for _ in range(4)] + x = random_tensor(4, *random_shape, requires_grad=True) + y = x ** 2 + y.sum().backward() + test_case.assertTrue( + np.allclose(x.grad.oneflow.numpy(), x.grad.pytorch.numpy()) + ) + + y = x ** 2 + x_grad = torch.autograd.grad(y.sum(), x)[0] + test_case.assertTrue( + np.allclose(x_grad.oneflow.numpy(), x_grad.pytorch.numpy()) + ) + test_case.assertTrue( + np.allclose(x.grad.oneflow.numpy(), x_grad.oneflow.numpy()) + ) + + @autotest(n=1, auto_backward=False, check_graph=False) + def test_no_grad_domain_call_backward(test_case): + random_shape = [random(1, 10).to(int).value() for _ in range(4)] + with flow.no_grad(): + x = flow.rand(*random_shape).requires_grad_() + with flow.enable_grad(): + y = x * 2 + flow.autograd.backward(y, flow.ones_like(y)) + test_case.assertTrue(np.array_equal(x.grad.numpy(), np.full(random_shape, 2.0))) + + @autotest(n=1, auto_backward=False, check_graph=False) + def test_acc_grad_inplace_update(test_case): + random_shape = [random(1, 5).to(int).value() for _ in range(4)] + x = flow.rand(*random_shape).requires_grad_() + y = flow.rand(*random_shape).requires_grad_() + + z = x / (x + y) + z.sum().backward() + id_x_grad = id(x.grad) + id_y_grad = id(y.grad) + + z = x / (x + y) + z.sum().backward() + test_case.assertEqual(id_x_grad, id(x.grad)) + test_case.assertEqual(id_y_grad, id(y.grad)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_autograd_function.py b/python/oneflow/test/modules/test_autograd_function.py index 7386dfae866037b4091f62090937d814c7d37314..b78dcd8aaec45176a054ac7d3c76079d7a298258 100644 --- a/python/oneflow/test/modules/test_autograd_function.py +++ b/python/oneflow/test/modules/test_autograd_function.py @@ -14,6 +14,8 @@ See the License for the specific language governing permissions and limitations under the License. """ +import re + import unittest import numpy as np @@ -110,6 +112,160 @@ class TestAutogradFunction(flow.unittest.TestCase): test_case.assertTrue(np.allclose(a.grad.numpy(), np_arr1)) test_case.assertTrue(np.allclose(b.grad.numpy(), np_arr0)) + @flow.unittest.skip_unless_1n1d() + def test_partial_inputs_requires_grad(test_case): + class MyModule(autograd.Function): + @staticmethod + def forward(ctx, x, y, z): + return x + y + z + + @staticmethod + def backward(ctx, out_grad): + return None, out_grad, None + + x = flow.randn(4, 5) + y = flow.randn(4, 5).requires_grad_() + z = flow.randn(4, 5) + # forward + res = MyModule.apply(x, y, z) + test_case.assertTrue( + np.allclose(res.numpy(), x.numpy() + y.numpy() + z.numpy()) + ) + # backward + res.sum().backward() + test_case.assertIsNone(x.grad) + test_case.assertTrue(np.allclose(y.grad.numpy(), np.ones((4, 5)))) + test_case.assertIsNone(z.grad) + + @flow.unittest.skip_unless_1n1d() + def test_dynamic_attr_for_ctx(test_case): + class MyModule(autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.scale = 2.0 + return x * ctx.scale + + @staticmethod + def backward(ctx, out_grad): + return out_grad * ctx.scale + + x = flow.randn(4, 5).requires_grad_() + # forward + res = MyModule.apply(x) + test_case.assertTrue(np.allclose(res.numpy(), x.numpy() * 2.0)) + # backward + res.sum().backward() + test_case.assertTrue(np.allclose(x.grad.numpy(), np.ones((4, 5)) * 2.0)) + + @flow.unittest.skip_unless_1n1d() + def test_backward_error_message(test_case): + class MyModule(autograd.Function): + @staticmethod + def forward(ctx, x, y, z): + return x + y + z + + @staticmethod + def backward(ctx, out_grad): + return None, out_grad + + x = flow.randn(4, 5) + y = flow.randn(4, 5).requires_grad_() + z = flow.randn(4, 5) + res = MyModule.apply(x, y, z) + with test_case.assertRaises(Exception) as exp: + res.sum().backward() + test_case.assertIsNotNone( + re.search( + r"RuntimeError: function MyModule returned an incorrect number of gradients \(expected \d, got \d\)", + str(exp.exception), + ) + ) + + @flow.unittest.skip_unless_1n1d() + def test_graph_test_multi_input(test_case): + class MyMul(autograd.Function): + @staticmethod + def forward(ctx, x, y): + z = x * y + ctx.save_for_backward(x, y) + return z + + @staticmethod + def backward(ctx, z_grad): + x, y = ctx.saved_tensors + x_grad = 2 * y * z_grad + y_grad = 3 * x * z_grad + return x_grad, y_grad + + class MyAdd(autograd.Function): + @staticmethod + def forward(ctx, x, y): + return 2 * x + y + + @staticmethod + def backward(ctx, z_grad): + x_grad = z_grad + y_grad = 2 * z_grad + return x_grad, y_grad + + model = flow.nn.Linear(5, 4, bias=False) + model.train() + + class MyGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.model = model + optimizer = flow.optim.SGD(self.model.parameters()) + self.add_optimizer(optimizer) + + def build(self, x, y): + x.retain_grad() + y.retain_grad() + self.model.weight.retain_grad() + z = MyMul().apply(x, y) + z = MyAdd().apply(z, self.model.weight) + z.sum().backward() + return z, x.grad, y.grad, self.model.weight.grad + + np_arr0 = np.random.randn(4, 5).astype(np.float32) + np_arr1 = np.random.randn(4, 5).astype(np.float32) + np_arr2 = np.random.randn(4, 5).astype(np.float32) + a = flow.tensor(np_arr0).requires_grad_() + b = flow.tensor(np_arr1).requires_grad_() + model.weight.copy_(np_arr2) + + c, a_grad, b_grad, w_grad = MyGraph()(a, b) + test_case.assertTrue(np.allclose(c.numpy(), 2 * np_arr0 * np_arr1 + np_arr2)) + test_case.assertTrue(np.allclose(a_grad.numpy(), 2 * np_arr1)) + test_case.assertTrue(np.allclose(b_grad.numpy(), 3 * np_arr0)) + test_case.assertTrue(np.allclose(w_grad.numpy(), 2 * np.ones_like(np_arr2))) + + @flow.unittest.skip_unless_1n1d() + def test_autograd_function_memory(test_case): + global_ctx = None + + class MyModule(autograd.Function): + @staticmethod + def forward(ctx, x): + z = x.clone() + ctx.save_for_backward(z) + nonlocal global_ctx + global_ctx = ctx + return z + + @staticmethod + def backward(ctx, out_grad): + (x,) = ctx.saved_tensors + return x + + x = flow.randn(5, 5).requires_grad_() + res = MyModule.apply(x) + test_case.assertTrue(global_ctx._is_data_valid()) + res.sum().backward() + + # ensure that global_ctx is released + test_case.assertFalse(global_ctx._is_data_valid()) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_avgpool.py b/python/oneflow/test/modules/test_avgpool.py index 0fc1abb3274f9539a57e047779a9a41c80c592e8..574bef09370c4a586f4cf0c965e7d8f26e9c3718 100644 --- a/python/oneflow/test/modules/test_avgpool.py +++ b/python/oneflow/test/modules/test_avgpool.py @@ -57,6 +57,31 @@ class TestAvgPoolingModule(flow.unittest.TestCase): y = m(x) return y + # TODO:(zhaoluyang) this test case has probability to fail in backward + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @autotest(n=5, rtol=0.001, atol=0.001, auto_backward=False) + def test_avgpool2d_with_half_data(test_case): + m = torch.nn.AvgPool2d( + kernel_size=random(4, 6), + stride=random(1, 3) | nothing(), + padding=random(1, 3) | nothing(), + ceil_mode=random(), + count_include_pad=random(), + divisor_override=random().to(int), + ) + m.train(random()) + device = gpu_device() + m.to(device) + x = ( + random_tensor( + ndim=4, dim2=random(20, 22), dim3=random(20, 22), requires_grad=False + ) + .to(device) + .to(torch.float16) + ) + y = m(x) + return y + @autotest(n=5) def test_avgpool3d_with_random_data(test_case): m = torch.nn.AvgPool3d( diff --git a/python/oneflow/test/modules/test_baddbmm.py b/python/oneflow/test/modules/test_baddbmm.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc5427b94cc609dd7097d8a2512dfd8b20fc016 --- /dev/null +++ b/python/oneflow/test/modules/test_baddbmm.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestBaddBmmModule(flow.unittest.TestCase): + @autotest(n=5) + def test_baddbmm_with_torch(test_case): + device = random_device() + input = random_tensor(ndim=3, dim0=2, dim1=4, dim2=4).to(device) + batch1 = random_tensor(ndim=3, dim0=2, dim1=4, dim2=3).to(device) + batch2 = random_tensor(ndim=3, dim0=2, dim1=3, dim2=4).to(device) + y = torch.baddbmm(input, batch1, batch2, beta=2.0, alpha=1.2) + return y + + @autotest(n=5) + def test_baddbmm_in_sd2_with_torch(test_case): + device = random_device() + input = random_tensor(ndim=3, dim0=2, dim1=2, dim2=2, requires_grad=False).to( + device + ) + batch1 = random_tensor(ndim=3, dim0=2, dim1=2, dim2=2).to(device) + batch2 = random_tensor(ndim=3, dim0=2, dim1=2, dim2=2).to(device) + y = torch.baddbmm(input, batch1, batch2, beta=0.0, alpha=1.2) + return y + + @autotest(n=5) + def test_baddbmm_no_attr_with_torch(test_case): + device = random_device() + input = random_tensor(ndim=3, dim0=2, dim1=4, dim2=4).to(device) + batch1 = random_tensor(ndim=3, dim0=2, dim1=4, dim2=3).to(device) + batch2 = random_tensor(ndim=3, dim0=2, dim1=3, dim2=4).to(device) + y = torch.baddbmm(input, batch1, batch2) + return y + + @autotest(n=5) + def test_baddbmm_broadcast_with_torch(test_case): + device = random_device() + input = random_tensor(ndim=1, dim0=4).to(device) + batch1 = random_tensor(ndim=3, dim0=2, dim1=4, dim2=3).to(device) + batch2 = random_tensor(ndim=3, dim0=2, dim1=3, dim2=4).to(device) + y = torch.baddbmm(input, batch1, batch2, beta=-1.98, alpha=1.34) + return y + + @profile(torch.baddbmm) + def profile_baddbmm(test_case): + input = torch.ones(10, 100, 100) + batch1 = torch.ones(10, 100, 100) + batch2 = torch.ones(10, 100, 100) + torch.bmm(input, batch1, batch2, beta=-1.98, alpha=1.34) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_batchnorm.py b/python/oneflow/test/modules/test_batchnorm.py index 238ab7b4494803b6632b1b9b629853ab9555c004..b43a1cdb430ddcc350ad29d5d840eaa71587b807 100644 --- a/python/oneflow/test/modules/test_batchnorm.py +++ b/python/oneflow/test/modules/test_batchnorm.py @@ -80,6 +80,63 @@ class TestBatchNormModule(flow.unittest.TestCase): y = m(x) return y + @autotest(rtol=1e-3, atol=1e-3, check_grad_use_random_data=False) + def test_functional_batchnorm_with_random_data(test_case): + device = random_device() + channel = random(1, 4).to(int) + x = random_tensor(ndim=5, dim1=channel, requires_grad=True).to(device) + running_mean = random_tensor(ndim=1, dim0=channel, requires_grad=False) + running_var = random_tensor(ndim=1, dim0=channel, low=0.0, requires_grad=False) + weight = random_tensor(ndim=1, dim0=channel) + bias = random_tensor(ndim=1, dim0=channel) + result = torch.nn.functional.batch_norm( + input=x, + running_mean=running_mean, + running_var=running_var, + weight=weight, + bias=bias, + training=random_bool(), + ) + return result + + @autotest(rtol=1e-3, atol=1e-3, auto_backward=False, check_graph=False) + def test_batchnorm2d_module_with_half_random_data(test_case): + device = random_device() + channel = random(1, 4).to(int) + m = torch.nn.BatchNorm2d( + num_features=channel, + track_running_stats=random().to(bool), + affine=random().to(bool), + ).to(device) + m.train(random()) + m.half() + x = random_tensor( + ndim=4, dim0=random(1, 4), dim1=channel, requires_grad=True + ).to(device) + x.half() + y = m(x) + return y + + @profile(torch.nn.functional.batch_norm) + def profile_batchnorm(test_case): + input = torch.ones(16, 128, 28, 28) + running_mean = torch.randn(128) + running_var = torch.randn(128) + weight = torch.randn(128) + bias = torch.randn(128) + torch.nn.functional.batch_norm( + input, running_mean, running_var, weight, bias, True + ) + torch.nn.functional.batch_norm( + input, running_mean, running_var, weight, bias, False + ) + torch.nn.functional.batch_norm( + input, running_mean, running_var, None, None, True + ) + torch.nn.functional.batch_norm( + input, running_mean, running_var, None, None, False + ) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_bernoulli.py b/python/oneflow/test/modules/test_bernoulli.py index db1335bd3fcf09f174dca7aa6355ed81275692ae..d7021fb028de514764090da84dff62fc496c289f 100644 --- a/python/oneflow/test/modules/test_bernoulli.py +++ b/python/oneflow/test/modules/test_bernoulli.py @@ -25,11 +25,18 @@ import oneflow as flow import oneflow.unittest -def _test_bernoulli(test_case, shape): +def _test_bernoulli(test_case, shape, p, dtype): input_arr = np.ones(shape) x = flow.tensor(input_arr, dtype=flow.float32, device=flow.device("cpu")) - y = flow.bernoulli(x) - test_case.assertTrue(np.allclose(y.numpy(), x.numpy())) + if p is None: + y = flow.bernoulli(x, dtype=dtype) + else: + y = flow.bernoulli(x, p=p, dtype=dtype) + test_case.assertTrue(y.dtype == dtype) + if p == 1 or p is None: + test_case.assertTrue(np.allclose(y.numpy(), x.numpy())) + elif p == 0: + test_case.assertTrue(np.allclose(y.numpy(), np.zeros(shape))) def _test_bernoulli_with_generator(test_case, shape): @@ -50,6 +57,8 @@ class TestBernoulli(flow.unittest.TestCase): arg_dict = OrderedDict() arg_dict["test_functions"] = [_test_bernoulli] arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)] + arg_dict["p"] = [None, 0, 1] + arg_dict["dtype"] = [flow.float32, flow.int64] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) @@ -59,6 +68,13 @@ class TestBernoulli(flow.unittest.TestCase): input = random_tensor(ndim=1).to("cpu") return torch.bernoulli(input) + """ + @profile(torch.bernoulli) + def profile_bernoulli(test_case): + torch.bernoulli(torch.ones(3, 3)) + torch.bernoulli(torch.zeros(3, 3)) + """ + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_binary_math_ops_dtype.py b/python/oneflow/test/modules/test_binary_math_ops_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..e11713533d12403836fc440b87f78c7c952bd04e --- /dev/null +++ b/python/oneflow/test/modules/test_binary_math_ops_dtype.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from itertools import product + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def get_dtype_str(dtype): + return str(dtype).split(".")[-1] + + +dtype_list = [ + torch.int8, + torch.int32, + torch.int64, + torch.float16, + torch.float32, + torch.float64, +] + + +@flow.unittest.skip_unless_1n1d() +class TestBinaryMathOpsDtype(flow.unittest.TestCase): + @autotest(n=2, auto_backward=False, check_graph=False) + def test_binary_math_ops_dtype(test_case): + device = random_device() + + for x1_dtype, x2_dtype in product(dtype_list, dtype_list): + x1 = random_tensor(2, 2, 3, requires_grad=False).to(device).to(x1_dtype) + x2 = random_tensor(2, 2, 3, requires_grad=False).to(device).to(x2_dtype) + + for op in ["+", "-", "*", "/"]: + y = eval(f"x1 {op} x2") + test_case.assertEqual( + get_dtype_str(y.oneflow.dtype), get_dtype_str(y.pytorch.dtype) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_bincount.py b/python/oneflow/test/modules/test_bincount.py new file mode 100644 index 0000000000000000000000000000000000000000..1066ba94b7f549f05741ecc4141a793f5d8a6fc3 --- /dev/null +++ b/python/oneflow/test/modules/test_bincount.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestBinCount(flow.unittest.TestCase): + @autotest(n=5, auto_backward=False, check_graph=False) + def test_bincount(test_case): + device = random_device() + x = random_tensor(1, 100, low=0, dtype=int).to(device) + result = torch.bincount(x) + return result + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_bincount_weight(test_case): + device = random_device() + x = random_tensor(1, 100, low=0, dtype=int).to(device) + weight = random_tensor(1, 100).to(device) + return torch.bincount(x, weights=weight) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_bincount_minlength(test_case): + device = random_device() + x = random_tensor(1, 100, low=0, dtype=int).to(device) + weight = random_tensor(1, 100).to(device) + minlength = random(1, 200).to(int) + return torch.bincount(x, weights=weight, minlength=minlength) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_bincount_0element(test_case): + device = random_device() + x = random_tensor(1, 0, low=0, dtype=int).to(device) + weight = random_tensor(1, 0).to(device) + minlength = random(1, 200).to(int) + return torch.bincount(x, weights=weight, minlength=minlength) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_bmm.py b/python/oneflow/test/modules/test_bmm.py index 960710ad0bc158e5553860aa13e05c5ebe98b539..bccfea9a484948c9fe261b6f60fae3193da55b07 100644 --- a/python/oneflow/test/modules/test_bmm.py +++ b/python/oneflow/test/modules/test_bmm.py @@ -105,6 +105,12 @@ class TestModule(flow.unittest.TestCase): y = torch.bmm(mat1, mat2,) return y + @profile(torch.bmm) + def profile_bmm(test_case): + mat1 = torch.ones(10, 100, 100) + mat2 = torch.ones(10, 100, 100) + torch.bmm(mat1, mat2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_broadcast_like.py b/python/oneflow/test/modules/test_broadcast_like.py index 4cf5396b2ce129c466c77005f691144d82e2a617..05c85082d8e11a9e92b6891f0eac5b890b3345f5 100644 --- a/python/oneflow/test/modules/test_broadcast_like.py +++ b/python/oneflow/test/modules/test_broadcast_like.py @@ -40,6 +40,54 @@ def _test_broadcast_like(test_case, device): test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) +def _test_broadcast_like_one(test_case, device): + input = flow.tensor( + np.ones(shape=(1, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 2, 3), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow.broadcast_like(input, like_tensor) + np_out = np.ones(shape=(1, 2, 3)) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_broadcast_like_different_dim(test_case, device): + input = flow.tensor( + np.ones(shape=(3, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(2, 3, 4), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow.broadcast_like(input, like_tensor) + np_out = np.ones(shape=(2, 3, 4)) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_broadcast_like_different_dim_with_input_axisvec(test_case, device): + input = flow.tensor( + np.ones(shape=(1, 5, 6), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 5, 6, 1, 6), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow.broadcast_like(input, like_tensor, broadcast_axes=(3, 4)) + np_out = np.ones(shape=(1, 5, 6, 1, 6)) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + def _test_broadcast_like_3dim(test_case, device): input = flow.tensor( np.ones(shape=(1, 3, 2), dtype=np.float32), @@ -72,6 +120,22 @@ def _test_broadcast_like_4dim(test_case, device): test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) +def _test_broadcast_like_empty_axisvec(test_case, device): + input = flow.tensor( + np.ones(shape=(1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(2, 3, 4), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow.broadcast_like(input, like_tensor) + np_out = np.ones(shape=(2, 3, 4)) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + def _test_broadcast_like_backward(test_case, device): input = flow.tensor( np.ones(shape=(3, 1, 1), dtype=np.float32), @@ -98,8 +162,12 @@ class TestBroadCastLike(flow.unittest.TestCase): arg_dict = OrderedDict() arg_dict["test_fun"] = [ _test_broadcast_like, + _test_broadcast_like_one, + _test_broadcast_like_different_dim, + _test_broadcast_like_different_dim_with_input_axisvec, _test_broadcast_like_3dim, _test_broadcast_like_4dim, + _test_broadcast_like_empty_axisvec, _test_broadcast_like_backward, ] arg_dict["device"] = ["cpu", "cuda"] diff --git a/python/oneflow/test/modules/test_broadcast_ops.py b/python/oneflow/test/modules/test_broadcast_ops.py index 61c871441965070243c20479543543e967db8432..8f0cc94bad868f09c993c24a5f56106131b61943 100644 --- a/python/oneflow/test/modules/test_broadcast_ops.py +++ b/python/oneflow/test/modules/test_broadcast_ops.py @@ -15,9 +15,13 @@ limitations under the License. """ import unittest -from oneflow.test_utils.automated_test_util import * + +import torch as ori_torch +import numpy as np + import oneflow as flow import oneflow.unittest +from oneflow.test_utils.automated_test_util import * binary_ops = [ torch.add, @@ -74,6 +78,30 @@ class TestBroadcastOps(flow.unittest.TestCase): out = op(x, y) return out + @autotest(n=5, auto_backward=False) + def test_cpu_scalar_tensor_auto_cast(test_case): + def check_output(test_case, output): + of_res = output.oneflow + torch_res = output.pytorch + # NOTE: torch's device has no device index bug oneflow has. + # e.g. torch gets "cpu" but oneflow gets "cpu:0" + test_case.assertTrue(str(torch_res.device) in str(of_res.device)) + test_case.assertTrue( + np.allclose(of_res.numpy(), torch_res.detach().cpu().numpy()) + ) + + op_idx = random(low=0, high=len(binary_ops)).to(int).value() + op = binary_ops[op_idx] + device = random_device() + x = torch.tensor(1.0) + y = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + + out = op(x, y) + check_output(test_case, out) + + out = op(y, x) + check_output(test_case, out) + @autotest(n=30, auto_backward=False) def test_broadcast_scalar(test_case): op_idx = random(low=0, high=len(binary_ops)).to(int).value() @@ -113,5 +141,25 @@ class TestBroadcastOps(flow.unittest.TestCase): torch.add(input0, input1) +@flow.unittest.skip_unless_1n1d() +class TestBroadcastOpsOther(flow.unittest.TestCase): + def test_broadcast_shapes(test_case): + shapes = (2,), (3, 1), (1, 1, 1) + test_case.assertTrue( + flow.broadcast_shapes(*shapes), ori_torch.broadcast_shapes(*shapes), + ) + + @autotest(n=3) + def test_broadcast_tensors(test_case): + device = random_device() + x = random_tensor(ndim=2, dim0=1, dim1=4).to(device=device) + y = random_tensor(ndim=2, dim0=3, dim1=1).to(device=device) + return torch.broadcast_tensors(x, y) + + def test_broadcast_to(test_case): + # see flow.expand, because broadcast_to is an alias of flow.expand + pass + + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_cast.py b/python/oneflow/test/modules/test_cast.py index c6017cb17c417b8df73efe843889454cfac4bff4..8689f3860d8f03b096db4587b46dbb7f0c3b960c 100644 --- a/python/oneflow/test/modules/test_cast.py +++ b/python/oneflow/test/modules/test_cast.py @@ -51,7 +51,8 @@ def _test_cast_with_non_contiguous_input(test_case, device, shape): output = flow.cast(input, flow.float32) np_out = np_arr.astype(np.float32).transpose(permute_dims) test_case.assertTrue(np.array_equal(output.numpy(), np_out)) - test_case.assertTrue(input.stride() == output.stride()) + # TODO:when cast kernel support stride + # test_case.assertTrue(input.stride() == output.stride()) def _test_cast_backward(test_case, device, shape): diff --git a/python/oneflow/test/modules/test_ceil.py b/python/oneflow/test/modules/test_ceil.py index ba5b96e4374292497efdf971ea3e12a6553e1b8d..65081eb128beb450e3bf12f3717806e1a45599e0 100644 --- a/python/oneflow/test/modules/test_ceil.py +++ b/python/oneflow/test/modules/test_ceil.py @@ -53,6 +53,11 @@ class TestCeilModule(flow.unittest.TestCase): y = torch.ceil(x) return y + @profile(torch.ceil) + def profile_ceil(test_case): + torch.ceil(torch.ones(4)) + torch.ceil(torch.ones(100000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_check_meta_consistency.py b/python/oneflow/test/modules/test_check_meta_consistency.py index 2577b032280a392e289c8cc2d084a548fe0505b5..d3dd316de50ec6d2678999b51884114a83574ec3 100644 --- a/python/oneflow/test/modules/test_check_meta_consistency.py +++ b/python/oneflow/test/modules/test_check_meta_consistency.py @@ -27,7 +27,7 @@ from oneflow.test_utils.test_util import GenArgList @flow.unittest.skip_unless_1n2d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCastModule_1n2d(flow.unittest.TestCase): +class TestGlobalCastModule_1n2d(flow.unittest.TestCase): def test_check_meta_consistency(test_case): if os.getenv("RANK") == "0": x = flow.ones((16, 16), device=flow.device("cuda"), dtype=flow.int32) diff --git a/python/oneflow/test/modules/test_checkpointing.py b/python/oneflow/test/modules/test_checkpointing.py new file mode 100644 index 0000000000000000000000000000000000000000..cbe8927546d5bf5907b8b540354c67e21aca1dd7 --- /dev/null +++ b/python/oneflow/test/modules/test_checkpointing.py @@ -0,0 +1,82 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import os + +import oneflow as flow +import oneflow.unittest +import numpy as np + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestCheckpointing(flow.unittest.TestCase): + def test_checkpointing(test_case): + relu_forward_num = 0 + relu_backward_num = 0 + + class MyReLU(flow.autograd.Function): + @staticmethod + def forward(ctx, x): + nonlocal relu_forward_num + relu_forward_num += 1 + y = flow.relu(x) + ctx.save_for_backward(y) + return y + + @staticmethod + def backward(ctx, dy): + nonlocal relu_backward_num + relu_backward_num += 1 + y = ctx.saved_tensors[0] + return dy * (y > 0) + + class M(flow.nn.Module): + def __init__(self): + super().__init__() + self.conv1 = flow.nn.Conv2d(3, 3, 3) + self.conv2 = flow.nn.Conv2d(3, 3, 3) + + def forward(self, x): + x = self.conv1(x) + if checkpointing: + x = flow.utils.checkpoint.checkpoint(MyReLU.apply, x) + else: + x = MyReLU.apply(x) + x = self.conv2(x) + return x + + x1 = flow.randn(1, 3, 8, 16).requires_grad_() + x2 = x1.detach().clone().requires_grad_() + + m = M() + + checkpointing = True + y1 = m(x1) + y1.sum().backward() + + checkpointing = False + y2 = m(x2) + y2.sum().backward() + + test_case.assertTrue(np.array_equal(y1, y2)) + test_case.assertTrue(np.array_equal(x1.grad, x2.grad)) + test_case.assertEqual(relu_forward_num, 3) + test_case.assertEqual(relu_backward_num, 2) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_chunk.py b/python/oneflow/test/modules/test_chunk.py index 80d5960605af135a26d30580c912015196ec4cfa..2e661bdd07aa62fb2e0fd1b2987096daf98cfb0e 100644 --- a/python/oneflow/test/modules/test_chunk.py +++ b/python/oneflow/test/modules/test_chunk.py @@ -103,6 +103,12 @@ class TestChunk(flow.unittest.TestCase): z = torch.cat(y, dim=-1) return z + @profile(torch.chunk) + def profile_chunk(test_case): + torch.chunk(torch.ones(16), 4) + torch.chunk(torch.ones(100000), 5) + torch.chunk(torch.ones(100, 100), 5, dim=1) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_clamp.py b/python/oneflow/test/modules/test_clamp.py index b92380559264f1f7cb1a6a71124dec1424603993..47bccd7f8641a331c8b300d88957eb4a09c90458 100644 --- a/python/oneflow/test/modules/test_clamp.py +++ b/python/oneflow/test/modules/test_clamp.py @@ -26,44 +26,46 @@ import oneflow.unittest from oneflow.test_utils.automated_test_util import * -def _test_clamp(test_case, shape, device): +def _test_clamp(test_case, shape, device, dtype): input = flow.tensor( - np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + np.random.randn(*shape), dtype=dtype, device=flow.device(device) ) of_out = flow.clamp(input, 0.1, 0.5) np_out = np.clip(input.numpy(), 0.1, 0.5) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) -def _test_tensor_clamp(test_case, shape, device): +def _test_tensor_clamp(test_case, shape, device, dtype): input = flow.tensor( - np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + np.random.randn(*shape), dtype=dtype, device=flow.device(device) ) of_out = input.clamp(0.1, 0.5) np_out = np.clip(input.numpy(), 0.1, 0.5) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) -def _test_clamp_scalar_min(test_case, shape, device): +def _test_clamp_scalar_min(test_case, shape, device, dtype): input = flow.tensor( - np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + np.random.randn(*shape), dtype=dtype, device=flow.device(device) ) of_out = flow.clamp(input, 0.1, None) np_out = np.clip(input.numpy(), 0.1, None) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) -def _test_clamp_scalar_max(test_case, shape, device): +def _test_clamp_scalar_max(test_case, shape, device, dtype): input = flow.tensor( - np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + np.random.randn(*shape), dtype=dtype, device=flow.device(device) ) of_out = flow.clamp(input, None, 0.5) np_out = np.clip(input.numpy(), None, 0.5) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) -def _test_clamp_integral(test_case, shape, device): - input = flow.tensor(np.random.randint(3, 10, shape), device=flow.device(device)) +def _test_clamp_integral(test_case, shape, device, dtype): + input = flow.tensor(np.random.randint(3, 10, shape), device=flow.device(device)).to( + dtype + ) of_out = flow.clamp(input, 1, 5) np_out = np.clip(input.numpy(), 1, 5) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) @@ -75,7 +77,7 @@ def _numpy_clamp_grad(arr, min, max): return grad -def _test_clamp_backward(test_case, shape, device): +def _test_clamp_backward(test_case, shape, device, dtype): x = flow.tensor( np.random.randn(*shape), dtype=flow.float32, @@ -100,11 +102,18 @@ class TestClampModule(flow.unittest.TestCase): _test_tensor_clamp, _test_clamp_scalar_min, _test_clamp_scalar_max, - _test_clamp_integral, _test_clamp_backward, ] arg_dict["shape"] = [(2,), (2, 3), (2, 4, 5, 6)] arg_dict["device"] = ["cpu", "cuda"] + arg_dict["dtype"] = [flow.float16, flow.float, flow.double] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + arg_dict["fun"] = [ + _test_clamp_integral, + ] + arg_dict["dtype"] = [flow.int8, flow.int, flow.long] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) @@ -131,6 +140,11 @@ class TestClampModule(flow.unittest.TestCase): ) return y + @profile(torch.clamp) + def profile_clamp(test_case): + torch.clamp(torch.ones(4), -1, 2) + torch.clamp(torch.ones(100000), -1, 2) + @autotest(n=5) def test_clip_flow_with_random_data(test_case): device = random_device() @@ -154,6 +168,11 @@ class TestClampModule(flow.unittest.TestCase): ) return y + @profile(torch.clip) + def profile_clip(test_case): + torch.clip(torch.ones(4), -1, 2) + torch.clip(torch.ones(100000), -1, 2) + @autotest(n=5, auto_backward=False, check_graph=True) def test_clamp_with_0_size_data(test_case): device = random_device() @@ -162,5 +181,127 @@ class TestClampModule(flow.unittest.TestCase): return y +def _test_clamp_min(test_case, shape, device): + input = flow.tensor( + np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + ) + of_out = flow.clamp_min(input, 0.1) + np_out = np.clip(input.numpy(), 0.1, None) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_clamp_min_integral(test_case, shape, device): + input = flow.tensor(np.random.randint(3, 10, shape), device=flow.device(device)) + of_out = flow.clamp_min(input, 1) + np_out = np.clip(input.numpy(), 1, None) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_clamp_min_backward(test_case, shape, device): + x = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + y = flow.clamp_min(x, 0.1).sum() + y.backward() + test_case.assertTrue( + np.allclose( + x.grad.numpy(), _numpy_clamp_grad(x.numpy(), 0.1, None), 1e-05, 1e-05 + ) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestClampMinModule(flow.unittest.TestCase): + def test_clamp_min(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_clamp_min, + _test_clamp_min_integral, + _test_clamp_min_backward, + ] + arg_dict["shape"] = [(2,), (2, 3), (2, 4, 5, 6)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + @autotest(n=5) + def test_clamp_min_flow_with_random_data(test_case): + device = random_device() + input = random_tensor().to(device) + y = torch.clamp_min(input, min=random().to(float)) + return y + + @autotest(n=5, auto_backward=False, check_graph=True) + def test_clamp_min_with_0_size_data(test_case): + device = random_device() + x = random_tensor(4, 2, 1, 0, 3).to(device) + y = torch.clamp_min(x, min=random().to(float)) + return y + + +def _test_clamp_max(test_case, shape, device): + input = flow.tensor( + np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + ) + of_out = flow.clamp_max(input, 0.5) + np_out = np.clip(input.numpy(), None, 0.5) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_clamp_max_integral(test_case, shape, device): + input = flow.tensor(np.random.randint(3, 10, shape), device=flow.device(device)) + of_out = flow.clamp_max(input, 1) + np_out = np.clip(input.numpy(), None, 1) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_clamp_max_backward(test_case, shape, device): + x = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + y = flow.clamp_max(x, 0.5).sum() + y.backward() + test_case.assertTrue( + np.allclose( + x.grad.numpy(), _numpy_clamp_grad(x.numpy(), None, 0.5), 1e-05, 1e-05 + ) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestClampMaxModule(flow.unittest.TestCase): + def test_clamp_min(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_clamp_max, + _test_clamp_max_integral, + _test_clamp_max_backward, + ] + arg_dict["shape"] = [(2,), (2, 3), (2, 4, 5, 6)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + @autotest(n=5) + def test_clamp_max_flow_with_random_data(test_case): + device = random_device() + input = random_tensor().to(device) + y = torch.clamp_max(input, max=random().to(float)) + return y + + @autotest(n=5, auto_backward=False, check_graph=True) + def test_clamp_max_with_0_size_data(test_case): + device = random_device() + x = random_tensor(4, 2, 1, 0, 3).to(device) + y = torch.clamp_max(x, max=random().to(float)) + return y + + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_clip_grad.py b/python/oneflow/test/modules/test_clip_grad.py index fcfc61316f0256e9b888790c34e61ebd816b7e48..ac625d4d47ddacb70e94dfbfe5e192d5655acee1 100644 --- a/python/oneflow/test/modules/test_clip_grad.py +++ b/python/oneflow/test/modules/test_clip_grad.py @@ -117,7 +117,7 @@ def _test_graph_clip_grad_value_impl(test_case, shape, device, clip_value): ) -def _test_clip_grad_norm_consistent_impl( +def _test_clip_grad_norm_global_impl( test_case, shape, sbp, placement, max_norm, norm_type ): of_input = flow.rand( @@ -169,20 +169,20 @@ class TestClipGrad(flow.unittest.TestCase): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestClipGradConsistent(flow.unittest.TestCase): +class TestClipGradGlobal(flow.unittest.TestCase): @flow.unittest.skip_unless_1n2d() - def test_clip_grad_consistent(test_case): + def test_clip_grad_global(test_case): arg_dict = OrderedDict() arg_dict["shape"] = [(2, 4), (2, 4, 3), (2, 4, 5, 6)] arg_dict["sbp"] = [flow.sbp.broadcast, flow.sbp.split(0), flow.sbp.split(1)] arg_dict["placement"] = [ - flow.env.all_device_placement("cpu"), - flow.env.all_device_placement("cuda"), + flow.placement.all("cpu"), + flow.placement.all("cuda"), ] arg_dict["max_norm"] = [0, 0.5, 1.0] arg_dict["norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5] for arg in GenArgList(arg_dict): - _test_clip_grad_norm_consistent_impl(test_case, *arg) + _test_clip_grad_norm_global_impl(test_case, *arg) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_comb2to2d.py b/python/oneflow/test/modules/test_comb2to2d.py index dc05016242a22d51e51f11ff2a07a43464ebd6c2..5b3a63a88ecd3d0645c7a54a273f363d9e3e5839 100644 --- a/python/oneflow/test/modules/test_comb2to2d.py +++ b/python/oneflow/test/modules/test_comb2to2d.py @@ -24,6 +24,9 @@ import numpy as np import oneflow.unittest +flow.boxing.nccl.enable_use_compute_stream(False) + + class _TestModuleDiffHierarchy(nn.Module): def forward(self, x): sbp_1ds = [ @@ -102,6 +105,8 @@ class _TestGraph(nn.Graph): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestLazyAllSbpCombinationTesting(flow.unittest.TestCase): def test_lazy_boxing_2d_all_combination(test_case): + os.environ["ONEFLOW_BOXING_DISABLE_MIDDLE_NODE_AND_CHECK"] = "0" + os.environ["ONEFLOW_BOXING_ENABLE_GENERAL_BASIC_COMMUNICATION"] = "0" x = flow.ones( 12, diff --git a/python/oneflow/test/modules/test_comm_ops.py b/python/oneflow/test/modules/test_comm_ops.py index fc71cf3b935d39b6aabe7e9639b220b765b818e4..355a17f13988024a28e5f972bea4c1849e886abd 100644 --- a/python/oneflow/test/modules/test_comm_ops.py +++ b/python/oneflow/test/modules/test_comm_ops.py @@ -44,6 +44,31 @@ class TestAllReduce(flow.unittest.TestCase): @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestAllGather(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_all_gather_into_tensor_1n2d(test_case): + device = "cuda" + tensor_in = ( + flow.tensor([[1, 2, 3], [4, 5, 6]], dtype=flow.int64, device=device) + + flow.env.get_rank() * 6 + ) + tensor_out = flow.zeros(4, 3, dtype=flow.int64, device=device) + flow.comm.all_gather_into_tensor(tensor_out, tensor_in) + test_case.assertTrue( + np.allclose( + tensor_out.numpy(), + np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]), + ) + ) + + tensor_out2 = flow.zeros(2, 3, 2, dtype=flow.int64, device=device) + flow.comm.all_gather_into_tensor(tensor_out2, tensor_in) + test_case.assertTrue( + np.allclose( + tensor_out2.numpy(), + np.array([[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]]), + ) + ) + @flow.unittest.skip_unless_1n2d() def test_all_gather_1n2d(test_case): if flow.env.get_rank() == 0: @@ -175,6 +200,37 @@ class TestReduceScatter(flow.unittest.TestCase): np.allclose(output.numpy(), tensor_list[0].numpy() * 4 + 6) ) + @flow.unittest.skip_unless_1n2d() + def test_reduce_scatter_tensor_1n2d(test_case): + tensor_in = flow.tensor( + [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], + dtype=flow.int64, + device="cuda", + ) + tensor_out = flow.zeros(2, 3, dtype=flow.int64, device="cuda") + flow.comm.reduce_scatter_tensor(tensor_out, tensor_in) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose(tensor_out.numpy(), np.array([[2, 4, 6], [8, 10, 12]]),) + ) + else: + test_case.assertTrue( + np.allclose(tensor_out.numpy(), np.array([[14, 16, 18], [20, 22, 24]]),) + ) + tensor_in2 = tensor_in.reshape(2, 3, 2) + tensor_out2 = flow.zeros(2, 3, dtype=flow.int64, device="cuda") + flow.comm.reduce_scatter_tensor(tensor_out2, tensor_in2) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose(tensor_out2.numpy(), np.array([[2, 4, 6], [8, 10, 12]]),) + ) + else: + test_case.assertTrue( + np.allclose( + tensor_out2.numpy(), np.array([[14, 16, 18], [20, 22, 24]]), + ) + ) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() diff --git a/python/oneflow/test/modules/test_concat.py b/python/oneflow/test/modules/test_concat.py index 2039e78c8596c4cca20eba7a58001826653db7aa..19adbe60a131f0db8eaccba61e05b0ab13e25a97 100644 --- a/python/oneflow/test/modules/test_concat.py +++ b/python/oneflow/test/modules/test_concat.py @@ -38,6 +38,40 @@ def _test_concat_origin(test_case, device): test_case.assertTrue(np.array_equal(of_out.numpy(), np_out)) +def _test_concat_with_empty_input(test_case, device): + input1 = flow.Tensor().to(flow.device(device)) + input2 = flow.tensor( + np.random.randn(2, 6, 5, 3), dtype=flow.float32, device=flow.device(device) + ) + of_out1 = flow.cat([input1, input2], dim=0) + of_out2 = flow.cat([input2, input1], dim=0) + of_out3 = flow.cat([input1, input2, input1, input1], dim=0) + + torch_input1 = torch.Tensor().to(torch.device(device)) + torch_input2 = torch.tensor( + np.random.randn(2, 6, 5, 3), dtype=torch.float32, device=torch.device(device) + ) + torch_out1 = torch.cat((torch_input1, torch_input2), 0) + torch_out2 = torch.cat((torch_input2, torch_input1), 0) + torch_out3 = torch.cat((torch_input1, torch_input2, torch_input1, torch_input1), 0) + + test_case.assertTrue( + np.array_equal(of_out1.numpy(), torch_out1.detach().cpu().numpy()) + ) + test_case.assertTrue( + np.array_equal(of_out2.numpy(), torch_out2.detach().cpu().numpy()) + ) + test_case.assertTrue( + np.array_equal(of_out3.numpy(), torch_out3.detach().cpu().numpy()) + ) + test_case.assertTrue( + np.array_equal(of_out1.numpy(), torch_out2.detach().cpu().numpy()) + ) + test_case.assertTrue( + np.array_equal(of_out1.numpy(), torch_out3.detach().cpu().numpy()) + ) + + def _test_concat_with_axis_one(test_case, device): input1 = flow.tensor( np.random.randn(2, 6, 5, 3), dtype=flow.float32, device=flow.device(device) @@ -119,16 +153,28 @@ def _test_concat_grad_and_no_grad(test_case, device): ) +def _test_concat_single_input_type(test_case, device): + torch_list = [torch.Tensor([1, 1, 9, 1])] + torch_list = [t.to(dtype=torch.int64, device=device) for t in torch_list] + + flow_list = [flow.Tensor([1, 1, 9, 1])] + flow_list = [t.to(dtype=flow.int64, device=device) for t in flow_list] + flow_cat_list = flow.cat(flow_list) + test_case.assertTrue(flow_cat_list.dtype is oneflow.int64) + + @flow.unittest.skip_unless_1n1d() class TestModule(flow.unittest.TestCase): def test_concat(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [ _test_concat_origin, + _test_concat_with_empty_input, _test_concat_with_axis_one, _test_concat_with_three_tensor, _test_concat_with_three_tensor_backward, _test_concat_grad_and_no_grad, + _test_concat_single_input_type, ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): @@ -189,6 +235,12 @@ class TestModule(flow.unittest.TestCase): x = random_tensor(4, 2, 3, random(0, 3)).to(device) return torch.cat((x,), 0) + @profile(torch.cat) + def profile_cat(test_case): + input = torch.ones(100, 100) + torch.cat((input, input), dim=0) + torch.cat((input, input), dim=1) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_0_dim_tensor.py b/python/oneflow/test/modules/test_consistent_0_dim_tensor.py deleted file mode 100644 index 5d7923c1ce4b01f29e26b002149a04b1586dd94e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_0_dim_tensor.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_0_dim_tensor(test_case, placement, sbp): - x1 = random_tensor(0).to_global(placement=placement, sbp=sbp) - x2 = random_tensor(0).to_global(placement=placement, sbp=sbp) - y1 = x1 * x2 - y2 = x1 + x2 - return y1 + y2 - - -@autotest(n=1, check_graph=False) -def _test_1dim_slice(test_case, placement, sbp): - x = random_tensor(1, random(1, 4) * 8).to_global(placement=placement, sbp=sbp) - return x[5] - - -class TestZeroDimensionTensor(flow.unittest.TestCase): - @globaltest - def test_0_dim_tensor(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=0): - _test_0_dim_tensor(test_case, placement, sbp) - for sbp in all_sbp(placement, max_dim=1): - _test_1dim_slice(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_TripletMarginLoss.py b/python/oneflow/test/modules/test_consistent_TripletMarginLoss.py deleted file mode 100644 index af1115ad7979b589a7e7d7fba278986016531860..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_TripletMarginLoss.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -import numpy as np - -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_global_triplet_marginloss_with_random_data(test_case, placement, sbp): - margin = random().to(float) - p = random().to(float) - swap = random_bool() - reduction = oneof("none", "sum", "mean", nothing()) - m = torch.nn.TripletMarginLoss(margin=margin, p=p, swap=swap, reduction=reduction) - m.train(random()) - anchor = random_tensor(2, 8, 16).to_global(placement, sbp) - pos = random_tensor(2, 8, 16).to_global(placement, sbp) - neg = random_tensor(2, 8, 16).to_global(placement, sbp) - y = m(anchor, pos, neg) - return y - - -class TestConsistentTripletMarginLoss(flow.unittest.TestCase): - @globaltest - def test_global_triplet_marginloss_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_triplet_marginloss_with_random_data( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_abs.py b/python/oneflow/test/modules/test_consistent_abs.py deleted file mode 100644 index 3ec8c2348daf8534071dbe660c3c7bbef51976cc..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_abs.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -from oneflow.test_utils.automated_test_util import * -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_abs_with_ndim_data(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.abs(x) - return y - - -class TestAbsModule(flow.unittest.TestCase): - @globaltest - def test_abs_with_ndim_data(test_case): - for placement in all_placement(): - ndim = random(0, 4).to(int).value() - for sbp in all_sbp(placement, max_dim=ndim): - _test_abs_with_ndim_data(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_adaptive_pool.py b/python/oneflow/test/modules/test_consistent_adaptive_pool.py deleted file mode 100644 index 89f90a2d6750468e11a0764f3907f85e9aacaee5..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_adaptive_pool.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from packaging import version -import unittest -from typing import Union, Tuple -import torch as torch_original - -import oneflow as flow -import oneflow.unittest -from oneflow.nn.common_types import _size_1_t -from oneflow.test_utils.automated_test_util import * - -NoneType = type(None) -# Not the same as those in PyTorch because 'output_size' cannot be NoneType (even in 'torch.nn.AdaptiveAvgPoolXd') -_size_2_opt_t_not_none = Union[int, Tuple[Union[int, NoneType], Union[int, NoneType]]] -_size_3_opt_t_not_none = Union[ - int, Tuple[Union[int, NoneType], Union[int, NoneType], Union[int, NoneType]] -] - - -@autotest(n=1, check_graph=False) -def _test_adaptive_avgpoolnd(test_case, ndim, pool_size, placement, sbp): - dims = [random(1, 3) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - if pool_size == 1: - m = torch.nn.AdaptiveAvgPool1d(output_size=random().to(_size_1_t)) - elif pool_size == 2: - m = torch.nn.AdaptiveAvgPool2d(output_size=random().to(_size_2_opt_t_not_none)) - elif pool_size == 3: - m = torch.nn.AdaptiveAvgPool3d(output_size=random().to(_size_3_opt_t_not_none)) - else: - raise ValueError("pool size should be 1, 2 or 3, but got %d" % pool_size) - m.train(random()) - y = m(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_adaptive_avgpoolnd_functional(test_case, ndim, pool_size, placement, sbp): - dims = [random(1, 3) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - if pool_size == 1: - return torch.nn.functional.adaptive_avg_pool1d(x, output_size=random().to(int)) - elif pool_size == 2: - return torch.nn.functional.adaptive_avg_pool2d(x, output_size=random().to(int)) - elif pool_size == 3: - return torch.nn.functional.adaptive_avg_pool3d(x, output_size=random().to(int)) - - -class TestAdaptiveAvgPool(flow.unittest.TestCase): - @globaltest - def test_adaptive_avgpool(test_case): - for placement in all_placement(): - ndim = 3 - for sbp in all_sbp(placement, max_dim=2): - _test_adaptive_avgpoolnd(test_case, ndim, 1, placement, sbp) - _test_adaptive_avgpoolnd_functional(test_case, ndim, 1, placement, sbp) - - ndim = 4 - for sbp in all_sbp(placement, max_dim=2): - _test_adaptive_avgpoolnd(test_case, ndim, 2, placement, sbp) - _test_adaptive_avgpoolnd_functional(test_case, ndim, 2, placement, sbp) - - # GPU version 'nn.AdaptiveAvgPool3d' has a bug in PyTorch before '1.10.0' - if ( - version.parse(torch_original.__version__) < version.parse("1.10.0") - and placement.type == "cuda" - ): - continue - ndim = 5 - for sbp in all_sbp(placement, max_dim=2): - _test_adaptive_avgpoolnd(test_case, ndim, 3, placement, sbp) - _test_adaptive_avgpoolnd_functional(test_case, ndim, 3, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_addcmul.py b/python/oneflow/test/modules/test_consistent_addcmul.py deleted file mode 100644 index 7716550dc4f3a3effcaeb47bce719b36a1d283ce..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_addcmul.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_addcmul(test_case, ndim, placement, sbp): - shape = [random(low=2, high=3) * 8 for i in range(ndim)] - - input = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp) - tensor1 = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) - tensor2 = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) - value = random(3, 6).to(int) - output = torch.addcmul(input, tensor1, tensor2, value=value) - return output - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_addcmul(test_case): - ndim = random(low=2, high=5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_addcmul(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_addmm.py b/python/oneflow/test/modules/test_consistent_addmm.py deleted file mode 100644 index e0b1035fc7680443d95f208e4451c61e19ef81f1..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_addmm.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_addmm_with_random_data(test_case, placement, sbp): - m = random(1, 3) * 8 - n = random(1, 3) * 8 - k = random(1, 3) * 8 - input = random_tensor(ndim=2, dim0=m, dim1=n).to_global( - placement=placement, sbp=sbp - ) - mat1 = random_tensor(ndim=2, dim0=m, dim1=k).to_global(placement=placement, sbp=sbp) - mat2 = random_tensor(ndim=2, dim0=k, dim1=n).to_global(placement=placement, sbp=sbp) - y = torch.addmm( - input, mat1, mat2, beta=random().to(float), alpha=random().to(float), - ) - return y - - -@autotest(n=1, check_graph=False) -def _test_addmm_broadcast_with_random_data(test_case, placement, sbp): - m = random(1, 3) * 8 - n = random(1, 3) * 8 - k = random(1, 3) * 8 - input = random_tensor(ndim=2, dim0=1, dim1=1).to_global( - placement=placement, sbp=[flow.sbp.broadcast for _ in range(len(sbp))] - ) - mat1 = random_tensor(ndim=2, dim0=m, dim1=k).to_global(placement=placement, sbp=sbp) - mat2 = random_tensor(ndim=2, dim0=k, dim1=n).to_global(placement=placement, sbp=sbp) - y = torch.addmm( - input, mat1, mat2, beta=random().to(float), alpha=random().to(float), - ) - return y - - -class TestAddmm(flow.unittest.TestCase): - @globaltest - def test_addmm(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_addmm_with_random_data(test_case, placement, sbp) - _test_addmm_broadcast_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_affine_grid.py b/python/oneflow/test/modules/test_consistent_affine_grid.py deleted file mode 100644 index b9d93572df655d196adcf80c2f7f9dd0b0d992e0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_affine_grid.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=False) -def _test_affine_grid_2d_with_random_data(test_case, placement, sbp): - N = random(1, 3).to(int).value() * 8 - C = random(1, 8).to(int).value() - H = random(1, 8).to(int).value() - W = random(1, 8).to(int).value() - align_corners = oneof(True, False).value() - dims = [N, 2, 3] - - theta = random_tensor(3, *dims).to_global(placement=placement, sbp=sbp) - output = torch.nn.functional.affine_grid( - theta, (N, C, H, W), align_corners=align_corners - ) - return output - - -@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=False) -def _test_affine_grid_3d_with_random_data(test_case, placement, sbp): - N = random(1, 3).to(int) * 8 - C = random(1, 8).to(int) - D = random(1, 8).to(int) - H = random(1, 8).to(int) - W = random(1, 8).to(int) - align_corners = oneof(True, False) - dims = [N, 3, 4] - - theta = random_tensor(3, *dims).to_global(placement=placement, sbp=sbp) - output = torch.nn.functional.affine_grid( - theta, (N, C, D, H, W), align_corners=align_corners - ) - return output - - -class TestAffineGrid(flow.unittest.TestCase): - @globaltest - def test_affine_grid(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_affine_grid_2d_with_random_data(test_case, placement, sbp) - _test_affine_grid_3d_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_argmax.py b/python/oneflow/test/modules/test_consistent_argmax.py deleted file mode 100644 index 207a7e91e2d43c8e6b4d39ac9ff5a3eba04ca178..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_argmax.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_argmax_with_random_data(test_case, ndim, placement, sbp): - dims = [8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.argmax(x, dim=random(0, ndim).to(int), keepdim=random().to(bool)) - return y - - -@unittest.skip("TODO: sometimes global TestArgmax fails on 2-GPU runs") -class TestArgmax(flow.unittest.TestCase): - @globaltest - def test_argmax(test_case): - ndim = random(1, 3).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_argmax_with_random_data(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_argmin.py b/python/oneflow/test/modules/test_consistent_argmin.py deleted file mode 100644 index 53c5f6aea5409245a941c035c0e0146ad86ed178..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_argmin.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_argmin_with_random_data(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.argmin(x, dim=random(0, ndim).to(int), keepdim=random().to(bool)) - return y - - -class TestArgmin(flow.unittest.TestCase): - @globaltest - def test_argmin(test_case): - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_argmin_with_random_data(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_argsort.py b/python/oneflow/test/modules/test_consistent_argsort.py deleted file mode 100644 index 5c2d8cb6188407acd4e6ea0906cbf167aa335049..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_argsort.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_argsort_with_random_data(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.argsort( - x, dim=random(low=-ndim, high=ndim).to(int), descending=random_bool() - ) - return y - - -@unittest.skip("argsort has bug not found at now.") -class TestArgsort(flow.unittest.TestCase): - @globaltest - def test_argsort(test_case): - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_argsort_with_random_data(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_argwhere.py b/python/oneflow/test/modules/test_consistent_argwhere.py deleted file mode 100644 index 223b991ab83b8896b0ba1cd4ffe949cf1b80b7e0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_argwhere.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import torch as torch_ori - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_argwhere_with_random_data(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - # PyTorch has no argwhere before v1.11, so we use nonzero instead of argwhere for PyTorch - # y = torch.argwhere(x) - y = x.clone() - y.oneflow = flow.argwhere(x.oneflow) - y.pytorch = torch_ori.nonzero(x.pytorch) - return y - - -class TestArgwhere(flow.unittest.TestCase): - @globaltest - def test_argwhere(test_case): - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_argwhere_with_random_data(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_avgpool.py b/python/oneflow/test/modules/test_consistent_avgpool.py deleted file mode 100644 index dd6d28dfd5d04ceb5d743e02b0d52e2f75bf57e7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_avgpool.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_avgpool1d_with_random_data(test_case, placement, sbp): - m = torch.nn.AvgPool1d( - kernel_size=random(4, 6), - stride=random(1, 3), - padding=random(1, 3), - ceil_mode=random(), - count_include_pad=random(), - ) - m.train(random()) - m.to_global(placement=placement, sbp=sbp) - ndim = 3 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = m(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_avgpool2d_with_random_data(test_case, placement, sbp): - m = torch.nn.AvgPool2d( - kernel_size=random(4, 6), - stride=random(1, 3), - padding=random(1, 3), - ceil_mode=random(), - count_include_pad=random(), - divisor_override=random().to(int), - ) - m.train(random()) - m.to_global(placement=placement, sbp=sbp) - ndim = 4 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = m(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_avgpool3d_with_random_data(test_case, placement, sbp): - m = torch.nn.AvgPool3d( - kernel_size=random(4, 6), - stride=random(1, 3), - padding=random(1, 3), - ceil_mode=random(), - count_include_pad=random(), - divisor_override=random().to(int), - ) - m.train(random()) - m.to_global(placement=placement, sbp=sbp) - ndim = 5 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = m(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_functional_avgpool1d_with_random_data(test_case, placement, sbp): - ndim = 3 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.nn.functional.avg_pool1d( - x, - kernel_size=random(1, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - ceil_mode=random_bool(), - count_include_pad=random_bool(), - ) - return y - - -@autotest(n=1, check_graph=False) -def _test_functional_avgpool2d_with_random_data(test_case, placement, sbp): - ndim = 4 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.nn.functional.avg_pool2d( - x, - kernel_size=random(1, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - ceil_mode=random_bool(), - count_include_pad=random_bool(), - ) - return y - - -@autotest(n=1, check_graph=False) -def _test_functional_avgpool3d_with_random_data(test_case, placement, sbp): - ndim = 5 - dims = [random(1, 3) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.nn.functional.avg_pool3d( - x, - kernel_size=random(1, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - ceil_mode=random_bool(), - count_include_pad=random_bool(), - ) - return y - - -class TestAvgPoolingModule(flow.unittest.TestCase): - @globaltest - def test_avg_pooling(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_avgpool1d_with_random_data(test_case, placement, sbp) - _test_functional_avgpool1d_with_random_data(test_case, placement, sbp) - for sbp in all_sbp(placement, max_dim=2): - _test_avgpool2d_with_random_data(test_case, placement, sbp) - _test_functional_avgpool2d_with_random_data(test_case, placement, sbp) - for sbp in all_sbp(placement, max_dim=2): - _test_avgpool3d_with_random_data(test_case, placement, sbp) - _test_functional_avgpool3d_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_cast.py b/python/oneflow/test/modules/test_consistent_cast.py deleted file mode 100644 index 6a189e7b1d9398a0c5b50f1a5ada61ffd2d1de20..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_cast.py +++ /dev/null @@ -1,1290 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -import oneflow as flow -import os - -import oneflow.unittest -from oneflow.test_utils.test_util import GenArgList - - -@flow.unittest.skip_unless_1n4d() -class TestConsistentCastModule_1n4d(flow.unittest.TestCase): - def test_to_global_flatten_hierarchy(test_case): - x = flow.ones((4, 4), dtype=flow.int32) - sbp = (flow.sbp.partial_sum,) - y = x.to_global( - placement=flow.placement("cpu", ranks=[[0, 1], [2, 3]]), - sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), - ) - placement = flow.placement("cpu", ranks=[0, 1, 2, 3]) - y = y.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_to_global_flatten_hierarchy_cpu_to_gpu(test_case): - x = flow.ones((4, 4), dtype=flow.int32) - sbp = (flow.sbp.partial_sum,) - y = x.to_global( - placement=flow.placement("cpu", ranks=[[0, 1], [2, 3]]), - sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), - ) - placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) - y = y.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_to_global_flatten_hierarchy_gpu_to_cpu(test_case): - x = flow.ones((4, 4), dtype=flow.int32) - sbp = (flow.sbp.partial_sum,) - y = x.to_global( - placement=flow.placement("cuda", ranks=[[0, 1], [2, 3]]), - sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), - ) - placement = flow.placement("cpu", ranks=[0, 1, 2, 3]) - y = y.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - - def test_to_global_broadcast_shape_dtype(test_case): - if int(os.getenv("RANK")) < 2: - x = flow.ones((4, 4), dtype=flow.int32) - else: - x = flow.zeros((1,), dtype=flow.float) - placement = flow.placement("cpu", ranks=[0, 1]) - sbp = (flow.sbp.split(0),) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_local_to_global_2d_sbp(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) - sbp = (flow.sbp.split(0), flow.sbp.partial_sum) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_local_to_global_sp_2_bb(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) - sbp = (flow.sbp.split(0), flow.sbp.partial_sum) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - y = y.to_global(sbp=(flow.sbp.broadcast, flow.sbp.broadcast)) - test_case.assertEqual(y.sbp, (flow.sbp.broadcast, flow.sbp.broadcast)) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32) * 2) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_local_to_global_ps0_2_s0s0(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - x = x * int(os.getenv("RANK")) - placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) - sbp = (flow.sbp.partial_sum, flow.sbp.split(0)) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - sbp = (flow.sbp.split(0), flow.sbp.split(0)) - y = y.to_global(sbp=sbp) - z = y.to_local() - if int(os.getenv("RANK")) < 2: - scale = 2 - else: - scale = 4 - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * scale) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_local_to_global_s0p_2_s0s0(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - x = x * int(os.getenv("RANK")) - placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) - sbp = (flow.sbp.split(0), flow.sbp.partial_sum) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - sbp = (flow.sbp.split(0), flow.sbp.split(0)) - y = y.to_global(sbp=sbp) - z = y.to_local() - if int(os.getenv("RANK")) < 2: - scale = 1 - else: - scale = 5 - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * scale) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_to_global_loop_broadcast_shape_dtype(test_case): - if int(os.getenv("RANK")) < 2: - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - a = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - else: - x = flow.zeros((1,), dtype=flow.float) - a = flow.zeros((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - sbp = (flow.sbp.split(0),) - for i in range(1000): - if i % 100 == 0: - print(i) - y = x.to_global(placement=placement, sbp=sbp) - b = a.to_global(placement=placement, sbp=flow.sbp.broadcast) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - - -@flow.unittest.skip_unless_1n2d() -class TestConsistentCastModule_1n2d(flow.unittest.TestCase): - def test_to_global_broadcast_shape_dtype(test_case): - if os.getenv("RANK") == "0": - x = flow.ones((4, 4), dtype=flow.int32) - else: - x = flow.zeros((1,), dtype=flow.float) - placement = flow.placement("cpu", ranks=[0]) - sbp = (flow.sbp.broadcast,) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - - def test_local_to_global_broadcast_data(test_case): - if int(os.getenv("RANK")) == 0: - x = flow.ones((4, 4), dtype=flow.int32) - else: - x = flow.zeros((4, 4), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - sbp = (flow.sbp.broadcast,) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue(np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32))) - - def test_cuda_global_to_global_cpu_s2b(test_case): - x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) - sbp = (flow.sbp.broadcast,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue(np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32))) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_s2b(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) - sbp = (flow.sbp.broadcast,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue(np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32))) - - def test_cuda_global_to_global_cpu_s2p(test_case): - x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) - sbp = (flow.sbp.partial_sum,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - if int(os.getenv("RANK")) == 0: - test_case.assertTrue( - np.array_equal( - z.numpy(), - np.concatenate( - ( - np.ones((4, 4), dtype=np.int32), - np.zeros((4, 4), dtype=np.int32), - ), - axis=0, - ), - ) - ) - else: - test_case.assertTrue( - np.array_equal( - z.numpy(), - np.concatenate( - ( - np.zeros((4, 4), dtype=np.int32), - np.ones((4, 4), dtype=np.int32), - ), - axis=0, - ), - ) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_s2p(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) - sbp = (flow.sbp.partial_sum,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (8, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - if int(os.getenv("RANK")) == 0: - test_case.assertTrue( - np.array_equal( - z.numpy(), - np.concatenate( - ( - np.ones((4, 4), dtype=np.int32), - np.zeros((4, 4), dtype=np.int32), - ), - axis=0, - ), - ) - ) - else: - test_case.assertTrue( - np.array_equal( - z.numpy(), - np.concatenate( - ( - np.zeros((4, 4), dtype=np.int32), - np.ones((4, 4), dtype=np.int32), - ), - axis=0, - ), - ) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_b2p(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.broadcast) - sbp = (flow.sbp.partial_sum,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - if int(os.getenv("RANK")) == 0: - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32)) - ) - else: - test_case.assertTrue( - np.array_equal(z.numpy(), np.zeros((4, 4), dtype=np.int32)) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_b2s(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.broadcast) - sbp = (flow.sbp.split(0),) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue(np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32))) - - def test_cuda_global_to_global_cpu_p2s(test_case): - x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) - sbp = (flow.sbp.split(0),) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * 2) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_p2s(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) - sbp = (flow.sbp.split(0),) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * 2) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_cuda_h2d(test_case): - x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - cuda_placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) - y = y.to_global(placement=cuda_placement, sbp=flow.sbp.partial_sum) - test_case.assertEqual(y.sbp, (flow.sbp.partial_sum,)) - test_case.assertEqual(y.placement, cuda_placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue(np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32))) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_cpu_p2b(test_case): - x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) - placement = flow.placement("cpu", ranks=[0, 1]) - cuda_placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) - import time - - y = y.to_global(placement=cuda_placement, sbp=flow.sbp.partial_sum) - sbp = (flow.sbp.broadcast,) - y = y.to_global(placement=cuda_placement, sbp=sbp) - y = y.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32) * 2) - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_cuda_global_to_global_p2b(test_case): - x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) - placement = flow.placement("cuda", ranks=[0, 1]) - y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) - sbp = (flow.sbp.broadcast,) - y = y.to_global(sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - test_case.assertEqual(y.dtype, flow.int32) - z = y.to_local() - test_case.assertTrue( - np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32) * 2) - ) - - -@flow.unittest.skip_unless_1n1d() -class TestConsistentCastModule_1n1d(flow.unittest.TestCase): - def test_to_global(test_case): - x = flow.ones((4, 4)) - placement = flow.placement("cpu", ranks=[0]) - sbp = (flow.sbp.broadcast,) - y = x.to_global(placement=placement, sbp=sbp) - test_case.assertEqual(y.sbp, sbp) - test_case.assertEqual(y.placement, placement) - test_case.assertEqual(tuple(y.shape), (4, 4)) - - -def _test_cpu_p2b_with_random_parameter(test_case, device_list): - gen_float = np.random.random - gen_int = np.random.randint - dtype_list = [ - flow.uint8, - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - flow.double, - ] - - def choose_shape_and_dtype(seed): - rng = np.random.default_rng(seed) - kdtype = rng.integers(low=1, high=len(dtype_list), size=1) - ndim = rng.integers(low=1, high=4, size=1) - shape = rng.integers(low=1, high=10, size=ndim) - return kdtype, shape - - for _ in range(10): - seed = flow.tensor(gen_int(1, 1000, 1)) - seed = seed.to_global( - placement=flow.env.all_device_placement(seed.device.type), - sbp=flow.sbp.broadcast, - ) - seed = int(seed.to_local().numpy()) - kdtype, shape = choose_shape_and_dtype(seed) - if kdtype <= 3: - np_arr = gen_int(1, 10, shape) - else: - np_arr = gen_float(shape) - tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) - cpu_tensor = tensor.to_global( - placement=flow.placement("cpu", device_list), sbp=flow.sbp.partial_sum - ) - cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.broadcast) - tensor = tensor.to("cuda") - cuda_tensor = tensor.to_global( - placement=flow.placement("cuda", device_list), sbp=flow.sbp.partial_sum - ) - cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.broadcast) - test_case.assertTrue( - np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) - ) - - -def _test_cpu_s2b_with_random_parameter(test_case, device_list): - gen_float = np.random.random - gen_int = np.random.randint - dtype_list = [ - flow.uint8, - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - flow.double, - ] - - def choose_shape_and_dtype(seed): - rng = np.random.default_rng(seed) - kdtype = rng.integers(low=1, high=len(dtype_list), size=1) - ndim = rng.integers(low=1, high=4, size=1) - shape = rng.integers(low=1, high=10, size=ndim) - return kdtype, shape - - for _ in range(10): - seed = flow.tensor(gen_int(1, 1000, 1)) - seed = seed.to_global( - placement=flow.env.all_device_placement(seed.device.type), - sbp=flow.sbp.broadcast, - ) - seed = int(seed.to_local().numpy()) - kdtype, shape = choose_shape_and_dtype(seed) - if kdtype <= 3: - np_arr = gen_int(1, 10, shape) - else: - np_arr = gen_float(shape) - tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) - cpu_tensor = tensor.to_global( - placement=flow.placement("cpu", device_list), sbp=flow.sbp.split(0) - ) - cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.broadcast) - tensor = tensor.to("cuda") - cuda_tensor = tensor.to_global( - placement=flow.placement("cuda", device_list), sbp=flow.sbp.split(0) - ) - cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.broadcast) - test_case.assertTrue( - np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) - ) - - -def _test_cpu_p2s_with_random_parameter(test_case, device_list): - gen_float = np.random.random - gen_int = np.random.randint - dtype_list = [ - flow.uint8, - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - flow.double, - ] - - def choose_shape_and_dtype(seed): - rng = np.random.default_rng(seed) - kdtype = rng.integers(low=1, high=len(dtype_list), size=1) - ndim = rng.integers(low=1, high=4, size=1) - shape = list(rng.integers(low=1, high=5, size=1) * 12) + list( - rng.integers(low=1, high=10, size=ndim - 1) - ) - return kdtype, shape - - for _ in range(10): - seed = flow.tensor(gen_int(1, 1000, 1)) - seed = seed.to_global( - placement=flow.env.all_device_placement(seed.device.type), - sbp=flow.sbp.broadcast, - ) - seed = int(seed.to_local().numpy()) - kdtype, shape = choose_shape_and_dtype(seed) - if kdtype <= 3: - np_arr = gen_int(1, 10, shape) - else: - np_arr = gen_float(shape) - tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) - cpu_tensor = tensor.to_global( - placement=flow.placement("cpu", device_list), sbp=flow.sbp.partial_sum - ) - cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.split(0)) - tensor = tensor.to("cuda") - cuda_tensor = tensor.to_global( - placement=flow.placement("cuda", device_list), sbp=flow.sbp.partial_sum - ) - cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.split(0)) - test_case.assertTrue( - np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) - ) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast(flow.unittest.TestCase): - def test_cpu_local_tensor_to_gpu_placement(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array([4, 6, 7, 8], dtype=np.float32) - else: - np_arr = np.array([0, 0, 0, 0], dtype=np.float32) - tensor = flow.tensor(np_arr, dtype=flow.float32) - placement = flow.placement("cuda", [0, 1, 2, 3]) - device = flow.device("cuda") - global_tensor = tensor.to_global(placement, flow.sbp.broadcast) - test_case.assertEqual(global_tensor.to_local().device, device) - test_case.assertEqual(global_tensor.placement, placement) - test_case.assertTrue( - np.array_equal( - global_tensor.to_local().numpy(), - np.array([4, 6, 7, 8], dtype=np.float32), - ) - ) - - def test_cpu_p2b_with_random_parameter(test_case): - arg_dict = OrderedDict() - arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] - for arg in GenArgList(arg_dict): - _test_cpu_p2b_with_random_parameter(test_case, *arg) - - def test_cpu_s2b_with_random_parameter(test_case): - arg_dict = OrderedDict() - arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] - for arg in GenArgList(arg_dict): - _test_cpu_s2b_with_random_parameter(test_case, *arg) - - def test_cpu_p2s_with_random_parameter(test_case): - arg_dict = OrderedDict() - arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] - for arg in GenArgList(arg_dict): - _test_cpu_p2s_with_random_parameter(test_case, *arg) - - def test_local_to_global_with_wrong_device(test_case): - np_arr = np.array([4, 6], dtype=np.float32) - tensor = flow.tensor( - np_arr, - device=flow.device("cuda:%d" % ((flow.env.get_rank() + 1) % 4)), - dtype=flow.float32, - ) - placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) - device = flow.device("cuda") - global_tensor = tensor.to_global(placement, flow.sbp.broadcast) - local_tensor = global_tensor.to_local() - test_case.assertEqual(local_tensor.device, device) - test_case.assertEqual(global_tensor.placement, placement) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast_S2S(flow.unittest.TestCase): - def test_global_to_global_s0_to_s1(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - split0_tensor = tensor.to_global(placement, flow.sbp.split(0)) - split1_tensor = split0_tensor.to_global(placement, flow.sbp.split(1)) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - split1_tensor.to_local().numpy(), - np.array( - [ - [4.0, 6.0], - [6.0, 2.0], - [3.0, 7.0], - [6.0, 8.0], - [2.0, 10.0], - [3.0, 9.0], - [4.0, 6.0], - [6.0, 8.0], - ], - dtype=np.float32, - ), - ) - ) - elif flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - split1_tensor.to_local().numpy(), - np.array( - [ - [5.0, 20.0], - [5.0, 7.0], - [5.0, 4.0], - [9.0, 4.0], - [10.0, 7.0], - [10.0, 5.0], - [6.0, 9.0], - [6.0, 4.0], - ], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_s1_to_s0(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - split_tensor = tensor.to_global(placement, flow.sbp.split(0)) - split1_tensor = split_tensor.to_global(placement, flow.sbp.split(1)) - split0_tensor = split1_tensor.to_global(placement, flow.sbp.split(0)) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - split0_tensor.to_local().numpy(), - np.array( - [ - [4.0, 6.0, 5.0, 20.0], - [6.0, 2.0, 5.0, 7.0], - [3.0, 7.0, 5.0, 4.0], - [6.0, 8.0, 9.0, 4.0], - ], - dtype=np.float32, - ), - ) - ) - elif flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - split0_tensor.to_local().numpy(), - np.array( - [ - [2.0, 10.0, 10.0, 7.0], - [3.0, 9.0, 10.0, 5.0], - [4.0, 6.0, 6.0, 9.0], - [6.0, 8.0, 6.0, 4.0], - ], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_s0_to_s1_cpu(test_case): - np_arr = np.random.randn(4, 12) - - cuda_device = flow.device("cuda") - cuda_tensor = flow.tensor(np_arr, device=cuda_device, dtype=flow.float32) - cuda_placement = flow.placement("cuda", ranks=[1, 3]) - cuda_split0_tensor = cuda_tensor.to_global(cuda_placement, flow.sbp.split(0)) - cuda_split1_tensor = cuda_split0_tensor.to_global( - cuda_placement, flow.sbp.split(1) - ) - - cpu_device = flow.device("cpu") - cpu_tensor = flow.tensor(np_arr, device=cpu_device, dtype=flow.float32) - cpu_placement = flow.placement("cpu", ranks=[1, 3]) - cpu_split0_tensor = cpu_tensor.to_global(cpu_placement, flow.sbp.split(0)) - cpu_split1_tensor = cpu_split0_tensor.to_global( - cpu_placement, flow.sbp.split(1) - ) - - if flow.env.get_rank() == 0 or flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - cuda_split1_tensor.to_local().numpy(), - cpu_split1_tensor.to_local().numpy(), - ) - ) - - def test_global_to_global_s1_to_s0_cpu(test_case): - np_arr = np.random.randn(4, 12) - - cuda_device = flow.device("cuda") - cuda_tensor = flow.tensor(np_arr, device=cuda_device, dtype=flow.float32) - cuda_placement = flow.placement("cuda", ranks=[0, 1]) - cuda_split_tensor = cuda_tensor.to_global(cuda_placement, flow.sbp.split(0)) - cuda_split1_tensor = cuda_split_tensor.to_global( - cuda_placement, flow.sbp.split(1) - ) - cuda_split0_tensor = cuda_split1_tensor.to_global( - cuda_placement, flow.sbp.split(0) - ) - - cpu_device = flow.device("cpu") - cpu_tensor = flow.tensor(np_arr, device=cpu_device, dtype=flow.float32) - cpu_placement = flow.placement("cpu", ranks=[0, 1]) - cpu_split_tensor = cpu_tensor.to_global(cpu_placement, flow.sbp.split(0)) - cpu_split1_tensor = cpu_split_tensor.to_global(cpu_placement, flow.sbp.split(1)) - cpu_split0_tensor = cpu_split1_tensor.to_global( - cpu_placement, flow.sbp.split(0) - ) - - if flow.env.get_rank() == 0 or flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - cuda_split0_tensor.to_local().numpy(), - cpu_split0_tensor.to_local().numpy(), - ) - ) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast_XToB(flow.unittest.TestCase): - def test_global_to_global_btb_gpu_to_gpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 1: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 2: - np_arr = np.array( - [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 3: - np_arr = np.array( - [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - global_tensor = tensor.to_global(placement, flow.sbp.broadcast) - new_placement = flow.placement("cuda", ranks=[0, 1, 2]) - broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(broadcast_tensor.placement, new_placement) - if flow.env.get_rank() != 3: - test_case.assertTrue( - np.array_equal( - broadcast_tensor.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_stb_gpu_to_gpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 1: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 2: - np_arr = np.array( - [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 3: - np_arr = np.array( - [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1, 2]) - global_tensor = tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) - broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(broadcast_tensor.placement, new_placement) - test_case.assertTrue( - np.array_equal( - broadcast_tensor.to_local().numpy(), - np.array( - [ - [4, 6, 5, 20], - [6, 8, 9, 0], - [3, 7, 5, 0], - [6, 8, 9, 0], - [2, 10, 10, 7], - [3, 9, 10, 5], - [4, 6, 6, 9], - [6, 8, 6, 4], - [9, 6, 5, 8], - [4, 9, 7, 0], - [2, 5, 7, 9], - [6, 8, 10, 0], - ], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_ptb_gpu_to_gpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 1: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 2: - np_arr = np.array( - [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], - dtype=np.float32, - ) - elif flow.env.get_rank() == 3: - np_arr = np.array( - [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1, 2]) - global_tensor = tensor.to_global(placement, flow.sbp.partial_sum) - new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) - broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(broadcast_tensor.placement, new_placement) - test_case.assertTrue( - np.array_equal( - broadcast_tensor.to_local().numpy(), - np.array( - [ - [15, 22, 20, 35], - [13, 26, 26, 5], - [9, 18, 18, 18], - [18, 24, 25, 4], - ], - dtype=np.float32, - ), - ) - ) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast_1ToN(flow.unittest.TestCase): - def test_global_to_global_1tob(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0]) - global_tensor = tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[0, 1]) - broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(broadcast_tensor.placement, new_placement) - if flow.env.get_rank() < 2: - test_case.assertTrue( - np.array_equal( - broadcast_tensor.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_1top(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", [0]) - global_tensor = tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[0, 1]) - partial_sum_tensor = global_tensor.to_global( - new_placement, flow.sbp.partial_sum - ) - test_case.assertEqual(partial_sum_tensor.placement, new_placement) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - partial_sum_tensor.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ), - ) - ) - elif flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - partial_sum_tensor.to_local().numpy(), - np.array( - [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_1tos(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0]) - global_tensor = tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) - split_tensor = global_tensor.to_global(new_placement, flow.sbp.split(0)) - test_case.assertEqual(split_tensor.placement, new_placement) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - split_tensor.to_local().numpy(), - np.array([[4, 6, 5, 20]], dtype=np.float32,), - ) - ) - elif flow.env.get_rank() == 1: - test_case.assertTrue( - np.array_equal( - split_tensor.to_local().numpy(), - np.array([[6, 2, 5, 7]], dtype=np.float32,), - ) - ) - elif flow.env.get_rank() == 2: - test_case.assertTrue( - np.array_equal( - split_tensor.to_local().numpy(), - np.array([[3, 7, 5, 4]], dtype=np.float32,), - ) - ) - elif flow.env.get_rank() == 3: - test_case.assertTrue( - np.array_equal( - split_tensor.to_local().numpy(), - np.array([[6, 8, 9, 4]], dtype=np.float32,), - ) - ) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast_NTo1(flow.unittest.TestCase): - def test_global_to_global_bt1(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - global_tensor = tensor.to_global(placement, flow.sbp.broadcast) - new_placement = flow.placement("cuda", ranks=[0]) - broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(broadcast_tensor.placement, new_placement) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - broadcast_tensor.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_st1(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - global_tensor = tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[0]) - partial_sum_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(partial_sum_tensor.placement, new_placement) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - partial_sum_tensor.to_local().numpy(), - np.array( - [ - [4, 6, 5, 20], - [6, 2, 5, 7], - [3, 7, 5, 4], - [6, 8, 9, 4], - [2, 10, 10, 7], - [3, 9, 10, 5], - [4, 6, 6, 9], - [6, 8, 6, 4], - ], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_pt1(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0, 1]) - global_tensor = tensor.to_global(placement, flow.sbp.partial_sum) - new_placement = flow.placement("cuda", ranks=[0]) - partial_sum_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(partial_sum_tensor.placement, new_placement) - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - partial_sum_tensor.to_local().numpy(), - np.array( - [ - [6, 16, 15, 27], - [9, 11, 15, 12], - [7, 13, 11, 13], - [12, 16, 15, 8], - ], - dtype=np.float32, - ), - ) - ) - - -@flow.unittest.skip_unless_1n4d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestConsistentCast_1To1(flow.unittest.TestCase): - def test_global_to_global_1to1_gpu_to_gpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[3]) - x = local_tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[2]) - y = x.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(y.placement, new_placement) - if flow.env.get_rank() == 2: - test_case.assertTrue( - np.array_equal( - y.to_local().numpy(), - np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_1to1_cpu_to_cpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cpu") - local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cpu", ranks=[0]) - x = local_tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cpu", ranks=[2]) - y = x.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(y.placement, new_placement) - if flow.env.get_rank() == 2: - test_case.assertTrue( - np.array_equal( - y.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_1to1_gpu_to_cpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cuda") - local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cuda", ranks=[0]) - x = local_tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cpu", ranks=[3]) - y = x.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(y.placement, new_placement) - if flow.env.get_rank() == 3: - test_case.assertTrue( - np.array_equal( - y.to_local().numpy(), - np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ), - ) - ) - - def test_global_to_global_1to1_cpu_to_gpu(test_case): - if flow.env.get_rank() == 0: - np_arr = np.array( - [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], - dtype=np.float32, - ) - else: - np_arr = np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ) - device = flow.device("cpu") - local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) - placement = flow.placement("cpu", ranks=[1]) - x = local_tensor.to_global(placement, flow.sbp.split(0)) - new_placement = flow.placement("cuda", ranks=[3]) - y = x.to_global(new_placement, flow.sbp.broadcast) - test_case.assertEqual(y.placement, new_placement) - if flow.env.get_rank() == 3: - test_case.assertTrue( - np.array_equal( - y.to_local().numpy(), - np.array( - [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], - dtype=np.float32, - ), - ) - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_chunk.py b/python/oneflow/test/modules/test_consistent_chunk.py deleted file mode 100644 index a17a8d14e9fb289f77705086fd86c8f4333f505f..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_chunk.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import numpy as np -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_chunk(test_case, ndim, placement, sbp): - dims = [random(1, 3).to(int) * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - dim = random(-ndim, ndim).to(int) - chunks = random(low=1, high=4).to(int) - y = torch.chunk(x, chunks=chunks, dim=dim) - z = torch.cat(y, dim=dim) - return z - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_chunk(test_case): - for placement in all_placement(): - ndim = random(1, 4).to(int).value() - for sbp in all_sbp(placement, max_dim=min(ndim, 2)): - _test_chunk(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_coin_flip.py b/python/oneflow/test/modules/test_consistent_coin_flip.py deleted file mode 100644 index 5c18efaa0e401fb67089519ecd23b88584da11e8..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_coin_flip.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_coin_flip( - test_case, batch_size, random_seed, probability, placement, sbp -): - m = flow.nn.CoinFlip( - batch_size, random_seed, probability, placement=placement, sbp=sbp - ) - x = m() - - test_case.assertEqual(x.shape[0], batch_size) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -def _test_graph_coin_flip( - test_case, batch_size, random_seed, probability, placement, sbp -): - class ConsistentCoinFlipGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - self.m = flow.nn.CoinFlip( - batch_size, random_seed, probability, placement=placement, sbp=sbp - ) - - def build(self): - return self.m() - - model = ConsistentCoinFlipGraph() - x = model() - - test_case.assertEqual(x.shape[0], batch_size) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -class TestCoinFlipConsistent(flow.unittest.TestCase): - @globaltest - def test_coin_flip_consistent(test_case): - arg_dict = OrderedDict() - arg_dict["batch_size"] = [8, 64] - arg_dict["random_seed"] = [None, 1, -1] - arg_dict["probability"] = [0.0, 0.5, 1.0] - for args in GenArgDict(arg_dict): - for placement in all_placement(): - # TODO: CoinFlip support cuda kernel - if placement.type == "cuda": - continue - - for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): - _test_consistent_coin_flip( - test_case, **args, placement=placement, sbp=sbp - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @flow.unittest.skip_unless_1n2d() - def test_coin_flip_graph(test_case): - arg_dict = OrderedDict() - arg_dict["batch_size"] = [8] - arg_dict["random_seed"] = [None, 1, -1] - arg_dict["probability"] = [0.0, 0.5, 1.0] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - # TODO: CoinFlip support cuda kernel - # flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - # TODO: CoinFlip support cuda kernel - # flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - placement = args["placement"] - for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): - _test_graph_coin_flip(test_case, **args, sbp=sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_concat.py b/python/oneflow/test/modules/test_consistent_concat.py deleted file mode 100644 index 7322bef6cf76cba6c23da84b2803ef2ed3318a66..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_concat.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_cat_with_random_data(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement=placement, sbp=sbp) - return torch.cat((x, x), random(0, 2).to(int)) - - -@autotest(n=1, auto_backward=False, check_graph=True) -def _test_concat_with_input_0_size_data(test_case, placement, sbp): - x = random_tensor(4, 8, 8, 2, 4).to_global(placement=placement, sbp=sbp) - y = random_tensor(4, 8, 8, random(0, 3) * 8, 8).to_global( - placement=placement, sbp=sbp - ) - z = torch.cat((x, y), dim=2) - return z - - -@autotest(n=1, auto_backward=False, check_graph=True) -def _test_concat_with_output_0_size_data(test_case, placement, sbp): - x = random_tensor(4, 8, 0, 2, 4).to_global(placement=placement, sbp=sbp) - y = random_tensor(4, 8, 0, 2, 4).to_global(placement=placement, sbp=sbp) - dim = random(0, 4).to(int).value() - z = torch.cat((x, y), dim=dim) - return z - - -@autotest(n=1, check_graph=False) -def _test_cat_only_one_tensor(test_case, placement, sbp): - x = random_tensor(4, 8, 8, random(1, 3) * 8, 8).to_global( - placement=placement, sbp=sbp - ) - return torch.cat((x,), 0) - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_cat_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - # TODO(): (S0, S1) will lead to wrong result, so skip it. - # Refer to the issue: - # https://github.com/Oneflow-Inc/OneTeam/issues/956#issuecomment-1011917987 - if sbp == (flow.sbp.split(0), flow.sbp.split(1)): - continue - _test_cat_with_random_data(test_case, placement, sbp) - - @globaltest - def test_cat_only_one_tensor(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_cat_only_one_tensor(test_case, placement, sbp) - - # TODO(): Support 0 size tensor for eager consistent - # @globaltest - # def test_concat_with_input_0_size_data(test_case): - # for placement in all_placement(): - # for sbp in all_sbp(placement, max_dim=2): - # _test_concat_with_input_0_size_data(test_case, placement, sbp) - - # TODO(): Support 0 size tensor for eager consistent - # @globaltest - # def test_concat_with_output_0_size_data(test_case): - # for placement in all_placement(): - # for sbp in all_sbp(placement, max_dim=2): - # _test_concat_with_output_0_size_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_constant.py b/python/oneflow/test/modules/test_consistent_constant.py deleted file mode 100644 index c09796317be6b7165dbcc09aeb79047942952c11..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_constant.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_constant(test_case, func, shape, placement, sbp): - func2 = None - if func == "ones": - func = flow.ones - np_res = np.ones(shape) - elif func == "zeros": - func = flow.zeros - np_res = np.zeros(shape) - elif func == "new_zeros": - func = flow.zeros - np_res = np.zeros(shape) - func2 = flow.new_zeros - else: - raise NotImplementedError - - x = func(*shape, placement=placement, sbp=sbp) - if func2: - x = func2(x) - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - x = x.to_global( - placement=placement, - sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], - ).to_local() - test_case.assertTrue(np.array_equal(x.numpy(), np_res)) - - -def _test_graph_constant(test_case, func, shape, placement, sbp): - func2 = None - if func == "ones": - func = flow.ones - np_res = np.ones(shape) - elif func == "zeros": - func = flow.zeros - np_res = np.zeros(shape) - elif func == "new_zeros": - func = flow.zeros - np_res = np.zeros(shape) - func2 = flow.new_zeros - else: - raise NotImplementedError - - class ConsistentConstantGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = func(*shape, placement=placement, sbp=sbp) - if func2: - x = func2(x) - return x - - model = ConsistentConstantGraph() - x = model() - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - x = x.to_global( - placement=placement, - sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], - ).to_local() - test_case.assertTrue(np.array_equal(x.numpy(), np_res)) - - -class TestConstantConsistent(flow.unittest.TestCase): - @globaltest - def test_constant_consistent(test_case): - shapes = [(8,), (8, 8,), (8, 8, 8)] - functions = [ - "ones", - "zeros", - "new_zeros", - ] - for func in functions: - for shape in shapes: - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(shape), except_partial_sum=True - ): - _test_consistent_constant( - test_case, func, shape, placement, sbp - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @flow.unittest.skip_unless_1n2d() - def test_constant_graph(test_case): - arg_dict = OrderedDict() - arg_dict["func"] = ["ones", "zeros", "new_zeros"] - arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - func = args["func"] - shape = args["shape"] - placement = args["placement"] - for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): - _test_graph_constant(test_case, func, shape, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_cumsum.py b/python/oneflow/test/modules/test_consistent_cumsum.py deleted file mode 100644 index b877b11b29303bd5a7a0858b2fd32e6313ff6ec0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_cumsum.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_cumsum_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - dim = random(0, ndim).to(int).value() - z = torch.cumsum(x, dim) - return z - - -@unittest.skip("This fails in multi-gpu") -class TestCumsumConsistent(flow.unittest.TestCase): - @globaltest - def test_cumsum(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=min(2, ndim)): - _test_cumsum_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_deconv2d.py b/python/oneflow/test/modules/test_consistent_deconv2d.py deleted file mode 100644 index 9cbd0cc9ed7a23105ce7753b55c99c1f5b1952a2..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_deconv2d.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_deconv2d_impl(test_case, placement, input_sbp): - ndim = 4 - in_channels = random(1, 5).to(int).value() * 8 - groups = random(1, 4).to(int).value() - out_channels = groups * 8 - kernel_size = random(1, 4).to(int).value() - stride = random(1, 5).to(int).value() - padding = random(1, 3).to(int).value() - dilation = random(1, 5).to(int).value() - padding_mode = constant("zeros") - m = torch.nn.ConvTranspose2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - padding_mode=padding_mode, - bias=False, - ) - m.train(random()) - - weight_sbp = random_sbp(placement, max_dim=2) - m.weight = torch.nn.Parameter( - m.weight.to_global(placement=placement, sbp=weight_sbp) - ) - - if m.bias is not None: - bias_sbp = random_sbp(placement, max_dim=1) - m.bias = torch.nn.Parameter(m.bias.to_global(placement=placement, sbp=bias_sbp)) - - batch = random(1, 3).to(int).value() * 8 - height = random(1, 5).to(int).value() * 8 - width = random(1, 5).to(int).value() * 8 - nchw = [batch, in_channels, height, width] - x = random_tensor(ndim, *nchw).to_global(placement=placement, sbp=input_sbp) - y = m(x) - return y - - -class TestDeconv2dConsistent(flow.unittest.TestCase): - @globaltest - def test_deconv2d(test_case): - for placement in all_placement(): - for input_sbp in all_sbp(placement, max_dim=2): - _test_deconv2d_impl(test_case, placement, input_sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_diag.py b/python/oneflow/test/modules/test_consistent_diag.py deleted file mode 100644 index d7b0e47433cb9d121f065b7847816317c36e83c4..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_diag.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_diag_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - return torch.diag(y) - - -class TestDiagConsistent(flow.unittest.TestCase): - @globaltest - def test_diag(test_case): - # random ndim in range [1,2] - ndim = random(1, 3).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_diag_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_diagonal.py b/python/oneflow/test/modules/test_consistent_diagonal.py deleted file mode 100644 index c93abe6272f0acd87220b2885baa183df9d8d158..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_diagonal.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_diagonal_impl(test_case, placement, sbp): - offset = random(-5, 5).to(int).value() - dim1 = random(-4, 4).to(int).value() - dim2 = random(-4, 4).to(int).value() - - x = random_tensor( - ndim=4, - dim0=random(1, 4) * 8, - dim1=random(1, 4) * 8, - dim2=random(1, 4) * 8, - dim3=random(1, 4) * 8, - ) - y = x.to_global(placement=placement, sbp=sbp) - z = torch.diagonal(y, offset, dim1, dim2) - return z - - -@unittest.skip("TODO: fix this test") -class TestDiagonalConsistent(flow.unittest.TestCase): - @globaltest - def test_diagonal(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_diagonal_impl(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_div.py b/python/oneflow/test/modules/test_consistent_div.py deleted file mode 100644 index 8bce10be8a383c8d86864788d82d954ca3ab98fa..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_div.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_div_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - x = x.to_global(placement=placement, sbp=sbp) - y = random_tensor(ndim, *dims) - y = y.to_global(placement=placement, sbp=sbp) - - z = torch.div(x, y) - return z - - -class TestDivConsistent(flow.unittest.TestCase): - @globaltest - def test_div(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_div_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_dot.py b/python/oneflow/test/modules/test_consistent_dot.py deleted file mode 100644 index f71cfd0aee39e98c974f9c4ad5bc7792c8b07739..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_dot.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_dot_impl(test_case, placement, sbp): - k = random(100, 1000) * 8 - x = random_tensor(ndim=1, dim0=k).to_global(placement=placement, sbp=sbp) - y = random_tensor(ndim=1, dim0=k).to_global(placement=placement, sbp=sbp) - z = torch.dot(x, y) - return z - - -class TestDotConsistent(flow.unittest.TestCase): - @globaltest - def test_dot(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - do_test_dot_impl(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_dropout.py b/python/oneflow/test/modules/test_consistent_dropout.py deleted file mode 100644 index a70ea11130eb85f369a5749e65d8c66831a99136..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_dropout.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, check_graph=False, atol=1e-5, rtol=1e-5) -def _test_dropout_p01(test_case, placement, sbp, ndim, p): - dims = [random(1, 5) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - m = torch.nn.Dropout(p=p, inplace=False) - return m(x) - - -@autotest(n=1, auto_backward=True, check_graph=False, atol=1e-5, rtol=1e-5) -def _test_dropout_eval_p01(test_case, placement, sbp, ndim, p): - dims = [random(1, 5) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - m = torch.nn.Dropout(p=p, inplace=False) - m.eval() - return m(x) - - -class TestDropoutConsistent(flow.unittest.TestCase): - @globaltest - def test_dropout_p01(test_case): - # random ndim in range [1,3] - ndim = random(1, 4).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=min(2, ndim)): - _test_dropout_p01(test_case, placement, sbp, ndim, p=0.0) - _test_dropout_p01(test_case, placement, sbp, ndim, p=1.0) - - @globaltest - def test_dropout_eval(test_case): - # random ndim in range [1,3] - ndim = random(1, 4).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=min(2, ndim)): - _test_dropout_eval_p01(test_case, placement, sbp, ndim, 0.0) - _test_dropout_eval_p01(test_case, placement, sbp, ndim, 1.0) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase1.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase1.py deleted file mode 100644 index 36149aec224979ecc1e6345871152f37a8762a8e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase1.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase1(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("hij, ijc->ihc", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase1(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase1(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase10.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase10.py deleted file mode 100644 index 5b1ffc696e0e0536deee016937990a713f6033ed..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase10.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase10(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) - y = random_tensor(ndim=4, dim0=dim0, dim1=dim2, dim2=dim1, dim3=random(1, 3) * 8) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("bhqk,bkhc->bqhc", g_x, g_y) - return z - - -@unittest.skipIf(True, "skip this test temporarily") -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase10(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_einsum_alphaflod_usecase10(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase11.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase11.py deleted file mode 100644 index 4474cfaaaf92ef2ba6b85d94f84e8172bbdc7bfd..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase11.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase11(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8, dim2=dim0,) - y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("bqa,ahc->bqhc", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase11(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase11(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase2.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase2.py deleted file mode 100644 index 1753ce8cb0ddde2d40d558ac864bca8a176e660d..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase2.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase2(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("rac,rab->rbc", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase2(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase2(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase3.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase3.py deleted file mode 100644 index 196c9dc956eefae2367cfc08d56cf0c245bc9ce6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase3.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase3(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ra,rab->rb", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase3(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_alphaflod_usecase3(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase4.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase4.py deleted file mode 100644 index bdde73c9f5fde022b0229dd05c77fc86621e7044..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase4.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase4(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) - y = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("qhc,khc->qkh", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase4(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase4(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase5.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase5.py deleted file mode 100644 index 21a6fa0fccbe28d094667c45cc75a798e2cd9344..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase5.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase5(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) - y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("nm, mrc->nrc", g_x, g_y) - return z - - -@unittest.skip("this case fails in multi gpu. TODO: depeng, shenghang") -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase5(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_alphaflod_usecase5(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase6.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase6.py deleted file mode 100644 index 87fea5b77d76751690de17cf37743bebe6e071e6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase6.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase6(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) - y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("abc,adc->bdc", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase6(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase6(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase7.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase7.py deleted file mode 100644 index 66eff986fb2d3efadd40cbff1211a4592ae5b6aa..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase7.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase7(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor( - ndim=4, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1, dim3=random(1, 3) * 8, - ) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("dceb,cef->dbf", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase7(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase7(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase8.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase8.py deleted file mode 100644 index b6f6626ec3932e2ffe04e1ee956517247eb5d1c1..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase8.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase8(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) - y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("acb,ade->dceb", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase8(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_alphaflod_usecase8(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase9.py b/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase9.py deleted file mode 100644 index 4292885e61d52c3945e3a7283d8868bdb6fd13b7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_alphaflod_usecase9.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_alphaflod_usecase9(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8, dim2=dim0,) - y = random_tensor(ndim=2, dim0=dim0, dim1=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("qkc,ch->hqk", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_alphaflod_usecase9(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_alphaflod_usecase9(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_attention.py b/python/oneflow/test/modules/test_consistent_einsum_attention.py deleted file mode 100644 index 2ae39cda7d14d796f3014954793e7cab7987b3f4..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_attention.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_attention(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) - y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b h i d, b h j d -> b h i j", g_x, g_y) - return z - - -@unittest.skipIf(True, "skip this test temporarily") -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_attention(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_einsum_attention(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul.py b/python/oneflow/test/modules/test_consistent_einsum_batch_matmul.py deleted file mode 100644 index 2809f1c4b47a1fcadc58e21e13c313b8654e236e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_matmul(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ijk,ikl->ijl", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_matmul(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_batch_matmul(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul2.py b/python/oneflow/test/modules/test_consistent_einsum_batch_matmul2.py deleted file mode 100644 index 42034add3abae331f5ef3daefbb0f02aada1d4d9..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul2.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_matmul2(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2) - y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=dim2, dim3=random(1, 3) * 8) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b h i j, b h j d -> b h i d", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_matmul2(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_einsum_batch_matmul2(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul3.py b/python/oneflow/test/modules/test_consistent_einsum_batch_matmul3.py deleted file mode 100644 index 9dfa1214d08c1c9cacf7a9ccdba0dcc807cd24d9..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul3.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_matmul3(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor( - ndim=4, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8, dim3=dim1, - ) - y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b x i d, b j d -> b x i j", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_matmul3(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_batch_matmul3(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul4.py b/python/oneflow/test/modules/test_consistent_einsum_batch_matmul4.py deleted file mode 100644 index 2eba0e465891d9a0123c995acf7abc8dcac5f686..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_matmul4.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_matmul4(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor( - ndim=4, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8, dim3=dim1, - ) - y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b x i j, b j d -> b x i d", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_matmul4(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_batch_matmul4(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_matrix_vector_multiply.py b/python/oneflow/test/modules/test_consistent_einsum_batch_matrix_vector_multiply.py deleted file mode 100644 index bbc553c0bd7021bf09c5446a56fc135f659b9908..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_matrix_vector_multiply.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_matrix_vector_multiply(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=dim2,) - y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b i d, b i j d -> b i j", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_matrix_vector_multiply(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_einsum_batch_matrix_vector_multiply(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_batch_permute.py b/python/oneflow/test/modules/test_consistent_einsum_batch_permute.py deleted file mode 100644 index d7560c342fb949cec95bc694dd3862bbbedb17ab..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_batch_permute.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_batch_permute(test_case, placement, sbp): - x = random_tensor( - ndim=5, - dim0=random(1, 3) * 8, - dim1=random(1, 3) * 8, - dim2=random(1, 3) * 8, - dim3=random(1, 3) * 8, - dim4=random(1, 3) * 8, - ) - g_x = x.to_global(placement=placement, sbp=sbp) - z = torch.einsum("...ij->...ji", g_x) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_batch_permute(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=5): - _test_einsum_batch_permute(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_bilinear_transformation.py b/python/oneflow/test/modules/test_consistent_einsum_bilinear_transformation.py deleted file mode 100644 index 875c93cd047e2baa9eb96ce056c3e06c9d4d8fee..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_bilinear_transformation.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_bilinear_transformation(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim1, dim2=dim2,) - w = random_tensor(ndim=2, dim0=dim0, dim1=dim2,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - g_w = w.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ik,jkl,il->ij", g_x, g_y, g_w) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_bilinear_transformation(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_bilinear_transformation(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_sum_row.py b/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_sum_row.py deleted file mode 100644 index abe7871d8f242b369241bb547183470170f0b35d..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_sum_row.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_eltwise_mul_sum_row(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("n d, n d -> n", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_eltwise_mul_sum_row(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_eltwise_mul_sum_row(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_then_reduce_sum.py b/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_then_reduce_sum.py deleted file mode 100644 index 795e14b304b164170c3a39d2117b34262e92d0ad..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_eltwise_mul_then_reduce_sum.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_eltwise_mul_then_reduce_sum(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - # NOTE(Liang Depeng): the same as 'ij,ij->' - z = torch.einsum("ij,ij", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_eltwise_mul_then_reduce_sum(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_eltwise_mul_then_reduce_sum(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_eltwise_multiply.py b/python/oneflow/test/modules/test_consistent_einsum_eltwise_multiply.py deleted file mode 100644 index 473d6f5a2a08fd3dfe64661f3a95bddc873c8647..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_eltwise_multiply.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_eltwise_multiply(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ij,ij->ij", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_eltwise_multiply(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_eltwise_multiply(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_get_diagonal.py b/python/oneflow/test/modules/test_consistent_einsum_get_diagonal.py deleted file mode 100644 index 37cb1ae33da42fb7413841fec096168f0912b5b7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_get_diagonal.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_get_diagonal(test_case, placement, sbp): - dim = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim, dim1=dim,) - g_x = x.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ii->i", g_x) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_get_diagonal(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_get_diagonal(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_matmul.py b/python/oneflow/test/modules/test_consistent_einsum_matmul.py deleted file mode 100644 index 912c14b8a7879923edafd3ec03c19d5ad621620b..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_matmul.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_matmul(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - dim2 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=2, dim0=dim1, dim1=dim2,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - # NOTE(Liang Depeng): the same as 'ik,kj->ij' - z = torch.einsum("ik,kj", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_matmul(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_matmul(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_matmul2.py b/python/oneflow/test/modules/test_consistent_einsum_matmul2.py deleted file mode 100644 index ba48ac59a6799b309da04b3c6cfb0644f45862ba..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_matmul2.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_matmul2(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) - y = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("i d, j d -> i j", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_matmul2(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_matmul2(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_matrix_column_sum.py b/python/oneflow/test/modules/test_consistent_einsum_matrix_column_sum.py deleted file mode 100644 index 050c3c961f7ea62c7e2be5172529c9259d12d4bb..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_matrix_column_sum.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_matrix_column_sum(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ij->j", g_x) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_matrix_column_sum(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_matrix_column_sum(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_matrix_transpose.py b/python/oneflow/test/modules/test_consistent_einsum_matrix_transpose.py deleted file mode 100644 index ed6895abaaf2ff0c2c01ec3786083314b43bec29..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_matrix_transpose.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_matrix_transpose(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8) - g_x = x.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ij->ji", g_x) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_matrix_transpose(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_matrix_transpose(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_matrix_vector_multiply.py b/python/oneflow/test/modules/test_consistent_einsum_matrix_vector_multiply.py deleted file mode 100644 index e2b73c0d0d937c2ec451ced770b46d1ac468f7bb..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_matrix_vector_multiply.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_matrix_vector_multiply(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) - y = random_tensor(ndim=1, dim0=dim1,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - # NOTE(Liang Depeng): the same as 'ik,k->i' - z = torch.einsum("ik,k", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_matrix_vector_multiply(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_einsum_matrix_vector_multiply(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_reduce_sum.py b/python/oneflow/test/modules/test_consistent_einsum_reduce_sum.py deleted file mode 100644 index 014c927d2822f6491d18104794b1ad739b7f15da..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_reduce_sum.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_reduce_sum(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - z = torch.einsum("ij->", g_x) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_reduce_sum(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_reduce_sum(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction.py b/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction.py deleted file mode 100644 index 4b495e2ecc171e1f98ad51740647a9be3b4411e7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_tensor_contraction(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - dim1 = random(1, 3) * 8 - x = random_tensor( - ndim=4, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1, dim3=random(1, 3) * 8, - ) - y = random_tensor( - ndim=5, - dim0=random(1, 3) * 8, - dim1=random(1, 3) * 8, - dim2=dim0, - dim3=random(1, 3) * 8, - dim4=dim1, - ) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("pqrs,tuqvr->pstuv", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_tensor_contraction(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_einsum_tensor_contraction(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction2.py b/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction2.py deleted file mode 100644 index 0d1a55788195df04385c024f61af148bfabdf297..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_tensor_contraction2.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_tensor_contraction2(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor( - ndim=4, - dim0=random(1, 3) * 8, - dim1=dim0, - dim2=random(1, 3) * 8, - dim3=random(1, 3) * 8, - ) - y = random_tensor(ndim=2, dim0=dim0, dim1=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - z = torch.einsum("b n h w, n d -> b d h w", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_tensor_contraction2(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_einsum_tensor_contraction2(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_vector_inner_product.py b/python/oneflow/test/modules/test_consistent_einsum_vector_inner_product.py deleted file mode 100644 index c3f1fd0dcd4baeb58cd1bfaebb8757bae6536c09..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_vector_inner_product.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_vector_inner_product(test_case, placement, sbp): - dim0 = random(1, 3) * 8 - x = random_tensor(ndim=1, dim0=dim0,) - y = random_tensor(ndim=1, dim0=dim0,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - # NOTE(Liang Depeng): the same as 'i,i->' - z = torch.einsum("i,i", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_vector_inner_product(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_einsum_vector_inner_product(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_einsum_vector_outer_product.py b/python/oneflow/test/modules/test_consistent_einsum_vector_outer_product.py deleted file mode 100644 index 79850f44ad644200c34827cabf9b0a4ce31e8022..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_einsum_vector_outer_product.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_einsum_vector_outer_product(test_case, placement, sbp): - x = random_tensor(ndim=1, dim0=random(1, 3) * 8,) - y = random_tensor(ndim=1, dim0=random(1, 3) * 8,) - g_x = x.to_global(placement=placement, sbp=sbp) - g_y = y.to_global(placement=placement, sbp=sbp) - # NOTE(Liang Depeng): the same as 'i,j->ij' - z = torch.einsum("i,j", g_x, g_y) - return z - - -class TestEinsumConsistent(flow.unittest.TestCase): - @globaltest - def test_einsum_vector_outer_product(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_einsum_vector_outer_product(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_empty.py b/python/oneflow/test/modules/test_consistent_empty.py deleted file mode 100644 index abe0ef3579ec132a7a316602ce070999214b9280..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_empty.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_empty(test_case, func, shape, placement, sbp): - func2 = None - if func == "empty": - func = flow.empty - elif func == "new_empty": - func = flow.empty - func2 = flow.new_empty - else: - raise NotImplementedError - - x = func(*shape, placement=placement, sbp=sbp) - if func2: - x = func2(x, size=shape) - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -def _test_graph_empty(test_case, func, shape, placement, sbp): - func2 = None - if func == "empty": - func = flow.empty - elif func == "new_empty": - func = flow.empty - func2 = flow.new_empty - else: - raise NotImplementedError - - class ConsistentEmptyGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = func(*shape, placement=placement, sbp=sbp) - if func2: - x = func2(x, size=shape) - return x - - model = ConsistentEmptyGraph() - x = model() - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -class TestEmptyConsistent(flow.unittest.TestCase): - @globaltest - def test_empty_consistent(test_case): - shapes = [(8,), (8, 8,), (8, 8, 8)] - functions = [ - "empty", - "new_empty", - ] - for func in functions: - for shape in shapes: - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(shape), except_partial_sum=True - ): - _test_consistent_empty(test_case, func, shape, placement, sbp) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @flow.unittest.skip_unless_1n2d() - def test_empty_graph(test_case): - arg_dict = OrderedDict() - arg_dict["func"] = ["empty", "new_empty"] - arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - func = args["func"] - shape = args["shape"] - placement = args["placement"] - for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): - _test_graph_empty(test_case, func, shape, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_eq.py b/python/oneflow/test/modules/test_consistent_eq.py deleted file mode 100644 index 5daa538ec36a427b6df92f83a6deb2cc2e602330..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_eq.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def do_test_eq_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - x = x.to_global(placement=placement, sbp=sbp) - y = random_tensor(ndim, *dims) - y = y.to_global(placement=placement, sbp=sbp) - - z = torch.eq(x, y) - return z - - -class TestEqConsistent(flow.unittest.TestCase): - @globaltest - def test_eq(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_eq_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_erf.py b/python/oneflow/test/modules/test_consistent_erf.py deleted file mode 100644 index 898d5504aeed420c986a8d07020126837ee15b91..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_erf.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_erf_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - z = torch.erf(y) - return z - - -class TestErfConsistent(flow.unittest.TestCase): - @globaltest - def test_erf(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_erf_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_erfc.py b/python/oneflow/test/modules/test_consistent_erfc.py deleted file mode 100644 index 2bc8b0069d9e2f713fa6d394562d64847427a010..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_erfc.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, rtol=1e-3, atol=1e-3, check_graph=False) -def do_test_erfc_impl(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - z = torch.erfc(y) - return z - - -class TestErfcConsistent(flow.unittest.TestCase): - @globaltest - def test_erfc(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_erfc_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_expand_op.py b/python/oneflow/test/modules/test_consistent_expand_op.py deleted file mode 100644 index 6c5b6ec0fad612ac95000e0a89445b9ae22c7985..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_expand_op.py +++ /dev/null @@ -1,235 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import os -import numpy as np -from collections import OrderedDict - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.test_util import GenArgList - -import torch - - -def _test_expand_new_dims_broadcast(test_case, device): - input_shape = (1, 4, 1, 1) - expand_dim = [2, 1, 2, 4, 2, 1] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - of_out = global_of_input.expand(*expand_dim) - of_out.sum().backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal(of_out.to_local().numpy(), torch_out.detach().cpu().numpy()) - ) - test_case.assertTrue( - np.array_equal(of_input.grad.numpy(), torch_in.grad.cpu().numpy()) - ) - - -def _test_expand_same_dim_broadcast(test_case, device): - input_shape = (4, 1, 2, 1) - expand_dim = [4, 1, 2, 1] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - - of_out = global_of_input.expand(*expand_dim) - loss = of_out.sum() - loss.backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal(of_out.to_local().numpy(), torch_out.detach().cpu().numpy()) - ) - test_case.assertTrue( - np.array_equal(of_input.grad.numpy(), torch_in.grad.cpu().numpy()) - ) - - -def _test_expand_same_dim_negative_broadcast(test_case, device): - input_shape = (2, 1, 4, 1) - expand_dim = [2, -1, 4, 4] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - - of_out = global_of_input.expand(*expand_dim) - loss = of_out.sum() - loss.backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal(of_out.to_local().numpy(), torch_out.detach().cpu().numpy()) - ) - test_case.assertTrue( - np.array_equal(of_input.grad.numpy(), torch_in.grad.cpu().numpy()) - ) - - -def _test_expand_new_dims_split(test_case, device): - input_shape = (4, 1, 2, 1) - expand_dim = [2, 1, 4, 1, 2, 1] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - global_of_input = global_of_input.to_global(sbp=flow.sbp.split(0)) - - of_out = global_of_input.expand(*expand_dim) - loss = of_out.sum() - loss.backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - of_out.to_local().numpy(), - torch_out.detach().cpu().numpy()[:, :, 0:2, :, :, :], - ) - ) - test_case.assertTrue( - np.array_equal( - of_input.grad.numpy(), torch_in.grad.cpu().numpy()[0:2, :, :, :], - ) - ) - - -def _test_expand_same_dim_split(test_case, device): - input_shape = (4, 1, 2, 1) - expand_dim = [4, 1, 2, 4] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - global_of_input = global_of_input.to_global(sbp=flow.sbp.split(0)) - - of_out = of_input.expand(*expand_dim) - loss = of_out.sum() - loss.backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - of_out.to_local().numpy(), - torch_out.detach().cpu().numpy()[0:2, :, :, :], - ) - ) - test_case.assertTrue( - np.array_equal( - of_input.grad.numpy(), torch_in.grad.cpu().numpy()[0:2, :, :, :], - ) - ) - - -def _test_expand_same_dim_negative_split(test_case, device): - input_shape = (2, 1, 4, 1) - expand_dim = [2, -1, 4, 4] - - input_nd = np.random.random(size=input_shape).astype(np.float32) - torch_in = torch.tensor(input_nd, requires_grad=True) - torch_out = torch_in.expand(*expand_dim) - torch_out.sum().backward() - - of_input = flow.tensor(input_nd, dtype=flow.float32, requires_grad=True) - global_of_input = of_input.to_global( - placement=flow.placement(device, ranks=[0, 1]), sbp=flow.sbp.broadcast, - ) - global_of_input = global_of_input.to_global(sbp=flow.sbp.split(2)) - - of_out = global_of_input.expand(*expand_dim) - loss = of_out.sum() - loss.backward() - - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.array_equal( - of_out.to_local().numpy(), - torch_out.detach().cpu().numpy()[:, :, 0:2, :], - ) - ) - test_case.assertTrue( - np.array_equal( - of_input.grad.numpy(), torch_in.grad.cpu().numpy()[:, :, 0:2, :], - ) - ) - - -@flow.unittest.skip_unless_1n2d() -class ExpandConsistentTestCase(oneflow.unittest.TestCase): - def test_expand_broadcast(test_case): - arg_dict = OrderedDict() - arg_dict["test_fun"] = [ - _test_expand_new_dims_broadcast, - _test_expand_same_dim_broadcast, - _test_expand_same_dim_negative_broadcast, - ] - arg_dict["device"] = ["cpu", "cuda"] - for arg in GenArgList(arg_dict): - arg[0](test_case, *arg[1:]) - - # NOTE(Liang Depeng): Run with the following command can pass the test locally, but will fail when run in ci. - # ONEFLOW_TEST_DEVICE_NUM=2 python3 -m oneflow.distributed.launch --nproc_per_node 2 test_global_expand_op.py - @unittest.skipIf(True, "skip for now") - def test_expand_split(test_case): - arg_dict = OrderedDict() - arg_dict["test_fun"] = [ - _test_expand_new_dims_split, - _test_expand_same_dim_split, - _test_expand_same_dim_negative_split, - ] - arg_dict["device"] = ["cpu", "cuda"] - for arg in GenArgList(arg_dict): - arg[0](test_case, *arg[1:]) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_expm1.py b/python/oneflow/test/modules/test_consistent_expm1.py deleted file mode 100644 index 2b63777df9dc62ae3a4f6545ec80b29e90063664..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_expm1.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_expm1_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - z = torch.expm1(y) - return z - - -class TestExpm1Consistent(flow.unittest.TestCase): - @globaltest - def test_expm1(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_expm1_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_eye.py b/python/oneflow/test/modules/test_consistent_eye.py deleted file mode 100644 index b08b2db520db2ac4fa018b38381ce86ee4d7a7c4..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_eye.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def do_test_eye_impl(test_case, placement, sbp): - n = random(1, 5).to(int).value() * 8 - m = random(1, 5).to(int).value() * 8 - x = torch.eye(n, m) - x.oneflow = flow.tensor( - x.pytorch.cpu().detach().numpy(), - requires_grad=x.pytorch.requires_grad, - placement=placement, - sbp=sbp, - ) - return x - - -class TestEyeConsistent(flow.unittest.TestCase): - @globaltest - def test_eye(test_case): - shape = random_tensor().shape - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - do_test_eye_impl(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_fill.py b/python/oneflow/test/modules/test_consistent_fill.py deleted file mode 100644 index f58020967f293e4890811be49f45ea030332ffef..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_fill.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_fill_(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 4 for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - value = random().to(float) - y = x + 1 - y.fill_(value) - return y - - -# TODO(zhongshsh): This test is not used, as we found that the value's grad is not recovered when switching from global to local -@autotest(n=1, check_graph=False) -def _test_fill_tensor_(test_case, ndim, placement, sbp): - dims = [random(1, 4) for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - value = torch.tensor(1.0, requires_grad=True).to_global( - placement=placement, sbp=[flow.sbp.broadcast for _ in sbp] - ) - y = x + 1 - y.fill_(value) - return y - - -class TestFillModule(flow.unittest.TestCase): - @globaltest - def test_fill_(test_case): - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_fill_(test_case, ndim, placement, sbp) - # _test_fill_tensor_(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_flatten.py b/python/oneflow/test/modules/test_consistent_flatten.py deleted file mode 100644 index ecaaad65953564c8c7d9dd8d34f4601342358aaa..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_flatten.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_flatten_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - start_dim = random(0, ndim).to(int).value() - end_dim = random(start_dim, ndim).to(int).value() - - z = torch.flatten(x, start_dim, end_dim) - return z - - -class TestFlattenConsistent(flow.unittest.TestCase): - @globaltest - def test_flatten(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_flatten_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_flip.py b/python/oneflow/test/modules/test_consistent_flip.py deleted file mode 100644 index f69881fcf59c275942345edfbddef16688e488c6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_flip.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_flip_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - new_dim = random(0, ndim).to(int).value() - z = torch.flip(y, constant([i for i in range(new_dim)])) - return z - - -class TestFlipConsistent(flow.unittest.TestCase): - @globaltest - def test_flip(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_flip_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_floor.py b/python/oneflow/test/modules/test_consistent_floor.py deleted file mode 100644 index ee6db9fa60173a74131d3a0cbaac356ad8cb0c2e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_floor.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def do_test_floor_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - z = torch.floor(y) - return z - - -class TestFloorConsistent(flow.unittest.TestCase): - @globaltest - def test_floor(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_floor_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_fmod.py b/python/oneflow/test/modules/test_consistent_fmod.py deleted file mode 100644 index b4fbc8b15ea4c1ec32b9c6cddf59d8b93ea0bad0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_fmod.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def do_test_fmod_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims) - x = x.to_global(placement=placement, sbp=sbp) - y = random_tensor(ndim, *dims) - y = y.to_global(placement=placement, sbp=sbp) - - z = torch.fmod(x, y) - return z - - -class TestFmodConsistent(flow.unittest.TestCase): - @globaltest - def test_fmod(test_case): - # random ndim in range [1,5] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_fmod_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_fold.py b/python/oneflow/test/modules/test_consistent_fold.py deleted file mode 100644 index 0ed80c37b02dd7024147e61c8ec9cdab331c6cc7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_fold.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_fold_impl(test_case, placement, sbp): - ndim = 3 - dims = [random(1, 4).to(int).value() * 8 for i in range(ndim)] - m = torch.nn.Fold( - output_size=constant(((dims[2] // 4) * 2, 4 * 2)), - kernel_size=constant(2), - dilation=constant(1), - padding=constant(0), - stride=constant(2), - ) - m.train(random()) - - x = random_tensor(ndim, *dims) - y = x.to_global(placement=placement, sbp=sbp) - z = m(y) - return z - - -class TestFold(flow.unittest.TestCase): - @globaltest - def test_fold(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_fold_impl(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_greater.py b/python/oneflow/test/modules/test_consistent_greater.py deleted file mode 100644 index cd6b04e501d370ddd25b35a00bbf786db4e36514..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_greater.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=10, auto_backward=False, check_graph=False) -def _test_greater_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x1 = random_tensor(ndim, *dims) - x2 = x1.to_global(placement=placement, sbp=sbp) - y1 = random_tensor(ndim, *dims) - y2 = y1.to_global(placement=placement, sbp=sbp) - - z = torch.gt(x2, y2) - return z - - -@unittest.skip("TODO: houjiang, yushun. this test might fail") -class TestGreaterConsistent(flow.unittest.TestCase): - @globaltest - def test_greater(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_greater_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_greater_equal.py b/python/oneflow/test/modules/test_consistent_greater_equal.py deleted file mode 100644 index 7dd94cda2b12334ab182f9114f51095dd72e3f81..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_greater_equal.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def do_test_greater_equal_impl(test_case, ndim, placement, sbp): - dims = [random(1, 4) * 8 for i in range(ndim)] - x1 = random_tensor(ndim, *dims) - x1 = x1.to_global(placement=placement, sbp=sbp) - x2 = random_tensor(ndim, *dims) - x2 = x2.to_global(placement=placement, sbp=sbp) - - z = torch.ge(x1, x2) - return z - - -class TestGreaterEqualConsistent(flow.unittest.TestCase): - @globaltest - def test_greater_equal(test_case): - # random ndim in range [1,4] - ndim = random(1, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - do_test_greater_equal_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_grid_sample.py b/python/oneflow/test/modules/test_consistent_grid_sample.py deleted file mode 100644 index 1603be15ca2b22a2f680383d6f25decc99fc97af..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_grid_sample.py +++ /dev/null @@ -1,128 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np - -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=False) -def _test_flow_grid_sample_cudnn(test_case, placement, sbp): - # cudnn only support 4D input, with mode = 'bilinear' && padding_mode = 'zeros' && align_corners - N = random(1, 3).to(int) * 8 - C = random(1, 3).to(int) * 8 - in_H = random(1, 8).to(int) - in_W = random(1, 8).to(int) - out_H = random(1, 8).to(int) - out_W = random(1, 8).to(int) - mode = "bilinear" - padding_mode = "zeros" - align_corners = True - theta = random_tensor(ndim=3, dim0=N, dim1=2, dim2=3).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=1) - ) - grid = torch.nn.functional.affine_grid( - theta, (N, C, out_H, out_W), align_corners=align_corners - ) - input = random_tensor(ndim=4, dim0=N, dim1=C, dim2=in_H, dim3=in_W).to_global( - placement=placement, sbp=sbp - ) - output = torch.nn.functional.grid_sample( - input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, - ) - return output - - -# This test may fail due to using ::floor in backward -# floor(1.99999988) = 1 and floor(2.000000) = 2, then select differente images pixel -@autotest( - n=1, - auto_backward=False, - rtol=1e-03, - atol=1e-04, - check_graph=False, - check_allclose=False, -) -def _test_flow_grid_sample_4d(test_case, placement, sbp): - N = random(1, 3).to(int) * 8 - C = random(1, 3).to(int) * 8 - in_H = random(1, 8).to(int) - in_W = random(1, 8).to(int) - out_H = random(1, 8).to(int) - out_W = random(1, 8).to(int) - mode = oneof("bilinear", "nearest", "bicubic") - padding_mode = oneof("zeros", "border", "reflection") - align_corners = oneof(True, False) - theta = random_tensor(ndim=3, dim0=N, dim1=2, dim2=3).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=1) - ) - grid = torch.nn.functional.affine_grid( - theta, (N, C, out_H, out_W), align_corners=align_corners - ) - input = random_tensor(ndim=4, dim0=N, dim1=C, dim2=in_H, dim3=in_W).to_global( - placement=placement, sbp=sbp - ) - output = torch.nn.functional.grid_sample( - input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, - ) - return output - - -@autotest(n=1, auto_backward=False, rtol=1e-03, atol=1e-03, check_graph=False) -def _test_flow_grid_sample_5d(test_case, placement, sbp): - N = random(1, 3).to(int) * 8 - C = random(1, 3).to(int) * 8 - in_D = random(1, 8).to(int) - in_H = random(1, 8).to(int) - in_W = random(1, 8).to(int) - out_D = random(1, 8).to(int) - out_H = random(1, 8).to(int) - out_W = random(1, 8).to(int) - mode = oneof("bilinear", "nearest") - padding_mode = oneof("zeros", "border", "reflection") - align_corners = oneof(True, False) - theta = random_tensor(ndim=3, dim0=N, dim1=3, dim2=4).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=1) - ) - grid = torch.nn.functional.affine_grid( - theta, (N, C, out_D, out_H, out_W), align_corners=align_corners - ) - input = random_tensor( - ndim=5, dim0=N, dim1=C, dim2=in_D, dim3=in_H, dim4=in_W - ).to_global(placement=placement, sbp=sbp) - output = torch.nn.functional.grid_sample( - input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, - ) - return output - - -class TestGridSample(flow.unittest.TestCase): - @globaltest - def test_grid_sample(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - if placement.type == "cuda": - _test_flow_grid_sample_cudnn(test_case, placement, sbp) - _test_flow_grid_sample_4d(test_case, placement, sbp) - _test_flow_grid_sample_5d(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_linear.py b/python/oneflow/test/modules/test_consistent_linear.py deleted file mode 100644 index 0f9acfc3e61403e9c3813eb3ada804c5f7a75a1c..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_linear.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_linear_with_random_data(test_case, placement, weight_sbp, input_sbp): - input_size = 8 - m = torch.nn.Linear(in_features=input_size, out_features=8, bias=random()) - m.train(random()) - m.weight = torch.nn.Parameter( - m.weight.to_global(placement=placement, sbp=weight_sbp) - ) - if m.bias is not None: - # bias is 1-d tensor - bias_sbp = random_sbp(placement, max_dim=1) - m.bias = torch.nn.Parameter(m.bias.to_global(placement=placement, sbp=bias_sbp)) - x = random_tensor(ndim=2, dim0=input_size, dim1=8).to_global( - placement=placement, sbp=input_sbp - ) - y = m(x) - return y - - -class TestLinearModule(flow.unittest.TestCase): - @globaltest - def test_linear_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_linear_with_random_data(test_case, placement, sbp, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_masked_fill.py b/python/oneflow/test/modules/test_consistent_masked_fill.py deleted file mode 100644 index 5d7886d2c19c5dbfac6080dbb2ef3cadc75dd255..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_masked_fill.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_masked_fill(test_case, placement, sbp): - k1 = random().to(int).value() * 8 - k2 = random().to(int).value() * 8 - input = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) - mask = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) - value = random().to(float) - return input.masked_fill(mask > 0.5, value) - - -@autotest(n=1, check_graph=False) -def _test_masked_fill_with_0dim_data(test_case, placement, sbp): - input = random_tensor(ndim=0).to_global(placement, sbp) - mask = random_tensor(ndim=0).to_global(placement, sbp) - value = random().to(float) - return input.masked_fill(mask > 0.5, value) - - -@autotest(n=1, check_graph=False) -def _test_masked_fill_with_broadcast_way(test_case, placement, sbp): - k1 = random().to(int).value() * 8 - k2 = random().to(int).value() * 8 - device = random_device() - input = random_tensor(ndim=2, dim0=k1, dim1=k2, dim2=1, dim3=k2).to_global( - placement, sbp - ) - mask = random_tensor(ndim=2, dim0=k1, dim1=k2, dim2=k1, dim3=1).to_global( - placement, sbp - ) - value = random().to(float) - return input.masked_fill(mask > 0.5, value) - - -class TestMaskedFill(flow.unittest.TestCase): - @globaltest - def test_masked_fill(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_masked_fill(test_case, placement, sbp) - # TODO() : fail at tensor slice - # _test_masked_fill_with_0dim_data(test_case, placement, sbp) - _test_masked_fill_with_broadcast_way(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_masked_select.py b/python/oneflow/test/modules/test_consistent_masked_select.py deleted file mode 100644 index c3304a4377f23e5ea56acd961c925b449d08dbd3..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_masked_select.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_masked_select(test_case, placement, sbp): - k1 = random(1, 2).to(int).value() * 8 - k2 = random(1, 2).to(int).value() * 8 - input = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) - mask = input.ge(0.5) - return torch.masked_select(input, mask) - - -@autotest(n=1, check_graph=False) -def _test_masked_select_broadcast(test_case, placement, input_sbp, mask_sbp): - k1 = random(1, 2).to(int).value() * 8 - k2 = random(1, 2).to(int).value() * 8 - input = random_tensor(ndim=4, dim0=k1, dim1=k2, dim2=1, dim3=k2).to_global( - placement, input_sbp - ) - mask = random_tensor(ndim=4, dim0=k1, dim1=k2, dim2=k1, dim3=1).to_global( - placement, mask_sbp - ) - return torch.masked_select(input, mask > 0.5) - - -class TestMaskedSelect(flow.unittest.TestCase): - @globaltest - def test_masked_select(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_masked_select(test_case, placement, sbp) - - @globaltest - def test_masked_select_broadcast(test_case): - for placement in all_placement(): - for input_sbp in all_sbp(placement, valid_split_axis=[0, 1, 3]): - for mask_sbp in all_sbp(placement, max_dim=3): - _test_masked_select_broadcast( - test_case, placement, input_sbp, mask_sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_math_op_higher_derivative.py b/python/oneflow/test/modules/test_consistent_math_op_higher_derivative.py deleted file mode 100644 index 39968e20607485461d4192d9b0148fc2cd48ae83..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_math_op_higher_derivative.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -def _consistent_math_op_grad_grad_impl(test_case, op_name, placement, sbp): - x = ( - random_tensor(2, dim0=8, dim1=8) - .to_global(placement=placement, sbp=sbp) - .requires_grad_(True) - ) - y = eval(f"x.{op_name}().sum()") - x_grad = torch.autograd.grad(y, x, create_graph=True)[0] - test_case.assertTrue( - np.allclose( - x_grad.pytorch.detach().cpu().numpy(), x_grad.oneflow.detach().numpy() - ) - ) - x_grad_grad = torch.autograd.grad(x_grad, x, torch.ones_like(x), create_graph=True)[ - 0 - ] - test_case.assertTrue( - np.allclose( - x_grad_grad.pytorch.detach().cpu().numpy(), - x_grad_grad.oneflow.detach().numpy(), - ) - ) - - -class TestConsistentMathOpHigherDerivative(flow.unittest.TestCase): - @globaltest - def test_consistent_sin_grad_grad(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _consistent_math_op_grad_grad_impl(test_case, "sin", placement, sbp) - - @globaltest - def test_consistent_cos_grad_grad(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _consistent_math_op_grad_grad_impl(test_case, "cos", placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_math_ops.py b/python/oneflow/test/modules/test_consistent_math_ops.py deleted file mode 100644 index d037f73683970706169bdc3d3d376d89bdbd4aab..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_math_ops.py +++ /dev/null @@ -1,190 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_sinh(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.sinh(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_sin(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.sin(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_inplace_sin(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = x + 1 - y.sin_() - return y - - -@autotest(n=1, check_graph=False) -def _test_cos(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.cos(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_log(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.log(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_sqrt(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.sqrt(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_exp(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.exp(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_rsqrt(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.rsqrt(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_square(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = torch.square(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_pow_with_scalar(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = random().to(float) - z = torch.pow(x, y) - return z - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_floordiv_with_scalar(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list,).to_global(placement, sbp) - y = random().to(float) - z = torch.floor_divide(x, y) - return z - - -@autotest(n=1, check_graph=False) -def _test_arccos(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list, low=-1, high=1).to_global(placement, sbp) - y = torch.arccos(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_acos(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list, low=-1, high=1).to_global(placement, sbp) - y = torch.acos(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_arccosh(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list, low=2, high=3).to_global(placement, sbp) - y = torch.arccosh(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_acosh(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list, low=2, high=3).to_global(placement, sbp) - y = torch.acosh(x) - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_floordiv(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = random_tensor(ndim, *dim_list).to_global(placement, sbp) - z = torch.floor_divide(x, y) - return z - - -@autotest(n=1, check_graph=False) -def _test_atan2(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = random_tensor(ndim, *dim_list).to_global(placement, sbp) - z = torch.atan2(x, y) - return z - - -class TestMathOps(flow.unittest.TestCase): - @globaltest - def test_math_ops(test_case): - ndim = random(1, 3).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_sinh(test_case, placement, sbp, ndim) - _test_sin(test_case, placement, sbp, ndim) - _test_inplace_sin(test_case, placement, sbp, ndim) - _test_cos(test_case, placement, sbp, ndim) - _test_log(test_case, placement, sbp, ndim) - _test_sqrt(test_case, placement, sbp, ndim) - _test_exp(test_case, placement, sbp, ndim) - _test_rsqrt(test_case, placement, sbp, ndim) - _test_square(test_case, placement, sbp, ndim) - _test_pow_with_scalar(test_case, placement, sbp, ndim) - _test_floordiv_with_scalar(test_case, placement, sbp, ndim) - _test_arccos(test_case, placement, sbp, ndim) - _test_acos(test_case, placement, sbp, ndim) - _test_arccosh(test_case, placement, sbp, ndim) - _test_acosh(test_case, placement, sbp, ndim) - - _test_floordiv(test_case, placement, sbp, ndim) - _test_atan2(test_case, placement, sbp, ndim) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_matmul.py b/python/oneflow/test/modules/test_consistent_matmul.py deleted file mode 100644 index fd61449c42088a639ad5c524618761cfa5c2a2cf..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_matmul.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_matmul(test_case, placement, x_sbp, y_sbp): - x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement=placement, sbp=x_sbp) - y = random_tensor(ndim=2, dim0=16, dim1=8).to_global(placement=placement, sbp=y_sbp) - return torch.matmul(x, y) - - -@autotest(n=1, check_graph=False) -def _test_tensor_broadcast_matmul(test_case, placement, x_sbp, y_sbp): - x = random_tensor(ndim=3, dim0=8, dim1=8, dim2=16).to_global( - placement=placement, sbp=x_sbp - ) - y = random_tensor(ndim=2, dim0=16, dim1=8).to_global(placement=placement, sbp=y_sbp) - return x.matmul(y) - - -class TestMatMulModule(flow.unittest.TestCase): - @globaltest - def test_matmul(test_case): - for placement in all_placement(): - for x_sbp in all_sbp(placement, max_dim=2): - for y_sbp in all_sbp(placement, max_dim=2): - _test_matmul(test_case, placement, x_sbp, y_sbp) - - @globaltest - def test_broadcast_matmul(test_case): - for placement in all_placement(): - for x_sbp in all_sbp(placement, valid_split_axis=[0, 1, 2, 3]): - for y_sbp in all_sbp(placement, valid_split_axis=[0, 1]): - _test_tensor_broadcast_matmul(test_case, placement, x_sbp, y_sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_max.py b/python/oneflow/test/modules/test_consistent_max.py deleted file mode 100644 index 7ed16f8b622b5e3c39c418246a325e6d5825fe0c..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_max.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np -import oneflow as flow -import oneflow.unittest -from collections import OrderedDict -from oneflow.test_utils.automated_test_util import * -from oneflow.test_utils.test_util import GenArgList - - -def _np_max(shape, dim, keepdims): - # np array result - input_arr = np.random.randn(*shape) - np_out = np.amax(input_arr, axis=dim, keepdims=keepdims) - np_out_grad = np.zeros_like(input_arr) - if dim == None: - arg_max = np.argmax(input_arr) - np.put(np_out_grad, arg_max, 1) - else: - arg_max = np.expand_dims(np.argmax(input_arr, axis=dim), axis=dim) - np.put_along_axis(np_out_grad, arg_max, 1, axis=dim) - - return np_out, np_out_grad, input_arr - - -def _test_max( - test_case, placement, sbp, np_out, np_out_grad, input_arr, shape, dim, keepdims -): - # of result - global_x = flow.tensor( - input_arr, - dtype=flow.float32, - requires_grad=True, - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - if dim is None: - of_out = flow.max(global_x) - else: - of_out = flow.max(global_x, dim, keepdims)[0] - test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) - of_out = of_out.sum() - of_out.backward() - - test_case.assertTrue( - np.allclose(global_x.grad.numpy(), np_out_grad, 0.0001, 0.0001) - ) - - -class TestMaxModule(flow.unittest.TestCase): - # backward formula is different from one of torch. - @globaltest - def test_eager_global_max(test_case): - arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_max] - arg_dict["shape"] = [(8,), (8, 8), (8, 8, 8, 8)] - arg_dict["dim"] = [None, 0, -1] - arg_dict["keepdims"] = [False, True] - for arg in GenArgList(arg_dict): - np_out, np_out_grad, input_arr = _np_max(*arg[1:]) - np_out = ( - flow.tensor(np_out) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - np_out_grad = ( - flow.tensor(np_out_grad) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - input_arr = ( - flow.tensor(input_arr) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=len(*arg[1:2])): - arg[0]( - test_case, - placement, - sbp, - np_out, - np_out_grad, - input_arr, - *arg[1:] - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_maximum_minimum.py b/python/oneflow/test/modules/test_consistent_maximum_minimum.py deleted file mode 100644 index 95fdafba8517401c68f8253741507332e8abe6ed..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_maximum_minimum.py +++ /dev/null @@ -1,109 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest -import torch as torch_original -from packaging import version -from oneflow.test_utils.automated_test_util import * - - -@autotest( - n=5, - auto_backward=( - version.parse(torch_original.__version__) >= version.parse("1.10.2") - ), - check_graph=False, -) -def _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp): - x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global( - placement, x_sbp - ) - y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global( - placement, y_sbp - ) - z = torch.maximum(x, y) - return z - - -@autotest( - n=5, - auto_backward=( - version.parse(torch_original.__version__) >= version.parse("1.10.2") - ), - check_graph=False, -) -def _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp): - x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global( - placement, x_sbp - ) - y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global( - placement, y_sbp - ) - z = torch.minimum(x, y) - return z - - -@autotest( - n=5, - auto_backward=( - version.parse(torch_original.__version__) >= version.parse("1.10.2") - ), - check_graph=False, -) -def _test_maximum_with_same_input(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp) - y = x.detach().clone() - y.requires_grad = True - z = torch.maximum(x, y) - return z - - -@autotest( - n=5, - auto_backward=( - version.parse(torch_original.__version__) >= version.parse("1.10.2") - ), - check_graph=False, -) -def _test_minimum_with_same_input(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp) - y = x.detach().clone() - y.requires_grad = True - z = torch.minimum(x, y) - return z - - -class TestMaximumMinimumOps(flow.unittest.TestCase): - @globaltest - def test_maximum_minimum_with_same_input(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_maximum_with_same_input(test_case, placement, sbp) - _test_minimum_with_same_input(test_case, placement, sbp) - - @globaltest - def test_broadcast_maximum_minimum(test_case): - for placement in all_placement(): - for x_sbp in all_sbp(placement, valid_split_axis=[0, 1, 2, 4]): - for y_sbp in all_sbp(placement, valid_split_axis=[0, 1, 3]): - _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp) - _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_maxpool.py b/python/oneflow/test/modules/test_consistent_maxpool.py deleted file mode 100644 index f8309db43cf4cd19712a22b5a934c4158335ca47..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_maxpool.py +++ /dev/null @@ -1,246 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import numpy as np -import oneflow as flow -import torch as ori_torch -import oneflow.unittest -from collections import OrderedDict -from oneflow.test_utils.automated_test_util import * -from oneflow.test_utils.test_util import GenArgList -from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t - - -@autotest(n=1, check_graph=False) -def _test_maxpool1d_functional(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random().to(int).value() * 8 - dim1 = random().to(int).value() * 8 - x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 22)).to_global( - placement, sbp - ) - y = torch.nn.functional.max_pool1d( - x, - kernel_size=random(4, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - dilation=random(2, 4).to(int), - ceil_mode=random().to(bool), - return_indices=return_indices, - ) - if return_indices: - return y[0] - else: - return y - - -@autotest(n=1, check_graph=False) -def _test_maxpool2d_functional(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random().to(int).value() * 8 - dim1 = random().to(int).value() * 8 - x = random_tensor( - ndim=4, dim0=dim0, dim1=dim1, dim2=random(20, 22), dim3=random(20, 22) - ).to_global(placement, sbp) - y = torch.nn.functional.max_pool2d( - x, - kernel_size=random(4, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - dilation=random(2, 4).to(int), - ceil_mode=random().to(bool), - return_indices=return_indices, - ) - - if return_indices: - return y[0] - else: - return y - - -@autotest(n=1, check_graph=False) -def _test_maxpool3d_functional(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random(high=4).to(int).value() * 8 - dim1 = random(high=4).to(int).value() * 8 - x = random_tensor( - ndim=5, - dim0=dim0, - dim1=dim1, - dim2=random(10, 12), - dim3=random(10, 12), - dim4=random(10, 12), - ).to_global(placement, sbp) - y = torch.nn.functional.max_pool3d( - x, - kernel_size=random(4, 6).to(int), - stride=random(1, 3).to(int), - padding=random(1, 3).to(int), - dilation=random(2, 4).to(int), - ceil_mode=random().to(bool), - return_indices=return_indices, - ) - - if return_indices: - return y[0] - else: - return y - - -@autotest(n=1, check_graph=False) -def _test_maxpool1d(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random().to(int).value() * 8 - dim1 = random().to(int).value() * 8 - m = torch.nn.MaxPool1d( - kernel_size=random(4, 6).to(_size_1_t), - stride=random(1, 3).to(_size_1_t), - padding=random(1, 3).to(_size_1_t), - dilation=random(2, 4).to(_size_1_t), - ceil_mode=random(), - return_indices=return_indices, - ) - m.train(random()) - x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 22)).to_global( - placement, sbp - ) - y = m(x) - if return_indices: - return y[0] - else: - return y - - -@autotest(n=1, check_graph=False) -def _test_maxpool2d(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random(1, 3).to(int).value() * 8 - dim1 = random(1, 3).to(int).value() * 8 - m = torch.nn.MaxPool2d( - kernel_size=random(4, 6).to(_size_2_t), - stride=random(1, 3).to(_size_2_t), - padding=random(1, 3).to(_size_2_t), - dilation=random(2, 4).to(_size_2_t), - ceil_mode=random(), - return_indices=return_indices, - ) - m.train(random()) - x = random_tensor( - ndim=4, dim0=dim0, dim1=dim1, dim2=random(20, 22), dim3=random(20, 22) - ).to_global(placement, sbp) - y = m(x) - if return_indices: - return y[0] - else: - return y - - -@autotest(n=1, check_graph=False) -def _test_maxpool3d(test_case, placement, sbp): - return_indices = random().to(bool).value() - dim0 = random(high=4).to(int).value() * 8 - dim1 = random(high=4).to(int).value() * 8 - m = torch.nn.MaxPool3d( - kernel_size=random(4, 6).to(_size_3_t), - stride=random(1, 3).to(_size_3_t), - padding=random(1, 3).to(_size_3_t), - dilation=random(2, 4).to(_size_3_t), - ceil_mode=random(), - return_indices=return_indices, - ) - m.train(random()) - x = random_tensor( - ndim=5, - dim0=dim0, - dim1=dim1, - dim2=random(10, 12), - dim3=random(10, 12), - dim4=random(10, 12), - ).to_global(placement, sbp) - y = m(x) - - if return_indices: - return y[0] - else: - return y - - -def _test_maxpool2d_channel_last( - test_case, placement, sbp, shape, kernel_size, stride, padding, dilation, ceil_mode -): - os.environ["ONEFLOW_ENABLE_NHWC"] = "1" - - tensor = random_tensor(len(shape), *shape, requires_grad=False).to_global( - placement, sbp - ) - # oneflow result - x1 = tensor.oneflow - m1 = flow.nn.MaxPool2d( - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - ceil_mode=ceil_mode, - ) - y1 = m1(x1) - - # pytorch result - x2 = tensor.pytorch.permute(0, 3, 1, 2).to(placement.type) - m2 = ori_torch.nn.MaxPool2d( - kernel_size=kernel_size, - stride=stride, - padding=padding, - dilation=dilation, - ceil_mode=ceil_mode, - ) - y2 = m2(x2).permute(0, 2, 3, 1) - - test_case.assertTrue( - np.allclose(y1.detach().cpu().numpy(), y2.detach().cpu().numpy(), 1e-4, 1e-4) - ) - os.environ["ONEFLOW_ENABLE_NHWC"] = "0" - - -class TestMaxPool(flow.unittest.TestCase): - @globaltest - def test_maxpool(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_maxpool1d_functional(test_case, placement, sbp) - _test_maxpool2d_functional(test_case, placement, sbp) - _test_maxpool3d_functional(test_case, placement, sbp) - _test_maxpool1d(test_case, placement, sbp) - _test_maxpool2d(test_case, placement, sbp) - _test_maxpool3d(test_case, placement, sbp) - - # @globaltest - # def test_maxpool2d_channel_last(test_case): - # arg_dict = OrderedDict() - # arg_dict["test_fun"] = [_test_maxpool2d_channel_last] - # arg_dict["shape"] = [(1, 16, 16, 3), (2, 224, 224, 3)] - # arg_dict["kernel_size"] = [3, (2, 3)] - # arg_dict["stride"] = [1, (1, 2)] - # arg_dict["padding"] = [0, (0, 1)] - # arg_dict["dilation"] = [1, 2] - # arg_dict["ceil_mode"] = [True, False] - # for arg in GenArgList(arg_dict): - # for placement in all_placement(): - # for sbp in all_sbp(placement, valid_split_axis=[1, 2]): - # arg[0](test_case, placement, sbp, *arg[1:]) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_mean.py b/python/oneflow/test/modules/test_consistent_mean.py deleted file mode 100644 index d459d7f7aef675326f68fac2ec283672dad69655..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_mean.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_mean(test_case, placement, sbp, ndim): - dim = random(1, ndim).to(int).value() - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list, dtype=float).to_global(placement, sbp) - return torch.mean(x, dim) - - -class TestMean(flow.unittest.TestCase): - @globaltest - def test_mean(test_case): - ndim = random(2, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_mean(test_case, placement, sbp, ndim) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_median.py b/python/oneflow/test/modules/test_consistent_median.py deleted file mode 100644 index ef39b986a5ddab9def6c8dbb27536177aa2f9934..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_median.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import torch -from functools import reduce -import operator -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_median(test_case, placement, sbp, ndim): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - return torch.median(x) - - -@autotest(n=1, check_graph=False) -def _test_median_with_indices(test_case, placement, sbp, ndim): - dim = random(1, ndim).to(int).value() - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = choice_tensor( - reduce(operator.mul, dim_list, 1), - dim_list, - replace=False, - dtype=float, - requires_grad=True, - ).to_global(placement, sbp) - return torch.median(x, dim) - - -class TestMedian(flow.unittest.TestCase): - @globaltest - def test_median(test_case): - ndim = random(2, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_median(test_case, placement, sbp, ndim) - _test_median_with_indices(test_case, placement, sbp, ndim) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_meshgrid.py b/python/oneflow/test/modules/test_consistent_meshgrid.py deleted file mode 100644 index b88cfba109c615543ea9fa56c638b00f53965005..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_meshgrid.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=5, auto_backward=False, check_graph=False) -def _test_meshgrid(test_case, placement): - x_sbp = random_sbp(placement, max_dim=1) - x = random_tensor(ndim=1, dim0=8, requires_grad=False).to_global(placement, x_sbp) - y_sbp = random_sbp(placement, max_dim=1) - y = random_tensor(ndim=1, dim0=8, requires_grad=False).to_global(placement, y_sbp) - res = torch.meshgrid(x, y) - return res[0], res[1] - - -class TestMeshGrid(flow.unittest.TestCase): - @globaltest - def test_meshgrid(test_case): - for placement in all_placement(): - _test_meshgrid(test_case, placement) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_min.py b/python/oneflow/test/modules/test_consistent_min.py deleted file mode 100644 index d6d0c3b738a7faaea5a646ec6b1174bc9b2bb930..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_min.py +++ /dev/null @@ -1,114 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np -import oneflow as flow -import oneflow.unittest -from collections import OrderedDict -from oneflow.test_utils.automated_test_util import * -from oneflow.test_utils.test_util import GenArgList - - -def _np_min(shape, dim, keepdims): - # np array result - input_arr = np.random.randn(*shape) - np_out = np.amin(input_arr, axis=dim, keepdims=keepdims) - np_out_grad = np.zeros_like(input_arr) - if dim == None: - arg_min = np.argmin(input_arr) - np.put(np_out_grad, arg_min, 1) - else: - arg_min = np.expand_dims(np.argmin(input_arr, axis=dim), axis=dim) - np.put_along_axis(np_out_grad, arg_min, 1, axis=dim) - - return np_out, np_out_grad, input_arr - - -def _test_min( - test_case, placement, sbp, np_out, np_out_grad, input_arr, shape, dim, keepdims -): - # of result - global_x = flow.tensor( - input_arr, - dtype=flow.float32, - requires_grad=True, - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - if dim is None: - of_out = flow.min(global_x) - else: - of_out = flow.min(global_x, dim, keepdims)[0] - test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) - of_out = of_out.sum() - of_out.backward() - - test_case.assertTrue( - np.allclose(global_x.grad.numpy(), np_out_grad, 0.0001, 0.0001) - ) - - -class TestMinModule(flow.unittest.TestCase): - # backward formula is different from one of torch. - @globaltest - def test_eager_global_min(test_case): - arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_min] - arg_dict["shape"] = [(8,), (8, 8), (8, 8, 8, 8)] - arg_dict["dim"] = [None, 0, -1] - arg_dict["keepdims"] = [False, True] - for arg in GenArgList(arg_dict): - np_out, np_out_grad, input_arr = _np_min(*arg[1:]) - np_out = ( - flow.tensor(np_out) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - np_out_grad = ( - flow.tensor(np_out_grad) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - input_arr = ( - flow.tensor(input_arr) - .to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=flow.sbp.broadcast, - ) - .numpy() - ) - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=len(*arg[1:2])): - arg[0]( - test_case, - placement, - sbp, - np_out, - np_out_grad, - input_arr, - *arg[1:] - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_movedim.py b/python/oneflow/test/modules/test_consistent_movedim.py deleted file mode 100644 index aa56409801c79e61ccc7327ec6b7e4f080413e97..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_movedim.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_movedim(test_case, placement, sbp): - x = random_tensor( - ndim=4, - dim1=random(1, 3) * 8, - dim2=random(1, 3) * 8, - dim3=random(1, 3) * 8, - dim4=random(1, 3) * 8, - ).to_global(placement, sbp) - z = torch.movedim(x, (0, 1), (2, 3)) - return z - - -class TestMovedim(flow.unittest.TestCase): - @globaltest - def test_movedim(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement): - _test_movedim(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_moving_average_max_min_observer.py b/python/oneflow/test/modules/test_consistent_moving_average_max_min_observer.py deleted file mode 100644 index 0e3537d000cbc030f4ff075e60ed5218d879b97a..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_moving_average_max_min_observer.py +++ /dev/null @@ -1,102 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import numpy as np -import oneflow as flow -from collections import OrderedDict -from oneflow.test_utils.test_util import GenArgList -from test_moving_average_min_max_observer import _check_moving_average_min_max_observer -from oneflow.test_utils.automated_test_util import * - - -def _run_test_moving_average_min_max_observer( - test_case, - placement, - sbp, - device_type, - dtype, - activation_shape, - quantization_bit, - quantization_scheme, - quantization_formula, - momentum, -): - moving_max_np = np.zeros((1,)) - moving_min_np = np.zeros((1,)) - - current_train_step_tensor = flow.tensor( - np.zeros((1,)).astype(np.float32), - dtype=flow.int64, - placement=placement, - sbp=sbp, - ) - for i in range(10): - of_activation = ( - random_tensor(len(activation_shape), *activation_shape, low=-0.5, high=0.5) - .to_global(placement, sbp) - .oneflow - ) - np_activation = of_activation.numpy() - - moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver( - training=True, - quantization_formula=quantization_formula, - stop_update_after_iters=1, - quantization_bit=quantization_bit, - quantization_scheme=quantization_scheme, - momentum=momentum, - ) - moving_average_min_max_observer = moving_average_min_max_observer.to_global( - placement, sbp - ) - (scale, zero_point) = moving_average_min_max_observer( - of_activation, current_train_step_tensor - ) - _check_moving_average_min_max_observer( - test_case, - np_activation, - scale.numpy(), - zero_point.numpy(), - moving_max_np, - moving_min_np, - quantization_bit, - quantization_scheme, - quantization_formula, - momentum, - ) - - -class TestMovingAverageMinMaxObserver(flow.unittest.TestCase): - @globaltest - def test_moving_average_min_max_observer(test_case): - arg_dict = OrderedDict() - arg_dict["device_type"] = ["cpu", "cuda"] - arg_dict["dtype"] = ["float32", "double"] - arg_dict["activation_shape"] = [(9, 48, 24, 10)] - arg_dict["quantization_bit"] = [8, 2] - arg_dict["quantization_scheme"] = ["symmetric", "affine"] - arg_dict["quantization_formula"] = ["google"] - arg_dict["momentum"] = [0.95] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, valid_split_axis=[1, 2]): - _run_test_moving_average_min_max_observer( - test_case, placement, sbp, *arg - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_mul.py b/python/oneflow/test/modules/test_consistent_mul.py deleted file mode 100644 index 54242a79eb2ea817b3fcf6e8fc86cd604ba8f925..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_mul.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_broadcast_mul(test_case, placement, sbp): - x = random_tensor(ndim=3, dim0=16, dim1=8, dim2=24).to_global(placement, sbp) - y_sbp = random_sbp(placement, max_dim=2) - y = random_tensor(ndim=2, dim0=8, dim1=24).to_global(placement, y_sbp) - z = torch.mul(x, y) - return z - - -@autotest(n=1, check_graph=False) -def _test_mul_with_scalar(test_case, ndim, placement, sbp): - dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dim_list).to_global(placement, sbp) - y = 2 - return torch.mul(x, y) - - -class TestMulModule(flow.unittest.TestCase): - @globaltest - def test_broadcast_mul(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_broadcast_mul(test_case, placement, sbp) - - @globaltest - def test_mul_with_scalar(test_case): - ndim = random(1, 4).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_mul_with_scalar(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_mv.py b/python/oneflow/test/modules/test_consistent_mv.py deleted file mode 100644 index 02bde993fa3ad38d94af5a3f252b7294e500fae9..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_mv.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_mv(test_case, placement, sbp): - dim = random(1, 6) - mat = random_tensor(2, dim1=dim).to_global(placement=placement, sbp=sbp) - vec = random_tensor(1, dim0=dim).to_global(placement=placement, sbp=sbp) - return torch.mv(mat, vec) - - -class TestMvModule(flow.unittest.TestCase): - @globaltest - def test_mv(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement): - _test_mv(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_narrow.py b/python/oneflow/test/modules/test_consistent_narrow.py deleted file mode 100644 index c1bd28fe7e580969ecb08514aff43531c3462438..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_narrow.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_narrow(test_case, ndim, placement, sbp): - dims = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - dim = random(-ndim, ndim).to(int).value() - start = random(0, dims[dim]).to(int).value() - length = random(1, dims[dim] - start + 1).to(int).value() - - return torch.narrow(x, dim=dim, start=start, length=length) - - -class TestNarrow(flow.unittest.TestCase): - @globaltest - def test_narrow(test_case): - for placement in all_placement(): - ndim = random(1, 4).to(int).value() - for sbp in all_sbp(placement, max_dim=min(ndim, 2)): - _test_narrow(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_ne.py b/python/oneflow/test/modules/test_consistent_ne.py deleted file mode 100644 index 4d72d9c95a079898b38e392eb2327375b56e15e2..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_ne.py +++ /dev/null @@ -1,38 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_ne(test_case, placement, sbp): - x1 = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement, sbp) - x2 = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement, sbp) - return torch.ne(x1, x2) - - -class TestNe(flow.unittest.TestCase): - @globaltest - def test_ne(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_ne(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_negative.py b/python/oneflow/test/modules/test_consistent_negative.py deleted file mode 100644 index ced1fd58ee21f894d178d9551470abfbb9c327eb..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_negative.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_negative(test_case, placement, sbp, ndim): - shape = [8 for _ in range(ndim)] - x = random_tensor(ndim, *shape).to_global(placement, sbp) - return torch.negative(x) - - -class TestNegative(flow.unittest.TestCase): - @globaltest - def test_negative(test_case): - ndim = random(2, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_negative(test_case, placement, sbp, ndim) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_nms.py b/python/oneflow/test/modules/test_consistent_nms.py deleted file mode 100644 index c9767a9df66866e4b18204f53409ab6de772343e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_nms.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import numpy as np -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * -from test_nms import create_tensors_with_iou -from test_nms import nms_np - - -def _test_nms(test_case, placement, sbp): - iou = 0.5 - boxes, scores = create_tensors_with_iou(800, iou) - - global_boxes = flow.tensor(boxes, dtype=flow.float32).to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.broadcast - ) - np_boxes = global_boxes.numpy() - global_boxes = global_boxes.to_global(placement=placement, sbp=sbp) - - global_scores = flow.tensor(scores, dtype=flow.float32).to_global( - placement=flow.env.all_device_placement("cpu"), sbp=flow.sbp.broadcast - ) - np_scores = global_scores.numpy() - global_scores = global_scores.to_global(placement=placement, sbp=sbp) - - keep_np = nms_np(np_boxes, np_scores, iou) - - keep = flow.nms(global_boxes, global_scores, iou) - test_case.assertTrue(np.allclose(keep.numpy(), keep_np)) - - -class TestNMS(flow.unittest.TestCase): - @globaltest - def test_nms(test_case): - for placement in all_placement(): - # TODO: nms only has cuda kernel at now. - if placement.type == "cpu": - continue - for sbp in all_sbp(placement, max_dim=1): - _test_nms(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_normal.py b/python/oneflow/test/modules/test_consistent_normal.py deleted file mode 100644 index 6b7fa39a745e3f6cf2a58fe29679dafca745f5d1..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_normal.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict -from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type -from oneflow.test_utils.automated_test_util import * -import oneflow as flow - - -def _test_consistent_normal( - test_case, placement, sbp, mean, std, shape, dtype, requires_grad -): - dtype = type_name_to_flow_type[dtype] - x = flow.normal( - mean, - std, - shape, - placement=placement, - sbp=sbp, - dtype=dtype, - requires_grad=requires_grad, - ) - - test_case.assertEqual(x.shape, shape) - test_case.assertEqual(x.dtype, dtype) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - test_case.assertEqual(x.requires_grad, requires_grad) - - -class TestNormalConsistent(flow.unittest.TestCase): - @globaltest - def test_normal_consistent(test_case): - arg_dict = OrderedDict() - arg_dict["mean"] = [-1, 0, 1] - arg_dict["std"] = [1, 2, 8] - arg_dict["shape"] = [(8, 8), (8, 8, 8), (8, 8, 8, 8)] - arg_dict["dtype"] = ["float32", "double"] - arg_dict["requires_grad"] = [True, False] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(arg[2]), except_partial_sum=True - ): - _test_consistent_normal(test_case, placement, sbp, *arg) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_normalize.py b/python/oneflow/test/modules/test_consistent_normalize.py deleted file mode 100644 index cd46d56f9a7f3ac5171a0db41d792352ec923970..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_normalize.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_functional_normalize(test_case, placement, sbp): - ndim = random(low=2, high=5).to(int).value() - shape = [random(low=2, high=3) * 8 for i in range(ndim)] - x = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) - dim = random(low=0, high=ndim).to(int).value() - y = torch.nn.functional.normalize(x, oneof(2, 3, 4), dim, 1e-12) - return y - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_normalize_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_functional_normalize(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_nozero.py b/python/oneflow/test/modules/test_consistent_nozero.py deleted file mode 100644 index 75b05d4651ed09fe03f63766071dfe16b41a7c1e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_nozero.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_nonzero(test_case, placement, sbp, ndim): - shape = [8 for _ in range(ndim)] - x = random_tensor(ndim, *shape).to_global(placement, sbp) - return torch.nonzero(x) - - -class TestNonZero(flow.unittest.TestCase): - @globaltest - def test_nonzero(test_case): - ndim = random(2, 5).to(int).value() - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=ndim): - _test_nonzero(test_case, placement, sbp, ndim) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_ones_like.py b/python/oneflow/test/modules/test_consistent_ones_like.py deleted file mode 100644 index 77091e1a64fba4d7463cbb0bba12d7b481a894b6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_ones_like.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict -import numpy as np -from oneflow.test_utils.test_util import GenArgList -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -def _test_ones_like_float(test_case, placement, sbp, shape, device): - x = flow.tensor( - np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) - ) - x = x.to_global(placement=placement, sbp=sbp) - y = flow.ones_like(x) - test_case.assertTrue(y.dtype is flow.float32) - test_case.assertTrue(y.shape == x.shape) - test_case.assertTrue(y.placement == placement) - y_numpy = np.ones(x.numpy().shape) - test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) - - -def _test_ones_like_int(test_case, placement, sbp, shape, device): - x = flow.tensor(np.random.randn(*shape), dtype=flow.int, device=flow.device(device)) - x = x.to_global(placement=placement, sbp=sbp) - y = flow.ones_like(x) - test_case.assertTrue(y.dtype is flow.int) - test_case.assertTrue(y.shape == x.shape) - test_case.assertTrue(y.placement == placement) - y_numpy = np.ones(x.numpy().shape) - test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_ones_like(test_case): - arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_ones_like_float, _test_ones_like_int] - arg_dict["shape"] = [(8, 8), (8, 8, 4), (8, 8, 5, 6)] - arg_dict["device"] = ["cpu", "cuda"] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - arg[0](test_case, placement, sbp, *arg[1:]) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_partical_fc.py b/python/oneflow/test/modules/test_consistent_partical_fc.py deleted file mode 100644 index 7ab42be66e4bdde839d970ff2b579f37563ccb81..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_partical_fc.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestParitalFC(flow.unittest.TestCase): - @globaltest - def test_parital_fc(test_case): - placement = flow.env.all_device_placement("cuda") - w = flow.randn(5000, 128, placement=placement, sbp=flow.sbp.split(0)) - label = flow.randint( - 0, 5000, (512,), placement=placement, sbp=flow.sbp.split(0) - ) - num_sample = 500 - out = flow.distributed_partial_fc_sample(w, label, num_sample) - test_case.assertTrue(out[0].shape == flow.Size([512])) - test_case.assertTrue(out[1].shape == flow.Size([500])) - test_case.assertTrue(out[2].shape == flow.Size([500, 128])) - - w = flow.randn(5000, 128, placement=placement, sbp=flow.sbp.broadcast) - label = flow.randint( - 0, 5000, (512,), placement=placement, sbp=flow.sbp.split(0) - ) - num_sample = 500 - out = flow.distributed_partial_fc_sample(w, label, num_sample) - test_case.assertTrue(out[0].shape == flow.Size([512])) - test_case.assertTrue(out[1].shape == flow.Size([500])) - test_case.assertTrue(out[2].shape == flow.Size([500, 128])) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_permute.py b/python/oneflow/test/modules/test_consistent_permute.py deleted file mode 100644 index ff207fe7bce2c1b6a24bc368a1339eda0b8da5ee..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_permute.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_permute4d_tensor_with_random_data(test_case, placement, sbp): - ndim = 4 - permute_list = [1, 2, 3, 0] - x = random_tensor( - ndim=ndim, dim0=8, dim1=8, dim2=random(2, 8).to(int), dim3=random(2, 8).to(int), - ).to_global(placement=placement, sbp=sbp) - y = x.permute(permute_list) - return y - - -class TestModule(flow.unittest.TestCase): - @globaltest - def test_permute4d_tensor_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_permute4d_tensor_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_rand.py b/python/oneflow/test/modules/test_consistent_rand.py deleted file mode 100644 index 0afb136f24c173747755076edca60f4daf642b1e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_rand.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_rand(test_case, shape, placement, sbp): - x = flow.rand(*shape, placement=placement, sbp=sbp) - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -def _test_graph_rand(test_case, shape, placement, sbp): - class ConsistentRandGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = flow.rand(*shape, placement=placement, sbp=sbp) - return x - - model = ConsistentRandGraph() - x = model() - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -class TestRandConsistent(flow.unittest.TestCase): - @globaltest - def test_rand_consistent(test_case): - shapes = [(8,), (8, 8,), (8, 8, 8)] - for shape in shapes: - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(shape), except_partial_sum=True - ): - _test_consistent_rand(test_case, shape, placement, sbp) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @flow.unittest.skip_unless_1n2d() - def test_rand_graph(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - shape = args["shape"] - placement = args["placement"] - for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): - _test_graph_rand(test_case, shape, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_randint.py b/python/oneflow/test/modules/test_consistent_randint.py deleted file mode 100644 index abc559e5bd72595b9dce16868c247f39a391d283..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_randint.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_randint(test_case, shape, placement, sbp, dtype): - x = flow.randint(1, 10, shape, placement=placement, sbp=sbp, dtype=dtype) - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - test_case.assertEqual(x.dtype, dtype) - - -def _test_graph_randint(test_case, shape, placement, sbp, dtype): - class ConsistentRandintGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = flow.randint(1, 10, shape, placement=placement, sbp=sbp, dtype=dtype) - return x - - model = ConsistentRandintGraph() - x = model() - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - test_case.assertEqual(x.dtype, dtype) - - -class TestRandintConsistent(flow.unittest.TestCase): - @globaltest - def test_randint_consistent(test_case): - shapes = [(8,), (8, 8,), (8, 8, 8)] - dtypes = [ - flow.uint8, - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - ] - for shape in shapes: - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(shape), except_partial_sum=True - ): - for dtype in dtypes: - _test_consistent_randint( - test_case, shape, placement, sbp, dtype - ) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @flow.unittest.skip_unless_1n2d() - def test_randint_graph(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] - arg_dict["dtype"] = [ - flow.uint8, - flow.int32, - flow.float32, - ] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - shape = args["shape"] - placement = args["placement"] - dtype = args["dtype"] - for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): - _test_graph_randint(test_case, shape, placement, sbp, dtype) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_randn.py b/python/oneflow/test/modules/test_consistent_randn.py deleted file mode 100644 index 481e29cda7178e28ec6b7d226ac250cb14186cc8..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_randn.py +++ /dev/null @@ -1,124 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import oneflow as flow -import numpy as np -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_randn(test_case, shape, placement, sbp): - x1 = flow.randn(*shape, placement=placement, sbp=sbp) - x2 = flow.randn(*shape, placement=placement, sbp=sbp) - test_case.assertTrue(not np.allclose(x1.numpy(), x2.numpy(), atol=1e-4, rtol=1e-4)) - test_case.assertEqual(x1.shape, flow.Size(shape)) - test_case.assertEqual(x1.sbp, sbp) - test_case.assertEqual(x1.placement, placement) - - -def _test_different_dtype(test_case, shape, placement, sbp): - x1 = flow.randn(*shape, dtype=flow.float32, placement=placement, sbp=sbp) - x2 = flow.randn(*shape, dtype=flow.float64, placement=placement, sbp=sbp) - test_case.assertTrue(not np.allclose(x1.numpy(), x2.numpy(), atol=1e-4, rtol=1e-4)) - test_case.assertEqual(x1.shape, flow.Size(shape)) - - -def _test_backward(test_case, shape, placement, sbp): - x = flow.randn(*shape, placement=placement, sbp=sbp, requires_grad=True) - y = x.sum() - y.backward() - test_case.assertTrue( - np.allclose(np.ones(shape), x.grad.numpy(), atol=1e-4, rtol=1e-4) - ) - - -def _test_with_generator(test_case, shape, placement, sbp): - gen = flow.Generator() - gen.manual_seed(0) - y1 = flow.randn(*shape, placement=placement, sbp=sbp, generator=gen) - gen.manual_seed(0) - y2 = flow.randn(*shape, placement=placement, sbp=sbp, generator=gen) - test_case.assertTrue(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) - - -def _test_randn_tuple_shape(test_case, shape, placement, sbp): - y1 = flow.randn(*shape, placement=placement, sbp=sbp) - y2 = flow.randn(*shape, placement=placement, sbp=sbp) - - test_case.assertTrue(not np.array_equal(y1.numpy(), y2.numpy())) - test_case.assertTrue(shape == y1.shape) - - -def _test_graph_randn(test_case, shape, placement, sbp): - class ConsistentRandnGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = flow.randn(*shape, placement=placement, sbp=sbp) - return x - - model = ConsistentRandnGraph() - x = model() - - test_case.assertEqual(x.shape, flow.Size(shape)) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -class TestRandnConsistent(flow.unittest.TestCase): - @globaltest - def test_randn_consistent(test_case): - shapes = [(8,), (8, 8,), (8, 8, 8)] - for shape in shapes: - for placement in all_placement(): - for sbp in all_sbp( - placement, max_dim=len(shape), except_partial_sum=True - ): - _test_consistent_randn(test_case, shape, placement, sbp) - _test_different_dtype(test_case, shape, placement, sbp) - _test_backward(test_case, shape, placement, sbp) - _test_with_generator(test_case, shape, placement, sbp) - _test_randn_tuple_shape(test_case, shape, placement, sbp) - - @flow.unittest.skip_unless_1n2d() - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @globaltest - def test_randn_graph(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - for args in GenArgDict(arg_dict): - shape = args["shape"] - placement = args["placement"] - for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): - _test_graph_randn(test_case, shape, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_randperm.py b/python/oneflow/test/modules/test_consistent_randperm.py deleted file mode 100644 index 9dc5dd82e3df1b828363926a9aa54c4638ecb02f..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_randperm.py +++ /dev/null @@ -1,100 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -import oneflow as flow -import numpy as np -from oneflow.test_utils.automated_test_util import * - -from oneflow.test_utils.test_util import GenArgDict - - -def _test_consistent_randperm(test_case, N, placement, sbp, dtype): - x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype) - # TODO:Synchronously get a global random seed, and then each rank sets its own seed in manual_seeds - test_case.assertEqual(x.dtype, dtype) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -def _test_graph_randperm(test_case, N, placement, sbp, dtype): - class ConsistentRandpermGraph(flow.nn.Graph): - def __init__(self,): - super().__init__() - - def build(self): - x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype) - return x - - model = ConsistentRandpermGraph() - x = model() - y1 = x.to_global(placement=placement, sbp=sbp) - y1_np_sort = np.sort(y1.numpy()) - y2 = np.arange(N) - test_case.assertTrue(np.allclose(y1_np_sort, y2, atol=1e-4, rtol=1e-4)) - test_case.assertEqual(x.dtype, dtype) - test_case.assertEqual(x.sbp, sbp) - test_case.assertEqual(x.placement, placement) - - -@unittest.skip("This fails in multi-gpu") -class TestRandpermConsistent(flow.unittest.TestCase): - @globaltest - def test_randperm_consistent(test_case): - RandNs = [i for i in range(10, 50, 10)] - # TODO support uint8,int8,int64,float32,float64,data type test - Dtypes = [ - flow.int32, - ] - for N in RandNs: - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): - for dtype in Dtypes: - _test_consistent_randperm(test_case, N, placement, sbp, dtype) - - @flow.unittest.skip_unless_1n2d() - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - @globaltest - def test_randperm_graph(test_case): - arg_dict = OrderedDict() - arg_dict["N"] = [i for i in range(10, 50, 10)] - arg_dict["placement"] = [ - # 1d - flow.placement("cpu", ranks=[0, 1]), - flow.placement("cuda", ranks=[0, 1]), - # 2d - flow.placement("cpu", ranks=[[0, 1],]), - flow.placement("cuda", ranks=[[0, 1],]), - ] - arg_dict["dtype"] = [ - flow.uint8, - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - ] - for args in GenArgDict(arg_dict): - N = args["N"] - placement = args["placement"] - dtype = args["dtype"] - for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): - _test_graph_randperm(test_case, N, placement, sbp, dtype) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_reshape.py b/python/oneflow/test/modules/test_consistent_reshape.py deleted file mode 100644 index 783381ecc9cc911ca3ca3b7d1964152847744a7f..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_reshape.py +++ /dev/null @@ -1,89 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_reshape_impl(test_case, pair, placement, sbp): - shape, to_shape = pair - x = random_tensor(len(shape), *shape) - y = x.to_global(placement=placement, sbp=sbp) - z = y.reshape(to_shape) - return z - - -def _test_reshape_like_impl(test_case, pair, placement, in_sbp, like_sbp): - shape, to_shape = pair - - nd_arr = np.random.rand(*shape) - np_out = nd_arr.reshape(to_shape) - - x = flow.tensor(nd_arr) - like = flow.empty(to_shape) - y = x.to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast).to_global( - placement=placement, sbp=in_sbp - ) - like = like.to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ).to_global(placement=placement, sbp=like_sbp) - z = flow._C.reshape_like(y, like) - local_z = z.to_global( - placement, sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ).to_local() - if flow.env.get_rank() == 0: - test_case.assertTrue(np.array_equal(np_out, local_z.numpy())) - - -class TestReshapeConsistent(flow.unittest.TestCase): - @globaltest - def test_reshape(test_case): - shape_pairs = [ - ((8, 16), (8 * 16,)), - ((8, 16), (8 * 4, 4)), - ((8, 16, 24), (64, 6, 8)), - ((8, 16), (64, 1, -1)), - ((8, 16), (-1,)), - ] - for pair in shape_pairs: - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=len(pair[0])): - _test_reshape_impl(test_case, pair, placement, sbp) - - @globaltest - def test_reshape_like(test_case): - shape_pairs = [ - ((8, 16), (8 * 16,)), - ((8, 16), (8 * 2, 8)), - ((8, 16, 24), (64, 48)), - ] - for pair in shape_pairs: - for placement in all_placement(): - for in_sbp in all_sbp(placement, max_dim=len(pair[0])): - for like_sbp in all_sbp(placement, max_dim=len(pair[1])): - _test_reshape_like_impl( - test_case, pair, placement, in_sbp, like_sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_rnn.py b/python/oneflow/test/modules/test_consistent_rnn.py deleted file mode 100644 index 7762592183726167375255fc96b1fb6d0f780889..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_rnn.py +++ /dev/null @@ -1,281 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -import numpy as np -import oneflow as flow -import oneflow.unittest -import torch -from oneflow.test_utils.automated_test_util.generators import * -from oneflow.test_utils.automated_test_util.torch_flow_dual_object import globaltest -from oneflow.test_utils.test_util import GenArgDict - - -def _compare_torch_and_oneflow( - test_case, m_torch, m_flow, placement, module_sbp, in_sbp, input_size -): - torch_state_dict = m_torch.state_dict() - new_dict = {} - for k, v in torch_state_dict.items(): - new_dict[k] = v.detach().numpy() - m_flow.load_state_dict(new_dict) - - m_flow = m_flow.to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ).to_global( - placement=placement, sbp=[module_sbp for _ in range(len(placement.ranks.shape))] - ) - - x = np.random.rand(32, 16, input_size).astype(np.float32) - x_torch = torch.tensor(x, dtype=torch.float32, requires_grad=True) - x_flow = ( - flow.tensor(x, dtype=flow.float32, requires_grad=True) - .to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) - .to_global(placement=placement, sbp=in_sbp) - ) - - out_torch, hid_torch = m_torch(x_torch) - out_flow, hid_flow = m_flow(x_flow) - - # check forward - local_output = out_flow.to_global( - placement=placement, - sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], - ).to_local() - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.allclose( - out_torch.cpu().detach().numpy(), - local_output.numpy(), - rtol=1e-05, - atol=1e-05, - ) - ) - - # check backward - out_torch.sum().backward() - out_flow.sum().backward() - local_x_grad = x_flow.to_global( - placement=placement, - sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], - ).to_local() - if flow.env.get_rank() == 0: - test_case.assertTrue( - np.allclose( - x_torch.cpu().detach().numpy(), - local_x_grad.numpy(), - rtol=1e-05, - atol=1e-05, - ) - ) - - -def _test_rnn_impl( - test_case, - placement, - module_sbp, - in_sbp, - input_size, - hidden_size, - num_layers, - nonlinearity, - bias, - batch_first, - dropout, - bidirectional, -): - rnn_torch = torch.nn.RNN( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - nonlinearity=nonlinearity, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - ) - rnn_flow = flow.nn.RNN( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - nonlinearity=nonlinearity, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - ) - _compare_torch_and_oneflow( - test_case, rnn_torch, rnn_flow, placement, module_sbp, in_sbp, input_size - ) - - -def _test_lstm_impl( - test_case, - placement, - module_sbp, - in_sbp, - input_size, - hidden_size, - num_layers, - bias, - batch_first, - dropout, - bidirectional, - proj_size, -): - lstm_torch = torch.nn.LSTM( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - proj_size=proj_size, - ) - lstm_flow = flow.nn.LSTM( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - proj_size=proj_size, - ) - _compare_torch_and_oneflow( - test_case, lstm_torch, lstm_flow, placement, module_sbp, in_sbp, input_size - ) - - -def _test_gru_impl( - test_case, - placement, - module_sbp, - in_sbp, - input_size, - hidden_size, - num_layers, - bias, - batch_first, - dropout, - bidirectional, -): - gru_torch = torch.nn.GRU( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - ) - gru_flow = flow.nn.GRU( - input_size=input_size, - hidden_size=hidden_size, - num_layers=num_layers, - bias=bias, - batch_first=batch_first, - dropout=dropout, - bidirectional=bidirectional, - ) - _compare_torch_and_oneflow( - test_case, gru_torch, gru_flow, placement, module_sbp, in_sbp, input_size - ) - - -class TestRNNConsistent(oneflow.unittest.TestCase): - @globaltest - def test_rnn(test_case): - arg_dict = OrderedDict() - arg_dict["input_size"] = [ - 1, - ] - arg_dict["hidden_size"] = [ - 1, - ] - arg_dict["num_layers"] = [ - 1, - ] - arg_dict["nonlinearity"] = ["tanh", "relu"] - arg_dict["bias"] = [True, False] - arg_dict["batch_first"] = [True, False] - arg_dict["dropout"] = [ - 0, - ] - arg_dict["bidirectional"] = [True, False] - - module_sbp = flow.sbp.broadcast - for args in GenArgDict(arg_dict): - for placement in all_placement(): - for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): - _test_rnn_impl(test_case, placement, module_sbp, in_sbp, **args) - - @globaltest - def test_lstm(test_case): - arg_dict = OrderedDict() - arg_dict["input_size"] = [ - 1, - ] - arg_dict["hidden_size"] = [ - 2, - ] - arg_dict["num_layers"] = [ - 1, - ] - arg_dict["bias"] = [True, False] - arg_dict["batch_first"] = [True, False] - arg_dict["dropout"] = [ - 0, - ] - arg_dict["bidirectional"] = [True, False] - arg_dict["proj_size"] = [0, 1] - - module_sbp = flow.sbp.broadcast - for args in GenArgDict(arg_dict): - for placement in all_placement(): - for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): - _test_lstm_impl(test_case, placement, module_sbp, in_sbp, **args) - - @globaltest - def test_gru(test_case): - arg_dict = OrderedDict() - arg_dict["input_size"] = [ - 1, - ] - arg_dict["hidden_size"] = [ - 1, - ] - arg_dict["num_layers"] = [ - 1, - ] - arg_dict["bias"] = [True, False] - arg_dict["batch_first"] = [True, False] - arg_dict["dropout"] = [ - 0, - ] - arg_dict["bidirectional"] = [True, False] - - module_sbp = flow.sbp.broadcast - for args in GenArgDict(arg_dict): - for placement in all_placement(): - for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): - _test_gru_impl(test_case, placement, module_sbp, in_sbp, **args) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_rnn_cell.py b/python/oneflow/test/modules/test_consistent_rnn_cell.py deleted file mode 100644 index 41fdf87ed17668eeac5a66912399f0a53b83b1fe..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_rnn_cell.py +++ /dev/null @@ -1,225 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_lstm_cell(test_case, placement, sbp): - batch_size = random(2, 3) * 8 - time_steps = random(2, 3) * 8 - input_size = random(2, 3) * 8 - hidden_size = random(2, 3) * 8 - has_bias = random().to(bool) - cx_requires_grad = random().to(bool) - m = torch.nn.LSTMCell( - input_size=input_size, hidden_size=hidden_size, bias=has_bias, - ) - - m.weight_ih = torch.nn.Parameter( - m.weight_ih.to_global(placement=placement, sbp=sbp) - ) - m.weight_hh = torch.nn.Parameter( - m.weight_hh.to_global(placement=placement, sbp=sbp) - ) - if m.bias_ih is not None: - # bias is 1-d tensor - bias_sbp = random_sbp(placement, max_dim=1) - m.bias_ih = torch.nn.Parameter( - m.bias_ih.to_global(placement=placement, sbp=bias_sbp) - ) - m.bias_hh = torch.nn.Parameter( - m.bias_hh.to_global(placement=placement, sbp=bias_sbp) - ) - - input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) - input = random_tensor( - ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size - ).to_global(placement=placement, sbp=input_sbp) - hx = random_tensor( - ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False - ).to_global(placement=placement, sbp=sbp) - cx = random_tensor( - ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=cx_requires_grad - ).to_global(placement=placement, sbp=sbp) - - for i in range(time_steps.to(int).value()): - res = m(input[i], (hx, cx)) - hx = res[0] - cx = res[1] - return res[0] - - -@autotest(n=1, check_graph=False) -def _test_rnn_relu_cell(test_case, placement, sbp): - batch_size = random(2, 3) * 8 - time_steps = random(2, 3) * 8 - input_size = random(2, 3) * 8 - hidden_size = random(2, 3) * 8 - has_bias = random().to(bool) - m = torch.nn.RNNCell( - input_size=input_size, - hidden_size=hidden_size, - bias=has_bias, - nonlinearity="relu", - ) - - m.weight_ih = torch.nn.Parameter( - m.weight_ih.to_global(placement=placement, sbp=sbp) - ) - m.weight_hh = torch.nn.Parameter( - m.weight_hh.to_global(placement=placement, sbp=sbp) - ) - if m.bias_ih is not None: - # bias is 1-d tensor - bias_sbp = random_sbp(placement, max_dim=1) - m.bias_ih = torch.nn.Parameter( - m.bias_ih.to_global(placement=placement, sbp=bias_sbp) - ) - m.bias_hh = torch.nn.Parameter( - m.bias_hh.to_global(placement=placement, sbp=bias_sbp) - ) - - input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) - input = random_tensor( - ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size - ).to_global(placement=placement, sbp=input_sbp) - hx = random_tensor( - ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False - ).to_global(placement=placement, sbp=sbp) - - for i in range(time_steps.to(int).value()): - hx = m(input[i], hx) - - return hx - - -@autotest(n=1, check_graph=False) -def _test_rnn_tanh_cell(test_case, placement, sbp): - batch_size = random(2, 3) * 8 - time_steps = random(2, 3) * 8 - input_size = random(2, 3) * 8 - hidden_size = random(2, 3) * 8 - has_bias = random().to(bool) - m = torch.nn.RNNCell( - input_size=input_size, - hidden_size=hidden_size, - bias=has_bias, - nonlinearity="tanh", - ) - - m.weight_ih = torch.nn.Parameter( - m.weight_ih.to_global(placement=placement, sbp=sbp) - ) - m.weight_hh = torch.nn.Parameter( - m.weight_hh.to_global(placement=placement, sbp=sbp) - ) - if m.bias_ih is not None: - # bias is 1-d tensor - bias_sbp = random_sbp(placement, max_dim=1) - m.bias_ih = torch.nn.Parameter( - m.bias_ih.to_global(placement=placement, sbp=bias_sbp) - ) - m.bias_hh = torch.nn.Parameter( - m.bias_hh.to_global(placement=placement, sbp=bias_sbp) - ) - - input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) - input = random_tensor( - ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size - ).to_global(placement=placement, sbp=input_sbp) - hx = random_tensor( - ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False - ).to_global(placement=placement, sbp=sbp) - - for i in range(time_steps.to(int).value()): - hx = m(input[i], hx) - - return hx - - -@autotest(n=1, check_graph=False) -def _test_gru_cell(test_case, placement, sbp): - batch_size = random(2, 3) * 8 - time_steps = random(2, 3) * 8 - input_size = random(2, 3) * 8 - hidden_size = random(2, 3) * 8 - has_bias = random().to(bool) - m = torch.nn.GRUCell(input_size=input_size, hidden_size=hidden_size, bias=has_bias,) - - m.weight_ih = torch.nn.Parameter( - m.weight_ih.to_global(placement=placement, sbp=sbp) - ) - m.weight_hh = torch.nn.Parameter( - m.weight_hh.to_global(placement=placement, sbp=sbp) - ) - if m.bias_ih is not None: - # bias is 1-d tensor - bias_sbp = random_sbp(placement, max_dim=1) - m.bias_ih = torch.nn.Parameter( - m.bias_ih.to_global(placement=placement, sbp=bias_sbp) - ) - m.bias_hh = torch.nn.Parameter( - m.bias_hh.to_global(placement=placement, sbp=bias_sbp) - ) - - input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) - input = random_tensor( - ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size - ).to_global(placement=placement, sbp=input_sbp) - hx = random_tensor( - ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False - ).to_global(placement=placement, sbp=sbp) - - for i in range(time_steps.to(int).value()): - hx = m(input[i], hx) - - return hx - - -class TestRNNCellConsistent(flow.unittest.TestCase): - @globaltest - def test_lstm_cell(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_lstm_cell(test_case, placement, sbp) - - @globaltest - def test_rnn_relu_cell(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_rnn_relu_cell(test_case, placement, sbp) - - @globaltest - def test_rnn_tanh_cell(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_rnn_tanh_cell(test_case, placement, sbp) - - @globaltest - def test_gru_cell(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_gru_cell(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_roi_align.py b/python/oneflow/test/modules/test_consistent_roi_align.py deleted file mode 100644 index d07851448de30fafa20c5a39983e6d8a82c1aeb3..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_roi_align.py +++ /dev/null @@ -1,159 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -import torch as pytorch -import torchvision -from oneflow.test_utils.automated_test_util import * - - -def _get_np_rois(): - random_img_idx = np.asarray( - [random(0, 2).to(int).value() for _ in range(200)] - ).reshape((200, 1)) - random_box_idx = np.asarray( - [random(0, 64 * 64).to(float).value() for _ in range(400)] - ).reshape((200, 2)) - - def get_h_w(idx1, idx2): - if idx1 > idx2: - idx1, idx2 = idx2, idx1 - h1 = idx1 // 64 - w1 = idx1 % 64 - h2 = idx2 // 64 - w2 = idx2 % 64 - return [x / 2 for x in [h1, w1, h2, w2]] - - zipped = zip(random_box_idx[:, 0], random_box_idx[:, 1]) - concated = [get_h_w(idx1, idx2) for (idx1, idx2) in zipped] - concated = np.array(concated) - rois = np.hstack((random_img_idx, concated)).astype(np.float32) - return rois - - -def _test_roi_align(test_case, placement, rois_sbp): - dims = [8, 8, 64, 64] - x = random_tensor(4, *dims).to_global( - placement=placement, - sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], - ) - x.oneflow = x.oneflow.detach().requires_grad_() - x.pytorch = x.pytorch.detach().requires_grad_() - - def get_h_w(idx1, idx2): - if idx1 > idx2: - idx1, idx2 = idx2, idx1 - h1 = idx1 // 64 - w1 = idx1 % 64 - h2 = idx2 // 64 - w2 = idx2 % 64 - return [x / 2 for x in [h1, w1, h2, w2]] - - np_rois = _get_np_rois() - of_rois = ( - flow.tensor(np_rois, dtype=flow.float) - .to_global( - placement=flow.env.all_device_placement("cpu"), sbp=[flow.sbp.broadcast,] - ) - .to_global(placement, rois_sbp) - ) - torch_rois = pytorch.tensor(np_rois) - - of_out = flow.roi_align(x.oneflow, of_rois, 2.0, 14, 14, 2, True) - torch_out = torchvision.ops.roi_align( - x.pytorch, - torch_rois, - spatial_scale=2.0, - output_size=[14, 14], - sampling_ratio=2, - aligned=True, - ) - - # compare output - of_local = of_out.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=[flow.sbp.broadcast,] - ).to_local() - test_case.assertTrue( - np.allclose( - of_local.numpy(), torch_out.detach().cpu().numpy(), rtol=1e-04, atol=1e-4 - ) - ) - - # compare backward - of_out.sum().backward() - torch_out.sum().backward() - of_input_grad = x.oneflow.grad.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=[flow.sbp.broadcast,] - ).to_local() - torch_input_grad = x.pytorch.grad.detach().cpu() - test_case.assertTrue( - np.allclose( - of_input_grad.numpy(), torch_input_grad.numpy(), rtol=1e-04, atol=1e-4 - ) - ) - - -def _test_roi_align_in_fixed_data_impl(test_case, placement, sbp): - from test_roi_align import input_np, rois_np, input_grad_np - - input = ( - flow.tensor(input_np, dtype=flow.float32) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp) - .requires_grad_() - ) - rois = ( - flow.tensor(rois_np, dtype=flow.float32) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - ) - of_out = flow.roi_align(input, rois, 2.0, 5, 5, 2, True) - of_out.sum().backward() - test_case.assertTrue( - np.allclose(input.grad.numpy(), input_grad_np, rtol=1e-04, atol=1e-4) - ) - - -class TestConsistentRoiAlign(flow.unittest.TestCase): - # TODO(wyg): It is a bug in pytorch-1.9.0, torchvision-0.10.0 and python3.7.10. - # Open this test after updating the versions of pytorch in CI. - - # @globaltest - # def test_consistent_roi_align(test_case): - # for placement in all_placement(): - # # TODO: roi_align only support gpu - # if placement.type == "cpu": - # continue - # for rois_sbp in all_sbp(placement, max_dim=0, except_partial_sum=True): - # _test_roi_align(test_case, placement, rois_sbp) - - def test_consistent_roi_align_in_fixed_data(test_case): - for placement in all_placement(): - # TODO: roi_align only support gpu - if placement.type == "cpu": - continue - for rois_sbp in all_sbp(placement, max_dim=0, except_partial_sum=True): - _test_roi_align_in_fixed_data_impl(test_case, placement, rois_sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_scatter_nd.py b/python/oneflow/test/modules/test_consistent_scatter_nd.py deleted file mode 100644 index 57ddae65dbdbda4900abc510098d116cdba0a8db..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_scatter_nd.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -def _test_scatter_nd(test_case, placement, sbp): - indices = ( - flow.tensor(np.array([[1], [6], [4]]), dtype=flow.int) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp) - ) - update = ( - flow.tensor(np.array([10.2, 5.1, 12.7]), dtype=flow.float) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp) - .requires_grad_() - ) - output = flow.scatter_nd(indices, update, [8]) - - # forward - of_local = output.to_global( - flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,] - ).to_local() - np_out = np.array([0.0, 10.2, 0.0, 0.0, 12.7, 0.0, 5.1, 0.0]) - test_case.assertTrue(np.allclose(of_local.numpy(), np_out, 1e-4, 1e-4)) - - # backward - output.sum().backward() - of_grad_local = update.grad.to_global( - flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,] - ).to_local() - test_case.assertTrue(np.allclose(of_grad_local.numpy(), np.ones((3)), 1e-4, 1e-4)) - - -class TestScatterNd(flow.unittest.TestCase): - @globaltest - def test_scatter_nd(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_partial_sum=True, except_split=True): - _test_scatter_nd(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_scatter_ops.py b/python/oneflow/test/modules/test_consistent_scatter_ops.py deleted file mode 100644 index 61d8557bf4d5faf5d3faa3e5519cbe9bf8be098f..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_scatter_ops.py +++ /dev/null @@ -1,84 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=10, auto_backward=True, check_graph=False) -def _test_scatter_random_data(test_case, placement): - input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=2) - ) - src = random_tensor(ndim=2, dim0=2, dim1=2).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=2) - ) - index = ( - torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp=random_sbp(placement, max_dim=2),) - ) - dim = random(0, 2).to(int).value() - return torch.scatter(input, dim, index, src) - - -@autotest(n=10, auto_backward=True, check_graph=False) -def _test_scatter_scalar_random_data(test_case, placement): - input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=2) - ) - index = ( - torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp=random_sbp(placement, max_dim=2),) - ) - dim = random(0, 2).to(int).value() - return torch.scatter(input, dim, index, 3.14) - - -@autotest(n=10, auto_backward=True, check_graph=False) -def _test_scatter_add_random_data(test_case, placement): - input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=2) - ) - src = random_tensor(ndim=2, dim0=2, dim1=2).to_global( - placement=placement, sbp=random_sbp(placement, max_dim=2) - ) - index = ( - torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) - .to_global(flow.env.all_device_placement("cpu"), [flow.sbp.broadcast,]) - .to_global(placement, sbp=random_sbp(placement, max_dim=2),) - ) - dim = random(0, 2).to(int).value() - return torch.scatter_add(input, dim, index, src) - - -@flow.unittest.skip_unless_1n2d() -class TestScatterOps(flow.unittest.TestCase): - @globaltest - def test_scatter_ops(test_case): - for placement in all_placement(): - _test_scatter_random_data(test_case, placement) - _test_scatter_scalar_random_data(test_case, placement) - _test_scatter_add_random_data(test_case, placement) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sign.py b/python/oneflow/test/modules/test_consistent_sign.py deleted file mode 100644 index 0ae4f17c31142a8996fe1471dff3eb96cc39486e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_sign.py +++ /dev/null @@ -1,42 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -from oneflow.test_utils.automated_test_util import * -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_sign_impl(test_case, ndim, placement, sbp): - dims = [random(1, 3) * 8 for i in range(ndim)] - x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) - y = torch.sign(x) - return y - - -class TestSign(flow.unittest.TestCase): - @globaltest - def test_sign(test_case): - for placement in all_placement(): - ndim = random(1, 4).to(int).value() - for sbp in all_sbp(placement, max_dim=ndim): - _test_sign_impl(test_case, ndim, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_slice.py b/python/oneflow/test/modules/test_consistent_slice.py deleted file mode 100644 index 7d0bfa9f9ac9706a8b8699c40f39cad53e15ec2f..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_slice.py +++ /dev/null @@ -1,193 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -def _check_forward_and_backward(test_case, input, of_out, torch_out): - # compare forward - test_case.assertTrue( - np.array_equal(of_out.numpy(), torch_out.cpu().detach().numpy()) - ) - - # compare backward - of_out.sum().backward() - torch_out.sum().backward() - torch_grad_local = input.pytorch.grad.cpu().detach() - test_case.assertTrue( - np.array_equal(input.oneflow.grad.numpy(), torch_grad_local.numpy()) - ) - - -def _test_slice_random_data(test_case, placement, sbp): - dims = [random(1, 2) * 8 for _ in range(2)] - input = random_tensor(2, *dims) - x = input.to_global(placement=placement, sbp=sbp) - slice_tup_list = [[None, None, None], [0, 5, 2]] - of_out = flow.slice(x.oneflow, slice_tup_list=slice_tup_list) - torch_out = x.pytorch[:, 0:5:2] - - _check_forward_and_backward(test_case, input, of_out, torch_out) - - -def _test_slice_empty(test_case, placement, sbp): - dims = [random(1, 2) * 8 for _ in range(2)] - input = random_tensor(2, *dims) - x = input.to_global(placement=placement, sbp=sbp) - slice_tup_list = [[3, 3, 1], [None, None, None]] - of_out = flow.slice(x.oneflow, slice_tup_list=slice_tup_list) - torch_out = x.pytorch[3:3:1, :] - - _check_forward_and_backward(test_case, input, of_out, torch_out) - - -def _test_slice_1dim(test_case, placement, sbp): - dims = [random(1, 2) * 8 for _ in range(2)] - input = random_tensor(2, *dims) - x = input.to_global(placement=placement, sbp=sbp) - of_out = x.oneflow[2] - torch_out = x.pytorch[2] - - _check_forward_and_backward(test_case, input, of_out, torch_out) - - -def _test_negative_index(test_case, placement, sbp): - dims = [random(1, 2) * 8 for _ in range(2)] - input = random_tensor(2, *dims) - x = input.to_global(placement=placement, sbp=sbp) - of_out = x.oneflow[-1:-6:1, :] - torch_out = x.pytorch[-1:-6:1, :] - - _check_forward_and_backward(test_case, input, of_out, torch_out) - - -def _test_slice_ellipsis_type(test_case, placement, sbp): - dims = [random(1, 2) * 8 for _ in range(2)] - input = random_tensor(2, *dims) - x = input.to_global(placement=placement, sbp=sbp) - of_out = x.oneflow[..., :] - torch_out = x.pytorch[..., :] - - _check_forward_and_backward(test_case, input, of_out, torch_out) - - -def _test_slice_with_bool(test_case, placement, sbp): - x = random_tensor(2, 8, 8).oneflow > 0.5 - x_numpy = x.detach().cpu().numpy() - - x = x.to_global(placement=placement, sbp=sbp) - y = flow.slice(x, slice_tup_list=[[0, 1, 1]]) - - test_case.assertTrue(np.array_equal(y.numpy(), x_numpy[0:1:1])) - - -@autotest( - n=2, auto_backward=False, check_graph=False, -) -def _test_slice_with_grad(test_case, placement): - sbp = random_sbp(placement, max_dim=2).value() - - # out_sbp - sbp_map = { - flow.sbp.broadcast: flow.sbp.broadcast, - flow.sbp.split(0): flow.sbp.split(0), - flow.sbp.split(1): flow.sbp.partial_sum(), - flow.sbp.partial_sum: flow.sbp.partial_sum(), - } - assert sbp is not None - out_sbp = tuple([sbp_map[in_sbp] for in_sbp in sbp]) - - x = random_tensor(2, 8, 16, requires_grad=True).oneflow - x_numpy = x.detach().cpu().numpy() - - class SliceWithGrad(flow.nn.Module): - def __init__(self): - super().__init__() - self.input_grad = flow.nn.Parameter(flow.zeros(8, 16)) - - def forward(self, input): - x = input + self.input_grad - x = x.to_global(placement, sbp) - return x[:, :8] - - slice_with_grad_m = SliceWithGrad().to_global( - placement, [flow.sbp.broadcast,] * len(sbp) - ) - - of_sgd = flow.optim.SGD(slice_with_grad_m.parameters(), lr=1.0, momentum=0.0) - - class SliceTrainGraph(flow.nn.Graph): - def __init__(self): - super().__init__() - self.module = slice_with_grad_m - self.add_optimizer(of_sgd) - - def build(self, x): - out = self.module(x) - test_case.assertEqual( - out.sbp, - out_sbp, - f"input sbp is {sbp}, but output sbp is {out.sbp} with placement: {placement}", - ) - z = out.sum() - z.backward() - return out - - graph = SliceTrainGraph() - - input = x.to_global(placement=placement, sbp=sbp) - y = graph(input) - - # output - test_case.assertTrue(np.array_equal(y.numpy(), x_numpy[:, :8])) - # input_grad - x_grad_np = np.zeros((8, 16)) - x_grad_np[:, :8] = 1 - test_case.assertTrue( - np.array_equal(-graph.module.input_grad.origin.numpy(), x_grad_np) - ) - - -class TestSlice(flow.unittest.TestCase): - @globaltest - def test_slice(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_slice_random_data(test_case, placement, sbp) - _test_slice_empty(test_case, placement, sbp) - _test_slice_1dim(test_case, placement, sbp) - _test_negative_index(test_case, placement, sbp) - _test_slice_ellipsis_type(test_case, placement, sbp) - _test_slice_with_bool(test_case, placement, sbp) - - @globaltest - def test_graph_slice(test_case): - for placement in all_placement(): - # TODO(wyg): It will be infer all broadcast sbp when 1n1d, - # slice_update will get error when doing inplace operator. - # Remove this judgement after refactor sbp infer method in Operator class. - if placement.ranks.size == 1: - continue - _test_slice_with_grad(test_case, placement) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_slice_update.py b/python/oneflow/test/modules/test_consistent_slice_update.py deleted file mode 100644 index e1acb85b0f1e10ffafac01b609404587ab053d35..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_slice_update.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -def _test_slice_update(test_case, placement, sbp): - input = random_tensor(2, 8, 16, requires_grad=True).oneflow - value = random_tensor(2, 8, 8, requires_grad=True).oneflow - x = (input + 0).to_global( - placement=placement, sbp=sbp - ) # add 0 to change to non-leaf tensor - y = value.to_global(placement, sbp=sbp) - x[:, :8] = y - - ref_np = input.detach().cpu().numpy() - value_np = value.detach().cpu().numpy() - - # forward - ref_np[:, :8] = value_np - test_case.assertTrue(x.sbp == sbp) - test_case.assertTrue(np.array_equal(x.numpy(), ref_np)) - - # backward - x.sum().backward() - # ref grad - ref_grad_np = np.ones((8, 16)) - ref_grad_np[:, :8] = 0 - test_case.assertTrue(np.array_equal(input.grad.numpy(), ref_grad_np)) - # value grad - value_grad_np = np.ones((8, 8)) - test_case.assertTrue(np.array_equal(value.grad.numpy(), value_grad_np)) - - -def _test_graph_slice_update(test_case, placement, sbp): - ref = random_tensor(2, 8, 16, requires_grad=True).oneflow - value = random_tensor(2, 8, 8, requires_grad=True).oneflow - - class SliceUpdateWithGrad(flow.nn.Module): - def __init__(self): - super().__init__() - self.ref_grad = flow.nn.Parameter(flow.zeros(8, 16)) - self.value_grad = flow.nn.Parameter(flow.zeros(8, 8)) - - def forward(self, ref, value): - x = ref + self.ref_grad - y = value + self.value_grad - x = x.to_global(placement, sbp) - y = y.to_global(placement, sbp) - x[:, :8] = y - return x - - slice_update_with_grad_m = SliceUpdateWithGrad().to_global( - placement, [flow.sbp.broadcast,] * len(sbp) - ) - - of_sgd = flow.optim.SGD(slice_update_with_grad_m.parameters(), lr=1.0, momentum=0.0) - - class SliceUpdateTrainGraph(flow.nn.Graph): - def __init__(self): - super().__init__() - self.module = slice_update_with_grad_m - self.add_optimizer(of_sgd) - - def build(self, x, y): - out = self.module(x, y) - z = out.sum() - z.backward() - return out - - graph = SliceUpdateTrainGraph() - - x = ref.to_global(placement=placement, sbp=sbp) - y = value.to_global(placement=placement, sbp=sbp) - z = graph(x, y) - - test_case.assertTrue(z.sbp == sbp) - - ref_np = ref.detach().cpu().numpy() - value_np = value.detach().cpu().numpy() - - # forward - ref_np[:, :8] = value_np - test_case.assertTrue(np.array_equal(z.numpy(), ref_np)) - - # backward - # ref grad - ref_grad = np.ones((8, 16)) - ref_grad[:, :8] = 0 - test_case.assertTrue( - np.array_equal(-graph.module.ref_grad.origin.numpy(), ref_grad) - ) - # value grad - value_grad = np.ones((8, 8)) - test_case.assertTrue( - np.array_equal(-graph.module.value_grad.origin.numpy(), value_grad) - ) - - -class TestGlobalSliceUpdate(flow.unittest.TestCase): - @globaltest - def test_slice_update(test_case): - for placement in all_placement(): - # TODO(wyg): It will be infer all broadcast sbp when 1n1d, - # slice_update will get error when doing inplace operator. - # Remove this judgement after refactor sbp infer method in Operator class. - if placement.ranks.size == 1: - continue - for _ in range(2): - sbp = random_sbp(placement, max_dim=2).value() - _test_slice_update(test_case, placement, sbp) - _test_graph_slice_update(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sparse_softmax_cross_entropy.py b/python/oneflow/test/modules/test_consistent_sparse_softmax_cross_entropy.py deleted file mode 100644 index 3caf07c2e54f6df2d09b7db989d05f60f30f4310..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_sparse_softmax_cross_entropy.py +++ /dev/null @@ -1,163 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import os -from collections import OrderedDict - -import numpy as np -import torch - -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type - -from oneflow.test_utils.automated_test_util.generators import * -from oneflow.test_utils.automated_test_util.torch_flow_dual_object import globaltest - - -def _compare_eager_global_with_torch( - placement, logits_sbp, labels_sbp, data_type, label_type, batch_size, num_classes, -): - data_type = type_name_to_flow_type[data_type] - label_type = type_name_to_flow_type[label_type] - np_labels = np.random.randint(0, num_classes, size=(batch_size,)).astype(np.int32) - np_logits = np.random.random((batch_size, num_classes)).astype(np.float32) - if flow.env.get_rank() == 0: - torch_logits = torch.tensor(np_logits, dtype=torch.float32, requires_grad=True) - torch_labels = torch.tensor(np_labels, dtype=torch.int64) - torch_output = torch.nn.functional.cross_entropy( - torch_logits, torch_labels, reduction="none" - ) - torch_output.sum().backward() - - of_logits = flow.tensor(np_logits, dtype=data_type, requires_grad=True).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_logits = of_logits.to_global(placement, logits_sbp) - - of_logits.retain_grad() - - of_labels = flow.tensor(np_labels, dtype=label_type).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_labels = of_labels.to_global(placement, labels_sbp) - - of_output = flow.nn.functional.sparse_softmax_cross_entropy( - labels=of_labels, logits=of_logits - ) - of_output.sum().backward() - of_logits_grad = of_logits.grad.to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_logits_grad = of_logits_grad.to_local() - of_output = of_output.to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_output = of_output.to_local() - - if flow.env.get_rank() == 0: - assert np.allclose( - of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 - ) - assert np.allclose( - of_logits_grad.numpy(), torch_logits.grad, rtol=1e-03, atol=1e-04 - ) - - -def _compare_lazy_global_with_torch( - placement, logits_sbp, labels_sbp, data_type, label_type, batch_size, num_classes, -): - data_type = type_name_to_flow_type[data_type] - label_type = type_name_to_flow_type[label_type] - np_labels = np.random.randint(0, num_classes, size=(batch_size,)).astype(np.int32) - np_logits = np.random.random((batch_size, num_classes)).astype(np.float32) - if flow.env.get_rank() == 0: - torch_logits = torch.tensor(np_logits, dtype=torch.float32, requires_grad=True) - torch_labels = torch.tensor(np_labels, dtype=torch.int64) - torch_output = torch.nn.functional.cross_entropy( - torch_logits, torch_labels, reduction="none" - ) - - class MyModule(flow.nn.Graph): - def __init__(self): - super(MyModule, self).__init__() - - # nn.graph no support get input.grad - def build(self, logits, labels): - output = flow.nn.functional.sparse_softmax_cross_entropy( - labels=labels, logits=logits - ) - return output - - of_logits = flow.tensor(np_logits, dtype=data_type, requires_grad=True).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_logits = of_logits.to_global(placement, logits_sbp) - - of_labels = flow.tensor(np_labels, dtype=label_type).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - of_labels = of_labels.to_global(placement, labels_sbp) - graph = MyModule() - of_output = graph(of_logits, of_labels) - of_output = of_output.to_global( - placement=flow.env.all_device_placement("cpu"), sbp=[flow.sbp.broadcast] - ) - of_output = of_output.to_local() - - flow._oneflow_internal.eager.multi_client.Sync() - - if flow.env.get_rank() == 0: - assert np.allclose( - of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 - ) - - -class TestConsistentSparseSoftmaxCrossEntropyWithLogits(flow.unittest.TestCase): - @globaltest - def test_eager_global_sparse_softmax_cross_entropy(test_case): - arg_dict = OrderedDict() - arg_dict["data_type"] = ["float32", "double"] - arg_dict["label_type"] = ["int32", "int64"] - arg_dict["batch_size"] = [64] - arg_dict["num_classes"] = [1024] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for logits_sbp in all_sbp(placement, max_dim=2): - for labels_sbp in all_sbp(placement, max_dim=1): - _compare_eager_global_with_torch( - placement, logits_sbp, labels_sbp, *arg - ) - - # TODO: Too many streams will cause bugs, open the graph mode after solving - # @globaltest - # def test_lazy_global_sparse_softmax_cross_entropy(test_case): - # arg_dict = OrderedDict() - # arg_dict["data_type"] = ["float32", "double"] - # arg_dict["label_type"] = ["int32", "int64"] - # arg_dict["batch_size"] = [64] - # arg_dict["num_classes"] = [1024] - # for arg in GenArgList(arg_dict): - # for placement in all_placement(): - # for logits_sbp in all_sbp(placement, max_dim=2): - # for labels_sbp in all_sbp(placement, max_dim=1): - # _compare_lazy_global_with_torch( - # placement, logits_sbp, labels_sbp, *arg - # ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_split.py b/python/oneflow/test/modules/test_consistent_split.py deleted file mode 100644 index 83d62f9d77e57dd85673e3cbdd616205a936b841..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_split.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_flow_split_with_random_data(test_case, placement, sbp): - k0 = random(2, 6) * 8 - k1 = random(2, 6) * 8 - k2 = random(2, 6) * 8 - rand_dim = random(0, 3).to(int) - x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( - placement=placement, sbp=sbp - ) - res = torch.split(x, 2, dim=rand_dim) - return torch.cat(res, rand_dim) - - -@autotest(n=2, check_graph=False) -def _test_flow_split_sizes_with_random_data(test_case, placement, sbp): - k0 = random(2, 6) * 8 - k1 = 16 - k2 = random(2, 6) * 8 - x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( - placement=placement, sbp=sbp - ) - res = torch.split(x, [6, 3, 4, 3], dim=1) - return torch.cat(res, dim=1) - - -@autotest(n=2, check_graph=False) -def _test_flow_split_sizes_neg_dim_with_random_data(test_case, placement, sbp): - k0 = random(2, 6) * 8 - k1 = 16 - k2 = random(2, 6) * 8 - x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( - placement=placement, sbp=sbp - ) - res = torch.split(x, [6, 3, 4, 3], dim=-2) - return torch.cat(res, dim=1) - - -class TestConsistentSplitModule(flow.unittest.TestCase): - @globaltest - def test_flow_split_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_split_with_random_data(test_case, placement, sbp) - - @globaltest - def test_flow_split_sizes_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_split_sizes_with_random_data(test_case, placement, sbp) - - @globaltest - def test_flow_split_sizes_neg_dim_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_split_sizes_neg_dim_with_random_data( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sqrt_square_sum.py b/python/oneflow/test/modules/test_consistent_sqrt_square_sum.py deleted file mode 100644 index 5815afa13f532d2b8bdbf15f43c4ae3c48709530..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_sqrt_square_sum.py +++ /dev/null @@ -1,61 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False, rtol=0.5, atol=0.5) -def _test_sqrt_sum_with_cpu_random_data(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=32, dim2=40, dim3=64).to_global( - placement=placement, sbp=sbp - ) - y = torch.linalg.norm(x) - return y - - -@autotest(n=1, check_graph=False, rtol=0.5, atol=0.5) -def _test_scalar_random_data(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=24, dim2=16, dim3=40).to_global( - placement=placement, sbp=sbp - ) - y = torch.linalg.norm(x) - return y - - -class TestConsistentLinalgVectorNorm2D(flow.unittest.TestCase): - @globaltest - def test_sqrt_sum_with_cpu_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_sqrt_sum_with_cpu_random_data(test_case, placement, sbp) - - @globaltest - def test_scalar_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_scalar_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_squeeze.py b/python/oneflow/test/modules/test_consistent_squeeze.py deleted file mode 100644 index 7e993d50f105a28e46b171d133235145866417a9..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_squeeze.py +++ /dev/null @@ -1,70 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_squeeze_1d_input(test_case, placement, sbp): - x = random_tensor(1, 16, dtype=float).to_global(placement, sbp) - y = torch.squeeze(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_flow_squeeze_with_random_data(test_case, placement, sbp): - x = random_tensor(2, 8, 16).to_global(placement, sbp) - y = torch.squeeze(x, random(0, 2).to(int)) - return y - - -@autotest(n=1, check_graph=False) -def _test_squeeze_with_0_size_data(test_case, placement, sbp): - x = random_tensor(3, 8, 16, 0).to_global(placement, sbp) - y = torch.squeeze(x) - return y - - -class TestConsistentSqueeze(flow.unittest.TestCase): - @globaltest - def test_squeeze_1d_input(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_squeeze_1d_input(test_case, placement, sbp) - - @globaltest - def test_flow_squeeze_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_squeeze_with_random_data(test_case, placement, sbp) - - @globaltest - def test_squeeze_with_0_size_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_squeeze_with_0_size_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_stack.py b/python/oneflow/test/modules/test_consistent_stack.py deleted file mode 100644 index 593d33211e4007cfd985e33a669a801b930609ce..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_stack.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=2, check_graph=False) -def _test_stack_with_random_data(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=8).to_global( - placement, sbp - ) - y = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=8).to_global( - placement, sbp - ) - out = torch.stack((x, y), dim=random(low=-5, high=5).to(int)) - return out - - -@unittest.skip("backward of stack with random diff has bug.") -class TestStackModule(flow.unittest.TestCase): - @globaltest - def test_stack_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_stack_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py b/python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py deleted file mode 100644 index 5f384d6470b5164f0ede7fcc92e302a2e951ba24..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_stateful_kernel_with_cache.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest - -import numpy as np -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -def _test_global_stateful_kernel_with_inpersistent_state(test_case, placement, sbp): - x = ( - flow.arange(64) - .reshape(8, 8) - .to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) - ) - x = x.to_global(placement, sbp) - y = x[0:3, 0:1] - y_np = np.array([[0], [8], [16]]) - test_case.assertTrue(np.array_equal(y.numpy(), y_np,)) - x = x.to_global(flow.env.all_device_placement("cpu"), sbp=flow.sbp.split(1)) - y = x[0:3, 0:1] - test_case.assertTrue(np.array_equal(y.numpy(), y_np,)) - - -class TestStatefulKernelWithInpersistentState(flow.unittest.TestCase): - @globaltest - def test_global_stateful_kernel_with_inpersistent_state(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_stateful_kernel_with_inpersistent_state( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_std.py b/python/oneflow/test/modules/test_consistent_std.py deleted file mode 100644 index f66f70d968973f5735e3d1516380e40dfd8072b2..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_std.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_std_flow_with_random_data(test_case, placement, sbp): - dim = random(low=0, high=4).to(int) - x = random_tensor( - ndim=4, - dim0=random(1, 4) * 8, - dim1=random(1, 4) * 8, - dim2=random(1, 4) * 8, - dim3=random(1, 4) * 8, - ).to_global(placement, sbp) - z = torch.std(x, dim=dim, unbiased=random().to(bool), keepdim=random().to(bool),) - return z - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_std_tensor_with_random_data(test_case, placement, sbp): - dim = random(low=0, high=4).to(int) - x = random_tensor( - ndim=4, - dim0=random(1, 4) * 8, - dim1=random(1, 4) * 8, - dim2=random(1, 4) * 8, - dim3=random(1, 4) * 8, - ).to_global(placement, sbp) - z = x.std(dim=dim, keepdim=random().to(bool),) - return z - - -class TestConsistentStd(flow.unittest.TestCase): - @globaltest - def test_global_std_flow_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_std_flow_with_random_data(test_case, placement, sbp) - - @globaltest - def test_global_std_tensor_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_std_tensor_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sub.py b/python/oneflow/test/modules/test_consistent_sub.py deleted file mode 100644 index 4212aed859401f22c7484de5fdb990438b7f84af..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_sub.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_sub(test_case, placement, sbp): - x = random_tensor(2, 8, 8).to_global(placement=placement, sbp=sbp) - y = random_tensor(2, 8, 8).to_global(placement=placement, sbp=sbp) - out1 = x - y - out2 = x - 2 - out3 = 2 - x - out4 = torch.sub(x, y) - return out1, out2, out3, out4 - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_sub_with_0_size_data(test_case, placement, sbp): - device = random_device() - x = random_tensor(2, 0, 8).to_global(placement=placement, sbp=sbp) - out1 = x - 2 - out2 = 2 - x - return out1, out2 - - -class TestConsistentSubModule(flow.unittest.TestCase): - @globaltest - def test_global_sub(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_sub(test_case, placement, sbp) - - @globaltest - def test_global_sub_with_0_size_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2, valid_split_axis=1): - _test_global_sub_with_0_size_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sum.py b/python/oneflow/test/modules/test_consistent_sum.py deleted file mode 100644 index c3611f7deaab7aa9e9de9a28782d68002337f8fb..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_sum.py +++ /dev/null @@ -1,57 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_global_sum_against_pytorch(test_case, placement, sbp): - x = random_tensor(4, 8, 16, 8, 24).to_global(placement, sbp) - y = torch.sum(x) - return y - - -@autotest(n=3, check_graph=False) -def _test_global_sum_with_0_size_tensor(test_case, placement, sbp): - x = random_tensor(4, 8, 16, 0, 24).to_global(placement, sbp) - y = torch.sum(x, dim=random(0, 3).to(int)) - return y - - -class TestConsistentSumModule(flow.unittest.TestCase): - @globaltest - def test_global_sum_against_pytorch(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_sum_against_pytorch(test_case, placement, sbp) - - @globaltest - def test_global_sum_with_0_size_tensor(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]): - _test_global_sum_with_0_size_tensor(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_tensor_ops.py b/python/oneflow/test/modules/test_consistent_tensor_ops.py deleted file mode 100644 index 00d0698c7ee709eb61303e6c5087902ada5c93e0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_tensor_ops.py +++ /dev/null @@ -1,196 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -def _test_type_as(test_case, shape, src_dtype, tgt_dtype, placement, sbp): - np_input = np.random.rand(*shape) - input = flow.tensor(np_input, dtype=src_dtype).to_global(placement, sbp) - target = flow.tensor(np_input, dtype=tgt_dtype).to_global(placement, sbp) - input = input.type_as(target) - test_case.assertEqual(input.dtype, target.dtype) - - -def _test_is_floating_point(test_case, shape, dtype, placement, sbp): - np_input = np.random.rand(*shape) - input = flow.tensor(np_input, dtype=dtype).to_global(placement, sbp) - output = input.is_floating_point() - if input.dtype in (flow.float, flow.float16, flow.float32, flow.double): - test_case.assertEqual(output, True) - else: - test_case.assertEqual(output, False) - - -@autotest(n=1, check_graph=False) -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -def _test_global_cuda(test_case, placement, sbp): - x = random_tensor(2, 8, 16).to_global(placement, sbp) - x = x.cuda() - y = x.sum() - return y - - -class TestConsistentCuda(flow.unittest.TestCase): - @globaltest - def test_global_cuda(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_cuda(test_case, placement, sbp) - - -@autotest(n=1, check_graph=False) -def _test_global_cpu(test_case, placement, sbp): - x = random_tensor(2, 8, 16).to_global(placement, sbp) - x = x.cpu() - y = x.sum() - return y - - -# PyTorch error if open auto_backward: -# element 0 of tensors does not require grad and does not have a grad_fn -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_long(test_case, placement, sbp): - x = random_tensor(2, 8, 16, requires_grad=True).to_global(placement, sbp) - y = x.long() - test_case.assertFalse(y.oneflow.requires_grad) - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_int(test_case, placement, sbp): - x = random_tensor(2, 8, 16, requires_grad=True).to_global(placement, sbp) - y = x.int() - test_case.assertFalse(y.oneflow.requires_grad) - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_float(test_case, placement, sbp): - x = random_tensor(2, 8, 16, dtype=int).to_global(placement, sbp) - y = x.float() - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_double(test_case, placement, sbp): - x = random_tensor(2, 8, 16, dtype=int).to_global(placement, sbp) - y = x.double() - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_item(test_case, placement, sbp): - x = random_tensor(ndim=1, dim0=1, dtype=int).to_global(placement, sbp) - y = torch.tensor(x.item()) - return y - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_global_tolist(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=32, dtype=int).to_global( - placement, sbp - ) - y = torch.tensor(x.tolist()) - return y - - -class TestConsistentTensorOps(flow.unittest.TestCase): - @globaltest - def test_global_cpu(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_cpu(test_case, placement, sbp) - - @globaltest - def test_global_long(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_long(test_case, placement, sbp) - - @globaltest - def test_global_int(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_int(test_case, placement, sbp) - - @globaltest - def test_global_float(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_float(test_case, placement, sbp) - - @globaltest - def test_global_double(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_double(test_case, placement, sbp) - - @globaltest - def test_global_item(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1, except_split=True): - _test_global_item(test_case, placement, sbp) - - @globaltest - def test_global_tolist(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_tolist(test_case, placement, sbp) - - @globaltest - def test_type_as(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(8, 16), (8, 16, 24), (8, 16, 24, 32)] - arg_dict["src_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] - arg_dict["tgt_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=len(arg[0])): - _test_type_as(test_case, *arg, placement, sbp) - - @globaltest - def test_is_floating_point(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(8, 16), (8, 16, 24), (8, 16, 24, 32)] - arg_dict["dtype"] = [ - # flow.uint8, nccl don't support uint8 - flow.int8, - flow.int32, - flow.int64, - flow.float32, - flow.float64, - flow.double, - flow.float, - flow.int, - ] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=len(arg[0])): - _test_is_floating_point(test_case, *arg, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_tensor_scatter_nd_update.py b/python/oneflow/test/modules/test_consistent_tensor_scatter_nd_update.py deleted file mode 100644 index 02e5b0e14c4704c8783ee3b33d4eebfc77911102..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_tensor_scatter_nd_update.py +++ /dev/null @@ -1,161 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -class TensorScatterNdUpdate(flow.nn.Graph): - def __init__(self): - super(TensorScatterNdUpdate, self).__init__() - - def build(self, origin, indices, update): - return flow.tensor_scatter_nd_update(origin, indices, update) - - -def _test_global_tensor_scatter_nd_update(test_case, placement, sbp, check_graph=False): - origin = random_tensor(1, 16, requires_grad=False).to_global(placement, sbp) - indices = choice_tensor(16, (8, 1), replace=False).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - update = random_tensor(1, 8, requires_grad=False).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - - np_origin = origin.oneflow.numpy() - np_indices = indices.oneflow.numpy().reshape(8) - np_update = update.oneflow.numpy() - - if check_graph: - tensor_scatter_nd_update = TensorScatterNdUpdate() - output = tensor_scatter_nd_update( - origin.oneflow, indices.oneflow, update.oneflow - ) - else: - output = flow.tensor_scatter_nd_update( - origin.oneflow, indices.oneflow, update.oneflow - ) - - np_origin[np_indices] = np_update - - test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) - - -def _test_global_tensor_scatter_nd_update_t( - test_case, placement, sbp, check_graph=False -): - - origin = random_tensor(2, 16, 4, requires_grad=False).to_global(placement, sbp) - indices = choice_tensor(16, (8, 1), replace=False).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - update = random_tensor(2, 8, 4, requires_grad=False).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - - np_origin = origin.oneflow.numpy() - np_indices = indices.oneflow.numpy().reshape(8) - np_update = update.oneflow.numpy() - - if check_graph: - tensor_scatter_nd_update = TensorScatterNdUpdate() - output = tensor_scatter_nd_update( - origin.oneflow, indices.oneflow, update.oneflow - ) - else: - output = flow.tensor_scatter_nd_update( - origin.oneflow, indices.oneflow, update.oneflow - ) - - np_origin[np_indices] = np_update - - test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) - - -def _test_eager_global_tensor_scatter_nd_update_backward(test_case, placement, sbp): - origin = random_tensor(1, 16,).to_global(placement, sbp) - origin.retain_grad() - indices = choice_tensor(16, (8, 1), replace=False).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - update = random_tensor(1, 8).to_global( - placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] - ) - update.retain_grad() - - np_origin = origin.oneflow.numpy() - np_indices = indices.oneflow.numpy().reshape(8) - np_update = update.oneflow.numpy() - - np_update_grad = np.ones(8) - np_origin_grad = np.ones(16) - np_origin_grad[np_indices] = np.zeros(8) - - output = flow.tensor_scatter_nd_update( - origin.oneflow, indices.oneflow, update.oneflow - ) - out_sum = output.sum() - out_sum.backward() - - np_origin[np_indices] = np_update - - test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) - test_case.assertTrue(np.allclose(update.oneflow.grad.numpy(), np_update_grad)) - test_case.assertTrue(np.allclose(origin.oneflow.grad.numpy(), np_origin_grad)) - - -class TestTensorScatterNdUpdate(flow.unittest.TestCase): - @globaltest - def test_global_tensor_scatter_nd_update(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_tensor_scatter_nd_update( - test_case, placement, sbp, False - ) # eager global - # skip lazy test - # _test_global_tensor_scatter_nd_update( - # test_case, placement, sbp, True - # ) # nn graph - - @globaltest - def test_global_tensor_scatter_nd_update_t(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_tensor_scatter_nd_update_t( - test_case, placement, sbp, False - ) # eager global - # skip lazy test - # _test_global_tensor_scatter_nd_update_t( - # test_case, placement, sbp, True - # ) # nn graph - - @globaltest - def test_global_tensor_scatter_nd_update_backward(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_eager_global_tensor_scatter_nd_update_backward( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_tensordot.py b/python/oneflow/test/modules/test_consistent_tensordot.py deleted file mode 100644 index cf0abaadd2ac0a339ada6a7f977f93f784eaaa43..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_tensordot.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False, atol=1e-3) -def _test_global_tensordot_against_pytorch(test_case, ndim, placement, sbp): - k = random(1, 2) * 8 - tensordot_dim = random(0, ndim + 1).to(int) - - x = random_tensor(ndim=ndim, dim0=k, dim1=k, dim2=k, dim3=k).to_global( - placement=placement, sbp=sbp - ) - y = random_tensor(ndim=ndim, dim0=k, dim1=k, dim2=k, dim3=k).to_global( - placement=placement, sbp=sbp - ) - z = torch.tensordot(x, y, dims=tensordot_dim) - return z - - -class TestTensorDotConsistent(flow.unittest.TestCase): - @globaltest - def test_tensordot(test_case): - for placement in all_placement(): - for ndim in range(1, 4): - for sbp in all_sbp(placement, max_dim=ndim): - _test_global_tensordot_against_pytorch( - test_case, ndim, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_tile.py b/python/oneflow/test/modules/test_consistent_tile.py deleted file mode 100644 index df349e1bc82c29364e7ed998e52e2447f750b9c7..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_tile.py +++ /dev/null @@ -1,65 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=3, check_graph=False) -def _test_global_flow_tile_with_random_data(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - reps = ( - random(1, 5).to(int) * 8, - random(1, 5).to(int) * 8, - random(1, 5).to(int) * 8, - ) - z = torch.tile(x, reps) - return z - - -@autotest(n=3, check_graph=False) -def _test_global_flow_tensor_tile_with_random_data(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - reps = ( - random(1, 5).to(int) * 8, - random(1, 5).to(int) * 8, - random(1, 5).to(int) * 8, - ) - y = x.tile(reps) - return y - - -class TestConsistentTile(flow.unittest.TestCase): - @globaltest - def test_global_flow_tile_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_flow_tile_with_random_data(test_case, placement, sbp) - - @globaltest - def test_global_flow_tensor_tile_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_flow_tensor_tile_with_random_data( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_transpose.py b/python/oneflow/test/modules/test_consistent_transpose.py deleted file mode 100644 index 20c4631ab5e807df3a4dd8474bdf62b65e5bc8ea..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_transpose.py +++ /dev/null @@ -1,130 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - -from oneflow.test_utils.automated_test_util import * -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - - -def _test_global_transpose(test_case, placement, sbp): - input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - input = input.to_global(placement, sbp) - of_out = flow.transpose(input, 0, 1) - np_out = input.numpy().transpose((1, 0, 2, 3)) - test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) - - -def _test_global_tensor_transpose(test_case, placement, sbp): - input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - input = input.to_global(placement, sbp) - of_out = input.transpose(0, 1) - np_out = input.numpy().transpose((1, 0, 2, 3)) - test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) - - -def _test_global_tranpose_negative_dim(test_case, placement, sbp): - input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( - flow.env.all_device_placement("cpu"), flow.sbp.broadcast - ) - input = input.to_global(placement, sbp) - of_out = flow.transpose(input, -4, -3) - np_out = input.numpy().transpose((1, 0, 2, 3)) - test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) - - -def _test_global_transpose_backward(test_case, placement, sbp): - x = flow.tensor( - np.random.randn(8, 16, 8, 16), dtype=flow.float32, requires_grad=True, - ).to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) - x = x.to_global(placement, sbp) - x.retain_grad() - y = flow.transpose(x, 0, 1).sum() - y.backward() - test_case.assertTrue( - np.allclose(x.grad.numpy(), np.ones((8, 16, 8, 16)), 1e-05, 1e-05) - ) - - -def _test_global_transpose_backward_v2(test_case, placement, sbp): - x = flow.tensor( - np.random.randn(8, 16, 8, 16), dtype=flow.float32, requires_grad=True, - ).to_global(flow.env.all_device_placement("cpu"), flow.sbp.broadcast) - x = x.to_global(placement, sbp) - x.retain_grad() - y = flow.transpose(x, 3, 1).sum() - y.backward() - test_case.assertTrue( - np.allclose(x.grad.numpy(), np.ones((8, 16, 8, 16)), 1e-05, 1e-05) - ) - - -@autotest(n=1, check_graph=False) -def _test_global_transpose_flow_with_random_data(test_case, placement, sbp): - x = random_tensor(4, 8, 16, 24, 8).to_global(placement, sbp) - y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) - return y - - -@autotest(n=1, check_graph=False) -def _test_global_transpose_with_0_size_data(test_case, placement, sbp): - device = random_device() - x = random_tensor(4, 8, 16, 0, 8).to_global(placement, sbp) - y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) - return y - - -class TestConsistentTranspose(flow.unittest.TestCase): - @globaltest - def test_global_transpose(test_case): - arg_dict = OrderedDict() - arg_dict["fun"] = [ - _test_global_transpose, - _test_global_tensor_transpose, - _test_global_tranpose_negative_dim, - _test_global_transpose_backward, - _test_global_transpose_backward_v2, - ] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - arg[0](test_case, placement, sbp) - - @globaltest - def test_global_transpose_flow_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_transpose_flow_with_random_data(test_case, placement, sbp) - - @globaltest - def test_global_transpose_with_0_size_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]): - _test_global_transpose_with_0_size_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_tril.py b/python/oneflow/test/modules/test_consistent_tril.py deleted file mode 100644 index 5bd6ac85dc7fde6cdbbd6ce5daa7699ad0ac39b6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_tril.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=2, check_graph=False) -def _test_global_tril_without_diag(test_case, placement, sbp): - x = random_tensor( - ndim=4, - dim0=random(1, 5).to(int) * 8, - dim1=random(1, 5).to(int) * 8, - dim2=random(1, 5).to(int) * 8, - dim3=random(1, 5).to(int) * 8, - ).to_global(placement, sbp) - y = torch.tril(x) - y = torch.exp(y) - - return y - - -@autotest(n=2, check_graph=False) -def _test_global_tril_with_diag(test_case, placement, sbp): - diagonal = random(-3, 3).to(int) - x = random_tensor( - ndim=4, - dim0=random(1, 5).to(int) * 8, - dim1=random(1, 5).to(int) * 8, - dim2=random(1, 5).to(int) * 8, - dim3=random(1, 5).to(int) * 8, - ).to_global(placement, sbp) - y = torch.tril(x, diagonal) - y = torch.exp(y) - - return y - - -class TestConsistentTril(flow.unittest.TestCase): - @globaltest - def test_global_tril_without_diag(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_tril_without_diag(test_case, placement, sbp) - - @globaltest - def test_global_tril_with_diag(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_tril_with_diag(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_triu.py b/python/oneflow/test/modules/test_consistent_triu.py deleted file mode 100644 index 5aa0ea847250e59aa03cc8679618b35db5cc0b75..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_triu.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=2, check_graph=False) -def _test_global_triu_without_diag(test_case, placement, sbp): - x = random_tensor( - ndim=4, - dim0=random(1, 3).to(int) * 8, - dim1=random(1, 3).to(int) * 8, - dim2=2, - dim3=4, - ).to_global(placement, sbp) - y = torch.triu(x) - y = torch.exp(y) - - return y - - -@autotest(n=2, check_graph=False) -def _test_global_triu_with_diag(test_case, placement, sbp): - diagonal = random(-3, 3).to(int) - x = random_tensor( - ndim=4, - dim0=random(1, 3).to(int) * 8, - dim1=random(1, 3).to(int) * 8, - dim2=2, - dim3=4, - ).to_global(placement, sbp) - y = torch.triu(x, diagonal) - y = torch.exp(y) - - return y - - -class TestConsistentTriu(flow.unittest.TestCase): - @globaltest - def test_global_triu_without_diag(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_triu_without_diag(test_case, placement, sbp) - - @globaltest - def test_global_triu_with_diag(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_triu_with_diag(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_unbind.py b/python/oneflow/test/modules/test_consistent_unbind.py deleted file mode 100644 index 75fa6f676c3ac90dc664580cbb552acbc515c6a0..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_unbind.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -import oneflow as flow -import oneflow.unittest -from oneflow.test_utils.automated_test_util import * - - -# TODO: the test is dependent on global select op(consistent tensor->stride()) -@unittest.skip("global select op is not currently supported") -@autotest(n=1, check_graph=False) -def _test_unbind(test_case, placement, sbp): - dim_size = random(1, 3).to(int).value() * 8 - rand_dim = random(0, 3).to(int).value() - x = random_tensor(ndim=3, dim0=dim_size, dim1=dim_size, dim2=dim_size).to_global( - placement, sbp - ) - return torch.unbind(x, dim=rand_dim) - - -class TestUnbind(flow.unittest.TestCase): - @globaltest - def test_unbind(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=3): - _test_unbind(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_unfold.py b/python/oneflow/test/modules/test_consistent_unfold.py deleted file mode 100644 index a4dc3919fe5b044ecf5c220a5b0408faf769f2d6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_unfold.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * -from oneflow.nn.common_types import _size_2_t - - -@autotest(n=3, auto_backward=True, check_graph=False) -def _test_unfold_with_random_data(test_case, placement, sbp): - m = torch.nn.Unfold( - kernel_size=random(1, 3).to(_size_2_t), - dilation=random(1, 2).to(_size_2_t), - padding=random(0, 1).to(_size_2_t), - stride=random(1, 2).to(_size_2_t), - ) - m.train(random()) - m.to_global(placement, sbp) - x = random_tensor(ndim=4, dim0=8, dim1=2, dim2=4, dim3=2,).to_global(placement, sbp) - y = m(x) - return y - - -class TestUnfold(flow.unittest.TestCase): - @globaltest - def test_unfold_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_unfold_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_unfold_tensor.py b/python/oneflow/test/modules/test_consistent_unfold_tensor.py deleted file mode 100644 index 9449d09efedcf961c5b88f685894c0b426678b7e..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_unfold_tensor.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * -import numpy as np - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_unfold_tensor_with_random_data(test_case, placement, sbp): - ndim = 4 - dim = random(0, ndim).to(int).value() - x = random_tensor( - ndim=ndim, - dim0=random(1, 3).to(int) * 8, - dim1=random(1, 3).to(int) * 8, - dim2=4, - dim3=4, - ).to_global(placement, sbp) - high = x.oneflow.size()[dim] - size = random(1, high).to(int).value() - step = random(1, high).to(int).value() - y = x.unfold(dim, size, step) - return y - - -class TestGlobalUnfoldTensor(flow.unittest.TestCase): - @globaltest - def test_global_unfold_tensor_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_unfold_tensor_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_unsqueeze.py b/python/oneflow/test/modules/test_consistent_unsqueeze.py deleted file mode 100644 index 0d33ebde5aa2c0df1c596c296098306e26b49bda..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_unsqueeze.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -import numpy as np - -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_flow_unsqueeze_with_random_data(test_case, placement, sbp): - x = random_tensor(2, 8, 16).to_global(placement, sbp) - y = torch.unsqueeze(x, random(0, 3).to(int)) - return y - - -@autotest(n=1, check_graph=False) -def _test_tensor_unsqueeze_with_random_data(test_case, placement, sbp): - x = random_tensor(2, 8, 16).to_global(placement, sbp) - y = x.unsqueeze(random(0, 3).to(int)) - return y - - -@autotest(n=1, check_graph=False) -def _test_unsqueeze_with_0_size_data(test_case, placement, sbp): - x = random_tensor(3, 8, 16, 0).to_global(placement, sbp) - y = torch.unsqueeze(x, random(0, 4).to(int)) - return y - - -class TestConsistentUnsqueeze(flow.unittest.TestCase): - @globaltest - def test_flow_unsqueeze_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_unsqueeze_with_random_data(test_case, placement, sbp) - - @globaltest - def test_tensor_unsqueeze_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_tensor_unsqueeze_with_random_data(test_case, placement, sbp) - - @globaltest - def test_unsqueeze_with_0_size_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_unsqueeze_with_0_size_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_upsample.py b/python/oneflow/test/modules/test_consistent_upsample.py deleted file mode 100644 index e364719275bbd07190d08419b1ae6ed828c4786b..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_upsample.py +++ /dev/null @@ -1,113 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_upsample2d_nearest(test_case, placement, sbp): - x = random_tensor(ndim=3, dim0=8, dim1=16).to_global(placement, sbp) - print(x) - m = torch.nn.Upsample(scale_factor=random().to(int), mode="nearest",) - y = m(x) - return y - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_upsample2d_linear(test_case, placement, sbp): - x = random_tensor(ndim=3, dim0=8, dim1=16).to_global(placement, sbp) - m = torch.nn.Upsample( - scale_factor=random().to(int), mode="linear", align_corners=random_bool(), - ) - y = m(x) - return y - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_upsample2d_bilinear(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=16).to_global(placement, sbp) - m = torch.nn.Upsample( - scale_factor=random().to(int), mode="bilinear", align_corners=random_bool(), - ) - y = m(x) - return y - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_upsample2d_bicubic(test_case, placement, sbp): - x = random_tensor(ndim=4, dim0=8, dim1=16).to_global(placement, sbp) - m = torch.nn.Upsample( - scale_factor=random().to(int), mode="bicubic", align_corners=random_bool(), - ) - y = m(x) - return y - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_global_upsample2d_trilinear(test_case, placement, sbp): - x = random_tensor(ndim=5, dim0=8, dim1=16).to_global(placement, sbp) - m = torch.nn.Upsample( - scale_factor=random().to(int), mode="trilinear", align_corners=random_bool(), - ) - y = m(x) - return y - - -class TestGlobalUpsample2d(flow.unittest.TestCase): - @unittest.skip( - "The nearest interpolate operation in pytorch has bug, https://github.com/pytorch/pytorch/issues/65200" - ) - @globaltest - def test_global_upsample2d_nearest(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_upsample2d_nearest(test_case, placement, sbp) - - @globaltest - def test_global_upsample2d_linear(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_upsample2d_linear(test_case, placement, sbp) - - @globaltest - def test_global_upsample2d_bilinear(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_upsample2d_bilinear(test_case, placement, sbp) - - @globaltest - def test_global_upsample2d_bicubic(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_upsample2d_bicubic(test_case, placement, sbp) - - @globaltest - def test_global_upsample2d_trilinear(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_global_upsample2d_trilinear(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_var.py b/python/oneflow/test/modules/test_consistent_var.py deleted file mode 100644 index 5bd3f2a8a8fe7fdaf21847d867506b30e13bf45a..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_var.py +++ /dev/null @@ -1,85 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest - -import oneflow as flow -from oneflow.test_utils.automated_test_util.generators import random -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -@autotest(n=1, check_graph=False) -def _test_flow_global_var_all_dim_with_random_data(test_case, placement, sbp): - x = random_tensor( - ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8, - ).to_global(placement, sbp) - y = torch.var(x) - return y - - -@autotest(n=1, check_graph=False) -def _test_flow_global_var_one_dim_with_random_data(test_case, placement, sbp): - x = random_tensor( - ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8, - ).to_global(placement, sbp) - y = torch.var( - x, - dim=random(low=0, high=2).to(int), - unbiased=random().to(bool), - keepdim=random().to(bool), - ) - return y - - -@autotest(n=1, auto_backward=True, check_graph=False) -def _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp): - x = random_tensor(3, 8, 0, 8).to_global(placement, sbp) - y = torch.var( - x, - dim=random(low=0, high=3).to(int), - unbiased=random().to(bool), - keepdim=random().to(bool), - ) - return y - - -class TestVar(flow.unittest.TestCase): - @globaltest - def test_flow_global_var_all_dim_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_global_var_all_dim_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_global_var_one_dim_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_global_var_one_dim_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_var_0_size_data_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2, valid_split_axis=[0]): - _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_where.py b/python/oneflow/test/modules/test_consistent_where.py deleted file mode 100644 index 0be2c1eb0b3c517f866e77978098f9a2fd3632ee..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_where.py +++ /dev/null @@ -1,371 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_global_where(test_case, placement, sbp): - x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - y = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - condition = random_tensor(ndim=2, dim0=8, dim1=16, high=2, dtype=int).to_global( - placement, sbp - ) - - condition = condition.to(torch.bool) - - z = torch.where(condition, x, y) - return z - - -@autotest(n=1, check_graph=False) -def _test_global_where_broadcast(test_case, placement, sbp): - x = random_tensor(ndim=3, dim0=8, dim1=16, dim2=1).to_global(placement, sbp) - y = random_tensor(ndim=3, dim0=8, dim1=16, dim2=8).to_global(placement, sbp) - condition = random_tensor( - ndim=3, dim0=8, dim1=16, dim2=1, high=2, dtype=int - ).to_global(placement, sbp) - - condition = condition.to(torch.bool) - - z = torch.where(condition, x, y) - return z - - -@autotest(n=1, check_graph=False) -def _test_global_where_scalar(test_case, placement, sbp): - x = random_tensor(ndim=0).to_global(placement, sbp) - y = random_tensor(ndim=0).to_global(placement, sbp) - condition = random_tensor(ndim=0, high=2, dtype=int).to_global(placement, sbp) - - condition = condition.to(torch.bool) - - z = torch.where(condition, x, y) - return z - - -# Close auto_backward because pytorch raise error: -# PyTorch error: element 0 of tensors does not require grad and does not have a grad_fn -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_where_x_y_none(test_case, placement, sbp): - condition = random_tensor(ndim=2, dim0=8, dim1=8, low=-1, high=1).to_global( - placement, sbp - ) - y = torch.where(condition) - return y[0], y[1] - - -@autotest(n=1, check_graph=False) -def _test_global_where_tensor_with_0dim_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random_tensor(ndim=0).to_global(placement, sbp) - y = random_tensor(ndim=0).to_global(placement, sbp) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, check_graph=False) -def _test_flow_where_tensor_broadcast_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=3, dim0=8, dim1=16, dim2=8).to_global(placement, sbp) - x = random_tensor(ndim=3, dim0=8, dim1=1, dim2=8).to_global(placement, sbp) - y = random_tensor(ndim=3, dim0=8, dim1=16, dim2=1).to_global(placement, sbp) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, check_graph=False) -def _test_flow_where_scalar_x_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random().to(float) - y = ( - random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) - .to_global(placement, sbp) - .to(torch.float64) - ) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, check_graph=False) -def _test_flow_where_scalar_x_broadcast_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) - x = random().to(float) - y = ( - random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) - .to_global(placement, sbp) - .to(torch.float64) - ) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_x_int_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random().to(int) - y = random_tensor(ndim=2, dim0=8, dim1=16, dtype=int).to_global(placement, sbp) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, check_graph=False) -def _test_flow_where_scalar_y_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = ( - random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) - .to_global(placement, sbp) - .to(torch.float64) - ) - y = random().to(float) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, check_graph=False) -def _test_flow_where_scalar_y_broadcast_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) - x = ( - random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) - .to_global(placement, sbp) - .to(torch.float64) - ) - y = random().to(float) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_y_int_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random_tensor(ndim=2, dim0=8, dim1=16, dtype=int).to_global(placement, sbp) - y = random().to(int) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_tensor_bool_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp).to(torch.bool) - y = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp).to(torch.bool) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_tensor_broadcast_bool_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp).to(torch.bool) - y = random_tensor(ndim=2, dim0=8, dim1=1).to_global(placement, sbp).to(torch.bool) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_x_bool_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random().to(bool) - y = ( - random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) - .to_global(placement, sbp) - .to(torch.bool) - ) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_x_broadcast_bool_with_random_data( - test_case, placement, sbp -): - cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) - x = random().to(bool) - y = ( - random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) - .to_global(placement, sbp) - .to(torch.bool) - ) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_y_bool_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = ( - random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) - .to_global(placement, sbp) - .to(torch.bool) - ) - y = random().to(bool) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_y_broadcast_bool_with_random_data( - test_case, placement, sbp -): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = ( - random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) - .to_global(placement, sbp) - .to(torch.bool) - ) - y = random().to(bool) - return torch.where(cond > 0, x, y) - - -@autotest(n=1, auto_backward=False, check_graph=False) -def _test_flow_where_scalar_xy_bool_with_random_data(test_case, placement, sbp): - cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) - x = random().to(bool) - y = random().to(bool) - return torch.where(cond > 0, x, y) - - -class TestGlobalWhere(flow.unittest.TestCase): - @globaltest - def test_global_where(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_where(test_case, placement, sbp) - - @globaltest - def test_global_where_broadcast(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_global_where_broadcast(test_case, placement, sbp) - - @globaltest - def test_global_where_scalar(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_global_where_scalar(test_case, placement, sbp) - - @globaltest - def test_where_x_y_none(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_where_x_y_none(test_case, placement, sbp) - - @globaltest - def test_global_where_tensor_with_0dim_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_global_where_tensor_with_0dim_data(test_case, placement, sbp) - - @globaltest - def test_flow_where_tensor_broadcast_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=1): - _test_flow_where_tensor_broadcast_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_x_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_x_with_random_data(test_case, placement, sbp) - - @globaltest - def test_flow_where_scalar_x_broadcast_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_x_broadcast_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_x_int_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_x_int_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_y_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_y_with_random_data(test_case, placement, sbp) - - @globaltest - def test_flow_where_scalar_y_broadcast_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_y_broadcast_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_y_int_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_y_int_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_tensor_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=2): - _test_flow_where_tensor_bool_with_random_data(test_case, placement, sbp) - - @globaltest - def test_flow_where_tensor_broadcast_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_tensor_broadcast_bool_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_x_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_x_bool_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_x_broadcast_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_x_broadcast_bool_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_y_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_y_bool_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_y_broadcast_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_y_broadcast_bool_with_random_data( - test_case, placement, sbp - ) - - @globaltest - def test_flow_where_scalar_xy_bool_with_random_data(test_case): - for placement in all_placement(): - for sbp in all_sbp(placement, except_split=True): - _test_flow_where_scalar_xy_bool_with_random_data( - test_case, placement, sbp - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_zeropad2d.py b/python/oneflow/test/modules/test_consistent_zeropad2d.py deleted file mode 100644 index 08a4cc92083e22957c68795f87f8421ff9cee9cd..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_consistent_zeropad2d.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import unittest -from collections import OrderedDict - -from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import * -import oneflow as flow -import oneflow.unittest - - -@autotest(n=1, check_graph=False) -def _test_global_ZeroPad2d(test_case, placement, sbp, padding): - x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=8, dim3=8,).to_global( - placement, sbp - ) - m = torch.nn.ZeroPad2d(padding) - y = m(x) - return y - - -class TestGlobalZeroPad2dModule(flow.unittest.TestCase): - @globaltest - def test_global_ZeroPad2d(test_case): - arg_dict = OrderedDict() - arg_dict["padding"] = [2, (1, 1, 2, 2)] - for arg in GenArgList(arg_dict): - for placement in all_placement(): - for sbp in all_sbp(placement, max_dim=4): - _test_global_ZeroPad2d(test_case, placement, sbp, *arg) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_constant.py b/python/oneflow/test/modules/test_constant.py index 73d7b231f86ddb31646dd141cc5fe35271a4298d..8ac32f008f3f46c4ced711191fc7ecb5d36c5419 100644 --- a/python/oneflow/test/modules/test_constant.py +++ b/python/oneflow/test/modules/test_constant.py @@ -50,6 +50,12 @@ class TestConstantModule(flow.unittest.TestCase): ).to(device) return y1, y2, y3, y4 + @profile(torch.zeros) + def profile_zeros(test_case): + torch.zeros(2, 3) + torch.zeros(32, 3, 128, 128) + torch.zeros(1000, 1000) + @autotest(n=10, auto_backward=False, check_graph=True) def test_flow_ones_list_with_random_data(test_case): device = random_device() @@ -61,6 +67,12 @@ class TestConstantModule(flow.unittest.TestCase): ).to(device) return y1, y2, y3, y4 + @profile(torch.ones) + def profile_ones(test_case): + torch.ones(2, 3) + torch.ones(32, 3, 128, 128) + torch.ones(1000, 1000) + @autotest(auto_backward=False, check_graph=True) def test_flow_zeros_like_list_with_random_data(test_case): device = random_device() @@ -68,6 +80,22 @@ class TestConstantModule(flow.unittest.TestCase): y = torch.zeros_like(x) return y + @profile(torch.zeros_like) + def profile_zeros_like(test_case): + input1 = torch.ones(32, 3, 128, 128) + input2 = torch.ones(1000, 1000) + input3 = torch.ones(2, 3) + torch.zeros_like(input1) + torch.zeros_like(input2) + torch.zeros_like(input3) + + @autotest(auto_backward=True, check_graph=True) + def test_flow_zeros_like_list_with_random_data_and_requires_grad(test_case): + device = random_device() + x = random_tensor().to(device) + y = torch.zeros_like(x, requires_grad=True) + return y + @autotest(auto_backward=False, check_graph=True) def test_flow_zeros_like_list_with_0dim_data(test_case): device = random_device() @@ -82,6 +110,22 @@ class TestConstantModule(flow.unittest.TestCase): y = torch.ones_like(x) return y + @profile(torch.ones_like) + def profile_ones_like(test_case): + input1 = torch.ones(32, 3, 128, 128) + input2 = torch.ones(1000, 1000) + input3 = torch.ones(2, 3) + torch.ones_like(input1) + torch.ones_like(input2) + torch.ones_like(input3) + + @autotest(auto_backward=True, check_graph=True) + def test_flow_ones_like_list_with_random_data_and_requires_grad(test_case): + device = random_device() + x = random_tensor().to(device) + y = torch.ones_like(x, requires_grad=True) + return y + @autotest(auto_backward=False, check_graph=True) def test_flow_ones_like_list_with_0dim_data(test_case): device = random_device() @@ -100,6 +144,13 @@ class TestConstantModule(flow.unittest.TestCase): ) return y + @profile(torch.Tensor.new_ones) + def profile_new_ones(test_case): + x = torch.Tensor(np.ones((1, 2, 3))) + x.new_ones((2, 3)) + x.new_ones((32, 3, 128, 128)) + x.new_ones((1000, 1000, 1000, 1000)) + @autotest(auto_backward=True, check_graph=True) def test_flow_new_ones_list_with_0dim_data(test_case): device = random_device() @@ -122,6 +173,25 @@ class TestConstantModule(flow.unittest.TestCase): ) return y + @profile(torch.Tensor.new_zeros) + def profile_new_zeros(test_case): + x = torch.Tensor(np.ones((1, 2, 3))) + x.new_zeros((2, 3)) + x.new_zeros((32, 3, 128, 128)) + x.new_zeros((1000, 1000, 1000, 1000)) + + @autotest(n=5) + def test_new_full(test_case): + device = random_device() + x = random_tensor().to(device) + y = x.new_full( + (random().to(int), random().to(int), random().to(int)), + random().to(float).value(), + device=device.value(), + requires_grad=constant(True), + ) + return y + @autotest(n=10, auto_backward=True) def test_full_with_random_data_int(test_case): device = random_device() @@ -129,6 +199,37 @@ class TestConstantModule(flow.unittest.TestCase): y = torch.full(shape, 2.0, requires_grad=True) return y + @autotest(n=5) + def test_full_with_random_data_numpy_scalar(test_case): + device = random_device() + shape = random_tensor(low=1, high=6, requires_grad=False).pytorch.shape + y = torch.full(shape, np.array([2.0])[0], device=device, requires_grad=True) + return y + + @autotest(n=5) + def test_full_with_scalar_tensor(test_case): + device = random_device() + shape = random_tensor(low=0, high=6, requires_grad=False).pytorch.shape + y = torch.full( + shape, + torch.tensor(2.0, requires_grad=random().to(bool)), + device=device, + requires_grad=True, + ) + return y + + @profile(torch.full) + def profile_full_with_scalar_tensor(test_case): + torch.full((2, 3), torch.tensor(3.141592)) + torch.full((64, 3, 128, 128), torch.tensor(3.141592)) + torch.full((1000, 1000), torch.tensor(3.141592)) + + @profile(torch.full) + def profile_full(test_case): + torch.full((2, 3), 3.141592) + torch.full((64, 3, 128, 128), 3.141592) + torch.full((1000, 1000), 3.141592) + @autotest(n=10, auto_backward=True) def test_full_with_random_data_float(test_case): device = random_device() @@ -136,6 +237,19 @@ class TestConstantModule(flow.unittest.TestCase): y = torch.full(shape, 2.0, requires_grad=True) return y + @autotest(n=10, auto_backward=True) + def test_full_like_with_random_data_float(test_case): + device = random_device() + x = random_tensor(low=1, high=6, requires_grad=False).to(device) + y = torch.full_like(x, 2.0, requires_grad=True) + return y + + @profile(torch.full_like) + def profile_full_like(test_case): + torch.full_like(torch.ones(2, 3), 3.141592) + torch.full_like(torch.ones(64, 3, 128, 128), 3.141592) + torch.full_like(torch.ones(1000, 1000), 3.141592) + def test_cast(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [ diff --git a/python/oneflow/test/modules/test_constant_pad.py b/python/oneflow/test/modules/test_constant_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..d713196c7d7c39a54f3cd8ab4fb9011408ddeec9 --- /dev/null +++ b/python/oneflow/test/modules/test_constant_pad.py @@ -0,0 +1,178 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + + +from random import choice +from oneflow.test_utils.automated_test_util import * +from oneflow.nn.common_types import _size_2_t, _size_4_t, _size_6_t +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestConstantPad1d(flow.unittest.TestCase): + @autotest(n=10, rtol=0.001, atol=0.001) + def test_constantpad1d_with_random_data(test_case): + m = torch.nn.ConstantPad1d( + padding=random(1, 6).to(_size_2_t), value=random().to(float) + ) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=3, dim1=random(1, 6), dim2=random(1, 6)).to(device) + y = m(x) + return y + + @autotest(n=10, rtol=0.001, atol=0.001, auto_backward=False) + def test_constantpad1d_with_random_int_data(test_case): + dtype = choice([int, bool]) + value = random(0, 2).to(bool) if dtype is bool else random().to(int) + m = torch.nn.ConstantPad1d(padding=random(1, 6).to(_size_2_t), value=value) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=3, dim1=random(1, 6), dim2=random(1, 6), dtype=int).to( + device + ) + if dtype is bool: + x = x.bool() + y = m(x) + return y + + +@flow.unittest.skip_unless_1n1d() +class TestConstantPad2d(flow.unittest.TestCase): + @autotest(n=10, rtol=0.001, atol=0.001) + def test_constantpad2d_with_random_data(test_case): + m = torch.nn.ConstantPad2d( + padding=random(1, 6).to(_size_4_t), value=random().to(float) + ) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor( + ndim=4, dim1=random(1, 6), dim2=random(1, 6), dim3=random(1, 6) + ).to(device) + y = m(x) + return y + + @autotest(n=10, rtol=0.001, atol=0.001, auto_backward=False) + def test_constantpad2d_with_random_int_data(test_case): + dtype = choice([int, bool]) + value = random(0, 2).to(bool) if dtype is bool else random().to(int) + m = torch.nn.ConstantPad2d(padding=random(1, 6).to(_size_4_t), value=value,) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor( + ndim=4, dim1=random(1, 6), dim2=random(1, 6), dim3=random(1, 6) + ).to(device) + if dtype is bool: + x = x.bool() + y = m(x) + return y + + +@flow.unittest.skip_unless_1n1d() +class TestConstantPad3d(flow.unittest.TestCase): + @autotest(n=10, rtol=0.001, atol=0.001) + def test_constantpad3d_with_random_data(test_case): + m = torch.nn.ConstantPad3d( + padding=random(1, 6).to(_size_6_t), value=random().to(float) + ) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor( + ndim=5, + dim1=random(1, 6), + dim2=random(1, 6), + dim3=random(1, 6), + dim4=random(1, 6), + ).to(device) + y = m(x) + return y + + @autotest(n=10, rtol=0.001, atol=0.001, auto_backward=False) + def test_constantpad3d_with_random_data(test_case): + dtype = choice([bool, int]) + value = random(0, 2).to(bool) if dtype is bool else random().to(int) + m = torch.nn.ConstantPad3d(padding=random(1, 6).to(_size_6_t), value=value,) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor( + ndim=5, + dim1=random(1, 6), + dim2=random(1, 6), + dim3=random(1, 6), + dim4=random(1, 6), + ).to(device) + if dtype is bool: + x = x.bool() + y = m(x) + return y + + +@flow.unittest.skip_unless_1n1d() +class TestFunctionalConstantPad2d(flow.unittest.TestCase): + @autotest(n=10, rtol=0.001, atol=0.001, check_graph=True) + def test_functional_constantpad2d(test_case): + device = random_device() + padding = random(-1, 6).to(_size_4_t) + value = random().to(float) + x = random_tensor( + ndim=4, + dim0=random(1, 6), + dim1=random(1, 6), + dim2=random(2, 6), + dim3=random(2, 6), + ).to(device) + y = torch.nn.functional.pad(x, pad=padding, mode="constant", value=value) + return y + + @autotest(n=10, rtol=0.001, atol=0.001, check_graph=True, auto_backward=False) + def test_functional_constantpad2d_int_data(test_case): + dtype = choice([bool, int]) + device = random_device() + padding = random(-1, 6).to(_size_4_t) + value = random(0, 2).to(bool) if dtype is bool else random().to(int) + x = random_tensor( + ndim=4, + dim0=random(1, 6), + dim1=random(1, 6), + dim2=random(2, 6), + dim3=random(2, 6), + ).to(device) + if dtype is bool: + x = x.bool() + y = torch.nn.functional.pad(x, pad=padding, mode="constant", value=value) + return y + + @profile(torch.nn.functional.pad) + def profile_pad(test_case): + tensor = torch.ones(32, 3, 128, 128) + pad = (1, 1) + torch.nn.functional.pad(tensor, pad) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_constantpad.py b/python/oneflow/test/modules/test_constantpad.py deleted file mode 100644 index 3217cbf9210517be8ecc96e37e07c0cf84bf1b13..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_constantpad.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np - - -from oneflow.test_utils.automated_test_util import * -from oneflow.nn.common_types import _size_2_t, _size_4_t, _size_6_t -import oneflow as flow -import oneflow.unittest - - -@flow.unittest.skip_unless_1n1d() -class TestConstantPad1d(flow.unittest.TestCase): - @autotest(n=10, rtol=0.001, atol=0.001) - def test_constantpad1d_with_random_data(test_case): - m = torch.nn.ConstantPad1d( - padding=random(1, 6).to(_size_2_t), value=random().to(float) - ) - m.train(random()) - device = random_device() - m.to(device) - x = random_tensor(ndim=3, dim1=random(1, 6), dim2=random(1, 6)).to(device) - y = m(x) - return y - - -@flow.unittest.skip_unless_1n1d() -class TestConstantPad2d(flow.unittest.TestCase): - @autotest(n=10, rtol=0.001, atol=0.001) - def test_constantpad2d_with_random_data(test_case): - m = torch.nn.ConstantPad2d( - padding=random(1, 6).to(_size_4_t), value=random().to(float) - ) - m.train(random()) - device = random_device() - m.to(device) - x = random_tensor( - ndim=4, dim1=random(1, 6), dim2=random(1, 6), dim3=random(1, 6) - ).to(device) - y = m(x) - return y - - -@flow.unittest.skip_unless_1n1d() -class TestConstantPad3d(flow.unittest.TestCase): - @autotest(n=10, rtol=0.001, atol=0.001) - def test_constantpad3d_with_random_data(test_case): - m = torch.nn.ConstantPad3d( - padding=random(1, 6).to(_size_6_t), value=random().to(float) - ) - m.train(random()) - device = random_device() - m.to(device) - x = random_tensor( - ndim=5, - dim1=random(1, 6), - dim2=random(1, 6), - dim3=random(1, 6), - dim4=random(1, 6), - ).to(device) - y = m(x) - return y - - -@flow.unittest.skip_unless_1n1d() -class TestFunctionalConstantPad2d(flow.unittest.TestCase): - @autotest(n=10, rtol=0.001, atol=0.001, check_graph=True) - def test_functional_constantpad2d(test_case): - device = random_device() - padding = random(-1, 6).to(_size_4_t) - value = random().to(float) - x = random_tensor( - ndim=4, - dim0=random(1, 6), - dim1=random(1, 6), - dim2=random(2, 6), - dim3=random(2, 6), - ).to(device) - y = torch.nn.functional.pad(x, pad=padding, mode="constant", value=value) - return y - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_contiguous.py b/python/oneflow/test/modules/test_contiguous.py index 1fa0072cc39ebd419ecf8ee96454e78856be87b2..4d589b551f159a895bb5b71bb58e4fb4ae3bb792 100644 --- a/python/oneflow/test/modules/test_contiguous.py +++ b/python/oneflow/test/modules/test_contiguous.py @@ -28,13 +28,37 @@ import oneflow as flow @flow.unittest.skip_unless_1n1d() class TestContiguous(flow.unittest.TestCase): - @autotest(n=10, check_graph=True) + @autotest(n=5) def test_transpose_with_random_data(test_case): device = random_device() x = random_tensor(ndim=4).to(device) y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) z = y.contiguous() - return y + return z + + @autotest(n=5, auto_backward=False) + def test_transpose_with_bool_data(test_case): + device = random_device() + x = random_tensor(ndim=4, requires_grad=False).to(device).to(torch.bool) + y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) + z = y.contiguous() + return z + + @autotest(n=5, auto_backward=False) + def test_transpose_with_int_data(test_case): + device = random_device() + x = random_tensor(ndim=4, requires_grad=False).to(device).to(torch.int) + y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) + z = y.contiguous() + return z + + @autotest(n=5, auto_backward=False) + def test_contiguous_with_half_data(test_case): + device = random_device() + x = random_tensor(ndim=4, requires_grad=False).to(device).to(torch.float16) + y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) + z = y.contiguous() + return z @autotest(n=10, check_graph=True) def test_permute2d_tensor_with_random_data(test_case): @@ -82,8 +106,13 @@ class TestContiguous(flow.unittest.TestCase): z = y.contiguous() return z + @profile(torch.Tensor.contiguous) + def profile_contiguous(test_case): + x = torch.ones(32, 3, 128, 128) + x.contiguous() + -def _tets_inplace_contiguous(test_case, device): +def _test_inplace_contiguous(test_case, device): arr = np.random.randn(4, 5, 6, 7).astype(np.float32) input = flow.tensor(arr, device=device) x = input.permute(0, 3, 2, 1) # x is non-contiguous tensor @@ -105,7 +134,7 @@ class TestInplaceContiguous(flow.unittest.TestCase): def test_inplace_contiguous(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [ - _tets_inplace_contiguous, + _test_inplace_contiguous, ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): diff --git a/python/oneflow/test/modules/test_conv.py b/python/oneflow/test/modules/test_conv.py deleted file mode 100644 index 001d35e1a929249630ae2fed154daedb5ee3b5e6..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_conv.py +++ /dev/null @@ -1,346 +0,0 @@ -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import * - -import oneflow as flow -import oneflow.nn as nn -import oneflow.unittest - -np_arr = np.array([[[1.28795946, -0.2921792, 0.20338029, 0.78604293, -1.89607573]]]) -input = flow.tensor( - np_arr, dtype=flow.float32, device=flow.device("cuda"), requires_grad=True -) -weight = np.array( - [ - [[0.10197904, 0.3372305, -0.25743008]], - [[0.27720425, -0.52435774, -0.38381988]], - [[0.56016803, -0.10063095, -0.10760903]], - ] -) -m = nn.Conv1d(1, 3, 3, stride=1, bias=False) -m.weight = flow.nn.Parameter(flow.Tensor(weight)) -m = m.to("cuda") -output = m(input) -np_out = np.array( - [ - [ - [-0.01954307, -0.16356121, 0.77392507], - [0.43217283, -0.48933625, 0.37196174], - [0.72899038, -0.2687211, 0.23886177], - ] - ] -) -if np.allclose(output.numpy(), np_out, 1e-06, 1e-06): - print("conv1d Passed") -output = output.sum() -output.backward() -np_grad = np.array( - [[[0.93935132, 0.65159315, -0.09726584, -1.03661716, -0.74885899]]] -) -if np.allclose(input.grad.numpy(), np_grad, 1e-06, 1e-06): - print("conv1d_back Passed") - - - -test_conv2d_weight = np.array( - [ - [ - [ - [0.8586049675941467, -0.2279418259859085, 0.2013147622346878], - [0.35005471110343933, 0.5360521078109741, 1.5194443464279175], - [1.9040879011154175, -1.5734431743621826, -0.14007866382598877], - ] - ], - [ - [ - [0.29670074582099915, 1.3111951351165771, 0.5035904049873352], - [-1.1894450187683105, -0.5502137541770935, -1.591875672340393], - [-1.1081947088241577, 0.07872020453214645, -0.9185634255409241], - ] - ], - [ - [ - [-0.7457143664360046, -1.2080862522125244, 1.8140212297439575], - [-1.5227429866790771, -2.515244960784912, -1.3549325466156006], - [-0.9574840068817139, -0.7248556613922119, 1.1119636297225952], - ] - ], - ] -) -test_conv2d_data = np.array( - [ - [ - [ - [ - 1.1630785465240479, - 0.4838046133518219, - 0.299563467502594, - 0.15302546322345734, - -1.168814778327942, - ], - [ - 1.5580710172653198, - -0.5459445714950562, - -2.3556296825408936, - 0.5414402484893799, - 2.678506374359131, - ], - [ - 1.2546343803405762, - -0.5487740635871887, - -0.6810643672943115, - -0.13531559705734253, - 0.37723132967948914, - ], - [ - 0.41016456484794617, - 0.5712682008743286, - -2.757962703704834, - 1.0762799978256226, - -0.6141325235366821, - ], - [ - 1.830764889717102, - -1.1468064785003662, - 0.053837940096855164, - -2.5074806213378906, - -0.5916498899459839, - ], - ] - ] - ] -) -test_conv2d_data_grad = np.array( - [ - [ - [ - [ - 0.4095913469791412, - 0.2847584038972855, - 2.803684800863266, - 2.3940934538841248, - 2.5189263969659805, - ], - [ - -1.9525419473648071, - -4.606781497597694, - -3.51521897315979, - -1.562677025794983, - 1.0915625244379044, - ], - [ - -2.1141327619552612, - -6.987950943410397, - -5.84306687861681, - -3.7289341166615486, - 1.1448840647935867, - ], - [ - -2.5237241089344025, - -7.272709347307682, - -8.646751679480076, - -6.123027570545673, - -1.3740423321723938, - ], - [ - -0.1615908145904541, - -2.381169445812702, - -2.32784790545702, - -2.1662570908665657, - 0.0533215403556824, - ], - ] - ] - ] -) -test_conv2d_weight_grad = np.array( - [ - [ - [ - [0.6277393400669098, -2.7888944894075394, -0.2910575419664383], - [-3.095237225294113, -4.835702538490295, -1.8706469237804413], - [-1.0139376372098923, -6.076017692685127, -5.780256435275078], - ] - ], - [ - [ - [0.6277393400669098, -2.7888944894075394, -0.2910575419664383], - [-3.095237225294113, -4.835702538490295, -1.8706469237804413], - [-1.0139376372098923, -6.076017692685127, -5.780256435275078], - ] - ], - [ - [ - [0.6277393400669098, -2.7888944894075394, -0.2910575419664383], - [-3.095237225294113, -4.835702538490295, -1.8706469237804413], - [-1.0139376372098923, -6.076017692685127, -5.780256435275078], - ] - ], - ] -) -test_conv2d_output = np.array( - [ - [ - [ - [0.9699610471725464, -0.20758534967899323, 2.3857712745666504], - [0.3666309118270874, 4.690882682800293, -8.203354835510254], - [2.6072847843170166, -1.9033538103103638, 2.331153154373169], - ], - [ - [2.519343852996826, 2.3757898807525635, -1.6613528728485107], - [0.5777544379234314, -3.5739502906799316, 5.349126815795898], - [0.729295015335083, 1.5791023969650269, 3.7627718448638916], - ], - [ - [-0.27685487270355225, 6.446267127990723, -2.762883424758911], - [-8.25644588470459, 9.616064071655273, 8.005367279052734], - [-0.6944921016693115, 3.866114854812622, 4.788446426391602], - ], - ] - ] -) -test_conv2d_with_bias_weight = np.array( - [ - [ - [ - [1.8271433115005493, -1.0446699857711792, 1.0062190294265747], - [0.5174201130867004, -0.806931734085083, 1.3769007921218872], - [0.205885112285614, 0.9943519234657288, -0.23580588400363922], - ] - ], - [ - [ - [0.29881811141967773, -1.9982075691223145, 0.3511354625225067], - [-0.7644741535186768, 1.2594351768493652, -0.9629734754562378], - [0.5080506205558777, 0.7561734318733215, 1.6839302778244019], - ] - ], - [ - [ - [1.2573646306991577, 0.13123232126235962, 1.6403018236160278], - [-1.2138012647628784, 2.399970531463623, -0.38509097695350647], - [-0.9878040552139282, 0.9585888385772705, -1.4976465702056885], - ] - ], - ] -) -test_conv2d_with_bias_bias = np.array( - [0.6605162620544434, -0.18903568387031555, -0.27302607893943787] -) -test_conv2d_with_bias_data = np.array( - [ - [ - [ - [ - -0.47827261686325073, - -1.1739492416381836, - -0.7921845316886902, - 0.9321041703224182, - -3.1557741165161133, - ], - [ - 2.1935296058654785, - -0.5385921001434326, - -0.8611332774162292, - -1.881519079208374, - -0.7205708026885986, - ], - [ - -0.35601571202278137, - -0.15963983535766602, - 1.797447681427002, - 0.19594945013523102, - -1.7376397848129272, - ], - [ - 0.047347065061330795, - 0.14580930769443512, - 0.32604914903640747, - 0.4578782916069031, - -0.8942581415176392, - ], - [ - 0.49383941292762756, - -0.9043426513671875, - -1.2140793800354004, - 2.1564064025878906, - 1.0938222408294678, - ], - ] - ] - ] -) -test_conv2d_with_bias_output = np.array( - [ - [ - [ - [-0.05607491731643677, -0.185230553150177, -3.8808679580688477], - [6.861937046051025, -2.3341472148895264, -0.5597308874130249], - [1.8299254179000854, -2.770848274230957, 2.1958212852478027], - ], - [ - [2.9348952770233154, 4.117504119873047, -6.278541088104248], - [0.2638452351093292, 3.998856782913208, 2.612290620803833], - [-1.9891828298568726, -1.6476304531097412, 3.39066219329834], - ], - [ - [-8.44466781616211, 0.5747121572494507, -8.501373291015625], - [-0.036642804741859436, -0.23458999395370483, -2.370849370956421], - [2.8372013568878174, -2.987276077270508, 1.8382092714309692], - ], - ] - ] -) - -to_device = flow.device("cuda") - -conv = flow.nn.Conv2d(1, 3, (3, 3), bias=True).to(to_device) -x = flow.tensor(test_conv2d_with_bias_data, dtype=flow.float32, device=to_device) -conv.weight = flow.nn.Parameter(flow.Tensor(test_conv2d_with_bias_weight)) -conv.bias = flow.nn.Parameter(flow.Tensor(test_conv2d_with_bias_bias)) -conv.to(to_device) -of_out = conv(x) -if np.allclose(of_out.numpy(), test_conv2d_with_bias_output, rtol=1e-4, atol=1e-8): - print("conv2d_bias Passed") - -conv = flow.nn.Conv2d(1, 3, (3, 3), bias=False).to(flow.device("cuda")) -x = flow.tensor(test_conv2d_data, dtype=flow.float32, device=to_device, requires_grad=True) -conv.weight = flow.nn.Parameter(flow.Tensor(test_conv2d_weight), requires_grad=True) -conv.to(to_device) -of_out = conv(x) -of_out.sum().backward() -if np.allclose(x.grad.numpy(), test_conv2d_data_grad, rtol=1e-4, atol=1e-8): - print("con2d_back_data_grad Passed") - -if np.allclose(conv.weight.grad.numpy(), test_conv2d_weight_grad, rtol=1e-4, atol=1e-8): - print("con2d_back_weight_grad Passed") - -conv = flow.nn.Conv2d(1, 3, (3, 3), bias=True).to(to_device) -x = flow.tensor(test_conv2d_with_bias_data, dtype=flow.float32, device=to_device) -conv.weight = flow.nn.Parameter(flow.Tensor(test_conv2d_with_bias_weight)) -conv.bias = flow.nn.Parameter(flow.Tensor(test_conv2d_with_bias_bias)) -conv.to(to_device) -of_out = conv(x) -if np.allclose(of_out.numpy(), test_conv2d_with_bias_output, rtol=1e-4, atol=1e-8): - print("conv2d_bias Passed") - -conv = flow.nn.Conv2d(1, 3, (3, 3), bias=False).to(flow.device("cuda")) -x = flow.tensor(test_conv2d_data, dtype=flow.float32, device=to_device, requires_grad=True) -conv.weight = flow.nn.Parameter(flow.Tensor(test_conv2d_weight), requires_grad=True) -conv.to(to_device) -of_out = conv(x) -of_out.sum().backward() -if np.allclose(x.grad.numpy(), test_conv2d_data_grad, rtol=1e-4, atol=1e-8): - print("con2d_back_data_grad Passed") - -if np.allclose(conv.weight.grad.numpy(), test_conv2d_weight_grad, rtol=1e-4, atol=1e-8): - print("con2d_back_weight_grad Passed") - - - - - - diff --git a/python/oneflow/test/modules/test_conv1d.py b/python/oneflow/test/modules/test_conv1d.py index 39cc86d2bba99789a9e9b1c4217461eaba797532..8f844abef64c0836b73038bf45fc1c40413ce39c 100644 --- a/python/oneflow/test/modules/test_conv1d.py +++ b/python/oneflow/test/modules/test_conv1d.py @@ -443,6 +443,39 @@ class TestConv1d(flow.unittest.TestCase): y = torch.nn.functional.conv1d(img, kernel, groups=3) return y + @profile(torch.nn.functional.conv1d) + def profile_conv1d(test_case): + inputs = torch.ones(40, 16, 30) + weight_16c = torch.ones(20, 16, 5) + weight_16c_4g = torch.ones(20, 4, 5) + weight_3k_16c = torch.ones(20, 16, 3) + weight_1k_16c = torch.ones(20, 16, 1) + torch.nn.functional.conv1d(inputs, weight_16c) + torch.nn.functional.conv1d(inputs, weight_16c, bias=torch.ones(20)) + torch.nn.functional.conv1d(inputs, weight_16c, bias=torch.ones(20), padding=2) + torch.nn.functional.conv1d( + inputs, weight_16c, bias=torch.ones(20), padding=2, stride=2 + ) + torch.nn.functional.conv1d(inputs, weight_16c_4g, groups=4) + torch.nn.functional.conv1d(inputs, weight_16c_4g, bias=torch.ones(20), groups=4) + torch.nn.functional.conv1d( + inputs, weight_16c_4g, bias=torch.ones(20), groups=4, stride=4 + ) + torch.nn.functional.conv1d( + inputs, weight_16c_4g, bias=torch.ones(20), groups=4, padding=2 + ) + torch.nn.functional.conv1d(inputs, weight_3k_16c) + torch.nn.functional.conv1d(inputs, weight_3k_16c, bias=torch.ones(20)) + torch.nn.functional.conv1d( + inputs, weight_3k_16c, bias=torch.ones(20), padding=1 + ) + torch.nn.functional.conv1d( + inputs, weight_3k_16c, bias=torch.ones(20), padding=1, stride=2 + ) + torch.nn.functional.conv1d(inputs, weight_1k_16c) + torch.nn.functional.conv1d(inputs, weight_1k_16c, bias=torch.ones(20)) + torch.nn.functional.conv1d(inputs, weight_1k_16c, bias=torch.ones(20), stride=2) + @autotest(n=5) def test_conv1d_with_random_data(test_case): channels = random(1, 6) diff --git a/python/oneflow/test/modules/test_conv2d.py b/python/oneflow/test/modules/test_conv2d.py index 52f75734ac2d42db3bfb1b303fd6ee86a7780cac..d4bcda5e6a3c56d100e8b207f3e32fcb0f41f882 100644 --- a/python/oneflow/test/modules/test_conv2d.py +++ b/python/oneflow/test/modules/test_conv2d.py @@ -1587,7 +1587,7 @@ class TestConv2d(flow.unittest.TestCase): device = random_device() img = torch.ones((1, 3, 224, 224), requires_grad=True).to(device) kernel = torch.ones((3, 1, 3, 3), requires_grad=True).to(device) - y = torch.nn.functional.conv2d(img, kernel, groups=3) + y = torch.nn.functional.conv2d(input=img, weight=kernel, groups=3) return y def test_conv2d(test_case): @@ -1895,116 +1895,130 @@ class TestConv2d(flow.unittest.TestCase): y = m(x) return y - # @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - # def test_conv2d_NHWC_with_random_data(test_case): - # in_channels = np.random.randint(6, 33) - # out_channels = np.random.randint(32, 66) - # kernel_size = np.random.randint(1, 5) - # stride = np.random.randint(1, 2) - # padding = np.random.randint(1, 3) - # dilation = np.random.randint(1, 3) - # spatial = np.random.randint(6, 64) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_conv2d_NHWC_with_random_data(test_case): + in_channels = np.random.randint(6, 33) + out_channels = np.random.randint(32, 66) + kernel_size = np.random.randint(1, 5) + stride = np.random.randint(1, 2) + padding = np.random.randint(1, 3) + dilation = np.random.randint(1, 3) + spatial = np.random.randint(6, 64) - # np_x = np.random.randn(4, in_channels, spatial, spatial).astype(np.float32) - # np_weight = np.random.randn( - # out_channels, in_channels, kernel_size, kernel_size - # ).astype(np.float32) - # np_bias = np.random.randn(out_channels).astype(np.float32) + np_x = np.random.randn(4, in_channels, spatial, spatial).astype(np.float32) + np_weight = np.random.randn( + out_channels, in_channels, kernel_size, kernel_size + ).astype(np.float32) + np_bias = np.random.randn(out_channels).astype(np.float32) - # flow_nchw_input = flow.tensor( - # np_x, device="cuda", dtype=flow.float32, requires_grad=True - # ) - # flow_nchw_weights = flow.nn.Parameter( - # flow.tensor( - # np_weight, device="cuda", dtype=flow.float32, requires_grad=True - # ) - # ) - # flow_nchw_bias = flow.nn.Parameter( - # flow.tensor(np_bias, device="cuda", dtype=flow.float32, requires_grad=True) - # ) + flow_nchw_input = flow.tensor( + np_x, device="cuda", dtype=flow.float32, requires_grad=True + ) + flow_nchw_weights = flow.nn.Parameter( + flow.tensor( + np_weight, device="cuda", dtype=flow.float32, requires_grad=True + ) + ) + flow_nchw_bias = flow.nn.Parameter( + flow.tensor(np_bias, device="cuda", dtype=flow.float32, requires_grad=True) + ) - # flow_nchw_conv = flow.nn.Conv2d( - # in_channels=in_channels, - # out_channels=out_channels, - # kernel_size=kernel_size, - # stride=stride, - # padding=padding, - # dilation=dilation, - # ).to("cuda") - # flow_nchw_conv.weight = flow_nchw_weights - # flow_nchw_conv.bias = flow_nchw_bias + flow_nchw_conv = flow.nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ).to("cuda") + flow_nchw_conv.weight = flow_nchw_weights + flow_nchw_conv.bias = flow_nchw_bias - # flow_nchw_out = flow_nchw_conv(flow_nchw_input) + flow_nchw_out = flow_nchw_conv(flow_nchw_input) - # os.environ["ONEFLOW_ENABLE_NHWC"] = "1" - # flow_nhwc_input = flow.tensor( - # np_x, device="cuda", dtype=flow.float32, requires_grad=True - # ) - # flow_nhwc_permuted_input = flow.permute(flow_nhwc_input, (0, 2, 3, 1)) - # flow_nhwc_weights = flow.tensor( - # np_weight, device="cuda", dtype=flow.float32, requires_grad=True - # ) - # flow_nhwc_permuted_weights = flow.nn.Parameter( - # flow.permute(flow_nhwc_weights, (0, 2, 3, 1)) - # ) - # flow_nhwc_bias = flow.nn.Parameter( - # flow.tensor(np_bias, device="cuda", dtype=flow.float32, requires_grad=True) - # ) + os.environ["ONEFLOW_ENABLE_NHWC"] = "1" + flow_nhwc_input = flow.tensor( + np_x, device="cuda", dtype=flow.float32, requires_grad=True + ) + flow_nhwc_permuted_input = flow.permute(flow_nhwc_input, (0, 2, 3, 1)) + flow_nhwc_weights = flow.tensor( + np_weight, device="cuda", dtype=flow.float32, requires_grad=True + ) + flow_nhwc_permuted_weights = flow.nn.Parameter( + flow.permute(flow_nhwc_weights, (0, 2, 3, 1)) + ) + flow_nhwc_bias = flow.nn.Parameter( + flow.tensor(np_bias, device="cuda", dtype=flow.float32, requires_grad=True) + ) - # flow_nhwc_conv = flow.nn.Conv2d( - # in_channels=in_channels, - # out_channels=out_channels, - # kernel_size=kernel_size, - # stride=stride, - # padding=padding, - # dilation=dilation, - # ).to("cuda") - # flow_nhwc_conv.weight = flow_nhwc_permuted_weights - # flow_nhwc_conv.bias = flow_nhwc_bias + flow_nhwc_conv = flow.nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ).to("cuda") + flow_nhwc_conv.weight = flow_nhwc_permuted_weights + flow_nhwc_conv.bias = flow_nhwc_bias - # flow_nhwc_out = flow_nhwc_conv(flow_nhwc_permuted_input) - # flow_nhwc_permuted_out = flow.permute(flow_nhwc_out, (0, 3, 1, 2)) + flow_nhwc_out = flow_nhwc_conv(flow_nhwc_permuted_input) + flow_nhwc_permuted_out = flow.permute(flow_nhwc_out, (0, 3, 1, 2)) - # test_case.assertTrue( - # np.allclose( - # flow_nchw_out.numpy(), - # flow_nhwc_permuted_out.numpy(), - # rtol=1e-4, - # atol=1e-4, - # ) - # ) + test_case.assertTrue( + np.allclose( + flow_nchw_out.numpy(), + flow_nhwc_permuted_out.numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) - # total_out = flow_nchw_out + flow_nhwc_permuted_out + total_out = flow_nchw_out + flow_nhwc_permuted_out - # total_out = total_out.sum() - # total_out.backward() - # test_case.assertTrue( - # np.allclose( - # flow_nchw_weights.grad.numpy(), - # np.transpose(flow_nhwc_permuted_weights.grad.numpy(), (0, 3, 1, 2)), - # rtol=1e-4, - # atol=1e-4, - # ) - # ) - # test_case.assertTrue( - # np.allclose( - # flow_nchw_input.grad.numpy(), - # flow_nhwc_input.grad.numpy(), - # rtol=1e-4, - # atol=1e-4, - # ) - # ) - # os.environ["ONEFLOW_ENABLE_NHWC"] = "0" + total_out = total_out.sum() + total_out.backward() + test_case.assertTrue( + np.allclose( + flow_nchw_weights.grad.numpy(), + np.transpose(flow_nhwc_permuted_weights.grad.numpy(), (0, 3, 1, 2)), + rtol=1e-4, + atol=1e-4, + ) + ) + test_case.assertTrue( + np.allclose( + flow_nchw_input.grad.numpy(), + flow_nhwc_input.grad.numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) + os.environ["ONEFLOW_ENABLE_NHWC"] = "0" - # @profile(torch.nn.functional.conv2d) - # def profile_conv2d(test_case): - # input = torch.ones(8, 128, 28, 28) - # weight = torch.ones(128, 128, 3, 3) - # bias = torch.ones(128) - # torch.nn.functional.conv2d(input, weight, padding=1) - # torch.nn.functional.conv2d(input, weight, padding=1, stride=2) - # torch.nn.functional.conv2d(input, weight, bias=bias, padding=1) - # torch.nn.functional.conv2d(input, weight, bias=bias, padding=1, stride=2) + @profile(torch.nn.functional.conv2d) + def profile_conv2d(test_case): + input = torch.ones(8, 128, 28, 28) + weight_128c = torch.ones(128, 128, 3, 3) + weight_128c_2g = torch.ones(128, 64, 3, 3) + weight_1x1_128c = torch.ones(128, 128, 1, 1) + weight_5x5_128c = torch.ones(128, 128, 5, 5) + bias = torch.ones(128) + torch.nn.functional.conv2d(input, weight_128c, padding=1) + torch.nn.functional.conv2d(input, weight_128c_2g, groups=2, padding=1) + torch.nn.functional.conv2d(input, weight_128c, padding=1, stride=2) + torch.nn.functional.conv2d(input, weight_128c, bias=bias, padding=1) + torch.nn.functional.conv2d(input, weight_128c, bias=bias, padding=1, stride=2) + torch.nn.functional.conv2d(input, weight_1x1_128c) + torch.nn.functional.conv2d(input, weight_1x1_128c, stride=2) + torch.nn.functional.conv2d(input, weight_1x1_128c, bias=bias) + torch.nn.functional.conv2d(input, weight_1x1_128c, bias=bias, stride=2) + torch.nn.functional.conv2d(input, weight_5x5_128c, padding=2) + torch.nn.functional.conv2d(input, weight_5x5_128c, padding=2, stride=2) + torch.nn.functional.conv2d(input, weight_5x5_128c, bias=bias, padding=2) + torch.nn.functional.conv2d( + input, weight_5x5_128c, bias=bias, padding=2, stride=2 + ) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_copy.py b/python/oneflow/test/modules/test_copy.py new file mode 100644 index 0000000000000000000000000000000000000000..a12f0c64a493ef448730786a750719cb20a687ee --- /dev/null +++ b/python/oneflow/test/modules/test_copy.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch as ori_torch + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class Test_Copy_module(flow.unittest.TestCase): + def test_copy_broadcast_tensor(test_case): + torch_base_grid = ori_torch.zeros(1, 2, 2, 3) + flow_base_grid = flow.zeros(1, 2, 2, 3) + torch_x_grid = ori_torch.ones(2) + flow_x_grid = flow.ones(2) + torch_base_grid[..., 0].copy_(torch_x_grid) + flow_base_grid[..., 0].copy_(flow_x_grid) + test_case.assertTrue( + np.allclose(torch_base_grid.numpy(), flow_base_grid.numpy()) + ) + + def test_non_contiguous_sliced_tensor_copy(test_case): + torch_tensor = torch.arange(24, dtype=torch.float32).reshape(1, 2, 3, 4) + flow_tensor = flow.arange(24, dtype=flow.float32).reshape(1, 2, 3, 4) + torch_copy = torch.tensor([3.1415]) + flow_copy = flow.tensor([3.1415]) + torch_tensor[:, 1:2, 1:2, ::2].copy_(torch_copy) + flow_tensor[:, 1:2, 1:2, ::2].copy_(flow_copy) + test_case.assertTrue(np.allclose(flow_tensor.numpy(), torch_tensor.numpy())) + + def test_non_contiguous_permuted_tensor_copy(test_case): + torch_tensor = torch.arange(24, dtype=torch.float32).reshape(1, 2, 3, 4) + flow_tensor = flow.arange(24, dtype=flow.float32).reshape(1, 2, 3, 4) + torch_copy = torch.tensor([3.1415]) + flow_copy = flow.tensor([3.1415]) + torch_tensor.permute(0, 2, 1, 3).copy_(torch_copy) + flow_tensor.permute(0, 2, 1, 3).copy_(flow_copy) + test_case.assertTrue(np.allclose(flow_tensor.numpy(), torch_tensor.numpy())) + + def test_copy_fp16(test_case): + x = flow.tensor([1, 2], dtype=flow.float16) + a = np.array([0, 9], dtype=np.float16) + x.copy_(a) + test_case.assertTrue(np.array_equal(x.numpy(), a)) + + @flow.unittest.skip_unless_1n1d() + def test_tensor_inplace_copy_with_diff_dtype(test_case): + np_arr = np.random.randn(4, 12) + x = flow.tensor(np_arr) + y = flow.tensor(np_arr, dtype=flow.int) + y.copy_(x) + a = ori_torch.tensor(np_arr) + b = ori_torch.tensor(np_arr, dtype=ori_torch.int) + test_case.assertTrue(np.array_equal(y.numpy(), b.cpu().numpy())) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_cosine_similarity.py b/python/oneflow/test/modules/test_cosine_similarity.py index 4aece7403dcf2ac807c5f96643cf85c8ffda2182..9665dafa1ad08d57135acc45fd723069a15c21c3 100644 --- a/python/oneflow/test/modules/test_cosine_similarity.py +++ b/python/oneflow/test/modules/test_cosine_similarity.py @@ -49,6 +49,26 @@ class TestCosineSimilarity(flow.unittest.TestCase): output = torch.nn.functional.cosine_similarity(a, b, dim=1, eps=1e-6) return output + @autotest(n=3) + def test_cosine_similartiy_module_with_nonequal_dim_data(test_case): + device = random_device() + a = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + b = random_tensor(ndim=3, dim0=10, dim1=10, dim2=128).to(device) + cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6).to(device) + cos.train(random()) + output = cos(a, b) + return output + + @unittest.skip( + reason="https://github.com/Oneflow-Inc/oneflow/issues/8881#issuecomment-1229682453" + ) + @profile(torch.nn.functional.cosine_similarity) + def profile_cosine_similarity(test_case): + input1 = torch.ones(100, 128) + input2 = torch.ones(100, 128) + torch.nn.functional.cosine_similarity(input1, input2) + torch.nn.functional.cosine_similarity(input1, input2, dim=0) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_ctc_loss.py b/python/oneflow/test/modules/test_ctc_loss.py index 3799e71624461d1de7c504647d78f0ddf351cdac..53ece9013582b7662f6adcb2540dfe6e48612521 100644 --- a/python/oneflow/test/modules/test_ctc_loss.py +++ b/python/oneflow/test/modules/test_ctc_loss.py @@ -180,6 +180,7 @@ def compare_with_np( device_type, device_num, data_type, + target_dtype, max_input_length, batch_size, num_classes, @@ -188,13 +189,13 @@ def compare_with_np( reduction, zero_infinity, ): - assert data_type in ["float32", "double"] + assert data_type in [flow.float32, flow.double] assert device_type in ["cuda", "cpu"] assert reduction in ["none", "mean", "sum"] assert zero_infinity in [False, True] log_probs = np.random.random( size=(max_input_length, batch_size, num_classes) - ).astype(np.float32) + ).astype(flow.convert_oneflow_dtype_to_numpy_dtype(data_type)) log_probs = log_softmax(log_probs, axis=2) targets = np.random.randint( 1, high=num_classes, size=(batch_size, max_target_length), dtype=np.int32 @@ -239,13 +240,13 @@ def compare_with_np( blank=blank, reduction=reduction, zero_infinity=zero_infinity ) log_probs = flow.tensor( - log_probs, - dtype=flow.float32, - requires_grad=True, - device=flow.device(device_type), + log_probs, dtype=data_type, requires_grad=True, device=flow.device(device_type), ) targets = flow.tensor( - targets, dtype=flow.int32, requires_grad=False, device=flow.device(device_type) + targets, + dtype=target_dtype, + requires_grad=False, + device=flow.device(device_type), ) input_lengths = flow.tensor( input_lengths, @@ -271,7 +272,8 @@ def gen_arg_list(): arg_dict = OrderedDict() arg_dict["device_type"] = ["cuda", "cpu"] arg_dict["device_num"] = [1] - arg_dict["data_type"] = ["float32"] + arg_dict["data_type"] = [flow.float32, flow.double] + arg_dict["target_dtype"] = [flow.float32, flow.double, flow.int32, flow.int64] arg_dict["max_input_length"] = [20] arg_dict["batch_size"] = [4] arg_dict["num_classes"] = [5] diff --git a/python/oneflow/test/modules/test_cum_ops.py b/python/oneflow/test/modules/test_cum_ops.py index 2088440a2926aec88fc812679d95ba5414a6e7a8..a7ffd5534a261305beac03f23cf075f08a2094d7 100644 --- a/python/oneflow/test/modules/test_cum_ops.py +++ b/python/oneflow/test/modules/test_cum_ops.py @@ -89,6 +89,48 @@ class TestCumOp(flow.unittest.TestCase): ) ) + def test_cumsum_graph_backward(test_case): + class CustomizedModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.layer = flow.nn.Linear(5, 5) + + def forward(self, input): + layer_out = self.layer(input) + loss = flow.cumsum(layer_out, -1) + loss = loss.sum() + loss.backward() + return loss + + class TestCumsum(flow.nn.Graph): + def __init__(self) -> None: + super().__init__() + self.my_module = CustomizedModule() + self.add_optimizer( + flow.optim.SGD(self.my_module.parameters(), lr=0.1, momentum=0.0) + ) + + def build(self, ids): + loss = self.my_module(ids) + return loss + + ids = np.random.randint(0, 10, (5, 5), dtype=np.int64) + ids_tensor = flow.tensor(ids, dtype=flow.float, requires_grad=False) + graph = TestCumsum() + loss = graph(ids_tensor) + + @profile(torch.cumsum) + def profile_cumsum(test_case): + input = torch.ones(100, 1280) + torch.cumsum(input, dim=0) + torch.cumsum(input, dim=1) + + @profile(torch.cumprod) + def profile_cumprod(test_case): + input = torch.ones(100, 1280) + torch.cumprod(input, dim=0) + torch.cumprod(input, dim=1) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_dataset.py b/python/oneflow/test/modules/test_dataset.py index f6562e7787eb85ba568f2d96901671257a4f8577..fb1dbfbc13b753635e2b709f28574b3c4fd22f81 100644 --- a/python/oneflow/test/modules/test_dataset.py +++ b/python/oneflow/test/modules/test_dataset.py @@ -87,7 +87,7 @@ class TestOFRecordModule(flow.unittest.TestCase): @flow.unittest.skip_unless_1n1d() -class TestConsistentOFRecordModule(flow.unittest.TestCase): +class TestGlobalOFRecordModule(flow.unittest.TestCase): def test_global_record(test_case): batch_size = 1 color_space = "RGB" diff --git a/python/oneflow/test/modules/test_ddp.py b/python/oneflow/test/modules/test_ddp.py index 2a5307236c0662e173289c26eb980a0d2207be4c..5b64e1e1e18a72b597a2e9f58c52eb2b00b8878a 100644 --- a/python/oneflow/test/modules/test_ddp.py +++ b/python/oneflow/test/modules/test_ddp.py @@ -15,6 +15,9 @@ limitations under the License. """ import unittest import oneflow as flow + +# Test import from oneflow.nn.parallel.distributed +from oneflow.nn.parallel.distributed import DistributedDataParallel from oneflow.nn.parallel import DistributedDataParallel as ddp import oneflow.unittest diff --git a/python/oneflow/test/modules/test_deconv2d.py b/python/oneflow/test/modules/test_deconv2d.py index bc69915861bf980c226e4640709bf7f43b0d2509..bb70a1880f24c7b39d2975a15183f5770c986b22 100644 --- a/python/oneflow/test/modules/test_deconv2d.py +++ b/python/oneflow/test/modules/test_deconv2d.py @@ -937,6 +937,36 @@ class TestDeconv2d(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.conv_transpose2d) + def profile_conv_transpose2d(test_case): + inputs = torch.ones(16, 128, 128, 128) + weights_4x4_64c = torch.ones(128, 64, 4, 4) + weights_6x6_64c = torch.ones(128, 64, 6, 6) + weights_8x8_64c = torch.ones(128, 64, 8, 8) + torch.nn.functional.conv_transpose2d( + inputs, weights_4x4_64c, stride=2, padding=1 + ) + torch.nn.functional.conv_transpose2d( + inputs, weights_4x4_64c, stride=2, padding=1, bias=torch.ones(64) + ) + torch.nn.functional.conv_transpose2d( + inputs, weights_6x6_64c, stride=3, padding=2, output_padding=1 + ) + torch.nn.functional.conv_transpose2d( + inputs, + weights_6x6_64c, + stride=3, + padding=2, + bias=torch.ones(64), + output_padding=1, + ) + torch.nn.functional.conv_transpose2d( + inputs, weights_8x8_64c, stride=4, padding=2 + ) + torch.nn.functional.conv_transpose2d( + inputs, weights_8x8_64c, stride=4, padding=2, bias=torch.ones(64) + ) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_default_dtype.py b/python/oneflow/test/modules/test_default_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..247788d5eb3ae1a4551fefaca864f09dab26454f --- /dev/null +++ b/python/oneflow/test/modules/test_default_dtype.py @@ -0,0 +1,102 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest + + +_source_op_list = [ + flow.ones, + flow.zeros, + flow.rand, + flow.randn, + flow.empty, + flow.Tensor, +] + + +class TestDefaultDTypeInferface(oneflow.unittest.TestCase): + def test_set_default_dtype(test_case): + flow.set_default_dtype(flow.float32) + test_case.assertEqual(flow.get_default_dtype(), flow.float32) + + flow.set_default_dtype(flow.float64) + test_case.assertEqual(flow.get_default_dtype(), flow.float64) + + for op in _source_op_list: + x = op((2, 3)) + test_case.assertEqual(x.dtype, flow.float64) + x = op(2, 3) + test_case.assertEqual(x.dtype, flow.float64) + + with test_case.assertRaises(Exception) as ctx: + flow.set_default_dtype(flow.int32) + test_case.assertTrue( + "only floating-point types are supported as the default type" + in str(ctx.exception) + ) + + def test_set_default_tensor_type(test_case): + flow.set_default_dtype(flow.float32) + test_case.assertEqual(flow.get_default_dtype(), flow.float32) + + # set default tensor type by TensorType + flow.set_default_tensor_type(flow.DoubleTensor) + test_case.assertEqual(flow.get_default_dtype(), flow.float64) + for op in _source_op_list: + x = op((2, 3)) + test_case.assertEqual(x.dtype, flow.float64) + x = op(2, 3) + test_case.assertEqual(x.dtype, flow.float64) + + # set default tensor type by TensorType string + flow.set_default_tensor_type("oneflow.FloatTensor") + test_case.assertEqual(flow.get_default_dtype(), flow.float32) + for op in _source_op_list: + x = op((2, 3)) + test_case.assertEqual(x.dtype, flow.float32) + + def test_behavior_for_oneflow_tensor(test_case): + # float32 scope + flow.set_default_dtype(flow.float32) + test_case.assertEqual(flow.get_default_dtype(), flow.float32) + + x = flow.tensor([1.0, 2]) + test_case.assertEqual(x.dtype, flow.float32) + + # float64 scope + flow.set_default_dtype(flow.float64) + test_case.assertEqual(flow.get_default_dtype(), flow.float64) + + x = flow.tensor([1.0, 2]) + test_case.assertEqual(x.dtype, flow.float64) + + # no affect for int type + x = flow.tensor((2, 3)) + test_case.assertEqual(x.dtype, flow.int64) + + # no affect for numpy array input + nd_arr = np.array([1, 2, 3]).astype(np.float32) + x = flow.tensor(nd_arr) + test_case.assertEqual(x.dtype, flow.float32) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_deform_conv2d.py b/python/oneflow/test/modules/test_deform_conv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..f41d026be03ed2f81dff6c41c3cb028a9ab6c26d --- /dev/null +++ b/python/oneflow/test/modules/test_deform_conv2d.py @@ -0,0 +1,253 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np +import torchvision.ops +import torch + +import oneflow as flow +from oneflow.test_utils.automated_test_util.torch_flow_dual_object import random_tensor +from oneflow.test_utils.test_util import GenArgList +import oneflow.unittest + + +def GetRandomData(max_batch_sz): + batch_sz = max_batch_sz + n_weight_grps = np.random.randint(1, 2) + n_offset_grps = np.random.randint(1, 2) + n_out_channels = n_offset_grps * np.random.randint(1, 15) + n_in_channels = n_offset_grps * np.random.randint(1, 15) + + random_stride_h = np.random.randint(1, 5) + random_stride_w = np.random.randint(1, 5) + random_pad_h = np.random.randint(0, 3) + random_pad_w = np.random.randint(0, 3) + random_dilation_h = np.random.randint(1, 3) + random_dilation_w = np.random.randint(1, 3) + random_in_h = np.random.randint(5, 30) + random_in_w = np.random.randint(5, 30) + + # BUG(yzm): Now use the rectangular convolution kernel is not aligned with PyTorch + # NOTE: Modify the following program after alignment using a rectangular convolution kernel + random_kernel_h = np.random.randint(1, 11) + random_kernel_w = random_kernel_h + # random_kernel_w=np.random.randint(1, 11) + + stride = (random_stride_h, random_stride_w) + pad = (random_pad_h, random_pad_w) + dilation = (random_dilation_h, random_dilation_w) + + return ( + batch_sz, + n_out_channels, + n_in_channels, + n_weight_grps, + n_offset_grps, + stride, + pad, + dilation, + random_kernel_h, + random_kernel_w, + random_in_h, + random_in_w, + ) + + +def GetFunArgs(device, max_batch_size): + out_w = 0 + out_h = 0 + while out_w <= 0 or out_h <= 0: + ( + batch_sz, + n_out_channels, + n_in_channels, + n_weight_grps, + n_offset_grps, + stride, + pad, + dilation, + random_kernel_h, + random_kernel_w, + random_in_h, + random_in_w, + ) = GetRandomData(max_batch_size) + stride_h, stride_w = stride + pad_h, pad_w = pad + dil_h, dil_w = dilation + weight_h, weight_w = (random_kernel_h, random_kernel_w) + in_h, in_w = (random_in_h, random_in_w) + out_h = (in_h + 2 * pad_h - (dil_h * (weight_h - 1) + 1)) // stride_h + 1 + out_w = (in_w + 2 * pad_w - (dil_w * (weight_w - 1) + 1)) // stride_w + 1 + + input_dims = [batch_sz, n_in_channels, in_h, in_w] + offset_dims = [batch_sz, 2 * n_offset_grps * weight_h * weight_w, out_h, out_w] + mask_dims = [batch_sz, n_offset_grps * weight_h * weight_w, out_h, out_w] + weight_dims = [n_out_channels, n_in_channels // n_weight_grps, weight_h, weight_w] + + input = random_tensor(4, *input_dims).to(device) + offset = random_tensor(4, *offset_dims).to(device) + mask = random_tensor(4, *mask_dims).to(device) + weight = random_tensor(4, *weight_dims).to(device) + bias_dims = [n_out_channels] + bias = random_tensor(1, *bias_dims).to(device) + return input, weight, offset, mask, bias, stride, pad, dilation + + +def _test_deform_conv2d_forward( + test_case, input, weight, offset, mask, bias, stride, padding, dilation, +): + torch_input = input.pytorch + torch_weight = weight.pytorch + torch_offset = offset.pytorch + torch_mask = mask.pytorch + torch_bias = bias.pytorch + + torch_out = torchvision.ops.deform_conv2d( + torch_input, + torch_offset, + torch_weight, + stride=stride, + padding=padding, + dilation=dilation, + mask=torch_mask, + bias=torch_bias, + ) + + flow_input = input.oneflow + flow_weight = weight.oneflow + flow_offset = offset.oneflow + flow_mask = mask.oneflow + flow_bias = bias.oneflow + + flow_out = oneflow.nn.functional.deform_conv2d( + flow_input, + flow_offset, + flow_weight, + stride=stride, + padding=padding, + dilation=dilation, + mask=flow_mask, + bias=flow_bias, + ) + test_case.assertTrue( + np.allclose( + flow_out.numpy(), torch_out.detach().cpu().numpy(), rtol=1e-5, atol=1e-5 + ) + ) + + +def _test_deform_conv2d_backward( + test_case, input, weight, offset, mask, bias, stride, padding, dilation +): + torch_input = input.pytorch.detach().requires_grad_() + torch_weight = weight.pytorch.detach().requires_grad_() + torch_offset = offset.pytorch.detach().requires_grad_() + torch_mask = mask.pytorch.detach().requires_grad_() + torch_bias = bias.pytorch.detach().requires_grad_() + + torch_out = torchvision.ops.deform_conv2d( + torch_input, + torch_offset, + torch_weight, + stride=stride, + padding=padding, + dilation=dilation, + mask=torch_mask, + bias=torch_bias, + ) + torch_out.sum().backward() + + flow_input = input.oneflow.detach().requires_grad_() + flow_weight = weight.oneflow.detach().requires_grad_() + flow_offset = offset.oneflow.detach().requires_grad_() + flow_mask = mask.oneflow.detach().requires_grad_() + flow_bias = bias.oneflow.detach().requires_grad_() + + flow_out = oneflow.nn.functional.deform_conv2d( + flow_input, + flow_offset, + flow_weight, + stride=stride, + padding=padding, + dilation=dilation, + mask=flow_mask, + bias=flow_bias, + ) + flow_out.sum().backward() + test_case.assertTrue( + np.allclose( + flow_input.grad.numpy(), + torch_input.grad.cpu().numpy(), + rtol=1e-5, + atol=1e-5, + ) + ) + test_case.assertTrue( + np.allclose( + flow_weight.grad.numpy(), + torch_weight.grad.cpu().numpy(), + rtol=1e-5, + atol=1e-5, + ) + ) + test_case.assertTrue( + np.allclose( + flow_offset.grad.numpy(), + torch_offset.grad.cpu().numpy(), + rtol=1e-5, + atol=1e-5, + ) + ) + test_case.assertTrue( + np.allclose( + flow_mask.grad.numpy(), torch_mask.grad.cpu().numpy(), rtol=1e-5, atol=1e-5 + ) + ) + test_case.assertTrue( + np.allclose( + flow_bias.grad.numpy(), torch_bias.grad.cpu().numpy(), rtol=1e-5, atol=1e-5 + ) + ) + + +def _test_forward_and_backward(test_case, device): + max_batch_size = 40 + for batch_size in range(1, max_batch_size): + input, weight, offset, mask, bias, stride, padding, dilation = GetFunArgs( + device, batch_size + ) + _test_deform_conv2d_forward( + test_case, input, weight, offset, mask, bias, stride, padding, dilation + ) + _test_deform_conv2d_backward( + test_case, input, weight, offset, mask, bias, stride, padding, dilation + ) + + +@flow.unittest.skip_unless_1n1d() +class TestDeformConv2d(flow.unittest.TestCase): + def test_deform_conv2d(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_forward_and_backward] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_diag.py b/python/oneflow/test/modules/test_diag.py index ce6df372a89adb8a017aec4cc59861cfddcda201..ff8fd8dc7fb088c2c5daea1b2686c6b32c47c82a 100644 --- a/python/oneflow/test/modules/test_diag.py +++ b/python/oneflow/test/modules/test_diag.py @@ -18,34 +18,55 @@ import unittest from collections import OrderedDict import numpy as np -from oneflow.test_utils.test_util import GenArgList +import torch as ori_torch import oneflow as flow import oneflow.unittest - from oneflow.test_utils.automated_test_util import * @flow.unittest.skip_unless_1n1d() class Test_Diag_module(flow.unittest.TestCase): - @autotest(check_graph=True) + @autotest(n=5) def test_diag_one_dim(test_case): device = random_device() x = random_tensor(ndim=1, dim0=random()).to(device) return torch.diag(x) - @autotest(check_graph=True) + @autotest(n=5) def test_diag_other_dim(test_case): device = random_device() x = random_tensor(ndim=2, dim0=random(), dim1=random()).to(device) return torch.diag(x) - @autotest(auto_backward=False, check_graph=True) + @autotest(auto_backward=False) def test_diag_one_dim(test_case): device = random_device() x = random_tensor(ndim=1, dim0=random()).to(device, torch.bool) return torch.diag(x) + def test_diag_0size_tensor(test_case): + torch_tensor = ori_torch.empty(0).diag() + flow_tensor = flow.empty(0).diag() + test_case.assertTrue( + np.array_equal(list(torch_tensor.shape), list(flow_tensor.shape)) + ) + torch_tensor = ori_torch.empty(0, 0).diag() + flow_tensor = flow.empty(0, 0).diag() + test_case.assertTrue( + np.array_equal(list(torch_tensor.shape), list(flow_tensor.shape)) + ) + torch_tensor = ori_torch.empty(0, 3).diag() + flow_tensor = flow.empty(0, 3).diag() + test_case.assertTrue( + np.array_equal(list(torch_tensor.shape), list(flow_tensor.shape)) + ) + + @profile(torch.diag) + def profile_diag(test_case): + torch.diag(torch.ones(1000)) + torch.diag(torch.ones(128, 128)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_diagonal.py b/python/oneflow/test/modules/test_diagonal.py index b3b67d66c8a83696c2eb31e2e2acdb47ba8fd903..9ab645d6927f5f0c7ddfd8245868856c7a90b286 100644 --- a/python/oneflow/test/modules/test_diagonal.py +++ b/python/oneflow/test/modules/test_diagonal.py @@ -57,6 +57,14 @@ class TestDiagonal(flow.unittest.TestCase): z = torch.diagonal(x, offset, dim1, dim2) return z + @profile(torch.diagonal) + def profile_diagonal(test_case): + input1 = torch.ones(128, 128) + input2 = torch.ones(16, 10, 128, 128) + torch.diagonal(input1, 0) + torch.diagonal(input1, 1) + torch.diagonal(input2, offset=-1, dim1=1, dim2=2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_div.py b/python/oneflow/test/modules/test_div.py index 98422e7be1ec374d439cc19bb84d73c8fff4dee7..21f86e2bf878cd6965f2361127a085f064b4575e 100644 --- a/python/oneflow/test/modules/test_div.py +++ b/python/oneflow/test/modules/test_div.py @@ -134,6 +134,37 @@ class TestDiv(flow.unittest.TestCase): z = x / y return z + @autotest(n=3) + def test_non_contiguous_inplace_div(test_case): + device = random_device() + x = random_tensor(2, 2, 4).to(device) + y = x + 1 + y = y[:, 1:3] + y /= random_tensor(2, 2, 2).to(device) + return y + + @autotest(n=3, check_graph=False) + def test_int_dtype_inplace_div(test_case): + num_elems = 20 + flow_out = flow.arange(num_elems) / num_elems + torch_out = torch.arange(num_elems) / num_elems + test_case.assertTrue(np.allclose(flow_out.numpy(), torch_out.numpy())) + + @autotest(n=5) + def test_scalar_div_with_random_devices(test_case): + x1_device = random_device() + x2_device = random_device() + x1 = random_tensor(2, 2, 3).to(x1_device).mean() + x2 = random_tensor(2, 2, 3).to(x2_device) + y = x1 / x2 + return y + + @profile(torch.div) + def profile_div(test_case): + input1 = torch.ones(16, 10, 128, 128) + input2 = torch.ones(16, 10, 128, 128) + torch.div(input1, input2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_dlpack.py b/python/oneflow/test/modules/test_dlpack.py new file mode 100644 index 0000000000000000000000000000000000000000..a970276377dd0d9943928efa039fb2c606bb631d --- /dev/null +++ b/python/oneflow/test/modules/test_dlpack.py @@ -0,0 +1,146 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import random +import unittest +import os + +import torch +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenCartesianProduct + + +test_device_args = ( + [("cpu",)] + if os.getenv("ONEFLOW_TEST_CPU_ONLY") + else [("cpu",), ("cuda", 0), ("cuda", 1)] +) +test_args = list( + GenCartesianProduct((test_device_args, [(torch, flow), (flow, torch)])) +) + + +def are_tensors_equal(a, b): + def are_devices_equal(a, b): + if a.type == "cuda" and b.type == "cuda": + return a.index == b.index + else: + return a.type == b.type + + return ( + np.array_equal(a.cpu().numpy(), b.cpu().numpy()) + and are_devices_equal(a.device, b.device) + and a.shape == b.shape + and a.stride() == b.stride() + and a.cpu().numpy().dtype == b.cpu().numpy().dtype + ) + + +@flow.unittest.skip_unless_1n2d() +class TestPack(flow.unittest.TestCase): + def test_same_data(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.randn(3, 4, 5, device=m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + test_case.assertEqual(tensor2.storage_offset(), 0) + + tensor2[1:2, 2:3, 3:4] = random.random() + # NOTE: OneFlow operations are asynchoronously executed, + # so we need to synchronize explicitly here. + flow._oneflow_internal.eager.Sync() + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + def test_use_ops(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.randn(3, 4, 5, device=m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + res1 = tensor1 ** 2 + res2 = tensor2 ** 2 + test_case.assertTrue(np.allclose(res1.cpu().numpy(), res2.cpu().numpy())) + + def test_more_dtype(test_case): + # PyTorch bfloat16 tensor doesn't support .numpy() method + # so we can't test it + # torch.bfloat16, flow.bfloat16 + dtypes = ["float64", "float32", "float16", "int64", "int32", "int8", "uint8"] + + for device_args, (m1, m2) in test_args: + for dtype in dtypes: + tensor1 = m1.ones( + (2, 3), dtype=getattr(m1, dtype), device=m1.device(*device_args) + ) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + def test_non_contiguous_input(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = ( + m1.randn(2, 3, 4, 5).permute(2, 0, 3, 1).to(m1.device(*device_args)) + ) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + def test_scalar_tensor(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.tensor(5).to(m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + def test_0_size_tensor(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.tensor([]).to(m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + def test_lifecycle(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.randn(2, 3, 4, 5).to(m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + value = tensor1.cpu().numpy() + del tensor2 + if device_args[0] == "cuda": + m2.cuda.synchronize() + # actually release the cuda memory + m2.cuda.empty_cache() + test_case.assertTrue(np.array_equal(tensor1.cpu().numpy(), value)) + + tensor1 = m1.randn(2, 3, 4, 5).to(m1.device(*device_args)) + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + value = tensor2.cpu().numpy() + del tensor1 + if device_args[0] == "cuda": + m1.cuda.synchronize() + m1.cuda.empty_cache() + test_case.assertTrue(np.array_equal(tensor2.cpu().numpy(), value)) + + def test_subview(test_case): + for device_args, (m1, m2) in test_args: + tensor1 = m1.randn(3, 4, 5, device=m1.device(*device_args)) + tensor1 = tensor1[1:, :, ::2] + tensor2 = m2.from_dlpack(m1.to_dlpack(tensor1)) + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + test_case.assertEqual(tensor2.storage_offset(), 0) + + tensor2[1:2, ::2, 3:4] = random.random() + test_case.assertTrue(are_tensors_equal(tensor1, tensor2)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_dot.py b/python/oneflow/test/modules/test_dot.py index 331b62c578e07c8e036eff5b1ef29a458a7831ee..cb360a0418c52862b1a737ddd61322afe97d491d 100644 --- a/python/oneflow/test/modules/test_dot.py +++ b/python/oneflow/test/modules/test_dot.py @@ -31,6 +31,12 @@ class TestDot(flow.unittest.TestCase): z = torch.dot(x, y) return z + @profile(torch.dot) + def profile_dot(test_case): + input1 = torch.ones(10000) + input2 = torch.ones(10000) + torch.dot(input1, input2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_dropout.py b/python/oneflow/test/modules/test_dropout.py index 8badc4b42b4d841d40f9009598f1d32396f4d2c2..d15b2dd3351d03fcfd15cc7f486e1882e13c5e19 100644 --- a/python/oneflow/test/modules/test_dropout.py +++ b/python/oneflow/test/modules/test_dropout.py @@ -239,7 +239,7 @@ class TestModule(flow.unittest.TestCase): def test_dropout_numpy_case(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [do_test_dropout_numpy_p0, do_test_dropout_numpy_p1] - arg_dict["shape"] = [[4, 127, 256], [2, 1024, 1024]] + arg_dict["shape"] = [[4], [4, 3], [4, 127, 256], [2, 1024, 1024]] arg_dict["device"] = ["cuda"] if os.getenv("ONEFLOW_TEST_CPU_ONLY"): arg_dict["device"] = ["cpu"] @@ -298,21 +298,81 @@ class TestModule(flow.unittest.TestCase): for arg in GenArgList(arg_dict): arg[0](test_case) - @autotest() + @autotest(n=5) def autotest_dropout_p0(test_case): device = random_device() x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) m = torch.nn.Dropout(p=0, inplace=random_bool()) return m(x) - @autotest() + @autotest(n=5) + def autotest_dropout1d_p0(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout1d(p=0, inplace=random_bool()) + return m(x) + + @autotest(n=5) + def autotest_dropout2d_p0(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout2d(p=0, inplace=random_bool()) + return m(x) + + @autotest(n=5) + def autotest_dropout3d_p0(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout3d(p=0, inplace=random_bool()) + return m(x) + + @autotest(n=5) def autotest_dropout_p1(test_case): device = random_device() x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) m = torch.nn.Dropout(p=1.0, inplace=random_bool()) return m(x) - @autotest() + @autotest(n=5) + def autotest_dropout1d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout1d(p=1.0, inplace=random_bool()) + return m(x) + + @autotest(n=5) + def autotest_dropout2d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout2d(p=1.0, inplace=random_bool()) + return m(x) + + @autotest(n=5) + def autotest_dropout3d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout3d(p=1.0, inplace=random_bool()) + return m(x) + + @autotest(n=5) + def autotest_functional_dropout1d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + return torch.nn.functional.dropout1d(x, p=1.0) + + @autotest(n=5) + def autotest_functional_dropout2d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + return torch.nn.functional.dropout2d(x, p=1.0) + + @autotest(n=5) + def autotest_functional_dropout3d_p1(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + return torch.nn.functional.dropout3d(x, p=1.0) + + @autotest(n=5) def autotest_dropout_eval(test_case): device = random_device() x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) @@ -320,7 +380,31 @@ class TestModule(flow.unittest.TestCase): m.eval() return m(x) - @autotest() + @autotest(n=5) + def autotest_dropout1d_eval(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout1d(p=1.0, inplace=random_bool()) + m.eval() + return m(x) + + @autotest(n=5) + def autotest_dropout2d_eval(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout2d(p=1.0, inplace=random_bool()) + m.eval() + return m(x) + + @autotest(n=5) + def autotest_dropout3d_eval(test_case): + device = random_device() + x = random_tensor(ndim=random(), dim0=random(1, 8)).to(device) + m = torch.nn.Dropout3d(p=1.0, inplace=random_bool()) + m.eval() + return m(x) + + @autotest(n=5) def autotest_0dim_dropout_eval(test_case): device = random_device() x = random_tensor(ndim=0).to(device) @@ -328,6 +412,11 @@ class TestModule(flow.unittest.TestCase): m.eval() return m(x) + @profile(torch.nn.functional.dropout) + def profile_dropout(test_case): + input = torch.ones(100, 128) + torch.nn.functional.dropout(input, p=0.3) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() diff --git a/python/oneflow/test/modules/test_dynamic_allocation_gradient_shuffle_shuffle_global.py b/python/oneflow/test/modules/test_dynamic_allocation_gradient_shuffle_shuffle_global.py new file mode 100644 index 0000000000000000000000000000000000000000..044a85c24782b073d8e1d0c723fc982a147c4762 --- /dev/null +++ b/python/oneflow/test/modules/test_dynamic_allocation_gradient_shuffle_shuffle_global.py @@ -0,0 +1,191 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "1" +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict +import numpy as np +import oneflow as flow + +parallel_num = 2 +max_id = 1000 + + +def get_tensors(batch_size, num_tables): + placement = flow.placement(type="cuda", ranks=list(range(parallel_num))) + ids = np.random.randint(0, max_id, (batch_size, num_tables), dtype=np.int64) + ids_tensor = flow.tensor(ids, requires_grad=False).to_global( + placement=placement, sbp=flow.sbp.split(0) + ) + table_ids = ( + ids % num_tables + ) # same id must have same table id, so in this case get table_ids from ids + table_ids_tensor = flow.tensor( + table_ids.astype(np.int32), requires_grad=False + ).to_global(placement=placement, sbp=flow.sbp.split(0)) + return ids_tensor, table_ids_tensor + + +def round_half_away_from_zero(x): + sign = np.sign(x) + abs_val = np.abs(x) + abs_val += 0.5 + floor_val = np.floor(abs_val) + out = floor_val * sign + return out + + +def _test_embedding_gradient_shuffle(test_case, enable_quantize, fp16, embedding_size): + np_tolerance = 0 + batch_size = int(1024 / parallel_num) + placement = flow.placement(type="cuda", ranks=list(range(parallel_num))) + num_tables = 26 + enable_quantized_comm = enable_quantize and embedding_size < 1025 + if enable_quantized_comm: + np_tolerance = 0.5 + os.environ["ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM"] = "1" + else: + if fp16: + np_tolerance = 1e-2 + else: + np_tolerance = 1e-4 + os.environ["ONEFLOW_ONE_EMBEDDING_ENABLE_QUANTIZED_COMM"] = "0" + embedding_grad = np.random.rand(batch_size, num_tables, embedding_size).astype( + np.float32 + ) + embedding_grad_tensor = flow.tensor(embedding_grad, requires_grad=False).to_global( + placement=placement, sbp=flow.sbp.split(0) + ) + + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, ids, table_ids, embedding_grad): + ( + num_unique_matrix, + inverse_unique_partition_indices, + cur_rank_num_unique, + cur_rank_unique_ids, + _, + cur_rank_inverse_indices, + ) = flow._C.one_embedding_id_shuffle(ids, table_ids, num_tables, "test") + if fp16: + embedding_grad = flow.cast(embedding_grad, flow.float16) + cur_rank_unique_embedding_grad = flow._C.one_embedding_embedding_gradient_shuffle( + embedding_grad, + num_unique_matrix, + cur_rank_inverse_indices, + inverse_unique_partition_indices, + "test", + ) + if fp16: + cur_rank_unique_embedding_grad = flow.cast( + cur_rank_unique_embedding_grad, flow.float32 + ) + return ( + cur_rank_unique_embedding_grad, + flow.cast(cur_rank_num_unique, flow.int32), + cur_rank_unique_ids, + ) + + graph = TestGraph() + for i in range(10): + ids_tensor, table_ids_tensor = get_tensors(batch_size, num_tables) + graph(ids_tensor, table_ids_tensor, embedding_grad_tensor) + ids_tensor, table_ids_tensor = get_tensors(batch_size, num_tables) + ( + cur_rank_unique_embedding_grad, + local_cur_rank_num_unique, + cur_rank_unique_ids, + ) = graph(ids_tensor, table_ids_tensor, embedding_grad_tensor) + cur_rank_num_unique = local_cur_rank_num_unique.to_local().to_global( + placement=placement, sbp=flow.sbp.split(0) + ) + global_ids = ids_tensor.numpy() + global_embedding_grad = embedding_grad_tensor.numpy() + np_unique_ids = np.unique(global_ids) + np_num_unique = np_unique_ids.size + np_cur_rank_unique_embedding_grad = np.zeros((max_id, embedding_size)) + if fp16: + global_embedding_grad = global_embedding_grad.astype(np.float16) + for k in range(np_num_unique): + unique_id = np_unique_ids[k] + np_data = sum( + global_embedding_grad.reshape(-1, embedding_size)[ + np.where(global_ids.flatten() == unique_id)[0] + ] + ) + # Quantize Embedding Gradient. + if enable_quantized_comm: + abs_max_factor = np.max(np.abs(np_data)) + int8_factor = np.full(abs_max_factor.shape, 127.0, dtype=np.float32) + quantize_factor = int8_factor / abs_max_factor + np_data = np_data * quantize_factor + np_data = round_half_away_from_zero(np_data) + np_data = np_data.astype(np.int8) + np_data = np_data.astype(np.float32) + dequantize_factor = abs_max_factor / int8_factor + np_data = np_data * dequantize_factor + + np_cur_rank_unique_embedding_grad[unique_id, :] = np_data + if fp16: + np_cur_rank_unique_embedding_grad = np_cur_rank_unique_embedding_grad.astype( + np.float32 + ) + + cur_rank_num_ids = batch_size * num_tables * parallel_num + of_unique_embedding_grad = np.zeros((max_id, embedding_size)) + for i in range(parallel_num): + num_unique_i = cur_rank_num_unique.numpy()[i] + unique_ids_i = cur_rank_unique_ids.numpy()[ + cur_rank_num_ids * i : cur_rank_num_ids * (i + 1) + ] + unique_embedding_grad_i = cur_rank_unique_embedding_grad.numpy()[ + cur_rank_num_ids * i : cur_rank_num_ids * (i + 1) + ] + for j in range(num_unique_i): + unique_id = unique_ids_i[j] + of_unique_embedding_grad[unique_id, :] = unique_embedding_grad_i[j, :] + + test_case.assertTrue( + np.allclose( + of_unique_embedding_grad, + np_cur_rank_unique_embedding_grad, + atol=np_tolerance, + rtol=np_tolerance, + ), + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class DataShuffleTestCase(flow.unittest.TestCase): + def test_embedding_gradient_shuffle(test_case): + arg_dict = OrderedDict() + arg_dict["enable_quantize"] = [True, False] + arg_dict["fp16"] = [True, False] + arg_dict["embedding_size"] = [128, 17] + for kwargs in GenArgDict(arg_dict): + _test_embedding_gradient_shuffle(test_case, **kwargs) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_eager_boxing.py b/python/oneflow/test/modules/test_eager_boxing.py index 284501400716c0a0fc08e1d7de63081e1ac1ba68..c1ca4fc291c1967aa06a1803a7f1ab81062eeaba 100644 --- a/python/oneflow/test/modules/test_eager_boxing.py +++ b/python/oneflow/test/modules/test_eager_boxing.py @@ -2663,7 +2663,7 @@ def _test_eager_boxing_b_to_s( ): np_arr = np.random.uniform(-1e-05, 1e-05, shape) # use cuda to avoid slice boxing here - placement_with_all_cuda_device = flow.env.all_device_placement("cuda") + placement_with_all_cuda_device = flow.placement.all("cuda") x = flow.tensor(np_arr, device="cuda", dtype=flow.float32) x = x.to_global(placement_with_all_cuda_device, flow.sbp.broadcast) @@ -2704,7 +2704,7 @@ def _test_eager_boxing_s_to_b( ): np_arr = np.random.uniform(-1e-05, 1e-05, shape) # use cuda to avoid slice boxing here - placement_with_all_cuda_device = flow.env.all_device_placement("cuda") + placement_with_all_cuda_device = flow.placement.all("cuda") x = flow.tensor(np_arr, device="cuda", dtype=flow.float32) x = x.to_global(placement_with_all_cuda_device, flow.sbp.broadcast) @@ -2730,7 +2730,7 @@ def _test_eager_boxing_p_to_s( ): np_arr = np.random.uniform(-1e-05, 1e-05, shape) # use cuda to avoid slice boxing here - placement_with_all_cuda_device = flow.env.all_device_placement("cuda") + placement_with_all_cuda_device = flow.placement.all("cuda") x = flow.tensor(np_arr, device="cuda", dtype=flow.float32) x = x.to_global(placement_with_all_cuda_device, flow.sbp.broadcast) @@ -2772,7 +2772,7 @@ def _test_eager_boxing_p_to_b( ): np_arr = np.random.uniform(-1e-05, 1e-05, shape) # use cuda to avoid slice boxing here - placement_with_all_cuda_device = flow.env.all_device_placement("cuda") + placement_with_all_cuda_device = flow.placement.all("cuda") x = flow.tensor(np_arr, device="cuda", dtype=flow.float32) x = x.to_global(placement_with_all_cuda_device, flow.sbp.broadcast) @@ -2801,7 +2801,7 @@ def _test_eager_naive_boxing_s_to_s( out_split_axis, ): np_arr = np.random.uniform(-1e-05, 1e-05, shape) - placement_with_all_cuda_device = flow.env.all_device_placement(device_type) + placement_with_all_cuda_device = flow.placement.all(device_type) x = flow.tensor(np_arr, device=device_type, dtype=flow.float32) @@ -3201,7 +3201,7 @@ class TestEagerNaiveBoxingSToS(flow.unittest.TestCase): @flow.unittest.skip_unless_1n2d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestEagerConsistentCastWithSamePlacementAndSBP(flow.unittest.TestCase): +class TestEagerGlobalCastWithSamePlacementAndSBP(flow.unittest.TestCase): def test_eager_global_cast_with_same_placement_and_sbp(test_case): x = np.ones((4, 8), dtype=np.int32) placement = flow.placement("cuda", ranks=[0, 1]) @@ -3218,7 +3218,7 @@ class TestEagerConsistentCastWithSamePlacementAndSBP(flow.unittest.TestCase): @flow.unittest.skip_unless_1n4d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestEagerConsistentCast1DTo2DSBP(flow.unittest.TestCase): +class TestEagerGlobalCast1DTo2DSBP(flow.unittest.TestCase): def test_eager_global_cast_1d_to_2d_sbp(test_case): x = np.ones((4, 8), dtype=np.int32) placement1 = flow.placement("cuda", ranks=[0, 1, 2, 3]) @@ -3241,7 +3241,7 @@ class TestEagerConsistentCast1DTo2DSBP(flow.unittest.TestCase): @flow.unittest.skip_unless_1n4d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestEagerConsistentCast2DTo1DSBP(flow.unittest.TestCase): +class TestEagerGlobalCast2DTo1DSBP(flow.unittest.TestCase): def test_eager_global_cast_2d_to_1d_sbp(test_case): x = np.ones((4, 8), dtype=np.int32) placement1 = flow.placement("cuda", ranks=[0, 1, 2, 3]) @@ -3309,7 +3309,7 @@ def _test_eager_global_cast_1d_uneven_split(test_case, device_type, shape): @flow.unittest.skip_unless_1n4d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestEagerConsistentCastOneDUnevenSplit(flow.unittest.TestCase): +class TestEagerGlobalCastOneDUnevenSplit(flow.unittest.TestCase): def test_eager_global_cast_1d_uneven_split(test_case): arg_dict = OrderedDict() arg_dict["device_type"] = ["cpu", "cuda"] @@ -3342,7 +3342,7 @@ def _test_eager_global_n_dim_reduce(test_case, device_type, src_sbp, dst_sbp): @flow.unittest.skip_unless_1n4d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestEagerConsistentCastNDimReduceBoxing(flow.unittest.TestCase): +class TestEagerGlobalCastNDimReduceBoxing(flow.unittest.TestCase): def test_eager_global_n_dim_reduce(test_case): arg_dict = OrderedDict() arg_dict["device_type"] = ["cpu", "cuda"] @@ -3352,7 +3352,7 @@ class TestEagerConsistentCastNDimReduceBoxing(flow.unittest.TestCase): _test_eager_global_n_dim_reduce(test_case, *arg) -def _test_eager_consistent_with_0_size_data( +def _test_eager_global_with_0_size_data( test_case, shape, in_device_type, @@ -3374,7 +3374,7 @@ def _test_eager_consistent_with_0_size_data( @flow.unittest.skip_unless_1n4d() class TestEagerNaiveBoxingSToS(flow.unittest.TestCase): - def test_eager_consistent_with_0_size_data(test_case): + def test_eager_global_with_0_size_data(test_case): arg_dict = OrderedDict() arg_dict["shape"] = [(8, 0, 4), (5, 0, 7)] arg_dict["in_device_type"] = ["cpu", "cuda"] @@ -3394,7 +3394,7 @@ class TestEagerNaiveBoxingSToS(flow.unittest.TestCase): (flow.sbp.partial_sum,), ] for arg in GenArgList(arg_dict): - _test_eager_consistent_with_0_size_data(test_case, *arg) + _test_eager_global_with_0_size_data(test_case, *arg) def _test_eager_boxing_one_to_n_with_diff_dim( diff --git a/python/oneflow/test/modules/test_empty.py b/python/oneflow/test/modules/test_empty.py index c1a29ca99ca91c65ef5a2e5b4120a1fde1bf54a0..01112d937bb176139fb867c5207385aa120b2c06 100644 --- a/python/oneflow/test/modules/test_empty.py +++ b/python/oneflow/test/modules/test_empty.py @@ -35,6 +35,18 @@ def _test_local_empty(test_case, shape, dtype, device, requires_grad): test_case.assertEqual(x.device, flow.device(device)) if dtype == flow.float32: test_case.assertEqual(x.requires_grad, requires_grad) + empty_like_x = flow.empty_like( + x, + dtype=dtype, + device=flow.device(device), + requires_grad=requires_grad if dtype == flow.float32 else False, + ) + test_case.assertFalse(empty_like_x.is_global) + test_case.assertEqual(empty_like_x.shape, flow.Size(shape)) + test_case.assertEqual(empty_like_x.dtype, dtype) + test_case.assertEqual(empty_like_x.device, flow.device(device)) + if dtype == flow.float32: + test_case.assertEqual(empty_like_x.requires_grad, requires_grad) def _test_new_empty(test_case, shape, dtype, device, requires_grad): diff --git a/python/oneflow/test/modules/test_eq.py b/python/oneflow/test/modules/test_eq.py index 483fc55b19013ef36b755232b7b080d01d215c8a..47b4ead7a323dad6cf4e202a30159538e859659a 100644 --- a/python/oneflow/test/modules/test_eq.py +++ b/python/oneflow/test/modules/test_eq.py @@ -50,7 +50,15 @@ class TestEq(flow.unittest.TestCase): shape = random_tensor().oneflow.shape x = random_tensor(len(shape), *shape, requires_grad=False).to(device) y = random_tensor(len(shape), *shape, requires_grad=False).to(device) - return torch.eq(x, y) + return torch.eq(x, oneof(y, random().to(int), random().to(float))) + + @autotest(n=5, auto_backward=False, check_graph=True) + def test_flow_tensor_eq_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape, requires_grad=False).to(device) + y = random_tensor(len(shape), *shape, requires_grad=False).to(device) + return x.eq(oneof(y, random().to(int), random().to(float))) @autotest(n=5, auto_backward=False, check_graph=True) def test_flow_eq_with_random_0d_data(test_case): @@ -86,6 +94,12 @@ class TestEq(flow.unittest.TestCase): x = random_tensor(ndim=0, requires_grad=False).to(device) return torch.eq(x, x) + @profile(torch.eq) + def profile_eq(test_case): + input1 = torch.ones(1000, 1280) + input2 = torch.ones(1000, 1280) + torch.eq(input1, input2) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_equal.py b/python/oneflow/test/modules/test_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..c5e6ee0c42d0083623e4b8eab3c3dcae2c282a8c --- /dev/null +++ b/python/oneflow/test/modules/test_equal.py @@ -0,0 +1,105 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestEqual(flow.unittest.TestCase): + @autotest(n=5, auto_backward=False, check_graph=False) + def test_eq_with_0_size_data(test_case): + device = random_device() + x = random_tensor(3, 2, 0, 3).to(device) + y = random_tensor(3, 2, 0, 3).to(device) + z = torch.equal(x, y) + return z + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_equal_with_0shape_0d_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=0).to(device) + z = torch.equal(x, y) + return z + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_equal_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape, requires_grad=False).to(device) + y = random_tensor(len(shape), *shape, requires_grad=False).to(device) + return torch.equal(x, y) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_tensor_equal_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape, requires_grad=False).to(device) + y = random_tensor(len(shape), *shape, requires_grad=False).to(device) + return x.equal(y) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_equal_with_random_0d_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(ndim=0, requires_grad=False).to(device) + y = random_tensor(ndim=0, requires_grad=False).to(device) + return torch.equal(x, y) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_equal_with_same_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape, requires_grad=False).to(device) + return torch.equal(x, x) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_equal_bool_with_random_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape, requires_grad=False).to( + device=device, dtype=torch.bool + ) + y = random_tensor(len(shape), *shape, requires_grad=False).to( + device=device, dtype=torch.bool + ) + return torch.equal(x, y) + + @autotest(n=5, auto_backward=False, check_graph=False) + def test_flow_equal_with_same_random_0d_data(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(ndim=0, requires_grad=False).to(device) + return torch.equal(x, x) + + @profile(torch.equal) + def profile_equal(test_case): + input1 = torch.ones(1000, 1280) + input2 = torch.ones(1000, 1280) + torch.equal(input1, input2) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_erf.py b/python/oneflow/test/modules/test_erf.py index 0cc394fe4c138614b62733d074319273a94ca310..db16c830c5358c939ebda4e9b0526cf0bd1d5e5f 100644 --- a/python/oneflow/test/modules/test_erf.py +++ b/python/oneflow/test/modules/test_erf.py @@ -43,6 +43,10 @@ class TestErfModule(flow.unittest.TestCase): y = torch.erf(x) return y + @profile(torch.erf) + def profile_erf(test_case): + torch.erf(torch.ones(100000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_erfc.py b/python/oneflow/test/modules/test_erfc.py index 13c958edfb44d0c3b105d28fd383beb9a2834b6d..dcca989b1000ed69547ecc6c1cd6a64ba6ff7261 100644 --- a/python/oneflow/test/modules/test_erfc.py +++ b/python/oneflow/test/modules/test_erfc.py @@ -43,6 +43,10 @@ class TestErfcModule(flow.unittest.TestCase): y = torch.erfc(x) return y + @profile(torch.erfc) + def profile_erfc(test_case): + torch.erfc(torch.ones(100000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_erfinv.py b/python/oneflow/test/modules/test_erfinv.py index e7b0db0f0ad960a4180912205ed4d73e9303648c..97ac4c06f959afe10aeb9190a47df5e965b98e71 100644 --- a/python/oneflow/test/modules/test_erfinv.py +++ b/python/oneflow/test/modules/test_erfinv.py @@ -62,6 +62,10 @@ class TestErfinvModule(flow.unittest.TestCase): y = torch.erfinv(x) return y + @profile(torch.erfinv) + def profile_erfinv(test_case): + torch.erfinv(torch.ones(100000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_expand_stride.py b/python/oneflow/test/modules/test_expand_stride.py new file mode 100644 index 0000000000000000000000000000000000000000..4199e133ee811f0bbe89bf40daddf4e096690ed4 --- /dev/null +++ b/python/oneflow/test/modules/test_expand_stride.py @@ -0,0 +1,238 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import numpy as np + + +import oneflow as flow +import torch + +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict + + +def _cmp_expand_stride( + test_case, input_shape, expand_shape, device="cuda", verbose=False, +): + input = np.random.randn(*input_shape) + torch_x = torch.tensor(input, dtype=torch.float32, device=device) + torch_y = torch_x.expand(*expand_shape) + + x = flow.tensor(input, dtype=flow.float32, device=device) + y = x.expand(*expand_shape) + + if verbose: + print("") + print(f" eager (view::Expand) (device={device}) ".center(50, "=")) + print(f" {input_shape} -> {expand_shape} ".center(50, "*")) + print(f"x: shape={x.shape}, stride={x.stride()}") + print(f"y: shape={y.shape}, stride={y.stride()}") + print(f"torch_y: shape={torch_y.shape}, stride={torch_y.stride()}") + print(" input ".center(50, "-")) + print(input) + print(" y ".center(50, "-")) + print(y) + print(" torch_y ".center(50, "-")) + print(torch_y) + + test_case.assertTrue(np.array_equal(y.stride(), torch_y.stride())) + test_case.assertTrue(np.array_equal(y.numpy(), torch_y.detach().cpu().numpy())) + + +def _cmp_expand_non_contiguous_stride( + test_case, input_shape, perm, expand_shape, device="cuda", verbose=False, +): + input = np.random.randn(*input_shape).astype(np.float32) + x = flow.tensor(input, device=device) + y = x.permute(*perm) + z = y.expand(*expand_shape) + + torch_x = torch.tensor(input, device=device) + torch_y = torch_x.permute(*perm) + torch_z = torch_y.expand(*expand_shape) + + if verbose: + print("") + print(f" non_contiguous (device={device}) ".center(50, "-")) + print(f" {input_shape}, {perm} -> {expand_shape} ".center(50, "-")) + print(f"x: shape={x.shape}, stride={x.stride()}") + print(f"y: shape={y.shape}, stride={y.stride()}") + print(f"z: shape={z.shape}, stride={z.stride()}") + print(f"torch_y: shape={torch_y.shape}, stride={torch_y.stride()}") + print(f"torch_z: shape={torch_z.shape}, stride={torch_z.stride()}") + print(" input ".center(50, "-")) + print(input) + print(" z ".center(50, "-")) + print(z) + print(" torch_z ".center(50, "-")) + print(torch_z) + + test_case.assertTrue(np.array_equal(z.stride(), torch_z.stride())) + test_case.assertTrue(np.array_equal(z.numpy(), torch_z.detach().cpu().numpy())) + + +def _cmp_lazy_expand_stride( + test_case, input_shape, expand_shape, device="cuda", verbose=False, +): + input = np.random.randn(*input_shape) + torch_x = torch.tensor(input, dtype=torch.float32, device=device) + torch_y = torch_x.expand(*expand_shape).contiguous() + # oneflow lazy must do this contiguous + + class MyGraph(flow.nn.Graph): + def __init__(self, expand_shape): + super().__init__() + self.expand_shape = expand_shape + + def build(self, x): + return x.expand(*self.expand_shape) + + expand_graph = MyGraph(expand_shape) + x = flow.tensor(input, dtype=flow.float32, device=device) + y = expand_graph(x) + + squeeze_y_stride = [] + for d, s in zip(y.shape, y.stride()): + if d != 1: + squeeze_y_stride.append(s) + + squeeze_torch_y_stride = [] + for d, s in zip(torch_y.shape, torch_y.stride()): + if d != 1: + squeeze_torch_y_stride.append(s) + + if verbose: + print("") + print(f" lazy (expand op/kernel) (device={device}) ".center(50, "=")) + print(f" {input_shape} -> {expand_shape} ".center(50, "*")) + print(f"x: shape={x.shape}, stride={x.stride()}") + print(f"y: shape={y.shape}, stride={y.stride()}") + print(f"torch_y: shape={torch_y.shape}, stride={torch_y.stride()}") + print(f"squeeze_y_stride={squeeze_y_stride}") + print(f"squeeze_torch_y_stride={squeeze_torch_y_stride}") + print(" input ".center(50, "-")) + print(input) + print(" y ".center(50, "-")) + print(y) + print(" torch_y ".center(50, "-")) + print(torch_y) + + test_case.assertTrue(np.array_equal(squeeze_y_stride, squeeze_torch_y_stride)) + test_case.assertTrue(np.array_equal(y.numpy(), torch_y.detach().cpu().numpy())) + + +@flow.unittest.skip_unless_1n1d() +class ExpandStrideTestCase(flow.unittest.TestCase): + test_shape_tuple_list = [ + ((1, 2), (2, 2)), + ((1, 2), (1, 1, 2)), + ((1, 2), (1, 2, 2)), + ((1, 2), (2, 1, 2)), + ((1, 2), (2, 2, 2)), + ((1, 2), (1, 1, 1, 2)), + ((1, 2), (1, 2, 1, 2)), + ((1, 2), (2, 1, 1, 2)), + ((1, 2), (2, 2, 1, 2)), + ((1, 2), (2, 2, 2, 2)), + ((2, 1), (2, 2)), + ((2, 1), (1, 2, 1)), + ((2, 1), (1, 2, 2)), + ((2, 1), (2, 2, 1)), + ((2, 1), (2, 2, 2)), + ((2, 1), (1, 1, 2, 1)), + ((2, 1), (1, 2, 2, 1)), + ((2, 1), (2, 2, 2, 1)), + ((2, 1), (2, 2, 2, 2)), + ((2, 2), (1, 2, 2)), + ((2, 2), (2, 2, 2)), + ((2, 2), (1, 1, 2, 2)), + ((2, 2), (1, 2, 2, 2)), + ((2, 2), (2, 1, 2, 2)), + ((2, 2), (2, 2, 2, 2)), + ((2, 1, 4), (2, 2, 2, 4)), + ((2, 1, 3), (2, 1, -1, -1, -1)), + ((2, 1, 3), (1, 2, -1, -1, -1)), + ((2, 1, 3), (2, 2, -1, -1, -1)), + ((2, 1, 3), (2, 1, -1, 2, 3)), + ((2, 1, 3), (1, 2, 2, 2, -1)), + ((2, 1, 3), (2, 2, 2, 2, 3)), + ((2, 3, 4), (1, 2, -1, -1, -1)), + ((2, 3, 4), (2, 1, -1, -1, -1)), + ((2, 3, 4), (2, 2, -1, -1, -1)), + ((), (1,)), + ((), (2,)), + ((), (1, 2)), + ((), (2, 1)), + ((), (2, 2)), + ] + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_on_cpu(self): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cpu"] + arg_dict["shapes"] = self.test_shape_tuple_list + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _cmp_expand_stride(self, input_shape, expand_shape, **kwargs) + + def test_stride(self): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cuda"] + arg_dict["shapes"] = self.test_shape_tuple_list + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _cmp_expand_stride(self, input_shape, expand_shape, **kwargs) + + def test_non_contiguous_stride(self): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shapes"] = [ + ((2, 1, 3), (0, 2, 1), (1, 2, -1, -1, -1)), + ((2, 1, 3), (0, 2, 1), (2, 1, -1, -1, -1)), + ((2, 1, 3), (0, 2, 1), (2, 3, -1, -1, -1)), + ((2, 1, 3), (0, 2, 1), (1, 2, -1, -1, 2)), + ((2, 1, 3), (0, 2, 1), (2, 1, -1, -1, 2)), + ((2, 1, 3), (0, 2, 1), (2, 3, -1, -1, 2)), + ((2, 3, 4), (0, 2, 1), (1, 2, -1, -1, -1)), + ((2, 3, 4), (0, 2, 1), (2, 1, -1, -1, -1)), + ((2, 3, 4), (0, 2, 1), (2, 2, -1, -1, -1)), + ] + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, perm, expand_shape = kwargs.pop("shapes") + _cmp_expand_non_contiguous_stride( + self, input_shape, perm, expand_shape, **kwargs + ) + + def test_lazy(self): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cuda"] + arg_dict["shapes"] = self.test_shape_tuple_list + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _cmp_lazy_expand_stride(self, input_shape, expand_shape, **kwargs) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_expm1.py b/python/oneflow/test/modules/test_expm1.py index 2e6c8e86827bed21a1edc934ad025437068f7412..2bb3c595a504304325ea97e502e261be60b26248 100644 --- a/python/oneflow/test/modules/test_expm1.py +++ b/python/oneflow/test/modules/test_expm1.py @@ -72,6 +72,10 @@ class TestExpm1Module(flow.unittest.TestCase): y = torch.expm1(input) return y + @profile(torch.expm1) + def profile_expm1(test_case): + torch.expm1(torch.ones(100000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_eye.py b/python/oneflow/test/modules/test_eye.py index 7afa556e5c4f354e962566a7275c68a93b933879..213434548558c28020880981282b15b11b6bffac 100644 --- a/python/oneflow/test/modules/test_eye.py +++ b/python/oneflow/test/modules/test_eye.py @@ -95,9 +95,14 @@ class TestEye(flow.unittest.TestCase): x = random_tensor(ndim=0).to(device) return x + @profile(torch.eye) + def profile_eye(test_case): + torch.eye(1000) + torch.eye(100, 1280) + @flow.unittest.skip_unless_1n2d() -class TestConsistentEye(flow.unittest.TestCase): +class TestGlobalEye(flow.unittest.TestCase): def test_eye_with_1n2d(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [_test_eye_with_1n2d] diff --git a/python/oneflow/test/modules/test_flatten.py b/python/oneflow/test/modules/test_flatten.py index 689bc55aa1d69b5efc748f7c52af48834ba48ff2..cf2ada9bbbdcad6b4174bcacb1aeef712538c9af 100644 --- a/python/oneflow/test/modules/test_flatten.py +++ b/python/oneflow/test/modules/test_flatten.py @@ -112,6 +112,10 @@ class TestFlattenModule(flow.unittest.TestCase): ) return y + @profile(torch.flatten) + def profile_flatten(test_case): + torch.flatten(torch.ones(1000, 1000)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_flip.py b/python/oneflow/test/modules/test_flip.py index 17491eecfdd751f11aeb1bc771ddf6024d857a82..2b3b21c2eb59daf1b4a49118e78b73c52c8018f1 100644 --- a/python/oneflow/test/modules/test_flip.py +++ b/python/oneflow/test/modules/test_flip.py @@ -55,6 +55,10 @@ class TestFlip(flow.unittest.TestCase): y = torch.flip(x, constant((0, 1, 2))) return y + @profile(torch.flip) + def profile_flip(test_case): + torch.flip(torch.ones(100, 100, 100), [0, 1]) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_floor.py b/python/oneflow/test/modules/test_floor.py index 87d10f02ce3f5d6290fd1305ac4c4be69892173e..743bfad209c0546743fc62115360037966223f0b 100644 --- a/python/oneflow/test/modules/test_floor.py +++ b/python/oneflow/test/modules/test_floor.py @@ -75,6 +75,10 @@ class TestFloor(flow.unittest.TestCase): y = torch.floor(x) return y + @profile(torch.floor) + def profile_floor(test_case): + torch.floor(torch.ones(100, 100, 100)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_fmod.py b/python/oneflow/test/modules/test_fmod.py index 1f00ff531d43d4d5cc2334d628bbc149489fcc2a..5809bcd9249365c2860fa71aa9abad7ad734f242 100644 --- a/python/oneflow/test/modules/test_fmod.py +++ b/python/oneflow/test/modules/test_fmod.py @@ -26,10 +26,18 @@ from oneflow.test_utils.test_util import GenArgList import oneflow as flow import oneflow.unittest +import torch as torch_original +from packaging import version + @flow.unittest.skip_unless_1n1d() class TestFmodModule(flow.unittest.TestCase): - @autotest(auto_backward=False) + # other.grad in torch.fmod(input, other) was not implemented before pytorch 1.11.0 + grad_implemented = version.parse(torch_original.__version__) >= version.parse( + "1.11.0" + ) + + @autotest(auto_backward=grad_implemented) def test_flow_fmod_element_with_random_data(test_case): device = random_device() dim1 = random().to(int) @@ -38,14 +46,14 @@ class TestFmodModule(flow.unittest.TestCase): other = random_tensor(ndim=3, dim1=dim1, dim2=dim2).to(device) return torch.fmod(input, other) - @autotest(auto_backward=False) + @autotest(auto_backward=grad_implemented) def test_flow_fmod_element_with_0dim_data(test_case): device = random_device() input = random_tensor(ndim=0).to(device) other = random_tensor(ndim=0).to(device) return torch.fmod(input, other) - @autotest(auto_backward=False) + @autotest(auto_backward=grad_implemented) def test_flow_fmod_broadcast_with_random_data(test_case): device = random_device() dim1 = random().to(int) @@ -63,13 +71,18 @@ class TestFmodModule(flow.unittest.TestCase): other = 3 return torch.fmod(input, other) - @autotest(auto_backward=False) + @autotest(auto_backward=True) def test_fmod_with_0_size_data(test_case): device = random_device() x = random_tensor(4, 2, 1, 0, 3).to(device) y = torch.fmod(x, 2) return y + @profile(torch.fmod) + def profile_fmod(test_case): + torch.fmod(torch.ones(100, 100, 100), 1) + torch.fmod(torch.ones(100, 100, 100), -0.5) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_fold.py b/python/oneflow/test/modules/test_fold.py index dea7722d30d74cbcc731ceed7d94200cd01b7699..88dca8c331486b72d4656136163322cd0a42c03e 100644 --- a/python/oneflow/test/modules/test_fold.py +++ b/python/oneflow/test/modules/test_fold.py @@ -40,7 +40,15 @@ class TestFold(flow.unittest.TestCase): ndim=3, dim0=constant(2), dim1=constant(36), dim2=constant(16) ).to(device) y = m(x) - return y + func_y = torch.nn.functional.fold( + x, + output_size=constant((4, 4)), + kernel_size=constant(3), + dilation=constant(1), + padding=constant(1), + stride=constant(1), + ) + return y, func_y @autotest(n=3, auto_backward=True, rtol=1e-4, atol=1e-4) def test_fold_with_random_data_2(test_case): @@ -58,7 +66,15 @@ class TestFold(flow.unittest.TestCase): ndim=3, dim0=constant(2), dim1=constant(36), dim2=constant(4) ).to(device) y = m(x) - return y + func_y = torch.nn.functional.fold( + x, + output_size=constant((4, 4)), + kernel_size=constant(3), + dilation=constant(1), + padding=constant(0), + stride=constant(1), + ) + return y, func_y @autotest(n=3, auto_backward=True, rtol=1e-4, atol=1e-4) def test_fold_with_random_data_3(test_case): @@ -76,7 +92,15 @@ class TestFold(flow.unittest.TestCase): ndim=3, dim0=constant(2), dim1=constant(72), dim2=constant(16) ).to(device) y = m(x) - return y + func_y = torch.nn.functional.fold( + x, + output_size=constant((8, 8)), + kernel_size=constant(3), + dilation=constant(1), + padding=constant(1), + stride=constant(2), + ) + return y, func_y @autotest(n=3, auto_backward=True, rtol=1e-4, atol=1e-4) def test_fold_with_random_data_4(test_case): @@ -94,7 +118,20 @@ class TestFold(flow.unittest.TestCase): ndim=3, dim0=constant(2), dim1=constant(9), dim2=constant(9) ).to(device) y = m(x) - return y + func_y = torch.nn.functional.fold( + x, + output_size=constant((8, 8)), + kernel_size=constant(3), + dilation=constant(2), + padding=constant(1), + stride=constant(2), + ) + return y, func_y + + @profile(torch.nn.functional.fold) + def profile_fold(test_case): + x = torch.ones(128, 128, 4) + torch.nn.functional.fold(x, output_size=(4, 4), kernel_size=(2, 2), stride=2) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_from_numpy.py b/python/oneflow/test/modules/test_from_numpy.py index 4a63b142e66d09db4e066d709ab8a8a1af93ceb3..2ac3c7a7775024dad5c1639f78c24fa5c74dc4bd 100644 --- a/python/oneflow/test/modules/test_from_numpy.py +++ b/python/oneflow/test/modules/test_from_numpy.py @@ -17,7 +17,9 @@ limitations under the License. import random import unittest +import torch import numpy as np + import oneflow as flow import oneflow.unittest @@ -58,11 +60,15 @@ class TestFromNumpy(flow.unittest.TestCase): test_case.assertTrue(np.array_equal(np_arr, tensor.numpy())) def test_non_contiguous_input(test_case): - np_arr = np.random.randn(4, 5) - np_arr = np_arr.transpose(1, 0) - tensor = flow.from_numpy(np_arr) - # TODO(wyg): support non-contiguous input - test_case.assertTrue(tensor.is_contiguous()) + np_arr = np.random.randn(2, 3, 4, 5).transpose(2, 0, 3, 1) + flow_tensor = flow.from_numpy(np_arr) + torch_tensor = torch.from_numpy(np_arr) + test_case.assertTrue(flow_tensor.shape == torch_tensor.shape) + test_case.assertTrue(flow_tensor.stride() == torch_tensor.stride()) + test_case.assertTrue( + flow_tensor.is_contiguous() == torch_tensor.is_contiguous() + ) + test_case.assertTrue(np.array_equal(flow_tensor.numpy(), torch_tensor.numpy())) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_from_torch.py b/python/oneflow/test/modules/test_from_torch.py index 5d3454f4dcc30c6adef23fc3bd083df84aec2341..117a66960c8d1b08983620bb548c5e95581f4c49 100644 --- a/python/oneflow/test/modules/test_from_torch.py +++ b/python/oneflow/test/modules/test_from_torch.py @@ -15,14 +15,24 @@ limitations under the License. """ import unittest import numpy as np +import os import oneflow as flow import oneflow.unittest import torch -@flow.unittest.skip_unless_1n1d() +def torch_device_to_flow(device): + if device.type == "cpu": + return flow.device("cpu") + elif device.type == "cuda": + return flow.device("cuda", device.index) + else: + raise NotImplementedError("Unsupported device type: {}".format(device.type)) + + class TestFromTroch(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n1d() def test_from_torch_cpu(test_case): torch_t = torch.rand(5, 3, 3) numpy_from_torch = torch_t.numpy() @@ -39,7 +49,7 @@ class TestFromTroch(flow.unittest.TestCase): # NOTE: oneflow and numpy shared the same memory, # so oneflow and torch cpu tensor shared the same memory, # which means oneflow can use torch's cpu tensor without cost. - flow_t = flow.utils.from_torch(torch_t) + flow_t = flow.utils.tensor.from_torch(torch_t) test_case.assertTrue( np.allclose(torch_t.numpy(), flow_t.numpy(), rtol=0.001, atol=0.001) @@ -48,16 +58,18 @@ class TestFromTroch(flow.unittest.TestCase): # NOTE: For the case of 0 size tensor, no memory addresses are compared. # Because the address of 0 size tensor is random at this time. + @flow.unittest.skip_unless_1n1d() def test_from_torch_cpu_with_0_size_data(test_case): torch_t = torch.rand(5, 0, 3) - flow_t = flow.utils.from_torch(torch_t) + flow_t = flow.utils.tensor.from_torch(torch_t) test_case.assertTrue( np.allclose(torch_t.numpy(), flow_t.numpy(), rtol=0.001, atol=0.001) ) test_case.assertEqual(torch_t.numpy().dtype, flow_t.numpy().dtype) + @flow.unittest.skip_unless_1n1d() def test_from_torch_cpu_with_0dim_data(test_case): torch_t = torch.tensor(5) numpy_from_torch = torch_t.numpy() @@ -66,13 +78,29 @@ class TestFromTroch(flow.unittest.TestCase): torch_t.data_ptr(), numpy_from_torch.__array_interface__["data"][0] ) - flow_t = flow.utils.from_torch(torch_t) + flow_t = flow.utils.tensor.from_torch(torch_t) test_case.assertTrue( np.allclose(torch_t.numpy(), flow_t.numpy(), rtol=0.001, atol=0.001) ) test_case.assertEqual(torch_t.numpy().dtype, flow_t.numpy().dtype) + @flow.unittest.skip_unless_1n2d() + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_from_torch_gpu(test_case): + for device in [torch.device("cuda", 0), torch.device("cuda", 1)]: + torch_t = torch.tensor([1, 2]).to(device) + + flow_t = flow.utils.tensor.from_torch(torch_t) + + test_case.assertTrue(np.array_equal(torch_t.cpu().numpy(), flow_t.numpy())) + test_case.assertEqual(torch_t.cpu().numpy().dtype, flow_t.numpy().dtype) + test_case.assertEqual(torch_device_to_flow(torch_t.device), flow_t.device) + + # Test oneflow tensor and pytorch tensor share the data + torch_t[0] = 5 + test_case.assertTrue(np.array_equal(torch_t.cpu().numpy(), flow_t.numpy())) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_functional_docstr.py b/python/oneflow/test/modules/test_functional_docstr.py index 32419309ab2e73ef4da3781c916fc5f94ec4ec67..42edef5d4c838e968e25c489700a1034a5755224 100644 --- a/python/oneflow/test/modules/test_functional_docstr.py +++ b/python/oneflow/test/modules/test_functional_docstr.py @@ -43,7 +43,22 @@ def _run_functional_doctest( for (name, fun) in r: if fun.__doc__ is not None: test = parser.get_doctest(fun.__doc__, {}, __name__, __file__, 0) - runner.run(test) + try: + runner.run(test) + except doctest.DocTestFailure as e: + print(f"\nGot error result in the docstring of {name}") + print(f"got output: {e.got}") + raise e + except doctest.UnexpectedException as e: + print(f"\nGot UnexpectedException in the docstring of {name}") + raise e.exc_info[1] + + if not raise_on_error: + test_case.assertEqual( + runner.failures, + 0, + f"{runner.summarize()}, please turn on raise_on_error to see more details", + ) @flow.unittest.skip_unless_1n1d() diff --git a/python/oneflow/test/modules/test_functional_scalar_tensor_param.py b/python/oneflow/test/modules/test_functional_scalar_tensor_param.py new file mode 100644 index 0000000000000000000000000000000000000000..67c6edae5ef6d65e25d7dea9ed723a321221c05e --- /dev/null +++ b/python/oneflow/test/modules/test_functional_scalar_tensor_param.py @@ -0,0 +1,54 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestFunctionalWithScalarTensorParam(flow.unittest.TestCase): + # NOTE: graph mode not support dynamic scalar tensor parameter + @autotest(n=2, auto_backward=False, check_graph=False) + def test_scalar_tensor_transfer_to_scalar(test_case): + device = random_device() + min = torch.tensor(0.0) + max = torch.tensor(0.5) + x = random_tensor(ndim=2, dim0=2, dim1=3).to(device) + return x.clamp(min=min, max=max) + + @autotest(n=2, auto_backward=False, check_graph=False) + def test_scalar_tensor_transfer_to_double(test_case): + device = random_device() + threshold = torch.tensor(0.5).to(device) + x = random_tensor(ndim=2, dim0=2, dim1=3).to(device) + return torch.nn.functional.threshold(x, threshold=threshold, value=0.5) + + @autotest(n=2, auto_backward=False, check_graph=False) + def test_scalar_tensor_transfer_to_int(test_case): + device = random_device() + start_dim = torch.tensor(1).to(device) + end_dim = torch.tensor(3).to(device) + x = random_tensor(4, *(2, 3, 4, 5)).to(device) + return x.flatten(start_dim=start_dim, end_dim=end_dim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_bias_add_dropout.py b/python/oneflow/test/modules/test_fused_bias_add_dropout.py index 670393eed032ba8e96d1f8150b840993cbff47a1..9b4a9cccc42a35c69f9b0041ce6ee1c70daadea9 100644 --- a/python/oneflow/test/modules/test_fused_bias_add_dropout.py +++ b/python/oneflow/test/modules/test_fused_bias_add_dropout.py @@ -77,16 +77,12 @@ class TestFusedBiasAddDropout(flow.unittest.TestCase): def test_fuse_bias_add_dropout(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [_test_fused_bias_add_dropout] - # arg_dict["shape"] = [(16, 64, 72), (32, 16, 48)] - # arg_dict["axis"] = [0, 1, 2, -1, -2, -3] - # arg_dict["drop_prob"] = [0.0, 1.0] - arg_dict["shape"] = [(2,3,4)] - arg_dict["axis"] = [1] + arg_dict["shape"] = [(16, 64, 72), (32, 16, 48)] + arg_dict["axis"] = [0, 1, 2, -1, -2, -3] arg_dict["drop_prob"] = [0.0, 1.0] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) - if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_fused_bias_add_scale_mask_softmax_dropout.py b/python/oneflow/test/modules/test_fused_bias_add_scale_mask_softmax_dropout.py new file mode 100644 index 0000000000000000000000000000000000000000..c1e8ce2831cc736b6f65e58fc851eb656e813e5f --- /dev/null +++ b/python/oneflow/test/modules/test_fused_bias_add_scale_mask_softmax_dropout.py @@ -0,0 +1,177 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import os +import numpy as np +from collections import OrderedDict + +import torch +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgDict + + +def _torch_bias_add_scale_mask_softmax_dropout(x, bias, mask, fill, scale, p): + masked = (x + bias) * mask * scale + unmask = (1 - mask.int()).bool() + masked.masked_fill_(unmask, fill) + softmax_y = torch.nn.functional.softmax(masked, dim=-1) + y = torch.nn.functional.dropout(softmax_y, p) + return y, softmax_y + + +def _test_bias_add_fused_scale_mask_softmax_dropout( + test_case, + input_shape, + bias_shape, + mask_shape, + input_dtype=flow.float32, + mask_dtype=flow.bool, + fill=-10000, + scale=1.0, + p=0.0, + device="cuda", +): + # print(f"{'=' * 40} test case {'=' * 40}") + # print(f"input_shap={input_shape}") + # print(f"bias_shape={bias_shape}") + # print(f"mask_shape={mask_shape}") + # print(f"input_dtype={input_dtype}") + # print(f"mask_dtype={mask_dtype}") + # print(f"fill={fill}") + # print(f"scale={scale}") + # print(f"p={p}") + + np_input = np.random.randn(*input_shape).astype(np.float32) + np_bias = np.random.randn(*bias_shape).astype(np.float32) + np_mask = np.random.randint(0, 2, size=mask_shape).astype(np.int32) + np_rand_init_grad = np.random.randn(*input_shape).astype(np.float32) + + torch_input = torch.tensor(np_input).to(device=device) + torch_bias = torch.tensor(np_bias).to(device=device) + torch_mask = torch.tensor(np_mask).to(device=device).bool() + torch_rand_init_grad = torch.tensor(np_rand_init_grad).to(device=device) + torch_input.requires_grad_(True) + torch_bias.requires_grad_(True) + torch_output, torch_softmax_output = _torch_bias_add_scale_mask_softmax_dropout( + torch_input, torch_bias, torch_mask, fill, scale, p + ) + (torch_output * torch_rand_init_grad).sum().backward() + torch_input_grad = torch_input.grad.detach().cpu() + torch_bias_grad = torch_bias.grad.detach().cpu() + torch_output = torch_output.detach().cpu() + torch_softmax_output = torch_softmax_output.detach().cpu() + + input = flow.tensor(np_input, dtype=input_dtype, device=device) + bias = flow.tensor(np_bias, dtype=input_dtype, device=device) + mask = flow.tensor(np_mask, dtype=mask_dtype, device=device) + rand_init_grad = flow.tensor(np_rand_init_grad, dtype=input_dtype, device=device) + input.requires_grad_(True) + bias.requires_grad_(True) + output, softmax_output = flow._C.fused_bias_add_scale_mask_softmax_dropout( + input, bias, mask, fill_value=fill, scale=scale, p=p, + ) + (output * rand_init_grad).sum().backward() + input_grad = input.grad.detach().cpu() + bias_grad = bias.grad.detach().cpu() + output = output.to(dtype=flow.float32, device="cpu") + softmax_output = softmax_output.to(dtype=flow.float32, device="cpu") + + def compare(a, b, a_name, b_name, atol=1e-5, rtol=1e-8): + test_case.assertTrue( + np.allclose(a.numpy(), b.numpy(), atol=atol, rtol=rtol), + f"\n{a_name}:\n{a.numpy()}\n{'-' * 80}\n{b_name}:\n{b.numpy()}\n{'*' * 80}\ndiff:\n{a.numpy() - b.numpy()}\n{a_name} vs. {b_name} max_diff:\n{np.max(np.abs(a.numpy() - b.numpy()))}", + ) + + if input_dtype == flow.float16: + compare(output, torch_output, "output", "torch_output", atol=1e-3, rtol=1e-2) + compare( + softmax_output, + torch_softmax_output, + "softmax_output", + "torch_softmax_output", + atol=1e-3, + rtol=1e-2, + ) + compare( + input_grad, + torch_input_grad, + "input_grad", + "torch_input_grad", + atol=1e-2, + rtol=1e-2, + ) + compare( + bias_grad, + torch_bias_grad, + "bias_grad", + "torch_bias_grad", + atol=1e-2, + rtol=1e-2, + ) + else: + compare(output, torch_output, "output", "torch_output") + compare( + softmax_output, + torch_softmax_output, + "softmax_output", + "torch_softmax_output", + ) + compare(input_grad, torch_input_grad, "input_grad", "torch_input_grad") + compare(bias_grad, torch_bias_grad, "bias_grad", "torch_bias_grad") + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test gpu cases") +class TestFusedBiasAddScaleMaskSoftmaxDropout(flow.unittest.TestCase): + def test_real_case(test_case): + args_dict = OrderedDict() + args_dict["input_shape"] = [[4, 12, 8, 8]] + args_dict["bias_shape"] = [[1, 12, 8, 8]] + args_dict["mask_shape"] = [[4, 1, 1, 8]] + args_dict["input_dtype"] = [flow.float16, flow.float32] + args_dict["mask_dtype"] = [flow.bool] + args_dict["fill"] = [-10000.0] + args_dict["scale"] = [1.0, 2.0, 4.0] + args_dict["p"] = [0.0, 1.0] + + for kwarg in GenArgDict(args_dict): + _test_bias_add_fused_scale_mask_softmax_dropout(test_case, **kwarg) + + def test_different_broadcast_dim(test_case): + _test_bias_add_fused_scale_mask_softmax_dropout( + test_case, [4, 2, 3], [1, 2, 3], [4, 1, 3] + ) + + def test_same_broadcast_dim(test_case): + _test_bias_add_fused_scale_mask_softmax_dropout( + test_case, [4, 2, 3], [1, 2, 3], [1, 2, 3] + ) + + def test_broadcast_bias(test_case): + _test_bias_add_fused_scale_mask_softmax_dropout( + test_case, [4, 2, 3], [1, 1, 3], [4, 2, 3] + ) + + def test_broadcast_mask(test_case): + _test_bias_add_fused_scale_mask_softmax_dropout( + test_case, [4, 2, 3], [4, 2, 3], [4, 1, 3] + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_center.py b/python/oneflow/test/modules/test_fused_center.py new file mode 100644 index 0000000000000000000000000000000000000000..524733dfeb4788b9dec022c7824a186e246fd7e5 --- /dev/null +++ b/python/oneflow/test/modules/test_fused_center.py @@ -0,0 +1,129 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def torch_center(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2): + return ( + (b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2 + ) / 4 + + +def _test_fused_get_center_dist_impl(test_case, device, shape): + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + x = [] + torch_x = [] + for _ in range(8): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2 = ( + x[0], + x[1], + x[2], + x[3], + x[4], + x[5], + x[6], + x[7], + ) + ( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + ) = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + torch_x[4], + torch_x[5], + torch_x[6], + torch_x[7], + ) + rho2 = flow._C.fused_get_center_dist( + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2 + ) + torch_rho2 = torch_center( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + ) + compare(rho2, torch_rho2) + + rho2.sum().backward() + torch_rho2.sum().backward() + compare(b1_x1.grad, torch_b1_x1.grad) + compare(b1_x2.grad, torch_b1_x2.grad) + compare(b2_x1.grad, torch_b2_x1.grad) + compare(b2_x2.grad, torch_b2_x2.grad) + compare(b1_y1.grad, torch_b1_y1.grad) + compare(b1_y2.grad, torch_b1_y2.grad) + compare(b2_y1.grad, torch_b2_y1.grad) + compare(b2_y2.grad, torch_b2_y2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetCenterDistModule(flow.unittest.TestCase): + def test_fused_get_center_dist(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_fused_get_center_dist_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(583, 1), (759, 1), (1234, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_dot_feature_interaction.py b/python/oneflow/test/modules/test_fused_dot_feature_interaction.py index 339be156292760de3b98fc18abd5af4db861fbac..dc86fc31afec590c698824560cbab0d91c4e103c 100644 --- a/python/oneflow/test/modules/test_fused_dot_feature_interaction.py +++ b/python/oneflow/test/modules/test_fused_dot_feature_interaction.py @@ -21,7 +21,7 @@ import numpy as np import oneflow as flow import oneflow.unittest import os -from fused_dot_feature_interaction import fused_dot_feature_interaction + def _test_fused_dot_feature_interaction( test_case, @@ -84,23 +84,15 @@ def _test_fused_dot_feature_interaction( output_concat_tensor = fused_feature_0_tensor else: output_concat_tensor = None - # fused_R = flow._C.fused_dot_feature_interaction( - # [ - # fused_feature_0_tensor.reshape(batch_size, 1, embedding_size), - # fused_feature_1_tensor, - # ], - # output_concat=output_concat_tensor, - # self_interaction=self_interaction, - # output_padding=output_padding, - # pooling="none", - # ) - fused_R = fused_dot_feature_interaction( - fused_feature_0_tensor, - fused_feature_1_tensor, + fused_R = flow._C.fused_dot_feature_interaction( + [ + fused_feature_0_tensor.reshape(batch_size, 1, embedding_size), + fused_feature_1_tensor, + ], output_concat=output_concat_tensor, self_interaction=self_interaction, output_padding=output_padding, - dtype=dtype + pooling="none", ) fused_loss = fused_R.sum() fused_loss.backward() diff --git a/python/oneflow/test/modules/test_fused_gelu_mul.py b/python/oneflow/test/modules/test_fused_gelu_mul.py new file mode 100644 index 0000000000000000000000000000000000000000..10607dfbf93fc7c8fd2bac11bbffe2fd694ea5b2 --- /dev/null +++ b/python/oneflow/test/modules/test_fused_gelu_mul.py @@ -0,0 +1,72 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import numpy as np +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgDict + + +def _test_fused_fast_gelu_mul(test_case, shape, dtype=flow.float32): + x = flow.randn(*shape).to(dtype=dtype, device="cuda").requires_grad_(True) + multiplier = flow.randn(*shape).to(dtype=dtype, device="cuda").requires_grad_(True) + y = flow.nn.functional.gelu(x, approximate="tanh") * multiplier + y.mean().backward() + x_grad = x.grad.detach().cpu() + m_grad = multiplier.grad.detach().cpu() + y = y.detach().cpu() + + fused_x = x.detach().clone().requires_grad_(True) + fused_multiplier = multiplier.detach().clone().requires_grad_(True) + fused_y = flow._C.fused_fast_gelu_mul(fused_x, fused_multiplier) + fused_y.mean().backward() + fused_x_grad = fused_x.grad.detach().cpu() + fused_m_grad = fused_multiplier.grad.detach().cpu() + fused_y = fused_y.detach().cpu() + + def compare(a, b, rtol=1e-5, atol=1e-8): + test_case.assertTrue( + np.allclose(a.numpy(), b.numpy(), rtol=rtol, atol=atol), + f"\na\n{a.numpy()}\n{'-' * 80}\nb:\n{b.numpy()}\n{'*' * 80}\ndiff:\n{a.numpy() - b.numpy()}", + ) + + # print(f"\n{'=' * 20} shape={shape} dtype={dtype} {'=' * 20}") + if dtype == flow.float16: + compare(fused_y, y, 1e-2, 1e-3) + compare(fused_x_grad, x_grad, 1e-4, 1e-3) + compare(fused_m_grad, m_grad, 1e-4, 1e-3) + else: + compare(fused_y, y) + compare(fused_x_grad, x_grad) + compare(fused_m_grad, m_grad) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test gpu cases") +class TestFusedFastGeluMul(flow.unittest.TestCase): + def test_fused_fast_gelu_mul(test_case): + args_dict = OrderedDict() + args_dict["shape"] = [[5], [7, 10], [4, 2, 3], [8, 3, 16, 16]] + args_dict["dtype"] = [flow.float16, flow.float32] + for kwarg in GenArgDict(args_dict): + _test_fused_fast_gelu_mul(test_case, **kwarg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_boundding_boxes_coord.py b/python/oneflow/test/modules/test_fused_get_boundding_boxes_coord.py new file mode 100644 index 0000000000000000000000000000000000000000..f9257423bab4059d44a4c89862359f5eb278d31b --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_boundding_boxes_coord.py @@ -0,0 +1,143 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_get_boundding_boxes_coord_impl(test_case, device, shape): + x = [] + torch_x = [] + for _ in range(8): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + x1, y1, w1, h1, x2, y2, w2, h2 = x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7] + ( + b1_x1, + b1_x2, + b1_y1, + b1_y2, + b2_x1, + b2_x2, + b2_y1, + b2_y2, + ) = flow._C.fused_get_boundding_boxes_coord(x1, y1, w1, h1, x2, y2, w2, h2) + torch_x1, torch_y1, torch_w1, torch_h1, torch_x2, torch_y2, torch_w2, torch_h2 = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + torch_x[4], + torch_x[5], + torch_x[6], + torch_x[7], + ) + torch_w1_, torch_h1_, torch_w2_, torch_h2_ = ( + torch_w1 / 2, + torch_h1 / 2, + torch_w2 / 2, + torch_h2 / 2, + ) + torch_b1_x1, torch_b1_x2, torch_b1_y1, torch_b1_y2 = ( + torch_x1 - torch_w1_, + torch_x1 + torch_w1_, + torch_y1 - torch_h1_, + torch_y1 + torch_h1_, + ) + torch_b2_x1, torch_b2_x2, torch_b2_y1, torch_b2_y2 = ( + torch_x2 - torch_w2_, + torch_x2 + torch_w2_, + torch_y2 - torch_h2_, + torch_y2 + torch_h2_, + ) + + def compare(a, b, rtol=1e-5, atol=1e-8): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + compare(b1_x1, torch_b1_x1) + compare(b1_x2, torch_b1_x2) + compare(b1_y1, torch_b1_y1) + compare(b1_y2, torch_b1_y2) + compare(b2_x1, torch_b2_x1) + compare(b2_x2, torch_b2_x2) + compare(b2_y1, torch_b2_y1) + compare(b2_y2, torch_b2_y2) + res = ( + (b1_x1 + 2 * b1_x2 + b1_y1 + b1_y2 + b2_x1 + b2_x2 + b2_y1 + b2_y2) * 2 + ).sum() + torch_res = ( + ( + torch_b1_x1 + + 2 * torch_b1_x2 + + torch_b1_y1 + + torch_b1_y2 + + torch_b2_x1 + + torch_b2_x2 + + torch_b2_y1 + + torch_b2_y2 + ) + * 2 + ).sum() + res.sum().backward() + torch_res.sum().backward() + compare(x1.grad, torch_x1.grad) + compare(y1.grad, torch_y1.grad) + compare(w1.grad, torch_w1.grad) + compare(h1.grad, torch_h1.grad) + compare(x2.grad, torch_x2.grad) + compare(y2.grad, torch_y2.grad) + compare(w2.grad, torch_w2.grad) + compare(h2.grad, torch_h2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetBounddingBoxesCoordModule(flow.unittest.TestCase): + def test_get_boundding_boxes_coord(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_get_boundding_boxes_coord_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(583, 1), (759, 1), (1234, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_ciou_diagonal_angle.py b/python/oneflow/test/modules/test_fused_get_ciou_diagonal_angle.py new file mode 100644 index 0000000000000000000000000000000000000000..20d343a5ca963b250e096dfdccd99abf95ce336d --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_ciou_diagonal_angle.py @@ -0,0 +1,98 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import math +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def torch_get_ciou_diagonal_angle(w1, h1, w2, h2, eps=1e-8): + return (4 / math.pi ** 2) * torch.pow( + torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2 + ) + + +def _test_fused_get_ciou_diagonal_angle_impl(test_case, device, shape): + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + x = [] + torch_x = [] + for _ in range(4): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + w1, h1, w2, h2 = ( + x[0], + x[1], + x[2], + x[3], + ) + (torch_w1, torch_h1, torch_w2, torch_h2,) = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + ) + v = flow._C.fused_get_ciou_diagonal_angle(w1, h1, w2, h2, eps=1e-8) + torch_v = torch_get_ciou_diagonal_angle(torch_w1, torch_h1, torch_w2, torch_h2,) + compare(v, torch_v) + + v.sum().backward() + torch_v.sum().backward() + compare(w1.grad, torch_w1.grad) + compare(h1.grad, torch_h1.grad) + compare(w2.grad, torch_w2.grad) + compare(h2.grad, torch_h2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetCiouDiagonalAngle(flow.unittest.TestCase): + def test_fused_get_ciou_diagonal_angle(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_fused_get_ciou_diagonal_angle_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(583, 1), (759, 1), (1234, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_ciou_result.py b/python/oneflow/test/modules/test_fused_get_ciou_result.py new file mode 100644 index 0000000000000000000000000000000000000000..36594d59fa19d875b3e6fd389457db4d8308075e --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_ciou_result.py @@ -0,0 +1,92 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_get_ciou_result_impl(test_case, device, shape): + eps = 1e-7 + x = [] + torch_x = [] + for _ in range(4): + tmp = flow.tensor( + np.random.uniform(0, 1, shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + v, iou, rho2, c2 = x[0], x[1], x[2], x[3] + y = flow._C.fused_get_ciou_result(v, iou, rho2, c2, eps)[0] + torch_v, torch_iou, torch_rho2, torch_c2 = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + ) + with torch.no_grad(): + torch_alpha = torch_v / (torch_v - torch_iou + (1.0 + eps)) + torch_y = torch_iou - (torch_rho2 / torch_c2 + torch_v * torch_alpha) + + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + compare(y, torch_y) + + res = y.sum() + torch_res = torch_y.sum() + res.backward() + torch_res.backward() + compare(v.grad, torch_v.grad) + compare(iou.grad, torch_iou.grad) + compare(rho2.grad, torch_rho2.grad) + compare(c2.grad, torch_c2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetCiouResultModule(flow.unittest.TestCase): + def test_get_ciou_result(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_get_ciou_result_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(492), (691, 1), (1162, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_convex_diagonal_squared.py b/python/oneflow/test/modules/test_fused_get_convex_diagonal_squared.py new file mode 100644 index 0000000000000000000000000000000000000000..a0d935d8604fb80764cc1be7d5406741fe609ccc --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_convex_diagonal_squared.py @@ -0,0 +1,134 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def torch_fused_get_convex_diagonal_squared( + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, eps +): + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) + c2 = cw ** 2 + ch ** 2 + eps + return c2 + + +def _test_fused_get_convex_diagonal_squared_impl(test_case, device, shape): + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + eps = 1e-8 + x = [] + torch_x = [] + for _ in range(8): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2 = ( + x[0], + x[1], + x[2], + x[3], + x[4], + x[5], + x[6], + x[7], + ) + ( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + ) = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + torch_x[4], + torch_x[5], + torch_x[6], + torch_x[7], + ) + c2 = flow._C.fused_get_convex_diagonal_squared( + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2, eps + ) + torch_c2 = torch_fused_get_convex_diagonal_squared( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + eps, + ) + compare(c2, torch_c2) + + c2.sum().backward() + torch_c2.sum().backward() + compare(b1_x1.grad, torch_b1_x1.grad) + compare(b1_x2.grad, torch_b1_x2.grad) + compare(b2_x1.grad, torch_b2_x1.grad) + compare(b2_x2.grad, torch_b2_x2.grad) + compare(b1_y1.grad, torch_b1_y1.grad) + compare(b1_y2.grad, torch_b1_y2.grad) + compare(b2_y1.grad, torch_b2_y1.grad) + compare(b2_y2.grad, torch_b2_y2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetCenterDistModule(flow.unittest.TestCase): + def test_fused_get_convex_diagonal_squared(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_fused_get_convex_diagonal_squared_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(583, 1), (759, 1), (1234, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_intersection_area.py b/python/oneflow/test/modules/test_fused_get_intersection_area.py new file mode 100644 index 0000000000000000000000000000000000000000..19fcebdc546758f91d14d03868b74c7fcd21abfa --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_intersection_area.py @@ -0,0 +1,128 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def torch_get_intersection_area(b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2): + return (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * ( + torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1) + ).clamp(0) + + +def _test_fused_get_intersection_area_impl(test_case, device, shape): + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}\n", + ) + + x = [] + torch_x = [] + for _ in range(8): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=True, + ) + ) + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2 = ( + x[0], + x[1], + x[2], + x[3], + x[4], + x[5], + x[6], + x[7], + ) + ( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + ) = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + torch_x[4], + torch_x[5], + torch_x[6], + torch_x[7], + ) + inter = flow._C.fused_get_intersection_area( + b1_x1, b1_x2, b2_x1, b2_x2, b1_y1, b1_y2, b2_y1, b2_y2 + ) + torch_inter = torch_get_intersection_area( + torch_b1_x1, + torch_b1_x2, + torch_b2_x1, + torch_b2_x2, + torch_b1_y1, + torch_b1_y2, + torch_b2_y1, + torch_b2_y2, + ) + compare(inter, torch_inter) + + inter.sum().backward() + torch_inter.sum().backward() + compare(b1_x1.grad, torch_b1_x1.grad) + compare(b1_x2.grad, torch_b1_x2.grad) + compare(b2_x1.grad, torch_b2_x1.grad) + compare(b2_x2.grad, torch_b2_x2.grad) + compare(b1_y1.grad, torch_b1_y1.grad) + compare(b1_y2.grad, torch_b1_y2.grad) + compare(b2_y1.grad, torch_b2_y1.grad) + compare(b2_y2.grad, torch_b2_y2.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetIntersectionAreaModule(flow.unittest.TestCase): + def test_fused_get_inter_intersection_area(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_fused_get_intersection_area_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(583, 1), (759, 1), (1234, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_get_iou.py b/python/oneflow/test/modules/test_fused_get_iou.py new file mode 100644 index 0000000000000000000000000000000000000000..9e5a08f491393f6d837b889717ecbb2ad4226f01 --- /dev/null +++ b/python/oneflow/test/modules/test_fused_get_iou.py @@ -0,0 +1,92 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_get_iou_impl(test_case, device, shape): + eps = 1e-7 + x = [] + torch_x = [] + for _ in range(5): + tmp = flow.tensor( + np.random.uniform(0, 1, shape), + dtype=flow.float64, + device=flow.device(device), + requires_grad=True if (_ < 2 or _ > 3) else False, + ) + x.append(tmp) + torch_x.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float64, + device=torch.device(device), + requires_grad=True if (_ < 2 or _ > 3) else False, + ) + ) + w1, h1, w2, h2, inter = x[0], x[1], x[2], x[3], x[4] + iou = flow._C.fused_get_iou(w1, h1, w2, h2, inter, eps) + torch_w1, torch_h1, torch_w2, torch_h2, torch_inter = ( + torch_x[0], + torch_x[1], + torch_x[2], + torch_x[3], + torch_x[4], + ) + torch_iou = torch_inter / ( + torch_w1 * torch_h1 + torch_w2 * torch_h2 - torch_inter + eps + ) + + def compare(a, b, rtol=1e-5, atol=1e-5, w1=w1, h1=h1, w2=w2, h2=h2, inter=inter): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + compare(iou, torch_iou) + + res = iou.sum() + torch_res = torch_iou.sum() + res.backward() + torch_res.backward() + compare(w1.grad, torch_w1.grad) + compare(h1.grad, torch_h1.grad) + compare(inter.grad, torch_inter.grad) + + +@flow.unittest.skip_unless_1n1d() +class TestGetIouModule(flow.unittest.TestCase): + def test_get_iou(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_get_iou_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(492), (691, 1), (1162, 1)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_glu.py b/python/oneflow/test/modules/test_fused_glu.py new file mode 100644 index 0000000000000000000000000000000000000000..1eaa40334699a793b4d8ac966f4c0a33ce0df756 --- /dev/null +++ b/python/oneflow/test/modules/test_fused_glu.py @@ -0,0 +1,324 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import time +import datetime +import numpy as np +from collections import OrderedDict + +import oneflow as flow +import oneflow.nn as nn +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + +is_profiling = False + + +class Glu(nn.Module): + def __init__(self): + super().__init__() + + def forward( + self, + x: flow.Tensor, + w: flow.Tensor, + b: flow.Tensor, + v: flow.Tensor = None, + c: flow.Tensor = None, + split_mode: bool = False, + activation: str = "none", + ) -> flow.Tensor: + # matmul + matmul_wx = flow._C.matmul( + input=x, other=w, transpose_a=False, transpose_b=True + ) + if split_mode: + matmul_vx = flow._C.matmul( + input=x, other=v, transpose_a=False, transpose_b=True + ) + + # add bias + matmul_wx_b = flow._C.add(input=matmul_wx, other=b) + if split_mode: + matmul_vx_c = flow._C.add(input=matmul_vx, other=c) + + # chunk + if split_mode: + hidden_state = matmul_wx_b + gate = matmul_vx_c + else: + hidden_state, gate = matmul_wx_b.chunk(2, dim=-1) + + # activation and element-wise product + if activation == "none": + return hidden_state * gate + elif activation == "sigmoid": + return hidden_state * flow.sigmoid(gate) + elif activation == "relu": + return hidden_state * flow.relu(gate) + elif activation == "gelu": + return hidden_state * flow.gelu(gate) + elif activation == "fast_gelu": + return hidden_state * flow._C.fast_gelu(gate) + elif activation == "silu": + return hidden_state * flow.silu(gate) + + +""" + @desp: profiling fused glu implementation with split weight matrix +""" + + +def _test_fused_glu_split_profiling(test_case, params: dict): + # config test data + m = params["m"] + n = params["n"] + k = params["k"] + act = params["act"] + + # generate random input + x = np.random.randn(2, m, k) + w = np.random.randn(n, k) # transpose + # w_t = np.transpose(weight).contiguous() #sync + b = np.random.randn(n) + v = np.random.randn(n, k) # transpose + c = np.random.randn(n) + + # transfer to gpu memory + input_tensor_x = flow.FloatTensor(x).to("cuda") + input_tensor_w = flow.FloatTensor(w).to("cuda") + input_tensor_b = flow.FloatTensor(b).to("cuda") + input_tensor_v = flow.FloatTensor(v).to("cuda") + input_tensor_c = flow.FloatTensor(c).to("cuda") + + # test: fused op + output_tensor_y = flow._C.fused_glu( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + v=input_tensor_v, + c=input_tensor_c, + activation=act, + ) + + +""" + @desp: check the functionality of fused glu implementation with split weight matrix +""" + + +def _test_fused_glu_split(test_case, params: dict): + # config test data + m = params["m"] + n = params["n"] + k = params["k"] + act = params["act"] + + # generate random input + x = np.random.randn(2, m, k) + w = np.random.randn(n, k) # transpose + # w_t = np.transpose(weight).contiguous() #sync + b = np.random.randn(n) + v = np.random.randn(n, k) # transpose + c = np.random.randn(n) + + # transfer to gpu memory + input_tensor_x = flow.FloatTensor(x).to("cuda") + input_tensor_w = flow.FloatTensor(w).to("cuda") + input_tensor_b = flow.FloatTensor(b).to("cuda") + input_tensor_v = flow.FloatTensor(v).to("cuda") + input_tensor_c = flow.FloatTensor(c).to("cuda") + + # test: fused op + output_tensor_y = flow._C.fused_glu( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + v=input_tensor_v, + c=input_tensor_c, + activation=act, + ) + + # test: naive result + flow_module = Glu() + origin_output_tensor_y = flow_module.forward( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + v=input_tensor_v, + c=input_tensor_c, + split_mode=True, + activation=act, + ) + + test_case.assertTrue( + np.allclose( + output_tensor_y.numpy(), + origin_output_tensor_y.numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + +""" + @desp: profiling fused glu implementation +""" + + +def _test_fused_glu_profiling(test_case, params: dict): + # config test data + m = params["m"] + n = params["n"] + k = params["k"] + act = params["act"] + + # generate random input + x = np.random.randn(2, m, k) + w = np.random.randn(n * 2, k) # transpose + # w_t = np.transpose(weight).contiguous() #sync + b = np.random.randn(n * 2) + + # transfer tensors to gpu memory + input_tensor_x = flow.FloatTensor(x).to("cuda") + input_tensor_w = flow.FloatTensor(w).to("cuda") + input_tensor_b = flow.FloatTensor(b).to("cuda") + + # test: fused op + output_tensor_y = flow._C.fused_glu( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + v=None, + c=None, + activation=act, + ) + + +""" + @desp: check the functionality of fused glu implementation +""" + + +def _test_fused_glu(test_case, params: dict): + # config test data + m = params["m"] + n = params["n"] + k = params["k"] + act = params["act"] + + # generate random input + x = np.random.randn(2, m, k) + w = np.random.randn(n * 2, k) # transpose + # w_t = np.transpose(weight).contiguous() #sync + b = np.random.randn(n * 2) + + # transfer tensors to gpu memory + input_tensor_x = flow.FloatTensor(x).to("cuda") + input_tensor_w = flow.FloatTensor(w).to("cuda") + input_tensor_b = flow.FloatTensor(b).to("cuda") + + # test: fused op + output_tensor_y = flow._C.fused_glu( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + v=None, + c=None, + activation=act, + ) + + # test: naive result + flow_module = Glu() + origin_output_tensor_y = flow_module.forward( + x=input_tensor_x, + w=input_tensor_w, + b=input_tensor_b, + split_mode=False, + activation=act, + ) + + test_case.assertTrue( + np.allclose( + output_tensor_y.numpy(), + origin_output_tensor_y.numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test gpu cases") +class TestFusedGlu(flow.unittest.TestCase): + def test_gather(test_case): + arg_dict = OrderedDict() + # set up test functions + if is_profiling: + # for profiling test + arg_dict["test_fun"] = [ + _test_fused_glu_profiling, + _test_fused_glu_split_profiling, + ] + else: + # for functionality test + arg_dict["test_fun"] = [ + _test_fused_glu, + _test_fused_glu_split, + ] + + # set up profiling functions + if is_profiling: + # set up test functions + arg_dict["params"] = [ + # for profiling + {"m": 256, "k": 1280, "n": 1280, "act": "none"}, + {"m": 256, "k": 1280, "n": 2560, "act": "none"}, + {"m": 256, "k": 1280, "n": 5120, "act": "none"}, + ] + else: + # for functionality test + arg_dict["params"] = [ + # m=256, k=1280, n=5120 + {"m": 256, "k": 1280, "n": 5120, "act": "none"}, + {"m": 256, "k": 1280, "n": 5120, "act": "sigmoid"}, + {"m": 256, "k": 1280, "n": 5120, "act": "relu"}, + {"m": 256, "k": 1280, "n": 5120, "act": "gelu"}, + {"m": 256, "k": 1280, "n": 5120, "act": "fast_gelu"}, + {"m": 256, "k": 1280, "n": 5120, "act": "silu"}, + # m=1024, k=640, n=2560 + {"m": 1024, "k": 640, "n": 2560, "act": "none"}, + {"m": 1024, "k": 640, "n": 2560, "act": "sigmoid"}, + {"m": 1024, "k": 640, "n": 2560, "act": "relu"}, + {"m": 1024, "k": 640, "n": 2560, "act": "gelu"}, + {"m": 1024, "k": 640, "n": 2560, "act": "fast_gelu"}, + {"m": 1024, "k": 640, "n": 2560, "act": "silu"}, + # m=4096, k=320, n=1280 + {"m": 4096, "k": 320, "n": 1280, "act": "none"}, + {"m": 4096, "k": 320, "n": 1280, "act": "sigmoid"}, + {"m": 4096, "k": 320, "n": 1280, "act": "relu"}, + {"m": 4096, "k": 320, "n": 1280, "act": "gelu"}, + {"m": 4096, "k": 320, "n": 1280, "act": "fast_gelu"}, + {"m": 4096, "k": 320, "n": 1280, "act": "silu"}, + ] + + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_matmul_bias.py b/python/oneflow/test/modules/test_fused_matmul_bias.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc1c47dd3a6e7cc71179da39da7b6961b5b468d --- /dev/null +++ b/python/oneflow/test/modules/test_fused_matmul_bias.py @@ -0,0 +1,120 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import numpy as np + + +def _matmul_bias(x, weight, bias, add_to_output): + return flow._C.add( + flow._C.bias_add( + flow._C.matmul(x, weight, transpose_b=True), bias, axis=len(x.shape) - 1 + ), + add_to_output, + ) + + +def _test_fused_matmul_add_bias( + test_case, batchsize, in_feature, out_feature, _add_to_output, dtype, device, +): + add_to_output = np.zeros((*batchsize, out_feature)) + if _add_to_output: + add_to_output = np.random.uniform( + low=-1, high=1, size=(*batchsize, out_feature) + ) + x = np.random.uniform(low=-1, high=1, size=(*batchsize, in_feature)) + weight = np.random.uniform(low=-1, high=1, size=(out_feature, in_feature)) + bias = np.random.uniform(low=-1, high=1, size=(out_feature)) + + naive_x = flow.tensor(x, dtype=dtype, requires_grad=True) + naive_weight = flow.tensor(weight, dtype=dtype, requires_grad=True) + naive_bias = flow.tensor(bias, dtype=dtype, requires_grad=True) + naive_add_to_output = flow.tensor(add_to_output, dtype=dtype, requires_grad=True) + + fused_x = flow.tensor(x, dtype=dtype, device=device, requires_grad=True) + fused_weight = flow.tensor(weight, dtype=dtype, device=device, requires_grad=True) + fused_bias = flow.tensor(bias, dtype=dtype, device=device, requires_grad=True) + fused_add_to_output = None + if _add_to_output: + fused_add_to_output = flow.tensor( + add_to_output, dtype=dtype, device=device, requires_grad=False + ) + + navie_y = _matmul_bias(naive_x, naive_weight, naive_bias, naive_add_to_output) + fused_y = flow._C.fused_matmul_bias( + fused_x, fused_weight, fused_bias, fused_add_to_output + ) + + y = navie_y.sum() + fused_y.sum() + y.backward() + + # TODO: relative error might be too high... + # Test output equality + if _add_to_output: + test_case.assertTrue( + np.allclose(navie_y.numpy(), fused_y.numpy(), atol=5e-2, rtol=1e-4) + ) + else: + test_case.assertTrue( + np.allclose(navie_y.numpy(), fused_y.numpy(), atol=5e-2, rtol=1e-4) + ) + + # Test grad equality + test_case.assertTrue( + np.allclose(naive_x.grad.numpy(), fused_x.grad.numpy(), atol=5e-2, rtol=1e-4) + ) + + test_case.assertTrue( + np.allclose( + naive_weight.grad.numpy(), fused_weight.grad.numpy(), atol=5e-2, rtol=1e-4 + ) + ) + test_case.assertTrue( + np.allclose( + naive_bias.grad.numpy(), fused_bias.grad.numpy(), atol=1e-4, rtol=1e-4 + ) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestFusedMatmulBiasAddRelu(flow.unittest.TestCase): + def test_fused_matmul_op(test_case): + args_dict = OrderedDict() + args_dict["test_fun"] = [_test_fused_matmul_add_bias] + args_dict["batchsize"] = [ + (1,), + (4,), + (8,), + (2, 4), + (2, 4, 8), + (2, 4, 4, 4, 8), + ] + args_dict["in_feature"] = [96, 128] + args_dict["out_feature"] = [512, 1024, 288, 1] + args_dict["_add_to_output"] = [True] + args_dict["dtype"] = [flow.float32, flow.float64] + args_dict["device"] = ["cuda", "cpu"] + + for arg in GenArgList(args_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_multi_head_attention_inference.py b/python/oneflow/test/modules/test_fused_multi_head_attention_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..26338ea8cd0c669c59c2a28e7797a85e703cc663 --- /dev/null +++ b/python/oneflow/test/modules/test_fused_multi_head_attention_inference.py @@ -0,0 +1,135 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +from oneflow.test_utils.test_util import GenArgList +import math + +import oneflow as flow + + +def _ref(query, key, value, num_heads, causal=False): + query = query.view(query.shape[0], query.shape[1], num_heads, -1).permute( + 0, 2, 1, 3 + ) + key = key.view(key.shape[0], key.shape[1], num_heads, -1).permute(0, 2, 3, 1) + value = value.view(value.shape[0], value.shape[1], num_heads, -1).permute( + 0, 2, 1, 3 + ) + scores = flow.matmul(query, key) / math.sqrt(query.shape[-1]) + if causal: + causal_mask = flow.triu( + flow.ones( + scores.shape[-2], scores.shape[-1], dtype=flow.bool, device="cuda" + ), + 1, + ) + scores = flow.masked_fill(scores, causal_mask, float("-inf")) + attn = flow.softmax(scores, dim=-1) + out = flow.matmul(attn, value) + out = out.permute(0, 2, 1, 3) + out = out.reshape(out.shape[0], out.shape[1], -1) + return out + + +def _fused_mha(query, key, value, num_heads, causal=False): + return flow._C.fused_multi_head_attention_inference( + query, key, value, num_heads, causal=causal + ) + + +def _test_fused_multi_head_attention_inference( + test_case, + batch_size, + num_heads, + query_seq_len, + kv_seq_len, + query_head_size, + value_head_size, + dtype, + causal=False, +): + + query = flow.randn( + (batch_size, query_seq_len, num_heads * query_head_size), + device="cuda", + dtype=flow.float, + ).to(dtype) + key = flow.randn( + (batch_size, kv_seq_len, num_heads * query_head_size), + device="cuda", + dtype=flow.float, + ).to(dtype) + value = flow.randn( + (batch_size, kv_seq_len, num_heads * value_head_size), + device="cuda", + dtype=flow.float, + ).to(dtype) + + ref_out = _ref(query, key, value, num_heads, causal).numpy() + fused_out = _fused_mha(query, key, value, num_heads, causal).numpy() + + test_case.assertTrue(np.allclose(ref_out, fused_out, atol=1e-2, rtol=1e-2)) + + +@unittest.skipIf(True, "skip test") +@flow.unittest.skip_unless_1n1d() +class TestFusedMultiHeadAttentionInference(flow.unittest.TestCase): + def test_multi_head_attention_inference(test_case): + args_dict = OrderedDict() + # test_case,batch_size, num_heads,query_seq_len, kv_seq_len,query_head_size,value_head_size,dtype + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 4096, 4096, 40, 40, flow.float16 + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 4096, 77, 40, 40, flow.float16 + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 1024, 1024, 80, 80, flow.float16 + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 1024, 77, 80, 80, flow.float16 + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 256, 256, 160, 160, flow.float16 + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 256, 77, 160, 160, flow.float16 + ) + + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 4096, 4096, 40, 40, flow.float + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 4096, 77, 40, 40, flow.float + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 1024, 1024, 80, 80, flow.float + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 1024, 77, 80, 80, flow.float + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 256, 256, 160, 160, flow.float + ) + _test_fused_multi_head_attention_inference( + test_case, 2, 8, 256, 77, 160, 160, flow.float + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_fused_scale_mask_softmax.py b/python/oneflow/test/modules/test_fused_scale_mask_softmax.py index 56bed5f94cc9fbdfba23ed6fc0112c7f53b27314..a67b93d6826971fd2164dbe32427cf5f4397387e 100644 --- a/python/oneflow/test/modules/test_fused_scale_mask_softmax.py +++ b/python/oneflow/test/modules/test_fused_scale_mask_softmax.py @@ -35,7 +35,7 @@ def _test_fused_scale_mask_softmax( if broadcast_dim: mask_size[broadcast_dim] = 1 - mask = np.random.randint(0, 2, size=mask_size, dtype=np.bool) + mask = np.random.randint(0, 2, size=mask_size, dtype=bool) fused_x_tensor = flow.tensor(x, dtype=flow.float32).to("cuda") fused_mask_tensor = flow.tensor(mask, dtype=flow.bool).to("cuda") fused_x_tensor.requires_grad = True diff --git a/python/oneflow/test/modules/test_fused_scale_mask_softmax_dropout.py b/python/oneflow/test/modules/test_fused_scale_mask_softmax_dropout.py index ea4a22254c026565ccf752656c0d76944513a875..ba9744669378d1bebf2e8504924518ff9f17276b 100644 --- a/python/oneflow/test/modules/test_fused_scale_mask_softmax_dropout.py +++ b/python/oneflow/test/modules/test_fused_scale_mask_softmax_dropout.py @@ -40,7 +40,7 @@ def _test_fused_scale_mask_softmax_dropout( mask_size = [batch_size, num_heads, seq_length, seq_length] if broadcast_dim: mask_size[broadcast_dim] = 1 - mask = np.random.randint(0, 2, size=mask_size, dtype=np.bool) + mask = np.random.randint(0, 2, size=mask_size, dtype=bool) fused_x_tensor = flow.tensor(x, dtype=flow.float32).to("cuda") fused_mask_tensor = flow.tensor(mask, dtype=flow.bool).to("cuda") diff --git a/python/oneflow/test/modules/test_fused_weighted_sum.py b/python/oneflow/test/modules/test_fused_weighted_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..0b95119c6490f6c7f8e6597ea9558b6823f88fdb --- /dev/null +++ b/python/oneflow/test/modules/test_fused_weighted_sum.py @@ -0,0 +1,79 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +from oneflow.test_utils.test_util import GenArgList +import math +import random +import os + +import oneflow as flow + + +def _ref(inputs, weights, alpha, init_grad, device, dtype): + inputs = [flow.tensor(t).to(device).to(dtype) for t in inputs] + for t in inputs: + t.requires_grad = True + init_grad = flow.tensor(init_grad).to(device).to(dtype) + out = inputs[0] * weights[0] + for i, w in zip(inputs[1:], weights[1:]): + out += i * w + out = out * alpha + out.backward(init_grad) + return out, [t.grad for t in inputs] + + +def _fused_weighted_sum(inputs, weights, alpha, init_grad, device, dtype): + inputs = [flow.tensor(t).to(device).to(dtype) for t in inputs] + for t in inputs: + t.requires_grad = True + init_grad = flow.tensor(init_grad).to(device).to(dtype) + out = flow._C.fused_weighted_sum(inputs, weights, alpha) + out.backward(init_grad) + return out, [t.grad for t in inputs] + + +def _test_fused_weighted_sum(test_case, shape, n, device, dtype): + inputs = [np.random.randn(*shape) for _ in range(n)] + init_grad = np.random.randn(*shape) + weights = [random.random() for _ in range(n)] + alpha = random.random() + out, grads = _fused_weighted_sum(inputs, weights, alpha, init_grad, device, dtype) + ref, ref_grads = _ref(inputs, weights, alpha, init_grad, device, dtype) + test_case.assertTrue(np.allclose(ref, out, atol=1e-5, rtol=1e-5)) + for (grad, ref_grad) in zip(grads, ref_grads): + test_case.assertTrue(np.allclose(ref_grad, grad, atol=1e-5, rtol=1e-5)) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestFusedWeightedSum(flow.unittest.TestCase): + def test_fused_weighted_sum(test_case): + _test_fused_weighted_sum(test_case, (1024, 1024), 1, "cuda", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 3, "cuda", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 8, "cuda", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 11, "cuda", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 21, "cuda", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 1, "cpu", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 3, "cpu", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 8, "cpu", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 11, "cpu", flow.float32) + _test_fused_weighted_sum(test_case, (1024, 1024), 21, "cpu", flow.float32) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_gather.py b/python/oneflow/test/modules/test_gather.py index 8765242adaa6458680abe0fc9d38259008825f69..ca7518ce77513ebdec699796f4c738c7036ccf2b 100644 --- a/python/oneflow/test/modules/test_gather.py +++ b/python/oneflow/test/modules/test_gather.py @@ -150,11 +150,11 @@ class TestGather(flow.unittest.TestCase): for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) - @autotest(check_graph=True) + @autotest(n=5) def test_flow_gather_with_random_data(test_case): device = random_device() input = random_tensor(ndim=4, dim1=3, dim2=4, dim3=5).to(device) - dim = random(0, 4).to(int) + dim = random(-4, 4).to(int) index = random_tensor( ndim=4, dim1=random(1, 3).to(int), @@ -164,7 +164,7 @@ class TestGather(flow.unittest.TestCase): ).to(device) return torch.gather(input, dim, index) - @autotest(auto_backward=False, check_graph=True) + @autotest(n=5, auto_backward=False, check_graph=True) def test_flow_gather_bool_with_random_data(test_case): device = random_device() input = random_tensor(ndim=4, dim1=3, dim2=4, dim3=5).to( @@ -180,6 +180,11 @@ class TestGather(flow.unittest.TestCase): ).to(device) return torch.gather(input, dim, index) + @profile(torch.gather) + def profile_gather(test_case): + t = torch.ones(1000, 1000) + torch.gather(t, 1, torch.ones(1000, 1000, dtype=torch.int64)) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_gelu_approximate.py b/python/oneflow/test/modules/test_gelu_approximate.py new file mode 100644 index 0000000000000000000000000000000000000000..88fd1cca959a759329c82b07d7919dd857042ffc --- /dev/null +++ b/python/oneflow/test/modules/test_gelu_approximate.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import math +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest +import torch + + +class NewGELUActivation(torch.nn.Module): + """ + Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see + the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 + """ + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return ( + 0.5 + * input + * ( + 1.0 + + torch.tanh( + math.sqrt(2.0 / math.pi) + * (input + 0.044715 * torch.pow(input, 3.0)) + ) + ) + ) + + +def _test_gelu_approximate(test_case, device): + torch_gelu = NewGELUActivation() + x = np.random.randn(2, 4, 3) + torch_x = torch.tensor(x, requires_grad=True, device=torch.device(device)) + oneflow_x = flow.tensor(x, requires_grad=True, device=flow.device(device)) + torch_y = torch_gelu(torch_x) + oneflow_y = flow._C.gelu_with_approximate(oneflow_x, "tanh") + test_case.assertTrue(np.allclose(torch_y.detach().cpu().numpy(), oneflow_y.numpy())) + torch_y_sum = torch_y.sum() + torch_y_sum.backward() + oneflow_y_sum = oneflow_y.sum() + oneflow_y_sum.backward() + test_case.assertTrue( + np.allclose(torch_x.grad.cpu().numpy(), oneflow_x.grad.numpy()) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestModule(flow.unittest.TestCase): + def test_gelu_approximate(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_gelu_approximate] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_generator.py b/python/oneflow/test/modules/test_generator.py index e30647a83b87cbe59ec35c0f3447b59e3e715835..e59a71072351016509dc15a20a518fe018ed03aa 100644 --- a/python/oneflow/test/modules/test_generator.py +++ b/python/oneflow/test/modules/test_generator.py @@ -103,6 +103,7 @@ class TestDefaultGenerator(flow.unittest.TestCase): cuda_gen = flow.Generator(device="cuda") state = cuda_gen.get_state() + @unittest.skip("the curandstate is no longer used by normal kernel") def test_generator_setstate(test_case): cpu_gen = flow.default_generator flow.randn(100, 100, dtype=flow.float32, device="cpu", generator=cpu_gen) @@ -157,7 +158,10 @@ class TestDefaultGenerator(flow.unittest.TestCase): new_state = flow.get_rng_state() test_case.assertTrue(np.allclose(new_state.numpy(), state.numpy())) - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + # NOTE: according to https://github.com/Oneflow-Inc/oneflow/pull/9102#discussion_r973811389 + # tensor init function fallback to `flow.default_generator.seed()`, and this test will be normal while tensor init functions reconstructed.(using op/kernel) + # @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @unittest.skipIf(True, "tensor init functions need to be reconstructed!") def test_tensor_init(test_case): flow.manual_seed(0) x = flow.ones(2) diff --git a/python/oneflow/test/modules/test_global_0_dim_tensor.py b/python/oneflow/test/modules/test_global_0_dim_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..18f4fc9fedce040cc2f9ed174293e78c41e70cf4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_0_dim_tensor.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_0_dim_tensor(test_case, placement, sbp): + x1 = random_tensor(0).to_global(placement=placement, sbp=sbp) + x2 = random_tensor(0).to_global(placement=placement, sbp=sbp) + y1 = x1 * x2 + y2 = x1 + x2 + return y1 + y2 + + +@autotest(n=1, check_graph=True) +def _test_1dim_slice(test_case, placement, sbp): + x = random_tensor(1, random(1, 4) * 8).to_global(placement=placement, sbp=sbp) + return x[5] + + +class TestZeroDimensionTensor(flow.unittest.TestCase): + @globaltest + def test_0_dim_tensor(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=0): + _test_0_dim_tensor(test_case, placement, sbp) + for sbp in all_sbp(placement, max_dim=1): + _test_1dim_slice(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_TripletMarginLoss.py b/python/oneflow/test/modules/test_global_TripletMarginLoss.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc7f312c77c2d410bcaec38f1d4a0d74a90d1e3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_TripletMarginLoss.py @@ -0,0 +1,52 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_global_triplet_marginloss_with_random_data(test_case, placement, sbp): + margin = random().to(float) + p = random().to(float) + swap = random_bool() + reduction = oneof("none", "sum", "mean", nothing()) + m = torch.nn.TripletMarginLoss(margin=margin, p=p, swap=swap, reduction=reduction) + m.train(random()) + anchor = random_tensor(2, 8, 16).to_global(placement, sbp) + pos = random_tensor(2, 8, 16).to_global(placement, sbp) + neg = random_tensor(2, 8, 16).to_global(placement, sbp) + y = m(anchor, pos, neg) + return y + + +class TestGlobalTripletMarginLoss(flow.unittest.TestCase): + @globaltest + def test_global_triplet_marginloss_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_triplet_marginloss_with_random_data( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_abs.py b/python/oneflow/test/modules/test_global_abs.py new file mode 100644 index 0000000000000000000000000000000000000000..be11cd0a3e6b7342921875e06956c62bf1090d06 --- /dev/null +++ b/python/oneflow/test/modules/test_global_abs.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_abs_with_ndim_data(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.abs(x) + return y + + +class TestAbsModule(flow.unittest.TestCase): + @globaltest + def test_abs_with_ndim_data(test_case): + for placement in all_placement(): + ndim = random(0, 4).to(int).value() + for sbp in all_sbp(placement, max_dim=ndim): + _test_abs_with_ndim_data(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_activation.py b/python/oneflow/test/modules/test_global_activation.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_activation.py rename to python/oneflow/test/modules/test_global_activation.py diff --git a/python/oneflow/test/modules/test_global_adaptive_pool.py b/python/oneflow/test/modules/test_global_adaptive_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..54d3b36cbae1616f9b6da30b41e6ba64cb4794a1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_adaptive_pool.py @@ -0,0 +1,90 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from packaging import version +import unittest +from typing import Union, Tuple +import torch as torch_original + +import oneflow as flow +import oneflow.unittest +from oneflow.nn.common_types import _size_1_t +from oneflow.test_utils.automated_test_util import * + +NoneType = type(None) +# Not the same as those in PyTorch because 'output_size' cannot be NoneType (even in 'torch.nn.AdaptiveAvgPoolXd') +_size_2_opt_t_not_none = Union[int, Tuple[Union[int, NoneType], Union[int, NoneType]]] +_size_3_opt_t_not_none = Union[ + int, Tuple[Union[int, NoneType], Union[int, NoneType], Union[int, NoneType]] +] + + +@autotest(n=1, check_graph=True) +def _test_adaptive_avgpoolnd(test_case, ndim, pool_size, placement, sbp): + dims = [random(1, 3) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + if pool_size == 1: + m = torch.nn.AdaptiveAvgPool1d(output_size=random().to(_size_1_t)) + elif pool_size == 2: + m = torch.nn.AdaptiveAvgPool2d(output_size=random().to(_size_2_opt_t_not_none)) + elif pool_size == 3: + m = torch.nn.AdaptiveAvgPool3d(output_size=random().to(_size_3_opt_t_not_none)) + else: + raise ValueError("pool size should be 1, 2 or 3, but got %d" % pool_size) + m.train(random()) + y = m(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_adaptive_avgpoolnd_functional(test_case, ndim, pool_size, placement, sbp): + dims = [random(1, 3) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + if pool_size == 1: + return torch.nn.functional.adaptive_avg_pool1d(x, output_size=random().to(int)) + elif pool_size == 2: + return torch.nn.functional.adaptive_avg_pool2d(x, output_size=random().to(int)) + elif pool_size == 3: + return torch.nn.functional.adaptive_avg_pool3d(x, output_size=random().to(int)) + + +class TestAdaptiveAvgPool(flow.unittest.TestCase): + @globaltest + def test_adaptive_avgpool(test_case): + for placement in all_placement(): + ndim = 3 + for sbp in all_sbp(placement, max_dim=2): + _test_adaptive_avgpoolnd(test_case, ndim, 1, placement, sbp) + _test_adaptive_avgpoolnd_functional(test_case, ndim, 1, placement, sbp) + + ndim = 4 + for sbp in all_sbp(placement, max_dim=2): + _test_adaptive_avgpoolnd(test_case, ndim, 2, placement, sbp) + _test_adaptive_avgpoolnd_functional(test_case, ndim, 2, placement, sbp) + + # GPU version 'nn.AdaptiveAvgPool3d' has a bug in PyTorch before '1.10.0' + if ( + version.parse(torch_original.__version__) < version.parse("1.10.0") + and placement.type == "cuda" + ): + continue + ndim = 5 + for sbp in all_sbp(placement, max_dim=2): + _test_adaptive_avgpoolnd(test_case, ndim, 3, placement, sbp) + _test_adaptive_avgpoolnd_functional(test_case, ndim, 3, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_add.py b/python/oneflow/test/modules/test_global_add.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_add.py rename to python/oneflow/test/modules/test_global_add.py diff --git a/python/oneflow/test/modules/test_global_addcdiv.py b/python/oneflow/test/modules/test_global_addcdiv.py new file mode 100644 index 0000000000000000000000000000000000000000..356a3b71b0ea3ae6bf9be3da936df40a938a09d0 --- /dev/null +++ b/python/oneflow/test/modules/test_global_addcdiv.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_addcdiv(test_case, ndim, placement, sbp): + shape = [random(2, 4) * 8 for i in range(ndim)] + input = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp) + tensor1 = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp) + tensor2 = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp) + value = random(2, 4).to(int) + output = torch.addcdiv(input, tensor1, tensor2, value=value) + return output + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_addcdiv(test_case): + ndim = random(2, 4).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_addcdiv(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_addcmul.py b/python/oneflow/test/modules/test_global_addcmul.py new file mode 100644 index 0000000000000000000000000000000000000000..8acad974e9995b3afa45cdb28046d04820ac9555 --- /dev/null +++ b/python/oneflow/test/modules/test_global_addcmul.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_addcmul(test_case, ndim, placement, sbp): + shape = [random(low=2, high=3) * 8 for i in range(ndim)] + + input = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp) + tensor1 = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) + tensor2 = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) + value = random(3, 6).to(int) + output = torch.addcmul(input, tensor1, tensor2, value=value) + return output + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_addcmul(test_case): + ndim = random(low=2, high=5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_addcmul(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_addmm.py b/python/oneflow/test/modules/test_global_addmm.py new file mode 100644 index 0000000000000000000000000000000000000000..4bee031692c56cfc6606d1d151cb4ff5ae9b3143 --- /dev/null +++ b/python/oneflow/test/modules/test_global_addmm.py @@ -0,0 +1,68 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_addmm_with_random_data(test_case, placement, sbp): + m = random(1, 3) * 8 + n = random(1, 3) * 8 + k = random(1, 3) * 8 + input = random_tensor(ndim=2, dim0=m, dim1=n).to_global( + placement=placement, sbp=sbp + ) + mat1 = random_tensor(ndim=2, dim0=m, dim1=k).to_global(placement=placement, sbp=sbp) + mat2 = random_tensor(ndim=2, dim0=k, dim1=n).to_global(placement=placement, sbp=sbp) + y = torch.addmm( + input, mat1, mat2, beta=random().to(float), alpha=random().to(float), + ) + return y + + +@autotest(n=1, check_graph=True) +def _test_addmm_broadcast_with_random_data(test_case, placement, sbp): + m = random(1, 3) * 8 + n = random(1, 3) * 8 + k = random(1, 3) * 8 + input = random_tensor(ndim=2, dim0=1, dim1=1).to_global( + placement=placement, sbp=[flow.sbp.broadcast for _ in range(len(sbp))] + ) + mat1 = random_tensor(ndim=2, dim0=m, dim1=k).to_global(placement=placement, sbp=sbp) + mat2 = random_tensor(ndim=2, dim0=k, dim1=n).to_global(placement=placement, sbp=sbp) + y = torch.addmm( + input, mat1, mat2, beta=random().to(float), alpha=random().to(float), + ) + return y + + +class TestAddmm(flow.unittest.TestCase): + @globaltest + def test_addmm(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_addmm_with_random_data(test_case, placement, sbp) + _test_addmm_broadcast_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_affine_grid.py b/python/oneflow/test/modules/test_global_affine_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..657224ba06d26a75a480acd987e90bdf6ca03967 --- /dev/null +++ b/python/oneflow/test/modules/test_global_affine_grid.py @@ -0,0 +1,68 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=True) +def _test_affine_grid_2d_with_random_data(test_case, placement, sbp): + N = random(1, 3).to(int).value() * 8 + C = random(1, 8).to(int).value() + H = random(1, 8).to(int).value() + W = random(1, 8).to(int).value() + align_corners = oneof(True, False).value() + dims = [N, 2, 3] + + theta = random_tensor(3, *dims).to_global(placement=placement, sbp=sbp) + output = torch.nn.functional.affine_grid( + theta, (N, C, H, W), align_corners=align_corners + ) + return output + + +@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=True) +def _test_affine_grid_3d_with_random_data(test_case, placement, sbp): + N = random(1, 3).to(int) * 8 + C = random(1, 8).to(int) + D = random(1, 8).to(int) + H = random(1, 8).to(int) + W = random(1, 8).to(int) + align_corners = oneof(True, False) + dims = [N, 3, 4] + + theta = random_tensor(3, *dims).to_global(placement=placement, sbp=sbp) + output = torch.nn.functional.affine_grid( + theta, (N, C, D, H, W), align_corners=align_corners + ) + return output + + +class TestAffineGrid(flow.unittest.TestCase): + @globaltest + def test_affine_grid(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_affine_grid_2d_with_random_data(test_case, placement, sbp) + _test_affine_grid_3d_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_argmax.py b/python/oneflow/test/modules/test_global_argmax.py new file mode 100644 index 0000000000000000000000000000000000000000..128728ea3ffc524d4b9ee02fd1712245ad8ef063 --- /dev/null +++ b/python/oneflow/test/modules/test_global_argmax.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_argmax_with_random_data(test_case, ndim, placement, sbp): + dims = [8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.argmax(x, dim=random(0, ndim).to(int), keepdim=random().to(bool)) + return y + + +@unittest.skip("TODO: sometimes global TestArgmax fails on 2-GPU runs") +class TestArgmax(flow.unittest.TestCase): + @globaltest + def test_argmax(test_case): + ndim = random(1, 3).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_argmax_with_random_data(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_argmin.py b/python/oneflow/test/modules/test_global_argmin.py new file mode 100644 index 0000000000000000000000000000000000000000..e05234d1d297c1622b17e8d6f6ceb0fa7370659d --- /dev/null +++ b/python/oneflow/test/modules/test_global_argmin.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_argmin_with_random_data(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.argmin(x, dim=random(0, ndim).to(int), keepdim=random().to(bool)) + return y + + +@unittest.skip("TODO: sometimes global TestArgmin fails on 2-GPU runs") +class TestArgmin(flow.unittest.TestCase): + @globaltest + def test_argmin(test_case): + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_argmin_with_random_data(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_argsort.py b/python/oneflow/test/modules/test_global_argsort.py new file mode 100644 index 0000000000000000000000000000000000000000..976e6b34f6a9528e897c4d73e369ed35993fec6a --- /dev/null +++ b/python/oneflow/test/modules/test_global_argsort.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_argsort_with_random_data(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.argsort( + x, dim=random(low=-ndim, high=ndim).to(int), descending=random_bool() + ) + return y + + +@unittest.skip("argsort has bug not found at now.") +class TestArgsort(flow.unittest.TestCase): + @globaltest + def test_argsort(test_case): + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_argsort_with_random_data(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_argwhere.py b/python/oneflow/test/modules/test_global_argwhere.py new file mode 100644 index 0000000000000000000000000000000000000000..e9ce285902cf6103065366dcaeb2478ffd3d0f27 --- /dev/null +++ b/python/oneflow/test/modules/test_global_argwhere.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import torch as torch_ori + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_argwhere_with_random_data(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + # PyTorch has no argwhere before v1.11, so we use nonzero instead of argwhere for PyTorch + # y = torch.argwhere(x) + y = x.clone() + y.oneflow = flow.argwhere(x.oneflow) + y.pytorch = torch_ori.nonzero(x.pytorch) + return y + + +class TestArgwhere(flow.unittest.TestCase): + @globaltest + def test_argwhere(test_case): + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_argwhere_with_random_data(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_atleast.py b/python/oneflow/test/modules/test_global_atleast.py new file mode 100644 index 0000000000000000000000000000000000000000..38f991c74be985808b6a7d918274629c0acea2dd --- /dev/null +++ b/python/oneflow/test/modules/test_global_atleast.py @@ -0,0 +1,73 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=False) +def _test_atleast1d_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=1, dim0=8).to_global(placement, sbp) + y = random_tensor(ndim=2, dim0=8).to_global(placement, sbp) + out = torch.atleast_1d([x, y]) + return out + + +@autotest(n=2, check_graph=False) +def _test_atleast2d_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=1, dim0=8).to_global(placement, sbp) + y = random_tensor(ndim=2, dim0=8).to_global(placement, sbp) + z = random_tensor(ndim=3, dim0=8).to_global(placement, sbp) + out = torch.atleast_2d([x, y, z]) + return out + + +@autotest(n=2, check_graph=False) +def _test_atleast3d_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=1, dim0=8).to_global(placement, sbp) + y = random_tensor(ndim=2, dim0=8).to_global(placement, sbp) + z = random_tensor(ndim=3, dim0=8).to_global(placement, sbp) + p = random_tensor(ndim=4, dim0=8).to_global(placement, sbp) + out = torch.atleast_3d([x, y, z, p]) + return out + + +class TestAtLeastModule(flow.unittest.TestCase): + @globaltest + def test_atleast1d_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_atleast1d_with_random_data(test_case, placement, sbp) + + @globaltest + def test_atleast2d_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_atleast2d_with_random_data(test_case, placement, sbp) + + @globaltest + def test_atleast3d_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_atleast3d_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_avgpool.py b/python/oneflow/test/modules/test_global_avgpool.py new file mode 100644 index 0000000000000000000000000000000000000000..4fd694ab8967f31776594a12d95c1bf9afede505 --- /dev/null +++ b/python/oneflow/test/modules/test_global_avgpool.py @@ -0,0 +1,144 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_avgpool1d_with_random_data(test_case, placement, sbp): + m = torch.nn.AvgPool1d( + kernel_size=random(4, 6), + stride=random(1, 3), + padding=random(1, 3), + ceil_mode=random(), + count_include_pad=random(), + ) + m.train(random()) + m.to_global(placement=placement, sbp=sbp) + ndim = 3 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = m(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_avgpool2d_with_random_data(test_case, placement, sbp): + m = torch.nn.AvgPool2d( + kernel_size=random(4, 6), + stride=random(1, 3), + padding=random(1, 3), + ceil_mode=random(), + count_include_pad=random(), + divisor_override=random().to(int), + ) + m.train(random()) + m.to_global(placement=placement, sbp=sbp) + ndim = 4 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = m(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_avgpool3d_with_random_data(test_case, placement, sbp): + m = torch.nn.AvgPool3d( + kernel_size=random(4, 6), + stride=random(1, 3), + padding=random(1, 3), + ceil_mode=random(), + count_include_pad=random(), + divisor_override=random().to(int), + ) + m.train(random()) + m.to_global(placement=placement, sbp=sbp) + ndim = 5 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = m(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_functional_avgpool1d_with_random_data(test_case, placement, sbp): + ndim = 3 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.nn.functional.avg_pool1d( + x, + kernel_size=random(1, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + ceil_mode=random_bool(), + count_include_pad=random_bool(), + ) + return y + + +@autotest(n=1, check_graph=True) +def _test_functional_avgpool2d_with_random_data(test_case, placement, sbp): + ndim = 4 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.nn.functional.avg_pool2d( + x, + kernel_size=random(1, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + ceil_mode=random_bool(), + count_include_pad=random_bool(), + ) + return y + + +@autotest(n=1, check_graph=True) +def _test_functional_avgpool3d_with_random_data(test_case, placement, sbp): + ndim = 5 + dims = [random(1, 3) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.nn.functional.avg_pool3d( + x, + kernel_size=random(1, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + ceil_mode=random_bool(), + count_include_pad=random_bool(), + ) + return y + + +class TestAvgPoolingModule(flow.unittest.TestCase): + @globaltest + def test_avg_pooling(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_avgpool1d_with_random_data(test_case, placement, sbp) + _test_functional_avgpool1d_with_random_data(test_case, placement, sbp) + for sbp in all_sbp(placement, max_dim=2): + _test_avgpool2d_with_random_data(test_case, placement, sbp) + _test_functional_avgpool2d_with_random_data(test_case, placement, sbp) + for sbp in all_sbp(placement, max_dim=2): + _test_avgpool3d_with_random_data(test_case, placement, sbp) + _test_functional_avgpool3d_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_batch_gather.py b/python/oneflow/test/modules/test_global_batch_gather.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_batch_gather.py rename to python/oneflow/test/modules/test_global_batch_gather.py diff --git a/python/oneflow/test/modules/test_global_bincount.py b/python/oneflow/test/modules/test_global_bincount.py new file mode 100644 index 0000000000000000000000000000000000000000..8357f27c78f8c4398ba13ca1d7608dbf086c5c95 --- /dev/null +++ b/python/oneflow/test/modules/test_global_bincount.py @@ -0,0 +1,39 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False, auto_backward=False) +def _test_bincount(test_case, placement, sbp): + x = random_tensor(1, 64, low=0, dtype=int).to_global(placement=placement, sbp=sbp) + weight = random_tensor(1, 64).to_global(placement=placement, sbp=sbp) + minlength = random(1, 100).to(int) + return torch.bincount(x, weight, minlength) + + +class TestBinCountModule(flow.unittest.TestCase): + @globaltest + def test_bincount(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, valid_split_axis=0): + _test_bincount(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_broadcase_like.py b/python/oneflow/test/modules/test_global_broadcase_like.py new file mode 100644 index 0000000000000000000000000000000000000000..6313962a3eab1e275ac59be80a001c2074c1560c --- /dev/null +++ b/python/oneflow/test/modules/test_global_broadcase_like.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_broadcast_like(test_case, placement, sbp): + like_shape = [8] * 4 + like = random_tensor(4, *like_shape).to_global( + placement, random_sbp(placement, max_dim=4) + ) + x = random_tensor(2, *(8, 8)).to_global(placement, sbp) + # oneflow + of_y = flow.broadcast_like(x.oneflow, like.oneflow) + # pytorch + torch_y = x.pytorch.broadcast_to(like_shape) + + test_case.assertTrue(np.allclose(of_y.numpy(), torch_y.detach().cpu().numpy())) + + +def _test_broadcast_like_expand_dims(test_case, placement, sbp): + like_shape = [8] * 4 + like = random_tensor(4, *like_shape).to_global( + placement, random_sbp(placement, max_dim=4) + ) + x = random_tensor(2, *(8, 8)).to_global(placement, sbp) + # oneflow + of_y = flow.broadcast_like(x.oneflow, like.oneflow, [1, 3]) + # pytorch + torch_y = x.pytorch.view(8, 1, 8, 1).broadcast_to(like_shape) + + test_case.assertTrue(np.allclose(of_y.numpy(), torch_y.detach().cpu().numpy())) + + +class TestGlobalBroadcaseLike(flow.unittest.TestCase): + @globaltest + def test_broadcase_like(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_broadcast_like(test_case, placement, sbp) + _test_broadcast_like_expand_dims(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_broadcast_matmul.py b/python/oneflow/test/modules/test_global_broadcast_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd67c5d7ae4eb16e41e20a59a1a67ea391c96d2 --- /dev/null +++ b/python/oneflow/test/modules/test_global_broadcast_matmul.py @@ -0,0 +1,92 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_flow_tensor_global_broadcast_matmul_with_random_data( + test_case, placement, x_sbp, y_sbp +): + batch_dim = random(1, 6) * 8 + k = random(1, 6) * 4 + x = random_tensor(ndim=3, dim0=batch_dim, dim2=k).to_global( + placement=placement, sbp=x_sbp + ) + y = random_tensor(ndim=2, dim0=k).to_global(placement=placement, sbp=y_sbp) + return x.matmul(y) + + +@autotest(n=1, check_graph=False) +def _test_flow_tensor_global_x_broadcast_y_matmul(test_case, placement, x_sbp, y_sbp): + batch_dim = random(1, 6) * 8 + k = random(1, 6) * 4 + x = random_tensor(ndim=2, dim1=k).to_global(placement=placement, sbp=x_sbp) + y = random_tensor(ndim=3, dim0=batch_dim, dim1=k).to_global( + placement=placement, sbp=y_sbp + ) + + return x.matmul(y) + + +@autotest(n=1, check_graph=False) +def _test_flow_tensor_global_broadcast_matmul_with_same_dims( + test_case, placement, x_sbp, y_sbp +): + k = random(1, 6) * 8 + batch_dim = random(1, 6) * 8 + x = random_tensor(ndim=3, dim0=batch_dim, dim1=4, dim2=k).to_global( + placement=placement, sbp=x_sbp + ) + y = random_tensor(ndim=3, dim0=batch_dim, dim1=k, dim2=4).to_global( + placement=placement, sbp=y_sbp + ) + return x.matmul(y) + + +class TestGlobalBroadcastMatmulModule(flow.unittest.TestCase): + @globaltest + def test_global_broadcast_matmul_with_random_data(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, max_dim=2, valid_split_axis=[0]): + for y_sbp in all_sbp(placement, max_dim=2, except_split=True): + _test_flow_tensor_global_broadcast_matmul_with_random_data( + test_case, placement, x_sbp, y_sbp + ) + + @globaltest + def test_global_x_broadcast_y_matmul(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, max_dim=2, except_split=True): + for y_sbp in all_sbp(placement, max_dim=2, valid_split_axis=[0]): + _test_flow_tensor_global_x_broadcast_y_matmul( + test_case, placement, x_sbp, y_sbp + ) + + @globaltest + def test_global_broadcast_matmul_with_same_dims(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, max_dim=2): + for y_sbp in all_sbp(placement, max_dim=2): + _test_flow_tensor_global_broadcast_matmul_with_same_dims( + test_case, placement, x_sbp, y_sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_broadcast_ops.py b/python/oneflow/test/modules/test_global_broadcast_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..4a31753735bb0c7fd77e399b0590e7935929bcb1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_broadcast_ops.py @@ -0,0 +1,60 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1) +def _test_global_broadcast_tensors( + test_case, input_shape, other_shape, placement, x_sbp, y_sbp +): + x = random_tensor(len(input_shape), *input_shape).to_global( + placement=placement, sbp=x_sbp + ) + y = random_tensor(len(other_shape), *other_shape).to_global( + placement=placement, sbp=y_sbp + ) + return torch.broadcast_tensors(x, y) + + +class TestGlobalBroadcastOps(flow.unittest.TestCase): + # flow.broadcast_shapes's input are shapes, so it can't be tested in global mode + # flow.broadcast_to is an alias of flow.expand, so its global tests are same as flow.expand's + + @globaltest + def test_global_tensors(test_case): + shapes = [((2, 2), (2, 2, 2)), ((1, 2), (3, 1))] + for input_shape, other_shape in shapes: + for placement in all_placement(): + for x_sbp in all_sbp( + placement, + max_dim=2, + valid_split_axis=[x for x in input_shape if x != 1], + ): + for y_sbp in all_sbp( + placement, + max_dim=2, + valid_split_axis=[y for y in other_shape if y != 1], + ): + _test_global_broadcast_tensors( + test_case, input_shape, other_shape, placement, x_sbp, y_sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_cast.py b/python/oneflow/test/modules/test_global_cast.py new file mode 100644 index 0000000000000000000000000000000000000000..4003a635133312b92af5e2fd6ed41fb020afba72 --- /dev/null +++ b/python/oneflow/test/modules/test_global_cast.py @@ -0,0 +1,1537 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import os + +import numpy as np + +import oneflow as flow +from oneflow import nn +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList +from oneflow import Tensor +from oneflow.nn.graph.util import ArgsTree + + +@flow.unittest.skip_unless_1n4d() +class TestGlobalCastModule_1n4d(flow.unittest.TestCase): + def test_to_global_flatten_hierarchy(test_case): + x = flow.ones((4, 4), dtype=flow.int32) + sbp = (flow.sbp.partial_sum,) + y = x.to_global( + placement=flow.placement("cpu", ranks=[[0, 1], [2, 3]]), + sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), + ) + placement = flow.placement("cpu", ranks=[0, 1, 2, 3]) + y = y.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_to_global_flatten_hierarchy_cpu_to_gpu(test_case): + x = flow.ones((4, 4), dtype=flow.int32) + sbp = (flow.sbp.partial_sum,) + y = x.to_global( + placement=flow.placement("cpu", ranks=[[0, 1], [2, 3]]), + sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), + ) + placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) + y = y.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_to_global_flatten_hierarchy_gpu_to_cpu(test_case): + x = flow.ones((4, 4), dtype=flow.int32) + sbp = (flow.sbp.partial_sum,) + y = x.to_global( + placement=flow.placement("cuda", ranks=[[0, 1], [2, 3]]), + sbp=(flow.sbp.partial_sum, flow.sbp.partial_sum), + ) + placement = flow.placement("cpu", ranks=[0, 1, 2, 3]) + y = y.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + + def test_to_global_broadcast_shape_dtype(test_case): + if int(os.getenv("RANK")) < 2: + x = flow.ones((4, 4), dtype=flow.int32) + else: + x = flow.zeros((1,), dtype=flow.float) + placement = flow.placement("cpu", ranks=[0, 1]) + sbp = (flow.sbp.split(0),) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_local_to_global_2d_sbp(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + sbp = (flow.sbp.split(0), flow.sbp.partial_sum) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_local_to_global_sp_2_bb(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + sbp = (flow.sbp.split(0), flow.sbp.partial_sum) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + y = y.to_global(sbp=(flow.sbp.broadcast, flow.sbp.broadcast)) + test_case.assertEqual(y.sbp, (flow.sbp.broadcast, flow.sbp.broadcast)) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32) * 2) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_local_to_global_ps0_2_s0s0(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + x = x * int(os.getenv("RANK")) + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + sbp = (flow.sbp.partial_sum, flow.sbp.split(0)) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + sbp = (flow.sbp.split(0), flow.sbp.split(0)) + y = y.to_global(sbp=sbp) + z = y.to_local() + if int(os.getenv("RANK")) < 2: + scale = 2 + else: + scale = 4 + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * scale) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_local_to_global_s0p_2_s0s0(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + x = x * int(os.getenv("RANK")) + placement = flow.placement("cuda", ranks=[[0, 1], [2, 3]]) + sbp = (flow.sbp.split(0), flow.sbp.partial_sum) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + sbp = (flow.sbp.split(0), flow.sbp.split(0)) + y = y.to_global(sbp=sbp) + z = y.to_local() + if int(os.getenv("RANK")) < 2: + scale = 1 + else: + scale = 5 + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * scale) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_to_global_loop_broadcast_shape_dtype(test_case): + if int(os.getenv("RANK")) < 2: + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + a = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + else: + x = flow.zeros((1,), dtype=flow.float) + a = flow.zeros((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + sbp = (flow.sbp.split(0),) + for i in range(1000): + if i % 100 == 0: + print(i) + y = x.to_global(placement=placement, sbp=sbp) + b = a.to_global(placement=placement, sbp=flow.sbp.broadcast) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + + +@flow.unittest.skip_unless_1n2d() +class TestGlobalCastModule_1n2d(flow.unittest.TestCase): + def test_to_global_broadcast_shape_dtype(test_case): + if os.getenv("RANK") == "0": + x = flow.ones((4, 4), dtype=flow.int32) + else: + x = flow.zeros((1,), dtype=flow.float) + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + + def test_local_to_global_broadcast_data(test_case): + if int(os.getenv("RANK")) == 0: + x = flow.ones((4, 4), dtype=flow.int32) + else: + x = flow.zeros((4, 4), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + sbp = (flow.sbp.broadcast,) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue(np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32))) + + def test_cuda_global_to_global_cpu_s2b(test_case): + x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) + sbp = (flow.sbp.broadcast,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue(np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32))) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_s2b(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) + sbp = (flow.sbp.broadcast,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue(np.array_equal(z.numpy(), np.ones((8, 4), dtype=np.int32))) + + def test_cuda_global_to_global_cpu_s2p(test_case): + x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) + sbp = (flow.sbp.partial_sum,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + if int(os.getenv("RANK")) == 0: + test_case.assertTrue( + np.array_equal( + z.numpy(), + np.concatenate( + ( + np.ones((4, 4), dtype=np.int32), + np.zeros((4, 4), dtype=np.int32), + ), + axis=0, + ), + ) + ) + else: + test_case.assertTrue( + np.array_equal( + z.numpy(), + np.concatenate( + ( + np.zeros((4, 4), dtype=np.int32), + np.ones((4, 4), dtype=np.int32), + ), + axis=0, + ), + ) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_s2p(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.split(0)) + sbp = (flow.sbp.partial_sum,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (8, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + if int(os.getenv("RANK")) == 0: + test_case.assertTrue( + np.array_equal( + z.numpy(), + np.concatenate( + ( + np.ones((4, 4), dtype=np.int32), + np.zeros((4, 4), dtype=np.int32), + ), + axis=0, + ), + ) + ) + else: + test_case.assertTrue( + np.array_equal( + z.numpy(), + np.concatenate( + ( + np.zeros((4, 4), dtype=np.int32), + np.ones((4, 4), dtype=np.int32), + ), + axis=0, + ), + ) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_b2p(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.broadcast) + sbp = (flow.sbp.partial_sum,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + if int(os.getenv("RANK")) == 0: + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32)) + ) + else: + test_case.assertTrue( + np.array_equal(z.numpy(), np.zeros((4, 4), dtype=np.int32)) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_b2s(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.broadcast) + sbp = (flow.sbp.split(0),) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue(np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32))) + + def test_cuda_global_to_global_cpu_p2s(test_case): + x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) + sbp = (flow.sbp.split(0),) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * 2) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_p2s(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) + sbp = (flow.sbp.split(0),) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((2, 4), dtype=np.int32) * 2) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_cuda_h2d(test_case): + x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + cuda_placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) + y = y.to_global(placement=cuda_placement, sbp=flow.sbp.partial_sum) + test_case.assertEqual(y.sbp, (flow.sbp.partial_sum,)) + test_case.assertEqual(y.placement, cuda_placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue(np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32))) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_cpu_p2b(test_case): + x = flow.ones((4, 4), device=flow.device("cpu"), dtype=flow.int32) + placement = flow.placement("cpu", ranks=[0, 1]) + cuda_placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) + import time + + y = y.to_global(placement=cuda_placement, sbp=flow.sbp.partial_sum) + sbp = (flow.sbp.broadcast,) + y = y.to_global(placement=cuda_placement, sbp=sbp) + y = y.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32) * 2) + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_cuda_global_to_global_p2b(test_case): + x = flow.ones((4, 4), device=flow.device("cuda"), dtype=flow.int32) + placement = flow.placement("cuda", ranks=[0, 1]) + y = x.to_global(placement=placement, sbp=flow.sbp.partial_sum) + sbp = (flow.sbp.broadcast,) + y = y.to_global(sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + test_case.assertEqual(y.dtype, flow.int32) + z = y.to_local() + test_case.assertTrue( + np.array_equal(z.numpy(), np.ones((4, 4), dtype=np.int32) * 2) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestGlobalCastModule_1n1d(flow.unittest.TestCase): + def test_to_global(test_case): + x = flow.ones((4, 4)) + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + y = x.to_global(placement=placement, sbp=sbp) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + test_case.assertEqual(tuple(y.shape), (4, 4)) + + +def _test_cpu_p2b_with_random_parameter(test_case, device_list): + gen_float = np.random.random + gen_int = np.random.randint + dtype_list = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + flow.double, + ] + + def choose_shape_and_dtype(seed): + rng = np.random.default_rng(seed) + kdtype = rng.integers(low=1, high=len(dtype_list), size=1) + ndim = rng.integers(low=1, high=4, size=1) + shape = rng.integers(low=1, high=10, size=ndim) + return kdtype, shape + + for _ in range(10): + seed = flow.tensor(gen_int(1, 1000, 1)) + seed = seed.to_global( + placement=flow.placement.all(seed.device.type), sbp=flow.sbp.broadcast, + ) + seed = int(seed.to_local().numpy()) + kdtype, shape = choose_shape_and_dtype(seed) + if kdtype <= 3: + np_arr = gen_int(1, 10, shape) + else: + np_arr = gen_float(shape) + tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) + cpu_tensor = tensor.to_global( + placement=flow.placement("cpu", device_list), sbp=flow.sbp.partial_sum + ) + cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.broadcast) + tensor = tensor.to("cuda") + cuda_tensor = tensor.to_global( + placement=flow.placement("cuda", device_list), sbp=flow.sbp.partial_sum + ) + cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.broadcast) + test_case.assertTrue( + np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) + ) + + +def _test_cpu_s2b_with_random_parameter(test_case, device_list): + gen_float = np.random.random + gen_int = np.random.randint + dtype_list = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + flow.double, + ] + + def choose_shape_and_dtype(seed): + rng = np.random.default_rng(seed) + kdtype = rng.integers(low=1, high=len(dtype_list), size=1) + ndim = rng.integers(low=1, high=4, size=1) + shape = rng.integers(low=1, high=10, size=ndim) + return kdtype, shape + + for _ in range(10): + seed = flow.tensor(gen_int(1, 1000, 1)) + seed = seed.to_global( + placement=flow.placement.all(seed.device.type), sbp=flow.sbp.broadcast, + ) + seed = int(seed.to_local().numpy()) + kdtype, shape = choose_shape_and_dtype(seed) + if kdtype <= 3: + np_arr = gen_int(1, 10, shape) + else: + np_arr = gen_float(shape) + tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) + cpu_tensor = tensor.to_global( + placement=flow.placement("cpu", device_list), sbp=flow.sbp.split(0) + ) + cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.broadcast) + tensor = tensor.to("cuda") + cuda_tensor = tensor.to_global( + placement=flow.placement("cuda", device_list), sbp=flow.sbp.split(0) + ) + cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.broadcast) + test_case.assertTrue( + np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) + ) + + +def _test_cpu_p2s_with_random_parameter(test_case, device_list): + gen_float = np.random.random + gen_int = np.random.randint + dtype_list = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + flow.double, + ] + + def choose_shape_and_dtype(seed): + rng = np.random.default_rng(seed) + kdtype = rng.integers(low=1, high=len(dtype_list), size=1) + ndim = rng.integers(low=1, high=4, size=1) + shape = list(rng.integers(low=1, high=5, size=1) * 12) + list( + rng.integers(low=1, high=10, size=ndim - 1) + ) + return kdtype, shape + + for _ in range(10): + seed = flow.tensor(gen_int(1, 1000, 1)) + seed = seed.to_global( + placement=flow.placement.all(seed.device.type), sbp=flow.sbp.broadcast, + ) + seed = int(seed.to_local().numpy()) + kdtype, shape = choose_shape_and_dtype(seed) + if kdtype <= 3: + np_arr = gen_int(1, 10, shape) + else: + np_arr = gen_float(shape) + tensor = flow.tensor(np_arr, device="cpu", dtype=dtype_list[int(kdtype)]) + cpu_tensor = tensor.to_global( + placement=flow.placement("cpu", device_list), sbp=flow.sbp.partial_sum + ) + cpu_tensor = cpu_tensor.to_global(sbp=flow.sbp.split(0)) + tensor = tensor.to("cuda") + cuda_tensor = tensor.to_global( + placement=flow.placement("cuda", device_list), sbp=flow.sbp.partial_sum + ) + cuda_tensor = cuda_tensor.to_global(sbp=flow.sbp.split(0)) + test_case.assertTrue( + np.allclose(cpu_tensor.to_local().numpy(), cuda_tensor.to_local().numpy()) + ) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast(flow.unittest.TestCase): + def test_cpu_local_tensor_to_gpu_placement(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array([4, 6, 7, 8], dtype=np.float32) + else: + np_arr = np.array([0, 0, 0, 0], dtype=np.float32) + tensor = flow.tensor(np_arr, dtype=flow.float32) + placement = flow.placement("cuda", [0, 1, 2, 3]) + device = flow.device("cuda") + global_tensor = tensor.to_global(placement, flow.sbp.broadcast) + test_case.assertEqual(global_tensor.to_local().device, device) + test_case.assertEqual(global_tensor.placement, placement) + test_case.assertTrue( + np.array_equal( + global_tensor.to_local().numpy(), + np.array([4, 6, 7, 8], dtype=np.float32), + ) + ) + + def test_cpu_p2b_with_random_parameter(test_case): + arg_dict = OrderedDict() + arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] + for arg in GenArgList(arg_dict): + _test_cpu_p2b_with_random_parameter(test_case, *arg) + + def test_cpu_s2b_with_random_parameter(test_case): + arg_dict = OrderedDict() + arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] + for arg in GenArgList(arg_dict): + _test_cpu_s2b_with_random_parameter(test_case, *arg) + + def test_cpu_p2s_with_random_parameter(test_case): + arg_dict = OrderedDict() + arg_dict["device_list"] = [[0, 1], [1, 2, 3], [0, 1, 2, 3]] + for arg in GenArgList(arg_dict): + _test_cpu_p2s_with_random_parameter(test_case, *arg) + + def test_local_to_global_with_wrong_device(test_case): + np_arr = np.array([4, 6], dtype=np.float32) + tensor = flow.tensor( + np_arr, + device=flow.device("cuda:%d" % ((flow.env.get_rank() + 1) % 4)), + dtype=flow.float32, + ) + placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) + device = flow.device("cuda") + global_tensor = tensor.to_global(placement, flow.sbp.broadcast) + local_tensor = global_tensor.to_local() + test_case.assertEqual(local_tensor.device, device) + test_case.assertEqual(global_tensor.placement, placement) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast_S2S(flow.unittest.TestCase): + def test_global_to_global_s0_to_s1(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + split0_tensor = tensor.to_global(placement, flow.sbp.split(0)) + split1_tensor = split0_tensor.to_global(placement, flow.sbp.split(1)) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + split1_tensor.to_local().numpy(), + np.array( + [ + [4.0, 6.0], + [6.0, 2.0], + [3.0, 7.0], + [6.0, 8.0], + [2.0, 10.0], + [3.0, 9.0], + [4.0, 6.0], + [6.0, 8.0], + ], + dtype=np.float32, + ), + ) + ) + elif flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + split1_tensor.to_local().numpy(), + np.array( + [ + [5.0, 20.0], + [5.0, 7.0], + [5.0, 4.0], + [9.0, 4.0], + [10.0, 7.0], + [10.0, 5.0], + [6.0, 9.0], + [6.0, 4.0], + ], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_s1_to_s0(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + split_tensor = tensor.to_global(placement, flow.sbp.split(0)) + split1_tensor = split_tensor.to_global(placement, flow.sbp.split(1)) + split0_tensor = split1_tensor.to_global(placement, flow.sbp.split(0)) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + split0_tensor.to_local().numpy(), + np.array( + [ + [4.0, 6.0, 5.0, 20.0], + [6.0, 2.0, 5.0, 7.0], + [3.0, 7.0, 5.0, 4.0], + [6.0, 8.0, 9.0, 4.0], + ], + dtype=np.float32, + ), + ) + ) + elif flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + split0_tensor.to_local().numpy(), + np.array( + [ + [2.0, 10.0, 10.0, 7.0], + [3.0, 9.0, 10.0, 5.0], + [4.0, 6.0, 6.0, 9.0], + [6.0, 8.0, 6.0, 4.0], + ], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_s0_to_s1_cpu(test_case): + np_arr = np.random.randn(4, 12) + + cuda_device = flow.device("cuda") + cuda_tensor = flow.tensor(np_arr, device=cuda_device, dtype=flow.float32) + cuda_placement = flow.placement("cuda", ranks=[1, 3]) + cuda_split0_tensor = cuda_tensor.to_global(cuda_placement, flow.sbp.split(0)) + cuda_split1_tensor = cuda_split0_tensor.to_global( + cuda_placement, flow.sbp.split(1) + ) + + cpu_device = flow.device("cpu") + cpu_tensor = flow.tensor(np_arr, device=cpu_device, dtype=flow.float32) + cpu_placement = flow.placement("cpu", ranks=[1, 3]) + cpu_split0_tensor = cpu_tensor.to_global(cpu_placement, flow.sbp.split(0)) + cpu_split1_tensor = cpu_split0_tensor.to_global( + cpu_placement, flow.sbp.split(1) + ) + + if flow.env.get_rank() == 0 or flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + cuda_split1_tensor.to_local().numpy(), + cpu_split1_tensor.to_local().numpy(), + ) + ) + + def test_global_to_global_s1_to_s0_cpu(test_case): + np_arr = np.random.randn(4, 12) + + cuda_device = flow.device("cuda") + cuda_tensor = flow.tensor(np_arr, device=cuda_device, dtype=flow.float32) + cuda_placement = flow.placement("cuda", ranks=[0, 1]) + cuda_split_tensor = cuda_tensor.to_global(cuda_placement, flow.sbp.split(0)) + cuda_split1_tensor = cuda_split_tensor.to_global( + cuda_placement, flow.sbp.split(1) + ) + cuda_split0_tensor = cuda_split1_tensor.to_global( + cuda_placement, flow.sbp.split(0) + ) + + cpu_device = flow.device("cpu") + cpu_tensor = flow.tensor(np_arr, device=cpu_device, dtype=flow.float32) + cpu_placement = flow.placement("cpu", ranks=[0, 1]) + cpu_split_tensor = cpu_tensor.to_global(cpu_placement, flow.sbp.split(0)) + cpu_split1_tensor = cpu_split_tensor.to_global(cpu_placement, flow.sbp.split(1)) + cpu_split0_tensor = cpu_split1_tensor.to_global( + cpu_placement, flow.sbp.split(0) + ) + + if flow.env.get_rank() == 0 or flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + cuda_split0_tensor.to_local().numpy(), + cpu_split0_tensor.to_local().numpy(), + ) + ) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast_XToB(flow.unittest.TestCase): + def test_global_to_global_btb_gpu_to_gpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 1: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 2: + np_arr = np.array( + [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 3: + np_arr = np.array( + [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + global_tensor = tensor.to_global(placement, flow.sbp.broadcast) + new_placement = flow.placement("cuda", ranks=[0, 1, 2]) + broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(broadcast_tensor.placement, new_placement) + if flow.env.get_rank() != 3: + test_case.assertTrue( + np.array_equal( + broadcast_tensor.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_stb_gpu_to_gpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 1: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 2: + np_arr = np.array( + [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 3: + np_arr = np.array( + [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1, 2]) + global_tensor = tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) + broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(broadcast_tensor.placement, new_placement) + test_case.assertTrue( + np.array_equal( + broadcast_tensor.to_local().numpy(), + np.array( + [ + [4, 6, 5, 20], + [6, 8, 9, 0], + [3, 7, 5, 0], + [6, 8, 9, 0], + [2, 10, 10, 7], + [3, 9, 10, 5], + [4, 6, 6, 9], + [6, 8, 6, 4], + [9, 6, 5, 8], + [4, 9, 7, 0], + [2, 5, 7, 9], + [6, 8, 10, 0], + ], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_ptb_gpu_to_gpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 8, 9, 0], [3, 7, 5, 0], [6, 8, 9, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 1: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 2: + np_arr = np.array( + [[9, 6, 5, 8], [4, 9, 7, 0], [2, 5, 7, 9], [6, 8, 10, 0]], + dtype=np.float32, + ) + elif flow.env.get_rank() == 3: + np_arr = np.array( + [[9, 4, 5, 8], [7, 2, 9, 5], [6, 3, 9, 2], [3, 7, 5, 8]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1, 2]) + global_tensor = tensor.to_global(placement, flow.sbp.partial_sum) + new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) + broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(broadcast_tensor.placement, new_placement) + test_case.assertTrue( + np.array_equal( + broadcast_tensor.to_local().numpy(), + np.array( + [ + [15, 22, 20, 35], + [13, 26, 26, 5], + [9, 18, 18, 18], + [18, 24, 25, 4], + ], + dtype=np.float32, + ), + ) + ) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast_1ToN(flow.unittest.TestCase): + def test_global_to_global_1tob(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0]) + global_tensor = tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[0, 1]) + broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(broadcast_tensor.placement, new_placement) + if flow.env.get_rank() < 2: + test_case.assertTrue( + np.array_equal( + broadcast_tensor.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_1top(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", [0]) + global_tensor = tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[0, 1]) + partial_sum_tensor = global_tensor.to_global( + new_placement, flow.sbp.partial_sum + ) + test_case.assertEqual(partial_sum_tensor.placement, new_placement) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + partial_sum_tensor.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ), + ) + ) + elif flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + partial_sum_tensor.to_local().numpy(), + np.array( + [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_1tos(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0]) + global_tensor = tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[0, 1, 2, 3]) + split_tensor = global_tensor.to_global(new_placement, flow.sbp.split(0)) + test_case.assertEqual(split_tensor.placement, new_placement) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + split_tensor.to_local().numpy(), + np.array([[4, 6, 5, 20]], dtype=np.float32,), + ) + ) + elif flow.env.get_rank() == 1: + test_case.assertTrue( + np.array_equal( + split_tensor.to_local().numpy(), + np.array([[6, 2, 5, 7]], dtype=np.float32,), + ) + ) + elif flow.env.get_rank() == 2: + test_case.assertTrue( + np.array_equal( + split_tensor.to_local().numpy(), + np.array([[3, 7, 5, 4]], dtype=np.float32,), + ) + ) + elif flow.env.get_rank() == 3: + test_case.assertTrue( + np.array_equal( + split_tensor.to_local().numpy(), + np.array([[6, 8, 9, 4]], dtype=np.float32,), + ) + ) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast_NTo1(flow.unittest.TestCase): + def test_global_to_global_bt1(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + global_tensor = tensor.to_global(placement, flow.sbp.broadcast) + new_placement = flow.placement("cuda", ranks=[0]) + broadcast_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(broadcast_tensor.placement, new_placement) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + broadcast_tensor.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_st1(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + global_tensor = tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[0]) + partial_sum_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(partial_sum_tensor.placement, new_placement) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + partial_sum_tensor.to_local().numpy(), + np.array( + [ + [4, 6, 5, 20], + [6, 2, 5, 7], + [3, 7, 5, 4], + [6, 8, 9, 4], + [2, 10, 10, 7], + [3, 9, 10, 5], + [4, 6, 6, 9], + [6, 8, 6, 4], + ], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_pt1(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0, 1]) + global_tensor = tensor.to_global(placement, flow.sbp.partial_sum) + new_placement = flow.placement("cuda", ranks=[0]) + partial_sum_tensor = global_tensor.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(partial_sum_tensor.placement, new_placement) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + partial_sum_tensor.to_local().numpy(), + np.array( + [ + [6, 16, 15, 27], + [9, 11, 15, 12], + [7, 13, 11, 13], + [12, 16, 15, 8], + ], + dtype=np.float32, + ), + ) + ) + + +@flow.unittest.skip_unless_1n4d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGlobalCast_1To1(flow.unittest.TestCase): + def test_global_to_global_1to1_gpu_to_gpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[3]) + x = local_tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[2]) + y = x.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(y.placement, new_placement) + if flow.env.get_rank() == 2: + test_case.assertTrue( + np.array_equal( + y.to_local().numpy(), + np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_1to1_cpu_to_cpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cpu") + local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cpu", ranks=[0]) + x = local_tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cpu", ranks=[2]) + y = x.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(y.placement, new_placement) + if flow.env.get_rank() == 2: + test_case.assertTrue( + np.array_equal( + y.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_1to1_gpu_to_cpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cuda") + local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cuda", ranks=[0]) + x = local_tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cpu", ranks=[3]) + y = x.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(y.placement, new_placement) + if flow.env.get_rank() == 3: + test_case.assertTrue( + np.array_equal( + y.to_local().numpy(), + np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ), + ) + ) + + def test_global_to_global_1to1_cpu_to_gpu(test_case): + if flow.env.get_rank() == 0: + np_arr = np.array( + [[4, 6, 5, 20], [6, 2, 5, 7], [3, 7, 5, 4], [6, 8, 9, 4]], + dtype=np.float32, + ) + else: + np_arr = np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ) + device = flow.device("cpu") + local_tensor = flow.tensor(np_arr, device=device, dtype=flow.float32) + placement = flow.placement("cpu", ranks=[1]) + x = local_tensor.to_global(placement, flow.sbp.split(0)) + new_placement = flow.placement("cuda", ranks=[3]) + y = x.to_global(new_placement, flow.sbp.broadcast) + test_case.assertEqual(y.placement, new_placement) + if flow.env.get_rank() == 3: + test_case.assertTrue( + np.array_equal( + y.to_local().numpy(), + np.array( + [[2, 10, 10, 7], [3, 9, 10, 5], [4, 6, 6, 9], [6, 8, 6, 4]], + dtype=np.float32, + ), + ) + ) + + +class GraphTestModel(nn.Graph): + def __init__(self, model): + super().__init__() + self.model = model + + def build(self, x): + return self.model(x) + + +@flow.unittest.skip_unless_1n2d() +class TestToGlobalAndLocal(flow.unittest.TestCase): + placement = flow.placement("cpu", ranks=[0, 1]) + sbp = None + model = nn.Sequential(nn.Linear(8, 4), nn.ReLU(), nn.Linear(4, 2)) + local_graph_model = GraphTestModel(model) + global_graph_model = None + + def __all_global(test_case, input, placement, sbp): + if type(input) == Tensor: + test_case.assertTrue(input.is_global) + # check placement + test_case.assertEqual(placement.type, input.placement.type) + test_case.assertListEqual( + list(placement.ranks), list(input.placement.ranks) + ) + # check sbp + test_case.assertTupleEqual(sbp, input.sbp) + elif isinstance(input, (dict, tuple, list)): + node_tree = ArgsTree(input) + for node in node_tree.iter_nodes(): + if isinstance(node, Tensor): + test_case.assertTrue(node.is_global) + # check placement + test_case.assertEqual(placement.type, node.placement.type) + test_case.assertListEqual( + list(placement.ranks), list(node.placement.ranks) + ) + # check sbp + test_case.assertTupleEqual(sbp, node.sbp) + + def __all_local(test_case, input): + if type(input) == Tensor: + test_case.assertFalse(input.is_global) + elif isinstance(input, (dict, tuple, list)): + node_tree = ArgsTree(input) + for node in node_tree.iter_nodes(): + if isinstance(node, Tensor): + test_case.assertFalse(node.is_global) + + def _test_any_input(test_case): + tensor = flow.zeros((3, 4)) + tensor_list = [flow.tensor([1, 2, 3]), flow.randn((2, 3, 4))] + tensor_tuple = (flow.zeros((2, 2)), flow.ones((2, 3)), flow.randn((3, 5))) + tensor_dict = {"tensor": tensor, "tensor_lt": tensor_list} + random_combination = [ + None, + 1, + "test_str", + tensor, + tensor_list, + tensor_tuple, + tensor_dict, + ] + + inputs = [ + None, + 100, + "test_str", + tensor, + tensor_list, + tensor_tuple, + tensor_dict, + random_combination, + ] + global_inputs = [] + for i in inputs: + ret = flow.utils.global_view.to_global( + i, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + test_case.__all_global( + ret, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + global_inputs.append(ret) + + for i in global_inputs: + ret = flow.utils.global_view.to_local(i) + test_case.__all_local(ret) + + def _test_any_input_get_sbp_func(test_case): + def __get_sbp(input, tensor): + return TestToGlobalAndLocal.sbp + + tensor = flow.zeros((3, 4)) + tensor_list = [flow.tensor([1, 2, 3]), flow.randn((2, 3, 4))] + tensor_tuple = (flow.zeros((2, 2)), flow.ones((2, 3)), flow.randn((3, 5))) + tensor_dict = {"tensor": tensor, "tensor_lt": tensor_list} + random_combination = [ + None, + 1, + "test_str", + tensor, + tensor_list, + tensor_tuple, + tensor_dict, + ] + + inputs = [ + None, + 100, + "test_str", + tensor, + tensor_list, + tensor_tuple, + tensor_dict, + random_combination, + ] + global_inputs = [] + for i in inputs: + ret = flow.utils.global_view.to_global( + i, placement=TestToGlobalAndLocal.placement, sbp=__get_sbp, + ) + test_case.__all_global( + ret, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + global_inputs.append(ret) + + for i in global_inputs: + ret = flow.utils.global_view.to_local(i) + test_case.__all_local(ret) + + def _test_tensor_to_global(test_case): + local_tensor = flow.ones((3, 4)) + + # local tensor -> global tensor + global_tensor = flow.utils.global_view.to_global( + local_tensor, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + test_case.assertTrue(global_tensor.is_global) + + # global tensor -> global tensor + global_tensor = flow.utils.global_view.to_global( + global_tensor, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + test_case.assertTrue(global_tensor.is_global) + + # passing no placement and sbp + with test_case.assertRaises(ValueError): + global_tensor = flow.utils.global_view.to_global( + local_tensor, placement=None, sbp=None + ) + + # wrong sbp type + with test_case.assertRaises(TypeError): + global_tensor = flow.utils.global_view.to_global( + local_tensor, + placement=TestToGlobalAndLocal.placement, + sbp=(TestToGlobalAndLocal.sbp, 0), + ) + + def _test_tensor_to_local(test_case): + # global tensor -> local tensor + global_tensor = flow.ones( + (3, 4), + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + local_tensor = flow.utils.global_view.to_local(global_tensor) + test_case.assertFalse(local_tensor.is_global) + + def __test_state_dict_to_global(test_case, local_state_dict): + # local state dict -> global state dict + global_state_dict = flow.utils.global_view.to_global( + local_state_dict, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + test_case.__all_global( + global_state_dict, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + + # global state dict -> global state dict + global_state_dict = flow.utils.global_view.to_global( + global_state_dict, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + test_case.__all_global( + global_state_dict, + placement=TestToGlobalAndLocal.placement, + sbp=TestToGlobalAndLocal.sbp, + ) + + def __test_state_dict_to_local(test_case, global_state_dict): + # global state dict -> local state dict + local_state_dict = flow.utils.global_view.to_local(global_state_dict) + test_case.__all_local(local_state_dict) + + # local input, display warning + local_state_dict = flow.utils.global_view.to_local(local_state_dict) + + def _test_eagar_state_dict(test_case): + test_case.__test_state_dict_to_global(TestToGlobalAndLocal.model.state_dict()) + global_model = TestToGlobalAndLocal.model.to_global( + placement=TestToGlobalAndLocal.placement, sbp=TestToGlobalAndLocal.sbp + ) + test_case.__test_state_dict_to_local(global_model.state_dict()) + + def _test_graph_state_dict(test_case): + test_case.__test_state_dict_to_global( + TestToGlobalAndLocal.local_graph_model.state_dict() + ) + test_case.__test_state_dict_to_local( + TestToGlobalAndLocal.global_graph_model.state_dict() + ) + + def test_to_global_local(test_case): + sbp_types = [ + (flow.sbp.broadcast,), + (flow.sbp.split(0),), + (flow.sbp.partial_sum,), + ] + for sbp in sbp_types: + TestToGlobalAndLocal.sbp = sbp + TestToGlobalAndLocal.global_graph_model = GraphTestModel( + TestToGlobalAndLocal.model.to_global( + placement=TestToGlobalAndLocal.placement, sbp=sbp + ) + ) + test_case._test_any_input() + test_case._test_any_input_get_sbp_func() + test_case._test_tensor_to_global() + test_case._test_tensor_to_local() + test_case._test_eagar_state_dict() + test_case._test_graph_state_dict() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_chunk.py b/python/oneflow/test/modules/test_global_chunk.py new file mode 100644 index 0000000000000000000000000000000000000000..992c9b346a252a6521f1938473e2eb645c4d1b83 --- /dev/null +++ b/python/oneflow/test/modules/test_global_chunk.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_chunk(test_case, ndim, placement, sbp): + dims = [random(1, 3).to(int) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + dim = random(-ndim, ndim).to(int) + chunks = random(low=1, high=4).to(int) + y = torch.chunk(x, chunks=chunks, dim=dim) + z = torch.cat(y, dim=dim) + return z + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_chunk(test_case): + for placement in all_placement(): + ndim = random(1, 4).to(int).value() + for sbp in all_sbp(placement, max_dim=min(ndim, 2)): + _test_chunk(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_clone.py b/python/oneflow/test/modules/test_global_clone.py new file mode 100644 index 0000000000000000000000000000000000000000..d04fa226265e30795f7acbca30b81c52d9bc99d4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_clone.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def do_test_clone_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = y.clone() + return z + + +class TestCloneConsistent(flow.unittest.TestCase): + @globaltest + def test_clone(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_clone_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_coin_flip.py b/python/oneflow/test/modules/test_global_coin_flip.py new file mode 100644 index 0000000000000000000000000000000000000000..6bcc61ba128e9a80b86d338a1e9af99c432e576c --- /dev/null +++ b/python/oneflow/test/modules/test_global_coin_flip.py @@ -0,0 +1,103 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_coin_flip( + test_case, batch_size, random_seed, probability, placement, sbp +): + m = flow.nn.CoinFlip( + batch_size, random_seed, probability, placement=placement, sbp=sbp + ) + x = m() + + test_case.assertEqual(x.shape[0], batch_size) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_coin_flip( + test_case, batch_size, random_seed, probability, placement, sbp +): + class GlobalCoinFlipGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + self.m = flow.nn.CoinFlip( + batch_size, random_seed, probability, placement=placement, sbp=sbp + ) + + def build(self): + return self.m() + + model = GlobalCoinFlipGraph() + x = model() + + test_case.assertEqual(x.shape[0], batch_size) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestCoinFlipGlobal(flow.unittest.TestCase): + @globaltest + def test_coin_flip_global(test_case): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [8, 64] + arg_dict["random_seed"] = [None, 1, -1] + arg_dict["probability"] = [0.0, 0.5, 1.0] + for args in GenArgDict(arg_dict): + for placement in all_placement(): + # TODO: CoinFlip support cuda kernel + if placement.type == "cuda": + continue + + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_global_coin_flip( + test_case, **args, placement=placement, sbp=sbp + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_coin_flip_graph(test_case): + arg_dict = OrderedDict() + arg_dict["batch_size"] = [8] + arg_dict["random_seed"] = [None, 1, -1] + arg_dict["probability"] = [0.0, 0.5, 1.0] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + # TODO: CoinFlip support cuda kernel + # flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + # TODO: CoinFlip support cuda kernel + # flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_graph_coin_flip(test_case, **args, sbp=sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_concat.py b/python/oneflow/test/modules/test_global_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..5dc2ccf986cd61a163260c75d3200b156ea4241e --- /dev/null +++ b/python/oneflow/test/modules/test_global_concat.py @@ -0,0 +1,88 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_cat_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement=placement, sbp=sbp) + return torch.cat((x, x), random(0, 2).to(int)) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_concat_with_input_0_size_data(test_case, placement, sbp): + x = random_tensor(4, 8, 8, 2, 4).to_global(placement=placement, sbp=sbp) + y = random_tensor(4, 8, 8, random(0, 3) * 8, 4).to_global( + placement=placement, sbp=sbp + ) + z = torch.cat((x, y), dim=2) + return z + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_concat_with_output_0_size_data(test_case, placement, sbp): + x = random_tensor(4, 8, 8, 0, 4).to_global(placement=placement, sbp=sbp) + y = random_tensor(4, 8, 8, 0, 4).to_global(placement=placement, sbp=sbp) + z = torch.cat((x, y), dim=2) + return z + + +@autotest(n=1, check_graph=True) +def _test_cat_only_one_tensor(test_case, placement, sbp): + x = random_tensor(4, 8, 8, random(1, 3) * 8, 8).to_global( + placement=placement, sbp=sbp + ) + return torch.cat((x,), 0) + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_cat_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_cat_with_random_data(test_case, placement, sbp) + + @globaltest + def test_cat_only_one_tensor(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_cat_only_one_tensor(test_case, placement, sbp) + + @globaltest + def test_concat_with_input_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_concat_with_input_0_size_data(test_case, placement, sbp) + + @globaltest + def test_concat_with_output_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_concat_with_output_0_size_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_constant.py b/python/oneflow/test/modules/test_global_constant.py new file mode 100644 index 0000000000000000000000000000000000000000..4ffea982f4142fb6930eeca5880679668abf2183 --- /dev/null +++ b/python/oneflow/test/modules/test_global_constant.py @@ -0,0 +1,200 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_new_full(test_case, shape, full_value, placement, sbp): + + np_res = np.full(shape, full_value) + x = flow.ones(shape) + y = x.new_full(shape, full_value, placement=placement, sbp=sbp) + + test_case.assertEqual(y.shape, flow.Size(shape)) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + + y = y.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + test_case.assertTrue(np.array_equal(y.numpy(), np_res)) + + +def _test_global_graph_new_full(test_case, shape, full_value, placement, sbp): + + np_res = np.full(shape, full_value) + + class GlobalNewFullGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self,): + x = flow.ones(shape) + y = x.new_full(shape, full_value, placement=placement, sbp=sbp) + return y + + model = GlobalNewFullGraph() + y = model() + + test_case.assertEqual(y.shape, flow.Size(shape)) + test_case.assertEqual(y.sbp, sbp) + test_case.assertEqual(y.placement, placement) + + y = y.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + test_case.assertTrue(np.array_equal(y.numpy(), np_res)) + + +def _test_global_constant(test_case, func, shape, placement, sbp): + func2 = None + if func == "ones": + func = flow.ones + np_res = np.ones(shape) + elif func == "zeros": + func = flow.zeros + np_res = np.zeros(shape) + elif func == "new_zeros": + func = flow.zeros + np_res = np.zeros(shape) + func2 = flow.new_zeros + else: + raise NotImplementedError + + x = func(*shape, placement=placement, sbp=sbp) + if func2: + x = func2(x) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + x = x.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + test_case.assertTrue(np.array_equal(x.numpy(), np_res)) + + +def _test_graph_constant(test_case, func, shape, placement, sbp): + func2 = None + if func == "ones": + func = flow.ones + np_res = np.ones(shape) + elif func == "zeros": + func = flow.zeros + np_res = np.zeros(shape) + elif func == "new_zeros": + func = flow.zeros + np_res = np.zeros(shape) + func2 = flow.new_zeros + else: + raise NotImplementedError + + class GlobalConstantGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = func(*shape, placement=placement, sbp=sbp) + if func2: + x = func2(x) + return x + + model = GlobalConstantGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + x = x.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + test_case.assertTrue(np.array_equal(x.numpy(), np_res)) + + +class TestConstantGlobal(flow.unittest.TestCase): + @globaltest + def test_constant_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + functions = [ + "ones", + "zeros", + "new_zeros", + ] + for func in functions: + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_constant(test_case, func, shape, placement, sbp) + + full_values = [2, 3, 4] + for full_value in full_values: + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(shape),): + _test_global_new_full( + test_case, shape, full_value, placement, sbp + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_constant_graph(test_case): + arg_dict = OrderedDict() + arg_dict["func"] = ["ones", "zeros", "new_zeros"] + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + + for args in GenArgDict(arg_dict): + func = args["func"] + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_constant(test_case, func, shape, placement, sbp) + full_values = [2, 3, 4] + shapes = [(8,), (8, 8,), (8, 8, 8)] + for full_value in full_values: + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(shape)): + _test_global_graph_new_full( + test_case, shape, full_value, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_ctc_loss.py b/python/oneflow/test/modules/test_global_ctc_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..26d9c9b4ddbe2e359603b8b34d93e949d7888738 --- /dev/null +++ b/python/oneflow/test/modules/test_global_ctc_loss.py @@ -0,0 +1,187 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest +import torch +from oneflow.test_utils.automated_test_util.generators import * +from oneflow.test_utils.automated_test_util.torch_flow_dual_object import globaltest +from oneflow.test_utils.test_util import GenArgDict + + +def log_softmax(logits, axis=0): + max_value = np.max(logits, axis, keepdims=True) + exp = np.exp(logits - max_value) + exp_sum = np.sum(exp, axis, keepdims=True) + dist = exp / exp_sum + return np.log(dist) + + +def _compare_torch_and_oneflow( + test_case, + torch_ctc_loss, + flow_ctc_loss, + placement, + module_sbp, + in_sbp, + max_input_length, + batch_size, + num_classes, + max_target_length, +): + log_probs = np.random.random( + size=(max_input_length, batch_size, num_classes) + ).astype(np.float32) + log_probs = log_softmax(log_probs, axis=2) + targets = np.random.randint( + 1, high=num_classes, size=(batch_size, max_target_length), dtype=np.int32 + ) + input_lengths = np.random.randint( + max_input_length / 2, high=max_input_length, size=(batch_size,), dtype=np.int32 + ) + target_lengths = np.random.randint( + max_target_length / 2, + high=max_target_length, + size=(batch_size,), + dtype=np.int32, + ) + + log_probs_torch = torch.tensor(log_probs, dtype=torch.float32, requires_grad=True) + targets_torch = torch.tensor(targets, dtype=torch.int32) + input_lengths_torch = torch.tensor(input_lengths, dtype=torch.int32) + target_lengths_torch = torch.tensor(target_lengths, dtype=torch.int32) + + log_probs_flow = ( + flow.tensor(log_probs, dtype=flow.float32, requires_grad=True) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + .to_global(placement=placement, sbp=in_sbp) + ) + targets_flow = ( + flow.tensor(targets, dtype=flow.int32) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + .to_global(placement=placement, sbp=in_sbp) + ) + input_lengths_flow = ( + flow.tensor(input_lengths, dtype=flow.int32) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + .to_global(placement=placement, sbp=in_sbp) + ) + target_lengths_flow = ( + flow.tensor(target_lengths, dtype=flow.int32) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + .to_global(placement=placement, sbp=in_sbp) + ) + + out_torch = torch_ctc_loss( + log_probs_torch, targets_torch, input_lengths_torch, target_lengths_torch + ) + out_flow = flow_ctc_loss( + log_probs_flow, targets_flow, input_lengths_flow, target_lengths_flow + ) + + # check forward + local_output = out_flow.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose( + out_torch.cpu().detach().numpy(), + local_output.numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + # check backward + out_torch.sum().backward() + out_flow.sum().backward() + local_x_grad = log_probs_flow.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose( + log_probs_torch.cpu().detach().numpy(), + local_x_grad.numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_ctc_loss_impl( + test_case, + placement, + module_sbp, + in_sbp, + max_input_length, + batch_size, + num_classes, + max_target_length, + blank, + reduction, + zero_infinity, +): + torch_ctc_loss = torch.nn.CTCLoss( + blank=blank, reduction=reduction, zero_infinity=zero_infinity + ) + flow_ctc_loss = flow.nn.CTCLoss( + blank=blank, reduction=reduction, zero_infinity=zero_infinity + ) + _compare_torch_and_oneflow( + test_case, + torch_ctc_loss, + flow_ctc_loss, + placement, + module_sbp, + in_sbp, + max_input_length, + batch_size, + num_classes, + max_target_length, + ) + + +@flow.unittest.skip_unless_1n2d() +class TestCTCLossGlobal(oneflow.unittest.TestCase): + @globaltest + def test_ctc_loss_global(test_case): + arg_dict = OrderedDict() + arg_dict["max_input_length"] = [20] + arg_dict["batch_size"] = [4] + arg_dict["num_classes"] = [5] + arg_dict["max_target_length"] = [10] + arg_dict["blank"] = [0, 4] + arg_dict["reduction"] = ["mean", "none"] + arg_dict["zero_infinity"] = [False, True] + + module_sbp = flow.sbp.broadcast + for args in GenArgDict(arg_dict): + for placement in all_placement(): + for in_sbp in all_sbp(placement): + _test_ctc_loss_impl( + test_case, placement, module_sbp, in_sbp, **args + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_cumprod.py b/python/oneflow/test/modules/test_global_cumprod.py new file mode 100644 index 0000000000000000000000000000000000000000..f56e4828e1c867192fa3fe4fee1b69c7d205e26d --- /dev/null +++ b/python/oneflow/test/modules/test_global_cumprod.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, auto_backward=True, check_graph=True) +def _test_cumprod_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + dim = random(0, ndim).to(int).value() + z = torch.cumprod(y, dim) + return z + + +class TestCumprodGlobal(flow.unittest.TestCase): + @globaltest + def test_cumprod(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=min(2, ndim)): + _test_cumprod_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_cumsum.py b/python/oneflow/test/modules/test_global_cumsum.py new file mode 100644 index 0000000000000000000000000000000000000000..c539e46f4bb31fe99dc51d297a855d4e7b293869 --- /dev/null +++ b/python/oneflow/test/modules/test_global_cumsum.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_cumsum_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + dim = random(0, ndim).to(int).value() + z = torch.cumsum(x, dim) + return z + + +@unittest.skip("This fails in multi-gpu") +class TestCumsumGlobal(flow.unittest.TestCase): + @globaltest + def test_cumsum(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=min(2, ndim)): + _test_cumsum_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_deconv2d.py b/python/oneflow/test/modules/test_global_deconv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..35e2aa1325352526b71c4f0104e0dd5fcb051b01 --- /dev/null +++ b/python/oneflow/test/modules/test_global_deconv2d.py @@ -0,0 +1,75 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_deconv2d_impl(test_case, placement, input_sbp): + ndim = 4 + in_channels = random(1, 5).to(int).value() * 8 + groups = random(1, 4).to(int).value() + out_channels = groups * 8 + kernel_size = random(1, 4).to(int).value() + stride = random(1, 5).to(int).value() + padding = random(1, 3).to(int).value() + dilation = random(1, 5).to(int).value() + padding_mode = constant("zeros") + m = torch.nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + padding_mode=padding_mode, + bias=False, + ) + m.train(random()) + + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight = torch.nn.Parameter( + m.weight.to_global(placement=placement, sbp=weight_sbp) + ) + + if m.bias is not None: + bias_sbp = random_sbp(placement, max_dim=1) + m.bias = torch.nn.Parameter(m.bias.to_global(placement=placement, sbp=bias_sbp)) + + batch = random(1, 3).to(int).value() * 8 + height = random(1, 5).to(int).value() * 8 + width = random(1, 5).to(int).value() * 8 + nchw = [batch, in_channels, height, width] + x = random_tensor(ndim, *nchw).to_global(placement=placement, sbp=input_sbp) + y = m(x) + return y + + +class TestDeconv2dGlobal(flow.unittest.TestCase): + @globaltest + def test_deconv2d(test_case): + for placement in all_placement(): + for input_sbp in all_sbp(placement, max_dim=2): + _test_deconv2d_impl(test_case, placement, input_sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_deform_conv2d.py b/python/oneflow/test/modules/test_global_deform_conv2d.py new file mode 100644 index 0000000000000000000000000000000000000000..83bf0256d5e7e14eb7366622670f682f76d75190 --- /dev/null +++ b/python/oneflow/test/modules/test_global_deform_conv2d.py @@ -0,0 +1,128 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +import torch as pytorch +import torchvision +from oneflow.test_utils.automated_test_util import * + + +def _test_deform_conv2d(test_case, placement): + input_sbp = random_sbp(placement, max_dim=4) + input_dims = [8, 8, 8, 8] + input = random_tensor(4, *input_dims).to_global(placement=placement, sbp=input_sbp) + + offset_sbp = random_sbp(placement, max_dim=2) + offset_dims = [8, 32, 5, 5] + offset = random_tensor(4, *offset_dims).to_global( + placement=placement, sbp=offset_sbp + ) + + mask_sbp = random_sbp(placement, max_dim=2) + mask_dims = [8, 4 * 4, 5, 5] + mask = random_tensor(4, *mask_dims).to_global(placement=placement, sbp=mask_sbp) + + weight_sbp = random_sbp(placement, max_dim=2) + weight_dims = [8, 8, 4, 4] + weight = random_tensor(4, *weight_dims).to_global( + placement=placement, sbp=weight_sbp + ) + + bias_sbp = random_sbp(placement, max_dim=1) + bias_dims = [8] + bias = random_tensor(1, *bias_dims).to_global(placement=placement, sbp=bias_sbp) + + flow_input = input.oneflow.detach().requires_grad_() + torch_input = input.pytorch.detach().requires_grad_() + flow_offset = offset.oneflow.detach().requires_grad_() + torch_offset = offset.pytorch.detach().requires_grad_() + flow_weight = weight.oneflow.detach().requires_grad_() + torch_weight = weight.pytorch.detach().requires_grad_() + flow_mask = mask.oneflow.detach().requires_grad_() + torch_mask = mask.pytorch.detach().requires_grad_() + flow_bias = bias.oneflow.detach().requires_grad_() + torch_bias = bias.pytorch.detach().requires_grad_() + + torch_out = torchvision.ops.deform_conv2d( + torch_input, torch_offset, torch_weight, mask=torch_mask, bias=torch_bias + ) + flow_out = oneflow.nn.functional.deform_conv2d( + flow_input, flow_offset, flow_weight, mask=flow_mask, bias=flow_bias + ) + + # compare forward + test_case.assertTrue( + np.allclose( + flow_out.numpy(), torch_out.detach().cpu().numpy(), rtol=1e-04, atol=1e-4 + ) + ) + + # compare backward + flow_out.sum().backward() + torch_out.sum().backward() + + flow_input_grad = flow_input.grad + torch_input_grad = torch_input.grad.detach().cpu() + flow_weight_grad = flow_weight.grad + torch_weight_grad = torch_weight.grad.detach().cpu() + flow_offset_grad = flow_offset.grad + torch_offset_grad = torch_offset.grad.detach().cpu() + flow_mask_grad = flow_mask.grad + torch_mask_grad = torch_mask.grad.detach().cpu() + flow_bias_grad = flow_bias.grad + torch_bias_grad = torch_bias.grad.detach().cpu() + + test_case.assertTrue( + np.allclose( + flow_input_grad.numpy(), torch_input_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + test_case.assertTrue( + np.allclose( + flow_weight_grad.numpy(), torch_weight_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + test_case.assertTrue( + np.allclose( + flow_offset_grad.numpy(), torch_offset_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + test_case.assertTrue( + np.allclose( + flow_mask_grad.numpy(), torch_mask_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + test_case.assertTrue( + np.allclose( + flow_bias_grad.numpy(), torch_bias_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + + +class TestGlobalDeformConv2d(flow.unittest.TestCase): + @globaltest + def test_deform_conv2d(test_case): + for placement in all_placement(): + for count in range(5): + _test_deform_conv2d(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_diag.py b/python/oneflow/test/modules/test_global_diag.py new file mode 100644 index 0000000000000000000000000000000000000000..24863ed64ed879b13a8ab70c72c31514636e4938 --- /dev/null +++ b/python/oneflow/test/modules/test_global_diag.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_diag_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + return torch.diag(y) + + +class TestDiagGlobal(flow.unittest.TestCase): + @globaltest + def test_diag(test_case): + # random ndim in range [1,2] + ndim = random(1, 3).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_diag_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_diagonal.py b/python/oneflow/test/modules/test_global_diagonal.py new file mode 100644 index 0000000000000000000000000000000000000000..bba9d403d77d22058049066c687fd8e59cabcb20 --- /dev/null +++ b/python/oneflow/test/modules/test_global_diagonal.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_diagonal_impl(test_case, placement, sbp): + offset = random(-5, 5).to(int).value() + dim1 = random(-4, 4).to(int).value() + dim2 = random(-4, 4).to(int).value() + + x = random_tensor( + ndim=4, + dim0=random(1, 4) * 8, + dim1=random(1, 4) * 8, + dim2=random(1, 4) * 8, + dim3=random(1, 4) * 8, + ) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.diagonal(y, offset, dim1, dim2) + return z + + +@unittest.skip("TODO: fix this test") +class TestDiagonalGlobal(flow.unittest.TestCase): + @globaltest + def test_diagonal(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_diagonal_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_div.py b/python/oneflow/test/modules/test_global_div.py new file mode 100644 index 0000000000000000000000000000000000000000..aadaa67d118a0b791cbe5074b30eb8370e466602 --- /dev/null +++ b/python/oneflow/test/modules/test_global_div.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_div_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + x = x.to_global(placement=placement, sbp=sbp) + y = random_tensor(ndim, *dims) + y = y.to_global(placement=placement, sbp=sbp) + + z = torch.div(x, y) + return z + + +class TestDivGlobal(flow.unittest.TestCase): + @globaltest + def test_div(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_div_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_dot.py b/python/oneflow/test/modules/test_global_dot.py new file mode 100644 index 0000000000000000000000000000000000000000..c2dd5bb0f8ac5b125e380e52f0f55da3a3942b0a --- /dev/null +++ b/python/oneflow/test/modules/test_global_dot.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_dot_impl(test_case, placement, sbp): + k = random(100, 1000) * 8 + x = random_tensor(ndim=1, dim0=k).to_global(placement=placement, sbp=sbp) + y = random_tensor(ndim=1, dim0=k).to_global(placement=placement, sbp=sbp) + z = torch.dot(x, y) + return z + + +class TestDotGlobal(flow.unittest.TestCase): + @globaltest + def test_dot(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + do_test_dot_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_dropout.py b/python/oneflow/test/modules/test_global_dropout.py new file mode 100644 index 0000000000000000000000000000000000000000..9899d49d209f6f8cb7447e26a5ba79fa0aef9c9a --- /dev/null +++ b/python/oneflow/test/modules/test_global_dropout.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=True, check_graph=False, atol=1e-5, rtol=1e-5) +def _test_dropout_p01(test_case, placement, sbp, ndim, p): + dims = [random(1, 5) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + m = torch.nn.Dropout(p=p, inplace=False) + return m(x) + + +@autotest(n=1, auto_backward=True, check_graph=False, atol=1e-5, rtol=1e-5) +def _test_dropout_eval_p01(test_case, placement, sbp, ndim, p): + dims = [random(1, 5) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + m = torch.nn.Dropout(p=p, inplace=False) + m.eval() + return m(x) + + +class TestDropoutGlobal(flow.unittest.TestCase): + @globaltest + def test_dropout_p01(test_case): + # random ndim in range [1,3] + ndim = random(1, 4).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=min(2, ndim)): + _test_dropout_p01(test_case, placement, sbp, ndim, p=0.0) + _test_dropout_p01(test_case, placement, sbp, ndim, p=1.0) + + @globaltest + def test_dropout_eval(test_case): + # random ndim in range [1,3] + ndim = random(1, 4).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=min(2, ndim)): + _test_dropout_eval_p01(test_case, placement, sbp, ndim, 0.0) + _test_dropout_eval_p01(test_case, placement, sbp, ndim, 1.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase1.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase1.py new file mode 100644 index 0000000000000000000000000000000000000000..42d155592a35fe1eb596ff900336bb2d0d00462a --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase1.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase1(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("hij, ijc->ihc", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase1(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase1(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase10.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase10.py new file mode 100644 index 0000000000000000000000000000000000000000..42b56ef02d373816fc3663dbfbf3686503474369 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase10.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase10(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) + y = random_tensor(ndim=4, dim0=dim0, dim1=dim2, dim2=dim1, dim3=random(1, 3) * 8) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("bhqk,bkhc->bqhc", g_x, g_y) + return z + + +@unittest.skipIf(True, "skip this test temporarily") +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase10(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_einsum_alphaflod_usecase10(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase11.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase11.py new file mode 100644 index 0000000000000000000000000000000000000000..37504e9839a0f55649a5b50ee35eafe9078aefa9 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase11.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase11(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8, dim2=dim0,) + y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("bqa,ahc->bqhc", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase11(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase11(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase2.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase2.py new file mode 100644 index 0000000000000000000000000000000000000000..ef78d8d4f3efb53501f5a4ed688a33855f24cbec --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase2.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase2(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("rac,rab->rbc", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase2(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase2(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase3.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase3.py new file mode 100644 index 0000000000000000000000000000000000000000..9d8f15e6384e0b765dd4c46de563aa95d43703cf --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase3.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase3(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ra,rab->rb", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase3(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_alphaflod_usecase3(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase4.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase4.py new file mode 100644 index 0000000000000000000000000000000000000000..0cfae41db1f811dcdd5fb3fa0315d2ac491112ed --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase4.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase4(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) + y = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("qhc,khc->qkh", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase4(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase4(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase5.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase5.py new file mode 100644 index 0000000000000000000000000000000000000000..eafad819bc241ae7bd5cc2f89a5edf69d77a8fa8 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase5.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase5(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) + y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("nm, mrc->nrc", g_x, g_y) + return z + + +@unittest.skip("this case fails in multi gpu. TODO: depeng, shenghang") +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase5(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_alphaflod_usecase5(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase6.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase6.py new file mode 100644 index 0000000000000000000000000000000000000000..262bc74df42f86eb47a98c3611615df45e3f9d7a --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase6.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase6(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) + y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("abc,adc->bdc", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase6(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase6(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase7.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase7.py new file mode 100644 index 0000000000000000000000000000000000000000..b63867c9b305da75a858bb51108c17ae456eb28a --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase7.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase7(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor( + ndim=4, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1, dim3=random(1, 3) * 8, + ) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("dceb,cef->dbf", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase7(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase7(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase8.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase8.py new file mode 100644 index 0000000000000000000000000000000000000000..4fafe566992a12cf81e06f1b4714f3d420316487 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase8.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase8(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) + y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("acb,ade->dceb", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase8(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_alphaflod_usecase8(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase9.py b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase9.py new file mode 100644 index 0000000000000000000000000000000000000000..63a4d9b4009dfc24da4979f5be578cf7b87cbeed --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_alphaflod_usecase9.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_alphaflod_usecase9(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8, dim2=dim0,) + y = random_tensor(ndim=2, dim0=dim0, dim1=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("qkc,ch->hqk", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_alphaflod_usecase9(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_alphaflod_usecase9(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_attention.py b/python/oneflow/test/modules/test_global_einsum_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..efaad79eea3ad474471cd8f922293bc3fbddeabc --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_attention.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_attention(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) + y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b h i d, b h j d -> b h i j", g_x, g_y) + return z + + +@unittest.skipIf(True, "skip this test temporarily") +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_attention(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_einsum_attention(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_matmul.py b/python/oneflow/test/modules/test_global_einsum_batch_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..c47f51ed49c649de7c2b6acc5c695caf93b70a1f --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_matmul.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_matmul(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ijk,ikl->ijl", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_matmul(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_batch_matmul(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_matmul2.py b/python/oneflow/test/modules/test_global_einsum_batch_matmul2.py new file mode 100644 index 0000000000000000000000000000000000000000..511d7aacc375837c8d397451b23bd33f1dd5ace3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_matmul2.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_matmul2(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2) + y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=dim2, dim3=random(1, 3) * 8) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b h i j, b h j d -> b h i d", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_matmul2(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_einsum_batch_matmul2(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_matmul3.py b/python/oneflow/test/modules/test_global_einsum_batch_matmul3.py new file mode 100644 index 0000000000000000000000000000000000000000..68f52a25c0bd1f8e59a2b57568ce30d05fa9571e --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_matmul3.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_matmul3(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor( + ndim=4, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8, dim3=dim1, + ) + y = random_tensor(ndim=3, dim0=dim0, dim1=random(1, 3) * 8, dim2=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b x i d, b j d -> b x i j", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_matmul3(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_batch_matmul3(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_matmul4.py b/python/oneflow/test/modules/test_global_einsum_batch_matmul4.py new file mode 100644 index 0000000000000000000000000000000000000000..5f84deaea9450997feb53cd7ad5dcabbe8fb83ba --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_matmul4.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_matmul4(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor( + ndim=4, dim0=dim0, dim1=random(1, 3) * 8, dim2=random(1, 3) * 8, dim3=dim1, + ) + y = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b x i j, b j d -> b x i d", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_matmul4(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_batch_matmul4(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_matrix_vector_multiply.py b/python/oneflow/test/modules/test_global_einsum_batch_matrix_vector_multiply.py new file mode 100644 index 0000000000000000000000000000000000000000..433937e10b599362bc5e71419b9697d94d33e8f3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_matrix_vector_multiply.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_matrix_vector_multiply(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=dim2,) + y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=random(1, 3) * 8, dim3=dim2,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b i d, b i j d -> b i j", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_matrix_vector_multiply(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_einsum_batch_matrix_vector_multiply(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_batch_permute.py b/python/oneflow/test/modules/test_global_einsum_batch_permute.py new file mode 100644 index 0000000000000000000000000000000000000000..9a4d4a67d9a4363863ca2e6ca9e52db87450c501 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_batch_permute.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_batch_permute(test_case, placement, sbp): + x = random_tensor( + ndim=5, + dim0=random(1, 3) * 8, + dim1=random(1, 3) * 8, + dim2=random(1, 3) * 8, + dim3=random(1, 3) * 8, + dim4=random(1, 3) * 8, + ) + g_x = x.to_global(placement=placement, sbp=sbp) + z = torch.einsum("...ij->...ji", g_x) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_batch_permute(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=5): + _test_einsum_batch_permute(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_bilinear_transformation.py b/python/oneflow/test/modules/test_global_einsum_bilinear_transformation.py new file mode 100644 index 0000000000000000000000000000000000000000..5f381c9913be6ee55cba27edbb5c26357d722c12 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_bilinear_transformation.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_bilinear_transformation(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=3, dim0=random(1, 3) * 8, dim1=dim1, dim2=dim2,) + w = random_tensor(ndim=2, dim0=dim0, dim1=dim2,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + g_w = w.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ik,jkl,il->ij", g_x, g_y, g_w) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_bilinear_transformation(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_bilinear_transformation(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_eltwise_mul_sum_row.py b/python/oneflow/test/modules/test_global_einsum_eltwise_mul_sum_row.py new file mode 100644 index 0000000000000000000000000000000000000000..06c20a9ae995864d649fa20f6b7a6bc999a6b75d --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_eltwise_mul_sum_row.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_eltwise_mul_sum_row(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("n d, n d -> n", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_eltwise_mul_sum_row(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_eltwise_mul_sum_row(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_eltwise_mul_then_reduce_sum.py b/python/oneflow/test/modules/test_global_einsum_eltwise_mul_then_reduce_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..d37634f44e867dcdfcc296ae9831b976dc15abc4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_eltwise_mul_then_reduce_sum.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_eltwise_mul_then_reduce_sum(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + # NOTE(Liang Depeng): the same as 'ij,ij->' + z = torch.einsum("ij,ij", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_eltwise_mul_then_reduce_sum(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_eltwise_mul_then_reduce_sum(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_eltwise_multiply.py b/python/oneflow/test/modules/test_global_einsum_eltwise_multiply.py new file mode 100644 index 0000000000000000000000000000000000000000..942ad7a0e4317f733c67cb94ca0a64793bb1dba3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_eltwise_multiply.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_eltwise_multiply(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ij,ij->ij", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_eltwise_multiply(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_eltwise_multiply(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_get_diagonal.py b/python/oneflow/test/modules/test_global_einsum_get_diagonal.py new file mode 100644 index 0000000000000000000000000000000000000000..9a070f5ba5646c8f8563a6300b82174022389aff --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_get_diagonal.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_get_diagonal(test_case, placement, sbp): + dim = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim, dim1=dim,) + g_x = x.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ii->i", g_x) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_get_diagonal(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_get_diagonal(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_matmul.py b/python/oneflow/test/modules/test_global_einsum_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..236463b64aaad71f931ca270a721ddaa5bd282d1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_matmul.py @@ -0,0 +1,48 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_matmul(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + dim2 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=2, dim0=dim1, dim1=dim2,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + # NOTE(Liang Depeng): the same as 'ik,kj->ij' + z = torch.einsum("ik,kj", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_matmul(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_matmul(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_matmul2.py b/python/oneflow/test/modules/test_global_einsum_matmul2.py new file mode 100644 index 0000000000000000000000000000000000000000..d0c9a5cbed17104e27901fa973b0b56666325441 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_matmul2.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_matmul2(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) + y = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=dim0,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("i d, j d -> i j", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_matmul2(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_matmul2(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_matrix_column_sum.py b/python/oneflow/test/modules/test_global_einsum_matrix_column_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..78994f9e86af1c89034680a0adc1d67451d6057a --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_matrix_column_sum.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_matrix_column_sum(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ij->j", g_x) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_matrix_column_sum(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_matrix_column_sum(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_matrix_transpose.py b/python/oneflow/test/modules/test_global_einsum_matrix_transpose.py new file mode 100644 index 0000000000000000000000000000000000000000..686f7811a0ab01f542e6ec94821ca03f87bcaff5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_matrix_transpose.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_matrix_transpose(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8) + g_x = x.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ij->ji", g_x) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_matrix_transpose(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_matrix_transpose(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_matrix_vector_multiply.py b/python/oneflow/test/modules/test_global_einsum_matrix_vector_multiply.py new file mode 100644 index 0000000000000000000000000000000000000000..71b4653c977c84843946e355d760164472ad7692 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_matrix_vector_multiply.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_matrix_vector_multiply(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor(ndim=2, dim0=dim0, dim1=dim1,) + y = random_tensor(ndim=1, dim0=dim1,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + # NOTE(Liang Depeng): the same as 'ik,k->i' + z = torch.einsum("ik,k", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_matrix_vector_multiply(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_einsum_matrix_vector_multiply(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_reduce_sum.py b/python/oneflow/test/modules/test_global_einsum_reduce_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..bb6c91f51b6a8783fd0ee1a4bab7d4a8e609fa08 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_reduce_sum.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_reduce_sum(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=random(1, 3) * 8, dim1=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + z = torch.einsum("ij->", g_x) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_reduce_sum(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_reduce_sum(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_tensor_contraction.py b/python/oneflow/test/modules/test_global_einsum_tensor_contraction.py new file mode 100644 index 0000000000000000000000000000000000000000..198b7d397c52d155430f82d05ba1204b1ee9eab1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_tensor_contraction.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_tensor_contraction(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + dim1 = random(1, 3) * 8 + x = random_tensor( + ndim=4, dim0=random(1, 3) * 8, dim1=dim0, dim2=dim1, dim3=random(1, 3) * 8, + ) + y = random_tensor( + ndim=5, + dim0=random(1, 3) * 8, + dim1=random(1, 3) * 8, + dim2=dim0, + dim3=random(1, 3) * 8, + dim4=dim1, + ) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("pqrs,tuqvr->pstuv", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_tensor_contraction(test_case): + for placement in all_placement(): + if len(np.array(placement.ranks).shape) > 1 and all( + dim != 1 for dim in np.array(placement.ranks).shape + ): + print( + f"[{flow.env.get_rank()}] skip TestEinsumConsistent.test_einsum_tensor_contraction with {placement}" + ) + continue + + for sbp in all_sbp(placement, max_dim=4): + _test_einsum_tensor_contraction(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_tensor_contraction2.py b/python/oneflow/test/modules/test_global_einsum_tensor_contraction2.py new file mode 100644 index 0000000000000000000000000000000000000000..8f549dfecb75ed954f707f12487c2b8de7aa34e2 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_tensor_contraction2.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_tensor_contraction2(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor( + ndim=4, + dim0=random(1, 3) * 8, + dim1=dim0, + dim2=random(1, 3) * 8, + dim3=random(1, 3) * 8, + ) + y = random_tensor(ndim=2, dim0=dim0, dim1=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + z = torch.einsum("b n h w, n d -> b d h w", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_tensor_contraction2(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_einsum_tensor_contraction2(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_vector_inner_product.py b/python/oneflow/test/modules/test_global_einsum_vector_inner_product.py new file mode 100644 index 0000000000000000000000000000000000000000..e96bc778ff7ed3b52de4c8d1f46948e21ce9ed4b --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_vector_inner_product.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_vector_inner_product(test_case, placement, sbp): + dim0 = random(1, 3) * 8 + x = random_tensor(ndim=1, dim0=dim0,) + y = random_tensor(ndim=1, dim0=dim0,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + # NOTE(Liang Depeng): the same as 'i,i->' + z = torch.einsum("i,i", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_vector_inner_product(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_einsum_vector_inner_product(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_einsum_vector_outer_product.py b/python/oneflow/test/modules/test_global_einsum_vector_outer_product.py new file mode 100644 index 0000000000000000000000000000000000000000..c64f522c1a569c1694110d5e422a2b9eaf52aab3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_einsum_vector_outer_product.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_einsum_vector_outer_product(test_case, placement, sbp): + x = random_tensor(ndim=1, dim0=random(1, 3) * 8,) + y = random_tensor(ndim=1, dim0=random(1, 3) * 8,) + g_x = x.to_global(placement=placement, sbp=sbp) + g_y = y.to_global(placement=placement, sbp=sbp) + # NOTE(Liang Depeng): the same as 'i,j->ij' + z = torch.einsum("i,j", g_x, g_y) + return z + + +class TestEinsumGlobal(flow.unittest.TestCase): + @globaltest + def test_einsum_vector_outer_product(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_einsum_vector_outer_product(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_empty.py b/python/oneflow/test/modules/test_global_empty.py new file mode 100644 index 0000000000000000000000000000000000000000..aaf52be72495bf7913b00e2372c3fc263027ade5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_empty.py @@ -0,0 +1,130 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_empty(test_case, func, shape, placement, sbp): + func2 = None + if func == "empty": + func = flow.empty + elif func == "new_empty": + func = flow.empty + func2 = flow.new_empty + elif func == "empty_like": + func = flow.empty + func2 = flow.empty_like + else: + raise NotImplementedError + + x = func(*shape, placement=placement, sbp=sbp) + if func2: + if func2.__name__ == "new_empty_op": + x = func2(x, size=shape) + elif func2.__name__ == "empty_like_op": + x = func2(x) + else: + raise NotImplementedError + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_empty(test_case, func, shape, placement, sbp): + func2 = None + if func == "empty": + func = flow.empty + elif func == "new_empty": + func = flow.empty + func2 = flow.new_empty + elif func == "empty_like": + func = flow.empty + func2 = flow.empty_like + else: + raise NotImplementedError + + class GlobalEmptyGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = func(*shape, placement=placement, sbp=sbp) + if func2: + if func2.__name__ == "new_empty_op": + x = func2(x, size=shape) + elif func2.__name__ == "empty_like_op": + x = func2(x) + else: + raise NotImplementedError + return x + + model = GlobalEmptyGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestEmptyGlobal(flow.unittest.TestCase): + @globaltest + def test_empty_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + functions = [ + "empty", + "new_empty", + "empty_like", + ] + for func in functions: + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_empty(test_case, func, shape, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_empty_graph(test_case): + arg_dict = OrderedDict() + arg_dict["func"] = ["empty", "new_empty", "empty_like"] + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + func = args["func"] + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_empty(test_case, func, shape, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_eq.py b/python/oneflow/test/modules/test_global_eq.py new file mode 100644 index 0000000000000000000000000000000000000000..f89a6c70d04cd11308fafc0469f87fcac114290f --- /dev/null +++ b/python/oneflow/test/modules/test_global_eq.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def do_test_eq_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + x = x.to_global(placement=placement, sbp=sbp) + y = random_tensor(ndim, *dims) + y = y.to_global(placement=placement, sbp=sbp) + + z = torch.eq(x, y) + return z + + +class TestEqGlobal(flow.unittest.TestCase): + @globaltest + def test_eq(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_eq_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_erf.py b/python/oneflow/test/modules/test_global_erf.py new file mode 100644 index 0000000000000000000000000000000000000000..3dba42f27fb43a2edbeb1c6658fff32e745c61c4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_erf.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_erf_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.erf(y) + return z + + +class TestErfGlobal(flow.unittest.TestCase): + @globaltest + def test_erf(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_erf_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_erfc.py b/python/oneflow/test/modules/test_global_erfc.py new file mode 100644 index 0000000000000000000000000000000000000000..f4b4f058a30f0cb25bb16ed19a1c95accb549c37 --- /dev/null +++ b/python/oneflow/test/modules/test_global_erfc.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=True, rtol=1e-3, atol=1e-3, check_graph=True) +def do_test_erfc_impl(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.erfc(y) + return z + + +class TestErfcGlobal(flow.unittest.TestCase): + @globaltest + def test_erfc(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_erfc_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_expand_op.py b/python/oneflow/test/modules/test_global_expand_op.py new file mode 100644 index 0000000000000000000000000000000000000000..4e59aee63e87d8d48218f59d79e4eaa6b7d216f7 --- /dev/null +++ b/python/oneflow/test/modules/test_global_expand_op.py @@ -0,0 +1,136 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np + +import oneflow as flow +import oneflow.unittest +import torch + +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_expand( + test_case, + input_shape, + expand_shape, + device="cuda", + sbp=flow.sbp.broadcast, + verbose=False, +): + # random input + input = np.random.randn(*input_shape) + if isinstance(input, np.ndarray): + input = input.astype(np.float32) + + # torch computation + torch_x = torch.tensor(input, requires_grad=True) + torch_y = torch_x.expand(*expand_shape) + torch_y.sum().backward() + + # oneflow computation + placement = flow.placement(device, np.array(range(flow.env.get_world_size()))) + x = flow.tensor(input, requires_grad=True) + global_x = x.to_global(placement=placement, sbp=flow.sbp.broadcast) + if global_x.sbp != sbp: + global_x = global_x.to_global(sbp=sbp, grad_sbp=flow.sbp.broadcast) + y = global_x.expand(*expand_shape) + y.sum().backward() + + y_b = y.to_global(sbp=flow.sbp.broadcast) + + if flow.env.get_rank() == 0: + out_a = y_b.to_local().numpy() + out_b = torch_y.detach().cpu().numpy() + grad_a = x.grad.numpy() + grad_b = torch_x.grad.cpu().numpy() + + if verbose: + print("") + print(f"{'=' * 10} {input_shape} -> {expand_shape} {'=' * 10}") + print(f"{'=' * 10} {device}, {sbp} {'=' * 10}") + print(f"{'-' * 20} compare out {'-' * 20}") + print(out_a) + print("*" * 20) + print(out_b) + print("") + print(f"{'-' * 20} compare grad {'-' * 20}") + print(grad_a) + print("*" * 20) + print(grad_b) + + test_case.assertTrue(np.array_equal(out_a, out_b)) + test_case.assertTrue(np.array_equal(grad_a, grad_b)) + + +@flow.unittest.skip_unless_1n2d() +class ExpandGlobalTestCase(oneflow.unittest.TestCase): + def test_global_expand(test_case): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["sbp"] = [flow.sbp.split(0), flow.sbp.broadcast()] + arg_dict["shapes"] = [ + ((2, 2), (2, 2, 2)), + ((2, 1, 3), (2, 1, -1, -1, -1)), + ((2, 1, 3), (1, 2, -1, -1, -1)), + ((2, 1, 3), (2, 1, -1, 2, 3)), + ((2, 1, 3), (1, 2, 2, 2, -1)), + ] + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _test_global_expand(test_case, input_shape, expand_shape, **kwargs) + + def test_split_expand(test_case): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cuda"] + arg_dict["sbp"] = [flow.sbp.split(0)] + arg_dict["shapes"] = [ + ((2,), (1, 2)), + ((2,), (2, 2)), + ] + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _test_global_expand(test_case, input_shape, expand_shape, **kwargs) + + def test_broadcast_scalar_expand(test_case): + arg_dict = OrderedDict() + arg_dict["verbose"] = [False] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["sbp"] = [flow.sbp.broadcast()] + arg_dict["shapes"] = [ + ((), (1,)), + ((), (2,)), + ((), (1, 1)), + ((), (1, 2)), + ((), (2, 1)), + ((), (2, 2)), + ((), (2, 1, 2)), + ] + for kwargs in GenArgDict(arg_dict): + assert "shapes" in kwargs + input_shape, expand_shape = kwargs.pop("shapes") + _test_global_expand(test_case, input_shape, expand_shape, **kwargs) + + +if __name__ == "__main__": + unittest.main() + +# ONEFLOW_TEST_DEVICE_NUM=2 python3 -m oneflow.distributed.launch --nproc_per_node 2 test_global_expand_op.py diff --git a/python/oneflow/test/modules/test_global_expm1.py b/python/oneflow/test/modules/test_global_expm1.py new file mode 100644 index 0000000000000000000000000000000000000000..77a846b94697e2d8b3981fc4aefbe8fb23bf1094 --- /dev/null +++ b/python/oneflow/test/modules/test_global_expm1.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_expm1_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.expm1(y) + return z + + +class TestExpm1Global(flow.unittest.TestCase): + @globaltest + def test_expm1(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_expm1_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_eye.py b/python/oneflow/test/modules/test_global_eye.py new file mode 100644 index 0000000000000000000000000000000000000000..4afd4ef841ba1558eb08249e73e199e64415e950 --- /dev/null +++ b/python/oneflow/test/modules/test_global_eye.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=False) +def do_test_eye_impl(test_case, placement, sbp): + n = random(1, 5).to(int).value() * 8 + m = random(1, 5).to(int).value() * 8 + x = torch.eye(n, m) + x.oneflow = flow.tensor( + x.pytorch.cpu().detach().numpy(), + requires_grad=x.pytorch.requires_grad, + placement=placement, + sbp=sbp, + ) + return x + + +class TestEyeGlobal(flow.unittest.TestCase): + @globaltest + def test_eye(test_case): + shape = random_tensor().shape + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + do_test_eye_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_fill.py b/python/oneflow/test/modules/test_global_fill.py new file mode 100644 index 0000000000000000000000000000000000000000..2b4619a77ef2f0779b719efefa9043643123ef16 --- /dev/null +++ b/python/oneflow/test/modules/test_global_fill.py @@ -0,0 +1,62 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_fill_(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + value = random().to(float) + y = x + 1 + y.fill_(value) + return y + + +@autotest(n=1, check_graph=False) +def _test_fill_tensor_(test_case, ndim, placement, sbp): + dims = [random(2, 4) * 8 for i in range(ndim)] + x = ( + random_tensor(ndim, *dims) + .to_global(placement=placement, sbp=sbp) + .requires_grad_() + ) + value = ( + torch.tensor(1.0) + .to_global(placement=placement, sbp=[flow.sbp.broadcast for _ in sbp]) + .requires_grad_() + ) + y = x + 1 + y.oneflow = y.oneflow.to_global(placement, sbp) + y.fill_(value) + return y + + +class TestFillModule(flow.unittest.TestCase): + @globaltest + def test_fill_(test_case): + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_fill_(test_case, ndim, placement, sbp) + _test_fill_tensor_(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_flatten.py b/python/oneflow/test/modules/test_global_flatten.py new file mode 100644 index 0000000000000000000000000000000000000000..9e3888722ba9926333425c4454d3ffc41ef97431 --- /dev/null +++ b/python/oneflow/test/modules/test_global_flatten.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_flatten_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + start_dim = random(0, ndim).to(int).value() + end_dim = random(start_dim, ndim).to(int).value() + + z = torch.flatten(x, start_dim, end_dim) + return z + + +class TestFlattenGlobal(flow.unittest.TestCase): + @globaltest + def test_flatten(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_flatten_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_flip.py b/python/oneflow/test/modules/test_global_flip.py new file mode 100644 index 0000000000000000000000000000000000000000..198b6cd89c149e3dd81ae39fab65dc485075df9e --- /dev/null +++ b/python/oneflow/test/modules/test_global_flip.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_flip_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + new_dim = random(0, ndim).to(int).value() + z = torch.flip(y, constant([i for i in range(new_dim)])) + return z + + +class TestFlipGlobal(flow.unittest.TestCase): + @globaltest + def test_flip(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_flip_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_floor.py b/python/oneflow/test/modules/test_global_floor.py new file mode 100644 index 0000000000000000000000000000000000000000..1090535e5f32ad368f00db61164a0ad1d9d6c7f6 --- /dev/null +++ b/python/oneflow/test/modules/test_global_floor.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def do_test_floor_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.floor(y) + return z + + +class TestFloorGlobal(flow.unittest.TestCase): + @globaltest + def test_floor(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_floor_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_fmod.py b/python/oneflow/test/modules/test_global_fmod.py new file mode 100644 index 0000000000000000000000000000000000000000..344bb62f1e5a6b46c7dda4615fbc4f1fdc813761 --- /dev/null +++ b/python/oneflow/test/modules/test_global_fmod.py @@ -0,0 +1,53 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + +import torch as torch_original +from packaging import version + +# other.grad in torch.fmod(input, other) was not implemented before pytorch 1.11.0 +grad_implemented = version.parse(torch_original.__version__) >= version.parse("1.11.0") + + +@autotest(n=1, auto_backward=grad_implemented, check_graph=False) +def do_test_fmod_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims) + x = x.to_global(placement=placement, sbp=sbp) + y = random_tensor(ndim, *dims) + y = y.to_global(placement=placement, sbp=sbp) + + z = torch.fmod(x, y) + return z + + +class TestFmodGlobal(flow.unittest.TestCase): + @globaltest + def test_fmod(test_case): + # random ndim in range [1,5] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_fmod_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_fold.py b/python/oneflow/test/modules/test_global_fold.py new file mode 100644 index 0000000000000000000000000000000000000000..afade52d7777a38791a8aa478c971990804e4ed9 --- /dev/null +++ b/python/oneflow/test/modules/test_global_fold.py @@ -0,0 +1,59 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_fold_impl(test_case, placement, sbp): + ndim = 3 + dims = [random(1, 4).to(int).value() * 8 for i in range(ndim)] + m = torch.nn.Fold( + output_size=constant(((dims[2] // 4) * 2, 4 * 2)), + kernel_size=constant(2), + dilation=constant(1), + padding=constant(0), + stride=constant(2), + ) + m.train(random()) + + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = m(x) + func_y = torch.nn.functional.fold( + x, + output_size=constant(((dims[2] // 4) * 2, 4 * 2)), + kernel_size=constant(2), + dilation=constant(1), + padding=constant(0), + stride=constant(2), + ) + return y, func_y + + +class TestFold(flow.unittest.TestCase): + @globaltest + def test_fold(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_fold_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_full.py b/python/oneflow/test/modules/test_global_full.py new file mode 100644 index 0000000000000000000000000000000000000000..a48258190695648dd861d83404708493bb58d410 --- /dev/null +++ b/python/oneflow/test/modules/test_global_full.py @@ -0,0 +1,121 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_full(test_case, shape, placement, sbp): + x = flow.full(shape, 1.0, placement=placement, sbp=sbp) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_global_full_tensor_scalar(test_case, shape, placement, sbp): + scalar_sbp = [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + x1 = flow.tensor(1.0, placement=placement, sbp=scalar_sbp) + x2 = flow.full(shape, x1, placement=placement, sbp=sbp) + test_case.assertEqual(x2.shape, flow.Size(shape)) + test_case.assertEqual(x2.sbp, sbp) + test_case.assertEqual(x2.placement, placement) + + +def _test_graph_full(test_case, shape, placement, sbp): + class GlobalFullGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.full(shape, 1.0, placement=placement, sbp=sbp) + return x + + model = GlobalFullGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_full_tensor_scalar(test_case, shape, placement, sbp): + class GlobalFullGraph2(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.full( + shape, + flow.tensor( + 1.0, + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ), + placement=placement, + sbp=sbp, + ) + return x + + model = GlobalFullGraph2() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestFullGlobal(flow.unittest.TestCase): + @globaltest + def test_full_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_full(test_case, shape, placement, sbp) + _test_global_full_tensor_scalar(test_case, shape, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_full_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [[8], [8, 8], [8, 8, 8]] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_full(test_case, shape, placement, sbp) + _test_graph_full_tensor_scalar(test_case, shape, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_full_like.py b/python/oneflow/test/modules/test_global_full_like.py new file mode 100644 index 0000000000000000000000000000000000000000..0b116cd7bbbfda44fdf5e0f626cce28b1d1259a8 --- /dev/null +++ b/python/oneflow/test/modules/test_global_full_like.py @@ -0,0 +1,86 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_full_like(test_case, shape, placement, sbp): + x_ = flow.randn(shape) + x = flow.full_like(x_, 1.0, placement=placement, sbp=sbp) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_full_like(test_case, shape, placement, sbp): + class GlobalFullLikeGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x_ = flow.randn(shape) + x = flow.full_like(x_, 1.0, placement=placement, sbp=sbp) + return x + + model = GlobalFullLikeGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestFillLikeGlobal(flow.unittest.TestCase): + @globaltest + def test_full_like_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_full_like(test_case, shape, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_full_like_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [[8], [8, 8], [8, 8, 8]] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_full_like(test_case, shape, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_greater.py b/python/oneflow/test/modules/test_global_greater.py new file mode 100644 index 0000000000000000000000000000000000000000..7407c8c6798d7a669882d213670f9ab91663d014 --- /dev/null +++ b/python/oneflow/test/modules/test_global_greater.py @@ -0,0 +1,53 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=10, auto_backward=False, check_graph=True) +def _test_greater_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x1 = random_tensor(ndim, *dims) + x2 = x1.to_global(placement=placement, sbp=sbp) + y1 = random_tensor(ndim, *dims) + y2 = y1.to_global(placement=placement, sbp=sbp) + + z = torch.gt(x2, y2) + return z + + +@unittest.skip("TODO: houjiang, yushun. this test might fail") +class TestGreaterGlobal(flow.unittest.TestCase): + @globaltest + def test_greater(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_greater_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_greater_equal.py b/python/oneflow/test/modules/test_global_greater_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..d02c655293754677c548dc5acd4d44ca93d95b8e --- /dev/null +++ b/python/oneflow/test/modules/test_global_greater_equal.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def do_test_greater_equal_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x1 = random_tensor(ndim, *dims) + x1 = x1.to_global(placement=placement, sbp=sbp) + x2 = random_tensor(ndim, *dims) + x2 = x2.to_global(placement=placement, sbp=sbp) + + z = torch.ge(x1, x2) + return z + + +class TestGreaterEqualGlobal(flow.unittest.TestCase): + @globaltest + def test_greater_equal(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_greater_equal_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_grid_sample.py b/python/oneflow/test/modules/test_global_grid_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..414584cb20c991e92bea0691f9062f59af97b5bb --- /dev/null +++ b/python/oneflow/test/modules/test_global_grid_sample.py @@ -0,0 +1,128 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, rtol=1e-03, atol=1e-04, check_graph=True) +def _test_flow_grid_sample_cudnn(test_case, placement, sbp): + # cudnn only support 4D input, with mode = 'bilinear' && padding_mode = 'zeros' && align_corners + N = random(1, 3).to(int) * 8 + C = random(1, 3).to(int) * 8 + in_H = random(1, 8).to(int) + in_W = random(1, 8).to(int) + out_H = random(1, 8).to(int) + out_W = random(1, 8).to(int) + mode = "bilinear" + padding_mode = "zeros" + align_corners = True + theta = random_tensor(ndim=3, dim0=N, dim1=2, dim2=3).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=1) + ) + grid = torch.nn.functional.affine_grid( + theta, (N, C, out_H, out_W), align_corners=align_corners + ) + input = random_tensor(ndim=4, dim0=N, dim1=C, dim2=in_H, dim3=in_W).to_global( + placement=placement, sbp=sbp + ) + output = torch.nn.functional.grid_sample( + input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, + ) + return output + + +# This test may fail due to using ::floor in backward +# floor(1.99999988) = 1 and floor(2.000000) = 2, then select differente images pixel +@autotest( + n=1, + auto_backward=False, + rtol=1e-03, + atol=1e-04, + check_graph=True, + check_allclose=False, +) +def _test_flow_grid_sample_4d(test_case, placement, sbp): + N = random(1, 3).to(int) * 8 + C = random(1, 3).to(int) * 8 + in_H = random(1, 8).to(int) + in_W = random(1, 8).to(int) + out_H = random(1, 8).to(int) + out_W = random(1, 8).to(int) + mode = oneof("bilinear", "nearest", "bicubic") + padding_mode = oneof("zeros", "border", "reflection") + align_corners = oneof(True, False) + theta = random_tensor(ndim=3, dim0=N, dim1=2, dim2=3).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=1) + ) + grid = torch.nn.functional.affine_grid( + theta, (N, C, out_H, out_W), align_corners=align_corners + ) + input = random_tensor(ndim=4, dim0=N, dim1=C, dim2=in_H, dim3=in_W).to_global( + placement=placement, sbp=sbp + ) + output = torch.nn.functional.grid_sample( + input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, + ) + return output + + +@autotest(n=1, auto_backward=False, rtol=1e-03, atol=1e-03, check_graph=True) +def _test_flow_grid_sample_5d(test_case, placement, sbp): + N = random(1, 3).to(int) * 8 + C = random(1, 3).to(int) * 8 + in_D = random(1, 8).to(int) + in_H = random(1, 8).to(int) + in_W = random(1, 8).to(int) + out_D = random(1, 8).to(int) + out_H = random(1, 8).to(int) + out_W = random(1, 8).to(int) + mode = oneof("bilinear", "nearest") + padding_mode = oneof("zeros", "border", "reflection") + align_corners = oneof(True, False) + theta = random_tensor(ndim=3, dim0=N, dim1=3, dim2=4).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=1) + ) + grid = torch.nn.functional.affine_grid( + theta, (N, C, out_D, out_H, out_W), align_corners=align_corners + ) + input = random_tensor( + ndim=5, dim0=N, dim1=C, dim2=in_D, dim3=in_H, dim4=in_W + ).to_global(placement=placement, sbp=sbp) + output = torch.nn.functional.grid_sample( + input, grid, mode=mode, padding_mode=padding_mode, align_corners=align_corners, + ) + return output + + +class TestGridSample(flow.unittest.TestCase): + @globaltest + def test_grid_sample(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + if placement.type == "cuda": + _test_flow_grid_sample_cudnn(test_case, placement, sbp) + _test_flow_grid_sample_4d(test_case, placement, sbp) + _test_flow_grid_sample_5d(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_groupnorm.py b/python/oneflow/test/modules/test_global_groupnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..1a10e53145152059e1372b9b8bd9df602e05cbad --- /dev/null +++ b/python/oneflow/test/modules/test_global_groupnorm.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False, atol=1e-3, rtol=1e-3) +def _test_global_group_norm(test_case, placement, input_sbp, affine): + if placement.type == "cpu": + return + batch_size = 4 + channel_size = 8 + num_groups = 2 + m = torch.nn.GroupNorm( + num_groups=num_groups, num_channels=channel_size, affine=affine + ) + m.train(random()) + m.to_global( + placement=placement, sbp=[flow.sbp.broadcast] * len(placement.ranks.shape) + ) + x = random_tensor( + ndim=4, + dim0=batch_size, + dim1=channel_size, + dim2=random(4, 16), + dim3=random(4, 16), + ).to_global(placement=placement, sbp=input_sbp) + y = m(x) + return y + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGroupNormModule(flow.unittest.TestCase): + @globaltest + def test_global_group_norm_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_group_norm(test_case, placement, sbp, True) + _test_global_group_norm(test_case, placement, sbp, False) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_gru_cell.py b/python/oneflow/test/modules/test_global_gru_cell.py new file mode 100644 index 0000000000000000000000000000000000000000..4efd21a4a536a35246579a91efb05c1ca385a12f --- /dev/null +++ b/python/oneflow/test/modules/test_global_gru_cell.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +import oneflow.framework.session_context as session_ctx + + +@autotest(n=1, check_graph=True) +def _test_gru_cell(test_case, placement, sbp): + batch_size = random(2, 3) * 8 + time_steps = random(2, 3) * 8 + input_size = random(2, 3) * 8 + hidden_size = random(2, 3) * 8 + has_bias = random().to(bool) + m = torch.nn.GRUCell(input_size=input_size, hidden_size=hidden_size, bias=has_bias,) + + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight_ih = torch.nn.Parameter( + m.weight_ih.to_global(placement=placement, sbp=weight_sbp) + ) + m.weight_hh = torch.nn.Parameter( + m.weight_hh.to_global(placement=placement, sbp=weight_sbp) + ) + if m.bias_ih is not None: + # bias is 1-d tensor + bias_sbp = random_sbp(placement, max_dim=1, except_partial_sum=True) + m.bias_ih = torch.nn.Parameter( + m.bias_ih.to_global(placement=placement, sbp=bias_sbp) + ) + m.bias_hh = torch.nn.Parameter( + m.bias_hh.to_global(placement=placement, sbp=bias_sbp) + ) + + input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) + input = random_tensor( + ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size + ).to_global(placement=placement, sbp=input_sbp) + hx = random_tensor( + ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False + ).to_global(placement=placement, sbp=sbp) + + for i in range(time_steps.to(int).value()): + hx = m(input[i], hx) + + return hx + + +class TestRNNCellGlobal(flow.unittest.TestCase): + @globaltest + def test_gru_cell(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_gru_cell(test_case, placement, sbp) + + session_ctx.GetDefaultSession().Reset() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_hann_window.py b/python/oneflow/test/modules/test_global_hann_window.py new file mode 100644 index 0000000000000000000000000000000000000000..9f8bae9c3f4537ab8072ff05e5e6b14c117bede3 --- /dev/null +++ b/python/oneflow/test/modules/test_global_hann_window.py @@ -0,0 +1,80 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from collections import OrderedDict + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_hann_window(test_case, placement, sbp): + x = flow.hann_window(8, placement=placement, sbp=sbp) + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_hann_window(test_case, placement, sbp): + class GlobalHannWindowGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.hann_window(8, placement=placement, sbp=sbp) + return x + + model = GlobalHannWindowGraph() + x = model() + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestHannWindowGlobal(flow.unittest.TestCase): + # TODO(wyg): It will be infer all broadcast sbp when 1n1d, + # slice_update will get error when doing inplace operator. + # Remove this judgement after refactor sbp infer method in Operator class. + @globaltest + def test_hann_window_global(test_case): + for placement in all_placement(): + if placement.ranks.size == 1: + continue + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_global_hann_window(test_case, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_hann_window_graph(test_case): + arg_dict = OrderedDict() + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_graph_hann_window(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_activation.py b/python/oneflow/test/modules/test_global_higher_derivative_activation.py new file mode 100644 index 0000000000000000000000000000000000000000..ccf8fa334d3e361aa2ae49ce3b833b6a7b6f7929 --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_activation.py @@ -0,0 +1,244 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin +from collections import defaultdict + + +def _assert_true(test_case, value1, value2): + test_case.assertTrue( + np.allclose( + value1.detach().cpu().numpy(), + value2.detach().numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_activation_grad_grad_impl(test_case, op_name, placement, *args, **kwargs): + x = random_tensor(ndim=2, low=-5, dim0=8, dim1=8).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + y = eval(f"torch.nn.functional.{op_name}")(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + init_grad_y = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _assert_true(test_case, dx.pytorch, dx.oneflow) + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +def _test_prelu_activation_grad_grad_impl( + test_case, op_name, placement, *args, **kwargs +): + x = random_tensor(ndim=2, low=-5, dim0=8, dim1=8).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + a = random_tensor(ndim=1, dim0=x.oneflow.shape[1]).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=1) + ) + y = torch.nn.functional.prelu(x, a) + + x_shape = x.oneflow.shape + a_shape = a.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + init_grad_y = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + init_grad_a = random_tensor(len(a_shape), *a_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=1) + ) + + dx_and_da = torch.autograd.grad(y, [x, a], init_grad_y, True, True) + dx, da = dx_and_da[0], dx_and_da[1] + _assert_true(test_case, dx.pytorch, dx.oneflow) + _assert_true(test_case, da.pytorch, da.oneflow) + + ddx_dda_ddy = torch.autograd.grad( + dx_and_da, [dx, da, init_grad_y], [init_grad_x, init_grad_a], True, True + ) + ddx, dda, ddy = ddx_dda_ddy[0], ddx_dda_ddy[1], ddx_dda_ddy[2] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, dda.pytorch, dda.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +def _test_hardswish_activation_grad_grad_impl( + test_case, op_name, placement, *args, **kwargs +): + x = random_tensor(ndim=2, low=-1, dim0=8, dim1=8).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + y = torch.nn.functional.hardswish(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + init_grad_y = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + + dx_pytorch = pytorch_origin.autograd.grad( + y.pytorch, x.pytorch, init_grad_y.pytorch + )[0] + dx_oneflow = oneflow_origin.autograd.grad( + y.oneflow, x.oneflow, init_grad_y.oneflow, True, True + )[0] + _assert_true(test_case, dx_pytorch, dx_oneflow) + + ddx, ddy = flow.autograd.grad( + dx_oneflow, [x.oneflow, init_grad_y.oneflow], init_grad_x.oneflow + ) + x, dx, init_grad_x, init_grad_y = ( + x.oneflow, + dx_oneflow, + init_grad_x.oneflow, + init_grad_y.oneflow, + ) + + zeros_grad = flow.zeros_like(x).to_global(placement=placement, sbp=x.sbp) + manual_ddx = flow.where( + ((x > -3.0) < 3.0), 1.0 / 3.0 * init_grad_x * init_grad_y, zeros_grad + ) + manual_ddy = dx / init_grad_y * init_grad_x + _assert_true(test_case, manual_ddx, ddx) + _assert_true(test_case, manual_ddy, ddy) + + +def _test_hardsigmoid_activation_grad_grad_impl( + test_case, op_name, placement, *args, **kwargs +): + x = random_tensor(ndim=2, low=-1, dim0=8, dim1=8).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + y = torch.nn.functional.hardsigmoid(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + init_grad_y = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement=placement, max_dim=2) + ) + + dx_pytorch = pytorch_origin.autograd.grad( + y.pytorch, x.pytorch, init_grad_y.pytorch + )[0] + dx_oneflow = oneflow_origin.autograd.grad( + y.oneflow, x.oneflow, init_grad_y.oneflow, True, True + )[0] + _assert_true(test_case, dx_pytorch, dx_oneflow) + + ddx, ddy = flow.autograd.grad( + dx_oneflow, [x.oneflow, init_grad_y.oneflow], init_grad_x.oneflow + ) + x, dx, init_grad_x, init_grad_y = ( + x.oneflow, + dx_oneflow, + init_grad_x.oneflow, + init_grad_y.oneflow, + ) + manual_ddx = flow.zeros_like(x) + manual_ddy = dx / init_grad_y * init_grad_x + _assert_true(test_case, manual_ddx, ddx) + _assert_true(test_case, manual_ddy, ddy) + + +class TestActivationHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_activation_grad_grad(test_case): + op_args = defaultdict(list) + op_kwargs = defaultdict(dict) + + # parameter name not same in pytorch and oneflow + op_args["leaky_relu"] = [random(-1, 1).to(float)] + + # some op only support kwargs, like celu in oneflow + op_kwargs["hardtanh"] = { + "min_val": random(-5, -1).to(float), + "max_val": random(1, 5).to(float), + } + op_kwargs["elu"] = {"alpha": random(0, 10).to(float)} + op_kwargs["celu"] = {"alpha": random(0, 10).to(float)} + op_kwargs["threshold"] = { + "threshold": random().to(float), + "value": random().to(float), + } + op_kwargs["softplus"] = { + "beta": random().to(float), + "threshold": random().to(float), + } + + op_names = [ + "mish", + "gelu", + "silu", + "selu", + "softsign", + "hardsigmoid", + "hardswish", + "relu", + "elu", + "celu", + "prelu", + "hardshrink", + "softshrink", + "leaky_relu", + "hardtanh", + "softplus", + "threshold", + ] + for op_name in op_names: + try: + functor = eval(f"_test_{op_name}_activation_grad_grad_impl") + except: + functor = _test_activation_grad_grad_impl + + print(f"| {op_name:-^60} |") + for placement in all_placement(): + for i in range(2): + functor( + test_case, + op_name, + placement, + *op_args[op_name], + **op_kwargs[op_name], + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_conv.py b/python/oneflow/test/modules/test_global_higher_derivative_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..1732b187cac9c4f92c65c2f96b3f086df9335bba --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_conv.py @@ -0,0 +1,147 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin + + +def _test_convnd_grad_grad_impl(test_case, ndim, placement): + x_shape = [8, 8] + [5 for _ in range(ndim)] + w_shape = [8, 8] + [3 for _ in range(ndim)] + y_shape = [8, 8] + [3 for _ in range(ndim)] + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + w = random_tensor(len(w_shape), *w_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_w = random_tensor(len(w_shape), *w_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_y = random_tensor(len(y_shape), *y_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + y = eval(f"torch.nn.functional.conv{ndim}d")( + x, w, stride=1, padding=0, groups=1, dilation=1 + ) + + dx = torch.autograd.grad( + outputs=y, + inputs=x, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + + test_case.assertTrue( + np.allclose(dx.pytorch.detach().cpu().numpy(), dx.oneflow.detach().numpy()) + ) + + dw = torch.autograd.grad( + outputs=y, + inputs=w, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(dw.pytorch.detach().cpu().numpy(), dw.oneflow.detach().numpy()) + ) + + # torch.autograd.grad in autotest does not support inputs/outpus/grad_outputs as a list + # so use the original pytorch/oneflow module + ddx_pytorch, ddw_pytorch = pytorch_origin.autograd.grad( + outputs=[dx.pytorch, dw.pytorch], + inputs=[x.pytorch, w.pytorch], + grad_outputs=[init_grad_x.pytorch, init_grad_w.pytorch], + create_graph=True, + retain_graph=True, + ) + ddx_oneflow, ddw_oneflow = oneflow_origin.autograd.grad( + outputs=[dx.oneflow, dw.oneflow], + inputs=[x.oneflow, w.oneflow], + grad_outputs=[init_grad_x.oneflow, init_grad_w.oneflow], + create_graph=True, + retain_graph=True, + ) + + test_case.assertTrue( + np.allclose(ddw_pytorch.detach().cpu().numpy(), ddw_oneflow.detach().numpy()) + ) + test_case.assertTrue( + np.allclose(ddx_pytorch.detach().cpu().numpy(), ddx_oneflow.detach().numpy()) + ) + + dgrad_dx = torch.autograd.grad( + outputs=dx, + inputs=init_grad_y, + grad_outputs=init_grad_x, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_dx.pytorch.detach().cpu().numpy(), dgrad_dx.oneflow.detach().numpy() + ) + ) + + dgrad_dw = torch.autograd.grad( + outputs=dw, + inputs=init_grad_y, + grad_outputs=init_grad_w, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_dw.pytorch.detach().cpu().numpy(), dgrad_dw.oneflow.detach().numpy() + ) + ) + + +class TestGlobalConvHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_conv1d_grad_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_convnd_grad_grad_impl(test_case, ndim=1, placement=placement) + + @globaltest + def test_conv2d_grad_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_convnd_grad_grad_impl(test_case, ndim=2, placement=placement) + + @globaltest + def test_conv3d_grad_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_convnd_grad_grad_impl(test_case, ndim=3, placement=placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_div.py b/python/oneflow/test/modules/test_global_higher_derivative_div.py new file mode 100644 index 0000000000000000000000000000000000000000..9b12872a7303e3a2658a8777ddb4e44134abeed5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_div.py @@ -0,0 +1,102 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_global_div_grad_grad_impl(test_case, placement): + x_shape = [8, 8, 8, 8] + y_shape = [8, 8] + if random_bool().value(): + x_shape, y_shape = y_shape, x_shape + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + y = random_tensor(len(y_shape), *y_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + z = torch.div(x, y) + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx_and_dy = torch.autograd.grad(z, [x, y], init_grad_z, True, True) + test_case.assertTrue( + np.allclose( + dx_and_dy.pytorch[0].detach().cpu().numpy(), + dx_and_dy.oneflow[0].detach().numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) + test_case.assertTrue( + np.allclose( + dx_and_dy.pytorch[1].detach().cpu().numpy(), + dx_and_dy.oneflow[1].detach().numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) + + ddx_and_ddy_and_ddz = torch.autograd.grad( + dx_and_dy, [x, y, init_grad_z], [init_grad_x, init_grad_y], True, True + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[0].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[0].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[1].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[1].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[2].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[2].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + + +class TestGlobalDivHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_div_grad_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_global_div_grad_grad_impl(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_loss.py b/python/oneflow/test/modules/test_global_higher_derivative_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8e6fa2ba0700ba386a6543de80f77feda00b1f --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_loss.py @@ -0,0 +1,234 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _assert_true(test_case, value1, value2, name=""): + is_equal = np.allclose( + value1.detach().cpu().numpy(), value2.detach().numpy(), rtol=1e-03, atol=1e-03, + ) + test_case.assertTrue(is_equal, f"{name} is not equal." if name else "") + + +def generate_grads_for_variables(variables): + if isinstance(variables, list): + shape_and_sbp = [(i.oneflow.shape, i.oneflow.sbp) for i in variables] + placement = variables[0].oneflow.placement + elif hasattr(variables, "pytorch"): + shape_and_sbp = [(i.shape, i.sbp) for i in variables.oneflow] + placement = variables.oneflow[0].placement + else: + assert False + grads = [ + random_tensor( + len(shape), *shape, requires_grad=random_bool().value() + ).to_global(placement=placement, sbp=sbp) + for shape, sbp in shape_and_sbp + ] + return grads + + +def calculate_and_compare_loss(test_case, input, target, model, order=2): + output = model(input, target) + _assert_true(test_case, output.pytorch, output.oneflow, "output") + + init_inputs = [input, target] + grad_inputs = [output] + grad_outputs = [] + for i in range(order): + inputs = [ + var for var in [*init_inputs, *grad_outputs] if var.pytorch.requires_grad + ] + outputs = grad_inputs + grad_outputs = generate_grads_for_variables(outputs) + if i == order - 1: + grad_inputs = torch.autograd.grad(outputs, inputs, grad_outputs) + else: + grad_inputs = torch.autograd.grad(outputs, inputs, grad_outputs, True, True) + for j in range(len(inputs)): + _assert_true( + test_case, + grad_inputs[j].pytorch, + grad_inputs[j].oneflow, + f"{i}-grad_inputs[{j}]", + ) + + +def generate_necessity_for_default_loss(placement): + shape = [8, 8] + ndim = len(shape) + input_requires_grad = True + target_requires_grad = random_bool().value() + return ( + random_tensor(ndim, *shape, requires_grad=input_requires_grad).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ), + random_tensor(ndim, *shape, requires_grad=target_requires_grad).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ), + ) + + +def generate_necessity_for_nll_loss(placement): + ndim = 2 + num_classes = 8 + batch_size = 8 + ignore_index = oneof(random(0, num_classes).to(int).value(), -100).value() + extra_dim = [random().to(int) for _ in range(ndim - 2)] + return ( + random_tensor(ndim, batch_size, num_classes).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ), + random_tensor( + ndim - 1, + batch_size, + low=0, + high=num_classes, + dtype=int, + requires_grad=False, + ).to_global(placement=placement, sbp=random_sbp(placement, max_dim=1)), + random_tensor(1, num_classes, low=0, high=3, requires_grad=False).to_global( + placement=placement, sbp=random_sbp(placement, except_split=True) + ), + ignore_index, + ) + + +def generate_necessity_for_bce_loss(placement): + ndim = 3 + num_classes = 2 + batch_size = 8 + extra_dim = [random().to(int) for _ in range(ndim - 2)] + input_requires_grad = True + target_requires_grad = False + return ( + random_tensor( + ndim, batch_size, num_classes, *extra_dim, requires_grad=input_requires_grad + ).to_global(placement=placement, sbp=random_sbp(placement, max_dim=1)), + random_tensor( + ndim, + batch_size, + num_classes, + *extra_dim, + low=0, + high=num_classes, + requires_grad=target_requires_grad, + ).to_global(placement=placement, sbp=random_sbp(placement, max_dim=1)), + random_tensor( + ndim, + batch_size, + num_classes, + *extra_dim, + low=0, + high=3, + requires_grad=False, + ).to_global(placement=placement, sbp=random_sbp(placement, max_dim=1)), + random_tensor(1, 1, low=1, high=3, requires_grad=False,).to_global( + placement=placement, sbp=random_sbp(placement, except_split=True) + ), + ) + + +def _test_smooth_l1_loss_grad_grad_impl(test_case, placement): + x, y = generate_necessity_for_default_loss(placement) + + m = torch.nn.SmoothL1Loss( + reduction=oneof("none", "sum", "mean", nothing()), beta=oneof(0.0, 0.5, 1) + ) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_kl_div_loss_grad_grad_impl(test_case, placement): + x, y = generate_necessity_for_default_loss(placement) + + m = torch.nn.KLDivLoss( + reduction=oneof("none", "sum", "mean", nothing()), + log_target=oneof(True, False), + ) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_bce_loss_grad_grad_impl(test_case, placement, with_logits=False): + x, y, weight, pos_weight = generate_necessity_for_bce_loss(placement) + + if with_logits: + weight = weight if random_bool().value() else None + has_pos_weight = random_bool().value() + pos_weight = pos_weight if has_pos_weight else nothing() + m = torch.nn.BCEWithLogitsLoss( + weight=weight, + pos_weight=pos_weight, + reduction=oneof("none", "sum", "mean"), + ) + if has_pos_weight: + y = y.detach().clone().requires_grad_(False) + else: + m = torch.nn.BCELoss( + weight=(weight if random_bool().value() else None), + reduction=oneof("none", "sum", "mean"), + ) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_nll_loss_grad_grad_impl(test_case, placement): + (x, y, weight, ignore_index) = generate_necessity_for_nll_loss(placement) + + m = torch.nn.NLLLoss( + weight=(weight if random_bool().value() else None), + reduction=oneof("none", "sum", "mean", nothing()), + ignore_index=ignore_index, + ) + + calculate_and_compare_loss(test_case, x, y, m) + + +class TestGlobalLossHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_smooth_l1_loss_grad_grad(test_case): + for placement in all_placement(): + _test_smooth_l1_loss_grad_grad_impl(test_case, placement) + + @globaltest + def test_kl_div_loss_grad_grad(test_case): + for placement in all_placement(): + _test_kl_div_loss_grad_grad_impl(test_case, placement) + + @globaltest + def test_nll_loss_grad_grad(test_case): + for placement in all_placement(): + _test_nll_loss_grad_grad_impl(test_case, placement) + + @globaltest + def test_bce_loss_grad_grad(test_case): + for placement in all_placement(): + _test_bce_loss_grad_grad_impl(test_case, placement) + + @globaltest + def test_bce_with_logits_loss_grad_grad(test_case): + for placement in all_placement(): + _test_bce_loss_grad_grad_impl(test_case, placement, with_logits=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_matmul.py b/python/oneflow/test/modules/test_global_higher_derivative_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..8ed7e9d648d4522e6a4e67ba30df737f10d4047b --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_matmul.py @@ -0,0 +1,138 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin + + +def _test_broadcast_matmul_grad_b_grad_impl(test_case, placement): + broadcast_dims = [np.random.randint(1, 5) * 8 for _ in range(2)] + m = np.random.randint(1, 5) * 8 + n = np.random.randint(1, 5) * 8 + k = np.random.randint(1, 5) * 8 + + a_shape = broadcast_dims + [m, k] + b_shape = [k, n] + y_shape = broadcast_dims + [m, n] + + a = random_tensor(len(a_shape), *a_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + b = random_tensor(len(b_shape), *b_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_a = random_tensor(len(a_shape), *a_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_b = random_tensor(len(b_shape), *b_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_y = random_tensor(len(y_shape), *y_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + y = torch.matmul(a, b) + + da = torch.autograd.grad( + outputs=y, + inputs=a, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + + test_case.assertTrue( + np.allclose(da.pytorch.detach().cpu().numpy(), da.oneflow.detach().numpy()) + ) + + db = torch.autograd.grad( + outputs=y, + inputs=b, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(db.pytorch.detach().cpu().numpy(), db.oneflow.detach().numpy()) + ) + + # torch.autograd.grad in autotest does not support inputs/outpus/grad_outputs as a list + # so use the original pytorch/oneflow module + dda_pytorch, ddb_pytorch = pytorch_origin.autograd.grad( + outputs=[da.pytorch, db.pytorch], + inputs=[a.pytorch, b.pytorch], + grad_outputs=[init_grad_a.pytorch, init_grad_b.pytorch], + create_graph=True, + retain_graph=True, + ) + dda_oneflow, ddb_oneflow = oneflow_origin.autograd.grad( + outputs=[da.oneflow, db.oneflow], + inputs=[a.oneflow, b.oneflow], + grad_outputs=[init_grad_a.oneflow, init_grad_b.oneflow], + create_graph=True, + retain_graph=True, + ) + + test_case.assertTrue( + np.allclose(ddb_pytorch.detach().cpu().numpy(), ddb_oneflow.detach().numpy()) + ) + test_case.assertTrue( + np.allclose(dda_pytorch.detach().cpu().numpy(), dda_oneflow.detach().numpy()) + ) + + dgrad_da = torch.autograd.grad( + outputs=da, + inputs=init_grad_y, + grad_outputs=init_grad_a, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_da.pytorch.detach().cpu().numpy(), dgrad_da.oneflow.detach().numpy() + ) + ) + + dgrad_db = torch.autograd.grad( + outputs=db, + inputs=init_grad_y, + grad_outputs=init_grad_b, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_db.pytorch.detach().cpu().numpy(), dgrad_db.oneflow.detach().numpy() + ) + ) + + +class TestGlobalMatmulHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_broadcast_matmul_grad_b_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_broadcast_matmul_grad_b_grad_impl(test_case, placement=placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_neg.py b/python/oneflow/test/modules/test_global_higher_derivative_neg.py new file mode 100644 index 0000000000000000000000000000000000000000..c6f549d82621be96a2af095e3dc61e3a68a54142 --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_neg.py @@ -0,0 +1,50 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _global_neg_grad_grad_impl(test_case, placement, sbp): + x = flow.randn(8, 8).to_global(placement=placement, sbp=sbp).requires_grad_(True) + init_grad = ( + flow.randn(8, 8).to_global(placement=placement, sbp=sbp).requires_grad_(True) + ) + init_grad_grad = ( + flow.randn(8, 8).to_global(placement=placement, sbp=sbp).requires_grad_(True) + ) + + y = x.neg() + x_grad = flow.autograd.grad(y, x, init_grad, create_graph=True)[0] + test_case.assertTrue(np.allclose(-init_grad, x_grad.detach().numpy())) + + dgrad = flow.autograd.grad(x_grad, init_grad, init_grad_grad, create_graph=True)[0] + test_case.assertTrue(np.allclose(-init_grad_grad, dgrad.detach().numpy(),)) + + +class TestGlobalNegHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_neg_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_neg_grad_grad_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_pool.py b/python/oneflow/test/modules/test_global_higher_derivative_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..7247cda758a00e0c4692479b30134cc49e9d7c1c --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_pool.py @@ -0,0 +1,168 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, name="", rtol=1e-5, atol=1e-5): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal, f"{name} is not equal" if name else "") + + +def _test_avg_pool_grad_grad_impl(test_case, placement, ndim): + x_shape = [8, 8] + [5] * ndim + + m = eval(f"torch.nn.AvgPool{ndim}d")(kernel_size=random(2, 5).to(int)) + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +def _test_max_pool_grad_grad_impl(test_case, placement, ndim): + x_shape = [8, 8] + [5] * ndim + + m = eval(f"torch.nn.MaxPool{ndim}d")(kernel_size=random(2, 5).to(int)) + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +def _test_adaptive_pool_grad_grad_impl(test_case, placement, ndim, mode): + x_shape = [8, 8] + [5] * ndim + + m = eval(f"torch.nn.Adaptive{mode.title()}Pool{ndim}d")( + output_size=random(2, 5).to(int) + ) + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +class TestGlobalPoolHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_max_pool_1d_grad_grad(test_case): + for placement in all_placement(): + _test_max_pool_grad_grad_impl(test_case, placement, 1) + + @globaltest + def test_max_pool_2d_grad_grad(test_case): + for placement in all_placement(): + _test_max_pool_grad_grad_impl(test_case, placement, 2) + + @globaltest + def test_max_pool_3d_grad_grad(test_case): + for placement in all_placement(): + _test_max_pool_grad_grad_impl(test_case, placement, 3) + + @globaltest + def test_avg_pool_1d_grad_grad(test_case): + for placement in all_placement(): + _test_avg_pool_grad_grad_impl(test_case, placement, ndim=1) + + @globaltest + def test_avg_pool_2d_grad_grad(test_case): + for placement in all_placement(): + _test_avg_pool_grad_grad_impl(test_case, placement, ndim=2) + + @globaltest + def test_avg_pool_3d_grad_grad(test_case): + for placement in all_placement(): + _test_avg_pool_grad_grad_impl(test_case, placement, ndim=3) + + @globaltest + def test_adaptive_avg_pool_1d_grad_grad(test_case): + for placement in all_placement(): + _test_adaptive_pool_grad_grad_impl(test_case, placement, ndim=1, mode="avg") + + @globaltest + def test_adaptive_avg_pool_2d_grad_grad(test_case): + for placement in all_placement(): + _test_adaptive_pool_grad_grad_impl(test_case, placement, ndim=2, mode="avg") + + @globaltest + def test_adaptive_avg_pool_3d_grad_grad(test_case): + for placement in all_placement(): + _test_adaptive_pool_grad_grad_impl(test_case, placement, ndim=3, mode="avg") + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_pow.py b/python/oneflow/test/modules/test_global_higher_derivative_pow.py new file mode 100644 index 0000000000000000000000000000000000000000..4427d632a5aed7f11828b9407a036d21d066c9e9 --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_pow.py @@ -0,0 +1,81 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, rtol=1e-3, atol=1e-3): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal) + + +def _test_global_pow_grad_grad_impl(test_case, placement): + x_shape, y_shape = [([8, 8], [8, 8]), ([8, 8, 8], [8, 8]), ([8, 8], [8, 8, 8]),][ + random(0, 3).to(int).value() + ] + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + y = random_tensor(len(y_shape), *y_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + z = torch.pow(x, y) + _check_equal(test_case, z.pytorch, z.oneflow) + + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx_and_dy = torch.autograd.grad(z, [x, y], init_grad_z, True, True) + _check_equal(test_case, dx_and_dy.pytorch[0], dx_and_dy.oneflow[0]) + _check_equal(test_case, dx_and_dy.pytorch[1], dx_and_dy.oneflow[1]) + + ddx_ddy_ddz = torch.autograd.grad( + dx_and_dy, [x, y, init_grad_z], [init_grad_x, init_grad_y] + ) + _check_equal(test_case, ddx_ddy_ddz.pytorch[0], ddx_ddy_ddz.oneflow[0]) + _check_equal(test_case, ddx_ddy_ddz.pytorch[1], ddx_ddy_ddz.oneflow[1]) + _check_equal(test_case, ddx_ddy_ddz.pytorch[2], ddx_ddy_ddz.oneflow[2]) + + +class TestGlobalPowHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_pow_grad_grad(test_case): + for placement in all_placement(): + for i in range(5): + _test_global_pow_grad_grad_impl(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_scalar_pow.py b/python/oneflow/test/modules/test_global_higher_derivative_scalar_pow.py new file mode 100644 index 0000000000000000000000000000000000000000..2be6efc91827bc450adf19f4b59853b8a7e15a51 --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_scalar_pow.py @@ -0,0 +1,74 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, rtol=1e-4, atol=1e-4, name=""): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal, f"{name} is not equal") + + +def _test_scalar_pow_grad_grad_impl(test_case, placement, reverse=False): + x_shape = [8, 8] + y = random().to(float if random_bool().value() else int).value() + + x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + z = torch.pow(x, y) if not reverse else torch.pow(y, x) + + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + + dx = torch.autograd.grad(z, x, init_grad_z, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, name="dx") + + ddx_and_ddz = torch.autograd.grad(dx, [x, init_grad_z], init_grad_x, True, True) + _check_equal(test_case, ddx_and_ddz.pytorch[0], ddx_and_ddz.oneflow[0], name="ddx") + _check_equal(test_case, ddx_and_ddz.pytorch[1], ddx_and_ddz.oneflow[1], name="ddz") + + +class TestGlobalScalarPowHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_scalar_pow_grad_grad(test_case): + for placement in all_placement(): + for i in range(10): + _test_scalar_pow_grad_grad_impl(test_case, placement) + + @globaltest + def test_global_scalar_reverse_pow_grad_grad(test_case): + for placement in all_placement(): + for i in range(10): + _test_scalar_pow_grad_grad_impl(test_case, placement, reverse=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_slice.py b/python/oneflow/test/modules/test_global_higher_derivative_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..1224c69ed41d2c6eb8b211d7eb8069379077b66c --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_slice.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _global_slice_grad_grad_impl(test_case, placement, sbp): + x = ( + random_tensor(ndim=3, dim0=8, dim1=8, dim2=8) + .to_global(placement=placement, sbp=sbp) + .requires_grad_(True) + ) + init_grad = ( + random_tensor(ndim=3, dim0=8, dim1=8, dim2=4) + .to_global(placement=placement, sbp=sbp) + .requires_grad_(True) + ) + init_grad_grad = ( + random_tensor(ndim=3, dim0=8, dim1=8, dim2=8) + .to_global(placement=placement, sbp=sbp) + .requires_grad_(True) + ) + + y = x[:, :, 2:6] + + x_grad = torch.autograd.grad(y, x, init_grad, create_graph=True)[0] + test_case.assertTrue( + np.allclose( + x_grad.pytorch.detach().cpu().numpy(), x_grad.oneflow.detach().numpy() + ) + ) + + dgrad = torch.autograd.grad(x_grad, init_grad, init_grad_grad, create_graph=False)[ + 0 + ] + test_case.assertTrue( + np.allclose( + dgrad.pytorch.detach().cpu().numpy(), dgrad.oneflow.detach().numpy(), + ) + ) + + +class TestGlobalSliceHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_slice_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_slice_grad_grad_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_higher_derivative_softmax.py b/python/oneflow/test/modules/test_global_higher_derivative_softmax.py new file mode 100644 index 0000000000000000000000000000000000000000..0867b6be303c71f6beac502e9114d6c2f700f4ab --- /dev/null +++ b/python/oneflow/test/modules/test_global_higher_derivative_softmax.py @@ -0,0 +1,86 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _assert_true(test_case, value1, value2): + test_case.assertTrue( + np.allclose( + value1.detach().cpu().numpy(), + value2.detach().cpu().numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_global_softmax_grad_grad_impl(test_case, op_name, placement, sbp): + ndim = 2 + data = random_tensor(ndim=ndim, dim0=8, dim1=8) + + for dim in range(ndim): + x = ( + data.detach() + .clone() + .requires_grad_() + .to_global(placement=placement, sbp=sbp) + ) + m = eval(f"torch.nn.{op_name}")(dim) + y = m(x) + _assert_true(test_case, y.pytorch, y.oneflow) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=sbp + ) + init_grad_y = random_tensor(len(x_shape), *x_shape).to_global( + placement=placement, sbp=sbp + ) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _assert_true(test_case, dx.pytorch, dx.oneflow) + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +class TestGlobalSoftmaxHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_softmax_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_softmax_grad_grad_impl( + test_case, op_name="Softmax", placement=placement, sbp=sbp + ) + + @globaltest + def test_global_logsoftmax_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_softmax_grad_grad_impl( + test_case, op_name="LogSoftmax", placement=placement, sbp=sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_inv.py b/python/oneflow/test/modules/test_global_inv.py new file mode 100644 index 0000000000000000000000000000000000000000..62a0a8a6d21e38e2d184f5c1f3820be6533cd5e6 --- /dev/null +++ b/python/oneflow/test/modules/test_global_inv.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_inv(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim - 2)] + square_dim = 8 + dim_list.extend([square_dim] * 2) + x = ( + random_tensor(ndim, *dim_list, low=-1) + .to(torch.double) + .to_global(placement, sbp) + ) + return torch.linalg.inv(x) + + +class TestInv(flow.unittest.TestCase): + @globaltest + def test_inv(test_case): + ndim = random(2, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_inv(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_linalg_cross.py b/python/oneflow/test/modules/test_global_linalg_cross.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6d6d26705ee3538f5213f83eed22fc188f457e --- /dev/null +++ b/python/oneflow/test/modules/test_global_linalg_cross.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1) +def _test_linalg_cross(test_case, index_size_equal_3, ndim, placement, sbp): + shape = [random(1, 4).to(int) * 8 for i in range(ndim)] + shape[index_size_equal_3] = 3 + x = random_tensor(ndim, *shape) + x = x.to_global(placement=placement, sbp=sbp) + y = random_tensor(ndim, *shape) + y = y.to_global(placement=placement, sbp=sbp) + return torch.cross( + x, y, dim=index_size_equal_3 + ) # TODO(peihong): will convert to torch.linalg.cross when PyTorch in ci is upgraded to 1.11 + + +class TestLinalgCrossGlobal(flow.unittest.TestCase): + @globaltest + def test_linalg_cross(test_case): + ndim = random(2, 5).to(int).value() + index_size_equal_3 = random(0, ndim).to(int).value() + for placement in all_placement(): + for sbp in all_sbp( + placement, + max_dim=ndim, + valid_split_axis=[i for i in range(ndim) if i != index_size_equal_3], + ): + _test_linalg_cross(test_case, index_size_equal_3, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_linear.py b/python/oneflow/test/modules/test_global_linear.py new file mode 100644 index 0000000000000000000000000000000000000000..018d52dfccbf3d4abf72a4e87036cab6c26e706f --- /dev/null +++ b/python/oneflow/test/modules/test_global_linear.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_linear_with_random_data(test_case, placement, input_sbp): + input_size = 8 + m = torch.nn.Linear(in_features=input_size, out_features=8, bias=random()) + m.train(random()) + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight = torch.nn.Parameter( + m.weight.to_global(placement=placement, sbp=weight_sbp) + ) + if m.bias is not None: + # bias is 1-d tensor + bias_sbp = random_sbp(placement, max_dim=1, except_partial_sum=True) + m.bias = torch.nn.Parameter(m.bias.to_global(placement=placement, sbp=bias_sbp)) + x = random_tensor(ndim=2, dim0=input_size, dim1=8).to_global( + placement=placement, sbp=input_sbp + ) + y = m(x) + return y + + +class TestLinearModule(flow.unittest.TestCase): + @globaltest + def test_linear_with_random_data(test_case): + for placement in all_placement(): + for input_sbp in all_sbp(placement, max_dim=2): + _test_linear_with_random_data(test_case, placement, input_sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_linspace.py b/python/oneflow/test/modules/test_global_linspace.py new file mode 100644 index 0000000000000000000000000000000000000000..9198b930679db1f2ae277f47ad9a656b3f2cfe93 --- /dev/null +++ b/python/oneflow/test/modules/test_global_linspace.py @@ -0,0 +1,81 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from collections import OrderedDict + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_linspace(test_case, placement, sbp): + x = flow.linspace(start=-10, end=10, steps=8, placement=placement, sbp=sbp) + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_linspace(test_case, start, end, steps, placement, sbp): + class GlobalLinspaceGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.linspace(start, end, steps, placement=placement, sbp=sbp) + return x + + model = GlobalLinspaceGraph() + x = model() + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestLinspaceGlobal(flow.unittest.TestCase): + @globaltest + def test_linspace_global(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_global_linspace(test_case, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_linspace_graph(test_case): + arg_dict = OrderedDict() + arg_dict["start"] = [-2, 0, 2] + arg_dict["end"] = [4, 8, 16] + arg_dict["steps"] = [8, 16, 24] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + start = args["start"] + end = args["end"] + steps = args["steps"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_graph_linspace(test_case, start, end, steps, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_logspace.py b/python/oneflow/test/modules/test_global_logspace.py new file mode 100644 index 0000000000000000000000000000000000000000..f12e60df2e9f30170a3ebee983cc73a3f0c03250 --- /dev/null +++ b/python/oneflow/test/modules/test_global_logspace.py @@ -0,0 +1,87 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +from collections import OrderedDict + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_logspace(test_case, placement, sbp): + x = flow.logspace(start=-10, end=10, steps=8, placement=placement, sbp=sbp) + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_logspace(test_case, start, end, steps, placement, sbp): + class GlobalLogspaceGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.logspace(start, end, steps, placement=placement, sbp=sbp) + return x + + model = GlobalLogspaceGraph() + x = model() + + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestLogspaceGlobal(flow.unittest.TestCase): + # TODO(wyg): It will be infer all broadcast sbp when 1n1d, + # slice_update will get error when doing inplace operator. + # Remove this judgement after refactor sbp infer method in Operator class. + @globaltest + def test_logspace_global(test_case): + for placement in all_placement(): + if placement.ranks.size == 1: + continue + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_global_logspace(test_case, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_logspace_graph(test_case): + arg_dict = OrderedDict() + arg_dict["start"] = [-2, 0, 2] + arg_dict["end"] = [4, 8, 16] + arg_dict["steps"] = [8, 16, 24] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + start = args["start"] + end = args["end"] + steps = args["steps"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_graph_logspace(test_case, start, end, steps, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_lstm_cell.py b/python/oneflow/test/modules/test_global_lstm_cell.py new file mode 100644 index 0000000000000000000000000000000000000000..01dcde1f31d819475a39025df1403032a1f82317 --- /dev/null +++ b/python/oneflow/test/modules/test_global_lstm_cell.py @@ -0,0 +1,84 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +import oneflow.framework.session_context as session_ctx + + +@autotest(n=1, check_graph=True) +def _test_lstm_cell(test_case, placement, sbp): + batch_size = random(2, 3) * 8 + time_steps = random(2, 3) * 8 + input_size = random(2, 3) * 8 + hidden_size = random(2, 3) * 8 + has_bias = random().to(bool) + cx_requires_grad = random().to(bool) + m = torch.nn.LSTMCell( + input_size=input_size, hidden_size=hidden_size, bias=has_bias, + ) + + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight_ih = torch.nn.Parameter( + m.weight_ih.to_global(placement=placement, sbp=weight_sbp) + ) + m.weight_hh = torch.nn.Parameter( + m.weight_hh.to_global(placement=placement, sbp=weight_sbp) + ) + if m.bias_ih is not None: + # bias is 1-d tensor + bias_sbp = random_sbp(placement, max_dim=1, except_partial_sum=True) + m.bias_ih = torch.nn.Parameter( + m.bias_ih.to_global(placement=placement, sbp=bias_sbp) + ) + m.bias_hh = torch.nn.Parameter( + m.bias_hh.to_global(placement=placement, sbp=bias_sbp) + ) + + input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) + input = random_tensor( + ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size + ).to_global(placement=placement, sbp=input_sbp) + hx = random_tensor( + ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False + ).to_global(placement=placement, sbp=sbp) + cx = random_tensor( + ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=cx_requires_grad + ).to_global(placement=placement, sbp=sbp) + + for i in range(time_steps.to(int).value()): + res = m(input[i], (hx, cx)) + hx = res[0] + cx = res[1] + return res[0] + + +class TestRNNCellGlobal(flow.unittest.TestCase): + @globaltest + def test_lstm_cell(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_lstm_cell(test_case, placement, sbp) + + session_ctx.GetDefaultSession().Reset() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_masked_fill.py b/python/oneflow/test/modules/test_global_masked_fill.py new file mode 100644 index 0000000000000000000000000000000000000000..6a8a3261c2e206a743baebe09359a60751bae311 --- /dev/null +++ b/python/oneflow/test/modules/test_global_masked_fill.py @@ -0,0 +1,68 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_masked_fill(test_case, placement, sbp): + k1 = random().to(int).value() * 8 + k2 = random().to(int).value() * 8 + input = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) + mask = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) + value = random().to(float) + return input.masked_fill(mask > 0.5, value) + + +@autotest(n=1, check_graph=True) +def _test_masked_fill_with_0dim_data(test_case, placement, sbp): + input = random_tensor(ndim=0).to_global(placement, sbp) + mask = random_tensor(ndim=0).to_global(placement, sbp) + value = random().to(float) + return input.masked_fill(mask > 0.5, value) + + +@autotest(n=1, check_graph=True) +def _test_masked_fill_with_broadcast_way(test_case, placement, sbp): + k1 = random().to(int).value() * 8 + k2 = random().to(int).value() * 8 + input = random_tensor(ndim=2, dim0=k1, dim1=k2, dim2=1, dim3=k2).to_global( + placement, sbp + ) + mask = random_tensor(ndim=2, dim0=k1, dim1=k2, dim2=k1, dim3=1).to_global( + placement, sbp + ) + value = random().to(float) + return input.masked_fill(mask > 0.5, value) + + +class TestMaskedFill(flow.unittest.TestCase): + @globaltest + def test_masked_fill(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_masked_fill(test_case, placement, sbp) + # TODO() : fail at tensor slice + # _test_masked_fill_with_0dim_data(test_case, placement, sbp) + _test_masked_fill_with_broadcast_way(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_masked_select.py b/python/oneflow/test/modules/test_global_masked_select.py new file mode 100644 index 0000000000000000000000000000000000000000..44c8c0477ff33c1a3fb7c1016bfdec9af42cd94c --- /dev/null +++ b/python/oneflow/test/modules/test_global_masked_select.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + +# Not check graph because of one reason: +# Reason 1, The implementation of the masked_select op calls argwhere with the lazy tensor as an argument, but lazy tensor can not be applied to argwhere. +# Please refer to File "python/oneflow/nn/modules/masked_select.py", line 54, in masked_select_op. +@autotest(n=1, check_graph="ValidatedFalse") +def _test_masked_select(test_case, placement, sbp): + k1 = random(1, 2).to(int).value() * 8 + k2 = random(1, 2).to(int).value() * 8 + input = random_tensor(ndim=2, dim0=k1, dim1=k2).to_global(placement, sbp) + mask = input.ge(0.5) + return torch.masked_select(input, mask) + + +# Not check graph because of one reason: +# Reason 1, The implementation of the masked_select op calls argwhere with the lazy tensor as an argument, but lazy tensor can not be applied to argwhere. +# Please refer to File "python/oneflow/nn/modules/masked_select.py", line 54, in masked_select_op. +@autotest(n=1, check_graph="ValidatedFalse") +def _test_masked_select_broadcast(test_case, placement, input_sbp, mask_sbp): + k1 = random(1, 2).to(int).value() * 8 + k2 = random(1, 2).to(int).value() * 8 + input = random_tensor(ndim=4, dim0=k1, dim1=k2, dim2=1, dim3=k2).to_global( + placement, input_sbp + ) + mask = random_tensor(ndim=4, dim0=k1, dim1=k2, dim2=k1, dim3=1).to_global( + placement, mask_sbp + ) + return torch.masked_select(input, mask > 0.5) + + +class TestMaskedSelect(flow.unittest.TestCase): + @globaltest + def test_masked_select(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_masked_select(test_case, placement, sbp) + + @globaltest + def test_masked_select_broadcast(test_case): + for placement in all_placement(): + for input_sbp in all_sbp(placement, valid_split_axis=[0, 1, 3]): + for mask_sbp in all_sbp(placement, max_dim=3): + _test_masked_select_broadcast( + test_case, placement, input_sbp, mask_sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_math_op_higher_derivative.py b/python/oneflow/test/modules/test_global_math_op_higher_derivative.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8e451396fe3d6bf0ad354e37cad60952597da7 --- /dev/null +++ b/python/oneflow/test/modules/test_global_math_op_higher_derivative.py @@ -0,0 +1,229 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _global_math_op_grad_grad_impl(test_case, op_name, placement, sbp): + x = ( + random_tensor(2, dim0=8, dim1=8, low=-2, high=2) + .to_global(placement=placement, sbp=sbp) + .requires_grad_(True) + ) + y = eval(f"torch.{op_name}")(x) + init_grad = random_tensor(2, 8, 8).to_global(placement, sbp).requires_grad_() + + x_grad = torch.autograd.grad(y, x, init_grad, create_graph=True)[0] + test_case.assertTrue( + np.allclose( + x_grad.pytorch.detach().cpu().numpy(), + x_grad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, + ) + ) + + x_grad_grad = torch.autograd.grad(x_grad, x, init_grad, retain_graph=True)[0] + test_case.assertTrue( + np.allclose( + x_grad_grad.pytorch.detach().cpu().numpy(), + x_grad_grad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, + ) + ) + + init_grad_grad = random_tensor(2, 8, 8).to_global(placement, sbp).requires_grad_() + dgrad = torch.autograd.grad(x_grad, init_grad, init_grad_grad, retain_graph=True)[0] + test_case.assertTrue( + np.allclose( + dgrad.pytorch.detach().cpu().numpy(), + dgrad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, + ) + ) + + +class TestGlobalMathOpHigherDerivative(flow.unittest.TestCase): + @globaltest + def test_global_sin_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "sin", placement, sbp) + + @globaltest + def test_global_cos_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "cos", placement, sbp) + + @globaltest + def test_global_tan_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "tan", placement, sbp) + + @globaltest + def test_global_sinh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "sinh", placement, sbp) + + @globaltest + def test_global_cosh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "cosh", placement, sbp) + + @globaltest + def test_global_tanh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "tanh", placement, sbp) + + @globaltest + def test_global_asin_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "asin", placement, sbp) + + @globaltest + def test_global_acos_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "acos", placement, sbp) + + @globaltest + def test_global_atan_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "atan", placement, sbp) + + @globaltest + def test_global_asinh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "asinh", placement, sbp) + + @globaltest + def test_global_acosh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "acosh", placement, sbp) + + @globaltest + def test_global_atanh_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "atanh", placement, sbp) + + @globaltest + def test_global_erf_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "erf", placement, sbp) + + @globaltest + def test_global_erfc_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "erfc", placement, sbp) + + @globaltest + def test_global_exp_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "exp", placement, sbp) + + @globaltest + def test_global_expm1_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "expm1", placement, sbp) + + @globaltest + def test_global_log_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "log", placement, sbp) + + @globaltest + def test_global_logsigmoid_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl( + test_case, "nn.functional.logsigmoid", placement, sbp + ) + + @globaltest + def test_global_log2_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "log2", placement, sbp) + + @globaltest + def test_global_log1p_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "log1p", placement, sbp) + + @globaltest + def test_global_reciprocal_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "reciprocal", placement, sbp) + + @globaltest + def test_global_rsqrt_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "rsqrt", placement, sbp) + + @globaltest + def test_global_sqrt_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "sqrt", placement, sbp) + + @globaltest + def test_global_square_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "square", placement, sbp) + + @globaltest + def test_global_sigmoid_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "sigmoid", placement, sbp) + + @globaltest + def test_global_abs_grad_grad(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _global_math_op_grad_grad_impl(test_case, "abs", placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_math_ops.py b/python/oneflow/test/modules/test_global_math_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..567e9f492a1893ebc1fc855ce713e4f882097b3e --- /dev/null +++ b/python/oneflow/test/modules/test_global_math_ops.py @@ -0,0 +1,190 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_sinh(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.sinh(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_sin(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.sin(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_inplace_sin(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = x + 1 + y.sin_() + return y + + +@autotest(n=1, check_graph=True) +def _test_cos(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.cos(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_log(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.log(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_sqrt(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.sqrt(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_exp(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.exp(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_rsqrt(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.rsqrt(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_square(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = torch.square(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_pow_with_scalar(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = random().to(float) + z = torch.pow(x, y) + return z + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_floordiv_with_scalar(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list,).to_global(placement, sbp) + y = random().to(float) + z = torch.floor_divide(x, y) + return z + + +@autotest(n=1, check_graph=True) +def _test_arccos(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list, low=-1, high=1).to_global(placement, sbp) + y = torch.arccos(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_acos(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list, low=-1, high=1).to_global(placement, sbp) + y = torch.acos(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_arccosh(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list, low=2, high=3).to_global(placement, sbp) + y = torch.arccosh(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_acosh(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list, low=2, high=3).to_global(placement, sbp) + y = torch.acosh(x) + return y + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_floordiv(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = random_tensor(ndim, *dim_list).to_global(placement, sbp) + z = torch.floor_divide(x, y) + return z + + +@autotest(n=1, check_graph=True) +def _test_atan2(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = random_tensor(ndim, *dim_list).to_global(placement, sbp) + z = torch.atan2(x, y) + return z + + +class TestMathOps(flow.unittest.TestCase): + @globaltest + def test_math_ops(test_case): + ndim = random(1, 3).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_sinh(test_case, placement, sbp, ndim) + _test_sin(test_case, placement, sbp, ndim) + _test_inplace_sin(test_case, placement, sbp, ndim) + _test_cos(test_case, placement, sbp, ndim) + _test_log(test_case, placement, sbp, ndim) + _test_sqrt(test_case, placement, sbp, ndim) + _test_exp(test_case, placement, sbp, ndim) + _test_rsqrt(test_case, placement, sbp, ndim) + _test_square(test_case, placement, sbp, ndim) + _test_pow_with_scalar(test_case, placement, sbp, ndim) + _test_floordiv_with_scalar(test_case, placement, sbp, ndim) + _test_arccos(test_case, placement, sbp, ndim) + _test_acos(test_case, placement, sbp, ndim) + _test_arccosh(test_case, placement, sbp, ndim) + _test_acosh(test_case, placement, sbp, ndim) + + _test_floordiv(test_case, placement, sbp, ndim) + _test_atan2(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_matmul.py b/python/oneflow/test/modules/test_global_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..4473cb01e63022f678cf5757223c2ca48975b4ab --- /dev/null +++ b/python/oneflow/test/modules/test_global_matmul.py @@ -0,0 +1,57 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_matmul(test_case, placement, x_sbp, y_sbp): + x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement=placement, sbp=x_sbp) + y = random_tensor(ndim=2, dim0=16, dim1=8).to_global(placement=placement, sbp=y_sbp) + return torch.matmul(x, y) + + +@autotest(n=1, check_graph=True) +def _test_tensor_broadcast_matmul(test_case, placement, x_sbp, y_sbp): + x = random_tensor(ndim=3, dim0=8, dim1=8, dim2=16).to_global( + placement=placement, sbp=x_sbp + ) + y = random_tensor(ndim=2, dim0=16, dim1=8).to_global(placement=placement, sbp=y_sbp) + return x.matmul(y) + + +class TestMatMulModule(flow.unittest.TestCase): + @globaltest + def test_matmul(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, max_dim=2): + for y_sbp in all_sbp(placement, max_dim=2): + _test_matmul(test_case, placement, x_sbp, y_sbp) + + @globaltest + def test_broadcast_matmul(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, valid_split_axis=[0, 1, 2, 3]): + for y_sbp in all_sbp(placement, valid_split_axis=[0, 1]): + _test_tensor_broadcast_matmul(test_case, placement, x_sbp, y_sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_max.py b/python/oneflow/test/modules/test_global_max.py new file mode 100644 index 0000000000000000000000000000000000000000..e76da898efee7a947154dbb0999fefcfd3594ed4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_max.py @@ -0,0 +1,105 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from collections import OrderedDict +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + + +def _np_max(shape, dim, keepdims): + # np array result + input_arr = np.random.randn(*shape) + np_out = np.amax(input_arr, axis=dim, keepdims=keepdims) + np_out_grad = np.zeros_like(input_arr) + if dim == None: + arg_max = np.argmax(input_arr) + np.put(np_out_grad, arg_max, 1) + else: + arg_max = np.expand_dims(np.argmax(input_arr, axis=dim), axis=dim) + np.put_along_axis(np_out_grad, arg_max, 1, axis=dim) + + return np_out, np_out_grad, input_arr + + +def _test_max( + test_case, placement, sbp, np_out, np_out_grad, input_arr, shape, dim, keepdims +): + # of result + global_x = flow.tensor( + input_arr, + dtype=flow.float32, + requires_grad=True, + placement=flow.placement.all("cpu"), + sbp=flow.sbp.broadcast, + ) + if dim is None: + of_out = flow.max(global_x) + else: + of_out = flow.max(global_x, dim, keepdims)[0] + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + of_out = of_out.sum() + of_out.backward() + + test_case.assertTrue( + np.allclose(global_x.grad.numpy(), np_out_grad, 0.0001, 0.0001) + ) + + +class TestMaxModule(flow.unittest.TestCase): + # backward formula is different from one of torch. + @globaltest + def test_eager_global_max(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_max] + arg_dict["shape"] = [(8,), (8, 8), (8, 8, 8, 8)] + arg_dict["dim"] = [None, 0, -1] + arg_dict["keepdims"] = [False, True] + for arg in GenArgList(arg_dict): + np_out, np_out_grad, input_arr = _np_max(*arg[1:]) + np_out = ( + flow.tensor(np_out) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + np_out_grad = ( + flow.tensor(np_out_grad) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + input_arr = ( + flow.tensor(input_arr) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(*arg[1:2])): + arg[0]( + test_case, + placement, + sbp, + np_out, + np_out_grad, + input_arr, + *arg[1:] + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_maximum_minimum.py b/python/oneflow/test/modules/test_global_maximum_minimum.py new file mode 100644 index 0000000000000000000000000000000000000000..d5e2d20df6d7404d2296558a03db4d19a1455a0c --- /dev/null +++ b/python/oneflow/test/modules/test_global_maximum_minimum.py @@ -0,0 +1,109 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest +import torch as torch_original +from packaging import version +from oneflow.test_utils.automated_test_util import * + + +@autotest( + n=5, + auto_backward=( + version.parse(torch_original.__version__) >= version.parse("1.10.2") + ), + check_graph=True, +) +def _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp): + x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global( + placement, x_sbp + ) + y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global( + placement, y_sbp + ) + z = torch.maximum(x, y) + return z + + +@autotest( + n=5, + auto_backward=( + version.parse(torch_original.__version__) >= version.parse("1.10.2") + ), + check_graph=True, +) +def _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp): + x = random_tensor(ndim=5, dim0=8, dim1=8, dim2=8, dim3=1, dim4=8).to_global( + placement, x_sbp + ) + y = random_tensor(ndim=5, dim0=8, dim1=8, dim2=1, dim3=8, dim4=1).to_global( + placement, y_sbp + ) + z = torch.minimum(x, y) + return z + + +@autotest( + n=5, + auto_backward=( + version.parse(torch_original.__version__) >= version.parse("1.10.2") + ), + check_graph=True, +) +def _test_maximum_with_same_input(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp) + y = x.detach().clone() + y.requires_grad = True + z = torch.maximum(x, y) + return z + + +@autotest( + n=5, + auto_backward=( + version.parse(torch_original.__version__) >= version.parse("1.10.2") + ), + check_graph=True, +) +def _test_minimum_with_same_input(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=8, dim2=8, dim3=8).to_global(placement, sbp) + y = x.detach().clone() + y.requires_grad = True + z = torch.minimum(x, y) + return z + + +class TestMaximumMinimumOps(flow.unittest.TestCase): + @globaltest + def test_maximum_minimum_with_same_input(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_maximum_with_same_input(test_case, placement, sbp) + _test_minimum_with_same_input(test_case, placement, sbp) + + @globaltest + def test_broadcast_maximum_minimum(test_case): + for placement in all_placement(): + for x_sbp in all_sbp(placement, valid_split_axis=[0, 1, 2, 4]): + for y_sbp in all_sbp(placement, valid_split_axis=[0, 1, 3]): + _test_broadcast_maximum(test_case, placement, x_sbp, y_sbp) + _test_broadcast_minimum(test_case, placement, x_sbp, y_sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_maxpool.py b/python/oneflow/test/modules/test_global_maxpool.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3e8ec9b9c2b600e832dab227f8f4ac544c174a --- /dev/null +++ b/python/oneflow/test/modules/test_global_maxpool.py @@ -0,0 +1,254 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +from pkg_resources import packaging +import oneflow as flow +import torch as ori_torch +import oneflow.unittest +from collections import OrderedDict +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList +from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t + + +@autotest(n=1, check_graph=True) +def _test_maxpool1d_functional(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random().to(int).value() * 8 + dim1 = random().to(int).value() * 8 + x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 22)).to_global( + placement, sbp + ) + y = torch.nn.functional.max_pool1d( + x, + kernel_size=random(4, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + dilation=random(2, 4).to(int), + ceil_mode=random().to(bool), + return_indices=return_indices, + ) + if return_indices: + return y[0] + else: + return y + + +@autotest(n=1, check_graph=True) +def _test_maxpool2d_functional(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random().to(int).value() * 8 + dim1 = random().to(int).value() * 8 + x = random_tensor( + ndim=4, dim0=dim0, dim1=dim1, dim2=random(20, 22), dim3=random(20, 22) + ).to_global(placement, sbp) + y = torch.nn.functional.max_pool2d( + x, + kernel_size=random(4, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + dilation=random(2, 4).to(int), + ceil_mode=random().to(bool), + return_indices=return_indices, + ) + + if return_indices: + return y[0] + else: + return y + + +@autotest(n=1, check_graph=True) +def _test_maxpool3d_functional(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random(high=4).to(int).value() * 8 + dim1 = random(high=4).to(int).value() * 8 + x = random_tensor( + ndim=5, + dim0=dim0, + dim1=dim1, + dim2=random(10, 12), + dim3=random(10, 12), + dim4=random(10, 12), + ).to_global(placement, sbp) + y = torch.nn.functional.max_pool3d( + x, + kernel_size=random(4, 6).to(int), + stride=random(1, 3).to(int), + padding=random(1, 3).to(int), + dilation=random(2, 4).to(int), + ceil_mode=random().to(bool), + return_indices=return_indices, + ) + + if return_indices: + return y[0] + else: + return y + + +@autotest(n=1, check_graph=True) +def _test_maxpool1d(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random().to(int).value() * 8 + dim1 = random().to(int).value() * 8 + m = torch.nn.MaxPool1d( + kernel_size=random(4, 6).to(_size_1_t), + stride=random(1, 3).to(_size_1_t), + padding=random(1, 3).to(_size_1_t), + dilation=random(2, 4).to(_size_1_t), + ceil_mode=random(), + return_indices=return_indices, + ) + m.train(random()) + x = random_tensor(ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 22)).to_global( + placement, sbp + ) + y = m(x) + if return_indices: + return y[0] + else: + return y + + +@autotest(n=1, check_graph=True) +def _test_maxpool2d(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random(1, 3).to(int).value() * 8 + dim1 = random(1, 3).to(int).value() * 8 + m = torch.nn.MaxPool2d( + kernel_size=random(4, 6).to(_size_2_t), + stride=random(1, 3).to(_size_2_t), + padding=random(1, 3).to(_size_2_t), + dilation=random(2, 4).to(_size_2_t), + ceil_mode=random(), + return_indices=return_indices, + ) + m.train(random()) + x = random_tensor( + ndim=4, dim0=dim0, dim1=dim1, dim2=random(20, 22), dim3=random(20, 22) + ).to_global(placement, sbp) + y = m(x) + if return_indices: + return y[0] + else: + return y + + +@autotest(n=1, check_graph=True) +def _test_maxpool3d(test_case, placement, sbp): + return_indices = random().to(bool).value() + dim0 = random(high=4).to(int).value() * 8 + dim1 = random(high=4).to(int).value() * 8 + m = torch.nn.MaxPool3d( + kernel_size=random(4, 6).to(_size_3_t), + stride=random(1, 3).to(_size_3_t), + padding=random(1, 3).to(_size_3_t), + dilation=random(2, 4).to(_size_3_t), + ceil_mode=random(), + return_indices=return_indices, + ) + m.train(random()) + x = random_tensor( + ndim=5, + dim0=dim0, + dim1=dim1, + dim2=random(10, 12), + dim3=random(10, 12), + dim4=random(10, 12), + ).to_global(placement, sbp) + y = m(x) + + if return_indices: + return y[0] + else: + return y + + +def _test_maxpool2d_channel_last( + test_case, placement, sbp, shape, kernel_size, stride, padding, dilation, ceil_mode +): + os.environ["ONEFLOW_ENABLE_NHWC"] = "1" + + tensor = random_tensor(len(shape), *shape, requires_grad=False).to_global( + placement, sbp + ) + # oneflow result + x1 = tensor.oneflow + m1 = flow.nn.MaxPool2d( + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ceil_mode=ceil_mode, + ) + y1 = m1(x1) + + # pytorch result + x2 = tensor.pytorch.permute(0, 3, 1, 2).to(placement.type) + m2 = ori_torch.nn.MaxPool2d( + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + ceil_mode=ceil_mode, + ) + y2 = m2(x2).permute(0, 2, 3, 1) + os.environ["ONEFLOW_ENABLE_NHWC"] = "1" + + test_case.assertTrue( + np.allclose(y1.detach().cpu().numpy(), y2.detach().cpu().numpy(), 1e-4, 1e-4) + ) + + +class TestMaxPool(flow.unittest.TestCase): + @globaltest + def test_maxpool(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_maxpool1d_functional(test_case, placement, sbp) + _test_maxpool2d_functional(test_case, placement, sbp) + _test_maxpool3d_functional(test_case, placement, sbp) + _test_maxpool1d(test_case, placement, sbp) + _test_maxpool2d(test_case, placement, sbp) + _test_maxpool3d(test_case, placement, sbp) + + @globaltest + @unittest.skipIf( + packaging.version.parse(ori_torch.__version__) + == packaging.version.parse("1.10.0"), + "skip when pytorch version == 1.10.0", + ) + # NOTE:pytorch maxpool2d nhwc has bug in version of 1.10.0, so skip it in CI. + # detail:https://github.com/pytorch/pytorch/pull/76597 + def test_maxpool2d_channel_last(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_maxpool2d_channel_last] + arg_dict["shape"] = [(1, 16, 16, 3), (2, 224, 224, 3)] + arg_dict["kernel_size"] = [3, (2, 3)] + arg_dict["stride"] = [1, (1, 2)] + arg_dict["padding"] = [0, (0, 1)] + arg_dict["dilation"] = [1, 2] + arg_dict["ceil_mode"] = [True, False] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, valid_split_axis=[1, 2]): + arg[0](test_case, placement, sbp, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_maxunpool.py b/python/oneflow/test/modules/test_global_maxunpool.py new file mode 100644 index 0000000000000000000000000000000000000000..dfb39613136795aa26f0f2de11ba36eca5b9e8bc --- /dev/null +++ b/python/oneflow/test/modules/test_global_maxunpool.py @@ -0,0 +1,193 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t + +# y = pool(x), z = unpool(y, indices), pool_input_shape is x.shape, pool_output_shape is y.shape. +# When `output_size` in unpool() is empty, the op will calculate the output size according to +# kernel_size, stride and padding. But when index in indices is outside the range required +# by output_size calculated by unpool op, the value of result and related grad will be unknown. +# To avoid the problem, this function calculate the output_size which will not cause unknown problems. +def _get_valid_output_size( + pool_input_shape, pool_output_shape, kernel_size, stride, padding +): + def convert_data(data, i, dst_data=None): + if not isinstance(data, (list, int)): + return dst_data + if isinstance(data, list): + return data[i] + return data + + _, _, *pool_input_hwd_shape = pool_input_shape.pytorch + batch_size, num_channels, *pool_out_hwd_shape = pool_output_shape.pytorch + unpool_output_shape = [batch_size, num_channels] + for i, (pool_input_size, pool_output_size) in enumerate( + zip(pool_input_hwd_shape, pool_out_hwd_shape) + ): + kernel_size_value = convert_data(kernel_size.value(), i) + stride_value = convert_data(stride.value(), i, kernel_size_value) + padding_value = convert_data(padding.value(), i, 0) + unpool_output_size = max( + pool_input_size, + (pool_output_size - 1) * stride_value + - 2 * padding_value + + kernel_size_value, + ) + unpool_output_shape.append(unpool_output_size) + return torch.Size(unpool_output_shape) + + +def _test_module_unpoolnd(test_case, placement, sbp, n): + device = random_device() + dim0 = random(high=4).to(int).value() * 8 + dim1 = random(high=4).to(int).value() * 8 + if n == 1: + _size_n_t = _size_1_t + MaxPoolNd = torch.nn.MaxPool1d + MaxUnpoolNd = torch.nn.MaxUnpool1d + x = random_tensor( + ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 31), requires_grad=False + ).to_global(placement=placement, sbp=sbp) + elif n == 2: + _size_n_t = _size_2_t + MaxPoolNd = torch.nn.MaxPool2d + MaxUnpoolNd = torch.nn.MaxUnpool2d + x = random_tensor( + ndim=4, + dim0=dim0, + dim1=dim1, + dim2=random(10, 21), + dim3=random(10, 21), + requires_grad=False, + ).to_global(placement=placement, sbp=sbp) + elif n == 3: + _size_n_t = _size_3_t + MaxPoolNd = torch.nn.MaxPool3d + MaxUnpoolNd = torch.nn.MaxUnpool3d + x = random_tensor( + ndim=5, + dim0=dim0, + dim1=dim1, + dim2=random(10, 14), + dim3=random(10, 14), + dim4=random(10, 14), + requires_grad=False, + ).to_global(placement=placement, sbp=sbp) + + kernel_size = random(4, 6).to(_size_n_t) + stride = random(1, 3).to(_size_n_t) + padding = random(1, 3).to(_size_n_t) + m = MaxPoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, return_indices=True, + ) + m.train(random()) + m.to(device) + y = m(x) + pooling_results = y[0] + indices = y[1] + pooling_results.requires_grad_() + output_size = _get_valid_output_size( + x.shape, pooling_results.shape, kernel_size, stride, padding + ) + unpool_module = MaxUnpoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, + ) + result = unpool_module(pooling_results, indices, output_size=output_size) + return result + + +def _test_functional_unpoolnd(test_case, placement, sbp, n): + device = random_device() + dim0 = random(high=4).to(int).value() * 8 + dim1 = random(high=4).to(int).value() * 8 + + if n == 1: + _size_n_t = _size_1_t + MaxPoolNd = torch.nn.MaxPool1d + max_unpool_nd = torch.nn.functional.max_unpool1d + x = random_tensor( + ndim=3, dim0=dim0, dim1=dim1, dim2=random(20, 31), requires_grad=False + ).to_global(placement=placement, sbp=sbp) + elif n == 2: + _size_n_t = _size_2_t + MaxPoolNd = torch.nn.MaxPool2d + max_unpool_nd = torch.nn.functional.max_unpool2d + x = random_tensor( + ndim=4, + dim0=dim0, + dim1=dim1, + dim2=random(10, 21), + dim3=random(10, 21), + requires_grad=False, + ).to_global(placement=placement, sbp=sbp) + elif n == 3: + _size_n_t = _size_3_t + MaxPoolNd = torch.nn.MaxPool3d + max_unpool_nd = torch.nn.functional.max_unpool3d + x = random_tensor( + ndim=5, + dim0=dim0, + dim1=dim1, + dim2=random(10, 14), + dim3=random(10, 14), + dim4=random(10, 14), + requires_grad=False, + ).to_global(placement=placement, sbp=sbp) + + kernel_size = random(4, 6).to(_size_n_t) + stride = random(1, 3).to(_size_n_t) + padding = random(1, 3).to(_size_n_t) + m = MaxPoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, return_indices=True, + ) + m.train(random()) + m.to(device) + y = m(x) + pooling_results = y[0] + indices = y[1] + pooling_results.requires_grad_() + output_size = _get_valid_output_size( + x.shape, pooling_results.shape, kernel_size, stride, padding + ) + return max_unpool_nd( + pooling_results, + indices, + kernel_size=kernel_size, + stride=stride, + padding=padding, + output_size=output_size, + ) + + +class TestMaxPool(flow.unittest.TestCase): + @globaltest + def test_maxpool(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_functional_unpoolnd(test_case, placement, sbp, 1) + _test_functional_unpoolnd(test_case, placement, sbp, 2) + _test_functional_unpoolnd(test_case, placement, sbp, 3) + _test_module_unpoolnd(test_case, placement, sbp, 1) + _test_module_unpoolnd(test_case, placement, sbp, 2) + _test_module_unpoolnd(test_case, placement, sbp, 3) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_mean.py b/python/oneflow/test/modules/test_global_mean.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f021023237c8e002b6865ddc8cdc06edf256e7 --- /dev/null +++ b/python/oneflow/test/modules/test_global_mean.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_mean(test_case, placement, sbp, ndim): + dim = random(1, ndim).to(int).value() + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list, dtype=float).to_global(placement, sbp) + return torch.mean(x, dim) + + +class TestMean(flow.unittest.TestCase): + @globaltest + def test_mean(test_case): + ndim = random(2, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_mean(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_median.py b/python/oneflow/test/modules/test_global_median.py new file mode 100644 index 0000000000000000000000000000000000000000..72ed9e638dcf45ec92244cc00009efe85e32906b --- /dev/null +++ b/python/oneflow/test/modules/test_global_median.py @@ -0,0 +1,57 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import torch +from functools import reduce +import operator +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_median(test_case, placement, sbp, ndim): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + return torch.median(x) + + +@autotest(n=1, check_graph=True) +def _test_median_with_indices(test_case, placement, sbp, ndim): + dim = random(1, ndim).to(int).value() + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = choice_tensor( + reduce(operator.mul, dim_list, 1), + dim_list, + replace=False, + dtype=float, + requires_grad=True, + ).to_global(placement, sbp) + return torch.median(x, dim) + + +class TestMedian(flow.unittest.TestCase): + @globaltest + def test_median(test_case): + ndim = random(2, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_median(test_case, placement, sbp, ndim) + _test_median_with_indices(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_meshgrid.py b/python/oneflow/test/modules/test_global_meshgrid.py new file mode 100644 index 0000000000000000000000000000000000000000..ba6cb944862b0c276a1f7d40877c0f326d15012e --- /dev/null +++ b/python/oneflow/test/modules/test_global_meshgrid.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=5, auto_backward=False, check_graph=True) +def _test_meshgrid(test_case, placement): + x_sbp = random_sbp(placement, max_dim=1) + x = random_tensor(ndim=1, dim0=8, requires_grad=False).to_global(placement, x_sbp) + y_sbp = random_sbp(placement, max_dim=1) + y = random_tensor(ndim=1, dim0=8, requires_grad=False).to_global(placement, y_sbp) + res = torch.meshgrid(x, y) + return res[0], res[1] + + +class TestMeshGrid(flow.unittest.TestCase): + @globaltest + def test_meshgrid(test_case): + for placement in all_placement(): + _test_meshgrid(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_min.py b/python/oneflow/test/modules/test_global_min.py new file mode 100644 index 0000000000000000000000000000000000000000..c08e1353a743eedbaaaa89e834e091c71a100fa5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_min.py @@ -0,0 +1,105 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from collections import OrderedDict +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + + +def _np_min(shape, dim, keepdims): + # np array result + input_arr = np.random.randn(*shape) + np_out = np.amin(input_arr, axis=dim, keepdims=keepdims) + np_out_grad = np.zeros_like(input_arr) + if dim == None: + arg_min = np.argmin(input_arr) + np.put(np_out_grad, arg_min, 1) + else: + arg_min = np.expand_dims(np.argmin(input_arr, axis=dim), axis=dim) + np.put_along_axis(np_out_grad, arg_min, 1, axis=dim) + + return np_out, np_out_grad, input_arr + + +def _test_min( + test_case, placement, sbp, np_out, np_out_grad, input_arr, shape, dim, keepdims +): + # of result + global_x = flow.tensor( + input_arr, + dtype=flow.float32, + requires_grad=True, + placement=flow.placement.all("cpu"), + sbp=flow.sbp.broadcast, + ) + if dim is None: + of_out = flow.min(global_x) + else: + of_out = flow.min(global_x, dim, keepdims)[0] + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + of_out = of_out.sum() + of_out.backward() + + test_case.assertTrue( + np.allclose(global_x.grad.numpy(), np_out_grad, 0.0001, 0.0001) + ) + + +class TestMinModule(flow.unittest.TestCase): + # backward formula is different from one of torch. + @globaltest + def test_eager_global_min(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_min] + arg_dict["shape"] = [(8,), (8, 8), (8, 8, 8, 8)] + arg_dict["dim"] = [None, 0, -1] + arg_dict["keepdims"] = [False, True] + for arg in GenArgList(arg_dict): + np_out, np_out_grad, input_arr = _np_min(*arg[1:]) + np_out = ( + flow.tensor(np_out) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + np_out_grad = ( + flow.tensor(np_out_grad) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + input_arr = ( + flow.tensor(input_arr) + .to_global(placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast,) + .numpy() + ) + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(*arg[1:2])): + arg[0]( + test_case, + placement, + sbp, + np_out, + np_out_grad, + input_arr, + *arg[1:] + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_min_max_observer.py b/python/oneflow/test/modules/test_global_min_max_observer.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_min_max_observer.py rename to python/oneflow/test/modules/test_global_min_max_observer.py diff --git a/python/oneflow/test/modules/test_global_movedim.py b/python/oneflow/test/modules/test_global_movedim.py new file mode 100644 index 0000000000000000000000000000000000000000..226f832f3584943e73871262b3c062460e226de5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_movedim.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_movedim(test_case, placement, sbp): + x = random_tensor( + ndim=4, + dim1=random(1, 3) * 8, + dim2=random(1, 3) * 8, + dim3=random(1, 3) * 8, + dim4=random(1, 3) * 8, + ).to_global(placement, sbp) + z = torch.movedim(x, (0, 1), (2, 3)) + return z + + +class TestMovedim(flow.unittest.TestCase): + @globaltest + def test_movedim(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement): + _test_movedim(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_moving_average_max_min_observer.py b/python/oneflow/test/modules/test_global_moving_average_max_min_observer.py new file mode 100644 index 0000000000000000000000000000000000000000..616e5663d3bfa13b03f32a994b35edb4ace11770 --- /dev/null +++ b/python/oneflow/test/modules/test_global_moving_average_max_min_observer.py @@ -0,0 +1,101 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList +from test_moving_average_min_max_observer import _check_moving_average_min_max_observer +from oneflow.test_utils.automated_test_util import * + + +def _run_test_moving_average_min_max_observer( + test_case, + placement, + sbp, + device_type, + dtype, + activation_shape, + quantization_bit, + quantization_scheme, + quantization_formula, + momentum, +): + moving_max_np = np.zeros((1,)) + moving_min_np = np.zeros((1,)) + + current_train_step_tensor = flow.tensor( + np.zeros((1,)).astype(np.float32), + dtype=flow.int64, + placement=placement, + sbp=sbp, + ) + for i in range(10): + of_activation = ( + random_tensor(len(activation_shape), *activation_shape, low=-0.5, high=0.5) + .to_global(placement, sbp) + .oneflow + ) + np_activation = of_activation.numpy() + + moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver( + quantization_formula=quantization_formula, + stop_update_after_iters=1, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + momentum=momentum, + ) + moving_average_min_max_observer = moving_average_min_max_observer.to_global( + placement, sbp + ) + (scale, zero_point) = moving_average_min_max_observer( + of_activation, current_train_step_tensor + ) + _check_moving_average_min_max_observer( + test_case, + np_activation, + scale.numpy(), + zero_point.numpy(), + moving_max_np, + moving_min_np, + quantization_bit, + quantization_scheme, + quantization_formula, + momentum, + ) + + +class TestMovingAverageMinMaxObserver(flow.unittest.TestCase): + @globaltest + def test_moving_average_min_max_observer(test_case): + arg_dict = OrderedDict() + arg_dict["device_type"] = ["cpu", "cuda"] + arg_dict["dtype"] = ["float32", "double"] + arg_dict["activation_shape"] = [(9, 48, 24, 10)] + arg_dict["quantization_bit"] = [8, 2] + arg_dict["quantization_scheme"] = ["symmetric", "affine"] + arg_dict["quantization_formula"] = ["google"] + arg_dict["momentum"] = [0.95] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, valid_split_axis=[1, 2]): + _run_test_moving_average_min_max_observer( + test_case, placement, sbp, *arg + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_mul.py b/python/oneflow/test/modules/test_global_mul.py new file mode 100644 index 0000000000000000000000000000000000000000..3c17b3deb16b1d3ad205a7990167ec632a2be7a5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_mul.py @@ -0,0 +1,55 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_broadcast_mul(test_case, placement, sbp): + x = random_tensor(ndim=3, dim0=16, dim1=8, dim2=24).to_global(placement, sbp) + y_sbp = random_sbp(placement, max_dim=2) + y = random_tensor(ndim=2, dim0=8, dim1=24).to_global(placement, y_sbp) + z = torch.mul(x, y) + return z + + +@autotest(n=1, check_graph=True) +def _test_mul_with_scalar(test_case, ndim, placement, sbp): + dim_list = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dim_list).to_global(placement, sbp) + y = 2 + return torch.mul(x, y) + + +class TestMulModule(flow.unittest.TestCase): + @globaltest + def test_broadcast_mul(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_broadcast_mul(test_case, placement, sbp) + + @globaltest + def test_mul_with_scalar(test_case): + ndim = random(1, 4).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_mul_with_scalar(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_mv.py b/python/oneflow/test/modules/test_global_mv.py new file mode 100644 index 0000000000000000000000000000000000000000..efb95fdef69adbcb0645a2250c8cf2057a910ca2 --- /dev/null +++ b/python/oneflow/test/modules/test_global_mv.py @@ -0,0 +1,39 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_mv(test_case, placement, sbp): + dim = random(1, 6) + mat = random_tensor(2, dim1=dim).to_global(placement=placement, sbp=sbp) + vec = random_tensor(1, dim0=dim).to_global(placement=placement, sbp=sbp) + return torch.mv(mat, vec) + + +class TestMvModule(flow.unittest.TestCase): + @globaltest + def test_mv(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement): + _test_mv(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_nansum.py b/python/oneflow/test/modules/test_global_nansum.py new file mode 100644 index 0000000000000000000000000000000000000000..153a80e880ebd35944f4822b25a13dc62dee2e12 --- /dev/null +++ b/python/oneflow/test/modules/test_global_nansum.py @@ -0,0 +1,61 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=False) +def _test_global_nansum_against_pytorch(test_case, placement, sbp): + x = random_tensor(4, 8, 16, 8, 24).to_global(placement, sbp) + mask = x < 0 + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x) + return y + + +@autotest(n=3, check_graph=False) +def _test_global_nansum_with_0_size_tensor(test_case, placement, sbp): + x = random_tensor(4, 8, 16, 0, 24).to_global(placement, sbp) + mask = torch.ones_like(x).bool() + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x, dim=random(0, 3).to(int)) + return y + + +class TestGlobalNanSumModule(flow.unittest.TestCase): + @globaltest + def test_global_nansum_against_pytorch(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_nansum_against_pytorch(test_case, placement, sbp) + + @globaltest + def test_global_nansum_with_0_size_tensor(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]): + _test_global_nansum_with_0_size_tensor(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_narrow.py b/python/oneflow/test/modules/test_global_narrow.py new file mode 100644 index 0000000000000000000000000000000000000000..fd79695af017ac9f6f44d8ad1098db461947fc2a --- /dev/null +++ b/python/oneflow/test/modules/test_global_narrow.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_narrow(test_case, ndim, placement, sbp): + dims = [random(1, 3).to(int).value() * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + dim = random(-ndim, ndim).to(int).value() + start = random(0, dims[dim]).to(int).value() + length = random(1, dims[dim] - start + 1).to(int).value() + + return torch.narrow(x, dim=dim, start=start, length=length) + + +class TestNarrow(flow.unittest.TestCase): + @globaltest + def test_narrow(test_case): + for placement in all_placement(): + ndim = random(1, 4).to(int).value() + for sbp in all_sbp(placement, max_dim=min(ndim, 2)): + _test_narrow(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_ne.py b/python/oneflow/test/modules/test_global_ne.py new file mode 100644 index 0000000000000000000000000000000000000000..c4b38190e7c0ec15435af265f5b734dc76c0ba93 --- /dev/null +++ b/python/oneflow/test/modules/test_global_ne.py @@ -0,0 +1,38 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_ne(test_case, placement, sbp): + x1 = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement, sbp) + x2 = random_tensor(ndim=2, dim0=8, dim1=8).to_global(placement, sbp) + return torch.ne(x1, x2) + + +class TestNe(flow.unittest.TestCase): + @globaltest + def test_ne(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_ne(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_negative.py b/python/oneflow/test/modules/test_global_negative.py new file mode 100644 index 0000000000000000000000000000000000000000..80c3be3b5b343f6d55358a8496bce5baff0bf013 --- /dev/null +++ b/python/oneflow/test/modules/test_global_negative.py @@ -0,0 +1,39 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_negative(test_case, placement, sbp, ndim): + shape = [8 for _ in range(ndim)] + x = random_tensor(ndim, *shape).to_global(placement, sbp) + return torch.negative(x) + + +class TestNegative(flow.unittest.TestCase): + @globaltest + def test_negative(test_case): + ndim = random(2, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_negative(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_nms.py b/python/oneflow/test/modules/test_global_nms.py new file mode 100644 index 0000000000000000000000000000000000000000..d55af50083eec20335f65f9376115c909fc3fa36 --- /dev/null +++ b/python/oneflow/test/modules/test_global_nms.py @@ -0,0 +1,60 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * +from test_nms import create_tensors_with_iou +from test_nms import nms_np + + +def _test_nms(test_case, placement, sbp): + iou = 0.5 + boxes, scores = create_tensors_with_iou(800, iou) + + global_boxes = flow.tensor(boxes, dtype=flow.float32).to_global( + placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast + ) + np_boxes = global_boxes.numpy() + global_boxes = global_boxes.to_global(placement=placement, sbp=sbp) + + global_scores = flow.tensor(scores, dtype=flow.float32).to_global( + placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast + ) + np_scores = global_scores.numpy() + global_scores = global_scores.to_global(placement=placement, sbp=sbp) + + keep_np = nms_np(np_boxes, np_scores, iou) + + keep = flow.nms(global_boxes, global_scores, iou) + test_case.assertTrue(np.allclose(keep.numpy(), keep_np)) + + +class TestNMS(flow.unittest.TestCase): + @globaltest + def test_nms(test_case): + for placement in all_placement(): + # TODO: nms only has cuda kernel at now. + if placement.type == "cpu": + continue + for sbp in all_sbp(placement, max_dim=1): + _test_nms(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_normal.py b/python/oneflow/test/modules/test_global_normal.py new file mode 100644 index 0000000000000000000000000000000000000000..836ca3d1b0034255e88359c907b550c003824cdd --- /dev/null +++ b/python/oneflow/test/modules/test_global_normal.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type +from oneflow.test_utils.automated_test_util import * +import oneflow as flow + + +def _test_global_normal( + test_case, placement, sbp, mean, std, shape, dtype, requires_grad +): + dtype = type_name_to_flow_type[dtype] + x = flow.normal( + mean, + std, + shape, + placement=placement, + sbp=sbp, + dtype=dtype, + requires_grad=requires_grad, + ) + + test_case.assertEqual(x.shape, shape) + test_case.assertEqual(x.dtype, dtype) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + test_case.assertEqual(x.requires_grad, requires_grad) + + +class TestNormalGlobal(flow.unittest.TestCase): + @globaltest + def test_normal_global(test_case): + arg_dict = OrderedDict() + arg_dict["mean"] = [-1, 0, 1] + arg_dict["std"] = [1, 2, 8] + arg_dict["shape"] = [(8, 8), (8, 8, 8), (8, 8, 8, 8)] + arg_dict["dtype"] = ["float32", "double"] + arg_dict["requires_grad"] = [True, False] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(arg[2]), except_partial_sum=True + ): + _test_global_normal(test_case, placement, sbp, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_normalize.py b/python/oneflow/test/modules/test_global_normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..3a8d2367f6cc20fce03a6aaca0606962aacadff5 --- /dev/null +++ b/python/oneflow/test/modules/test_global_normalize.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_functional_normalize(test_case, placement, sbp): + ndim = random(low=2, high=5).to(int).value() + shape = [random(low=2, high=3) * 8 for i in range(ndim)] + x = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp) + dim = random(low=0, high=ndim).to(int).value() + y = torch.nn.functional.normalize(x, oneof(2, 3, 4), dim, 1e-12) + return y + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_normalize_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_functional_normalize(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_nozero.py b/python/oneflow/test/modules/test_global_nozero.py new file mode 100644 index 0000000000000000000000000000000000000000..9da616f9311c27f769a07c9c242d3adfb41cbe2a --- /dev/null +++ b/python/oneflow/test/modules/test_global_nozero.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +# Not check graph because of one reason: +# Reason 1, lazy tensor cannot call numpy(), tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. +# Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. +@autotest(n=1, auto_backward=False, check_graph="ValidatedFalse") +def _test_nonzero(test_case, placement, sbp, ndim): + shape = [8 for _ in range(ndim)] + x = random_tensor(ndim, *shape).to_global(placement, sbp) + return torch.nonzero(x) + + +class TestNonZero(flow.unittest.TestCase): + @globaltest + def test_nonzero(test_case): + ndim = random(2, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_nonzero(test_case, placement, sbp, ndim) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_ones_like.py b/python/oneflow/test/modules/test_global_ones_like.py new file mode 100644 index 0000000000000000000000000000000000000000..6629570f328c87ffb227bb2768cd323ff21d121c --- /dev/null +++ b/python/oneflow/test/modules/test_global_ones_like.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import numpy as np +from oneflow.test_utils.test_util import GenArgList +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def _test_ones_like_float(test_case, placement, sbp, shape, device): + x = flow.tensor( + np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + ) + x = x.to_global(placement=placement, sbp=sbp) + y = flow.ones_like(x, placement=placement, sbp=sbp) + test_case.assertTrue(y.dtype is flow.float32) + test_case.assertTrue(y.shape == x.shape) + test_case.assertTrue(y.placement == placement) + y_numpy = np.ones(x.numpy().shape) + print("y_numpy: ", y_numpy) + print("y.numpy()", y.numpy()) + + test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) + + +def _test_ones_like_int(test_case, placement, sbp, shape, device): + x = flow.tensor(np.random.randn(*shape), dtype=flow.int, device=flow.device(device)) + x = x.to_global(placement=placement, sbp=sbp) + y = flow.ones_like(x, dtype=flow.int, placement=placement, sbp=sbp) + test_case.assertTrue(y.dtype is flow.int) + test_case.assertTrue(y.shape == x.shape) + test_case.assertTrue(y.placement == placement) + y_numpy = np.ones(x.numpy().shape) + test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) + + +class TestModule(flow.unittest.TestCase): + @unittest.skip("TODO: global ones_like test will fail!") + @globaltest + def test_ones_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_ones_like_float, _test_ones_like_int] + arg_dict["shape"] = [(8, 8), (8, 8, 4), (8, 8, 5, 6)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + arg[0](test_case, placement, sbp, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_pad.py b/python/oneflow/test/modules/test_global_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..9536c1634a112bda4bef3eb89d022910890987dd --- /dev/null +++ b/python/oneflow/test/modules/test_global_pad.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +@autotest(n=5, check_graph=False) +def _test_pad_1d_impl(test_case, placement, sbp): + pad = [random(0, 5).to(int) for i in range(2)] + x = random_tensor( + ndim=3, dim0=8, dim1=random(2, 8).to(int) * 8, dim2=random(2, 8).to(int) * 8 + ).to_global(placement=placement, sbp=sbp) + y = torch.nn.functional.pad(x, pad, mode=oneof("constant", "reflect", "replicate")) + return y + + +@autotest(n=5, check_graph=False) +def _test_pad_2d_impl(test_case, placement, sbp): + pad = [random(0, 5).to(int) for i in range(4)] + x = random_tensor( + ndim=4, + dim0=8, + dim1=8, + dim2=random(2, 8).to(int) * 8, + dim3=random(2, 8).to(int) * 8, + ).to_global(placement=placement, sbp=sbp) + y = torch.nn.functional.pad(x, pad, mode=oneof("constant", "reflect", "replicate")) + return y + + +class TestPad(flow.unittest.TestCase): + @globaltest + def test_pad_1d(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_pad_1d_impl(test_case, placement, sbp) + _test_pad_2d_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_partical_fc.py b/python/oneflow/test/modules/test_global_partical_fc.py new file mode 100644 index 0000000000000000000000000000000000000000..9114677af6a3dd8f2932c91d6d22fe8d423fd269 --- /dev/null +++ b/python/oneflow/test/modules/test_global_partical_fc.py @@ -0,0 +1,52 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestParitalFC(flow.unittest.TestCase): + @globaltest + def test_parital_fc(test_case): + placement = flow.placement.all("cuda") + w = flow.randn(5000, 128, placement=placement, sbp=flow.sbp.split(0)) + label = flow.randint( + 0, 5000, (512,), placement=placement, sbp=flow.sbp.split(0) + ) + num_sample = 500 + out = flow.distributed_partial_fc_sample(w, label, num_sample) + test_case.assertTrue(out[0].shape == flow.Size([512])) + test_case.assertTrue(out[1].shape == flow.Size([500])) + test_case.assertTrue(out[2].shape == flow.Size([500, 128])) + + w = flow.randn(5000, 128, placement=placement, sbp=flow.sbp.broadcast) + label = flow.randint( + 0, 5000, (512,), placement=placement, sbp=flow.sbp.split(0) + ) + num_sample = 500 + out = flow.distributed_partial_fc_sample(w, label, num_sample) + test_case.assertTrue(out[0].shape == flow.Size([512])) + test_case.assertTrue(out[1].shape == flow.Size([500])) + test_case.assertTrue(out[2].shape == flow.Size([500, 128])) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_permute.py b/python/oneflow/test/modules/test_global_permute.py new file mode 100644 index 0000000000000000000000000000000000000000..6e06d191a8ae45b5ad6ee3cefbbd0c2d57de1e0e --- /dev/null +++ b/python/oneflow/test/modules/test_global_permute.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_permute4d_tensor_with_random_data(test_case, placement, sbp): + ndim = 4 + permute_list = [1, 2, 3, 0] + x = random_tensor( + ndim=ndim, dim0=8, dim1=8, dim2=random(2, 8).to(int), dim3=random(2, 8).to(int), + ).to_global(placement=placement, sbp=sbp) + y = x.permute(permute_list) + return y + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_permute4d_tensor_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_permute4d_tensor_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_rand.py b/python/oneflow/test/modules/test_global_rand.py new file mode 100644 index 0000000000000000000000000000000000000000..5f0660ceba58fdf2dde750305634ba0e648bb99d --- /dev/null +++ b/python/oneflow/test/modules/test_global_rand.py @@ -0,0 +1,84 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_rand(test_case, shape, placement, sbp): + x = flow.rand(*shape, placement=placement, sbp=sbp) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_rand(test_case, shape, placement, sbp): + class GlobalRandGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.rand(*shape, placement=placement, sbp=sbp) + return x + + model = GlobalRandGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestRandGlobal(flow.unittest.TestCase): + @globaltest + def test_rand_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_rand(test_case, shape, placement, sbp) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_rand_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_rand(test_case, shape, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_randint.py b/python/oneflow/test/modules/test_global_randint.py new file mode 100644 index 0000000000000000000000000000000000000000..a2dfe2c4b98d724e46428b9af6fe100bc8040d5e --- /dev/null +++ b/python/oneflow/test/modules/test_global_randint.py @@ -0,0 +1,101 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_randint(test_case, shape, placement, sbp, dtype): + x = flow.randint(1, 10, shape, placement=placement, sbp=sbp, dtype=dtype) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + test_case.assertEqual(x.dtype, dtype) + + +def _test_graph_randint(test_case, shape, placement, sbp, dtype): + class GlobalRandintGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.randint(1, 10, shape, placement=placement, sbp=sbp, dtype=dtype) + return x + + model = GlobalRandintGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + test_case.assertEqual(x.dtype, dtype) + + +class TestRandintGlobal(flow.unittest.TestCase): + @globaltest + def test_randint_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + dtypes = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + ] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + for dtype in dtypes: + _test_global_randint(test_case, shape, placement, sbp, dtype) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_randint_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["dtype"] = [ + flow.uint8, + flow.int32, + flow.float32, + ] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + dtype = args["dtype"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_randint(test_case, shape, placement, sbp, dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_randint_like.py b/python/oneflow/test/modules/test_global_randint_like.py new file mode 100644 index 0000000000000000000000000000000000000000..cf269ef30b0b217a72f3df41552bfefb1d2fa997 --- /dev/null +++ b/python/oneflow/test/modules/test_global_randint_like.py @@ -0,0 +1,105 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_consistent_randint_like(test_case, shape, placement, sbp, dtype): + x_ = flow.randint(1, 10, shape) + x = flow.randint_like(x_, 1, 10, placement=placement, sbp=sbp, dtype=dtype) + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + test_case.assertEqual(x.dtype, dtype) + + +def _test_graph_randint_like(test_case, shape, placement, sbp, dtype): + class ConsistentRandIntLikeGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x_ = flow.randint(1, 10, shape) + x = flow.randint_like(x_, 1, 10, placement=placement, sbp=sbp, dtype=dtype) + return x + + model = ConsistentRandIntLikeGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + test_case.assertEqual(x.dtype, dtype) + + +class TestRandIntLikeConsistent(flow.unittest.TestCase): + @globaltest + def test_randint_like_consistent(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + dtypes = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + ] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + for dtype in dtypes: + _test_consistent_randint_like( + test_case, shape, placement, sbp, dtype + ) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n2d() + def test_randint_like_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["dtype"] = [ + flow.uint8, + flow.int32, + flow.float32, + ] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + dtype = args["dtype"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_randint_like(test_case, shape, placement, sbp, dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_randn.py b/python/oneflow/test/modules/test_global_randn.py new file mode 100644 index 0000000000000000000000000000000000000000..92ae7ec3217cd759e15e47ab472a97b1c3771928 --- /dev/null +++ b/python/oneflow/test/modules/test_global_randn.py @@ -0,0 +1,124 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import numpy as np +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_randn(test_case, shape, placement, sbp): + x1 = flow.randn(*shape, placement=placement, sbp=sbp) + x2 = flow.randn(*shape, placement=placement, sbp=sbp) + test_case.assertTrue(not np.allclose(x1.numpy(), x2.numpy(), atol=1e-4, rtol=1e-4)) + test_case.assertEqual(x1.shape, flow.Size(shape)) + test_case.assertEqual(x1.sbp, sbp) + test_case.assertEqual(x1.placement, placement) + + +def _test_different_dtype(test_case, shape, placement, sbp): + x1 = flow.randn(*shape, dtype=flow.float32, placement=placement, sbp=sbp) + x2 = flow.randn(*shape, dtype=flow.float64, placement=placement, sbp=sbp) + test_case.assertTrue(not np.allclose(x1.numpy(), x2.numpy(), atol=1e-4, rtol=1e-4)) + test_case.assertEqual(x1.shape, flow.Size(shape)) + + +def _test_backward(test_case, shape, placement, sbp): + x = flow.randn(*shape, placement=placement, sbp=sbp, requires_grad=True) + y = x.sum() + y.backward() + test_case.assertTrue( + np.allclose(np.ones(shape), x.grad.numpy(), atol=1e-4, rtol=1e-4) + ) + + +def _test_with_generator(test_case, shape, placement, sbp): + gen = flow.Generator() + gen.manual_seed(0) + y1 = flow.randn(*shape, placement=placement, sbp=sbp, generator=gen) + gen.manual_seed(0) + y2 = flow.randn(*shape, placement=placement, sbp=sbp, generator=gen) + test_case.assertTrue(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) + + +def _test_randn_tuple_shape(test_case, shape, placement, sbp): + y1 = flow.randn(*shape, placement=placement, sbp=sbp) + y2 = flow.randn(*shape, placement=placement, sbp=sbp) + + test_case.assertTrue(not np.array_equal(y1.numpy(), y2.numpy())) + test_case.assertTrue(shape == y1.shape) + + +def _test_graph_randn(test_case, shape, placement, sbp): + class GlobalRandnGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.randn(*shape, placement=placement, sbp=sbp) + return x + + model = GlobalRandnGraph() + x = model() + + test_case.assertEqual(x.shape, flow.Size(shape)) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +class TestRandnGlobal(flow.unittest.TestCase): + @globaltest + def test_randn_global(test_case): + shapes = [(8,), (8, 8,), (8, 8, 8)] + for shape in shapes: + for placement in all_placement(): + for sbp in all_sbp( + placement, max_dim=len(shape), except_partial_sum=True + ): + _test_global_randn(test_case, shape, placement, sbp) + _test_different_dtype(test_case, shape, placement, sbp) + _test_backward(test_case, shape, placement, sbp) + _test_with_generator(test_case, shape, placement, sbp) + _test_randn_tuple_shape(test_case, shape, placement, sbp) + + @flow.unittest.skip_unless_1n2d() + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @globaltest + def test_randn_graph(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8,), (8, 8,), (8, 8, 8)] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + for args in GenArgDict(arg_dict): + shape = args["shape"] + placement = args["placement"] + for sbp in all_sbp(placement, max_dim=len(shape), except_partial_sum=True): + _test_graph_randn(test_case, shape, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_random_op_data.py b/python/oneflow/test/modules/test_global_random_op_data.py new file mode 100644 index 0000000000000000000000000000000000000000..6b20c10e45f4758493cbd536624b469054ae8deb --- /dev/null +++ b/python/oneflow/test/modules/test_global_random_op_data.py @@ -0,0 +1,120 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow +import numpy as np +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +_fn_param = { + "normal": lambda shape, placement, sbp: flow.normal( + size=shape, mean=0.0, std=1.0, placement=placement, sbp=sbp + ), + "rand": lambda shape, placement, sbp: flow.rand( + size=shape, placement=placement, sbp=sbp + ), + "randint": lambda shape, placement, sbp: flow.randint( + low=0, high=2, size=shape, placement=placement, sbp=sbp + ), + "randn": lambda shape, placement, sbp: flow.randn( + size=shape, placement=placement, sbp=sbp + ), +} + + +def _test_data_consistent(test_case, shape, placement, sbp, fn): + # lazy result + class GlobalRandnGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self): + flow.manual_seed(233) + x = fn(shape, placement, sbp) + return x + + model = GlobalRandnGraph() + lazy_x = model() + + # eager result + flow.manual_seed(233) + eager_x = fn(shape, placement, sbp) + + test_case.assertTrue( + np.array_equal(lazy_x.to_local().numpy(), eager_x.to_local().numpy()) + ) + + # different data + eager_x2 = fn(shape, placement, sbp) + + test_case.assertFalse( + np.array_equal(eager_x.to_local().numpy(), eager_x2.to_local().numpy()) + ) + + +class TestGlobalRandomOpData(flow.unittest.TestCase): + @globaltest + def test_random_op_data_consistent_with_eager_and_lazy(test_case): + shape = (8, 8) + + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2, except_partial_sum=True): + for _, fn in _fn_param.items(): + _test_data_consistent(test_case, shape, placement, sbp, fn=fn) + + @globaltest + @oneflow.unittest.skip_unless_1n4d() + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_random_op_data_correctness(test_case): + shape = (8, 8) + sbp = [flow.sbp.split(0), flow.sbp.broadcast] + + for device in ["cpu", "cuda"]: + placement = flow.placement(device, [[0, 1], [2, 3]]) + + for _, fn in _fn_param.items(): + flow.manual_seed(233) + local_tensor = fn(shape, placement, sbp).to_local().cpu() + + # broadcast local data for each rank + rank_to_tensor = [ + local_tensor + if rank_id == flow.env.get_rank() + else flow.empty(local_tensor.shape, dtype=local_tensor.dtype) + for rank_id in range(4) + ] + for rank_id in range(4): + flow.comm.broadcast(rank_to_tensor[rank_id], rank_id) + + np_local = [x.numpy() for x in rank_to_tensor] + # rank0 == rank1 + test_case.assertTrue(np.array_equal(np_local[0], np_local[1])) + # rank2 == rank3 + test_case.assertTrue(np.array_equal(np_local[2], np_local[3])) + # rank0 != rank2 + test_case.assertFalse(np.array_equal(np_local[0], np_local[2])) + # rank1 != rank3 + test_case.assertFalse(np.array_equal(np_local[1], np_local[3])) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_randperm.py b/python/oneflow/test/modules/test_global_randperm.py new file mode 100644 index 0000000000000000000000000000000000000000..d9af660b94ea5ebdf576b19d87d6c08bfd3018c6 --- /dev/null +++ b/python/oneflow/test/modules/test_global_randperm.py @@ -0,0 +1,100 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import oneflow as flow +import numpy as np +from oneflow.test_utils.automated_test_util import * + +from oneflow.test_utils.test_util import GenArgDict + + +def _test_global_randperm(test_case, N, placement, sbp, dtype): + x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype) + # TODO:Synchronously get a global random seed, and then each rank sets its own seed in manual_seeds + test_case.assertEqual(x.dtype, dtype) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +def _test_graph_randperm(test_case, N, placement, sbp, dtype): + class GlobalRandpermGraph(flow.nn.Graph): + def __init__(self,): + super().__init__() + + def build(self): + x = flow.randperm(N, placement=placement, sbp=sbp, dtype=dtype) + return x + + model = GlobalRandpermGraph() + x = model() + y1 = x.to_global(placement=placement, sbp=sbp) + y1_np_sort = np.sort(y1.numpy()) + y2 = np.arange(N) + test_case.assertTrue(np.allclose(y1_np_sort, y2, atol=1e-4, rtol=1e-4)) + test_case.assertEqual(x.dtype, dtype) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +@unittest.skip("This fails in multi-gpu") +class TestRandpermGlobal(flow.unittest.TestCase): + @globaltest + def test_randperm_global(test_case): + RandNs = [i for i in range(10, 50, 10)] + # TODO support uint8,int8,int64,float32,float64,data type test + Dtypes = [ + flow.int32, + ] + for N in RandNs: + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + for dtype in Dtypes: + _test_global_randperm(test_case, N, placement, sbp, dtype) + + @flow.unittest.skip_unless_1n2d() + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @globaltest + def test_randperm_graph(test_case): + arg_dict = OrderedDict() + arg_dict["N"] = [i for i in range(10, 50, 10)] + arg_dict["placement"] = [ + # 1d + flow.placement("cpu", ranks=[0, 1]), + flow.placement("cuda", ranks=[0, 1]), + # 2d + flow.placement("cpu", ranks=[[0, 1],]), + flow.placement("cuda", ranks=[[0, 1],]), + ] + arg_dict["dtype"] = [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + ] + for args in GenArgDict(arg_dict): + N = args["N"] + placement = args["placement"] + dtype = args["dtype"] + for sbp in all_sbp(placement, max_dim=1, except_partial_sum=True): + _test_graph_randperm(test_case, N, placement, sbp, dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_reciprocal.py b/python/oneflow/test/modules/test_global_reciprocal.py new file mode 100644 index 0000000000000000000000000000000000000000..c383e53700d8b6be0b467c62ca2db717c73f97ec --- /dev/null +++ b/python/oneflow/test/modules/test_global_reciprocal.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=False) +def _test_reciprocal_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.reciprocal(y) + return z + + +class TestReciprocalGlobal(flow.unittest.TestCase): + @globaltest + def test_reciprocal(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_reciprocal_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_reflection_pad2d.py b/python/oneflow/test/modules/test_global_reflection_pad2d.py new file mode 100644 index 0000000000000000000000000000000000000000..fb55820c2def3136994e9f8d9c519cad83145924 --- /dev/null +++ b/python/oneflow/test/modules/test_global_reflection_pad2d.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_reflection_pad2d_impl(test_case, padding, placement, sbp): + m = torch.nn.ReflectionPad2d(padding=padding) + dims = [random(2, 4) * 8 for _ in range(4)] + x = random_tensor(4, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = m(y) + return z + + +class TestReflectionPad2dGlobal(flow.unittest.TestCase): + @globaltest + def test_reflection_pad2d(test_case): + padding = [ + (2, 2, 1, 1), + 1, + (1, 0, 1, 0), + (0, 1, 0, 1), + ] + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + for pad in padding: + _test_reflection_pad2d_impl(test_case, pad, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_repeat.py b/python/oneflow/test/modules/test_global_repeat.py new file mode 100644 index 0000000000000000000000000000000000000000..09ac2bd703276e8272252c6fcabafd26a70da83a --- /dev/null +++ b/python/oneflow/test/modules/test_global_repeat.py @@ -0,0 +1,45 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=False) +def _test_repeat_impl(test_case, ndim, placement, sbp): + dims = [random(1, 4).to(int).value() * 8 for _ in range(ndim)] + repeat_size = [random(1, 3).to(int).value() for _ in range(ndim)] + x = random_tensor(ndim, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = y.repeat(repeat_size) + return z + + +class TestRepeatGlobal(flow.unittest.TestCase): + @globaltest + def test_repeat(test_case): + # random ndim in range [1,3] + ndim = random(1, 4).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_repeat_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_replication_pad2d.py b/python/oneflow/test/modules/test_global_replication_pad2d.py new file mode 100644 index 0000000000000000000000000000000000000000..d356bba135fa17c6d7d4e0421e3af24225178503 --- /dev/null +++ b/python/oneflow/test/modules/test_global_replication_pad2d.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_replication_pad2d_impl(test_case, padding, placement, sbp): + m = torch.nn.ReplicationPad2d(padding=padding) + dims = [random(2, 4) * 8 for _ in range(4)] + x = random_tensor(4, *dims) + y = x.to_global(placement=placement, sbp=sbp) + z = m(y) + return z + + +class TestReplicationPad2dGlobal(flow.unittest.TestCase): + @globaltest + def test_replication_pad2d(test_case): + padding = [ + (2, 2, 1, 1), + 1, + (1, 0, 1, 0), + (0, 1, 0, 1), + ] + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + for pad in padding: + _test_replication_pad2d_impl(test_case, pad, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_reshape.py b/python/oneflow/test/modules/test_global_reshape.py new file mode 100644 index 0000000000000000000000000000000000000000..de40ed9e552e237520211f079b9ab4e0ab53da6f --- /dev/null +++ b/python/oneflow/test/modules/test_global_reshape.py @@ -0,0 +1,89 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_reshape_impl(test_case, pair, placement, sbp): + shape, to_shape = pair + x = random_tensor(len(shape), *shape) + y = x.to_global(placement=placement, sbp=sbp) + z = y.reshape(to_shape) + return z + + +def _test_reshape_like_impl(test_case, pair, placement, in_sbp, like_sbp): + shape, to_shape = pair + + nd_arr = np.random.rand(*shape) + np_out = nd_arr.reshape(to_shape) + + x = flow.tensor(nd_arr) + like = flow.empty(to_shape) + y = x.to_global(flow.placement.all("cpu"), flow.sbp.broadcast).to_global( + placement=placement, sbp=in_sbp + ) + like = like.to_global(flow.placement.all("cpu"), flow.sbp.broadcast).to_global( + placement=placement, sbp=like_sbp + ) + z = flow._C.reshape_like(y, like) + local_z = z.to_global( + placement, sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ).to_local() + if flow.env.get_rank() == 0: + test_case.assertTrue(np.array_equal(np_out, local_z.numpy())) + + +class TestReshapeGlobal(flow.unittest.TestCase): + @globaltest + def test_reshape(test_case): + shape_pairs = [ + ((8, 16), (8 * 16,)), + ((8, 16), (8 * 4, 4)), + ((8, 16, 24), (64, 6, 8)), + ((8, 16), (64, 1, -1)), + ((8, 16), (-1,)), + ] + for pair in shape_pairs: + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(pair[0])): + _test_reshape_impl(test_case, pair, placement, sbp) + + @globaltest + def test_reshape_like(test_case): + shape_pairs = [ + ((8, 16), (8 * 16,)), + ((8, 16), (8 * 2, 8)), + ((8, 16, 24), (64, 48)), + ] + for pair in shape_pairs: + for placement in all_placement(): + for in_sbp in all_sbp(placement, max_dim=len(pair[0])): + for like_sbp in all_sbp(placement, max_dim=len(pair[1])): + _test_reshape_like_impl( + test_case, pair, placement, in_sbp, like_sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_rnn.py b/python/oneflow/test/modules/test_global_rnn.py new file mode 100644 index 0000000000000000000000000000000000000000..b6b2974f6fa2706527630835a3a2b3a6fa147446 --- /dev/null +++ b/python/oneflow/test/modules/test_global_rnn.py @@ -0,0 +1,279 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest +import torch +from oneflow.test_utils.automated_test_util.generators import * +from oneflow.test_utils.automated_test_util.torch_flow_dual_object import globaltest +from oneflow.test_utils.test_util import GenArgDict + + +def _compare_torch_and_oneflow( + test_case, m_torch, m_flow, placement, module_sbp, in_sbp, input_size +): + torch_state_dict = m_torch.state_dict() + new_dict = {} + for k, v in torch_state_dict.items(): + new_dict[k] = v.detach().numpy() + m_flow.load_state_dict(new_dict) + + m_flow = m_flow.to_global(flow.placement.all("cpu"), flow.sbp.broadcast).to_global( + placement=placement, sbp=[module_sbp for _ in range(len(placement.ranks.shape))] + ) + + x = np.random.rand(32, 16, input_size).astype(np.float32) + x_torch = torch.tensor(x, dtype=torch.float32, requires_grad=True) + x_flow = ( + flow.tensor(x, dtype=flow.float32, requires_grad=True) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + .to_global(placement=placement, sbp=in_sbp) + ) + + out_torch, hid_torch = m_torch(x_torch) + out_flow, hid_flow = m_flow(x_flow) + + # check forward + local_output = out_flow.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose( + out_torch.cpu().detach().numpy(), + local_output.numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + # check backward + out_torch.sum().backward() + out_flow.sum().backward() + local_x_grad = x_flow.to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ).to_local() + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.allclose( + x_torch.cpu().detach().numpy(), + local_x_grad.numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_rnn_impl( + test_case, + placement, + module_sbp, + in_sbp, + input_size, + hidden_size, + num_layers, + nonlinearity, + bias, + batch_first, + dropout, + bidirectional, +): + rnn_torch = torch.nn.RNN( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + nonlinearity=nonlinearity, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + ) + rnn_flow = flow.nn.RNN( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + nonlinearity=nonlinearity, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + ) + _compare_torch_and_oneflow( + test_case, rnn_torch, rnn_flow, placement, module_sbp, in_sbp, input_size + ) + + +def _test_lstm_impl( + test_case, + placement, + module_sbp, + in_sbp, + input_size, + hidden_size, + num_layers, + bias, + batch_first, + dropout, + bidirectional, + proj_size, +): + lstm_torch = torch.nn.LSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + proj_size=proj_size, + ) + lstm_flow = flow.nn.LSTM( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + proj_size=proj_size, + ) + _compare_torch_and_oneflow( + test_case, lstm_torch, lstm_flow, placement, module_sbp, in_sbp, input_size + ) + + +def _test_gru_impl( + test_case, + placement, + module_sbp, + in_sbp, + input_size, + hidden_size, + num_layers, + bias, + batch_first, + dropout, + bidirectional, +): + gru_torch = torch.nn.GRU( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + ) + gru_flow = flow.nn.GRU( + input_size=input_size, + hidden_size=hidden_size, + num_layers=num_layers, + bias=bias, + batch_first=batch_first, + dropout=dropout, + bidirectional=bidirectional, + ) + _compare_torch_and_oneflow( + test_case, gru_torch, gru_flow, placement, module_sbp, in_sbp, input_size + ) + + +class TestRNNGlobal(oneflow.unittest.TestCase): + @globaltest + def test_rnn(test_case): + arg_dict = OrderedDict() + arg_dict["input_size"] = [ + 1, + ] + arg_dict["hidden_size"] = [ + 1, + ] + arg_dict["num_layers"] = [ + 1, + ] + arg_dict["nonlinearity"] = ["tanh", "relu"] + arg_dict["bias"] = [True, False] + arg_dict["batch_first"] = [True, False] + arg_dict["dropout"] = [ + 0, + ] + arg_dict["bidirectional"] = [True, False] + + module_sbp = flow.sbp.broadcast + for args in GenArgDict(arg_dict): + for placement in all_placement(): + for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): + _test_rnn_impl(test_case, placement, module_sbp, in_sbp, **args) + + @globaltest + def test_lstm(test_case): + arg_dict = OrderedDict() + arg_dict["input_size"] = [ + 1, + ] + arg_dict["hidden_size"] = [ + 2, + ] + arg_dict["num_layers"] = [ + 1, + ] + arg_dict["bias"] = [True, False] + arg_dict["batch_first"] = [True, False] + arg_dict["dropout"] = [ + 0, + ] + arg_dict["bidirectional"] = [True, False] + arg_dict["proj_size"] = [0, 1] + + module_sbp = flow.sbp.broadcast + for args in GenArgDict(arg_dict): + for placement in all_placement(): + for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): + _test_lstm_impl(test_case, placement, module_sbp, in_sbp, **args) + + @globaltest + def test_gru(test_case): + arg_dict = OrderedDict() + arg_dict["input_size"] = [ + 1, + ] + arg_dict["hidden_size"] = [ + 1, + ] + arg_dict["num_layers"] = [ + 1, + ] + arg_dict["bias"] = [True, False] + arg_dict["batch_first"] = [True, False] + arg_dict["dropout"] = [ + 0, + ] + arg_dict["bidirectional"] = [True, False] + + module_sbp = flow.sbp.broadcast + for args in GenArgDict(arg_dict): + for placement in all_placement(): + for in_sbp in all_sbp(placement, max_dim=3, valid_split_axis=1): + _test_gru_impl(test_case, placement, module_sbp, in_sbp, **args) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_rnn_cell.py b/python/oneflow/test/modules/test_global_rnn_cell.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd01b38e4767b36db223817c395db10fb04773c --- /dev/null +++ b/python/oneflow/test/modules/test_global_rnn_cell.py @@ -0,0 +1,135 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +import oneflow.framework.session_context as session_ctx + + +@autotest(n=1, check_graph=False) +def _test_rnn_relu_cell(test_case, placement, sbp): + batch_size = random(2, 3) * 8 + time_steps = random(2, 3) * 8 + input_size = random(2, 3) * 8 + hidden_size = random(2, 3) * 8 + has_bias = random().to(bool) + m = torch.nn.RNNCell( + input_size=input_size, + hidden_size=hidden_size, + bias=has_bias, + nonlinearity="relu", + ) + + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight_ih = torch.nn.Parameter( + m.weight_ih.to_global(placement=placement, sbp=weight_sbp) + ) + m.weight_hh = torch.nn.Parameter( + m.weight_hh.to_global(placement=placement, sbp=weight_sbp) + ) + if m.bias_ih is not None: + # bias is 1-d tensor + bias_sbp = random_sbp(placement, max_dim=1, except_partial_sum=True) + m.bias_ih = torch.nn.Parameter( + m.bias_ih.to_global(placement=placement, sbp=bias_sbp) + ) + m.bias_hh = torch.nn.Parameter( + m.bias_hh.to_global(placement=placement, sbp=bias_sbp) + ) + + input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) + input = random_tensor( + ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size + ).to_global(placement=placement, sbp=input_sbp) + hx = random_tensor( + ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False + ).to_global(placement=placement, sbp=sbp) + + for i in range(time_steps.to(int).value()): + hx = m(input[i], hx) + + return hx + + +@autotest(n=1, check_graph=False) +def _test_rnn_tanh_cell(test_case, placement, sbp): + batch_size = random(2, 3) * 8 + time_steps = random(2, 3) * 8 + input_size = random(2, 3) * 8 + hidden_size = random(2, 3) * 8 + has_bias = random().to(bool) + m = torch.nn.RNNCell( + input_size=input_size, + hidden_size=hidden_size, + bias=has_bias, + nonlinearity="tanh", + ) + + weight_sbp = random_sbp(placement, max_dim=2, except_partial_sum=True) + m.weight_ih = torch.nn.Parameter( + m.weight_ih.to_global(placement=placement, sbp=weight_sbp) + ) + m.weight_hh = torch.nn.Parameter( + m.weight_hh.to_global(placement=placement, sbp=weight_sbp) + ) + if m.bias_ih is not None: + # bias is 1-d tensor + bias_sbp = random_sbp(placement, max_dim=1, except_partial_sum=True) + m.bias_ih = torch.nn.Parameter( + m.bias_ih.to_global(placement=placement, sbp=bias_sbp) + ) + m.bias_hh = torch.nn.Parameter( + m.bias_hh.to_global(placement=placement, sbp=bias_sbp) + ) + + input_sbp = random_sbp(placement, max_dim=3, valid_split_axis=1) + input = random_tensor( + ndim=3, dim0=time_steps, dim1=batch_size, dim2=input_size + ).to_global(placement=placement, sbp=input_sbp) + hx = random_tensor( + ndim=2, dim0=batch_size, dim1=hidden_size, requires_grad=False + ).to_global(placement=placement, sbp=sbp) + + for i in range(time_steps.to(int).value()): + hx = m(input[i], hx) + + return hx + + +class TestRNNCellGlobal(flow.unittest.TestCase): + @globaltest + def test_rnn_relu_cell(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_rnn_relu_cell(test_case, placement, sbp) + + session_ctx.GetDefaultSession().Reset() + + @globaltest + def test_rnn_tanh_cell(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_rnn_tanh_cell(test_case, placement, sbp) + + session_ctx.GetDefaultSession().Reset() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_roi_align.py b/python/oneflow/test/modules/test_global_roi_align.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6eef9d2674496e1b2dfcd5fe21c360b062c15c --- /dev/null +++ b/python/oneflow/test/modules/test_global_roi_align.py @@ -0,0 +1,157 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +import torch as pytorch +import torchvision +from oneflow.test_utils.automated_test_util import * + + +def _get_np_rois(): + random_img_idx = np.asarray( + [random(0, 2).to(int).value() for _ in range(200)] + ).reshape((200, 1)) + random_box_idx = np.asarray( + [random(0, 64 * 64).to(float).value() for _ in range(400)] + ).reshape((200, 2)) + + def get_h_w(idx1, idx2): + if idx1 > idx2: + idx1, idx2 = idx2, idx1 + h1 = idx1 // 64 + w1 = idx1 % 64 + h2 = idx2 // 64 + w2 = idx2 % 64 + return [x / 2 for x in [h1, w1, h2, w2]] + + zipped = zip(random_box_idx[:, 0], random_box_idx[:, 1]) + concated = [get_h_w(idx1, idx2) for (idx1, idx2) in zipped] + concated = np.array(concated) + rois = np.hstack((random_img_idx, concated)).astype(np.float32) + return rois + + +def _test_roi_align(test_case, placement, rois_sbp): + dims = [8, 8, 64, 64] + x = random_tensor(4, *dims).to_global( + placement=placement, + sbp=[flow.sbp.broadcast for _ in range(len(placement.ranks.shape))], + ) + x.oneflow = x.oneflow.detach().requires_grad_() + x.pytorch = x.pytorch.detach().requires_grad_() + + def get_h_w(idx1, idx2): + if idx1 > idx2: + idx1, idx2 = idx2, idx1 + h1 = idx1 // 64 + w1 = idx1 % 64 + h2 = idx2 // 64 + w2 = idx2 % 64 + return [x / 2 for x in [h1, w1, h2, w2]] + + np_rois = _get_np_rois() + of_rois = ( + flow.tensor(np_rois, dtype=flow.float) + .to_global(placement=flow.placement.all("cpu"), sbp=[flow.sbp.broadcast,]) + .to_global(placement, rois_sbp) + ) + torch_rois = pytorch.tensor(np_rois) + + of_out = flow.roi_align(x.oneflow, of_rois, 2.0, 14, 14, 2, True) + torch_out = torchvision.ops.roi_align( + x.pytorch, + torch_rois, + spatial_scale=2.0, + output_size=[14, 14], + sampling_ratio=2, + aligned=True, + ) + + # compare output + of_local = of_out.to_global( + placement=flow.placement.all("cpu"), sbp=[flow.sbp.broadcast,] + ).to_local() + test_case.assertTrue( + np.allclose( + of_local.numpy(), torch_out.detach().cpu().numpy(), rtol=1e-04, atol=1e-4 + ) + ) + + # compare backward + of_out.sum().backward() + torch_out.sum().backward() + of_input_grad = x.oneflow.grad.to_global( + placement=flow.placement.all("cpu"), sbp=[flow.sbp.broadcast,] + ).to_local() + torch_input_grad = x.pytorch.grad.detach().cpu() + test_case.assertTrue( + np.allclose( + of_input_grad.numpy(), torch_input_grad.numpy(), rtol=1e-04, atol=1e-4 + ) + ) + + +def _test_roi_align_in_fixed_data_impl(test_case, placement, sbp): + from test_roi_align import input_np, rois_np, input_grad_np + + input = ( + flow.tensor(input_np, dtype=flow.float32) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp) + .requires_grad_() + ) + rois = ( + flow.tensor(rois_np, dtype=flow.float32) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + ) + of_out = flow.roi_align(input, rois, 2.0, 5, 5, 2, True) + of_out.sum().backward() + test_case.assertTrue( + np.allclose(input.grad.numpy(), input_grad_np, rtol=1e-04, atol=1e-4) + ) + + +class TestGlobalRoiAlign(flow.unittest.TestCase): + # TODO(wyg): It is a bug in pytorch-1.9.0, torchvision-0.10.0 and python3.7.10. + # Open this test after updating the versions of pytorch in CI. + + # @globaltest + # def test_global_roi_align(test_case): + # for placement in all_placement(): + # # TODO: roi_align only support gpu + # if placement.type == "cpu": + # continue + # for rois_sbp in all_sbp(placement, max_dim=0, except_partial_sum=True): + # _test_roi_align(test_case, placement, rois_sbp) + + def test_global_roi_align_in_fixed_data(test_case): + for placement in all_placement(): + # TODO: roi_align only support gpu + if placement.type == "cpu": + continue + for rois_sbp in all_sbp(placement, max_dim=0, except_partial_sum=True): + _test_roi_align_in_fixed_data_impl(test_case, placement, rois_sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_roll.py b/python/oneflow/test/modules/test_global_roll.py new file mode 100644 index 0000000000000000000000000000000000000000..f9a24961370cfd6f100a27965e33e2ba0b2436a2 --- /dev/null +++ b/python/oneflow/test/modules/test_global_roll.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=3, check_graph=False) +def _test_roll_impl(test_case, placement, sbp): + shifts = ( + random(-100, 100).to(int).value(), + random(-100, 100).to(int).value(), + random(-100, 100).to(int).value(), + random(-100, 100).to(int).value(), + ) + dims = (0, 1, 2, 3) + x_dims = [random(2, 4) * 8 for _ in range(4)] + x = random_tensor(4, *x_dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.roll(y, shifts, dims) + return z + + +class TestRollGlobal(flow.unittest.TestCase): + @globaltest + def test_roll(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_roll_impl(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_round.py b/python/oneflow/test/modules/test_global_round.py new file mode 100644 index 0000000000000000000000000000000000000000..2ea7edd8447a1c424a76592473254479d94fa929 --- /dev/null +++ b/python/oneflow/test/modules/test_global_round.py @@ -0,0 +1,44 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_round_impl(test_case, ndim, placement, sbp): + x_dims = [random(2, 4) * 8 for _ in range(ndim)] + x = random_tensor(ndim, *x_dims) + y = x.to_global(placement=placement, sbp=sbp) + z = torch.round(y) + return z + + +class TestRoundGlobal(flow.unittest.TestCase): + @globaltest + def test_round(test_case): + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + _test_round_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_scatter_nd.py b/python/oneflow/test/modules/test_global_scatter_nd.py new file mode 100644 index 0000000000000000000000000000000000000000..c9f30dc22db20673851da47c6548d60edf5f13d8 --- /dev/null +++ b/python/oneflow/test/modules/test_global_scatter_nd.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_scatter_nd(test_case, placement, sbp): + indices = ( + flow.tensor(np.array([[1], [6], [4]]), dtype=flow.int) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp) + ) + update = ( + flow.tensor(np.array([10.2, 5.1, 12.7]), dtype=flow.float) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp) + .requires_grad_() + ) + output = flow.scatter_nd(indices, update, [8]) + + # forward + of_local = output.to_global( + flow.placement.all("cpu"), [flow.sbp.broadcast,] + ).to_local() + np_out = np.array([0.0, 10.2, 0.0, 0.0, 12.7, 0.0, 5.1, 0.0]) + test_case.assertTrue(np.allclose(of_local.numpy(), np_out, 1e-4, 1e-4)) + + # backward + output.sum().backward() + of_grad_local = update.grad.to_global( + flow.placement.all("cpu"), [flow.sbp.broadcast,] + ).to_local() + test_case.assertTrue(np.allclose(of_grad_local.numpy(), np.ones((3)), 1e-4, 1e-4)) + + +class TestScatterNd(flow.unittest.TestCase): + @globaltest + def test_scatter_nd(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_partial_sum=True, except_split=True): + _test_scatter_nd(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_scatter_ops.py b/python/oneflow/test/modules/test_global_scatter_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f728557aa7fa50dc658014d126fd404853573167 --- /dev/null +++ b/python/oneflow/test/modules/test_global_scatter_ops.py @@ -0,0 +1,84 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=10, auto_backward=True, check_graph=True) +def _test_scatter_random_data(test_case, placement): + input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + src = random_tensor(ndim=2, dim0=2, dim1=2).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + index = ( + torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp=random_sbp(placement, max_dim=2),) + ) + dim = random(0, 2).to(int).value() + return torch.scatter(input, dim, index, src) + + +@autotest(n=10, auto_backward=True, check_graph=True) +def _test_scatter_scalar_random_data(test_case, placement): + input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + index = ( + torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp=random_sbp(placement, max_dim=2),) + ) + dim = random(0, 2).to(int).value() + return torch.scatter(input, dim, index, 3.14) + + +@autotest(n=10, auto_backward=True, check_graph=True) +def _test_scatter_add_random_data(test_case, placement): + input = random_tensor(ndim=2, dim0=2, dim1=2).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + src = random_tensor(ndim=2, dim0=2, dim1=2).to_global( + placement=placement, sbp=random_sbp(placement, max_dim=2) + ) + index = ( + torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64) + .to_global(flow.placement.all("cpu"), [flow.sbp.broadcast,]) + .to_global(placement, sbp=random_sbp(placement, max_dim=2),) + ) + dim = random(0, 2).to(int).value() + return torch.scatter_add(input, dim, index, src) + + +@flow.unittest.skip_unless_1n2d() +class TestScatterOps(flow.unittest.TestCase): + @globaltest + def test_scatter_ops(test_case): + for placement in all_placement(): + _test_scatter_random_data(test_case, placement) + _test_scatter_scalar_random_data(test_case, placement) + _test_scatter_add_random_data(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_searchsorted.py b/python/oneflow/test/modules/test_global_searchsorted.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_searchsorted.py rename to python/oneflow/test/modules/test_global_searchsorted.py diff --git a/python/oneflow/test/modules/test_global_sign.py b/python/oneflow/test/modules/test_global_sign.py new file mode 100644 index 0000000000000000000000000000000000000000..75018db5d2083ee9c7883bb9da813e6f2fd9bba0 --- /dev/null +++ b/python/oneflow/test/modules/test_global_sign.py @@ -0,0 +1,42 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_sign_impl(test_case, ndim, placement, sbp): + dims = [random(1, 3) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp) + y = torch.sign(x) + return y + + +class TestSign(flow.unittest.TestCase): + @globaltest + def test_sign(test_case): + for placement in all_placement(): + ndim = random(1, 4).to(int).value() + for sbp in all_sbp(placement, max_dim=ndim): + _test_sign_impl(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_slice.py b/python/oneflow/test/modules/test_global_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..c28cd4f9249e31cad572f91cb5d7ab2cc7ee4563 --- /dev/null +++ b/python/oneflow/test/modules/test_global_slice.py @@ -0,0 +1,188 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_forward_and_backward(test_case, input, of_out, torch_out): + # compare forward + test_case.assertTrue( + np.array_equal(of_out.numpy(), torch_out.cpu().detach().numpy()) + ) + + # compare backward + of_out.sum().backward() + torch_out.sum().backward() + torch_grad_local = input.pytorch.grad.cpu().detach() + test_case.assertTrue( + np.array_equal(input.oneflow.grad.numpy(), torch_grad_local.numpy()) + ) + + +def _test_slice_random_data(test_case, placement, sbp): + dims = [random(1, 2) * 8 for _ in range(2)] + input = random_tensor(2, *dims) + x = input.to_global(placement=placement, sbp=sbp) + slice_tup_list = [[None, None, None], [0, 5, 2]] + of_out = flow.slice(x.oneflow, slice_tup_list=slice_tup_list) + torch_out = x.pytorch[:, 0:5:2] + + _check_forward_and_backward(test_case, input, of_out, torch_out) + + +def _test_slice_empty(test_case, placement, sbp): + dims = [random(1, 2) * 8 for _ in range(2)] + input = random_tensor(2, *dims) + x = input.to_global(placement=placement, sbp=sbp) + slice_tup_list = [[3, 3, 1], [None, None, None]] + of_out = flow.slice(x.oneflow, slice_tup_list=slice_tup_list) + torch_out = x.pytorch[3:3:1, :] + + _check_forward_and_backward(test_case, input, of_out, torch_out) + + +def _test_slice_1dim(test_case, placement, sbp): + dims = [random(1, 2) * 8 for _ in range(2)] + input = random_tensor(2, *dims) + x = input.to_global(placement=placement, sbp=sbp) + of_out = x.oneflow[2] + torch_out = x.pytorch[2] + + _check_forward_and_backward(test_case, input, of_out, torch_out) + + +def _test_negative_index(test_case, placement, sbp): + dims = [random(1, 2) * 8 for _ in range(2)] + input = random_tensor(2, *dims) + x = input.to_global(placement=placement, sbp=sbp) + of_out = x.oneflow[-1:-6:1, :] + torch_out = x.pytorch[-1:-6:1, :] + + _check_forward_and_backward(test_case, input, of_out, torch_out) + + +def _test_slice_ellipsis_type(test_case, placement, sbp): + dims = [random(1, 2) * 8 for _ in range(2)] + input = random_tensor(2, *dims) + x = input.to_global(placement=placement, sbp=sbp) + of_out = x.oneflow[..., :] + torch_out = x.pytorch[..., :] + + _check_forward_and_backward(test_case, input, of_out, torch_out) + + +def _test_slice_with_bool(test_case, placement, sbp): + x = random_tensor(2, 8, 8).oneflow > 0.5 + x_numpy = x.detach().cpu().numpy() + + x = x.to_global(placement=placement, sbp=sbp) + y = flow.slice(x, slice_tup_list=[[0, 1, 1]]) + + test_case.assertTrue(np.array_equal(y.numpy(), x_numpy[0:1:1])) + + +@autotest( + n=2, auto_backward=False, check_graph=True, +) +def _test_slice_with_grad(test_case, placement): + sbp = random_sbp(placement, max_dim=2).value() + + # out_sbp + sbp_map = { + flow.sbp.broadcast: flow.sbp.broadcast, + flow.sbp.split(0): flow.sbp.split(0), + flow.sbp.split(1): flow.sbp.partial_sum(), + flow.sbp.partial_sum: flow.sbp.partial_sum(), + } + assert sbp is not None + out_sbp = tuple([sbp_map[in_sbp] for in_sbp in sbp]) + + x = random_tensor(2, 8, 16, requires_grad=True).oneflow + x_numpy = x.detach().cpu().numpy() + + class SliceWithGrad(flow.nn.Module): + def __init__(self): + super().__init__() + self.input_grad = flow.nn.Parameter(flow.zeros(8, 16)) + + def forward(self, input): + x = input + self.input_grad + x = x.to_global(placement, sbp) + return x[:, :8] + + slice_with_grad_m = SliceWithGrad().to_global( + placement, [flow.sbp.broadcast,] * len(sbp) + ) + + of_sgd = flow.optim.SGD(slice_with_grad_m.parameters(), lr=1.0, momentum=0.0) + + class SliceTrainGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.module = slice_with_grad_m + self.add_optimizer(of_sgd) + + def build(self, x): + out = self.module(x) + test_case.assertEqual( + out.sbp, + out_sbp, + f"input sbp is {sbp}, but output sbp is {out.sbp} with placement: {placement}", + ) + z = out.sum() + z.backward() + return out + + graph = SliceTrainGraph() + + input = x.to_global(placement=placement, sbp=sbp) + y = graph(input) + + # output + test_case.assertTrue(np.array_equal(y.numpy(), x_numpy[:, :8])) + # input_grad + x_grad_np = np.zeros((8, 16)) + x_grad_np[:, :8] = 1 + test_case.assertTrue( + np.array_equal(-graph.module.input_grad.to(flow.Tensor).numpy(), x_grad_np) + ) + + +class TestSlice(flow.unittest.TestCase): + @globaltest + def test_slice(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_slice_random_data(test_case, placement, sbp) + _test_slice_empty(test_case, placement, sbp) + _test_slice_1dim(test_case, placement, sbp) + _test_negative_index(test_case, placement, sbp) + _test_slice_ellipsis_type(test_case, placement, sbp) + _test_slice_with_bool(test_case, placement, sbp) + + @globaltest + def test_graph_slice(test_case): + for placement in all_placement(): + _test_slice_with_grad(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_slice_update.py b/python/oneflow/test/modules/test_global_slice_update.py new file mode 100644 index 0000000000000000000000000000000000000000..f4b86e6d79403ad7df51ed0138c4eecc9c2a13d1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_slice_update.py @@ -0,0 +1,129 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def _test_slice_update(test_case, placement, sbp): + input = random_tensor(2, 8, 16, requires_grad=True).oneflow + value = random_tensor(2, 8, 8, requires_grad=True).oneflow + x = (input + 0).to_global( + placement=placement, sbp=sbp + ) # add 0 to change to non-leaf tensor + y = value.to_global(placement, sbp=sbp) + x[:, :8] = y + + ref_np = input.detach().cpu().numpy() + value_np = value.detach().cpu().numpy() + + # forward + ref_np[:, :8] = value_np + test_case.assertTrue(x.sbp == sbp) + test_case.assertTrue(np.array_equal(x.numpy(), ref_np)) + + # backward + x.sum().backward() + # ref grad + ref_grad_np = np.ones((8, 16)) + ref_grad_np[:, :8] = 0 + test_case.assertTrue(np.array_equal(input.grad.numpy(), ref_grad_np)) + # value grad + value_grad_np = np.ones((8, 8)) + test_case.assertTrue(np.array_equal(value.grad.numpy(), value_grad_np)) + + +def _test_graph_slice_update(test_case, placement, sbp): + ref = random_tensor(2, 8, 16, requires_grad=True).oneflow + value = random_tensor(2, 8, 8, requires_grad=True).oneflow + + class SliceUpdateWithGrad(flow.nn.Module): + def __init__(self): + super().__init__() + self.ref_grad = flow.nn.Parameter(flow.zeros(8, 16)) + self.value_grad = flow.nn.Parameter(flow.zeros(8, 8)) + + def forward(self, ref, value): + x = ref + self.ref_grad + y = value + self.value_grad + x = x.to_global(placement, sbp) + y = y.to_global(placement, sbp) + x[:, :8] = y + return x + + slice_update_with_grad_m = SliceUpdateWithGrad().to_global( + placement, [flow.sbp.broadcast,] * len(sbp) + ) + + of_sgd = flow.optim.SGD(slice_update_with_grad_m.parameters(), lr=1.0, momentum=0.0) + + class SliceUpdateTrainGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.module = slice_update_with_grad_m + self.add_optimizer(of_sgd) + + def build(self, x, y): + out = self.module(x, y) + z = out.sum() + z.backward() + return out + + graph = SliceUpdateTrainGraph() + + x = ref.to_global(placement=placement, sbp=sbp) + y = value.to_global(placement=placement, sbp=sbp) + z = graph(x, y) + + test_case.assertTrue(z.sbp == sbp) + + ref_np = ref.detach().cpu().numpy() + value_np = value.detach().cpu().numpy() + + # forward + ref_np[:, :8] = value_np + test_case.assertTrue(np.array_equal(z.numpy(), ref_np)) + + # backward + # ref grad + ref_grad = np.ones((8, 16)) + ref_grad[:, :8] = 0 + test_case.assertTrue( + np.array_equal(-graph.module.ref_grad.to(flow.Tensor).numpy(), ref_grad) + ) + # value grad + value_grad = np.ones((8, 8)) + test_case.assertTrue( + np.array_equal(-graph.module.value_grad.to(flow.Tensor).numpy(), value_grad) + ) + + +class TestGlobalSliceUpdate(flow.unittest.TestCase): + @globaltest + def test_slice_update(test_case): + for placement in all_placement(): + for _ in range(2): + sbp = random_sbp(placement, max_dim=2).value() + _test_slice_update(test_case, placement, sbp) + _test_graph_slice_update(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_sort.py b/python/oneflow/test/modules/test_global_sort.py new file mode 100644 index 0000000000000000000000000000000000000000..67c73665ecce4c0996bc735756857fed1b39a5af --- /dev/null +++ b/python/oneflow/test/modules/test_global_sort.py @@ -0,0 +1,46 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=5, auto_backward=False, check_graph=False) +def _test_sort_impl(test_case, placement): + sbp = random_sbp(placement, max_dim=4) + x_dims = [random(2, 4) * 8 for _ in range(4)] + x = random_tensor(4, *x_dims) + dim = random(0, 4).to(int).value() + descending = random().to(bool).value() + + y = x.to_global(placement=placement, sbp=sbp) + sort_result = torch.sort(y, dim=dim, descending=descending) + value = sort_result[0] + return value + + +class TestSortGlobal(flow.unittest.TestCase): + @globaltest + def test_sort(test_case): + for placement in all_placement(): + _test_sort_impl(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_sparse.py b/python/oneflow/test/modules/test_global_sparse.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_sparse.py rename to python/oneflow/test/modules/test_global_sparse.py diff --git a/python/oneflow/test/modules/test_global_sparse_softmax_cross_entropy.py b/python/oneflow/test/modules/test_global_sparse_softmax_cross_entropy.py new file mode 100644 index 0000000000000000000000000000000000000000..01b493b82d3061be486dbae5f87e6cad897206e1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_sparse_softmax_cross_entropy.py @@ -0,0 +1,161 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import os +from collections import OrderedDict + +import numpy as np +import torch + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type + +from oneflow.test_utils.automated_test_util.generators import * +from oneflow.test_utils.automated_test_util.torch_flow_dual_object import globaltest + + +def _compare_eager_global_with_torch( + placement, logits_sbp, labels_sbp, data_type, label_type, batch_size, num_classes, +): + data_type = type_name_to_flow_type[data_type] + label_type = type_name_to_flow_type[label_type] + np_labels = np.random.randint(0, num_classes, size=(batch_size,)).astype(np.int32) + np_logits = np.random.random((batch_size, num_classes)).astype(np.float32) + if flow.env.get_rank() == 0: + torch_logits = torch.tensor(np_logits, dtype=torch.float32, requires_grad=True) + torch_labels = torch.tensor(np_labels, dtype=torch.int64) + torch_output = torch.nn.functional.cross_entropy( + torch_logits, torch_labels, reduction="none" + ) + torch_output.sum().backward() + + of_logits = flow.tensor(np_logits, dtype=data_type, requires_grad=True).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + of_logits = of_logits.to_global(placement, logits_sbp) + + of_logits.retain_grad() + + of_labels = flow.tensor(np_labels, dtype=label_type).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + of_labels = of_labels.to_global(placement, labels_sbp) + + of_output = flow.nn.functional.sparse_softmax_cross_entropy( + labels=of_labels, logits=of_logits + ) + of_output.sum().backward() + of_logits_grad = of_logits.grad.to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + of_logits_grad = of_logits_grad.to_local() + of_output = of_output.to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + of_output = of_output.to_local() + + if flow.env.get_rank() == 0: + assert np.allclose( + of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 + ) + assert np.allclose( + of_logits_grad.numpy(), torch_logits.grad, rtol=1e-03, atol=1e-04 + ) + + +def _compare_lazy_global_with_torch( + placement, logits_sbp, labels_sbp, data_type, label_type, batch_size, num_classes, +): + data_type = type_name_to_flow_type[data_type] + label_type = type_name_to_flow_type[label_type] + np_labels = np.random.randint(0, num_classes, size=(batch_size,)).astype(np.int32) + np_logits = np.random.random((batch_size, num_classes)).astype(np.float32) + if flow.env.get_rank() == 0: + torch_logits = torch.tensor(np_logits, dtype=torch.float32, requires_grad=True) + torch_labels = torch.tensor(np_labels, dtype=torch.int64) + torch_output = torch.nn.functional.cross_entropy( + torch_logits, torch_labels, reduction="none" + ) + + class MyModule(flow.nn.Graph): + def __init__(self): + super(MyModule, self).__init__() + + # nn.graph no support get input.grad + def build(self, logits, labels): + output = flow.nn.functional.sparse_softmax_cross_entropy( + labels=labels, logits=logits + ) + return output + + of_logits = flow.tensor(np_logits, dtype=data_type, requires_grad=True).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + of_logits = of_logits.to_global(placement, logits_sbp) + + of_labels = flow.tensor(np_labels, dtype=label_type).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + of_labels = of_labels.to_global(placement, labels_sbp) + graph = MyModule() + of_output = graph(of_logits, of_labels) + of_output = of_output.to_global( + placement=flow.placement.all("cpu"), sbp=[flow.sbp.broadcast] + ) + of_output = of_output.to_local() + + flow._oneflow_internal.eager.multi_client.Sync() + + if flow.env.get_rank() == 0: + assert np.allclose( + of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 + ) + + +class TestGlobalSparseSoftmaxCrossEntropyWithLogits(flow.unittest.TestCase): + @globaltest + def test_eager_global_sparse_softmax_cross_entropy(test_case): + arg_dict = OrderedDict() + arg_dict["data_type"] = ["float32", "double"] + arg_dict["label_type"] = ["int32", "int64"] + arg_dict["batch_size"] = [64] + arg_dict["num_classes"] = [1024] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for logits_sbp in all_sbp(placement, max_dim=2): + for labels_sbp in all_sbp(placement, max_dim=1): + _compare_eager_global_with_torch( + placement, logits_sbp, labels_sbp, *arg + ) + + # TODO: Too many streams will cause bugs, open the graph mode after solving + # @globaltest + # def test_lazy_global_sparse_softmax_cross_entropy(test_case): + # arg_dict = OrderedDict() + # arg_dict["data_type"] = ["float32", "double"] + # arg_dict["label_type"] = ["int32", "int64"] + # arg_dict["batch_size"] = [64] + # arg_dict["num_classes"] = [1024] + # for arg in GenArgList(arg_dict): + # for placement in all_placement(): + # for logits_sbp in all_sbp(placement, max_dim=2): + # for labels_sbp in all_sbp(placement, max_dim=1): + # _compare_lazy_global_with_torch( + # placement, logits_sbp, labels_sbp, *arg + # ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_split.py b/python/oneflow/test/modules/test_global_split.py new file mode 100644 index 0000000000000000000000000000000000000000..01c81f0be74c829fd9f98dcae68c0cb8b2e6a65c --- /dev/null +++ b/python/oneflow/test/modules/test_global_split.py @@ -0,0 +1,88 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_flow_split_with_random_data(test_case, placement, sbp): + k0 = random(2, 6) * 8 + k1 = random(2, 6) * 8 + k2 = random(2, 6) * 8 + rand_dim = random(0, 3).to(int) + x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( + placement=placement, sbp=sbp + ) + res = torch.split(x, 2, dim=rand_dim) + return torch.cat(res, rand_dim) + + +@autotest(n=2, check_graph=True) +def _test_flow_split_sizes_with_random_data(test_case, placement, sbp): + k0 = random(2, 6) * 8 + k1 = 16 + k2 = random(2, 6) * 8 + x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( + placement=placement, sbp=sbp + ) + res = torch.split(x, [6, 3, 4, 3], dim=1) + return torch.cat(res, dim=1) + + +@autotest(n=2, check_graph=True) +def _test_flow_split_sizes_neg_dim_with_random_data(test_case, placement, sbp): + k0 = random(2, 6) * 8 + k1 = 16 + k2 = random(2, 6) * 8 + x = random_tensor(ndim=3, dim0=k0, dim1=k1, dim2=k2).to_global( + placement=placement, sbp=sbp + ) + res = torch.split(x, [6, 3, 4, 3], dim=-2) + return torch.cat(res, dim=1) + + +class TestGlobalSplitModule(flow.unittest.TestCase): + @globaltest + def test_flow_split_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_split_with_random_data(test_case, placement, sbp) + + @globaltest + def test_flow_split_sizes_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_split_sizes_with_random_data(test_case, placement, sbp) + + @globaltest + def test_flow_split_sizes_neg_dim_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_split_sizes_neg_dim_with_random_data( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_sqrt_square_sum.py b/python/oneflow/test/modules/test_global_sqrt_square_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..53c37e8804bdeed6f36c089c4d9e72ac7839067c --- /dev/null +++ b/python/oneflow/test/modules/test_global_sqrt_square_sum.py @@ -0,0 +1,61 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True, rtol=0.5, atol=0.5) +def _test_sqrt_sum_with_cpu_random_data(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=32, dim2=40, dim3=64).to_global( + placement=placement, sbp=sbp + ) + y = torch.linalg.norm(x) + return y + + +@autotest(n=1, check_graph=True, rtol=0.5, atol=0.5) +def _test_scalar_random_data(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=24, dim2=16, dim3=40).to_global( + placement=placement, sbp=sbp + ) + y = torch.linalg.norm(x) + return y + + +class TestGlobalLinalgVectorNorm2D(flow.unittest.TestCase): + @globaltest + def test_sqrt_sum_with_cpu_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_sqrt_sum_with_cpu_random_data(test_case, placement, sbp) + + @globaltest + def test_scalar_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_scalar_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_squeeze.py b/python/oneflow/test/modules/test_global_squeeze.py new file mode 100644 index 0000000000000000000000000000000000000000..08085d8b0d247b36df6d67d38aa6aeb2b2fadbfc --- /dev/null +++ b/python/oneflow/test/modules/test_global_squeeze.py @@ -0,0 +1,70 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=False) +def _test_squeeze_1d_input(test_case, placement, sbp): + x = random_tensor(1, 16, dtype=float).to_global(placement, sbp) + y = torch.squeeze(x) + return y + + +@autotest(n=1, check_graph=False) +def _test_flow_squeeze_with_random_data(test_case, placement, sbp): + x = random_tensor(2, 8, 16).to_global(placement, sbp) + y = torch.squeeze(x, random(0, 2).to(int)) + return y + + +@autotest(n=1, check_graph=False) +def _test_squeeze_with_0_size_data(test_case, placement, sbp): + x = random_tensor(3, 8, 16, 0).to_global(placement, sbp) + y = torch.squeeze(x) + return y + + +class TestGlobalSqueeze(flow.unittest.TestCase): + @globaltest + def test_squeeze_1d_input(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_squeeze_1d_input(test_case, placement, sbp) + + @globaltest + def test_flow_squeeze_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_squeeze_with_random_data(test_case, placement, sbp) + + @globaltest + def test_squeeze_with_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_squeeze_with_0_size_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_stack.py b/python/oneflow/test/modules/test_global_stack.py new file mode 100644 index 0000000000000000000000000000000000000000..8f929febf56a7f1203f9035eaba479e8eee66fc7 --- /dev/null +++ b/python/oneflow/test/modules/test_global_stack.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=2, check_graph=True) +def _test_stack_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=8).to_global( + placement, sbp + ) + y = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=8).to_global( + placement, sbp + ) + out = torch.stack((x, y), dim=random(low=-5, high=5).to(int)) + return out + + +@unittest.skip("backward of stack with random diff has bug.") +class TestStackModule(flow.unittest.TestCase): + @globaltest + def test_stack_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_stack_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_stateful_kernel_with_cache.py b/python/oneflow/test/modules/test_global_stateful_kernel_with_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..afb9d8b02d4621c2cc7305ca9e4ecc733e5cc6ef --- /dev/null +++ b/python/oneflow/test/modules/test_global_stateful_kernel_with_cache.py @@ -0,0 +1,51 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_global_stateful_kernel_with_inpersistent_state(test_case, placement, sbp): + x = ( + flow.arange(64) + .reshape(8, 8) + .to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + ) + x = x.to_global(placement, sbp) + y = x[0:3, 0:1] + y_np = np.array([[0], [8], [16]]) + test_case.assertTrue(np.array_equal(y.numpy(), y_np,)) + x = x.to_global(flow.placement.all("cpu"), sbp=flow.sbp.split(1)) + y = x[0:3, 0:1] + test_case.assertTrue(np.array_equal(y.numpy(), y_np,)) + + +class TestStatefulKernelWithInpersistentState(flow.unittest.TestCase): + @globaltest + def test_global_stateful_kernel_with_inpersistent_state(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_stateful_kernel_with_inpersistent_state( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_std.py b/python/oneflow/test/modules/test_global_std.py new file mode 100644 index 0000000000000000000000000000000000000000..7c5527f2668328b9e262364549dde792aeb84634 --- /dev/null +++ b/python/oneflow/test/modules/test_global_std.py @@ -0,0 +1,66 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_std_flow_with_random_data(test_case, placement, sbp): + dim = random(low=0, high=4).to(int) + x = random_tensor( + ndim=4, + dim0=random(1, 4) * 8, + dim1=random(1, 4) * 8, + dim2=random(1, 4) * 8, + dim3=random(1, 4) * 8, + ).to_global(placement, sbp) + z = torch.std(x, dim=dim, unbiased=random().to(bool), keepdim=random().to(bool),) + return z + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_std_tensor_with_random_data(test_case, placement, sbp): + dim = random(low=0, high=4).to(int) + x = random_tensor( + ndim=4, + dim0=random(1, 4) * 8, + dim1=random(1, 4) * 8, + dim2=random(1, 4) * 8, + dim3=random(1, 4) * 8, + ).to_global(placement, sbp) + z = x.std(dim=dim, keepdim=random().to(bool),) + return z + + +class TestGlobalStd(flow.unittest.TestCase): + @globaltest + def test_global_std_flow_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_std_flow_with_random_data(test_case, placement, sbp) + + @globaltest + def test_global_std_tensor_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_std_tensor_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_sub.py b/python/oneflow/test/modules/test_global_sub.py new file mode 100644 index 0000000000000000000000000000000000000000..0e30f3e281238f2194e0e0f28d572c0f03ef3b60 --- /dev/null +++ b/python/oneflow/test/modules/test_global_sub.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_sub(test_case, placement, sbp): + x = random_tensor(2, 8, 8).to_global(placement=placement, sbp=sbp) + y = random_tensor(2, 8, 8).to_global(placement=placement, sbp=sbp) + out1 = x - y + out2 = x - 2 + out3 = 2 - x + out4 = torch.sub(x, y) + return out1, out2, out3, out4 + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_sub_with_0_size_data(test_case, placement, sbp): + device = random_device() + x = random_tensor(2, 0, 8).to_global(placement=placement, sbp=sbp) + out1 = x - 2 + out2 = 2 - x + return out1, out2 + + +class TestGlobalSubModule(flow.unittest.TestCase): + @globaltest + def test_global_sub(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_sub(test_case, placement, sbp) + + @globaltest + def test_global_sub_with_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2, valid_split_axis=1): + _test_global_sub_with_0_size_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_sum.py b/python/oneflow/test/modules/test_global_sum.py new file mode 100644 index 0000000000000000000000000000000000000000..2288ba85b74eeb344e8f464a3655256c1b5cf538 --- /dev/null +++ b/python/oneflow/test/modules/test_global_sum.py @@ -0,0 +1,57 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=False) +def _test_global_sum_against_pytorch(test_case, placement, sbp): + x = random_tensor(4, 8, 16, 8, 24).to_global(placement, sbp) + y = torch.sum(x) + return y + + +@autotest(n=3, check_graph=False) +def _test_global_sum_with_0_size_tensor(test_case, placement, sbp): + x = random_tensor(4, 8, 16, 0, 24).to_global(placement, sbp) + y = torch.sum(x, dim=random(0, 3).to(int)) + return y + + +class TestGlobalSumModule(flow.unittest.TestCase): + @globaltest + def test_global_sum_against_pytorch(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_sum_against_pytorch(test_case, placement, sbp) + + @globaltest + def test_global_sum_with_0_size_tensor(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]): + _test_global_sum_with_0_size_tensor(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_tensor_ops.py b/python/oneflow/test/modules/test_global_tensor_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..0a574c0b567d0f47f0985b40c0ee19eca1273eeb --- /dev/null +++ b/python/oneflow/test/modules/test_global_tensor_ops.py @@ -0,0 +1,222 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def _test_type_as(test_case, shape, src_dtype, tgt_dtype, placement, sbp): + np_input = np.random.rand(*shape) + input = flow.tensor(np_input, dtype=src_dtype).to_global(placement, sbp) + target = flow.tensor(np_input, dtype=tgt_dtype).to_global(placement, sbp) + input = input.type_as(target) + test_case.assertEqual(input.dtype, target.dtype) + + +def _test_local_to_global_type_as( + test_case, shape, src_dtype, tgt_dtype, placement, sbp +): + np_input = np.random.rand(*shape) + input = random_tensor(ndim=len(shape)).oneflow.to_local() + target = flow.tensor(np_input, dtype=tgt_dtype).to_global(placement, sbp) + input = input.type_as(target) + test_case.assertEqual(input.dtype, target.dtype) + test_case.assertEqual(input.placement, target.placement) + test_case.assertEqual(input.sbp, target.sbp) + + +def _test_global_to_local_type_as( + test_case, shape, src_dtype, tgt_dtype, placement, sbp +): + np_input = np.random.rand(*shape) + input = flow.tensor(np_input, dtype=tgt_dtype).to_global(placement, sbp) + target = random_tensor(ndim=len(shape)).to(random_device()).oneflow.to_local() + input = input.type_as(target) + test_case.assertEqual(input.dtype, target.dtype) + test_case.assertEqual(input.device, target.device) + + +def _test_is_floating_point(test_case, shape, dtype, placement, sbp): + np_input = np.random.rand(*shape) + input = flow.tensor(np_input, dtype=dtype).to_global(placement, sbp) + output = input.is_floating_point() + if input.dtype in (flow.float, flow.float16, flow.float32, flow.double): + test_case.assertEqual(output, True) + else: + test_case.assertEqual(output, False) + + +@autotest(n=1, check_graph=True) +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +def _test_global_cuda(test_case, placement, sbp): + x = random_tensor(2, 8, 16).to_global(placement, sbp) + x = x.cuda() + y = x.sum() + return y + + +class TestGlobalCuda(flow.unittest.TestCase): + @globaltest + def test_global_cuda(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_cuda(test_case, placement, sbp) + + +@autotest(n=1, check_graph=True) +def _test_global_cpu(test_case, placement, sbp): + x = random_tensor(2, 8, 16).to_global(placement, sbp) + x = x.cpu() + y = x.sum() + return y + + +# PyTorch error if open auto_backward: +# element 0 of tensors does not require grad and does not have a grad_fn +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_long(test_case, placement, sbp): + x = random_tensor(2, 8, 16, requires_grad=True).to_global(placement, sbp) + y = x.long() + test_case.assertFalse(y.oneflow.requires_grad) + return y + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_int(test_case, placement, sbp): + x = random_tensor(2, 8, 16, requires_grad=True).to_global(placement, sbp) + y = x.int() + test_case.assertFalse(y.oneflow.requires_grad) + return y + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_float(test_case, placement, sbp): + x = random_tensor(2, 8, 16, dtype=int).to_global(placement, sbp) + y = x.float() + return y + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_double(test_case, placement, sbp): + x = random_tensor(2, 8, 16, dtype=int).to_global(placement, sbp) + y = x.double() + return y + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_global_item(test_case, placement, sbp): + x = random_tensor(ndim=1, dim0=1, dtype=int).to_global(placement, sbp) + y = torch.tensor(x.item()) + return y + + +@autotest(n=1, auto_backward=False, check_graph=False) +def _test_global_tolist(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=24, dim3=32, dtype=int).to_global( + placement, sbp + ) + y = torch.tensor(x.tolist()) + return y + + +class TestGlobalTensorOps(flow.unittest.TestCase): + @globaltest + def test_global_cpu(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_cpu(test_case, placement, sbp) + + @globaltest + def test_global_long(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_long(test_case, placement, sbp) + + @globaltest + def test_global_int(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_int(test_case, placement, sbp) + + @globaltest + def test_global_float(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_float(test_case, placement, sbp) + + @globaltest + def test_global_double(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_double(test_case, placement, sbp) + + @unittest.skip("TODO: sometimes global item will result to segment fault!") + @globaltest + def test_global_item(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1, except_split=True): + _test_global_item(test_case, placement, sbp) + + @globaltest + def test_global_tolist(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_tolist(test_case, placement, sbp) + + @globaltest + def test_type_as(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8, 16), (8, 16, 24), (8, 16, 24, 32)] + arg_dict["src_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] + arg_dict["tgt_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(arg[0])): + _test_type_as(test_case, *arg, placement, sbp) + _test_local_to_global_type_as(test_case, *arg, placement, sbp) + _test_global_to_local_type_as(test_case, *arg, placement, sbp) + + @globaltest + def test_is_floating_point(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(8, 16), (8, 16, 24), (8, 16, 24, 32)] + arg_dict["dtype"] = [ + # flow.uint8, nccl don't support uint8 + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + flow.double, + flow.float, + flow.int, + ] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=len(arg[0])): + _test_is_floating_point(test_case, *arg, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py b/python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py new file mode 100644 index 0000000000000000000000000000000000000000..7c7acc8d9d324b5966b81f1c5d689ace00da9d8c --- /dev/null +++ b/python/oneflow/test/modules/test_global_tensor_scatter_nd_update.py @@ -0,0 +1,161 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +class TensorScatterNdUpdate(flow.nn.Graph): + def __init__(self): + super(TensorScatterNdUpdate, self).__init__() + + def build(self, origin, indices, update): + return flow.tensor_scatter_nd_update(origin, indices, update) + + +def _test_global_tensor_scatter_nd_update(test_case, placement, sbp, check_graph=True): + origin = random_tensor(1, 16, requires_grad=False).to_global(placement, sbp) + indices = choice_tensor(16, (8, 1), replace=False).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + update = random_tensor(1, 8, requires_grad=False).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + + np_origin = origin.oneflow.numpy() + np_indices = indices.oneflow.numpy().reshape(8) + np_update = update.oneflow.numpy() + + if check_graph: + tensor_scatter_nd_update = TensorScatterNdUpdate() + output = tensor_scatter_nd_update( + origin.oneflow, indices.oneflow, update.oneflow + ) + else: + output = flow.tensor_scatter_nd_update( + origin.oneflow, indices.oneflow, update.oneflow + ) + + np_origin[np_indices] = np_update + + test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) + + +def _test_global_tensor_scatter_nd_update_t( + test_case, placement, sbp, check_graph=True +): + + origin = random_tensor(2, 16, 4, requires_grad=False).to_global(placement, sbp) + indices = choice_tensor(16, (8, 1), replace=False).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + update = random_tensor(2, 8, 4, requires_grad=False).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + + np_origin = origin.oneflow.numpy() + np_indices = indices.oneflow.numpy().reshape(8) + np_update = update.oneflow.numpy() + + if check_graph: + tensor_scatter_nd_update = TensorScatterNdUpdate() + output = tensor_scatter_nd_update( + origin.oneflow, indices.oneflow, update.oneflow + ) + else: + output = flow.tensor_scatter_nd_update( + origin.oneflow, indices.oneflow, update.oneflow + ) + + np_origin[np_indices] = np_update + + test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) + + +def _test_eager_global_tensor_scatter_nd_update_backward(test_case, placement, sbp): + origin = random_tensor(1, 16,).to_global(placement, sbp) + origin.retain_grad() + indices = choice_tensor(16, (8, 1), replace=False).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + update = random_tensor(1, 8).to_global( + placement, [flow.sbp.broadcast for _ in range(len(placement.ranks.shape))] + ) + update.retain_grad() + + np_origin = origin.oneflow.numpy() + np_indices = indices.oneflow.numpy().reshape(8) + np_update = update.oneflow.numpy() + + np_update_grad = np.ones(8) + np_origin_grad = np.ones(16) + np_origin_grad[np_indices] = np.zeros(8) + + output = flow.tensor_scatter_nd_update( + origin.oneflow, indices.oneflow, update.oneflow + ) + out_sum = output.sum() + out_sum.backward() + + np_origin[np_indices] = np_update + + test_case.assertTrue(np.allclose(output.numpy(), np_origin, 0.0001, 0.0001)) + test_case.assertTrue(np.allclose(update.oneflow.grad.numpy(), np_update_grad)) + test_case.assertTrue(np.allclose(origin.oneflow.grad.numpy(), np_origin_grad)) + + +class TestTensorScatterNdUpdate(flow.unittest.TestCase): + @globaltest + def test_global_tensor_scatter_nd_update(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_tensor_scatter_nd_update( + test_case, placement, sbp, False + ) # eager global + # skip lazy test + # _test_global_tensor_scatter_nd_update( + # test_case, placement, sbp, True + # ) # nn graph + + @globaltest + def test_global_tensor_scatter_nd_update_t(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_tensor_scatter_nd_update_t( + test_case, placement, sbp, False + ) # eager global + # skip lazy test + # _test_global_tensor_scatter_nd_update_t( + # test_case, placement, sbp, True + # ) # nn graph + + @globaltest + def test_global_tensor_scatter_nd_update_backward(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_eager_global_tensor_scatter_nd_update_backward( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_tensordot.py b/python/oneflow/test/modules/test_global_tensordot.py new file mode 100644 index 0000000000000000000000000000000000000000..25d76c98507e7e6fbba920e42c18b6eb7de84a33 --- /dev/null +++ b/python/oneflow/test/modules/test_global_tensordot.py @@ -0,0 +1,50 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True, atol=1e-3) +def _test_global_tensordot_against_pytorch(test_case, ndim, placement, sbp): + k = random(1, 2) * 8 + tensordot_dim = random(0, ndim + 1).to(int) + + x = random_tensor(ndim=ndim, dim0=k, dim1=k, dim2=k, dim3=k).to_global( + placement=placement, sbp=sbp + ) + y = random_tensor(ndim=ndim, dim0=k, dim1=k, dim2=k, dim3=k).to_global( + placement=placement, sbp=sbp + ) + z = torch.tensordot(x, y, dims=tensordot_dim) + return z + + +class TestTensorDotGlobal(flow.unittest.TestCase): + @globaltest + def test_tensordot(test_case): + for placement in all_placement(): + for ndim in range(1, 4): + for sbp in all_sbp(placement, max_dim=ndim): + _test_global_tensordot_against_pytorch( + test_case, ndim, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_tile.py b/python/oneflow/test/modules/test_global_tile.py new file mode 100644 index 0000000000000000000000000000000000000000..c3ae03a20979d9418b30c029da9f1061401b72b4 --- /dev/null +++ b/python/oneflow/test/modules/test_global_tile.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=3, check_graph=False) +def _test_global_flow_tile_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + reps = ( + random(1, 5).to(int) * 8, + random(1, 5).to(int) * 8, + random(1, 5).to(int) * 8, + ) + z = torch.tile(x, reps) + return z + + +@autotest(n=3, check_graph=False) +def _test_global_flow_tensor_tile_with_random_data(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + reps = ( + random(1, 5).to(int) * 8, + random(1, 5).to(int) * 8, + random(1, 5).to(int) * 8, + ) + y = x.tile(reps) + return y + + +class TestGlobalTile(flow.unittest.TestCase): + @globaltest + def test_global_flow_tile_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_flow_tile_with_random_data(test_case, placement, sbp) + + @globaltest + def test_global_flow_tensor_tile_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_flow_tensor_tile_with_random_data( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_transpose.py b/python/oneflow/test/modules/test_global_transpose.py new file mode 100644 index 0000000000000000000000000000000000000000..5a729c75be3b691c56dd8ae8724f24167d47054a --- /dev/null +++ b/python/oneflow/test/modules/test_global_transpose.py @@ -0,0 +1,130 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_global_transpose(test_case, placement, sbp): + input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + input = input.to_global(placement, sbp) + of_out = flow.transpose(input, 0, 1) + np_out = input.numpy().transpose((1, 0, 2, 3)) + test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) + + +def _test_global_tensor_transpose(test_case, placement, sbp): + input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + input = input.to_global(placement, sbp) + of_out = input.transpose(0, 1) + np_out = input.numpy().transpose((1, 0, 2, 3)) + test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) + + +def _test_global_tranpose_negative_dim(test_case, placement, sbp): + input = flow.tensor(np.random.randn(8, 16, 8, 16), dtype=flow.float32).to_global( + flow.placement.all("cpu"), flow.sbp.broadcast + ) + input = input.to_global(placement, sbp) + of_out = flow.transpose(input, -4, -3) + np_out = input.numpy().transpose((1, 0, 2, 3)) + test_case.assertTrue(np.array_equal(of_out.numpy().flatten(), np_out.flatten())) + + +def _test_global_transpose_backward(test_case, placement, sbp): + x = flow.tensor( + np.random.randn(8, 16, 8, 16), dtype=flow.float32, requires_grad=True, + ).to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + x = x.to_global(placement, sbp) + x.retain_grad() + y = flow.transpose(x, 0, 1).sum() + y.backward() + test_case.assertTrue( + np.allclose(x.grad.numpy(), np.ones((8, 16, 8, 16)), 1e-05, 1e-05) + ) + + +def _test_global_transpose_backward_v2(test_case, placement, sbp): + x = flow.tensor( + np.random.randn(8, 16, 8, 16), dtype=flow.float32, requires_grad=True, + ).to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + x = x.to_global(placement, sbp) + x.retain_grad() + y = flow.transpose(x, 3, 1).sum() + y.backward() + test_case.assertTrue( + np.allclose(x.grad.numpy(), np.ones((8, 16, 8, 16)), 1e-05, 1e-05) + ) + + +@autotest(n=1, check_graph=False) +def _test_global_transpose_flow_with_random_data(test_case, placement, sbp): + x = random_tensor(4, 8, 16, 24, 8).to_global(placement, sbp) + y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) + return y + + +@autotest(n=1, check_graph=False) +def _test_global_transpose_with_0_size_data(test_case, placement, sbp): + device = random_device() + x = random_tensor(4, 8, 16, 0, 8).to_global(placement, sbp) + y = torch.transpose(x, dim0=random(1, 3).to(int), dim1=random(1, 3).to(int)) + return y + + +class TestGlobalTranspose(flow.unittest.TestCase): + @globaltest + def test_global_transpose(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_global_transpose, + _test_global_tensor_transpose, + _test_global_tranpose_negative_dim, + _test_global_transpose_backward, + _test_global_transpose_backward_v2, + ] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + arg[0](test_case, placement, sbp) + + @globaltest + def test_global_transpose_flow_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_transpose_flow_with_random_data(test_case, placement, sbp) + + @globaltest + def test_global_transpose_with_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]): + _test_global_transpose_with_0_size_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_tril.py b/python/oneflow/test/modules/test_global_tril.py new file mode 100644 index 0000000000000000000000000000000000000000..8f72bb3dbae3bc0fcb935fd22e2b4d18c80834f1 --- /dev/null +++ b/python/oneflow/test/modules/test_global_tril.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=2, check_graph=True) +def _test_global_tril_without_diag(test_case, placement, sbp): + x = random_tensor( + ndim=4, + dim0=random(1, 5).to(int) * 8, + dim1=random(1, 5).to(int) * 8, + dim2=random(1, 5).to(int) * 8, + dim3=random(1, 5).to(int) * 8, + ).to_global(placement, sbp) + y = torch.tril(x) + y = torch.exp(y) + + return y + + +@autotest(n=2, check_graph=True) +def _test_global_tril_with_diag(test_case, placement, sbp): + diagonal = random(-3, 3).to(int) + x = random_tensor( + ndim=4, + dim0=random(1, 5).to(int) * 8, + dim1=random(1, 5).to(int) * 8, + dim2=random(1, 5).to(int) * 8, + dim3=random(1, 5).to(int) * 8, + ).to_global(placement, sbp) + y = torch.tril(x, diagonal) + y = torch.exp(y) + + return y + + +class TestGlobalTril(flow.unittest.TestCase): + @globaltest + def test_global_tril_without_diag(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_tril_without_diag(test_case, placement, sbp) + + @globaltest + def test_global_tril_with_diag(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_tril_with_diag(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_triu.py b/python/oneflow/test/modules/test_global_triu.py new file mode 100644 index 0000000000000000000000000000000000000000..fcc6c3d922b49a491ab400452fb6f0f759917a0e --- /dev/null +++ b/python/oneflow/test/modules/test_global_triu.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=2, check_graph=True) +def _test_global_triu_without_diag(test_case, placement, sbp): + x = random_tensor( + ndim=4, + dim0=random(1, 3).to(int) * 8, + dim1=random(1, 3).to(int) * 8, + dim2=2, + dim3=4, + ).to_global(placement, sbp) + y = torch.triu(x) + y = torch.exp(y) + + return y + + +@autotest(n=2, check_graph=True) +def _test_global_triu_with_diag(test_case, placement, sbp): + diagonal = random(-3, 3).to(int) + x = random_tensor( + ndim=4, + dim0=random(1, 3).to(int) * 8, + dim1=random(1, 3).to(int) * 8, + dim2=2, + dim3=4, + ).to_global(placement, sbp) + y = torch.triu(x, diagonal) + y = torch.exp(y) + + return y + + +class TestGlobalTriu(flow.unittest.TestCase): + @globaltest + def test_global_triu_without_diag(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_triu_without_diag(test_case, placement, sbp) + + @globaltest + def test_global_triu_with_diag(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_triu_with_diag(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_unbind.py b/python/oneflow/test/modules/test_global_unbind.py new file mode 100644 index 0000000000000000000000000000000000000000..1018a621da042aba8811b9c87fa6e4437b1430df --- /dev/null +++ b/python/oneflow/test/modules/test_global_unbind.py @@ -0,0 +1,43 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +# TODO: the test is dependent on global select op(global tensor->stride()) +@unittest.skip("global select op is not currently supported") +@autotest(n=1, check_graph=True) +def _test_unbind(test_case, placement, sbp): + dim_size = random(1, 3).to(int).value() * 8 + rand_dim = random(0, 3).to(int).value() + x = random_tensor(ndim=3, dim0=dim_size, dim1=dim_size, dim2=dim_size).to_global( + placement, sbp + ) + return torch.unbind(x, dim=rand_dim) + + +class TestUnbind(flow.unittest.TestCase): + @globaltest + def test_unbind(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=3): + _test_unbind(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_unfold.py b/python/oneflow/test/modules/test_global_unfold.py new file mode 100644 index 0000000000000000000000000000000000000000..91fdf18235996e679df16a9fea350aa04687baca --- /dev/null +++ b/python/oneflow/test/modules/test_global_unfold.py @@ -0,0 +1,58 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * +from oneflow.nn.common_types import _size_2_t + + +@autotest(n=1, check_graph=True) +def _test_unfold_with_random_data(test_case, placement, sbp): + ndim = 4 + dims = [random(1, 4).to(int).value() * 8 for i in range(ndim)] + m = torch.nn.Unfold( + kernel_size=random(1, 3).to(_size_2_t), + dilation=random(1, 2).to(_size_2_t), + padding=random(0, 1).to(_size_2_t), + stride=random(1, 2).to(_size_2_t), + ) + m.train(random()) + + x = random_tensor(ndim, *dims).to_global(placement, sbp) + y = m(x) + func_y = torch.nn.functional.unfold( + x, + kernel_size=random(1, 3).to(_size_2_t), + dilation=random(1, 2).to(_size_2_t), + padding=random(0, 1).to(_size_2_t), + stride=random(1, 2).to(_size_2_t), + ) + return y, func_y + + +class TestUnfold(flow.unittest.TestCase): + @globaltest + def test_unfold_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_unfold_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_unfold_tensor.py b/python/oneflow/test/modules/test_global_unfold_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..adcc8eb6d8ea4d661781da6d74f6e9f792e31771 --- /dev/null +++ b/python/oneflow/test/modules/test_global_unfold_tensor.py @@ -0,0 +1,52 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * +import numpy as np + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_unfold_tensor_with_random_data(test_case, placement, sbp): + ndim = 4 + dim = random(0, ndim).to(int).value() + x = random_tensor( + ndim=ndim, + dim0=random(1, 3).to(int) * 8, + dim1=random(1, 3).to(int) * 8, + dim2=4, + dim3=4, + ).to_global(placement, sbp) + high = x.oneflow.size()[dim] + size = random(1, high).to(int).value() + step = random(1, high).to(int).value() + y = x.unfold(dim, size, step) + return y + + +class TestGlobalUnfoldTensor(flow.unittest.TestCase): + @globaltest + def test_global_unfold_tensor_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_unfold_tensor_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_unique.py b/python/oneflow/test/modules/test_global_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..f55c679f16f3d93d9b9c431a63604557c100d64f --- /dev/null +++ b/python/oneflow/test/modules/test_global_unique.py @@ -0,0 +1,97 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import torch as torch_ori +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_unique_unsorted(test_case, placement, sbp): + input = random_tensor(ndim=1, dim0=64, high=20).to_global( + placement=placement, sbp=sbp + ) + oneflow_output = flow.unique( + input.oneflow, sorted=False, return_inverse=True, return_counts=True + ) + torch_output = torch_ori.unique( + input.pytorch, sorted=False, return_inverse=True, return_counts=True + ) + + oneflow_result, oneflow_indices, oneflow_counts = oneflow_output + torch_result, torch_indices, torch_counts = torch_output + + test_case.assertTrue( + np.allclose( + np.sort(oneflow_result.to_local().numpy()), + np.sort(torch_result.detach().cpu().numpy()), + ) + ) + test_case.assertTrue( + np.allclose( + oneflow_result[oneflow_indices].numpy(), + torch_result[torch_indices].detach().cpu().numpy(), + ) + ) + test_case.assertTrue( + np.allclose( + oneflow_counts.numpy()[np.argsort(oneflow_result.numpy())], + torch_counts.detach() + .cpu() + .numpy()[np.argsort(torch_result.detach().cpu().numpy())], + ) + ) + + +def _test_unique_sorted(test_case, placement, sbp): + input = random_tensor(ndim=1, dim0=64, high=20).to_global( + placement=placement, sbp=sbp + ) + oneflow_output = flow.unique( + input.oneflow, sorted=True, return_inverse=True, return_counts=True + ) + torch_output = torch_ori.unique( + input.pytorch, sorted=True, return_inverse=True, return_counts=True + ) + + oneflow_result, oneflow_indices, oneflow_counts = oneflow_output + torch_result, torch_indices, torch_counts = torch_output + + test_case.assertTrue( + np.allclose( + oneflow_result.to_local().numpy(), torch_result.detach().cpu().numpy(), + ) + ) + test_case.assertTrue( + np.allclose(oneflow_indices.numpy(), torch_indices.detach().cpu().numpy(),) + ) + test_case.assertTrue( + np.allclose(oneflow_counts.numpy(), torch_counts.detach().cpu().numpy(),) + ) + + +class TestUniqueModule(flow.unittest.TestCase): + @globaltest + def test_unique(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement): + _test_unique_unsorted(test_case, placement, sbp) + _test_unique_sorted(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_unsqueeze.py b/python/oneflow/test/modules/test_global_unsqueeze.py new file mode 100644 index 0000000000000000000000000000000000000000..0ab295f6a1001463bb0902aa66739851a1694422 --- /dev/null +++ b/python/oneflow/test/modules/test_global_unsqueeze.py @@ -0,0 +1,69 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=False) +def _test_flow_unsqueeze_with_random_data(test_case, placement, sbp): + x = random_tensor(2, 8, 16).to_global(placement, sbp) + y = torch.unsqueeze(x, random(0, 3).to(int)) + return y + + +@autotest(n=1, check_graph=False) +def _test_tensor_unsqueeze_with_random_data(test_case, placement, sbp): + x = random_tensor(2, 8, 16).to_global(placement, sbp) + y = x.unsqueeze(random(0, 3).to(int)) + return y + + +@autotest(n=1, check_graph=False) +def _test_unsqueeze_with_0_size_data(test_case, placement, sbp): + x = random_tensor(3, 8, 16, 0).to_global(placement, sbp) + y = torch.unsqueeze(x, random(0, 4).to(int)) + return y + + +class TestGlobalUnsqueeze(flow.unittest.TestCase): + @globaltest + def test_flow_unsqueeze_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_unsqueeze_with_random_data(test_case, placement, sbp) + + @globaltest + def test_tensor_unsqueeze_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_tensor_unsqueeze_with_random_data(test_case, placement, sbp) + + @globaltest + def test_unsqueeze_with_0_size_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_unsqueeze_with_0_size_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_upsample.py b/python/oneflow/test/modules/test_global_upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..7fce5153df4024fb05c533a215172382b0ffb364 --- /dev/null +++ b/python/oneflow/test/modules/test_global_upsample.py @@ -0,0 +1,113 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_upsample2d_nearest(test_case, placement, sbp): + x = random_tensor(ndim=3, dim0=8, dim1=16).to_global(placement, sbp) + print(x) + m = torch.nn.Upsample(scale_factor=random().to(int), mode="nearest",) + y = m(x) + return y + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_upsample2d_linear(test_case, placement, sbp): + x = random_tensor(ndim=3, dim0=8, dim1=16).to_global(placement, sbp) + m = torch.nn.Upsample( + scale_factor=random().to(int), mode="linear", align_corners=random_bool(), + ) + y = m(x) + return y + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_upsample2d_bilinear(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=16).to_global(placement, sbp) + m = torch.nn.Upsample( + scale_factor=random().to(int), mode="bilinear", align_corners=random_bool(), + ) + y = m(x) + return y + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_upsample2d_bicubic(test_case, placement, sbp): + x = random_tensor(ndim=4, dim0=8, dim1=16).to_global(placement, sbp) + m = torch.nn.Upsample( + scale_factor=random().to(int), mode="bicubic", align_corners=random_bool(), + ) + y = m(x) + return y + + +@autotest(n=1, auto_backward=True, check_graph=True) +def _test_global_upsample2d_trilinear(test_case, placement, sbp): + x = random_tensor(ndim=5, dim0=8, dim1=16).to_global(placement, sbp) + m = torch.nn.Upsample( + scale_factor=random().to(int), mode="trilinear", align_corners=random_bool(), + ) + y = m(x) + return y + + +class TestGlobalUpsample2d(flow.unittest.TestCase): + @unittest.skip( + "The nearest interpolate operation in pytorch has bug, https://github.com/pytorch/pytorch/issues/65200" + ) + @globaltest + def test_global_upsample2d_nearest(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_upsample2d_nearest(test_case, placement, sbp) + + @globaltest + def test_global_upsample2d_linear(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_upsample2d_linear(test_case, placement, sbp) + + @globaltest + def test_global_upsample2d_bilinear(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_upsample2d_bilinear(test_case, placement, sbp) + + @globaltest + def test_global_upsample2d_bicubic(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_upsample2d_bicubic(test_case, placement, sbp) + + @globaltest + def test_global_upsample2d_trilinear(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_global_upsample2d_trilinear(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_var.py b/python/oneflow/test/modules/test_global_var.py new file mode 100644 index 0000000000000000000000000000000000000000..1cbb459c0882550dca59ec3b6d56b15f664d0349 --- /dev/null +++ b/python/oneflow/test/modules/test_global_var.py @@ -0,0 +1,85 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util.generators import random +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=True) +def _test_flow_global_var_all_dim_with_random_data(test_case, placement, sbp): + x = random_tensor( + ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8, + ).to_global(placement, sbp) + y = torch.var(x) + return y + + +@autotest(n=1, check_graph=True) +def _test_flow_global_var_one_dim_with_random_data(test_case, placement, sbp): + x = random_tensor( + ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8, + ).to_global(placement, sbp) + y = torch.var( + x, + dim=random(low=0, high=2).to(int), + unbiased=random().to(bool), + keepdim=random().to(bool), + ) + return y + + +@autotest(n=1, auto_backward=True, check_graph=False) +def _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp): + x = random_tensor(3, 8, 0, 8).to_global(placement, sbp) + y = torch.var( + x, + dim=random(low=0, high=3).to(int), + unbiased=random().to(bool), + keepdim=random().to(bool), + ) + return y + + +class TestVar(flow.unittest.TestCase): + @globaltest + def test_flow_global_var_all_dim_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_global_var_all_dim_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_global_var_one_dim_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_global_var_one_dim_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_var_0_size_data_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2, valid_split_axis=[0]): + _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_vector_matrix_product.py b/python/oneflow/test/modules/test_global_vector_matrix_product.py new file mode 100644 index 0000000000000000000000000000000000000000..c5adf5c267cae99d8d3b45d6e487f1e236377259 --- /dev/null +++ b/python/oneflow/test/modules/test_global_vector_matrix_product.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@autotest(n=1, check_graph=False) +def _test_vector_matrix_product(test_case, placement, sbp): + dim = random(1, 6) + vec = random_tensor(1, dim0=dim).to_global(placement=placement, sbp=sbp) + mat = random_tensor(2, dim0=dim, dim1=constant(4)).to_global( + placement=placement, sbp=sbp + ) + return torch.matmul(vec, mat) + + +class TestGlobalVectorMatrixProduct(flow.unittest.TestCase): + @globaltest + def test_vector_matrix_product(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement): + _test_vector_matrix_product(test_case, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_consistent_view.py b/python/oneflow/test/modules/test_global_view.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_view.py rename to python/oneflow/test/modules/test_global_view.py diff --git a/python/oneflow/test/modules/test_consistent_weight_norm.py b/python/oneflow/test/modules/test_global_weight_norm.py similarity index 100% rename from python/oneflow/test/modules/test_consistent_weight_norm.py rename to python/oneflow/test/modules/test_global_weight_norm.py diff --git a/python/oneflow/test/modules/test_global_where.py b/python/oneflow/test/modules/test_global_where.py new file mode 100644 index 0000000000000000000000000000000000000000..9af1a8d5c19b613e946a439fcceb91daf0d32c82 --- /dev/null +++ b/python/oneflow/test/modules/test_global_where.py @@ -0,0 +1,374 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +from oneflow.test_utils.test_util import GenArgList +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_global_where(test_case, placement, sbp): + x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + y = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + condition = random_tensor(ndim=2, dim0=8, dim1=16, high=2, dtype=int).to_global( + placement, sbp + ) + + condition = condition.to(torch.bool) + + z = torch.where(condition, x, y) + return z + + +@autotest(n=1, check_graph=True) +def _test_global_where_broadcast(test_case, placement, sbp): + x = random_tensor(ndim=3, dim0=8, dim1=16, dim2=1).to_global(placement, sbp) + y = random_tensor(ndim=3, dim0=8, dim1=16, dim2=8).to_global(placement, sbp) + condition = random_tensor( + ndim=3, dim0=8, dim1=16, dim2=1, high=2, dtype=int + ).to_global(placement, sbp) + + condition = condition.to(torch.bool) + + z = torch.where(condition, x, y) + return z + + +@autotest(n=1, check_graph=True) +def _test_global_where_scalar(test_case, placement, sbp): + x = random_tensor(ndim=0).to_global(placement, sbp) + y = random_tensor(ndim=0).to_global(placement, sbp) + condition = random_tensor(ndim=0, high=2, dtype=int).to_global(placement, sbp) + + condition = condition.to(torch.bool) + + z = torch.where(condition, x, y) + return z + + +# Close auto_backward because pytorch raise error: +# PyTorch error: element 0 of tensors does not require grad and does not have a grad_fn +# Not check graph because of one reason: +# Reason 1, lazy tensor cannot call .numpy(), tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. +# Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. Because nonzero_op is called by where. +@autotest(n=1, auto_backward=False, check_graph="ValidatedFalse") +def _test_where_x_y_none(test_case, placement, sbp): + condition = random_tensor(ndim=2, dim0=8, dim1=8, low=-1, high=1).to_global( + placement, sbp + ) + y = torch.where(condition) + return y[0], y[1] + + +@autotest(n=1, check_graph=True) +def _test_global_where_tensor_with_0dim_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random_tensor(ndim=0).to_global(placement, sbp) + y = random_tensor(ndim=0).to_global(placement, sbp) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, check_graph=True) +def _test_flow_where_tensor_broadcast_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=3, dim0=8, dim1=16, dim2=8).to_global(placement, sbp) + x = random_tensor(ndim=3, dim0=8, dim1=1, dim2=8).to_global(placement, sbp) + y = random_tensor(ndim=3, dim0=8, dim1=16, dim2=1).to_global(placement, sbp) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, check_graph=True) +def _test_flow_where_scalar_x_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random().to(float) + y = ( + random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) + .to_global(placement, sbp) + .to(torch.float64) + ) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, check_graph=True) +def _test_flow_where_scalar_x_broadcast_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) + x = random().to(float) + y = ( + random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) + .to_global(placement, sbp) + .to(torch.float64) + ) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_x_int_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random().to(int) + y = random_tensor(ndim=2, dim0=8, dim1=16, dtype=int).to_global(placement, sbp) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, check_graph=True) +def _test_flow_where_scalar_y_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = ( + random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) + .to_global(placement, sbp) + .to(torch.float64) + ) + y = random().to(float) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, check_graph=True) +def _test_flow_where_scalar_y_broadcast_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) + x = ( + random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) + .to_global(placement, sbp) + .to(torch.float64) + ) + y = random().to(float) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_y_int_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random_tensor(ndim=2, dim0=8, dim1=16, dtype=int).to_global(placement, sbp) + y = random().to(int) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_tensor_bool_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp).to(torch.bool) + y = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp).to(torch.bool) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_tensor_broadcast_bool_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp).to(torch.bool) + y = random_tensor(ndim=2, dim0=8, dim1=1).to_global(placement, sbp).to(torch.bool) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_x_bool_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random().to(bool) + y = ( + random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) + .to_global(placement, sbp) + .to(torch.bool) + ) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_x_broadcast_bool_with_random_data( + test_case, placement, sbp +): + cond = random_tensor(ndim=2, dim0=1, dim1=16).to_global(placement, sbp) + x = random().to(bool) + y = ( + random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) + .to_global(placement, sbp) + .to(torch.bool) + ) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_y_bool_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = ( + random_tensor(ndim=2, dim0=8, dim1=16, dtype=float) + .to_global(placement, sbp) + .to(torch.bool) + ) + y = random().to(bool) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_y_broadcast_bool_with_random_data( + test_case, placement, sbp +): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = ( + random_tensor(ndim=2, dim0=8, dim1=1, dtype=float) + .to_global(placement, sbp) + .to(torch.bool) + ) + y = random().to(bool) + return torch.where(cond > 0, x, y) + + +@autotest(n=1, auto_backward=False, check_graph=True) +def _test_flow_where_scalar_xy_bool_with_random_data(test_case, placement, sbp): + cond = random_tensor(ndim=2, dim0=8, dim1=16).to_global(placement, sbp) + x = random().to(bool) + y = random().to(bool) + return torch.where(cond > 0, x, y) + + +class TestGlobalWhere(flow.unittest.TestCase): + @globaltest + def test_global_where(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_where(test_case, placement, sbp) + + @globaltest + def test_global_where_broadcast(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_global_where_broadcast(test_case, placement, sbp) + + @globaltest + def test_global_where_scalar(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_global_where_scalar(test_case, placement, sbp) + + @globaltest + def test_where_x_y_none(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_where_x_y_none(test_case, placement, sbp) + + @globaltest + def test_global_where_tensor_with_0dim_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_global_where_tensor_with_0dim_data(test_case, placement, sbp) + + @globaltest + def test_flow_where_tensor_broadcast_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=1): + _test_flow_where_tensor_broadcast_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_x_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_x_with_random_data(test_case, placement, sbp) + + @globaltest + def test_flow_where_scalar_x_broadcast_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_x_broadcast_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_x_int_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_x_int_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_y_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_y_with_random_data(test_case, placement, sbp) + + @globaltest + def test_flow_where_scalar_y_broadcast_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_y_broadcast_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_y_int_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_y_int_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_tensor_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + _test_flow_where_tensor_bool_with_random_data(test_case, placement, sbp) + + @globaltest + def test_flow_where_tensor_broadcast_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_tensor_broadcast_bool_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_x_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_x_bool_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_x_broadcast_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_x_broadcast_bool_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_y_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_y_bool_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_y_broadcast_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_y_broadcast_bool_with_random_data( + test_case, placement, sbp + ) + + @globaltest + def test_flow_where_scalar_xy_bool_with_random_data(test_case): + for placement in all_placement(): + for sbp in all_sbp(placement, except_split=True): + _test_flow_where_scalar_xy_bool_with_random_data( + test_case, placement, sbp + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_zeropad2d.py b/python/oneflow/test/modules/test_global_zeropad2d.py new file mode 100644 index 0000000000000000000000000000000000000000..2e3c2bdef9d7b4e0e34ed15231eec941135a48ff --- /dev/null +++ b/python/oneflow/test/modules/test_global_zeropad2d.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +from oneflow.test_utils.test_util import GenArgList +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@autotest(n=1, check_graph=True) +def _test_global_ZeroPad2d(test_case, placement, sbp, padding): + x = random_tensor(ndim=4, dim0=8, dim1=16, dim2=8, dim3=8,).to_global( + placement, sbp + ) + m = torch.nn.ZeroPad2d(padding) + y = m(x) + return y + + +class TestGlobalZeroPad2dModule(flow.unittest.TestCase): + @globaltest + def test_global_ZeroPad2d(test_case): + arg_dict = OrderedDict() + arg_dict["padding"] = [2, (1, 1, 2, 2)] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=4): + _test_global_ZeroPad2d(test_case, placement, sbp, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_global_zeros_like.py b/python/oneflow/test/modules/test_global_zeros_like.py new file mode 100644 index 0000000000000000000000000000000000000000..a3d089f9b001bb42fd987126f196b32e419782ad --- /dev/null +++ b/python/oneflow/test/modules/test_global_zeros_like.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict +import numpy as np +from oneflow.test_utils.test_util import GenArgList +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def _test_zeros_like_float(test_case, placement, sbp, shape, device): + x = flow.tensor( + np.random.randn(*shape), dtype=flow.float32, device=flow.device(device) + ) + x = x.to_global(placement=placement, sbp=sbp) + y = flow.zeros_like(x, placement=placement, sbp=sbp) + test_case.assertTrue(y.dtype is flow.float32) + test_case.assertTrue(y.shape == x.shape) + test_case.assertTrue(y.placement == placement) + y_numpy = np.zeros(x.numpy().shape) + test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) + + +def _test_zeros_like_int(test_case, placement, sbp, shape, device): + x = flow.tensor(np.random.randn(*shape), dtype=flow.int, device=flow.device(device)) + x = x.to_global(placement=placement, sbp=sbp) + y = flow.zeros_like(x, dtype=flow.int, placement=placement, sbp=sbp) + test_case.assertTrue(y.dtype is flow.int) + test_case.assertTrue(y.shape == x.shape) + test_case.assertTrue(y.placement == placement) + y_numpy = np.zeros(x.numpy().shape) + test_case.assertTrue(np.array_equal(y.numpy(), y_numpy)) + + +class TestModule(flow.unittest.TestCase): + @globaltest + def test_zeros_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_zeros_like_float, _test_zeros_like_int] + arg_dict["shape"] = [(8, 8), (8, 8, 4), (8, 8, 5, 6)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=2): + arg[0](test_case, placement, sbp, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_glu.py b/python/oneflow/test/modules/test_glu.py index 2c00ef16960aeda94f351bf75e1edacecc5ced32..0ffbf37af6278f607ea5642238396afad1f02825 100644 --- a/python/oneflow/test/modules/test_glu.py +++ b/python/oneflow/test/modules/test_glu.py @@ -43,6 +43,11 @@ class TestGluModule(flow.unittest.TestCase): y = m(x) return y + @profile(torch.nn.functional.glu) + def profile_glu(test_case): + input = torch.ones(1000, 1000) + torch.nn.functional.glu(input) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_greater.py b/python/oneflow/test/modules/test_greater.py index ff21d27fd943b4a832ed5521a0684651124e30a6..aae206ceb853e61d72d5b7ff2b0a799b3190bbde 100644 --- a/python/oneflow/test/modules/test_greater.py +++ b/python/oneflow/test/modules/test_greater.py @@ -150,6 +150,13 @@ class TestGreater(flow.unittest.TestCase): y2 = x1 > x2 return (y1, y2) + @profile(torch.gt) + def profile_gt(test_case): + input = torch.ones(1000, 1000) + other = torch.ones(1000, 1000) + torch.gt(input, other) + torch.gt(input, 0) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_grid_sample.py b/python/oneflow/test/modules/test_grid_sample.py index 27ab3d3c8babc5a3e9cba66b475c8b5f36eb44a7..3dd6f82d6ea1df1ca97c537a8d970728d0a6588b 100644 --- a/python/oneflow/test/modules/test_grid_sample.py +++ b/python/oneflow/test/modules/test_grid_sample.py @@ -142,6 +142,17 @@ class TestGridSample(flow.unittest.TestCase): ) return output + @profile(torch.nn.functional.grid_sample) + def profile_grid_sample(test_case): + input = torch.ones(32, 3, 128, 128) + grid = torch.ones(32, 64, 64, 2) + torch.nn.functional.grid_sample(input, grid) + torch.nn.functional.grid_sample(input, grid, align_corners=True) + torch.nn.functional.grid_sample(input, grid, mode="nearest", align_corners=True) + torch.nn.functional.grid_sample(input, grid, mode="bicubic", align_corners=True) + torch.nn.functional.grid_sample(input, grid, padding_mode="border") + torch.nn.functional.grid_sample(input, grid, padding_mode="reflection") + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_grouped_matmul_bias.py b/python/oneflow/test/modules/test_grouped_matmul_bias.py new file mode 100644 index 0000000000000000000000000000000000000000..a528fee9df95429f51dd33ff27bf8ad1bbb481db --- /dev/null +++ b/python/oneflow/test/modules/test_grouped_matmul_bias.py @@ -0,0 +1,77 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import numpy as np +from oneflow.test_utils.test_util import GenArgList +import math +import os + +import oneflow as flow + + +def _ref(xs, weights, biases): + if biases is None: + return [ + flow._C.matmul(x, w, transpose_a=False, transpose_b=True) + for x, w in zip(xs, weights) + ] + else: + return [ + flow._C.matmul(x, w, transpose_a=False, transpose_b=True) + b + for x, w, b in zip(xs, weights, biases) + ] + + +def _grouped(xs, weights, biases): + if biases is None: + return flow._C.grouped_matmul(xs, weights) + else: + return flow._C.grouped_matmul_bias(xs, weights, biases) + + +def _test_grouped_matmul_bias(test_case, dtype, problems, bias): + + xs = [flow.randn((m, k), device="cuda", dtype=dtype) for (m, n, k) in problems] + ws = [flow.randn((n, k), device="cuda", dtype=dtype) for (m, n, k) in problems] + bs = [flow.randn((n), device="cuda", dtype=dtype) for (m, n, k) in problems] + + ref_out = _ref(xs, ws, bs if bias else None) + grouped_out = _grouped(xs, ws, bs if bias else None) + for (ref_y, grouped_y) in zip(ref_out, grouped_out): + test_case.assertTrue( + np.allclose(ref_y.numpy(), grouped_y.numpy(), atol=1e-2, rtol=1e-2) + ) + + +@flow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestGroupedMatmulBias(flow.unittest.TestCase): + def test_grouped_matmul_bias(test_case): + problems = [(2, 1280, 1280)] * 12 + [(2, 1280, 640)] * 4 + [(2, 1280, 320)] * 5 + _test_grouped_matmul_bias(test_case, flow.float16, problems, True) + _test_grouped_matmul_bias(test_case, flow.float16, problems, False) + problems = ( + [(2 * 77, 768, 1280)] * 6 + + [(2 * 77, 768, 640)] * 5 + + [(2 * 77, 768, 320)] * 5 + ) + _test_grouped_matmul_bias(test_case, flow.float16, problems, True) + _test_grouped_matmul_bias(test_case, flow.float16, problems, False) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_groupnorm.py b/python/oneflow/test/modules/test_groupnorm.py index ddbe5d6525a94ab33cb0285acd9ecab275857735..990f372cfc9d3b6c71a0060f92da1a804c5bdd1b 100644 --- a/python/oneflow/test/modules/test_groupnorm.py +++ b/python/oneflow/test/modules/test_groupnorm.py @@ -255,6 +255,52 @@ def _test_groupnorm_backward(test_case, device): ) +def _test_groupnorm_backward_fp16(test_case, device): + input_arr = np.array( + [ + [ + [ + [-0.8791, 0.2553, 0.7403, -0.2859], + [0.8006, -1.7701, -0.9617, 0.1705], + [0.2842, 1.7825, 0.3365, -0.8525], + ], + [ + [0.7332, -0.0737, 0.7245, -0.6551], + [1.4461, -0.1827, 0.9737, -2.1571], + [0.4657, 0.7244, 0.3378, 0.1775], + ], + ], + [ + [ + [1.8896, 1.8686, 0.1896, 0.9817], + [-0.0671, 1.5569, 1.1449, 0.0086], + [-0.9468, -0.0124, 1.3227, -0.6567], + ], + [ + [-0.8472, 1.3012, -1.1065, 0.9348], + [1.0346, 1.5703, 0.2419, -0.7048], + [0.6957, -0.4523, -0.8819, 1.0164], + ], + ], + ], + dtype=np.float16, + ) + x = flow.tensor( + input_arr, dtype=flow.float16, device=flow.device(device), requires_grad=True + ) + m = ( + flow.nn.GroupNorm(num_groups=1, num_channels=2) + .to(device=flow.device(device)) + .to(flow.float16) + ) + y = m(x) + z = y.sum() + z.backward() + test_case.assertTrue( + np.allclose(x.grad.numpy(), np.zeros(shape=input_arr.shape), 1e-03, 1e-03) + ) + + def _test_groupnorm_backward_3d(test_case, device): input_arr = np.array( [ @@ -327,6 +373,101 @@ def _test_groupnorm_backward_3d(test_case, device): ) +def _test_groupnorm_backward_3d_fp16(test_case, device): + input_arr = np.array( + [ + [ + [ + [ + [1.04569761, 0.22863248, 1.42439335, 1.62249689], + [-0.80578825, -0.27276461, 1.04556507, 0.56864134], + [-1.24085419, -1.23960097, 0.33451416, -1.84820402], + ], + [ + [-1.511261, 1.06157517, -0.26715858, -1.32888141], + [1.17976881, -0.07931171, 0.33910684, -1.93458573], + [-1.72659647, 0.79049652, 0.39102785, -1.16264882], + ], + ], + [ + [ + [0.30067973, -1.2912226, -0.61508225, 0.56454001], + [0.87074187, -1.69257376, 0.36119148, -0.31014289], + [0.20776964, 1.26195488, -1.37122193, -0.17945234], + ], + [ + [-0.31112407, -0.80682631, 0.8233194, 0.6384975], + [0.57617527, 0.45505028, 1.68286151, -1.09590744], + [-1.18127546, -1.07529277, 0.52779943, 1.21755926], + ], + ], + ], + [ + [ + [ + [-0.12832351, 1.05625455, -0.23253249, -0.64747611], + [-0.00738123, -1.41390089, -1.92664144, -0.21427625], + [-0.94631219, -0.86493989, 0.21026905, 0.24989732], + ], + [ + [1.3859182, 1.72002107, 0.50091892, 1.04198896], + [0.71694594, 1.66417023, -1.63030052, 0.77182641], + [0.71545083, 1.96458366, -1.99031931, 1.3196714], + ], + ], + [ + [ + [1.80091702, 0.02834973, 0.82259214, -1.05597501], + [-0.58212207, 0.44205949, -0.14740003, -0.994508], + [1.14678114, -0.39196097, 1.2554798, -0.41829324], + ], + [ + [-1.0153903, -0.25755713, -1.81756333, -1.06781159], + [1.79680841, -1.9107133, -0.64325796, -1.94640775], + [1.30671156, 1.20445339, -1.26262901, -0.79494188], + ], + ], + ], + ], + dtype=np.float16, + ) + x = flow.tensor( + input_arr, dtype=flow.float16, device=flow.device(device), requires_grad=True + ) + m = ( + flow.nn.GroupNorm(num_groups=2, num_channels=2, affine=False) + .to(device=flow.device(device)) + .to(flow.float16) + ) + y = m(x) + z = y.sum() + z.backward() + test_case.assertTrue( + np.allclose(x.grad.numpy(), np.zeros(shape=input_arr.shape), 1e-03, 1e-03) + ) + + +def _test_groupnorm_nhwc(test_case, shape, num_groups): + (n, c, h, w) = shape + x = flow.tensor( + np.random.uniform(low=0.0, high=1.0, size=shape).astype(np.float32) + ).to("cuda") + gamma = flow.tensor( + np.random.uniform(low=0.0, high=1.0, size=(c)).astype(np.float32) + ).to("cuda") + beta = flow.tensor( + np.random.uniform(low=0.0, high=1.0, size=(c)).astype(np.float32) + ).to("cuda") + y = flow._C.group_norm(x, gamma, beta, True, num_groups, 1e-5) + x_nhwc = x.permute(0, 2, 3, 1).contiguous() + y_nhwc = flow._C.group_norm( + x_nhwc, gamma, beta, True, num_groups, 1e-5, "channels_last" + ) + test_case.assertTrue( + np.allclose(y_nhwc.permute(0, 3, 1, 2).numpy(), y, 1e-03, 1e-03) + ) + + @flow.unittest.skip_unless_1n1d() class TestGroupNorm(flow.unittest.TestCase): def test_groupnorm(test_case): @@ -341,6 +482,19 @@ class TestGroupNorm(flow.unittest.TestCase): for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) + def test_groupnorm_grad_fp16(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [ + _test_groupnorm_backward_fp16, + _test_groupnorm_backward_3d_fp16, + ] + # cpu test will raise error: var only support floating point dtypes + # https://github.com/Oneflow-Inc/oneflow/issues/9559 + # arg_dict["device"] = ["cpu", "cuda"] + arg_dict["device"] = ["cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + @autotest(rtol=1e-03, atol=1e-03, check_graph=True) def test_group_norm_with_random_data(test_case): channels = random(5, 20) @@ -357,6 +511,10 @@ class TestGroupNorm(flow.unittest.TestCase): y = m(x) return y + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_groupnorm_nhwc(test_case): + _test_groupnorm_nhwc(test_case, (16, 64, 128, 128), 32) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_gumbel_softmax.py b/python/oneflow/test/modules/test_gumbel_softmax.py new file mode 100644 index 0000000000000000000000000000000000000000..5f789e9b2281070a7264232ea13933eb80e024ff --- /dev/null +++ b/python/oneflow/test/modules/test_gumbel_softmax.py @@ -0,0 +1,116 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.nn.functional as F +import oneflow.unittest + + +def _test_gumbel_softmax(test_case, tau, dim, device, dtype): + dtype = type_name_to_flow_type[dtype] + x = flow.tensor(np.random.randn(20, 32), dtype=dtype, device=flow.device(device),) + y_soft = F.gumbel_softmax(x, tau=tau, dim=dim) + y_hard = F.gumbel_softmax(x, tau=tau, dim=dim, hard=True) + test_case.assertEqual(x.shape, y_soft.shape) + test_case.assertEqual(x.shape, y_hard.shape) + test_case.assertEqual(x.dtype, y_soft.dtype) + test_case.assertEqual(x.dtype, y_hard.dtype) + + +def _test_gumbel_softmax_hard(test_case, tau, dim, device, dtype): + dtype = type_name_to_flow_type[dtype] + x = flow.tensor(np.random.randn(45, 23), dtype=dtype, device=flow.device(device),) + y_hard = F.gumbel_softmax(x, tau=tau, dim=dim, hard=True) + test_case.assertEqual(y_hard.min(), 0) + if dim == -1: + test_case.assertEqual(y_hard.sum().item(), 45) + elif dim == 0: + test_case.assertEqual(y_hard.sum().item(), 23) + + +def _test_gumbel_softmax_backward(test_case, tau, dim, device, dtype): + dtype = type_name_to_flow_type[dtype] + x_np = np.random.rand(10, 10) + x_soft = flow.tensor( + x_np, dtype=dtype, device=flow.device(device), requires_grad=True, + ) + x_hard = flow.tensor( + x_np, dtype=dtype, device=flow.device(device), requires_grad=True, + ) + y_soft = F.gumbel_softmax(x_soft, tau, dim=dim) + y_hard = F.gumbel_softmax(x_hard, tau, dim=dim, hard=False) + + y_soft.mean().backward() + y_hard.mean().backward() + + np.testing.assert_allclose( + x_hard.grad.numpy(), x_soft.grad.numpy(), rtol=1e-5, atol=1e-5, verbose=True + ) + + +def _test_gumbel_softmax_half(test_case, tau, dim, device): + x = flow.tensor(np.random.randn(20, 32), device=flow.device(device),).to( + flow.float16 + ) + y_soft = F.gumbel_softmax(x, tau=tau, dim=dim) + y_hard = F.gumbel_softmax(x, tau=tau, dim=dim, hard=True) + test_case.assertEqual(x.shape, y_soft.shape) + test_case.assertEqual(x.shape, y_hard.shape) + test_case.assertEqual(x.dtype, y_soft.dtype) + test_case.assertEqual(x.dtype, y_hard.dtype) + + +@flow.unittest.skip_unless_1n1d() +class TestGumbelSoftmaxModule(flow.unittest.TestCase): + @autotest() + def test_gumbel_softmax(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_gumbel_softmax, + _test_gumbel_softmax_hard, + _test_gumbel_softmax_backward, + ] + arg_dict["tau"] = [1, 2, 0.5] + arg_dict["dim"] = [0, -1] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["dtype"] = ["float32", "double"] + + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + @autotest() + def test_leakyrelu_module_with_half_random_data(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_gumbel_softmax_half, + ] + arg_dict["tau"] = [1, 2, 0.5] + arg_dict["dim"] = [0, -1] + arg_dict["device"] = ["cuda"] + + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_hann_window.py b/python/oneflow/test/modules/test_hann_window.py new file mode 100644 index 0000000000000000000000000000000000000000..b9f2f1417ca638326c53ad721f2e501942ca0037 --- /dev/null +++ b/python/oneflow/test/modules/test_hann_window.py @@ -0,0 +1,57 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestHannWindow(flow.unittest.TestCase): + @autotest(n=5, auto_backward=False, check_graph=False) + def test_hann_window(test_case): + device = random_device() + window_length = random(1, 8).to(int).value() + periodic = random_bool().value() + output = torch.hann_window(window_length, periodic, device=device) + return output + + def test_hann_window_global(test_case): + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + window_length = random(1, 8).to(int).value() + periodic = random_bool().value() + output = flow.hann_window(window_length, periodic, placement=placement, sbp=sbp) + test_case.assertEqual(output.sbp, sbp) + test_case.assertEqual(output.placement, placement) + + def test_hann_window_dtype(test_case): + device = random_device().value() + window_length = random(1, 8).to(int).value() + periodic = random_bool().value() + dtype = flow.float64 + output = flow.hann_window(window_length, periodic, device=device, dtype=dtype) + test_case.assertEqual(output.dtype, dtype) + + @profile(torch.hann_window) + def profile_hann_window(test_case): + torch.hann_window(128000, periodic=True) + torch.hann_window(128001, periodic=False) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_activation.py b/python/oneflow/test/modules/test_higher_derivative_activation.py new file mode 100644 index 0000000000000000000000000000000000000000..b50332beb4ac303444456abdc306cdd9e42ddfc8 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_activation.py @@ -0,0 +1,200 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin +from collections import defaultdict + + +def _assert_true(test_case, value1, value2): + test_case.assertTrue( + np.allclose( + value1.detach().cpu().numpy(), + value2.detach().numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_activation_grad_grad_impl(test_case, op_name, *args, **kwargs): + x = random_tensor(ndim=2, low=-5) + y = eval(f"torch.nn.functional.{op_name}")(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_y = random_tensor(len(x_shape), *x_shape) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _assert_true(test_case, dx.pytorch, dx.oneflow) + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +def _test_prelu_activation_grad_grad_impl(test_case, op_name, *args, **kwargs): + x = random_tensor(ndim=2, low=-5) + a = random_tensor(ndim=1, dim0=x.oneflow.shape[1]) + y = torch.nn.functional.prelu(x, a) + + x_shape = x.oneflow.shape + a_shape = a.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_y = random_tensor(len(x_shape), *x_shape) + init_grad_a = random_tensor(len(a_shape), *a_shape) + + dx_and_da = torch.autograd.grad(y, [x, a], init_grad_y, True, True) + dx, da = dx_and_da[0], dx_and_da[1] + _assert_true(test_case, dx.pytorch, dx.oneflow) + _assert_true(test_case, da.pytorch, da.oneflow) + + ddx_dda_ddy = torch.autograd.grad( + dx_and_da, [dx, da, init_grad_y], [init_grad_x, init_grad_a] + ) + ddx, dda, ddy = ddx_dda_ddy[0], ddx_dda_ddy[1], ddx_dda_ddy[2] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, dda.pytorch, dda.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +def _test_hardswish_activation_grad_grad_impl(test_case, op_name, *args, **kwargs): + x = random_tensor(ndim=2, low=-1, dim1=4) + y = torch.nn.functional.hardswish(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_y = random_tensor(len(x_shape), *x_shape) + + dx_pytorch = pytorch_origin.autograd.grad( + y.pytorch, x.pytorch, init_grad_y.pytorch + )[0] + dx_oneflow = oneflow_origin.autograd.grad( + y.oneflow, x.oneflow, init_grad_y.oneflow, True, True + )[0] + _assert_true(test_case, dx_pytorch, dx_oneflow) + + ddx, ddy = flow.autograd.grad( + dx_oneflow, [x.oneflow, init_grad_y.oneflow], init_grad_x.oneflow + ) + x, dx, init_grad_x, init_grad_y = ( + x.oneflow, + dx_oneflow, + init_grad_x.oneflow, + init_grad_y.oneflow, + ) + manual_ddx = flow.where( + ((x > -3.0) < 3.0), 1.0 / 3.0 * init_grad_x * init_grad_y, flow.tensor(0.0) + ) + manual_ddy = dx / init_grad_y * init_grad_x + _assert_true(test_case, manual_ddx, ddx) + _assert_true(test_case, manual_ddy, ddy) + + +def _test_hardsigmoid_activation_grad_grad_impl(test_case, op_name, *args, **kwargs): + x = random_tensor(ndim=2, low=-1, dim1=4) + y = torch.nn.functional.hardsigmoid(x, *args, **kwargs) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_y = random_tensor(len(x_shape), *x_shape) + + dx_pytorch = pytorch_origin.autograd.grad( + y.pytorch, x.pytorch, init_grad_y.pytorch + )[0] + dx_oneflow = oneflow_origin.autograd.grad( + y.oneflow, x.oneflow, init_grad_y.oneflow, True, True + )[0] + _assert_true(test_case, dx_pytorch, dx_oneflow) + + ddx, ddy = flow.autograd.grad( + dx_oneflow, [x.oneflow, init_grad_y.oneflow], init_grad_x.oneflow + ) + x, dx, init_grad_x, init_grad_y = ( + x.oneflow, + dx_oneflow, + init_grad_x.oneflow, + init_grad_y.oneflow, + ) + manual_ddx = flow.zeros_like(x) + manual_ddy = dx / init_grad_y * init_grad_x + _assert_true(test_case, manual_ddx, ddx) + _assert_true(test_case, manual_ddy, ddy) + + +class TestActivationHigherDerivative(flow.unittest.TestCase): + def test_activation_grad_grad(test_case): + op_args = defaultdict(list) + op_kwargs = defaultdict(dict) + + # parameter name not same in pytorch and oneflow + op_args["leaky_relu"] = [random(-1, 1).to(float)] + + # some op only support kwargs, like celu in oneflow + op_kwargs["hardtanh"] = { + "min_val": random(-5, -1).to(float), + "max_val": random(1, 5).to(float), + } + op_kwargs["elu"] = {"alpha": random(0, 1).to(float)} + op_kwargs["celu"] = {"alpha": random(0, 1).to(float)} + op_kwargs["threshold"] = { + "threshold": random().to(float), + "value": random().to(float), + } + op_kwargs["softplus"] = { + "beta": random().to(float), + "threshold": random().to(float), + } + + op_names = [ + "gelu", + "mish", + "silu", + "selu", + "softsign", + "hardsigmoid", + "hardswish", + "relu", + "elu", + "celu", + "prelu", + "hardshrink", + "softshrink", + "leaky_relu", + "hardtanh", + "softplus", + "threshold", + ] + for op_name in op_names: + try: + functor = eval(f"_test_{op_name}_activation_grad_grad_impl") + except: + functor = _test_activation_grad_grad_impl + + print(f"| {op_name:-^60} |") + for i in range(10): + functor(test_case, op_name, *op_args[op_name], **op_kwargs[op_name]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_conv.py b/python/oneflow/test/modules/test_higher_derivative_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..17196da65fb6681a248bd21204b633ff968ccd94 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_conv.py @@ -0,0 +1,136 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin + + +def _test_convnd_grad_grad_impl(test_case, ndim): + minibatch = np.random.randint(1, 5) + groups = np.random.randint(1, 5) + in_channels = np.random.randint(1, 5) * groups + out_channels = in_channels * np.random.randint(1, 5) + padding = np.random.randint(1, 3) + stride = np.random.randint(1, 3) + dilation = np.random.randint(1, 3) + + x_shape = [minibatch, in_channels] + [np.random.randint(8, 12) for i in range(ndim)] + w_shape = [out_channels, in_channels // groups] + [ + np.random.randint(2, 5) for i in range(ndim) + ] + + x = random_tensor(len(x_shape), *x_shape) + w = random_tensor(len(w_shape), *w_shape) + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_w = random_tensor(len(w_shape), *w_shape) + + y = eval(f"torch.nn.functional.conv{ndim}d")( + x, w, stride=stride, padding=padding, groups=groups, dilation=dilation + ) + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape) + + dx = torch.autograd.grad( + outputs=y, + inputs=x, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(dx.pytorch.detach().cpu().numpy(), dx.oneflow.detach().numpy()) + ) + + dw = torch.autograd.grad( + outputs=y, + inputs=w, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(dw.pytorch.detach().cpu().numpy(), dw.oneflow.detach().numpy()) + ) + + # torch.autograd.grad in autotest does not support inputs/outpus/grad_outputs as a list + # so use the original pytorch/oneflow module + ddx_pytorch, ddw_pytorch = pytorch_origin.autograd.grad( + outputs=[dx.pytorch, dw.pytorch], + inputs=[x.pytorch, w.pytorch], + grad_outputs=[init_grad_x.pytorch, init_grad_w.pytorch], + create_graph=True, + retain_graph=True, + ) + ddx_oneflow, ddw_oneflow = oneflow_origin.autograd.grad( + outputs=[dx.oneflow, dw.oneflow], + inputs=[x.oneflow, w.oneflow], + grad_outputs=[init_grad_x.oneflow, init_grad_w.oneflow], + create_graph=True, + retain_graph=True, + ) + + test_case.assertTrue( + np.allclose(ddw_pytorch.detach().cpu().numpy(), ddw_oneflow.detach().numpy()) + ) + test_case.assertTrue( + np.allclose(ddx_pytorch.detach().cpu().numpy(), ddx_oneflow.detach().numpy()) + ) + + dgrad_dx = torch.autograd.grad( + outputs=dx, + inputs=init_grad_y, + grad_outputs=init_grad_x, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_dx.pytorch.detach().cpu().numpy(), dgrad_dx.oneflow.detach().numpy() + ) + ) + + dgrad_dw = torch.autograd.grad( + outputs=dw, + inputs=init_grad_y, + grad_outputs=init_grad_w, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_dw.pytorch.detach().cpu().numpy(), dgrad_dw.oneflow.detach().numpy() + ) + ) + + +class TestConvHigherDerivative(flow.unittest.TestCase): + def test_conv1d_grad_grad(test_case): + _test_convnd_grad_grad_impl(test_case, 1) + + def test_conv2d_grad_grad(test_case): + _test_convnd_grad_grad_impl(test_case, 2) + + def test_conv3d_grad_grad(test_case): + _test_convnd_grad_grad_impl(test_case, 3) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_div.py b/python/oneflow/test/modules/test_higher_derivative_div.py new file mode 100644 index 0000000000000000000000000000000000000000..8eca261f5df7bd5ea8b34c0bdea361ea7748da08 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_div.py @@ -0,0 +1,94 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +from numpy.random import randint + + +def _test_div_grad_grad_impl(test_case): + y_shape = [randint(2, 5) for _ in range(randint(0, 6))] + x_shape = [randint(2, 5) for _ in range(randint(0, 6 - len(y_shape)))] + y_shape + if random_bool().value(): + x_shape, y_shape = y_shape, x_shape + + x = random_tensor(len(x_shape), *x_shape).requires_grad_(True) + y = random_tensor(len(y_shape), *y_shape).requires_grad_(True) + z = torch.div(x, y) + + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape) + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape) + + dx_and_dy = torch.autograd.grad(z, [x, y], init_grad_z, True, True) + test_case.assertTrue( + np.allclose( + dx_and_dy.pytorch[0].detach().cpu().numpy(), + dx_and_dy.oneflow[0].detach().numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) + test_case.assertTrue( + np.allclose( + dx_and_dy.pytorch[1].detach().cpu().numpy(), + dx_and_dy.oneflow[1].detach().numpy(), + rtol=1e-4, + atol=1e-4, + ) + ) + + ddx_and_ddy_and_ddz = torch.autograd.grad( + dx_and_dy, [x, y, init_grad_z], [init_grad_x, init_grad_y], True, True + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[0].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[0].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[1].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[1].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + test_case.assertTrue( + np.allclose( + ddx_and_ddy_and_ddz.pytorch[2].detach().cpu().numpy(), + ddx_and_ddy_and_ddz.oneflow[2].detach().numpy(), + rtol=1e-3, + atol=1e-3, + ) + ) + + +class TestDivHigherDerivative(flow.unittest.TestCase): + def test_div_grad_grad(test_case): + for i in range(10): + _test_div_grad_grad_impl(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_loss.py b/python/oneflow/test/modules/test_higher_derivative_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..5ef136726a181300881abf8e6a713a194e01401f --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_loss.py @@ -0,0 +1,237 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _assert_true(test_case, value1, value2, name=""): + is_equal = np.allclose( + value1.detach().cpu().numpy(), value2.detach().numpy(), rtol=1e-04, atol=1e-04, + ) + test_case.assertTrue(is_equal, f"{name} is not equal." if name else "") + + +def generate_grads_for_variables(variables): + if isinstance(variables, list): + variables_shape = [i.pytorch.shape for i in variables] + device = torch.device(str(variables[0].pytorch.device)) + elif hasattr(variables, "pytorch"): + variables_shape = [i.shape for i in variables.pytorch] + device = torch.device(str(variables.pytorch[0].device)) + else: + assert False + + grads = [ + random_tensor(len(shape), *shape, requires_grad=random_bool().value()).to( + device + ) + for shape in variables_shape + ] + return grads + + +def calculate_and_compare_loss(test_case, input, target, model, order=2): + output = model(input, target) + _assert_true(test_case, output.pytorch, output.oneflow) + + init_inputs = [input, target] + grad_inputs = [output] + grad_outputs = [] + for i in range(order): + inputs = [ + var for var in [*init_inputs, *grad_outputs] if var.pytorch.requires_grad + ] + outputs = grad_inputs + grad_outputs = generate_grads_for_variables(outputs) + if i == order - 1: + grad_inputs = torch.autograd.grad(outputs, inputs, grad_outputs) + else: + grad_inputs = torch.autograd.grad(outputs, inputs, grad_outputs, True, True) + for j in range(len(inputs)): + _assert_true( + test_case, + grad_inputs[j].pytorch, + grad_inputs[j].oneflow, + f"{i}-grad_inputs[{j}]", + ) + + +def generate_necessity_for_default_loss(): + ndim = random(2, 6).to(int).value() + device = random_device() + shape = [random().to(int) for _ in range(ndim)] + input_requires_grad = True + target_requires_grad = random_bool().value() + return ( + random_tensor(ndim, *shape, requires_grad=input_requires_grad).to(device), + random_tensor(ndim, *shape, requires_grad=target_requires_grad).to(device), + ) + + +def generate_necessity_for_nll_loss(): + ndim = random(2, 6).to(int).value() + device = random_device() + num_classes = random(low=2).to(int) + batch_size = random(low=2, high=5).to(int) + ignore_index = ( + random(0, num_classes).to(int) | nothing() + if num_classes.value() > 2 + else nothing() + ) + extra_dim = [random().to(int) for _ in range(ndim - 2)] + return ( + random_tensor(ndim, batch_size, num_classes, *extra_dim).to(device), + random_tensor( + ndim - 1, + batch_size, + *extra_dim, + low=0, + high=num_classes, + dtype=int, + requires_grad=False, + ).to(device), + random_tensor(1, num_classes, low=0, high=3, requires_grad=False).to(device), + ignore_index, + ) + + +def generate_necessity_for_bce_loss(): + ndim = random(2, 6).to(int).value() + device = random_device() + num_classes = 2 + batch_size = random(low=2, high=5).to(int) + extra_dim = [random().to(int) for _ in range(ndim - 2)] + input_requires_grad = True + target_requires_grad = False + return ( + random_tensor( + ndim, batch_size, num_classes, *extra_dim, requires_grad=input_requires_grad + ).to(device), + random_tensor( + ndim, + batch_size, + num_classes, + *extra_dim, + low=0, + high=num_classes, + requires_grad=target_requires_grad, + ).to(device), + random_tensor( + ndim, + batch_size, + num_classes, + *extra_dim, + low=0, + high=3, + requires_grad=False, + ).to(device), + random_tensor( + 1, + oneof(extra_dim[-1] if ndim > 2 else num_classes, 1).value(), + low=1, + high=3, + requires_grad=False, + ).to(device), + ) + + +def _test_smooth_l1_loss_grad_grad_impl(test_case): + x, y = generate_necessity_for_default_loss() + + m = torch.nn.SmoothL1Loss( + reduction=oneof("none", "sum", "mean", nothing()), beta=oneof(0.0, 0.5, 1) + ) + m.to(x.device) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_kl_div_loss_grad_grad_impl(test_case): + x, y = generate_necessity_for_default_loss() + + m = torch.nn.KLDivLoss( + reduction=oneof("none", "sum", "mean", nothing()), + log_target=oneof(True, False), + ) + m.to(x.device) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_bce_loss_grad_grad_impl(test_case, with_logits=False): + x, y, weight, pos_weight = generate_necessity_for_bce_loss() + + if with_logits: + weight = oneof(weight, nothing()) + has_pos_weight = random_bool().value() + pos_weight = pos_weight if has_pos_weight else nothing() + m = torch.nn.BCEWithLogitsLoss( + weight=weight, + pos_weight=pos_weight, + reduction=oneof("none", "sum", "mean"), + ) + if has_pos_weight: + y = y.detach().clone().requires_grad_(False) + else: + m = torch.nn.BCELoss( + weight=oneof(weight, nothing()), reduction=oneof("none", "sum", "mean"), + ) + m.to(x.device) + + calculate_and_compare_loss(test_case, x, y, m) + + +def _test_nll_loss_grad_grad_impl(test_case): + (x, y, weight, ignore_index) = generate_necessity_for_nll_loss() + m = torch.nn.NLLLoss( + weight=oneof(weight, nothing()), + reduction=oneof("none", "sum", "mean"), + ignore_index=ignore_index, + ) + m.to(x.device) + + calculate_and_compare_loss(test_case, x, y, m) + + +@flow.unittest.skip_unless_1n1d() +class TestLossHigherDerivative(flow.unittest.TestCase): + def test_smooth_l1_loss_grad_grad(test_case): + for i in range(5): + _test_smooth_l1_loss_grad_grad_impl(test_case) + + def test_kl_div_loss_grad_grad(test_case): + for i in range(5): + _test_kl_div_loss_grad_grad_impl(test_case) + + def test_nll_loss_grad_grad(test_case): + for i in range(5): + _test_nll_loss_grad_grad_impl(test_case) + + def test_bce_loss_grad_grad(test_case): + for i in range(5): + _test_bce_loss_grad_grad_impl(test_case) + + def test_bce_with_logits_loss_grad_grad(test_case): + for i in range(5): + _test_bce_loss_grad_grad_impl(test_case, with_logits=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_matmul.py b/python/oneflow/test/modules/test_higher_derivative_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..08e8da0a0459970a11326efd0f14b762713993b5 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_matmul.py @@ -0,0 +1,129 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + +import torch as pytorch_origin +import oneflow as oneflow_origin + + +class TestMatmulHigherDerivative(flow.unittest.TestCase): + def test_broadcast_matmul_grad_b_grad(test_case): + broadcast_dims = [ + np.random.randint(2, 10) for _ in range(np.random.randint(1, 3)) + ] + m = np.random.randint(2, 10) + n = np.random.randint(2, 10) + k = np.random.randint(2, 10) + + shape_a = broadcast_dims + [m, k] + shape_b = [k, n] + shape_y = broadcast_dims + [m, n] + + a = random_tensor(len(shape_a), *shape_a).requires_grad_(True) + b = random_tensor(len(shape_b), *shape_b).requires_grad_(True) + + y = torch.matmul(a, b) + + init_grad_a = random_tensor(len(shape_a), *shape_a).requires_grad_(True) + init_grad_b = random_tensor(len(shape_b), *shape_b).requires_grad_(True) + init_grad_y = random_tensor(len(shape_y), *shape_y).requires_grad_(True) + + da = torch.autograd.grad( + outputs=y, + inputs=a, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(da.pytorch.detach().cpu().numpy(), da.oneflow.detach().numpy()) + ) + + db = torch.autograd.grad( + outputs=y, + inputs=b, + grad_outputs=init_grad_y, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose(db.pytorch.detach().cpu().numpy(), db.oneflow.detach().numpy()) + ) + + # torch.autograd.grad in autotest does not support inputs/outpus/grad_outputs as a list + # so use the original pytorch/oneflow module + dda_pytorch, ddb_pytorch = pytorch_origin.autograd.grad( + outputs=[da.pytorch, db.pytorch], + inputs=[a.pytorch, b.pytorch], + grad_outputs=[init_grad_a.pytorch, init_grad_b.pytorch], + create_graph=True, + retain_graph=True, + ) + dda_oneflow, ddb_oneflow = oneflow_origin.autograd.grad( + outputs=[da.oneflow, db.oneflow], + inputs=[a.oneflow, b.oneflow], + grad_outputs=[init_grad_a.oneflow, init_grad_b.oneflow], + create_graph=True, + retain_graph=True, + ) + + test_case.assertTrue( + np.allclose( + ddb_pytorch.detach().cpu().numpy(), ddb_oneflow.detach().numpy() + ) + ) + test_case.assertTrue( + np.allclose( + dda_pytorch.detach().cpu().numpy(), dda_oneflow.detach().numpy() + ) + ) + + dgrad_da = torch.autograd.grad( + outputs=da, + inputs=init_grad_y, + grad_outputs=init_grad_a, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_da.pytorch.detach().cpu().numpy(), + dgrad_da.oneflow.detach().numpy(), + ) + ) + + dgrad_db = torch.autograd.grad( + outputs=db, + inputs=init_grad_y, + grad_outputs=init_grad_b, + create_graph=True, + retain_graph=True, + )[0] + test_case.assertTrue( + np.allclose( + dgrad_db.pytorch.detach().cpu().numpy(), + dgrad_db.oneflow.detach().numpy(), + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_neg.py b/python/oneflow/test/modules/test_higher_derivative_neg.py new file mode 100644 index 0000000000000000000000000000000000000000..8a354ad902a86cdef5a12de3476d03a53b36c658 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_neg.py @@ -0,0 +1,50 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +class TestNegHigherDerivative(flow.unittest.TestCase): + def test_neg_grad_grad(test_case): + x = random_tensor(ndim=2).requires_grad_(True) + y = torch.neg(x) + np_arr = np.random.rand(*x.oneflow.shape) + init_grad = torch.tensor(np_arr).requires_grad_() + + x_grad = torch.autograd.grad(y, x, init_grad, create_graph=True)[0] + test_case.assertTrue( + np.allclose( + x_grad.pytorch.detach().cpu().numpy(), x_grad.oneflow.detach().numpy() + ) + ) + + init_grad_grad = torch.tensor(np_arr).requires_grad_() + dgrad = torch.autograd.grad( + x_grad, init_grad, init_grad_grad, create_graph=False + )[0] + test_case.assertTrue( + np.allclose( + dgrad.pytorch.detach().cpu().numpy(), dgrad.oneflow.detach().numpy(), + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_pool.py b/python/oneflow/test/modules/test_higher_derivative_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..a96f0aa85a0dc9e45d773ea47becfb3233101994 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_pool.py @@ -0,0 +1,177 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, name="", rtol=1e-5, atol=1e-5): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal, f"{name} is not equal" if name else "") + + +def _test_avg_pool_grad_grad_impl(test_case, ndim): + device = random_device() + minibatch = random(1, 5).to(int).value() + channels = random(1, 5).to(int).value() + padding = random(0, 3).to(int).value() + ceil_mode = random_bool().value() + count_include_pad = random_bool().value() + divisor_override = random().to(int).value() + kernel_size = random(4, 6).to(int).value() + stride = random(1, 3).to(int).value() + x_shape = [minibatch, channels] + [ + random(8, 12).to(int).value() for i in range(ndim) + ] + + kwargs = { + "kernel_size": kernel_size, + "stride": oneof(stride, nothing()), + "padding": oneof(padding, nothing()), + "ceil_mode": ceil_mode, + "count_include_pad": count_include_pad, + } + if ndim != 1: + kwargs["divisor_override"] = divisor_override + + m = eval(f"torch.nn.AvgPool{ndim}d")(**kwargs) + m.to(device) + + x = random_tensor(len(x_shape), *x_shape).to(device) + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to(device) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to(device) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +def _test_max_pool_grad_grad_impl(test_case, ndim): + device = random_device() + minibatch = random(1, 5).to(int).value() + channels = random(1, 5).to(int).value() + padding = random(0, 3).to(int).value() + dilation = random(1, 3).to(int).value() + ceil_mode = random_bool().value() + return_indices = random_bool().value() + kernel_size = random(4, 6).to(int).value() + stride = random(1, 3).to(int).value() + x_shape = [minibatch, channels] + [ + random(10, 12).to(int).value() for i in range(ndim) + ] + + m = eval(f"torch.nn.MaxPool{ndim}d")( + kernel_size=kernel_size, + stride=oneof(stride, nothing()), + padding=oneof(padding, nothing()), + dilation=oneof(dilation, nothing()), + ceil_mode=ceil_mode, + return_indices=return_indices, + ) + m.to(device) + + x = random_tensor(len(x_shape), *x_shape).to(device) + if return_indices: + y_and_indices = m(x) + y, indices = y_and_indices[0], y_and_indices[1] + else: + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to(device) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to(device) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +def _test_adaptive_pool_grad_grad_impl(test_case, ndim, mode): + device = random_device() + x_shape = [random(5, 10).to(int).value() for i in range(2 + ndim)] + output_size = [random(2, 1 + x_shape[2 + i]).to(int).value() for i in range(ndim)] + + m = eval(f"torch.nn.Adaptive{mode.title()}Pool{ndim}d")(output_size) + m.to(device) + + x = random_tensor(len(x_shape), *x_shape).to(device) + y = m(x) + _check_equal(test_case, y.pytorch, y.oneflow, "y") + + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).to(device) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape).to(device) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, "dx") + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x, True, True) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + + _check_equal(test_case, ddx.pytorch, ddx.oneflow, "ddx") + _check_equal(test_case, ddy.pytorch, ddy.oneflow, "ddy") + + +class TestPoolHigherDerivative(flow.unittest.TestCase): + def test_max_pool_1d_grad_grad(test_case): + _test_max_pool_grad_grad_impl(test_case, 1) + + def test_max_pool_2d_grad_grad(test_case): + _test_max_pool_grad_grad_impl(test_case, 2) + + def test_max_pool_3d_grad_grad(test_case): + _test_max_pool_grad_grad_impl(test_case, 3) + + def test_avg_pool_1d_grad_grad(test_case): + _test_avg_pool_grad_grad_impl(test_case, ndim=1) + + def test_avg_pool_2d_grad_grad(test_case): + _test_avg_pool_grad_grad_impl(test_case, ndim=2) + + def test_avg_pool_3d_grad_grad(test_case): + _test_avg_pool_grad_grad_impl(test_case, ndim=3) + + def test_adaptive_avg_pool_1d_grad_grad(test_case): + _test_adaptive_pool_grad_grad_impl(test_case, ndim=1, mode="avg") + + def test_adaptive_avg_pool_2d_grad_grad(test_case): + _test_adaptive_pool_grad_grad_impl(test_case, ndim=2, mode="avg") + + def test_adaptive_avg_pool_3d_grad_grad(test_case): + _test_adaptive_pool_grad_grad_impl(test_case, ndim=3, mode="avg") + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_pow.py b/python/oneflow/test/modules/test_higher_derivative_pow.py new file mode 100644 index 0000000000000000000000000000000000000000..21fe3c08c4f7714a9870ea24da56a7145ca25494 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_pow.py @@ -0,0 +1,71 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, rtol=1e-3, atol=1e-3): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal) + + +def _test_pow_grad_grad_impl(test_case): + y_shape = [random().to(int).value() for _ in range(random().to(int).value())] + x_shape = y_shape[random(0, 5).to(int).value() :] + if random_bool().value(): + x_shape, y_shape = y_shape, x_shape + + x = random_tensor(len(x_shape), *x_shape) + y = random_tensor(len(y_shape), *y_shape) + + z = torch.pow(x, y) + _check_equal(test_case, z.pytorch, z.oneflow) + + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape) + init_grad_y = random_tensor(len(y.oneflow.shape), *y.oneflow.shape) + + dx_and_dy = torch.autograd.grad(z, [x, y], init_grad_z, True, True) + _check_equal(test_case, dx_and_dy.pytorch[0], dx_and_dy.oneflow[0]) + _check_equal(test_case, dx_and_dy.pytorch[1], dx_and_dy.oneflow[1]) + + ddx_ddy_ddz = torch.autograd.grad( + dx_and_dy, [x, y, init_grad_z], [init_grad_x, init_grad_y] + ) + _check_equal(test_case, ddx_ddy_ddz.pytorch[0], ddx_ddy_ddz.oneflow[0]) + _check_equal(test_case, ddx_ddy_ddz.pytorch[1], ddx_ddy_ddz.oneflow[1]) + _check_equal(test_case, ddx_ddy_ddz.pytorch[2], ddx_ddy_ddz.oneflow[2]) + + +@flow.unittest.skip_unless_1n1d() +class TestPowHigherDerivative(flow.unittest.TestCase): + def test_pow_grad_grad(test_case): + for i in range(10): + _test_pow_grad_grad_impl(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_scalar_pow.py b/python/oneflow/test/modules/test_higher_derivative_scalar_pow.py new file mode 100644 index 0000000000000000000000000000000000000000..6dc13c954e7fcfb164727e9c449a7abf43267222 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_scalar_pow.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _check_equal(test_case, lhs, rhs, rtol=1e-4, atol=1e-4, name=""): + is_equal = np.allclose( + lhs.detach().cpu().numpy(), + rhs.detach().cpu().numpy(), + rtol=rtol, + atol=atol, + equal_nan=True, + ) + test_case.assertTrue(is_equal, f"{name} is not equal") + + +def _test_scalar_pow_grad_grad_impl(test_case, reverse=False): + x_shape = [random().to(int).value() for _ in range(random().to(int).value())] + y = random().to(float if random_bool().value() else int).value() + + x = random_tensor(len(x_shape), *x_shape) + z = torch.pow(x, y) if not reverse else torch.pow(y, x) + + init_grad_z = random_tensor(len(z.oneflow.shape), *z.oneflow.shape) + init_grad_x = random_tensor(len(x.oneflow.shape), *x.oneflow.shape) + + dx = torch.autograd.grad(z, x, init_grad_z, True, True)[0] + _check_equal(test_case, dx.pytorch, dx.oneflow, name="dx") + + ddx_and_ddz = torch.autograd.grad(dx, [x, init_grad_z], init_grad_x, True, True) + _check_equal(test_case, ddx_and_ddz.pytorch[0], ddx_and_ddz.oneflow[0], name="ddx") + _check_equal(test_case, ddx_and_ddz.pytorch[1], ddx_and_ddz.oneflow[1], name="ddz") + + +class TestScalarPowHigherDerivative(flow.unittest.TestCase): + def test_scalar_pow_grad_grad(test_case): + for i in range(10): + _test_scalar_pow_grad_grad_impl(test_case) + + def test_scalar_reverse_pow_grad_grad(test_case): + for i in range(10): + _test_scalar_pow_grad_grad_impl(test_case, reverse=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_slice.py b/python/oneflow/test/modules/test_higher_derivative_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..c06842c02c5e1e15c04664a43250647fb7d380c8 --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_slice.py @@ -0,0 +1,74 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def random_index(dim): + start = np.random.choice(list(range(dim))) + stop = np.random.choice(list(range(1, dim + 1))) + if start >= stop: + start, stop = stop - 1, start + 1 + step = np.random.randint(1, dim) + return f"{start}:{stop}:{step}" + + +def random_slice(dim_vec): + slice_index = ", ".join(random_index(dim) for dim in dim_vec) + return slice_index + + +def _test_slice_grad_grad_impl(test_case): + ndim = np.random.randint(2, 5) + x_shape = [np.random.randint(3, 8) for _ in range(ndim)] + x = random_tensor(len(x_shape), *x_shape).requires_grad_(True) + + slice_index = random_slice(x_shape) + y = eval(f"x[{slice_index}]") + + init_grad = random_tensor(len(y.oneflow.shape), *y.oneflow.shape).requires_grad_() + x_grad = torch.autograd.grad(y, x, init_grad, create_graph=True)[0] + test_case.assertTrue( + np.allclose( + x_grad.pytorch.detach().cpu().numpy(), x_grad.oneflow.detach().numpy() + ) + ) + + init_grad_grad = random_tensor( + len(x_grad.oneflow.shape), *x_grad.oneflow.shape + ).requires_grad_() + dgrad = torch.autograd.grad(x_grad, init_grad, init_grad_grad, create_graph=False)[ + 0 + ] + test_case.assertTrue( + np.allclose( + dgrad.pytorch.detach().cpu().numpy(), dgrad.oneflow.detach().numpy(), + ) + ) + + +class TestSliceHigherDerivative(flow.unittest.TestCase): + def test_slice_grad_grad(test_case): + for i in range(10): + _test_slice_grad_grad_impl(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_higher_derivative_softmax.py b/python/oneflow/test/modules/test_higher_derivative_softmax.py new file mode 100644 index 0000000000000000000000000000000000000000..29fd114436360df14b4e2310558fb03a335488cd --- /dev/null +++ b/python/oneflow/test/modules/test_higher_derivative_softmax.py @@ -0,0 +1,68 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def _assert_true(test_case, value1, value2): + test_case.assertTrue( + np.allclose( + value1.detach().cpu().numpy(), + value2.detach().cpu().numpy(), + rtol=1e-05, + atol=1e-05, + ) + ) + + +def _test_softmax_grad_grad_impl(test_case, op_name): + ndim = random(low=2).to(int).value() + data = random_tensor(ndim=ndim) + + for dim in range(ndim): + x = data.detach().clone().requires_grad_() + m = eval(f"torch.nn.{op_name}")(dim) + y = m(x) + _assert_true(test_case, y.pytorch, y.oneflow) + + x_shape = x.oneflow.shape + init_grad_x = random_tensor(len(x_shape), *x_shape) + init_grad_y = random_tensor(len(x_shape), *x_shape) + + dx = torch.autograd.grad(y, x, init_grad_y, True, True)[0] + _assert_true(test_case, dx.pytorch, dx.oneflow) + + ddx_ddy = torch.autograd.grad(dx, [x, init_grad_y], init_grad_x) + ddx, ddy = ddx_ddy[0], ddx_ddy[1] + _assert_true(test_case, ddx.pytorch, ddx.oneflow) + _assert_true(test_case, ddy.pytorch, ddy.oneflow) + + +@flow.unittest.skip_unless_1n1d() +class TestSoftmaxHigherDerivative(flow.unittest.TestCase): + def test_softmax_grad_grad(test_case): + _test_softmax_grad_grad_impl(test_case, op_name="Softmax") + + def test_logsoftmax_grad_grad(test_case): + _test_softmax_grad_grad_impl(test_case, op_name="LogSoftmax") + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_hub.py b/python/oneflow/test/modules/test_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..0bba0b0277f0a5298f98a4ed34e1969b56f9c479 --- /dev/null +++ b/python/oneflow/test/modules/test_hub.py @@ -0,0 +1,55 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import numpy as np +import oneflow as flow +import oneflow.unittest + + +@unittest.skip(reason="network fluctuations can cause downloads to fail!") +class TestHub(flow.unittest.TestCase): + def test_hub_list_api(test_case): + entrypoints = flow.hub.list("OneFlow-Inc/vision", force_reload=False) + test_case.assertEqual("alexnet" in entrypoints, True) + test_case.assertEqual("densenet121" in entrypoints, True) + + def test_hub_help_api(test_case): + help_info = flow.hub.help("Oneflow-Inc/vision", "resnet18", force_reload=False) + print(help_info) + + def test_hub_load_api(test_case): + repo = "Oneflow-Inc/vision" + model = flow.hub.load(repo, "resnet18", pretrained=True) + x = flow.randn(1, 3, 224, 224) + y = model(x) + test_case.assertTrue(np.array_equal(y.size(), (1, 1000))) + + def test_hub_download_url_to_file__api(test_case): + flow.hub.download_url_to_file( + "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip", + "/tmp/temporary_file", + ) + + def test_hub_load_state_dict_from_url_api(test_case): + state_dict = flow.hub.load_state_dict_from_url( + "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip" + ) + test_case.assertEqual("layer3.1.bn2.bias" in state_dict.keys(), True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_index_add.py b/python/oneflow/test/modules/test_index_add.py new file mode 100644 index 0000000000000000000000000000000000000000..0cb23105ceb743b67098fbf2925dce0dea367314 --- /dev/null +++ b/python/oneflow/test/modules/test_index_add.py @@ -0,0 +1,82 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import numpy as np +import torch as torch_origin +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList +import unittest +from oneflow.test_utils.automated_test_util import * + + +def _test_index_add(test_case, device): + torch_origin_x = torch_origin.ones(5, 3).to(device) + torch_origin_t = torch_origin.tensor( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch_origin.float + ).to(device) + torch_origin_index = torch_origin.tensor([0, 4, 2]).to(device) + torch_origin_y = torch_origin.index_add( + torch_origin_x, 0, torch_origin_index, torch_origin_t + ) + torch_origin_y_alpha = torch_origin.index_add( + torch_origin_x, 0, torch_origin_index, torch_origin_t, alpha=-1 + ) + + flow_x = flow.ones(5, 3).to(device) + flow_t = flow.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=flow.float).to(device) + flow_index = flow.tensor([0, 4, 2]).to(device) + flow_y = flow.index_add(flow_x, 0, flow_index, flow_t) + flow_y_alpha = flow.index_add(flow_x, 0, flow_index, flow_t, alpha=-1) + test_case.assertTrue( + np.allclose(torch_origin_y.cpu().numpy(), flow_y.cpu().numpy(), 1e-05, 1e-05) + ) + test_case.assertTrue( + np.allclose( + torch_origin_y_alpha.cpu().numpy(), flow_y_alpha.cpu().numpy(), 1e-05, 1e-05 + ) + ) + + # check inplace + torch_origin_x.index_add_(0, torch_origin_index, torch_origin_t) + flow_x.index_add_(0, flow_index, flow_t) + test_case.assertTrue( + np.allclose(torch_origin_y.cpu().numpy(), flow_y.cpu().numpy(), 1e-05, 1e-05) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestIndexAdd(flow.unittest.TestCase): + def test_index_add(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_index_add] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + @profile(torch.index_add) + def profile_index_add(test_case): + torch.index_add( + torch.ones(50, 30), + 0, + torch.arange(30), + torch.arange(1, 901, dtype=torch.float32).reshape(30, 30), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_info.py b/python/oneflow/test/modules/test_info.py new file mode 100644 index 0000000000000000000000000000000000000000..df66a0ff01c37617e01d41005282526e2f14b0da --- /dev/null +++ b/python/oneflow/test/modules/test_info.py @@ -0,0 +1,66 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest +from collections import OrderedDict + + +def _test_finfo(test_case, dtype): + # test finfo without input params + if dtype is None: + finfo = torch.finfo() + else: + finfo = torch.finfo(dtype) + torch_finfo = finfo.pytorch + flow_finfo = finfo.oneflow + test_case.assertEqual(torch_finfo.max, flow_finfo.max) + test_case.assertEqual(torch_finfo.min, flow_finfo.min) + test_case.assertEqual(torch_finfo.bits, flow_finfo.bits) + test_case.assertEqual(torch_finfo.eps, flow_finfo.eps) + test_case.assertEqual(torch_finfo.tiny, flow_finfo.tiny) + test_case.assertEqual(torch_finfo.resolution, flow_finfo.resolution) + + +@flow.unittest.skip_unless_1n1d() +class TestIInfo(flow.unittest.TestCase): + @autotest(n=3, check_graph=False) + def test_iinfo_max(test_case): + for dtype in [torch.uint8, torch.int8, torch.int32, torch.int64]: + return torch.iinfo(dtype).max + + @autotest(n=3, check_graph=False) + def test_iinfo_min(test_case): + for dtype in [torch.uint8, torch.int8, torch.int32, torch.int64]: + return torch.iinfo(dtype).min + + @autotest(n=3, check_graph=False) + def test_iinfo_bits(test_case): + for dtype in [torch.uint8, torch.int8, torch.int32, torch.int64]: + return torch.iinfo(dtype).bits + + @autotest(n=3, check_graph=False) + def test_finfo_min(test_case): + # TODO(WangYi): support bf16 + dtype = random_dtype(["None", "float", "half"]) + _test_finfo(test_case, dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_initializer.py b/python/oneflow/test/modules/test_initializer.py new file mode 100644 index 0000000000000000000000000000000000000000..34e6a6b7f0a60b775f443924f8fd5f8ec8d1b176 --- /dev/null +++ b/python/oneflow/test/modules/test_initializer.py @@ -0,0 +1,186 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +class DataChecker: + check_list = [ + "mean", + "std", + "min", + "max", + "value", + "lambda_func", + ] + + def __init__(self, **kwargs): + self.checkers = {} + for key in self.check_list: + if key in kwargs: + self.checkers[key] = kwargs[key] + + def __call__(self, test_case, tensor): + for func in ["mean", "std"]: + if func in self.checkers: + of_res = eval(f"tensor.{func}")().numpy() + checker_res = self.checkers[func] + test_case.assertTrue( + np.allclose(of_res, checker_res, rtol=1e-1, atol=1e-1), + f"{func} not equal, {of_res} vs {checker_res}", + ) + + if "min" in self.checkers: + test_case.assertTrue(np.all(tensor.numpy() >= self.checkers["min"])) + + if "max" in self.checkers: + test_case.assertTrue(np.all(tensor.numpy() <= self.checkers["max"])) + + if "value" in self.checkers: + test_case.assertTrue(np.all(tensor.numpy() == self.checkers["value"])) + + if "lambda_func" in self.checkers: + test_case.assertTrue( + np.allclose( + tensor.numpy(), + self.checkers["lambda_func"](tensor.shape), + rtol=1e-4, + atol=1e-4, + ) + ) + + +# NOTE(wyg): register initializers to this list +check_func_list = [ + # oneflow.nn.init.normal_ + { + "func": flow.nn.init.normal_, + "params": {"mean": 0.0, "std": 1.0}, + "checker": DataChecker(mean=0.0, std=1.0), + }, + # oneflow.nn.init.xavier_normal_ + { + "func": flow.nn.init.xavier_normal_, + "params": {"gain": 1.0}, + "checker": DataChecker(mean=0.0, std=0.0625), + }, + # oneflow.nn.init.kaiming_normal_ + { + "func": flow.nn.init.kaiming_normal_, + "params": {"mode": "fan_in"}, + "checker": DataChecker(mean=0.0, std=0.0883883476), + }, + { + "func": flow.nn.init.kaiming_normal_, + "params": {"mode": "fan_out"}, + "checker": DataChecker(mean=0.0, std=0.0883883476), + }, + { + "func": flow.nn.init.kaiming_normal_, + "params": {"mode": "fan_in", "a": 2.0, "nonlinearity": "leaky_relu"}, + "checker": DataChecker(mean=0.0, std=0.0395284708), + }, + { + "func": flow.nn.init.kaiming_normal_, + "params": {"mode": "fan_in", "a": 2.0, "nonlinearity": "linear"}, + "checker": DataChecker(mean=0.0, std=0.0625), + }, + # oneflow.nn.init.trunc_normal_ + { + "func": flow.nn.init.trunc_normal_, + "params": {"mean": 0.0, "std": 1.0, "a": -5.0, "b": 5.0}, + "checker": DataChecker(min=-5.0, max=5.0), + }, + # oneflow.nn.init.uniform_ + { + "func": flow.nn.init.uniform_, + "params": {"a": 0.0, "b": 1.0}, + "checker": DataChecker(min=0.0, max=1.0, mean=0.5, std=0.28849875926971436), + }, + # oneflow.nn.init.xavier_uniform_ + { + "func": flow.nn.init.xavier_uniform_, + "params": {"gain": 1.0}, + "checker": DataChecker( + min=-0.10825317547305482, max=0.10825317547305482, mean=0.0, std=0.0625 + ), + }, + # oneflow.nn.init.kaiming_uniform_ + { + "func": flow.nn.init.kaiming_uniform_, + "params": {"mode": "fan_in"}, + "checker": DataChecker( + min=-0.15309310892394865, max=15309310892394865, mean=0.0, std=0.0883883476 + ), + }, + { + "func": flow.nn.init.kaiming_uniform_, + "params": {"mode": "fan_out"}, + "checker": DataChecker( + min=-0.15309310892394865, max=15309310892394865, mean=0.0, std=0.0883883476 + ), + }, + { + "func": flow.nn.init.kaiming_uniform_, + "params": {"mode": "fan_in", "a": 2.0, "nonlinearity": "leaky_relu"}, + "checker": DataChecker( + min=-0.06846531968814576, + max=0.06846531968814576, + mean=0.0, + std=0.0395284708, + ), + }, + { + "func": flow.nn.init.kaiming_uniform_, + "params": {"mode": "fan_in", "a": 2.0, "nonlinearity": "linear"}, + "checker": DataChecker( + min=-0.10825317547305482, max=0.10825317547305482, mean=0.0, std=0.0625 + ), + }, + # oneflow.nn.init.eye_ + { + "func": flow.nn.init.eye_, + "params": {}, + "checker": DataChecker(lambda_func=lambda size: np.eye(*size)), + }, +] + + +@oneflow.unittest.skip_unless_1n1d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestInitializer(flow.unittest.TestCase): + def test_initializer(test_case): + default_shape = (256, 256) + for device in ["cpu", "cuda"]: + for check_func in check_func_list: + tensor = flow.empty(*default_shape, device=flow.device(device)) + check_func["func"](tensor, **check_func["params"]) + try: + check_func["checker"](test_case, tensor) + except AssertionError as e: + print( + f"Failed: {check_func['func'].__name__} {check_func['params']}" + ) + raise e + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_interpolate.py b/python/oneflow/test/modules/test_interpolate.py index 868d571055bd6ddc92ae1daa05c5fbbb5283b687..801535e4112170b3ec5904d6119e8527a57d99f2 100644 --- a/python/oneflow/test/modules/test_interpolate.py +++ b/python/oneflow/test/modules/test_interpolate.py @@ -653,6 +653,27 @@ def _test_interpolate_area_3d(test_case, device): test_case.assertTrue(np.allclose(input.grad.numpy(), np_grad, 1e-05, 1e-05)) +def _test_interpolate_output_size_arg_with_scalar(test_case, device): + mode = "bicubic" + x = flow.Tensor(8, 32, 64).to(device) + + window = 16 + t = x.shape[2] + x = x[:, None] + + np_center = np.random.randint(window, t - window, (1,))[0] + np_warped = np.random.randint(np_center - window, np_center + window, (1,))[0] + 1 + + center = flow.tensor(np_center) + warped = flow.tensor(np_warped) + + res = flow.nn.functional.interpolate( + x[:, :, :center], (warped, x.shape[3]), mode=mode, align_corners=False + ) + test_case.assertTrue(np.array_equal(res.size()[0], 8)) + test_case.assertTrue(np.array_equal(res.size()[1], 1)) + + @flow.unittest.skip_unless_1n1d() class TestInterpolate(flow.unittest.TestCase): def test_interpolate(test_case): @@ -670,6 +691,7 @@ class TestInterpolate(flow.unittest.TestCase): _test_interpolate_area_1d, _test_interpolate_area_2d, _test_interpolate_area_3d, + _test_interpolate_output_size_arg_with_scalar, ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): diff --git a/python/oneflow/test/modules/test_inv.py b/python/oneflow/test/modules/test_inv.py new file mode 100644 index 0000000000000000000000000000000000000000..da2a6f8ad4f47501f7150c5a7dc0082d5fec8072 --- /dev/null +++ b/python/oneflow/test/modules/test_inv.py @@ -0,0 +1,53 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import time +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestLinalgInv(flow.unittest.TestCase): + @unittest.skip("TODO: peihong, fix this test") + @autotest(n=5, rtol=1e-2) + def test_inv_3by3_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=2, dim0=3, dim1=3, low=-1).to(device) + return torch.linalg.inv(x) + + @autotest(n=5, rtol=1e-2) + def test_inv_batch_3by3_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=3, dim0=random(), dim1=3, dim2=3, low=-1).to(device) + return torch.linalg.inv(x) + + @autotest(n=5, rtol=1e-2) + def test_inv_random_square_with_random_data(test_case): + device = random_device() + square_dim = random() + x = random_tensor(ndim=4, dim2=square_dim, dim3=square_dim, low=-1).to(device) + return torch.linalg.inv(x) + + @profile(torch.linalg.inv) + def profile_linalg_inv(test_case): + torch.linalg.inv(torch.randn(1, 32, 4, 4)) + torch.linalg.inv(torch.randn(16, 32, 4, 4)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_isclose.py b/python/oneflow/test/modules/test_isclose.py new file mode 100644 index 0000000000000000000000000000000000000000..fe609bef31c7a7bb4a1c9a6d7906ac25958e26d7 --- /dev/null +++ b/python/oneflow/test/modules/test_isclose.py @@ -0,0 +1,79 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + +rtol = 1e-3 + + +def _perturbate(x): + shape = x.oneflow.shape + device = x.device + diff = ( + random_tensor(len(shape), *shape, low=-1, high=1, requires_grad=False).to( + device + ) + * rtol + * 2 + ) + return x + diff + + +@flow.unittest.skip_unless_1n1d() +class TestIsClose(flow.unittest.TestCase): + @autotest(n=10, auto_backward=False, check_graph=False) + def test_isclose_with_random_data(test_case): + device = random_device() + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = torch.isclose(x1, x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_isclose_with_0dim_data(test_case): + device = random_device() + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = torch.isclose(x1, x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_tensor_isclose_with_random_data(test_case): + device = random_device() + x1 = random_tensor(requires_grad=False).to(device) + x2 = _perturbate(x1) + y = x1.isclose(x2, rtol=rtol) + return y + + @autotest(n=10, auto_backward=False, check_graph=False) + def test_isclose_broadcast(test_case): + device = random_device() + shape = random_tensor(2, 2, 4).oneflow.shape + x1 = random_tensor(len(shape), *shape, requires_grad=False).to(device) + x2 = _perturbate(x1[:, :1]) + y = torch.isclose(x1, x2, rtol=rtol) + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_jit_script_api.py b/python/oneflow/test/modules/test_jit_script_api.py new file mode 100644 index 0000000000000000000000000000000000000000..680b06404aea695c09137436eed22f55060501f4 --- /dev/null +++ b/python/oneflow/test/modules/test_jit_script_api.py @@ -0,0 +1,59 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + + +def _test_jit_script_api(test_case): + @flow.jit.script + def add2(x): + return x + x + + x = flow.randn(2, 3) + y = add2(x) + test_case.assertTrue(x.size(), y.size()) + + +def _test_jit_ignore_api(test_case): + @flow.jit.ignore + def add2(x): + return x + x + + x = flow.randn(2, 3) + y = add2(x) + test_case.assertTrue(x.size(), y.size()) + + +@flow.unittest.skip_unless_1n1d() +class TestJitScriptApi(flow.unittest.TestCase): + def test_jit_script(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_jit_script_api, _test_jit_ignore_api] + for arg in GenArgList(arg_dict): + arg[0](test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_layer_norm.py b/python/oneflow/test/modules/test_layer_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..f753caef9844667f016ec24857354812b17aa73a --- /dev/null +++ b/python/oneflow/test/modules/test_layer_norm.py @@ -0,0 +1,215 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import numpy as np +import unittest + +import oneflow as flow +import oneflow.unittest +import torch + + +def _layer_norm(x, normalized_shape, weight=None, bias=None, eps=1e-6): + begin_norm_axis = len(x.shape) - len(normalized_shape) + begin_params_axis = len(x.shape) - len(normalized_shape) + + if weight is not None and bias is not None: + return flow._C.layer_norm_affine( + x, + weight, + bias, + begin_norm_axis=begin_norm_axis, + begin_params_axis=begin_params_axis, + epsilon=eps, + ) + else: + return flow._C.layer_norm( + x, + begin_norm_axis=begin_norm_axis, + begin_params_axis=begin_params_axis, + epsilon=eps, + ) + + +def _test_layer_norm( + test_case, + shape, + normalized_shape, + affine=True, + eps=1e-6, + dtype=flow.float32, + device="cuda", + backward=True, +): + np_x = np.random.randn(*shape).astype(np.float32) + if affine: + np_weight = np.random.randn(*normalized_shape).astype(np.float32) + np_bias = np.random.randn(*normalized_shape).astype(np.float32) + + # torch process + torch_dtype = torch.float16 if dtype is flow.float16 else torch.float32 + torch_x = torch.tensor(np_x).to(device=device, dtype=torch_dtype) + if backward: + torch_x.requires_grad_(True) + torch_weight = None + torch_bias = None + if affine: + torch_weight = torch.tensor(np_weight).to(device=device, dtype=torch_dtype) + torch_bias = torch.tensor(np_bias).to(device=device, dtype=torch_dtype) + if backward: + torch_weight.requires_grad_(True) + torch_bias.requires_grad_(True) + torch_y = torch.nn.functional.layer_norm( + torch_x, normalized_shape, torch_weight, torch_bias, eps + ) + + if backward: + np_rand_init_grad = np.random.randn(*tuple(torch_y.shape)).astype(np.float32) + torch_rand_init_grad = torch.tensor(np_rand_init_grad).to( + device=device, dtype=torch_dtype + ) + (torch_y * torch_rand_init_grad).sum().backward() + + torch_x_grad = torch_x.grad.detach().cpu().numpy() + if affine: + torch_weight_grad = torch_weight.grad.detach().cpu().numpy() + torch_bias_grad = torch_bias.grad.detach().cpu().numpy() + + torch_y = torch_y.detach().cpu().numpy() + + # oneflow process + x = flow.tensor(np_x).to(device=device, dtype=dtype) + if backward: + x.requires_grad_(True) + weight = None + bias = None + if affine: + weight = flow.tensor(np_weight).to(device=device, dtype=dtype) + bias = flow.tensor(np_bias).to(device=device, dtype=dtype) + if backward: + weight.requires_grad_(True) + bias.requires_grad_(True) + y = _layer_norm(x, normalized_shape, weight, bias, eps) + + if backward: + # np_rand_init_grad = np.random.randn(*tuple(y.shape)).astype(np.float32) + rand_init_grad = flow.tensor(np_rand_init_grad).to(device=device, dtype=dtype) + (y * rand_init_grad).sum().backward() + + x_grad = x.grad.detach().cpu().numpy() + if affine: + weight_grad = weight.grad.detach().cpu().numpy() + bias_grad = bias.grad.detach().cpu().numpy() + + y = y.detach().cpu().numpy() + + def compare(a, b, a_name, b_name, atol=1e-5, rtol=1e-8): + test_case.assertTrue( + np.allclose(a, b, atol=atol, rtol=rtol), + f"\n{'=' * 80}" + f"\n{a_name}:" + f"\n{a}" + f"\n{'-' * 80}" + f"\n{b_name}:" + f"\n{b}" + f"\n{'-' * 80}" + f"\ndiff:" + f"\n{a - b}" + f"\n{'*' * 80}" + f"\nshape={shape}" + f"\normalized_shape={normalized_shape}" + f"\naffine={affine}" + f"\ndtype={dtype}" + f"\ndevice={device}" + f"\n{a_name} vs. {b_name} max abs diff: {np.max(np.abs(a - b))}", + ) + + if dtype is flow.float16: + compare(y, torch_y, "y", "torch_y", 1e-2, 1e-2) + if backward: + compare(x_grad, torch_x_grad, "x_grad", "torch_x_grad", 1e-2, 1e-2) + if affine: + compare( + weight_grad, + torch_weight_grad, + "weight_grad", + "torch_weight_grad", + 1e-2, + 1e-2, + ) + compare( + bias_grad, + torch_bias_grad, + "bias_grad", + "torch_bias_grad", + 1e-2, + 1e-2, + ) + else: + compare(y, torch_y, "y", "torch_y") + if backward: + compare(x_grad, torch_x_grad, "x_grad", "torch_x_grad") + if affine: + compare( + weight_grad, torch_weight_grad, "weight_grad", "torch_weight_grad", + ) + compare( + bias_grad, torch_bias_grad, "bias_grad", "torch_bias_grad", + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestLayerNorm(flow.unittest.TestCase): + def test_no_affine(test_case): + _test_layer_norm( + test_case, shape=[4, 16], normalized_shape=[16], affine=False, + ) + + def test_warp_impl(test_case): + _test_layer_norm( + test_case, shape=[32, 1024], normalized_shape=[1024], dtype=flow.float16, + ) + _test_layer_norm(test_case, shape=[16, 512], normalized_shape=[512]) + _test_layer_norm(test_case, shape=[15, 512], normalized_shape=[512]) + _test_layer_norm(test_case, shape=[16, 511], normalized_shape=[511]) + _test_layer_norm(test_case, shape=[13, 499], normalized_shape=[499]) + + def test_block_smem_impl(test_case): + _test_layer_norm( + test_case, shape=[16, 2048], normalized_shape=[2048], dtype=flow.float16, + ) + _test_layer_norm(test_case, shape=[8, 1536], normalized_shape=[1536]) + _test_layer_norm(test_case, shape=[8, 2048], normalized_shape=[2048]) + _test_layer_norm(test_case, shape=[7, 1536], normalized_shape=[1536]) + _test_layer_norm(test_case, shape=[8, 1533], normalized_shape=[1533]) + _test_layer_norm(test_case, shape=[7, 1533], normalized_shape=[1533]) + + def test_block_uncached_impl(test_case): + _test_layer_norm( + test_case, + shape=[16, 1024 * 1024], + normalized_shape=[1024 * 1024], + dtype=flow.float16, + ) + _test_layer_norm( + test_case, shape=[8, 1024], normalized_shape=[1024], dtype=flow.double + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_linalg_cross.py b/python/oneflow/test/modules/test_linalg_cross.py new file mode 100644 index 0000000000000000000000000000000000000000..bfa4d8505a57bc448179aa898a1bcbd697aabadc --- /dev/null +++ b/python/oneflow/test/modules/test_linalg_cross.py @@ -0,0 +1,88 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestLinalgCross(flow.unittest.TestCase): + # TODO(peihong): PyTorch 1.10 has no torch.linalg.cross, so uncomment the below code when PyTorch in ci is upgraded to 1.11. + # @autotest(n=5) + # def test_linalg_cross_with_random_data(test_case): + # device = random_device() + # ndim = np.random.randint(2, 6) + # shape = list(np.random.randint(16, size=ndim)) + # index = np.random.randint(ndim) + # shape[index] = 3 + + # x = random_tensor(ndim, *shape).to(device) + # y = random_tensor(ndim, *shape).to(device) + # return torch.linalg.cross(x, y, dim=index) + + # @autotest(n=10) + # def test_linalg_cross_with_random_data_broadcast(test_case): + # device = random_device() + # ndim = np.random.randint(3, 6) + # shape = list(np.random.randint(16, size=ndim)) + # indexes = list(np.random.choice(ndim, 3)) + # shape[indexes[0]] = 3 + # x_shape = shape + # y_shape = shape[:] + # x_shape[indexes[1]] = 1 + # y_shape[indexes[2]] = 1 + + # x = random_tensor(ndim, *x_shape).to(device) + # y = random_tensor(ndim, *y_shape).to(device) + # return torch.linalg.cross(x, y, dim=indexes[0]) + + # @autotest(n=1) + # def test_linalg_cross_with_random_data_broadcast_different_num_axes(test_case): + # device = random_device() + # x = random_tensor(4, 4, 5, 3, 5).to(device) + # y = random_tensor(3, 1, 3, 5).to(device) + # return torch.linalg.cross(x, y, dim=2) + + # @autotest(n=5) + # def test_linalg_cross_with_random_data_default_dim(test_case): + # device = random_device() + # ndim = np.random.randint(2, 6) + # shape = list(np.random.randint(16, size=ndim)) + # index = np.random.randint(ndim) + # shape[index] = 3 + + # x = random_tensor(ndim, *shape).to(device) + # y = random_tensor(ndim, *shape).to(device) + # return torch.linalg.cross(x, y) + + @autotest(n=5) + def test_cross_with_random_data_default_dim(test_case): + device = random_device() + ndim = np.random.randint(2, 6) + shape = list(np.random.randint(16, size=ndim)) + index = np.random.randint(ndim) + shape[index] = 3 + + x = random_tensor(ndim, *shape).to(device) + y = random_tensor(ndim, *shape).to(device) + return torch.cross(x, y) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_linear.py b/python/oneflow/test/modules/test_linear.py index a7e06b8c1915ed0bd6bf136bd5bc0142e00564f4..23f3567bafb854e24748b5d1e8c05f79344852db 100644 --- a/python/oneflow/test/modules/test_linear.py +++ b/python/oneflow/test/modules/test_linear.py @@ -193,6 +193,23 @@ class TestLinear(flow.unittest.TestCase): y = m(x) return y + @autotest(n=5) + def test_linear_with_device_and_dtype(test_case): + input_size = random() + device = random_device() + m = torch.nn.Linear( + in_features=input_size, + out_features=random(), + bias=random() | nothing(), + device=device, + dtype=torch.float, + ) + m.train(random()) + m.to(device) + x = random_tensor(ndim=2, dim1=input_size).to(device) + y = m(x) + return y + @autotest(n=5) def test_nn_functional_linear_with_random_data(test_case): input_size = random() diff --git a/python/oneflow/test/modules/test_linspace.py b/python/oneflow/test/modules/test_linspace.py index 1e9ed197ad4e2185bc35053f787e8411067751eb..678d0e6d254303bf9c4fd7ae3acb1ec2779b1721 100644 --- a/python/oneflow/test/modules/test_linspace.py +++ b/python/oneflow/test/modules/test_linspace.py @@ -48,6 +48,14 @@ class TestLinspace(flow.unittest.TestCase): x.to(device) return x + @autotest(n=5, auto_backward=False) + def test_linspace_with_scalar_tensor_as_params(test_case): + start = random_tensor(2, 3, 4, requires_grad=False).mean() + end = start + random_tensor(2, 3, 4, requires_grad=False).mean() + steps = random(0, 10).to(int) + y = torch.linspace(start=start, end=end, steps=steps) + return y + def test_global_naive(test_case): placement = flow.placement("cpu", ranks=[0]) sbp = (flow.sbp.broadcast,) diff --git a/python/oneflow/test/modules/test_logaddexp.py b/python/oneflow/test/modules/test_logaddexp.py new file mode 100644 index 0000000000000000000000000000000000000000..9dd962b49ff06be53d3cd05238e31b3dc15d6490 --- /dev/null +++ b/python/oneflow/test/modules/test_logaddexp.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestLogAddExpModule(flow.unittest.TestCase): + @autotest(n=3, check_graph=True) + def test_log_add_exp_against_pytorch(test_case): + device = random_device() + dim1 = random(1, 5) + dim2 = random(1, 5) + x = random_tensor(2, dim1, dim2).to(device) + y = random_tensor(2, dim1, dim2).to(device) + z = torch.logaddexp(x, y) + return z + + @autotest(n=3, check_graph=True) + def test_log_add_exp_with_0dim_tensor(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(2, random(1, 5), random(1, 5)).to(device) + z = torch.logaddexp(x, y) + return z + + @autotest(n=3, check_graph=True) + def test_tensor_log_add_exp_against_pytorch(test_case): + device = random_device() + dim1 = random(1, 5) + dim2 = random(1, 5) + x = random_tensor(2, dim1, dim2).to(device) + y = random_tensor(2, dim1, dim2).to(device) + z = x.logaddexp(y) + return z + + @autotest(n=3, check_graph=True) + def test_tensor_log_add_exp_with_0dim_tensor(test_case): + device = random_device() + y = random_tensor(ndim=0).to(device) + x = random_tensor(2, random(1, 5), random(1, 5)).to(device) + z = x.logaddexp(y) + return z + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_logical_reduce.py b/python/oneflow/test/modules/test_logical_reduce.py index 431547e87438b80217cf5d1df7e61cbfc2925690..80c35f93b64af6317012ba91500f9eb20d0939ee 100644 --- a/python/oneflow/test/modules/test_logical_reduce.py +++ b/python/oneflow/test/modules/test_logical_reduce.py @@ -136,6 +136,24 @@ class TestLogicalReduce(flow.unittest.TestCase): ) return torch.all(x, dim) + @autotest(auto_backward=False, check_graph=False) + def test_max_bool_input_with_random_data(test_case): + device = random_device() + dim = random(1, 4).to(int) + x = random_tensor(ndim=4, dtype=float, requires_grad=False).to( + device, dtype=torch.bool + ) + return torch.max(x, dim) + + @autotest(auto_backward=False, check_graph=False) + def test_min_bool_input_with_random_data(test_case): + device = random_device() + dim = random(1, 4).to(int) + x = random_tensor(ndim=4, dtype=float, requires_grad=False).to( + device, dtype=torch.bool + ) + return torch.min(x, dim) + @autotest(n=5, auto_backward=False) def test_any_bool_input_with_random_data(test_case): device = random_device() @@ -148,7 +166,7 @@ class TestLogicalReduce(flow.unittest.TestCase): @autotest(n=5, auto_backward=False) def test_reduce_all_0dim_tensor(test_case): device = random_device() - x = torch.empty(0).to(device) + x = random_tensor(ndim=0, requires_grad=False).to(device) return torch.all(x) @autotest(n=5, auto_backward=False) diff --git a/python/oneflow/test/modules/test_logspace.py b/python/oneflow/test/modules/test_logspace.py new file mode 100644 index 0000000000000000000000000000000000000000..5e299a31ffff647c228d3a1fdf10c9262b8c90cb --- /dev/null +++ b/python/oneflow/test/modules/test_logspace.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +@flow.unittest.skip_unless_1n1d() +class TestLogspace(flow.unittest.TestCase): + @autotest(n=5, auto_backward=False) + def test_logspace_int_with_random_data(test_case): + start = random().to(int) + end = start + random().to(int) + steps = random(0, end - start).to(int) + x = torch.logspace(start=start, end=end, steps=steps) + device = random_device() + x.to(device) + return x + + @autotest(n=5, auto_backward=False) + def test_logspace_float_with_random_data(test_case): + start = random() + end = start + random() + steps = random(0, end - start).to(int) + x = torch.logspace(start=start, end=end, steps=steps) + device = random_device() + x.to(device) + return x + + @autotest(n=5, auto_backward=False) + def test_logspace_with_random_base(test_case): + start = random() + end = start + random() + steps = random(0, end - start).to(int) + base = random(1, 4).to(float) + x = torch.logspace(start=start, end=end, steps=steps, base=base) + device = random_device() + x.to(device) + return x + + def test_global_naive(test_case): + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + x = flow.logspace(start=0, end=10, steps=2, placement=placement, sbp=sbp) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_logsumexp.py b/python/oneflow/test/modules/test_logsumexp.py new file mode 100644 index 0000000000000000000000000000000000000000..21eb0183ec9aa5a2975f23ff2873fe11b852bcae --- /dev/null +++ b/python/oneflow/test/modules/test_logsumexp.py @@ -0,0 +1,91 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestLogSumExpModule(flow.unittest.TestCase): + @autotest(n=3, check_graph=True) + def test_log_sum_exp_against_pytorch(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + y = torch.logsumexp(x, dim=np.random.randint(0, 3)) + return y + + @unittest.skipIf(True, "pytorch-1.10.0 dose not support big_value of logsumexp") + @autotest(n=3, auto_backward=False, check_graph=True) + def test_log_sum_exp_with_big_value(test_case): + device = random_device() + x = torch.tensor([100, 200]).to(device) + y = torch.logsumexp(x, dim=0) + return y + + @autotest(n=3, auto_backward=False, check_graph=True) + def test_log_sum_exp_with_0_size_tensor(test_case): + device = random_device() + x = random_tensor(4, 4, 3, 0, 2).to(device) + y = torch.logsumexp(x, dim=np.random.randint(0, 3)) + return y + + @autotest(n=3, auto_backward=False, check_graph=True) + def test_log_sum_exp_with_0dim_tensor(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = torch.logsumexp(x, dim=0) + return y + + @autotest(n=3, check_graph=True) + def test_tensor_log_sum_exp_against_pytorch(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + y = x.logsumexp(dim=np.random.randint(0, 3)) + return y + + @unittest.skipIf(True, "pytorch-1.10.0 dose not support big_value of logsumexp") + @autotest(n=3, auto_backward=False, check_graph=True) + def test_tensor_log_sum_exp_with_big_value(test_case): + device = random_device() + x = torch.tensor([100, 200]).to(device) + y = x.logsumexp(dim=0) + return y + + @autotest(n=3, auto_backward=False, check_graph=True) + def test_tensor_log_sum_exp_with_0_size_tensor(test_case): + device = random_device() + x = random_tensor(4, 4, 3, 0, 2).to(device) + y = x.logsumexp(dim=np.random.randint(0, 3)) + return y + + @autotest(n=3, auto_backward=False, check_graph=True) + def test_tensor_log_sum_exp_with_0dim_tensor(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = x.logsumexp(dim=0) + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_loss.py b/python/oneflow/test/modules/test_loss.py index dd4282f320719ea63ef8969156eb3d771e7b7092..3758b72a3f1ce7bd11bf9a5272d9e69a2fb8485d 100644 --- a/python/oneflow/test/modules/test_loss.py +++ b/python/oneflow/test/modules/test_loss.py @@ -15,12 +15,18 @@ limitations under the License. """ import unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList from oneflow.test_utils.automated_test_util import * + +import numpy as np import oneflow as flow import oneflow.unittest +import torch as torch_original +from packaging import version -def generate_necessity_for_cross_entropy_or_nll_loss(dim: int): +def generate_necessity_for_cross_entropy_or_nll_loss(dim: int, prob: bool = False): if dim > 5 or dim < 2: raise ValueError("dim should be less than 5 or greater than 1. ") device = random_device() @@ -28,13 +34,17 @@ def generate_necessity_for_cross_entropy_or_nll_loss(dim: int): batch_size = random(low=10, high=100).to(int) ignore_index = ( random(0, num_classes).to(int) | nothing() - if num_classes.value() > 2 + if num_classes.value() > 2 and not prob else nothing() ) extra_dim = [random().to(int) for _ in range(dim - 2)] - return ( - random_tensor(dim, batch_size, num_classes, *extra_dim).to(device), - random_tensor( + + if prob: + target_tensor = random_tensor( + dim, batch_size, num_classes, *extra_dim, requires_grad=False, + ).to(device) + else: + target_tensor = random_tensor( dim - 1, batch_size, *extra_dim, @@ -42,7 +52,10 @@ def generate_necessity_for_cross_entropy_or_nll_loss(dim: int): high=num_classes, dtype=int, requires_grad=False, - ).to(device), + ).to(device) + return ( + random_tensor(dim, batch_size, num_classes, *extra_dim).to(device), + target_tensor, random_tensor(1, num_classes, low=0, high=3, requires_grad=False).to(device), ignore_index, device, @@ -81,18 +94,22 @@ def generate_necessity_for_bce_loss(dim: int): ) -def _test_cross_entropy_loss(dim=int): +def _test_cross_entropy_loss(dim: int, prob: bool = False): ( x, target, weight, ignore_index, device, - ) = generate_necessity_for_cross_entropy_or_nll_loss(dim) + ) = generate_necessity_for_cross_entropy_or_nll_loss(dim, prob) m = torch.nn.CrossEntropyLoss( reduction=oneof("none", "sum", "mean", nothing()), ignore_index=ignore_index, weight=oneof(weight, nothing()), + # TODO(wangyi): PyTorch under 1.12 has bug here, which returns wrong result when ignore_index >= 0 and label_smoothing > 0 + label_smoothing=random(low=0, high=1) + if version.parse(torch_original.__version__) >= version.parse("1.12.0") + else 0, ) m.train(random()) m.to(device) @@ -101,14 +118,14 @@ def _test_cross_entropy_loss(dim=int): return y -def _test_nn_functional_cross_entropy_loss(dim=int): +def _test_nn_functional_cross_entropy_loss(dim: int, prob: bool): ( x, target, weight, ignore_index, device, - ) = generate_necessity_for_cross_entropy_or_nll_loss(dim) + ) = generate_necessity_for_cross_entropy_or_nll_loss(dim, prob) y1 = torch.nn.functional.cross_entropy(x, target) y2 = torch.nn.functional.cross_entropy(x, target, weight) return y1 + y2 @@ -118,24 +135,45 @@ def _test_nn_functional_cross_entropy_loss(dim=int): class TestCrossEntropyLossModule(flow.unittest.TestCase): @autotest(n=5) def test_cross_entropy_loss_with_random_data_dim_2(test_case): - return _test_cross_entropy_loss(2) + return _test_cross_entropy_loss(2, prob=False) @autotest(n=5) def test_cross_entropy_loss_with_random_data_dim_3(test_case): - return _test_cross_entropy_loss(3) + return _test_cross_entropy_loss(3, prob=False) @autotest(n=5) def test_cross_entropy_loss_with_random_data_dim_4(test_case): - return _test_cross_entropy_loss(4) + return _test_cross_entropy_loss(4, prob=False) @autotest(n=5) def test_cross_entropy_loss_with_random_data_dim_5(test_case): - return _test_cross_entropy_loss(5) + return _test_cross_entropy_loss(5, prob=False) @autotest(n=5) def test_nn_functional_cross_entropy_with_random_data_dim(test_case): dim = random(2, 6).to(int).value() - return _test_nn_functional_cross_entropy_loss(dim) + return _test_nn_functional_cross_entropy_loss(dim, prob=False) + + @autotest(n=5) + def test_cross_entropy_prob_loss_with_random_data_dim_2(test_case): + return _test_cross_entropy_loss(2, prob=True) + + @autotest(n=5) + def test_cross_entropy_prob_loss_with_random_data_dim_3(test_case): + return _test_cross_entropy_loss(3, prob=True) + + @autotest(n=5) + def test_cross_entropy_prob_loss_with_random_data_dim_4(test_case): + return _test_cross_entropy_loss(4, prob=True) + + @autotest(n=5) + def test_cross_entropy_prob_loss_with_random_data_dim_5(test_case): + return _test_cross_entropy_loss(5, prob=True) + + @autotest(n=5) + def test_nn_functional_prob_cross_entropy_with_random_data_dim(test_case): + dim = random(2, 6).to(int).value() + return _test_nn_functional_cross_entropy_loss(dim, prob=True) def _test_nll_loss(dim=int): @@ -184,10 +222,13 @@ def _test_bce_loss(dim=int, with_logits: bool = False): weight=oneof(weight, nothing()), reduction=oneof("none", "sum", "mean", nothing()), ) + pos_weight_for_testing_broadcast = random_tensor( + 1, 1, low=1, high=3, requires_grad=False, + ).to(device) if with_logits: m = torch.nn.BCEWithLogitsLoss( weight=oneof(weight, nothing()), - pos_weight=oneof(pos_weight, nothing()), + pos_weight=oneof(pos_weight, pos_weight_for_testing_broadcast, nothing()), reduction=oneof("none", "sum", "mean", nothing()), ) m.train(random()) @@ -197,6 +238,95 @@ def _test_bce_loss(dim=int, with_logits: bool = False): return y +def _test_nn_functional_binary_cross_entropy(dim=int): + (x, target, weight, pos_weight, device) = generate_necessity_for_bce_loss(dim) + y = torch.nn.functional.binary_cross_entropy( + x, + target, + weight=oneof(weight, nothing()), + reduction=oneof("none", "sum", "mean", nothing()), + pos_weight=oneof(pos_weight, nothing()), + ) + return y + + +def _test_nn_functional_binary_cross_entropy_with_logits(dim=int): + (x, target, weight, pos_weight, device) = generate_necessity_for_bce_loss(dim) + y = torch.nn.functional.binary_cross_entropy_with_logits( + x, + target, + weight=oneof(weight, nothing()), + reduction=oneof("none", "sum", "mean", nothing()), + ) + return y + + +def _test_nn_functional_binary_cross_entropy_with_logits_different_dtype_float_first( + test_case, shape, reduction, device +): + def compare(a, b): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), + b.detach().cpu().numpy(), + rtol=1e-5, + atol=1e-5, + ) + ) + + arr = np.random.randn(*shape) + + flow_pred_mask = flow.Tensor(arr).float().to(device) + flow_pred_mask.requires_grad = True + flow_gt_mask = flow.Tensor(arr).double().to(device) + flow_loss = flow.nn.functional.binary_cross_entropy_with_logits( + flow_pred_mask, flow_gt_mask, reduction=reduction + ) + flow_loss.sum().backward() + torch_pred_mask = torch_original.Tensor(arr).float().to(device) + torch_pred_mask.requires_grad = True + torch_gt_mask = torch_original.Tensor(arr).double().to(device) + torch_loss = torch_original.nn.functional.binary_cross_entropy_with_logits( + torch_pred_mask, torch_gt_mask, reduction=reduction + ) + torch_loss.sum().backward() + compare(flow_loss, torch_loss) + compare(flow_pred_mask.grad.data, torch_pred_mask.grad.data) + + +def _test_nn_functional_binary_cross_entropy_with_logits_different_dtype_double_first( + test_case, shape, reduction, device +): + def compare(a, b): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), + b.detach().cpu().numpy(), + rtol=1e-5, + atol=1e-5, + ) + ) + + arr = np.random.randn(*shape) + + flow_pred_mask = flow.Tensor(arr).double().to(device) + flow_pred_mask.requires_grad = True + flow_gt_mask = flow.Tensor(arr).float().to(device) + flow_loss = flow.nn.functional.binary_cross_entropy_with_logits( + flow_pred_mask, flow_gt_mask, reduction=reduction + ) + flow_loss.sum().backward() + torch_pred_mask = torch_original.Tensor(arr).double().to(device) + torch_pred_mask.requires_grad = True + torch_gt_mask = torch_original.Tensor(arr).float().to(device) + torch_loss = torch_original.nn.functional.binary_cross_entropy_with_logits( + torch_pred_mask, torch_gt_mask, reduction=reduction + ) + torch_loss.sum().backward() + compare(flow_loss, torch_loss) + compare(flow_pred_mask.grad.data, torch_pred_mask.grad.data) + + @flow.unittest.skip_unless_1n1d() class TestBCELossModule(flow.unittest.TestCase): @autotest(n=5) @@ -215,6 +345,11 @@ class TestBCELossModule(flow.unittest.TestCase): def test_bce_loss_with_random_data_dim_5(test_case): return _test_bce_loss(5) + @autotest(n=5) + def test_nn_functional_binary_cross_entropy(test_case): + dim = random(2, 6).to(int).value() + return _test_nn_functional_binary_cross_entropy(dim) + @flow.unittest.skip_unless_1n1d() class TestBCEWithLogitsLossModule(flow.unittest.TestCase): @@ -234,6 +369,25 @@ class TestBCEWithLogitsLossModule(flow.unittest.TestCase): def test_bce_with_logits_loss_with_random_data_dim_5(test_case): return _test_bce_loss(5, True) + @autotest(n=5) + def test_nn_functional_binary_cross_entropy_with_logits(test_case): + dim = random(2, 6).to(int).value() + return _test_nn_functional_binary_cross_entropy_with_logits(dim) + + @autotest(n=5) + def test_nn_functional_binary_cross_entropy_with_logits_different_dtype(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [ + _test_nn_functional_binary_cross_entropy_with_logits_different_dtype_float_first, + _test_nn_functional_binary_cross_entropy_with_logits_different_dtype_double_first, + ] + arg_dict["shape"] = [(24, 16, 80), (42, 160), (4, 54, 32, 56)] + arg_dict["reduction"] = ["sum", "mean", "none"] + arg_dict["device"] = ["cpu", "cuda"] + + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + @flow.unittest.skip_unless_1n1d() class TestL1LossModule(flow.unittest.TestCase): @@ -252,6 +406,19 @@ class TestL1LossModule(flow.unittest.TestCase): y = m(x, target) return y + @autotest(n=5) + def _test_nn_functional_l1_loss(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + + x = random_tensor(len(shape), *shape).to(device) + target = random_tensor(len(shape), *shape, requires_grad=False).to(device) + + y = torch.nn.functional.l1_loss( + x, target, reduction=oneof("none", "sum", "mean", nothing()) + ) + return y + @flow.unittest.skip_unless_1n1d() class TestSmoothL1LossModule(flow.unittest.TestCase): @@ -290,6 +457,19 @@ class TestMSELossModule(flow.unittest.TestCase): y = m(x, target) return y + @autotest(n=5) + def _test_nn_functional_mse_loss(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + + x = random_tensor(len(shape), *shape).to(device) + target = random_tensor(len(shape), *shape, requires_grad=False).to(device) + + y = torch.nn.functional.mse_loss( + x, target, reduction=oneof("none", "sum", "mean", nothing()) + ) + return y + @flow.unittest.skip_unless_1n1d() class TestKLDivLossModule(flow.unittest.TestCase): @@ -302,8 +482,8 @@ class TestKLDivLossModule(flow.unittest.TestCase): target = random_tensor(len(shape), *shape, requires_grad=False).to(device) m = torch.nn.KLDivLoss( - reduction=oneof("none", "sum", "mean", nothing()), - log_target=oneof(True, False), + reduction=oneof("none", "sum", "mean", "batchmean", nothing()), + log_target=oneof(True, False, nothing()), ) m.train(random()) m.to(device) @@ -311,6 +491,20 @@ class TestKLDivLossModule(flow.unittest.TestCase): y = m(x, target) return y + @autotest(n=5) + def test_nn_functional_kl_div(test_case): + device = random_device() + shape = random_tensor().oneflow.shape + x = random_tensor(len(shape), *shape).to(device) + target = random_tensor(len(shape), *shape, requires_grad=False).to(device) + y = torch.nn.functional.kl_div( + x, + target, + reduction=oneof("none", "sum", "mean", "batchmean", nothing()), + log_target=oneof(True, False, nothing()), + ) + return y + @flow.unittest.skip_unless_1n1d() class TestMarginRankingLossModule(flow.unittest.TestCase): diff --git a/python/oneflow/test/modules/test_loss_consistent.py b/python/oneflow/test/modules/test_loss_consistent.py deleted file mode 100644 index 8c9ed934c8feeadbff481fdc12561ad2e266102c..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_loss_consistent.py +++ /dev/null @@ -1,147 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import numpy as np -import oneflow as flow -import oneflow.unittest -from collections import OrderedDict -from oneflow.test_utils.test_util import GenArgList - - -def get_sbp(device: str): - return flow.env.all_device_placement(device), flow.sbp.split(0) - - -shapes = {2: (128, 8), 3: (16, 8, 64), 4: (16, 8, 32, 32), 5: (16, 8, 16, 16, 16)} - - -def compare_loss(device_type, dim, reduction, cls, data_generator): - x, y, x1, y1 = data_generator(dim, device_type, *get_sbp(device_type)) - reduce_loss_func = cls(reduction=reduction).to(device_type) - none_loss_func = cls(reduction="none").to(device_type) - - loss_mean = reduce_loss_func(x, y) - loss_none = ( - flow.mean(none_loss_func(x1, y1)) - if reduction == "mean" - else flow.sum(none_loss_func(x1, y1)) - ) - - loss_mean.backward() - loss_none.backward() - - assert np.allclose( - loss_none.to_local().numpy(), - loss_mean.to_local().numpy(), - rtol=1e-05, - atol=1e-05, - ) - assert np.allclose(loss_none.numpy(), loss_mean.numpy(), rtol=1e-05, atol=1e-05,) - assert np.allclose( - x.grad.to_local().numpy(), x1.grad.to_local().numpy(), rtol=1e-05, atol=1e-05, - ) - - -def generate_necessity_default(dim: int, device: str, placement, sbp): - shape = shapes[dim] - x_np = np.random.uniform(0, 1, shape) - y_np = np.random.uniform(0, 1, shape) - - def f(x, requires_grad): - t = flow.tensor(x, device=device, requires_grad=requires_grad).to_global( - placement=placement, sbp=[sbp] - ) - if requires_grad: - t.retain_grad() - return t - - return f(x_np, True), f(y_np, False), f(x_np, True), f(y_np, False) - - -def generate_necessity_for_cross_entropy_or_nll_loss( - dim: int, device: str, placement, sbp -): - shape = shapes[dim] - y_shape = (shape[0],) if dim == 2 else (shape[0], *shape[2:]) - x_np = np.random.uniform(0, 1, shape) - y_np = np.random.randint(0, shape[1], y_shape) - - def f(x, requires_grad): - t = flow.tensor(x, device=device, requires_grad=requires_grad).to_global( - placement=placement, sbp=[sbp] - ) - if requires_grad: - t.retain_grad() - return t - - return f(x_np, True), f(y_np, False), f(x_np, True), f(y_np, False) - - -class TestBCELossOrWithLogitsConsistent(flow.unittest.TestCase): - @flow.unittest.skip_unless_1n2d() - def test_bce_loss(testcase): - arg_dict = OrderedDict() - arg_dict["device_type"] = ["cuda", "cpu"] - arg_dict["dim"] = [2, 3, 4, 5] - arg_dict["reduction"] = ["sum", "mean"] - arg_dict["cls"] = [flow.nn.BCELoss, flow.nn.BCEWithLogitsLoss] - arg_dict["data_generator"] = [generate_necessity_default] - for arg in GenArgList(arg_dict): - compare_loss(*arg) - - -class TestCrossEntropyOrNllLossConsistent(flow.unittest.TestCase): - @flow.unittest.skip_unless_1n2d() - def test_cross_entropy_loss_or_nll_loss(testcase): - arg_dict = OrderedDict() - arg_dict["device_type"] = ["cuda", "cpu"] - arg_dict["dim"] = [2, 3, 4, 5] - arg_dict["reduction"] = ["sum", "mean"] - arg_dict["cls"] = [flow.nn.CrossEntropyLoss, flow.nn.NLLLoss] - arg_dict["data_generator"] = [generate_necessity_for_cross_entropy_or_nll_loss] - for arg in GenArgList(arg_dict): - compare_loss(*arg) - - -class TestKLDivLossConsistent(flow.unittest.TestCase): - @flow.unittest.skip_unless_1n2d() - def test_kl_div_loss(testcase): - arg_dict = OrderedDict() - arg_dict["device_type"] = ["cuda", "cpu"] - arg_dict["dim"] = [2, 3, 4, 5] - arg_dict["reduction"] = ["sum", "mean"] - arg_dict["cls"] = [flow.nn.KLDivLoss] - arg_dict["data_generator"] = [generate_necessity_default] - for arg in GenArgList(arg_dict): - compare_loss(*arg) - - -class TestSmoothL1LossConsistent(flow.unittest.TestCase): - @flow.unittest.skip_unless_1n2d() - def test_smooth_l1_loss(testcase): - arg_dict = OrderedDict() - arg_dict["device_type"] = ["cuda", "cpu"] - arg_dict["dim"] = [2, 3, 4, 5] - arg_dict["reduction"] = ["sum", "mean"] - arg_dict["cls"] = [flow.nn.SmoothL1Loss] - arg_dict["data_generator"] = [generate_necessity_default] - for arg in GenArgList(arg_dict): - compare_loss(*arg) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_loss_global.py b/python/oneflow/test/modules/test_loss_global.py new file mode 100644 index 0000000000000000000000000000000000000000..12c314420df52b24fb44df222e7ddf925ad2003f --- /dev/null +++ b/python/oneflow/test/modules/test_loss_global.py @@ -0,0 +1,147 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList + + +def get_sbp(device: str): + return flow.placement.all(device), flow.sbp.split(0) + + +shapes = {2: (128, 8), 3: (16, 8, 64), 4: (16, 8, 32, 32), 5: (16, 8, 16, 16, 16)} + + +def compare_loss(device_type, dim, reduction, cls, data_generator): + x, y, x1, y1 = data_generator(dim, device_type, *get_sbp(device_type)) + reduce_loss_func = cls(reduction=reduction).to(device_type) + none_loss_func = cls(reduction="none").to(device_type) + + loss_mean = reduce_loss_func(x, y) + loss_none = ( + flow.mean(none_loss_func(x1, y1)) + if reduction == "mean" + else flow.sum(none_loss_func(x1, y1)) + ) + + loss_mean.backward() + loss_none.backward() + + assert np.allclose( + loss_none.to_local().numpy(), + loss_mean.to_local().numpy(), + rtol=1e-05, + atol=1e-05, + ) + assert np.allclose(loss_none.numpy(), loss_mean.numpy(), rtol=1e-05, atol=1e-05,) + assert np.allclose( + x.grad.to_local().numpy(), x1.grad.to_local().numpy(), rtol=1e-05, atol=1e-05, + ) + + +def generate_necessity_default(dim: int, device: str, placement, sbp): + shape = shapes[dim] + x_np = np.random.uniform(0, 1, shape) + y_np = np.random.uniform(0, 1, shape) + + def f(x, requires_grad): + t = flow.tensor(x, device=device, requires_grad=requires_grad).to_global( + placement=placement, sbp=[sbp] + ) + if requires_grad: + t.retain_grad() + return t + + return f(x_np, True), f(y_np, False), f(x_np, True), f(y_np, False) + + +def generate_necessity_for_cross_entropy_or_nll_loss( + dim: int, device: str, placement, sbp +): + shape = shapes[dim] + y_shape = (shape[0],) if dim == 2 else (shape[0], *shape[2:]) + x_np = np.random.uniform(0, 1, shape) + y_np = np.random.randint(0, shape[1], y_shape) + + def f(x, requires_grad): + t = flow.tensor(x, device=device, requires_grad=requires_grad).to_global( + placement=placement, sbp=[sbp] + ) + if requires_grad: + t.retain_grad() + return t + + return f(x_np, True), f(y_np, False), f(x_np, True), f(y_np, False) + + +class TestBCELossOrWithLogitsConsistent(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_bce_loss(testcase): + arg_dict = OrderedDict() + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["dim"] = [2, 3, 4, 5] + arg_dict["reduction"] = ["sum", "mean"] + arg_dict["cls"] = [flow.nn.BCELoss, flow.nn.BCEWithLogitsLoss] + arg_dict["data_generator"] = [generate_necessity_default] + for arg in GenArgList(arg_dict): + compare_loss(*arg) + + +class TestCrossEntropyOrNllLossConsistent(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_cross_entropy_loss_or_nll_loss(testcase): + arg_dict = OrderedDict() + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["dim"] = [2, 3, 4, 5] + arg_dict["reduction"] = ["sum", "mean"] + arg_dict["cls"] = [flow.nn.CrossEntropyLoss, flow.nn.NLLLoss] + arg_dict["data_generator"] = [generate_necessity_for_cross_entropy_or_nll_loss] + for arg in GenArgList(arg_dict): + compare_loss(*arg) + + +class TestKLDivLossConsistent(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_kl_div_loss(testcase): + arg_dict = OrderedDict() + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["dim"] = [2, 3, 4, 5] + arg_dict["reduction"] = ["sum", "mean"] + arg_dict["cls"] = [flow.nn.KLDivLoss] + arg_dict["data_generator"] = [generate_necessity_default] + for arg in GenArgList(arg_dict): + compare_loss(*arg) + + +class TestSmoothL1LossConsistent(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_smooth_l1_loss(testcase): + arg_dict = OrderedDict() + arg_dict["device_type"] = ["cuda", "cpu"] + arg_dict["dim"] = [2, 3, 4, 5] + arg_dict["reduction"] = ["sum", "mean"] + arg_dict["cls"] = [flow.nn.SmoothL1Loss] + arg_dict["data_generator"] = [generate_necessity_default] + for arg in GenArgList(arg_dict): + compare_loss(*arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_lr_scheduler.py b/python/oneflow/test/modules/test_lr_scheduler.py index bb2e5a664e5f39f90ae6ddeaba1f09948028b1fe..38375f658b64816b52833254c581f462b186b9a1 100644 --- a/python/oneflow/test/modules/test_lr_scheduler.py +++ b/python/oneflow/test/modules/test_lr_scheduler.py @@ -749,6 +749,44 @@ class LinearLRTestCase(flow.unittest.TestCase): f"\nexpected_lrs: {expected_lrs}\nvs.\ncalculated lrs: {lrs}", ) + def test_end_factor(test_case): + param = flow.nn.Parameter(flow.ones(3, 4)) + optimizer = flow.optim.SGD([param], lr=0.1) + linear_lr = flow.optim.lr_scheduler.LinearLR(optimizer, 0.1, 0.9, 10) + expected_lrs = [ + 0.01, + 0.018, + 0.026, + 0.034, + 0.042, + 0.05, + 0.058, + 0.066, + 0.074, + 0.082, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + 0.09, + ] + lrs = [linear_lr.get_last_lr()[0]] + for _ in range(len(expected_lrs)): + linear_lr.step() + lrs.append(linear_lr.get_last_lr()[0]) + + lrs = lrs[:-1] + test_case.assertTrue( + np.allclose(lrs, expected_lrs), + f"\nexpected_lrs: {expected_lrs}\nvs.\ncalculated lrs: {lrs}", + ) + @flow.unittest.skip_unless_1n1d() class ChainedSchedulerTestCase(flow.unittest.TestCase): diff --git a/python/oneflow/test/modules/test_masked_fill.py b/python/oneflow/test/modules/test_masked_fill.py index 551ef0db0e568d62ecd96075e49cdd63d9d11984..bf41bf71919487880a0557ae39bd7905c0d3683d 100644 --- a/python/oneflow/test/modules/test_masked_fill.py +++ b/python/oneflow/test/modules/test_masked_fill.py @@ -16,8 +16,6 @@ limitations under the License. import unittest -import numpy as np - from oneflow.test_utils.automated_test_util import * import oneflow as flow @@ -26,7 +24,7 @@ import oneflow.unittest @flow.unittest.skip_unless_1n1d() class TestMaskedFill(flow.unittest.TestCase): - @autotest(check_graph=True) + @autotest(n=3) def test_flow_masked_fill_with_random_data(test_case): k1 = random(2, 6) k2 = random(2, 6) @@ -36,7 +34,7 @@ class TestMaskedFill(flow.unittest.TestCase): value = random().to(float) return input.masked_fill(mask > 0.5, value) - @autotest(check_graph=True) + @autotest(n=3) def test_flow_masked_fill_with_0dim_data(test_case): device = random_device() input = random_tensor(ndim=0).to(device) @@ -44,7 +42,7 @@ class TestMaskedFill(flow.unittest.TestCase): value = random().to(float) return input.masked_fill(mask > 0, value) - @autotest(check_graph=True) + @autotest(n=3) def test_flow_masked_fill_broadcast_with_random_data(test_case): k1 = random(2, 6) k2 = random(2, 6) @@ -54,7 +52,7 @@ class TestMaskedFill(flow.unittest.TestCase): value = random().to(float) return input.masked_fill(mask > 0.5, value) - @autotest(check_graph=True) + @autotest(n=3) def test_flow_masked_fill_int_with_random_data(test_case): k1 = random(2, 6) k2 = random(2, 6) @@ -64,7 +62,7 @@ class TestMaskedFill(flow.unittest.TestCase): value = random().to(int) return input.masked_fill(mask > 0.5, value) - @autotest(auto_backward=False, check_graph=True) + @autotest(auto_backward=False, n=3) def test_flow_masked_fill_bool_with_random_data(test_case): k1 = random(2, 6) k2 = random(2, 6) @@ -76,6 +74,15 @@ class TestMaskedFill(flow.unittest.TestCase): value = random().to(bool) return input.masked_fill(mask > 0.5, value) + @autotest(auto_backward=False, n=3) + def test_flow_masked_fill_inplace_with_random_data(test_case): + device = random_device() + input = random_tensor(ndim=2, dim0=10, dim1=20).to(device).clone() + mask = random_tensor(ndim=2, dim0=10, dim1=20).to(device) + value = random().to(float) + input.masked_fill_(mask > 0.5, value) + return input + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_masked_select.py b/python/oneflow/test/modules/test_masked_select.py index 3ae15808da7dac966aa70de6c52cf585e950663a..10cdd8402964c49a1592446794f7952f85102892 100644 --- a/python/oneflow/test/modules/test_masked_select.py +++ b/python/oneflow/test/modules/test_masked_select.py @@ -82,11 +82,55 @@ def _test_masked_select_broadcast(test_case, device): test_case.assertTrue(np.allclose(x.grad.numpy(), np_grad, 1e-05, 1e-05)) +def _test_masked_select_input_zero(test_case, device): + x = flow.tensor( + [[26, 14, 18, 14, 5, 18, 5, 18, 4, 18, 15, 18, 22, 18, 0]], + device=flow.device(device), + dtype=flow.int64, + ) + f_mask = flow.tensor( + [ + [ + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + True, + ] + ], + device=flow.device(device), + dtype=flow.bool, + ) + y = x.masked_select(f_mask) + test_case.assertTrue( + np.allclose( + y.numpy(), + [26, 14, 18, 14, 5, 18, 5, 18, 4, 18, 15, 18, 22, 18, 0], + 1e-05, + 1e-05, + ) + ) + + @flow.unittest.skip_unless_1n1d() class TestMaskedSelect(flow.unittest.TestCase): def test_masked_select(test_case): arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_masked_select, _test_masked_select_broadcast] + arg_dict["test_fun"] = [ + _test_masked_select, + _test_masked_select_broadcast, + _test_masked_select_input_zero, + ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) diff --git a/python/oneflow/test/modules/test_math_op_higher_derivative.py b/python/oneflow/test/modules/test_math_op_higher_derivative.py index 22e0ea16ccd925718c2cd3ecfde07b18816465f4..89f22a96ac717821698198af258097dd6e35fde9 100644 --- a/python/oneflow/test/modules/test_math_op_higher_derivative.py +++ b/python/oneflow/test/modules/test_math_op_higher_derivative.py @@ -22,21 +22,44 @@ from oneflow.test_utils.automated_test_util import * def _test_math_op_grad_grad_impl(test_case, op_name): - x = random_tensor(ndim=2).requires_grad_(True) - y = eval(f"x.{op_name}().sum()") - x_grad = torch.autograd.grad(y, x, create_graph=True)[0] + x = random_tensor(ndim=2, low=-2, high=2).requires_grad_(True) + y = eval(f"torch.{op_name}")(x) + np_arr = np.random.rand(*x.oneflow.shape) + init_grad = torch.tensor(np_arr).requires_grad_() + + x_grad = torch.autograd.grad(y, x, init_grad, retain_graph=True, create_graph=True)[ + 0 + ] test_case.assertTrue( np.allclose( - x_grad.pytorch.detach().cpu().numpy(), x_grad.oneflow.detach().numpy() + x_grad.pytorch.detach().cpu().numpy(), + x_grad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, ) ) - x_grad_grad = torch.autograd.grad(x_grad, x, torch.ones_like(x), create_graph=True)[ - 0 - ] + + x_grad_grad = torch.autograd.grad(x_grad, x, init_grad, retain_graph=True)[0] test_case.assertTrue( np.allclose( x_grad_grad.pytorch.detach().cpu().numpy(), x_grad_grad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, + ) + ) + + init_grad_grad = torch.tensor(np_arr).requires_grad_() + dgrad = torch.autograd.grad(x_grad, init_grad, init_grad_grad, retain_graph=True)[0] + test_case.assertTrue( + np.allclose( + dgrad.pytorch.detach().cpu().numpy(), + dgrad.oneflow.detach().numpy(), + atol=1e-4, + rtol=1e-4, + equal_nan=True, ) ) @@ -48,6 +71,78 @@ class TestMathOpHigherDerivative(flow.unittest.TestCase): def test_cos_grad_grad(test_case): _test_math_op_grad_grad_impl(test_case, "cos") + def test_tan_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "tan") + + def test_sinh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "sinh") + + def test_cosh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "cosh") + + def test_tanh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "tanh") + + def test_asin_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "asin") + + def test_acos_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "acos") + + def test_atan_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "atan") + + def test_asinh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "asinh") + + def test_acosh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "acosh") + + def test_atanh_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "atanh") + + def test_erf_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "erf") + + def test_erfc_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "erfc") + + def test_exp_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "exp") + + def test_expm1_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "expm1") + + def test_log_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "log") + + def test_logsigmoid_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "nn.functional.logsigmoid") + + def test_log2_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "log2") + + def test_log1p_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "log1p") + + def test_reciprocal_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "reciprocal") + + def test_rsqrt_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "rsqrt") + + def test_sqrt_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "sqrt") + + def test_square_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "square") + + def test_sigmoid_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "sigmoid") + + def test_abs_grad_grad(test_case): + _test_math_op_grad_grad_impl(test_case, "abs") + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_math_ops.py b/python/oneflow/test/modules/test_math_ops.py index 508e0051d01d4089016eb8e28843d44c2929fd8e..3c81cdbcc791382af7e46522b61ebd879b9effa1 100644 --- a/python/oneflow/test/modules/test_math_ops.py +++ b/python/oneflow/test/modules/test_math_ops.py @@ -28,6 +28,9 @@ from oneflow.test_utils.test_util import ( type_name_to_np_type, ) +import torch as torch_original +from packaging import version + @flow.unittest.skip_unless_1n1d() class TestSinh(flow.unittest.TestCase): @@ -207,6 +210,15 @@ class TestPow(flow.unittest.TestCase): y = random_tensor(ndim=2, dim1=2).to(device) return torch.pow(x, y) + @autotest(n=5) + def test_scalar_pow_with_random_devices(test_case): + x1_device = random_device() + x2_device = random_device() + x1 = random_tensor(2, 2, 3).to(x1_device).mean() + x2 = random_tensor(2, 2, 3).to(x2_device) + y = torch.pow(x1, x2) + return y + @flow.unittest.skip_unless_1n1d() class TestAsin(flow.unittest.TestCase): @@ -276,6 +288,14 @@ class TestAtan(flow.unittest.TestCase): z = torch.atan2(x, y) return z + @autotest(n=5) + def test_flow_atan2_with_1elem_data(test_case): + device = random_device() + x = random_tensor(ndim=1, dim1=1).to(device) + y = random_tensor(ndim=3, dim1=random(1, 6).to(int)).to(device) + z = torch.atan2(x, y) + return z + @autotest(n=5) def test_flow_atanh_with_random_data(test_case): device = random_device() @@ -307,6 +327,22 @@ class TestTopk(flow.unittest.TestCase): return y[0], y[1] +@flow.unittest.skip_unless_1n1d() +class TestTopkReturnValues(flow.unittest.TestCase): + @autotest(auto_backward=False) + def test_flow_topk_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dim1=8, dim2=9, dim3=10).to(device) + result = torch.topk( + x, + random(low=1, high=8).to(int), + dim=random(low=1, high=4).to(int), + largest=random_bool(), + sorted=constant(True), + ) + return result.values, result.indices + + @flow.unittest.skip_unless_1n1d() class TestPow(flow.unittest.TestCase): @autotest(n=5) @@ -387,19 +423,21 @@ class TestAtan2(flow.unittest.TestCase): class TestMinimum(flow.unittest.TestCase): @autotest(n=5) def test_flow_elementwise_minimum_with_random_data(test_case): + device = random_device() k1 = random(2, 6) k2 = random(2, 6) - x = random_tensor(ndim=2, dim0=k1, dim1=k2) - y = random_tensor(ndim=2, dim0=k1, dim1=k2) + x = random_tensor(ndim=2, dim0=k1, dim1=k2).to(device) + y = random_tensor(ndim=2, dim0=k1, dim1=k2).to(device) return torch.minimum(x, y) @autotest(n=5) def test_flow_broadcast_minimum_with_random_data(test_case): + device = random_device() k1 = random(2, 6) k2 = random(2, 6) k3 = random(2, 6) - x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1) - y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3) + x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1).to(device) + y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3).to(device) return torch.minimum(x, y) @@ -407,19 +445,21 @@ class TestMinimum(flow.unittest.TestCase): class TestMaximum(flow.unittest.TestCase): @autotest(n=5) def test_flow_elementwise_mximum_with_random_data(test_case): + device = random_device() k1 = random(2, 6) k2 = random(2, 6) - x = random_tensor(ndim=2, dim0=k1, dim1=k2) - y = random_tensor(ndim=2, dim0=k1, dim1=k2) + x = random_tensor(ndim=2, dim0=k1, dim1=k2).to(device) + y = random_tensor(ndim=2, dim0=k1, dim1=k2).to(device) return torch.maximum(x, y) @autotest(n=5) def test_flow_broadcast_maximum_with_random_data(test_case): + device = random_device() k1 = random(2, 6) k2 = random(2, 6) k3 = random(2, 6) - x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1) - y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3) + x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1).to(device) + y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3).to(device) return torch.maximum(x, y) @@ -441,12 +481,112 @@ class TestFloorDiv(flow.unittest.TestCase): return torch.floor_divide(x, y) -if __name__ == "__main__": - # unittest.main() - x = np.array([[1, 3, 8, 7, 2], [1, 9, 4, 3, 2]], dtype=np.float32) - input = flow.tensor( - x, dtype=flow.float32, device=flow.device("cuda") +@flow.unittest.skip_unless_1n1d() +class TestFmod(flow.unittest.TestCase): + # other.grad in torch.fmod(input, other) was not implemented before pytorch 1.11.0 + grad_implemented = version.parse(torch_original.__version__) >= version.parse( + "1.11.0" ) - (values, indices) = flow.topk(flow.Tensor(x), k=1, dim=0) - print(values) - print(indices) \ No newline at end of file + + @autotest(auto_backward=grad_implemented) + def test_elementwise_fmod_random_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + y = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + + return torch.fmod(x, y) + + @autotest(n=5, auto_backward=grad_implemented) + def test_flow_broadcast_fmod_with_random_data(test_case): + device = random_device() + k1 = random(2, 6) + k2 = random(2, 6) + k3 = random(2, 6) + x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1).to(device) + y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3).to(device) + return torch.fmod(x, y) + + @autotest(auto_backward=grad_implemented) + def test_tensor_fmod_scalar_random_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + y = random().to(int) + return torch.fmod(x, y) + + +@flow.unittest.skip_unless_1n1d() +class TestPow(flow.unittest.TestCase): + @autotest(auto_backward=False) + def test_elementwise_pow_random_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + y = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + + return torch.pow(x, y) + + @autotest(n=5) + def test_flow_broadcast_pow_with_random_data(test_case): + device = random_device() + k1 = random(2, 6) + k2 = random(2, 6) + k3 = random(2, 6) + x = random_tensor(ndim=3, dim0=k1, dim1=1, dim2=1).to(device) + y = random_tensor(ndim=3, dim0=1, dim1=k2, dim2=k3).to(device) + return torch.pow(x, y) + + @autotest(auto_backward=False) + def test_tensor_pow_scalar_random_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dim0=2, dim1=4, dim2=8, dim3=3).to(device) + y = random().to(int) + return torch.pow(x, y) + + +@flow.unittest.skip_unless_1n1d() +class TestAbsModule(flow.unittest.TestCase): + @autotest(n=5) + def test_abs_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return torch.abs(x) + + +@flow.unittest.skip_unless_1n1d() +class TestCoshModule(flow.unittest.TestCase): + @autotest(n=5) + def test_cosh_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return torch.cosh(x) + + +@flow.unittest.skip_unless_1n1d() +class TestLgammaModule(flow.unittest.TestCase): + # TODO: Add lgamma backward. + @autotest(n=5, auto_backward=False) + def test_lgamma_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return torch.lgamma(x) + + +@flow.unittest.skip_unless_1n1d() +class TestLog2Module(flow.unittest.TestCase): + @autotest(n=5) + def test_log2_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return torch.log2(x) + + +@flow.unittest.skip_unless_1n1d() +class TestLog10Module(flow.unittest.TestCase): + @autotest(n=5) + def test_log10_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return torch.log10(x) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_matmul.py b/python/oneflow/test/modules/test_matmul.py index 5d0075566398ec03be4dcecc4d5850e949b5737f..73a4061cb66dec5fcdad8791d12c4bd7742aeb49 100644 --- a/python/oneflow/test/modules/test_matmul.py +++ b/python/oneflow/test/modules/test_matmul.py @@ -15,10 +15,10 @@ limitations under the License. """ import unittest +import numpy as np import oneflow as flow import oneflow.unittest - from oneflow.test_utils.automated_test_util import * @@ -41,7 +41,21 @@ class TestModule(flow.unittest.TestCase): y = random_tensor(ndim=2, dim0=k).to(device) return x.matmul(y) - @autotest(check_graph=True) + @autotest(n=5, check_graph=False) + def test_flow_tensor_matmul_with_random_int_data(test_case): + x = np.random.randint(10, 21, size=5) + y = np.random.randint(1, 14, size=(5, 4)) + torch_x = torch.from_numpy(x).to(torch.int) + torch_y = torch.from_numpy(y).to(torch.int) + torch_output_numpy = torch_x.matmul(torch_y).numpy() + flow_x = flow.tensor(x).to(flow.int) + flow_y = flow.tensor(y).to(flow.int) + flow_output_numpy = flow_x.matmul(flow_y).numpy() + test_case.assertTrue( + np.allclose(flow_output_numpy, torch_output_numpy, 1e-05, 1e-05) + ) + + @autotest(n=5, check_graph=True) def test_flow_tensor_broadcast_matmul_with_random_data(test_case): device = random_device() k = random(1, 6) @@ -49,6 +63,22 @@ class TestModule(flow.unittest.TestCase): y = random_tensor(ndim=2, dim0=k).to(device) return x.matmul(y) + @autotest(n=5, check_graph=True) + def test_flow_tensor_x_broadcast_y_matmul(test_case): + device = random_device() + k = random(1, 6) + x = random_tensor(ndim=2, dim1=k).to(device) + y = random_tensor(ndim=4, dim2=k).to(device) + return x.matmul(y) + + @autotest(n=5, check_graph=True) + def test_flow_tensor_broadcast_matmul_with_same_dims(test_case): + device = random_device() + k = random(1, 6) + x = random_tensor(ndim=4, dim1=1, dim3=k).to(device) + y = random_tensor(ndim=4, dim0=1, dim2=k).to(device) + return x.matmul(y) + @autotest(check_graph=True) def test_flow_mm_with_random_data(test_case): device = random_device() @@ -58,6 +88,7 @@ class TestModule(flow.unittest.TestCase): z = torch.mm(x, y) return z + @autotest(n=5, check_graph=True) def test_flow_mv_with_random_data(test_case): device = random_device() k = random(1, 6) @@ -70,6 +101,15 @@ class TestModule(flow.unittest.TestCase): def profile_mv(test_case): torch.mv(torch.ones(32, 64), torch.ones(64)) + @autotest(n=5, check_graph=True) + def test_flow_vector_matrix_product_with_random_data(test_case): + device = random_device() + k = random(1, 6) + x = random_tensor(ndim=1, dim0=k).to(device) + y = random_tensor(ndim=2, dim0=k).to(device) + z = torch.matmul(x, y) + return z + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_max.py b/python/oneflow/test/modules/test_max.py index 919eabec6bcbce7b4658589b0eb49981bae98fb8..57078a70fb62ceaf0a24271eaf96286387a6c405 100644 --- a/python/oneflow/test/modules/test_max.py +++ b/python/oneflow/test/modules/test_max.py @@ -106,6 +106,12 @@ class TestMaxModule(flow.unittest.TestCase): y = random_tensor(2, 1, 4) return torch.max(x, y) + @autotest(n=3, auto_backward=False) + def test_max_return_type(test_case): + x = random_tensor(3, 4) + result = x.max(1) + return result.values, result.indices + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_maxpool.py b/python/oneflow/test/modules/test_maxpool.py index 477ba8024bb7606a1507ea2854dad8a723424329..e60e2e4313a5b431645477066cd3aa3f7eb4ead7 100644 --- a/python/oneflow/test/modules/test_maxpool.py +++ b/python/oneflow/test/modules/test_maxpool.py @@ -15,6 +15,7 @@ limitations under the License. """ import unittest from collections import OrderedDict +from pkg_resources import packaging import numpy as np import torch as pytorch @@ -50,10 +51,10 @@ def _test_maxpool2d_channel_last( ceil_mode=ceil_mode, ) y2 = m2(x2).permute(0, 2, 3, 1) + os.environ["ONEFLOW_ENABLE_NHWC"] = "0" test_case.assertTrue( np.allclose(y1.detach().cpu().numpy(), y2.detach().cpu().numpy(), 1e-4, 1e-4) ) - os.environ["ONEFLOW_ENABLE_NHWC"] = "0" @flow.unittest.skip_unless_1n1d() @@ -106,6 +107,32 @@ class TestMaxPooling(flow.unittest.TestCase): else: return y + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @autotest(n=5, auto_backward=False) + def test_maxpool2d_with_half_data(test_case): + return_indices = random().to(bool).value() + m = torch.nn.MaxPool2d( + kernel_size=random(4, 6).to(_size_2_t), + stride=random(1, 3).to(_size_2_t) | nothing(), + padding=random(1, 3).to(_size_2_t) | nothing(), + dilation=random(2, 4).to(_size_2_t) | nothing(), + ceil_mode=random(), + return_indices=return_indices, + ) + m.train(random()) + device = gpu_device() + m.to(device) + x = ( + random_tensor(ndim=4, dim2=random(20, 22), dim3=random(20, 22)) + .to(device) + .to(torch.float16) + ) + y = m(x) + if return_indices: + return y[0] + else: + return y + @autotest(n=5, auto_backward=True, check_graph=True) def test_maxpool3d_with_random_data(test_case): return_indices = random().to(bool).value() @@ -132,21 +159,28 @@ class TestMaxPooling(flow.unittest.TestCase): else: return y - # def test_maxpool2d_channel_last(test_case): - # arg_dict = OrderedDict() - # arg_dict["test_fun"] = [_test_maxpool2d_channel_last] - # arg_dict["device"] = ["cuda"] - # # CPU pool is very slow, so don't run it with CUDA - # if os.getenv("ONEFLOW_TEST_CPU_ONLY"): - # arg_dict["device"] = ["cpu"] - # arg_dict["shape"] = [(3, 14, 27, 3), (5, 9, 14, 10), (2, 224, 224, 3)] - # arg_dict["kernel_size"] = [3, (2, 3), (3, 4)] - # arg_dict["stride"] = [1, (1, 2), 2] - # arg_dict["padding"] = [0, (0, 1)] - # arg_dict["dilation"] = [1, (1, 2), 2] - # arg_dict["ceil_mode"] = [True, False] - # for arg in GenArgList(arg_dict): - # arg[0](test_case, *arg[1:]) + @unittest.skipIf( + packaging.version.parse(pytorch.__version__) + == packaging.version.parse("1.10.0"), + "skip when pytorch version == 1.10.0", + ) + # NOTE:pytorch maxpool2d nhwc has bug in version of 1.10.0, so skip it in CI. + # detail:https://github.com/pytorch/pytorch/pull/76597 + def test_maxpool2d_channel_last(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_maxpool2d_channel_last] + arg_dict["device"] = ["cuda"] + # CPU pool is very slow, so don't run it with CUDA + if os.getenv("ONEFLOW_TEST_CPU_ONLY"): + arg_dict["device"] = ["cpu"] + arg_dict["shape"] = [(3, 14, 27, 3), (5, 9, 14, 10), (2, 224, 224, 3)] + arg_dict["kernel_size"] = [3, (2, 3), (3, 4)] + arg_dict["stride"] = [1, (1, 2), 2] + arg_dict["padding"] = [0, (0, 1)] + arg_dict["dilation"] = [1, (1, 2), 2] + arg_dict["ceil_mode"] = [True, False] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) @flow.unittest.skip_unless_1n1d() diff --git a/python/oneflow/test/modules/test_maxunpool.py b/python/oneflow/test/modules/test_maxunpool.py new file mode 100644 index 0000000000000000000000000000000000000000..57186c1f491f003c8b2a628937e15506c3a02d31 --- /dev/null +++ b/python/oneflow/test/modules/test_maxunpool.py @@ -0,0 +1,224 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import random as random_util +import unittest + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * +from oneflow.nn.common_types import _size_1_t, _size_2_t, _size_3_t + +# y = pool(x), z = unpool(y, indices), pool_input_shape is x.shape, pool_output_shape is y.shape. +# When `output_size` in unpool() is empty, the op will calculate the output size according to +# kernel_size, stride and padding. But when index in indices is outside the range required +# by output_size calculated by unpool op, the value of result and related grad will be unknown. +# To avoid the problem, this function calculate the output_size which will not cause unknown problems. +def _get_valid_output_size( + pool_input_shape, pool_output_shape, kernel_size, stride, padding +): + def convert_data(data, i, dst_data=None): + if not isinstance(data, (list, int)): + return dst_data + if isinstance(data, list): + return data[i] + return data + + _, _, *pool_input_hwd_shape = pool_input_shape.pytorch + batch_size, num_channels, *pool_out_hwd_shape = pool_output_shape.pytorch + unpool_output_shape = [batch_size, num_channels] + for i, (pool_input_size, pool_output_size) in enumerate( + zip(pool_input_hwd_shape, pool_out_hwd_shape) + ): + kernel_size_value = convert_data(kernel_size.value(), i) + stride_value = convert_data(stride.value(), i, kernel_size_value) + padding_value = convert_data(padding.value(), i, 0) + unpool_output_size = max( + pool_input_size, + (pool_output_size - 1) * stride_value + - 2 * padding_value + + kernel_size_value, + ) + unpool_output_shape.append(unpool_output_size) + return torch.Size(unpool_output_shape) + + +def _test_module_unpoolnd(test_case, n): + device = random_device() + if n == 1: + _size_n_t = _size_1_t + MaxPoolNd = torch.nn.MaxPool1d + MaxUnpoolNd = torch.nn.MaxUnpool1d + x = random_tensor(ndim=3, dim2=random(20, 31), requires_grad=False).to(device) + elif n == 2: + _size_n_t = _size_2_t + MaxPoolNd = torch.nn.MaxPool2d + MaxUnpoolNd = torch.nn.MaxUnpool2d + x = random_tensor( + ndim=4, dim2=random(20, 31), dim3=random(20, 31), requires_grad=False + ).to(device) + elif n == 3: + _size_n_t = _size_3_t + MaxPoolNd = torch.nn.MaxPool3d + MaxUnpoolNd = torch.nn.MaxUnpool3d + x = random_tensor( + ndim=5, + dim2=random(20, 31), + dim3=random(20, 31), + dim4=random(20, 31), + requires_grad=False, + ).to(device) + + kernel_size = random(4, 6).to(_size_n_t) + stride = random(1, 3).to(_size_n_t) | nothing() + padding = random(1, 3).to(_size_n_t) | nothing() + m = MaxPoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, return_indices=True, + ) + m.train(random()) + m.to(device) + y = m(x) + pooling_results_dtype = random_util.choice( + [torch.int, torch.long, torch.float, torch.double] + ) + indices_dtype = random_util.choice([torch.int, torch.long]) + pooling_results = y[0].to(pooling_results_dtype) + indices = y[1].to(indices_dtype) + pooling_results.requires_grad_() + output_size = _get_valid_output_size( + x.shape, pooling_results.shape, kernel_size, stride, padding + ) + unpool_module = MaxUnpoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, + ) + result = unpool_module(pooling_results, indices, output_size=output_size) + return result + + +def _test_functional_unpoolnd(test_case, n): + device = random_device() + + if n == 1: + _size_n_t = _size_1_t + MaxPoolNd = torch.nn.MaxPool1d + max_unpool_nd = torch.nn.functional.max_unpool1d + x = random_tensor(ndim=3, dim2=random(20, 31), requires_grad=False).to(device) + elif n == 2: + _size_n_t = _size_2_t + MaxPoolNd = torch.nn.MaxPool2d + max_unpool_nd = torch.nn.functional.max_unpool2d + x = random_tensor( + ndim=4, dim2=random(20, 31), dim3=random(20, 31), requires_grad=False + ).to(device) + elif n == 3: + _size_n_t = _size_3_t + MaxPoolNd = torch.nn.MaxPool3d + max_unpool_nd = torch.nn.functional.max_unpool3d + x = random_tensor( + ndim=5, + dim2=random(20, 31), + dim3=random(20, 31), + dim4=random(20, 31), + requires_grad=False, + ).to(device) + + kernel_size = random(4, 6).to(_size_n_t) + stride = random(1, 3).to(_size_n_t) | nothing() + padding = random(1, 3).to(_size_n_t) | nothing() + m = MaxPoolNd( + kernel_size=kernel_size, stride=stride, padding=padding, return_indices=True, + ) + m.train(random()) + m.to(device) + y = m(x) + pooling_results_dtype = random_util.choice( + [torch.int, torch.long, torch.float, torch.double] + ) + indices_dtype = random_util.choice([torch.int, torch.long]) + pooling_results = y[0].to(pooling_results_dtype) + indices = y[1].to(indices_dtype) + pooling_results.requires_grad_() + output_size = _get_valid_output_size( + x.shape, pooling_results.shape, kernel_size, stride, padding + ) + return max_unpool_nd( + pooling_results, + indices, + kernel_size=kernel_size, + stride=stride, + padding=padding, + output_size=output_size, + ) + + +@flow.unittest.skip_unless_1n1d() +class TestMaxUnpooling(flow.unittest.TestCase): + @autotest(n=3, check_graph=False) + def test_max_unpool1d_with_random_data(test_case): + return _test_module_unpoolnd(test_case, 1) + + @autotest(n=3, check_graph=False) + def test_functional_max_unpool1d_with_random_data(test_case): + return _test_functional_unpoolnd(test_case, 1) + + @autotest(n=3, check_graph=False) + def test_max_unpool2d_with_random_data(test_case): + return _test_module_unpoolnd(test_case, 2) + + @autotest(n=3, check_graph=False) + def test_functional_max_unpool2d_with_random_data(test_case): + return _test_functional_unpoolnd(test_case, 2) + + @autotest(n=3, check_graph=False) + def test_max_unpool3d_with_random_data(test_case): + return _test_module_unpoolnd(test_case, 3) + + @autotest(n=3, check_graph=False) + def test_functional_max_unpool3d_with_random_data(test_case): + return _test_functional_unpoolnd(test_case, 3) + + @profile(torch.nn.functional.max_unpool1d) + def profile_max_unpool1d(test_case): + max_pool_results = torch.randn(1, 32, 64) + max_pool_indices = torch.arange(64).expand(1, 32, 64) + torch.nn.functional.max_unpool1d(max_pool_results, max_pool_indices, 2) + + max_pool_results = torch.randn(32, 32, 64) + max_pool_indices = torch.arange(64).expand(32, 32, 64) + torch.nn.functional.max_unpool1d(max_pool_results, max_pool_indices, 2) + + @profile(torch.nn.functional.max_unpool2d) + def profile_max_unpool2d(test_case): + max_pool_results = torch.randn(1, 16, 32, 32) + max_pool_indices = torch.arange(32).expand(1, 16, 32, 32) + torch.nn.functional.max_unpool2d(max_pool_results, max_pool_indices, 2) + + max_pool_results = torch.randn(32, 16, 32, 32) + max_pool_indices = torch.arange(32).expand(32, 16, 32, 32) + torch.nn.functional.max_unpool2d(max_pool_results, max_pool_indices, 2) + + @profile(torch.nn.functional.max_unpool3d) + def profile_max_unpool3d(test_case): + max_pool_results = torch.randn(1, 4, 32, 32, 32) + max_pool_indices = torch.arange(32).expand(1, 4, 32, 32, 32) + torch.nn.functional.max_unpool3d(max_pool_results, max_pool_indices, 2) + + max_pool_results = torch.randn(16, 4, 32, 32, 32) + max_pool_indices = torch.arange(32).expand(16, 4, 32, 32, 32) + torch.nn.functional.max_unpool3d(max_pool_results, max_pool_indices, 2) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_mean.py b/python/oneflow/test/modules/test_mean.py index 3b137ecc9434052820f58d2f4adcec4c621b2c1b..6bf95895485d09019fd5a7c7d1c666b08f6d14c1 100644 --- a/python/oneflow/test/modules/test_mean.py +++ b/python/oneflow/test/modules/test_mean.py @@ -86,6 +86,21 @@ class TestMean(flow.unittest.TestCase): x = random_tensor(ndim=4, dtype=float).to(device) return torch.mean(x, dim) + @autotest(n=5) + def test_mean_with_scalar_data(test_case): + device = random_device() + x = random_tensor(ndim=4, dtype=float).to(device).mean() + y = x.mean(-1) + return y + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @autotest(n=5, atol=1e-3) + def test_mean_with_float16_data(test_case): + device = gpu_device() + dim = random(1, 4).to(int) + x = random_tensor(ndim=4, dtype=float).to(device=device, dtype=torch.float16) + return torch.mean(x, dim) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_median.py b/python/oneflow/test/modules/test_median.py index a6e557e011d2800f53c7cc837a2d1141c47e7f2e..a74821c32ac8863512a5afe0c159e6838c8c592d 100644 --- a/python/oneflow/test/modules/test_median.py +++ b/python/oneflow/test/modules/test_median.py @@ -56,6 +56,12 @@ class TestMedianModule(flow.unittest.TestCase): x = random_tensor(ndim=3, dim1=0, requires_grad=False).to(device) return torch.median(x, 0) + @autotest(n=5, auto_backward=False) + def test_median_return_type(test_case): + x = random_tensor(3, 4) + result = x.median(1) + return result.values, result.indices + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_min.py b/python/oneflow/test/modules/test_min.py index 6ef5e67e291d8f7826a43c3ba1b530743d74bb9c..fc8693b499219700369e07b6d53be98dfeb51f9d 100644 --- a/python/oneflow/test/modules/test_min.py +++ b/python/oneflow/test/modules/test_min.py @@ -98,6 +98,12 @@ class TestMinModule(flow.unittest.TestCase): y = random_tensor(ndim, *b_dims, dtype=int).to(device) return torch.min(x, y) + @autotest(n=3, auto_backward=False) + def test_min_return_type(test_case): + x = random_tensor(3, 4) + result = x.min(1) + return result.values, result.indices + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_module.py b/python/oneflow/test/modules/test_module.py index 7c538d7cb3c33ac58386c67b9c70bced2cacdb35..211992b14f16ebb7046bc8a01b771b1a78177b94 100644 --- a/python/oneflow/test/modules/test_module.py +++ b/python/oneflow/test/modules/test_module.py @@ -23,12 +23,22 @@ from typing import Tuple, Union, List from collections import OrderedDict import numpy as np +import torch import oneflow as flow import oneflow.nn as nn import oneflow.unittest +class CustomModuleForSaveLoad(flow.nn.Module): + def __init__(self): + super().__init__() + self.param = flow.nn.Parameter(flow.randn(1, 3, 3, 3)) + + def forward(self, x): + return self.param + x + + def np_relu(np_arr): return np.where(np_arr > 0, np_arr, 0) @@ -175,6 +185,49 @@ class TestModule(flow.unittest.TestCase): net.apply(get_module_num) test_case.assertEqual(module_num, 2) + @flow.unittest.skip_unless_1n1d() + def test_load_map_location(test_case): + x = flow.ones(1, 2, 3) + y = flow.ones(2, 3, 4) + with tempfile.TemporaryDirectory() as save_dir: + flow.save({"x": x, "y": y}, save_dir) + loaded = flow.load(save_dir, map_location="cuda") + assert np.array_equal(loaded["x"].numpy(), x.numpy()) + assert loaded["x"].device == flow.device("cuda") + assert np.array_equal(loaded["y"].numpy(), y.numpy()) + assert loaded["y"].device == flow.device("cuda") + + with tempfile.TemporaryDirectory() as save_dir: + flow.save({"x": x, "y": y}, save_dir) + loaded = flow.load(save_dir, map_location="cpu") + assert np.array_equal(loaded["x"].numpy(), x.numpy()) + assert loaded["x"].device == flow.device("cpu") + assert np.array_equal(loaded["y"].numpy(), y.numpy()) + assert loaded["y"].device == flow.device("cpu") + + x = x.to_global(sbp=flow.sbp.broadcast, placement=flow.placement("cuda", [0])) + y = y.to_global(sbp=flow.sbp.broadcast, placement=flow.placement("cuda", [0])) + + with tempfile.TemporaryDirectory() as save_dir: + flow.save({"x": x, "y": y}, save_dir, global_dst_rank=0) + loaded = flow.load( + save_dir, global_src_rank=0, map_location=flow.placement("cuda", [0]) + ) + assert np.array_equal(loaded["x"].numpy(), x.numpy()) + assert loaded["x"].placement == flow.placement("cuda", [0]) + assert np.array_equal(loaded["y"].numpy(), y.numpy()) + assert loaded["y"].placement == flow.placement("cuda", [0]) + + with tempfile.TemporaryDirectory() as save_dir: + flow.save({"x": x, "y": y}, save_dir, global_dst_rank=0) + loaded = flow.load( + save_dir, global_src_rank=0, map_location=flow.placement("cpu", [0]) + ) + assert np.array_equal(loaded["x"].numpy(), x.numpy()) + assert loaded["y"].placement == flow.placement("cpu", [0]) + assert np.array_equal(loaded["y"].numpy(), y.numpy()) + assert loaded["y"].placement == flow.placement("cpu", [0]) + @flow.unittest.skip_unless_1n1d() def test_save_state_dict(test_case): class CustomModule(flow.nn.Module): @@ -262,6 +315,105 @@ class TestModule(flow.unittest.TestCase): def test_save_and_load_global_from_nested_dict_2n2d(test_case): test_case._test_save_and_load_global_from_nested_dict() + @flow.unittest.skip_unless_1n1d() + def test_load_pytorch_weights(test_case): + for device in ["cpu", "cuda"]: + for map_location in [None, flow.device("cuda:0")]: + conv_torch = torch.nn.Conv2d(3, 3, 3).to(device) + + conv_flow1 = flow.nn.Conv2d(3, 3, 3).to(device) + with tempfile.NamedTemporaryFile() as f: + torch.save(conv_torch.state_dict(), f.name) + conv_flow1.load_state_dict( + flow.load(f.name, map_location=map_location) + ) + test_case.assertTrue( + np.array_equal( + conv_torch.weight.detach().cpu().numpy(), + conv_flow1.weight.numpy(), + ) + ) + + conv_flow2 = flow.nn.Conv2d(3, 3, 3).to(device) + with tempfile.NamedTemporaryFile() as f: + torch.save({"weights": conv_torch.state_dict()}, f.name) + conv_flow2.load_state_dict( + flow.load(f.name, map_location=map_location)["weights"] + ) + test_case.assertTrue( + np.array_equal( + conv_torch.weight.detach().cpu().numpy(), + conv_flow2.weight.numpy(), + ) + ) + + @flow.unittest.skip_unless_1n2d() + def test_load_pytorch_weights_global(test_case): + for device in ["cpu", "cuda"]: + for map_location in [None, flow.placement.all("cuda")]: + conv_torch = torch.nn.Conv2d(3, 3, 3).to(device) + + all_placement = flow.placement.all(device) + conv_flow1 = flow.nn.Conv2d(3, 3, 3).to_global( + all_placement, flow.sbp.broadcast + ) + with tempfile.NamedTemporaryFile() as f: + if flow.env.get_rank() == 0: + torch.save(conv_torch.state_dict(), f.name) + conv_flow1.load_state_dict( + flow.load(f.name, map_location=map_location, global_src_rank=0) + ) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + conv_torch.weight.detach().cpu().numpy(), + conv_flow1.weight.numpy(), + ) + ) + + conv_flow2 = flow.nn.Conv2d(3, 3, 3).to_global( + all_placement, flow.sbp.broadcast + ) + with tempfile.NamedTemporaryFile() as f: + if flow.env.get_rank() == 0: + torch.save({"weights": conv_torch.state_dict()}, f.name) + conv_flow2.load_state_dict( + flow.load(f.name, map_location=map_location, global_src_rank=0)[ + "weights" + ] + ) + if flow.env.get_rank() == 0: + test_case.assertTrue( + np.array_equal( + conv_torch.weight.detach().cpu().numpy(), + conv_flow2.weight.numpy(), + ) + ) + + @flow.unittest.skip_unless_1n1d() + def test_save_load_module_directly(test_case): + x = flow.randn(1, 3, 3, 3) + + m = CustomModuleForSaveLoad() + + with tempfile.TemporaryDirectory() as f: + flow.save(m, f) + new_m = flow.load(f) + res = m(x) + new_res = new_m(x) + test_case.assertTrue(np.array_equal(res.numpy(), new_res.numpy())) + + m = flow.nn.parallel.DistributedDataParallel(m) + test_case.assertTrue(m._is_ddp_module) + + with tempfile.TemporaryDirectory() as f: + flow.save(m, f) + new_m = flow.load(f) + test_case.assertTrue(new_m._is_ddp_module) + res = m(x) + new_res = new_m(x) + test_case.assertTrue(np.array_equal(res.numpy(), new_res.numpy())) + @flow.unittest.skip_unless_1n1d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") def test_module_cpu_cuda(test_case): @@ -328,6 +480,20 @@ class TestModule(flow.unittest.TestCase): output = model(input, "conv", "relu") test_case.assertEqual(output.shape, flow.Size([4, 10, 30, 30])) + @flow.unittest.skip_unless_1n1d() + def test_module_delattr(test_case): + class ConvBNModule(nn.Module): + def __init__(self): + super(ConvBNModule, self).__init__() + self.conv = nn.Conv2d(1, 2, 1, 1) + self.bn = nn.BatchNorm2d(2) + + def forward(self, x): + return self.bn(self.conv(x)) + + m = ConvBNModule() + delattr(m, "bn") + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_module_to.py b/python/oneflow/test/modules/test_module_to.py index 7d656fab009338bcaca4e73b77e0eab2858405b3..d9bd15bb26d260d2d80728c5f02ddf6e40af1485 100644 --- a/python/oneflow/test/modules/test_module_to.py +++ b/python/oneflow/test/modules/test_module_to.py @@ -37,6 +37,7 @@ class DummyModule(flow.nn.Module): super().__init__() self.register_buffer("dummy_buf", flow.Tensor(dummy_val)) self.dummy_para = flow.nn.Parameter(flow.Tensor(dummy_val)) + self.dummy_para_int = flow.nn.Parameter(flow.Tensor(dummy_val).to(flow.int32)) def forward(self, x): return self.dummy_para * x + self.dummy_buf @@ -84,12 +85,29 @@ def _test_dummy_module_to(test_case): @flow.unittest.skip_unless_1n1d() @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestModuleTo(flow.unittest.TestCase): - def test_module_to(test_case): + def test_module_to_device(test_case): arg_dict = OrderedDict() arg_dict["test_fun"] = [_test_dummy_module, _test_dummy_module_to] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) + def test_module_to_dtype(test_case): + m = DummyModule() + m.to(flow.float64) + test_case.assertEqual(m.dummy_buf.dtype, flow.float64) + test_case.assertEqual(m.dummy_para.dtype, flow.float64) + test_case.assertEqual(m.dummy_para_int.dtype, flow.int32) + + def test_module_to_tensor(test_case): + m = DummyModule() + m.to(flow.zeros(1, dtype=flow.float16, device="cuda")) + test_case.assertEqual(m.dummy_buf.dtype, flow.float16) + test_case.assertEqual(m.dummy_para.dtype, flow.float16) + test_case.assertEqual(m.dummy_para_int.dtype, flow.int32) + test_case.assertEqual(m.dummy_buf.device.type, "cuda") + test_case.assertEqual(m.dummy_para.device.type, "cuda") + test_case.assertEqual(m.dummy_para_int.device.type, "cuda") + def test_module_to_with_var_reuse(test_case): class ReuseVarModule(flow.nn.Module): def __init__(self): diff --git a/python/oneflow/test/modules/test_module_to_consistent.py b/python/oneflow/test/modules/test_module_to_consistent.py deleted file mode 100644 index 6702d3a1e3fb2a3bf4eabc4f4858be1d5f6de15a..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_module_to_consistent.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import os -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import GenArgList - -import oneflow as flow -import oneflow.unittest - - -@flow.unittest.skip_unless_1n2d() -@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") -class TestModuleToCosistent(flow.unittest.TestCase): - def test_module_to_global(test_case): - rank = flow.env.get_rank() - P = flow.placement("cuda", ranks=[0, 1]) - B = flow.sbp.broadcast - - class ReuseVarModule(flow.nn.Module): - def __init__(self): - super().__init__() - self.linear1 = flow.nn.Linear(3, 4) - self.linear2 = flow.nn.Linear(3, 4) - self.linear2.weight = self.linear1.weight - - reuse_var_m = ReuseVarModule() - - test_case.assertTrue(reuse_var_m.linear1.weight is reuse_var_m.linear2.weight) - test_case.assertEqual( - reuse_var_m.linear1.weight.device, flow.device("cpu", rank) - ) - - test_case.assertTrue(reuse_var_m.linear1.bias is not reuse_var_m.linear2.bias) - test_case.assertEqual(reuse_var_m.linear1.bias.device, flow.device("cpu", rank)) - - reuse_var_m.to_global(placement=P, sbp=B) - - test_case.assertTrue(reuse_var_m.linear1.weight is reuse_var_m.linear2.weight) - test_case.assertEqual(reuse_var_m.linear1.weight.placement, P) - test_case.assertEqual(reuse_var_m.linear1.weight.sbp[0], B) - - test_case.assertTrue(reuse_var_m.linear1.bias is not reuse_var_m.linear2.bias) - test_case.assertEqual(reuse_var_m.linear1.bias.placement, P) - test_case.assertEqual(reuse_var_m.linear1.bias.sbp[0], B) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_module_to_global_or_local.py b/python/oneflow/test/modules/test_module_to_global_or_local.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e876816d1d8d7ad5c868e37d1ced90c4a26a25 --- /dev/null +++ b/python/oneflow/test/modules/test_module_to_global_or_local.py @@ -0,0 +1,119 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n2d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestModuleToGlobalOrLocal(flow.unittest.TestCase): + def test_module_to_global(test_case): + rank = flow.env.get_rank() + P = flow.placement("cuda", ranks=[0, 1]) + B = flow.sbp.broadcast + + class ReuseVarModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear1 = flow.nn.Linear(3, 4) + self.linear2 = flow.nn.Linear(3, 4) + self.linear2.weight = self.linear1.weight + + reuse_var_m = ReuseVarModule() + + test_case.assertTrue(reuse_var_m.linear1.weight is reuse_var_m.linear2.weight) + test_case.assertEqual( + reuse_var_m.linear1.weight.device, flow.device("cpu", rank) + ) + + test_case.assertTrue(reuse_var_m.linear1.bias is not reuse_var_m.linear2.bias) + test_case.assertEqual(reuse_var_m.linear1.bias.device, flow.device("cpu", rank)) + + reuse_var_m.to_global(placement=P, sbp=B) + + test_case.assertTrue(reuse_var_m.linear1.weight is reuse_var_m.linear2.weight) + test_case.assertEqual(reuse_var_m.linear1.weight.placement, P) + test_case.assertEqual(reuse_var_m.linear1.weight.sbp[0], B) + + test_case.assertTrue(reuse_var_m.linear1.bias is not reuse_var_m.linear2.bias) + test_case.assertEqual(reuse_var_m.linear1.bias.placement, P) + test_case.assertEqual(reuse_var_m.linear1.bias.sbp[0], B) + + def test_module_to_local(test_case): + rank = flow.env.get_rank() + device = "cuda" + P = flow.placement(device, ranks=[0, 1]) + B = flow.sbp.broadcast + S = flow.sbp.split(0) + + class ToLocalModule(flow.nn.Module): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(3, 4, False) + + to_local_m = ToLocalModule() + flow.nn.init.uniform_(to_local_m.linear.weight) + + to_local_m.to_global(placement=P, sbp=B) + origin_w_np = to_local_m.linear.weight.numpy() + + to_local_m.to_global(placement=P, sbp=S) + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np) + ) + + # When wight SBP is split(0) + to_local_m.to_local() + test_case.assertTrue(to_local_m.linear.weight.is_local) + if rank == 0: + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np[:2]) + ) + elif rank == 1: + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np[2:]) + ) + + # local to global from split(0) + to_local_m.to_global(placement=P, sbp=S) + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np) + ) + + # When wight SBP is broadcast + to_local_m.to_global(placement=P, sbp=B) + test_case.assertTrue(not to_local_m.linear.weight.is_local) + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np) + ) + + # When wight SBP is broadcast + to_local_m.to_local() + test_case.assertTrue(to_local_m.linear.weight.is_local) + test_case.assertTrue( + np.array_equal(to_local_m.linear.weight.numpy(), origin_w_np) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_moving_average_min_max_observer.py b/python/oneflow/test/modules/test_moving_average_min_max_observer.py index e57e821f253daf83b09c5002f64790d208caefec..ab4ecc4163a263fb3ca719bfc6af59a4f1ddd3b8 100644 --- a/python/oneflow/test/modules/test_moving_average_min_max_observer.py +++ b/python/oneflow/test/modules/test_moving_average_min_max_observer.py @@ -155,7 +155,6 @@ def _run_test_moving_average_min_max_observer( activation, dtype=flow.float32, device=flow.device(device_type) ) moving_average_min_max_observer = flow.nn.MovingAverageMinMaxObserver( - training=True, stop_update_after_iters=1, quantization_formula=quantization_formula, quantization_bit=quantization_bit, diff --git a/python/oneflow/test/modules/test_mul.py b/python/oneflow/test/modules/test_mul.py index 281028db5996fd99f4b9075612cc040a72587014..3aa65fd56983021e2a82f2c6aa6b331248bb3b59 100644 --- a/python/oneflow/test/modules/test_mul.py +++ b/python/oneflow/test/modules/test_mul.py @@ -176,6 +176,15 @@ def _test_inplace_mul_scalar(test_case, device): ) +def _test_mul_inplace_0size_tensor(test_case, device): + targets = flow.randn((0, 6), device=flow.device(device)) + height, width = 640, 640 + targets[:, 2:] *= flow.tensor( + (width, height, width, height), device=flow.device(device) + ) + test_case.assertTrue(np.array_equal(targets.size(), (0, 6))) + + @flow.unittest.skip_unless_1n1d() class TestMulModule(flow.unittest.TestCase): def test_mul(test_case): @@ -184,6 +193,7 @@ class TestMulModule(flow.unittest.TestCase): _test_mul_impl, _test_inplace_mul_tensors, _test_inplace_mul_scalar, + _test_mul_inplace_0size_tensor, ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): @@ -198,6 +208,24 @@ class TestMulModule(flow.unittest.TestCase): x.mul_(y) return x + @autotest(n=3) + def test_non_contiguous_inplace_mul(test_case): + device = random_device() + x = random_tensor(2, 2, 4).to(device) + y = x + 1 + y = y[:, 1:3] + y *= random_tensor(2, 2, 2).to(device) + return y + + @autotest(n=5) + def test_scalar_mul_with_random_devices(test_case): + x1_device = random_device() + x2_device = random_device() + x1 = random_tensor(2, 2, 3).to(x1_device).mean() + x2 = random_tensor(2, 2, 3).to(x2_device) + y = x1 * x2 + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_multi_tensor_yolov5_weight_update.py b/python/oneflow/test/modules/test_multi_tensor_yolov5_weight_update.py new file mode 100644 index 0000000000000000000000000000000000000000..01f76bacb471ccf5927c2db7acdd7946f76eb181 --- /dev/null +++ b/python/oneflow/test/modules/test_multi_tensor_yolov5_weight_update.py @@ -0,0 +1,96 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +import torch +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_multi_tensor_weight_update_impl(test_case, device, shape, n, d): + def compare(a, b, rtol=1e-5, atol=1e-5): + test_case.assertTrue( + np.allclose( + a.detach().cpu().numpy(), b.detach().cpu().numpy(), rtol=rtol, atol=atol + ), + f"\na\n{a.detach().cpu().numpy()}\n{'-' * 80}\nb:\n{b.detach().cpu().numpy()}\n{'*' * 80}\ndiff:\n{a.detach().cpu().numpy() - b.detach().cpu().numpy()}", + ) + + weight = [] + torch_weight = [] + weight_update = [] + torch_weight_update = [] + for _ in range(n): + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=False, + ) + weight.append(tmp) + torch_weight.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=False, + ) + ) + tmp = flow.tensor( + np.random.randn(*shape), + dtype=flow.float32, + device=flow.device(device), + requires_grad=False, + ) + weight_update.append(tmp) + torch_weight_update.append( + torch.tensor( + tmp.numpy(), + dtype=torch.float32, + device=torch.device(device), + requires_grad=False, + ) + ) + for i, v in enumerate(torch_weight): + v = v * d + v = v + (1 - d) * torch_weight_update[i] + torch_weight[i] = v + + flow._C.multi_tensor_yolov5_weight_update(weight, weight_update, d) + for i in range(n): + compare(weight[i], torch_weight[i]) + + +@flow.unittest.skip_unless_1n1d() +class TestMultiTensorWeightUpdateModule(flow.unittest.TestCase): + def test_multi_tensor_weight_update(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_multi_tensor_weight_update_impl] + arg_dict["device"] = ["cuda"] + arg_dict["shape"] = [(20, 1), (30, 1), (55, 1)] + arg_dict["n"] = [5, 10, 292] + arg_dict["d"] = [0.22, 0.5] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_multinomial.py b/python/oneflow/test/modules/test_multinomial.py new file mode 100644 index 0000000000000000000000000000000000000000..d265b554f7732013a6b25bc82922634900b8499f --- /dev/null +++ b/python/oneflow/test/modules/test_multinomial.py @@ -0,0 +1,89 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import random +import numpy as np +from collections import OrderedDict +import torch + +import oneflow as flow + +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + + +def _test_multinomial(test_case, device, seed, replacement, dtype): + n_dists = random.randint(8, 64) + n_categories = random.randint(8, 64) + num_samples = random.randint(4, n_categories) + + weights_torch = torch.rand( + n_dists, + n_categories, + device=device, + dtype=torch.float32 if dtype == "float" else torch.float64, + ) + weights_oneflow = flow.tensor( + weights_torch.cpu().numpy(), + device=device, + dtype=flow.float32 if dtype == "float" else flow.float64, + ) + + torch.manual_seed(seed) + flow.manual_seed(seed) + + torch_res = torch.multinomial( + weights_torch, num_samples, replacement=replacement, generator=None + ) + flow_res = flow.multinomial( + weights_oneflow, num_samples, replacement=replacement, generator=None + ) + + test_case.assertTrue( + np.allclose(torch_res.cpu().numpy(), flow_res.cpu().numpy(), atol=1e-8,) + ) + + torch_gen = torch.Generator(device=device) + torch_gen.manual_seed(seed) + oneflow_gen = flow.Generator(device=device) + oneflow_gen.manual_seed(seed) + + torch_res = torch.multinomial( + weights_torch, num_samples, replacement=replacement, generator=torch_gen + ) + flow_res = flow.multinomial( + weights_oneflow, num_samples, replacement=replacement, generator=oneflow_gen + ) + + test_case.assertTrue( + np.allclose(torch_res.cpu().numpy(), flow_res.cpu().numpy(), atol=1e-8,) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestMultinomial(flow.unittest.TestCase): + def test_multinomial(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda", "cpu"] + arg_dict["seed"] = [0, 2, 4] + arg_dict["replacement"] = [True, False] + arg_dict["dtype"] = ["double", "float"] + for arg in GenArgList(arg_dict): + _test_multinomial(test_case, *arg[0:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_nansum.py b/python/oneflow/test/modules/test_nansum.py new file mode 100644 index 0000000000000000000000000000000000000000..675f0a9b88480de10f9dfe57ee29e73656ada648 --- /dev/null +++ b/python/oneflow/test/modules/test_nansum.py @@ -0,0 +1,90 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np + +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestNanSumModule(flow.unittest.TestCase): + @autotest(n=5, check_graph=True) + def test_nansum_without_nan(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + y = torch.nansum(x) + return y + + @autotest(n=5, check_graph=True) + def test_nansum_with_partial_nan(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + mask = x < 0 + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x) + return y + + @autotest(n=5, check_graph=True) + def test_nansum_with_total_nan(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + mask = torch.ones_like(x).bool() + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x) + return y + + @autotest(n=5, check_graph=True) + def test_nansum_with_partial_nan_dims(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + mask = x < 0 + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x, dim=random(0, 4).to(int)) + return y + + @autotest(n=5, check_graph=True) + def test_nansum_with_total_nan_dims(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + mask = torch.ones_like(x).bool() + x = x.masked_fill(mask, float("nan")) + y = torch.nansum(x, dim=random(0, 4).to(int)) + return y + + @autotest(n=5, auto_backward=False, check_graph=True) + def test_sum_with_0_size_tensor(test_case): + device = random_device() + x = random_tensor(4, 4, 3, 0, 2).to(device) + y = torch.nansum(x, dim=np.random.randint(0, 3)) + return y + + @autotest(n=5, auto_backward=False, check_graph=True) + def test_sum_with_0dim_tensor(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = torch.nansum(x) + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_nonzero.py b/python/oneflow/test/modules/test_nonzero.py index c5df31454b53edaebb095b332f378eadcea56b0b..5e19a9b756b32620561fa6b86d0991444cc4c35a 100644 --- a/python/oneflow/test/modules/test_nonzero.py +++ b/python/oneflow/test/modules/test_nonzero.py @@ -60,7 +60,7 @@ class TestNonzero(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, lazy tensor cannot call .numpy(). tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. # Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. - @autotest(auto_backward=False, check_graph="ValidatedFlase") + @autotest(auto_backward=False, check_graph="ValidatedFalse") def test_nonzero_with_random_data(test_case): device = random_device() x = random_tensor(ndim=random(2, 5).to(int)).to(device) @@ -70,7 +70,7 @@ class TestNonzero(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, lazy tensor cannot call .numpy(). tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. # Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. - @autotest(auto_backward=False, check_graph="ValidatedFlase") + @autotest(auto_backward=False, check_graph="ValidatedFalse") def test_nonzero_bool_with_random_data(test_case): device = random_device() x = random_tensor(ndim=random(2, 5).to(int)).to(device=device, dtype=torch.bool) @@ -80,7 +80,7 @@ class TestNonzero(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, lazy tensor cannot call .numpy(). tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. # Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. - @autotest(auto_backward=False, check_graph="ValidatedFlase") + @autotest(auto_backward=False, check_graph="ValidatedFalse") def test_nonzero_with_0dim_data(test_case): device = random_device() x = random_tensor(ndim=0).to(device) @@ -90,7 +90,7 @@ class TestNonzero(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, lazy tensor cannot call .numpy(). tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. # Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. - @autotest(auto_backward=False, check_graph="ValidatedFlase") + @autotest(auto_backward=False, check_graph="ValidatedFalse") def test_nonzero_tuple_with_random_data(test_case): device = random_device() x = random_tensor(ndim=random(2, 5).to(int)).to(device) diff --git a/python/oneflow/test/modules/test_norm.py b/python/oneflow/test/modules/test_norm.py index 120cafd02fd1b6de1f3cb35acc9c9316643f5566..70924e20b5107d668b01fd4995c90385ae00b12a 100644 --- a/python/oneflow/test/modules/test_norm.py +++ b/python/oneflow/test/modules/test_norm.py @@ -269,7 +269,8 @@ class TestNormModule(flow.unittest.TestCase): input = random_tensor().to(device) keepdim = random_bool() m = torch.linalg.norm(input, keepdim=keepdim) - return m + n = torch.norm(input, keepdim=keepdim) + return m, n @autotest(n=5) def test_one_dim_norm_with_random_data(test_case): @@ -280,7 +281,8 @@ class TestNormModule(flow.unittest.TestCase): ord = oneof(float("inf"), float("-inf"), k, None) keepdim = random_bool() m = torch.linalg.norm(input, ord, dim, keepdim) - return m + n = torch.norm(input, ord, dim, keepdim) + return m, n @autotest(n=5) def test_no_dim_one_shape_norm_with_random_data(test_case): @@ -290,7 +292,8 @@ class TestNormModule(flow.unittest.TestCase): ord = oneof(float("inf"), float("-inf"), k) keepdim = random_bool() m = torch.linalg.norm(input, ord=ord, keepdim=keepdim) - return m + n = torch.norm(input, p=ord, keepdim=keepdim) + return m, n @autotest(n=5) def test_no_dim_two_shape_norm_with_random_data(test_case): @@ -305,7 +308,6 @@ class TestNormModule(flow.unittest.TestCase): def test_tuple_dim_norm_with_random_data(test_case): device = random_device() input = random_tensor(ndim=2).to(device) - k = random(low=-2, high=1).to(int) dim = oneof((-2, -1), (0, 1), (-1, 0)) ord = oneof(float("inf"), float("-inf"), "fro", 1, -1, None) keepdim = random().to(bool) @@ -321,6 +323,17 @@ class TestNormModule(flow.unittest.TestCase): m = torch.linalg.vector_norm(input, ord=0, dim=dim, keepdim=keepdim) return m + @autotest(n=5) + def test_ord_random_data(test_case): + device = random_device() + ndim = random(1, 3).to(int) + input = random_tensor(ndim).to(device) + p1 = random(-5, -1).to(int).value() + p2 = random(2, 6).to(int).value() + m = input.norm(p1) + n = input.norm(p2) + return m, n + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_normalize.py b/python/oneflow/test/modules/test_normalize.py index 0d6a3fafef05d05b1c1e54dbf2219d08c79a4b4e..f3000d5414ef04238e142ce1ffd060c7d74c7feb 100644 --- a/python/oneflow/test/modules/test_normalize.py +++ b/python/oneflow/test/modules/test_normalize.py @@ -15,14 +15,42 @@ limitations under the License. """ import unittest -from oneflow.test_utils.automated_test_util import * +from collections import OrderedDict + +import numpy as np + import oneflow as flow import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList, type_name_to_flow_type +from oneflow.test_utils.automated_test_util import * + + +def _test_functional_normalize_double_dtype(test_case, device, dtype): + dtype = type_name_to_flow_type[dtype] + x = flow.ones(2, 2, dtype=dtype).to(device) + y = flow.nn.functional.normalize(x, p=2, dim=0) + test_case.assertEqual((2, 2), y.shape) + out = np.array( + [ + [0.7071067690849304, 0.7071067690849304], + [0.7071067690849304, 0.7071067690849304], + ] + ) + test_case.assertTrue(np.allclose(y.numpy().tolist(), out, 1e-05, 1e-05)) @flow.unittest.skip_unless_1n1d() class TestFunctionalNormalize(flow.unittest.TestCase): - @autotest() + def test_functional_normalize_naive(test_case): + arg_dict = OrderedDict() + arg_dict["fun"] = [_test_functional_normalize_double_dtype] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["dtype"] = ["float32", "double"] + + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + @autotest(n=5) def test_functional_normalize(test_case): device = random_device() ndim = random(low=2) diff --git a/python/oneflow/test/modules/test_one_embedding_adagrad.py b/python/oneflow/test/modules/test_one_embedding_adagrad.py index d1d7bcee47959dcaf12bbc9f90dbc7a817e88509..f5dd7326e0889c21b3830cc7b79f7439c067fc93 100644 --- a/python/oneflow/test/modules/test_one_embedding_adagrad.py +++ b/python/oneflow/test/modules/test_one_embedding_adagrad.py @@ -19,6 +19,9 @@ from collections import OrderedDict import tempfile import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import numpy as np from oneflow.test_utils.test_util import GenArgDict from optimizer_test_util import clip_grad_norm_np @@ -48,6 +51,45 @@ def compare_with_numpy_adagrad( down_scale_by = 10 epsilon = 1e-5 + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build( + self, + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + train_step, + ): + # add id shuffle to set num_unique in op, and use it in update + (_, _, num_valid, _, _, _,) = flow._C.one_embedding_id_shuffle( + ids, table_ids=None, num_tables=1, embedding_name="" + ) + return flow._C.one_embedding_adagrad_update( + num_valid, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + train_step, + 0, + 0.0, + scale, + weight_decay, + lr_decay, + epsilon, + line_size, + embedding_size, + "", + ) + + graph = TestGraph() + def adagrad_by_oneflow(): unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( "cuda" @@ -56,30 +98,25 @@ def compare_with_numpy_adagrad( np.array(learning_rate).reshape(1,).astype(np.float32) ).to("cuda") down_scale_by_tensor = flow.tensor( - np.array(down_scale_by).astype(np.float32) + np.array(down_scale_by).reshape(1,).astype(np.float32) ).to("cuda") - def train_one_iter( - num_valid, unique_embeddings, embedding_grad, skip_if, train_step - ): - return flow._C.one_embedding_adagrad_update( - num_valid, + def train_one_iter(ids, unique_embeddings, embedding_grad, skip_if, train_step): + return graph( + ids, unique_embeddings, embedding_grad, lr_tensor, down_scale_by_tensor, skip_if, train_step, - scale, - weight_decay, - lr_decay, - epsilon, ) for i in range(1, train_iters): - num_valid_tensor = flow.tensor( - np.array(num_valid_seq[i]).reshape(1,).astype(np.int32) - ).to("cuda") + np_ids = np.zeros(num_rows) + np_ids[0 : num_valid_seq[i]] = np.arange(num_valid_seq[i]) + # add ids of num_valid unique to use id_shuffle out_put num_unique as grad input + ids = flow.tensor(np_ids.astype(np.int32)).to("cuda") grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") skip_if_tensor = flow.tensor( np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) @@ -88,11 +125,7 @@ def compare_with_numpy_adagrad( "cuda" ) updated_tensor = train_one_iter( - num_valid_tensor, - unique_embeddings_tensor, - grad_tensor, - skip_if_tensor, - step_tensor, + ids, unique_embeddings_tensor, grad_tensor, skip_if_tensor, step_tensor, ) unique_embeddings_tensor[0 : num_valid_seq[i]] = updated_tensor[ 0 : num_valid_seq[i] diff --git a/python/oneflow/test/modules/test_one_embedding_adam.py b/python/oneflow/test/modules/test_one_embedding_adam.py index e5b9ec853af36dce451dacc208a7ddf23ac2a173..8816cc42e3d74b05f9cf47c2f193bed1facc1f68 100644 --- a/python/oneflow/test/modules/test_one_embedding_adam.py +++ b/python/oneflow/test/modules/test_one_embedding_adam.py @@ -19,6 +19,9 @@ from collections import OrderedDict import tempfile import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import numpy as np from oneflow.test_utils.test_util import GenArgDict from optimizer_test_util import clip_grad_norm_np @@ -36,6 +39,7 @@ def compare_with_numpy_adam( do_bias_correction, beta1, beta2, + use_optional_tensor, ): num_rows = 500 @@ -53,27 +57,40 @@ def compare_with_numpy_adam( init_value = np.random.uniform(size=(num_rows, line_size)).astype(np.float32) down_scale_by = 10 + + """ + In OneFlow's optimizer, learning_rate is passed by attr in eager mode, and passed by tensor in lazy mode. + in this test, if use_optional_tensor is True, we also pass lr_tensor/down_scale_by_tensor/skip_if tensor for unittest. + if use_optional_tensor is False, we only pass lr by attr, and not have down_scale_by_tensor/skip_if, so mul down_scale_by to scale and skip skip_if's test. + """ + bias_correction1_val = 1.0 + bias_correction2_val = 1.0 + if use_optional_tensor: + scale_val = scale + else: + # if pass as attr instead of tensor, mul down_scale_by to scale_value + scale_val = scale / down_scale_by epsilon = 1e-5 - def adam_by_oneflow(): - unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( - "cuda" - ) - lr_tensor = flow.tensor( - np.array(learning_rate).reshape(1,).astype(np.float32) - ).to("cuda") - down_scale_by_tensor = flow.tensor( - np.array(down_scale_by).astype(np.float32) - ).to("cuda") + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() - def train_one_iter( - num_valid, + def build( + self, + ids, unique_embeddings, embedding_grad, + lr_tensor, + down_scale_by_tensor, skip_if, bias_correction1, bias_correction2, ): + # add id shuffle to set num_unique in op, and use it in update + (_, _, num_valid, _, _, _,) = flow._C.one_embedding_id_shuffle( + ids, table_ids=None, num_tables=1, embedding_name="" + ) return flow._C.one_embedding_adam_update( num_valid, unique_embeddings, @@ -83,23 +100,69 @@ def compare_with_numpy_adam( skip_if, bias_correction1, bias_correction2, - scale, + learning_rate, + scale_val, weight_decay, beta1, beta2, + bias_correction1_val, + bias_correction2_val, epsilon, do_bias_correction, + line_size, + embedding_size, + embedding_name="", ) - for i in range(1, train_iters): - num_valid_tensor = flow.tensor( - np.array(num_valid_seq[i]).reshape(1,).astype(np.int32) + graph = TestGraph() + + def adam_by_oneflow(): + unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( + "cuda" + ) + if use_optional_tensor: + lr_tensor = flow.tensor( + np.array(learning_rate).reshape(1,).astype(np.float32) ).to("cuda") - grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") - skip_if_tensor = flow.tensor( - np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) + down_scale_by_tensor = flow.tensor( + np.array(down_scale_by).reshape(1,).astype(np.float32) ).to("cuda") - if do_bias_correction: + else: + lr_tensor = None + down_scale_by_tensor = None + + def train_one_iter( + ids, + unique_embeddings, + embedding_grad, + skip_if, + bias_correction1, + bias_correction2, + ): + return graph( + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + bias_correction1, + bias_correction2, + ) + + for i in range(1, train_iters): + np_ids = np.zeros(num_rows) + np_ids[0 : num_valid_seq[i]] = np.arange(num_valid_seq[i]) + # add ids of num_valid unique to use id_shuffle out_put num_unique as grad input + ids = flow.tensor(np_ids.astype(np.int32)).to("cuda") + grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") + if use_optional_tensor: + skip_if_tensor = flow.tensor( + np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) + ).to("cuda") + else: + skip_if_tensor = None + if do_bias_correction and use_optional_tensor: bias_correction1 = 1.0 - np.power(beta1, i) bias_correction2 = 1.0 - np.power(beta2, i) bias_correction1_tensor = flow.tensor( @@ -112,7 +175,7 @@ def compare_with_numpy_adam( bias_correction1_tensor = None bias_correction2_tensor = None updated_tensor = train_one_iter( - num_valid_tensor, + ids, unique_embeddings_tensor, grad_tensor, skip_if_tensor, @@ -135,7 +198,7 @@ def compare_with_numpy_adam( bias_correction1 = 1.0 bias_correction2 = 1.0 - if do_bias_correction: + if do_bias_correction and use_optional_tensor: bias_correction1 = 1.0 - np.power(beta1, step) bias_correction2 = 1.0 - np.power(beta2, step) @@ -156,7 +219,7 @@ def compare_with_numpy_adam( return (model, state_m, state_v) for i in range(1, train_iters): # if step = 0, bias_correction2 is 0 - if skip_if_seq[i] > 0: + if skip_if_seq[i] > 0 and use_optional_tensor: pass else: (x, m, v) = np_train_one_iter( @@ -192,6 +255,7 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["do_bias_correction"] = [True, False] arg_dict["beta1"] = [0.9, 0.8] arg_dict["beta2"] = [0.9, 0.8] + arg_dict["use_optional_tensor"] = [True, False] for arg in GenArgDict(arg_dict): compare_with_numpy_adam(test_case, **arg) diff --git a/python/oneflow/test/modules/test_one_embedding_ftrl.py b/python/oneflow/test/modules/test_one_embedding_ftrl.py index 63e2f5c06eea4663ffbb45048a86126238c05856..d794de2a311b00614b536bf241ad7fc2199e1f09 100644 --- a/python/oneflow/test/modules/test_one_embedding_ftrl.py +++ b/python/oneflow/test/modules/test_one_embedding_ftrl.py @@ -18,6 +18,9 @@ from collections import OrderedDict import tempfile import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import numpy as np from oneflow.test_utils.test_util import GenArgDict from optimizer_test_util import clip_grad_norm_np @@ -36,6 +39,7 @@ def compare_with_numpy_ftrl( scale, learning_rate, train_iters, + use_optional_tensor, ): num_rows = 500 embedding_size = 128 @@ -52,18 +56,34 @@ def compare_with_numpy_ftrl( down_scale_by = 10 - def ftrl_by_oneflow(): - unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( - "cuda" - ) - lr_tensor = flow.tensor( - np.array(learning_rate).reshape(1,).astype(np.float32) - ).to("cuda") - down_scale_by_tensor = flow.tensor( - np.array(down_scale_by).astype(np.float32) - ).to("cuda") - - def train_one_iter(num_valid, unique_embeddings, embedding_grad, skip_if): + """ + In OneFlow's optimizer, learning_rate is passed by attr in eager mode, and passed by tensor in lazy mode. + in this test, if use_optional_tensor is True, we also pass lr_tensor/down_scale_by_tensor/skip_if tensor for unittest. + if use_optional_tensor is False, we only pass lr by attr, and not have down_scale_by_tensor/skip_if, so mul down_scale_by to scale and skip skip_if's test. + """ + if use_optional_tensor: + scale_val = scale + else: + # if pass as attr instead of tensor, mul down_scale_by to scale_value + scale_val = scale / down_scale_by + + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build( + self, + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + ): + # add id shuffle to set num_unique in op, and use it in update + (_, _, num_valid, _, _, _,) = flow._C.one_embedding_id_shuffle( + ids, table_ids=None, num_tables=1, embedding_name="" + ) return flow._C.one_embedding_ftrl_update( num_valid, unique_embeddings, @@ -71,25 +91,60 @@ def compare_with_numpy_ftrl( lr_tensor, down_scale_by_tensor, skip_if, - scale, + learning_rate, + scale_val, weight_decay, lr_power, lambda1, lambda2, beta, + line_size, + embedding_size, + embedding_name="", ) - for i in range(1, train_iters): - num_valid_tensor = flow.tensor( - np.array(num_valid_seq[i]).reshape(1,).astype(np.int32) + graph = TestGraph() + + def ftrl_by_oneflow(): + unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( + "cuda" + ) + if use_optional_tensor: + lr_tensor = flow.tensor( + np.array(learning_rate).reshape(1,).astype(np.float32) ).to("cuda") - grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") - skip_if_tensor = flow.tensor( - np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) + down_scale_by_tensor = flow.tensor( + np.array(down_scale_by).reshape(1,).astype(np.float32) ).to("cuda") + else: + lr_tensor = None + down_scale_by_tensor = None + + def train_one_iter(ids, unique_embeddings, embedding_grad, skip_if): + return graph( + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + ) + + for i in range(1, train_iters): + np_ids = np.zeros(num_rows) + np_ids[0 : num_valid_seq[i]] = np.arange(num_valid_seq[i]) + # add ids of num_valid unique to use id_shuffle out_put num_unique as grad input + ids = flow.tensor(np_ids.astype(np.int32)).to("cuda") + grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") + if use_optional_tensor: + skip_if_tensor = flow.tensor( + np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) + ).to("cuda") + else: + skip_if_tensor = None updated_tensor = train_one_iter( - num_valid_tensor, unique_embeddings_tensor, grad_tensor, skip_if_tensor, + ids, unique_embeddings_tensor, grad_tensor, skip_if_tensor, ) unique_embeddings_tensor[0 : num_valid_seq[i]] = updated_tensor[ 0 : num_valid_seq[i] @@ -125,7 +180,8 @@ def compare_with_numpy_ftrl( return (model, accumulate, z) for i in range(1, train_iters): - if skip_if_seq[i] > 0: + # when use_optional_tensor is False, not pass skip_if to op + if skip_if_seq[i] > 0 and use_optional_tensor: pass else: (x, accumulate, z) = train_one_iter( @@ -166,6 +222,8 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["scale"] = [1, 0.1] arg_dict["learning_rate"] = [0.3, 1.5] arg_dict["train_iters"] = [10] + arg_dict["use_optional_tensor"] = [True, False] + for arg in GenArgDict(arg_dict): compare_with_numpy_ftrl(test_case, **arg) diff --git a/python/oneflow/test/modules/test_one_embedding_sgd.py b/python/oneflow/test/modules/test_one_embedding_sgd.py index c72a27448d1cd71dc1ae0318f125e7f421fbe734..eb6c7eaa2b92d3a8246cbf5b8d5e77f00a0440be 100644 --- a/python/oneflow/test/modules/test_one_embedding_sgd.py +++ b/python/oneflow/test/modules/test_one_embedding_sgd.py @@ -19,6 +19,9 @@ from collections import OrderedDict import tempfile import os + +# dynamic memory allocation can't be tested in unittest +os.environ["ONEFLOW_ONE_EMBEDDING_USE_DYNAMIC_MEMORY_ALLOCATION"] = "0" import numpy as np from oneflow.test_utils.test_util import GenArgDict from optimizer_test_util import clip_grad_norm_np @@ -28,9 +31,15 @@ from oneflow.nn.parameter import Parameter def compare_with_numpy_sgd( - test_case, momentum, weight_decay, scale, learning_rate, train_iters, + test_case, + momentum, + weight_decay, + scale, + learning_rate, + train_iters, + use_optional_tensor, ): - + # if use_optional_tensor, pass lr as tensor to sgd_update, else pass as attr. num_rows = 500 embedding_size = 128 model_shape = (num_rows, embedding_size) @@ -45,42 +54,105 @@ def compare_with_numpy_sgd( init_value = np.random.uniform(size=(num_rows, line_size)).astype(np.float32) + """ + In OneFlow's optimizer, learning_rate is passed by attr in eager mode, and passed by tensor in lazy mode. + in this test, if use_optional_tensor is True, we also pass lr_tensor/down_scale_by_tensor/skip_if tensor for unittest. + if use_optional_tensor is False, we only pass lr by attr, and not have down_scale_by_tensor/skip_if, so mul down_scale_by to scale and skip skip_if's test. + """ down_scale_by = 10 + if use_optional_tensor: + scale_val = scale + else: + # if pass as attr instead of tensor, mul down_scale_by to scale_value + scale_val = scale / down_scale_by + + class TestGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build( + self, + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + ): + # add id shuffle to set num_unique in op, and use it in update + (_, _, num_valid, _, _, _,) = flow._C.one_embedding_id_shuffle( + ids, table_ids=None, num_tables=1, embedding_name="" + ) + return flow._C.one_embedding_sgd_update( + num_valid, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + learning_rate, + scale_val, + weight_decay, + momentum, + line_size, + embedding_size, + embedding_name="", + ) + + graph = TestGraph() def sgd_by_oneflow(): unique_embeddings_tensor = flow.tensor(init_value, requires_grad=False).to( "cuda" ) - lr_tensor = flow.tensor( - np.array(learning_rate).reshape(1,).astype(np.float32) - ).to("cuda") - down_scale_by_tensor = flow.tensor( - np.array(down_scale_by).astype(np.float32) - ).to("cuda") - - def train_one_iter(num_valid, unique_embeddings, embedding_grad, skip_if): - return flow._C.one_embedding_sgd_update( - num_valid, + if use_optional_tensor: + lr_tensor = flow.tensor( + np.array(learning_rate).reshape(1,).astype(np.float32) + ).to("cuda") + down_scale_by_tensor = flow.tensor( + np.array((down_scale_by,)).astype(np.float32) + ).to("cuda") + else: + # pass by attr + lr_tensor = None + down_scale_by_tensor = None + + def train_one_iter( + ids, + unique_embeddings, + embedding_grad, + lr_tensor, + down_scale_by_tensor, + skip_if, + ): + return graph( + ids, unique_embeddings, embedding_grad, lr_tensor, down_scale_by_tensor, skip_if, - scale, - weight_decay, - momentum, ) for i in range(train_iters): - num_valid_tensor = flow.tensor( - np.array(num_valid_seq[i]).reshape(1,).astype(np.int32) - ).to("cuda") + np_ids = np.zeros(num_rows) + np_ids[0 : num_valid_seq[i]] = np.arange(num_valid_seq[i]) + # add ids of num_valid unique to use id_shuffle out_put num_unique as grad input + ids = flow.tensor(np_ids.astype(np.int32)).to("cuda") grad_tensor = flow.tensor(random_grad_seq[i]).to("cuda") - skip_if_tensor = flow.tensor( - np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) - ).to("cuda") + if use_optional_tensor: + skip_if_tensor = flow.tensor( + np.array(skip_if_seq[i]).reshape(1,).astype(np.int64) + ).to("cuda") + else: + skip_if_tensor = None updated_tensor = train_one_iter( - num_valid_tensor, unique_embeddings_tensor, grad_tensor, skip_if_tensor + ids, + unique_embeddings_tensor, + grad_tensor, + lr_tensor, + down_scale_by_tensor, + skip_if_tensor, ) unique_embeddings_tensor[0 : num_valid_seq[i]] = updated_tensor[ 0 : num_valid_seq[i] @@ -94,19 +166,28 @@ def compare_with_numpy_sgd( def train_one_iter(num_valid, grad, model, state): grad[0:num_valid] = grad[0:num_valid] * (scale / down_scale_by) next_state = ( - momentum * state[0:num_valid] if momentum > 0 else 0 - ) - learning_rate * grad[0:num_valid] + (momentum * state[0:num_valid] + grad[0:num_valid]) + if momentum > 0 + else 0 + ) if momentum > 0: state[0:num_valid] = next_state - model[0:num_valid] = ( - model[0:num_valid] - + next_state - - learning_rate * weight_decay * model[0:num_valid] - ) + model[0:num_valid] = ( + model[0:num_valid] + - learning_rate * next_state + - learning_rate * weight_decay * model[0:num_valid] + ) + else: + state[0:num_valid] = 0 + model[0:num_valid] = ( + model[0:num_valid] + - learning_rate * grad[0:num_valid] + - learning_rate * weight_decay * model[0:num_valid] + ) return (model, state) for i in range(train_iters): - if skip_if_seq[i] > 0: + if skip_if_seq[i] > 0 and use_optional_tensor: pass else: (x, vt) = train_one_iter( @@ -139,6 +220,7 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["scale"] = [1, 0.1] arg_dict["learning_rate"] = [1, 0.9] arg_dict["train_iters"] = [10] + arg_dict["use_optional_tensor"] = [True, False] for arg in GenArgDict(arg_dict): compare_with_numpy_sgd(test_case, **arg) diff --git a/python/oneflow/test/modules/test_optim_adadelta.py b/python/oneflow/test/modules/test_optim_adadelta.py new file mode 100644 index 0000000000000000000000000000000000000000..ce3603c8efd916fa5bd698e1c33a979a698d20d5 --- /dev/null +++ b/python/oneflow/test/modules/test_optim_adadelta.py @@ -0,0 +1,235 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import tempfile +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList +from optimizer_test_util import clip_grad_norm_np + +import oneflow as flow +from oneflow.nn.parameter import Parameter + + +def compare_with_numpy_adadelta( + test_case, + device, + x_shape, + learning_rate, + train_iters, + rho, + eps, + maximize, + weight_decay, + reload_state_step, + save_load_by_pickle, +): + random_grad_seq = [] + for _ in range(train_iters): + random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + init_value = np.random.uniform(size=x_shape).astype(np.float32) + + def train_by_oneflow(): + x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + adadelta = flow.optim.Adadelta( + [{"params": [x], "lr": learning_rate, "weight_decay": weight_decay,}], + rho=rho, + eps=eps, + maximize=maximize, + ) + + def train_one_iter(grad): + grad_tensor = flow.tensor( + grad, requires_grad=False, device=flow.device(device) + ) + loss = flow.sum(x * grad_tensor) + loss.backward() + adadelta.step() + adadelta.zero_grad() + + for i in range(train_iters): + train_one_iter(random_grad_seq[i]) + if i == reload_state_step: + state_dict = adadelta.state_dict() + adadelta = flow.optim.Adadelta([x]) + if save_load_by_pickle: + with tempfile.TemporaryDirectory() as save_dir: + flow.save(state_dict, save_dir) + state_dict = flow.load(save_dir) + adadelta.load_state_dict(state_dict) + return x + + def train_by_numpy(): + x = init_value + square_avgs = np.zeros_like(x) + acc_deltas = np.zeros_like(x) + + def train_one_iter(grad): + grad = grad if not maximize else -grad + grad = grad + weight_decay * x + new_square_avgs = square_avgs * rho + (1.0 - rho) * grad * grad + std = np.sqrt(new_square_avgs + eps) + delta = np.sqrt(acc_deltas + eps) / std * grad + new_acc_deltas = acc_deltas * rho + delta * delta * (1 - rho) + param = x - learning_rate * delta + return (param, new_square_avgs, new_acc_deltas) + + for i in range(1, train_iters + 1): + (x, square_avgs, acc_deltas) = train_one_iter(random_grad_seq[i - 1]) + return x + + oneflow_res = train_by_oneflow().numpy() + numpy_res = train_by_numpy() + + test_case.assertTrue( + np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-4, atol=1e-4) + ) + + +def compare_with_numpy_adadelta_clip_grad( + test_case, + device, + x_shape, + learning_rate, + train_iters, + rho, + eps, + maximize, + weight_decay, + clip_grad_max_norm, + clip_grad_norm_type, + reload_state_step, + save_load_by_pickle, +): + random_grad_seq = [] + for _ in range(train_iters): + random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + init_value = np.random.uniform(size=x_shape).astype(np.float32) + + def train_by_oneflow(): + x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + adadelta = flow.optim.Adadelta( + [ + { + "params": [x], + "lr": learning_rate, + "weight_decay": weight_decay, + "clip_grad_max_norm": clip_grad_max_norm, + "clip_grad_norm_type": clip_grad_norm_type, + } + ], + rho=rho, + eps=eps, + maximize=maximize, + ) + + def train_one_iter(grad): + grad_tensor = flow.tensor( + grad, requires_grad=False, device=flow.device(device) + ) + loss = flow.sum(x * grad_tensor) + loss.backward() + adadelta.clip_grad() + adadelta.step() + adadelta.zero_grad() + + for i in range(train_iters): + train_one_iter(random_grad_seq[i]) + if i == reload_state_step: + state_dict = adadelta.state_dict() + adadelta = flow.optim.Adadelta([x]) + if save_load_by_pickle: + with tempfile.TemporaryDirectory() as save_dir: + flow.save(state_dict, save_dir) + state_dict = flow.load(save_dir) + adadelta.load_state_dict(state_dict) + return x + + def train_by_numpy(): + x = init_value + square_avgs = np.zeros_like(x) + acc_deltas = np.zeros_like(x) + + def train_one_iter(grad): + total_norm, grad = clip_grad_norm_np( + grad, clip_grad_max_norm, clip_grad_norm_type + ) + grad = grad if not maximize else -grad + grad = grad + weight_decay * x + new_square_avgs = square_avgs * rho + (1.0 - rho) * grad * grad + std = np.sqrt(new_square_avgs + eps) + delta = np.sqrt(acc_deltas + eps) / std * grad + new_acc_deltas = acc_deltas * rho + delta * delta * (1 - rho) + param = x - learning_rate * delta + return (param, new_square_avgs, new_acc_deltas) + + for i in range(1, train_iters + 1): + (x, square_avgs, acc_deltas) = train_one_iter(random_grad_seq[i - 1]) + + return x + + oneflow_res = train_by_oneflow().numpy() + numpy_res = train_by_numpy() + + test_case.assertTrue( + np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-4, atol=1e-4) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestAdadelta(flow.unittest.TestCase): + def test_adadelta(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["x_shape"] = [(10,)] + arg_dict["learning_rate"] = [1, 1e-3] + arg_dict["train_iters"] = [10] + arg_dict["rho"] = [0.9, 0.6] + arg_dict["eps"] = [1e-6, 1e-4] + arg_dict["maximize"] = [False] + arg_dict["weight_decay"] = [0.0, 0.1] + arg_dict["reload_state_step"] = [5] # save and load optim state + arg_dict["save_load_by_pickle"] = [False, True] + + for arg in GenArgList(arg_dict): + compare_with_numpy_adadelta(test_case, *arg) + + def test_adadelta_clip_grad(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda"] + if os.getenv("ONEFLOW_TEST_CPU_ONLY"): + arg_dict["device"] = ["cpu"] + arg_dict["x_shape"] = [(10,)] + arg_dict["learning_rate"] = [1e-3] + arg_dict["train_iters"] = [10] + arg_dict["rho"] = [0.9, 0.6] + arg_dict["eps"] = [1e-6, 1e-4] + arg_dict["maximize"] = [False] + arg_dict["weight_decay"] = [0.0, 0.1] + arg_dict["clip_grad_max_norm"] = [0, 0.5, 1.0] + arg_dict["clip_grad_norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5] + arg_dict["reload_state_step"] = [5] # save and load optim state + arg_dict["save_load_by_pickle"] = [False, True] + + for arg in GenArgList(arg_dict): + compare_with_numpy_adadelta_clip_grad(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_optim_adagrad.py b/python/oneflow/test/modules/test_optim_adagrad.py index b046c4de9cff81956669c76fb29ec7d31f407af2..c9c3aae08c971e1d83a161ef41096941fa073a64 100644 --- a/python/oneflow/test/modules/test_optim_adagrad.py +++ b/python/oneflow/test/modules/test_optim_adagrad.py @@ -104,7 +104,7 @@ def compare_with_numpy_adagrad( ) -def compare_with_numpy_adam_clip_grad( +def compare_with_numpy_adagrad_clip_grad( test_case, device, x_shape, @@ -228,7 +228,7 @@ class TestAdagrad(flow.unittest.TestCase): arg_dict["save_load_by_pickle"] = [False, True] for arg in GenArgList(arg_dict): - compare_with_numpy_adam_clip_grad(test_case, *arg) + compare_with_numpy_adagrad_clip_grad(test_case, *arg) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_optim_adam.py b/python/oneflow/test/modules/test_optim_adam.py index 59abf061288e6d37317106d963d5ee29d3d3a77f..1d7541af4429182b2298f9c3f2dea2d8791a6b1e 100644 --- a/python/oneflow/test/modules/test_optim_adam.py +++ b/python/oneflow/test/modules/test_optim_adam.py @@ -39,18 +39,34 @@ def compare_with_numpy_adam( amsgrad, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) + adam = flow.optim.Adam( [ { - "params": [x], + "params": x, "lr": learning_rate, "betas": betas, "eps": eps, @@ -59,16 +75,19 @@ def compare_with_numpy_adam( ], do_bias_correction=do_bias_correction, amsgrad=amsgrad, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() adam.step() adam.zero_grad() @@ -77,7 +96,7 @@ def compare_with_numpy_adam( train_one_iter(random_grad_seq[i]) if i == reload_state_step: state_dict = adam.state_dict() - adam = flow.optim.Adam([{"params": [x],}],) + adam = flow.optim.Adam([{"params": x,}],) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -85,8 +104,8 @@ def compare_with_numpy_adam( adam.load_state_dict(state_dict) return x - def train_by_numpy(): - x = init_value + def train_by_numpy(tensor_idx): + x = init_value_seq[tensor_idx] vt = np.zeros_like(x) st = np.zeros_like(x) max_st = np.zeros_like(x) @@ -117,14 +136,25 @@ def compare_with_numpy_adam( return (param, v, s, max_s) for i in range(1, train_iters + 1): - (x, vt, st, max_st) = np_train_one_iter(i, random_grad_seq[i - 1]) + (x, vt, st, max_st) = np_train_one_iter( + i, random_grad_seq[i - 1][tensor_idx] + ) return x - oneflow_res = train_by_oneflow().numpy() - numpy_res = train_by_numpy() - test_case.assertTrue( - np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3) - ) + oneflow_res = train_by_oneflow() + numpy_res = [] + for i in range(tensor_num): + numpy_res.append(train_by_numpy(i)) + + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) + ) def compare_with_numpy_adam_clip_grad( @@ -142,18 +172,34 @@ def compare_with_numpy_adam_clip_grad( clip_grad_norm_type, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) + adam = flow.optim.Adam( [ { - "params": [x], + "params": x, "lr": learning_rate, "betas": betas, "eps": eps, @@ -164,16 +210,19 @@ def compare_with_numpy_adam_clip_grad( ], do_bias_correction=do_bias_correction, amsgrad=amsgrad, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() adam.clip_grad() adam.step() @@ -183,7 +232,7 @@ def compare_with_numpy_adam_clip_grad( train_one_iter(random_grad_seq[i]) if i == reload_state_step: state_dict = adam.state_dict() - adam = flow.optim.Adam([{"params": [x],}]) + adam = flow.optim.Adam([{"params": x,}]) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -192,7 +241,7 @@ def compare_with_numpy_adam_clip_grad( return x def train_by_numpy(): - x = init_value + x = init_value_seq vt = np.zeros_like(x) st = np.zeros_like(x) max_st = np.zeros_like(x) @@ -203,37 +252,44 @@ def compare_with_numpy_adam_clip_grad( total_norm, grad = clip_grad_norm_np( grad, clip_grad_max_norm, clip_grad_norm_type ) - grad = grad + weight_decay * x - bias_correction1 = 1.0 - bias_correction2 = 1.0 + for i in range(tensor_num): + grad[i] = grad[i] + weight_decay * x[i] - if do_bias_correction: - bias_correction1 = 1.0 - np.power(beta1, step) - bias_correction2 = 1.0 - np.power(beta2, step) + bias_correction1 = 1.0 + bias_correction2 = 1.0 - v = beta1 * vt + (1 - beta1) * grad - s = beta2 * st + (1 - beta2) * grad * grad - max_s = np.zeros_like(x) + if do_bias_correction: + bias_correction1 = 1.0 - np.power(beta1, step) + bias_correction2 = 1.0 - np.power(beta2, step) - if amsgrad: - max_s = np.maximum(s, max_st) - denom = np.sqrt(max_s) / np.sqrt(bias_correction2) + eps - else: - denom = np.sqrt(s) / np.sqrt(bias_correction2) + eps + vt[i] = beta1 * vt[i] + (1 - beta1) * grad[i] + st[i] = beta2 * st[i] + (1 - beta2) * grad[i] * grad[i] - param = x - ((learning_rate / bias_correction1) * v / denom) - return (param, v, s, max_s) + if amsgrad: + max_st[i] = np.maximum(st[i], max_st[i]) + denom = np.sqrt(max_st[i]) / np.sqrt(bias_correction2) + eps + else: + denom = np.sqrt(st[i]) / np.sqrt(bias_correction2) + eps + + x[i] = x[i] - ((learning_rate / bias_correction1) * vt[i] / denom) for i in range(1, train_iters + 1): - (x, vt, st, max_st) = train_one_iter(i, random_grad_seq[i - 1]) + train_one_iter(i, random_grad_seq[i - 1]) return x - oneflow_res = train_by_oneflow().numpy() + oneflow_res = train_by_oneflow() numpy_res = train_by_numpy() - test_case.assertTrue( - np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3) - ) + + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) + ) @flow.unittest.skip_unless_1n1d() @@ -251,6 +307,8 @@ class TestAdam(flow.unittest.TestCase): arg_dict["amsgrad"] = [True, False] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [False, True] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgList(arg_dict): compare_with_numpy_adam(test_case, *arg) @@ -270,6 +328,8 @@ class TestAdam(flow.unittest.TestCase): arg_dict["clip_grad_norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [False, True] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgList(arg_dict): compare_with_numpy_adam_clip_grad(test_case, *arg) diff --git a/python/oneflow/test/modules/test_optim_adamw.py b/python/oneflow/test/modules/test_optim_adamw.py index c59f917b0b80224d2d15c5f6976d27c977e2b989..ebcc8ff79c89bb4166dadf0af8c0af9e8955cb84 100644 --- a/python/oneflow/test/modules/test_optim_adamw.py +++ b/python/oneflow/test/modules/test_optim_adamw.py @@ -39,19 +39,34 @@ def compare_with_numpy_adamw( amsgrad, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) adam = flow.optim.AdamW( [ { - "params": [x], + "params": x, "lr": learning_rate, "betas": betas, "eps": eps, @@ -60,16 +75,19 @@ def compare_with_numpy_adamw( ], do_bias_correction=do_bias_correction, amsgrad=amsgrad, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() adam.step() adam.zero_grad() @@ -78,7 +96,7 @@ def compare_with_numpy_adamw( train_one_iter(random_grad_seq[i]) if i == reload_state_step: state_dict = adam.state_dict() - adam = flow.optim.AdamW([x]) + adam = flow.optim.AdamW(x) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -86,8 +104,8 @@ def compare_with_numpy_adamw( adam.load_state_dict(state_dict) return x - def train_by_numpy(): - x = init_value + def train_by_numpy(tensor_idx): + x = init_value_seq[tensor_idx] vt = np.zeros_like(x) st = np.zeros_like(x) max_st = np.zeros_like(x) @@ -118,15 +136,23 @@ def compare_with_numpy_adamw( return (param, v, s, max_s) for i in range(1, train_iters + 1): - (x, vt, st, max_st) = train_one_iter(i, random_grad_seq[i - 1]) + (x, vt, st, max_st) = train_one_iter(i, random_grad_seq[i - 1][tensor_idx]) return x - oneflow_res = train_by_oneflow().numpy() - numpy_res = train_by_numpy() - - test_case.assertTrue( - np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3) - ) + oneflow_res = train_by_oneflow() + numpy_res = [] + for i in range(tensor_num): + numpy_res.append(train_by_numpy(i)) + + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) + ) def compare_with_numpy_adamw_clip_grad( @@ -144,18 +170,34 @@ def compare_with_numpy_adamw_clip_grad( clip_grad_norm_type, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) + adam = flow.optim.AdamW( [ { - "params": [x], + "params": x, "lr": learning_rate, "betas": betas, "eps": eps, @@ -166,16 +208,19 @@ def compare_with_numpy_adamw_clip_grad( ], do_bias_correction=do_bias_correction, amsgrad=amsgrad, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() adam.clip_grad() adam.step() @@ -185,7 +230,7 @@ def compare_with_numpy_adamw_clip_grad( train_one_iter(random_grad_seq[i]) if i == reload_state_step: state_dict = adam.state_dict() - adam = flow.optim.AdamW([x]) + adam = flow.optim.AdamW(x) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -194,7 +239,7 @@ def compare_with_numpy_adamw_clip_grad( return x def train_by_numpy(): - x = init_value + x = init_value_seq vt = np.zeros_like(x) st = np.zeros_like(x) max_st = np.zeros_like(x) @@ -206,37 +251,44 @@ def compare_with_numpy_adamw_clip_grad( total_norm, grad = clip_grad_norm_np( grad, clip_grad_max_norm, clip_grad_norm_type ) - v = beta1 * vt + (1 - beta1) * grad - s = beta2 * st + (1 - beta2) * grad * grad - bias_correction1 = 1.0 - bias_correction2 = 1.0 + for i in range(tensor_num): + vt[i] = beta1 * vt[i] + (1 - beta1) * grad[i] + st[i] = beta2 * st[i] + (1 - beta2) * grad[i] * grad[i] - if do_bias_correction: - bias_correction1 = 1.0 - np.power(beta1, step) - bias_correction2 = 1.0 - np.power(beta2, step) + bias_correction1 = 1.0 + bias_correction2 = 1.0 - max_s = np.zeros_like(x) - if amsgrad: - max_s = np.maximum(s, max_st) - denom = np.sqrt(max_s) / np.sqrt(bias_correction2) + eps - else: - denom = np.sqrt(s) / np.sqrt(bias_correction2) + eps + if do_bias_correction: + bias_correction1 = 1.0 - np.power(beta1, step) + bias_correction2 = 1.0 - np.power(beta2, step) - lr = learning_rate / bias_correction1 / denom - g = lr * v + learning_rate * weight_decay * x - param = x - g - return (param, v, s, max_s) + if amsgrad: + max_st[i] = np.maximum(st[i], max_st[i]) + denom = np.sqrt(max_st[i]) / np.sqrt(bias_correction2) + eps + else: + denom = np.sqrt(st[i]) / np.sqrt(bias_correction2) + eps + + lr = learning_rate / bias_correction1 / denom + g = lr * vt[i] + learning_rate * weight_decay * x[i] + x[i] = x[i] - g for i in range(1, train_iters + 1): - (x, vt, st, max_st) = train_one_iter(i, random_grad_seq[i - 1]) + train_one_iter(i, random_grad_seq[i - 1]) return x - oneflow_res = train_by_oneflow().numpy() + oneflow_res = train_by_oneflow() numpy_res = train_by_numpy() - test_case.assertTrue( - np.allclose(oneflow_res.flatten(), numpy_res.flatten(), rtol=1e-3, atol=1e-3) - ) + + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) + ) @flow.unittest.skip_unless_1n1d() @@ -254,6 +306,8 @@ class TestAdamW(flow.unittest.TestCase): arg_dict["amsgrad"] = [True, False] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [False, True] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgList(arg_dict): compare_with_numpy_adamw(test_case, *arg) @@ -272,6 +326,8 @@ class TestAdamW(flow.unittest.TestCase): arg_dict["clip_grad_norm_type"] = ["inf", "-inf", 0.0, 1.0, 2.0, 3.5] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [False, True] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgList(arg_dict): compare_with_numpy_adamw_clip_grad(test_case, *arg) diff --git a/python/oneflow/test/modules/test_optim_add_param_group.py b/python/oneflow/test/modules/test_optim_add_param_group.py new file mode 100644 index 0000000000000000000000000000000000000000..34357f6fa89b79368b4980934f77a6abfac37657 --- /dev/null +++ b/python/oneflow/test/modules/test_optim_add_param_group.py @@ -0,0 +1,49 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from oneflow.test_utils.test_util import GenArgList +import oneflow as flow + + +def _test_sgd_add_param_group(test_case): + w1 = flow.ones(3, 3) + w1.requires_grad = True + w2 = flow.ones(3, 3) + w2.requires_grad = True + o = flow.optim.SGD([w1]) + test_case.assertTrue(o.param_groups[0]["lr"] == 0.001) + test_case.assertTrue(o.param_groups[0]["momentum"] == 0.0) + test_case.assertTrue(o.param_groups[0]["weight_decay"] == 0.0) + test_case.assertTrue(o.param_groups[0]["nesterov"] == False) + test_case.assertTrue(o.param_groups[0]["maximize"] == False) + o.step() + o.add_param_group({"params": w2}) + test_case.assertTrue(o.param_groups[1]["lr"] == 0.001) + test_case.assertTrue(o.param_groups[1]["momentum"] == 0.0) + test_case.assertTrue(o.param_groups[1]["weight_decay"] == 0.0) + test_case.assertTrue(o.param_groups[1]["nesterov"] == False) + test_case.assertTrue(o.param_groups[1]["maximize"] == False) + o.step() + + +class TestAddParamGroup(flow.unittest.TestCase): + def test_sgd_add_param_group(test_case): + _test_sgd_add_param_group(test_case) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_optim_sgd.py b/python/oneflow/test/modules/test_optim_sgd.py index 6480c21804d6bad659c41fb85600ede875469921..7484362714124b584d84bd9224e191b67364e8b4 100644 --- a/python/oneflow/test/modules/test_optim_sgd.py +++ b/python/oneflow/test/modules/test_optim_sgd.py @@ -17,6 +17,7 @@ limitations under the License. import unittest from collections import OrderedDict import tempfile +import os import numpy as np from oneflow.test_utils.test_util import GenArgDict @@ -31,38 +32,57 @@ def compare_with_numpy_sgd( device, x_shape, momentum, + dampening, + nesterov, + maximize, weight_decay, learning_rate, train_iters, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) + sgd = flow.optim.SGD( - [ - { - "params": [x], - "lr": learning_rate, - "momentum": momentum, - "weight_decay": weight_decay, - } - ] + [{"params": x, "lr": learning_rate, "weight_decay": weight_decay,}], + momentum=momentum, + dampening=dampening, + nesterov=nesterov, + maximize=maximize, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() sgd.step() sgd.zero_grad() @@ -72,7 +92,7 @@ def compare_with_numpy_sgd( # test state_dict/load_state_dict if i == reload_state_step: state_dict = sgd.state_dict() - sgd = flow.optim.SGD([x]) + sgd = flow.optim.SGD(x) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -80,27 +100,49 @@ def compare_with_numpy_sgd( sgd.load_state_dict(state_dict) return x - def train_by_numpy(): - x = init_value + def train_by_numpy(tensor_idx): + x = init_value_seq[tensor_idx] vt = np.zeros_like(x) def train_one_iter(grad): grad = grad + weight_decay * x - v = momentum * vt - learning_rate * grad - param = x + v + if momentum > 0.0: + next_momentum = momentum * vt + (1 - dampening) * grad + v = next_momentum + + if nesterov: + grad += momentum * next_momentum + else: + grad = next_momentum + + alpha = -learning_rate + if maximize: + alpha = learning_rate + next_model = x + alpha * grad + param = next_model + else: + v = learning_rate * grad + param = x - v return (param, v) for i in range(train_iters): - (x, vt) = train_one_iter(random_grad_seq[i]) + (x, vt) = train_one_iter(random_grad_seq[i][tensor_idx]) return x - oneflow_res = train_by_oneflow().numpy() - numpy_res = train_by_numpy() - test_case.assertTrue( - np.allclose( - oneflow_res.flatten(), numpy_res.flatten(), rtol=0.0001, atol=0.0001 + oneflow_res = train_by_oneflow() + numpy_res = [] + for i in range(tensor_num): + numpy_res.append(train_by_numpy(i)) + + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) ) - ) def compare_with_numpy_sgd_clip_grad( @@ -108,6 +150,9 @@ def compare_with_numpy_sgd_clip_grad( device, x_shape, momentum, + dampening, + nesterov, + maximize, weight_decay, learning_rate, clip_grad_max_norm, @@ -115,35 +160,60 @@ def compare_with_numpy_sgd_clip_grad( train_iters, reload_state_step, save_load_by_pickle, + fused, + tensor_num, ): random_grad_seq = [] + init_value_seq = [] + + for i in range(tensor_num): + init_value_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) + for _ in range(train_iters): - random_grad_seq.append(np.random.uniform(size=x_shape).astype(np.float32)) - init_value = np.random.uniform(size=x_shape).astype(np.float32) + random_grad_seq_per_iter = [] + for i in range(tensor_num): + random_grad_seq_per_iter.append( + np.random.uniform(size=x_shape).astype(np.float32) + ) + random_grad_seq.append(random_grad_seq_per_iter) def train_by_oneflow(): - x = Parameter(flow.Tensor(init_value, device=flow.device(device))) + x = [] + for i in range(tensor_num): + x.append( + Parameter(flow.Tensor(init_value_seq[i], device=flow.device(device))) + ) + sgd = flow.optim.SGD( [ { - "params": [x], + "params": x, "lr": learning_rate, - "momentum": momentum, + "dampening": dampening, + "nesterov": nesterov, + "maximize": maximize, "weight_decay": weight_decay, "clip_grad_max_norm": clip_grad_max_norm, "clip_grad_norm_type": clip_grad_norm_type, } - ] + ], + momentum=momentum, + dampening=dampening, + nesterov=nesterov, + maximize=maximize, + fused=fused, ) def train_one_iter(grad): - grad_tensor = flow.tensor( - grad, - dtype=flow.float32, - requires_grad=False, - device=flow.device(device), - ) - loss = flow.sum(x * grad_tensor) + loss = 0.0 + for i in range(tensor_num): + grad_tensor = flow.tensor( + grad[i], + dtype=flow.float32, + requires_grad=False, + device=flow.device(device), + ) + loss += flow.sum(x[i] * grad_tensor) loss.backward() sgd.clip_grad() sgd.step() @@ -154,7 +224,7 @@ def compare_with_numpy_sgd_clip_grad( # test state_dict/load_state_dict if i == reload_state_step: state_dict = sgd.state_dict() - sgd = flow.optim.SGD([x]) + sgd = flow.optim.SGD(x) if save_load_by_pickle: with tempfile.TemporaryDirectory() as save_dir: flow.save(state_dict, save_dir) @@ -163,30 +233,49 @@ def compare_with_numpy_sgd_clip_grad( return x def train_by_numpy(): - x = init_value + x = init_value_seq vt = np.zeros_like(x) def train_one_iter(grad): total_norm, grad = clip_grad_norm_np( grad, clip_grad_max_norm, clip_grad_norm_type ) - grad = grad + weight_decay * x - v = momentum * vt - learning_rate * grad - param = x + v - return (param, v) + + for i in range(tensor_num): + grad[i] = grad[i] + weight_decay * x[i] + if momentum > 0.0: + next_momentum = momentum * vt[i] + (1 - dampening) * grad[i] + vt[i] = next_momentum + + if nesterov: + grad[i] += momentum * next_momentum + else: + grad[i] = next_momentum + + alpha = -learning_rate + if maximize: + alpha = learning_rate + x[i] = x[i] + alpha * grad[i] + else: + vt[i] = learning_rate * grad[i] + x[i] = x[i] - vt[i] for i in range(train_iters): - (x, vt) = train_one_iter(random_grad_seq[i]) + train_one_iter(random_grad_seq[i]) return x - oneflow_res = train_by_oneflow().numpy() + oneflow_res = train_by_oneflow() numpy_res = train_by_numpy() - test_case.assertTrue( - np.allclose( - oneflow_res.flatten(), numpy_res.flatten(), rtol=0.0001, atol=0.0001 + for i in range(tensor_num): + test_case.assertTrue( + np.allclose( + oneflow_res[i].numpy().flatten(), + numpy_res[i].flatten(), + rtol=0.0001, + atol=0.0001, + ) ) - ) @flow.unittest.skip_unless_1n1d() @@ -196,11 +285,16 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["device"] = ["cpu", "cuda"] arg_dict["x_shape"] = [(10,)] arg_dict["momentum"] = [0.0, 0.9] + arg_dict["dampening"] = [0.0, 0.9] + arg_dict["nesterov"] = [True, False] + arg_dict["maximize"] = [True, False] arg_dict["weight_decay"] = [0.0, 0.9] arg_dict["learning_rate"] = [1, 0.1] arg_dict["train_iters"] = [10] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [True, False] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgDict(arg_dict): compare_with_numpy_sgd(test_case, **arg) @@ -209,6 +303,9 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["device"] = ["cpu", "cuda"] arg_dict["x_shape"] = [(10,)] arg_dict["momentum"] = [0.0, 0.9] + arg_dict["dampening"] = [0.0, 0.9] + arg_dict["nesterov"] = [True, False] + arg_dict["maximize"] = [True, False] arg_dict["weight_decay"] = [0.0, 0.9] arg_dict["learning_rate"] = [1, 0.1] arg_dict["clip_grad_max_norm"] = [0, 0.5, 1.0] @@ -216,9 +313,28 @@ class TestOptimizers(flow.unittest.TestCase): arg_dict["train_iters"] = [10] arg_dict["reload_state_step"] = [5] # save and load optim state arg_dict["save_load_by_pickle"] = [False, True] + arg_dict["fused"] = [True, False] + arg_dict["tensor_num"] = [1, 4] for arg in GenArgDict(arg_dict): compare_with_numpy_sgd_clip_grad(test_case, **arg) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_eager_global_zero_grad_sbp(test_case): + x = flow.nn.Parameter( + flow.zeros((10,)).to_global( + sbp=flow.sbp.broadcast, placement=flow.placement("cuda", [0]) + ) + ) + x.grad = flow.ones_like(x) + t = x.grad + test_case.assertEqual(len(t.sbp), 1) + test_case.assertEqual(t.sbp[0], flow.sbp.broadcast) + optimizer = flow.optim.SGD([x]) + optimizer.zero_grad() + test_case.assertTrue(np.allclose(t.numpy(), 0.0)) + test_case.assertEqual(len(t.sbp), 1) + test_case.assertEqual(t.sbp[0], flow.sbp.partial_sum) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_pairwise_distance.py b/python/oneflow/test/modules/test_pairwise_distance.py new file mode 100644 index 0000000000000000000000000000000000000000..6a62c7a58c8c18e8b4b5192fce482da10674d421 --- /dev/null +++ b/python/oneflow/test/modules/test_pairwise_distance.py @@ -0,0 +1,64 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +from oneflow.test_utils.automated_test_util import * + +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestPairwiseDistance(flow.unittest.TestCase): + @autotest(n=3) + def test_pairwise_distance_module_with_random_data(test_case): + device = random_device() + a = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + b = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + cos = torch.nn.PairwiseDistance(p=2, eps=1e-6).to(device) + cos.train(random()) + output = cos(a, b) + return output + + @autotest(n=3) + def test_pairwise_distance_module_with_nonequal_dim_random_data(test_case): + device = random_device() + a = random_tensor(ndim=1, dim0=128).to(device) + b = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + cos = torch.nn.PairwiseDistance(p=2, eps=1e-6).to(device) + cos.train(random()) + output = cos(a, b) + return output + + @autotest(n=3) + def test_pairwise_distance_functional_with_random_data(test_case): + device = random_device() + a = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + b = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + output = torch.nn.functional.pairwise_distance(a, b, p=2, eps=1e-6) + return output + + @autotest(n=3) + def test_pairwise_distance_functional_with_nonequal_dim_random_data(test_case): + device = random_device() + a = random_tensor(ndim=1, dim0=128).to(device) + b = random_tensor(ndim=2, dim0=10, dim1=128).to(device) + output = torch.nn.functional.pairwise_distance(a, b, p=2, eps=1e-6) + return output + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_parital_fc.py b/python/oneflow/test/modules/test_parital_fc.py index 7b056128bc02e4749a3179514fb0ae62b5ec9cbe..f22b18464eb9e4750ee3bee1832a4a69abb5c705 100644 --- a/python/oneflow/test/modules/test_parital_fc.py +++ b/python/oneflow/test/modules/test_parital_fc.py @@ -25,7 +25,7 @@ import oneflow.unittest @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") class TestParitalFC(flow.unittest.TestCase): def test_parital_fc(test_case): - p = flow.env.all_device_placement("cuda") + p = flow.placement.all("cuda") w = flow.randn( 50000, 128, placement=p, sbp=flow.sbp.broadcast, requires_grad=True ) diff --git a/python/oneflow/test/modules/test_qat_conv_modules.py b/python/oneflow/test/modules/test_qat_conv_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..97c2a90626ff117e4df2b68c4a1179ef5c22106e --- /dev/null +++ b/python/oneflow/test/modules/test_qat_conv_modules.py @@ -0,0 +1,204 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict +import random + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + + +def _test_qat_conv1d( + test_case, + device, + quantization_formula, + quantization_bit, + quantization_scheme, + weight_quant_per_layer, + input_quant_momentum, +): + batch_size = random.randint(1, 5) + input_channels = random.randint(1, 3) + output_channels = random.randint(1, 3) + spatial_size = random.randint(8, 16) + kernel_size = random.randint(1, 3) + stride = random.randint(1, 2) + padding = random.randint(0, 2) + + qat_conv1d = flow.nn.QatConv1d( + in_channels=input_channels, + out_channels=output_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ).to(device) + + qat_input = flow.rand( + batch_size, + input_channels, + spatial_size, + dtype=flow.float32, + requires_grad=True, + device=device, + ) + + qat_out = qat_conv1d(qat_input) + qat_out.sum().backward() + qat_out.numpy() + qat_input.grad.numpy() + + +def _test_qat_conv2d( + test_case, + device, + quantization_formula, + quantization_bit, + quantization_scheme, + weight_quant_per_layer, + input_quant_momentum, +): + batch_size = random.randint(1, 5) + input_channels = random.randint(1, 3) + output_channels = random.randint(1, 3) + spatial_size = random.randint(8, 16) + kernel_size = random.randint(1, 3) + stride = random.randint(1, 2) + padding = random.randint(0, 2) + + qat_conv2d = flow.nn.QatConv2d( + in_channels=input_channels, + out_channels=output_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ).to(device) + + qat_input = flow.rand( + batch_size, + input_channels, + spatial_size, + spatial_size, + dtype=flow.float32, + requires_grad=True, + device=device, + ) + qat_out = qat_conv2d(qat_input) + qat_out.sum().backward() + qat_out.numpy() + qat_input.grad.numpy() + + +def _test_qat_conv3d( + test_case, + device, + quantization_formula, + quantization_bit, + quantization_scheme, + weight_quant_per_layer, + input_quant_momentum, +): + batch_size = random.randint(1, 5) + input_channels = random.randint(1, 3) + output_channels = random.randint(1, 3) + spatial_size = random.randint(8, 16) + kernel_size = random.randint(1, 3) + stride = random.randint(1, 2) + padding = random.randint(0, 2) + + qat_conv3d = flow.nn.QatConv3d( + in_channels=input_channels, + out_channels=output_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + quantization_formula=quantization_formula, + quantization_bit=quantization_bit, + quantization_scheme=quantization_scheme, + weight_quant_per_layer=weight_quant_per_layer, + input_quant_momentum=input_quant_momentum, + ).to(device) + + qat_input = flow.rand( + batch_size, + input_channels, + spatial_size, + spatial_size, + spatial_size, + dtype=flow.float32, + requires_grad=True, + device=device, + ) + qat_out = qat_conv3d(qat_input) + qat_out.sum().backward() + qat_out.numpy() + qat_input.grad.numpy() + + +@flow.unittest.skip_unless_1n1d() +class TestQatModules(flow.unittest.TestCase): + def test_qat_conv1d(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda", "cpu"] + arg_dict["quantization_formula"] = ["google"] + arg_dict["quantization_bit"] = [4, 8] + arg_dict["quantization_scheme"] = ["symmetric", "affine"] + arg_dict["weight_quant_per_layer"] = [True, False] + arg_dict["input_quant_momentum"] = [0.95] + + for i in range(5): + for arg in GenArgList(arg_dict): + _test_qat_conv1d(test_case, *arg) + + def test_qat_conv2d(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda", "cpu"] + arg_dict["quantization_formula"] = ["google"] + arg_dict["quantization_bit"] = [4, 8] + arg_dict["quantization_scheme"] = ["symmetric", "affine"] + arg_dict["weight_quant_per_layer"] = [True, False] + arg_dict["input_quant_momentum"] = [0.95] + + for i in range(5): + for arg in GenArgList(arg_dict): + _test_qat_conv2d(test_case, *arg) + + def test_qat_conv3d(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda", "cpu"] + arg_dict["quantization_formula"] = ["google"] + arg_dict["quantization_bit"] = [4, 8] + arg_dict["quantization_scheme"] = ["symmetric", "affine"] + arg_dict["weight_quant_per_layer"] = [True, False] + arg_dict["input_quant_momentum"] = [0.95] + + for i in range(5): + for arg in GenArgList(arg_dict): + _test_qat_conv3d(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_quantile.py b/python/oneflow/test/modules/test_quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..d73e34982184f4aea944fda54a7a2eae45620bd0 --- /dev/null +++ b/python/oneflow/test/modules/test_quantile.py @@ -0,0 +1,47 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +from oneflow.test_utils.automated_test_util import * +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +@autotest(n=3, check_graph=True) +def _test_quantile(test_cast, q): + device = random_device() + a = random_tensor(2, random(2, 5), random(2, 5)).to(device) + out = torch.quantile(a, q, dim=1, interpolation="linear") + return out + + +@unittest.skipIf(True, "pytorch-1.10.0 will cause oneflow cudnn or cublas error") +@flow.unittest.skip_unless_1n1d() +class TestQuantile(flow.unittest.TestCase): + def test_quantile(test_case): + arg_dict = OrderedDict() + arg_dict["q"] = [0.2, 0.6, 0.8] + for arg in GenArgList(arg_dict): + print(arg) + _test_quantile(test_case, *arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_quick_gelu.py b/python/oneflow/test/modules/test_quick_gelu.py new file mode 100644 index 0000000000000000000000000000000000000000..9c75b06a8ce00f564f0a277e828275a51a2dc595 --- /dev/null +++ b/python/oneflow/test/modules/test_quick_gelu.py @@ -0,0 +1,65 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest +import torch + + +class QuickGELUActivation(torch.nn.Module): + """ + Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs + """ + + def forward(self, input: torch.Tensor) -> torch.Tensor: + return input * torch.sigmoid(1.702 * input) + + +def _test_quick_gelu(test_case, device): + torch_quick_gelu = QuickGELUActivation() + x = np.random.randn(2, 4, 3) + torch_x = torch.tensor(x, requires_grad=True, device=torch.device(device)) + oneflow_x = flow.tensor(x, requires_grad=True, device=flow.device(device)) + torch_y = torch_quick_gelu(torch_x) + oneflow_y = flow._C.quick_gelu(oneflow_x) + test_case.assertTrue(np.allclose(torch_y.detach().cpu().numpy(), oneflow_y.numpy())) + torch_y_sum = torch_y.sum() + torch_y_sum.backward() + oneflow_y_sum = oneflow_y.sum() + oneflow_y_sum.backward() + test_case.assertTrue( + np.allclose(torch_x.grad.cpu().numpy(), oneflow_x.grad.numpy()) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestModule(flow.unittest.TestCase): + def test_quick_gelu(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_quick_gelu] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_rand.py b/python/oneflow/test/modules/test_rand.py index a4921ceb547890648c9457d9195a3a131ab7582d..bcf9cd2c45a505014922bc845e44748d0b81ad99 100644 --- a/python/oneflow/test/modules/test_rand.py +++ b/python/oneflow/test/modules/test_rand.py @@ -114,6 +114,13 @@ class TestRandModule(flow.unittest.TestCase): for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_half_rand(test_case): + for device in ["cuda", "cpu"]: + x = flow.rand(2, 3, dtype=flow.float16, device=flow.device(device)) + test_case.assertTrue(x.dtype == flow.float16) + test_case.assertTrue(x.shape == flow.Size((2, 3))) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() diff --git a/python/oneflow/test/modules/test_randint_like.py b/python/oneflow/test/modules/test_randint_like.py new file mode 100644 index 0000000000000000000000000000000000000000..c4d0f5eb5c75271778aebeedb74c9434aa8d2014 --- /dev/null +++ b/python/oneflow/test/modules/test_randint_like.py @@ -0,0 +1,162 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.test_util import GenArgList + + +def _test_randint_like(test_case, device, shape, low, high): + x = flow.randn(shape) + y1 = flow.randint_like(x, low, high, device=flow.device(device)) + y2 = flow.randint_like(x, low, high, device=flow.device(device)) + test_case.assertFalse(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) + test_case.assertTrue(shape == y1.shape) + + +def _test_0d_randint_like(test_case, device, shape, low, high): + x = flow.randn(shape) + y1 = flow.randint_like(x, low, high, device=flow.device(device)) + y2 = flow.randint_like(x, low, high, device=flow.device(device)) + test_case.assertTrue( + np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4) + ) # 0d is [] and [] + test_case.assertTrue(shape == y1.shape) + + +def _test_different_dtype(test_case, device, shape, low, high): + for dtype in [ + flow.uint8, + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + ]: + x = flow.randint(low, high, shape, dtype=dtype) + y = flow.randint_like(x, low, high, dtype=dtype, device=flow.device(device)) + test_case.assertTrue(y.dtype == dtype) + test_case.assertTrue(y.shape == shape) + + +def _test_with_generator(test_case, device, shape, low, high): + gen = flow.Generator() + gen.manual_seed(0) + x = flow.randn(shape) + y1 = flow.randint_like( + x, low, high, dtype=flow.float32, device=flow.device(device), generator=gen + ) + gen.manual_seed(0) + x = flow.randn(shape) + y2 = flow.randint_like( + x, low, high, dtype=flow.float32, device=flow.device(device), generator=gen + ) + test_case.assertTrue(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) + + +def _test_high(test_case, device, shape, low, high): + x = flow.randn(shape) + y1 = flow._C.randint_like(x, high, device=flow.device(device)) + y2 = flow._C.randint_like(x, high, device=flow.device(device)) + test_case.assertFalse(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) + test_case.assertTrue(shape == y1.shape) + + +def _test_0rank(test_case, device, shape, low, high): + x = flow.randn(shape) + y1 = flow.randint_like(x, low, high, device=flow.device(device)) + test_case.assertTrue(y1.shape == shape) + + +@flow.unittest.skip_unless_1n1d() +class TestRandIntLike(flow.unittest.TestCase): + def test_global_different_types(test_case): + for dtype in [ + flow.int8, + flow.int32, + flow.int64, + flow.float32, + flow.float64, + ]: + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + x_ = flow.randn((10, 1)) + x = flow.randint_like(x_, 0, 16, placement=placement, sbp=sbp, dtype=dtype) + test_case.assertEqual(x.dtype, dtype) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + def test_randint_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [ + _test_randint_like, + _test_different_dtype, + _test_with_generator, + ] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)] + arg_dict["low"] = [i for i in range(10)] + arg_dict["high"] = [10 + np.random.randint(10, 20) for i in range(10)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + def test_0d_randint_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_0d_randint_like] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [(2, 0, 4), (2, 0, 2)] + arg_dict["low"] = [i for i in range(10)] + arg_dict["high"] = [10 + np.random.randint(1, 20) for i in range(10)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + def test_high_randint_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_high] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [(2, 3, 4), (2, 5, 2)] + arg_dict["low"] = [i for i in range(10)] + arg_dict["high"] = [10 + np.random.randint(10, 20) for i in range(10)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + def test_0rank_randint_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_0rank] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [()] + arg_dict["low"] = [i for i in range(10)] + arg_dict["high"] = [1000 + np.random.randint(1, 10) for i in range(10)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class TestRandIntLikeOnNonDefaultDevice(flow.unittest.TestCase): + def test_non_default_device(test_case): + x_ = flow.randn((2, 3)) + x = flow.randint_like(x_, low=1, high=2, device="cuda:1") + test_case.assertEqual(x.device, flow.device("cuda:1")) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_randn.py b/python/oneflow/test/modules/test_randn.py index 9e674aa3f642872f97ebddeda0f788733f7bf853..eccf4f5b7001f5b69c49d09f1ba36fb65873a7bd 100644 --- a/python/oneflow/test/modules/test_randn.py +++ b/python/oneflow/test/modules/test_randn.py @@ -125,6 +125,13 @@ class TestRandnModule(flow.unittest.TestCase): for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_half_randn(test_case): + for device in ["cuda", "cpu"]: + x = flow.randn(2, 3, dtype=flow.float16, device=flow.device(device)) + test_case.assertTrue(x.dtype == flow.float16) + test_case.assertTrue(x.shape == flow.Size((2, 3))) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") @flow.unittest.skip_unless_1n2d() diff --git a/python/oneflow/test/modules/test_randn_like.py b/python/oneflow/test/modules/test_randn_like.py new file mode 100644 index 0000000000000000000000000000000000000000..00d6f8ffc8e03f11423f1ae0593ba4d994c71ad8 --- /dev/null +++ b/python/oneflow/test/modules/test_randn_like.py @@ -0,0 +1,128 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +from collections import OrderedDict + +import numpy as np +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.test_util import GenArgList + + +def _test_randn_like(test_case, device, shape): + x = flow.randn(shape) + y = flow.randn_like(x, device=flow.device(device)) + test_case.assertTrue(x.shape == y.shape) + + +def _test_0d_randn_like(test_case, device, shape): + x = flow.randn(shape) + y = flow.randn_like(x, device=flow.device(device)) + test_case.assertTrue(x.shape == y.shape) + + +def _test_different_dtype(test_case, device, shape): + for dtype in [ + flow.float16, + flow.float32, + flow.float64, + flow.double, + ]: + x = flow.randn(shape, dtype=dtype) + y = flow.randn_like(x, dtype=dtype, device=flow.device(device)) + test_case.assertTrue(x.shape == y.shape) + + +def _test_with_generator(test_case, device, shape): + gen = flow.Generator() + gen.manual_seed(0) + x = flow.randn(shape) + y1 = flow.randn_like( + x, dtype=flow.float32, device=flow.device(device), generator=gen + ) + gen.manual_seed(0) + x = flow.randn(shape) + y2 = flow.randn_like( + x, dtype=flow.float32, device=flow.device(device), generator=gen + ) + test_case.assertTrue(np.allclose(y1.numpy(), y2.numpy(), atol=1e-4, rtol=1e-4)) + + +def _test_0rank(test_case, device, shape): + x = flow.randn(shape) + y = flow.randn_like(x, device=flow.device(device)) + test_case.assertTrue(x.shape == y.shape) + + +@flow.unittest.skip_unless_1n1d() +class TestRandIntLike(flow.unittest.TestCase): + def test_global_different_types(test_case): + for dtype in [ + flow.float16, + flow.float32, + flow.float64, + flow.double, + ]: + placement = flow.placement("cpu", ranks=[0]) + sbp = (flow.sbp.broadcast,) + x_ = flow.randn((10, 1), dtype=dtype) + x = flow.randn_like(x_, placement=placement, sbp=sbp, dtype=dtype) + test_case.assertEqual(x.dtype, dtype) + test_case.assertEqual(x.sbp, sbp) + test_case.assertEqual(x.placement, placement) + + def test_randn_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [ + _test_randn_like, + _test_different_dtype, + _test_with_generator, + ] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + def test_0d_randn_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_0d_randn_like] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [(2, 0, 4), (2, 0, 2)] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + def test_0rank_randn_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_0rank] + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["shape"] = [()] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n2d() +class TestRandIntLikeOnNonDefaultDevice(flow.unittest.TestCase): + def test_non_default_device(test_case): + x_ = flow.randn((2, 3)) + x = flow.randn_like(x_, device="cuda:1") + test_case.assertEqual(x.device, flow.device("cuda:1")) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_reduce_sum_like.py b/python/oneflow/test/modules/test_reduce_sum_like.py new file mode 100644 index 0000000000000000000000000000000000000000..283ac33281774fea1d9fe8a73ea8a8c5ae41feb0 --- /dev/null +++ b/python/oneflow/test/modules/test_reduce_sum_like.py @@ -0,0 +1,162 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest + + +def _test_reduce_sum_like(test_case, device): + input = flow.tensor( + np.ones(shape=(3, 3, 3), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(3, 1, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(1, 2)) + np_out = np.full(shape=like_tensor.shape, fill_value=9) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_one(test_case, device): + input = flow.tensor( + np.ones(shape=(1, 2, 3), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(1, 2)) + np_out = np.full(like_tensor.shape, 6) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_different_dim(test_case, device): + input = flow.tensor( + np.ones(shape=(2, 3, 4), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(3, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(0, 2)) + np_out = np.full(like_tensor.shape, 8) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_different_dim_with_input_axisvec(test_case, device): + input = flow.tensor( + np.ones(shape=(1, 5, 6, 1, 6), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 5, 6), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(3, 4)) + np_out = np.full(like_tensor.shape, 6) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_3dim(test_case, device): + input = flow.tensor( + np.ones(shape=(3, 3, 2), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 3, 2), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(0,)) + np_out = np.full(like_tensor.shape, 3) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_4dim(test_case, device): + input = flow.tensor( + np.ones(shape=(3, 3, 2, 3), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + like_tensor = flow.tensor( + np.ones(shape=(1, 3, 2, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(0, 3)) + np_out = np.full(like_tensor.shape, 9) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + + +def _test_reduce_sum_like_backward(test_case, device): + input = flow.tensor( + np.ones(shape=(3, 3, 3), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + like_tensor = flow.tensor( + np.ones(shape=(3, 1, 1), dtype=np.float32), + dtype=flow.float32, + device=flow.device(device), + requires_grad=True, + ) + of_out = flow._C.reduce_sum_like(input, like_tensor, axis=(1, 2)) + of_out = of_out.sum() + of_out.backward() + np_grad = np.full(input.shape, 1.0) + test_case.assertTrue(np.allclose(input.grad.numpy(), np_grad, 1e-05, 1e-05)) + + +@flow.unittest.skip_unless_1n1d() +class TestReduceSumLike(flow.unittest.TestCase): + def test_reduce_sum_like(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [ + _test_reduce_sum_like, + _test_reduce_sum_like_one, + _test_reduce_sum_like_different_dim, + _test_reduce_sum_like_different_dim_with_input_axisvec, + _test_reduce_sum_like_3dim, + _test_reduce_sum_like_4dim, + _test_reduce_sum_like_backward, + ] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_reflection_pad.py b/python/oneflow/test/modules/test_reflection_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..299177899cfa142bb724ebc64223379963eaaa17 --- /dev/null +++ b/python/oneflow/test/modules/test_reflection_pad.py @@ -0,0 +1,175 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import ( + Array2Numpy, + FlattenArray, + GenArgList, + Index2Coordinate, +) + +import oneflow as flow +import oneflow.unittest +from oneflow.test_utils.automated_test_util import * + + +def gen_numpy_test_sample(input, padding): + (c_idx, h_idx, w_idx) = (1, 2, 3) + pad_left = padding[0] + pad_right = padding[1] + pad_top = padding[2] + pad_bottom = padding[3] + pad_shape = ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)) + + def _np_reflection_pad2d(input, pad_shape): + numpy_reflect = np.pad(input, pad_shape, "reflect") + return numpy_reflect + + def _np_reflection_pad2d_grad(src, dest): + (dx_height, dx_width) = (input.shape[h_idx], input.shape[w_idx]) + (dy_height, dy_width) = (output.shape[h_idx], output.shape[w_idx]) + numpy_src = np.ones(src.shape, np.int32) + numpy_dest = np.zeros(dest.shape, np.int32) + array_src = FlattenArray(numpy_src) + array_dest = FlattenArray(numpy_dest) + src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx] + dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx] + elements_num = src.shape[0] * src_num + for iter_n in range(elements_num): + coords = Index2Coordinate(iter_n, src.shape) + (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx]) + ip_x = ip_y = 0 + if j < pad_left: + ip_x = pad_left * 2 - j + elif j >= pad_left and j < dx_width + pad_left: + ip_x = j + else: + ip_x = (dx_width + pad_left - 1) * 2 - j + if i < pad_top: + ip_y = pad_top * 2 - i + elif i >= pad_top and i < dx_height + pad_top: + ip_y = i + else: + ip_y = (dx_height + pad_top - 1) * 2 - i + ip_x = ip_x - pad_left + ip_y = ip_y - pad_top + src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j + dest_index = ( + n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x + ) + array_dest[dest_index] += array_src[src_index] + numpy_dest = Array2Numpy(array_dest, dest.shape) + return numpy_dest + + output = _np_reflection_pad2d(input, pad_shape) + grad = _np_reflection_pad2d_grad(output, input) + return (output, grad) + + +def _test_reflection_pad2d(test_case, shape, padding, device): + np_input = np.random.randn(*shape).astype(np.float32) + of_input = flow.tensor( + np_input, dtype=flow.float32, device=flow.device(device), requires_grad=True + ) + if isinstance(padding, int): + boundary = [padding, padding, padding, padding] + elif isinstance(padding, tuple) and len(padding) == 4: + boundary = [padding[0], padding[1], padding[2], padding[3]] + else: + raise ValueError("padding must be in or list or tuple!") + (np_out, np_grad) = gen_numpy_test_sample(np_input, boundary) + layer = flow.nn.ReflectionPad2d(padding=padding) + of_out = layer(of_input) + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001)) + of_out = of_out.sum() + of_out.backward() + test_case.assertTrue(np.allclose(of_input.grad.numpy(), np_grad, 0.0001, 0.0001)) + + +@flow.unittest.skip_unless_1n1d() +class TestReflectionPadModule(flow.unittest.TestCase): + def test_reflection_pad2d(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)] + arg_dict["padding"] = [2, (1, 1, 2, 2)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + _test_reflection_pad2d(test_case, *arg) + + @autotest(n=5) + def test_reflection_pad_1d_with_3d_input(test_case): + c = random(1, 6).to(int) + w = random(1, 6).to(int) + m = torch.nn.ReflectionPad1d(padding=random(low=0, high=5).to(int)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=3, dim1=c, dim2=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_reflection_pad_1d_with_2d_input(test_case): + w = random(1, 6).to(int) + m = torch.nn.ReflectionPad1d(padding=random(low=0, high=5).to(int)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=2, dim1=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_reflection_pad_2d_with_random_data(test_case): + c = random(1, 6).to(int) + h = random(1, 6).to(int) + w = random(1, 6).to(int) + m = torch.nn.ReflectionPad2d(padding=random(low=0, high=5).to(int)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_functional_reflection_pad_1d_with_random_data(test_case): + c = random(1, 6).to(int) + w = random(1, 6).to(int) + pad = [1, 2] + device = random_device() + x = random_tensor(ndim=3, dim1=c, dim2=w).to(device) + y = torch.nn.functional.pad(input=x, pad=pad, mode="reflect") + return y + + @autotest(n=5) + def test_functional_reflection_pad_2d_with_random_data(test_case): + c = random(1, 6).to(int) + h = random(1, 6).to(int) + w = random(1, 6).to(int) + pad = [0, 1, 2, 3] + device = random_device() + x = random_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device) + y = torch.nn.functional.pad(input=x, pad=pad, mode="reflect") + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_reflection_pad2d.py b/python/oneflow/test/modules/test_reflection_pad2d.py deleted file mode 100644 index c66cb9d79550aaa4d6466410aa69c5efb11ae584..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_reflection_pad2d.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import ( - Array2Numpy, - FlattenArray, - GenArgList, - Index2Coordinate, -) - -import oneflow as flow -import oneflow.unittest - - -def gen_numpy_test_sample(input, padding): - (c_idx, h_idx, w_idx) = (1, 2, 3) - pad_left = padding[0] - pad_right = padding[1] - pad_top = padding[2] - pad_bottom = padding[3] - pad_shape = ((0, 0), (0, 0), (pad_top, pad_bottom), (pad_left, pad_right)) - - def _np_reflection_pad2d(input, pad_shape): - numpy_reflect = np.pad(input, pad_shape, "reflect") - return numpy_reflect - - def _np_reflection_pad2d_grad(src, dest): - (dx_height, dx_width) = (input.shape[h_idx], input.shape[w_idx]) - (dy_height, dy_width) = (output.shape[h_idx], output.shape[w_idx]) - numpy_src = np.ones(src.shape, np.int32) - numpy_dest = np.zeros(dest.shape, np.int32) - array_src = FlattenArray(numpy_src) - array_dest = FlattenArray(numpy_dest) - src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx] - dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx] - elements_num = src.shape[0] * src_num - for iter_n in range(elements_num): - coords = Index2Coordinate(iter_n, src.shape) - (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx]) - ip_x = ip_y = 0 - if j < pad_left: - ip_x = pad_left * 2 - j - elif j >= pad_left and j < dx_width + pad_left: - ip_x = j - else: - ip_x = (dx_width + pad_left - 1) * 2 - j - if i < pad_top: - ip_y = pad_top * 2 - i - elif i >= pad_top and i < dx_height + pad_top: - ip_y = i - else: - ip_y = (dx_height + pad_top - 1) * 2 - i - ip_x = ip_x - pad_left - ip_y = ip_y - pad_top - src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j - dest_index = ( - n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x - ) - array_dest[dest_index] += array_src[src_index] - numpy_dest = Array2Numpy(array_dest, dest.shape) - return numpy_dest - - output = _np_reflection_pad2d(input, pad_shape) - grad = _np_reflection_pad2d_grad(output, input) - return (output, grad) - - -def _test_reflection_pad2d(test_case, shape, padding, device): - np_input = np.random.randn(*shape).astype(np.float32) - of_input = flow.tensor( - np_input, dtype=flow.float32, device=flow.device(device), requires_grad=True - ) - if isinstance(padding, int): - boundary = [padding, padding, padding, padding] - elif isinstance(padding, tuple) and len(padding) == 4: - boundary = [padding[0], padding[1], padding[2], padding[3]] - else: - raise ValueError("padding must be in or list or tuple!") - (np_out, np_grad) = gen_numpy_test_sample(np_input, boundary) - layer = flow.nn.ReflectionPad2d(padding=padding) - of_out = layer(of_input) - test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 0.0001, 0.0001)) - of_out = of_out.sum() - of_out.backward() - test_case.assertTrue(np.allclose(of_input.grad.numpy(), np_grad, 0.0001, 0.0001)) - - -@flow.unittest.skip_unless_1n1d() -class TestReflectionPad2dModule(flow.unittest.TestCase): - def test_reflection_pad2d(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)] - arg_dict["padding"] = [2, (1, 1, 2, 2)] - arg_dict["device"] = ["cpu", "cuda"] - for arg in GenArgList(arg_dict): - _test_reflection_pad2d(test_case, *arg) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_repeat.py b/python/oneflow/test/modules/test_repeat.py index 39cbdb9c6caf2db5f00fd04ccc15b291aee6692e..c3b3b51272c99ef462bc138fb230cb4039e67e2d 100644 --- a/python/oneflow/test/modules/test_repeat.py +++ b/python/oneflow/test/modules/test_repeat.py @@ -23,27 +23,41 @@ from oneflow.test_utils.automated_test_util import * @flow.unittest.skip_unless_1n1d() class TestRepeat(flow.unittest.TestCase): - @autotest(check_graph=True) + @autotest(n=10) def test_flow_tensor_repeat_with_random_data(test_case): x = random_tensor(ndim=2, dim0=1, dim1=2) sizes = (random(1, 5).to(int), random(1, 5).to(int), random(1, 5).to(int)) y = x.repeat(sizes) return y - @autotest(auto_backward=False, check_graph=True) + @autotest(n=10, auto_backward=False) def test_flow_tensor_repeat_bool_with_random_data(test_case): x = random_tensor(ndim=2, dim0=1, dim1=2).to(torch.bool) sizes = (random(1, 5).to(int), random(1, 5).to(int), random(1, 5).to(int)) y = x.repeat(sizes) return y - @autotest(check_graph=True) + @autotest(n=10) def test_flow_tensor_repeat_with_0dim_data(test_case): x = random_tensor(ndim=0) sizes = (random(1, 5).to(int), random(1, 5).to(int), random(1, 5).to(int)) y = x.repeat(sizes) return y + @autotest(n=5, auto_backward=False) + def test_complicated_repeat_case(test_case): + x = torch.ones(224, 224) + y = torch.triu(x, diagonal=1).repeat(32, 1, 1) + z = y.byte() + return z + + @autotest(n=5) + def test_flow_tensor_0size_with_random_data(test_case): + x = random_tensor(ndim=2, dim0=3, dim1=1) + sizes = (1, 0) + y = x.repeat(sizes) + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_replication_pad.py b/python/oneflow/test/modules/test_replication_pad.py new file mode 100644 index 0000000000000000000000000000000000000000..9cae47c3cb7a83639d2d8b3a55801f60d5dd644f --- /dev/null +++ b/python/oneflow/test/modules/test_replication_pad.py @@ -0,0 +1,172 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import numpy as np +from oneflow.test_utils.test_util import ( + Array2Numpy, + FlattenArray, + GenArgList, + Index2Coordinate, +) + +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +def _np_replication_pad2d_grad(src, dest, padding): + (c_idx, h_idx, w_idx) = (1, 2, 3) + pad_left = padding[0] + pad_right = padding[1] + pad_top = padding[2] + pad_bottom = padding[3] + (dx_height, dx_width) = (dest.shape[h_idx], dest.shape[w_idx]) + (dy_height, dy_width) = (src.shape[h_idx], src.shape[w_idx]) + numpy_src = np.ones(src.shape, np.int32) + numpy_dest = np.zeros(dest.shape, np.int32) + array_src = FlattenArray(numpy_src) + array_dest = FlattenArray(numpy_dest) + src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx] + dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx] + elements_num = src.shape[0] * src_num + for iter_n in range(elements_num): + coords = Index2Coordinate(iter_n, src.shape) + (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx]) + ip_x = ip_y = 0 + if j < pad_left: + ip_x = pad_left + elif j >= pad_left and j < dx_width + pad_left: + ip_x = j + else: + ip_x = dx_width + pad_left - 1 + if i < pad_top: + ip_y = pad_top + elif i >= pad_top and i < dx_height + pad_top: + ip_y = i + else: + ip_y = dx_height + pad_top - 1 + ip_x = ip_x - pad_left + ip_y = ip_y - pad_top + src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j + dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x + array_dest[dest_index] += array_src[src_index] + numpy_dest = Array2Numpy(array_dest, dest.shape) + return numpy_dest + + +def _test_ReplicationPad2d(test_case, shape, padding, device): + np_input = np.random.random(shape).astype(np.float32) + of_input = flow.tensor( + np_input, dtype=flow.float32, device=flow.device(device), requires_grad=True + ) + if isinstance(padding, int): + np_boundary = ((0, 0), (0, 0), (padding, padding), (padding, padding)) + boundry = [padding, padding, padding, padding] + elif isinstance(padding, (tuple, int)) and len(padding) == 4: + np_boundary = ( + (0, 0), + (0, 0), + (padding[2], padding[3]), + (padding[0], padding[1]), + ) + boundry = [padding[0], padding[1], padding[2], padding[3]] + else: + raise ValueError("padding must be in or list or tuple!") + layer = flow.nn.ReplicationPad2d(padding=padding) + of_out = layer(of_input) + np_out = np.pad(np_input, np_boundary, mode="edge") + test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + of_out = of_out.sum() + of_out.backward() + np_out_grad = _np_replication_pad2d_grad(np_out, np_input, boundry) + test_case.assertTrue(np.allclose(of_input.grad.numpy(), np_out_grad, 0.001, 0.001)) + + +@flow.unittest.skip_unless_1n1d() +class TestReplicationPadModule(flow.unittest.TestCase): + def test_ReplicationPad2d(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)] + arg_dict["padding"] = [2, (1, 1, 2, 2)] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + _test_ReplicationPad2d(test_case, *arg) + + @autotest(n=5) + def test_replication_pad1d_with_3d_input(test_case): + c = random(1, 6).to(int) + w = random(1, 6).to(int) + pad = random(low=0, high=5).to(int) + m = torch.nn.ReplicationPad1d(padding=pad) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=3, dim1=c, dim2=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_replication_pad1d_with_2d_input(test_case): + w = random(1, 6).to(int) + m = torch.nn.ReplicationPad1d(padding=random(low=0, high=5).to(int)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=2, dim1=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_replication_pad2d_with_random_data(test_case): + c = random(1, 6).to(int) + h = random(1, 6).to(int) + w = random(1, 6).to(int) + m = torch.nn.ReplicationPad2d(padding=random(low=0, high=5)) + m.train(random()) + device = random_device() + m.to(device) + x = random_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device) + y = m(x) + return y + + @autotest(n=5) + def test_functional_replication_pad_1d_with_random_data(test_case): + c = random(1, 6).to(int) + w = random(1, 6).to(int) + pad = [0, 1] + device = random_device() + x = random_tensor(ndim=3, dim1=c, dim2=w).to(device) + y = torch.nn.functional.pad(input=x, pad=pad, mode="replicate") + return y + + @autotest(n=5) + def test_functional_replication_pad_2d_with_random_data(test_case): + c = random(1, 6).to(int) + h = random(1, 6).to(int) + w = random(1, 6).to(int) + pad = [0, 1, 2, 3] + device = random_device() + x = random_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device) + y = torch.nn.functional.pad(input=x, pad=pad, mode="replicate") + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_replicationpad2d.py b/python/oneflow/test/modules/test_replicationpad2d.py deleted file mode 100644 index 4a79ceba216dcb7430ff54a6fc26919ce357925c..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_replicationpad2d.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -from collections import OrderedDict - -import numpy as np -from oneflow.test_utils.test_util import ( - Array2Numpy, - FlattenArray, - GenArgList, - Index2Coordinate, -) - -import oneflow as flow -import oneflow.unittest - -from oneflow.test_utils.automated_test_util import * - - -def _np_replication_pad2d_grad(src, dest, padding): - (c_idx, h_idx, w_idx) = (1, 2, 3) - pad_left = padding[0] - pad_right = padding[1] - pad_top = padding[2] - pad_bottom = padding[3] - (dx_height, dx_width) = (dest.shape[h_idx], dest.shape[w_idx]) - (dy_height, dy_width) = (src.shape[h_idx], src.shape[w_idx]) - numpy_src = np.ones(src.shape, np.int32) - numpy_dest = np.zeros(dest.shape, np.int32) - array_src = FlattenArray(numpy_src) - array_dest = FlattenArray(numpy_dest) - src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx] - dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx] - elements_num = src.shape[0] * src_num - for iter_n in range(elements_num): - coords = Index2Coordinate(iter_n, src.shape) - (n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx]) - ip_x = ip_y = 0 - if j < pad_left: - ip_x = pad_left - elif j >= pad_left and j < dx_width + pad_left: - ip_x = j - else: - ip_x = dx_width + pad_left - 1 - if i < pad_top: - ip_y = pad_top - elif i >= pad_top and i < dx_height + pad_top: - ip_y = i - else: - ip_y = dx_height + pad_top - 1 - ip_x = ip_x - pad_left - ip_y = ip_y - pad_top - src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j - dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x - array_dest[dest_index] += array_src[src_index] - numpy_dest = Array2Numpy(array_dest, dest.shape) - return numpy_dest - - -def _test_ReplicationPad2d(test_case, shape, padding, device): - np_input = np.random.random(shape).astype(np.float32) - of_input = flow.tensor( - np_input, dtype=flow.float32, device=flow.device(device), requires_grad=True - ) - if isinstance(padding, int): - np_boundary = ((0, 0), (0, 0), (padding, padding), (padding, padding)) - boundry = [padding, padding, padding, padding] - elif isinstance(padding, (tuple, int)) and len(padding) == 4: - np_boundary = ( - (0, 0), - (0, 0), - (padding[2], padding[3]), - (padding[0], padding[1]), - ) - boundry = [padding[0], padding[1], padding[2], padding[3]] - else: - raise ValueError("padding must be in or list or tuple!") - layer = flow.nn.ReplicationPad2d(padding=padding) - of_out = layer(of_input) - np_out = np.pad(np_input, np_boundary, mode="edge") - test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) - of_out = of_out.sum() - of_out.backward() - np_out_grad = _np_replication_pad2d_grad(np_out, np_input, boundry) - test_case.assertTrue(np.allclose(of_input.grad.numpy(), np_out_grad, 0.001, 0.001)) - - -@flow.unittest.skip_unless_1n1d() -class TestReplicationPad2dModule(flow.unittest.TestCase): - def test_ReplicationPad2d(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(1, 2, 3, 4), (8, 3, 4, 4)] - arg_dict["padding"] = [2, (1, 1, 2, 2)] - arg_dict["device"] = ["cpu", "cuda"] - for arg in GenArgList(arg_dict): - _test_ReplicationPad2d(test_case, *arg) - - @autotest(n=5) - def test_replication_pad2d_with_random_data(test_case): - c = random(1, 6).to(int) - h = random(1, 6).to(int) - w = random(1, 6).to(int) - m = torch.nn.ReplicationPad2d(padding=random(low=0, high=7)) - m.train(random()) - device = random_device() - m.to(device) - x = random_tensor(ndim=4, dim1=c, dim2=h, dim3=w).to(device) - y = m(x) - return y - - -if __name__ == "__main__": - unittest.main() diff --git a/python/oneflow/test/modules/test_reshape.py b/python/oneflow/test/modules/test_reshape.py index 14e321883037616cf9be9701c30ebd5f20f6ba8d..707359c7c0a8d63bde4b987822a6f69ebd60faac 100644 --- a/python/oneflow/test/modules/test_reshape.py +++ b/python/oneflow/test/modules/test_reshape.py @@ -125,6 +125,38 @@ class TestModule(flow.unittest.TestCase): y = torch.reshape(x, shape=(-1,)) return y + @autotest(n=2, auto_backward=False, check_graph=False) + def test_reshape_like(test_case): + device = random_device() + shape = [random(1, 5).to(int).value() for _ in range(4)] + like_shape = np.random.choice( + np.array(shape), len(shape), replace=False + ).tolist() + x = ( + random_tensor(4, *shape, requires_grad=False) + .to(device=device) + .requires_grad_() + ) + y = ( + random_tensor(4, *like_shape) + .to(device=device) + .requires_grad_(random_bool()) + ) + # forward + of_z = flow._C.reshape_like(x.oneflow, y.oneflow) + torch_z = torch.pytorch.reshape(x.pytorch, like_shape) + test_case.assertTrue( + np.array_equal(of_z.numpy(), torch_z.detach().cpu().numpy()) + ) + # backward + of_z.sum().backward() + torch_z.sum().backward() + test_case.assertTrue( + np.array_equal( + x.grad.oneflow.numpy(), x.grad.pytorch.detach().cpu().numpy() + ) + ) + @profile(torch.reshape) def profile_reshape(test_case): torch.reshape(torch.ones(50, 20), (20, 50)) diff --git a/python/oneflow/test/modules/test_reshape_sbp.py b/python/oneflow/test/modules/test_reshape_sbp.py new file mode 100644 index 0000000000000000000000000000000000000000..3ac50f21664be4379543d3d97b695ed8bfdf8dc0 --- /dev/null +++ b/python/oneflow/test/modules/test_reshape_sbp.py @@ -0,0 +1,36 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import os +import oneflow.unittest +import oneflow as flow + + +@flow.unittest.skip_unless_1n2d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestReshapeSbp(flow.unittest.TestCase): + def test_reshape_sbp(test_case): + input = flow.rand( + 9, 9, 8, placement=flow.placement("cuda", [0, 1]), sbp=flow.sbp.split(0) + ) + + output = input.view(81, 8) + test_case.assertTrue(output.sbp[0] != flow.sbp.split(0)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_rmsnorm.py b/python/oneflow/test/modules/test_rmsnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..07c9ae0871ed59f1dcc89dbef6b82c97e2d0f990 --- /dev/null +++ b/python/oneflow/test/modules/test_rmsnorm.py @@ -0,0 +1,192 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import os +import numpy as np +import unittest +from collections import OrderedDict + +import oneflow as flow +import oneflow.unittest +import torch + + +def _get_norm_dims(shape, normalized_shape): + lpad = len(shape) - len(normalized_shape) + assert lpad >= 0 + return tuple(range(lpad, len(shape))) + + +def _torch_rmsnorm(x, weight, normalized_shape=None, eps=1e-6): + if weight is not None: + normalized_shape = weight.shape + else: + assert normalized_shape is not None + norm_dims = _get_norm_dims(x.shape, normalized_shape) + root_mean = torch.mean(x * x, dim=norm_dims, keepdim=True) + rms = torch.rsqrt(root_mean + eps) + normed = x * rms + return normed * weight if weight is not None else normed + + +def _test_rmsnorm( + test_case, + shape, + normalized_shape, + affine=True, + eps=1e-6, + dtype=flow.float32, + device="cuda", +): + np_x = np.random.randn(*shape).astype(np.float32) + np_weight = ( + np.random.randn(*normalized_shape).astype(np.float32) if affine else None + ) + + torch_dtype = torch.float16 if dtype is flow.float16 else torch.float32 + torch_x = torch.tensor(np_x).to(device=device, dtype=torch_dtype) + torch_weight = ( + torch.tensor(np_weight).to(device=device, dtype=torch_dtype) if affine else None + ) + torch_x.requires_grad_(True) + if affine: + torch_weight.requires_grad_(True) + torch_y = _torch_rmsnorm(torch_x, torch_weight, normalized_shape, eps) + + np_rand_init_grad = np.random.randn(*tuple(torch_y.shape)).astype(np.float32) + torch_rand_init_grad = torch.tensor(np_rand_init_grad).to( + device=device, dtype=torch_dtype + ) + (torch_y * torch_rand_init_grad).sum().backward() + + torch_y = torch_y.detach().cpu().numpy() + torch_x_grad = torch_x.grad.detach().cpu().numpy() + if affine: + torch_weight_grad = torch_weight.grad.detach().cpu().numpy() + + x = flow.tensor(np_x).to(device=device, dtype=dtype) + weight = flow.tensor(np_weight).to(device=device, dtype=dtype) if affine else None + x.requires_grad_(True) + if affine: + weight.requires_grad_(True) + y = flow._C.rms_norm(x, weight, normalized_shape, eps) + # np_rand_init_grad = np.random.randn(*tuple(y.shape)).astype(np.float32) + rand_init_grad = flow.tensor(np_rand_init_grad).to(device=device, dtype=dtype) + (y * rand_init_grad).sum().backward() + + y = y.detach().cpu().numpy() + x_grad = x.grad.detach().cpu().numpy() + if affine: + weight_grad = weight.grad.detach().cpu().numpy() + + def compare(a, b, a_name, b_name, atol=1e-5, rtol=1e-8): + test_case.assertTrue( + np.allclose(a, b, atol=atol, rtol=rtol), + f"\n{'=' * 80}" + f"\n{a_name}:" + f"\n{a}" + f"\n{'-' * 80}" + f"\n{b_name}:" + f"\n{b}" + f"\n{'-' * 80}" + f"\ndiff:" + f"\n{a - b}" + f"\n{'*' * 80}" + f"\nshape={shape}" + f"\normalized_shape={normalized_shape}" + f"\naffine={affine}" + f"\ndtype={dtype}" + f"\ndevice={device}" + f"\n{a_name} vs. {b_name} max abs diff: {np.max(np.abs(a - b))}", + ) + + if dtype is flow.float16: + compare(y, torch_y, "y", "torch_y", 1e-3, 1e-2) + compare(x_grad, torch_x_grad, "x_grad", "torch_x_grad", 1e-2, 1e-2) + if affine: + compare( + weight_grad, + torch_weight_grad, + "weight_grad", + "torch_weight_grad", + 0.1, + 0.1, + ) + else: + compare(y, torch_y, "y", "torch_y") + compare(x_grad, torch_x_grad, "x_grad", "torch_x_grad") + if affine: + compare( + weight_grad, + torch_weight_grad, + "weight_grad", + "torch_weight_grad", + 1e-5, + 1e-4, + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestRMSNorm(flow.unittest.TestCase): + def test_real_example(test_case): + _test_rmsnorm( + test_case, + shape=[512, 4, 768], + normalized_shape=[768], + affine=True, + dtype=flow.float16, + device="cuda", + ) + + def test_no_affine(test_case): + _test_rmsnorm( + test_case, shape=[4, 16], normalized_shape=[16], affine=False, + ) + + def test_warp_impl(test_case): + _test_rmsnorm( + test_case, shape=[32, 1024], normalized_shape=[1024], dtype=flow.float16, + ) + _test_rmsnorm(test_case, shape=[16, 512], normalized_shape=[512]) + _test_rmsnorm(test_case, shape=[15, 512], normalized_shape=[512]) + _test_rmsnorm(test_case, shape=[16, 511], normalized_shape=[511]) + _test_rmsnorm(test_case, shape=[13, 499], normalized_shape=[499]) + + def test_block_smem_impl(test_case): + _test_rmsnorm( + test_case, shape=[16, 2048], normalized_shape=[2048], dtype=flow.float16, + ) + _test_rmsnorm(test_case, shape=[8, 1536], normalized_shape=[1536]) + _test_rmsnorm(test_case, shape=[8, 2048], normalized_shape=[2048]) + _test_rmsnorm(test_case, shape=[7, 1536], normalized_shape=[1536]) + _test_rmsnorm(test_case, shape=[8, 1533], normalized_shape=[1533]) + _test_rmsnorm(test_case, shape=[7, 1533], normalized_shape=[1533]) + + def test_block_uncached_impl(test_case): + _test_rmsnorm( + test_case, + shape=[16, 1024 * 1024], + normalized_shape=[1024 * 1024], + dtype=flow.float16, + ) + _test_rmsnorm( + test_case, shape=[8, 1024], normalized_shape=[1024], dtype=flow.double + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_round.py b/python/oneflow/test/modules/test_round.py index 732df645bd8199c16419bee0d247b1a05f0dd97c..487845272750ec0a7af2beb965dada7ce760d5d2 100644 --- a/python/oneflow/test/modules/test_round.py +++ b/python/oneflow/test/modules/test_round.py @@ -40,6 +40,18 @@ class TestRound(flow.unittest.TestCase): y = torch.round(x) return y + @autotest(check_graph=True) + def test_flow_round_half_to_even(test_case): + device = random_device() + random_shape = [random(1, 10).to(int).value() for _ in range(4)] + random_tenosr = np.random.randint(-99999, 99999, size=random_shape) + x = torch.tensor(random_tenosr).to(device) + y = torch.full(x.shape, 0.5).to(device) + y += x + y = y.requires_grad_() + z = torch.round(y) + return z + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_saved_tensor_hooks.py b/python/oneflow/test/modules/test_saved_tensor_hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..6a04fe638c105fcb44e6c8fa039e26fe675779ac --- /dev/null +++ b/python/oneflow/test/modules/test_saved_tensor_hooks.py @@ -0,0 +1,85 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest + +import numpy as np + +import oneflow as flow +import oneflow.unittest + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestSavedTensorHooks(flow.unittest.TestCase): + def test_normal_saved_tensor_hooks(test_case): + x = flow.ones(1, 2, 3).to("cuda").requires_grad_() + y = flow.zeros(1, 2, 3).to("cuda").requires_grad_() + tensor_list = [] + + def pack(x): + tensor_list.append(x) + return len(tensor_list) - 1 + + def unpack(x): + return tensor_list[x] + + with flow.autograd.graph.saved_tensors_hooks(pack, unpack): + z = x * y + z.sum().backward() + test_case.assertEqual(len(tensor_list), 2) + test_case.assertTrue(np.array_equal(tensor_list[0], y)) + test_case.assertTrue(np.array_equal(tensor_list[1], x)) + test_case.assertTrue(np.allclose(x.grad, y)) + test_case.assertTrue(np.allclose(y.grad, x)) + + def test_saved_tensor_hooks_in_autograd_function(test_case): + x = flow.ones(1, 2, 3).to("cuda").requires_grad_() + y = flow.zeros(1, 2, 3).to("cuda").requires_grad_() + tensor_list = [] + + def pack(x): + tensor_list.append(x) + return len(tensor_list) - 1 + + def unpack(x): + return tensor_list[x] + + class MulFunction(flow.autograd.Function): + @staticmethod + def forward(ctx, x, y): + ctx.save_for_backward(x, y) + return x * y + + @staticmethod + def backward(ctx, dz): + x, y = ctx.saved_tensors + dx = dz * y + dy = dz * x + return dx, dy + + with flow.autograd.graph.saved_tensors_hooks(pack, unpack): + z = MulFunction.apply(x, y) + z.sum().backward() + test_case.assertEqual(len(tensor_list), 2) + test_case.assertTrue(np.array_equal(tensor_list[0], x)) + test_case.assertTrue(np.array_equal(tensor_list[1], y)) + test_case.assertTrue(np.allclose(x.grad, y)) + test_case.assertTrue(np.allclose(y.grad, x)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_scatter_ops.py b/python/oneflow/test/modules/test_scatter_ops.py index 476280e3c0b5ec335cccfc719588013bd2d78f46..4227c943c0837161a9a4ac56a8ce1e827b659ba1 100644 --- a/python/oneflow/test/modules/test_scatter_ops.py +++ b/python/oneflow/test/modules/test_scatter_ops.py @@ -39,7 +39,7 @@ def _get_indexes(device): ) -def _test_scatter_random_data(test_case, test_scalar: bool, dim: int): +def _test_scatter(test_case, test_scalar: bool, dim: int): device = random_device() input = random_tensor(ndim=2, dim0=2, dim1=2).to(device) src = 3.14 if test_scalar else random_tensor(ndim=2, dim0=2, dim1=2).to(device) @@ -47,7 +47,7 @@ def _test_scatter_random_data(test_case, test_scalar: bool, dim: int): return y -def _test_scatter_add_random_data(test_case, dim: int): +def _test_scatter_add(test_case, dim: int): device = random_device() input = random_tensor(ndim=2, dim0=2, dim1=2).to(device) src = random_tensor(ndim=2, dim0=2, dim1=2).to(device) @@ -55,39 +55,31 @@ def _test_scatter_add_random_data(test_case, dim: int): return y -@flow.unittest.skip_unless_1n1d() -class TestScatterOpsModule(flow.unittest.TestCase): - @autotest(n=5) - def test_scatter_random_data_at_dim_0(test_case): - return _test_scatter_random_data(test_case, False, 0) - - @autotest(n=5) - def test_scatter_random_data_at_dim_1(test_case): - return _test_scatter_random_data(test_case, False, 1) - - @autotest(n=5) - def test_scatter_random_data_at_negative_dim1(test_case): - return _test_scatter_random_data(test_case, False, -1) - - @autotest(n=5) - def test_scatter_scalar_random_data_at_dim0(test_case): - return _test_scatter_random_data(test_case, True, 0) - - @autotest(n=5) - def test_scatter_scalar_random_data_at_dim1(test_case): - return _test_scatter_random_data(test_case, True, 1) +def _test_scatter_reduce(test_case, dim: int): + device = random_device() + input = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + src = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + y = torch.scatter( + input, dim, oneof(*_get_indexes(device)), src, reduce=oneof("add", "multiply") + ) + return y - @autotest(n=5) - def test_scatter_scalar_random_data_at_negative_dim1(test_case): - return _test_scatter_random_data(test_case, True, -1) - @autotest(n=5) - def test_scatter_add_random_data_at_dim0(test_case): - return _test_scatter_add_random_data(test_case, 0) +@flow.unittest.skip_unless_1n1d() +class TestScatterOpsModule(flow.unittest.TestCase): + @autotest(n=10) + def test_scatter_with_random_data(test_case): + return _test_scatter(test_case, oneof(True, False), oneof(0, 1, -1)) @autotest(n=5) - def test_scatter_add_random_data_at_dim1(test_case): - return _test_scatter_add_random_data(test_case, 1) + def test_scatter_add_with_random_data(test_case): + return _test_scatter_add(test_case, oneof(0, 1)) + + @autotest( + n=5, auto_backward=False + ) # peihong: pytorch dose not support backward when reduce is add or multiply + def test_scatter_reduce_with_random_data(test_case): + return _test_scatter_reduce(test_case, oneof(0, 1)) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_single_threaded_vm.py b/python/oneflow/test/modules/test_single_threaded_vm.py new file mode 100644 index 0000000000000000000000000000000000000000..d505e4d54d0a2033fc9c8e9ea38039154c889448 --- /dev/null +++ b/python/oneflow/test/modules/test_single_threaded_vm.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import subprocess +import sys +import os +import unittest +import oneflow as flow +import oneflow.unittest + + +class TestSingleThreadedVM(flow.unittest.TestCase): + @flow.unittest.skip_unless_1n2d() + def test_ddp_in_single_threaded_vm(test_case): + # Environment variables of current process like ONEFLOW_TEST_DEVICE_NUM + # and environment variables about distributed training (i.e. MASTER_ADDR, + # MASTER_PORT, WORLD_SIZE, RANK) are all in `env`. + env = os.environ.copy() + env["ONEFLOW_VM_MULTI_THREAD"] = "0" + p = subprocess.run( + [sys.executable, "test_ddp.py"], + cwd=os.path.dirname(os.path.realpath(__file__)), + env=env, + ) + test_case.assertEqual(p.returncode, 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_slice.py b/python/oneflow/test/modules/test_slice.py index a0cb1f8cc16985dec655de5ad844efcb4f34b9d8..d639f5d8ffa0cd4434d57f149713fa29be831dee 100644 --- a/python/oneflow/test/modules/test_slice.py +++ b/python/oneflow/test/modules/test_slice.py @@ -16,13 +16,14 @@ limitations under the License. import unittest from collections import OrderedDict +from random import randint import numpy as np from oneflow.test_utils.test_util import GenArgList -from oneflow.test_utils.automated_test_util import util import oneflow as flow import oneflow.unittest +from oneflow.test_utils.automated_test_util import * def _test_slice(test_case, device): @@ -168,12 +169,48 @@ class TestSliceUpdate(flow.unittest.TestCase): input[-1] = 1 test_case.assertTrue(np.array_equal(input.numpy(), np_arr)) + def test_slice_update_scalar_integer_tensor_index(test_case): + np_arr_a = np.random.rand(133, 1, 15) + np_arr_b = np.random.rand(133, 2, 1) + + a_torch = torch.tensor(np_arr_a) + b_torch = torch.tensor(np_arr_b) + pos_torch = torch.tensor(0) + a_torch[:, 0, pos_torch] = b_torch[:, 1, 0] + + a_flow = flow.tensor(np_arr_a) + b_flow = flow.tensor(np_arr_b) + pos_flow = flow.tensor(0) + a_flow[:, 0, pos_flow] = b_flow[:, 1, 0] + + test_case.assertTrue( + np.allclose(a_flow.numpy(), a_torch.cpu().numpy(), rtol=1e-5, atol=1e-5,) + ) + + def test_slice_update_scalar_boolean_tensor_index(test_case): + np_arr_a = np.random.rand(2, 1, 2) + np_arr_b = np.random.rand(2, 2, 1) + + a_torch = torch.tensor(np_arr_a) + b_torch = torch.tensor(np_arr_b) + pos_torch = torch.tensor(True) + a_torch[:, 0, pos_torch] = b_torch[:, 1, 0] + + a_flow = flow.tensor(np_arr_a) + b_flow = flow.tensor(np_arr_b) + pos_flow = flow.tensor(True) + a_flow[:, 0, pos_flow] = b_flow[:, 1, 0] + + test_case.assertTrue( + np.allclose(a_flow.numpy(), a_torch.cpu().numpy(), rtol=1e-5, atol=1e-5,) + ) + def test_slice_update_negative_index_graph(test_case): np_arr = np.zeros(shape=(2, 3, 4)) input = flow.tensor(np_arr, dtype=flow.float32) np_arr[-1] = 1 - @flow.nn.Graph.to_graph + @flow.nn.Graph.trace def test_func(): input[-1] = 1 return input @@ -181,6 +218,15 @@ class TestSliceUpdate(flow.unittest.TestCase): out = test_func() test_case.assertTrue(np.array_equal(out.numpy(), np_arr)) + def test_slice_update_different_dtype(test_case): + x = np.array([1, 1, 1, 1, 1]).astype(np.float32) + for value_type in [np.int32, np.float64]: + input = flow.tensor(x) + update = flow.tensor(np.array([2, 3, 4]).astype(value_type)) + output = np.array([1.0, 2.0, 3.0, 4.0, 1.0]) + flow.slice_update(input, update, slice_tup_list=[[1, 4, 1]]) + test_case.assertTrue(np.array_equal(input.numpy(), output)) + def test_slice_update_ellipsis_type(test_case): np_arr = np.zeros(shape=(2, 3, 4, 5, 6)) input = flow.tensor(np_arr, dtype=flow.float32) @@ -193,7 +239,7 @@ class TestSliceUpdate(flow.unittest.TestCase): input = flow.tensor(np_arr, dtype=flow.float32) np_arr[0, ::1, ..., 2:3] = 1 - @flow.nn.Graph.to_graph + @flow.nn.Graph.trace def test_func(): input[0, ::1, ..., 2:3] = 1 return input @@ -245,18 +291,79 @@ class TestSliceUpdate(flow.unittest.TestCase): value_grad = np.array([1.0, 1.0, 1.0]).astype(np.float32) test_case.assertTrue(np.array_equal(-test_m.value_grad, value_grad)) - @unittest.skip("TODO:(zhaoluyang) test when slice_update support stride") - def test_slice_update_with_stride(test_case, device): - arr = np.arange(24).reshape(2, 2, 2, 3).astype(np.float32) - np_in = arr - np_out = np_in.transpose(1, 0, 2, 3) - np_out[0:1, 1:2, :, 1:2] = 3.1415 - - input = flow.tensor(arr, device=flow.device(device)) - output = input.permute(1, 0, 2, 3) - output[0:1, 1:2, :, 1:2] = 3.1415 - - test_case.assertTrue(np.array_equal(output.numpy(), np_out)) + def test_random_nd_slice_update_in_non_contiguous_tensor(test_case): + def get_random_slice_tuple(shape): + slice_tup = [] + slice_size = [] + for i in range(len(shape)): + start = randint(0, shape[i] - 1) + end = randint(start + 1, shape[i]) + step = randint(1, end - start + 1) + slice_tup.append(slice(start, end, step)) + slice_size.append((end - start + step - 1) // step) + return tuple(slice_tup), tuple(slice_size) + + def get_random_update_shape_and_perm(shape): + perm = flow.randperm(len(shape)).tolist() + no_perm_shape = [shape[i] for i in perm] + inv_perm = [0] * len(shape) + for i in range(len(shape)): + inv_perm[perm[i]] = i + return no_perm_shape, inv_perm + + def compare_result_between_oneflow_and_numpy(test_case, shape): + device = random_device().value() + # non-contiguous ref + ref = ( + flow.rand(shape, dtype=flow.float32) + .to(device) + .permute(flow.randperm(len(shape)).tolist()) + ) + ref_np = ref.detach().clone().numpy() + shape = ref.shape + # slice param + slice_tup, slice_size = get_random_slice_tuple(shape) + # non-contiguous update + no_perm_shape, perm = get_random_update_shape_and_perm(slice_size) + update = ( + flow.rand(no_perm_shape, dtype=flow.float32).to(device).permute(perm) + ) + update_np = update.detach().clone().numpy() + + ref_np[slice_tup] = update_np + # non-inplace update + # NOTE: should test non-inplace first + def slice_tuple_to_slice_list(slice_tup): + # NOTE: oneflow.slice_update don't support passing slice parameters. + slice_list = [] + for i in range(len(slice_tup)): + slice_list.append( + (slice_tup[i].start, slice_tup[i].stop, slice_tup[i].step) + ) + return slice_list + + of_res = flow.slice_update( + ref, update, slice_tuple_to_slice_list(slice_tup) + ) + test_case.assertTrue(np.array_equal(of_res.numpy(), ref_np)) + # inplace update + ref[slice_tup] = update + test_case.assertTrue(np.array_equal(ref.numpy(), ref_np)) + + for dims in (2, 3, 4): + for _ in range(10): + shape = [randint(1, 21) for _ in range(dims)] + compare_result_between_oneflow_and_numpy(test_case, shape) + + def test_slice_update_expand_value(test_case): + ref_np = np.random.rand(2, 3, 4) + ref_of = flow.tensor(ref_np) + update_np = np.random.rand(3,) + update_ref = flow.tensor(update_np) + + ref_of[:, :, 1] = update_ref + ref_np[:, :, 1] = update_np + test_case.assertTrue(np.array_equal(ref_of.numpy(), ref_np)) if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_softmax_cross_entropy b/python/oneflow/test/modules/test_softmax_cross_entropy deleted file mode 100644 index 017d01a2271770bb55b73b4d54995db0bc8bba6d..0000000000000000000000000000000000000000 --- a/python/oneflow/test/modules/test_softmax_cross_entropy +++ /dev/null @@ -1,174 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import unittest -import oneflow as flow -import oneflow.unittest -import numpy as np -import torch - -class TestSoftmaxCrossEntropyError(flow.unittest.TestCase): - def test_softmax_cross_entropy_prediction_numaxes_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - prediction = flow.randn(10) - label = flow.randn(1, 10) - flow._C.softmax_cross_entropy(prediction, label) - test_case.assertTrue( - "The dimension of prediction must be greater than or equal to 2, but found" - in str(context.exception) - ) - - def test_softmax_cross_entropy_prediction_shape_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - prediction = flow.randn(1, 10) - label = flow.randn(1, 11) - flow._C.softmax_cross_entropy(prediction, label) - test_case.assertTrue( - "must match the size of prediction" in str(context.exception) - ) - - def test_softmax_cross_entropy_dtype_err(test_case): - with test_case.assertRaises(TypeError) as context: - prediction = flow.randn(1, 10, dtype=flow.float32) - label = flow.randn(1, 10, dtype=flow.float64) - flow._C.softmax_cross_entropy(prediction, label) - test_case.assertTrue( - "label and prediction are expected to have the same dtype, but found" - in str(context.exception) - ) - - def test_softmax_cross_entropy_grad_prob_numaxes_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - dy = flow.randn(10, 5) - label = flow.randn(10, 10, 5) - prob = flow.randn(10) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - test_case.assertTrue( - "The dimension of prob must be greater than or equal to 2, but found " - in str(context.exception) - ) - - def test_softmax_cross_entropy_grad_dy_numaxes_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - dy = flow.randn(10, 10, 5) - label = flow.randn(10, 10, 5) - prob = flow.randn(10, 10, 5) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - test_case.assertTrue( - "The dimension of dy is expected to be less than that of prob by 1, but found" - in str(context.exception) - ) - - def test_softmax_cross_entropy_grad_dy_i_shape_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - dy = flow.randn(10, 8) - label = flow.randn(10, 10, 5) - prob = flow.randn(10, 10, 5) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - test_case.assertTrue("must match the size of label" in str(context.exception)) - - def test_softmax_cross_entropy_grad_prob_shape_err(test_case): - with test_case.assertRaises(RuntimeError) as context: - dy = flow.randn(10, 10) - label = flow.randn(10, 10, 5) - prob = flow.randn(10, 10, 6) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - test_case.assertTrue("must match the size of prob" in str(context.exception)) - - def test_softmax_cross_entropy_grad_label_dtype_err(test_case): - with test_case.assertRaises(TypeError) as context: - dy = flow.randn(10, 10, dtype=flow.float64) - label = flow.randn(10, 10, 5, dtype=flow.float32) - prob = flow.randn(10, 10, 5, dtype=flow.float64) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - test_case.assertTrue( - "label and prob are expected to have the same dtype, but found" - in str(context.exception) - ) - - def test_softmax_cross_entropy_grad_dy_dtype_err(test_case): - with test_case.assertRaises(TypeError) as context: - dy = flow.randn(10, 10, dtype=flow.float32) - label = flow.randn(10, 10, 5, dtype=flow.float64) - prob = flow.randn(10, 10, 5, dtype=flow.float64) - flow._C.softmax_cross_entropy_grad(dy, label, prob) - print(str(context.exception)) - test_case.assertTrue( - "dy and prob are expected to have the same dtype, but found" - in str(context.exception) - ) - - -if __name__ == "__main__": - - np_prediction = np.random.random((1, 10)).astype(np.float32) - np_label = np.random.random((1, 10)).astype(np.float32) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cuda"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cuda"), dtype=flow.float32) - of_output = flow._C.softmax_cross_entropy(of_prediction, of_label).to("cuda") - of_output.sum() - - torch_prediction = torch.tensor(np_prediction, dtype=torch.float32, requires_grad=True) - torch_label = torch.tensor(np_label, dtype=torch.float32) - torch_output = torch.nn.functional.cross_entropy( - torch_prediction, torch_label, reduction="none") - torch_output.sum() - - if np.allclose( - of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 - ): - print("test_softmax_cross_entropy Passed") - else: - print("test_softmax_cross_entropy Failed") - - np_prediction = np.random.random((1, 10, 2)).astype(np.float32) - np_label = np.random.random((1, 10, 2)).astype(np.float32) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cpu"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cpu"), dtype=flow.float32) - of_output = flow._C.softmax_cross_entropy(of_prediction, of_label) - of_output.sum().backward() - print("of cpu res:") - print(of_prediction.grad.numpy()) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cuda"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cuda"), dtype=flow.float32) - of_output = flow._C.softmax_cross_entropy(of_prediction, of_label).to("cuda") - of_output.sum().backward() - print("of gpu res:") - print(of_prediction.grad.numpy()) - - torch_prediction = torch.tensor(np_prediction, dtype=torch.float32, requires_grad=True) - torch_label = torch.tensor(np_label, dtype=torch.float32) - torch_output = torch.nn.functional.cross_entropy( - torch_prediction, torch_label, reduction="none") - torch_output.sum().backward() - - print("*************************") - print(torch_prediction.grad) - # if np.allclose( - # of_prediction.grad.numpy(), torch_prediction.grad, rtol=1e-03, atol=1e-04 - # ): - # print("test_softmax_cross_entropy_grad Passed") - # else: - # print("test_softmax_cross_entropy_grad Failed") - - - - - - diff --git a/python/oneflow/test/modules/test_sort.py b/python/oneflow/test/modules/test_sort.py index 5e0cfa561ac9b6b4a01769607b89d5a2d25c7e5c..6f57b6509814907afa8d899c51e8f2b750f65a74 100644 --- a/python/oneflow/test/modules/test_sort.py +++ b/python/oneflow/test/modules/test_sort.py @@ -84,12 +84,29 @@ class TestSort(flow.unittest.TestCase): y = torch.sort(x, dim=random(low=-4, high=4).to(int), descending=random_bool()) return y[0], y[1] + @autotest(n=5, auto_backward=False, check_graph=True) + def test_sort_return_type_with_random_data_(test_case): + device = random_device() + x = random_tensor(ndim=4).to(device) + result = torch.sort( + x, dim=random(low=-4, high=4).to(int), descending=random_bool() + ) + return result.values, result.indices + @autotest(n=10, auto_backward=False, check_graph=True) def test_sort_bool_with_random_data(test_case): x = random_tensor(ndim=4).to(device="cpu", dtype=torch.bool) y = torch.sort(x, dim=random(low=-4, high=4).to(int), descending=random_bool()) return y[0], y[1] + @autotest(n=10, auto_backward=False, check_graph=True) + def test_sort_return_type_bool_with_random_data(test_case): + x = random_tensor(ndim=4).to(device="cpu", dtype=torch.bool) + result = torch.sort( + x, dim=random(low=-4, high=4).to(int), descending=random_bool() + ) + return result.values, result.indices + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_sparse.py b/python/oneflow/test/modules/test_sparse.py index a5031d9afeefd02ba6ba41b623f8eb91a84980db..2fa0626430747fb6e6bb0505e076bf5930485dbd 100644 --- a/python/oneflow/test/modules/test_sparse.py +++ b/python/oneflow/test/modules/test_sparse.py @@ -70,7 +70,7 @@ def _test_embedding_padding_idx(test_case, device): embedding(indices).sum().backward() after = (embedding.weight + embedding.weight.grad)[padding_idx] embedding.zero_grad() - test_case.assertTrue(flow.equal(after, pre).all()) + test_case.assertTrue(flow.equal(after, pre)) def _test_embedding_scale_by_freq(test_case, device): @@ -185,7 +185,7 @@ class TestEmbedding(flow.unittest.TestCase): # Graph mode do not support inplace op with flow.no_grad() # See this issue: https://github.com/Oneflow-Inc/OneTeam/issues/1382 @unittest.skip("still have error in ci test. TODO(Yao Zihang)") - @autotest(n=5, rtol=1e-03, atol=1e-03, check_graph="ValidatedFlase") + @autotest(n=5, rtol=1e-03, atol=1e-03, check_graph="ValidatedFalse") def test_embedding_renorm(test_case): device = random_device() emb_size = random(low=2) * 16 diff --git a/python/oneflow/test/modules/test_sparse_softmax_cross_entropy.py b/python/oneflow/test/modules/test_sparse_softmax_cross_entropy.py index 683138d1049622937b159317f166ddbb3acce370..c3d81e6ca843c945f15850591796582ebb42e7aa 100644 --- a/python/oneflow/test/modules/test_sparse_softmax_cross_entropy.py +++ b/python/oneflow/test/modules/test_sparse_softmax_cross_entropy.py @@ -258,63 +258,4 @@ class TestSparseSoftmaxCrossEntropyMsWithLogits(flow.unittest.TestCase): if __name__ == "__main__": - # unittest.main() - np_label = np.random.randint(0, 10, size=(2,)).astype(np.int32) - np_prediction = np.random.random((2, 10)).astype(np.float32) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cuda"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cuda"), dtype=flow.int32) - of_output = flow.nn.functional.sparse_softmax_cross_entropy( - labels=of_label, logits=of_prediction).to("cuda") - of_output.sum() - - torch_prediction = torch.tensor(np_prediction, dtype=torch.float32, requires_grad=True) - torch_label = torch.tensor(np_label, dtype=torch.int64) - torch_output = torch.nn.functional.cross_entropy( - torch_prediction, torch_label, reduction="none") - torch_output.sum() - - if np.allclose( - of_output.numpy(), torch_output.detach().numpy(), rtol=1e-03, atol=1e-04 - ): - print("test_softmax_cross_entropy Passed") - else: - print("test_softmax_cross_entropy Failed") - - np_label = np.random.randint(0, 10, size=(2,)).astype(np.int32) - np_prediction = np.random.random((2, 10)).astype(np.float32) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cpu"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cpu"), dtype=flow.int32) - of_output = flow.nn.functional.sparse_softmax_cross_entropy( - labels=of_label, logits=of_prediction) - of_output.sum().backward() - print("of cpu res:") - print(of_prediction.grad.numpy()) - - of_prediction = flow.tensor( - np_prediction, device=flow.device("cuda"), dtype=flow.float32, requires_grad=True) - of_label = flow.tensor(np_label, device=flow.device("cuda"), dtype=flow.int32) - of_output = flow.nn.functional.sparse_softmax_cross_entropy( - labels=of_label, logits=of_prediction).to("cuda") - of_output.sum().backward() - print("of gpu res:") - print(of_prediction.grad.numpy()) - - torch_prediction = torch.tensor(np_prediction, dtype=torch.float32, requires_grad=True) - torch_label = torch.tensor(np_label, dtype=torch.int64) - torch_output = torch.nn.functional.cross_entropy( - torch_prediction, torch_label, reduction="none") - torch_output.sum().backward() - print("*********torch res*********") - print(torch_prediction.grad) - - if np.allclose( - of_prediction.grad.numpy(), torch_prediction.grad, rtol=1e-03, atol=1e-04 - ): - print("test_softmax_cross_entropy_grad Passed") - else: - print("test_softmax_cross_entropy_grad Failed") - + unittest.main() diff --git a/python/oneflow/test/modules/test_squeeze.py b/python/oneflow/test/modules/test_squeeze.py index 353d706a6ff2ef85d6f4c2c7bf4719338f990cb8..867499c521e858b68153d70043ac9be9bec39be2 100644 --- a/python/oneflow/test/modules/test_squeeze.py +++ b/python/oneflow/test/modules/test_squeeze.py @@ -111,6 +111,13 @@ class TestSqueeze(flow.unittest.TestCase): y = torch.squeeze(x, random(1, 3).to(int)) return y + @autotest(n=10, check_graph=False, auto_backward=False) + def test_inplace_squeeze_with_random_data(test_case): + device = random_device() + x = random_tensor(requires_grad=False).to(device) + y = x.squeeze_(random(1, 3).to(int)) + return y + @autotest(auto_backward=False, check_graph=True) def test_squeeze_with_0_size_data(test_case): device = random_device() diff --git a/python/oneflow/test/modules/test_stack.py b/python/oneflow/test/modules/test_stack.py index cd1f209ce76df0dda11154f0e26a43be1fde4f00..9ed23d6d4a433bc2732b6b6f953c25888e4498b1 100644 --- a/python/oneflow/test/modules/test_stack.py +++ b/python/oneflow/test/modules/test_stack.py @@ -44,6 +44,99 @@ class TestStackModule(flow.unittest.TestCase): out = torch.stack((x, y), dim=random(low=1, high=4).to(int)) return out + @autotest(check_graph=True) + def test_column_stack_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=1, dim0=10).to(device) + y = random_tensor(ndim=2, dim0=10, dim1=5).to(device) + z = random_tensor(ndim=2, dim0=10, dim1=5).to(device) + out = torch.column_stack((x, y, z)) + return out + + def test_column_stack_with_0dim_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=1, dim0=1).to(device) + out = torch.column_stack((x, y)) + return out + + @autotest(check_graph=True) + def test_row_stack_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=1, dim0=10).to(device) + y = random_tensor(ndim=2, dim0=5, dim1=10).to(device) + z = random_tensor(ndim=2, dim0=5, dim1=10).to(device) + out = torch.row_stack((x, y, z)) + return out + + def test_row_stack_with_0dim_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=1, dim0=1).to(device) + out = torch.row_stack((x, y)) + return out + + @autotest(check_graph=True) + def test_hstack_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=1, dim0=5).to(device) + y = random_tensor(ndim=1, dim0=5).to(device) + out = torch.hstack((x, y)) + return out + + @autotest(check_graph=True) + def test_hstack_with_0dim_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=0).to(device) + # test 1-dim simultaneouslsimultaneouslyy + z = random_tensor(ndim=1, dim0=1).to(device) + out = torch.hstack((x, y, z)) + return out + + @autotest(check_graph=True) + def test_vstack_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=2, dim0=3, dim1=4).to(device) + y = random_tensor(ndim=1, dim0=4).to(device) + z = random_tensor(ndim=2, dim0=3, dim1=4).to(device) + out = torch.vstack((x, y, z)) + return out + + @autotest(check_graph=True) + def test_vstack_with_0dim_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=0).to(device) + out = torch.vstack((x, y)) + return out + + @autotest(check_graph=True) + def test_dstack_with_random_data(test_case): + device = random_device() + x = random_tensor(ndim=2, dim0=1, dim1=4).to(device) + y = random_tensor(ndim=3, dim0=1, dim1=4, dim2=1).to(device) + z = random_tensor(ndim=1, dim0=4).to(device) + out = torch.dstack((x, y, z)) + return out + + @autotest(check_graph=True) + def test_dstack_with_0dim_data(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = random_tensor(ndim=0).to(device) + z = random_tensor(ndim=0).to(device) + out = torch.dstack((x, y, z)) + + @autotest(auto_backward=True, check_graph=True) + def test_stack_kMaxInputCount_inputs(test_case): + kMaxInputCount = 128 + 1 + stack_list = [ + random_tensor(ndim=2, dim0=3, dim1=4) for _ in range(kMaxInputCount) + ] + out = torch.stack(stack_list, 0) + return out + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_stateful_kernel_with_cache.py b/python/oneflow/test/modules/test_stateful_kernel_with_cache.py index 76893f4680bdb646ceafc3e4229fba970f01974c..f29ba1237e06f902a5d924d36003dc194179617e 100644 --- a/python/oneflow/test/modules/test_stateful_kernel_with_cache.py +++ b/python/oneflow/test/modules/test_stateful_kernel_with_cache.py @@ -27,7 +27,7 @@ import oneflow.unittest class TestStatefulKernelWithInpersistentState(flow.unittest.TestCase): def test_stateful_kernel_with_inpersistent_state(test_case): x = flow.arange(4).reshape(2, 2) - x = x.to_global(flow.env.all_device_placement("cuda"), flow.sbp.split(0)) + x = x.to_global(flow.placement.all("cuda"), flow.sbp.split(0)) y = x[0:3, 0:1] y_np = np.array([[0], [2], [0]]) test_case.assertTrue( diff --git a/python/oneflow/test/modules/test_stft.py b/python/oneflow/test/modules/test_stft.py new file mode 100644 index 0000000000000000000000000000000000000000..0fe7f66ab9fb23a8cf7a54ac6d40c261f15defed --- /dev/null +++ b/python/oneflow/test/modules/test_stft.py @@ -0,0 +1,111 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from numpy import random + +import unittest +from collections import OrderedDict + +import numpy as np +import re + +import oneflow as flow +from oneflow.test_utils.test_util import GenArgList + +from oneflow.test_utils.automated_test_util import * + + +def getRandBoolvalue(): + value = np.random.randint(0, 2) + if value == 1: + return True + else: + return False + + +def getRandFFtvalue(): + pow = np.random.randint(2, 10) + result = 1 + for i in range(pow): + result = result * 2 + return result + + +def is_cufft_available(): + if flow.cuda.is_available(): + (major, _minor) = flow.cuda.get_device_capability() + return major >= 7 + else: + return False + + +class TestStft(flow.unittest.TestCase): + @autotest( + n=20, check_graph=False, check_grad_use_random_data=False, auto_backward=False, + ) + def test_stft_with_1D_random_data(test_case): + if is_cufft_available(): + device = random_device() + else: + device = cpu_device() + rand_fft = getRandFFtvalue() + rand_size = np.random.randint(rand_fft, 30000) + input_dims = [rand_size] + win_dims = [rand_fft] + x = random_tensor(1, *input_dims).to(device) + win = random_tensor(1, *win_dims).to(device) + onesided_value = getRandBoolvalue() + center_value = getRandBoolvalue() + normalized_value = getRandBoolvalue() + y = torch.stft( + x, + n_fft=rand_fft, + window=win, + return_complex=False, + onesided=onesided_value, + center=center_value, + normalized=normalized_value, + ) + return y + + def test_stft_with_2D_random_data(test_case): + if is_cufft_available(): + device = random_device() + else: + device = cpu_device() + row_rand_size = np.random.randint(1, 50) + rand_fft = getRandFFtvalue() + col_rand_size = np.random.randint(rand_fft, 30000) + input_dims = [row_rand_size, col_rand_size] + win_dims = [rand_fft] + x = random_tensor(2, *input_dims).to(device) + win = random_tensor(1, *win_dims).to(device) + onesided_value = getRandBoolvalue() + center_value = getRandBoolvalue() + normalized_value = getRandBoolvalue() + y = torch.stft( + x, + n_fft=rand_fft, + window=win, + return_complex=False, + onesided=onesided_value, + center=center_value, + normalized=normalized_value, + ) + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_sub.py b/python/oneflow/test/modules/test_sub.py index 84c7a60a9fb830370632710117bbc4098d0fb337..1e9ab44d52f1aa01823911432dab30c6e619cdb8 100644 --- a/python/oneflow/test/modules/test_sub.py +++ b/python/oneflow/test/modules/test_sub.py @@ -156,6 +156,24 @@ class TestSubModule(flow.unittest.TestCase): z3 = torch.sub(s, x3, alpha=alpha) return z1, z2, z3 + @autotest(n=3) + def test_non_contiguous_inplace_sub(test_case): + device = random_device() + x = random_tensor(2, 2, 4).to(device) + y = x + 1 + y = y[:, 1:3] + y -= random_tensor(2, 2, 2).to(device) + return y + + @autotest(n=5) + def test_scalar_sub_with_random_devices(test_case): + x1_device = random_device() + x2_device = random_device() + x1 = random_tensor(2, 2, 3).to(x1_device).mean() + x2 = random_tensor(2, 2, 3).to(x2_device) + y = x1 - x2 + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_sync_batchnorm.py b/python/oneflow/test/modules/test_sync_batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..2967ac08d0367ab903891027293fd841a0ca7b05 --- /dev/null +++ b/python/oneflow/test/modules/test_sync_batchnorm.py @@ -0,0 +1,99 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import numpy as np + +import oneflow as flow +import oneflow.unittest +from sync_batchnorm_test_util import ensure_datas + + +@flow.unittest.skip_unless_1n2d() +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +class TestSyncBatchNorm(flow.unittest.TestCase): + def test_sync_batchnorm3d(test_case): + data_path = ensure_datas() + os.environ["ONEFLOW_ENABLE_NHWC"] = "0" + channel = 8 + input_np = np.load( + f"{data_path}/sync_bn3d_nchw_input_rank{flow.env.get_rank()}.npy" + ) + torch_out = np.load( + f"{data_path}/sync_bn3d_nchw_torch_output_rank{flow.env.get_rank()}.npy" + ) + torch_grad = np.load( + f"{data_path}/sync_bn3d_nchw_torch_grad_rank{flow.env.get_rank()}.npy" + ) + + of_input = flow.tensor(input_np, requires_grad=True, device="cuda") + of_bn = flow.nn.BatchNorm3d(channel) + of_bn = flow.nn.SyncBatchNorm.convert_sync_batchnorm(of_bn).cuda() + of_res = of_bn(of_input) + of_res.sum().backward() + + test_case.assertTrue(np.allclose(torch_out, of_res.numpy(), atol=1e-8)) + test_case.assertTrue(np.allclose(torch_grad, of_input.grad.numpy(), atol=1e-8,)) + + def test_sync_batchnorm2d(test_case): + data_path = ensure_datas() + os.environ["ONEFLOW_ENABLE_NHWC"] = "0" + channel = 8 + input_np = np.load( + f"{data_path}/sync_bn2d_nchw_input_rank{flow.env.get_rank()}.npy" + ) + torch_out = np.load( + f"{data_path}/sync_bn2d_nchw_torch_output_rank{flow.env.get_rank()}.npy" + ) + torch_grad = np.load( + f"{data_path}/sync_bn2d_nchw_torch_grad_rank{flow.env.get_rank()}.npy" + ) + + of_input = flow.tensor(input_np, requires_grad=True, device="cuda") + of_bn = flow.nn.BatchNorm2d(channel) + of_bn = flow.nn.SyncBatchNorm.convert_sync_batchnorm(of_bn).cuda() + of_res = of_bn(of_input) + of_res.sum().backward() + + test_case.assertTrue(np.allclose(torch_out, of_res.numpy(), atol=1e-8)) + test_case.assertTrue(np.allclose(torch_grad, of_input.grad.numpy(), atol=1e-8,)) + + def test_sync_batchnorm1d(test_case): + data_path = ensure_datas() + os.environ["ONEFLOW_ENABLE_NHWC"] = "0" + channel = 8 + input_np = np.load( + f"{data_path}/sync_bn2d_nchw_input_rank{flow.env.get_rank()}.npy" + ) + torch_out = np.load( + f"{data_path}/sync_bn2d_nchw_torch_output_rank{flow.env.get_rank()}.npy" + ) + torch_grad = np.load( + f"{data_path}/sync_bn2d_nchw_torch_grad_rank{flow.env.get_rank()}.npy" + ) + + of_input = flow.tensor(input_np, requires_grad=True, device="cuda") + of_bn = flow.nn.BatchNorm1d(channel) + of_bn = flow.nn.SyncBatchNorm.convert_sync_batchnorm(of_bn).cuda() + of_res = of_bn(of_input) + of_res.sum().backward() + + test_case.assertTrue(np.allclose(torch_out, of_res.numpy(), atol=1e-8)) + test_case.assertTrue(np.allclose(torch_grad, of_input.grad.numpy(), atol=1e-8,)) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_t5_layernorm.py b/python/oneflow/test/modules/test_t5_layernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..10d81ed748d8ef7628cc88384a4e55303b7bfcfe --- /dev/null +++ b/python/oneflow/test/modules/test_t5_layernorm.py @@ -0,0 +1,92 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import math +import numpy as np +from oneflow.test_utils.test_util import GenArgList + +import oneflow as flow +import oneflow.unittest +import torch + + +class TorchT5LayerNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Construct a layernorm module in the T5 style. No bias and no subtraction of mean. + """ + super().__init__() + self.weight = torch.nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + + # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean + # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated + # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for + # half-precision inputs is done in fp32 + + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + res = self.weight * hidden_states + return res + + +def _test_t5_layer_norm(test_case, device): + torch_t5_layernrom = TorchT5LayerNorm(3) + oneflow_t5_layernorm = flow.nn.RMSLayerNorm(3) + torch_t5_layernrom.to(device) + oneflow_t5_layernorm.to(device) + x = np.random.randn(2, 4, 3) + torch_x = torch.tensor(x, requires_grad=True, device=torch.device(device)) + oneflow_x = flow.tensor(x, requires_grad=True, device=flow.device(device)) + torch_y = torch_t5_layernrom(torch_x) + oneflow_y = oneflow_t5_layernorm(oneflow_x) + test_case.assertTrue( + np.allclose( + torch_y.detach().cpu().numpy(), oneflow_y.numpy(), rtol=1e-4, atol=1e-4 + ) + ) + torch_y_sum = torch_y.sum() + torch_y_sum.backward() + oneflow_y_sum = oneflow_y.sum() + oneflow_y_sum.backward() + test_case.assertTrue( + np.allclose( + torch_x.grad.cpu().numpy(), oneflow_x.grad.numpy(), rtol=1e-5, atol=1e-5 + ) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestModule(flow.unittest.TestCase): + def test_t5_layernorm(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [_test_t5_layer_norm] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_tensor_ops.py b/python/oneflow/test/modules/test_tensor_ops.py index 07d3252a6142c84879fc772135255801f3a17a04..8510cf8d3e1e0c127e422ef499a58772ba43cae2 100644 --- a/python/oneflow/test/modules/test_tensor_ops.py +++ b/python/oneflow/test/modules/test_tensor_ops.py @@ -18,6 +18,7 @@ import unittest from collections import OrderedDict import numpy as np +from random import shuffle from oneflow.test_utils.test_util import GenArgList import oneflow as flow @@ -26,14 +27,6 @@ import oneflow.unittest from oneflow.test_utils.automated_test_util import * -def _test_type_as(test_case, shape, device, src_dtype, tgt_dtype): - np_input = np.random.rand(*shape) - input = flow.tensor(np_input, dtype=src_dtype, device=device) - target = flow.tensor(np_input, dtype=tgt_dtype, device=device) - input = input.type_as(target) - test_case.assertEqual(input.dtype, target.dtype) - - def _test_is_floating_point(test_case, shape, device, dtype): np_input = np.random.rand(*shape) input = flow.tensor(np_input, dtype=dtype, device=device) @@ -154,6 +147,16 @@ class TestTensorOps(flow.unittest.TestCase): y = x.long() return y + @autotest(n=5, auto_backward=False) + def test_long_with_non_contiguous_input(test_case): + device = random_device() + permute_list = list(range(4)) + shuffle(permute_list) + input = random_tensor(ndim=4).to(device) + x = input.permute(permute_list) + y = x.long() + return y + @autotest(n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph=True) def test_int(test_case): device = random_device() @@ -210,12 +213,36 @@ class TestTensorOps(flow.unittest.TestCase): y = x.double() return y + @autotest(n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph=True) + def test_bool(test_case): + device = random_device() + x = random_tensor().to(device) + y = x.bool() + return y + + @autotest(n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph=True) + def test_bool_0dim(test_case): + device = random_device() + x = random_tensor(ndim=0).to(device) + y = x.bool() + return y + + @autotest(n=5, auto_backward=False) + def test_bool_with_non_contiguous_input(test_case): + device = random_device() + permute_list = list(range(4)) + shuffle(permute_list) + input = random_tensor(ndim=4).to(device) + x = input.permute(permute_list) + y = x.bool() + return y + # Not check graph because of 2 reason. # Reason 1, nn.Graph.build()'s input/output item only support types: Tensor/None. # Reason 2, This op needs to convert the EagerTensor to a numpy array，so this op only supports eager mode. # Please refer to File "oneflow/api/python/utils/tensor_utils.h", line 49, in EagerTensorToNumpy. @autotest( - n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFlase" + n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFalse" ) def test_item(test_case): device = random_device() @@ -228,7 +255,7 @@ class TestTensorOps(flow.unittest.TestCase): # Reason 2, This op needs to convert the EagerTensor to a numpy array，so this op only supports eager mode. # Please refer to File "oneflow/api/python/utils/tensor_utils.h", line 49, in EagerTensorToNumpy. @autotest( - n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFlase" + n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFalse" ) def test_item_0dim(test_case): device = random_device() @@ -241,7 +268,7 @@ class TestTensorOps(flow.unittest.TestCase): # Reason 2, This op needs to convert the EagerTensor to a numpy array，so this op only supports eager mode. # Please refer to File "oneflow/api/python/utils/tensor_utils.h", line 49, in EagerTensorToNumpy. @autotest( - n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFlase" + n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFalse" ) def test_tolist(test_case): device = random_device() @@ -254,7 +281,7 @@ class TestTensorOps(flow.unittest.TestCase): # Reason 2, This op needs to convert the EagerTensor to a numpy array，so this op only supports eager mode. # Please refer to File "oneflow/api/python/utils/tensor_utils.h", line 49, in EagerTensorToNumpy. @autotest( - n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFlase" + n=20, auto_backward=False, rtol=1e-4, atol=1e-4, check_graph="ValidatedFalse" ) def test_tolist_0dim(test_case): device = random_device() @@ -262,14 +289,12 @@ class TestTensorOps(flow.unittest.TestCase): y = torch.tensor(x.tolist()) return y + @autotest() def test_type_as(test_case): - arg_dict = OrderedDict() - arg_dict["shape"] = [(1, 2), (3, 4, 5), (2, 3, 4, 5)] - arg_dict["device"] = ["cpu", "cuda"] - arg_dict["src_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] - arg_dict["tgt_dtype"] = [flow.int64, flow.int32, flow.float32, flow.float64] - for arg in GenArgList(arg_dict): - _test_type_as(test_case, *arg) + input = random_tensor().to(random_device()) + target = random_tensor().to(random_device()) + input = input.type_as(target) + return input def test_is_floating_point(test_case): arg_dict = OrderedDict() @@ -417,6 +442,7 @@ class TestTensorOps(flow.unittest.TestCase): flow.IntTensor: [flow.int32, flow.device("cpu")], flow.LongTensor: [flow.int64, flow.device("cpu")], flow.HalfTensor: [flow.float16, flow.device("cpu")], + flow.Tensor: [flow.float32, flow.device("cpu")], flow.FloatTensor: [flow.float32, flow.device("cpu")], flow.DoubleTensor: [flow.float64, flow.device("cpu")], flow.cuda.CharTensor: [flow.int8, flow.device("cuda")], @@ -448,6 +474,18 @@ class TestTensorOps(flow.unittest.TestCase): for arg in GenArgList(arg_dict): _test_type_noargs(test_case, *arg) + @autotest(n=3, auto_backward=False) + def test_bincount(test_case): + device = random_device() + len = random(1, 100) + input = random_tensor(1, len, dtype=int).to(device) + weight = random_tensor(1, len, dtype=float).to(device) + min_length = random(1, 100) | nothing() + return ( + input.bincount(minlength=min_length), + input.bincount(weight, minlength=min_length), + ) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_tensor_scatter_nd_update.py b/python/oneflow/test/modules/test_tensor_scatter_nd_update.py index b141e3efae3e004e979e38c14c43227aeb018df4..f9bb1bbe2c3e2c58e78893a6f0217fea8cf2e019 100644 --- a/python/oneflow/test/modules/test_tensor_scatter_nd_update.py +++ b/python/oneflow/test/modules/test_tensor_scatter_nd_update.py @@ -37,6 +37,28 @@ def _test_tensor_scatter_nd_update(test_case, device): test_case.assertTrue(np.allclose(output.numpy(), np_out, 0.0001, 0.0001)) +def _test_tensor_scatter_nd_update_with_non_contiguous_input(test_case, device): + # non-contiguous tensor with shape (2, 3, 4) + origin = flow.tensor( + np.ones((4, 3, 2)), dtype=flow.float, device=flow.device(device) + ).permute(2, 1, 0) + # indices with shape (3, 2) + indices = flow.tensor( + np.array([[0, 0], [1, 0], [1, 1]]), dtype=flow.int, device=flow.device(device) + ) + # non-contiguous update with shape (3, 4) + update = flow.tensor( + np.zeros((4, 3)), dtype=flow.float, device=flow.device(device) + ).T + output = flow.tensor_scatter_nd_update(origin, indices, update) + + np_res = np.ones((2, 3, 4)) + np_res[0, 0] = 0 + np_res[1, 0] = 0 + np_res[1, 1] = 0 + test_case.assertTrue(np.array_equal(output.numpy(), np_res)) + + def _test_tensor_scatter_nd_update_t(test_case, device): origin = flow.tensor( np.arange(15).reshape(5, 3), dtype=flow.float, device=flow.device(device) @@ -92,6 +114,7 @@ class TestTensorScatterNdUpdate(flow.unittest.TestCase): arg_dict = OrderedDict() arg_dict["test_fun"] = [ _test_tensor_scatter_nd_update, + _test_tensor_scatter_nd_update_with_non_contiguous_input, _test_tensor_scatter_nd_update_t, _test_tensor_scatter_nd_update_backward, ] diff --git a/python/oneflow/test/modules/test_to_torch.py b/python/oneflow/test/modules/test_to_torch.py index de65b41373543b4ab76fb0fe36a88d3913f7ef07..cd9d2591ac6de5ff1e92fca7086190c2179a9fac 100644 --- a/python/oneflow/test/modules/test_to_torch.py +++ b/python/oneflow/test/modules/test_to_torch.py @@ -15,6 +15,7 @@ limitations under the License. """ import unittest import numpy as np +import os import oneflow as flow import oneflow.unittest @@ -28,7 +29,7 @@ class TestToTroch(flow.unittest.TestCase): flow_t = flow.rand(5, 3, 3) numpy_from_flow = flow_t.numpy() - torch_t = flow.utils.to_torch(flow_t) + torch_t = flow.utils.tensor.to_torch(flow_t) test_case.assertEqual( torch_t.data_ptr(), numpy_from_flow.__array_interface__["data"][0] @@ -48,7 +49,7 @@ class TestToTroch(flow.unittest.TestCase): def test_to_torch_cpu_with_0_size_data(test_case): flow_t = flow.rand(5, 3, 0) - torch_t = flow.utils.to_torch(flow_t) + torch_t = flow.utils.tensor.to_torch(flow_t) test_case.assertTrue( np.allclose(flow_t.numpy(), torch_t.numpy(), rtol=0.001, atol=0.001) @@ -59,7 +60,7 @@ class TestToTroch(flow.unittest.TestCase): flow_t = flow.tensor(5) numpy_from_flow = flow_t.numpy() - torch_t = flow.utils.to_torch(flow_t) + torch_t = flow.utils.tensor.to_torch(flow_t) test_case.assertEqual( torch_t.data_ptr(), numpy_from_flow.__array_interface__["data"][0] @@ -70,6 +71,30 @@ class TestToTroch(flow.unittest.TestCase): ) test_case.assertEqual(flow_t.numpy().dtype, torch_t.numpy().dtype) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_to_torch_gpu(test_case): + flow_t = flow.rand(5, 3, 3).to("cuda") + + torch_t = flow.utils.tensor.to_torch(flow_t) + + flow_t[0][0] = flow.tensor([1, 2, 3]).to(flow.float32) + # NOTE: OneFlow operations are asynchoronously executed, + # so we need to synchronize explicitly here. + flow._oneflow_internal.eager.Sync() + test_case.assertTrue(np.array_equal(torch_t.cpu().numpy(), flow_t.numpy())) + + test_case.assertEqual(flow_t.numpy().dtype, torch_t.cpu().numpy().dtype) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_to_torch_global(test_case): + flow_t = flow.rand(5, 3, 3).to_global( + placement=flow.placement.all("cuda"), sbp=flow.sbp.broadcast + ) + + torch_t = flow.utils.tensor.to_torch(flow_t) + + test_case.assertEqual(flow_t.numpy().dtype, torch_t.cpu().numpy().dtype) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_triu.py b/python/oneflow/test/modules/test_triu.py index fddfed33ba56e51b60a4b4ea1d991d747c353d19..84df177b7b4faa0eded78e85bb21769ea976f20f 100644 --- a/python/oneflow/test/modules/test_triu.py +++ b/python/oneflow/test/modules/test_triu.py @@ -27,38 +27,58 @@ import oneflow.unittest from oneflow.test_utils.automated_test_util import * -def _test_triu(test_case, diagonal, device): +def _test_triu(test_case, diagonal, device, dtype): arr_shape = (4, 4, 8) - np_arr = np.random.randn(*arr_shape) + flow_dtype, np_dtype = dtype + np_arr = np.random.randn(*arr_shape).astype(np_dtype) input_tensor = flow.tensor( - np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True + np_arr, dtype=flow_dtype, device=flow.device(device), requires_grad=True ) output = flow.triu(input_tensor, diagonal=diagonal) np_out = np.triu(np_arr, diagonal) test_case.assertTrue(np.allclose(output.numpy(), np_out, 1e-06, 1e-06)) output = output.sum() output.backward() - np_grad = np.triu(np.ones(shape=arr_shape, dtype=np.float32), diagonal) + np_grad = np.triu(np.ones(shape=arr_shape, dtype=np_dtype), diagonal) test_case.assertTrue(np.allclose(input_tensor.grad.numpy(), np_grad, 1e-06, 1e-06)) +def _test_triu_(test_case, diagonal, device, dtype): + arr_shape = (4, 4, 8) + flow_dtype, np_dtype = dtype + np_arr = np.random.randn(*arr_shape).astype(np_dtype) + input = flow.tensor(np_arr, dtype=flow_dtype, device=flow.device(device)) + np_out = np.triu(np_arr, diagonal) + test_case.assertFalse(np.allclose(input.numpy(), np_out)) + input.triu_(diagonal=diagonal) + test_case.assertTrue(np.allclose(input.numpy(), np_out)) + + @flow.unittest.skip_unless_1n1d() class TestTriu(flow.unittest.TestCase): def test_triu(test_case): arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_triu] + arg_dict["test_fun"] = [_test_triu, _test_triu_] arg_dict["diagonal"] = [2, -1] arg_dict["device"] = ["cuda", "cpu"] + arg_dict["dtype"] = [(flow.float32, np.float32), (flow.float16, np.float16)] for arg in GenArgList(arg_dict): arg[0](test_case, *arg[1:]) - @autotest(auto_backward=False, check_graph=True) + @autotest() def test_triu_with_0_size_data(test_case): device = random_device() x = random_tensor(4, 2, 1, 0, 3).to(device) y = torch.triu(x) return y + @autotest() + def test_triu_with_0_size_data_fp16(test_case): + device = random_device() + x = random_tensor(4, 2, 1, 0, 3).to(device, torch.float16) + y = torch.triu(x) + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_trunc.py b/python/oneflow/test/modules/test_trunc.py new file mode 100644 index 0000000000000000000000000000000000000000..3db61c203931f2f24c7d3f28530111a289bd0107 --- /dev/null +++ b/python/oneflow/test/modules/test_trunc.py @@ -0,0 +1,41 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestTrunc(flow.unittest.TestCase): + @autotest(n=5, check_graph=True) + def test_trunc(test_case): + device = random_device() + x = random_tensor( + ndim=4, + dim0=random(1, 5).to(int), + dim1=random(1, 5).to(int), + dim2=random(1, 5).to(int), + dim3=random(1, 5).to(int), + ).to(device) + y = torch.trunc(x) + + return y + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_trunc_divide.py b/python/oneflow/test/modules/test_trunc_divide.py new file mode 100644 index 0000000000000000000000000000000000000000..dfc1d8b4630227b86cf3804a5ff7f2e152ed231d --- /dev/null +++ b/python/oneflow/test/modules/test_trunc_divide.py @@ -0,0 +1,115 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np + +from oneflow.test_utils.automated_test_util import * +import oneflow as flow +import torch as torch_original +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestTruncDivide(flow.unittest.TestCase): + @autotest(n=5, check_allclose=False, check_graph=False) + def test_elementwise_trunc_divide_random_data(test_case): + device = random_device() + dim0 = random(1, 8) + dim1 = random(1, 8) + dim2 = random(1, 8) + dim3 = random(1, 8) + x = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=dim2, dim3=dim3).to(device) + y = random_tensor(ndim=4, dim0=dim0, dim1=dim1, dim2=dim2, dim3=dim3).to(device) + + x.oneflow = x.oneflow.detach().requires_grad_() + x.pytorch = x.pytorch.detach().requires_grad_() + y.oneflow = y.oneflow.detach().requires_grad_() + y.pytorch = y.pytorch.detach().requires_grad_() + + oneflow_out = flow._C.trunc_divide(x.oneflow, y.oneflow) + torch_out = torch_original.div(x.pytorch, y.pytorch, rounding_mode="trunc") + + test_case.assertTrue( + np.allclose( + oneflow_out.detach().cpu().numpy(), + torch_out.detach().cpu().numpy(), + rtol=0.0001, + atol=1e-05, + ) + ) + + oneflow_out.sum().backward() + torch_out.sum().backward() + + test_case.assertTrue( + np.allclose( + x.oneflow.grad.detach().cpu().numpy(), + x.pytorch.grad.detach().cpu().numpy(), + rtol=0.0001, + atol=1e-05, + ) + ) + test_case.assertTrue( + np.allclose( + y.oneflow.grad.detach().cpu().numpy(), + y.pytorch.grad.detach().cpu().numpy(), + rtol=0.0001, + atol=1e-05, + ) + ) + + @autotest(n=5, check_allclose=False, check_graph=False) + def test_tensor_truncdiv_scalar_random_data(test_case): + device = random_device() + x = random_tensor( + ndim=4, + dim0=random(1, 8), + dim1=random(1, 8), + dim2=random(1, 8), + dim3=random(1, 8), + ).to(device) + x.oneflow = x.oneflow.detach().requires_grad_() + x.pytorch = x.pytorch.detach().requires_grad_() + + scalar = random().to(float).value() + + oneflow_out = oneflow._C.trunc_divide(x.oneflow, scalar) + torch_out = torch_original.div(x.pytorch, scalar, rounding_mode="trunc") + + test_case.assertTrue( + np.allclose( + oneflow_out.detach().cpu().numpy(), + torch_out.detach().cpu().numpy(), + rtol=0.0001, + atol=1e-5, + ) + ) + + oneflow_out.sum().backward() + torch_out.sum().backward() + + test_case.assertTrue( + np.allclose( + x.oneflow.grad.detach().cpu().numpy(), + x.pytorch.grad.detach().cpu().numpy(), + rtol=0.0001, + atol=1e-5, + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_unfold.py b/python/oneflow/test/modules/test_unfold.py index ba4089b232a45fbcd05a64e367f801182eee89dd..a944535d03ea3f0a08046e0142067f3a9047c686 100644 --- a/python/oneflow/test/modules/test_unfold.py +++ b/python/oneflow/test/modules/test_unfold.py @@ -43,7 +43,14 @@ class TestUnfold(flow.unittest.TestCase): dim3=random(10, 20), ).to(device) y = m(x) - return y + func_y = torch.nn.functional.unfold( + x, + kernel_size=random(1, 3).to(_size_2_t), + dilation=random(1, 2).to(_size_2_t) | nothing(), + padding=random(0, 1).to(_size_2_t) | nothing(), + stride=random(1, 2).to(_size_2_t) | nothing(), + ) + return y, func_y if __name__ == "__main__": diff --git a/python/oneflow/test/modules/test_unique.py b/python/oneflow/test/modules/test_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..953f84b3623f9d54592b358d911db9f78dd2e6c3 --- /dev/null +++ b/python/oneflow/test/modules/test_unique.py @@ -0,0 +1,157 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import random as random_util +import torch as torch_ori +from collections import OrderedDict + +from oneflow.test_utils.test_util import GenArgList +from oneflow.test_utils.automated_test_util import * + +import numpy as np +import oneflow as flow +import oneflow.unittest + + +def _test_unique_unsorted(test_case, device, return_inverse, return_counts): + dtype = random_util.choice([torch.int8, torch.int, torch.float, torch.double]) + input = random_tensor(ndim=3, dim0=random(), dim1=random(), dim2=random(), high=20) + input = input.to(device).to(dtype) + oneflow_output = flow.unique( + input.oneflow, + sorted=False, + return_inverse=return_inverse, + return_counts=return_counts, + ) + torch_output = torch_ori.unique( + input.pytorch, + sorted=False, + return_inverse=return_inverse, + return_counts=return_counts, + ) + + if not return_inverse and not return_counts: + oneflow_result = oneflow_output + torch_result = torch_output + else: + oneflow_result = oneflow_output[0] + torch_result = torch_output[0] + + test_case.assertTrue( + np.allclose( + np.sort(oneflow_result.numpy()), + np.sort(torch_result.detach().cpu().numpy()), + ) + ) + test_case.assertEqual(list(oneflow_result.shape), list(torch_result.shape)) + + if return_inverse: + oneflow_indices = oneflow_output[1] + torch_indices = torch_output[1] + test_case.assertTrue( + np.allclose( + oneflow_result[oneflow_indices].numpy(), + torch_result[torch_indices].detach().cpu().numpy(), + ) + ) + test_case.assertEqual(list(oneflow_indices.shape), list(torch_indices.shape)) + + if return_counts: + oneflow_counts = oneflow_output[-1] + torch_counts = torch_output[-1] + test_case.assertTrue( + np.allclose( + oneflow_counts.numpy()[np.argsort(oneflow_result.numpy())], + torch_counts.detach() + .cpu() + .numpy()[np.argsort(torch_result.detach().cpu().numpy())], + ) + ) + test_case.assertEqual(list(oneflow_counts.shape), list(torch_counts.shape)) + + +def _test_unique_sorted(test_case, device, return_inverse, return_counts): + dtype = random_util.choice([torch.int8, torch.int, torch.float, torch.double]) + input = random_tensor(ndim=3, dim0=random(), dim1=random(), dim2=random(), high=20) + input = input.to(device).to(dtype) + oneflow_output = flow.unique( + input.oneflow, + sorted=True, + return_inverse=return_inverse, + return_counts=return_counts, + ) + torch_output = torch_ori.unique( + input.pytorch, + sorted=True, + return_inverse=return_inverse, + return_counts=return_counts, + ) + + if not return_inverse and not return_counts: + oneflow_result = oneflow_output + torch_result = torch_output + else: + oneflow_result = oneflow_output[0] + torch_result = torch_output[0] + + test_case.assertTrue( + np.allclose(oneflow_result.numpy(), torch_result.detach().cpu().numpy(),) + ) + test_case.assertEqual(list(oneflow_result.shape), list(torch_result.shape)) + + if return_inverse: + oneflow_indices = oneflow_output[1] + torch_indices = torch_output[1] + test_case.assertTrue( + np.allclose(oneflow_indices.numpy(), torch_indices.detach().cpu().numpy(),) + ) + test_case.assertEqual(list(oneflow_indices.shape), list(torch_indices.shape)) + + if return_counts: + oneflow_counts = oneflow_output[-1] + torch_counts = torch_output[-1] + test_case.assertTrue( + np.allclose(oneflow_counts.numpy(), torch_counts.detach().cpu().numpy(),) + ) + test_case.assertEqual(list(oneflow_counts.shape), list(torch_counts.shape)) + + +@flow.unittest.skip_unless_1n1d() +class TestUnique(flow.unittest.TestCase): + @autotest(n=5) + def test_unique(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cpu", "cuda"] + arg_dict["return_inverse"] = [False, True] + arg_dict["return_counts"] = [False, True] + for arg in GenArgList(arg_dict): + _test_unique_unsorted(test_case, *arg) + _test_unique_sorted(test_case, *arg) + + @profile(torch.unique) + def profile_unique(test_case): + input = torch.randint(0, 1000, (1000,)) + torch.unique(input) + torch.unique(input, return_inverse=True, return_counts=True) + input = torch.randn(1000,) + torch.unique(input) + torch.unique(input, return_inverse=True, return_counts=True) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_unsqueeze.py b/python/oneflow/test/modules/test_unsqueeze.py index ed6968c4a17d48fd5643d2f21e7e230516516e84..f02dfac3fab6fac40ff3b67dbcb40556952a3d92 100644 --- a/python/oneflow/test/modules/test_unsqueeze.py +++ b/python/oneflow/test/modules/test_unsqueeze.py @@ -84,6 +84,13 @@ class TestUnsqueeze(flow.unittest.TestCase): y = torch.unsqueeze(x, random(1, 3).to(int)) return y + @autotest(n=10, check_graph=False, auto_backward=False) + def test_inplace_unsqueeze_with_random_data(test_case): + device = random_device() + x = random_tensor(requires_grad=False).to(device) + y = x.unsqueeze_(random(1, 3).to(int)) + return y + @autotest(auto_backward=False, check_graph=True) def test_unsqueeze_with_0_size_data(test_case): device = random_device() diff --git a/python/oneflow/test/modules/test_upsample.py b/python/oneflow/test/modules/test_upsample.py index 9cc0d679813927f026f02e0f7e557afebcc7658f..40a5c8240d29734fbb86132151088332353fcc56 100644 --- a/python/oneflow/test/modules/test_upsample.py +++ b/python/oneflow/test/modules/test_upsample.py @@ -384,6 +384,17 @@ class TestUpsample2d(flow.unittest.TestCase): y = m(x) return y + @unittest.skip( + "The nearest interpolate operation in pytorch has bug, https://github.com/pytorch/pytorch/issues/65200" + ) + @autotest() + def test_upsample2d_nearest_half(test_case): + device = random_device() + x = random_tensor().to(device=device, dtype=torch.float16) + m = torch.nn.Upsample(scale_factor=random().to(float), mode="nearest") + y = m(x) + return y + # The forward and backward result in cpu and cuda of bilinear interpolate operation in PyTorch is different # in some corner cases. OneFlow has the same cpu and cuda results with PyTorch's cuda result. # So here we only test cuda device forward result. diff --git a/python/oneflow/test/modules/test_util_ops.py b/python/oneflow/test/modules/test_util_ops.py index 7d205dad688289c3a0549b6afd764f3a1ada7277..8df2ae27b96b635a95ba06c71447673650f72e4e 100644 --- a/python/oneflow/test/modules/test_util_ops.py +++ b/python/oneflow/test/modules/test_util_ops.py @@ -39,11 +39,22 @@ def _test_isinf(test_case, shape, dtype, device): test_case.assertTrue(np.allclose(res.numpy(), np.isinf(of_tensor.numpy()))) +def _test_isfinite(test_case, shape, dtype, device): + np_array = np.random.randn(*shape) + inf_mask = np.random.choice([1, 0], np_array.shape, p=[0.1, 0.9]).astype(bool) + nan_mask = np.random.choice([1, 0], np_array.shape, p=[0.1, 0.9]).astype(bool) + np_array[inf_mask] = np.inf + np_array[nan_mask] = np.nan + of_tensor = flow.tensor(np_array, dtype=dtype, device=device) + res = flow.isfinite(of_tensor) + test_case.assertTrue(np.allclose(res.numpy(), np.isfinite(of_tensor.numpy()))) + + @flow.unittest.skip_unless_1n1d() class TestUtilOps(flow.unittest.TestCase): def test_util_ops(test_case): arg_dict = OrderedDict() - arg_dict["test_fun"] = [_test_isnan, _test_isinf] + arg_dict["test_fun"] = [_test_isnan, _test_isinf, _test_isfinite] arg_dict["shape"] = [(2, 3, 4), (1, 2, 3)] arg_dict["dtype"] = [flow.float, flow.int] arg_dict["device"] = ["cpu", "cuda"] diff --git a/python/oneflow/test/modules/test_utils.py b/python/oneflow/test/modules/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1911d69c221870180198d977fd9c620f35190587 --- /dev/null +++ b/python/oneflow/test/modules/test_utils.py @@ -0,0 +1,85 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import numpy as np +import oneflow as flow +import torch +from torch._utils import _flatten_dense_tensors as torch_flatten_dense_tensors +from torch._utils import _unflatten_dense_tensors as torch_unflatten_dense_tensors +from oneflow._utils import _flatten_dense_tensors, _unflatten_dense_tensors +from collections import OrderedDict +from oneflow.test_utils.test_util import GenArgList + + +def _test_flatten_dense_tensors(test_case, device): + torch_x = torch.randn(6, 6, device=device) + x = flow.utils.tensor.from_torch(torch_x) + torch_x_flatten = torch_flatten_dense_tensors([torch_x]) + x_flatten = _flatten_dense_tensors([x]) + test_case.assertTrue(np.array_equal(torch_x_flatten.size(), x_flatten.size())) + torch_x_flatten = torch_flatten_dense_tensors([torch_x, torch_x, torch_x]) + x_flatten = _flatten_dense_tensors([x, x, x]) + test_case.assertTrue(np.array_equal(torch_x_flatten.size(), x_flatten.size())) + test_case.assertTrue( + np.allclose( + torch_x_flatten.cpu().numpy(), x_flatten.cpu().numpy(), 1e-05, 1e-05 + ) + ) + + +def _test_unflatten_dense_tensors(test_case, device): + torch_flat = torch.randn(6, 1, device=device) + torch_x1 = torch.randn(2, 1, device=device) + torch_x2 = torch.randn(2, 1, device=device) + torch_x3 = torch.randn(2, 1, device=device) + torch_tensors = [ + torch_x1, + torch_x2, + torch_x3, + ] + tensors = [ + flow.utils.tensor.from_torch(torch_x1), + flow.utils.tensor.from_torch(torch_x2), + flow.utils.tensor.from_torch(torch_x3), + ] + torch_outputs = torch_unflatten_dense_tensors(torch_flat, torch_tensors) + outputs = _unflatten_dense_tensors( + flow.utils.tensor.from_torch(torch_flat), tensors + ) + for i in range(len(outputs)): + test_case.assertTrue(np.array_equal(torch_outputs[i].size(), outputs[i].size())) + test_case.assertTrue( + np.allclose( + torch_outputs[i].cpu().numpy(), outputs[i].cpu().numpy(), 1e-05, 1e-05 + ) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestUtilsFunction(flow.unittest.TestCase): + def test_utils_function(test_case): + arg_dict = OrderedDict() + arg_dict["test_fun"] = [ + _test_flatten_dense_tensors, + _test_unflatten_dense_tensors, + ] + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgList(arg_dict): + arg[0](test_case, *arg[1:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/modules/test_var.py b/python/oneflow/test/modules/test_var.py index 90a1a134e8662292dad76afa35d592c9b6262287..942f2892d9c50c51f3c50b434e5583b3d518b11d 100644 --- a/python/oneflow/test/modules/test_var.py +++ b/python/oneflow/test/modules/test_var.py @@ -24,6 +24,7 @@ from oneflow.test_utils.automated_test_util import * @flow.unittest.skip_unless_1n1d() class TestVar(flow.unittest.TestCase): + @autotest(check_graph=True) def test_flow_var_all_dim_with_random_data(test_case): device = random_device() x = random_tensor().to(device) @@ -42,6 +43,21 @@ class TestVar(flow.unittest.TestCase): ) return y + # In fp16 mode, variance op backward has a gap of 1e-3 between the gradient of PyTorch + # and OneFlow for some unknown reason. However, it is not important now, because both in + # PyTorch and OneFlow variance op don't need support fp16 backward in amp train. + @autotest(n=5, auto_backward=True, check_graph=True, rtol=1e-3, atol=1e-3) + def test_flow_var_one_dim_with_random_half_data(test_case): + device = random_device() + x = random_tensor(ndim=4).to(device).to(torch.float16) + y = torch.var( + x, + dim=random(low=-4, high=4).to(int), + unbiased=random().to(bool), + keepdim=random().to(bool), + ) + return y + @autotest(auto_backward=False, check_graph=True) def test_flow_var_0_size_data_with_random_data(test_case): device = random_device() @@ -54,6 +70,25 @@ class TestVar(flow.unittest.TestCase): ) return y + @autotest(n=5, auto_backward=False, check_graph=True) + def test_flow_var_0_size_data_with_random_half_data(test_case): + device = random_device() + x = random_tensor(4, 2, 3, 0, 4).to(device).to(torch.float16) + y = torch.var( + x, + dim=random(low=-4, high=4).to(int), + unbiased=random().to(bool), + keepdim=random().to(bool), + ) + return y + + @autotest(n=5) + def test_flow_var_all_dim_with_random_data_n5(test_case): + device = random_device() + x = random_tensor(ndim=4, dim0=5, dim1=1, dim2=16, dim3=16).to(device) + y = torch.var(x, dim=[0, 2, 3]) + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/modules/test_weight_norm.py b/python/oneflow/test/modules/test_weight_norm.py index 99f1bdbc559796bcea48bc6419cd7c2fff97aa8d..5fc096d5a9a370f0b15d2b221686c02af73c6faa 100644 --- a/python/oneflow/test/modules/test_weight_norm.py +++ b/python/oneflow/test/modules/test_weight_norm.py @@ -146,7 +146,7 @@ class TestWeightNorm(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, Graph's build input nn.modules.linear.Linear type is not supported. # Please refer to issue: https://github.com/Oneflow-Inc/oneflow/issues/7466 - @autotest(n=10, auto_backward=True, check_graph="ValidatedFlase") + @autotest(n=10, auto_backward=True, check_graph="ValidatedFalse") def test_weight_norm_with_random_data(test_case): device = random_device() diff --git a/python/oneflow/test/modules/test_where.py b/python/oneflow/test/modules/test_where.py index 08c32035e5e17f44aace7842217de668920f87f7..c32d00495583804ca5852e430072018dec0eeea8 100644 --- a/python/oneflow/test/modules/test_where.py +++ b/python/oneflow/test/modules/test_where.py @@ -73,8 +73,18 @@ def _test_where_scalar(test_case, device): y = 2.0 condition = flow.tensor(np.array([1]), dtype=flow.int32) of_out = flow.where(condition, x, y) + test_case.assertTrue(of_out.dtype == flow.float32) np_out = np.array([0.5]) test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-05, 1e-05)) + flow.set_default_dtype(flow.double) + of_out = flow.where(condition, x, y) + test_case.assertTrue(of_out.dtype == flow.double) + flow.set_default_dtype(flow.float16) + of_out = flow.where(condition, x, y) + test_case.assertTrue(of_out.dtype == flow.float16) + flow.set_default_dtype(flow.bfloat16) + of_out = flow.where(condition, x, y) + test_case.assertTrue(of_out.dtype == flow.bfloat16) def _test_where_dim4(test_case, device): @@ -191,6 +201,14 @@ def _test_where_x_y_none(test_case, device): ) +def _test_where_scalar(test_case, device): + x = flow.randn(5, 5) + y = flow.where(x > 0, x, 0.0) + test_case.assertTrue(np.array_equal(y.size(), (5, 5))) + y = flow.where(x > 0, 0.0, x) + test_case.assertTrue(np.array_equal(y.size(), (5, 5))) + + @flow.unittest.skip_unless_1n1d() class TestWhere(flow.unittest.TestCase): def test_where(test_case): @@ -204,6 +222,7 @@ class TestWhere(flow.unittest.TestCase): _test_where_broadcast_backward, _test_where_broadcast_x_backward, _test_where_x_y_none, + _test_where_scalar, ] arg_dict["device"] = ["cpu", "cuda"] for arg in GenArgList(arg_dict): diff --git a/python/oneflow/test/profiler/test_profile_lenet.py b/python/oneflow/test/profiler/test_profile_lenet.py index 07d3a3c0ca002bc89f1caeba79217ecf12e08c49..fefbd78de8518db350c4ef7451f647025728fec5 100644 --- a/python/oneflow/test/profiler/test_profile_lenet.py +++ b/python/oneflow/test/profiler/test_profile_lenet.py @@ -1,148 +1,148 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -import os -import unittest -import oneflow.unittest -import oneflow as flow -import oneflow.nn as nn -import oneflow.nn.functional as F -import oneflow.profiler -from oneflow.profiler.events import CustomEvent, KernelEvent - - -class LeNet(nn.Module): - def __init__(self): - super(LeNet, self).__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - out = F.relu(self.conv1(x)) - out = F.max_pool2d(out, 2) - out = F.relu(self.conv2(out)) - out = F.max_pool2d(out, 2) - out = out.view(out.size(0), -1) - out = F.relu(self.fc1(out)) - out = F.relu(self.fc2(out)) - out = self.fc3(out) - return out - - -def get_event(events, name: str, input_shapes: str = "-"): - for item in events: - if isinstance(item, CustomEvent): - if item.name == name: - return item - if isinstance(item, KernelEvent): - if item.name == name and item.input_shapes == input_shapes: - return item - return None - - -def _test_lenet( - test_case, - on_cuda: bool, - record_shapes: bool, - record_bandwidth_for_cuda: bool = False, -): - x = flow.randn(2, 3, 32, 32) - lenet = LeNet() - if on_cuda: - x = x.to("cuda") - lenet.to("cuda") - activities = [oneflow.profiler.ProfilerActivity.CPU] - if on_cuda: - activities.append(oneflow.profiler.ProfilerActivity.CUDA) - with oneflow.profiler.profile( - activities=activities, - record_shapes=record_shapes, - record_bandwidth_for_cuda=record_bandwidth_for_cuda, - ) as prof: - with oneflow.profiler.record_function("lenet_forward_total_time") as f: - for _ in range(2): - eager_res = lenet(x) - with oneflow.profiler.record_function("lenet_backward_total_time") as f: - eager_res.sum().backward() - events = prof.key_averages(group_by_input_shape=True) - print(events) - conv_event = get_event( - events, "conv2d", "[(2,3,32,32), (6,3,5,5)]" if record_shapes else "-" - ) - test_case.assertIsNotNone(conv_event) - - if on_cuda: - test_case.assertGreater(conv_event.cpu_time, 0.0) - test_case.assertGreater(conv_event.cpu_time_total, 0.0) - test_case.assertGreater(conv_event.cuda_time, 0.0) - test_case.assertGreater(conv_event.cuda_time_total, 0.0) - else: - test_case.assertGreater(conv_event.cpu_time, 0.0) - test_case.assertGreater(conv_event.cpu_time_total, 0.0) - - test_case.assertEqual(conv_event.count, 2 if record_shapes else 4) - if record_bandwidth_for_cuda and on_cuda: - test_case.assertNotEqual(conv_event.bandwidth, -1) - - relu_grad_event = get_event( - events, "relu_grad", "[(2,6,28,28), (2,6,28,28)]" if record_shapes else "-" - ) - test_case.assertIsNotNone(relu_grad_event) - if on_cuda: - test_case.assertGreater(relu_grad_event.cpu_time, 0.0) - test_case.assertGreater(relu_grad_event.cpu_time_total, 0.0) - test_case.assertGreater(relu_grad_event.cuda_time, 0.0) - test_case.assertGreater(relu_grad_event.cuda_time_total, 0.0) - else: - test_case.assertGreater(relu_grad_event.cpu_time, 0.0) - test_case.assertGreater(relu_grad_event.cpu_time_total, 0.0) - - test_case.assertEqual(relu_grad_event.count, 1 if record_shapes else 4) - if record_bandwidth_for_cuda and on_cuda: - test_case.assertNotEqual(relu_grad_event.bandwidth, -1) - - test_case.assertIsNotNone(get_event(events, "lenet_forward_total_time")) - test_case.assertIsNotNone(get_event(events, "lenet_backward_total_time")) - - -class TestProfileLenet(flow.unittest.TestCase): - def test_lenet_cpu(test_case): - _test_lenet(test_case, on_cuda=False, record_shapes=True) - _test_lenet(test_case, on_cuda=False, record_shapes=False) - - @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") - def test_lenet_cuda(test_case): - _test_lenet( - test_case, on_cuda=True, record_shapes=True, record_bandwidth_for_cuda=False - ) - _test_lenet( - test_case, - on_cuda=True, - record_shapes=False, - record_bandwidth_for_cuda=False, - ) - _test_lenet( - test_case, on_cuda=True, record_shapes=True, record_bandwidth_for_cuda=True - ) - _test_lenet( - test_case, on_cuda=True, record_shapes=False, record_bandwidth_for_cuda=True - ) - - -if __name__ == "__main__": - unittest.main() +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import oneflow.unittest +import oneflow as flow +import oneflow.nn as nn +import oneflow.nn.functional as F +import oneflow.profiler +from oneflow.profiler.events import CustomEvent, KernelEvent + + +class LeNet(nn.Module): + def __init__(self): + super(LeNet, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + out = F.relu(self.conv1(x)) + out = F.max_pool2d(out, 2) + out = F.relu(self.conv2(out)) + out = F.max_pool2d(out, 2) + out = out.view(out.size(0), -1) + out = F.relu(self.fc1(out)) + out = F.relu(self.fc2(out)) + out = self.fc3(out) + return out + + +def get_event(events, name: str, input_shapes: str = "-"): + for item in events: + if isinstance(item, CustomEvent): + if item.name == name: + return item + if isinstance(item, KernelEvent): + if item.name == name and item.input_shapes == input_shapes: + return item + return None + + +def _test_lenet( + test_case, + on_cuda: bool, + record_shapes: bool, + record_bandwidth_for_cuda: bool = False, +): + x = flow.randn(2, 3, 32, 32) + lenet = LeNet() + if on_cuda: + x = x.to("cuda") + lenet.to("cuda") + activities = [oneflow.profiler.ProfilerActivity.CPU] + if on_cuda: + activities.append(oneflow.profiler.ProfilerActivity.CUDA) + with oneflow.profiler.profile( + activities=activities, + record_shapes=record_shapes, + record_bandwidth_for_cuda=record_bandwidth_for_cuda, + ) as prof: + with oneflow.profiler.record_function("lenet_forward_total_time") as f: + for _ in range(2): + eager_res = lenet(x) + with oneflow.profiler.record_function("lenet_backward_total_time") as f: + eager_res.sum().backward() + events = prof.key_averages(group_by_input_shape=True) + + conv_event = get_event( + events, "conv2d", "[(2,3,32,32), (6,3,5,5)]" if record_shapes else "-" + ) + test_case.assertIsNotNone(conv_event) + + if on_cuda: + test_case.assertGreater(conv_event.cpu_time, 0.0) + test_case.assertGreater(conv_event.cpu_time_total, 0.0) + test_case.assertGreater(conv_event.cuda_time, 0.0) + test_case.assertGreater(conv_event.cuda_time_total, 0.0) + else: + test_case.assertGreater(conv_event.cpu_time, 0.0) + test_case.assertGreater(conv_event.cpu_time_total, 0.0) + + test_case.assertEqual(conv_event.count, 2 if record_shapes else 4) + if record_bandwidth_for_cuda and on_cuda: + test_case.assertNotEqual(conv_event.bandwidth, -1) + + relu_grad_event = get_event( + events, "relu_grad", "[(2,6,28,28), (2,6,28,28)]" if record_shapes else "-" + ) + test_case.assertIsNotNone(relu_grad_event) + if on_cuda: + test_case.assertGreater(relu_grad_event.cpu_time, 0.0) + test_case.assertGreater(relu_grad_event.cpu_time_total, 0.0) + test_case.assertGreater(relu_grad_event.cuda_time, 0.0) + test_case.assertGreater(relu_grad_event.cuda_time_total, 0.0) + else: + test_case.assertGreater(relu_grad_event.cpu_time, 0.0) + test_case.assertGreater(relu_grad_event.cpu_time_total, 0.0) + + test_case.assertEqual(relu_grad_event.count, 1 if record_shapes else 4) + if record_bandwidth_for_cuda and on_cuda: + test_case.assertNotEqual(relu_grad_event.bandwidth, -1) + + test_case.assertIsNotNone(get_event(events, "lenet_forward_total_time")) + test_case.assertIsNotNone(get_event(events, "lenet_backward_total_time")) + + +class TestProfileLenet(flow.unittest.TestCase): + def test_lenet_cpu(test_case): + _test_lenet(test_case, on_cuda=False, record_shapes=True) + _test_lenet(test_case, on_cuda=False, record_shapes=False) + + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + def test_lenet_cuda(test_case): + _test_lenet( + test_case, on_cuda=True, record_shapes=True, record_bandwidth_for_cuda=False + ) + _test_lenet( + test_case, + on_cuda=True, + record_shapes=False, + record_bandwidth_for_cuda=False, + ) + _test_lenet( + test_case, on_cuda=True, record_shapes=True, record_bandwidth_for_cuda=True + ) + _test_lenet( + test_case, on_cuda=True, record_shapes=False, record_bandwidth_for_cuda=True + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_autocast.py b/python/oneflow/test/tensor/test_autocast.py new file mode 100644 index 0000000000000000000000000000000000000000..6288ff09253da692d68f7d3fef34819b53028ebf --- /dev/null +++ b/python/oneflow/test/tensor/test_autocast.py @@ -0,0 +1,63 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "skip test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestAutoCast(flow.unittest.TestCase): + @autotest(n=1, auto_backward=True, check_graph=False) + def test_autocast_half_mm(test_case): + a = random_tensor(2, 2, 3).to("cuda") + b = random_tensor(2, 3, 4).to("cuda") + with torch.autocast("cuda"): + x = torch.mm(a, b) + return x + + @autotest(n=1, auto_backward=True, check_graph=False) + def test_autocast_half_mm_add(test_case): + a = random_tensor(2, 2, 3).to("cuda") + b = random_tensor(2, 3, 4).to("cuda") + c = random_tensor(2, 2, 4).to("cuda") + with torch.autocast("cuda"): + x = torch.mm(a, b) + y = x + c + return x.float() + y.float() + + def test_autocast_graph(test_case): + class LinearGraph(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear = flow.nn.Linear(3, 4, bias=False).cuda().half() + + def build(self, x): + return self.linear(x) + + x = flow.Tensor(3, 3).cuda() + + with flow.autocast(device_type="cuda"): + linear = LinearGraph() + y = linear(x) + test_case.assertTrue(y.dtype == flow.float16) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_bfloat16_activation.py b/python/oneflow/test/tensor/test_bfloat16_activation.py new file mode 100644 index 0000000000000000000000000000000000000000..196f626fbd6042533c742627c246c0c6b634f2ca --- /dev/null +++ b/python/oneflow/test/tensor/test_bfloat16_activation.py @@ -0,0 +1,681 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import unittest +import numpy as np +import oneflow as flow +import oneflow.unittest + + +@flow.unittest.skip_unless_1n1d() +class TestBfloat16Activatian(flow.unittest.TestCase): + def test_tan_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.tan(x) + fp32_y = flow.tan(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_tanh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.tanh(x) + fp32_y = flow.tanh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_sin_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.sin(x) + fp32_y = flow.sin(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_sinh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.sinh(x) + fp32_y = flow.sinh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_cos_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.cos(x) + fp32_y = flow.cos(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_cosh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.cosh(x) + fp32_y = flow.cosh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_atan_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.atan(x) + fp32_y = flow.atan(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_atanh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.atanh(x) + fp32_y = flow.atanh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_asin_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.asin(x) + fp32_y = flow.asin(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_asinh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.asinh(x) + fp32_y = flow.asinh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_acos_with_random_data(test_case): + np_array = np.random.uniform(-1, 1, (4, 4)) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.acos(x) + fp32_y = flow.acos(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_acosh_with_random_data(test_case): + np_array = np.random.uniform(1, 5, (4, 4)) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.acosh(x) + fp32_y = flow.acosh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_sqrt_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.sqrt(x) + fp32_y = flow.sqrt(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_square_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.square(x) + fp32_y = flow.square(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_exp_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.exp(x) + fp32_y = flow.exp(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_ceil_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.ceil(x) + fp32_y = flow.ceil(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_erf_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.erf(x) + fp32_y = flow.erf(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_erfc_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.erfc(x) + fp32_y = flow.erfc(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_floor_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.floor(x) + fp32_y = flow.floor(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_expm1_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.expm1(x) + fp32_y = flow.expm1(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_lgamma_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.lgamma(x) + fp32_y = flow.lgamma(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_log_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.log(x) + fp32_y = flow.log(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_log2_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.log2(x) + fp32_y = flow.log2(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_log1p_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.log1p(x) + fp32_y = flow.log1p(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_sigmoid_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.sigmoid(x) + fp32_y = flow.sigmoid(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_round_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.round(x) + fp32_y = flow.round(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_rsqrt_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.rsqrt(x) + fp32_y = flow.rsqrt(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_softplus_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.softplus(x) + fp32_y = flow.softplus(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_softsign_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.softsign(x) + fp32_y = flow.softsign(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_softshrink_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.softshrink(x) + fp32_y = flow.softshrink(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_silu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.silu(x) + fp32_y = flow.silu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_selu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.selu(x) + fp32_y = flow.selu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_mish_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.mish(x) + fp32_y = flow.mish(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_gelu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + y = flow.gelu(x) + fp32_y = flow.gelu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_elu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + elu = flow.nn.ELU() + y = elu(x) + fp32_y = elu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_celu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + celu = flow.nn.CELU() + y = celu(x) + fp32_y = celu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_hardswish_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + hardswish = flow.nn.Hardswish() + y = hardswish(x) + fp32_y = hardswish(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_hardswish_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + hardsigmoid = flow.nn.Hardsigmoid() + y = hardsigmoid(x) + fp32_y = hardsigmoid(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_hardshrink_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + hardshrink = flow.nn.Hardshrink() + y = hardshrink(x) + fp32_y = hardshrink(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_hardtanh_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + hardtanh = flow.nn.Hardtanh() + y = hardtanh(x) + fp32_y = hardtanh(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_leakyrelu_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + leakyrelu = flow.nn.LeakyReLU(0.1) + y = leakyrelu(x) + fp32_y = leakyrelu(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_threshold_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + th = flow.nn.Threshold(threshold=0.5, value=0.2) + y = th(x) + fp32_y = th(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + def test_logsinmoid_with_random_data(test_case): + np_array = np.random.rand(4, 4) + x = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + fp32_x = x.float() + logsigmoid = flow.nn.LogSigmoid() + y = logsigmoid(x) + fp32_y = logsigmoid(fp32_x) + test_case.assertTrue( + np.allclose( + y.float().numpy(), + fp32_y.bfloat16().float().numpy(), + atol=1e-4, + rtol=1e-4, + ) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_consistent_tensor.py b/python/oneflow/test/tensor/test_global_tensor.py similarity index 100% rename from python/oneflow/test/tensor/test_consistent_tensor.py rename to python/oneflow/test/tensor/test_global_tensor.py diff --git a/python/oneflow/test/tensor/test_global_tensor_and_ndarray_compatibility.py b/python/oneflow/test/tensor/test_global_tensor_and_ndarray_compatibility.py new file mode 100644 index 0000000000000000000000000000000000000000..13c484da19215097ad630b3651ffcbd8c3ba12fb --- /dev/null +++ b/python/oneflow/test/tensor/test_global_tensor_and_ndarray_compatibility.py @@ -0,0 +1,78 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +from collections import OrderedDict + + +import torch +import oneflow as flow +import unittest +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * + + +import unittest +import oneflow as flow +import oneflow.unittest + +from oneflow.test_utils.automated_test_util import * +import numpy as np + +np.random.seed(233) + +test_compute_op_list = [ + "+", + "-", + "*", + "/", + "**", + "//", + "%", +] + + +def do_test_compute_op(test_case, ndim, placement, sbp): + dims = [random(1, 4) * 8 for i in range(ndim)] + x = random_tensor(ndim, *dims, dtype=int, low=0, high=5) + x = x.to_global(placement=placement, sbp=sbp) + x = x.to("cpu") + flow_input = x.oneflow.detach() + torch_input = x.pytorch.detach() + + for op in test_compute_op_list: + if op not in ["**"]: + random_numpy = np.random.randint(1, 30000, size=list(flow_input.shape)) + else: + random_numpy = np.random.randint(1, 5, size=list(flow_input.shape)) + + z_flow = eval(f"flow_input {op} random_numpy") + z_torch = eval(f"torch_input {op} random_numpy") + test_case.assertTrue(np.allclose(z_flow.numpy(), z_torch.numpy())) + + +class TestGlobalTensorAndNdarrayCompatibility(flow.unittest.TestCase): + @globaltest + def test_tensor_and_ndarray_compatibility(test_case): + # random ndim in range [1,4] + ndim = random(1, 5).to(int).value() + for placement in all_placement(): + for sbp in all_sbp(placement, max_dim=ndim): + do_test_compute_op(test_case, ndim, placement, sbp) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_global_tensor_indexing.py b/python/oneflow/test/tensor/test_global_tensor_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..e290dc672a9c3cdf73e2d783eb9e25a02d081ec7 --- /dev/null +++ b/python/oneflow/test/tensor/test_global_tensor_indexing.py @@ -0,0 +1,993 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +# This test code is referenced from: https://github.com/pytorch/pytorch/blob/cd41c8f032dd06c445bf97fc76fb82008b19afcb/test/test_indexing.py + +import unittest + +import numpy as np + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest + + +def _randint(low, high): + """ + Get a random integer in the range [low, high). + """ + return random(low, high).to(int).value() + + +def _cpu_global_tensor(tensor): + return tensor.to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + + +def _assert_tensor_equal(test_case, tensor1, tensor2, atol=0.0, rtol=0.0): + test_case.assertTrue( + np.allclose(tensor1.numpy(), tensor2.numpy(), atol, rtol), + f"{tensor1.numpy()} vs {tensor2.numpy()}", + ) + + +def global_broadcast_consec(size, start=1): + """ + Generate a arithmetic progression with given size and start value. + """ + sequence = flow.ones([int(np.array(size).prod(0)),]).cumsum(0) + sequence.add_(start - 1) + return _cpu_global_tensor(sequence.view(*size)) + + +def _test_basic_slice(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + + ref_sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, ref_sbp) + + # empty tensor indexing + _assert_tensor_equal( + test_case, + reference[ + _cpu_global_tensor(flow.LongTensor()).to_global( + placement, broadcast_for_placement + ) + ], + flow.empty(0, 8, 8), + atol=0, + rtol=0, + ) + + _assert_tensor_equal( + test_case, reference[0], global_broadcast_consec((8, 8)), atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[1], global_broadcast_consec((8, 8), 65), atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[2], global_broadcast_consec((8, 8), 129), atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0, 1], global_broadcast_consec((8,), 9), atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0:2], global_broadcast_consec((2, 8, 8)), atol=0, rtol=0 + ) + test_case.assertEqual(reference[2, 2, 2].item(), 147) + _assert_tensor_equal( + test_case, reference[:], global_broadcast_consec((8, 8, 8)), atol=0, rtol=0 + ) + + # indexing with Ellipsis + _assert_tensor_equal( + test_case, + reference[..., 2, 2], + flow.tensor([19, 83, 147, 211, 275, 339, 403, 467]), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + reference[0, ..., 2], + flow.tensor([3, 11, 19, 27, 35, 43, 51, 59]), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, reference[..., 2], reference[:, :, 2], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0, ..., 2], reference[0, :, 2], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0, 2, ...], reference[0, 2], atol=0, rtol=0 + ) + test_case.assertEqual(reference[..., 2, 2, 2].item(), 147) + test_case.assertEqual(reference[2, ..., 2, 2].item(), 147) + test_case.assertEqual(reference[2, 2, ..., 2].item(), 147) + test_case.assertEqual(reference[2, 2, 2, ...].item(), 147) + _assert_tensor_equal(test_case, reference[...], reference, atol=0, rtol=0) + + reference_5d = global_broadcast_consec((8, 8, 8, 8, 8)).to_global( + placement, sbp=random_sbp(placement, max_dim=5).value() + ) + _assert_tensor_equal( + test_case, reference_5d[..., 1, 0], reference_5d[:, :, :, 1, 0], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, + reference_5d[2, ..., 1, 0], + reference_5d[2, :, :, 1, 0], + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + reference_5d[2, 1, 0, ..., 1], + reference_5d[2, 1, 0, :, 1], + atol=0, + rtol=0, + ) + _assert_tensor_equal(test_case, reference_5d[...], reference_5d, atol=0, rtol=0) + + # LongTensor indexing + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.LongTensor([2, 4])).to_global( + placement, broadcast_for_placement + ) + _assert_tensor_equal( + test_case, reference[idx], flow.stack([reference[2], reference[4]]) + ) + + # None indexing + _assert_tensor_equal(test_case, reference[2, None], reference[2].unsqueeze(0)) + _assert_tensor_equal( + test_case, reference[2, None, None], reference[2].unsqueeze(0).unsqueeze(0) + ) + _assert_tensor_equal(test_case, reference[2:4, None], reference[2:4].unsqueeze(1)) + _assert_tensor_equal( + test_case, + reference[None, 2, None, None], + reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0), + ) + _assert_tensor_equal( + test_case, + reference[None, 2:5, None, None], + reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2), + ) + + # indexing 0-length slice + _assert_tensor_equal(test_case, flow.empty(0, 8, 8), reference[slice(0)]) + _assert_tensor_equal(test_case, flow.empty(0, 8), reference[slice(0), 2]) + _assert_tensor_equal(test_case, flow.empty(0, 8), reference[2, slice(0)]) + _assert_tensor_equal(test_case, flow.tensor([]), reference[2, 1:1, 2]) + + # indexing with step + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, reference[1:5:2], flow.stack([reference[1], reference[3]], 0) + ) + _assert_tensor_equal( + test_case, + reference[1:6:2], + flow.stack([reference[1], reference[3], reference[5]], 0), + ) + _assert_tensor_equal( + test_case, reference[1:9:4], flow.stack([reference[1], reference[5]], 0) + ) + _assert_tensor_equal( + test_case, + reference[2:4, 1:5:2], + flow.stack([reference[2:4, 1], reference[2:4, 3]], 1), + ) + _assert_tensor_equal( + test_case, + reference[3, 1:6:2], + flow.stack([reference[3, 1], reference[3, 3], reference[3, 5]], 0), + ) + _assert_tensor_equal( + test_case, + reference[None, 2, 1:9:4], + flow.stack([reference[2, 1], reference[2, 5]], 0).unsqueeze(0), + ) + _assert_tensor_equal( + test_case, + reference[:, 2, 1:6:2], + flow.stack([reference[:, 2, 1], reference[:, 2, 3], reference[:, 2, 5]], 1), + ) + + # random check + lst = [ + list(range(i, i + 16)) for i in range(0, 256, 16) + ] # arange(64).reshape(8, 8) + tensor = _cpu_global_tensor(flow.DoubleTensor(lst)) + for _ in range(5): + sbp = random_sbp(placement, max_dim=2).value() + cur_tensor = tensor.to_global(placement, sbp) + + idx1_start = _randint(0, 16) + idx1_end = idx1_start + _randint(1, 16 - idx1_start + 1) + idx1_step = _randint(1, 14) + idx1 = slice(idx1_start, idx1_end, idx1_step) + if _randint(0, 2) == 0: + idx2_start = _randint(0, 16) + idx2_end = idx2_start + _randint(1, 16 - idx2_start + 1) + idx2_step = _randint(1, 14) + idx2 = slice(idx2_start, idx2_end, idx2_step) + lst_indexed = [l[idx2] for l in lst[idx1]] + tensor_indexed = cur_tensor[idx1, idx2] + else: + lst_indexed = lst[idx1] + tensor_indexed = cur_tensor[idx1] + _assert_tensor_equal(test_case, flow.DoubleTensor(lst_indexed), tensor_indexed) + + # error check + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + test_case.assertRaises(RuntimeError, lambda: reference[1:9:0]) + test_case.assertRaises(RuntimeError, lambda: reference[1:9:-1]) + + test_case.assertRaises(IndexError, lambda: reference[1, 1, 1, 1]) + test_case.assertRaises(IndexError, lambda: reference[1, 1, 1, 1:1]) + test_case.assertRaises(IndexError, lambda: reference[3, 3, 3, 3, 3, 3, 3, 3]) + + test_case.assertRaises(IndexError, lambda: reference[0.0]) + test_case.assertRaises(RuntimeError, lambda: reference[0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, :, 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, ..., 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, :, 0.0]) + + +def _test_advanced_indexing(test_case, placement, dtype): + broadcast_for_placement = [flow.sbp.broadcast] * len(placement.ranks.shape) + + # pick a random valid indexer type + def ri(indices): + choice = _randint(0, 3) + if choice == 0: + return _cpu_global_tensor(flow.LongTensor(indices)).to_global( + placement, broadcast_for_placement + ) + elif choice == 1: + return list(indices) + else: + return tuple(indices) + + def validate_indexing(x): + _assert_tensor_equal(test_case, x[[0]], global_broadcast_consec((1,))) + _assert_tensor_equal(test_case, x[ri([0]),], global_broadcast_consec((1,))) + _assert_tensor_equal(test_case, x[ri([3]),], global_broadcast_consec((1,), 4)) + _assert_tensor_equal(test_case, x[[2, 3, 4]], global_broadcast_consec((3,), 3)) + _assert_tensor_equal( + test_case, x[ri([2, 3, 4]),], global_broadcast_consec((3,), 3) + ) + _assert_tensor_equal( + test_case, x[ri([0, 2, 4]),], flow.tensor([1, 3, 5], dtype=dtype), + ) + + def validate_setting(x): + x[[0]] = -2 + _assert_tensor_equal(test_case, x[[0]], flow.tensor([-2], dtype=dtype)) + x[[0]] = -1 + _assert_tensor_equal(test_case, x[ri([0]),], flow.tensor([-1], dtype=dtype)) + x[[2, 3, 4]] = 4 + _assert_tensor_equal( + test_case, x[[2, 3, 4]], flow.tensor([4, 4, 4], dtype=dtype) + ) + x[ri([2, 3, 4]),] = 3 + _assert_tensor_equal( + test_case, x[ri([2, 3, 4]),], flow.tensor([3, 3, 3], dtype=dtype), + ) + x[ri([0, 2, 4]),] = _cpu_global_tensor(flow.tensor([5, 4, 3], dtype=dtype)) + _assert_tensor_equal( + test_case, x[ri([0, 2, 4]),], flow.tensor([5, 4, 3], dtype=dtype), + ) + + # 1d tensor and integer index setitem and getitem + sbp = random_sbp(placement, max_dim=1).value() + reference = global_broadcast_consec((8,)).to_global(placement, sbp) + validate_indexing(reference) + validate_setting(reference) + + # reference is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([1, 9, 17], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([1])], + flow.tensor([2, 10, 18], dtype=dtype), + ) + _assert_tensor_equal( + test_case, reference[ri([0]), ri([0])], global_broadcast_consec((1,)) + ) + _assert_tensor_equal( + test_case, reference[ri([2]), ri([1])], global_broadcast_consec((1,), 18) + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0]), ri([0, 1])]], + flow.tensor([1, 2], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 1, 1, 0, 2, 7]), ri([1])]], + flow.tensor([2, 10, 10, 2, 18, 58], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], + flow.tensor([1, 2, 9, 9], dtype=dtype), + ) + + rows = ri([[0, 0], [1, 6]]) + columns = ([0],) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[1, 1], [9, 49]], dtype=dtype), + ) + + rows = ri([[0, 0], [1, 6]]) + columns = ri([6, 0]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[7, 1], [15, 49]], dtype=dtype), + ) + rows = ri([[0, 0], [1, 2]]) + columns = ri([[0, 1], [3, 7]]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[1, 2], [12, 24]], dtype=dtype), + ) + + # setting values + reference[ri([0]), ri([1])] = -1 + _assert_tensor_equal( + test_case, reference[ri([0]), ri([1])], flow.tensor([-1], dtype=dtype), + ) + reference[ri([0, 1, 2]), ri([0])] = _cpu_global_tensor( + flow.tensor([-1, 2, -4], dtype=dtype) + ).to_global(placement, broadcast_for_placement) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([-1, 2, -4], dtype=dtype), + ) + reference[rows, columns] = _cpu_global_tensor( + flow.tensor([[4, 6], [2, 3]], dtype=dtype) + ).to_global(placement, broadcast_for_placement) + _assert_tensor_equal( + test_case, reference[rows, columns], flow.tensor([[4, 6], [2, 3]], dtype=dtype), + ) + + # Tests using less than the number of dims, and ellipsis + # reference is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + reference[ri([0, 2]),], + flow.tensor( + [[1, 2, 3, 4, 5, 6, 7, 8], [17, 18, 19, 20, 21, 22, 23, 24]], dtype=dtype + ), + ) + _assert_tensor_equal( + test_case, + reference[ri([1]), ...], + flow.tensor([[9, 10, 11, 12, 13, 14, 15, 16]], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + reference[..., ri([1])], + flow.tensor([[2], [10], [18], [26], [34], [42], [50], [58]], dtype=dtype), + ) + + # verify too many indices fails + with test_case.assertRaises(IndexError): + reference[ri([1]), ri([0, 2]), ri([3])] + + # test invalid index fails + sbp = random_sbp(placement, max_dim=1).value() + reference = _cpu_global_tensor(flow.empty(8, dtype=dtype)).to_global(placement, sbp) + for err_idx in (10, -11): + with test_case.assertRaisesRegex(IndexError, r"out of bounds"): + reference[err_idx] + + +def _test_combined_indexing(test_case, placement, dtype): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + + def tensor_indices_to_np(tensor, indices): + # convert the flow Tensor to a numpy array + npt = tensor.numpy() + + # convert indices + idxs = tuple( + i.tolist() if isinstance(i, flow.LongTensor) else i for i in indices + ) + + return npt, idxs + + def get_numpy(tensor, indices): + npt, idxs = tensor_indices_to_np(tensor, indices) + + # index and return as a oneflow local Tensor + return flow.tensor(npt[idxs], dtype=dtype) + + def set_numpy(tensor, indices, value): + if not isinstance(value, int): + value = value.numpy() + + npt, idxs = tensor_indices_to_np(tensor, indices) + npt[idxs] = value + return npt + + def assert_get_eq(tensor, indexer): + _assert_tensor_equal(test_case, tensor[indexer], get_numpy(tensor, indexer)) + + def assert_set_eq(tensor, indexer, val): + pyt = tensor.clone() + np_ref = tensor.clone() + pyt[indexer] = val + np_ref = flow.tensor(set_numpy(np_ref, indexer, val), dtype=dtype) + _assert_tensor_equal(test_case, pyt, np_ref) + + def assert_backward_eq(tensor, indexer): + # compare gradient between cpu and cuda + cpu = ( + tensor.float() + .clone() + .detach() + .to_global(placement, broadcast_for_placement) + .requires_grad_() + ) + outcpu = cpu.clone()[indexer] + outcpu.sum().backward() + dev = ( + cpu.detach() + .to_global( + placement, random_sbp(placement, max_dim=len(tensor.shape)).value() + ) + .requires_grad_(True) + ) + outdev = dev[indexer] + outdev.sum().backward() + _assert_tensor_equal(test_case, cpu.grad, dev.grad) + + def get_set_tensor(indexed, indexer): + set_size = indexed[indexer].size() + set_count = indexed[indexer].numel() + set_tensor = _cpu_global_tensor( + flow.arange(set_count, 0, -1).view(set_size).to(dtype) + ).to_global(placement, broadcast_for_placement) + return set_tensor + + # Tensor is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + + indices_to_test = [ + # grab the second, fourth columns + [slice(None), [4, 6]], + # first, third rows, + [[0, 6], slice(None)], + # TODO(wyg): only support getitem but not setitem + # # weird shape + # [slice(None), [[0, 1], + # [2, 3]]], + # negatives + [[-1], [0]], + [[0, 7], [-1]], + [slice(None), [-1]], + ] + + # test getitem + get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]] + get_indices_to_test = indices_to_test + [ + [slice(None), [[0, 1], [2, 3]]] + ] # TODO: test setitem + for indexer in get_indices_to_test: + assert_get_eq(reference, indexer) + if placement.type != "cpu": + assert_backward_eq(reference, indexer) + + # test setitem + for indexer in indices_to_test: + assert_set_eq(reference, indexer, 44) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + + ######################### + # test more dims tensor # + ######################### + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8), 0).float().to_global(placement, sbp) + + indices_to_test = [ + [slice(None), slice(None), [0, 3, 4]], + [slice(None), [2, 4, 5, 7], slice(None)], + [[2, 3], slice(None), slice(None)], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0], [1, 2, 4]], + [slice(None), [0, 1, 3], [4]], + [slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [[0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4], slice(None)], + [[0, 1, 3], [4], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 3]], slice(None)], + [[[0, 1], [2, 3]], [[0]], slice(None)], + [[[2, 1]], [[0, 3], [4, 4]], slice(None)], + [[[2]], [[0, 3], [4, 1]], slice(None)], + # non-contiguous indexing subspace + [[0, 2, 3], slice(None), [1, 3, 4]], + # less dim, ellipsis + [[0, 2],], + [[0, 2], slice(None)], + [[0, 2], Ellipsis], + [[0, 2], slice(None), Ellipsis], + [[0, 2], Ellipsis, slice(None)], + [[0, 2], [1, 3]], + [[0, 2], [1, 3], Ellipsis], + [Ellipsis, [1, 3], [2, 3]], + [Ellipsis, [2, 3, 4]], + [Ellipsis, slice(None), [2, 3, 4]], + [slice(None), Ellipsis, [2, 3, 4]], + # ellipsis counts for nothing + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, slice(None), [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), [0, 3, 4], Ellipsis], + [Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 212) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + if placement.type != "cpu": + assert_backward_eq(reference, indexer) + + sbp = random_sbp(placement, max_dim=4).value() + reference = ( + global_broadcast_consec((8, 8, 8, 8), 0).float().to_global(placement, sbp) + ) + + indices_to_test = [ + [slice(None), slice(None), slice(None), [0, 3, 4]], + [slice(None), slice(None), [2, 4, 5, 7], slice(None)], + [slice(None), [2, 3], slice(None), slice(None)], + [[1, 2], slice(None), slice(None), slice(None)], + [slice(None), slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), slice(None), [0], [1, 2, 4]], + [slice(None), slice(None), [0, 1, 3], [4]], + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [slice(None), [0, 2, 3], [1, 3, 4], slice(None)], + [slice(None), [0], [1, 2, 4], slice(None)], + [slice(None), [0, 1, 3], [4], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)], + [slice(None), [[0, 1], [3, 2]], [[0]], slice(None)], + [slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)], + [slice(None), [[2]], [[0, 3], [4, 2]], slice(None)], + [[0, 1, 2], [1, 3, 4], slice(None), slice(None)], + [[0], [1, 2, 4], slice(None), slice(None)], + [[0, 1, 2], [4], slice(None), slice(None)], + [[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)], + [[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)], + [[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [[[2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 4], [1, 3, 4], [4]], + [slice(None), [0, 1, 3], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 5], [3], [4]], + [slice(None), [0], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1]], + [slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]], + [[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)], + [[2, 0, 1], [1, 2, 3], [4], slice(None)], + [[0, 1, 2], [4], [1, 3, 4], slice(None)], + [[0], [0, 2, 3], [1, 3, 4], slice(None)], + [[0, 2, 1], [3], [4], slice(None)], + [[0], [4], [1, 3, 4], slice(None)], + [[1], [0, 2, 3], [1], slice(None)], + [[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)], + # less dim, ellipsis + [Ellipsis, [0, 3, 4]], + [Ellipsis, slice(None), [0, 3, 4]], + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4], Ellipsis], + [Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4]], + [[0], [1, 2, 4], slice(None)], + [[0], [1, 2, 4], Ellipsis], + [[0], [1, 2, 4], Ellipsis, slice(None)], + [[1],], + [[0, 2, 1], [3], [4]], + [[0, 2, 1], [3], [4], slice(None)], + [[0, 2, 1], [3], [4], Ellipsis], + [Ellipsis, [0, 2, 1], [3], [4]], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + indices_to_test += [ + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]], + [slice(None), slice(None), [[2]], [[0, 3], [4, 4]]], + ] + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + if placement.type != "cpu": + assert_backward_eq(reference, indexer) + + +def _test_single_int(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + v = _cpu_global_tensor(flow.zeros(8, 7, 3)).to_global(placement, sbp) + test_case.assertEqual(v[2].shape, (7, 3)) + test_case.assertEqual(v[6].shape, (7, 3)) + + +def _test_multiple_int(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = _cpu_global_tensor(flow.zeros(8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual(v[4, :, 1].shape, (8,)) + + +def _test_none(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = _cpu_global_tensor(flow.zeros(8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual(v[None].shape, (1, 8, 8, 8)) + test_case.assertEqual(v[:, None].shape, (8, 1, 8, 8)) + test_case.assertEqual(v[:, None, None].shape, (8, 1, 1, 8, 8)) + test_case.assertEqual(v[..., None].shape, (8, 8, 8, 1)) + + +def _test_step(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + v = _cpu_global_tensor(flow.arange(8)).to_global(placement, sbp) + _assert_tensor_equal(test_case, v[::1], v) + test_case.assertEqual(v[::2].tolist(), [0, 2, 4, 6]) + test_case.assertEqual(v[::3].tolist(), [0, 3, 6]) + test_case.assertEqual(v[::11].tolist(), [0]) + test_case.assertEqual(v[1:6:2].tolist(), [1, 3, 5]) + + +def _test_step_assignment(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + v = _cpu_global_tensor(flow.zeros(8, 8)).to_global(placement, sbp) + v[0, 1::2] = _cpu_global_tensor(flow.tensor([3.0, 4.0, 5.0, 6.0])).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(v[0].tolist(), [0.0, 3.0, 0.0, 4.0, 0.0, 5.0, 0.0, 6.0]) + test_case.assertEqual(v[1:].sum(), 0) + + +def _test_bool_indices(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=3).value() + v = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + boolIndices = _cpu_global_tensor( + flow.tensor( + [True, False, True, True, False, False, False, True], dtype=flow.bool + ) + ).to_global(placement, broadcast_for_placement) + test_case.assertEqual(v[boolIndices].shape, (4, 8, 8)) + _assert_tensor_equal( + test_case, v[boolIndices], flow.stack([v[0], v[2], v[3], v[7]]) + ) + + +def _test_multiple_bool_indices(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + v = global_broadcast_consec((8, 8, 4)).to_global(placement, sbp) + # NOTE: these broadcast together and are transposed to the first dim + mask1 = _cpu_global_tensor( + flow.tensor([1, 0, 1, 0, 0, 1, 0, 0], dtype=flow.bool) + ).to_global(placement, broadcast_for_placement) + mask2 = _cpu_global_tensor(flow.tensor([1, 1, 1, 0], dtype=flow.bool)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(v[mask1, :, mask2].shape, (3, 8)) + + +def _test_int_indices(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual(v[[0, 4, 2]].shape, (3, 8, 8)) + test_case.assertEqual(v[:, [0, 4, 2]].shape, (8, 3, 8)) + test_case.assertEqual(v[:, [[0, 1], [4, 3]]].shape, (8, 2, 2, 8)) + + +def _test_int_indices2d(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + rows = _cpu_global_tensor(flow.tensor([[0, 0], [6, 3]])).to_global( + placement, broadcast_for_placement + ) + columns = _cpu_global_tensor(flow.tensor([[0, 2], [0, 7]])).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(x[rows, columns].tolist(), [[1, 3], [49, 32]]) + + +def _test_int_indices_broadcast(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + rows = _cpu_global_tensor(flow.tensor([0, 7])).to_global( + placement, broadcast_for_placement + ) + columns = _cpu_global_tensor(flow.tensor([7, 2])).to_global( + placement, broadcast_for_placement + ) + result = x[rows[:, None], columns] + test_case.assertEqual(result.tolist(), [[8, 3], [64, 59]]) + + +def _test_empty_index(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + # TODO:(wangyinggang): masked_fill support sbp:partial_sum + sbp = random_sbp(placement, max_dim=2, except_partial_sum=True).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.tensor([], dtype=flow.long)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(x[idx].numel(), 0) + + # empty assignment should have no effect but not throw an exception + y = x.clone() + y[idx] = -1 + _assert_tensor_equal(test_case, x, y) + + mask = _cpu_global_tensor(flow.zeros(8, 8).to(flow.bool)).to_global( + placement, broadcast_for_placement + ) + y[mask] = -1 + _assert_tensor_equal(test_case, x, y) + + +def _test_empty_ndim_index(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=1).value() + x = global_broadcast_consec((8,)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + x[ + _cpu_global_tensor(flow.empty(0, 2, dtype=flow.int64)).to_global( + placement, broadcast_for_placement + ) + ], + flow.empty(0, 2), + ) + + sbp = random_sbp(placement, max_dim=1).value() + x = _cpu_global_tensor(flow.empty(8, 0)).to_global(placement, sbp) + test_case.assertEqual(x[[1, 2]].shape, (2, 0)) + test_case.assertEqual(x[[], []].shape, (0,)) + test_case.assertEqual(x[[[]]].shape, (0, 0)) + test_case.assertEqual(x[[[[]]]].shape, (1, 0, 0)) + test_case.assertEqual(x[[1], []].shape, (0,)) + test_case.assertEqual(x[[], [2]].shape, (0,)) + with test_case.assertRaisesRegex(IndexError, "for dimension with size 0"): + x[:, [0, 1]] + + +def _test_empty_ndim_index_bool(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=1).value() + x = global_broadcast_consec((8,)).to_global(placement, sbp) + test_case.assertRaises( + IndexError, + lambda: x[ + _cpu_global_tensor(flow.empty(0, 2, dtype=flow.uint8)).to_global( + placement, broadcast_for_placement + ) + ], + ) + + +def _test_empty_slice(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + x = global_broadcast_consec((8, 8, 8, 8)).to_global(placement, sbp) + y = x[:, :, :, 1] + z = y[:, 1:1, :] + test_case.assertEqual((8, 0, 8), z.shape) + + +def _test_index_getitem_copy_bools_slices(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + false = _cpu_global_tensor(flow.tensor(0, dtype=flow.uint8)).to_global( + placement, broadcast_for_placement + ) + + sbp = random_sbp(placement, max_dim=1).value() + tensor = global_broadcast_consec((8, 8)).to_global(placement, sbp) + + _assert_tensor_equal(test_case, flow.empty(0, *tensor.shape), tensor[False]) + _assert_tensor_equal(test_case, flow.empty(0, *tensor.shape), tensor[false]) + + +def _test_setitem_scalars(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + zero = _cpu_global_tensor(flow.tensor(0, dtype=flow.int64)).to_global( + placement, broadcast_for_placement + ) + + # non-scalar indexed with scalars + a = global_broadcast_consec((8, 8)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + a_set_with_number = a.clone() + a_set_with_scalar = a.clone() + b = global_broadcast_consec((8,), 233).to_global( + placement, random_sbp(placement, max_dim=1).value() + ) + + a_set_with_number[0] = b + a_set_with_scalar[zero] = b + _assert_tensor_equal(test_case, a_set_with_number, a_set_with_scalar) + a[1, zero] = 7.7 + value = a[1, 0].numpy() + test_case.assertEqual(np.array(7.7, dtype=value.dtype), value) + + np_x = np.zeros((8, 8)) + np_x[0, 6] = 1.0 + x = _cpu_global_tensor(flow.tensor(np_x)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + x[0, 6] = 1.0 + test_case.assertEqual(x.numpy().all(), np_x.all()) + + # scalar indexed with scalars + r = _cpu_global_tensor(flow.tensor(1.0)).to_global( + placement, random_sbp(placement, max_dim=0).value() + ) + with test_case.assertRaises(IndexError): + r[:] = 8.8 + with test_case.assertRaises(IndexError): + r[zero] = 8.8 + r[...] = 9.9 + test_case.assertEqual(r, 9.9) + + # scalar indexed with oneflow.Size([1]) + np_x = np.zeros((8, 8)) + np_x[0, 6] = np.ones(1) + x = _cpu_global_tensor(flow.tensor(np_x)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + x[0, 0] = _cpu_global_tensor(flow.ones(1).to(flow.float64)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(x.numpy().all(), np_x.all()) + + +def _test_basic_advanced_combined(test_case, placement): + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + _assert_tensor_equal(test_case, x[1:2, 3:5], x[1:2, [3, 4]]) + test_case.assertEqual(x[1:2, 1:3].tolist(), [[10, 11]]) + + # Check that it is a copy + unmodified = x.clone() + x[1:2, [1, 2]].zero_() + _assert_tensor_equal(test_case, x, unmodified) + + # But assignment should modify the original + unmodified = x.clone() + x[1:2, [1, 2]] = 0 + test_case.assertFalse(np.array_equal(x.numpy(), unmodified.numpy())) + + +def _test_ellipsis_tensor(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.tensor([0, 7])).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual( + x[..., idx].tolist(), + [[1, 8], [9, 16], [17, 24], [25, 32], [33, 40], [41, 48], [49, 56], [57, 64]], + ) + test_case.assertEqual( + x[idx, ...].tolist(), + [[1, 2, 3, 4, 5, 6, 7, 8], [57, 58, 59, 60, 61, 62, 63, 64]], + ) + + # Test scalar ellipsis getitem + x_scalar = _cpu_global_tensor(flow.tensor(9.9)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(x_scalar[...], 9.9) + + +class TestGlobalIndexing(flow.unittest.TestCase): + @globaltest + def test_global_slice(test_case): + for placement in all_placement(): + for _ in range(5): + _test_basic_slice(test_case, placement) + _test_advanced_indexing(test_case, placement, dtype=flow.float32) + _test_combined_indexing(test_case, placement, dtype=flow.float32) + _test_single_int(test_case, placement) + _test_multiple_int(test_case, placement) + _test_none(test_case, placement) + _test_step(test_case, placement) + _test_step_assignment(test_case, placement) + _test_bool_indices(test_case, placement) + _test_multiple_bool_indices(test_case, placement) + _test_int_indices(test_case, placement) + _test_int_indices2d(test_case, placement) + _test_int_indices_broadcast(test_case, placement) + _test_empty_index(test_case, placement) + _test_empty_ndim_index(test_case, placement) + _test_empty_ndim_index_bool(test_case, placement) + _test_empty_slice(test_case, placement) + _test_index_getitem_copy_bools_slices(test_case, placement) + _test_setitem_scalars(test_case, placement) + _test_basic_advanced_combined(test_case, placement) + _test_ellipsis_tensor(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_lazy_tensor_indexing.py b/python/oneflow/test/tensor/test_lazy_tensor_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..197928688d89ba6ec10e2c195a42c2b5afad342e --- /dev/null +++ b/python/oneflow/test/tensor/test_lazy_tensor_indexing.py @@ -0,0 +1,950 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest + +import numpy as np + +import oneflow as flow +from oneflow.test_utils.automated_test_util import * +import oneflow.unittest +import oneflow.framework.session_context as session_ctx + + +def get_graph_output(*args, func): + def generate_graph(func): + class Graph(flow.nn.Graph): + def __init__(self): + super().__init__() + + def build(self, *args): + return func(*args) + + return Graph() + + graph = generate_graph(func) + return graph(*args) + + +def setitem_and_return(ref, idx, value): + ref[idx] = value + return ref + + +def _randint(low, high): + """ + Get a random integer in the range [low, high). + """ + return random(low, high).to(int).value() + + +def _cpu_global_tensor(tensor): + return tensor.to_global(flow.placement.all("cpu"), flow.sbp.broadcast) + + +def _assert_tensor_equal(test_case, tensor1, tensor2, atol=0.0, rtol=0.0): + test_case.assertTrue( + np.allclose(tensor1.numpy(), tensor2.numpy(), atol, rtol), + f"{tensor1.numpy()} vs {tensor2.numpy()}", + ) + + +def global_broadcast_consec(size, start=1): + """ + Generate a arithmetic progression with given size and start value. + """ + sequence = flow.ones([int(np.array(size).prod(0)),]).cumsum(0) + sequence.add_(start - 1) + return _cpu_global_tensor(sequence.view(*size)) + + +def _test_basic_slice(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + + ref_sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, ref_sbp) + + # empty tensor indexing + empty_index = _cpu_global_tensor(flow.LongTensor()).to_global( + placement, broadcast_for_placement + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[empty_index]), + flow.empty(0, 8, 8), + atol=0, + rtol=0, + ) + + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[1]), + global_broadcast_consec((8, 8), 65), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[0, 1]), + global_broadcast_consec((8,), 9), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[0:2]), + global_broadcast_consec((2, 8, 8)), + atol=0, + rtol=0, + ) + test_case.assertEqual( + get_graph_output(reference, func=lambda x: x[2, 2, 2]).item(), 147 + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[:]), + global_broadcast_consec((8, 8, 8)), + atol=0, + rtol=0, + ) + + # indexing with Ellipsis + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[..., 2, 2]), + flow.tensor([19, 83, 147, 211, 275, 339, 403, 467]), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[0, ..., 2]), + flow.tensor([3, 11, 19, 27, 35, 43, 51, 59]), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[0, 2, ...]), + reference[0, 2], + atol=0, + rtol=0, + ) + + reference_5d = global_broadcast_consec((8, 8, 8, 8, 8)).to_global( + placement, sbp=random_sbp(placement, max_dim=5).value() + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference_5d, func=lambda x: x[2, ..., 1, 0]), + get_graph_output(reference_5d, func=lambda x: x[2, :, :, 1, 0]), + atol=0, + rtol=0, + ) + + # LongTensor indexing + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.LongTensor([2, 4])).to_global( + placement, broadcast_for_placement + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, idx, func=lambda x, y: x[y]), + get_graph_output(reference, func=lambda x: flow.stack([x[2], x[4]])), + ) + + # None indexing + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[None, 2, None, None]), + reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[None, 2:5, None, None]), + reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2), + ) + + # indexing 0-length slice + _assert_tensor_equal( + test_case, + flow.empty(0, 8, 8), + get_graph_output(reference, func=lambda x: x[slice(0)]), + ) + _assert_tensor_equal( + test_case, + flow.empty(0, 8), + get_graph_output(reference, func=lambda x: x[2, slice(0)]), + ) + _assert_tensor_equal( + test_case, + flow.tensor([]), + get_graph_output(reference, func=lambda x: x[2, 1:1, 2]), + ) + + # indexing with step + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[2:4, 1:5:2]), + get_graph_output( + reference, func=lambda x: flow.stack([x[2:4, 1], x[2:4, 3]], 1) + ), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[3, 1:6:2]), + get_graph_output( + reference, func=lambda x: flow.stack([x[3, 1], x[3, 3], x[3, 5]], 0) + ), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[None, 2, 1:9:4]), + get_graph_output( + reference, func=lambda x: flow.stack([x[2, 1], x[2, 5]], 0).unsqueeze(0) + ), + ) + + +def _test_advanced_indexing(test_case, placement, dtype): + broadcast_for_placement = [flow.sbp.broadcast] * len(placement.ranks.shape) + + # pick a random valid indexer type + def ri(indices): + choice = _randint(0, 3) + if choice == 0: + return flow.LongTensor( + indices, placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast, + ).to_global(placement, broadcast_for_placement) + elif choice == 1: + return list(indices) + else: + return tuple(indices) + + def validate_indexing(x): + _assert_tensor_equal( + test_case, + get_graph_output(x, func=lambda x: x[ri([3]),]), + global_broadcast_consec((1,), 4), + ) + _assert_tensor_equal( + test_case, + get_graph_output(x, func=lambda x: x[ri([2, 3, 4]),]), + global_broadcast_consec((3,), 3), + ) + + def validate_setting(x): + # x[[0]] = -2 + x = get_graph_output(x, func=lambda x: setitem_and_return(x, [0], -2)) + _assert_tensor_equal(test_case, x[0], flow.tensor([-2], dtype=dtype)) + # x[[0]] = -1 + x = get_graph_output(x, func=lambda x: setitem_and_return(x, [0], -1)) + _assert_tensor_equal(test_case, x[0], flow.tensor([-1], dtype=dtype)) + # x[[2, 3, 4]] = 4 + x = get_graph_output(x, func=lambda x: setitem_and_return(x, [2, 3, 4], 4)) + _assert_tensor_equal( + test_case, x[[2, 3, 4]], flow.tensor([4, 4, 4], dtype=dtype) + ) + # x[ri([2, 3, 4]),] = 3 + x = get_graph_output( + x, func=lambda x: setitem_and_return(x, [ri([2, 3, 4]),], 3) + ) + _assert_tensor_equal( + test_case, x[[2, 3, 4]], flow.tensor([3, 3, 3], dtype=dtype), + ) + # x[ri([0, 2, 4]),] = _cpu_global_tensor(flow.tensor([5, 4, 3], dtype=dtype)) + value_tensor = _cpu_global_tensor(flow.tensor([5, 4, 3], dtype=dtype)) + x = get_graph_output( + x, func=lambda x: setitem_and_return(x, [ri([0, 2, 4]),], value_tensor) + ) + _assert_tensor_equal( + test_case, x[[0, 2, 4]], flow.tensor([5, 4, 3], dtype=dtype), + ) + + # 1d tensor and integer index setitem and getitem + sbp = random_sbp(placement, max_dim=1).value() + reference = global_broadcast_consec((8,)).to_global(placement, sbp) + validate_indexing(reference) + validate_setting(reference) + + # reference is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0, 1, 2]), ri([0])]), + flow.tensor([1, 9, 17], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0, 1, 2]), ri([1])]), + flow.tensor([2, 10, 18], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0]), ri([0])]), + global_broadcast_consec((1,)), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([2]), ri([1])]), + global_broadcast_consec((1,), 18), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0, 0]), ri([0, 1])]), + flow.tensor([1, 2], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0, 1, 1, 0, 2, 7]), ri([1])]), + flow.tensor([2, 10, 10, 2, 18, 58], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + get_graph_output( + reference, func=lambda x: x[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])] + ), + flow.tensor([1, 2, 9, 9], dtype=dtype), + ) + + rows = ri([[0, 0], [1, 6]]) + columns = ([0],) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[rows, columns]), + flow.tensor([[1, 1], [9, 49]], dtype=dtype), + ) + + rows = ri([[0, 0], [1, 6]]) + columns = ri([6, 0]) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[rows, columns]), + flow.tensor([[7, 1], [15, 49]], dtype=dtype), + ) + rows = ri([[0, 0], [1, 2]]) + columns = ri([[0, 1], [3, 7]]) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[rows, columns]), + flow.tensor([[1, 2], [12, 24]], dtype=dtype), + ) + + # setting values + # reference[ri([0]), ri([1])] = -1 + reference = get_graph_output( + reference, func=lambda x: setitem_and_return(x, [ri([0]), ri([1])], -1) + ) + _assert_tensor_equal( + test_case, reference[ri([0]), ri([1])], flow.tensor([-1], dtype=dtype), + ) + + value_tensor = _cpu_global_tensor(flow.tensor([-1, 2, -4], dtype=dtype)).to_global( + placement, broadcast_for_placement + ) + reference = get_graph_output( + reference, + func=lambda x: setitem_and_return(x, [ri([0, 1, 2]), ri([0])], value_tensor), + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([-1, 2, -4], dtype=dtype), + ) + + value_tensor = _cpu_global_tensor( + flow.tensor([[4, 6], [2, 3]], dtype=dtype) + ).to_global(placement, broadcast_for_placement) + reference = get_graph_output( + reference, func=lambda x: setitem_and_return(x, [rows, columns], value_tensor) + ) + _assert_tensor_equal( + test_case, reference[rows, columns], flow.tensor([[4, 6], [2, 3]], dtype=dtype), + ) + + # Tests using less than the number of dims, and ellipsis + # reference is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([0, 2]),]), + flow.tensor( + [[1, 2, 3, 4, 5, 6, 7, 8], [17, 18, 19, 20, 21, 22, 23, 24]], dtype=dtype + ), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[ri([1]), ...]), + flow.tensor([[9, 10, 11, 12, 13, 14, 15, 16]], dtype=dtype), + ) + _assert_tensor_equal( + test_case, + get_graph_output(reference, func=lambda x: x[..., ri([1])]), + flow.tensor([[2], [10], [18], [26], [34], [42], [50], [58]], dtype=dtype), + ) + + +def _test_combined_indexing(test_case, placement, dtype): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + + def tensor_indices_to_np(tensor, indices): + # convert the flow Tensor to a numpy array + npt = tensor.numpy() + + # convert indices + idxs = tuple( + i.tolist() if isinstance(i, flow.LongTensor) else i for i in indices + ) + + return npt, idxs + + def get_numpy(tensor, indices): + npt, idxs = tensor_indices_to_np(tensor, indices) + + # index and return as a oneflow local Tensor + return flow.tensor(npt[idxs], dtype=dtype) + + def set_numpy(tensor, indices, value): + if not isinstance(value, int): + value = value.numpy() + + npt, idxs = tensor_indices_to_np(tensor, indices) + npt[idxs] = value + return npt + + def assert_get_eq(tensor, indexer): + _assert_tensor_equal( + test_case, + get_graph_output(tensor, func=lambda x: x[indexer]), + get_numpy(tensor, indexer), + ) + + def assert_set_eq(tensor, indexer, val): + pyt = tensor.clone() + np_ref = tensor.clone() + pyt = get_graph_output(pyt, func=lambda x: setitem_and_return(x, indexer, val)) + np_ref = flow.tensor(set_numpy(np_ref, indexer, val), dtype=dtype) + _assert_tensor_equal(test_case, pyt, np_ref) + + def get_set_tensor(indexed, indexer): + set_size = indexed[indexer].size() + set_count = indexed[indexer].numel() + set_tensor = _cpu_global_tensor( + flow.arange(set_count, 0, -1).view(set_size).to(dtype) + ).to_global(placement, broadcast_for_placement) + return set_tensor + + # Tensor is 1 2 3 4 5 6 7 8 + # 9 10 11 12 13 14 15 16 + # 17 18 19 20 21 22 23 24 + # 25 26 27 28 29 30 31 32 + # 33 34 35 36 37 38 39 40 + # 41 42 43 44 45 46 47 48 + # 49 50 51 52 53 54 55 56 + # 57 58 59 60 61 62 63 64 + sbp = random_sbp(placement, max_dim=2).value() + reference = global_broadcast_consec((8, 8)).to_global(placement, sbp) + + indices_to_test = [ + # grab the second, fourth columns + [slice(None), [4, 6]], + # first, third rows, + [[0, 6], slice(None)], + # TODO(wyg): only support getitem but not setitem + # # weird shape + # [slice(None), [[0, 1], + # [2, 3]]], + # negatives + [[-1], [0]], + [[0, 7], [-1]], + [slice(None), [-1]], + ] + + # test getitem + get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]] + get_indices_to_test = indices_to_test + [ + [slice(None), [[0, 1], [2, 3]]] + ] # TODO: test setitem + for indexer in get_indices_to_test: + assert_get_eq(reference, indexer) + + # test setitem + for indexer in indices_to_test: + assert_set_eq(reference, indexer, 44) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + + ######################### + # test more dims tensor # + ######################### + sbp = random_sbp(placement, max_dim=3).value() + reference = global_broadcast_consec((8, 8, 8), 0).float().to_global(placement, sbp) + + indices_to_test = [ + [slice(None), slice(None), [0, 3, 4]], + [slice(None), [2, 4, 5, 7], slice(None)], + [[2, 3], slice(None), slice(None)], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0], [1, 2, 4]], + [slice(None), [0, 1, 3], [4]], + [slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [[0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4], slice(None)], + [[0, 1, 3], [4], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 3]], slice(None)], + [[[0, 1], [2, 3]], [[0]], slice(None)], + [[[2, 1]], [[0, 3], [4, 4]], slice(None)], + [[[2]], [[0, 3], [4, 1]], slice(None)], + # non-contiguous indexing subspace + [[0, 2, 3], slice(None), [1, 3, 4]], + # less dim, ellipsis + [[0, 2],], + [[0, 2], slice(None)], + [[0, 2], Ellipsis], + [[0, 2], slice(None), Ellipsis], + [[0, 2], Ellipsis, slice(None)], + [[0, 2], [1, 3]], + [[0, 2], [1, 3], Ellipsis], + [Ellipsis, [1, 3], [2, 3]], + [Ellipsis, [2, 3, 4]], + [Ellipsis, slice(None), [2, 3, 4]], + [slice(None), Ellipsis, [2, 3, 4]], + # ellipsis counts for nothing + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, slice(None), [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), [0, 3, 4], Ellipsis], + [Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 212) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + + sbp = random_sbp(placement, max_dim=4).value() + reference = ( + global_broadcast_consec((8, 8, 8, 8), 0).float().to_global(placement, sbp) + ) + + indices_to_test = [ + [slice(None), slice(None), slice(None), [0, 3, 4]], + [slice(None), slice(None), [2, 4, 5, 7], slice(None)], + [slice(None), [2, 3], slice(None), slice(None)], + [[1, 2], slice(None), slice(None), slice(None)], + [slice(None), slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), slice(None), [0], [1, 2, 4]], + [slice(None), slice(None), [0, 1, 3], [4]], + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [slice(None), [0, 2, 3], [1, 3, 4], slice(None)], + [slice(None), [0], [1, 2, 4], slice(None)], + [slice(None), [0, 1, 3], [4], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)], + [slice(None), [[0, 1], [3, 2]], [[0]], slice(None)], + [slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)], + [slice(None), [[2]], [[0, 3], [4, 2]], slice(None)], + [[0, 1, 2], [1, 3, 4], slice(None), slice(None)], + [[0], [1, 2, 4], slice(None), slice(None)], + [[0, 1, 2], [4], slice(None), slice(None)], + [[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)], + [[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)], + [[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [[[2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 4], [1, 3, 4], [4]], + [slice(None), [0, 1, 3], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 5], [3], [4]], + [slice(None), [0], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1]], + [slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]], + [[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)], + [[2, 0, 1], [1, 2, 3], [4], slice(None)], + [[0, 1, 2], [4], [1, 3, 4], slice(None)], + [[0], [0, 2, 3], [1, 3, 4], slice(None)], + [[0, 2, 1], [3], [4], slice(None)], + [[0], [4], [1, 3, 4], slice(None)], + [[1], [0, 2, 3], [1], slice(None)], + [[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)], + # less dim, ellipsis + [Ellipsis, [0, 3, 4]], + [Ellipsis, slice(None), [0, 3, 4]], + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4], Ellipsis], + [Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4]], + [[0], [1, 2, 4], slice(None)], + [[0], [1, 2, 4], Ellipsis], + [[0], [1, 2, 4], Ellipsis, slice(None)], + [[1],], + [[0, 2, 1], [3], [4]], + [[0, 2, 1], [3], [4], slice(None)], + [[0, 2, 1], [3], [4], Ellipsis], + [Ellipsis, [0, 2, 1], [3], [4]], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + indices_to_test += [ + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]], + [slice(None), slice(None), [[2]], [[0, 3], [4, 4]]], + ] + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + + +def _test_single_int(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + v = _cpu_global_tensor(flow.zeros(8, 7, 3)).to_global(placement, sbp) + test_case.assertEqual(get_graph_output(v, func=lambda x: x[2]).shape, (7, 3)) + + +def _test_multiple_int(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = _cpu_global_tensor(flow.zeros(8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual(get_graph_output(v, func=lambda x: x[4, :, 1]).shape, (8,)) + + +def _test_none(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = _cpu_global_tensor(flow.zeros(8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[None]).shape, (1, 8, 8, 8) + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[:, None]).shape, (8, 1, 8, 8) + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[:, None, None]).shape, (8, 1, 1, 8, 8) + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[..., None]).shape, (8, 8, 8, 1) + ) + + +def _test_step(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + v = _cpu_global_tensor(flow.arange(8)).to_global(placement, sbp) + _assert_tensor_equal(test_case, v[::1], v) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[::2]).tolist(), [0, 2, 4, 6] + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[::3]).tolist(), [0, 3, 6] + ) + test_case.assertEqual(get_graph_output(v, func=lambda x: x[::11]).tolist(), [0]) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[1:6:2]).tolist(), [1, 3, 5] + ) + + +def _test_step_assignment(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + v = _cpu_global_tensor(flow.zeros(8, 8)).to_global(placement, sbp) + value_tensor = _cpu_global_tensor(flow.tensor([3.0, 4.0, 5.0, 6.0])).to_global( + placement, broadcast_for_placement + ) + v = get_graph_output( + v, func=lambda x: setitem_and_return(x, [0, slice(1, None, 2)], value_tensor) + ) + test_case.assertEqual(v[0].tolist(), [0.0, 3.0, 0.0, 4.0, 0.0, 5.0, 0.0, 6.0]) + test_case.assertEqual(v[1:].sum(), 0) + + +def _test_multiple_bool_indices(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + v = global_broadcast_consec((8, 8, 4)).to_global(placement, sbp) + # NOTE: these broadcast together and are transposed to the first dim + mask1 = _cpu_global_tensor( + flow.tensor([1, 0, 1, 0, 0, 1, 0, 0], dtype=flow.bool) + ).to_global(placement, broadcast_for_placement) + mask2 = _cpu_global_tensor(flow.tensor([1, 1, 1, 0], dtype=flow.bool)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(v[mask1, :, mask2].shape, (3, 8)) + + +def _test_int_indices(test_case, placement): + sbp = random_sbp(placement, max_dim=3).value() + v = global_broadcast_consec((8, 8, 8)).to_global(placement, sbp) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[[0, 4, 2]]).shape, (3, 8, 8) + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[:, [0, 4, 2]]).shape, (8, 3, 8) + ) + test_case.assertEqual( + get_graph_output(v, func=lambda x: x[:, [[0, 1], [4, 3]]]).shape, (8, 2, 2, 8) + ) + + +def _test_int_indices2d(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + rows = _cpu_global_tensor(flow.tensor([[0, 0], [6, 3]])).to_global( + placement, broadcast_for_placement + ) + columns = _cpu_global_tensor(flow.tensor([[0, 2], [0, 7]])).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual( + get_graph_output(x, func=lambda x: x[rows, columns]).tolist(), + [[1, 3], [49, 32]], + ) + + +def _test_int_indices_broadcast(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + rows = _cpu_global_tensor(flow.tensor([0, 7])).to_global( + placement, broadcast_for_placement + ) + columns = _cpu_global_tensor(flow.tensor([7, 2])).to_global( + placement, broadcast_for_placement + ) + result = get_graph_output(x, func=lambda x: x[rows[:, None], columns]) + test_case.assertEqual(result.tolist(), [[8, 3], [64, 59]]) + + +def _test_empty_index(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.tensor([], dtype=flow.long)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[idx]).numel(), 0) + + # empty assignment should have no effect but not throw an exception + y = x.clone() + y = get_graph_output(y, func=lambda x: setitem_and_return(x, idx, -1)) + _assert_tensor_equal(test_case, x, y) + + # TODO(wyg): support eager bool indices tensor in lazy mode + # mask = _cpu_global_tensor(flow.zeros(8, 8).to(flow.bool)).to_global( + # placement, broadcast_for_placement + # ) + # y = get_graph_output(y, func=lambda x: setitem_and_return(x, mask, -1)) + # _assert_tensor_equal(test_case, x, y) + + +def _test_empty_ndim_index(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=1).value() + x = global_broadcast_consec((8,)).to_global(placement, sbp) + index = _cpu_global_tensor(flow.empty(0, 2, dtype=flow.int64)).to_global( + placement, broadcast_for_placement + ) + _assert_tensor_equal( + test_case, get_graph_output(x, func=lambda x: x[index]), flow.empty(0, 2), + ) + + sbp = random_sbp(placement, max_dim=1).value() + x = _cpu_global_tensor(flow.empty(8, 0)).to_global(placement, sbp) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[[1, 2]]).shape, (2, 0)) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[[], []]).shape, (0,)) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[[[]]]).shape, (0, 0)) + test_case.assertEqual( + get_graph_output(x, func=lambda x: x[[[[]]]]).shape, (1, 0, 0) + ) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[[1], []]).shape, (0,)) + test_case.assertEqual(get_graph_output(x, func=lambda x: x[[], [2]]).shape, (0,)) + + +def _test_empty_slice(test_case, placement): + sbp = random_sbp(placement, max_dim=1).value() + x = global_broadcast_consec((8, 8, 8, 8)).to_global(placement, sbp) + y = get_graph_output(x, func=lambda x: x[:, 1:1, :, 1]) + test_case.assertEqual((8, 0, 8), y.shape) + + +def _test_setitem_scalars(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + zero = _cpu_global_tensor(flow.tensor(0, dtype=flow.int64)).to_global( + placement, broadcast_for_placement + ) + + # non-scalar indexed with scalars + a = global_broadcast_consec((8, 8)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + a_set_with_number = a.clone() + a_set_with_scalar = a.clone() + b = global_broadcast_consec((8,), 233).to_global( + placement, random_sbp(placement, max_dim=1).value() + ) + + a_set_with_number = get_graph_output( + a_set_with_number, func=lambda x: setitem_and_return(x, 0, b) + ) + a_set_with_scalar = get_graph_output( + a_set_with_scalar, func=lambda x: setitem_and_return(x, zero, b) + ) + _assert_tensor_equal(test_case, a_set_with_number, a_set_with_scalar) + + # a[1, zero] = 7.7 + value = get_graph_output( + a, func=lambda x: setitem_and_return(x, [1, zero], 7.7) + ).numpy() + test_case.assertEqual(np.array(7.7, dtype=value.dtype), value[1, 0]) + + np_x = np.zeros((8, 8)) + np_x[0, 6] = 1.0 + x = _cpu_global_tensor(flow.tensor(np_x)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + # x[0, 6] = 1.0 + res = get_graph_output(x, func=lambda x: setitem_and_return(x, [0, 6], 1.0)) + test_case.assertEqual(res.numpy().all(), np_x.all()) + + # scalar indexed with scalars + r = _cpu_global_tensor(flow.tensor(1.0)).to_global( + placement, random_sbp(placement, max_dim=0).value() + ) + # r[...] = 9.9 + res = get_graph_output(r, func=lambda x: setitem_and_return(x, [...], 9.9)) + test_case.assertEqual(res, 9.9) + + # scalar indexed with oneflow.Size([1]) + np_x = np.zeros((8, 8)) + np_x[0, 6] = np.ones(1) + x = _cpu_global_tensor(flow.tensor(np_x)).to_global( + placement, random_sbp(placement, max_dim=2).value() + ) + value_tensor = _cpu_global_tensor(flow.ones(1).to(flow.float64)).to_global( + placement, broadcast_for_placement + ) + # x[0, 0] = value + res = get_graph_output( + x, func=lambda x: setitem_and_return(x, [0, 0], value_tensor) + ) + test_case.assertEqual(res.numpy().all(), np_x.all()) + + +def _test_ellipsis_tensor(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=2).value() + x = global_broadcast_consec((8, 8)).to_global(placement, sbp) + idx = _cpu_global_tensor(flow.tensor([0, 7])).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual( + get_graph_output(x, func=lambda x: x[..., idx]).tolist(), + [[1, 8], [9, 16], [17, 24], [25, 32], [33, 40], [41, 48], [49, 56], [57, 64]], + ) + test_case.assertEqual( + get_graph_output(x, func=lambda x: x[idx, ...]).tolist(), + [[1, 2, 3, 4, 5, 6, 7, 8], [57, 58, 59, 60, 61, 62, 63, 64]], + ) + + # Test scalar ellipsis getitem + x_scalar = _cpu_global_tensor(flow.tensor(9.9)).to_global( + placement, broadcast_for_placement + ) + test_case.assertEqual(get_graph_output(x_scalar, func=lambda x: x[...]), 9.9) + + +def _test_bool_indices(test_case, placement): + broadcast_for_placement = [flow.sbp.broadcast,] * len(placement.ranks.shape) + sbp = random_sbp(placement, max_dim=1, except_partial_sum=True).value() + v = global_broadcast_consec((8,)).to_global(placement, sbp) + boolIndices = _cpu_global_tensor( + flow.tensor( + [True, False, True, True, False, False, False, True], dtype=flow.bool + ) + ).to_global(placement, sbp) + _assert_tensor_equal( + test_case, + get_graph_output(v, func=lambda x: setitem_and_return(x, boolIndices, 6.6)), + flow.tensor([6.6, 2.0, 6.6, 6.6, 5.0, 6.0, 7.0, 6.6]), + ) + + +class TestGlobalIndexing(flow.unittest.TestCase): + @globaltest + @unittest.skip( + "TODO(wyg, zwx): test these cases after supporting clear session interface to avoid" + "geting 'stream_id.h:33 Check failed: stream_index <= kMaxStreamIndex (4096 vs. 4095)' error" + ) + def test_global_slice(test_case): + for placement in all_placement(): + for _ in range(5): + _test_basic_slice(test_case, placement) + _test_advanced_indexing(test_case, placement, dtype=flow.float32) + _test_combined_indexing(test_case, placement, dtype=flow.float32) + _test_single_int(test_case, placement) + _test_multiple_int(test_case, placement) + _test_none(test_case, placement) + _test_step(test_case, placement) + _test_step_assignment(test_case, placement) + _test_int_indices(test_case, placement) + _test_int_indices2d(test_case, placement) + _test_int_indices_broadcast(test_case, placement) + _test_empty_index(test_case, placement) + _test_empty_ndim_index(test_case, placement) + _test_empty_slice(test_case, placement) + _test_ellipsis_tensor(test_case, placement) + # TODO: cpu variable don't support common net + if not placement.type == "cpu": + _test_setitem_scalars(test_case, placement) + + session_ctx.GetDefaultSession().Reset() + + @globaltest + def test_bool_indices(test_case): + for placement in all_placement(): + for _ in range(2): + _test_bool_indices(test_case, placement) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_new_tensor.py b/python/oneflow/test/tensor/test_new_tensor.py index c13fde2b9bd4a95510beba4413d4af2807d56c1b..9fd83268e6abcb0a456f6eb58361ab5b581b3a91 100644 --- a/python/oneflow/test/tensor/test_new_tensor.py +++ b/python/oneflow/test/tensor/test_new_tensor.py @@ -63,6 +63,25 @@ class TestNewTensor(flow.unittest.TestCase): test_case.assertEqual(new_tensor.placement, placement) test_case.assertEqual(new_tensor.sbp, (flow.sbp.broadcast,)) + @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") + @flow.unittest.skip_unless_1n1d() + def test_new_cuda_bfloat16_local_tensor_with_numpy(test_case): + from oneflow import sysconfig + + if sysconfig.get_cuda_version() < 11000: + return + np_array = np.random.rand(4, 4) + tensor = flow.tensor(np_array, dtype=flow.bfloat16, device="cuda") + test_case.assertEqual(tensor.dtype, flow.bfloat16) + test_case.assertEqual(tensor.device, flow.device("cuda")) + + @flow.unittest.skip_unless_1n1d() + def test_new_cpu_bfloat16_local_tensor_with_numpy(test_case): + np_array = np.random.rand(4, 4) + tensor = flow.tensor(np_array, dtype=flow.bfloat16, device="cpu") + test_case.assertEqual(tensor.dtype, flow.bfloat16) + test_case.assertEqual(tensor.device, flow.device("cpu")) + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/tensor/test_parameter.py b/python/oneflow/test/tensor/test_parameter.py index d0b31c1cd5ddf0415d3897d2bbce3fbc325866ba..418915b16a29eb9371b30f58b9d34e903faf199a 100644 --- a/python/oneflow/test/tensor/test_parameter.py +++ b/python/oneflow/test/tensor/test_parameter.py @@ -44,7 +44,7 @@ class TestParameter(flow.unittest.TestCase): # Reason 1, x.data return a new tensor but share storage with the origin tensor, this is not well dealed in nn.Graph. # Reason 2, inplace operation mul_ can works well inside nn.Graph but will not change the value in free eager tensor. # Please refer to test case: test_graph_return_inplace_free_eager_tensor - @autotest(n=1, check_graph="ValidatedFlase") + @autotest(n=1, check_graph="ValidatedFalse") def test_parameter_inplace_modify_data(test_case): x = torch.nn.Parameter(torch.ones(2, 3)) x.data.mul_(2) diff --git a/python/oneflow/test/tensor/test_tensor_and_ndarray_compatibility.py b/python/oneflow/test/tensor/test_tensor_and_ndarray_compatibility.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd51b3384b387d09a69ba25b1aa9432625d415e --- /dev/null +++ b/python/oneflow/test/tensor/test_tensor_and_ndarray_compatibility.py @@ -0,0 +1,121 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +import unittest +from collections import OrderedDict + +import oneflow as flow + +from oneflow.test_utils.test_util import GenArgDict +import numpy as np +import torch + + +test_compute_op_list = [ + "+", + "-", + "*", + "/", + "**", + "//", + "%", +] + +test_login_op_list = [ + "^", + "&", + "|", +] + +test_compare_op_list = [ + "==", + "!=", +] + + +def _test_compute_operator(test_case, shape): + random_tensor = np.random.randn(*shape) + x_flow = flow.tensor(random_tensor) + x_torch = torch.tensor(random_tensor) + random_numpy = np.random.randn(*shape) + + for op in test_compute_op_list: + if op in ["**", "//", "%"]: + random_tensor = np.random.randint(1, 100, size=shape) + random_numpy = np.random.randint(1, 10, size=shape) + else: + random_tensor = np.random.randn(*shape) + random_numpy = np.random.randn(*shape) + + x_flow = flow.tensor(random_tensor) + x_torch = torch.tensor(random_tensor) + + z_flow = eval(f"x_flow {op} random_numpy") + z_torch = eval(f"x_torch {op} random_numpy") + test_case.assertTrue(np.allclose(z_flow.numpy(), z_torch.numpy())) + + # TODO:support for "+=" compatibility + if op not in ["**", "+"]: + exec(f"x_flow {op}= random_numpy") + exec(f"x_torch {op}= random_numpy") + test_case.assertTrue( + np.allclose(z_flow.numpy(), z_torch.numpy(), 1e-05, 1e-05) + ) + + +def _test_logic_operator(test_case, shape): + random_tensor = np.random.randint(100, size=shape) + x_flow = flow.tensor(random_tensor, dtype=flow.int64) + x_torch = torch.tensor(random_tensor, dtype=torch.int64) + random_numpy = np.random.randint(100, size=shape) + + for op in test_login_op_list: + z_flow = eval(f"x_flow {op} random_numpy") + z_torch = eval(f"x_torch {op} random_numpy") + test_case.assertTrue(np.allclose(z_flow.numpy(), z_torch.numpy(), 1e-05, 1e-05)) + + +def _test_compare_operator(test_case, shape): + random_tensor = np.random.randint(100, size=shape) + x_flow = flow.tensor(random_tensor, dtype=flow.int64) + x_torch = torch.tensor(random_tensor, dtype=torch.int64) + random_numpy = np.random.randint(100, size=shape) + + for op in test_compare_op_list: + flow_bool_value = eval(f"x_flow {op} random_numpy") + torch_bool_value = eval(f"x_torch {op} random_numpy") + print(flow_bool_value) + print(torch_bool_value) + test_case.assertTrue(flow_bool_value, torch_bool_value) + + +@flow.unittest.skip_unless_1n1d() +class TestTensorAndNdarrayCompatibility(flow.unittest.TestCase): + def test_op_compatibility(test_case): + arg_dict = OrderedDict() + arg_dict["shape"] = [(2, 3), (2, 3, 4), (2, 3, 4, 5)] + + for arg in GenArgDict(arg_dict): + _test_compute_operator(test_case, **arg) + # TODO(yzm):support compare operator Compatibility + # _test_compare_operator(test_case, **arg) + + # TODO(yzm):fix the logic op bug + # _test_logic_operator(test_case, **arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_tensor_exponential.py b/python/oneflow/test/tensor/test_tensor_exponential.py new file mode 100644 index 0000000000000000000000000000000000000000..5423c2f63ee360080c0a3881083d7ceaf3678385 --- /dev/null +++ b/python/oneflow/test/tensor/test_tensor_exponential.py @@ -0,0 +1,98 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest +import random +import numpy as np +from collections import OrderedDict +import torch + +import oneflow as flow + +import oneflow.unittest +from oneflow.test_utils.test_util import GenArgList + + +def _test_exponential(test_case, device, seed, lambd, dtype): + torch.manual_seed(seed) + flow.manual_seed(seed) + + dim1 = random.randint(8, 64) + dim2 = random.randint(8, 64) + + torch_arr = torch.zeros( + dim1, device=device, dtype=torch.float32 if dtype == "float" else torch.float64 + ).exponential_(lambd=lambd, generator=None) + oneflow_arr = flow.zeros( + dim1, device=device, dtype=flow.float32 if dtype == "float" else flow.float64 + ).exponential_(lambd=lambd, generator=None) + + test_case.assertTrue( + np.allclose(torch_arr.cpu().numpy(), oneflow_arr.cpu().numpy(), atol=1e-8,) + ) + + torch_arr = torch.zeros( + dim1, device=device, dtype=torch.float32 if dtype == "float" else torch.float64 + ).exponential_(lambd=lambd, generator=None) + oneflow_arr = flow.zeros( + dim1, device=device, dtype=flow.float32 if dtype == "float" else flow.float64 + ).exponential_(lambd=lambd, generator=None) + + test_case.assertTrue( + np.allclose(torch_arr.cpu().numpy(), oneflow_arr.cpu().numpy(), atol=1e-8,) + ) + + torch_gen = torch.Generator(device=device) + torch_gen.manual_seed(seed) + oneflow_gen = flow.Generator(device=device) + oneflow_gen.manual_seed(seed) + + torch_arr = torch.zeros( + dim1, device=device, dtype=torch.float32 if dtype == "float" else torch.float64 + ).exponential_(lambd=lambd, generator=torch_gen) + oneflow_arr = flow.zeros( + dim1, device=device, dtype=flow.float32 if dtype == "float" else flow.float64 + ).exponential_(lambd=lambd, generator=oneflow_gen) + + test_case.assertTrue( + np.allclose(torch_arr.cpu().numpy(), oneflow_arr.cpu().numpy(), atol=1e-8,) + ) + + torch_arr = torch.zeros( + dim1, device=device, dtype=torch.float32 if dtype == "float" else torch.float64 + ).exponential_(lambd=lambd, generator=torch_gen) + oneflow_arr = flow.zeros( + dim1, device=device, dtype=flow.float32 if dtype == "float" else flow.float64 + ).exponential_(lambd=lambd, generator=oneflow_gen) + + test_case.assertTrue( + np.allclose(torch_arr.cpu().numpy(), oneflow_arr.cpu().numpy(), atol=1e-8,) + ) + + +@flow.unittest.skip_unless_1n1d() +class TestExponential(flow.unittest.TestCase): + def test_exponential(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cuda", "cpu"] + arg_dict["seed"] = [0, 2, 4] + arg_dict["lambd"] = [1, 0.5, 0.1] + arg_dict["dtype"] = ["double", "float"] + for arg in GenArgList(arg_dict): + _test_exponential(test_case, *arg[0:]) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_tensor_indexing.py b/python/oneflow/test/tensor/test_tensor_indexing.py index 02d586abc0fb8ce87c143a54d0c89a27cf9bc60c..cd57cd2fbb940fc6b82c1feabf7d00101e955f6b 100644 --- a/python/oneflow/test/tensor/test_tensor_indexing.py +++ b/python/oneflow/test/tensor/test_tensor_indexing.py @@ -68,18 +68,14 @@ def _test_numpy_scalar_advance_indexing(test_case, numpy_x, np_scalar): test_case.assertTrue( np.allclose( numpy_x[ - [ - [np_scalar(0), np_scalar(1)], - [np_scalar(0), np_scalar(1)], - [np_scalar(1), np_scalar(0)], - ] + [np_scalar(0), np_scalar(1)], + [np_scalar(0), np_scalar(1)], + [np_scalar(1), np_scalar(0)], ], x[ - [ - [np_scalar(0), np_scalar(1)], - [np_scalar(0), np_scalar(1)], - [np_scalar(1), np_scalar(0)], - ] + [np_scalar(0), np_scalar(1)], + [np_scalar(0), np_scalar(1)], + [np_scalar(1), np_scalar(0)], ].numpy(), ) ) @@ -143,6 +139,8 @@ def _test_basic_slice(test_case, numpy_x): ) +# NOTE: When numpy>=1.23.0, the list of index will be seemed as basic indexing, +# and tuple of index will be seemed as advanced indexing. def _test_advanced_indexing(test_case, numpy_x): x = flow.tensor(numpy_x) @@ -152,19 +150,20 @@ def _test_advanced_indexing(test_case, numpy_x): ) test_case.assertTrue( np.allclose( - numpy_x[[[0, 1], [0, 1], [1, 0]]], x[[[0, 1], [0, 1], [1, 0]]].numpy() + numpy_x[tuple([[0, 1], [0, 1], [1, 0]])], + x[[[0, 1], [0, 1], [1, 0]]].numpy(), ) ) - test_case.assertTrue(np.allclose(numpy_x[[[0], [1]]], x[[[0], [1]]].numpy())) + test_case.assertTrue(np.allclose(numpy_x[tuple([[0], [1]])], x[[[0], [1]]].numpy())) test_case.assertTrue( np.allclose( - numpy_x[[[[0], [1]], [[0], [1]], [0, 1]]], + numpy_x[tuple([[[0], [1]], [[0], [1]], [0, 1]])], x[[[[0], [1]], [[0], [1]], [0, 1]]].numpy(), ) ) test_case.assertTrue( np.allclose( - numpy_x[[[[0, 1], [1, 1]], [[0, 0], [1, 1]], [0, 1]]], + numpy_x[tuple([[[0, 1], [1, 1]], [[0, 0], [1, 1]], [0, 1]])], x[[[[0, 1], [1, 1]], [[0, 0], [1, 1]], [0, 1]]].numpy(), ) ) @@ -396,6 +395,54 @@ class TestTensorIndexing(flow.unittest.TestCase): numpy_x = np.arange(0, 720, 1).reshape([8, 9, 10]).astype(np.float32) _test_mask_setitem(test_case, numpy_x) + def test_combined_mask_setitem(test_case): + np_in = np.random.rand(5, 4, 3, 2) + np_mask_dim1 = np.array([False, True, False, True]) + np_mask_dim3 = np.array([True, False]) + np_update = np.random.rand(2, 5, 3) + np_in[:, np_mask_dim1, :, np_mask_dim3] = np_update + + flow_in = flow.tensor(np_in) + flow_mask_dim1 = flow.tensor(np_mask_dim1) + flow_mask_dim3 = flow.tensor(np_mask_dim3) + flow_update = flow.tensor(np_update) + flow_in[:, flow_mask_dim1, :, flow_mask_dim3] = flow_update + test_case.assertTrue(np.array_equal(flow_in.numpy(), np_in)) + + def test_non_contiguous_combined_mask_setitem(test_case): + np_in = np.random.rand(5, 4, 3, 2) + np_mask_dim1 = np.array([False, True, False]) + np_mask_dim3 = np.array([True, False, False, True, True]) + np_update = np.random.rand(4, 2, 3) + + flow_in = flow.tensor(np_in).permute(3, 2, 1, 0) # (2, 3, 4, 5) + flow_mask_dim1 = flow.tensor(np_mask_dim1) + flow_mask_dim3 = flow.tensor(np_mask_dim3) + flow_update = flow.tensor(np_update).permute(2, 1, 0) # (3, 2, 4) + flow_in[:, flow_mask_dim1, :, flow_mask_dim3] = flow_update + + np_in = np_in.transpose(3, 2, 1, 0) + np_update = np_update.transpose(2, 1, 0) + np_in[:, np_mask_dim1, :, np_mask_dim3] = np_update + test_case.assertTrue(np.array_equal(flow_in.numpy(), np_in)) + + def test_combined_indexing_setitem(test_case): + np_in = np.random.rand(2, 3, 4) + np_in[[0, 1], 1:2, [0, 1]] = 1.0 + + flow_in = flow.tensor(np_in) + flow_in[[0, 1], 1:2, [0, 1]] = 1.0 + test_case.assertTrue(np.array_equal(flow_in.numpy(), np_in)) + + def test_expand_dim_setitem(test_case): + a = flow.tensor(1.0) + a[True, ...] = 0.0 + test_case.assertTrue(np.array_equal(a.numpy(), 0.0)) + + a = flow.tensor(1.0) + a[False, ...] = 1.0 + test_case.assertTrue(np.array_equal(a.numpy(), 1.0)) + def test_advanced_indexing_with_scalar_index(test_case): index = flow.tensor([0, 2]) x = flow.randn(5) diff --git a/python/oneflow/test/tensor/test_tensor_indexing2.py b/python/oneflow/test/tensor/test_tensor_indexing2.py new file mode 100644 index 0000000000000000000000000000000000000000..db9f429c164d682959e954aa0e98627e98a84c1e --- /dev/null +++ b/python/oneflow/test/tensor/test_tensor_indexing2.py @@ -0,0 +1,951 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +# This test code is referenced from: https://github.com/pytorch/pytorch/blob/cd41c8f032dd06c445bf97fc76fb82008b19afcb/test/test_indexing.py + +from collections import OrderedDict +import random +from random import randrange +import unittest + +import numpy as np + +import oneflow as flow +from oneflow.test_utils.test_util import GenArgDict +import oneflow.unittest + + +def _assert_tensor_equal(test_case, tensor1, tensor2, atol=0.0, rtol=0.0): + test_case.assertTrue(np.allclose(tensor1.numpy(), tensor2.numpy())) + + +def consec(size, start=1): + """ + Generate a arithmetic progression with given size and start value. + """ + sequence = flow.ones([int(np.array(size).prod(0)),]).cumsum(0) + sequence.add_(start - 1) + return sequence.view(*size) + + +def _test_basic_slice(test_case, device, dtype): + reference = consec((3, 3, 3)).to(device=device, dtype=dtype) + + # empty tensor indexing + _assert_tensor_equal( + test_case, + reference[flow.LongTensor().to(device)], + flow.empty(0, 3, 3), + atol=0, + rtol=0, + ) + + _assert_tensor_equal(test_case, reference[0], consec((3, 3)), atol=0, rtol=0) + _assert_tensor_equal(test_case, reference[1], consec((3, 3), 10), atol=0, rtol=0) + _assert_tensor_equal(test_case, reference[2], consec((3, 3), 19), atol=0, rtol=0) + _assert_tensor_equal(test_case, reference[0, 1], consec((3,), 4), atol=0, rtol=0) + _assert_tensor_equal(test_case, reference[0:2], consec((2, 3, 3)), atol=0, rtol=0) + test_case.assertEqual(reference[2, 2, 2].item(), 27) + _assert_tensor_equal(test_case, reference[:], consec((3, 3, 3)), atol=0, rtol=0) + + # indexing with Ellipsis + _assert_tensor_equal( + test_case, + reference[..., 2], + flow.tensor([[3.0, 6.0, 9.0], [12.0, 15.0, 18.0], [21.0, 24.0, 27.0]]), + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, reference[0, ..., 2], flow.tensor([3.0, 6.0, 9.0]), atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[..., 2], reference[:, :, 2], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0, ..., 2], reference[0, :, 2], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, reference[0, 2, ...], reference[0, 2], atol=0, rtol=0 + ) + test_case.assertEqual(reference[..., 2, 2, 2].item(), 27) + test_case.assertEqual(reference[2, ..., 2, 2].item(), 27) + test_case.assertEqual(reference[2, 2, ..., 2].item(), 27) + test_case.assertEqual(reference[2, 2, 2, ...].item(), 27) + _assert_tensor_equal(test_case, reference[...], reference, atol=0, rtol=0) + + reference_5d = consec((3, 3, 3, 3, 3)).to(device) + _assert_tensor_equal( + test_case, reference_5d[..., 1, 0], reference_5d[:, :, :, 1, 0], atol=0, rtol=0 + ) + _assert_tensor_equal( + test_case, + reference_5d[2, ..., 1, 0], + reference_5d[2, :, :, 1, 0], + atol=0, + rtol=0, + ) + _assert_tensor_equal( + test_case, + reference_5d[2, 1, 0, ..., 1], + reference_5d[2, 1, 0, :, 1], + atol=0, + rtol=0, + ) + _assert_tensor_equal(test_case, reference_5d[...], reference_5d, atol=0, rtol=0) + + # LongTensor indexing + reference = consec((5, 5, 5)).to(device=device, dtype=dtype) + idx = flow.LongTensor([2, 4]).to(device) + _assert_tensor_equal( + test_case, reference[idx], flow.stack([reference[2], reference[4]]) + ) + + # None indexing + _assert_tensor_equal(test_case, reference[2, None], reference[2].unsqueeze(0)) + _assert_tensor_equal( + test_case, reference[2, None, None], reference[2].unsqueeze(0).unsqueeze(0) + ) + _assert_tensor_equal(test_case, reference[2:4, None], reference[2:4].unsqueeze(1)) + _assert_tensor_equal( + test_case, + reference[None, 2, None, None], + reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0), + ) + _assert_tensor_equal( + test_case, + reference[None, 2:5, None, None], + reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2), + ) + + # indexing 0-length slice + _assert_tensor_equal(test_case, flow.empty(0, 5, 5), reference[slice(0)]) + _assert_tensor_equal(test_case, flow.empty(0, 5), reference[slice(0), 2]) + _assert_tensor_equal(test_case, flow.empty(0, 5), reference[2, slice(0)]) + _assert_tensor_equal(test_case, flow.tensor([]), reference[2, 1:1, 2]) + + # indexing with step + reference = consec((10, 10, 10)).to(device=device, dtype=dtype) + _assert_tensor_equal( + test_case, reference[1:5:2], flow.stack([reference[1], reference[3]], 0) + ) + _assert_tensor_equal( + test_case, + reference[1:6:2], + flow.stack([reference[1], reference[3], reference[5]], 0), + ) + _assert_tensor_equal( + test_case, reference[1:9:4], flow.stack([reference[1], reference[5]], 0) + ) + _assert_tensor_equal( + test_case, + reference[2:4, 1:5:2], + flow.stack([reference[2:4, 1], reference[2:4, 3]], 1), + ) + _assert_tensor_equal( + test_case, + reference[3, 1:6:2], + flow.stack([reference[3, 1], reference[3, 3], reference[3, 5]], 0), + ) + _assert_tensor_equal( + test_case, + reference[None, 2, 1:9:4], + flow.stack([reference[2, 1], reference[2, 5]], 0).unsqueeze(0), + ) + _assert_tensor_equal( + test_case, + reference[:, 2, 1:6:2], + flow.stack([reference[:, 2, 1], reference[:, 2, 3], reference[:, 2, 5]], 1), + ) + + lst = [list(range(i, i + 10)) for i in range(0, 100, 10)] + tensor = flow.DoubleTensor(lst).to(device=device, dtype=dtype) + for _ in range(10): + idx1_start = randrange(10) + idx1_end = idx1_start + randrange(1, 10 - idx1_start + 1) + idx1_step = randrange(1, 8) + idx1 = slice(idx1_start, idx1_end, idx1_step) + if randrange(2) == 0: + idx2_start = randrange(10) + idx2_end = idx2_start + randrange(1, 10 - idx2_start + 1) + idx2_step = randrange(1, 8) + idx2 = slice(idx2_start, idx2_end, idx2_step) + lst_indexed = [l[idx2] for l in lst[idx1]] + tensor_indexed = tensor[idx1, idx2] + else: + lst_indexed = lst[idx1] + tensor_indexed = tensor[idx1] + _assert_tensor_equal( + test_case, flow.DoubleTensor(lst_indexed).to(dtype), tensor_indexed + ) + + test_case.assertRaises(RuntimeError, lambda: reference[1:9:0]) + test_case.assertRaises(RuntimeError, lambda: reference[1:9:-1]) + + test_case.assertRaises(IndexError, lambda: reference[1, 1, 1, 1]) + test_case.assertRaises(IndexError, lambda: reference[1, 1, 1, 1:1]) + test_case.assertRaises(IndexError, lambda: reference[3, 3, 3, 3, 3, 3, 3, 3]) + + test_case.assertRaises(IndexError, lambda: reference[0.0]) + test_case.assertRaises(RuntimeError, lambda: reference[0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, :, 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, ..., 0.0:2.0]) + test_case.assertRaises(IndexError, lambda: reference[0.0, :, 0.0]) + + +def _test_advanced_indexing(test_case, device, dtype): + # pick a random valid indexer type + def ri(indices): + choice = random.randint(0, 2) + if choice == 0: + return flow.LongTensor(indices).to(device) + elif choice == 1: + return list(indices) + else: + return tuple(indices) + + def validate_indexing(x): + _assert_tensor_equal(test_case, x[[0]], consec((1,))) + _assert_tensor_equal(test_case, x[ri([0]),], consec((1,))) + _assert_tensor_equal(test_case, x[ri([3]),], consec((1,), 4)) + _assert_tensor_equal(test_case, x[[2, 3, 4]], consec((3,), 3)) + _assert_tensor_equal(test_case, x[ri([2, 3, 4]),], consec((3,), 3)) + _assert_tensor_equal( + test_case, + x[ri([0, 2, 4]),], + flow.tensor([1, 3, 5], dtype=dtype, device=device), + ) + + def validate_setting(x): + x[[0]] = -2 + _assert_tensor_equal( + test_case, x[[0]], flow.tensor([-2], dtype=dtype, device=device) + ) + x[[0]] = -1 + _assert_tensor_equal( + test_case, x[ri([0]),], flow.tensor([-1], dtype=dtype, device=device) + ) + x[[2, 3, 4]] = 4 + _assert_tensor_equal( + test_case, x[[2, 3, 4]], flow.tensor([4, 4, 4], dtype=dtype, device=device) + ) + x[ri([2, 3, 4]),] = 3 + _assert_tensor_equal( + test_case, + x[ri([2, 3, 4]),], + flow.tensor([3, 3, 3], dtype=dtype, device=device), + ) + x[ri([0, 2, 4]),] = flow.tensor([5, 4, 3], dtype=dtype, device=device) + _assert_tensor_equal( + test_case, + x[ri([0, 2, 4]),], + flow.tensor([5, 4, 3], dtype=dtype, device=device), + ) + + # 1d tensor and integer index setitem and getitem + reference = consec((10,)).to(device=device, dtype=dtype) + validate_indexing(reference) + validate_setting(reference) + + # reference is 1 2 + # 3 4 + # 5 6 + reference = consec((3, 2)).to(device=device, dtype=dtype) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([1, 3, 5], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([1])], + flow.tensor([2, 4, 6], dtype=dtype, device=device), + ) + _assert_tensor_equal(test_case, reference[ri([0]), ri([0])], consec((1,))) + _assert_tensor_equal(test_case, reference[ri([2]), ri([1])], consec((1,), 6)) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0]), ri([0, 1])]], + flow.tensor([1, 2], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 1, 1, 0, 2]), ri([1])]], + flow.tensor([2, 4, 4, 2, 6], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], + flow.tensor([1, 2, 3, 3], dtype=dtype, device=device), + ) + + rows = ri([[0, 0], [1, 2]]) + columns = ([0],) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[1, 1], [3, 5]], dtype=dtype, device=device), + ) + + rows = ri([[0, 0], [1, 2]]) + columns = ri([1, 0]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[2, 1], [4, 5]], dtype=dtype, device=device), + ) + rows = ri([[0, 0], [1, 2]]) + columns = ri([[0, 1], [1, 0]]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[1, 2], [4, 5]], dtype=dtype, device=device), + ) + + # setting values + reference[ri([0]), ri([1])] = -1 + _assert_tensor_equal( + test_case, + reference[ri([0]), ri([1])], + flow.tensor([-1], dtype=dtype, device=device), + ) + reference[ri([0, 1, 2]), ri([0])] = flow.tensor( + [-1, 2, -4], dtype=dtype, device=device + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([-1, 2, -4], dtype=dtype, device=device), + ) + reference[rows, columns] = flow.tensor([[4, 6], [2, 3]], dtype=dtype, device=device) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[4, 6], [2, 3]], dtype=dtype, device=device), + ) + + # Test non-contiguous(by transpose) reference + # Transposed: [[0, 4, 8], + # [1, 5, 9], + # [2, 6, 10], + # [3, 7, 11]] + reference = flow.tensor( + [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], dtype=dtype, device=device + ).T + + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([0, 1, 2], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([1])], + flow.tensor([4, 5, 6], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[ri([0]), ri([0])], + flow.tensor([0], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[ri([2]), ri([1])], + flow.tensor([6], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0]), ri([0, 1])]], + flow.tensor([0, 4], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 1, 1, 0, 3]), ri([1])]], + flow.tensor([4, 5, 5, 4, 7], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]], + flow.tensor([0, 4, 1, 1], dtype=dtype, device=device), + ) + + rows = ri([[0, 0], [1, 2]]) + columns = ([0],) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[0, 0], [1, 2]], dtype=dtype, device=device), + ) + + rows = ri([[0, 0], [1, 2]]) + columns = ri([1, 0]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[4, 0], [5, 2]], dtype=dtype, device=device), + ) + rows = ri([[0, 0], [1, 3]]) + columns = ri([[0, 1], [1, 2]]) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[0, 4], [5, 11]], dtype=dtype, device=device), + ) + + # setting values + reference[ri([0]), ri([1])] = -1 + _assert_tensor_equal( + test_case, + reference[ri([0]), ri([1])], + flow.tensor([-1], dtype=dtype, device=device), + ) + reference[ri([0, 1, 2]), ri([0])] = flow.tensor( + [-1, 2, -4], dtype=dtype, device=device + ) + _assert_tensor_equal( + test_case, + reference[ri([0, 1, 2]), ri([0])], + flow.tensor([-1, 2, -4], dtype=dtype, device=device), + ) + reference[rows, columns] = flow.tensor([[4, 6], [2, 3]], dtype=dtype, device=device) + _assert_tensor_equal( + test_case, + reference[rows, columns], + flow.tensor([[4, 6], [2, 3]], dtype=dtype, device=device), + ) + + # Tests using less than the number of dims, and ellipsis + # reference is 1 2 + # 3 4 + # 5 6 + reference = consec((3, 2)).to(dtype=dtype, device=device) + _assert_tensor_equal( + test_case, + reference[ri([0, 2]),], + flow.tensor([[1, 2], [5, 6]], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[ri([1]), ...], + flow.tensor([[3, 4]], dtype=dtype, device=device), + ) + _assert_tensor_equal( + test_case, + reference[..., ri([1])], + flow.tensor([[2], [4], [6]], dtype=dtype, device=device), + ) + + # verify too many indices fails + with test_case.assertRaises(IndexError): + reference[ri([1]), ri([0, 2]), ri([3])] + + # test invalid index fails + reference = flow.empty(10, dtype=dtype, device=device) + for err_idx in (10, -11): + with test_case.assertRaisesRegex(IndexError, r"out of range"): + reference[err_idx] + + +def _test_combined_indexing(test_case, device, dtype): + def tensor_indices_to_np(tensor, indices): + # convert the flow Tensor to a numpy array + tensor = tensor.to(device="cpu") + npt = tensor.numpy() + + # convert indices + idxs = tuple( + i.tolist() if isinstance(i, flow.LongTensor) else i for i in indices + ) + + return npt, idxs + + def get_numpy(tensor, indices): + npt, idxs = tensor_indices_to_np(tensor, indices) + + # index and return as a flow Tensor + return flow.tensor(npt[idxs], dtype=dtype, device=device) + + def set_numpy(tensor, indices, value): + if not isinstance(value, int): + if device != "cpu": + value = value.cpu() + value = value.numpy() + + npt, idxs = tensor_indices_to_np(tensor, indices) + npt[idxs] = value + return npt + + def assert_get_eq(tensor, indexer): + _assert_tensor_equal(test_case, tensor[indexer], get_numpy(tensor, indexer)) + + def assert_set_eq(tensor, indexer, val): + pyt = tensor.clone() + np_ref = tensor.clone() + pyt[indexer] = val + np_ref = flow.tensor( + set_numpy(np_ref, indexer, val), dtype=dtype, device=device + ) + _assert_tensor_equal(test_case, pyt, np_ref) + + def assert_backward_eq(tensor, indexer): + cpu = tensor.cpu().float().clone().detach().requires_grad_(True) + outcpu = cpu[indexer] + grad = flow.rand(outcpu.shape) + outcpu.backward(grad) + dev = cpu.to(device).detach().requires_grad_(True) + outdev = dev[indexer] + outdev.backward(grad.to(device)) + _assert_tensor_equal(test_case, cpu.grad, dev.grad) + + def get_set_tensor(indexed, indexer): + set_size = indexed[indexer].size() + set_count = indexed[indexer].numel() + set_tensor = flow.randperm(set_count).view(set_size).to(dtype).to(device) + return set_tensor + + # Tensor is 0 1 2 3 4 + # 5 6 7 8 9 + # 10 11 12 13 14 + # 15 16 17 18 19 + reference = flow.arange(0.0, 20, device=device).to(dtype).view(4, 5) + + indices_to_test = [ + # grab the second, fourth columns + [slice(None), [1, 3]], + # first, third rows, + [[0, 2], slice(None)], + # TODO(wyg): only support getitem but not setitem + # # weird shape + # [slice(None), [[0, 1], + # [2, 3]]], + # negatives + [[-1], [0]], + [[0, 2], [-1]], + [slice(None), [-1]], + ] + + # test getitem + get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]] + get_indices_to_test = indices_to_test + [ + [slice(None), [[0, 1], [2, 3]]] + ] # TODO: test setitem + for indexer in get_indices_to_test: + assert_get_eq(reference, indexer) + if device != "cpu": + assert_backward_eq(reference, indexer) + + # test setitem + for indexer in indices_to_test: + assert_set_eq(reference, indexer, 44) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + + ######################### + # test more dims tensor # + ######################### + reference = flow.arange(0.0, 160, device=device).to(dtype).view(4, 8, 5) + + indices_to_test = [ + [slice(None), slice(None), [0, 3, 4]], + [slice(None), [2, 4, 5, 7], slice(None)], + [[2, 3], slice(None), slice(None)], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0], [1, 2, 4]], + [slice(None), [0, 1, 3], [4]], + [slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [[0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4], slice(None)], + [[0, 1, 3], [4], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 3]], slice(None)], + [[[0, 1], [2, 3]], [[0]], slice(None)], + [[[2, 1]], [[0, 3], [4, 4]], slice(None)], + [[[2]], [[0, 3], [4, 1]], slice(None)], + # non-contiguous indexing subspace + [[0, 2, 3], slice(None), [1, 3, 4]], + # less dim, ellipsis + [[0, 2],], + [[0, 2], slice(None)], + [[0, 2], Ellipsis], + [[0, 2], slice(None), Ellipsis], + [[0, 2], Ellipsis, slice(None)], + [[0, 2], [1, 3]], + [[0, 2], [1, 3], Ellipsis], + [Ellipsis, [1, 3], [2, 3]], + [Ellipsis, [2, 3, 4]], + [Ellipsis, slice(None), [2, 3, 4]], + [slice(None), Ellipsis, [2, 3, 4]], + # ellipsis counts for nothing + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, slice(None), [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), [0, 3, 4], Ellipsis], + [Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)], + [[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 212) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + if device != "cpu": + assert_backward_eq(reference, indexer) + + reference = flow.arange(0.0, 1296, device=device).to(dtype).view(3, 9, 8, 6) + + indices_to_test = [ + [slice(None), slice(None), slice(None), [0, 3, 4]], + [slice(None), slice(None), [2, 4, 5, 7], slice(None)], + [slice(None), [2, 3], slice(None), slice(None)], + [[1, 2], slice(None), slice(None), slice(None)], + [slice(None), slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), slice(None), [0], [1, 2, 4]], + [slice(None), slice(None), [0, 1, 3], [4]], + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]], + [slice(None), slice(None), [[0, 1], [2, 3]], [[0]]], + [slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]], + [slice(None), [0, 2, 3], [1, 3, 4], slice(None)], + [slice(None), [0], [1, 2, 4], slice(None)], + [slice(None), [0, 1, 3], [4], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)], + [slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)], + [slice(None), [[0, 1], [3, 2]], [[0]], slice(None)], + [slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)], + [slice(None), [[2]], [[0, 3], [4, 2]], slice(None)], + [[0, 1, 2], [1, 3, 4], slice(None), slice(None)], + [[0], [1, 2, 4], slice(None), slice(None)], + [[0, 1, 2], [4], slice(None), slice(None)], + [[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)], + [[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)], + [[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [[[2]], [[0, 3], [4, 5]], slice(None), slice(None)], + [slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 4], [1, 3, 4], [4]], + [slice(None), [0, 1, 3], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1, 3, 4]], + [slice(None), [2, 3, 5], [3], [4]], + [slice(None), [0], [4], [1, 3, 4]], + [slice(None), [6], [0, 2, 3], [1]], + [slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]], + [[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)], + [[2, 0, 1], [1, 2, 3], [4], slice(None)], + [[0, 1, 2], [4], [1, 3, 4], slice(None)], + [[0], [0, 2, 3], [1, 3, 4], slice(None)], + [[0, 2, 1], [3], [4], slice(None)], + [[0], [4], [1, 3, 4], slice(None)], + [[1], [0, 2, 3], [1], slice(None)], + [[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)], + # less dim, ellipsis + [Ellipsis, [0, 3, 4]], + [Ellipsis, slice(None), [0, 3, 4]], + [Ellipsis, slice(None), slice(None), [0, 3, 4]], + [slice(None), Ellipsis, [0, 3, 4]], + [slice(None), slice(None), Ellipsis, [0, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4]], + [slice(None), [0, 2, 3], [1, 3, 4], Ellipsis], + [Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)], + [[0], [1, 2, 4]], + [[0], [1, 2, 4], slice(None)], + [[0], [1, 2, 4], Ellipsis], + [[0], [1, 2, 4], Ellipsis, slice(None)], + [[1],], + [[0, 2, 1], [3], [4]], + [[0, 2, 1], [3], [4], slice(None)], + [[0, 2, 1], [3], [4], Ellipsis], + [Ellipsis, [0, 2, 1], [3], [4]], + ] + + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + assert_set_eq(reference, indexer, get_set_tensor(reference, indexer)) + indices_to_test += [ + [slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]], + [slice(None), slice(None), [[2]], [[0, 3], [4, 4]]], + ] + for indexer in indices_to_test: + assert_get_eq(reference, indexer) + assert_set_eq(reference, indexer, 1333) + if device != "cpu": + assert_backward_eq(reference, indexer) + + +def _test_single_int(test_case, device): + v = flow.randn(5, 7, 3, device=device) + test_case.assertEqual(v[4].shape, (7, 3)) + + +def _test_multiple_int(test_case, device): + v = flow.randn(5, 7, 3, device=device) + test_case.assertEqual(v[4].shape, (7, 3)) + test_case.assertEqual(v[4, :, 1].shape, (7,)) + + +def _test_none(test_case, device): + v = flow.randn(5, 7, 3, device=device) + test_case.assertEqual(v[None].shape, (1, 5, 7, 3)) + test_case.assertEqual(v[:, None].shape, (5, 1, 7, 3)) + test_case.assertEqual(v[:, None, None].shape, (5, 1, 1, 7, 3)) + test_case.assertEqual(v[..., None].shape, (5, 7, 3, 1)) + + +def _test_step(test_case, device): + v = flow.arange(10, device=device) + _assert_tensor_equal(test_case, v[::1], v) + test_case.assertEqual(v[::2].tolist(), [0, 2, 4, 6, 8]) + test_case.assertEqual(v[::3].tolist(), [0, 3, 6, 9]) + test_case.assertEqual(v[::11].tolist(), [0]) + test_case.assertEqual(v[1:6:2].tolist(), [1, 3, 5]) + + +def _test_step_assignment(test_case, device): + v = flow.zeros(4, 4, device=device) + v[0, 1::2] = flow.tensor([3.0, 4.0], device=device) + test_case.assertEqual(v[0].tolist(), [0.0, 3.0, 0.0, 4.0]) + test_case.assertEqual(v[1:].sum(), 0) + + +def _test_bool_indices(test_case, device): + v = flow.randn(5, 7, 3, device=device) + boolIndices = flow.tensor( + [True, False, True, True, False], dtype=flow.bool, device=device + ) + test_case.assertEqual(v[boolIndices].shape, (3, 7, 3)) + _assert_tensor_equal(test_case, v[boolIndices], flow.stack([v[0], v[2], v[3]])) + + v = flow.tensor([True, False, True], dtype=flow.bool, device=device) + boolIndices = flow.tensor([True, False, False], dtype=flow.bool, device=device) + uint8Indices = flow.tensor([1, 0, 0], dtype=flow.uint8, device=device) + test_case.assertEqual(v[boolIndices].shape, v[uint8Indices].shape) + test_case.assertEqual(v[boolIndices], v[uint8Indices]) + test_case.assertEqual( + v[boolIndices], flow.tensor([True], dtype=flow.bool, device=device) + ) + + +def _test_multiple_bool_indices(test_case, device): + v = flow.randn(5, 7, 3, device=device) + # NOTE: these broadcast together and are transposed to the first dim + mask1 = flow.tensor([1, 0, 1, 1, 0], dtype=flow.bool, device=device) + mask2 = flow.tensor([1, 1, 1], dtype=flow.bool, device=device) + test_case.assertEqual(v[mask1, :, mask2].shape, (3, 7)) + + +def _test_int_indices(test_case, device): + v = flow.randn(5, 7, 3, device=device) + test_case.assertEqual(v[[0, 4, 2]].shape, (3, 7, 3)) + test_case.assertEqual(v[:, [0, 4, 2]].shape, (5, 3, 3)) + test_case.assertEqual(v[:, [[0, 1], [4, 3]]].shape, (5, 2, 2, 3)) + + +def _test_int_indices2d(test_case, device): + x = flow.arange(0, 12, device=device).view(4, 3) + rows = flow.tensor([[0, 0], [3, 3]], device=device) + columns = flow.tensor([[0, 2], [0, 2]], device=device) + test_case.assertEqual(x[rows, columns].tolist(), [[0, 2], [9, 11]]) + + +def _test_int_indices_broadcast(test_case, device): + x = flow.arange(0, 12, device=device).view(4, 3) + rows = flow.tensor([0, 3], device=device) + columns = flow.tensor([0, 2], device=device) + result = x[rows[:, None], columns] + test_case.assertEqual(result.tolist(), [[0, 2], [9, 11]]) + + +def _test_empty_index(test_case, device): + x = flow.arange(0, 12, device=device).view(4, 3) + idx = flow.tensor([], dtype=flow.long, device=device) + test_case.assertEqual(x[idx].numel(), 0) + + # empty assignment should have no effect but not throw an exception + y = x.clone() + y[idx] = -1 + _assert_tensor_equal(test_case, x, y) + + mask = flow.zeros(4, 3, device=device).to(flow.bool) + y[mask] = -1 + _assert_tensor_equal(test_case, x, y) + + +def _test_empty_ndim_index(test_case, device): + x = flow.randn(5, device=device) + _assert_tensor_equal( + test_case, + flow.empty(0, 2, device=device), + x[flow.empty(0, 2, dtype=flow.int64, device=device)], + ) + + x = flow.randn(2, 3, 4, 5, device=device) + _assert_tensor_equal( + test_case, + flow.empty(2, 0, 6, 4, 5, device=device), + x[:, flow.empty(0, 6, dtype=flow.int64, device=device)], + ) + + x = flow.empty(10, 0, device=device) + test_case.assertEqual(x[[1, 2]].shape, (2, 0)) + test_case.assertEqual(x[[], []].shape, (0,)) + test_case.assertEqual(x[[[]]].shape, (0, 0)) + test_case.assertEqual(x[[[[]]]].shape, (1, 0, 0)) + test_case.assertEqual(x[[1], []].shape, (0,)) + test_case.assertEqual(x[[], [2]].shape, (0,)) + with test_case.assertRaisesRegex(IndexError, "for dimension with size 0"): + x[:, [0, 1]] + + +def _test_empty_ndim_index_bool(test_case, device): + x = flow.randn(5, device=device) + test_case.assertRaises( + IndexError, lambda: x[flow.empty(0, 2, dtype=flow.uint8, device=device)] + ) + + +def _test_empty_slice(test_case, device): + x = flow.randn(2, 3, 4, 5, device=device) + y = x[:, :, :, 1] + z = y[:, 1:1, :] + test_case.assertEqual((2, 0, 4), z.shape) + # this isn't technically necessary, but matches NumPy stride calculations. + test_case.assertEqual((60, 20, 5), z.stride()) + test_case.assertTrue(z.is_contiguous()) + + +def _test_index_getitem_copy_bools_slices(test_case, device): + true = flow.tensor(1, dtype=flow.uint8, device=device) + false = flow.tensor(0, dtype=flow.uint8, device=device) + + tensors = [flow.randn(2, 3, device=device), flow.tensor([1.0], device=device)] + + # TODO: compare tensor_storage after exporting the inferface + for a in tensors: + # test_case.assertNotEqual(a.data_ptr(), a[True].data_ptr()) + _assert_tensor_equal(test_case, flow.empty(0, *a.shape), a[False]) + # test_case.assertNotEqual(a.data_ptr(), a[true].data_ptr()) + _assert_tensor_equal(test_case, flow.empty(0, *a.shape), a[false]) + # test_case.assertEqual(a.data_ptr(), a[None].data_ptr()) + # test_case.assertEqual(a.data_ptr(), a[...].data_ptr()) + + +def _test_setitem_scalars(test_case, device): + zero = flow.tensor(0, dtype=flow.int64) + + # non-scalar indexed with scalars + a = flow.randn(2, 3, device=device) + a_set_with_number = a.clone() + a_set_with_scalar = a.clone() + b = flow.randn(3, device=device) + + a_set_with_number[0] = b + a_set_with_scalar[zero] = b + _assert_tensor_equal(test_case, a_set_with_number, a_set_with_scalar) + a[1, zero] = 7.7 + value = a[1, 0].numpy() + test_case.assertEqual(np.array(7.7, dtype=value.dtype), value) + + np_x = np.random.rand(2, 3) + np_x[0, 0] = 1.0 + x = flow.tensor(np_x) + x[0, 0] = 1.0 + test_case.assertEqual(x.numpy().all(), np_x.all()) + + # scalar indexed with scalars + r = flow.tensor(1.0).to(device) + with test_case.assertRaises(IndexError): + r[:] = 8.8 + with test_case.assertRaises(IndexError): + r[zero] = 8.8 + r[...] = 9.9 + test_case.assertEqual(r, 9.9) + + # scalar indexed with oneflow.Size([1]) + np_x = np.random.rand(2, 3) + np_x[0, 0] = np.ones(1) + x = flow.tensor(np_x) + x[0, 0] = flow.ones(1).to(flow.float64) + test_case.assertEqual(x.numpy().all(), np_x.all()) + + +def _test_basic_advanced_combined(test_case, device): + x = flow.arange(0, 12, device=device).view(4, 3) + _assert_tensor_equal(test_case, x[1:2, 1:3], x[1:2, [1, 2]]) + test_case.assertEqual(x[1:2, 1:3].tolist(), [[4, 5]]) + + # Check that it is a copy + unmodified = x.clone() + x[1:2, [1, 2]].zero_() + _assert_tensor_equal(test_case, x, unmodified) + + # But assignment should modify the original + unmodified = x.clone() + x[1:2, [1, 2]] = 0 + test_case.assertFalse(np.array_equal(x.numpy(), unmodified.numpy())) + + +def _test_ellipsis_tensor(test_case, device): + x = flow.arange(0, 9, device=device).view(3, 3) + idx = flow.tensor([0, 2], device=device) + test_case.assertEqual(x[..., idx].tolist(), [[0, 2], [3, 5], [6, 8]]) + test_case.assertEqual(x[idx, ...].tolist(), [[0, 1, 2], [6, 7, 8]]) + + # Test scalar ellipsis getitem + y = flow.tensor(1.0).to(device) + x_scalar = flow.tensor(9.9) + y = x_scalar[...] + test_case.assertEqual(y, 9.9) + + +@flow.unittest.skip_unless_1n1d() +class TestIndexing(flow.unittest.TestCase): + def test_slice(test_case): + arg_dict = OrderedDict() + arg_dict["device"] = ["cpu", "cuda"] + for arg in GenArgDict(arg_dict): + dtype_list = [flow.float32, flow.float16] + from oneflow import sysconfig + + if not sysconfig.get_cuda_version() < 11000: + dtype_list.append(flow.bfloat16) + + for dtype in dtype_list: + _test_basic_slice(test_case, **arg, dtype=dtype) + _test_advanced_indexing(test_case, **arg, dtype=dtype) + _test_combined_indexing(test_case, **arg, dtype=dtype) + _test_single_int(test_case, **arg) + _test_multiple_int(test_case, **arg) + _test_none(test_case, **arg) + _test_step(test_case, **arg) + _test_step_assignment(test_case, **arg) + _test_bool_indices(test_case, **arg) + _test_multiple_bool_indices(test_case, **arg) + _test_int_indices(test_case, **arg) + _test_int_indices2d(test_case, **arg) + _test_int_indices_broadcast(test_case, **arg) + _test_empty_index(test_case, **arg) + _test_empty_ndim_index(test_case, **arg) + _test_empty_ndim_index_bool(test_case, **arg) + _test_empty_slice(test_case, **arg) + _test_index_getitem_copy_bools_slices(test_case, **arg) + _test_setitem_scalars(test_case, **arg) + _test_basic_advanced_combined(test_case, **arg) + _test_ellipsis_tensor(test_case, **arg) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test/tensor/test_tensor_part_1.py b/python/oneflow/test/tensor/test_tensor_part_1.py index 0feb5d77faecb296c25184efdc629d5d056acf0c..491e9378c5d4fbe790f92b349325e5a41181d600 100644 --- a/python/oneflow/test/tensor/test_tensor_part_1.py +++ b/python/oneflow/test/tensor/test_tensor_part_1.py @@ -44,6 +44,12 @@ class TestTensor(flow.unittest.TestCase): np.allclose(tensor.numpy(), np.ones(shape, dtype=np.float32)) ) + shape = flow.Size((2, 3)) + tensor = flow.Tensor(shape) + flow.nn.init.eye_(tensor) + test_case.assertTrue(tensor.dtype == flow.float32) + test_case.assertTrue(np.allclose(tensor.numpy(), np.eye(2, 3))) + @flow.unittest.skip_unless_1n1d() def test_tensor_deepcopy(test_case): shape = (2, 3) @@ -151,30 +157,40 @@ class TestTensor(flow.unittest.TestCase): return input.gather(dim, index) def _test_tensor_init_methods(test_case, tensor_creator, get_numpy): - shape = (2, 3, 4, 5) - x = tensor_creator(*shape) - np_ones = np.ones(x.shape) - np_zeros = np.zeros(x.shape) - random_fill_val = 923.53 - x.fill_(random_fill_val) - test_case.assertTrue(np.allclose(get_numpy(x), random_fill_val * np_ones)) - flow.nn.init.ones_(x) - test_case.assertTrue(np.allclose(get_numpy(x), np_ones)) - flow.nn.init.zeros_(x) - test_case.assertTrue(np.allclose(get_numpy(x), np_zeros)) - flow.nn.init.constant_(x, random_fill_val) - test_case.assertTrue(np.allclose(get_numpy(x), random_fill_val * np_ones)) - z = tensor_creator(5, 4, 3, 2) - flow.nn.init.kaiming_normal_(z, a=0.1, mode="fan_out", nonlinearity="relu") - flow.nn.init.kaiming_uniform_(z) - z.requires_grad_() - flow.nn.init.xavier_normal_(z) - flow.nn.init.xavier_uniform_(z) - flow.nn.init.orthogonal_(z) + for dtype in [flow.float32, flow.float16]: + shape = (2, 3, 4, 5) + x = tensor_creator(*shape).to(dtype) + np_ones = np.ones(x.shape) + np_zeros = np.zeros(x.shape) + random_fill_val = 2.0 + x.fill_(random_fill_val) + test_case.assertTrue(np.allclose(get_numpy(x), random_fill_val * np_ones)) + flow.nn.init.ones_(x) + test_case.assertTrue(np.allclose(get_numpy(x), np_ones)) + flow.nn.init.zeros_(x) + test_case.assertTrue(np.allclose(get_numpy(x), np_zeros)) + flow.nn.init.constant_(x, random_fill_val) + test_case.assertTrue(np.allclose(get_numpy(x), random_fill_val * np_ones)) + z = tensor_creator(5, 4, 3, 2) + flow.nn.init.kaiming_normal_(z, a=0.1, mode="fan_out", nonlinearity="relu") + flow.nn.init.kaiming_uniform_(z) + z.requires_grad_() + flow.nn.init.xavier_normal_(z, flow.nn.init.calculate_gain("relu")) + flow.nn.init.xavier_uniform_(z, flow.nn.init.calculate_gain("relu")) + flow.nn.init.xavier_normal_( + z, flow.nn.init.calculate_gain("leaky_relu", 0.2) + ) + flow.nn.init.xavier_uniform_( + z, flow.nn.init.calculate_gain("leaky_relu", 0.2) + ) + flow.nn.init.trunc_normal_(z, mean=0.0, std=1.0, a=-2.0, b=2.0) + flow.nn.init.normal_(z, mean=0.0, std=1.0) + flow.nn.init.orthogonal_(z) + x = tensor_creator(*shape).to(dtype=flow.int32) np_ones = np.ones(x.shape, dtype=np.int32) np_zeros = np.zeros(x.shape, dtype=np.int32) - random_fill_val = -51 + random_fill_val = -2 x.fill_(random_fill_val) test_case.assertTrue(np.allclose(get_numpy(x), random_fill_val * np_ones)) flow.nn.init.ones_(x) @@ -188,23 +204,75 @@ class TestTensor(flow.unittest.TestCase): test_case.assertEqual(flow.nn.init.calculate_gain("conv2d"), 1) test_case.assertEqual(flow.nn.init.calculate_gain("tanh"), 5.0 / 3) + def _test_non_contiguous_tensor_init_methods(test_case, tensor_creator, get_numpy): + shape = (8, 8) + x = flow.zeros(shape) + sliced_x = x[::2, 1::2] + not_sliced_x = x[1::2, ::2] + random_fill_val = 923.53 + np_zeros = np.zeros((4, 4)) + # ones + flow.nn.init.ones_(sliced_x) + test_case.assertTrue(np.allclose(get_numpy(sliced_x), np.ones((4, 4)))) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # constant + flow.nn.init.constant_(sliced_x, random_fill_val) + test_case.assertTrue( + np.allclose(get_numpy(sliced_x), np.ones((4, 4)) * random_fill_val) + ) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # eye + flow.nn.init.eye_(sliced_x) + test_case.assertTrue(np.allclose(get_numpy(sliced_x), np.eye(4))) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # kaiming_normal_ + flow.nn.init.kaiming_normal_( + sliced_x, a=0.1, mode="fan_out", nonlinearity="relu" + ) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # kaiming_uniform_ + flow.nn.init.kaiming_uniform_(sliced_x) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # xavier_normal_ with relu gain + flow.nn.init.xavier_normal_(sliced_x, flow.nn.init.calculate_gain("relu")) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # xavier_uniform_ with relu gain + flow.nn.init.xavier_uniform_(sliced_x, flow.nn.init.calculate_gain("relu")) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # trunc_normal_ + flow.nn.init.trunc_normal_(sliced_x, mean=0.0, std=1.0, a=-2.0, b=2.0) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # normal_ + flow.nn.init.normal_(sliced_x, mean=0.0, std=1.0) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + # orthogonal_ + flow.nn.init.orthogonal_(sliced_x) + test_case.assertTrue(np.allclose(get_numpy(not_sliced_x), np_zeros)) + @flow.unittest.skip_unless_1n1d() def test_local_tensor_init_methods(test_case): - test_case._test_tensor_init_methods( - lambda *args, **kwargs: flow.Tensor(*args, **kwargs), lambda x: x.numpy() - ) + for device in ["cpu", "cuda"]: + test_case._test_tensor_init_methods( + lambda *args, **kwargs: flow.Tensor(*args, **kwargs, device=device), + lambda x: x.numpy(), + ) + test_case._test_non_contiguous_tensor_init_methods( + lambda *args, **kwargs: flow.Tensor(*args, **kwargs, device=device), + lambda x: x.numpy(), + ) @flow.unittest.skip_unless_1n2d() - def test_consistent_tensor_init_methods(test_case): - test_case._test_tensor_init_methods( - lambda *args, **kwargs: flow.Tensor( - *args, - **kwargs, - sbp=flow.sbp.broadcast, - placement=flow.placement("cuda", range(2)) - ), - lambda x: x.to_global(sbp=flow.sbp.broadcast).to_local().numpy(), - ) + def test_global_tensor_init_methods(test_case): + for device in ["cpu", "cuda"]: + test_case._test_tensor_init_methods( + lambda *args, **kwargs: flow.Tensor( + *args, + **kwargs, + sbp=flow.sbp.broadcast, + placement=flow.placement(device, range(2)) + ), + lambda x: x.to_global(sbp=flow.sbp.broadcast).to_local().numpy(), + ) @flow.unittest.skip_unless_1n1d() def test_tensor_with_single_int(test_case): @@ -442,7 +510,7 @@ class TestTensor(flow.unittest.TestCase): test_case.assertTrue(np.allclose(z.numpy(), 5 * np.ones(z.shape))) @flow.unittest.skip_unless_1n1d() - def test_mirrored_tensor_and_op(test_case): + def test_local_tensor_and_op(test_case): x1 = flow.Tensor([[1.0, 2.0]]) test_case.assertEqual(x1.dtype, flow.float32) test_case.assertEqual(x1.shape, flow.Size((1, 2))) @@ -553,16 +621,25 @@ class TestTensor(flow.unittest.TestCase): compare_setitem_with_numpy(x, se[1, :, 2], v) @flow.unittest.skip_unless_1n1d() - @autotest(n=5) + @autotest(n=5, auto_backward=False) def test_setitem_with_random_data(test_case): device = random_device() - x = random_tensor(low=0, high=0, ndim=1, dim0=16).to(device) + x = random_tensor(low=0, high=0, ndim=1, dim0=16, requires_grad=False).to( + device + ) y = random_tensor(low=-2, high=2, ndim=1, dim0=16).to(device) idx = random_tensor( low=0, high=15, ndim=1, dim0=20, dtype=int, requires_grad=False ).to(device) - z = y[idx] - x[idx] = z + + getitem_of = y.oneflow[idx.oneflow] + getitem_torch = y.pytorch[idx.pytorch] + test_case.assertTrue( + np.allclose(getitem_of.numpy(), getitem_torch.detach().cpu().numpy()) + ) + + x.oneflow[idx.oneflow] = getitem_of + x.pytorch[idx.pytorch] = getitem_torch return x @flow.unittest.skip_unless_1n1d() @@ -756,6 +833,24 @@ class TestTensor(flow.unittest.TestCase): y = x.argmax(dim=random(0, ndim).to(int), keepdim=random().to(bool)) return y + @autotest(auto_backward=False, check_graph=False) + def test_max_bool_input_with_random_data(test_case): + device = random_device() + dim = random(1, 4).to(int) + x = random_tensor(ndim=4, dtype=float, requires_grad=False).to( + device, dtype=torch.bool + ) + return x.max(dim) + + @autotest(auto_backward=False, check_graph=False) + def test_min_bool_input_with_random_data(test_case): + device = random_device() + dim = random(1, 4).to(int) + x = random_tensor(ndim=4, dtype=float, requires_grad=False).to( + device, dtype=torch.bool + ) + return x.min(dim) + @flow.unittest.skip_unless_1n1d() @autotest(n=5) def test_tensor_tanh_with_random_data(test_case): @@ -855,6 +950,14 @@ class TestTensor(flow.unittest.TestCase): y = x.sort(dim=random(low=-4, high=4).to(int), descending=random_bool()) return y[0], y[1] + @flow.unittest.skip_unless_1n1d() + @autotest(auto_backward=False, check_graph=True) + def test_sort_tensor_return_type(test_case): + device = random_device() + x = random_tensor(ndim=4).to(device) + result = x.sort(dim=random(low=-4, high=4).to(int), descending=random_bool()) + return result.values, result.indices + @flow.unittest.skip_unless_1n1d() @autotest(auto_backward=False, check_graph=True) def test_argsort_tensor_with_random_data(test_case): @@ -888,6 +991,12 @@ class TestTensor(flow.unittest.TestCase): x = random_tensor().to(device) return x.log2() + @autotest(n=5) + def test_log10_tensor_with_random_data(test_case): + device = random_device() + x = random_tensor().to(device) + return x.log10() + @autotest(n=5) def test_neg_tensor_with_random_data(test_case): device = random_device() @@ -925,12 +1034,24 @@ class TestTensor(flow.unittest.TestCase): device = random_device() x = random_tensor(ndim=4, dim1=8, dim2=9, dim3=10).to(device) y = x.topk( + random(low=1, high=8).to(int), + dim=random(low=1, high=4).to(int) | nothing(), + largest=random_bool() | nothing(), + sorted=constant(True) | nothing(), + ) + return y[0], y[1] + + @autotest(auto_backward=False, check_graph=True) + def test_tensor_topk_return_type(test_case): + device = random_device() + x = random_tensor(ndim=4, dim1=8, dim2=9, dim3=10).to(device) + result = x.topk( random(low=1, high=8).to(int), dim=random(low=1, high=4).to(int), largest=random_bool(), sorted=constant(True), ) - return y[0], y[1] + return result.values, result.indices @autotest(auto_backward=False, check_graph=True) def test_flow_fmod_element_with_random_data(test_case): @@ -1155,27 +1276,35 @@ class TestTensor(flow.unittest.TestCase): def test_tensor_constructor(test_case): x = flow.tensor([1, 2, 3]) test_case.assertTrue(np.array_equal(x.numpy(), [1, 2, 3])) - test_case.assertEquals(x.dtype, flow.int64) + test_case.assertEqual(x.dtype, flow.int64) x = flow.tensor([1.0, 2.0, 3.0]) test_case.assertTrue(np.array_equal(x.numpy(), [1.0, 2.0, 3.0])) - test_case.assertEquals(x.dtype, flow.float32) + test_case.assertEqual(x.dtype, flow.float32) x = flow.tensor([1.0, 2.0, 3.0], dtype=flow.float64) test_case.assertTrue(np.array_equal(x.numpy(), [1.0, 2.0, 3.0])) - test_case.assertEquals(x.dtype, flow.float64) + test_case.assertEqual(x.dtype, flow.float64) np_arr = np.array([1, 2, 3]) x = flow.tensor(np_arr) test_case.assertTrue(np.array_equal(x.numpy(), [1, 2, 3])) - test_case.assertEquals(x.dtype, flow.int64) + test_case.assertEqual(x.dtype, flow.int64) np_arr = np.array([1, 2, 3], dtype=np.float64) x = flow.tensor(np_arr) test_case.assertTrue(np.array_equal(x.numpy(), [1.0, 2.0, 3.0])) - test_case.assertEquals(x.dtype, flow.float64) + test_case.assertEqual(x.dtype, flow.float64) x = flow.tensor(np_arr, dtype=flow.float32) test_case.assertTrue(np.array_equal(x.numpy(), [1.0, 2.0, 3.0])) - test_case.assertEquals(x.dtype, flow.float32) + test_case.assertEqual(x.dtype, flow.float32) x = flow.tensor(np_arr, dtype=flow.int8) test_case.assertTrue(np.array_equal(x.numpy(), [1.0, 2.0, 3.0])) - test_case.assertEquals(x.dtype, flow.int8) + test_case.assertEqual(x.dtype, flow.int8) + x = flow.tensor([flow.tensor([1, 2])] * 3, dtype=flow.float32) + test_case.assertTrue(np.array_equal(x.numpy(), [[1, 2], [1, 2], [1, 2]])) + test_case.assertEqual(x.dtype, flow.float32) + + def test_tensor_contains_magic_method(test_case): + x = flow.tensor([[1, 2, 3], [4, 5, 6]]) + y = 1 in x + test_case.assertEqual(y, True) @profile(torch.Tensor.fill_) def profile_fill_(test_case): diff --git a/python/oneflow/test/tensor/test_tensor_part_2.py b/python/oneflow/test/tensor/test_tensor_part_2.py index dd426eba5c6f48db502b2e50fe4d5216283b4743..3bada76577a09b549f2a54d33d4fe841d5c79483 100644 --- a/python/oneflow/test/tensor/test_tensor_part_2.py +++ b/python/oneflow/test/tensor/test_tensor_part_2.py @@ -81,6 +81,13 @@ class TestTensor(flow.unittest.TestCase): np_out = np.equal(arr1, arr2) test_case.assertTrue(np.allclose(of_out.numpy(), np_out)) + def test_tensor_equal_bool_dtype(test_case): + np_bool = np.random.randint(0, 2, size=()).astype(bool).item() + input = flow.tensor(np_bool, dtype=flow.bool) + input2 = flow.tensor([np_bool], dtype=flow.bool) + test_case.assertTrue(input == np_bool) + test_case.assertTrue(input2 == np_bool) + def test_tensor_detach(test_case): shape = (2, 3, 4, 5) x = flow.tensor(np.random.randn(*shape), dtype=flow.float32, requires_grad=True) @@ -259,6 +266,16 @@ class TestTensor(flow.unittest.TestCase): ) return y + @autotest(auto_backward=False) + def test_clamp_maxnone_tensor_no_grad_with_random_data(test_case): + device = random_device() + input = random_tensor(low=-2, high=2).to(device) + y = input.clamp( + min=random(low=-1, high=-0.5).to(float), + max=random(low=0.5, high=1).to(float) | nothing(), + ) + return y + @autotest(n=5) def test_clamp_inplace_maxnone_tensor_with_random_data(test_case): device = random_device() @@ -270,6 +287,77 @@ class TestTensor(flow.unittest.TestCase): ) return y + @autotest(auto_backward=False) + def test_clamp_inplace_maxnone_tensor_no_grad_with_random_data(test_case): + device = random_device() + x = random_tensor(low=-2, high=2).to(device) + y = x + 1 + y.clamp_( + min=random(low=-1, high=-0.5).to(float), + max=random(low=0.5, high=1).to(float) | nothing(), + ) + return y + + @autotest(n=5) + def test_clamp_min_tensor_with_random_data(test_case): + device = random_device() + input = random_tensor(low=-2, high=2).to(device) + y = input.clamp_min(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(n=5) + def test_clamp_min_inplace_tensor_with_random_data(test_case): + device = random_device() + x = random_tensor(low=-2, high=2).to(device) + y = x + 1 + y.clamp_min_(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(auto_backward=False) + def test_clamp_min_tensor_no_grad_with_random_data(test_case): + device = random_device() + input = random_tensor(low=-2, high=2).to(device) + y = input.clamp_min(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(auto_backward=False) + def test_clamp_min_inplace_tensor_no_grad_with_random_data(test_case): + device = random_device() + x = random_tensor(low=-2, high=2).to(device) + y = x + 1 + y.clamp_min_(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(n=5) + def test_clamp_max_tensor_with_random_data(test_case): + device = random_device() + input = random_tensor(low=-2, high=2).to(device) + y = input.clamp_max(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(n=5) + def test_clamp_max_inplace_tensor_with_random_data(test_case): + device = random_device() + x = random_tensor(low=-2, high=2).to(device) + y = x + 1 + y.clamp_max_(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(auto_backward=False) + def test_clamp_max_tensor_no_grad_with_random_data(test_case): + device = random_device() + input = random_tensor(low=-2, high=2).to(device) + y = input.clamp_max(random(low=-0.5, high=0.5).to(float)) + return y + + @autotest(auto_backward=False) + def test_clamp_max_inplace_tensor_no_grad_with_random_data(test_case): + device = random_device() + x = random_tensor(low=-2, high=2).to(device) + y = x + 1 + y.clamp_max_(random(low=-0.5, high=0.5).to(float)) + return y + @autotest(n=5) def test_clip_tensor_with_random_data(test_case): device = random_device() @@ -466,7 +554,7 @@ class TestTensor(flow.unittest.TestCase): # Not check graph because of one reason: # Reason 1, lazy tensor cannot call .numpy(). tensor.numpy() is not allowed to called in nn.Graph.build(*args) or called by lazy tensor. # Please refer to File "python/oneflow/nn/modules/nonzero.py", line 29, in nonzero_op. - @autotest(n=5, auto_backward=False, check_graph="ValidatedFlase") + @autotest(n=5, auto_backward=False, check_graph="ValidatedFalse") def test_tensor_nonzero_with_random_data(test_case): device = random_device() ndim = random(2, 6).to(int) @@ -791,7 +879,7 @@ class TestTensor(flow.unittest.TestCase): return y @flow.unittest.skip_unless_1n4d() - def test_construct_consistent_tensor_by_numpy(test_case): + def test_construct_global_tensor_by_numpy(test_case): x = np.ones((4, 4), dtype=np.int32) placement = flow.placement("cuda", [0, 1, 2, 3]) y = flow.tensor( @@ -818,7 +906,7 @@ class TestTensorNumpy(flow.unittest.TestCase): @flow.unittest.skip_unless_1n2d() def test_1d_sbp_tensor_numpy_1n2d(test_case): ori_x = flow.tensor([1, 2, 3, 4]) + flow.env.get_rank() - placement = flow.env.all_device_placement("cpu") + placement = flow.placement.all("cpu") x = ori_x.to_global(placement=placement, sbp=flow.sbp.split(0)) test_case.assertTrue(np.allclose(x.numpy(), [1, 2, 3, 4, 2, 3, 4, 5])) @@ -828,7 +916,7 @@ class TestTensorNumpy(flow.unittest.TestCase): x = ori_x.to_global(placement=placement, sbp=flow.sbp.partial_sum) test_case.assertTrue(np.allclose(x.numpy(), [3, 5, 7, 9])) - placement = flow.env.all_device_placement("cuda") + placement = flow.placement.all("cuda") x = ori_x.to_global(placement=placement, sbp=flow.sbp.split(0)) test_case.assertTrue(np.allclose(x.numpy(), [1, 2, 3, 4, 2, 3, 4, 5])) @@ -967,6 +1055,26 @@ class TestTensorNumpy(flow.unittest.TestCase): z = x.repeat_interleave(y, 1, output_size=2) return z + @flow.unittest.skip_unless_1n2d() + @globaltest + def test_global_tensor_detach(test_case): + device = random_device().value() + placement = flow.placement(device, [0, 1]) + a = flow.ones(4, 8).to_global(placement, flow.sbp.broadcast) + test_case.assertTrue(a.is_leaf) + b = a.float().clone().detach() + test_case.assertTrue(b.is_leaf) + + @flow.unittest.skip_unless_1n1d() + @autotest(n=5) + def test_tensor_nansum(test_case): + device = random_device() + x = random_tensor(4, random(0, 5), 2).to(device) + mask = x < 0 + x = x.masked_fill(mask, float("nan")) + y = x.nansum() + return y + if __name__ == "__main__": unittest.main() diff --git a/python/oneflow/test/tensor/test_tensor_part_3.py b/python/oneflow/test/tensor/test_tensor_part_3.py new file mode 100644 index 0000000000000000000000000000000000000000..cf23c0ecbec1a497380bb2a15a16dacada4b848a --- /dev/null +++ b/python/oneflow/test/tensor/test_tensor_part_3.py @@ -0,0 +1,82 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import unittest + +import oneflow as flow +import oneflow.unittest +import numpy as np + +from oneflow.test_utils.automated_test_util import * + + +def _get_indexes(device): + return ( + constant( + torch.tensor(np.array([[0, 1], [1, 0]]), dtype=torch.int64, device=device) + ), + constant( + torch.tensor(np.array([[1, 0], [0, 1]]), dtype=torch.int64, device=device) + ), + constant( + torch.tensor(np.array([[1, 0], [1, 0]]), dtype=torch.int64, device=device) + ), + constant( + torch.tensor(np.array([[0, 1], [0, 1]]), dtype=torch.int64, device=device) + ), + ) + + +@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases") +@flow.unittest.skip_unless_1n1d() +class TestTensor(flow.unittest.TestCase): + @autotest(n=10) + def test_scatter_random_data(test_case): + device = random_device() + input = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + src = oneof(3.14, random_tensor(ndim=2, dim0=2, dim1=2).to(device)) + inplace = oneof(True, False) + dim = oneof(0, 1, -1) + if inplace: + y = input + 1 + y.scatter_(dim, oneof(*_get_indexes(device)), src) + return y + return input.scatter(dim, oneof(*_get_indexes(device)), src) + + @autotest( + n=10, auto_backward=False + ) # peihong: pytorch dose not support backward when reduce is add or multiply + def test_scatter_add_or_multiply_random_data(test_case): + device = random_device() + input = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + src = random_tensor(ndim=2, dim0=2, dim1=2).to(device) + inplace = oneof(True, False) + reduce = oneof("add", "multiply") + dim = oneof(0, 1) + if inplace: + y = input + 1 + y.scatter_( + dim, oneof(*_get_indexes(device)), src, reduce=reduce, + ) + return y + return input.scatter(dim, oneof(*_get_indexes(device)), src, reduce=reduce) + + def test_tensor_element_size_api(test_case): + x = flow.ones(2, 1, dtype=flow.float) + test_case.assertEqual(x.element_size(), 4) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/oneflow/test_utils/automated_test_util/generators.py b/python/oneflow/test_utils/automated_test_util/generators.py index 15602299c933cae68515e6054a3aa16b1f94cee5..9215b8490aedae211a3fb966f5734a9627fe717c 100644 --- a/python/oneflow/test_utils/automated_test_util/generators.py +++ b/python/oneflow/test_utils/automated_test_util/generators.py @@ -195,6 +195,9 @@ class oneof(generator): return self.args[i].value() raise RuntimeError() + def __call__(self, *args: Any, **kwds: Any) -> Any: + return self._calc_value()(*args, **kwds) + def size(self): return sum([x.size() for x in self.args]) @@ -402,6 +405,49 @@ class gpu_device(generator): return random_util.choice(["cuda"]) +@data_generator(torch.dtype) +class random_pytorch_dtype(generator): + none_dtype_seq = [None] + bool_dtype_seq = [torch.bool] + floating_dtype_seq = [torch.float, torch.double] + half_dtype_seq = [torch.half] + bfloat16_dtype_seq = [torch.bfloat16] + signed_int_dtype_seq = [torch.int8, torch.int32, torch.int64] + unsigned_int_dtype_seq = [torch.uint8] + int_dtype_seq = [torch.int8, torch.int32, torch.int64] + image_dtype_seq = [torch.uint8, torch.float] + index_dtype_seq = [torch.int32, torch.int64] + arithmetic_dtype_seq = [*floating_dtype_seq, *int_dtype_seq] + pod_dtype_seq = [*arithmetic_dtype_seq, *unsigned_int_dtype_seq, *bool_dtype_seq] + all_dtype_seq = [*arithmetic_dtype_seq, torch.half, torch.bfloat16] + + seq_name_to_seq = { + "None": none_dtype_seq, + "bool": bool_dtype_seq, + "float": floating_dtype_seq, + "half": half_dtype_seq, + "bfloat16": bfloat16_dtype_seq, + "signed": signed_int_dtype_seq, + "unsigned": unsigned_int_dtype_seq, + "int": int_dtype_seq, + "image": image_dtype_seq, + "index": index_dtype_seq, + "arithmetic": arithmetic_dtype_seq, + "pod": pod_dtype_seq, + "all": all_dtype_seq, + } + + def __init__(self, seq_names): + super().__init__([]) + # concat related dtype_seq for name in seq_names + self.data_type_seq = [ + dtype for name in seq_names for dtype in self.seq_name_to_seq[name] + ] + + def _calc_value(self): + return random_util.choice(self.data_type_seq) + + class all_placement(generator): def __init__(self): super().__init__([]) @@ -574,6 +620,7 @@ __all__ = [ "random_pytorch_tensor", "random_bool", "random_device", + "random_pytorch_dtype", "cpu_device", "gpu_device", "random_placement", diff --git a/python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py b/python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py index 159ad0a8d47688e6e07c9d18ca4de1f6d4a085a4..a059e2758effb60c56e802ef4cf823a6d0606b22 100644 --- a/python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py +++ b/python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py @@ -24,6 +24,7 @@ import gc import numpy as np import oneflow as flow from oneflow.test_utils.automated_test_util import profiler as auto_profiler +from oneflow.test_utils.test_util import type_name_to_flow_type flow.backends.cudnn.deterministic = True @@ -44,6 +45,7 @@ from .generators import ( Nothing, generator, random_pytorch_tensor, + random_pytorch_dtype, choice_pytorch_tensor, rng, ) @@ -292,6 +294,25 @@ def get_fake_program_more_detail(oneflow, mode, func, args=None, kwargs=None): print("\n\n") +# NOTE(lixiang): When the graph global test is executed, the func is used to get the device type. +def get_global_test_device(oneflow_args, oneflow_kwargs=None): + # The case when the parameter input of Op only has kwargs. + if not oneflow_args: + return oneflow_kwargs["placement"].type + # The case when the parameter input of Op is tensors. + elif isinstance(oneflow_args[0], flow.Tensor): + return oneflow_args[0].placement.type + # The case when the parameter input of Op is tensor. + elif isinstance(oneflow_args[0], flow.placement): + return oneflow_args[0].type + # The case when the parameter input of Op is tuple. For example: test_0_dim_tensor. + elif isinstance(oneflow_args[0], tuple): + return oneflow_args[0][0].placement.type + # When oneflow_args[0] is int or float, etc. + else: + return oneflow_args[1].placement.type + + # NOTE(lixiang): When oneflow is of type nn.Module, build the following Graph for testing. # graph_train_oneflow: is a deepcopy of oneflow. def get_module_graph_test(graph_train_oneflow, oneflow, verbose, oneflow_args, *args): @@ -317,9 +338,9 @@ def get_module_graph_test(graph_train_oneflow, oneflow, verbose, oneflow_args, * res = self.test_module(*args) forward_res = res if global_backward and graph_train_parameters_len: - if isinstance(self.test_module.origin, flow.nn.LSTMCell): + if isinstance(self.test_module.to(flow.nn.Module), flow.nn.LSTMCell): res = res[0] + res[1] - elif isinstance(self.test_module.origin, flow.nn.LSTM): + elif isinstance(self.test_module.to(flow.nn.Module), flow.nn.LSTM): res = res[0].sum() + res[1][0].sum() + res[1][1].sum() elif isinstance(res, (tuple, list)): res = res[0] @@ -369,19 +390,40 @@ def get_functional_graph_res( return graph_functional_oneflow(*graph_args, **graph_kwargs) try: - # When the tensor on the cpu executes to to the cpu in nn.Graph, a check error will be reported. + # In graph mode, when the tensor on the cpu executes the to("cpu") method, a check error will be reported. if oneflow.__name__ == "to" or oneflow.__name__ == "_to": if isinstance(oneflow_res, flow.Tensor): - if (oneflow_args and oneflow_res.device.type == oneflow_args[0]) or ( - oneflow_kwargs - and oneflow_res.device.type == oneflow_kwargs["device"] - ): - test_g_res = oneflow_res + # The global tensor needs to obtain the device type through placement.type. + if is_global(): + if ( + oneflow_args and oneflow_res.placement.type == oneflow_args[0] + ) or ( + oneflow_kwargs + and oneflow_res.placement.type == oneflow_kwargs["device"] + ): + test_g_res = oneflow_res + # The tensor needs to obtain the device type through device.type. + else: + if ( + oneflow_args and oneflow_res.device.type == oneflow_args[0] + ) or ( + oneflow_kwargs + and oneflow_res.device.type == oneflow_kwargs["device"] + ): + test_g_res = oneflow_res else: pass + # nn.Graph donot deal with Module type. EX: m.to_global(placement, sbp). + elif oneflow.__name__ == "to_global": + test_g_res = oneflow_res elif oneflow.__name__ == "Parameter": # nn.Graph donot deal with Parameter creation. test_g_res = oneflow_res + # When doing the global op test, get_global_test_device() will be executed, and temporarily skipping the graph autotest on cpu device. + elif is_global() and ( + get_global_test_device(oneflow_args, oneflow_kwargs) == "cpu" + ): + test_g_res = oneflow_res else: test_g = TestGraphOfFunctional() test_g_res = test_g() @@ -422,8 +464,12 @@ def get_tensor_graph_res( return graph_tensor_oneflow(*tensor_graph_args, **tensor_graph_kwargs) try: - test_g = TestGraphOfTensorMethod() - test_g_res = test_g() + # Set test_g_res = None, check_eager_graph_tensor will return True, the purpose is to temporarily skip the Graph global test on cpu. + if is_global() and (get_global_test_device((oneflow,)) == "cpu"): + test_g_res = None + else: + test_g = TestGraphOfTensorMethod() + test_g_res = test_g() except Exception as e: if not verbose: get_fake_program_more_detail( @@ -479,8 +525,14 @@ def oneflow_eager_run_with_graph_check( test_g = get_module_graph_test( graph_train_oneflow, oneflow, verbose, oneflow_args, *args ) - # When testing module methods, kwargs are not considered. - test_g_res = test_g(*graph_args) + # When doing the global op test, get_global_test_device() will be executed, and temporarily skipping the graph autotest on cpu device. + if is_global() and ( + get_global_test_device(oneflow_args, oneflow_kwargs) == "cpu" + ): + test_g_res = oneflow_res + else: + # When testing module methods, kwargs are not considered. + test_g_res = test_g(*graph_args) elif oneflow.__name__ in ignore_apis_list: find_check_module_func = False # 1. "oneflow.nn.modules" not in oneflow.__module__: For avoid run nn.Module branch graph test, like fold op call Fold Module actually. @@ -943,8 +995,7 @@ class DualObject: ) else: oneflow = oneflow.to_global( - placement=flow.env.all_device_placement("cpu"), - sbp=[flow.sbp.broadcast,], + placement=flow.placement.all("cpu"), sbp=[flow.sbp.broadcast,], ) if testing: dual_modules_to_test.append(self) @@ -1137,8 +1188,8 @@ def autotest( ): verbose = os.getenv("ONEFLOW_TEST_VERBOSE") is not None - if check_graph == "ValidatedFlase": - # check graph is intentionally closed and threre is a validated reason. + if check_graph == "ValidatedFalse": + # check graph is intentionally closed and there is a validated reason. check_graph = False def deco(f): @@ -1308,7 +1359,7 @@ def random_tensor( flow_tensor = flow.tensor( pytorch_tensor.detach().cpu().numpy(), requires_grad=(requires_grad and dtype != int), - placement=flow.env.all_device_placement("cpu"), + placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast, ) else: @@ -1321,6 +1372,15 @@ def random_tensor( return GetDualObject("unused", pytorch_tensor, flow_tensor) +def random_dtype(seq_names): + pytorch_dtype = random_pytorch_dtype(seq_names).value() + if pytorch_dtype is None: + flow_dtype = None + else: + flow_dtype = type_name_to_flow_type[pytorch_dtype.__str__().split(".")[-1]] + return GetDualObject("DualDType", pytorch_dtype, flow_dtype) + + def choice_tensor( a, size=None, replace=True, p=None, dtype=int, requires_grad=False, ): @@ -1339,7 +1399,7 @@ def choice_tensor( flow_tensor = flow.tensor( pytorch_tensor.detach().cpu().numpy(), requires_grad=(requires_grad and dtype != int), - placement=flow.env.all_device_placement("cpu"), + placement=flow.placement.all("cpu"), sbp=flow.sbp.broadcast, ) else: @@ -1352,4 +1412,4 @@ def choice_tensor( torch = GetDualObject("", torch_original, flow) -__all__ = ["autotest", "globaltest", "random_tensor", "choice_tensor"] +__all__ = ["autotest", "globaltest", "random_tensor", "random_dtype", "choice_tensor"] diff --git a/python/oneflow/test_utils/test_util.py b/python/oneflow/test_utils/test_util.py index 1b01ea8b25cb8d25c1dc153b393123a1b939ec85..f49887b33c2363f66f75089cb9d3ad06a8dacb63 100644 --- a/python/oneflow/test_utils/test_util.py +++ b/python/oneflow/test_utils/test_util.py @@ -65,10 +65,13 @@ type_name_to_flow_type = { "float16": flow.float16, "float32": flow.float32, "double": flow.double, + "float64": flow.double, "int8": flow.int8, "int32": flow.int32, "int64": flow.int64, "uint8": flow.uint8, + "half": flow.half, + "bfloat16": flow.bfloat16, } type_name_to_np_type = { "float16": np.float16, diff --git a/python/oneflow/test_utils/throttle.py b/python/oneflow/test_utils/throttle.py new file mode 100644 index 0000000000000000000000000000000000000000..39a846dc07f497b7e49fe3dc0135ca58309fd1b4 --- /dev/null +++ b/python/oneflow/test_utils/throttle.py @@ -0,0 +1,60 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import argparse +import hashlib +import subprocess +import portalocker +import os + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Control when the script runs through special variables." + ) + parser.add_argument( + "--with-cuda", type=int, default=1, help="whether has cuda device." + ) + parser.add_argument("cmd", type=str, nargs="...", help="command to run") + return parser.parse_args() + + +def hash_cli2gpu(cmd: list): + import pynvml + + pynvml.nvmlInit() + slot = pynvml.nvmlDeviceGetCount() + hash = hashlib.sha1(" ".join(cmd).encode("utf-8")).hexdigest() + gpu_id = int(hash, 16) % slot + return [gpu_id] + + +def main(): + args = parse_args() + if args.with_cuda: + cuda_visible_devices = [str(i) for i in hash_cli2gpu(args.cmd)] + with portalocker.Lock( + ".oneflow-throttle-gpu-" + "-".join(cuda_visible_devices) + ".lock", + timeout=400, + ): + env = dict(os.environ, CUDA_VISIBLE_DEVICES=",".join(cuda_visible_devices)) + return subprocess.call(args.cmd, env=env) + else: + return subprocess.call(args.cmd) + + +if __name__ == "__main__": + returncode = main() + exit(returncode) diff --git a/python/oneflow/utils/__init__.py b/python/oneflow/utils/__init__.py index aefafb337a0dbdf142b0b71b7a9b7a1ce1b093eb..40acb6e6a1c0e5302f6daa8e5cec1cb17fc6480c 100644 --- a/python/oneflow/utils/__init__.py +++ b/python/oneflow/utils/__init__.py @@ -14,4 +14,6 @@ See the License for the specific language governing permissions and limitations under the License. """ from oneflow.framework.config_util import api_load_library as load_library -from oneflow.utils.torch.from_or_to_torch_tensor import from_torch, to_torch +from oneflow.utils import tensor +from oneflow.utils import global_view +from . import checkpoint diff --git a/python/oneflow/utils/checkpoint.py b/python/oneflow/utils/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..053758ba230bfc68f013365a9abcde1ce920aaa8 --- /dev/null +++ b/python/oneflow/utils/checkpoint.py @@ -0,0 +1,111 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +# This file is mostly copied from PyTorch + +import oneflow as flow +from typing import List, Union + + +def _checkpoint_without_reentrant(function, *args): + """Checkpointining without re-entrant autograd + Args: + function: describes what to run in the forward pass of the model or + part of the model. It should also know how to handle the inputs + passed as the tuple. For example, in LSTM, if user passes + ``(activation, hidden)``, :attr:`function` should correctly use the + first input as ``activation`` and the second input as ``hidden`` + *args: Arguments to pass in to the given ``function``. + """ + + storage: List[Union[flow.Tensor, None]] = [] + counter = 0 + + def pack(x): + nonlocal counter + counter += 1 + return counter - 1 + + # TODO(jianhao): support restoring rng state once we have flow.random.fork_rng + def unpack(x): + if len(storage) == 0: + + def inner_pack(inner): + storage.append(inner) + return None + + def inner_unpack(packed): + raise RuntimeError( + "You are calling backwards on a tensor that is never exposed. Please open an issue." + ) + + with flow.enable_grad(): + with flow.autograd.graph.saved_tensors_hooks(inner_pack, inner_unpack): + _unused = function(*args) + + return storage[x] + + with flow.autograd.graph.saved_tensors_hooks(pack, unpack): + output = function(*args) + + return output + + +def checkpoint(function, *args): + r"""Checkpoint a model or part of the model + + Checkpointing works by trading compute for memory. Rather than storing all + intermediate activations of the entire computation graph for computing + backward, the checkpointed part does **not** save intermediate activations, + and instead recomputes them in backward pass. It can be applied on any part + of a model. + + Specifically, in the forward pass, :attr:`function` will run in + :func:`flow.no_grad` manner, i.e., not storing the intermediate + activations. Instead, the forward pass saves the inputs tuple and the + :attr:`function` parameter. In the backwards pass, the saved inputs and + :attr:`function` is retrieved, and the forward pass is computed on + :attr:`function` again, now tracking the intermediate activations, and then + the gradients are calculated using these activation values. + + The output of :attr:`function` can contain non-Tensor values and gradient + recording is only performed for the Tensor values. Note that if the output + consists of nested structures (ex: custom objects, lists, dicts etc.) + consisting of Tensors, these Tensors nested in custom structures will not + be considered as part of autograd. + + + .. warning:: + If :attr:`function` invocation during backward does anything different + than the one during forward, e.g., due to some global variable, the + checkpointed version won't be equivalent, and unfortunately it can't be + detected. + + .. warning:: + Preserving rng states is not supported now, so that the behavior of + checkpointing does not fully align with PyTorch. + + Args: + function: describes what to run in the forward pass of the model or + part of the model. It should also know how to handle the inputs + passed as the tuple. For example, in LSTM, if user passes + ``(activation, hidden)``, :attr:`function` should correctly use the + first input as ``activation`` and the second input as ``hidden`` + args: tuple containing inputs to the :attr:`function` + + Returns: + Output of running :attr:`function` on :attr:`*args` + """ + return _checkpoint_without_reentrant(function, *args) diff --git a/python/oneflow/utils/data/__init__.py b/python/oneflow/utils/data/__init__.py index fb8219c4846dad972d0fa707e4f14b135ddbc670..27d775ed57039d2bf2de36288c7daaff4362430d 100644 --- a/python/oneflow/utils/data/__init__.py +++ b/python/oneflow/utils/data/__init__.py @@ -29,7 +29,11 @@ from oneflow.utils.data.dataset import ( random_split, ) from oneflow.utils.data.dataset import IterableDataset as IterDataPipe -from oneflow.utils.data.dataloader import DataLoader, _DatasetKind +from oneflow.utils.data.dataloader import ( + DataLoader, + _DatasetKind, + get_worker_info, +) from oneflow.utils.data.decorator import ( functional_datapipe, guaranteed_datapipes_determinism, diff --git a/python/oneflow/utils/data/_utils/pin_memory.py b/python/oneflow/utils/data/_utils/pin_memory.py index 54b5efec48e60e73dc1c623b3a26a274f7e5f5be..b3f3ca4a46f8f0bdba19da22f884924eb312b285 100644 --- a/python/oneflow/utils/data/_utils/pin_memory.py +++ b/python/oneflow/utils/data/_utils/pin_memory.py @@ -26,7 +26,7 @@ import collections.abc import queue from . import MP_STATUS_CHECK_INTERVAL -from .worker import ExceptionWrapper +from oneflow._utils import ExceptionWrapper container_abcs = collections.abc string_classes = (str, bytes) diff --git a/python/oneflow/utils/data/_utils/worker.py b/python/oneflow/utils/data/_utils/worker.py index 215bd913b28d04dc43f593b7e64c1253a835f17c..cdf6d4d7f39ee2f5218fe73492f753e67be811b5 100644 --- a/python/oneflow/utils/data/_utils/worker.py +++ b/python/oneflow/utils/data/_utils/worker.py @@ -30,44 +30,7 @@ import signal import oneflow as flow from . import signal_handling, MP_STATUS_CHECK_INTERVAL, IS_WINDOWS, HAS_NUMPY - - -class KeyErrorMessage(str): - r"""str subclass that returns itself in repr""" - - def __repr__(self): - return self - - -class ExceptionWrapper(object): - r"""Wraps an exception plus traceback to communicate across threads""" - - def __init__(self, exc_info=None, where="in background"): - # It is important that we don't store exc_info, see - # NOTE [ Python Traceback Reference Cycle Problem ] - if exc_info is None: - exc_info = sys.exc_info() - self.exc_type = exc_info[0] - self.exc_msg = "".join(traceback.format_exception(*exc_info)) - self.where = where - - def reraise(self): - r"""Reraises the wrapped exception in the current thread""" - # Format a message such as: "Caught ValueError in DataLoader worker - # process 2. Original Traceback:", followed by the traceback. - msg = "Caught {} {}.\nOriginal {}".format( - self.exc_type.__name__, self.where, self.exc_msg - ) - if self.exc_type == KeyError: - # KeyError calls repr() on its argument (usually a dict key). This - # makes stack traces unreadable. It will not be changed in Python - # (https://bugs.python.org/issue2651), so we work around it. - msg = KeyErrorMessage(msg) - elif getattr(self.exc_type, "message", None): - # Some exceptions have first argument as non-str but explicitly - # have message field - raise self.exc_type(message=msg) - raise self.exc_type(msg) +from oneflow._utils import ExceptionWrapper if IS_WINDOWS: @@ -278,7 +241,6 @@ def _worker_loop( auto_collation, collate_fn, drop_last, - generator, base_seed, init_fn, worker_id, @@ -308,7 +270,7 @@ def _worker_loop( flow.set_num_threads(1) seed = base_seed + worker_id random.seed(seed) - generator.manual_seed(seed) + flow.manual_seed(seed) if HAS_NUMPY: np_seed = _generate_state(base_seed, worker_id) import numpy as np diff --git a/python/oneflow/utils/data/dataloader.py b/python/oneflow/utils/data/dataloader.py index 33e4aa24de59439b3eb42f69899b364b79bd3896..118b4234ea715baf21fa577681004ad8fa322119 100644 --- a/python/oneflow/utils/data/dataloader.py +++ b/python/oneflow/utils/data/dataloader.py @@ -13,8 +13,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ -import sys -import traceback import warnings import os import threading @@ -25,40 +23,9 @@ from typing import Any, Callable, TypeVar, Generic, Sequence, List, Optional import multiprocessing as python_multiprocessing import oneflow.multiprocessing as multiprocessing +from oneflow._utils import ExceptionWrapper import oneflow as flow -from oneflow.utils.data import _utils - - -class ExceptionWrapper(object): - r"""Wraps an exception plus traceback to communicate across threads""" - - def __init__(self, exc_info=None, where="in background"): - # It is important that we don't store exc_info, see - # NOTE [ Python Traceback Reference Cycle Problem ] - if exc_info is None: - exc_info = sys.exc_info() - self.exc_type = exc_info[0] - self.exc_msg = "".join(traceback.format_exception(*exc_info)) - self.where = where - - def reraise(self): - r"""Reraises the wrapped exception in the current thread""" - # Format a message such as: "Caught ValueError in DataLoader worker - # process 2. Original Traceback:", followed by the traceback. - msg = "Caught {} {}.\nOriginal {}".format( - self.exc_type.__name__, self.where, self.exc_msg - ) - if self.exc_type == KeyError: - # KeyError calls repr() on its argument (usually a dict key). This - # makes stack traces unreadable. It will not be changed in Python - # (https://bugs.python.org/issue2651), so we work around it. - msg = KeyErrorMessage(msg) - elif getattr(self.exc_type, "message", None): - # Some exceptions have first argument as non-str but explicitly - # have message field - raise self.exc_type(message=msg) - raise self.exc_type(msg) - +import numpy as np string_classes = (str, bytes) @@ -129,13 +96,13 @@ class DataLoader(Generic[T_co]): Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset. - The :class:`~flow.utils.data.DataLoader` supports both map-style and + The :class:`~oneflow.utils.data.DataLoader` supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching (collation) and memory pinning. - See :py:mod:`flow.utils.data` documentation page for more details. + See :py:mod:`oneflow.utils.data` documentation page for more details. - In consideration of compatibility, the design of our dataloader is consistent with pytorch, ref:https://github.com/pytorch/pytorch/tree/v1.7.0 + In consideration of compatibility, the design of our dataloader is consistent with pytorch, ref: https://github.com/pytorch/pytorch/tree/v1.7.0 Args: dataset (Dataset): dataset from which to load the data. @@ -402,7 +369,7 @@ class DataLoader(Generic[T_co]): if self.persistent_workers and self.num_workers > 0: if self._iterator is None: self._iterator = self._get_iterator() - else: + elif not self._iterator._status_reset: self._iterator._reset(self) return self._iterator else: @@ -535,19 +502,22 @@ class _BaseDataLoaderIter(object): self._timeout = loader.timeout self._collate_fn = loader.collate_fn self._sampler_iter = iter(self._index_sampler) - self._generator = loader.generator - self._base_seed = flow.tensor([0], dtype=flow.int64).uniform_().numpy().item() # self._base_seed = flow.empty((), dtype=flow.int64).random_(generator=loader.generator).item() + self._base_seed = flow.randint( + 0, np.iinfo(np.int64).max, (), generator=loader.generator + ).item() self._persistent_workers = loader.persistent_workers self._num_yielded = 0 self._profile_name = "enumerate(DataLoader)#{}.__next__".format( self.__class__.__name__ ) + self._status_reset = True def __iter__(self) -> "_BaseDataLoaderIter": return self def _reset(self, loader, first_iter=False): + self._status_reset = True self._sampler_iter = iter(self._index_sampler) self._num_yielded = 0 self._IterableDataset_len_called = loader._IterableDataset_len_called @@ -559,6 +529,7 @@ class _BaseDataLoaderIter(object): raise NotImplementedError def __next__(self) -> Any: + self._status_reset = False if self._sampler_iter is None: self._reset() data = self._next_data() @@ -575,6 +546,7 @@ class _BaseDataLoaderIter(object): if self._num_workers > 1: warn_msg += "Multiprocessing dataloader is not support yet!" warnings.warn(warn_msg) + return data next = __next__ @@ -920,7 +892,6 @@ class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter): def __init__(self, loader): super(_MultiProcessingDataLoaderIter, self).__init__(loader) - assert not flow.env.rdma_is_initialized(), ( "RDMA is initialized! Could not create _MultiProcessingDataLoaderIter any more. " "Please make sure Dataloader is created before invoking oneflow.env.init_rdma(). " @@ -963,7 +934,6 @@ class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter): self._auto_collation, self._collate_fn, self._drop_last, - self._generator, self._base_seed, self._worker_init_fn, i, @@ -1241,9 +1211,15 @@ class _MultiProcessingDataLoaderIter(_BaseDataLoaderIter): # Called when shutting down this `_MultiProcessingDataLoaderIter`. # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on # the logic of this function. - python_exit_status = _utils.python_exit_status + + # See (2) of the note. If Python is shutting down, do no-op. + try: + python_exit_status = _utils.python_exit_status + except AttributeError: + # Python is shutting down and `_utils` has been freed + assert _utils is None + return if python_exit_status is True or python_exit_status is None: - # See (2) of the note. If Python is shutting down, do no-op. return # Normal exit when last reference is gone / iterator is depleted. # See (1) and the second half of the note. diff --git a/python/oneflow/utils/data/distributed.py b/python/oneflow/utils/data/distributed.py index 80466117abe4952f4d9865362c435ad4c5386ab0..a8207f02738a72d516f989955d1c705f75eb8f89 100644 --- a/python/oneflow/utils/data/distributed.py +++ b/python/oneflow/utils/data/distributed.py @@ -85,12 +85,7 @@ class DistributedSampler(Sampler[T_co]): num_replicas = flow.env.get_world_size() if rank is None: rank = flow.env.get_rank() - print( - "flow.env.get_world_size() >>>>> ", - flow.env.get_world_size(), - "flow.env.get_rank() >>>>>", - flow.env.get_rank(), - ) + if rank >= num_replicas or rank < 0: raise ValueError( "Invalid rank {}, rank should be in the interval" diff --git a/python/oneflow/utils/global_view/__init__.py b/python/oneflow/utils/global_view/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ac403a45a7cbcafbcdcaf53821d01d3cbdb39b63 --- /dev/null +++ b/python/oneflow/utils/global_view/__init__.py @@ -0,0 +1,25 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from oneflow.utils.global_view.to_global import to_global +from oneflow.utils.global_view.to_local import to_local +from oneflow.utils.global_view.global_mode import global_mode, current_global_mode + +__all__ = [ + "to_global", + "to_local", + "global_mode", + "current_global_mode", +] diff --git a/python/oneflow/utils/global_view/global_mode.py b/python/oneflow/utils/global_view/global_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..86f3dffcab406d918a861059a45bbb88f2eace47 --- /dev/null +++ b/python/oneflow/utils/global_view/global_mode.py @@ -0,0 +1,114 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import oneflow._oneflow_internal.global_view as internal_global_view + + +class global_mode(internal_global_view.global_mode): + r"""Create a scope to provide global information for the computation process within it. + + It provides convinience for converting from local execution to global execution, especially for converting to ddp global execution. + + 1) Make the source op create the global tensor directly. + 2) Make it legal for the "to(device)" API of the global tensor. + 3) Make it legal to use ".device" to get the device type of the global tensor. + + Note: + Both placement and sbp are required if the global mode is enabled. + + Args: + enabled (bool): whether the global mode is enbaled. + placement (oneflow.placement, optional): the desired placement of the input. Default: None + sbp (oneflow.sbp.sbp, list/tuple of oneflow.sbp.sbp, optional): the desired sbp of the input or self-defined functions in order to specify SBP. Default: None + + Example:: + + .. code-block:: python + + class LinearEvalGraphWithDDP(flow.nn.Graph): + def __init__(self): + super().__init__() + self.linear_dp = linear_dp + + def build(self, x): + with global_mode(True, placement=P, sbp=B): + device = self.linear_dp.weight.device + + x = x.to(device) + + out = self.linear_dp(x) + + # The local tensor will be converted to global + sample = flow.randn(out.shape, device="cpu").to(device) + out = out + sample * 100 + out = out - sample * 100 + + return out + + .. code-block:: python + + with global_mode(False): + # The tensor will be keeped as local. + sample = flow.randn(out.shape, device="cpu").to(device) + out = out + sample * 100 + out = out - sample * 100 + """ + + def __init__(self, enabled, placement=None, sbp=None) -> None: + if not enabled: + super().__init__(enabled) + else: + super().__init__(enabled, placement, sbp) + + def __enter__(self): + pass + + def __exit__(self, type, value, traceback): + pass + + +class current_global_mode(internal_global_view.current_global_mode): + r"""Get the current global mode information. + + Use the current_global_mode to get the information of global mode, including enabled, placement and sbp. + + Note: the sbp property is supposed to return a list/tuple of oneflow.sbp.sbp. + + Example:: + + .. code-block:: python + + with global_mode(True, placement=P, sbp=B): + # Get the global mode info. + cur_global_mode = global_view.current_global_mode() + test_case.assertTrue(cur_global_mode.is_enabled) + test_case.assertEqual(cur_global_mode.placement, P) + test_case.assertEqual(cur_global_mode.sbp[0], B) + """ + + def __init__(self) -> None: + super().__init__() + + @property + def is_enabled(self): + return super().is_enabled + + @property + def sbp(self): + return super().sbp + + @property + def placement(self): + return super().placement diff --git a/python/oneflow/utils/global_view/global_utils.py b/python/oneflow/utils/global_view/global_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..722c7b7a21f1f6f265728866b3b7358a4a2be09e --- /dev/null +++ b/python/oneflow/utils/global_view/global_utils.py @@ -0,0 +1,91 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import warnings +import pickle + +import oneflow as flow +from oneflow.framework.tensor import Tensor +from oneflow.nn.graph.util import ArgsTree + + +def to_global_tensor(input_tensor, placement=None, sbp=None, **kwargs): + # specific operation for None + if input_tensor is None: + return flow.local_to_global( + input=input_tensor, placement=placement, sbp=sbp, **kwargs + ) + + if input_tensor.is_global: + return flow.global_to_global( + input=input_tensor, placement=placement, sbp=sbp, **kwargs + ) + else: + if "grad_sbp" in kwargs: + del kwargs["grad_sbp"] + return flow.local_to_global( + input=input_tensor, placement=placement, sbp=sbp, **kwargs + ) + + +def to_local_tensor(input_tensor, copy): + if not input_tensor.is_global: + warnings.warn("The tensor should be global, local tensor will remain the same.") + return input_tensor + return flow._C.to_local(input_tensor, copy) + + +def check_input_global(input): + is_input_global = False + if input is not None: + if isinstance(input, Tensor): + is_input_global = input.is_global + elif isinstance(input, (dict, tuple, list)): + is_first_tensor_in_input = True + input_tree_for_is_global = ArgsTree(input) + for arg in input_tree_for_is_global.iter_nodes(): + if isinstance(arg, Tensor): + if is_first_tensor_in_input: + is_input_global = arg.is_global + is_first_tensor_in_input = False + else: + assert ( + arg.is_global == is_input_global + ), "Tensor(s) in the input must be all local or all global." + + return is_input_global + + +def check_placement_on_all_ranks(placement): + # Determine whether the ranks of placement are same as all ranks + is_placement_on_all_ranks = False + all_ranks = flow.placement.all("cpu").ranks + if ( + all_ranks.shape == placement.ranks.shape + and (all_ranks == placement.ranks).all() + ): + is_placement_on_all_ranks = True + + return is_placement_on_all_ranks + + +def src_sbp_broadcast(obj, src: int = 0): + rank = flow.env.get_rank() + if src == rank: + obj_bytes = pickle.dumps(obj) + obj_bytes = flow._oneflow_internal.cpu_broadcast(obj_bytes, src) + else: + obj_bytes = flow._oneflow_internal.cpu_broadcast(None, src) + return pickle.loads(obj_bytes) diff --git a/python/oneflow/utils/global_view/to_global.py b/python/oneflow/utils/global_view/to_global.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b2afef6e57fe08a277cfae82a9150d7502a2aa --- /dev/null +++ b/python/oneflow/utils/global_view/to_global.py @@ -0,0 +1,178 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import warnings +import pickle +import types + +import oneflow as flow +from oneflow.framework.tensor import Tensor +from oneflow.nn.graph.util import ArgsTree +from oneflow.utils.global_view.global_utils import ( + to_global_tensor, + check_input_global, + check_placement_on_all_ranks, + src_sbp_broadcast, +) + + +def to_global(input, placement=None, sbp=None, warn_on_non_tensor_leaf=True, **kwargs): + r"""Converts the input tensor or input tensor(s) in list/tuple/dict to global tensor(s). + + Note: + Both placement and sbp are required if the input is local, otherwise at least one of placement and sbp is required. + + Args: + input (oneflow.Tensor/None/list/tuple/dict): the input that needs to be converted. + placement (oneflow.placement, optional): the desired placement of the input. Default: None + sbp (oneflow.sbp.sbp, list/tuple of oneflow.sbp.sbp or Callable[[Tensor], oneflow.sbp.sbp], optional): the desired sbp of the input or self-defined functions in order to specify SBP. Default: None + warn_on_non_tensor_leaf (bool, optional): whether to warn when the leaf is not a tensor. Default: True + + Returns: + The converted input. + + For a tensor input: please refer to the examples in :func:`oneflow.Tensor.to_global`. + + For an input of other type (take a state dict as an example): + + .. code-block:: python + + >>> # Run on 2 ranks respectively + >>> import oneflow as flow + >>> from oneflow import nn + >>> placement = flow.placement("cpu", ranks=[0, 1]) # doctest: +SKIP + >>> sbp = (flow.sbp.broadcast,) # doctest: +SKIP + >>> model = nn.Sequential(nn.Linear(8, 4), nn.ReLU(), nn.Linear(4, 2)) # doctest: +SKIP + >>> global_state_dict = flow.utils.global_view.to_global(model.state_dict(), placement, sbp) # doctest: +SKIP + >>> for val in state_dict.values(): # doctest: +SKIP + >>> print(val.is_global) # doctest: +SKIP + + .. code-block:: python + + >>> # results on rank 0 + True + True + True + True + + .. code-block:: python + + >>> # results on rank 1 + True + True + True + True + + Note: + For the input of dict type, such as the state dict of the model, the unified sbp cannot be used when calling the to_global method, and the sbp needs to be specialized. + Usually used for making graph models's state dict global. + + If you want to do the `split(0)` operation, but there are tensors that cannot be split by dim 0, then these tensors can specify sbp. + It is worth noting that, for a tensor of shape `(1, n)`, you can specify SBP is `oneflow.sbp.split(1)`. + For example: + + .. code-block:: python + + flow.utils.global_view.to_global(state_dict, placement=placement, sbp=get_sbp) + # Defines a function to return the specified SBP. + def get_sbp(state_dict, tensor): + if tensor is state_dict["System-Train-TrainStep"]: + return oneflow.sbp.broadcast + if tensor is state_dict["module_pipeline"]["m_stage3.linear.weight"]: + return oneflow.sbp.split(1) + if tensor is state_dict["module_pipeline"]["m_stage3.linear.bias"]: + return oneflow.sbp.broadcast + return oneflow.sbp.split(0) + + """ + is_input_not_tensor_or_none = False + if (input is not None) and (not isinstance(input, (Tensor, dict, tuple, list))): + is_input_not_tensor_or_none = True + + if ( + (not is_input_not_tensor_or_none) + and (placement is not None) + and (not check_input_global(input)) + and (not check_placement_on_all_ranks(placement)) + ): + src_rank = placement.ranks.flat[0] + cur_rank = flow.env.get_rank() + + if cur_rank == src_rank: + # Replace tensor(s) in the input with None, in order to reduce communication cost + if isinstance(input, Tensor) or input is None: + mapped_input_none = None + else: + input_tree_none = ArgsTree(input) + + def leaf_fn_to_none(node): + if isinstance(node, Tensor): + # Ensure that each rank has a tensor instance, which can avoid the situation of none is none in the user-defined get_sbp function. + return flow.empty(0, 1) + else: + if warn_on_non_tensor_leaf: + warnings.warn( + "Non-Tensor type: {} encountered, it will remain the same.".format( + type(node) + ) + ) + return node + + mapped_input_none = input_tree_none.map_leaf(leaf_fn_to_none) + + obj_input = pickle.dumps(mapped_input_none) + flow._oneflow_internal.cpu_broadcast(obj_input, src_rank) + else: + if cur_rank in placement.ranks: + # Participating in the broadcast process but retaining original value + flow._oneflow_internal.cpu_broadcast(None, src_rank) + else: + # The input of other ranks will be always overwritten no matter what is passed in + input = pickle.loads( + flow._oneflow_internal.cpu_broadcast(None, src_rank) + ) + + if isinstance(input, (Tensor, dict, tuple, list)): + input_tree = ArgsTree(input) + + def leaf_fn(node): + if isinstance(node, Tensor) or node is None: + if isinstance(sbp, types.FunctionType): + + return to_global_tensor(node, placement, sbp(input, node), **kwargs) + + else: + return to_global_tensor(node, placement, sbp, **kwargs) + + else: + if warn_on_non_tensor_leaf: + warnings.warn( + "Non-Tensor type: {} encountered, it will remain the same.".format( + type(node) + ) + ) + return node + + mapped_input = input_tree.map_leaf(leaf_fn) + return mapped_input + + else: + if warn_on_non_tensor_leaf: + warnings.warn( + "Non-Tensor type: {} encountered, it will remain the same.".format( + type(input) + ) + ) + return input diff --git a/python/oneflow/utils/global_view/to_local.py b/python/oneflow/utils/global_view/to_local.py new file mode 100644 index 0000000000000000000000000000000000000000..ab26b55268b7c9de40fb3891828ae860f2126daf --- /dev/null +++ b/python/oneflow/utils/global_view/to_local.py @@ -0,0 +1,87 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import warnings + +import oneflow as flow +from oneflow.framework.tensor import Tensor +from oneflow.nn.graph.util import ArgsTree +from oneflow.utils.global_view.global_utils import to_local_tensor + + +def to_local(input, *, copy=False): + r"""Returns the local part of the input. + + Returns: + The converted input. + + For a tensor input: please refer to the examples in :func:`oneflow.Tensor.to_local`. + + For an input of other type (take a state dict as an example): + + .. code-block:: python + + >>> # Run on 2 ranks respectively + >>> import oneflow as flow + >>> from oneflow import nn + >>> placement = flow.placement("cpu", ranks=[0, 1]) # doctest: +SKIP + >>> sbp = (flow.sbp.broadcast,) # doctest: +SKIP + >>> model = nn.Sequential(nn.Linear(8, 4), nn.ReLU(), nn.Linear(4, 2)) # doctest: +SKIP + >>> model = model.to_global(placement=placement, sbp=sbp) # doctest: +SKIP + >>> local_state_dict = flow.utils.global_view.to_local(model.state_dict()) # doctest: +SKIP + >>> for val in local_state_dict.values(): # doctest: +SKIP + >>> print(val.is_global) # doctest: +SKIP + + .. code-block:: python + + >>> # results on rank 0 + False + False + False + False + + .. code-block:: python + + >>> # results on rank 1 + False + False + False + False + """ + if isinstance(input, Tensor): + return to_local_tensor(input, copy) + elif isinstance(input, (dict, tuple, list)): + input_tree = ArgsTree(input) + + def leaf_fn(node): + if isinstance(node, Tensor): + return to_local_tensor(node, copy) + else: + warnings.warn( + "Non-Tensor type: {} encountered, it will remain the same.".format( + type(node) + ) + ) + return node + + mapped_input = input_tree.map_leaf(leaf_fn) + return mapped_input + else: + warnings.warn( + "Non-Tensor type: {} encountered, it will remain the same.".format( + type(input) + ) + ) + return input diff --git a/python/oneflow/utils/tensor/__init__.py b/python/oneflow/utils/tensor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..566b75f43b16d5eea9790bd412a2be696a788c54 --- /dev/null +++ b/python/oneflow/utils/tensor/__init__.py @@ -0,0 +1,21 @@ +""" +Copyright 2020 The OneFlow Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +from oneflow.utils.tensor.from_or_to_torch_tensor import from_torch, to_torch + +__all__ = [ + "from_torch", + "to_torch", +] diff --git a/python/oneflow/utils/torch/from_or_to_torch_tensor.py b/python/oneflow/utils/tensor/from_or_to_torch_tensor.py similarity index 75% rename from python/oneflow/utils/torch/from_or_to_torch_tensor.py rename to python/oneflow/utils/tensor/from_or_to_torch_tensor.py index 631ded91c9aefd68a7d7614e62810e5b4a93e58b..c1e6cbab3b2a2d8390930c82d1c53072a97cdd86 100644 --- a/python/oneflow/utils/torch/from_or_to_torch_tensor.py +++ b/python/oneflow/utils/tensor/from_or_to_torch_tensor.py @@ -37,8 +37,6 @@ def from_torch(torch_tensor): The returned tensor and torch tensor share the same memory. .. note:: - Currently only cpu tensor, local tensor is supported. - This function can be used in special data processing stages, torch's some cpu ops can be used. Args: @@ -55,7 +53,7 @@ def from_torch(torch_tensor): import torch torch_t = torch.tensor([[1, 2, 3], [4, 5, 6]]) - flow_t = flow.utils.from_torch(torch_t) + flow_t = flow.utils.tensor.from_torch(torch_t) This feature ``from_torch`` is at Alpha Stage. """ @@ -64,12 +62,7 @@ def from_torch(torch_tensor): except: print_error_msg() assert isinstance(torch_tensor, torch.Tensor) - device = torch_tensor.device.__str__() - assert ( - device == "cpu" - ), "Only supports conversion of torch tensor whose device is cpu, need to call torch_tensor.cpu() first" - np_data = torch_tensor.cpu().detach().numpy() - return flow_from_numpy(np_data) + return flow.from_dlpack(torch.to_dlpack(torch_tensor)) def to_torch(flow_tensor): @@ -81,7 +74,7 @@ def to_torch(flow_tensor): The returned tensor and oneflow tensor share the same memory. .. note:: - Currently only cpu tensor, local tensor is supported. + Currently only local tensor is supported. Args: input (oneflow.Tensor): Input Tensor @@ -97,7 +90,7 @@ def to_torch(flow_tensor): import torch flow_t = flow.tensor([[1, 2, 3], [4, 5, 6]]) - torch_t = flow.utils.to_torch(flow_t) + torch_t = flow.utils.tensor.to_torch(flow_t) This feature ``to_torch`` is at Alpha Stage. """ @@ -106,8 +99,9 @@ def to_torch(flow_tensor): except: print_error_msg() assert isinstance(flow_tensor, flow.Tensor) - assert ( - flow_tensor.is_cuda == False - ), "Only supports conversion of oneflow tensor whose device is cpu, need to call flow_tensor.cpu() first" - np_data = flow_tensor.cpu().detach().numpy() - return torch.from_numpy(np_data) + if flow_tensor.is_global: + print( + "WARNING: `to_torch` received a global tensor. A PyTorch CPU tensor which is a copy of its data will be returned." + ) + return torch.from_numpy(flow_tensor.numpy()) + return torch.from_dlpack(flow.to_dlpack(flow_tensor)) diff --git a/python/oneflow/utils/torch/__init__.py b/python/oneflow/utils/torch/__init__.py deleted file mode 100644 index 60e94617b17edb953ec469fcab21fddc211eeb17..0000000000000000000000000000000000000000 --- a/python/oneflow/utils/torch/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Copyright 2020 The OneFlow Authors. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" -from oneflow.utils.torch.from_or_to_torch_tensor import from_torch, to_torch - -__all__ = [ - "from_torch", - "to_torch", -] diff --git a/python/setup.py b/python/setup.py index 5255b42fc447a75dbb0ad8a0a7e9eefb40d2fd77..ca492bc13387d012d4846e21296e23867e2a65f0 100644 --- a/python/setup.py +++ b/python/setup.py @@ -39,6 +39,19 @@ parser.register("type", "bool", lambda v: v.lower() == "true") parser.add_argument("--package_name", type=str, default="oneflow") args, remain_args = parser.parse_known_args() sys.argv = ["setup.py"] + remain_args + + +def get_version(): + import importlib.util + + spec = importlib.util.spec_from_file_location( + "version", os.path.join("oneflow", "version.py") + ) + m = importlib.util.module_from_spec(spec) + spec.loader.exec_module(m) + return m.__version__ + + REQUIRED_PACKAGES = [ f"numpy>={np.__version__}", "protobuf>=3.9.2, <4.0", @@ -47,6 +60,12 @@ REQUIRED_PACKAGES = [ "pillow", "rich", ] + +ONEFLOW_VERSION = get_version() +if "cu11" in ONEFLOW_VERSION and "cu112" not in ONEFLOW_VERSION: + REQUIRED_PACKAGES.append("nvidia-cudnn-cu11") + REQUIRED_PACKAGES.append("nvidia-cublas-cu11") + # if python version < 3.7.x, than need pip install dataclasses if sys.version_info.minor < 7: REQUIRED_PACKAGES.append("dataclasses") @@ -76,17 +95,6 @@ def get_oneflow_internal_so_path(): package_data = {"oneflow": [get_oneflow_internal_so_path()] + include_files} -def get_version(): - import importlib.util - - spec = importlib.util.spec_from_file_location( - "version", os.path.join("oneflow", "version.py") - ) - m = importlib.util.module_from_spec(spec) - spec.loader.exec_module(m) - return m.__version__ - - setup( name=args.package_name, version=get_version(), @@ -98,4 +106,7 @@ setup( zip_safe=False, distclass=BinaryDistribution, cmdclass={"install": InstallPlatlib}, + entry_points={ + "console_scripts": ["oneflow-mock-torch=oneflow.mock_torch.__main__:main"] + }, ) diff --git a/tools/check_src.py b/tools/check_src.py index 74ceb391cc7884b57edb82d26c8bc19a6af204a0..8f4887c4fa720dfc36094571ae17733a3a6e35ca 100644 --- a/tools/check_src.py +++ b/tools/check_src.py @@ -58,6 +58,7 @@ check_unwanted_test_scripts( "tensor", "exceptions", "expensive", + "ddp", "misc", "profiler", ], diff --git a/tools/functional/generate_functional_api.py b/tools/functional/generate_functional_api.py index 77a989156045f56ac3200c186ffa41e3e7f684ba..aa800071484f72f21326d621a639ac2178b222f1 100644 --- a/tools/functional/generate_functional_api.py +++ b/tools/functional/generate_functional_api.py @@ -123,13 +123,45 @@ pybind_source_fmt = ( #include "oneflow/api/python/functional/python_arg.h" #include "oneflow/api/python/functional/python_arg_parser.h" #include "oneflow/api/python/functional/python_frame.h" +#include "oneflow/api/python/functional/python_return_types.h" #include "oneflow/core/common/maybe.h" #include "oneflow/core/common/optional.h" #include "oneflow/core/functional/functional.h" +namespace {{ +// This return type template code is referenced from: +// https://github.com/pytorch/pytorch/blob/master/tools/autograd/gen_python_functions.py +using oneflow::one::functional::returned_structseq_repr; +{2} + +std::unordered_map& get_namedtuple_types_map() {{ + static std::unordered_map namedtuple_types_map = {{ +{3} + }}; + return namedtuple_types_map; +}} + +PyTypeObject* get_namedtuple(const std::string& name) {{ + static auto& namedtuple_types_map = get_namedtuple_types_map(); + return namedtuple_types_map[name]; +}} + +}} // namespace + + namespace oneflow {{ namespace one {{ namespace functional {{ + +PyObject* WrapTensorTuple(const TensorTuple& tensortuple, + const std::string& name) {{ + PyObjectPtr r(PyStructSequence_New(get_namedtuple(name))); + if (!r) {{ throw py::error_already_set(); }} + for (int i = 0; i < tensortuple.size(); ++i) {{ + PyTuple_SET_ITEM(r.get(), i, CastToPyObject(tensortuple[i])); + }} + return r.release(); +}} {0} }} // namespace functional }} // namespace one diff --git a/tools/functional/generator.py b/tools/functional/generator.py index 6a0054a655a20909249b03a8506b3830f0a1e9bb..8a0093eac8896e03bb00f416c46b91534b8bd307 100644 --- a/tools/functional/generator.py +++ b/tools/functional/generator.py @@ -156,6 +156,7 @@ return_type_aliases = { "Tensor": "Maybe", "TensorTuple": "Maybe", "String": "Maybe", + "Shape": "Maybe", **{k: "Maybe<{0}>".format(v) for k, v in generic_type_aliases.items()}, } @@ -182,6 +183,11 @@ def _normalize(fmt): return re.sub(r"\s+", " ", fmt) +def _remove_square_brackets_and_content_inside(fmt): + # "TensorTuple[values], TensorTuple[indices]" -> "TensorTuple, TensorTuple" + return re.sub(r"\[[^()]*?\]", "", fmt) + + def _std_decay(fmt): fmt = fmt.strip() fmt = re.sub(r"(const|&)", "", fmt) @@ -196,12 +202,13 @@ def parse_function_params(fmt): raise ValueError('Missing "(" in function def: ' + fmt) header = _normalize(fmt[0:open_paren]) - items = header.split(" ") + items = _normalize(_remove_square_brackets_and_content_inside(header)).split(" ") if (len(items)) != 1: raise ValueError( "Missing return type or more than 1 return type in function def: " + fmt ) - params.append(items[0]) + + params.append(header) close_paren = fmt.rfind(")") if close_paren == -1: @@ -233,6 +240,29 @@ def render_file_if_different(target_file, content): f.write(content) +def generate_return_types_named_tuple(return_names, func_name, block_name): + param_names = ", ".join( + [ + '{{const_cast("{}"), const_cast("")}}'.format(x) + for x in return_names + ] + ) + code = f"""PyTypeObject* Get{func_name}NamedTuple() {{ + static PyStructSequence_Field NamedTuple_fields[] = {{ {param_names}, {{nullptr}} }}; + static PyTypeObject {func_name}NamedTuple; + static bool is_initialized = false; + static PyStructSequence_Desc desc = {{ const_cast("oneflow.return_types.{block_name}"), nullptr, NamedTuple_fields, {len(return_names)} }}; + if (!is_initialized) {{ + PyStructSequence_InitType(&{func_name}NamedTuple, &desc); + {func_name}NamedTuple.tp_repr = (reprfunc)returned_structseq_repr; + is_initialized = true; + }} + return &{func_name}NamedTuple; +}} +""" + return code + + class Argument: def __init__(self, fmt, keyword_only=False): self._keyword_only = keyword_only @@ -304,7 +334,7 @@ class Argument: class Return: def __init__(self, fmt): - self._type = _normalize(fmt) + self._type, self._return_names = self.check_named_tuple(_normalize(fmt)) assert self._type in types_allowed, "Unknow type: " + self._type if self._type in return_type_aliases: @@ -319,6 +349,15 @@ class Return: def to_string(self, to_cpp=False): return self._cpp_type if to_cpp else self._type + def check_named_tuple(self, fmt): + matches = re.match(r"(.*?)\s*\[(.*?)\]", fmt) + if matches is None: + type, return_names = _normalize(fmt), None + else: + type = matches.group(1) + return_names = [_normalize(x) for x in matches.group(2).split(",")] + return type, return_names + class FunctionSignature: def __init__(self, fmt): @@ -445,6 +484,9 @@ class Generator: schema_fmt = "" module_fmt = "" header_fmt = "" + + return_type_fmt = "" + map_pairs = [] for name, blocks in self._blocks.items(): schema_types = [] max_args_count = 0 @@ -535,6 +577,7 @@ class Generator: name ) schema_fmt += " HANDLE_ERRORS\n" + schema_fmt += ' OF_PROFILER_RANGE_GUARD("{0}");\n'.format(name) schema_fmt += " PythonFrameGuard pf;\n" schema_fmt += ' static PythonArgParser<{0}> parser("{1}");\n'.format( ", ".join(schema_types), name @@ -549,9 +592,20 @@ class Generator: for j in range(len(signature._args)): cpp_type = _std_decay(signature._args[j]._cpp_type) params.append("r[{0}].As<{1}>()".format(j, cpp_type)) - schema_fmt += " return CastToPyObject(functional::{0}({1}));\n".format( - signature._name, ", ".join(params) - ) + if signature._ret._return_names is None: + schema_fmt += " return CastToPyObject(functional::{0}({1}));\n".format( + signature._name, ", ".join(params) + ) + else: + schema_fmt += ' return WrapTensorTuple(functional::{0}({1}).GetOrThrow(), "{2}");\n'.format( + signature._name, ", ".join(params), signature._name, + ) + return_type_fmt += generate_return_types_named_tuple( + signature._ret._return_names, signature._name, block._name, + ) + map_pairs.append( + f' {{"{signature._name}", Get{signature._name}NamedTuple()}},' + ) schema_fmt += " }\n" i += 1 schema_fmt += " Py_RETURN_NONE;\n" @@ -562,5 +616,8 @@ class Generator: target_pybind_header_file, pybind_header_fmt.format(header_fmt) ) render_file_if_different( - target_pybind_source_file, pybind_source_fmt.format(schema_fmt, module_fmt) + target_pybind_source_file, + pybind_source_fmt.format( + schema_fmt, module_fmt, return_type_fmt, "\n".join(map_pairs) + ), ) diff --git a/tools/generate_pip_version.py b/tools/generate_pip_version.py index 556e5b76d9906873082accbd3d08a4fbddd0c0bf..37ffb69fdc53334f7653139f4411f211dc8cde48 100644 --- a/tools/generate_pip_version.py +++ b/tools/generate_pip_version.py @@ -12,7 +12,7 @@ parser.add_argument("--out", type=str, required=False) args = parser.parse_args() local_label = "" -version = f"0.8.0" +version = f"0.9.1" # set version if release of nightly assert ( diff --git a/tools/oneflow-tblgen/op_schema_emitter.cpp b/tools/oneflow-tblgen/op_schema_emitter.cpp index fea5ebf50856f2016661121162b550289aadedaf..946da4b385aa6a10b4a8cd195e25599fc857174f 100644 --- a/tools/oneflow-tblgen/op_schema_emitter.cpp +++ b/tools/oneflow-tblgen/op_schema_emitter.cpp @@ -141,6 +141,9 @@ void OpSchemaEmitter::run(raw_ostream& os) { emitBit(def, "has_output_arg_modify_fn", &op); emitBit(def, "has_output_blob_time_shape_infer_fn", &op); emitBit(def, "has_sbp_signature_infer_fn", &op); + emitBit(def, "has_get_nd_sbp_fn", &op); + emitBit(def, "has_dump_nd_sbp_signature_for_op_conf_fn", &op); + emitBit(def, "has_compute_complexity_fn", &op); emitBit(def, "has_check_fn", &op); ops[op_name.str()] = op; } @@ -188,6 +191,8 @@ void OpSchemaEmitter::emitAttrs(const Record* def, json* op) const { AS = A->getValueAsDef("baseAttr")->getNameInitAsString(); } auto NS = attrs->getArgName(i)->getAsUnquotedString(); + // FlatSymbolRefAttr:$callee, + if ("callee" == NS && "FlatSymbolRefAttr" == AS) { continue; } json attr{{"name", NS}, {"type", emitType(AS)}}; if (auto DV = A->getValueAsOptionalString("defaultValue")) { attr["default"] = DV.getValue(); } diff --git a/tools/oneflow-tblgen/op_schema_header.inc b/tools/oneflow-tblgen/op_schema_header.inc index 1080fe3ad1732f3d2c789190290f9fdc8f13251c..b4ea6b16658e88633d1d844e894816fa32b04d1e 100644 --- a/tools/oneflow-tblgen/op_schema_header.inc +++ b/tools/oneflow-tblgen/op_schema_header.inc @@ -2,12 +2,17 @@ R"OP_SCHEMA_INC( #include "oneflow/core/common/data_type.h" #include "oneflow/core/common/shape.h" #include "oneflow/core/common/symbol.h" -#include "oneflow/core/framework/op_base.h" +#include "oneflow/core/operator/op_conf.pb.h" +#include "oneflow/core/job/sbp_parallel.pb.h" +#include "oneflow/core/framework/op_definition.h" #include #include #include +class OperatorConf; +class NdSbpSignature; + namespace oneflow { class Device; @@ -24,6 +29,8 @@ class InferSbpSignatureFnContext; class InferOutputBlobTimeShapeFnContext; class InferNdSbpFnContext; class DeviceAndStreamInferContext; +class ComputeComplexityFnContext; +class GetNdSbpSignatureListContext; } // namespace user_op using GetInputArgModifier = @@ -32,7 +39,7 @@ using GetOutputArgModifier = std::function; {% for opname, op in ops %} -class {{opname}} : public OpBase { +class {{opname}} : public OpDefinition<{{opname}}> { public: virtual ~{{opname}}() = default; {% if op.has_nd_sbp_infer_fn -%} @@ -41,6 +48,12 @@ class {{opname}} : public OpBase { {% if op.has_get_sbp_fn -%} static Maybe GetSbp(user_op::SbpContext* ctx); {% endif -%} + {% if op.has_get_nd_sbp_fn -%} + static Maybe GetNdSbpSignatureList(user_op::GetNdSbpSignatureListContext* ctx); + {% endif -%} + {% if op.has_dump_nd_sbp_signature_for_op_conf_fn -%} + static Maybe DumpNdSbpSignatureForOpConfFn(const NdSbpSignature& nd_sbp_sig, OperatorConf* op_conf); + {% endif -%} {% if op.has_logical_tensor_desc_infer_fn -%} static Maybe InferLogicalTensorDesc(user_op::InferContext* ctx); {% endif -%} @@ -56,6 +69,9 @@ class {{opname}} : public OpBase { {% if op.has_sbp_signature_infer_fn -%} static Maybe InferSbpSignature(user_op::InferSbpSignatureFnContext* ctx); {% endif -%} + {% if op.has_compute_complexity_fn -%} + static Maybe GetComputeComplexity(user_op::ComputeComplexityFnContext* ctx); + {% endif -%} {% if op.has_input_arg_modify_fn -%} static Maybe ModifyInputArg(const GetInputArgModifier&, const user_op::UserOpConfWrapper&); {% endif -%} @@ -75,7 +91,7 @@ class {{opname}} : public OpBase { virtual void set_{{attr.name}}(const {{attr.type}}& {{attr.name}}) = 0; {% endfor -%} - const HashSet& AttrNames() const; + static const HashSet& AttrNames(); }; namespace schema { @@ -88,7 +104,7 @@ class {{opname}} : public oneflow::{{opname}} { {% endfor -%} - Maybe GetAttr(const std::string& attr_name) const override; + Maybe Attr(const std::string& attr_name) const override; private: {% for attr in op.attrs -%} diff --git a/tools/oneflow-tblgen/op_schema_source.inc b/tools/oneflow-tblgen/op_schema_source.inc index a60e52652562397cf3e32b573e9a89e69e0bfa4d..be852ee9e62d0a9b90e1f23fa575831b70a6b9b6 100644 --- a/tools/oneflow-tblgen/op_schema_source.inc +++ b/tools/oneflow-tblgen/op_schema_source.inc @@ -11,16 +11,16 @@ R"OP_SCHEMA_INC( namespace oneflow { #define REGISTER_OP_SCHEMA(op_type, schema) \ - REGISTER_CLASS_CREATOR(std::string, op_type, OpBase, ([]() { return new schema; })) + REGISTER_CLASS_CREATOR(std::string, op_type, OpDefinitionBase, ([]() { return new schema; })) {% for opname, op in ops %} -const HashSet& {{opname}}::AttrNames() const { +/*static*/ const HashSet& {{opname}}::AttrNames() { static const HashSet attr_names = { {%- for attr in op.attrs -%}"{{attr.name}}", {%- endfor -%} }; return attr_names; } namespace schema { -Maybe {{opname}}::GetAttr(const std::string& attr_name) const { +Maybe {{opname}}::Attr(const std::string& attr_name) const { {% for attr in op.attrs %}if(attr_name == "{{attr.name}}") { return CastAttrValue(&{{attr.name}}_); } @@ -76,6 +76,15 @@ REGISTER_USER_OP("{{op.name}}") {%- if op.has_get_sbp_fn -%} .SetGetSbpFn(&{{opname}}::GetSbp) {%- endif -%} +{%- if op.has_get_nd_sbp_fn -%} + .SetGetNdSbpSignatureListFn(&{{opname}}::GetNdSbpSignatureList) +{%- endif -%} +{%- if op.has_dump_nd_sbp_signature_for_op_conf_fn -%} + .SetDumpNdSbpSignatureForOpConfFn(&{{opname}}::DumpNdSbpSignatureForOpConfFn) +{%- endif -%} +{%- if op.has_compute_complexity_fn -%} + .SetComputeComplexityFn(&{{opname}}::GetComputeComplexity) +{%- endif -%} {%- if op.has_logical_tensor_desc_infer_fn -%} .SetLogicalTensorDescInferFn(&{{opname}}::InferLogicalTensorDesc) {%- endif -%} diff --git a/version_script.lds b/version_script.lds deleted file mode 100644 index 665a2f9af475e1795ad209c91bd01e762926e124..0000000000000000000000000000000000000000 --- a/version_script.lds +++ /dev/null @@ -1,7 +0,0 @@ -{ - global: - *; - local: - *llvm*; -}; -